sk_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import sklearn.linear_model\n",
    "import sklearn.preprocessing\n",
    "import sklearn.decomposition\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import math\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "from cycler import cycler\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (5, 4)\n",
    "plt.rcParams['figure.dpi'] = 80\n",
    "plt.rcParams[\"axes.prop_cycle\"] = cycler('color', sns.color_palette(\"muted\")) # chooses a nicer default set of colors.\n",
    "plt.rcParams['text.usetex'] = False\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data_set(target_directory, target_filename, verbose = True):\n",
    "    X_data = np.genfromtxt(os.path.join(target_directory, target_filename+\"_trainX.csv\"), delimiter=',')\n",
    "    Y_data = np.genfromtxt(os.path.join(target_directory, target_filename+\"_trainY.csv\"), delimiter=',')\n",
    "    \n",
    "    if verbose:\n",
    "        print('Succesfully imported dataset with of size {0}'.format(X_data.shape))\n",
    "    \n",
    "    return (X_data, Y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Succesfully imported dataset with of size (1943053, 20)\n",
      "Succesfully imported dataset with of size (194614, 20)\n",
      "Succesfully imported dataset with of size (187030, 20)\n"
     ]
    }
   ],
   "source": [
    "(X_data, Y_data) = get_data_set(target_directory = './resources/run_2', target_filename = 'full')\n",
    "(X_data2, Y_data2) = get_data_set(target_directory = './resources/run_1', target_filename = 'small')\n",
    "(X_data3, Y_data3) = get_data_set(target_directory = './resources/run_3', target_filename = 'small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"\",\n",
    "):\n",
    "    \n",
    "    \"\"\"\n",
    "    Compares the predicted output against the training output via a violin plot.\n",
    "    \n",
    "    :param Y_data Output from training set.\n",
    "    :param Y_predicted Output predicted by machine learning algorithm.\n",
    "    :return None\n",
    "    \"\"\"\n",
    "    \n",
    "    y_buckets = sorted(set(Y_data))\n",
    "    \n",
    "    d=dict()\n",
    "    for b in y_buckets:\n",
    "        d[b] = Y_predicted[Y_data == b]\n",
    "    \n",
    "    plt.rcParams['figure.figsize'] = (10, 8)\n",
    "#     plt.rcParams['figure.dpi'] = 300\n",
    "   \n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.violinplot(d.values(), positions = d.keys(), showmedians = True, showextrema = False)\n",
    "    ax.set_xlabel('Control input selected by optimal controller (rad/s)')\n",
    "    ax.set_ylabel('Control input selected by reactive controller (rad/s)')\n",
    "    ax.set_ylim([min(y_buckets), max(y_buckets)])\n",
    "    ax.set_aspect('equal')\n",
    "    \n",
    "    major_locator = ticker.MultipleLocator((max(y_buckets) - min(y_buckets))/(len(y_buckets) - 1.))\n",
    "    ax.xaxis.set_major_locator(major_locator)\n",
    "    ax.yaxis.set_major_locator(major_locator)\n",
    "    \n",
    "    \n",
    "    fig.suptitle(\n",
    "        fig_title,\n",
    "    )\n",
    "    \n",
    "    plt.savefig('optimal_reactive.pdf', dpi=600, facecolor='w', edgecolor='w',\n",
    "        orientation='portrait', papertype=None, format='pdf',\n",
    "        transparent=False, bbox_inches=None, pad_inches=0,\n",
    "        frameon=None)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data, \n",
    "    fit_intercept = True, \n",
    "    Y_weighting_function = None,\n",
    "    X_transform = (lambda x: x),\n",
    "    Y_transform = (lambda x: x), \n",
    "    Y_inverse_transform = (lambda x: x),\n",
    "    x_labels = np.arange(0, 20)/19.0*math.pi-math.pi/2.0,\n",
    "    plot_coefs = False,\n",
    "    fig_title = \"\",\n",
    "):\n",
    "    \n",
    "    assert np.allclose(Y_inverse_transform(Y_transform(Y_data)), Y_data), \"Composing transform and inverse transform does not return identity function.\"\n",
    "        \n",
    "    if Y_weighting_function == None:\n",
    "        sample_weight = None\n",
    "    else:\n",
    "        sample_weight = Y_weighting_function(Y_data)\n",
    "    \n",
    "    Xt_data = X_transform(X_data)\n",
    "    Yt_data = Y_transform(Y_data)\n",
    "    \n",
    "    lr = sklearn.linear_model.LinearRegression(fit_intercept = fit_intercept)\n",
    "    lr.fit(Xt_data, Yt_data, sample_weight)\n",
    "    \n",
    "    print (\"Regression weights for features = \")\n",
    "    print lr.coef_\n",
    "    \n",
    "    print (\"Intercept = \")\n",
    "    print lr.intercept_\n",
    "    \n",
    "    print (\"r-value\")\n",
    "    print lr.score(Xt_data, Yt_data, sample_weight)\n",
    "    \n",
    "    if plot_coefs:\n",
    "        assert Xt_data.shape[1] == len(x_labels), \"The number of features does not match the length of the labels.\"\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.scatter(x_labels, lr.coef_)\n",
    "        plt.xticks(\n",
    "            [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2,],\n",
    "            [r'$-\\frac{\\pi}{2}$', r'$-\\frac{\\pi}{4}$', '$0$', r'$\\frac{\\pi}{4}$', r'$\\frac{\\pi}{2}$',],\n",
    "        )\n",
    "        \"\"\"\n",
    "        We're inverting the x axis here to align the plot to the sign convention for the bearing.\n",
    "        Negative values for the bearing correspond to the sensors on the right of the robot.\n",
    "        \"\"\"        \n",
    "        ax.invert_xaxis()\n",
    "        ax.set_xlabel('Bearing of rangefinder sensor')\n",
    "        ax.set_ylabel('Coefficient')\n",
    "        \n",
    "        fig.suptitle(\n",
    "            fig_title,\n",
    "        )\n",
    "        \n",
    "        plt.show()\n",
    "    \n",
    "    Yt_predicted = lr.predict(Xt_data)\n",
    "    Y_predicted = Y_inverse_transform(Yt_predicted)\n",
    "\n",
    "    return Y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression weights for features = \n",
      "[-0.03095658 -0.01949426 -0.02677003 -0.03462618 -0.04171019 -0.05536625\n",
      " -0.07269059 -0.08088806 -0.07962131 -0.03693672  0.03636901  0.08357396\n",
      "  0.08290121  0.0689061   0.05494826  0.04255617  0.03428646  0.02521712\n",
      "  0.0174142   0.02976362]\n",
      "Intercept = \n",
      "0.06311237307\n",
      "r-value\n",
      "0.593026257835\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear features\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data, \n",
    "    Y_weighting_function = lambda Y_data: np.abs(Y_data)**6,\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for linear features determined via linear regression\",\n",
    ")\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of control input selected by learned reactive controller \\n and optimal controller\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cubed objective, no centering of features\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data, \n",
    "    Y_transform = lambda y: np.power(y, 3),\n",
    "    Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    "    plot_coefs = True,\n",
    "\n",
    "    fig_title = \"Coefficients for linear features with cubed goal determined via linear regression\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of learned reactive controller \\n to optimal controller\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression weights for features = \n",
      "[-0.12933211 -0.1168573  -0.18053814 -0.29548549 -0.43497715 -0.64520728\n",
      " -0.93109957 -1.14716128 -1.19673222 -0.62145319  0.61387357  1.24814345\n",
      "  1.16823557  0.89885198  0.64761157  0.430246    0.28741517  0.17543379\n",
      "  0.10411869  0.13474329]\n",
      "Intercept = \n",
      "0.0\n",
      "r-value\n",
      "0.507933296073\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAKACAYAAABkGxwyAAAgAElEQVR4Xux9B3gc1bn2e2YluUlyb7Itq+yekSzbEExCwBibnoQWCJDe201u7k276SHlJiQ3IZXcP8lNgQAJafTeMSUQWgi2hTRn1pJsjI2xsbEsV2nn/M+3mlFG611pZ7W7mpW+8zx5Ylanvt+ZmfOerwlwYQQYAUaAEWAEGAFGgBFgBBgBRqBICIgijcPDMAKMACPACDACjAAjwAgwAowAIwAmILwJGAFGgBFgBBgBRoARYAQYAUagaAgwASka1DwQI8AIMAKMACPACDACjAAjwAgwAeE9wAgwAowAI8AIMAKMACPACDACRUOACUjRoOaBGAFGgBFgBBgBRoARYAQYAUaACQjvAUaAEWAEGAFGgBFgBBgBRoARKBoCTECKBjUPxAgwAowAI8AIMAKMACPACDACTEB4DzACjAAjwAgwAowAI8AIMAKMQNEQYAJSNKh5IEaAEWAEGAFGgBFgBBgBRoARYALCe4ARYAQYAUaAEWAEGAFGgBFgBIqGABOQokHNAzECjAAjwAgwAowAI8AIMAKMABMQ3gOMACPACDACjAAjwAgwAowAI1A0BJiAFA1qHogRGBoB0zTXaq1X9fX11Xd0dGweq3hJKb8L4MMAqgF8USn1o3RrjUajqw3DeBDA75VS78k3HlLK9wK4EsA3lFL/ne/+s+2vubl5cSKR6ASwVil1SrbtRqOeaZoXA7hMa10D4Hal1JszzUNK6QDoUko1jMZcwzKmlPJ3AN6jtV5j2/bDYZlXvuchpaRn6b1jfZ35xi3X/rz3oxDid5ZlfYD6GS97LVfMuF24EGACEi55jNnZNDQ01JaXl39Ca306gDoAEwBsA9AO4Jqqqqrrn3nmmd4xC0AWCzNN8yKt9aLy8vJftba29mTRpOSqmKb5Bq31HQBe1Fr/H4CHMx3KikRArgDwzdEkIKZpVmmtPyyEeMGyrL+GVah1dXXTKioqtgIQQoifAVhnWdbvmYAMLTH3YE4E5OSxQkCklB9xHMeKx+MPeauPRqNnGIaxFMCflVIvhnUfF2teUsrXCSGOtyzrp4UYMwMBIRI4pvZaIbDjPsOBABOQcMhhTM8iFov9hxDiMgARuuUVQvwdwGEA9VrrcwDMBPCkEOICy7LogMNljCJgmuYXtNbfAfBJpdT/DrXMIhGQUdeAlIqoY7HYcUKIx4UQN1iWdeFw82YNSD9CY5CAGFLKPaQJG03iPtz+G+2/m6b5S631GYXSADIBGW0J8/gjRYAJyEgR5PZDIiCl/BCAXwHYpLU+37btf/ob1NTUTK6srKSb8HcCeEYp9ToAejzB2tLSUtHa2kqEbMwXKeU3AFwC4P1KqauZgBRO5PneV01NTWscx3nAb/Ix1OzDQEDyjUEu0hopAVmzZk3Z2rVr+3IZuxBtYrHY0UKIf4y26WIh1pbPPqWUzwKYmgUBMUirCCARZPxiEpCw7cEgOHHd8CLABCS8sin5mTU0NEwtKyvbAqAMwGuUUmRula7QjRppRcodx3lnPB5/3qsUi8XOEkJ8CsAxACoBbNda32sYxrctyyK7+WTxHY5+lUgkLjMM43IAK92X+r19fX0fra2t3bd161ay9X8HgHkANgohvm5Z1nVeP1LKqwC8WwhBROgox3E+JYRoBLAfwH2JROJzGzdufMG/iGg02mgYxtcAnAxgLoBDANYDuFwp9Wd/XdfP46S+vr66srKy7wN4E4DfKKU+4/+b5wMSjUYXGobxBQBkurYQwEEANoArlFK/TgEzYpommbm9G4Dpapw66ca6rKzse36zLinlN4kICCEuTiQSLxqG8S0AK0gGAJ5wHOe/4vE4fUCHLY2NjYsikchXAZwJYD6AfQCo7f8qpW6kDnx+DvSfRDC9d09G/wu/BiQSiXyL5ArgRACTAbQCuFQpdVPqBGOx2AVCiE8CONpdTxeAv+zfv/97W7ZsOeCT9YAPCP0dwA8AnABgIpkXCSG+bFnWA/7+6+vr55aXl5Os3wBggRCCDg3tWuvfKqV+7uub9uZiIcTCdFo90zRv0VqfDeCsSCTyfAYfkCDyJGL3NSHE27TW5HPxGdeki2SasSxcuHDS5MmTP6e1vsjd57QeW2t9bXV19U89s0gpZXI9KbIb0mclEwHJVj40adc87UsAzndNN+nnDnd+3/ebbbrkNi0GsVjs/UKI32qtP28Yxq2O43xfCEHvhykAngPwFaXU/X6g6NC1bdu2z2qt306KDNLaCiGeJ9NBpRS9JwYVKeUqAKTde437DnjEMIzPO45D88/KLMbFrF0IcRaAP2itVwghLrIs6xYaTEpJsqX9dxqA2QB2AXjIcZxvxeNxeiYGFTLrdBzn40IIehZora8AeDCRSHxt48aN8dT6TU1NZ9Kz774L6DlYL4S41Dd+0s/Dvw88Qur3P4hEIpsdx+mgvaSUondRaolIKV+mZ7mvr29eR0cHaVQQZG9k2tREOg8fPvx5IQTJrZ7e3Vrru8rKyr7S1ta2yd8uGo0uMQyD3l2rAcwC0O2+/y7zm5e52JNP0/M1NTVHb926ld6f1H+NEIK+Sb9TStEzqH3vLf97TiulIr734F2GYVziOA7h2SSEONayrHU0TlNT0wmJROILZL5FBMaV2SO0t5RStFeTJQgBCbJvhtuDQ71P+G+MQBAEmIAEQYvrBkLANb0i+1c6nJHTccaS7qbSNM1Paq1/DOBlrfUNhmFs01q3ALgAwB7DMFa2t7cr/8sYwJ+01scahvGA1prqXyiEWALgWjoUCCFeo7W+TWu9UAhBH1InEom0tLW10aF+wFyCSAGA8wCQTT75qqxxP/pdQoijLMva69ZfQJob14zsjwDa3EM4OU2Tk/W/KaVIA5QssVjsQSHESQDosHosgFuEEM9alnWX9zfPCb2lpWVGb28vkbHpWuu/0uFHCDGZNEl0phdC/MCyrM97fUspbwBATsEbANwjhDjgOM6JQgj6uD5TXl6+0tO0SCm/7h5Yv6O1/pgQ4q+O42wRQtAB/I2E+cSJExvXrVtHZCJjccnX4zRHALcBIA0XmdTRPBbSgc+27R94fg5kkiCEIDJFxOxpx3Eei8fjRD6PKL4P+d0umXgMwFP00ScNCoBJAM5VSpFPiYfvV4UQRDJJTtc7jnNACHEqAPqYP1FeXr7ah4FHQMgPhGR9h3vAJDt20sgdjEQizd6hpaWlpbK3t/dJl9zRgfApIQTJ5l0u8fwfpdSX3X3xPwA+B+A//MSE/ub2s4MOO0qpmubm5oXpCEgu8nT3Fa2F1rTVv/dSAaZnrre3l5yiyVb971rrtQDoNpbwooPvbZZlnUvtTNP8oNaaLgE+5srgz8P5rKQjILFYLGv5EIE2TfN+rfVJWmvyNbif9j+AiwDQpcCflFJ0mZAs3p5Oh4EbcIAwoffR2+gywfU/WwbgrfSs9PX1NfkuF4xYLEb7gfYrYXOvewFC+5pMRy+3bZsuRpIlGo2+xjAM2p+E31WEDV1gaK3peSIyTr5Pw/qAeIc/8pGigydpGrTWt9DlTXNz89JEIkEHUSIGf9VaKyFEjOZP7zHHcU6Nx+P0LHp4eNrnrUKIax3H2SOEeD2RXgA7I5HIUW1tbfRu856dfxNC0HuJCOifDcMQWmvCirD+LwoW4fp5vI8w1FrTO+Yex3E2xOPxe1I1Pe6l0msdx1nmv1SiwWKx2OlCCHqub1JK0fucfguyN9K9Mug3Ija0jwl3ekbpeSUSQu9jepe9VilFxCh50Hcc5x5qQ5c07n6oITIOYJoQ4h2WZdHFhIcnERC6RKN3UIsQ4mYA5L9FeMzUWn/Rtu3L3EsjCtZAFxpEEImUgvDzERBaOxH6R4UQG8vKyq5qbW19ySVg9G7cp7W+HgBdIDXSRZHWms5rZNL1qLvnkkE6UpzQj/AByWHfeOs8Yg9mAp1/ZwRyQYAJSC6ocZusEDBN8zo6LAshzvdu0LJq2H+YoIP9Rjqk9fb2Luvs7NzutTVN8wNaayIIdyql6GM6cBsEgBzZP+FpB3yOs2VCiKcrKytX+251k4dE78NB/XiRXGjcRCKxzK/tiMViRALoY/k5L3KTG9GJSAAdPr/izdF3eN6ilKr1feTpg7GaSEdlZeXr/Te4qQSEHD0B/FII8R3LsuiWLllWrFhRvnfvXvoIzjh8+LDs6uo66Dqw04eLbqXpgD+gzpdSkqnTO1PWSQSE/ndIa32m3zk2FovdLISg2/kLPQ1GJrlJKW93tQEfVUqRTJKlsbFxTiQSISJULYRo8LQAvkNiEBMsLYT4jN+Z09WM3UqERylFB2PaAy2GYdAtYuvEiROP95Mn3+HoC0SIXFl7BITMW97t11ZJKf8fkUchxGcty/oJ1Y/FYh+lA5oQ4k+WZRFBSRaXhBHxTOzfv38GaVncAykR0/tdeQxAGIvF3iGEIOftnyulPpEuCtYI5PkqEVvvkDXU8xaLxT4nhPge3bQrpUhr5hXSSNIBnfbp2zzH+HQ3rkP1n0pAgsrHvY2/E8DflFKkXUgW95mmd8M0Eou3Vndv0Z4+AgNfxDMydfywUuoarz/TNH+qtf6Ee8imCw96D/w7AHK0v0opRWQ3WchkdMqUKU/QpYYQ4jjLsp5269NefJMQ4sOWZRHRSRbvGaab8QAEZB9dCHiRjXx90WH6GCLwlmVRdLhk8bS/WutW27aJUHlj0wFyrkuikxcs7py8Q+qltm2TNgWuZo+0XC/29PQctXXrVtL40t6uNgyDLkFmG4ZR097e/kqm6HFpCMinAfxQa32JbduXeuO7c6BLmQ8KId5KGuige8Pfl//fpml+Smv9I631ZbZtk+Y4WXzP081KKbrAIdmQxqiJNLdKKdrvHj70G12k7HUcZ2E8HieNNtWngzntn0eUUmd4psK+Z329UuooXz9HRIHzPesUZOQHfh+aurq6iRUVFWQxMMVxnBV+0ialpEsBIsHPK6XogiRrDYiUMui+oXmn3YOZcOffGYFcEGACkgtq3CYrBKSUZCd8VCQSafI0DFk17H/ZJz9eqYdvt72QUtLN3cy+vr5ZpL73Hfh31dTUzPPbTJum+YzWmuyW3+6/0YpGo+cYhkG3WL9QStGBw68B+aX3mzdn3wHsXsuyyNwoefiMRCKxsrKyf9INVspHdidpBhzHmR6Px0m1P6AB0Vp/3bbtb/vrpxIQ0zQ/r7X+H631t72Dgq8+3bTSh8L7wJJJD5GxQR9Td450MCeTsH8opUjr4t0W02HtiAOyp3nSWtNhncye0pampqaZjuMQMdymlFqUWsk0TQrX+hm/w3mOBORVpRSZmwyykTZNk/yKFkYikYV0kyulpHC+ZHr1jjSmb3SzSRoe0jYlzZK8g5T/N9/hgQ4pdAP5EzKPc3EkrVmz1rojHo/TAXigSCnpIPoa0rB5phRSSovOy4cPH57b1dVFh+JkcTUb55EmzLKsv2UgILnK8wGlFJnnDFuklETWWtI9nx7B8zucj5SABJVPNBqdHYlElgkhdrS3t9P+HSjpLjd8BOQIDDxZa63jtm2TOZW/r/O01mQq6H8P0LtreXl5+cLU59pHIH+slPqseyFAz3evUoo0gf59Su8qei/MCkBAyHRntXfTTRNtampa5jgOmd/crZQiDeWgIqWkm/xTDcM42sXKiEajxxmGMU0pRSRuoMRisbOFEKQdGNA+SCmJgP3UNTskUurHh27zyYzq92T2GoCA0CUShRN/1nvvuJ0SwaX390THcebQAT/o3si0ub3nsLy8fEGK3EgOl5K5FF1k+EjD40opMsUbVLzLJq31ebZtk2bXIyB0GZJ8bv0NpJS7hRAVlmWRmVuypNMA+p51nUgk6vwXXFLK5DuHzAtt2yataqqM6dLpGFdztSEbE6wc9s3AOlP3YCbM+XdGIFcEmIDkihy3GxaBWCxGJgKkPl4UNLqVlJJuKOmmOK32REpJH1Uyj0h+DHwE5AmlFJnb+D+gyfwa9PL229D6bg6vtG37g+5HI3k7mM5J2r0lpA/nZqUUhRIeVMikhQiRYRjlpC53HIfMJWoSicT8jRs3kr2zn4Cca9s2aQ8GShoCcqzWmkwq6EDyM631NalO/L6PHZl81PjJTgoGpNKPKKXIbEl7h7VUMy53ju8TQlzh+seQb0ja4ruVGzjI+CtKKck0hMzSBkzwciQgaQ/VnvbFMIxT29vb10opCe8TtNZvKCsrS5rmecWVB5l6TVdKkRlPwneQOsJE0DTNk7XWZP4zsDf8/ZF/wI4dO2Y5jjPB7ZvWSX5Dxyul6MaRZP3fQgjSir3Pu3GnG/SqqqodWuuXlVJkGuL3jxnwqZBS5irPH1qWRaZfQxbX/IpuuckMbEZqZd9e36iUIjOftDeuQw2SegALKp+Uvo3GxsZZFRUVkwhv8gcibaT/UsG3p4/AwCfr65RSdKAeKKnvAR82r0YikSN8aBzHaXD3xkO2bZ8spaQbc9ISUBCN16Zi4r2rghCQiRMnVvs1eK4J3K+FEL8yDIPy6Awq5Jvmmsd9IJ1/CmkyHMeZNmHCBMKO3o9kknqXUop80Giv/t71mTjHb9KYTr7ZEhBqK6UkE7+V/sO292wBuFopReZLVC/Qs5tuXj657VJKzRlqb3p4+i8Y/PXdaH3f9Wtv3P2se3p6qjwNkddGSkl+ZovIz8P321AakFfcS5WBYU3T/JbW+sta60/btk0+jIOKaZq/0Fp/RAjxHsuy/pANAcll33jrTN2Dw71T+O+MQFAEmIAERYzrZ42Ap3nQWi+1bZtMVLIusVjsLjI1MAxjVXt7O9lWDyreB9MN3Xuzj4AMfFS9BqkHe+/3oV7g5ARKfhn+Qd0PHDmB06GNzD+okJ06fTQoERTZ9KYWuuk6goCQb4bfXpsapZunaZoXaq0pXC1pAOh5Jbvwe7TWpKGhj3aySClpXoZSqiIdyFJKUu3P7+vrm0EaI58PyJcsyyJn+IGSbYI+KSXZh9NBhpzoyVxsUJFS0k083cz6zR6SvicBo2BRXgFy+EztP0lSAbyFnNGllGSfTYflTO+1ZHQ1Tx4+DcggXxqqk+m2X0r5cQD/mWEc6n+AgLhmJXRzP0DQfKYgA/4i6TQgucoTwFeVUkccUFOxa2lpmdfb27s1nUaA6hLB2rp1K5mb7HFv9fNBQALJh+ZBiQ/JdJC0Ea5/hX8pdBs9oNX0kdsjMPBpQH5p2zbJcKCkytrDJsXh/ojnGoCllFoSjUaPNwyDbsTvc01zBtX13lUBCAhpUihPkv+ZJAzIl8Dv2DwIC/oPIcTA80yOx1rr7wkhSFtLATxS65M2JUlAPA1KuvdSmucubQLPdNG+PFM2rfWnvEO1Z95oGMab2tvbyReCxg+8N1LnZZpmUss5hON7OjxpryR9NFLwTvrPaK1/ats2aeM9zcBB9wIjtT6Zr9UGICBHOOe7YXs/TEFEbNum9+qgYprmt7XWX/LMUbMhIFLKXPYNEacj9mDqfPi/GYGRIsAEZKQIcvuMCEgp6VaYDhBpX6hDQZeq4UjzMqYIMW/zNCT5JiCO47yRHCv947o2unRrTLfXFEVrIPOsa2pwhdbaNgwj6bittaYoVbPSERD/QdUbIxNRokhFU6ZMOYvmJISg6Es0Nj27fqdniu5UppSiKFZHFCkl2YPP8zQkeSIgR2g4/AOnczTNUQMyyNnYh1fy1tYjoVJKIrlSCEFkiJy805ZEInG3a/aRPEil0wJl+LgnI02RAy9pdYQQZEOeNK1zHIfMOyjYwQABod9jsRhFEao/fPjwLPLVkVKSn86FPlOZTBqQvMkzHQjpNBz+eq5ZEdm+k/lbUkOSBxOsQPKJxWJJTZzrPEyaSXL6J7zJn4JM7dakIyD+Q7i3piAExIfNy47jfDgSiaT9Tmqt91HkLNeZmZyJB0wz/VhKKekw+dYABOSIQ653I0+O9+SDlGlvG4bRRuaurg8Wkd9ZQoj7tNY3a623G4bRSxdCAMj8068B8S58ktrEYd7NWRMQdx6UdPRR0hbRe8u9DCFtLAWTSJqRBn12083PRxzTaqj9bdJpOFJkRkFTKDz8gAmmqxnIFwFpJ/KaMqdBGo7UNZIJGYAvehqSbAhI0H3jyoJkknadQ+0L/hsjEBQBJiBBEeP6WSPgOWBqrW+1bZsi82QsdAustW7y3YhRmEuyg03ebqc2NE3zbq31ad6NXb4JCDltK6UGfeibm5vnU8haCk1JzobuB4/+e09vb2+z31HefZFTyMtpIyUgKWsXsViMHPt/SxFYhBAtlmVZnj/E4cOHZ/r9Ddy29NEn4kShIMn8aCBi0FCHteHi/PtMKShKD0UHGlRisRiRgz8A+LVS6qP+cYNoQCjajm3bRLwGFSklRb+i2901pA2SUlKkJIr6NeAcPNSeC6gBoeg6FNFmsuM4R6VG9fH8ndIQEIrsQyE73+I4zp2GYZD51Sa/s3AGH5Ckf0s+5JkOA5dgEFHe79PmDVT17fWBm9o8EJCg8kkSFv9NuTdBnx/NERqQkRIQHzaHlFJVw73w3FCuFHDhKaXUcWn2KTk4k0lftlGwjjj8eWGEvcAFw83J8x9zs5IP0h6apnmu1preqQMExGfymnQKz+a5SX0/ZMp34gY0OMkwjPmO4zQDILMsCtFNmsRkCfrsppufq6GmPU1yS9X4pL6bvLDMg6KZ+eZDoZPpwE/hmZMaxUITEDc0Or0vBgJf+CctpSRC9CGKumfb9h+zISBB981w6xxu3/HfGYEgCDABCYIW1w2EgBtulJwQKRLSoMgtKR0J0zTv0lqfTmFzbdu+wXWKvDw1monbjg6D5Pxc2dPTM4PscQtAQMjBlOLhDxTP58ELT+plhqa4+kopilLir0v+AORzkNYEKxsNSDQanRCJROqIYKQ51JBZFoXPTR4YPKdcAGencTr1Eoc9ppSiPBp5ISBuNCLSNOxQSpHD6aAEkr7oQhSKOJmzJEcNCPVPWp9B/XtmZRTpRym1U0pJ0a0+44X+TcXMNM16f+6YIATEdyveqZSisKQDxQ27SU7plO9mkAakubk5lkgkSH5XOI5zsxv04MtKKYrAliwZCEgyglw+5JnpofUcdtOZSKZziM0DAQkqn7RaoOXLl085ePAghVKl2/28ExB3n3pBBY4gs6QJnTRp0nQvhK2rGSXNDJE5ckIf2KeuKRv5f00dCQFxQ6muS4105cmWzI/Kysp2eiGmPX8Bv/+RVzcWi1Ekt39LISDJoB+pxMDFgiKkkb8NmSr+LYgPiNuetAkUze+9WmvKkfKpVFOvoM/uEHuaHPWXOo7z2ng8ToEE/O/k35Hju1LqbR6emUijFwmQ/Mls26boUwUnIL7gAH9VSpF2eVCRUlJkrmVe0IhsCEjQfTPcOjPhzr8zArkgwAQkF9S4TdYI+CLGUKIpik40KCKLG+aRwrdSvg5/dCmKgNOltaZDyFFKKdI0JIuXX8QfLSTPBIRMDMjXgvJ9bPXGlVLSreE5WuuP27b9f24Cvk1uPgRKEJaMfuOGEKa65BMy0zCMZi9fiS8PyKCDqruuZI4QLw+IlJKinix3HOeY1CRjUkr6KJ7iOM5KyqPh+3j9raqq6hR/eF8fORkIlZsPEyyasy8a0Scsy/qFh5WLDUVZikyYMGHx+vXrd7vY5OIDQiY3Scx9e+AtFKoUwEAUG9M0l2utKefCi4lE4ljP8d+dp3fr6490FMQEi0jvfiFEX29vb42XOM0lYTQPMqcgE7cjTPdcXyjy4aG9/6FIJNLgT4iWjoDkU55DHNYo8hE5u/6FDmVePdIA9PT0PKS1ptv8gahqIyUgOciHSF2d//lxk8xRgATKy0NJLz/m5TrJZk+T79RwPiDuPvWwIS3BOSlhremgTglK/eQnqeWgQ7ZlWRRiOVm8sLABw/BmMvPxSFHSCdkbw41GR35y0/bv319HYaBN07xEa02atwEzTXddpKkkmVNEt79blkX5Mrx3FiUm3OsPe05k78CBA88KIWojkUi9G20u6fslhPiZZVlkCpcsmTQgbj4jigRG5od0MUOmooNIfNC9McSe/iwAitz3e6UUBRPxvhnJvCP+3DYeAad3ut/xPhqNHmMYBl0eUR4deq97ZmIZTZPcRJ2pPiDkl0ekdCDIg+9ZP8IEy9W8kTM75VE6LiXpoBexceASKRsC4sol633j1mcTrEwbjH/PKwJMQPIKJ3eWDgGKxEEfPa01RWB6gnJVkP20YRjkJEmmWdMo0WBvb++7yU7e90FL3pyRz4WbEJBMmiijLx0IXohEIid4t5B5JiD04SIH5zcIIW50E/Sd4mY6bzt8+PAKb55u9nKKsPUYZdsVQtBNPZk8/EAIsYyyKJMJETk0kmYnCAFxk34RkemlpHrkMAyAnMxJ20LRnu71myb5ItmQ7bdH9OigRh/9e1yH0+TtrC9i0BdzdUKnfhoaGmrLysroYz2bzDooD4fjOPOFEG+hD6kQ4n3+A5kvVCpFhqL8JBmLT6aEwWu11n8XQmwQQhCxI5v6csdxTvdnLPbMGNw9c63WmsJj0q0rJdQjG/gT29vb6SPvD8OblRO6m+mZboPJ3OZ6IQQlIaMkdndorWlvktnGkxSpyJ8LwmcOQ8krN6SG/UxHQGh++ZLnEBB7if5o/z4uhHiQnlHKC+P6s1B0sIHgAiMlIC7mdCimnDb0TA8nH/K5uQQAZa+m55E0TJSHhxx+aU/Qu4GinZGJ3w+z2dPZEhA3oR09t/T8kLxvouhbQgh6D9Cz91B1dfUZvpxChOEDRFSEEOSvQg7+tO/ogE8+FReNRANC2LmhYylbO5mFUYhouu0nEkaJ8+b4SXpTU5N0HIfeA/S8kzkrYUgRuigJKpkzUjI7+v7/MBKJXEF+I274bQplTZc95F+V0FpTxLCoP0eKSxboNp7MncjPjzSUX8lEQFy5k7kkhbsls9FBeY187/us90amPU1aY8MwCG9657ANBlEAACAASURBVFEUu4fofSGEoDxIB92M47R/CE8iGiQzcvj/ixCC3q+UZPJC2muUC8myLPp7suRggpUMQ+8G4qDvVTIXipt09AgC4o5BQQEoKSJ9BynZ5xatNZmt0b7vcRxntXcZlS0BCbJvhlsnn3AYgXwiwAQkn2hyX0MdJhcahkG3imSzTwdIipdO5jsUPYbsgQciOvk7cZOR0a0W5a8g/wV6Id+qtf5OPB4fcDR2X8b0saAby2RyQq+4h/5VfX19DR0dHWQSlixeG63171LD8JLJmNZ6gRCCsh03EWGiTNmJROLz/pt118mSbkTpYEIHA0sI8RO6oXQ/1OQ0Sv4tFLXqs95c6GDihWsdap6maVIo3s8IIVZqrSm0JCXNs7TWf6qoqLjcM7lw+yBfD4rwQ4nTKDQoFUpAdk1NTc3l/twoWdwW0y3zNyzLyhiG15s3mX+QczaZK7gO8nTQpuzRlAyM7L0HShATLDc8Kh24fu44zk8MwyCcVwkhKBP0c4lE4hupgQJoIDebMO01OgDSntkmhLgrkUh8Ox6PU5ScZPGyY2utf+BPWpZpb7imP2QPTmSG8jrQQebXFN3HNcMiUkLmH3fYtk2HwmRxCQaZDNFh8D9TM6P7/k5heP2mfHmRpx//1H/TgU0IQaaGFNCBbqX7iORRZDM/icqEyVB9uwe2I0zWspWPeyNMYYzfKYTwIhxdW1VV9Z2enp6JdGkhhCAyQD5Zr89mTxNpyZDf5wH/e4DWReN3d3d/kg6vbtQzQ2tNWpk/9/b2/sB/WeLic0YkEiHSRBnQDwkhHiVzQCEEZZH/jOM4p/jJcjrsCDPKyu7PJ5Hy/DQIIb5K/m9keugmjHtKa03O0oO0y67JKDmbt7iZtR81DOOrZNIZi8X+i3xlXHlf4OW1ME2TcqJQ3pujhBAUTny91vpbqSHD3ZCxH3Ozst9OJkPDEBDSNlJAAe36UFEAhyNKtntjqH3nBu2gyIREnigJbI/WmnwGv+h//qkP0zRNx3EuEUKQg/wsN3M5+Sp916+BcN8XGWXjakAoDC+R5GQxTZPe2aTdp3DbpAE6JRKJJBKJBL0LiICQXI4oUkoiTyQbImzVLlmnrPOX+k1Ih/p+pZJdiogWYN8MuQeHwp7/xggEQYAJSBC0uO6YR8D7iLo+KwO3X2N+4bxARoARYAQYAUaAEWAEioQAE5AiAc3DlAYCTEBKQ048S0aAEWAEGAFGgBEoXQSYgJSu7HjmBUCACUgBQOUuGQFGgBFgBBgBRoAR8CHABIS3AyPgQ4AJCG8HRoARYAQYAUaAEWAECosAE5DC4su9lxgCTEBKTGA8XUaAEWAEGAFGgBEoOQSYgJScyHjCjAAjwAgwAowAI8AIMAKMQOkiwASkdGXHM2cEGAFGgBFgBBgBRoARYARKDgEmICUnMp4wI8AIMAKMACPACDACjAAjULoIMAEpXdnxzBkBRmCcIRA0G3kh4XETDXYppRoKOU4p9Z0uq72UkpITfg3A+5RSV5fSeniujAAjwAgUCgEmIIVClvtlBBiBUUfANM3PA7jLsqx1oz6ZgBOgjMhCiOMty/qp19TNuH6x4zgb0mWBDzjEiKqXIgFpamqSjuO8XSn1zREtPkPjDATk6y4BeT8TkEKgzn0yAoxAKSLABKQUpcZzZgQYgWERqK+vn1teXr6tVG+eTdP8pdb6jLBqGEqRgEgpvwjgUqVUZNgNlEMFJiA5gMZNGAFGYFwiwARkXIqdF80IjH0EpJRvBnA9gJK8eZZSPgtgKhOQ/O1VKeVNAM7JhoCsWbOmbO3atX1BRi8mAVmxYkX5M8880xtkflyXEWAEGIGwIMAEJCyS4HkwAoxA3hCIxWIPCiFWA9AAvPfcN5RS/02DtLS0zOjt7f2y1vpcIcQiAIeFEK1a6yuUUr/JdiLRaHSJYRhfBUBjzQLQDeAJx3Eui8fjD3n9NDU11TmO0wHgjkgk8uFEIvETAKcAqNRax4UQlyulfk31PT+PlLlrOjSn8wGRUpI50SUA3mEYxm7Hcb4lhGjRWu8WQlxpWdYlLS0t83p7e38E4FQAVVrrJyKRyH+2t7ev9681Go0eE4lECJeVAGYC2Afgaa31923bvtdfN6AGREgp/10I8X4ATVrrXiHEw0KIr6TOobGxcVEkEiFMzwQw350DkbH/VUrd6J+DaZqbtNYzlVKVUsrPAvgQgDoArxL53L9//+e2bNlywEcMqLm3J3RfX199R0fHZnct7UKIswD8QWu9QghxkWVZt7gyyUrOQQhIY2PjnEgkQnKjMRe4e+fvQoj/sSzrbylYdwGYU15evrCvr++PWutVWusv2rZ9ebZ7lesxAowAIxAmBJiAhEkaPBdGgBHICwKmaV5Eh1Gt9WkA/kyHaMdxHovH4393yccTABqEEPc5jvOEEGIKgDcBMAH8XCn1ieEm0tTUdILjOPcAiAghbgDQDqBGa01jTxNCvMOyrL9QP76D6SMAZmutNwshHicyQCZiAGZorT9p2/bPPD8PAD8AsAvAd6gPpdSPMhAQz8fgxwDeCuAaAIZ7GJ8O4D8AfFwI0aG1JjJxghDiDACdSqmoeyCHlPIoAH8TQgit9e/p7wAaAbwbQJlhGGe1t7ff7eEShIBIKf/ozm0tgPuEEHO11u8FMFFrvdq2bZIHka9GwzAIF5r3bQD+6RIh0mYt1Fp/3rZtwiVZpJQ0x7kALnP7J1mXu/+uB/ALpdS/m6ZJpOvDAL5CsgHwX0RMy8vLf9Xa2trjERAALwJ4RQjxD631LUqp9hzlvFYpRQST5niED4hLCJ+k/SKEuMlxnH8ahkHreweAyVrri23bpj2Vuk4iR4sMw3ggkUg8TPt5uH3Kf2cEGAFGIIwIMAEJo1R4TowAIzBiBNId/NwD4f8D8G9CiO9alkU37clSU1MzubKy8hk6MwJ4vVLqqaEmIaVspdt8uqlXSt3nOyzSb3Rw3us4zsJ4PH7IR0C01vpntm1/yqtvmuZyrfU/6Na+pqZmnmf2k+6APwQBoUPuQQDH0KGZ+jZN8w1a6zsAkJkOaQ9IQ+AdaOmQ/zr/Ol2ScDFh42ljXLyIKFwJ4FGl1Em+PhwAw0bBck3h6DD9F6XU23ztX0vaIgDPKaVe4451O4A3APioXxPlags2AKgWQjRYlrXVrU8EhDRY6x3HeT1hTb+79V8AcEApRYTDWzfVr001wXKx3ieE+KtlWR/wyz1HOQ9JQGKx2F+FEBdorT9o2/bvfHvB1FrT3uk+fPjw4q6uLpKpR7QWArhHKUUaEy6MACPACJQ0AkxASlp8PHlGgBHIhEAGAkKmQKRVmFheXj6bbr9TDpv/DuBnAH6klKJb8rQlGo2+xjAMIiuPK6XIXGlQ8R0wz7Nt+zYfAXF6e3sXdHZ2bvc3cE3GTnIc58R4PE7kgA6dRxzwhyIgWutbbds+z3eYpVv/PaTh6O3trfGPKaX8IYBPCSHeZlnWX6lNNBptEULUGYbxsGVZe71+WlpaKnt7e8m07FWl1Azv92w1IKZpXqe1Pt9xnJWpN/bkFE4Kl8rKyh/t27ev2nEcwmWbUopIxaBimuZlWuvPAPikUup/XYyShEII8R7Lsv6QIksy21ruOM68eDy+w18/AwEh06zVSqlHvX5GIOeMBKSurm5aRUXFTgBxpRSR1UFFSvkrAB90HOfN8Xj81pR1vteyLNJOcWEEGAFGoKQRYAJS0uLjyTMCjEAmBNIRENfExyYNhVLqmNS2sVjsONc06n6l1OmZ+jZN84Naa/LZ+IlSig7FqYflL2itv6u1vsS27Ut9BCStxsA0zV9orT/ivxEPSkAAfE8p9aWUQziRmD1KKTJpGihubgryP6DcFGSyNajU1dVNLCsrm1VeXh7RWgvXf+WgUmqyVzFbAiKl3EK+HD09PVVbt27dP4S8yD+F/ExuUkpdkFpPSknmZWTK9VulFJlTeZqBWsdxlsXj8ef9bUzTXEu+EolEom7jxo2kDRmon4mATJw4sXrdunXk95IsI5BzRgIipfTWSf5ARHgHlUQiQWZYlwL4phcu2DU1o3Uuj8fjpHnjwggwAoxASSPABKSkxceTZwQYgSEOtEfY3kej0dcbhvEY+SEopcgPYlBpbGyMRiIR5TcLSte/G86VfDO+qpRK+mj4i5SSnKF/pbX+qW3bn/YRkCeVUq9PU5/6+IIQ4rOWZZGDelANyNeEEF+yLOv7KfNIayaVjpyRCdqUKVMuFULQQX9emnXnSkAOkxZGKTVhqN0qpSTzrGsB/EYp9ZE0GJE/D/nc3KyUOt/FKKkBSSQS8zdu3Piyv42nVfIczf31MxCQ3tQ5jkDOQxEQb53+AAmpyyVTvf+zbfvj/nlHIpGFbW1tFFqaCyPACDACJY0AE5CSFh9PnhFgBDIhkO6QPZyGo7m5OZZIJKxMGhJvLNM0B2k40hyW6Yb+/zwNiUdAhBB/tyzrhDT1/wfA57TW/27b9i9Hg4D4Ioe1CSGuIv8O168EWmsKX5srASE/hjKlFDmH06E7bUmn4UghFKcLIcgJfkBD4mkG8kRABq2Pxs5VzgCGIiCeJucBin6WCQ+t9Sal1HPDES1+AzACjAAjUIoIMAEpRanxnBkBRmBYBNIRENM067XWGwGsU0odndpJNBo93jAMigR1r2VZFAY2bYnFYu8XQvxWa32536HcqyylJFMoMqP5ilLquz4NiMpg909khbQmF3qhZgOaYI1IA0JZ1wH8nUICV1RUHOP3jYlGo9WGYVBY21wJCJk/1VC0KqUU+T6kLaZpnqy1vh8ARZ+iqFeDSiwWe7sQgvw8fq2U+uhwB/McNCBHEJARyDkjAfGt8w6l1NnDbmSf6Vg6opVNe67DCDACjEDYEGACEjaJ8HwYAUYgLwgMEQWLHJKr+vr65nZ0dJCT9kCRUn4awA+FEN/xR8hKnVBzc/PSRCKxDsBTSqnj0hyWbxZCnK21fgPlz/ARkL7Dhw/P6erqogO9f1wKz0thfV8bj8cpIlZRTbA87YPW+krbtj/on5tpmhdrrf+UKwGJxWIeFhf5Q8vSGKZpfl9rHevr63uf4ziioqKCZLNDKUV5MQZpS0zT/KnWmsIjD0TpKrQGZARyHs4Jndb5qhclzY93NBqdPXny5P1+X5Sh1pmXh4U7YQQYAUagyAgwASky4DwcI8AIFAcBn/3+Z5VSlCMjWaSUlEfiM1rry2zb/oL3e0NDw9SysjI6/NcCWOaFs800Wynl0wAofCxl1qZwt8lCyfwMw6D8DFvdLOaOPwwvgO/7ncWbmppWOI5DOSG86E/Jg7eUkkyX9vsjTw2VB2QkPiBSylUAHko1EYvFYs0UPlcIQTk1JjmOM9kLdZutE3osFnsLhbd1zZLIsT/h4tRiGAbhbSmllruEJBkxSwjxCcuyfuFh6iYnJMIXmTBhwuL169fvdjEK6gNCIYpjEyZMmOX14SN7R2hA3L/lIuchw/B6kcEogaTfh4ic/ysqKsgRf0UikTBTnedZA1KcdwePwggwAoVHgAlI4THmERgBRmAUEDBN81zXd2EHJQrUWttuMj8yKSKCQEkHyaeAEhFSIsBzXfIx6FCYaeou0XgAADlX0yE9DoBMvC4knwfSgFiWRX/3JyIkokHJBynhnZeIkPJsUK6KDyilyPfCI0p0OKfkgOR4/UIkErm0t7eXwuRSlvffefkqXE3P14UQXwzohE5O+hQF6+oVK1aUd3d3twohKPHgnQDIUZ8yir9Va/0xwzA+obUmTQ8lViQn8fuzJSDuIZ7ygFCI4KeFEJRgcJbWmhIcTtJar/ESETY0NNSWlZWRbChZ402GYaxzHGe+EOItlJBQCPE+fxjaHDQg3jwo4/g6N/P9U+5a0hKQHOWcSkAGsCY8XEJF2c5J03OXmwBymtaa1km4U0SzL/v2QkaiNQqPFg/JCDACjMCIEWACMmIIuQNGgBEIKQKU84NC5VJmcnrXke9AMhkfaTvKy8u/QrkWhBCUc4K0DZQ34nKlFDlcZ1VM0zQdx7lECHEyHardzOUPAfiu50CcQkAe7e3tvai8vJy0MKQNqCYNgNb6+7ZtU4jZgWKa5kqt9W+I1JB2BMCpjuMks2BrrX/nmUp5pmZDaEAo4zkRi4GSzjxNStmgtf6xEOJ4N5v4OjdZ411u6NjfUsZ2AF8njZJ7aD+i7wzAGa5523uJ5Gite7XWpHH5QqqmyTRN8hf5GpmvudG4KCcJ+aeQxurhlHUkExEmEomaDFGwVvX19TV0dHRsdokQ5d2gPBotJCshxMWWZf2N1iKEOGBZ1pR08w8o5w5X20PhdtNmQndJyJyysrKvaK3JD4SICCVRJMx/mSanScZ1ZrVRuRIjwAgwAiFDgAlIyATC02EEGIGxh4DPBGtQNvGxt1JeESPACDACjAAjMDwCTECGx4hrMAKMACMwIgSYgIwIPm7MCDACjAAjMMYQYAIyxgTKy2EEGIHwIcAEJHwy4RkxAowAI8AIjB4CTEBGD3semRFgBMYJAkxAxomgeZmMACPACDACWSHABCQrmLgSI8AIMAK5I+ASEHJOJh+Q1bn3xC0ZAUaAEWAEGIHSR4AJSOnLkFfACDACjAAjwAgwAowAI8AIlAwCTEBKRlQ8UUaAEWAEGAFGgBFgBBgBRqD0EWACUvoy5BUwAowAI8AIMAKMACPACDACJYMAE5CSERVPlBFgBBgBRoARYAQYAUaAESh9BJiAlL4MeQWMACPACDACjAAjwAgwAoxAySDABCRFVFLKtwG4VgjxO8uyPjCUJGOx2DuEEJ8D0ASgB8Cdvb29n+vs7NxeMjuAJ8oIMAKMACPACDACjAAjwAgUEQEmID6wGxoappaVlbUBmCuEuGooAhKLxd4uhPgDgF8IIa51HIfa/ADA3pqamhVr167tK6IceShGgBFgBBgBRoARYAQYAUagJBBgAuITk2mav9BanwCgWgjx4FAEREpJMf27lFKneF2YprlSa/2I1vqDtm1fWRI7gCfJCDACjAAjwAgwAowAI8AIFBEBJiAu2NFo9PWGYTwihDhDa33FUARESnkUgGe11h+3bfuXfnlJKV8UQjxjWda5RZQjD8UIMAKMACPACDACjAAjwAiUBAJMQPrFFJFS/oNIhVLqfVLKzmEIyLsB/A7AGqXUIykE5F4AMaVUXUnsAJ4kI8AIMAKMACPACDACjAAjUEQEmIAAME3z81rrzxuGYba3t7+SBQH5LIDva62X2rZNPiMDRUpJDuznWZY1pYhy5KEYAUaAEWAEGAFGgBFgBBiBkkBg3BOQpqamOsdxNmit/8Pz2xiOgMRisa8KIb7pOI6Mx+MbUwjIVQDepZSKlMQO4EkyAowAI8AIMAKMACPACDACRURg3BMQKeXtQogplmWt8XAfjoBIKZMaEMdxlsXj8edTCMifAJytlKosohx5KEaAEWAEGAFGgBFgBBgBRqAkEBjXBMQ0zYu01tcYhrGyoqKinSSWSCREb2/vBgAPT5w48WPr1q07SD/7pWma5ju11lc7jnNKPB5/yP+3WCz2oBBioVIqVhI7gCfJCDACjAAjwAgwAowAI8AIFBGBcU1ApJQUKvc9AFJx0O5v9P/vV0pd7ZdJNBptMQxjvdb6k7Zt/yxFA7IDwP1KKUpoGLi8/HI3jZlVmTGj381k1659WdUPayVax6233oyVK08L6xSzmtfBg3uwaVMnTPPorOqHsRLJ4pZbbsKJJ54exullPaexsA5aw2OP/Q3Tps3FvHnzs1572CqOred7I0zzmLBBnPV8+p+LP+LYY8/AxIkTs24Xtoq7dnXipZeeRnPzhRCiNI8xO3bEUV29EVu37sbUqadjxoyZYYM5OZ/du3dh1657MWdOddr5VVZOSP7e03Mo4/xffrkbM2acjunTZ4RyjTSpXM5Tc+ZUl+bmC4kUxjV40Wi0saysbG6qLBzHuQ7APxzHudQwDFsptTO1jpTyeSHEq5ZlUd6QZJFSvhHA7VrrC23bviEXGY9fAnITVq4s7UPv2CEgN+LEE8/IZfuGpg0TkNCIIvlhHzsXDExAwrCzdu/ehK1bn8CSJReVMAFRqK7uTBKQ6urTMXNmOAlIZ2cbpkyxMGlSP9FILdkQkAMHDmH//ibU1TWFYfuknQMTkOKLZlwTkExwp/qASCm/BuASIYS0LKuT2sVisbOFEDcJIa7WWl+ptV4khLgMQFwptTpXUTIByRW50W/HBGT0ZeDNgAlIuGQxVghIV1ccTU0rwgNuwJmMNQ1IU9NbYBhGQBTCUf3ll9sxbdrmJAGZMuUUzJ49OxwTS5lFa+vDqK3NbGWRDQGhLjdvnoKWlpNCuUaaFBOQ4ouGCUgazCnLudb6Qdu2P0h/llJ+nQgI/VMpRRnQkyUajZ5jGAaRkxYAewBc7zjOl+PxeHeuomQCkityo9+OCEhX10Y0NZW6icaNSW1UqZo2eB+TUjclYxOs0X+m/TPof76ZgIRBKjt3dmDnzmcRi70ZkUhpBpzctq0Vs2dvxbZtr6KiYhXmzp0XBmiPmMO6dXegvj7zUTFbAtLZqbF8+ZtCuUYmIKMjFiYgo4N7xlHHKwEp9cMiCbRfA9IB03xNyHZV9tPpvyFlApI9YoWryQSkcNjm0jMTkFxQK0wb8p/Yvfs5NDSch7KyssIMUuBeX3zxOcyfvxPbtu1GJHIc5s1bVOARg3efSCTQ2noD6uszB/XMnoD0YOnS8GqsWAMSfH+MtAUTkJEimOf2TEDyDGgRu2MCUkSwhxnKI1Kl7MvCBCQ8+8m7YBgbGpBrsWLFGZg0aVK4AA4wmx07FF59dQPq6s5BeXl5gJbhqbply7NYsGB3koBovQILFtSFZ3LuTPbseRU7dtyNuXPTO6BTtWwJyPbt3Zg9+0xMnTotdOukCTEBKb5YmIAUH/MhR2QCEjKBBJjO2CEgN2DlyjPGgAlWaTvTMwEJ8PAVoerY0YCUPgEh/4m9e9tQW3t2CROQZ7BgwR5s374Hhw8vRW1t+CL3d3W1Y9KkNkyenDliWrYEZP/+gzh4cAkWLzaL8LQGH4IJSHDMRtqCCchIEcxzeyYgeQa0iN0xASki2KwBCQ/YWciCndDDIa5+zWDpE5Dt29vQ09OGRYvORkVFRTjADTiLF154CgsX7sWOHd2hjRDV2voIamt7hlxZtgREa40XXqhCS8uqgEgVpzoTkOLg7B+FCUjxMWcNSAoCY8FchpbEBCQ8D9NY2FOsAQnPfvKe77FggnXrrdfiNa85HZMnTw4XwAFms3XrevT2bsTcuW8o2Xwmmzc/gUWL9uGVV3qwZ08dGhuXB0CgOFXXrbsd9fVDRxnLloDQjMPsiM4EpDh7iglI8XHOesTxqwEpbXOZsXRAueUWNsHK+oEtYEUmIAUEN4eux4oJ1q23/gFHH306pkzpT2RbimXLln8C2ITp008r2XVs2vQ31NYeQnf3AWzbNhtNTa8LlSj6+vrQ1nYj6uoyO6DThIMQkK6uHjQ3nx/KwAFMQIq//VgDUnzMWQMypjUgYyFRGROQMLwWmICEQQr/msNYISC33fYHHHVUqROQZ2EYm1FdfQoqK6vCtVGynM2mTQ+htjaRzCC+ZctUNDcP5DTOsofCVtu5cwf27r0fs2ZNHXKgIARk585uVFefipkzZxV28jn0zgQkB9BG2IQJyAgBzHfz8asBuQEnnnhmvuEsan/9JlhMQIoKeobB2AQrDFLon8NYyoQ+Fkywbr/991i27HRUVg59sx2eHXTkTF544WmUlb2QTOBXXT30ATms6+jqegCLFyNJQF54oRJLloTLNyIe/2cyUeKECUNHGQtCQA4d6sWrry5GNHpU6MTCBKT4ImECUnzMWQOSVgPCBCQMW7H/4M4akLDI4rHH/oZp0+Zi3rz5YZhSTnNgApITbAVpRLK4447fY+nS00pWc0DAvPDCk6io2IoJE1Zj2rTpBcGq0J12dt6DurqyJAHZvHkiWlpOLvSQgfrfsOFBLF58cNg2QQgIdbZp00QsXRqutXoXJfT/u3ZlzvqeCsacOdV8hh52h2SuwOCNALxCNGUNSCFQLU6frAEpDs7ZjMIakGxQKk4dJiDFwTmbUUgWt99+NZYuPR1VVZlzO2TT12jW2bTp75g0aRvKyk7CjBkzR3MqOY+9cePtaGiYlCQgmzZFsHTpGTn3VYiGzz13ExoaJgzbdVAC0tFxCEcd9eZh+y12BdaAFBtxgAlI8TEfckQmICETSIDp9NuI22hqOjZAq3BVZQ1IeOTBPiDhkQXNZKz4gNxxxzVoaTmtpAnI5s39BESIVZg1K3z+BMPtXApJ29FxKxoaKpMEpKsLWLbsjcM1K9rf9+/fj82bb8OCBcP71wQlIFu2dGPx4nNCF4WNCUjRttfAQExAio85E5AUBLxD79jwAYnDNFeEbFdlPx0mINljVeiaTEAKjXCw/scOAbkaS5acVrK+EyS1zZsfw5QpLyOROAFz5swJJsgQ1D506BBeeuku1NZWuQSkD8uWnROCmfVPYcuWDhjGs6iqGj5Uc1ACsnfvfjjOMVi4sD4066WJMAEpvjiYgBQfcyYgaQjIzTdfj1Wr3hAyaQSbTr8JFhOQYKgVpjabYBUG11x6ZROsXFArTJt+H5DSJyAUwraqaid6e4/D3LnzCgNWAXvt6dmL7u4HMH9+PwHp7DyA5csvKOCIwbpubX0UixZ1Q4jhj4hBCUh/QsJqtLScGGxSBa7NBKTAAKfpfvjdVfw5jesRx6sJ1s03X4dVq8Kjgs5lE7IJVi6oFaYNE5DC4JpLr0xAckGtMG1IFnfeeQ2am08taQ3Ipk2Porp6Fw4fXoG5cxcUBqwC9rpr1yvo63sYs2dPdTUgPViy5AJEIpECjpp919kkIPR6C0pAqF1np4Ply8/KfkJFqMkEpAggpwzBBKT4mA85IhOQkAkkwHSYgAQAvA/lswAAIABJREFUq8BV+wnITTjxxNMLPFLhumcTrMJhm0vPY8UEiwhIU9MpmDp1Wi4whKLNpk2PYNq0V7F//1GYP782FHMKMont219ERcUzmDat3weE/CLq6s7FpEmTgnRTkLqHDx+GUjdh8eLh/T9oArkQkE2bemCab0Z5+dAhfguywAydMgEpJtr9YzEBKT7mTEBSEKAHnzUg4diIY8sHhAlIGHYVa0DCIIX+OfRrQK5GU9OpJU1AuroewowZe7F37zIsWLA4PABnOZPNmxVmzIhjypSJSQLy0kt7MHfuG0Ohldq+fSsOHXoU06cXjoDs2tWNiRNPwty54QkvzgQky82bx2pMQPIIZj66Yg1IPlAcnT7ohrSzU6G5+bWjM4E8jMoEJA8g5qkL1oDkCcg8dcMakDwBmYduNm16GNOnd5csAbHtf2Dx4p0oL+/PA0IH8smTT8bs2aPvUN/e/iTmzduBSMTISlK5aEASCQcvvTQbTU2vy2qMYlRiAlIMlAePwQSk+JizBiStBuSvWLXqTSGTRrDp9B9QFJqaSp+AnHDC6TCM7D5AwVAqTm02wSoOztmM0q8BuQkrV5auORytc6zk+RkbPiClTUCef/5vaG4+lHx8iID09BxAX99RWLSoMZtHqqB11q+nBImJrMfIhYBQ511dESxbFp7cJ0xAshZ53ioyAckblPnpaPxqQP6CVavC5ZQWVKJMQIIiVrj6TEAKh23QnpmABEWscPXHjhP6I5g+fU/JakA2bHgALS39ciYC0tvbh1deWYhY7DWFE34WPTuOgw0bbkB9/ZQsavdXyZWAdHbux9Kl54fmoosJSNYiz1tFJiB5gzI/HY1fAvJnrFp1dn5AHKVemICMEvBphuUoWOGSBWtAwiGPsROGt98JvadneUn6gKxffzeWLu13wCYCQmXz5qpRD0376qu7sXPnPZg7tzrrDZsrAdm+vRuzZp2BadOmZz1WISsyASkkuun7ZgJSfMyHHJEJSMgEEmA6TEACgFXgqkxACgxwgO5ZAxIArAJX7Scg12DJklIPw9tPQPbtW46amtJzQl+37hYsW1Y5iIBs2jQRS5eeXOAdMHT3HR0bUF29ERMmVGQ9j1wJyMGDh7F3bxQNDa4qKOsRC1ORCUhhcB2qVyYgxcecCUgKAv1RsFgDEoat6Dmhjw0fkBtx4onhsTEOKt+x5YTOPiBB5V+I+h4BaWk5DVVV2d9yF2IuI+mTnNCnTdtTshqQ5567DsuXzxhEQLq6DCxbduZIYBlx29bWh1Bbuz9QP7kSEBpk8+bJaGlZHWi8QlVmAlIoZDP3ywSk+JgzAUlLQP6EVavOCZk0gk2nXwNihSqyR7AV9IfpvOWWG8AEJChy+a/PBCT/mI6kx7ESBYs0IGOBgPRHwVqKBQvqRiLWorc9dOgQNm68Bc3NMwcRkI6OwzjqqPOKPh//gOvW3YH6+mDHwpEQkM5OjeXLwxF8hglI8bdesJ1W/PmNuxHHqwnWTTf9ESeddG5Jy5sJSHjExyZY4ZIF+4CEQx5jxwSL8oD0YM+eZixc2BAOcLOcBWVBf/XVe1Ff3x9y1/MB6eoa3WzouTig0/xHRkD2YenSC0LhiM4EJMsNnMdqTEDyCGY+umICkg8UR6cPJiCjg3u6UZmAhEsWTEDCIY+x44S+FjNn7sPu3U2hCF0bRLqbN9uoqNiAuXOnDiIglIxw3rw3jZppXE/PXmzdegfmzw9mmjcSArJtWzdqat6Eysrskh4GwTloXSYgQREbeX0mICPHMK89jF8Cci1OOml01c8jFSQTkJEimL/2TEDyh+VIe2In9JEimL/2Y4eAPIiZMw9g164Yamtj+QOoCD21tT2BmpqdqK6eNIiAdHfvg2G8DvPn1xZhFkcO8eKLmyDE06iqmhxo/JEQkL1790PrY0MRyYwJSCCx56VyKAiIlHIWANKjztJaTxVC7AGwE0CHUor+f9yU8UtA/oCTTnpzScuZCUh4xMcEJFyyYA1IOOQxVvKAdHU9iFmzDuCVVxqxeLEZDnCznAXlAFm8+NARpkv9uUAWIBY7Jsue8lutre1J1NTsCGwONRICEqaM6ExA8rufsult1AhIU1OT1Fp/SGtNYWqWAkg3Fw1gg9b67rKyst+0tbXZ2SyqlOuMVwJy441/wOrVTEBGe++yE/poS+Bf47MTenhkQTPhTOjhkUdn5/2YO/cwXn65DnV1zeGZWBYzWbfudtTXG2l9J0YzKlRr61rU1h7IYgWDq4yEgFBPmzdPQkvLmsDj5rsBE5B8Izp8f0UnIE1NTTMdx/khgHcBMAC8orVeK4SIu1oP0n6QcSRpQ6JCCIrRRhoSRwhxtRDic+3t7a8Mv7TSrDF+Ccg1WL36gtIUmjtrOqB0drajufm4kl1Hf0jk67Fy5RmBb8LCtGjWgIRHGmPJBKurK46mphXhATfgTEgWd911DUzzFEydOi1g6/BU7+y8BwsW0OF1DqLRo8MzsWFmkkgk0NpKmcYr0xKQ0YwKtWHDPVi8OBEYy5ESkE2bIli6dPTDpTMBCSz6ETcoKgGJRqOrDcP4EwCKP/cnx3F+HI/Hnx1uFdFo9BjDMD4N4K0AdmmtL7Zt++Hh2pXi38cvAbkaq1e/pRRFNjDnsWKCxQQkHNuQNSDhkIM3i7GjAbkKpnlqaDJQ5yLljo47sXhxOZSqRnPz63LpYlTa7Ny5A3v3PoBZs6rTEhCKhNXU9GaUl/dnSS9mWb/+dtTV0Z1wsDJSAtLV5WDZsrOCDVqA2kxACgDqMF0WlYBIKQ8DeNgwjA+1t7d3BV2uaZr1WuvfADhRKTUhaPtSqD9+CchVWL36wlIQUcY5MgEJj/hYAxIuWbAPSDjk0a8BuQqx2CmYPr0/EV4plo0bb0NDw2S0tU3AkiUrS2YJSj2DWbO2ory8LC0B2bVrLyZNOglz5swr+prWrbsR9fUTA487UgLS2XkQy5efH3jcfDdgApJvRIfvr6gExDTNL1iW9X0A5NuRaxGmaX7esqzv5dpBmNuNXwLyO6xefVGYRTPs3JiADAtR0SowASka1MMONJZMsDZt2gjTHB0n4WGBzqLCWCAglK+iq+u2pBlTW1sZliw5KYuVh6PK+vX3oK6u38wp3cG9ry+B7dvnFD2ZLZmGPf/8DairqwwM1EgJCGl9WlreMuomv0xAAot+xA2KSkACzDZimmaL1vqwUqo9QLuSrzpeCcgNN1yBNWvIwq50CxOQ8MiOCUi4ZMEakHDIg56Lu+++Co2NJ2PGjP5M3KVWKJP4Sy/dhdraKjz/vEBLy8klsQStNdatuyGpuclEQOj3zk4Dy5efWdQ17d+/H5s334YFC4Ln4xgpAXnxxb2orT0bkycHC/+bb4CYgOQb0eH7G20CEonFYj8TQlQqpd5D0yUzK8dx7hZCNLrTX7t///6zt2zZEjw8w/DrD10NJiChE0nWE2ICkjVUBa/IBKTgEGc9AGtAsoaq4BX7CcgVaGg4DTNnliYB2bdvH1599T7U1FShtbUvFA7M2Qiuu3sPXn75Lsyd25/oL9PBvbOz+NnB9+7txksv3Yl584IlIRxqHdlgQnVeeqkb8+ePfjJCJiDZSix/9UaVgEgpvwTg2wAuUUp9h5YlpbwHwKlCiB9qrfcD+AKA7ymlvpG/ZYe3p/FLQH6DNWveHl7BZDEzjoKVBUhFqsIEpEhAZzHMWCIgXV02mpqOzWLV4axCsrjnnitRW7sGc+bMCeckh5nVrl2voK/vYcyePRUbNhwIhQNzNkDG489h2rQuTJhQMSQB2bmzG5WVJ2P27OLJJ5UcZbMer85INSDbt3dj7tw3jloGeG8dTECCSD0/dUeVgMRisfUAnrRt+4O0nObm5sWJRKITwG+VUh+m30zT/L7W+myl1JL8LDncvYxXAnL99b/GySe/I9zCGWZ2TEDCIz4mIOGSxdgxwYrDNEs7DO99912JBQtOxNy5NeHZJAFmQhm7KyvXobp6Ctav3xMKB+Zspr9u3V2or/+X+2umgzv5uLz44rSiOtfv2fMqdu68G3PmFF8D8vLL3Zg168xRDwvNBCSbXZzfOqNKQKSUe4UQH7Ms6/e0rFgs9lEhxM+FEKdblvWAS0DeSZGvlFKT8rv0cPY2HgnI9OmTccMNv8KaNe+EEKO6JUe0KZiAjAi+vDZmApJXOEfUGWtARgRfXhuTLO6//0rMn3885s1blNe+i9VZR8d61NRsxYQJ5diw4RUsWXIBIpFIsYbPaZwDBw6gq+tWLFz4Lx+LoTQHHR2HsHz5eUX7Hu7evQu7d9+T1CoFLSPVgOzYsQczZpw56mGhmYAElfzI64/qaU9KuQvAp5RSV7tk4zrKjF5VVTXzmWee6XVJyfuEEJcrCvhdwGKa5sVaa8o14mlantRaf2uYfCOGaZof1Vp/EkAdmTMCWAvgK0qpF3OZ7ngkINOmTcKNN/4Kq1e/c9QjYeQiM68NE5CRoJfftkRq6db9xBNHP8FVrivjPCC5IleYdv0+XqVvgvXAA1di9uzjsGDB4sIAVeBe29r+DtPclzycK7UTCxeehSlTphR41JF139GxAVVVGzFxYr/5FZWhDu4vv7wX06adgpkzKQdz4QuZte3Zcy9mzSo+Adm5cw+mTj191IMiMAEp/D5LHWG0CchTWmvLtu13NTU11TmO06a1vs227YF4rFLK/wOwUim1tFDwSCk/AuCXAH4thKBEiUR2vqa1XubmHHky3dixWOwnQohPaK2/I4S4VwgRc/99aO/evc1bt24lH5ZAZTwSkKlTJ+Kmm4iAvIsJSKDdkv/K9BIeC4kImYDkf2/k2uPY0oAoNDW9NlcoRr0dyeLBB6/EzJnHYuHChlGfTy4TaG1diyVLnGTTrq6XMW3aGaHPaZJqfjUcAUkkHGzbNgPNzcfnAlHgNq+88gr27r0PM2cGv+cdqQbklVe6UVU1+kERmIAE3jYjbjDaBMQ7+G8AsBAA6SeJbCQP/KZpflJrfRmAS5VS3xzxatN0sGbNmrKtW7e+DOBxpdRAOs7GxsZFkUiEkiWS+ddHU5vW19fPLS8v3wrg50qp//D+7mpS/gjgfUqpa4LOeTwSkOrqCbj55l9h1ap3oqysLChkoanPGpDQiAJMQMIjCyYg4ZLFQw9dialTj0ZtrQzPxALMhHJpLF3a/53Ytm03tD421NqcgwcPoqPjVixaNDjHxnAH92KaYY2uBqQbU6eexhqQAM/AWKk6qgTEIxkA3qq1Jt0kEY0bPXCllERMXigvLz+vtbWVsqjnvdTU1Eyuqqp6byKReDYej//dP4CUkkyqnlVKvTF14KamppmJROJMrfUT8Xh8o4+AmFrrNgBf9SJ7BZn0eCQgVVUVuP323+D449+O8vLyIHCFqi4RkI6ONixZ8vpQzSvIZPo1INdh5cozS1obxQQkiNQLW3csEZDOznY0Nx9XWMAK2Ds9F48+ehUqK5dh8eKmAo5UuK7XrbsFy5b1H+Z3796HXbsWIRo9unADjrBnMr+qrIxj0qQJg3oajoCQGdbUqWswa1bho2G9+upu7Np196j4gJAT+syZZ7APyAj3WSk2LyoBaWhomNrR0bEnW6Cam5vnt7W1bcu2fj7rtbS0zOjt7d2mtf65bdvkG5JVkVK+GcANQoi3WZb1l6wa+SqNRwJSWVmOO+/8LY477m2oqPiXjWxQ7Ea7/tjRgDABGe29ROOzD0gYpPCvOYyFPD/kb/fYY1dj0qQW1NeXXmBJSua3fv11WLasP4dJT88hbN5chZaWE8O1WdzZ9CcfvA0NDUdq9ocjIBQN64UXpmDp0jUFXxvlAdm+/c6BHCVBBhxuHcP1xWF4h0No7P69qAREStkH4B8A7tdaP9Db2/tIV1fXwZDBG5FSLhVC/FRrTX4pJ8bj8S3ZzJG0KZWVlc8AmOI4Tiwejx/Kpp2/zngkIFOmlOGee67Asce+FRMmDL4lCorfaNZnAjKa6A8emzUg4ZFFf0QyCghwengmlcNMxgIBIX+7J574PSoqmtDQUDC3yhzQza6Jl7HbNPuds4mAbNpUhqVLw7m3duzYjr17H8Ls2UdmGM/m4N7V1QMpzy34d7Gnpwdbt96O+fOL7wOybVs3amrOQmXlYBO17HZE/mqxD0j+sMy2p6ISkFgs9jkhxCkAVgGYDOCwEOLvREaEEPdblkUmUIlsJ5/veq7PyY8BaCHEI1rrDyilOrIZp66ubmJFRcX1lEQRwOlKqUeyaZdaZzwSkMmTI6DY9MccczEmTpyYC2yhaMMEJBRiSE6in4DciBNPPDM8kwo4k7GkAWECElD4BapO/nbPPHMtDCOGxsblBRqlcN2Ss3R3932oq5s9QEA6OxNYvvzswg06gp7Xr38QixcfSBtONxsCcvDgYXR31xdcVkTsXnjhtmR2+aAlm3UM1eeLL+5Fbe3ZmDyZjoSjV5iAFB/7ohIQb3krVqwo7+7uPl4IQYd1IiSvA0A6Sooa9ajWOqkhicfjpC0pWiG/jr6+vlpyiI9EIh/WWq9yHOet8XicsrNnLNFotNowjFsBHCeEuNiyrFtynfR4JCCTJhlYu/YqLF9+ISZNKt10L0xAct31+W/HBCT/mObaI2tAckUu/+3I3+7ZZynQY0Oo/SYyrfyFF+IoL1+PuXP7w8WSBqSzcx+WLXtL0XJmZCuVQ4cOwbZvweLF6W/2sz24d3T0Yvnycwq6PnKU7+y8ZVCekmzXme06MvW3Zcte1NefO+qXj0xAspV4/uqNCgFJnf7y5cunHDx4cLWrPSBC4l3N7AbwEJlsKaV+nr9lZ9eTlPJ+AE1KqQWZWri+Ig8CqDUM4/z29nbKA5Jz6etL/CtV6jC9GEa/+Bwn6yY5z6uQDQ8ePJAkICed9J5RvwUZyTpfemkbyEn1+ONPHkk3o9qW9hQ5oZ911vkl7YRO+SxvueVGnHPO+aOK50gGJ1k89tijqK2tR01NaWatpvX37ymSBbnHlW4ZC893b+9hrFv3F0ye3ITm5mNLThitrU9g9uxtA9ESyceCDrBSnh+6yyvLehaVlR2Dcn/4AfeS7tIahirkjD5z5smYM2d+weSVSCTwz3/+BXV1wc2gsl1HpsmTmdnRR1886skkczlPlZVFQnGGLtjGKHDHoQSPNBFa61Ppf66GpEEpVZBUp1JKIhcU5eqBVHMrKeUPKVGi4zjz4vH4jlRZRKPRCZFI5EGtdYMQ4gzLstaNVF7jkYAcOLAfjzxyNVaufHfoE0oNJV86oHR0PI8TTqBtW5qFCUh45MYEJDyyoJn0P99tOOEEuiMrzUIEZP36v2DiRBNLlpRePpNnn70XtbUHB7QBdHjfubMb06efhjlz5oZGKDSvp566AY2NmYOqZHtwp742bZqIFSsKm1T1qaf+jIaG4GZQ2a4jk3A6Ovbjta9966jLjglI8UUQSgKSCkNDQ0NtR0fH5kLAY5qmFzb3f5VS/+kfQ0r5OGVGV0rNSOebIqX8fwDeQ6Zatm3/Mx/zG48mWBUVGo8//nuY5vmj7og2Ehn2h+F9HkuWFCd51Ejmmqnt2ArDyz4ghdgjQftkE6ygiBWuPvnbtbZeh8OHF8E0S08DQhGl6usjg7KIHzpEfhKNoXKq37p1E/r6nsT06Zk1CkFMl0jLs2jRmwr6fVy37ibU1wcPAhNkHel2dmfnISxfPvraUTbBKtx7J1PPRSUgsVhs0AE/yHJt2748SP0gdaWUVwJ4F4CfCiHuEEKUO47zfgCUkf2bSqn/llJ+DcAlQghpWVZnc3NzLJFIUL6PPxqG8ZM04+1tb29XQeZBdccjASkvd5KRWUiNXlkZ3AkuKMaFqs8EpFDIBu+XfUCCY1aoFmOJgJR6HhAiIM8/fx0OHlxYchndKSzthg3Xo76+chABoX27eXMlWlootk04yj//eQcaG4c+XgU5uPf29mH79lkFzYy+fv3tqKszAgMYZB3pOu/qcrBs2UAO6MDj56sBE5B8IZl9P0UlIFJKhyJMAfCP6zeATPc7/aYLZYJFUFE29G3btn3WcZx3CSEaAewDEAdwhVLq11RHSvl1IiD0TzLVklK+l/4+BNQPKaUC6+rHIwEpK0vg6af/gMbGN6OqKngYwOy3e2FrMgEpLL5Beh8rBOTxxx/D1KlzMG9e4ey/g+CaS10mILmgVpg2REDa2q7HgQMLSo6AUK6Kl166A/PmTT2CgHR1GVi2LBwR73bv3oWdO+/FvHlDf8uCHtzJ2b65+byCJevdsOFuLF5MR7RgJeg6UnvftMnA0qWjLzsmIMHkno/axSYgdGhPLe8B0EzJ+wC0CSEOOI5TKYSgIOWkl3saAJlH3ZGPBYe9j/FIQCKRPvzjH9eivv48VFf3RzcpxcIEJDxSYwISHlkwAQmPLCjioGXdgH37atDcTMEnS6e8+OImCPE0qqomH0FAOjoO4KijLgjFYjZseBCLFu0fNohH0IP7/v0H0dMTRWPjsoKss7X1PtTW9gbuO+g6UgfYvLkcLS2nBR433w2YgOQb0eH7KyoBSZ2OlPIjAP6tvLz8pNbW1p7Uv9fV1U2rqKh4SGv9C9u2fzn8ckq/xngkIEIcxnPP/RG1tedi2rTpJStEJiDhER0TkPDIgglIeGQxcaKAbd+Inp7SIyBtbU9g/vydiESMIwjIiy92Y9Gis0c9iMmBAwfQ0XEramuHNyXO5eDe0XEYy5adMyy5yWXHtbY+jNpaMv4IVnJZh3+EzZunoKXlpGCDFqA2E5ACgDpMl6NNQJ4XQvzKsqx0PhTJqUspPw3go0qppuLDU/wRxyMBAQ6htfXPqKk5G9Onk79/aRYmIOGRGxOQ8MhibBGQNjQ3vz484AacyYQJQEfHzejunlty61i37g7U1/cfWVIPvfv2HcShQ0uweLEZEJH8VrespzFr1jZUVFBas6FLLgf3Xbv2YsKE4zFv3qLhug/897a2J1FTsyM9uTnUC3E4vXYkm3XoinJgQvn/Z+89oBu50jPRr0CCOZNNspkQmgQIIlBSa0axp1saxVGe0djPnrWfvX67Tntm/MbZuw5j73rfHnttz6z3PHv8bE1Okjq3OqgltVqtHFoECZIokEjMOYCZIOqdCzS7GRCqgIpk1Tlz5qh573/v//+3Cv93/7RrTySvZ3j4gCy8cSoA4XxkMp4gNQBZZhjmdzwezz8n4uSGl+SbNE0rt0MdBzXtRwDCMCvo6fkZDh5UPgDx+ZRtoKhVsDi8rAIPJbpQc0AEFjIH8rFGo8p+v0nBj2DwNGZnq2Gx3MuBe2mHxpr6nYJOF/MsxDN6/f5c2O2c0y55Y4yUy3U6T8FoZFdJio3hvnNzZA2/PwcOB/8hS35/LwoKepGfv3v/5d98EeXfeiltWc189XnMfI3U9Nn+LC+vYnm5FTqd9PfLKgBJW71pT5QagPhIAYtIJPJIX1/f6k4uSMf0UCh0kSSGu91uXdpcKmjifgQgkcgyaPpFHDjwBCorKxWkre1b3QsGigpA5HP8iC7eeedtlJXV7IEkdFISWdg+BkJrLvZ+dwtaiUhoHki+3dDQWUxPV6Gt7X6hl+ONPsn/AD5ESUlhQgBC8kAcjucE7RiejKHx8REsLr6FqqrU4VeJQBQbgQUCIbS0PINc4s7i8RkbG8baGvne7C4dLBQAmZ1dQE7O/aipkb7IhgpAeDxMLElJDUD+CMBfAxgCQJLMfSQJHUBeJBLRURT1BQDE1xgthcuSJ0UP248AJBxeRH//S6isfAJVVVWK1Z8KQOSjur0SgkUASHl5LWpqauUjXI47iYVgqQCEo9gEGU7y7UglqampSrS1yadsbSpmSX5CY+PCTXARz3swNjaPysqHJQvjJcnnTU3LrAFQOh4QIifiNQiFjDh0yJFKbJz+Pj8/h7Gx89EqYzsfoQDI6Ogcamoel0XxGRWAcDouvAyWFIAQDsxm8x8yDPP7AOIF/4cA/D1N09+4Ub6XF6blTGQ/ApD19RD8/pMoLX0U1dXVclZP0r2pAEQ+qlMBiHx0sVcAyPLyLPx+4gFRTujSzlNAwl0nJs5jcrICbW1H5XNIUuyko4OENt3qKh7PeF9bC2N6ugEtLbeLztf6+jp6e09Br495aNg86QIQQtvr3UB7+5NslmE9hoS59fefRmNjnOaJAuWADAws4NChp3n35rBmestAFYCkI7XM5kgOQG5sP6u5ubmdoii9RqMpALDCMEywuLj4+scff8y9LlxmMpF09n4EIGtr8xgYOI2ioocUfdOrJqFL+upsW1wFIPLRhQpA5KML4m2enX0Vo6OlsNkekM/GkuxkdnYaExPb+2okMt69Xgbt7SRwQtyH5Abl5/eisDCP9cKZAJDR0XlUVfHv7enoeBlGIzHB2D+Z8CGn8skqAGGvc75GygWAJOTHZDIdoSjqebfb/TW+mJYznf0IQFZWpjEycgEFBUdRU1MvZ/Uk3ZvqAZGP6lQAIh9d7CUAQnJA2tqU6wFZWprA+vp7CAZJwrYycnK6uq6ioSEULb+7+SQyesfGQqisfEj0MCyn8xIMhg1OL10mhvvGRgQjI6Qz+l2c1kw1uLPzPPT6VKO2/z0TPvx+wG5/nNuCAo1WAYhAgk1CVhYAhCSbLywsGAFsy6piGIZcJ5AyvM/QNM0NlosvS15W3I8AZGlpEpOTl6HV3ouDB5t4kaMURFQAIoXU46+pAhD56GLvAJCZaBUsJQOQ2dkgtFoXvF75GH7JTura2hrcbhLatD0sKJHRS8q6DgwUwWYTN7yso+M4jEZuhTozMdyJzPz+LN5BJMlj0elWOH08MuEjEMiTjSdOBSCc1M7LYEkBiNVqzVlfX/87AKQhYVYCjsgeP6BpWrnF1zmoaj8CkMXFMczOvgngLtTXK7fY2V6okqNWweLwsgo8VK2CJbCAOZJfXlY+AJmYcKO0NAC3mzS04zeHgKM4WQ3v6+tAaakfeXlq2OMfAAAgAElEQVS38j/IxGRGr9+/AJNJvLyCpaUlBINnUF9fwoqnVF4ctkS83hW0tz/Hdjircb29H6C2dmKbtynVxHQBCPHijI4eQGvrZ1MtIcrfVQAiipi3LSIpADGZTJtVsD4FQAP4OQAXSaGHqDUKEK/HX2m12u+4XK5p8cUj/or7EYCEQiNYWHgbGxt3oKGBOMKU+agARD5620seELUMrzzO1V4AIIODn6KubgpdXfPRkrVyfmJ9NU5vSz5nY7yvrq5hZqZJtGT00dEBhMPvobQ0TvJ2EgGna7hvkhwaCqGp6UkUFPAXHDI46IVGcx3FxexppstHKLSESOR22fzmqwBE/K+B1ACki6KoDrfb/RW9Xl+Wk5MzzTDMMY/Hc/VGWNafMwzzaDgcfsjr9c6JLx7xV9yPAGRubggrK+9jddWOpqYW8YXO04oqAOFJkDyQUQEID0LkicTeCcGavhGCdR9PkhGfzMDAR2homEdn5xTs9udZl4wVf6cAMexXV99DRcVuwz6V0ev1rsJufzp+V2+emUnHa0C2kIqHVNucm1tAdjbpit6Qaijrv8/OzmBq6hKqq9l7c9LlY3yclE1+BGVl5az3J+RAFYAIKd34tKUGIAsAfpOm6e83NzeXaDSaWY1G82Bvb++Vze2azeaXGIYZo2n6t8UXj/gr7kcAMjMTwMbGdSwuyqMjarpaVwFIupLjf54KQPiXaboUVQCSruT4nxcIvBvtVdHdPQWT6VlotVr+F+GJ4qefvoJDh+KbKKmM3rm5RTDMbWhsbOZpN4nJdHW9Cp0uzHmdVDykIihECBMpJ+x2n4ROx96bky4fgcACzGb5nEEVgKQ6cfz/XWoAEmIY5qsej+cFkgNiMplIN/Sv0DT9001WTSbTLwH47zRN8wfz+ZcjbxT3IwCZnPQiO7sLMzPNMBjaeJOl2IT2EgC5995HkJWVKC1LbMlyX08FINxlJtSMvQRAvF4XrFblNPDbqVO/nyQZM+jrm0Bt7eMoLmZ/0y3U+YhHd2QkGPWKV1XFN4TZGL39/cQL8pTg3zGn8ywMBu7fSjY8pJJ5MJgPq/VYqmGc/t7RcRJGI/su6+ny4fWuob39GU57E3KwCkCElG582pICELPZ/E4kElldX19/3O/3r5hMpgEAH9E0fTM4taWl5b9QFPX7NE3vbs8pvrwEX3E/ApCJCQ/y82mMjTXy3t1VcIVtWWAvNCrbTEJXAYiYJyf+WmoSuvQ62LqD5eWpGyFY98trYxx24/VehMGgxcDAJPLyjqK6uobDbHGGkkpWHR1n0Ny8PfF86+psjN7FxRUsLh4S/Dels/MV6PXcTSk2PKSSOKn4xXdH+87OS9Dr2ZcUTpcPIap4pZJXsr+rACQT6aU3l/tbk946cWeZzeavMAzzfYqiXnW73Y+aTKZ/JCFZJBGdYZjXKYrSURT17wF86Ha7+YX5PPLBJ6n9CEDGxnpQUhLA4GCNaImDfOpsk5YKQISQano0VQ9IenITYtZe8YCQcuF+PynDq0wPCDHs/f5zMBgKMT4+h5UVC3S6ViFUnhHNQMAd9YiXlCROhGZr9Pp8S7BYnhY01Kyr6yJ0ughnntnykIzwwEAJ2tr4zUnq6XkH9fWzrPOD0uGDFBgYGiqDxSKfnjoqAOF8hDOeICkAIbs3mUy/xTCMzuPx/KHRaCzVarXnGYbZWnJ3iKKoJ9xutzNjbhVAYD8CkJGRLlRVjaC/vwytrZ9RgJbib1EFIPJRXVlZPs6ePYn7739UPpviuBPVA8JRYAIPVzoAWV5ejiYY19cXY35+GcPDB2CxyKME6qbqwuEwXK4zMBqTdxRna/SSiliTkwcFLfXa1XUJOh17j8Emr2x5SHSsY0Z8BSwWfjsU+P29KCjoRX4+uzCsdPhYWlrBykobdDqzwG8te/IqAGEvK75GSg5A4jHS3Nx8t0aj0Ws0mtGVlZX3SHgWXwzLnc5+BCCbpSHd7kLeP6Zi6nvvAJAXce+9jwoeOy2kblQAIqR0udGOeUCOKxoMEo5JF3FinCnVAzIzM421tavRCkcLC6sYGChEW9vnuClT4NEez6coKfGhoIAfAEK26/eH0Nz8FPLzuTUKZMuqy/UamprW2A6/OS4dw33rIiQJfWysBmbznZzXTjZhamoK8/OvoqqKXdR7OnxMTs6hpORhVFZW8rr3TIipACQT6aU3VzIAcuzYsezh4eFf3NjYeKe/v78vve3vvVn7E4BcR339DHp782TlkuV6ukifAK+3G1Yrvy5xrvvIZHwsB0QFIJnIkK+5e8sDonwAsrg4DhIepFQAMjQUQHFxZ7THAwEgcovBJxWYenpORUPEUj1cjN719TBGRsphtQqTu+NyXYlWFuP6cOEhHu21tTBmZ3W857iQ7vM0TSphFbNiKR0+AoEQzObnBA2NY7X5LYNUAMJVYpmPlwyAkK2bTKYlAL+6tepV5iwpm8J+BCCbtel7enLQ1ibMj4QYp2IvNCqLAZCf4Z57HkV2drYYYhNkDdUDIohY0yK6Vzwgi4tjCARoxQKQ3t4P0dw8G/VsEgBCemW0tz+blk6FmNTXR7wfflahP1yNXlLylXhB8vKSe1bS4au7+200NMyxzpnYXIMrDzv3trCwjHC4HY2Nh9LZdtI5HR2n4jaAjDcpHT7kVgGL8KUCEN6PUUqCUgOQM6TrOU3TPw+ASbnbfTBgfwKQD9DQsICeniy0tR1VrJZjAKSb96RAMQWiAhAxpZ18LdUDIh9dkJ0sLIxiYMADi0WZSehdXW/Aao39zBIAIqc+DBsbG+jqIkYvuzAprkYv8RaMj9cIkvMSDPZBq3WiqIjd3vkCIKOj84KVUubS24SrLgj/gUA2bLaHZfWCqwBEfHVICkCam5vvoSjq6xRFNQE4wTBMUKPRkOaEux63231afPGIv+J+BCDB4LtobCTNsShYrQ+IL3SeVlxeVn6nZBWA8HQYeCCjAhAehMgjiVBoBIODfYoFIE7nOdjtMSOZABA5daIm/VXy892sjfh0jF6fbwGtrc8gJydxed90jksoNI+RkfM4eJBbT5V0eNi6P1Lhy27/ImfPCxsee3reR13dJKtO8lz5INXYhoerYLHcxWYroo1RAYhoor65kKQAxGQykdp15EqG7CORByT6N5qmuXf6EV+eGa+4HwFIIHAtmsTX3c3Aav18xjKUisDeASA/xT33PKb4EKwzZ07gyJHHpDoOGa+rApCMRcgrgfn5YQwP96O1VXkeEFIxqbPzZdjtFTcBiJAhPFwETwxSp/M0Dh1iV3WJ0OZq9JI5pCLWzEwjWlru4LK9lGNjsj0Bg0FcD4jfr4HdLkyVv4GBfmRnd7AChFx1IZdzt1OxKgBJedR5HyA1APkLtqFXNE1/g3fuZUhwfwKQt9DUtA6XKwyb7REZaoXdlvZCo7KYB+SnuPvuR2WVIMhOA7dGkRwQFYBwlZow4/dKDsjc3BBGRryKBCCLi4sYHDwLk+nATQAil5voYNCDrKwOlJSkTj7fPKFcjd7NeV7vEqzWZ3i/XOHavC9dELXJh9C6m5+fw9jYedTWpq6ExVUXY2PzqK5+DCUlqWkL80WKT1UFIGJKO7aWpABEfHblv+L+BCBvoKmJgcu1DptNmBsdMTSvAhAxpMxuDRWAsJOTGKNioPZlRXujiJzm5gYxOuqD2aw8D8jo6ADW199DQ0Os7CkJwSJPIKCFzfaQGMcg4RpOJ+nOzq2RH1ejd3PxUGgJkcgdaGgw8Mozl5ClTEEUmT8/vwjgM6iv1/HKx1aA09V1nPeKZIS+z7cIm+2LrMK7BGEuAVEVgIgpbQkAiMlk+t+RSOTrfX19sa9fGk9zc3OuRqP5O5qmfzuN6bKfsh8BiN//GnQ6Cl1dy7Dbn5C9jhJtUOmNyghfqgdEPsdvL4Vg7QUAMjsbxPh4ACaT8gBIT8+7qKubRknJrRwQctKJR8DhECaPgM2bRG7yu7pI+FLirufx6KQLQITqwD0yEsTGxvsoLS1iw3Z0TLo8kLlDQyE0NT2JggJucmO9OQCdneeh16eewZUPvx+w2x9PTVjkESoAEVngYntATCbTHLl0iUQiv97X1/cuV3ZbW1vvjUQi/wSgiabpMq7zlTB+PwIQn+8S9PpsdHYuwOF4WglqirtHFYDIR3WqB0Q+utgrHpCZGT8mJgYUCUCczldgMFC7jN7R0TkcOPAoysrKJTkwJIF7dPQVVqE+WzfI1ejdOtfvp2C385sbtrq6iv7+02hsjANAVtdBra3vki8bHpgcLZCr3TVXjDK2LhcJjY5bE2jbftjwsXVCMFgEq1V+IF4FIOJ/AkQNwWpubm7TaDQvkxYgDMNc1Wg0/wDgdbfbHUrEutlsLmYY5vMURf0OwzDk1LojkcjzfX193eKLS/gV9yMA8XrPw2DIRWfnHByO54QXskArKL1RGRFLzFj8Ce6++7E9kANyHEeOyO+mje3xi3lArqGsrBa1tQfZTpPduNiZeknRuiBCnZryYnp6CC0t8jOekimdNPhzu0ljuaJdAIQkZs/NGXhvZsf2EJLGjnl53Sk7n++kx9Xo3TpfqBCgjo6zMBp318op/+aLKP/WS2xFsm3czFefx8zXvrxrbiCQD5vtWFo02U7y+3tRUNCbsi8LF10sLa1gedkCvb6V7TZEG6cCENFEfXMhUQEIWZWEUGVlZf0BwzC/A4B4MTYAXKcoinRDn4xEInMajYZkJ1UxDNMM4HYA5K2eZRjm7xmG+ZtMQrjEFzG3FfcjAOnvJx/uAnR2TsPheJ6bwGQ0WumNylQAIqPDdAMMEgBSXn4QNTW18toch93sFQAyOdmPmRkCQD7HgXvph46NDWF19W2UlxfHDfsJBHJgs0lTfdDluoqmJpLPwO3hYvTupEySoKuqHuHd6+NyXUNj4/yusrh8A5DFxRWsrrZBpzNzExrH0TMz05iefhXV1cnLC3PRhVCy58ha3OEqAOFDitxoiA5ANrdntVqL1tbW/j1FUSTrmLS/Lo6zdeIZeYthmIs5OTkvuFyu1P5AbvzLbvR+AyAkBtjnOwOjsRidnZOw2Z6XXXIa20Oi9EZlmwDk5MkfRz0gfNfLZytHPsbFQrD2hgdEBSB8nIjMaUxM9GFubgTNzcrygPT0vIe6uqnodzWesej1Lkc9zxQlvjlAepMYDBrOyuFi9O4kvry8isVFMwwGC+d1k00YHPRBo/kExcXb8zL4BiDDwyHU138BRUXs803SYZQ0h3S5SCJ68nW46IL0YrFav4isLPl1VVABSDqnJLM54n9x4u9XY7VaqyORCPF6lFAUNa/RaCZdLtc4AG7lMTKTh+Sz9xsAIeEBw8Pn0dRUhO7uKZhMzyo29CfWqIx0SlbWDenWQ08+wioAkfwzEN3AZgiWCkDkoY+JCRrz82M4dEhZAKSj4xyMxpiRH89YJLfSlZUPo7w81iNEzMfpfJlzAnoiPrjse2CgBG1t93GZknLs8vIy/P4zaGjYcZfKcw6I17uK9vZnU+6HjwFsEtG5ABAh8m/44HPze0v+f3qavUeuurpELjY0X2IQlY4qPFHFnXqx/QZAyEd7auoS6uuL0ds7Ab3+aeTnc2volFqq4oxQcp+ATQmpAEScs8JmFRWAsJGSeGPGxnqwuDgJo1E5AIT0/xgYOIv6+lgYTTxjMRzewOjoAUk6UzudL7Eq9bpTy1yM3ngnRAgAQtYhDRUNht1J4/H2kC4PgUAebLYHRDn43d1vo6FhLql3jC0fpALZ4GAp78CPL0GoHhC+JMmejgpA2MtKlJH7DYAsLIQwP/86Dh4shsczgbq6JwR3LQulSCX3CdgKQEgS+l13PaqGYAl1UFjSVQEIS0GJNGxkxIWVlRkYDCRiWBlPX9+nKCsLIDc3JyEAIX/wetfR3i5+BUKn88WUIT58Gu+btAYGitHWxr8eu7regE63wupwsDXctxJbXw9jaqoBLS0kNVb4J1FY2daV2fIhVA8WvqSgAhC+JMmejgpA2MtKlJH7DYDMzc1iZeVKNNHN651AVdWjKC1VZoVlJfcJ2A5AfozPfvZR5ObminLmhViktDQPZ8+eUHTlpb1VBetFHDnyBSFULRrN4WEn1tfnodPxb7gKxYTTSSoM3qKeyFgcHSXdqcX99pIb8c5OAkDipX8mlwhbozcRFaEAiMdzHZWVg9Bqs1OqNB0epqfnUVDwAA4cqE5Jn48BxIM2OHgOdXWJdcSWD5K70tDwBAoL2Xe854MHtjRUAMJWUvyNUwEIf7LkhdJ+AyBTU1OIRK6iqqoUfv84SkoeRmVlrFuv0p6ZmQAmJoKK7BOgAhD5nTYVgMhLJ4OD17GxsQCdThkhWEtLS9GchMbGW1WMEhmL5GZ9fLwGra2fFU3osSaEJAeEezI1W6M3MQApQlsb/3ocHx/F8vKbqKhIXjmK7CsdHgYGFnDo0NOiXg51dJyE0Zj4MootH2LmrqRziFUAko7UMpujApDM5Mf77P0GQMbGhqHVfhgtETk4OIWcnPtRU6PMngfT0z5MTg4qHoCcPv1jfOYzqgeE95ebI8G9BUB+hiNHnuAoAXkNjwGQEHQ6ZRSZ6O/vREmJF3l5sfCrVEav17uB9vYnRRM6qbLU3X0cer0UAKQQbW386zFpQ8IdkmVruG+dJkYDwp0HoKvrMnS63Y0U2ZyprbSkLPfM5lCrAISNlPgdowKQG/I0mUwPAfgLAHcACFEU9WkkEvkzj8fzPluRm83mf2IY5j8C+BWapr/Hdt7WcfsNgAwNBVBc3BktXTgyMgPgTtTV6dIRneRzSKOyqSkCQPj/YROLuVjPhh9FAUheXp5Yy/K+jhqCxbtI0yYYO1N7AYB8fAOACNsALm1B75jY0XEeRuP2f0xm9A4Pz+PgwcdQUkLacAn/hMNh9PaeiDZI5PqkY7xvN4aFS+Tu6DgFo/EW6EvEWzo8SGHEezyfoLJyKGFYGRs+xM5d4XqeyHgVgKQjtczmSAZADh8+rA2FQhcikcj/6Ovru5QZG5nNNpHar8BxhmFepijqnyiKKmQY5k8BODQazb29vb0fp1qhubn5Ho1G8xYAItNfVQFIKonF/h4MelBRQaOwMB9jY3NYX7ejsZH0n1Teo9RGZVslrQIQ+Zy7veUB+SmOHBHvdl0ILQ4MfIhweB4GgzRN+7jwRMKvAoGzu0rCJjMWiZE4MVELs/kzXJZKeywpwU7TJ6Ml2Lk+bIzeZDSFrCSVymOwuS+uPGxsRDA6Wo3WVnH0s7nPiYlxLC5eQWVl/DwQNnxMTYVQWHhMtNwVrudJBSDpSCzzOZIBELJ1k8k0xjDMH3k8nhcyZyV9CiaTqRvAGk3TpLQEQyhZrdba9fX1IYZhvuPxeH4tBfUsk8n0CbGnAZBMSxWAsFSHz9eNmpoA8vNzMTkZwuKiCXo9vw2iWG4l42FKbVSmApCMVS8IARWACCLWtIkGg+8jEglBryfOcnk/fX0dKCvz36x+xdbo9XrDaG9/ShTmSLiS13tqd98MFquzMXqTAxDhur/39LyPurrJlA11ufJAqkgxzJ2orxc3QoAARbf7FHS6+MnjbPjw+xfQ2irvHl+qB4TFi8fzEEkBSEtLy28A+LpGo3ne7XY7eeaNNTmz2fw8wzCjNE1f2zrJZDKNAOikafqRZMTMZvMfMgzzexqN5rFIJPKhGoLFWvTo7+9AQ8MocnK00QZAs7M6HDrkYE9ARiOV2qhsJwAhOSB33vmIGoIl8dlSAYjECtixfDD4TjQJ3WBI+nMgi007na/AYNj9857KWBwdncOBA4+irKxccD5ijftOo6EhdcL2zs2k4iPV5oPBHFitwniyiFc/J6cLhYXJQ1i58kB0U1v7BRQXc5dXKnmk+nuyjvVs+PD5InA45J0DpgKQVKeA/79LCkBMJtPfAdADIL557w0PwkIcNhmapr/EP/uJKZpMpioABIB8m6bp3040srW1VR+JRLoYhvlP2dnZb2xsbPhUAMJeU273Rzh0aAZZWVmYm1vG2FgNzOY72ROQ0cjx8V4sLEwoqlHZTvGRj/Dp0z/C4cOPKLYhJOFJzQGRz4sRC+v7CY4cEedmXSjOA4Fr0RwQo/FxoZbghe7CwkK0dOpm88GtRFMZi2I2JSRhYqRJYrISr4kEkoqPVIIMBLJhsz2calhaf5+Zmcb09CVUVyfPpeHKg8+3AKv1i9HfSrEfl+sqmpridwhnw0cwWASrlf+qY3zKQQUgfEqTHS2pAUiE3TZBAIiob53JZCJJ5L9IUdQdybwzJpPpFYZh8j0ezwMWi0WnAhCWGr0xrKfnPbS2LkX/KxRawdBQBSyWu7kRkcnoWKfkKRiNyukToAIQmRyeONvYSx6Qkyd/jM99TvxGd3xqNxB4M+oBMRrlfZNLelGUlw8gN3d3R242xiKptORwPJ20+zUfciVAaWiI9JjgfqPPho9kewwEsmCzCePJioUskeT65P1NuPLg84XhcEgD4v3+XhQU9EZDpXc+qfhYWlrByooFOl0rH8dGMBoqABFMtAkJSwpAiMHOluWenp4A27GZjjOZTH8G4M8B/Geapv+fRPTMZvPPMQzzPYqi2t1ut1sFINwl39PzDlpbY51jFxZWMThYCovlXu6EZDBjdLQby8vTiuqUHA+AnDnzI9xxh+oBkfpIqQBEag1sX9/vf+OGB+QpwY3zTDjv6DgHo1ETl0QqY5FMGhubR2Xlwygvr8hkGynnhkLzGBs7j5oa8QGI30/Bbn8s5R7THZCqdwahy0YXW9cPBLSw2aTJP4p5dV6NNgzmCkDGx+dRUSH8eUpXV5vzVACSqQS5z5cUgHDfrvAzWlpa/p6iqK8C+Buapv8o0YrNzc0lGo2mh2GYf/V4PASwgA8AEg5vRJPg2TwaTUx9kQjrKWzIijrG6byKhob56JqkM+7QUBkcDnm7ahMJqLv7A6yuzuD22x8VVYZ8LkbOFAEgDzzwDAoKCvgkLSothong7NnjeOqp50Vdl8/FiC7eeecampoMqKur45O0qLQIHydO/AjPPPMLoq7L92LXr58CsAy7/XlkZ6fudM33+mzokbAmj+dEwrwKior9ZpBvbaInVjK1DlbrXWyWTHvM9PQ0xsdfwYED3Mv+suEj2cYCAQZ33CGcR+769fNoagonlQ0XHkjTxtHRA7DbpbmcIz1brl//Wdymkan48PsXcdttX5YkdIzL4UzHnsrOzlJtaC5C3jFWFsJrbW09FolEjjIM0wTgbz0eTw/ZZ0tLy20ej+fTDPjjMpW6EXZFfiX/kKbp/5lssslk+t8AHtFoNHfn5OREr/CXlpZ0Go2mC8Cv5+Xl/cjpdMYPmkxCeL8BkI6OK2hsjKX9kB/FwcEStLcf5aI32Yx1ud7H2tqs4gHI6dM/xIMPPqsCEIlPlgpAJFbAjuU/+eQ4srNXYTI9J9sCDR5PJ/Ly3CgoiN+5OpWxuMkySRq+885nBFXA2Ngo5udfY9U1fOdG2PKRiAG/P4zDh58TjD+n8xrq6qaTesq48EAqYGk0d6Kp6ZBge05F+OOPT0Gv3+1ZS8WH3x/B4cPCnqVUe2fzdxWAsJESv2MkBSBWqzVnfX39JwDI6SR7YRiGecDj8Vx1OByFq6ur4wzDvBGJRL7U19e3yi/r26nd8Hz8NkVRv+p2u3+Yai2TyUSSzQlg2ilDcrUU5SUcDhu8Xi8pzcv62W+NCLu7r8Jiid0UkRCsgQFhOtSyVkAGA4eGOhAOk07Jys4BOXv2R7jttocVDUBiSejHceQIqYqtzGdvhWD9EJ/7HGm3pNzH6z2HvLwwSkoeRFFR8vh+qbh0Oi/BYNhIuDzbsJ+hoXk0Nj6JwsL4pVf54G94mERVfxRtQsv1YctHIro+3wocDuEACJtKWFx4GB2dR23t45JUwNqUYVfXG9DpYuHSW59UfASD+bBa5d+8Uw3B4voWZj5eUgBiMpn+GMB/BfBNiqLOMAzzGsMwxwgAIeBkbW3tDyiK+gaAb9A0/ZeZsxufgtls/jLDMD8F8Bs0TX+bzTrNzc13ZGdnb6uzt7GxUUtR1EsURf03iqLOFxYWfvjxxx+vs6G3OWa/ARCX6wra2mK1CAgAUcrHKp5OBwc/jSapKh2AnDnzQ9x++yMqAOHy4gowVgUgAgg1TZIkBMbvP4vCwghyc4+KUqaW61ZJ8nNv70no9Ykb+6UyFjfXXFlZw/y8EYcO2blug/X4YJBGbq4LBQXJy9XGI8iWj8QAZAEOx5dZ75XrwLm5WUxMXEya38KFB59vETbbF1P2FuG6Ty7j+/o+RVlZcFdxg2R8rK6uY25Or4jS+ioA4XIa+BkrNQAh4Uqf0DT9y0ajsTQ7O3tmE4Bssmc2m/+NYZh7aZoWqoQCaSLYR4J7NRrNL8UTK+mEfiMx/U8pijK53W7i/dj18JEDst8ASFfX67BaY6IkACQQyIXN9iA/p1tkKoOD128AEGXmsBBxkY8wASDEAyLk7afQqlE9IEJLmD19cqZOnPghjh5VrgdkbW0NIyPnUVjIIBK5B9XVNewFINLIwUEfNJpPknoUuBm9GjgcwuWzeb2dKCnxxa3WlUpkXPiIR4s0xmtrE66kLcmZ6O4+zgsYJPuXQx+N8fExLC9f2RUyl0wX09PzyM8/Jsv3Zee5UAFIqreO/79LDUCWGIb5LY/H851EAMRkMv3yjV4c3K9JWMjrBmggPUjiPdFQKlIC2GQykapYf0oauNM0HXf8FlpqJ3QWsidDXK7X0dZ2C4AEg7mwWpUMQEgI1udYci+/YSoAkY9O9pIH5MSJH+DoUeFCXoTW2uIiaZL6GoqKKCwttePgQRJ9K6/H5XoTTU2xkuaJHi6GOzHSLZbnBEu4JzfqpFxwTg73hH4ufMSTRSAQgtn8HLTa3aWK+dKq03kWBkPi7gFceJBDZAB5B0h/mZ19W5LxMTwcQkPDE4q4zFIBCF8nn2z+aHYAACAASURBVD0dqQHIHIDfo2n6X5J4QL7GMMxf0DQtfGtW9nITbOR+84C4XK+hrS12DGMhWEoGIJ9Ey3TqdMpMoic6UAGIYK82Z8J7C4B8H0ePfpGzDOQygYTUkNvfoqIcTE0ZZdnTIJXBS2TJxeidnJxHSclDqKysFEQN/f1OlJb696QHhAgsUc7EpjDZ6oKEMc3ONqG5+TZB9MCWKCkS09n50q5KWMn4IM0TSdW4zUR1tmtJMU4FIOJLXWoAcglAAU3TR4xGY8nOEKzm5uYGjUZzDQBN07QwXYPEl3nSFVUAkgOr9fMy0wq77QwOEgAyD51O/gl3iTiKAZAf4LbbHlHErVUiPtQQLHZnVoxRsRCs7+Ho0S+JsZwga5Dwk6ysd1FSUgifrwIm02FB1kmXaKz53UnodInzP7gCEKHj973eLpSU9CM3Nyc+26vroNbip1CyMd6ZHC0QpxkjWYx4d6zWLwmaUxELMfMm5I8ND2SvExNzKC19WDAgyOXMxQO5yQFIBA6HvBt3bvKvAhAuJ4GfsVIDENJV5wJFUdcikcjLFEV9E8BfUhQ1xjDMPQCIzz6PoqhH3G73G/ywLG8q+w+AXEZbW6y0X8wDomQA8vEND4iyAcjZsz9Ee7uaAyL1lyLmAXkLZWUHUVt7UOrtpL3+XgAgAwP9KCvrRVFRPrq7yTdKXpXuJibGsbh4BZWV/HbfDgTyYLM9kLbuk030+bpRVORBXl58AFL+zRdR/q2X0l575qvPY+Zr8RPNxbiZn56ewszM5bjN+7iAQdJHo63tOVn00ejqugydbjsoTAZAAoEc2GzKuFBUAUjar1raEyUFIGTXN7qJ/y8AB25wsdkhiextgqKo/+R2u19Mm0OFTVQBiBZWqzTdXjM9KgMDHyESISFYwvxgZ7o/NvPJR/js2R/A4XgYRUXJb1PZ0JNqTMwDcgJHjjwu1RYyXncTgJSX16GmpjZjelIRiAGQ7+LoUeU2hezv70BDwyhycrRwuSjBjPJ0deTxXEdl5SC02uT5FGxv3Tf34fOtw+EQpmFfINCL/Hzyv/g9S4QFICE4HD+XrrhZzSOJ6C7X8bjN+7gBEMBul8d3rKfnXdTXz2wLqUp0pmKNhcthsZC7ZPk/KgARX0eSAxDCsl6vz8vLyyONCFsjkQixekIURXUXFxe/ybWMrfgi5HfF/QZAurpehdUaS9SLVcHKhs32ML9CFYnawMCH0T4gBoMyk+iJmGIA5PtwOB5RAYhI5ybRMnsLgHwHR48KV/ZUaFX19LwHs3kxani5XGHYbPKKCCbVBHW61K2yuAKQYDCE5uZnkJsbHyRkIvdUZXiFBSDCluHdlEtn5wXo9fG7zrPRRaw5byna2u7LRNS8zSVeq+Jiz7awskR8kFLOCwstMBhuVJnhbRfCEFIBiDByTUZVFgBEfLblu6IKQJQMQD6IluHV65UNQM6d+wHsdtUDIvVXYm8BkBdw9KiwN85C6mtrw9SurhXY7fJqcNnRcQpGY4Jcii2CYWP0bpVjrIzqUVRX8++BGxkJIhL5IJpXE/cRMAfE611Be7vwVdl6et5BXd1M3FwTNrogHdAjkTvQ0GAQ8nizpj06Oohw+F2Ult7yjifiY25uAVrtvaipqWdNX8qBKgARX/qiApAbJXXT4pKm6e+lNVFhk/Y7AAkGlRyCRQBICHq9MmJe470a5CMcAyAPybbbM5tXWg3BYiMlccaQM3X8+L/h2LGfF2dBAVZxuW7lqjmd82hvl09Pk9XVVfT1nUJTU+ru7GyM3q3iW18PY2qqAS0tt/MuVVJZbHLyYsIciWQLcuVjJy2fj4HDITyIHBoi3d4/jAuy2PAgtzK2s7MzmJ6+iAMHSm+KNBEf4+PzqKx8RJZNOxP99pF/n55eZH3Wq6tLRLWhWW9MIQNFFZ7JZCItr+P7I4F4eyFjb/biUIhMM9qmCkCUC0CCwfejOSB6vTJzWMjBJcbiK6/8ADabCkAyepF5mLyXPCBKByCdnedgs+VHtdrVNSVoEzuuR4dU6FpaIgnoJSmnsjF6dxIRKhGdAKf+/tNobOSea5YOH1v5Eqvh7fLyMvz+M2ho2A0O2fDg9a7KCuzG+DmNhoZbZy0RH0NDJB/yKeTnx94buT+qB0R8DYkNQP7PdFmkafq76c5V0rz9BkC23izGqmApHYDMQ69XZg7LVgBCSiEXF6c2aOT6bu2dMrxvYS8koR8//q84duz/kOtxSbkvp/M47Pay6DianpRVczWP51NUVLBr6MfG6N0pDJ8vDIfjqZQy4jog1leCJGkXcJ3KqZ/JTuJi51U4nadhMOxueMhGF35/Lux2+YT0RiIRdHW9vC2xPhEfpNKYzSZsqWPOByfJBBWA8ClNdrREBSDstrS/R6kARMkA5L1oHxCDQV4JqlzeqJgH5PvRSmQqAOEiOf7H7qUyvC+//C944IFf5F9IIlCM9dg4Aau1KrpaIDCBoqLPo6oq9t9SP6ka3m3dHxujdyc/gcACzOZnBekazqZ5Yjz5psPHJh2h+5vs3K/LdQVNTcu72EjFw+rqGubnD8FotEl9xLat39FxEkbjraIEifiQm/cmlRBVAJJKQvz/XQUg/Ms0I4oqAFFuH5BA4B1EIguKByDnzn0vWolMBSAZvcoZT95LIVhKBiCh0DxGR19Bc3N1VKdjY3NYX3egsfFQxjrmgwAXIz6V0RtvP6QjenHxg6iq2qyUz8euYzQ6Oy9Br9/gTDAdPjYXmZ9fRFbWXaitbeS8bjoT/P5eFBTsLjecioexMZJD8TDKyyvSWVawOZ2dr0Cvv2U6JuLD72dkV6whmVBUACLYkUlIWFQAkiIHJBn3DE3TyQuciy87QVbcfwDkNbS1xY6h0hsRBoPvRKtgqR4QQV4NTkRLSnJx7hzpAyJ8oimnjXEYvLcAyD/jgQf+HQfu5TN0bGwEq6tvobEx5vGYnV3CxMRBWXRD55KATvaeyuiNJ/W1tTCmp4VJRO/peR8HD04iKyvWjJbtkw4fm7TFTuzeBLC1tbcSt9nowudbhM32RUG7tbOV99ZxXV2XoNPdAo2JdBEIZMmuXLUKQNLRuHBzRAUgZrP5SiQSSZSEnpRLj8ej3O5uHPSnApBcWK3yiXnloDoEAm9Hk9ANhse4TJPV2FgI1vfQ1vYQSkq2/2DKaqMpNrN3AMhVlJfXK7oRYXl5AY4f/zaOHfvKtgZmSjlPmx27q6piicSxi5JCWK2fk5yFwUEfNJpPUFzMLo8iXcPd59PA4XiUd37Hx0extHQ1ZQf3nQunyweh4/Wuob39Gd55SUQwlutyAgbD9mTsVDz4/Vmw2+UXzruz50xiAJILm005v+WqB0S0V+LmQqICEPHZU96K+w+AvI62G32KYj/sSgYg16JleI1GeXStTef0k4/w+fPfh8XyeRWApCNAHufEPCDKByBlZfk4ceKfcfTov5PdbS4bdZFeDvX1syguzrsJQPx+Dex2/g1yNvvZfhv9BnS6FdbTUhm9iQgFAoswm5/hPQ8kHA6jp+ck9PoEvUASbChdPggYGBgoEh08dnZehF5PioDeepLxEOsiXgaL5V7WuhVroMt1FU1Nt0rVJuJDLiCdrVxUAMJWUvyNUwEIf7LkhdL+AyBvoK0t5hSLAZA8WK3KdHYFAm/dACDKDvs5f/57aG39PEpLY1V/lPioHhD5aI1UJDt58tuKBSBdXZeh061vC1+SQ4ItMVKdTpIQHANGbJ50DffZ2QVkZ9+Fgweb2CzDaYzTeQEGA7fAiHT5kKqxX3f322homNvmAUzGw8LCcjTPqKmpmZMsxRjc0/Mu6utnbvISj48YgKqAxXK3GFviZQ0VgPAiRk5EJAcger2+TKvVfp2iKGK1kaw+chUSAtAL4HgkEvnHvr6+VU5cKXiwCkCUDECu3gAgTyj2BMY8IHsFgBzHkSPK1gXxgJSV1aG29qBizxQBg6dP/wuOHPkKsrKyFMeH03kOBoNmGwDx+xck7wUyMzONqalXUVPDvlx2uoZ7zHNQDKv1CO/6c7s/woEDI9Bq2ad5psvH4CAJkX0aeXnsQRsfDAcCbuTldaOg4Na6yXgYHZ1DTc3jsvRCezzXUVk5eFNf8fgQsoElH/qIR0MFIEJJNjFdSQGI2WyuYxjmbQA6AMSPTNqGknp1BIToAZDi2R3hcPiY1+udE1884q+43wAIKSFptd7ygAjV9EoMTfr9V6JJ6IcOPSnGcoKsEQMg34XZ/HnFdLCNJ4iYB2RvABCl54AUF+fg3Ln/D/fc8wu8h/AI8hJsIRqL3yd9Dwq3AZDR0XnU1j4uaaU4mv4YVVXDohjuRCRCeX2mpiYxN3d5W3ftVHpNF4B4vRtobxf/+0zA4vT0q9u6vifjQc49NPz+HhQW0sjLy4mqKR4fy8urWFpqhV7fmkqVsvm7CkDEV4WkAMRkMn0bACkO/5vFxcU/+fjjj9c3RaDX6/O0Wu2vUBT1DwD+kabp3xNfPOKvuN8AiMu1MwQrH1brMfEFz8OKgcAVhMN7AYB8B2bzQ4oGIMTofeUVAkDENzZ4OEpREnslB6SoSIvz5/8Vd9+tPABCOj8HAmdQX1+8zdCam1uAVnsvamrq+VI3Zzpihi6RzQ0Pz6O+/gsoKtrd1Zvz5rdMIM3tOjtPCR5KRm7lx8dr0Nr62Uy2m9bcjY0NdHcfh15/q+t7cgASgcMhT+/t4KAXWVmfoqgollQfjw+pQt3SUs6NSSoAyUR66c2VGoAMAPg+TdN/kmj7LS0tf09R1NM0Tcuj6Hp6cmY9a/8BkCtoa4sl58VyQJQMQN64AUD47xrM+gBlOJB8hC9c+C5aWh6UXf15LqypAISLtIQdW1iYjUuX/g133vnzyM291cBM2FX5oT4xMY7FxTdQWVmyzdAipWlnZhrR3HwbPwtxpBLrSE26iIuTvE22t7i4grU1G5qaWjjuNvVwUo63pmactTcnHQ/I0NB8tIN9UdEtEJB6Z/yNcDrPwGC4FWaWjAc5RwKMjQ1jbe1tlJXF5BiPD5IzlJNzH2pq6vgToMCUVAAisIDjkJcagKxSFPVrbrf7B4lYN5lMvwzg2zRNixu0Kb4uoiuqAES5AMTvf/1GI8KnFFlulJw/FYBI9OLHWXaveEAKCrJw+fILuOOOnxM99j5Tbfp8PSgqioWb7DS0pKzyMz09hZmZy9tCetjwmo7hvpWuUDzPz89hZOQC6urY5bOkw4fXC7S3S1ehkG3/jFgCdzkslnvYqFT0MVNTU5iffxVVVbEy7fF0MTk5h5KSh1FZWSn6/tJdUAUg6Uou/XlSA5BxhmG+5fF4/msSAPJHAH6Xpmn+27CmLzfBZu43ANLd/SYsllhTI+IBGRgoQFvbUcHkKyRhv/81RCKL0OufVGS50VsA5Dtoafm86gER8rCwoL1XAEh+vgZvvPEdtLd/Gfn523shsBCDpEO2Vi/aaWhJWYq3v9+J0lI/cnNJmiT7Jx3DfSt1n28DDocwYY0dHa/AaGRnknDlg4QEhcOkqhT/3hu20ne5rqCpiaS4xp5EPKyurmNuTo9DhxxsSYs6bm5uFhMTF28WP4jHB+niXl39mCyT6BMJSwUgoh6j6GLs3naB9mUymV4GcE8kEnmor6+ve+cyra2t9kgkcpGiqHfcbvfzAm1DVmRVAFKItjbpG3ylcyj8/stgmCU0Nn4B2dnsK7qks5ZQc8hH+OLF7+LQoQdQUaGc26ud8tg7IVhvoqysXtFVsPLyKFy9+j3YbF9CQQG7hnlCnW+udLu6XoVOF45rMAqVlM1mjzubwbGZk8zoZTs/GAyhufkZQULp/P5e5OWRBOfUwQ5cAQjpKt7W9qyk32XST4aU4k0FQOSeP7G4uIjBwXOoq4vlAsXThdjd5tme32TjVADChxS50ZAUgFgsFtvGxsa7AMgXh/x/L8MwSxRFkcBW0p7uLnIxzjDMPR6Pp4cba8ocvf8AyFVYLLEf+JgHRLkAxOe7BIpajiZqarXcbiblclpjAOQ7MBofVJT7fKf8YonPJAld2fk477zzJsrLGxTdCT0nh8E77/wAFssXUVjILWdB6vfC6TwNgyH2Lu8OwVpAS8szyMmJVQMS8+noIP0/uOfTcDXcd/JEYvu12ntQW9vAO7vr6+vo7T3FqikhFz5IvgxpPmizSetZ7+v7FOXlA8jJiV1OJeJhamoeRUWfR1VVFe8y5oPg6uoqvN5TaGhIDEAGBkI4dEgYoMoHD/FoqABEKMkmpispACHbamlpuY2iqL8BQLrPabZslcTlXGAY5g/2C/ggvKsApAhtbfzXmhfj1SIARKNZRm3tY4LcEIrBA/kIX7r0AvT6B2X7A8hGDioAYSMlccZotRG8//4PYDI9x3sFJSE52Fm5aKfBODExh/LyR0QPVSSGutt9Ejod92RqLoZ7PNmS5PvZWZ1g4UFdXVfQ2LiYMoSVCx/j4yGUlZELFWkN+oGBfmRndyStHkVkPjIyj7o6/quN8fWupHovyDqkT47V+qWUeuRrT3zQUQEIH1LkRkNyALK5XYfDUbi8vNyi0WgKGYZZWFpaogcHB28FTHLjS7Gj9x8AeQsWS6z6cswDomQAcgHZ2as4cOBRxSXbbr4wMQ/Iv8FgkO8NHJuXWwUgbKQkzpjs7A189NGPojeixcXskozF2VnyVUhi9NjYedTWxk+2XVpaweqqFU1NJlG3G6+kK9sNcDHc49FcXV1DKNQMg8HKdklO40hy/fT05ZTNFbnw4fVG0N4ufUnb8fFxLC+/gYqK2DuQiAcS5tbS8qwknjW2ynI6X7pZgS0eHyTkzeFQVtS8CkDYap+/cZIBkMOHD2tDodAfMwxz2uPxfMofS8qmpAKQYrS13a9IJXq955Gbu47y8ocUF+u+FYAQD0hT0zFUV1crUg+xH3c1BEsuytNo1vHppz+GTvc0SkvL5LKtlPsIBj3Iyem6mZOw09Ai1YoGB0vR1nZfSlp8Dog1R3wRBgP3fhxcDPd4eyYN5paXW6HTCddgrqPjLIzGrKQiY8sHAYmkIZ7BYOFTBWnRIuBqbi559ShCOBBYhMXyHLKykssgrU3wNMnpPAmDIRYCGB+ArMLheJan1cQhowIQceS8dRXJAAjZhMlkCgH4DzRN/0R81uW54v4DINdgsaxFlRHzgCgZgJxDbm4YZWUPKS7WfSsAefXVF9DYeBTV1TXyfElY7EoFICyEJNIQilqD0/ljNDY+rajmliRpuL5+9mZJ7XiGllSVsLbeQHNRI1vDPRFN0gtkfd2Bxkbh2nL5fN3Iz3cnTUZnywfpKG6xPCuLnLzZ2RlMTV26WTo5EQ8+Xwh2+5dlXcq9s/Ms9PoYQIr/XmzAbhemWhqX885lrApAuEiLn7GSAhCz2fxvDMPolpaWntyP4VbxVKgCEOUCkP7+sygoCKO4+POKinXfeg7JR5j0bKivP4KamoP8fGUkoLJXAMjbb1+JJqHX1ipXFwyzgp6en6G29glFVVbr7LwIvT7WJDXxTe8y7PbnRDcWnU7ShJB7SWO2hnuiV5ZUaGKYO1FfrxPsrSY5Lj095IY9cY4LGz5iyefFsNnkUVUxFJqPhvTV1CQPwVJC+JLT+QoMhpj5GB+AAHa7dD1X0jmcKgBJR2qZzZEUgJhMpucYhvlFiqI+Q1EUSTgPkvyPeCx5PJ5vZcaqMmbvNwDicl1DW9stD0gwWAyrVZkhWP39Z6IApLDwQUXVP48PQO5XVBfbnW/3XgIgFRWNiq6CtbGxhL6+F1FZ+YRiChuQMCen8wSMxltGvpzKjZK9GQwJytWuroNai+XV7X4vYmEzxNuc6GFytECC/iJzcwvIzhamCtbW/bhcb6GhYT5hEjMbAEJ6UVRWPix6kYBEcl1YWMDQEClfmwqALMPh+KKsDZbOzkvQ62P9u+IDkCzY7Y/Imoedm1MBiPjqkhqAkOslhkU/EoamafkGRPKot/0GQEijL4sl9mMYC8EqET2mmg/1kds2n+8MiooY5OYeVVSoSTwAUld3ryClNvmQNRsahYXZuHDhhOLL8BIPiNIByNraPILBUygpeUQxYX1LS0sIBs+ivv5WnkU8Q2t+nlRs+iwOHmxicyx5G+N0nofBEJ9c+TdfRPm3Xkp7rZmvPo+Zr3057vzRUdJg7lHBc3kmJsawuPgmKivj57mwASBe7zra259OWw58T1xeXkYgcObmmUocgiX//Imurjeg060kBCCBQB5sNlLYVDmPCkDE15WkAKSlpeVXKIoiACTlQ9P0d1MO2gMD9hsAIXHWra2xDxkBICSp02K5V3GaDIfDGBw8j8LCDWRlHVFUqMlOAPLaay+gtvZu0Y0qPpW+dwAIqZrTpGgPyOrqDIaHz6Og4JhivGqjowMIh99DaemtMKB4BuPGRgQjIwdgsXyWz+ObkpbHcz3aUyJeJ3QhAYhYRj250OnqImFm8fvGpAIgZP7QUJmsLrPY9M8givf51uFwyAc4xTuM3d3X0NhIUnjje0CUmMupApCUnx3eB0gKQNhwc+zYsezBwcGsvr6+xD5jNoQUMmb/AZB30doaq7YcAyBlsFjuUYi2bm1zbW0tamQVFhKn3v04cOCA4nggGyYf4ddffwEHDtwlaJy30MJRAYjQEmZPf2lpEpOTl6HV3qsYUNvT8wEOHpxAVtat1lSJjN5AIBs228PsBcLDyJ3hPFtJCgVAwuENjI4SsEX6Awv/dHZehl6fXijZ9HQIeXnyCiPd2b8l0Xny++WfwE3ej7q6iWiI3E4+CPgbHhYflGd6IlUAkqkEuc+XFICYTCavRqP5zd7e3ouJtm42m/+AYZhfp2lauLIb3OUm2Iz9B0DeQ2vr0hYAUg6L5W7B5CsU4ZWVFYyPX0BREYX19bsUe2NNPsJXrryAioo70dBgFEpcgtNVAYjgIma9wMLCKObn30IkQs5Ugrgh1tTEGdjV9Sp0uvC2xRIZjF7vCtrbnxNnY1tW6eg4B6Nxa+/eG38UKAeE5FRUVDwkmnfX7+9Ffn4PCgp257qk8oCQRG6rVV6lbIlh7nK9DL0+5lVLDEAY2O1fEP08cVmwv9+JsrJAtKv7Tj5WV9cxN6cXrFkll31yGasCEC7S4mes1ACEXBd/iabpE4nYaWlp+QeKon6DpukEGXf8CEIuVPYjADGbF6NVZEKhFQwNVYp2w8anzknM+PT0q1EAsrJyO2prG/kkLxqtTQBSXn5Y0FKbQjNEAMjFiydw//1PCb2UYPSJLt5++3WUlzcpugrW3NwgVlc/wMqKHU1NLYLJi0/CHR0kAX37T04ig3FoKISmpidF7/3jdn+EqqqRqBHI9klluCej4/WS3IRnRKv4Rb6pgcBZNDTszgNJxYffnw27XVyvFBsdOJ2kf0tyABIIULDZHmNDTrIxW3vk7NRFrFSzHY2NzZLtL52FVQCSjtQymyM6ADGbzV9jGOZrN7atBzAOIHYFvvshJUhINzSapmnpOwllJmtWs/cfAPkAJlOs2sn8/LIiXbdEsSQkYn7+NRQVZSEUsqG+nhxt5T3kI/zmmy+gtPQ20Ts88ymtvQRAKip0ivWoEZ1OT/tAUZ2YnSUdtNv4VLMgtOIloJOFEhm9pDJUVtZdooeXkU7tIyMXblZVYiOMVIZ7IhqxXBfinRY3Py+RlycZH6RZ4uKiSZZnzel8GQZDQVTMiUP6smCzybuC1NjYMNbW3kZZWdEuPmZnF5CTc59i8r02z7wKQNh8QfgdIzoAaW5utmo0GlJi4zMACMyfTAJASJ03L4D/QtP0B/yyLk9q+w2A9PZ+iObm2WjX17m5ZYyN1cBsvlOeykmyK1LjfXHx9WgH7pkZs+JufzZZKy8vwLVr30FhoQN6vXDdjoVWcEFBFi5dOrknPCBKByATEzTy8z0YG2tURFjGyEgQkcgHKCnZngCdyGCUKhGdvEMdHWdgNArvAZmYmEdJyYOoqhI3t21rtaWt34xkAGRykuz1IVRWVgr9meFMf2v5ZDnlFHFlZGZmGjMzl3DgQOkuADIxMYfy8kdkU/6YLW8qAGErKf7GiQ5Atm7dZDJFGIZ53uPxHOePpfQotba2Ho5EIqQj+6FwOKz3er3BVJTMZrOBYZi/BfDgjbHvRiKR3+/r63Olmpvo7/sNgHg8n0Cvn0J2dhZmZhYxNdWAlpbb0xWfZPPm5maxsnIl+jGenDRAp1Om8V5Wlo933/0+8vLaZHmDyFbBMQBCQrDkXU0mGT+bIVhKByCjo90oKxtAMFgFk+kwWxVKNi5eAnqyG2vyNykS0cm6vb0foLp6DFotOxCSrgeE5Lk4HM+KFn61qfyenndRXz+za91kfAwPz6O+/gkUFSVuZCjV4XI6T8Ng0EaXT8RDMJgLq3XTpJBqp8nX3VoEYScfw8MhNDQ8gcLC+BXM5MlRrAALeaanF1lvsbq6RFIbmvVGZTpQFR4Ak8n0dQB/fSMcrD4cDhtSARCLxXJwY2PjYwD9kUjkr7KysvIZhvkfAMojkYitr69vIh2d7zcA0tf3KZqaxqM/oOTFn5lpRHPzbemITtI509NTCIevorg4HyMjDTAabZLuJ93FS0py8fHHP4JG06KI2+pEfBIAcvHicRw58ky6opB8HvlBvHbtNVRW6hUdgjU87ERNzThoukT0crXpKLGr6zXodLHmqFufZEav17uG9nbxz9rs7AwmJy/d7K6dit90AAhJnibl0aVoEEt+H0i54Z15Lsn4CAQW0Nr6HLKz2YGyVDLj8++dneehvxGdm4gHJZSwJVUfPZ6TaGoq3gWkgsEFtLQ8g5ycHD5FJzgtFYAILuJdC0gOQJqbm0s0Gs2DFEUdjEQisauBOI9QndBbW1uPRSKRUxRF/V8AHAzD/AkbAGIymb5NUdSji4uLrYODg9E6ss3NzXdoNJofAfg6TdOvpKPO/QZAvN5O1NUNR+vZT02RPAoDjEZ7OqKTHWdAMwAAIABJREFUdM7EBMGb11BaWohgsFqRIIoIkISQdXT8FJGIQZGeqM1DsFc8IHsBgAwMfIKGhln09OShrU3cHIJ0PgpO5xkYDLuN11RGr9n8LLTahD9h6WyF1ZyOjlMwGtkZe+kAkMnJEAoLP4fq6lpW++Fz0OCgDxrNJygujuVNbD7J+PD55NtJ3OW6jKamWGnheDwopYQtwzDo7CQJ9bsBiM+3ALv9edG9ZZmeOxWAZCpB7vMlBSDNzc1HNRoNqYBVmqIbumCd0C0WS7QsS09Pj8dsNv8VGwBy+PBhbSgUIhbn39E0/ZfcxZ54xn4DID6fC7W1A8jLy8HUVAihUIsiQ3/Gxkah1b6PkpIC9PeXKzKPhZxKYrh3d7+ElZUGtLaSNC1lPnsHgFxGRYVe0VWwBgY+QkPDPHp6ctDWdr+sD9TGxga6u4/fLJXK1uglce9lZQ+LVqJ26756et7DwYNT23qWJBJyOgDE612C3f5ctFCI2A+52CG5dZWVJawBiJz7aHR3v4XGxoWEAGRlZQ0LC8r4DdysFLfzTElVljrTs6kCkEwlyH2+pADEZDJ9SLwOAL7HMMxHFEXFWmLHecTohM4WgJhMpnYA1xmGedTj8bzKXewqANmUAKn1fuCADwUFuSCJjktLrYpMfiadk/PyrkcTV93uYkWEmsQ7hXl5FPr6TiIUOqhYHjaB1F7IAbl27VVUVhoVHYI1MPABGhoW0NOThba2o3x+LnmnRXK5JiYuxg1pSma8Ly2tYGWlDTqdmfc9pSJIwj9nZl5DdfXucrU753IFIOSme2CgCFbr51JtQ5C/LyyEMDJyHrW123lL7o2SbxWpnp73UV8/FfUOxONhfn4RGs1nRa+olo7yOjvPQa/f3YjQ74/Abn8iHZKSzlEBiPjilxqAEMBBvAh/Ij7ru1dkC0DMZvMzDMMcZxjmAYqifhUAaTZAfMQfajSar/f29pLckLSe/eYBCQZpVFT0obAwD+Pjc1hdtSmmV8BWBQ8NBVBU5IwCkN7eAkU2UyT85OQw8PlOYW6uWhHhMolesvx8DV59lVTBUnYS+l4AIMHgO2hsXEF3N2SfXDsw0I/s7A4UFZEK8NufZEYvMdSHhspEL1NLdkjWdjpJGFZuyt8crgCElFTVau+WrK/R4uIiBgdfQV3d9oTy5ABE/M70KQV/Y4DX24WSkn7k5ubEBSCTk8STpowKUp2dF6DXM7v48Psp2O3y7mMST18qAGF7ivkbJzUAIT1Afpem6e/zx1L6lDgAkK8wDPN9hmH6APwkKyvrAsMw9QzDkHAsPUVRt7ndbnc6O9lvAGRw0IvS0p7oD/7o6Cw2Nm5TZAduEqtcUuKKxir39ubDYrknHfVLPic7ewODg2cwPX0AbW33Sb6fdDdAAMjlyydx333KBiBvvXUp6gGprT2YrigknxcIvBWNe3e5wrLvb0AqYNXVTcQNN0plvEtVCYso2OW6Gg3tITfryZ5UPOyc6/cvwmJ5VrKE7li1JQJA9oYHhFxUUdRH0d+JeLogFbwaGp5URAWprq7L0OnWd/ERCGhhsz0k+XeH6wZUAMJVYpmPlxqAEOCxRNP0r2fOSuYU2AIQk8n08wB+DODvaZr+3c2VW1tb9ZFIhACP4zRN/0I6O9pvAIR8kIuLO6Mf5JGRGTDMnaiv16UjOknnDAz0obzcjcLCfPT05CrYeF/F2Nh5TE1Voq3tiKQyzWTxGAA5gfvuE78yUSb73jqX/CDGAIgBtbV1fJEVnU4gcAVNTRG4XOuw2R4VfX0uCybqO0FopDLefb4NOBxPclmOt7HkO8owH6C0NHnp2VQ87NyQz5cFh0O6pngEgAwPn8PBg+xzQAIB+YZgkaplU1OXUF1dEvc8kQRum+1LkuTbcD2M3d1vorFxaRcfAwMFsg+1jMerCkC4noDMx0sKQKxWa0U4HD7LMMyFjY2Nf+rv7yceEcketgDkRuWs1wF8iaZpkkR/82lpaemkKCqLpum0Wv6GwxsMWwFoNDH1RSKsp7AlLdq4oaEg1tbeif5wzsyEkJ9/P+rqGkVbn6+FvN5eaLVd0VCywcESOBzSxExnys/k5ASmpy9haakWt932+UzJSTZ/ZWUZr712Ek88kdY9gGT73roweb/feusiDAYr6urqZbGndDZx/foZ6HQUSGz4HXfI2yP1ySdnodPF/55uehdIyFO8hyRr33nnz6X0QqQjw1Rz1tfX4XS+FDd5fuvcVDxsHUs6ii8tmWEySVeVMBQKIRg8sysHJBkfwWA2br/98VQik+TvpHxtV9dL0OmKb56TrefJ5wvjzjufk2RvXBd1Ot9Cff3sLj5IKKLDobzLq3TsqezsLEltaK46k9t4SYVnMpk+AkDqFm42TSDlbHcXYAdIFSzB25pyACCVkUhkDMBv0TT97a1KNZlMPQBWaZpOq5nFfgMgo6PDWFx8E+XlxZienkdR0QOKDDfp73chL68X+fm50aTN9vZjcnvXWe1nbIzo4yrm5ipw++3S3Xyy2mySQQSAEA/Ik0/+YqakJJu/CUD0egvq65UHyjcF98knJ6DXa+HzreHw4S9KJk82C3/00cswGOKXtE1lvA8MhNDa+kXk5eWxWYr3MZ988gp0uo2kdFPxsHXy0NA8jMZnJG3oNz8/j4EBAkC2e0CSA5As3H77F3iXL18EP/zwZzAa8+MCkGBQi9tvV0b+hMtFmmCOICsrKyoaAqRIFbmJiTpYrZ/lS1yi0VEBiGiivrmQpADEbDYHGIaJsGGbpmkDm3GZjGELQMgaJpPpMoB8mqZvBsqTkr4bGxvdDMP8i8fj+a109rLfQrDGx8eh0bwTLbMYDE6goOBBHDhwIB3RSTrH5+tGbW0wWk64p0er2PClxcVxbGx8gGAwR/bx+skUTqp5vf76Sdx7r9JDsM7fyAFpkPR8Z7K413sOBkM+Ojtn4HB8KRNSgs5NVoKXLJwqfImU4i0vly6BmDTtKysLRBOcEz2peNg6z+sNo72d1FeR7gmF5qMhoTU1XEKwNLIO9evquhD1ssXTRTBIKo4pw3tAeniVlPhQWRkL+1tYWMXq6rpie3mpIVjiv+eSAhDx2d29oslkOkJRlOkGgn8WwBcoivpjAFPk39xu97+aTKY/A/CnZJzb7faRf29tbT0ciUTeoijqlY2Njf+l0WhIl6ZvACANFe/o6+vrT4e//QZApqamEIlcRVVVKfz+cZSUPIzKSsGdXemoJumcvQJA5uYGkZXlhM+nUWQlk00l7SUAQvqAHDzYxPuZFYMgMeoDAQJAitDVNQWrVb7x7YnyDTbllMp4D4WWwDCHUV9/o9W1GALeskYsYfsc6uq2G+tbt5GKh82x4fAGRkcPwGK5S2Quti+313JACHcu1xtoalrZBUDW1sKYmWlUTBPbYNCD3NwuVFeTNm4xALK4uIK1NWVWslQBiPivugpATKYXAPxyAtGT0K9sk8n05wSAEMcHTdPezbHNzc13azSavwZAOrYR3/eVSCTyn/v6+lzpqnK/AZCZmWmsr7+JAwdK4fONS3qDmK7OyLy9AkAmJ70oLKTR17ehyFrumzrMzUXUA3LffeROQZkP+UG8du08Skt1iizMQKS+srKC8fELaGwsQW/vJAyGpyULUUp1Csi3aGbmUvRbFO9JZbyvrq5hfr4ZRqM11VKC/b2j4xyMxsQNA1PxsLmxsbF5VFY+jPLyCsH2yobw8vIy/P7TaGhg7wEh3lurVb75a5uV1kpKYqWeieFOHiX1ACH7HRkJIhIhVeNiZ4TwQXjIyrpLsrLNbM5UojEqAMlEeunNlRyAHDt2LHtkZIQAgC8wDHOIYZhCjUYTAtAbiUSOezyel9NjTZmz9hsAIY2/VlauRKuCeL2TqKp6BKWlZYpT3l4BIGNjPaioGERPzzIcDnknDCc7JHsFgLz99nkUFzcosjR1zCgJIRR6LRrD7/FMoK7uCygqSt0wT4oPAPkWTU5ejH6L0gEgJGl7ebkVOl2rFNuPrunxXEd5+QByc0lq5e6HLQDxetfQ3i59+CJJrqfpk2hqYt8HJBjMg9X6gGQ6SLUwab5bUEAa8MbO2SYAIaDvwIFHFfP7t9mlXqeLhUwTPqam5lFYqMwwahWApDq5/P9dUgBiNBpLs7OzSS7FHQDIXshVAElEJ1cDpKsSKTdySavVPuNyueIlp/MvEYkp7jcAQmJ8Fxdfj8b49vdPoKbmcRQXJw4hkFg9CZffKwBkcPBT1NVNoatrHg6HMqqxxFPKXgEg77xzHgUFBxXZnJPoRUkezkT5BpvnK5XxLofwExKyNDh4FvX16XlxCK9yCb8ie4lEItGqUQYD+z4gpAiInEuIj4+PYWWFlLCt2gZABgYW0Nz8DHJyEufwyOn3b7OksNF4C4CMj8+jqko5IGqrPFUAIv7pkhSAmEym/wngqxRF/WU4HP5Of3//wKYImpubD2k0mv8A4PcpivoLt9v9V+KLR/wV9xsAIT+Y8/Ov4eDB4hs3pE9IWnUlXY1vByDZaGtTZhnewcFPoqUVOzun4XA8n644JJ9HAMgbb5AkdGWHYL377nnk5dVApzNLLtN0NjA2NoScnI9RVlaEwcEp5OTch5oaefY0Id6a4eFXdvWcYAtASA5IJHIHGhoEr5eSVBXJwrBSgShCWC7hV5tMkvLCBkPhNp6T8TEwUCLrPkyb56yl5ZbhTpgjZZzb2+VbpGHnodvMOTKZbvFBGinW1yvzN1wFIOl84TObIzUA6aco6qzb7f5aIjZMJtO/ALifpmlLZqwqY/Z+AyCLi4uYmbmM+vpi0PQkGhqeUEQX2J2niQCQmppAtAyvkqtgDQx8gIaGBdknDKd6m3NyGLz55incc4+yAch7752HVlsFgyGttkKpxCT434NBGhUVfdH+OOPjc1hdtaKpKVrzQ3YP+RYNDpKu2/Gb+aUy3gkAoajPSF4wIFkYVioeYobwOtrb5RN+6XSegMGwvbRxIj5IKdihoUrJk+eTHe7Nams2W6zYymYIls8XgcPxhOzei0QbIvldPt9ptLbe8uQMDoZkneeVTLgqABH/6EkNQFYA/Eeapr+XiPWWlpZfoSjq/6VpOpaxtcef/QZASJIh6QxLAIjbPQm9Xr5JqsmOHonrPXDAh4ICAkBy0NZ2vyJPajD4Lhobl9HdPYmWlmcVEw6wU9habQRXrxIAotwwMvKD+P7755GVVQ6jUbpmcJkcZGIM63QT0GqzMTOziKmpBrS03J4JScHmJkp43lwwlfE+O7sgCw8PyWWZmLi4q3Qt4SMVD2SM3ErBOp2vwGDYbqok4mNpaQUrKxZJ83DYHNCOjpNwOLbngAQCWthsD7GZLosx4XAYvb0nYLXeAlJ+/wIslueQnZ0tiz1y2YQKQLhIi5+xUgOQaYZh/trj8fxtInZMJtP/TSpQ0TQtbTkOfuSdksp+AyCrq6sYGyNVcorR2zsFo/Fp5JL4GYU9gYAblZX90ZteJQOQQOAamprWomBQp3sK+fnKxP3Z2Ru4du007r5b2QDkww8vgGGKFVOac+dr29PzLlpbSVpf7KZ3cLAMFss9sny7iUHV00OaJqbnAZmcnJNFGfHYDTvhY3vYEhsAQhLpFxfNMBjkE3Dgcl1BU1PsDKUCg3LRQaoD3tl5HjZb7Hdu0wMSDBbDalXOxRXxNnV2vgi7/ZYHxOcLwW7/8s0mi6nkIKe/qwBEfG1ICkBaWlouADAyDHNfX1/fxE72DQZDjVarvUq8wjRNPy6+eMRfcb8BEFLlZHj4fLTKics1CbP5OWi18Su4iK8N9iuSmugVFZ4bACRX1jHIybgKBK6iqSkMmp5QbCwv4S8GQE7h7rvl3Xk7mS7ID+JHH11AOFwAk+kw+8Moo5Hd3W/CYol15yaG1sBAoazzo8jNtNEY/wIklfdATuEnTifpvbK7HG8qHkgzxbKyh1FRIZ9eTMSLVlk5GPWipQIgwWBIEZ7brq7LsFpj+iHvxcZGBKOj1WhtJRX9lfM4ncdht8eqVhI+fD5SPVGZ31wVgIh/7iQFIKQJIIDXACwBOEX6/lEUtRSJREgp3jaGYZ4k1bAikcgDfX1974ovHvFX3G8AhNzWBYPnoreOLtd01H2blZUlvuAzXHFgoB/l5b0oLMxHT4+SAQhpksWgr288WpGspCR+NZ0MxSX49KysMN5+mwAQ5SR17hQK+UH85JMLWF7OhcXyWcFlJsQCLtdltLXdMrSCQS2sVvmGmXR2XoJeHwNMO59UxrvXu4z2dnkYX11db0CnIxHO259UPJAQmtbWZ2V1CTQ6Oohw+F2Ult7yTCXiw+tdRXu7/PO+XK6raGsL3zTcV1bWsLDQorhcL6fzFOz2WIWyGABZg8MhffnmdL5lKgBJR2qZzZEUgJCtm83mxyKRyLcoimqOw0oXRVFfc7vdb2TGpnJm7zcAQty4Pt+ZaKfkzs4p2O3PK9J9OzjoRUlJN4qLC9DbmweL5V7lHLotO/X7X4NORym6KSRhR6NZx7vvnsZddykbgFy/fh5LSzmwWO5W5Hnq6roIqzXm0SQGSiCggc32qGx5cbneQlPTQloAxO8H7HZ5OOrjeQ0IU6kAiM8XhsPxlKz0E6/DeyI+/P4s2O2PyGr/8TbT0/MezObF6G8deS/kUsCAq+CczrOw2wu2AJANOBzk3lh5jwpAxNeZ5ABkk+WWlhYLRVFmiqJI4OqCRqPp7unp8YgvEmlX3G8AhEi7v/8MjMbCKABxOL4srQLSXH1oKIDi4s4bAKRAsQajz0dugLMRCEwotqEUUSFFreG99wgAUW4pYfKDeP36K1ha0ioW0HZ2noXNdstA8fs3YLfL10Dp73eitNQft5FfKuM9GMyH1XoszS8Iv9PGx0exvPwmKirYdxAnOwgEcmGzPcjvZjKkFusF8nL0kmrziacLcplFSvAqIY+CnLOGhhHk5GijAITkrhQXP4Sqqlg+hVKenbksgQAFm+0xpWx/2z5VACK+2mQDQG40IiSNB6NPXV1dwfDwMAnN2lfPfgQgXi+JV85HZ+cMHA5l3liPjASRn/8pSkoK4XYXKhaAeL3nYTDkyr5nQ+qPwio+/PAMPvMZpQOQc1hcJH1llJOculU3pISq3R4L44uFaKzIusHl2NgIVlffQnn57m7tyQAIad43MXFQNrk68bwGRAepQJRciwR0dl6AXn/TPIjLhxwaQab+LsVGkJDdsrJeFBXlR9+L0dEQDh58HEVFu88dW5pSjOvqugSrNZabE/NwZsFmk78HKp6sVAAi/gmSHICYTKZfAvDXFEU94Xa7nZsiMJvNv8YwzH+jKOp33W73/8/em0C3dV3nwt8FCM7zPJMggQuSICnJkm150OAxjmfHTpp07utq//6v82s6t2nTl9f2b/O/f7XvtX1N28hN4tiOB03WZFmDNVm2ZoIgiQuSADjPE0AQxHT/tQFCoihSAijw4h4SZ62sFYv3nnP2Pudc7O/svb/9lvSqic2IGxuATMvaOLnbjqA45aSkq8jMTIUgZDIbsx8CIP3940hIeATFxeWxOQj3OaoounD5MgEQNj1qJH4wB+RgwAMi58rOKy1VkCXnfTQ13aLptFgcsg6zDBruVAskMgBCITSiuA1lZVX3uXOj87rb7UZn535UVNzO6HU3AOLxeGVLk9zefgGlpZNQKIL5RMvJESyC9zwTRjxVQ1cqPwt4qILkDA7U1rLHAHlnjlci9PqnorOJJe4lDkAkVviC10H6URdG1Ol0L4uiuI+oxwE8JwhCxyIA8pgoilSEkEoAvygIwpGYTVTCgTcmAAneuhsMM2huln8C4XLbYXh4ACrVpQAA6ezMZo7NJCRTdzdx7idjYGACCsVDMS+qttqj5/M5ce3aR9i27Wur7SLm7wUByP4FALIr5vOJdALEcCcI+9DQcAuA2Gx2WTPdLRfuE5L7bsb78PA0CgqeQ1ZWkBEo1i0E/haHLa1kuIfmShS8c3N1sqyhQSGuHHc5EOK6khxEAtDc/BoTOYSTkxNwu8+gsDAIQCj5X69//SbAivX+CXd8o/EUGhqCnimSo6cnGXr9E+G+Lqvn4gBE+uWIqQeE5/lLAOwqleo5o9HoXir+7t27EwYGBo4ByBIEYZv06pF+xI0IQCyWY6iuVsFgsDPLoBG60aIQLKs1D1rtA9JvniiMGAqHGxyclNWNbqSieTwO3LhxmHkAcuXK3gAAYfFH3el0orf3I/D8rToBAwN2VFS8iNTUoCEpx9bScgBq9Z1U4HcDID09Dmi1r8iqcGckFcRpHeScCE17yWY7iPLyYE7LcmvBSgJ60Fi3w24/geLiIACxWJxMhh+3tZ1Ffb3nJgDp7U1n0ltLAsQBiPRf41gDkDlRFP9vs9n85kqixyuhr7wpVnNgpN9i9x4xlPjc2jqLpiZ5MbDce/bBJ8bGxiCKZ5GVlQabrUC21Z7vJc/tAGQrysqq7/WKLP/udk+DGJgeeIBtD8iVKx8uABB5JQaHs+gzM9MYGTmK2tqCmwbK8PAMCgufkzW980pUvHcDIHKi4A2tjcFwMEAosbjdTYbx8RlZE08srtGyVA4KH6McHJ2OjXvKubk5jI9/jLKyjAUAMs+k97+t7Tzq6+cXAZBMZmtgrcaeKizMjKkNHc53WM7PxFR5PM+PiKL4LbPZ/H9WUpJOp/ttekYQBPlURlrDFd2IHhCr9RNUVSnQ2jqHpqYX1lC7a9f1+Pg4fL4zgRCs/v4S1NY2r91ga9jzYgDi929BeXnNGo62dl27XBNob/8EW7awDUAuX34vAEAaG59ZO2WtUc8Eyh0OonW+BUDIyE1Pf0rWbD8rUfHezXiXEwXvLQByBNVL7g/uJsPQ0LSsa/+0tp5EVVXQ2F0qx/i4Hampu1BYWLRGuzm63VJ4Yn//EVRVpS+EYMmbHW4l6RfTCdvtLvT35zJLwBIHINHd4+H0FlMAotPp3hdFscHj8TxhsViGl054gZr3YwBXBEFgMzkgnFVY9MzGBCD0wwK0trrQ1PR8hBqTx+MU0+vxfIqMjBQMDpajpqZRHhOLcBadnQdRW5uGoaEpeL2bUFFRG2EP8njc6RyFIJzC5s2sA5CfLAAQ9phlhoZ64fN9jtLS3Js3pDMzs1Aqt8ua3KCz8zpycnqRmBi+98BmS0Zjo7xi36nadlVVMDwm1O4GQORUyX25r4jZfBV5ef2BiuhL5WClAnpILsrR6e4m+vkgAGGVvtZkuoza2slA8eDp6TkMDxcx44VausfiAET63+6YAhCe5+sAfMFxnNLv959VKBQWAG5RFCmTrx7AVgqXVCqVj7W3t7dKrx7pR9yYAISq9oqgNCBWOcSnpiYxP38GGRmJGBlRo7qatjZ7bb2EYM3ODqOz81Ns2sQuAMnJScXly+9gbi6RyXPR02NGUlIrCgtv0fA6nS7MzzeislIr28OxXOVtmuxKxrvP58fQUAHq6uRVrZ7CYyoqZsIGIHLPQyCKZJfrTIA5auladHe7sWkTWxW4u7oOoaYmSMPLKn3t4nom4+MOTE9XM+v9jwMQ6T/JMQUgJK5Op9OJovhdAHTFtzjzzwfguEKh+MOOjg6D9KqJzYgbEYDYbKdRWemH0eiRdZXku+0Iind3Ok8hPT0R4+O1qKoi8jb2WogFi5LQOe5BZlmw7PZBWCxn0dzMLgDJzEzCjRvvweGg6s7shSaScZKdbbuZ3EmG1vy8R/ZGCiUIDwwcRklJeEX8KHnb738A5eVqWR347m4DMjMttxVVvJsHxGLxo7lZvvvM5XLBYjmA8vKMOwCIzUYgnS361xD5SpA9ik36Wqu1AwUFFqSmJmF0dAZOZx2zl29xACL95yvmACQksl6vT5+fn9cmJCSkeDyeEZ/P12e1Wl3SqyS2I25MAHIGlZVeGI1eZosY2e0zmJ09ifR0FSYn65gNXVpcB0SlehRFRWWxPRCrHH16ug89PRfQ1MQuAKG9ZDS+j5kZBZPkDO3tX6C0dBSZmSmBVSRDi2huBwYKZF0nZyUq3pWMd7nmTiznybkbALHZKNfo6VWeOGleCyWiL5aDikBS6E9d3YPSTCJKo1itx1FVpVygr02BXr87Sj1L183igooUtuvzbWY2bzAOQKTbN6GRZANApBddniNuTAByDpWVbhiNPiaTbYPGlR0zMwRAlJiZ0cvuNjTc3R66levrG0di4uMoKioJ91VZPTc5acXg4CXU17/BRF2A5ZSXkqKA2bwXExMikww5RiOda/sdt9W9vTJmypmfB+eeDzCoETHG4raS8d7TM4u6upegSEkFkpJkcw7IKzs8fATFxcEQOGp3pxLOgF7/uGzmv9xEWlvJaPfeJsfUlAOJiexdllitwdzHYCFCNulrBwd7kJJyHVlZ6czXjooDEOmPfhyASK/zu464kQFIW5vIbBXV2dlZTE19gvR0BZzOTcyGLoUokXt6RpGS8gQKCwtldkLCm87YWBfGx69Do3k1kCDJYlOp/IHaB2NjXkZrBHyKigrnMgAkFQ0N8iysmPp3f4207/7tqrbL7Df/CM4/+JNVvbsWLxHTksm0F1VVt6q6rwRA3G4vJicroNFsXoupRK3P5bxq/f12VFbKu7bMcgoIhR4HAYiMQfldVm94eAgJCRcDeTm9vWNIStqJoqLiqK23lB3FAYiU2g6OFQcg0us8DkCWaIBCZSoqXGhrA/R69uodkDghXve0NA5u91ZmQ5dCYQE22ygyMp5GXh6b7NejowKmplpRXf0SVKo7i8rJ7NgvOx2Oc2No6DCGhz1oamLPk2M0ngh4Nu9kLJJvvPt6AiC0qW7c2IuamuSb+2slADI97UBCwiOyZicjIZYjNrBYqH7U68x5Om22oOefZfpaop/3+88gPz8LrP9mxAGI9L+KcQAivc7jAOQOAHIxcFPa1sYxWfGZxKEEyZGRY0hPBzyeh5m9BbJaqW4DB4tlBDlpXXYxAAAgAElEQVQ5zyInJ0ihylobGmqD02lCWdnzSJJRWEwkevT5nJiYOIbhYS90uleZA1KtrR+jqsq3TMKwUra5XusNgBgMx1Bd7b8nABkcnEFp6fNIT7/lLYlkr0r17OjoaCDXbnFtGauVzRoaoYs3lulriX7e7T6DwsLMwG9Gbu6XkJ2dI9V2iOo4cQASVXWG1VkcgISlJuke2oghWD09n6OiYhZtbQomE/Fod7jdbgwOHkFamghRfAwFBcHia6w1m+0UKitFdHWNyr5i9d10OzBggNfbFZAhOfnWDTBL6zE/Pwmn8yyGh12orn6ZOTkMhsOorubuACBWqyjfej8LOSB2ux0jIx8HDKtQW8574HTOw+NpCCTeiolJssoBoXkbjUTwMXtPAGKxONDY+DoUitvzXuR2XijUta/vEHg+PzC1IIVtApO5g6HfvYkJCt+tYpK+lvKMCBBSnhH9ZhQVfRkZGbezx8ltD600nzgAkX6l4gBEep3fdcSNCEB6e79AebkD7e0JaGjYKbMVCW86Xq8XfX2HkZrqQ0LCTuTmshm6ZLN9ispKH8zmUZSWvoB0cukw2Pr6rgOwISfnaaSlpTEoARlXQ/D7L2NoaJbJtWhpOQC1WnUHALFYPGhuflnWa7IcE9ZyAGR4eAYFBV9CVhaVrpJfW1pUcaUQLIuF8oxekp8AS2YUWpempuD3leUE7tDv3tiYHQ6HFmp1g+z1v3SCDocDMzMnUFKSAUEYRVkZu78ZcQAi/faTBQCpq6vb7ff7d4miWAngu2azuZ1UodVqN5vNZrIkNkzbmADkEsrL7WhvT0RDg7xZWFbaiD6fDzbbIaSl+ZCYuIvZ0KVQXLIgjKGigr3EztD69PVdg0LRg8zMp5gFUZOTNiQmtmFwcBoFBc/J1shd6Uy0tOyFWp28DABxobn5Ndl/01taDkGtvuURWM54t1odaGj4imyJDoaH++F2X0B2dvAiYSUAwgIFb2jD3LixH83NwVCxqSknRkdLwPNUs5it1td3BWVl0xgZmZZ9cc6VNBvKfSwry4DJNIaqqpeQkhKk3WatxQGI9CsWUwCi1+sTPR7POwCohCnNRRRF8Qmz2Xymubk5bX5+fkQUxVN+v//1zs7OeenVI/2IGxGAhD7ELAMQURRhsRxEWpofycm7mTMWQzs9FJfc0TGGmppXmM2f6Ou7ioSEHqSlPclsSMDoaCcyM7swMDCJrKxnmPOqtbR8CLU6ZRkAMofm5q9I/3GNcMTW1lOoqrpVimo5413ungO6oe7vP4TS0mBYzHIy0Lervz8H9fWPRKih2DxOeS2NjUFiCZZrT9AlSVnZJKjoqyhuRVlZdWwUeh+jzs/PY3j4KCoqMsD6b0YcgNzHRljlqzEFIDzP/zGA7wD4B47jDoqieEIUxd0EQAicuN3uP+A47tsAvi0Iwl+tUkamXtuYAOQqysqm0NGRjPr6R5lar8WT7eoiAOJj2ui12T5DZeUcjMYx6HSvMZf4HFqP3t7LUKn6kJLCLhikRPr8/P4AAElO3oXCwiKmzkZLy/tQq9OWASCzaG5+Q/ayCMIV5OcPQKVKWNF4t9mS0NgoX+a+paFky+exuOByNaCqSif7NaEJGo0n0bAQrRSkC9/N3NkgOQYGWlBcPBqon8ECA9lym4OongcGjqCyMh1G4ziTZBkhueIARPrjH2sA0grgqiAIP19TU5OVkJAwGQIgIVXodLrvi6L4qCAIddKrR/oRNyYACd4EdXSkMHMLt9zO6Or6CKmpXmRkUNiPvNlkVtrZobjk1tZx6PXyT0q9mxyJiQNITNzJbDgc5bGUlo4HbkiVyodRXFwh/QdplSPSrbrB8B7U6oxlAIgdTU1flT1t6tBQL7zei4Eia8t5D1io6k7zNhg+QnV1sBbOcgBkfHwG6elPIT8/mNgt99bWdhb19Z7ANLu6RpgMT6S50wVDXl5/4HyzeMFAMoRCj9XqdLD+mxEHINKf/FgDEKcoiv/VbDa/uRIA4Xn+5wF8TxAENqlsIlzTjQlAgoaWyZSG+vrtEWpMPo93dx9CUpIX2dnsJj6T56C8fAYGwxiam78mH+VGOBNimElOHoBSuZPZWiYURkaeQRbDTMg4Nxo/QHV1+jIsWA4mwO3SSuJLjffZWRfc7kZUVmoj3J3SPm40foLKyqDBvhwAoUJ+LMXut7dfhE43GwCwLOeqUa2irCxrwMPJas0lumjo7j6Impr0wG8GCxcLK52+OACR9rtEo8UagEwD+KYgCP92Fw/Ib4ui+JeCILBJLh3hmm5EAEKUqYWFQzCbs1Bf/1CEGpPP493dR5CY6EZu7jNITU2Vz8QimEkoLtlgmGSy+nZI1J6ez5CaOsw0JXJvb5CcYXR0BnNzdaiqYscJTKxwHR1UhftOAGKzzaKu7lUkJARDm+TagpXE9wVkWM54HxubCXg75e45aGs7j4qKmRUBCGuF/IjZq6JiGImJKqbDfsbGupGWJgQuGLKz2a25RJ7/mppUGAwTTIRWxgGIfL64sQYgHwNIFQRhR01NTebSECyNRlOuUCjOARAEQXhWPmpbu5lsRABCruicnF5YrXnQ6batnXLXuGcCICqVO0DLyWrtCQr7KSubgMEwzQRT0UpLSsn0aWkj8Hq3M1sUMlQnYHzcgenpaqbqBNwdgDhQV/ea7AEI7a3FlcSXeg/6+mYC9VnkzvrT3W1AZqYFSUl3UiKTjBaLH83NL6zx1zF63VM19NxcM9LSkmEwjKO5+avR61zCnkIsd0NDM8jPly+V871UQp5/IpswGKaYIJeIA5B7rah0f481AHkawFGO4875/f4POI77BwB/xXHcsCiKRMlBXI3JHMc9azKZTkmnltiNtBEByPBwOzIzbejrK4RW+0DslH+fI1ssH0OhmENx8XPMskcNDhpRWDgIo5EShYmcjs1ms51HRsYYPJ4HUVRUyqQQ5MWpqJjD5OQsxsfLodVuYUaOYPIzJaEvlwPCRtE7UrbBcBTV1eKy3gNWPAfDw4Nwu88FqHiXC8FiiYKXFoKohRMTrwTkMRhm0Nz8KjPnYvFEp6f7oVTewNCQnekCfnTxplYnMX9pFQ/Bkv4YxRSAkLg6ne5roij+LwCh0tHBr30wPGyU47jfMJlM70mvmtiMuBEBCMXCpqR0YmioFBrN5tgoPgqjBgGIC6WlX2aWPWpkpAPZ2T3o6KBaDfIuFne3JaN6JllZE3C5tjCVvL1YphAl8vT0HEZGipmrdXDjxnuB2PClRm93twObNrFxa72YinepHBaLD83NL0bhy7G2XTgcdgwOHkZxceYdaxGk4M1min1wbGwMongW+flZaG11oqlJ/muw3AqzXmg0JJPFQiA9EQaDnelLqzgAWdvv0HK9xxyA0KSqq6uTk5OTqRBhnd/vp4BbO8dxbRkZGZ9euXIlmD23QdrGBCCdSE42YWSkgqkwk6Vb0mo9DmAO5eXPMxFestyRCtWeEAQvmprYCctYKovNdhbZ2VNwOJpRVlbF5NeDvDiVlfOYmZnD4GAB6urYyo+6ceN91NTcScPb3e3Epk2vM7Em7e1foLR0FAqF4g7j3WZLQGPjM7KXg8Lh2tv3LksIMD/vxsxMLWpqGmUvR2iCU1OTmJ8/g8LCDLS2zqOp6cvMzH3xRJ3OsUCRyOFhF8rLX0BaWhqTctDFW3V1AlpbZ9HU9BKTMtCk4wBE+qWLKQDhef5X3G73e1ardUp60eU54kYEIJSMp1IZMT6uZuqH8E4AcgJ+/yyqql6QbWXke+36iQkLUlJM6Oz0o6np+Xs9Ltu/22xnkJtrx/R0A8rL1bKd590mFqpKb7e70N+fh/r6h5mSg/VK6KRsi6UN6elmJCcn3gFAenrSoNfvZGJNbtzYh5qapDtkmJpyIDHxURQVlTEhB02SPDp2+4mAR8do9KKxkc300Pn5STidZwMAhCUWsuUu3qqqlEx7o+IAJDbHP9YAxA/ALYriMY7jfux0Og/09fXNxUIVOp1umyiKfwOAfuUVAC5yHPdHJpPp8krz0el0GaIo/ulCJXe6ZnUCOKtQKL7V0dFhWI0cGxGAkNELtGBmhkd1df1q1CaLd6zWk/D7HVCrX5J9jYOVFDY11QuVqhUWC4fGxudkodfVTMJqPYX8fCcmJ3WoqNCspouYv0NeHKJPdTjm0ddHYTJsVKoOKa6lZT/U6jsNd4vFzUyoxtBQH7zezwK1QBaHYLFSAyS0FqFclqVhZMPDM8zlHzidTkxMHEdZWQba2vzQ6ymVlL3mds/A4TiF4eF51NS8wmzeoMXyCaqrFTAY5pgiM1i6Y+IeEOnPUKwByK8CoGDgXQCoUhLVBdkP4O2ysrJjp0+f9kqhktraWo1SqbwK4DKBEI7jCBj9EYERv9+/pbOzs2u5eWi12lMcx20C8Gccx90AUCqK4l8AKPf5fE1dXV29kc5/IwIQYgPx+a5hdpYtqtGla2uznYLXa0dtLbu5E3b7IDjuKqxWFbM3i7QuNttp5OXNMg5APkVlpW8BgBBF9aORfk5i+nxLy0Go1QnLFCL0ormZjVANCvcZH/8YhYW350/Mzc3D6axDdTUb1MihXJalAMRmczBXvdrtdmNwkKpvEwDhoNc/EdN9vtrBvd5ZTE0dx/CwG1rtq0hMTFxtVzF9z2I5gepqDgYD5Q2y6zWPAxDpt1FMAUhIXL1en+v1el8TRfENAE8CIIL4SY7j3vf5fG93dnZ+upaq4Xn+TQCvchxXYTKZ7DSWXq9P93g8PQAOCoLwC0vH53megMc1URS/ZTabvxP6O8/zOwB8ynHcr5tMpn+JdN4bEYDQrbvbfQkuFxX14iNVmWyeJ6M3CEDYMK6WU9zs7Ah8vi/Q05O4LgDIxAQv+0JxK21gm41tALLSrbvVyqGpiQ3vmsvlgtV6IHDbvth4D4YuPcYMwxoV7yN67YyMYD1f8qpR6+6ew6ZNX5HNNzSciSyuvt3enoCGBjbC4JbK5vfPYXz8KIaHvczQUi+3PnTxVlkpwmh0M+01jwOQcE5fdJ+RBQBZLFJ1dXW2SqV6VaFQfEUURfKtJgEYEAShIrqi3+qN5/kJAB8LgvD1xWPodLq3RFF8QRCE7KVj63Q6nSiK7aIo/qHZbP770N+1Wu1mjq6Qgd8UBOGfIp3zRgQgREfocl3E/Hwzs8YirTMZjEEAwiYrC8kwNzeO+fnz6OtLYSLBdmXjnTwgFKqhZXZPUR5LZaWXWQ9Ia+txVFV5mU3epr1FLFEGA9EJ3x6CRaFLhYXPITMzK9JPfEyep1yWjAwz8vIybgMgrNUACSmvq+tAgGGtvT0RDQ2Px0Sn9z/oPEZGDgcASEPDV5jNG6SLt8pKP4xGDxobv3T/aolRD3EAIr3iZQdAQirQaDS1HMd9heO4bwLIFwSBQrSi3mprayuUSqWN47i/MJlM/33xAFqt9k85jvsrhUJR29HRYV06OM/zhwE0KhSKr3d0dHzO83wxgO8B2KRQKDZ1dHSMRzrhjQhAKOxndvY8PJ4tqKiojVRlsnmeDEafz46aGnbZo9zuaTgcpzE4mA69/inZ6DbSiQRDsNYTAGEvB2TlsJ9kNDayEzazXAJ3X58davXLzBQcHRrqhddLXpC82wAIK0xeS89/qPhdR0cyc6GJIVk4zo2hIQIgHjQ1vcFs3mDIU8syIQCtSRyARPore//PywqA1NXVbRVFkUKxqABhKLjWKIriu2az+X/cv7h39kBj+v3+S8uFTPE8TzkqFEa1XRCES0vf1mg0SRzH/TPHcb+06G+UC/K6IAjdq5nvRgQgxIc+M3MWfv82ZhmLaK0padjnm2EagNyKS85iNrY6uBankJc3x7gHhO0k9La286iomLnDA9Lbm4mGhsdW83mMyTsGwxFUV+M2OSwWB1NGI+WyTEwcg1pdeBsA6e1NR0MDRQ2z1bq7D0OtTkZHRyrq67ezNfmF2SqVXvT3f4SREQ+z1dyD39qgpzYOQJjchjGddKwBCKfRaHZyHPcax3FUzpTCrGhOAoB3RVF8x2w2t6+lhnieJ//tGY7jfsVkMv3H4rG0Wu0vcRz376IoPmE2m88snQfP8wSK/psoit9RKpWnvF5vnkKhoHwQld/v39XZ2Tka6dw3IgChvIPJyVPguO3M1mwIARCvdwa1tex6QIJxyccwMpINvX53pNtXNs8HWbAIgGiYzSu6nQUrhzlDK1RDIzMz5abRyxp7FE3caPwkwEa2OAfEYplnqgL33NwcbLaD0OnymV6L0AeGit9VVibAbM5CXd2DsvnuRDKRhAQf+voOYmSESBko/ZXNFvpOtbZS7Sg2KZFJ83EPiPT7L6YAhOf5EQDkE6Z5EBfru/Q/QRDIiyBJC3lAAPyGIAj/vASA/BrHcf/EcdzDS+l4dTpdsyiK1ziO+2OTyfR3offUanWRSqWyiqL4r2az+XciFcLr9YUqwd/zVYUiuHx+f9iv3LPPWDwwMNCD0dFPkJf3FMrL2SwaR3q7ceNj+HwOPPAAW0mdi9ecbkqHhw/D5SrFpk3shMks3bfXrx9Gbu4s/P7NqK7WxmJb3/eY168fCxi+wWrVuWhuZsdrQMILQgvS0sxISaE0vmA+hcvlxuwsD55vum/9SNXBjRunUF7uuBkiQ3LYbMADD7BDNkHA7+rVd1FTE8wBIRmcznm43Q2orW2QSpVRG+fatX0oLQUmJytQV7c1av1K2dHsrCNAcDA3l4Bt274m5dBRHYt+9yoq3LDZVNiyhd0ckNXYUwkJypja0FFdyBh0FlPl8TxPNLUh0HFHiJMU+uB5niow0Ty+LQjCtxePyfM8Uep+C0ClIAj9S/726wD+EcADSwETz/PXAHgEQYi4dPFGBCCDg70YHiaqy2dRWrpmXANrvp1u3Di+AEAogpDNNjMzjf7+Q3C7CYCw6wG5fv1IoBAhsBWVlWzmFYV+2Ml4HBoqRGMjW6EmPT1d8PsvIzMzWOGZjF673Qml8kFUVNQwc0BaWs6jtHQ8UA09JAexxLFmbF2+TJXpgyxYtBaTk3akpe1ESUk5M2sRmui1awdQXOyD3V4Lnm9mbv404bk5J7q79y8AEKpGwGaj372KinnYbHQm2PWAxAGI9PsvpgBEenGXH3HBE3NWEITXl4CMDwA8JggCJZff1rRa7Tc5jvt/lvOO8DxPeSt+s9kc8TXfRgzBcrkmMDh4FKmpu5iqyLt0T/T0XAgAELWa3Y8wxSVTWMDERAH0elbZZYJxyTk5M7DbG1FWVi2XT01E86D9VFHhwvT0HIaHi6DTbYvo/Vg/PDo6AqeTKDoLAlMh6teJiRmkpj6JgoLgv7HQurpakJVlRV5e+k05entT0dBA5avYaQbDYTQ23qLhHRmZQV7es8jOzmFHiIWZUtHXgoJ5jI6qmanFslTJyckcOjv3YWLCj+Zmdi+tQt+ptjYqX0BVFNhs8RAs6ddNUgDC8zx5E34iCEIHibrw3/eUmuM4cSlD1T1fiuABrVZLieQ/p1KpqoxGI1Hy0twoWJaYr74vCMJvLe1Op9M9IYriCQB/KggCVVAPtAWPSrcoij8ym82/HME0Ao9uRAAyPz+FgYFDSE19ghle/eXWtafnswALFssARKXyo6fnACYnC5hKFF66HhSXnJ09hdnZZpSWshnW19NzERUVVM19FhMTFdBoNkf6OYnp8+RNGxk5itraWwBkaGg6UHmbFfpaUmBfnwUKxVWUlAQNdVYr0xMtsl4fJJMkGfr77aisfBGpqakx3SerGXw9sNylpiphMn2IyUmgufmV1ahBFu/Q715FxRzTRSFJkXEAIv12khqA+KnYoNls/nDBWKeK4+E0ca1oeGlwjUZTrlAoKJ/DzHHct0VRJL18SxTFWpVKtcloNA4tgKU/5ziON5lMlK9CYIMAyCOiKP4PURTPKZXKIlEU/wQAxRc8FAJa4QgYemajAhAK+0lLewpFRXc4myJRX0yfJYPR77ejuvqZmM7jfgZPTBQDccnT04Wor3/kfrqK6bs22zlkZU3A5dqC4mI2w/p6ej5HRcUsxscdmJlRo6YmYodqTNeAEp9pL9XV3QIgZPRWV7NDX0sKHB0dhdN58qYnh1VA2Nb2KerrfTcBiNXqYLb+BFG/5uTYMTOjZ5Y5MS0tAW1tH2B6mkNz88sxPav3M3joO9XWpmCauCQOQO5nF6zuXUkBiEaj2aVQKIyCIIwtGP5h+7DXuhq6RqNpUCgUVFAwEHciiuJphULxByaTybQANigf5M8Jd4Qodqurq5MTExPp3ymDjAJpKej8rEKh+FZHR4dhNUuyEQGI2z2D3t4DSE9/mnEA8vkCAKH6mWy2pCSgq2s/7PYi5liXFmucAEhm5gTc7q3MhvX19l5CebkdY2P2QOJ2dXU9U5uKcleMxg/Q2Hir9gQZvXr96zfzKVgQaKknZ3h4Gm53I3MFLtvbP4NO5wwk05MHxGKZQ3Mzm4QZ5OHMyprE3NxmlJRUsrCN7phjRkYiDIb3YLcr0dTEbvHaOABhcvvJYtKSApClEtfU1FT6/f4Rq9XqWkkbdXV1PBUi7OjouCALja3xJDYiAPF4iA1kL7KyvoTCwiBPPYutt/eLQAhWdTW7BfxSUhSBsIDZ2VLU10fMoSCbZaO45LS0EXi925kFtb29l1FePoORkWm4XA2oqtLJRr/hTqSl5UM0NWUHHmfV6HW5XAFPTojCtq9vHImJj6OoqCRcNcjiuc7O66ioGEZioiqwFlarj1nD12Y7j4yMMXg8DzIbtpuVlYzr138Ch4MACLvU7fS7Ryxx7e0qJmvKhA5n3AMi/WcqpgCE53mfKIpfDYVkLSc+z/O/C+APl0sEl15daz/iRgQgVPyuu/sD5OY+j/z8IE89iy0IQByormY3EY/iko3G9+F2VzCX9Lx4z1BcckrKEDhuB7N7qq/vGsrKJjE4OAm/fwvKy9lhjgqthcFwEI2NQRYsVo1e8uS0tn6ApqagJ6e7e5TJ5O3e3i5kZ3cgPT1lYS0UaGpikzbVZgteMIjiY0wRGiz+RmVnpwSokWdnE9DU9DyLP3mBOYcuStrbE9HQwC5xSRyASL8FJQcgGo0m0+/3B67EEhISKMn717xe79HlRFepVCmiKP6/AJ4SBCFYzWqdt40IQHw+Jzo730Ne3gvMGovBD/GlBQDCbv2M9HQVbtx4F36/GlrtFmZPG4UFJCcPICFhJ3Jzg4Yja62//wZKSsYCAESpfJjJXJbW1mPQ61U3AYjNpkBjI3tGb0vLXjQ1ZQXkoAjiigr2krdHRoahVH6G3NzMAADp6UlilrVoPZxvMngvXXoHc3MqNDY+x9rn6eZ8+/quoKxsGu3tSUwTl8QBiPRbUHIAslBbg/Imwq2eR3M8LQgCu9fKEazrRgQgVH3bZPoJCgtfRF4em8Zi6CaIktCrqtgFIBSXfPXq21AoNKit3RTBzpXXo+SNSkwcQGLiTuTk5MprcmHOZmioDfn5/RgYmERy8i4UFhaF+aZ8HjMaT6ChIfgzEzR6E6HXsxeiaDB8hMbGIFuU0TiOurrX6AJNPooOYybT01NwuU6jsDAIQHp7M5i9sV4P55sM3i++eAcuVxIaG9mlbu/tvYry8qk4AAnjDMYfuV0DkgOQ5ubmNLfb/aAoig+Jovi3BC4A2FZYGKLs6FYoFP/a0dExvhEWbyMDkKKiF5m9raa9STdBlANSVcVuAb/MzCRcvvxjJCTwzLEuLf4+kDdKpepHSspuZGUFcxBYa6OjncjM7AoAkKysZ5g8G21tZ1Ff77kJQHp705mME1/syTEYJtDc/AZr2wlOpxMTE8dRVpYBu92F/v58ZvO8KOxHpeoLULezROm8eNMQALl8+SdwOskDsh4ASAoaGthlTox7QKT/pEkOQBaLqNVqT3Ec9y1BEM5KL7o8R9yIAEQUXWhvfxfFxawDkKsLACRscjfZbUKKS/7ssx8iObkBanWD7OYX7oQIDCYk9CIt7UlkZGSG+5qsnpuctCIpqR2Dg+wWjGtvvwidbjbAvBQ0enOZZFdb7MkxGGbQ3PyqrPZKOJPxeqnI6GFUVaUHqJ2np6tRW8tmFfG+vqtQKnuQkfEU0tMzwhFfds8EAcj7gRAsvZ5d5kQKFS0uHoUgZDILaGlzxAGI9EckpgAkJG59fb1WpVINtLS0zIb+TaPR1AJI6uzsbJNeLbEbcSMCEGAeRuM7KC19idlwmaAH5NoCANkZuw10nyPn5KTi3Lk3kZbWzGyF4dBaKBQ9yMwkAyVYwZq1NjMzAIXiGgYH7Sgufp5JILWYeWliYhZTU5VMhva1tZ1Bfb03sIUMBqKvZZO1qKvrI9TUpGJoaApe7yZUVNDPLHuNvrUc14Ps7KeRlhYkOWCtkcF75cqHcDoTmAxLDOl7cNCIvLx+dHfnoK7uQdaW4eZ84wBE+qWLNQBRarXa73Ec94t+v//Bzs7OqyEV6HS6XxZF8XsLlcj/LwDhFi2UXotRHHGjApDW1ndQVsY6ALm+AEB2RHFHSNsVfYQ//XQPMjM3MUn7GtJWX9/1QGRnTg67BorTOQqP5yKGhlwoL3+eSUOrp8eM3FwBaWkpYLV+Bu2p9vYLqKsLssW3tnqYZY+yWI6hulqF3t4xJCXtZJaiemCgBT5fN/Lzv4SUFDb5aYIAZG8gBEuvZzdvcGSkA2lpFgwMFDNNXBIHINLaGjRaTAEIz/O/B4CK//1EqVT+bnt7+2BIBTzPE+fkHwP4LwC+KQjC/ye9eqQfcSMCEI5zo6XlbVRUvIzs7BzplR6lEckV7fXOoKqKbQBy+vQe5OQ8gIoKTZQ0I303AwMGeL1dKChg10Bxu6fhcJzG8PA81Gq2qoeHVnx4uB8q1WXk5GQgWD/jMSbrNgjCFajVE0hIIJpqoLGRTU4Uq/UkqqoAi2UEublfYvZ7SwQNc3MmlJY+jySqnspgI4P36tV9mJ0lABjwGWUAACAASURBVMJu3iDlqiUldWB0lLybbIb00faJAxDpD1GsAUgXgJOCIPzKSqLzPE9ekCcFQWDXGopgXTciAFEoPLh+/ceoqnqF2YRhWmIyeslorK5mmwudAEhu7jYm606Ejtp6MFCIHW58/CiGh71Msi7RWkxOTsDj+RQFBVkBozcn51kmwyyt1g4UFlqQkpKEtjYKmWEzzNJmO4PKSi8EYRRlZS8wG55It+52ezsqK1+EShWkeWatBQHIgUAdEL2e3bzBiQkLgBZMT2uZzhuMAxDpT1CsAcg8x3H/xWQyvbWS6Dqd7mdEUfwPQRCSpVeP9CNuVABy7drbUKtfYZbRhHYKxcK6XJNQq9kGICdP7kFBwcMoK6uS/gBEaUQyUGZm2lBV9RKzBgp5BoeGDmN42IPGxtehUCiipB3punE47JiZOYmSkgyYzaMoLWXT6O3vtyIjoxUZGano6EhBfT2bbD89PRdQUUGkH2OorX2FWe8B3bpPTt5ATc0rzNEhh05fEIB8FMgBaWhg12s+NdULt/sSXK5GVFby0n1cojxSHIBEWaFhdBdrAEKFCP9VEIS/uQsA+Y4oir8oCEJ5GPIw/8hGBCBKpRdXrvw48GPCKqUibTy6dXc6J1BTwzYAOXFiD4qLt6OkpJLZ87QeDJSEBB/6+j7CyIiHSdpX2jzz8/MYGjqKysoMtLePo7b2ZSaN3uHhISQkXERmZhosllzw/FYmzwbVzygvd8BgGGcW1JLi6dZ9aOgy6urYBOYkAxm8164dCnhAWK4gbrcPwuE4D693C7OkBqH1CO6tm1xI9zzjhYWZMbWh7zlBmT8QU+XpdLp/EEXx5zmO+wWTyfTR4kRzqpjOcdxPcxz3PwHsEQTh12Wuy6hMbyMCEDK0Ll16CxrNq0wy/YQWnm7dHY5R1NSwe5tFP4oEQEpLKVa/LCp7OhadrAcDhULbu7sPYHzch+bm12Khxvse0+/3w2r9CGp1esDobWp6I0DJy1qjUDK3+1NkZqZgaKgCarWeNREC8yX2qLKySRgMU2hu/gqTMtCk6da9r+8iGhu/yqwMQQByOJCEXl//KLNyzM6OYGLiFJTK7SgtZddrHveASL8FY/pLUFNTk5WQkEA1QOhrPgegF4Cb47hsURSLAVCp2Q6VSrXDaDROSK8e6UfciABEpfLj88/fAs+/yiynO+2U0VEBMzPDqK1lG4B88skelJWxy5ATNFB60Nt7EU1NX5P+EEdpxNRUJTo6PsTUFNDc/EqUepW+m+7uQ1CrU5g2eimUzG4/gfR0Faan65nNjyIvbX5+P9raiEr4Zek3Q5RGJIpqq/UcmpvZPd9BAHJ0AYBsj5JmpO9mfn4SAwOHA0Uhi4pKpZ9AlEaMA5AoKTKCbmIKQGieGo0mSaFQ/AbHca+LokhAhDj1RgBYOY47YLfb//fAwIAzApmYfnQjApDERBGfffYj6HSvMZsUGQQgnZieHoRGwzYA+fjjPais3I3CwkJmz9L0dD9stvNMGygZGYkwGN6H3a5AU9OLzK6FxfIxqqsT0No6i6aml5iUw+VyYXT0GKjkhMfzILOG1thYN9LSBHR2+tDUxGYtE9pADscQurrOYNOm9QBAkpgu4Ofx2GG17kNW1peY/s2IAxDpP80xByD3Enn37t0JnZ2dqr6+PvKQrPu2EQEIhZqcP/9D1Nd/hclaB6FNST/uk5P90GrZByBq9VPIy8tj9rytBwOFqtJfvfpuIEa8qel5ZtciRP3a2jqPpqYvMykHVRHv7T2EtDQ/EhJ2IjeXzbNBwFypvAGrVYnGxi8xuRY0aadzDIJwCps3sx2CdeXKUczPJzNdwI/Y+gThPRQUvMD0b0YcgEj/OZA9ANHpdD8riuJfC4LAbkZsBOu6EQFIcjKHM2d+EEiKTE1NjUBb8nqU8g7GxvrA82wDkKNH34RG8ySzRlbIQDGZTmLLFrZvSC9degdzc4lMG4s221lUVnpgNHrR2PisvA5tmLMRRRHd3QcDAIRCTVglyyDD3eP5DL29yWhsfCZM6eX3GIX9tLUdZ/58X758BD5fOrTaB+Sn5LBnNA8qJFxeznYdrzgACXvBo/ZgzAEIJZsrlco3RFHUAritopAoiskcx9HVX64gCJlRk1rGHW1EAJKSosDp0/8ZYPphtaotbanJSRtGR3vWAQD5T2i1TzJZryF0tMlAMRqP44EH2AYgly//JBAjzqrhTuvR0/MZKirm0NbGMV3xmXJZkpK8yM5+mllPLYXLzMycwNBQFtNr4XbPwGA4iq1b2T7fly4dhShmQKPZLGOr5O5TWy8slnEAIv0WjCkA0Wg0tQqF4jQAylyiuYhLqrPTf4scx/2tyWT6M+nVI/2IGxGAULItMS/RbXVyMrvlXoiZZXjYCp2ObQ/IkSM/QF3dU0wXhSRDq6XlCPMGypUrHwQAiF7/lPQfoyiN2Nt7CeXldrS3q5iud2CxHINS6UJh4ZcY/k7NY2TkMMbG8tDQwGYxRdqWXu8srl37CA8++FNR2qXSd0MG7xdfHIVCkY2amibpJxClEYMkMj8Cz1MOZ0aUepW+mzgAkV7nMQUgPM+/DeA5juP+kpLORVHcK4ri7ygUCvr/VBqUvi6/IgjCEelVE5sRNyIASUtLwPHj38fWrT/FZI2A0E6h+OrBwW7U1bENQA4f/gEaGp5mNsyE1sPncwYMlG3b2L4hvXJl7wIAeSI2H6QojBqifu3oSGaabtRq/QSi6ERFxfPMFr8L1pY5iPHxfOj17NYroryDK1cIgLCdA3Lx4hGoVPlMVxCnHM5z5+g343VmPYP0mYsDkCh87CPsIqYARKfT2fx+/7+ZzebvLFDyToqiuNtsNp8hOXQ6XbMoip/4/f5XOjs7P4tQNiYf34gAhKgtjx79Dzz00NeRmJjI5LrRpKkgU39/5zoAID+EXv800zVZRNGFy5fJQHmD2f0UrJR8YKFSMt3HsNkGBgwoKCDWomymk21ttlPweh2orWWTyYt2DxmLXV37YLcXo76eXepXYB5ffHEQDz3E9vkmAJKUVIiqqjo2DzfRlqYocOrUm9i06atMh1DHAYj0WzCmAITn+XkAvyAIwjt6vT7d4/HM+P3+5zo7Oz8OqYLnefKO7BQE4Unp1SP9iBsRgBDd6KFD/4ZHHvlpqFQq6ZUepRGJeamnx8x0mAl9hA8d+hGamp5m2p2+XgyUq1c/CnhAWK6UTPTUyckmjIxUoLa2OUqnTfpubLYz8PnsqKlhl76Wwl3b2t6Hy1XONBjkODcuXjyIhx9+XfqNEKUR6Vt74cIRpKYWo7KSj1Kv0ndDEQwff/x9bNvGdgRDHIBIv3diDUCGRFH8rtls/i6JzvM8Ue3+viAI/zukCp1O9zOiKP6TIAjZ0qtH+hE3IgDJzEzCgQP/hh07fgZKpVJ6pUdpRKoIa7OZmAcgH330I2za9CzT7nSFwoPPPiMDhd1qz8FCZYcwO0sA5LEo7VLpu6HcKI/nMubm9EwbWjbbOfj9DqjVz0mvxCiNSJc91669A6CG+cTnCxcOMH++L1w4irS0UlRU1EZphaXvhvbU4cP/ju3bv8H0BWIcgEi/d2IKQHQ63VuiKD7Ncdyvmkym/TzPXwOQ4fP5nujq6uqlGiADAwPvAXhIEIQy6dUj/YgbEYBQvYO9e/8Vu3b9LBQKhfRKj9KIRHFpsbRBr2c3uZM+wgcPvoXNm59hGoBQrPu5c/uxfTvrAIT9SskEzCcmTkGp3I7S0qoonTbpuyE2L/KAqNVsUgmTxrKyknHx4o+QnFwPtZrq/rLZ1sv5PnfuCDIzK1BermZzIRb2VOj3m+ULxDgAkX4LxhSAaLXaeo7jzgK4JAjCl3me/wUAewC4AXQCKAaQA+BfBEH4DenVI/2IGxGA5OSk4sMP/xW7d/8sOC6mW/K+FpwAiNXazrwH5ODBH+OBB55lOp53vRgoV68exdwc25WSiTK1p2c/MjKeQVERfdLZbD09n8Pvt6O6+mk2BVhItP300z3IytrMtDeKmJfOnNmPRx55jem1IACSlVWFsjJ2gXnwAvF7zP9+xwGI9Ecp5taeRqMpT0hI0Hd0dBwj8bVa7a9xHPe7AOhEDgF43+FwfGtgYMApvXqkH3EjAhA6+B988D088cTPSK/wKI44NzcOi4UACLvsMrQWBw68jW3bnmWYahQIApAD2L6dbQNlPVRKJkYys5kqJb/IdKVkohP2+RyormaXkYzO96lTe5CX9yDTt+6UTH/q1D48+uirUfyCS9sVrcXZs0eQm1uNkhJ26yyvl9/vOACRdv/TaDEHINKLLO8RNy4A+Tc88cRPy3tx7jG7ubmJBQDCbrw+fYT37/8xHnyQ5VoH6weAXLtGIViJqK9/mOGzEayUXFb2EtPFLfv6rgZCsKqq2GUkCwGQ/PyHUFZWzeyeIgBy8uQ+PPYY+wAkL68GxcXlzK4F7akPP/x37N79DWZloInHAYj0yycbAFJfX1/l8/kqk5KSWg0Gw6T0qpDHiBsVgHz44X9g9+6vy2MRVjmLubnJQA4IywnDcQCyysVfg9eCSehHAixY9fWPrMEI0nRJhADXr7+NqqqXmS5uSQDE651eFx6Q3NytTCc+JydzOHFi7zoAIMcWPCCsA5DvY/dudotCxgGINL8FS0eJOQDheZ6uMP7nQsgVRFF8YlEdkAuiKP6DIAjvxkY90o+6UQHI3r17sGsXu0XjaKcEAQiFYD0q/caJ0ojrJQSLYsTPnqUkdLZDsNYDCxaFw12+/GPU1r7CdG0ZSkL3eomGl90kdDrfp0/vQXb2FlRWaqP01ZC+myAAIQ/IK9IPHqURgyFYxxD0gJRGqVfpuyE59u59E7t2sVsUMg5ApN83NGJMAQjP808BOMZxXL8oipQD8sshAKLX63PdbvenHMfVA3hpo1RD37gAhP0P2Nzc1IIHhH0AQiFYSRTnwGhTKr04f54ACNt1Aqiae5CGl928osREERcu/BB1dV9Beno6ozsKsNlOLwCQF5klyyDCj7Nn30RGxiZUVemYXQsqfvfJJ+QBYR2AfLwAQEqYXYsgAPkBdu1i91sbByCx2X6xBiBUcLA4OTn5kZmZGVViYuLE4kro5eXlKampqRcATMYLEd65QVYTsxibbXb3UdfLB8zlmg4AEJbDZYIhWG/joYfYBiBUFLKz80ygOi+rzGrBSugHFwoR7pDj0Q1rTgQGv/jiLfD8a0x7QCyWY1AqXSgqeo5ZcE6MRRcu/ACpqU2orma7+vbx4/vw+OMvh7UH5fgQne9z544jN1eN4mLWAciPsGsXu97mOACJzQmJNQAhwPGXZrP5H2tqarISEhImFwMQUgnP80S/+9eCIGTGRkXSjrpxPSA/xK5d7NZsoF0SBCDtqK/fLu2mieJo6wWA9PffgM9nQX7+s0hNTY2ihqTrijwHgnAQU1MimppeZLZI58BACzIz+9Dbm4/6+oekU2AURxJFERbLR0hN9SE19QlkZmZFsXfpuqKirwQGExN1qKlpkm7gKI9EHpD1AkAoBItlemr6zdi37y3s3MkuIUAcgET5gIbZXawBiIvjuF82mUxv3QWA/ByA7wmCkBKmTEw/tnEByFvYtYvtD1gQgHQwzVi0XpLQqWp1RsY4PJ4HUVTEXnw1GbxdXadRXe3FxMQsxsdLmTTefT4frNajqKlJQUvLFBobX2ESSLlcLoyMHEV6OsfsngpekkxgdvYc+vtT0Nz8DLO/leslB+TcuU+Ql6deBwDkbezcya43Kg5AYvMpiDUAaQNwQhCE37wLADkIQC0IQmNsVCTtqBsVgOzb92Ps3MluPG/IA2K1mlBXx+Ytb+gjvB4KEXZ3H0N5OQebrQBa7RZpD3EURqNQvpycbmRlpcLhmMfAgB35+buRl1cYhd6l66K3twsZGW3IykrDwMAE/P4tzLEvERjs6PgC5eXjSEpKQFubiMbGp5CQkCCdIqM0ks12ERUVs2hrmwTPv4zExMQo9SxtN+uFhvf8+ZPIyalkPgRr//53sGPHS9JugiiPtpqQ9sLCzJja0FFWgeTdxVR5Wq32TzmO+3MAvwlgL4ARALucTufltLS0R/x+/29zHPcigD8VBOFvJddODAbcuACE/RsU8oCsDwDyFjZvfgZpaWkxOAH3P6TH40F//xFUVaXDaOTQ2MhW4TiHw4Hu7sNoasoPKIMASNAj4kFzM1uhWNevH8amTck35ejqErF58/P3v8gS9UB6b2u7gOzsIZSW5gRGHR+3o68vCXr9U1CpVBLN5P6HIW+UzXYEanUqBgcn4fU2M8uEReGJp0/vZ74Q4foBIO9ixw4y1dhtcQAi/drFFIDs3r07YWBggIDHCwD8C6xcHgChrzoniuLBsrKy10+fPu2VXj3Sj7hRAch6uEEJhmCZmAyVCe10+ggfOvQjNDU9wyxj0cCADUlJN5Cbm46Wlhk0Nr7ETNgPGbw3bhxDdbUPWVnBqFMCINTm5uaZCsWamprAyMhxaLUFN+UYHJxBYeEzyM7Olf7jGuGIfr8fRuN55OWNIiMjBenpQVY4Wo/5eQ96e5XQ659hxotA5yI5+QZyctIDMlgsSjQ3s0kpTDTbZ87sxyOPsJv4TN/a8+dPISenYh14QN7Djh3sXCws9ymIA5AIP5BReDymACQ0f61W+zrHcUQiTbyAxNNoF0WxjeO49wVB2BcFOe/ZhU6n2yaK4t8AoJLDCgAXOY77I5PJdPluL2u12p/mOO73ARCliAPAEY/H8/sWi2X4noMu88DGBSDs36AEPSBm1NVtW83Sy+Id+ggfPvxD6PVPM8lY5PV6YTAcwObN2QF9jo7OYGKiDHV1D8pCv/eaBJEYqFTGQMjSYoM39B6FYhUUPInc3KB3RK5tZmYagnAWGg13G5Dy+fzo6iJykR2y3l8EPgyGT1FUNIW0tKAHZ+l6eDxe2GxAQ8OzTLBiGQynoNf7A6xwBEBsNge02peZmPvSfU61Zc6dO8B8nZ/z508jN7eC+RyQ/fvfx44dX5br5yisecUBSFhqiupDsgAgUZVoFZ3V1tZqlErlVQCXCYRwHEfemD8iMOL3+7d0dnZ2LdetVqv9BsdxbwH4F47jfuz3+4s4jvsuAajS0tKtq/HabFwA8hPs2EGOMHYbARCbrRM63VZmhQgCkB+goeFpJpl+2ts/R37+EPLzMwJrQIZWX58dZWXPIjMzCErk2NxuN7q6rkGp7EVJSTD0bTkAQh4Sq3UWaWl1UKv1UCjorkQ+jbwePT3XkZg4huLiNCiVijvkIBAyOOiAx1OAysrNsvOGUKiSwXASpaWzSEm5VQtnufXwen2wWHyoq3tG1mxrtL9MpgNobAx6nuhczM66MDeng1rdIJ8NFOZM1kudHwIgOTnl68ADEgcgYW7d+GOLNBAzALJ161aV3W7vAPB7Unk5Vlp5nuffBPAqx3EVJpPJTs/p9fp0j8fTA+CgIAi/sNy7PM93A7AurlGi0+keE0XxrCiKv2w2m/dEuts2LgBh34UbBCBd0OkeiHTZZfM8AZAjR96ETvc0srODMe+stMnJcQwPn0BZWcZtRi/dZnd3c9i8+cuyqwkSZIkyYnbWhNLSJCQl3copWM7gDa2FwzGH4WEF8vObUVamjrlcY2Mj6O+/juTkSRQXp98GjFaSg9ZlaMgBlysH5eVbkJcXDNWKZSMPWmvrJygrm0dy8u0J2ivJQYCqu3sePP+sbMMWyZtTWjqD0C1vKKzPYiEvyItISWGLZJLj3Lh48QAefviNWG6X+xqb1uLChU+RnV22DgDIB9ix47n70kesX457QKRfgZgBEBKV53ky8P9WEIR/ll70WyPyPD8B4GNBEL6+eB46ne4tURRfEAThjqtTnuc3AbgmiuJ/NZvN/2fxezzP93Mcd8VkMkXMS7dxAQj7NyjrBYAcPboHGs1TyM3Ni+WxjGhsMmZv3DiM2lpFwBhfaizOzDjhdjdAra6PqN+1epg8GX193RgfN6C4WLwZ5rN4vLsBkNBzExMOTE6mobx8K/Lzi9Zquiv2Ozzcj8HBG8jIcKCgIH1ZIHQvOUgXIyN2OBwZKCnZhKKiMsnloAFnZqbQ2fkpKiu524BgaDJ3k4P2n8XiREnJIygqKo/J/FcadHDQBpfrC+Tnp99xLsiDY7MlBUgnWGqi6MKlSx/hoYdYByBnkJ1dyjwAOXDgQzz++JdY2kJ3zDUOQKRfvlgDECr88F2O4/6byWQ6BMAntQpqa2srlEqljeO4vzCZTP998fgLLF1/pVAoajs6OqxLQAbVJyHPyW6Bgp0XNZ7njwPQCoJQHak8cQASqcbk83wwB6QTdXVsh2B9/PEeVFU9gYKC2N9Ih7u63d0GJCcLyMgIFh1czliUy23v6OgQ+vuvIDd3Djk5KzON3ctwD+kmaMA7MDubi5qah9Y8t4LGGxzswchIK7KyZm+Gu620VuHKQe+PjdkxPZ2GwsJGlJRUSubZsVo74HAYUFmZtuKY4chBSfY+nxo63TZZhMfNz8+jo+MgampWDuujOjMcR4xYfLjHLebP+XxOXLv2EbZt+1rM57LaCQQ9IHEAslr9Rfu9OACJtkbv3V+sAciHHMeliaJIPJnzoigOchxHidy3NY7jRJPJtCZWXV1d3Va/33+J47hfN5lM/7IESPwq5XcA2C4IwqUlf/s9AH8nimKj2WxuX/K3H3Mc94rJZIqYx3TjAhD2XbhBD0g3dDr26k6E9i99hD/5ZA/Kyh5npoBfMGb/Q9TWEn9FsK0Urz80lIv6+kfv/WVcgyfoht1iuYT09EkUFgZzVO7WwjF4F79Pt/ADA7MQxTLU1GxFcnIweTpajfrv77dgfJxqlLgCLGPhtEjloD6Dnp1k5OU1BELM1irXhSib29vPIStr/GZ40koyhSsH5VYMDiZCq90V85Asg+E0SkvtSEwM1ixZSQaLxQ6N5kVZ57EsXhePx46WliPYujUOQMI5g2v5DP1mxD0ga6nh9dt3rAEIJXuH00RBEJThPBjpMzzPPw7gDMdxv2Iymf5j8ftarfaXOI77dwJIZrP5zJK//RnHcd/2+/380iR1nuf/E8DPrmbOcQAS6QrK5/n1AkBOnNiD4uLtgRtoFtrk5AQmJo6jsDDz5nRXNrRENDdLSxdJVbS7ui5DoehHaent+RHRBCChvtxuL/r7XUhJ0UCtbopKwbzZ2VmYTB+jsJDogSO7VwnXcF9OF9PTsxgZUUKnezbqdWkmJkZhtZ5FVZXqpoEerfUgsGazzSI3dyvKy2tjcoyIdtfl+hwFBbfA7kprEQzFSsTmzWzQ8s7PT6Gt7WNs2RIHIDHZXIsGDQKQvXj8cTb2zkr6intApN9JMQUg0ot754ghDwiA31iai6LVan+N47h/4jju4aV0vDzPBzwgfr+/qbOzkyq632w8z78D4EVBEMK7Ilz07kYFIOvhBmW9AJBTp/YgP/8hlJVFHEEYkyPd3W1ERkbnbUnDKxlaxCDV0PCapHVBrlyh4m9eqFSRVc6+H8OdFsJud2J6uiIqdWkcDjsGBg6jpOQWyLttsec94NxUwunOFo4cYqIKWJSAv7gXCmsqLX0e6en39hqFswGDRR1vwOMxBQgLKGconBaOHEv7GR21w+ksRV3d9qgAwXDmSc8Ec6L2QqMJhiSG2t1koAKLKhUb535ubhwdHSfiACTcDbGGz8UByBoqd513Hd6Xdx0rged5ynjsBfBtQRC+vQRI/AWAbwGoFAShf/HfdDrdz4ii+AO/3/9kJ2UuLmparfYUx3HlgiBoI1Wd1+sTw31HoQgun98f9ivhdi3pcyTH/v0f4KWXviLpuNEebGhoEFZrN7ZvfyzaXUvWH63FyZN7UFu7ExUVNZKNez8DXbt2ApWVztu6CBmVZGwubiMjVAjvOeTlSZdg39vbjfn5zwMF4CJpK8kQbh+9vTOorX0ZGRn3b7gTO9SNG++hunp5GdL+/m2kfffdcKd2x3Oz3/wpzP7+N5Z932p1YNOmr0bFgCdvVEvLCRQWzgaKC0bSVrseLpcbvb0i6uqekpRZ7tKlD1BTczuT191kIACSmbkbxcWlkaglJs8ODvaio+MknnhiWYLKmMwp0kHpW3v+/FlUV9eipKQk0tdl83zw93svXnqJUnrZbauxpxISlBvehr6fFY8rL8jGNQLgrCAIry9WJs/zHwB4TBCE4qVK1mg0eoVCYRBF8bfNZvP/WvLeKIATS1m1wlmoOAAJR0vyfGa9AJATJ74PjWY3KirU8lT0klldubIP1dW3R2iuZGiRMTg7y4PnmySTjW6jL1/ei9ra243Be01gtQYv9Utj9vSkYuvW6LEbXbr0Hmpqls8rWUsA0t3twoMPUp3a+2/d3R2w2y+jvDwr4s7uZz0mJ2dht5diy5adEY+72hciAeY0BhUmbGp6AyrVLSro1Y691u8NDfWhre0TPPnkL671UGvWfwiAVFXVoLRU/qBvJUXEAciabZF133EcgBBdlVb7zxzH/ZxKpaoyGo1EyUughEoNE/PV9wVB+K3ldgLP81StfcpkMt3MauV5nsqBHhJF8Q2z2fxhpDsoHoIVqcbk8/x6CcEiD0hh4cMoLa2Sj3JXmAklEZtM+1BVdfvN/N1CTWy2ZDQ2Eu+FdM1iaUNKimlZut2VZrGakJ9QX8PDduTmRpdKuaXlMNTq5X8ycv7hPeT84/urVujkb72Byd9eHmRYrUBTU/SqLBMt7ejo5wFvTrjhVyTYatdjcNAOpbIOtbXNq9bPal6kPZeebg4rNJH6t1j8aG5moxjs/Pwk2tqOr4MQrLPIySllvhJ6PAdkNSc0/k4cgADQaDTlCoXiGsdxZkosF0WR9PItURRrVSrVJqPROMTzPIVi/TnHcbzJZLLQ1tFqtS9yHLeP47gfiKK4RxTFCo7j/h5ApyAIu1azveIAZDVak8c76wWAsJSETsnR7YiI4wAAIABJREFUVutBVFbefqN9t+J3VqsKmzZJmzBJIUxG437U1Nwek3+3nbtag5f67OpCoPBiNFtr60lUVc0v3+Ua5oBQnYrGxiejKcpCzY+TqK5WhZ2bE+l6BKvWO1BYuB3FxdITOszMTGNk5CiKiu5NzkBz7e3NhF5PnCzybwRAjMbjeOAB1pPQ4wBELrstnoQu/UrEAciCzjUaTYNCoSDwEPgCi6J4WqFQ/IHJZDLRf/M8T/kgf07/VxAEqoAeaBqN5iWFQkHgRA9gGsAHfr//Tzo7O2dWs5wbFYBQDgjrlVTXEwChgmrFxRWr2cKSv9PeTkXWBsO66e3ttaO8/FlkZt5RW3TN520yXUZe3sCyRe6WGzxSgzfUB8XyJyVFn8WM9Jye3oPsbOlYsKamZuFwVEYlkX6pjqlGRnv7SZSUuJCaem/K4kjWw+Pxwmbzorb2iZjstYXfMBgMe6FW38p1WUkGIizw+x9AeTkbYZfEgmU0frwOAMgZ5OSUxT0ga/71vfcAcQBybx1F+4mYAhCNRpO5WkM92oqQS39xACKXlYh8HuulEjrVASktfRTFxfKq6LzSipB3wWA4CI3mlhG5nKFF9RlmZqrA8w9EvrhReIMMXqPxGIqLPTcLJt6t20gM3lvgw4Hp6Vw0Nz8ZUXhROOLRLTnVARkbMyI31xV2Uv1q5JicdGBiIhn5+fpAHZBIQqXCkSX0DOXKdHRcRGpqP/Ly7k4SEK4ctM+GhlLQ0PAkEhMjy/uJZO7hPNvS8jGqq7039beSDATM1eqXkJISWWJ+OHNYi2fc7hkYDEfXRR2Q9QFA4pXQ12Kfr/c+YwpAeJ4n6pr9HMf9p4lI5oFw64Ks23XZuADkfezYEd2QEak3yXoBIMeP70F5+U6mbuVGRwcxNXUWxcVBxqelhhYZz52dHmze/NKaFbULZ7+RwWuxtGJ21oTy8uS7hv+Ea/DSuHNz8xgYEFFQsGXN6ZNJl1RjYnTUEFZBwkjkCBYgTEFBQWMgB2mtgMfStaJ8ifn51gAt70otHDnGx2fhdJahru7hmO6zkAxTUxMYHDyO8vJgGNZyMszPuzE6WoT6+u3hbGFZPOP1zuL69UProhJ6HIDIYkvdLEQ6MTEb9oQKCzNjakOHPVGZPhhT5fE8T8X9KIGb5jEMgCqI/8BkMrXIVF9rPq04AFlzFa/ZAOsFgBw79n1UVz+JgoKCNdPVWnTc2noOxcUTgRCnpYbWwIAdBQVPIjeXuCVi34gOtrPzcyQmDgVA03KGdjgGLwGavr5ZJCTUoLZ2c1SoasPVDgGRwcEejIwYkJ3tRF7e8sZ7OHJQ2NjUVCoKC5sCBTClAh6LZR0ZGcDQ0LkVk9PvJcfAgAOJiXqo1Q3hqlCS5zo6LiE3tx8pKUnLApDOTheaml6SdO/cr+Ci6MLlyx/hwQffuN+uYvY+hfxcuHAG2dmlKC5ml4Y3Xgk9ZluI+YFjCkBIe2q1ukilUhH9LX1JdgBQADAA+E+Px/Nji8VCwGTDtI0LQN7Djh3SVqiO9qYiAGK1dqKubmu0u5asP/oxOXr0TWi1TyEnJ1eycaMxkNvtRlvbQdTWpt5maBH17tiYPG94x8aG0dv7BYqK3HeEZd3L4B0bo3CrLGg0j0StSN9q1oGAyNBQL4aHDcjKciI/P3xGsqAMqSgqagrkHMUCeCyW2W6fhtl8Ytnk9JXWI5RsXlT0KIqK5Be26PP50NLyETSaxDsACLGlZWQ8hqIiKofFTuM4Nz7//AAeeoh9ABL3gMhj38VzQKRfh5gDkMUiL1DfvgaAqC12Uy44gOMcx72Znp6+78qVK8uX2pVeb2s24sYFIO9ix44X10yvUnQc9IB0QqdjG4AcOfKDQNG0rCzpE7Xvd516e7ugUFxDaWkQPDkc8+judkKvf0W2N7zkxbBa2+FwtN0WlrWSwet0ujA4yKGo6IGAt0AujQzx4eE+DA21ICtrFvn5y4fD0XzHxuyYnk5HSUkzCgvLYg48FuuQcnXa2k6grMwd8BqE2nLr4fX6YLHMQ6N5KmbJ5uGsPwHd8fFPEWSXD54Lt9uLwcFsNDbSvR9bTan04sKFA3j4YXaL1wY9IJ8iJ6ecqXDXpTuF5Ni/n/0Q6jgAkf4bICsAslh8rVZbz3Hc9wE8tPDvoxzH/ZPP5/u7zs7OFbggpVdgtEeMA5Boa1S6/oIeEDPq6rZJN2iUR6KP8KFDP0Bj4zPIyLhF3xnlYdasO0pIb2vbi6amYKXzmZk59PURvaj8jaxgWNYlqFSDKClJR0ZGMKmejEVqPh+FWzmRlKRBTU0zlMrbiy+umVJX0fHwcD8GB2+gpGQehYVBimSSIwieklBSsknWt+7kNWhrO4ecnFFkZQVZv5YCEPKs9fUloL7+KSQn35tFaxVqjOorbW0XoFbPBNjigsB8FvX1L8c8UX41QiYk+HDu3AFs3073lWw2+taeP38aubkVcQAigyWMAxDpF0FWAKS5uTnN5XKRT/XrAIj4nUqydoiiSLkhDwOgGB2zUqn8Unt7u016da39iBsXgLyDHTteWnsFr+EIQQAioK7uwTUcZW27DgKQH6Gp6Rmkp9+dFWhtZ7L63g2Gk2hsDL5vtY4hLW0nCguLV9+hxG9OTIyhr+/UTRAVAiCC4IFO9yQz67IUDJIcFsssGhpela03avFSk0fHbL6KhIRuFBSk3wZA7PY5jI3lQK/fKWsguFgeKtrZ03M8UM2+t3ccCsVWlJfXSLy7ozNcYqKI06f34tFH2faArB8Awn4IdRyAROdsRtKLHAAIV1dX96zP5/s5juNeBUCVuuwA3vX7/d/v7Oy8GBKI5/mnAOwDcE4QBLYpk1ZYpY0KQPbtexs7d74cyd6V3bNBAGJCXV3IaSe7Kd5zQvQR/uijHwUK9aWlRVbv4Z6dS/RAf78VaWmUj5CGlpYpNDW9KgtGokjEb2//DBqNAwkJysBtdbBOwxbmDEaD4RT0en8gxMpud6GnJ0XyKvSR6H25Z3t7O2G3X0V9fTB8yWodx/x8NXS6bbIKHQtHTpvtIiornTAax6HTvQqViu742GtJScCpU/vw6KNkMrDZgh6QU8jNrVwHHpCfYMeOF9hciIVZxwGI9MsXUwDC8/x3AXwDQOB6UhTFMxzH7XE6ne/19fXNLacOnuf/BsDvCILABmF5hGu6UQHI3r1vYdcudn9MaJnXCwA5ePAtbN78DLMAhG7ee3qOoKoqBR0dSUyEXy39TBB9qst1JlDFmjXPwWJZKCdEpboaqBnS0zOG5GR5Jmrf6zPd22tBWloLMjNT0dGRisbGx+71iiz/PjTUhry8frS1ObFp0yuynGM4kyIAcvLkPjz2GLu/GUEAchI5OZXMs2Dt389+DmccgIRz8qL7TKwBCNX96KM6IKIo7llcYXwlMbVa7Tc4jvsLQRDqoqsKefS2cQHI/8/em4C3dV3nov8BwHmeZ4AAiQOSIEBLHmI7iSXP8RQPcToknZJ7e2/fS19v+9rX5va+Tkl7e5v09rW3Q3qbNs7gOE1sy7JkzbJkybLlQZQsghMOSGLkCI4gCBIkgfO+BRASxRGkQOBsEvv78iURzzl777X3OVj/Xmv9/8s4cIDdfN4IALFau9HYSNmCbDb6CBMA2bfvMWRmUjCSzWazvYfs7FEEg/egtJRNisve3tPQaBRLkQNyeomXg60W1j05AY0mg9loFFnc6yW2rrPIyaH1aN5xrZWdWuW5uXHMzl5Cfz9Foh7dqW52/Lm7CYAUFqp2QQQkCUB2fNPvwg4SCkDq6+sf6+npObPEdrULzbv1Ke1dAPJDHDhAbMzsNoqAkKBZY+N9zE4iDEBewf79jzGjiryWscfHrRgc/BiNjS8yl34Vmc/ISDdyc+0YGppCZuZnmQVSTucVlJdPgILWev1nmHw3wkxlx5CZGUBGxkEmGeLI8FQ70dt7GF5vBVPCgys3TXo6h3Pn3sT997Obtkvf2kuXzqKoSM08ANkNKdTJCEj8P80JBSCR6fI8f48oig9wHEeckumiKPpkMpkVwPm9Jkq4dwHID3DgALuc7rSXZ2cnYbMRACFtTTZbWFTqFdx5J9sAZGqqH3b7ezAaidGbzUanvBbLYUxMiDAYnmUWSC0uTkMQ3kRp6WMoLi5lczEAWK10VjaLmponmSiiX8vQBQWZeOed76Ow8E7U1NQxuxYZGTKcPUspWKwDkDMoKtLsAgDyCh54gN2UPnoRkgAk/p+DhAIQvV5fuLCw8BqAA0tq6CstQDogxzmO+5LZbKbC9F3f9i4AeQkHDrDrLIYByASs1i40NbEOQH6Cu+56jAlq0fU+CNPTg7Ba32UagNAP4pUrb8LnS0FzM30i2Wx06n758svQ6Z5nhsFrLUvb7ZcQDM5ArX6czYVYcrKOH/8+GhoeQX5+AbPzIABy5swb+Mxn2HV6wxGQ3QFAdkMNZxKAxP9zkFAAwvP8DwD8siiKr3McR0DEKpPJZgOBQKZMJqsPBoM/x3EccbN+VxCE/xx/88S/x70KQA4d+jccPEjsy+y2MADpRFMTmwWqkVOgo0d/gv37H2U6BWtmZhg9PRfQ0sIuqKUfxGvX3sLMTAqamthMXaI9RaJxra2voK7uOSa1ZSJfJJfrGhYXPaitZRcM0p46fvxH0OsfYXotKAXr7bcpAsI2AHn33dOhCEh5OZt1apHfjDfeYL+GMwlA4u97JRqAjHEc96bZbP7qelPX6XQ/FkXxMUEQSuJvnvj3uFcByOuvfxcPPvil+Bs8hj3Ozo4vRUBYByDsF6H7fG4IwnnccQfbAOTq1aOhCEhTk/SFFNd7lThuHibTv6O6+hmmT93d7h5MTQ2ivp7dtQgDkB+iqekR5OaGBSJZbLulCP3dd0+hsFCNiooqFpchNGbaU2+8wX4NZxKAxH8LJhqAeAF8TRAEioSs2bRa7a9xHPcPgiCwqYq2xTXdiwCE8pIPHfoXHDz4ZeZ49Zcv7+zs2FIEhG0HhXUaXloTWovu7rexbx/rAOQIfD4FmprYPXUPBmchCK+itPQpFBaGFepZbG63AI9nGHV1bL/fJ078KKTezjoAOX+eitBZj4CcChWhl5ezDkDYr+FMApD4f5UTCkB0Ot37oiieEQThTzaIgPyRKIpPCoLALrXQFtZ1LwKQ/PwMvPHG/8aBA7/EbKEtLbHPNwqbjWpA2HZQ3nrrx2hpYVcHZHcBkMOhFCy9nj0K3shnb2FhGnb7YeTmPo7SUnaL0ElDw+cbh0bDbjocOVknTvwQDQ0PM8vkRfsqrIROQoTsUrfTWrz77omlFKzqLXgJ0ro0HAH5Pg4c+KK0BrbF0SQByBYNFoPLEwpA6uvrD8hkspdFUXzRYrF8uHI+9fX1+2Uy2evBYPDXeiihew+0vQhAcnPT8Oab/4LPfvbLzLLL0NakugO7XWAegBw79mMYDI8wXTDs9Q6ht/ci8zUgra1vhFKw9PoHmf36kfbE4OBJZGQ8gPJydh2tgQET5uenUFubBCCJ3owpKUFcvPgm7ruPfQBSWFiLigoiAGWzkeN+6ND3cPDgz7M5gaVRJwFI/JcvrgBEp9N9b+UURVHUAbgXQJcoil0cx80AIJVzHoARwCUAVwRB+N34myf+Pe5FAJKTk4rjx/8V9977i0hJSYm/0WPUIzm9TqcFjY1sR0COHXt5CYDkxMgy8X/M1JQLDsf7MBjYTsFqbX0dMzMKNDc/En8jxqhHei+mpy8hENiP6mpNjJ4a/8e4XJ8gEJiGSsX2+33ixA+g0z3MdD2OQhHApUtHcO+9bAOQS5dOIj9ficpKVfw3dIx6DAOQf8XBg78Yoycm5jFJABJ/u8cVgPA8T8rn22miIAjy7dzI2j17EYBkZSlw+vT3cNddP480qi5ktJH2xOBgHxoa2HZQdgNLzuhoH9zuq9DpXmA2rS9Mw/uzJRrexxh9KwASheQ4EyYn66FWNzE7D2LBCgS8zAOQkyd/AK32IRQUFDK7FsSs9t57b+Lee9kVrw3T8J5EXl4NqqpqmV0LmsduIJFJApD4b8G4ApDGxsZtwXxRFLnu7m5b/M0T/x73IgAhTvdz574fKhhOT0+Pv9Fj1OPkpBMjI3bwPOspGuwXqVLB8NRUO1SqZ5iNqlFt1NWrPw3VgBgMT8Rol8b/McPDXSFFd5erDFrtvvgPIEY9Op2tCASIhpfddDhysk6d+h40mkdQVMQuIYBMtoAPPjiKe+55IUarG//H0Fq8995J5ORUMR0Z3C0kMkkAEv93IK4AJP7TY6/HvQhAiNP9woUfwGB4EZmZmewt2tKIJyZscLud4Hm2IyC7IUVjcLADfr+A8vInmAW1lJrY3v4aPB4ZDIanmX0vKHWpsnIMZnMWGhsp25bN5nR+HIqAsA5ATp9+CUrlQaYJAQA/PvqIAMiLbG6mJfra998/iaysSqZV6XcLiUwSgMT/VUoCkPjbfMMe9yIAIUaT999/GQ0NbCslU9rPxEQ/tFq2AchuSNEgpxewoaCAXTYvAuY9PYcxPi7CaHxOYl+q6IfjcrWiqmoKXV2pTAsqOhwfIhicRm0tu/U45GSdPfsSqqo+g7KyyugXUWJXiuIcPv6YAAi7zEu0FpcvEzlDGZRKKnlls+XlpePw4X/BAw98GXI5u5nySQAS//2XBCDxt3kSgKywABUUfvTRj8HzBEDYLXx2uy3weIaY1wk4der7qKt7iGnNBnJ6FQonsrIeYlbxmd4Lp/Mo3O5FGI3snvSS415TM4OuLjnTeiYOx+VQEbpazW49DjlZb7/9Eioq7kN5eY3Efv2iHw5py7S2HsXdd7NNMvHBByeQllYKlaoh+slL7EpisTx69Lv49Ke/xDSLZRKAxH9jJQFI/G2eBCArLEAFha2tr0CjeZZpcayRkW54vW5oNGxHQChHXK1+GMXFxRJ7O6IfjtP5EdLS+pGScoDhYls/hoePY2RkEXr9F5gtprfb34NS6Udnpwi9/uHoF1FiV9rtlxAMeqFWf05iI4t+OORknT//EoqL72G68HlhwYvr14/jrrvYByApKcVMkzNQquixY9/Fffd9idl6O3qDkgAk+u9IrK5MApBYWTJGz9mLKVgcN4+2tp+gpubzTFNDUt3B3NwE1Gq2i9DPnHkJNTUHUFpaFqNdHf/H2O3vIytrBKL4aZSUlMR/ADHocX7eA6/3PIaH/SFwzipDnN3+DpTKIDo6FtHczG70wG5/N1SErtE8FYPVTcwjyMl6552XUFBwJ9N1B6THYjKdwp13sg1APvzwBOTyQmg0zYnZEDHoNcJieffdv4DU1NQYPDExj0gCkPjbPQlA4m/zZARkhQUon7ez86eorHyG4dNqICxUNonaWrYjILshR5ycxby8CczN7WM21YT0M0SxFYODXlRWPsWsMKTNdhYqlQzt7bMwGNh13glILS5Oo67uGYn9akQ/HGIsunjx+8jNbYFKRRJcbDYSt+zqOhtiTmS1kcP70UcnwXF5qKsjyTM2W2amPFRXtH8/2yyWSQAS//2XBCDxt3kSgKywQCDgg8XyKkpKnmaaGnK3CJVRjnh5+b1Mq/PabO+gqGgGk5MNzJ70EqlBVpaAwcFJFBQ8xiw4t1pPorY2FSaTh+liepvtXCgFq7b2aWbT4Yix6P33f4iMDD3TaT8+nxuCcB533ME2ALly5SSCwRzU198hMU8k+uEQjf75899HS8sXkZFBGtJstiQAif+6xRWA8Dw/vt0pCoLArmrSFia9F1OwFhamYbcfRm7u40xTQ4aFykgp+YEtrLi0Lt0tOeJW69soK5vH8LCKWUeLUvpKSgZCACQ19bMoKyuX1maJcjS9vW9Bo8mEyTTOdDG9zXYGwCyqqp5gNtedCoavXPkx5HKe6VN3ig729l5ESwv7ACQQyIJWuz/Kt0l6lxFb38WLP0Rz8xeYptFPApD47614AxASExRXTJOSBiuW/m0s9IUHsgHkL/0b3TMqCMI98TdP/HvciwDE75/EwMAxZGY+yDQ1pMt1NZQjrlIdjP/GiVGPuyVH3Go9jaoqwG4vYVb8jgBtVdUEhoYmEQjcwaRY2eLiIlyu41CpstHePobGxueZZcqxWk9BLp9DaenjzGrLZGen4Pr1nyIYVDP7XtCnzuMZgM12CUYj2wCktfUk/P50NDTcHaMvePwfE6HRb2x8AVlZWfEfQIx6TAKQGBlyC4+JKwBZOa76+vpqmUx2TBTFlzmOe0kQhNHINY2NjRWLi4tf5Tju5wOBwFO9vb3OLcyL2Uv3IgDx+UYxOnoWqamfZjZfnzac03klpBOgUrGtlHzhwkvM54j39R1HbW0a0+J3xORVXe3F2Ng0PJ46JgtVfT4fxsfPoKoqB2bzaEiZntU0DdpTqakLKCx8lNmTXsrX7+h4DX5/NdNO7+SkE07nZRgMbAOQa9dOwedLRWMju+erKSlBfPjhy8zT6CcBSPzd5oQCEJ1O95ooigOCIPzWelPnef4fAFQKgvBC/M0T/x73IgCZmRnG1NRFiOLdTFNDksMYVkp+KP4bJ0Y97oYccVEU0ddHaT9ZTIvfEe2rUjkPj2cWAwMlTDopHs8UfL7zKCvLRW+vG6Wln2OWaptSyTIzF5GT8zCzekWULmOxvAGvt5LJ/RT5zE1M2DA4+DEaG18ExyXUjdn2l5cc3jAAUaCx8d5tPyfRN5Je0ZUrr6Cu7llmNZfIhkkAEv+dlNA3d6km5HcEQfjBelPXarW/xnHc3yRrQFZbaDsvTPy32OY9Ujh9dvYy5ufvYLZgmGZJgmuUgqVWP7r5pCV6RThH/BXI5Vpmc8SXp/10dsqg17OZEme3n4dSKcLr9cPpzGFSRdztdoPj3kNRUS7sdndIGJJFWmQCtVbrUWRlBZGefhB5eZEMYYm+yOsMi9JlrNYj8HhK0dh4H1uDXzZaImgYHb0GrfY5ZtW3wwDk5BIAYXctZLIFXLv2E6jVbOt4bcefKi3NTagPzewLvDTwhBqP53kvgP8lCMIfrmdInU7356Io/rYgCFQXsuvbXoyATE46sLDQCp+viWlqSIfjAywukk4Au1oHuyFHfHZ2FqOjp1Bdncu0+B3VsdTWKkIAxOFIh17PXmpff78NOTntyMnJxODgBETxLlRVqZj7jodB7YlQBESheACFhUXMzYEGTKfVTudRjI8XQ69nV6/I7RYwOWlCbe3nmSUECAOQ4/D5UtDYeD+T+4kGvVt0vJIAJP5bMKEARKfTnRJJKQz4A7lcfqirq2swYoK6urpSmUz2Asdx3wLwkSAIj8TfPPHvcS8CkPFxKzjOhMnJemYZi8IRkPdDKVhqNbsAJJIjPj9fA53urvi/ADHocXqaBPzOobw8l2nxuwh7FAEQu13OpIif1dqB8nIn0tNTMTo6Da9Xy+Q77vf7MTR0EllZIoLB+5gV6aTTaiL8GBsrgl7Prl7R8HAXvN4u1NQ8zaz4XRiAHMPMTAqamsgNYrX50d7+76iqYlvHKwlA4r//EgpAeJ5vAHAeQOnS1BeWWLDSARA7Fo1vjOO4h81mc1v8zRP/HvciAHG7e5CW1g23W8ls2g/tFMrZJ50Atfpz8d84MeqRON0F4RDTOeLj42NYXLyIkpI8ZsXvFhYW0N9/IsQeRQDEZgvAYHg6Rqscv8d0d3+E+vqpUJrM1NQshoZKmSx+jhTTZ2Vx8Pv3o7y8On5GjGlPfgwPnwgBkKYmdgEIUVT7/RaUl3+OWUayMAB5awmAsBuNCgZnYTb/DGVlTzMbGaRXLAlAYvqhiephCQUgNML6+vpcmUz2HzmOOyiKYi2ATABzdKAM4CKAf13OjhXVrBi+aC8CkJGRbmRlWTEwUM40NSSpb5MOiEbzJLM7MC0N6Ot7E1NTJWhqYjMtYGjIibS0a8jPz4bJNAWj8Xnm1mNmhkQUz6KyMicEQPr6fGhp+QJz8+jouISmpvnQuMOpZDlMpv54vV54PG8jO1uO6elmZskyRHEObvcJjIzko7mZvZS+yAswMNCGQKAPxcWPM8uqRg7v1atHQylYLIPBxcUZ9Pa+hqKip1BcXMzcNyoy4CQAif/SJRyAxH/Kt/ao1+uzFxYW/hIAeSklAGwcx33HbDb/7WZj0+l0L1J9CoBGABkcx3WJovgdQRD+dbN71/v7XgQgdJpVWOiCzVbEbNpPOAJyIZSCpdE8td3lT/h9kRzxiQkCIGymBdjtZhQV9SIrKx0m0xgMBvaYcpZHccIREC+aml5gruC2vf089Pqw9FM4lSydScc3wuaVnZ2K8fF6KJV8wt/V7QwgECBa5FMhAMJiTVFkzi7XJ+A4B/LzH2ZWeyIMQI4sARB2xWtJSNhmO4y8vMeYTU1MRkC28zW5/XskAUAqKyszMzMz75bJZEq5XH42Ugty5513prS2tlJa1o61pTqUu0RR/D1RFLs4jnuE47g/5Tjuv5nN5r9ar2OtVvsVjuP+DcD3APxk6bpfAPAfOI77v6MBMGs9ey8CEDrNKisbgSDkMk0Nabe/g4UFD+rrP79j+3WnHxzJEaci1aYmNtMCLJZrUKncSElRoLNzDDz/HHOFqv39dmRntyE3NyvkuA8MTKO6+inmnC2T6TSamxU3AIjNJofBwF6N1MTEOObnLyI3Nx1DQ0om61hoEebnPZiePoehoVzo9ezShZNIp0zmQG4uUSKzyU9DAKS19XAIgLDK1Ed7ioSE+/uPhRjuysoimtI7/UsV++cnIyCxt+lmT0w4ANFqtb/FcdxfLKVeQRTFBy0WC6VeyXie712KRlAhesxbQ0PDwWAweA7AV5ZTAfM8/10Av5Cenl7e1tY2s1bHPM9fA5AjCEL98r/zPG8BMC4Iwqe2M+C9CEDoNKuqahzd3ZlM86HbbOeXakCeZpabHqAc8eNLOeJsnsp1dX0AnW4mtAakbcqi497X146KCleoeJsAyOjoFPLzH0NBQeF2PisJu6fWkbbeAAAgAElEQVSt7RgMhowbAMRqDcJoZC9CODY2hmCQAEgmXK4KZmvVyFmcmbmAwcFs6PUPJ2xf3G7HLtdVKBSOkNObk5N7u49LyP3hCMjhUA0IywBkbm4cg4MnkZHxAMO1UckakES8BAkFIDqd7udEUfx3AK2iKL7Jcdw3RFE8SABEo9HkpaSk/EwURYpI/KrZbH451gbSarV/y3Hc19LT0/OXAw2tVvsox3EnRVH8osViObQOALkMIFcQBP0KAELAZFEQhLu3M969CECczlZUV0+hqyuN2bQfWmub7W0EgzNQqZ5iLlUmslepoHBs7CRGRgqY/VHs7HwXjY3hwKnVOoKCAvYc9+UgigCIxzMDmeweVFQot/NZSdg9bW1vwmDIWQZA5mE0Ppuw8Wy345GRYcjll0MRKUoV1Wr3b/dRCb3P5xvF/Pz7cLky0NzMrl6R03kFKSkuZGSwq8kSjoC8sRQBYbceh/bU6OhZpKTcz9z3afnLmIyAxP/TlFAAwvP8JQCiIAgP1NbW5qWmpo5HAMiSKTitVnuO47gMQRBiLhXK8/zbAGoFQahbASKqADgBfEMQhD9da1m0Wu0vchz3I1EUv56RkfGd8fHxYFZW1q+Jovj3oij+isVieWU7y7k3AcgVVFd70NmZymSBamSdbbYzIRK3qqonmEv5icyB8nmp2HZwMBfNzWymaHR0nEVTkyw0pf7+sdAPY1kZvdLstM7Oi2hsXLzhuPv9pIheB42mmZ1JAGhrew0GQzhqQ0DKap2B0fgiU3OgwQ4NuZCWdjUUAWE5VXRmZgSBwEdwOFKZpHWObByn8yOkpg4gLe0A8vMLmNtPNOAwADm0BEDY/NbSPGZmhjE5eQHAPcySM0TWg/57fHzNpJc191hSiPD2Xr1EA5ApjuO+bjabv0MRD4VCMbECgIDn+V/nOO5vzWZz1u1NdfXdWq3WxHGcb2W6lF6vT11YWCAmLioo/9p6/Wq12i9wHEc1IOEjPoB27m9upOy+2Rz2JgD5GNXV0+jqYpsNhITjZLJZVFQ8wSw3vd8/AZ/vXQwMZDGbomEynUBzc1roVXO7PfD5GlBbS4zf7LSVtROkxN3fX4TGxm1ldiZk4kQlbDYfhl4fFu0LF6F7odOxWZOTk2MKCSqynCrq9Q5BFFthsylgMDyekH0Ri04JgKSl9TMtChkGIK8vARB20+Gmpwfh9b6HQGA/qqs1sVjehDwjGQGJv9kTDUB8AP6zIAg/Wg+ALBV7//1WlNAPHjyoGBwcvCWqsdK0HMeNBoPBDwEMCoKwkhCd43k+wHHc981m81fXWhae5+8BcEIUxUscx/2zTCabosiHKIoUBfmyxWJ5fTvLuRcBiMPxIWpqZtDVJUdT04HtmE0S91itJ6FQ+FFS8jiz3PQ+nxsLCx/A6SS2IjZTNK5fPwyjMZwXzqr2xMraCZqLw5EJvZ6d94OohF2uY+D5MDVnpJiexOMyM4ltnZ3mdPagoMCMrKwMpiO15Cxy3FXYbClMR0Acjg+QkUFz+Syz1K/k8F658hpmZ6kGhF2d5ampfszNfQi/3wClUsvOS71ipEkAEv+lSzQAuQrAJAjCr64DQOQ8z7/DcVym2Wy+M1rzNDY2qgKBgJXSu9a7RxRFKnx/nuO4WUEQCEzcaLW1tempqakEjv5JEITfXAeA0NjT1qgBOQzg3pycnJrtMHjtTQByGTU1s+js5JimhuzrO4HU1HkUFj7KnIMV2eORE1K7nU0HhSIFJhOl/dw8dXc6c5hj9Lp+/RCMxvwbjjv9D9YYpCYnJzA2dhoaDbGbhwHIyIgnpN2QlxeeGyvNbu9GcbEVmZlp6OpKZW4/RexMzqJc/kkoAtLczG4ExOF4H1lZlE7Grip9GIC8Cp+P1oI9ZrjInpqcdGBhoRU+XxNUKh0rr/SqcSYBSPyXLqEARKfT/R+iKP4jgL8MBoOvyWSyVo7jng8Gg4JMJrtPFMX/E8A++m+LxfK/Y20enucpab9eEAT18mdHAIwoin9ksVgIqNzSlrRDPKIo/p3FYvmd5X/U6XT/RRTFvwkGg4aenp7OrY55cTGwLmha+SyZLLx8wWDUt2x1OHG5/vr1M6ipofSMVOzbx+6HuLX1VaSnL0KleoZZaki7XYBM1obR0Qzs28ceW9Hc3By6uw9BqQxHQAiQUDTnjjvYieYsLi7i+vVXoVaHMztpDtTs9iD272engHtoaDBE+VpUlHdjHuPjnhB1allZeVy+LbHqpKenHZmZQoiVzOnMRkvLwVg9Oq7PsdstkMmuM/t+R4xFvxnZ2aPIzHwAFRVs1XdF5kC/3x9//DPIZDnYt+9zcd0Hseysr68DotgGUTSgvp6tGrXldtiOP6VQyBPqQ8dyHRPxrIQbj+f5fwBAQIN+ZWk8y71p+v//IAjCb+2EcXQ63bdJSHBxcbG4r69vKtJHfX39MzKZjFi5njSbzSdX9s2HcwpG1oqQaLXa/4fjuP8hk8nu6e7ubt3quPcmADmFmpoF0Kn7vn3snsq1tv4MGRmLqKl5Bjk5kbKgre6AxF5PPyZpad0YHk7F/v3PJHYw2+h9amoKLtdRVFSET9jJeXc4OOzb9/Q2npaYW0h122p9E1VVNx13Gklf3yzuvvvnEjOobfTqcPSG6g2obiKyFl7vLDjuTiiVG2bIbqO3nb1FEKj+owdpaSlwOrPQ0sImaxHrBww3Acgp5OZOICXlPlRXq3Z28Xfo6WEA8lPIZNnYt+/JHepl5x/b02OCQtGJ+fkm8Lxh5zvcoR6SAGSHDLvBYxMOQGhsDQ0NdwaDwS8CoPgdqQpNcxzXSVERi8XyyU6ZZamO44OlCMs/R/rhef6nAB7KycmpXC+NSqfT2UVRpAJ2UkG/0XiePw7ggfn5+WKbzUaF7FtqezEFy2a7AJUqgPb2BaYLI3t7jyIzczHETZ+bG3YeWWtudw9ycnphsSzCYGAvAjI8PID5+fdQXX0zBctqZYv6dXR0FF7v21CpbqYu0T5irYC7r8+E3FwriorCQnGUguX3L8Dj0TDH5tXb24bq6kGkpqags1PGLEX15KQTCoUJNpsMzc3snrrTb0Zh4TSmp5uZZV6ilJ+PP/53zM4SIxm7B2/0m5GebobbrYRGwy4ASaZgxd9bkQQA2Wja9fX1JQqFIqu7u9u2E+bhef5nACjv5/dlMll7IBB4luO43+M47j+ZzWZSOodGo1EqFIo+AN8UBOHP6N94nv8VAC+JovgWgO9yHDfHcdyLoij+uiiKf7xW6lY049+LAIQUxJXKIDo6Fpj9ENNJu9V6FFlZQaapIUdGulFQ4ER39xwMBvYiIDZbNzIzu1FSEk7BClO/etHc/AXIZGFqXqk3l6svlKdfXn5rDcjgoAeVlU8iO5uN6FpX10eorHQjN/emEGEwGMTAQAkaG28pu5P6kqCn5xMolSNISVEwXau2WwAI/WYUFc1gYqIBNTVsRdMim/0mAKF6O3bBoNstICOjB0NDlaivv0Py7/J6A0wCkPgvXUIBCDFNbST2t+To/y6A3xYEoWYnzEOUu/Pz89/kOO4XAdCRIymZf5uYuSL9LdWEEAD5c0EQ/iTy7zqd7llRFH8fgBGAHEA3x3H/GAEu2xnvXgQgNts5qFRAe7sfBsMT2zFbwu8JBAJwOI4hKyuAlJQDzClWRww4ONiO0tIhdHSQXgM79QaR8a/l9Pb3T4fqcjIywo6w1JvFcg1FRS4UFISZxwlEUaP6iczMB1FSUir1KYTG19n5HmpqPMjODlMiR+bhcuWhsfF+JuYQGaTFchW1tWNQKORMA5CpKRfk8rYQoQHLp+52+3kUF89hdFTDbOFzQUEmWlv/PaSEzurvHr0fdGiVnW1Ff385tNp9TL3XywebBCDxX7pEA5AggC8IgvDGWlOvr69Pk8lk3wXwc4IgpMffPPHvcW8CkLNQqWRob59lMu2HdglpHvT3n0BmZgBy+QMoKgqnALHWXK5PUFU1DpNpCkbj86wNHx0dF6BU+m5xet3uKabU0Ds6LkGpnF7luPt8c/D7m5mhuiQxRaLXXglAqIaiqekBpvaW2XwFdXUTkMsJgAB6PZvCcWEWrOvMAxA6tCorm8fQkBJqdRNTeyky2Pz8jCUAooDRyF66a2QeQ0Odoai53V4Mno+arFRya5YEIPFfkrgDEJ7nKYLwx1uc6jVBEO7a4j1MXr4XAUhf32mo1QqYTF4YjZ9nct3m5+cxOHgCWVkiRPHTKCkJ5++z1lyuq6iqmoTJNAGj8QusDR8k4FdbG7jF6Z2e9oHj7kZFhZKJ+bS3EyBfWOW4BwJBDA2VoqHhbibm0dFxDkqlf9U8HI505ui2BaEVGs34EgBhly58t+iA2GxnUV4ewMBAJbN1B3l56fjkk5/B65Uze/BGHyKKmhcXD6K3Nw8NDWylVi7/kCYBSPx/VhIBQKpEUaSai7s5jvsNitSLouhea+ocxwWI/CUYDP5VT09Pb/zNE/8e9yYAOQG1Og1tbR60tDwXf6PHoEeifx0ZOYnsbA4LC59ijmY0YgKn8wqqqz0wmUZhNLLDuBQZf1vbW1Cr5bc4vVT4PDVVi7o6ypSUfiMRQrVatspxp5E7HKRQz0b0oL39dIhcYmUExG6n9B+26LZ3SwoW6zo/kbfXaj2Nqip6H0qZrTvIzaXfvFcxPc02ABkYaENZ2QgEIZe52q4kAEns72HcAcjy6fI8b5XJZL/R3d19KrFmkE7vexGA9PYeg0aTgba2SbS0vCCdxdjCSGZnZ0Oia1lZHObn70RZGZvc9E7nR6iu9qK9fQxNTS+ETnxZaWERwkNQqzOZrjsgEUJ6H1Y67rQOLIkRtrefhEolrgFAOOaKbqkuR6VyM1+ETgAkGLwCh4OYl9gCgcu/Q1YrUbfL0NtbAJ2OzeSInJxUtLe/Bo9HBoOBHZrwlb8HkbTd7u5MNDbey8rPxapxJiMg8V+6hAKQyHRJ2A/AfEdHx3zk39RqdZlcLp/t6enxxN8sietxLwKQvj468c1gNu2HdsvMzAwmJ88iO1sGn6+FmXSflTvd4Yio0o9Cq30OqampiXsZttiz3+9Hb+8R1NRkr+H0pqG5Wfp5+yRC2N39BlSq1XMIA5AgM+kaJtMx1NaujuSwNIfIFryVBYtdGt6ZmWEEAh/vAgBC4DYFZnMOs6fu2dkp6OiIABD2GAcj74bLdQ1VVRPo7s5AY+N9W/xqS+fyJACJ/1okHIDodLo/EkXxv4qieP9yzQ+dTvcfRFH8J1EUv7FdStv4m/P2e9yLAIScRo0mGybTGIxGkoNhr5F4nMfzNrKz5fB49KiuVrM3iZDWBBVAz8NsHmWKOYqMPTU1idHRUygtzV0FQKxWEUaj9MW+pqc9GBo6gfLy1XOgOVqtPhgML4DjEv7p3nR/m0xHUVurWLUWNhtpzLDlcO0WHRCfbxQLC5fhdKajufnRTddQqhdEDq1YPnXPzJSju/sQJifBJOPgagCSzhy73fL9nQQg8X/bE/orptVqv8Jx3L9xHPcBgC+bzWZrxAQ6nc4oiuL/JEFAAF8RBOGH8TdP/HvcawCEdAFsNsrbzw6l/ej17Og1LN8d5DjOzJwDnWqNj/PMMBWt3OF2O7FIBdDT40ZFBTuaEzSPoSEnAoEPkZubtcrp7eubQ0uL9Fm9hocHMT9/Cfn5a0dAXK5paDTPIi0tTG0r5dbW9ibU6tQ1wOACc2QTJKpYWTkQUkJnWYhwbm4cs7OXMDCQCb3+ESlvnw3HdhOAsOv0ZmTIQASgExN0OMJm7SMtUoS4pKsrDU1Nn2Z2TyUBSPyXLqEAhOf5DtLOEARhXbodnU73miiKjYIg6ONvnvj3uNcASJi+9jhUqhx0dIxBp3sOKSkp8Tf8bfZIp+9zcxeQnZ3KNDd9RJPFah1BYeHjyM8vuE3LxO92OqXOy7OFnMSV9RM2mxeNjc9DoVDEb0Db6Mlq7UR2tgXp6asdd3qc2+1BYeFjTKxLW9vhELnEyrWwWv3MOVwkcFlaakVGRhq6ulLQ1PTZbaxu4m+Zn5+C1/sOBgdzmKUSJiv29h6FRpOFrq5UNDV9JvGG3cYI0tM59Pa+ibGxAJOU55Epu1ytqKqaYnotaC5JALKNTXybtyQagMwB+PXlon8r58Pz/C+T0nhSB2T1Sm/nhbnN/RLz2ylvf3j4JGpqctDdPcrM6e5KQ0xMjGN+/iJyc9OZ5qYndhlKm7Hb3cjKeogpOuGurvdRXT0VWpqVTi8rKuJdXZdD+dSUYrVWETpRCoviXaiqUsX8XYz1A9va3oBanb4GAJljzuFyOCwoLBSQlZUBlk96FxamQ6miQ0N5zFEhR/YnRc2tVgIgOUyDQQpi9vURAAky9z4s/1ZEmBNZBoNJABLrr390z0s0AOnnOO5bZrP579YbLs/zXwfwO4IglEU3Jbav2msREJ/Ph/HxM6iqymGy7iCy28bHx7C4SAAkE/39FcxQvq58W/r6wpTI/f1jSEm5nyk2r46Ot0P1K2sBkMlJL1JTP4OysgpJfyDa289DpaJzmdUgiv5tYWER4+M1TFCPtrW9viYjGdWxsKYx099vQ05OO3JyMpkutg0E6Ht7CiMj+cwCECJqcDqPo7Y2m+l0uNRUETbbEYyOUgSETfZH+iY5nR+junqaaTCYBCCJ+VlMNAB5CQBVwj0lCML1lSbQ6XSfE0XxxwCOC4JAkZBd3/YaAIkUb1dU5MBicaOy8ilkZxMpGlvN7XaD494LARC7vQRa7T62JgCAaGz7+t4KpTaMjEwxpbpNxo7oZ6zlvPv98/B46qDRNEt6XUymE6itDQ9xrQgI/bvDQekz0k87aWt7DWr16nocq3UGRuOLkl6HlYMbGnIhLe0q8vKymGZeEsU5uN0n4HYXQK8/yNQaRAZLoq8DAyeW0naJ0vlBJuehUATgcr2FkRGqiWLrfVhu8Ah1e1eXAk1NbGgUrbVhtpNRUlqam1AfmsmNv2zQCTWeTqerFEXxCgCKbliI5EUUxXmO4/IB8ABKAQwHAoFP9fb2Olk3djTj32sAJFK8XVaWi95eN8rKnkBOTm40ppLUNcPDQ0hJoQLoTGa56cNq7iehVGZjYmKGmZN22gjLNUDWct7p7y5XnuSLJK9ffx0aTeaGAIQVLZD1IyCzzJ340vutUHwQIjiwWgvB83dK6vsT/WAo5fU4xsaKmHUWSfTV7T6F6mqqGwyiuZnNYnq5fBH9/REAwib7I+27mwBEjqamA9FvRYldmQQg8V+QhAIQmi7P88UA/gQAxSAj+REigEFRFI8oFIpvdnV1DcbfNInpca8BkMnJCfj9F0LUqVarm5kC29UnpE6kp18LOShmczYaGz+VmA10G71SNGpq6iwqK3MxPT2H/v4CZnjdySmxWo+EnJL1ogd2O4mvPXwbFtrZW0nMklIyqqvDAHy9CEhfnx8tLdJnzdlNNSBjY2MIBC6EIiAuVznq6lp2djPs0NNlsgUMDh7D2Fgxs8Xby9N2OzoCzNIJR9ZieHgeBsMXmaDWXmtbOhwfoqZmhul0OJpXEoDs0Edng8cmHIAsH5vBYCiYn5/PyM7Odre2ti7E3xyJ73GvAZDwD/tFlJTkwWYbQV7eoygsLEr8QmxxBLshR5wK6RcWLoTWwuv1w+nMYuaUlMY+MXE6NPb1nHerlYo9n9riysbv8pGRYczOvoPCwo0BiMMxzYRI5PoAhD0WrMhBSU5OGkZG1KitbYjfxohhT+G0n6MYHy+RfDRwvWkvT9vt6FhkWNGdolEnQACkuflFyGSyGK50/B7lcHyAmhofOjs5ZuuKkgAkfvtleU9SAiCyurq6Kr/fP+pyuWYTY47E97rXAAg5XXL55ZDT5XSOIj39AEpL2eMbCLPkWJCVlc4sHeHwcD9SU1tDGhQEQByOFGa0ApzOXigU15GdnbEuAOnro9qDL0j2pHE5jfBGEZDR0Snk5j6KoiJpA/X1dUDmmRNeW67zMzHRgJqausT/WGxjBJHC56mpUmaimyun6fFMhTSXysvz0NGxgObmx7dhicTfEqnHGR5eRFPTC5DL5Ykf1DZG4HBcRk3NbBKAbMN2e/2WhAMQrVb7KZlM9lekhA5ALorigxaL5SItDM/zPwHwj4IgXNorC7XXAEikuJOcXhaZlyL7kvQbysrsTOsE2O3dKCrqC4EoAiA2G2AwPMHEq9fd/RHKy92Qy8OniGulL5GIn1r9eaSnp0tyTh0dF6FUztwY23opWLOzfvh8DZI/hW9rOwK1erUmi9XKnhDhzMwMJibOIidHBp+vBRUVSknuoc0GRdSvvb2HMT1djsbGeze7XJJ/X562azJRPZF0o5obGTDCSEYAhFX9K5qfw/E+amrm0NkJprVlkilY8X/dEwpA6uvr98tkMgIXi6Iovs9x3KMRANLQ0FAUDAbbAeQHg8EHe3p6SC1917e9BkCWpy4NDk5AFO9EVdUSDRBDq02n19XVg0hNTUFHB5vMLF1dH4HnPaFUAAIgrNQa0DZZTl+7HgAZHfUgJ+dhFBdT2Zn0msl0ErW1VP4WbusBECqodzpzJc+EZTK9hdpa+RqikEEYDGw5jVRjNDJyCkTQt7DwKZSVlUtvA0UxIlLfNpsPYWamEo2N90Rxh/QuiVCeU7ple/ssc3spYtGFhXDNHbFg1dc/i9TUVOkZO4oR2e3vQan0o7NThF4v3Rq7zaaSBCCbWSj2f08oAOF5/g0Ad3Ac92m/3+9LTU0dF0Xx4LIICHkKl5fU0p+J/fSl98S9BkAcDgGFhT2hU/fh4SnMzzdDqdRKb2E2GZEgtEKtHodCIUdHB9Dc/BBzcyAnXq8PO8AEQOx2LzMnc8vpa9dz3n2+OczNNUGl0klrbfx+iHOUwnAUKlWYAWsjAEJ/czgo3/oRiKlpAB1rS7C1t5+ESiWuAiB2OwH0z0lwxOsPaWFhAf39J5CZGYBc/oDk09/Wm0lWlgLt7a9iYUHJLJMXUZ4Dl1BcTABkBgYDm65BRJV+eNgv6cjsZi+q3X4ppL/U2RlkJmV3rTklAchmKx37vycagLhFUfyWxWL5tkajyVMoFBPLAQhNl+f53wXw3wRBKIz99KX3xL0GQJanLo2OTsPr1UKtbpLewmwyIooe6HTTofoCVplZyIlvbg47swRAhoamQrTIubnhwm4pt+vXD0GjCdd/rOe8k4LywECx5BjKMr/135H11/9jW+ad+b2vw/f7f7ite3f6pvb2M1CpFtcAIArmmIso6kTq21lZQaSlHUB+fsFOm29Hnp+dnYJPPvkpAA0TYpZrGSFCeV5QkAOTyQuj8fM7Yqudfujc3DhmZy9heHgOKtUzyMi4+f3a6b5j+Xy7/V0olQtgmxAgyYIVyz0R7bMSDUD8oih+xWKxvLIeANHpdF8WRfHfBEGQZuJ2tJaO8rq9BkB6e6+junoolLo0Pj6DyUkVkyriXV3vo6EhrGDN6oeYnHijkSR4wgDE45mBTHaP5PPd/X4/enuPoKbmpoDleulLdnua5KJTuxWAdHSch1I5twqAOBzpTLLl9Pa+hcxMAlQPMalVRO91bm4arlx5BQoFD43GEOWvkrQuW143aDJ5YDRKn5J6LQv6fKNYWLiMoaFZ1NQ8jczMm9FPaVl849HY7VS7tsjs715kdskISPx3XaIBSA+AVwVB+K8bREB+AOB+QRDYy8vZxnruNQBiNl+BRjMRSl2anPTB7a5gMjWgs/MSGhvnlwAI0Sqyl2IiCIfR1BRmViIAMj+/iImJGsmflK6k4F0vAkL/LkUq3t0KQDo730VNjXcVAHE6c5jUoOjrO47U1AUUFj7KrLOYn5+B99//ITIy9ExGmukd7u+3Izu7LaS5ZDJNwWh8fhu/tIm/xesdQjB4BYODM6iqegrZVGDEYLPb34FSGUwCEAbXLtFDTigA0el03xZF8T+JovhiIBD4iFKwABwQBOFdnuc1AH4bwNcAfItASqKNFY/+9xoA6er6EDqdN5S6FBa/oxQZ9oojOzreQVNTMLRFWCyMnJqahNt9EnV1pTcACP0PhyMbev1n47H1t92Hy2WFTHYVOTmb109YrRJU4fb70XH1TahUt9JwrhfFiRjK6UxH4x2PSLYGpKvrMqqrJ1cBEJcrn0kKWKv1FOTyOZSVfQ5pEq272ewlKijIxLvvfh85OUaoVGxqmbhcfcjL6wpRbptMEyFqbRbb9PQgOO4qBge9qKh4EtnZYRFV1prdTpFOkWlKZLJ5MgIS/52XUACi1+uzFxYW3gGwD8AQAKIWcQKgZEgqQKfxtaakpDzY0dHhjb954t/j3gMgN1OX6NTd5cpDYyMxMrPVOjrOoWmpdIXFvGS73Yz09E6UlobrPWgtqNlsHAwGaUdzurs/RlnZcCiKFmnrOe9ExavRPCspB3J+fh4Wy5tQKm89Ad0MgPT1zaOl5VnJvihEzFBSMoj8/DAwpD21uBjA6GgltNr9kh33egOz2c4AmEV19ZNQKBTMjT/iZL3zzkvIz9/HJNkHzcHp7EFBgRlZWQRAxmE0vsjkWkxOOqFQmDA46GGm1m4tQ9ts50NkE+3tfmZo29eaRxKAxP81SigAoenW19enyWSy3wRAXxE6kqFf4WkAnRzHvaZQKP6po6MjnNuyB9peAyDLU5fC6ttspmdQwa1eH3aAWUwL6Oi4hJoaoqkNl1pFAIjVOoPm5hckrdLb0XEBSqXvlq/Des672z2FgoLHUFAgHU4Lt3sEMzPnUVQUVkDfDERF/u5weKHVSpe+02brRlaWGcXF4ZNd2lNzc/OYmdFJXsNkbUfrHIJBL9TqZyQrZrnZTyQ5WefPv4SiortQXU1JBuy1sOirwDwAmZiwIy2tMwRAiosfR15euP6OtWaznYNKRZH/ORgMT7I2/BvjTQKQ+C9dwgFI/Kcs7R73HgC5gMbGwA0HxenMRFPTAWkv0hqja28/Bb0+JTAC5CcAACAASURBVPQXFk/lIjS2Kx334WEPSks/J2kmLJPpFGprw+lvmznv09M+BIP7UV2tlswe6+m5joICB1JTbz1V3ywCInVdE8rV57grqKgIM0YRACH7i+JdqKpSScb+0Q6Ect0XF72oq3s62lskdx05WefOvYSSkk8xuQZk0N0CQEZHSfRVwODgpOQORbaycW22s1CpZEymHi+fZxKAbGXVY3NtEoDExo4xe8peAyDElNPUdFN7glWGHJPpGJqbwzSK7e1jaGp6AXL5rTn9MdskMX4Q0dO2tx+CWp21Kl9/ZmZO8tos168fhkZzqxbGes57IBDE8HAZdLq7YmzF7T+O6oeUytlVD9gMgPj98/B46qDRNG+/8x28k/QafL5zUCpLbgCQsTEPsrIeQklJ+N9YakQ3GghQCh/bp7xvv/0SysvvlTy73Xp7Y7cAELe7B7m5vRgYmEBe3qMoLAwTgLDW+vpOQ61WwGSagdHIpiYL2TwJQOK/8+IKQHie79vmFEVBEOq2eS9Tt+09AHIWTU2yGw6Kw5HCpJhRW9ubMBjCqSbd3aOSqzPY6CXweKYwPHwC5eV5qwCI1FW3w+DpdajV0ddPOJ3ZaGqSTmF9W9sxqNXhd2B52wyA0LUORxb0+gck+Y0LExucQl3dTQDCQkRtPWOS4nMwOA21Wto1URttBnKyCIBUVn4aZWVVktw3mw0qDEAsIfFaFqPNkfmNjHQjP98RAiDZ2Q+juJjKXtlrVutp1NYSAGFXkyUJQBKz7+INQGwAwsfdW2yCIEgnZ2KLY9/K5XsNgLS3n4ZeH049Catvy9Hc/NhWTCaJa9vaDsFgCOfwCsIoqqufQlZWliTGttkgqACdcpHpB30tp1fKheg+nw8Ox1uoqrqVQWYj511KIHc9AEVrFg0Asdkg2cJPWhun8y3wfNixove7v98DpfIZJmlsHY7LoQiIWs3e9ynyDSAAcvbsS6iqegBlZcT5wl5bHgFpaxtDS8sX2ZsEse4MdaK4uD8EQNLTD6C0tIzJeVitJ1FbmwqTaRpGo3RJMTYzbjICspmFYv/3uAKQ2A9/9z1xrwGQlerbdjvHnIYGRQlMpldhMIQdrb6+URQXP8ZMUWFHB+k1hFXc13J6rVYvmpu/IMlC9LU0QDZz3m02UTLFkhR9Ghk5ibKyWwvQN5tD5Msn5bVZWFiA2XwYev1NbRm73Qud7jmkpITrpVhqDseHoQhIbe0jLA37lrGSk3XmzEuoqWHX4XU6e5Gf3808De/gYDtKS4dCACQ1lSJSlUzuq76+E1Cr05gkX1lu8CQAif/2SwKQ+Nt8wx73HgC5WTtBJ6Q2WxAGw1MSW5WNhxN2tN6AXh8GIHa7m6k89+UpQGsBkJERT0h8TUrMUZEVGRpyIhD4MCRKtrxtFD2wWv2SUU9eS8MkMo9oIiADAx5Ji5hdv/4qjMabAKSvz8vsibXT+XEoAlJb+xBT36eVTtapU9+DWs1uys9uESLs77+OiopRDA5OQC7/FMrLa5jcV319lEJKlMiTMBpfYHIONOgkAIn/0iUcgCwpoP+OKIoHOI5TBoPBX+rp6blMptBqtb8hiuLrPT097vibJjE97jUA0tZ2BAZDOH+fAIjVugCj8fOJMf42e52ZmYHLdexGqgn9oHDc3UwUeS4uLqK7+zBUqrADv5bTK+ViZ6u1Ezk5FqSlpW4BgEyjuflFSUR0urs/Qnm5G3L59mpAJie9kj49JYIAozEc3aH3u6/Pj5aW57b5piX2NqfzyhIAeTCxA7mN3snJOnXqJWg0D6OoiM2i5+HhfqSmtiI/Pxsmk0cyhwlbXRaX6xqqqiZCAIRVZjia800Awq4oZBKAbHX3xub6hAIQtVpdlpKS8j4Aqu8g7Y9sURQftFgsFxsaGoqCweAw1VkC+KwgCP2xmbK0n7L3AMgbMBhuit9ZrXMwGp+X9iKtGN3k5ATGx09BrQ6riI+OTmNmhkdtbaPk5zE66sb09DkUF4edxPVO3W22dBgM0nO8uro+QlXV6Cpdho2iB/3901Aqn5ZEHUJ7O4l4za25T6KJgMzPL2Jiogb19XdIcq+ZTMfR3HxTW0ZK6W9bNRg5jBQBUamkWfQfzXzCAOQHqKt7kFnWpZGRYcjll1FYmIv29hkYDGwyL0UAyNDQJAKBfZKiBo9mL0Wu6e09Co0mCybTGIxGNutxkgBkKyseu2sTCkB0Ot13RFH8pWAw+KW0tLT3FhYWRkVRPEgAhKbI8zxR1RwF8KogCL8eu2lL90l7D4C8BoMhLAoXjoAQlR9byrbDwwOYn38P1dXhE8WpqVkMDZWioeFu6W60pZFZLNdQWOi6oUGxntPb17eAlhbpRabWc+A3ct6JCjYz86Akij7b2o5DrV77MxwNAKFldDiyoddLh9Vr+aY3mU6jufkmyYTNJofBwGYRt8v1yRIAkaato/nYEAA5efIH0GofkmRKZTRzGBsbQyBwASUl+UxrT7hcV1FVNYnh4SksLBhRU8Me0SfVP1qtR0MshCbTKAyGLzIt0kn7b3x8JpptGLqmtDQ3oT501AOV6IUJNZ5Op7OLovgTQRC+vpSKNbEcgCyBkL8A8GuCILDJGbjFhd9LACTCAGQw3MwRp6Jag+FFpj5ixMqSlkYFhTcjOawoure3k4qt/8YuXc/plarqNqXwqdWrC5o3ct4XFhYxNlYNrXbfFt/O2F5OP95tba+HTg/XatECEJtNBoPh8dgOLkZPI4Co19/U+bHb09HcLL1IWjTTHRgwYWFhCirVZ6K5XJLX7AYAQsQT8/MXQt9bltW3Xa5WVFURCcUU/P5mKJVaSe6ZjQZFv+E221shAEL6V3q9NMlKojFssgYkGivF9pqEAhCe5/0cx33VbDb/eD0AotVqv8Rx3EuCINyqNBZbO0jmaXsJgKws3g7T8LLHkkNRhKIiFwoKwo4kzYMVQcWVGhTrOb1u9xTy86UlluX3+9HbewQ1NbdqgNAabOa82+1paG5ObDHx7OwsbLYjqK5ezYAVzRwiHy0p11V0dl5CY+P8jfeCFWC+1g8C0abOzo5DrWYbgJw48UPodA8hPz+sUM9am572YGbmXIg5rqNjAc3N0gTfm9mVaoqqqz1wuz2YnW2AStWw2S2S+zvVELpcx6FSZaOjYwwNDc9DoQhHPFlrSQAS/xVLNAAZEkXxry0Wy1+vB0B0Ot03RVH8j4IgVMTfPPHvcS8BkLm5uZADptPd1AlwuYhn//NITw/njbPQurreR3X11C1Or5RPpSM2DdMHk4jfzRP49Rz3sCK6AUplvWSWZGRkCLOzF0K54CvbZgBECmQHVDs0NnbqRuRsq3OIXC/lqCHV6PC8J1Tw7/HMYmCgBI2N90hmD21lIG63AI9nGHV1bKdgnTjxIzQ0PMQMTfjKNSLSj8nJs6iszEFHRwDNzY9uZRklcy2xqlVXTzNVM7jSePPz8xgcPAGlMgednWPgeTYptmleSQAS/1cjoQBEp9P9WBTF+1NSUu6bnZ2dVSgUt6RgabXaFziO+yGANwRB+OX4myf+Pe4lAOL1etHff/wWobKBgWmmRPxoh3R0nIdSOXcLAJGCg7vZ7g2L+B1FVdVNB349x51C7VJzHgWhFcXFA0hJWX3ithkAcTq9qKv7PNLSEhdYpVSSycnTKC4Op+5tF4DYbF40Nb0AuVy+2ZLH/e99fSZUVg4gLS0FY2NeeDxqaDSGuI8jFh2OjvZicnIA9fVsA5Djx3+IpqZHkJu79r6Lha128hkU+RwePomaGnJ6Ab0+sZHM7c6VdGVqamYwNjYNj6cOGk3zdh+VsPvoEHFk5FRoLbq7R6HRPJvQb+rtGCIJQG7Hetu7N6EApK6url4ul38EYBHAeQBUfUxF55SU/ikARIw9CeBuQRD6tjfFze8yGo1Zc3Nz3wHwSwD+VBCEb2x+F3Dw4EHFwMDAH1KNCgCSlbVyHPc3ZrP536K5f61r9hIAiYiw1dWVhExBqUtDQ1MoK3uCqR9Hk4mUYMUVAET6tSzEJjM7+84tEYSNHHep5e+3t78NlSqc3rNV53183IP0dFKDTlxglYppPZ4ztw1AKG1RqqkPy0Xjwmw/d6C6WrPdz2NC7xsft2JsrB9aLdspWMeP/wh6/SPIyVk79S+hRo6i80AgAIfjGGprs9HVJUdT04Eo7pLeJTcBCLvAnNJIx8ZOo6oqB2bzKFSqZ5CRkSE9Y0cxoiQAicJIMb4koQCE5rIEQv4/AJTIufwok0DJ8UAg8Hu9vb09MZ73jcc1NDTcGQwGXwFAbw0Vuv9ZtACE53mKzjwN4A8AdAP4KoBfFUXxqxaL5fvbGfNeAiCRE+Da2jB9LQGQ0VGqNXiMKYYW0jrQaNJuASBE9Sr1jzFpaGRnW5CeflNDY2MBvyCMRmmIRFJExmQiu6/9Y7dZBCQQCGJwsBiNjXTOkZjmdrvh851bM4WMRrTZHCKjJoIAqaY+DA8PQaH4IDRHp3MU6ensKnBPTNjhdjsQJmdks5GTdfz4y2hufgTZ2TlMToJSR/v63gqRN3R1paKpiU1AuByATE3Voq7OyNx6LE+HE4QxVFc/iaystUk1pD65JACJ/wolHIBEprwUhajnOI4qSqf9fr9gs9nWJsiPoZ14nrdxHHdOJpP9TSAQaIs2AtLQ0HB/MBi8FAwGn+3p6aGoTajxPP82gRFBEL62nWHuJQBCJ/BzcxdQU3OzBoROpjMyHkRpaRiUSL3RaVxn56HQadxyh5GKtgsKpA2kurouh4SwOO7mZ2Ajp7evz4eWli9IYknc7mF4ve/c0C9ZOahonPe+vkW0tCROQyCy/wsK1nYEo5kDzVsK6WTrbQqqc/H7ibEoF319bhQVPcZs8fPUlAtDQ1bodGwDkGPHXobBwC4Aob0WEb/r7s5AY+N9kvgmbXUQuwGAeL2UPnYOFRU5sFjcqKx8CtnZq0lBtmqbRFyfBCDxt7pkAMjKqdfX1+dmZmYG2traoidl3ob96uvrnyEAUV9fXyeTySzRApAlDZPHBUGIaT7BXgIgg4MOiOLHqKgIs7FQBMTjmYFc/imUl1P2nfQb1bEMDBxDRUXuLQBketqHYHC/pMWliKGopob0P2+2jSMgXskITVHhf2XlxLpq5tE47wMDHlRUfC5h6X4R/RhSdF6rRTMHuk/KxA10QjoxcSZUZyQIblRXP83sCen09CD6+3vQ0MA2AHnrrZdhND7KrKNIe95qpboDGXp68pnQW1rr/Y4AENKdmJxUMRkBWc5I1tvrDqVPs5ralwQg8fe3Eg5AdDrd74uiWCYIwu/S9EkBPRAIvMZxHMnNBkRR/JHFYiERwuBOmmerAITn+Wscx3WazeYvx3JcewmAkH5Gamo7yspu6mcQ29LCggE1NdJhW9pofZfXUSx3GKWiNbHR2Ds730VNjXdLAEQKGi1h/Ywj0Ghupo6tnGc0zjupiI+PJ04PZK0anGjB4PLrHI5paLXPITV1fXvE8hu1lWfdypIzKtlxRjOnmZlh2O0CmppYByA/htFIERA2T6pprWy2s6ioCMDlqmDScac5OBwfoKbGFxK+m5ioQX39HdFsQ0ldMzU1ibm5d0IRTgIgJSWPM8uulgQg8d9aCQUgWq32NziO+yeO4/7ebDb/F5q+Vqt9leM4yvN4DYAPALFf/Z4gCFQnsmNtGwCEGLv+heM4B4D/C0AtgAFRFP/WYrH8PYCw+tYW214CIFZrB3JyelBUFE5BoQiI3z+P6Wkt1OqmLVouMZfbbN3IzOxGRsatNSDhH5gs6PWEo6XZOjsp/Y1esZtt4yL0GTQ2Pp9wtiVK6xkdPR3SAVivRQNA6F6rFTAan0jIAlF0wOU6FqITXatFO4e+vlm0tLyQkDls1inV6pBSskaTA5NpDM3N7AqV+Xxu0PvOPgB5BS0tjzAbiaI9Z7OdR0nJHEZH1UzqZ4R/H9gHIGFRyIshAGK1jkg+7Xij71USgGz2NY/93xMKQHiev0LvoSAIoV/Purq6Urlc3i+K4mGLxfJF+jedTvfPoijeIwjC/minT+xUg4ODdRtdz3HcaHd391jkmm0AkAVKv15ivvr2EnPXL4uiSIxY3xAE4U+jHe/y6/YSALFYroZoVPPzM0MmIADCQuRg+Xotr6NY6TDabHIYDI9tZxvE5Z4IfXD0AGQaOt3zSElZrTwelwEvddLd/TFKS4fWpN+NjCNa553Sl2prE8PcQs55ezvpsNxeCpbVKsJofDKeS7ClviL5+ibTBIxGadQQbWkCSxf7fKOw2bqYByBHj/4Yd9zxKNMAxG6/iIICD6anm1FVRWd/7LVbU7CUqKtrYW4SxOQXCFxESUkebLYR5OVJS6x2KwZNApCtWCs21yYagEyKovjbEcYonud/BcBLoig+a7FY3qIp8jz/yxzH/bPZbI6aWqGxsVEVCASsG0UhRFH8C4vF8se3AUBmAYwGg8H6np4eog0ONZ7nzwD4DMdxpWaz+dYE+yjWbHExEHXkRCYLL18wGPUtUYwgfpe0t19Gebn7Rh4/pdbQf4aGStDczEZh4bVrZ0IaINQixdw0B2pW6yLuuuv5+Bl0iz198skZ1NTcyvOwcg7LH0nFzo2NLySc5/3KlSNQqzf+dG00j+VzCqf86VFXl5iI25Urb0CtXls5ONo5OBwZ2LfvkS2ufvwuv3qVSBpSYbX6ceed7AKQgQFHCIDcfz+bytu04vSbQQDk4MFnmQYg16+fQXb2KDIzH0BFBZFXstfa2s6junoGc3Pz8HrrwfPssWARk9/ERFjLyO2eRGHh4ygpCdPqs9a2408pFPKE+tCs2XjleBNqPJ7npziO+5rZbH55yXknWtsver3eooGBgVBuCAEQAN8RBGFHE1a3EQGxiaJ43WKxPLvcqDzPUy3LtwDcJwgCaZxsqe0lANLWdhFVVVOrHPf+/nwYjWzkWbe2HkFtbfg1Wg1AvNi//+fXLZTe0sbYgYuvXTsNpfIGdl5zDsu7pWiBTvdCQlXqSfiqq+tQSHl3oxat807PsNvTsH9/YiJV166dhFJJwdTVLZo5UBSFALvBcP8O7JDYPPLatSNQqWSw2QLYv/+Wz2VsOojTUwYHnbBaCYAkZq/EYpphAPIKHnzwWWRmhiPPLDZy3tPTB0IOb3FxmEWRtRYBILOzfszM8OB59gQ6h4YG4fWeD9Fsj41NITf3YZSVkSQaey0JQOK/ZokGIER7+44gCL9FxefBYJDEBt8TBOFGPgHP8/9TFMVnLBYLv5Pm2SoA0Wq1b8pkshKz2XzLL79Op/sDURT/uyiKd1oslk+2Oua9lILV0fEOlMrZVXoHTmcmM+JS16+/Do0m/EO+Mu2HWJaqqqRLS9jRcW4VANkodUkKdK8kbCeXf4KcnI2dp2hTsGjdrFYfmpufTwhQ7Oh4F0rlrUQAkW9GNHPw+ebg9+uhVO7o53Grn7Fbrrfbz0OpFNHRMY/m5s/d1rMSefPs7HgIgDQ1fTqRw7itvinNhCIg+/Y9xjQAcTo/QmrqANLSDjBL60xzqK72YnR0Gl4vO3WPyzcgMfmlpHwMohJ3ucaQmvpplJVV3tYeTdTNyRSs+Fs+oQBkiQHrLzmOOyaKYiMATTAYfKKnp+c0mYLnecpfoajIv0RYsnbKRFsFIDqd7pdEUXwJgEEQBBIhDDWe5y8AaMnJySlpbW1d+2hzg0nsLQDyNpTK+VWOu8ORCr3+4Z1a6pg91+/3o7f3CGpqwsG5lQ5jWNPkIEpLy2LWZywftNUaECkI3rW3n4dKtbk8UDTOe8SWo6Me5OQ8hOLi+KcO9Pa2IS/PhrS01XU10cyBhDtzcx9FUVFRLLdGTJ9F+fpK5SI6OhbR3Mxu9GB2dgIk3sk+AHkF+/Y9yjQAcblaoVA4kZX1ELO0r07nx6iunobb7YHP14Da2oaYvnfxeNjQkBPp6deQl5eNgYFxpij0V9onCUDisWNu7SOhAISKxfv7+7/NcdzPA0gVRfEbFovlfy1z5i2iKM4FAoHP9PX1TcXaPHq9vnxxcTEk7SyKIinf/YUoikdlMtkR+rfFxcULpMKu0WiUCoWCojPfFAThz5bGIeN5/j2O4wju/64oihMAqIaFgMnXLRYLFaZvue0lANLefhoqVWCV4263y5lwVIgDfXj4xA02ppUOI9UXzM83Q6nUbnkfxOOGSARqeV8bOb0223SoBkShWLtmYafHHKbfJfXz9E27isZ5jzxkcTGAoaFSNDbes+lzY33BRlS80cxByhS8EVvZ7e+FIm2dnSITBwvrrXEYgFAERLrpbpvtz3AEZDcAkGvgODvy8tilE3Y6r6C62oORkSnMzTVBpdJttnyS+3t/vx05OaZQRHpwcAKieBeqqlSSG2c0A0oCkGisFNtrEgpANpuKVqu9w2KxmEgPZLNrt/P3+vr6AzKZ7PwGxepfEQThh0tF7QRA/lwQhD+J9EViiTKZ7C8BEItXPgCB47i/M5vN39vOeOievQRATKbjofqJ1exRIgwG6bL6RNbW7R6BzxfOf6W2ch5SZ/Tq7LyImppbdT43BiBe6PWJo1ElysexsTMb0u9G1iYa5335O9rXF0RLS+gsIq6NalpstiOoqlpd0xLNHPr6/GhpeS6uY95qZxG60c5ODnr9g1u9XTLX7yYAQjVPGRkZkrHtVgfS338dwaAVxcWPMzsPl+sqqqomMTw8xZT21fK1crn6kJfXhezsDAwNTSIQuAPV1THVZt7q1tj29UkAsm3TbftGSQOQbc+K4Rv3EgAhMTm1OmWV4261LsBo/LzkV5HqERSK66GP71oAhP7N5cpDY6M0T0y3LkQ4DYPhizeK7eO9QF1dH6KsbGRD+t3tAhCXawo1NU8nRJzt+vVD0GhWO4PRABCpUz3TekRy3bu6FGhqkq4uzmb7eXZ2EjZbp2Tf583GT3+PREBYByBDQ53w+cyoqnoy4ax80dh9rWtcrmuoqppYihzcySSdsNPZg4ICM7KyMpaAlBE1NRsqIGzXXDt+XxKA7LiJV3WQBCDxt/mGPe4tAEIaCJlrAJBZGI3SFFZbvng9PZ+goMCJ1NRwStJaDiNRpOr1ByW2y8LD6ei4AKUyeiFCq3Uazc0vJqRYO5x+9SY0mrSobBmN8778QaSKPjpaAZ3urqieH8uLTKZTqK0NrnrkZnMgmxBjnFQBbmRClK9PbHddXaloavpMLE0X12fNzU2FakAaG9mgCF/LOGEA8hPs3/8os5EDmpfb3YPJyetQq59NWEro7W4+l+sTVFWNh2onZLJ7UFGhvN1Hxv1+h0NAYWEPsrLSQ6lkUifE2MhASQAS9+2DJACJv82TAIRy6gIBdHaSPkD2GilYXjQ1vZBwxe3NtsZyEcL1AIiU61lMptOorb01u3Ejp7e/fxpK5dMJKV51u4fh9b6D4uL11c+Xr9dmzvtaa9vXNx+KvEXobzdb/1j9vaPjEpTK1ZJBm82BBQYsspHTeRXV1ZPo7s5g2nnfLQDkyJFXcOedbKdgjY72we2+GqIFl8lksXoV4/qcgQETysqGMTAwwSx7lM3WjZISKzIz00LF9LOzDcwq0ycBSFy3f6izJACJv82TAATA7Ows7Pajodz3lY4WObq1tZ9PqN5ENNtiZQ3FWg6jzSbdepa2tmNQq2/98d7I6SXGpfz8x1BQUBiNeWJ6TXv7O6F6lWidjc2c97UGNzzsQUHBwygqiq+uAP2IZ2Z2IyPj1ujOZnOg8RYVPZqQ9djK4g4MtKG0dBgWC6Ujxr/Qfytj3ejaMADpQmPjvbF6ZNyfQ07WkSM/wV13PSb57+tGxpmYsGNg4EM0NSUuJfR2F294uAuFha5QBCQj40GUlhIPDluNIoLl5Q6kp6cyTSdMVk8CkPjvvSQAib/NkwAEwHIGqZWO1tCQB+XlT0ieXnEli9TaACQAg+Fpie2y8HDWqj3YyOmdnvYlhOVkcXERnZ2HoVZnRW3HzZz3tR5Eon5Us6PXxzdNyOOZwsjIyVXF9ZvNwWqdQXOz9E+AKV8/L88Bl6sUWu3+qNdQaheGAUg3Ghs/JbWhRT0ecrLefPMV3H3340wDkMlJJ5zOyzAYfi7quUvtQrdbQG6uFYODk8jJeUTSVNrr2a6vz4SKiv4QABkbm4bHo4FGw56gYhKAJObtSCgA0Wq1D8jl8o7u7u6x9abP8/w9HMepzGbzq4kxUXx73Ss1IGNjY/B4zqC4OG9VBIQFbQPaFSt1NNYGIIswGJ6J7yaKoreFhQWYzYehUoU1TCJtI6fX71/A1FQt6uqMUfQQu0uiFR+Mdh4bjayvj0QJn4tr+h/VcphMh0L1UFuZg80mg8HweOwMvUNPcrstyMgQMDxcE/e9E8spEQCx2cxoaGA3irNbAMj09CCs1ndhNLILQCiNLCtLCAGQgoLERJZv9/0gHaPq6kGkpqZgbMybkN+H251D5P5kBCRWloz+OQkFIDzPB0RR/KLFYjm03pB1Ot1vi6L4/wqCEN+8iOhtGNMr9woAGRx0IBj8CLm5WasAiMczw4SgUUdHWEhxI+fdaqW6gmdjukdi8TCvdxoDA8dRUXFrTcVGACRRRc8m0xnU1i5uadqbRQ/We9j4+DTS00nNt2pL/d3uxVutx6FozcBAMROn8ePjVnCcCZOT9VCrm27XVAm7f7cAkN2QguX1DqG39yJaWtgFIJRGlpraicFBD0pKHsf/z957h7l1nneivwNM74XTC4DhDIApACVThRSrrF4pyZK9drzOTZ6b3OQma6ds4mzWa6dukk3uPnGc7LOxvXFJHBdJ7GIRKYliU6MoTcHM4GA4AKb3immYGZz7fABBDmdQzjk4FTznefyHxe97v7d8Z/D+ztvy80knf3U9pBFLTc1oEIBMTS1geroG9fX3qEuIm9xqAER6s0kOQMhQP71ebySiUhR1gaKobwUCATI9fMuj1+szA4HAN8lkcZqmRv7rQQAAIABJREFU7/xUK72uJDnxbgEgXm83MjK6kJWVsQWAkOJaNQxm6ug4B4PhtmMcyel1u5dht78oyd3hcggZIMUwHwYn2G584jnubrcedrt006xJpKa7+2iwWQGXJ54c0WgRkNXfn4PmZmnbxXZ3f4jy8nHo9bdrcmLJMDPjQ2rqLpSX13BRiyxrSbrM6uo1LC01o7bWLAsPQhyaTACEpGClp7PrKCeE7oSmsbAwBpfrAu65R70AZHZ2EHr9pxge9qki5TiSDV2uT2AwjAdbo09PL2BqSgMgQt/1ZKYnOQBpaGj4rxRF/XmM4X+b9U14PErTtPL7sgpwU+4WAEL+cBUXDwT/cKltgF/YzOFJ7uH/HxmAKLOlcEfHO6itXdrS8Sme4+71LsBiOYTU1FQBbnt8EgMD5Ov5x8FI2ZZnZRWUfzUikXhykE1MWiqQvlWO3t4l2GwvsC54jy9F/BWjo4NYWbmCwsLbAwljydDfP4+6OuU3aiCSk6/V8/OXsb7+GdUOKSNyJAsAOXbsp3jgAXUDkKWlSXR3v4V771UvAFlYGMX6+kcYHl5EdfUzyM5mX+MW/y+KNCtcruswGieRkqIPApDJySrV1nlpERBp7szGUyQHIORwq9VqZBjmAYZhfgaA1HZ0RhKdYRjSI7Q3LS3tNYfDcTvXRXo9SXbi3QJASAtb0pqTPJsdrVCqT5Hiu82wAyCLsNs/J9n9YXMQSd9pbz+Curo7aw4i2WIzPVKIHggQR9LE5qiE17S3vw2jcSUincJvv4rCf3iN9xnTX30Z0197Zcv+iYl5ZGfvR2lpOW/aXDf6/X64XEdRW8sOgKhlWCfRA/laPT39DnS6XaisNHBVjWLWhwAIDav1fsXwxJWRUA3Iz/DAA4+rOgKysjKNzs5zqgYgBESRjw4jI8swGJ5T5VwWp/Matm+fDtbMzcwsYny8AmbzTq7XUhHrNQAivRlkASBhMc1m8w8CgcB3enp6rksvujJPvFsAiMNxEbW1CxEBCPmPfX3ZkqfBcL0RagUgoZka72LbttvOblh2NpEDjycDNtvDXNXFeX287ldiARC5umG1tZ2EyaS/padYtvB6M9DSIr4NOBstwgbiaI2OvomMjH0oL68WgqQsNEJdsGg0NqofgDz44BNIS0uTRY9CHLqyMgOH40185jPqjYD4/XPw+d7B6OgK6uoOqRIQdnd/hPr6mSAAmZ1dwshIqWoBugZAhHgzudGQFYBwY/XuWH23AJCOjrdgMISCWpEcLa83DS0tjyja6B0dZ2Ew3J5gHTkFawF2+8uKkqOz8wqqqmYiphixASCk/Wtz84uid4oijQrW1j5AQUHk+g+xAAgxVm8vqd15QdKhhJsn00ezxcqKH3Nz9aira1bUvYrGjN8/i4GBk8jOfgRlZdJFlYRWTigC4oLVep/QpCWjR5yso0d/il27nlQ1ACHOe3v7GezcqV4Asra2gJmZcxgdXYXZ/IJkaa1CXraurg9hNs8Ff0vm5pYwPFyi2i5xGgAR8mawoyUrADGbzawjHzRNq7eBPDtbBFfdLQCkvf0sjMaQ8x65fa3yW4yqEYCQ9La2tmOoq4tcgMoGgIQ6Re1FWVklh5vNfSlp8VhQQDrFpETeLFINCDnM6/XBan0RKSlRzuYuTtwdmwcSRrOFWgYQhgVeXfXB4zmCgoInUVJSElcPSl1AAIjX2wOLRZ0pJkSv4RQstUdAyJ1qbT2F++5TLwBhmGWMj5/G6OgamppeEv2DjhjvFQEgFst88EMNASBDQyWqHTaqARAxbkhsmnIDkNufj6PzOUhRVMDpdKo3eZiDXe8eAHIKRmPo+qltgnjYnOwAiA92+9Y6Aw5XQtCl4+OkIPgiSkq2pl9Fs8VmBkiKUl9fluhpWG63A7m5PUhP554qwgZIxVKs2z2PlpaXJS1EJwMJR0dPo7w8P+p7Qf5BLQMIw/oNBJbgdP4CpaXPqnLYWliOZAIgJAIiVSMJQf+A3SS2vr6I69dP4P77vyAGeUloUpQfIyOnghGQlpbPSfq3RigBNwKQ+fllDA6S1uDqnJOjARChbgV7OrICkGhs1tfXk8+z23U63W8BMGVkZLzS1tYWKhhI8uduASBtbcdhMoU6EEVOXVqF3f68oq3d0XEGBgNzi8fIcigLgLS1nYfR6I+aWsTWce/rI12YxC2cJK2aMzPJ/7i3C2UrR7QLRgCI1EPOCLDr6CADCUPdcKLJoJYBhLd1uwKH42eorHwOhYVFin6nYzGXPADk59i16wlVAxACaj/+mAAQ9UZA9Po1DA6exNgY+a1TzkcqLi9oV9f7sFoXg1t8vhUMDJDmMQ9yIaGYtRoAkd4UigQgG9VgNpt/TFHUgtPp/E3p1SP9iXcPACGOVmYMAKLM+Rkbb0R7++0oTnQg5VPM1y0yfLC//xSqq+8cPrhRJraOu9+/hvHxMlHzfckE9NTUNmRnZ3B+EdnKER2AyFO7Ey81Ua5hkJwNsGED+dLb1vZT1NQ8j4KCwkRIybo3BEBuwGJRbzZwKAUrWQDISdx/vzodd3KRU1NJJPk4xsfXFdcpke2LthWAFCq+e2U02TQAwtbqwq1TAwD5CoC/oWm6QjixlUvpbgAgxIlqb38VJlMoDShy5GAeNtsrkhYBc70V7e0nYDTerhGIJEdfnw8NDYcUUfDZ1XUV5eVTwZ7t0R4ujntv7yKamw+JVidBhiVS1DXk5m5tFxzPVlzkiETL7ZZnfovDcRm1tfNR34uFhWX4/S2orW2IpwLF/LtOt4pPP/0pjMZDyMsLpZep8dEAiHKsRuonPvroBB54QL0AhMyB7O09homJdezYoc4xZ6SdvtW6FLwYoQhIARobdyvnonDgRAMgHJQl0FLFA5Cbgwu/TtN09M+2AilDCWTuBgBCpls7nUdgMEQHIKQI2GJRdmeQtrYjMJluf52P5PQODc2jpuZZZGVxd6KFvI9E511dR2EyxZ4ozsVx9/mWsLLSBKPRKiSrt2iNjPRjbe39LdPa2RzGRY7IAESeCJzb3YmcHBcyMtIiAvOxsTkUFz+uqkhCSso6rl37CbZvfwG5uer9M64BEDZvnlRrVoIA5P77ldVhkIv0GRkUenqOYmqKCXbcU+OjAZA8xfvQSr5XsirPYrFETfJnGCaLoqgWhmG+CqCLpml1JhZytP7dAECWlpbg9Z5AVVV0ADI4OK/44UxtbSSKc9uhj+T0ko5FZWVPye54kY5Subm9cespuDruN26sYseO50SJVJGUscHB06isjFwwH+vV4irHZlputw52+xMc397El09MTGB+/i1s25YXpTnDfLA7l5oKiEmqyQcf/CTYajQnh7stE9eqMBSSCYDs3v2kaJFLYbQdj8oKPvyQREDUC0CysvTo7n4dMzMU7PZD8QRW5L+TqLrVurwhApKPxsaHFMlrPKa0CEg8DQn/77ICELPZTLpg3a7i3Sof4W+FoqinnU7nO8KLrzyKdwMAIY7l8DDp9hMdgAwPz6Gy8hnk5MT+Yi+XBcmQvK6uwzAaY0+unpiYQX7+4ygqKpaLVYRa7x5HXV38blJcHffQ1PB9KC0VJ0OytZXwHWpWwOXhKsdG2isrq5iZqUV9/T1cjhRk7crKCm7cOI6ampyIAKS3148dO9TlrKSlMbh69d+CwEmp7zMb4yUPAPkFdu9+QtUAhNQVvf/+cTz4oHoBSE5OKhyO1zA3R1rOP8fmCipuDZkp1di4ogEQxVlGHQzJDUD+JAYAWWUYZlin0511Op1D6lBn4lzeDQBkdnYGExNnUVoaSseI5CySVJNt255Afn5B4koVgcLy8jI8nuO3ojjR5JiZ8SEtbY/oMzNiiTg6OoClpasoLo7/9Zmr407AjceTBrv9URG0DMQamhjrQK5ybKRFwG9FhXxRq9bWo8E5LZEHdKagpeUxUXQtFlGS637p0o/R3Pw5ZGeHOnyp8dEAiHKsRuqK3nuPAJDPKYcpjpzk5qYFayHn5/Ww2Z7luFsZyzcDkP7+PDQ17VEGcxy50CIgHBUmwHJZAYgA/CcdibsBgExOTmJ+/jyKi6MDkMnJOeTmPqrYuQEERI2Pn0FZ2e2i2kgO4+LiMpaXG2EwiFMnweYFaG19EybTGqs0KT6Oe3//PIxGcepcRkeHsLx8GUVF8cHTRl3wkSO8v7d3BTt2yJeTHe6EtVmGUAcs0mVGXUWeJNf9woUfYceOV5CZGep8p8ZHAyDKsVoyAJD8/Ax8+ukv4PMRAPKMcpTLgRMNgGg1IByuy5aligAgO3fuTJ2dnbXpdLoyAPk6nW46EAiM0DTdDoDNsMJEdKCovXcDACFOpd9/BQUFofSqSM5iKHJApm2Lk9qTqNEHBz2gqI/v6NAUSQ65nUafz4e+vjdQU8Ou+JeP4y5mS9719XU4HKRlM7dUPD5ykDtB7EW+4jU37030ivDe73BcRG3twpb3Qglglo9QmZk6vP32D3HvvZ9HRgb3lsp8zhRjDwEgHs8NWK1qb8Or/hQsMkPj6lUSAVFn9yhyPwsKMnH9+s+DAMRuVysAuYzGRn/wdSNdsPr7c9HUJN/fzkTeey0Ckoj2+O2VG4BQZrP5mwB+H0Ck2PwMwzB/5XK5/o6feOrbdTcAkM3OeyRncX5+EQxzH6qqDIo0Ynf3RygvH4Ner7vFXzSn1+tNQ0vLI7LI0dX1AcrKxpCaertdcCxG+Druvb1LaGk5BL0+eotfvgpobz8Ho3GN03a+chDgm5q6C+XlNZzOE3JxT8+nKCzsR/gHkfywk2diYhY5OY+gpKREyONEp0WKbc+d+wHuu+8LSCf5WCp9QhGQXlgs96pUAgTv1LFjyQFArlw5hl271JuCRWzx0Uc/xdIS+X14UpV3qrNTAyCqNJxCmJYVgJjN5j8G8BcABiiKOgVgEMAcwzAkr6UOwFMAtgH4A5qm/6dCdCYqG3cDAPF6ncjI6ERWVuhraPTUpSYYDBZR9c2XuMNxAbW1of7n4Sea0+t2k0FT0uf4kuhBRwepJ2DfApiv4z43t4BA4F7U1Gznq9Ko+3p7O5CbeyPYmpbtw1cOj8cHq1Xe9s8jIwNYW3sPVVWhxgVhADIwMAej8XnVpTFlZ6fg7Nl/wQMP/AdFzMNhe4c2r0seAPIqHnroCVE+FvDVLdd9pLXz5csEgKg3AhICID+7CUCk77jHVeeR1msAREvBSuQeyQ1APAC8fr//CY/HE+rltuGprq7OzMrKOgegkqZpAkiS/rkbAAhpCVtQ4EVaWuirfCRnkXQimp01Yvt2uyJt3t5+BkbjnQ3cogMQH2y2l1nVYAgpbKh+4hKKitilX0WzBVuePJ5U2GzCF6OH2vGeQmWl+HLI1X53o47n5+cwMnIa9fWhSEcYgLjdC7DZPif5PWJr/2jrSLHtG298D7t3f0lV7YM1AJKo5cXbnywA5Nq1X2BxMRUtLY+LpywRKXd2XkJj4+qtv1P9/Tloaton4onikdZSsMTTbTTKcgMQAjp+nabpH0dj0Gw2/zKA/03TtHqrFznY9W4AICR9qaJiDDpdKH0pkuO+vh7AyEgprNb7OWhPuqWtrYdRV3fnlYwGQIaG5lBd/azkHYC6ut5HZeXkLT2z0Q7fyAGhTdKw7PYXRXGQw52h2MjAF0iRWpapqWo0NMibYkMiV52dh9HScmcExO0OqDJXnACQEye+i717v6zq1q9aBITt2yf+uhAAOY5du14U/zCRTiAOrwZARFIuD7IaAOGhtAS3yA1AnAC+Q9P0P0aTw2Kx/A7DML9G03RzgrKqYvvdAEDI8KLq6tlb9ojm9A4MFCiy48/CwgL6+0+iqurOL/LR5Jid9UGvfxAVFbWS3sG2tlMwmbi94okAEDJ0cds2caZ0k8Lsmhofa3DDRw7Cf3HxYygsLJLUTpEOa209Brs91PkrHAGRs5YoEYXk5aXj2LHvYv/+L6s67UcDIIncAmH3kuGWly6RFCy1A5DXsLiovtbaYWt2dl5EY2OoPi9UhK5FQIS96clNjZt3IrAuLBbLbzIM8/9QFLXP6XTObyZvt9uzl0kOSSgC8l2Bj1ckubsBgJCwLXEmw080Z1GpHTUGBtzQ6a7f0QEr1lf3QCCAoaFiNDbukuzOra6uwuk8CoNBmu5RRLCVFT9mZ02ipM0NDnoBfIS8PHZzJPgAkN7eZdjtL7AGOWIak7TibWkJDWAkP+xyd1NLRFbSbvTIkX/GgQPqByDJ0QVL/TUgBIBcvHgMu3erG4B8/PHrwRSs5mZ5mpQk8l6TvQ7Hu2hqWr/1d6qvLwvNzQcSJSvLfi0CIr3aZQUgZrOZ/PX4VQAPMAzzOkVRnQzD+HQ6XWYgEDBTFPUKgC4A/7pZNbHStqRXo3An3g0AZHMBdzRnsa8vE83NB4VTrkCUSO9zEsGhqDtfn1hOr8dDpt1KV2g4OjoIv//qrVbHbEXn47hvpO3xpMBmE35Qnt/vB00TQMVuHggfOTyeDNhsD7NVlajrNv+wE3A3P98Ak6lJ1HPFIE7ajYYBSDjtUoxzxKapRUDE1jB7+mlpDN5992gSAJDDNwHIZ9kLr6CVnZ3vorHxNgDp789CU5MGQBRkIkWzIjcAITM+SCVvPD42VvuStQxN08L3+1SAqe4OAHIetbWhwjXyRAcg5MuQ8EXNiZq5vf00jMatVGI5vaSAuKXlJU71GInw2dX1Hiorpzifx8dx38hnb+8i7PaXRIkitLWdgMkkTjthpTU9IHVS9fUzwZQlEgEJzcXZg7KyykSuhSx7QwDkuzh48Mui3AuphNIAiFSajn8OASAXLhzFQw+pPQJy5CYAUcaHj/iav3OFBkC0Llhc78zG9fEc/0Rox93b0NDwf1EUdWcrobi7Qgtomv4Ry6WqWnY3AJCOjjMwGG6bPfr8DEpx/dFDBcJHYDRuTQWK5byPjc2hqEi6+oKOjvMwGG6DPLYvQaIAZGBgHibT86IMnOMiE1c5pqfnkZ6+TzGDL/v7b6CwsBvZ2ZlBADIyMofy8qeQm8u+Exhbm4u9rrAwC4cPfxcPP/xlsY8Slb4GQERVLyfiIQByDA899AKnfUpaTFJ+Pv5Y3QCEZDM0NYVmRYdqQLQIiJLumNJ5kRWAKF05cvB3NwCQtrbjMJlC+e3kieYsejxrsNmek8MMUc+cnJzA3Nxb2LZtqyMYy+klKTRzc3Woq7NJIk9Hx1kYDKEfBi4PV8d9M23iKFdUPI2cHHapUlx4I0MVKysnWEV1uMoxNDSP6upnJO9UFk3+yclJBAKXgveM/LCT+SSNjS+qsosUcbRef50AkF/iYm7FrU0eAKL+QYRknuU775AIiLoByPXrR7GwQCL9yks1ZvMCbgYgSk2bZiOLVgPCRkvCrlEMACEzPzIyMm57pZvk7OnpmRNWdGVSuxsASGvra6irux1BiD4/g6TzKGvSrct1HcXFgxEni8dzej2eNNhs0hQbtre/AaPx9pR2trc9ngzx6JBp3QUFj4vSSaq/vwcpKW3IyYnfkZurHEqbsbG4uIipqXOoqsoNAhC3eyVYIK/GJwRAvoeHH/6SGtm/xbMGQJRjvhAAIRGQQ8phiiMn5L24fv14sAuWWusmNACipWBxvPZ3LJcVgNTX1+fpdLpvAyC/rLFyC0jNB7vk70S0oYC9yQ5A1tbW0NVFUphud2eKHgHxoanpJUW17mxrexMmU6jobvMTz+mVsstSW9tRmEzpnG90PBniERSzVmFmZhqTk2+itDR+GhJXOdxuBnb70/HEk+zfSec0t/sE6upCAMTjYWCzKYc/Loogjtbhw9/HwYNf5LJNcWsJAEmOLljJEgFJBgBy4iYA2a+4+86GIQ2AaACEzT2JtkZWAGKxWH7CMAz5VSLRjS6GYbZMQw8z7nK51FmlxdE6yQ5AyJfdvr4Td8zQiOYsDg7Oo7b2WWRlZXHUojjLiVPY0XEYJlPkVrDxnF4p8/jb2l6Lymcs7cSTIZ5mfb4lrK3tQE3N9nhLOf87l9bCXOXwejPQ0qKsPzG9vaeDIJIAkL4+ZTZkYGPEEAD5Pzh48D+wWa7YNaEIiBsWyz2K5TEeY8QWx44lBwB5++2j2LNHnVFBYqdQBOTkTQCizunhGgDRAEi8vzmx/l1WAGI2m6cYhvlwaWnpxYGBgaVEBOG7t7m5udzv9/8lRVGk2IB4ur0Mw3zf5XL9E4DIn7ojHFZfX79bp9NdAXCBpmnePfWSHYCEvmKfRWlp/i0tRnMWSeF2cbE4g+343BdS/zE7+xZKSiJ/gY/n9C4vk1aq9airE3emZniS9sYo0x3yrqyC8kcuUI8nA6HDpKUC6ZGzJUO1LttRV9fCR8Vx97DthMVGjvBhpAPWzEwt6uuV5Vh6PG/BYKBuFndmo6lJnV9JQwDkX3Dw4Bfi2lfJCzQAohzrkBSsZAAgn3xyMlgD0tS0VznK5cCJBkA0AMLhumxZKjcAIdPofkuujlZ1dXX5KSkpnwSdKob5BsMwgxRFvUBR1NcA/D1N07/HRrkHDx5MGRwc/ISiKNKk/10NgETXGhkoR1HX7hjiF81ZnJ9fBMPch6oqAxsziL4mVv0HOZyN0ytFHQhJc+vuPhJ1CGHht19F4T+8xltf0199GdNfIyN6tj7EmZ+bM4lWbN/R8Q4MhqiB0rigNhLPpG4lN/cRbNtWwlsnYmz0ei8F21XPzy9jcLBI0kGWQspDAMiRIz/AgQOfF5Ks5LQ0ACK5yqMemDwA5I2bAGSPcpTLgRMNgGgAhMN1URYAsVgsVwOBwBGXy/W3iQjBd6/FYvlDhmH+SqfTPdDd3f1xmI7ZbD5FUdSBnJycgo8//jhuL1OLxfJ1hmF+FwAZ17ygAZDoFnG5PkFx8cAdRdzRHPfV1TVMTlajoeFeviYWdF+s+g+2AESKOpBQqtjrMJkiT0EXE4CQKM/CggVGo1VQ3YeJ9fa2Iy+vF+npaTHpswGDYQJe7wKs1hcU12Gqr+8D1NQsYHLSJyqoE8VQG4iGAMgPceBAZNAq9vlC0dcAiFCaTJxO8gCQU1hYIEXoGgBJ/FYkRkHrgpWY/vjsljUCYrFYHmYY5geBQOCRnp6eG3wESGRPfX39Z3Q6nW1zBMZsNv8VgD+kKKrG6XQOxTrDarUaA4FAB8Mw/wnAV8hcEw2ARNdYZ+dl1NTM37EglrPY35+riPB0yKk/ApMpej0KG6d3eHgOlZXitKndqNS2tldlASCLi8tYWWlBbW1DIq9m1L1TU5OYmTmHkpLbKXyRFrOxRXif2x2A3f6MKPwmQnRg4BNUVU1jeHgaDLMTVVURpl8mcoBEezUAIpGiWRyj1YCwUJJES4gtPvnkdLAGpLHxIYlOFfYYLQKiRUASuVGyAhCz2fzbAO4HQKoTzwPwAFiJIBBx6n8/EUG57DWbzUcBPELTdNxhBiRawjBMJimSb2hoeEcDILE13dFxDgbDGmsA4vWmoKXlMS7mE2Xt7OwMxsbOoLw8uuPLxuldWCAOejMMBrMofIaJtrWRYvko7WpFrAEJpc2J5yzHGgS5UaFsbBFe39eXjeZm5dVXjIx0Bls+Dw1NITPzIEpLy0S9M2IRDwGQH+HAgZfFOkISuloERBI1szokeSIgZ24CkN2s5FbaIpIS29wcGipMmmUosZkHW51pERC2mhJundwAhO2kNAJA9MKJHZ2S2Wx+FMCZmzUg/znWmRaL5fMMw/yIoqh7nE6nUwMg8S3U3n4SRuOdpozlLHo867DZno1PWOQVbncncnJcyMiInvrD1umVwuFtayPRmgzOWmErQzTCc3ML0Ot3oby8mvPZbDe0tZ2CyRT7TxdbOUjEZmmpUbSUMbYyRVo3OdmLrCwaw8MzKCwUZ7ZKIvyx3RsCID/GgQPKmunDlv/wuuQBIK9i9+7HFZdyyMUeyQNAzt4EILu4iK+YtZ2d76KxMdSrJzQJXd3NMogcU1MLrPVbWqpFQFgrK8JCWQGI1Wo9uLa2FoLPcZ6enp53460J/zspCh8eHo7ZB5SiqInu7u7JjTRvpmSdpSiqb2FhYW+szlw3Z5h0Afg+TdPfInQ0ABLbQtFqE2I5i263Dy0tn2M1/Zrt/eCzjk3xM1un1+1eh90uLqhqazsGkyl2nUQkPbCVIZoOp6fnkZ6+H2Vl5XzUzGpPV9eHKC8fQ0pK9G8SbOUYGJiDwfCcYlo9b1TA/PwwgOsYHvZJkrbHSvk8FoUAyL/iwIGXeOxWzpZkAiAPPfSEouYrcbVycgGQNDQ2PsBVBYpY39l5EY2NoYyGEADJQVOTOlsKaxEQ6a+UrABELHEbGxsN6+vrbtLcKtoZDMP8pcvl+mb43+vr6w/odLpjJA0sEAg81tPTMx6Lv4aGhv9FUdQjqampNofD4dcASHxrzs/PYWTk1JY0pljOopSzM2JJ0Np6DHV1whQ+9/X5UF//PNLJr6hIT6RIE5uj2Dru0WhNTs4hN/dRFBcXszmO15rp6SlMTp5DWVn0gYRs5VDaAMKNCllZmcbCwkWMja3AaHweGRncI1q8FCzwphAA+TccOPCiwJSlJacBEGn1Heu05JmEfhbLy2mwWtUKQC6hsTHUpycEQJRRs8nnpmoAhI/WEtuTlACEq0oaGhqepSjqVYqiPlhZWXnB4/HMxAEfD1IUdYVhmBcyMzPfIWvX19ep1dXVUwT0ZGRkPJ2amupn00Fr8zlra+usIkJkn04XMl8gwHoLV9UIur6v7wYCgTtb8JIDKCokB8NslYPUFOj196Ompk5QXrgQW15eRnf366ipiT2BO5YcG8+bmppHTs5+VFSIl6Z0/frx4AwJrg9bGaLRHRubRVnZ0ygqKuJ6NOv15J5cu3YEdXWRZ5HEu1Phg0Itgw2wWneyPlvKhaTgfmLiDObnddix4xXVpsyQv1MEgBw69EtSqk/ws0ZGhoODCB98UJ0Fw+HfjGPHXsXTT7+o6gjI8vIS3nrrKJ55hsxcuH31AAAgAElEQVQxVudD3otr184gI6MITU2kFFZ9T2vru7eaypC/y4OD+bDblVdPx0azfPyplBQ99x9ZNszcJWskVZ7ZbF5nGOYVl8t1mOjXbDaTGhA23jOpAUkRwyb19fW79Hr9WwzDvJmamvqFcDQj1llms5mkXIWjJ5F0SGT6U5qm/4wrz8kMQNrbr6K8fHxLOlUsp5ekbY2MlMBmk+9Hf3h4IPglurAwdk8Cts772to6JierRP3RIRN2DQY2r9adN5StDNHu9ego6fL1DAoKCrhefU7r29uvoKxsPKoTxUaOwcE5mEzPIzc3bq8JTrwJtZhEDAcGTsDn0+P++9U7RTwEQH6CQ4e+JJRqZKGjARBZ1B7x0GQBIB9+eBrZ2dvQ2HifcpTLgZO2tkuorp4N7ggBkALY7epMwdIACAfDC7RUUgBys0bimzRNXyL8WyyWCwGWn+9JlymBZL5F5mYdRyfDMK68vLzH2UYs6uvrq1NSUmo38xMIBP6RdMFaX18n3b36e3p6BrjynMyT0Ds6zsNg2DpWJV66jNebipYW0htAnsfpvIbS0pGYNQeEs3hybOTe601DS8sjognU0fEmDIZQcSCXh4sMkeiSlLmKCvHbDCc6lZ7w3tsbwI4dymu/G9YrSTPp7T2GyUnSJli96UskteHo0X/H/v2HuFxFxa0lKVgeTy+sVmXMJeKjoFAb3tfw0EOPqzoCkpFBBSMge/ao904RW3z00WkEArmKmXXF9U51dl5BY2OocWkoBStPtTNNtBQsrtZPfL2kACRxdoWlYLFY/oJhmP8C4Es6na5nM3W9Xu92OBxTdXV1tSkpKb0A/pym6T+NxoVWhB7bPtHqKOI5vb29fuzYId8PTUfHWzAYgmU+MZ94cmzcLLZMcgGQoaF5VFc/g+zs7HjqSujfyde2trajqKuLXBcRzxZkyOXYWJmic6+zsvRwOg9jehqw2+W7/wkZCkAIgPwU+/c/nygpWfdrAERW9d9xePIAkDNgmFzU19+jHOVy4KSr631YrYu3AMjAQCEaG9XZ0UsDIBwML9DSuxqA3AQMsRIWf4Wm6R/fLGonAOQvwh2vIun/Jr0ATdO8P20nawRkZWUFPT3HUFu7Nd0lnrPY1zeP+vpDohZtx3qf2tqOw2SKXm8Q3htPjo1niC2Tw3EetbVbo03x/m5wkSESLdJVihRMZ2ZGmUESjwEO/06+vlVVzUTskBZPDjIQsrz8SeTlxR5oyIEdwZfm5qaho+M1zM3pFNGKmq+AGgDhqznh94UjIHv2PCF7Z8FEpMvM1OH8+SOqj4B8+OEZUFQ+tm+3J6IO2faSjoRm81zwLs3NLWF4uETRH3ViKUoDINJfo7sagEiv7vgnJisAGR0dxsrKpYh1FPGcxampOWRkkNauFfEVKPAKv98Pl+toROC0+ah4cmxcT7pFZWc/jJKSUoE5DpGLNPCRzUFcZIhEb3BwHrW1z0rS1nZiYhxzc2+jpGRrc4B4cvT2rmPHDnFbIbPRd6w1BQWZuH7951hYSIHN9nSi5GTbrwEQ2VS/5eDkAiAkBUu9UTViiw8+OA29vgh1dS3KuSQcOHG5rsNonAymJ09PL2Byslq16WQaAOFgeIGWagBEIEUKRSZZAUh394coK4s8uyGes0iKtkdHSbqM9J1CxsfHsLBwAcXF8QuV48mx8Y74/WuYmhLvj7V8KVhzqKp6Bjk5OUK9ElHphNKwSHvkre2MY9mC6H5iogIWi7ILP0M54j/D0hKpF3pCdH2KdUDI6f0Z9u17TqwjJKGbPClYr2PPnsdVHwE5d+4I9u5Vd2ri+++fRmrqNphMTZLcYaEPuXGjDdXVw0hLS8XkpA+zs0bVRnM0ACL07YhPTwMg8XUk6YpkBSCxvsizcdy93hS0tDwmqS3IYT09n6KwsB9pafGbsLGRY6MAXm8GWloE761wMwJyFgYDaTLH7eEqw2bqpAtWWdlTyM2N3bKYG1fRV3d1XUVFxTT0et0di2LJMTREOnVJxyNfWckP4rVrv8DiImnC8DhfMrLvCwGQn2PfPmVHnOIpSgMg8TQk3b+TFKxz545i7151R0AIAElLK4XRaJVOeQKe5HZ3ory8DxkZaZiYmIfP16BaMKUBEAEvBktSGgBhqSipliUjAAl9qSZzGyLXBbBxent7l4KdgMLtVaWyB5sJ6GFe2MixkW8xJ6K3t5+G0chdS1xl2HzC2NgciosfR0FBIffDeewgszKmps5vGUoYSw6ld78Kq4H8IH788etBANLczLusjIdWhd2iARBh9ZkItZAt1B8BIQ0a3nyTREDUDUDee+80MjJKYTCoE4D09dEoKupBdnYGyAyolZVm1NaaE7misu3VAIj0qtcAiPQ6j3liMgIQn8+HwcE3UFkZ+as4G6eXdFeqqiLtXcVP7dlooLa2N2Ay3fl1PZoB2cixca/H40Nj44uiDJhrb38DRiM7vjfyxFWGzbqYmJhFfv5jKCoSbxL6xjND4Pb4lin10eQgwwenp2tUkaccAiBHbgIQcSJlUvz5Czm9v8C+fcptecxGD6FJ6B5YLDvYLFfkmmQCIGfPHsa+fepOwSIAJDOzXLVO++CgB7m5HcjNzcLw8DQY5j5UVRkUeffjMaUBkHgaEv7fJQUgZrP5K3xFIN2o+O5V075kBCD9/TeQktKKnBz+ERCfbwlraztQU7NdMnOur6+js/MwjEZ2oIer8z4+PouCAnGcdbaduzYrk6sMm/eHius/i5KSEsnsRNKwKiun78hpjyYHSb8KAdn4NT2SCRDlIPKDeP36USwskAjIQbnZ4X2+BkB4q07wjcQWx48fxkMPPabqGhASAUkGAHL16mlkZVWgtrZBcFtLQXB0dBBpaR+joCAHAwOTSEvbg7KySimOFvwMDYAIrtK4BKUGIGwnn29knPBIJqHr40qTBAuSEYA4HJdQUzMfNX2KjdNLvnSTIUfNzXsls/L09BSmps6htJRdPQMbOTYyv7S0gsVFqyj5v+3tJ2E0cn9luMoQCYDk5j6K4mJpIiDk/PHxUfh8F7Bt2207RZOjt3cVO3aoI20jBECOByMgTU2xuoVL9krwOigEQF7Fvn3q7eRFBE+WCEiyAJBkSMG6evUMsrMrJf2wxusljrJpfHwcFHUFxcV58HrHJf/4JKQsGgARUpvsaEkNQH6ZHVtbV9E0/SO+e9W0LxkBSGvrG6iri54OxNbplTp33+3uQna2E5mZW7ssRbpTbOXYuLevLxvNzcI7l3dLChbRZSAQQEfHEZhMWbdUG8kWZN3QEBmU9ZAq/iSEAMhJLC6moKlpnyp4jsRkuPXrvn1PqVYGDYAoy3TZ2Sk4e5bUgKi3sxp5L65cOY3c3GpUV9cpS8EsuSEf6fz+d1Famg+3ewyFhY+jsLCI5W5lLdMAiPT2kBSASC+e+k5MNgCyuroKp/MoDIboaUxsHXev1weL5QWkpsYfCiiE5R2Od1FbG5ryyuZhK8dGWm53AHa78LnxcgEQklYmx49Qe/vbMBpXYgIQ0qUlJ+cASkrK2JhT9jW3AQiJgEgX+RNacA2ACK1R/vTCKVikDa/UDT34c711Z/IAkDM3AYhJSPVIRmtubhaLi+8Em4DcuDEuaQdEoYXUAIjQGo1PTzEApLGxsSUQCGwPBALZDMPMp6amdnd1dbnii5BcK5INgJABhMvLF1FUFD2Nia3jTgYSZmYeQGlpuSRGb2s7CZOJfRoTWzk2Mk8K0a1W4UFVe/spGI3cX28+MmyUR+ouWOGzSZ2RXv9psBiSPJHkcLsX0dLyompy38kP4iefnAqmYKklahPpxUwmAOLxuGG13iPJ3x8xDkkmAHLmzBFVz5Yhtrh8+Qzy82tQVcWjZaEYF4QjzYWFBczMnEdlZS5oegLV1c8gOzubIxVlLNcAiPR24O6hCMxjQ0PD5yiK+v8A1GwmzTBMJ8Mwv93T0/OuwMcqllyyAZBYAwjDRmDr9IYGEpbCan1AdPuRCeg0TSI37IuV2cqxkfmJiTnk5Ag/Eb2j4wwMBoaznvjIsPEQMgektPRJ5OXlcz47kQ0rKyvo6TmO2tpQpC2SHB5POmy2zyZyjKR7QwDkTDAFq7Fxt6RnC3lYMgGQZOiCRWpAkiECkhwA5DTy8w2q7Ry1vLyMsbGzqKnJRXf3BOrqDiE9nV3KspB/Y4SgpQEQIbTIjYasAMRqtT4RCAROAZhiGOa4TqejA4HAEkVRJArSTFEUmVyVAWAfTdMfcRNNnauTDYC0tb0Jk2k9pjG4OL1utx52u/hD2cbGRrC09O7WyM3KKij/akR54snBpKUC6Xemj62urmFyUviJ6O3tZ2E0Sj+IcHiYDPmTp8vUxpbJm22xsuLH3Nx21NW1qOYPw20AQiIgu1TD92ZGNQCiHNMlSwQkJycVp0+TNrzqrgEhEZCCglpUVqqzde3a2hoGBk4FU6wdjqlgND8lJf7QXuW8Ebc50QCI9FaRFYCYzea3ABT7/f6DHo9nZrP4JpOpLDU19SJFUU6n06mO1jUJ2jCZAEhoRsNh1NXdLg6OpJ54jvvGPb29i7DbXxI9f9npvIbS0hGkpNyZglX47VdR+A+v8bLy9FdfxvTXXtmy1+tNQ0uLsIPmIrWmZcM0F1tEoud2L6Gl5QVZ0pwcjouorV2IGAEhqWFFRY+pqkAyVANyBktL6WhsFD/qx+Z+8FkTnj2xb9+TfLYrZk8ydcFSewQkWQDIpUunUVRkREVFrWLuORdGyG+8201SlbPR3j4Bm+0V0X+bufDHZa0GQLhoS5i1cgOQGYqivuV0Or8dTZyGhoY/oCjqj2ialq6vpzC65UUlmQCIzzePoaFTqKiI3caWi9Mr1RyHjo5zMBjWtthQDADS27uCHTte4HVfom0aGenH6ur7wf7sXB4utohE1+NJgc32GJcjBVvr8XQjK6s72LVssxxutw/NzS9Br2df0yMYYzwJkR/Ejz8+A78/ExbLfTypyL9NAyDy2yDMgRYBUZYt1A5AiDZ7e8mw3ky0t88EPw6q9dEAiPSWkxuALDMM8xsul+uH0UQ3m82kde8/0zRNUrGS/kkmANLX50JqanvUAYRhY3JxeslAwtVVO2pr60W7C6HIzRHU1W0dnCgGABkcnENNzbOCFu+FaliOxew+FkmBXGyxeT+ZMj47a8T27XbRbBOL8OzsDMbGzqC8PH8LAPF4AJtNXW1gyQ/itWtnsb6ehYaGz8iiUyEODTu9e/c+IQQ52WhoERDZVL/lYBIBOXXqdezfr97ECPJehACICRUVW0pglaPsOJy43WdgNKahvX0OdruwH9KkVIIGQKTUdugsuQGIE8BFmqZ/LQYA+S7DMAddLpdZevVIf2IyARCH4zJqaubihmS5OL1SDCT0+XwYHHwDlZURIjcC14CQGzY/vwiG2Sl4J5R481eEBiAjI7MoKXkCBQWF0r84t+aBHA6mA2y8U1LcGTEEJj+IH310BgyTi/p69Xde0gCIGLeEG00tAsJNX2KuThYA4vGQbAE9OjoWYbORsl11PhoAkd5ucgOQvwTwdQB/C+B7NE33hlXQ2NjYEAgEfp1hmN+lKOqvnE7nf5NePdKfmEwApK3tFEym+FeMCwAhFnG7Gdjt4k1VJi1dU1Ja40ZuNt8OrnKE9xMHeWAgH01NewS9cF1d76OycpJTPQZfGUJ2WYLN9mJcwCmokJuIhYvvN8pBomZraztUN204DECAfNmiSkLYSouACKFFYWhoAEQYPQpBJXkAyNswGICOjmXYbOL9Lguh81g0NAAitoa30o/vHYrIU2VlZVZOTs5p0uUKAOkZSpLulwCQ3BfSSoHwdz4QCDzX09Nze8qYiDzJTTpZAMj6+jocDvI1On4NAlenV+x8fraRG6EACKEjRorQ2NgwFhYuYds2cVsJE/4JiPJ6M2Rvc+twXEJtre+OCAhpDUwiM/n5BXK/3pzOJz+IH3xwGnp9IerqbJz2KmmxBkCUY41kASC5uWnBFCy1d8G6dOnMzRSsauVcEo6ceL1kYC/5vV9DS4v4HSo5ssd6uQZAWKtKsIWyApCbUugsFssXGYYhyZwWAGSKjY+iqM5AIHDY5XK9Lpi0KiCULACE5OOPj58NTkiN93AFICTVh8yaEMuhZBu5ERKAiAGqCCj49NMTqK9nPzmeqy3COhgdnUdh4cMoLi6JZ25R//3GjTbk53tQXBwCvj7fCjyeeTQ2vqS69pDkB/H9908jNXUbTKYmUfUmJnENgIipXW60kwuAkDa86k75SQ4Achm1tX44HOtoaZGnAQm3tyDyag2ACKFFbjSUAEC4cZzkq5MFgPT10UhPdyArK37vAK5O78LCMvz+FtTWNgh+G4jT3t7+GqvIjZAAhHylLyt7Crm58QEbF6EHB91YX7+GwsL4kShCl6styJ5QK0Y9duyQv8h4dHQIfv8VVFeHmuYRACJGlzEuNuC7NgxA0tJKYTRa+ZKRfZ8GQGQ3wS0GNACiLFskAwDp67uKmppldHZSaG5+WDkK5siJBkA4KkyA5bIBkJ07d6bOz893A/h9moyc1p6gBpIFgHBJY+Lq9IpZVLy0tASP5ziqq7kDAa5ybLzyMzM+pKU9hLKyKkHfBKKr1tYT2L6dXRSEjwxjY/PIzz+AbdvKBOWdD7FQA4FTMJu33QIgHo8eNpv6UgPID+J7751GRkYpDAYNgPC5D0Lu0bpgCanNxGiFUrCSIQJy9mYKlrB/9xPTLrfd/f0forrah64uPZqaDnDbrKDVGgCR3hiyARAiqtls7gPw1zRN/y/pRVfmickCQLhM4ubj9Ho8Othswn9xn5ycxNzcOWzbls/5gvCRI3yImJO6SVE98AkKCkh2Y+yHqwwE4PT2UrjnHmW0uA0EAujoOAybregWAOnry0Vz8954oivu38kP4tWrp5GVVSFKtE8qgZMpAuLxuGG1qr8jmdoHEWoARKq3N/45/f3XUF09h66uNDQ1qe/vbFhCDYDEt7XQK+QGIKRp9N9RFPV7TqfzDQDrQguoNnrJAEBCaUxHgsOJ2DxcnV5CU6yJ6Hw7YBGe+MgR1g/R2eBgARobH2KjMk5riFPe1kaiIGlx93GVgUQ/8vL2o6SkPC5tqRa0tZ2EzZYVPG5ych5zc3WqLOImP4hXrpxGTk6V6jp4bbR1MgEQr9cDi2WHVFdZ8HO0FCzBVcqbYKgLlvojIAMDn6Cqahrd3Rmi/H7xVjDHjRoA4agwAZbLDUAOUxSVzTAMSRxcYRhmmKIo32a5KIpinE7nTgHkVTyJZAAgCwsLGBggczTYdV/i6vQSI4oxvI/QdTqvobR0BCkp3Cdm85Fj44X0etPQ0vKIKHeUDIXU6VqRnx87CsJFhlD0A7jnHmW1XnQ4zqOpSRfU4+DgJFJThU9tE8VIm4iSH8TLl08hL68G1dV1UhwpyhkaABFFrbyIagCEl9pE2ZQ8AOTTYLt3pzMbjY27RNGVFEQ1ACKFlu88Q24AEmArMk3TIY8iyZ9kACCjo4Pw+6+ioEC8wufp6Xmkp+9FWVmloDfC4biI2toFXjS5OO+RDnC7A7Dbn+F1drxNbDticZFBKZ2vNsve2XkZjY3+4H++cWM82DEtL497Sl08nYr972EAkp9vEHxIpdi8b6QfcnqPYO9e9dXhbJRDqwGR8tbEPit5UrDevFkDIuzvmJSWGh52oLh4EG53ESyW+6Q8WtCzNAAiqDpZEZMVgLDi8C5blAwAxOX6BMXFA0hNJaNc4j9cnN4wNb9/DVNT1WhouDf+ARxWOBykp/kihx23l/KRY+NBHg8j6iCnkZF+LC29F3MuCFsZSFqXx5OOHTse5aUrMTe5XNdhNE4Go1idnRNoaHgBaWnx08/E5IkP7RAAOY0QADHwIaGIPRoAUYQZgkxoERBl2eLSJfUDkPFxJzIzb2BkpBL19equjyK3Y2qK/QfI0tI8zYdO4JWSVXkNDQ379Xq9o7u7ezKaDGaz+X6KooxOp/PVBORUzdZkACAOxwXU1pJ5kuwetk7vZmp9fVlobha260Zn5xXU1MyxY3zTKr5yhMl4vXrRBzl9+ukp1NUh6rRytjIMDMyhquoJ5OUpb7jfwEAv8vO7gpPs29qmsWPH53jZU+5NoRSN0ygqMqKiolZudnifrwEQ3qoTfKMGQARXKW+Cofdb/QBkYqIXKSkdmJ7eDpOpmbc+5N6oRUCkt4CsAMRsNq8zDPOKy+U6HE10i8XyOwzDfIOm6VBfzSR/kgGAcB3kx9bp3Wx6MaaHkxqQsrJR6PXcM/74yhGWq68vHc3NnxX1hk9OjmNq6h2Ul0euz2Ejw9raOgYH89DSsl9UXvkSJ53MAgEyAT4P7e1LoqW18eWP7b7bAMSEiooattsUt04DIMoxSbIAkLy8dLzxBmnDK07KqhQWSxYAMjPTh7W161hYsKq+XTixuxYBkeL2h86QHIDU1dXV6vV6Y/BwirpAUdS3AoHAu5FE1uv1mYFA4JsAdtA0za6gQDrdiXKS2gFIqAPW6zCZ4rd8DSuQjdMbSdlu9yJstpeifs3nYyC3uxO5uS6kp3NP2eErR5jP/v5sNDWJ79S3tp6H0bgCnW4ryGIjg9frQ13dM8jKCnWaUtpDmiBMT59HVVUuOjrWVDkDhOg05KCcQXFxHcrL1TsnQAMgynlDNACiLFskQwRkdnYQy8vvw+/fgZqaeuUomCMnWgSEo8IEWC45AGloaPivFEX9ORmgzJJ/wuNRmqZfYrle1cvUDkAWFxfR13cCVVXsB/mxcXojGXVwcB61tc8K6ggPDLih011Hbi5355qvHGHZBgZIG97dot/fublZDA2diWijeDKsrq5hdLRE0d1O1tbWMDBwCgZDDjo7dWhuPii6TsU4INwlp7jYlAQA5Cj27n1MDDVJRlMrQpdM1XEPSpYIyOXL54JF6OXlFXFlVuoCn28Ec3OXwDD3q75ZBtGxFgGR7qZJDkCIaFar1cgwzAMMw/wMAKnt6IwkMsMwZC5Ib1pa2msOhyPU1ibJH7UDkPHxMSwsvIPiYvEByOTkHLKzH0ZJSalgt2JsbBRLSxdQVMSe//Dh8Zz3WEyStKbx8QqYzdJ0m25tPYu6uq1N6OLJMDBAQN8zyM5mH+ESzDgcCN24cQJ1ddno6kpHU9MeDjuVs/Q2ACEREPV2yQl9ddcAiBJuVnJFQI5g3z5ltQDnYuNQkwn1A5DFxXFMTr6NlJTdqq9V0wAIlxuc+FpZAEiYbbPZ/INAIPCdnp6e64mLkhwU1A5ASApTTo4LGRnsU5jiOb3RLLu0tILFRSuMRqtgxl9ZWYHLdQwGA7sZJhsP5isHoTExMYecnIMoKSkTTJZYhIaH++D3v4+iojvljCUDSa/zeFJhtyv/S3Zv72kYDKmg6Tw0Nj4giU6FPiScIx6KgGgARGj9cqWnRUC4aky89aEIiAZAxNMwe8orK9MYGjqNrKwDKCtTd6qoBkDY212IlbICECEESDYaagcgXV1XUVU1w6kug6/jLtb08La20zCZuN8svnKQk9zuBbS0vBSxLoM7J/F3kDa67e3HUVeXfsfiWDKQqeKZmWSoX3X8A2Re4fGcQ0VFAAMDFdi+3S4zN/yOvw1ASAREvSkaWgSEn/3F2KVFQMTQKj+ayRIB8fvn0N9/HDk5j6KsrJyfMhSwS6sBkd4IsgMQs9n8ywBeBEB+YVMjqUCbhB75YvB5YcS+Yh0d52EwrHI6JhHH3etNRUuLsLMours/Ck5DZzvHJCxsInKQyILNJqwc8YxA5rUUFvbdUXAfS4beXj/s9uc5gct4PIj1717vBRQXL2JqqgG1tQ1iHSMq3bCDUlhI2vBqERBRlc2COImAeDxuWK3qnnVw/Phh7NnzuCre42hm0SIgLC6sREvW1hbQ2/s6CgufQklJiUSnCn8MH39KmwOSmB1kBSBms/lPAPw3Ft24GJqm9YmJqo7dao+AtLWdgMnEbgChEI67270Ou/1ZQY07NTWJ6em3UFrKLQ2LLwAhqWQLCxaYTI2CyhGP2PLyMnp6TgSLtePZgvA4N2dCff2OeGQV8e9e72Xk509heflelJers4VtGICQLlhq/7KYLDUgGgBRxOuN/PwMnDxJUrCeUgZDPLhIlgjI+voiXK5XUVLyLIqLi3loQhlbNAAivR3kBiADABYoivrV9fX1az09PSvSq0BZJ6oZgPBpwUu0z9dxJ3tJ6pLN9jlBv+YROdrajqCuLpPT5eArx+DgHGpqnpWlsLu9/e1gS954AMTrnUdDwyGkp9+ZssVJQRIu7uu7iuzsMayv70ZpqTR1NUKLpwEQoTWaGD0tApKY/oTcrQEQIbWZGC2GWUZ3989RVvYsioo0AJKYNu+u3XIDkGWGYX7H5XL977tL7dGlVTMAIQXcvb3HUF0dIXKwsgrKHzk1i43jzqSlAulbM/T6++exfbvwjnF7+zswGJY4ARs2ckSyfG/vKnbseF6WV8DrdSIjoxNZWRnB86PJ4HYzsNvV03Gmr+8DZGYOQ6fbp9qvchoAkeWViHqoBkCUY4/kASDnUVRkVHWNF7ACh+PnqKx8FoWFRcq5JBw50SIgHBUmwHK5AUg3wzB/LycA2b59e71er/8zAGQCHJm27qYo6odOp/NvAWztU7pB6VardSfDMP+dYRgyvGENgAPAf6dp+jRf26gZgMzOzmB8/CzKyra2sC389qso/IfX+KoF0199GdNfe2XL/tHROZSUPIH8/ALetCNtHB8fwfz8uygpYd+Olw8AWV72Y3bWKFtqExnaNzDwBiorQ6Axkgzr6wEMD29DY+ODgupYTGL9/R8iLW0IaWn7VfujGAIg50G6YGkpWGLeFna0kweAHMGePY9x+rjCTkPSrdIAiHS6jn/SCjo6fobq6udRUFAYf7lCV2gARHrDyA1AfgvAVyorK/dcuHCBOPCSPvX19dU6ne4agJ2/mtkAACAASURBVDGGYf5Er9dPMQzzIsMw/wnAP9I0/dVoDJnNZtLb8yKAqxRF/Y/19fWAXq//fYZhHqMo6gWn03mcjzBqBiCjo4NYXb2K/PytQ+vFAiAzMz6kpZHOTMK2/wulYZ1EXR37ehY+AMTrXUBDw3Oypja1th5DXV2obXIkGUj3q6ys/SgtVU+Hk/7+a0hNHUBW1sPIy8vn8yrKvicMQNT+hTSZumAlRw2IBkBkf7kBJMsHBp1uFZ9++u8wGA4J/iFQSjtpAERKbYfOkhWANDY2tqyvr38NwD6GYf6PTqfzgMTzIjx8HfpYKjWbzd8B8Ot6vd7Y1dU1HF5rNpvPA3iIpmkybS3ixHaz2XwKwGcWFxdNAwMDS2RvdXV1ZlZWVj+AVpqmH+FjTjUDEDIDJDfXdUdXpbAOxAIgJILg8zXAZGrio+6Ye3p7HcjOpm+lJ8U7gCsAIa1w+/tz0NJyIB5pUf/d4biImhpf8ItoJBk8ngU0Nb0IvV49fSAGBq5Dr+9Dbu4jyMnh1kxAVGVzIB5yUN5CUZFB1SkaGgDhYHSRl4ZsoQEQkdXMinz4/S4uNqo6wqnXr+H69X+HyXRItR97iME0AMLq2gq6SFYAYjabSYoTcfAJHxEd/fC/idEFy2w2P0VRVIXT6fyXjVo1m81/DeAPVldXK91u92gkjd/cyzidzjOb9r7HMEyxy+Uy87GUmgFIV9f7qKqaihzaF6kGJDQLpFiU9KDV1VV0dR2FybQ1ohPJtlwBCEkfKy5+TPYUoYEBN3S668jNzYoCQPSw2R7nc51l2zMw8CkoyouCgkdlKe4XQnANgAihReFoaClYwukyUUrJk4Kl/g8MBIB8/PG/Y/v2F5Cbyz5lOdE7IPR+DYAIrdH49OQGIH8ar84iLAJN02StJI/FYnmNYZiHaZomNSHRgFEkXvRms5lEUj6iafoZPsyqGYB0dpIv6QucxebquG8+oL8/G01NpIRH+MfhuIyqqlno9bq4xLnKceMGg3vukb+we2lpCR7PcVRX520BIKT+Y2SkBFaruqaJDw62gmE8KCp6DFlZWXFtp8QFGgBRllU0AKIce2gARDm2SElZx4cf/gQNDRoAUY5V1MGJrABEaSqqr68v0el0/y+AbzAM82sul+uHXHi0WCx/zjDMHzMMc8jlcp3ksje8Vs0AxOF4C7W1fs5ic3XcNx/Q15eG5mZeGW9xeSWF9SMjZ1FZGf/LDhc55ucXsbZmV8yQvNbW11FXtzUCQvhkmPtQVWWIqyslLRgaasfa2g2Ulj6JjIxQhy+1PRoAUZbFNACiHHskCwC5cuVtFBbWqjrFMjU1gA8++DeYzS+qNt2V3GwtAiL9+60YAGKxWHIZhqnT6XSk7mJ+eXnZ5fF4lvmo5ODBgynDw8PbY+2lKGqiu7t7kqypq6vLT0lJmb65foJhmK+5XK6fcjm7oaHhVyiK+h5FUd93Op2/wWXvxrVqBiAdHW/CYFjnLDoXxz0Sca9Xj5YW8VKEPv30NLbHvE0hrrjI0du7hJaWQ4qpq2hvPwOjkdkiw8jIHMrLn1JdaH142IHlZRqVlU/LWuDP+WXYsCFZcsS1GpBEboGwe8M1IHv3ivf3UliOI1PTAIgUWmZ3Rloag6tX/xVW60vIyWGXrsyOsrSrNAAirb7JabIDkPr6+t06ne5vAOzZJD7xZM8yDPOHLperi4tqGhsbDevr6+5Y6VMMw/yly+X65k26OovF0sIwDGmX81kAf3SzFe9vsjnXbDb/HoC/ZRjmpy6X6yts08oi0V5bW2ed8qXThcwXCLDewkYc3muuXz8GgyF+qtLmA0jxM3lIPQefx+sN4DOfOcRnK6s9g4NeLC5eRVFR7D+ubOVYWVnFzEwNmpruZ3W+FIs+/fSdW4XoG21BBj1+5jOfh07H3a5S8B3tjO7uj7Gw0AWb7fNISwt1+FLbQ95vUoRuMjWioqJCbezf4pfIQSahP/useO+oFMoZGRkG6YK1a9dDUhwnyhnEFseOHcFzz70gCn2piDJMACdPHsZzz70s1ZGCnxN6v8kgWAsqKysFpy8VwaWlRVy69GPs2fMfVVtvR3TFx59KSdHL7kNLZWcxzpFVeTdb2b5LbA/gMkVRNMMwpKMUiYKQtkZkvsYcRVG7nU6nUwwFRKJpNpsJiPhhIBB4sqen581Y55rN5r8E8F8Yhvm2y+X63UR5VDMAuXbtNZhM3Cdls3Xco+nW4/Fj587PJar6qPsJMLp2jUxG3zoIceMmtnJ4vb5gVyklpQY5na3IyelBZmbIfmEw6PEEsHOn+hxHp/M65uY6cc89X0Bqamy7iXZxEiQcBiBGo1XVDooGQBK8CAJu1wCIgMpMkFQyAZCLF3+Mffu+otp6Ow2AJHiZeW6XG4CcANAM4FGapns3y1BfX9+s0+kIAHiXpukv8ZQx6jaz2fwFhmEWNtdrWK1WWyAQaGUY5usul4sMJIz4NDQ0fJWiqL8H8Mc0TZPOWQk/ak3BIg5rezsBINxDsFxSlyIp2O32wWZ7WdTBWr29HcjOdsVsyctGjlDr3Wy0tBxM+K4ISWBsbBSLixdgMJQEyfp8oW7YXm8GWloeFvIoSWiNj/dgZqY12BoyJYX9LBdJmGN5CEkJIDnipA2v2gcRnjhxNDj8Ts1PMtWAaClY8t/E8Put9hqQjAwK7777Y9jtLyMzM1N+xfLkQEvB4qm4BLbJDUBIvcXfxHHyv0FR1G/TNC34FDSz2fwegFqfz9cwNDS0GNZjQ0PDb1AU9U8URX3R6XT+IpJ+6+vrd+l0ussA/gdN03+cgA3u2KpWALK2tobu7qMwGEjwitvDxnGPRdHj8aGx8UVRHc1QS95jMJmiy8dGDlJTsW2b/K13N+sz3AnLar0NQAhYGhoqQWOjujpgEdkmJnoxMfFJsDOLmuaXbLRLsjgoRA4NgHD7myjWaq0GRCzNcqebLO93ZqYOb7/9Q9x77+cVFdXnahENgHDVWOLr5QYg5DPrr9E0/eNoopjN5v8I4Ps0TXPP7YmjH4vF8lmGYUiE5QMAf0f8FoZhdlEU9Q0Ag6mpqfc4HA5/XV1dbUpKConQ/Hm4HbDZbCbgwxwIBJ5PSUlZ3XxUeXl5K5/p7moFICsrK+jtPYbqau5D39g47rFM2d/vw/btz4tebNzZeQWVlTNRW/KykUMprXc36zMUwXodNltR8J9IBGRhYRmrq3bU1LCowE/8b5GgFKam3BgZuYbGRnEjY4IyvYlYsjgoGgAR85Zwo60BEG76EnN1srzfWVl6nD//g2CtoJLSirnaTgMgXDWW+Hq5AYiHYZgjsWonzGYzAQYv0zRtTFzcrRQsFsvDgUDg6xRFPUBRVDrDMH0A3gbwLZqmJ8iOm0XtBID8BU3T3yL/bcMQxYhsra2tmXp7ewktTo9aAcji4iL6+0+islJ6ADI0NI+ammdFzz+dmZnG+PibKC+P3JI3HgDx+Zbg9zfDYLBwuhNSLW5rOwmbLTQzgwCQiYlZ5OU9huLiYqlYEOycmZl+9Pe/FyxCV+uTLA6KBkCUcwOTBYAUFGTixInD2LfvKeUolyMnoff7HRQW1qi6DW92dgrefPNfcN99XxD9IyBHFXNargEQTuoSZLHcAOQ7AP5vhmF+Z3V19Ucb2+5WV1dnZmVl/SqJTFAU9V2n0/k1QSRWOBG1AhCfbx7Dw6eiOuex1B7PcY9nMpLWVFHxtCQ9yFtb30BdXeSOUPHkILUqjY0vKLYouqPjPJqbQ7IRADIwMA+T6XlVftWanR2E13sFdrsGQOK9P2L/uwZAxNYwe/oaAGGvK7FXJgsAyclJxenT38eDD35RtR0Hia01ACL2jd9KX1YAYrVaiwOBwBUADQBWSfSBoihSi0ES7WsBkOpRh9/v3+/xeGakV4/0J6oVgJDowOTkmygtjT+wb7NW4znu8awwNkbqKp5Afn5BvKUJ/zspRt/YLWojwVhyKLX4fCP/JMXMal0OFvMTAOJ2L8Jme0nU4v6EDRKFgM83gt7eS7DbXxHrCNHpahEQ0VXM6QCtCJ2TukRdnEwRkKKiWlU3mcjNTcOpU9/Hrl1fVOzHNTaXUQMgbLQk7BpZAQgRxW63Zy8tLf0ORVHPAyC5KQR8+AB0Ajjs9/v/ie9AQmFVJQ01tQKQyclJzM2dw7ZtZJQKtydRACJlqhCpdXG5jsNg2NrtK5Yc4+NzyM09iJKSMm7KkXA1AVcVFQPIyEgLAhDSgtdme0ZCDoQ7amFhDC7XBdxzj9ojIOpP0dAiIMLd60QpaRGQRDUo3P7QB4YLKCysVnUKVl5eOk6c+B727PmSqI1ghNN8ZEoaABFbw1vpyw5ApBdZ2SeqFYBMTEzA53sLxcXSR0AmJ4lz/6hktQptbW/BaFzZEhmIBUB6e/2w259XdDRhdHQIqakfobAwNwhA+vrS0Nz8iLJfmCjcLS1Nwul8G/fco/YIiAZAlHIBtQiIUiwBJE8E5AKKimpUHQEhU+mPHv0u9u//JdV2HCQ3WwMg0r/fsgKQ+vr69J6entDAgU2PxWKpdDqdQ9KrRN4TNQDCXf9SA5Dh4T6srX2AgoI7oyDRAMjq6hrGxspgtSq7ne3s7AyWly8E0+gIAOnvz0VT017uBlHAjpWVGTgcbwY7s6j1CX8hVbuDokVAlHMDwxEQMpMlPDhVOdyx5yQEQI5g374n2W9S2Mpkeb+JLY4c+WccOPBl6HSR6yMVpvqI7GgARHoryQJACLhgGOZHAC7RNP1nm8XeuXNn6vz8PA3gU5/P90sbZ3RIryJpT9QACHd9Sw1AyEwQMvPEaGQHQMbG5lFY+AiKipTdTWp5eRnj42eDrZRnZ5cwMlIKq/V+7gZRwI7V1Xm0tp7GffepH4CoPUVDAyAKeCFusqABEGXZgqRgqf0DQwiAfBcHD35Z1aBWAyDSvxuSAxCj0ZiRlpZGZmh8hqKof3Y6nb+5Wezm5uac1dVV0iHrlwG8QdP0c9KrRp4TNQDCXe9SAxDCYVvbmzCZ1u9gNloEpLd3GXb7C4r/40xmgbjdJ4PDFknNytKSFQaDlbtBFLBjfX0Rn3xyUgMgCrCFBkAUYAQNgCjHCBtscfXqu8EakLIywecsSyZvYWEWDh/+Lh5++MuSnSnGQRoAEUOrsWlKDkDMZvOvAfhnAP+Zpun/GYs9i8XyhwzD/FUgEHihp6fnhPTqkf5EDYBw17kcAOTGjTbk57uRnp52i+FIAIQ49X19mWhpeZi7YDLscLvPwmhMxcDAJNLS9qKsrEIGLhI/kmGW8dFHJ/DAA+quASEOSkFBlaqLVDUAkvh9FoqCFgERSpOJ0yG2SAYAQuR4/fXv4eGHv5S4UmSkoAEQ6ZUvOQCxWCwXAKQ5nc6H2IhrNpvfAzBD07R6Jw6xEfTmGrUCENIFa37+/F1RhE5MReaeDA6eQmXl7aL7SABkbm4BDLMT1dUmDrdAvqVe7zuorWVw48Y4SkufRF4e965m8nG/8eQVfPghASAvK4MdHlwkk4Ny4sRRkLoDNT+kCN3r9cBi2aFaMTQAohzThd/vZPjAcPjw93Hw4BeVo1wenGgAhIfSEtwiOQAxm80DFEV9x+l0/g0b3s1m8+8D+COapkvYrFf7GrUCEFLAPDFxVpY5IKOjcygpkWYOyMb71dp6HHV1qbf+UyQA4vX6YDYfUs2AJq/3Mmpr/XA6J2AwPIfMzExVvlIU5cf775/Agw9+TpX8E6Y1AKIs02kARDn2SJYi9GSJgBw+/C84ePALyrkgPDjRAAgPpSW4RQ4AsswwzK+4XK6fsuG9oaHhSxRF/YCm6XQ269W+Rq0AZGFhAQMDb6CyMpezCRKdAzI0NI/q6meQnU1GyEj3OByXUFMzf6u2I5IcbjcDu/1p6ZhK8KS+vvdRU7OIzs5JmM3KndoeT0ydbhXvvUcAyEvxlir23zUAoizTaABEOfYgAOTkyaPYu/cJ5TDFkZNker81AMLR+NryoAbkACAjFEX9tdPp/Hs2NrhZB/K7NE2rMxmdjZAb1qgVgJDOUE7nERgM0gMQEmWwWKR3lr1eJzIyOpGVlRG04GYAQuo/+vvz0Nysnla2/f0fobp6Hu3tk7DZXlZ84Xy010uvX8PVq8eTAIBcRGFhlaqLVJOpBiRZUrD27n2c4y+TspZrAEQ59iDv95EjP8SBA+qttyPa1CIg0t8pOQDIGxRFZTudzoNsxLVYLB8zDDNC07Q6RzKzETIJAAhxttvbX4PJtHVCeDwVJBoBcbt9sNul/+M3NzeLsbEzKCsL1YFslmNhYRl+fwtqaxviqUAx/z4w8AmqqqbR3j4Du1290YOUlHVcvnwMu3apV4bQF1INgCjl5UimCIgGQOS/Vcn0fh858iMcOKDeejsNgMjzPkgOQCwWy5dvzgD5TZqmvxtLbLPZ/KcAvgHgSzRN/1weFUl7qlojIERLra1HUVfHPVMuUQDS27uCHTtekNZQAAKBADo6Dgfb1kYCICMjsygvfxq5udynw0suzM0Dh4c7UFo6AodjAXb7IbnYSPjcEAA5jl27XkyYllwEwg5KQUFlEnTBOoY9ex6VS5WCnKtNQhdEjYIQSZ4ISHJ8YDhy5Mc4cEC99XYaABHkteRMRHIAQtK+zGbzJQC7AfxAr9f/fVdXV8cGzimr1bp7fX396xRFPQvgbZqm1d0+hYNZ1AxA2tregMnEfRJqogDE7Q7AbpcnQNbefgZGIxMRgLjdC2hpeUlV02HHx13Iy+sFTa/BZpNHpxxel6hLU1MDuHjxGHbv1gCIEPpMhEYoBUsDIInoUKi94S5YWgREKI3yp5NMHxg0AML/HtzNO+UAILBarcUMw7zOMMx+AMR7mwUwDIB4r1UAyCdlwtvpQCDwxZ6enrm7xUhqBiAdHedhMKxyNlWiAMTrTUVLizxfVx2Oy6ipmQvWSmyWw+OhYLM9yVkfcm6YmnIjI6MbPT3ygToh5CcA5NIlkoKlARAh9JkIDQ2AJKI9YfdqAERYfSZCLblSsP4NBw6o92+tFgFJ5Cbz3ysLALnJLtXQ0PASRVG/RFHUToZhSm+CkSGKoj6gKOrH3d3dZ/mLps6dagYgXV3vBesHiDPO5UkEgJA0qKGhbWhsfJDLkYKt9Xi6kZXVjczM9C0ApK8vB83N+wQ7SwpCMzN9SE11wO2m0NKiLvC0UT8aAJHitrA7QwMg7PQkxSoNgEihZXZnJBcA+QkOHJA+DZqdptmt0orQ2elJyFXcPEUhT9ZoRdSAmgFIXx+N9HTHra5QbE2cCACRu9B7fHwMCwvvBAcwbpRjdXUNk5PVaGi4l60aFLFudnYAen0bPB49WlrU2+JSAyCKuE5BJjQAoixbHD9O2teqO6s5mWpAkqHG6+jRn2D/fg2AKOdNVwcnGgBRmJ3UDECmpiYxM3MOJSXcpmcnAkDGx2dRUPAYioqKZbHkxvknG+UgE9B1ugdQUVErC198D52fHwZFXYfHQ9La1NuqUytC53sDhN+nARDhdcqXYigCogEQvvoTcl8y1YAcPfpT7N//vJDqkZyWFgGRXOXSzwGRXkR1nahmAOL3++FyHUVtLbdZIIkAkL4+Hxoa5Js0vrET1kY5SAessrKnkJfHDYzJfVt9vhEEAtfQ15emARCZjZFMDopWhC7zZbp5vAZAlGGHcGSQtNlOjgiIBkCUc7PUw4kWAVGYrdQMQIgq+bTiTQSAyNWCd+O1aWs7AZMp5Y4ULLmGIyZ6nRcWxrC29gH6+9M1AJKoMhPcn0w54skCQJJjEKEWAUnw1RRkezJ9YDh27GfYt+85QfQiFxEtAiK95jUAIr3OY56odgDCpxNWIgBEzg5YYUN2dJyDwbB2BwBRAjDic7UXFyfg91/FwEAmWlrUmyeuDSLkY31x9iRTCpYGQMS5I1ypajUgXDUm3nryfh879nPs20emJqj30QCI9LbTAIj0Ok9qAOJ0XkNJyTBSU1NYa5YvACGF3hMTlTCbd7I+S4yFDscl1Nb67gAgHg9gsz0lxnGi0lxamsTKyhUMDmahuVme1sZCCKgBECG0KAwNDYAIo0chqGgpWEJoURgayRUB0QCIMLfi7qKiARCF2VvtEZCxsREsLl5EcTH7OhC+AGRych7Z2QdRUkI6OMv3dHd/hPLyMeTnZwaZ8PlWoITIDB+NLC9PYWnpMoaG1A1A9Po1XLlC5oCodzqvloLF5waLt4dMQk+GCMiJE0exZ496o5vEwloERLx7zpWyFgHhqjFtfVgDGgBR2F1QOwBZXV2F03kUBkMOa83yBSBKqbPweskckG6UlOTdAiBqnAFCmF9Zmcbi4iUMDWWjufkR1jZU2kICQK5ePY4HH3xJaayx5kcDIKxVJclCDYBIomZWh2gAhJWaJFkUAiC/wL59z0hynliHaClYYmk2Ol0NgEiv85gnqh2AEOHa2t6AyUSG2rN7+AIQt1sZ07pHR4fg919BdXWoFfDs7BJGRkphtd7PTgEKWrWyMoOFhXcxPEyGKKoXgOh0q3jvvRNJAEDeRUFBFcrLKxR0S7ixkkwpWB6PG1brPdwUoKDVIVtoERAlmCS5UrBexb59TytBrbx50AAIb9Xx3qgBEN6qE2djMgAQh+MiamsXWCuILwDp6yNf6fezPkeshbOzMxgfP4vt20uCR4yNzWJ5uQkGg0WsI0Wj6/fPwue7kBQA5P33T+CBB7QIiGiXhSVhDYCwVJQEyzQAIoGSWR6RXADkNezbp76ax42m0gAIy4sr4DINgAioTCFIJQMA6etzIS2tA9nZGaxUwgeAyD0BfaNgy8vLcLuPw2rdFvzPAwOTSEvbg7KySlbyK2nR6uo85ubewshIPpqbH1YSa5x4SZ4IyEUUFlahrKyck/xKWqwBEOVYQwMgyrJFsswBOXZMAyDKuVnq4UQDIAqzVTIAEJ/Ph8HBN1BZGaqJiPfwASBDQ/OoqnoaOTnsa03i8cH330PDCF+HzRZKwertHUdx8eMoKCjkS1K2fevri5iaOouxsUI0Nx+UjY9ED9ZqQBLVoHD7NQAinC4TpZQsAKSwMAsnThzB3r1PJKoS2fZrERDZVB/xYC0CIr09NAAivc5jnpgMAIQIyGUgIR8AorQ5G21tR2Czhaae0/QEqqufQXZ2tsJuV3x2GGYZExNnMD5eiKamA/E3KHRFqAvWcezapaVgyW0iDYDIbYHb52sARFm20CIgyrIH4WZqin36eGlpnuZDJ2BCTXkJKE+MrckCQDo63obBsMJKRXwAiNdLJnV/lhV9KRa1tR2HzRaKxjgcE7BYXkRqaqoURwt6BkX5MTJyCpOTxWhq2icobSmJaXNApNR27LM0AKI0W6i/CF2LgCjrTh079jr27XtSOUzx4ESLgPBQWoJbNACSoAKF3p4sAOTGjTbk53uQnh7fCecKQFZW/JidNWH7drvQ6udNr739DFpa0oL729v///a+PE6Oouz/eXp2N5tjwyVXCBCS6epNlgQEFZBD8AAPFPAWD1BeT/D14lIED8ALRUEFf54IKnigHAqoyCEgIgKSsOx29eQAIVEQEAIh2Z2p5/f5Tqr37Uxmdmc2u7Oz4al/CDvdXU99q6r7+dZzPUYLF76RmCff9kLsxMqVv6PHH38eLVhwwKjxmOgb1xMQWECOmmhRRt3/5uSicfXVV9L++0/ewpaYRKTh1SxYo17OY3qjEpAxhXOTHrY+Da8SkE0C8Tl68+TTkDbzidpcCMgTTzxOjz9+PW233cgFCRslII88spq23vrltNVWW7fMarjvvuupp2d96uElS56iRYuObBnZGhEEivs//3k1PfHEtrRgwf6N3NpS125OBESD0FtjaWkdkNaYB0ihBKR15gIE5Kqrfj2p43GAplpAmr+mlIA0H/Nhe9xcCMj6wOxf0267jRwH0SgBWb78Gdp999dTENRfa2S8p7m39yZasMCVu7nvvmdp4cLJWZSpo0NoxYqr6Mknt6P58/cbb9jG7fnt7Y7+/Ocrab/9Jr8FRAnIuC2Thh6sBKQhuMb1YiUg4wpvQw9XAtIQXHpxBgElIC22HDYXArLeEvB7mjNnvVI+XGuUgKxYEdDCha2V/aS391ZasGCgPMze3kHafffWkm+kOUh/7+xkSpLf0NNP70jz5+9T720tdx2I1M03g4BMTktUeiK3uQSpbi4uWA88sIKiaI+WW+/1CrR5BaFfQQcccGi9Q2+56zYnF0u1gLTc8poUAikBabFp2pwISF/f7TRr1uMjWioaISCwrKxc+byWU477+v5KUfRMOe6jt7dEu+/+ihZbWfWJM21ajvr6Lqdnn91pUlZyT0c5ZQrRjTdeSS9+8RH1DbwFr9qcFBQlIK2xwJSAtMY8bG4HDEpAWmddTSZJlIC02GxtTgRk1aoHybm/0cyZw7thNUJAnnzyacrl9qEdd9ylpWbO2rto7tzHKZfL0f33E/X0tE6GrkaAmj69jRYv/gU5N4fCcK9Gbm2pa0FAbrgB2X7UAjLRE7M5ZcFSC8hEr6b1/a93wVILSCvMhrpgtcIsTE4ZlIBk5m3u3LlbtLW19RPRdtba3EhTOm/evHwulzuHiFAwAWW/F4vI6UmS/HGke2v9vjkRkGeffZZWrLiaZs8ePhC9EQLy0EOrac6c19LUqVNHC/G43Lds2RKaNWtlOetXX19u0tbQ6OrqoLvvvpSCIE/z5k1eVxO4kt1wAywgrxuX+W7GQ9UC0gyU6+9DY0Dqx2q8r1yv9IKATE5Ls1pAxnuFNP58DUJvHLNNvUMJSAbBKIq+IyLvIyIZiYAsXLhwq3Xr1t1PRI+IyGlE9BQzf4CI3sjMh8RxfNtoJmdzIiAY/733Xklz565PT1urNUJAli0boD32aD23mgcftLT11gWaPr2T+vo6Jm0K2y226KQ77vgJdXR009y5u49mCbfEPVOnBnT99bCAKAGZ6AlRC8hEz8D/9b+5e3q2fwAAIABJREFUuGApAWmtNaUuWK0zH5NJEiUgfrby+fx+QRDcQkTXEdGrRiIgxpjPEtFpzGziOF7uHxMYY3pBSqy1oyojvbkRkHoKEjZCQFqtAGG62R9+eAV1dd1HXV3TqL9/6qTNILXlllPp9tsvoc7OBbTbbgsm07tsA1mVgLTO1CkBabW5mPyFCDcfAnILbbnljrTDDju2ziJpUJL1c/GbSZ0QILVI4b9aCb3BBbAJlysBWQ9ezhhzNxHdQ0QgE2fUQUBw7Tpr7b5Z/I0xZxPRKUEQbN/f3/9Yo3OzuRGQJLmbttnmYWpvb9tkC8jgYJEee2w2heHzG4V13K9HvMu0afeWCUgcz2i5IPl6AQAB+ctfLqZp03anOXPm13tby12nBKR1pkQJSKvNhRKQVpiR9S6WSkBaYS6UgEzMLCgBIaIoik4RkRODIOh2zp1QBwEBYVkjIj9JkuS47NRFUfR2Ebk4CIKX9ff339TotG5uBOSRR/5Fzz57M2299cxNJiCPPfYUTZt2MG233faNwjru1//73w9TR8ddNHPmNLJ2Js2f/6Jx73M8Ovg/ArKQ5szpHo8umvJMEJA//hGncq3nrlcvABoDUi9SzblOY0Cag3M9vagFpB6UmnONWkCag/Pm2MtznoB0d3fPcc7dJyIfTpLkR8aYz4xEQLq7u7dxzj0qIuckSXJKdmHk8/lDgyC4jpnfEsfxLxtdNJsbAVm3bh0VClfSLrvUDkSv1wXrwQefpjA8gjo6ho8paRTzsbj+3//+F7W331EmIEuXbkVR9IKxeGzTn4HsMrfe+mOaMWMh7brrZCcgCFLVGJCmL6KKDtUCMtEz8H/9awxIa83F5mMBmdwJAdQCMjH7YrMkIAcffHDbqlWr5g0HKTP/By5SxpjfMfP0OI4PxvX1EJB8Pj87CIIHmfnsOI5Pz/YTRdEhIvInIjrWWntxo9O6uREQjH/x4qtot93aa0JRLwFZvnyQFi1qTYXykUceoSD4S5mAPPDAti3pJlbPWgQBueWWi6ira5ESkHoAG8dr1AIyjuCO4tGwgKxYsZy6u/ccxd2tccvmRECQhnf//Sd7FqzNxQVLCUhr7PDJJcVmSUDmz5+/a6lUQiyH1JoOETk7CAJYPi5m5j3iOI7rJSCpBYSZvxrH8ckVBOSVInINM785juNfNbocisVSTZkrnxUE66fPubpvaVScMbn+nnuup112ebbms1C8D01k+HE8+OBUev7zXz4mMo31Qx599FF64ok/lAnIk0/OoShqvTiVesaMNXXzzT+inXbal+bOnbwWkHXr1pZdsA4//G31DLslr8Fc3Hbbn2nXXefRrFmzWlLGeoTCOK666ko6/PDWPDyoZwy45l//WlUmIPvu++J6b2m569bPxRV0+OGT1zURoG4O41i/v2+hXXedO+n395VX/oZe+9rJW3MpXVON6lNtbbnNUodu1ovrOQteFEVdIoKaH5d0dnaemQK+du1apNQ9pbOzc+ZTTz1VWrFixdoqk4FsV88Q0WXW2ndnfzfGHENEP2Tmg0abirdZk9+sfvbem/Y6/XTadcstqTjaPv/7X2o780x64K67CMkCWrE97/zz6bCddqI1Z51Fy+65h+5tRSHrkImPPpqOWbKE/rpkCWF/TNY2de5cOmrZMvrZZB0A5O7qogNXr6alRLRyMo+jo4OOGBigKyfzGIhoVlsbzSkW6S+TeRybyVzQ5jCOri46wO/vVZN5TU2bRkeuWUNXTOYxqOzNR+A5S0Dy+fxLgiC4wUNeC4ebrLVVS1obY+5k5iCO470rCMi5RHR8e3v7Nr29vU83f0q1R0VAEVAEFAFFQBFQBBQBRaB1EXjOEhBYQJh5YeXUlEql45j5WOfcASguWCgUUNdjoxZF0cki8gURWZgkSR8u6Onp6SgWiwnq78VxPLn9DVp3zapkioAioAgoAoqAIqAIKAKTGIHnLAGpNWfVgtDnzp27S1tb2zIiOtNa+zncu2jRoulr165djFogRPQpZn5KRD5GRIcEQbBff3//kkm8LlR0RUARUAQUAUVAEVAEFAFFYFwQUAJSAasnIKdba4cq5/mgdhCQs6y1SNNbbsaYnYjoq0R0KBFNIaK/EdGp1lr8V5sioAgoAoqAIqAIKAKKgCKgCFQgoAREl4QioAgoAoqAIqAIKAKKgCKgCDQNASUgTYNaO1IEFAFFQBFQBBQBRUARUAQUASUgugYUAUVAEVAEFAFFQBFQBBQBRaBpCCgBaRrU2pEioAgoAoqAIqAIKAKKgCKgCCgB0TWgCCgCioAioAgoAoqAIqAIKAJNQ0AJSNOg1o4UAUVAEVAEFAFFQBFQBBQBRUAJiK4BRUARUAQUAUVAEVAEFAFFQBFoGgJKQJoG9dh2NHfu3C3a2tr6iWg7a21upKdHUfRGEfkoEc0noqnM3CciF1prvz/SveP1e6NjmDdvXj6Xy51DRC8hok4iWiwipydJ8sfxkrHWc70snyeig4joeUS0nJkviuMY8rnh5Onu7t5bRL4gIvsRUZGIeonoC9baa5s9jp6enh0GBgbOZubXEtE0IlomIt9PkuTbRFSqV558Pr9fEAS3EdFN1tqX1nvfWF3nC4NeSETvIKLPWmsxNyO2gw8+uG3lypWfIqJjiWgHP4/nxnH8gxFvHuMLenp6ZgwODn6RiI4iom2JaAUzXxjH8TdG6mqi9ncURS8QEci8DxEFRPRXZj41juO/DydzGIZHM/NJRNRNRE8T0bWDg4MnLV++/N8jjXU8fh/NOKIo6hKR04joCCLalYjWENEtQRCcMVGFaPFucc5dRkTzisXinGXLlj04El5RFO0mIqhnle7b251zJxUKBbyXmt6MMS/HHiaivYhoNTP/wzl3RpIkd9QrTBRF3xGR92FfW2svrve+sbwuiqI3++LEC/xz/yYiZyZJ8udh+gmiKHq/iHyEiOYQ0b/wTiWi06y1D4+lfI0+yxjzViL6mf/OvWcy7e+srPWOoxX3d6Nz1urXKwFp9RmqIV/mBSsjEZAwDN/NzFCofkhEl/pH4mVyHDN/vB4FZzxgamQMCxcu3GrdunX3E9Ej/qP/FDN/gIjeyMyHxHEM5bcpLZ/Pzw6CAAoWZPlsLpd7XESOEpEPE9G3rLX/W0sQY8yLiAgfoL8w81dKpZLL5XKfEJFXMPORcRxf1ZRBEJEngPegPxH5tIg8DBmYGR+/b1hrP16PLFDiH3744XuYGR/am5tNQLzS9TMQayJCcdDP1UtAjDFQTg4nolOICIQeH9ZjROQ9SZJcVM/4x+qaKIp+LyJQ6E8UkT5mfjkzf5aZT4vj+Mu1+pmo/e1J+N1E9HeQEGYG8T4VZMQ59/xCobC0msxhGL6NmX9KRBcy88+cc9szMxTg1bNmzdr7pptuAilvWtuEcdzIzHsQ0aeZ+V4imiUiKFQ7u1QqLVy6dOk/mzaI9YVxsV+/gPcS9kGxWNxtJAIyf/78HUul0l1EtNQ5d2Yul5sqIlhrWznndi8UCo82eQxHEtGvReRyZv4OM0/HIRMRLQqC4MX9/f2QddjmD0NuISLoN++eCAJijAH5+Q4RfY+ZQQhnEtEZIrKQiA6oVaw4DMNvMPMJOKBi5j8yc+j/vW716tXzV65cCZLb9Oa/FX1EhL364ziOaxKQVtvfWbAaHEdL7e+mT3oTOlQC0gSQx7qLzAv2OiJ61UgExBgDJbPLWpvPymKMSYjocWstTi+b2kYxBpyIncbMJo7j5V7YwBiDU7pHrLWwijSlGWO+SUTvy+Vyc/r6+lalnRpjrieiF1trp0OnryaMMeYanOytWbNmt4ceeuhZXDN79uyp06ZNg7Jyr7X2ZU0ZBBFFUXQyFMcgCF6U/bBDRmZ+yYwZM7a86667BkeSJ4qiU/xJ3wNE9EyzCYgxBpaCG4IgOLdUKi2u1wLS3d39Yufcrc65IwqFwtWZefwTyIi19viRxj5Wv3d3dx/snLvBK0w/zsjyPSJ6a2dn5w6LFy9+psaampD9bYwBQQNh3TmO49WQzVtxcOp+tbX2mBryLoN1J7tOoijaX0RuEZHjkiT50VjhWs9zRjMOYwyIxz0igpP5szLzdSBIODMfH8cxLHJNaX79XMnM/wNlXUQ+VQ8BMcZ8l5kPe+aZZ7rT91E+n98rCAIQ+o9ba/G+alozxuCQacBa+/z0HQor7eDg4MMiclGSJMeNIEzOGANSjDX46okgIN6qChJ4u7X2Nam88+bN2zmXy60gou9ba99fOY7ddttt+/b29pVEdIG1FodZ5eYtKTg4hDXnkqZNRqajKIouFJEXg0gx843DERBjTEvt7yxe9Y6j1fb3RMx5M/pUAtIMlMe2j/QFC6UDivgZdRCQ2/HisNb2ZEXxxKRorX3h2Io44tNGMwaMd521dt+KMZyN0+sgCLbv7+9/bMSex+ACY8yrmHnHOI5hURpqxpgvEdFJg4ODs2q5kvh7JY5jkMfsvbeLyDZJkpgxELGuR3hFY6G1dkjhxY3GGLjUnOwVS3wQa7bu7u45zrn7vPXnXcwMi1xTXbDy+fxrQSDy+fy8IAhAqutywfIfo8OstXPrAmwcL/Inn8d3dnZumSUaYRjCMnadiLwpSZJfVxPBGDMh+9sY8zgR/cFaC2vqUIui6Kci8hpr7ZaV8mY+7B9KkgQnxNk9AAvcXXEcv24cod7o0aMZRxRFEaxUInJKkiRwuyy3MAz3ZGYowB+21sKNsSlt/vz5ITrq6+tLoig6sx4Csvfee7evXr0aFo5z67UYjvdgvCvhv6y1t1asDRz0LLHWHjqcDP4w5MQgCF7pnLtzIlywZs2aNa2rq+uYUql0T6FQ+GvFOOBSdY+19lWV4+ju7t6mVCodJiJ3ZK2H6VqDpc1aCwtXU1s+n983CIJbmPlQEfnhcASkFfd3ClYj42i1/d3UCW9iZ0pAmgj2WHSVecF2O+dOqIeAeJPoJSJy6tSpUy98/PHH3fTp048VkW+KyLuSJMFpV9PaKMYAwrJGRH5SeQIWRdHbReTiIAhe1t/fD1/ZCWtRFP1KRA6x1iImpKoFpIZwGB8+sHdmT8wmaiDGmCuI6GXW2q6RZIC1RESmJklySBiGMFk3nYBkPjANERAQcGa+P47jt480zvH+3RgDq8sca+28CoUFLmWwjn3eWgsr4EZtIva3P819gJk/E8fxmVmhwjCEpfLzQRDM6+/vx4nvUDPGvJOIYDk52FoLN5nsb4jlCq218H1vShvtOCCct2buHgTBW/v7++8wxiCG6LtEtEcQBHs060CkEqh6CUhGWTxsIuLo6p1gYwzep3g/fnc4q2TmMOSEtra2G0ulEg7oJiwGpHJ8PT09Ww8ODq4SkQuSJPlYA+Mvu6Ux81vjOP5FvfeN0XVDh4XW2mONMYh1rGkBabX9ncGgoXG08v4eo3lticcoAWmJaahPiOxpM9wUjDHwNx7RAoKnh2H4BmbGiX2qVMKd44TK0+/6JBn9VaMZA06GnHOPisg5SZLAV3+o5fP5Q4MguI6Z3xLH8S9HL9no78zn89sGQfAhnFCJyHsbjR1IFQYROSJJkt+OXpJNv9MHgMI6gxiQE4d7oncN+DEz7xnHcTwJCcgTIgIXFLhrwOUBiu9KEflGkiRws2uERG4S+GEYLmHmNZXukD09PR2Dg4NrES8xnPLV7P3t427urOZq5P3f4X60r7UWp9BDzRjzCSL6iojsniQJfMqzvyHA9Yg4juHC2JQ22nFAuHw+P4WZL2Dmd2eERSzIG6y1cEOZkFYvAYmi6AgRQbzFIX4MaSKKO4Mg+Hg98RbNGKCP00LSgr3iOIaLZdWWPQyZP3/+ri1EQKD87s7M54kILMYHFAqFh+rBDtaUGTNmIO5lunMuLBQK6+q5b6yu8W66JwdBEIFQ10FAWmp/pzg0Oo5W3t9jNbet8BwlIBM8C/AXXbVq1QannpUiMfN//Ob/HYLy4jg+GNfUS0B84PO1InIrAvuCIHgSlg8RgRXk7UmSXL4pMIz3GHzQ94PMfHYcxwhIHGpRFB0iIjg93uSTrkbGAQF8QNsTXpj/IHNJkiRpkH9dkPoAYgQqfj+OYwTVb3JrdBxph94l6/dQyJ955pkDUp/wagLl8/mZQRBAgYQ/M4gwSO6YWUBGM4ZGXbCMMYhvgXUBp3pwo8HH/Z3YF8NZHBqZoHrH4ZxDhp9V1lrEEGQbG2NKw2WeGe/9XW28xpgDkEyBmd9bmTHMr+nvQ7GtzPgThiECtj/nnDOVQerGGLgCvmMkl9JG8B/p2tGOw79/4f75cRE5K5fL3VgsFrcJggDxIO3OuZc0O4A7o2zV5YLlrcewjBeI6LJcLgdXv51EBBnk5qQHCyNhOJ6/G2POICK8X5AFCi6uVZs/DLkYSQFwGNIqBCSKoo+IyNdxmMHMiHF6T73kdM6cOZ0dHR34NiMu8BWVFsPxxB3Prjws9Gt+WAtIq+3v0Y6jlff3eM97M5+vBKSZaFfpK/OirHnaKiJnB0EAP/uhF6zfIHVZQHxQ3pQqMSBwtdm3q6tr53qCjWtBNd5jSC0gyJQTx/HJWTmiKHqliCBo+s1xHP9qU6az3nEkSYKPIhpSJu4uIlv4FJanekXxg/XI4bPWnCMilyZJ8q6R0vfW80xcM4px4DT3JUEQXIngYOfcK0ZSnsIwxMnvy9rb2xf29vYOoN+xJCCjHEOjLlhIAvAf51w+e7JojIEr0AHMvF0aXF0v9pXX1TsOpN5l5mettciSNtS8EoLMNwhMhcvlRm2893e1PlPLgbeiXpC9JgzDDzDzt5l5n8p0vKkFxDm3sFAoIOB4qBljkC3ocGvtjNHi3eh9ox1HFEUI9IYL3yfjOP5K2q8PJF4hIv8vSRKkPW96q9cCYox5i8+K+HVrLU6uy80rnjHcfqy1b2v6AHyHYRh+nZmRUfAcay2yq1Vt6WGIiPwgfTe3CgHBt6tYLO6CXCO5XO69InKgc+4thULhD8Ph6seExBj7+G9b07IjpnIZYzY48MTf67WAtMr+9jI3PI5W3t8TtR/Ho18lIOOB6hg/0+ejRorQSzo7O4f8rdeuXYsc9Kd0dnbOfOqpp0orVqyAq8YGzWeleUpEzqv0O/WnM+dWe1mM8RCQyQM580c1Bij6xhi4jF1mrc26O+CFiEw7CIw7qJmpeKvhY4wBibjIOffKkT4wxhicnn6y2ryMNfYjPS8Mw8OZ+ZfMfMe6deuOXLFixX+HuycMQ3wUbxORI6dOnXojri2VSjw4OIiMOdLZ2fnq9vb2gU0htSPJXO33UVhAoCjemyQJ6jgMtVRJJqL9aqXLHI18w93jSU/eWrtb9rpUkfL1brBmWmJ/G2PS2BSkPP5cBX7lgxEi2qWydkEas+Wce2mhULg5e58nsLOtteWA6ma00Y7DGIMMaecjo521Fm5X2fWDhBmDlWSyGeNBH/USkEzmNbiM/aZiLuASmLPWpjUsmiU++oHVD+mxQX5OsdZ+bYS9g2B/uOLu29HRUf4GrlmzZlcc2hHR+zs7O39WK4NcMweFvnysV7e1FvunavOxIniv7hIEwVETEdsYRdGbROSSIAj27+jowHc7fccD0z93dnZ+cPHixcB6g1pRrba/RzuOVt7fzV6z49mfEpDxRHeMnu1Pp5Gis/xyrvHYqgXgfAAfUgJudIIahuFJzPylyjSsYyT2Bo/ZlDH4Fzf8zYM4jveu+NifS0THt7e3b9Pb24uCZuPecHIoIs9Uxmt0d3cvdM7dW5kZp1KgMAz/l5lRXO5Tw7kVjPtA1vux75vL5f4kIn9ob29/S2rNGOGDnyqYtdYjrHl11+IYq3E2SkDCMLwyCIJt4zhGesmh5pMkoFDk3kmS/GOs5BvuOVEUwRL20WKx+Lxly5Y9mV6LDF+wTDHzqyszp/l9gQDdCdnfxhj0e4u19g0VexJuI/tbaxGUXfke6AmCYIl3V0SczVAzxiAj058qs2qNN/6jGUcYhicy85drWHl6RcQlSYKaD01vDRAQxNah8OOHrLUIns/OBdwrkXVwz2YPwFs+jkdcShzHqBczbMOpPJT1Kt9GvIfwvZR6UhKP1E+9v3tSiyxXN1S6WxljQKY+6pzboZqVGXFFcOcTkbnIOjVczEu98ozmOmMMUmHjQK1S3xjCtFqK43w+31L7e7TjaOX9PZr5bNV7lIC06sxk5IL1gJk3+piVSiUUEjwWQW1E9FStyrVRFD0gIghwRRX07EcGJ9YHDQwMPK+a9WQsoRmDMaBmBZTChWnwKgJ0i8Ui0q7e28zUnT7t6S5PP/10mC0MlXE9eVutbCU+FSBSTH7FWosK3BPWvJn/fhFJZs6ceWi9FgvE5LS1teGDv0Fzzn0LWbBKpRJchf5Zb6DlWAHQKAGJougdIoIPLVIRl0/50IwxOJnfo6ura9t6MdnUMfg4jr+KyAbpaY0xP4d7X1dX16xaskzU/vZueO9sb2/ftbe3Fyl5gR0IETJf/bBWQU7UemDm/2aJH9JTE9HvROSNtdINbyrGte4fzTgysWeITUDa6nTt4GR7WbWMfeMlf+Vz6yUgfr5Qu2iqtXb/9DlI6VsqlfBe+F6SJEiu0bTmT6yx5j9QSYpqCYHYtba2ts7s76VSaQdm/hXiBpn52unTp9/ZrL2cSZu7UVFa/+1YYK3dutJ64OcD1hzEZx7YrMOParjiXdrW1rZ9lXc83Jzvds7BLTyx1v6n8ppW2t+jHUcr7++mbcYmdKQEpAkgj1cX1YLQ586du0tbWxsysJyZukZ416AfiQgyLCHgeS0zvxEZm3whrY1cO8ZL5iovq43iWKqNYdGiRdPXrl2LDCgIFP4UM8OtDKkMDwmCYL/+/v4lzZI5iqKXwmJARAgcRgVnBKDvy8yfJqKH29vb94QlocZcgHwY59zr2traNiryt8MOO9zbrErQURSdJSKfJKKjgyBAIOoGLZfLLYdiWW0c1bAeyxiQeucSRcqKxWK52JeIbEdEZ4vI1UEQlH2mi8XizUuXLi3UGANc+25j5llEhGr0SCiAUz8Qk1Oz9R3qlWdTrjPGIMUm6hwg68x9pVLpCGbGSfv70kDvVtrfPjkE4iASBJaLCL4nqPY8r729fY/e3t5/+SDi07MFRL3L3xXMfDEIoIjs7JMAFJpZUDSdq9GOw7vT7IcYPST4yOVy26P+BvJTENGLsqR2U9ZFPfcaYw4Exn4fIG3rqxGfQkTl2khYP9XmwsfAoMbDNaVS6ZtBEMBqBZe6HZ1ze9WqZl+PTKO4Btmi8B56NggCpGveqCEzV7VxVF440TEg/uT9HUR0HrBlZiQmgPvwm1LrcOU4PPGD5enSIAhgIa9sq/v7++0ocB2zWypjQCbD/q42+HrG0Ur7e8wmsMUepASkxSakEXE8ATndWtuW3udfvCAgZ6XZifCbT7mIAO5FRJRDpWcEilZmsGmk/7G4tpExeNM2FH4oaVOI6G9EdGqz/PSz48UJiXPuFGZ+ETNPERGkcoWb3GfSU6Fqc2GMccOld22mq4AnDAcNM4/vttZeXGtNVd7nn+eaWc3du/bBX7pWEodhx+CtQDjBfj0RoXCeRbrMyiKTY7HWR3oGLHoDAwNnMjN837clIlj3EIA7VP241fZ3Pp9fEAQBMojBCgsSeFMQBCcjExH+P93f+GfWHcW7liFOBMVR4XJ2uXPuU4VC4amRcBqP30czDp8gAAcob0aQMRGhGvwtQRCc0cwDEY9z6jJTDR7U5mkbZi5QaA4F7lCQFj79NznnTqtlUR8P/PHMzNqu1kXZlQoZ0mqNI3tT5lnl/T9eMtd6rs9+9wnn3DuYGVkuEcMIcgXL4Peq7Y00nnEYWW9udpHXSllQ5VxEbkzrcU2W/T2acbTS/m72+m1Wf0pAmoW09qMIKAKKgCKgCCgCioAioAgoAjUDmhUaRUARUAQUAUVAEVAEFAFFQBFQBMYcAbWAjDmk+kBFQBFQBBQBRUARUAQUAUVAEaiFgBIQXRuKgCKgCCgCioAioAgoAoqAItA0BJSANA1q7UgRUAQUAUVAEVAEFAFFQBFQBJSA6BpQBBQBRUARUAQUAUVAEVAEFIGmIaAEpGlQa0eKgCKgCCgCioAioAgoAoqAIqAERNeAIqAIKAKKgCKgCCgCioAioAg0DQElIE2DWjtSBBQBRUARUAQUAUVAEVAEFAElILoGFAFFQBFQBBQBRUARUAQUAUWgaQgoAWka1NqRItAaCBhjjiGiHxHRsdbai2tJFYbhjcx8kLU21xqST7wUxpgVROSstXMnXprGJfDyz7TWbl3r7vnz5+9aKpWWM/NFcRy/p/FeWvOOiV7Pk33tbOqsVltXURTdJCJ4xwSb+vxG7o+i6HQROcM5d1ChULi9kXtHc206TufcloVC4ansM4wxvczcF8fxG0d6dk9PT8fg4OAdRMRr1qzZ76GHHnp2pHv0d0WgVRFQAtKqM6NyTRQCHEXR20XkbUS0NxFtSUT/YeaCiPx0YGDgkhUrVqxthnBhGJ5WKpUuWbZs2YNj2Z8nID8koncPR0CMMQeKyLZJkvx6LPvf1GeNFy71yGWMWU5EMp4ExBizExH9j7X2c/XI1Mg1Xv4tNncCEobha4IgaIvj+MoUn4lez81YO42shVrX4v1XKpUeKhQKN4/F89JnVCMgE0EKoyg6RESuBwFJkuTssRxjrWel43TObZUlID09PTsMDg6uZObj4zi+sB5ZjDHdRPR3Efl5kiTH1XOPXqMItCICSkBacVZUpglBoLu7exvnHBSW/Zj5FiK6wjn37yAIdiGiI0RkXyK6m5lfF8fxyvEUsru7e45zbpmIHJwkyZ/Hsq96CchY9jlWzxpPXOqRsRlKZBiG72bm74+H5em5QkBw4kxEy1rJgtOMtVPPGh7pGm+p+aG19vMjXdvI7y1CQAJjTB8Rlay1PThMaGQMo722FgHxh10XM/PZcioiAAAe+klEQVSCOI7jep8fhuGnmflzQRC8rL+/H2tdmyIw6RBQAjLppkwFHicEYPm4UUQOJKIPWmu/W9mPMebjRPRVIvqTtfYV4yRH+bFRFL1ZRC4VkUOGIyB77713+1133TXYiCyTmYDUi0sjeDRybTOUyDAML2Dm9ysBaWRmNrgWSuZ/mflXSkAaw3DevHnb5XK5fxHRZ0cgIHCZgvJetwLfDAJy8MEHt910003FWqOOougdIgK3U1h/f1zrutG8V4dDuhYBCcPwB8x8mLV2diMzNXfu3C3a2tpgGV9irT2gkXv1WkWgVRBQAtIqM6FyTCgCYRi+gZl/SUQ4+fufYT5gF4rIKufclwuFwjpc5y0nnyWi1xLRjkS0mojgV3ymtfZv6bOMMXCpOT2Xyy0qlUovIaIPE9EcuHgREU688QwyxiA+A3Ea+Lhjj0pKRIwxDpYZIsJH9HwiGrTWzsN9s2bNmjZjxozTiAi+xLsSEVzF7gqC4Cv9/f2/z8iBZ4/oglXpn93d3X2wc+4GIvpfZu4lorNFZBERDTDztaVS6YOpe0EYhscyM/oArjuIyEe9TFBuvmut/WKqvBhjMO4zmPnIOI6vymLvx/sPa+1ew+FSbb6gjKxaterjIvIO3zfwhCvd95Mk+U72nnnz5uXb2to+JyIvI6KtmBlyXjMwMPDZ5cuX/zuD3UYuWD09PTOKxeJnROQoItqZiODjfatz7sxCoXB3tp8oimaJCNw+DiWibYhoqYh8NUkSzDnmHs/H3A3NfZaIRFH0QRF5L5Yd5h4KCDOfH8fxLypw62bm80Rkfz8/N5RKpY8GQQDLXt0uWKVS6ce5XO6Lfp6x3n/b3t7+sd7e3sfz+fyhQRBcR0TnWmtPrJwDYwwsdy9wzu1Q6fdegQmsiyD3exJRBxEBg8va29u/1NvbO+D3WNkiyMzfLJVKvw6C4EvMvEhE1hDRlVOmTDlpyZIlT+Tz+ZcEQXBjBj9KY1lqrWdmxhpBbM9ZRDRXRB4ios8kSXJpFEXHiciJzLyLiEAu7OmfV8j/diI6XkQW+v2KZ/2kvb393FT+zNzW5b5njHkXEZ1ARAvS/RUEwaf6+voeyPSdC8Pw48z8TiLKExEU714R+WaSJD9Lr6t332Ziw9K1h0eUiYjHbhEz7ycilxFRT7FYzKfuoR6n90NeZmYRsSLywyRJvpnKUS8Bwb59+OGHT2Fm4IpYK8zx34noy9baP2X2Yvm94ZzbH9gQEfbuB0ZwK8V+nG2txXu6lHkW5uzxIAhOKJVKFzHzXGttG35fuHDhVgMDA58UEbxX4R75X+DMzGfGcYy1NtSwv51z5zEzDqgAxV9LpRLm6FuIp6t0wfLWplustZhD2m233bZvb28/nYheiVc6ET3NzPdg7HEc492b7Qv7+wTn3MJCoXB/tXeg/k0RaGUElIC08uyobE1DwBiDOIcjcrncHn19fffV2/GiRYumr1279i4ojSLyHXwsRGR7Zv4gyIhz7qVpkKMx5jP4YHrlH8pTWelk5o95BeI9OJXL5/N7BUFwCogEM3/WOdeby+Vu7u/vfwwKOTP/UURAOs4lopXWWhASnPriYwiF80ciclsQBFuIyLvxDRWRNydJcrlXhOoiIJX+2Rnl7hIieomIfJuZH0G8DDNDof6xtRb9QZFO+7jGK+Xf94o5fJZhZfpcGuOQ4sLMRw1HQIbDpdp8hWH4dWb+XyKCnH9zzrXBfY6IjmTmU+M4/gruA/nI5XIgik9iTEEQrPLK5PFE9EixWNxr2bJlT1ZTIvP5/JQgCG4jopCILhCRJVBSmPlDRLS9J44IGgUmzyOixQhiZ+ZzRORxyEJERzHzZ+I4PrO7u/sw59zXiGg+5l9EOI3BMcZ8i4jwXOB/PTPPEJGjMecgeEmSgJBSPp+fGQQB3Dm6PEnFvxHPBNILGabWEwPiSTTcD0Ekofxjzo8lopustS/F0jXGQCHO+RPcodNwr0g9TES/tNYinqpqM8aATP0/EGVm/qlzDpYLyHmMiFydJMkRuDFVXokIShiU7e960vAyZsaaK8sEjEUEpBf+9LBofhvXgQgOs54vw3wHQXABCJCIQAGc6ffq0SICi9RUIvo0EU0vFosmVbyNMZiPb4kI4iXgSlNk5lf6GLILrLUgEeVWr/XMu9d8npkvdc79zq+nk0BEBgcH904JcRiGP2FmyHc5M/+BiNqZGZbTg4joRGst3g9YDykpG3bfzp07d5e2tjbMLxT7X4jIL5j5fmttv8duD2a+k4juxDtp6tSpVy1evPiZKIrOEhEQAJCD33hi/Boiwl77lrUWe3BoDrPJDarFgBhjriYiKPDfgwLvnNuGmXEoBDL2Rv++A57l96mI/Bqch4guL5VKdy1durRQbbHl8/ltgyDAwcKvrLVvyV7j5wbEIhCRnwRBsDKO45/6ecP+3YuZvy4iIDA4OMDhEd7B+2cOmXLGGJAF7F2sP3wXcFDwZiJ6BqQtS0Dy+fy8IAgSEXlPkiQXYT9FUYTYjjkici4zL/V9ld/hzPzSOI7xrim3MAwPZ+arQKDjOP5Gvd8svU4RaBUElIC0ykyoHBOKQBRFD4hI13CKWQ0l9zRm/ryIfChJEihSqbKBkzv4Gt9hrYVCkH4w8dG0s2bN2j11FYiiCAokTqavstZCIc1+XDdwwfIWASg5b8wq696XGAoGTgk/mcoxZ86cLTs6OhJ8AK21sLZkycGwQejDKGyDzrkFhUIBH0iaPXv21GnTpq3ywdlbZfoAwXqUmfNxHMMqRHPmzOns6OiwCO7v6uraBu5j9RKQ4XCpNjfGGAR3/j2OYyhCQy2KIpwcFq21n/DPhMKzT3t7+6Le3l4oKOWWz+dfGwQBYoI+n7FObWABMcbgGV8RkVcmSfLH9F5v6ehn5rvjOD7Y94PTYCise1lr781c+xcR2bO9vX273t7ep6spZd3d3Xs756D8fdFaCytX2kACQJ66n3766e1Xrly5xrsKngNLlbX22+mFYRiexMxfxglunQQElrcXJ0lSJlBoYRj+kplfjzgpKF5RFJ0J5TMIgldXWNkwzm8y82viOIaVZKOWWTeP+fVUtij6fuCaAivaK3DymyEg4pw7IJu5KJUp/XutLF7DrOe1pVJpztKlSx/x85S6Wv7XOTcnY9WDJeTLzPy+OI5/4OX8chAEe65bt+6IbHIKYwwU1d27urqmpy6S9RAQ7wIFC8xvrbXAudyiKAKpARn5EuY/8874eZbgwXVo9erVILk7t7e374D1lCEgI+7bzLUbuGB57HBwcF66byDXvHnzds7lctgTf7XW4vchEmqMuZ6IYDWN8K6oxwKSsUTDBfF76fhx0LNu3br7RQSxG+UMdP69gffpndZaxOcN6w4WRREIPUjVRgHffm52grUrJfLoI5/Pz87lcjhYuCubFMIYswcRgWz8xFoLaxX2xuvh9sfMX4vjGIQxnbtyv5AvS0DCMHw/M19QLBZ3A6Ht7u42zrn+LHnEA/w783IR+aUnKuXn+oMGHGJca62F9V2bIjCpEFACMqmmS4UdLwSMMUhnuMwHJtbdjTEGytkeAwMDW1ZmxzLG4ETwYGbeEgp45sTu1CRJoCCmHxKcoqN/fEj3yXxccbpXjYCATCA7V9aFAG4hcBEIrbXLsgPwfsbHptademNAhlHY8MHDCedQM8bA5exFa9asmYHUkKk7B6xCSZJAGR1qURR9B25EzrkX4mR6HAkIXNueCIJgX1iPqk2qt2A9gZPbYrH4vuw1U6dOzQ0ODi7HqWeSJIf4eakkIBj3LsViEaezQ805x+3t7Zfh1NJa2wmrhzEGFoHV1lqcimbx2M05t0VHR8f9cNmpcSr8JSI6yTkHl6ah+fX9nICAVG9tuzmKoqtE5DW5XG52X18fiGG5eQsMlOx6CQhOv3evmGecHCM26eQkSb5qjIEyiFPcS5Mkgatb2hcsFd3eMgK3wY2aMeZVRASlGm5NUCSHGjJWERGsCmX3rgwBia21OGHOXvtWIoLLUfnUv1ECwsy/zZLUMAxfwcxwWRyy6KGzzN8/ba39QpUh5fL5PCwkQUdHB1z83pQNLq6HgKRKKRG93VoLV6e0Bfl8fh+4hy1duvSfURSB9H4iJWgV6wkpZj8rIkckSfLbDKkYcd+OQEDgQrR/oVD4a9qfdwmEe9F74ziGpSw7L3ArQpwFiPC36iEgxhiMGe+xHYrF4lBsG9Z5R0cHLJbH5XK5uXBFy1iUT7PWYn8M24wxp3q30Q0OC3CTn5td2tvbd8oeQlQ+EKS5Y31rc8496g+Y9vPPgAXy+EqC7K3TIApdWQJijME7G4cRsJ7CQrR7qVRazMy/juMY+2zo/V5rYMYY7O8nrLUbvH9GwkJ/VwRaAQElIK0wCyrDhCNgjIGf8QOVys1IgiHYVUQeSZLEVF4bRRHiRaDUlk+8M4r2W+M4RrxJ9mMNJa0c6+A/Zql7wUYEREQKlf0ZY/6Bb5i1dkoVOU4RkS/g5BppSceAgFxorYV70lCrDLLM+JOXlY/stT4HP9w8DrfWXjteBCQMQ8SqfB1+1IgTgNtSqVS6Lj3p9jinJ5lZv/esuPj7ijTOplKJNMaAvMBdp9q7tHwii9P1adOmPb1u3TqQoN9ba6F412w1CAhcW8ruSDUa+kJdl0uMMTgBN574bHC5MQZKE1ymRqwDAteeJEneVLFOX4TT7qxrjZf3hZ2dndvDJccTHShGOC3fKDYkfV4URR8Vka+JyDuzMQv43btw4RlXWmuPyiivUM42qJeQkhUROSdJklMaJSBwncu6SqVKODN/IY5juF2VWzXl3AcDI6YHc4O4gmw9C2Hm58dxjPmoywXLW+dOqLQ8Vc65MQZul69l5p0rM/IZY8okEa6dcRyfl5F7xH07EgGp7C8Mw28w84eDIDiwv7//LxXvhH2Y+XYROS9Jko/VSUBgVUBcWc39hEMda+0tmQOdjdZPtT1ijEECkY+BxBcKBfQz1Py+nlXt/ZnP5/cLggAZwbD24daYbUPv7JT4VyMxxhhYL/eqICA4DPiNtRaxM+VmjIGbLFxEYQW7olQqXb927drra9X7CMMQMWDbW2u3G+bdoD8pAi2JgBKQlpwWFarZCBhj4De8nbV2i5FM+RUfLpzS9VprEUC7QTPGwAf7I+nHeSwU7WxQdoUccLPa1ltGNpAjVcRTRW8MCMg3rLVwUxlqtQhIxr956Nooij4CH+f0lHcscKm1XnygNALgYcFAgLMTkStyudwHYBXJur95mTZ6lIgMZuJ4Ki0gmH9YNt6JeI1qcjz77LN3Tp8+fSucXmfjGmrJXIOAwL3rpSCRzjmQno1aW1tbAouHX8vbWGvL7nAV6wSZc2bUQ0AQ02CtRUzAUOvu7l7onIP7GJImlC1GPmAaPuzlzEI+rgPxUEPKdzV5Uc8F7otIVJD69afXefcS+ORfb609NGMB2Uim1B2Gmb8bx/EHRkFANljPtZTwan+Pogjuc7BaInnCZUEQ/LtUKiHGB26QhzZKQIwxyL53XDUluWIey+thYGBgmxUrVgCn7P56HdY4YlZgqcnIPeK+HYmAVAmihpvUe0Rk7yRJcAiSlQNxbvhbea3USUDwHkMih1fU2k+dnZ1LkHBguPdGtfWWYlvNSlwrPXUURRgDXByRDAExIPh32Z2UmZH+NntoBJezQ4rF4tZpzFgqh0/IsH+KX8aF620VSQ0QR4I4OcRG4TAK7xTEj5xvrUVs0gbWRGPMrUT0wmrEqdb7Rf+uCLQKAkpAWmUmVI4JRSCKop+KyFurneRlBUPGo87OznUZv24og/9JzegVSgJiQv4HPuL9/f1LxkLRHoaA4ERvQbUPkTEGytBZzrkjC4XC1c0kIDX8rRGI/5k0bqAWLj7AG65pI1qGRlo8yBA2ffp0BE4fw8xvQNAw3Kq8ggElCcHSGwSm1lBiKgkIXCuQiWz74WTwvvnISnartRZB1jVbDQJSTpIgIrsnSYLYoprNGIMkCvOstQic3qAZYyAv1UlANsIkDMP0VPv8JElA7NIYIMTO/AXWHWMMlGOQ4Y1IeVaYlIgy87vSgN/09/nz5+9YKpVA7HBC/IYMAakm00FeGSzHPzWLgOTz+X2DIMCp/3XW2ldX7P3yfDVKQHxA9yeZ+eWVGZYqng+LGGKbdrHWAqehFoYhkkLgffYRZKEaTwKSJnpAhqdsgDSE8ZYDBE1/HXEjdRIQxM7skbpyjrDOy1biaskrauzdkSwgG2WHM8aU3+FEdHSWKGQIcvbd9FsietXg4OCsbOY8yOJjgvbIEJBy7JjPEAer5EYNVsC2trZX+YQWL8gm7kgvVgvIcCtEf2t1BJSAtPoMqXxNQcBnH7p2pBNqnwoWcR37w/XBGIMP7AvWrFmzZaWZ3BiDwPJ9BgYGZiI+ZDwJSEqgcrlcd19fH04RswoJsuW8Lf2tmQQkdb+oUJ5wWg7/8NQ1DRl0zqz0e8/n8wuCIIAyvckEpKJ/BIS+AcpAsVgc6OjoQNpcZBMrB+lnG1IsZ+NHqrhg4QQSPuAbxd5UuRduFXB/gqvOUAMJwinmwMDAb6G4VCMgGcV0I197uAEtW7YMp7Ll01FjDDKPHVYqlXbMuptllPp6Y0DutdY+v0LWt4vIJal7T/qbP11G5qo9mRkuRydba+H+VrNlYioqA+sxBqRUBZEp/5YhIBvJlKZ8TmVqFgExxpRjT9J4mHSgnmwiresOjRIQYwyUXVhBTrDWIitXdh8jKP8Ra+01xhi4fZ1amfwAF6drJf1tPAlIJovZBkHjXg6kMIaFpPxbPQTEZ/YCgSonH8iOv6enZ2ukf86suUYJyEgxIBsRkDAMr/MpdeHmhJiycsskqBh6N6X1e9IEDem1SCu8cuVKHFRNyxAQxD7tbK3F3h+2+fuxnjD3ZRfdDAYaAzISgPp7yyKgBKRlp0YFazYCxhh8FJB//azKoFj/QT1ZRBDsWPZLx9/SLEiVGYfy+XxPEARwVRnKUNIIAclUuq3MLrRBrEiKUZo9pjIDi/elj0Xkn0mSoE7BWGTBGtGVIxMDsrKzszNCbAD69pmP8DFts9ZuC6XZV/5G1qMNfO6NMcjghHTG2Y98WgF4A1wq10oYhlCEEWcDRRinxdmPNgjQ0Z2dnVv5mIWyP72IvClNeetxQiA0UrmekNYNqSQgPo4B7mRD6Ub9vUh3i5S8SA9bTkObKiiVmaG8xQABvtsi45LPHnSIc25aWmvGjwenw3f7RAVpgCpSd14nIqarq8v4rGJQtL7gM7MN1TvxLk8gevUSEGTtKScKyKyzK5kZ6T83cK/KWAOQlGHvwcHBnStPgSvnqKenp2NwcBCn988MDAx0V2SRQjAygrj3ieP47xVZsDaQKd23qazIXBQEAVzNNsgQNUxShVG5YGXqa3zbWou0rOXmCQBipBAbVM4W5tfERjVkKjExxqDOxHKfve3FmWeWM+Ux81fjOD45zYpWGafj9xdqQszwsQgDjRAQYwyK2qF+ywbZ9GoV0uvp6dlhcHAQqZjvsdaCiKeZqLAub4NrVi6XmwPXwHoIiDEGWQB/XXkQhHFNnTr1b0EQrI7juIxLoy5YdWTBqmYBKR+WBEHwov7+fqTVLdcFWbdu3bWw1GCu0gDwlJDCsmGtxR5M10OZtKdZsGbPnr0GhMTXI0IK9vQ6uLKdkcvl9ssmj/BB7DhUetRn+ypfr1mwKneP/v9kQ0AJyGSbMZV33BDwL3QEAcLXHm45P3fOPcTMOLFG4CuCEK95+umn34J0pxDEp0iEgoFMJlCY7xWRXX0dkKkoBJe6zKRpI0cquOeV1aOZ+SeoxQBFGnU9rLV31nLB8h9k1AKAmxGy0eDjv53Pn7+rc+6wQqGArEIpAUGKXAQto6Bh1VavwublvTFbaCtDQJAJDHn8LxYRnALCtxnFzE5BFiWvsKE4H1L6DooI0pz+k4he7mtWgDThw1s++QvDsBIXuP0MFXvMDARF2v7BzLBq4ES57J8OyxUycInIRUmSwNc6rQMCxXkKgtadc33M3IOMNihO5pzbp1AolN0kKgmIV6JhBYN8sDT9QUR2QEE08C34sltrYSVBlhu4FUGJQaIAZEF7mJnhVnUUFI8kSXCqjT7KcQBEhDoE9zrnLkb/xphylh24OqHIGzO3oQ4IcEfMQVrXxAeBw00L4znfOVdgZrhwIB30DKRArtMFCxa8XVBQ0jn3zyAI4JcPMvW7amk/jTFQfCPvkrRBlrRaa8xXpoaSBxdC/HediBzmU/0idqAcoJshIMB6Z2ZGGtxlIoJ6EUiDeo219nBc6wtQoqYL4nOwnv6NDE31rud6Y0D83k+YeWvn3BeZ+QE/nygkeSkRYX0j0xEIyq31ZMHy848EDfD3RyYuBJPvKCJQVHH4sEd6Em+MKcdfoP6FiEAh7mJmZCJDzYpjUre2BgkICBDI20pYL4MgWBHH8a9qERC/J9NYHlgskKY5h1osPu4KcSgoOlp3HZAwDMskF3OKeiQonOn3A94FyA5WLgQ53Pu02nrL1AG53FqL2hxDrVYMSFprg4juE5GvB0Ew0zl3POrMAGtfoBOuiFe0t7c/Ojg4iEMH1PFA7ZhyYhBfBwQJKMpB6EEQwOrxZ+fcEXCJTYWYP39+WCqVEKyOmB7UuQERnYGU6yKC92E5yUR6vdYBGTdVQB/cJASUgDQJaO1m8iDgs8hAqYFSiWxBcNH5u3PuguwHIx0Ram1MmTIFVbTTTDhQtG9AatQ4jlEErtxGsICU8MGK4xgF48grtjgFRoG/tSA0yJzlCxHek16XRRUxE7lc7tPOubegcjNOlpkZRARpTvFhS+VopBDhgWlFYK/IQMnAiXG5hkbmYwgCcqBzbmt/ij/UBxEhDe1HfFVjuAzAWlAukpa2KIoO8dYlfLAh9xVTpkw50ef+fywlILVwqba6fIYipDJGtqBZvkIz5uMSa+152YBOn04Wlerxocecw90CChDqIQz52HtFxaVZsfxczRgcHEwr0COAFuT0Nufc2dmUpbjW101AClfMKxQrEC+cNg8RQV+gDEoWUuA+6uOSYDUCAXt/EATvE5HIjwdZcL4VxzHIahZPBM8CY9RHQADtTaVS6SO5XA4EOz8cAenu7kbVccj1DefcVag6joNfIkI8Doq+nZjWdalYA2XrFDNvlOVtuN0fhuFrmBmFN2G1gvKKtL4/qFVF2xfwhFKLGJM1zHx5W1vbSah5kVlPOE1G5rcu1M+AwpnWshhpPafrHEVAURwyfWa1v3v3ufNFBK5qcIO7slgsfsqncMb6QfXw78Vx/JFqa6cWLr6yONIrGxEBKbsGGFXGe6DmC4qNotYOyBuK3znnvlwoFHAYUW6N7Ftc77PUgfC0QxHGXk+xS/d3pdz+wAFFF9MUyUsqK7J7EgnSOET+K+cEz/UuR8ieBjKFYn8gkqiEfg6y5qV9N2oB8WO7S0Tg+lRZCR3WqZnWWhQZ3KAZYzAuWLhAzpaCiKAeRxRFODzAYQFweh1Ipi/miHcLDoLQYLX5uHMOhPL1xWJxm7a2NrwL8Z5+XlpjJjNXC3K53Gm+mCQsxNhzdzvnvlH57UkPJLQS+nBvF/2tlRFQAtLKs6OyKQKTFIF640wm6fBU7CoI+ArWL+zq6to5TdIwVkDViusYq+frc54bCKQFW6tl55tMCPjDFViqEBNVLnSrTRGYbAgoAZlsM6byKgKTAAElIJNgksZQRB80/odKq8FYdaEEZKyQfM4/J/CuguJjN4atnt6qaKW1lNLio60qp8qlCAyHgBIQXR+KgCIw5ghkYkCGjTMZ8471gU1FwKd8hU873FQeds7tkQbOj6UgSkDGEs3n9rO8u+cfvYvskIvdZEElDMP5zAyX2sustciapk0RmJQIKAGZlNOmQisCrY2AWkBae37GSjpjTD8RIej6JhR37OvrQ0akMW/V4gfGvBN94HMGAZ9l8Azn3EGVcVqtDIKPgUPCDFmzZs3+tSqkt/IYVDZFIEVACYiuBUVAEVAEFAFFQBFQBBQBRUARaBoCSkCaBrV2pAgoAoqAIqAIKAKKgCKgCCgCSkB0DSgCioAioAgoAoqAIqAIKAKKQNMQUALSNKi1I0VAEVAEFAFFQBFQBBQBRUARUAKia0ARUAQUAUVAEVAEFAFFQBFQBJqGgBKQpkGtHSkCioAioAgoAoqAIqAIKAKKgBIQXQOKgCKgCCgCioAioAgoAoqAItA0BJSANA1q7UgRUAQUAUVAEVAEFAFFQBFQBJSA6BpQBBQBRUARUAQUAUVAEVAEFIGmIaAEpGlQa0eKgCKgCCgCioAioAgoAoqAIqAERNeAIqAIKAKKgCKgCCgCioAioAg0DQElIE2DWjtSBBQBRUARUAQUAUVAEVAEFAElILoGFAFFQBFQBBQBRUARUAQUAUWgaQgoAWka1NqRIqAIKAKKgCKgCCgCioAioAgoAdE1oAgoAoqAIqAIKAKKgCKgCCgCTUNACUjToNaOFAFFQBFQBBQBRUARUAQUAUVACYiuAUVAEVAEFAFFQBFQBBQBRUARaBoCSkCaBrV2pAgoAoqAIqAIKAKKgCKgCCgC/x8H3dYIVqrYagAAAABJRU5ErkJggg==\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cubed objective with centering of features: this forces the controller to do nothing when there are no obstacles in view.\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    fit_intercept = False,\n",
    "    X_transform = lambda x: x - np.max(x),\n",
    "    Y_transform = lambda y: np.power(y, 3),\n",
    "    Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for centered linear features with cubed goal determined via linear regression\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of learned reactive controller\\n to optimal controller\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = lambda x: x**(-1.0),\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for inverse features determined via linear regression\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n using inverse features\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# attempt at inverse controller. It's not so good.\n",
    "\n",
    "def capped_inverse_features(X_data):\n",
    "    X_features = X_data**(-1)\n",
    "    cap = np.array([1])\n",
    "    X_capped = np.minimum(cap, X_features)\n",
    "    return X_capped\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = capped_inverse_features,\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for capped inverse features determined via linear regression\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n using capped inverse features\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def capped_inverse_features(X_data):\n",
    "    X_features = X_data**(-1)\n",
    "    cap = np.array([1])\n",
    "    X_capped = np.minimum(cap, X_features)\n",
    "    return X_capped\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = capped_inverse_features,\n",
    "    Y_transform = lambda y: np.power(y, 3),\n",
    "    Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Sandbox\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Sandbox\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cubed features. \n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = lambda x: x**3,\n",
    "    fit_intercept = False,\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for cubed features determined via linear regression\",\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n using cubed features\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = lambda x: sklearn.preprocessing.PolynomialFeatures(degree = 2).fit_transform(x),\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n for linear regression, with second-degree polynomial features\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    X_transform = lambda x: sklearn.preprocessing.PolynomialFeatures(degree = 2).fit_transform(x),\n",
    "    Y_transform = lambda y: np.power(y, 3),\n",
    "    Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n for linear regression, with second-degree polynomial features and cubed objective\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skpca = sklearn.decomposition.PCA().fit(X_data)\n",
    "skpca2 = sklearn.decomposition.PCA().fit(X_data2)\n",
    "skpca3 = sklearn.decomposition.PCA().fit(X_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_principal_components(\n",
    "    pca, \n",
    "    selected_principal_components,\n",
    "    fig_title,\n",
    "    x_labels = np.arange(0, 20)/19.0*math.pi-math.pi/2.0,\n",
    "    n_max = None,\n",
    "):\n",
    "    \n",
    "    # NOTE: x_labels default value is hardcoded for the specific sensor we have\n",
    "    if n_max == None:\n",
    "        n = len(selected_principal_components)\n",
    "    else:\n",
    "        n = n_max\n",
    "    \n",
    "    colors = sns.color_palette(\"husl\", n)\n",
    "    \n",
    "    lines = []\n",
    "    scatters = []\n",
    "    legend_text = map(lambda x: round(x, 3), (pca.explained_variance_ratio_[i] for i in selected_principal_components))\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    for i in selected_principal_components:\n",
    "        # TODO: I'm sure there's a better way to do this joined line plot, but I just can't figure it out yet\n",
    "        line, = ax.plot(\n",
    "            x_labels, \n",
    "            pca.components_[i], \n",
    "            color = colors[i],\n",
    "        )\n",
    "        lines.append(line)\n",
    "        scatter = ax.scatter(\n",
    "            x_labels, \n",
    "            pca.components_[i], \n",
    "            s = pca.explained_variance_ratio_[i]*400, \n",
    "            color = colors[i]\n",
    "        )\n",
    "        scatters.append(scatter)\n",
    "\n",
    "    box = ax.get_position()\n",
    "#     ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.8])\n",
    "\n",
    "    ax.legend(\n",
    "        scatters, \n",
    "        legend_text, \n",
    "#         bbox_to_anchor=(.0, -0.3, 1., .102), \n",
    "        loc='upper center',\n",
    "        ncol=5, \n",
    "        mode=\"expand\", \n",
    "        fontsize = 16,       \n",
    "        borderaxespad=0.\n",
    "    )\n",
    "    \n",
    "    fig.suptitle(\n",
    "        fig_title,\n",
    "        fontsize = 20,\n",
    "    )\n",
    "    \n",
    "    plt.xticks(\n",
    "        [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2,],\n",
    "        [r'$-\\frac{\\pi}{2}$', r'$-\\frac{\\pi}{4}$', '$0$', r'$\\frac{\\pi}{4}$', r'$\\frac{\\pi}{2}$',],\n",
    "    )\n",
    "    \n",
    "    ax.set_ylim([-0.4, 0.4])\n",
    "    \n",
    "    ax.invert_xaxis()\n",
    "    \"\"\"\n",
    "    We're inverting the x axis here to align the plot to the sign convention for the bearing.\n",
    "    Negative values for the bearing correspond to the sensors on the right of the robot.\n",
    "    \"\"\"\n",
    "    \n",
    "    plt.xlabel(\n",
    "        'Rangefinder bearing corresponding to basis vector',\n",
    "    )\n",
    "    plt.ylabel(\n",
    "        'Value of eigenvector',\n",
    "    )\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "visualize_principal_components(\n",
    "    skpca, \n",
    "    range(5), \n",
    "    fig_title = \"Principal component analysis on rangefinder readings \\n aggregated from 10 obstacle environments\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "visualize_principal_components(\n",
    "    skpca2, \n",
    "    range(5),\n",
    "    fig_title = \"Principal component analysis on rangefinder readings \\n from single obstacle environment with 7 obstacles\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "visualize_principal_components(\n",
    "    skpca3, \n",
    "    range(5),\n",
    "    fig_title = \"Principal component analysis on rangefinder readings \\n from single obstacle environment with 20 obstacles\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "visualize_principal_components(\n",
    "    skpca3, \n",
    "    range(1),\n",
    "    fig_title = \"Principal component analysis on rangefinder readings \\n from single obstacle environment with 20 obstacles\",\n",
    "    n_max = 5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "production_pca = sklearn.decomposition.PCA(n_components = 10)\n",
    "X_pca = production_pca.fit_transform(X_data)\n",
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    fit_intercept = False,\n",
    "    X_transform = lambda x: sklearn.preprocessing.PolynomialFeatures(degree = 2).fit_transform(\n",
    "        sklearn.decomposition.PCA(n_components = 6).fit_transform(x)\n",
    "    ),\n",
    "#     Y_transform = lambda y: np.power(y, 3),\n",
    "#     Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n for linear regression, with second-degree polynomial principal components as features\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    fit_intercept = False,\n",
    "    X_transform = lambda x: sklearn.preprocessing.PolynomialFeatures(degree = 2).fit_transform(\n",
    "        sklearn.decomposition.PCA(n_components = 6).fit_transform(x)\n",
    "    ),\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n for linear regression, with second-degree polynomial PCA-ed features and cubed objective\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    Y_data,\n",
    "    fit_intercept = False,\n",
    "    X_transform = lambda x: sklearn.preprocessing.PolynomialFeatures(degree = 3).fit_transform(\n",
    "        sklearn.decomposition.PCA(n_components = 2).fit_transform(x)\n",
    "    ),\n",
    "    Y_transform = lambda y: np.power(y, 3),\n",
    "    Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    ")\n",
    "\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n for linear regression, with second-degree polynomial PCA-ed features and cubed objective\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# linear features to predict |u| - sadly, it doesn't seem to work!\n",
    "\n",
    "Y_predicted = run_linear_regression(\n",
    "    X_data, \n",
    "    np.abs(Y_data),\n",
    "#     fit_intercept = False,\n",
    "#     X_transform = lambda x: x - np.max(x),\n",
    "#     Y_transform = lambda y: np.power(y, 3),\n",
    "#     Y_inverse_transform = lambda y: scipy.special.cbrt(y),\n",
    "    plot_coefs = True,\n",
    "    fig_title = \"Coefficients for linear features determined via linear regression for magnitude of control\",\n",
    ")\n",
    "evaluate_predictions(\n",
    "    Y_data, \n",
    "    Y_predicted,\n",
    "    fig_title = \"Comparison of behavior of reactive controller to optimal controller \\n using linear features and only magnitude of control\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}