GMM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Gaussian Mixture Model (GMM)\n",
    "Is a probabilistic model, that can be used as a clustering model. One different with K-Means is that here we have probabilities of one point of belonging to a cluster, (KMeans assing each point to a cluster directly) but GMM take the maximum probability and returns the cluster to which that point belongs, however you can use the method **predict_proba** to see the probability of each cluster.\n",
    "\n",
    "It's based in the covariance matrix of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score\n",
    "from mpl_toolkits import mplot3d\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
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       "      <td>Feb</td>\n",
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       "      <td>Returning_Visitor</td>\n",
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       "      <td>154.216667</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.024561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
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       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
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       "      <th>9</th>\n",
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      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0             0.0                      0.0            0.0   \n",
       "1             0.0                      0.0            0.0   \n",
       "2             0.0                     -1.0            0.0   \n",
       "3             0.0                      0.0            0.0   \n",
       "4             0.0                      0.0            0.0   \n",
       "5             0.0                      0.0            0.0   \n",
       "6             0.0                     -1.0            0.0   \n",
       "7             1.0                     -1.0            0.0   \n",
       "8             0.0                      0.0            0.0   \n",
       "9             0.0                      0.0            0.0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0             1.0                 0.000000   \n",
       "1                     0.0             2.0                64.000000   \n",
       "2                    -1.0             1.0                -1.000000   \n",
       "3                     0.0             2.0                 2.666667   \n",
       "4                     0.0            10.0               627.500000   \n",
       "5                     0.0            19.0               154.216667   \n",
       "6                    -1.0             1.0                -1.000000   \n",
       "7                    -1.0             1.0                -1.000000   \n",
       "8                     0.0             2.0                37.000000   \n",
       "9                     0.0             3.0               738.000000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0     0.200000   0.200000         0.0         0.0   Feb                 1   \n",
       "1     0.000000   0.100000         0.0         0.0   Feb                 2   \n",
       "2     0.200000   0.200000         0.0         0.0   Feb                 4   \n",
       "3     0.050000   0.140000         0.0         0.0   Feb                 3   \n",
       "4     0.020000   0.050000         0.0         0.0   Feb                 3   \n",
       "5     0.015789   0.024561         0.0         0.0   Feb                 2   \n",
       "6     0.200000   0.200000         0.0         0.4   Feb                 2   \n",
       "7     0.200000   0.200000         0.0         0.0   Feb                 1   \n",
       "8     0.000000   0.100000         0.0         0.8   Feb                 2   \n",
       "9     0.000000   0.022222         0.0         0.4   Feb                 2   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  \n",
       "5        2       1            3  Returning_Visitor    False    False  \n",
       "6        4       3            3  Returning_Visitor    False    False  \n",
       "7        2       1            5  Returning_Visitor     True    False  \n",
       "8        2       2            3  Returning_Visitor    False    False  \n",
       "9        4       1            2  Returning_Visitor    False    False  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_online_customers = pd.read_csv('C:/Users/alberto.rubiales/PycharmProjects/Pycharm/Gaussian Mixture Model/online_shoppers_intention.csv')\n",
    "df_online_customers[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ProfileReport(df_online_customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset have a huge quantity of outliers, so i will create a function that drop the outliers and apply several times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_cleaner(df, sigma=3):\n",
    "    '''\n",
    "    A function to clear all the outliers of a dataframe\n",
    "    \n",
    "    :param df: The dataframe to clean outliers\n",
    "    :param sigma: The z_score threshold to remove a value, by default 3\n",
    "    :return: the dataframe with the outliers removed\n",
    "    '''\n",
    "    for col in df.columns:\n",
    "        column_data = df[col]\n",
    "        mean = np.mean(column_data)\n",
    "        std = np.std(column_data)\n",
    "        \n",
    "\n",
    "        outliers = []\n",
    "        counter = 0\n",
    "        \n",
    "        for data in column_data:\n",
    "            z_score = (data -mean)/std\n",
    "            if np.abs(z_score) >= sigma:\n",
    "                outliers.append(counter)\n",
    "            counter += 1\n",
    "            \n",
    "        df.drop(df.index[outliers], inplace=True)\n",
    "\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape (9916, 3)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational_Duration</th>\n",
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      "text/plain": [
       "   ProductRelated_Duration  Administrative_Duration  Informational_Duration\n",
       "0                 0.000000                      0.0                     0.0\n",
       "1                64.000000                      0.0                     0.0\n",
       "2                -1.000000                     -1.0                    -1.0\n",
       "3                 2.666667                      0.0                     0.0\n",
       "4               627.500000                      0.0                     0.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_online_customers = df_online_customers[['ProductRelated_Duration', 'Administrative_Duration', 'Informational_Duration']]\n",
    "df_online_customers = outlier_cleaner(df_online_customers)\n",
    "df_online_customers = outlier_cleaner(df_online_customers)\n",
    "df_online_customers = outlier_cleaner(df_online_customers)\n",
    "df_online_customers.dropna(inplace=True)\n",
    "print('Shape', df_online_customers.shape)\n",
    "df_online_customers[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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qA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQAH/z5sAAAJ6SURBVIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAg8I7WAieaY/xrAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<meta charset=\"UTF-8\">\n",
       "\n",
       "<style>\n",
       "\n",
       "        .variablerow {\n",
       "            border: 1px solid #e1e1e8;\n",
       "            border-top: hidden;\n",
       "            padding-top: 2em;\n",
       "            padding-bottom: 2em;\n",
       "            padding-left: 1em;\n",
       "            padding-right: 1em;\n",
       "        }\n",
       "\n",
       "        .headerrow {\n",
       "            border: 1px solid #e1e1e8;\n",
       "            background-color: #f5f5f5;\n",
       "            padding: 2em;\n",
       "        }\n",
       "        .namecol {\n",
       "            margin-top: -1em;\n",
       "            overflow-x: auto;\n",
       "        }\n",
       "\n",
       "        .dl-horizontal dt {\n",
       "            text-align: left;\n",
       "            padding-right: 1em;\n",
       "            white-space: normal;\n",
       "        }\n",
       "\n",
       "        .dl-horizontal dd {\n",
       "            margin-left: 0;\n",
       "        }\n",
       "\n",
       "        .ignore {\n",
       "            opacity: 0.4;\n",
       "        }\n",
       "\n",
       "        .container.pandas-profiling {\n",
       "            max-width:975px;\n",
       "        }\n",
       "\n",
       "        .col-md-12 {\n",
       "            padding-left: 2em;\n",
       "        }\n",
       "\n",
       "        .indent {\n",
       "            margin-left: 1em;\n",
       "        }\n",
       "\n",
       "        .center-img {\n",
       "            margin-left: auto !important;\n",
       "            margin-right: auto !important;\n",
       "            display: block;\n",
       "        }\n",
       "\n",
       "        /* Table example_values */\n",
       "            table.example_values {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .example_values th {\n",
       "                border: 0;\n",
       "                padding: 0 ;\n",
       "                color: #555;\n",
       "                font-weight: 600;\n",
       "            }\n",
       "\n",
       "            .example_values tr, .example_values td{\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "                color: #555;\n",
       "            }\n",
       "\n",
       "        /* STATS */\n",
       "            table.stats {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats th {\n",
       "                border: 0;\n",
       "                padding: 0 2em 0 0;\n",
       "                color: #555;\n",
       "                font-weight: 600;\n",
       "            }\n",
       "\n",
       "            .stats tr {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats td{\n",
       "                color: #555;\n",
       "                padding: 1px;\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Sample table */\n",
       "            table.sample {\n",
       "                border: 0;\n",
       "                margin-bottom: 2em;\n",
       "                margin-left:1em;\n",
       "            }\n",
       "            .sample tr {\n",
       "                border:0;\n",
       "            }\n",
       "            .sample td, .sample th{\n",
       "                padding: 0.5em;\n",
       "                white-space: nowrap;\n",
       "                border: none;\n",
       "\n",
       "            }\n",
       "\n",
       "            .sample thead {\n",
       "                border-top: 0;\n",
       "                border-bottom: 2px solid #ddd;\n",
       "            }\n",
       "\n",
       "            .sample td {\n",
       "                width:100%;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* There is no good solution available to make the divs equal height and then center ... */\n",
       "            .histogram {\n",
       "                margin-top: 3em;\n",
       "            }\n",
       "        /* Freq table */\n",
       "\n",
       "            table.freq {\n",
       "                margin-bottom: 2em;\n",
       "                border: 0;\n",
       "            }\n",
       "            table.freq th, table.freq tr, table.freq td {\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "            }\n",
       "\n",
       "            .freq thead {\n",
       "                font-weight: 600;\n",
       "                white-space: nowrap;\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "\n",
       "            }\n",
       "\n",
       "            td.fillremaining{\n",
       "                width:auto;\n",
       "                max-width: none;\n",
       "            }\n",
       "\n",
       "            td.number, th.number {\n",
       "                text-align:right ;\n",
       "            }\n",
       "\n",
       "        /* Freq mini */\n",
       "            .freq.mini td{\n",
       "                width: 50%;\n",
       "                padding: 1px;\n",
       "                font-size: 12px;\n",
       "\n",
       "            }\n",
       "            table.freq.mini {\n",
       "                 width:100%;\n",
       "            }\n",
       "            .freq.mini th {\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "                white-space: nowrap;\n",
       "                max-width: 5em;\n",
       "                font-weight: 400;\n",
       "                text-align:right;\n",
       "                padding-right: 0.5em;\n",
       "            }\n",
       "\n",
       "            .missing {\n",
       "                color: #a94442;\n",
       "            }\n",
       "            .alert, .alert > th, .alert > td {\n",
       "                color: #a94442;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Bars in tables */\n",
       "            .freq .bar{\n",
       "                float: left;\n",
       "                width: 0;\n",
       "                height: 100%;\n",
       "                line-height: 20px;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                background-color: #337ab7;\n",
       "                border-radius: 3px;\n",
       "                margin-right: 4px;\n",
       "            }\n",
       "            .other .bar {\n",
       "                background-color: #999;\n",
       "            }\n",
       "            .missing .bar{\n",
       "                background-color: #a94442;\n",
       "            }\n",
       "            .tooltip-inner {\n",
       "                width: 100%;\n",
       "                white-space: nowrap;\n",
       "                text-align:left;\n",
       "            }\n",
       "\n",
       "            .extrapadding{\n",
       "                padding: 2em;\n",
       "            }\n",
       "\n",
       "            .pp-anchor{\n",
       "\n",
       "            }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>4 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>9916 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>310.0 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>32.0 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>4 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_Administrative_Duration\"><code>Administrative_Duration</code></a> has 5556 / 56.0% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_Informational_Duration\"><code>Informational_Duration</code></a> has 9028 / 91.0% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_ProductRelated_Duration\"><code>ProductRelated_Duration</code></a> has 717 / 7.2% zeros <span class=\"label label-info\">Zeros</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Administrative_Duration\">Administrative_Duration<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>1849</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>18.6%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>35.358</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>-1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>256</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>56.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-2621568212912192917\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAB0UlEQVR4nO3dMY7TUBSG0euI1kkfxYtAYgtILIgOiY6SpdDT0rOLRFmALQoKYgoITTS/JtGMHTPnlLbk%2Byy9T69w4WYcx7Emdjgcquu6qceycPv9vna73aQzX0067a%2B2bavqzwuv1%2Bs5lsCC9H1fXdf92zdTmiWQpmmqqmq9Xl8E8ubD16uf9/3TuydZF/ftvG%2BmtJp8IiyIQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAMMt/0p%2Baf6vzXJwgEPwXJ8gtnDo8xosN5BbXRiWo5RPIM3JKLZ9A7swtUV1LhI83SyDjOFZVVd/3F/d%2B/fwx9XJenNfvv8y9hAd9%2B/j24tp5n5z3zZRmCWQYhqqq6rpujvHcsc3nh%2B8Nw1CbzWa6xVRVM86Q5el0quPxWG3bVtM0U49nYcZxrGEYarvd1mo17ZeJWQKBpfChEAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHgN9dOT3v32k1XAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-2621568212912192917,#minihistogram-2621568212912192917\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-2621568212912192917\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-2621568212912192917\"\n",
       "                                                  aria-controls=\"quantiles-2621568212912192917\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-2621568212912192917\" aria-controls=\"histogram-2621568212912192917\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-2621568212912192917\" aria-controls=\"common-2621568212912192917\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-2621568212912192917\" aria-controls=\"extreme-2621568212912192917\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-2621568212912192917\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>-1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>54</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>170.04</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>256</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>257</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>54</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>57.584</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.6286</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>2.5155</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>35.358</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>44.115</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.798</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>350610</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>3316</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>77.5 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-2621568212912192917\">\n",
       "            <img 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VsmyrAb1NWXKFA0bNkwDBw702V9UVKTa2lolJSXZ%2B9xut%2BLi4rRx40ZJUkFBgeLi4uR2u%2B02gwcPVnV1tYqKiuw2P%2B7jfJutW7eqtrb2nzp/AADQtAX7uwCXy6W7775b%2B/fv17fffqutW7dqzZo1ateund58803dcccdF3xudna2ioqKtHXr1jrHPB6PQkNDfWa1JCkqKkoej8duExUV5XM8IiJCoaGhF20TFRWlM2fO6OjRo4qOjq7z2tXV1aqurrYfn58NAwAAVwe/fYrw6NGjWrBggbp3765%2B/fqpoqJCH3/8sfbv36/vvvtOycnJ%2Bvd///cLPr%2BsrEwPP/ywli9frubNmzf4dS3LUlBQkP34xz83tM352bX6niv9cMO90%2Bm0t5iYmAbXBwAAAp9fAta//du/qX379lq8eLFSUlJUVlamP//5zxoyZIiCgoL0i1/8Qo8%2B%2Bqj2799/wT6KiopUUVGh%2BPh4BQcHKzg4WPn5%2BXrppZcUHBysqKgo1dTUqLKy0ud5FRUV9oyUy%2BWyZ6rOq6ysVG1t7UXbVFRUKDg4WG3atKm3tpkzZ8rr9dpbWVnZPzxGAAAgcPnlEmF4eLg%2B//zzi17%2Bi46O1u7duy94/O6779b27dt99j3wwAO65ZZb9NhjjykmJkYhISHKy8vT6NGjJUnl5eUqLS3VvHnzJEmJiYl69tlnVV5ebl/qy83NlcPhUHx8vN3mk08%2B8Xmd3NxcJSQkKCQkpN7aHA6HHA7HJUYBAAA0VX4JWMuWLbtkm6CgIN14440XPN6qVSvFxcX57AsLC1ObNm3s/Q8%2B%2BKCmT5%2BuNm3aqHXr1poxY4a6detm3xCflJSkLl26KCUlRc8//7z%2B9re/acaMGUpLS1N4eLgkadKkSVq4cKEyMzOVlpamgoICvf7663rvvff%2B2dMHAABNnF8uET7yyCNauHBhnf2vvPKKzxpUP9eLL76okSNHavTo0erXr59atmypTz75RM2aNZMkNWvWTKtWrVLz5s3Vr18/jR49WiNHjvRZgiE2NlarV6/W2rVrddttt%2BmZZ57RSy%2B9pHvvvddYnQAAoGkJshq6HoJBHTp00Mcff6yEhASf/du2bat3tfVAV1VVJafTKa/Xa8%2BMmZIwK8dof5fT1meH%2BLsEAMBV5HL%2B/b0Uv8xgHT16tM7yCdIP92YdPXrUDxUBAACY45eAdeONN%2Bqzzz6rs/%2Bzzz5TbGysHyoCAAAwxy83uWdkZCgjI0PHjh3TXXfdJUlas2aN5s2bx1fQAACAgOeXgJWWlqbTp09rzpw5evLJJyX9cF/WSy%2B9pN/%2B9rf%2BKAkAAMAYv31VzrRp0zRt2jSVl5erRYsWuvbaa/1VCgAAgFF%2B/y7C%2Br7LDwAAIJD55Sb3I0eO6IEHHtB1112n5s2bKzQ01GcDAAAIZH6ZwUpNTdW3336r//zP/1R0dPQFvzQZAAAgEPklYK1bt07r1q1Tjx49/PHyAAAAl5VfLhF26NCBWSsAANBk%2BSVgvfjii5o5c2aT%2B0ocAAAAyU%2BXCFNSUnT8%2BHF17NhR4eHhCgkJ8TleUVHhj7IAAACM8EvAeu655/zxsgAAAI3CLwHrwQcf9MfLAgAANAq/3IMlSfv27dPs2bOVkpJiXxLMzc3Vzp07/VUSAACAEX4JWOvXr1fXrl2Vn5%2BvDz74QCdOnJAkbdu2TX/4wx/8URIAAIAxfglYjz32mGbPnq0vvvjCZ%2BX2u%2B66S4WFhf4oCQAAwBi/BKyvvvpK9913X539kZGROnLkiB8qAgAAMMcvAevaa6%2BVx%2BOps7%2BkpETt27f3Q0UAAADm%2BCVg3X///Xr88cd15MgRe0X3TZs2acaMGRo/frw/SgIAADDGLwFrzpw5crlcio6O1okTJ9SlSxf17dtXvXr10u9//3t/lAQAAGCMX9bBCg0N1fvvv69vvvlG27Zt07lz59SzZ0/dcsst/igHAADAKL8ErPNuvvlm3Xzzzf4sAQAAwDi/BKz/%2BI//uOjx1157rZEqAQAAMM8vAau8vNzncW1trXbs2KHjx4/rzjvv9EdJAAAAxvglYH3yySd19p05c0YPPfSQOnfu7IeKAAAAzPHbdxH%2BVHBwsGbMmKHnn3/e36UAAAD8LFdMwJKkPXv2qLa21t9lAAAA/Cx%2BuUT46KOP%2Bjy2LEvl5eVauXKlxo0b54%2BSAAAAjPFLwCooKPB5fM0116hdu3Z67rnnlJaW5o%2BSAAAAjPFLwFq/fr0/XhYAAKBRXFH3YAEAADQFfpnB6tWrl/0lz5eyefPmy1wNAACAWX4JWL/85S/1pz/9STfffLMSExMlSYWFhdq1a5cmTpwoh8Phj7IAAACM8EvA%2Bv777zVlyhTNmTPHZ/%2BsWbN0%2BPBh/c///I8/ygIAADDCL/dgffDBB3rggQfq7E9NTdWf//xnP1QEAABgjl8ClsPh0MaNG%2Bvs37hxI5cHAQBAwPNLwEpPT9ekSZOUkZGh7OxsZWdnKyMjQ5MnT9bDDz/coD4WLVqkW2%2B9VeHh4QoPD1diYqI%2B/fRT%2B3h1dbWmTZumtm3bKiwsTCNGjNDBgwd9%2Bjhw4ICGDx%2BusLAwtW3bVunp6aqpqfFpk5%2Bfr/j4eDVv3lw33HCDFi9e/PMHAAAANGl%2BuQdr1qxZio2N1R//%2BEe98cYbkqTOnTtryZIlGjt2bIP66NChg5577jn9y7/8iyRp2bJl%2BtWvfqXi4mJ17dpVGRkZ%2BuSTT5Sdna02bdpo%2BvTpSk5OVlFRkZo1a6azZ89q2LBhateunTZs2KBjx45pwoQJsixLL7/8siRp7969uueee5SWlqZ33nlH//u//6vJkyerXbt2uvfeey/P4AAAgIAXZFmW5e8iTGndurWef/553XfffWrXrp3efvttjRkzRpJ06NAhxcTEaPXq1Ro8eLA%2B/fRTJScnq6ysTG63W5KUnZ2t1NRUVVRUKDw8XI899phWrlypnTt32q8xadIkffnll3VWo7%2BYqqoqOZ1Oeb1ehYeHGz3nhFk5Rvu7nLY%2BO8TfJQAAriKX8%2B/vpfhtodGqqiotXbpUf/jDH1RZWSlJ%2BvLLL1VeXv4P93X27FllZ2fr5MmTSkxMVFFRkWpra5WUlGS3cbvdiouLs%2B/9KigoUFxcnB2uJGnw4MGqrq5WUVGR3ebHfZxvs3Xr1ot%2BKXV1dbWqqqp8NgAAcPXwyyXC0tJSDRw4UC1btlRZWZlSU1MVERGhDz74QAcPHtSyZcsa1M/27duVmJio06dP6xe/%2BIVWrFihLl26qKSkRKGhoYqIiPBpHxUVJY/HI0nyeDyKioryOR4REaHQ0NCLtomKitKZM2d09OhRRUdH11vX3Llz9dRTTzXoHAAAQNPjlxmsRx55RGPHjtW3336r5s2b2/uHDRumdevWNbifTp06qaSkRIWFhXrooYc0YcIEff311xdsb1mWzwry9a0mf6k256%2BoXmwl%2BpkzZ8rr9dpbWVlZg88JAAAEPr/MYG3ZskWLFi2qE1Lat2//D10iDA0NtW9yT0hI0JYtW/THP/5RY8aMUU1NjSorK31msSoqKtS3b19Jksvl0qZNm3z6q6ysVG1trT1r5XK57NmsH/cRHBysNm3aXLAuh8PBchMAAFzF/DKDFRoaqhMnTtTZv3v3brVt2/af7teyLFVXVys%2BPl4hISHKy8uzj5WXl6u0tNQOWImJiSotLfUJdLm5uXI4HIqPj7fb/LiP820SEhIUEhLyT9cJAACaNr8ErBEjRuiZZ57RmTNnJP1wue27777T448/rlGjRjWojyeeeELr16/Xvn37tH37ds2aNUtr167VuHHj5HQ69eCDD2r69Olas2aNiouLNX78eHXr1k0DBw6UJCUlJalLly5KSUlRcXGx1qxZoxkzZigtLc3%2BpMGkSZO0f/9%2BZWZmaufOnXrjjTf0%2Buuva8aMGZdnYAAAQJPgl0uEL7zwgoYMGSKXy6VTp07prrvu0qFDh9SrV6863094IYcPH1ZKSorKy8vldDp16623KicnR4MGDZIkvfjiiwoODtbo0aN16tQp3X333Vq6dKmaNWsmSWrWrJlWrVqlyZMnq1%2B/fmrRooXGjh2r%2BfPn268RGxur1atX65FHHtErr7wit9utl156iTWwAADARfltHSzLspSXl6dt27bp3Llz6tmzpwYPHnzRm8cDFetg/YB1sAAAjcmf62A1%2BgxWbW2t7rnnHr366qtKSkqqs84UAABAoGv0e7BCQkJUXFzcJGeqAAAAJD/d5D5%2B/Hi9%2Beab/nhpAACAy84vN7lL0sKFC/X5558rISFBYWFhPsfmzZvnp6oAAAB%2BPr8ErKKiIt16662SpK%2B%2B%2BsrnGJcOAQBAoGvUgLVnzx7FxsZq/fr1jfmyAAAAjapR78G66aabdOTIEfvxmDFjdPjw4cYsAQAA4LJr1ID10yW3Vq9erZMnTzZmCQAAAJedXz5FCAAA0JQ1asAKCgqqcxM7N7UDAICmplFvcrcsS6mpqXI4HJKk06dPa9KkSXWWafjoo48asywAAACjGjVgTZgwwefx%2BPHjG/PlAQAAGkWjBixWbwcAAFcDbnIHAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC9iANXfuXPXq1UutWrVSZGSkRo4cqV27dvm0qa6u1rRp09S2bVuFhYVpxIgROnjwoE%2BbAwcOaPjw4QoLC1Pbtm2Vnp6umpoanzb5%2BfmKj49X8%2BbNdcMNN2jx4sWX/fwAAEDgCtiAlZ%2BfrylTpqiwsFB5eXk6c%2BaMkpKSdPLkSbtNRkaGVqxYoezsbG3YsEEnTpxQcnKyzp49K0k6e/ashg0bppMnT2rDhg3Kzs7Whx9%2BqOnTp9t97N27V/fcc4/uuOMOFRcX64knnlB6ero%2B/PDDRj9nAAAQGIIsy7L8XYQJR44cUWRkpPLz83XnnXfK6/WqXbt2evvttzVmzBhJ0qFDhxQTE6PVq1dr8ODB%2BvTTT5WcnKyysjK53W5JUnZ2tlJTU1VRUaHw8HA99thjWrlypXbu3Gm/1qRJk/Tll1%2BqoKCgQbVVVVXJ6XTK6/UqPDzc6HknzMox2t/ltPXZIf4uAQBwFbmcf38vJWBnsH7K6/VKklq3bi1JKioqUm1trZKSkuw2brdbcXFx2rhxoySpoKBAcXFxdriSpMGDB6u6ulpFRUV2mx/3cb7N1q1bVVtbW28t1dXVqqqq8tkAAMDVo0kELMuylJmZqdtvv11xcXGSJI/Ho9DQUEVERPi0jYqKksfjsdtERUX5HI%2BIiFBoaOhF20RFRenMmTM6evRovfXMnTtXTqfT3mJiYoycJwAACAxNImBNnTpVX331ld57771LtrUsS0FBQfbjH//c0Dbnr6rW91xJmjlzprxer72VlZU16DwAAEDTEPABa9q0aVq5cqW%2B%2BOILdejQwd7vcrlUU1OjyspKn/YVFRX2jJTL5bJnqs6rrKxUbW3tRdtUVFQoODhYbdq0qbcmh8Oh8PBwnw0AAFw9AjZgWZalqVOn6qOPPtJf/vIXxcbG%2BhyPj49XSEiI8vLy7H3l5eUqLS1V3759JUmJiYkqLS1VeXm53SY3N1cOh0Px8fF2mx/3cb5NQkKCQkJCLtfpAQCAABawAWvKlCl655139O6776pVq1byeDzyeDw6deqUJMnpdOrBBx/U9OnTtWbNGhUXF2v8%2BPHq1q2bBg4cKElKSkpSly5dlJKSouLiYq1Zs0YzZsxQWlqaPes0adIk7d%2B/X5mZmdq5c6feeOMNvf7665oxY4bfzh0AAFzZAjZgLVq0SF6vVwMGDFB0dLS9vf/%2B%2B3abF198USNHjtTo0aPVr18/tWzZUp988omaNWsmSWrWrJlWrVql5s2bq1%2B/fho9erRGjhyp%2BfPn233ExsZq9erVWrt2rW677TY988wzeumll3Tvvfc2%2BjkDAIDA0GTWwbqSsQ7WD1gHCwDQmFgHCwAAoAkhYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLGrdDEAAA%2BLSURBVDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWEAHrHXr1mn48OFyu90KCgrSxx9/7HPcsizNnj1bbrdbLVq00IABA7Rjxw6fNpWVlUpJSZHT6ZTT6VRKSoq%2B//57nzbbt29X//791aJFC7Vv315PP/20LMu67OcHAAACU0AHrJMnT6p79%2B5auHBhvcfnzZunrKwsLVy4UFu2bJHL5dKgQYN0/Phxu83YsWNVUlKinJwc5eTkqKSkRCkpKfbxqqoqDRo0SG63W1u2bNHLL7%2Bs%2BfPnKysr67KfHwAACEzB/i7g5xg6dKiGDh1a7zHLsrRgwQLNmjVLo0aNkiQtW7ZMUVFRevfddzVx4kTt3LlTOTk5KiwsVO/evSVJS5YsUWJionbt2qVOnTpp%2BfLlOn36tJYuXSqHw6G4uDh98803ysrKUmZmpoKCghrtfAEAQGAI6Bmsi9m7d688Ho%2BSkpLsfQ6HQ/3799fGjRslSQUFBXI6nXa4kqQ%2BffrI6XT6tOnfv78cDofdZvDgwTp06JD27dvXOCcDAAACSpMNWB6PR5IUFRXlsz8qKso%2B5vF4FBkZWee5kZGRPm3q6%2BPHr/FT1dXVqqqq8tkAAMDVo8kGrPN%2BegnPsiyfffVd4rtUm/M3uF/o8uDcuXPtm%2BadTqdiYmL%2B6foBAEDgabIBy%2BVySao7y1RRUWHPQLlcLh0%2BfLjOc48cOeLTpr4%2BpLqzY%2BfNnDlTXq/X3srKyn7eyQAAgIDSZANWbGysXC6X8vLy7H01NTXKz89X3759JUmJiYnyer3avHmz3WbTpk3yer0%2BbdatW6eamhq7TW5urtxut66//vp6X9vhcCg8PNxnAwAAV4%2BADlgnTpxQSUmJSkpKJP1wY3tJSYkOHDigoKAgZWRkaM6cOVqxYoVKS0uVmpqqli1bauzYsZKkzp07a8iQIUpLS1NhYaEKCwuVlpam5ORkderUSdIPyzg4HA6lpqaqtLRUK1as0Jw5c/gEIQAAuKCAXqZh69at%2BuUvf2k/zszMlCRNmDBBS5cu1aOPPqpTp05p8uTJqqysVO/evZWbm6tWrVrZz1m%2BfLnS09PtTxuOGDHCZ10tp9OpvLw8TZkyRQkJCYqIiFBmZqb9WgAAAD8VZLEk%2BWVXVVUlp9Mpr9dr/HJhwqwco/3h/9v67BB/lwAA%2BBku59/fSwnoS4QAAABXIgIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCygFxoFLqdAW2OMdbsA4MrBDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMBYaBZqIQFoYlUVRATR1zGABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYXyKEECjC6RPPEp86hHAP44ZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABjGTe4AcAnclA/gH8UMFgAAgGEELAAAAMO4RAgATUwgXdLkciaaKmawAAAADCNgAQAAGMYlQgCA3wTS5UyJS5poOAIWAAANRCBEQ3GJEAAAwDBmsAAAaKICacatqc22MYPVQK%2B%2B%2BqpiY2PVvHlzxcfHa/369f4uCQAAXKEIWA3w/vvvKyMjQ7NmzVJxcbHuuOMODR06VAcOHPB3aQAA4ApEwGqArKwsPfjgg/rd736nzp07a8GCBYqJidGiRYv8XRoAALgCcQ/WJdTU1KioqEiPP/64z/6kpCRt3Lix3udUV1erurrafuz1eiVJVVVVxus7W33SeJ8AADS2y/E38nyflmUZ7/tSCFiXcPToUZ09e1ZRUVE%2B%2B6OiouTxeOp9zty5c/XUU0/V2R8TE3NZagQAINA5X7h8fR8/flxOp/PyvUA9CFgNFBQU5PPYsqw6%2B86bOXOmMjMz7cfnzp3T3/72N7Vp0%2BaCz/k5qqqqFBMTo7KyMoWHhxvv/2rDeJrHmJrHmJrHmJrn7zG1LEvHjx%2BX2%2B1u9NcmYF1C27Zt1axZszqzVRUVFXVmtc5zOBxyOBw%2B%2B6699trLVuN54eHh/KNgEONpHmNqHmNqHmNqnj/HtLFnrs7jJvdLCA0NVXx8vPLy8nz25%2BXlqW/fvn6qCgAAXMmYwWqAzMxMpaSkKCEhQYmJiXrttdd04MABTZo0yd%2BlAQCAK1Cz2bNnz/Z3EVe6uLg4tWnTRnPmzNH8%2BfN16tQpvf322%2Brevbu/S7M1a9ZMAwYMUHAwmdkExtM8xtQ8xtQ8xtS8q3VMgyx/fHYRAACgCeMeLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwApwr776qmJjY9W8eXPFx8dr/fr1/i4pYMyePVtBQUE%2Bm8vlso9blqXZs2fL7XarRYsWGjBggHbs2OHHiq8869at0/Dhw%2BV2uxUUFKSPP/7Y53hDxrCyslIpKSlyOp1yOp1KSUnR999/35incUW51JimpqbWed/26dPHp011dbWmTZumtm3bKiwsTCNGjNDBgwcb8zSuGHPnzlWvXr3UqlUrRUZGauTIkdq1a5dPm4aM14EDBzR8%2BHCFhYWpbdu2Sk9PV01NTWOeyhWjIWM6YMCAOu/T%2B%2B%2B/36dNU//dJ2AFsPfff18ZGRmaNWuWiouLdccdd2jo0KE6cOCAv0sLGF27dlV5ebm9bd%2B%2B3T42b948ZWVlaeHChdqyZYtcLpcGDRqk48eP%2B7HiK8vJkyfVvXt3LVy4sN7jDRnDsWPHqqSkRDk5OcrJyVFJSYlSUlIa6xSuOJcaU0kaMmSIz/t29erVPsczMjK0YsUKZWdna8OGDTpx4oSSk5N19uzZy13%2BFSc/P19TpkxRYWGh8vLydObMGSUlJenkyZN2m0uN19mzZzVs2DCdPHlSGzZsUHZ2tj788ENNnz7dX6flVw0ZU0lKS0vzeZ/%2B6U9/8jne5H/3LQSsf/3Xf7UmTZrks%2B%2BWW26xHn/8cT9VFFiefPJJq3v37vUeO3funOVyuaznnnvO3nf69GnL6XRaixcvbqwSA4oka8WKFfbjhozh119/bUmyCgsL7TYFBQWWJOv//u//Gq/4K9RPx9SyLGvChAnWr371qws%2B5/vvv7dCQkKs7Oxse993331nXXPNNVZOTs5lqzVQVFRUWJKs/Px8y7IaNl6rV6%2B2rrnmGuu7776z27z33nuWw%2BGwvF5v457AFeinY2pZltW/f3/r4YcfvuBzrobffWawAlRNTY2KioqUlJTksz8pKUkbN270U1WBZ/fu3XK73YqNjdX999%2BvPXv2SJL27t0rj8fjM74Oh0P9%2B/dnfBuoIWNYUFAgp9Op3r1722369Okjp9PJOF/E2rVrFRkZqZtvvllpaWmqqKiwjxUVFam2ttZn3N1ut%2BLi4hhTSV6vV5LUunVrSQ0br4KCAsXFxfl8YfDgwYNVXV2toqKiRqz%2ByvTTMT1v%2BfLlatu2rbp27aoZM2b4zFxfDb/7V9eyqk3I0aNHdfbs2TpfOB0VFVXni6lRv969e%2Butt97SzTffrMOHD%2Bu//uu/1LdvX%2B3YscMew/rGd//%2B/f4oN%2BA0ZAw9Ho8iIyPrPDcyMpL38QUMHTpUv/71r9WxY0ft3btXv//973XXXXepqKhIDodDHo9HoaGhioiI8Hke/zb8cE9gZmambr/9dsXFxUlSg8bL4/HUeR9HREQoNDSUMa1nTCVp3Lhxio2NlcvlUmlpqWbOnKkvv/zS/l7fq%2BF3n4AV4IKCgnweW5ZVZx/qN3ToUPvnbt26KTExUTfeeKOWLVtm3zTM%2BP58lxrD%2BsaTcb6wMWPG2D/HxcUpISFBHTt21KpVqzRq1KgLPo8xlaZOnaqvvvpKGzZsuGRb3qcNc6ExTUtLs3%2BOi4vTTTfdpISEBG3btk09e/aU1PTHlEuEAapt27Zq1qxZnaRfUVFR5/%2B00DBhYWHq1q2bdu/ebX%2BakPH95zVkDF0ulw4fPlznuUeOHGGcGyg6OlodO3bU7t27Jf0wpjU1NaqsrPRpd7W/d6dNm6aVK1fqiy%2B%2BUIcOHez9DRkvl8tV531cWVmp2tpaxrSeMa1Pz549FRIS4vM%2Bbeq/%2BwSsABUaGqr4%2BHh7uvW8vLw89e3b109VBbbq6mrt3LlT0dHR9tT2j8e3pqZG%2Bfn5jG8DNWQMExMT5fV6tXnzZrvNpk2b5PV6GecGOnbsmMrKyhQdHS1Jio%2BPV0hIiM%2B4l5eXq7S09KocU8uyNHXqVH300Uf6y1/%2BotjYWJ/jDRmvxMRElZaWqry83G6Tm5srh8Oh%2BPj4xjmRK8ilxrQ%2BO3bsUG1trf0%2BvSp%2B9/1zbz1MyM7OtkJCQqzXX3/d%2Bvrrr62MjAwrLCzM2rdvn79LCwjTp0%2B31q5da%2B3Zs8cqLCy0kpOTrVatWtnj99xzz1lOp9P66KOPrO3bt1u/%2Bc1vrOjoaKuqqsrPlV85jh8/bhUXF1vFxcWWJCsrK8sqLi629u/fb1lWw8ZwyJAh1q233moVFBRYBQUFVrdu3azk5GR/nZLfXWxMjx8/bk2fPt3auHGjtXfvXuuLL76wEhMTrfbt2/uM6aRJk6wOHTpYn3/%2BubVt2zbrrrvusrp3726dOXPGj2fmHw899JDldDqttWvXWuXl5fb297//3W5zqfE6c%2BaMFRcXZ919993Wtm3brM8//9zq0KGDNXXqVH%2Bdll9dakz/%2Bte/Wk899ZS1ZcsWa%2B/evdaqVausW265xerRo4fPe7Cp/%2B4TsALcK6%2B8YnXs2NEKDQ21evbs6fMxWVzcmDFjrOjoaCskJMRyu93WqFGjrB07dtjHz507Zz355JOWy%2BWyHA6Hdeedd1rbt2/3Y8VXni%2B%2B%2BMKSVGebMGGCZVkNG8Njx45Z48aNs1q1amW1atXKGjdunFVZWemHs7kyXGxM//73v1tJSUlWu3btrJCQEOu6666zJkyYYB04cMCnj1OnTllTp061WrdubbVo0cJKTk6u0%2BZqUd9YSrLefPNNu01Dxmv//v3WsGHDrBYtWlitW7e2pk6dap0%2BfbqRz%2BbKcKkxPXDggHXnnXdarVu3tkJDQ60bb7zRSk9Pt44dO%2BbTT1P/3Q%2ByLMtqvPkyAACApo97sAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AHe7qhLKBX1KAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-2621568212912192917\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">5556</td>\n",
       "        <td class=\"number\">56.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.0</td>\n",
       "        <td class=\"number\">41</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.0</td>\n",
       "        <td class=\"number\">40</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11.0</td>\n",
       "        <td class=\"number\">38</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14.0</td>\n",
       "        <td class=\"number\">34</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15.0</td>\n",
       "        <td class=\"number\">30</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9.0</td>\n",
       "        <td class=\"number\">28</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (1839)</td>\n",
       "        <td class=\"number\">4018</td>\n",
       "        <td class=\"number\">40.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:72%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-2621568212912192917\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">5556</td>\n",
       "        <td class=\"number\">56.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.333333333</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3.0</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">255.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">255.5166667</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">255.75</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">255.8666667</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">256.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Informational_Duration\">Informational_Duration<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>272</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>2.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>2.5831</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>-1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>70</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>91.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram4779681948868304302\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABrUlEQVR4nO3XMcoTQRjH4XfD1%2B6mD9lDCF5B8EB2H9hZehR7W3tvkZAD7GJhYdZCYxP8w4cym4XnKXdg3lmYXzHdsixLNXY%2Bn2scx9Zj2bjT6VTH47HpzKem037r%2B76qfv3wMAxrHIENmaapxnH8c29aWiWQruuqqmoYhrtAXj9/fvF%2BXz%2B8/S/n4rHd7k1Lu%2BYTYUMEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKB4GmNocuyVFXVNE13az%2B%2Bf3vxfq/effrnM/EYvrx/c/ftdk9u96alVQKZ57mqqsZxXGM8D2z/8e9r8zzXfr9vd5iq6pYVsrxer3W5XKrv%2B%2Bq6rvV4NmZZlprnuQ6HQ%2B12bV8FqwQCW%2BGRDoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUDwE4BoQHH8%2BzTyAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives4779681948868304302,#minihistogram4779681948868304302\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives4779681948868304302\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles4779681948868304302\"\n",
       "                                                  aria-controls=\"quantiles4779681948868304302\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram4779681948868304302\" aria-controls=\"histogram4779681948868304302\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common4779681948868304302\" aria-controls=\"common4779681948868304302\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme4779681948868304302\" aria-controls=\"extreme4779681948868304302\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles4779681948868304302\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>-1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>21.212</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>70</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>71</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>10.149</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>3.929</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>19.998</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>2.5831</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>4.7291</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>4.4451</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>25614</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>103</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>77.5 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram4779681948868304302\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nO3df1RVdb7/8RcKHH%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%2BM7yZw9xugQAABoEK1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhjXagHXp0iX97//%2BryIjI9WyZUt16NBBL774oi5fvmzPsSxLs2bNUnh4uFq2bKkBAwZo7969XvspKytTQkKC3G633G63EhISdPbsWa85u3fvVv/%2B/dWyZUvdfPPNevHFF2VZVoP0CQAAGp9GG7B%2B97vfafHixcrIyND%2B/fs1d%2B5cvfzyy3rttdfsOXPnztX8%2BfOVkZGh7du3y%2BPxaNCgQaqsrLTnjB49Wjt37tT69eu1fv167dy5UwkJCfb2iooKDRo0SOHh4dq%2Bfbtee%2B01zZs3T/Pnz2/QfgEAQOPh73QB39e2bdv005/%2BVEOHDpUk3XbbbXr33XeVn58v6crq1YIFCzRz5kyNHDlSkrR8%2BXKFhoZq1apVGj9%2BvPbv36/169crLy9PvXr1kiQtXbpUsbGxOnDggDp16qSVK1fq4sWLeuutt%2BRyuRQVFaWDBw9q/vz5Sk1NlZ%2BfnzMvAAAA%2BNFqtCtYffv21SeffKKDBw9Kkr788ktt2bJFDz74oCTpyJEjKikpUXx8vP0Yl8ul/v37a%2BvWrZKuhDS3222HK0nq3bu33G6315z%2B/fvL5XLZcwYPHqxTp07p6NGj16ytqqpKFRUVXjcAANB0NNoVrKlTp6q8vFydO3dW8%2BbNVVtbq9mzZ%2BsXv/iFJKmkpESSFBoa6vW40NBQHTt2zJ4TEhJSZ98hISH240tKSnTbbbfV2cfVbZGRkXUen56erhdeeOGHNQgAABqtRruCtXr1aq1YsUKrVq3Sjh07tHz5cs2bN0/Lly/3mvfNQ3iWZXmNXesQ3/XmXD3B/dsOD06fPl3l5eX2raio6N9rDgAANGqNdgXrN7/5jaZNm6ZHH31UktStWzcdO3ZM6enpGjt2rDwej6Qrq0xhYWH240pLS%2B0VKI/Ho6%2B%2B%2BqrOvk%2BfPu015%2Bpq1tf3IdVdHbvK5XJ5HVIEAABNS6NdwfrnP/%2BpZs28y2/evLl9mYbIyEh5PB7l5OTY26urq5Wbm6s%2BffpIkmJjY1VeXq4vvvjCnvP555%2BrvLzca86mTZtUXV1tz8nOzlZ4eHidQ4cAAABSIw5YDz30kGbPnq0PP/xQR48eVVZWlubPn6%2Bf/exnkq4cvktJSVFaWpqysrK0Z88eJSYmqlWrVho9erQkqUuXLhoyZIjGjRunvLw85eXlady4cRo2bJg6deok6cplHFwulxITE7Vnzx5lZWUpLS2NTxACAIBv1WgPEb722mv67W9/q6eeekqlpaUKDw/X%2BPHj9dxzz9lznn32WV24cEFPPfWUysrK1KtXL2VnZ6tNmzb2nJUrV2rSpEn2pw2HDx%2BujIwMe7vb7VZOTo6Sk5MVExOj4OBgpaamKjU1teGaBQAAjYqfxSXJ611FRYXcbrfKy8sVFBRkdN8xM9cb3V99yp89xOkSAABNSH3%2B/r2eRnuIEAAA4MeKgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMa9QB6%2BTJkxozZozatm2rVq1a6e6771ZBQYG93bIszZo1S%2BHh4WrZsqUGDBigvXv3eu2jrKxMCQkJcrvdcrvdSkhI0NmzZ73m7N69W/3791fLli11880368UXX5RlWQ3SIwAAaHwabcAqKytTXFycAgIC9PHHH2vfvn165ZVXdMMNN9hz5s6dq/nz5ysjI0Pbt2%2BXx%2BPRoEGDVFlZac8ZPXq0du7cqfXr12v9%2BvXauXOnEhIS7O0VFRUaNGiQwsPDtX37dr322muaN2%2Be5s%2Bf36D9AgCAxsPf6QK%2Br9/97neKiIjQm2%2B%2BaY/ddttt9p8ty9KCBQs0c%2BZMjRw5UpK0fPlyhYaGatWqVRo/frz279%2Bv9evXKy8vT7169ZIkLV26VLGxsTpw4IA6deqklStX6uLFi3rrrbfkcrkUFRWlgwcPav78%2BUpNTZWfn1%2BD9g0AAH78Gu0K1tq1axUTE6OHH35YISEh6tGjh5YuXWpvP3LkiEpKShQfH2%2BPuVwu9e/fX1u3bpUkbdu2TW632w5XktS7d2%2B53W6vOf3795fL5bLnDB48WKdOndLRo0fruUsAANAYNdqAdfjwYS1atEgdO3bUhg0blJSUpEmTJuntt9%2BWJJWUlEiSQkNDvR4XGhpqbyspKVFISEidfYeEhHjNudY%2Bvv4c31RVVaWKigqvGwAAaDoa7SHCy5cvKyYmRmlpaZKkHj16aO/evVq0aJF%2B%2Bctf2vO%2BeQjPsiyvsWsd4rvenKsnuH/b4cH09HS98MIL/2ZHAADAVzTaFaywsDB17drVa6xLly46fvy4JMnj8Uiqu8pUWlpqr0B5PB599dVXdfZ9%2BvRprznX2odUd3XsqunTp6u8vNy%2BFRUV/bvtAQCARqzRBqy4uDgdOHDAa%2BzgwYO69dZbJUmRkZHyeDzKycmxt1dXVys3N1d9%2BvSRJMXGxqq8vFxffPGFPefzzz9XeXm515xNmzapurranpOdna3w8HCvk%2Bq/zuVyKSgoyOsGAACajkYbsJ555hnl5eUpLS1Nf/3rX7Vq1SotWbJEycnJkq4cvktJSVFaWpqysrK0Z88eJSYmqlWrVho9erSkKyteQ4YM0bhx45SXl6e8vDyNGzdOw4YNU6dOnSRduYyDy%2BVSYmKi9uzZo6ysLKWlpfEJQgAA8K0a7TlYPXv2VFZWlqZPn64XX3xRkZGRWrBggR577DF7zrPPPqsLFy7oqaeeUllZmXr16qXs7Gy1adPGnrNy5UpNmjTJ/rTh8OHDlZGRYW93u93KyclRcnKyYmJiFBwcrNTUVKWmpjZcswAAoFHxs7gkeb2rqKiQ2%2B1WeXm58cOFMTPXG91ffcqfPcTpEgAATUh9/v69HkcOEa5YsUIXL1504qkBAADqnSMBKzU1VR6PR%2BPHj/c6wRwAAMAXOBKwTp06pTfeeEPFxcXq27ev7rzzTr3yyis6ffq0E%2BUAAAAY5UjA8vf318iRI7V27VodP35cY8eO1RtvvKH27dtr5MiR%2BvDDD8WpYQAAoLFy/DINHo9H9913nwYMGCA/Pz/l5%2Bdr9OjR6tixozZv3ux0eQAAAP82xwLWmTNntGDBAnXv3l1xcXEqLS3VBx98oGPHjunkyZMaNmyY11feAAAANBaOXAfrZz/7mT766CNFRkbqV7/6lcaOHaubbrrJ3v6Tn/xEzz77rBYuXOhEeQAAAD%2BIIwErKChIGzduVL9%2B/b51TlhYmA4dOtSAVQEAAJjhSMBavnz5def4%2Bfnp9ttvb4BqAAAAzHLkHKxnnnnG6%2BtorvrDH/6gyZMnO1ARAACAOY4ErD//%2Bc/q3bt3nfHY2FitXr3agYoAAADMcSRgnTlzRsHBwXXGg4KCdObMGQcqAgAAMMeRgHX77bdrw4YNdcY3bNigyMhIByoCAAAwx5GT3FNSUpSSkqK///3vGjhwoCTpk08%2B0dy5czVv3jwnSgIAADDGkYA1btw4Xbx4UWlpaXr%2B%2BeclSe3bt9fChQv1xBNPOFESAACAMY4ELEmaOHGiJk6cqOLiYrVs2VI33HCDU6UAAAAY5VjAuiosLMzpEgAAAIxy5CT306dP6/HHH9ctt9yiFi1aKDAw0OsGAADQmDmygpWYmKi//e1v%2Bs1vfqOwsDD5%2Bfk5UQYAAEC9cCRgbdq0SZs2bVKPHj2ceHoAAIB65cghwvbt27NqBQAAfJYjAevVV1/V9OnTdeLECSeeHgAAoF45cogwISFBlZWVuvXWWxUUFKSAgACv7aWlpU6UBQAAYIQjAWvOnDlOPC0AAECDcCRgPfnkk048LQAAQINw5BwsSTp69KhmzZqlhIQE%2B5Bgdna29u/f71RJAAAARjgSsDZv3qw777xTubm5eu%2B993Tu3DlJ0o4dO/Tcc885URIAAIAxjgSsqVOnatasWfr000%2B9rtw%2BcOBA5eXlOVESAACAMY4ErF27dum///u/64yHhITo9OnTDlQEAABgjiMB64YbblBJSUmd8Z07d%2Brmm292oCIAAABzHAlYjz76qKZNm6bTp0/bV3T//PPPNWXKFI0ZM8aJkgAAAIxxJGClpaXJ4/EoLCxM586dU9euXdWnTx/17NlTv/3tb50oCQAAwBhHroMVGBio1atX6%2BDBg9qxY4cuX76se%2B65R507d3aiHAAAAKMcCVhX3XHHHbrjjjucLAEAAMA4RwLW//zP//zL7UuWLGmgSgAAAMxzJGAVFxd73a%2BpqdHevXtVWVmpe%2B%2B914mSAAAAjHEkYK1bt67O2KVLl/TrX/9aXbp0caAiAAAAcxz7LsJv8vf315QpU/Tyyy87XQoAAMAP8qMJWJJ0%2BPBh1dTUOF0GAADAD%2BLIIcJnn33W675lWSouLtbatWv12GOPOVESAACAMY4ErG3btnndb9asmW666SbNmTNH48aNc6IkAAAAYxwJWJs3b3biaQEAABrEj%2BocLAAAAF/gyApWz5497S95vp4vvviinqsBAAAwy5GA9V//9V/64x//qDvuuEOxsbGSpLy8PB04cEDjx4%2BXy%2BVyoiwAAAAjHAlYZ8%2BeVXJystLS0rzGZ86cqa%2B%2B%2Bkp/%2BtOfnCgLAADACEfOwXrvvff0%2BOOP1xlPTEzUn//8ZwcqAgAAMMeRgOVyubR169Y641u3buXwIAAAaPQcOUQ4adIkJSUlqbCwUL1795Z05RyspUuXasaMGU6UBAAAYIwjAWvmzJmKjIzU73//e73xxhuSpC5dumjp0qUaPXq0EyUBAAAY40jAkqTRo0cTpgAAgE9y7EKjFRUVeuutt/Tcc8%2BprKxMkvTll1%2BquLjYqZIAAACMcGQFa8%2BePbr//vvVqlUrFRUVKTExUcHBwXrvvfd04sQJLV%2B%2B3ImyAAAAjHBkBeuZZ57R6NGj9be//U0tWrSwx4cOHapNmzY5URIAAIAxjqxgbd%2B%2BXYsWLarzdTk333wzhwgBAECj58gKVmBgoM6dO1dn/NChQ2rXrp0DFQEAAJjjSMAaPny4XnrpJV26dEmS5Ofnp5MnT2ratGkaOXLk99pnenq6/Pz8lJKSYo9VVVVp4sSJateunVq3bq3hw4frxIkTXo87fvy4HnroIbVu3Vrt2rXTpEmTVF1d7TUnNzdX0dHRatGihTp06KDFixd/rxoBAEDT4EjAeuWVV3Tq1Cl5PB5duHBBAwcOVIcOHdSiRYs630/4XWzfvl1LlizRXXfd5TWekpKirKwsZWZmasuWLTp37pyGDRum2tpaSVJtba2GDh2q8%2BfPa8uWLcrMzNSaNWs0efJkex9HjhzRgw8%2BqH79%2BqmwsFAzZszQpEmTtGbNmh/2IgAAAJ/lyDlYbrdbW7duVU5Ojnbs2KHLly/rnnvu0eDBg%2Bucl3U9586d02OPPaalS5fq//7v/%2Bzx8vJyLVu2TO%2B8847uv/9%2BSdKKFSsUERGhjRs3avDgwcrOzta%2BfftUVFSk8PBwSVfCX2JiombPnq2goCAtXrxYt9xyixYsWCDpygVR8/PzNW/ePP385z839IoAAABf0uArWDU1NRo0aJD%2B%2Bte/Kj4%2BXtOmTdOMGTM0ZMiQfztcSVJycrKGDh1qh6irCgoKVFNTo/j4eHssPDxcUVFR9vcgbtu2TVFRUXa4kqTBgwerqqpKBQUF9pyv7%2BPqnPz8fNXU1Pzb9QIAAN/X4CtYAQEBKiws/F5h6psyMzNVUFCg/Pz8OttKSkoUGBio4OBgr/HQ0FCVlJTYc0JDQ722BwcHKzAw8F/OCQ0N1aVLl3TmzBmFhYXVee6qqipVVVXZ9ysqKr5fgwAAoFFy5BysMWPG6M033/xB%2BygqKtLTTz%2BtlStXel1L63osy/IKd9cKetebY1nWtz5WunLCvdvttm8RERHfuT4AAND4OfZdhBkZGdq4caNiYmLUunVrr21z58697uMLCgpUWlqq6Ohoe6y2tlabNm1SRkaGNmzYoOrqapWVlXmtYpWWlqpPnz6SJI/Ho88//9xrv2VlZaqpqbFXrTwej72a9fV9%2BPv7q23bttesbfr06UpNTbXvV1RUELIAAGhCHAlYBQUF9if%2Bdu3a5bXtux46vO%2B%2B%2B7R7926vsccff1ydO3fW1KlTFRERoYCAAOXk5GjUqFGSpOLiYu3Zs8cOcLGxsZo9e7aKi4vtQ33Z2dlyuVx2cIuNjdW6deu8nic7O1sxMTEKCAi4Zm0ul0sul%2Bs79QEAAHxPgwasw4cPKzIyUps3b/7B%2B2rTpo2ioqK8xlq3bq22bdva408%2B%2BaQmT56stm3b6sYbb9SUKVPUrVs3%2B4T4%2BPh4de3aVQkJCXr55Zf1j3/8Q1OmTNG4ceMUFBQkSUpKSlJGRoZSU1M1btw4bdu2TcuWLdO77777g3sAAAC%2BqUHPwerYsaNOnz5t33/kkUf01Vdf1dvzvfrqqxoxYoRGjRqluLg4tWrVSuvWrVPz5s0lSc2bN9eHH36oFi1aKC4uTqNGjdKIESM0b948ex%2BRkZH66KOP9Nlnn%2Bnuu%2B/WSy%2B9pIULF3KJBgAA8K38rKtnbDeAZs2aqaSkRCEhIZKurEJ9%2BeWX6tChQ0OV4IiKigq53W6Vl5fbK2OmxMxcb3R/9Sl/9hCnSwAANCH1%2Bfv3ehz5FCEAAIAva9CA5efnV%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%2BebO/jyJEjevDBB9WvXz8VFhZqxowZmjRpktasWdPgPQMAgMbBz7Isy%2BkiTDh9%2BrRCQkKUm5ure%2B%2B9V%2BXl5brpppv0zjvv6JFHHpEknTp1ShEREfroo480ePBgffzxxxo2bJiKiooUHh4uScrMzFRiYqJKS0sVFBSkqVOnau3atdq/f7/9XElJSfryyy%2B1bdu271RbRUWF3G63ysvLFRQUZLTvmJnrje6vPuXPHuJ0CQCAJqQ%2Bf/9eT6Ndwfqm8vJySdKNN94oSSooKFBNTY3i4%2BPtOeHh4YqKitLWrVslSdu2bVNUVJQdriRp8ODBqqqqUkFBgT3n6/u4Oic/P181NTXXrKWqqkoVFRVeNwAA0HT4RMCyLEupqanq27evoqKiJEklJSUKDAxUcHCw19zQ0FCVlJTYc0JDQ722BwcHKzAw8F/OCQ0N1aVLl3TmzJlr1pOeni63223fIiIijPQJAAAaB58IWBMmTNCuXbv07rvvXneuZVny8/Oz73/9z991ztWjqtd6rCRNnz5d5eXl9q2oqOg79QEAAHxDow9YEydO1Nq1a/Xpp5%2Bqffv29rjH41F1dbXKysq85peWltorUh6Px16puqqsrEw1NTX/ck5paan8/f3Vtm3ba9bkcrkUFBTkdQMAAE1How1YlmVpwoQJev/99/WXv/xFkZGRXtujo6MVEBCgnJwce6y4uFh79uxRnz59JEmxsbHas2ePiouL7TnZ2dlyuVyKjo6253x9H1fnxMTEKCAgoL7aAwAAjVijDVjJyclasWKFVq1apTZt2qikpEQlJSW6cOGCJMntduvJJ5/U5MmT9cknn6iwsFBjxoxRt27ddP/990uS4uPj1bVrVyUkJKiwsFCffPKJpkyZonHjxtmrTklJSTp27JhSU1O1f/9%2BvfHGG1q2bJmmTJniWO8AAODHrdEGrEWLFqm8vFwDBgxQWFiYfVu9erU959VXX9WIESM0atQoxcXFqVWrVlq3bp2aN28uSWrevLk%2B/PBDtWjRQnFxcRo1apRGjBihefPm2fuIjIzURx99pM8%2B%2B0x33323XnrpJS1cuFA///nPG7xnAADQOPjMdbB%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%2BruLhYd955pxYsWKB%2B/fo5XRbqEYEQAPB9EbC%2Bg9WrVyslJUWvv/664uLi9Mc//lEPPPCA9u3bp1tuucXp8gBJjS8Qov4QtgHn%2BVmWZTldxI9dr169dM8992jRokX2WJcuXTRixAilp6df9/EVFRVyu90qLy9XUFCQ0dr4pQoA8AX18R%2BD%2Bvz9ez2sYF1HdXW1CgoKNG3aNK/x%2BPh4bd269ZqPqaqqUlVVlX2/vLxc0pW/aNNqq84b3ycAAA2tPn5HXt2nE2tJBKzrOHPmjGpraxUaGuo1HhoaqpKSkms%2BJj09XS%2B88EKd8YiIiHqpEQCAxs79Sv3tu7KyUm63u/6e4BoIWN%2BRn5%2Bf133LsuqMXTV9%2BnSlpqba9y9fvqx//OMfatu27bc%2BxqSKigpFRESoqKiowZdEndIUe5aaZt9NsWepafbdFHuWmmbf9dWzZVmqrKxUeHi4sX1%2BVwSs62jXrp2aN29eZ7WqtLS0zqrWVS6XSy6Xy2vshhtuqLcav01QUFCTeXNe1RR7lppm302xZ6lp9t0Ue5aaZt/10XNDr%2B2pZ9IAAAjZSURBVFxdxYVGryMwMFDR0dHKycnxGs/JyVGfPn0cqgoAAPyYsYL1HaSmpiohIUExMTGKjY3VkiVLdPz4cSUlJTldGgAA%2BBFqPmvWrFlOF/FjFxUVpbZt2yotLU3z5s3ThQsX9M4776h79%2B5Ol/atmjdvrgEDBsjfv%2Blk6KbYs9Q0%2B26KPUtNs%2B%2Bm2LPUNPv2tZ65DhYAAIBhnIMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYPub1119XZGSkWrRooejoaG3evNnpkozatGmTHnroIYWHh8vPz08ffPCB13bLsjRr1iyFh4erZcuWGjBggPbu3etQtWakp6erZ8%2BeatOmjUJCQjRixAgdOHDAa05VVZUmTpyodu3aqXXr1ho%2BfLhOnDjhUMVmLFq0SHfddZd94cHY2Fh9/PHH9nZf7Pmb0tPT5efnp5SUFHvMF/ueNWuW/Pz8vG4ej8fe7ovva0k6efKkxowZo7Zt26pVq1a6%2B%2B67VVBQYG/3xb5vu%2B22On/Xfn5%2BSk5OluRbP98ELB%2ByevVqpaSkaObMmSosLFS/fv30wAMP6Pjx406XZsz58%2BfVvXt3ZWRkXHP73LlzNX/%2BfGVkZGj79u3yeDwaNGiQKisrG7hSc3Jzc5WcnKy8vDzl5OTo0qVLio%2BP1/nz//%2BLvlNSUpSVlaXMzExt2bJF586d07Bhw1RbW%2Btg5T9M%2B/btNWfOHOXn5ys/P18DBw7UT3/6U/sXjC/2/HXbt2/XkiVLdNddd3mN%2B2rfd955p4qLi%2B3b7t277W2%2B%2BL4uKytTXFycAgIC9PHHH2vfvn165ZVXvL71wxf73r59u9ff89WLeD/88MOSfOzn24LP%2BM///E8rKSnJa6xz587WtGnTHKqofkmysrKy7PuXL1%2B2PB6PNWfOHHvs4sWLltvtthYvXuxEifWitLTUkmTl5uZalmVZZ8%2BetQICAqzMzEx7zsmTJ61mzZpZ69evd6rMehEcHGz96U9/8vmeKysrrY4dO1o5OTlW//79raefftqyLN/9u37%2B%2Beet7t27X3Obr76vp06davXt2/dbt/tq39/09NNPW7fffrt1%2BfJln/v5ZgXLR1RXV6ugoEDx8fFe4/Hx8dq6datDVTWsI0eOqKSkxOs1cLlc6t%2B/v0%2B9BuXl5ZKkG2%2B8UZJUUFCgmpoar77Dw8MVFRXlM33X1tYqMzNT58%2BfV2xsrM/3nJycrKFDh%2Br%2B%2B%2B/3Gvflvg8dOqTw8HBFRkbq0Ucf1eHDhyX57vt67dq1iomJ0cMPP6yQkBD16NFDS5cutbf7at9fV11drRUrVuiJJ56Qn5%2Bfz/18E7B8xJkzZ1RbW1vnC6hDQ0PrfFG1r7rapy%2B/BpZlKTU1VX379lVUVJSkK30HBgYqODjYa64v9L1792795Cc/kcvlUlJSkrKystS1a1ef7jkzM1MFBQVKT0%2Bvs81X%2B%2B7Vq5fefvttbdiwQUuXLlVJSYn69Omjv//97z77vj58%2BLAWLVqkjh07asOGDUpKStKkSZP09ttvS2oa/5598MEHOnv2rBITEyX53s%2B3b1yPHjY/Pz%2Bv%2B5Zl1Rnzdb78GkyYMEG7du3Sli1brjvXF/ru1KmTdu7cqbNnz2rNmjUaO3ascnNzv3V%2BY%2B%2B5qKhITz/9tLKzs9WiRYvv/LjG3vcDDzxg/7lbt26KjY3V7bffruXLl6t3796SfO99ffnyZcXExCgtLU2S1KNHD%2B3du1eLFi3SL3/5S3uer/X9dcuWLdMDDzyg8PDwfzmvsfbMCpaPaNeunZo3b14n5ZeWltb5H5CvuvqpI199DSZOnKi1a9fq008/Vfv27e1xj8ej6upqlZWVec33hb4DAwP1H//xH4qJiVF6erq6d%2B%2Bu3//%2B9z7bc0FBgUpLSxUdHS1/f3/5%2B/srNzdXCxculL%2B/v0JDQ32y729q3bq1unXrpkOHDvns%2BzosLExdu3b1GuvSpYv9oSRf7fuqY8eOaePGjfrVr35lj/na%2B5qA5SMCAwMVHR1tfyLjqpycHPXp08ehqhpWZGSkPB6P12tQXV2t3NzcRv0aWJalCRMm6P3339df/vIXRUZGem2Pjo5WQECAV9/FxcXas2dPo%2B77WizLUlVVlc/2fN9992n37t3auXOnfYuJidFjjz1m/9kX%2B/6mqqoq7d%2B/X2FhYT77vo6Li6tzuZWDBw/q1ltvleS7/55d9eabbyokJERDhw61x3zufe3QyfWoB5mZmVZAQIC1bNkya9%2B%2BfVZKSorVunVr6%2BjRo06XZkxlZaVVWFhoFRYWWpKs%2BfPnW4WFhdaxY8csy7KsOXPmWG6323r//fet3bt3W7/4xS%2BssLAwq6KiwuHKv79f//rXltvttj777DOruLjYvv3zn/%2B05yQlJVnt27e3Nm7caO3YscMaOHCg1b17d%2BvSpUsOVv7DTJ8%2B3dq0aZN15MgRa9euXdaMGTOsZs2aWdnZ2ZZl%2BWbP1/L1TxFalm/2PXnyZOuzzz6zDh8%2BbOXl5VnDhg2z2rRpY//b5Yvv6y%2B%2B%2BMLy9/e3Zs%2BebR06dMhauXKl1apVK2vFihX2HF/s27Isq7a21rrlllusqVOn1tnmSz/fBCwf84c//MG69dZbrcDAQOuee%2B6xP8rvKz799FNLUp3b2LFjLcu68tHm559/3vJ4PJbL5bLuvfdea/fu3c4W/QNdq19J1ptvvmnPuXDhgjVhwgTrxhtvtFq2bGkNGzbMOn78uHNFG/DEE0/YP8s33XSTdd9999nhyrJ8s%2Bdr%2BWbA8sW%2BH3nkESssLMwKCAiwwsPDrZEjR1p79%2B61t/vi%2B9qyLGvdunVWVFSU5XK5rM6dO1tLlizx2u6rfW/YsMGSZB04cKDONl/6%2BfazLMtyZOkMAADAR3EOFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4fYH5IoB%2BbdPwAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common4779681948868304302\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">9028</td>\n",
       "        <td class=\"number\">91.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.0</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9.0</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.0</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10.0</td>\n",
       "        <td class=\"number\">19</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.0</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">12.0</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11.0</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (262)</td>\n",
       "        <td class=\"number\">706</td>\n",
       "        <td class=\"number\">7.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme4779681948868304302\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">9028</td>\n",
       "        <td class=\"number\">91.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">10</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">69.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">69.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">69.5</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">69.6</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ProductRelated_Duration\">ProductRelated_Duration<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7293</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>73.5%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>719.55</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>-1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>3564.6</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>7.2%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram4016025667388981877\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAB30lEQVR4nO3cMa4SURiG4X%2BI7UBPmEWYuAUTF2R3EztLl2Jva%2B8uICyAiYWFjIViQ/wi5DLDcJ%2BnZALnkJw3hzkk0wzDMNTIdrtddV039rDM3Ha7rc1mM%2BqYr0Yd7Y%2B2bavq9xdeLpdTTIEZORwO1XXd33UzpkkCaZqmqqqWy%2BVZIG%2Bevlz8ed8%2BvnuWeXHfTutmTIvRR4QZEQgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEkzx69Ll5XCm3YgeBQCAQCAQCgUAgEAgEAsFDHPNew9Ew/8MOAoFAIBAIBC/2HuQal963uGeZPzsIBAKBwE%2BsG3KUPH92EAjsIHfGrnNfBPIAnK7djkBeoGt2qWs8QoiTBDIMQ1VVHQ6Hs2s/f3wfezrcyOv3ny9%2Bz9cPb89eO62T07oZ0ySB9H1fVVVd100xPHds9enf1/q%2Br9VqNd5kqqoZJsjyeDzWfr%2Bvtm2raZqxh2dmhmGovu9rvV7XYjHuweskgcBc%2BB8EAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeAX0lZVf/jvt%2BQAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives4016025667388981877,#minihistogram4016025667388981877\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives4016025667388981877\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles4016025667388981877\"\n",
       "                                                  aria-controls=\"quantiles4016025667388981877\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram4016025667388981877\" aria-controls=\"histogram4016025667388981877\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common4016025667388981877\" aria-controls=\"common4016025667388981877\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme4016025667388981877\" aria-controls=\"extreme4016025667388981877\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles4016025667388981877\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>-1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>136.15</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>447.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>1056.3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>2428.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>3564.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>3565.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>920.12</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>770.93</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.0714</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>1.6104</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>719.55</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>598.63</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.4472</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>7135000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>594340</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>77.5 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram4016025667388981877\">\n",
       "            <img 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9/vf7yl780Wv6Xv/xFcXFxLlQEAABgjysXuc%2BYMUMzZszQV199paFDh0qS/vM//1NPP/20lixZ4kZJAAAA1rgSsNLS0nT69GktXLhQTz75pCSpW7duWrp0qX7xi1%2B4URIAAIA1rgQsSZo2bZqmTZum0tJStW/fXtdcc41bpQAAAFjlWsBq0KVLF7dLAAAAsMqVi9yPHj2qn//857r22mvVrl07hYaG%2Bj0AAAACmSszWJMnT9b//u//6l/%2B5V/UpUsXBQUFuVEGAABAs3AlYG3evFmbN2/Wrbfe6sbuAQAAmpUrpwi7devGrBUAAGi1XAlYzz33nObMmaNDhw65sXsAAIBm5copwtTUVFVXV6t79%2B6KiIhQSEiI3/ry8nI3ygIAALDClYC1aNEiN3YLAADQIlwJWFOmTHFjtwAAAC3ClWuwJOnzzz/X/PnzlZqa6pwSzM7O1ieffOJWSQAAAFa4ErC2bNmiW265Rbm5uXr33Xd14sQJSdKOHTv061//2o2SAAAArHElYD3xxBOaP3%2B%2BNm7c6Hfn9qFDh2rbtm1ulAQAAGCNKwFr9%2B7d%2BslPftJoeefOnXX06FEXKgIAALDHlYB1zTXXqKysrNHyoqIide3a1YWKAAAA7HElYD3wwAOaPXu2jh496tzR/aOPPtKsWbP0s5/9zI2SAAAArHElYC1cuFAxMTHq0qWLTpw4oV69emnAgAG67bbb9Ktf/cqNkgAAAKxx5T5YoaGheuedd/TXv/5VO3bs0NmzZ9WvXz/ddNNNbpQDAABglSsBq0HPnj3Vs2dPN0sAAACwzpWA9c///M/fuf6VV15poUoAAADscyVglZaW%2Bj2vq6vT3r17VV1drTvvvNONkgAAAKxxJWB98MEHjZadOXNGDz30kG6%2B%2BWYXKgIAALDHte8iPFdwcLBmzZqlxYsXu10KAADAJblsApYkffbZZ6qrq3O7DAAAgEviyinCxx9/3O%2B5MUalpaVas2aNJk6c6EZJAAAA1rgSsPLz8/2et2nTRp06ddKiRYuUlpbmRkkAAADWuBKwtmzZ4sZuAQAAWsRldQ0WAABAa%2BDKDNZtt93mfMnzhXz88cfNXA0AAIBdrgSsu%2B66S//6r/%2Bqnj17KikpSZK0bds27d%2B/Xw8%2B%2BKA8Ho8bZQEAAFjhSsA6fvy4HnnkES1cuNBv%2Bbx583TkyBH94Q9/cKMsAAAAK1y5Buvdd9/Vz3/%2B80bLJ0%2BerD//%2Bc8uVAQAAGCPKwHL4/EoLy%2Bv0fK8vDxODwIAgIDnyinC6dOnKz09XTt37lT//v0lfXMN1ooVKzR37lw3SgIAALDGlYA1b948xcXF6YUXXtCrr74qSbr55pu1YsUKTZgwwY2SAAAArHElYEnShAkTCFMAAKBVcu1Go1VVVXr99df161//WhUVFZKkXbt2qbS09KJen5mZqdtuu03h4eHq3Lmz7rvvPu3fv99vTE1NjaZNm6aoqCiFhYVpzJgxOnTokN%2BYgwcPavTo0QoLC1NUVJSmT5%2Bu2tpavzG5ublKSEhQu3bt1KNHDy1fvvwSOgcAAK2dKwGruLhYPXv21FNPPaXMzEwnYL377ruaPXv2RW0jNzdXjzzyiLZt26acnBydOXNGycnJOnnypDNmxowZWr16tbKysrR161adOHFCo0aNUn19vSSpvr5eI0eO1MmTJ7V161ZlZWVp1apVmjlzprONAwcO6N5779WgQYO0c%2BdOzZ07V9OnT9eqVassviMAAKA1CTLGmJbe6T333KPevXvrmWeeUUREhHbt2qUePXooLy9PEydO1IEDB773No8eParOnTsrNzdXd955pyorK9WpUye9%2BeabGj9%2BvCTp8OHDio2N1bp165SSkqIPP/xQo0aNUklJiXw%2BnyQpKytLkydPVnl5uSIiIvTEE09ozZo1%2BuSTT5x9paena9euXY2%2BtPp8qqqq5PV6VVlZqYiIiO/d23dJnLfe6vaaU8GC4W6XAAC4gjTn398LcWUGa/v27Xr44YcbfV1O165dL/oU4bkqKyslSR06dJAkFRYWqq6uTsnJyc4Yn8%2Bn%2BPh45xYR%2Bfn5io%2BPd8KVJKWkpKimpkaFhYXOmG9vo2FMQUGB6urqflCtAACgdXPlIvfQ0FCdOHGi0fJPP/1UUVFR33t7xhhlZGTojjvuUHx8vCSprKxMoaGhioyM9BsbHR2tsrIyZ0x0dLTf%2BsjISIWGhn7nmOjoaJ05c0bHjh1Tly5dGtVTU1Ojmpoa53lVVdX37gkAAAQuV2awxowZo9/%2B9rc6c%2BaMJCkoKEhffvmlZs%2BerbFjx37v7U2dOlW7d%2B/W22%2B/fcGxxhi/mbOmvnT6QmMazqqe7wurMzMz5fV6nUdsbOxF9QEAAFoHVwLWM888o8OHDysmJkanTp3S0KFD1aNHD7Vr167R9xNeyLRp07RmzRpt3LhR3bp1c5bHxMSotrbWuYC%2BQXl5uTMjFRMT48xUNaioqFBdXd13jikvL1dwcLA6duzYZE1z5sxRZWWl8ygpKflePQEAgMDmyilCr9ervLw85eTkaMeOHTp79qz69eunlJSU884KncsYo2nTpmn16tXatGmT4uLi/NYnJCQoJCREOTk5GjdunCSptLRUxcXFevrppyVJSUlJWrBggUpLS51TfdnZ2fJ4PEpISHDGfPDBB37bzs7OVmJiokJCQpqszePx8JU/AABcwVo8YNXV1enee%2B/VSy%2B9pOTk5EYXkF%2BsRx55RH/605/0H//xHwoPD3dmmbxer9q3by%2Bv16spU6Zo5syZ6tixozp06KBZs2apd%2B/euvvuuyVJycnJ6tWrl1JTU7V48WJ9/fXXmjVrltLS0pxPG6Snp2vZsmXKyMhQWlqa8vPztXLlyos6HQkAAK5MLX6KMCQkRDt37rzomarzefnll1VZWakhQ4aoS5cuzuOdd95xxjz33HO67777NG7cOA0cOFBXXXWVPvjgA7Vt21aS1LZtW61du1bt2rXTwIEDNW7cON13331asmSJs424uDitW7dOmzZt0t/93d/pt7/9rZYuXar777//kuoHAACtlyv3wZoxY4bCwsK0YMGClt61K7gP1je4DxYAoCW5eR8s176LcNmyZdqwYYMSExMVFhbmt67hGikAAIBA5ErAKiwsVJ8%2BfSRJu3fv9lt3qacOcfkKpNk2iRk3AMAP16IB67PPPlNcXJy2bNnSkrsFAABoUS16kfsNN9ygo0ePOs/Hjx%2BvI0eOtGQJAAAAza5FA9a519OvW7dOJ0%2BebMkSAAAAmp0rd3IHAABozVo0YAUFBTW6iJ2L2gEAQGvTohe5G2M0efJk52tkTp8%2BrfT09Ea3aXjvvfdasiwAAACrWjRgTZo0ye/5z372s5bcPQAAQIto0YD12muvteTuAAAAXMFF7gAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy4LdLgC4XCXOW%2B92Cd9LwYLhbpcAAPj/mMECAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALAvogLV582aNHj1aPp9PQUFBev/99/3WG2M0f/58%2BXw%2BtW/fXkOGDNHevXv9xlRUVCg1NVVer1der1epqak6fvy435g9e/Zo8ODBat%2B%2Bvbp27aqnnnpKxphm7w8AAASmgA5YJ0%2BeVN%2B%2BfbVs2bIm1z/99NN69tlntWzZMm3fvl0xMTG65557VF1d7YyZMGGCioqKtH79eq1fv15FRUVKTU111ldVVemee%2B6Rz%2BfT9u3b9fvf/15LlizRs88%2B2%2Bz9AQCAwBTQd3IfMWKERowY0eQ6Y4yef/55zZs3T2PHjpUkvfHGG4qOjtaf/vQnPfjgg/rkk0%2B0fv16bdu2TbfffrskacWKFUpKStL%2B/ft144036q233tLp06f1%2Buuvy%2BPxKD4%2BXn/961/17LPPKiMjQ0FBQS3WLwAACAwBPYP1XQ4cOKCysjIlJyc7yzwejwYPHqy8vDxJUn5%2BvrxerxOuJKl///7yer1%2BYwYPHiyPx%2BOMSUlJ0eHDh/X555%2B3TDMAACCgtNqAVVZWJkmKjo72Wx4dHe2sKysrU%2BfOnRu9tnPnzn5jmtrGt/dxrpqaGlVVVfk9AADAlaPVBqwG557CM8b4LWvqFN%2BFxjRc4H6%2B04OZmZnORfNer1exsbE/uH4AABB4Wm3AiomJkdR4lqm8vNyZgYqJidGRI0cavfbo0aN%2BY5rahtR4dqzBnDlzVFlZ6TxKSkourRkAABBQWm3AiouLU0xMjHJycpxltbW1ys3N1YABAyRJSUlJqqys1Mcff%2ByM%2Beijj1RZWek3ZvPmzaqtrXXGZGdny%2Bfz6brrrmty3x6PRxEREX4PAABw5QjogHXixAkVFRWpqKhI0jcXthcVFengwYMKCgrSjBkztHDhQq1evVrFxcWaPHmyrrrqKk2YMEGSdPPNN2v48OFKS0vTtm3btG3bNqWlpWnUqFG68cYbJX1zGwePx6PJkyeruLhYq1ev1sKFC/kEIQAAOK%2BAvk1DQUGB7rrrLud5RkaGJGnSpEl6/fXX9fjjj%2BvUqVN6%2BOGHVVFRodtvv13Z2dkKDw93XvPWW29p%2BvTpzqcNx4wZ43dfLa/Xq5ycHD3yyCNKTExUZGSkMjIynH0BAACcK8hwS/JmV1VVJa/Xq8rKSuunCxPnrbe6PQSuggXD3S4BAC4rzfn390IC%2BhQhAADA5YiABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwL6BuNAvg/gXRPNO7ZBaC1YwYLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlwW4XAODKkzhvvdslfC8FC4a7XQKAAMMMFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL%2BKocALgAvtoHwPfFDBYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGffBAoBWJpDu28U9u9BaMYMFAABgGQELAADAMgIWAACAZVyDBQBwTSBdLyZxzRguHjNYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyL3AEAuEhclI%2BLRcACAKCVCqRA2NrCIKcIAQAALCNgAQAAWEbAAgAAsIyAdZFeeuklxcXFqV27dkpISNCWLVvcLgkAAFymCFgX4Z133tGMGTM0b9487dy5U4MGDdKIESN08OBBt0sDAACXIQLWRXj22Wc1ZcoU/fKXv9TNN9%2Bs559/XrGxsXr55ZfdLg0AAFyGuE3DBdTW1qqwsFCzZ8/2W56cnKy8vLwmX1NTU6OamhrneWVlpSSpqqrKen31NSetbxMAgJbWHH8jG7ZpjLG%2B7QshYF3AsWPHVF9fr%2BjoaL/l0dHRKisra/I1mZmZ%2Bs1vftNoeWxsbLPUCABAoPM%2B03zbrq6ultfrbb4dNIGAdZGCgoL8nhtjGi1rMGfOHGVkZDjPz549q6%2B//lodO3Y872t%2BiKqqKsXGxqqkpEQRERHWtns5o%2Bcro2fpyuybnum5tXKrZ2OMqqur5fP5WmyfDQhYFxAVFaW2bds2mq0qLy9vNKvVwOPxyOPx%2BC275pprmq3GiIiIK%2BY/0gb0fOW4Evum5ysDPbeMlp65asBF7hcQGhqqhIQE5eTk%2BC3PycnRgAEDXKoKAABczpjBuggZGRlKTU1VYmKikpKS9Morr%2BjgwYNKT093uzQAAHAZajt//vz5bhdxuYuPj1fHjh21cOFCLVmyRKdOndKbb76pvn37ul2a2rZtqyFDhig4%2BMrJyvR85bgS%2B6bnKwM9t35Bxo3PLgIAALRiXIMFAABgGQELAADAMgIWAACAZQQsAAAAywhYAeqll15SXFyc2rVrpzwGKjMAAArdSURBVISEBG3ZssXtkn6w%2BfPnKygoyO8RExPjrDfGaP78%2BfL5fGrfvr2GDBmivXv3%2Bm2joqJCqamp8nq98nq9Sk1N1fHjx1u6lfPavHmzRo8eLZ/Pp6CgIL3//vt%2B6231uGfPHg0ePFjt27dX165d9dRTT7nyHVzShXuePHlyo%2BPev39/vzE1NTWaNm2aoqKiFBYWpjFjxujQoUN%2BYw4ePKjRo0crLCxMUVFRmj59umpra5u9v6ZkZmbqtttuU3h4uDp37qz77rtP%2B/fv9xtjq6fc3FwlJCSoXbt26tGjh5YvX97s/TXlYnoeMmRIo2P9wAMP%2BI0JtN/vl19%2BWX369HFunJmUlKQPP/zQWd/ajrN04Z5b43G%2BJAYBJysry4SEhJgVK1aYffv2mUcffdSEhYWZL774wu3SfpAnn3zS3HLLLaa0tNR5lJeXO%2BsXLVpkwsPDzapVq8yePXvM%2BPHjTZcuXUxVVZUzZvjw4SY%2BPt7k5eWZvLw8Ex8fb0aNGuVGO01at26dmTdvnlm1apWRZFavXu233kaPlZWVJjo62jzwwANmz549ZtWqVSY8PNwsWbKkxfr8tgv1PGnSJDN8%2BHC/4/7VV1/5jUlPTzddu3Y1OTk5ZseOHeauu%2B4yffv2NWfOnDHGGHPmzBkTHx9v7rrrLrNjxw6Tk5NjfD6fmTp1aov1%2BW0pKSnmtddeM8XFxaaoqMiMHDnSXHvttebEiRNWe/rss8/MVVddZR599FGzb98%2Bs2LFChMSEmL%2B/d///bLsefDgwSYtLc3vWB8/ftxvO4H2%2B71mzRqzdu1as3//frN//34zd%2B5cExISYoqLi40xre84X0zPrfE4XwoCVgD6%2B7//e5Oenu637KabbjKzZ892qaJL8%2BSTT5q%2Bffs2ue7s2bMmJibGLFq0yFl2%2BvRp4/V6zfLly40xxuzbt89IMtu2bXPG5OfnG0nmv//7v5u3%2BB/g3LBhq8eXXnrJeL1ec/r0aWdMZmam8fl85uzZs83d1nc6X8D68Y9/fN7XHD9%2B3ISEhJisrCxn2ZdffmnatGlj1q9fb4z5JsS1adPGfPnll86Yt99%2B23g8HlNZWWm5i%2B%2BvvLzcSDK5ubnGGHs9Pf744%2Bamm27y29eDDz5o%2Bvfv39wtXdC5PRvzzR/eRx999LyvCfTf7waRkZHmD3/4wxVxnBs09GzMlXOcLxanCANMbW2tCgsLlZyc7Lc8OTlZeXl5LlV16T799FP5fD7FxcXpgQce0GeffSZJOnDggMrKyvz69Xg8Gjx4sNNvfn6%2BvF6vbr/9dmdM//795fV6A%2BI9sdVjfn6%2BBg8e7Pc9mCkpKTp8%2BLA%2B//zzlmnme9q0aZM6d%2B6snj17Ki0tTeXl5c66wsJC1dXV%2Bb0vPp9P8fHxfj3Hx8f7fZFrSkqKampqVFhY2HKNnEdlZaUkqUOHDpLs9ZSfn9/o34CUlBQVFBSorq6uWXu6kHN7bvDWW28pKipKt9xyi2bNmqXq6mpnXaD/ftfX1ysrK0snT55UUlLSFXGcz%2B25QWs%2Bzt/XlXE71Vbk2LFjqq%2Bvb/RF09HR0Y2%2BkDpQ3H777fq3f/s39ezZU0eOHNHvfvc7DRgwQHv37nV6aqrfL774QpJUVlamzp07N9pu586dA%2BI9sdVjWVmZrrvuukbbaFgXFxdnu/RLMmLECP3jP/6junfvrgMHDuhXv/qVhg4dqsLCQnk8HpWVlSk0NFSRkZF%2Br/v273pZWVmj9y0yMlKhoaGuH3tjjDIyMnTHHXcoPj5ekqz11NSY6OhonTlzRseOHVOXLl2aq63v1FTPkjRx4kTFxcUpJiZGxcXFmjNnjnbt2uV8x2ug/n7v2bNHSUlJOn36tK6%2B%2BmqtXr1avXr1UlFRUas9zufrWWq9x/mHImAFqKCgIL/nxphGywLFiBEjnJ979%2B6tpKQkXX/99XrjjTeci54v1G9TvQfae2Kjx6a2cb7Xum38%2BPHOz/Hx8UpMTFT37t21du1ajR079ryvC5RjP3XqVO3evVtbt2694NjWcqzP13NaWprzc3x8vG644QYlJiZqx44d6tevn6TA7PnGG29UUVGRjh8/rlWrVmnSpEnKzc097/jWcJzP13OvXr1a7XH%2BoThFGGCioqLUtm3bRv93Xl5e3uj/dAJVWFiYevfurU8//dT5NOF39RsTE6MjR4402s7Ro0cD4j2x1WNMTEyT25Aaz45djrp06aLu3bvr008/lfRNP7W1taqoqPAbd%2B77cm7PFRUVqqurc7XnadOmac2aNdq4caO6devmLLfV0/mOdXBwsDp27NgcLV3Q%2BXpuSr9%2B/RQSEuJ3rAPx9zs0NFQ/%2BtGPlJiYqMzMTPXt21cvvPBCqz7O5%2Bu5Ka3lOP9QBKwAExoaqoSEBGfKtUFOTo4GDBjgUlV21dTU6JNPPlGXLl2c6eZv91tbW6vc3Fyn36SkJFVWVurjjz92xnz00UeqrKwMiPfEVo9JSUnavHmz38e8s7Oz5fP5Gk25X46%2B%2BuorlZSUOKc9EhISFBIS4ve%2BlJaWqri42K/n4uJilZaWOmOys7Pl8XiUkJDQsg3om//Tnjp1qt577z3913/9V6PTGbZ6SkpKavRvQHZ2thITExUSEtJc7TXpQj03Ze/evaqrq3OOdWv5/TbGqKamplUe5/Np6LkprfU4X7SWu54etjTcpmHlypVm3759ZsaMGSYsLMx8/vnnbpf2g8ycOdNs2rTJfPbZZ2bbtm1m1KhRJjw83Oln0aJFxuv1mvfee8/s2bPH/PSnP23yFgZ9%2BvQx%2Bfn5Jj8/3/Tu3fuyuk1DdXW12blzp9m5c6eRZJ599lmzc%2BdO59YaNno8fvy4iY6ONj/96U/Nnj17zHvvvWciIiJc%2B3jzd/VcXV1tZs6cafLy8syBAwfMxo0bTVJSkunatatfz%2Bnp6aZbt25mw4YNZseOHWbo0KFNftR92LBhZseOHWbDhg2mW7durt2m4aGHHjJer9ds2rTJ76Pqf/vb36z21PDx/ccee8zs27fPrFy50rWP71%2Bo5//5n/8xv/nNb8z27dvNgQMHzNq1a81NN91kbr31VqdnYwLv93vOnDlm8%2BbN5sCBA2b37t1m7ty5pk2bNiY7O9sY0/qO84V6bq3H%2BVIQsALUiy%2B%2BaLp3725CQ0NNv379/D4SHWga7vkUEhJifD6fGTt2rNm7d6%2Bz/uzZs%2BbJJ580MTExxuPxmDvvvNPs2bPHbxtfffWVmThxogkPDzfh4eFm4sSJpqKioqVbOa%2BNGzcaSY0ekyZNMsbY63H37t1m0KBBxuPxmJiYGDN//nzXPtr8XT3/7W9/M8nJyaZTp04mJCTEXHvttWbSpEnm4MGDfts4deqUmTp1qunQoYNp3769GTVqVKMxX3zxhRk5cqRp37696dChg5k6darfR7xbUlP9SjKvvfaaM8ZWT5s2bTK33nqrCQ0NNdddd515%2BeWXW6LFRi7U88GDB82dd95pOnToYEJDQ831119vpk%2Bf3uieZ4H2%2B/2LX/zC%2BTe4U6dOZtiwYU64Mqb1HWdjvrvn1nqcL0WQMYF4e1QAAIDLF9dgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACw7P8BrtYUVXpxKnoAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common4016025667388981877\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">717</td>\n",
       "        <td class=\"number\">7.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">17.0</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11.0</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15.0</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">12.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">19.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13.0</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (7283)</td>\n",
       "        <td class=\"number\">9039</td>\n",
       "        <td class=\"number\">91.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme4016025667388981877\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">-1.0</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">717</td>\n",
       "        <td class=\"number\">7.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.333333333</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3556.5166670000003</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3556.61241</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3559.375</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3561.139286</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3564.590227</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_index\">index<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>9916</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>4969.6</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>9929</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram3972063556753175160\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABnUlEQVR4nO3XMa7TQBSG0evotXb6KN4Fi2BP1JTsh0Wwi0RZgC0KCjIUEBp4fzmTSOeUnuKOpfsVM7XWWnV2vV5rXdfeY3lxl8ulzudz15lvXaf9Mc9zVf3%2B4WVZRlyBF7JtW63r%2BndvehoSyDRNVVW1LMuwQD58%2BjpkLtm3zx/fPXvsTU9DAkksLs/kMPoC8MwEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBG8jhrbWqqpq27Z/zn7%2B%2BN77OjyR/%2B3E49tjb3oaEsi%2B71VVta7riPE8seOX98/2fa/j8djvMlU1tQFZ3u/3ut1uNc9zTdPUezwvprVW%2B77X6XSqw6Hvq2BIIPAqPNIhEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCH4Bo8tB2sZda10AAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives3972063556753175160,#minihistogram3972063556753175160\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives3972063556753175160\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles3972063556753175160\"\n",
       "                                                  aria-controls=\"quantiles3972063556753175160\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram3972063556753175160\" aria-controls=\"histogram3972063556753175160\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common3972063556753175160\" aria-controls=\"common3972063556753175160\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme3972063556753175160\" aria-controls=\"extreme3972063556753175160\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles3972063556753175160\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>495.75</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>2492.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>4971.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>7450.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>9433.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>9929</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>9929</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>4957.5</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>2865.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.57658</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.1982</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>4969.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>2480.9</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.0023788</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>49278965</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>8210500</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>77.5 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram3972063556753175160\">\n",
       "            <img 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2BP1wQcfaPTo0Ro/frx%2B//vfS5JKSkokSbGxsUGvi42NdbaVlJQoJiamxr5jYmKcmvNVVlaqoqIi6AEAAK4ergSsd955R927d68x3qNHD7311lvWjlNdXa2bb75ZGRkZ6tq1q0aNGqWRI0dq4cKFQXUhISFBz40xQWPnb79QzTdlZmY6F8R7vV61bdvWwtkAAID6wpWAdfjwYTVr1qzGeHR0tA4fPmztOK1atVKXLl2Cxjp37qz9%2B/dLOnuLCEk1VqJKS0udVS2fz6dDhw7V2HdZWVmNla9zpk2bpkAg4DyKioou%2B1wAAED94UrA%2BtGPfqQPPvigxvgHH3yguLg4a8fp1auX9uzZEzT26aefqn379pKkuLg4%2BXw%2B5eTkONurqqqUm5urnj17Sjq7qhYIBIJ%2B0mfjxo0KBAJOzfk8Ho%2Bio6ODHgAA4OrhyrcI09PTlZ6err///e%2B64447JEkffvih5syZo7lz51o7ziOPPKKePXsqIyND99xzjzZt2qRFixZp0aJFks5%2B9Jeenq6MjAzFx8crPj5eGRkZatKkiVJTUyWdXfHq37%2B/Ro4cqZdeekmS9NBDD2ngwIF8gxAAAFyQKwFr5MiROnnypDIyMvSb3/xGktSmTRs999xzeuCBB6wd55ZbbtGqVas0bdo0zZw5U3FxcZo/f76GDh3q1EyZMkUnTpzQmDFjVF5erm7duik7O1tRUVFOzeuvv67x48c73zYcPHjwBS/SBwAAkKQQ4/KP/hUXFysyMjLo5p8NTUVFhbxerwKBgPWPC5OmZ1ndHwAAbtgyq7/1fdbm%2B%2B/FuLKC9U2tWrVyuwUAAACrXLnIvaysTL/4xS/Url07NW7cWBEREUEPAACA%2BsyVFawRI0bob3/7mx599FG1atXqW%2B8nBQAAUB%2B5ErDWrl2rtWvXqmvXrm4cHgAAoFa58hFhmzZtWLUCAAANlisB69lnn9W0adP05ZdfunF4AACAWuXKR4RpaWk6evSo2rdvr%2BjoaIWHhwdtLy0tdaMtAAAAK1wJWLNnz3bjsAAAAHXClYD14IMPunFYAACAOuHKNViS9MUXX2jGjBlKS0tzPhLMzs7W7t273WoJAADAClcC1rp163T99dcrNzdXb7/9to4dOyZJ2rp1q5544gk3WgIAALDGlYD12GOPacaMGfroo4%2BC7tx%2Bxx13aMOGDW60BAAAYI0rAeuTTz7Rv/7rv9YYj4mJUVlZmQsdAQAA2ONKwLrmmmtUUlJSY7ywsFCtW7d2oSMAAAB7XAlY9913n6ZOnaqysjLnju4bN27U5MmTNWzYMDdaAgAAsMaVgJWRkSGfz6dWrVrp2LFj6tKli3r27KlbbrlFv/71r91oCQAAwBpX7oMVERGht956S59%2B%2Bqm2bt2q6upq3XzzzerUqZMb7QAAAFjlSsA659prr9W1117rZgsAAADWuRKwHnrooe/cvmjRojrqBAAAwD5XAlZxcXHQ81OnTmnnzp06evSobrvtNjdaAgAAsMaVgPWHP/yhxtjp06f1y1/%2BUp07d3ahIwAAAHtc%2By3C84WFhWny5Ml6%2Bumn3W4FAADgslwxAUuSPv/8c506dcrtNgAAAC6LKx8RTpkyJei5MUbFxcVavXq1hg4d6kZLAAAA1rgSsPLz84OeN2rUSD/84Q81e/ZsjRw50o2WAAAArHElYK1bt86NwwIAANSJK%2BoaLAAAgIbAlRWsW265xfmR54vZtGlTLXcDAABglysB6/bbb9dLL72ka6%2B9Vj169JAkbdiwQXv27NGoUaPk8XjcaAsAAMAKVwLWkSNHNHbsWGVkZASNT58%2BXYcOHdJ//ud/utEWAACAFa5cg/X222/rF7/4RY3xESNG6J133nGhIwAAAHtcCVgej0d5eXk1xvPy8vh4EAAA1HuufEQ4fvx4jR49Wtu2bVP37t0lnb0Ga/HixXr88cfdaAkAAMAaVwLW9OnTFRcXp//4j//QK6%2B8Iknq3LmzFi9erNTUVDdaAgAAsMaVgCVJqamphCkAANAguXaj0YqKCr366qt64oknVF5eLkn6%2BOOPVVxc7FZLAAAAVriygrVjxw7deeedatKkiYqKijRixAg1a9ZMb7/9tr788kstXbrUjbYAAACscGUF65FHHlFqaqr%2B9re/qXHjxs74gAEDtHbtWjdaAgAAsMaVFazNmzdr4cKFNX4up3Xr1nxECAAA6j1XVrAiIiJ07NixGuOfffaZWrZs6UJHAAAA9rgSsAYPHqx///d/1%2BnTpyVJISEhOnDggKZOnaohQ4a40RIAAIA1rgSsZ555RgcPHpTP59OJEyd0xx13qGPHjmrcuHGN3ycEAACob1y5Bsvr9SovL085OTnaunWrqqurdfPNNyslJaXGdVkAAAD1TZ0HrFOnTunuu%2B/WCy%2B8oOTkZCUnJ9d1CwAAALWqzj8iDA8P17Zt21ipAgAADZYr12ANGzZMS5YscePQAAAAtc613yJcsGCB1qxZo6SkJDVt2jRo25w5c1zqCgAA4PK5ErAKCgp04403SpI%2B%2BeSToG18dAgAAOq7Og1Yn3/%2BueLi4rRu3bq6PCwAAECdqtNrsOLj41VWVuY8v/fee3Xo0KG6bAEAAKDW1WnAMsYEPX///fd1/PjxumwBAACg1rnyLUK3ZGZmKiQkROnp6c5YZWWlHn74YbVs2VJNmzbV4MGD9eWXXwa9bv/%2B/Ro0aJCaNm2qli1bavz48aqqqqrr9gEAQD1RpwErJCSkxkXsdXVR%2B%2BbNm7Vo0SLn4vpz0tPTtWrVKi1fvlzr16/XsWPHNHDgQJ05c0aSdObMGQ0YMEDHjx/X%2BvXrtXz5cq1YsUKTJk2qk74BAED9U6cXuRtjNGLECHk8HknSyZMnNXr06Bq3aVi5cqXV4x47dkxDhw7V4sWL9dvf/tYZDwQCevnll/Xaa6/pzjvvlCQtW7ZMbdu21Zo1a5SSkqLs7Gzt2rVLRUVF8vv9ks7%2BluKIESM0a9YsRUdHW%2B0VAADUf3W6gjV8%2BHDFxMTI6/XK6/Vq2LBh8vv9zvNzD9vGjh2rAQMGOCHqnIKCAp06dSro53r8fr8SEhKUl5cnScrPz1dCQoITriQpJSVFlZWVKigouODxKisrVVFREfQAAABXjzpdwXLj7u3Lly9XQUGBtmzZUmNbSUmJIiIi1KxZs6Dx2NhYlZSUODWxsbFB25s1a6aIiAin5nyZmZl68sknLZ0BAACobxr0Re5FRUWaMGGCXn/9dTVu3PiSX2eMCbo27ELXiZ1f803Tpk1TIBBwHkVFRf948wAAoN5q0AGroKBApaWlSkxMVFhYmMLCwpSbm6vnnntOYWFhio2NVVVVlcrLy4NeV1pa6qxa%2BXy%2BGitV5eXlOnXqVI2VrXM8Ho%2Bio6ODHgAA4OrRoANWv379tH37dhUWFjqPpKQkDR061Pnv8PBw5eTkOK8pLi7Wjh071LNnT0lSjx49tGPHDhUXFzs12dnZ8ng8SkxMrPNzAgAAVz7Xfuy5LkRFRSkhISForGnTpmrRooUz/uCDD2rSpElq0aKFmjdvrsmTJ%2BuGG25wLohPTk5Wly5dlJaWpqefflpfffWVJk%2BerJEjR7IyBQAALqhBB6xL8eyzzyosLEz33HOPTpw4oX79%2BunVV19VaGioJCk0NFTvvfeexowZo169eikyMlKpqamaO3euy50DAIArVYg5//drYF1FRYW8Xq8CgYD1Va%2Bk6VlW9wcAgBu2zOpvfZ%2B1%2Bf57MQ36GiwAAAA3ELAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsa9ABKzMzU7fccouioqIUExOjn/70p9qzZ09QTWVlpR5%2B%2BGG1bNlSTZs21eDBg/Xll18G1ezfv1%2BDBg1S06ZN1bJlS40fP15VVVV1eSoAAKAeadABKzc3V2PHjtWGDRuUk5Oj06dPKzk5WcePH3dq0tPTtWrVKi1fvlzr16/XsWPHNHDgQJ05c0aSdObMGQ0YMEDHjx/X%2BvXrtXz5cq1YsUKTJk1y67QAAMAVLsQYY9xuoq6UlZUpJiZGubm5uu222xQIBPTDH/5Qr732mu69915J0sGDB9W2bVu9//77SklJ0R//%2BEcNHDhQRUVF8vv9kqTly5drxIgRKi0tVXR09EWPW1FRIa/Xq0AgcEn1/4ik6VlW9wcAgBu2zOpvfZ%2B1%2Bf57MQ16Bet8gUBAktS8eXNJUkFBgU6dOqXk5GSnxu/3KyEhQXl5eZKk/Px8JSQkOOFKklJSUlRZWamCgoI67B4AANQXYW43UFeMMZo4caJ69%2B6thIQESVJJSYkiIiLUrFmzoNrY2FiVlJQ4NbGxsUHbmzVrpoiICKfmfJWVlaqsrHSeV1RU2DwVAABwhbtqVrDGjRunTz75RG%2B%2B%2BeZFa40xCgkJcZ5/87%2B/reabMjMz5fV6nUfbtm2/f%2BMAAKDeuSoC1sMPP6zVq1fro48%2BUps2bZxxn8%2BnqqoqlZeXB9WXlpY6q1Y%2Bn6/GSlV5eblOnTpVY2XrnGnTpikQCDiPoqIiy2cEAACuZA06YBljNG7cOK1cuVJ/%2BtOfFBcXF7Q9MTFR4eHhysnJccaKi4u1Y8cO9ezZU5LUo0cP7dixQ8XFxU5Ndna2PB6PEhMTL3hcj8ej6OjooAcAALh6NOhrsMaOHas33nhD//3f/62oqChnJcrr9SoyMlJer1cPPvigJk2apBYtWqh58%2BaaPHmybrjhBt15552SpOTkZHXp0kVpaWl6%2Bumn9dVXX2ny5MkaOXIkwQkAAFxQgw5YCxculCT17ds3aHzJkiUaMWKEJOnZZ59VWFiY7rnnHp04cUL9%2BvXTq6%2B%2BqtDQUElSaGio3nvvPY0ZM0a9evVSZGSkUlNTNXfu3Lo8FQAAUI9cVffBcgv3wQIA4LtxHywAAAB8JwIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYl%2BiFF15QXFycGjdurMTERK1bt87tlgAAwBWKgHUJ3nrrLaWnp2v69Onatm2bbr31Vv3TP/2T9u/f73ZrAADgCkTAugTz5s3Tgw8%2BqH/7t39T586dNX/%2BfLVt21YLFy50uzUAAHAFCnO7gStdVVWVCgoKNHXq1KDx5ORk5eXlXfA1lZWVqqysdJ4HAgFJUkVFhfX%2BzlQet75PAADqWm28R57bpzHG%2Br4vhoB1EYcPH9aZM2cUGxsbNB4bG6uSkpILviYzM1NPPvlkjfG2bdvWSo8AANR33mdqb99Hjx6V1%2ButvQNcAAHrEoWEhAQ9N8bUGDtn2rRpmjhxovO8urpaX331lVq0aPGtr/k%2BKioq1LZtWxUVFSk6OtrafnEW81u7mN/axfzWHua2dtmcX2OMjh49Kr/fb6m7S0fAuoiWLVsqNDS0xmpVaWlpjVWtczwejzweT9DYNddcU2s9RkdH8z95LWJ%2BaxfzW7uY39rD3NYuW/Nb1ytX53CR%2B0VEREQoMTFROTk5QeM5OTnq2bOnS10BAIArGStYl2DixIlKS0tTUlKSevTooUWLFmn//v0aPXq0260BAJQDOgwAAAq9SURBVIArUOiMGTNmuN3ElS4hIUEtWrRQRkaG5s6dqxMnTui1117TTTfd5HZrCg0NVd%2B%2BfRUWRlauDcxv7WJ%2BaxfzW3uY29rVEOY3xLjx3UUAAIAGjGuwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBq5564YUXFBcXp8aNGysxMVHr1q1zu6UrTmZmpm655RZFRUUpJiZGP/3pT7Vnz56gmsrKSj388MNq2bKlmjZtqsGDB%2BvLL78Mqtm/f78GDRqkpk2bqmXLlho/fryqqqqCanJzc5WYmKjGjRurY8eOevHFF2v9/K40mZmZCgkJUXp6ujPG/F6eAwcOaNiwYWrRooWaNGmiH//4xyooKHC2G2M0Y8YM%2Bf1%2BRUZGqm/fvtq5c2fQPsrLy5WWliav1yuv16u0tDQdOXIkqGb79u3q06ePIiMj1bp1a82cOdOV326rS6dPn9avfvUrxcXFKTIyUh07dtTMmTNVXV3t1DC/l27t2rUaNGiQ/H6/QkJC9O677wZtr8u5XLFihbp06SKPx6MuXbpo1apVtXPSF2NQ7yxfvtyEh4ebxYsXm127dpkJEyaYpk2bmn379rnd2hUlJSXFLFmyxOzYscMUFhaaAQMGmHbt2pljx445NaNHjzatW7c2OTk5ZuvWreb22283N910kzl9%2BrQxxpjTp0%2BbhIQEc/vtt5utW7eanJwc4/f7zbhx45x9fP7556ZJkyZmwoQJZteuXWbx4sUmPDzc/Nd//Vedn7NbNm3aZDp06GBuvPFGM2HCBGec%2Bf3%2BvvrqK9O%2BfXszYsQIs3HjRrN3716zZs0a89e//tWpmT17tomKijIrVqww27dvN/fee69p1aqVqaiocGr69%2B9vEhISTF5ensnLyzMJCQlm4MCBzvZAIGBiY2PNfffdZ7Zv325WrFhhoqKizNy5c%2Bv0fOvab3/7W9OiRQvzP//zP2bv3r3mnXfeMT/4wQ/M/PnznRrm99K9//77Zvr06WbFihVGklm1alXQ9rqay7y8PBMaGmoyMjLM7t27TUZGhgkLCzMbNmyo/Uk4DwGrHvrJT35iRo8eHTTWqVMnM3XqVJc6qh9KS0uNJJObm2uMMebIkSMmPDzcLF%2B%2B3Kk5cOCAadSokcnKyjLGnP2j0ahRI3PgwAGn5s033zQej8cEAgFjjDFTpkwxnTp1CjrWqFGjTPfu3Wv7lK4IR48eNfHx8SYnJ8f06dPHCVjM7%2BV57LHHTO/evb91e3V1tfH5fGb27NnO2MmTJ43X6zUvvviiMcaYXbt2GUlBby75%2BflGkvnLX/5ijDHmhRdeMF6v15w8edKpyczMNH6/31RXV9s%2BrSvGgAEDzAMPPBA0NmTIEDNs2DBjDPN7Oc4PWHU5l/fcc4/p379/UD8pKSnmvvvus3%2BiF8FHhPVMVVWVCgoKlJycHDSenJysvLw8l7qqHwKBgCSpefPmkqSCggKdOnUqaC79fr8SEhKcuczPz1dCQkLQD4WmpKSosrLS%2BagmPz%2B/xr9HSkqKtmzZolOnTtXqOV0Jxo4dqwEDBujOO%2B8MGmd%2BL8/q1auVlJSkn/3sZ4qJiVHXrl21ePFiZ/vevXtVUlISNDcej0d9%2BvQJml%2Bv16tu3bo5Nd27d5fX6w2q6dOnT9Dvp6akpOjgwYP64osvavks3dO7d299%2BOGH%2BvTTTyVJH3/8sdavX6%2B7775bEvNrU13O5bf9vXDj/ZGAVc8cPnxYZ86cqfFD07GxsTV%2BkBr/xxijiRMnqnfv3kpISJAklZSUKCIiQs2aNQuq/eZclpSU1JjrZs2aKSIi4jtrYmNjdfr0aR0%2BfLi2TumKsHz5chUUFCgzM7PGNub38nz%2B%2BedauHCh4uPj9cEHH2j06NEaP368fv/730uSMz/f9begpKREMTExNfYdExNz0fn95jEaoscee0z333%2B/OnXqpPDwcHXt2lXp6em6//77JTG/NtXlXH5bjRtzXX/vQX%2BVCwkJCXpujKkxhv8zbtw4ffLJJ1q/fv1Fa8%2BfywvN68VqzP%2B/6LIh/5sUFRVpwoQJys7OVuPGjS/5dczvpamurlZSUpIyMjIkSV27dtXOnTu1cOFC/fznP3fqLva3gPm9sLfeekvLli3TG2%2B8oeuvv16FhYVKT0%2BX3%2B/X8OHDnTrm1566mssr5f2RFax6pmXLlgoNDa2RxktLS2ukdpz18MMPa/Xq1froo4/Upk0bZ9zn86mqqkrl5eVB9d%2BcS5/PV2Ouy8vLderUqe%2BsKS0tVVhYmFq0aFEbp3RFKCgoUGlpqRITExUWFqawsDDl5ubqueeeU1hYmGJjY5nfy9CqVSt16dIlaKxz587av3%2B/pLPzItVcBTl/fg8dOlRj32VlZRedX6nmikND8uijj2rq1Km67777dMMNNygtLU2PPPKIsxrL/NpTl3P5bTVuzDUBq56JiIhQYmKicnJygsZzcnLUs2dPl7q6MhljNG7cOK1cuVJ/%2BtOfFBcXF7Q9MTFR4eHhQXNZXFysHTt2OHPZo0cP7dixQ8XFxU5Ndna2PB6PEhMTnZrz/z2ys7OVlJSk8PDw2jo91/Xr10/bt29XYWGh80hKStLQoUOd/2Z%2Bv79evXrVuK3Ip59%2Bqvbt20uS4uLi5PP5guamqqpKubm5QfMbCAS0adMmp2bjxo0KBAJBNWvXrg26NUZ2drb8fr86dOhQW6fnuq%2B//lqNGgW/BYaGhjq3aWB%2B7anLufy2vxeuvD/W8UX1sODcbRpefvlls2vXLpOenm6aNm1qvvjiC7dbu6L88pe/NF6v1/z5z382xcXFzuPrr792akaPHm3atGlj1qxZY7Zu3WruuOOOC95GoF%2B/fmbr1q1mzZo1pk2bNhe8jcAjjzxidu3aZV5%2B%2BeWr4jYCF/LNbxEaw/xejk2bNpmwsDAza9Ys89lnn5nXX3/dNGnSxCxbtsypmT17tvF6vWblypVm%2B/bt5v7777/gV99vvPFGk5%2Bfb/Lz880NN9wQ9NX3I0eOmNjYWHP//feb7du3m5UrV5ro6OgGdxuB8w0fPty0bt3auU3DypUrTcuWLc2UKVOcGub30h09etRs27bNbNu2zUgy8%2BbNM9u2bXNuH1RXc/m///u/JjQ01MyePdvs3r3bzJ49m9s04B/zu9/9zrRv395ERESYm2%2B%2B2bn1AP6PpAs%2BlixZ4tScOHHCjBs3zjRv3txERkaagQMHmv379wftZ9%2B%2BfWbAgAEmMjLSNG/e3IwbNy7oa8LGGPPnP//ZdO3a1URERJgOHTqYhQsX1sUpXnHOD1jM7%2BX5wx/%2BYBISEozH4zGdOnUyixYtCtpeXV1tfvOb3xifz2c8Ho%2B57bbbzPbt24Nq/v73v5uhQ4eaqKgoExUVZYYOHWrKy8uDaj755BNz6623Go/HY3w%2Bn5kxY0aDvYXAORUVFWbChAmmXbt2pnHjxqZjx45m%2BvTpprKy0qlhfi/dRx99dMG/t8OHDzfG1O1cvvPOO%2Ba6664z4eHhplOnTmbFihW1eu7fJsSYBnY7WQAAAJdxDRYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMv%2BH%2BP3OIjmQTtXAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common3972063556753175160\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2047</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9510</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7473</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5424</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9518</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3371</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1322</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7465</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5416</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3363</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (9906)</td>\n",
       "        <td class=\"number\">9906</td>\n",
       "        <td class=\"number\">99.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme3972063556753175160\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">9925</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9926</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9927</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9928</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9929</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAKBCAYAAABDF/2oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nOzdeViU9f7/8RfIKqiYguSKmeAvNwZRNJcS9ZiaSuaBMrdM7WTinkvlFmlq1qnc8nAsc9c0za30ZKa2SZaZy8F9QVFRXEGQbX5/EHOcwBGbIfT%2BPh/XNRfMvc37/oDOm9d87hkns9lsFgAAAIACORd3AQAAAMC9jIYZAAAAsIGGGQAAALCBhhkAAACwgYYZAAAAsIGGGQAAALCBhhkAAACwgYYZAAAAsIGGGQAAALDBpbgLAID/K06fPq1WrVrddr2rq6u8vb0VEBCgxx9/XN27d5e3t/dfWCEAoCBOfDQ2APw1bm2YAwMD8zXDmZmZunTpks6cOSNJqlixoubPn69q1ar95bUCAP6HhhkA/iK3NswLFixQWFhYgdvt3LlTAwYMUEpKikwmk5YtW/ZXlgkA%2BAPmMAPAPSYsLEzDhg2TJO3evVv79u0r5ooA4P82GmYAuAe1adPG8v2ePXuKsRIAABf9AcA9qFSpUpbvU1NTrdb99NNPWrhwoX755RdduXJFpUuXVnBwsHr06KEmTZoUeLxr165p2bJl2rZtm44cOaKUlBR5enqqatWqatmypXr27KkyZcpY7RMUFCRJ%2Bu677zRlyhRt2bJFzs7Oql27tj766CO5uLhoz549mj9/vg4cOKCzZ8/K3d1d1atXV%2BvWrdWtW7cCL1pMT0/XsmXLtHHjRh05ckSZmZmqUKGCHn30UfXp00cBAQFW2%2B/cuVM9e/ZU/fr1tXjxYi1cuFBr1qzRyZMn5erqqtq1a6tHjx5q3br1nxlqALgjGmYAuAedPHnS8r2/v7/l%2B%2BnTpys2NlaSVKZMGQUGBiopKUlbtmzRli1b1LdvX73yyitWxzpx4oR69%2B6ts2fPysXFRVWrVlWlSpV05swZ7d%2B/X/v379eGDRu0atUqeXl55aslOjpau3fvVmBgoC5duiRfX1%2B5uLho8%2BbNGjp0qLKyslS2bFk9/PDDSk1N1W%2B//aY9e/Zo7dq1WrZsmVXTfO7cOT3//PM6duyYJCkgIEBeXl46evSoli9frjVr1mjKlClq3759vjoyMzPVr18//fDDDypbtqxq1Kih48eP68cff9SPP/6oCRMm6Nlnn7Vv4AGgIGYAwF8iISHBHBgYaA4MDDT/%2BOOPNrcdOXKkOTAw0Fy7dm3zhQsXzGaz2bx06VJzYGCgOTQ01Pz5559bts3JyTFv2LDBHBwcbA4MDDSvWLHC6ljdu3c3BwYGmiMjI83nz5%2B32m/16tXmWrVqmQMDA82LFi2y2i%2Bv1jp16pjj4uLMZrPZnJ2dbb58%2BbI5Ozvb3LRpU3NgYKA5NjbWnJWVZdlv37595saNG5sDAwPNc%2BfOtSzPysoyd%2B7c2RwYGGhu27at%2Bb///a9l3fXr182vvfaa5Zx//fVXy7off/zRUktwcLB57dq1lnXXrl0z9%2BrVyxwYGGhu1KiROTMz0%2Ba4AsCfwRxmALhHpKen68CBAxo/frzWrFkjSerdu7fKly%2BvjIwMzZgxQ5I0efJkderUybKfk5OT2rdvb0mWZ8yYoaysLElScnKyDh8%2BLEmKiYmRn5%2Bf1X4RERFq1KiRJOngwYMF1tWuXTs1bNhQkuTs7CwfHx9dunRJFy5ckCRFRkaqRIkSlu1r166toUOHqnXr1vLx8bEs//LLL/Xf//5X7u7uio2NVa1atSzrvL299eabb6p58%2BbKzMzUP//5zwJrGTRokDp27Gi5X6pUKct5X7lyRcePH7/N6ALAn8eUDAAoBj179rzjNn//%2B981ePBgSbnvlnHx4kV5eXnd9sNPOnXqpJiYGJ0/f14HDhxQvXr1VK5cOf34449KT0%2BXh4dHvn2ys7MtUybS09MLPG6DBg3yLStbtqzKlCmjq1evasSIEXrppZdUv359OTvn5jCRkZGKjIy02ufrr7%2BWJIWHh6tKlSoFPtbzzz%2BvHTt2KC4uTtevX7eayy1JLVu2zLdPjRo1LN9fu3atwOMCgD1omAGgGPzxg0ucnJzk7u4uHx8fBQUFqXXr1nr44Yct6/NS4szMTD333HO3PW6JEiWUk5OjY8eOqV69epblHh4eOnv2rPbs2aNTp04pISFBR48e1X//%2B1/duHFDkpSTk1PgMX19fQt8nBEjRmjs2LHatm2btm3bpjJlyigsLExNmzbV448/bjX3WpIl/a1du/Zt689bl52drZMnT6pOnTpW6ytUqJBvn1v/EMjOzr7tsQHgz6JhBoBi8Prrr9/2g0sKcv36dUlSRkaGfvnllztuf2vSeuzYMU2bNk3btm2zaoq9vb0VGhqqpKQkxcfH3/ZYBSXTUm6KXK1aNX388cf6/vvvdfXqVW3evFmbN2%2BWk5OTHn/8cU2YMMHSOKekpEhSvtT4Vrf%2BEfHHdweRcj8%2B3BYzn8UFoAjQMAPAfcDT01NSbgL72WefFXq/5ORkde/eXcnJyapYsaIiIyP1yCOP6KGHHlLlypXl5OSk4cOH22yYbQkLC1NYWJjS09O1a9cu/fTTT9qxY4f279%2BvrVu36uzZs1qzZo2cnJws78CR1/wX5NZGv6B37ACA4kDDDAD3gerVq0vKfYu4rKwsubjk/%2B/bbDZr586d8vf3V8WKFeXm5qZVq1YpOTlZPj4%2BWrVqlR544IF8%2B50/f/6u68nIyFBCQoJSUlJUv359eXh4qFmzZmrWrJmGDh2qDRs2aNiwYYqPj9fBgwdVq1YtPfTQQzpw4ID2799/2%2BPu3btXUu4UlapVq951XQBQFHiXDAC4DzRs2FClSpVSamrqbRPmdevWqVevXmrXrp3OnTsnSTp9%2BrQkqWLFigU2y0eOHNGvv/4q6e7m/27fvl3t27dX//79lZGRkW/9o48%2Bavk%2B77h5F%2Bx9/fXXSkhIKPC4CxYskCQFBwerdOnSha4HAIoSDTMA3AdKliyp/v37S5ImTZqkVatWWc1H/uqrrzR%2B/HhJuW8Dl5fOPvTQQ5Kk%2BPh4bdq0ybK92WzW9u3b1bdvX2VmZkqS0tLSCl1PixYtVLZsWV25ckWjRo3SlStXLOtSU1M1depUSdKDDz6omjVrSpKeeOIJBQUF6ebNm%2BrXr5/VNJCUlBSNHTtW3377rVxcXDRixIjCDw4AFDGmZADAfaJfv35KSEjQihUr9Oqrr%2Brtt99W5cqVdf78eSUlJUmSQkJC9Oabb1r26dq1q5YsWaKTJ09q0KBBqlSpksqWLauzZ88qOTlZrq6uatSokeLi4u5qaoabm5vef/99vfDCC9q4caO2bNmiqlWrytnZWQkJCbpx44Y8PT01ZcoUubm5SZJcXFw0e/Zs9evXT8eOHVPnzp2tPukv763vJk6cqNDQUMcOHgDYgYYZAO4TTk5OiomJUdu2bbVs2TL9%2Buuvlg8CCQ4O1pNPPqmoqChLgyrlvuvEypUrFRsbq61bt%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%2B/Y2tzty5IgCAwOtlj388MOWTwv943pXV1cFBARYfZqovfjgEhiPk1NxV%2BBY1atLhw9LNWtKx48XdzWOc/p0cVfgWCVKSBUqSOfPS9nZxV2N4/j4FHcFjuXkJHl6SmlpkpE%2BhsDDo7grcDxnZ%2BmWj383hBIliu%2Bxi%2BC5cfkHH2jmzJlWywYOHKjo6OhC7e/r61uo7VJTU%2BXp6Wm1zMPDQzdu3CjUekegYQbudT4%2Buf/JGq1xMRpn59wnJGdnYzXMRuPk9L%2BbkRpmIzJa%2BGFAUVFRCg8Pt1pW2Cb4bnh6eio9Pd1qWXp6ury8vAq13hFomAEAAIzO2fGzcP38/OTn5%2Bfw4/5RYGCg9u/fb7XsyJEjqlOnjiSpZs2aOnz4sFq2bClJyszM1IkTJ/JN47AHc5gBAABwz%2BrUqZPi4uK0ceNGZWVlaePGjYqLi1Pnzp0lSU8//bQWLVqk%2BPh43bx5U%2B%2B8847Kly%2Bv0NBQh9VAwgwAAGB0RZAwFyWTyaSJEyeqU6dOqlGjhmbNmqXp06frtddeU6VKlTRjxgxVr15dktS1a1ddv35dL7/8si5duqS6detq7ty5cnV1dVg9TmZHvkkdcC8w2rw3k0n65RcpJETavbu4q3Eco1305%2Boq%2BflJSUlSZmZxV%2BM4Rps77%2Bz8v4v%2BjHRBmREv%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%2B7BhDk5OVljx45VXFycSpQooU6dOmnUqFFycbFuT/v27auff/7ZatmNGzcUFRWlN954QxcvXlTTpk1VsmRJy/qyZcvq66%2B/dlitNMwoUMWKFbV79%2B7iLgMAADjCPdgwDxkyRBUqVNCOHTt08eJFvfTSS5o/f7769u1rtd2///1vq/srV67UzJkzNXDgQEnS3r17ValSJYc2yH90740e7gmnT59WUFCQ5evChQvVtm1bmUwmPfPMMzp48KBl2y1btqhDhw4KDg7Wiy%2B%2BqMuXL1sda8OGDerYsaMaNGigLl266Ntvv5UkXb58WS1atNC0adMkSVlZWXrmmWc0bNiwv%2B5EAQDAX%2B7kyZOKi4vTK6%2B8Ik9PT1WpUkUDBgzQ4sWLbe537NgxxcTEaPr06fLz85OU2zDXqVOnSOslYUahbNiwQYsWLZKHh4cGDRqkadOmad68eTp27JgGDx6syZMnq3379vrmm280aNAgderUSZK0bds2jR8/XnPmzFFISIi2b9%2Bu6OhorVixQjVr1tTbb7%2BtF154QW3atNHWrVt1%2BfLlfH9J2pKUlKQLFy5YLfOtXl1%2BPj4OPf9iVauW9VejcHUt7gocK%2B8lRBeD/bd6D6ZSdnFy%2Bt9Xo50bYEsR/L4X%2BBzs62tpZG05fPiwfHx8VKFCBcuyGjVqKDExUdeuXVPp0qUL3G/ixImKiIhQaGioZdnevXt19epVPfnkk7p48aLq1q2rUaNG6eGHH/6TZ5afwf5nR1Hp0aOHfH19JUnt2rXT3LlzJUkbN25UnTp1LA1y69at1bJlS8t%2BixYt0rPPPquGDRtKklq2bKnw8HAtW7ZMY8eOVVhYmF544QUNGTJE165d09KlS%2BXt7V3oupYvX66ZM2daLRs4eLCiBw%2B263zvSUuWFHcFKIwHHijuClAYHh7FXQEKo0SJ4q4ANhT4HDxwoKKjo%2B%2B4b2pqqjw9Pa2W5d2/ceNGgQ3zrl27tGfPHk2fPt1qeenSpfXwww%2BrX79%2BcnNz0/vvv6/nn39eGzduVKlSpe72tApEw4xCKV%2B%2BvOV7FxcXmc1mSdL58%2BfzXRRYtWpVy7SMM2fOKC4uTkuXLrWsz87OVuPGjS33u3Xrpn//%2B98ymUyqdZcpalRUlMLDw62W%2BXbsKH3yyV0d555Wq1Zus9ytmxQfX9zVOM6XXxZ3BY7l4pLbLF%2B6JGVlFXc1juOgJ5t7hpNTbrOcni79/v%2BYIbi5FXcFjleihJSdXdxVOFZx/gFQBAlzgc/Bv4drd1KyZEmlpaVZLcu77%2BXlVeA%2By5cvV7t27fI9xjvvvGN1f8yYMVq1apV27dplFeLZg4YZdvH399c333xjtezcuXNyd3e3rI%2BIiFD//v0t6xMTE%2BVxS7ozduxYNW/eXHv37tWSJUvUrVu3Qj%2B%2Bn59f/pd%2Bjh%2B/%2BxO5H8THS0a6EDMzs7grKBpZWcY6t5yc4q7AsfKaBrPZeOcG2FIEDXOBz8GFVLNmTV25ckUXL160hHJHjx6Vv79/galwVlaWtmzZolmzZlktT0lJ0axZs9S9e3dVqlRJUm4wl5WVZdVr2IsJXLBLp06ddOjQIa1YsUJZWVn69ttv9Z///MeyPjIyUgsWLNBvv/0mKXeeUZcuXbR%2B/XpJ0ieffKIDBw7orbfe0htvvKGpU6fq8OHDxXIuAADgrxEQEKAGDRpo8uTJSklJUUJCgmbPnq2uXbsWuP3Bgwd18%2BZNhYSEWC339vbW999/r6lTp%2Br69etKTU1VTEyMKleubDXP2V40zLBLlSpV9OGHH2rx4sVq0KCBZs%2BerTZt2ljWP/HEExo2bJheffVVhYSEaPDgwerdu7d69Oih%2BPh4TZ8%2BXTExMSpbtqxatWql9u3ba9iwYbp582YxnhUAAAbj7Oz4m50%2B%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%2Bffuqbt26MplMltv27dslSdnZ2Zo6daoeffRRmUwmvfTSS0pKSrK7vlvRMAMAABjdPdgwDxkyRCVLltSOHTu0cuVK/fDDD5o/f36B2%2B7bt0/z5s3T7t27LbcWLVpIkubMmaPvvvtOq1at0o4dO%2BTh4aHXX3/d7vpuRcMMAACAv9TJkycVFxenV155RZ6enqpSpYoGDBigxYsX59s2ISFBV69e1SOPPFLgsT799FP169dPDz74oLy9vfXaa69p%2B/btSkhIcFi9XPQHAABgdEVw0V9SUpIuXLhgtczX11d%2Bfn533Pfw4cPy8fFRhQoVLMtq1KihxMREXbt2TaVLl7Ys37t3r7y8vDR06FDt3btX5cuXV%2B/evdW1a1ddv35d586dU2BgoGX78uXLq0yZMjp48KCqVKnigDOlYYYRnT5d3BU4lqtr7tcvv5QyM4u3FkeqXLm4K3Ask0n65RfpiSek3buLuxrH2bGjuCtwLC%2Bv3J9VfLyUmlrc1ThOuXLFXYFjeXhI1atLp05J6enFXY3j/L//V9wVONTy5cs1c%2BZMq2UDBw5UdHT0HfdNTU2Vp6en1bK8%2Bzdu3LBqmDMyMhQcHKyhQ4eqZs2a2rlzp6Kjo%2BXl5SWTySRJKlmypNWxPDw8lOrAf%2BM0zAAAAEZXBAlzVFSUwsPDrZb5%2BvoWat%2BSJUsqLS3NalnefS8vL6vlERERioiIsNxv1qyZIiIi9MUXX%2BjRRx%2B12jdPenp6vuPYg4YZAADA6IqgYfbz8yvU9IuC1KxZU1euXNHFixdVvnx5SdLRo0fl7%2B%2BvUqVKWW27cuVKeXl5qV27dpZlGRkZcnd3V5kyZVShQgUdOXLEMi3jwoULunLlitU0DXtx0R8AAAD%2BUgEBAWrQoIEmT56slJQUJSQkaPbs2eratWu%2BbVNSUhQTE6MDBw4oJydH33zzjdavX6%2BoqChJUpcuXTRnzhwlJCQoJSVFkydPVqNGjVS1alWH1UvCDAAAYHT34Cf9ffDBB3rjjTfUqlUrOTs7KyIiQgMGDJAkmUwmTZw4UZ06dVKvXr1048YNDRw4UMnJyapSpYqmTp2q0NBQSdLLL7%2BsrKwsPffcc0pNTVVYWJjee%2B89h9bqZDabzQ49IlDczpwp7gocy9VV8vOTkpK46O9elnfRX0gIF/3dy/Iu%2Btu9m4v%2B7mV5F/0dP85Ff44yaZLjj/naa44/5j2KhBkAAMDo7sGE%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%2BzhDhw7VmjVrLPcLqj0kJETPPfecDhw4UKhj7ty5U0FBQYXa9uLFi2rbtq0uXbr0p%2Bo/ffq0goKCdPr06UJtHxQUpHr16lnOzWQyKSoqSl999dWfevzCunWcExMTZTKZlJiYWKSPCQAA7pKzs%2BNvdkpOTtaAAQMUGhqqsLAwTZo0SVlZWQVuu3TpUrVt21Ymk0lt27bV4sWLLetycnKs%2Bp%2B8myNDy7uekjFx4kR16dLFcv/gwYPq3bu3PD09NWjQIIcVZq/Lly/nW/bH2i9evKjXX39dAwcO1FdffSVnB75ckZ6e/peny7GxsQoLC5MkpaWlad26dRoyZIimTp2qDh06FMlj3jrOFStW1O7du4vkcQAAgB3uwSkZQ4YMUYUKFbRjxw5dvHhRL730kubPn6%2B%2BfftabffVV1/p3XffVWxsrOrXr69ff/1V/fv3V/ny5dW2bVsdOXJEmZmZ%2BuWXX%2BTm5lYktdo9ekFBQWrYsKEOHDigHj16aPTo0WrZsqUef/xxpaSk6ODBg%2BrXr58aNWqkFi1aaMKECbp%2B/bpl/08//VStWrWSyWTSqFGjlJaWZlk3evRojR49Ot/j7dy5U5J06dIljRgxQg0bNlRYWJiGDh2qq1ev6rXXXtOuXbs0d%2B5c/eMf/7ht7eXLl1dUVJTOnDmjK1euSMptokeMGKGmTZuqWbNmGjdunFJSUgrc/%2Buvv9YzzzyjJk2aqH79%2BurevbtOnDih7OxsPfnkk5KkJ598Uhs3bpQkbdiwQR07dlSDBg3UpUsXffvtt5ZjpaSkaNSoUWrQoIGaN2%2Buzz///G5%2BDPl4enoqMjJSL7zwgqZOnSqz2Vxgaj1jxgz16NFDkvTZZ5%2BpS5cu6tOnj0JDQ7Vu3TqdP39eQ4YMUXh4uOrXr69WrVpp5cqVkpRvnP94/DNnzmjIkCFq0qSJmjZtquHDhyspKUlSblofHh6uOXPmqHnz5mrUqJGio6NvO9YAAMA4Tp48qbi4OL3yyivy9PRUlSpVNGDAAKvkOM/58%2BfVr18/BQcHy8nJSSaTSWFhYfrpp58kSXv37lVQUFCRNcuSnRf95XXzP/74o6Kjo7V582Z9//33%2BvTTT%2BXp6anMzEz17NlTXbp00YwZM3T9%2BnWNGDFCI0eO1Jw5c/TDDz/ojTfe0L/%2B9S81bNhQK1as0Jo1a/TYY48V6vEHDx4sLy8vbd68Wa6urho8eLAmTpyod999V6dOnbI0Ybdz9uxZLVq0SHXr1tUDDzygnJwcDRgwQAEBAdq0aZMyMzM1ZswYjRs3Tu%2B%2B%2B67VvufOndPgwYP1/vvvKzw8XJcvX9bAgQM1a9Ysvf322yd8J9cAACAASURBVFq/fr1atWql9evXq3Llytq2bZvGjx%2BvOXPmKCQkRNu3b1d0dLRWrFihmjVr6o033tCpU6e0efNmOTs7a/jw4fb8aCwef/xxffjhhzp27Jjc3d3vuP3%2B/fs1ZcoUffjhh8rJyVF0dLR8fHy0YcMGubm5acGCBYqJiVG7du00adIkq3G%2BtRHPzMxUnz59VKdOHW3evFlms1kTJ07UP/7xD61YsUJSbkN9/vx5/ec//9H58%2Bf13HPPacmSJerfv3%2Bhzy8pKUkXLlywWuYryc/Xt9DHuOe5uFh/NQqTqbgrcKxatay/GoWXV3FX4FientZfjcLDo7grcKy8xqcIG6D/c4ogYS7wOdjXV35%2Bfnfc9/Dhw/Lx8VGFChUsy2rUqKHExERdu3ZNpUuXtix/7rnnrPZNTk7WTz/9pDFjxkjKbZhv3rypp59%2BWmfOnFGNGjU0fPhwhYSE2HN6Vv7UlIzJkydb7vv7%2B%2Bv5559X9%2B7dtXnzZrVo0cJy8itXrpSrq6tGjBihEiVKyMPDQ2PHjlWHDh104cIFrV27Vn/729/UpEkTSVK3bt306aefFqqOM2fOKC4uTl9%2B%2BaXKli0rSZoyZYolKbZVe1ZWljIzM%2BXv7682bdroxRdflCTt27dP%2B/fv18cffyyv358kRo0apSeeeEJjx461OtYDDzygDRs2qGrVqkpJSdG5c%2BdUtmxZnT9/vsDHXrRokZ599lk1bNhQktSyZUuFh4dr2bJlGjVqlL744gt9%2BOGHKleunCRp5MiR6ty5c6HGwpa8sbly5YrVL%2BXtuLq6qnPnzpbpKW%2B%2B%2Baa8vLzk6uqqxMREeXl5KT09XVevXrWMUUF27dqlhIQErVq1St7e3pJyx79Ro0bat2%2BfZbuXX35ZHh4eqlatmsLCwnT8%2BPG7Or/ly5dr5syZVssGvvyyou%2Bh6UEO88ADxV2BY/3yS3FXUDSWLCnuClAYRvvDxqgqVSruCmBDgc/BAwfaDCvzpKamyvMPf7jm3b9x44ZVw3yrCxcu6MUXX1SdOnUsr%2BZ7eHioXr16Gjx4sMqUKaPFixfrhRde0Nq1a1WlSpU/c2r53HXDPH78eKt5wH90618VycnJqlixokqUKGFZVrlyZUn/Sxdr165ttX9hTyzvL5pKt/xj8vX1la%2BNZDGv9oyMDC1YsEAffvihHnvsMUtTefr0aWVnZ%2BdLuN3c3PK9E4Srq6vWr1%2BvZcuWycnJSYGBgUpJSZHLbVLAvAZ/6dKllmXZ2dlq3LixLl%2B%2BrIyMDD344IN3PQ53kpycLEmWRvxOfH19reZyJyQkaNq0aTpx4oQCAgJUrVo1SbrjRZ7JyckqW7aspVmWJG9vb/n4%2BOjMmTMqX7685fHyuLq6ymw2F%2B7EfhcVFaXw8HDrc5Ck36d%2BGIKLS26zfOmSdJuLIe5LTzxR3BU4Vq1auc1yt25SfHxxV%2BM48%2BYVdwWO5emZ%2B7OKj5dumQJ43/PxKe4KHMvNLbdZPnNGysgo7mocp3r14nvsIkiYC3wOLuQrvCVLlrSahivJcv92gdyvv/6qwYMHKzQ0VG%2B99Zal5/rj9N0XXnhBn332mbZt26bu3bsXqp47cfhrvE5OTpbvK1WqpMTERGVnZ1ua5lOnTknKHVB/f/98jei5c%2BdUs2ZNSZKzs7Nu3rxpWXfrO07kNZeJiYkKCAiQJB05ckTr16/XkCFDbNbo5uamvn376urVqxowYICWLl2qWrVqyd/fXx4eHtq5c6el3oyMDCUkJKhatWr6%2BeefLcf44osvtGjRIi1dutTSRMbExOjQoUMFPqa/v78iIiKsphskJibKw8ND3t7ecnd3V0JCgh566CHLODjC1q1bValSJVWrVs1yzMzMTMv6P14ceevPLzMzUy%2B%2B%2BKKGDRumbt26ycnJSfv27dPatWvv%2BLiVKlXS5cuXlZKSYmmar1%2B/rsuXL8vX1/euG%2BPb8fPzy//Sz5kz0i3naBhZWcY6L6NeIBofb6xzS00t7gqKRlqasc7NaFMy8mRkSOnpxV2FMRRBw1zgc3Ah1axZU1euXNHFixctIdrRo0fl7%2B%2BvUqVK5dt%2B5cqVevPNNzVo0CD16dPHat0///lPtW3bVo888ohlWUZGRqGmohZWkV4ymZfUTp8%2BXenp6bpw4YImTZqkxo0bq1KlSnr66af11VdfaevWrcrKytLq1au1Z88ey/41atTQrl27dP78eaWnp2vWrFmWhq5ChQpq2rSppk2bpmvXriklJUVvv/22pQF3c3OzuriwIEOGDFFQUJCGDRum9PR01atXT9WqVdOUKVOUmpqq9PR0TZ48Wb1791Z2drbVvtevX5ezs7M8PDxkNpu1fft2rVmzxtKM5v2Q8i5ii4yM1IIFC/Tbb79Jyp1v06VLF61fv15ubm6KiIjQ%2B%2B%2B/r3Pnzun69et6%2B%2B237Rr7GzduaMmSJVq4cKHGjBkjJycnlStXTmXKlNGGDRtkNpu1f/9%2Bffnll7c9RmZmptLT0%2BXh4SEnJyclJiZa6so7z9uNc926dfXwww9r/Pjxun79uq5fv64JEyaoatWqDp1TBAAA7j8BAQFq0KCBJk%2BerJSUFCUkJGj27Nnq2rVrvm03bdqkCRMmaMaMGfmaZUk6dOiQJk2apAsXLigjI0MzZ85USkqK2rRp47B6i7RhLlWqlD7%2B%2BGMdOnRIjz32mJ588klVqlRJ77//viSpQYMGmjZtmqZMmaLQ0FBt2rRJTZs2tewfFRUlk8mkTp06qU2bNnrwwQdVsWJFy/rp06fL29tb7dq1U6tWrfTAAw9o4sSJkqSIiAitWrVK3bp1u219JUqU0Ntvv63z589r6tSpcnFx0dy5c3Xx4kX97W9/U7NmzXTq1Cl9/PHH%2Bf5Keeqpp/Too4%2BqQ4cOaty4sebMmaNevXrp%2BPHjysjIUPny5dWmTRtFRUVp6dKleuKJJzRs2DC9%2BuqrCgkJ0eDBg9W7d2/LO1S89tprqlevnjp27Ki//e1vql%2B//l2Pd79%2B/SzvPdi6dWtt2bJFc%2BbMsfzCuLm5KSYmRl988YVCQkI0ZcoURUZG3vZ4JUuW1OTJkzVr1iyZTCb17NlTTZs2Vfny5S1J%2Bu3GOW8ss7Ky1LZtW7Vs2VKZmZn6%2BOOPbzttBQAAFJF78H2YP/jgA2VlZalVq1aKjIxU8%2BbNNWDAAEmSyWSyvKI9c%2BZMZWdna9CgQVbvszxu3DhJ0ltvvaWqVauqc%2BfOCgsLU1xcnD7%2B%2BGP5OHCqkpPZUa%2BNA/eKM2eKuwLHcnWV/Pxy52UbaUrG79czGIbJlHshY0iIsaZk7NhR3BU4lpdX7s9q925jTcko5HUq9w0Pj9z5vsePG2tKxv/7f8X32N995/hj3hJyGh1RHwAAgNHdgx9ccj%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%2BclSatXr9bChQs1b9487dy5U7Vr19agQYNkNpvtrjEPDTMAAAD%2BUidPnlRcXJxeeeUVeXp6qkqVKhowYIAWL16cb9vVq1crNDRUrVu3louLi9q3b6%2BGDRtq%2BfLlkqQVK1aoW7duqlmzptzd3TV8%2BHAlJiZq586dDquXi/4AAACMrggu%2BktKStKFCxeslvn6%2BsrPz%2B%2BO%2Bx4%2BfFg%2BPj6qUKGCZVmNGjWUmJioa9euqXTp0pblR44cUWBgoNX%2BDz/8sOLj4y3r%2B/XrZ1nn6uqqgIAAxcfHq3Hjxn/q3P6IhhnG4%2BNT3BU4Vt5/cqVKSTk5xVuLI%2B3YUdwVOJaXV%2B7XefOk1NTircWRmjcv7gocy2SSfvlFeuEFaffu4q7GcX77rbgrcKy8eaxZWf/7Hvec5cuXa%2BbMmVbLBg4cqOjo6Dvum5qaKk9PT6tlefdv3Lhh1TAXtK2Hh4du3LhRqPWOQMMMAABgcGY5OfyYUVFRCg8Pt1rm6%2BtbqH1LliyptLQ0q2V5973yAojfeXp6Kj093WpZenq6Zbs7rXcEGmYAAACDK4oXKP38/Ao1/aIgNWvW1JUrV3Tx4kWVL19eknT06FH5%2B/urVKlSVtsGBgZq//79VsuOHDmiOnXqWI51%2BPBhtWzZUpKUmZmpEydO5JvGYQ8u%2BgMAAMBfKiAgQA0aNNDkyZOVkpKihIQEzZ49W127ds23badOnRQXF6eNGzcqKytLGzduVFxcnDp37ixJevrpp7Vo0SLFx8fr5s2beuedd1S%2BfHmFhoY6rF4aZgAAAIPLyXH8zV4ffPCBsrKy1KpVK0VGRqp58%2BYaMGCAJMlkMmnt2rWSci8GnDVrlubOnauGDRtq9uzZmjFjhqpXry5J6tq1q3r37q2XX35ZjRs31oEDBzR37ly5urraX%2BTvnMyOfJM64F5gpAuupNyL/jw9pbQ0Y130Z6QLrqTci/5MptzzMtLvoFEv%2BgsJMdbvoNEu%2BvPwkGrWlA4flv4wN/W%2BVrdusT10Zqbjj%2BnAfvSexxxmAAAAgzNS3lIcaJgBAAAMjobZPsxhBgAAAGwgYQYAADA4Emb70DADAAAYHA2zfZiSAQAAANhAwgwAAGBwJMz2IWEGAAAAbCBhBgAAMDgSZvvQMAMAABgcDbN9mJIBAAAA2EDCDAAAYHAkzPYhYQYAAABsIGEGAAAwOBJm%2B9AwAwAAGBwNs32YkgEAAADYQMIMAABgcCTM9iFhBgAAAGwgYQYAADA4Emb70DADAAAYHA2zfZiSAQAAANhAwgwAAGBwJMz2IWEGAAAAbCBhBgAAMDgSZvvQMAMAABgcDbN9mJIBAAAA2EDCDAAAYHAkzPYhYQYAAABsIGEGAAAwOBJm%2B9AwAwAAGBwNs32YkgEAAADYQMIMAABgcCTM9iFhBgAAAGwgYQYAADA4Emb70DADAAAYHA2zfZiSAQAAANhAwgwAAGBwJMz2IWEGAAAAbCBhBgAAMDgSZvvQMAMAABgcDbN9mJIBAAAA2EDCDAAAYHAkzPYhYb6NEydOFOv%2B95v/a%2BcLAACKzo0bNzRmzBiFhYWpQYMGGjlypFJTU2%2B7/aZNm9S5c2eFhIQoPDxcM2fOVM4tfyW0a9dO9evXl8lkstyOHj1a6HqKtGFevHixgoKCNH/%2BfJvb7dy5U0FBQX/qMcaNG6dx48YVatu%2Bffvqww8/vON2ixcv1tixY/9UPZJ04MABPfnkk3%2BqRnv06NFDderUsfwi1K9fX%2B3atdO//vUvq18aR/vjeHXo0EFr164tsscDAAB3JyfH8beiFBMTo7Nnz2rTpk3avHmzzp49q%2BnTpxe47b59%2BzRy5EgNGTJEu3btUmxsrD777DNL/5mSkqLjx49r48aN2r17t%2BVWo0aNQtdTpFMyFi9erGeffVYLFixQ9%2B7d5eLi%2BId74403Cr3tv//970Jtd%2BnSpT9bjiTp%2BvXryszMtNy/mxrt9eKLLyo6OlqSlJ2drbi4OI0ePVpnzpzRxIkTi%2BQx/zheGzZsKJLHAQAAf879NCUjLS1N69at04IFC%2BTj4yNJGjFihHr27KmRI0fK09PTavszZ87omWeeUcuWLSVJNWrUUJs2bfTTTz%2BpT58%2B2rdvn3x8fFSpUqU/XVORJcw//PCDkpOTNXr0aOXk5GjTpk2WdUlJSfrHP/6hkJAQtWrVSt99951l3enTpxUUFKQ1a9aoZcuWCg4O1pgxY7Rr1y516tRJJpNJvXr1sjRpo0eP1ujRoyVJM2bM0KBBgzRixAiFhoaqRYsWeueddyzH7tGjh2bMmCFJOnz4sJ577jk1bNhQLVu21KhRo5SSkqLVq1dr7ty52rVrl0JDQyVJ4eHhGjdunJo2baqIiAjl5ORo5cqV6tKli8LCwmQymfTiiy/q0qVLSkhIUL9%2B/SRJJpNJu3fvttSYkpIik8mkb7/91lLTtWvXVK9ePf3222%2BScpvNjh07qkGDBurSpYvVtnerRIkSatKkiSZPnqzly5fr2LFjlvP57LPPLNvdmvDnjf%2BUKVPUsGFDTZw4URkZGZo6daratWsnk8mkJk2aKCYmRmaz%2BbbjlXf89PR0TZs2TY899pgaNmyoHj16WM5VkoKCgrRw4UK1bdtWJpNJzzzzjA4ePPinzxkAANz70tPTdfLkydveMjMzFRgYaNm%2BRo0aSk9PL3AKaNu2bTVmzBirY3/zzTeqXbu2JGnv3r3y9PRU9%2B7dFRYWpi5dumjr1q13VW%2BRJcwLFy5UZGSkPDw81K1bN3300Ufq0KGDJGno0KEqW7astm/fruvXr%2Bull17Kt/%2B2bdu0ceNGJSQkKCIiQgcOHFBsbKxcXV31zDPPaMmSJRo4cGC%2B/TZv3qwpU6Zo6tSp%2Bvbbb/Xiiy%2BqVatWCg4Ottpu4sSJatKkiRYtWqTLly%2BrV69e%2BvTTT/X888/r9OnTiouL08KFCy3b//bbb/riiy8k5Ub/b775phYsWKB69erp3Llz6tWrlxYsWKAhQ4YoNjZWPXv21O7duyVJy5cvlyR5e3urXbt2Wr16tZo1ayZJWr9%2BvapVq6Z69epp27ZtGj9%2BvObMmaOQkBBt375d0dHRWrFihWrWrPmnfxaPPvqo3NzctHPnTj300EOF2ic1NVXfffed0tPT9cknn2jHjh365JNP5Ofnp927d6t79%2B5q3bq1nnrqqQLHK8%2BECRN04MABLViwQA8%2B%2BKCWLl2q3r17a/369apYsaKk3D8SFi1aJA8PDw0aNEjTpk3TvHnzClVnUlKSLly4YLXM19tbfn5%2Bhdr/vuDk9L%2Bvzga67MDLq7grcKy8xOMPycd9z2Qq7gocq1Yt669G4eFR3BU4lru79VfYrSgS5gKfg319C/UcvGfPHvXs2bPAdYMHD5YklSxZ0rIsL1W2NY9Zyp1%2BMXjwYHl4eKh3796SJCcnJ9WtW1fDhg1TxYoV9eWXXyo6OlqLFi3K1x/eTpE0zGfOnNGOHTss83YjIyM1a9YsxcXFqVKlStq1a5c2bdokb29veXt7a%2BDAgXr55ZetjtGnTx95enoqMDBQvr6%2Beuqpp1ShQgVJUnBwsM6cOVPgYwcEBCgiIkKS9Nhjj8nX11cnTpzINyDu7u7asWOHatSooSZNmujzzz%2BXs41mpG3btipdurQkKTAwUOvXr1flypV19epVJSUl6YEHHtD58%2BfvODZ///vf9fzzzyslJUXe3t5avXq1unbtKklatGiRnn32WTVs2FCS1LJlS4WHh2vZsmV2zal2cnJSmTJldOXKlULvExERITc3N7m5uSkyMlJPPfWUypUrp6SkJKWnp8vLy%2BuO53vz5k2tX79es2bNUrVq1SRJvXr10rp167R%2B/Xr1799fUm7y7%2BvrKyl3Uv7cuXMLXefy5cs1c%2BZMq2UDX35Z0YMGFfoY9w2jPSEarRHLY7RG7JdfiruCorFkSXFXgMKoWrW4KzCMomiYC3wOHjjQMjXUlrCwsNu%2BonzgwAG9//77SktLk9fv4UpaWpqk3PDxdo4dO6ZBgwapXLlyWrBggWXbvn37Wm3XqVMnrV%2B/Xps2bSrehnnJkiXKyspS586dLcuysrL00UcfWZqkvHRRkqoW8A8ib86KlDu1IK9ZlSRnZ2eZzeYCHzuv8crj6upa4AVv7733nmbMmKF//vOfGjZsmEJCQjRhwoTbJrm3/rXk7OysBQsWaN26dSpZsqSCgoKUkpJy25puZTKZVLlyZcsPKT4%2BXrGxsZJy/9CIi4vT0qVLLdtnZ2ercePGdzyuLVlZWbp27ZrKlStX6H1uPd%2B0tDS98cYb%2Bumnn%2BTv769HHnlEZrP5jhcSXr16VZmZmapcubLV8sqVK%2Bv06dOW%2B%2BXLl7d87%2BLiUqhxzBMVFaXw8HCrZb7e3tLv/7AMwckpt1lOT5fuYmzuefHxxV2BY3l65jbL8fHG%2Bv174YXirsCxatXKbZa7dTPW7%2BDvr2Qahrt7brN86pR082ZxV%2BM4drxafC8q8Dn4D33Yn1G9enW5urrqyJEjql%2B/viTp6NGjcnV1VUBAQIH7bNu2TcOGDVNkZKSGDx9udd3cvHnz9Mgjj6hJkyaWZRkZGXK/i1cwHN4w37x5UytXrtSkSZP06KOPWpYfOnRI/fv31wu//%2BebkJBguTrx3Llz%2BY7jlPcydBHIycnRgQMHFB0drVdffVVnz57VW2%2B9pdGjR2vVqlUF7nNrPfPnz9d3332ndevWWZq9f/zjH4V%2B/K5du2r9%2BvU6efKkWrdubfnjwN/fXxEREZY/KiQpMTFRHnYmi99//71u3rxp%2BUVxdna2uijx8uXL%2Bfa59Xxff/11lSlTRt9%2B%2B63c3d2Vk5NjScFtKV%2B%2BvNzd3a1%2B1pJ06tSpfP/A/iw/P7/8L/2kpt5fVzfcSd4rH2azsc7rDi%2Br3bfS0ox1br9PLTOc%2BHhjnVt6enFXUDRu3jTuuf3FiuLpo8DnYAfw9PRUu3btNH36dL3//vuSpOnTp%2BvJJ58ssCf69ddf9fLLL2vChAmWV%2B1vdfbsWX366aeKjY3Vgw8%2BqDVr1mj37t139WYIDp8QuW7dOjk5Oaljx47y9/e33Fq0aKHAwECtXbtWzZo101tvvaWrV6/qwoUL%2BeL8oubs7Kw333xT7733nm7evKkHHnhA7u7uKlu2rKTc6Rq2EuOUlBS5uLjI1dVVWVlZ%2Bvzzz7Vjxw5LE5r3F8v169cL3D8iIkK//vqr1qxZo7///e%2BW5ZGRkVqwYIHlori9e/eqS5cuWr9%2B/Z86z6ysLG3fvl3jxo1Tr169VKVKFUm5E%2Be3bNmi9PR0XbhwQQsWLLB5nJSUFLm7u8vZ2VkpKSmaNm2aUlJSrM63oPFydnbW008/rXfffVcnT55URkaGPvnkEx05csQynx0AAOCPxo8fr4CAAHXs2FFPPPGEKleubPUWvR06dLC8VfCHH36orKwsTZo0yep9lvOmYowcOVItWrRQt27dFBoaqmXLlulf//qXZbpoYTg8YV6yZIk6duwoV1fXfOuioqI0depUbd26VW%2B%2B%2BaZatmwpb29vdenSRXv27HF0KTa99957iomJUbNmzSyJaUxMjKTcucNLly5VgwYN9M033%2BTbt0%2BfPjp06JBatmwpd3d3PfLII%2BrWrZt%2B/PFHSblznBs0aKDmzZtb/jK6lY%2BPj8LDw7V7926rlweeeOIJ3bhxQ6%2B%2B%2BqoSExPl4%2BOj3r17q0ePHoU%2Br7lz5%2Bqjjz6SlDsdpXLlyurfv7%2B6detm2WbEiBGaMGGCmjZtKj8/P/Xq1Us///zzbY/5%2Buuva9y4cWrUqJG8vLz0%2BOOPq3nz5jp06NAdx2vkyJGaMWOGevfurStXrigoKEjz5s1T9erVC31OAADAPvfbC5Te3t6KiYmx9GZ/dOtb2N7pMzbc3Nz06quv6tVXX/3T9TiZ72bCKHA/MNLL4VLulAxPz9yX%2Bu%2B3//FsMdLL4VLuu36YTLnnZaTfwebNi7sCxzKZci9kDAkx1u/gLW/XaQgeHrnzfQ8fNtaUjLp1i%2B2hb3l3X4dp29bxx7xXGeg9qgAAAADHK9JP%2BoPjdOnSRcePH7/t%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%2Bfj4qFy5choxYoQ%2B%2B%2BwzpaWl5ds%2BIyNDhw4dUp06dQo83ooVK9StWzfVrFlT7u7uGj58uBITE7Vz585C18QcZgAAAPyl0tPTHs75kQAAIABJREFUdf78%2BQLXpaWlKTMzU4GBgZZlNWrUUHr6/2/v7uNqvvs/gL9O6U6opKTcZFtmc2FJhTZtYQ1FbhvCsMWIxbZr%2Bkk3CnO7sVyomcnddg2zCLuRi9wl6iKbki6RoprSutXROb8/6KzT/R2f0/F6Ph4eW5/vt9MrB%2Bfd%2B7y/n28JUlNT8corryidn5iYiEePHmHjxo24dOkS2rZti3HjxuH999%2BHhoYGbty4gQ8%2B%2BEBxvpaWFiwtLZGYmIgBAwbUKy8LZiIiIiI19zQ6wllZWcjOzlZaMzExgampaZ2fe/nyZUybNq3aYx999BEAoHXr1oo1PT09AKh2jjk/Px92dnaYOnUq1q9fj2vXrmHevHnQ0NDA%2B%2B%2B/j8LCQsXnl9PV1UVRUVGdOcuxYCb1o6srOsHToa0tOkHzMjYWnaB5lf%2B5MzRUrz%2BDV66ITtC8yp%2Bb778HSkrEZmlOffqITtC8rK2BuDjA3R2Ijxedpvk0YGa2uT2Ngvn7779HSEiI0pqXlxfmz59f5%2Bfa29sjKSmp2mN//PEHNmzYgOLiYujr6wOAYhSjTZs2Vc53cHCAg4OD4uM%2Bffpg%2BvTpOHLkCN5//33o6emhpNLf95KSEsVj1wcLZiIiIiJqMHd3dzg5OSmtmZiYNPlxu3fvDi0tLdy4cQN9%2B/YFAKSkpChGKSr77bff8Oeff%2BLdd99VrJWWlkL3yQ/IVlZWSE5OxltvvQUAkEqlSE1NVRr5qAsv%2BiMiIiJSc0/joj9TU1P06tVL6Vd9xjHqoqenh%2BHDh2Pt2rXIyclBTk4O1q5dCxcXF0URXJFcLsfKlStx7tw5yOVyxMfHIzw8XLFLxrhx47Br1y4kJibi4cOHWLduHTp06ID%2B/fvXOxM7zERERESkUvz9/bFq1Sq4urpCKpViyJAhWLp0qeL4yJEj4erqijlz5mDYsGHw8fFBQEAAMjMz0aFDB8yfPx%2BjR48GAIwfPx75%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%2B%2BmDJlyjNIVruFCxfi4MGDAICMjAxYW1sjIyPjqX/d%2Bv5%2BAcDixYvRq1cvWFtbw9raGn369MGQIUOwdu1alJSUPLWMUVFRmDVrluLj999/H1u2bHlqX4%2BIiIjUX1BQEO7evYuff/4Zv/zyC%2B7evYu1a9dWe%2B6yZcsQHx%2Bv%2BPXVV1%2BhXbt2WLx4MQDgxo0bkEqluHDhgtJ59S2WARXtMF%2B7dg2nT59GTEwMDA0NRcdBbm6u4v/Nzc0RHx8vME3NXF1d8fnnnwMA5HI5EhISsGTJEiQlJSEsLOypfM0HDx5ALpcrPv7666%2BfytchIiKixmtJHebi4mIcOnQI4eHhijrwk08%2BwbRp0/DPf/4Tenp6NX5uTk4OPvnkEyxZsgRWVlYAgISEBLz88svQ1tZudCYhF/0dOHAAkyZNQnBwMAYMGICBAwdiyZIlkEql%2BO233zBjxgwAwFtvvYUffvgBMpkMoaGhGDp0KGxsbDB%2B/HhER0crHs/JyQl%2Bfn5wcHCAm5sbzp07BycnJ3z99ddwcHCAjY0N1q9fj%2BPHj8PZ2RnW1taYP38%2BSktLAQCZmZnw9vaGk5MT%2BvbtiyFDhmDfvn0AgCVLluDixYvYunUr5syZgzt37uDll1/GnTt3AADp6enw9vbGwIED4eDggI8//hhZWVkAgJiYGDg5OWHz5s144403YGdnh/nz56OgoAAAUFpailWrVmH48OGwtrbGwIEDERQUpFSANpZEIkGfPn2wYcMGREdH4/Tp0wCqdq0rfz8vv/wygoODYW9vjzlz5kAulyM0NBSurq7o378/bG1t8fHHH6OkpAQxMTHw9/dXdN0zMzOVHr8%2Bz9vWrVvh5uYGa2truLm54fz5803%2B3omIiEi1lZSU4NatWzX%2Bkkql6NGjh%2BL8F198ESUlJUhNTa31cdeuXYt//OMfGDVqlGItISEBDx8%2BxLhx4zBgwABMmTIFcXFxDcorrMMcFxeHwYMHIzo6GteuXcP06dMxaNAgjBw5EmFhYZg2bZqik/vVV19h3759%2BNe//oWXX34Zv/zyC%2BbOnYvdu3ejT58%2BAIArV67g6NGjAB53qNPT05GdnY3//Oc/OHv2LDw9PeHg4IB///vf%2BOuvvzBu3DgcOXIEbm5u8PX1haGhISIjI6GtrY3w8HAEBQVh%2BPDhWL58OW7fvq0odssLSwCQSqWYOXMm/vGPf%2BCXX36BXC5HYGAg5syZg3//%2B98AHhfUmZmZ%2BPXXX5GZmYkpU6Zgz5498PT0xI4dOxAdHY0dO3bA1NQU8fHx8PDwwNChQzFw4MBm%2BX1%2B4YUX0K1bN5w/fx6vv/56vT7n9u3b%2BM9//gOpVIqjR48iPDwcu3btgqWlJVJSUjB58mQcOnQIEyZMQGBgIEJCQhAVFVXlcTZt2lTn87Z//36EhYXB1NQUgYGBCAgIwLFjx%2Br9/WVlZSE7O1tpzcTYGKYmJvV%2BDBJEV1d0guZV3rloQgdDJT16JDpB89LRUf6vurC2Fp2gefXsqfxfarKn0WGu9jXYxASmpqZ1fu7ly5cxbdq0ao999NFHAKA0MlHeVS4sLKzxMdPS0hAREYEffvhBaV1XVxd9%2BvTBRx99BAMDA%2BzevRuzZs1CREREva9PE1Yw6%2BrqYs6cOYpO6Msvv4ybN29We%2B7%2B/fvh6emJXr16AQBGjBiBn3/%2BGfv27VMUXs7OzmjXrp3S582ePRtaWlqKQnHSpEkwMDCAgYEBrKysFMVvcHAw9PX1oaWlhYyMDOjr66OkpAR5eXnVDpeXu3jxItLS0rB//360adMGABAYGAg7OztcvXpVcd68efOgq6uLbt26wd7eXvF9Tpw4EWPGjIGxsTGysrJQUlICfX19ZGZmNua3tEZGRkZ48OBBvc93cXGBnp4e9PT0MHjwYPTr1w9mZmbIyclBbm4uDA0N65WxPs/b%2BPHj0a1bNwCPR0rKZ8Xr6/vvv0dISIjSmte8eZi/YEGDHqdF0NQUnaB5de8uOsHTYWEhOgHVR9euohM0rwZ2y1qMPXtEJ1AbT6NgrvY12MsL8%2BfPr/Nz7e3tkZSUVO2xP/74Axs2bEBxcbGiDisuLgYARb1Vnf3798Pa2hqvvPKK0nr5LHO5WbNm4cCBAzh58iQ8PDzqzAoILJiNjY2VrlTU0tKqcRThzz//rPITQOfOnZGYmKj4uLqfZoyMjAAAmk8KjYoFtYaGhuLrpaWlYfXq1YrdHsoLOFkdf7ru378PIyMjpSevTZs2MDQ0RHp6Ojp06ADg8U9b1X2fxcXFWLZsGWJjY2FmZoZXX30Vcrm8zq/bUDk5OTA2Nq73%2BRV/L%2BVyOb744gucOHEC7du3xyuvvAKpVFqvsZH6PG/lv0cA0KpVqwaPo7i7u8PJyUlpzcTYGCgra9DjqDxNTfX7nm7fFp2geWlrPy6W09OBJ%2BNeakEdO8xduz7%2B8/fwoeg0zcfdXXSC5tWz5%2BNiefJkoMJrRounZj/YVPsa3Azv8Hbv3h1aWlq4ceMG%2BvbtCwBISUmBlpZWrbty/fLLL5g5c2aV9S%2B%2B%2BALOzs549dVXFWulpaXQacA7TSp50V9lFhYWSEtLU1pLS0tTKuwqFt%2B1rVUmlUoxe/ZsLFq0CJMnT4ZEIsHVq1cRERFRr1y5ubkoKChQFM35%2BfnIzc2FiYlJncWfr68vDAwMcPr0aejo6EAmk8HW1rbOr9sQKSkpuHXrlmLEQ0NDA1KpVHG84gWN5Sr%2Bvq1duxYZGRmIiopSfI%2Burq71%2Btr1ed6aytTUtOrjqVthqa6e4u4tQpWWqtf3pm4Fc7mHD9XreVLRi9GbLDFRfb%2B3Z%2BxpdJirfQ1uBnp6ehg%2BfDjWrl2LDRs2AHhcj7i4uEC3hnG%2B3NxcpKSkVFtHXb9%2BHRcvXsSXX34JAwMDhIaGoqCgAMOGDat3phZxp78JEyYgNDQUv//%2BO8rKynD06FFERUVhzJgxTX5sqVSKkpIS6OrqQiKRICMjA2vWrFEcAwBtbW3k5%2BdX%2BdzevXvjpZdegr%2B/P/Lz85Gfn4%2BAgAB07doV/fr1q/NrFxQUQEdHBxoaGigoKMDq1atRUFCgVNA2lkwmQ1xcHLy9vTFs2DAMGDAAwOOh%2BejoaPz111/Iz8%2Bvc/eM8oyampp4%2BPAhvvnmG1y/fl2RUUdHB8XFxXhUzYvq03zeiIiISH35%2B/vD0tISrq6ueOedd9C5c2f4%2Bfkpjo8cOVJpG9vyMduOHTtWeayVK1eia9euGD16NOzt7XHhwgVs3769QTuxtYgO84wZMyCTybBw4UJkZ2ejW7duWL9%2BPezs7Jr82K1bt8aKFSuwYcMGBAcHw9jYGBMnTsSNGzdw/fp1dO/eHW5ubggICMDVq1exevVqxee2atUKW7duxeeffw5nZ2eUlpZi0KBB2L59O1q1qvu31tfXF35%2BfrCzs4O%2Bvj7efPNNvPHGG7h%2B/XqjvpdDhw7h559/VmQzMzPDyJEj8f777yvOmT17NpYsWYIhQ4agbdu2WLBggeJzquPt7Q0fHx8MGjQIrVu3ho2NDUaPHq3IaGtrC2NjY9ja2uK7775T%2Btyn%2BbwRERFR/bWkbeWAxyOuQUFBCAoKqvZ4ZGSk0se9e/eucSba0NAQK1eubFIeibw59jAjUiXqOJKhjjPMjfzBUGXp6j6%2BkPHmTfV6q1/dRjJ0dQErKyA5Wb2epycXUqsNa%2BvH8779%2BqnXSIbAkquZNt9Scu5c8z%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%2BPb1AOFsxEREREpHKSk5MxZcoU/Pe//631vLy8PHh6esLNzQ2xsbFYvnw5Vq5ciStXrgAAYmJiEBQUhM8//xyxsbEYNWoUPvzwQxQXF9c7CwtmIiIiIjXX0jrM586dw/Tp0zFmzBiYm5vXeu4vv/wCQ0NDTJkyBa1atcLAgQPh6uqK3bt3AwB%2B%2BOEHjBw5EjY2NtDS0sJ7770HIyMjHDlypN55OMNMRERERM9USUkJMjMzqz1mYmKCnj174sSJE9DR0cH27dtrfazk5GT06NFDae2ll17Cvn37AAA3btzAuHHjqhxPTEysd14WzERERERq7ml0hLOyspCdna20ZmJiAlNT0zo/9/Lly5g2bVq1xzZt2oShQ4fWO0dhYSH09PSU1nR1dVFUVFSv4/XBgpnUj6am6ATNKisrC99//z3c3d3r9Y9Qi/HKK6ITNKusrCx8/9VXj5%2Bn7t1Fx6EaKD1PVlai4zQfuVx0gmaleJ6OHVOvf/cEehp/RL766nuEhIQorXl5eWH%2B/Pl1fq69vT2SkpKaJYeenh7y8/OV1kpKSqCvr684XlJSUuW4kZFRvb8GC2YiFZednY2QkBA4OTnxhUOF8XlqGfg8tQx8nloGd3d3ODk5Ka2ZmJg88xw9evTAmTNnlNZu3LgBqyc/FFtZWSE5ObnK8cGDB9f7a/CiPyIiIiJqMFNTU/Tq1Uvpl4gfcIYNG4Y///wT3377LaRSKc6fP49Dhw4p5pbHjx%2BPQ4cO4fz585BKpfj2229x//59DBs2rN5fgwUzEREREbUoI0eOxJYtWwAARkZG%2BOabb3Ds2DHY29vD19cXvr6%2BGDBgAABg4MCB8Pf3R0BAAOzs7BAZGYmwsDAYGhrW%2B%2BtxJIOIiIiIVFZUVFSVtcjISKWPe/fuje%2B%2B%2B67Gxxg9ejRGjx7d6AyaAQEBAY3%2BbCJ6JvT19WFnZ6e4gIFUE5%2BnloHPU8vA54lUiUQuV7NLa4mIiIiImhFnmImIiIiIasGCmYiIiIioFiyYiYiIiIhqwYKZiIiIiKgWLJiJiIiIiGrBgpmIiIiIqBYsmImIiIiIasGCmYiIiIioFiyYiYiIiIhq0Up0ACKqv9LSUmhra4uOQdRiJCcnY/Xq1UhNTYVMJlM6dvz4cUGpiKilYcFMpII%2B/vhjBAcHQ09PT7GWkpKCRYsW4aeffhKYjKhl8fPzg56eHjw9PdGqFV/yiKhx%2BK8HkQpKT0/H2LFjsXHjRlhZWWHv3r1YtWoVRowYIToaVVBYWIg9e/ZU271cuXKloFRUUVJSEk6dOoU2bdqIjkJ1iImJQWBgIFJTUyGXy5WOXbt2TVAqosdYMBOpoN27d2PDhg1wd3dHr169cPPmTaxfvx5OTk6io1EFPj4%2BiI%2BPh729PbS0tETHoWqYmpqitLRUdAyqh88//xx9%2B/aFr68v3w0glcM/kUQqSFNTE05OToiIiEB8fDwGDx4MGxsb0bGokpiYGOzbtw9dunQRHYVq4OHhgXnz5mHatGno0KGD0jFbW1tBqag6qamp%2BO6776CjoyM6ClEVLJiJVNCaNWsQHh6O999/H5MmTYKvry9cXFwQHBwMR0dH0fHoCR0dHXTs2FF0DKpFcHAwACA%2BPl5pXSKR8G1%2BFWNpaYmsrCz%2BAEoqSSKvPChERMI5OjpizZo1sLOzU6zt2LED69evx%2BXLlwUmo4q2bNmCrKwseHl5oX379qLjELVooaGh2L9/P8aPHw8TExOlY25uboJSET3GgplIBT148ACGhoZV1hMTE9GzZ08Biag6Tk5OyMjIgEQiqXKM3UvVce/ePRw6dAjp6ekwNTWFi4sLunbtKjoWVVLTNRoSiYRbAJJwLJiJVFRKSgr27t2Le/fuISgoCJGRkfDw8BAdiyq4cOFCjccqvjtA4iQkJOC9997DCy%2B8gM6dO%2BP27dtISUnBtm3beF0AEdUbC2YiFXTmzBnMnz8fb731Fk6cOIHIyEiMHTsWM2bMgKenp%2Bh4VIFMJsPVq1dx584dmJqaol%2B/ftDQ4E1UVcW0adMwdOhQTJs2TbG2Y8cOHDt2DHv37hWYjKpz9epV7Nu3D%2Bnp6TAxMcHYsWPRv39/0bGIWDATqaJx48ZhwYIFcHR0hK2tLWJjY5GQkABvb2%2B%2BNalCsrOzMWfOHCQmJsLQ0BC5ubmwtLTEN998AzMzM9HxCIC9vT3OnDmjtE2ZVCrFgAEDcOnSJYHJqLLTp09j7ty5cHJyUrwbcOLECXzxxRcYOnSo6Hj0nOMuGUQq6NatWxg8eDAAKOZje/fujby8PJGxqJJVq1bB0tIS4eHh0NfXR35%2BPgICArBy5Ups2LBBdDwCoKenh7t37yrtvHD37l0YGBgITEXV2bhxI1atWoXhw4cr1o4ePYp//etfLJhJOL5vSKSCzM3NERcXp7SWkJCATp06CUpE1Tl//jwCAwOhr68PAGjbti0CAgJw7tw5wcmo3IgRIzB//nxER0fj5s2bOHnyJBYsWMC7ZqqgmzdvwtnZWWnN2dkZqampYgIRVcAOM5EKmj17Nj788ENMmjQJUqkUYWFh2LlzJxYtWiQ6GlUgk8mq7JAhkUh41z8V8tFHHyEnJwdz586FVCqFjo4Oxo0bBy8vL9HRqBJDQ0Ncv35daSegxMTEKlvMEYnAGWYiFXXy5Ens3r0b6enpMDMzw8SJE6t0X0ishQsXolWrVggMDETr1q1RWFgIf39/lJWV4YsvvhAdjyooLS1FXl4eOnToUO02gCReaGgo9u7di9mzZytmmMPCwjB58mR88MEHouPRc44FMxFRI2VkZGDGjBlIT0%2BHoaEhHjx4gJdeeglbt27lHQAFO3z4MFxcXHDw4MEaz%2BHNMFSLXC5HSEgIDhw4gD///BMWFhaYMGECZsyYwZ1nSDgWzEQqxMfHp85zVq5c%2BQySUH09evQIsbGxyMnJgYWFBXr37g1NTU3RsZ57Li4uOHz4MG%2BGQUTNgjPMRCooNzcX0dHReOutt9ClSxdkZmbi119/xdtvvy06GuHxnePMzMyQkZEBAOjWrRu6desGAMjMzATw%2BMJNEufw4cMAgKioqGqP5%2BfnP8s4VIvQ0FB4enoiJCSkxnM4c06isWAmUiHl3eM5c%2BZg48aNGDJkiOLY6dOnsWXLFlHRqIIRI0YgLi4OTk5OVeZh5XI5JBIJb42tIuzs7Kq9I%2BNbb72FixcvCkhElcXGxsLT0xMxMTHVHufMOakCjmQQqSBra2tcunRJaW6vrKwM/fv3R3x8vMBkBDzex7dTp05IT0%2Bv8RwLC4tnmIgqunXrFvz8/CCXy3Hx4sUqd4orKCjAgwcPauw%2BkxjZ2dnV7oiRnJwMKysrAYmI/sYOM5EKsrCwwNGjRzFy5EjF2oEDBxRv%2B5NY5fthBwcHY/PmzVWOe3h4YNeuXc86Fj3RrVs3vP3228jNzUVcXBzs7OyUjmtra9c420ziODs7V9l/vqysDO7u7lXWiZ41FsxEKmjhwoX46KOPsHv3bnTq1Al37tzB9evXOZKhAu7cuaPYeeH06dNV5i4LCgqQlJQkIhpVMGXKFABA586duRuGCrt16xZmzZoFuVyO4uJipTE0ACgpKeG7NaQSOJJBpKL%2B97//4ciRI8jKyoKZmRlcXV2Vbu9LYshkMixcuBA5OTm4dOkSbGxslI7r6OjAzc0NLi4ughJSZefPn0dmZibKX%2B6kUimSkpLg6%2BsrOBkBwIkTJ5Cbm4uAgAAEBgYqHdPR0YGtrS1vXkLCsWAmImokX19fBAcHi45BtQgODsZ3332nuH15WVkZCgsL8cYbbyA0NFRwOqrowoULVcZniFQFC2YiFZScnIzVq1cjNTUVMplM6Rj3jlUtxcXFyMvLUzxPUqkU169fx7BhwwQnIwBwcHDApk2bUFxcjIiICKxYsQKrVq1CUVERli1bJjoeVVBaWopDhw4hMzOzyt%2Bn6q4VIHqWOMNMpIL8/Pygp6cHT09PtGrFv6aq6sCBA1i2bBkePnyotG5sbMyCWUUUFxfjtddeQ3Z2Nn7//XdIJBJ4eXlhxIgRoqNRJf/3f/%2BH6OhoGBkZQSqVonXr1khOTuYMOqkEvhITqaCkpCScOnUKbdq0ER2FarF582Z4e3tDX18fsbGxmD59OtasWQMHBwfR0egJMzMz3L9/HyYmJrh37x6kUil0dXVRUFAgOhpVEh0djb179yInJwd79%2B7FunXr8M033%2BDKlSuioxGBN2cnUkGmpqYoLS0VHYPqkJ2djenTp2PgwIG4ffs2evXqhRUrVuCHH34QHY2ecHR0xHvvvYecnBzY2tri//7v/xAQEABLS0vR0agSmUyGF154AS%2B88ILixj9TpkzhDWZIJbDDTKSCPDw8MG/ePEybNg0dOnRQOmZraysoFVVmbGwMqVSKTp064ebNmwAe3xL7/v37gpNRuUWLFsHY2BhaWlrw8/ODr68v8vPzebGmCjIzM0NaWhq6dOmC%2B/fvo6ioCBoaGigsLBQdjYgX/RGpop49e1a7zlsuq5aFCxdCR0cHS5cuxcyZM%2BHm5gZdXV2EhITw4kwVERQUhIULF3K8qQUIDQ3Fzp07sW/fPqxfvx737t2Djo4OiouLsXPnTtHx6DnHgplIhdy7dw9mZmZIT0%2BHRCJB5b%2BeEonCtipBAAAc60lEQVQE5ubmgtJRZVlZWYqt5W7fvo05c%2BagpKQEK1euhKurq%2Bh4BMDOzg7nzp2Dpqam6ChUD0ePHoWjoyNkMhnWrFmDgoICeHt7cw96Eo4FM5EK6devH%2BLi4tCzZ09IJJJqz2GHWXVERkZi6NCh0NHRAQA8evQIUqkUenp6gpNRuVWrVqGwsBBjx46FiYmJ0t8r/vCpWj788EOsWbOG7waQSmLBTKRC7t69i06dOiE9Pb3Gc3ibWNVhZ2eHM2fOQEtLS3QUqkHF8abyYlkul3O8SQUNGDAAp06dgra2tugoRFWwYCYiaqRZs2Zh1KhRGD16tOgoVAP%2B8NlyBAcH486dO3B1da3ybgAvdibRWDATETXSuHHj8Pvvv0NbWxsdOnRQeoHnRX9EDcOLnUmVcVs5IqJG8vDwEB2B6sDrAVqOxMRE0RGIasQOMxERqa0LFy4ofZyTk4OdO3di9OjRmDhxoqBUVJ2MjIwaj/ECTRKNBTMRUSNNnTq1xu5leHj4M05D9ZWdnY333nsPkZGRoqNQBeXvBpSXJRX/bvHdABKNIxlERI1kb2%2Bv9HFubi6OHTsGd3d3QYmoPtq1a4fMzEzRMaiSynP/OTk5%2BPrrrzFkyBBBiYj%2Bxg4zEVEz%2Bv3337F69Wrs2LFDdBQCcPDgQaWPpVIpjh8/jsLCQt49rgXIz8/HmDFj8Ntvv4mOQs85dpiJiJpRr169cPXqVdEx6ImNGzcqfaypqYkXX3wR/v7%2BghJRQ/3111%2BiIxCxYCYiaqzKFylJpVJERkaiU6dOghJRZVFRUaIjUD2FhIQofSyVShEdHY3XXntNUCKiv7FgJiJqJCcnJ8VFSuX/NTAwQHBwsOho9ERqaipiYmLw4MEDGBsbY%2BDAgbxhiYqKiYlR%2BlhTUxPW1taYPXu2oEREf%2BMMMxFRI1W%2Bi5ympiaMjY15q2wVIJfLsWzZMnz33XfQ1taGgYEB/vrrL5SWlmL69On47LPPREckohaEHWYiokaysLDAzZs3Fd1Ldi5Vx9dff40TJ05g27ZtGDRoEACgrKwMZ86cgb%2B/PywtLbmbiYo5e/Yszp8/jwcPHqB9%2B/ZwcHDgLbFJZbDDTETUCEeOHMHq1auRmZmpGMmwsLCAj48Pt8FSASNHjsTSpUsxYMCAKsdOnTqFjRs3Yt%2B%2BfQKSUWUPHz7E/PnzER0djW7dusHIyAg5OTm4desW3n77bXz55ZfQ0NAQHZOecyyYiYga6Pjx4/D29sbMmTPx1ltvwcjICPfv30dUVBR2796NzZs3V1uo0bNjbW2NuLi4am8sU1ZWhkGDBlWZmSUx1q1bh//85z/YuHEjunfvrlhPSUnBwoUL4eLiAk9PT4EJiTiSQUTUYGFhYfDz88OECRMUa926dUO/fv1gbm6Or7/%2BmgWzYBKJpMa7MGpqakIqlT7jRFSTY8eOYf369UrFMgC8%2BOKLCAgIQEBAAAtmEo7vcRARNVBycjJGjRpV7bExY8YgMTHxGSciarmys7PRu3fvao9ZW1vzroykEthhJiJqIJlMBh0dnWqP6enpoaio6BknospKS0ur7OtbETvMqqO2%2BWSJRMLnilQCC2Yiogaq6a1%2BUh3W1ta1zijzZhhE1BAsmImIGkgmk%2BHixYuo6ZppmUz2jBNRZTt37qz3uZcuXYKNjc1TTEO1KS0thY%2BPT63HiURjwUxE1EAlJSXw8PCo8Tg70C3LBx98gLi4ONExnluurq5NOk70LHBbOSIieq5ZW1sjPj5edAyqh8OHD8PFxUV0DHoOcZcMIqKnqF%2B/fqIjUB34jkDL4efnJzoCPadYMBMRPUV8E4%2Bo%2BfDvE4nCgpmI6Cli95Ko%2BfDvE4nCgpmIiIiIqBYsmImIiIiIasGCmYiInmuWlpaiIxCRimPBTEREai0lJQXBwcHw8vJCbm4udu3apXT8wIEDgpJRQ/GiPxKFBTMR0VPEF3ixzpw5g4kTJyI3Nxdnz55FSUkJNm3ahNDQUNHRqBEcHBxER6DnFG9cQkTUQBkZGXWeY25uDgDIyclB%2B/btn3YkqsG4ceOwYMECODo6wtbWFrGxsUhISIC3tzeOHz8uOh4BCAkJqfMcLy%2BvZ5CEqGa8NTYRUQM5OTlV2d5KLpcrrV27dg0AWCwLduvWLQwePBjA31uS9e7dG3l5eSJjUQUxMTG1HudWcqQKWDATETVQeWfyp59%2BwqVLl/Dpp5%2Bia9euuHv3LtauXYvXXntNcEIqZ25ujri4ONjY2CjWEhIS0KlTJ4GpqKKdO3eKjkBUJ45kEBE1kqOjIyIiImBgYKBYy8/PxzvvvIMzZ84ITEblIiMjERgYiEmTJiE8PBxz587Fzp07sWjRIri5uYmOR5WcP38emZmZitl/qVSKpKQk%2BPr6Ck5Gzzt2mImIGqmwsBAymUxpraioCFKpVFAiqmzkyJFo06YNdu/eDXNzc5w/fx5LliyBs7Oz6GhUSXBwML777jvo6%2BsDAMrKylBYWIg33nhDcDIiFsxERI02ZMgQzJ07FwsWLECnTp2QlpaGDRs2wMXFRXQ0euLq1atwdHSEo6Oj6ChUh6NHj2LXrl0oLi5GREQEVqxYgVWrVqGoqEh0NCKOZBARNVZhYSECAwNx7NgxlJaWQkdHB6NHj4avry%2B0tbVFxyMAffv2haWlJSZMmIDRo0ejbdu2oiNRDfr164e4uDhkZ2dj1qxZiIiIQEFBAUaMGIFTp06JjkfPORbMRERNVFpaigcPHsDIyAhaWlqi41AF%2Bfn5OHToEA4ePIikpCQMHToU48ePx8CBA0VHo0pGjBiBnTt3wtjYGHZ2djhz5gwkEgns7OwQFxcnOh4951gwExE1QUpKCvbu3Yt79%2B4hKCgIkZGR8PDwEB2LqpGSkoKIiAj89NNP0NLSwq%2B//io6ElWwatUqnD59Gjt27MDSpUvRunVr6Ojo4I8//uDdGEk43umPiKiRzpw5gwkTJvAuci1AUVERrly5goSEBOTl5aFPnz6iI1ElixYtwujRo6GlpQU/Pz/k5ubixo0bCAoKEh2NiB1mIqLG4l3kVN/Zs2fx448/4rfffkPnzp0xfvx4uLm5KW0FSERUF%2B6SQUTUSLyLnOqbN28eRowYge3bt/OGMiqusLAQe/bsQWpqapXtGleuXCkoFdFjLJiJiBqJd5FTfadPn1bs60uqzcfHB/Hx8bC3t%2BfFs6RyWDATETXS7Nmz8eGHH2LSpEmQSqUICwtT3EWOxAoICEBAQACCg4NrPIddS9USExODffv2oUuXLqKjEFXBgpmIqJF4FznVxctzWh4dHR107NhRdAyiavGiPyKiRjp69CiGDx9eZf3777%2BHu7u7gERU2eXLl9G3b98q66dOnVLMn5Nq2LJlC7KysuDl5YX27duLjkOkhAUzEVEDFBcXIzc3F8DjDvORI0eUupn5%2Bfl49913ER8fLyoiVVB%2B97iKCgoK8MYbb/A5UjFOTk7IyMhQXEBb0bVr1wQkIvobRzKIiBqgoKAAI0eORElJCYDHL/JyuRwSiUTx36FDhwpO%2BXy7desWRo4cibKyMsjlcrzyyitVzunXr5%2BAZFSbzz//XHQEohqxw0xE1ED3799HcXExXF1dcfjwYaVjOjo66NChg6BkVO7atWv466%2B/4OnpibCwMKVjOjo66NGjB/T09ASlo5rIZDJcvXoVd%2B7cgampKfr16wcNDd5jjcRjwUxE1EgymazaF/NHjx6hVSu%2BgacK0tLSuOtCC5GdnY05c%2BYgMTERhoaGyM3NhaWlJb755huYmZmJjkfPORbMRESNdPv2bWzatAmZmZmKGy1IpVLcvHkT58%2BfF5yOACA3Nxc7d%2B6s8hxdv34dERERgtNRRZ988gnkcjmWLVsGfX195OfnIyAgAI8ePcKGDRtEx6PnHFsgRESNtGTJEsjlchgZGeH%2B/ft49dVXcfDgQbz33nuio9ETPj4%2BSE1NRfv27VFYWIhOnTrh9OnTmDJliuhoVMn58%2Bdx7NgxxY1m2rZti4CAAAwZMkRwMiKAg0FERI109epVbNq0CXPnzkXbtm3h6%2BuL9evX49y5c6Kj0ROxsbHYsWMHFi9ejC5dumDLli1Yvnw5/ve//4mORpXIZLIqO2RIJBLe9Y9UAgtmIqJG0tPTg4GBAbp27Yrr168DAAYPHsxiTIW0atUKHTt2hKWlJZKSkgA83g7wjz/%2BEJyMKrO3t0dAQACKiooAAIWFhQgICICdnZ3gZEQsmImIGq1r1644efIk9PX1IZPJkJaWhszMTDx69Eh0NHrCwsICV69eRbt27VBYWIicnBwUFRUptgUk1fHpp5/iypUrsLOzw%2Buvvw57e3vcuHEDixcvFh2NiDPMRESN5enpiQULFuDw4cNwd3fHu%2B%2B%2BC01NTc5cqpDJkydj6tSpiIyMhIuLC6ZPn45WrVrB1tZWdDSqxNzcHJGRkYiNjUVOTg4sLCzQu3dvaGpqio5GxF0yiIiaIjMzE%2B3bt4eWlhaOHDmCgoICuLm5QVtbW3Q0euLKlSvo2bMnJBIJtm/fjsLCQsycORMGBgaioxGAe/fuwczMDBkZGTWeY25u/gwTEVXFgpmIiNTW2LFjER4ejjZt2oiOQjUov315%2BQ81FZXfPZO3xibROJJBRNRA1b2wV8YXeNWQlZUlOgLVITIyEgBw/PhxwUmIasYOMxFRA124cKHOc3hlv2rw9/dHQkICnJ2dYWpqqvSDjpubm8BkVNmHH36IzZs3V1n38PDArl27BCQi%2BhsLZiKiJsrLy0NaWhpeffVVPHr0iPPLKsTJyanadYlEwo6mCrhz5w4OHjwIANi6dStmz56tdLygoAD79%2B9HbGysiHhEChzJICJqpMLCQvj5%2BSEyMhK6uro4cOAAZsyYge3bt%2BOFF14QHY8AREVFVbuen5//jJNQdczNzZGcnIycnByUlZUhJiZG6biOjg78/f0FpSP6GzvMRESN5O/vj6ysLPzzn//ExIkTcfbsWSxfvhxpaWnYtm2b6HiEx6Mx1Y3Q9O/fHxcvXhSQiGri6%2BuL4OBg0TGIqsWCmYiokQYPHoxDhw7BwMBAUZiVlJRg8ODB9Zpzpqfj1q1b8PPzg1wux8WLF9G/f3%2Bl4wUFBXjw4EGN3WcSp7i4GHl5eZDJZAAAqVSK69evY9iwYYKT0fOOIxlERI0kk8kU88rlvYeKayRGt27d8PbbbyM3NxdxcXFVLsDU1taucbaZxDlw4ACWLVuGhw8fKq0bGxuzYCbhWDATETXSgAEDsGzZMvj5%2BSl2X/jyyy%2B5Q4YKmDJlCgCgc%2BfO3A2jhdi8eTO8vb2hr6%2BP2NhYTJ8%2BHWvWrIGDg4PoaETQEB2AiKil8vHxQUpKCmxtbZGfnw9ra2vExsbis88%2BEx2Nnhg2bBi%2B/fZbAMCNGzcwYcIEzJ49G5mZmWKDURXZ2dmYPn06Bg4ciNu3b6NXr15YsWIFfvjhB9HRiDjDTETUFHK5HAkJCUhPT4eZmRn69OkDTU1N0bHoicWLF%2BPatWv46aef4OHhAWNjY%2Bjo6CA/P7/aPX9JnCFDhuDo0aPQ1NTEoEGDFDtm2NjY4NKlS4LT0fOOIxlERI304MEDGBoaok%2BfPujTpw8AoLS0FCtWrMDSpUsFpyPg8U1mDhw4gLy8PMTFxeHEiRMwNDTE66%2B/LjoaVdKnTx/4%2Bflh6dKlsLS0xN69e6GrqwtDQ0PR0Yg4kkFE1FDXrl3DkCFDMHDgQLi7uyMvLw8AkJSUhDFjxiAiIkJwQipXWFgIQ0NDnDt3Dl26dEHHjh0hkUjqvLU5PXs%2BPj7IyclBYWEhPv30U6xbtw5Lly6Ft7e36GhEHMkgImooDw8PtG3bFu7u7ti5cyd69OgBR0dHzJ07Fy%2B//DLWrFmDzp07i45JePxcDRo0CKdOncJrr70GLy8vfPnll0hJScH27dtFx6NaPHr0CFKpFHp6eqKjELFgJiJqKBsbG/z6669o37497t27Bw8PD/z1119499134e3tDQ0NvnmnKm7cuIHAwEDo6Ojgyy%2B/xB9//IGgoCBs3LgR3bt3Fx2PKpBKpThy5AjS09MV%2BzCX8/LyEpSK6DEWzEREDWRtbY34%2BHjFx//4xz%2BwaNEizJw5U2AqopZtwYIFiImJgZWVldLIjEQiQXh4uMBkRLzoj4iowSrPv2ppaWHq1KmC0lB1QkND4enpiZCQkBrPYddStZw%2BfRoREREcZyKVxIKZiKiJtLS0oKWlJToGVRAbGwtPT0/F1mSV8aI/1WNiYsIdMUhlcSSDiKiB%2BvTpg2XLlik%2BDgwMhL%2B/v9I5vLscUcNERUVh9%2B7dmDx5Mtq1a6d0zNbWVlAqosdYMBMRNZCTk1OtxyUSCY4fP/6M0lBdrly5gps3b6Lyyx1/qFEtX3zxBbZu3VplXSKR4Nq1awISEf2NBTMREamt9evXIywsDCYmJmjV6u8pRP5Qo3psbW2xbt06vP7669xphlQOZ5iJiBrpww8/rPb2yh4eHti1a5eARFRZREQEtmzZAkdHR9FRqA46OjpwcHBgsUwqiQUzEVED3LlzBwcPHgTw%2BKr%2ByrswFBQUICkpSUQ0qkZhYSEGDx4sOgbVw4wZM7B27VrMmTMHBgYGouMQKWHBTETUAObm5khOTkZOTg7Kysqq7MKgo6NT5QJAEufNN9/EoUOHMGrUKNFRqA67d%2B9GRkYGvv322yrHOMNMonGGmYiokXx9fREcHCw6BtViwYIF%2BO2332BpaYkOHTooHePNMFTL%2BfPnaxzHsLOze8ZpiJSxw0xE1Ej//Oc/8fHHH2Pu3Ll48cUXsWHDBty5cwcBAQHQ19cXHY8A9OjRAz169BAdg%2Bph9erVCA8PR5s2bURHIaqCHWYiokb6%2BOOPkZeXh1WrVsHY2BgpKSlYs2YNjI2NsXz5ctHxiFqU119/HceOHWPBTCqJBTMRUSMNGDAAx48fV%2BomFxQUYNiwYTh37pzAZBQQEICAgAD4%2BPjUeM7KlSufYSKqi7%2B/PxISEuDs7AxTU1OluzFyz2wSjSMZRESNJJPJUFZWprQml8uhqakpKBGVYy%2Bo5YmOjgYAfP/990rrEomEBTMJxw4zEVEjffLJJygsLISPjw86deqEu3fvYvXq1WjTpg0%2B//xz0fGIiKiZsGAmImqknJwcfPTRR4iNjVW8fTxo0CCsXbsWRkZGgtMRADx48AB79uxBeno6ZDKZ0jGOZKieq1evYt%2B%2BfUhPT4eJiQnGjh2L/v37i45FxIKZiKipMjIykJ2dDTMzM3Ts2FF0HKrgvffew927d/Haa69V2bKMBbNqOX36NObOnQsnJyd07twZt2/fxokTJ/DFF19g6NChouPRc44FMxFRI8XGxtZ4zNbW9hkmoZpYW1vjxIkTMDQ0FB2F6jBx4kTMmDEDw4cPV6wdPXoUYWFhOHDggMBkRLzoj4io0aZOnVplTUNDA506dcLx48cFJKLKunbtCqlUKjoG1cPNmzfh7OystObs7IwlS5YISkT0NxbMRESNlJiYqPRxTk4ONm3aBAsLC0GJqDI/Pz94enrCzc0NBgYGSse484JqMTQ0xPXr19GzZ0/FWmJiIkxMTASmInqMIxlERM2opKQEzs7OOHnypOgoBMDHxwcREREwMTFRmmGWSCR8F0DFhIaGYu/evZg9e7ZihjksLAyTJ0/GBx98IDoePefYYSYiakZ5eXl4%2BPCh6Bj0xLFjx/DTTz/hpZdeEh2F6vDBBx/g4cOH2Lp1K/78809YWFjAw8MDM2bMEB2NiAUzEVFjVb6LnFQqxaVLlzBo0CBBiagyIyMjdO3aVXQMqsXUqVOxc%2BdOHDhwAPPnz8f8%2BfNFRyKqgiMZRESNVLlg1tDQwIsvvgh3d3el22WTOAcPHkR0dDRmzZoFAwMDpdstm5ubC0xG5aytrXHy5Em8%2BeabiIuLEx2HqFosmImISG1VvICsvFiWy%2BWQSCS4du2aqFhUwaxZs3D27Nlaz%2BFzRaKxYCYiaqCQkJA6z/Hy8noGSagu6enpNR7jbiaqobi4GH/88QdmzpyJsLCwas%2Bxs7N7xqmIlHGGmYiogWJiYgA83hEjISEBr776Kjp37ozMzExcvnwZDg4OghNSORbFqk9PTw82NjZYt24dC2NSWewwExE10uLFi9G3b19MmjRJsfbjjz/il19%2BwebNmwUmo549eyrNK1eHb/OrlrKyMvz8889ITU2FTCZTOsZ3bEg0FsxERI3Ur18/XLx4UWl/37KyMvTv3x/x8fECk9GFCxcAAGfOnMGpU6fg5eWFrl274u7du9i0aRMcHBywYMECwSmpIl9fX0RGRqJnz55o1ervN8AlEgnCw8MFJiPiSAYRUaO1b98esbGxsLe3V6ydPn0apqamAlMR8PfM65IlS7Br1y507NgRAGBlZYVXXnkFEyZMYMGsYqKiohAeHo7evXuLjkJUBQtmIqJGmj17Nj744AM4OzvD3NwcaWlp%2BO2337Bq1SrR0eiJnJwctGvXTmlNR0cH%2Bfn5ghJRTeRyOV599VXRMYiqpVH3KUREVJ0JEyZg69ataNWqFa5evQo9PT18%2B%2B23GD58uOho9IStrS0%2B%2B%2BwzpKWlQSqV4n//%2Bx8%2B%2BeQTODo6io5Glbi4uGDbtm2iYxBVizPMRERNdP/%2BfaSnp8PExASdOnUSHYcqyM7Ohre3Ny5duqS4CNDBwQGrV69G%2B/btBaejiiZPnoy4uDjo6elVeW6OHz8uKBXRYyyYiYgaqaCgAJ999hmioqIUawMHDsSXX35ZZQyAxMrIyEBmZibKysoQERGBiIgI/Pe//xUdiyr48ccfazw2ZsyYZ5iEqCrOMBMRNdK6detQWFiIw4cPo3Pnzrh16xZWrFiBNWvWICgoSHQ8qiAjIwPbtm3DyZMnYWVlhU8//VR0JHqiPjcCIhKNHWYiokZ68803sX//fhgbGyvWsrOzMWrUKJw7d05gMgIAmUyGY8eOYfv27UhOTsajR4%2BwefNmvPHGG6KjUQVTp06t9Ti3lSNVwA4zEVEjFRcXo23btkpr7dq1q3LTBXr2duzYgfDwcMhkMkyaNAlhYWF455130KNHD9HRqJKdO3eKjkBUJ%2B6SQUTUSH379sWGDRtQ/kadXC7Hhg0buI%2BsCli5ciUcHR3x888/w9PTE4aGhqIjEVELxpEMIqJGSkpKwrRp06CtrQ0LCwukp6dDIpFg%2B/btePHFF0XHe67t3r0be/bsQU5ODiZOnIjJkyfDzc0NBw8eVNzEhIiovlgwExE1UlFREUpLS3H8%2BHHcv38fFhYWcHR0RJs2bURHoyfOnTuHXbt2ITo6GmVlZVi%2BfDlcXV2hqakpOhoRtSAsmImIGsnJyQkREREskFuA9PR07NmzB/v374eGhgZGjRqFxYsXi45FRC0EZ5iJiJqguLhYdASqBwsLC3z66ac4deoUFi1ahAsXLoiOREQtCDvMRESN5OPjg3PnzmHw4MEwNTVVOubl5SUoFRERNTduK0dE1Eh37txBly5dcPPmTdy8eVOxXn4LZiIiUg8smImIGiEkJARt2rTB66%2B/jilTpoiOQ0RETxFnmImIGmj16tXYs2cPtLS0sHHjRoSGhoqORERETxFnmImIGmjw4MHYtm0brKysEBMTg%2BDgYBw6dEh0LCIiekrYYSYiaqD8/HxYWVkBAGxsbJCZmSk4ERERPU0smImIGkhD4%2B9/Olu14qUgRETqjgUzEVEDcZKNiOj5wtYIEVEDPXr0CAcPHlR8LJVKlT4GADc3t2cdi4iInhJe9EdE1EBOTk61HpdIJDh%2B/PgzSkNERE8bC2YiIiIiolpwhpmIiIiIqBYsmImIiIiIasGCmYiIiIioFiyYiYiIiIhqwYKZiIiIiKgWLJiJiIiIiGrBgpmIiIiIqBYsmImIiIiIavH/ZwKKQUCPJk4AAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
       "    <img 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j333%2B3%2Bcr98KTCaNasmb755psC7zef15EjRxQYGGiz7L777tOBAwcKXO/m5qbq1atb1zsDH1wC4zHaJ37dc490%2BLBUs6Z0/HhxV%2BM8cXHFXYFzlSgh%2BftLiYlSVlZxV%2BM8vr7FXYFzmUySj4909apkpI8hcDXY07nJJHl4SOnpxvo9eXkV32MXwXPjsunTNXPmTJtlQ4cOVWRkZKH29/f3L9R2V69elddfxs7T01OpqamFWu8MBvsXBhiQn19OM%2BbnV9yVwB6T6X9fuHPl/T0ZqREzIv4t3fG6deumsLAwm2WFbYJvhZeXl9LS0myWpaWlycfHp1DrnYGGGQAAwOhcnD8Lt3z58ipfvrzTj/tXgYGB%2Bv33322WHTlyRHXq1JEk1axZU4cPH1bLli0lSRkZGTpx4kS%2BaRyOYA4zAAAA7lidOnVSTEyM1q9fr8zMTK1fv14xMTHq3LmzJOnxxx/XokWLdODAAaWnp%2Butt95SuXLlFBIS4rQaSJgBAACMrggS5qJkNps1fvx4derUSTVq1NCsWbM0bdo0/fe//1WlSpU0Y8YM3XPPPZKkrl27Kjk5Wc8%2B%2B6wuXLigunXrau7cuXJzc3NaPSaLM29SB9wJjDbvzWyWdu2SgoOl3buLuxrnMdpFf66u/7voLzOzuKtxHqNd9Ofi8r%2BL/rKzi7sa5zHiRX%2BenlJamrHmmhfnRX9ObB6tMjKcf8w7lMH%2BhQEAACCff1jCfKehYQYAADA6GmaHMHoAAACAHSTMAAAARkfC7BBGDwAAALCDhBkAAMDoSJgdQsMMAABgdDTMDmH0AAAAADtImAEAAIyOhNkhjB4AAABgBwkzAACA0ZEwO4SGGQAAwOhomB3C6AEAAAB2kDADAAAYHQmzQxg9AAAAwA4SZgAAAKMjYXYIDTMAAIDR0TA7hNEDAAAA7CBhBgAAMDoSZocwegAAAIAdJMwAAABGR8LsEBpmAAAAo6NhdgijBwAAANhBwgwAAGB0JMwOYfQAAAAAO0iYAQAAjI6E2SE0zAAAAEZHw%2BwQRg8AAACwg4QZAADA6EiYHcLoAQAAAHaQMAMAABgdCbNDaJgBAACMjobZIYweAAAAYAcJMwAAgNGRMDuEhhkAAMDoaJgdwugBAAAAdpAwAwAAGB0Js0MYPQAAAMAOEmYAAACjI2F2CA0zAACA0dEwO4TRAwAAAOwgYQYAADA6EmaHMHoAAACAHSTMKFBcXJzat2%2BvdevWqWLFire07%2BjRoyVJkydPLorSAADArboDE%2BakpCS9/PLLiomJUYkSJdSpUyeNGjVKrq627emAAQP0yy%2B/2CxLTU1Vt27d9Nprr%2Bn8%2BfNq2rSpvL29retLly6t//u//3NarTTMKFDFihW1e/fu4i4DAAA4wx3YMA8fPlwVKlTQtm3bdP78eT3zzDNasGCBBgwYYLPdhx9%2BaPPzypUrNXPmTA0dOlSStG/fPlWqVMmpDfJf3XmjhzvC6dOnFRQUZP2%2BcOFCtW3bVmazWU888YQOHjxo3fbbb79V%2B/bt1aBBAw0ePFgXL160Oda6devUsWNHNWzYUF26dNH27dslSRcvXlSLFi00depUSVJmZqaeeOIJjRgx4u87UQAA8Lc7efKkYmJi9MILL8jLy0tVqlTRkCFDtHjxYrv7HTt2TBMmTNC0adNUvnx5STkNc506dYq0XhJmFMq6deu0aNEieXp6atiwYZo6darmz5%2BvY8eOKSoqSpMmTdKjjz6q7777TsOGDVOnTp0kSVu2bNErr7yi2bNnKzg4WFu3blVkZKSWL1%2BumjVr6s0339RTTz2lNm3aaPPmzbp48WK%2BV5L2JCQkKDEx0WaZ/z33qLyfn1PPv1jVqmX73ShcDfbfT%2B75GO287sBUyiG552O08zKZirsC58o9H6OdV3Eqgr/zBT4H%2B/tbG1l7Dh8%2BLD8/P1WoUMG6rEaNGoqLi9OVK1d01113Fbjf%2BPHjFR4erpCQEOuyffv26fLly%2BrQoYPOnz%2BvunXratSoUbrvvvtu88zyM9j/7CgqvXv3lr%2B/vySpXbt2mjt3riRp/fr1qlOnjrVBbt26tVq2bGndb9GiRerevbsaNWokSWrZsqXCwsL06aef6uWXX1ZoaKieeuopDR8%2BXFeuXNHSpUvl6%2Btb6LqWLVummTNn2iwbGhWlyKgoh873jrRkSXFXgMIoXbq4K0BheHkVdwUoDA%2BP4q4AdhT4HDx0qCIjI2%2B679WrV%2BX1l3%2BHuT%2BnpqYW2DDv3LlTe/fu1bRp02yW33XXXbrvvvs0cOBAubu767333tOTTz6p9evXq2TJkrd6WgWiYUahlCtXzvpnV1dXWSwWSVJ8fHy%2BiwKrVq1qnZZx5swZxcTEaOnSpdb1WVlZaty4sfXnHj166MMPP5TZbFatW0xRu3XrprCwMJtl/h07Sp98ckvHuaPVqpXTLPfoIR04UNzVOM%2BGDcVdgXO5uuY0yxcvSpmZxV2N8%2BS5iMYQXFxymuVr16Ts7OKuxnlKlCjuCpzLZMppltPTpT%2BfbwzB07P4HrsIEuYCn4P/DNduxtvbW9euXbNZlvuzj49PgfssW7ZM7dq1y/cYb731ls3PY8aM0apVq7Rz506bEM8RNMxwSEBAgL777jubZefOnZPHn6lAQECAwsPDNWjQIOv6uLg4eeb5T%2BPll19W8%2BbNtW/fPi1ZskQ9evQo9OOXL18%2B/1s/x4/f%2Bon8Exw4IBnpQkwjNZV5ZWYa69yM1FTmlZ1trHMz2hSTXBaLsRrm4lQEf0cKfA4upJo1a%2BrSpUs6f/68NZQ7evSoAgICCkyFMzMz9e2332rWrFk2y1NSUjRr1iz16tVLlSpVkpQTzGVmZtr0Go4y6L8w/F06deqkQ4cOafny5crMzNT27dv1zTffWNdHREQoOjpav/76q6SceUZdunTR2rVrJUmffPKJ9u/frzfeeEOvvfaapkyZosOHDxfLuQAAgL9H9erV1bBhQ02aNEkpKSmKjY3V%2B%2B%2B/r65duxa4/cGDB5Wenq7g4GCb5b6%2Bvvrhhx80ZcoUJScn6%2BrVq5owYYIqV65sM8/ZUTTMcEiVKlU0Z84cLV68WA0bNtT777%2BvNm3aWNc/8sgjGjFihMaOHavg4GBFRUWpX79%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%2BwQRg8AAACwg4QZAADA6EiYHcLoAQAAAHaQMAMAABgdCbNDaJgBAACMjobZIYweAAAAYAcJMwAAgNGRMDuE0QMAAADsoGEGAAAwOhcX5385KCkpSUOGDFFISIhCQ0M1ceJEZWZmFrjtgAEDVLduXZnNZuvX1q1bJUlZWVmaMmWKHnzwQZnNZj3zzDNKSEhwuL68aJgBAACM7g5smIcPHy5vb29t27ZNK1eu1I8//qgFCxYUuO1vv/2m%2BfPna/fu3davFi1aSJJmz56t77//XqtWrdK2bdvk6empl156yeH68qJhBgAAwN/q5MmTiomJ0QsvvCAvLy9VqVJFQ4YM0eLFi/NtGxsbq8uXL%2Bv%2B%2B%2B8v8FgrVqzQwIEDdffdd8vX11f//e9/tXXrVsXGxjqtXi76AwAAMLoiuOgvISFBiYmJNsv8/f1Vvnz5m%2B57%2BPBh%2Bfn5qUKFCtZlNWrUUFxcnK5cuaK77rrLunzfvn3y8fHRc889p3379qlcuXLq16%2BfunbtquTkZJ07d06BgYHW7cuVK6dSpUrp4MGDqlKlihPOlIYZRhQXV9wVOJfrn/9MN2yQbjC36x%2BpYsXirsC5zGZp1y6pbVtp9%2B7irsZ5li8v7gqcy89PatNG%2BuEH6dKl4q7GeapWLe4KnMvbW6pbVzp8WEpNLe5qnCc0tLgrcKply5Zp5syZNsuGDh2qyMjIm%2B579epVeXl52SzL/Tk1NdWmYb5%2B/boaNGig5557TjVr1tSOHTsUGRkpHx8fmc1mSZK3t7fNsTw9PXX16tXbOq%2BC0DADAAAYXREkzN26dVNYWJjNMn9//0Lt6%2B3trWvXrtksy/3Zx8fHZnl4eLjCw8OtPzdr1kzh4eH66quv9OCDD9rsmystLS3fcRxBwwwAAGB0RdAwly9fvlDTLwpSs2ZNXbp0SefPn1e5cuUkSUePHlVAQIBKlixps%2B3KlSvl4%2BOjdu3aWZddv35dHh4eKlWqlCpUqKAjR45Yp2UkJibq0qVLNtM0HMVFfwAAAPhbVa9eXQ0bNtSkSZOUkpKi2NhYvf/%2B%2B%2BratWu%2BbVNSUjRhwgTt379f2dnZ%2Bu6777R27Vp169ZNktSlSxfNnj1bsbGxSklJ0aRJk/TAAw%2BoqhOnKpEwAwAAGN0d%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%2Be2r9/f6GOuWPHDgUFBRVq2/Pnz6tt27a6cOHCbdV/%2BvRpBQUF6fTp04XaPigoSPXq1bOem9lsVrdu3bRp06bbevzCyjvOcXFxMpvNiouLK9LHBAAAt8jFxflfDkpKStKQIUMUEhKi0NBQTZw4UZmZmQVuu3TpUrVt21Zms1lt27bV4sWLreuys7Nt%2Bp/cL2eGlrc8JWP8%2BPHq0qWL9eeDBw%2BqX79%2B8vLy0rBhw5xWmKMuXryYb9lfaz9//rxeeuklDR06VJs2bZKLE9%2BuSEtL%2B9vT5Xnz5ik0NFSSdO3aNa1Zs0bDhw/XlClT1L59%2ByJ5zLzjXLFiRe3evbtIHgcAADjgDpySMXz4cFWoUEHbtm3T%2BfPn9cwzz2jBggUaMGCAzXabNm3S22%2B/rXnz5ql%2B/fras2ePBg0apHLlyqlt27Y6cuSIMjIytGvXLrm7uxdJrQ6PXlBQkBo1aqT9%2B/erd%2B/eGj16tFq2bKmHH35YKSkpOnjwoAYOHKgHHnhALVq00Kuvvqrk5GTr/itWrFCrVq1kNps1atQoXbt2zbpu9OjRGj16dL7H27FjhyTpwnDJMY0AACAASURBVIULGjlypBo1aqTQ0FA999xzunz5sv773/9q586dmjt3rp5%2B%2Bukb1l6uXDl169ZNZ86c0aVLlyTlNNEjR45U06ZN1axZM40bN04pKSkF7v9///d/euKJJ9SkSRPVr19fvXr10okTJ5SVlaUOHTpIkjp06KD169dLktatW6eOHTuqYcOG6tKli7Zv3249VkpKikaNGqWGDRuqefPm%2BuKLL27l15CPl5eXIiIi9NRTT2nKlCmyWCwFptYzZsxQ7969JUmfffaZunTpov79%2ByskJERr1qxRfHy8hg8frrCwMNWvX1%2BtWrXSypUrJSnfOP/1%2BGfOnNHw4cPVpEkTNW3aVM8//7wSEhIk5aT1YWFhmj17tpo3b64HHnhAkZGRNxxrAABgHCdPnlRMTIxeeOEFeXl5qUqVKhoyZIhNcpwrPj5eAwcOVIMGDWQymWQ2mxUaGqqff/5ZkrRv3z4FBQUVWbMsOXjRX243/9NPPykyMlIbN27UDz/8oBUrVsjLy0sZGRnq06ePunTpohkzZig5OVkjR47Uiy%2B%2BqNmzZ%2BvHH3/Ua6%2B9pg8%2B%2BECNGjXS8uXL9fnnn%2Buhhx4q1ONHRUXJx8dHGzdulJubm6KiojR%2B/Hi9/fbbOnXqlLUJu5GzZ89q0aJFqlu3rsqUKaPs7GwNGTJE1atX14YNG5SRkaExY8Zo3Lhxevvtt232PXfunKKiovTee%2B8pLCxMFy9e1NChQzVr1iy9%2BeabWrt2rVq1aqW1a9eqcuXK2rJli1555RXNnj1bwcHB2rp1qyIjI7V8%2BXLVrFlTr732mk6dOqWNGzfKxcVFzz//vCO/GquHH35Yc%2BbM0bFjx%2BTh4XHT7X///XdNnjxZc%2BbMUXZ2tiIjI%2BXn56d169bJ3d1d0dHRmjBhgtq1a6eJEyfajHPeRjwjI0P9%2B/dXnTp1tHHjRlksFo0fP15PP/20li9fLimnoY6Pj9c333yj%2BPh49ezZU0uWLNGgQYMKfX4JCQlKTEy0WeZvMqm8v3%2Bhj3HHc3W1/W4UZnNxV%2BBctWrZfjcKP7/irsC5Spa0/W4U3t7FXYFzeXrafofjiiBhLvA52N9f5cuXv%2Bm%2Bhw8flp%2BfnypUqGBdVqNGDcXFxenKlSu66667rMt79uxps29SUpJ%2B/vlnjRkzRlJOw5yenq7HH39cZ86cUY0aNfT8888rODjYkdOzcVtTMiZNmmT9OSAgQE8%2B%2BaR69eqljRs3qkWLFtaTX7lypdzc3DRy5EiVKFFCnp6eevnll9W%2BfXslJibqyy%2B/1L///W81adJEktSjRw%2BtWLGiUHWcOXNGMTEx%2Bvrrr1W6dGlJ0uTJk61Jsb3aMzMzlZGRoYCAALVp00aDBw%2BWJP3222/6/fff9fHHH8vHx0eSNGrUKD3yyCN6%2BeWXbY5VpkwZrVu3TlWrVlVKSorOnTun0qVLKz4%2BvsDHXrRokbp3765GjRpJklq2bKmwsDB9%2BumnGjVqlL766ivNmTNHZcuWlSS9%2BOKL6ty5c6HGwp7csbl06ZLNX8obcXNzU%2BfOna3TU15//XX5%2BPjIzc1NcXFx8vHxUVpami5fvmwdo4Ls3LlTsbGxWrVqlXx9fSXljP8DDzyg3377zbrds88%2BK09PT1WrVk2hoaE6fvz4LZ3fsmXLNHPmTJtlQ599VpF30PQgp/nzd2kYu3YVdwVFY8mS4q4AhdG4cXFXgMKoWbO4K4AdBT4HDx1qN6zMdfXqVXl5edksy/05NTXVpmHOKzExUYMHD1adOnWs7%2BZ7enqqXr16ioqKUqlSpbR48WI99dRT%2BvLLL1WlSpXbObV8brlhfuWVV2zmAf9V3lcVSUlJqlixokqUKGFdVrlyZUn/Sxdr165ts39hTyz3FU2lSpWsy/z9/eVvJ1nMrf369euKjo7WnDlz9NBDD1mbytOnTysrKytfwu3u7p7vThBubm5au3atPv30U5lMJgUGBiolJUWuN0gBcxv8pUuXWpdlZWWpcePGunjxoq5fv6677777lsfhZpKSkiTJ2ojfjL%2B/v81c7tjYWE2dOlUnTpxQ9erVVa1aNUm66UWeSUlJKl26tLVZliRfX1/5%2BfnpzJkzKleunPXxcrm5uclisRTuxP7UrVs3hYWF2Z6DyST95RXvP5qra06zfPGidIOLIf6R2rYt7gqcq1atnGa5Rw/pwIHirsZ5pkwp7gqcq2TJnGb5p5%2BkPNMD//ECAoq7Aufy9Mxplg8fltLSirsa56lbt/geuwgS5gKfgwv5Dq%2B3t7fNNFxJ1p9vFMjt2bNHUVFRCgkJ0RtvvGHtuf46ffepp57SZ599pi1btqhXr16FqudmnP4er8lksv65UqVKiouLU1ZWlrVpPnXqlKScAQ0ICMjXiJ47d041/3xF6eLiovT0dOu6vHecyG0u4%2BLiVL16dUnSkSNHtHbtWg0fPtxuje7u7howYIAuX76sIUOGaOnSpapVq5YCAgLk6empHTt2WOu9fv26YmNjVa1aNf3yyy/WY3z11VdatGiRli5dam0iJ0yYoEOHDhX4mAEBAQoPD7eZbhAXFydPT0/5%2BvrKw8NDsbGxuvfee63j4AybN29WpUqVVK1aNesxMzIyrOv/enFk3t9fRkaGBg8erBEjRqhHjx4ymUz67bff9OWXX970cStVqqSLFy8qJSXF2jQnJyfr4sWL8vf3v%2BXG%2BEbKly%2Bf/62fs2eN1Vjmysw01nkZ9QLRAweMdW523rX7R0tONta53SCN%2B8dLS5MMfnvWv00RNMwFPgcXUs2aNXXp0iWdP3/eGqIdPXpUAQEBKlnAlKmVK1fq9ddf17Bhw9S/f3%2Bbde%2B8847atm2r%2B%2B%2B/37rs%2BvXrhZqKWlhFeslkblI7bdo0paWlKTExURMnTlTjxo1VqVIlPf7449q0aZM2b96szMxMrV69Wnv37rXuX6NGDe3cuVPx8fFKS0vTrFmzrA1dhQoV1LRpU02dOlVXrlxRSkqK3nzzTWsD7u7ubnNxYUGGDx%2BuoKAgjRgxQmlpaapXr56qVaumyZMn6%2BrVq0pLS9OkSZPUr18/ZWVl2eybnJwsFxcXeXp6ymKxaOvWrfr888%2BtzWjuLyn3IraIiAhFR0fr119/lZQz36ZLly5au3at3N3dFR4ervfee0/nzp1TcnKy3nzzTYfGPjU1VUuWLNHChQs1ZswYmUwmlS1bVqVKldK6detksVj0%2B%2B%2B/6%2Buvv77hMTIyMpSWliZPT0%2BZTCbFxcVZ68o9zxuNc926dXXffffplVdeUXJyspKTk/Xqq6%2BqatWqTp1TBAAA/nmqV6%2Buhg0batKkSUpJSVFsbKzef/99de3aNd%2B2GzZs0KuvvqoZM2bka5Yl6dChQ5o4caISExN1/fp1zZw5UykpKWrTpo3T6i3ShrlkyZL6%2BOOPdejQIT300EPq0KGDKlWqpPfee0%2BS1LBhQ02dOlWTJ09WSEiINmzYoKZNm1r379atm8xmszp16qQ2bdro7rvvVsWKFa3rp02bJl9fX7Vr106tWrVSmTJlNH78eElSeHi4Vq1apR49etywvhIlSujNN99UfHy8pkyZIldXV82dO1fnz5/Xv//9bzVr1kynTp3Sxx9/nO9VymOPPaYHH3xQ7du3V%2BPGjTV79mz17dtXx48f1/Xr11WuXDm1adNG3bp109KlS/XII49oxIgRGjt2rIKDgxUVFaV%2B/fpZ71Dx3//%2BV/Xq1VPHjh3173//W/Xr17/l8R44cKD13oOtW7fWt99%2Bq9mzZ1v/wri7u2vChAn66quvFBwcrMmTJysiIuKGx/P29takSZM0a9Ysmc1m9enTR02bNlW5cuWsSfqNxjl3LDMzM9W2bVu1bNlSGRkZ%2Bvjjj284bQUAABSRO/A%2BzNOnT1dmZqZatWqliIgINW/eXEOGDJEkmc1m6zvaM2fOVFZWloYNG2Zzn%2BVx48ZJkt544w1VrVpVnTt3VmhoqGJiYvTxxx/Lz4kXLZssznpvHLhTnD1b3BU4l6ur5O%2BfMy/bSFMy8rz4NQSzOedCxuBgY03J%2BPOuNobh5ye1aSN9842xpmRUrVrcFTiXt3fOfN99%2B4w1JePPz0ooFt9/7/xj5gk5jY6oDwAAwOjuwA8u%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%2BbNNuvnzZunFi1aqEGDBurdu7eOHTvmcH150TADAAAY3R3YMA8fPlze3t7atm2bVq5cqR9//FELFizIt92JEycUGRmpqKgo7dy5U5GRkRo%2BfLji4%2BMlSatXr9bChQs1f/587dixQ7Vr19awYcNksVgcrjEXDTMAAAD%2BVidPnlRMTIxeeOEFeXl5qUqVKhoyZIgWL16cb9vVq1crJCRErVu3lqurqx599FE1atRIy5YtkyQtX75cPXr0UM2aNeXh4aHnn39ecXFx2rFjh9Pq5aI/AAAAoyuCi/4SEhKUmJhos8zf31/ly5e/6b6HDx%2BWn5%2BfKlSoYF1Wo0YNxcXF6cqVK7rrrrusy48cOaLAwECb/e%2B77z4dOHDAun7gwIHWdW5ubqpevboOHDigxo0b39a5/RUNM4zH17e4K3Cu3P/kvL2l7OzircWZli8v7gqcy88v5/uUKdKlS8VbizNFRBR3Bc5lNktt2kijRkm7dxd3Nc7zww/FXYFz5b6VbrEY6/89g1m2bJlmzpxps2zo0KGKjIy86b5Xr16Vl5eXzbLcn1NTU20a5oK29fT0VGpqaqHWOwMNMwAAgMFZZHL6Mbt166awsDCbZf7%2B/oXa19vbW9euXbNZlvuzj4%2BPzXIvLy%2BlpaXZLEtLS7Nud7P1zkDDDAAAYHBFEdSXL1%2B%2BUNMvClKzZk1dunRJ58%2BfV7ly5SRJR48eVUBAgEqWLGmzbWBgoH7//XebZUeOHFGdOnWsxzp8%2BLBatmwpScrIyNCJEyfyTeNwBBf9AQAA4G9VvXp1NWzYUJMmTVJKSopiY2P1/vvvq2vXrvm27dSpk2JiYrR%2B/XplZmZq/fr1iomJUefOnSVJjz/%2BuBYtWqQDBw4oPT1db731lsqVK6eQkBCn1UvDDAAAYHDZ2c7/ctT06dOVmZmpVq1aKSIiQs2bN9eQIUMkSWazWV9%2B%2BaWknIsBZ82apblz56pRo0Z6//33NWPGDN1zzz2SpK5du6pfv3569tln1bhxY%2B3fv19z586Vm5ub40X%2BiSkZAAAA%2BNuVK1dO06dPL3Dd7r9clNu8eXM1b968wG1NJpP69%2B%2Bv/v37O73GXDTMAAAABsfNRhxDwwwAAGBwNMyOYQ4zAAAAYAcJMwAAgMGRMDuGhhkAAMDgaJgdw5QMAAAAwA4SZgAAAIMjYXYMCTMAAABgBwkzAACAwZEwO4aGGQAAwOBomB3DlAwAAADADhJmAAAAgyNhdgwJMwAAAGAHCTMAAIDBkTA7hoYZAADA4GiYHcOUDAAAAMAOEmYAAACDI2F2DAkzAAAAYAcJMwAAgMGRMDuGhhkAAMDgaJgdw5QMAAAAwA4SZgAAAIMjYXYMCTMAAABgBwkzAACAwZEwO4aGGQAAwOBomB3DlAwAAADADhJmAAAAgyNhdgwJMwAAAGAHCTMAAIDBkTA7hoYZAADA4GiYHcOUDAAAAMAOEmYAAACDI2F2DAkzAAAAYAcJMwAAgMGRMDuGhhkAAMDgaJgdw5QMAAAAwA4SZgAAAIMjYXYMCTMAAABgBwkzAACAwZEwO4aGGQAAwOBomB3DlAwAAADADhJmAAAAgyNhdgwJ8w2cOHGiWPf/p/n/7XwBAEDRSU1N1ZgxYxQaGqqGDRvqxRdf1NWrV2%2B4/YYNG9S5c2cFBwcrLCxMM2fOVHaeVwnt2rVT/fr1ZTabrV9Hjx4tdD1F2jAvXrxYQUFBWrBggd3tduzYoaCgoNt6jHHjxmncuHGF2nbAgAGaM2fOTbdbvHixXn755duqR5L279%2BvDh063FaNjujdu7fq1Klj/YtQv359tWvXTh988IHNXxpn%2B%2Bt4tW/fXl9%2B%2BWWRPR4AALg12dnO/ypKEyZM0NmzZ7VhwwZt3LhRZ8%2Be1bRp0wrc9rffftOLL76o4cOHa%2BfOnZo3b54%2B%2B%2Bwza/%2BZkpKi48ePa/369dq9e7f1q0aNGoWup0inZCxevFjdu3dXdHS0evXqJVdX5z/ca6%2B9VuhtP/zww0Jtd%2BHChdstR5KUnJysjIwM68%2B3UqOjBg8erMjISElSVlaWYmJiNHr0aJ05c0bjx48vksf863itW7euSB4HAADcnn/SlIxr165pzZo1io6Olp%2BfnyRp5MiR6tOnj1588UV5eXnZbH/mzBk98cQTatmypSSpRo0aatOmjX7%2B%2BWf1799fv/32m/z8/FSpUqXbrqnIEuYff/xRSUlJGj16tLKzs7VhwwbruoSEBD399NMKDg5Wq1at9P3331vXnT59WkFBQfr888/VsmVLNWjQQGPGjNHOnTvVqVMnmc1m9e3b19qkjR49WqNHj5YkzZgxQ8OGDdPIkSMVEhKiFi1a6K233rIeu3fv3poxY4Yk6fDhw%2BrZs6caNWqkli1batSoUUpJSdHq1as1d%2B5c7dy5UyEhIZKksLAwjRs3Tk2bNlV4eLiys7O1cuVKdenSRaGhoTKbzRo8eLAuXLig2NhYDRw4UJJkNpu1e/dua40pKSkym83avn27taYrV66oXr16%2BvXXXyXlNJsdO3ZUw4YN1aVLF5ttb1WJEiXUpEkTTZo0ScuWLdOxY8es5/PZZ59Zt8ub8OeO/%2BTJk9WoUSONHz9e169f15QpU9SuXTuZzWY1adJEEyZMkMViueF45R4/LS1NU6dO1UMPPaRGjRqpd%2B/e1nOVpKCgIC1cuFBt27aV2WzWE088oYMHD972OQMAgDtfWlqaTp48ecOvjIwMBQYGWrevUaOG0tLSCpwC2rZtW40ZM8bm2N99951q164tSdq3b5%2B8vLzUq1cvhYaGqkuXLtq8efMt1VtkCfPChQsVEREhT09P9ejRQx999JHat28vSXruuedUunRpbd26VcnJyXrmmWfy7b9lyxatX79esbGxCg8P1/79%2BzVv3jy5ubnpiSee0JIlSzR06NB8%2B23cuFGTJ0/WlClTtH37dg0ePFitWrVSgwYNbLYbP368mjRpokWLFunixYvq27evVqxYoSeffFKnT59WTEyMFi5caN3%2B119/1VdffSUpJ/p//fXXFR0drXr16uncuXPq27evoqOjNXz4cM2bN099%2BvTR7t27JUnLli2TJPn6%2Bqpdu3ZavXq1mjVrJklau3atqlWrpnr16mnLli165ZVXNHv2bAUHB2vr1q2KjIzU8uXLVbNmzdv%2BXTz44INyd3fXjh07dO%2B99xZqn6tXr%2Br7779XWlqaPvnkE23btk2ffPKJypcvr927d6tXr15q3bq1HnvssQLHK9err76q/fv3Kzo6WnfffbeWLl2qfv36ae3atapYsaKknBcJixYtkqenp4YNG6apU6dq/vz5haozISFBiYmJNsv8vb1Vvnz5Qu3/j%2BDiYvvdKP5MDQyjZEnb70ZhNhd3Bc5Vq5btd6Pw8SnuCpwrN0H8S5KI21cUCXOBz8H%2B/oV6Dt67d6/69OlT4LqoqChJkre3t3VZbqpsbx6zlDP9IioqSp6enurXr58kyWQyqW7duhoxYoQqVqyor7/%2BWpGRkVq0aFG%2B/vBGiqRhPnPmjLZt22adtxsREaFZs2YpJiZGlSpV0s6dO7Vhwwb5%2BvrK19dXQ4cO1bPPPmtzjP79%2B8vLy0uBgYHy9/fXY489pgoVKkiSGjRooDNnzhT42NWrV1d4eLgk6aGHHpK/v79OnDiRb0A8PDy0bds21ahRQ02aNNEXX3whFzsNSdu2bXXXXXdJkgIDA7V27VpVrlxZly9fVkJCgsqUKaP4%2BPibjs1//vMfPfnkk0pJSZGvr69Wr16trl27SpIWLVqk7t27q1GjRpKkli1bKiwsTJ9%2B%2BqlDc6pNJpNKlSqlS5cuFXqf8PBwubu7y93dXREREXrsscdUtmxZJSQkKC0tTT4%2BPjc93/T0dK1du1azZs1StWrVJEl9%2B/bVmjVrtHbtWg0aNEhSTvLv7%2B8vKWdS/ty5cwtd57JlyzRz5kybZUOffVaRw4YV%2Bhj/GEZ74mjTprgrKBqNGxd3Bc5l1N/TkiXFXQEKw4GwCLaKomEu8Dl46FDr1FB7QkNDb/iO8v79%2B/Xee%2B/p2rVr8vnzxeC1a9ck5YSPN3Ls2DENGzZMZcuWVXR0tHXbAQMG2GzXqVMnrV27Vhs2bCjehnnJkiXKzMxU586drcsyMzP10UcfWZuk3HRRkqpWrZrvGH550qcSJUpYm1VJcnFxkcViKfCxcxuvXG5ubgVe8Pbuu%2B9qxowZeueddzRixAgFBwfr1VdfvWGSm/fVkouLi6Kjo7VmzRp5e3srKChIKSkpN6wpL7PZrMqVK1t/SQcOHNC8efMk5bzQiImJ0dKlS63bZ2VlqbGDT8CZmZm6cuWKypYtW%2Bh98p7vtWvX9Nprr%2Bnnn39WQECA7r//flkslpteSHj58mVlZGSocuXKNssrV66s06dPW38uV66c9c%2Burq6FGsdc3bp1U1hYmM0yf29v6SavQP9RXFxymuVr1/5Zk9Bu5ocfirsC5ypZMqdZ/uknKTm5uKtxnlGjirsC56pVK6dZ7tFDOnCguKtxnptcXP%2BP4%2BWV0ywfPpzzf59R1KtX3BU4VYHPwX/pw27HPffcIzc3Nx05ckT169eXJB09elRubm6qXr16gfts2bJFI0aMUEREhJ5//nmb6%2Bbmz5%2Bv%2B%2B%2B/X02aNLEuu379ujw8PApdk9Mb5vT0dK1cuVITJ07Ugw8%2BaF1%2B6NAhDRo0SE899ZQkKTY21np14rlz5/Idx2QyObs0q%2BzsbO3fv1%2BRkZEaO3aszp49qzfeeEOjR4/WqlWrCtwnbz0LFizQ999/rzVr1libvaeffrrQj9%2B1a1etXbtWJ0%2BeVOvWra0vDgICAhQeHm59USFJcXFx8vT0vJ3TtPrhhx%2BUnp5u/Yvi4uJic1HixYsX8%2B2T93xfeukllSpVStu3b5eHh4eys7OtKbg95cqVk4eHh83vWpJOnTqV7x/Y7Spfvnz%2Bt36Sk43VWOb6Oy5L/jvdwjse/yjJycY6tz%2BnlhnOgQPGOjcjhQR5Xbtm3HP7mxXF00eBz8FO4OXlpXbt2mnatGl67733JEnTpk1Thw4dCuyJ9uzZo2effVavvvqq9V37vM6ePasVK1Zo3rx5uvvuu/X5559r9%2B7dt3QzBKdPilyzZo1MJpM6duyogIAA61eLFi0UGBioL7/8Us2aNdMbb7yhy5cvKzExMV%2BcX9RcXFz0%2Buuv691331V6errKlCkjDw8PlS5dWlLOdA17iXFKSopcXV3l5uamzMxMffHFF9q2bZu1Cc19xZJ8g5QpPDxce/bs0eeff67//Oc/1uURERGKjo62XhS3b98%2BdenSRWvXrr2t88zMzNTWrVs1btw49e3bV1WqVJGUM3H%2B22%2B/VVpamhITExUdHW33OCkpKfLw8JCLi4tSUlI0depUpaSk2JxvQePl4uKixx9/XG%2B//bZOnjyp69ev65NPPtGRI0es89kBAAD%2B6pVXXlH16tXVsWNHPfLII6pcubLNLXrbt29vvVXwnDlzlJmZqYkTJ9rcZzl3KsaLL76oFi1aqEePHgoJCdGnn36qDz74wDpdtDCcnjAvWbJEHTt2lJubW7513bp105QpU7R582a9/vrratmypXx9fdWlSxft3bvX2aXY9e6772rChAlq1qyZNTGdMGGCpJy5w0uXLlXDhg313Xff5du3f//%2BOnTokFq2bCkPDw/df//96tGjh3766SdJOXOcGzZsqObNm1tfGeXl5%2BensLAw7d692%2BbtgUceeUSpqakaO3as4uLi5Ofnp379%2Bql3796FPq%2B5c%2Bfqo48%2BkpQzHaVy5coaNGiQevToYd1m5MiRevXVV9W0aVOVL19effv21S%2B//HLDY7700ksaN26cHnjgAfn4%2BOjhhx9W8%2BbNdejQoZuO14svvqgZM2aoX79%2BunTpkoKCgjR//nzdc889hT4nAADgmH/aG5S%2Bvr6aMGGCtTf7q7y3sL3ZZ2y4u7tr7NixGjt27G3XY7LcyoRR4J/ASPNHpZw5zD4%2BOW9L/tP%2Bx7Pn66%2BLuwLn8vPLuUDum2%2BMNSUjIqK4K3Aus1natUsKDjbWlAyjXRPg45Mz3/fXX401JSNPSPZ3y3N3X6dp29b5x7xTGew%2BVQAAAIBzFekn/cF5unTpouPHj99w/bx586wfHAIAAJCXkd6gLA40zP8QeT%2BZDwAAAH8fGmYAAACDI2F2DA0zAACAwdEwO4aL/gAAAAA7SJgBAAAMjoTZMSTMAAAAgB0kzAAAAAZHwuwYGmYAAACDo2F2DFMyAAAAADtImAEA%2Bo9dcwAAIABJREFUAAyOhNkxJMwAAACAHSTMAAAABkfC7BgaZgAAAIOjYXYMUzIAAAAAO0iYAQAADI6E2TEkzAAAAIAdJMwAAAAGR8LsGBpmAAAAg6NhdgxTMgAAAAA7SJgBAAAMjoTZMSTMAAAAgB0kzAAA4P%2B1d%2B/xOdf/H8efl5mZOYzZcqYDKaFhm0NRQ75OWQk5JzUSQkc/a4aVHFLEVyzJub4hTUMHfHOIGds3lHOOG7NstM1ml22/P%2BTKtZMd8Ll2edxvt910fT6f69pzu7au117X6/P%2BwM7RYS4aCmYAAAA7R8FcNIxkAAAAAHmgwwwAAGDn6DAXDR1mAAAAIA90mAEAAOwcHeaioWAGAACwcxTMRcNIBgAAAJAHOswAAAB2jg5z0dBhBgAAAPJAhxkAAMDO0WEuGgpmAAAAO0fBXDSMZAAAAAB5oMMMAABg5%2BgwFw0FMwAAgJ2jYC4aRjIAAACAPNBhBgAAsHN0mIuGDjMAAACQBzrMAAAAdo4Oc9HQYQYAALBzGRm3/uN2unz5ssaOHSsfHx81bdpUb731lpKTk3M9fvz48XrkkUfk6elp%2Bfjqq68s%2B0NCQtS6dWs9%2Buij6t%2B/v/74448C5aFgBgAAgE2ZNGmSzp49q%2B%2B//14//PCDzp49q%2BnTp%2Bd6/L59%2BzRp0iRFRUVZPnr16iVJ%2Buabb7RkyRItWLBA4eHhatCggUaOHKnMzMx856FgBgAAsHPFqcOckpKitWvXauTIkXJ1dZWbm5veeOMNrV69WikpKdmOT0tL0%2BHDh/XII4/k%2BHj/%2Bc9/1KdPH9WtW1dOTk56/fXXFRMTo/Dw8HxnYoYZAAAAd1RqaqpiY2Nz3JeSkiKz2ax69epZtt1///1KTU3ViRMn9NBDD1kdf/DgQV29elWzZs3Snj17VK5cOXXv3l0vvfSSSpQooaNHj%2Brll1%2B2HO/o6Kg6dero4MGDat68eb7yUjADAADYudvRET5//rzi4uKstrm7u8vDw%2BOm9/311181YMCAHPe99tprkqQyZcpYtjk7O0tSjnPMiYmJ8vb2Vv/%2B/TVjxgwdOHBAr776qkqUKKGXXnpJycnJlvtfV7p0aV2%2BfPmmOa%2BjYIb9KWlnP9Ym07V/HRykEnY0RVWrltEJbq3r/2OvUkUqX97YLLfSL78YneDWcnG59u8XX0h5nEBU7LRsaXSCW8vTU4qMlF54QYqKMjrNrVOAmdlb7XYUzF999ZVmz55ttW348OEaMWLETe/r4%2BOjQ4cO5bjv999/18yZM5WSkiKXv39nr49ilC1bNtvxrVq1UqtWrSy3GzVqpIEDB2rdunV66aWX5OzsrNTUVKv7pKamWh47P%2ByssgAAAMCd0KtXL/n6%2Blptc3d3L/Lj3nvvvXJ0dNTRo0fVuHFjSdKxY8csoxRZ/fTTT/rzzz/1/PPPW7alpaWpdOnSkqS6devqyJEjevLJJyVJZrNZJ06csBr5uBk7alcBAAAgJ7fjpD8PDw81aNDA6iM/4xg34%2BzsrI4dO2r69OmKj49XfHy8pk%2Bfri5duliK4BtlZmZq8uTJ2rFjhzIzMxUVFaXFixdbVsno3r27li5dqoMHD%2BrKlSv68MMPVblyZTVr1izfmegwAwAAwKaMHz9eU6ZMUdeuXWU2m9W2bVu9%2B%2B67lv2dO3dW165dNXToULVv315jx45VUFCQYmNjVblyZY0YMULdunWTJD333HNKTEzUq6%2B%2Bqvj4eDVs2FDz5s2To6NjvvOYMguyCB1QHOSw5EyxZjJJpUtLqamGzr/dcnv3Gp3g1ipTRmrYUNq3TyrAiSQ2z94uD%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%2Bnp6YqJiVHNmjWNjmKTTpw4oTp16hgdAwAA5KK4FcyXL1/WpEmTtGnTJl29elVt27bV%2BPHj5eLiku3YwMBArV271mpbamqqWrZsqQULFigjI0NNmzZVZmamTCaT5Zjt27erTJky%2BcpzR0cy%2Bvfvr08%2B%2BeSmx12%2BfFmDBw9W48aN1bdv3zuQLG%2BjR4/WmjVrJEkxMTHy9PRUTEzMbf%2B8%2Bf1%2BSdI777yjBg0ayNPTU56enmrUqJHatm2r6dOnKzU19bZl3LRpkwYPHmy5/dJLL%2BnTTz%2B9bZ8PAADYv0mTJuns2bP6/vvv9cMPP%2Bjs2bOaPn16jsdOnDhRUVFRlo9PPvlE5cuX1zvvvCNJOnr0qMxms3bt2mV1XH6LZclGO8wHDhzQtm3bFB4eLldXV6PjKCEhwfLf1apVU1RUlIFpcte1a1d98MEHkqTMzEzt27dP48aN06FDhxQSEnJbPufFixeVmZlpuf3ZZ5/dls8DAAAKrzh1mFNSUrR27VotXrzYUge%2B8cYbGjBggN566y05Ozvnet/4%2BHi98cYbGjdunOrWrStJ2rdvnx588EGVKlWq0JkMOelv9erV6t27t4KDg9W8eXO1aNFC48aNk9ls1k8//aRBgwZJkp588kl9/fXXysjI0Pz589WuXTs1bdpUzz33nLZu3Wp5PF9fXwUGBqpVq1by8/PTjh075Ovrq88%2B%2B0ytWrVS06ZNNWPGDG3cuFEdOnSQp6enRowYobS0NElSbGysRo0aJV9fXzVu3Fht27bVypUrJUnjxo3T7t27NW/ePA0dOlRnzpzRgw8%2BqDNnzkiSoqOjNWrUKLVo0UKtWrXS66%2B/rvPnz0uSwsPD5evrq7lz5%2Brxxx%2BXt7e3RowYoaSkJElSWlqapkyZoo4dO8rT01MtWrTQpEmTrArQwjKZTGrUqJFmzpyprVu3atu2bZKyd62zfj0PPviggoOD5ePjo6FDhyozM1Pz589X165d1axZM3l5een1119XamqqwsPDNX78eEvXPTY21urx8/O8zZs3T35%2BfvL09JSfn5927txZ5K8dAADYttTUVJ08eTLXD7PZrHr16lmOv//%2B%2B5WamqoTJ07k%2BbjTp0/XI488oqefftqybd%2B%2Bfbpy5Yq6d%2B%2Bu5s2bq2/fvoqMjCxQXsM6zJGRkWrdurW2bt2qAwcOaODAgWrZsqU6d%2B6skJAQDRgwwNLJ/eSTT7Ry5Ur9%2B9//1oMPPqgffvhBw4YN07Jly9SoUSNJ0t69e7V%2B/XpJ1zrU0dHRiouL03//%2B1/98ssv8vf3V6tWrfSf//xHf/31l7p3765169bJz89PAQEBcnV1VVhYmEqVKqXFixdr0qRJ6tixo9577z2dOnXKUuxeLywlyWw268UXX9QjjzyiH374QZmZmZowYYKGDh2q//znP5KuFdSxsbH68ccfFRsbq759%2B2r58uXy9/fXokWLtHXrVi1atEgeHh6KiopSv3791K5dO7Vo0eKWfJ/vu%2B8%2B1a5dWzt37tRjjz2Wr/ucOnVK//3vf2U2m7V%2B/XotXrxYS5cuVZ06dXTs2DH16dNHa9euVY8ePTRhwgTNnj1bmzZtyvY4c%2BbMuenztmrVKoWEhMjDw0MTJkxQUFCQNmzYkO%2Bv7/z584qLi7Pa5l6%2BvDzc3fP9GDbv%2BrzVDXNXdqEAb4UVC6VLW/9rL27BH/A25XpnKo8OVbHk6Wl0glurfn3rf1Fkt6PDnONrsLu7PDw8bnrfX3/9VQMGDMhx32uvvSZJViMT17vKycnJuT7m6dOnFRoaqq%2B//tpqe%2BnSpdWoUSO99tprqlChgpYtW6bBgwcrNDQ03%2BenGVYwly5dWkOHDrV0Qh988EEdP348x2NXrVolf39/NWjQQJLUqVMnff/991q5cqWl8OrQoYPKly9vdb8hQ4bI0dHRUij27t1bFSpUUIUKFVS3bl1L8RscHCwXFxc5OjoqJiZGLi4uSk1N1aVLl3IcLr9u9%2B7dOn36tFatWqWyZctKkiZMmCBvb2/t37/fctyrr76q0qVLq3bt2vLx8bF8nT179tQzzzwjNzc3nT9/XqmpqXJxcVFsbGxhvqW5qlixoi5evJjv47t06SJnZ2c5OzurdevWatKkiapUqaL4%2BHglJCTI1dU1Xxnz87w999xzql27tqRrIyXXZ8Xz66uvvtLs2bOttg1/9VWNGDmyQI9TLDg5GZ3g1mrY0OgEt8ffbwHCxtnb81TAblmxsXy50Qnsxu0omHN8DR4%2BXCNGjLjpfX18fHTo0KEc9/3%2B%2B%2B%2BaOXOmUlJSLHVYSkqKJFnqrZysWrVKnp6eeuihh6y2X59lvm7w4MFavXq1fv75Z/Xr1%2B%2BmWSUDC2Y3NzerMxUdHR1zHUX4888/s/0FUKNGDR08eNByO6e/ZipWrChJcnBwkCSrgrpEiRKWz3f69GlNnTrVstrD9QIu4yY/XRcuXFDFihWtnryyZcvK1dVV0dHRqly5sqRrf23l9HWmpKRo4sSJioiIUJUqVfTwww8rMzPzpp%2B3oOLj4%2BXm5pbv42/8XmZmZuqjjz7S5s2bValSJT300EMym835GhvJz/N2/XskSSVLlizwOEqvXr3k6%2Btrtc29fHnpNp7oeMeZTNeK5StX7Kvbd%2BSI0QlurdKlrxVhR47Y18%2BfPf3MSdc6y9efp79fgO3CCy8YneDWql//WrHcp490w2tGsWdnf9jk%2BBp8C97hvffee%2BXo6KijR4%2BqcePGkqRjx47J0dExz1W5fvjhB7344ovZtn/00Ufq0KGDHn74Ycu2tLQ0ORWgEWWTJ/1lVb16dZ0%2Bfdpq2%2BnTp60KO1MOb1fntC0rs9msIUOGaMyYMerTp49MJpP279%2Bv0NDQfOVKSEhQUlKSpWhOTExUQkKC3N3db1r8BQQEqEKFCtq2bZucnJyUkZEhLy%2Bvm37egjh27JhOnjxpGfEoUaKEzGazZf%2BNJzRed%2BP3bfr06YqJidGmTZssX2PXrl3z9bnz87wVlYeHR/bHS0mxvxd56drXZE9f1%2BXLRie4PVJT7etrK05nChVESoqUx1u7xY6NnoxeZAcP2u/Xdofdjl/lHF%2BDbwFnZ2d17NhR06dP18yZMyVdq0e6dOmi0rmMvSUkJOjYsWM51lGHDx/W7t279fHHH6tChQqaP3%2B%2BkpKS1L59%2B3xnKhZX%2BuvRo4fmz5%2Bv3377Tenp6Vq/fr02bdqkZ555psiPbTablZqaqtKlS8tkMikmJkbTpk2z7JOkUqVKKTExMdt9GzZsqAceeEDjx49XYmKiEhMTFRQUpFq1aqlJkyY3/dxJSUlycnJSiRIllJSUpKlTpyopKcmqoC2sjIwMRUZGatSoUWrfvr2aN28u6drQ/NatW/XXX38pMTHxpqtnXM/o4OCgK1eu6PPPP9fhw4ctGZ2cnJSSkqKrV69mu%2B/tfN4AAID9Gj9%2BvOrUqaOuXbvqX//6l2rUqKHAwEDL/s6dO1stY3t9zPaee%2B7J9liTJ09WrVq11K1bN/n4%2BGjXrl1auHBhgVZiKxYd5kGDBikjI0OjR49WXFycateurRkzZsjb27vIj12mTBm9//77mjlzpoKDg%2BXm5qaePXvq6NGjOnz4sO699175%2BfkpKChI%2B/fv19SpUy33LVmypObNm6cPPvhAHTp0UFpamlq2bKmFCxeqZMmbf2sDAgIUGBgob29vubi46IknntDjjz%2Buw4cPF%2BprWbt2rb7//ntLtipVqqhz58566aWXLMcMGTJE48aNU9u2bVWuXDmNHDnScp%2BcjBo1SmPHjlXLli1VpkwZNW3aVN26dbNk9PLykpubm7y8vPTll19a3fd2Pm8AACD/itubRWXLltWkSZM0adKkHPeHhYVZ3W7YsGGuM9Gurq6aPHlykfKYMm/FGmaALbGnuUTp2gxz6dLX3uq3p1/XvXuNTnBrlSlz7UTGffsYybBlLi5So0bXfv7saSSjZUujE9xanp7X5n2bNLGvkQwD/x9%2BixbfsrJjx61/TFtVLEYyAAAAAKMUi5GMu92rr76qX375Jdf9EyZMsFqgGwAA4Eb29mbRnUbBXAzMmTPH6AgAAAB3LQpmAAAAO0eHuWgomAEAAOwcBXPRcNIfAAAAkAc6zAAAAHaODnPR0GEGAAAA8kCHGQAAwM7RYS4aCmYAAAA7R8FcNIxkAAAAAHmgwwwAAGDn6DAXDR1mAAAAIA90mAEAAOwcHeaioWAGAACwcxTMRcNIBgAAAJAHOswAAAB2jg5z0dBhBgAAAPJAhxkAAMDO0WEuGgpmAAAAO0fBXDSMZAAAAAB5oMMMAABg5%2BgwFw0FMwAAgJ2jYC4aRjIAAACAPNBhBgAAsHN0mIuGDjMAAACQBzrMAAAAdo4Oc9FQMAMAANg5CuaiYSQDAAAAyAMdZgAAADtHh7lo6DADAAAAeaDDDAAAYOfoMBcNBTMAAICdo2AuGkYyAAAAgDzQYQYAALBzdJiLhg4zAAAAkAc6zAAAAHaODnPRUDADAADYOQrmomEkAwAAAMgDHWYAAAA7R4e5aOgwAwAAAHmgwwwAAGDn6DAXDR1mAAAAO5eRces/7oSUlBT16tVLq1evzvO4X3/9VT169JCnp6d8fX319ddfW%2B3/5ptv1L59ez366KN69tlnFRUVVaAcFMwAAACwOUeOHFHfvn31v//9L8/jLl26JH9/f/n5%2BSkiIkLvvfeeJk%2BerL1790qSwsPDNWnSJH3wwQeKiIjQ008/rVdeeUUpKSn5zkLBDAAAYOeKW4d5x44dGjhwoJ555hlVq1Ytz2N/%2BOEHubq6qm/fvipZsqRatGihrl27atmyZZKkr7/%2BWp07d1bTpk3l6OioF154QRUrVtS6devynYcZZgAAANxRqampio2NzXGfu7u76tevr82bN8vJyUkLFy7M87GOHDmievXqWW174IEHtHLlSknS0aNH1b1792z7Dx48mO%2B8FMwAAAB27nZ0hM%2BfP6%2B4uDirbe7u7vLw8LjpfX/99VcNGDAgx31z5sxRu3bt8p0jOTlZzs7OVttKly6ty5cv52t/flAww/5k%2BaUo7s6fP6%2BvQkLUq1evfP1PqNjw8TE6wS11/vx5ffXJJ/b3PNkZu32eMjONTnBLWZ6nDRvs63ky0O34Efnkk680e/Zsq23Dhw/XiBEjbnpfHx8fHTp06JbkcHZ2VmJiotW21NRUubi4WPanpqZm21%2BxYsV8fw4KZsDGxcXFafbs2fL19eWFw4bxPBUPPE/FA89T8dCrVy/5%2BvpabXN3d7/jOerVq6ft27dbbTt69Kjq1q0rSapbt66OHDmSbX/r1q3z/Tk46Q8AAAAF5uHhoQYNGlh9GPEHTvv27fXnn3/qiy%2B%2BkNls1s6dO7V27VrL3PJzzz2ntWvXaufOnTKbzfriiy904cIFtW/fPt%2Bfg4IZAAAAxUrnzp316aefSpIqVqyozz//XBs2bJCPj48CAgIUEBCg5s2bS5JatGih8ePHKygoSN7e3goLC1NISIhcXV3z/fkYyQAAAIDN2rRpU7ZtYWFhVrcbNmyoL7/8MtfH6Natm7p161boDA5BQUFBhb43gDvCxcVF3t7elhMYYJt4nooHnqfigecJtsSUmWlnp9YCAAAAtxAzzAAAAEAeKJgBAACAPFAwAwAAAHmgYAYAAADyQMEMAAAA5IGCGQAAAMgDBTMAAACQBwpmAAAAIA8UzAAAAEAeShodAED%2BpaWlqVSpUkbHAIqNI0eOaOrUqTpx4oQyMjKs9m3cuNGgVACKGwpmwAa9/vrrCg4OlrOzs2XbsWPHNGbMGH377bcGJgOKl8DAQDk7O8vf318lS/KSB6Bw%2BL8HYIOio6P17LPPatasWapbt65WrFihKVOmqFOnTkZHww2Sk5O1fPnyHLuXkydPNigVbnTo0CFt2bJFZcuWNToKbiI8PFwTJkzQiRMnlJmZabXvwIEDBqUCrqFgBmzQsmXLNHPmTPXq1UsNGjTQ8ePHNWPGDPn6%2BhodDTcYO3asoqKi5OPjI0dHR6PjIAceHh5KS0szOgby4YMPPlDjxo0VEBDAuwGwOfxEAjbIwcFBvr6%2BCg0NVVRUlFq3bq2mTZsaHQtZhIeHa%2BXKlapZs6bRUZCLfv366dVXX9WAAQNUuXJlq31eXl4GpUJOTpw4oS%2B//FJOTk5GRwGyoWAGbNC0adO0ePFivfTSS%2Brdu7cCAgLUpUsXBQcHq02bNkbHw9%2BcnJx0zz33GB0DeQgODpYkRUVFWW03mUy8zW9j6tSpo/Pnz/MHKGySKTProBAAw7Vp00bTpk2Tt7e3ZduiRYs0Y8YM/frrrwYmw40%2B/fRTnT9/XsOHD1elSpWMjgMUa/Pnz9eqVav03HPPyd3d3Wqfn5%2BfQamAayiYARt08eJFubq6Ztt%2B8OBB1a9f34BEyImvr69iYmJkMpmy7aN7aTvOnTuntWvXKjo6Wh4eHurSpYtq1apldCxkkds5GiaTiSUAYTgKZsBGHTt2TCtWrNC5c%2Bc0adIkhYWFqV%2B/fkbHwg127dqV674b3x2Acfbt26cXXnhB9913n2rUqKFTp07p2LFjWrBgAecFAMg3CmbABm3fvl0jRozQk08%2Bqc2bNyssLEzPPvusBg0aJH9/f6Pj4QYZGRnav3%2B/zpw5Iw8PDzVp0kQlSnARVVsxYMAAtWvXTgMGDLBsW7RokTZs2KAVK1YYmAw52b9/v1auXKno6Gi5u7vr2WefVbNmzYyOBVAwA7aoe/fuGjlypNq0aSMvLy9FRERo3759GjVqFG9N2pC4uDgNHTpUBw8elKurqxISElSnTh19/vnnqlKlitHxIMnHx0fbt2%2B3WqbMbDarefPm2rNnj4HJkNW2bds0bNgw%2Bfr6Wt4N2Lx5sz766CO1a9fO6Hi4y7FKBmCDTp48qdatW0uSZT62YcOGunTpkpGxkMWUKVNUp04dLV68WC4uLkpMTFRQUJAmT56smTNnGh0PkpydnXX27FmrlRfOnj2rChUqGJgKOZk1a5amTJmijh07WratX79e//73vymYYTjeNwRsULVq1RQZGWm1bd%2B%2BfapatapBiZCTnTt3asKECXJxcZEklStXTkFBQdqxY4fByXBdp06dNGLECG3dulXHjx/Xzz//rJEjR3LVTBt0/PhxdejQwWpbhw4ddOLECWMCATegwwzYoCFDhuiVV15R7969ZTabFRISoiVLlmjMmDFGR8MNMjIysq2QYTKZuOqfDXnttdcUHx%2BvYcOGyWw2y8nJSd27d9fw4cONjoYsXF1ddfjwYauVgA4ePJhtiTnACMwwAzbq559/1rJlyxQdHa0qVaqoZ8%2Be2bovMNbo0aNVsmRJTZgwQWXKlFFycrLGjx%2Bv9PR0ffTRR0bHww3S0tJ06dIlVa5cOcdlAGG8%2BfPna8WKFRoyZIhlhjkkJER9%2BvTRyy%2B/bHQ83OUomAGgkGJiYjRo0CBFR0fL1dVVFy9e1AMPPKB58%2BZxBUCDfffdd%2BrSpYvWrFmT6zFcDMO2ZGZmavbs2Vq9erX%2B/PNPVa9eXT169NCgQYNYeQaGo2AGbMjYsWNveszkyZPvQBLk19WrVxUREaH4%2BHhVr15dDRs2lIODg9Gx7npdunTRd999x8UwANwSzDADNighIUFbt27Vk08%2BqZo1ayo2NlY//vijnnrqKaOjQdeuHFelShXFxMRIkmrXrq3atWtLkmJjYyVdO3ETxvnuu%2B8kSZs2bcpxf2Ji4p2MgzzMnz9f/v7%2Bmj17dq7HMHMOo1EwAzbkevd46NChmjVrltq2bWvZt23bNn366adGRcMNOnXqpMjISPn6%2Bmabh83MzJTJZOLS2DbC29s7xysyPvnkk9q9e7cBiZBVRESE/P39FR4enuN%2BZs5hCxjJAGyQp6en9uzZYzW3l56ermbNmikqKsrAZJCureNbtWpVRUdH53pM9erV72Ai3OjkyZMKDAxUZmamdu/ene1KcUlJSbp48WKu3WcYIy4uLscVMY4cOaK6desakAj4Bx1mwAZVr15d69evV%2BfOnS3bVq9ebXnbH8a6vh52cHCw5s6dm21/v379tHTp0jsdC3%2BrXbu2nnrqKSUkJCgyMlLe3t5W%2B0uVKpXrbDOM06FDh2zrz6enp6tXr17ZtgN3GgUzYINGjx6t1157TcuWLVPVqlV15swZHT58mJEMG3DmzBnLygvbtm3LNneZlJSkQ4cOGRENN%2Bjbt68kqUaNGqyGYcNOnjypwYMHKzMzUykpKVZjaJKUmprKuzWwCYxkADbqjz/%2B0Lp163T%2B/HlVqVJFXbt2tbq8L4yRkZGh0aNHKz4%2BXnv27FHTpk2t9js5OcnPz09dunQxKCGy2rlzp2JjY3X95c5sNuvQoUMKCAgwOBkkafPmzUpISFBQUJAmTJhgtc/JyUleXl5cvASGo2AGgEIKCAhQcHCw0TGQh%2BDgYH355ZeWy5enp6crOTlZjz/%2BuObPn29wOtxo165d2cZnAFtBwQzYoCNHjmjq1Kk6ceKEMjIyrPaxdqxtSUlJ0aVLlyzPk9ls1uHDh9W%2BfXuDk0GSWrVqpTlz5iglJUWhoaF6//33NWXKFF2G2POPAAAfIklEQVS%2BfFkTJ040Oh5ukJaWprVr1yo2Njbb71NO5woAdxIzzIANCgwMlLOzs/z9/VWyJL%2Bmtmr16tWaOHGirly5YrXdzc2NgtlGpKSk6NFHH1VcXJx%2B%2B%2B03mUwmDR8%2BXJ06dTI6GrL4v//7P23dulUVK1aU2WxWmTJldOTIEWbQYRN4JQZs0KFDh7RlyxaVLVvW6CjIw9y5czVq1Ci5uLgoIiJCAwcO1LRp09SqVSujo%2BFvVapU0YULF%2BTu7q5z587JbDardOnSSkpKMjoasti6datWrFih%2BPh4rVixQh9%2B%2BKE%2B//xz7d271%2BhogLg4O2CDPDw8lJaWZnQM3ERcXJwGDhyoFi1a6NSpU2rQoIHef/99ff3110ZHw9/atGmjF154QfHx8fLy8tL//d//KSgoSHXq1DE6GrLIyMjQfffdp/vuu89y4Z%2B%2BfftygRnYBDrMgA3q16%2BfXn31VQ0YMECVK1e22ufl5WVQKmTl5uYms9msqlWr6vjx45KuXRL7woULBifDdWPGjJGbm5scHR0VGBiogIAAJSYmcrKmDapSpYpOnz6tmjVr6sKFC7p8%2BbJKlCih5ORko6MBnPQH2KL69evnuJ1LLtuW0aNHy8nJSe%2B%2B%2B65efPFF%2Bfn5qXTp0po9ezYnZ9qISZMmafTo0Yw3FQPz58/XkiVLtHLlSs2YMUPnzp2Tk5OTUlJStGTJEqPj4S5HwQzYkHPnzqlKlSqKjo6WyWRS1l9Pk8mkatWqGZQOWZ0/f96ytNypU6c0dOhQpaamavLkyeratavR8SDJ29tbO3bskIODg9FRkA/r169XmzZtlJGRoWnTpikpKUmjRo1iDXoYjoIZsCFNmjRRZGSk6tevL5PJlOMxdJhtR1hYmNq1aycnJydJ0tWrV2U2m%2BXs7GxwMlw3ZcoUJScn69lnn5W7u7vV7xV/fNqWV155RdOmTePdANgkCmbAhpw9e1ZVq1ZVdHR0rsdwmVjb4e3tre3bt8vR0dHoKMjFjeNN14vlzMxMxptsUPPmzbVlyxaVKlXK6ChANhTMAFBIgwcP1tNPP61u3boZHQW54I/P4iM4OFhnzpxR165ds70bwMnOMBoFMwAUUvfu3fXbb7%2BpVKlSqly5stULPCf9AQXDyc6wZSwrBwCF1K9fP6Mj4CY4H6D4OHjwoNERgFzRYQYA2K1du3ZZ3Y6Pj9eSJUvUrVs39ezZ06BUyElMTEyu%2BzhBE0ajYAaAQurfv3%2Bu3cvFixff4TTIr7i4OL3wwgsKCwszOgpucP3dgOtlyY2/W7wbAKMxkgEAheTj42N1OyEhQRs2bFCvXr0MSoT8KF%2B%2BvGJjY42OgSyyzv3Hx8frs88%2BU9u2bQ1KBPyDDjMA3EK//fabpk6dqkWLFhkdBZLWrFljddtsNmvjxo1KTk7m6nHFQGJiop555hn99NNPRkfBXY4OMwDcQg0aNND%2B/fuNjoG/zZo1y%2Bq2g4OD7r//fo0fP96gRCiov/76y%2BgIAAUzABRW1pOUzGazwsLCVLVqVYMSIatNmzYZHQH5NHv2bKvbZrNZW7du1aOPPmpQIuAfFMwAUEi%2Bvr6Wk5Su/1uhQgUFBwcbHQ1/O3HihMLDw3Xx4kW5ubmpRYsWXLDERoWHh1vddnBwkKenp4YMGWJQIuAfzDADQCFlvYqcg4OD3NzcuFS2DcjMzNTEiRP15ZdfqlSpUqpQoYL%2B%2BusvpaWlaeDAgXr77beNjgigGKHDDACFVL16dR0/ftzSvaRzaTs%2B%2B%2Bwzbd68WQsWLFDLli0lSenp6dq%2BfbvGjx%2BvOnXqsJqJjfnll1%2B0c%2BdOXbx4UZUqVVKrVq24JDZsBh1mACiEdevWaerUqYqNjbWMZFSvXl1jx45lGSwb0LlzZ7377rtq3rx5tn1btmzRrFmztHLlSgOSIasrV65oxIgR2rp1q2rXrq2KFSsqPj5eJ0%2Be1FNPPaWPP/5YJUqUMDom7nIUzABQQBs3btSoUaP04osv6sknn1TFihV14cIFbdq0ScuWLdPcuXNzLNRw53h6eioyMjLHC8ukp6erZcuW2WZmYYwPP/xQ//3vfzVr1izde%2B%2B9lu3Hjh3T6NGj1aVLF/n7%2BxuYEGAkAwAKLCQkRIGBgerRo4dlW%2B3atdWkSRNVq1ZNn332GQWzwUwmU65XYXRwcJDZbL7DiZCbDRs2aMaMGVbFsiTdf//9CgoKUlBQEAUzDMd7HABQQEeOHNHTTz%2Bd475nnnlGBw8evMOJgOIrLi5ODRs2zHGfp6cnV2WETaDDDAAFlJGRIScnpxz3OTs76/Lly3c4EbJKS0vLtq7vjegw24685pNNJhPPFWwCBTMAFFBub/XDdnh6euY5o8zFMAAUBAUzABRQRkaGdu/erdzOmc7IyLjDiZDVkiVL8n3snj171LRp09uYBnlJS0vT2LFj89wPGI2CGQAKKDU1Vf369ct1Px3o4uXll19WZGSk0THuWl27di3SfuBOYFk5AMBdzdPTU1FRUUbHQD5899136tKli9ExcBdilQwAuI2aNGlidATcBO8IFB%2BBgYFGR8BdioIZAG4j3sQDbh1%2Bn2AUCmYAuI3oXgK3Dr9PMAoFMwAAAJAHCmYAAAAgDxTMAIC7Wp06dYyOAMDGUTADAOzasWPHFBwcrOHDhyshIUFLly612r969WqDkqGgOOkPRqFgBoDbiBd4Y23fvl09e/ZUQkKCfvnlF6WmpmrOnDmaP3%2B%2B0dFQCK1atTI6Au5SXLgEAAooJibmpsdUq1ZNkhQfH69KlSrd7kjIRffu3TVy5Ei1adNGXl5eioiI0L59%2BzRq1Cht3LjR6HiQNHv27JseM3z48DuQBMgdl8YGgALy9fXNtrxVZmam1bYDBw5IEsWywU6ePKnWrVtL%2BmdJsoYNG%2BrSpUtGxsINwsPD89zPUnKwBRTMAFBA1zuT3377rfbs2aM333xTtWrV0tmzZzV9%2BnQ9%2BuijBifEddWqVVNkZKSaNm1q2bZv3z5VrVrVwFS40ZIlS4yOANwUIxkAUEht2rRRaGioKlSoYNmWmJiof/3rX9q%2BfbuByXBdWFiYJkyYoN69e2vx4sUaNmyYlixZojFjxsjPz8/oeMhi586dio2Ntcz%2Bm81mHTp0SAEBAQYnw92ODjMAFFJycrIyMjKstl2%2BfFlms9mgRMiqc%2BfOKlu2rJYtW6Zq1app586dGjdunDp06GB0NGQRHBysL7/8Ui4uLpKk9PR0JScn6/HHHzc4GUDBDACF1rZtWw0bNkwjR45U1apVdfr0ac2cOVNdunQxOhr%2Btn//frVp00Zt2rQxOgpuYv369Vq6dKlSUlIUGhqq999/X1OmTNHly5eNjgYwkgEAhZWcnKwJEyZow4YNSktLk5OTk7p166aAgACVKlXK6HiQ1LhxY9WpU0c9evRQt27dVK5cOaMjIRdNmjRRZGSk4uLiNHjwYIWGhiopKUmdOnXSli1bjI6HuxwFMwAUUVpami5evKiKFSvK0dHR6Di4QWJiotauXas1a9bo0KFDateunZ577jm1aNHC6GjIolOnTlqyZInc3Nzk7e2t7du3y2QyydvbW5GRkUbHw12OghkAiuDYsWNasWKFzp07p0mTJiksLEz9%2BvUzOhZycOzYMYWGhurbb7%2BVo6OjfvzxR6Mj4QZTpkzRtm3btGjRIr377rsqU6aMnJyc9Pvvv3M1RhiOK/0BQCFt375dPXr04CpyxcDly5e1d%2B9e7du3T5cuXVKjRo2MjoQsxowZo27dusnR0VGBgYFKSEjQ0aNHNWnSJKOjAXSYAaCwuIqc7fvll1/0zTff6KefflKNGjX03HPPyc/Pz2opQAC4GVbJAIBC4ipytu/VV19Vp06dtHDhQi4oY%2BOSk5O1fPlynThxIttyjZMnTzYoFXANBTMAFBJXkbN927Zts6zrC9s2duxYRUVFycfHh5NnYXMomAGgkIYMGaJXXnlFvXv3ltlsVkhIiOUqcjBWUFCQgoKCFBwcnOsxdC1tS3h4uFauXKmaNWsaHQXIhoIZAAqJq8jZLk7PKX6cnJx0zz33GB0DyBEn/QFAIa1fv14dO3bMtv2rr75Sr169DEiErH799Vc1btw42/YtW7ZY5s9hGz799FOdP39ew4cPV6VKlYyOA1ihYAaAAkhJSVFCQoKkax3mdevWWXUzExMT9fzzzysqKsqoiLjB9avH3SgpKUmPP/44z5GN8fX1VUxMjOUE2hsdOHDAgETAPxjJAIACSEpKUufOnZWamirp2ot8ZmamTCaT5d927doZnPLudvLkSXXu3Fnp6enKzMzUQw89lO2YJk2aGJAMefnggw%2BMjgDkig4zABTQhQsXlJKSoq5du%2Bq7776z2ufk5KTKlSsblAzXHThwQH/99Zf8/f0VEhJitc/JyUn16tWTs7OzQemQm4yMDO3fv19nzpyRh4eHmjRpohIluMYajEfBDACFlJGRkeOL%2BdWrV1WyJG/g2YLTp0%2Bz6kIxERcXp6FDh%2BrgwYNydXVVQkKC6tSpo88//1xVqlQxOh7uchTMAFBIp06d0pw5cxQbG2u50ILZbNbx48e1c%2BdOg9NBkhISErRkyZJsz9Hhw4cVGhpqcDrc6I033lBmZqYmTpwoFxcXJSYmKigoSFevXtXMmTONjoe7HC0QACikcePGKTMzUxUrVtSFCxf08MMPa82aNXrhhReMjoa/jR07VidOnFClSpWUnJysqlWratu2berbt6/R0ZDFzp07tWHDBsuFZsqVK6egoCC1bdvW4GSAxGAQABTS/v37NWfOHA0bNkzlypVTQECAZsyYoR07dhgdDX%2BLiIjQokWL9M4776hmzZr69NNP9d577%2BmPP/4wOhqyyMjIyLZChslk4qp/sAkUzABQSM7OzqpQoYJq1aqlw4cPS5Jat25NMWZDSpYsqXvuuUd16tTRoUOHJF1bDvD33383OBmy8vHxUVBQkC5fvixJSk5OVlBQkLy9vQ1OBlAwA0Ch1apVSz///LNcXFyUkZGh06dPKzY2VlevXjU6Gv5WvXp17d%2B/X%2BXLl1dycrLi4%2BN1%2BfJly7KAsB1vvvmm9u7dK29vbz322GPy8fHR0aNH9c477xgdDWCGGQAKy9/fXyNHjtR3332nXr166fnnn5eDgwMzlzakT58%2B6t%2B/v8LCwtSlSxcNHDhQJUuWlJeXl9HRkEW1atUUFhamiIgIxcfHq3r16mrYsKEcHByMjgawSgYAFEVsbKwqVaokR0dHrVu3TklJSfLz81OpUqWMjoa/7d27V/Xr15fJZNLChQuVnJysF198URUqVDA6GiSdO3dOVapUUUxMTK7HVKtW7Q4mArKjYAYA2K1nn31WixcvVtmyZY2Oglxcv3z59T9qbnT96plcGhtGYyQDAAoopxf2rHiBtw3nz583OgJuIiwsTJK0ceNGg5MAuaPDDAAFtGvXrpsew5n9tmH8%2BPHat2%2BfOnToIA8PD6s/dPz8/AxMhqxeeeUVzZ07N9v2fv36aenSpQYkAv5BwQwARXTp0iWdPn1aDz/8sK5evcr8sg3x9fXNcbvJZKKjaQPOnDmjNWvWSJLmzZunIUOGWO1PSkrSqlWrFBERYUQ8wIKRDAAopOTkZAUGBiosLEylS5fW6tWrNWjQIC1cuFD33Xef0fEgadOmTTluT0xMvMNJkJNq1arpyJEjio%2BPV3p6usLDw632Ozk5afz48QalA/5BhxkACmn8%2BPE6f/683nrrLfXs2VO//PKL3nvvPZ0%2BfVoLFiwwOh50bTQmpxGaZs2aaffu3QYkQm4CAgIUHBxsdAwgRxTMAFBIrVu31tq1a1WhQgVLYZaamqrWrVvna84Zt8fJkycVGBiozMxM7d69W82aNbPan5SUpIsXL%2BbafYZxUlJSdOnSJWVkZEiSzGazDh8%2BrPbt2xucDHc7RjIAoJAyMjIs88rXew83boMxateuraeeekoJCQmKjIzMdgJmqVKlcp1thnFWr16tiRMn6sqVK1bb3dzcKJhhOApmACik5s2ba%2BLEiQoMDLSsvvDxxx%2BzQoYN6Nu3rySpRo0arIZRTMydO1ejRo2Si4uLIiIiNHDgQE2bNk2tWrUyOhqgEkYHAIDiauzYsTp27Ji8vLyUmJgoT09PRURE6O233zY6Gv7Wvn17ffHFF5Kko0ePqkePHhoyZIhiY2ONDYZs4uLiNHDgQLVo0UKnTp1SgwYN9P777%2Bvrr782OhrADDMAFEVmZqb27dun6OhoValSRY0aNZKDg4PRsfC3d955RwcOHNC3336rfv36yc3NTU5OTkpMTMxxzV8Yp23btlq/fr0cHBzUsmVLy4oZTZs21Z49ewxOh7sdIxkAUEgXL16Uq6urGjVqpEaNGkmS0tLS9P777%2Bvdd981OB2kaxeZWb16tS5duqTIyEht3rxZrq6ueuyxx4yOhiwaNWqkwMBAvfvuu6pTp45WrFih0qVLy9XV1ehoACMZAFBQBw4cUNu2bdWiRQv16tVLly5dkiQdOnRIzzzzjEJDQw1OiOuSk5Pl6uqqHTt2qGbNmrrnnntkMpluemlz3Hljx45VfHy8kpOT9eabb%2BrDDz/Uu%2B%2B%2Bq1GjRhkdDWAkAwAKql%2B/fipXrpx69eqlJUuWqF69emrTpo2GDRumBx98UNOmTVONGjWMjglde65atmypLVu26NFHH9Xw4cP18ccf69ixY1q4cKHR8ZCHq1evymw2y9nZ2egoAAUzABRU06ZN9eOPP6pSpUo6d%2B6c%2BvXrp7/%2B%2BkvPP/%2B8Ro0apRIlePPOVhw9elQTJkyQk5OTPv74Y/3%2B%2B%2B%2BaNGmSZs2apXvvvdfoeLiB2WzWunXrFB0dbVmH%2Bbrhw4cblAq4hoIZAArI09NTUVFRltuPPPKIxowZoxdffNHAVEDxNnLkSIWHh6tu3bpWIzMmk0mLFy82MBnASX8AUGBZ518dHR3Vv39/g9IgJ/Pnz5e/v79mz56d6zF0LW3Ltm3bFBoayjgTbBIFMwAUkaOjoxwdHY2OgRtERETI39/fsjRZVpz0Z3vc3d1ZEQM2i5EMACigRo0aaeLEiZbbEyZM0Pjx462O4epyQMFs2rRJy5YtU58%2BfVS%2BfHmrfV5eXgalAq6hYAaAAvL19c1zv8lk0saNG%2B9QGtzM3r17dfz4cWV9ueOPGtvy0Ucfad68edm2m0wmHThwwIBEwD8omAEAdmvGjBkKCQmRu7u7Spb8ZwqRP2psj5eXlz788EM99thjrDQDm8MMMwAU0iuvvJLj5ZX79eunpUuXGpAIWYWGhurTTz9VmzZtjI6Cm3ByclKrVq0olmGTKJgBoADOnDmjNWvWSLp2Vn/WVRiSkpJ06NAhI6IhB8nJyWrdurXRMZAPgwYN0vTp0zV06FBVqFDB6DiAFQpmACiAatWq6ciRI4qPj1d6enq2VRicnJyynQAI4zzxxBNau3atnn76aaOj4CaWLVummJgYffHFF9n2McMMozHDDACFFBAQoODgYKNjIA8jR47UTz/9pDp16qhy5cpW%2B7gYhm3ZuXNnruMY3t7edzgNYI0OMwAU0ltvvaXXX39dw4YN0/3336%2BZM2fqzJkzCgoKkouLi9HxIKlevXqqV6%2Be0TGQD1OnTtXixYtVtmxZo6MA2dBhBoBCev3113Xp0iVNmTJFbm5uOnbsmKZNmyY3Nze99957RscDipXHHntMGzZsoGCGTaJgBoBCat68uTZu3GjVTU5KSlL79u21Y8cOA5MhKChIQUFBGjt2bK7HTJ48%2BQ4mws2MHz9e%2B/btU4cOHeTh4WF1NUbWzIbRGMkAgELKyMhQenq61bbMzEw5ODgYlAjX0QsqfrZu3SpJ%2Buqrr6y2m0wmCmYYjg4zABTSG2%2B8oeTkZI0dO1ZVq1bV2bNnNXXqVJUtW1YffPCB0fEAALcIBTMAFFJ8fLxee%2B01RUREWN4%2BbtmypaZPn66KFSsanA6SdPHiRS1fvlzR0dHKyMiw2sdIhu3Zv3%2B/Vq5cqejoaLm7u%2BvZZ59Vs2bNjI4FUDADQFHFxMQoLi5OVapU0T333GN0HNzghRde0NmzZ/Xoo49mW7KMgtm2bNu2TcOGDZOvr69q1KihU6dOafPmzfroo4/Url07o%2BPhLkfBDACFFBERkes%2BLy%2BvO5gEufH09NTmzZvl6upqdBTcRM%2BePTVo0CB17NjRsm39%2BvUKCQnR6tWrDUwGcNIfABRa//79s20rUaKEqlatqo0bNxqQCFnVqlVLZrPZ6BjIh%2BPHj6tDhw5W2zp06KBx48YZlAj4BwUzABTSwYMHrW7Hx8drzpw5ql69ukGJkFVgYKD8/f3l5%2BenChUqWO1j5QXb4urqqsOHD6t%2B/fqWbQcPHpS7u7uBqYBrGMkAgFsoNTVVHTp00M8//2x0FEgaO3asQkND5e7ubjXDbDKZeBfAxsyfP18rVqzQkCFDLDPMISEh6tOnj15%2B%2BWWj4%2BEuR4cZAG6hS5cu6cqVK0bHwN82bNigb7/9Vg888IDRUXATL7/8sq5cuaJ58%2Bbpzz//VPXq1dWvXz8NGjTI6GgABTMAFFbWq8iZzWbt2bNHLVu2NCgRsqpYsaJq1apldAzkoX///lqyZIlWr16tESNGaMSIEUZHArJhJAMACilrwVyiRAndf//96tWrl9XlsmGcNWvWaOvWrRo8eLAqVKhgdbnlatWqGZgM13l6eurnn3/WE088ocjISKPjADmiYAYA2K0bTyC7XixnZmbKZDLpwIEDRsXCDQYPHqxffvklz2N4rmA0CmYAKKDZs2ff9Jjhw4ffgSS4mejo6Fz3sZqJbUhJSdHvv/%2BuF198USEhITke4%2B3tfYdTAdaYYQaAAgoPD5d0bUWMffv26eGHH1aNGjUUGxurX3/9Va1atTI4Ia6jKLZ9zs7Oatq0qT788EMKY9gsOswAUEjvvPOOGjdurN69e1u2ffPNN/rhhx80d%2B5cA5Ohfv36VvPKOeFtftuSnp6u77//XidOnFBGRobVPt6xgdEomAGgkJo0aaLdu3dbre%2Bbnp6uZs2aKSoqysBk2LVrlyRp%2B/bt2rJli4YPH65atWrp7NmzmjNnjlq1aqWRI0canBI3CggIUFhYmOrXr6%2BSJf95A9xkMmnx4sUGJgMYyQCAQqtUqZIiIiLk4%2BNj2bZt2zZ5eHgYmArSPzOv48aN09KlS3XPPfdIkurWrauHHnpIPXr0oGC2MZs2bdLixYvVsGFDo6MA2VAwA0AhDRkyRC%2B//LI6dOigatWq6fTp0/rpp580ZcoUo6Phb/Hx8SpfvrzVNicnJyUmJhqUCLnJzMzUww8/bHQMIEclbn4IACAnPXr00Lx581SyZEnt379fzs7O%2BuKLL9SxY0ejo%2BFvXl5eevvtt3X69GmZzWb98ccfeuONN9SmTRujoyGLLl26aMGCBUbHAHLEDDMAFNGFCxcUHR0td3d3Va1a1eg4uEFcXJxGjRqlPXv2WE4CbNWqlaZOnapKlSoZnA436tOnjyIjI%2BXs7Jztudm4caNBqYBrKJgBoJCSkpL09ttva9OmTZZtLVq00Mcff5xtDADGiomJUWxsrNLT0xUaGqrQ0FD973//MzoWbvDNN9/kuu%2BZZ565g0mA7JhhBoBC%2BvDDD5WcnKzvvvtONWrU0MmTJ/X%2B%2B%2B9r2rRpmjRpktHxcIOYmBgtWLBAP//8s%2BrWras333zT6Ej4W34uBAQYjQ4zABTSE088oVWrVsnNzc2yLS4uTk8//bR27NhhYDJIUkZGhjZs2KCFCxfqyJEjunr1qubOnavHH3/c6Gi4Qf/%2B/fPcz7JysAV0mAGgkFJSUlSuXDmrbeXLl8920QXceYsWLdLixYuVkZGh3r17KyQkRP/6179Ur149o6MhiyVLlhgdAbgpVskAgEJq3LixZs6cqetv1GVmZmrmzJmsI2sDJk%2BerDZt2uj777%2BXv7%2B/XF1djY4EoBhjJAMACunQoUMaMGCASpUqperVqys6Olomk0kLFy7U/fffb3S8u9qyZcu0fPlyxcfHq2fPnurTp4/8/Py0Zs0ay0VMACC/KJgBoJAuX76stLQ0bdy4URcuXFD16tXVpk0blS1b1uho%2BNuOHTu0dOlSbd26Venp6XrvvffUtWtXOTg4GB0NQDFCwQwAheTr66vQ0FAK5GIgOjpay5cv16pVq1SiRAk9/fTTeuedd4yOBaCYYIYZAIogJSXF6AjIh%2BrVq%2BvNN9/Uli1bNGbMGO3atcvoSACKETrMAFBIY8eO1Y4dO9S6dWt5eHhY7Rs%2BfLhBqQAAtxrLygFAIZ05c0Y1a9bU8ePHdfz4ccv265dgBgDYBwpmACiE2bNnq2zZsnrsscfUt29fo%2BMAAG4jZpgBoICmTp2q5cuXy9HRUbNmzdL8%2BfONjgQAuI2YYQaAAmrdurUWLFigunXrKjw8XMHBwVq7dq3RsQAAtwkdZgAooMTERNWtW1eS1LRpU8XGxhqcCABwO1EwA0ABlSjxz/86S5bkVBAAsHcUzABQQEyyAcDdhdYIABTQ1atXtWbNGstts9lsdVuS/Pz87nQsAMBtwkl/AFBAvr6%2Bee43mUzauHHjHUoDALjdKJgBAACAPDDDDAAAAOSBghkAAADIAwUzAAAAkAcKZgAAACAPFMwAAABAHiiYAQAAgDxQMAMAAAB5oGAGAAAA8vD/LlI9wnIQLDMAAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational_Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x2df994908d0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ProfileReport(df_online_customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GMM Hyperparameters\n",
    "* n_components: the number of clusters (here are called components) that we want\n",
    "* covariance_type: there are 3 types of covariance matrix:\n",
    "    * diag: each component has its own diagonal covariance matrix, this helps plain clusters\n",
    "    * tied: all components share the same general covariance matrix, this helps eliptical clusters\n",
    "    * spherical: each component has its own single variance, this helps sheprical clusters\n",
    "    * full: each component has its own general covariance matriz, this helps the most eliptical and tiny clusters\n",
    "* reg_covar:the regularization added to the covariance matrix in order to avoid negative number\n",
    "* init_params: the method that initialize the components, could be:\n",
    "    * kmeans: the kmeans algorithm initialize the components\n",
    "    * random: in the first interation the components are located ramdomly\n",
    "* weights_init: to pass weights to the algorithm\n",
    "* means_init: to pass the initial means to the algorithm\n",
    "* precisions_init: to pass the initial inverse of the convariance matrix\n",
    "* warm_start: True/False, if True, the last fit iteration is pased to the next iteration, instead of the init param. This is to speed up convergence\n",
    "\n",
    "![covariance types](https://scikit-learn.org/0.15/_images/plot_gmm_classifier_0011.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components=2,\n",
    "                      covariance_type=\"diag\",\n",
    "                      max_iter = 1000,\n",
    "                      random_state=2018)\n",
    "preds = gmm.fit_predict(df_online_customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.55303858e+05, 3.02385419e+03, 1.00000000e-06, 1.00000000e-06],\n",
       "       [7.70189061e+05, 5.08375845e+03, 3.92576422e+02, 1.00000000e-06]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Covariance matrix of each cluster\n",
    "gmm.covariances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.75491057e+02, 3.21088534e+01, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.16744911e+03, 6.83870465e+01, 2.88443000e+01, 1.00000000e+00]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Means matrix of each cluster\n",
    "gmm.means_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91044776, 0.08955224])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Weights of each cluster\n",
    "gmm.weights_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_online_customers['preds'] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2066744735106361"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(df_online_customers, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
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       "\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",
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       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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O2I2QAIkQAIkQAIkQAIkQAKmCFB+TWHSdhHlVxtaZ1RM+XXGOLAXJEACJEACJEACJEACJED5tXcOUH7t5a+9dcqvdsRsgARIgARIgARIgARIgARMEaD8msKk7SLKrza0zqiY8uuMcWAvSIAESIAESIAESIAESIDya+8coPzay19765Rf7YjZAAmQAAmQAAmQAAmQAAmYIkD5NYVJ20WUX21onVEx5dcZ48BekAAJkAAJkAAJkAAJkADl1945QPm1l7/21im/2hGzARIgARIgARIgARIgARIwRYDyawqTtosov9rQOqNiyq8zxoG9IAESIAESIAESIAESIAHKr71zgPJrL3/trVN+tSNmAyRAAiRAAiRAAiRAAiRgigDl1xQmbRdRfrWhdUbFlF9njAN7QQIkQAIkQAIkQAIkQAKUX3vnAOXXXv7aW6f8akfMBkiABEiABEiABEiABEjAFAHKrylM2i6i/GpD64yKKb/OGAf2ggRIgARIgARIgARIgAQov/bOAcqvvfy1t0751Y6YDZAACZAACZAACZAACZCAKQKUX1OYtF1E+dWG1hkVU36dMQ7sBQmQAAmQAAmQAAmQAAlQfu2dA5Rfe/lrb53yqx0xGyABEiABEiABEiABEiABUwQov6YwabuI8qsNrTMqpvw6YxzYCxIgARIgARIgARIgARKg/No7Byi/9vLX3jrlVztiNkACJEACJEACJEACJEACpghQfk1h0nYR5VcbWmdUTPl1xjiwFyRAAiRAAiRAAiRAAiRA+bV3DlB+7eWvvXXKr3bEbIAESIAESIAESIAESIAETBGg/JrCpO0iyq82tM6omPLrjHFgL0iABEiABEiABEiABEiA8mvvHKD82stfe+uUX+2I2QAJkAAJkAAJkAAJkAAJmCJA+TWFSdtFlF9taJ1RMeXXGePAXpAACZAACZAACZAACZAA5dfeOUD5tZe/9tYpv9oRswESIAESIAESIAESIAESMEWA8msKk7aLKL/a0DqjYsqvM8aBvSABEiABEiABEiABEiAByq+9c4Dyay9/7a1TfrUjZgMkQAIkQAIkQAIkQAIkYIoA5dcUJm0XUX61oXVGxZRfZ4wDe0ECJEACJEACJEACJEAClF975wDl117+2lun/GpHzAZIgARIgARIgARIgARIwBQByq8pTNouovxqQ+uMiim/zhgH9oIESIAESIAESIAESIAEKL/2zgHKr738tbdO+dWOmA2QAAmQAAmQAAmQAAmQgCkClF9TmLRdRPnVhtYZFVN+nTEO7AUJkAAJkAAJkAAJkAAJUH7tnQOUX3v5a2+d8qsdMRsgARIgARIgARIgARIgAVMEKL+mMGm7iPKrDa0zKqb8OmMc2AsSIAESIAESIAESIAESoPzaOwcov/by19465Vc7YjZAAiRAAiRAAiRAAiRAAqYIUH5NYdJ2EeVXG1pnVEz5dcY4sBckQAIkQAIkQAIkQAIkQPm1dw5Qfu3lr711yq92xGyABEiABEiABEiABEiABEwRoPyawqTtIsqvNrTOqJjy64xxYC9IgARIgARIgARIgARIgPJr7xyg/NrLX3vrlF/tiNkACZAACZAACZAACZAACZgiQPk1hUnbRZRfbWidUTHl1xnjwF6QAAmQAAmQAAmQAAmQAOXX3jlA+bWXv/bWKb/aEbMBEiABEiABEiABEiABEjBFgPJrCpO2iyi/2tA6o2LKrzPGgb0gARIgARIgARIgARIgAcqvvXOA8msvf+2tU361I2YDJEACJEACJEACJEACJGCKAOXXFCZtF1F+taF1RsWUX2eMA3tBAiRAAiRAAiRAAiRAApRfe+cA5dde/tpbp/xqR8wGSIAESIAESIAESIAESMAUAcqvKUzaLqL8akPrjIopv84YB/aCBEiABEiABEiABEiABCi/9s4Byq+9/LW3TvnVjpgNkAAJkAAJkAAJkAAJkIApApRfU5i0XUT51YbWGRVTfp0xDuwFCZAACZAACZAACZAACVB+7Z0DlF97+WtvnfKrHTEbIAESIAESIAESIAESIAFTBCi/pjBpu4jyqw2tMyqm/DpjHNgLEiABEiABEiABEiABEqD82jsHKL/28tfeOuVXO2I2QAIkQAIkQAIkQAIkQAKmCFB+TWHSdhHlVxtaZ1RM+XXGOLAXJEACJEACJEACJEACJED5tXcOUH7t5a+9dcqvdsRsgARIgARIgARIgARIgARMEaD8msKk7SLKrza0zqiY8uuMcWAvSIAESIAESIAESIAESIDya+8coPzay19765Rf7YjZAAmQAAmQAAmQAAmQAAmYIkD5NYVJ20WUX21onVEx5dcZ48BekAAJkAAJkAAJkAAJkADl1945QPm1l7/21im/2hGzARIgARIgARIgARIgARIwRYDyawqTtosov9rQOqNiyq8zxoG9IAESIAESIAESIAESIAHKr71zgPJrL3/trVN+tSNmAyRAAiRAAiRAAiRAAiRgigDl1xQmbRdRfrWhdUbFlF9njAN7QQIkQAIkQAIkQAIkQAKUX3vnAOXXXv7aW6f8akfMBkiABEiABEiABEiABEjAFAHKrylM2i6i/GpD64yKKb/OGAf2ggScQqBSqdR0pSLr/+4Sj8fdVTfrqurqTl5MAiRAAiRAAtuTAOXX3nGn/NrLX3vrlF/tiNkACdhCoFZiK5Xyah8gtrVyKxIOBySfL0qhUKz5Ha5xqXuq9Xg8XvH5vD0/SycRHh4OSblclnQ6t2EbLqNbTaVT/T13vEquHlu/1a3dj+dxuz1SLJYsq1M3C8s6yopIgARIgASaCFB+7Z0UlF97+WtvnfKrHTEbIIGeCbQWWBGv16WktFSqSu26pDbKba3AVjviqjHI6elxSaUy6n8bFciv1+vp+Vk63RiJhKVUKnWU3071bLXfgymePRZLSrmsx7CtkGFdLx3w8iWXK9TN5doxtKLv7eaE1XVbXd9Wm8vsLwmQgDUEKL/WcOy1Fspvr+S2yH2U3y0yUOzmliXQTmBF1sW1+nDr19YvPUYUtraeiYlRFSmMx5N9c3GW/CLym+37mbZSBZshv07mMTYWUS9esPpg0IoZGY5GhySTybV9fl0vHTqxNtP3TnWYfeng9bqlWGz+e2i2fp19NdsHXkcCVhKg/FpJs/u6KL/dM9tSd1B+t9RwsbM2EWgvsM3LiBv3zKLLLldFpqYmVHQvm11f1lsbge3m0cbHoypKivr6LZTffgn2dz/ld3Dl18zMGB0dVqsd8vmCmcsH7hrkERgZGZJ4PNU2+t/tQ1shw+1eOqAvVtTf+Ew+n0fcbrdks3rnQae+d/p9t2PB63sjQPntjZtVd1F+rSLp0Hoovw4dGHbLcgLtEzm1FtihoaD6MrYuq837YA2xbbMJteYJcMnMzKSsrMQlm833/WxWyu/U1Lik005Z9szIb9+TY4tVMMiRXzNDQfm1Xn7NcHfaNVj+7/F4JJFI29q14uAtwLCVZ6+NU357JWfNfZRfazg6thbKr2OHhh1rQ6BdFNbnc6toaO2+ydbLiI2K62W4WWIhmFYtLab8dp7OkUhISqUKlz13RjVQV2x3+TWeP8vIr4WR3634AXGC/CLqW7Iu795WHAbH9Jnya+9QUH7t5a+9dcqvdsRsoAWB/vfBNgvs7OykJBIpy+TJSvlFb9G/lZVE3bLnXifHIEZ+jWzPlN9e58RWvY/yS/m1etnzVvwshMNBdZScnZFfyq9zZg7l196xoPzay19765Rf7YgHtoF+9sEOD4fVf+ixZ7VTBNYswJmZCcvl16p9tZTfzqNI+dWX7bkzffuuoPxSfim/OHIuKG63S5LJjbPu6/ykUn510u2ubspvd7ysvpryazVRh9VH+XXYgGxyd8wIbCDgUxG5+oQs1WzEve2DjUaH1f6mpaWYZU88PT0hyaS1kV+nyu/Y2Ig6E9eKhFdTU2MqWm73UUeUX8qvZX8MtlBFXPZs7PmNxVLqb9p2LU6QX7Dnnl9nzEDKr73jQPm1l7/21im/2hFvSgO1e1vr97nWnxvaKhOxcS6skbSp9vfVRE44VgfiiyXFVhXKb38kDfmtSCyW6K8iEaH89o2wrwqY7Xl7Z3um/FJ+8QeE8tvXn9GBu5nya++QUn7t5a+9dcqvdsRdNwABrVRq34CbOw92aCgkoVBA5ueX69o0k414o05uHfkdl2QybemeX2dHfq2TX5wzCnYbFY/HKxA1XYWRX0Z+dc0tJ9e73Zd9V4862u6RX5wugP9W27nsmZFf5/yloPzaOxaUX3v5a2+d8qsdcdcNlMtFwf9qixmBhfzi7XGj/HbdgYYbJiaiUigU1TmMVpWRkWElUtYue4b8ZtSxPVYUK5cWoz9IeIVILUSz32J15Jfy2++I9H4/I7+M/GLLQT6/Pc+YwfaXkZHwtl/2TPnt/W/oIN5J+bV3VCm/9vLX3jrlVzvirhuoyq8Z4a2t3JDfkMzPL3Xd5kY3GFmPt4b84ktkp72rZuFYKZiU387UGfll5LfzLBm8K7Z75Hf95c/23vNL+R28z3Y/T0T57Yde//dSfvtn6OgaKL/OGx5nym9J4vGkZbB0RX63k/xieTyOTuq3YM8vI7/9Uuz9fkZ+GfndzpHf7T7/q385KL+9/w0dxDspv/aOKuXXXv7aW6f8akfcdQPlcknK5YLa/9NNQeQX/5ub0xH5tVp+h8Tn88riopXZnsdV1He7RH4HUX7xTKlUtptpv+Wv3e5f/rd75HO7P/92n/+U3y3/J1zLA1B+tWA1XSnl1zSqrXkh5dd540b57W1MpqbG1X5fK+XXKsHEE+EcYkTPrdrza1XfJifHJJt1RsIrym99dvbePglb667tLn/b/fkpv8bnFZFfnLpg1X+/ev0rwKOOeiVn7X2UX2t5dlsb5bdbYlvsesqv8wasV/nFfl/sm9QR+bUy6zGIRyJD4vf7ZHFxxbIB2Brym5JMpv/IJvYjb778esTr9W44XtlSRuayc1KsFCToCclMcFY8LnMZojF3Kb+UX8v+IGyRiii/HolEkPBqe+55r4382i2/lYpIqbRFPjgD3k3Kr70DTPm1l7/21im/2hF33UDv8huU4eEw5TdlXbZnqwRzPfJrjfyOjo7gVGZL9vy2ivy63W5xu11S/f8ul1tl58a/4wuScTb0esHPkvmkXEpeEI/Lq5bsl8tlcbtccn/0QfG4qvfVfxxq68IxXag3nc6vXrTehnFdteAoMOOfMT7dbg/o+gOp+YbtHvna7vK33Z9/u8//dfkNqb9/dm77oPxq/mPfRfWU3y5gabiU8qsBqpOqpPw6aTSMvvQnv0MyN7do6UNZfeQPOqcn8jumzvi1atmYlYJpp/xCDhtl1vh3Q3ADgYA6VxpffKrC2ziBjLOnaw3UJY1b0s8un5V8uSquq3O5Upap4JTsjuy2bE6WK2W5sHJBFrOLgn8e9g3LfaP3SdgXbugj5Fgpcl3bjSJtSHSjlDdHYDeuq52oG4LezHO9XxiHYNCvVgSU1ZHexvXNt61Lf219Zvplti7LBqmLira7/G3356f8Gh8W5Oug/Hbxh2PAL6X82jvAlF97+WtvnfKrHXHXDfQjv5DKe/d0yG9FnVFrVdElv2ayFpt9Bh3ym0iklKD3WgyRdcnISETJJ+paj9DWR2shVbi+MTJaFVlEZsvlilrKjGXtuVxe/Tt+Dhmu/jP+P+5xuz0qSVm7cjp2SkV6G0vAE5SDw4fWflx/iXE9fmZEfmV1T3SzXFevOx8/L7F8TNwuPJ9RLfr3xMRTdT9b70e7uur7ut6v9Z83P05zXcY13da1/tzGc7kcE8VulHUzLxBa+H1H6a+OD7Y/4BxxzDtjLBtnkH7pb/3MqjdrnTHbr24/25RfLnvGnHHCtg9Gfrv99Oq7nvKrj62Zmim/Ziht4Wsov84bPMpvb2Ni9sges7WPjkaU1KysxM3esuF1SHjVKL/tIrLtorWtlvhWJdaQVkNe18W1XmKr19R21GzCKyx9zuOUtEcAACAASURBVLoykimmJeoblaAXCVrWy/nEOSlX6jeMoT8R34jsDu/pyNDMl79iuSgnVj5RS6trS6lSkj2hPTITmu3YjtMu6BT56izl9SK9kfRvLOrrEq9P+o02UsWEnFs5J6liSoK+oOwd2is7wjvrhsbMc7dKiG/3MvhOkf5Gwfd43FIqGZ/dJu3vsGqgsa5+Vw2YWRWBNsy87Fi/ZuMXCB6PIb8rK8mWDJz2edXVHzN//3S1Xa2X8qubsPn6Kb/mWem4kvKrg6qD6qT8OmgwVrvSq/yGQkEZGdET+bVy7yseE192AgG/LCxYmfDK3Hm1Zke8W/lFBBaCWLtXtnZ5cTAYWI2qbry8uF5eq5HYqtQaMoslcviiYoWYT06OqqhvIpFuiwZyeSlzUYpSVPt3S5WyjPnHZf/Q/rV7lvJLcjt9U7zudTEtVkpy3/D94vf4O2I38+UvV8rJZ7FPxedqrm/cPy77avrTsUGHXNBJfh3STcu6kS1l5aOlD1R9EFUs+c7mcnIofFh2hHZZ1k5tRWZE2uhPffOtRDpWWJab6VtK1PCyZSowpT7zRmm3aqD9Sgb8Xcjn1yPfnfparlTkVuqmJAtJGQ+Oy0xopuUKj/rn6bZf3R2zZ/WgdfsCoRvpN7OSAc9jJtJvZsuB2bpCIb9qs/Y0gM4vENptkehtRCi/vXHTcRflVwdV83VSfs2z2pJXUn6dN2zbQX6RmAtfeq2UX7MRTDMjji+9o6PDSmZTqbTaH9tabI39tGaWF2PJMJZ35vOFhugsIimG1Lb60teqvxBztLm83H9U2oz8Xk1fkUw5o5JeVUuxXJK94b0yHphY+9lyflkWcvNSLBcEy513BHdIyBs2g9zUsj/wObHyaVN9hXJBjo48pPb/brWy3eT3XPyMzOXm1pbj4+8APheeik+eGX/W0cN3LXVV8FnwipHUDVnNZ4M75b7I/T33G8uek8mMYtCp4OXPpyvHJVfOidfllWKlKBFvRB4bfUIt+ddZel010OoFQm1diHxjy4OR6AkyZ34lw7rgb7ztwMxKBqOuZulvfG4zddm9+sAQ7na5DtonEiwUSpJI5HROI9ZtkgDl1yQoTZdRfjWBdUq1lF+njMR6P3qX34CMjAxbvufX6r2veFJDfgOysLBs2QBsJL/1y4tb742tjdJ2Wl5siCqEtXlZ8fq+2fovH9PT45JMpvva81uF1U5+8YXnTu6WXE1fVUuD94X2yUxgdsOMyIb8FtSS7HbldOILtecXX1RrS8gdlkORw5aMoZnILxpazC3I5dQl8YhXEEWcy98Tn8srs6Edsj98UCZqZNySjpmoRC03l7K4xXgR0k2xWn7Rl5uZ67JSiKnw1ZB3WPaG99VF5Lvpn9XXfr5yQuLF9Zc2Vfktl8ry4uTLVjdnWX1Ycv/B0rvikvrPAAT06bFnJeRFwqLuSzfy+0XspKzkl+vmGFZl7Avv35KrHkCrOv+3w7LnjV4ghMPYRlKRTKY+aSBeBrRe3g961kp/sViWTKbQ/STmHZYToPxajrSrCim/XeHaehdTfp03Zv3IbzQakbt3Fyx9KGP5L5bYWpfwqh/5rU3wVLs3FkuBsXdOHbGzmsm4Gq1tBNJpbyzE1vgyYuz5hdD2W3TJb7wQU5GgUd+YHI99JFfTl1VEqCwVJWMPDh+VJ0efbtt9U/KbRDIrd5P8hj1Ddcms+mFkVn7RRraYUQJ8PnlWIODV/cfI/vxI9PFNFeBLyUtyPX1VEJULeoJyaOiw7BnaaxqF1fJ7KXlREsX42vnKmOs4a/noyMNdi3n1Ie5m7shSflHF5dQe7tDuniON6N+tzM21+6vy668E5Mnx9vPUNFBNF0I6serA765fco85t2/ogOwxsa+9Vde6kd/3Ft6VsjQfxDrkGZLHxp7Y8MkxD2J4ISIiUV+057lgNV6fz6Nehm4H+d2IHf7+4b8z/SRE7HdsuOy5X4LW3U/5tY5lLzVRfnuhtoXuofw6b7CcKb/WJX4C8ar8Li6utMxWvC61zVHa1iKLbMTG2bLVzLG12YqNCG11abFxrE+nghcJqNOKpcVoy5DfjKTT/Z9DjBcSOFbovZvvS6aUUV9kc8WsXMteRTqa1cWDiAoYD/rVyR+WyeB0y0eemBhVS7E3ivwikhgvJcTnrdnPWy7KweHDMuLDmcP9l27kF619tnJClvOLTV/ih70ReXJscyTqRvqaIBrndq0vB0fSr6fGnpGpNrwbSVkpv1j+fTL2ufhq9l2jPSxDPzB0WEb9o10P1NXUVVnKLYjHbTwjZC/kCckDI0e7rqval4+WPlRLhvFyKhDwSS6fl6PDD9ctoe+pco03ZYoZ+WjpffG6fXWt4DkeHnmk5753I78fLr0vGOPGMuKLyrHoI22ffim3KJ+ufCKpUkr9ScD2gMdHn5Qx/5hGYuaqpvwanCi/5ubLdrmK8mvvSFN+7eWvvXXKr3bEXTfQq/xiGTGkSE/kt7P8YmlW/b7Y1smfIGrI8Glc37xEtPVS4vaZjKt7m8zsXe1mMJwqv8liUkqBvNzN3JVEOrnG8Fb2pszn5tRy59r9f5CVI0P3y4vjX+pZflHHjdx1SZVTggW+WHKMRDuzwR3dIN3w2m7lFyKQKTUn6fK7/PL8ZOtntayzqxV9b+G7gvFoLGO+MXlm4jlTzVkpv+liWs4kTjVFJ/EZmQpMy45QfUblTh3EUt/PY582ZdculItqn2uvLz4gcFdSlxW7seER2eHfJcGKub3hnfqs8/efLX8qiWJi7fMFrkjmhpcd3S53r/azG/m9mroi11PX1l5EqJcJlaIcG3lUxgPjLR8dn9037n1TvWyoLX53QL5v+vt77rdVnJELAZ99Rn4Z+bVqTg1CPZRfe0eR8msvf+2tU361I+66AafIb3V5MTJIQ2rrz5Q1Ej3V7qVtLbLryZxqo6/4wo8vPbGYcbxF7e+6BrZ6g5nlu93UrUN+U6mM4H+9FHyJPZU4KYuFRQn4/YKlqFgqWt3Tey93T+5kb6kvs17XenQKkcj7ww/I8xO9yy/6iz2/4qlIvpRXy4yxlNbKgmXrCEsZiW86l5Mrn8lifqHuyzvmEo5henzsyc4VWHDFW/Nvqn3HjQVJiF4wKeBWyi/2f36x8pkxVjUFsvlg5Kjp5GPVWyHTp+InW8r0dHBGdlqQnXkrnXMLvhcS52Q5v6S2FSDiev/wA6aymbeabnj3hxeWZhNeYX5fTF6Qe7m7goRzAbdP9oT3bXiUGP5OfLT8gUqQVVsgzc9PvCiTgSkLPgm9V0H5NdjhBARs2+Gy597n0iDdSfm1dzQpv/by19465Vc74q4b0CG/7c6NrQpuq0zGjR03JHVdVJv3zdZLLIS2XcGyZ2T4nJ+3LuGVmeW73QxGNDqs5N7KZc/9yO+V9CW5nrmupNPv98ntzG0pFotK9sZ942o55JnkKbUs1be6LxF7lxHhQWTo4cixlo9vlhuECl9UdZVu5RdihugvhBnzG/MRi7yfGHtK7WncjPLJ8scyl13PXIw20Q+ca/xwtDXvxn5ZKb+o+07mttzN3llLcFUqF2XEN9rT3mwj8nui6UUHfo7IL/b/9lu2kvz2+6yN93crv9X7IeEYA+w/7hRxvpW5JZ8sfdSU8AzR+2cnnpeZ4IzVj9VVfevymzC1HaWryrfQxYb8liSdti/bMvf8OmfCUH7tHQvKr738tbdO+dWOuOsGOsmvIbLNZ8pCiLD0OZvNNUVk2y8vbp2tuDaTMaTEqmN1qjBQJxJKOV1+sTx7aclIEtNvwZ7ffuQXyaywvxcF+yQXcguSyWdVpuOdwd3q5/PZOVkozEtJjBcPQVdAdof2yQPDD8qEf/1Iotpn2aryi2eAAF9KXpBsOSsBd1AODB2UiM9I0LYZBftA3118R+2/xlJzSEnYE5YXJ18SX8Pe0Hb9sVp+0U68EFdL4CHi2Oc74Z/sKEnt+nc9dVXmcwviXY0mG884JA+MPGgJYifKb7aUUXunIZgPjRyTEX//kt8KVq/y2w14PMMbc98UrACpLTiP+/XpL/ecuKybPmx0LeXXoEP5tWpGDUY9lF97x5Hyay9/7a1TfrUjNtVALpeTkyc/k1hsRZaXlySdTojP55O/+3d/SUXbzC0vrqhsvLlcvuEInnrBrZ4pa6pjyAxqceIntGvIb0jm55fMdqPjdWYlrmNFqxcg8mul/E5NjatkV70uez6+8rFkysYeV8gvll3eSdxR0ZKdwZ1qafqu4C4Z8UblXPKslFbPAN0R2Clj/tb7AVGXWW6bE/mVnvmYHVerr0Myqcupy5IqJlXEGZl/u1kSrkN+rX7Ge9m7spRfUjKNfb5Y7mzVubLd7Hm1+rla1XcxcUG+t/hdSRaM7PZI7oVl9DqW0m+G/OIZkF0b2wSw1BkFK0MejT6mjgezvXgqcqd4UxbjyxLxRmV3eHdXnx/b+29RByi/FoEckGoov/YOJOXXXv7aW6f8akdsqoH/8B/+b/l3/+7fqGu9Xq+Mj4/Lvn375Gtf+zfi9weUzDbuja1NDGUIkV/GxkbUOb+NB9yb6kSbi6xe/otmtpr8gufd3F1JlZJqjy32dHZb+pXfa5mrci1zRdziUfKLaHw6m5Fp/6xMB6aV9Dbu69uoj9i3eCd3R/xhj7jLHhmtjMt4m+gw6tEvv8bRUr2+HOg0Htibey11VdKllErgNB2YkdnQbKfbtP9+K8ivTghOkl9Etf/4+n+SlUL9doyQJyw/s+fnJey1NinXZskvxg8vaW5mbqjjz3aGdjvi3OdUMSUnYsfF7XVJPl9UKydC7pA8Pf6sI/qnc9431k353Uzazm+L8mvvGFF+7eWvvfXtJL/f+MbX5V/9q9+sY1oo4LgNl7z55nvy3nvvyB/8we/L7du3ZGZmVn7lV35VXnzxJe1jgAaw1+fevbsSjUYlFApJZfUNfTeN65RfKyOgVfmFAM/NWRv5xXjG46lusLW9thr5vT1/Vz6OfSRYCmksbS3LjuBOeWi4u3NT+5VfCPiZ5GmZL8wJjgfB0TORclQeHH6o6yWtOAf2YuqC+oI5FA5JqVySZDotB0OHJNrmOByz8ot+LuTmVCZffLbGfRMy4u+8B3doyBr5jeVX5Hb2thTKeRXh2hncJUPeIfls5VM11tUtAIiC7QrukV3hXZbMl14r6UV+scx6ObckQW9o0/Y39/p8ne5zkvzey9yV/3LrT6QxBz3mNBKYWR39xVwcHR02nfCqE8ut9vsTy59IohRXOQyyubzqPnIW7A7tkSOR+7ba4/TVX8hvsViSTIZ7fvsCOSA3U37tHUjKr738tbe+neS3Eeb8/Jz88i//z/Irv/KP5OjRh+UXf/Hn5Nd+7TfkhRe+JG+99ab85m/+mvzRH/2ZTE21Ph9V1+AYy5Kbz3Ls1B7ld1SslN+RkWGBmHzzyhsSKyzXCSYiFMcij6oosNkyNTWmMnn2G9lEBNMVKstocFQyceMLY6uSK+XkRvaaknacTbrDv3NNbC+kzku2bOwfrspvNpsXv/jlvuEHWsq0Wfm9nLwomVJ2beki9hzOBmfanjNc7bsV8hvPx1Q2XE/NObdoH5H6eDHWYqmuSx4ffaLrlwdmx9zMdd3K7/X0NbmXvSMucasTnQPugDwQObphxmEkQ4sXYjLkHZagBy8ZnFOcJL9YDfEnN/6zikA2FhwL1OvZxu1ob3f5xVFhFVdZbe2pyi9YDXsi8uT4U86ZpJvQE8rvJkDeQk1Qfu0dLMqvvfy1t75d5Rdv8n/1V/++7Nq1W/7JP/m/1JLjM2dOye/+7tfWmP/jfwwpfkh+6Zf+F+3jUNtAv/I7N7eosjJbVaoSaFXiJ/QL+31xtqO1kd+oFApFyyK/eG7sof6zC38mFVczz3HfpDw68phpzFX5vRebFyxhRuQx6h2RPaF9Xe9x67QfGbJzMvGZWkZcjXQi6+/B8CG1tPlM4rQUxXjBAvktFItyJ3lHCqW8TAVnZcgzJPuC++uEyoz8JgpxuZa+WnfUEtrAnIY4bJSZFvKL3+PYl17L+cRZJd6NJV5YUeLXWDAGOKPVqv2rZvuN8cERQiv5ZRn2R+TF3c9LIW3szd+oxAoxORc/U5dMC3/Lwt7WCajUaoHEKbmRui6FSlHNs5nAjDw29sSmP3O753KS/KKP//Xmn6qM2bVzdcw/Jj+9++csX4q73eX3g8X3pCD5OvnFnEWOgkdHzf9tNfu5c/J1lF8nj87m943yu/nMa1uk/NrLX3vr21V+/+qv/rt87Wu/J3/8x38m4fCQ/LN/9n/Kzp275B/+w/99jfnv//7vyu3bN+W3fut3tI+DNfLrk7GxqGxX+R0fj6plY/F40pLxqkr/n134b1KW+kgQvqBN+Wfk2MgjptuC/F5ZuibH5z5WUTt88UWEadgzLE9En+pKRjrJ79X0FVkqLDXJpt/lk4cixwS/RyQUfYD83kzeknQuLUFPSCb9k2rPuNvlkaM1S6rNyC+OX0KEsbFA9u4feaDpvNja66yQ31Oxk1JsEbVDMirsh8Yy79qC/dOPjT1uegytuBAR+b+++z8EImtId0WGA8Py2uTrMuId3bAJZLZGJufGgnn05NjTTeN9I31DTqqjitafG1l/9w8dlAdHjlrxOH3X4TT5Bd9vz31TZVPHEtxx/7h8afIVLfvD1+U3LYVCc7S5b7gOrwAvZS5nLkjQH1yL/GILBpaX44VDp3I9dU3tYy5WCurM5QeGj6ozyLdiGRkJqznAZc9bcfSs7zPl13qm3dRI+e2G1ha8djvKLxJF/fzP/5T85E/+Lfnpn/5ZNWq/+qu/IseOPSK//Mv/69oo/vt//wfyxRcn5fd+z0hEtVnFSGzVfjlru35g3xQEENHUjc7Y7fY59ER+g4Kzfq2M/OqS37evvSP3cnfq5BSy8XT0WXXGrtkyOTkq37r2hiRy9XKOrMxHwvfJrtAes1VJpzFBtuf0ambo2koxt56IPin5Ul7Ops5IRcoSCPnleuK6VIoVlQSqekRPsVySA+GDa3tKzcgvlo3eztyqky20D16QrY0irFbIL+QwUTD2GlcLnnnYOyypUkrtA672AeecHhw6JFPBKdPcrbjww6X35Hz8/Fof0VX0F/vIX516fcMmLiUvtny5AEl7cvzppnsRWVsprDT9POgOyivTr1nxOKoOnCt8OvaFOoor5A2rTMITgUlT9XeSX4zfnextJaM4kxZztNPZtqYa7nAR9qyDK5bM62pvu8svhuBW7rrcKdyWRDopIXdYDgwdkBkTWaiRlRt5C6qZ1TFP8LfrpclXLI/QWzGfOtVB+e1EaHv9nvJr73hTfu3lr7317Si/H3/8oYr0/vmf/7VKLoXyT//p/yG7d++Vf/AP/rc15kbk95b81m/9tvZxaBQUZ8nvkMpAbe2yZ8jvkIpSW1Wsl98htRxvbmFJPo+fkMXCojorU50nGzoge8P7u+r6xGRU/uLyX0qpaJzBW1uQFOqhyMOm6+skv8gKvZhvFfn1r7UDIUW256IvJ7eSt2TYFalbfo0v/rOBHUo2UMzIL76AYulxuVJZX25dKcmYf1RlmN2oWCG/2A99Jn5qLbKO/kDwj448rL4Q48iXRCGh/hmyiWN7Nrt84+7XZT43v9Ys5BdHmYXdQ/KjO39c/RzscbxQrpxTY1CNZiULSTmdOKXOdq4WLCmP+EbkSOT+pkd5f/FdFWFuLNgn/Or091ny6Hcyd+Tt+TfXjtEB84DHL98/85UNj9iqNr6R/CJD8Vvz35Hl/LJKQoXtB5P+KXlp6pWutwpY8rAWV0L5FfH7verM96XluOnVL5hj35l/o2mVB/6mHRm+Tw4NH7Z4pPRXR/nVz3grtUD5tXe0KL/28tfe+naU39/+7X8pmUxa/sW/+PU1vv/2335Nzp8/J7/zO/967WfY8/vAAw/K3/t7f1/7ONQ24LzIryGBi4vNX6J7BYMvOzrkF1mzYzGrlj3juX2yuGhEziAi+XJOwp6hnr54I/L79at/Jdl8fTZPld00uEcODZn/wtZJfrHM+IvEZ1Kp2fNbVnt+DzcJSXRsWD6695Hkc/VJ1rAf9sGhhyTgCZiWX1yIBFNY/pwuJdWX2VHfqEyZiNaZkV9kOYa8hj1hCXmNF1eNBcuK72RvSa6UVxK2M7h7w2RQvc7hXu/7zty35Wb6xno00SXicbtlzDsuX5n9qmDf9GcrJwRj6Ha51HnOe0P71PNCmlPq/FmXeN0ehIxVcqD7Ive3jHZdTl6Ss/EzKjN4tWC+7Qrtkkcs2lP5rXvfUKLe+DdsT3ivqejyRvJ7fPljuZK8VCdF6D+W0B+LPtrrEDjmPsqvIb/I/L+8bJyrbKbgpcgbc99qKcvY027V3DbTF6uuGRkZUjkruOzZKqJbux7Kr73jR/m1l7/21rej/P7iL/6s/NRP/az8yI/82Brfa9euyt/5O39b/vk//zV55ZXXVLbn3/iNX5M//MP/JHv37tM+Do1fHPuJ/M7PL0mp1Bxd7PUhIpEhdRRFVQJ7raf2Psgv6sWZxFYVRH51ym+3/YQ8QmAQZYMETk6OyedzJ+X88vm6pXoQnGdHX6hLYtSprU7yi/vzpZzczN6QTNnI9rwzsFMi3uZIJ7jdiN+U84sXxOvyKClDcqQp35Q6cqRazER+O/V7o99vJL94IXQheV4tf4X8uMSl9iYfjT5sOlrUT9+svHc+OydvzH1zPaOwS5TIPjX2rHoB8uHSB5Itptfk2Fj2e0eGvRGVqdmIZldUdAvR63YvAdBnXPvJ8sdyL3fXyA5dKauM38+OP2/Z0tC/vP3/tYwuY9nzD85+tSO6jeT3m3f/WnAsV2MZ9Y8Jsi9v9VKV30QirfIVbMfSi/xiXr89/x3JV+q3B2ELCTKf7x86sOVQGvJbkEym+y1PVj1spYJjF62qjfX0Q4Dy2w+9/u+l/PbP0NE1bEf5/YEfeEl+/df/pTz//It1Y/PBB++pc35v3bops7M45/cfyfPPf2nTx6/fyO9WkN9QKCj4j72T5bed9GN8VorLggjjhH+ySVrx+3PJM+o8XkRB/e6Aitw9vudRyWSycmr+jNzN3xFkX0YG4kPhw2pfYTfFjPyiPkSrL6UuSEnKcih0SGUFbizV5eJ3l+ZkvjCvBAnPVdsnPFOilJCMZCTsDanfW70PciP5xT7iC4nzdRFMLHHcHdoth4aPdIPOEdfiuCIk50oWU4rno9OPyF7fQckWs4LjX/ASolrSxbTcy91T+5Zr95jjpcqLky+ZGgckcVrIzav928j2beXYvXHvm2rPb23BfNk3tF8tT+5UNpJf1B1rsWd5wj8hr1i0bLtT/xp/H8vH5ErqkqRLGfG5vTIT3CF7w3u7rUZdT/ntLfILdkh2hUzmSMyHgpdiQ56wvDD50pZ7IYb+47+H+XxBcOScXYXyaxf55nYpv/aOBeXXXv7aW9+O8qsdap8N9Cq/WJo8MTEqlF9rlj23kt9MMSOfJo5LupRWo4wsugdDB2Vfzf7fS+mLKgNpbXKncqUor+x9Ve2rTSaNe/sp+KLUaSn69cx1ORE/rgQcBZFbZG++b6h+b2invdL4UnkpfUFy5bwEfH5BIix86b8/8qBl0UP0byP5PbH8iSRLzePqc/nk2Ynn+0Fp+70ej0ew3y8WS0m+mJN3Fr4rHpUF2ihY6oz5huOnkNG2WiD/2Lfrd/ttfQZEst+cf0MlUYPM4e8X9ih/eeaHTO2p3kh+z8ZPyxfxk+KWdR7YT/742BO27OvEy4lPVj5aEy6AxzjsDe8TLPPutmy2/GJs8CJpMb+gVoHsHer+mLVun7HT9VhVhM9+N8ueq3XOZe+po9XwN27UP6r2+2KVy1YslN+tOGr6+kz51cfWTM2UXzOUtvA1lF/nDV7/8ruslv9aVXQse9YR+R0bG1FZrq3a89vquT9a+UCQBbY2claSkjwffWEtqvr+8rtSqNTvn8WY7hnbrY5G2gz5xRfyv57/uuTK9WfeIjPqV6a+qpZiV0un5eJ3s7dVsi+P26vOPUbB8yDJkpXLCzeS30+Xj6tszY1l0OQX8xfPin2/1TmGs4CRsXk2uKNOuvD7V6Ze62n/uVV/G6r1LOYW5GTsc8mWM2pPPM5oNZsJfSP5xTz7dOW43EhfV6sYkKUaEeVHoo9ZGr02ywOrDxBBb4yc43P19PizZqtZu24z5Rd/E95b+J6KpOOzjOR94PnCxEu2Hg/Uj/x2DdzBN1B+HTw4NnSN8msD9JomKb/28tfeOuVXO+KuG3Ci/AYCPllYaD4ypeuHW70hFAqo43qsXPZsyG9FYjHziVM26n+j/GL/7neX3mpaUofx2hncKfcPP6iqe3fpHYEQN5Yd0Vl5LPq4hfLbfh/2XG5O3l56s0mMEMV9dORxOTy0vlS4k/xeTJ1XMu9yudfkF8+GaBz23FpVNpJfyA+SNzUmbkJym/tHDO7tSqqYkrOJM0ooISnTgWmVGXmjY5eseiYz9dRGfiG/WE5/MvaZOs8XmapxfEsin5BgTYKvspRlR3CHPBw1f860mb7YcU2no47QJyQ6y5TSSqyrR3HZ0dfTsVPqfOzGgmj0C5P122jM9G8z5fdc/IxcStUnD8PfLnweWh2TZab/VlxD+TUoRqNDkstx2bMVc2oQ6qD82juKlF97+WtvnfKrHXHXDfQrvwsLy5YmT4lEwhII+C2X32g0InfvLnTNp90NOuS3VvrNyu/J+GeyXFiuiw4hyvLY7KMqqzOS2/RbjGXP7eUX0cI3Fr/ZJHiQpmeiz8me0F4VAVrIz0t4OChR76iE8sMto2nY35itZJvk1+vyygMjR/t9lLX7OyW8OhX/Yi3qhs8Ijil6dPTxDSOfWA753uL3jCRZq2f/IgIGabZS3PuB0Ci/1bpwZi76P+QdUiIMgU8WE2o/MLJndzo3uZ8+WXEvOOOFxUphWTBXTeyIzgAAIABJREFUDg4fahkNNiO/VvTHijqQpRvLbKtny1brRAT1sbEnum5iM+UXn4PWx1755TUbk4dBfsPhgKysWLNdpetBcMgNlF+HDIRDukH5tXcgKL/28tfeOuVXO+KuGzDkF8fh4GRL86W659dq+R0eDkswGBDUa1VB5FeH/KpkVCvWRX4bI97tlz2/KGFvWOFB5O7T+MeSLWfFLR4pS0lGfePy+oFXJZ8vSiLRvHy3W66dlqKDw7cXkSwoVpc1GEcEfXnqh+Re9o5czVxRy2gnImNSLBXFWwzURYSrfYoVY3I9c1V8bv9a5Bf7fneGdsp00DgD2Ipi5qgjJH9azi9KxBc1tZ/0WuqaXEyeV0cGYSyqAgw+X5p62RFLhtvJbyum6DeKlQmrrBi7xjrwsgGyhag7IuzV85YfiT4qs6GddZdvJfnFc51Y+UTwYgICrP5WS1keHjmmsmh3W9xul0Sjw+qFmO5szx8tfSCL+ebs+mFPSF6eeq3brlt2fbqSkivZixJLJVQCwPuHH7B1GbZlD9ZlRZTfLoEN+OWUX3sHmPJrL3/trVN+tSPuuoFe5dfr9QrOkqX8WiW/zRHvbCkjn8Y/kZRKvuRSUTicnYtszrUFX5Lv5u6o/cGTgUl1huvU1Jha1rYZ8ou+oO2PYh+oyBuOxhnxRuXJ6FPiEY98b/m76md4hoDfLxOBccGpIfcNPaC+gDaWhTwyQS9IxVVRX/qR7XlXeFfXc3ujG8zIbzcNIvL4zsLbMp+dxy5l1W9kTA54goJzQpEpGf9sd+lGfqt9xd+IW5mb6nzdoCckRyL32Z74qpbjtdRVOZc4U7dHGb9HhBQvHWrLVpJf9BufbSSNihdi6oUQVlEgwVcvZTPldy47Jx8vf1A3JuVySZ0RfThyXy/d7/se7J/+JPaxOue6UCyplwnYx485gmO9tlNxgvyWyyL4H4v9BCi/9o4B5dde/tpbp/xqR9x1A73Lr0edJbsV5BeR5NFR65c9Wxn5bRfxNo46WpF8OSdj3jF1pBEyIe8K7q5LJNU48MjEjaMsrJDfQNgrt/O3ZTkWExz7MuYbbzvPEKVCdCrsNiLTn8Y+kTv522vH6WDFAL7QT7qn1f6/ncHdLevCnl+XFxmuPRvulwUfRPywTxhLkxuXiLbrqNXyeyV5Se5k78rtzM3141CkLBO+ccmUsjLmH1X6PxmYksPDR2zbA4wkYljGjmzP2PPbqVSjqvO5OcUW/459sM+NvyjjgfbzoFO9Vv4e2bkX8s1bGnCM1uszX1assaT7cuqS5L0ZCVWGZV9wv6XZw618Hl11bab84hmupC6rpei5UlbNmV3h3fJg5KGuVxLczdyR29lbki8XJOQJyf7wAYn61zORm+X17sI7kion1YqSfMHISo+/Hzhf/Njo1t/PbpYDrqP8dkNr8K+l/No7xpRfe/lrb53yqx1x1w1QfrtGpm4YHR1RET6rlj13Wu6NPbOfxI6r/bBucan/m/HPyqh/TGYDsxJw10curJLf5fySXCteFp/HJ5lsTkkEBPhIwxFGrShCSk8nv1B7fasJnyC/iJKGKkPy0PAxGfOPtRwAHJWEazcq1WRNqWJSiSW+YB8cOiQ7Gpa6tqojHA5KoZKXa0s3JOgOqX70urwXQoikUVjqjGOn0B8s8YZ8YTk6vrAjCoxSgvgHpuTp8Wd6m3h93tWt/F5NXZYTy5/WJf9S8983Ki9Nvdpnb6y5/XzirFxNXW16oYAjsl6efE29GHlz7ltqdQKOz8oXC+oop1enXpeAZz0TuTW9cW4tmy2/IIHPBhKI4bNp9sVULUGI76XkBZUxulrw9+OJ0ackVJOUzQz1N+59Q8qucp38VufycxMvmKliYK6B/GazBcnl7Dvnl5Ff50wnyq+9Y0H5tZe/9tYpv9oRd91Av/K7uLgihdW36F033uIGSCD26M7PW7fnV0fk1yr5hUziaB/shZ2JTAt4NhaM0TvL35Vs2UhehS+U8/k5yZZwHEtAZeY9HD4ij42sJ8GxSn5PxD8Rj8/IvJzOGEcZoc8PDj8k0ZpzYFuNfbqYkjOp05IoJCRTTis5gdBiD++oe1Sejj7XVjjNyO+ny58o0aw7CqpSlGfGnqvLVtyqb3eKtwQJhQr5opSlopbIHos+0pMM4cv4yZXPVSQRY4UlqoliQi31ThbiTfszS5WivDDxck/Rq34/Y93K74dL76vlzo0FY/nV2R/p+YWB2efAvuvPV06ojOYPjTysXvY0FsjVd+ffako0dmjosByOHJGPlz6U6+lrqq8+r1cdzVYql9V5uU/Z9BLC7PNbeZ0d8ttv/z9Z/ljJc23BZwyrDnD2dzcF2xLwd6gx8otM5r0kEOumbaddi73f2Wye8uu0gbGpP5Rfm8CvNkv5tZe/9tYpv9oRd93A9pBfv4rU3ru3IKs5fLrm1HgDllFjD+vKSrznuq6kL8uVzGVBZmef1yPjoQl50Pdwk4BhKfFb6igh49zb+fy8QCzxZd7r8qn9l0hX9vL4a7IzaOyNtUJ+C+W8HI99LEPBsHi8bkmn18/xHfeOy8Ghw03PjqRXWKI96htTWXdPJU8q0cCRLTg+xuf3qr2LTw8/t+Gy7U7yi3207y5+rymahPmMyO+h4ea+VTuLPp5NnRK/1y/5nHFGMu4b9g33fJwPjnYpVIyllNWCLNiL+QUV+a0teHlxX+QBlZF4s0u38otzgHH0U2NUHHPuK7Nf1dp9RHTVUtWikbQNe10fiz7R8qicZCEpZxOnVzNUG0tssTwW/UbEL140PqdV+cVxQSPeEbUsGmN/N3tHlvPLgmjxrtCetYRyWh9wkyvfivL7/uJ7aoVNY0FW8mPRR7siiL3TJ+MnxOV2SSJrZHtG4sCXp15tmXugq8q32MWG/OZUXgi7CiO/dpFvbpfya+9YUH7t5a+9dcqvdsRdN9DrUUdInIOkSlZHfoeGQoIlqdZGfqvyu6i+6FpR+pVfnAP7/sq7a/tDfT6PuN1uCZWH5Mno03VdzJVz8p3FN9aWdSL5UPVsXyRsgUziuZAI60vjRoIfK+QXEc3jsY8kFAjWyS/kbWdgp+wO7V3rJwQeS5zTpbQSDlfFpUR83Dchl9MX1dJTlEh4WA5GDokn69twGDrJL9pDht/GpZTgMBOcVUmZ2hVIVVISqp9V+cW1OCLquYkXe4pmIkJ5MXlBRXtxJnGxUlR7kC8kz6l/ry2InD8/+aKM+Td/z2y38psqJOXb82/UCQjGH8vLj412Jx/dfO4w9/74+n+UlUL9Sgi8SPiZPX+7Kzl9e/7NtczDtfKLuQnxORn73JDm1ch9sVKS+yMPqORxg1S2ovx+tnJCvTSrLZh/s8EdPb08OpP8QnB+cq6YV8muMMaPjD4uE4GJQRrqjs8yOjosmQzltyOobXIB5dfegab82stfe+uUX+2Iu27AmfIbkvn5pa6fpd0NODcY5/Leu2et/EKelpd7i/x+kTipok3ViBrkFy8UsBTttYnXm/Yvvrf8PUkU4+r6WvkNecJq/y/KnuDeOvktFAoSj/d31NH51DnJuFJquXI18os583j0SRXZrZbTiVMqK3VthBDR2Ycjj8iQZ1iSpaRalrp3apdIxSWx2MZZssECGcU3KseXP5JMMdPQZlGeHn92Qzm6kDgn8UpMvWyok18py3PjL/Qkv4Y8l2Uht6DGKeKJyGRwSi3ZvZO9vSbpuAZR8ecm7dlj2K384rnuZG7LmcRpSalzf/3q2KlHoo/1zMnMB/t25rb82a0/XZvb1Xsw916cekkeGzV/zi2WPGPpc/2y54o8OfakividS5xTEd/a4nF55cmxp8x0dctcs9nymy1m5GbmphrD3eE9PW0piOVj8kX8c/UCCeOHzw/+7jwx9lTXCcvwwux47EOVcR5/Z6sFORSeGX9e63x22iSh/DptROztD+XXXv6UX3v5a2+d8qsdcdcN9C6/bpmaGtcU+bVHfrFXc7GwIBO+SRnpsJ8VkV898puTVydeb4po4tgjLEGGWC0U5tX5vkjWgy/pKNDfl8ZfVVmgURD5tUJ+1VErlRsSK65IMpWWkDcse4P71F5jvzuwdq7qh7H3Wya0GfWO1Z3ni5cQKKlURskn/gchq/3/xs+bz51ujNon8yn5eO4jyZaya5J0MHpIDo0cbFreXntvIp+Q4wsfqyReyHiMtQD4/ag/KsfGH2m4t7JhXesLCSpqTM7EzgiiwFiqiS/pe0L7ZC53Vy4kzku+jGhwRJ4df17Gg+M19RqrEeoXJTS32/WHu8UNvciv0beKima3y759L3NXEKUrSF4lYsMeSkRSqwWRXIwTjpQxk/hoKbcof3rzj1RytMby+vQPyP0j3e33PJ84J5eTF6XkLopXfHIgfEgdu4MXIcuF5vwChXJRnh1/rmvBsmKMdNWxmfJ7I31DsVXnLuMTVqnIAyMPyo5Q90eWZYppuZa+phLU4UUa9mrXzi2zvG6lb8iN3PUm+c2X8vLE2JMS8Rl/m7ZDofxuh1E2/4yUX/OsdFxJ+dVB1UF1Un4dNBirXelXfpeWYupIHasKlj3jf3Nzmxf5heB9sPKezOXvqS/5+MI27Z+RZ0afa3skTb/yi+zNH6ysC2N12XOgFJanR9tnAkaCJywt/jR+XC3lxLFCAbdfnf/7RHQ9UjUxEVWJyPqN/GJcI5Gw+Pw+mV9YkhuZ6yp7M0QIGVyn/bMy7huTzxOfid/jV9Kqlj27DHmdCk3LwxMPrYluo9RCPvG/Uqn+/0MEEf1FWa1qVfFr/92YdThbF0vDZ0IzEvD4165bv9foy3o9IrfSt+Rq4orkSnklzlH/iDw2+Xjdl2rj+uZ78bPauqrPemLhhKQLxrLvasnkM5Kv5JUIV3+OZc9PTD8hU6Gprj42tQJviPL6Ev5aCVe/qVndX3sfuoaIOuZG9edm712vt77d0ytfyHvz74mrIuJyuwXnueIomh/d/eNKdE/FvlDZmPOlnHphsn94vzwUfXi1j+3F/7/e/FMVda5yU4mO/BPyt/b+nCmBboZbkaGRoOTSRSkWDam+nrqmjtGpZiOv3oO2nhlvn5Ctq4FzyMWbJb+Y399deFvctR+S1emKM3XNvPzQgQyJ27AFIxgI1EV+ERF+auyZrpbS6+jfZtZJ+d1M2s5vi/Jr7xhRfu3lr711yq92xD01UCrlur4PESREfgdBfk8nvpALqfN10oIvvzjO52jkoZZsotGIEr1elz2j0oupC3Itc0UdfwOeY6FROeo/JsGGBEmtOoD+LRQWVGZh7K1tTKo0Ph5VX/DjcSOxS68F4mEcw+SXM/fOqaN8EDHFz7FP8m7mrgz5hiRbzCpx3B3Zrb7con/5Yl6enHhKkJymKrehUFDJLvqFn7Urnfb89vo81fuwr1xcZbmzPKf2TCPxjZmC52p1JBK+8H+6cnx9KfiqOCMiWpGyjPpHV6t3KZ8Oe0LyxDheVtSLNC6qrX9j8W+8t6KizjhmCcfAtLpXLf31GfJbW2pF3+hD9bcbyz94/OHZP1RR79pSKpfk2dlnZcQ/IsfnjquXH9WC8X965mk5MnpkQ+TxfFz+4spfyL30PRU9nAhOyFf2fkVmw7N14oxKagW+cVt/rfyrbOPF0toZx4VSUd6be0+qCw1wL6LNO8I75MjIEdNR//o+qH9reAFh/KxaNnrh0HivmXlp5prNkl8kljoTP90UnYVkPhZ9XG0HsKPgJSeWPXu9HsmuHvGDuRFwB9Qy6l6POrPjWfptk/LbL8HBup/ya+94Un7t5a+9dcqvdsQ9NdCL/OLL7PS09fIbDodkeFhP5HdublHK5eaEV99Z/LbKRtxYot5ReWXitZZMrZBfVIxjPObz92RsaEz2ju2RhQVrjnjqJL/4oocvw4iubrT8GJmbTy+dFuy9u528rZZaQ0IgDZfjl1TUFEuvx3yjMp9dWIuau1xumfSNi9vlVfI35puQMd+Y2nuNL5ydzkfeDPn1eFySSGQ6fmbwpRnHImHJORZJh90h2R3eW7eH0ZDfj1X27dqCpZZut1edi1tbsIfRzL5fSCXGIOwd2jBitpxbkvPJc4LM4BBXLA99eOQRlSG5tvS67LkdJMj2f7n5Jy2jfNPBacGe9FafLRxZ9PL0K03C0UrC0QbGIOJHhvVq2filAa5qFbn3+31KfmuFGJmiz8fOC/6/1+WRmfCMHI4eFszh9Xpat7tZwmRF1B9PUH3xYVXU36BSH72fz87JJ0vHVUKpasEVxXJenp54Tr0Iql25sBnL/av9yEhaLqbPyXIqpv52Yc83XnA2fk46/lHY4hdAftPpnKWrtrpFwmzP3RLTdz3lVx9bMzVTfs1Q2sLXUH6dOXjOkt+gijRau+zZJ2NjUWknv28tvqn2tDaWqG9UXhlvJ7/DShr7ifzWtmdkue5/rzO+8KNfONoJETYcZQHJM/bUQnSNf66NxKEfjcuP8e+QhA8WPhBxV9RS2StLl6UoJQm5kWRL1PExkJJipSABd1CJjAdSN/aiip7dzt0Wj3jUz3EN9lI/vutRB8mvWxKJ+kyyrT6hV5JXJF1K1S2NxXM/OHK07menYiebziRF4qZhz5D41HLs9YLoPvYZtivgdzZ+ZvWIHmP/8O7Q7pZ7JnHt+wvvNq1cwBf6J8fqM4dbLb9IavbHN7A3t6hkoiqDmD84bgr7NJPF5tUHEW9EXpn+vk39g4jPBrYrJJMZS88mb3wIs8vlrYz6t3ppgDmKI52wIgO/X4/6Y8n3+kvARoHfaBVA40rmVvKPdt+4+YYUSoW6JevY7/3a7te6jrA27vXfaMn/RlF/40WG8dIvnk2IS9xqD3qjvFeFfqO6uoncd7NFQVfUv3GOUn439U+P4xuj/No7RJRfe/lrb53yqx1xTw0Muvwi2oNIaDv5RSbbc6kzzXIz/JDcP/xAS6Y4JxEyiWXfVpRO8luV2nYJoqpJo8xIbeP+WpX0qcUJUHO5ObmSuaSOOoL83ly5JUi8hUuxzxhSky6nVYTH+BIpUioX5ZGRxyVdTqkvl7UFAvz8zucl7As5JPLbWX4R0T0TPyVed31EF7KnjnKqOSIlVUjJh8vvS7IYF5/LL2P+MZX86WbulrEfdtUccO/RkWMbHq+CZEE45qd2L6rKnh19pOlMUkSXL6cuN0WG8+WCPDfxXN0yeqvl92rqinxv/h2Jo694weLyqmWkOJP6R3f+hFrafz11temztW/ogDw6+pgVHx3TdWyW/JrukKYLsbz4zblvqWzyOLZp3D8uX5p8RaZD0xKNDkk8npZSqTmRmBXdqYozouhfxE5KqmRkdY94R+TY6CNqi0S1mJXuVCGtzsvGcxjR/96i/mi3+neyuue7KsTrfVr7J/UP9bLfeq+/Fdw2qsOqqH+thGMFAP47YGw9ad53380LB/Td7LaD9WsrUirhf7rpsX4zBCi/Zijpu4byq4+tI2qm/DpiGJo60Zv8umR6ekKWl2MqumhVwV7M4eEhJapWlXX5XVrb61dbN/5D/3HsQ7mTu60ilvgCj3Nscd5uu2WNVslvVWohv8FgQJLJ9NoXtMYMyLV9xvLt9Wgt9jDiiwSSRhn/jOh5v3t+r6WvqiRgOCoK56MmUimZy8+pxEWT/im5mbmuEm6FPUOKEzhi/+yUf1o8Lpf43fVLbvH7gxP7Ze/wXgfIb0C9vOgU+cWy47OJM+J310du8Sxj/tG1SCzmDY7TwdJoCAekGft6cewSvqx/sPiuIMmZ3xNQ82rnUPust2o+Ln/UtJRYZaT2jcnhSP1eWQgolmU3Jm3Cknq0jz3X1WKl/C7mFuTz2GfqGJrbmZvqZQjmw7A3IsjIPBvaIRB2JD/C3nTsBQcnrKj40uTLPWXs7edvgm75xfJsHGsV9oTVObSNfzswfkv5JbU6YCowpe35/8edv5BLyYtNicJ+bt8vyMRYVKv8No5PrpRV8x/bJbot4IXjzDC3S1JUe9lnArPy3MQLbRMRdmoDuQsCAZ/EYv0dAdeundZRfzNJ85Rqr1Vrds/9uqCbu3f9aD1Dfhslt9+ofyf+tb9PJrOSyVj33aGbtnltPQHKr70zgvJrL3/trVN+tSPuqYH+5DcuudXkIT013nAT5DcSGVJn8lpVOslvtZ10MSUrxZiMeqNqj+VGxYz8tjrCp9XP2kttfQbk2qzIjV9aGvuKSDeiO7FY7wmvIDMnE5/LUDCk5DeVzqgvS9hXijOFERXGMTHGOcNIBOWRHYEdqisQMSyFri0qKjx7TCaDk5sqvzgLeCE3LwFPUCYDk6pv4bA5+cXzInlPo8ggunZ4+MhaoqwryUtyOXWpLvqKeyGCYBQrxNaOhcKZsi9OviSTgfrEP9VkWvj/Hy19IB63ke26tox4o3Ikcl/dzyDoHyy9V3fuMi6AbDZmLLZSfnGGMaLTjQVLtF+Y/NLaj/E8tzI3ZDG/JBP+CdkV2t310lcr/hbolN/T8S/kRvq6Ehgs+8Ve52fHn1VHg6EgEvq9xbcVL/CAIB+LPioHhg9a8WhrdUDA//P1/0cy5fq97Gjz1enX5eX9L6oM8Bslm7O0Q31UhtUPJ2OfN6waqMiRyBHFrpeiW3576ZMd94yNRdRxc/l8feK7fvrSrTgXCog8t1hy1E8neG9PBCi/PWGz7CbKr2UonVkR5deZ49KL/EIGZmYQ+bVWfpENeGTEHvk1OzpVgUDkMJ3Otj2rtra++khts9RC0CH+Vu11tkJ+0f/zybOSdCUk5A8q+UVEc1/ogOwI7pCVwrKcSZxSS58RLR/xjqgkQe6KS335T9Xsk8WXb4/bLV/a/aK6ZmUlviFuqxJeXUleFhxxgn2P1aj+0ZGHZHJkzFTkF51EtA7Rp+pxRRBfJLDaO7Rv7Rk+XT6uIruNZSm/rNptjMpCAl9d3fOK/s3l7qk9koiQ7QztlruZ24pfrXQjivrAyFEVOW0s6B/kG1FYZEZGe3jOMf943aVWyu+JlU9VRLexoA840sZpRZf8IgL+4dIHdZFczPeoLyrPTjyvMLxx7xtqHtVl8ha3/I0dP2wqu7tZlkv5Rfmj6/9RReBrC/qDjMY/fOSrW0Z+3577jiwVml+C4oXS98982SySuusovwYOHfLb7YAw4VW3xPRdT/nVx9ZMzZRfM5S28DWUX2cO3iDLL6Iwl3LnJSYrks8UZE9wn+wJ7W05EL1EavGlsnEPbfXfGyO1kKab2etKhvaE9qm9kdVidZZrq+QXz5f2JiRWXpFUIqOWc2L/XrWchRyX4kp+URDd3Rc+IBO+CXWMU6wYU5EuZFXdH9wv0xOTKjpmtfyijfncPbmRuan2Ix8Zvk+y5Zzar4vziGtLwBWQZ3Y+bVp+cS+OcprPzSupgPjiHNvacnLlMxVdbowQ38vea9ovjPv8Lp/8yK4fE2TGxdnJEOtqwQuG/UMH5Hr6mnoG7J/FPJ4OzMqB4QNt/4hgOS3OxUVdWHLc6jxVK+UXe43PJ86Jx73ed2M5+Lg8MtpbZE7nX0hd8vvZygn1gqXVMmdIGs6g/u+3/7x2Vat6TLC6P/KAPGLh3mfME8hvozRiX/5P7vkZOTJzYAvJ75uyVDDOe8eLoVwlJ36XX+2zp/z290mh/PbHb9DupvzaO6KUX3v5a2+d8qsdcU8NOEt+AzIyMmzJsmd8ufze8tsyX5hXR3yUcMSJiBwbPSZPTD3RdMRPLbxWUlsrs4hQQyTMJrxC8qiTiRNKfFd3gMl9Q/crUURxqvyib9g/HAoFZH6++RgmcMI+YGTLhmzN+mebEjLVckW2XavlV+2RXfpQrmeui1tcKvIJ4d0R2CWrp9XUfS4gly/vfkn8Xn/HPb9mP1CJQkI+XHpfiWq14PxmRGsTxWSTGA17h+Qrs1+V07EvpFhpXnqIZfeHhg7LSn5ZsuWsEspqUrGN+mTsK10QRJxx9vOO0M46CbZSftEWlqUu5OdVVu8yMoF7wvLE6FPib8hubZajzusgp8hya3W2ZzDAS4emBE4i8vr0lyVbzshf3v7zlnJ8IHxI/R6yihdIu4K7VSKwfo5QwgufdxfekXTJyGSOlyEPjDwor89+eTXhVW/LnvFyB/vLse0Be+CxfH13eI+2ITsfP6vmF/62YGk/Ptf424H90j+x+6c3PPqrXacY+TXIUH61TdstWTHl195ho/zay19765Rf7Yh7asBp8oszdO/eXej4LLUZjltFbRGN+fr1r9d/kayIWlr6k/t/SlwVd9MRP9Wobac9tRB0r9dctmdEY95e+o467qdOsEXUUUr4Imkk+rLuiCecpwtZ72fPb7WvRibqYEv57ThIDRdAfvHFvvGIKAjgnewtyZSz6rgkRDknhxAl7lzuZu7K9xbeEiyVNvYfGwVjuDu0p+lYp1K5pOTX5/VZJr9oD1Hey8mLSjog3zjXeDm/LJdSF5TYhL04IsqI4h6LPqKEBBFj40t9fcF+6ftHWmcab0cEz/uFkgVkm64u8/bIo9HHJOgNqduslN9qP+KFuMzn5tSKAIhJt+KG/dB4CYDjpLAvG1H7meBs54Hv8gpd8psoxOXdxXdUQqb1uVdWe7qx1BjlG3f/Si2Lr2VTKYv4PT6VKKz680qlLAeHD8vzEy92+XT1ly/nl+Tz2AkplItqb/q+8H719wpbSnrZ84sx/nTleNMKhfsi97U8fquvztd8fv/i9n9TWw6QdhmfbGRdx35pHDNWZdtNW5Rfgxbk1+qXQN2MA67lsuduiem7nvKrj62Zmim/Ziht4Wsov84cvN7kV2RmBomL4pLN5i17MEQYIb+Li6tHp3iMM2lbiW5to7WR2mqE9qP5D+Vs7Iy4Vu8vFkpKiBB9/YHJH1R78not+BKJaPLiYuejjvCl993ld5qW30KCjgzdLwfCB1fl17os14b8ViQWM44a6ad0Ooapm7pbyS/G41zyjJLAtXNiKyXZO3xApoL1SaEGkhwMAAAgAElEQVRq24I0QDZPLH8qqWJSyS+ioxGPIdhYoozssKFV8VNfuCplQdKox2cf7WrZc7tnRBQZy1rRbnWZMdq4l7krH618IKVyWWX3TRRjaq8z9koj2gfxRR/PJ85LdjVCV20D90Ocuo2q3c7cEuxxxv7m2oJszzgiCaUqv70IUDfjbPZaJIJ6a/7bKvq9JoYVkafHn5MdISN5mlWllfxi/NT51DXi2kt711JX5WLyguTKWXGLRx1z9eTYU2tL3vE34J35tyVZTAnOvcLyXWRMv565quZFbcES5b+56yeaMoz30q/ae/oZexxbhFUIjS82sHXjqfFn+u1a2/u/efevBKtmMD8Qwa6OE/a9/9COv9F1u5RfAxnlt+upM9A3UH7tHV7Kr738tbdO+dWOuKcGepFfNDQ7a2TtzWZzptrtFKmt/r62slZSW7v8uPrPrSK1NzM35IPYe+oLk9fnkar8Igr3Q1NfVcfy9FoM+fUpSe9UIGnvLL/VlI0X0vdQ5JjsDu5R8mtlluutJL+ImM4X7tZFziAEPo9XHooea4kX0nI2flouJM+rTMpYiomoGQr2oA55hiToCajzTW9lbioBRZ0j3ojcP/KgRIbCfckv5tvp+Cm5m72tvpj7XQGVAOvQ8GHVhzfvvaH2O9cWSC2SUEF8qyVbysrZxGl11CaSVGFOIGr84MhDXQvZqdhJFUVsLHipgONhFBuPu+foX6d53svvjy99JPicNkoV9lW/PPVaL1W2vadWflcyMbmcuijIjoyYYsQXlSPDR/o6fghjh+XvAY9fLf9uGodKRW5nbwmO/9kd3iufr3wm5xJnmp4d8+RHdv7NlonN+gHSz9h/uvyJisw3Fix3f27SmFs6yrfufUPtpW8so/5R+cHZ3uQXyQXx8mc7F8rvdh795men/No7Hyi/9vLX3jrlVzvinhroV37z+cJaZHYjwa3tXDupRZQXkcaFheWW5xB284Bo443Fb6qzV31+r5JffEFFtuKnok93U1XTtd3IL/rx7vJ31XLY2i/5kPKXx19TkrOd5RfnCafKSRWVr54XHC/GJVaKyaHwYTkYObR2pFB1IO5k7giOF7qZvaH2AS5m56UkZayOVPtPscR4zD8hL05+ScZ840qOERGtntdr9qijdpPkUuKCnEuck3gxJkhkhiWvEW9EnauLiCWWa+LneB4sda6WmeBM05JWiPzd7B0VQYa0TwdnejrH9Gz8TMuM0+jb06vRuX4EqK8PTJubvzv/liyvJjWqvQTLvr8y+0MdmwQ7fK6wv7kxsVnjzVX5jSdS8uHc+2opbbUYxw8NydHoQx3btOoC7BP+9ty3muQ36A7Kj+788b5EvFUf+xn7C4kL6kVPbdZyMBvxjViasKux39i/fGLlk7rPEF7m4CXSo6OPdz0UiPxSfhn57XriDPgNlF97B5jyay9/7a1TfrUj7qmBdvJbu9y4ldRi2W9jwRei2shsq8zH+Fm7PbXBYECwNNbMnl8zD1so5+Xz1AlJuZJSzBVlxr9Djg4/3PW+xMa2EKXFlygzkV/cizOET8Q/kXgJy5CNL9rHhh9ZO4qmnfwiYokv95CiTl/ua/uIyC8YIzLfb9G57BlRrpPxz+Re/o6SWCz5TBQTKrMrnhdLlLHZD/v7II7VcjN9XS4lL6klkZCaZCEumVJWSlISr3hUVmocC4SlkocjR5oQ9Cu/353/jlxMXlQJrarCjsjywaFD6kv5N+99XWVqhvqiD1h6XKmI7B/aL4+PPdnvkLS8P1lIyImVE4JzhKsFUeldwV2yf/U82X4ESEenjy9/pPZ0NkZ+8cLipalXNmwS+4SvpK+oSGo1AdNjo0+0/WxX5ffS/GW5ELvYJJfYH/vE2JOWLzdu9xD4fEJ+IZW1S5+xH/yhEWPFQ+MRWf2MQT9jj5cMx5c/lFw5r17W4XOLuY25jLmtq4ARzrBG5nP8LceeXyQFwznZ3e4tRx8pv8Y7H/w3lnt+dc3arVcv5dfeMaP82stfe+uUX+2ITTdw8eIFuXXrpiwszMvKyqLE4yvyC7/wP8mRI0fWori1lbWSWkhROp1Re343Wn5sulPqy4m18ou2IekTE6MqmlwslrrpTttru5XfakUQWXxxhMzWfnlrPN8YvE8lT6pEQhAYLNHeEdipMkSb+dJntfxirK04gxj7ud3u9YRXl9OXJFlMqIzReCmAPaCJUkLCnpAM+yJqLy1YIBL48vSrazIQy8fkcvKC2mfpcnskU8ysJo6qCDIpQx6qQnFf5P6mccTeciQASiSMjLjdlv/35p/IUm6xaSyQjOdw5D5Zzi3J3dzd1QRcFRWNxvFI3zf95aYo9kZt49lvZ27LSmFJzZugJyx7wnvaZn7GEvLr6asqOy6Wf0/5p9RS7Oqc6UeAumVk5nosO/7O/LeV2NS+RHhu/HkVAW9XrqeuyvGV4+pc42opV0pquXjtsvLa+6vye+reWbmdvNU0dvlSXh4be7zlkmUzz9LLNRjT0/Ev5G72rpLK/eEDkiwlBUm0ULCa4L7IAypJX7+l37HHqhm8dMLLKXwe9w3t37QXBZgnOCd5zDcqQ77hnlGEQn61XWU7L3um/PY8fQb2RsqvvUNL+bWXv/bWKb/aEZtqANL7Mz/zY+par9crk5NTMjMzLf8/e+8BJUd2nWne9N6XRaEAFLz3DTTaoD2bnkNKK4mH3CPKUpQocXY5ZyRK4ohLDYdzdqSVuJJGR5rdGc5KokiKIik2TYtsw240ugF0w6PgXaGA8lXpvdvz36jISleZkZkRlVlAPB6w0ZUR772472V1fO/e+99//+9/l7Zs2VpVAbmapxY5vxBUisel5fxKmRxO5t1up2ye34Vn7Az4XcwG5fALheCRxEjJyz1ePjfbNtOApX55EaXgFzA+m57lkkI9pl5yGzxSlrVwTTH8Iiz4YmS4oEyMkibjyXEChCA312laECQDJCAHFOHMaNiP8AZdC1/hWqDpbIqVbSFuBWVb1ALlUFa9hQatqyvm2Cr8fnv0nypqqUKzGUJG210Abw3NJKZoLu2nLHKCtUZ6puc58pqkKViLEwZs+NN+FlESW55yNXOC8dzpfJo9zuWew1YBqKHFlngxxMqQP41/AqoAez6Tr+bdEI+aTc1WXOMwOOjpnmer3ivC73Rgjk7OvFPh+WVPpnufpMMliY/W8GWn/Sf54GJB/Vko27XP21qaBibSiWvfsIFavEGFX3h+lSn51ejSqGrPjVpMuetV+FXOtlJ6VuFXipWW8TUPGvyGQkH6ylf+lN566ygD5Z49e+mzn/0cdXV10fDwBfrzP/8vdPv2TXK7PfSLv/jL9P73C0C6FG1iYoLMZjM5nQiPRZ3RynIr9eahJPxOTs5wmKgcDR6+ri5PR3h+F3uecvg9FniTIIZU3lBOZp9LKJ9Sq+EAAWtaK+wZkBTNRhkS7Xr7oi/98Priz1u3jjP4AqrQ4JFeYR5gwS6prRh+E9k4XYpeIpNmQXgM4lQQigLE2os8PLk80dO9z1R4mlCCZSx+l2aSMyxq5TN1cf6nUE9ZQ+ts68hQpeZsq/D7xtTrdC4k1G1GA2RatFZyG1yFEONim8BW+zwPNSRiBeAfDl6oAFiM2Wfuq/CMiqGotSID7hcAem3qVQqkK8XmbDorPdv3fNXtuPDSH6NrgeucZw2wFBXg19k3UFeDhxNS972U66LpCJ0OnKpIb0jl0lwaCyJPrbT7Ze1bsQG+94gEerA9vyL8xiidlicSqpk1UeG3Gaspc48Kv8rYVWqvKvxKtdQyve5Bg9/f/u1PksPhpM997j/wqfuXvvQFSqfT9Id/+EX6hV/4MP3Kr3ySPvShj9DZs6fpc5/7d/Tnf/5XtHXr9iVf3WwWpYoaJ83eXh+FQhFZPb8mk5HgtZycnF00L7hRAykFvyaTgWZm6qs9S5mvWOJJzHV+y3+UBZDKm11np/3u+qVF6sEvvGyXIpcokYvz2iPXdrVlLfWaeisgGOBLxhy9cvM1zidFnmsoE+ZSQnrS0+O+JyTnIxfDL2DtUmS4pDYvwsJvxW6yR9moN/DjZ/M5rh9br6QKIAZe0ngmSkatmb2Hi+VMVoNfhAxPJSfZi4ww0xWWgUJOdvE6YJzvjX2X7sVGKafJww3NHyNE+5D3MYpkwxX2gOdXLDckZT/w4UIuTReCF6ra1ql30qBtFXeFWsI4AECZHSiZo4TOgHVl1WHuFwCCsjXKRJULMA3aBvmQoVorhl+89CNHGjnjCJ1eYR1YshDexdZ/NjlLEHgqz+3HwQlq9bZa+xhrb7Tp6MjIUd4ziFKAl13uklJS93c7rlPhd8Hzi5QPudKAmllLFX6bsZoy96jwq4xdpfaqwq9USy3T6x4k+L18+RL95m/+Kr3wwr+SzSbkKMETPDMzQ8PD5+kf/uH/o69//duFlfyTP/kyJRIJ+sM//D+WfHVbg98oxeOVHspmH0JZ+A1QJgMvd+vN4bAS5qoU/F4KD3MIcPHLPXIaUSMWdYHrtVrwC3g7GXyHMvk058lOJMdZVAvhtH3GFbTNsZ36zSsKQ1itFprNTdHwxCXOzZxOT5Mmr2FIhrfVa/DQSstqculd5DN21RTpcbnsXLfZ7xdyGmdSM3QvMcoQjf7S+QzlczkKZYOUJIjraKnb2EN7vPvqek3hEcX8kB9dr25rOfxOJ6a55qpeIwA3WjaXpo3OzWTXO0rMDYGmn069wvCPwwPR4wr4/dmVv0BTiQmGKnjIcQ3Unrc4tzYsDIR1wj4oj4DAuiE31GV0c6meS6GLJSG8CLNGPeF+y0DFNrlf4BdrfXTmCHv8Aa95ypKTyyM9uehBTDn8VvsOIeQYfdr0Vi41JCW/vt53UernEJV6e+54xfcnk8/SAe9ByQdMi42X02TpJ9Mvkj+2cGCn1WjogOcQrbEPSZ3msr5OgF8dhULN5fo3+/D4LrPaP2kayvlvdrxa94nfAxV+lbDu8uxThd/2rpsKv+21v+KjP0jw+53vfIteeOG79Nxz76bvfvdblEjE6eDBR+jTn/639NWv/j80OTlBX/rSfynY/Fvf+jp9//vfo69+9WuKr0P5AK3AbzgcpVhsecAvlJnTaTnh18Sh1HK0cs8vXu5PBd/hUjqAJ0Cqz+CjXc49kl7IoeYJmeRAQIDM4oZasFCehocJdUfx7yw2BBVqrYXMOgs943uO/4kG+E3qYvTm6HHyp2YIL+NoADuIVaGmqdfgY7gzaUy01bF9Ufgsh1/0g/BnCF7l8nkGaafBJcxHl2eA1GkX8l2r2ZpFoRL3CCJYmBPykb1GH/VZ+hddmnL4vRQcplQ+XXE9hMnK1aLPB8/SucCZinUAvLyn7/3Ube6haCbK9UlNWhP/ez0YX2yi8NDdiY7w/bAJwBZ2Rogu2s3IDULodzmk4dAEobLl7X6BXzwX1l3w1k/weg9YVtb8btSD36uhK3QnPsKHQAiEcRic7EVuRGW91d8Fd2N3aSR6kz34aBnK0Crrav7TahsOnafh8HlCOexiDQeXwUXv6X9/q90vi/vbAb+BVIDOBk6xUBgaykPtdT9UktaxlMZT4Xcprb08xlLht73rpMJve+2v+OgPEvz+z//5/9J//+9/S+9//4fot37rM+zV/eM//g+sNOn1+jj8+fOf/2LB5t///nfp7/7uq/SNb3xX8XUoH0CF38ZNbrdbWZlaKfgVX+4DGT/nNgIu8ZIqtS0Gv3jpRWjvcPgCh3neiF8reCcBWBathUOgUV93u1OAJ8Cv3W6hl66/QiPx24Uw5RjyhYnIprdz3m6XqYe9oN3GLlplWVN1qoBfnU5Hc3PBmo8C7zC+K1LadGKKppPTpC+C5EwuS/2WFeQxVhfkKoffc4Gz82rRpSMCvodsazmPWARMvMz+cPwFIoQ8FzWA7gdXfKQqLMGjCKIy6cxSHqnkmkQmzmuGMk4O9q77CnOB4Fcsi9D10pbP52i3Z2/Fz+8n+G3UkLXgFwcVEJuCQrbY8F2BravZsdGxG7keByfj8Xu8HxGBAdVzOdrR2SM0nrxXUT8dqQwfGfg5SYdqcsyjnX0sNfzi9+ErUy9TqkzEDEJ8T3Q/3Rabq/Dbzh3YmWOr8NvedVHht732V3z0Bwl+v/a1v6O/+Zu/pB//+HUymYQyFZcuDdOv//on6L3v/QBFoxH6j//x/yzYHJ7fH/zge/Q//sfy8fz29PgoElHG8zs1NUs5qBzJ0ABb3d0erskrl+d3KeC3lUevBr8cIhsZpmhOeLlGTjG8nQLU5RlqEbYMoh20rKK988JaqEGM5x2bmKLXZl+mONevJQ75NWktgscY3l+joNAreH+3VZ2+EvB7PQyArxRuMWiMNDRf37Z8MuUvwdfCVzkssRh8UDIono0zzMK7vdmxmYbs6/iSN2feYK8ryjYBklBWabtzB+107y4ZCtB7PXKV+2H41Zq5DwiMydEm4mM0lhiv8CxbdIJqcnmTC37hcZ1LCXn5OGBAXeWlDBFuxna14Pds4AzNpmYqukV4/5M91SElmA4SDl5wiLTCsoJr0Ja3qcQUQbk9nonxwcca2xB7qNvRzgZP0ZXI5QrPL8opvW/FB9sxpSUfc6nhdyI+Tu/4TxQ8+eIDI4/7sa7Hq2oKKG0U/M7C72E17FlpSy+f/lX4be9aqfDbXvsrPvqDBL9vvfUG/e7v/u/0ox+9Usj5vXDhPH3qU79Mn/nMZwlh0f/wD98q2Bw5v/F4jD7/+T9WfB3KB2jW89vT46VIJCZz2LOBPB4XPWjwK3d9Y8AvXvbF3FpACsKoU3mhnupEYozw8p7IJxh6ccygIy3nt5q1ZnrE8xiXMkIT4NfGa4Lw4nuJuwxb8EZmKMsA1GvqK3g8ETq9yb5lyeAX4MqhqmUN+btr52G1/LPyl2Aoa18OX+T8WoQMj8ZGGO6grr0AdXk63P00eY1efuZb0Zs0GrvDn2+wb2RPc3m7EDzPwlXFYIh7AcnlYlwI2x5LjFEqlySjxkC9ln4eq1aDZ+ly6BKHjsNriX8HZG92bCHLfFmo4vvlgN/b0ZucFyuG5iIU221wc33jTm714Bfe33KAx3fjySoeuqvhK1yDGwcjos23OLZxHrbYUOsZ4FOat5+j7a5dbRGZylCa/nXqhxROREqWCeWdNjkrD0o6eS2bnVurKu+NjovfD2cDp6vAb5oO1qll3ehYUq/vFPjNZgtagVKnrl6nkAVU+FXIsBK7VeFXoqGW62UPEvxCWOnjH/9faP36DfT7v/8FSiaT9IUv/D6D8O/+7h/Qz//8h+mXf/nX6CMf+Tk6d+4M/d7vfZb+83/+U9q7t34ZG7nXvzX4jVMsVhl22ewcjUaEhQN+57g8lBxNKc8vXqSmp+XJ+VUafqHufDp0suCdGk+OsVhSNBth8IUnFw3iQcgr3u86UAABwK/DYWMFbjQA8HRqisOxEaLpMXpZ6Rgtm0/TWssG9gTfjF3nUkoQreox9tKAeZAA5VLDnnPaHKHOLQvFaLQMWNXyOsfi9yiUDpZARjQTI4vWzKrHyN0sb9U8QMjZxaFAMpuic8EzbBTAj9gArSssK+mg7+GK/gC4KNOEvOWV1kGGIth3OITw8lKPIEKyh2xDXItYbMi7BlAhBFVsmA9Crt2LhG4XzwvCYeF0mMeCF1Zf1E/xZBuFX9Ri9qfmWEQM9ofHCvmLuiJhMPSfyadou3MXl6iq18QyVOVQiL4Rcq+UB7kW/EJp+ZT/7cqwZ1MX7XbvKTwSQPeM/zRdiVwir8HLayPOF3Pf49lXuPaU/x2aS85WPA+8/gd9j9Qzk+yfY+3JnKWfjrzOeeI4YFlrX0/r7OtlH6tTO1xq+MX35+Wpn1RULtBrdPR073NNawG0Yl8Vflux3v15rwq/7V1XFX7ba3/FR3+Q4BfGnJmZpr/4i/+Lzpw5Rclkih577DB95jP/jhwOB12+fJG+8pU/oRs3bpDb7aZPfOJXORy6He3+h18tdXd7ZQ97Xk7wCzA6Ez7JEIM2mZigufQshyxDaRkle/Bij9zdg55HaL1NEFRCK4ff4j06lrjLYlVQIDaQkfpM/dRl6qZ3gif48EIEA4AN6gHv6t8hCX7BnNfjV0vAAdDk1Ltota00nxjzhhcW3lsd6Wg8cY/zmFHyB/+06q20wb6pBI7rhT/+YOwFSudRAqy0wRv+SNdjJT9EaOOpwEnCAQPmAvCFoA1qxkKtGWGxxQ3PsdKyinrMPYUfI3Q7nqtUoDVqTLJ65RqBXxwE4Nlw8ACoR/5zt6mH7sRGKvKa8UyD1lXUaxaiBaq1QGqOrkeuc46yXqPlfbLevpG97PAkw4Os1xppwDLQclmfauPXE7xCBAGeTYBzCBO5aJ9nf+HACPvre2PfpvHEOHePgxHk4W9ybOG9BdA54H24YJtjM2/y4VJ5M2pN9Hj3E0v+q15c+2AwKtvB4pI/RIsDLjX8Yroj0dt0MXSBv0NoEPDb4dxBA1bp9dFbfOyS21X4ldOa90dfKvy2dx1V+G2v/RUf/UGDX8UNKtMArcBvNBon/JGrKeP5FeAXIkupVKWibzNzRw5sJ8NvcT1dPB+8lm8HjzMMosFTOhofJaT7IsyZ/5LPE/L/IFYF76/YasGv2DeAQVQkHo3fYdXcSoVjDT236lkyGPQlgle4F/V5ARyo74v7/Jk5ms5MVfQBr+k2V3U1aShPj8ZGKZwOlZQTAZCi31U2QTE3no2RwaQnj8VF4XD1vfvG9Osc1lrshcQ8d7v3lnjK0Pe/TvyIgmk/1yMW7KDlMMcPrfgI51gu+I4Fi+IZdrl3l3hnEbpcDbbhid/m2tHMFq16j1T4hcjWpfDFSvGuvGA/QGpxw+HHVuf2Rcs5Iff5xNxxto3YxDrRWO9iRW8clGx2bGVV3GoN5bVuR29xePca21rJ9XnrwS/GAsAi3B053uXj/2TyRboYGq6Y0krLIIN/NpejA76DhT17PniOJvnwoDR6AB7/vZ6lj/BBxIXTaSUVfnWc77qUDft/JHqL98Jq25DkPavEHEX4RbmnLGKP29TUsOc2Gb7KsCr8tnctVPhtr/0VH12FX8VN3NQAzcIvgBIhz0rA7/T0HKuSytHwwi83/NpsFvaIIuwZwBPOhPiFxqqzNTVls9lIqM07OTlTUde1mQ7L4Rd9IHz1SvQyqwYDhm/HbnJoLwAL3j2r1sphn7scu2jQslBapR78ls/vauQKe5XLGzx7z656jixGcwF+Ayk/jSbuLMRd5zUstgUxrShVes1Qx3eLc/uiL48oV5PKQ1m5tKFc1Hr7Bs6/Q66zTq8lu8FGQ+b17M0sb5F0hI7MvMYh1yidBA+56PUtDteFoNErUz9hQSvUYcaD4Fq03a49tM6xQRDGmgcgAN8qyyrqNvdyaSaILOE+hKGi3/KyToAwzFuuJhV+kauItSkPQQYcYo+jbAugFXMPpP0cPbDesYn6LH1VQzlvRK7ReLy0bjWeCcJZKONTxIf8qAgNribYhRrLZ4OnKZsTXtoB09tdu2n1/MFGLTu1qnL7j3f+nvPcyxvqASPHGgcsm50Lue4Anrdm32CgxtqKwmgP+w6xQvpSNxV+EcVi4siTpYbfpV7rWuNBSd/lslEoFJXtv7HNPJ8Kv81YTZl7VPhVxq5Se1XhV6qllul1Kvx25sLd7/CL/9hDnEtOz68AvxY6MfIOXYpc5PBGhIWiFu8hz6OF8GKpK74Av4KCbqutGvyiT4A619TNZSiZS9HlKESe8gQbwYvp0rvoEc/jJSHCjcLvRHKcbsSukY4W8lcFUNHR04NPFzy/GO9i5EKF8BPCA9dYh+hucpQMutI+wJVbnNsWzQtF6R+oWJc3jI3Qb4QmA4J0engbtZRIpejxrieqlieCrW5FbjDo9ZsRittbMS5yRV8Y+y6H7BYTHEK+By2D9O4V7yPk7kJMCeAL0EZYNFSCJxITBe9vKptisOo2dTMAY01gH4TUSsmjlbpfpMIvIBOHJZXwm+H6wQj9hWccodFmjYV8pi6GfsAo1qc85/hy6CLNpeYqpjkWH2MFZPFwQLwApaXQT3HDevxk4kXOOy5uWo2O3tX77kXznMVr68EvQp4R+oz9g1rK25zbOTRbbN8c/UcaT4xVPINb7+Yc3o2O0tB6XAjwvRFB7nuEzFoLrbOtJ7O+8XJXxYNiPyHKAeHTCOmX2lT4VeEXe0WFX6nfmAfnOhV+27vWKvy21/6Kj67Cr+ImbmqA5uHXw0rPcnp+ERLr87lJTs+vUvCb0afpm1e/Oa/2Kpge0IK810e8pXmh9RZGfvi180uOqPa82Pioj3s7fpOBArWEkeuLXEaABmAewGCxmMnpXBC8qvcsgLazodPsDYXHC166cDZEXYYuWu1dTasdqykWSrLH8E5ihMcpbulchiEhlA9yCDNACnZFuPCgbbBQUqnaPACjyCEthi88C7xy1yJXCUIzaAX4TSZZVEosYVTv2co/x7z+/vZXKTmvoi1+rs1r2KP5TN+7KrrEPZdCFyvAEgBs1BnYHqgZ3GdeQUZdaXhxo/Mrv14q/ALaLgTPlRwKYN4o1wPIQ0OecnF5KHH/o/SRGGIujj+dmOb85/K1xvoCMMvDy1dYBipKAiH/+K3ZoxUHFdi7+9wP0aBtVU3z1IJf5GWe9L9dcj+g+umeZwvhz8dm36QTc8dK6kHjgOeZnudoi2trq0sj6f6x2D0WnBMUplFj28YHJIsJnBV3qtfryOFA2HNEtjJykibdQRepnl8VfjtoO3bMVFT4be9SqPDbXvsrProKv4qbuKkBWoHfeDzJ5Y7kassJfs+ETtH5qQsVEIMX/Pf2fKAhJU+TyUgeD8Ke5fL8SoPf4nXjUOj4LYLnNp1PM4ANmlfROs+6huAXfQI4kVPsT83SdHqKXHo3e6nwnFlNlrpyPRxufTd5t0ThGPcCvCBQ5Ta7GZADCFMmLRZvtUoAACAASURBVPWYesgsQU0YXkuoHyMkF+HOgDGELB+dPUKGedAW4RdCdBCeEYGumX18ZPo1uha5wjm/aFh/p85JQ461VXM78XyXQsNVowNQWmmlVbk6sFLhF8+Bg4R78VE+GEHWqhiCLdazBRyLIj7FdkP4f3H4rwjFuB653bAPw5uGaKNtM91LCGPAO48DDpveyl7f8lJQ8J6/MfN6BUDj3oe9j1Cfpb9p+H158icUTAdK7sf3AcrdB32H+Of4959Ov8Liash7RlkwhDuLnzezdxq5B1ELw9g3RYdFmJPN4KBN8wcStfrrFPjFnJFXHcqEuERaI97revZCqDmiDNA3Ujm6jd20wbGxsJdU+CVq5HdAPXu38rka9tyK9eS9V4Vfee3ZaG8q/DZqsWV2vQq/nblgnQm/ftnEOJTy/KJu7vB0pQcPoZ/v6f5A1VDaxXaAEvArpaRQ8XwAq8gDRjiw2OC13de9n4a6VxdKHTWyi0fitzjHVvTsxTVRCmciqI1DPkMXBTNBVmUu9vxp8hoWTzIam/d6IjQ0mU2QSWditV68cL85e5TS8yHRIvzGkwl6xPdY1Zq4Up8T3vOLgfMUzIQoR1kOb3UYHLTauobhqbwB/OD5LYc7hE6vMA+UlECSOgep1zX64ou5xjMQuDKwLYsbICOVqxSQA7yuq5KnjDVAjjOUnaE6Pmgd5NxtjDGVmGQVaJfByZ79auWOcP9LUz8miHGJnwveaBM92/t8hT3LbVLL8/vD8RdYLby8dRm76HDPUyU/BmAhjNmpdxaUoKXav5Xrbkau83epvOHQBarU9UpEifAbCITZ81vv+lbmuti9OPg5OnOE88kFvQEd7wMIybU6H+wFRAZAEE3sC3sLqQTbXTt5Sir8qvCrxL5e7n2q8NveFVTht732V3x0FX4VN3FTAzQLv11dHkoklPL8ygm/Gurp8ZHfH6RkUh61Z+T8RjQh+s71fylR88ULmNfopSd9zzS0Fp0AvyeDb1MyVwkAPouPnlhzmCYmZhp6Jlx8OXKZ0vMCVLFslML5MOl1OkonM5xHm8jGKcU5loIAEEr7rLGuIZvBTgZDeX1cKPH62VMMkaFyeKw3ObxwQywJNXn1etSTJVppXE1r7etq3oqXaVYkng+ZrnYx8kVRPgrQjetQ5mjItm7RF3qIP80kpwrwJIghadiT2CoE1HqYRuG3Wl8cgp7PsGDXKKt6F9cmTtNG52YWrKrWIOyF3F94L3vNfZLCdYv7AfydnDvBAnPIjIenfK97H3lM3nrLz3Z1u+0sdpTJlKrcvjb9Ks0mS/c3nnPIXt17X3cwBS5YHH6ztM/zUN19E8j46Vr8Ms1EZtnuOGjBIZOS+63cDG/PHafx+FhFmPt+z4GqB0VSzHgnOsKh4Kg7HkgF+Pdv8UENAPixrsN8GAn9AnwHHmTBKzl+B0hZl3rXqJ7fehZaus9V+F06W1cbSYXf9tpf8dFV+FXcxE0N0InwOzPjr3hBberhWOBDhN8Q11uWowF+8eel66/QzdgN9l7lNXmy6ex0yP0o1/9spHUC/B73H6MsVR4OuMxuemboqabg90b0GsXm69cibJX0edJoNZRLQT25j02EkNp11vX8N7NOEAOCt74YfuEZBFwiDxO2hnd6yLq2YY8twrHx8k36HA25higdX1xR3A8l6tgICyABtCEGtta+flHoRt/wCsKbWSsHM56J0+3YLZqKT1IyHyezzspe4gHLyoZhsJE9hmtbffGFmvZw6DxFs1EukYVwcnhqESZv1ppohWUFuYyeqtOC8BMOIOBFFnJWc7Tevom95I00QCnGR6kjfN+kwls5/OJQA95eq87K4fVvzByhbC7D/WEMhNg/0/1cywJVjTxbrWuRH30pdKkkTQDztOptFWHm5f1gX7468xKrnKfTGZiO0wLWWNfSdrfgFV2K9uLEDwmK7eUN+6iZ8HHkat+M3uADJ+wtCIHhUKTP3F/4LmG8Q75H+YBNhd/WfwfItU9U+JXLkq33o8Jv6zZspQcVflux3jK4V4Xfzlyk+x1+8TLb2wvPr3zwC6Vnu91CU1NzFMlEaDQxQlatjcv0NOqRxK4Q4XdqalYWMRqn004Ic4TCtdQ2HD7PEFAME4CUIecQ7Vu5tyn4RZ7i1ZggNIXwYI0+z4cEzryLoY+BTKOhnZ7dDGZarY7/ibkDgPFyn8wkadh/oai2rKCGjZDJrZ6tJerYglC28PmCaHa+pHwUh8qa9KTT6Skahae78nqA0anZU6wOjRq2UNaFN9Nt9JTU+ZVqW/E6FgMLnK6wMUJo1zs2Ntpdw9e3Ar9j8Xv00uSPS9SWIY4Gr92OOgAFjy3KHRV7iTF55P+ibvNSNBF+UeLl5Mw7NJmcZE89vISINugx99HF0AVKZZNkNzhoq2N7x4CvaJ+7sTustA27Yy/hsAj50fBq1moXgudpJHaLUEddhF/R/s/1vlvyAUKr6yQ3/AqlpAT1b0SQoHQWGspxeeejAVCq7NGuw/x7WYBfzaL1vVt9vuVwfyu/A+R8PhV+5bRma32p8Nua/Vq9W4XfVi3Y4fer8NuZC5TLpSk/L9bTyAy7utzsSZUzhEyv1xP6ldPzqwz8mslutzL8ytE6AX7hHTodOkmJXIIFfVL5NDl1Dnp24Dnq9viagl/YJpKN0ERqnGv/xilG3ZYuzosF3JImT16zlza6BfBDiSDUd8Y/BQjXEBRuZxMzhX8X7Z3Op2indxcrIi/UidWU1IyV6hUsX8OLsxfp9PRpVvbFSzNAA4AxYB+gQ32HCgccUmFb7H88OkG3w8ir1ovMzfCdzmVpf9f+grK1eH1x//hZo+OVX48Xf4ejuRqfAF942sptihDvD674cM2vwe2okPtd3hCCvtuztyFxuGa/byL8Hhs9Qbcit7gsk9iQ277Xs596zL3Ndr9k9yFvFodUJq25oERda3BEXByZOULhTIAMOgNZNbYCLOMg6N3971sS+2OO78ydoHvxuyUHhPBAH/Aeon7LioZt+Pr0q/OxI8KteFYokKNUls/YxeXF1tvW0yrbGv4c8ItIoEgk3vBY98sNIvwGg1H+PduupsJvuyxfOa4Kv+1dCxV+22t/xUdX4VdxEzc1QGvwm6ZwGCGI8jR4/JBLvDzg10bjk1N0MXyBZtMzDEoeg5e223c2HL5qMhnI43FROz2/WEGEVh4PvMXeTpPOQhathewmOz255jDNTJWq4YorjpcpiGtV+ycAt1ic6Ir/Cs3EZxDxSulshiw6M623beJ/B/SKNY6h1AovFRrCnSGSVO5RBwRsrVJTttZOFOdiNhs4rFp8CS6H51cnXmHAEIKyhYYDIoROvmvF85y3LLTFYbsakN8O36KZ5GyhT7F3CEft794372ks7bN4jGZhvpZNiutK14Lr79z6Nt2N3i3tKk/kNrnpY+s/Pm8jtlTR34V/vx0e4drGvIZFZayh7rzXt5d/XstTv7AG1fuXUhpbhN/vXasuboVSX/u8D8nzi6xDegEMnvafIuTbz6Zmed9mcznyGrwMwNjPT3SXCnopOXV42lEyCmrs+D5hDkLo9Y6mhkUOMUqqFbdwOsjpEF3GHlppGSx4gHGNCr8LYc8q/Da15e7Lm1T4be+yqvDbXvsrProKv4qbuKkBmoVf1ONNpR5s+P3+1R/wi5wIZvAQug1uzvttBFQAel4v4HdOltP4ZsKesXkA8mKOrriZkCe43reOejT9VQF3AUzyBc9tNptlmBX+CH/P5bJkt9soq8nQ7elRsumsBaGr8o1bDL94YRZqxC6EdgLYkFu73rGhqT2PusqwOUJgq7Wj00doKjVV4h3EdfBYv6vv3U2NiZsQRg2l5/KcYISx7nTtbmjPoD8BsAWEluL9xgEFAAD1uRegtx7AC/2/OXmUzsyWhmyjj7XOdfS+1e8r2KREuXt+fjioODn9zvxzC+MhRxriRJu8Qt1gOVsp0C/QNg5jvn/rBxV5p4BnhMk+2veohDB6oezRwr7H3+qH2jd7fSt2OTF7jMLIg83naTqFHPMkIcjHqDGwSNg+zwHqbYO3G4dsSBdBvni5ingjz4tyXOeDZ7ikGfYdflfA8wuFZ/x+WGNby6XOxKbCL+BXR04n6j2rnt9G9tr9fK0Kv+1dXRV+22t/xUdX4VdxEzc1QGfCb4AyGSGXq9Umhj0HAiFKJOQRvMJLFJly9K0r365SsibL8IsXe6mtXfAL2xR7bI/PHWMwAXwiPFAEGbvBTvu695XB7ALYioBb73kRcotnnZ2t7kUW7y+GX/wsmApwuCQ80oA9hDWusQ6RQddcOaR68HvGf4omkxNcfgfhsUJagIbW2IZol3tPvces+fmN8HWaS8+xcBGgBBCIl3SEDyvdWsn3A8D+aOL7NJ2YLohCOQ1Oenffe8lhcNaduqAOfYdLULGAmMHN9iz26DcK84tdj8mUHwZgL2Pd37h7lCZiE/wMoucda7Ddu4PWOaH8Le0wQMphQ12jSLygWdh+6d5LLMLGLZ9n4Iykoyzy9b7B95PNYJv/qDGYB+wXe9sbnZ/Ex5Z0WTQTJYTVY18hLx2h3RDAwpxylKddrl3UNx9SbbOZed0f5LDnTqn3rIY9S9reS3KRCr9LYuZFB1Hht732V3x0FX4VN3FTA3Qi/AKOWJhFhoaX1N7eLpIbfkOaAL1468cs2lPc4P3dZt/OCr5Sm/zwayODQU9+f3gebktDk+EBgwcAgCs2vCweHTtKiUySYQ+1QIWXWg31u3qpNzMg9XEWva5Z+BXe3fOcj4wXW3h1Gm1Q9h2LjxHq6XbbumjQtXLRfHUoQo/EbhNyQeFJAgCbdRauFwrgq9Uwz7nULKvPmnTmEuVZ8T4oSeMa9Ntn6mdl4aVorcAv5gdIvBK6xOJlsMM2146G1wLeOQBvM8JwrdhIDHueDQbo6OQb7IUX87m7TT2c89tItIaUuTQC52J/1Tzn+EwKbJeP99bkW4S9JjjgNaTVaDjsuc/aR/u78bz8AQ+90D/+vvB7QW6biN/lhd87/BP+11ZC328Er9PFAOpniykJwggQBXuyTwjtFlMpRNX/VsaTEmovZY8s9TUq/C61xTt/PBV+27tGKvy21/6Kj67Cr+ImbmqAToJfAFl3t4c9g3LBL4zS1ycv/FosZjLZ9PSNS98sTmGcf4HL0WHvU2TTC14VKa1Z+C322opAi58BfNmzVfQSK4pJLYQkl4YmA9omEuN0O36rJCwX75KHVz1O0bl5D5KUB1rkmlbgt9lhUSbpTmyUJhPj7PFyGlysOu21eGm1fu2iwHN3/h6oyRp1JlplXUVdpu6a04ANEaKNerbIZ+SSPnliSLTqBXXrdrZW4bedc2917OJSR/jdAs8+vNEQuSoOjW1kHIA8DkngfXQbXbSySbX3RsYsvxZ7DuV+8N216Ky0zbm9EEqMA5h3/G/zHhRV1NPpLB3yPtJwmTBxXLm884vDNkaSHsovzufE1AkuHya0hfvxHfzgmg+VHPQpCfSL5c1jVnLBduUY5YcHi4fmYx+gTB+0OrLZYo9/UTJ+KxtS4r2q51eioZbgMhV+l8DINYZQ4be99ld8dBV+FTdxUwM0D78uBtTF8iabmYyy8BumRKJ1gMNzAX6dThu9ev01hkXR25DLZWjAMki7nI2FxorwOz09x6HFYlsQkqoUlSoWk8L1xfm1ej3ClrUUCkUKubfFoYm11mYsMUaTyXGu3Yrw4nXO9bS2d3XTas/FYy01/CIcEmJZEM3KUZZFyVCfts/WT3lNjlYaVpGvTrgxXp6leikn4xNcw7c8pxdjAoDb3VT4tbO3P5PJtrwUqNd8fE4Qh8P3HxEFKLFz0HuoYcG7ZieDvfny5E8Y5AXRsLzg6ex+hnwmH3eL/FrUWE5Tijw2N63UreYohvutXQlfYjXy8u8qtAIg6gXgrRb2rDTMM4qXeNKbg3uhH3HV5FG1r7UHGg1lb/T6aDRJbRSbvt+2f0vPo8JvS+Zr+WYVfls2YWd3oMJvZ65Ps/ALgSa8RAKw5Gp4Oe/u9irk+ZUfficnZ+le4h6NJe4yWPWb+mmleZWk8MnifFvAL07jEY6HUGSx3q1oV85fmy8DVOm5FTy4zUCmlHUzm03kdjvaCr94ycefcqisNX+U0TkTOMOhnqOxOyWiZChZZDVayJS10Dp7c6JZ1cbGCzjyKssbwoUPeB+WYm5Fr1HhV4DfZDpF8OwDDJEjusqyumFPKPLCoaBcXhd70DpImxxbFF1HsXOIp530nygJ9cXvih5TLz3X93zJHMRw10AgUiLYtSQTXYJBUJv7yPRrhNJJ4prge7fetoHWzQvjPQg5v/VgHgeqsAPynhfU9YvD3JWFc3F+gQAEt5ZgY6hD1LWACr91TaToBSr8Kmre9neuwm/716DaDFT4bXxdLBYTuVy1gbAYYqt5cLnO7XzDSwhe2KCejQOFYsAVobeRWaIGcYwiFAxEyK5zSILxxfqHSJDb7aTJSZQnaWQWldfC84uyTjMz0gSvEFZ6MTRM/jRUsLNkNzhos2MrOQyOuhNB6DFgFDnZEFoS5Y1wSOEz+8hldpKLPDRoXV23L6kX3AhfI3/aX2nvPLWljA7sJwiFZWiVdTWZDSaOWEC0RvmByWLPiL2JcjII40bJnHOBswz4EOxaZV1DG+wbW9pfUm3b6nVcisztoGA4Qm9Pn+BnWhBGytEO1w5yNyBS9/r0a/MCbKUzs+vsdMC3NAcdr029SvcSZeWniMikNdHPrPy5Bwp+8bCimnokE2Z1+JXWQRZVE9uDAL/1viedcgiihj3XW6ml+1yF36WzdbWRVPhtr/0VH12FX8VN3NQAnQi/c3NBBkG5GnJ+g8EwxeNyhT0L8Ivc5Moat0KIcvV8W6HkT3GIMkBXr9dzqaPysOdmnh9iRGdjpyiUDlIumyOX3k0H3Yc417WZtgC/sy17jBqFX3jXAulAwWsLEENY4yNdjzG41GrwBJ0LnGFvMUqi4MUYawIPMjy/AMHN5m2k1y6UUGrGPsX3IBT2bPAMg6HYAKC95n5aY5MugNbqPHA/cp3PBE4RagjDr4PQ3J2enbS9f6tk+L0YHKbh4HmC0jNK0kD8y6HHYYpwcAMvGw4jtri2tjRlhKVPJiYpm0+TWWelQesqsuvtLfUp3gwQuhMdYeVuwE8sHqdoKlYZmq6z0F7PPsljvjVzlOK50hqzuNlt8DTUj+QBq1yIOSDftzyH1aaz04cGPlxyR6dATyvP2+q9KvwSdco+UOG31d0s3/0q/Mpny2Z6UuG3Gasto3tU+O3MxWoFfgFuwaD8Yc+dAL/FYlLFgCsISwl1JcVWLyS5Xr4tBKpQN7lV+EWY34vTP6AUpXh+2WyGgdWpd9GzvudpJHGbppKTnBe4ybaF/1mvyQu/VjKZjDU9v2LYsM1so7dmj1ZALp5xg30DrbSuqjd1uh6+RqFMkL2+/tQceyzhCV7vXk+bvBspn6gN0LUGwDxQRgbK08Xh2HPJWboTG6FkDiV9dNRl7OJSRkqI7Cw2PwD+K1MvMbQWN4SAf2TjhykRTdf0/OL+ozNHGHyzXOZJUP1G8RioPLsMC7VTzToTPd/33rprsdgFAN/R2Ajpiuo4I392h2tXSzVgMR4OHnAYAVVtLpFlMdIN/02iHJG7qP4rrsWzPdp1WPJzAKgvhS/y9XoNDru0nPe7172/bh45DmagwmzVWSSViVpsUigB9uLEDylLCznMWLutzu0VAC4n9ETSEc4zxu8VlOhayr0teYGqXKjCrwq/reyf+/VeFX7bu7Iq/LbX/oqPrsKv4iZuaoBOgl+AZU+Pl5SGX3CrUOqnUkgKYFspJlWqjIxrIHo1M+PnEOVWw4EX4Ffor9kG5dnjwbc4j1GEX/SFskUuvZNC2RC/pAOIAW2PuB+rq2AsN/wmNQm6PHGNvZE9xr6CKjbCUG/GrlMyl2RY1eg0NJuc4VDn4oa5r7AM0AbHxrpmwrUI+4X3GGGvTr2TVloGyWa1cPh1MBit20e1C+7FRjnfE2AIoPQYvOytLIYA5BwDfhcTy8LcAMiAcTm9z5jvXHKOjs6+zqGfwDqEPWM8nVZHB1ccoF7tiprweyk0TCfn3qZQJlQIF0cfaAh/7jevKCjqolrW+/v/TdMABO+8ANgLDXP1mjx8aNBKuxu7S1PzYlAi/I4G71IgGaR+c3/JnHUaPR2UGK6M+SGk/lrkKoVSQcpr4PF10x7PXha8q9VuRm6wABu+AOjHqrfRdueOhvLZi/vHQcv54FkCkOI7jTD+fVXKNskBv5jvqcBJwv7H9wl7Cwchj3Y9zvui0xvgF4aPRis99p0+d7nmZzDoCGkx7c79Vj2/cq1o6/2o8Nu6DVvpQYXfVqy3DO5V4bczF6lZ+PV4nCzCJKfnVy74LRaTAqgi3DaTwcu7hr225fm2CEMWvLeVIcnVciPlFIFioCh4fluD3+vRq3Q6dJL0OgMLZwnPTEKtWtJXeNK8Bi895Xu25saEpxZrDXGveh7sejt8jqZpPDVGqYQAY/CerjQPMkxdDF8oeLA0JHjXb0Sukc/UXQKQUNd9yPOwpLzfxeYDoG8WfhGeez18lRVzRc85nqPX3Eu95r56JuDPpxNTrAoN0NeSlr2Qmxyb64ZyS+qciEv4vD79KgNWEOHveeFABYcKh1ceprWmDTXh943p1+la5AqhNrLYctDLzmcZ1gcsKwvwi/DkZ3qfkzq1iusA2eW1WXGRTW+ljY7NTfeLG29Fb/Dz87NrNIR1j8RjdCt8g/rMKwr7Ch7b1dYhWmWTlv99K3KT7sThrdbxPsb/UBN6m2s7l05arOEw52rkcomXG/e7DS7a5GxeJAt94HACa7OYF1YO6IGa8unAyZJ9irH7LSvoIe/BltZqKW6GqCCaCr8q/C7FflsuY6jw296VUuG3vfZXfHQVfhU3cVMDLEf4BdhVhiLrGJiqwS0MAyEplGYqB1xAb6OtU+EX4ZQ/mvo+5bS5EvgFxJi05ooXY3gGP9hT22snBX6RXzydnuZClj5jF+c9ljeE4F5KXCCz0UTR2ILnBWGaW+zb6HLkYsF7xPCr1xLCOiOZaCH/Ey/48PpucbaWY9os/F4JXaLjc8cono0xtDoMLtri2MpeO71WJ0nlN5GJs/esOFQaNoD3eJOzNdgTbQ4g+en0K6xoPF/ylD9C+O8m7yba5zxAlFsI2y9fq6PTR7h+bQDiXUUdCIBl4DUQRdoOeA9Qv2Wg0a9Q4fqLwQuUzKVK7gesI096pRWQ3XxDndtrkWucgy3CbzKZpkQ6wV5SCHjB695n6q/w3Nca9cTcMcJ3rbwhH3qXe/EyZ5dDFymUCVfpOq+4Grgc8Is0hJnkdMX8YctWQt+bX+HG7hTgN0/R6MKhTmM9LP+rcdBqt1va7vmdP5dd/ga9D55Ahd/2LqIKv+21v+Kjq/CruImbGqA1+M2zkJRcDVDb0+OjYDBEmUyuipiUALi1820FD67oyUXIb2+vj0V+4nF5XnrkgF/Unz0TPkUQ5HHo7TTkG6Ih3XrS5BZUoBezK2D2duwmC+6ghuyQdW2hdueN6HW6GD/PniB4fgG9Dp2TZtMzFfBr0VrovT0fqLl89eAXarMTybH5EFvi+sA9xl4uHyM2iE0BbqczU2QymEifNRaAFlC82rKGVWvF0EkRfgGFXoOPUrkkZfJZ6jP3k8foaTrEVpxPM/ALEH9h/F8YfIWQTyFsFbC/xbmNwXKzBChHzVUARLmHDs+KGrFy5U9OxMfp5amfcN4rafJk0Bipz9JHDoudhszrqcvQvei6I6T3SugyTSWnGPKAyXhW7KVB22r2ygJ4oPRcnjvb6O8CeKnhZdaSIBIHO2DAbmM36bR66jX1Nh0SjLlcDV+hYDpARp3g7Y8l4rTOup5cZTm/jcz7+OyblJ4PAy++z6az0Z4aolmLwy/RAYU9p3LA77HZN2k6OVVhKuyLd/W9uxETtuVaQB/2sQq/gN9wy+k6rSyiCr+tWE/ee1X4ldeejfamwm+jFltm16vw25kL1gr84kUC/xFttC0mJiX+vLg/ISS5PBx5IURZSigu4DccjlIs1hnweyVyic6Fz3K5FdH/ptfpGVA2WTfTJvsWhotqLZGN0zvBE5SbL4/EHjjS0F7X/kL+rMGqpZuxGxSPJGnIuo7Da1+e+TFlSAiDRgNkbLRtol3OxT1VuK4W/AKwz4UgKFQqHIWf73TsIoPWyKJQl6MXWXxpJjtNRr2BEskkWbRWBNLyz4csmGOcNPPln0T4zeSytNu9pyX4qWbDZuAXyrqXw5c4hBjhv+LCwRO6z/0Q+Uw+Lq1SrwHG4FEtb4DUg75DsoU+wxsPtewcpJzw/aEsH5bgOwZPe5exh5/FZXBXBXEoRd+K3KBoNsZrZNPbaItjG21zyauQDTvAG4562elchvOgk9k46TVGPmSAENVa+3ryNlCGqNi2+H5A7CyY8ZPT4SBP3kfa/IIad731qvb5peAwzaRKD5PwfVptXUOra6h6Ixf7Svgi6YryY/mAx+hpOcS73nPIAb/3YnfpHa4rvHBAh/mjjFYt6K83t6X6XIVfIcVG8Pyq8LtU+67Tx1Hht70rpMJve+2v+Ogq/Cpu4qYGkBt+F/JtF8SktFoxJFn4p9jwYlqslIy/22xWBtVEItWS+FOxMeSH3+Zr36IczhH/T2kqNSlAaC5HyXySX/MNGgNZdTYWZjrsfZJsVcq9DIfPs9hSsYcQdvQYPLTDuYv7hKCJ0WSg4yNv02x6mvsGUIYzIYpmo+xhHTCvpF2OPXU9jbXgF0JSyDMuF7sBHA6aV1G3qYdzI+F5w3ynMhOU1+QpnkzwPFx6Fyvl9psGKJ5LzIMfah5rCYcBeKnuNi/uoWxqwxNx7me9nN9Aao6FskC5CEk+HzpL18JXCWJpgDS89OPgAiJSj/gO0ybHpkXFrYrnUEvDVQAAIABJREFU6U/Oce1iiJIVN3jPdrl3N/tIFfdhTyA8F7m6yDGOZiOcW4sDh0w2QwatiXOW7Xob7XbvrSp8FsvE+F6sJzzu8PLK5Zmu9qAQCTsdOM25qyUAS3na497b0tiIKnG57BQOxzgFopXG8/Sf4rBp2BQ5wx6jlxWqFxM4E8e7HblF48kx4fCKiNWet3LkQPPK41KeRa5wVyiA34reojTy1TV66jZ1y3poI+VZmr1Ghd8F+PX7Gz+0btbuVb/rC+ewcnar9tWEBVT4bcJoMt6iwq+MxuzErlT47cRVAXylKV+mtlprpgLc6sjptPHLKOrxFntyq4lJFefZlpcFKh4L/QFU/f4QJZOVOXXNWhCh1JGInJ5fEX4bF4FCLuXFyAWaTk3xizLCShFCCdVgHenIrLWQRWehFaYBOug5VPHIbwdPELy/5c2oNdFB98P8Y8DvudBZuu0fIR2JNVlzXKN1t3Mfv2hLhRgRfqemZlk1urgB5Icj5yvgF6HPm2ybyaF30s3odYrlYnybzqDl8OvxyAR7HREe3YXw1nnhIMwP9Yjhreyx9SgGBPXgF7my8M6KEAZPtiavpXcCJ4pCngUBqB5TL32wrKZqvX0KiEbJKfQPOEX+8HbXzoLnvt79Uj9HzutZ/xm6G7/DYeMQC2Pvf17DkQU4nMA+wAHEs73PK2ZvqfOdjE/QaHy0Yh7pfJK2OnaQ3dB87V854RfPg8MFpC7Aw+4zdZFrvo42QtqxL7rNi+9feNJD6RB/z616q1TztHSdXPCLSXCppuQcpy7YWliTlh6oiZtV+FXht4ltc9/fosJve5dYhd/22l/x0VX4VdzETQ1QDr9iHdsFQalSDy5eIsXGir2LKCSLCsqNTEo5+PVSJBKTLey5Xh5srWdGztyp0EmaSU0xSCF/FwDEhwqkY2CEJ9Wus9OzXc9XdHU2dKbgSS3+UADbvfwjvUVLr0//lNLJ0uN1ABDCo+ElltpqwS/6uBy+yOG0oscLewLhztsc23mI8cQ4Tacm+XP0hZfwazPXOccT5WaKG+7bYNvInl+jUbnSKbXgF2HaUOSFF764AYBRJ/h65Cp7frF2UOl9pud5chldUs1ZuA6labAXTDoTq0Qr5fm7Fr5G5wKnKZwN8xjIvxZKc+VZ8Ri5sPBc7nbtlax23PDDSrwBMHk7eqNEDRm3pnMp2uHexbDYbJMbfsvnEUgF6EzgJCur4zckPOvw6EIcrBOanPDbCc/TzBxU+O0M+MXvnxYq+jWz9Oo9NSygwm97t4cKv+21v+Kjq/CruIklD3D37iidO3eGJibGaXJyjKampujgwYP06U//Vh0xqYXcW3gXERIaCIQkj1vvwgcBfgGHb/rfoMnkBIeipvIp9hSxGq3WQg6dg/NJPXovPeF7qsJkCCE+Gz5TosKLENztjp3kM/r4+qQhTidmj1EuXeqpxXVrbetZYEpqqwe/mPtI/BaFMkGWgcL811jXFrym+PxK9DIhVFRn1FI4G6Kx0ASHdneZusk4D5mwi8PgoFWWNS3BL/JHZ1Nz3JfT4Kz6mLXgF+VoJhLjld7HXHq+7myeIFqFcNUh29q6Ya5S7azUdXiWVyZf4j0FbzsAHg1htzgEcRpdvP9Qa3atfZ1S05DUL/bnmcDpkr2NfQFl7J1uIaS/2aYk/GKOr8/8lIXZSpuGnup+WvY6zs3YoFNyPZuZu1z3qPBLZDTqCarX7Qx7VuFXrh0tTz8q/Mpjx2Z7UeG3Wcstk/tU+O2chfrkJ3+JhofPk9fro/7+fhoYGKCnn36GnnnmmSJPbq5mXVe326EY/AKokfMrV+vpgec3TrGiEjut9N2K5xfjwtt7IXyOJlLjHDaZyCXIbrSTkUzCtPLEHtpixeTi+c6lZul2/DYl5tWeV1tWk8+4kBtrtOrZ85sssyEg54D7YbLrsXbSGnJjPR4XVQt7ltYDMVzdjY/QSGqE9HodTYdnKMI5qFpaYR5gZV94pREqDU9oM55fAMhw6ALdjY9yHjVyc71GH+33HqgA2WL4xX0I44RXFJAVzUTpZuQ6e98BiwB6fJbKpbkWL+a3nBps8Y3Rrwnh1RoNhTNhrkmLXFVEC7iMbobN53qfrwhfb8dzAs5vRm7wdwLGR1gwVKVbtbuS8BtOh+jI9GsVedzY9yiFtdo21A5Tloypwi/SQVS1ZxV+2/5V7LgJqPDb3iVR4be99ld89AcRfl9++cf0xS9+nozGBeXew4efpM9//o/prbfeoL/+67+gsbF71NvbR7/5m5+hRx99XPF1wACpVEooXWIyNZzzK04Q8AtvJfJz5WoAlt7eLvYmyw2/0Wic8EeO1ir8inMQlarvpUZpJHOLAtEAmTRmWmtdT2ttzXvh4JW/FrtCl6aucAkeNKj9dhl6aOe8KJZUOyzA7xxDZbMN6tNpXZKuR65RIpVkGEPuI9o66zra6dxTCGvFvir+zkgZE3m654NnSVcklgRPIsJOy72GIvzemb7HsAw1bAAgDgXg/URO7nj8HiXYk5fnMk4DlpW01bVNylQ67po3pl6nieQ4Q35em6NYOkbavI7zNZ0GB211bqc1bQa02eQsjSXucuj/GtvaQl56ueo5Do6uhC9ziTCsC0TR+i2l4fPVFkBZ+A3zYRPKYAHeoVCNHHwoae9w7VDht0O+ESr8qp7fDtmKHTUNFX7buxwq/LbX/oqP/iDC71/+5Z9TKBSk3//9Pyqx7+joHfrFX/wofeELX6JHHnmMXnvtVfpP/+kL9PWvf4e6u3sUX4viARoVvBLvdbkchBdKOeEXfff1AX7DlEiUhxA2bxZ4fpWA31a8ocVPA29oV5eHZmb8LSvRol/ALwDv3N1hmkyOs/cS9WhXmVdLFroS54fcW68Xnt/G4BdgOxq/w6GgPeZemkxM0mxummZS05TL5vnwBcJYaPBaP+N9lkSxtGbg9+3Z4+SvUkII8PRkz9MlmweHFzoj0dE7x0rKKEHdGB5GCEDNJKYpmUeNWw2ZdSZy6l20072748Ocq31LICQFhWkoU2NfoNYtxMrW2zdyaZ7Fymo1/41r7M4LwfM0Ghsp8dBDAKw8Xxah82/MHOEcYFGwLZvP0VbHNhq0rao5qJLwi7387Xv/RAiZL9QuyxOH/n9s9S+27LVuzJrVr16Onl+UqELYPg4R+s0rGv7dVW4JwC9E++QqeSfHuix1H6rnd6kt3vnjqfDb3jVS4be99ld89AcRfj/96V+np556ln7mZ36uxL5/+7f/lS5dGqY/+7O/Kvz8s5/9Hdq6dRv9yq98UvG1eNDgt7vbyyHPcnt+5Ybf2dkApdOlIlWAyFuxGxwmDBGmNdYhFsWq1QC/FouJpqcr68k2urmagV+UCDobOi0IeZGGw56hOp3Rpln5OZVOzXtVEVKs5frGOx2758WuNPyS26jnt1H4nUpP0O25OwyzqLE7m5rhf2YhZoWca6OnxM4QvFplXVW1JFCjNm3H9VDnHU2MEqK2jVkzDVnWLnkuKjzxY/F7XCIIYlvIyYbq8ZuzRypC07Ennuh+qgR44JG/Fb1RcQBh0pro8e4n2wa/2N/fufvP5E/NUnZ+zwuHJmZ62HdI8Rq+UvbTcoJfHCa87T9Ok4kJVkLHAQdy+B/terylgxoVflXPr5TvyoN2jQq/7V1xFX7ba3/FR3/Q4Bchos8//yTt2rWbbt26ySGjDz/8KH3qU79NX/7yF2nFigH67d/+3wp2/4u/+DMaG7tLX/7ynyq+FvLAr509dcvB8ys//LaeB1u8BlDW7u72UDn8AnxPBI5RBh5IjZa9pfDi7nTs4nzWxRoETaxWc9vgF3OGAnRxS2YThKpLsVyUYqkYqyXjf2atmUNEUdpJrzVw7d+VlkGymxsra4Ow5wvBc5zLKrbFwp7h+R1L3aWxwARfCtVlgC+X7soKEQfwkvYYe0ivFVIW0FePuadj1Hub+SWB76vLZaNQKEpQY1/Khlzqk/63KZ6F1zlGSMpea13LecaIECgvvYX1ONz9VEn5pzP+Uxw5UN4g3oWc5Vrlu5T0/KL02Atj/8J7BPNG2DOUyzEfHFYd8AklyNrZRPhtp9CR1Oe/FbnJ32V8B8WG33095j56yHtAajcV16nwC/g1kM1mVgWvmt5F99+NKvy2d01V+G2v/RUf/UGD37m5Wfr853+P3vOe99Ozzz5PgUCAvvSlPyKz2cy1cXfs2Em/+qu/UbD7f/tvf00XLpynr3zlvyq+FsUDNB/2bOd6v3NzQVnni7DnYDBM8bh8Yc8AS4S6yeX5bcYbWstIi8HvtehVupcYrfB02XQ22uda/CVQDvhlBWp4r0wmcrptdPrOOYKwD8Sf+kz9HIpYreG+1+d+WjU82Gv2ssjXRGSC9Fode7I511bnYIEjePtEheat7q3U30CZGLwcXwidY88iPEUoN+Mxeukh78EKryLgN6NL0Tv3TnKO8Hh8bCHkOq9hjzUaPHfu+bJQOIjY7trRESGszX7h2gm/78ydoLuxu+z1hbddOPrI0ZB1HQUzgYo1AtA+2f10ib2vh6/SzaqeXzM93v1ETbMoCb/Yez8Y/x6XOSr53ZrP0UHfwzTUQv5+s2tdft9ygt+3Zt4kf3q24tFxoIBDjmabCr8C/FqtJgoEBNX3djRV7bkdVl98TBV+27seKvy21/6Kj/6gwW81gyLU+dd//RO0d+9+2rBhE3360/+2cJng+b1HX/7ynyi+Fir8tmbipYLfM8FTFMpWHi5o8lp63Lf4y74Avxaanp5r+EFZ7Th2naLZGKsDe00e0piJguEQcTwwxLPyGVptWUs9psr8dHi/3ph7Tby0MD4AYcg1RLt6d9I/X/lnhl2IG7n1bppKTzEEI/cUId0YRafT0yNdjzVc/xa5rFDDRimixUodQcTLbDbRhfFLNJWYZM8vvHQYFzV3Ucd3OjFFNr2VXAYPZSjNwD9gXSnZnqInWan6vZInUnRhu+AXa/+TyX+lmeR0xaGIVWcju95GCCsXPbfYQ15jV4WXDzZ9Y+Y1FkpbyPnN0lbH9rbm/MLEV0KX6GzwTMHawjN46dne5zsiT3w5we/x2bc4DaG8Ibz9md53NbP1+R6Hw8oRDw92zq8Kv01voPv0RhV+27uwKvy21/6Kj/6gwe/169foJz95kX7jNz5deFE7e/YM/c7vfJI++tH/la5du0p/+qf/d8HuyPndvHkL/dqvfUrxtZADfp1OO5etWS6eX3iSI5FSz0yzhpYffrWE0OzysOdL4WGaSk1WhHOiHjBKFi3WmoVfeG1PB08uiPYQsacuo0uTV+tjsRixIVd3l3Nv1SkMhy/wvOHhE1s+n6OnVzxDPqeXzo6ep9n0DMNvMBOkqeQkj+k1+ASVX4QZa3K027O3Znh3s+snwm8wGCHUBT4fPMfeYqfRyTmGaMlcgkWudFo9l2Ky6C2ShsPhwWhslBDmjcMDm97OolKdAMHthN/vjX2X1ZCLG6DYorPQo12H6VrkCpdhwp5Bveqdrt0lYmTifclskq5GLlMoFeKQadgWBxb1mpKeX3FshN6jTFY6n+FngGgXymfJ2WAziEDhAACHMVL31XKCX+T6npg9Tjrtwu8P/G6CBx3RF802FX5Vz2+ze+d+vk+F3/aurgq/7bW/4qM/aPA7NTVJH/vYz9InPvGr9PM//zGamZmhP/qjz9HQ0FqG31/6pY/RH/zBF+iJJ55itecvfekL9NWvfo1WrVqt+Fp0Mvz29vooFIrIHva8HOAXBwkIiRcbcgmPB45xzVqxAdI22jbTgHmg8DOoFCPH1ql3MhA0C78TyTEaiZeq7gayAUprktSj6yV4nAEfCAuGIvJ+d2VIMSYFr9fFyDB7b3KUJbPWSuutG2iNdxV7pCEUNpa4R/7MHAXTIZpNT/PckftLeQTD5om0eTrgfZg9uHK3YvhF3/Ao3oreZGBFg1ex19RLXebGlNf5uUMXSoCES4rpzLTOvl7ux2i8Pw1R2hjnQyCn3i0ZnBofqPKO16d+ymWlig9EYC+o+D7WfZj3LdSckdsuFegamddSwG+9+UDc61r4CodHY0+sta1tSEANatIvwYOO71U+x2H9B70P0wbHpnpDU7PwC+jEWFCurpVTXXcCDV5wNXyF6z3jEAoHCIi82OPZ15IXXYDfLMVi8qXUNPhYbb8cv/sghqiGPbd9KTpmAir8tncpVPhtr/0VH/1Bg18Y9PTpk/Q3f/NXdPPmDVavffbZd9GnPvU7XF/3+PG3uM7vvXt3qa8PdX5/hw4dekzxdSgfoNmcX6U8vwL8RikeF0BEjoYyQiidJLfnF2HFcggH6XSC57ccfvHs4UyIbsSuUywTZRGdleZBBgY0vJgOh89TIO1nYAQ0QDhqZ88OBmCUJ2qkjcRuV3iao7kIRXJh9vzOpf2UyadJAzbVaOkh18M1vW6YH/5wbi+8oDZLybwAhvj8ZPBtDntFvV0IYmVzOTLrTbTXvY8GrIOyv3Qv9gKIkGmUX7LpbfzCj5rEkXSIRbS6TT00aF1Vcy4IlZ5MTlTAG7x0qKUrtxdwsbWFXa+EL9FkcpLVqxFKDhGxqfQk4dnxXcjnNbTGtoYBaikaDk1+OPEC4UAHOX/ItfYYPNRvXUE7XDsVn0K74TeaidCx2Tf52YvDu3d79vDeqtewpt+6+w0aT4xxioDYcDj00cGP181FbxR+sf9RT3kuNcNREWadhdbZ1pPPtLjQXr1naPRzHEpFs1EWxUMJslabCr/E338VflvdSffX/Sr8tnc9Vfhtr/0VH/1BhF/FjSrDAM3Dr429CbOz8gpeqfC74Pmtt7xXIpcrwouzuSzt6d5D67qGGoZfhOyeD58tgTRAw0xmmrLpLGVyWX5xh+cXIcE2nZVDn6XWiS2HX/H5MO658BkGR0A1XnZXWlcJisvmbi6LI2er9wKIF38oE4t5u3zQkMtSn6WP1tk3LDqV8fg98qf8FYCcyqVps2OLpBd4QM69+CjXRcaBhsfgprX29Q15vFA3F5AkelDzuRzXQN7g2Eg2q4XhFyHseM6lrF0cSPkZqBCpAE+vy+imLY6tS3Io0G74PRs4w3nk5d5Tu95OB32H6m5v7Kuvj/4978niBtmwx3yHaa93f80+GoVfHJ5MJaZKDnJwUPWQ5yCZ9ea68+3EC1T4VeG3E/dlu+ekwm97V0CF3/baX/HRVfhV3MRNDdAa/Bo4T1XOBvgNh6NNiZLA03U7doPLAa2yrKYV86HBy9nzW8u2x/xvUpayFZd0Wbvo8cFHG4ZfdAQ13akU1JgNXJ4rlo9Sl91HV+aukiFvIIPeSHaIFOkcXHqp29RNgxZpofqLwS/GvRO7TaFMiHN/AUYQvEIDLGx1bpNzi9X1fkzEx+lG5HpJqRVMAHnLB3yHFg3LTWQTdDV8medfCijEkCclbBThnlCsFsEVgIoap3vc+yTdD0B5bfoVWK4whVQuSf6kn/rMfTToWVmAX4DUGtsQeZfQm4dJCfnQugo7ybrIZZ21G36Pzx6jSCZc8YhGraFujWLcBBG3r9/5B46UKIffQ75HWdW8VjMa9Rx1UVzqCN/f88GzdC9+jw+0vAYv7fM+xJEab86+UeJhFvZ/nnotvbTevlHJpVKsbxV+VfhVbHMt445V+G3v4qnw2177Kz66Cr+Km7ipAQC/uXmPXiMdOBw2LpsgN/z29PgoEmkcfi+GL9Dl6MVCWCFe1NZa19Ee1z6SG37hRfH53KymLEfYM4SIenqqhz3Xht+jlJ0vy1N8XZfFR4+veqwp+EU/oXSQplNTNJ4cI4POQE6bg67OXCcN5VmAyqYTavDCxl2mbj5okNJqwe/N6HUO8YbAVDH8wrPVishNtXnV8/wCfKEAXd4Qmv2Q5wDnay7WAK7TyWkyaPTzxXxytNqyhpxGV10TIef16MwbFV5eQOou9x7ymuqHKCO8+PXpV9mOYkvnUhRIB8htcNM679oC/ELVeq19HXmMnrpzW4oLsJ/QpBwSNDqfZuGXQ28zEQ77bSX0FpA5Ga8Ur0Ou+0O+2uAqfte+efcfWZ28uCFE/6OrPk4WnbWmSarBL1SVcdgF8TpxDKfBTe/qfTfDb7Xca4TJb3FubdT8HXG9Cr8q/HbERuywSajw294FUeG3vfZXfHQVfhU3cVMDdB78ejk3t5FyFHi5/9H0DzkftbhpSUPPdb2HVvcNUDKZonBYHrXnBfj1s4BKq61Z+IUa9Ex6pgSWkOO507eLNvasbxp+8TwIk70avUwmvYmsVjPdnL0lhAGThvpNKxhQAIM7HLu4Hq6UBrEr1Nosz0WeSU3T9eh1CmUCrLYMyOi19PNYVr2VAa2RNpec4xBq3D9gWcme0+K2GPymcil+xngmSpcil1jkp7ihP3jYikWbqs0LYb3+1CwDaJexW3JYLzyD8BCWh5EDCldaV3L4c72Ga4/MvF76XcjnGciRs9zj7CrAL1Bzp2uXsJYo75Sc5Lxyn7FLEQBdbO4T8Qm6HrnKQlAQPRu0rKR1jsXDy+vZoNrnzcDv7ehtmoiPcXQF/Ohuo5fD1+utf7XxkU/+1txRjqaAvUXQ3+vZJ9nzjnrUr0y9xF5gHAoh9WC/90DVwyFEDMDjb9KaeTzAL77Hl8av8sEOYP5c4CzlqDSMGvWVH/E9yvsFOfjFDfnw25zbGxLpamatlLpHhV8BflHmDUr37Wpqnd92Wb76uCr8tnc9VPhtr/0VH12FX8VN3NQA9wP8ovTHEf9PWUSnuOEFcLdzLz28+iFKJtMcTi1H6xT4BXyeC52hcDZcCFHsMfbSnu7dBM98o4JXxba5FbtJc+k5ghgXXpqD4TCXJEplUyy4BTAcNA9Sr7m/pknj2TjdTYxymRuLyUIr3SvIHLMX4AqgeD12jT2lM+lpylBGyPs1mFhMa6NzM5l0Rh5j3jFYAAfhZ4K3UPhHnq6Fr9JYDGHDwl6AjYZsaxn8xFYOv/C4otwRvKOACpTfQb/4n+j94trG1rUMoUq1RT2/+Qztcu1uCJIuhM7zQQLAJ5tLk13vJLvBTlojUTKRIr3GwIcKdr2Dla6RJwwxKoRLoz7yI77HKg4N5HhuhO0C4gC6OBhAOagTc8dKD3DyWdrk2ExrbGvlGJL7aBR+oayM/GSE/4sNv08Q5t+scnc8E6NrgPwM1J5NfJjhMtSPCCg2AuyHyAQc1Gywb6zqjT4xe4zHwXcOpbYgtravey+d8B+j8dAk6bU6wvcSJYXgeUZuvdiw7zc6NnNu+/nAGU4hwaEPvkc+UxfDrxKeedkWukZHgN9MJitrJYGlmLecY5hMRjKbjSr8ymnUZd6XCr/tXUAVfttrf8VHV+FXcRM3NUAulyH8afSFRrmwZ3h+4xSL4UVcWkMJkZdmXyzJc8SdeFl93PMkbR3YxCWE5IbfmRk/v0y12pr1/GJcvKwiVxZeQ9TJRT1awKrdbm0JfscT9+gqXtTzUdIbdEQZLbn1bvYGbbJt5vDneiVpRDVq0VOGMHmdQUv2nIvWONZwneg7sTsUzgRJpxNewCF+FU/Hue8nVjzBuaoAJYfRwWG/tfYp7nt97HUO1S5ugFj0VT5fBtw80fHJY3Q3cpefDbmogD9EDaxxDFE4LdSeRU3VLrOgdFsfwvmqwnXFfxchfaGfUni/HLw0L1YlwDv2MESR9nr2N/QdxbxvR28ytEBNGB5w7DOdNU+RcIxMGgv3h/zbH43/gHM+iwHIbXTTM73vanVrl9wP6DsTOMVzAphjf0QzYdJrjQzBsERqvpSWy+Cmw91PyjZ+o/B7MThMkWyldwxVqPd5D8g2L7k7Ohc4Q0dnjpRoASCHd5dnF0VyEcpmhHWGh/du7A7v7eLICPz8ye6nqc/Sz6Wn7sWF70WXsYfD4xv974Tcz9dKfyr8wvML+DVQMCjPQXAz66F6fpuxmnL3qPCrnG2l9KzCrxQrLeNrVPjtzMVrHn6t/B/SmRl5Ba+Q+xqNxvlPI+212VcJ4bPiyxnABjmOT/ue45zfzoZfDSHX2e8Psoe61SbALzy/s013hZqsJ4LH2Ctk0OsplUaJIw1tc+yk9bba4bfwFgNmUTd4OjVDep2OPchYG1aLhnBUzwEOAb3qv07BZEBQH87l+E8+l6esNseqzwAkeKoQlotc2w2ODaQhLdcPBsyiLqxYB3k0NkojkZsMsEV6T4Sw+L2+h8hjcrM9AN3w3icSKa5b/M8j/8T9AwQAYIDlLpOPNrs200bPQg3V4hd/YUw8j2jihb+3AgjYt7dCt2gsOsZ28pl9tMm9qeAZFcG7FKgFgEYTPeHC3/n/CxCOueI7i+eGvfHZOf85uui/UAE1gKD3D3yQw87LvesLfZf2X2+znQmcpkg6XDLWvdgomXVW9i4izxwedvwdod9P9TxHg9bBet1K+rxR+B0ODlO0CvwKoe+dC7/fvvtPXEO7vMHDvNKxsqSOOBSkkd+Pgw40rDmiLQC/rexhSQvShotU+CX2+iLyRYXfNmzADh1Shd/2LowKv+21v+Kjq/CruImbGqBZ+IVnEf8h7RT4RS3VU6F3GIDxtu8x+Givaz/no0KcannAb4hzk1ttgF945icnm4NfwM7bwePsFQznwpTX5tjDbdQYaYd9F620DjLMAnDxBzCJfxf+CSAViPCK/wr5E8iLBtTmOfQU1wTDIdpm38nwGs6E6Xb8JofhFhEcxSnOIaHFL+GAQYAvvITwHiL0Gvmp3WahTupscpaGQ+crVITT2TQd8D1cyE0uDnu+E71Nb8wcYcAublrS0U7XbtroXIBfES4hOhTIBFgVF7aoVepJhGTcW/n3UnEn8VmrAXUxaEuFcGFMYS3EsbFOOGAQPzs5dZKG54arwG+Ofnb9z3KodCNtcVDO00t3Xy5SEBaAfSY+Q6F0mEWlFu7Nc540vO3P9D9LVoMg5lTaN/9EsncddnA6bZz3LyVaYzI+IYhJnvbSAAAgAElEQVRBFQmHYXyP0c1hwZ3avjn6NZqqItaG8GbAbzpdmuObzqYItYJRy7fX3EtbnNuaymnuVHsUz8vptFI6/WCHPavwuxx26tLOUYXfpbV3+Wgq/LbX/oqPrsKv4iZuaoDW4NdECP2Vs3V3eznkuVHPb605yA2/er2eurrc/OxSXqTr2Qcv5ijx5Pd3BvwCLFFGCQACWEW4Mp4TAIVSJ3t695R42AG3+BziX/gj/n06Pk2j8TuFnEL0A89jLJygLY6F8kUIsYaHGDAMwDXqDKTV6kinFVRo0eCVCqYDFE1HaZV9IX8Xnlurzk7d5m5y6Bz0TgD1edOF+Ylhw1BLFlsx/CK89WJouEL4BzWNP9D/IXIYnYX7AD/IpYX3ErbBvyNcGGWMULO20xvCnl0uG4VCsYJQG/JQX5z8EZdxKm4Aouf73z3/o8U83Pi4FK6rg7sA4a9PvMZedbHhTtRPHomMcHi5RgshKOFaj8FDBp2e9vc8RJsL3nd5vOvFz1nqKS8OQSe6ODdMk7FJ0mhxYJDlg4Ddvj0l4fPl3nXpIe7lXvrS8HfMsZqXv94e+/HEjzhXufiABM+41bWV8tp8IewZ/WRzOVrv2EAbHaUHPPXGWK6fq/Cren6X695Vct4q/Cpp3fp9q/Bb30bL+goVfjtz+fBSB9GrRsPcBM/v8oHfdDpNoZA8eU4AQoRSdwr8wuuNkF0xp7URzy+ASPTYih5ceAdfn3i9pKYoXqDTmQwN2Ydo0LKqCHZLoakcLK5Fr1A8J+TwItRYZ9RSV6aX3GXldfAMLLCl0ZHP4KM76REOgUULpyMMvgjRBFxBKAne3kBK+BlA1Gv0slLwKssa9iTj5wAz5ClutG8uUVwuhl+IB92JjtBUapIBEGGtgpKum9674v0lX1rRG4jxihs8v8Vw3ZnfdIg+VcIv5nojco2GQxc4BBwNOcaHfI8VwmHLn8efnKOpFEpB5XktIIQkpV0LX6GJxESFuJVNZ6NL4YvzJde0ZNVZ+ZpcPkt7PPvrqn1X864vQLgA04g6QKmteDzJkQhSves4HJhLzpLd6CyEzZeGu1d61/lIYD4cvtRLv1B7WYq9Frumlnc9nIzQN25+XYB2TCJPtMK2gn5h/UcJwoAXZoYpkYlzdMRK20ra4dlVMkwr3vVmYL1RO+Aw61LoIod260jHewO1qqU0FX5V+JWyTx60a1T4be+Kq/DbXvsrProKv4qbuKkBWoFfi8VE09Nye349XOZIXs+vi8P97jf4BRBei16lWC7GAk1ug5e22LeS3WrjEE8x7LkUcIVw5fIQZbz0AgpEz+2t0E26FrrGqskIlcOaQKRon+uhukJXxRsRL6sIRUd4s9VsoQ3d6ygyl6h44YYidDAjqC0jnNlqsLHQDoB0IjnBYwZSc2TTO4Qw4zxRmpI8P4RC95h72ROLMHd4smrVjC2GX6jmvj13gv2X4XSIgR/wBaXhNfbSl2p41ASoLm3wEj/sO9TwAVJTX9gWbloMftElDh/uxUe5NE6fuX/RZ7kTu81q2qIKMqIEek19NGSvr8yMvQAbziRn+GAD9yIcF15elO2CHUtgkTT07r73kllvaeGphVsbzfltecA6HUgPcWeU5t5q5ZkLnwvXoeTbqdlT5E/MUa+1l3Z5d5NBb+DvPNI/8L0y6ozzOfNC/3Lnrhc/vtSc8WpeePRT7F3/17sv0kj0diF8HodVOz27aX/X/sJ1C/eUetetVhNlMjnOeedfIE1615XeG0r2r4Y9K2nd5dm3Cr/tXTcVfttrf8VHV+FXcRM3NUAnwi+8M6j1K1fzel2UySgBvwHut1oDRAGmjDoTw1St1kzYMwSajgXeWvAw4QVZkyefuYse7j9YCFXGC2+xCFgx4BaHKOPn5Q21cqfSk2SyGEiT0tFq4xC/NDfbFvNII+xZ9PoW3vK1UDl20lj8HpeG4XIseSqU7wH4o4wLfoiapaInGXbZ5d5dM2+xvNQR1MJvRW5QNBvl3ONecw+truJNuhm5STPJqUowzBM95DvYrFmW7L5a8CtlEggnP+l/p+LwAxALNepauc/F/ScyCTo2d5SVvXGogXzTTC7FIe+oqQsswWHOLtceWu/cKGVqda/pNPitO2GZL0DKgc1mJr8/3FTPUrzrYsexTJSuhC7z9wnRGGsd66jXIuTlL+YJlwL2E7EJ+pfb3y0JnUefEGX7+MaPs8p7oxFMixmjlnd9wcNdWWoN/VUqutcKcec7JOeut+pdB/xiL8h1ENzUZsIBTfX/bDbbnXpfCxZQ4bcF48lwqwq/Mhixk7tQ4bczV6dZ+EUIIWBGCc+vMvCbpVCosnRJM6sihj3PzgYqBGTQ3534CF2PXqNkLk4ajY5LBMFjilDDak0q/BZ7cEdit+h27BYL8uDFXmwQM3p25XNk1BvYW1sNcAHmKXh/tKa6Xlw5Q7wXg9/LkYulZtFoWCHaqrNx/u5c2k8IjUUoLGqTApEgkmTVWdjzixBosZxSluF3ISe5mr3L4bfWHgik/ISarznKk01vY69TcdgzclbhKS33Ejezr5S+p1X4nUvO0ZXwpYp9jP2EWsrwvktpZ/ynaSIxVgIq6VyG+sx93De8eagzC3vL1VT4bQ1+AYP4LiBSwmvyLfq7DAcbb86+wQcZIoji7ztdu6jX0tfScp7yv0MXgucqABe5yx9Y8W/IZxJKkYmt3LuOVB3oEQiigsX547ijMe+6VPE67lkhZfjiZ5XqXRdTAGCHVkLcMXb9km+Le9dV+G3pqyDrzSr8ymrOhjtT4bdhky2vG1T47cz1UuG38XWpBb+RTITe9B/hEjxiw0uGz+hjAK7WRPgNBEKUSmUq1JPFMOViD+7ZqbM0GrnLbyAQg0J5IPwTysZP9j9JPZ6uqmrPIwhbTd7jMFdAXI+xl9ZZ1y/qMVkK+L0YHmYF56K3VgZ6fzpAVp2Zw2R1pOdavy69m2sap7JpymtyrAgtur9hZ3iB1ts31FxUeD4QAhkI1D4MGYuP0WRivBDiC7AGbEPmCrWHkXOIGrqrbWtk8zg1vhul39Eq/CL/9WzwDIelFzccUGxz7SypF1trVq9OvcyHL+XNZfBw+LgSTXx2qWrPSsyhnX224vlNZBN0KTRMyVyKdFwOLE8D5kEasA5UPNKF4HmaiI9XfB9seis97Hu0JRMgN//V6ZdLf1egdJnGQB8e+BmOAKnVkAqC9BccrnZia8S7XitsvpZ3Hf8twXdBiFiqfQAAGymRuw7w9vvli+zqxLVcTnNS4be9q6XCb3vtr/joKvwqbuKmBmgNfi00PT3X1LiL3QQhqURCibDnpfH8DofP073EvYqXP6DdU75n2dO64MEV82+FvFrAm9QQ5WA6SGdCp0q8kLApyu8c7jtMbreTJiZmSsyMEihXo5dLvL3w2q22/P/svWdwJVl6Hfg97x3w4FEooLxBOZTp6u5qN9NmemY4hiJDWm0ouFqZXTFEcne5P6TQj+UfkqGQGCORWpIhaTckrYKiEcmJHg45093Ttqqrq8t0WZQDUPD+ee83zpfIh3wOzyYeqivvRE13A5l57/1uvlf33HO+8+2mXabdZZdkE/xWlnjX/ALocvQwPk4L/iXSqbTUbxikPea9NBN7QrEMWPK8SxCtp9bJqrGQXW9nYysAXq57a3CTVWujPZa95E/5aCm2SBmCZFtgZvda9pfEpHh8tYBfxOWO/3beqVp8BoDeQdthMmlMZQEvzISi2Rgz1QaNoebQbMeFzYJfjPFe4A7LlUWmHe8s5nncebLmKXy8+gHFs4V537jZpevgklRytFbMXY5xIX6JbJwPFMQ8ajn6aQb83vXfpmSusAQbDs/A5pq0hSkd17xfEL6bihu+917t/lpTU0Osfrz0DnmTnoLvSeTnv9z1atVnC+A3RbFY8+Xkqna2Qy8wmfSk07VO9lw7CGcozVHBOsbjmR0aoWdvWAr4be+aK+C3vfGXvXcF/Moe4oY6eFbAL+S/gUBrZM84Pe/qclE52fOd0C1aii8xO4HSLdgciH++O/xdMuoNZQEulwCKximRSGw4KVd2URYX+kH4Pq0kl5gVFUvIHLGO0qCjnxwOWwH4xYbjTvAWRbKljtcwORpznN4S/FaSeIs3LcUXOW8XrCiAH8C0lCEEmPw08DGbVMF0Bw1gdr/lAO0x7aOp2CQb9eAe5JAGswHqMcB4SeghnctQLB0jm9ZGh+yHCySVqBEL0F+rMVIt4Bd5xnAgLs5jRRy7DF3UZ+oviBfmMh2Z5nq1AIaIg13roF3mobpYYTFXXKfSk0XXOtkvBitKf6Wljur90sD4psKTFEwHeBNr1zn4IKLYARvPRZ3oqcgUy5jhyivGEm69TyJTzCKKDXm/yPEtxybWO8Zy1+9E8AuGFKqHSCrMhyxQEZxynuZYAlxCZoy8dqgZmm21vPPl+kAOPfK8dUUO51j7Dr2b3d+l7V7gLh9IFefeWrUWeq7zhWanwY7kX3g/ZxM9KC/6TQOcby4exmzVAcAvvnsEw6tns8GkEq77Ss7vs7n+5WatgN/2vgsK+G1v/GXvXQG/soe4oQ6aAb/I+11dfTqYXznAr88HAECE0kCiNBkb1o+WPypgDJGH69Q76ULXy5JauIKzsth6e93k94eY9a61YQMaSAcIxlTYMO8yDjHAwAZHBL9gTCejE2w+A7Mm5FRCXgpAIjYAxzPOc2W7rZbfjJuQ47y+UacX/41xYbMMIC5ugqciE3Q/Nk4GnX4j507oTktaerXz6zxuX8pH8WyMnDonLSTnS1hXAEwA6+EanIW3imEtQADgA/mFxWwcfr7Xuk+QW0sajLnAUEs34XA07jH2kbvGUkCr8WWajs5QJpuiHKnYKO2w/UjLGOR68l6xhjjQWI2v8vgHTIN1gXgYiN0J3GYzK15ntZZOOk/xYQDW8ab/Bq3EV/jAg0tUmXfzXOVqOw38Qkr8wep7kqrHwmFQn7GPTBoz4fMCoAdJb7exm57rfL5qfr40dlg/MKRriVVy6Tto0DbIhlfVpP7F8cdhx3Xf1RKpu5DK4S7Jdce8Lnsucg3hvIqFcmxCB3DfzqaAX+K/G3Q6Ddf6bmdTcn7bGf3CvhXw2961UMBve+Mve+8K+JU9xA110Cj4NZtNZLXKAX6dDI6Qm9eq5nLZCQC0Eea3VKIslAnSajfzHovLBN3y3KSp0BTn4CK+2MyecZzb0sCnEfBbKT4i+F1YWqGr/s95g42NqD/lZwdqyIMhMRWBKqTEBy2HGgK/LA8O3mRjL2lDfuyweYQ69IIJDSTay6lFPiQIx8OcPwhmEEZSMAQDu3XQfJjBEYDSQnKBYpkIjxt9AIRBbus2dJJT38FsY6Oy4lrAL8YMd2cwnGL9ZKwzxnbEfrQkVo9CDxm8FDeAerCe1RpAw03/dQY7YkN/kFePOo9Xu73s7zGeuegcgyAw6sPWYRrqGuDPFvLuihtyemdjs5w/Ph+fZfkqwDxMvQCgXul6tSZpLg4Ifrr813ngK/aDg5e3et/OxxPgDrnTqCtcyQyuoYmXuWmngV/Ix59EnpQcKOAzgQ8su5tvNKwj3MfHXOXVGcXTxfVXvVfYqI3XjzLkMrjojeHXKRKs/XBNfO544B7FMtEig7IUG8uhtFjpexSjR+GHBNdnvP8wQ4NJVrubAn4V8Nvud3An9q+A3/auigJ+2xt/2XtXwK/sIW6og8bBr5Hgntl65hfgN0WhUKk8t6EJIpewCvjFxhjsrVj7VvpPgK9wMkw31m5QLB2nIcsQ7TbvJoB/jBESOimDK44REt2lxAKZNBbqMfRUleXJAX6vPrlOc/G5gvzMteQKA88B4wCDE4Crk7axsrJVgM6sOkMD3b3k8wbLOlsjX/FO8E4JeAGoQY1em85ORrWRctksPYo/pEQuTolUgnM+sUnH/2waO2+SwUDvM++nQ/YjpNVp6br3GgVSPgZhYKp7TD3k0DmZWVarNHTMcbwuNlJcm1rzH9HPQmye+4eMGSBtl3l3WQauXvCL2M5GZimUDvLzEIu4NO95Y7CQgJ9xnauaxywFzJAUL8bmOfcchz4AHga1gedwpv80uchdAn4fBR/S3eBtji1ASywHibk9D24wPoAYSEyrtenIE7rhu16mJFKKLnS+3LTrb7X+y/1+p4HfL303+N0qlgcDsELaXyzjBch8q/ebNU39ceghPQ6jRvemrBzpA8POYTpgqJ9dT2aS9CA0zgAYB2k4SMEhVY+xOffmmibTwoscDgv/3aLInhXmt4Wv1VP/KAX8tncJFfDb3vjL3rsCfmUPcUMd7DTw29np5Lys1oPfHEWjsRKAK7hfbhpxiHVwxRJBc6E5+nT9E5YgYqMKADFgHKSfP/R98vtC+fzVhoIvuake8AtwAkMobJKdWlfJBtpoNJDTaaOLk5/xddIGcIO8VOTkdug6yK3vKrkf1zxEPnFqmVLZJGn1WurTDtIB08GSawGK7oRucmVWsQH4epJr1GPozYOndCZNa+kV8qa9lM5kWN6MfuCeDNYPcuYszLeMwwwws2o4KkcomorSQnye6yXDJEssp4NcRMiPxfq+9cS/VvBbzzOR5+hLehmUiw0AF2WQikuwYN6Q/SayiTzI8cTXWf7r0BfKqSELPu06VzMz+iQySZPhSXZT9iQ8+fzjXkM/l7+yGi101nGepHWdPXEPvb/60/xYIN1PU4YPIzr0Hfk1x/v2es9bVcOyEFugK57PeG2lDTLwV7u+1hYWcKeB37X4Cl32XC6R9geTgbI5vng33u77VtXY44LLnksULDKdwvytBgs973ippmeUuwh58GD1rTprXRLshjts8Y0K+BWYXxzutlJZVe8yIVVIkvFT7+3K9S2OgAJ+WxzQOh+ngN86A/a0Xa6A3525Ys2BXwutrnpaOrFmwG81BhcDBfAAGwbZ52YN3M3/Lp7Mu2s/IX/aV/TjHH1z5JvUke1qKfgNBEJbluHA2O+Hx2k1uUJqUjFratFY6ZRdMMkRmwh+7889onuhuyWsIZib0/azFVnTycgEzcafUDgdYYAGg5RkKkmDxt10pky94sX4Ii0nFvNgZzm+xJt6MN5iw9hjFKUIhWk1vM6SZrh/YtwA0FaNleczZBxiIx0wzgDFzKDHF/k6gEm3oZsMaj3/+4BpF/XUWFtWuoBygF/MbzY6TaF0iCiHw5Qcy7MHzYMln4+V2DIhxtI1S2dSbJSG3FcpGwhGvB4n5Yvrn/KBBYA4HLTFdx6ycrfRTWqtil5wXSB1bvN9+WztEs3FZvLgN5DyUyqXInVOzYcLeo2en4MyU1/reb3q5x3r+e7K31Ais2kshPhAbv96z5sNsfVVO61ywU4Dv4jHl/7rNB+d3zigyPHhzqBpkB6FHxWAS1wLgzXk/dbSrngusxO6tPH8jXY6Z2/edKqWMezEaxTwq4DfnfhetntMCvht7woo4Le98Ze9dwX8yh7ihjpoBvzabJaytWQbGsjGTdXAL1ha0VxK+s9iBlcKcAF2stkc5/yWkygXjxfACswJpLYfeN9jp2FpA7Q57D5MY5az2wp+l+IL9DDykLQSdlE0njliGy0Bv0tLa3Q/fI9dmAG0GPjnsnTQepjcenfFZbrsu8S5rsF0kDfmAL+oj2lUGWmfZT/fX9y8SS+tp9b4+WB9IZktlnQmKEYGo4HikSRNx6YYWENGCdcws8bC1x8wHyCDxsjML8CvkLc6K7DuuRzLqCE/BvMLUNhIrqgc4FeMBxQCmJdZY6qYHwtZKsy9SmKY8DKbzwcClGWp8mHbkZJyMlt9vlBDFw253TBDU28w8gaNnllzg15Pz7suUDbDked2ce0TWohBHi+w1pBfhzNhzj1FPjivQzZHx5zH6YDtYE0fb1/Cyy7BkHWjJxiEnes4X3Mt4Jo6qeOiTfAbKWC963hE1UvZgC7lZ+d1J0zlpLWrK9wNmTPkz3BcR244DqZgMDUfm+P3XXgWzPJeKXEerzSg+egc3Q7cKpA951Q5GnUfpUHNcNV5fFUvUMCvAn6/qu92M/NSwG8z0Wv+XgX8Nh/DHf0EBfzuzOVpFPyaTEaCgcjKSuuZX6EWY6KqRLmQwRXck0VGVxpt5PxiIwk35WoNzsWQ/EYyUWZX/Wk/gxDpRhbPOtN3mg4bRrcV/N4K3qRQunwNTQAasYnML+r8YqwoC+JJwfRIQwOGXWTSmrYMw0XvJ+RNeVjiCFNoyOQSqSSDX7j+nnac3fL+R5GHXLtUbBiDJ7lOWo2WYqooZZMQj+eYnQJLqVPreWPfa+glh9bFAHsuObMB2InW4mu0llzl9Rg072JA3mvsL8uqVltf/F5O8FtL/8jHnYnOlEpHc0SnnGPkTflYcuzS1wagpH2iDAyMwZBvDQYecUb8HXon2bRW2t+5j4YNewtyfu/57xDKZsHYSnzPw6kQs+vIsTZojTRkHuJSRLUAOum6RzJhPkRCyZ52NrnBL5y+x4N3+eADDd8ZR+xHmP1vpAVTQVqOL3L8uw09dcUd/T0I3qfZ6AyPR6/SsdvzCwPPUyDQOi+FRubVznsU8EtkNhv48FiRPbfzTdxZfSvgt73roYDf9sZf9t4V8Ct7iBvqoF3gtxKDC6CVL5FRIlGuDHC3mrzTaWcZajXwi1zaD70CcyY2OCQjf1K6eYfs9u8d/XsUD6a2FfyiTm8g7S+ZKhyCz7s25YxGo54w55UVgN/6XwuA7JnoFKVyaUrkEpSFX2w2y8ZVYKFed7+1Zc5fOB2micgjBqlYy/XEOsdwyDZEOr2Wpn2zzC52aDsplo2SitTk0rm4ZmyvoY9zCtcza7SeWGP5bjKbZKYXOcFWnZ2OOU5Qh2ETVADsrSaWKZlJsUQXINq0RW1UvV5LKNPl81U/DKk/etXvAKi84b3G7K74rsMdu9+0i3Zbdld/wBZX+JI++tJ/jaXXyLGF0zNOMFAPtd/cT6cHTlE4HCsAv8gNfhi6T6uJFc5tR0OO6cudr5JOq9uSxW5qsNt4s5zgF+qEzz2f8XpKGw5rzne+WNXsTq4w4DOD/HuYZdlMFs73rLfUkVxja8dzFfCrgN92vHc7vU8F/LZ3hRTw2974y967An5lD3FDHTQOfg1kt1u3ZH4rS5TVhM0oWnEOrl6vp0wmzXUIa5Eo1zLpWsEv3G6nY4XlRzA+SIABfgFabBobnXSM0bGhI+T1BloGfnt6OikYDG+Z84uanffCdwtkz9h49xj66KB1s1RRLeAXIGcuPssMLSSXqBEslg7iWp3+S7QcW6QkpUgDN2zSkEVtJZPGSHvM+2mvZd+WoUe+KfKAk9kU/xOllYx6A0ExgHkirpCGDpp2lTwHgBDvwXT4CcE5WK1SMZiFJFgEZmL5IADfqfAk1xUWG2Tq+6z7yKgpz3C3G/xinIj/dGSKUIdZrdJy7jIAaiMNMYBJFeTiMKjiZ0cFWTliDGdeOEpvVecXsvWV+DKh3BGeA2dn5Ak30wC+HoUekDcJiTdyoF10yHaYGX1vwkMriRVmqO06J/Wb+mUFiXKCX0iXITMuluCnsykadZyouc5zM7Gudq/BoFPAr+L2rDC/1T4oz+DvFfDb3kVXwG974y977wr4lT3EDXXQDPh1OGxseFWceyuUCtoEuBiYVJKcTgsMrvgz6cA7OhzMSgEgwV30YeQ+m+/sNo5Qt6G7bvkfng3nY7RqzO/t4E2C7LmctPP1zrfYxAk5eQAR3d2d5PMFuHRGK5oAfiMUi23Khcs9dyo6SQvxOWZCwax26DrpqO1YAXDYBL8eBpnFDXG9E7rFclQxlxby5uO2kwWlbT7wvM9yZeSJqjMaZn479W7OIz3lqK3mKA4M7oZuc9xQvsi8AX4xJuQFD5lKmc48+I08YYYYTZwHfgdwO+o4xj+Hu7HIVkrnadSYadhSPr+xEvjlA4GYwEoD7LsNXdRl7G5oeUOpEINbMNtg5uH63Ci4rTQA0WQLpZi0ah0fzoBt3GdF3rSh5LatwG9Dk6xy0w3k/KaEvHFxDXGI0Wcc4FJMGDN/N+QyZNGY6YhjM2+91eORE/yux9foTvB2CfhF2sAx+3FyG7taPZ26n1cr+IUTPJQYzR581D3AbbhBYX4V5ncbXrOnrgsF/LZ3yRTw2974y967An5lD3FDHdQCfosZXIBdAAj8U9pqBbhbDVQEv+Mr43Q18AVLXtEA1IbNI3TOeb7ueQrgV0V+f3DLewEiPvF+yJt15D9GsmEGmSaNmV7rfJ069Z18/yb4DVIiseloW/fAJDfUCn5xC8YUzoS4Ri9YW7Gx4U7aTzFVhHZ1DlKZ9GC+9EF4nJBTWJzHDEnzAQmDjNxd9KMzaiiVyNCmqlNFY1uAX4DIhcQ8lzPSkpYimQjn9ErBLw409pr359lcaexE8Is6uJ7EKq0m1rjGKNhLlAJy67vpiOMo3/Iw+IDLTxU3lNnZbztQdknKgV8G6YE7OAPIxwVx7jMNUG+d9UzBnMPJF+NFE+sFIy5gsSHvHnOdbcipWjohf9JLs9G5Ejdvg0pPe237S+a+neA3kgrTFd/n7GAsbZCmI+e8mJVHSacD1oMNla6q5XMnJ/iF+gLlhWBOVfAeE9HznRdkZbRrmTuuqQZ+kWLwmecSBcDSq1T8Xfey+1Wy6oSDw69CA/iNx1Mt+85+GmNiNhtJo1FRKCQcKrajKaWO2hH1yn0q4Le966GA3/bGX/beFfAre4gb6gCyw0wmSQDBRqMxbzK1yeaWZ3CFDZWefL5gWQa3ocEQEcAvnIX/dPJPKJQpzMkEAP5a5xt5EFprHwC/AFQYa7UGYPg48pi8yXVkujJjiRIvMGUC4ENOarvBb7k5ALwhJxgSbT6cMOjIkDLRqO14yeb7y8ANAtgobgCoJ+yn8j8G6zsVmySnzU7RaJzXGUDOrrXTfmt5118ARkizpSASTGo2R2QzWslqMpM34KcOfWdZ1lvHdcoAACAASURBVBedi+A3nAzTT5b/mlI5HDAAWAglhPaY99FzXULZlyeRKUpk4swegrUC6DWpTWTRWWl3HczvYnRBMNXaYCmlsUGOcT1tIvSYGXPxcAFuvsuxZQa+IiMLFv3n+r5PFp2lnkcXXAtJOHI6ixvyh485S8e8neB3Lb5KdwO3S4A5s/QqFa8/3gsuDUWC2/eQebjpnOdKwZQT/KJP5E3fD97b+FypODXhsP0ou2XvhLYV+MVn54cLfy443G84VONz3mXopm/3f3cnDL8lY3A4rBSPJ1sKfuPpGI2H7hGUHvj+RDpGT52HZS2ZXI0PAfjF9wDy/tvVFPDbrsiX71cBv+1dDwX8tjf+sveugF/ZQ1xzB8vLy/TOO39BCwtzNDc3R4uL85ROp+nSpUtktVrL1r8tlihLHYVr7riGCwF+/XE//fHkH5dcjQ3ZfssBGnOcqeFJm5fUA35xF1i7x9FHvHFnp2cGXcSg70LHK7xBBFMLMN1K5jcUijDIbKRNRh/TVGSCyxMxaNdqSZvV06h1lPYU5efeC90pAU2ILUoISUsmYRwz8SmKaMMUjyUJtWhNajMdth3lQ4FybS42R76Up0Q6jpqxg7ZdpDdrKBfSbCmrRHzBEl/1fE4PQw8Y2KLhAAKMoVVroTd732agCgn3Df81iqQiDC7BwgFcPdfxPDn1zrJjLMf8ToWnKFJ02IKbAeZPOE/Vxd6NB+4xY46GuAouwAKAh/mQ+PMj9tGaa7eWm8hsZIbBY7FMHzEQZeHS+7YT/EKx8dn6xYJyOxgLcsAtGgvFsnEKpLz58kqQCO+x7KVTrtrk9PV+RuQGvxgP4g4GFQ2S+XIHKfWOu1XXbwV+J8MT9PHaB2XH+92+nyeXxFyuVeNpx3NaDX7xzqK0GBzrxc9gNpejE86TnGe/E5sCfnfiqrR3TAr4bW/8FfDb3vjL3rsCfmUPcc0d/OQnP6Z//+9/n/r7B2hwcBeNjOymAwcO0tjY6QIX2K0eKCf4jSaj9EcTf0TpXLpgCNhcHrefpCNWQfJaa0NuMjb+tTC/eOYV/2Vm7oobys+80fWNHQl+P/deopm4kK+MP8i5Rskol7aD3ux+u2AqMEd6EBlnllRsyKM9ahtlgC9tAA02l4merE6TJqvneq1btYnIBMWypeVUoDA4132e869RgmmrhvEvp5boTuAWMyogo8AcQ3cNyTAk6W/1vc3GTGBe7gRuUzQb5Z/jwALXgP09aN80AZP2Vw78rsZXaDG+UOJijfzmoxv5xVuNGSCZ467S0Fxklubjc/zvAL8wQwKAxyEK6u2Kbbd5hF7r/nqtr3HJdZj7w9BD0mmE3FmOUC5LTp2DBstsvrcT/GIsE+HHHAutWpB/4/PcbxzkNbrtv8k59GiIEWoMd+ndfNCwlVN3o8HaDvDb6Ni2476twC/k/le9n5ccomRyWfp233d2NJNZT+wE8JtomU/DvcBd9hwoPuQwa8z0ahOf63rmVO+1CvitN2Jf/esV8NveNVbAb3vjL3vvzzL4BWv6a7/2T6ivr5/+xb/4DY715csX6Q/+4PdocXGBenp66Zd/+dfoxRdfkn0dijsAKMminmudTTRVqgZk6nwsoSYv6vf+1ZO/osX4YsGGDFLRt7u+zezm/fA9gqMwmLTDlqNbOqrWC36FUj9PKJKNcLkYNoDRmNns6eWOV2UBvzDQCocbZ35/svrXnO8rBb8wDtORjr7f9wsly4ASOPOJeTaLMmoMNGjYRa4yNUkBGrq7O2p2tp6PzZEntV6yIdSq9HTKfbIm8IvBPo4/ouXYEq0klvPu1gBJYH+R6/xm7zfYMGk+Okco74N8Sw1LlgWWHqwM2E8AreJWDvwCNAqyVcFIjMFaNs3SaUh0Sz83OY414jcfm6VYOsaMM96TfuMA3QveYaM2PAs1V6OZCI9dGCNAao7OdzzftMkTXJrhyo2+8J7icGLIvLusadt2g1/MEzWa4faNtesx9rFpHeTplzwXmTHDukFdIb57cH0eKOMAXu/3SPH1Cvit7PaM79G/mP+zjfSCzchZNVb6/uAvssEdSo7hOnwHGrWbPgPNrst23u90WtlNv1UmhV94Puca6sUNn8O3+761nVOruS+LxcjfDYrsueaQfeUvVMBve5dYAb/tjb/svT/L4Pc//sc/pP/yX/5feuutbzL4nZubpV/6pf+BfuM3fpNeeOECffzxh/Rbv/Ub9Md//JfU1dWYu2yjCyiUGqrftKmao3Cj4xHAb448Ph9dCXxGKO8DBgKM5En7aS6784nnI86tE52KwchecL1SMb8OJ/7Y/NbK/CL37YcrfyEAXzEHNJejk/ZTdNJxekeC3088H9JSYqmE+YWjMg4MyjlY17JG1cAv8jYXEwuUyqZZjgwzqofhByzxFftMZzM0YhqhXntPTfWHcbjxZfAaA9jVxCrjWQBepP0CTO63HqBTHacZUIEdBmsLYhhMKwx6IKvF+zHqOF62HnElt2ews0uxRULpILCVyN2zbpRXEmO1El/hmsIYG8BsIh0nm37TFAhjAqOO3L+F6BzLkmEuNhl+LABUlmbnqNvQwxtk0RSrlrWodA36xLPxrK2eJ4JfuIon08L17WiQRN/wXS8oTyUcWIAZ7qflxBIlM8mN0kiH8q7QzYw1p8rRfHqaloOrXKar3IFGM8/f6fdWM7y6479FNwNfEsozoUFVgdQBlCK7FfiSgS+UCzisGDAN0j6rYKiG0lg4oMJnocfQW/awaafEphj84vO+GFtkMz38/YLPe63fk/jMXVr/hJ5EnvDnCKXwxHJrbJC4Q5lfBfzulLdx54xDAb/tXQsF/LY3/rL3/qyC3+vXr9IPfvCvaGRkDxtKAfxCcnz//j36wQ/+73zcf/3Xf5WOHDlK/+Af/C+yr4W0g0bBL8yuAFRXVsqX02l0EiL4DQSEnElsUPAHZiJoF72fMJNU7FTca+inCx0vl+22XvB7L3SXHocfUpSZ3wwDYLNaYH5f6XwtD37hHg0DlVY0sKvhcLThnN+F2AJ9EbgsuGOrcpzzq8vomYU84zzX8BC3Ar8w13oUfsiSVQHUZQkHEQfMB2k5uUQxuD2jzI+hl12da6k/DOb1fnicAhkfPw+bboBHcUM+bB6mY87jnPsL0IvatMvx5TyQw3phnew6O+0r43iM5yRVccrpMqSO60vK02wVqPXEOs1Ep/MOxnB1Ro4n2EqpVDeVSXG5ISlDFkgG2JAMTHG3sYeO2I9uP/hU5Wgq+ZjmAgtcS9uitdBB25GKudENvzRFN7LMPnSfaxrr1QZmphG7ULqwDBLyt6FIAMAS3ye8N691v14VVKEPlKkCgMaBz4hlJH8Pfvfeyk/Jn/ZRLiuoBwDeLrhfrhnsVIsFABQONRDTndLw+cH7isO8EfsIDXUMkt8frjg8fM4eBJEOoWFFAg6cbvlvcrkq6fctDn6OO07ycxfj83zYk6UsKw8O246wI/tObFLwi8/hTT+M/9I8X6RMwIUcsvtqh0L4O/Oq7woflOF9xb0qtYprahvVBjrmOElDltISbjshJgr43QmrsLPGoIDf9q6HAn7bG3/Ze38Wwa/P56V//I//J/qt3/rX9Kd/+kccY4Dff/7P/0/Ot/2VX/nf83H/vd/7ARtP/fZv/47sa7HTwS82F5Vq8r6//lOW9xY3u8ZJb3S9VRH8wgHZ6w3UFNur/s9prYycDZuit7q+KSP4jVE02pgLJ2J2K/QlrSXWON8XhxO5BNjqM2xk1WjbCvyOh+5Sgp2YN1s2l6E+Qx/ndha3WtQCy/El8qbWKavOsaRQxSbPKmZjD9oO02H7kfxGfCL0iBnfcCpE3pSX5fJgicHCnOt4rgQwARiPB+5SNBchvV5HiXiKegx9NGIdqSk89wP3mGEVWyQdpWDazxveXlN//ucAByOWvTsKCGFwdwO3yJ/zUSadoxxitdEAAsWauzUFoo6LAGg/WfuID2TEBiXHYdth/k9/ys+HHACsc9HZvFGYeC1+h4OCQ/YjFXtdiS3Tg9B4wSEMpNRnOs4xkPnx0ju0EJ0ntUbN4BcNQO2t3rebNibCu/fp+scE+TlAO8qFPd/5YttdnhF3mFhBeQDZMgQsw/YRGrOerRnw4zvl4vonJSkM+Dk+Y3AaF9lOcXFghDfmqs+QsI7XqalLpeB3PHiPAkkhTUT6rvUZ+2nEumfLfnDgdtV7hd8tpEZAqYLvBb1KT692fY36zQNNjVPOmxXwK2d0n85nK+C3veumgN/2xl/23p818IuN+K//+q9wHu8v/MLfod/8TSHXF+D3137tl+nYseP0D//h/5qP+3/4D39Ad+/eoX/7b39f9rWQdrATmd+twO8l76e0lFiom/mtB/zCDflJdKpk02fRWDeYX6KeHjcDdBiotKIJzG/j4BdjQNzARPhyHupyuMmedJEqJ+SYig0AEOwwmPRqDAfu2SzrFCjIlUNfn/k+5U2fQY26rZt5gJAdH7CUlkKqBfwifxYlrsBcA/iE02Fm38EoA8xIc3gfhR4wiBHnnsgmBBYnJ9RmzlKGxwVVgF6jp0ehh5y7qNPquE418v+weT1oO0QdhtK83uJ1RekedpPeaPh3sM4Gtb5gw5vJZumQ/XBb3H4RK5RVimXgfq1hFhxMHH7+6fpHZNDrKZlMsUs2r53KwPWQ91j3teI1LnkGmEPIvzmPehNnkFFtole7v1Zw/Y8Wf5h39pb+AnJUAMpK7Yrn85J8Vcx3xLyHBsyD9Eez/x+XVQI7J4Jfdo63HWxKnopn/HjpR+RJbJa1ws9sOht9b+Bv8ZxxaINyXHq1jg9EdOpNczJZAr7x0E/XPmZVhAjuMHfkDZywj9HeGteapb2eT/Nu9+J4+e+MXIal0cUy4VQmTWc6zuZLesk5x3qfLQW/Vz1X2BW/uCFn/7jz5JaPxju9GJsvuQbv3Os9bxV8F9Y7RrmvV8Cv3BF++p6vgN/2rpkCftsbf9l7f9bA73/+z/8PjY/fpX/5L3/AsZWC33/2z/4PGhwcon/6T/+3fNwF5neBfvu3/7XsayHtYKeBX6cTbsOVmV9fykefeD/Ml5dgEySVnl7qfJU6dB1lY2e3W7l+ca3ML8DhR54PCkpYgC86bjtBu0xCCYve3taD30gkRvjTbAOriZJRq6sbbOjGA6ej07SSXGLAxzmt+l7abRrZkgkqV9MYm7z7oXGaiT0R5KmUJb3KQG59F/eEdRg2l7KptYBfSGOnYlNk0hWa6mhIQ4cdhS7fs9FZdnuWbsDBwKlJRY6NMkd4P3KUZaCDPFO8W1wHeQP8YrxgHQFWqzWUhAHTJ+0PktB0Lks9RiFXH7FFj2DFAMKRM3zAemhbTIIwVwD8YtOuPlM/y84vej5m8LsQWuRxIocTIAZGVK/3vFkzI1gtTuLvl2JLdHn9UwpnwkLqgMacZ8PVpGHTMml7d/lvypbgGraMVCyBhDkD1APoFze8h2CM/9vsf2XDsWLwizq8L3W9Uut0Sq7zJbz0o6Uf5k3WxAvwvr3kfo0S2Tg9DI0T6prinQDwPddxflvck3GQwCkQGw1zh4rDremiC13l00PKBQJO0Dgwkjoa4/1CvXMfl6oqPFxDfv/ZjnPbBvLrWTwp+MV3QTJbenBp1dpp1DG65WMfhx4RDt6K5453/PXuN6tK9OsZc6uvBfjFKVQr/p5pdGxKnd9GIyfPfQr4lSeutT5VAb+1Ruopve5ZA79/9+/+LVpfX2fmDC0eF+q3Iu/3+9//RXr06CH9zu/8bn41kfN76NBh+kf/6J9s6wo3Dn515HIBYHnYoKpVrRr4RT/+lI/GQ/colo0yg4TatC6dq+IQ6gW/eBDce8cjd9mZFjmCw+Y9LOcV29MGflcSSzQZnSxge5GrNmIeoT7jALPGkBkH0gFmMvsMA7yJKwd+n0QnKZAKcA6nP+3nTSA291aNjSXWo9bjZZmfWvPE5+JzFMoGeAMNdhW5lPts+0rMp1KZJD2JPuFrwPhCbgzmu8vQXSJn7NR3EsB/M+AXeYLIicRcMWccAiBGey37KZASpPj4pzQHmQ2w1Bo63/lCTUx7M58jb8LLjJSmjMP1Qesh+tz3Ga2lVgilxMRPLMBvl76bTrrGWgrKAAxvBW5yPPBHNPoCCDdrzfyuFIMwAIp7gTt5kznEDjLar/e8WbEuNK753HuZDxqkDe8EzJpgPPazlXcJBxdS2TMOzL478P2yDue1rgEk+n+z9OOSdUXfJ51jNB2dLAHG7FTe83bLDxqKxwypN/KqxQbwy/XJdX30grsyi178HBySwFQO+f2CmZuOdpt3c577Vd/VgjrOohy6XI3pWmMq53VS8LsQm6fpDbMqsU98nlF726Wv/HcJroUp2Aer7wsHSBuyadwLIzC56lS3Ki4K+G1VJL86z1HAb3vXUgG/7Y2/7L0/a+C3OKBS5ndmZpr+/t//H1kC/corr7HbM37/n/7TH9HQ0PYaZTQKfjfZxVaDX8E5t1LObyMvaiPgt1o/Txv4vR26xa6mxQ2HB8dsx7l0FMAsgBM272AFj1qPkVVvIZRhglN2IiEwSTBuAtuLBuYTzB7+GyZU5xznK+YY1wp+eYOpSZEn4WEADDBbrmQRrsNYkTcKYApmcSE2VzZ/1aIxUyQTrUv2DDANKTUAiygRxyYXOabxbJzMGgt1G7vzDBDG8pnnIoMEacM9eyx7ZTfBmY/OUihdamiEeSBvFoZP76/+NH9YhcMPzAvlkfp54z5W7bWv+feo4wvAhJjMRec2nNNVLMPvNLhZGot1LW6PQw9pJjrDrBxY81H7ceowlFd0iPfORmYIBzJi3WrEH4cTz3Wc53cB8/9g7T12JU6kE2TXOui48wSB+W2mYV3/cv7PmVWWNryzMH6C3LlYFoyYv9H7DTZkk7Nd912jKQD+DWaWmV+Vil50vURdxp66u4Z8G58FuCKLn0V/0ksT4UlmuHHEi/rah2xHdizzWez2PBOZoeW4UCYMOeK7TLsKcve3ChIO/+4FblMgFeTPUJ+xj+uBF7PBdQda5hssFhMfAEYimwcjMndZ8niF+d3uiG/dnwJ+27seCvhtb/xl710Bv5s5vwj2lSuXuc7vwsI89faizu+v0vPPX5B9HYo7aB78Fkprm52A0wnwqyI4KbeqyQV+4UiNvNFWtK6uDja7aoUcrZzs+WbwS96kFjfIleHGPBufKWGwTGozjdqPUU9PIfi9G7pTwrThuQDSYBgrtVrBL8BKgPxs1OM2dDHTCgawlo3lo9Aj3txJWyabpl5TH8ubUcsXhleQ/8KpG+VZig1u8JkAw4syS5xvrNbRoGmQRqx7t1xqyEwvrX/KYy1uXYauLU2bWvEOwY0abGRxLjdqeQPowQjtc/8llj0HEkHKEOoQa9i9GqDmdMc5zpFtRYOsNJoRgDgAMOToKF8EgyTkRdpaDP7gdr4U2yi5pbNyOSMwzGLD3NWmHK36PWTXOFrGwoNRvur9nBIZ4WAIwPCE8ySDqTuB2yXvLGLxjd5vVWSya429kA8fIg1pyaIrdZjG7694LtNSfInSuSSXADvmHqVd2trM3WodB64Dw4x3brvymesZm/RagN9oNME572LDZx2fcYy/1jJH0mfi/kbua3QOzd5ntZr4cE4Bv81G8qtzvwJ+27uWCvhtb/xl7/1ZB7+yB7jBDnYi+MVmotaavLVM2263kE6nJY+nNrfnWp4J5vdpAr9T0UlmvqQAEhvkbn0PMzpryVWeNljO/DW5HJ11nS8Bv4vxRVpKLJJWkmeJPMAR017q0Fdm6WoBv+vJFZpNzJFOoyN/wsfO3h16N7OEXUY39RoFV+VidlVcMzi4gjlcTazk80eR+/lC5wXOeURLUIxIn61Y6giABsZbUhCJXEawpyhTJPZdvOnFzy97Pis5GEBscC+Mm+RsWM+HwfsM/fM1lnMprimMviFhf5KYoOsrNyiUCuUNqHBfr7GPnYphKiaCmGY29hOhx1wGR/q+4XkoB3PMeULOMJR9NsAvvgdQ4ziT2TQta8VAUI4LhyVYZzCfMLyCNPYny3/NP5MCLcz/tZ6vN9XtWnyNZmMzlMmmeK1h7gbTNoPGUPJcKCKgUnBbO8hsNFIgUMhSNzWQp+zmcuD3KZtC08NVwG/TIfzKPUABv+1dUgX8tjf+sveugF/ZQ9xQBwL4BXspsWKt4Ukiu7i25m3pZhLMb63gF3m5d8K3uF4o1+407+e6tsVNAL868nhKSyTVMNWylzwN4Fe6NmA37oRuUzQd4RxUsa7loGGQrgeucU1eSJ3xvw59pyD1JTWNOc+WgF+8M2CKPcl1dlRGLV+A6HKxlwZPCn7xc5iQiX80Gi2ROkdfrF3h8S2FlwibfBGAdpu62Syp19JLB10H2b1ZCirw72wslMvSu7Pv0nrcgwpJzMLqNQYasgzRCfcJBq4AwWqNilJJAZiIKmUR1L638B5B0pkGI0QasuosLOkGyHDonMy4IU5gL3ebh/N5j3gOcgkfBR8QDJ3wHiPuyHNF+ZftYIiQh7gYW2Dgj9hhLTs3nKwBfr2qNbo09xmtJFZ4DgCnkIvjT6ehi4bMu0it0tJaYoXg3GvQ6KnfNFB3bizGcd13leLZGI8DucVgKU+7zpJRC+nl9jY5wW+lmcBt+ab/SwpznVw1uXQdnPuNerKNNqgiUJ9Wqi7AewvgC9ltpQazOYNB90yDX5fLxoynlPltdB3kvg8HUvgco0Y1vot3W4ZbUo5MAb9yr9zT93wF/LZ3zRTw2974y967An5lD3FDHew08Otw2Jihqsb8prJJen/9XYrAxXXDdARlb07Zx7jEibTZbBau67qzwa+LotF4S2XPxQcTWGtP0kPBTIDsGjtvxm8Er1E6l6KV5ArLjAU0SMwWAswC3BXLnsXYAthhM46ySZUkyVgbEeAC/JI2R/64n1wmV56pSqczhD9zwTl6Ep6idCpNs/HZ/HlMKpNigIk+nIYOchvcNGjZRbssgxRLQ8qdY0ABgLEcW6Ibnmv5jaJYxhMM2RsDb1AkFWWjG5S+wXx7TL00Yh/OmyyFkkF6Z/qdgrMgbERdBhfPdcS+6Y6NeKKU0tHOwtxRT9xDTwJP2BCn09RJe+x78uViBKCdKwHcAggX5NrF15S7vpFrEIv13AotBJZoMjTBYxLlnphjp85NMAaLZCMF4ArzOGA/VHe9aDwTcuRQOkAmjYVzKivlbjf05VXHTe0Av+KasjxZpW1a6oznoRbyWnwl/76KIcC7CbOlStLjpwX84p15HH5Eq/EVPlw6ah9tmZz6aQG/+F695v2C1Rk4CBQN/c52PNd07XAF/NbxpfGMXKqA3/YutAJ+2xt/2XtXwK/sIW6og0bBL2TEnZ1OajXzWyv4BYv5IDxeArqQu/iGu7CEihzgF4AwGAy3MOfXxc8Kh0tNqepd2FpZedQnfRx9xIAEZlhwehZL9RwwH2QXbbCklcCvdFybIFdbxOgKJWhQ9/ra6jWuQZtIJkmVU1G3vpsOmJEjLKgOwCSjvjJYS08KzK0ABmG2A/YD1zn1LpbwxtNRcsDhe0OwACYMDCXcjmGwVMyyprNJOttxnl1/9TrU+dVxjeZ0JkM9pj4aMgslrGC4NBWe5PrC0mdgQ4pSTt2mnk2NhIoYEI86j+WBfP4ghscFh10hStJntfqaWt4PjH/CP0HTwWkKJoPsZo5DC3EsOFjA3KxaS16qm2fEifjnB50HS4B5MWAvB96lEvVKAL+SjL2WuRVfg3f4SWSSgukgA3yH1kn7HPvI6bDJIntuZIyN3oPyXuvx1dL6utkknXSd5jUt154G8It1g1R8NbHMU8A7AROtt3q/2RKDsEbAL+ph4wAHuerI+4eBmtwNRmVTkamCtAshZcBJYx1nmupeAb9Nhe8rebMCftu7rAr4bW/8Ze9dAb+yh7ihDnYe+LUy4KrG/F72XaTFxGLJnCHB/U739wo2hwC/AOv3Fu/TQnyeWZj9lgN1M1nSzgTwG6FYrDWumV1d2w9+vSkvPQzfL2BWwDJgE3rUNsoSXy6P0tPJBmQwiAIrr9UKABf1cgVWV8tmSgxUkxFajiyTQWUgh8ZFmYzA6k4EJ2guMUNmk4ljxo6fcEE276Eh0zDfi75vBW9ulFDyMQgTa+aihBJ+D+ALAAz2y6qzU7dBqC2MlgF7qe+kL/3X886/4u8wjyHTbi67gzGL4FcAeCoac53mS2GIBQMfMGxwHIaaAE7WANd7rftK6slCgXDAepDMWsu2yJrLfcgFgL0JtItBNz7jX/quc3kqg05Pq9E1iqUgR1azfDxLOTYEO9V5kiZDkxxzAbOr8ocLYNaPuo5ugPmN31YA+M3Ku5tlwG+s3+A1xNqJ75Vdb6UzfWf4gAkHMcVSd5FJx/WV+2/oK7alNyGH93bgS041kDYcYB1zVM6lfhrA7w3fNX5Ppe8P1mLIPExv9L7VdBwF8Buj5Ea6Q7UHToYnaTY6zakP4mfiqON4Po2g2v2N/h5xwGe1uCGV4pXuVxt9LN8H8IvShFAZtaspbs/tinz5fhXw2971UMBve+Mve+8K+JU9xA110Dz49THAaVVzOKwMqrzerc2pUG7nYeRBCeCAnPeNrlLm9wvP5/TQ+0ioScsOnWo6aT/FgKiRtpPBb62sPOIA2TM2dtKmJhWddp7jAwMAW+RMp9NpIVd2wzQK94pyZQHgpmki8JhW42sMFMUauAfNhzjn9pr/C0qqEgTpswh+0adZbabTzrP57iERfRx5REvJJZYmowGcqTbQitvYwwAsno6T2+gmt96dvxd99hh6aCIyQb6kL8+cIC8ZdZoxT5agatQF4Bf3QVKIhvI0waTw7kUyYWZIUdLIpe/g/NVi5mctscp5xih5olWpacg8wkxwseNyI+9Yq+5B/d9b/i/ZRAx5n4lEinO/4VB82H6YJe52vYO7mwg9uu4k8wAAIABJREFUyhuFif3j0AGu24PmXQVDiqdjtJ5c53xhxKcc6C0G5pvXlLLi4rXopNJ91a4JJAN0y3NzkwHdAOg4pDjfe76pfFuMaxMYyytR30rqDmn/bARpAZDP5wg1iw/YDm0pq34awC9YXyg3ihtKU/3Crr/d0McB7+hcbI4Peo71H6VUPFMT+AXje8V7ucS5Hd9tyNtu9oBnq8nc8t8kqHKK+9CrDfSiu7mKEAr4beg1+krfpIDf9i6vAn7bG3/Ze1fAr+whbqiDpxX8oqzM++s/5Y36Zs6vik7ZxmjEUliSJqGL0c+W32OwJm0wMXqj862GNjIAv6FQpGUn6G63i2W4rZA9b4Lf6gcT4UyIJmOPKZkDq6sms95EJ90nOSdX3OwjvjCJQZ1fKeCVxtKf8tFUdIrlgWLDuwW58n7LQbrqv0IpVbIM+DUx0C5u3oyPVhNLDDgXIgvsWOvSu3iMyAEGMzLqOE5miXkQQGw3mGGdk6XL3qSHDzsGTIPUZ+rn+rxwgjZIZM/A1NhUjm6YBaEcz3jwLg9HPCgB83vEPkrriTUC2AUAFvKn1zmHGgyxwAypmJ2GOc35jufrkkhi7CgHBCANk6pW5sYivxfOyzjwAfgF84V+kpkUPdd5nixaaz78YEwfhMY57xvzF0s9camkDXdvzH0i/JjLKuEazBmfpeOOE2Udhxv6YmrwJsjqZ2PTJYcPmVyazvafIW3KkDfoa0Z+Xk7SXo2BLwfqK0njq00f6oy12Boz99I6yIUMNn+CmeWGYgPzFb8Da5GfN3ONFLxXm4v4+5+tvEfTZWojw+39ewM/X+tj8tfNRKbpUfgh5/bj82k06Omw9Sh1arv484tSZvhMIy67TEPs0i22ydBjljsXA1DUBD7f8UJL8rcrTQjfbVe9VwreYXwO91r3lvzdVm9QFPBbb8S++tcr4Le9a6yA3/bGX/beFfAre4gb6kAAv0KNynqaCLDW130loLKe5xRfW09NXpzOw+05mAqSXq2jveb9NGgqZKfw/IfJcTb4KQa/ADWvd77VkPx5p4FfrON6co2Nq7QaDR3s2U/aqDHPyoPtFKTKhTm5AJO4N5AIMCgwqUz8TwHkplkiqrbnaGJ1ilLpNHXpuso6/wL4RjKhkqXHpu2kfYwmIo9pObVAJqMge8bmHbm8yKMFOHbpXAUbTZ0O+ahEOOSYj87SVGSSATpAmSBnzZFD76RuQ3e+zzA/z83AEQCyuPQL5jkOWXMuxmVfMA5IpQ/ZDhfUnUWZmqX4IktnUa+1zzSQl4bDhAaAF4QijLNQIgp5v9JmUpvopOsUDVsKjdcqfS7g6IqNej6ZGFJPy3DdDsuVno8NPmrOwgxJCn4hf3zB/WIJSMfaANiiBJZVY6UuY3dBbr034aF7wbsFAB2xhUHRMcfxZj7+Td8LRcAtyIIlhzB4aI6y9OrwKxQLJ1vqTt/0gMs8oBq7jVvqvUb47KsplRIczuvJQW+W5axVxj4fnqcfzf6Iy0RJ2zn3c3Sm62yJHJ3XtYKBHFQNH698IMxzQ9iiN+gom87Rix0v04Pgff7sgsnF+HA4st92gPpNQp3rmcgMTUcmS9IckIqBsmnlykq18l1Yji3yd14sE+cDxT7jAO2z7m/ooFY6LpsN3++K7LmVa/W0P0sBv+1dQQX8tjf+sveugF/ZQ9xQB42CX8hh3W4ntRP81jrhhcwsfem/wRsfaYO8982ub1Y0idnq+TsN/AJcLiVgzKIjjVZDKi3RgHGQRjuPMuAF84MGwANQK+bi4t8BdCvVPkU90YguSOlkhtKpDOfhAnDuNo0UhGc6+oSC6UDJ5iyby9EJ+0lmB+9F71BEFaJQJEyhdIgZx13GXZShLFk0Fhox7cnfD/Arjhm5d+lckpDviOdg4wkZqyfhpV7UzlWp2OAIhyHYzAKwuo1dtN96kJx6Z8E48b77Mx6Ka2KUjKYZ2FYyCar2fn289iG7S+MQRdrQP+qublV6Rrye4xK4UxI3gHIAyUou2tXGJv095nzN9wWhHq3JaGDmN51Jc+wO2g/X8yi+FnnR/lRp2TCwyc93Xqhrgw6gjTm2Yp7iRMD6Cwy9oEJAH4PmQRrtP/LUG17VvVgbN7RK9twcuy2VumNgpXnq4757dNNzk8KpMBm0Rtpv30cv9m7W6K4soy+MDMzd7nnvsVuytEHZMeYeo/t+wetAarQG5cYr/a/yuwh1yUeLH26Y8Ql/b+C706az0rnu84IlflnwzT/Ng/Ly1xTeW+4a4WcA5Zm8I3ujay+9z2Yz83d/NIryhu1pSs5ve+JeqVcF/LZ3PRTw2974y967An5lD3FDHTQOfjUEqe7TAH6NZj39zdKPKRgL5TfmAB19hj56zvVCQ3Hr7u6kcLh9sudNsykNpSlFHy9/TKFMkFlIbPgA+lBX9cWuC6TJafJyZTC5tTauKRq8QU67XXBGTgmycQBg5EujtrLYADwfhO8xkMUmEgAVMbZq7GTWmFh2uMu2i7QWFX345BMyqQ0slRUbAMpu0zC7u6KJ4DeVSdKtwE0KpYI8L6vWli/3oSUt56IC7IAlQU6r2ND3Xst+ljMXM1dQLUD+5/eHCza/tcZFvO669yozSBnKbMi9Vfw8xH6PZS8dsh+p+khIHOHuKpaowcYQG3yOh2WEpd5iQx7zncAtAcRqTGxwhN/jWmySpe7NxR3j95ORxxRXRymVzJBb52YjoUZYvQfBcZZoF9+LsT9fYz4k3LTno3OUgDGVSsVuvhhPq0DwegKS9DV+F7v03eQ2uzl33eP30UJ4kYPcZ+ytS5pedTHbcAHW9V7gLkvwcZgHQ7iDtsMla9Mq8LsdU8RnCKqDrUqolRuHVMa+FFuiO/5bG+AXbwGRTq+lWDJOg8ZB8qV8RTFSUSqXpBe6XiSrXkgDgMJh3D/Oh2p4Nj7XpzpPkUFr2Ey12cgpx/W1AvNaYigF5c3Iz4ufA88F/B2ANBbR9K3eUmq1jH+raxTw22wEW3u/An5bG896n6aA33oj9pRdr4DfnblgOw/8WthkqZrhVT3RtFrNFFNF6cOZjyiYCrD5SZe+h045TjdsTNTd3cH5ua1yzayU81vsqizWzBU3etjIPPZO0M8W3t8IiQDANFo12chOx20nqUsiDa4nbsiZnYxOlAW/I6a9XG9XbJAQ3g+Nkz/tZ9CLjSsk0qgpjI0smklrpPMD52li7QlpaZORAcsJMGRQ62jYtIecOhfp9QbKqbL0KPSAWZjV5CqvWzaX4Zxeo8bMeXowanpvGbnfm/We0RdiYNFa6CX3qyUSxVaAX8hrL3su0mRkkhlpbK8xZ+QN7jbvpgP2w9Sh76ga7nAqxHVNMX/In3GwACCMOR5zniTHxmEATKs+WH1fcKBWCWuM6/Za9pJqIxcX/Q+YBtgNu1wDkw5DuVAo2lSqAgD7bT+kxYWHDZCaH64B8AOsQ34uNQXjgxKtlUastUnFqwZ24wLEyZfycm6s2pijOyt3KZcTVBD43X7bfj5AeRobxn9p/VMGcuKhAT4fyHEfcxWWxHmawG8r1gLv06drH/FhnNj0ei1pszp2l38cEkq8SRsOEp7vfLHA/R4xht8A3tVKChGkb8xEn7CBHA6lhi0jXP9b2prJL69Hoo4+K/XVaH558XoUg3Hhs8T/X8VFXbgmkUhTItE6k8xWvC/P8jMU8Nve1VfAb3vjL3vvCviVPcQNddA8+PWzjLZVDewMwInHs7Xbcz39AfwajQZmqbEpAlBphPGS9imA3xhFo4IjcbMNEnLRTEoEuGB3xXFCliy6KovXCTm5ObodvMnAU7sh8cM9kD7n0jl63f2NhnKaMR8wHndDt8nlcBQwv2CEUQpJNEpCXi4YYpSQQm4lNpHYcy4k51nOLDbkFxsNBnLmOpmJgUkWWMxIJkJGlZHBIp5pVBvpoOMIrafW2PUUG/tENs4mV4JMVktjzlN5h+J3l39KsQzq8gqlbcRmVBnoaz1vlmxyGwG/+Jwsxhe5xBIoHk9ijTLZDI99LbHGjDvmBObttOssDVsLZeGV3g8897P1T2klsUpqsSjwRl3kr/W8kZdtQ2K9EIVplQDc0B/WBxLrHlMPl2OB+RvA8yHbETrqGC1hUVsFftE/SkHhDwyAYCgEkA6Zdy0u12DlkIdczPKCebNrnXyQARazU++mHlNvwx+thegcXfF+Tv6kn1RqFek0WsKhjVSxgPif7TjXMsa50mDxmXgSnqIgy8VVnM8NY7RmmG6w25+tXyyR9gJgvN79JhkkAOxZA79YB6z7veBt9hZgBYDNTaPWE6TOauiK5zJ/T+UPEXNZ9h0YddaXsw5FCtyZsY7ioRT6HnOeIaO2EAA3/CK38MZi2XM9MnYMo1EXdum98XiKYrHCvO4WTlF5VJ0RUMBvnQFr8eUK+G1xQHfa4xTwu9NWZHM8mUz9+T8AaGArPR5/3kSlFTMUwK+On9uqBvBrMhlobc3XqkdSo+BXrIsrAlyxRu4myBXq4koBLv5dKl8rnsSt4Jc0HXuSB2DYoYD51Wa19O3u7zU15/HQPdKYiZKJFMuecXgAduOIbTT/XDgog/mVHihgwzkXn+G6u4LoEI6zalJrVeQgF83H5klDamYyUcoI8ztgOUBGrYk3pYOW3WxMhZI8xQ2Aa9S+KWeGM+pibIEvy8cxm6FB8xADm+Km02kI70Q9sufxwF1aSSwzwEcMZqLTDNhTBNC7ySTatHbOLwXzVusBy+W1S7SaXGFgj6ZRa8mhcVCXqYsdrdF+svRjZvjQEB+UgWKWWKXnAwNfysMydxyAAIzuMg/R17rfKBhDK8GvOA4w1wBZxUzXVi8dDMxQGqq4zUbxvrjzzBtMiLoMPbTbUn85MhwO/MX8nzGQ5rFSlvPELRobHZLkOeO6I/YjLTMXqzTv237I1aN5sIs1hMJBOpZ6P6iPQw/pQehB6SFCNkUX3C8XKDMAflHbGrXJn6WG7xUcsOFgqc/dzQeWMP2CcgM1zuE9gM8LVAsHbAfrPozAuqJ8mvSzjj5xkFdL2sN2rwXAL/4+Qb3rdjVk3tSRfdOuYT4z/Srgt71LrYDf9sZf9t4V8Ct7iBvuoBHwC1ayq6v14Ndms/Am7WkAv5FIjPCnuOF0vNBVWXBYhttqeZCbJovFxPVXUT6p3nY/PM45f/60T5DgqlRkM1qpS9NNJ22n631cwfXYpPv167QUWOZNo03roBHzSAHDByC4hvxKCXMJ0DoTm86zzmAotRotJSjOjDSclNO5DINf1PqF5NWkMVOvsY/7t+rtZNfZuBxJMZsIqCk1agok/XTdd5UZHuTfwmTLrrXRq91f53uRn4r+Og0dnGdcL/iNpaP0uffyZt3gXJZZT8i5MS8pe2fT2hhIoW5wrYDwc89nDGhF8CvKMVFf+JRrjOPx0eoHDPARY8wF8lYw7pgPQCKAMEA4WE3MGcZfr3S9VlCbt9Xgt9EXK5IO00RoooCRB0gFq9634bYrPhvzPOkaq4lRlo4HebCfey5tyoEBfnMpUuVUdNg+ml8b5JQfc57gnGO5Gtzo7wVul+QXp7JpOu0607BzMHLAL65/XOJIrMoRvd77VoFM91kFv9I1dblsefDbqrW+6rlCaSpVPhnVpvxnt1V9teI5CvhtRRS/Ws9QwG9711MBv+2Nv+y9K+BX9hA33MFXHfwCWJrNxpYzvwC+MA6Rsrhijq64GMWuyiKjW8zkdnY6+VmNgF/IX78MXeONPVjUSDZMGp2aRk3HqVff3/B7Id7Y0WWnqbVpQkWs4pJEuAYs5O3QTdKoNnNA4+kYTcUnSU/Cz5BmCcY4o0qTRWWhZC5F2PzHM4JDs0PnFBhL0xCzwC5jB+2yDHHOr2ACJbCrAHn9poGCfFpc/yQyxTVCweSBPTzhOkUAWde8X9B6cp3BJfrZbR6m8z3nyWV3lDC/eA5kqSiZhHzaToObQddidJ4ehh8WALCl2CKh7BGA6qZZVZZ2mXdzbvLZjvM1gxqYWAWShU7ZbMhm6ufyJmiQCX+4+jOeHyTgyJMGMATDhPkJDeBXx7JxAPK91n10rgPOtELbKeAXY5mNTBPAG/KGMVcc3hjUpQwyDkeOO0/yu1NPu+m7QTd81zYPZDbKZmGNUbMYBy1o4MlPd5yt59F1X4t3Bax2icwb6QP2US7Z1WjDwQmcrdUbed+Q4kNOfcJ5suCRcoFfxBP/a0a+3ejc67kPXx9OZ+vB703flxTLRguGgpiIaQD1jHE7rlXA73ZE+enqQwG/7V0vBfy2N/6y966AX9lD3HAHjYFfNXV1dbRc9iwH89ss+AXwKga4YKdFQMYlKbg2rlA2SPyTyaQljppbL08z4BdPhnzvbvAWLSaWSKfWkNviplyKaMg4zICy0bYUXyK/bp0S8SQlUynSqXR00HKYzFoBPIhtMb5A8/E5BmDZbIa8KQ+BBV1JrFAilyCwUZBj73XtofWIh4Ej4GwKrGU2TZ26Tn4myjMB4B5xjJJJJ0igkSMKySgkvZ26LrLrC1k6uD0D6G6a/mR5nMvxZVpJLudlyTzWHNGoa5ReHHqhAPxiDVG3F6wqAC0AGTb1w5ZhSmZTDKKlBjkAoUuxBR4fgAf6BuOLwwEwvqdctTPufHjhv8aMNZ6DZ4JRRu6wtM+1+BrdDd4m5HpifF16N0UyUULskWsN5hdMMIx5AIzPdDzHJZfE1gj4RT9gnJFvjbEMmYbIVLT2jb5bkIv6EgDAWq7TPRWZIq3kAAXPbZT5xfvy5/N/xlJnbkA/qhypc2raazlAqPwFAAxn5OJ32Zf00oPQfb7tsO1oSbmseucL5cBN1B5G/TFJwwHGGdeZphynsT6QP3PaAampz9hPI5bNkmFid60Gv/i8oHwWPjNQIMClfcx5mgbMQp3cndbkAr8wohsP3uFUBbHhAAKKDdETYSfFwm43UyqlyJ530pq0eywK+G3vCijgt73xl713BfzKHuKGO2gG/MKVGYxlqxrAr8Ggo/X11uX8CuDXRGtr3i2HuQlyBZmymJ8LuTKaAHIFcKvX6ymRSLDsGf/dbGsW/GITfMX/OaM7gByTych5XXBKfs75fIGDaa1jhdnUtcBVsphNpM8aSJcTShuBrTtmKzWGASAE6EWckO8LYAYQksgl2Q4UwKbD4iRv1MfuxmKuLDbPepWeWVnkCPcY+qjT5M7X+d1qvLgX+bgi+wpAAaYNDr/hZIhUajWZNea8KRB+32Pspp/b922KhpL5XGpPwsMMGpjeQMrPpZUAfrGBPdNxjsAkcn7ghqkW4u3QOsimt3OpJQBO3Iu+jtqP1WR2g7FDpg5WE33NRqcplkmQTWtluXIl8yixvA3KBGE8cIsGOLVqLQzEEX/IyL/V/52CZ9QLfjFHmPlAkoyx8PtPWTpiO0odhupO1pXWjc3DYvPMWGO8OPDA8yBVxuGDGGPIubuNPTRkrj/nF33fDdymm/4vKZlJsPLAaXDQ8x0XqEMnGK5JDaHEsd7236TbgVuUkeRfn3KeZgOxZtrD0APyJTx5kIQ1xHs+UqMxWjN9495Wg18cBt0O3CwYFpj77/R/nx3Pd1qTC/xiniiJNBud5RQOHHztNo+QQy+UbNtpTQG/O21F2j8eBfy2dw0U8Nve+MveuwJ+ZQ9xwx3sLPBrJtQilBP8AgTAaKqYzYUhkwhyAWjLSZbFIIP1htNzuZzfRhaiWfALwAbjK+R8FoPfPea91G8cqGtYC/EFGg/dZYkt1iORSpBFZWWTHuROnrSPVSz9gVzYz32XBddeSR4w5+OqMwyiAZRhRAOWBMwfGKsT1lNcGimSCXN+MOSgyAHeyjgKIHVSUicXubh4Lp6PslYAlZwDrbXzP5kxNXbRt/d+i+JhMPM5dk0eD9xhhjqeSeRrJSNgGN8u8y5mCAEy/UkcoKhYbrx/wyAHz+Q6xCotl1eqZnSFPh+Exmk+NifMX6WjYcse2mfbV/Ma4RlwmQZ7alabaCmxRAuxeWaNUSbpjOs5sug2nbbxYK5V6qy91BEOEQDsUV9Z2vRqA43VwWxL78W47wRuE8poZSlL0VSEgekx+zEasgzTfGx2QxWgok5DF/UYe2qOSbkLcfjyMPiADFojnRk4RfFIilUa5RoOE/5y4c8ZxEgbQN3PD/5iQwdI4nMw75X4Msu74UreZehquARZIwFpNfj97/N/wp+v4rWFydOL7pcaGaKs94jvvmh4JWtnO/jhCvjdwYvTpqEp4LdNgd/oVgG/7Y2/7L0r4Ff2EDfcwU4Cv0JZotaAX4BZAFzk+wLAwbAJ/10McgWZ8qZkGaC3WoPZF2r8thL8plKpht1YIfu9HrjGG3Qp+E2mk3TIeriujTYA1HX/NYpkQhTOhLk8Eccnk6E+Qz/nFZ+yj20JBj5a/xnXxyyoUalWkdPkIG/EyxJfNMiTewxCORvkqgoyYjUbhqHOL0ArchgrNUim7wXvsKQUBlqT4ceCdDibYVAby8YItZBB2KIcEIDcqOMYvbn3DZY9g/Gdj84wqxtLx2k9ucYHCHkDJHZu7aQ9lr3kaoLtlI5/MjSxUZpqUyqJeUA22m+u75Ci2nsq/X294Pd+8D4FNhympc/BGr3QeaEqyC83NtQyvum/QZF0lA85kOeNhkOKr3e/Th2S2tH1zK3atVBvwEkebseVwC9qv15a/6QkfxXz/XrPmw0z0NXGth2/N5n07KLfCrdnAPn/NvtfKZ4tNfvDIc7Xul/fjinV1YcCfoVwKeC3rtfmmbhYAb/tXWYF/LY3/rL3roBf2UPccAeNgF8ASJT7abXsWVqTt9YJiSC3kM0tBLl4VjyeKCghVGkTXEu/AL+QFYfDhWYntdxb7howv82AX2xIbwSvsYwWG33IngHOYYJ11vncloY0MKyajc9SOptiZncpvsiyZeQQoo6ty+QkyqoolU6TS+tgRk5a6qh4PmBC7wbvcL4tarcC5+rUWuoyuqnb3k2+UIAS6QSPSXRETmXSfCih28iLBPhFbVaMCQZFoqy5XOxQz1UwtcrSk8gkjxs5swaNno2wIKUFK6tVaTgndsA8QN/f/z0KBsD43gWRy/Lw5cQSAZwhP9SqtfFY4LgMGSfyeRuV3xaP+aNVHAyUlhpBTeSXul5p9BWqel+94Bes73J8qeTdQXyf69w00qraseSCx6HHNBed4UMGGIOJLZ3N0LBlhA26qjHn9fQnXlsL+F2ILdD7yz/dqGVa2Mvbfd+u6wCp1jHi0AMHNoF0gA8CcBAEyXsrYoDvBNTSxuex39FLBr2hZvCLe7caw4+X3mEmW9pwz/nOF7je805rm+A3yjmvz2rDARD+nonFNnLh2xAIpdRRG4K+RZcK+G3veijgt73xl713BfzKHuKGO3hawC82sMUAFyAJTCdaNouc3ELTKTCWBoOBrFYTra5unfNbTwBrAb/If52LzbK0FrLebn1PxQ1lZ6eDmelmmBkA3/uRexTNhslgNJAmpaP9pgNbGq94kh66F77LhlTYIMI4B6ZAAITI80zmkpSiJJnUZspkszRgGKAT9lMVnYwh5fWkPOyaDOCMuqrIPwV4ValztMc9QrmommYi02yUg46MagPZNQ4u1YR+IYXNqLNk1lp4DPus+8iqs3EckduK+1x6VwEgRjkj5N49DN1nGbVJbWJ5qVCSKM7mRgCyZq3gGnyq9yQ5skKeqQisUSrpSXiKWUjk8LoNXWzaBUYZY3DqXfW8IhWvfXf5Jyz5LW6IA8ozydXqBb+Qjl/1flEoXUcNZtMg7baMNDRMSKlhlCSU5Np8BJQAA6YBGnOdLVsiCmNB3iyktljfXaZd1GeqnSWvBfzi/Xpn8YcsbZea2cH1+1t932kJIC0Gi8idjRbV/0Wd6H3WAw3FV7wJ7uGQ1gufMSKT3kTH3KNkSBUa1RWPB5L0udgMm7yhXNgxx0nqNnaXjAUmdB+uvs85/YgVYtdr6qdv9H6z7rJUTU20xpsV8CsECuAXHh3xuAJ+a3x1vvKXKeC3vUusgN/2xl/23hXwK3uIG+6gMfCrou7uTtmYX5T8Kc7JFTekkLFKXZVFyTJ+Xq7B7Gq7we9UdIImIhNs6oRxQzo5aByko7byrEhHh4PnFAyGG15H8caMKkMA05FAnAF1pYYN69XAF/kcRxgMcb1eUjFLCjMi/DvKJpnJQla1jU47K5eFAROE3FONWs2bYW/Ky9LWTp2bJc1Oo5PODZ0hz3qAZdSQRYP9BUsLdvbLwHXuH2wxfg6QY9c66JXu13gKE+FHDETBkAE8Ih9UrAuM36O80lXfFzQfm2fGG3OAbBsSZpRSEhsOS3Y7h2hEt59NkaTGUnBvBVvm0DrZVRqsGXI+D9uPtAz8fLZ+iU21pMwaXI17DX001nGm6fWv9IB6wS+eA1fuqcgk529DWg5mcsjSmAEVnod4/mzlXS5zhPdE+Jlg0AV5+ZjrTAnLj8/OZ+sX8zWscQ9+dsB2kNlitGpMZTgbIm9ujWKxOPUaBrgUTbmG+X7m+ZRzkoXc7k56yf1KiSN0KxZpNb6al+xLn4eEgBfdFxoGkfy59l3hWItNp9Py+3bCWrlmMszNHoXu86GR2KCYeKvn7bLzj6TCbA6Gz3GvsZfz4ndqyaNG3v1WrPFOe4YCfnfairR/PAr4be8aKOC3vfGXvXcF/Moe4oY7aAb8+nwBSiQac3vWaERHZfxz04CqPMjdZHQrgdxKAUDOL+TUrWR+3W4Xy6jLyZ5RqucT74f5vFZxXNiMXnC9XHYj2Urwi7iCmfZ4/FuCX4zzM+/F/IY1mUmSJ73Om24wp/gn5Lk6nYZZ6+PmU1vKj++H7lGaCsE2GDsQfIetR8lmtBHk3WtrPjYTk7ZoOkKfej9mthj9QvIMgGvT2GjUcZxNrDBeaYPx1iHrITJqTQx+rvuu8j2QMAfTfkpns2wIBcfgQnCRpbP9Z8iZddNseIbLMUWGEniqAAAgAElEQVQzYa47DNBMlKVe0wCXD0IJlwHTYEs39eFUmHNLAeAE+jNHepWBnu98scSkquEPdZkbdwoAQC72+6vvUSqb4MMVOGoLpa4sdMRxtGTkUAmA9S12v4aUHk7MM9FpiqZjDKZR4xmu4dKDBZTjwTVmo5GSCaG+9G7zbhqx7q0YXry3aFvJ7cWbsY7T4ScUzoRo2LynZqdflChCOa7ils6l6LmOFxoG3GDHAWSlYwf4xWdjt35PWSYXY/irxXdK8njxudpr2SfroUwr3/FKz9op7/52zHWrPhTw2+4V2Hn9K+C3vWuigN/2xl/23hXwK3uIG+6gEfCLzURPTyf5fEFKJLaWUBUzuCLoLQW5aRJ/B+AGGXMrmgB+LbS6CjanNW0r8AvWCGWHijfOAL/7LQdoxLynZBDtAL8Yz2XfJZb5orGDcHKV/xvMmEvXyT83mLXsxJxJbL0e90J3ysp5DSoDHbAeImzAK4FfyMNnogAQAKEpzv81aAwEZ2FIxjOULqkBi/E69U7OkQRjC/BdHHM4IkPSLZa1wZxteiu9tedNzvmNp+J0w3+NjbHwPINaTw6ti/rN/QS5q1wNAHAqPMEln+xaOw1b99QNdiAdngg/plA6yCASDtR7rfsrAvWdBADgjo26vsg3F92491sPFNQ1FmN/x3+LVhOrZUEinK1hUCYFom59d76EEHLGP/Nc4hrReoOOwS+YZhygPe9+sSZwu9U7AAb/k7WPmRnHwQnmgjq7KI9VrYl1m4vr/+K+F5pgfhsBv3j3f7jw5wRH9uIGp3iM52luO+ndb2ccFfDbzujvzL4V8NvedVHAb3vjL3vvCviVPcQNd9Aq8CvWxd0EuxoGsyLIhcGU1FVZ/HdsvMQm1OQ1MjvYqobnoX7wysr2gF8Am4u+j1k2LG2QFY/Zz3KZk+LWWvCrJpRiqsb8YgyPIg/Z6El03cUmPpgO0pBxN9ckBXg40neIetR9bKC1VZuOPqFgOlAk581Sl76bSy2J4Hd93VdSG3k2OkNzMN3KCYwb6vMKuYRZZl7B8qIkkLThvUHu74B5kM13UPIIUmlpA5DuMfSQP+VnyTTA0iHnIXI57BQIhGkhsiCU1pEmoLLgVUX7bPtb9Qq2/Dki0w3DpPwhEmoP6x10xF6+Ju1OBADIScW7V7xu0oBVYn6RbwoZdnEpJhxwnHad5bhAWjwevEt6ja4A/CJuKMsDuW4z7Wcr7xHqYUvfHxweveJ+jXpMWz9bXEPkmm/WNkZprSHaa6297FXx+CvLnolOWE9XlFO/v/IuS/6lDbEEu77f1lwOcjMxbsW9O/Hdb8W86n2Gw2FhpZaS81tv5L661yvgt71rq4Df9sZf9t4V8Ct7iBvuoB7wC4wAQAsg43DY2DwDLB0MZTZBLkoHiX825cpSkFtpsAL4NdHaWuvMqbYb/GJuV/1XyJfy5Te1mLtJY6IXXS+VzR0F+IUUGICs2Ya1APitxYkb45qIPqK1xCqlKUNWjZV2GXZROBth4Nlt6KW9A7sJOdjVwC9kyQ/DDzhnFxJVOPiaNSbabznIcdgK/AJwPw4/pEDavyF7FvIOAXgP2g8RZNHIC5aCDABb5OJigw4A8iB0n6w6awHzifmBhZNKZnE4Y7OZOdbToRk2xCpukIgetB1qdimavh/jBzuMgxNIsEWQtJ5YYylwMWuINTjb8VzZGsxPCwAAMF2IzrNkvcfYx+z+ZQ9yfpOb3zG5DFm1FnbvLm6QNZ/pOMtrHkwFWQ4P528p84s+TjrH+NmNNjzjncW/YJdxacOaoTzXc53PV300JNNw1cbhDA4B+kx91F+HkVelDqSGVzjIMemNNOo+SoZUYe1n6f04QEJuNd4hvCv4XHXqO+m17tdbKvuvGhQZLnha3n0Zpl7wSAX8yh3hp+/5Cvht75op4Le98Ze9dwX8yh7ihjvIZCBbLpS0YrNQyOBu5uRKO4KhEgCwlNGtBeRuJ/hF2R/IvVrL/DpZ7h0KlS91hE3t3dAdguMzy3N1TjpqPVbRJbld4FdcB3HNihlQ/B7ydin4jaVjlMwluBxQcR6mYHTlYQdb5Osa1SZaS63yhtpldNGBvr3k9cDwqlReuRCbp9nYLMWyYcqqkAerp722AzRiGeH82KnIBNeHFUCxlksWIU/Zl/RyKSPIzSGPRawhmcY9eyz7qLeIgZOC39XoKpdJKp4Hnj9iLZWnN/whq/NGsIGzkWk27wIoQrwRwz7jAAMrHAbAObl4vcCkjrlOl3X4fhoAAOpVf7T2AfmTfgZfAG59xj664H6FVQoAszAyGzQPsVEapOPFrDHW7rjzJEdcZFdxwGEw6ln2DNdyyODPus41ZWKG9+udxb8kgOBi8It3B4cQ7WyYu1DqKLdR6gh1frcuzRZJRbgGdTKbYIXKHuu+ho232jn34r5hcudwWPn7utx3z04aq5xjUcCvnNF9Op+tgN/2rpsCftsbf9l7V8Cv7CFuqINQKEQzMxO0tLRAFy68RD093Qx6we6KrRyLm81m2O3Z7w+2VEIF5hd/WmlOJR/4TTEobEVrN/gV54ANPfIwkWsr5s9incPhCIUiEWZnkZcLaScAIxyKIWku10LpEE1FJ0lDGxJmVY66nW7qyw1SpRrLyGNFzVO9Vk8dhg7SaQqlzvg9wIZJa2LgCydiaZ4vwB8Y9wHjAPWZ+suWzZGC33Qmw7WAwSJjPgAMWYL0dDdZtJVZslaseaVnoBbucnyRlmNLDHgh+UbetJZjkaMhE8ZmI19qvUQKjmcCdJVz3X0awC9qIKM8lXT8eCePO07SSddYCci8H7pPyB+G+RXWTnSBlpalwnvyKPKAohTmgzqb1kGHbIdLDqJwmIADFpirwZ18t3mEdlmGtlzqS2ufEg5tEFu8Q3j/dCod/Vz/d8muL+8oLee7U+nZJpOBjeuqgd92jG07+lTArxBlBfxux9v2dPWhgN/2rpcCftsbf9l7V8Cv7CGuuQMA3t/8zf+L7t8fJ49nne+DdPl3f/f36KWXXiqQLEOKK0nJLeijt9f9lIBfA9ntVhmY39aBX5fLziY8UtkzpJCzsWlmwHoMfdRr6K2JqapH9ixd0NnYDC0lFjnPF/nKcM7dbz5APT1uBr+3125z+SJs7kXGEUD0oPUQ2bT2kvfvQfg+y3XFhnHpzTrqyHSRUyPUzAVYwTMBHFw6J9fjRdPp9Pn6zZVebIBDMIHF7CfyhlEntZJTrxT8wlQN8UWN4GgmRjo15t1Fes2miVLNH6wWXAgDpdv+W/wk1EyGiRLYULCbVp2dpehug5vBr0lt5IMCUfqMecNtGDnQ5dpOB78Ar382/yfsAl3cUG/5OwM/X/Jz3LOeXCN/0scHAZUOPPDuWW0mNjkrp0yBjPyG73oB6MZ7ccxxbMt6wnhvL659Qk+iU1yKCWuB0lpwGH+h80JFpUcLXpW6HqGAX4X5FcFvPJ6qalJZ18tV58WoSFihKmGdT1Iub0UEFPDbiig2/gwF/DYeu6fiTgX87pxlikYj9Pu//7vkcDhpZGQP7d49RAMDA2QwGOoapAB+Q1zyp1VNjpq82PghP3l5WQD6rWhwLQaL1Crmtxj8ojQL2CpIXgFaYNYE+ecx24mqw8dBRnd3bTm/4sM8yTWWlUI+LDZs/vsM/XR2eIwerU3QpZWLghRVpSaL2kwuXQecoZhJ22suLBsDgHE7dIslqmJTA4BYzERxNe0y7CYYgz2JTTGbiXmyNFrXSYOmwRLwC5CM2rCohwtXY61aR3DLhdS5mOUEeD9gP1RRrlkMfqsGdBsvQB4vgDjYXuS9It7xrOCIjJrHaJ2GTnLqXNRv7GdTMjhaqyEPNg0w8KrUngbw+9/n/piSuVL3eEjsv1sG/ErninduMb5IwZSf+o2DBSWHAH6R+hAIRPiQqbhd837BebfFDaC7Wu4ucnbvBIQDC4BfSLXRuow9dLYG1+dy6yUoELItkxwr4FcBv3jPIP2G2VW1Cg1yfuUp4FfO6Nb/bAX81h+zVt6hgN9WRnMHPutZBL/Xr1+lP/zDf0czM9NkNBrptde+Tr/8y79KBoOR7t27S//m3/wrmp6eIqfTRb/0S/8zffv/Z+89oyTJzuvAm9678qa7urraezc9fqYxM4QbOAIUSawOj0SBosSjFVdcCnsOqEPsiiRoRIIrgEfY/cEfBERCAkQsCQ1mBhgDYHyPae+9qS5v03uz535RkZWuqjKzMjurp+M1Gt1TGRHvxfcist999373+/QvtmTmKuX8VjOQ5oDfxtfkvXfAbw6BQEjYUNa8JdArbPz5Q95HlwU4PL4e8Hs+dA6RTLnZFtm0h9Y/gGMTxzESHskv7LPIwWVwwmPySm7vJke5M/K50Jn88TIug16cvM1JG3rNfSKhLi2tksqlMWTbBK/Nl2d+afg0HLkNnS4HnYDkDHqsveL0fDV0pQggEDRYjVYM2Dcs+QivZfDL0kXTcaWsD/N9CeSljI5OB4/RK3LzIecmAcVbXdtXfBYKg7DWwS/H+vPJV5U85wL3bd4rnZmXy6GNp2N4Z/YtUGpP+Tqfz35bPw56H5DYrQR+j868LTWmS5vVYJF84+XauzPvyCYM5dKJbELYeb3eIJsVn+r7zJKn8n0m6KZLeiabRqe5W0oK8b9nElNipMUayNvdO6T02GqaBn418LsIfhPi+NyqpoHfVkW+cr8a+G3tfGjgt7Xxb3rv9xv4nZ+fxy/90qfx5S9/BZ/4xKcwNzeH3/3d/xVHjjyNX/7l/wVf/OLn8Ru/8a/xuc99AadPn8Tv/d6X8Y1vfAs7d1YuU9LMCVoN+CVYi8Uayfxq4JcL+Hfm3xR5cWEjsGMu6lbHtmUfh3rAL4EqTapKG4HE+rZ+TIdnMR4ZE2mnKjMmW9th6cQW+7aKi/PR+CjIKKtmUgS/VpsZg4ZNyGZyuBg+VzFn1WlwYaN7k4Bf3vOV0CVhmAsbHX23u7YjkU1Kfiz/JONGsEDmuFLOq3r+3QK/BDeUZdPl22qwVvUKE8Qd838gDCLdracSkyJ7Juink3WnpUvqAjMne5entu+KewH8prJJ/Hj8BVD+DR3vWifyezoO08Rsqfbu7FEBjIUS+Ew2I+ZfNMdaCfyeDZyRHOuikkW5nEjMD/gOLTt378+9izuRYYWhh+JSzsbnnmz1UnP/04mXcT1yrejalOpz46bQxItpJ493PCFqh3obwS+f+6UM+uq97r1ynpbzq8yUwvxq4PdeeW7vxjg18Hs3orx0Hxr4bW38m977/QZ+GVDKi+12hyzgb968jv/wH/4P/PIvfxFmswXf/e5/xfe+9w/5uH/963+KeDyO3//9P2j6XJR2sPbArwNTU42ryWu1WuD1KrJnsjNsBCSrac2QPdME6sT4Sam7eyc2DLPeJOyRCh4Jpna59ixpMKXeTyH4HQmNyPWYD2o3OLDRtqkiiBiNj+BO7HZRbWKljm6blAWaj/oRT8QxlZyWUkbMRWXb694vBkyVGs9nDvF8ahbpXBZOkx17+/YgEUojmUrhQug8jPriWsiKM7YPA65BAb+JTBxXQlekTmthU8qwdKLL2iU/TmVSwr4RwHNkzI9lmZZK7tUrgV+OIZCahz8ZEFlxl6Wr5tzNS8GLAtrJJpp0Zqkn+0jHY1XJWKfik7gZuSksIqEUAZVFb5axENRTirvBsbGqaxXG7F4Avxwv4z8SHcZschY0rmLd21I37tLn7SfjLyKDYtdlHtNh7hTZ8krgl5s6LKeUlHx3vTDrlOw/0v4YbEYlD32pxhJBr068JOkAaqNouc3Ujh2enRh0lLuG01Tuu7f/DplcqugZjWcS6LP0ocPWIcZuUp86mxZlxaE2hcWup2ngVy9mT/e727PX65TNao35rect+nCeo4Hf1s6rBn5bG/+m934/gl81qJ///LOYnp7Cvn0H8PWv/xX++q//H0xOTuCP//gv8nH/wQ++h+effw7f/vZ/a/pclHaw1sCvy9XYskQEv7Bl8MLVF/N5fZQRHvY8XLeckOA3lUohGGyM2zNzfk/OnMRN/y0YdHox8SHzx0U/F/CsRGU2WKRO8HKsJudWBb9nRy/ganBRFkxQwQX6Ic8DZfVh+dnFyHmJjwEGkSPbDXbsdu1FxpHA5ZkryKaVclgcFxk6spDbnTurfl4JOjs6fJiZ8UtprNvRmwIOC+9HMdDaAbvFIeCX7rkEkap5FUFuMBUQeWuHuQPt1g5hBjnuWCZasFGQhdPorFgzdSXwy/I5inx2wUEYGWy0DxXlkC5308xFfm36p2VgfcixCYfbqyt/w/ngRg0ZQLO+MeZbjQK/3IThPHFc9YKxqh+aKg98eeLHkidd2BhDyuOlzvMKOb88jxLzG5EbUl+Yzz7L/FQb+5cmXpScdNUsjrnX/bb1cBmd2OzaWnYXNNj6/+78D3lX84A5B6k5zQ2vLmsnJuITCzn2OlFIbHFuxcPtj9YVcw38KuCXbtc0caRR2VhsVNII+ukxsApWvcpHdE0cpoHfNTENa2oQGvht7XRo4Le18W967/cz+E0k4qDD8h/8we8L69vZ2SnA6atf/cN83J9//of427/9Nr7//R82fS4aBX5Z/zUYDDdc9txo8GuxmPHyzI8xFZrOLxy5MHaZXPh4x7N1LSbb2z1gjeNGgV+Xx45XRl8WEy02jm8+NYdYNga3wSOL+F3O3bBWwVgTNLI80U+uvYREutg8iIxWn6UfG+yDFZ8zAsv59DxcBjd8Jp/EprPThw/GjmEmMitgjMZUlGTvcOyqyRW5FPwSRJFtltJJuZwsQDk2t8lTZHjF0j9kyAhKCDBCqZBSO9niU8yvYEAql4HHXGz2xAXuZueWMrnocuA3kPSLcy/BRmHjhsQO966q3s2js2+LDLYUGHLuWAKnVW214Jcxvxa+gpnErOSjU4Y8YB/Ms++tui/2e2r+hKglikskZcVxmdLlasDvasZ/IXgBCcn5TUrZJcrWuUFA1prOz6WN5Zf+7vZ3ythqMtDdlm7EcwnZYFK/C/hO8Bl8uP2xihs6K41dA78q+I1gLDyGK+HLeT8CAmBuLPTYelcK4z3/uQZ+7/kpbPgNaOC34SGt6YIa+K0pXPfewfcz+FVn68KFc/hX/+rX8U/+yRcxMzOFr33tz/MTSeb3hReew9/8zb3D/CrgN4JYLN6wB7IZNXmD8OPV6ZfzzKU6WLqpPuE7gl5rX83jZ11e1j8m+G9EMzkN+PnYT8vGKOyVpU/KCVXbCH47On14/uoLyJUb24px0nbnjmovJ87RkUgMY/4J+FPzwop5jB5MJieFbaPhFdnplVhAFfzOzvpl44CNYJx1eSkD9Rp9cJgcch1uEpEUY75jKpPA9fANMQJKpuNIIyMbFya9Wc6n6VYkHSoDBQQf6+zrJQ+4sC0HfmmsFUwHy2JDIL3Hs7coF3OpALL0zXh8rOxjsogrORZXPSl1HLga8OtP+oXNJiNuN9gk1mTGGeN93gMtq4mshkExj/oA04lJ2Zxhnd5Nzi3YssC61gN+CURZA5pSesnFtQ3AY/ZWjDxN2ejUzRxlAnDmpLNONOsJL/VevDzxE9yK3FTS2XUAa05TOW3RWRHLRuVZowqD+d183tnW2dYLk11r08CvAn7nA0G8M/WWEvCCRgBMVn0leX2tcV9rx2vgd63NSOvHo4Hf1s6BBn5bG/+m936/gd+zZ0/jT//0D/Gd73wPJpPCItHY6nd+59/g3/27L+Pv//6/47vf/UE+7sz5jcWi+OpX/6jpc1HaQb2y53sF/E5nJ/DGzOsKEFSUu9IU9+RHMGjfWHPMGw1+3R4nfjr2CmKJ4o2EdDaDfe4Dkr9abVOZ35euv4J4qvh6vOf11vVYv0SebqU+OjvbEI3GBACz0YDpSvSyxJILfbov2/U2kSuvZDRF2bMKfmlUdTV6FYlcDEaDCQ6THRvdG7GzY2fZdWZiM7gVvIWZ+ExRnVbGp93aBn6+ybMpX5OamwbMc97i3iZMnFqrmj9nfKgGYO4b6/zyRtTPxyKjmKJxkro4Xnhe6Ma727u3yNBoqfkgoHlv9t0ix2L222vrxROdH5HTaIQ1k5gB3YS7rT13ZdFdL/gdj43hlcmfIJAKCJDjvRDY7XDvhB4GyQvf6lrehK3aZ3e1x9EwzJ/2S91pjlFttYJfbnYcm39f7lW9Z8m5d+9Gm6Xyu0jJ80R8XKT63JBpWyLnvPD75+jMO6J+IBPM63dYO2GCEay5zY0dlnhSgS9dpDc5t2Kvd+VyZ2ofHP+NyHXMpWdgNBjQY+yXWsj3W6O8nOD3xtQtnJk/UyZz5txR2dG54CHwYY2PBn4/rDNb/31p4Lf+2DXiTA38NiKKa/ga9xv4jUaj+LVf+2Upb/Rbv/XbmJ2dwVe/+hVs27YDv/mbv4Vf/dXP40tf+k184Qu/gjNnTuErX/n3+LM/+0scPPjAXZ/F1YBf1rmNRhvJ/Da+Jq/RrMPzkz9COBEpAr9kc57t/Exd+V6NBr805LoevI4zU2dhWMgDZKkTr9GL/e6DK7KqhQ8NF+vcmLg0dgUX/BdgWDCV4kKYcswDnkMVwVYoHRRmlwtusrAqY1UKfi+FL4gZU2Hjwr3fug7dlp4ln18CTuY2E3TOJ+dwzn8WZMwImAk+uQB16V0CzNc5BgSkKv4+OnBsw+HbCKXDiKaZJ6wwNwTeffY+hJMhtNna8/dF4OAyuzDgWi/nq9eRq9FFeMFJupSVI5N5YvpEEcNL1+A2a5uA60JQwb+nMmmRp5Kd45h4H4zz6+OvCVhnY9kdj9mDj/V9FDajA5f8FzEZmxTJOK9NoL3Hu0/AfyFI57m8lvKn/H/Z57V8WdQLfn809j8xGrsjjGrh/ffZ+sR9nMz/Dk91kvBaxlvrsXPJOdwIX0UsE5fcdubbbnftFHl2reCXJadoZFW6mUNGeSX351rHTaB9Yv4YKLnX6ZXnmnnjkXREnkOW9GIjK/nR7k9U7RzOc+hEzc0Ls8kkz1kilcI21zYpHXU/NXX+b8+M4NTcibK0BipY9nr2yUbOh7kR/EajiXx6TSvuVSt11IqoL92nBn5bOx8a+G1t/Jve+/0GfhnQmzdv4K/+6i9x8eIFOJ1OfOxjn8Sv//q/hNlsxqVLF/DNb34d169fh9frlZ8/++zSNSGbOUH1gl/mlYbDax/8EnSN50bw+vAbItNko8vwbtc+bHGUm9FUE+tmgF+CtBtTNzESvyM1Psn2rrcO1AR8OXYV/M7PBzERmsBYYhSsn+s0OLHBNlgG9gmwLkcuCfAlOCaQZX7qbtceWXCXgt8TgeOSf1ja7AYntji2CLA0Go3y22QyLPzdkL8PGs6cmTqHm6EbAh7TmbTkNmezGVnsb7Rvwl7f/nydXwX85SRPj+wX2TUBtHKvNATrQL+1X1yoCY75c5fJvSTzZjAY4HbbEQhEkOVKbKGpQJh1dUeid8RwiuCnzdImuZuqORGBNMHoldBlTMWmkIVi/rTBNYj1Ds6XzALmErO4Fb4Nn9krjDY3IWbjszg/f07ykPPCSx0E0BzuPlzNo1d0zErguPhzwGw2IplM5++78vnFIPtvb3xHSg/xuaA8VG1ekxdDjs0iL+6ukNda882s4gS+18fm3ytzK2dcKcuuFfye8Z+uWPeaQ9zh2gkCbc45c/ELyxLVewvvzR5FMB3In855oTKAm0ziuG50YY9nX0351dxgenP6dXmGDUYKsnWSqqGC6EaMu977vdvnLc5/GEen35F/B9T3XTYF9UY82PZwzd+1d/s+VtufBn5XG8EP3/ka+G3tnGrgt7Xxb3rv9yP4bXpQG9RB/eC3DeFwtCnM7/Fbp3EjelWYvTZTm5T4ITCrp6mM483RO7gauQKWIdls37qqPEWCX4K4QKAxOb9kfgmY/P7yfNNa77kQ/CYSxYZXla41Fh8VwF2Y7yZljkw+KbFSCn7PBE/lQRABIReWYoxl78CO9h0guGSjpJiuzvzNHF8xqfK6RfZ80X8R44lRqRvMPFIiQYGzOUh5m490PgOLqdjlmMeOxO4gnAojlA4IcCZ76jS5hK3uNHfAbfasGK6lwG/piSowrJSzeTN8A4xbYczIjO5y78kzdZUGcjV0WeTDpY2L8QfbHxagovangmi1us3izxXYXM/nJpNRnluC95XOV8f4nYvfwXh0XOacTDUbY9Np78RD3Q9hf8f+vDSYnyUXTNaUurTlbHU1gFvpQ/5/Sda78HOWRroTvVPG1JJVfcD3IOxmG9xuR9mGx1IPCx3GCT5L5z6YDMjzSdUI1QUc3zbXzrw0ufB6rBt8NXxV6v8yT36rczs6rZ0Vu7wYPI/hKEuNFbo/8x1sx37fgbrUKbzmtdBVuYdC8MtyTk93/QK8S+Qvr/gC3YMHqOCXHhXBRAiMDZ3m2RwGSvh3rerfg3slJD6fC5FIXGN+75UJuwvj1MDvXQjyMl1o4Le18W967xr4bXqI6+5gdeA3JvmgjWosS3Qnc0tYWpVlItvnM7fjmfaPrljmp9I4VPA7OTlblC+6mjE3A/xykUq2drWtVvB7IXROmNfSRrOd/Z6D4vZMaTudqMnmjidGMJ6YgMW4CE6Zs0iGzQSTAF0CXtYtLmwEnbwWwe+d0B3Ja2QNYhqPqYCKGxx0ux1wDmKDs7IjNYEi2dnR2GjRhgjBJxlaljhartXKAla61gdz7wkTWtgI6lhCa6dn95Ld3whfx2wFUMUYHPY91FTmqVbZswqOzwXO4OjM23nZM8dq1pnxib5nsdE1tChNTwXx9tRbwoqSZ2QZqiM9R2A3sU6uKj1XlAls1YL8at6Ha/5rGAmPFMWP80Hw+0j3o7AZbbJJQ+ZzEXwvLSmPp+N4b/rogsKA483JpgtNvhZNr5TzjToz9nr3FknSJ+OT4DNS2Hjbj7Q9LkqC0kbJ/zszb4nRlV5nEGDNvh5uexRO0/LP82eiHWEAACAASURBVFLxodyZsmdu0BSCXx5P+TTl4K1uHOPV0BX5/rHprWJQ1mvrb/iwCsGv+r1Ekz22ahz0Gz6gFl1QAb8xUX+0qmUy6sZWq0ag9VsYAQ38tvZ50MBva+Pf9N418Nv0ENfdwVoCvwSqL079CHPR+aL74WLwsPchDNkX8y6rveH7BfwSjN2O3kIwE0Cntx29unXQZxQWdrl2MXwR0cwigy1MkUEvrOojPY+IOVShRJAA4rr/OsbD40hkkjDDjH7LOjH5IbCYS80inInApreJJFllswrBbyKZxM3odVwIXxC5MuXHlAV3mbtF7m012rDNs7TD9e3ILTEWKm1mvQkbHMsbmDUC/FKmWigBVsfhMDixx7t3yXATVJ0KMOdwUcXAZ5vSVv6eiI2J4RZzEB0GuzDam11b5M/VtlrBb2F/Z/1ncDV8CfFMQljOg94H0G9flz+E8/7C+HNShqrwWSHQ+3jPs8sOnZsZNGXiuXajHZtK6usuxVAXAmqqAj6YfR8mQ6E6hPVxDXio62GRrFssJtnAqYb1Zp+RVBRXApcRTUdF4sx7VDdqCm+Imy6P9T5WpAJ49c6rInFnW2Soc+ix9+CJ3icXfl4MvqlkuB68BjprMw6cdwLUWhjwQqad7xQduoPJIOKIyfiNMGPQMViXY/Rqn7/S86fjU3hvjhsMi40RebDtITGBa2SrBH4bef175Voa+L1XZurujVMDv3cv1pV60sBva+Pf9N418Nv0ENfdwWrAL3eRVRfgugdQeKIph+fG/xGJVDmwYb7qg96Ha+5GBb9TU7ML7r41X6LsBBo3cXHZSNnzaphfAt/35o8iklXYKavFLLWk9zkPwmtSDHMqNZb98WfmcTVyFRajSUACx8HrrXOsk3rAlMoSNFDiTuBb2AjcVHDLc65ELkmeopo7zLFsd+xQyrYUML9qqaNzwXMIZfziGkzgqOYhOs1ObHJtXnLcZFBLmVcezPqzG52bBKws1RoBfs8HzoE1kQtlsQRxG51D6LctgsJKY6CcVsB7Lg4d3ZJNPgE5lO4GUkGpZ8xm0Vtl84AmSA+1PVxWr7jWp3g14HelvkZjI3h96udlygyafX2y51NLSsHJzL418wYSmbicy+eJz8Aj7Y/XLEOl7JnSYeajc2NCB73k59JsrFqp+3L3eStyC3NJ1gpflCYTJGdzORxuf1CeBfV5eGX8ZTFmU4E7TdFoYMX62Fu929Fn68GAa3AZ6TlHsmjUVip9X2k+1M9puvbS8EuIpWKSWkAG/IHOwyJVr1ZSXlm6zh6Wk6RXZtULx02mey6pbBAUNtbufrTjiWpvsarjNPCrhEkDv1U9LvfVQRr4be10a+C3tfFveu8a+G16iOvuoF7wW5oLWvcACk40W4x4fvI5BGLF8l8uilnyp5b6tOplyfj4fB40HvzmEAgoQGW1zeNxicFTvbLn65FruBm7vgA+dLBZLUimUrDDIYw5F+UEsQS7yp/K39XF+uXZK7gTGkY8nYAeengMPgxZNym5vJ0+BCIhXJu9Ls7GbqOnYl3fkfgI5pIzZXmLNOsZtA9VBL+sqUvAUmi+k8mlpUQR3ZqXajSkIiPH8RFkkC0LpP0w6cxS29dudKDf1l/RGbcR4JdM42n/SWGfKStlbjoNoHZ79lQlXSYLp57Le6A8lfnOzGlWG8F9l6VLnKR5T4MLjDb7nklMiVxzpXI6hfFrJvhljizr7Ja6I5MVZX4p6wJXamcDZzAWLZcrs+TPIV/tBmCM6WRsQjYKuqxdeTZ2OfDL0l1T8UmpB91p6Vpy/njt0/MnYNCXsPamtrKNmndn3xHHZsacGzI0DGNjCSbOpbij29bJ39XGYwiwpeSRpQPrbUub3RWz4ZUl5XzGfjbxqmxGiYM8x5LNyvWf7n0GbrN7SfDNMfGdYmxY/sdmssowSyXrtX7vFUrOfzz8YwST/P6kRfrilSjz/vi6T8gPlnI5rzVvnJt6Lpdd6tKXpmPUeg/38vEa+L2XZ685Y9fAb3PiWu1VNfBbbaTu0eM08Lt2J24tgV+r1YyL8fP4YPxYvtYqFzrM4/x457N11UM1m01gju7U1FyRu+9qZkRhfmsHv4oseA7pbBIdlsXF+WrB78nAMcynFak4F6hWi0XMiciofm7os3kTKsmDlJzcTN6ISs2D5KI4kY0LgKShj9osbgOOT55AKqk4pJLhpEnMdufOIqBwOXxJJMyljYBop3O3SKm5YaLW+VWP86f8mElOSX4mGdtOSzfabR1Fbs+l10xlkrgRuaEA35RfSiFx/UwASuOsHluv5J3SibjUtKgR4Jfj4YYMczspGacxEUvSVDLHWuk5I4hnTVmy5Xeiw3kAqTz3DnjNbQJyt7t3gIz3SGxY7pWf28TN+GBV5W9WC37Znz85L67EZKUJilTDr3gmjh+N/bCMjSdw/2zf55Zkrcn+qUx3YZzIhB/pfHql0FX9eSXwy/u5FLoo7uHMb8+ALudWHPAeWjKevH+mFlD+LU7gJp8oDUpBP03N3p55U95B5qdzw4Ky/o32IUknYOMm08E2pbQdS0md9Z8VsJmGYg7HsmEf6Xq6rmeK1yTT/Pr0z0QJYqTbs06HVErJeaaiY4encrkj5h+f8p+UMmTq89xn7Zd83NK2kmnaciD96NRRjEdHlT4WHNI5tj5HLx7vUZjfakB+1Q9BwYHVg+flS42tLElvXKmyeu6z9BwN/DYiih+ua2jgt7XzqYHf1sa/6b1r4LfpIa67g2yWeXDF5kTVXEw1Qmqk7JkSZTofv3H9LQzHh5HOpeBhrVvXQcmDq6etFfDLhe3J4AlEM2QsaZRjwhb7VqyzrUct4JdSwTvxYckLZf7lNs82kS1Pxsehp/PyQhEdSj8tOiuOdH1kAehmxOm31nYHNxGKh4scQsnoDVg3oMvSnb/c+dA5TCen5FkiiHAYXFL7lvdJxr4U/HIBOhofwa3oTVn8bnfsFMaLzWQyLwt+eQxBuCzSU2GRoxr1ZpEP2/V2YX4JHNfZB8rks2tNAsn7eH/uqMh06SCtuAgr4Jqgl/dE+a7ZYMHJ+eNFLLkCkF044Du44rTyeZjCGEb9Y8Ko9VnXodfWu+J5PID9XA5dEiCnStoJ3ra5d+SB4tnAaVAOrgILyo8JzLe5l87dpikUS/KUNt7Tox2PVzW2ag6qBH6Zc3oueLYo/5pjd5vc2L9CPAkQaUxVCnoLx8Ln8nLoIkZiI8Judpq78sBXnV86fLPP16dfk/eXWcULSBBUQNBEbrdn6Rzy5e6dgPtnU6/IOBWVBwT8cpNru2sHhpyV/RPO+c9gPjVfJumnqqHDUtmtupo5KD2G4PyN6dekvjcBsOQk6414ouMjdZl8VTJQC6XCoGEbN6gcFgeG7JsXnvmlJeXVAu6lc9Grj0ZlEF5Y37sSeK7koC5v6YrO6HQ8p9uzmnai9l/9iFd/pGZ4tfoYNvIKGvhtZDRrv5YGfmuP2T11hgZ+1+50rTXwS1a1kc7MzQK/woT5q5c9v+t/ZyEPMF/hVRZ8j7U9iS5fu+TbLiV7VmvnzqVncT54DiajUVmcSskaHfb59uPN8TcV0JTNwmI2I5aMY8iyGYP25Q2gihbs6RAmE5Pyox5Lj0hBL6bOIZfJlZXHYN3gzQt1kglgLoUvwJ8OSM4ljYHIILeb2kXeyYW/Cn7n5gJyLZrd3IhdU7rP6fg/7HTuFIdpk0nJP16p0YjpTvS2sFSUoxJ4k43ss/YKY9pnW1dWhmatgV/eI2sGTyemJPd1OjEt80ogQBkuc1b3evYL+OQxpewyZbWPdxxZFojxOTs+fwwxXRjZTE5UCxnJUd4kBlMrtcn4BMZixaWdeI6VJXwKGEE6PV8LXZExbnPuEGntcm02MY0PRC69+E6wxvVez170F0iCVxrfSp8T/FH2WljbmaBoPllsrLcAIfBEx5G6GdfSsdCVnPnchWWxeAzZc5qj0XX4J+MviuOxunGlXsNpdONTvZ+ueyyUX1O+zFQHFfwSsD/V+cyS9Ynfmn5Dxd9Ft6LI+usD4kvNTzwdw6XwJfleZDmoba4dkpfciBZLR/Gz6Z8ilUkt1B43iOLlkJi1LcrNG9FX4TUqgXB+XioZXxlky1kL7Hft59d6X8u7oNcPuEul64mEUmpNa2sjAhr4be08aOC3tfFveu8a+G16iOvuYDXgNxZLiBFSo1oznJnXAvjlAvet+TfKFsBccAzYNuBQ30GRJhMYcqGu5uSq+bkEbGyvj7yOcCqCbCYrzCCBDKWVLA/kM7bhcvQSYpkY2l0+dOl70GdcV/XCmXLa0fioSFQ5LjJPzDuMmkKSP0zAqjZ+7jO15YH1+dBZAbx07A1ngiCAYRuybcY2l8L8FYLfmcisuHoXYB45hov/z/X8EpwWZxH4Fbl4clYYKTr49lh6xRH67ek3BfhS9qwaEREAt5naxbBrs3NrGShsNPglczseHxMQQ1bUbfQikWMZFb04V1dTUob3RzktyyDFs3GRybZbOtFl6UCPtU/m8HLwojhBl4PfLB7veHJZ8MvrnvQfh91qE9aH4FeN9xOdR1ZMJ5ByNJny95xsMtnJ1TSC6uvha3J9AkLmNm9wVC5zVW8/lcAvWerCslN8n8S0CjowJo1qvC6BNt9LAmDONd8VsvmsS83nh5Jx5uYWNnEBN7Xjoz0frdvsjKkEJ+aPIZRlWkAWFtiw2723Yrklte+3p9+QjajCxjHTiGqXZ0+jwlLVdfge8LnlJoVRb5CNtB2uXVV9p/G+hyPDypxSaWNUwK/b6MZHup6pqv97+aBK4JrMbzSaEAXQyuC7/nri8t1S+uW+EMz5+QjS6dqVZvfyXKzlsWvgt7Wzo4Hf1sa/6b1r4LfpIa67g/sF/E5PzzXM7MTrJaNVPfPLnMi35l8vARnKomzIvQkHe/bnnZbVRQMXKGp+rlo7901ZmJZvm5thEvCnsEdAr68Hm83bkFvEq8s+H1wknwwel/zA4gV4Bps7N2EkOIJManHBQtZwl2uvSF4pozwTOpWXj7LmL2Nj0psFCA7aFSBTCH6PzxzHudDpcoOkbBoP+x7Fdu/OIvBLZpTuymRDFWCeEdnmtdBVAcRK7d8odFy16yAOyofbHhRwUdh43lhiFFljCj5dB9yG4s9rfYnY78XgeXEXJqNGlo1gvN/eD4vOIkZYNHvymr1VX5pzQTbXYrAikAwIsE5mE+IqTAbWYXLkr8VYsLbwXu9+6ZsAmudT8r3RsSkPvGlIRYCZNWSQSWdh1Vtlfngs5cVL1Ubm/d2O3sZw5JbcIwFQodSXz+oez76q7225A3kv9eRMV9P5IvgN54E/83IJkPhMcGOF8lsyUp2WTjzT87H8u0oQSmDKfPRS9raavnmMkh8+Dhq8URFBEFe4KfLOzJu4Gb4pecFsjAXjPOQYwgNtq6//bLIakNNlkIquHGNuCtC4rtDVms8B1QeVahRXG4Naj2PMXp78saQ1qM8Ff0alwkGfkiu9XHtr+k3MJmfkEIpIuLlI8Mv4f7L3Uyud/qH7nP/WMKUoHI7lZc934yZL5eGp1CKLfDf61/pYPgIa+G3tE6KB39bGv+m9a+C36SGuu4N6wW9Hhw/xeHOY30Y6M6vMbyvBL4Hfu/NHEctGBQRyYUkpMxdznxz4ZL6eZyQSzQPeSvlYx/3HEBdWcbFR5hzKBIWJVZvdboUhbcRuR3XAhIzirdjNssU9AcGD/YcxG53DSGBE2GC6DK+zDcBjVIAjx3kmdFqcoOdp5pVLiSETWdBt9h15Fq8Q/F7xX8U7FZhwArwn2p7ERvemPPglSCH4NRW47MqCFnoYYUQkE0EkE0Y6S8uirNTHZe7wdnexoQ9zrt+bfRfJXEJk4YlkAt2WPuz3HqgbdNF1msCJQIVAki7MehoM6Y3os/ZJfHhPW13bVuyDAONq+IqYHhFoEoilc8miGr8sk0QGj/0xR5hO2gJ8U36cD5yRmKj5kwRSD7U9Iqwhc4UvhS7BZrbIBhA3VgiibXo7nuw8UpFZ5IbNsbn3wfxuAkSypDRCo1kS748/oylTn72yk3PdX0hNOLES+GU3t8I3QGkwnxsyvlaDBe3mDqm5zLjeityUTYN4NiZliriRscu9R+6dca4FDDOefJ+Yj16aK8x3+JXJl+BPzcvccxxtxk5scW9Br015jlbT5PvAoEMoVPzdUemafA7PBE4J28qYcK7pS7BUjvBqxrXcuTcjN3Bs7j3JWS5sfAaf7fnMkrJt9Vg+87cjt2WeCsEv3eppJHa/tVaB39I4azm/a+vJ08Bva+dDA7+tjX/Te9fAb9NDXHcHawv8Nr4sEaXD7e1eNBb8KmV4KuX8FpYTUssLMX81mAzirbG3EEqElHqmMGGLYyt6TH1wu50iy6PsebnGfNBLkfP5BSGBJxlPs04xe1Kbw2FDJBbFAecDYoxFYymCUrvBITJrAtPCFkjO423/WyK5ZWP+XYe5UyTVRzY8CWPWJPJ2YXV1KKujS5fic+EzRWCAi/g+Sz/2uBUAXgh+o/EYfjD+PwSIkqlWxqMTc6pPdX0WVrM1D34JQAgKSlsqk8Y6+zoxiRIQkoNISwmEB2yDslgvLKFEkEMmlZsOJrNR3KtT2bS4+1Zr/FQ6BsqB6ZDNFkwF8+WX+N8D9gH5eSKbxBbnFmFyl2s0R4qlY3mQPBWfEsZxnW1dEViig3KXtVuYZZUFfn/2PSSyJZsiuSz6bevlfBo7sYxSTp8V5lN+59LY6tqOPZ69FcHv+cBZEGyrrBvBWygVhN1gQ69tHdotbbIJ0iy2tu4vtAonquB3en4O14LXZMxDjk2Sx3wrckOcnvkMqWA2k81im2sbTviPF8WeOdnc/PEaffJzsu6bXFuWBcEEvJeCFxHOKP4AZNz5DtItu7ARUN+JDIuEnxsovdYetFnaGxIGgl8+92T9Vmqj0RF5pyKpiADx9fYBbHZtWem0hn9OVp4AuLTJhmHvp5dUK6jHM5/4Z9OvIplJCfBXmd/DvgfRu0TprYbfxBq6oAZ+19BkrKGhaOC3tZOhgd/Wxr/pvWvgt+khrruD+sGvF/F4sqE5v83Iz20e+NUhGo1Jfq7JtJinqzJvimy5uKQQf0aZLgFph7kjv2iuFvxykhfdntOw620CfCeTk0WLdILfUCyMQfMmDMduKUyh1BzNCtA84DlUtGA/HvgA1yJXhcWV8ZN90lux2b4FHxl6EsFoCMcnTiglhXQ5YQy3OLYJ88hGw6uzodOIU56LrIyJ0mO2rY5tYpzFDYCurjbMzM7j/Px5AYs3olcXALdOjn/M9wTaLR1FhlfjsbFFgFvwlGezGez3HRIpsPxOTAgj12Zqg93olHugRJKSXt4XmTU2LgJV8KvKXA/4DlV8f3jecPSWgCQ2snAb7BvzgO9i4Dyuha8ilUsLiGfOKjchCKJYZ5iNc73VuW3ZvE2yxqf9p4rY7fHYuDDtdB/2mBZl05Sm7/LsLhrvG9Ovg7NW2twmjzhyT8QnAF0Oc5kZhBJhqefMe9tg3yBMJGXZZHQLgex7s0fL8lB5fcp/ySjfC6BXjQfB72h6GG+OvCW52Ww2gx29ln5koLgNFzZurLCMVKHrMUtS8d0jG8pn1GlwwWv2wWV0iuv1Uu1C4Ly4ZBf2QXZ1v/dgVfngdX+xF5xYLfglu385eEnM4/LpF9k0BhwbRKp9N9tobAQshVXKkvP5e7b3M1Wx7nSUPh88KxuEbpsTA+ZBdJiLNx2qvSe+o0wfYPoF2WjWkWb98nvlPeA4vV6n/HvNf5da1TTmt1WRr9yvBn5bOx8a+G1t/JveuwZ+mx7iujtYDfhNJJIIhRpneNVc8DtfV6kfBpasgQpwCaY5znweWjYnpYT4WwW7/Hstjpa1gN/SiRZ5auD9osUgwW86mYE+Y0CypPYuAXCvhSBOycWl0+ob868hl80hkg2LzFXuGUY80/ExbO0bwpsjbyMQ8+fvWQHJBhz2PCiL01kpv3S7jFHmgpEA2mVy58HvpbErGI0obC2vQ3aTUl673iVsGxvdngkax+JjoGsr3XKdJle+3JWAXHOb5LWyzSfmRKJqWqihqsaIUlWW2mE/LPtCI65C8EsGlDWB93n3V3x/Ts2fwHD0dp5pZy4umTCCZcpC6YwbSPnl3nLZLJK5lJiPESSroJtgeINj0XGbwIdMNiXKBE8EU2R5uUhnrWB1sT8TnwbnlvfNn7PxPpwmp9QvLmwEqswLLmw8ttvai25rt0h3TQYTbDYL5iMBTMfpKK0ww4qEOSubMf32RYBzfO4DkZSXNm548P6T2aTUHeZmCk2qCln2ur+MmnRiTp/Bc2M/RCTJ76rFTQJu0rSZO2A2LNa15hBYnotHBRYUB3QMnknOyLvBeWPJHz4L3JTg/Bz0Hqy4ucG5PuH/oCyXnnPD3OLC56JJty6XrQb88l39ycSLCKb8cu9WvQ291j55bsw604rlnxo9fsbotemfivpgEQDnsNO1Z8kaxUuNYSnZe7Vj5ljoTp/MJBY3BXIZ9Fh7y9Irqr3m3T5OA793O+L3Rn8a+G3tPGngt7Xxb3rvGvhteojr7qBe8EspMR2AQ6HyBXK9g2k1+FVcQY0lbK4xX3NWZXONRr2AW78/2BATrdWAX8Z6OHYbt2O3YIBBGE+C3822rTg/d76sfAqPJ4DZ4dwl0zQRH8eJ4LE8cFVzjcng7nbtwbbeLXht5HXk0sXMIs2cNtu3CtgjGD0XKpY9C1hDFruce2XxqjK/x++cFOl3aSPLSRMtNp1Bh6uRK3lAT7n1VGJCat1y7MzJLGQqb4VvIpwJV7hmCns9Sk7vaf9JkE016PV55pey7sc6nhBwXtoIun869Uq+7qr6OeP7TNdHcTpwEjPxGWSREbk1NxUYO5psbXJskrxuAmAyZurincwaS9+wHBSvQ0n0XHxWNigIojm2LkuPAHsCLspPWY+X4CzvEuzela+tq45pPDYqpZBU2a7MoU4nDC2ZMgJrzhFB0EhgFLGU4kxN4K+OjX/udC8yypOxCVwInS/aVOE1KJNmHjZrLKdk4yInLCr7akR+ar3fHcuddyt6A+/MvQVkyfAuPsecf7Ly0Szjobw7/JSOyGR5r4SVmFIuTzdqyuTNBjO8C6oGlmgis8688UqydoLfY/MflOWrc36YW7zROdSM2y27ZjXg963p1zEcHRblhry7uZw8d4P2TTDpDDjYdviujLWwEz5vNJRTATC/a0RdIsCzDz3WnqqY19WCXyonLgbPlW3u8Wl5tP2JNb3xo8ZTBb/crKbxV6uaxvy2KvKV+9XAb2vnQwO/rY1/03vXwG/TQ1x3B2sR/DYyP1eVPc/MzBf9o08wVsjmEvQqZlQ6WfhxgVDI5tJxWQWGdM3kcUvV5a11MlYLftkf5ZzjiTEYdCYc2LAH0VAcb0+8nWdy8+Atl5N83k0Opb4r80zfmP95GUgmcH3c9xF4fU68NfY2ULJeIthbbx2QvE82YX9jZEmVskxcGG6wDgq7yaaC37MjFzAdozFUaR1fHbY7FfnoTHoGgbS/7BiypGRySxuNp8SgZ0G+ynkKp0PCaq+3bxDmlPmTp+dPYjY1DZ1RJ3nMm+xbsN6hjL/SNQlcSvOjuSAn03clzPI/BL0ZAZaUipIVpEyZpYekaE6BnJZA6ELwfBEQuhK6IvPGGqqUTousPJfDZscWiVe7qQM6vU5KHxF09Vn7l5TKEqzeiQ2L8RZzp8kO80825p3eit5ExpTCpZnLYk5GZo9jdpmcAuY43tJSNqOxO1Iuhuy8RW/BoGNQSjA9P/bDsueKAPjTvZ+rCozU+n6s9viR+DDemH5N6kkXg98sPtbzSVj0ZsnLpTyWDssDC6WWmCdOST/zdROZpMStzdxeZMJE6TjLPS0lfz3jP41UifqCc0SXbHV+qr2/eDqOazRFy0TkuaQRVbetp+LpBOuxdFyeR7eTaQdL5/wyl/u16Z8hnkmIuZdab5jvOPtgbj3zw1vZmA/NTRwavTHWZOH5Puz3rVxqa7Xgl4Z7E7Gxcnl8NoWH2x/LK1JaGZ+V+tbA70oRuj8/18Bva+ddA7+tjX/Te9fAb9NDXHcH9wv4JUNNQKEaUvHvbHRaJdBVywmp5YWWC6jH45LFZK3gl8CGv7i4pMnO1fBl+NMB2KwWrHP2oz3dXTN4oPx0LDHGJaHImemG2t3djmAwgpvzN3EjdkNYLTalf+CAuzjf8HTwlJQAMmAhJsii29wjucG+Njd+PvIaEknF2EltZM0OeR4sAmNckM4mp2WBSmar0A1XBb9TM7M4O6+wxGR0p1M0dopLWSTmEZPdGU+NSS4wTbpohKUCUGGHK9Qa5XUuhs4J8GcjUxRNh4XRJfDlWAnCWT82q0vDbDeCqZ8L5YiL7osxYg7xcOQ2roQvy/3ZDLY8A0ywe6TzaZwNnMZo/I6wgQpYyIlcdqNjCA+2P1z2+MwkpuW6KujneWSTOB80TmI8ssjJZgQNrQ55H4DVuLxJVi0vPRe/YYMfZybPSlkkdRw0dyLTTBaY8uVKrbAMEfMeaUZUunlBt+2P93xS8mHrbdwgoHlaBqwxvaFhObF8/FlLNyiKA4X5FZbe6BHzpKOzb4sUnXPAZ4VSZm5g8LmjMoLGZlJKC3pEM2EBv6wnTVb9kY7HRImwVKOC4GLogmxKMGaUmNOEbJ19fU1h4vnvzh5FbsFpmidTfUFQquaXy8+yaXww9x74vPE7hnnouzt2YYdvx5KGV1PxSYkBncGpQGApMyoX+CcdrlkXl98rjWzKezaOudSclCjrXsbgiyCc4Jz3VvQdlEsL8+opKWlWOs7Vgt/ZxKwoPUwlRoF87x/teGLZGtuNjNlqrqWB39VE78N7rgZ+Wzu3Gvhtbfyb3rsGfpse4ro7+DCBX/4DrzosK0ZUlDAb8oBykc1ljq4CeAl+a231gN/JxCRuYLAHCgAAIABJREFURK8JW8hFNRfTzEllSRqz2SjApwOd2O4qLtGz3Ngoi+U1VaaGUtet9u3YPbBdwG8sFhcjqPHEONLZJGwGBzbYBsucUrkQZakjGmdRz91p7sKQndJdnThl3wncwanp0wKw2WQBbx0QwyQuTMn6xrNRAbAEcpWaCn7paB2MBTEcG8aVyCUZu81oh01vEwZ1k30zcgbgRuSaXJuNC2/mWZJ93OLaWvH6wWQAo/FRhFMhsHSTKo9WyVeCzd2ePbCaLHC7HRIflv0pbdfDV0WazDmiQzLZY8qtCZQ4nnZLu0ilz/pP40zgdNFmBfN+uRiuJGedS8wWGXcRzFwMXpDNEBX8qqBswD5YVV4jr8ENAQKclYx3+Pnt1HWEYxFMxiYRy8SFqabM1a634omup6oyEWL+8Ptz74p0uxiI5PCJ7k/CZ1kaCC73LE8npvD+7LsiqWacCRS3ubbjUAPktvwOCOn8eOX2TxFIMnedGw4+PNb+OMbj47gZuV4EYPg+9NnW4YDvoAyZ4zkfPCfPAuNNszY+kw+3PQqHybni1wevx3eEzDE3Bwiaa22saU0mvnTTge7RD7c/mr8cc7WZK19Uj1mfw9N9z8CaWawRXdg/lQGvTr2cf9/I9PN94Rx/ovtTRbWlax33UseTaSerruaKE9gy53zAvqHsFNbOfn3q52UAnHHl8Ts8y39nquDX7w/n1Tu13Af7OeU/Ic+m+tzzu2rQsUnUEPdC08DvvTBLd3+MGvi9+zEv7FEDv62Nf9N718Bv00NcdwerAb+pVEpARKNaLc7MlCgXAlyeS2MqFUCokmWCW6fTjtnZ+Ya5XNYKfkPpED7wHxU2hY1GRrPJGamV6zJ5YDYZoTfoxT2bjsfVsCxcLL7nPwrmHRZ9mUKHz2z7NMKhmIDf1TY1t3s2MC+Laua49ph7RepHA5jLkUv5UjFcMLsMTmx2bC0DY2TKu7raMT8fQCKRwvXINQHmpaCNGwLMm51OTkmeJcEGmTbWPn3Q9yjc5vL83MJ7nI5PKeBVX1wflPGiwVCHrb0M/JIpvhO9LRsTdJemkRHvjywg3Z4pM6Wsl+Vpnup8RhjJ84FzwqAyF5mMN0EI2asOczu2LBh3FY6LAIqy50LQSDklJc10W1Yb4/tkx9PLSimZu3w2cAaxTAQWnVXGpUiSl2ZdGeebqauYDSvlfsSBGDrJnaRx2Da3Yja2UmMcXxh/Ll/iSX3f6Cz9iZ5PrQjCK11fdeOeSkwWye/Jrn6k82msW0KavtJY1c8XwU9I8rTzZmM6Hd6aeV3AbGnjZsxT3b+Q/zHHyDxgMqN8FjjX5dL9akdU+3HcbKlU8oubF092PJWP+0sTLwojXNgMeh3WOddht6OysRuPvRq6isuhC4uSZ2SlHFQlpUXtoy8+g7WsFXfzYjaZ78gDbQ+WbcJQ3fKzqVdl3gob37s9nj2S2rBcWy345bU5NprfzSXnZN77revQae1cbSju2vn8/vV4nGJQqeX83rWwr/mONPDb2inSwG9r49/03jXw2/QQ191BveC3rc0jObHNAb/FzsyF5YQKa+fypsneKUB3kc2lMVXhwrejw4fSnN+6A0aZqkcp3VOt7Pls8Aymk5P5BWowHQTLcFBm2W3pyYPfUCyCR7yPrVjDkmMfj4/hevRq2UKRbODTQ0/BnLIiGq0O/Kpy7EqL+eWMza5HrgowLMptzaXRb1knRkCFrRT8ng+dQzBdXteYtVR77f0gExhOB4URp/SZksPH2p+Ez6rkEFdqBGa3I7cEWJMlJqOqMksE5jvcO2E32eHxLDK/ZJ8IZMnAE4hS5kpQ2GnpQigVQixLuasOBHdkkwl8H/A9KEBWNQcqHItV2OnKQJK5lXdilEonF06h0/OkGF7pcpAcXLKdS8mPedLtyG2c9p8QwM1xcVHOMTHeez37K0qlyezx2mR+f3zrx0ghtWDtRFdvAw56H8CjnY9X/UowRsfm3pccZc4Nc5Yf7Xg8X5KJecyqEVY1FyX4JKBWmX71HMWxugcf7fl4NZdZ8pjlZK8sp1MJVNKs7MnOp1bVbyNPprM2N2jKS/9Y8FD7I/mufjL+AjILplX570CDHr32Xux1Kkz2Us2fnJfauiqj2tEkcMf8XX5/lW580bGcJaBYGq20kdFmnfO8p0AuJ07U3BxZaROiEeC3kXPZimtp4LcVUV/7fWrgt7VzpIHf1sa/6b1r4LfpIa67g9WB3wyCwXKX3XoHo7o9sxYhmV2VzV26di5ly+X1TQv758JHAb9+AcmNaAS/ZJkp4a2mnQwchz89nz+UskLKILloY56uyvyydNTjviNVyU8JDi+GL4iBEhfvgXRQABHBzDMDT2PQMrQi+OUi92bsBsi4kS2yG2wYtA1JDqjalgO/Z4KU/ZZHgKwZ2d/Ctgh+g+B90oBpND5StnAlALIaLYhkomLUFMuqTspZqbH7YEd5Pi37IfA94z8l0kSaXZEtJitO8MRNBqfRJXV/uWlRCH5pSBRZcIqmlHE0qoxJcpKzSfk783zpwkyQyRjvdu9GNBPLs0DqfWayKayzD4hEe6nGmDOfmRJyOgTzv5mfyb5Yloh5l0s1xoZljQQ4LEjQeSxBeJe5W8y7CoEzSxrRJIjx4P1MJSeFrS5tXqMXv7T+VyRG1TaOmxsHjA+Zcr6jrIV7OXRRGHQ2q8Eqz4HLpBjELdW4EfTc6D+WbSbwfpkD/rHeT1Y7rIrHLQd+aZZ2yn+8yMSK/W5378TmkpJSqxrEKk8Wpcfc0YV8XDX9IIMdrp3otvXmr/7uzDtSlqko3vocHut+FF7Un4+9yuEXnT6XmMPl8MUyMzm+W9xYqqR8EUf5wBkllzmXlY0tpjFUAsqlY9XALw0HNea3kc/wh+VaGvht7Uxq4Le18W967xr4bXqI6+6gVeBXqZ2r5OQqfypuy7KYX6idu2hCpTgv19NU8Ds76xd2uBGtVvB7I3IdN2LXCkrR0B15GjmWSjF3CfilEW23vqcMNC4HhN4PvAuyNZQIL3guibSa0l4a9jizy0uEb0ZvYDQxIoBZbVxYHnI/AJuRJk+QnN+l5O3ngmeR05XnzToMDgzZFTdptZWCXy50TwSOCWhVNzfoML3ZsQ3XooqTsj89JzVSuRgm2HIZ3Hio45EimbB6feaL3o7eUsAqTcxyKWFr7QY7Njo2i3w6maPEuA3bezaLLJyqATJKhbWQWV+XgJvGSFxw62FQTMSsiqsux8HcRJZaYl5yMBXKG175zO0NywEUJ+lsBnajIw9kyKi+N/suZpLTZRJQMpUE90NOJe5ksZnHqTqUk1Wj4VZp3WfJudbb8GD7I9jr2VeXbFmdgxNzx0QCr7ZgKoBoOoYB+3rJN2fclnI4fn7sf8qzrKYGKNfQyUbDviocfZd7r1cyPKKJ183ITSSzcZGv08F8l3v3qmLRiO+Z0mtwTvldEkmHZJNkwLZBXMwLWzwdwzuzb8uGhxrLzZ4hPNB1GJFIdUqQZoy98Jp8Jll6jBs+Kkjnu+YztVV0cy89V56MZTZTSsdPV3+mvtSb89vseNyN62vg925E+d7rQwO/rZ0zDfy2Nv5N710Dv00Pcd0dNBv8cpFSCHDV8kL5RU9GdVrOKADH5WioRHktgF8u7I7530cwExTnZTJLBJwsOcTSIlarFYPuQTiTlc2ilppc5hK/NP2CmGexkd2zGxywmM3oMnXjAddDyz4X7/uPCotSurhkOReW26Hp03Lgdyw+IiZZheWACGaF7SthETnfdKGmVJzMLxsl2qxPTOaV12DpJALWd/xvSn4dy9Uq7thAh7VLgOyAY4Pk21EyTaBCyS2BInMdi+4DgFlnkpxWAj9eRHHbzaDd6cNO+x5xe74QVHJ384vpHITRZL4p2V06LtPNV2VaCdoPeg/lawMTbDD+ZKDIDK+2EeCwZi/zeXnnvIeNzk2Ka3UuI8wvwS/zkdXGOWw3t+Ng2wN5w7HzgbOgVFYFQMzxZb6q+qwUMsc08yKLtt21Q5yO62nhVBhnAot5nDSWimXjUuuZ9VjJKtMReqd7V8W6qKFkED+ZfFHmkmMjuOP78UjHo1WlASw35pXAL89lbJmLT7m8KpXnz8VNPTqKNFLwmtokzrUAr3piudpz+D3K2rRMG+i19qPb2yFjDocVRn4tNH5PqJtHVEFwU2qjY2NTYquBX6XUnKJ4iaIwLehuPwtand+7HfHl+9PAb2vnQwO/rY1/03vXwG/TQ1x3B6sBv/xHNBBYlD0rtXOL2VwuPNnU2rkqm6u6LavMFI8h+9vR4W0o+CXD3NnpQyOZ33rq8hLwEizOp+alxuqgfVDAGxsBv8ViEml2re3V6ZcQyrCEiwLuuFhP61Iww4yNts1YZ+0XyW6l9s78m1K+RW0Eo3OpWQGdXZYu+EzteHjgsNT4rZTbzbm7E78t90TZtFlvQo+lD53mTgETNPVifjNzXHO6HNq9PrgzXthyiussmUyCHYfBKXVs2ZhHylzmW7FbIjGm5NgII/R6g9Qc5T16pTSQSWSyBEpcSNOlOZVLFS2eGXOy0B6zIstlk80Ykx69pn70W9eDpWhO+U8qIHuhxjOPPOB9ABOJcckhVsEQASeZNgLF1TbmNt+O3paYkWlU+zjrP1PEiLEfxvKA96AccyN8TcowsUSMFK7K6WDQ6bDXcxBDrqH8sFiOaCw2lv9vcSnOBOBP+PNxk2cGBskzJrimhLYa9+JK9x5IBkRiLSw9WMZmUmTnnAM+DzQx4987LB2yIVGpqfNIx26WqaJBWSM2FKoBv5XvyS8bEXzmpLZsNi0Sb8ZrLQNgyorpYE15Pct07e3aA6/Fu6bA72rfn1rO579JTqftPmd+i9M9aolfI4/VwG8jo7n6a2ngd/UxXM0VNPC7mujdA+dq4HftTlK94Le93SMgiWC21ISKstNCAypKlqtxmFzMz52v6vhqorpWwO9yY3W57LBYzHWB33fn3xGQpizOU2JAZTQYxUm6w9QpbNt25w6pu1vaTgVOLEh8lc2J4dhtpHIsiWSHWWeW31s7tmC/78CyxmYENeJ4rDMpDFM6JLnEdIOeSc4K6+oxurGuvR/BcBh9pnUCsun6SmBBsOQ1+gTccgwxxDARG8dIbFhANe+L13AZ3dLHFvc2uI2Lkm46PJPZ9Kf8AoYXpZRpkVIWAjp+xo0Gc9aGXa7dEhKyrXR1Jliw6u0YsA/kQRedoCfiY8KQ08mZJXBWC3wUZ92Lwory3sk2HvIdlvtjSRV1U0SdL24A9NvWodfWJ/M0GhsRCTMNuUwGM/a495a5YNO1WilJZFwA/UA0FxEJOxlO1TCLUmRemwFejeyZzwAl5Jwn/p155Eq+dE4YPdWUyGlwCXt/N1u94JeOxEqt6cVG47Stzq1lcuO7eT/L9cWSWsf9x/KyeD4vZD6f7D0CXVJ5Fu63poHfQua3com3u/VMaOD3bkW6un408FtdnJp1lAZ+mxXZNXJdDfyukYmoMIyVwO9i7VzWzTUIO6vWzuXCqthtma7Lmbpq53JozQG/enR2trWc+W0W+CXQfHP+dQFwBIAEizazVer55jKKGZhN78Be976yIZBlPRs6I4CKwJF5yHRQJUAhiCQwc1qc+Hj/J5CMVJ8v/c7sm5hJz8p4KMUmEGfb3LEJ6UQWgZhfZMKFLq1kVXutfXJOOBfBWGxEmONYOoasLidjpDySYNZsNAv7pgI71l8djg6DbGo8mxCZtFVvFQMps9Eq+b5q4/PMuspOeLDVWV15n0a+vWSafzb9ioDNwsbxPtr++IJ0uFg+TTDZa+tdAKnVjYbx+mD+PSndpMQpC4vZIrnYgXgAs4kZ2EWqbZYNhU2uLauWF/Oa18JX5bmhMRH/pPmXWvuZwHHQvhEec23y/urueOmj6gG/kqow935FiTaf581L1Jte7VhXe/4Hs++J2qKwGYx6dNu6scOubPbcb20R/IZkw/Z+bKVGf62KgQZ+WxX5yv1q4Le186GB39bGv+m9a+C36SGuu4NC8FtcO1cBu4W1cwsNqGw2iwDfQECR3DaiNcOZmffUDPDL2MzOVuf2vFJsaMZitVpE7l1Po3v0lfAl0MCKJXvWefqRTrEElMJamXRmHPQ8UPHSlCTfid/BxdB5kSCb9WbJQyaIZl1Ngt/Prv8cxAOqinYpfBEng8elni3HRRUA8ze7TF3o8nbClnVgLDyKHuuiQ616WTKgvZZ+MbwaTtxGLquAKOJEgmgjTFK/l3mpbWbKZxU592R8HFadDRPJcSQzvOec5D5/pOtpqcNLebEqKyb4NZh02OPYL/Lz1TbFrXlGQDslvSu5z14KXsS18JUy9piS2me6P4rr4WtlediZXAr7vYeqqv9ceD9K7ue4uEPTWfpQ/35k4opag4CaJYYYD7Loq2Wz1X55H2R9KV2mCoHPkyoZdhrdGHIuSrNXG/tqz68H/DJ2x+Y/yNfR5n2RLWduOhn4gRVqy1Y7tqWO49yMxUZl44YScLLzfAdWam9Ovy6y+cJmNBngMjlx0PXgSqd/KD/XwC/ETNLtXizx1qqJrtO3slXD/dD3q4Hf1k6xBn5bG/+m966B36aHuKYOEok4rl69imvXruD69Su4ceMaDh48iK985StyHebyqjm5Sg3dTJlJhs/nFlfmZoDfRubnquCXZYmSyWIJY2nQmPMaSAfgMNhF+rtU4yKCC6q1An7VcRIAU07scNiRTFJqnhLGlEY9lD4v186FzkreLE25uOBW818pof6nG38N+oRRrsU8W4LiSmCJn7868xJmUyzjpEMym5KcXbItbpMbm9s2wZK1YTo6VVGGbdFbsdE+BH/WjzenXxPwTDMsAiirwSYmT2QTCQZ8Zq+Y+XChz/JGZHnZP52aCVA4RnEXNjgkb5fScDKPDqMNe3p2w5ZxrlpaT7adJk/8k8Akg7TU293i3LYkmLwWuiI1giVfd8GkjGCd5lr7PAdhgF5k1jSqMtNAK5fDgGMA3RU2C2p56TlfXq9T8j4b5Xq+Uv/JTBLTC2W0yP6qJZFWOq/Rn9cDfjkGGoaxpNh8cl6k4lKMGZB6ykPOTY0eZv56lPFfj1yXMmZs3KjgO7DHs2/FmrZkq/k+FDYyvz22bmzXmN/7lvnVwG/TXtd7+sIa+G3t9Gngt7Xxb3rvGvhteohr6uB3fuff4Nix94XV3bBhENu3b8PHPvYJPPTQw1JSaKXaueyM4Ffksv7GMb/NyM+tFvxeCl/AjegNJLJxAU+d5i4c9j5UkW1RwK9JpNSNaKtlftUxMMf2VOgkLDYTMuksEsmELJb3uQ5KzdXl2nRiGm/OvSbgn8CVjQBNQKt7M2xZJ8YTY2L6QxOdTfYtYopV2AhWfzrzsjB+XLDzCvwZG1nWB/sOIxZNwAa7MKWFsmfmJvdZ+6XG8FxmDqyNHE/HEUz7xRWawJgA06a3Sm5ur20dOi0dkptLABxOh4XJdBjtaDN1CAPebunI1yzms8pfRpbYsutxfeaWlBKiGdhKsSmNm1rj9qT/hOQjE4yr90J2cJtzBzqtlWv9BpNBvDjxo4X4KLnW/MUSQP22fozERiRXmvfrNDnxSPtjVTkws8burcgNkX0TKK23rRejL7VxSr1eV93glzEmKLMZ7fCZfA1jihvx/qx0jXrBL+eG7tqTiQmZI+bA0/DMSAduxxDaS8oMrTSOaj8/OX+8rOYx3ztKxrttlc3r1GsTqEvu9YK5m+T8mo040nsESKzMHFc7xnvpOJX5nZ9v3L9V99L9c6wa+L3XZuzujFcDv3cnzkv1ooHf1sa/6b3fr+D36tUr+Na3voHLly8JWDp8+CH89m//LrxeL86fP4dvfOMvcOvWDXi9Pvzzf/4lfPrTv9j0uWAHw8O3EYvFMDi4ESaTXsq61Nq8XhoOrX3wy1ynrq42LMf8TiYm8a7/nSIwRvA2YB3AAc+hstDQndlsXnvglwPlIjlkmcdcZB66tEHKB9GBt5r2yvRPMJOayefIErB2OjoQSUZhgUUAG8vXcN4tOgsea3tCStiojZ//bPZV2UBgSR3mTRI0ECAO2AZxqP+AuD2nk1kMx24JYKXlkz89LxsOA9YN4gx8O3FLLnkueAaBVFBMuAR8wCIgkaCQoJW1bSlBPTrzlgBiOheTabYZrAKO93n3512k1TESyExnJ5BJ5hbqAacxYB8U5rhS49h5H2a9Jc+Gs4zQfGoO4/EJiQXzoynjVuPM0kE7PbvKLkcgcil0QUD6neiwjJnAnmWv6CDNGq5qvWayfqyNy5JRj3Q8tuz0sdzSycCJotq/HPNO904pIaMAofrB75XQZdyK3EQWCovPMT3Q9mDNmwbVPIPNOKZe8MuxnPGfkvmXedYtOqOz7NZW1/aqhsv5VspXRcXcjMqANosyL6WNfX0w917Fd5bMOTceKGXnJgvVEAO2AdmQKGysl8xniWw1x0m3Z5fZjUikytyFqu7q3jlIA78a+L13nta7O1IN/N7deJf2poHf1sa/6b3fj+CX0uJf+ZVfxGc/+3n8s3/2JUSjEXzta/+XLKB+//f/AF/84ufxG7/xr/G5z30Bp0+fxO/93pfxjW98Czt33l1TkpUMr5Z6OJoDfhufn1sN+H3f/67Uqy1tBHgf6/xE2c+bAX6ZQz09XV/OrzpALpy58O3t6oIxZUY0SqBafaP7M+XBGWQEkBn0RgXkx2Ykp5aMruqmzFzebksvnmw/UtTB1cgVXIlcyhtmERATIB9pfwp9PV3w+4OIx5WcRLKgJ4IfCLAlcKSjMn/12fqRyMZwOXhJzK6kBms6LuCLubwd1g7JSebC3pAziDSatXx1CzmRlG13W3rweOeTRYZF3Bg4EzgJh92ORCKVN2bLZLPiaF2YU8lxE6DynglamWtMSXM8k5CcXeYQj0RHBBSxkW3ttvbIsawLvN1dLjNnLdyb0euSg817Zqkn5gsrTTEdy5dkgk5qHhPMf6TzmWVL/jBOrE1cKkWXMjfe/XJ1Ai+n2wZdwliT3JtM4ntz7+TNxWSkco8+HG5bvo506ZPHmLJ+czAVEBdobl4wd1Ytc1X9k1rbkasBv6cXymCV9mjT27HNvTL4Ze7z27NvSsw4P4wBn5iHfA+hvYI6gMed8B8ru0G+A3a9E7EsTeQWXZv5fOzzHlh2g8vhoOpDp4FfjflFIBCp25Cytreu8tFazm8joti4a2jgt3GxrOdKGvitJ2r30Dn3I/gdHr6Fb37z/8af//l/zptGvfXW6/ijP/o/8W//7f+O7373v+J73/uH/Cx+/et/ing8LsD4brb6wS8ZP52AmUa1ZphTVQN+3/MfxVRyquw2zDoTPt75bNnP1yL4ZQ3h27HbAhQJpMmSbjXvqKlOKnMFGYtCEMWauIlUAuFUVNjDwmbWWfDxzk8K8Ctsw7FhjMRvS46tx+jFTtcucRXu7u4oAr/nQ+cwl5wp6o/uzlaTFYlcUoC8OpZA0i/MJuXMBJlslErT4Zk1ZElJEgATWRgNZimttMu7B23m9vzQCDRvR2/CWQJ+yb5ucW6F1+zLHztC4JuOFqkBWEc4mU4ilovKcXQ3ptxYHeN6+4CUxtnrOSBy8dJGBpD5x4VM/FRiSmTOZPLovq1ei4ZhNIgikD/S+dSyOehkJ2lWVtr0OgN2u/fgeuSqxMZqs4CVe9ZZBook0cu9v7z2RHyigsw5h1/o+nhN8mduGjBeqkRcHLx1eux0767pOrV+36wG/LJeLjcASiX6GxyD6LRUlrYXju/N6Tek5i5BKoEvn19uIJHVf7zjcfTa+stuZyR6R8pZqSZtApwlP1ypbV3YqNqhvH/QsXHJsDgcirGbxvxqsmcN/Nb67fHhPl4Dv62dXw38tjb+Te/9fgS/lYJK4Ds9PYXNm7dgcnICf/zHf5E/7Ac/+B6ef/45fPvb/63p81HYwVoEv9WYU1UbpGrAL8HaycCxvKxRNXvqsfTgQe/DZV01C/zemRgX1jScCYsMeJ11HfqsrMG6fCNgPBH6IM8I2W1WJFMpmLMW7HUrzF+1jU6916JXJSeXUt8dHdsxHLyNG8GbYCzVRuauzdSG3a696LYoZlP8VQgSeCzLrtyJDYsUut3rQzs64cwpwJBAO4009HqdXJu/CbAjqYiAobnEnBxH12d/0i/MLK/f6+hTGNecwh63WduK2DACYdb2Pdh2UAHGC2ylPxHApcAF2K02xfE4q7C2BJ+7PHtEUk1Zr8iTgxeKr7lwDZaD4lnqM8Jcabpj87xBxyDW2Qawzl55znjdi8ELRTGKZ6LgNcgqXw5fytfGdRmd4pLNEkiPdzy5LDikpHY+Uc78khlX2EayjoDNbkEinkIqnRJGuHSuKj0jS4FfgrFnuj5WNWjlBsO5wJkyhpIbJKwDXLjxUO2zWu1xqwG/3Ewisx7NhOX7gc9Mu6UNQ87NK3bPzZHnRv8RqVxaVAtUHjDmdqNDNoyY477Psx++ChJolqjiu8j+ado26BiS+BVKr9UB0KBvWwWlQf7z+xz8srQZNwC0nF9HS5lffkey1JHW1k4ENPDb2rnQwG9r49/03u938MtF71//9f+Lf/iHv8e3vvXX+Pu//+9IpVL46lf/MB/755//If72b7+N73//h02fj8IOVgN+ubCen28889tY8KtDV1c75ucDInWt1Ogi/OPpFzCTmhIAR8lvl7kbT3d8tIzV5PkEvxaLCTMzjTG84sLMbDXiuSs/QjKXygMKskVb7NuwwT647DNxPXINU8nJ/HkEvwR38WQCD/serapESmkHqkyzrc2D8dA4nht+blHynMvBKmWJ+rDZvhXBTADzqVlZqFv1NJxqQzAdkHzdqdSELN4pfaaxVywexw7PDvS6enF0+h1hAtnIilFKTdAuEmddBpPxSTEZshgtiCQjSOQSwmS7zTSYMsCoMwg4JJtGQKoadVE+TZOuvR17BVizqWACNj7kAAAgAElEQVT12PQxcY1WGVYxBDKY8EDXYiko/uzM7BmFeVuoC8qfyXWgw0hkZAEYL4DnVAodtg7sadsrY1AOZW1iIaTzf+fPAgmF/SVjx2tRBOs1ehHNxjAaGcF0cgpWnRV02SazfsB7sCI4Kpwvxos5vzQjVu+Lc7HZsQXc2FHKDS2C32Q6hXX29VUxl2Tf360ge6YxGfN+q22cZ+Y7l6oEOO8E/j228tJX1V57peNWA37Va1OyTgk8a/xajcubx6nnvDH9mpSuYjkxMvuqwzcNsygbZ11rr9krLs7VNIJfOqgXfX8zftauZUsvKcxvDpFIbWkQ1YzpXjhGA7/M+TXA7bYjEAhXZWjZjHnVwG8zorq6a2rgd3XxW+3ZGvhdbQTX+Pn3M/iNRML4kz/5AzG9+k//6T9j06bN+MY3vo6ZmSl87Wt/np85Mr8vvPAc/uZv7h3mt9HgtxqWttZHneBHAb9BJBLF9S/VaxE4jMbviKSQ7sRkXcmaPd3+0Yq5dEuB31A6JBJTMoF0691k3yx5m8s1gqprycu4Er6M+di81GMleFQZHkqvH/Mtz/yxT8q2OR80uYnqwkimkrLIYX3fDbbBqhm60rES/LL01TsjR6VeLBfxzLul6RGZSbfBLaWNCOZmU6x3GxOWjKwW/YEIWHM6wGN2w2tV3Idtehv2+fbjRuAmzs+fgy6nF5QoNWKTU+Ii7TS7MBGbQDDlF2OfSDoirs40DCKQ45/Mnf1o98dlXGSXuXFgYh1Wa58wYZVcnGmeNZoZxlx4HiR+nUYHNju3CMDO59vqdLgZui79cR4UubdOzKk2ugcRTkXkc+b/igzb1o2dvp1yvvJbOZ6SbIIVt9mV/4yfSG52wi+AX2GtF114CWRvBW/JeNY71ivAnqx6CQhXwbj6ZyQVxfUgx6TUat5g3yA52+fmybZaBPyYLSakkmmkMilRFHDcpeBc7afwObgWuirSXeZZs1HS/YDvwZok9bxXgrdStpnM7w7XzqoBZa3vv+RrG7nwJ+t1dxf+L028KIoF5jjze0XdgOF8b3Zuhc1og9PgxH7fwapuixsRV0JKvjmbukG117OvKLe99GJ3G/yy9BfLrkXTMal9TWfsalQGVQWhjoOqAb+jsVFcD10RQz9+P21xbRUzvQ9La8QG0GpjoYHf1Uaw8edr4LfxMa3lihr4rSVa9+Cx9yv4HR0dwZe//L+hu7sH//E//om4PLM999w/4vvf/y6++90f5GeTOb+xWBRf/eof3dUZrpf59Xhcwqo1kvltBvjlgrO7e2nwSyDC2rSKo+ti42J9i2MbtjnLTW1cLjssFnMR80vgS+MsnqcucskCPuB5CKxxulQ75n8fo6k7krMZT7MsUE7AS4+5T5x/ucB9qv2ZZReP0XQEJ4LHhVmiVJImVZl0RtyPO0yd4ghLOW41jeMfi4/Cn5oX8LS7e6cYE835A2LWFEwreXOUCA9aNuJc5IyAwpnUtIA+LvKZG0sTIy7S7Ua7gHDex4BnPRLxJLLpXN5F+3b0FkYTo8KORdJh2WzwWHx5FpeS5LnUHNqM7Qik/QLuyOzSDXmjYzN6bD2QXNtsWsCqSWcSJnepxmfW43FiPhAU4ycVSJQezxq1t6I3hSVWa/h6TD70LSyIY6mYLPB5v9ywGLQP5vNo6RD9wdz7mE3OLJQtsmGvb5+Mk20RIC8ytYXAWzlm+c+Eg654jJwtn3GxeWLmuOIgXdDSmTQOdx9eFjAtAm4FeHN+pqLTMp/ceAilQrAbbLAZHQXMtvK8shX+qQLqiegERqN35Lli45yRQabbtnp8Nc9oNccsmmsFodPn0O5qQ0euW6T8d6u9MfWaMPm8T75PzFHnvDAPfoNzUL5zaPhFcFhtC6dDGIvxfUnJJtR6+3rZhFquOZ02ie/dYH4DKdbofl3eRQJevqt0qD7S9VRdCpRq47LccaXgl/PBONIQju/uZHxCHLYLvQ4Yr4faHl2yZFkjxnU3r6GB37sZ7XunLw38tnauNPDb2vg3vff7EfwGg0H8i3/xT3Ho0GF85StfLcqXDAT8+NVf/Ty+9KXfxBe+8Cs4c+YUvvKVf48/+7O/xMGDi/LLpk8M5abZVF2ljgggCFbvdfBL8PLqzMul+EBC329dh33uA2XToNblnZqexUh8BJF0CFz0hSQvcBFpcAFFw6VDnspzyr5fnn4RIEOaTSCUXDg/l0OnpUtMjvQ5Pbwmn7CplPp2WroxZBsqY3JH4ndwOnACsWwMRqNRpNtthnYBOJRZHvIcXvZxIqikTJkliMjW0nBJJMEWI3Z798CWcuZBDUuH8PfN6A2c9p+ShThloQTtPIfssAFGAY6UPHPcOWSxqX1InJ4NGSO6LJ0CztvNHQvy3yzOBs8gkYtBp2durxJHjiuWicNr8irSaDH8yYEOzWRmCJaHnJswHKEzc1iuRVaast5KbJMKfkOh6Iqux7wXmlRxIU+2k5JrNjJb788dzTNvPI7yZTJwrAPL2rBT8QlEFso9KYHT4UjnEfgs7bgdvS0GVCzJtMG+sWklg5gLzjmigRLj2eXqBNJ6rLOtR4elM29gVgi8VdaaMR6PjWMyRrMrPTY4N0h5nlvhm+JKzeeD88Eax1u8WyTW6rmVQLn68BE0T0SUurl07SbzXdgWQbMErYyZ5kbKbHwOzInlM7bBsR7WhVI/Mg9UIMRncSt0S8ZDFYdOrwcBUCyewC73bhlrJTZ9uReEsaS6gMCJUmWWtlqJzWRd5Ldm3pBnlu8CN8j4fG5wbBDZPFMBDvgONh0U3k3wS+A7k+BG2OL3INM3aGq23b3zbvyTVtZHIfjl8zseH13Y7NRJrjnN9FhurbTxu5t1tj8MTQO/H4ZZbPw9aOC38TGt5Yoa+K0lWvfgsfcj+P3e9/4O/+W/fANWq7UMqLzyypu4dOkCvvnNr+P69evCCP/6r/9LPPvsZ+767K4t8Ltyfm6tAVqJ+eUi+LW5n0kpmMLGBdth78NSMqe0Efym9Em8eOPHiEn5H70wO2aDRViOQqqN7MJjvicqDpsggqyzxWiGTq/DXGxOmBKuG1kr1qV3I6fLgmVV1MUkF9I9ll70mHsxmhiRnNcBGxfTBpwJnsJMclpK+RizJqTVuhI5HR7yPVJxDLz/K5HLmBPJclRYVo5ZpNfQwWq1wKQ345GOR4VR5iKKYxkODeOW/xZGIqMIJAKIpZlPyDxWgqKMMIQsH00GmD9jqaAB33pMhaalZjBl4YSMBMCbHFsEsF4Mn5dNBL3BmAe/ZMTFNdrkFuChNgJsGm2xxqmaU5mPUS4r9zDk2JyPG9lYGg/ZjFb4vG5UA36XetZohjUVX8yxVo+jg+9+7wG8PPljATqFucVEcXRvdhtd8pyo6gCee8B7qEz2y9q9l0IXxTCMQKmHmx7OxftZ6T0gWKOUngw555gGZgazHruce2CB4v67XDsfOIeJ+JhsnvB8Ppft5nZhbZlrrTaCTZ+pDX32ctfiwusXAmOVmV5kr3nkoly8+NhCplyHC/PnMRYbE1DLxvdhd9tu9Dn6BMRcnLuIZC6JsYgy9h57D1xmpRY1n58Nrg3otleu6VzOWvOsHGZiszg1c1I2cQjpyOC6TC4c6mANcP6kcn43GW8+J5T2SyqEzirOzLwON7R6pLZ0CS2/0sTU8fndBL8vjP1I6nKXNm7mPdZR+Xuwjluq6RQV/F6fuImLwYtFigc+v2R+K6lFnAaXMNYfhqaB3w/DLDb+HjTw2/iY1nJFDfzWEq178Nj7EfzeK9O0GvBLEw2aUzWqVZOfW2tfK4FfXm88MY5TgeN5yTLliAS9hz0PVcyVJfh9e+ZNjARG85+zTBDZ2w5zh+Soqs1n9OKQt7IxEBfjP5l5ETldBnqDHolkUuTPBI9kBH2GNsylZ8uYIQJVmiER8BJAGvVm7HDswrXIJcmZtVqssOns4C82h8GJPe7KpjojsTsYiZPRymAuPUdPWsnVZa5yu61N1uYc51O9TwMZnRi10UzrnP+cLHKZYzyeGEUwFVKYMKmJS5dijs8o7Pl8ag6UDPe2dWHMPw5jrlimyRJGHB+liGdDp2EwmPLgl6wyWVP2RSBo0ZuFMSMw54KaknI6MBdKP8djoxiPT4hZltPkEtBGMEy2kYBoqGMQrqx3ReZ3qWft5PxxGWtpI5B/sP1h/GT8BYlHFum8nJ7sqQlmYU/JHKqNgKu0LjCfv3dm3hTQvwjoM+i3ra9YP7jSOG+GbyCWLd7Qsdut0KeNWG/ZIM8Zr824lDay+O/NHc3PAeeVz8BsYlb658YD69dSQkxGnM/hcm7Dtb6zlY6PpaNg7jE3argZQ4djPg/MBeezsN21Q0A61Sgc62RiQsZFcDPkGoLNZkUsFofP1I51jnVF+d3srxI4V3/2/7P35kGSXdl538l9razK2nvf9270CqCxNoDhDMDhiJQom1IoLEuW7LBCjvA/cmixxaBtkWGGRSlIhh2SRQU1lkSLFIej0QzAmQEw2AY70Gig967u6n2rfct9dfzOq5eVmZVVuVRmZ3Xj3YmeQle+vPe+c19m3+9+3/nOp2MfS5L8b50YWnPjM0Ge97ogeaHlwH1Bsl7rvVeSiVeSkNOf6Uhu/He5zFx/W3KNz2coMmIxQGk5m17rDGu77vX7P9GDFnNuiVxCcrmsOqA/3/dSw94DtY1e+SoO7ah1/MH1j7WcWHm7G7+tz1S5agd2vx5Tt5XMsdXvNcHv9HSk6SkGtc7dyvmtNVIP7joL/D64WFcayQK/7Y1/y0e3wG/LQ9zwAIBfNifF//DX0hmy52aD31qAai1zK77G7JN6xEhul2rkzV6JXdE6rQDftZ51mmMKqEWaS96r2TCQee3ejySWWKitCkjjehhNrjd2fyJHu44pM7ZUOzd3RoYTlzWWgEqMo/o9g8oWX4pelLFkaf1hwOb91D3pc/er9JTGBn8uOytr3OtkLD0q4sirc7JfAgpiKUcEs1rcAAlIl7+Y+lxuAX7zGXVZpn/cbDF96fcOiNOB4ZLIId/REpknzr1pMZxnAQLk7ibycWWi+10DEs3FpNvVo+ws8USenPBH5Mr4sGQzpbVKiR1zxKBKSyMlb6osGIYU2fR4clQimagyvBhqAUJ2deyWdf71CmgxvDJNo2BxrkevqQyZ3FQOEmAt94T2aixUCuuxy3rXJj0gaKRdmDmn61L+mfE6/PJ49xPyzshbxhxshqOzuUbEBgOgziLwy2v8vtj06Gbshlyeu6QMYWmzyfN9J6rKZGF9P578QM22MG7DWVhL7Pi9MhuLSjKdkNg8MPbbAwpoYTLNNjx3WU7PfKW52zyPGvM85mUYjrkVKOB6DOuKIReGavu7HmsklFXfQ43hzyY+Fuohc9CDyRnlq6ZSk7q2xIjn35DX29WoiOd1JHlP++Yggee4P9Qrs9E52RHYraqEWht9fzj+vt5neYO95blarj2o/O7yHPBa70+/pgqGaouB8WJQvrz52rmpc3Ju+ox+5hYOiGya706+/O6OBy99NsHv+9c/qnholc6mJJKL6PeYqcjgMO253hPqbfAoNAv8Pgqr2Px7sMBv82NaT48W+K0nWg/htRb4Xb2L1ij4DYWCKoFtJvPbGvArMjDQK9XAb/EKsWH+bPoTlQJrCR2xK4A7HDqqmyPA75/fe1WiiVJmDZMkmMUud7f4bB6V3S4HfM0xR3L35W7qlkRiMQW1OwO7dCNP/dcLkTNin5d4cj0mSsiTNWdzvoNIJqKybdhiwFbGmZJYKiaSs8kL3d8Qn8urQNflQrZs5Os6HAZw/pOhP5FIKqL/ncvmNJ+TDT9AkTrHTrdDNgY2yoCUyloBnCMpg10zN9DTmWkj19jdo4cH5UZf6UBcLo5dUsOr4gbDeSD0mIJVmtPp0vmx8T4z85Uxt3zOYCt53e6Wjf6NCmrIv704e0FcDkMGi6MwQJmTBxh4wLyRe92t7CTvNxhQh2zwLl9CaqlPLZLkTyY/1r54HpgbucEDvjW63m67U94ZfWt+M20XkGOH05Bt97n7xO8KFMpGMTfA8N7QPp3naPK+nJsx5N+Y8RQzs9lcRp7rO6G/X6pR7/ermVM6n6xkdW4cyFBL1x/wyqXxS8rCMxa5mcqc2mxqurQntE+Z3M8mPpGr0SsqceZ1XKs5NbDl5022xKayXQ4rVA7t6ZZnep9v+pccDPQP7/4nlZADdM2cb+SoyXxC5fQcqgCK+R1z5VAEF2U+IwBk5ud1uFVZ4c57ZUdwpx6a1NqIEzncedB/UWMuuIqTb74aWzGTzfPO5yEex718ofRXJXk597L4mkrma3rlItM14nVq7JR8NvqZ8H1I2sNgYFDCnrBgtLavZ19Bdl4tv7tSqbBKrueljuWL2XC+8/x+jwzdH5ahuYvqgWA25kDeL+XkDIfqqPidQdkV3CVeZ/X0gNW49pXmZIHfh2WlHuw8LfD7YONdPpoFftsb/5aPboHfloe44QFyuYzwp17m92EBvwRmcBDwOyeJBG7K1dvZudNyM36jhOlk8707sFcBLbLnL6Y/l6GJyyXXwEI+Fz5RtbxR+Qz8fp+Qlzc6OlnyEhuzr2ZPaR1dJLX8nbJCdrGVsFewpZgywcBhbITEE2MgwNK3Nn1LXE5jswcbDLts/okn4/L2+Jsyl8FMywDD5KlSzxTGEIford2bZVvHNi0TU9yYy9XYFZnJzOh7FTA7O7W801LPUm9/l7xz/d0S5ldNtRwe2RfcV+jeBL8cQnw1/UXJZtW8CJC2ed4l93bspkymJvXg4cspTL8Shuuz3aWAmQbzva/zQAH8StpgLBttU6kJGY5cVYaafG9ypDF/ojFmLE3+9ISk8kkJOkKaewsTRqzJf2YDznswNTsUPiwBZ1BrwuJ4DYhDjg64QD6OdJvGQQNs1HKfVVxrGZ8c5ynyp+fBObV0Az6fzMRm9VCAXFQAolFrOKuu4B6nV3PDz86ellsxQw0ACOe5VkmxzaH3gUSe2GJCBhAPOUPyVG/zjYFILbgwe17nYch1yT3O6pyJBXEgjswJKTwqDcy8ejw9Gq9UNq2O3b3uHnV7lrRdPxPI5TcFttS89Fcjw2qwhVrCbDDix7qfrFgHvOaOH9CFDzLnV79nJCufjH8sDru9RL0A2EaFsKtzT0WZOe9dYMpNKXll13PTT6vcJb1aSM9OnJV7sXt6oKmKAVdAjvUdK9Ttriwpp9dSVrxyibBSVryWMmLV5tus1y3w26xIPlr9WOC3vetpgd/2xr/lo1vgt+UhbngAC/wuDt17E28XZKHFr1Ki5KnwMwp+3R6n/GT4dTWYYkMOQ7fdv0M2+2svW2L2DTNDn+XgVzeS86WHyJtFetnn7JMvI1/qZoy6qwCpZC6hAGVTaJMaZ2nLizjFqTJcSvoAeLPZUrkxTOTJmU9VUhrJzunGkzxZSqjsCuxRNjAcDino4PCgUsONOZKNSMAe1PcjoYaFBhB3OcOa82tuUDmEuDF2Uy5ODSlYJ26Auz3BfSU1Yysxv8Vj8761vnUy4DXMyLgPZMKYdd2L3TUcr+fL6ZCHDaAf9K3R+yE6bp9T+uz9EnJgTlZfm03NyvnZs5LL58XtcCkrigs0INZsSK5H4vdkQ2DjPBDNKHsJk095ILNcE3F5pud5WedfJ7FMTIbm5g2qROR69GoBuKt6wIbUe4+WtlmqsU7vjr1dOMhATk7+JfHisODgwGNycfyS5lBfiwzrIQEgjs5ZJ7/LL+t86xXwAjCRkAP6qNOMwRSA1zgkyav8GEdtzTvPizzZc7y+QNZw9c9G3lDwyvpy+KQse854hnUeNpse0iB55rVsPqMHNurmrOA3qfcR8nSIx+uWeDypc+UzdSRcKuNfbjrEFRk7nxPSIsihh/ElpvRVmE8N99SOSx40+CVen019rJqZ4sbvOXjYGqz/O7LWuC2AYd5hAGiULl6vW6JRDsKohR7Xw7KgK6isr+FOvnD9Ujncla9ZXfndS4FywC//xlg5v7U+SV+P6yzw2951tsBve+Pf8tEt8NvyEDc8QOPgN6CbiomJ5hle1ZqfW8vNwqBhUoP0dt2agbqY33cn3l5kFsSYgLnj4adV9gxgHRubUpMr/gQdwaqlT5aa93Lgt/g9bGCI+cnpk0LeKbJCADF5pLjaRtMxseVsek0qnZItnh1qwLVce2v8Da3dy8YOWSJgBonwE13HlSGsBn7NvgFYF+bOirplzTcACxLbTb5N+hvA78zMnEzNzcjZyGl1wGU8xtnu31nISzbBL+8h5/MOhjTz0m820JhH7e88oPGmdM652bMKyPi7yZpSIgqmjnmpIVNwj4JUDL3Wdg3KgH3tosOAagDoWvSqXJg9V2BC6Y9yO7gLA0rN/F76mUhOahxNOTYMJjLdUFFurQkGDnQdlNHEqIwk7hWeIQ4zJpLjWn6KvOkdHbvULKtSQ/YOSAWIX40Oq3wcF2yzMQ71kI+uPyxvXHtTZpKzKmteuEB0DZCSIwkGNBslpURux24rY818YFdn03ze85qX3unuVPBHjju5ts1ulNH6VOXlNp0T4/IdgYQcwAvDbZafYr4bfBvlie7j8/NlDcZlSutV20rAbyafkoOdR0oOXOqdO88VjDDx4LCD3NAt/q0quV5t7UGDX+4fc7Kx1FhJfjqHUEe7jz1wttzjcYnP51Hg18rWzPzuxZJyZl5qrFavWosempnfXUmCboyh/19iumb+Pptt5QpYfdcbAQv81hux5l5vgd/mxnPV9WaB31W3JIUJNQp+OzoCWvpmYmK6aTcHAKs3P7d8cHJAP575UKZSU8oEsSl9ZsPT0ieDNcuez8+dlevx6wW3W8Zgk783uF82+7fMg1+fjI2VypQbDYQBfgMyOjpR6AIDLLOervmTzc6dyB35YvQLzecF/EJx5XN5WeNZq+67s5kZ6eoIyXrvesknFpv0FM8RNvnC3DkFCBkxyggBNLqdPfJsj5HD2dWFlHdp5tfsbzQ5oq7ZZg6w+ftMLiW7g/uMGrzBnMRiCbk1d8uov1pUC5Sd0pHOxxX8FYNf+gHE3I/DQt7RGsSdLuqsDmrpnzPTX+nvihvPgAaG+bvC6gSMxBewRH7t2t5+LXWUSCXVFZjrYaSKTZ/K13IkMSI4KI8lRwrmRwrE9dnIS5+3v8QUjXsjJ5T30f/t+E0FpOWFbZjf4fBRBe1X5i6VuFYzB/Kh93fuX/R7c37kNGseo92QtiNnhmGmpJIJgHl2n+p5SgZ7+2Vo9Iq8fvenWhYIQGtIhyktZdfDh28OvKJGalciVwzzH8kLoJ+UV2TdzBP5M5JtDl5wqibPFsnxlciQYTDlGTBY9uL1beDDAeP75/d/pAcDpuQZifP+0GOyt3O/HhbcTdzRTTY1n8nnLR5zMjkpw9EhzTt1e1zK/JqliSgvtZL5kWeuZc6KDnt45B7rOrToEIyYkV8NU8x6r2TcBsKoKRXELxqlHNmDaYyHjB+PAg4mvHaf5p3DtD7o9qDA74O+L3O8aoy0fsadDmW/+f7V7/mi3G/z70tJyWup313t3nkeSMWZnDScwK22OiJggd/2roMFftsb/5aPboHfloe44QFwesb0qt4NWSvALzdRb35u+Y1/MPlzGUuVOvF6PR75Zu/LWuallgZY+GLmc+0H0yDAAQZOBzoOFgyvyNNtFviFSUaShizPBLpmfqEhWTbKC/Hn1ORJZWrLGzLUx+dLKvX2dkkymVKAt1wbjl4RTKposIYqQ3Z6FcwdDR0zzLtqBL+34jc1/9dsbHaQIceyEfHaPJKx5WRTeINkUzm5PDukebCAAbOR17nVt136Pf2LwK/KKCc/UeBluu4CjGBWcSJG+l3ekKjvCu3WX6sbdnpOgTk1X7u6gnJ3ckROT55WsyR+D7MIAF6KwQTswPKZJXQWbtTAU6gCYEJpzG2tf71Kl3GFBsCiRIClZM5mI947grtkU8DIPb48N6SSzEL+dT6n9Y3L81Ppnxq8SKUpP8RnFwdkGrG6Fb0p05lJBRx+Z0AOdR5Wk6Gurg6JROLy/evfk8nUhJqBAWQpMcb7kFf/6vr/UvsjZ/h+YkSy+bQyu1xLbrnmDjsDytob+bYuuRG9Ll9Of6Eg1JQfI0l/uufZur9XyteRe8XEDEBP/A52Hlrklr3UM849YZhGvWTKPsE8EpOnup+RLR2NS295Vrhf7r24MdfNgS1qRmeuxdnZMyqBB0Dk9AChQ46FH1/yMKOW76d6r2kH+DXnqCZlecqx1fbdW++91XL9ow5+a4kB/67wHJC+YjK2tbyv2jX11O/m8xiPL9Rqr9a39XrrI2CB39bHeLkRLPDb3vi3fHQL/LY8xA0P8CiBXzalPx37cwWsxY1/+Lf6t8lu74KpUi0Bw/mTDT8mLTjmmg2wyp9KObrV+gUcLDC6rhLn5WzWyM0t/mM6opr9fjX7pbKo5Q2AjlSZZoDftMzNLX/Kfit+Q+4nRxYxVQDCI6FjRt5ul2G2VCnnl00t7DFxd9tdcjNhMLo0wBW5yFzDPQMwYd86HWG5MYeZmE2drU2gx3Ww1Wu96xczv4lx+XIGsOEu3DbAbyY1pe/HdIlyQRwYIC3mmQaI7ujYrUD0enRYUnmjLBO5uU9teEJO3v1C4plSJkxrt3buU2a5vF2cvSiJbEzuJO6UsLeM2O3uU3k55lbcJwD+ZuyWULLIvD/A/URyzCib5QrqvGChj4WfKInB3fhdiWgtUluB4S4+mOJ5IL8ZqS2/v5+4p7mtSJLpD4dkcnaJ73r/Rr0NQN+u0C7p7Q4r+P3J7R+rSRfgW92wNYfXJduDO7ROcT2N+bw+8hPtp7jR99M9z2iudTvbT+/9udzCPMyeF9JzHWKXHk+v/PLav9QwMCen/cz0aWWUi5spYycXmnYndluVFcVqCK7p9fTLY12ldbdhqU/PfKku3XyWWLt9of0Nz+wVvWAAACAASURBVLF4Xu0Ev+1ce3NsC/waec88B1NTlb0bHsQ6WXV+H0SU6xvDAr/1xavZV1vgt9kRXWX9WeB3lS1I0XQeJfCLidJPxl7TzXxx4x/+jdQi9dVWi5QN6u3ELUNSactLv6tftvi3FTaitYLfUqBrlhgyWMpi52WYNxyaR0YWZM9LPTHIZ6/Hr5aUP2K+Pe4e2R00amj29HRJKrUAfgGW5G0iaQYkmWBKGazZUyXgF9DS4+qRbYHt2pcBfm0yMTWlQNZkuwCfV2KXNYcW51RKyuTVgMutf7+bvKPvB5TiiAwI9Ho9IlmbjMZH1cCo19Wvbqs0ANqh0GEZT43LdHZSfK6ADLgHJZaLyN3YXZlOTYvH6VGpN5J2fg+ogPFGzgz767Z7JZmNK0g+2HlY7xMAGi7KlUV2HPD7ZC4WUQft8tbl6pYdHTsW/R7mF/kvjCjj+Z1ISUWZ1cc6H5M+70DJe94afUOSyNKLmrLMYpd1/o0qG0aqW6/iYjo1JXfjd3QtcIQmFnr44PDIno496gZOw+ys19unTDJ5yZSF2rt2p4Lf4ZlhZdLNxvODbPqb/d+SgGuhnnUt31oAwZ/e/7EeOpQ3coiRdLerwfb/4M739DNsmGUZ3wt8P7zY940C417v/IgXzG/52qWzaTnQ+VihRM6pqZN6yFDeHDanPN/3QuHXqC6IoeFqbSgHWDcODg51HVFFQr3PSfGYFvh9MDm/9T5HD/J6C/w+yGg/PGNZ4Le9a2WB3/bGv+WjW+C35SFueICVgF9O1MfHm5fzy02YpkhGfl797Z2Jt3TDWbxZ5B9+Nru+rAG0qjUj5/daCSPX7x6QY/Oy4qXAb3mOLnm75qY7k4HRXZAvFzsvA3xDoUBN4JeN97m5MzKZmdSauoBVZKgHQ4cLrGsx+MXV9Grsspp/MRfA6O7AHgVtNFyIb8SvaU4suZ5hZ3cJ0O/o9MvQzJDcm7mvbCPuzJt8WwSZM8C3uAFM13jWqVP27cRNCTvDCtJuJY2yOTlbVrK5nATtHcqo++w+6XJ36ZzWezfKncRtmc1MKxBjPhhJ7QzuUmkyTshG3mdG3ZyN7GTMjzo0pxAXZhJT3Xa3AuIB34DmV+LQG3aFxTXP0gF+UaumUplCvMx7MJk7gCJuyMR2Q2CT1v4kjtSNxVyKe0rnUsrgrfevVxBO2R+YV7O9OfL6vEnTQoToXw3FPD1acgcGeHtgp0rNa20YWyG/vhwZUpBEQ1KN8zeSalhw4oLpFdJks1EH95Xt35J4LKXKgtuxW9oH7DzuxftDB0oOCWqZD0AcEEhuJ/JpjKjMwxFid6DzgBp1tavdj9+TP7//qh6MlIBf8svDxxaxr/XMcywxJjdiV7VEDn1TVmnAW1pCaWnw65Ln+04Uhvty+pQMz13WfsgPR6LN+R2xJHe6292rioRGG+AX4G/mezbaz2p8H5+Be4k7ksqlZdC7Rr8Ly1sx80tu+s3odf289Hn61N18JQcLqzEmleZkgd+HZaUe7Dwt8Ptg410+mgV+2xv/lo9ugd+Wh7jhARoHv37xeNyrDvyyufl4+kMt8aJAx+aQw4OHZKd3j8Tj1Q1fADVvTby5iD0G8Dwbfl5r+Jp1eZEVG4DXpYYiahCUzxdkywbgzWipoeVaPeDX7If7pI4stXVhEYs3cCb4nZ6dlZPTn5aY8pgs3+HQkZL3qDxZ3URLLZnuyW2ZSc1IMrHAYrLRhOeDbSxu9A04xgYKqSldUUpnLjunTFYil1C2Falwn2dAnbjZvD7e9aSaFw1FL4rD4dJ5YNzFT8AsJX6+nD4pkUxUvA6v1iE21xbgbJR9ohxOXgElTY273N2SzKVLauUyH5srL66sW82oYKyRCFMyB8OwAe8auTR3vnBbMNKA8Q2mhDifUYAC4PQ7KZWykOcL+GUzTTs5+Zn2XxzP2fS0Mu+AXxrxIhaP9xxfZBS21PPCWlyau6CsrymnpR9i67F5tG/uRV2O582YOLQI2oOyo3+brHGu12dypQ0TsjdHfiqJbFINx5BeI3nnQICf5Od+c+DltuZ6wtJ//8739BCgGPw6bQ6VPZt1mRuNRSKTkPvJe3oYQp5vcQ47fVIq6vT0VyU56VxLma49oQUw+9H4B3IvcVefB3XTLnwEbWrWxrONkRafl0baowp+MW0jp5qYGuqTjLqVbwuWKjdM8Hvu3kWtG81nTnPTc1n9vKzU/KyRNXnQ77HA74OO+MMxngV+27tOFvhtb/xbProFflse4oYHaBT8YtCEjHV8fLGsr+HJNIH5ZWyVhCZuab3ZDd5NsmXdBpmdjdYEfnEn/XDq/RIzGwW1kpcD3Y/Jgd79CnhNoGvW0DXzdAG89bZGwO9yY5jg9/L4Fa2TWu7ADKN6qONIRZakuF/Yu0upcwr+ig8OABUwoGwcTeBF/VPktZR9QoINCzyWHlW5MmY35DIChp0OpzpVBxx+NXJCnkuJmAuR8wqSaYwLwEUqnctnZGfHbhnH8VkBgigjzD0B7pDbInfn7wBeSs0wJ4AsYCSeiUmHu1PZ5en0tEqwXS6HOPMuSWfwuGa9YGSdsjOwW67HhwvyU3Mu3MPejn0FsA/TDKsI61xcfghjK/JmAX7M6aOJD2SO+yYPGbY4n1I2GuYafEM9ZWKzybdZawLX0gzzr0/lduxmwfyLvsllhinE8Xg4Miy5eVYe4AswgM0O+L2yy7+3KeD3ZyOvaxkkE9zDICO/5p62B7fLwa4jy7pn13KvtVzD4QkqAoC/KaEvfh+GV19Nn1J1gFmChefpqZ5nKnZPLGHXeb5ZGxy7/UVlo2qZU/E15Gffit3S+sCsA5+Zg12HSz6TlAU6NX1S3wb4NfPEYe9xNGfNWb9tQSMVod72KIJf83NAXItbOp+Wo13HJFhUUgzwi9Pxq1deK/ls875MLqvSfMrFPYjG9yAHfXxeOQQx17rVY7vdTvWpsHJ+Wx3ph6t/C/y2d70s8Nve+Ld8dAv8tjzEDQ+w2sDvwECPzM5G5suSNHxbJW+kT1jaWmR/AKu3J94Uh8OujCn5uDQ2xS+seVG6XT0K4ijzhNvzUs6ZXH81NqyGUIC0dd4NMuAZrHhD1KDs7OyQ+/fHC6+zuWMD7rDbxWf31yXNM8Hv0PhluR69Jul8Ul2RVSbs6JC8XeRgx+GS0jyVJgaYGUpdEJejFPxyLXMDUN5JIjnENEkUWK7zrNccRRrvJwZs6AGndqdd3IJJkE3r9gIEu129sjOwS8EvJlk2dR7OSSRL7WGHFo1k048h0ExmWmXSgFhYH1P6zLhep1+CjoCyP8wFKW/QGdBNMPOAKSe30ulwSY8/LIlUQtKZrPR7+xVE4lxMLi851cUuvqwD4BfwgaySxv2OxEc0Z7e4ritrzr1jqETjvZSAYuwOR0h+Pv6OOj/7nF6DicznlAnGIZhyQbU2zKUAn0YtXkPKbtS7zcszvc/L5dlLMpoir5ocbbfWFgaA+/2epoHfH939geYblzdAxLfX/IVab2VF1wEqb8Vu6pprLrczqDm3Ztkns3MUEsOJIYknkiojNln88sGJ17mZM3pIgWLEkNnjyL1DOldQoofnj88AgKcSkGbcd8bekvHEmD73kjeYyfW+9cpOA/C3BbfJGt+6huL1KIJfPVwa/2CR47Z5UFDs2g74zTtz8urwa0KprOLG9cR1T8jwS2hVY5zzs2dVDcBBG59dTOYA3pWk2s2ehwV+mx3RR6M/C/y2dx0t8Nve+Ld8dAv8tjzEDQ+wOsFvbSxtrTe9FPhdynn5vTvvyc3ITbHlDZMc5HGAl6e7jNItlerylm+oPp/5REv/FJet2ebfrq7T5a0c/AIqLkXPG7mteZtujvYHDxTydKvdtwl+p2Zn5PWxn6jsk/xgNmDkz5JD+Hz3CzUB6uHMJZUGFzO/sCvb/Dvkk+mPDGbTRokb+kc6LbLZv1UBGcDvRuKGgk/Arsp87Xbx2nx6T4D6Tf7Nsj2wQ2ZTM/LRzIe6mQWkI9tNZZLid/mk292jgBeTrE5nWHOKkacTH7fNU6i1G3J3KLOjRla2vOYLb5037vps6pN5gNQhSUdMosmYZDM58Tt8Ep6XIaMUoJawvax0UiQdVWbIBPVsvMn/Jd8XYEzeNI17htUzpdfmOl2NDMuVyGW5Fbuhv9JDCFdImUWAzTM9z8laf33ABnZXc07F2MwD0mCdNwY26Zxg/E3nbV5XuW14oGmy59fv/0Sdpsuf+43+TfLiwDeqPaI1v06sOcDhJ5JynNdpkfSckCtbXEKH54u47u88UNI/KQkdHX6Zno7oM7hUuxenbvDtRUoJ4ri/szazvJpvrOxC1g8GGEk76glKSnGgoZ8Zm10e736yYZbwYQW/fDZMp/jyuPLahxMfCBL24sZzvs6/QWsKmw3w63Q75EfDry6qs813G2oJs9xYo+tX7X1jiVE5M3u65DPJ2nI4dzR8rNrbV/y6BX5XHMJHsgML/LZ3WS3w2974t3x0C/y2PMQND7AS8AtoGxtrruzZYH6bC377+3skEolq7q3Tabgu84dNsQKDXK6kvFAylZILc+cVvAEXyand27G/sHEB/FLneCl3ZkyWvpz9YlH9WUAZoLNc6lYMftkEfzz1oYJUs7FJYiP8eOfxmgAr4BdzLWrZfjL9ocxlZg3/6zw50HYty/N0+Lma+sp7s3Jx9rwk4rBhdgH49rr6ZINvo7w7+baCKmIEuKamL/mw5L5yWHA9dlXSYuTI6j3bKDdj3AvGWoDJI53HCmCD0ksXIxdUxgpEhIEjfzXg9CtrhlkWZmawyNxP2BNWkEBeMDJgTK2YD39f79uo4JQWTUfl/fH31KALcG5z2sSWxSnapcZQJlMLkGKTWmyYRuxhT490HVMWn/5hUmPpuNxK3FAXbdaVGPA/ACiyWrPhiv3RxPsK3qjJq/mn8LA2h4ScIXHYnXKi90W9l3obEso78dvaH+tRLPu9E7slY0lTSZAXn9Mvj68/UjC8qnes8uvvxu7IO+NvFUy3eJ0SPb/Q/7K6TDejIXXHNConWb1HA9islwOdB9UAjbzj8hx1rkHSXPz7WsHvlbkhiVQoIwbQOtb9RDNuqWofzB8DMe4NdhBpPeDM6/RVfe9SFzxs4DeeiWvZJw7WOFmj9NhjnQfn1Q0Ld3l6mmtQiCz4FABmj/c8VcII402B7PmDmx/pgY2ZC28Y0Dnl2b7na865b3QRzsyckanUYjd/1vtE34s1fRc3OrZ+Ni3Z80rC98i+1wK/7V1aC/y2N/4tH90Cvy0PccMDrEbwW6tEebmbLnZe1hI7gJ75ciemEZXpvlzsvFxLIKuB34uR84VSP8X9kV/2bPfzCuiKWzH4ZXN2MXqhhNEAEI+mRlTKC7DDeXp/x2NLMkE9PZ0K5k/fP6Nux5R6wT2ZDFlAJNLbI51HxeeovqFGjg1ovDRyWYFvj6tXN+TM6d2JtxaZZqnE1xkUh80lI8n7CgjdNrdEcxGVNJPj22EPyUbfJtkfemxRDdTL0UsKmDOSVkaVjSrMO8wo0l2MnpCewxwjVzYbrPCWwDZlmTHFMg8Y2OCenTktI8kRZY5BvzwbE7FJdZ0e8A7q9UhnYX+8Dp98OfWFlgyCycbgjI13uTkSgP698Xe09JEB/JAXY+CVlUNdhxRs0sj5JDeW2AGe6RNmT+sa+zboxh4wVyyfrvYMck/Emf46nZ0SdpcanpnvJxYwpNwf8wmHO7TUUTMMrxiDOrbnZs9KPBsVvyMoB7oOyqC3srS/2j2Vv849cmBR7FjNNTwLT/Y8rQdTsO/ljTU+3vNMyWejVvB7I3pDJlNjBYM43KwxWWMuu0N7VDJdzDTXe0/1Xs+4zXAi7ujwSTb7cLg9m+vO58u8d37H5/2p3tI8bQ4leP6oa833DMqL7cGd0jOv5DDjbYBflzL/MOt8J/Ic4QmwO7T3gciOL8yc03EXrWde5Lm+E01Z5+WeL9J0AgFvW3N+mV8Dlhj1fmys6+uIgAV+6whWCy61wG8LgrqaurTA72pajdK5NAp+Mc8ABDab+TVZ2lryc807YXO7AHZLnZdjyZh8MvaJ3Ivel2yGGra9JWWBGlmZauCX+sDnImcLDIM5BuzVc90vLCqxAzinni45v4C74djlEiffO8nbyp4CrpBgApwGPWvkyfBTFadvgt+hscqGV0iDj3U+sShfrlJngF/ynqemYGNL2+fTnxquzEXMCxvSrb5tWrZoPD1uGFHZbEKeatqWUvZ2wD0gJ8IvLSrxAygcjl4Wj9OruY84KhtmY6K5sZ2ukAJiShkV504aubMBWTvvtFw8S8DfpcglzR0eT44p+GUzHE3EpMfZIwO+QQXq5P4CEs0GuGfTvRTYQZoOI16cH6zgLJ+VTf4tMugzQCCSZ2S7gHHM1NRV28Z1Odnq36r3taeOMja8H3CupaZyBphHFv5s7/OL5orZF7HjkIM4Nhv8NvLZqfU9PO9vj75ZcsDBe1kT1pm8XZi/8vxsZKTk/Ra3WsEvhwWnZ77Szy3PCsw6DYaefGKeD+ruNgOQ1hqHZlyH5JsDvnq+U5sxbiN9jCdG5eT0yUXfkRxOPdXzbEUTNVQVfO4Av5XWBtYX6fPMTLSRKTXlPZF0RD6b+liZZrOZHgH7Ovc3ZYzlOrHAb8tD/FAOYIHf9i6bBX7bG/+Wj26B35aHuOEBVgZ+fWr61MxWDfxiREVpoQXpslPBGZvicudlmF2kuVGZ09xd5M0wb73ufmVgG23V3JnZ1Hw09UGhxIoBinKy3rNe9nQsrtdZDH7ZgH88/YHKPGkwtpRTATxR1sj8Pa99s/dlZSrLW3d3p8ZiemZWTs58prExN4XMDbZxd3BPydvYPNLKnaGXA78A2i9mP18oPSQO2ezfonnNV6NXZCw1JveTd7XGcFayat6FPHq7d6d4nB7ZFdhTslklj/dKdEjcTq9KXWGOmQ/yzy5nWHNpKWdE3V5ygmkwPuTtbvBvqsiEwwqRSwlI4tpoJipOj10kY5ft/h2FskP1PguAMyS55eCYgwUYbTPvl/V8f/xdw/GZesTpWS0L5Ha4NY8UNrE85svN5Wrkqnwx9Zneq/ksAILXBTbIUz1P61tZl5ux6yq1NtbUqeZJG/vXNZX5rTdm9VzPIcpbo2/qQUFx4/mF4d/ZsUuuR67pIQuN36OoIKaG+ddCqxX86uctE9PDiouzFzRtAffqjnnnYJyFKbtFWaOHqa0W8MsaIVPms7iUgzZ58RdmLyz6TPA5Otb9eEOxXw3gl+cFP4Hh6LAa75H2gCs+z+uDcHwG/GJ4B/vdzmYxv+2M/uKxLfDb3vWwwG9749/y0S3w2/IQNzzA6gO/3RKJxJSlwBypWL7Mf/M7WinQTQtS5nIvG5i29ybfEY/brcwH4JcGGPxGz7caLsVSDfwyBkCO/FVMrwA3/e5BZfrKmQkAyoX4OYlLVDLJnGwP7FR57pXYZWUrkbbSR9AZVMbTbGwGX+h5SVm/8maCX3XNzsZkOHpFIrmI2PN2NQzCrAogiHQUJhX21shYFXVM5nUTQHR2BjXmlZhfM5YAUeSpfe4+zY+lUdf3cuyy5u1RdopDB5/HJ1v828SZc6lcmBI/sNnF7Wp0WHK2nOZjw67iKk2JGErOAB7X+dZJlzusTA9ABblxOdgp7o8NN2weUFGbTcTv80oskZB9HQcWbbL1ECWfMdyp5+vkLvXhujR7QY24TPDKWDCE5UzOaGJU5Za4SbP+SJUxxlpu3kuN+c7o2zKRGtOYFDfmSu1a+kdqrZnY+bxKrFELAJSPb3pc4tFU02TPDX/p1PhGaiWPc6/F65C3yYn+F3Xdjc9ZWvNjOQTCCbxSqwf88n6eq5NTnxXGKO6TPPWtwcWmdTXeUlsuWw3gl9JgN2LXJa3O3DZVauwN7V+knOBQ6edj7y76nuS5fr73hYZk56sF/Jrfl3wPcABQrhpp5cNhgd9WRvfh7dsCv+1dOwv8tjf+LR/dAr8tD3HDA6wE/CJ9Hh1tDvNrOi8j/wWowuY6HEZOJ38383PNerrLubaawaDMzlezpxaBXyR0x7uelbXetQ3FjRzdUCi4pOGV2SkgCubRazfcjcsbG/efTbwusWxUnC6nghI2hkdDj8ta7zq5l7yn4PVi9PwisAPg+mbvKxU3UMXg1xzTjJcpQT4XOa1sNOwsQNTj8Eqfy2C0AEsHOw/rfwN+WYfbo/cUfMKuVQOFhfvPZeRW4qY6D/O+teFBSaUykkql5xn4PjWsKm7E5G7qjqQkpeANEEdNYJiiHndvQ4CR3FDYPGVKkT17XbLOtV5CjlKTKQypMM6idJIyM54elTAvJXNlfjCsuCsTGxh13Kv573LwzLUYadEo8dSodPZnI29o2ZzixkEAhwwwkswbIy9yfK9Hryr7awLu72z/JQnmjHxwmFXmaYLIhj4ILX4Tc8T4COMrDoQ4WKAkTb3Ma73gl3h+OvnJwoHJ/H3C/O7p2F9S27nFIajYPeqFC7Pn1WwNk7EN/s2ywb9hyakY4DcrsViyHdMVDKyoY1xcgkqdjiscFDFBlBooR9S1XdUdOdnZsUc2BTY1NP/VBH4buoEmvAnZN/9uWcxvE4L5CHVhgd/2LqYFftsb/5aPboHfloe44QEaBb9+v09wEW0E/CJlNFyXi+XL8xsdamtmc5JIJAsOzCZjW+9NkjMKuARYFjO/bKpe7vvFhjf+leryls/tWuyqXI8b4AOWDsBzOHS0hLk4N3dWzkfOqrzS4XQUGDlyDAG2Zvts+hO5m7xd2AzCMMIQ7+uonCtWCfwWz+/83DmJ52IqRx5Ljej86JM5YmYFO7M7sEc6XZ3iCTjl4txFGZ81nEphdjd6N1ZknCutD/2ej5xTMBjqCEgimVLwC8jd6N0k05kpZSdhQXrdfYYDssMpebvhIl0MzgAlbKSRDNcL2gBSMIQ4Tm8b2CLJWKaEAaXfs1qKxFW4DaTXSORr3XRTpxWJOoZi5I3CylOeB5D5+eSnMobRjoK4gOwL7df6ooCA+4m7MpGaVJMzXKrLSyWVrN3MeTlXVDKFKI1japYXZT4BCnPJiGRtWS0vVSx339K5Rb41+IsyNDWkwAmmnzjiFB1yG/VkYd4B1xyGUIKqHiOuej+ftV7P4QwHNcy1kUMDl8shwWD1UkfF8zEl1Wa5KMC33xmUg12Hap12S67j++S9sbc139uMBbHhUIB60ZVau8Evee9jSYzESjXs2VxGnug5XlH2T3oANZx5yybflhIn83oCy7MzkR0Vl9sloWx3Q8xxPeOt1mst8LtaV6a987LAb3vjb4Hf9sa/5aNb4LflIW54gAcBfovLC5klhtTIKJ8vKTEEu0uZnmg0rn+a0b6Y+VzuZm5LPmeUNEJ+i6z3QOhgw91XA78wfJ9Nf6o5rmbjXgF3B0MGo0ojL/hu8o7YbbYS8MuG+5f7DQkrjfdej1+Te8m7ClQ3+DbJOm/lurCMPSL3DFMs21oJu7oX3Sc1iMlBRX6HNNxkcj12t4I9mPEtvm3KsF3LXNGSQ/H4AmuUyWflYMehmjeSSFfvJe5Id2dYwW8sEZOgo0Pr9RbXooUp3+DdKGFvd6EMlTn5u/G7MpK4r+CRqIbcnVrDt1YW2uyHkKIuKHc9ZoMOg7sYXOXlcPho1WeFXEZq+brsC4Y2AJR+74CamN2lfnCJjNomL/X9glyJXlawqeWblOnOKss14C1lxM0J8Ay/O4b02ShjhDIAJpDcVA4FaDDdHGDAtptwg4MESiFRLzaeNuTXZgM87erYLR9NfKBydeZp5Inb5XjP03oIshoac9LDJJu9LsloI+CX+x2J3zdcwsk5d3UJNYzrfd6aHTcY3xvzBmrFfVOy60T/SxWHazf4hcmdSE3UBX6bETek1hz08bFDSZRL51Vqjcnd161Z4PfrtuK13a8FfmuLU6uussBvqyK7Svq1wO8qWYgK02gc/HqVTSlnfkudl6mla7BPC4ZU6QLgJW+3vPX1dUss1hzwi9Pv/dQ9cfudMhodk0w6qyV2kBSvpFUDv2dmT8to6v7ivDWxywvdLxV+f3r2SxmKXjKMoIqY36AjKC/3fbvuKQ5FLslQ9IIy6nqwkMnKjsDOReZWHAgAhgDIAGrkwFyPKzDSYgAmLDWb/qHUBfE43SVOsbxvwDNYVxzJo8wEEhJPJMWXDchselrLyBQzk4Bfj8MtO4K75WbyutYnRkqMMcy1yNUS2SRzIH+Z8kS1Nu5RQaM3K922PrHnFkolXabGa2axGQyM39Hux6sOAXhGvr642eRG7JrGurgxlz5Pn4LU4gMArgEIP9G9dE1n+oIVA+TyU8FzEegmVxqJLnmwhilWXqXQdoddGWe3rdQQiv6imZhMpydLwB1zpIzSkz2VXcWrBqWJF3AwQd40BnDcE2B0f+eBkmdiqeEAv76ARy7cuyQzqRlVOKzxrW2IRW7iLTXU1cnJzzXnu1L75sArFe+p3eAXp2Pk6+WyZ4zqKI/VisZ32Htj7+izT9oGRokoTjj0e75vca31VsxhNfVpgd/VtBqrZy4W+G3vWljgt73xb/noFvhteYgbHmBl4Dcgs7NzBfkyQNd0XibHzMzPNX/WMslmgF827Z/OfKyMG5sgt8uQ1B4PPbMIaNQyp/JrqoFfQO14ekzL+iDrpcYrG3af3aeMrmnaBYv1xvhPJJFLqLGX5vzmRQ6EDiloradhsPXm+OsKWOnLPGxg3F/ofblEJgwYG0mNKMjC8GomMy2SE60fDEBa792ozDLzG0qdV2fm4jIpBnDrl3Xe9fVMUfr7uwusPrJw+gfw3ozdkFg2Il5lKm1a4xcQDKBj/WBQNy/higwAIp/XrPna5e6WLYEtCuLon7rJpiT4k4kPZTI1KXaHTWXwg55BrbHJphxJMowaZY+KG+VtkJTSeC/9EWuP3atMoOlaC3hO5hKL4sHcb8SvJ63K4AAAIABJREFUl7h0cxExdDs8KvMub8QExrUWaffVyBX5ePLDgiSevpD74/hMTiXNTj3kvIjP7dXnyiEL7LQ5l3vEiYegrCED/8bAN+ta5+UujqajamIFqKbOai0NOfZHE+8bbmXzTXNGXR1ypAZWPu/ICgc+U5FpLZ1Ef5SYggVvZd1eDhUA7RxuICtvRhuauySseTkDzWfnmd5n5V78rio6OOCD6actB37vxu5oTj6fMw6ZdoX21OU+Xus98Rm/E8eZ26b525Ql2hc6sKjcWa39Vbvufvy+nFHA7SwBv6QkHO46Kn3eh8uxu9r9Vnt9NYBfvoezi8+7q03der2FEbDAbwuDW0PXFvitIUgP8yUW+F29q4cMOJdLV53gYudllwJdWrHzMq7LgLhaDKkqDdrXF1agtRLZ8+XIkJyaO1kwiYKNBoRs8m7R+rYrbcWliSr1hUHPF7OfyUR6XDLztViJBzmY+0MH5UDHQh1S2MZzsTOSsMWEyjSbfVtki39r3VOktvBnM5/oRrsY/Bos7hGVShcDB8zAJtPjmksJG+K1e9R5ecA9WFKG5Er6kuRtpTVCAWcHOg4uCc4w+Rqnpq3kpMPRoaZWbNYBv9Nzs3Jz6paWQJpOzwrzxikaBlhNz2Ao+Z/NpuCQuEWzEXHbPbo5H/AO6n8bLa8GT4xnggHut9MVlgHPgFyLDhcOO4YjlzXHGfCDu3U6m1b2G1n4k91PyY6OnTI0d1FmM7Oa92vIq22yO7RX44EZFq7Z5OWaDXBzoPOgmnAhyQYYF5ctYu7k97Lx55ChuNE/JXtMyXPxa8zrqZ5napLYMsYbIz9RUygzBvlcXrK2nGRzaY0PoJZDhRPrT0g8mZR0pvTzDiCg/BLO4uWyb+TUHBCspJlMNXV5ed6JF3HiAAWQXw7kyAf/ZPIjGUsgORZBju9z+MueN1y5s/JM73NVDwkuRM7KZGZCzdYAXuQ688213rdBwV4r2p34HTkz/aWWAeN57nR36XNWyfiunvEB7j8ff1fXy1TUcD/kZ/NZIkUBTwPTGZ3ndynwC5BGRs1njWaoKbrlud4TLWHFOYyifjLAl3Eayd+uNVY8O19On1oEfvnuOhp+Qrrdi9NBau37YbwO8Mu/WzMz7St1ZIHf1ffkWOC3vWtigd/2xr/lo1vgt+UhbniASuDXdF4uLjNU6rycUeDh8bhV9two0G0V+H134m0ZS40Wugf8ah1QW0Be6fulhmNlvrEa+OW6Dybfl+HYUIGtotZql7NLcxVf6v2FEgaaOIbDIXWPbjSWSF3fn3pHwQ4GX9CdHEoAsp4JP6dy5vKmrGoeYyTXkhtRu0+Ekj6z0UjhmnWe9QpCK7XR5KjcS94pGEcBNjBP2uHfKf6wW06PnJF0yjgcoUYwwMs8RGETjwkO4JYNOS7ZkeycMnUwOIBzQNzWwDZlXoklMuZi9g6Dm+n0jNjyNgUa1GjlPWdnz2guMxtwmHgQECBTWTlXp/zl9b+msm/iOJOe0jlwjyaYPTtzulA317xvQxYclq3B7Xo/HLrEqCM8z1jz3p3B3WouBTsLqGMuPIsbAhvlsc5D8tnkJ6V1mCUr/Z5BrWNbayM+52bPKADmfgDyHDhQ4olcY5/dqyB+/7o9cndyRIZmhhT8KcueZ7x+ZVFhV4vrSHMwAjtXq+FXpfmqc/LER3I3cXc+p5px7VquisMF1rJc+vrG/Z/I/cRC2gAsNgdHlBgidxSWHxDDvT7T85zsDO0uDG0C7cnUhILqQe9auRa/IhlbWm7P3tWDFlMGjrrhhYFfqDXMNV+nio6RH+vBUvGzgoT/hSXycmvufL6806W5izKXnlEDOmpFX4lckVgmIql8Sj/TSN6JxbHw47KhZ90it2de49DErAVtjs+cHw8/Iev89ak66pn/g7iW+3t/7F2jvniR7JmDrWd7n28p8H4Q91fvGBb4rTdiX4/rLfDb3nW2wG9749/y0S3w2/IQNzxALBaVK1cuyOzsjLz88itCPUDAIg1znXLpsum8XEut20YmBfOLuRK1fhtt7028I6OpkcLbTfAbsDWWS1s+j1rA77m5M3Itdk03ow6xK5jiUAEA9kLPNyTgWCh91AzwC/h6b/JtZRhxNgX8snbIO090v9jwZo+STsiEr43c0PxU5n0tfk1ux29KVjLS7eqR/R0HpMMZUhB3LnJ2kcQXoEJN35Q3JtFETJLJlDKAF6MXJJU1SvHgjgxwx8AJGTJgh6aAEVZYALMAWRG/MyA7O3bq2ABOE/zSF4ZfvIfGdVyP5B3jG+TKSEJpRl1jwK9LmcUj4WP6Z6l2curzItHtwlUwkqYsmvufSc8ITrW4JMNUm6wmcxqOXFGJO4xjr8c4jMCs6kpkSKXK3L/WkQ1sa3i9lvvMhMOG0VcilZSxxJjmBfe6e8Xr9OnbRhMjAhtIXi2S682BzSo3X0mj5jDMN6y4CbQ45ODggrxdmOViWfVMalpevffD+arTxsioNuK5uLJ1AF+jIrUB3nm+vznwshqLEX9yS4trL3MwkMwnZC47K5EUrNeCdNptc8uvbfirNeUN1xODi7MX5OIcjGppLeZcPi8vD76iLHYzG/f41ugbejDA4Y/GxmZXSf224A45vv6JReCX5+6NkZ/q5668bfBvkkNdC8Z81eaKt8Kp6S9UxYBaAxZ6S3Blz021MWt5Hck5h1YJiauvgivv0QOnWuX2tYzxsFzDvzFer0tmZir5EjyYu7CY3wcT53pGscBvPdFq/rUW+G1+TFdVjxb4XVXLITdv3pB//++/Kxcvnpfr168pyO3p6ZFXX31NPB7vPOBNa17kUq3WWrf13nkzwC+S3s9nPi2RPbNB3OrdLkc6lwY4tc6VupFdXTC145pPWqnhEvvR1M910w67hvQRZjHk6JQXe79RIo9tBvhlDjBkX86elIhtTpnNgHSo5BkWqN4GkCBf2evzyECwX2anowpUv5g9Kbfi17X+LqCUZ4dN9rM9J1TOeGGO0k2G63BxCzk6JOvLSD6TV/CLARbgF4aGAwHAEA1wCmiALWYjj3yTEFOCCeYM1hYA/q3BVySRSchnU58UwO9kckKvRhJKvAF1ACUA1nhyRFld1sNsvEb+Kw7Nx7qfkKPhpY2tzs6c0fq/xY0Ydbt7ZUuwcomZemPe6utN8MuhyINqlHjCuIxcVEpamQ3g2uPp1bV8qSin+Gbsprw9+uaivFOeC1QqsJpmw+EYdn1jYLO82P8NmU5NyZfTXywCsxyIoBAo/qzSF8/tY12HlSlvZjs/e1aGZi8pO819cjijn5V8VjClol5xMxsx+dObfywZYbyFxu8Pdh2WZzc8swj88n0I88scixvvOdR1ZMmySeXzTmaT8uN7rxpGZAUHcZscDR+rS73QzHiUf0bTzoS4nC6RhKMlh0r1zJ343owaaRCUuNvo27xsabN6+l7u2tVQ69gCv81azeb1Y4Hf5sWykZ4s8NtI1B6i91jgd3Ut1vvvvyf/9t/+oWzfvkP27Nkre/bslg0bNqozc62tmulTrf2UX9fbG9Yavythfk1J7c34DWWbvG6vuhM/HnyyKWYuC+B3aZky7N+Px36keaYLJYtyssO/S57qfqbktk3wOzo6IbncEmi6joB2dgXVbCsy2xh7DjAdil1S8OnxeNQptTffr8CRWriR7IIEmmkhMUZ6eSR0TGC8YTBLN9QZWedZJzFPRB23Ab+sEeBXQQ0uX5q/a9Qb7vZ0K+iFuYH1w7iHP0iYaWFPWE6sfUH7+B/+3t8VWewzpdft/K92SjaSlWQ0KV3BsCS7ExKVqAJiQImRW2wXR94hf3ng1yTk7VTlA3JxctxNYzL6gpFEamqyzKY8/bGuQ3WV3THjQk3gC3PnFXzATO/t3NeUZ3O5x6Qd4PfLqS8ECTKsILWMzfxSnhGY323B7bK/cyEHHjD1g7t/VmDvzftRhjJvUxk8a1cs1R/0rtH6xcNzVzTvtTyXNJ6JyWR2QhLppAJQxqYMFMB7Y2BTyfh1fMyWvJR5vDv+juTngTopD7CNlIx6qf+bTQdgfE6/f/tP9buu+N75LB3sPCzPbHxan+nicmVM/sz0V1rXuZArns9rfjtzrLWk0xdTnwtMd3nMud9fWvPLzQjnivvw+dxqyjg72z7Wk5sA+J6aOqmHQGY5MdaIlARTCbLim12iAwv8tiqyD3e/Fvht7/pZ4Le98W/56F938Ds1NSV/5+/8N/IP/sE/liNHDObx3Lmz8ru/+0/l+vWr0tUVlr/xN/6WfOc7f7Hla1E+AJv4HE5LdbbVDH7NW6FmKYzP5v6N4sn7VgSoi8NTC/jF8Zn8VzbrbHbYsCPD7XZ2y9Pdz5ZEm3yscLhTmgV+yR8GRM/MlJos1brEZ+dOFxghgDngdzYSUQZ1KHpRpdzlba1nrTzbfUIdXTH6YsNP4/kC8OwJ7pOYd07uR+4rWCYPLy1puTh9QWODLJgSPHs79komn1NWhNgNzw2rUjXk7FD355/98zdrvQ29zrXJJZIy5pGZzIh3m1cCOwKSS+bE7jHq2c59OSfpW4Zc1DB2c+kfn8+nr6dSC/dr89r0QIDNvj1jF4/bI263W3w+v/7MZNLS2dkloVCHXL58RQIBvxw//rSsWUNpHbtEoxG5Ebsh51KnJZOFdbMp0Ee2i3y3VtBRVxDmL64EfpGsjiaNFAEk1x01OjDXOj5s+8mpzzS/dyw5pvLurORUroysGhOo8nsGUJ2fPVcYgjVA5o5U/nbsVgnQ4rU9nfu0NBRSbnKfyx2c1cXakZe5RNQAv8xGUxByau7U6W5eHWPUCB9M/FzZblOizWcR1/Bvr/mOdHt6ag1dzdcBft8efUsNyzg84H75/HU6O2V7x055fN3RiuDXzFPnMwsw4zDCNHCrdXDKCRkuzqWNwwny6FtpalXrHPm3Cv+KdoPfW9Gb6vxebIrHPfC9d7R75Yqk5eJhgd9an5av13UW+G3velvgt73xb/noX2fwe/r0l/Jbv/W/yp07t+X3f/9fKvidnZ2Vv/pX/5L87b/938uv/MqvyldfnZJ/9I/+J/nd3/2/Ze/e/S1fj+IBHmXwa95nb2+Xso1zc7UzocQFiS0b8/I6rLXIlCmtMk0JobLmEIec6Hmx5LetAL8qW56uH/wC1E/PfqVSYJoBfh0SjcZUUnwvfkeiuYX6vFwDmN3s3yKPdz2p7xlJjMhUhtzMnIQ8Idka2ip+r083oNcj12UyPimpbFpceZesca+RdDpnlJ9xBA1QIjmZzI5JMpeWP/jNf9GSz4M9aFfQKW0wP3VvcYtnnUfLSiVHk5JP58UZdIon4pFwPizhcFiOHTumIHpo6JJcvXpVAoGg9Pf3y82bN/X7w+/3yeDgGlVsdHV16bXJZFzZve7usExOTing6enpFq/XJyhSQ6GAfgbMdIbbsdtyL3GnwFxzALHGu07WN9ns6H78rgxHh4XDqEw2KyFXh7KtfL5uxK4r+0+OMaWnery9yupSeomSPmgCNge2yCb/ZnX1Jk8VUzFDRmy4E//imu/o+3nmkVmTV22CLkDdWv8aWRdeK+/d/LkqCdR0TPKy0b9RYO6b2Sh5dTt2cx5cZ1XeCtjBvO1E/4tVnakbnctnE5+osRrPNHHhOwuzp6d6npXBcF9F8NvoWMXv+2r6lJASUH6AQYoCYH81tNUCfs/PnDPKypU1DmSe7nmupQcFFvhdDU/i6puDBX7buyYW+G1v/Fs++tcV/P74x6/Kv/7X/1L+7t/9H+U3fuN/LoDfH/3oB/JHf/Rv5Y//+PuF2P/O7/wfkkgk5B//4/+t5evRDPBbi+lTIzfSCFCtNo7RZ1rm5mqTvcEWn4+cU+YRsyryOg+FjhRAcC3g90r0slyPXyvZFLI5h4051lVabgmpbXc3zO+k5tCutMH8Ngp+kU5+NXuqAIhM8BuJRqXH2Sv3UrflZvxWwTfIKOcTlGfCz2oOp9tNqSWX/kRGb+YFk2cK+EXSPjsbUdatnAG5ePGi/If/8O9Wevur//2m15lNlH2GbQaw5JN5yc/VL3uHoSbOfH9Uen5Yh1AINUCucI3L45LOg52aEgCTzR+n0yW9/T1yrPdxGR8dl3Q6LYFAQF/z+wPS2dmpfRhycLJL8/N5tMZP/qRSSf3JM238zrifsfiIfDr1qZaZMiTPnZoDb5atiqRnZTI9JUFnSFn+bk+v7O7YvQhUIWHGORtJPC7dxykf5FrIoYUFBYDO6Dh2lZTv7Nqp5X5GJiZUGo1yAeALcObQhZxz05BupQ8PhmF3ythp+iTvH5aZMk+taIlMXL6YPqkGaqwMKomtge3q1M29V5I9N2MeHJj8+N5rCrzNAwcSCp7oflLduVdDM8CvQ2YbTANp1j0MR4blfuLuomeaA9Eneo43a5iK/Vjgt6XhfWg7t8Bve5fOAr/tjX/LR/+6gt+JiXFlZNh8PvvssQL4/f3f/2cyMnJffuu3/mkh9t/73h/Lq6/+UL773f+v5eux+sFv7UC1lmD19HRJKlVbnzgCvz3xluQkW9i4kyNK6RizRnAx+KW8zJ3ELQUDG32bVDpI0xIv0x9rfiwgz5T/Hu18Qo12ils7wC8A/1rsKrawyr5qDVxKw3jWyJXYZTV3YjNrgt+5SEQeCx1SkEau7u34LcnZspqrdrT/mKwLrVGGWDf6mawCJ+qq8pO/08rNzL73vf8oZ858VcsSPlrXgB3NMw68wTwitqxN8on8wu9bfcdOEdeAS3KZnEhWxGa3icPjEEfQIbnRnLhShvSb9VOJetqQhQOEYaZV9m23y9jYmMrCTb8AwLHNYZPOUKf09vQqSz09My32IwifbZKP58XpdUrem5dgOChr7GvE2+GTkfR9yWfzYkvapcfWI96QVzb1bJLdXXtkZmZGSB0JBgNyx3ZX84eV2czCc2Zld2iP1namVSoVxqFLMOiT6elI4XWjBNPH6kJN3jWGarDAa33rVhT5eCYuH078fFG9Z58zIMd7nlpR39XerAde6Wn97FLazJR/txL8MieA91czXyozT+mlXcHdsta/sjhWu9d6Xl8t4JeDAkqb0YqVCagaNvg31nNLdV8L+OXfmXZKvy3Dq7qXreVvsMBvy0O87AAW+G1v/Fs++tcV/BYHthj8/vZv/xPdTP76r//vhUteffUH8u/+3XflT/7kBy1fj+IBGpU9t4r5rQeo1hqoevocil6SochFlaeZpVlwIg47u+Vbfb+ojKgJft8dfl+ux64WXKWRUu4I7JStfoPxYIN9K35T+6IuLb9Hnlnemg1+caKGlVtK9oxp1RA1iPN5zUem5A2GYD2uXpVKrvdskInMuLpHuz0u8Tq90icDMhgY1A2UUf/ZpfV5VR6eNkCuCXaXMu36jd/4X2pdsq/fdQaR+mDbPAkJ8BWniN1pF3KaM2MZkZUYQqOYB9znjE2+AmpHTuxeu9jdajMmtoBN7D67pO6lFCw7OhySnc4a+eDikEB3QHwhv2xybVQnesDvpauXZHJ6UnOr3R63BLoC4nQ79T171u+RwcFByWQyyk4DyMmxHhwcUFl4b2+v/j2bzUo0GpXPpj/V3GvmQu41tafdDrf80ubvqEFXJpeTNb5BzYE2AXXxT2Mjn9Wxyo0CcfMdjl7Wzz9LiuT5SPhI012ea31YQiG/pNOLDa9qff/Dft1qAb/EEWYeOX80G9PUEsza1vlaX1PZAr8P+1Pcmvlb4Lc1ca21Vwv81hqph/Q6C/xKCfP7u7/7OzI+Piq/+Zv/Z2FFYX5fe+2H8m/+zcPC/FYv99PI41oPUK21/3r6BBiemvtCktlEoRas1oMVp/wXg39FOt1dCn49Qaf82aXvL6ppC7t1ouelRXnCy821GviF6boWv1rIjVzv3aBM9FJtOfBL3uVPx3+ixj9s+mM5o0QJLNEO/05lrMBhlP1J2VLi9jmlx98tDrvB6sK2FQPdpcrmxONx+e3f/s1al8i6rl0RYFlZcLeIw+UwSkvNNBGF20RsPpvYXXZlmd29xuFPLpGTfC4vmbmMAm8hHZ95FJWdVUOyKJLqZebD9XkRh8MpTgelbOySI68X0O1wzKsO8vrfgGZVYDgc4nvKK/6wX+cQuRWRVCSlf7JzWaH+b+dAp4TWhcQ155Kwg9rjcfF6vSofRwp+69YtZaRhvzs6OmT9+vWak71jxw6dCwdK96L39bCrz9tXWN1kMqmfIfpiLsatlcvHjfstB9sL19X3sFjgd3XInutbteZebYHf5sbzUenNAr/tXUkL/LY3/i0f3QK/peD3hz/8T/Inf/JH8kd/9L1C7Mn5jcdj8uu//k9avh7FAzTK/NaS99rIjdQDVGvtv54+b8dvyk/HfmwYx6gtjlEWh5/rPOvlW72/KCF/UMZto/LR7Y8LrK85F3IOj3U9qbmGtTYT/I6NTS6qrQxTcGr2c93Um43N/a7gniUBcFeXURKoEvNLDeRTMydV6kn+pNYGtXGHNtkZ2iXdvm597em1T0nAGShs0MmXBvQuVfv5gw/ek9df/2mtt2xdt1oiYD5W/KTU8kIZ5ObOEJY5J2IPGE7LmpucFQW3AgCHaeanKQdvIv6udCOudS6xOW1adxpgjhw7G89KNp2tKwYmqCYfGkfvSCQinZ0hCYU61fH7woWLMjU1qaZjfr9fNmxYLx0dIZWPb9myWVnjkfERuRa7JvG5uGwMbBRbzqb51YBqwHZ3d7eMj4/LBx98oKw17922bZu+hgR9bi4isVhUenv7lOWmwUrH4wlVaWCOhhqDg6pysM3nGZYcJcdCSbZyUN7cR+FB9wbz63Q66jI8fNBzbPV4q6HckyV7bvUq19+/BX7rj1kz32GB32ZGcxX2ZYHfUvA7MzMtf+Wv/CX5W3/rv5Nf/dVfExyh/+E//Hvy27/9zwqlkB7UMn5dwC/ArZZ8J8q+vDb6I0nnUwp4zYYUs9PZJbs79six3mOScMfk9atvLDJtwk32me7nJehcMOGptpbLgd8LkXMykZpY5ATqQ0rZWbk8hgF+bTI9Pbto6PuJ+/LexFuSFIyJFhytuddNHZtkrW+tJDJJNfiyZx0SDFLCxyUTE6UupX/4h/9abty4Vu3WrNetCBgRgNV1iiGBThqsrwJeo9rTA5d8O7odkkvNA3DmkhexeWySj6FnbnzRAMPd3T1a8mp2Frd14zsEMEoDAIfD3XqQlE6nxDXoEvsOh8TvxTQOmemM2KZtykCbkmrKaPFZ5cARwzhMxXjN7fZIPp9T4Aobzc+enl4JBoPqEM5cAOL8HWdwgDAAfdu2rQrST548KUNDQwqs6Q/Z+MGDBxVYk6s9OTmpPwcGBmTnzp3zB3CmuRlpFdNy69ZNGR+f0PsCjG/ZskWN00yWuhqzXen1xqNf+Z0W+BVZDY7XFvht9pO98v4s8LvyGK6kBwv8riR6D8F7LfBbCn5ZsosXz8vv/d7vyPDwsJ7W/82/+d/Kt7/9Fx74aq4+8NupDEUtQLXWYPX01N4nrOdroz+UsdSYbixpuMbCjJIX2+3qkRMDLwiOyt+78H2tW6pSYZuR/xpydsqT4fqMbZYDv1/NfCGR3GKXasD48fDTFUNQDn5hPYodmP/foe/KTGpG9+Xk/NIwECKvea1nnbrw7u0wSm51dAQU/P79v/8PJRarv3RSrWtkXfc1iQCqZwDvyk3NVxYwmG6viODjxVzApk0C4ZpG4HQWTMIqTRT2FjbWtckpOYy7UjnJRrNq+pVL50QStd2e3e5QVtNgbu2SSMQ175m/MwcDNMN8OlWOzd8Bp93dXXL79h1VG5m/Q4pNmSz6oY9YLKbybIAtTPTLL7+i/Vy5MiRXrlyVubkZBcdci6ldX1+vstoHDz6mEnDyrF0upOY5uXLlioyOjur3JK9t37593jV88X2acm/6vXv3rjLYa9dSJ9umYN2UoG/btl1ZbTP/enR0RJlxQLzPZ6hWaDjPI0+PxRIVZea1Rfrhvmo15D1b4Hf1PUMW+G3vmljgt73xb/noFvhteYgbHsAAvwAgdn61t9bJnmsHqrXOljJCbM4osVNLuxi5IB9Pf6CGNWajBi3ML7m2T/Yel3PJ03Jh7KLMpGf0EkoYbfBtlAMdBwtlgmoZi2tgZJBmV5I9Y8AFG21KEovnczB0eNEQXBcOd+hmD0kjffPfrDObSUypPhv9TKg5Gc/GdAOOcZXfEVCQvzuwR7pTPfJ//d7v1Tp96zorAg9vBEzZ9wMG41reyp4XZ9c8A43RN6A31djBgKYumHLyCqsBCwyDbOZDk3vM94HZzMM7QDOAFeYasGL8zCuzjKzaZJthfaPRiEqmuY460jSPxwDLSLZhk/k74HxgYI32wXWA2Z4ealkbJd+Kv9uQbwPOYcVPnvxcGWpyq6mcAOiFVQ8E/AroAd6bN2+Rrq5OZa/n5uaEsl/cG+O/9NJL6kZ+//59/Q4kL5vfA+jJ/zbbYkMzI9/a+LcxV8J4LzDaBrBmToB7ZO1mavpSudrL5q63+BNkgd8WB/gh7d4Cv+1dOAv8tjf+LR/dAr8tD3HDA6wU/I6OTiiAalarF6jWMm4jfV6cuyBfzH2uRwIAQ68dmkjkeNczcit1Qy7HL6mBj7nhgRt+oecb0u3uqWVKJdcsB35T2aScnP1M6+KaG1Q2YI91HNI6p2zqTAdmfrLRNK+jtrEhrzTcmM25UuboSnRI7Dan3t+189fk7E9P1z1v6w1WBKwIrCwCzn6nkW+czBrAt0WN7wlAH7WgTWC31FAL3zOVr0DeDDs8Nzdb6Ms8YIMZNmTPHOq5VHqtJmN2m2zbtkNzkwHMyMB37NgpBw48poD2zJnT+gfQC0g2TME86vJNzjQAGLDOOGpu5jTk5YBdpNf0SeN9DoddGWpAPNccPnxQpqamlUWGmYaRNgGyzZaXrq6wzgNAzfzu3r0nd+/e0WsAtrznscceU/bbOGTApjsyAAAgAElEQVQQiUZjCs6RtkcicxpX4jIxYaSooObaunWrHDp0SONgtjt37qjay6yhvXfvPgXzxcC5mlScvhbM0hb+OxbjcNcuPh9maqVlvyzw26IP1kPerQV+27uAFvhtb/xbProFflse4oYHaBz8uiQc7pSHEfwiVb4UvSiJXFx89oCynT6nb1EM78Rvy/X4NUnmEuJz+GVnYJfWz/zp+J9LQgx5YfGmBeZ3KSnycgu0AH6nCrmBxdcDgK8nrks8GxWv0yc7OndI2B9WOd9CbV2D1WVTxaaRNjW1OOeX3//BH/w/cvv2zYafGeuNVgSsCDQnAvagocpQ46+HqBWbYxVP22SgkWMDePkJ28z3lKkyQWVkssnBYIemVty/P6KA1awfDavL+zEOg/UF3JpMtenkDagEoKp5WoXGmEYedM+8Qz3lr2zS2zsguVxm3jCsW8cF+HZ2dinoHRsbVcaZ361fv0Hnjpz6+PGnFTgz9//8n/+TjI+PKTCH/QWQA4BRRAHQAeX9/Uau9NGjj8ulSxfUIXxkZET6+voVoAPsOcj89re/I319fQVgza0wnqbR6EEDvzFAtxn3Yracft5++20F97Q1a9bIK6+8oocIJlA2w2McVJuGZsUgemnH8cpgXHsukZFXY7Yt2fPq+4Bb4Le9a2KB3/bGv+WjW+C35SFueIBGwW+18jyNTqgRlrbaWMV9IlP+aOp9dTQ2GQ7KkTwbPiF+p79aV/r6a2M/lIwtXQJ++f2gZ1Ce7T5RUx/FF5ngd3QUt2ejTItud2w2BbhGvq5RX9ecM2xuKmWwuvws3niQ88t1gF82Z//qX/0Lzd+zmhUBKwKrMAKUelpJXeVVeEume7ThDp9XmTHg0Mg7zhUO+QCKZgNsqgnfvKs84NfMW+Z3jTTz+5Jx+GO45ueVBQfUwvryncsfgObatevk+vVrOtdsNqPzBcgCvLmW15n/qVMn1XwM6bfBsuYKIJxxmDfAH6m1CaCHhy/rtQB57o25AJwBqYDkYDCkwPv+/Xs6NjJ14tbb26+s85YtW3WOw8NXlCHv7x9UM7I/+7M/1e95s761AYAH5a/9tb9eAM5m3jP/VhT/+1IKrPVfnapgu9o6LCX75nA2Gm2hvKHaxKzXF0XAAr/tfSgs8Nve+Ld8dAv8tjzEDQ+wGsEvG5GZmdryc2u5ccCv2ecn0x/JWNIwXjEbMVjjXStHOx+vpTv5YPo9GUuPap8F5ldycrDjiOwM7qqpj+KLIvk5uZG5KmORcXHZ3bI+sE6ODBwuyOUWauuaEuald8onT34mP/zhD+qeg/UGKwJWBKwINCMCpuGX4TtgHOYBzEz3ahPIat5zEWtrAlUToJpGXOQdN6MVy7mNg0Xq/xoHioBQM9+ZOZs5wRwa4pbNH0AroBMJtnEvLgWpNOPfgqLqAFpzGubbLps2bVLPCQAqlwCszVxqk4k10lfcCvwBzoBzxuAnsmuAL/LqdDqrDt5cz8EnDPLVq8NqXGZIvw3XNsb563/9v5b16zfq/JrleG3KvheD5OWBM//cMvd4/BE75WnGg9nGPizw28bg82nNV9NLtHd+1ugrjIAFflcYwBa+3QC/9Z/GtpL5bSX4fXviTYllF7OguDQ/VyNrG5eYfDDznkzFpsQmdslLTvo9g/Jc+ISaRtXa2HiJIy/vjr+jTIDZ0pm0bPRvlF3BvcvW1uX6n/3sTXnvvbdrHdK6zoqAFQErAhUjYDhCO1RKvJJm5OXiQu0sGEYZxliGPLme7V61/OOVzJM5FvdvzDevgBY21jT8gpE22FoAZ7qQX6xwbwmjMRP4cq8AZ8zCjLxlSmAZudKLG2DZYIQBi2aeNrWcBwfXyuQk/ho5NfkyG1LnkZH780ZmC//2cA/79u2TX/mVv9xU8LuSeFuy55VErzXvtcBva+Jaa68W+K01Ug/pdRb4Xb0Lt1LwW8mheCV3Swkh/oFvJvNb3OeHk+/LVGayZIrEABfnJ7qO1zT1udyM3Mxdl2uT13WDtDuwV7YHti9yZC7ujI0MYNeUL5uMw5ejX8qVmSvidDhVvmzKBO3ikOe7Xyjpk43Xa6/9UE6d+qKmeVoXWREQ9sMP2MnYivrDFwHDPMqrAM1gJiswdAAyr13EIZKLLH6oTIMr5Lrd3d3KVOLADKAE9FXsc5lQ0Q/va1TyXG0VyoF1MWDlv7kfpMvFMm1TIl2tbxMYm7JuALAZB1jmas2cC+8fGDDkzRh7cTBBHjSMMDnFXMe/B8Xu1fTNv6H79u2XX/7lv6hD+f0ePZCYm2tf+osFfqut+oN/3QK/Dz7mxSNa4Le98W/56Bb4bXmIGx7g6wZ+R5L35fOZT0viRQ3f4+FnpNvVXTWO48lR+SpySlxulxpMscmgzu/TXc+WmGYV19Y1am46tG/kbwZ7YJhTfTl5SsYz4+L1khOHu6jBCOTyedmT2Sff/cM/lGSy+map6sStC74+EQDwkkfKIwdGAccsVO36+sThEb/TWlnRateZMmWTxTTyUkvZX1vQJs6gUxwBh9icNsmn8pK4k1jkUG3k1drULAq3ZfJlMYYqlwXXsjRmLWSkxtXuoZb+ql1jMq5cB1AEUFLCCNYXF2hTol0rc22m1gCgqUfMT/pColytGXMxJMzEEPdoDibIQSb/lznw8ubNW4Vc4kgkWlAPsX7kBu/Zs1defPEbFvitFuyv8esW+G3v4lvgt73xb/noFvhteYgbHqBR8LtceZ6GJyMiBkubl5mZuZV0U/Le8j7vJe/JcHRIErmk+Ow+dXHu8/TXNB45w5HcnDK4lBLSjVA+L+sD6+Xx/ieU3S2urWuUGVowpyp3Jr2XuCfno2ck4PPLxc8vyrWfXatpHtZFVgSWjUBQxOF2SD6dl9zcw0f9Aj74Y5awWepeHwQoehSetOXiZDgx5+YNociFdZcY5Nn8NvGu9y4qBZ+NZCV1ZyFlhjEAaoZE2KGu8/ycmZmuS+psgk89BMyRv8rzawDBas3Mm60VoBb3Z7CtyLWpiezTXFu/PyC3bt1o2DDQlIDjam3UDLYpI27Oj0OBSm0h/9mmdZOZF4cAzAcWmUYfOFQ/99xz8tZbb8nsrFFzHqCMo/SmTZuV/aVZzG+1J+fr+boFftu77hb4bW/8Wz66BX5bHuKGB1g5+K1cnqfRCT0I8Nvo3HjfuxNvSdaWFZfbqRJlOzlfdpt0e7rlxXUvFhhd04l5qbHYCH3/+/9Rzpw5s5LpPDrvZW8LW8lP9oMWU/norG2dd2IyZqZj8HI5qAC3pQBELcM+zOC53DBqufuFLaR8znLSY6M+r+HMjELFZH99u3zG59EQrxRaPpuXxPWEKgtM1thYO0MyjIwadQtlg5Zr5WsA0Ovo6FC2lb5Mc6jq62lTAyi+WxsBv/TPvAGUhvO0aK3ec+fOydTUxJLDL/cMGXm7Pi2BBHBlXmYZIxjg5STdrEcwaOQecwDAnIgHwJx7ZJ127twlL7zwkgwNXZJr167qwQPgHUn04cNH9UDDAL+4W1ObuTrrXD3OjV1hyZ4bi1sr32WB31ZGt3rfFvitHqOH+goL/K7u5ctm6zc4qVabttE7BvyyQZiebi7zu5I+TTYXtve9kXeFckk04+AAg5Ss9LsH5UDwsSVvm43OD37wA7l48VyjoXm032fs65TcsTvtknflJT9dnel5tIPy9bw7jIdgt8gXNT5jzWOuTaACaDYdfGvJwTRXwqxXa5o3PegVahSsG+V3SNUwvuuJsSlFNssA8QE0a/ISdxMo+w/4VT6fSxvMpbasiCPkkMhXEbHnF4yWiksXmY7Fy5VZM2vikmdsuDs7Ncd1enpawS+Aj8OP5Q44eA9zbTQ2xWsIy7pz526VDQMiKW1ETV8AfCVAjQyZmC0HtgHU3MeaNWv1vvg7OdGA1ViMlBbje664D4At18E+h8PdEo3OFfwgtm7dXvhcHDx4SBleGjEAWBN3AygvNMAvZlqRSPtSaCzw+6C/LaqPZ4Hf6jFq5RUW+G1ldFdB3xb4XQWLsMwUVgJ+x8enlCVoVuvqCulmoF3glw2UaUpVqbbuzemb8snEJ+J2uSSVTEsWuaDNrjm/xXWCyXN77bUfaRkKq1WJgEPE7sc1O685hYG9AUlcTkhqNCX5pAWAH5XnxwROjbJyxXFYKdABdAUCwRIJarU4Gw7GLgUXhpFT8773lh67XO5rOAKbBwK1Mt9mXi+A3wRyJrgFPJrgCzUL33u8Zo5h99ml66UuSY+kJRvNCiZqzi6jjm3gakBjSP+AVN5jAlVixe+Xy3HF1MoA37gc21Su29fXp9+b0WisIHk2WODSZj5PMKH8d7kTM1eXsuOlsavUH+wstXm3bt0mR44clbfeelPOnj1TYGuNOOn/KzhdirllPgBjXJdNVtzIgfbLxo0b9Dm6evWK5vFy6FDuhE0ZJtYWkAy45QABV2fWirJH9Ivx1XPPnSiwu8s9vxb4rfbp/nq+boHf9q67BX7bG/+Wj26B35aHeEUDPOrgdzlAbRibkKfr0p/mRpANnGlKxc9iueB0dkru5W/LdGRGPDavbPPtkDOffiU/+9kbTWWpVrSoD9ObnSJ2j11cYZfB/oSdkpnMSGYqI7l481i/hykkzJVn08z1W2ruJuu1mu/NzHtslWtvI/euoEjyYnPMM25Vyo+aJk4GqHEs7YhcYTImG9oog23m5NJ1cQ1cw0HeAGDLMaNGLivsrAH+DBbYWQCL/J3PnXkoUUlO7dngkcC+gB5O5XN5cUQcsim6ZV7yLHLr1i0t4QPQZS6m8RUHBfwdZrdS4/V169bJt771iqxbt0Hr7dI++uhD+fDD9xUUMq/i3O/ieJrMMQCRAwkckc1Wfh/LHb6Y6wvjunv3Xjlw4DHZtm27fPe7fyhjY6Nao7e8Gew581v6OwqQa8TXkC0D1AGvNKTKzJnXi9cP0NzVhamVTQYHB9XgikZsA4EO6enpkd7eXgXoxUy7BX4b+Sb4er/HAr/tXX8L/LY3/i0f3QK/LQ/xigZoBPwiCevtDUvzmd8OvZdmMr9dXUafmGixWTRkzAbYNTd+Ro4bZTUMc6rlNqqwEH/6p38sV69eXVHcrTfPRwDm12NXNsnmsIk9YJdcNCfp8fQDBb+m/LPekiytWkcTQFQCNmzWYX7Ih0TqWK3Vkx+6XF/1MK6GisItPT29yg4SV8BNM5jfavdb7XW72y65bE7z9W1um+QSuUV55uVgybx3Mx/UAPMLoHG5MRuNPwCL9xI7QDDNdALGUIoGwIJ1BWDCCJbHl/cVy8eN7z63Ai5AZfF3nVn7lt+ZvzeZS9IS3GGPOPMO2dC/QTo6OmXjxo36Xfrzn7+nzCTzNMc315944WTPs1o81gIj6pITJ16Ul176hUII33jjJ/Lpp5/M31O24vcxcUGezM+NGzfJ+Pi4PmcGm2q04liY+cgot8u/3821JS4bNmzU/Nw1a9bI6dOnZXSUOrqVAS5rsRz4NesdY3hljstciQnxun37dkGubTg42/RgYvv2HbpG5O7y+ea/yT/etm1HtUe74usW89tQ2B75N1ngt71LbIHf9sa/5aNb4LflIV7RAKsP/Npkenp2RffEm9kUAXCDQUNaZ2622MgUOzDz38ttyHErfeONn8q5c2cfeWa3LUwi9UP988wvcsIel+QzeYmdj9Vi8Lri58TsoJhJa5SlWzwZ5Jylm/CVTth4lu1qdjM7Oy337t2r+lxyPYY3ywF7U6bZLPALKAsE/PrZQvJplnmBzTZNjB40EFbm1umQnCen5Xr4HwBY5wG+Ac8u0UyARPxhLPkJmK/FcKtW8Ft8Hf/d1RVWl18AULEjs2kGxnqazwOfXaTY/I5Ys548e5XW/P9n70zAbqraN/7QIDRIJV/9mzShAYWUOYWQiAylTEmU+nxNGjSLlFKGRp8o9cqYKCkNmlEimlVfaRJCmUv/617Lc84+5z3DPsN693517+t6L7xnn7XW/q11jnWvZ8IcYD1g7PGu0xoPrP15LcIVK/7LuCbDMvp//3eIEZ+w+A4bNjQibr1zirZUBEOg61gignoHa7RbuXIV+e2334yY/v339eaZIRAxxkTPgLbhIo2DyyOOOEJ++OEHE9NsXdK3J8wUrv2q0IwX/nALxz04sIGlFTG56bJV2wRhUTdx7/LB3MC9HlbaVatWmkMKWKnVw2j58q9l27YtZqx4Dljmka356KOPlZNPrmUEeD6usmX32JFpmjG/+eC5s7RB8RvsTFL8Bsvfee8Uv84R59RBbuJ3bcoNdaYDg5UWm4/ffstc/MIabeN0rVVXLSVaLgPJPmCtSRej/MMPK+Sxxx6Vv/8uipi+TAntpPfvIsbt2cQTVthVNn2xqVAN0aJ4cmxWYZXJJAlS/LjycYCQykUT7cN9EhaklSt/MbGRiWIi48eVzmprvSBsZtlU79UkRH7mA59FXLB84fOIhD/WzdP6GfsRjn76SSFZjchT11JjBd39b/uz7W9BxmJkMS6JhYfSOhvss8eL8vj5AH9k8VVLp7cObKKxpGKPgwFcakn2vt8eIJQ1otYmjrKHKRC2EF2bN28yXPEdh3tslmWM6y8j3rwxuInWKdrGM9ifvyLWSTy/ekJA6CMGFa64tWrVMeVzdF7R5gcfLJBZs2aasSQ6NLLj+VNKldojMiblqazRx2GHHWG+v3H9/LONb0V8MtpVl/l4qzKEYpUqx5nPK8TlunXrjZswRLM3y7Q3wRfaVxdkCGxdgxgnmOICd3wOYLHesmVz0theDU3wZqTGc+L5IMTBEMIXGaTxDChHhDGDE0Rx9eo1zDh//PEH8zscLOBQAbG+fl2a/Xw+wiB+YTzPY+46P4/Ne9IQoPgNdolQ/AbL33nvFL/OEefUQS7id/XqtcaKmq/Lr/jF5gLuy5qUCpsxnJpjc2Rr626LxOzuvbc9zU8kqHH///73rcyZM1sgeovLlc7drrg8R6ZCIf5+3UTjz1ystfagBGVEbHmWVOV1UrHFhhVted0/M50LY53c4eLqdT/VdrCRR1baI444Un766UfZsOEPs/n38/z+BHBhV159LjCCcMDGHpz8Wm7Ll9/PJBL67LNPzWPoWPH+TARwVDTZ+NVU74VAhPDFd8O+++4rq1evtlbEEn+J7LpD+G6XiACGRXi37bsljU/Vz5y6puLwASV5wAHzAKtlMh7WKmtdie13l40FxTwfdND/ySGHHCLvvvt2JH5XxaeWtEGiJHzfqVBFaEaZMqUjiZj0gA/PjPvgEg+xDDdgFZiJxC/mEtmVsX7WrFm9w2K8SySrM8Qg2oR78znnnGueVy+M7dNPP5X33ntLvvnmm0j26Ph+bKkfm+1Ya+bqPGLcGK++DgGKCy7cONgBn99//yOSpVrXrz3gLCUnnniiGRe4QCQvXLhAPv54ifz6668x77Fjsoml0CbcmrEW8DnHGgFvjEHnyStqcR9YJvp8qXeDimisEXw2MT68DwcS8NCApR1W6lNPrbvDyrvNzFE+BW6q7xmK30y/hf8Z91P8BjvPFL/B8nfeO8Wvc8Q5dRAm8bvPPnuZTW28UNVNrCangpUXGyEkHMHm0+vGHA/DK6jhwvbii7Pkyy+/yKvFOqcJyOLNqSyDZptXYocr5462vf8Ou3DWOOxkm3YvLmxWsdHMxVKL9jS2EpveXNrS5ES5ivFES0JFMf6E9QiCBFYjuHvaeE9Y7nI7BIhfO143XHBWq/OmTUhg9LcvVjaOtKRUqVJF1qxZYxII4VKBEB93msXHIeFb4JILwYVaqRAv69f/bgTRho0bZGuJrSK7iey6566yddVW2b51u5SEO/QWWH5t7GWy9aei9dhjq5j7wB7trly5MiazsSaj0rhPZamuyhB6EGFwbcZYkQApXqyqWNYsyLBowtKPNiB+t2zZWiibsg33gNj8l7Eogm/UdRmHPDaDurV+ljFWxl9++SViYdXxYnywYuLfJ510ktSr1zDCGbVvZ86cboQpvpuTHRap0Ee5nuOPP8EIyHfeedu4SEOMwi0eFlBYqWHx1IReeB/WNvpG5mfvYYkePiAeFjVu4YKtFzwLnn22wLgqw3VaLd96mKD3oQ37WQELa+WGkLXjsiEzEPqYH03kpdZnvA+WavwbnweIe1iH0Qes55gX235JqV37FKlXr0EkljdfazvTdih+MyX2z7if4jfYeab4DZa/894pfp0jzqmDbMQvNiUHHABrSn4tvyp+//hjY8R9GZtIbLBwWauuTUpls5ymzgYMN7NXX31Zli9fbjYxxeWK33ynE7vpnkuFk3cT6ddql67tTF5PZ3n0iiL8PRNrpp+2U43VWvGsBSnbS90mvdlps20r/nnwmVPrFDLkHnfccSbL7jfffCsoraVCKdW8aoxmujElilFVQXDggQca8fXjjz9GrL+p5kmTKFWuXNlYvyAoYCnDZ/q00+qZtiZMeMo8gwrh+Pa8WdiTPZ8emqgVr0KFCpFawbBeQrDA+gwPj3Xr18nmbZvl7xJ/G+Fr4pJ3SV66xsvLlPgpW0Zq1DhZjj32WHnjjdd3uB5vl9WrkXTpDyOCILohTrWMj1r5IJYhLGFxxe/0PZhvFZE619YyiZI3NsMvxgkRD7EIy+F3331bSHjqurHWTesFo8zUowCuwRDUeBZYrVWMqqsu2jj44P8zzDAXJ55YTapVq2EwLFw4X1566UUzh9pX8oRQ8KYoY+YdbcDyiUzOEPqasAuuyugXY0SbsM7CFRxzVb36ScYrB9/jmjEafWLcdevWM1mivdZTlCZavHixEaXwiEB7Kmjx2YH7MX6HAy78v6Ix0jq/ygfu4BDsYIzDGvzfoaWc4G20997lZN99y+2w4tuY359++sEIeXiP4P8l1Ahu3rxFJL433WfO5esUvy7pFt+2KX6DnTuK32D5O++d4tc54pw6CFr8YnOocbo2kUz0VD7eqptqY4/EMJ9//rlMnz7VJIkprpfd3Nn4y3zVEo1aOoIrHaTiKd6NUDfQuvHUecvEHTbXucYY4K6IGD+NR03UZiqBra/lIsKTvVcPP2BlgtcDNvLIkAsBNXv2LCPa1679LVKCJ9HnBIIAP3gt3lJnkyZF3YjjMwSrtQ39Nm3azFgo586d69tlGe+vV6+eXHDBBZHSNTgoUOECIf3OO+/Ixx9/bKy06pKqCZkgRlQcqYXb+4z6mVFOKAeD15H5V9cVPk8QXBA/GrOqGY/18+En0zfuRYKmXr16G6H73HPTYmJg8TuUxoHARfIlPQhRCzg+0/ieg+US1kEVn4Xjc+H+bi2SuB/iF7HTOMCAtf/HH1ckddHG2oX112Z1xoHO5ohQ23vvvUzfeP2MM86UCRPGG3EHFlYog9Mess8+e5vETxCa7dq1N/dj3Tz55DhZseI781zWemoPD5JdGANEIQQ3xCTq5y5Z8pGxlGM9ofTPhx8ujMyLNlWq1O5mHHAnBwN8Pi1DeA3sLu3bd5D99z/AdIv+UWpu6dIlZs71sA+HECp+y5UrL3/+udU8K54JFlpNWKbfNVgXsEBjXLgggM88s5lxwcZnB+sK1mLMCz6LyDD9xRefGWaI7YVQhsUZBwsolRT/nZbr91S274f4haV9w4bsD/ay7Vvfx5jfXAnm//0Uv/lnmkmLFL+Z0CqG91L8hnvSshO/JeWAA8pnZfnF5l3FrnWltCU8sAHRjQ+yPadLTIWN2PTp0+STTz4OLWC/QsgrTrExwwEANlvYOGZrsfbbdzw8rwUt32CtKyVKn2yObMh1nN5kNplYpeOT2SQbswok3eiq8FKLKTaqdg0W3sjjXriH2gQ8hWuW6nu9fWfDP9F7VNCp6yb4QRRASGC8XhGpcaUQFtj0a+wxxIK1qNmEQrhP40fVdRz9qOVV2ai4wf3WGgkRtp+sXbvOuJbGX4k44B60A8F16qmnGqsfPrsQtLAmwoKt6x/P8+mnn8jSpcuMkDEW2d12M6/jPTo/6l7uXTPgovdWr15dPvroI3O/vkefxQrD3U2bmhkZv8MzJ6tF7J0D3AshV69efWnQoKEUFDxtLLxWhG035Wtg+cMcwfrojf/W8eI1CF8wQKwtfo9nsvkTousP/dq43AONADz99Cby6qtzTV9ICoX2E114H9rWQw28F6VyIGBhZYWrdc2atcw4R416ILLmMQ7LwNaYRVwzklxVrHig6QaxvQgb+fXXXyLiN9UhlR64QERiPFhfOBiAaMUYIRbVMq2fQ3hggA0OWCCa1RMDY4OoRDvg1759x8jhyVdffWnEL54XruFoUwWwPbjResg2Ezba0jnVz4OuQbCB14DOc9u27fP9NVjk7ZUti1hqWNYT11suigFR/BYF5cz6oPjNjFe+76b4zTfRkLVH8RuyCYkbTi7id82adcYFOdllT+ltbV1NTqXiym48o8mpsPnZZ589zSYQ7cZf2LRNmTJFPvtsWSBAsRk866xWxo0alhe/ViK/Qg7ugdbqbZOm4N+bN8P9e3djDcMmWa9kIgOvZyO6tF3N8KpujH7cjvW93mQx8SICG3iUEdGyINgwwyqWTGz4nWAr0hCfiWy3qa3a6n4bLQ9j4zqVWaLkUvo6NtxwNUbSJIxbLxVWmONElkjcp67GUYsl5si2AB7ar4qIRPOnbsj4E2sDbqS4D5anvfba21gZ9YL7MIQifmfLC202B0xqWVROat3FnyqKVRhg7cF7AgdQsFTi2WzW392N8MP6T8Q7mugndi40ThbiRbP34pAHQufss88xLq7ea8aM50y8qgpDFeYqavQ7RMegAh4MwQMuu8uXfxWTgVi5gh/utyXObM4APF8yERe1Iv4ZSVaFeGtcZ555phFc8+bNM6/BqgwrNuYchyTaNuZD28H7tAQQfofPtrcP71qCaIMFFPG7OJPRdYD7bbKzxDWe0T4smPjOtRm8/zalhE499bQYzqilO2/e65GDKLxohWFJua5W0jEAACAASURBVPTSPsby670wH9OmTTUu6lp+Kdn3m86RzUBt1yzeh+eFGzcOZ3CYpOx1DWJ+1OMHa9hmfbafORy+4HNYv35DUzJIL5SiQ+JCWIlxIKDcMQZYa7GOkZFb15H2qYJXxbCuc4h+/P2YYyqbQ47iflH8FvcZdDN+il83XP22SvHrl1QxvY/iN9wTl0/xa5Ot2CRE2HhpYipsUmNr61r3uvhLxS9iiX/44Xt55ZWXzQYYMXtIllOUhV/RJzae2Gy1bNnauMBNnTrJJMvC8/hN1ONXjGKTByGC8hfoCy6t77zzlhEysJB4EzH5bTPTlYd2IXK0zIgf4W7HgvIrtm6oWg31vVgLBx10kJlD/B0becSqYoP6/fffmU1mplZfFf+ZJO9SK5RabqNWMesKjPFBmCV6ZjwjNvAQS+qK67UqeoWgbqiVPfrFWtG4VW0f7wFrZPq12W1XmnUVn0UZ7emhBNrAIQKyF+NCgh+4YcIaiwvCHsIUIhVusrjQNqyEycQd7rVjgyXUHmRBlEJI4NJnUyGB/hCnqvy8z6Nr2OtWHR+L612TeDaIMriwfvLJMpPcqHz5feW777A+tu4oDwMvEAhViZR08lpzrWUPa2t3E2+JeYLAgmUa74u38EGYwgqNOYFLMA7vYI30Wv11DtXibb+rbGIkWC31sAEJtVq2bGVYYfzo8+uvvzbWScvcZsK3dXdRW3eXHcIXVmZrgdTDGP278kRfODTCWPAdhH9jLnUd4xAGfcSvPbwfCdHwmdML95x//gU71g0+b1bkQrTDQo51pEmb8BlFbGz37t1NfLY+u/2cisyYMd0Ibxy8eMsJxX/XQLjj+TFuWHARI4u1qZ8f3K/rGgxU/OrnRBNK2c+ZzZINC3i3bj0jbsna52uvvSrLli2JrFn9PdqvU6eOfP75FyZuGN8XmDsIZB27zgn6AScwBzuskRYtzo5xac/0+zQs9++5Z+kdYQO0/IZlTsIwDorfYGeB4jdY/s57p/h1jjinDrIRv9hUVKhQXtat+2NHDFZsbV2ctGtSKojedC7M0c2KyD333J21q282IGBNgHUr3gqJZ7QbwX2MxaF27Try9ttvmY2/Zjj1Lw6Tx8TFiwEIoooVDzLJdJYuXSq//PJTxqVzUlmG0zHC82IDCitnskvFgW7Q1ZKi8Y3KBePAZlPFBng2bNhIFi360GSYVcseNr+p3I6944iPR033PPo6xBE2zxgj+oXogxsonhVxenB3xO/9lClSCyzcXVUweteCJr7CZhrPD9Gi8X/eGElYpSAMcEF4QzQiZjF+DJppGX3A/RqiBBf6Visi2ofoQ5kXuG1aMVHCCDL8Lv7SAxQV3+p2imfD3HvHaUXKLuazAO8ECB970BW1XOPzg3nGhRhJvbxCUj9TeE0t0Rg3fo/3q0DEnxBwiKtUrvgTon/Fiu/NQRAYoX+IWYgjFU1gCEuhNyETRA+s7Sr0cdABC6OWbcLriJnWpEYYH/rDQQMOa3AwAeGHMenhDu7Bay1bnh15Vrxn8uSJJikV2tMDK6+3AcYLcYzntTV8cZiw2oizeG8ScLDfB/8y84i1qwIY79XkTdEDkl2kUqUjzPeVdYm2+RSQrbl27dqROsE6J+hz3LhxZvxq/cTfsS67du0aeS6M6+WXX5b//e9/kXhssEOpIzyHjS22Lua40L5lawU+2gNbrE/8G2OKuuTb2ux66IPnxXP/8ccGs9b0svNfXi67rF+hWFpYlMeOHWNi9tWrAvfjIOTggw+S8uX3NwdtehCC13766SfjAq7xvfgdBDvG2rRpCznssENjDk7sc8V8W6f56rE3R8dT+Hbv8/n9HsvmPorfbKjt/O+h+A12jil+g+XvvHeKX+eIc+rgr78Qr5ZenGHj4LXqwrKrmyavVRei148ojB803nPPPUOSxrHl9JA73qzuhdhEwd0SrnM///yT/Pe/j5nNmFpRdPzYCGEzhqt0aZS9WBcp1eLnGa3LN9yWE7sn6kbRa+3Be+AWCGuSxm0mena1qGn9ShUzXksh7sGG1I+LtvaBzSA2osnciLHpvvnmmyNi9amnnjJWeWzg0U+8BQ3iz4rj7VKu3D4R6y+sK2+++abZfELwqeVLN9Cp+Oozpoo39DKzbreof2qFh3W9LB9xw8bmGb+HmMXhRip3bLV0I+Mv4m4xT7DieTf+2LyjL/yJdn/5BVZdKwzwXHgNfA8//AjTRrVq1eWll16QX39dFTkQ8I4f7UCI4RAJwhYbaivO9pK2bdsZEYJnAMsvv/w8kgUd99gyNrY0UTwTcEHWXIwFTGDRw+9WrkT2aFs6CQICVkH1CoBVGMLBMo3G66MNuC+DA+JevQcg1s3YxmFifeO1+NhL/A5talIurUFr1zkOEcpI48aNTbZgeA6gb5S5gfCC9RJM0AfWIdrS/lRsqRiz2Zox15tNRl64AiOOFs+LdWytjOArggzV4Is+UaPYK3zxvGB33HHHyzffLJcFCxaYQzusM/td8pc5BEB7SB4FoYqxYt3pGqpfv4EReR98MN9Yce3nDhyipcp0nWKd4e+w+KplHS7eapFHdulKlY4yWZXxzHADRp9IvOR1i4//Llm8+CNzGKXiE2vzzDOb7ojLNd9QJongd9/9b8fz2yRXhx9+uHEtx2cXl9cTAv/Gs+phmLrD69xjzOo9owc7+p2DQwnEhr/77rtxc7i7sR63atXKlE2yYh3iEgczJeShhx6Sr776KlLyC/ONwwp8bs46q6U899x0z2fQfn7sQdga836MF4c3zZufZWKLi+L6+2+EuSTuKfr7JDfEvC0qtOM9quB9teeeNgM1Y36LYlaLTx8Uv8HOFcVvsPyd907x6xxxTh0kE7/YXEbFrs0Uazc51oV5jz2QjOkP2bgxP65USISDzKOuLmzq4DIXH8c2aVKB2diqVcAbxwnXUbUMYJNqYyg3Jh2iFWWIodzVbDjhIomEKU8+OdaIEFxq4VSXSpvoxrqbqjjH39MLsMIHFiqAsaGElQybZFifNDOrH0EJsQ6LSCLxCxaIBYRFDpzA54kn/mtqj0Lgq0UOY8cGF0IJv8M4sH5sbVHUwUTsamkjFtTlUa1CuA8HDmoBTQbbaz1Kt2asqLIZYyHAsUHs2LGTGT/YjB//RCRpEf6NpDnJxD/awoYarGHFRpbXSZMmRpJ4qYiDqIXghBjC88O6rGsLLu0QGEcddXRk6Ii/fPPNN8xm3Ouqq0L/9NPPMBt0CD8ww8EMMiir5Vgbmj//vR1i0CZ0wrPowYK3XTwH5uKqq6417U6bNsXEq+KC1VIPTPAaLImYX1iccWgBsY+kV/gs4JmsBRiifi/TFyzNKqbwXrgj2zhOezCm7t0qgrzj0u8ZzFWtWrXMeyBijjnmGJk6dapZT3o/1kmTJk2MVVIt1hpDqy74XsGNtjB3mj0a42jb9lwBexXNGot+2GGHyQUXXLgjEdU2mTlzpilnY63MsLAeZcQ4ROvUqVNiDpjsGrBJutAHfv71r4Nlv/2seMW4IZqPPvoYw3vZso/lueeeixG9annHYQdErH4+7PeyLa+DQ7zu3Xvm7JqLw5Gvv/7KfK5xIOMV+TgMKih4JuFHDBwRm43/D/TwAc9uD9Ds78Abv8NnHesNBzZ4FltmCNZi6xquVm5N1IX3aykiXYNor06dU6VRo0aeqgA22/Rnn30mzz03wxyU6MEM1mTVqlWkdevWxuUaGcXBH99LENg4rMGa/+STT8zvkCgNfaE9+4ORRf+e7Hf2Hiuo9Ts2VRvpvq9yfR0H0/pjD0BxuIXv4cL5ObIV2YZMgjPzZNZsJrzKdVbz/36K3/wzzaRFit9MaBXDeyl+wz1pEL/YZCC+VZNS4U+1LGKDgk2jJqfCf6T4D65CBVgn1yX8DzWbJ3799VfltddsJtN8XSoGsbGCWGjUqInZdHqvyZOflc8++yTyK92sYvOi7qP43erVa4zFzm6Sowl9IG7shh2xdojFtO5zeH+VKlWlQYNGZvMLgQRrDDZ7YKiufrppxADUHVEFQjIOmBt9tviYP7xHN38QHhArEFsLFy5IaYHG+/B8aBfxcInE3wknnCC///6HiRHEGsBBAjaT2CCr6MPmEQIFVimsKbjIqpDyjlvjV21tz7/N5hPzhEQ92CjDdRUiKlUiIrsB8+e1AN5YA9iYw2rVuXPnCN4PP/xQXnvttcg4rUukddmM54sxQphhbVxxxRWm5M8HH3wQaUvHA47YyCPhlB3ndiOQcbhw8cW9Ylx69c1YI8h2rDGuttQNLNb7SNeu3SPx2OAIq5s9tClpRBQscxDY4A53XPwbn11YJXEgY62Q1pqLS+PLYdnE9cwzE3bE1SNGdYuZA6C1hxYoS7SLtGnT1oh9MJk48Wn59ttvzVjxnLhPM09D3GBdgLlaXJHwCP1jbeFHx+L1FAA7Fb94jiuv7B8RdjNnzjAxwV6hjPtRKge/w6EARLtaGyG0ME5NNoVnVDdpbxuwIkJAv/rqq0bIQtgeeeSRAqss+HvvxQEWfiCM4QKO69ln8f3xWcx9+HyACyy1euEz36xZMzn55JMjhxHIaQBrJdyqtQQThDGeXQVVvXoNZMaMaZHnwDPjOweHW/hc4xDH5YX1+PLLcwq5GuN7DImhUDfZ1l+HC749yIOVHm7a+BNrB+sIc4O5hlVVD/zsfEGQ2WzXWEeYN10bmpVbvxfxZ8+ePQ17vA8/ELh6vfXWPDMXWAP4jCBJIdzSbRZw8w274zvWftfaf0e/d73fwfr72Hv8teFnPrzi2H5nZCqgo/ejPxv3bn/wbDb0aKth5DfsyM+4090Ta83WubGs//4bCQp5hYUAxW+wM0HxGyx/571T/DpHnHEHECsff7zY/MDq8Pnnn8ngwYOlZcuWOzYyNhGMzYiaqPSLit/1EYtZxoOIewMsVA89NNKXmInvC//xYyOoGwq4Muu4sanCJhSi47zzOsVsSNHO559/KlOmTIoRORqjhxhRbCSwmYegw6YLG1tNjoL3a0ywrau5m9n0Y8MPEXfGGU1jNo0a97Zu3W8mmZe16FiLsiZbUTGQSvTZrLvljIs4xoYLz2s3ibDoWVdtCJZGjRoba8njjz8q33zzddJpwpiRWAfzDvfX6ObMzj/aRr/qTqqJd/BM3nhdvA/3wVqHTZcKYzseuLBa64jeg9/jWQ855FCT+Aj9TJ48Sb777lsjxvyI23RrT0U3+oS17IILupg/vRtiiO2PP15ifodxv/XWmzsEYNSao4cK2EzXqHGSdOrUSV5//XV57713Y6xlGA/6atOmjbFKYs3gwuFC8+bN5eijoxZfr6Vo+fLlUlAw0VhWsY7AAocCEESwuqIdiAlsJO3rdqwYO/qzSbmsBbZDh85mbcKavGTJErO+ENOsCYdwKIO1odcLL8w0yZpU7GGe1VqN+cVBCp4bh0cQ/kgypFYjvEcTb2EcEPmw3Fs3YlvXVrNFw7UUz2GtzDbLsvfAAH2hvRo1asg555wbGR9KCiHxW/y1334HGAH4wQcLZOHChaZPrH9YMXVtafblePd/9A3r33HHnWDmRN244/tIJJyU06RJzxoB6L00Xh7hC/azY1+FqD7//PPN8z3zzDOybNmySI1dPWRRjwnMO6zd+N5B/WOvxRu8IBJhAYWbbiLxlMhqmU5gedvBeHDYgIMg1OW1WdXthfWJuGZ4b0DU68GfLaFU04SSfPjhBzEHAlhPOFDBPVhHSDIGDt7SYfg3PpeYhyOOOCLigaHfGVWrHm8OJVJd+D6E2z0OB3DgEsQVFetm9mMEdzYivHAbflyh7ZOntkAnE9z+rNi2TjJi723uhB09CgSwXS/enyBmgn2mIkDxG+z6oPgNlr/z3il+nSPOqANsOtq1a2UEFxKpIEasevVq0rRpcyMQ/Vz4z/3AA2H5zZ/4Rb///e+jJqlKogsbbmxSIdgh8DQ2UOM3IX6PPbayiXGDCyfcqCFGYU3Adcghh5nYr/gLmzzcH42VtMKsQoWKO4QC3Fz3kh9/XGE2ZRAdal1CWxChsHjUrHmKEUTY2KNPWAeTXdiQwAq8ePEiI4CxicRzwEpqrao2Js4rgMEc/dukQzYZl403ta5sai1RN0+1TMEK2L17Dxk9epR88cUXScUkLGBwrcW4Zs9+IdI3xqGiVf+umyp9Prg2anwx+seY0D+eyWtF9opfiBOINL3gIg43U1wQngsWvG+ez29cr244Na5Q3UaxOcd8Yk7UWo5N8YUXdi0kWG0N6l3N76dMmWwsuhqLqIcLGB828Jde2tfMMVx/n3xyfMyawL2wDrZu3cbMI6ySsPzCpdQmqEpsbYKIGDHiQfnuu+8MB+0T49EfPB/WDMQkuENQqKULG359xuOPP146duxo2nj//feNRQzPgs/JGWc02eHeqZZzJF1aI8888/SO8jk2rhMx7vhOsKIweggCd3Hcr88BUQm3Sqzjpk2bmQMBfM9obKpawTF2xNlDjCKO1q5d9GXjoXVd4yAE8xPdUIs8//xz5vAm3vILpuCMC0Lq2WcLjPDWtYZDHVj/PvlkqYmN1vcjwRjWJ0Q9vBgwpy1atDRlkjK5cAhgD02igsRmqLefVa/VEmK2ffvzzCHE6NEPme8SLcGDPsFcwwOaNWtuavEikRO+EzREQMcGhtddNyCSERq/TyW6vBbMdM+Hz9348ePl559/MetWs1XjebCeYKm2llqbrA3zhznr2rWbeR0HEOPGjS3kPYL3n3RSTbOusCbRNuYqGmdtk4xhffXufal5bsRS4xAW3+1aYird+HfW1+GJYBP12fKBYG2t7vYHn1FdA5msh8RWbo0jTi60TznllMjBnq5be8hla5HjB5+v887rLA0bRg/adtb5KW7PRfEb7IxR/AbL33nvFL/OEWfUAf7DhLUXm2C4oPlNeOXtxJX4RT3CF1+cZWKzsOGCuGjc+AypVq2acSXGBTH17rtvm801NgKVKx9nEtN4L2wiFy36wAhpbBIqVaokVaocX0js6HsgsJYtW2oELjb71aufZFxkvW6izzzzlHHdg0UFFjHNTgtLBaxHKIeUzHIUtcqqu53ZmkTEg258UUYJ7p0YP8aBZ9RyLWCBH7gaIlkPkiN5YzO9whQbEVhe7TwdKH379jVZXSHmvGVo9Plxzw033GA2K+A+cuRI496pWZjxJ543UekYtIGNODY5KpA0nhn3281tVNyoJQLWeOWF30HEtGljLX14Hw4kINggUhJd3k0eXkcbGuscnVcr4LBJjxVN241gglu6ugrivZpoyCZw2yqvvPKKEeLW8lnKjAvr8Nxz2xnLvl6wkCETOJ4VAhqHSjho0XjxZB9QtIcNPjb8YI91BbGDNa5WSp0vFXPaFuYYYgKiSK2nsBbqARbmFK7SXqGdzuoEMfjOO2+b58CBD1zl493flbP+Hu9R13Vwwo8ehHgtrTperBX8XhM3aWwongsHV2effbZJ+KZzateLGJdkrAlNvoXfY81hHnHAoxcEMGJ48dmBJRwCEmsNjN544zXjqg2m8EjAXHoFNj7z3br1iFkr6b5creB+Rn77ba1xk8ZY4TYOBt5Mwvh9q1Znm3WO+NOnn37K3Of9PFqLbhlzUINYXrBCvC0yvmu7+plEsq8WLVqlG16Kz45+B3m/h+zfcQgSPWiwybdg7YU1Fs8Lq72N3Y8KIzzvv//9b2Nxxe8RRoCkVbo28UzwhID1G2t2+PDh5jV8r3jXO7634F6P+Nt4S3XUmp3cMpnICp4VpJC8Cd8nKnhtvW6bzA3J+ZJ5ZuV76Pa7Nna94P8vfD8j5htrAuEdGzYgrnhzJP8B1ja+v886q5WplsArXAQofoOdD4rfYPk7753i1yLGZmzo0EFGlGGDiHIKl112Zczmy/lkJOggTOJ3r73KmlNt1PnN15WvNjF/cFW2m2ZYCHczVqJjjqliarXGW6RirTC6cfCK38RPCCGF+GebATmaHRgbadQVhasqLCs4JED5DrXKYPOp5UMgpDSj8IknnihnntlMkNV1zpyXTLveZFrYxJ51Vgs55ZQ6kWeABfrVV18xlmg8JzZacLHGptUrhlTkagZbzTxdt25ds/mH0IArLza5EO6aEVbjRNVKifFecMEFxu1anxnWpCVLPjZuw4gfxJhxX8OGDWX27JeMxdXLHDQhIjEWJCfDhfsxb1aYx5YqOeOMM+T000+Ps54Ujo+DyyncPiHyIC5huYIFL/6yltL1ZqOayuqv73v//feMayjmA3OAAxqIZsy9JhxCn+oWquvJ6x6L7xGdc7wHYlgPFHBI0q7deVl9jOy8bJeHHx4dJ85s7DKEtU3gVsIcwKiVHRYfTVKkc6vjxrggbBAnjvd4Ra8OEvGwbdu2TTpmiK433njDcIaYggvs//0f1ow3/jHWlTPe/RefF7B//fXXCh2GoZ2uXXtk5C5rBcl2WbBgvrHo4nMHi9icOS+btW/j3/cSxMvXqXOaeTaMYezYx03SMFuSyF7WZX0fU+u4Y8fzzTMiJOXll1+K+dxZa1qHQsn7sprsuDfhM/rww6MiserescHlHWvsvffeKfTZgyiD5debKRmf26VLPzafQxwo4rsYh004cHj00UfNIYA+t36P9enT1/DXz3ZqF2I37r9+XMYT35OPGRDDyCb5srk3sL4QjqKCNz+9ZN5KNAKKLs2Z0wvfOyh+g50Tit9g+TvvneLXIu7Xr7fZGFx77Y1GQA0Y8B9zInr++Rc5n4NUHeQifteuXS+bN6NUUn6ufAlV72jy3SbcFA84oLz8+effkXhna9m1ZTeicZD239lc2IDCVRYba5Rj8WZfRXvYHMHKhI3l7NmzIxZZbKaxUcIG3CYlq2jcLCGcMUYIKySwQdIqiCZsZNu1a29EdaILbWHzCUE+ffo0Y/3BZl7FDja1mlVaN2kYL0SXrTMrRjzNnfuyEUsYE9zP4ZIL6xxiVWFhPO2008xBQjJ3YJtgzLp9455BgwYZ65NywbPZuO8DpX///vLll1+a/iEoH3zwQWNl9F64t0+fPsa6j027WqN1I56JmPK+1+9cf/PNNzJlyrM7XBXtu/CMSFwFt3Pd+MMiphmSVQR4+9P7rLVLzFyAESyarVq1NpY6v5dNWIWEObCG2rhblLGCq7Adn20J8bRwSZ479xXjRg+Bg3vRL0Q8WOt4tG+8js08Ejk1aNBQlixZHEmupffgPbD6Vq5cpVCcJPqBGzwOAuBpAddqlL1Jl8DIa62aOfN5efvtt8161Nh1DYnwjuHKK6806yj5WrCupRgvDkYgcBGSUbv2KRGLt/18iLGIw/MAlmwN09DDnbffflPmz59vXtfDKIwLFw4RNFcBDnvw+uLFi82BDxiiPBM+Zy4ujB2HHppLwMsGAv600+rKE0+MjdS31tdxsIFs+t7vKmu1tHV81U0X3zs2r8DPMmPGdFm1yiYQxPcAchOg7Feml1om7ech0yRWUffeRBbOeA+TdGPTz6fOc7o4a80mrV4T+tlTwYtEk4irjf1OSjeKfL2O70Y9YKDgzRfVsLRD8RvsTFD8Bsvfee8UvyIrVnwvnTq1lenTXzTlb3DNnTtHRo9+UKZMmel8DlJ1kJ34FTnwQFhw8i1+bXbQVavCZ/n1MqxYcX8jfDXZi25MvBvm5JtnfxmKo8JBM3naDaR3cwTr2bp162XevHmCJFp77bWPcQFF8jAIWoipeOGMTS0S0mBDjg2rnwvPN2fO7EgmYggICAdYp2ClQcwjDnQgYBGbF9+nnz4yuQefJ5RYUtGA7LcQ4hC7F1540Q7LvI3dRfwpMvl6XbaxwT777NYZuQT7GV+6ja9ai5DsaOnSpTtEbjShHL4bIBZQZxf7eIgnjS/3WrnV4m5Fpt3s4++YE8Qan3wyrNOxoQDx48fhCUQJBC/EMg4m0K63ZjfE5gsvzJIffrDllRAXC2srMj7jgjCGiNeNuy0FFnVT91p/ERN60kknS61atU2IAQ5EIIJwD/pFBm4cmsSvHawtxKArBzynLfHTwyQB83Ph4GbhwvmFRDlcjDU5HNpFMq6ePS82LDTBE7wrMA9VqlQ2Fn2MD+sOlksIX1wYPz53vXr1MoyiMZSpR/f555+bJF1IKgUOODhAvK3XFRuisF+/y6VsWcTHRw9qkokr/B6HVJg7v3ziRzlt2lT56qsvY6y7+N45//wuhhFc/MFT3dpx6IHkfsccc2ykxA7WFtaYd03F14DHa/gsY80ga7eKfz9zGsQ9ha3Q/uOsEwlrTcSHZ4n3Ykn3fH6/a1Id5KEPhAbBEwPeBDgktSWSSpvvU4RQ2APc6Och3bj4evEhQPEb7FxR/AbL33nvFL8ib775ugwefIe88EK0lA9KlXTt2klefPE14xYX1JWN+MVYIQDXrv1dNm/ekreh77lnGVM/OEzi11p1vdYBxLju6SmDEp/N058rXlQwx9eRjFqRsQmPurui7A5qYyIRli2XlEhge2Pe8jYxOxpCoptPP11mNvhwgfRu0vPdV7r23nnnLUGJIljDIL4gPi666CJjzbYxytEfWNHhugyLOsQ5DgUy3Wymswp5N8aJLFBeKyQSZKlF1fucsERfeumlpp4tLNsaM23rptqYa7WwepOuaRvIWKxJh+I3vbhHs1VDyGgMsS1lFo3pTlQ5CkIK/eF7Kp4bYlchYPB7HKzYmtJI0BX9zOCQpX//q2OmFDG8yNCM54Iwrl69RsKYeRxyxGdTxrOdcsqpplZyugtjv+eeITFZhfU9NkO2jV3FwQMSY8GKi7HD6o7kWXDN1bJFEP4ID4Ar/htvvF4o1KFq1aoxruapskRjDB98sFA++WSZWZdwU//oo0VxXgpg+Lc0aNBAWrVCfG+sVTN+LtDOpEmTTCwmBCXWE8o4aXklvx4NiN+cOLHAzKtNXlfOHFrg2VWAw2L70UcfGsGE36N+dmyZHVvX2SZiSuaVQQAAIABJREFU4qWfP7gyQ2DqdyfWFurv2rJN9js93XdJ+nvMHTvaSV7WCXfVqVPHHLgkujRuHcIY9ep79uzNidyJCFD8BjuZFL/B8nfeO8WvyEsvvSCPPDJKpk6dFeGNDV3Hjm3M73CaHtQVPvFbSlatSvyfcTaM4PaMDYdfQa1iN7pxtb1m6v6WbgPj3RjvumvJSKKgePdW3bB6Xfv8CjevdSBqNcosWYy6sCazNGQzJ9m+B88dTVC1qxFa2OjDFRRWZxW98W632fbn6n1IJIU4Tm/5GIwZidvgpo7fQ8hAhKFer01Ctd5YHmHVhTUUZYnsurSjxPsRa92yZauYTa+64vtdM9pWbExj8phaZHl+7rlpsmLFD8aCBLEJQeS14CLc45JLLo1JYOSX7YgRw02Mb/wFK+N553VM2wwsmHDT9dbm1jdBgCDDOCzmyCRsLV82JhVJ3+Bu7b1gDbv44kvkpZdmR6y+3tcPOABZivumHRNuQPz9/PnvRZLB6ZqNz7iP36PsE4R5sku/G1588QV57733IrdhbWDNYMyoeZssq2+yRGirVv1qxDjY+LHKpjrQi64ru5ay/T7yBTdEN9lkeVHBC0ZaozjeEl6Uwy5RAvNQ0ny2EKsOTyYceuDzrEmr4OGjf69WrYbgh9fOQ4DiN9i5pPgNlr/z3il+xWQZHTr0Tpk1q7Dld/bs102inqCunV/8pnal9opdr0DIVOz6nb+ogIu6MeO9NvNp1FoZn2U3vv1UVqXEwju7WLh0z5VOYCdPHpM6MZG2a8VurMt3LCtYTArXok437iBfx9xOnTrZxFLD8oq5hEDs0KGTiddOd+HgDKWYYJ1U12K4n8MV2GZ8BjOUVIKIhiuztcIhftBm5E7srZBu3RS2PGKkti14Jfz666/yyCOP7CiXZJ8CryG5WLNmzWIeK5lQio+RfOih0Sb+XS91961b9zRp1szWuE3elpikbSNHPhCT5M07kCZNzpD27dtH3L4hSHDQcP/9wxLGvcLKCUEIl+X4C94HPXv2Sjd95oDgoYdGmXhf74XDm2hpKfsKDkCwLpAJO901atQIgWD1XmADd3MtB5WujUSvY33a2F2U2bHhBFjDWLsYs13DhTMCe79D0x8G+vOYwfjy4fKbLqlVNpz0PbbsjxW8EL/R+F1r4Q3u0hhebxxvdnkpgnsG9pwvAhS/+SKZXTsUv9lxKzbvovgVk5W3c+dzZcaMlyJlPBDzO2rUAzHW4CAmNRfxu27d77JpU77dnvNr+bWu1NE2E4vd2LJD+ZwHbF5VvNlEVaiZinI+Vuz++ad1DQyjgEsusOPdL/FEhQVV8tI6mfH2Wp8tp3jhbMVvNpviIK3EiLdDPWq42qLOcrJSWYnWI1wVUfIL1pqKFSsYgQkLJvhgTUHoQvCqe3w+13SqtpYv/9LEhGJ8iKmtUqWKyVDutUBnIpQWLVok06ZNi2SdxnxB6CMxVXzCqkTjwv3XX399QtdOjAPj69v3MnP45E1UBfGLWN94IYmkTHCbnzRpYoxLL9qCWzQSeqW74Er92GMPF3IhxxhgXdbvArinIwHYOee09eWmjzHHC2qMBSEK7dt3SDesmNchcG38pz14wvPhOwveFhBw+LuLy+vhkk3yqnTlvPx6QPg9nFERje/5kiWRbM4mi7OHA0juZQ8HvOEo+j3lgl/hNil4i4Zz8euF4jfYOaP4DZa/894pfi3ivn0vNtad66670dT2vO66/tKoUZPA42jCJn5Lly4lv/6aP7dniN8yZUqbTVt8kph0mTijGxZ11Uv/ccGm0euaGyt2rdAtqvqM6Ucb/B3eOrvRjePfRoxg0wiLIubNn8tm4ZqlmW12k1sR/bt0JnbrzAdpr9u3WpUwLnuQYgWvt7ZuPvrMtg1v3els29D3of414mNRMxTx5o0bn27iUFMJJbU0Yn099tijJjNz/IV7UAqsV69LCrU1atRIkzXcu34QH4xs4sgG/eabb5rs0fguhwhHBnMkUbNX6sMZlOK6//77TLZz74XvDri9I7Ye8dOVK1eVI444PMY9PhXLJ58cJ8glEb/mkfkb7vLpLpvxW110d4mUAVM33XTeKOnaD8vrfr1m0nlIWMGLDP/24M97kJb5944mQoxdO2Y1ebwb4kU0kpshaRoOl3DIC+s83PdtAiskrirtSVwVlhngOMJAgOI32Fmg+A2Wv/PeKX4tYsTV3HffUFm0yP5H1bx5S+nTp19G1h4Xk5Wt+D3wwP1k/fo/8m75zVX8WstutOwQNnRly+4RY3lKtqlJxzeZi6UKM90I6X2wulkRh1IVsZuYZDFv6cZQ3F/HYUC0pA5cKGEl8Vorkcwrv1alTCyNUVdNf9ZtzIefja7Xeq1COpE1KN4dE20ja64mqbKuzBC7VvDCyos8OUFasMO0Jm2N1KjLKbhMnTrNJKmKj/vFWoRLMazu8RdqaU+e/KzJnK7lw1Cnt379+hGxg3mAdRhhK2odTeUa7hXryPj9/vvvR7rFOI866ii54oorEq6ndOEFaAgWZSRTg6u3ruNjjz3WxDVbcabC3B7yYKzRckRRd2ZvTGqY5jboseC7CrHPXmu4zY5ureHxXhaZfu+kXjuJE1dhDSHRX6oL/y9BBN94463SsGHjoDGy/5AQoPgNdiIofoPl77x3il/niHPqIDfxu8G4XebrKlu2tJQps4dvy69u+L3xZCpG/AiS+HHHx6VpG97fa8ZcW97FnvpHN6ZRq18iEeWHU3xb6QVSZpYCP2PI5z1aUkctvJplOBrf7M6FMp/Pka6tdFZI77pMZ8UGI2+25HR96+u5Wqf9CHS/Yymq+6Ilm6LuuRClECNatgmJe8aOHWOyF+sz4vOLjNFIEJbsguj95puvTV1sJCODS3K+LrSNXBBIXIbxHnTQQdK8eQtjtUtW7zrZuvGuLVjHYZVGzW+UZ4Jl2687feFDmeSJzrJNWpUvfkXVDtaXV/CCkVfwBhuuYg8wUHIQWe81aRUyfSOuXJNVabb2xo2bGO8zXiQAAhS/wa4Dit9g+TvvneLXOeKcOgif+C0tv/66JuEzWatubNmhXMSuH3DYOHrdmGNLxNgNtlp207WXyhIQ64qX2H039fv9JYzxY0FK7GbnL0GVun2rdSTKy7p7h8k1N918FcXrmFNlpbVRdYOtrsxqCY895PEXY53oAMf+Ll8luvIjkLyHPKm4Y+g2ht7WKMbnMypIYkvGeNuBdQ6xyLDkQsxACOy7775FMcWB95HIGm4zo1uvlOh3qNe6mPl3kJ8HTXZAU9hVHK0VLgPn5z4/40h1j7WGWwsv/o4xW2s4LLzw4gkuwV60a629yxq8uc73P/X9FL/BzjzFb7D8nfdO8esccU4d5CJ+f/99g2zcmG/Lb1T8qtjNtexQJoCw2cFmEe7S+DNevKnFMpM2i/LeeOt1Ile6VOIn8fuzT1CFOUycpCrx5rY4Wh8znV8VI7rO8H5NjhN0bVQ/6yexcPbvJp6OV6IDmui4NLv09h1JhWxoQbo428Lx/ulGUXxf1wMVe6iym/EigEuuuue6rL2b/fpJX9/WrzePn++QeBFuPXqiJee8Cassr8IeNkW1Qih4i4r0P6sfit9g55viN1j+znun+HWOOKcOwiR+4fKMurybN2+JiZHVB0zkXux11cvmRF7FLjaJELzYKEYtb9ZaGZYkQjlNdJ7ebLPARg8H1KVSE1Rhk42NY2o3YGNriqtHa//t50rlnpl64+vPeu1nDJncY9eYdctNVoIom7WbyRjCcq+/ZEM2FtWGFuwS+UziEMVmZLYeIKncgNM9bzoPiER1jhNZIlO5/6YbQz5ft7H00WRV9kDFun/beNT8xtHnc+yZtlV47jMv46Z5IfB9r4JdP4OZfQ+l8nzwl4UeWd9RPgsJq5CoClZnm7BqD9lll113JDyjhTfTdcL7UxOg+A12hVD8Bsvfee8Uv84R59TB9u3bCiWC8dNghQr7CbKW5mL5jd/EYsOLzKklS0atAInKXaQbX7xIjnepxObGm6kTr3uTU/3553aPy11wJ/7pnrMoXger+IzMurFWK2W+s1f7sR5lbr32rqn05FKtoXTCCRtriBEVcLFxzipGsMZ4KQE9IFBXU8yvirdMreGZlOiKrqNYy2M667afmUscPuBNfOdPHMVbrbVd60Fg3XNhtcThgHWVtz//lAMVP3OBe9Qijhq8OIyya+xP2bKl8AFBqhATbSvXOH+0g8OcGjVqmBjdRBfGiWRViDevVesUueGGW/w+Lu8jgZQEKH6DXSAUv8Hyd947xa9zxDl1kL34LS9//LExI/EbFbv2pN5embnUZho3ay1H1qXNe8IfdXuLltHJ7cQ/lVUxfVIqvzGPOU22jzdjXqzLdzSmElxshmFbOzbouDcfj5Hylkw3tpkIpMzWEIapcY3p10iyDOHxIqs4iB4VIuoCjs+oV7xhnQWbTCj1Kst0DRUOP/DmLsitRJeKqGQi2V9yKnclunL9vObyfq1XDMGL7zVcXsEbZPkmPaRZs2atrFmzyiSsggjesGGj+TM+adXhhx8hTZo0zQUH30sCEQIUv8EuBorfYPk7753i1zninDrITfxuko0bNyXt34rdaNkhI3V3/DunQad4c+K6sdsj9XUh4FK5ACZLPKWiJt4qmV4YFXbJS/fs2VqMvAI6VRvx/SdiBku417Ib5CYxHa8gXrc1Ue0hgdYn1nq7UVf51EmDis567a0h6tf1u/B98esrU+7RsjpeZkiCFq4axZk+l6v7sT7imamXCgQcvsdsDezsklP5OajJJn62cFhC6lq1+eaHA0+IXetFsOuOMJZtEQtvsIdD+Fx5XZi9iavyTYLtkUByAhS/wa4Oit9g+TvvneLXOeKcOshF/G7YsEnwg8sbLxW1imTmaprpg3itlDZ50C5mIwih5i2lE19/MdN+8n1/YgHtdbtMtZlVq3muMbOxmbMxfxpTqXGVfkR4IqtSvnmFob1EJXV0nWlZnaI8IIiNd00f85j+kCZ2/flhHvWeKGy91gRBNqYy6nmB9+CQwP78aT6r+v3hFU3BChQ/T+/uHo3dxZ/WIo5kVfC6yH+24Vxdd1N74mQWw5/esyGxdRprxdbCtrkI4l3AYVEN9kokeEsGOyT2/o8nQPEb7BKg+A2Wv/PeKX6dI86pg2zF7wEHlN/hPrbVWB9046KDcbGRjY0/tWVOrEuuFbt//mlLD4VN7OY0QVm8OV5c2/hTWCltAiG8rhYkbKxVgBTeyGbnjulHFCUW1v5cx7NAkvFbvG656UoQZdx4MXlD/DpKlDk8PjYWwqNkSZukSmOdo+K2KEIMolbGRGVxcrVeu5g6dc3Vklc2FjWarKq4J9zzrqPEie5ANXHpreh7kx/6+ZmTTL+T0ic3i35Xbd26xRzwqFu1HQ8Fr5954T3BEaD4DY69+cb7+598xBss+yLpneK3SDBn3Ylf8RuN17UblXLl9opkrvXjPhfdAMfWbkxUosQrprGRtiUobBkKXLGW3dRuzFmDKcZvTFabWGN27UFB9tlfC1uL/JW5SeY6Hr/xTYc+mStm4d8ntxQlaiM287dda2EpQZSOSVCvQ+RqSR0tq2MtlTaJUKpkaJlarzMRTpl9J6lYiY+7Tu/6rYc4sd9vXgGeeGYS1ZK1dbCT1yoOao7D0q8eSKllPHpIsDVy6JksA3jyzOCFv7sSeUkkY7B27Vpp3LixbNy40YRAIDHV7ruXktKlS5tszfonMjmfcEI16dKlW1hwFptx/PbbGhk6dJAsWvSB2QM0bdpCLrvsyrjDhmLzOKEZKMVvsFNB8Rssf+e9U/w6R5xTB8nEr1fsejeSehIf36n/WNlULpo2C7P3tN/vw6UWRKk2sf5e8zuOIO6zLrk2/tRbm9jr+p2L2C3qZ/LjSpmL67if54laplOVMkkeS5vusMfPGMJ6T7ROsY3dta7MEG5WvIWprE72VsfcRJF37rwCGb/XMdlQA9Qptj9+3X7DaL12tVbt4YrNaK1lwqKHBFuLzMsnWQZxzMWCBQvk119/NQmqNm2yCas2bdokW7bYpFWbN28yfx511DHSq1cfV6h22nb79estBxxQQa699kZZvXqVDBjwHznrrFZy/vkX7bTPXBQPRvFbFJST90HxGyx/571T/DpHnFMHKn4Ti93MMjFnOhBsbLz1T9WyaxMHRd2YEYuavSBKtYn1/3yp3OaSWRzjN6mpYmgzYQc3SW+SKohfjaX0JqnKpM1/wr1aozj+kADCDWsOoi261mLdMBO5/EYtRPlKauYvGVWygx6veMrnfEbdcu0BSzTUIL11N5/jCGNbqQ798P3mjUPVfARaCzsqhM3fCtW91tfTPXfh76bMXL9TWa/VMyfdGPL5Or7PvIIX4/MK3uCzgHtdmpmwKp9zH9/WihXfS6dObWX69Bdl//0PMC/PnTtHRo9+UKZMmemy652+bYrfYKeY4jdY/s57p/h1jjinDrZv37ojAzM2THYDFn+VKGHrURa+/CU0wfuwgfnpp59MX0cffZQRb7Fi17pI5rtmrF846RK/ZGPZzqSMkzdGOpEFCDFlNo4yGktprUaaPAiZXxO5lKvrr18SO8990RjxaNkm7yEBrJQuLOLJrER+3b79HPSkmyXvWkgcv5i4xqwKaw0z0FrFsdbdorO4pXvOsL2uVnFbe9daxa1wQ7Kq7Mo3ZRtmkPrAJrNkhKm+W/y4fsd7UsSvSc1qDdGr3gTqAh6GmsU2FAeXV+z6//8vbOu0uIznzTdfl8GD75AXXpgbGfLy5V9J166d5MUXX5O99tqruDxK6MZJ8RvslFD8Bsvfee8Uv84R57mDRELX7+9ih7Ju3Tp57bW5snjxIvnoo4/k119XStWqVeXZZyfFlB4SsRlfY6+da2PhZwPrtSSq+IDVDYJXDxB0E5mpa7gfMZSsHqgfl/I8L8Ksm0tXggiW8ehGNutuAn9jLm7fyUS49/fpHjDdYU38ek1VgzbqIeF1MU83gnC87o1D1ZhnLROm4i0cI00+Cv8He1YwJ6pZnOzAx8jFBAeqiUaj3zM223xq63UmBzrZWq8peINfuS+99II88sgomTp1VmQwP/ywQjp2bGN+V6HCgcEPspiOgOI32Imj+A2Wv/PeKX6dIw5tB8OH3yvTpk2SY46pLDVqnCQ1a9aS448/QcqU2TNuzPHiOpmlOdmjpr4/seU6POIae0Ov+7dmDYVV15Y4SZ04SDeYXoFdWOAUTfmkWJHjL3uzH5fyZDOfvgRRdta20H6o8jSw6JqLtYqre6n1wNgeiU/NxO07uRDKJczAryuvP9fxXDBat1xws1ZKfNa8yarCFPOcy3Pm87363aTJ0fCnuoFHy15Fw1uiuSXyH3vt/Y6aM+dlmTTpWZOoao89SpvvYSSswt/1d/g7Eledemo9WhqTLAq4It9++0DzmdCrQYNGMnDgHfLuu2/JQw+NkB9//EEOPLCi9O17pdStWz9y34QJ42Ty5Iny++/rpXLlqnLttTfIoYcebl5/443XZOjQO2XWrMKW39mzX5c994zfS+Rz1e7cbVH8Bju/FL/B8nfeO8Wvc8Sh7cCKt61m41A0l18LdSbiOhthbeRo0kdGMpRPPllmSjM1bdo0soEGL2+SquBj26KPkNjSmHhjmtg65LUa5RIji/mIfb9NHIQfuIDDaoSfeBGU2s23aNZnsL3ADXf33aOJ0TBPNr4+GrtbVCPMLMzA66LrQgylSmpmX4P3hdcbQ93At22z/ApbK4uKZLj7Uct4qVJIWGUFr02Otk22bMlfcrTsQg1E3n33XZk6dUokWRWyNuNny5YtMUmrMN9XXz1A2rRpH27gAY1u5Mjhsn79OrnhhltiRvD9999J166d5dZbB8lpp9UzYvauu26VgoJpJonViy/OlEcfHS3Dho2Qgw/+P/P3+fPflfHjJ5q1gvd37nyuzJjxkpQvv59pG0J71KgHYqzBAT12se6W4jfY6aP4DZa/894pfp0jZgdFRiB7cb1+/XqZNKlAPvzwA1m2bKnZAMIF/OmnCyKZcrXervdxwm61zgf6dG6XNmkQahWjfmxJ06XWJ9YDglRxsn7GGB+TGI1j9JcNPJUbsJ/+XdyjMc9qbdPEaFqCyMagJgo5cDGaom0zddy0PZxK5zquIQd6n7rlZuLGG38Ik6j2cNSFN77UUtTancgNuGiJ+u9NE6RB8KoXC77vIHbDseYS1eDVWN7Cz4k5xCFuqVKl/EP4h915+eWXSOPGZ0i7dh1inhxi9tNPl8n994+K/P6qq66QqlWPk549e0ufPj3l1FPrykUX9TCvY520aNFEhgwZJiedVNP8rm/fi41Qvu66GwWlpa67rr80atTEvJ9X9gQofrNnl493Uvzmg2KI26D4DfHkcGhFRgAn3vfdd7dx+z7ppFpSq1ZtOeSQwzzxcMkszO5cwvORyMwFQFtv17rjYvMMseGNo8w0KVqqerLphLc3tjpZO+kYZJONNxoHW1gAxcc7evuPsrNliNS6awWvdZ/nlZiAtYxj3dmyOpocTZNV2XJE9vK/bvxarXPxhvCW3PKul+R1rhOJ8Pg1l8k6UXYqeG2iL2vdDUPCKvu8XoHLLM2ZzG+qe3GA1qxZI6lWrbp8883X5kCtTp260qdPPxk8+HY56KCDpV+//pEmRoy4X378cYUMHjxMmjdvLDfddJvUq9cg8nqPHl2kefMW0qHD+eZ3a9aslvvuGyqLFi0UJH5s3rylaVsTZubrOf5p7VD8BjvjFL/B8nfeO8Wvc8TsgAR8EMjeap288Wxcwgu7g69YsUIWL/5QSpbcRS644Hxj3YVFN1q2Ce6R4bVQprYyWlGTqJRN6sRTmcXHRkVZiUjWb60hG5vILJV1MbH7r4/FVWxv8cagwjKu606TVam1N4gHzCTUIJMkVNlYrhPXIIYruM0+jx89LNDSYekyPLtnmkjwWs8RXvkjAHE6cOAAU3v3jDOaGevsoEG3mJhpfI5OOOFEufjiSyMdPvbYQ7J06cfywAOjpWHDU+S++0bKySfXirwOS2/t2nWkW7eL8zdItlSIAMVvsIuC4jdY/s57p/h1jpgdkEAICGQmrhEf9uijD8v7778nP/30oznFb9asmdx552CzYbIliPIjrkMAJ6chxMfHWusuYnd3NaJDLeO2TrGNd/YKp3QuvukG5ydTONqIL4eTvgRO0ZfhQuyuJqsCP8sOuQmKPu45HXfXr8eLaz8CGp9THBLouvMK3PhY/3Tj95PoLn5NeUU4YnNRRQBJliC08Cdck0uVsgmqkLgKlkIRCt5Uc/Hbb7/JpZd2l+uuuyniaozQnOHD75Fvv/1aypXbV7p27SGtWrWJNINY3SeeeFxWr14lhx12hPTvf40cf/yJ5nV8ngYNulXmzHnRrJN//esgGTXqcdl///3N69by+4MMHnyvNGvWUG6++c6YBFjW8ttSOnTonG4J8fUcCFD85gAvD2+l+M0DxDA3QfEb5tnh2EggGAKfffaJDBt2t4n9qlXrFKlR42QpWzZZ5k4XLuHZCGuwKvos4Zo0SN1xrWV8e0yG4VwslNmIoHh38FSW7XQrLJXASSy8M7NeR2vI2hhU65Jrs6jDLXdnjXtOx93P63bt7WqEpWZoxsGUPSzYmrJOduGkZoWTlaU7mPG2ES+uCwoK5JZbYhMseZ8J92vG5rPPbiOXXNLXzyP/o+5ZsuQjI1RRPujBBx824hf5KTp1amtias8551xTqvD666+W4cNHSdWqx8uHHy6UAQOuknvvfcD825YielZmznzFHDr897+PCkoU4VCzfftO8sorL8mxx1aWe+55wLBFzG/lylWkV68+0qdPD6lbt4F06dLNvKYxv3fddY/UrFn7HzUXRf2wFL9FTTy2P4rfYPk7753i1zlidkACJJATgcys1v66ykZcW2GNDSAsLwsWzJdKlY6QDh06RFxKNTOztYwXjytdYqlcRZIfCt7EU1bsFs4Gnl2daz+9F697ElnHo6WctoYkBMG6NMPyuHnzFpOteePGTbJp0+ZIlmZk1d+yxf77xBOrmwM2XlECsN4+/vjD0rfvFXLLLTdExO/zz0+XCRPGS0HB1MjN99472DBGfC5KGpUqtYdJQIVr5cpfpF27VtKo0elyyy2DzN9RHgrlDTt3vlC6dTvfVDaYMGGyfPnlF0ZsP/HE03LooYfJzJnTZcyYR2Xo0OHm30iQ9fbb8+SppyZFkqVxztwQoPh1w9VvqxS/fkkV0/sofovpxHHYJEACeSLgR1z/Lb/+ulKGDx8mCxcukA0bNkj58uWle/ce0qnT+abec6x1N3txnaeHCkUzGk+ttXeRKE0TfcH9EocEiBfPtmaxn4dMlCk8mbhOnMgsVWIqPyPI/R5v7WJN9uUVvGEou2YPJ3B5k1UVvSdG7rTD0QIODvbZp5wRmfXq1YyI3wcfHCa//PKzDBp0T2SgkycXyMyZM4xo7d79fGnZsrWx6up12WW9ZMWK702ZqE2bNsqZZzaTAQNuNm7o77//rlxzzZWmH5QzgthGzWRc+DwUFEyQqVMnydq1v0mVKlXl6quvN0KYl1sCFL9u+aZrneI3HaFi/jrFbzGfQA6fBEigSAjAKvLIIyNN7ByypR5zzLGR0k7JB+BHWJttZgbPkI2wVlGSQTc53orNtGZnhuBVd2aN382HO7P/rM6FszUnzxTuzQCdGoIeeCSKp06dsTl9iS6v4FV+yi4cGZohjih4c/yY+Hq7V/wOGXKHCQkYOPD2yHthoX3yySdk4sTp0rFjG7nwwu7SqtU5kdfvuGOg7Lbb7tKjxyVy7rktzX0Qunrhd717XybNmrXwNR7e5J4Axa97xql6oPgNlr/z3il+nSNmByRAAiTgiEBYxHUJE4sIV3BYxtu0OUfq1q1r4nWjyaq2ecSSIxx5bjZdpnBzpFAWztjZAAAgAElEQVRCaxKnL5uUTdIpczTyN8Sy/kT/nc1rXlGeOS7N0KyHKSxJlDnDzN/hFb/Dh98rq1atlDvvHBppCJbfWbNmyNixT0vXrp2ldes20q5dx8jrN954jRx4YEXp1q2XtGhxuowfXyCVKh0VeR21e6+/fqDUr98o88HxHU4IUPw6weq7UYpf36iK540Uv8Vz3jhqEiABEnBLwF8is+XLv5T77x8mH3+82GSSrVy5slx11bWmhEri2Ofkluuw1rZ2xTk2WdpugnheTZam2cGTJZ1KZvX2ZoVON+5kmcLHjRtnDjGQIAlJqXbddTcTR1q6dJlIkqo99igjpUvvISefXFvKlCmTrqt/9OuJMjYjTheCFR4Sel1+eX+TxArXhAnjZPLkifL77+tNPO+NN95qyhXNmDFNnnnmSTnhhGry1lvz5K+//pTy5fcznii33TZYbrvtJilbtqxxT9brggvaS+fOXUxG6LZtW8jll/9bmjRpal6Ge/U55zQvZA3+R09YCB6e4jfYSaD4DZa/894pfp0jZgckQAIksNMSWLz4I4HlCbVATzutnlSocKDPZw2P1drngPNym1fwRjM0/2ks5MhuDdGbrytVNuZUicwKCp6Rt99+2ySjws+GDRuNAMPfN2/Gz+bIEPv16y8dO16QryHvdO0kytiMh+zZ80Jp376jEbTxF5JdIbnUsGEjjHtykyZ1TUmiZ599TlCGDmLVlih6zBw6oY7vaafVlyFDkJNgvtxwwzXm70gkhkzPY8c+LhMnTpO9995HUMf3zTdflyFD7pNy5crJ3XffKatXr5aRIx/d6dgX5wei+A129ih+g+XvvHeKX+eI2QEJkAAJkECRE/AjrjOJtcYDZBNvXcKUlXnnnbfl888/k5tuukn23XdfkzVcBW8+4p9zx6suzZqkKrlLs3Vn32pE8D777GNcv3kVJpAsYzPYoYbumDFPSaVKRxZ6Y58+PeXUU+vKRRf1MK/B7RmW93vuGW7KF+G9hx12uPz8889GwJ55ZnMpKHhKZs2aayzzKGU0btwYk6Tv8MMryb//fY0cd9zxpi2sOwhg1PnduHGDKZ907bU3yr77lucUhogAxW+wk0HxGyx/571T/DpHzA5IgARIgAR2KgL+XMKRSffhh0eZEjLIdF23bj255ZbbZPfd94jLDp6tsMb7chGe/gXvTjV9RfQwyTI2f/LJUundu7s0aNBIlixZbGqot2rVWs4//yKTRK9588ambFG9eg0iI+3Ro4s0b95CatSoaTI6z5nzhpQpU9a8DhHbtGlDGTt2ghx99LFF9HTsxiUBil+XdNO3TfGbnlGxvoPit1hPHwdPAiRAAiQQUgJz5syW999/W047rYGceuppEbGSfrh+rNbpBXNsP/Ftei28JdMPiXfkRMCbtGrBgvdMdubu3XuZ2N0vvvjMuCp36NDZCOCGDU+R++4baUIJ9Orb92KpXbuOEb+XXXaxzJs3P5JtHq7yeM+oUY9LtWrVcxon3xwOAhS/wc4DxW+w/J33TvHrHDE7IAESIAESIIGQEFARnIvFOCSP4ngYsNiPGjXcuKvDcl+r1inSr99/jKvxsmVLZfjwe+Tbb7+WcuX2la5de5iEUnrB5fmJJx43CaUOO+wI+fzzTyO1eiFWH354pMyePcu4jp98ck058sij5b333pExY540bs0333yn1K1bP9Ketfy2lOrVawj+PmfOvEiisajl92k5+uhjHFNh80VBgOK3KCgn74PiN1j+znun+HWOmB2QAAmQAAmQAAkUIwJbtmyWDh3aSOvWbU3sLQTmnXfeIiVKlDQuyZ06tZWePXub7MyLFy+S66+/WoYPH2Vicj/8cKEMGHCV3HvvA+bfU6ZMlBEj7pd7731Q6tQ5TW655QZBIqyHH/6v7LnnnjJ06CD54ovPTdzt6NGPS58+PaRu3QbSpUs3QwxxuihHdNdd95g6482bNzJWXo3jhRC//PJeMnv2ayY2mFfxJ0DxG+wcUvwGy9957xS/zhGzAxIgARIgARIggWJE4LvvvpUHHrhPhg69X3bZZRcz8rfeekPuuONmQUmiCRPGS0HB1MgToXQRrLgQxrffPtCI0OuuuzHyOtye4dLct+8V0rJlE9mwYYMRw3Btfvfdt+Taa/ubEkSdOnWRmTOny5gxj8rQocPl0EMPM5mf3357njz11CRTGumOOwbKypUr5fbbh5j2b755gFSs+C9TDonXzkGA4jfYeaT4DZa/894pfp0jZgckQAIkQAIkQALFnACELzIoH3XU0fLLLz/LoEH3RJ4I5b5mzpwhTzzxtElI1bJla2nfvlOM+G3Y8HS5/vqbjeW2a9ee8uqrL5v2UKd3zZrVxtUZSbBQf7mgYIJMnTpJ1q79TapUqWrq9kII44IVesSI4UYQb9u2TerXbyj9+19r6jLz2jkIUPwGO48Uv8Hyd947xa9zxOyABEiABEiABEigmBKAGEV5IIhR1NadNOkZIzoHDrw98kSw1iKJ1cSJ06VjxzZy4YXdpVWrcyKvw1q72267S48el8i557Y096GGr174Xe/el0mzZi2KKSUOO58EKH7zSTPztih+M2dWrN5B8VuspouDJQESIAESIAESKCICGzb8IXfddZtJenX33ffLkUceJcOH3yurVq2UO+8cGhkFLL+zZs2QsWOflq5dO0vr1m2kXbuOkddvvPEaOfDAitKtWy9p0eJ0GT++QCpVOiryOmJ6r79+oNSv36iInozdhJkAxW+ws0PxGyx/571T/DpHHEgHv/22xiTRWLToAxOv1LRpC7nssitNvBAvEiABEiABEtjZCaTK1owYXYhV7/+JiOVFAitcEyaMk4kTnxb8X4o6vIjPPf74E8xrkydPNJZg/N/6119/Sr16DU026G3btsrAgXfIbbfdJGXLljWuynpdcEF76dy5i8kI3bZtCxPf26RJU/MyMkKfc07zQtbgnX1++HzJCVD8Brs6KH6D5e+8d4pf54gD6aBfv95ywAEV5NprbzT/sQ4Y8B8566xWJuEGLxIgARIgARLYmQmkytaMJFY9e14o7dt3NP8vxl8oU4RSRCJ/S82ap8g++5QT1OYdP36ilChRQm699UZ57bVXpGfPS+Sss86Wa665Ur799htTm/ekk2rKwoXzTd3eIUOGyYknVpepU5+VsWMfl4kTp8nee+9jhPObb74uQ4bcZ8om3X33nbJ69WoZOfLRnXlK+GwZEKD4zQCWg1spfh1ADVOTFL9hmo38jGXFiu9NGYbp01+U/fc/wDQ6d+4cGT36QZkyZWZ+OmErJEACJEACJBBSAqmyNT///Mumlu6YMU9JpUpHFnqCPn16Gsst6u7usYctHYRMzrvvvruULFnSlB6CVXfWrOdk+fLlpt4uElPNnv165P6XXnpBxo0bYxJaHX54Jfn3v6+JlCbC+yGA58x50SSvgmDGQTVKHfEiARCg+A12HVD8Bsvfee8Uv84RF3kHOFEePPgOeeGFuZG+ly//Srp27SQvvvia7LXXXkU+pqLsEEIfpSawUdELGTThjoaSEg89NEJ+/PEHE3/Vt++VUrdu/aIcHvsiARIgARIIgIBma7700suld+/uJrPykiWLjVtzq1atjWcUxG3z5o1NyaJ69RpERtmjRxdp3ryF1KhR02RznjPnDSlTpqx5HQK2adOGMnbsBDn66GMDeDJ2ubMRoPgNdkYpfoPl77x3il/niIu8A5w4P/LIKJk6dVak7x9+WGEyUOJ3FSocWORjKsoOR44cLuvXr5Mbbrglptvvv//OJCK59dZBctpp9eSNN16Tu+66VQoKphkXcV4kQAIkQAI7H4H4bM1r1qwymZm7d+8lJ5xQTb744jPjptyhQ2cjgBs2PMW4MKMGr159+14stWvXMeL3sssulnnz5huhjOuvv/4y7xk16nGpVq36zgeQT1TkBCh+ixx5TIcUv8Hyd947xa9zxEXeAUTd0KF3yqxZhS2/cMvac889i3xMRdnh5ZdfIo0bnyHt2nWI6fbRR0fLp58uk/vvHxX5/VVXXSFVqx4nPXv2Lsohsi8SIAESIIEUBD74YIGJu/3f/741rsSNGzeRvn2vkFKl9pBly5bK8OH3yLfffi3lyu0rXbv2MImk9ELM7hNPPG7yXRxyyKHGsvvzzz+ZbM2HH36EaXf27FnGlfnkk2vK1VffIHPmvCBz574sY8Y8aVyiUXPX6xVkLb8tpXr1GoK/z5kzz7g744pafp+Wo48+hvNKAjkToPjNGWFODVD85oQv/G+m+A3/HGU6Qlg4O3c+V2bMeEnKl9/PvB2uwKNGPRBjDc603eJw//bt26VZs0bm9P2bb74W/LtOnbrSp08/GTz4djnooIOlX7/+kUcZMeJ++fHHFTJ48LDi8HgZj/G3336TSy/tLtddd5OJK8OVycbxsMOOkP79r5Hjjz8x4775BhIgARLIhgC+t9q1ayVXXz3ACM41a9bIf/5zmTRseLqcd15nk9MCB5bIzLx48SK5/vqrZfjwUVK16vHy4YcLZcCAq+Teex8wiapgpf3jjz+koGC6VKxYUf7730flhReel9atz5V27c4zVRE2btwoxx13gsyf/56MHv249OnTQ+rWbSBdunQzw0eMLkoR3XXXPea7sHnzRsbKe9xxx0e+Uy+/vJfMnv2aEee8SCBXAhS/uRLM7f0Uv7nxC/27KX5DP0VZDRAuWnDlve66G2Xt2rVy3XX9pVGjJju9hXPNmtUycOAAk8HzjDOamWcfNOgWYznYunWbnHDCiXLxxZdGmCLpyNKlH8sDD4zOinOY37RkyUcyaNCtApf3Bx982Ijf9evX+944YiM5ZcpEefLJsTJ58sxIIpcwPzPHRgIksHMQgDUVMbVwWf7mm+XGLfm88zrJ7ruXkgkTxktBwdTIg6JsEay4iNNFvgcIUBx4IjYXrsv4jr/ggoukZcvWcu65Lc3/hc89N8VYgo84opK0aXOW8YhCUqpmzVrIzJnTZcyYR2Xo0OFy6KGHCbyG3n57njz11CRTGumOOwbKypUr5fbbh5gx3HzzAKlY8V9y44237hzw+RSBE6D4DXYKKH6D5e+8d4pf54gD6QAi8L77hsqiRQulRAkk8GhpNgOoS/hPu+DqfMkl3Yz4QzIS1FfUy1p+f5DBg+/dqbDA7e/xxx82boK33HJDRPw+//x0XxtHHJrohfqUiIPDxpEXCZAACRQlAdTERcbkatVqmFq7jz02Wn755WcZNOieyDAmTy6QmTNnyBNPPG0EL76rYK1F/gd78LnVlChCLV6I5HHjCuTjjxfLxIkTTNtbtmyRs89uYzIu44LgLiiYIFOnTjJZnKtUqWqyO0MI44IwHzFiuBHE27Ztk/r1G0r//tdK6dKlixIN+9qJCVD8Bju5FL/B8nfeO8Wvc8TsoAgJfPXVl/Lyy7MF2Tyx2cG1ePFHcsUVvaVz5wvlyy+/kGHDHoyMCDG/lStXkV69+hThKN13hVg3uPzBSlGvXs2I+H3wwWG+No7t23eKDPLGG6+RChUqypVXXuV+4EXQQyJXcFiOZs2aYXjpdfnl/Y1bJa4JE8bJ5MkT5fff10vlylXl2mtvkEMPPbwIRssuSOCfTQD1en///Xe57babjNX3gAMOMIJz4MDbI2BgqUUCq4kTp5vEjhde2F1atTon8jostbvttrv06HGJsfzivoMP/r/I6/hd796XGasvLxIIAwGK32BngeI3WP7Oe6f4dY6YHRQhgZUrfxFYKrt1u1g6drxAVq1aJbfccr1xbYP47d79AuOa1rBhY5PtGW7BsBboiX4RDrXIuvKK3yFD7sh64zhgwMAiG7OrjhK5gqOvnj0vlPbtOxp3+fgLVnS4PQ4bNsJsmPH3+fPflfHjJ0YOWFyNl+2SAAlYAp98stR48OBgbtWqlXLnnUMjaGD5xeHV2LFPm4z+rVu3kXbtOkZexwEeStt169ZLWrQ4XcaPL5BKlY6KvI543uuvHyj16zcibhIIBQGK32CngeI3WP7Oe6f4dY6YHRQxgUWLPjClnr7+ermp9XvGGU2lTx9kCS0l77//rqnzizhYJD+BW/Cpp9Yr4hEWbXde8Tt8+L1ZbxyvuKJ4W36TuYLDJRLZXceMeUoqVTqy0OT06dNTTj21rlx0UQ/zmia/GTJkWCSJWNHOKHsjgXATSJWp2Y+XxTPPPCXr1q2VE0+sbvJWwMsCia2uvLKPHHtsFZO1Hy7G9eo1lKuuGiCjRz8gmzZtNLXcYSEuW7ascVPWCweinTt3MRmh4UaN0JcmTZqal+Elc845zQtZg8NNmKPb2QlQ/AY7wxS/wfJ33jvFr3PE7IAEAiXgFb8zZkwzcW4TJkyOjAmbUb8bx0AfJMfOk7mCw6LUu3d3adCgkSxZstiURWnVqrWJc0Ydz+bNG5tEOvXqNYiMwJY9aSEdOpyf46iCfztCAUaNGi6ff/6ZiYmsVesU6dfvP1KuXDlmBg9+eordCFJlakaGZj9eFojnhbV2333Lm3jde+4ZLjfffL1s2PCH+R3CWyBmFyx4X/baa2+T4VkPoxYunG+SY+HfEM9Tpz4rY8c+LhMnTpO9995HkOTwzTdflyFD7jNr/O6775TVq1fLyJGPFjvWHPDOS4DiN9i5pfgNlr/z3il+nSNmByQQKAGv+IU1pWPHttKjRy8599wOAjdglAXxu3EM9EHy2LmXyYIF75l4we7de8kJJ1STL774zGyeO3TobARww4anyH33jTRZY/VCNvXatesY9/rifCGeskOHNtK6dVtj2UYinzvvvMUkyYPg91tShpnBi/MqyP/Yk2VqPvvstr69LFCqDrV8YUWG4EVIwrPPPi0jRjxiDmkeeOBe+eKLL2Tr1i3Gyqvx+Xial156QcaNG2OSWR1+eCWTxVnLEsFzAwJ4zpwXzXpHIkQkukIfvEggLAQofoOdCYrfYPk7753i1zlidkACgRLwCj0M5LPPPjEbx+XLlxvLBwRcixZnR8aYauMY6IPksfN4JvFNP/30eJk792UZM+ZJs1m/+eY7pW7d+pHbrOW3pRHIxfn67rtv5YEH7pOhQ++PZIJ/66035I47bhYk/PJTUoaZwYvzCnA79vhMzShZlK2XRY0aNU0m5zlz3jAlkHBBvDZt2lDGjp1gMvnzIoGdhQDFb7AzSfEbLH/nvVP8OkfMDkiABEJGwCt+5817XVAarE2bdpFRPvHE4zJ//nsyevTj0qdPD6lbt4F06dLNvK4xv3fddY/UrFk7ZE+W+3AgfGExO+qoo//xmcFTuYT7iV39p2cIj8/U3KnT+Vl7WUD8XnbZxTJv3nwTjoDrr7/+Mp4Zo0Y9LtWqVc998bMFEggJAYrfYCeC4jdY/s57p/h1jpgdkAAJhIyAV/wi6/ftt98kd999v3FtXrbsYxkw4D8m7hWlT1BGZcyYR2Xo0OEmKziyPaO+51NPTYopjRSyR8x4OKhtCndQ1DYdNeoxmTTpmX90ZvBULuGwlPuJXWWGcLsMNVPzCy+8KnvvvXfM2vTrZVG9eg2Bx8WcOfOkTJkypo2o5fdpOfroYzJe83wDCYSVAMVvsDND8Rssf+e9U/w6R8wOSIAEQkYg3u15+vQpJhEYLJ7ly+8nnTp1kXPPPc+MGqKwoGCCEYVr1/4mVapUNTGGO1N5LCQSuuuu20zSKxwCHHnkUfJPzgyOeU/lEv788y/7jl1FW/+kDOEff7xYBg++XcaNKzCxubiQqfnf/+4rN910u6mVnY2XxfHHnyjNmzcyVl6N3122bKlcfnkvmT37NSlVao+QfctwOCSQPQGK3+zZ5eOdFL/5oBjiNih+Qzw5HBoJkAAJOCaAsl9XX32FqYN66613mThwXP/kzODJkKtL+KWXXp517OrOkCE81ZLcuHGjdOlynjRu3EQuvbSfKSU0cOAAU6IImcRz8bK4446BsnLlSrn99iFmCDffPEAqVvyXqd3OiwR2JgIUv8HOJsVvsPyd907x6xwxOyABEiCBUBJYv369SSIEd+8BAwZGYikxWGYGj05ZvEv4mjWrso5dLe4Zwv0sZGRqfvDBYfLpp5/InnvuKU2bnmUS66Huei5eFnBzHjFiuAk72LZtm9Sv31D697/W1PzlRQI7EwGK32Bnk+I3WP7Oe6f4dY6YHZAACZBAKAkUFDwlI0cOlz322ENKlCgRM8aXX36TmcFFTG3ZeJfwRJPpN3a1uGcID+VC5qBIYCcjQPEb7IRS/AbL33nvFL/OEbMDEiABEiCBYkggmUs4M4QXw8nkkEmgGBGg+A12sih+g+XvvHeKX+eI2QEJkAAJkEAxI5DKJZwZwovZZHK4JFDMCFD8BjthFL/B8nfeO8Wvc8TsgARIgARIoJgRSOcSnkvsajFDweGSAAkUMQGK3yIGHtcdxW+w/J33TvHrHDE7IAESIAESIAESIAESIAFfBCh+fWFydhPFrzO04WiY4jcc88BRkAAJkAAJkAAJkAAJkADFb7BrgOI3WP7Oe6f4dY6YHZAACZAACZAACZAACZCALwIUv74wObuJ4tcZ2nA0TPEbjnngKEiABEiABEiABEiABEiA4jfYNUDxGyx/571T/DpHzA5IgARIgARIgARIgARIwBcBil9fmJzdRPHrDG04Gqb4Dcc8cBQkQAIkQAIkQAIkQAIkQPEb7Bqg+A2Wv/PeKX6dI2YHJEACJEACJEACJEACJOCLAMWvL0zObqL4dYY2HA1T/IZjHjgKEiABEiABEiABEiABEqD4DXYNUPwGy9957xS/zhGzAxIgARIgARIgARIgARLwRYDi1xcmZzdR/DpDG46GKX7DMQ8cBQmQAAmQAAmQAAmQAAlQ/Aa7Bih+g+XvvHeKX+eI2QEJkAAJkAAJkAAJkAAJ+CJA8esLk7ObKH6doQ1HwxS/4ZgHjoIESIAESIAESIAESIAEKH6DXQMUv8Hyd947xa9zxOyABEiABEiABEiABEiABHwRoPj1hcnZTRS/ztCGo2GK33DMA0dBAiRAAiRAAiRAAiRAAhS/wa4Bit9g+TvvneLXOWJ2QAIkQAIkQAIkQAIkQAK+CFD8+sLk7CaKX2dow9EwxW845oGjIAESIAESIAESIAESIAGK32DXAMVvsPyd907x6xwxOyABEiABEiABEiABEiABXwQofn1hcnYTxa8ztOFomOI3HPPAUZAACZAACZAACZAACZAAxW+wa4DiN1j+znun+HWOmB2QAAmQAAmQAAmQAAmQgC8CFL++MDm7ieLXGdpwNEzxG4554ChIgARIgARIgARIgARIgOI32DVA8Rssf+e9U/w6R8wOSIAESIAESIAESIAESMAXAYpfX5ic3UTx6wxtOBqm+A3HPHAUJEACJEACJEACJEACJEDxG+waoPgNlr/z3il+nSNmByRAAiRAAiRAAiRAAiTgiwDFry9Mzm6i+HWGNhwNU/yGYx44ChIgARIgARIgARIgARKg+A12DVD8Bsvfee8Uv84RswMSIAESIAESIAESIAES8EWA4tcXJmc3Ufw6QxuOhil+wzEPHAUJkAAJkAAJkAAJkAAJUPwGuwYofoPl77x3il/niNkBCZAACZAACZAACZAACfgiQPHrC5Ozmyh+naENR8MUv+GYB46CBEiABEiABEiABEiABCh+g10DFL/B8nfeO8Wvc8TsgARIgARIgARIgARIgAR8EaD49YXJ2U0Uv87QhqNhit9wzANHQQIkQAIkQAIkQAIkQAIUv8GuAYrfYPk7753i1zlidkACJEACJEACJEACJEACvghQ/PrC5Owmil9naMPRMMVvOOaBoyABEiABEiABEiABEiABit9g1wDFb7D8nfdO8escMTsgARIgARIgARIgARIgAV8EKH59YXJ2E8WvM7ThaJjiNxzzwFGQAAmQAAmQAAmQAAmQAMVvsGuA4jdY/s57p/h1jpgdkAAJkAAJkAAJkAAJkIAvAhS/vjA5u4ni1xnacDRM8RuOeeAoSIAESIAESIAESIAESIDiN9g1QPEbLH/nvVP8OkfMDkiABEiABEiABEiABEjAFwGKX1+YnN1E8esMbTgapvgNxzxwFCRAAiRAAiRAAiRAAiRA8RvsGqD4DZa/894pfp0jZgckQAIkQAIkQAIkQAIk4IsAxa8vTM5uovh1hjYcDVP8hmMeOAoSIAESIAESIAESIAESoPgNdg1Q/AbL33nvFL/OEbMDEiABEiABEiABEiABEvBFgOLXFyZnN1H8OkMbjoYpfsMxDxwFCZAACZAACZAACZAACVD8BrsGKH6D5e+8d4pf54jZAQmQAAmQAAmQAAmQAAn4IkDx6wuTs5sofp2hDUfDFL/hmAeOggRIgARIgARIgARIgAQofoNdAxS/wfJ33jvFr3PE7IAESIAESIAESIAESIAEfBGg+PWFydlNFL/O0IajYYrfcMwDR0ECJEACJEACJEACJEACFL/BrgGK32D5O++d4tc5YnZAAiRAAiRAAiRAAiRAAr4IUPz6wuTsJopfZ2jD0TDFbzjmgaMgARIgARIgARIgARIgAYrfYNcAxW+w/J33TvHrHDE7IAESIAESIAESIAESIAFfBCh+fWFydhPFrzO04WiY4jcc88BRkAAJkAAJkAAJkAAJkADFb7BrgOI3WP7Oe6f4dY6YHZAACZAACZAACZAACZCALwIUv74wObuJ4tcZ2nA0TPEbjnngKEiABEiABEiABEiABEiA4jfYNUDxGyx/571T/DpHzA5IgARIgARIgARIgARIwBcBil9fmJzdRPHrDG04Gqb4Dcc8cBQkQAIkQAIkQAIkQAIkQPEb7Bqg+A2Wv/PeKX6dI2YHJEACJEACJEACJEACJOCLAMWvL0zObqL4dYY2HA1T/IZjHjgKEiABEiABEiABEiABEqD4DXYNUPwGy9957xS/zhGzAxIgARIgARIgARIgAU4G4h4AAAiDSURBVBLwRYDi1xcmZzdR/DpDG46GKX7DMQ8cBQmQAAmQAAmQAAmQAAlQ/Aa7Bih+g+XvvHeKX+eI2QEJkAAJkAAJkAAJkAAJ+CJA8esLk7ObKH6doQ1HwxS/4ZgHjoIESIAESIAESIAESIAEKH6DXQMUv8Hyd947xa9zxOyABEiABEiABEiABEiABHwRoPj1hcnZTRS/ztCGo2GK33DMA0dBAiRAAiRAAiRAAiRAAhS/wa4Bit9g+TvvneLXOWJ2QAIkQAIkQAIkQAIkQAK+CFD8+sLk7CaKX2dow9EwxW845oGjIAESIAESIAESIAESIAGK32DXAMVvsPyd907x6xwxOyABEiABEiABEiABEiABXwQofn1hcnYTxa8ztOFomOI3HPPAUZAACZAACZAACZAACZAAxW+wa4DiN1j+znun+HWOmB2QAAmQAAmQAAmQAAmQgC8CFL++MDm7ieLXGVo2TAIkQAIkQAIkQAIkQAIkQAIkEBYCFL9hmQmOgwRIgARIgARIgARIgARIgARIwBkBil9naNkwCZAACZAACZAACZAACZAACZBAWAhQ/IZlJjgOEiABEiABEiABEiABEiABEiABZwQofp2hZcMkQAIkQAIkQAIkQAIkQAIkQAJhIUDxG5aZ4DhIgARIgARIgARIgARIgARIgAScEaD4dYaWDZMACZAACZAACZAACZAACZAACYSFAMVvWGaC4yABEiABEiABEiABEiABEiABEnBGgOLXGVo2TAIkQAIkQAIkQAIkQAIkQAIkEBYCFL9hmQmOgwRIgARIgARIgARIgARIgARIwBkBil9naNkwCZAACZAACZAACZAACZAACZBAWAhQ/IZlJjgOEiABEiABEiABEiABEiABEiABZwQofp2hZcMkQAIkQAIkQAIkQAIkQAIkQAJhIUDxG5aZ4DhIgARIgARIgARIgARIgARIgAScEaD4dYaWDZMACZAACZAACZAACZAACZAACYSFAMVvWGaC4yABEiABEiABEiABEiABEiABEnBGgOLXGVo2TAIkQAIkQAIkQAIkQAIkQAIkEBYCFL9hmQmOgwRIgARIgARIgARIgARIgARIwBkBil9naNkwCZAACZAACZAACZAACZAACZBAWAhQ/IZlJjgOEiABEiABEiABEiABEiABEiABZwQofp2hZcMkQAIkQAIkQAIkQAIkQAIkQAJhIUDxG5aZ4DhIgARIgARIgARIgARIgARIgAScEaD4dYaWDZMACZAACZAACZAACZAACZAACYSFAMVvWGaC4yABEiABEiABEiABEiABEiABEnBGgOLXGVo2TAIkQAIkQAIkQAIkQAIkQAIkEBYCFL9hmQmOgwRIgARIgARIgARIgARIgARIwBkBil9naNkwCZAACZAACZAACZAACZAACZBAWAhQ/IZlJjgOEiABEiABEiABEiABEiABEiABZwQofp2hZcMkQAIkQAIkQAIkQAIkQAIkQAJhIUDxG5aZ4DhIgARIgARIgARIgARIgARIgAScEaD4dYaWDZMACZAACZAACZAACZAACZAACYSFAMVvWGaC4yABEiABEiABEiABEiABEiABEnBGgOLXGVo2TAIkQAIkQAIkQAIkQAIkQAIkEBYCFL9hmQmOgwRIgARIgARIgARIgARIgARIwBkBil9naNkwCZAACZAACZAACZAACZAACZBAWAhQ/IZlJjgOEiABEiABEiABEvj/9uuYBAAAAIFg/9ameHC4AiLnJAECBAgQyASc34xWMAECBAgQIECAAAECBAi8CDi/L0voQYAAAQIECBAgQIAAAQKZgPOb0QomQIAAAQIECBAgQIAAgRcB5/dlCT0IECBAgAABAgQIECBAIBNwfjNawQQIECBAgAABAgQIECDwIuD8viyhBwECBAgQIECAAAECBAhkAs5vRiuYAAECBAgQIECAAAECBF4EnN+XJfQgQIAAAQIECBAgQIAAgUzA+c1oBRMgQIAAAQIECBAgQIDAi4Dz+7KEHgQIECBAgAABAgQIECCQCTi/Ga1gAgQIECBAgAABAgQIEHgRcH5fltCDAAECBAgQIECAAAECBDIB5zejFUyAAAECBAgQIECAAAECLwLO78sSehAgQIAAAQIECBAgQIBAJuD8ZrSCCRAgQIAAAQIECBAgQOBFwPl9WUIPAgQIECBAgAABAgQIEMgEnN+MVjABAgQIECBAgAABAgQIvAg4vy9L6EGAAAECBAgQIECAAAECmYDzm9EKJkCAAAECBAgQIECAAIEXAef3ZQk9CBAgQIAAAQIECBAgQCATcH4zWsEECBAgQIAAAQIECBAg8CLg/L4soQcBAgQIECBAgAABAgQIZALOb0YrmAABAgQIECBAgAABAgReBJzflyX0IECAAAECBAgQIECAAIFMwPnNaAUTIECAAAECBAgQIECAwIuA8/uyhB4ECBAgQIAAAQIECBAgkAk4vxmtYAIECBAgQIAAAQIECBB4EXB+X5bQgwABAgQIECBAgAABAgQyAec3oxVMgAABAgQIECBAgAABAi8Czu/LEnoQIECAAAECBAgQIECAQCbg/Ga0ggkQIECAAAECBAgQIEDgRcD5fVlCDwIECBAgQIAAAQIECBDIBJzfjFYwAQIECBAgQIAAAQIECLwIOL8vS+hBgAABAgQIECBAgAABApmA85vRCiZAgAABAgQIECBAgACBFwHn92UJPQgQIECAAAECBAgQIEAgE3B+M1rBBAgQIECAAAECBAgQIPAi4Py+LKEHAQIECBAgQIAAAQIECGQCzm9GK5gAAQIECBAgQIAAAQIEXgSc35cl9CBAgAABAgQIECBAgACBTMD5zWgFEyBAgAABAgQIECBAgMCLgPP7soQeBAgQIECAAAECBAgQIJAJDNsywysAccUCAAAAAElFTkSuQmCC\" width=\"959\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = plt.axes(projection=\"3d\")\n",
    "ax.scatter3D(df_online_customers['ProductRelated_Duration'], df_online_customers['Administrative_Duration'], df_online_customers['Informational_Duration'], c=preds, cmap='Accent_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions of our data\n",
    "We can see that there is  2 clusters in our data, so our clients are segmented in 2 kinds of differents customers:\n",
    "* Cluster 0: are our clients with less informational duration\n",
    "* Cluster 1: are our clients with more informational duration\n",
    "\n",
    "This model is not good, we have a low silouthe coefficient as a we see in the plot, some points are very near one each other\n",
    "## Conclusions of GMM algorithm\n",
    "\n",
    "### Pros\n",
    "* Easy to understund \n",
    "* Is one of the most knowleged cluster algorithm\n",
    "* Clusters (Components are callçed in this algorithm) don't have to be spherical\n",
    "\n",
    "### Cons\n",
    "\n",
    "* We need to say to the cluster the number of cluster that we want. there are methods to infer the number of clusters but they are not at all good (we will talk about this methods in another notebook).\n",
    "* The algorithm does not discard points, all points of the dataset belong to a cluster, even if they are extremely far away, sometimes the algorithm segment noise as other cluster.\n"
   ]
  },
  {
   "cell_type": "code",
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