tutorial_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('../data/macrodata.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1959.0\n",
       "1      1959.0\n",
       "2      1959.0\n",
       "3      1959.0\n",
       "4      1960.0\n",
       "        ...  \n",
       "198    2008.0\n",
       "199    2008.0\n",
       "200    2009.0\n",
       "201    2009.0\n",
       "202    2009.0\n",
       "Name: year, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      1.0\n",
       "      ... \n",
       "198    3.0\n",
       "199    4.0\n",
       "200    1.0\n",
       "201    2.0\n",
       "202    3.0\n",
       "Name: quarter, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', length=203, freq='Q-DEC')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflindex = pd.PeriodIndex(year = data.year, quarter=data.quarter, freq='Q-DEC')\n",
    "inflindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "          ... \n",
       "2008Q3   -3.16\n",
       "2008Q4   -8.79\n",
       "2009Q1    0.94\n",
       "2009Q2    3.37\n",
       "2009Q3    3.56\n",
       "Freq: Q-DEC, Name: infl, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = inflindex\n",
    "data.infl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    177.146\n",
       "1959Q2    177.830\n",
       "1959Q3    178.657\n",
       "1959Q4    179.386\n",
       "1960Q1    180.007\n",
       "           ...   \n",
       "2008Q3    305.270\n",
       "2008Q4    305.952\n",
       "2009Q1    306.547\n",
       "2009Q2    307.226\n",
       "2009Q3    308.013\n",
       "Freq: Q-DEC, Name: pop, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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/j36oap6oGZRH0bMrhFAAAEEEEAAgdQT4Po79az9eU80AP25OjaOjROQjYtLagjYXODXiAPqM2m9meW4x2soLKSQzbP27/T+WXdYL/60xgyybP5smt27qfn3fWOWaO2BM+bfb90fosfqFPfvpIgOAQQQQAABBBDwkgDX316CtPg0NAAtXkCrhs8JyKqVI24EEHj1t3WatOqgCdG5QUkNuacSMD4U+GHFPvX/c6MZQY3iufRHz4bm3w+PX6bwPafMv9+8p5KealDShxGzawQQQAABBBBAIPUEuP5OPWt/3hMNQH+ujo1j4wRk4+KSGgI2F3ju+1WatvGomeWDNYvqvYeq2jxr/05v3Pxdemf6VjPIZhXyaWKXOubfnb5coUU7Tph/9293p55tUtq/kyI6BBBAAAEEEEDASwJcf3sJ0uLT0AC0eAGtGj4nIKtWjrgRQODJr8K1cHukCRFWuaDGPVETGB8KGM0/owkY87unamF9/Fh18++uE1dqztbj5t+vtamg55uX9WHE7BoBBBBAAAEEEEg9Aa6/U8/an/dEA9Cfq2Pj2DgB2bi4pIaAzQUe+mypVu49bWbZpHw+ffu0424zm6fvs/QuX7sRfRdfoZyZVLlITpc4+v+5QT+s2G8ue6JecQ2/L8T8O/Zdmy+1LKeXW5X3WS7sGAEEEEAAAQQQSE0Brr9TU9t/90UD0H9rY+vIOAHZurwkh4CtBWJ/UbZmidz6/bkGts7Z18nduBml9p8sNr/k+/b9IXrU6SMexgdAjA+BxPx6NiujPm2Dzb9f+nmN/lrrWP9cszJ63Wm9r/Nj/wgggAACCCCAQEoKcP2dkrrWmZsGoHVqZatIOQHZqpwkg0BACTQbNU97T14ycw4umF3TezUJKIPUTnbRjkh1+jLc3G35Atk082XHV36f+ipcC5wey34jLFg9mpYxx7/22zr95vThlq6NSmng3RVTOw32hwACCCCAAAII+ESA62+fsPvdTmkA+l1JAiMgTikjMtkAACAASURBVECBUWeyRMCOArVHzFbk+StmasXuyKxFfVrYMVW/yenrJXv05j+bzXgypEujbcPaKigoKHpZh7FLtGb/GXP9yA4h6li3uPl37EeEO9UroWH3Vfab/AgEAQQQQAABBBBISQGuv1NS1zpz0wC0Tq1sFSknIFuVk2QQCCiBSoOm6+LVG2bOebJm0KqBrQLKILWTffOfTfp6yV6X3UYMCFXebBmjl7UcPV+7Ii+a6z/tWF13Vyls/h17+0dqFdM7D1ZJ7TTYHwIIIIAAAggg4BMBrr99wu53O6UB6HclCYyAOAEFRp3JEgG7CURFRal0v6mKinJkltG4G214mN1S9at8nvkmQrO3HHOJ6e8XGqpK0VzRy2LflWl8lMX4OEvM761pWzR+wW7z7w7Vi+iDR6r5VY4EgwACCCCAAAIIpJQA198pJWuteWkAWqtetomWE5BtSkkiCASUwH9Xb+jOQdPj5LxzRJjSpU0TUBapmWybDxZq27HzLrv87Imaalu5YPSyCgOm6cr1m+b6yc83VLVit5qDxu/9mdv08dyd5t93hRTSmMdrpGYK7AsBBBBAAAEEEPCZANffPqP3qx3TAPSrcgROMJyAAqfWZIqAnQROXLiiWsNnx0lp3eDWypk5vZ1S9ZtcjLsuKw6aof+uOR67NoIb3L6iujQspcvXbih4oGtTdu4rTVU6XzYzh0/n7tB7M7ebf4feWUBfPFXLb3IkEAQQQAABBBBAICUFuP5OSV3rzE0D0Dq1slWknIBsVU6SQSBgBPafvKQmo+bFyXfpGy1UOFfmgHFIzUSND64Yj/jG/j3buJT631Ux+oMssdc7vx/Q2G7Cwl0aOXWrOYXxeLDxmDA/BBBAAAEEEEAgEAS4/g6EKieeIw3AxI0YkQICnIBSAJUpEUAgxQW2HDmnsI8WxdnP7N5NVDZ/9hTffyDuYNW+03pg3NI4qd9VpZDGdKyhXZEX1HL0Apf124a3VcZ0ac1lE5fs0RCnrwjXK32Hfu5WPxA5yRkBBBBAAAEEAlCA6+8ALHo8KdMA5DjwiQAnIJ+ws1MEEEimwKp9p/TAuGVxZon9zrlk7obNnQQmrzmkXr+sjWNSo3gu/dGzodbsP60OYx0Nwkzp02jrMNePsvy4Yr/6/bnBnCNmW6ARQAABBBBAAIFAEOD6OxCqnHiONAATN2JECghwAkoBVKZEAIEUF1i4PVJPfhUeZz8/PlNXDcrmTfH9B+IOPp6zQ+/Pcry/L8agYI5MWt6vpRZsj9RTTjXJnz2jwvuHulBNWnVQr/62zlxWuUgOTXmxcSBykjMCCCCAAAIIBKAA198BWPR4UqYByHHgEwFOQD5hZ6cIIJBMgekbj6rH96vizDKhU021rnTri7T8vCtgNO6MBl7sX5ogadvwMBk1efGnNebqsvmzaXbvpi7D/153WP9zGlO+QDbNfNl1jHejZjYEEEAAAQQQQMB/BLj+9p9a+DISGoC+1A/gfXMCCuDikzoCFhb4Y/VB9f7VcSdZTCofPlJN91UvYuHM/Df0h8cvU/ieU/EGuPj15tF3APb/c6O5Pr7He2M3bkvmyaL5rzX336SJDAEEEEAAAQQQ8KIA199exLTwVDQALVw8K4fOCcjK1SN2BAJX4Lvl+zRwsqPZFCMx/L7KeqJeicCFScHM6781R0fOXo53D7/1qK+Ivaf1znTHF36bVciniV1cv/A7b+txdZm40pyjcM5MWtq3ZQpGzdQIIIAAAggggID/CHD97T+18GUkNAB9qR/A++YEFMDFJ3UELCwwfsEuvTXN0WyKSaVfu2B1a1LGwpn5Z+iXr93QnYOmKyoq/vg+erSath49r3Hzd5kD7qlaWB8/Vt1lgyU7T+jxL1aYy/Jmy6CIAa38M2miQgABBBBAAAEEvCzA9beXQS06HQ1AixbO6mFzArJ6BYkfgcAUMD5GYXyUIvbvfy3LqXer8oGJkoJZ74q8oJajFyS4h9fbBuvg6Uv6YcV+c8wT9Ypr+H0hLtus3HtKD33m+Hpz9kzptGFImxSMnKkRQAABBBBAAAH/EeD6239q4ctIaAD6Uj+A980JKICLT+oIWFhg+JTN+mLxnjgZdG1USgPvrmjhzPwz9HnbjqvL145Hd2NH+WT9Ejp96Zr+WXfYXNWzWRn1aRvsMnTdgTO6d8wSc1nGdGmiPyDCDwEEEEAAAQQQCAQBrr8DocqJ50gDMHEjRqSAACegFEBlSgQQSHGBfn9u0I9Od5vF7PCxOsX01v1VUnz/gbaDb5ft1aC/NiWYduidBXTtxs3oD4HE/N4IC1aPpq6PY285ck5hHy0yxwQFSbtHtlOQ8R/8EEAAAQQQQAABmwtw/W3zAruZHg1AN6EY5l0BTkDe9WQ2BBBIHYFeP6/R5LWOu81i9tq+amF9Euu9c6kTkb33EvuOyzRB0k2n9wFWLJRDGdOn0Zr9Z0yIkR1C1LFucReY+B4l3jEiTOnTprE3INkhgAACCCCAAAKSuP7mMDAEaAByHPhEgBOQT9jZKQIIJFPg2W8jNGvzsTiztAzOry87107m7GweW6DbtxGa6eRdpWhOrT941hyWO0t63ZE1g3ZFXjSXfdqxuu6uUthlqgOnLqnxu/Nclm16s42yZkwHOgIIIIAAAgggYHsBrr9tX2K3EqQB6BYTg7wtwAnI26LMhwACqSHw+BfLtWTnyTi7qlvqDv3SvX5qhBBQ+2j74cLor/zG/Lo1Ka0JC3e7GGTPmE7nr1w3l337dB01KZ/PZczxc5dVZ+Qcl2VrBrZS7qwZAsqTZBFAAAEEEEAgMAW4/g7MusfOmgYgx4FPBDgB+YSdnSKAQDIF7huzRGsPOB43jZkupEhO/fNio2TOzubOAlFRUao8eIYuXr1hLv7yqVrq+k3EbaEmP99Q1Yrlchlz5tJVVRs6y2XZin4tVSBHJtARQAABBBBAAAHbC3D9bfsSu5UgDUC3mBjkbQFOQN4WZT4EEEgNgdYfLND2Yxfi7Kp03qya+2qz1AghYPZx8sIV1Rw+2yXfxa83110fL9bZ/64l6DD3laYqnS+by/pLV6+r4qAZLssW9WmuYndkCRhPEkUAAQQQQACBwBXg+jtwa++cOQ1AjgOfCHAC8gk7O0UAgWQKNHpnrg6e/i/OLAVyZNSKfqHJnJ3NnQXW7D+tDmOXmovSpQnStuFhuvuTxTK+6pvQL2JAqPJmy+iy+vqNmyrbf5rLstm9m6psftdGIRVAAAEEEEAAAQTsKMD1tx2r6nlONAA9N2MLLwhwAvICIlMggECqC9QYNkunLl6Ns99sGdNp45ttUj0eO+/wr7WH9NLPa80US+bJovmvNVfXiSs1Z+vxBFPfNrytMqZLG2d96b7/unxBeOr/Gqti4Rx2JiQ3BBBAAAEEEEAgWoDrbw4EQ4AGIMeBTwQ4AfmEnZ0iELACRtNu0qoDypM1ozpUL6I0aYKSZFFhwDRduX4zzrZBQdLuke0UZPwHP68IfDp3h96bud2cq3G5vPqua10NnLxR3y3fF+8+MqVPo63DwuJdF7t28b0r0CuBMwkCCCCAAAIIIOBnAlx/+1lBfBQODUAfwQf6bjkBBfoRQP4IpJ6A8fhn89HzdeDUrUd3jS/J9mt3p8cBxPcYqfMkm4e2UZYM6Tyelw3iF+gzaZ1+jThorny8bnGN6BCisfN36t3p2+LdKF/2jFrZP/5HsUOGzND5y46vBf/avb7qlLoDfgQQQAABBBBAwPYCXH/bvsRuJUgD0C0mBnlbgBOQt0WZDwEEEhJYvf+07nd6l1z+7BkVnkCT6HaK5y5fU5UhMxMcEt6/pfJn56uy3joSH52wTMt3nzKn6xsWrO5Ny2jymkPq9Yvj0WDn/ZXJl1VzXon/Yyy1hs/SiQuOx7e/71pXjcrl9Va4zIMAAggggAACCPitANfffluaVA2MBmCqcrOzGAFOQBwLCCCQWgILt0fqya/Czd0ZT+nuHNFOaT18DPjo2cuq99acBMOe/2ozlcybNbXSsv1+Gr49V4fOOD64Mu7xGgoLKaTwPaf08Phl8eZfvXgu/dmzYbzrGrw1R4fPXjbXfdW5lloEF7C9IwkigAACCCCAAAJcf3MMGAI0ADkOfCLACcgn7OwUgYAUmLPlmLp+E+GS+6oBocoT60uxieHsjrygFqMXJDhsyouNVLlIzsSmYb0bAlev31SFgdMUFeUYHON74NQlNX53XryzNC2fT988XSfedc1GzdPek5fMdZ89UUNtKxdyIxqGIIAAAggggAAC1hbg+tva9fNW9DQAvSXJPB4JcALyiIvBCCCQDIHpG4+qx/erXGaY9XITlSuQ3aNZNx46q7s/WZzgNrxTziPO2w7ec+Kimr8332XM+iGtlSNTel27cVPlB7g2B2MGtq9aWJ88Vj3euVt/sEDbj10w1330aDXdW62I94JmJgQQQAABBBBAwE8FuP7208Kkclg0AFMZnN3dEuAExJGAAAKpJfDPusN68ac1Lrv7uVs91Sudx6MQVuw+qUcmLE9wm68711bz4Pwezcng+AUWbI/UU06PbefKkl5rB7U2B9cdOVvHzl2Js3HMh0Lim/XuTxZp46Fz5qpRD1bRQ7WKUQIEEEAAAQQQQMD2Alx/277EbiVIA9AtJgZ5W4ATkLdFmQ8BBBIS+GP1QfX+dZ3L6rGP11C7EM8e/5y39bi6TFyZIPSnHavr7iqFKYQXBL5bvk8DJ280ZwopklP/vNjI/LvD2CVas/9MnD0916yMXm8bHG8E949dotVO24zoUFmP1y3hhWiZAgEEEEAAAQQQ8G8Brr/9uz6pFR0NwNSSZj8uApyAOCAQQCC1BH5deUB9fl/vsrth91ZSp/olPQphyvrDeuFH1zsJnSd454EQPVK7uEdzMjh+gZFTt2jCwt3myrtCCmnM4zXMv5//YbX+3XAkzsZG889oAsb3i/1V4cHtK6pLw1KUAAEEEEAAAQQQsL0A19+2L7FbCdIAdIuJQd4W4ATkbVHmQwCBhAS+X75PA5zuJjPG9Qotp16h5T1Ci6+R6DzBwLsrqmsjGkoeoSYwuMd3qzR901FzbY+mZfRGmOPOvuFTNuuLxXvibH27u/qML0EbX4SO+fUNC1b3pvE3C72RA3MggAACCCCAAAL+IsD1t79Uwrdx0AD0rX/A7p0TUMCWnsQRSHWBiUv2aMg/m132+2T9Ehp6b2WPYolvHucJXmlVXi+2LOfRnAyOX6DdR4u0+YjjfX0jO4SoY13H3ZVfLd6joVNca2rMZHwAxPgQSHy/Z76J0Owtx8xV1IujDwEEEEAAAQQCRYDr70Cp9O3zpAHIceATAU5APmFnpwgEpMDnC3drxNQtLrnfVaWQxnR0PFLqDsyYeTs1asa2BId2b1pafcPudGcqxtxGICoqSlWGzNT5K9fNUd93ratG5fKaf0/feEQ9vl8dZ5Zvnq6jpuXzxTt77MeGX2xRVq+0rkAtEEAAAQQQQAAB2wtw/W37EruVIA1At5gY5G0BTkDeFmU+BBBISGDs/J16d7pr465+6Tz6qVs9j9BGzdiqMfN2JbhNp3olNOw+z+4q9CiAABl8+uJVVR82yyXbha81V/E8Wcxl6w+e0T2fLokj8mfPBqpePHe8Ui//slZ/rjlkruvepLT6tqNhGyCHFWkigAACCCAQ0AJcfwd0+c3kaQByHPhEgBOQT9jZKQIBKfDR7B36YPZ2l9wrFMiuGS838chjyN+bNHHp3gS3ub96Eb3/SDWP5mRwXIGNh87q7k8WmyvSpgnS1mFtlT5tGnNZ5Pkrqj1idpyN57zSVGXyZYuX9fVJ6/VLxAFzXecGJTXknkqUAAEEEEAAAQQQsL0A19+2L7FbCdIAdIuJQd4W4ATkbVHmQwCBhARGz9ymT+budFmdN1tGRQwI9Qitz6R1+jXiYILbtKlUQOM71fJoTgbHFVi664Q6fr7CXJE7S3qtGdTaZeDNm1EKHjhdV2/cdFm+sn+o8mXPGC/rwMkb9d3yfeY6452CxrsF+SGAAAIIIIAAAnYX4Prb7hV2Lz8agO45McrLApyAvAzKdAggkKDAW9O2aPyC3S7rjbvKdgwPU5o0QW7LPf/jav27/og5PnP6tPrv2g3z70Zl8+r7Z+q6PR8D4xeYs+WYun4TYa4skiuzlrzRIs7gpqPmad/JSy7LjTsFM6VPG+/Ew6Zs1pdOXw5+sGZRvfdQVcqAAAIIIIAAAgjYXoDrb9uX2K0EaQC6xcQgbwtwAvK2KPMhgEBCArEbPzHj1g5qpVxZMrgN9/TElZq79bg5vkSeLC4NqOrFc+nPng3dno+B8Qv8ve6w/vfTGnNlufzZNKt30ziDH52wTMt3nzKXZ0yXRtuGhyXI+s70rRo33/EOx3urFdZHj1anDAgggAACCCCAgO0FuP62fYndSpAGoFtMDPK2ACcgb4syHwIIJCQw6K+N+naZ49HPmHG3e19cfHM9Mn6ZVuxxNJxqlcitiH2nzaHlC2TTzJfjNqqojGcCv6zcr9d/32BuVLVYLv31fNzGau9f1+qP1Y6PehiP/hqPACf0+2DWdn00Z4e5OqxyQY17oqZnwTEaAQQQQAABBBCwoADX3xYsWgqETAMwBVCZMnEBTkCJGzECAQS8I9D3jw36KXx/nMl+61FftUve4fZO2n+yWBsOnTXHtwspqKkbjpp/J/Soqts7YGC0wFeL92jolM2mRkJfbH5vxjZ9Os/xbscy+bJqzivNElQcM2+nRs1wfA26ZXB+fdm5NuoIIIAAAggggIDtBbj+tn2J3UqQBqBbTAzytgAnIG+LMh8CCCQk8Opv6zRpVdyPd3z2RE21rVzQbbgWo+drd+RFc/xT9UvoG6c7C3NlSa+1sT5W4fbkDDQF3G3Uxb5TsE6pO/Rr9/oJSn6xaLeG/7vFXN+4XF5915V3NnLoIYAAAggggID9Bbj+tn+N3cmQBqA7SozxugAnIK+TMiECCCQg0OvnNZq89nCctcYXYI0vwbr7qzdyjo6eu2wOf7V1eb03c7v5d/q0Qdoxop270zEuAYF3p2/VWKd39bWvWlifPBb3XX1nLl1V43fn6fzl69EzvX1/iB6tk3A9v122V4P+2mTuNbGGIQVCAAEEEEAAAQTsIsD1t10qmbw8aAAmz4+tkyjACSiJcGyGAAIeCzz/w2r9u8Hx9d6YCYwG3gstyrk9X8iQGWazydjo3QeqqM/v61223z48TBnSpXF7TgbGFRjy9yZNXLrXXPFIrWJ658Eq8VIdPvOfpm44ojL5s6l5hfy35fw5fL/e+MPxbsFqxXJpcjzvFqQmCCCAAAIIIICA3QS4/rZbRZOWDw3ApLmxVTIFOAElE5DNEUDAbYFnv43QrM3H4ozv0rCkBrev5NY8UVFRKtd/mq7fjDLHf925trpMXOmy/ZqBrZQ7q/tfFnZr5wE2qM+kdfo1wvHItid1uh3VH6sPqvev68whFQvl0NSXGgeYLukigAACCCCAQCAKcP0diFWPmzMNQI4DnwhwAvIJOztFICAFunwdrnnbIuPkfm+1wvro0biPlsaHdOX6DVUYMN1l1ZQXG+nuTxa7LFv8enMVzZ0lIJ29lfQLP67WlPWOOzafb15Gr7UJTvb0U9Yf1gs/rjHnKZs/m2b35qvNyYZlAgQQQAABBBDwewGuv/2+RKkSIA3AVGFmJ7EFOAFxTCCAQGoJPPHFCi3eeSLO7jz5CMTpi1dVfdgslzlW9GupuiPnuCyb0auJKhTMnlqp2XI/T09cqblbj5u5vdamgp5vXjbZuc7cdFTdvltlzlP8jixa2Kf5bee9dPW6xs7bpQOnL+nJ+iVUs4T7X41OdsBMgAACCCCAAAIIeEmA628vQVp8GhqAFi+gVcPnBGTVyhE3AtYTeGT8Mq3YcypO4HcWyqFpbj4CevD0JTV6Z57LHBvfbKM6I2br0tUb5vI/ejZQjeK5rYfkRxE/OmGZlu921Gtw+4rq0rBUsiOcv+24On/teGS7YI5MWt6v5W3nffOfTfp6ya33ERofeVnet6XyZMuY7FiYAAEEEEAAAQQQSE0Brr9TU9t/90UD0H9rY+vIOAHZurwkh4BfCdw/dolW7z8TJ6YCOTJqRb9Qt2Ldfuy8Wn+w0GXsrpHtou8APHHhirn8u6511LhcPrfmZFD8Avd+uljrDp41VxofW3m4drFkcy3ddUIdP19hznNH1gxaPbDVbedt/O5cHTj1nznm047VdXeVwsmOhQkQQAABBBBAAIHUFOD6OzW1/XdfNAD9tza2jowTkK3LS3II+JXAPZ8u1nqnhlJMcMYdXcZXe4OCghKNd83+0+owdqk5LnP6tNoyrK2ajZqnvScvmcs/e6KG2lYulOh8DEhYIPT9Bdp5/II54JPHqqt91eQ33VbtO6UHxi0z582WMZ2MuzgT+hkffjHe+3j1xk1zyMgOIepYtzjlQwABBBBAAAEELCXA9belypViwdIATDFaJr6dACcgjg8EEEgtgbYfLtTWo+fj3d36Ia2VI1P6RENZuvOEOn7huHssb7YMihjQSu0+WqTNR86Z249+qKoeqFk00fkYkLBAw7fn6tAZx113X3WupRbBBZJNtuHgWbX/1PHRlgxp02j7iLAE5z154YpqDp/tsr5vWLC6Ny2T7FiYAAEEEEAAAQQQSE0Brr9TU9t/90UD0H9rY+vIOAHZurwkh4BfCcS+o8w5uPmvNlPJvFkTjXfW5mN69tsIc1zMByQe/myZwvc63lc39N5KerJ+yUTnY0DCAtWGztSZS9fMAT89W0/1y+RJNtm2o+fV5kPXx7j3vNUuwTtANx0+q7s+dv3K84styuqV1hWSHQsTIIAAAggggAACqSnA9XdqavvvvmgA+m9tbB0ZJyBbl5fkEPArgaaj5mmf02O6zsH9/lwD1SyR+Ec7/lp7SC/9vNbcNLhgdk3v1URdvg7XvG2R5vLX2wbruWbcIZacA6D8gGm6et3x2O3fLzRUlaK5kjNl9LZ7TlxU8/fmu8yzbXhbZUyXNt6552w5pq7fOJq+xqDODUpqyD2Vkh0LEyCAAAIIIIAAAqkpwPV3amr7775oAPpvbWwdGScgW5eX5BDwK4HYj5Q6B/f5k7XUqmLij5f+uGK/+v25wdy0RvFc+qNnQz3/42r9u/6IufyF5mX1ahvuEEvqAXD9xk2V7T/NZfPZvZuobP7sSZ3S3M54rNg4Fpx/G4a0VvYEHgH/YcU+9f9zo8v4B2oU1eiHqyY7FiZAAAEEEEAAAQRSU4Dr79TU9t990QD039rYOjJOQLYuL8kh4FcCtUfMVuR5x5d6nYN754EQPVI78Y86fLFot4b/u8XctHG5vPqua129Pmm9fok4YC7v0rCkBrfnDrGkHgDnLl9TlSEzXTZf+kYLFc6VOalTmtsZx4BxLDj/Vg0IVZ5sGeOd+/2Z2/Tx3J0u61pXLKAJT9ZKdixMgAACCCCAAAIIpKYA19+pqe2/+6IB6L+1sXVknIBsXV6SQ8CvBKoPnanTTu+Ucw6uT9sK6tmsbKLxfjR7hz6Yvd0c16ZSAY3vVEtv/rNJXy/Zay5/uFZRvfsgd4glCprAgKNnL6veW3Nc1q4d1Eq5smRI6pTmdmf/u6aqb7o2F5f1baFCOeNvLr722zr9tuqgy34blMmjH5+tl+xYmAABBBBAAAEEEEhNAa6/U1Pbf/dFA9B/a2PryDgB2bq8JIeAXwlUHjxDF65cjzemZxqV0oC7KyYa71vTtmj8gt3muPurF9H7j1TT6Jnb9InTXWJ3VSmkMR1rJDofA+IX2B15QS1GL3BZebv39HniePnaDQUPnO6yyYLXmqlEnvg/AtPpyxVatOOEy/iQIjn1z4uNPNktYxFAAAEEEEAAAZ8LcP3t8xL4RQA0AP2iDIEXBCegwKs5GSPgK4EKA6bpitNHJZzjiGnkJRbbwMkb9d3yfeawJ+oV1/D7QjRu/i69M32rubxZhXya2KVOYtOxPgGBjYfO6u5PHF/eTZcmSDtGhCX4pV5PIG/ejFLpflNdNpn1chOVKxD/+wVbf7BA249dcBlfKm9WzXu1mSe7ZSwCCCCAAAIIIOBzAa6/fV4CvwiABqBflCHwguAEFHg1J2MEfCVQuu+/uhkV/96bls+nb55OvGHX+9e1+mP1IXOS7k1Kq2+7O/Xdsr0a+Ncmc3ntkrn1W48GvkrV8vsN33NKD49fZuaRPVM6bRjSxmt5le03VdedDoYpLzZS5SI5450/ZMgMnb/seudo3mwZFDGgldfiYSIEEEAAAQQQQCA1BLj+Tg1l/98HDUD/r5EtI+QEZMuykhQCfidw42aUysS668s5SHcf6ezx3SpN33TU3PTl0PJ6KbScfl91UK/8ts5cXrFQDk19qbHfOVgloPnbjqvz1yvNcAvmyKTl/Vp6LfyKg6br0tUb5nx/9GygGsVzx5n/4pXrqjR4RpzlGdOl0bbhYV6Lh4kQQAABBBBAAIHUEOD6OzWU/X8fNAD9v0a2jJATkC3LSlII+J1AfO99cw6ySK7MWvJGi0Tjjv0+uP7t7tSzTUpr+saj6vH9KnP7EnmyaMFrzROdjwHxC0zdcEQ9f1htriydN6vmevGR22pDZ+qM0wdhfu5WT/VK54kTzK7IC2oZ612EMYO2Dw9ThnRpKCECCCCAAAIIIGAZAa6/LVOqFA2UBmCK8jJ5QgKcgDg2EEAgNQTOX76mkCGuX3513m+m9Gm0dVjid3Q9MG6pVu07bW46okNlPV63hBbvOKEnvlxhLs+bLaMiBoSmRmq23MekVQf1qtMdlZWL5NCUF713R2WdEbN1/PwV0+7bp+uoSfl8cSyX7jyhjl846uo8YPXAVroja/K/SmzLApIUAggggAACCPilANffflmWVA+KBmCqk7NDQ4ATEMcBAgikhsDpi1dVfdis2+5q89A2ypIh3W3HcnWDywAAIABJREFUhH20SFuOnDPHfPhINd1XvYhW7z+t+8cuNZdnTp9WW4a1TY3UbLmPb5ft1SCndyrWKXmHfu1R32u5Nnpnrg6e/s+c74snaym0YoE48/+x+qB6/+p4tNt5wO2+HOy1QJkIAQQQQAABBBDwogDX317EtPBUNAAtXDwrh84JyMrVI3YErCNw/Nxl1Rk557YBL+rTXMXuyHLbMU1HzdO+k5fMMRM61VTrSgW1/dh5tf5gocu2u0a2U9o0QdZB8qNIP1uwS29PS7mvKrcYPV+7Iy+aGY99vIbahRSKIzBm3k6NmrEtXpnbfTjEjygJBQEEEEAAAQQQMAW4/uZgMARoAHIc+ESAE5BP2NkpAgEncOjMf2r49tzb5v3X8w1VtViu246pNXy2TlxwPDr6wzN11bBsXh08fUmN3pnnsu2GIa2VPVP6gLP2RsLvz9ymj+fuNKdqF1JQYx+v6Y2po+do++FCbT163pwv5k7O2DsY9NdGfbtsX7z7/enZeqpfJu57A70WJBMhgAACCCCAAAJeFuD628ugFp2OBqBFC2f1sDkBWb2CxI+ANQT2nrioZu/NdwnWeH/bqYtXzWVfda6lFsGOx0CNpl6BHJmUPq3jQw+xvx77Z88Gql48t85cuqpqQ10fMV7Rr2X09vw8Fxg+ZbO+WLzH3PDBmkX13kNVPZ8ogS3u+XSx1h88a65994Eqerh2sTiju30boZmbj8U7y+dP1lKreB4b9lqQTIQAAggggAACCHhZgOtvL4NadDoagBYtnNXD5gRk9QoSPwLWENh5/LxC33d9RDe4YHaXu8BGPVhFD9Uqpms3burJL8O1bPdJFciRUd93ratyBbLr5s0ole431SXhGb2aqELB7Lp6/abKD5jmsm7OK01VJl82awD5WZR9/9ign8L3m1E9Wb+Eht5b2WtRPjhuqSKcPuYy7L7K6lSvRJz5YzcKnQe8/3BV3V+jqNdiYiIEEEAAAQQQQCClBbj+Tmlha8xPA9AadbJdlJyAbFdSEkLALwU2Hz6ndh8vMmMz3s3XoEweLdpxwlzWNyxY3ZuW0Q8r9qn/nxvN5fdULayPH6uui1euq9LgGS75Ob83sHz/abp646a5/u8XGqpK0ds/UuyXWH4QVK+f12jy2sNmJD2altEbYcFei6zj58u1dNdJc76Bd1dU10al4swf+2vBzgOG3ltJT9Yv6bWYmAgBBBBAAAEEEEhpAa6/U1rYGvPTALRGnWwXJScg25WUhBDwS4H1B8/onk+XmLFlTJdGbSsX1F9OTabuTUqrb7s79eRX4Vq4PdIcWyJPFi14rbkiz19R7RGzXfJbPbCVjEeJjV+1oTN15tI1cz3viEv6ofDstxGa5fTobe9W5fW/luWSPmGsLTt/Ha752xw1fr1tsJ5rVsZllHEnqHFXZ1RU/Lt9rU0FPd+8rNdiYiIEEEAAAQQQQCClBbj+Tmlha8xPA9AadbJdlJyAbFdSEkLALwVW7TulB8YtM2PLnjGdHqxVVF8v2WsuM94zN+SeSqoxdJbLnXzGh3y3DGuro2cvq+ko1/cIbh3WVpnSp42ew/jIiPGxkZjfF0/WUijviEvS8fDEFyu0eKfj7swBd92pZxqXTtJc8W0U+91+L4eW10uhrg3Gw2f+U4PbfDjG23clei05JkIAAQQQQAABBBIQ4PqbQ8MQoAHIceATAU5APmFnpwgEnMDy3Sf16ITlZt65s6SPfuTzvZnbzWUtgvPr4VrF1OP7VXF8Zr7cRNdvRMV5jHjniDAFBQVFj2/zwUJtO+b4suxHj1bTvdWKBJy1NxLuMHaJ1uw/Y041skOIOtYt7o2po+d44cfVmrL+iDnf883L6LU2ro8Yr95/WvePXZrgPh+vW1wjOoR4LSYmQgABBBBAAAEEUlqA6++UFrbG/DQArVEn20XJCch2JSUhBPxSYNGOSHX6MtyMLX/2jOoVWl79/txgLqtaLJfK58+m31YdjJPDuMdrKF/2jHrwM6e7CDOl04YhbcyxKd208kvYFAqq7YcLXT7Q8uEj1XRfde81U3v/ulZ/rD5kRv9s41Lqf1dFl2ymbjiinj+sTjDDe6sV1kePVk8hAaZFAAEEEEAAAQS8L8D1t/dNrTgjDUArVs0GMXMCskERSQEBCwjM23pcXSauNCMtkiuzjA8/ON/tZyy7cv2GTly4GiejV1uXV0jRXHrqK0cT0fhC8Ip+oebYTl+ucPmoiLcfW7UAs9dCbPLuPO0/dcmcb0KnmmpdqaDX5u/7x3r9FH7AnO+p+iX0ZqyvDH+1eI+GTtmc4D6NO0a/6lzbazExEQIIIIAAAgggkNICXH+ntLA15qcBaI062S5KTkC2KykJIeCXAjM2HVX37xyP9pbMk0WjHqqqh5zu6Ltd4B2qF1HrigX0nNMdYaXzZtXcV5uZm3X/LkIzNh0z/+4VWi76LkN+ngvUGj7LpRH7fde6alQur+cTJbDF4L826ptl+8y1j9Upprfur+Iy+q2pWzR+4e4E91mn5B36tUd9r8XERAgggAACCCCAQEoLcP2d0sLWmJ8GoDXqZLsoOQHZrqQkhIBfCkxZf1gv/LjGjK1s/mwa36mmWo5e4Fa8VYrm1FP1S+qV39aZ4ysXyaEpLzY2/479WGm3JqXVr92dbs3PIFeBioOm69LVG+bCP3o2UI3iub3GNOLfzfp80R5zvvtrFNH7D1dzmf9/P63R3+sOm8vyZsuoExeumH8HF8yu6b2aeC0mJkIAAQQQQAABBFJagOvvlBa2xvw0AK1RJ9tFyQnIdiUlIQT8UmDymkPq9ctaM7Y7C+XQz8/WU9WhM92KN2uGtHo9LFiD/tpkjq9T6g792t1xB9jAyRv13XLHXWXGRyuMj1fw80zg5s0ole431WWj6b0aK7hgDs8mus3oUTO2asy8XeaIu6sU0qcda7hs8fD4ZQrfc8pcVr90Hi3bfdL8u2juzFr8eguXbSLPX9GHs7fr8rWberFFWZXMm9VrMTMRAggggAACCCCQXAGuv5MraI/taQDao46Wy4ITkOVKRsAIWFLg14gD6jNpvRm7cUffX883VLn+03T9ZpRbOXVuUFITl+41xzavkE9fd6lj/v32tK36bIGjqcRHItxijTPo0tXrqjhohsvyRX2aq9gdWZI2YTxbfTR7hz6Y7fgCtPF494Qna7mMbDpqnvaddLyH8Mn6JfSt02PDOTOn17rBrV22eWzCcrNJaLxT0og7TZpbX4nmhwACCCCAAAII+FqA629fV8A/9k8D0D/qEHBRcAIKuJKTMAI+EfhxxX6XL/7WLJFbvz/XQLVHzJZx11bsX64s6XX1+k2Xx1CNbVbtO20OvatKIY1xumvskzk7NHqWo6kUemcBffGUa1PJJ8lbbKdGPYy6OP8iBoTKeATXW79x83fpnelbzemaVciniU7N3KioKFUYOD36GIj5Dbu3kgY63QGaNk2Qdo4IU1DQrQbff1dv6M5B011CnPVyE5UrkN1bYTMPAggggAACCCCQLAGuv5PFZ5uNaQDappTWSoQTkLXqRbQIWFXgm6V7Nfhvx+O79UrfoZ+71VfbDxdq69HzcdIyPvqx4/h5bTx0zlyXOX1a/XfN8V66h2oWjf6QSMwv9ldjjUdGf+pWz6pkPot7/8lLajJqnsv+twxtq8wZ0notpi8X79Ewpy/8NiiTRz8+66jV6YtXVX3YLJf9/fRsPT32+fIE4zpw6pIav+sat/GIuPGoOD8EEEAAAQQQQMAfBLj+9ocq+D4GGoC+r0FARsAJKCDLTtIIpLrAF4t2a/i/W8z9Ni6XV991rauOny/X0l2O97rFDPi0Y3XN3HTM5SMQsYM2Hgkeck8lc/EvK/fr9d83mH9XNR4zfqFRqudq9R1uOXJOYR8tMtMwbrDbPbKdeaedN/Iz3tVovLMx5lerRG5Neq6B+Xd8Mazo21J1Rs5x2X14v5bKnyNT9LK1B87ovjFLXNYbH5ppU6mgN0JmDgQQQAABBBBAINkCXH8nm9AWE9AAtEUZrZcEJyDr1YyIEbCiQOxHPlsE59dXnWvrhR9Xa8r6Iy4ppUsTpNWDWunrxXtd3hMXO++ezcqoT9tgc3HsLw2XyZdVc15pZkUun8ZsPGb9wLilZgzGB1g2DW3r1Zh+XXlAfX53vBMydrN23tbj6jJxpbnPfNkzasnrLVR+wDSXOOa80lRl8mWLXjZ36zE9PTHCZf27D1TRw7WLeTV2JkMAAQQQQAABBJIqwPV3UuXstR0NQHvV0zLZcAKyTKkIFAFLC8R+P1/MRx8G/7VR3zh92MFIsmHZPPrhmXqK3dCLDfBamwp6vnlZc/G8bcfV5WtH06hQzkxa1relpd18EfyiHZHq9GW4uWvj3X/GOwC9+Yv9Vejggtk1vVcTcxc/he9X3z8cd3OGFMmpf15spAoDpumK03sBJz/fUNWK5YreLvaHZoxl/doFq1uTMt4MnbkQQAABBBBAAIEkC3D9nWQ6W21IA9BW5bROMpyArFMrIkXAygLvz9ymj+fuNFOI+YBH7K/BGgMG3V1RTzcqpdiPgcbOf3D7iurSsJS5OHzPKT08fpn5d/ZM6bRhSBsrs/kk9hmbjqr7d6vMfRe/I4sW9mnu1Vimbjiinj+sNucsnS+r5jrdrfn+rO36eM4Oc33MB11qDZ+tExccH435vmtdNSqXN3pc7LtMjWWx7xL1NIm/1h7SD8v3q1TerOp3150yvjzMDwEEEEAAAQQQSKoA199JlbPXdjQA7VVPy2TDCcgypSJQBCwtYHzx1WjQxPzuq1ZYHz5aXbHfBWesX/BaM5XIk1WXr936qmtUVPypx368c9Phs7rr48Xm4NhfibU0YCoGn9jded4IZfbmY3rmW8fjukVzZ9bi11uYU78+ab1+iThg/t2pXgkNu6+ymr83X3tOXDSXj3u8hsJCCkX/PXzKZn2xeI9LeB3rFtfIDiFJCtnYj7G/mF+XhiU1uL3jnZNJmpSNEEAAAQQQQCCgBbj+Dujym8nTAOQ48IkAJyCfsLNTBAJOIHZzJuYLvgu3R+rJrxyPm5bLn02zejc1fRq9M1cHT/8Xr9cnj1VX+6qFzXV7T1xUM6eGjbFi67C2ypTee1+vDYTC/bBin/r/6fhAR43iufRHz4ZeTT123fNnz6jw/o7HjJ/6KlwLtkea+4x53PueTxdr/cGz5vJ3H6yih2vdesffy7+s1Z9rDrnEeVdIIY15vEaSYv8t4oBem+R4T2HlIjk05cXGSZqLjRBAAAEEEEAAAUOA62+OA0OABqDFjoPjx48rPDw8+t/KlSuj/508eetLlk899ZQmTpzoUUbTpk3ThAkToueJjIxUvnz5VLt2bXXr1k1hYWEezeXJYE5AnmgxFgEEkiow5O9Nmrh0r7n5Y3WK6637Q3T9xs3ox3ZX7z+j9GmDNO7xmgqtWMAcF7sR5Lz/rzrXUotgx9jI81dUe8RslxCNd9cZ77Dj575AQl9sdn+GxEcu331Sj05Ybg7MlSW91g5qbf7d5oOF2nbsvPn3ew9V1YM1i8b5avTAuyuqa6Nbj4EbjWSjsej8a1Amj358tl7iAcUzInYjtEKB7JrxsuM9hUmalI0QQAABBBBAIKAFuP4O6PKbydMAtNhxEBQUlGDEnjQAb968Gd3k+/LLLxOc75lnntH48eOVJk0arytxAvI6KRMigEA8Av3+3KAfV+w31zxVv4TevLdy9N9Xr9+U8fhuwZyZVChnZpeth/6zWV8tcX2sM2bAz93qqV7pPOb4S1evq+KgGS7bL3ytuYrnyUJNPBCI/V7GmA+2eDBFokNX7z+t+8c6vjScJUNabXb60nDVN2fq7H/XzHl+eKauGpbNq+7fRWjGpmPm8pdDy+ul0HLRf9/18SJtOnzOZd8VC+XQ1JeSdtfexCV7NOSfzeZ8pfNm1dxX+ap0osVlAAIIIIAAAggkKMD1NweHIUAD0GLHgXMDsHjx4goODtbMmTOjs/CkAdi3b1+9/fbb0dtVr15dffr0UZkyZbRr1y69++67WrNmTfQ6Y9zIkSO9rsQJyOukTIgAAvEI9Jm0Tr9GHDTXGHdtGXdvJfaLfReW8/h/XmikkKI5zUVRUVEq3W+qyzsDp/6vsSoWzpHYbljvJPDWtC0av2C3uaRD9SL64JFqXjXaeOis7v7E8b7GdGmCtHNku+h9/Hf11rsfnX+zezdV2fzZ9Mqv6/T7asdx9EyjUhrw/8dRvZFzdPTcZZftiuTKrCVvON4t6EkSse+ELHZHZi3qk7S5PNkvYxFAAAEEEEDAvgJcf9u3tp5kRgPQEy0/GDt48ODoR3SNfwUKFNDevXtVqtStx5DcbQBu375dlSpV0vXr11WrVi0tXLhQmTM77n65dOmSmjZtqoiICKVLl05btmxR2bJlvZo9JyCvcjIZAggkIBD7/Ww9mpbRG2HBiXrFflTUeYM5rzRVmXzZXOaoPHiGLly5bi6b1KO+apW8I9H9MMAhMHDyxuiPs8T8kvMhjYRcdxw7r1YfLHRZvXtkO6VJExT9kQ/nj28Ygza+2UbZMqZT7EfJH61dTG8/UEVG87f8gGm6dsP1izFZM6TVJqc7Cz2p89j5O/Xu9G3mJgVzZNLyfi09mYKxCCCAAAIIIICAiwDX3xwQhgANQIsfB0lpAPbs2VPjxo2LznzZsmWqVy/ue4qWL1+u+vXrR48xxo8ZM8arUpyAvMrJZAggkIDA8z+u1r/rj5hrX2xRVq+0rpCo1/Hzl1VnxJx4xy3v2zL6sWHnX92Rs3Xs3BVz0cQutdWsQv5E98MAh0Dsu+yebVxK/e9K/G5NTwz3nbyopqMcX9g1to35YMuyXSf12OeO9wNmz5hOG95sEz396Jnb9MncneauYj7yYTwubDw2HN9v+/AwZUjn+Ss0Pp6zQ+/P2m5OmTdbBkUMaOVJmoxFAAEEEEAAAQRcBLj+5oAwBGgAWvw48LQBaNytULRoUR0+fDj68WHj7r6Efsb6bdu2qUiRIjpw4IBu9/5BTxk5AXkqxngEEEiKQOx3t/VuVV7/a3nr3W23+xnnyipvztT5y467+mLGrxvcWjkzp3fZvMXo+dodedFcNvbxGmoXUiix3bDeSeC571dp2saj5hKjTka9vPk7cvY/1X9rrsuUMfWcvOaQev2y1lxnPPprPAJs/CYs3KWRU7ea65qUz6dvn64T712DMYNW9g9VvuyefwgmdrMxR6Z0Wj/kViOSHwIIIIAAAgggkBQBrr+Toma/bWgAWrymnjYAd+/eHf2uP+PXvXt3ffbZZwkKGOuNLwQbP2O7mEeNvUHGCcgbisyBAAKJCTw9caXmbj1uDuvTtoJ6NnPvlQb3jVmitQfOxNnFjhFhSp/W9c6u9p8s1oZDZ82x7z5YRQ/XKpZYeKx3Eoj95eW+YcHq3vTW/6+89Tt54YpqDnf9YnNMo27c/F16Z7qjyde4XF5917Vu9K6ND8kYH5SJ+VUvnkt/9myoiL2n9OBny+INb9bLTVSuQHaPQ3972lZ9tmCXuV3sD5V4PCEbIIAAAggggEDAC3D9HfCHQDQADUCLHweeNgCnTJmi9u3bR2f9wQcfqFevXgkKGOt79+4dvf7ff/9Vu3a3XpTujR8nIG8oMgcCCCQm0OnLFVq044Q5bMBdd+qZxqUT2yx6fexHUo1lxiOdxqOdsX+PTlim5btPmYuHtK+ozg1vvZ+Vn3sCD322VCv3njYHD7u3kjrVL+nexm6OOn/5mkKGuD6ya3ysw/hoR+z3/D1Ys6jee6hq9Mz/rDusF3+69XEs41cufzbN6t1U0zceVY/vV8W799961FftJLwHctiUzfpyseML1OnTBmnHCO/9/9dNKoYhgAACCCCAgI0EuP62UTGTkQoNwGTg+cOmnjYAjTv+nnvuuejQf/vtNz344IMJpjFp0iQ99NBD0euN7Yw7At39GSeY2/2OHDmiOnXqRA8xHi82HkvmhwACCHhbIDmNuTHzdmrUDMfHGIzYcmdJrzWDWscJs+vElZrjdKfha20q6Pnm7t1p6O2crTrfXR8v0qbD58zwRz9UVQ/U9O7/G65cv6EKA1y/9Dvv1WYqlTerYj8u/kLzsnq1za33Rc7bdlxdvl5pxhbzYY7bfS368ydrqVXFAh6XY9BfG/XtMsfHUIwJ9rzVzquv4fA4KDZAAAEEEEAAAUsL0AC0dPm8FjwNQK9R+mYiTxuAo0aNUp8+faKDnTZtmtq2bZtg4Mb6mLv+3nvvPb3yyituJ+nJ+wJpALrNykAEEPBQ4MFxSxWxz3FX2YgOlfV43RJuzRLf3V3GnWLGHWOxf8//sFr/bvD8YyNuBRIgg4wv8Bpf4o35jXu8hsK8/B5F492OpfpOdRGd0auJKhTMrtiPfA+7r7I61bt1rKzad0oPjHM86mt8Gdj4QvAnc3ZotNMHO5wnHvVgFT2UhMfA+/6xQT+F73eJMb7HzgPksCBNBBBAAAEEEPCCAA1ALyDaYAoagBYvoqcNwGHDhmnQoEHRWc+ZM0ctWsS9kI0hmTt3rlq2bBn9p7HdgAED3NaiAeg2FQMRQCAFBe79dLHWHXR6N98DVfRwbffezbfz+HmFvr/QJbryBbJp5su3Pgzh/EuNL9imIJNfTB37S8rfPF1HTcvn83ps5ftP09UbN815/3mhkUKK5lTDt+fq0Jn/zOUTOtVU60oFo//edvS82nzoeizsGtlOxuO6E5fujTdGTx43d57g1d/WadIq17votwxtq8wZ0nrdggkRQAABBBBAIDAEaAAGRp0Ty5IGYGJCfr7e0wZgat0ByCPAfn7gEB4CASLQ7qNF2nzE8VjpB49UVYfq7j1WevX6Td05aLpu3IwytaoWy6W/nm8YR6//nxv0wwrHXVvGnWPGHWSB/DPutpux6ajOXLqm9lULK2vGdLflCBkyw+Wry0l9h15i5pUHz9CFK46vO//+XH3VKJ5b5QdM07UbjlobdTbqbfwOn/lPDd52/Xrw+iGt1e+PDZqy3nHnp/O+nR8hTiwm5/W9fl6j/2PvPKCiuLo4/rf33hUbShEERRHBgqKoaDQaY0mMJhp7L7HEjj3RaIzGGo01MXaNiqAgKioWLIgiKGBXVKxYENt3Hvl2Zmd2FnZhZtlZ7pzznfOx8959//t742Tf3ffu3XXhnqCLVOVpY2xSWyJABIgAESACRCBrE6AAYNaef433FABU+XNgbADQVDkA08JKL6C0CNF9IkAE5CDQYsERXHv4kjP1ezcXtHUub7DpZr8cRpzWsdQG1Urg777uOv3FhRs617XCvP8XkDB4MAtrOGf/Faw4EpfiFSuasX1QAxTOm0vSSxYsrD5xvyDYundoI9SsUER2KnVmHMSTV8mc3b/71od92cJgn2tfoeOboVyRfCkfvUh6B2eJ4iGjt4QjNO6xpMbu7pUws4OT0frFx8mZgbOTvFGiYB6jbVEHIkAEiAARIAJEgAgwArT+pueAEaAAoMqfA2MDgFQFWOUTTvKJABEwioA4r9zy7nXhU/O/Y52GXH3WnUHglYdcU+8aZbDqO1edrvMCorAkOJb7vK1zOfzerY4hQ1hsG3GgrUPt8lj4lYukv6kV55AbkPvsIMS/SOLMru1VD2UK50Xr30K4z7JlQ0q151w5sqd89vHjJ1hP0M0dOHTTOVx9wAeYtbV+5lwOS9LxDPRbH4YDkQ8Ebp8c3xxli+SVGwXZIwJEgAgQASJABLIIAQoAZpGJTsNNCgCq/DkwNgAYFxeHatWqpXjNqvqyHYH6LnZ/5cqVKbdZv6pVq8pGi15AsqEkQ0SACKRCQJzX7c+ermhmb3hl1p/2R2H5ET6w18XVCnM71dIZUVwMQl+gMKtMFjs+zY7Uiq+FXWujg0sFnc+fvkqGi2gH3qkJzVMCc3JfnnODcevJa84sy/WXJ1cOfPfnae6zkgVzI2xSC8HQ4qPD2wZ4YMDGs0h4ye8m1O7QqHpJbOxT32j5vdacRnD0I0G/kLFeqFg8v9G2qAMRIAJEgAgQASJABBgBWn/Tc8AIUABQ5c+BsQFAdszKysoK9+7dg729Pa5cuaKXQI0aNRAVFYUKFSqAVeo1prBHWljpBZQWIbpPBIiAHATEhSXWf+8GTyMKS8Q9eplS/IHlhsueDVj/fX00simpI21VSBxm7uPfp41tSmJDb+ODP3L4bA42HiYmwW1WkI6UQnlywm94Y51gFiu+wYK12leEb0sU0nNkOCM+ei84ghjRsfDXyR8wdttFzmyNcoWxf3hjwTAec4Jw/zm/c3D1d67ouz4MWikiBe1rViiMvUOFNgzR3X3VKRyLSRA0PfRDE1iXKmhId2pDBIgAESACRIAIEAEdArT+poeCAoAW8AwYGwBkLg8aNAjLli1L8T40NBTu7rr5rE6ePAkPD4+UNqz9kiVLZKVFLyBZcZIxIkAE9BAQH0Pd1NcdHtVKGMUr9tFLHI5+BJdKRVOKRUhdG07exORdl7hb9aoUw9YBDYwax5IaX32QiJa/CqvmavyrW7kYNvdzR87/H69ln197kIgWovasym4OFnWV+RIXhlnQpVZKYG9eQDQ3UlO7Uljby00wsjifpG87B/juidSrzqpYPhwb18xo9V1XhOLU9SeCfgdGesK2TCGjbVEHIkAEiAARIAJEgAgwArT+pueAEaAdgCp/DtITALx69SocHBzw4cMHuLq64ujRo8iX779E5+x68+YNPD09ERYWhpw5cyIyMhI2NjaykqIXkKw4yRgRIAJ6CIgry7Jjm65VisvOa2vYbYzR2kHmVKEI9gxtJPs4ajF4Ku4xuq48qVfuSG9bDPfm/7sSfvsZ2i85zrXPkzM7ome2VsTdDkuO48LtZ5ztnzo6pVSKXh96k/tM6qh3x6XHce4W369ngypYe+KGXo1st2PEtFZG+yAehxlQqiCK0eKoAxEgAkSbiq98AAAgAElEQVSACBABIqBKArT+VuW0yS6aAoCyI1XW4LFjxxATE8MNkpCQgDFjxqT83bBhQ/Tp00cgoGfPnpKCxo8fj59++inlnouLC8aNG5eSGzA2NhY///wzzp8/n3KPtZs9e7bsTtELSHakZJAIEAEJAvaT9yPp3Ufuzu7BDVGrYlHZWe0Jv4ehm/57b7KreumCCBzVRPZx1GLQ/1J8Sn48fRfb2belvwfYbkB2nYhNQLc/TnHNi+XPhfNTWiribpcVoTittcNuentHnIh5DP/L8dx4Q5tVxw8t7QTjf/vnaRy9yufm87IrpZOrTyz42iy+kIihznz++zFcvPNc0HzX4IaorcBza6gmakcEiAARIAJEgAiomwCtv9U9f3KppwCgXCRNZIcF9NatW2fwaCznn9T18eNH9O3bF3/++adeW717904pApI9+39VEOW86AUkJ02yRQSIgD4C1Sb44YNWkrZ9wxrBsXwR2YEFRj5An/VhnN30Hv+UXVgmGfzn9C38uCOCG71Egdx49uadYC5syxREwAjPlPyyQVceoPc6nl+Fovlw/Efjj88a4m6P1acQco3PsTfpsxrYF3Ef57V2981o74geHlUE5gb/fQ77Lt7nPrMuWQBxCa+4vwvlzYnEpPeCPmGTvFGyYB5DZHFtfBYeRVR8oqDP1gEeqKfAzlWjhFFjIkAEiAARIAJEQLUEaP2t2qmTVTgFAGXFqbwxuQKAGqV+fn4pQb4zZ86A7SYsWbIk6tWrl1IhuHVrZY5fsbHpBaT8s0IjEIGsTuDjx0+wnuAnwHBwpCdsFMilduxaArqv5newsaAPC/5k1WvZ4Vj87B/Fud/EtlTKbr8FB68KkGiKW/wbfg/DtHZQ2pQuiIMK7aDsvfYMgqIecjrGtLLD36dugRUi0VwretRFK8eyAq0/br+If87c5j7LmT0b3msFl9nOUnaUWftiu0DZblBjLnGREtb377710aCabvEZY+xSWyJABIgAESACRCDrEqD1d9ade23PKQBIz0GmEKAXUKZgp0GJQJYi8Pb9B9hN8hf4HDy6KaqWLCA7h7AbT9BpeShnN73532QXlkkG5/hdwYqjcdzoHWqXx/wutcGKsjx/8477XFOVefOZWxi3nd8xyIJp7Li2EteADWcFx32HNbfB8sOxSP7AHxXfOagBXEQFX2bti8QfIdf1SvJxLIuQa4/wKvkD12b7QHbM2bick03mBePm49eCcdZ97wYWRKWLCBABIkAEiAARIALpIUDr7/RQs7w+FAC0vDlVhUf0AlLFNJFIIqBqAq/evofj1ACBD8fGecGqWH7Z/bp09znaLj7G2WW7w2Jmt5F9HLUYHLstHFvC7nByWcEM388d0XZxCC7dfcF9PvsLJ3SrXwlrjl/HNK2Kuh7WJbCpn26Fejn8ZzsN2Y5DzfW1WyVsOn1LYPrEj81QvihfHIvd/C3wGn4NFO5g1O7E/DgS/Uiwk3D1d65oXqOMUbIbzAnCvedJgj7psWPUoNSYCBABIkAEiAARsGgCtP626Ok12DkKABqMihrKSYBeQHLSJFtEgAhIEXj2Ohm1px8U3Do1oTnKFM4rO7CYh4nwXnBUYDdmVmvkzCF/DlXZxStgsO/6MByMfMBZ1lT97b8hDAGX+c8He1XDmFb2WBIcg3kB0Vx77xqlseq7egooA0ZvDce2s3xwsrFNSUFOQDbo1ZmtkTuncO7+PHYd0/dG6tU0rFl1BF55mFJRWHPN71wLX9a1MsqPerMC8SjxraDP8u514FOznFF2qDERIAJEgAgQASJABDQEaP1NzwIjQAFAeg4yhQC9gDIFOw1KBLIUARZEYcEU7evc5BYoXiC37BzuPH2NRj8HC+xemtYKBfPklH0sNRjsvPwEztx4yklllXa/9aiCGXsjsfoYf4y2fe3y+O0rF8wLiMKS4Fiufbta5bH4axdFXJ2wMyIl55/mYkfCr2sV82AFS85ObqEz9paw2xi77aJeTdM+d0TA5XiciH3MtZnc1gG9G1U1yo/a0w/g2Wv+mDTrzFgwJnQRASJABIgAESACRCA9BGj9nR5qlteHAoCWN6eq8IheQKqYJhJJBFRN4N6zN2jw0yGBDxd9W6Jw3lyy+5Xw8i1cZwqDjempACu7sEwyKC5ksehrF3xeqzzEu+hcKxfDtoEN4PvvZaw9cYNT29W1In7u5KyIevFYuXNkF+T/sy9bCP4jPHXG9r90HwM2ntOr6fduLvCLuA+/iHiuzdBm1fFDSzuj/Kg5NQAv3wqrCS/oUgsd6xi3k9CoQakxESACRIAIEAEiYNEEaP1t0dNrsHMUADQYFTWUkwC9gOSkSbaIABGQInDr8Wt4zhPuyoua4YO8uXLIDowFbFjgRvtSKt+g7OIVMOg68yASXiZzljf0dkNjm1I4cDke/Tac5T4vWzgvTk5ojnHbLmJzGF9ht1fDKpjazlEBZYC4QIl4EE/bUmDFScSXuNKz+P6mvu7Yc/GeYHdhD/fKmNGhplF+2E7aj+T3fEES1nnul87oUq+iUXaoMREgAkSACBABIkAENARo/U3PAiNAAUB6DjKFAL2AMgU7DUoEshSBmIcvwXaiaV9K5eV7/+Ejqk/cLxgrcJQnqpculKWYM2c/ffoEm4n78f7jJ873vUMboWaFIrh87zk+W8QXS8mWDYie0RqjtlzA3ov3ufaa3IBKwJt/IBqLD8XoNd25rhXmda6lcz/89jO0X3Jcb78DIz2x6/xdLD2csaPM1uP3QQtdyngzO9REd/fKSuAgm0SACBABIkAEiEAWIEDr7ywwyQa4SAFAAyBRE/kJ0AtIfqZkkQgQASGBqPgX8FkYwn3Igk1xs9sgG/s/Clw2E/3w7oNu0EuBocza5Iukd3D2PSDQqNkN+fzNO9SaJrx3ZExTTN8TiaCoh1yfMa3sMNiruiJ+Lg66hvkH9Vfz1Rd8jH30Es3nCwPK2gLPTvLG9nN3MNsvivuYFRjZ0Lu+wX58+PgJ1Sb46bT3beeAng2NyyVo8KDUkAgQASJABIgAEbB4ArT+tvgpNshBCgAahIkayU2AXkByEyV7RCBrEmC7zUKuJeDjp09oYltKENyLuPMc7X7nd5uxqq6suqtSl9PUACRq5W7bNsADrlWKKzWc2dqVOnp9eVorFPh/QRQxp7/71MeiQ9dwMu4J59PUdg7opVDAa8WRWMzZzwfpxCA1BUvEnz9MTILbrCBJ7tmzAddmtcH2s3cwdjtfKMTZqgj+HdLI4LlKevcB9pP9ddpPbFMDfT2tDbZDDYkAESACRIAIEAEioE2A1t/0PDACFACk5yBTCNALKFOw06BEwOIIjN9xEZtO/5c7rlNdK/yidXTz3K2n6Lj0BOdzgdw5cHm6j2IMWBEQVgxEc23sXR+NbEoqNp65GhYflWVFNqJn+nDBWZ+FRxEVn8jJZ/nt/jp1E+F3ngs+Uyrn3Zrj1zFtT6RefMu714FPzXI6998kf0CNKbrBOdawZMHcCJvUIqUKcH+tHIeViufH0bFeBk9VYtI7OIl2T7LOY33sMKipMjsiDRZHDYkAESACRIAIEAHVEqD1t2qnTlbhFACUFScZM5QAvYAMJUXtiAAR0EfgdfL7lOOkmmO3ObJnw4UpLVDo/1V+T8U9RteVJ7nuRfPnwoUpLRUD2ujnQ7jz9A1n/49vXdHCoYxi45mr4cPRD9FzzRlOXulCeXB6ojf3d591ZxB4hT/uO6xZdfhdigfL2ai5Fn/tgna1yiviIgs2Ttx5Sa/tHYMaoE6lYjr3pXIbahrZlSmEgJGeED9zhfLmRIRvK4P9ePIqGXVmHNRpP9LbFsO9bQy2Qw2JABEgAkSACBABIqBNgNbf9DwwAhQApOcgUwjQCyhTsNOgRMCiCMQ/T4L7HOGRzJCxXqhYPH+Kn8djEvDNqlOczyUL5kHYJD4QJTcMVnDEVEEsubXLaY8Vwhix+QJnUhMc03zg++9lrD1xg7vfsU4FnIp7grvP+ODpnz1d0cxemeDp1rDbGLONP6Yr9j216s21px/As9fvdHB5WJfApn7uuPogES1/PSq4b0zhmYcvkuA2W/eY8RCv6hjdyk7OaSJbRIAIEAEiQASIQBYiQOvvLDTZqbhKAUB6DjKFAL2AMgU7DUoELIrA9YRX8PrlsMCngyM9YVPmv8q7wdEP0UtrJ1r5InlxYnxzxRi0W3wMEXf5Y6zzOjmjs2tFxcYzV8PiI7b1qxbH5v4enNw/jsZhlt8V7m+3qsVTAmfagbVNfd3hUa2EIi7uvnAXw//hA5TiQdhx5Tw5c0iO3XjuIdx+wgcqNY3aOpfD793qQCqAd25yCxQvkNsgX+48fY1GPwfrtO3fxBrjW9cwyAY1IgJEgAgQASJgqQRYMbGJOyNw/tazlFMWE9rUAMvxTFfaBGj9nTajrNCCAoBZYZbN0Ed6AZnhpJAkIqAyApH3XqDNIr7KL5O/Z0gjOFkVSfHkwOV49MtAPjZjcXRefgJnbjzlus3oUBM93Csba0b17RccvIpFQdc4P3wcy2J5j7rc3/sj7mPgX+e4vysUzYdHL98i+f1H7rN/hzSEs1VRRVj4X7qPARv58bUHKZY/F86ncky89W8huHL/hY6ung2qwPdzR0gV8Tj0QxNYlypokC83El6hqSiozTp+37AqprRzMMgGNSICRIAIEAEiYKkElh2Oxc/+fCGvFT3qopVjWUt1V1a/aP0tK07VGqMAoGqnTt3C6QWk7vkj9UTAHAiIi3wwTVsHeKDe/yvv+kXcxyCtQFO1UgUQ9ENTxaT3WH0qpSKx5pr0WQ30aZz1KrdO2X0J60Nvchy+dquIOR2dub/F1ZlZBd2Pn4TTEjjKE9VL/7eTU+7rUNQDfL82TNKsfdlC8B/hqXfILitCcfo6X61Y0/CHFrYY2vy/HH01JvvjzbsPnA19OQWlBrn2IBEtREeIWbtvPSpjevuacqMge0SACBABIkAEVEVg0F9n4RcRz2mmFBmGTx+tvw1nZcktKQBoybNrxr7RC8iMJ4ekEQGVEDgRk4BuWjn+mOwNvd3Q2KZUigfio55pBXcy6nafdWEIvPKAM6MdFMqobTX1H7rpPPaE3+MkD2xaDeN87Lm/9RW60PbxxI/NUL5oPkXcPnYtAd1X87khtQdpbFMSG3rX1ztu77VnEBTFFzDRNJz9hRO61a+U8qfHnCDcf57E2VjTsx687Esb5Mvle8/x2aJjOm3FQVSDjFEjIkAEiAARIAIWRkD8XYtSZBg+wbT+NpyVJbekAKAlz64Z+0YvIDOeHJJGBFRCIOjKA/ReJ9zJtbJHXbT8/1GQbWfvYPTWcM4bpwpFsGdoI8W8G/L3Oey9eJ+zP9irGsa04gNfig1sZobFOyEntLFHP89qnEpWTddxagBeJ/O75MQusGrORfMbljfPWPfZDj62k0/q6lTXCr90rqXX5Ih/zmPXBT64qWm4vHtd+NT87wiSz8KjiIpP5Gws6FILHetYGSQz/PYztF9yXKdtWroMMk6NiAARIAJEgAionMB3f57GkauPOC96N6qKyW0pRYYh00rrb0MoWX4bCgBa/hybpYf0AjLLaSFRREBVBPZdvI/BfwtzuS362gWf1yqf4sem07cwfkcE55NLpaLYOaihYj6O2RqOrWfvZPkvpW0Xh+DSXT5P3twvndGlnrAYSosFR3Dt4Uu9c5FaIY6MTuCF28/QQSLIxuwOaloNY7V2K4rHmrQrAhtP3tKRsG2AB1z/f/T8q5WhOBnHHxOe0tYB3zeqapDssBtP0Gm5bnCyfe3y+O0rF4NsUCMiQASIABEgApZKoNsfJ3Ei9jHn3ncelTGNUmQYNN20/jYIk8U3ogCgxU+xeTpILyDznBdSRQTUREC8w49pn9vJGV3+X3l3Q+gNTN59mXOJVZvdolWNVm5fJ++6hA0n+dx339SvhFlfOMk9jNnba/jTIdx9xlfK1d6VqRHfa81pBEfzv+BrO5UzezZcm9Ua2bJlU8RXqeIxmoF82zmgZ0P9wTqWeJwlIBdfwaObomrJAikfD9x4Fvsv8fmJhjW3wagWtgb5Ehr7GF//cVKn7WdO5bDkmzoG2aBGRIAIEAEiQAQslYC44BpLv8HScNCVNgFaf6fNKCu0oABgVphlM/SRXkBmOCkkiQiojAALtrGgm/Y1vb0jvvWokvLR6mPXMWNvJHe7YfUS+KuPu2JeztoXiT9CrnP2O9apgAVdais2nrkadpzij1dax3u1C7NoNOvbScfuF8qbExG+rRRzL+bhS3gvOCJpf9k3ddDaqZzesZcEx2BeQLTO/Yu+LVE4b66Uz8fvuIhNp29zbYzZnXD06iN8++dpHfstHMrgj29dFWNChokAESACRIAIqIEAS5PB0mVori6uVpjbSX/qDjX4ZCqNtP42FWnzHocCgOY9Pxarjl5AFju15BgRMBmBVSFxmLnvimA87XxzK47EYs7+KO5+U7tSWNvLTTF98w9EY/GhGM5+Vty1lfz+I2wn7Rcwlqrou/xILH7SmhvtDmUL58XJCc0Vm6fbT16j8dxgSfvbB3qgbuXiescW7yplDXPnyA52ZFmzY5H5xfzTXMYc39VXodjLrhTWKPjsKgabDBMBIkAEiAARkJFAm99CEHmfTzPS0aUCFnTNej+2pgcprb/TQ83y+lAA0PLmVBUe0QtIFdNEIomAWRNYHHQN8w9eFWhkRy3ZkUt2/X7oGn45wN/3rlEGq75TbheVeHdYc/vSWN2znlkzlFvcwxdJcJsdJDAbNskbJQvmEXzGqgSzasFSl3XJAjg0uqnc0jh7D14kob5Io+ZmyFgvVCyeX+/YO8/fwcjNfGEZ1lAcsBQHN5vYlsK67w0LPPtfiseAjWd1xm9UvSQ29tFfnVgxWGSYCBABIkAEiIAZERDnEG5XqzwWf005cg2ZIlp/G0LJ8ttQANDy59gsPaQXkFlOC4kiAqoiMC8gCkuChfnYBjathnH/L+Lw68Gr+C3oGudTG6eyWPpNXcV8NPWRY8UcyYDh6PhEtFp4VGAhZlZr5MyRXfDZ+VtP8cXSE5Ij1axQGHuHNs6AitS7Pn2VDJcZByUbRc3wQd5cOfQaCIx8gD7rhZWnHcsXxr5hvN5/Tt/Cj1rFZ2pZFcHuIYZVn9YXGK1ftTg2K5i/UjHYZFjVBLaE3QbbaV2haL6UfKbli+ZTtT8knggQAfUTaDovGDcev+YcaV2zLJZ1V+67nfqJ8R7Q+tuSZjP9vlAAMP3sqGcGCNALKAPwqCsRIAIpBKbvicSfx/mce+yzXg2rYGo7x5T7c/2jsFSrYAOrDsyqBCt1/XXqJibu5HMS1q1cDNsHNlBquAzZTXr3AWO2XUTApXjUrlgUv3/jgtKF8mbIJut8Mu4xvlrJF7HQl8/vYWIS3GYJdwpqBnerUhxbBnhkWIs+A6/evofj1ACd20Xz58KFKS1THVfsH2vsaVsK67V2+Plfuo8BG/nq1JVL5MeRMV4G+SO1w5B1rFOpKHYoWMHaIHHUKEsRYIV8Gv18CJ8+/ee20u/PLAWXnCUCRCDdBMSFxpQ+3ZFuoWbYkdbfZjgpmSCJAoCZAJ2GBOgFRE8BESACGSUwfkcENp2+JTDztVslzOn4XzW42X5XsPJoHHf/yzpWmN9FuUTR28/ewQ9b+eOhDuUKw2+4cjvZMsJv94W7GP7PBc7E0GbV8UNLu4yYTOkrDn5VKp4fR8fqBr8+ffoE+8n+ePv+o86YSudqfPfhI2wmCvMUMhG2ZQriwMgmqTK4fO85Plt0TNBGnH9IXMm3SL5cCJ+aemBRY3DLmdsYu/2ijgZnqyL418BdhBmeRDJABAD4RdzHoL/4QLZVsXw4Nq4ZsSECRIAIZCqBerMC8SjxLadB6e8MmeqszIPT+ltmoCo1RwFAlU6c2mXTC0jtM0j6iUDmExi1+QJ2nL+rNxjj++9lrD1xg7v/tVtFzOnorJjwfRfvY/Df/ILZulQBHPpBuVx2GXFEXKhCrl/QjTn+2uyXw4hLeKXjhtJHtVnw0XqCH7ezSSOgsU1JbOidep69W49fw3OesIBI38ZVMfEzB86PqPgX8FkYwv2dLRsQM6sNcmTPluaUiXeRajrYly0E/xGeafanBkRALgJbw26n7BLWXCyPJ8vnSRcRIAJEIDMJ1J5+AM9ev+MkUI5cw2eD1t+Gs7LklhQAtOTZNWPf6AVkxpND0oiASggM3HgW+y/FC9Rq54KZtCsCG0/yOwR7uFfGjA41FfNOXMGV5c06/qN57piZvOsSNpy8ybHwsC6BTf3cM8xm2eFY/OzPV15OrQBGj9WnEHItQWfMTnWt8Etn5XZqsgHtJu3X2X3YsU4FLOiSeiVBqfyBLOckyz2pueKfJ8F9jvB484UpLVA0f+40+a49fh2+eyJ12lUvXRCBo1LfnZimcWpABIwgsD70Bqbsvsz1KJQnJyKmtTLCAjUlAkSACMhPwHGKP14lf+AMU45cwxnT+ttwVpbckgKAljy7ZuwbvYDMeHJIGhFQCYGea07jcPQjgVovu1JY0+u/iqvjtl3E5rDb3P3vG1bFlHb8Ti253TwRk4Buq05xZksUyI2zk1vIPYws9kZuvoCdWrsn5TpiOsfvClZoHbvuULs8Fn4lnXdx/I6L2HSanx+NY996VMb09soFatk4Tr4BSEx6L2CpXUBGH2Sp48NzOzmji2tFrgvLr8iON2tfh0c3RZWSBdKcO1ZwYea+KzrtjMkjmOYg1IAIGEBAHMzPnSM7rs5qbUBPakIEiAARUI6A7cT9SP7Apw8x53zLylFIn2Vaf6ePm6X1ogCgpc2oSvyhF5BKJopkEgEzJvDVylCcjHsiUKi9k018RLi/pzXGt6mhmEdnbz7Fl8v4yrb5c+dA5HQfxcbLiOG+68NwMPIBZ6JaqQIIkuG48tht4dgSdoez27NBFfh+/l9RFvG1JDgG8wKidT4f0KQafmxtnxH30uzrOvMgEl4mC9pNbeeAXg2rptnXfvJ+JL3jFx9/9nRFM/sygn7iHYa7BjdMKbaS1rX0cAzm+usyMefdpGn5RPfVSWDBgWgsOhQjEB8727Cj7Or0mFQTASJg7gRYCo+q4/0EMmtZFcFuypFr0NTR+tsgTBbfiAKAFj/F5ukgvYDMc15IFRFQE4H2S44j/PYzgWQWZGHBFnYN3XQee8LvcfeHeFXH6FYZL3Shj5G4QARL+cYWzNlYEjgzu7r9cRInYh9zqsoWzouTE5pnWKU4sDjS2xbDvW0k7e46fxcjNvOFSDSNRrWwxbDm0n0yLPD/BhrMCcK950kCc0u/qYM2TuXSHOLz34/h4p3nKe3Y1IaM9YJVsfyCfvVnB+LBCz5J+Zpe9eBlVzpN278FXsOvgVd12pUqlAdnJlL+tTQBUgPZCMzYG4nVx4RV1iOnt0L+3DllG4MMEQEiQASMISC1C9+cC64Z4xtre+1BIsLvPAc71lyxuPB7hbG2pNrT+lsOiuq3QQFA9c+hKj2gF5Aqp41EEwGzIuCz8Cii4hMFmrSLJQzYcBb+l/kcgSO8bTDC21YxH+IevUSz+UcE9q/ObI3cObMrNmZ6DWsHsZgNufJ7dV5+AmduPOVkTW/viG89qkjKDLvxBJ2Wh+rcm/RZDfRpbJ1e1wzq13ReMG48fi1ou22AB1yrFE+z/4nYBAz5+zyevU5OCVRKPVOtfj2K6Af8s7mwa210cKmQpu35B6KxWLTrinUqlj8Xzk8xrJJwmoNQAyJgAAGpKuvnJrdA8QJp57I0wDw1IQJEgAgYTeB18ns4TAkQ9LMtUxAHRqo/Ry77TtTtj1Mpx5vZd7LdQxrCulRBoxml1oHW37LiVK0xCgCqdurULZxeQOqeP1JPBMyBQJN5wbgpCuJULVkAwaP/q7zbZ90ZBF55yEkd08oOg72qKyb93rM3aPDTIYH9i74tUThvLsXGTK/hZvMPI+4RX4FXrt2K3guOIObhS07Woq9d8Hmt8pIy7z9/A485Ql6s4ewvnNCtfqX0umZQv5a/HsHVB7xO1ont5DP0F/cPHz+B/U9fcLfrilCcus4fT/dt54CeBhwvnrP/ClYcidPxoWCenLhEBRgMmltqJA+BEf+cx64L/A5qZvXEj81Qvmg+eQYgK0SACBABIwk8f/0OtaYfEPSyLlkAh/7/vc9Ic2bVfOLOCPx1ii9cp8R3Vlp/m9WUZ5oYCgBmGvqsPTC9gLL2/JP3REAOAm6zAvEwkT9myWyWK5IXoeP/O8r67Z+ncfQqXyRkQht79PPkq7XKoUHbxpNXyagz46DA7OmJzVG6UF65h8qwPSl2V6b7IF/uHBmyLc6tt6G3GxrblJK0yQJoLJ/euw+fBPcN3S2XEaFtF4fg0t0XAhNRM3yQN1fG/NcY7L8hDAGX+RyLhu4+lTp2yWzmyZkd0TOpAENG5pz6GkdAfJyf9T70QxPZd6QYp4paEwEikJUJPEp8i3qzAgUIKhbPh5CxzVSPZdBfZ+EXwZ9aUSJvNa2/Vf+YyOIABQBlwUhGjCVALyBjiVF7IkAExAScfQPwQlTJtWj+XLjw/6OS4jx3hhZ5SC9pqaMpR8d4oVIJ+fO4pFejpp/jFH+8Sv4gMMNyzLFcc+m9WHJum4n78f4jH9DbO7QRalYootek59xg3HoiPIq7skddtHQsm14ZBvXruPQ4zt3i80cWzpsTF31bGdTXkEbiCtSpFUPRtjdl9yWsD72pMwTboRk35zNDhqY2REAWAt1XncKxmASBLb9hjeFQvrAs9skIESACRMBYAlInLbR/+DXWnjm17732DIKi+FMrvRtVxeS2DrJKpPW3rDhVa4wCgKqdOnULpxeQuueP1BMBcyBgO3F/Sq4U7StvruyImvHfTilxProZHWqih3tlxaR//PgJ1hOE1ekOjPSEbZlCio2ZHsNSOpmdI2OaonKJAukxmbZ5V/QAACAASURBVNLnRdI7OPsKj+YcG6dbIEN7AHGQlt3b2Ls+GtmUTLcOQzqKK0jblC6Ig6PkyyE0x+8KVhzlj/J+4VIBv3atnaY0qbxrmk5UgTVNfNRARgLiIDkzvWNQA9SpVEzGUcgUESACRMBwAjcfv0KTeYcFHUoWzIOwSeovktVj9SmEXON/dDH0h0PD6QG0/jaGluW2pQCg5c6tWXtGLyCznh4SRwTMngA7PlpNFGzTiL4+57/Kux2WHMcFrSrBP3/phK71lM0tZztpP5Lf80HJf4c0hLNVUbPimZj0Dk6iQB0TuG9YIziW179bLy0nbj1+Dc95wYJml6e1QoE8+quGjtkajq1n7wj6mCLIID4e3rB6CfzVxz0tFw2+v/RwDOb6R3Ptm9qVwtpebmn2H701HNtEPDSd5DyinKYQapDlCUgVWfq7b300qKZscD7LgycARIAI6CUQ8zAR3guOCu5rn/xQM7ouy0Nx+gafO5j9YM1+uJbzovW3nDTVa4sCgOqdO1UrpxeQqqePxBOBTCfw6u17OE4VVoITB0o+WxSCy/f4PG/zO9fCl3WtFNUuPpa8pb8H3KqmXVlWUVEi4/HPk+A+J0hnyIxqDb/9DO2XHOfs5s7B8tb5pARj9V2/BV7Dr4FXBbf9RzSGfVlljxn2WReGwCt8jr6OLhWwwIAdeobO09+nbmHCzgiuee2KRbFrcMM0uw//5zx2iwovaDqxIiCsGAhdRMAUBKSO56/pWQ9e9qVNMTyNQQSIABHQIRB57wXaLAoRfG4pRbLa/34M4Xeec7597VYJczo6yfoU0PpbVpyqNUYBQNVOnbqF0wtI3fNH6olAZhNIePkWrjOFiaA1mi5MaYGi+XOj1a9HEf0gkZOaWkVaufwRF9dY970bmthKF8GQa0xj7Uj9gs5srOlVD1526V/cH45+iJ5rznByShfKg9MTUz+Ww3a7sV1v2pcx1XiN9V3Tfuim89gTzlc4HdCkGn5sbZ9eczr9/CLuY9Bf57jPtatTpzbI4L/OYV/Efckm5ye3QLECuWXTSIaIQGoE2PuVvWe1r2Xf1EFrp3IEjggQASKQKQTEPzQyEZZSJEu867qLqxXmdqolK2daf8uKU7XGKACo2qlTt3B6Aal7/kg9EchsAneevkajn4XHTTWaTo5vjrJF8qLZL4cRl/CKk2qKxat418yKHnXRSuGCFsbOBTsWzY5Hi6/fu7mgrXN5Y81x7Xedv4sRmy9wf9uVKYSAkZ6p2jsZ9xhfrTwpaMNy+bCcPkpeW8JuY+y2i9wQ2wd6oG5l+XZqnohNQLc/TnH2i+XPhfP/L06Tml9SlVc17c21orSS80S2M4+AwxR/vBYVCvq1ay184aLsLurM85hGJgJEwNwJhN14gk7LQwUyc2TPBpYjV+2X+DtrxzoVsKBL2rmDjfGb1t/G0LLcthQAtNy5NWvP6AVk1tND4oiA2RPQt4uNCT88uimqlCyAxnMP4faTN5wvq751hbdDGUV9E+86/O2r2mhfu4KiYxpr/HhMAr5ZxQenNP0zmiNxzfHrmLYnkpNTv2pxbO7vkao88XHk3DmzI8K3JfLkzGGsW0a1ZxWLN568ibM3n8KnZln41JR3V5P4mBKr4hszqw2ys/+TytVrzWkERz+SbHH8x2aoUDSfUX5SYyKQHgLs3wcraPSJL+idYuanjk74yk3ZPKrp0Ut9iAARyBoExD+uabyOm532f1/NnVDDnw7h7jP+O2uH2uWx8CsXWWXT+ltWnKo1RgFA1U6duoXTC0jd80fqiUBmE4i48xztfj8mKWP/8MaoUa4w3GcHIf5FEtdmba96aJqBI66G+CzO4TL3S2d0qVfRkK4ma+N/KR4DNp7VGW9KWwd836hqunUsOHgVi4Kucf19HMtieY+6adrrvyEMAZf/y8enRM6bNAUo0IB9iWdf5rWv8KktUSRfrlRH677qFI7F8FUAtRtntEqzAm6SSQsl8Cb5A2pM8dfxzredA3o2TP87wkJxkVtEgAiYiMCRq4/w3Z+ndUZj+YaV/uFQaRfFaRfaOpfD793qyDosrb9lxalaYxQAVO3UqVs4vYDUPX+knghkNoEzN56gs+gYiEbTzkEN4FKpGFxnHkTCy2RO6t996qNBdWUrWHZZEYrT1/kqbtPbO+JbjyqZjUsw/vazd/CDKO8ea/BDC1sMbW6Tbq1Tdl/C+tCbXP+v6lXET186p2mPVXQ+GBmPnNmzo5l96TR3yaVp0AwaPH/9DrWmHxAo0RxNT01e1xWhOKX1/Gi3DRzlieqlC5mBdyTB0gnoy7HK8mSyfJl0EQEiQAQyg0Bg5AP0WR+mM/Tlaa1QQOVFspx8A5CY9J7zrXXNsljWPe0fUY2ZB1p/G0PLcttSANBy59asPaMXkFlPD4kjAmZP4OjVR/hW4ldgJnxTX3d4VCsBcUXerQM8UK+KfHnepCAxTUyb5hrf2h79zWzBvO7EDUz997KO/P5NrDG+dY10z73ShTXSLSwTOia//wjbSfsFIx/6oQmsSxVMVU3Hpcdx7tYzyTZ+wxrDobyy1ZEzARUNaYYEbj95jcZzdXOsjvC2wQhvWzNUTJKIABHICgT2R9zHQK0CWxqfw6e0RJH8qe+wN3c+7DsD++6guVo4lMEf37rKKpvW37LiVK0xCgCqdurULZxeQOqeP1JPBDKbQMDlePTfoHuMlela07MevOxLo8Zkf7x594GTqtkZqKT2fuvDcCDyv+Os7BrpbYvh3unfVaeE1iXBMZgXEK1juod7ZczoUDPdQ/ZYfQoh1/jjq+YY/Ey3c+noWH2CH95/5JOo7R3aCDUrFEnV0ue/H8PFO88l2/w7pCGcrYqmQwl1IQLGEYiOT0SrhUd1OsldLds4VdSaCBCBrE5g94W7GP4PX2xMw8MUxcOUZM/yrlYd7ycYorl9aazuWU/WYWn9LStO1RqjAKBqp07dwukFpO75I/VEILMJ6PsSyHRpqv3aTPTDuw/GBWAy6tfwf85j94V7nJmBTathnI99Rs3K2n+ufxSWHo7VsdnRpQIWdE1/xbm2i0Nw6e4Lzq455j+UFWQaxsTHeQzZgeqz8Cii4hMlLctdqdiULGgsdRE4d+spOi49oSO6Z4Mq8P3cUV3OkFoiQAQshsC2s3cwWiKFiSEpNswZwtv3H2A3SZh3tYltKaz73k1W2bT+lhWnao1RAFC1U6du4fQCUvf8kXoikNkENp+5hXHbIyRl/Nq1FjrUrqDza2rACE/YlVU2h9q4bRexOew2p6tXwyqY2s68FsziXH0asS0dymBlBo6biCvYrexRFy0dy2b2o5Jp49efHYgHL95y47Mv8uwLfWpX8/mHEfvolWSTf/q5w926RKb5QwNnHQL6KoV/7VYRczqmndcz65AiT4kAETAlgU2nb2H8Dt3vfiFjvVCxeH5TSpF1rBdJ7+DsK8wb3Kh6SWzsU1/WcWj9LStO1RqjAKBqp07dwukFpO75I/VEILMJ6Mtjx3TN6eiETnWtYDPR+BxsGfVr6u5LWKdVCMMcF8yjtlzAjnN3dVzN6JdNxyn+eJXMH7k2ZMdbRnmbc3+vXw7jegIfzFvevQ58apZLVXKTecG4+fi1ZJsNvd3Q2Cb1AKI58yBt6iFwMPIB+kok2v/CpQJ+zcAuYfUQIKVEgAiYI4H1oTcwZbduDuPg0U1RtWQBc5RskCapwkse1iWwqZ+7Qf0NbUTrb0NJWXY7CgBa9vyarXf0AjLbqSFhREAVBJYficVP+6MktU5p64Cv3CrCYUqA4L4pfiGe43cFK47GceOa44JZnKdQI7Z2xaLYNbhhuuZfquhFVq9a2+a3EETe549Ez+9cC1/WtUqVb4M5Qbj3PEmyjSa3ZbomiDoRASMI6EuxoERVSiNkUVMiQASyOIFVIXGYue+KDoWDIz1hU0bZEx5Kor/77A3YKQrty61KcWwZ4CHrsLT+lhWnao1RAFC1U6du4fQCUvf8kXoikNkEfj14Fb8FXZOUMdbHDt/Ur4xa04THKUyRI0asyxwXzN+sOonjMY912NmULoiDo5qka2ofvkiC2+wgQV+1J+VOFwitTp2WnUDYzafcJ6zACiu0ktpVb1YgHiXyx4a1267oURetsvCR6ozOB/U3nIC+Y3ZedqWwppe8OakMV0UtiQARyOoElh2Oxc/+uj/++g1rDIfyhVWLJ+7RSzSbf0Sgv06lotgxKH0/yuoDQetv1T4isgqnAKCsOMmYoQToBWQoKWpHBIiAFIE5+69gxRF+p512m2HNbfCtR2W4zgw0eUBK/OXUHBfM7ZccR/jtZzpYKxTNh+M/NkvXAydVNTRmVmvkzJE9XfYsoZO4KvKENvbo51ktVddqTz+AZ6/fSbZZ0q0OPnNO/QixJXAjHzKfwOpj1zFjb6SOECWOpGW+t6SACBABtRBYFHQNCw5e1ZH775CGcLYqqhY3dHRGxb+Az8IQwee1rIpg95BGsvpE629ZcarWGAUAVTt16hZOLyB1zx+pJwKZTUCca09bTz9Pa3zfsCrc5wh3pIVPbYki+XIpKn3N8euYtodfOJvjgllfoQnGhjFKz3Uy7jG+WnmS61oob05E+LZKjymL6dN/QxgCLj/g/BnhbYMR3rap+ldzagBevn0v2WZh19ro4FLBYviQI+ZLYHHQNcyXWGS7VCqKnTLvSDFfCqSMCBABcyMw/0A0Fh+K0ZG1fWAD1K1czNzkGqyH/SjLfpzVvmpWKIy9QxsbbMOQhrT+NoSS5behAKDlz7FZekgvILOcFhJFBFRDYOy2cGwJuyOpl+3+69vYGo3nBgvuR05vhfy5cyrqo/joXEby6ikl1H12EOJf6OaZy5k9G67Nao1s2bIZPbT/pfsYsPEc169S8fw4OtbLaDuW1GHk5gvYeZ4vttLf0xrj29RI1UXbSfvB8ilKXfM6OaOza0VLQkS+mCkBdsSO7WYWXzXKFcb+4fIuSM0UAckiAkTADAnoO/2xuZ876luXMEPFhkk6ff0JuqwIFTS2L1sI/iM8DTNgYCtafxsIysKbUQDQwifYXN2jF5C5zgzpIgLqIDBs03n8G35PUmwXVysMaFJNJ58KC27lUvhI6s7zdzByczinS4kvcBmdIaepAUjUs8sseqYP8uTMYfQQ4sCnEkdXjBaVyR0m7IzA36ducSpY/j+WBzC1q+r4ffj0SbrF7C+c0K1+pUz2iobPCgR8/72MtSdu6LhqXaoADv3QNCsgIB+JABEwQwLT90Tiz+PXdZT91ac+GlYvaYaKDZN07FoCuq8+JWickbzM+kal9bdh82HprSgAaOkzbKb+0QvITCeGZBEBlRDouz4MByP545Xasj+vVR6Dvaqj1cKjAm+uz2mTrt1txiAR74SrWrIAgkebz4L506dPsJ7gpzfIdG5yCxQvkNsYl1PaLj0cg7n+0Vy/JralsO77rF0sYObeSKw6xi9Uvqxjhfldaull++HjJ1Sb4Kf3/vT2jvjWo4rRc0MdiICxBMZsDcfWs7o7rDOSJ9RYDdSeCBABIiAmMHnXJWw4eVMHzNpe9dDUrrRqgQVdeYDe68IE+q1LFsAhmb8/0vpbtY+IrMIpACgrTjJmKAF6ARlKitoRASIgRUBcYEG7TQuHMhje3AZtFx/jPs6Vgx1vbaM4zODoh+i15gw3TrkieRE6vrni4xo6wKu37+E4NUBv85CxXqhYPL+h5rh2s/2uYOVRvihLh9rlsfArF6PtWFKHBQeisUgrV1Ebp7JY+k1dvS4mvfsA+8n+eu9P+qwG+jS2tiRE5IuZEhj89znsu3hfR12JArlxdnILM1VNsogAEbB0Aj9uv4h/ztzWcXPVt67wdiijWvf9Iu5j0F98GhXmSOUS+XFkjLypVGj9rdpHRFbhFACUFScZM5QAvYAMJUXtiAARkCLQefkJnLnxVBJOY5uSGNXCFl8sPcHdz5crB67M8FEcZmjsY3z9B18Mo2j+XLgwJX2FNZQQ++BFEurPFhZH0R7Hf0Rj2JctbPTQ4h1DPRtUge/njkbbsaQO4orQTe1KYW0v/bsiE5Pewcn3gF4EP7a2TznaThcRUJrA92vP4FDUQ51hCuTOgcvTlX+PKu0f2ScCRECdBEZtuYAd5/jcuhovlnevA5+a5dTpFIBd5+9ixOYLAv1K7Lim9bdqHxFZhVMAUFacZMxQAvQCMpQUtSMCRECKQNvFIbh094UkHLcqxTHGxw6dl/MJlQvnzYmLJqhKe/7WU0HgMW+u7Iia0dpsJjH20Us0n39Er57tAz1Qt3Jxo/X2WReGwCv8keyR3rYY7m1jtB1L6rA+9Aam7L7MueRWtTi29PfQ6+KTV8moM+Og3vs/tLDF0OZZm6klPR/m7EvXFaE4df2JjsQc2bMhdrbyO6nNmQ1pIwJEIPMIDN10Hnsk8j8v/toF7WqVzzxhGRx585lbGLc9QmClbOG8ODlB3hMktP7O4ERZSHcKAFrIRKrNDXoBqW3GSC8RMC8C3guOIObhS0lRThWKYHxre3RbxSdUNtXRtaj4F/BZGCLQZYrcg4bOzsU7z/D578f1Nl//vRs8bUsZao5r12nZCYTd5HdkUr46YGvYbYzZdpFjxJ7LPUMb6WX78EUS3FLZnTmsuU3Kzla6iIDSBD7//Rgu3nkuOYwpiikp7R/ZJwJEQJ0EBmw4C//L8Trif+1aC1+4WKnTKQDiHwyZIyUL5kHYJG9ZfaL1t6w4VWuMAoCqnTp1C6cXkLrnj9QTgcwm0PCnQ7j77I2kDFY5beJnNdBTKxefEr+kSg1+I+EVmv5yWHAraoYP8uYyvrKuEoxPxCQIAqPiMZZ9UwetnYw7RsMKizT6OVgwH4u+dgErxpKVL5ZDjeVS01zVShVAUCoVVO88fZ3CUd81sGk1jPOxz8pIyXcTEWg+/zBiH72SHC3CtyUK5c1lIiU0DBEgAkSAJ9B77RkESaQnmNvJGV1cK6oW1aqQOMzcd0WgnxVkY4XZ5Lxo/S0nTfXaogCgeudO1crpBaTq6SPxRCDTCdSdcRCPXyVL6qhYPB+mtnVEn/V8RTX2WcjYZorrjn+eBPc5whx74VNaokh+fsF88/ErPEp8izqViiF79myKa9Ie4MDlePTbcFbvmPM6OaOzkV+iL997js8W8QVXmPE9QxrByaqISX0zt8GCox6i11q+IEz5InlxIpWCMNcTXsFLFDzW9qlv46qY+JmDublJeiyQQIM5Qbj3PEnSszMTvVGqUB4L9JpcIgJEwNwJ6CsAN/sLJ3SrX8nc5evVtyQ4BvMCogX3lUhdQ+tv1T4isgqnAKCsOMmYoQToBWQoKWpHBIiAFAHHKf54lfxBEg5bnE7/3BEDtSqqWZcsgEOjmyoO89nrZNSeLszjdnJ8c5Qtkjdl7N0X7mLUlnB8+PgJHtYl8Fef+iYNAu44dydlfH3XtM8d8V2DKkZx+ml/FJYfieX6sMTVx8Z5IVs20wY3jRJtgsYn4x7jq5WGF4S59iARLX49qlcZFVYxwaTRECkEak8/gGev30nSSG+lcEJLBIgAEcgoga9WhuJknG5+UrWnHVlwIBqLDsUI8ChRdInW3xl9Ai2jPwUALWMeVecFvYBUN2UkmAiYDQF25LTaBD98/CQtqVCenJjV0QnDNp3nGtiVKYSAkZ6K+5D07gPsJ/sLxjk8uimqlCyQ8lmb30IQeZ8vXsICgA2rl1Rcl2aADaE3MFmrMIV44DGt7DDYq7rBethcNJ4bjDtP+ePY/ZtYY3zrGgbbsNSG4nyLuXNmx9WZ+gvCSO2k1GbzTf1KmPWFk6XiIr/MiIDtxP1I/vBRUlHgKE9UL13IjNSSFCJABLIKgS+XncBZrXzDGr8nt3VA70ZVVYthzv4rWHEkTqA/T87siE7lO0N6nKX1d3qoWV4fCgBa3pyqwiN6AalimkgkETBLAm/ff4DdJGGQTVtorhzZ8FNHZ/ywld/p5li+MPYNa6y4PywgVnW8n2Ac/xGNYV+2cMpnTlMDkPj2PXff2IBbRh1YdjgWP/tH6TUzqGk1jDUiz5y46jEzvHdoI9SskLWP/zIOMQ8T4b1AuKOPVVBllVSlrvDbz9B+if4CLV1crTC3U62MPgLUnwikSuD9h4+oPnG/3jb075seICJABDKLgL4CRazwW/8m1TJLVobH9f33MtaeuCGww77LXpslb9V1Wn9neKoswgAFAC1iGtXnBL2A1DdnpJgImAuB52/eoda0A6nKmfVFTUzceYlrU7tiUewa3NAkLthP3o+kd/zuGTYuGz/5/UfYThIurH0cy2J5j7om0cUGmRcQhSXB/HFd8cDGHjOdsTcSq49d58xUZUetf2iS5Y//MiCsSA0rVqN9pVZAIezGE3RaHqr3WejoUgELutY22bNCA2VNAi+S3sHZV//7ddsAD7hWKZ414ZDXRIAIZCoBn4VHERWfqKPB1D+myg1h/I4IbDp9S2CWZVG5PuczWYei9besOFVrjAKAqp06dQunF5C654/UE4HMJPDgRRLqzxYW2hDrYb8Gz9nP73SrV6UYtg5oYBLZ4vxZm/q6w6NaCUjptirG8uUpX5xE47jUr8zaUDrVtcIvnQ3bZfaR5TH8KQgPXrzlTAxrVh2jWtqZhLO5D/L0VTJcZgjzQZ6a0BxlCv+XD1J8hcY+xtd/8DkDxffbOpfD793qmLvbpE/lBKQKGWm7tLF3fTSyMV3aApXjJPlEgAjISEBfhfIR3jYY4W0r40imNfXDlnBsP3dHZ9C42W1kzRNN62/Tzqu5jkYBQHOdGQvXRS8gC59gco8IKEiAVdFtMu9wqiMM8aqO34P5hMoNqpXA333dFVTFm3afHYT4F3wFzTW96sHLrjSu3H+B1r+F6Gi4MKUFiubPbRJt+r5kagZvXbMslnU3bEfiqbjH6KpV5ILZODjSEzZlKD8YYyGVDzJ4dFOwXZJS19Grj/Dtn6f1Pgem3i1qkgeSBjE7AnGPXqLZ/CN6da3+zhXNa5QxO90kiAgQAcsn4Dk3GLeevNZxlH3nG91KvT8+Dvn7HPZevK/jF8sbzPIHy3XR+lsukuq2QwFAdc+fatXTC0i1U0fCiUCmE4iKfwGfhbqBNG1h7Cirdj4VT9tSWP+9m0m0N50XjBuP+S+oy7vXgU/NcjgRk4Buq07paDDljpoBG87C/3I8pyF/7hx4rVVN2RhOk3ddwoaTNzlbpiq0YpJJlGEQqWI1+4Y1gmN56fyIQVceoPe6ML0jN7cvjdU968mgjEwQAf0ELt19jraLj+ltsKRbHXzmXI4QEgEiQARMTsBjThDuP+d/YNUIUHvxsb7rw3Aw8oEOz6gZPsibK4dsnGn9LRtKVRuiAKCqp0+94ukFpN65I+VEILMJiAtP5M6RXadiZcc6FbDj3F1OqneN0lj1nWmCJ+IcNQu71kYHlwrYe/EehvzNVybWiPuxtT0GmCh5dY/VpxByLYHjYl2yAOISXnF/161cDNsHpn1UmhUKYMewH79K5vqObmmLIc1sMvvxMKvxa04NwEutoi+p5U/zvxSPARvP6tVvTHDWrCCQGFUROH39Cbqs0J+Lcn7nWviyrpWqfCKxRIAIWAYB15kHkfCS/96h8YpVAGaVgNV6fffnaRy5+khH/qVprVAwT07Z3KL1t2woVW2IAoCqnj71iqcXkHrnjpQTgcwmIM6VVrxAbrx48w7vP37ipLVwKCP4NdWUxyc7LDmOC7efcVp+6uiEr9wqYX3oDUzZfVkHnylzu4m11a9aHKeuP+E02ZctBP8RnmlO8bFrCei+Wrib8fDopqii53hrmgYttEG9WYF4lMjnSGS7UFkgT+raE34PQzfpBog1bT2sS2BTP9McY7fQ6SC3DCBwOPoheq45o7clK7D0Tf3KBliiJkSACBABeQk4+wbgRdJ7HaPfeVTGtPY15R3MhNa+WhmKk3H8dzHN0OFTWqJI/lyyKaH1t2woVW2IAoCqnj71iqcXkHrnjpQTgcwmEBz1EL3W8gvUCkXzpQQAE7V2WrlbFxd8mWpXqzwWf+1iEuniL3JT2zmgV8OqWBh4FQsDr+loYDnhWG44U1wtFhzBtYcvuaE+r1Ue/4bf4/6uWDwfQsamXZRk3LaL2Bx2m+vnVKEI9gxtZAoXVDVGk3nBuCk4Dl4XPjXLSvqw8/wdjNwcrtc/UxayURVkEisrgf0R9zHwr3N6bbJdNmy3DV1EgAgQAVMTqDHZH2/efdAZtlv9Spj9hZOp5cg23hdLj+P8Lf6HY43hs5O8UaJgHtnGofW3bChVbYgCgKqePvWKpxeQeueOlBOBzCbgF3Efg7QWqNalCuDFm/dIeMnvtHIsXxiX773gpHZ0qYAFXWubRHrPNadxOJo/yjHOxx4Dm1bD1N2XsC6Uz5mnLeaib0sUzivfr7z6HBXnz+nnaY2VR+O45mw35bnJLVLllPz+I9gxHO1f4Se0sUc/z2om4aumQcTHwX/tWgtfuEgfn9xy5jbGbr+o171aFYti9+CGanKftKqQwLazdzB6q/5A9JhWdhjsVV2FnpFkIkAE1E6g+gQ/wWkPjT9dXK0wt1Mt1br32aIQwXdWjSOnJzZH6UJ5ZfOL1t+yoVS1IQoAqnr61CueXkDqnTtSTgQym8COc3cwagu/QGXBvudv3uHO0zectErF8wsqxXV1rYifOzmbRPrAjWex/xJfaGN4cxuMbGELfVXemKh/+rnD3bqE4vqcfAOQqHV8ZtJnNTBz3xVuXFZtjlWdS+06FPUA368VFqs4/mMzsJ2YdAkJfLnsBM7efMp9mNrxyb9O3cTEnZf0ImTP+b5hjQkxEVCUwIbQG5gskapAM+iwZtUxqqV6q20qCo+MEwEioBiBjx8/wXqCn6R9U/7Iq4SD3guOIEbrdIZmjNDxzVCuiHzfrWj9rcTsqc8mBQDVN2cWoZheQBYxjeQEEcgUAuJATjNonAAAIABJREFUiWvlYikBQO2jrcXy58LT1+84fd/Ur4RZJjoeMnLzBew8zxcg0VSn+2bVSRyPeSzJjAXi+jS2VpSnVFVadixanHfu2qzWyJUju14tP2wJx/Zzd7j7hhYOUdQ5MzUuLrqS2jyvPX4dvnsi9XpiW6YgDoxsYqaekixLIbDiSCzm7I/S6w7bNTyhTQ1LcZf8IAJEQCUEkt59gP1kf0m1pkzzogSuxnMP4fYT/kdszRghY71QsXh+2Yak9bdsKFVtiAKAqp4+9YqnF5B6546UE4HMJrD62HXM2MsHShpVL5kSAIy4+5yTljN7NsExkZ4NqsD3c0eTSB+/4yI2nebz42mSU7f+LQRX7vPHkrXFdKhdHgu/UjZH4evk93CYEiBgsKW/h07Fz7SSTrddHIJLd3k/TBG8NMnEKTBIv/VhOBD5gLM8qoUthjWXrpT8x9E4zPLjd2OK5ZgyV6QCKMikSggsOHgVi4J0c5Vq5H/rURnTVZxsXyXTQDKJABEQEUhMegcn3wOSXFrXLItl3euqlln92YF48IJPY6NxRO7iarT+Vu0jIqtwCgDKipOMGUqAXkCGkqJ2RIAIiAksCY7BvIBo7mPvGmXw/E0yztzgj1qK+/RtXBUTP3MwCUzffy9j7Ykb3Fia48fus4MQ/yJJUkP10gUROErZ3V0PE5PgNitIMP6RMU3RZN5hwWdpHecVf1Fd07MevOxLm4St2gYZ8c957LrAF1kZ0KQafmxtL+nG0sMxmOvPP9fiRlbF8uHYuLQLtKiNEek1LwKz9kXij5DrekWpPdeWedEmNUSACBhK4MmrZNSZcVCyOfseuOo7V0NNmV272tMP4JnWqRWNwKAfmqBaqYKy6aX1t2woVW2IAoCqnj71iqcXkHrnjpQTgcwm8EtANH4PjuFktHUul7IDMORagl5pg5pWw1gf6cCL3P78tD8Ky4/Ecmbbs919XWvDbpI/kj98lBwuWzbg8rRWyJ87p9xyOHtxj16i2fwjAvtXpvugxhThkZqDIz1hU6aQpA6Wg8dm0n58+PiJu79nSCM4WRVRTLeaDY/fEYFNp29xLmh2g0r59FvgNfwaeJW7lT93DrxO5qsdlimcB6cmeKsZB2lXAYGJOyPw1yn+mRVLZpXDF5mooroKcJFEIkAETETgwYsk1J8t/BFTM3RTu1JY28vNRErkH0ZfdeMDIz1hq+f7WHpU0Po7PdQsrw8FAC1vTlXhEb2AVDFNJJIImCWBmXsjseoYv0Olc10rPHvzDge1jlqKhWsKcZjCIXEgp5VjGczvUhs1pwqP34q1bBvgAdcqxRWTGHHnOdr9foyznytHtpSCH+xY8Jt3fKBp56AGcKlUTFKH1C/wJ8c3R9ki8lWpUwxAJhhmR9XZkXXNxZ7VeZ2lKxXOPxCNxYf4wHbJgrmR8DKZ61uiQG6cTaNCcya4SENaGAFxDlOxey0dymDlt+rdaWNh00XuEIEsQ+D2k9doPDdY0l+WCmZjn/qqZWE9fh+0flfl/PAb1hgO5QvL5hetv2VDqWpDFABU9fSpVzy9gNQ7d6ScCGQ2AfEOFZaTihX82BPOH7UUaxzd0hZDmknnXpPbH3ES/Sa2pTCzQ02dL66sau7dZ3zSZ992DujZsKrccjh7obGP8fUfJ7m/i+bPhQtTWsJ15kFBoGlj7/poZFNSUkd0fCJaLTwquJdW0RDFHFKBYXFQ7zPncljSrY6k8jn7r2DFkTjuXsXi+QRJwQvlzYkI31Yq8JokqplA/w1hCLjM560skDsHXmntRGXvs3Xfq3enjZrnhrQTgaxMQOoUg4ZH/arFsbm/hyrxvP/wEdUn7pfULvcJC1p/q/IRkV00BQBlR0oGDSFALyBDKFEbIkAEpAiM2nIBO87xVXZZVcpnr5OxJYyvTCvuN761Pfo3qWYSoOtO3MDUfy9zY7lVLY6JbWqg/ZLj3Ge5c2QHq1qnXU23U10r/KJnd5gcwtkOyb7rwzhTmpxyTeYF4+bj19znK3rURSvHspJDHruWgO6rT3H3WLXl81NayiHPIm2I8/o1sy+NP3vWk/RVvFvQrkwhRD9I5Nrmy5UDV2b4WCQncsp8CIgrV4sD0WpeaJsPZVJCBIiAsQSkfoDU2KhbuRi2D2xgrEmzaP/q7Xs46jkhsmtwQ9SuWFQ2nbT+lg2lqg1RAFDV06de8fQCUu/ckXIikNkEBv91Dvsi7nMyWFVVFgBcH3pTr7TJbR3Qu5Fyu+u0B9585hbGbY/gPqplVQQjvG3Ra+0Z7jOWz21gk2rw3cNXM7YvWwj+IzwVw7vr/F2M2HyBs68Zr81vIYjUqk68oEstdKxjJalDbMO2TEEcGKls8RLFgJjA8Nrj1wVz7G5dHP/0k96lMGX3JcEzzL70X7j9jFPJKlvHzG5jAtU0RFYm8OWyEzh7ky+oxN5f4Xf4Cuu1KhbF7sENszIi8p0IEIFMIHDp7nO0XcynMdGWwN5Tu4c0ygRVGR8yteIm2wd6oG5l+VLD0Po74/NlCRYoAGgJs6hCH+gFpMJJI8lEwEwIfL/2DA5FPeTUjPOxx9PXyVh5lD8+KZY6o70jenhUMYkHuy/cxfB/+EAb28nFdin+sDWcG79GucJgmjotD+U+y5E9W0ohkLy5ciiic8PJm5i86xJnW/OLeeflJwQVlFNj9cfROMzyu8LZaFi9BP7q466IXkswuuXMbYzdfpFzxdmqCP7Vs0gZv+MiNp2+zbVtUK0ETsQ+FmC4PqcNsrGKMXQRAYUItP4tBFe0fhDwsiuF4OhH3GhK/1ChkFtklggQAZUTOHfrKTouPSHphUO5wvAb3liVHsY/T4L7HOniJpv7uaO+dQnZ/KL1t2woVW2IAoCqnj71iqcXkHrnjpQTgcwm0O2Pk4LAyNR2Dnj6KhmLtAooiDXO6eiEr90qmUR6wOV49N9wlhurcon86OFeGTP3CQNnK3u4oqZvAD7xBXUh93EPbYdZZWJWoVhzaXJ59VxzGoe1Fvg/trbHAD3HpWf7XREEWlmF49++cjEJVzUOwvJSDt10npNevXRBBI6S3jE5ems4tp3lj7F71yiNwCt8oJsZYUVbcufMrkYUpFklBJrOC8YNrZQAHetUEKRcqFIiPw6P8VKJNySTCBABSyFwKu4xuq7k8xhr+6Xm0wg3H79Ck3mHJafp7z710aC6dE7m9Mwrrb/TQ83y+lAA0PLmVBUe0QtIFdNEIk1M4NOnT7S7xwDmXyw9jvO3+KORP3V0wpPXyZjrH623N8utx3LsmeI6cvURvvvzNDcUO+7LjtQuOxzLfdbWuRx+71YHzecfRuyjV9znMzrUTAkWKnHpFKRwKocl39TB4L/PYd9F/kj10GbV8UNLO0kJozZfwI7zfP7FPo2qYlJbByXkWoTNoCsP0Hsdn3eRFX45/mMzSd+G/3Meuy/whWxYjkhxYZvI6a2QP3dOi2BDTpgngXqzAvEo8S0njv0b1666XrZwXpyc0Nw8xZMqIkAELJaAOAextqPWJQvg0OimqvT96oNEtPxVWFxN48iG3m5obFNKNr9o/S0bSlUbogCgqqdPveLpBaTeuSPl8hNgiy2W1y7s5hO0cCiTsqNKqWOg8qs3vUWfhUcRFc8XR1jYtTZYDpXpe/l8emJVv31VG+1rVzCJWPGv1IXz5kQbp3L45wx/vPM7j8qY1r4mRvxzHru0gj5f1auIn750VkSn77+XsfbEDc52V9eK+LmTM8ZuCxcUUOnVsAqmtnOU1CAuEGDK4iqKQFHY6InYBHT7w7CiKeLclmx+NofxzwyTGj6lJYrkz6WwajJvaQTY+/HDx08oVShPmq7VnBqAl2/fc+3GtLLDvAD+xxVN9fA0DVEDIkAEiICMBIKjHgpyKWubZsWKQsZK/7gmowRFTKWW23BNr3rwsist27i0/pYNpaoNUQBQ1dOnXvH0AlLv3JFy+QksOBAtOL7685dO6FrPNMdV5fdGeYtevxzG9QR+19zy7nVTAoATdvKFN8Qqln5TJyUIZ4or/PYzYcXfnNnR1LYUDkQ+4IYf4W2TUhhkVUic4GhwzQqFsXeoMnlsxmwNx1atI6bfN6yKKe0cMG3PZaw5rhsYlGIlDr7O71wLX5poZ6Up5k7uMVgRjw5a1Z/z5MyO6JmtJYdhFZpZpWbN1bNBFUHAln0eNskbJQumHcSR2w+yp14CLPcnC/5//PQJo1vaYbBXdb3OsF3o1Sb44aNWWgKWPmH8Dv7dmjdXdkTNkH6G1UuJlBMBImDuBMTpVbT1liuSF6Hj1bkz+ezNJ/hyGZ8PWtuvVd+6wtuhjGxTQ+tv2VCq2hAFAFU9feoVTy8g9c4dKZefQJ91ZwS5vjQ7s+QfyTIsus8OQvyLJM6Zdd+74cmrtxi5mS+yIfZ0ZY+6aOlY1iQApI5zsIIb2pU1p7d3xLceVRAa+xhf/8HntMmVI1tKlVjWXu5r4Maz2H8pnjM7rFl1jGppB52jwc7lsKRbHcnhXWceRMLLZO7e+u/d4Gkr3/EUuX3ObHtSz0Ls7DZgBV/EV681pwXFFgY1rYalWsfGWfvQ8c1Qrki+zHaLxlcJgfcfPsJ1ViCevX6Xopjljzw7yRuF8krvIk169wH2k/0F3rEfWAZs5HOasptUjEYlDwDJJAIWRGDvxXsY8jefU1fbNfbDGPuBTI3XiZgEdFvFnxTQ9oG9f31qyvfdldbfanxC5NdMAUD5mZJFAwjQC8gASNQkyxDouPQ4zmnltHOrWhxb+ntkGf+NdbTWtAN4/ua/BS27WJU0tgNw4F/n9JqS+xhFappvPX4Nz3nBgibli+TFved80HLx1y5gOd5eJL2Ds+8BQVtW5PU7jyoY3coOBfPIl+9NfHx3Qht79POslpKb8Gd/vjhIU7tSWNvLTcdFFkywmbRfULRk//DGYBWN6ZImcPvJazSeK3wWLk1rJTmv3VedwrGYBM7Q6Ja2+OXAVYHhkLFeqFg8P+EmAgYRYO/FOjMOCtr6j2gM+7LS/2al2m8d4IHOWtXKmbGoGT6UpsKgGaBGRIAIyEVg5/k7en/oVXNqguDoh+i15owkJrlPr9D6W66nUd12KACo7vlTrXp6Aal26ki4AgTEVRdZ0YhTE9T5S6YCeHRM2k3aj7fvP3Kf/zukIR6/TNabG4Y13Ni7PhrZyFdJLTU/H75IgtvsIEETtuFL+1iddmW3L5edEOwO1HRkQcNZXzjBy16e/C/iQPOsL2rim/qVsSH0BibvvszpdatSHFsG6Aagpfw6M9HboLxipnguzHEMqYDK6YnNUbpQXh25XVaE4vT1J9znk9s6YIYor2XQD01QrVRBc3SVNJkhAakA9Ka+7vCoVkJS7Z2nr9HoZ2HAmlWt9l5wRNCeclGa4WSTJCJg4QS2nLmNsdsvSnrJfixlP66p8fK/FK+zy1rjx6KvXfB5rfKyuUXrb9lQqtoQBQBVPX3qFU8vIPXOHSmXn4CzbwBeJPFJ19kIVO1TmvPHj59gPcFPcPPgSM+UY6naR2nFvdkuwfrW0oteuWeU7U5kuxRTu7R3zrFF+sC/zuLS3ReSXUZ622K4t02GZbb89QiuPnjJ2dEURtlx7g5GbeGPTzuUKwy/4bp5CC/fe47PFh3j+rOg5rVZ0sdZMyzWQgxIHak8MqYpKpcooOOhOEA7+wsnnbyWASM8YVe2kIXQITeUJiB1BD21HSVS7dmR4bozAwVST01ojjKFdYPYSvtD9okAEci6BDaevIlJuy5JAkgtv665E9t94S6G/3NBUuavXWvhCxcr2Vyg9bdsKFVtiAKAqp4+9YqnF5B6546Uy0vgHTtWOXG/jlE6WinN+U3yB9SYIsxRxY5FJrx8iy+WntA7OdsHNlAkr57UgMnvP8J2ku6carcVL6DZ8do/j1/HgoNXkfSO393I+rC8gBemtESBDB4HbvjTIdx99oaTsfo7VzSvUQbiX58rl8iPI2O8dFw7HP0QPbWOqbCKomwHIF36CbCiCixg/UmrqIK+f9vtFh9DxN3nnLF5nZxTAoDvPvCd9w5thJoVihByImAQgfO3nuq8FzU7f6UMiIvWsIIf5ye31Hnn6gtiGySKGhEBIkAE0kFgzfHrmLYnUrIny6vL8uuq8doadhtjtknvbGTfAzq7VpTNLVp/y4ZS1YYoAKjq6VOveHoBqXfuSLm8BB4lvkW9WcLdFWyEZd/UQWsTVa2V1yNlrUkdqWRBKBYAbP1biN7B9wxpBCcr0wROpCppioVdndk6JSG/+Lr5+FVKxc0TsY8Ft+RYcIt3mv7Tzx3u1iVw7FoCuq/mE1DrS6Yt/pLKcv+xYBZdqRNwnOKPV8kfuEb6gtHiCstshyZ7Fl5r9d05qAFcKslfIIbm0DIJHI9JwDei5PJjWumvBCxORl+8QG6ETfTW2XV9YKQnbMvQTlTLfGrIKyJgngRWHo3FbD8+X7FYpVqLE6W2s/HnL53QtV4l2SaE1t+yoVS1IQoAqnr61CueXkDqnTtSLi+B6PhEtFp4VMfoOB97DGxaLc3Bnr5KxvKjsXjyMhmtncrCy640srEqEhZ6sR1sbCeb9hXh2zIlB2DTXw7r9drUOyodpvgLAjfawgrlyYmIVHLVGLNrjBUROXj5AdhCvbFNSeTMoRtUZGMzm9Un7scHrUSEmt1k4l1C+XLlwJUZPjosxcVCWPVfVgWYrtQJuM4MTAlQay59+Sibzz+M2EevuHasEvP4HRcF6QFYcSBWJIguImAIgYORD9B3fZigaZ9GVTGprYNk98DIB+ij1d6qWD4cG9csZUcz29msuVjeVWerooZIoDZEgAgQAVkILAmOwbyAaL229P2wKsvgGTRy4HI8/g2/B6cKRdCnsTXYjkXNtfrYdZ18v5p7qe3YTo8kWn+nh5rl9aEAoOXNqSo8oheQKqaJRJqAQGjsY8ncdV1dK+LnTs5pKhi15QJ2nLvLtWNBmSltHVC9tGUWCoh99BLN5wsT0l+b1TolAOg+R1h4QxseS2RvSias8ibbrSh1VSqeH0fH6h6x1W4rDiDuGNQAdUQ7v1g+xHa/H8Ple//lDrQvWwi+nzum7OoTX1K56A6PbooqJQtAKu8XO0qj/QWV2Zu+JzLlmLLm+rKOFeZ3qZXmM5rVG3jODcatJ685DCt71EVLx7I6WJrMC8bNx8J27Agwy2+pubSLx2R1ruR/2gSkckt1rFMBC7rUluzMFqjDNp3n7tmWKYgDI5tAvHuYAtFps6cWRIAIyEvg14NX8VvQNb1GzTV39qW7z9F2MZ8/mX1H/75RVc4P8Y+r2g5Ob++Ibz2qyAaS1t+yoVS1IQoAqnr61CueXkDqnTtSLi8Bv4j7GPTXOR2j9asWx+b+upVYtRuy3VwsH572zgx2P2f2bOjVsAqGNrdB4by55BWcydbEX6RYfjxWiOLZ62TUnn5Qrzo5jtAa43qDOUG49zxJskutikWxe3DDVM3VnXEQj7UCiFKBHzELjcG2zuUwoU0NlC+ajxtD6qh52CRvsOO++nZVFhI9O0M3ncee8Huczf5NrDG+dQ1jsGTJtlJHe9vXrqDDQvzMrOlZL+UIcPwL/jla26semtrJUxU6S05GFnP671O3dArJNLMvjT971pMk8c/pW/hxRwR3T/OucpsViIeJ/C5WtvOX/dhEFxEgAkTAVATm+kdh6eFYvcOFT22JIvnM7zvv0sMxmOvP71z0siuFNb340xMLA69iYaB0YFMcLMwoa1p/Z5SgZfSnAKBlzKPqvKAXkOqmjAQrRGDDyZuYLFHVrEzhPDg1IfUCC1K74bRlliz4P/auAyqqowt/KvbeG3ZUxIoFFBELqNh+e9eoscbYjcbeS2I3GmONGlvURGOMiooFG4pdbAgi9t57S/5zF99782bfwgJvl12YOSfnxN0pd755zL65c+/3pcCc1q7wLJrNQtZbv9sTEU/QYmGgPLCUTqsV4cZad2RYLZVDzNKW15q5H+FMOic7XlQHcKmeKcEOtp8DVx7iq1+DNKdC5P3f1nBC75pOhki+iEevjVKkL0/0RarkyTSdp0eHeyNXRrXKZ5vFgTga/kQeb1SDEoZUFlGiRqDpgsM4feOZXGlqs9Jo62bM6aOVKjx88zncfKIItyz9qiJ8XHIKyAUCZiGw9GA4Jm27pKrrmj8TNvfWvoD49dA1TPhHIdn3KJIVa7tXhrlRrGYZJSoJBAQCAoFYIDB520UsOahkIfBdkGJ51nQpY9GzZZtM3XEJiwLC5UEqFcyMjb085H//6HcZFAWoVfR+zxLnb8uutb30LhyA9rJSCcxOsQElsAUV04k1Aj/tCTUov2qV6NIZTEUPsn2lS+kAivQiR09CKLxghaRESxx3hYZvNzlFEgqhutYq9ecexMW7kam5fDEnddZnVgDCHrySm85v54qGZfKoutp54R56rjoZ5ZSIR5L4JPloQXIKhk2uZ+CL1FKi1kqZ5m0ikQqtSDZrYWwv47RfehSHwxRRl9ENXdCVSf+R5lFuwi48e/NRntb6HpUNEYDhjxReQCEOZC+rrr+dFOlNKugZUjuYzfOq9ftSMGsa7NdQ+SaLeY4tnxI5sLRTJdSZHYAr95X96Ke2rvhfWfV+pP+MRY8CAYGAQEBBYNzfF7DiSIRJSI6N8EbODOqLS1vAj6g8KBpbKryA2sR/LoJ4ALXKsHrO6FU9ej5wc+cpzt/mIpWw6wkHYMJeX5udndiAbHZphGFWRiCqF5rohCt4PhSK+Hvx9hM+fFbI2mk6ktiDladmkeF4UnuWT8959A68+6ieu2TEmTG1kSlNCovYpNVpswWHcYqJ+mLrdK9WCCMbaJPwS/UazTuE4NvP5WbTW5RBy4r5VENp8XvxtuRIT5Gk3jh27QnaLD4qf01pMpQuIxVzSP7Ljt+F528VB5XgozPvceq28gT8L92XK39Xpxj61Cpq1FhLLXjEpmCE3H8p1xWOF/MwT2i1Lt55gW4rjxtoBeqXzoX5bcsjKUMib2q+fOQJ1eP/9tm2fIodOfnomeP3o2ktyqAVtx8lNMzFfAQCAgHbQoB3pPHWHfq+Jhwzp7EtowHw9CkFsqZBAHMJM+qvYKw+qjgI2QlEpdoem4mK83dsUEt4bYQDMOGtqV3MSGxAdrFMwkgrIND/99PYckbhVWOHXNihPHxL5TZpRa9VJ+F34Z78/ddVC+GrKgUMwhAv332SP1/V1Q3ViiYMvibioKOXKdlx9YWknv7NR1CxwF0YXxdpUzpYYUUjh2i35CiOXFWivtiBh/oWR+8aTlHa0mphIIIilHTbiY1LoiNHBL0u6IYhQozFomLBLKqbZvqO0p8v3X2BrisVNdC8mVLj8LBaclvXCbvwlIk+W9vdHR5FlNTx958+o/goP5XNuwd6oWjO9FbD1F4HIlEFEleQihSVyc+Hd8Ju7eOJYZvOySIvVH9my7JoXsHRXqEQdscSgW/XnMK24Lty65Vfu6G6GRx8Y7acx2+B11Wjkkh86KR6morh/IVUm0r58EPzMmi58AiORzyV+9Haj2I5NdFMICAQEAiYhcCQjWex8eQtk3UlYTOzOrNipc7Lg7A/5KE8InEvU2aOVKKa10CfYujvY3xhGFvzxfk7tsglrHbCAZiw1tNuZiM2ILtZKmGohRHouOwYDoY+0hyFUjfJWWCq1JyxH9eY9MBpzcugVaV8RulaCSlVc8OJmxj6xzkZkrKOGbGlj6fh31Wm7sFdE8IbIZN8kdLBemnQXVccx57LDzSX7sfmpdG6kjEHHFuZuP2I408qI+o7o4eX+lnQ4uta3dUdrhN3qyL1fm5XHp/+/Rf9fz8j91c8Z3rsHOgl/9vzx7249VThmlvyVUXUZrjm7jx7C48f9qrmY+2oSgv/KVqs+2F/nsPvx2/K/Xf2KGhQa+ZLoeHb8N9/yqcUAUwO3jM3Ff5Ac54di01EdBxvCHjP3I+rDKfo8HrO6GlGWtjgDWfx5ynjA7Mprqzv/ziH9SeUZ5XEpMY2Kgn+d2pk/RLo7iX4P+PtgRADCwQSIQIDfj+Nv0xcmBMc/oO84JTD9i4lm/9yBCevKxcoaVIkw8UJvvIKRhUI0M+7KAbVLqbbaovzt25Q2nVHwgFo18tnv8aLDch+105Yri8CDX46qIrwYXuXoi+0RiQuKJexfiqHASnLkmpj60WBhpRPqYxt5IIuVQvpa3g89fZbYATGbLkgj+5WKAs2fFFL5h2irInhU+qblTKn17T4iB2238UdK6BOyVxRDtVz1QnsvKCkjWrdAvN8Xd7OObCscyV0+jUIAYzzsJtnIRTKnhYjN5+XxyyfPxM2MUIA0SnVnr35DI1/Piy3J/XlK5MiOQRFiRqB8VsvYPlhhbeoVUVHTGtRVtWIFL2LjFBzWBIP4/BN59SRV01KoWPlAgLyRIYAn35P0d5jGkVNI0AQ9V5zEtuDlShxCTYtjk/6rs/aU/jnnBJp2KemE76rWxx8Gvvg2sUMKvOiCAQEAgIBayEQ1XsV2RAdbY617OTH4TlU6Xv2nZTP5mHbf1uzCIbUddbNdHH+1g1Ku+5IOADtevns13ixAdnv2gnL9UXAY+oeA6+TVnEvlAXrvzi3+O+Dbz03pPpKhfwwlOaaJoUDvll9EjvOK4e+frWcMKhOcX0Nj6feFh+4iinbL8ujUxocpcNR4Z1YUiUSvLg6pb5VLR604Qw2nbqtOeYfvaqAUnWjKvxNN5FAExk0W2bsDMH8fWHyRw3L5Mb8duUxx/8K5viHyp+Ts69uyVyYukPBrVrRbFjV1V2uw3MWTm5aCu3dFUeT/8X76PabkkKcO2MqBA73tiqm9jrY9J2X8fM+ReFPWid2Ploq1gFDahgiANlU8oTkzLfX9bS23ST+QenhbNF6hrTs4i8DpDqm9iA+clnin+Idg3ofSq2NqRhPICAQsD8Euv92AsQDbaoQbUZpx4w2NzF0hL0UAAAgAElEQVSt7JTz4+uCRPqodFkehH1MijA7gZ7VC2N4vRK6zUmcv3WD0q47Eg5Au14++zVebED2u3bCcv0QIOVa59F+eP9JW7giV4ZUODpC28my8cRNDGFSYVlS4ZGbg7GGURxr754fk5uW1s/weOxprn8oZvsrqsm+JXNhYccKBouaLjiM0xrCG6mSJ8XlifWsanVUZNV7B1dH4ezporSHIr/WBUWdNsorx7Ws4IjpLcsaov/o4C+VFA5JQRFDCwMUJxQJCSxoH4kbFT7Fj085/j3oBoYxfINlHDPi7y+p11YF1g4HMxWpyU7lxbuPKDNul2p2xN1ImEeXCm6HkAiTY4DA3edvUWWqOv3erWAWbOhVJdpeeO4+qQGf4i993nbxUQSGK9ylksP5u41n8QfDvUUq1qRmLYpAQCAgELAWAjyXHj/upt4eKJ8/s7XMMXucUmN34tV7hZebGgaN8EaOL4rF7ZcexeEwbc5oyuAYpeNeK87fZi9bgq4oHIAJenltd3JiA7LdtRGWWQ+BNx8+wWXMzigHvDTBF6lTGHPXTfrnIpYeuia3reOSE4u/qmj498xdIZi3V4kM45091puh/iP9sOOyypHVpFwezGnjahiIP7xKo6dP6YDg8XX1NyaKHidsvYhfDyvrw1Y1hzuPTxttXTEffmxRRjUi72TsVKUAxjcuZeD/o5RBtlQokFnFQSM5C6U6fNRof++iGMjwzszbE4qZuxXHay3nHPi1cyWrYmqvg/FcjVUKZ8W6HpVV03ny+gPKT9yt+uz4SB9DCrD/JYVLUm9FQHvFNDHZzUd709x5FUlTeJiimJD4Yvl2jecfwtlbivq4VI9XqexQOT8mNUkYl0qJ6VkScxUI2DMCUTnKaF5EB0O0MLZUtOg9yD72IrjFL0dwguEIZO2XeFj1mpM4f+uFpH33IxyA9r1+dmu92IDsdumE4ToicOvpG3j+uC/KHv0GVINzrgxGdfiILTbNl3c4RJVKrON0rNIVr1LZ1i0fpjaLdIyZSqPIkjYFTo2ubRX7pEH4tE/p86RJgLDJ0fMRTvO7jAX7lYi9/5XNg5/aRjo6pTJw/RlsPq2kGbOpIj6zAhD24JVcl9Kg6UVUKvxLJS8WwN8682qiWg5JqwJsR4OtP34D3/+pqDUTTyfxdbLl/ot3cJ+yR/UZOYopBZhN5x/gUxQDfPQjBLcjGBOtqfsuP0CXFcdV86eoZrocio6D0xQvqikRkdqzAhDK7Bvz2rqiUdk84C+cWlRwxIyWah7LRLtAYuICAYGAVRBotTAQQREKvzU/6Npu7vBwymYVW8wdROtCltr+09cTpfJGpis3mncIwbeVixe276+qFMCExqXMHS7aeuL8HS1EiaKCcAAmimW2vUmKDcj21kRYZH0E+MgOh6RJQM6qBy/fy8Ys7FAevqVyGxnnNtlfVW9+O1c0LJPHUG/LmdsqxddiOdNh18Dq1p+gBUbkFVVZR5Ypwvsc6VMiaKSPBawx3SUfMSfVzJo2BU6a4YycvzcUM3YpEXekyEtpe2zhiaNZ59CQjWexkUnZ4y3tW8sJgxleyLFbzmNl4HW5GutYpQ95bCVxAKuCaqeD/X32DvqtOy1br/X3qHUZQJyelAK89ewdua3gXrPThyAOZvPK51JX58bVQYZUyaPsmf+dkCprcYrSd1V/2IvbzxQ18GWdKsK7RE6Y4huNw7REU4GAQEAgECMEmvx8GGduPjPZhvigiRfaloqpi/71PSrDvXBWg6l1Zx9AyP2Xmma3c8+PKTpS+Ijzty09HfFni3AAxh/2iXpksQEl6uUXk/+CwP6QB+i8XInsyJ4+JQpnS6tS8CXhBzqsseXp6w9w5dIF/Qd5wSlHekM14gz7iuGAy5YuJU6Msq4DzFKL3P/309hyRnGIfFOjCL73jRTHGLT+DDYxEXGSDXkzpcbhYbUsZZJmv0sOhGPy9ktG3xXNkQ67B0XvjF16MByTtintedEO6pgn+Gd5+9YeuwFKETZV+OcquohDnktsXCMXdE4gytKWfjB4ARXHzKlx6Hv183jt0WtQtBZbQib5GiIAWTGZnl6FMby+foTglp676D/uCCzYH4ZpfiFGHbF7vqlRSo/diZcc9xTVNRXB6zphF56++Sh3t657ZVQpkhX8hYRPiZxY2kl9IRH3mYoeBAICAYGAaQRMURpILaQLC1vC8NLdF6g396CRSb92rohazjkNn9eYvg8Rj99omq13toU4f9vS0xF/tggHYPxhn6hHFhtQol5+MfkvCGw+fQsD15+V8XDOlR5lHTNh/QlF/KFNpXz4obma+y3w6mO0XXJUbkciDxfH14VDsqSGz87ffo6G8xSFYEr/DJ1UD0kp/9TOS89VJ7DzgqICN6h2MfTzLmqYFTlL1gXdMJphwaxpsH9ITavOfFVgBEZvuWA0JvHTEE9NdGXNsesYufm8XK1igcz44xsPVTM+HWZi45LoWKWgoY6pl06pg0lNSqFDZUXlNzqhCv4F9ed25dGgjHFkanTzSozfHwl7hHZLj8lT14oCDb3/ErVnH1DBEz6lvuGZZvcDvfmAEuN62NucTfGJrunmjqpRpLuRyFSREdvBZP7LU2c5Y1k8io/aoRKlolR1Slk350LC3nAV9goEBAL2hUCd2QG4cl+hNuGtX9ihAnxL5bKpSR2PeIKWCwONbJLoFegLLZVgqYHedAvi/G1Tj0e8GSMcgPEGfeIeWGxAiXv9xewjEVh26BpIyVUqJA7gVSw7fvS7LH9WuXAW/N5D7TBaeSQCY/9WnEsuuTNge/9qcps7z97C4we1auTZMXWQMU3U6WL2sC4U2WhKFZUXzpDmY27UnZ7zN5W2x6oWRzXeplO3MGiD4hwumScDtvVT1pja8rwx01uUQcuK+QzdEt9f6XE78ebDZ81h5rQuhyaueeXvVhy+hnFblWeR543kVexskWxbz/XTs6/TN56i6YIjcpepkyfDpYm+qiEu3HmOBj+pnfZXp9QHL77QsXIBTGyiHx+QnvMUfVkGAUofpzRyvsxqVRbNyjuaHPTdx88GlXmtoqUirEVWv3ugF4rmTI9VR69j9F/KhUSlgpmxsZf6QsIysxe9CgQEAgKBSARMcZpK+LBUOLaC2d7L9/H1ihNG5vzYvDRaV8pv+LzCxN14/PqDpslNXfNidutyuk1HnL91g9KuOxIOQLtePvs1XmxA9rt2wnLTCPz773+IePwaWdOlRMbU0TvbeKGIBqVzo2GZ3PhmzSl5kFwZUuHoCG/VoHykG/+CoHXw2/ddDRTKltbul6/VokAEXVNIoNmoNz6NVZpsidwZsINxkFoDBOJt68vwvkljmsvnsiP4ruo5KJw9LfYOrqEy3Xvmflx9+Fr+jI/KM6WKTA2WflURPi6R6SdUNp64iSF/nJP/XTpvRmzt62n4t5ZaNatgZw087XmMkHsvUXeOcXQfG5F79uYzNP75sDxNEnm4PLEeeNEbrYhge8ZG2B49Au2WHMWRq4+NKmrRQ7CVtJSlpe+1LkVevvuI0uPU6uFEnUAUCvyFBrs/RD8DUUMgIBAQCMQdAZ6jlO+Rv9iM+4hx74Hn5JZ6HNPQBV97FjL8k79gZUclESaKFtSriPO3Xkjadz/CAWjf62e31osNyG6XThhuAoFPn/818O7RQY2cf8RFUrFglijx4h15HSrnR3v3AkZ8IaT2mDpFMrmvZgsO49QNhQhZ6yDoMsZPFf315zdVUKFA1PbYw+L+b/4hnLulqKWxUW8/7QnFrN2KcIY0n7KOGbGlT6Qzy1pl98X76P6b8a2vueIZ+0IeoAvDD5knYyocGa52BPMvwyynDM3TlBIxfSdxe0l4bA++i96M45m4KPd+F+lwvPH4Dbymq9Wqg8fVQfpoBAishbWtj3PzyRtUm6bG7+KEukiTwkE2/UTEE7Rg0oTSp3JA8Li6mLL9EhYfCJfrNS/viJmthPqqra+5nvaZSnuLLh1c67mT7MqWLgVOjFIroz948Q5unBL16dG1kTltCkMEIitkEx9R1XpiKvoSCAgE7A8BU6JG0kzY90FbmR0fPS3ZNbh2MfT9Ql9TdOR2fPz8n6bJFBjwc/vyuk1HnL91g9KuOxIOQLtePvs1XmxA9rt2wnJtBPZdfoAuKxRBjxrFs2NFF7co4eJVXInLrlf1wnAZs1PVzm9ANTjnymD4jHidKErjFUPsvrxLJdQsnkPVhncOkYIsKcnae6k9KwChDxQOGJZHZfGBq5iyXUmfluZaoUBm/Mnx51kah4OhD9FxWZDRMKMbuqDrl1vfqGw4Gv4YbRYrPI+Z0yTH6TF1VE3KT9wNivKRytru7vAokk3+Ny8+wTbe2scTpR0zyh8FXHloEBWRCqucfPL6UzT/RUlhpeg0ckonSWL/nJKWfg6o/0ev3qPiJH/VUCTKQ+I8Ujly9RHaLVF4AkkN/NTo2ohOnMUa9osx4hcB/u9csia6g6FW5KnUlhTnQyfXU/0NRzx6jRqcEM3lib5IlTwZdl24hx6rTspA5MuSGgeHWldYKX5XQYwuEBAIxDcCvEgRb8/UZqXR1i0yrdZWiikRJ0mJnTKHCo/YbtLcuiVzYlFH/QSXxPnbVp6M+LVDOADjF/9EO7rYgBLt0ifYifMk6awDxdSk+XRWSVnVfYo/7r94LzdjiY1vPX0Dzx/V0URHhtVCnkypVcPw/HDTmpdBq0qR/HD2XKpN24ubT97KU2BTWU0Jb2jxKFoaA1PEz7Nbl0VTV9O8XZJdplJCWbudR+/Au4//yh/99W1VlMuXSf63luNJ+pJPCecj0NKldMD58XUN1f3O30Ov1crhX0vF1tJ42nP/WinUB4fWRL4saeRp8crdOTOkxLERPpi9+wrm7gmV69UvnQsL2lewZziE7TFAgCLLi47agf80gkO0hIHYrnnuSX7Yc+PqIAMTxavFQxn2xUnIP5+kWH98ZMJQlo/BcoiqAgGBQDwiwKfKJk+WRBU5x1LCxKOZqqGJ0/uX/VeNzPmqSgFMaFwKUXG1UiOfEjmwtFMl3aYjzt+6QWnXHQkHoF0vn/0aLzYg+107Ybk2AjN2hmD+vjDVl3yaH9/SZ1YAwphotrltyqFxubzgHYNsiu+eS/fRdaWSWkqpgufG1jGKxqJoLorqksr3vs74pkYRu18+iqQix5ZUWCVMU8Ib1Ypmw6qu7ladO6/ELA2+8ms3VC+WPVpbrtx/iToaqrASb5zWrfHOAV4oniu9qm+vaftw48kbo/GCRnojR/pU8ue8ajAF95EKLUX58SksrvkzYXPvqtHOQVSIRMCcteL/rol3jfjX5u8NxYxdSlq7T4mcWNpJv2gAsUa2jcCDl+/gNnmPppH5s6TBgaGm1c0Phz1Ce0Z9mu8kYEgNFMiq8MIapaGndEDwl0sA4l2l3yWpSCnqto2esE4gIBBISAgUG7UDHz4pl54ZUjngxbtP8hRZXj1bmTcv5CXZ1ax8XsxqVQ7P33xE2Qlq7lXWdnOyiWIyV3H+jglaCbeucAAm3LW16ZmJDciml0cYFwsERm4OxppjN1QtSXiCBChMFV75a3VXd3gWzYbv/ziH9Sduys3auuXD1GZlDP/+eV8Ypu8Mkb8zpcY4cP0ZbD59W67Xw6swRtQvEYuZ2VYT/gaY5Tbkeaoky2s558CvnfW7QTUHkbAHL+EzSy38QO341FtTfWnxd7FckG8/fEaJMWqFzwNDaiJ/ViWqjPru//tpbDljrCDK80pqjXdhfF2kTelgFIVWxyUnFn8lnFDmPAdSnRKj/fD2o6LIvKm3B8rnzyx3wUdZkmAPRWkuCriKqTuUtHa9DwMxmYOoa30ELt55gfo/HdQcOKUDCcWYTsU3xUMqdcZHDJuKQqX65249w//mKyI1KZIlxZXJ9awPiBhRICAQSJQIEP0Npcqy0dAkknfvxTsZjxH1ndHDy7Yuuk29g/mWzIWFHSsgqksempjeF9ji/J0o/3yMJi0cgOI5iBcExAYUL7CLQS2IwLdrTmFb8F3VCAs7lIdvqdyao1JUkNPI7fiXSe3a1s8TJfNkNKQLUNqAVNgUVv5lgoRDJjUpbTTGxH8uYtmha/LnLSo4YkZL+xcPcBqxHZ8Y0CTMaKKmDrx6c6iY8xiZIuA/9H1NOGZWO+m0+nv48j0qTVbzxhEnHHHDUXn86j0qcLxylJJHqXlsWXH4GsZtvaj6LGkS4OqX6D7pC63+pCjBEZuDsZZxbpurZGwOTomlDu/sZyNXCQNeNVoSWaC/YfpblkpVp6xY061yYoEt0c+Td8rxgJwdUwcZ02grzptSn5T64Llj/c7fRa/VigK95ISm+loRybSHJKPNRBSBgEBAIGBhBIgOwWnkDtUoJFYW/ui1/NmQusXxbU0nC1sSs+6/XnEcey8/MGokOfaiEmuiRh5FsmJtd/1+88X5O2brl1BrCwdgQl1ZG5+X2IBsfIGEeTFGoO3iowgMf6xqF1Xa7dPXH+A6cbeqfuDwWsidMTV2BN/FN4wia+6MqRD4RQHWd84BXL73Um5nivOEjxSMjyi4GIMYTYOPxIfFvQDuGVwdRbKnM7Q0JbzRoExu/NxOPxU1c+al5cCjdtGlhUt9k8gLRTuyhVJCKTWUihYXJHH2EXcfW3guQfpOK31Pi4dG4gkkNWNyrkqlv3dRDKxdzBwYRJ0vCHj+uBe3nirclbwoz+bTtzBw/VkZL5fcGbC9fzXwvJZuhbJgQ88qAtdEgsCmU7cwaIPyXPDT3jXQC8VyqtP+pTrktCfnvanC85HyY0nPILXXUgLno4gTyZKIaQoEBALxgIBW1kPJPBlw4c4L2ZqBPsXQ36doPFhnesiWC4/geMRTowrl82fCpt5VDTRARAdkquj9my/O3zb1eMSbMcIBGG/QJ+6BxQaUuNc/Ic6ed8zRHNtUyocfmkem7vLl6sNX8J6p/tGXFBd5PjZqS4cth2RJ4DLGT0V6TM4AekHgC3/4I3EISvmy5/Ly3UeDAjJbWAEUU8IbTV3zYnbrcladupatpJ57eaJ5aXNat93+g7zglCPysK+VYqwVkUN8OaXH7cR7hjcnT8ZUOPLFoSyBQuk15Fxloyv/6euJUnkzoumCwzh945mM36QmpdChcgGr4mnvg9WZHYAr9xX1aonvU5rXhuM3MfTPc/I0yzpmxJY+nlgXdAPDNylOHMG/aO9PQszsN6VsLvUi0UZo9coLU/F1eEXy1UevY9Rf5+VqLL3Egxfv4DZFzUV4enRtZP4SkRyzWYnaAgGBgEAgZgg8f/sRZcer3/9oj2Kda31qOuG7usVj1rGFa2udDWhI51zp4TfAC1HRPFC9CgUy489vPHSzUpy/dYPSrjsSDkC7Xj77NV5sQPa7dsJybQQqT9mj4iKhWlGpz/KE62lTJMOFCb6GzrVUQ/0GVEMSJEHdOWpeOVMpYDynWHSE8fawrlpcKewhNPjWczSaf8hoKi0rOGK6ldOftaIVtRxvUeFebOQOfPisEF6z/IFGnFwOSXFlkrZzscUvR3DiunIDLaWX8mOXGbdTRai9vkdluBfOCj56jVWltofnxhZsbPzzYVA0plR+aFYabdzyy//mnS+SwusfJ2/hu41KBFjpvBmxta+nLUxJ2GAFBKZsv4TFB8JNjjSzZVk0r6CtKv7TnlDM2q0IyPCd9K3lhMF1lMMy72z0KpYdv33tZmimdfiWItatAIMYQiAgEEjkCJD4G4nAsYX2KKJJkErP6oUxvJ5tcV1X/WEvbj9Tov8lW/NlSY2DQ2shOrV2vS/vxfk7kf8hfZm+cACK5yBeEBAbULzALga1EAIUPVV8tJ9KnYyGYlN3+aF3XbiHHqtOyh87Zk6NQ9/Xkv/tPsUf918oarc50qcEHf73MFwiRIB8dIS35qyMVBsZRUcLwWDxbqNLQzMlvBFfnHU8XyGlq2zrV81snHiHHBvteSz8MVovPir3lTF1cpwdW0ez78nbLmLJQYUP0tQLpcfUPbjzXCHU/rVzRdQsngPOo/1UEYR0G0230qKYj0C7JUdx5KpCEcCrFfJcjVUKZ8W6HpXB87hJUQPmjyxq2jMCg9afwSZGzImfy1Df4uhdQ5vzauqOS1gUYNp5yPPHzvG/gjn+ofIQEkk9ffD+02cUH6UWHZIoAuwZX2G7QEAgYB8I3H3+FlWm7lUZS3uU34V78mddPQuBIpttqVAGxktGqViyjficidf5aPhjtGHe5Xjb9b70E+dvW3o64s8W4QCMP+wT9chiA0rUy5/gJq8VsSdNUkrr5Sf9e9ANDGNS+8o4ZsTffZTInjaLA3E0/EmUWFUvlh0rv0Ro8BW1nGEUIZbCIand4q9FRB8+pT6SfiGiN0Wm3KlKAYxvXMrq8+YVi2Oq5sY7gWmtac2p7A95gM7Lj8tzisoZfOTqI7RbckyuawqP2rMCEPpASVP9qa0rahbPbpR2fXBoTeTLEr2QidUBt+EBu608Dv9LChE4T1a+5EA4Jm+/JM9Aela2B99Fb4YPtEj2tNgzuIYNz1SYpicCHZcdw8HQRya77OxREOP+V1Lz+zFbzuO3wOsm2zYonRs/t1e4Uflow2bl82JWq0jqBC0FzuhU7vXEQfQlEBAIJG4EtC6Am7nmVV2QxNe7nqmVIbG/IiPVysVSXVJxD5lUzxDB+NWvQSYXt0TuDKC9Vq8izt96IWnf/QgHoH2vn91aLzYgu106YbgGAhTeT2H+WmXnAC8Uz2VM0r5gfxim+YXITXhnHp/6p9V3T6/CGF5fO93hyesPKM+JjBwb4Y2cGVLZ7RryghY8p54p4Y1unoUwKh5uhStO2o1Hrz7IeDculwdz27iajX+N6fsQ8fiNXJ9NveWFYkgNb+93ph1DFN1DPHP0LE5rUdZILZgG0UpTJX7JWhxXpSD/N3sJ5Yp91502KP1K5duaRTCkrrP8b34/8HbOgWWdK4GPFE4IqfwxRy/xtqg39yCIE1YqOTOkVEWG1y+dCwvaV9AEaPCGs/jz1C35O4ekSVQcn7y65Ki/grH66A25Ph8h6Dx6B959VCgJNvf2gGt+EQmceJ9OMXOBgPUQ0BLLaO+eH2uOKXtWfGV7mEJBiwuarRs2uR72hTwECa2ZKsVypsOugdV1A1qcv3WD0q47Eg5Au14++zVebED2u3bCcmMEzt9+jobzjLnnqOaijhVQt2Quo0Z8WibdZM7ihCp2XriHRQFXcYoRYGA74lUc2e8+//sfnLibR3uP2OBTJTKlSY4zY5S0Vy3lXMKkV/UiGFZPcbZY6xnmufOiitbRsoknj57TuhyauOY1VI1KsTO282u/9CgOhylpqqMalDCknbOpxqQyTGrDosQMge//OIf1J27KjbpULYixjZTIrbn+oZjtr/C11S2ZE4s6VsS+kAfowkR6xpRHMmZWitq2hkClyf6giw2pkJr7XoYGIiqC+G9Wn8SO80p6HNFMsErUfGTJoA1nsOnUbXmsHl6FMYK5YCo3YReevfkof7+ue2VUKZLV1iAT9ggEBAIJEAFeHI8SPzp7FMKvhxV6k1YVHQ0XnLZS7jx7Cw8TwQFk47lxdQwRgH3WnjZpst5R/+L8bStPR/zaIRyA8Yt/oh1dbECJdukT5MQPhj5Ex2XaIfwj6jujh1cRo3nz0RlRcZeQStiaY9fx1+nbeP3hs6GvrGlTIGBoTZBDxlRxnbALT5kD29pu7vBwyma3a8CnvfIci1rKuTTZfrWcMIghu7cWAD6zAkC31lIZVLsY+nkXNXt4Xn13arPSaPtFOIKeh5GbFcVOSTTC7M41Kvb47QR2Xbwvf0O2Fs+ZHt+uPSV/VihbWhD3lygxQ2Dc3xew4kiE3IhXCJ+5KwTz9obJ3zcskxvz25XHodBH6LBMSd/Oli4lTozyidngorZdIkDpY0VH7QBd5kiFhDvY54TnjmUn2unXIAQwBPnuhbLg2DWFVoKnDei16qSKT2uAT1EM8Ckmd8kLXS3vUsnAESqKQEAgIBCwNAK88Bml0NKl6iJGJEnrIt3SdkXVf8i9l0bCfWx9ElIKvPoYgzYoQl98fwWzpsH+ITV1m4Y4f+sGpV13JByAdr18sTc+SZIkZjWuXr069u/fb1bdmFQSG1BM0BJ1bR2Bv8/eQb912jd4plISuiwPMoT+S4XnBNOaM0W4UUogRYRQJFh06bzeM/fj6sPXclfz2rqiUdk8tg6nSfv8zt9Fr9VRO6N44Q3qLKaON70AajTvEIJvP5e7m9ikFDpWLmB291EJRyw9GI5J24w548zuXKPiwPVnsJkRHKAbduL6u86kIVcqmBkbe3nEZZhE2Xaa32Us2H9Vnvv/yuYBcSxKhRdsaOqaF7NblwMv9sJHvSZKMBPJpB+/eo8KnOrlwg7lVXtgimTEI+ULrXe6lguP4HiEov5NnH5shJ/EQSXBSTxUrKImf3llTElQHr6lcieS1RDTFAgIBOITgZPXn6D5L4GyCelTOuArjwL4eZ/yu0rvt/SeayvlRMQTtFio2Mzb5T+oOo5HPMFwhg+crxPVJU9s5inO37FBLeG1EQ7AhLemZs1IOADNgklUEgiYhcBvgREYs+WCZt2qTlmxpltlo+94vjU2ususQc2oxB8AJzQuia+qFDSjpW1WoQjIAevPyMZpkSOXHrsTL99/Uk3ge19nfFPDOArT0rOc+M9FLDukpKfsHVwdhbOnM3vYriuOq1SfWSfxvD2hmLlbSRmt7ZITS76qaHbfWhWnbr+kuk3XqhMV51icBk/gjefvDcWMXcp6+ZTIiaWdlPWasPWiZirTyetP0fyXIzI6aVMkw4UJvgkcLTE9QkAreiRgSA1Un66+lD09ujYyp01hBFr9uQdxkeEPpEjon5goU2rA8nnyvxf8hUVUlARixQQCAgGBgCURoEi5tkuOykOQii5dqM7doyiX1yuVC7900OZEtaRtpvrmKTz4eqoFaBQAACAASURBVH/3qYpT159i3NaLJs3jM13iOg/hAIwrggmjvXAAJox1jPEsJAfgN998g969e5tsnzZtWhQqVCjG/UfXQGxA0SEkvrcnBEhgYY6/8hLC2p43U2ocHlbLaDpe0/bhxhNtgQe95t5z1QnsvKCkdPIpXXqNY61+1gXdUN2Uls+fCZt6V1UNX3GSPx69Ujiz6EvisutWrbC1zJTHIQLoKdsv4+rDVyCy6sblIvn7zC2Uervt3F25OqX/Df6SysxHlMVUYETLBuLYabkwEBRpaqrYmsqeuVjGdz1yBJNDWCr8xQCv2ErPy+SmpRF86zkazVf4RUnFm9S8RUn4CBwOe4T2S5X0b4r+DBrhg2Kjdqgmb0poquaM/bj2SIkA/7F5aXz/Z7Cq7ZFhtZAnU2rDZ7zDcFarsmhW3lGuryUS1OYLJUHCXw0xQ4GAQCA+EeDVckkQiRyAUV2sxae9NHZU2UH0/dru7iAOcXpPlAov1pQjfUoEjdSP9kOcv+P7qbCN8YUD0DbWwepWSA7AsWPHYty4cVYfX2xAVodcDGhBBHh+L3YoyranKItUyZOpLCg1dqfK0bKhZxWQ4qqeZfimc1gXpAgPfFWlACY0LqXnEFbt69dD1zCBcaLwKpZkDC+8QZ+N/19JdPKwv8jH7zaeBalBS4VVM46OUy62CxPx6DWWHAxX8U2yfY2sXwLdvazvTI3tfGylHe+8ds2fCZsZ5zX/tyoJxvDE57SfXJvawFamJeywIAJbztxG/9+ViGeJDL7CxN14/FpRF//tazd4FctuZInbZH88YAREVnSphK4rT6g4Bf/p64lSeTMa2vIOQ0o3ZlN8Wy8KVHEIjmvkgs5V9b8gtiCkomuBgEDAThHYc+m+Yf+SCqXGdqhcAD/sUJxnNYpnx4oubjYzQ56rmTds6VcVDSrvbDZH+lQOePlOuYQlvu+To2vrNidx/tYNSrvuSDgA7Xr5Ym+8cADGHjvRUiDAI9D/99PYcuaOSWD8B3nBKUd6+fv3nz6j+Cg/VX2+jh4o81FikrAA2/fley+QyiEZCmRNo8kjpYcdevWxYH8YpvmFyN15O+fAss6VVN3XnhWAUEZ4g76c3LQU2rubz72nl71x7Wf0X+ex6uh1uRspKow+GPbnOfx+XHHuxlRhODrbKAqQ+ADXHL2Oy/deGqpTBNKugV7IkT5VdM3F9xwCvDPHOVd6+A3wkmvxzt7u1QphZAMXg4gMicmw5eqU+khGBI06l9vP3oL2DIqg7eZZGDWdbUfggfZMirImTkQSnuhd08kiGOgMaZy646NGScRjfc8q4FNxp7cog5YV8xmNxV8y/dGrCnquOqlyHq7u6g7PopHCUO5T/HH/hRI9vfJrN1RnHIu8qMjwes7oWd361ApxAlU0FggIBOwSAZ4DunC2tCCObZYL2dMpG1Z3c7eZ+S0MuKpyUPKGzW1TDqH3X2H+PkUAjCIb2X04Y+rkODu2jm5zEg5A3aC0646EA9Culy/2xgsHYOyxEy0FAjwCHZcdw8HQRyaBoVs+H5ec8vf3X7yD+5Q9qvqnRtcGcZroWXihCD7tkHUiueTOYLhNpVTStFEoC+tpX0z7mrUrRMVh1aBMbvzcrryqG154g76c1qIMWmkckGM6vrXr85x8ROI/q1U5gxkkOkPpJVLpXaMIhvo6627if//9ZxAyoXR1jyLZdH9GdTfYRjsk8Z4eq07K1uXPkgYHhirKfvwlAnFWEnfl9cevjTjfLk80jijWY9rfrD6JHefvGbpKnTyZwdlLIjC2UH4PuoFhDFE6H51mCzbqbcOPfpfxCyMcI+13vCNOS0CK/m6LjNgORkAY2/tVQ991p0wKQ/H8qeQwrFhQiUrnKSUG+hRDfx/zVc31xkf0JxAQCCQeBPh02uI50xscgGP/Vvi3pUsSW0Fl+s7LKpES3q4pTUsj4vFrLGaUjMmxGc5QN6RL6YDz4+vqNiXhANQNSrvuSDgA7Xr5Ym+8cADGHjvRUiDAI6DldGLr8Bx0Wml9YZP1j+rZdOoWBm04K5vCRh1RqmeNGcYK3/SyQQqklC5cNKcStWgLqz5520UsOaiIajQv74iZrcqqTOOJ7OnLOa3LGVST7a3M3n1FRXDNCnB0/+0Edl9U+B0H1y6Gvt7iMG6ra3wo9BE6LFP43LKlS4ETo5S0nm/XnMK2YIXvsZ93UYN69Z1nb+Hxw17VtILH1UH6VMl1nernf/9DiTF++PDpX7lfEo0Y9IVzUtfBYtFZ33WnsZVxeEsRkrHoym6aDNl4FhsZCgApynfoH2ex4YRCDaDFy/nu42c4j1ZHme//rgaG/HFWpQwsCUORw9Bp5A5VevC2fp4omScyPZiKKSe13QAqDBUICATsFoE/T97C4I3K+2ypvBnQ1i0/Rm4+L8+pQoHM+PMbD5uZI8/tyxtGZ4ObT95gZaCS6VEyTwZcuPNCrkqXcZcm6if8JRyANvN4xKshwgEYr/DH3+CSA9DFxQX04hcREYFkyZIhV65c8PDwQOfOnVGzphKdoLelYgPSG1HRX3wiwPPOUcj+87cfZZM6VM6PSU1Ky//myd0zp0mO02P0C/GXBuIVyCi14NiISDLh6MiJiYh4dutyaFQ2T3xCqxqbT4nlcaXKWtGY89u5omEZ25mHuYDy6SM1i2fH8i/8Nvw840voxNy5JPZ6vJpvmhTJcJFR8zXl0H3w8h3cJls+WpgOIdWm7VMtEwkYHRxaE0ktkG4c0+ehw9JjOBSmRFnrnfIeU3usUb/L8iDsC3koD/VdnWLoU6soZu4KwTxGzde3ZC4s7KhWvnzy+gPKT9ytMjNopLfhsMxeHEhRfFq0FOQwLJgtrdzH93+cw/oTCu1Al6oFMbZRSWtAIcYQCAgEEjkCfBQ48ei2rZQfQ/88JyNT1jEjtvTxtBmkBq4/Y6BSMVVo/737/K2KzsWtYBYERTyRm6RIlhRXJusn/CXO3zbzeMSrIcIBGK/wx9/gkgMwKguaNGmCFStWIGNG5QbYXItpg4mq3L17F25ukUStN2/ehKOjojRn7hiiXsJG4NPnf7Fg/1VQ5ExVp2z4tmYROCRLapOT5rmWiJCdFMukUq1oNqzqqvCSUCQLRbRIpXD2tNg7uIbuczt36xn+N/+w3G/yZEkMCqL09z9jZ4iKd0Rr8BK5M2BH/2q62xXbDqMSxZD65B0p9PmijhVQt2Su2A4bb+1WHolQpbdULpwFv/eoYrCn+S9HQE4lqdgrz2G8gWvlgbWifsOn1Jd5NzsvD8J+xtkzrJ4zelUvgmdvPqDcBM6RM8IbOTLoy8PIKyxK8KzrXhlVimS1MlrGw/EKtVrO/3g3UmcD+MjyH5qVBqnurgqMwOgtStobLyhjeK/ScOhSGtmErRdU0YOSI/Xp6w9w5R2G3HM2dst5VaQKRd9MbaZcbOk8fdGdQEAgIBCQEeD3PRLNa1MpnyrLhahsttvQO2u3lcfhf+mByVXs6VXYINTEOgmJdzWAOT/Q/Vu4jsJfwgEo/qgIAeEATKTPQdq0afG///0P3t7ecHZ2Rrp06fDw4UMEBARg4cKFePz4sQGZ6tWrY/fu3UiePGbpRuY4GCXohQMwkT6E0UybJ8231TROSpkrNmqHajb9vYuqUjfzZaFImlpynd8CIzCGOcBVKpgZG3vpn7agdQiU0ge7rTwB/0tKCilFBz58+V7FGZUhlQPOjTPmHqF0QeKn+uv0bYOCJHHsZUuX0uIPcp+1p/DPOSVNsm8tJwzmUhT5VEEy6tfOFVHLWeFgtLihOg2w4fhN9e12vkzY8m1VQ++8Q2RWq7JoVl5cpOgEve7daHH5kTp46hSR6uB8hNvohi7o6lkIr99/QsmxO1X2HPq+Jhwz68vNt+LwNYzbetFo3i0qOGJGS3Wave7gmNFhlal7cPf5O7kmHfx+aF7GjJb2W4Wfs8Qlu/PCPYOYh1QoUvPwMOX3hT4PufcSdeccUE2exGNI5GURwzfVpFwezGnjChKAqRpNqvmU7ZdUXFXNXPNiVutITlJRBAICAYGAJRHgRZFI8KNVpXwGPmSpFMuZDrsGVrekGTHqu9WiQARdU6L5kiQB/vtP6YKE3Z69+aii/6hXKpfMxSvVvDZVuSyMkQEalYUDMK4IJoz2wgGYMNYxxrN49uwZMmXKpNnu/v37qFevHk6fjtxU586di379+sVoDOEAjBFcorIGAny6UeuK+fBjC+0DH0UvPHz1HkSea+0owQcv3sGNE/RY3qUSuiw/Ls+KbvAuT6yHFA6REYw8t1sdl5xY/FVF3Z+DNx8+wWWM2nlwYEhN5M+aBtWm7cXNJ2/lMWe2LIu8mVOjzeKjKrvp0Mj/PR8MfYiOy4LkenSLObx+Cd3t5zvkb1O1yO953izqY1VXN1Qrmt3i9uk9gBbp9c6BkcqxtWbsVxFF/9K+POqVzq23CaI/nRDQSuU9OcoHWb84zvmDgsTNZk5qph4m8tFdUp+Uqnx8pE+8CgMRTQnx2b1n+Am1+D/1wMFW+qA5k1L8h88KJ+Nf31ZFuXyZcObmMzT5WTuyW7L/9I2naLrgiDwdiUeKpxWgaPXfvnZD2IOX8Jll7DBk1aZ5ESaWk9RWcBN2CAQEAgkTAS1KFBJ3+2bNKXnCdAbY+53+2TSxRbTe3IOg6H+p5Eif0hDxJxXi23757qMqSpB+2/48pc6iC5tcT7ezjXAAxnY1E1Y74QBMWOup22zCw8MNkYEfP36Ek5MTQkNDY9S3SAGOEVyisgYCfISaqcMGpUF+veK4gXOvfP5MWNu9MlIlj4yqsUbRirQ4Pbq2UTrV3sHVUTh7OoNJPDGwpaJZtA7Om3t7oFjO9EZRRf/09QRxF/I8YBfG1zU6/M/fG4oZu67I8BJnyYZekampliztlx7F4bDI6GQqUpQUO6YW6bKtpDHGFBvi6qKUZqmwyrGVp+zBvRdKRNSKLpVQo3iOmA4h6lsJAa1IPuLXk1R2my04jFM3nsnWUGolpVj+++9/KDxiu8rK3QO9dBfoiUrJnC4HmlfQJ7p0UcBVUGp7gaxpDZHD5qgMa11kSJFrVlo+qw/z/M1HlJ2wSzWuFPlJnFFVpqqFYXgVeZ5nNmvaFDg5ujbWH7+B7/8Mlvst45gRf/fxxMnrT9D8l0D585QOSREySc079fO+MEzfGSLXqeWcA792rmR1bMSAAgGBQOJDYN6eUMzcrbx30sV5y4r5VO9IfLZNfKPE84PTfnvu1nPZLJrD24+fcTBU4bclAb7fGFEQqnx5oq9u5xrhAIzvp8I2xhcOQNtYB5u0okGDBti+PfLgcfv2beTJox+JvtiAbHLJbcoo/kDM8+hJxvZecxLbg+/Jti/sUAG+pazH9xZ49THaLlGi5qS0WSJgJyJ2qSzvXAk1nSMdNHwqa+8aRTDU19ki+PNpZJQOmzlNClV0CEUokiABKUfyfGNHh3sjV0Y139jUHZewKCBcttdSHIY8IDzv3ZSmpdHOPb+qGp+mRl/++U0VVCiQxSL4WrJTXjk2e/qUhmgsKmXH71IJzazvURnuheOfq82SeNhz31qOvF0DvQzOeCo839v0FmUMhxsqRUZsV6mzbu9XDS55MugKB6V/UhqoVvEoktVwsRLXwl+WmJtCqkVl0KBMbvzcrnxcTbLZ9mEPXsFnVoDKPukQ+PFzJO0Em0pGXK3E2SqVXRfuoQeTJiwdjPn0Yelz/nBdMGsa7B+iFoLjU/D0ei5sdhGEYQIBgYDNIMBHINNvAFFUsNk2uTOmQuBwb5uxmX9Pq+2SUyXCVNUpKz5+/k+VJtyzemHV+zVNRusiPraTFOfv2CKXsNoJB2DCWk9dZzNkyBDMmDHD0GdQUBAqVdLvpldsQLouVYLsrMb0fYh4/EaeW1mG/4ydcIOfDuLCHSXEXisqzJIA7Qi+q0pBKJA1DQKG1ATvwBzT0AVfexYymMJHsllSwZXHh/i8SGBl2CYlCkRy4NHBsuhINZ+h/yAvOOWIdFJIZcTmYKw9dkP+N0UOnh2rv4oxv248793s1mXR1FUdmcS/JFIfxJtHz4+9FT4qJ31KBwSPj+RkJAcA8U9K5e8+VVHG0f7maG9rEhd7nUfvwLuPximd1KfvnAO4fO+l3P3cNuXQuFxew7/5dno/z+T4LzHGT+VQYudJvEWHvq8F4pqLS+Gjz8xN1+LFjAx4aSjfxsU2W2t7NPyxio4hXUoHkIiHVCpO2o1Hr5QLJj4CmOfQdc6VHn4DvHA84glaLlQi/aR+G88/hLNMZIqWyvKaY9cNKsJSoYj7Tb0jOUlFEQgIBAQClkTghx2XQWnAUqH0WUqX7bDsmPwZcVGfGBV5SRrfhTJwnEbuUF3edaxcAKuOXpdNI0oHogQ8e1OJ/idl4Nn+SqQjVab3a3rP1qOI87ceKNp/H8IBaP9raLEZDB06FNOnTzf0LxyAFoNZdGwCgdLjduLlu0/yt6aizHguu+/qFEOfWkWthis5wsghJhXJUTlo/RlsOn1b/rxTlQIY37iU4d/8YV/PFDt+4nxq34j6zgYy/eWHI+SqbHp1idF+hpQEqWzq7YHy+TOrutUS2gidXA/JLazSbA7v3YL9YZjmp6SpkeGWiJiyxgN24c5zNPjpkDyUQ9IkCJtS3/BCSVFhbNFy1FrDRjGG+QjwUcFru7vDo0g2QwfeM/fj6sPXcmcU3UYRDlRKj92Jl++VvfCPXlVQsaB+Ea2X772A75yDqomQY+gVM6Ye++riA1cxZftleZxMaZLjzJjoLw72hzxAZ4ZTlTrwKZEDSzvpdylp/ipap+Y/5+6gz1qF3J6PyOMvQyidmviwpML/LklKwVpcf5Ra7PnjPtXE1nRzR1WnyGdTKn+evIXBG8/K/7Y1xU3rrIwYRSAgEIgPBCb+cxEUhSyVVhUdDcJnLG+1ub8p1rBfi/aDeKtZGgUSLUmaJInq8o8CAiZtu6QykWiFMqdNoYvZwgGoC4x234lwANr9ElpuAg0bNsS2bdsMA9CGkTdvZDSCHkVsQHqgmHD70FLWNXWzx4fYf1uzCIbU1T+dlpwuSQAkpXxZpvC8SDWKZ8eKLm6Y6x+qusWrXiw7Vn7tZmjpPsUf918oRMBserDeq0oKaSQmIZVe1YuAImqOXFW49Ab4FMUAn2KGKpUm+xvUgKVCBPFEFM+WzsuDsD/koeqzoBHeyJFBnSqs91w8pu7BHUYJlMRWanK8d78euoYJ/6jVTC3Bmab33LT6C3/4CrVmqtMAydFKYgilrKAMa405JqYx+DTbZZ0qwrtEpDq117R9uPFEiXhe3LEC6pSMpDLgHYd6c1ryUcy5MqQy0BWsC1KifAsRufrg6kaCQDFZvxk7QzB/X5jchCILwybXBys0odXf5tO3MHC94niiOux+GhMb7KUur8pcsUBm/PGNohTfZXkQ9jF7MO+gXXowXHWIJMXM1d3c8ejVe1Sc5K+Cgfb/Of4Kz3P6VA4gTkH+Qmfbubv4di1DuJ+dngnbIdy3l7UVdgoEBAIxR4DndyYF3Wbl86q4S/lI6ZiPol+Le8/fofLUPaoO6bKfvUShqPqUyZMinLn8m9y0lCrSmjog6heigNGjiPO3Hijafx/CAWj/a2iRGVy7ds0gAvLhwwcUKVIEYWHKS7seA4oNSA8UE24f91+8gzunrKtFSk4h9hQJ9S/F0H8pX1cthDGNXHQFhyJX6NYufarkmNO6nMohNumfi1jK3EpKvFZ8CpYUwaGl7qh3Sh87+XF/X8CKI0q0H6kp+1+6j8cMP+HCDuXhWyoy2qjWzP2ql5EF7cujPqcuy3PxUTtrRNm5TtiFp28+ytPTcoTwkS9Uef93NVAwW1pdnwlrdKZF9n9uXB28//ivwVHLFlZR1hq2iTFijkDtWQEIffBKbjivrSsalY3k1o3Kue022V+lHKjllI+5NUoL/hKDuN0G1ykO+jtnS1y5NEf/dV6V/kR9mxPZwHPPUTvJoRWXedtKW0r/OnbtsSEatFTejAazeGcpn/I87M9z+P34TXkKlFo2sUlkhDmVn/aEYhZHmE9K80T/QGlpbHHMnBq3nir8j/RM0rPJlz2X7qPrSkWUiA6vh4fVshUYhR0CAYFAAkZg+KZzWBek7HlEU0BpwI0ZRXStc0J8QRJ6/yVqz1Yrq6/u6q5KWaaIxbQpHFT8u0Rtw194aXFxx3Ze4vwdW+QSVjvhAExY62nWbLZu3Yp69erBwcFBs/79+/cN358+HZl+MnPmTAwaNMisvs2tJDYgc5FKnPUu3nmB+j+pU9IIiSuT6iGFQ1IZFEpR4yOhSDmTFDT1KnSLV/XHvTKPB5+KPHjDWfx56pY8nOSAPHPzGZowLyaUvnlpYqTQRulxanVHVg1UL7ulfviDIKWCnWbURqnevu9qgCJ8qPBcUD82L43WldRCG3VnH0DIfYWvjNrRi41nUXXKmN5z4dOT//q2KohDhS1a0UKSeqbe9li6v2dvPhiJslCkJfHIeU1Xp+xdmuCL1Cmsp35t6bknxP75v61pzcugVaXItE2KyqLoLKmwf0985CAJ+dRyjowc1KPwexhFVkxqUsoQfXrtkZKWHNe9lY9GJttZdXRTc5m+8zJ+3qdwP1E990JZsL6n5ZXH9cA3qj4CrjxE1xXH8enf/5A8WRL83oMEizKDd/B1qExrovyu8VynpCZJDj6p8EJNrGpymXE78YKh1+DtY/kn2e94ZeFs6VLgxKjaloZI9C8QEAgIBMD/TvXwKozG5fKoaFIomvzqlPo2gdbJ609Vl2ipkyfDhp5V0Gi+QuuSIllSZEidXPXbv6hjBfRkBJxoMnq+w4rzt008HvFuhHAAxvsSWN+AggUL4uPHj2jevDmqVKkC+nfq1Knx6NEj7N+/H4sWLTL8PxVPT0/4+/sjZUp9Qo+l2YoNyPrrbk8j8uqnku18lJNWhBTdCM5uXU636e6+eB/df1OiHqhjisLKkCqSkJcOb3suP5DHG1y7GPp6F4WW84Yi0Yjvg3feELk7pS5YohDhMEXeSIVekCidWSqpkifFhfG+chqeOQIlfLQS9WXq0KjXnLTw9B9UHU450qmG4NMZ6UtrpCfrNU+2H3IWO4/2U3VNzxClANedo75ZDp9S3yg93RI2iT5jj0CbxYE4Gv5E7mBcIxd0rhopDFRuwi48Y6JbWVVnXhBJb6VzXrBIEiWavzcUM3YpZOSUGkoE6ykdYudo7vRrEMjhxRZzogqHbwpWpSNTe3KS/cmkxMZ+VZSWNx6/wYxdIYb9sZ93URTPpRY/0mMM/u+blH7Z6DtShVzTrTK6rTxhiNSWCkvTQJ+tPnodo5h9nS5C6EJEKny0Jamlk2o6lerT9+E6I7DF2kQXVSdH19YknOdFiWwp3U7vtRH9CQQEAraFAH+BRHQ/JJRVh4uyuza1fpyoKvSaNc9dmyN9Svzeo7IRrQs5Blne7VVd3dBxWZDKjANDaiJ/1jS6mCbO37rAaPedCAeg3S9hzCdADr/r1xUVIlM9kINw6dKlyJRJf2VJsQHFfN0SUws+fVaaO5/KGXLvpZEjpG7JnFjUUYmEiCtuiwKuYuoOhbie+vunr6ecqtV0wWFVRB1FznSoXMAwLH+oJ6XGTGlSqCID6QYwZJKvxV5YtgffRe81Cm8Tj0fpvBmxta+n/HGvVSfhd+Ge/O/+3kUxsHYkP6BUeFEC+tzS6stHwh6h3VJF7Y0iQS+Mr2vEU7Xv8gN0WXFcZS/xWWXRiUA5rs9TTNpTunjhEdtV6qw7+lczOADZ6FJ6gaToUlFsG4GvVxzHXuayYKhvcfSu4WQwuuQYP7z+oIjvkHOLnFxU+NTh+e1c0bBMZOqwHoXfpyRO0tvP3sLzx72q54/d+2I6Nr9XUvulX1WEj0vU0Yz8nkTtTKnCx9Qmqf7bD59BzjiaMxVSKN410AsOFhQ24nliJVv8BlTDsD+DQVHkUmF/V+gz/mIqT8ZUODLcW67PR8t0r1YIIxtEUmNQyhyrOsliJjkgtXA8f/s5Gs4zFiWKLeainUBAICAQMBeBb1afxI7zyrspXYr8r2weI4canylkbv961+OFnIpkT4u13Ssb0Rvx49JvP0+/YU6kvLn2i/O3uUgl7HrCAZiw11dzdgEBAaD/AgMDER4eboj2e/HiBdKlS4d8+fLBw8MDnTp1MkQHWqqIDchSyCaMfpcfvobxW9VCDjSzrX08UdoxkiOJyvGIJ2i5MFA1aRKsII4svQqfikX9sgfwmjP2q9LkWPVO/qA1tpELCmZNq3JQEeH+0RHKwU0vu6V+Aq8+RtslR01226KCI2a0LCt//93Gs/jjpJLS3NWzkMG5J5V/SYF2pNopRd9ZSnxFGnfJgXBM3q4oo5VxzIi/+yiOS6nekauP0G6J4iikz4PH1THwN9pjcRnjhzeMY4hUmSkykJ0jOTfJySmKbSNAAgokpCCVvrWcDFx7VIqN2gESP5IKu9fVm3sQl+6+kL8jjqCmro66TPbp6w9wnbhb1Rd70VJ5yh7ce/FO/t4ch50pw3h+Uao3vUUZtGTUa7XatloUiKBrSuQk1SmZJwO29aumCwbUybw9oZjJcObRZ3/3qYoyjvpfgFLft56+gffMAIMzny/E03r46iNVZCAf9UlCTv+bf1huSpF7dPCVRKr4wzJFNA76cpHDC4iw49NvVJcvUam8XWEPXhmcpGwJm1zPok5S3RZYdCQQEAjYNQLdVh6H/yUl24YUdckBWG2amg7l4oS6SJPCMhk1JM5EAQESH3hUtDckokXR61KhKG2K7uMpgPhFoUte+s1ni55CduL8bdd/BroZLxyAukEpOooJAmIDiglaia/uzF0hmLfXWHhmbTd3eDgpPHN7L9/H1yvU6bluBbNgQy/9nNetFgYiKEJ9+KQXj29rRkbu8NEzrI39fz+NLWcUBV5Kv82TKRVuPlEI10vkzgD6mvOZ7QAAIABJREFUwbdUuXL/pVGKBDvWyPol0N2rsPzR+K0XsPywWjTkxxZl5O9fvPuIMhyHIX3ZplI+/NBcqaf3fAauP4PNp2/L3Zoa7/SNp2i6QC1ecHmiL1Ilj13aot7ziGl/FSbuVgm20PP17tNn1XMvyPhjimr81B+y8Sw2ajjXTUV60t5AxYg7sEUZtIrGaWbuDPm0TuKhIz5JKfKNIk3ZSDQSmiDBidiUipN249GrD6qm/P6j1S8fAUl1nHOlh98Ar9iYYdSGVM8pzZqNwKRKYxq64GvPyBRtvQvvoGP7N/Dc/gd8+Kw4B/lUaS2FSUrPzpYukq6FT7ceXs8ZPasXMXw3aP0ZbGL2UnbsqPhobz55Y3TYtiR9hd6Yi/4EAgIB+0Xgq1+DcIChkKDfjoZlc6PK1L2qSZ0dW0eTwiCuM6ffCY8f9uDj50gKHaKfIRoaU4XEA6dsV7KHqhXNhhVd3AzChVEVivajixZW3JDOCNL7QFznIc7fcUUwYbQXDsCEsY52NwuxAdndklnV4JGbg7Hm2A2jMVm1Wvryr9O3MWD9GVU9PqU1robz5PzUX8sKjpjesqyBK8qJi4Zjf6hn776CuXtCozQhqpSruNpO7emlhVeMZfvlFUV5cvn6pXNhQfsKcpM7z97C4wf1Cxd96VMiJ5Z20i/1mp97ndkBuHJfUVCd2LgkOlYpaAQRRUrxt6f2HKXCC0As61TRIAJC0WRSie5FVI/nSPQRdwTGbjmPlYEK/YYkqqGlzMryW/Kq25OblkJ799g54fhZbDxxE0P+OCd/TGlKewbXkP/Np9/2qemE7+pGRi3GpJCTk6IcpcOT1LZ3jSIY6uscZVdajkPezpjYwtcd9VcwVh81/r1pUCY3KKJb78KLaZjTf8CQGiiQVVEyp2em6KgdqvRsVom95cIjOB7xVO6addxO2HoRvx6+ZjRsdE5Vrd8S1ulozjxEHYGAQEAgEBsE2i4+isDwx3JT4tBtUCaP0fstzxUem7G02vCcflQnqstlPpChQenc+Ll9efBidvxYpKxec/p+1QVQXKg3+P7F+VuvJ8K++xEOQPteP7u1XmxAdrt0VjHcVHTENC7y5bfACIzZckFlk57OEFPRblKU4ZPXH1CeS587OtwbuTKmMtgUXfQd1WlUNg/mtXW1GK4f6aA4cofJ/nmBDD7Vlm4tV3V1l9tr8S7Sl+XzZ8Km3goJvZ4TopTXkmN3qsRLWI40diyqSw7Pl19ULkvlzYB/+louwlLPeWr1RTfBlHonFUo/J74y1mmjt9Pb0nNKrP3/sOMyFgYoarakYDi3jashpZsXe2FJv3nxkPH/K4lOHsbO79jgOs3vMhbsV2ziHfnj/r6AFUeUiODm5R0xs5VCGWDumG8+fILLmJ1G1du65cPUZqYjh4lygBxdrHARdVIgaxoEDKlp7vAm69HfFgnq8P1TA6JnCBxeS1d+VtqP6YKC/ZvOnCY5KhbMYuD1M1WI7zQtJxRF+xw55aQicTfSv+vPPYiLTNr4zJZl0bxCZNo4L+4itWdT0rXsePnuo1H6mp7qlHFeTNGBQEAgkGARaPHLEZy4rlxq0EUYOdXKTVBTWBwb4Y2cGSLfwfUsqwIjMJo7b0S1//G/nVLWitaFFmsnXapU+3GfShhky7dVDby3ehRx/tYDRfvvQzgA7X8N7XIGYgOyy2WzmtFanE80OC808fO+MEzfGaKyS890SCJKJx4/vpCaV9BIH1x9+MrA48QW/kbwWPhjwwGbbi5Zji+pDZtObCmAy47fhedvPxp1nylNcpweXVt1wNXiLWHVJU9EPEELjneROtbrQK6FAb8OSZIA58cZH4iltiQE8qPfZUPaL5Hnl8qr8EZaCmNL9dto3iEE334ud0+caeQwYl9E9U57t9RcEnu/P+0JxSyGZ662S04s+aoitC4ajgyrhTyZUhsg67jsGA6GPpLhk1R69cCTj/Dr6VUYw+uXkLvmRZBiG7GslbJKg/iWzIWFHZUIY35OWurfVEevfZ5X2+XHp2gMGkuvsvRgOCZtU7hMqV9S5yVOU1Zggx2PlNopLTsJbXxMaTjvIM7fVrghf2hWGm3c8htq8Ny0bPQ8ryAsdRndIVPrMklLiV0vrEQ/AgGBgEBAQkCLCqN+6dwoNVZ9sWSpS4nJ2y5iyUF15PTm3h5wzR8p1sWXQRvOYNMphbZGEmLymrYPN568Mbmw58bVgcfUvXj1/pNcx9SFd2yeDnH+jg1qCa+NcAAmvDW1ixmJDcgulinejOSjniRDSPVrgI+iSDt1+yUsOhCuslNPQQStFGNpMCIapnTT5r8oIiRRqbFSBMyRsMfYG/IA5KC6+/wd3AplAZG7W1qhlj8MSnNwL5QF63uq+RK3nr2DvutOy5jyEZVaKrtUOV1KBxAflCXK2mM3MGKzQqZcOHta7GXSFC0xpq30yXNQUuoziYKwytTVi2XHSh2Fb2xl7gnNDt754+mUDau7uUMrkvj4SB9kTx/J58arB3/v64xvakTyucW11J19ACH3X8rdsE4k+pBXZI9t6q2pyGGtPYidU/jDV0Yqj/R9zgwpcWyET5ymT5czrRebFkiizn9q62ogmtejULRerRn78ZI52FGE8pZvPUH8sK0XBeIYJ3ZC4zpmTo1D39cyMoEnxWd/H90m++MBEx1I+wPtE1RIiIalEJDwDBzmLYuImJov8Vex0ZJ6pqbpgbHoQyAgEEiYCPBiWHNal4NvqVxG0fOsiJWeSPT47QR2cVHaiztWQJ2SuTSH6f7bCVVUN4kwkRgTPw++ccgkX7hN3qO6tN/Qs4rhvKBHEedvPVC0/z6EA9D+19AuZyA2ILtcNqsZTWm1dCjmC69IO3zTOawLuqmqFpUTLqYT4Pnw2PZ+A6oZxDzoR14q5kalEB/Wf/8h2sNWTO01Vb/ZgsM4deOZ0dedqhTA+MalVJ/zPCe8SjHvEGAbW0psg+eEtHTatF6469EPT3w9or6zwQE4x1/hlowuikoPO0QfcUdgzbHrGLn5vNyRlDZ//8U7uE/ZoxrgzJjayJQmheEz/uAhHSTiahE5ckqM8VNFJvMHDd5JliZFMlA6Kh+NFp0tpOJLkd18KZ4zPXYONC3mwYuUSO2zpUuBE6Nir3xNqcVNFxzG2VtKdG3WtClQJEc6leKw1h4Z3VxNfc8rrFM9NrJj54V76LnqpFFzUo9ko7ClCnQpQpcjUmHTqSkqho0g+aNXFUOaMRUtpfR27vkNkYjRlZJj/FRiKbw4SXTtxfcCAYGAQCA2CPCBAcTPSg5AXlTDf5AXnHKkj80QUbbxnXMAl+8pl2VUOSo+Xp66Q1JY51OZ+UGvTa0P4h5/zJyB1nWvjCpFsuoyJ3H+1gVGu+9EOADtfgntcwJiA7LPdbOG1VrCGtK4rSo6YloLhX+KohgomkHrBzSmB1StufVZewr/aPRPdSlyj1L3hjIE+iXzZMC2frbHN2cqzU3r5YU/cKelA/8EXxke3onB4samLer5rNBB/TTjwNQzAkpPOy3RV89VJ7DzgsINNtCnGN58/IRFAUrka1PXvJjdupwlhhd96ogAH1EsiS7cevoGnj/uU43Ecr7x+1y/Wk4YVCfmQhz8VLRUXdnIQ6p/4/EbeE1X20YpShlSJY8RMsRvx16WSI0lOgVTnZlqlzF1cpDaY2zL32fvoB8T6Uz9UHTti3efVLQSenGInrrxFM04dfJm5fNiVivl75Z++yham08PMyWwxKeU13LOgV87VwJdMNGhmFWRZAVCtISSWP7AqDDlVcnXdHNHVadssV0G0U4gIBAQCJiFQPXp+3D9sZI6K0XfFRq+TSWGpKdirmQY7al0qcIrxfNZSexEGvx0EBfuKBQNM1qWRYsKjuAvddk2pAB/ZVI98BHcq7u6w7OoPvusOH+b9bgl+ErCAZjgl9g2Jyg2INtcF1uw6vGr96gwyV/TlHqlcuGXDgpfFM+NJTXSKxKNJ1JnjRpezxlEyTRl+2X5YymlzxZwZG0Y+sdZbDhxy8gsregNLeGSq1PqG1LUqJCIAYkZaJWtfTxR2lFfvj06FNOL19uPn+UheeViW8NbT3sG/H4af525I3fZq3oRUDr5b4yarLnRO3raJfqKOQJ8hJfEm3nt0WuD44ctlAaU0iGZ4SOtZ2BYvaiVc82x7sCVh4bDiFTSp3QAOffYyxMtgZJdA71QLGfMIiz+OHkLFAHHl+TJkhgOPKYubNYfv4Hv/1TS/6X2/MWEOfNl6/BpWIWzpTVEIp68/hRtmLRg2vfOja1jJMARk/Eo2rDJgsM4x0QbEmXC3u+qI0d6NVn9r4euYcI/F1XdmxJK2XD8Job+qSg4u+TOgO39q2mKyrBpcRQZSI6895/+NYyTIZWDgdOWOFOjK1qq5N4lckbXTHwvEBAICATihIDH1D248/yd3MfyLpVQs3gOFBu5Q6WYa4n3UFPnkvbu+THZROQ0z/VHQQMUsdh7zUlsD76niUX6VA4IHlcXVabuMdAESWVFl0qoUTxHnPCTGovzty4w2n0nwgFo90tonxMQG5B9rps1rA69/xK1Zx/QHIonoG/y82GcuWmc2npqdO048+rRjR+pVrKOJ9aotm75QVEorKqnraam8uqj0jyCx9VBei6S586zt/D4Ya8Kf4q0oblSmb7zMn7ep6iGshX1fEmR+g178BI+s9TPA6mkZUsXyY+W0MvwTcEgYRapdPYoiNfvP2HjScWhy6fGJ3RM7HV+B0MfouMyxeFGHH8UcafldA+fUl+mCBiy8axqvbt5FsKohi5xhmHF4WsYt1VxNpV1zIgtfTyN+uUpGWLjgNdybEkDEXcoOcS0yi/7rxoEffiS0iEpQibVixUGJMZUbJRaGV0SySCF7dLjduITEz63tps7POIQ5fZ70A0M26R2Yo6sXwLdvQob2U9Ku1U4AnhT6rwBVx6iE+PApRTmk6NrQ+uwGjTSW+VsJEVLcjSS029qs9JoWMY8nsNaM/cj/OFr2e4F7cuDiPhFEQgIBAQClkSA0mIfvVJUz6XoY5cxfgZaFKls6u2B8iaEOWJr3+kbT9GUi+Cmvuq45MTirypqdus6YReevlHE96TfES0qCKkDeq+l91vPH/fi1tO3cr/LOlWEXhct4vwd26cgYbUTDsCEtZ52MxuxAdnNUlnd0KPhj1URGKwBpJT4N3NA5Q8jUl09lBtNqVZKY3gUyYr8WdLg9+MKB+FXVQpgAsepZ3UANQZcfOCqKlKRqpjiK9RSJGXxHLPlvCr6jB1uZsuyaF7BUdcp85yDepD/62qghTubsPUifj2sKM+1rpgPrz98UqWm96nphO/qxj0l1MJTSfTd8+n1knDOhTvP0eCnQzI+DkmTIGxKffnfvBNYL166sVvOYyUTSdqkXB7MaeNqtE58JPS05mXQqlK+GK3n7N1XMHePwlvJNj44tCbyZUmj2d+U7ZewmBN6oooUmUeRybEpWpccpIaeOW0k5yKvNjm4djH09S6qGoqi6CgoOk0KbcelVPn5m4+oOXO/itOWhFR29PcCpXtplRk7QzB/X5j8lSkFyMv3XsB3zkFVFxRNSZyS1aaZTimXGnz6/K/B0WlO5J/Uhn8WZrUqi2bl9d3zY7Omoo1AQCCQsBEoO36XpjBGmXE7DdQNUtFTMEPq0xT3tWv+TNjcu6oR8BRAUHTkDtVFkhSZyP/uso2l9/Ia0/chQiPdWY8VFudvPVC0/z6EA9D+19AuZyA2ILtcNqsYvT34LnqvOaU5VqFsabHvuxryd/yNoPSF/6DqIPXauJQjYY/Qbukxk13QDzXxQ7H8bKTwRQT9tlY2nriJIQxXIdkn8UXxtlK6WuER21Uf7xzgheK5IlP+Bq4/g82nb2tOkQQqenjpo04qDcArPZuy29Yw18sePuKSFEkpAnDP5QfyEEPqFse3NZ30GlL0YyEEtJRwyWFz8e4LUDSzVFIlT4rLE5XoNv7AoFfKN0+hYEpcpOuK46rnjXgo+/uoHWLRQTbu7wtYcSRCs1pUKVtRRUsQWXpsuF4papzFm09DHr/1ApYfVmxlVbbpYDdp2yUsO3TNEBU9p005QxqaqaI171Vd3VCtaKQir1Yh2oPpO0NAUSeNy+UFrbdWefbmA8pN2K366tD3NfH6/WfUnaOOmmYjSqNbq6i+5wWlSDjElH1xGUe0FQjENwL0OztmywVQFkKHygXQsmLMLj3i2/6ENj4f6be5twdc82c20BmwghlxjdjWwm3enlDM3H3F6Kt8WVLj4FBjhXaKJCeBLbZINAwU0U6R7VqFqCj2flcD3jP34yoTaf1L+/Kop1OktTh/J7S/jNjNRzgAY4ebaBVHBMQGFEcAE3DzVUevY/RfilImO1Ve+bH4qB0yjxFb75++niiVN25cdKuPXscoxg5KOZM4k2gs4v8r65hJlYIsqXzZ2vLsvXwfX69Q1IrJvm9qFAGJaWiVqBQku608Dv9LivOJbd+zemEMr1dC1+l3WHoMh8IeyX2aSofTdVAb6mz+3lDM2KW8eNZ2yWlwAB65+li2ckxDF3ztWciGrBamaCHw9PUHuE5UO2xIOIci0losVBRyJR4gqY+J/1w0OJyk0rKCI6a3VMSQYos2z+c2r60riMaAL7wKtylOuqjsGLT+DDaZuDiIKqWYdz6yY4ROrofkybSj6KKyhRcWyZMxFY4M95abkLAUCa9Ihdbj7Jg6hpRsv/P30Gu1otQrHdi0xqMIPYrsJIeeVOqWzIlFHbVTxmK6juSMdB7tp/pdIl5XcoqygiOpkyfDpYmKkFNMx2Hrt1tyVOw9cQFQtLUbBAZtOINNpyIvO+l9b/dAy6jL2g0gVjKUuHIpEKCMYyYQ5YnEP1105HZ8/KzspdJ7vvsUf9x/oaQGr/zaDXRpo2fhaTikvumy7tIEX6OLqAcv3sFtyh6VCSdH+SBrupT4eV+YSmiKrSQJg9WZHYAr91/JX5n6bY7NHMX5OzaoJbw2wgGY8NbULmYkNiC7WKZ4MXKufyhm+xvftJExkkIW/b8WOb1k8MZeVVCpYJY42c+nXtYsnh37Qh6q+iR7iE9KKnPblDNEbNha0eIvicpWnoBYIlumebVeFIhj155oTpEUzkjpTK9CB1wShHny+oPcpZ43oXrZacl+lh4MN0QcSaVa0Wyg9ENWFZk4vIiTUhTbRoCe5+Kj/FSE5X99W9Ug6tJuiRJtnCVtChCPqVR4Dk89VJ9p/6QIhf+U8xRMXZzw0Q+0Fy7v4hYjsKO6OIhqL+IVwNlB6eCVOkX0whW8obySedl8mbDlWyWN6+7ztwYePraQ8AlFoNeZfQAk2sIWLcoJWmsSE2H3SrpEouh0U+nOMQL0S2WeaP7nduWRKU1ytGei1/mLs9iMI7XpsjxI9Ts41Lc4etcQ0cdxwVS0tT0EHr58bxBiYLlAxUWb5dfp3K1n+N98JRp+UpNShuhL2k8LDVdnpkhiVNYQJorqvVeLSzvswSv4zApQAUbR/nRmWH74GsYz3LtsJYmHlxepmtO6HJq46nO2EOdvyz/H9jCCcADawyolQBvFBpQAF1WnKUXFj0FDSAq/9IJWabK2WrAeN4D8QadfLSesDbqpIiHmp6zHuDrBqOrm+uPXqD5drTK6o381lMidQXO42rMCEPpAuX38qa0rKPWUSlTKyLFxDEQ1X62D+IEhNZE/qzZfmCWwi+8+eWdFxQKZDQ7Ay/deyqbZquM5vrGzxfH5w8rijhUMHGysGi/PczlzVwjm7VU44RqUyQ1y9MSlaPHHXRhfV1PtlqcQkKIUYjJ+y4VHcDziqWaTcY1c0LmqdgRr9en7cJ3hQmI70Dp4mWMTz0dIUbVLOCJ3fp0o1fXTv/8aUgL5ovX3xwu+UJv+3kUxUGeKiFYLAxEUoVzIkJPCMXNq9FilRCmaSlMzByu+zjerT2LHeUXB0lZpL2IzN9FGICAhoCU+JMS2LP98zPG/gjn+Clest3MOLOtcyXDRzgs3ER0QXcrwfHmS2q6e1vIKxGzfewdXR+Hsasoh/tKdFa3i1dvZvtwKZsGGXlXQaN4hBN9+Ln+lJ7+2OH/r+WTYb1/CAWi/a2fXlosNyK6Xz6LG91l7SiVwwA9Gqpmknhn+8BVqzVTfsEl19XgB4F8qZrcuizVHb+DEde1DLI39d5+qhrQFWysfP/9ruM1+9Coyko7wO/x9LZMk9FHxPFWbthc3nyjqZOxcTamIxhYP/4v30e03JXU5fUoHnBtXJ1a8X7G1Ib7bbTp1C4M2nJXNKJkng8EByDpFFnWsgLolc8W3qWJ8MxDgI9omNikFSkHtulJ5znmBnp/2hGIWwz8UmzRSUk+kyDVKWaU0pB3Bd/ENw7WaK0MqHB2hpMGyUzkU+ggdlikRihRhdmZMHTNmq1ThU5rYxlE5xkiR9yVD8M62Y4U7YmLMiM3BWHtMUdbW4lTst+40/j57R+6WMD8R8VTFNSV9qSX+xHP/0ZruGVw9RoIb5syp77rT2MrY2dOrMFzyZED/38/IzWPjsDU1Ns8BS+MNr68v7YM58xZ1BAKWQoB4kEm4h7948C2ZCws7VrDUsKJfAKP+Csbqo8reLIlsEO1JybE7VRgR36lj5jSGSDuKuJPK/HauZquamwP6+0+fDVQLbLQ82259j8pwL5xV1dWBKw9Vl3qSui9V4ikm2IaU4bGqq7uBo5a4aqUSG+EtU3MT529zVj3h1xEOwIS/xjY5Q7EB2eSy2IRRPMcQb5R028YTubP14houT7eNlB7HcjdRqt5vgREyJ4wWWFGpWcY3uMQDSNErSZMkwfjGJaMkrqdoJHqBkcrwes7oWT1S3KPchF149uaj5nRMKQvHdu6848OtUBaQwltiKryjpnD2tHj17hMevFQ4b6LiUEtMWNnDXHuuOqEWDqrlZHDY9FqtcM7xYkcL9odhml+IPL2YCuGciHhicOC9+xhJV0BOQIo6JPERqZCq+drulTUhJBJ8n1lqUQkpEttczHmeJradKVVjurggJUVTRboMMtcGqV63lSfgf+m+3GyAT1EM8FGLN608EoGxfxtH+2mN5ZI7A7b3r6b6qu7sAwi5r0TpflenGPrUiplwijnzmvTPRSxl+CEbl8sD90JZQU5OqZTPnwmbNJQqzemfrzN80zmsC1KU7/VSpI6NLaKNQMASCBwOe6RKoZfGKOOYEX/38bTEkKLPLwj0XnMS24OVCGPpt1BL8ChohDdyZEgF3zkHVBkRcX3/5xfj6sNX8DYRbEB1tRyOvJghyxW7L+QBuiw/rrnmUsRj81+O4CQTbKCn2JI4f4s/N0JAOADFcxAvCIgN6P/sXQeUFEXXfT8555xzWjJLziwZEQNJAQFFRMQACErO8AlmEEFRMKCggCICS845s+Scc0Yy6H9uDzVd/aZ68s7OzPY7x/N9bFd4VdXT3fXqvnvjZNpDolP+MudOg6sJnE2qFCtR1ldONBV/x64hDQmbQhmJw33bM6wRpUqaKCTm2ZmTb03fTvNjztuLCOEN8LAUGrDQEBiV2+Hqpb5OBA+WdK6ej4Y0j/C12ZCqzz8WgRYDIur2g8f2cczqVpUifeS8DKlJCWFnIXAEoSNhbSJzU43CmQhILmFFsqaixT1r2/+t4oEESsAdw28WQhRysE9Vr13lPDTq+VLKJoE4hTCQbELR0B0fUKbYoIX2ACSvA3oB0Axwu3T7PlUaZSRSl8ts6FePsqdN7q4L9nItvlpHuyR0xajnS1K7ynkN7ew5e5OeGb/WrbYT/B/R7qH6s19FTzGnezUqnye9W+15UojfG5XzZyCkNMu8oTUKZaKfu7h3v7jqmyMbcf9+1LK0q2rWdWsGQmYGIAAElBa3jCmT0DaJmzVkBhRCjraevIE2SxzTAm2uehcIBDhPlx3XsrRTxWYc7M/efoYgygVlZ3DuOjNnATvUU4n/zdxyij6YrR/CyNkxGB/GqbKmpbLRxHYVtOvyPIxoEUEdqubzy0pa+2+/TGPIN2IFAEN+CUNzANYDKDTXLRBeg9cPGygz+/m1ytqG2RmM3ley5sV7Lxg4lASJ+tydZw2pVbKPiRP+H4HkFwqMoW4fzt5NM7boKA8osQ19NoLuPbQJBzizfcMbUYok/gmC8nRjCIxAaCQ+2cZjVzUxAWHpUyTWAoAyObk/VK/j05zG5VhVghpQ3pXTvDmijKPRqhTIQDO6uoeEdXZQIs/DwGbFqUvNAqZTU2rIIkPQ+dfXq1DVgsa0J7PKSKGC+ImZibQnfv3ghdvU6HMj8lAu4y3imvM5gf8PQTPZHj/5l0oPW0x3Hz5xcBuqlHjKy7/Bn16rRDUL25QnkTqMFGJhOBTaObgBJfJCsdjVvYr0Xzl4DMQMRGLkg6qGJbLSN4zj0FW7Zte5IA0Qh1+0dQzeetu+Vc+agbicAVAlgC5FVpuV/fFWeCguxxRKffN0XnxOHxnVlC7cuk/gZZVNHLhzWg1XAABZ4C9PhhQELkGhNKyaK2T+qLhfRdnudQpS38bFDFX5wYx8CLP33E3tUE5lQuCLZ0I548n1dH2t/benMxae5a0AYHiua9CPynoABf0SxYmDQKsg5UveWHFHhArsr5tPUb85+gmbXK5Po6L0Vl3vlQknrzpKYxYesDcpiHmBGgF6RGVZUielzQPqx8m8+btTnlb2Yvlc9EnrMnTp1n2qNNockQM/vN2U8zHcvPeIygxbbPjzgndqaumS8cn4PZcowf85/D5UJNTxaY5CaaycABycjh2r5qO+s3fbh8FVaVVCMLPerObWsNtP2URrj1xxWVYgq80KcmEgcKI+X869YLwzwSb0VzJnGvr7bWMKLf6+4ehVeulbPfjNfRMk8C4HJxXAOwZk8vIG32zsZnQU7avkob3nbhmUuGUeQ54m62nKtifj2XLiGrWapKNJkkNQpmpemrz6mL0Zf6hGi8Y4Sb83fJSAdoMyAAAgAElEQVSejM8qa81AIGeAf/vxvqHiXSiLUfABZc7euEf3Hj6mgplThcUhcCDnXO6r/IgldO2OjataGJB++B6s87FRyO7gyMaUNFFC4kJIztByeP7ju/KWxCv7R/dqVM4JOnvE3/voO4lmgc9Nqwq5aFyrMoY/4wAGFDbCmpTMRl+3t/FHnrhyx2EsopxAVHf4bhOtOay/t10d0HmyXtb+25PZCt+yVgAwfNc2qEdmPYCCennizLmbdx9RmeHGoA/Ic3EqK0yQ4X6z+iiNXqAH6WSne9QtRO83Kur1ODgCTryUVf6JTopmTU2Letbyus9gqvjF0sP02dJDdpfEJk+VGg3ko7yZdvUx5e44+eY/ScIEtHd4I0ocCygad32Ki3KHLt6mhp+Zo6Dgk7epkHExnvjeJ08nwvMNHHQD/9xjnxooPcsBvt+2nqa+s6QAYa60NNcNLqqYMzep+QQj0gDp/Eh5gqDF1pPXNIQbApCunpd8Q/JB42L0Zh0bL6grUz035Dpm3KHOUN6ov6RnLSqcNbWr7g3XkfZVbsQSw9/Wf1iPcqRzTCX+dPFB+lJSX0allEkS0so+denbNcfoGynIVr1QRprexcahWGvsCjp17a69D39u3vhgT129S7XGrTD8+bmyOejPnbqAiUrkxKNJkwpPWnWUgAIUVrtIZvrh1UreNmfVs2YgaGYAwSEIy0Esycxwr+Oel23G5lM04M89GjVK24q56X8vWinx3iwq5q/QgAUOYhs44MS1Buw76NjoppQgwf8RP6hxlgGk+oZ3xaH8+o9back+nTM2SaIEmiqxsDpFM9O0zsZn4LB5e2nquhP2Mq0jc9HYlrYgoTNqCyEo1WnqZlp5UM3D7c3cynWs/bevMxge9a0AYHisY8iNwnoAhdySBcRhlbJvhbzpDWS4YjP18aKDNGHFEaVfr9XIT4OeKeG1z/xE0R0RDHAvzQwTgQqcduLUU5gQCNhx6jo9P3G9/e9JEyUg8OKcu3nf/rfvOkZSVHFjOp03C8F9MEMJedN2KNU5fe0u1Rxr3OBz/5FemC6Fcx6bUBpzOPvK03+Q4jSgaXEDZ1vVAhnp1666IMcfO85Qz5m6ErRKdEI1Z1xRHcFGKCdCAMRT6/P7Lvp92xl7NZX4A57faJsH00BmDlJzM0NQbe/wxg6XwZUIzkQzW/huTSqe3TNEsCqtGNQN2NRxW3nwEnViZO29GxSht6MK06K9F+iNn7bZq6RIkpB2D2lI52/ed/i9xiZy+f4jm0KlbOCb2nXmpv1Pr9fMTwOaef8+lNuetu44DZ2nvxvC6b3n6W/CKh9eM6BCHON3LdMAcDEGBA2RFSHT1vgrCyK8Ztf1aHDQHzlyqUPB2W9WI/BLy2mzyIQ4MrqpVpaL1vVvWoy61lIfTqm4XVUUELITnJccB3RbJYEOoPjnv2NEsL//+y6aJb0v5T2JStFY9Cee1V1+2EJL91+yu+FrVpM8Hmv/7fpejA8lrABgfFjlIByj9QAKwkUJApegVtlSSmfCx1etwpkpeq+uCvZOVGHq1aAIDZ67h37coJPpy+77injAR4iMOvymQwVqGJFN64ITyIt+G0dko0kdbBD/UDeepijU76AMjI8tYQgoZE+bjGLO6ptNgdD0dQ56/bbToLgsn6D62nYo1XeVPomxiFSYUBpXfPVVtcnpUiO/QcmVc+L9vfsc9fhF55QrnCUVLemli4So5hLIsDofr6B//9Ov+rKJ4Gg4zis3ZO4e+mHDSYIgxrAWJalDFV1UwxWJutk9zFXA+Tjn9ahBpXKl9ehWWnv4iqaILAycmjsGN1S2gY0a0HxXn6ak4Vm3vHcdSp4kofZ+4JtVcHHuO3fLkM6toS0H1NeQKrFl5YYvpuuSMnvqpIkMfI1yerKvPvB3Q6mcaWne25Yyqq/zatWP+xkAbyf4O4Uh1RecmjL6i2eXqGhRBE913I8otDwwy3bAoXLGVEnpOYl+B1QH+0fYDo1em7aFlh1wL1gWvecCdftZP7hBfZWKr5g5BHgjhiwyBIHfqFXAQLGgov/hAnay0vy///5HBRVIR/T5Vt2C1KdRMeL1xcGTP1bU2n/7YxZDvw0rABj6axiSI7AeQCG5bLHuNEdVIDUM6DMZeSKUYHvO3El/7Dir9OkFkKC3KeuVv7fuP6LSQ41pyEt71aJCWWypZu/O2EFzpfQq0clLlfIQyIfDwRbGnKc3p2+3D6VAppS0/P06tCDmPHVnf8+dIQWtOqSnKvRtXJS61/Gef1F0yk9dhz0bQR2r+UcFLZTWSKXAKvuPuMLR0U0t3qEQWVR8/IODTuY5BT/Qwj36IUdUsSz0XaeK9hHxTQs2peC/c2ZcbRgou/UfRlHaFIm9minOQyirGnJOIwS9tg2sb78nuXgSp3WAQ5v6R1HWNMkMvnHFWe64N8q6c7afMQiuuKJuWH/kCn2y5BBhwwkhJJn/q+7HKw3pgiBq33n6hiH9tlmp7PRVu/Jezbm7lfizkteTEezutmlWLnrPeer2s/5uyJsxBa3qU9fXZq361gzE6QyAd67K6GX08Ime2oksEiDwp63XUzk5n6YKNfhF27LUomzOOB1PKHZuxvkKVd98mVIauE7TJEukKa/DeLCsZ/0i9G79wsop4OIcKPRp6zL0Qnk1n63qoGdyhwoG9DcERIAil4VEeFoy7iWgAIVFDI6mOwqBKYAbAHJ4a/p2mh+jK1H78xDH2n+H4q/D/z5bAUD/z6nVohszYD2A3JikeFgEXCofSsIeQJ5F5s1A3687bp8NqMBCDZaf+snT5Qsaj4suIMByYISeIsbJfUW/KiWwUF1CrhyKDfvWgfVp5pZT9MFsXXgFQYCCWVIZkHpAMw30If0acwbV0IjBiwxBkt+7VaWK+TKE6pR67TfUSAsNWGha3yx90usOrYqxPgPYaELVUBiec7ullE3+/Fp+4CK9Om2rvbwZZ54ocPWfB1Ttf8vpgcRT5Ovvkvsgox74cxt+IAAI1AaMqyiWz5OOtp+6YZjn6PdqUrFsxnRes8MWUdGbZwLnsJPVGT1deJ7m9Uzp7LTp+DVDOuDI50pSewkN6Wkf7pTv+P1mwyEMrzPiOSMi0502zcqsP3qFXv7WPQSlL/1Yda0ZCOQM8MAQKAE29Yui2dvPGOgZhCCc8I0fjODvQ5qXoM7V9WBPIMcRyn2Zcb6CIgNpti9P0Z87mVIloa0DG2jDfeuX7YS6wsBz27uhmgNcdajkTDWY01eA8xoHHni/yobvY3wnC2s+fq0xM6ZlaWodmdt+veKopYb3hLggDmug7A6Fd2G+8prLvlr771D+lfjPdysA6L+5tFryYAasB5AHkxWPin614giNW3TQPmKQ65bNnY4+X6qraQlBilaT1tOWE9eVs+MLMTnn2+IIB44gEQ7gI+X1WgXCYrWAYpHTLcC/giAo/0gG8X1EjrQGMnx/KE5ynjRMaszQhpQ6mXfopVBflCIDFhqQCfJ4RHA21McYn/xvMWGtgaMNPJoizRTzgEDShJd11BhPvXelOM4PKcCXtOaDupQ9raPQhbvzjtTWpl+usRcHdyFQDxDl6f3bLm2jLBvSYUvmtKXnjl92WEPRCatfPIv27Iayo7BfX69CVQtmNLTBhUe4r7+8XpmqFczk7hC0clzR0Re0OA984jl5/5GOIEJ/gVDo/mDWbpq59bTpPHzSqgy9WME9xWZXk8k5tIB6OTKqiYVAdjVx1vWgngH+rIGQzudtyxHPhuCHLyqFWH8Ga4J60vzsHD8oEs3jcL1S/gwGPlbQMWzoF6UVeW/GDgPqulvtgvRhk2JK77r8sJWW7tcFPVAIyO1OJgHbP3ecpfdm7rS3BfT94p61NBT/fxK9BuejrTNuBZ24qgtBTWpfnhqXzG5vh6PHxQXhC89wcjYmT5fB2n97OmPhWd4KAIbnugb9qKwHUNAvUZw46LA5K59TCzCpBCmcpT3hY+E3LwU5ONdV3aKZaaqk8LXt5DV68esNDvMDVCLQieFgKtXOw6Oa0PjlRwi8XMKAVCqbJ51BFZLzl3kzH79vPU19JNXTPBlS0Oq+8TfNrPTQRXTr/mPlVObOkJzW9K3nzTRbdeJoBlSbENkVHpTiqVFIsd0+yIZ+4Hb34WMNnXBD4oR7sXwu+qS1TYHQW1Op5677sB5hQ1x73Ao6KW120MeUVyKpfgmbGNDIv/cZOA5fKJ+Ttp+8btggfd2uPDUppW+QUK/Zl2to77lbpi7/9FolqlnYqMjpanwcWfFG7QLUr0lxV9WU149cuk31PzVX6MYmFQrD/4doaSyaSq1Y7o5vPn1xRSVKtGdYI0qVNJEvzVp1rRmIsxkAzxv4POVDGPE82n3mBj07YZ3dNwS8D45oTIkS2kSDOk/dTCsktVb8LZzoYAK5KJ8tOURfSN+Xom/MZ71iWQhqvMLkb0KOxHaGdlftG5xRJHAeWgEuqDBiieF+4UrC/Pr0LpWpeiH9sMrs3SZEZviYutYqQP2bevee4mto7b8DeVcHb19WADB41yasPbMeQGG9vF4Pjp96QRGrcNbU1FcKBgnS8WpjlhnUZ+VOhWiFO45cvHVfQ6LggwIKllw589Xq+Wlwc11B0UypzF/qt+74HNtlVMTWOwY1oC+XH6ap63Q+nFYVcmkns3KwTqWI5qm/w+ftM6R9C9Snp+2ES/nKo5fSxVsPlMMpkjUVLe7pXBAiXOYhXMbR/48Y+mXTKdPhcMEbLo4EkYeYYTb+I24/bzxJA5lyroZYyGrjMPXWsEkuOiiaHkppxVBnzJ0+uaaCyW1EiwjqUNXG2dl31i76bauOEASP645TNzS+PGGjni9J7SrrwiH4u7NnPK5P7VSR6hbL4tGQ2n6zgTYeu2avI1TlPWrkaWHMSfkRSwwCHHI7CHR+2to7LlpP/FGlIcr1+ebUk7Z5WbwrywwzcuQiyMmVn33pw6przUAgZwDfgJXZM2x1n7qUJ2MKAp1CBaZMCyX1XOlTaC5CJOjUNR3phb/F9+8VzAGejWeu39MyF/JmSGEPmDpbV85bK8qCI7d5mRwG/umCmVPSst42Htx+c3bTr5t1BHSnavk0vlZu8An83rcfGA9TBe+eyjceiIO4FSgVeCCR8wjyrI25b1WnMrnT2btoPWkDbT6hv4fEBYHW/nD2bpqxRR8T34f48vuw9t++zF741LUCgOGzliE1EusBFFLLFTBnoTCLdDdhHzQuRvkzpTCQjufLmIJW9qlLpYYscniRi3ruqGSi7A/rT9CweXs1pUyc7KJtqK7KaCvOn4SPiFJDFxPEGWTDZrhC3vQBm6vY7AgoohKDFxm6WNO3rob+kwVZ8FECxF/naVvsZbOlSUYb+9tSM7y1l77ZSBuOXbVXlxXUvG0zlOvxdBJ5LPioxMelZaEzA18sPUyfLdVTYrnn7SrnoVHP64JCnJdUpOSrRtxr5k6aI4kjATnxvSQo4ssscaTfVy+XJ4DbZGEg0b7MicpJ2kFoDmSNjJxRKRQXG7TQIaVW9l9WZ3d3XPU+WUnHLt+xF//ypXL0bJkc7lZ3KNflhy20dL+uQCkXCBQqfNn+i/TaDzo6hjs5q1tVivQTf6pKwVLF3+j1hFoVfZoBpCyC5xKCOgiCIGXRMuczsOLAJcM3DA5Ydg1pqCl343uv+OBow3MI2SU4+ARXcfFB0QaldfRUMV96+r1bNb9OO94BEL3DQU7birljVVXcH473mxNDv262HXKBT7FQ5lRULFtqKpotNUUVz2IX1ZP74sIX4lqVAhk0VOW7M/RUXLQV/V4trQgPHPL3p2jn5t1HVGa48fAC14TyrmrcrSdvoM3H9UCdoPrhKeMyivD+oydUbFC0oTlOBaFCjqKCUCTmh4Qdq+alYS1K+mNpyNp/+2UaQ74RKwAY8ksYmgOwHkChuW6x7fUz49fQnrN6ytfYF0tTzvTJqZ1E/ov0ty0D6lPB/gtM3XFFko+Kj578q6E3bpukVorGf+lSmapJ0H38XQXfDwTXU2zPv2gfH70QnniCyOhTm/9ODRq/7AhF79XVSrGRR4ChxVd6igxIksEN5m3aG/ouN2KJIYURqmuNIrIFavhB14+zdHd8HM/oWjXofLYcMp8BbIywQTIzjmDgnJg4rIDys8q6/riVFu/TOY6coRs8XaM2kzdoIhfCgJ47e+OeARUsrslcoBx1N/iZErTn3E2n4kH3Hj7RNt6ygctQVk9WpQ27GhM/OFJxD7pqQ77+9cqj9FH0AWWVQCHjOC8fd2bBOzWpRA6jwIonY+RlSw1dZHhvzuxahSoXMPI3+tK+Vde7GQB1R5MvVtOjJ7b3dv3iWWlKx0jvGotHtSYsP0wfL9YPZLjQR9QnK+modGjwWZsy9Hy5XHTo4m1q+JkjBUCBzClp+VN0mj+m8cz1u1Rn3Er7s88X1LI//HHVBleF5+UhrAdKCsyhbPz9Iq4h2AcFXTnTRM7ywSG+nJnSJjI3fdSytIObZs9JZynDXLBrUvsK1LhkNuIHbXIbABFA5EM27Fkyp9ZFQrhwiSgrDrUGz91DP244aW+ifZU8NPI5/VDQ1Ro4u/73hhhqXs02P6dPn6ZcucKDusiXOYmPda0AYHxc9SAYsxUADIJFCEIXeMoXeKSypElq4GBBgGnrgAbKkzwxJJDqbzPhyBJljl+5QyDidWUb+tUjTp6vOqncObgBpUuRxFVzIXMdaV4yST82eUgBXndER+bhQxQfQzU+WmEY1+6hDSmNl4Id52/eo6pjjAprQB/mzmBLuYmP9vzEdVrKpMo4R2V8nJ9QG7MrxBaoDwY002kHDl+8TQ3YRvPY6KZKFAhHJvhTnIir8mLTg4BgzNmbDksgB6abfLGG9p/XD3aQ5rTv/C36bq2u7s55ChFYrM6UFsGpd/6mrp7sKXpPFVRc1rs2FcycyutbaMuJa9RqkiMnLJBXK963pajFtqk2nHKfq/rUobwZ/YcEw7pgfYR9+0okNXjK9xjbY7XaN58BBKIRkBYGpPD+4Y29PoyLL3Pdffo2WhCjH2zyAxj+TH2/YRHqUa8wRe85b8hOEfOVLkVi2jm4od+mj6f4xwbC0G/OEpnOi9yHjOATf2/w6So6fOkfB1eypklK70YVIaDihCHbBlk3sNEL9huE6MyoF6L3XKBuP29zaF+k9fILKiSfOEzhfbYom4O+aFtOa+LY5X+o3ierDM0dGNFYoxgSxmkxxN9/eLUSgWeQBzVfqpSbxrzgGNT0dN0QtG7x0Vw68FkHraoVAPR0BsOnvBUADJ+1DKmRWAHAkFqugDgL5Bdg8w8kjqk53atRhhRJqA4L1C3tVcsp+XqKJAlp3/DGTv3maR+qwvhIgboXR7ONjT5AE6UPbZxoHhml3pAHZPJioRO+yUMwFgHA3Wf0zf5HL5aiZ8vkdEDqYOPrberR8gMX6dVpejob0nEQUPQWURgLUxPwJl/+diOtP6oHXmUHmpXKTl+10xVjA+6c1aHHM+AKsSWnz6Jx1WHFwZGNKWkifUMhnODq6JzCwGNnpQpjFu6nyauO2f+C4POqQ5cdUuBQQFZPVz1LDly4ZUDdRBXLQt9JqcoxZ25S8wlr7X0B9Yj0NzmQKJA47o7p1NW7VGuc8bDCV3VxbBLBKwWuK9lerpyHQOgeCENabtFBC+3IL97n5gFRlCV1Mr+50vSLNVoAV5g/VYb95mQ8awjfTzXHrtB412Tz99qH47Ryig1knrSumNs+VM4xJ4IxX604QuMWHVROCUTToJDuD5u48giNjdb7ETQ4/mg7NtqYvOoojVmoRkWL/pImSkAIisnfdVw4Q5RFCnH/JsVo6Lx9dnflAyb+PQ5KBxwOcZuy5hiNnL/f4e+cc1cUUInhCcGjb1cfo1EL9LaqFcxIv7xeRavKKTuSJExAh0Y1MfQ79K+9NG29zqctLs7oWoWqFMjoIJwFvu1xrXwT8jp34x69+PV6On36DJ39upPWpRUAjI1fQGi0aQUAQ2Odws5LKwAYdkvq84DuPHhMEUOMvHNALkBdkJMwT+tckTpN1XnneOfgpQJCxlnQaOq64zRM+qAAbyDSDA5cuE1HL/+jofl6NyhC+RQcOr9tOU19Z++2d5s+RWLa4ccTX58n0w8N8LTTz9uU1RTaEIwQNrFdeWpaKjuVGBxNdx8+sf/dF84p/lEd7Kfdfphql0044xnzh8KrSwesAn6dAZXIjtzBO1GFCam7wpACxlG2e4c1opQK5dXm49caEHnjWpamVpH6ZtaXgUxbd9ywCcPGhge+RPvYtEEtE89gnnYLDi0gEWSxknJ50tEf3XUuSwQWO36/2e5uplRJKWe6ZLRLOoAY27I0tfZgbFzB3V8IqRcmrqPtDKELfsRmpY2qxr7Mvau6PMgqlze7V1y1aXZdldL9ao383jYX7+shMH3h1n2NO87bg65tJ69rm3tu4cRNHBs3CricS7Lvzr/frkElc6a1d8dThMF7/NNrlan3b7to9nZd3Ej2z5+B13GLDtBXK3RkZ+pkiShmqFoEKjbmyNM2OX9drSKZqWqBjA5UCdsG1qeMqWxpsaCbKTxggfIwCdfxPvx0iZ6mLdYA1/B38FMLa1oqG01sV8HBbbOg23Nlc9DnT9F7ciUOEpAzi8C1+d5MnZNQ5h1fe/gKtf9uk70pVUbSx4sO0oQVRxx8BOihfJ70xA/bXiiXkz5t472g1I27D6nlpA2EoObjW1esAKCnN3UYlrcCgGG4qKEwJCsAGAqrFFgfT1+7q51gywZ0BlAuRQYuNPwdyAo5HUDlKYfc8zJD5u6hHySODbOPAFXbnPtFPv0L7KzFXm8OSKIWEfTFsiMEFWRhP71WiWoWzkw1xy6n09d05IEvnH2cG+WVqnlpuJ/Ij2NvtmK3Za5MLffmT26Y2B2F1bqYAVebHZFiJsqrVCrNKAfqf7pK+8gXJkjF/TH7ZilUZm1vH9SA0iZP7MDXuui9WpqP+K0L4ymzfINVNGtqSpk0oSHQhvcAkHbu2sKY8/TmdL1PKL+v7lvX3eqm5cYs2E+TV+vISBSUN7c+d+BGA85oAszSxd1oVlmE80zGpUgTNrZQps6Sxn8IR2/nxZt6s7adISiNwnyhc+DfM8IXHNw9Vy6nN67FizpcYR08o0B5yemaf+w4Qz1n2tYIJjj+nP3m/CmMo1pbMwR4MCwaT5mGwNMbtQpoKvIyr/S8HjWoVC5boFWltiyPBd85P2+0iYrA6hfPQlM6VtT+//hlh+kTKTgIOgLQEnDr8sNWWrpf58cV16Ey/HV7x4AhRAKH/LXX3kzZ3Onoz6eCa+uOXDFwk8tp3/w9o0JsclSn6ARc2xE50hIP+sopxp6uMVDq4FHHIQHMCgB6OoPhWd4KAIbnugb9qKwAYNAvUcAd3Hn6Bj0niUkAXYKPHJyIFx240JAaDNUu+UQU6QRy6jCc3zGoAaVPac7JxxWHPd3E4AUNvh3wAyIFEx8H4WRcpaxv46IE9VJ5nvExhHFj3bB+wjzdmMvzxlU6x7xQSlOAi8/W5/ddBvVleS661ipA/ZsWj8/TE5JjrzRqKV26rQfT5UF82KQYdatd0P6na3ceaoJFsnFScXGNI8G+6xhJUcWz+mWO+DPaVaNA0kCQCaI+sm3sF0XHrvxDL3+royQ4b9b3a4/T8L+NKV/QJJIVGUe0iKAOVfO5csN+nW/oZB4ptxtRFFx58JIBkV4qZ1qa93YNX5r0uO6bP2+jhXt0HjPRQPLECWn/COd0GJ52xp9HUIMf3FznrPS0PW/L/7ThBA3+ay/9959zJU9v24/teghcRo5cQrckITI5KOJu/4+f/EtVxiyjK/88dKjiTxEgd/0JpXI/4h6aqwd5VNx0eOZADVaYQA6XHb7EwJMsj1slHuftvKiQhniGZksbnEHvWmNX0Klrd+3DFVytnONbPihW8dzK89WwRFaDuJUctONCTGaBdDMxNQjZfS/RT4h+h8/bR9+v03lq5dRilQCMCMry9OAyudLS3B7G9wF/F4k+l/aqTYWypHJANQJNDlS5p4ZnQ7eftxsCn1YA0NNZDM/yVgAwPNc16EdlBQCDfokC7iDnfsuWJhlt7B+l+RE5cqkBeQa03p87z9l9BJJD/uDABVcKjPwj5Yu2ZalFWc9OysG9lAAEgGFo7/y6g/7apc8xhAm+XaN/DGHIgkD/tWlbaNmBS/ZZQOr021GFPZ4VkPRHDIk2pIGIIKPHjYVRBa4IJw+Np4uG0bDDeihc8Vwe7KBnSmh0BMJu3X+k8czJZvZ84zxK/tyIqpCIzhYJvKHYzHAOVwgTnLh6hyAOIgy0DeBRBdcf7JPFB2n8cj1FCmldECWSRYigJuxJ6ilPuzJDfnh644F/7c2ft2sK6SmTJKRpr1aiivkyeNqMT+XN0tsypUpCWwc28KltXnnE3/sMAi4tK+Sij33kp/LUwd1nbtDzE9cbEEW/d6sa8Hn31G+5PP/m0e77VmXoxQqeqXKuPnSZcKCpMn9wh/kyxmCoCzTxgpjzmlL1q9XzGdKsP5i1m2ZuPW13U5VqqRIkcsVDPf6lctS8TA6/DL/bT9u0Z4tsPE3ZLx35oZFHT/7VuLxlpN/ct6pTmdzpqOXX62nrUxQauhrSvAR1rm57z208dpXafrPR1APUB7eeMDkYx7n95PRguUGuXi6uVS+UkaZ3sfH3ycYRgz3qFqL3GxXVily/89DhYEu8k3k9lYCHjPyV+xSCdzhs/2ypnvLcOCIbTergiFJ0tWSfLTmkUffIlv6/W7Rz7MvanywOQFczGL7XrQBg+K5tUI/MCgAG9fLEiXO/bz1NfWbpvHolsqehBe/W1Hyp9/FKOiZxz4ErZ8sJG5wdxv+NvzlTd8TJe7FBCw2BJvGREieDD8JOOY8LTiDn7z5v8HRz/ygt9YormnEVPXeHxxFGiAXsHdaYkidxFDtwt81wKEcZ8E8AACAASURBVKdKMRTj4mixcBhvfBjDq9O20HIpaC6PeXiLCHpFQrap1AhX96lLeTI6KmNHDI6mOxIfp+AU8secYlMHOgZ5cye3C+GPk1d15AcQeti4PTthnb0YVNwPjWxCF2890FBLssmobf78aVc5D52+fo8Q7BDmqcIxf051rJqXhvmRXgBcblCtl9MH/THv7rTBUTCijr/SnGUf+ObULOXOHb+9KYP3N7guD168bagOHsk5b1bzmkfPG198qaNCdvWsX4Tere/Z4RlSiBFQUFnl/Blo5htVfXEzpOvyFF+encA5Uwc2K05dahYwjBkoKp6+isMHGaHMJ2nYsxHUsZr76GRnk8xTalFWqMUG2+KcvHqHao9baXBL0FW8/esOmicdKsvZC/i2lCkh+LhypE1G5yQFeJn7mHPTygIhoh0cHpUZZjxEE9ci86anWU8VheV+G3222vCMkTlnVcJL2EMA/Q3EO/oTpkrD52nCoqz4puZc2PWLZ6UpHR3Tmp2tPw6mwJ+ODAJh4I8c/2weqlvBhti2AoDB9gsKnD9WADBwc231JM2AFQC0bgc+A1w5TD7FazFhrYH8nX8MID1gxcFLBhVEZyekxy7/Q/U+WWVwYdfghpQ2RWJrYZ7OACchxuZqByO6B5IHwbmPom3p0MJw8o0TcE/tl02nDNyOgmvH03bCrbzqFFeM0Z8bjXCbt2AeD1eWlH3lae/YgBYaYORBFalCcj188BfsbyRSX/BOTSqRI43fpqLqmGV0XtqIiYYLZk5JRbOlpgUxOlIFasZVC2akDt/JYh42RJoqqCkf2vCU1rfrFaJ9524ZkMagJehep5DbY+s0dTOtPKgHEDnXotsNBWFBzlMmXFSlNPrqPhfQCnSQiZP+y+MJtPiKt3P54PETLbPhtpT+i7Y8RVPid1QR7Tx4rHQF30rr+9kyKeKj8TTOIllT0eKetbWpAFoNwnMIKAv75fXKVK1gJoep4tQKSDNdIT1LeIV36hWiXg1taDFfTcU16KkCuq8+uFufo1HTJEtEu58KlvCDzGdKZ6cJT9Nakc4/SErF5v3h4OjRk//sf5ZRdT9vPGkQlFIF9Paeu0nNvtRV5eX2VZQNeJeWGLyI7j3Sxe2EQq+oy9+FQLznTJ/cgGxHWRVan4tciTbFPmTSqqP0P0lJ2SxN2dm63L7/iEqxzIEfX61EBVI8oNy5bcJgVgDQ3Ts7/MpZAcDwW9OQGJEVAAyJZQqok/zjQCa95SegQIaBD0oYPpoX7b1g+Jh2pkTLU2/CUcXX18XjyndIyYZSoTCQZR8e1URDW/AUDG9FUQb9uYd+2njS3oe3vCe+jj3Y6vOPQdm/sS+WptYV/aPyGmzjDmd/nAUxkE6JZ5owbEYK9F+gcZ0JU5HMI6hQdGC0YdpWvF+HILDhLzMjvm9bMTelSJLIwJn0fLmcFFU8C/X4ZYe9ezmoz9XDZ79ZlSrktaXOtpm8gTYdv2avB8QN0sQW79NJ3D3lN2v25Rrae+6Wvc1w+u2sP3rFwKkoBlkeqDhJXdkf98Gc7Weo12+6KELx7Glo4VO0vtw+uNPAj4iASo3CjkEVb3xBEPjZCWvpsfwBIDUExOOSXrU08bBAGQJIoxfs1+7PpqWyE4LVrtR8l+2/SK/9sNXBRU+DqdF7zmscX2aG1PqDI5oQVLnjo73x01ZatNco/AARIhxWHLxwmxp9vtowLWbiSq0nbaDNJ/TnEbg15eAQn1sglkc9X8ovU97ws1V06KIu7IRGVUhFv3TmYyM8GFcyZxr6+21bJg/nvZOfTZ8vPUSfLzWmqjpzRRaH+23Laeo7W88eAuocaDzZsD9446dtyibloLAooBIlWfdhPY3TVhieQ7slVXoc3D149ISGztO5a3NnSE5r+tZz6Jcr0osC4lCdf1NDSRnBO09MJawIgZsbly9YAUBPJjJMy1oBwDBd2GAflhUADPYVCrx/PI2lc/V8NKR5hOZI9+nbDMgS7h3KIoVAJtXHyxIvTZV9t/Y4gcdIGNBtf/h5kxT4GfRvj/xjjQdd5aApR59AsXNRz1oeO8Q5YqAe91Zd9xE+HncYIhXMCKPhvj+5hkJkOsLCTb5Rkgel4iMtMmAhPXyiI1VUYgE37z6iMsONaU7+Jos3exYjaAlepFEL9tuHglQsoIEH/LFH+azlqBogKOqXsAmW8E0v0qiW7LtI82N0GgJP+S8rjlpKlyXhlamdKlLdYlnC4n5SodoxMDM+LF8GvXTfReryox68wqYYm2PZ5NRLvDsmd4gkpAqrDEisCzfvawj8NMnMUfgoB8EpOYirao9zaPoyVnfq8s26O8/kXjN30pwdZx2aV82lMx/47zEiRxqH+fH3IYA7cxIsZXiKL/xCgLZ3w6LEv1uczX3PmTvpD8V6iXHy7yN/8Yuiff6cxN/erFOQPmhcLFim2e4HAuHfSIrozUpl10TyYIv3XqCuUhAue9pktOEpOtUZz7FqkODIxe8cxg8k8BuY/44t6CiMf/PL10BdsaqPUQ0edA61xq0wtCGCc+KPnPsaB1L7z98yiDGZIXpRTubAFW0eHW3jweUiWGY8hc5ugD1nb9Iz43XUozi0P3v2rBUADLpfTuAdsgKAgZ9zq0cisgKA1m3AZ4BzYsnpWZyomdd9N6ow/bnzrIF/SlYY4+U50kxF/BzfV2j2tjPU+3cd5cHnQ+aW4mkfmVIlpa0D63s0heBUKT1sMf0jpTJ93ymS6hXzj4KpR84EWWF+wi27JwdNgsxtyx0nM4Bg1utSEEUuOrFdeQ1JJJs73H4qkQ5/UxvwlDrh46o+dSjm7E0D2g8bq7YV82gUAcJqF8ms8VfBOCJvXMvS1CrShmblwk840Jm9/QzNlcSfkGLc181NMHgLCw8wpkcHK5G+Nz+cOw8ea+mM3ECP8c0rnnFHueqfq6KmTpqIYoY1MlQbMncP/bBBR3MjuIIUb86PiE1qp6lb7CJfaCtHuuSUI10yypU+BRXOmopwoFQsWxr6edNJGrfooKEfBHPWH71K2yRxAShKr3q/bsAoPfjBVZ2imWlaZ3O0jpb+O0KdtotA0sGRTShxQteIPaT44XfyQEphBcfd2EUH6MZdnYMsWPniXN1n/rgO9XSZAw1t5suYghAURbBKFjZzxrPGBYS4b0C67Tmro4sr5c9Av/mJexHcdTKnHPpuHZmLxrYs448p8msbHHEpByp5QAr3OvhgEyVMQG9N32443HHlVLfaBQn8xzCI1UG0TpjqAHrYvL00dd0JZbOy4KAooFL5PTa6qUH078PZu2nGFl1Apn2VPLQw5gJdlTj3ZN5AuXNVgBFpzodHNdWKcXVqT5HBaGPt4SvU/rtN9m6FIJS1/3Z1d8WP61YAMH6sc9CN0noABd2SxLlDLb5aZ1D5ksmaR/69j6asNSrQyg7jJBAiIgcu6KTgzlR9eUqxp6lkcT5ZAXDAWcoEupdFWlQfdlD09EQhWfVBtKFfPcqeVk+5CMCwg7IL/oErOzm9S2WqXsg/6XVBOfgwdQqKhnjmqeybDhWoYUQ2wyW+CZzZtYqmainbiSt3HBR3D4xo7FdRim9WH6XRC/SAHvrPnDopgbx8+6nr9OLXG+wuIe0Q6OzJq47Z/yarN7afsonWHrlivyZEPZDyDM5DWWwEwbrv1x2nOdt11NQbtQpQv6bF3bpDLt2+T5VGGUVHNg+Ioiypk7lVPxQKlRqyyIELDmnYn7Up61f3VamTArkiOlIpl/ZvWoy61ipo9+Xuw8fU+PM1dOqaLhzjiaNI3Zv3dg3C+0e+79CGLDDgSZuelgUqseSQRYYgXJKECWjH4AaUMmkiZXMcQckLmQn88HL8kA4Iny0D6muKwAjGCxv5XElqXyWvp0ML+fK4v8DjpjI8TxAARPBYmDNE8a+bT1G/OTGmc4LnnBxggvo5eFp9NTwLCw9Y6JDyXr94FprSsaKvzfu9fuPPVxu+w//3QilqWymP1g8CsQjIyibSatt+s4E2HtNTrHGIfOWfB6b+yRyLXFBDxR2NwzYcuqksQ8oktH2QUSkdKuOyeBXeZQhWysaV6rHmRy4ZU7XNfssYG4L3skFBfu/wxtqfpm86aUDOQ+jw927VPFqvv3efMxzIgad3We86FgDHo1kM38JWADB81zaoR2YFAIN6eeLEuZpjl9Ppa/fsfU9qX54al7ShYLjqIHcQp2z4QJNFKuQPD16e9+UsWBgnkxEEnZpxSgnX5BNJpG9xRU98UOHDyl2L3nOBuv2sc7QgxRhtuOJScrf9UC7nDC3mT5XXUJ6jUPP9/M17VHXMcqXbUztXpLpFjampHBGnCvzytCLwfwG14M/fkCoY3bRUNprYrgKdu3GPqv3POKbGEdkoeq8uDNKhSl4a8VxJbdw9ftlOf0vK4gItokplxkbxy6WHaeZWHXHxavX8NLi5LQ3MlakOKYC2QLpVuFj9T1c5bEBfrpyHcJjmT1PduxxpyjnT0D8EAZBql/7pe8EMTeqOr1g2cBuWzZ1OK85FYxCEA+IwdwZHpWx32ne3DL+vRD35+4W35Sqd1N1DnY7fbyaICQiLKpaFvutU0QFN5Umg3N1xh0I5BGPwm1AZ5gTPEhkpOal9BWpc0njwIuryLAfeJsRnZBVbVVDJmzlTiSWhHdz3fzKeO2/a92cdBCsRDJdV6GVRFVwvNijaECwXXN2c8gHflzIHLPezd4Mi9HaUTS2bfx+plM+Rbov3o8rkwJu4zlHOspiJKMNRerxtpDhDAET1/lWtq3zPzNxyij6YrQecvaEp4jQjFfKmp9lvVrMCgP686UO4LSsAGMKLF8quWwHAUF692PGdp7ghfQJpFDDOh8E9wIcbXsbyae6Q5iWoc/X8Ds6CsLvYoIUGEZG/elSn0rlsGwnLbDMQc+YmNZ+gVk3DdTldBnNaZCBXKa1FhbKkdns6udKtt0IibncYQgV5KofsOsj3QcJvWWjNAJBD+M3Iwh5iBD+/VtlBNIErDqqChEDgvTBxvX0iUiRJSPueIgr8NTtbTlyjVpN0lB/ahUDHqzXyE9SKMSZZnwECJMev3LF336NuIXq/kU0dk/M+CWVHlK/78UqDy+BfGjF/H0EpXJhMBO9qfFCJ7zx1i70YUItAS4WTtZuykdYd0RFNGNvrNfPTgGbuBUndnQtVujFHukR9spKOXtbXXbQtgrbgCGw1eYPy/nfHDx7UAvoVwR5ZHERGm7rTpjdlzLg8Xyifkz5t7Yi8xMYfwXyZ6oL3+9GLpahNRRtqyszAtxk5aqkBJSsOMqEeCuEoYSo+OgRjrvzzkPCMMEMqejMfwVTHTGkVPqZNntghrXZN37qmAeOjl/+hqE/UwcSMKZPQz10qGzjdcPiCLAhfDxhUYhTw30xcgs8/fqs4aNl+6gY1L5Odhj9b0qPMDE/WU4VqW/tBXS2VX1i9j1fSMel9IO7ZyJFLtPtRGERUpkvPeu4H0n+RBgzjz3aV8nWpoYscFLdFm0DOHhltS70VxgO+WYByZ+8LjjzkPspChvwafn8F+xspKWRORGQ09ZmlC5uUzpWW/upRw5PloK9WHDFQJogDAmv/7dE0hm1hKwAYtksb3AOzHkDBvT6B9k51Gob0CUDqYbO2nSGIhJjZL10qE0h+lx24ZC9iJiChOhXePbShU/LxQM9HMPSnSieU/eK8ifwDa0bXKlSFpSg6G1fXH7caFD5lkudgmI+49MFMMQ4+rXy/DuXzo8prXI4zvvXNNz1i/Kr03lpjVxhSJb99xVFUYf2RK/TyFJ3zBxvTbSy1ydc5VikLyoIkVUYvM6iFYyMsBznlNFCuhAy04KQOFTQ+txe/1gOZyRInoAMjmhDnlfME3cZ5NGUKA1/nJFjq9/ptpyFFGn6BH7dngyJ+ddEsRbtkzrT2fsoOX2xAV4kL4LkCQT9ShOVAQNJECTTePLR99sY9OnfjPh2/8o+WTojgy6MnugQ20DC/vl7FIbV96F97adp6I8+XJwhpBJ7/3HFW++54pnR2t5CzXLxMjBMIdgSYwW8mGxdCwO+jTK50tPP0DXsxOUhutnAc9YTfyLaBtrRjnj4oK7GiPXAQgnNZBIsRTCqaNQ0Vy5aaimVPTfWKZdEUvUPdXKXtyuMD9yS+A83Q0vcePqHig40K66I+0jOBAKw02kgxAB5kpLL6YipqFLSnQq2p+kHaMuZBmDNubHf9PHP9LuGbnR/w8uc2fut4bstBUH5IgUBe15oFqBDjZ8Wh0nBJqI/7Jgv98ANSfrgD/kRQaDizI6NsXITC+O9UhSqUhY5Ubcs0RqrrnLJBFiPhAjXevK9Gzd9n4LgUhxLW/tvdOz28y1kBwPBe36AdnfUACtqliRPHVClFOwY1sKcKueKjA5/L16uOakrAwoTSGx8Q59+JjU1ynEyinztVnebKXXSsmpeGtbCl8sGA2JGRPvggblbaKGTgzEWelg1VUSioWUa099xNavalGo0J7rUsacKHxyw+rbdZWpIqaFHvk5V0TEJUqYRClh+4SK9Oc67O6uv8Qqynzscr7cFIbIyW965t3zxBpVUOZvD+ZHTT1HXHadg8XY1d0ArwZ7RAdEC5HQc9wjwhwudoCFdCDb7OU1zUHxt9gCau1JFf8KFfk2L0xlOkjD994uIKOISr9pSLFEhQcDiamQp9xfkB5bpAy+LdAu5BcHFBSIaLiaA8OMZqj1thQPogVfKP7tVcBvOAtKo5dgXdffhE69pdJWFV2rXwXc5iEH97d8YOg5ANshxK5kir8VsKc4YcEmU4yq9qgYz0a9cq2uU1hy9Th+8229vj6Yu/bT1NfSV0EV8nBFCQmpk3Y0p/3jIBb8uVcIfskDuiHRVGLDEIPIj6bSJz08jnS2pcfbIt7lmLimR1PwtCNUH7zt2ipl+uUc6dK35XUClUHrOU7j/S1eN95cYEshT3Howf0vKgVYFMKWn5+3UMvvf5fRf9vu2M/W/4jny3fhEHbkCIPoHL0sxGtIigDlXzaZc3HrtKbb/ZaC+K4PuOwQ3t/3b2/SQK7RveyBD05lQX4Btd3NPI6Xjy6h2qPc6IVJf9lUEMqnHwwzK5D94/gvPR79Xy6DfE51qgr639t0fTGLaFrQBg2C5tcA/MegAF9/oE2jvOo4MTw8Mjm9hTFVzx0SH9aPzyw4YPiy418tPAZxxTn6asOUYj5++3D1HwYgR6zMHenxn3jPD7rboFqU8jmwobjCshyh9orsYKNcNSQ40ntPPfqUEROXREias2wvn6scv/UD2T9CMLvRq6K99p6mZaeVDn8BIjkRF14m+cXF3FW4oDEJmHSpB++3uGsKESm8C+jYpRqVz677T79G20IEbn/ON9y9xoQFu9N3OnvYhQb+QBCoFgGrNgP01erQuKmKVZqsbL0YOtKuSica2CT0XTl7X6Yf0JGvLXXkMT4FsE76K/rc64FXTiqi7eIa/r5dsPqOIoI8G9s/7L5E5Hc96s5nO6JPr4dvUxGrVAf7/jb+5w/PJUXncQN7fu25BFqjR+9MuDLXinIogkc6QNbxGhpfHKgXB3vklaT95A4CkTJqMGVYEJmaPRFQch2jT7fvL3fRSb7fFxIngMuhKVdaqWj4Y+G+HUnWcnrKXdZ3RxFVFYBK9LD11Et+4/trcBlGrVgkahJk/Hq6JcEG0IAQ2zNvkBC8r5IgoErufqHy23p50DvYq0aZHiy7m6VYcsHPUNKpkPmxSl+p+uNgwDqcM1PlphOl0yxzfPkOCq5BxAAH7Qh0+M94EMOECn/B2kSsE1U15HfSA/twyIcnrwwA/1SuVMq4kawRbEnKfu07fbx++NqAwXPhFih9b+29NfYXiWtwKA4bmuQT8q6wEU9EsUUAc534aQqxdOmBFti+t4eX+29BD9uOGk3W9wiIxSkJ8P/DOGft6op0R4sokM6KQEQWdFBix0+FASbskcLPgbT+HNmS454aMpR7rk2n850yWz/3+gLuVUG/6RC06WvcMbUdJECYNgFuLeBWeCEYdHNaHELM0s7j22PHBnBvrO2kW/bdXREKJO9Hs1qVg2I6/jM+PX0J6zOom5CiHLqRJ46p87PvlaxpWwg0wKzzm6ROqWjDKBPzULZ6KfXqtM4xYdoK9W6Ag3FccbNvjggwViDJvdyHw2HlkuEsEPMHwddzDU50JK8OnT1mXohfL+R1LzYIiM7Dxw4Zam7isbUGhycERcw2b873dq+IyUEu1h/SEoIAcnwa21vHcdSp7E/H3C04eRUrtvWGOnfGnrjlyhdlLKPb8HwH8JdKx41/FABAIom/pH0a7TNwmbdWEqvjG5bZXy8PedIqlesaxaMVwvyrg45UMFjrZX3bt1i2amqZ0rBcNt7bUPPEj6UqU8hnRYueFxLUtTq8jcTvvizxBReMorkVS/RFafsyBUnXN+O7mMM+5qpNI3+Gy1gyhQ9UIZaXoXG1LUU1OJ8UGAr/XTeeMUBCqO1hmbT9GHkppyRI40GodsGwnBhwAeqCs4r7Ts7yetytCLTzNEdp2+QS2+Wme/LCgjxB+AGgd6XBiQdocuGtV6N/aLomxp9UyKnzacoEFz9cOUSvky0G/dqjpMWYnB0XbUsHyxWans9FW78k6nuPn4tQa1bjnwz5/leJasYGhKV+vHhZhw2PBK1XyWCIiriYsn160AYDxZ6GAbphUADLYViVt/OBKEw+1dQe3B3zFu0UG30CHtp2yitUeu2Acsq4nF7SwEX+88zUv2cNTzJaldZR1ZwrlmnI0GJ/FI6xPBQaRuLZf4G71Jdwi+2fOfRzfuPqSyw5c4NAiOHSiZWhaaM2CWoqZKHXp+4jqXKuc/bTxJg/7cY58McFP93q1aQCdHhcCSHZCRvbvP3KBnJ+gbN9zPh0Y20URGtp68bq8mUCscPSLUh+X2v1x2mFAOBoGDuW9Vp8JZUzsglIc9G0Edq9lSyMLFkHqNFGzZnKnR+jLuDt9tojWH9ffogKbF6fVaBbQmORdluhSJCQg1GXkv+n6/YRHqUc+m5ukv4/xdaLdn/SL0bn3zfvh4UMcVwoqnlauCnOK3jIBMp6lbDKq9VQpkoBldq5IqYOosvZMHPOArRzBV/99yjUtRmKAMuHTrvgNXHfiSce+AV1BYXBweeLr+SJP8cf0JypMxBeH+y8j49vgc4LfwxbIjSjVYdzIORv69j6ZIFATCXwR5C2ROpfGWggdPmCdZEGZj/3v3Oerxyw7lZZUQlCjI02LF3wtnSUVLehlTWd2Zd6T1I0X+/M37huIyopBngQxsVpy61LQ9E4Txw36k6+KgXka75cuYglb2qUtcGFBu58uXyhEOgGA8xZeLegybt5emrtO5QSGKs3CPEaW+qk8dQ8o7f4+JQyg+V6AcOCkhocV1d94vbb/ZQBuP6SheWfiO02C4K/oi+8eVlQUS2tp/u3PHh38ZKwAY/msclCO0HkBBuSxx5hQ/oRMfxsIhBIgQjFKZIEPmKrIq5TvU5x+F418qR82ffkjE2QQEacdmHzdwV/4Aw79dbfw9GaIvaSqe9BMqZc3SsVMnS0QxQxuFyjAsP9kMAKk2WEIZ2DdJfepqm1rZOJpFtbnk9AZmm5bYXAhnG1b0KyttqgRFgKbpIqGhUEegHccvO0yfPA3u4e8NS2Slb16JNAwHSBAESIQJhDcXUVFxKMbmvASibaToVRljFCIAl1atIpn93v1b07fT/Bg15+68Xefo7V/1oAVS0Re8W1NT6T19TQ9KAf3z51vV/Y5gRrANyLz1R3VF5OSJE9Ly92tT9rTJlXPBvwtQyNXcdflhKy3drwfNgHgCyu/irQf2PgRSXiVIIdKzoQhccsgig1/LetemgpltImjceGpngcxAGhq51l76ZiNtOKaPX/gRvec8dftZTy0E2mrnkIaEwEznabpKtisUot9vKA8bBMIXStNCcRw8fB+1LG1vBQGrooOiDSrJQMwhaI3DYtlw8LB3WGONX9KZqVJqEWzaP6Kxdg/zlEt/CPDM3HKKPpgdo3TLGU8yfn/4HXID/+auITo/nrvTrgqqo262NMloQ796GsoVaf9I/xemEqo6cum2Q7ovAoXy4UD5POloTvfqDt/qsq/ywcahi7ep4WfGFOLjY5rakbd8XYD+lpHkaJfzNfJ3TYMSWQnj4caDnuK6CsXP6742bYtBuFBG3XLkJzJqcCDhiVUatZQuSevxw6uVNP5Ua//tySyGb1krABi+axvUI7MeQEG9PAF3jqd2cfg8Ulo4wbJwEh8gG/tHEU8bU/GPIJACJTeZswcCIrJ6YcAHH8QdNvtyDe09p6cdyq7y0+frdx5S++82mZb3ZJjOCOE9aSdcymJDW6D/AgeuqWDfpIXL/MfWOFQpm+hr/Yf1NHSsbFw9EWlTr9bIbyjj7qYltsaDdp0pVuO6zFmp4v4EcbysDouND1KfsDn/euVR+ijaRkAPg1rp950qGobDUQ/YoK/9oJ6WnnfvkU3gAQaRA5EeHJvzEci2EfRA2pwIiqDv2W9WpQp5bWnQ/rR+c3bTr5tP25uU0/2mrTtOQyVxF5E+t/LgJXrth61aUAZouZlvVKXi2Y2p7v7ycf/5W4T3lzwXXLle9HX34WMqMdgYgMO1oc1LUKfqxt+YqINncsVRywhiWcI+a1OGtpy4Tr9s0ilGIvOm1wJTz3y51nD/ZUiZRLuvEZCBlRu+mK7ffWRva1rnilSnaBbldPT4ZTv9LQmeqfgsOb2AUMzmKDZxSMBpVhL8H2noclnB1V9r4492+D3GlV/P3bhH1f633NDVtoH16c6DJ1RrnJFbDvfgwndrunRLFQSTg6/8NwHuTQR5fTF+OC63ZSbwg3uy6phlBuVsud7BkY09plfp+P1mA3pVbg8ISATWuUqySgRFxZv3XNkc9OdOPVgJXsApHSOJ017IfX7XMZKiittS3lUcyUCSi4AuF9sCXcGgP/canSrlvAAAIABJREFU6G34PoDvSQASAFiAmyotHIjn7QMbOKUPQDs8SNsoIitN7mALMnIhn6xpktKm/vXdvpXwfEIAXOa8BBoefKvW/tvtaQzrglYAMKyXN3gHZz2Agndt4sKzD2fvphlb1JsJ4U/xQdGGD2jxd5EuzNE0QlFSHs/hi7c1XhTZ9gxrRKmSJoqLYQd9n20mb6BNEtG47LBqA42PjiOX/qEz1+9p6Uf4CLf9d1/794Vb9w0n8mYTgI/x2NoYBv2kmzio4poRqTKhOqb47vf2U9fphYnrHaZhy4D6hA2tbFwwRLX54yqwKo682J5z1cZb9ImgwpFRTe0bIzwvcLDzWI7SMAdlEQuOMgayDSgt2bgaOa5hI470aNl4yldsz0ug2peRX0iB3jqwvkHd0l9+cEEWbOI/b2vbIH+6+CB9ufyIvavGEdloUocK2r+R7gqEZrWCmSh3BiPK1V++iXZUtBRAHEIZ2PANcPYmPTPeUWXdWQDnzPW7DiIFCIScvHaXOk/VkXTg+SuSJTUdvHjb0KccwMAFzqnoTLyFoxVlQQTRyYTlh+njxbZUeJgI9HEqgffqF6b36hehS7fvU6VRRvTo5gFRlCV1cCrM829GjHH7oAaEwCqM8wqDF27/8MYaKoyjhF8sn4s+ae1aEEjFRS2CVejT1UG2N/e3TGnA679RqwD1a1rcodmJK4/Q2GgjylEuBIENIdzhjk+nrt6l2h+vMBW7AR1MZN4M1Ohz47c15lvFuwnhnJv39GA3EMJHJYV7geZUpeULf2V0rgpJLqv6lhq6yKAMPr1LZer20za6/UAXbOEHJVxx3kw0avDcPQbucfhnhhbkc83vYfl9zWkUOC+6q3W799AGdpBNvPOs/ber2Ysf160AYPxY56AbpbsPIGwQEDhIljihpqpkmXoGdpy6rinCVS+UKSTRbDyVRsXXw+HsYiZwwj7rzWoOql1lcqWluT1silrCwHEjk23jnsIGyTL1DPB1kUsteq8WFc2W2qOpA/IDGw0ECc7esP2v+A//hkG8pX0sqFZ65GgQFoZ65NU7Dw2eWVyJQbhQHrikCiKguqzYKZrjv0Xwdr1Vt5ChN851xNPiPHDN66IqFJpoDMiInYON6Wc8bUzuGKiHVX3qau9/GE/BkzmTRD1VKidQTHj2yGa2OfV64EFSEamRw+ft1TbYCOzERvovhsr572Q0Zv8/YgwoODNBrtieMiCh6o5badjoq0QQ5u48S+/O0NWo7feSE8EErrgNJN/OwQ3oweN/NbqSuw91tCkfpyqwyFOq36hdgPo1cQzuqNK8VUgrPiYcFkW/V4sQDHn0RP8t/PRaJapZOLP2+yg8YIEBMekOL15sr6FZ+zyIh3Ky6i4fv4zU49fEHLgai4qLVw7CuaKycdW+6joPtMtlVIHLf//9T0M44hDWzOZ0r0bl86R32x2groG+NrNmpbNTizI5qOtP2+xFnGUncEV7BMnlrJzudQpS38bFHBBycv/yWqtE0pDmjN8knoMIOMqGQBj4Gq/8o39P/dKlMlUrlMlebMAfMTRdQvKaHQZw1D0aUHEfKtd24X6avEpXtRcoXZTddOyqQRhF9e50toDKORnckNKmSGwhAN2+88O7oBUADO/1DdrRuRMAxIus9++76I8dZwn8LUivaFwyu09jQkAR8u9QnpNVSH1qNI4r46QTSC3sb7DRmfNmNQ3mHUrGT6VHPlfSIQgEvhf5lFCMT/BmcN4hLiSC8hxBIoKHoTRXgfS158yd2u9PZao0xUD6Ft/6UgU2yuVJR390rx7fpiJsxvvg8RMqOtB4So/B7R3WiFIyVHL36dtoQYxOXK7il+KIp07V8tHQZyMCPl9VRi/T0L7c8mRIQav71jX8mafsyhd5mrM7yoxmB0Vyu+A9ixlmcWf6cmNwwRn5XQp0TfRe/V59p14h6tWwqC/deV33m9VHafQCPW0cKFSg/lMk0VH/HLEoOhP0IqrOR83fR9+uOW6/JKNR+fjl+oWypCIo8nJkFA/0mKmILog5bxBMAA8sDgwSYGCScXQxeO6gpN1WUltFFQRKUiezpSHzYLwzkQm5Lxxk9J21m05cuUMdquajN+sU9Hq93KmIYCU4E+WUftSTU7Z5gJrzoYKTce3hK1S/RBZ6vpx7KtnYP6DfO1Jwd+yLpal1RZt6sCsxO3fGxsvwQJR8XaXUjDR7iM3IhrWXg76T2legxiWzueUO3lHVxiw3HD7iYEbmucyYMgl1q12QRi3Yb2/TmQAV577jjogAmgpdJ8rKiD3wDuLelQ3p3hCF4QIhCDYeHNFEo4SQRXKmdqpIdYvpKfe9f9tFs7efsTf5es38NKBZCYc546rGKCArbjubZAA3Xvh6vT34KTj6UIdTaXjK9wwKBKQ+C8O4jz5F37uz/3br5rAKhfQMWAHAkF6+0HXenQcQ59sAYfT8d1zzdJjNCl4SXX/aqikp4hQYL0Hx4RO6M0k08M8Y+nmjzjnzWo38NOgZxxdVMI+Ri02olAuhbgilOm4tyuagL9qWI66alSt9co37STaOTGhZIZdGMG+ZegacfYDFDNU3Dtb8xf4MgEAf6dWyqdAsse+J1YM/Z4Bzf6Ftmb9I9PXOrzsIqpfCQGTep1Exgys8YI9NGcj/A21mz+pSOdPSvLeNqGwubiJ8RcrTmr71DIESLqQgyOLl8TlTLhflwDO4/H2jaEKg5yjU++MoKlldtPWkDbT5hK5u6YxLL7bnAalwEUOinfIicvSd7JMqGI/rrSat1/j+7L/PqMLUq0ER7Z+ztp2h93/f5TA0HDz/8VY1isiR1uEaD6iWzpWW/mIZDKjEUxNVafAoB/Rj5EhjUAQ8jT9u0FPhOfcd5/yVg1vO1qnXzJ00RzoojG3kIFCuCOBwe6lSbhrzgk0IhH/rta2Ym/73oi4S4u19137KJlp7RFe/lhXbucKtp2mbKp/em7HDwI8nl1HdI1zwokT2NBoXnvzt7Cy9nPuAdw7ePbJ93ymSXp221fA3UO7IdDHO0qr5noX3CbAHgrJc9V0uB0GX0rlsQAcVMnNT/yjKmiYZ8X1k9rQQLYnS7h/cR8L4nuOtX7YTUL7C3q5XiHorDjG4WIcQ1XGXOxMADtw3FfNlMKC1uaI76Bz2DW/s9m274ehVeunbjfbyMoLQnf232x1ZBUN2BqwAYMguXWg77s4D6OVvNxpU3Dx9APIZ4lBtd2TaQ2GWu/64lRbv05XoAMf/6uXyoeC63UecqkIJT9jv3apqL0TZzPhABDR/3ZErmvKfMNXHF7+nVGl0ITVxsews5xQT3cmnibHsgtX80xloPn4txZy9aZiP+sWz0JSORhEEa8JCawYafbbagR9MVjAUo+GIhK61ClB/xv/EkUeC3yvQM6IiRocPNQplop+7VDa488ZPW2nRXv39JS6qhIB+33qa+szaba+v2gCXGrLIkPKpGruKHzbQcxTq/XGkkUxSX++TlXRM4vQCeT5I9OPKeMrhkOYlqLMk7sGvy36qRMIgSoZU2vuP/rUXlRFE1+48pMiRSwxBRxR0lhrI5zN9isS0g6XLow0eXFfRpaAc0GoRQxYZUpHRpiw00r5KHhr5XCn7GDpP3UwrDl62//v9hkWoR73CLpet5tjlBnXn4S0i6JWq+VzW87bAwpjz9OZ0XclYtCMj4jlnau8GRejtKNdjceUTEGVIFz9/4x51r1vIQMOgElKROU9dta267oyGhSvDIu0TmQIy2wH4+VYdvGzYI3iCyOU80FUKZNBSrWt8tMKAoOO+Ixj+jsl8u+IoFEg4leqy6EfmiVapaAuew+/XHqfhf++zuyeQifw3/0XbstSibE57uS4/bKGl+y/Z/222V8ABQ/WPlhN+8zB/ATBiztyk5hN0XlIEcXEw6K5xtW+ZL9qd/be7/VjlQncGrABg6K5dSHvu6gGkknXHgL1RrxITxU90wgX9xU+i8YKe0bVqyNwfUOYtNsiYBresd20qmDmVYQxmp/SCL4SnvKRMkpD2shOzamOW0bmbemoaAqUImFqmngGuuilKeZqOYM2v7zOgQkqZKdP53pvVQqBmgB9sQLX2yOimDt1zwnBVei9XajRTiYztsQ2ft4++X6enR4r+VGmNXDkTZRGoAHqbp0H/seMM9ZypI6uAblnA1DuLDlyo8bA5s2dKZ6cJIXZIFttr5mn7/H0Lmpb9I2wIlbLDF9MNSdGW82t52pev5YHGAypP2Avlc9Knrctq/0Q6KcjyZbVMuT8eGMA1nlaIv4mUQ1GXoyCRgvpD50qmyqBAdwPlLRsXKMO3Eufw+/m1ylSjsM5dJtdXHS7I1z9vU5aeK6cHPbhycMeqeWlYC+cqtqDqKTpooSHF1Iy/0Nd1FPXNUrbxzYc5A71Pg09X0WEJMf9JqzL0YgX3Un299VPFuyYLk3jTrizqw+vLwia4xtOeMR+bBtSn/y3cb8gSchcNqRLNE8F8fiDFfeP3lnydp0rzuiLo7qycjLxU7SFWvl+H8mVKSfxd9Hy5nPRZm7LUYsJa2nVGP1DlaFeO9HQWvIcIyYwtpzTEYZuKuT1WWFbdF/vO3aKmX+opvEAUHlV8F5jdUzw1GbRQUAGGudp/e3OfWnVCbwasAGDorVlYeOzqAWTGe+GLKhnfaKkIxENxcnlqIDhm8HIMFQMPB04tZVOR4KtU31AHKW5IdeOcF+C3wQtTcD2qPhJiO1UlVNbAzE+eliTK5UibjNb3iwr14YWU/zy4A+dbR+aisS2tFPaQWkjmLN9IYVN3YITjSf+gP/cYlGw5eke7HyZv0MSghMU2Esds3jnXqignp+iJv6kI5s3QFu7wvBboN98BecX9fLV6fhrcPLRoMoLtHlcFrIBQwXu30ICFBne9EYzy53h/3HCCBs/da29STleGwilEE8wMKCaR2ivKTN90kgb8scdeRcVtufHYVXrlu80a5zQEKICaQoDAzFTfJ9Hv1aRi2dLYq2w9cY1aTtpg/zeQ+LslDj/etjP0GMqu6VvXoMT88aKDNGGFrt7cpGQ2+rq9Tb3ZzC7duk+VRhvVg2NbfZynucq+YUygf+FcfTO6VqEqBTL687ZyaEvF6bq0Vy0qlMUzsTS5YRXyX74up6hz5LV43nIl4ahiWei7Tq4zB4b+tZemrT9h7w5cf0ifBRpt9rYzGke7mTkTGuECF7yNDf3qUfa0yUnFZyjKru5Tl/JktKmII4hfsP8CQzNi3nmGlEjl5QH6ES0iNP5KYS2/Xk9bT+op/ipe8ti8mVQgGFVmgJkPk1Ydpf8t1LlPaxfJTEBWwlztv2NzXFbbwTMDVgAweNYiXnni7AF08+4jqjJmmQPBLybIl5cpf5Hmz5SSVoQBDxBXBzVLHYmrGwzQ+L92nqVc6VNQVPEsDuIru8/coGcnrLO7BwTM4VFNHMqNXrCfvlmtK2aJCkhxaFc5r0ZAXYfxwsiIUdULlZ+wx9UcBWu/ZiewRbOmpkU9awWr22Hpl4pY3h2ERlhORhgNigfAzNC17ij8OqAaWpam1pE2gvpA2t+7z1GPX4y8UehfhQziyplpkiWidR/WU/Lz8tQ/zuWn2ggmTZTAAREoDo0COSfh1hfU3CuNMgZ+tg6sT//+95/D37cMqE+ZUyeNsyngaEUEzvYMtQntLD9w0YHPTHZURanS5/dd9LuEKDQLeEGx99jlf6h07nSUion6qCaDC9h8+0okNSiR1V6UC5q4UoE3Q+KiQai0gidNFsPjgdIKedPT7DerOV03CBk8P3G9oYwzAQh/3AQ85Vhuc8orkRSZLz2VHb7E0BUPdvrDD1UbnIJgZtcqVNmHwGO9j1fSMYmrjvcpj4sLKn30YilqUzEPce5UFReraiyVRy81iH1A3OWDxjZO2XM37lE1dnAvt8ERsfI1IOZqjjUPuovv9l2nbxDUnlUmgoTiWv5+8w1KwiJFuOkXa2jf+Vv2JsSccEAIR/g9M34N7Tmr1wNXOLLGAmVHL/9DUZ8YEcEANLjLLYjgH4KAwgRXOv5tBQADtYrB3Y8VAAzu9Qlb75w9gKasOUYj5+tqUvIkzOpWlSIZN5y7k1Rr7Ao6de2uvTg2BgdGNA5pNWCkXxQasMAB8XBkVBNKlDCBu1MTa+Vwqg2y3fNP025VKWmcRFfmEpId4xyO4ppISbh46z5VZifROwc3oHQpkmhFF+29QG/8tM3eJDYk2JhYZj4Dy/ZfpNd+MJI9o7Slnhz4u0alyBxXIg+BH3349jht3XEaOk/nKMqQMgkhbYwbVwmV0xhFWb4B/PKlcoTgRKCNKxiK/vs2Lkrd6xQyuAMEON7NCN7BOD+bXHjJvosE9I8wjrxSoagQJP9BEj5A3UCkAwZ6zgPdn2qul/eurSHeGn+up67Br7j+HoGv4MMT9xh8EjzDZmhVMZ9cKAN/5+mlXK3a27V4YeI62n5KFzrjvwXOl/ly5Tw0+nmdw4/364xDTYXu47xhuTMk14R4nBmEEkCvIxvnpvN2PlT1bt9/RKWGLjZtEuhhoJ2eGa/zpwGVenBkE0ocgG9iLmj3dbvy1KSU9zQzrlTN/+hejcrlSU+Pn/xLJQYv0n5/wsR+iX/HmX1jy5MKbjukxssm8+7h73ysoiyC3RCJk4PLcjvg0CwycKEhYCfXxcE8zBk6F4cNmVLphwpFBiw0jF0o8fIgpuDqdMXxxzOrJrxcjp4pHbh3qStAg6vfFKfWkA+LrQCgq9mLH9etAGD8WOegG6XZAwgfaHU+XmEgFJadx+lefelE1JOBlRm2mG7ee2So4uyUypO246os0JJlhjt+DJmlSuPjCdw8+EBLgK+iWDYedCuYOSUt621UX+SKeSpeJ7jJN8rCdUEYjLXFGssmnxJOXnWUxkiQ+Er5MtBv3UKHKzGWl0rZvFmqRt2imWlqZ1s6gWWBmYF+c2K0k3zZzAjgA+OR1Ys/ZmBBzHnqLhHam23Oxi06QF+t0E/0VfyPHBnDEUT+8NedNszQIQKtzdsApxrQxiVzpjUQsfNy/LCIUxHcuv+ISrPgwJKeteiFiesNwiBANQHdZJlvM8D5Fv98qzrdffCYXpbEuIIlI4GT/g96poRG2M+pRRBUlg+KkZK/b1hj+/cSvqFKD1tsCF746356d8YOmrtTV/qWU9Uh6lFx1DJN3VeYq0C22QEe6qs4zXjgnnPMqe4W1YE9UEpIB3cXrQQ06X//kdMUadG32eGCuI4DD3B8dpUOe4Xyq293u3u1n5+4jnZIQVxfU0cjBkfTnYdPTDv/rmMkRRXPqsyA2TGoAaVPmYS4oIQ76wPkaj2GQOOK2Cr+VjiqCprzAfDAnLieN2MKWtWnrvZP1fNclNs1pCGlTZ7Y3myJwdEGwRukIJfLnY6KDow2BAbBgwc+PM4pzlP9a3y0nM5cv2dv35e9p3t3jrGUCiW5b3gjSpEkkVvN8XRweXxWANCtKQz7QlYAMOyXODgHaPYAWrrvInWRTvi5997CsIGUKzhggcOJk0rhLThnTO0VZOyBsOPGT+pwHWkw4IRBSi7UGKd1rhjrKEHOQ6H6oOSBOZBl//SaUSkS/ptxjgiuEZwqFmbcQ0AkFHgqJsI/Viz+NNd3Oicilj+ygS6yLHAzoErnUimlBs4jqyd/zADSBKEiKNBJDUtkpW9eiXRo+vOlh+jzpYftf1cheCJHLjUECJwJBPjDd7M2gEYBwkNWo0RZX9Vg1xy+TB2+22zvFmmMmyUU99V/HlCFkUsNbgEpsvrQZer1m42vSqhYmqFTYnNewq3tiqOW0uXbekAKh3E4iHvnVz39W3XoFxfzwAUuhBgAF1HrUiM/TVlrFLARiqLwe/2RK4YAZ+KE/0cxQxtRssQJfR4W5+BD+i+C+DBVQEAIHZh1rBJxEGUFckyuq+pj99CGlCaZHmjhfXFqAnGdp2ia+QgE5uiF+wnH0e83ckQI83o/bzxJA//U+Rf5ddCTtK2Um4ZJqGp3Upl9XrynDXBkmTM1XFd9Yt9SgHHbJUmYwBDQEuIVPNgrB95V2TEcQcd9WXfkCrWTAvmgZtg91IbMEzZ351lNEZlb44hsNKmDc+5IrmYt2pCVnBH0xjf9Y/4iIaL9wxtT8iT6b6700EV06/5juyu/vVGVSuRIo3FByiZSpnvN3Elzdpy1X+L0FHH9LlUJygBVmdrJb1EeJxePkVHKVgDQ1S8vfly3AoDxY52DbpRmDyCuvMQdF6e2ng5IhQ5DG5M7VKBGEdk8bS5oym87eZ1e/NrIvwLnfnqtEtUsnNngJydOFlD42ByMCrXEBT44t99zZXPQ520dg0uL914wnOoKv2U1sEL9Fxg+FmSRD/5CVKWjxeZchGLbZlwtrlKPQnGswe4zR4DB3xHPlaQOVfIGu+uWfy5mYM72MwTF7Wxpk2kpfbkz2MjNZeMKj/WLZ6EpHY1E7px/avabValC3gxxMv9VRi+jC7d0xXU48eOrlahWEeN7yRPnNhy9Si99u9FehadLI5gK/mDZBFIEiBb4UyV/xoCg3z0ZV6iW5WlyCPAiCCuntAcL0p4LWonAZPkRS7RDUWFA+oBu4fYDPZggsgxQZsLyw/Tx4kP28qVzpaW/etTwyxJy5U4ZScX5eHHvI4PFWSBblcYJRyHiAA5E/K9sqrRu+ftKNUgVNy3KuYOKRH+Y/7tPEW4IbiF7RdC2qPob+GeMQdEW2SygERAGDun2VfIaxCtiW5RE9tMbJWWzm+efB48dAlgQlDl2+Y69Cjj5wM3H+SFlmhYcyBRmKbcL3qmpBcjM7Petp6nPrN32yyq+SZUADCq8UasA9Wta3Olvovv0bbQg5oJDGf5e44E4UYHTCnAudCiP4z3KuQYF7zcHBHSqlo+GPhth94e/S32hn/Lm4aDiWJUpjVy12eSLNZoworBPW5ehF8rbOAytAKCr2Ysf160AYPxY56AbpeoBpDqtBGLs/iOd0+KdeoWoV8OiHo/HLJAxtHkJ6lQ9v8ftBUsFM8Tk523K0nPlchrcrDNuBZ24qnMgOpO199f42k3ZSOuOXDU0t7hnLSqSVVdF6/XbTpqzXT+JQ1oOAr3c+OZPXJfTnZ1tgKuOWWbnIkTdie3KU1MfuFn8NUfB3M71Ow+p3Agjmbb2gVe7APVr4vwDL5jHFYq+8Y0nxuAtIjoUxx/ffeapdkBx/9zFiJR2dgAS6PlTITxE+pW3vmw5cY1aSSqoXDBF9Z7nSBFv+7bqOc4A56xDijeCsOOXe6YkG4i53Xn6BuGeFAYhECiJ8gABUHVIxd115qa9rMzFx+9rfwoxcdRV6qRAXdm41AbP3UM/SlyW9YtnpSkdHZHCfC45ShPXnYl0cCTVL69XpmoFM5ku0bMT1tJuaa5EQXc401Q8Z993iqR6xXThE94xV2cFD64sdoDyPEgmi1fE9r3GxReQjjzh5fJedatC7oF+ZcXBy/b2gFgd+EwJh1T2NpG56aOWpe3lIkcuoSv/6IFuZADVKZrF1C+uHGxG+8IPAdCgGdWD3NnIv/c5IG1xnWfmcL5NlMFv99jopobgN08pRtAeKEhZYBABZgiM4PfEFY5fqpSHxryg82kWHrCAHj2x8dLCAp0tZoZml3kPnd1U1cYso3NPuddRTv5dWQFAr36OYVfJCgCG3ZKGxoBUDyB+socTzjpFMxuCQ69UzUvDW5T0eJB7zt40kAKLBtw5qfK4swBW4Kd0omse3AOUHgTB9x7pXCI96hbSUi5i01RqbRwF0mnqZlopfdCYIfPAE9XsS53YWfgNIReRfsM/dgUSEul1SEmTScAFSXBsjj/U21alVWNMINp+q66RzD/Uxxrs/qu4lr56uTxBpdKy8J8BruRYNnc6AueaMNVvdVnv2lTwKQVCoGeIcxChf1cpi6585IqjKZIkpH3DG9urqZQT41qAwtWYQvk6f3cDjXT6+l36ZZPOVdquch4a5USoIlDjB9oM6YByOiHeY+MWHbS7gAABeLb6ztptSA8Eyhpoa1VanowO9HUsKtEDgfpp9uUa2ntOR/S4m8GADBFkisjmTDwq6pOVdFRCmH3RtqxTXk4zhJY79BQqPr+36hakPo1sSrPc8B0LARAg44Qhk2XAHzGGYAevF0ikPH9PVyuYkX55vYpXt4bqecZFjcxS2fn8c0TYWBcK8RwhZ5b1MejPPQR0rWzuUE9w9XdRnwdrW0/aQJtPXDO0D+QqOCZlq/6/5QYkKLgRwXXYaeoWezGZX5dnHsmiWio1+aW9alGhLDpwwasF9aCSit8dqt1Z0yRzqxXOiSgjcq0AoFtTGPaFrABg2C9xcA6QP4AyZc1uSAWA193rFNQ4hOTTPRWUHx8Fk1Yd00jEi2dPTcOeLUlpUxg5S/jJqpgVX07ngmFmOezf7CWqItNtXyUPjXzOXEHO1/FhQ1psULQh6IY2+YdH8/FrKeasftouOE14/yp0h3yih/Jc6fmbDhWoYUQ2jacIwUHZNvePoixuvkx9nYtQrl9s0EIDChdjGd4igl6pmi+UhxVyvk/fdJIG/GHkPgpEGn/ITVSYOjxv1zl6W+JWK5wlFS3pVds+WtUzfv2H9ShHuuRxMiMqzkqoG+Ngz1vjB3l4/h8apW8ED1y4ZVCgVSFFvO3bquc4A+D6+2uXLlqBwBJQXdF79dQ+b7M2YmO+m36xhvZJaXGFsqSiI5f+sXdVJGsqWtyztkOarwjicFVd8KJtHdjAIZXWW9/xzQRhFZnyDMijf//7T0Mvyn+f2bUKVS6Q0WVX783YQX9KwiKo4EwciFOlOMsUUaUMC4d4SqXKUS4ShzKV82egmW+oxdlU34DgGgTVjHyIzPsK5HsSdA6CbxR+qFJnXS7a0wK7z9xwQLD1bFCEPoo+YG8ClAo4VOep7EIcRBTs+P1mWnVIRw66OsTl5d9vWIR61Cvs4PrCmPP0piRihQJA1ubJ6EhjIVfmitPiGr864P59AAAgAElEQVTfuv64lRbvu2joFyrDQilYXOAZTpPaV6B7jx5Tz5k27le+Fp8sPmhAKuMgFQeqsDsPHmuq4bIJ7kB3187Xcqr0b3ff5w8eP9HET2STDwOtAKCvqxMe9a0AYHisY8iNgj+AbvxfagNCDx/u6z6op31cAlIvrHaRzIQTV9k2H79GrSdvsP9JdYLIlRZF4UCQAz98/C/NjzlHyRMnpIYlsvmVf4inG4hxtaqQi8a1KmOfkyOXblP9T1cb5k1+4cXGDaRK70A//EOCQ9XN1LZu3H1IZYcb01EzpUqifYAL40p/4vSai1ng/jo8skmsi6DExrwGuk1VCtFnbcrQ8+VsfCKWBWYG+MYCvf76ehWqWtD1JjAwHlq9xOYMrDhwiTpP09EMXNlSxcckVCBj0y+ztkHuP2rBfsNlX9F44DQCkkUYhOyPjWlm/zdXu1QhReJiLsK1T47+AUoIVC5bTuiIs2HPRlDHasFxWMQVf/m6CGEdHtTIliYZbewfRW0mb6BNx3U00gvlctKnbcr6dXk5kgl0LhNWHDEEKoF83TawgUEEwcyJTxcfpC+llGyUcxaI50rEXWsVoP4mfG5m33jow0zMSPZTdagF2h+IqiROaOQnRD3OAw0VWCAk/xd9gCavOma6Dpx2xq8LxhpbefCSAXWWOXVS2vJUqOjuw8eaIFHCBAkoqlgWl3uB9Uev0MvfbrL3gJRWcOsBoSosIkcaTTQPAUDZVrxfh/JnSmn/0/u/76JZ287Y/+0qQNvws1V06KIeHDdTnAZNDHhXHzy2UTXhm3xjvyiX39a7Tt+gFlJKvnBM5qrD3z6YtZtmbj1tGJtKWZynIiMF/eKtBzTi7332ujIak1OqyCn1qvRbmWooNu8f0bYquO5uEFLFHwi+0IypkmrNWwHAQKxg8PdhBQCDf43C0kP+ADp+L6lB3S8jCI4HNSBOigz5dvAIycYf5CrSaaSk9P8jxmEu+SYqNiZbFjbxt3iC2QdtvWJZ6PtOOkG8CgFZvVBGmt7Fu9QEd+YJHzqvfK8rNoo6MvIQ6M2ig6IJQVJhKnU6XAORcSGm8osPHHzoCOP8PB+9WIraVMyjnXziRFMYDxy6M574Wqbexyvp2BWddBrz4AxBEF/nKbbHrTppRwooUkEtC/8Z4AddnP9OlT4o0yMEeob+3n2Oevyiq8GqUBue+qQ6yAIXVAJEAom0VEdZFMsffXrqY3wqz4WJkFEBhJ0sUuCr8rM/59OVgqygRTl44TY1+tx4YLqqTx2q+/FKAwpPZBj400ceZITS9SVJaRl9vVe/ML1Xv4hb3fKDowKZUtJy6ZuJNzJq/j76do2ugmwmyoZ6PEAlt1UqZ1qa97ZzcRTOMyfq/9WjOpXO5fhe4+UFWvCPHWcMSC8+JiH84NaE+ViIH0JAlESkq0KIb9mBS1oPIq3cWXdL9l0k1BGWK31yLfvi1Wn637CHwW+spcSNCmVqcJ8mkoKoY6MP0MSVR+1tNS2VjSa2M1fq5XzazrggQU8BxDcew2NblnGLlkQVpIJznJtwzML9DsFdrv6OevzwH4FzoHsRPBcmgx74AVXNwpm0QCoMojIIxMvmSg3bx9vGoTr2RKAtko0Hdc36VPHpy4dvVgDQ36sVmu1ZAcDQXLeQ95o/gLZfTUBIJxEmFNo4TDxfxhS0sk9dw/iHzdtLU9edsP8NaR1QLpMNCosybF5cwwsLL2f5RenPyT159Q7VHrfS3iQQCXuHqU83velXBY9HO2VypaW5kjKdCj1UInsaWvBuTW+6dasOV90TlRqUyKoFkGAqmLuzUy7Oa8HHydNXhMgLTj5xAirMl7QMtwYfRoVaTFhrIETH0GZ0rUJV3Eg/CqNpiPOhrDh4iTpLfDZwKJDIhjifgHjuAEcx4911VCJCVwUtjo8xEqUHcgqPX7mjBUyE+QNtz9tE2yB1T5ooodbNxmNXqe03ukqwCikSyDkI974mrzpKY6QMDaQjIm3xxt1H9qFDjbNaIXMRiUDOEU+p5H0L8TSgb/CtIafc4vBW5jYECg9IOsE/7K9x9P5tF83eriO1eLsIrM3pXk2JkFP5AGoAUKOINRn8TAl6tYa58B2nlXF2UMy/q+T+3TlkHTJ3D/0gCZuI+mY+cuVYgWIz44dGe0AJQgk8UKYKHgGliOf3y1N0NB/2AjFDG9qfXSr/uPIzvlv/92Jpg5gNaBAQFPxwjg5w4PQQaHvauuMGdW5ZJZj3ffv+I41rUTZX/K0IWCHYKQ5jXM33v//+p1EEPXyiH/6jDhfbAAWUnAWGMlB+XvdhPUMXnEpoXMvStOP0DcNvVg66/rjhBA2eu9fehpx6ruJexD6Rq2a7GqMv1zE/BfovMDThLg+hg1hW0kQUM6yRvS0rAOjLyoRPXSsAGD5rGVIj4Q+gFacf0yDpYSxeTioI/I7Bxpc556ER6EF5QsxSZVHGXV4FbyZYhYLzJ5S81aT1hnQb4SN/QaoCoDnSJqP1/aK8GZZbdfhJsqhUOlda+utpcJIHSFEGJNwpkiRS9sGVvuRTO1R4ddoWWv70hBX/FkTZfPy8nlsDiqeFVErO89+pQRE50sbTGYmbYfPgBrxwNyUkbjy2evXnDKgQfvKzkqucIpXuwAgjUbo//XGnLaQfAoGRIWVSTXW9Uv4M7lQzLaPiAJPnYM3hy4ZMAhVSxCcHrMqGGeAZGiVzpqE9Z3WhChRe9F4tKpotcOT5zpYI3FgQApHVPeXysjAY5xNGoEUOVjQrlZ2+aueduqszHz9feog+X3pYWQQBiPlv16DCWT2bz3M37tH83ee1lNCo4lkM6qm8Ix50UgWTRJ3xyw7TJ0sOmQ7HFQL5rV+2a35xM6On4dkIIsMD6wqRO1nkTbRZPHsaWhiLB93cd1XqJvjXgF6cy7gYEcTCt7qZccQqDlEQpObK1VCwBQpPWOOIbDSpgxHdx2mQ8mZMQasYmELUP3TxNjX8zIiAdbWW3jwa+W8MbfD92Mwtp+iD2cbsLZ75g3rPT1xHO07dsLsBRV/svxbuUfOR8nZlUS0eUP5/9q4DuoqiC3/Se+8dEhI6hB5KIPSqghSlKE26ShGQ3kFB0B9RERFQUBEERCAkEHoJhFBD772H3ut/7ouzOztv972X5PXMnOPx8N7MnTvfbN7u3rn3+8TDtvisNT5jCg9djTeqELHNv6ViqTxljm4bogZMZQAwPrvhfWNkAND79tQjViT+AC07/gTTuQeJesVzYM5HlUynZk1maDl/Tk1US35osXyJLf2bfqzFPkQSzN8geZD+7hmIioUS9mJiBLr4gGztgZi4Jyj4lTpFbEaDtSYqtrH+KZPRy1+s3D01UfKePhMVFK3NFdfvjbITeSUusWSLeBKPjldVHcU5RZ4PsYxBfKBkBORiluh75fNhWhuVIzGua0tM/XssiELYYS0Jsww8Of8K0OPMIW4h4hiSzfsRuP3ouRnPE3+YFHE6Bh/87H7Zb3HNDLG0k9fuPTXxTfGNsnsoy4fahmPXNeVxepki3n+lOG+FYlAhfcpkeMAptJIn7vYb1ey7rWZBSoYYVWekTRl7+CgqHIuoOqq0eemeSxjIVSvw81oS5LDXru84dUuTqWYpg05UihV9sJY1JpY7s/F61DxPnr9CidGhmoAI0QERLRA18dmQ2eK53eyFkTU7YqUK8Vr3/mOvhuqGbFij8NDLsJ3Vobwp2Mk3UcxGjwddzAqjZ206PGHvCLw9kcdQL6nCGga2fP/+7AjsPKNV+BUDjXpCMUysh59DVAse/05JrDp4VcPZObp5CXSuHpv9Kga6+UCx+F7i6HclI6yKDg/RHFbYevC+OOqihieSDmZWfaJWe8kAoC1Xp/f3kQFA799jt1yh+AP0y757mjLeVhXy4evWZUEnl9UELgb+gZ8WJ3I/0GeUcp8pjao22Of3vVgdbX7SSH1nfBAAUhd2RNMjYNbj0iAuvP5/7TeptVHpBClY2RKUrDB+HWIePdd1nec9EUsn2AC+fMre69fbF5qDL7sWT6qsvbC1/GE79nKnfO9Xym8qiWBNJDr+uGZhDG9aAn3/2Gt6GGCN1Aq/aFzM3kv2Snsipnp/X165cDdblN6pPP/C6mbuSnfsjICest+GgbVQJHs600xiibijM7ztvDybzOmpufPk5qGHrqHnwj2KLb1MEZsmkp1sQmDbyVvo8Ita1qg3KKHCLzY5EodORkErMehE4gG/bFO58PgpKBOPyn+JY9LebdeZGLTlytiZfcqeXfRxVZtLLOPrlx7PplH2F3E8U5aVUbNW/m0UtCN7YnaceABGZ9tHxjZSDsuNnvGtiV3EFydL42pO2YCLt58oXUh8YsfpGLMh1riUxfcHduBdfGQonrx4ZeiCnmCHXrUNlSCnTxV7eMI3SpagpAnWxACSvTAb8Nd+LNt3WTGXNkVSHB6nTQAQuW+pMwmfrP5US1/U7uedGoypjHzR7gsaIRMmCkg2RE7lItnTYsPAWD5xMQhOyvX09+7sRorgTFyF5jbixhT9EvkNa/hmw8JusfyG1GQA0Nk76Z7zyQCge+6L13sl/gBN234Ly7kbAQvckGqWeNolSsxXnhhuRpLMS54TmGKWIA/w0MbF0KOWj0Mw1+NzIal5KnHgm3jixKtVGTlGHBG+w0M0PDV8XyKtLpg1VgWMiNFpDrHtGlYXOTOkitfaKbODSq6IA4f44JL+R8ROxiigWXJ0GB4/139IYfOKDxoip5/oGAl5kKAHa+w6Yf8WVQmZ4Ih40jyyWQl0tcCDEy9AvHSQXvboyYmNbeYg8lJYnL6sR89eosKEdXj6IpYzhwI8VNZhK+eO0x2WE9odATEjgC9ZNHuhsUL2b3fnnGBQTwk+clhd5PjvHrbywBV8wnEJ62WKOMHNRDOFNU49d+RgNBKEE1+S9RRq2cayChVHbLTeoTcFRkL7BSF/ljSOmFJj897jFyg7Tsv/tm1IMPJlNp+7/vTNOHlDVYoVnaNDfDrMN2oB49biDscXyfcTD+bFkk1RzMRIUGRYk2LoHuSY53ujdZG6LQUsrbWJLUqhfZWCht1IWGPudjUI3bpCPkxtXdYkUEFcg0ZNL7OQMiiLjwrVDBHfk9iXYuCR5+22tqa4fC+KCBXIkgZbBms53vUELfhyXTafGIymfZ+95SxuPXymuPRbl8ognlJqlrLFxe9cdZhWclQoHnHvUMT9Wb5AZqsQi7iKJfUyAGgVwkTRQQYAE8U2u98ixR+gkeuuYONxNbAzqKE/+gT7mgJJpITEc7bwLz30fdHha/CSZ2sGIJb1igSxPCIfBRbE2HdKOQQkUZSCJqHU9I6BhTTziYEwW4iL9V6GeKNLe1UDcYZQq/HVBly6Y/7AENqvJorlyhCvtfMnrp2rF8Lo5iUVO3qZGvwkrHTj+42nMDXsuPKVqF4sOiYG+EQsJ4UcxewtZ5RhrNS3zrRNGmVCR2Z9xgtMNx4kPgxaK9N246V4vGu/7jiHCauPgPio6EWgSWntQYLHL1AuwCIC4gvznx9XRaBPVtMYUejJ0SJPrtgqPXJ6PlNIxEAvU8QVfnvrnHpZRfxamZibO61fVGllvonPgXqcq6wvCQy0rpjfIcsiHrvy49fh3hNVSIX4zIjnzRmNnqn9SZzhpSrOoBd4oH7Ep8gHKCgjkoTdWBtY3w+f1C2q6/bLV6/hO1yrcsp3FDP3xINIkf5Fr1SU7OkduDsaR5GL2mg+a2rOQ/4+iL+iLirDGSZ6wmz8HEaKtaKyr5GYm1j14ah3JJEiSU+YRO9donKhLFjcM1ADa9f5uxWFZfqC3iGJVornheQFRsTs5WzpUiJqRD2TTZHawJpytqOupzJjwnD/qfr3ZCtd1fDl0fh9l8oJ2b5KAUxsUVpxUwYAHbVjnmVXBgA9a7+8xlvxB6jvivMgEnPW+JOxihPCNac4C7pWRs2isac4eqeV9PnsjhXQoGQuxZ4e2Sz70lGnW2S/1tSNOB/zWLNvejf9mRtO4uu1WjJlsdRZ3PwzNx+izrTNhtcEw8D0QDfCXG2LBvIvkHG5uESVLEr+I3EWxsW05/xtvPdjhKHJnzpWQMOSuSBy87ETTqOBpHTZfs4u0/VA5NSLewQic1q11Jtu+HQSzBp7SCw9JgwPuBtpfNcdF4y8pa+oCiiJ9V27s+zFzJmKdK5dsZydISAe5PAlZGLGkj1Ud90NeT2Cfb4iwBKxu7utxRv8ufPoOQLGrzNcCpWt0j3anRqV0pcevdZMfZSUVD/kDmZvPHiKyhO1fJO0DlI6pUABTzFj7/WRYuu4VUdM1R2kPjzx3VIWhTvsPb/4O0OUNI1Kqc/TpmfvJy9Qdqw2U5DE1baevKW480Hl/JjcUqVo4f00wpf1EctORb64AfX98CkXXDQKRi/vXQ0BNmRN2RNDPdoUPftiYEbsI3Ja9w32xecN/dF5XqQmYYIfZ+n5TBRRMToIF4XfiC6HaHPs3ej3g641FkQe/24pkFIv3168em1K8uCbnjK1yFXdrUZhzBFK+PnDIjOl3FTJED0mVilXPEhytpAMWysdBBD3L2tGAVtr143ICSkDgPa+kj3TngwAeua+ebzX4g9Qhz9P4hwXKCPFQJbdIvKE8OTLRkEwpg7GgKIHFf5ElQfQUVkCRjL3eqdpemWW/GmV3oaLZcNin0ktSpseHi09pBOhcKNScc8iWrz7IgYvPaiZ8tculVHrv/T65fsuof9fBwyvU5a5Jyo428LNR/t4+c4TEGcHlR/zbdbm0yDFZ9aC/bPjxw4VUGykbaUPHv+H5YAFiCVT7pjV4YBlS5MSAbdDQORVJUXIdwPymvwkvjLiLWNNLGl0u8XEwyG9rCG+jG3BzvOgLHHW9DJF4jGtHGKAgLUsrsalcpnuv+7W9CpCRL46OjgtM3at5uCQ1kFBrgVdVT4tR62NFK9JddjnP45PR82jZ1fkWtarWjl69T4a/08V6CNOvk+CfTFjwynFJD0P0nOhXhMF/sQ+RClzcHQDkygL7UW5cdqsSPGQn563iXZG5MbjhZKcheHkkKP4iatEMZq3QYmcmP1hRUO3RCGaIY2KoVdtH1gKMAYWyYo/u1fVtSlS4RiJyoiBQp47z94Y0nX+74ErICETSgrQa2LmYm3/7JjfWXtdicFS4nUnu3w7Ok7ljBTpC+hA9cSExqbuYkVWQIFMWN67ur2XbtWemPzye7cqqO6bzeo4kfJqeJPi+DioiDJOBgCtQpgoOsgAYKLYZvdbpPgD1GzuEdzluEB4oQyRv44/JRJPcdhK2Y2S/k0PBj7DQzTqYTwijiJ4NTrhbFYmN2a2K6/ZFOItIv4ivlkLzoUfuY5uv0UZbi4rvzh27T4afas+qPED4ltaIpYmkM3P6hZF//p+JvPfhp/At+FqJp7oJDuREk8axRtVXK9cKpEc/e9hZVjVIlkwtVVZ1JyyUWPKqEQirvMlhv7i31jDkjnxU0fjh9bEgIlco0TAFQi0+nEHojguV/5eKNIpuEL90tGYUCCg8NAQzTRh/YLgnyu96bO522Izp1jTyxRxtI+JzT6VgfJln/z6rWU4uQqrYcujQQdbfNPjQ9bjcpvwbil0ELKUXLUOR80rZlN9WscXAxr4a6YTedIo84yeu3kFY6rSWDeglq6bxB/d8ZdI5TtSkKbgHU/nw4Kyetl9WwcHm3EiiqWxFNQ5Nq6R03lyRfVeo32yFlhqPWsHdp9TubtZIHbymqP4abNKdWPr35z4ntGjVhEMbVxc4x79xhJXIOMapi+X9AxEpUJZHHW5WbUriqroVW31W7TPJKLIGj378wrDqZInwbHxsQE+alRN1PDbLZq5z05uYsq0nbf9LMauVO8jloKqVp1PQIeqk9bj2v2nigU+ycKSWVHpfEqrMmjDURbIAGACNsWLhsoAoBdtpicthf8BOnf+Amr/eFAToAv5tCZK5InlptPjdiB+QGqi6h/DgBeHMCoT5vEyUjlLCKZU0vzu99vNTOi9lIgKVjTIWjBMlHoXJ2J8ISSaQeIZem1wI3/0rh2LZVyaHvkzfzIuqnuJtpnKs5jRMr1NWbQsb0wabc1HMTOxbP5MIDUwCiKzlpIeCsc3cmpJjTW/3fl7eiAcsPiASaSHlBKp7LBU3ozu7LL0TSLglQiIIkh8adbXYccxc6OafaN30OQNoPgOC9EECVZ/WgMl88T+Hokv3nqZIt6AgTutodrk9bhyT31J5X2jEk0q1XS3Jmb4EHcdKaLSyz/fBizej2V7VZVS+poChTnSx084zd1wMPJH5Fp+v1J+fPmetpRXzLalZ60hjfzR7mdVFZpwPTQ2tqxSbP/su4x+f+1XPibBHqroOHjpnvIZHWLTs/5H8yI1pcVkl7IDRQGswX8fwOKoS8r4QlnTYNMgraiEM/ZgSdRFDPpbWyFD86ZJkVQjjJc3U2qT2rFRowxLyrRkjT0fi7Qs/Hh63u1iIHAnKlu3DMiL6W3LaabXqxgyEoFxBpY0x9szt2mui6alc+P79tokCjErkiqEztx8pLgoCnmcu/UItb/epFnC8QmNkDJZUvy46TS+CtVWEs0TMg6dsXZR7GVep0oILpbD6tRiCb+YLSsDgFYhTBQdZAAwUWyz+y2S/wGKPn4azeYe1TgZMbQOcmdMbfpMfAjjg3tGSm1M/IHGX4h5jKCp2gwwEZENA2uhiJ1LLUQiWTZnsVzpTYpufGv4zRYcv/5A89mHgQUxzoI4iaWHADLEXgD1ynXZRN2DimBYE+0JoLWrxSigmj5VMhwYFftQJmZtZk+fEkTmyxoLFlaaGK753NYTLiMfKeWfyopZ88+Z3pSV2HPhHuWzfJlTm9RTZYsbAkTATw9HknsubrjJ3hIBeyEgljl9UscXA//LzBFf7qzxqdrLJ2fb8R+xBs84gQImKEV+iFy6juT3dfa63XU+8RCP93Ps2yXxUTWt4Jk7rIMUVGtP3aiIyxmVKosB5cRSUv7d+pOYtk7lpNYTZ6MACQVKWCO+ZcoArDVVG1QxqraYs/UMJqxWn/ur+WQ1ZfLO235OsUkBfKIy4PvRl3oBSfqcuBPHcJlblkqQHXkdbjx2A53n7zabgt5dft6qqvrSs9RxC4fRInc5485euueSJtOSn8jSM7RIkaNHE3H4yj00nbFNMUlBbyqNTZ40iSMhs2hbVPh9t1wefPt+gGbM0GUH8WekKphCGaUPOEEakerp6r0nCJy8QWODXavfrDuB/3Fc4q6iMhA55HnOX0uAiSXTxMNKfKysyQCgyy5lt5pYBgDdajsSjzP8D9C2/cfQ/k81c4FQ4DPyxq08grnb1Zsm/2IjPqgwBPkHFlH1jUic6SSOV1eylVshLjskPuCwsTkzpMSuYbFqU6xVnLAOtx6qZK/0OfHXWTp1Iq47uqEbNUqBX9Q9EEYY0bj4vCRuPH4DneeZP9yQvbX9g+CXMz3EwB6d2K2Ovqq4Sqe9oZ8FoeiINYYqXXHBmvVdd+Q6PubKovNnSY3uQT4aXihrZRfxmVeOkQhIBCQCjkbASBWS5hXLGq0dIDnaV0fZF0tOebV7UQRKL1PEUX4lVrttZkUg8txt3eXzfM3uhg8d0P689Ywpq31Us5LIldE8q4+yoShLiHiHiZOOxMP4F2l3W5O9/BHVWUVBDppHLLnsWqMwqKKEBOf4FtqvJorliq3m4Zv4/Nq8bB4QvUjfP9QD3NTJk5qeD4kLkbVcGVJhzWc1NeJv7Lu7j5+bhPFIOIGE6YiqhA4BnN0OXLwLKh/nG1ENkYqrKNxnSeyvwvh1iOFEINh7iqVncEvZeqK4BT2Hr+2vLdEWqYX03lecjedni/ZhBVfeq/feImatij6KwU66Rkhkg2+7h9cDJSuIJdYtAvLiGyFT0hkYiFyM1mihyCc90RT2XsZ8lgFAZ+ye+88hA4Duv0de6SH/AxSyMxq9lp9X1ilyNfwv/CS+CVdPI/lTfT3xDDJULn8m/NMnlrRVlHvPmjaF6Uf+2DU1425qqzJozXEk2AN0UeGW2Uye9C3TiRorN6EHnKLDQ0yKb3yzJrYgvgxmSpNcw6PI+FdG/BONhTu1fDdsnvjwRE1fe1xD9Kx5qGtZGm+Xy4MSo8I0a6FMAJ6bL0OqZNg8KNhMQXDn0Lq6D+K27sf2U7dMKsGsZUuXEu0q59f4a4142da5ZD+JgERAIuBMBCwdhomZ8j2CimBoHLO7nbmW+M5VbtxazX3ur+5VUaVIVpM5MStJL1MkvvPKcfoIdPs1CuFHr+t+yXM5eyp+VPFAAReivSChgsTQRH4/vSCQGPgd2awEKAgoHmYblS0OWnIAS/ao5bpEWUOcdGJWFo83ZaP93rUKqlkQQqDAzqbjN0wH0a6iKiFhC5F3mlRpScFXFKQLH1DL8LryG7EGz7lsZ3qnoXcbMamBYUTvTkfGGnMeiu9CmdMkx75RDTSX9IKIcxi5QuXR5t+lXHXti8Fivcolo/ct5jMFmOlAgrVHz16aRGP4xoKn4nulJTVrR2IiUi3NbBeAZmXyWJwy5uEzVJgQrukTSbQFGdQDDhkAdOSueY5tGQD0nL3yKk/5H6BFG/diSKhK3konfDuH1VXWKwo70AkspTRT0xPPoM8LZk1jCjBRW33wKqh0ijXihiiYJQ02Hr+pfNa/nh8+q1fUrhiLRMq8ceKbSZ8quekjvR9s+twaVx1lulHGG2uVCmXWEAYzcROxH+8HjVnSs1qc1i0Kd/CD21TMZ+IfEUVHSNG42XdqWQGNWdm3BprP1H7GODji5BDXWVRGJq6Y5mVza0oDOlQtgAnvlo7vFHKcREAiIBFwCQJihhtfmtT79z0Iib6m+OWu/GsJBU4MMPDZ+xNWHcGcbfrVAgmdV47XR0AMPPO9eIEWiZ/nIHDo8j3N8xplP9KhNf2fNZFn7Mf25dG4dG4zvjYj0ZQu83djw7Ebir3PG/ihb5Y5SoYAACAASURBVJ2iEHnPeNQ85VDj6YtXoOy9R89fKe6v6x+EojnTo8yYME31EWWVBvrEHmDwTS+TK3xAEHxzpAeVsBNOYiueO4MpO9Konbj+AA2+0Qpf0L7ytC56pd0/tHetkjf5/c7M7SaRGMoKJVESMbg7KeQoZltQXv4osCDGcpRKlhTlv1h6EIt2q+XEjE/d2X/BIr2CLWrMp28+RN1pmzWuihz3MgDo7J10z/lkANA998XrveJ/gH5aHYlJW9QHAZEjb8X+y/hskUoWTLxuYf1jOfT0xDPoc+J/iP6PfJjU3qg8ijUqAS2ROwN+51TgjDhFErIRzb/bhujLKqExb2vzoNoomDWt6SM9NSrWVzy54W2ICmEfVC5gkq9njU5LT05obOLjO8ARK/M26ESbTiBtbZStSA8w/IMNP5ayDumUs8cClXOP1OE2fF4bVLrFt6/eK40hS9V9IQ7B6DH6hNG2+nfkyn00maEqHtMDa22/7FjPPWg6Ithrq3+yn0RAIiARiC8CIu8rL7zUeV6k5lCL+Lh61faJ71RuO86SMuLoFYfwa4RaTUD3RFK6l81xCBhVYdCMrKTOcbNLy45A4Mb9p6g8ab3GNL+X9BxIXJy8Yi/j4hQPvvsE+2BQw2JmboqKvfR3Sn+vxOFMXM5iIw635b2rewwHMU+907OWD0iwiVrdaZtwmhOnmPFBAN4ua57VReXM5cZpS1QZNzoFGMVMQrJtTfhJT+CDREhIjIQ1vdJuyu50daOsyqjzt1E2XyZdvvYpocfwA8dJKfrbr15R9KunFSTyGRaioSBi4pMiBvz+ORMHUc3XFpFEMQmCAqZHxzfSuC0DgM7cRfedSwYA3XdvvNoz/gdoytLt+D5SlboXJdcpnb8TxznHlyNYIqBmJ1uiohMRC5Ok/dSw4wrG/IuUvYAX+Tt4u8t6V0P5AplNH+04dQvtuLJVvt/SXoGoUFAlb+W/Ex8kSP1r3CpVup76Rg6vi7e/266RkudtZEuXAlEj6tu8ZDHApjfw0zq+mpJblmUocjfRiRz/slY4W1ps/Ly2zb7odTx76xGCBWUvOhXlldQmtSiNdlUKJGgeOVgiIBGQCDgbAfEwq3yBTFjWO5bq4v3ZEdh5RuViG9O8BDpVL+xsFx0+n5gh9MtHFVG3eCzP19Bl0ZpDMDHrw+HOJcIJRMJ8BgE7gEzmQvGARLgddlkyZUcRP/MbjpaGV9u+fv8pqggBQnrWJHVksRRTT2mWnBT/jpnAgVjxQ32ptHXVJzU9rgT71I2HIM5xqkhilD/i7/SIpsXRrWYRs327dOcxanylFS/kBVVKjgo1O4j/rG5Rk+idUXvzhgK3oRpOxeW9qyHgv3cRGtfmpwhEnlXvI0b+2eVCs6MRMTteND3unZL4MFArSFR8ZKgpq5A1hkXPBXsQeljNptcLHtrRdUNTYpB8SqsyaGOFqkos3xfVj2kyGQB0xu65/xwyAOj+e+SVHvI/QCMWbsKC6IfKOkXFpX0X7qDFDzuU76k09viExqZ/V5wQjlsPVXVZHiyWPSfyR7xTLg8oCNj/rwNK97hmwlnbFKMTOjaOf2kRlWt529+2LYd3A/LqTkcEtsR3wtr8zpVMymP8QxuV3hIZMZ3Y6jV6ODk5UeUjtLauBTvPawQ16OZy69FzDU9JoaxpcC7msWKKKTLXmbYJZ7iTT7FkuWLBzPi7V9zKkUV/r917iqqTtSfXaVMk1Two2aqkZQ0L+b1EQCIgEXAmAmI2PE/i/u7327H/4l3FHcqwblvJ+w46SL2Vv78wZUxa+OdLDuBvjleMeLdGuEH2ijOvEWfP9cu2syAFarHp8Ys52zc5X/wREJ+t6fmytn8Ok8G9F+6gJfdMniJpEpNwX5Ikb+HnLWcwMURV961SOAv++o+yh3lDgSjKYOPVvNmhuFh+TGMmtiiF9lUKxn8xbjRSpC0i3sOhjYubeahXGXR6UhOlDFtUCCYDRtmEvHEx8Dq7YwU0KJlL6VJzygZcvP1E+fcP7cujSencboSgviuWxA5phJ4gkcgnu6h7VVQtkhWd5kViE0cRRdmblAXo7Nbyh+3Ye0G9p7MsWUt+iEIvemXhMgDo7J10z/lkANA998XrveJ/gD75eS3+PaUGssSyHb2sLnrYoIcOUUWWB46pj4ny8KSOSDe092fvVLpTkOjQ2IbKKV1CN+DMzYdmal+8Tf4kZ972sxi70vwBmvoPrO+HT+qacxO+fv0GvoJwCJ3QdvwlUhMU/KZtWU2gU29d/KmitXX3/2s/lu+7rHQjdazzMY80NynRxoD6fiA+KrFcm/j5Hj57qXQnBThSbUtII7W+smPXWjTBiJQTMo8cKxGQCEgEnI3A+qPX0fXXKGVaKt2iEi5q8eELcrb/9piv3vTNoMwa1r5vVx5Ny8S+oIpqkVQCTaXQsjkOgSVRFzHo74NmE1gTMXOcR9KyPRBo8r+tOHL1vmKKf2ZddfCKRq2X59wWvyuQJQ22DI7l42aNnvtESpgtg4JRIGsaUHCQhNx2nI4xdacDezoIZxl09libK21QsJyC5qy1LJ8X09uUM3PJWilnix+2Yx8XHCIDdOBvTfiEEgJIpZg1nqOR3iv8R67Bi1dqwoCYIehK7CzNLVZ6iX15rlj2XZVJ4bh+X00g+bVLZdTyy+422fSi0I4Rnya/1rnbzmoqwcSKOuorA4DuehU71y8ZAHQu3nK2/xDgf4A+nBGCzZdfK9j0ru2DwdxDux5vxa5hdU0BwABBxp0HmCnQieTon9TxResK+RE0VZtef2BUA2RMEyvMkdAmqm2J9oY2LoYe/50ofR12HDM3ntKdUk/unjoa8YN8+EskTnIvRx/XLIyft6oPG3qTbB0cjPxZ0ti05FpTN+I8l903/p2Spn/zxOuiIUZcKwYPxX724GsixTRSTrPUdnxRB3k4zhObFi47SQQkAhIBFyOw60wM2nIHV6SmfvA/3lTxt5nPjHOx23ad3lKgM7EIodgV0AQaCzt8TcP5y8zxYm0JnEIOdwECYhbUoIb+6BPsa/JEzPKrWiQLFnWPFeYTswOTJ30Lx8c3NmUHskaHxrWmbtKs6vDYhkibMpnps2cvX2FN9DWkTpEU9Yvn1Ix1ARR2nXLW5tOgqiTWjOiHtpy4iQ/nRir9sqVLiagR9ZR/d/t1N8KPqtzp9MWRcQ2RJkUshkZNFAXkxaJuPHiKyhO1FTT0rpWTU5C1Kxh2NDZn6xlMWK1mnoqmGb8f/7mYRcmqg8Rs+i9blsb7lZ2fTS+Wi499uyQ+qqYtYxbXaUkojPWVAUA7XngebEoGAD148zzZdf4HqMVX/2Lv7STKcoY3KY6Pg1RODCpfJbJWvpG6HAk8UDaAUWOZAaJqLXFadAwsaCpB4MtlST2L0qXt0RbvvojBS81PxZltXs1MzFDk5+cfrPjP9TIMKSuSHtp4Hqjqvlmx/VTsSSo1UliOefRMc8L3b9/qKJMvk9VlU6k1lYXwjbIOKQDY+3dVZVk0xEo7xFJssR8FZgc28Lfqh6UOdHrsO3yNYckzjU2o0nCCHJSDJQISAYlAPBHQU+c89R+FQ+WJ4bjxQM1m+K1LZQT5ZY/nTO47TCRGn9a6LN6rkM/kcLdfoxB+9LriPFMWdd/VeL5nO8/EaKop2IpEKhfPX2niWsGgJQewhCun5/k0ReEXPotNj4aF8QMyBPecv433foxQANUTKvBWtJfuuYSBS1T6IVH0kK17TfRV9OKeq4laZ9MgNZNyyN8H8VeUqlTLZ4Nbwm748miNAOIHlfNjcssypiGUGUgZgqwRRRDRLfHqz+66L/O3n8UYg0oq8nnn0LrIlTGVxv360zdrEiZmtgtAszJ5zLLpLVExORKPDnN2YdupW8oUJMbStYZlXl9zISx1f5khGQB05K55jm0ZAPScvfIqT/kfoOCxf+PMY/WHWY/otPSYMDx4qpaLLu4RaCoV4LMhRIAoO61jYCGILwzMfqWJ4bjJvTDxvHwJBduIGJvZ5TP7xBM5fm6jm7rRA1SfP/Zi9cGriolMaZLj7uMXyr/L5s+Ey3eeaHgTWdq7tTWvPXwN3Tl1XyqbPjC6AW49fG7Gu8fb2jOiHrKmSwlLpc7U316k9aLYCO8L4bF/VANrS5XfSwQkAhIBt0NAL3OGDn5SJU9qUme/z90jl/QMNIldeVsTS9h4rsOP5kZi84mbypL5THtvw8Fd1mMkDNa+SgFMbCEVmN1ln+Lqx9SwY/h+42llWJPSufBD+wqmf4tKv32DffF5w9jDW0sKwcyYmDWaL3NqbBsSS2Xg7U3M7MuaNgX2jDQX4hNL60kFefWnNRV4RNVbW4UM/xd+Et+En1Ds1CueA3M+qmT6d+ihq+i5UD3MtzWo6A57tnDneYz455ChK+w+yXcwOkwiIUGinmJtVofyaFTK+TyI4v1sWJNi6B5kmYtQpMHQUzCWAUB3uGJd74MMALp+DxKlB/wPUIUvFuHWm3QKDnoiDSIxLRHX0oMGf0ImAtm/nh8+q1cU4lhWGiW+SIx/txQ6VtUnGqYy5K9Cj5nUdOkEpmZRy5kV4ump6FvdYjnwS6fYm65I9Mr3paoJOoFLLijphR+5jm6/mXNBiSez4rwNSuQ03dj4MmFWomvtQpy85ih+2nxG6VbNJyv++Liq6d/VJq/HlXtPzUwQz1/0mAYm/hbxRFPsrEfSa80nve8tCcMUzZEO6wbUio9ZOUYiIBGQCLgUAb0s7N3D6yF7+pTwG75Go+5oCx+USxcTz8lb/bgDUefvKKN5gQCRZ3ZUsxLoYiVjIp5uyGH/IaCnVkpf8aWFEizPQ0BU4+VF2pp/tw3Rl+8pi5rUojTaVVFLJEWhiR/bl0djTkhCVDOng+kVfWLVzL29Hb16H43/t1VZJqlln9B5xhcz2sSSejHzliU8WMNPxL5Mvoz4t28N0zCRP46E+pb0TJgwnzV/7PX9X7svYMjSaF1zaVIkxZFxjcy+e+/HHSCuRdbYdRw4eT2ucu8z8zpXQvB/Ajj28tcWO13n78b6Y2qZN/HZEq+tpSYGDfUETGQA0Bb0vb+PDAB6/x675Qr5H6Cin/2G56nUTIW/ewaiopC5ID5wUBYf8b1ZOvHpVK0Qxrxd0iwzgik99Vq4B2sOqVLvIvcgD9ynf+4DqfVSo3KFzYNqI4cFXgyx7JiIkC/cVpVxAwpkwvLesQ88IneTuGGMHJn/fHHURQzmiLdL5c2AVZ/UhDUlLApwkrpY5LnbijlbM+9az9qB3efUmyVfsitmHjLjJXJnQMhnsaeWIqmxuE7G2ZjQC1ZPHY3Z5IOWCZ1HjpcISAQkAs5EQE9dftPntUEZNER9wLfwAUHwzZHeme45ZS5LvEjiPcrSoZ5TnE0Ekzx4+gKlx5gLb9nCV5UI4PHYJYoHtryYR8UJ60yVH6yJARLx71AsXRSfU/kDcY8FzEbHYx4+QwWBSkePZ+/7jacwNey4YrVOsRyY+1/SAH1IFVBL915G6KFroPeJ7kFFzBIF9FwSkweIFmjnsLqmrhNXH9Fwhr9dNo9JWdgTmlhazftslMkoHhiNbl4CnasXRsC4tbjDVU79+XFVBPpkdToMYnWYLZQWYmKLHn+hDAA6fSvdckIZAHTLbfF+p/gfoLy95iNZhmzKovVeXDr+sgtbT6pcCMQT+Pj5K00qu4ga3byIu8FneIiG64+pA49beQRzt6sCGe+S2tj7+je7CuPXIeaR+sDDZx3o7ZaYQk7lEyHRarCRV02zVLJKtvXUq37afBqTdYiExdM90Te6gRy8dA9rj6g8Sf3qFUW/en4WLzoKtlIZ9rOXqlgL/9BHqmakbia2RiVzYVbH2LKRy3efgE6GjRrxOvrnSvgLa8NvtuD49Qe601jaY+//q5MrlAhIBDwZAXrpKzp8DV6+VlUaKdOvULa0Zqqa24YEI19m28SdPAkTkReJOH271YzlDLZUHuxJa/QkX0k51Hd4CLhL0uQ+49PypLVIX1UEos7dRqtZKk9fquRJcHRcI9MzIPFn821t/yD45VSf3cQyRKqaoSAga2KlSpuK+TClVdlEAT/9vRQdoeWp1svWpoojUrZlrXnZPKAqmYS2g5fu4u2ZWp4/ykAkkRbxIL9HrSIY2rh4Qqd0yvgV+y/js0X7decqnTcjVn4Sm+XIt87zIrHxuEoZwbLlio8MxZMXr5SurlJCFpNUbHlXqz11I85xQo165csyAOiUS9LtJ5EBQLffIu900FIAkJSuSPGKb+KNiThH6OT514jzhgARJ8bMD8qj7Djt6XTE0DrInTE1RNUoI9W6l69em27YvGBIszK5MbNded256SXNf2SoKUORtcGN/DElVD3NS0+lsWMbQi+jg7j1Hj1Xbz6TW5YGKeTyTSzHZSd1Rop8bCxlTu45d0dDHswyJS1dafsv3gUpY/GNV00Wld9YP17sxJpCr96+x+fqF18CeRukijy8qfogGh/7coxEQCIgEXAVAmXHrsW9JyqvK2W0+2RPB+K05RvjXnWVn46aV1Qn5Xn+mvxvK45cva9MPb1NWbQsHysQIpvjEBCvSZrJXhn9jvNaWraEwIWYxwiaulHThehcKPOPDrj5Rp+nT5Vc+UgUfOP5A6mT+DxvqfrGG3epyqRwXL+vCjbN61QJwcVyaJY6asUh/Ma939A7AL0LJLTpibSwe4VIR+RJWbwh0VcNxQhJDItEscQmclkSbdSndX1RZJg2acSeApFx2b++f+zFKo7T/dM6vhhgRSix3Li1Gt53vexFGQCMyy54b18ZAPTevXXrlVkKAJKqYTKB805UrupQtYApRZsXvMiZIaXmpkrlp7M6VDB7iDkyriHSpEgG8YaRP0tqbB1sTkR84/5TVJ60XoNntnQpQNxLxG0nNj2eJrr5fDg3UtOVTt1uPHiKGl9pH7JI+ZdX8tV7OBIVwFgQz1qZLQl+7Dh1Cz9tUbn83imXB/8zyHxkDosZfr450iGc49J79vIVSo9eq+GgorFipqRYOsLsE4ynJjaxi9qYWCLGg24Lia5b/+FI5yQCEoFEjYDIr0XiVZR9U3OK9j7C7nPeBla3X3cj/KjKizSooT/6BPualimqOtqLV9bbMLT3ekSeZbJvr4x+e/sq7dmGgN7h9PqBtXD93lO0m7NLMZI+FfE8N9QY/S3iHEatOKx8Vi5/JvzDcfyJz2i2qJva5rVn9BLFJ3ghI7aCAYv3Y9ney8qC7HV4/eLVa/gJCQ0swCVy3xHXeoOSuTwCVFGkkHfaqPKHp3ai/vSuRbzx/iO0Ga4bP6+NwtnSOh2Hfov24Z/9sdRTzL/BjYoZ+kHZpWLFm17wUgYAnb6VbjmhDAC65bZ4v1NGAUC9hwlCQ1Qkowy8mIfPEXEmRgGrum9WbD+l/jt3xlQgwQ8+3T150rdMhLsUuBOz2ui74+NjU+H5dujyPTT7bpvZpohlD6yDmGKfNMlboKzDyhO1QUTi/aDTOMpYY418+DCwECjgxppe6r/IDcEET/RUInnHqfx547GbJkET1oxOx/hx4olt24r58VWrMhpM9MRMFnatghpF1fJuMUODGTBSQovPX4KY1s/b+KZtWbQIkBkh8cFVjpEISARcj4BIcUAiTnTYVf+bLRrnzkxqYnYvc733Cfeg54I9CD2s0mmwex9ZNi9/qoBGpTzjBTbhyLjOghjQIE/sldHvulXJmYn25QGnLE7ZRCT6Mojjny6WKz1C+wVpwBJ55nKkT4nI4fWUPmKg3lYhOm/ZEfEZVY/bTcxOs6X801Z8REojSgyo7pPVFBjkS/k9SUhq47Eb6Dx/ty4EnasXwujmJc2+E8Uau9UojE/qFgVlNPONVY3Ziq+9+olBYL6iSm+Oe49fGFa88f1lANBeO+TZdmQA0LP3z2O9NwoAGmXh/bzlDCaGHFXWS+W91+8/xYnrD5XPKAtu/o5zyr9TJkuCOR9VRMdf1Mw7ytyLGlHf1Ecvsy9yWF0zcY9Nx2+g0zzzG4tRenzooavouXCv4gcR0G4ZHGziyeHLiOlk5uq9J+gyX1XzJUJeUnka/a/x6SkZFpUQx71T0hQ4fPjspRkXFH+R7BtZH1Qm/MUyVS2LVwHTu6CopDlw8gaTAjJreieWE1YdwRwucEl9RQGTLvN3YwOnasXs+eVMh7X97aPO2+f3vVgdfVX3b0MMSHrsH5B0XCIgEUiUCIgHLZRlTRxH/EFXimRJTAdd3tjEsihejErMjpzbqSLqFMvpjTC41ZpEMn17ZvS71UITmTN1pm3CmZuPlFXTwTvRD/B83MH+2TGvs7a88vCVe2g6Qz00p+uBDtfpd4la+fHrcJvj1E5sz2ViBc9HgQUx9p1SmqvLEtdpQi/DRt9uwbFrKk82lWgTNY7I0e1JNBJbT97UvOvxGA2s72cK7IltxD/RWLjzgvIxiSTS/USs+KL3psxpUyQU9jiPH/z3ASyOuqSME7k0RYN6CSDE25k6RVJNVxkAjPNWeOUAGQD0ym11/0UZBQCNglGLd1/E4KUHlYVRvyt3n2iUyEjxb+Q/hzSLJ847Xi23SPa02DCwtqkPpUv7j1yDF69UQnU9stclURc1J55sgoYlc+KnjhXNwBbLZSsXyoLFPQPNlKVI3IOEMXj/SubJgIEN/DRBQT5oySarO20TTnMPZqzUiYJ1RNDMi3WwMSmSJsHxCY0Qdvg6ei7co/jNq7vpXTnbT91Ce67kg/qs6x+EohzpM30mllQnS/IWjo1vpCnnHrrsIP6MvGg2DZU9L+oeaJcLd+DiA1i6V71p8kaNsjbtMrE0IhGQCEgEHIwAUUlsOaESlxMHHpXYtZ29U5k5Y+rkODC6gYM9cY15sSyqZy0fEHk7NeJBvPlA5dZa0LUyahbN7hpHE9GsYlZm5jTJsW+Ud15/iWhbYYlOheHQvkoBTGyh5aa7+/g5yo1bp4Fq6+Bg5M+SBnqc2q7iWHPVXn4ddhwzN55Sphc5EukL4tymKiXW9LjA4+u/SKlEdpqWzq05OKcECnp+16M5iu+8jhwXcToGH/ys3gP5uSa8WwodqhY0m56EC/lqKxKj6Rtc1Iw2Si+I5si1MNtDl0Xjz0g1QGmNr/3AxbuaijIKuB/X2UMZAHTG7rn/HDIA6P575JUeGgUAjcpRRXGLfJlTmwKAfLo6kaG/z70EEXDE6fADp6QVUCATlveurmAqctf80L48mpTOrcGclLj4kln2Jb1k0cmQWDIs3lQY/4R4mkpBu0t3npiV445sWtysnEvkcxJPUHmlYDELgvlLmG0bUge7zsRoXhaNyq7ZuK7zd2M9l7VXJFtaEBeM+GBA6ec1pmxQSkZq+WUHlRbw7dvwE/g2/KTZNd20TG58byCqEtc/APFUjx/vqpO8uK5B9pcISAQkAnoI9P59j0ZRnojBKxTKgo84jlnKJN85rK5XAvj5kgP4e496wMNzY4kE6PRMULVIVq/EwZ0WJWY0iRzB7uSr9MV2BEQ1X72RPAcn+54OokuMCtMoqf7RrQqq+WYz8V6LdDjEp509vVb4z3YvPa/nrzvOaap8KhXKjCU9q2kWIpZJz/ggACT2Z4928fZjNJ2xFfe58m7RbqGsabBpULA9pnOKDVG1mp9U772Ovp8Sekzzfkh86MQn28BN6DQooWXBTlXokjIUKdHFqG0+cVPzHCCW3rNxMgDolEvS7SeRAUC33yLvdNAoAGgkSCEGrYimjw/+EUrE01Dzq414yX1BJ2sh0SpfUG3/7JjPlSu0+SkCkWdvKyCPaFoc3WoW0YAuBvT4L/U4MkTpdibi0XrWDuw+d0cZTiXEdCPmy2ZbBuQ1naYWH6UloeUJtV+9foOiw0M061/9aQ2UzJPRZPudmdtw4NI9swunQsHMWNqrGo5fe4CG32r5ovSEV8jA2VuPzBTfxr9TEh0DC+lemDvPxGDW5tPInCYFhjQqhlwZU2n60WkWnWqJTa8EIr5X/sTVR/DzVpVDkdkx4niM7zxynERAIiARcDYCIm9Rl+qFQQr2fFY3EZYTcbk3ti+WHsSi3WoWOZ8VUXJUKB49f6Usm+53dN+TzbEIiPdcuh4X97BPRr9jPZfWLSGwYv9lfLZov2EXeg5f9UlNlMiTwayPyPdMz4NEb3P06n00/t9WpT+VB5+cYC785807I1bL6AXbqk1ejyv3VNode9MZbDh2XVNpJOIdWCQr/uxe1WO2QeR05x3XU8Kl7/8XfhLfhJ9QujYqmQu9g33chk5jzL+HNbRW1pSgxb9XI2olGQD0mMvaoY7KAKBD4ZXGjRAwCgAaBYL0glaibeI8qv7VBk0JEBEU81wXYoBRPOEkEtgRzUpoTItKUfyXegFDMQDH0s9FUt9P6xY1BQCX7zNX+qo4IRykJszanA8rol6JWC6jO4+eI2C8tryCJ6k14tljZQZ63IdGhN2jVhzCbxHqCVSGVMkQMbQu0qZMFq+L24iod0B9PxAe9mjT1x7HjA1qeQWzSaIw5LtsEgGJgETAUxEYu/Iw5m1XuW6pbKmaTzb0+0t9Udcj5vfU9Yp+6/E2sawIv+FrNEr0K/vWQOl8sQdjsjkOAZH2hA4yp7ct57gJpWWnIbB0zyVsPH4DpB7Lt7QpkqFF+byGJfZi9lK94jlNnNzbTt5Ch19UFeEsaVNg78hYXu7E0nafu43WsyKU5aZJkRRHxjXSLL/MmDBNht5f3auiip2zmaevO4EZ680rcsiRluXzYnobz/kbNhJrpLUYKZJTssKXa1RBROKz7FXbF5QYwhq98xwUVK6ddZ3qlShPaVXWcHpRfZvRT4kDZADQWTvo3vPIAKB774/XemcUAPysblH0r+9ntm5Sy606Wauiy3diZawiuW3q5Ek1ZQgfBhbEOI5sd/Kao/hp8xnFFJEczxRKUUWCa37eusVy4JdOlTT+isG7eZ0qIbhYDoj8d5TOfS7mkYZQ2b2nkgAAIABJREFUmficetTygUj0Prp5CXSuXtg0z5mbD1Fn2mbNnMTVkSp5LNGrmCHCOrJMiecvX5vUvvgWPiAIvjnSaz6jkl7C/MkLNaOiR60iGNq4eLyvyyNX7qPJDPX0lxkiIvv2Vcw5OuIzkVHJtjWxk/jMJcdIBCQCEgFnIjBt7XF8xx1wEHcTKa3zmdUi1YUz/XP0XOZZEfkxuWUZUNlh4aEhmulJ9b5YLvPsJEf7mNjs06Fi0++2mQ5fUyVPAhJ1qFgoS2KDQa6XQ+CffZc1hxIU6CNRiRX7r2g+L5ojHdYNsI8AnKdswLlbj1D7600adw+PbagcrNNvme/wNaBqH9YcochL9kk5l+eUZfP1DfbF5w39PQVS3com5ryeuCN9N2/7WYxdeURZYzWfrCBOWeLZZc2ojNYZwEwOOYqftqjvp++Vz4dpbYwDgBTMpaAua/VL5MTPH5rz1MsAoDN2z/3nkAFA998jr/TQKAA4pnkJdPov0MUv/OmLVyZxC6PGSp4+mL0TEWdiDPsRX9KABupNTbwB2MLFwRtPlzIZ9o+qrwhd6PnJTp+mhh3D9xtPK8Ppxe3MrUemkgjWvm5dFq0q5IOYmUhlXqOax2Ym7jl/G+/9aHx6SCdadLIlNlaCQZ+LpVJ/9ww0e2CfveU0JoWop2NJk7xlUjMmVeP4tpiHz1BhQrjZ8FkdyqNRKS33YnznmL/9LMZwN3Vmp17xHJjzkTZYG9855DiJgERAIuAKBH7afBqTuawF4s2lzAXxReaPjz2nfCsuOIpq83S/pPsmZSgVHa492CKuWp/s6eJiXvaNJwKk6rrn/B3450yPAlnTxNOKHOYtCFB1S80pGzXL2TCwFjYcu4EJq48qn3taqak99ufRs5coOTpMY2rT57VRKFta02d67xH89/bwgdmgiqJm320zCRLyzZ6H8vb018jW6ZsPUVdIjGB9qTqMKVDz4//YdQHDlquUREQX0SOoCLovUEUS82dJja2D6zhjCWZziByFjE/eyJlxK49g7naV/qh1hXyY2to8YCgDgC7ZTrebVAYA3W5LEodDRgHAb9uWw7sBeXVB8B+xRlfdljozfrs+v+/VKFmJhsSS3TXRV9Hr971KN70f+4Bxa3Hn8QvDjfmnT3WTCiM1vZO96DENkD5VcszZesbswYduWjc41cJ5nSsh2D8HRJUw/iRn3ZHr+Pi3KMUfCsht/0K9QYnzsI7T25RFy/L5TP8UhULolIjmYI2U2mpN3aR5KLCHUAedbFL2Ia+8THMu6RmISnbKGPhr9wUMWWrOM/hB5dhMEdkkAhIBiYCnIrBw53mM4NTu6d5Xt3gOTAk9rixJLzPdU9cr+i0ecLUIyItv2pbD4+cvTcIDfGPKo96ydrkOiYCnIEDPelUmrdc8305tVcZ06E1VGqzpVd14yhoT4qd4CM8/AxP9D1US8c2RQikHL91Fqx8jNPQJxOFJXJ6e0i7EPDZT7yXf06dMhuixDXWXQWJSJCrFWqm8GdA9yAdE+8SaKzNUxWx/a38rAxbvx7K95pRS4uJlANBTrmrH+ikDgI7FV1o3QMAoADi/cyXU9s+hO6ryxHDNwwTfqUGJnJj9YUVYUoCl/vQA0rpifmXo3gt30PKHHcq/UyRNguMTGikKt3pZBVTi8vSFyofCZ9btOHUL7eao/Ca8wu7yfZfQ/y/1ZkMErWduPtKIljDOIjGIxXM6LY66iMF/H9TctIiImTWx9IJ9zisFN/tuKw5dVjMPp7QqgzYcLqsPXkWfP9TAKNlY1rsayhdIOKG6nkoxnQwXsVOmhhFxtZj9Kf84JQISAYmApyEg/r7TvYHufzzvqT0Oa9wVF/FwjL0U3XvyAmXHrtW4vXNoXTMhKnddl/RLIuBtCIiCeO9Xyo/Xb95gcZSq4s2L+Hjb+i2tp/bUjTgX81jpwivVno95ZDqA59vRcY2QOkUszY8j2r8HrmDw3wdM7zYklkjURW+RQouHtCt3n6DalxvMvC2QJY2pckmvrTxwBZ9wwT56JyMRSP79qnTejFj5SQ2XoPBt+Al8G65yNDYulQs/dqhg6EvX+bux/tgN5Xs9hW76UgYAXbKdbjepDAC63ZYkDoeMAoAr+lRH2f+y6UQkGnyzGSeuP9QFiGV3GQlAsEGzO1ZAg5K5FBt6Nw3iKcmaLqWpjx73IL1cUYCMtZpFs2FB1yqmfy6JuohBXHCOD9xtOn4DnebtVsalTJbELKNxxxd1kCdTaoiBxLQpkuLQ2IamG7JYAsbPT8a3nryJjr+oHBZsQp7nr8OcXdh26pbiy7AmxUwnX6y99+MOUzkPa5ThSJmO9miibbJ5YHQDZEyd3B7mIWZIMqOW1IvtMrE0IhGQCEgEHIxA+JHr6CZkgJPAE698bo0ryMEuOtS80UuRXtYMfy93qFPSuERAImCGgFiNQtlU+bOkMZUBszawvh8+sZMAnCdtQetZO7D7nPqMzdMfiYIWpLZ8elIThwfkbjx4CirlpzJ+Twr+0b6T75UnmvPE0/skvVfqtbWHr2nKfQtmTQMSghy54rDSXY8WylnX2cwNJ/H1WpXTjyW6GM0vvlsxAUqxvwwAOmsH3XseGQB07/3xWu+MAoBbBgUb8se0mRWByHO3dTHpE+yDQQ2LwYj/jQ0SlbQow49KizmuXaz+tAZK5olVDhRvxMSDN+P9AE12HAmNUACLOCZEWfk6xXJg7n8iIdGX7qH5zG0W95SJeejxp5BSGhEpi8Ilb5fNgxkfBCh2jYQ2Do5pgAypYoNsff/Yi1VcELNXbR9QJiO1/Rfv4t3vt2v8JPs0jz1a79/3ICT6mmIqedK3QBwd9nrgEFXm2ET25Bm0Bw7ShkRAIiARiCsCEadj8MHPO5VhmdIkB2XBLdx5QfmsQ9UCmPBu6bia9oj+3288halharkzUxe9eu8JAidrM0D4e55HLE46KRHwIgT2XbiDFlyFDS2tUNY0msy3SS1Ko12VAl60atuWIj4Hs3cYGr3rTAzazlZ/4/lKItusJ75exGUYMH6d2cKJH3de58q6gGw+cRMfcYIfuTKkQtcahTExROWoFBMsnImsKGhojdqj7rRNOH3zkeLizHYBaFbG/L1NBgCduYvuO5cMALrv3ni1Z0YBQEuZYMR7R9ldem1UsxLoUqMwjMo/2Rg9VUCxtHhup4qoUyyWD2/jsRsmlSzWSBEqtF8Qygs3GsbfMeTvg/gr6qLuixiR7FL5q1HjuSqIg89/ZKhGBYxlR1KavqUSClLkqzxJexKWJkVSkMoYC7KN/OcQFuw8r7jyQeUCmNwy9oVRFCDJnTGVKYU+edIkdrkmx648jHnbzym2cmZIiV3D6tnFNhkRRVKY4aW9qpm4ImWTCEgEJAKeioB4kEQHKG+XzYule9Wyuo9rFsbwprGiUd7WSOCKeABZo3K1+Z0rQ48Dih2oeRsGcj0SAU9A4PnL1yg9JsyQu5vWIFbleMK67OGjqGbepmI+TGkVK9iw4dh1dJmv8nzTM3jE0Lr2mNZrbTx4+gKlx2gpIGixLcvnxfQ25XTXvfNMDN7nAq2Z0yQHCS5O45R02QGTK4AThRhr+WXHr130g5nkX6WJ4SYldtZ+61IZJBImNhkAdMVuut+cMgDofnuSKDzSCwBSdt2picaZYETWSqSteu1/75fDO+XyGpa/sjERQ+sgd0atiu3bM7fh4KV7iln+RFLk2yuZJwNWf1oTjf+3VaPeO6C+Hz6tWxQdf9mFrSfV0trBjfzRu7avyfaT569QfJSxkjGdjG4apHJV1JyyARdvq8pc7DRHDIT2r+eHz+oVVfyn4KGvoIbIVJJZJ7FUmnFLUEZkmTFr8eTFK8Uez3Foj4tTfIErkTsDQj5TOQwTOsfhK/fQdIZ5pqUkhE8osnK8REAi4GoEzt56hOCvtfxQ9JISflQ9HPNmvlOxrJBlaJy68RD1pm/WbA+VzdFzhWwSAYmAaxCwVLlDHiXWg1kxk5nPVCM+Pl6IwjdHOoQPqOWaDfSQWfWUk8l1Kukd0Uz/MEzMUKVEic7VC+H7je4hUvPLtrMYv+qIsgM1fLNhYbdYuim9Vm7cWtzlBCsXda+KqkWymnWVAUAPuagd7KYMADoYYGleHwG9AGDWtCmwZ2R9Q8gmrDqCOdtUiXO+48KuVVCjaDazkl3R2JFxDZEmRTLNx2JAjX95Em/S7ARGlFsn7ghS76UbN3FosMYCk+zfxUeGaoJrvCMVC2bG372qKR+1+3kndpyOUf7NAnGtftyBKI6fT4/bTlQurlI4C/7qEajYmrvtLMZxN5aqRbJgUfdA6JUpR42oh2z/cSLa43qOPHsbbX6KUEzx2Yf2sH/m5kPUmaZ9ESS7jiZRtofv0oZEQCIgEbCEgB7XEXG0EnUDa0bk396ArEjzwe5dR6/eNx3MsUZxvzOTm3rDkuUaJAIei4Co2i0uZPOg2iiYNa3Hri++ji/efRGDl+qL+f0ZeQFDl0Urpi3x2MV3fm8bp5f4QGu0dC8U6ZLosIhEaSjwxlqrCvnwdevYzExnt193nMPof1U+wsAiWfFn96qGbpQeHYYHz14q3y/tFYgKBc2VnGUA0Nk76Z7zyQCge+6L13ulFwAskj0tNgysbbh2kRCV7xjyaU2UyJMBRkpQ1NeIa04sh+VT8cVyVUaubiQ0ITrPSoPZ53oKuOy7hiVz4qeOFRUTYjkxC5TVmbbJpB7M2ncfBKC5wM9HmRCUEcGayBO4bO8lDFisKhIzsRLxhlMkW1ps+Nx4T+Jzob558wZfhR4HKR0XzZke37Qth7yZtFmZ8bHLxugJt/Dl1QmxLcdKBCQCEgFXIqCXSZ4vc2pcuqNmizNKDFf66ai5F+48jxH/HFLMM5L2g5fu4u2ZKnctiWwdn9DYUW5IuxIBiYANCIiiReIQErdLl1J7KG+DWY/vItIL8VQ4YpZzdd+s+L2bceDH48GwwwLovaLw0BAzS0RtRO9Oeu30zYeoKyQLkFL1ot36NE52cDNOJsR7XeVCWbC4p5rIIRorNnKNScWZNRJupMNBsckAYJy2wWs7ywCg126tey9MLwBI/GxUDmDUiLOOgnV6LXJYXeTIkApGaeA0Jlu6FIgaYZ5hKGb5EWcCcSdQE8UyetbywReNi+HekxegLDtePETPL6bqy75r/t02RF9Wy435MUSETOXHrIkBT+IfXNe/Fmp/vRF3uDTv37tVQXXfbJrp2/4UgV1nVcEUkRPK6OFD5P/zRDXJe49foOw4LReIIwKZ7v0XJr2TCEgEvBEBetHxGRaiufekSp5E8+Bv6aXH0zGhg6MhS9XsGKZQL3K/ykMfT99p6b83IEAVMSJnNlsXBemJp9NeAnCehJeewCCJ4VEWmqh0bk391ZPW7Uhfiw4PwYtXbzRTWBL/u3TnMWp8tVHTn+iQ1hxSRQotlRA7ci1kW8wELV8gE5b11lc0pv7i+ld9UgOl8sYKWvJNBgAdvXOeYV8GAD1jn7zOS70AYL3iOTDno0qGaxV5MfiOJyc2VkQqSowKxePnKocd6+eTPS3W62QYLt1zCQOXqNlw/jnTI6x/kGnY+7MjsPOMGkgb0bQ4utUsYvpu6LKD+DNSPSkSHQ8okAnLhR/rD+dGYsuJm7prFHmbRCUwGkSEtv/su6x5+WPZj7zRUSsO4bcIVeSDUtgplZ01kfuCZUuIvINGMvLufEE+e/kK/iO0XItiCbQ7+y99kwhIBCQClhAgYv0HT9VSH7Hvt23L4d2AvF4JIvEAEx8wa6XyZsCqT2pCVEfOkjYF9lqgFPFKcOSiJAJuiECdrzfhzC21aoW5SJUf27+o44YeO94lPbE+RrczcfUR/LxVLUNtGZAX09vqC1k43lPPmUGPYumv7lVRRYcHj1alR6dB2ZbbT6nUS32DffF5Q3+XgCBy0JfNlxEr+tbQ9UUvA1JP9JIGywCgS7bT7SaVAUC325LE4ZBeANAa1wIFziiAJraMqZOD1INZMyqzNTo92X7qFtrP2aWMz5AqGQ6OaWj6t1hKy3P6PX7+EisPXMH+i+YZfXkzpUKHqgWRKU0Kjbv9/9qP5fsu627y2LdL4qNqhZTv6Ae907zdIKl6S23n0LrIlTGVpsu5W49M6sVEGE8cSfM6VUbqFEmVPvR9bYFInkQyak7RnobpBRfd/QrVy5BpViY3ZrYr7+6uS/8kAhIBiYBVBKpNXo8r954a9rOU9WDVuJt3WLH/Mj5btF/xktFXiM8H9laXd3NYpHsSAbdFYNCSA1iiI+BnKaDhtouxk2PEWVd0xBq84RLW1nxWE8VzZzDx/1H2F2sdqxbE+HdL2Wlm7zWjdzC2rn+QiWpIr1ElV9mx2mohOlA6dPm+0v3zBn7oW0cVWXQmeiJVEzvs0vPh1evYygC+kXAMCciITQYAnbmL7juXDAC67954tWd6AUBrqdYixw8DSCzvNCqz5VW2eHD11AOZYATdHOgmwdof3aqgmlBuG5eNIkUnnmCWH8tUfvnPiNOwwTdb8JAjdhXnoxKKVMnV4B77/vnL13jw9AUyp0mBJIISol6ZLAUgecLZtCmSmgKhnqiiWHJUKB5xWaCk7DW6ecm4bJXsKxGQCEgE3BKB+tM34yTH8So6Ob9zJdT2z+GWvifUqVUHr6DvH/sUM0whc/3R6+j6a5TyOfEibhuSOLOLEoqxHC8RsCcCiyIv4AtO1ILZrlMsB+Z2Mq76sacP7mir4oR1uPVQFQ0k6iGiICIFYKp4Yo1RD7njGtzJJyo150UYyTdLIoZ6lFH5s6TGxdsqny5f9eXstRoddun5obeWTZ/XRqFs5gI7MgDo7J10z/lkANA998XrvdILAFpTLrwQ8xhBU7UZagQUIwFnoBmV2b5bLg++fT/ADFsKrpUaHab5fOPntZEnUyqzUlJLp0m2bJrIN8iP+fPjqgj0MZds/2PXBQxbrnIe8WNItv7IuEa2TK3p8/r1G/gO1/JI0YMHX55czScr/vjYM4mHK04Ix62Hz5Q1D27kj961feOMkxwgEZAISATcDYEWP2zHvguq6q/on6WyJ3dbS1z9CT10DT0X7lGGFc6WFnS/Dj10FT0X7lU+l7yvcUVW9pcIOAaBUzceoN70LWbGW1fIh6kuUlh1zErjZrXRt1tw7NoDZdC01mXxXoV86Dp/N9Yfu6F87sostLityLW9K08Mx40H6nM/eXNqYmMkS5pE1zG9slmqALvP0WtQ5iVlYLqiiYddRXOkw7oBtXRdefTsJUoK77HbhgQjX+Y0Zv1lANAVu+l+c8oAoPvtSaLwSC8AOLFFKbSvYvxDq5e1RmCJ6rn9Fu3DP/vV0zMG6EeBBTH2Hf00elE+nYJxBbOmQbUvN2j2Y9/I+sicVlvWG5cNE0ld+bFGwUW6SVGJ8o7TKi8FG5cQDhUSMeHFRFIkTYLnr1QFKVdyX8QFU72+Ipfh1FZl0Lpi/oSaleMlAhIBiYDLEej4yy5sPXnL0I9/+1ZHmXzm6n8ud9wODhhl+okcwTyXrx2mlSYkAhKBeCJAB84B49dpqmnIVK/aPhjSqFg8rXr+MPF3nLAgTEQRv9HNS6Bz9cKev2AHr0CkfxLpofSm9xuxBlQtxdpbb0FTlu3Kd4e4HGrpvR/r0UPROmUA0MEXooeYlwFAD9kob3NTLwD4Q/vyaFI6t+FS6SHCZ3iI5seZOpPEO6kesjZu5RHM3a4S6LLPRZENfiKR6++btmXhkz0d3p65XemWLMlbIJUusZw2LnsjZi/wY4mwnIjL9drF249NpcBPXmjFTSxxQljzy4iYmY2b26ki6hTLac2MW37/8W9RWHfkuuJbWL8g+OfS5wFxywVIpyQCEgGJgAECPRfsQehhValQ7JbQTHV3Bn7T8RsmblzWcmVIhZ3D6kIU80rIvdGd1y99kwh4IgKd50Vi43Etn7UryyvdAcOBiw9g6d5LiitdqhfGqOYl0Oy7rRoeuimtyqCNPMC2umW1pm7E+ZjHSj+WHW5poDVBre8+CEDzsnmszu2IDvQOQ+8yrFFSyuZBwbpTxTx8hgoTwjXfGZU/ywCgI3bL82zKAKDn7ZlXeKwXALSFX6/cuLW4+1jl5CMwPqnji4ENVJWmmRtO4uu1J8xwsvSw0WHOLmw7pWZU0EmcX850Gk4h9qKRkA3Yfe42Ws+KMDNBPHsnrQQX528/izErj2jG1iyaDQu6VomXSy1/2I69FsrIEprtGC+n7DTo+LUHJh6Vi3ceo3tQEfSr52cny9KMREAiIBFwLQKkgktquEaNBJ3yZzEv/XGt1/aZXRTtypYupYnnSeQZCyiQCct7V7fPpNKKREAikCAE9OhvvFmt3BawvlxzDLM2n1a6UqCJAk7BX28yCfixZi05wpa5EkOfutM24fRNFTcj4UceC5EuSMTp5w8ron4J1yRCbDx2wyTmyJqliq/r95+iyqT1Gvf3j6pvJkRJHWQAMDH8NVhfowwAWsdI9nAAAnoBQFsUZ2tP3Yhz3AkPuSamx/++6zyGLz9k5rWlVG7xJK5TtUIonjs9hixVuffskVGgJzhCjmZPnxK7h9eziDRlQLadHYHd5+4o/dpVKYBJLdTsx7hslcgzwo+V/ElxQVL2lQhIBCQCzkNgzL+HMX/HOcMJ6V5C9xRvbDvPxOD92TuVpWVKkxz7RzXAgohzGLnisPJ55cJZsLhHoDdCINckEfA4BCJOx+CDn9W/W1rAgq6VUbNodo9bi70cJkFAEgZkrWqRLFjUPRCVJobjJsdl92uXyqjll3hxshVvkVOxXvEcmPORZZEZsWxYnMuV1+jmEzfx0dxIxSVLSSiX7z4BrYVvh8Y2RLqUyczgkwFAW68o7+4nA4Devb9uuzq9AGDE0DrInTG1RZ/fmbkNBy7d0/SZ8UEA3uZStNdEX0Wv31UycNZ5dscKaFAyl679r8OOY+bGU8p3jUrmQul8GTE17LjymZGKcFxAvvPouYkLRWzFcqVHaL8gq6bOxzwy8QFeuvMERFb7Z/eqKJkno9Vxeh0GLN6PZXsv6459r3w+TGtTNl525SCJgERAIiARcBwC4v1KnMnowd9xHjnP8p7zt/Hej2oWffqUyRA9tiHEl+kavtmwsFv8suOdtxo5k0QgcSDw+PlLlBmzFi9fv1EWHD4gCL45Ei81i8hbWiALlXjWNok5PH6u0v0s7RWICgWzJI4LJQGrFEunbRGZqTNtE85wWYPi9Et6BqJSIddgb5TtrgcRvRvWmrpJ89Wx8Y2QKnlSs+4yAJiAi8yLhsoAoBdtpictRS8AeHRcI6ROYf5jxa9LT+H3925VUN03m9JNzBBgX1hSRlyw8zxG/qNmDZbLnwn0H59lYcvNxNoe6Knv0pi4vKwQ2evRa/dNHIUJyfKgk0d6adJrE94thQ4uUr6yhqH8XiIgEZAIJGYEftx0Gl+FHjOEwJLyoafjduDiXbzzvcrNmyp5Ehwb39hUSkcldazZ48DO07GS/ksE3AkBesamZ21qVJ65tFc1vEWqC4m07ToTg7ZcNjPBMLxJcUwMOapBRHJY23aBvPv9duy/eFfpTPQ/w5oUtzhYzBoUO6/sW8OUDOKKJr7LEkc8ccXrtdM3H6LutM2ar4yeA2QA0BW76X5zygCg++1JovBIDACmzZIDxyc0trp24nWjUzO+hfariWK5Migfnbj+wCSYITZLN1GRbJVSrSsUyozVB68qZuylWFZh/DrEPHquce+dcnnwv/cDrK7fnh2MuBJpDlvKse3pi7QlEZAISAQkArYhIJa78qOSJ30LJyc2sc2QB/Y6dPkemn23TfGcxLlOTWqC79afxLR1KvdvgxI5MfvDih64QumyRMA7EaADcBIvevjsJZqXyWP1wN87UVBXReqz1b7cgFsPnykfJnkL4JIkTZ9vGxKMfJm9k9PVnnvcetYODUUSU1W2NAcdJtGhklFzpaBW1LnbaMVxxlPV18ExDXVdJd7zht9q33vPTm6iG2CXAUB7XnWea0sGAD137zzaczEAmC9fPpOSn7U2asUh/BYRe4LIWuTwusiRPpXyb7qZErGr2Iwk0alf9KV7aD5TfakgUY6A/JkQdV7l2xvVrAS61ChszUWr39efvhknbzzU9GPqX1YH27HDwp3nMYLLemSm06ZIarrJEAaySQQkAhIBiYB7IbB83yX0/+uArlPpUyVDtMFLgnutIn7eGL3ofLPuBGZsUGk8mpbJje/blY/fJHKUREAiIBFwAgKi0IPelEZiDk5wz6OmGL48Gr/vuqD4/FuXygiywp3Y5qcIRJ69bbhOVwpq7b1wBy1/2KH4Ru9mh8c10vX18JV7aDrD/GBMr7MMAHrUZe0wZ2UA0GHQeo7h8+fPY8aMGVi9ejUuXryIlClTwsfHB23atEGfPn2QJo39T57EAGApv8I2ceBNW3sc33EP+YSymOb88tVr+A5fY7YBlkqMbzx4isoTtQpKxC304NlLxY7INRjfHda74Qxq6I8+wb7xNRmvcZTd2OcPc67Eaj5Z8cfHVeNlUw6SCEgEJAISAccisPbwNXRfsEd3khzpUyLSiqCUY71zrHU9IS16Bvh67QmNomaLgLz4pm05xzojrUsEJAISgQQiMJ0OL9afNLRycmJjJE+aJIGzeP/wCzGP0XPhHhy7dh+tK+TH5JalkcRKIkPHX3Zh68lbhuCICSbORPHgpbt4e6ZKd5EyWRLDSjmRGsNSXxkAdOYuuu9cMgDovnvjFM9WrlyJDh064P79+7rz+fn5mQKDvr72DU6JAcAa5fxM6lfW2pytZzBhtcqPwRQAxXFlx67FvScvlI9TJKUfzkaGfCNUmuA3Yo2GoFi0+efHVRHok9Wai1a/77VwD9Ycuqbp99V7pdG2UgGrY+3ZYcepW2g3Z5eZyb7Bvvi8ob89p5K2JAISAYmARMBOCBj9dpP5glmJSD7YTjO5nxkjsnMpL4OJAAAgAElEQVQS7OI5bdtUzIcpraSQlfvtoPRIIiAR4BF49foNOs/fjS0nbpoBkyJZEpywgR5JIho/BLr9GoXwo9cNBx8c0wAZUiWPn/EEjhKz+izRe+w5fwfv/ahmC5L6L4mB6TUZAEzgxnjJcBkA9JKNjM8y9u3bh+rVq+PJkydIly4dhg4diuDgYNO/Fy1ahJ9//tlkloKAUVFRSJ/efmpdYgCwebVS+LFDBavL+HvPJXy+RC19KpI9LTYMrG02LvjrTTh765HyebZ0KRE1op5F+9bk4O2lWDZseTT+4NLUyalfPqqIusVzWl2/PTuINxdme26niqhTzLm+2HNd0pZEQCIgEfBmBMTMAH6ttirKeyo+l+8+Ad2r+UYvOlNCj2noQdpXKYCJLUp76jKl3xIBiUAiQuDOo+cmblP6feObJeGHRASPw5ZKVVA817s4EQVfKQjriibSXZBeztnJTXVdEQVlMqZOjgOjG+j2lQFAV+ym+80pA4DutydO8ygoKAhbt25FsmTJsGXLFgQGajPwpk6disGDB5v8GT16NMaMGWM338QAYMd65U3p2tYa3RxrfLUBb97E9uxUrRDGvF3SbFjLH7Zj7wWV2NUne1qs1wkU8gPFMaLRA6MaIGOahJ8E6ZUx/9Onukl12Jntyt0nJgJise0bWR+Z06ZwpityLomAREAiIBGwEQE9xT82tGz+TFjRp7qNljyv2/X7T1FlkpaugziySBX5z8iLyoKMng08b8XSY4mARCAxIEAHO61+jMDzV6+V5ebPkhpbB9dJDMt3yRoHLN6PZXsv685N1cOnJ+kLaTjD2VM3HqDedK2wx5lJTXTLmsWqgGzpUiBqhL5isAwAOmP33H8OGQB0/z1yiIeRkZGoUqWKyXaPHj0wa9Yss3lev36NUqVK4ejRo8iUKRNu3LiB5MkTHgCjicQA4CdvVwEpNtnS1h+9bjrpL5Q1DQY29NdNzxbTussXyIRlvS2/FPX+fQ9CorWlucwfayXEtvjN+szbfhZjVx7RDHEF0eyT569QfFSoxo8i2dJiw+fmGZVxWZ/sKxGQCEgEJAKOQ+DG/aeoLATB2GxVi2SxiU7Dcd451nLMw2eoIIh8UXb/5JBjWLr3kjJ596AiGNakuGOdkdYlAhIBiYAdEfgz8gKGLotWLDYvmwfffRBgxxmkKR4Bwpow12tpUiTFEQPRDWegSFVsVM3GN6OMxM0nbuKjuZFK15wZUmLXMP2qNxkAdMbuuf8cMgDo/nvkEA+HDRuGyZMnm2zv3LlTCQaKk3355Zem0mBqYWFhaNBAP6U4rk6KAcBR79dA9yCfuJox7D/47wNYHKW+DAT7Z8e8zpUt2h+78jDmbT+n2yd3xlSIGGpdpdiWBazYfxmfLdqv6XpkXEOkSZHMluF27eM/Yg2evVRPG98rnw/T2kjeJLuCLI1JBCQCEgE7IvDo2UuUHB2ma9GWe50dXXG6qXuPX6DsuLWaeXcOrYtJIUfx74Eryud9gn0wqKFth4pOX4ScUCIgEZAI6CDw5s0bLNx5HnO2nUW+zKnxZcsyyJ/F/kKMEvxYBMb8exjzd+i/97m6/Pri7ceoOWWjZquOjW+EVMmTmm3fhmPX0WV+lPJ53kypsf0L/cxRGQCUVz8hIAOAifQ6YOW/adOmxd27d01lwHotIiIC1apVM301atQojB071i6IiQHA6V3qoE3F/HaxTUbEMltbCMFnbzmNSSHHdH0oky8j/u1bwy7+bT15Ex1/UU9qXHnKVHXSely7/1RZ14R3S6FD1YJ2Wac0IhGQCEgEJAL2R4BeEosMC1GoMPgZmpTOhR/aW+fTtb9XzrGoF/zcNiQYE1cf1Yhr9atXFP3q+TnHKTmLREAiIBGQCHgcApPXHMVPm8/o+p0nYyrssFPiR3yA0aNpIr5bEvgQW9jha+ixYI/ysSUxMBkAjM9ueN8YGQD0vj21aUXZs2fHrVu3ULZsWezfr81G4w3cuXMHWbJkMX3UunVrLF682Cb71jqJAcB5fRuhfgn7CU8QeWqz77bixas3IOLUhV2roLpvNotu6WXmsQF1iuXA3E6VrC3Lpu8fPH1h4jB6/PyVqT+t++cPK9o01t6d+v+1H8v3xfJfENHtps9rI0+m1PaeRtqTCEgEJAISATsiUHp0GB48e2lmsWVAXkxvW86OM7mXqWcvX8F/hJa6gu5bE1YfQfjRG4qzgxr6o0+wr3s5L72RCEgEJAISAbdBYPra45ix4ZSuP66mRNLjuzXiog+Jvorev+9V1mGJ914GAN3m8nOpIzIA6FL4XTP506dPkTp1bJCnadOmWLVqlUVHSCH40aNHqFq1Kigj0JZGPzCW2tWrV1G5cmxJbt5e87F8cHNUKhQbaLRXO3btPraeuIXyBTOjQsHMVs2KKkr8gLYV8+OrVmWs2rC1w84zMfhh02lkTZsCXzQuhpwZUtk61K79SHmMSqeu3HuCLtULO12J2K6LkcYkAhIBiUAiQUDM3mbLblelACZ5sfrtq9dv4DMsRLPL4QOCMG7VUWw5cVP5fHiT4vg4qEgiuRrkMiUCEgGJgEQgrgh8v/EUpoYd1x1WPHcGrPmsZlxN2q3/rYfPUFHgu90zoh6ypktpNoeYwOKfMz3C+gfp+iIDgHbbIo82JAOAHr198XP+5s2byJEjh2lw27ZtsWjRIouGcubMaRIAIUGQ6GiVnNbSoLco7c7GRgHATWNawjdHehtHOKbb+ZhHqDVVS7jKZpJ8Qo7BXFqVCEgEJAISgbgjUG/6Zpy68dBsYNcahTGyWYm4G/SgEYWHrtaUP4f2q4mx/x5BxJkYZRVjmpdAp+qFPWhV0lWJgERAIiARcCYCc7aewYTVR3WnDCiQCcutiEc60te7j5+j3Lh1mikih9dFjvTmCSPL9l7CgMUHlL4l82TA6k/1g5cyAOjIXfMc2zIA6Dl7ZTdPL168iAIFCpjsdezYEb/99ptF29SXxvj4+ODUKf1UadFAXAOA+6e8j2w6pxp2W7QNhp6+eIViI7WlRWzY6OYl0Fm+TNiAouwiEZAISAQkAo5G4J3vt+PAxbtm0/QN9sXnDf0dPb1L7RcdHmKi92Bt1Sc1TGTuUefvKJ9NbFEK7atIPluXbpScXCIgEZAIuDECCyLOYeSKw7oeBhbJij+7V3WZ90QXVXqMVvAqYmgd5M5oTtO0ePdFDF56UPG1bL6MWGHAWy8DgC7bUreaWAYA3Wo7nOOMMzIA41oCfO67jkiWNIlzALAwS8C4tbjz+IVZj5ntAtCsTB6X+ycdkAhIBCQCEgGJQPs5O7H9lJrxxhBJDNx3xUeG4smLWA5dav/0qY7RKw7hwKV7ymdTWpWxq7CYvOIkAhIBiYBEwLsQEANn/OqC/bNjXudYqipXtMfPX6LEqDDN1FsHB+uqQv+x6wKGLVcr9Ij2ammvWAFPsckAoCt20/3mlAFA99sTh3vkDA5Aa4vgf4C6zFyDX/o0sjbEKd83+nYLjl17YDbXou5VUbVIVqf4ICeRCEgEJAISAYmAJQR6LIhC2OHrZl1GNC2ObjW9m/uu9JgwPHiqCqD83TPQlMVx9Op9BY9v25bDuwF55UUkEZAISAQkAhIBXQQsiT82LpULP3ao4DLkjASvCmVLa+bTbxHnMIrLZKxcOAsW9wjU9V0GAF22pW41sQwAutV2OM+ZbNmyISYmxi1UgKm8OF++fM5bvIWZOs2LxKbjKpE467p+YC34ZE/nFj5KJyQCEgGJgEQgcSMwYPF+LNsbq+DOt8RQ+ipm6v/5cVWMXHFIw4kos/YT99+HXL1EQCIgEbCGQOihq+i5UFXP5fu3CMiLb9qWs2bCYd/rCV4ZvYvO3XYW41YdUXyp7psVv3fTL1+WAUCHbZlHGZYBQI/aLvs5GxQUhK1btyJt2rS4e/cukiVLpmucVH+rVYtNIx41ahTGjh1rFyfc9Qfoi6UHsWj3RbM1HhjdABlTJ7fL2qURiYBEQCIgEZAIJASBUSsO4beI82Ymprcpi5bl3eNALSHrszS20sRw3HzwTOmysGsVDP8nGudjHiuf/dSxAhqWzOUoF6RdiYBEQCIgEfBwBDYeu4HO83frruL9Svnx5XtlXLbCN2/eoPBQreL92v5B8MtpLpg5e8tpTAo5pvga5Jcdv3XRL1921/dvlwGdSCeWAcBEuvHDhg3D5MmTTavfuXMnqlSpoovEl19+iaFDh5q+CwsLQ4MGDeyCmLv+AH2z7gT+t/6kZo0pkiXB8fGNEBdhE7uAJI1IBCQCEgGJgERAB4Epocfww6bTZt/82L48GpfO7dWYBU5ej6v3niprnNe5EoYvi8YV/rNOlRBcLIdX4yAXJxGQCEgEJALxR2DHqVtoN2eXroFO1QphzNsl42/cDiN9hoWAMgFZC/m0JkrkyWBm+YdNpzAl9Ljyed1iOfBLp0q6Hrjr+7cd4JIm4oCADADGASxv6hoZGakE/Xr06IFZs2aZLe/169coVaoUjh49ikyZMuHGjRtIntw+WXDu+gO0KPICvlimEqkSKHkzpcb2L+p40/bLtUgEJAISAYmAByPw/cZTmBqmPvCzpcxLBIGvmlM24OLtJ8ruzfmwoum+feuhNiuwRtFsHrzD0nWJgERAIiARcCQCUeduo9WsCN0petQqgqGNiztyequ2/UaswfOXr5V+K/vWQOl8Gc3GzVh/EtPXnVA+b1AiJ2Z/WFEGAK0inHg7yABg4t17sDJgKv/dsmULAgO1hKFTp07F4MGDTQiNHj0aY8aMsRta7hoA3Hj8BjrP06aDW5JTtxsg0pBEQCIgEZAISARsREAk/WbDiA8v0Me7BavqfL0JZ249UpCa1aE8hiyNxr0nL5TP/upeFVWkcJeNV5PsJhGQCEgEEh8C0ZfuofnMbboL/6xuUfSv7+dSUEqMCsXj51rF+3L5M5n5RME/CgKy1rR0bnzfvrwMALp099x7chkAdO/9cah3+/btQ/Xq1fHkyROkS5cOVBYcHBxs+veiRYswe/Zs0/x+fn6IiopC+vTmvAPxddBdA4CkItj4f1s1y6pXPAfmfKSfSh3f9ctxEgGJgERAIiARiC8CS/dcwsAlB8yG/9OnOvReEOI7jzuOqz99M07eeKi49t0HARiy9KDmRWlZ72ooXyCzO7ovfZIISAQkAhIBN0DgxPUHaPDNFl1PhjQqhl61fVzqZenRYXjwTFW8X9orEBUKZjHzSaQEebtsHsz4IEAGAF26e+49uQwAuvf+ONy7lStXokOHDrh//77uXBT8W716NXx9fe3qi7sGAO88eo6A8es0a3U1EaxdgZfGJAISAYmARMDjEQg9dA09F+4xW0dYvyD457LfYZ07AkWHdHRYx9q3bcth0N8H8OKVypW06pMaKJXXvFTKHdcjfZIISAQkAhIB5yNwPuYRak3dpDvx6OYl0Ll6Yec7xc1Ybtxa3H1sPbN9csjR/7N3H9BRVd3Dhzcd6b3XQIDQa+i9BxApooAIKIgKgoKg9A4KCi8iIogCIqAgKCK910DohE7ovUrv5VuT/8cNQ8pMJjOZc8/9zVrvetcM556797PvnHh2Jndk0vrjxpFNS2aVMS0i/gZjVfffXoW24MlpAFqw6K+mfOrUKRk3blxoo8+2MCRMmDC04ffmm29Kly5dJEmSJG5XUnUBsn3rUv7+S+3uufBJjbzSo05+txswIQIIIIAAAq4IbAq5Kq0juHn5+p7VJUda9//MdiVGTx3TaPxGCT5305h+VPOi0uvPvXans0Ij1FO+zIsAAghYQeDizQdSbuSqCFMd2bSItPTP4VWG0sNWyNU7j4wYZnUoKxXyhr+37ZCFB+SXTSeMcS1KZ5NRzYtFGLuq+2+vQlvw5DQALVh0FVJWeQGqOnqNnLp2z2Aa/HohaVshlwpsxIAAAggggIDsPnND3piwKZxEUJ+akiFFYq2FmvywSXadvmHkOKRxIRmwYL9dzqt7VBWf9Mm0diA5BBBAAAHXBSL6q68Xs419q5g0KZHN9cndcGTZESvl0q2wL7ea8b6/VPZNH27mgQv2yfTAU8brrcrmkBFNitAAdEMNdJ2CBqCulVU8L5UbgN1+3yULdp83BLmXkOIXE+EhgAACFhMIuXxHao1ZFy7rvYPqSIrECbTWaPFjoASdvG7k2Lt+ARm55JBdzht6VZfsafT+JKTWRSY5BBBAwMMC9x49kYIDlkV4lomtS0r9Ipk9HEHU01cYuUrO33xgDJravoxUz58h3EF9/gqWWVtPG6+/Wz6nDGlcmAagV6un9slpAKpdH22jU7kBeOLqXek6e5ccv3JH3imXU76sX0DixImjbS1IDAEEEEDAXAKR/enS0eH1JUG8uOZKJprRtpy8RQKPXzOOsn1b47iXvgHR9g9b+9SUjJp/EjKabAxHAAEEEHhJ4MnTZ5K375IITaa2KyPVC4RvtsUmYOVRq+XM9fvGKae8W1pqFcwYLoQv/twrf2w/Y7z+XsXcMqBRQRqAsVksk52LBqDJCqZLuCo3AHUxJg8EEEAAAT0Fbj94LEUGLbdLLn7cOBIyIkDPhF/Kqs3PW2XD0avGK52q+NjdAN32Dzv715Y0SRNqb0GCCCCAAAKuC+Tts1iePAv7AqkXM83qWFYq5Al/vz3XzxT9I6t/s1ZsH0p58fjxnVJSr3CmcBN1n7Nb5u88Z7z+QRUf6RPgRwMw+uSWOYIGoGVKrVaiNADVqgfRIIAAAgiYR+DZs+fi02exXcDJEsWXfYPrmicJFyNtPzVI1hy+YhzdplxOmbEl7P5Htn8IHlRHkmv+p9Au8nEYAggggMD/Fyg0YKncffQ0nIcKt3+y3ebDdruPF48fWpeUgAj+LPnVW1d9XC2P9KpXgAYgV3mkAjQAuTi8IkAD0CvsnBQBBBBAQBOBVzcu6ZIlku39ammSXeRpdPx1u6w4cMkY0KxkNpm386zdAYeG1pPECeJpb0GCCCCAAAKuC5QYslz+u/c43ASLu1aWgllSuD6xG46sO3a9HL5025jpu5Yl5PViWcLN3HnmTlkUfMF4vWuNvNK9Tn4agG6oga5T0ADUtbKK50UDUPECER4CCCCAgNICr35DYPY0r8mGXjWUjtkdwX08c4csDr5oTNWgSGa7zY/tH46NCJB4cbl3rzu8mQMBBBDQVaDciFVy8VbYF228yFOFb5IPGLdBDly4ZdBH9s3EnWZsl2X7w34p1r12Pula05cGoK4XrRvyogHoBkSmiL4ADcDom3EEAggggAACLwRqfLtWjl8Juz9QvozJZPlnVbUH+mT2Llm457yRZ/X86e3+JNjW+LM1AHkggAACCCAQlUDV0Wvk1LV74YZs/rKGZEn1mlfxXv9+o+w9e9OIYXTzovJm6ezhYnp/2jZZdeiy8XrPuvmlc/W8NAC9Wj21T04DUO36aBsdDUBtS0tiCCCAAAKxIPDq5qBotpTyT5dKsXBm756i+x+7Zf6usBuel/NJI1uOXzeCSpwgrhwaWt+7QXJ2BBBAAAHlBeqMXSdHLoXdZ+9FwCp8kVSTHzbJrtM3DMOvmxWRt8rkCGfa9pcgWXck7L64fQIKyAdV8tAAVP7q816ANAC9Z2/pM9MAtHT5SR4BBBBAIIYCrX7aIpuPXTNm8c+dRuZ0Kh/DWdU/vOfcPTJ3R9g9/4plTyV7zoRtkpInji/Bg/T/MhT1K0WECCCAgNoCjcZvlOBzYZ+yexHtgSF1JUnC+F4NvvnEzbL91H9GDMObFJbWZXOGi6n1lC2yKSTsvwX6Nywo71fKTQPQq9VT++Q0ANWuj7bR0QDUtrQkhgACCCAQCwKvfhlG1XzpZfp7/rFwZu+eovf8YJkddNoIwvanzy9/giNt0oSyo39t7wbJ2RFAAAEElBd4tcn2IuDjIwIkrpfvI/vWpEDZeiLs0+1DGheSd8vnCmfq7Djbgey/lb8kYyVAGoCxwsxJXhVgAeKaQAABBBBAwHWBz/7YLX+99Kew9Qplkh/blHJ9QpMcOWDBPvk18JQRre3LT85cv288z5QisWzpU9Mk2RAmAggggIC3BF799JwtjoTx4sqR4d6/jcSrsQ1sVFDaVwz/yb5mEzfLDic+KUgD0FtXmXrnpQGoXk0sERENQEuUmSQRQAABBDwk0P/vfTJjS1gjrEmJrDL2reIeOps60w5euF+mbjppBJQuWUK5eueRXUPQCt+GrE5FiAQBBBAwp8B707bJ6pe+QMOWhSq3kXj3lyBZ/9K9/fo18JMOlX3CQTeesMnuNhijmhWVFmXCf1kIDUBzXqOeiJoGoCdUmdOhAA1Ah0QMQAABBBBAIFKB37ackn5/7zP+Papv/tOJccTigzJ5/XEjpaQJ48ndR0+N5z7pk8rqHtV0SplcEEAAAQQ8IPDRbztkyb6LdjOnT55ItvWt5YGzRW/K9lOD7L7h/sv6BeTDquG/3KPBdxtk//lbxuTfvllMmpXKFuHJ2H9Hrwa6jqYBqGtlFc+LBUjxAhEeAggggIDSArcfPJYus3aFfvuff640MvGdkpI2WSKlY3ZHcF8vPSQT1x4zpooTR+T587CZC2RKLks/reKOUzEHAggggIDGAt1+3yULdp+3y9B2WwkVPkXeYfp2WXnwkhFbZL/kq/e/9XLo4m1j3Li3i0vj4llpAGp83cY0NRqAMRXkeJcEaAC6xMZBCCCAAAIIGALP/3/nK46tC2aRx5jlh+W71SGRZlska0pZ+Ekli2iQJgIIIICAqwK9/twjc7aHfau8bR7fDMlkRfeqrk7ptuM+nLFDlu4P+3Ri99r5pGtN33Dz1/x2rRy7ctd4fUKrktKgaGYagG6rhH4T0QDUr6amyIgGoCnKRJAIIIAAAggoJfDdqqMyZsWRSGMqmSOVzP+4olIxEwwCCCCAgHoCr95L1xahKr9E6jxrpyzae8FAszX/bE3AVx/VRq+Rk9fuGS9PalNK6hbKRANQvctNmYhoACpTCmsFQgPQWvUmWwQQQAABBNwhMGFNiIxedjjSqcrmTiN/dCrvjlMxBwIIIICAxgLD/j0gUzaesMuwTK7UMvfDCl7PuuvsXfLPnrA/T+5cPY/0rFsgXFwVv1ot527cN17/uW1pqemXkQag1yuobgA0ANWtjdaR0QDUurwkhwACCCCAgEcEJq8/JiMWH4p07sq+6WTG+2U9cm4mRQABBBDQR2D0skMyYU3YPWVtmanyM6T7H7tl/q5zBnanqj7Su75fOPxyI1bJxVsPjNenv+cvVfOlpwGoz2Xq9kxoALqdlAmdEaAB6IwSYxBAAAEEEEDgZYGfN56Qof8eiBSlRoEM8ku7MqAhgAACCCAQpUBEt5So5ZdRprQt7XW5nnP3yNwdYfcn7Fg5t/RtUDBcXKWHrZCrdx4Zr8/sUFYq5k1HA9DrFVQ3ABqA6tZG68hoAGpdXpJDAAEEEEDAIwK/Bp6UAQv2Rzp33UIZZVIb72/ePJI8kyKAAAIIuE1g0rpjMnKJ/SfKGxbNLN+3Kum2c7g6Ue/5e2V20Bnj8PYVc8nARoXCTVd8yHK5ce+x8fofH5STsj5paQC6Cm+B42gAWqDIKqZIA1DFqhATAggggAACagvM2npa+vwVHGmQqmze1FYkOgQQQACBaZtOyKCF9p8ob14qm3zzZjGv4/T9K1hmbj1txPFu+ZwypHHhcHEVHrhM7jx8Yrw+76PyUipnGhqAXq+gugHQAFS3NlpHRgNQ6/KSHAIIIIAAAh4RmLPtjPSatzfSuZuWyCpj3irukXMzKQIIIICAPgKzg05L7/n2v1B6p1wOGfZGEa8nOXDBPpkeeMqIo1XZHDKiSfi4CvRfIg8ePzPGLehcUYplT0UD0OsVVDcAGoDq1kbryGgAal1ekkMAAQQQQMAjAvN3npXuc/ZEOvdbpbPL182LeuTcTIoAAgggoI/AvB1npcdc+58nHSrlln4Nw99rL7azHrLwgPyyKewbit8uk12+ahb+Z1vePovlybPnRnj/flJJCmdNSQMwtgtmovPRADRRsXQKlQagTtUkFwQQQAABBGJHYMHuc9Lt992RnkyVT2/EjgZnQQABBBBwVeDfveely6xddod3qZ5XPq+b39Up3XbciMUHZfL648Z8Ef1p8vPnzyV378V251z2aRXJnyk5DUC3VUK/iWgA6ldTU2REA9AUZSJIBBBAAAEElBJYHHxBPp65M9KYIrtRulJJEAwCCCCAgNcFVhy4JB1/3W4Xx+d18kmXGr5ej+2rJYfkx3XHjDialMgqY1+5vcWTp88kb98ldrGu7F5V8mZIRgPQ6xVUNwAagOrWRuvIaABqXV6SQwABBBBAwCMCy/dflA9m7Ih07k5VfKR3gJ9Hzs2kCCCAAAL6CKw/ckXe/SXILqF+DfykQ2Ufryf5zbLD8v2aECOORsWyyPiWJezievD4qRTov9TutXU9q0nOtElpAHq9guoGQANQ3dpoHRkNQK3LS3IIIIAAAgh4RGD1oUvy3jT7T2y8fCJV/nzLI8kzKQIIIICA2wS2Hr8mb03eYjff0DcKS5tyOd12DlcnGrviiIxbddQ4vEGRzDKhdUm76Wzf/mv7FuCXHxu/qC7ZUiehAegqvAWOowFogSKrmCINQBWrQkwIIIAAAgioLRDRJzZejvizWvmkWy3v//mW2opEhwACCCCw+8wNeWPCJjuI0c2Lypuls3sdZ/yqo/LtiiNGHHULZZRJbUrbxXXz3mMpNmS53Wtb+9SUjCkS0wD0egXVDYAGoLq10ToyGoBal5fkEEAAAQQQ8IjA5pCr0mrK1kjn7lUvv3xcLa9Hzs2kCCCAAAL6CBy8cEvqj9tgl5Dtz2xtf27r7ceENSEyetlhI4xafhlkStsydmFdvfNQSg9baffa9n61JF2yRDQAvV1Ahc9PA1Dh4ugcGg1AnatLbggggAACCHhGIOjEdWkxKTDSyVW5fzDHfCgAACAASURBVJNnsmdWBBBAAAF3CRy/ckdqfLvObrrJbUpJnUKZ3HUKl+eZtO6YjFxyyDi+Wv70Mq29v918l249kLIjVtm9tmdAHUmZJAENQJfl9T+QBqD+NVYyQxqASpaFoBBAAAEEEFBaYOfp/6TpD5sjjXHw64WkbYVcSudAcAgggAAC3hc4d+O+VPxqtV0gv77nL1Xypfd6cFM2HJdhiw4acVT2TScz3i9rF9fZ/+5Jpa/X2L22b3BdSZYoPg1Ar1dQ3QBoAKpbG60jowGodXlJDgEEEEAAAY8I7D17Q17/3v6eTS+faESTItKqbA6PnJtJEUAAAQT0EYjoT2jndCov/rnTeD3JaZtOyKCFB4w4KuRJK7M6lrOL69S1u1J19Fq71w4NrSeJE8SjAej1CqobAA1AdWujdWQ0ALUuL8khgAACCCDgEYED529JwHf292x6+USq3MDdI8kzKQIIIICA2wRuPXgsRQfZf4nGP10qStFsqdx2DlcnmhF4Uvov2G8cbmtK2pqTLz9CLt+RWmPs/4Q5ZHh9iR8vLg1AV+EtcBwNQAsUWcUUaQCqWBViQgABBBBAQG2BI5duS52x6yMNctzbxaVx8axqJ0F0CCCAAAJeF3j45Knk77fULo7ln1WRfBmTez22WVtPS5+/go04SuVMLfM+qmAX1+GLt6Xu/+x/Hp4YGSBx4sShAej1CqobAA1AdWujdWQ0ALUuL8khgAACCCDgEYGIbtr+8ol+aF1SAopk9si5mRQBBBBAQC+BhuM3yL5zt0KTSp0kgQT1rSUJIvkEXWxmPmfbGek1b69xyuLZU8nfnSvahbDv3E1pOH6j8Vr8uHEkZERApGGy/47NCqp7LhqA6tZG68hYgLQuL8khgAACCCDgEYEz1+9J5VH2Nz1/+UQ/vVtaahfM6JFzMykCCCCAgF4CBy/ckkH/7JcHj5/KF/UKSIW86ZRIcN6Os9Jj7h4jliJZU8rCTyrZxbbnzA1pPCHsnriJE8SVQ0Pr0wBUooLqBkEDUN3aaB0ZDUCty0tyCCCAAAIIeETg/I37UuGVb218+UTT2peRavkzeOTcTIoAAggggEBsCPy965x8+sdu41R+mVPIkm6V7U6949R1aTYx0HjN9u2/tm8BjuzB/js2Kqf+OWgAql8jLSNkAdKyrCSFAAIIIICARwUu334g/sNXRXqOWR3KKvMJDo9CMDkCCCCAgLYCC/ecl09m7zLyy5cxmSz/rKpdvluPX5O3Jm8xXkuVJIHsHlCHBqC2V4V7EqMB6B5HZommAA3AaIIxHAEEEEAAAQTk+t1HUnLoikgl5n5YXsrkSoMUAggggAACphVYEnxBPpq504g/T/qksqpHNbt8NoVcldZTthqvpUuWULb3q00D0LRVj53AaQDGjjNneUWABiCXBAIIIIAAAghEV+DWg8dSdNDySA+z3STddrN0HggggAACCJhVYPn+i/LBjB1G+LnSJpG1PavbpbPuyBVp+0uQ8VqmFIllS5+aNADNWvRYipsGYCxBcxp7ARqAXBEIIIAAAgggEF2B+4+eit+ApZEetqhrJSmUJWV0p2U8AggggAACygisOnhJ3p++3YgnW+rXZOMXNezie3VM1lSvyaYv7ce8fAD7b2XK69VAaAB6ld+6J2cBsm7tyRwBBBBAAAFXBR49eSb5+i2J9PAVn1UR34zJXZ2e4xBAAAEEEPC6wNrDl6Xd1G1GHJlTJpbA3vaf7lu2/6J0cvApQRqAXi+lcgHQAFSuJNYIiAagNepMlggggAACCLhT4Nmz5+LTZ3GkU679vJrkSpfUnadkLgQQQAABBGJVYOPRq/LOz2H398uQPJEE9a1lF8OivRek86yo7xNIAzBWy2aKk9EANEWZ9AuSBqB+NSUjBBBAAAEEYkPAp/ciefY84jPZ/vzJ9mdQPBBAAAEEEDCrQOCxa9Lyp7Bv+E2bNKHs6G//BR8Ldp+Tbr/vNlIskCm5LP20SqQps/8269Xg3rhpALrXk9mcFGABchKKYQgggAACCCBgJ2D7E2DbnwJH9AjqW1MyJE+MGAIIIIAAAqYVCDpxXVpMCjTiT/laAtkzsI5dPvN2nJUec/cYrxXKkkIWda1MA9C0VY+dwGkAxo4zZ3lFgAYglwQCCCCAAAIIuCJQaMBSufvoaYSH7h5QW1IlSejKtByDAAIIIICAEgI7Tv0nzSZuNmJJlii+7Btc1y62P7adli/mBRuvFcueShZ0rkgDUIkKqhsEDUB1a6N1ZDQAtS4vySGAAAIIIOAxgaKDlsmtB08inN+2QbJtlHgggAACCCBgVoE9Z25I4wmbjPBfSxBPDg6tZ5fOzK2npO9f+4zXSuVMLfM+qkAD0KxFj6W4aQDGEjSnsRegAcgVgQACCCCAAAKuCJQaukKu3X0U4aGHh9WTRPHjuTItxyCAAAIIIKCEwL5zN6Xh+I1GLAnjxZUjw+vbxTZ980kZ+M9+47WyudPIH53K0wBUooLqBkEDUN3aaB0ZDUCty0tyCCCAAAIIeEzAf/hKuXz7YYTzHx8RIHHjxvHYuZkYAQQQQAABTwscvHBL6o/bYJzG9mPt+MgGdqf9eeMJGfrvAeO1innTyswO5WgAero4Jp+fBqDJC2jW8GkAmrVyxI0AAggggIB3BSp+tVrO3bgfLoj4ceNIyIgA7wbH2RFAAAEEEIihwNFLt6X22PV2s5wYGSBx4oT9gmvSumMycskhY0zVfOll+nv+NABjaK/74TQAda+wovnRAFS0MISFAAIIIICA4gJVR6+RU9fuhYsyonskKZ4K4SGAAAIIIBBO4PiVO1Lj23V2r4cMry/x48U1XpuwJkRGLztsPK9ZIIP83K4MDUCupygFaABygXhFgAagV9g5KQIIIIAAAqYXqPHtWjl+5W64PFIkji97B9l/S6LpkyUBBBBAAAHLCZy6dleqjl5rl/ehofUkcYKwe9yOW3lUxq48YoypWyijTGpTmgag5a6W6CVMAzB6Xox2kwANQDdBMg0CCCCAAAIWE6g7dr0cvnQ7XNbpkiWU7f1qW0yDdBFAAAEEdBM4+989qfT1Gru09g+uK0lf+pb7McsPy3erQ4wxDYpklgmtS9IA1O1icHM+NADdDMp0zgnQAHTOiVEIIIAAAgggYC8QMG6DHLhwKxxL5pSJJbB3TbgQQAABBBAwtcDFmw+k3MhVdjnsGVhHUr6WwHjt66WHZOLaY8bzxsWzyLi3S9AANHXlPR88DUDPG3OGCARoAHJZIIAAAggggIArAo2/3yh7zt4Md2iONElkfa/qrkzJMQgggAACCCgjcOX2QykzfKVdPLv615bUSRMar41YfFAmrz9uPG9aMquMaVGcBqAyVVQzEBqAatZF+6hoAGpfYhJEAAEEEEDAIwLNJm6WHaf+Czd3nvRJZVWPah45J5MigAACCCAQWwLX7z6SkkNX2J1uW99akj55IuO1wQv3y9RNJ43nb5XOLl83L0oDMLaKZNLz0AA0aeHMHjYNQLNXkPgRQAABBBDwjkCLSYESdOJ6uJMXyJRcln5axTtBcVYEEEAAAQTcJHDz/mMpNni53WxbeteUTCkTG68NWLBPfg08ZTxvVTaHjGhShAagm2qg6zQ0AHWtrOJ50QBUvECEhwACCCCAgKICradskU0h18JFVzRbSvmnSyVFoyYsBBBAAAEEnBO4+/CJFBq4zG7wxi+qS7bUSYzXes8PltlBp43nbcvnlMGNC9MAdI7YsqNoAFq29N5NnAagd/05OwIIIIAAAmYVePeXIFl/5Eq48EvlTC3zPqpg1rSIGwEEEEAAgVCBB4+fSoH+S+001vesLjnShjUAe/25R+ZsP2uMea9ibhnQqCANQK6hKAVoAHKBeEWABqBX2DkpAggggAACphd4f9o2WXXocrg8yvmkkd8/KG/6/EgAAQQQQMDaAo+fPhPfvkvsEFb3qCo+6ZMZr3X/Y7fM33XOeN6pio/0DvCjAWjtS8dh9jQAHRIxwBMCNAA9ocqcCCCAAAII6C/QacZ2Wbb/UrhEK/umkxnvl9UfgAwRQAABBLQWePbsufj0WWyX44rPqohvxuTGa11n75J/9pw3nn9cLY/0qleABqDWV0bMk6MBGHNDZnBBgAagC2gcggACCCCAAALSeeZOWRR8IZxEzQIZ5Od2ZRBCAAEEEEDA9AI+vRfJs+dhaSzpVln8MqcIa/jN3CGLgy+GNQRr+kr32vloAJq+8p5NgAagZ32ZPRIBGoBcGggggAACCCDgikC333fJgt1hn3p4MUe9QpnkxzalXJmSYxBAAAEEEFBKIF/fJfLo6TMjpn8/qSSFs6Y0nn/w63ZZfiDs0/C25l/Xmr40AJWqonrB0ABUryaWiIgGoCXKTJIIIIAAAgi4XaD7nN0yf2fYfY9enKBRsSwyvmUJt5+PCRFAAAEEEIhtgQL9l8iDx2ENwAWdK0qx7KmMMN6btk1Wv3Q/3F718svH1fLSAIztQpnsfDQATVYwXcKlAahLJckDAQQQQACB2BX44s+98sf2M+FO2rRkVhnTonjsBsPZEEAAAQQQ8IBA4YHL5M7DJ8bMtm+5t33b/YvHu78EyfojV4znfQP8pGMVHxqAHqiFTlPSANSpmibKhQagiYpFqAgggAACCCgk0PevYJm59XS4iN4uk12+alZUoUgJBQEEEEAAAdcEig1eLjfvPzYOntOpvPjnTmM8b/XTFtl87JrxfEDDgvJepdw0AF3jtsxRNAAtU2q1EqUBqFY9iAYBBBBAAAGzCAxcsE+mB54KF26bcjll6BuFzZIGcSKAAAIIIBCpQKmhK+Ta3UfGv8/uWE7K50lrPG8xKVCCTlw3ng9tXEjalM9FA5BrKkoBGoBcIF4RoAHoFXZOigACCCCAgOkFhv57QH7eeCJcHu9VzC0DGhU0fX4kgAACCCCAQJnhK+XK7YcGxG/vl5VKvumM501/2CQ7T98wno9oUkRalc1BA5BLhwYg14B6AjQA1asJESGAAAIIIGAGgZGLD8qk9cfDhdqpqo/0ru9nhhSIEQEEEEAAgSgFyo9cJRduPjDGTGtfRqrlz2A8b/z9Rtlz9qbxfFTzotKidHYagFxXNAC5BtQToAGoXk2ICAEEEEAAATMIjF52SCasORYu1E9q5JUedfKbIQViRAABBBBAIEqBSl+vlrP/3TfG/Ny2tNT0y2g8Dxi3QQ5cuGU8H9OimDQtmY0GINcVDUCuAfUEaACqVxMiQgABBBBAwAwCY1cckXGrjoYLtXvtfNK1pq8ZUiBGBBBAAAEEohSoNnqNnLx2zxgzuU0pqVMok/G87tj1cvjSbeP5dy1LyOvFstAA5LqiAcg1oJ4ADUD1akJECCCAAAIImEFg/Kqj8u2KI+FC/aJeAfmoWh4zpECMCCCAAAIIRClQ49u1cvzKXWPMxNYlpX6RzMbzV//9h9YlJeClf391cvbfXHA2Ab4EhOvAKwIsQF5h56QIIIAAAgiYXmDi2mPy9dJD4fLo18BPOlT2MX1+JIAAAggggECdsevkyKU7BsT4liWk0Uuf8Ks6eo2ciuITgjQAuYYiEqAByHXhFQEagF5h56QIIIAAAgiYXuCn9cdl+OKD4fIY0riQvFs+l+nzIwEEEEAAAQTqj9sgB1+6x9+4t4tL4+JZDZiKX62WczfC7hH4S7vSUqNA2D0CaQByDdEA5BpQRoAGoDKlIBAEEEAAAQRMJTB10wkZvPBAuJhHNi0iLf1zmCoXgkUAAQQQQCAigUbjN0rwubBv+f32zWLSrFTYl3yUHbFSLt16aBz663v+UiVf+kgx2X9zndkE+AQg14FXBFiAvMLOSRFAAAEEEDC9wIwtp6T/3/vC5fHNm8Wk+UubI9MnSgIIIIAAApYVaDxhk+w5c8PIf1SzotKiTHbjeamhK+Ta3UfG81kdykqFvOloAFr2inEucRqAzjkxys0CNADdDMp0CCCAAAIIWERgdtBp6T0/OFy2r/55lEU4SBMBBBBAQEOBZhM3y45T/xmZjWhSRFqVDfuUe7HBy+Xm/cfGv8/pVF78c6ehAajhteDOlGgAulOTuZwWoAHoNBUDEUAAAQQQQOAlgbnbz0jPP/eGM3n1GxJBQwABBBBAwKwCLSYFStCJ60b4Q98oLG3K5TSeFxqwVO4+emo8n/dRBSmVMzUNQLMWPJbipgEYS9Ccxl6ABiBXBAIIIIAAAgi4IvDXrrPy2R97wh065d3SUqtg5DdAd+VcHIMAAggggIA3BFr9tEU2H7tmnHpQo4LSrmJu43n+fkvk4ZNnxvN/ulSUotlS0QD0RrFMdE4agCYqlk6h0gDUqZrkggACCCCAQOwJLNxzXj6ZvSvcCae/5y9Vo7gBeuxFyJkQQAABBBCImUCbn7fKhqNXjUn6BvhJxyo+xvM8fRbL02fPjeeLulaSQllS0gCMGbv2R9MA1L7EaiZIA1DNuhAVAggggAACqgss3XdBPvxtZ7gwZ3UsKxXyRH4DdNXzIj4EEEAAAQReCHSYvl1WHrxkgPSsm186V88b+vz58+eSu/diO6zln1WRfBmT0wDkEopSgAYgF4hXBGgAeoWdkyKAAAIIIGB6gRUHLknHX7eHy+PPD8tL6VyR3wDd9ImTAAIIIICAZQQ6z9opi/ZeMPLtWiOvdK+TP/T546fPxLfvEjuLVT2qSp70yWgAWuYKcS1RGoCuuXFUDAVoAMYQkMMRQAABBBCwqMCaw5el/dRt4bJf0LmiFMse+f2PLMpF2ggggAACJhToPme3zN95zoi8UxUf6R3gF/r8weOnUqD/Urus1vesLjnSJqEBaMJax2bINABjU5tzGQI0ALkYEEAAAQQQQMAVgQ1Hr0ibn4PCHbq4a2UpmCWFK1NyDAIIIIAAAkoJ9J4fLLODThsxtauQSwa9Xij0+e0Hj6XIoOV28W76soZkTfUaDUClqqheMDQA1auJJSKiAWiJMpMkAggggAACbhcIPHZNWv60Jdy8K7tXkbwZIr//kdsDYUIEEEAAAQQ8JDDon/0ybfNJY/aW/jlkZNMioc9v3HskxYessDtzUJ+akiFFYhqAHqqHLtPSANSlkibLgwagyQpGuAgggAACCCgisP3kdWn+Y2C4aNb1rCY50yZVJErCQAABBBBAwHWBkYsPyqT1x40JmpbMKmNaFA99fuX2QykzfKXd5Dv61ZK0yRLRAHSd3BJH0gC0RJnVS5IGoHo1ISIEEEAAAQTMILDr9H/S5IfN4ULd/GUNyRLFnz+ZITdiRAABBBBAwCYwZvlh+W51iIHRoGhmmdCqZOjzizcfSLmRq+yg9gysIylfS0ADkMsnSgEagFwgXhGgAegVdk6KAAIIIICA6QX2nbspDcdvDJfHtr61JH3yyD/9YPrESQABBBBAwDICE9aEyOhlh418a/lllCltS4c+P3P9nlQetcbOYv/gupI0UXwagJa5QlxLlAaga24cFUMBGoAxBORwBBBAAAEELCpw8MItqT9uQ7js9wyoIymTRP7pB4tykTYCCCCAgAkFflp/XIYvPmhEXiVfevn1Pf/Q5yev3pVq36y1y+rwsHqSKH48GoAmrHVshkwDMDa1OZchQAOQiwEBBBBAAAEEXBEIuXxbao1ZH+7QA0PqSpKEkX/6wZVzcQwCCCCAAALeEPg18KQMWLDfOHXZ3Gnkj07lQ59H9HPw2IgAiRc3Dg1AbxTLROekAWiiYukUKg1AnapJLggggAACCMSeQESffLCd/ejw+pIgXtzYC4QzIYAAAggg4CGBP7adli/mBRuzF8+eSv7uXDH0+aGLt6Te/8I+CR8njsiJkQ2ijIT9t4cKZbJpaQCarGC6hMsCpEslyQMBBBBAAIHYFYjo3ke2CE6MDJA4tl0QDwQQQAABBEwu8Peuc/LpH7uNLPwyp5Al3SqHPn/1XrgJ4sWRo8MDaACavOaxET4NwNhQ5hzhBGgAclEggAACCCCAgCsCEX37YcJ4ceXI8PquTMcxCCCAAAIIKCewJPiCfDRzpxGXT/qksrpHtdDnu8/ckDcmbDL+7bUE8eTg0Ho0AJWronoB0QBUryaWiIgGoCXKTJIIIIAAAgi4XeDK7YdSZvhKu3mTJown+4dEvflxeyBMiAACCCCAgIcEVh28JO9P327Mni31a7Lxixqhz7efvC7Nfww0/i15ovgSPLguDUAP1UKnaWkA6lRNE+VCA9BExSJUBBBAAAEEFBK4ce+RFB+ywi6iVEkSyO4BdRSKklAQQAABBBBwXWDj0avyzs9bjQnSJ08k2/rWCn2+5fg1eXvyFuPfUidJILsc/Axk/+16LXQ6kgagTtU0US4sQCYqFqEigAACCCCgkMCdh0+k8MBldhG9vDFSKFRCQQABBBBAwCWBbSevy5svfcovReL4snfQ/33K79XmYLpkiWR7v/9rDkb2YP/tUhm0O4gGoHYlNUdCLEDmqBNRIoAAAgggoJrAg8dPpUD/pXZhZU31mmz68v/+NIoHAggggAACZhfYe/aGvP592H3+EsWPK4eH/d+9btceviztpm4zUsycMrEE9q4ZZcrsv81+RbgnfhqA7nFklmgKsABFE4zhCCCAAAIIIBAq8OTpM8nbd4mdRq60SWRtz+oIIYAAAgggoIXAoYu3pN7/Ntjl8uLb7lceuCQdfo34/oCRJc/+W4vLIsZJ0ACMMSETuCLAAuSKGscggAACCCCAwPPnzyV378V2EL4ZksmK7lXBQQABBBBAQAuBE1fvSvVv1trlcmRYfUkYP64s3XdRPvxth/FvudMllTWf/983BNMA1KL8HkuCBqDHaJmYBYhrAAEEEEAAAQQ8IZC3z2J58uy5MXXBzClkcbfKnjgVcyKAAAIIIBDrAudv3JcKX622O2/woDqSPHEC+Xfveekya5fxb3kzJJOVDn4JxgdwYr2ESp6QBqCSZdE/KBYg/WtMhggggAACCHhKoED/JfLg8TNj+mLZUsqCLpU8dTrmRQABBBBAIFYFrt15KKWGrbQ7p+2LPmxf+LFg9znp9vtu498KZEouSz+tEmV87L9jtXzKnowGoLKl0TswFiC960t2CCCAAAIIeFLA9i3Atm8DfvEonTO1/PlRBU+ekrkRQAABBBCINYGIvvHe9mVXti+9+nPHWfl87h4jlsJZU8i/n0T9KXj237FWOqVPRANQ6fLoGxwLkL61JTMEEEAAAQQ8LVB8yHK5ce+xcZryPmll9gflPH1a5kcAAQQQQCBWBB49eSb5+tl/4ZXtPn+2+/39se20fDEv2IijePZU8nfnilHGxf47Vsqm/EloACpfIj0DZAHSs65khQACCCCAQGwIlB62Qq7eeWScqkq+9PLre/6xcWrOgQACCCCAgMcFbF945dNnsTwPu92tLP20shTIlEJ+23JK+v29z4jBmU/Bs//2eMlMcQIagKYok35BsgDpV1MyQgABBBBAILYEyo1YJRdvPTBOV8svg0xpWya2Ts95EEAAAQQQ8LjAq/e7XdC5ohTLnkqmbz4pA//Zb5y/nE8a+f2D8lHGw/7b4+UyxQloAJqiTPoFyQKkX03JCAEEEEAAgdgSqPT1ajn7333jdPULZ5KJ75SKrdNzHgQQQAABBDwuUGzwcrl5P+x2F3M6lRf/3GlkyobjMmzRQeP8lfKmk986lKUB6PGKmP8ENADNX0NTZkAD0JRlI2gEEEAAAQSUEKg2eo2cvHbPiOX1Ylnku5YllIiNIBBAAAEEEHCHQJnhK+XK7YfGVDPe95fKvull0rpjMnLJIeP1avnTy7T2Ud8Gg/23Oypi/jloAJq/hqbMgAXIlGUjaAQQQAABBJQQqDVmnYRcvmPE0qxkNvm2RTElYiMIBBBAAAEE3CHw6qfdf25bWmr6ZZQJa0Jk9LLDximcuQ0G+293VMT8c9AANH8NTZkBC5Apy0bQCCCAAAIIKCFQ73/r5dDF20YsLf2zy8imRZWIjSAQQAABBBBwh0DNb9fKsSt3jal+aF1SAopklnErj8rYlUeM1+sVyiQ/ton6Nhjsv91REfPPQQPQ/DU0ZQYsQKYsG0EjgAACCCCghEDD8Rtk37lbRizvls8pQxoXViI2gkAAAQQQQMAdAgHjNsiBC2E/68a+VUyalMgm3y4/LONXhxinaFA0s0xoVTLKU7L/dkdFzD8HDUDz19CUGbAAmbJsBI0AAggggIASAm9M2CS7z9wwYnm/Um7p37CgErERBAIIIIAAAu4QaPLDJtl1Ouxn3VdNi8jb/jnk66WHZOLaY8Yp3iieRf73dtT3wWX/7Y6KmH8OGoDmr6EpM2ABMmXZCBoBBBBAAAElBJpP3CzbT/1nxPJh1TzyZf0CSsRGEAgggAACCLhD4K1JgbL1xHVjqiGNC8m75XPJ8EUH5KcNJ4zXnbkPLvtvd1TE/HPQADR/DU2ZAQuQKctG0AgggAACCCgh8PbkQNlyPGxT1LVGXuleJ78SsREEAggggAAC7hB495cgWX/kijFV3wA/6VjFRwYv3C9TN500Xn+7THb5qlnU98Fl/+2Oiph/DhqA5q+hKTNgATJl2QgaAQQQQAABJQTembJVNoZcNWLpUTuffFLTV4nYCAIBBBBAAAF3CHT8dbusOHDJmOrzOvmkSw1f6f/3Ppmx5ZTxeuuyOWR4kyJRnpL9tzsqYv45aACav4amzIAFyJRlI2gEEEAAAQSUEPhwxg5Zuv+iEcvARgWlfcXcSsRGEAgggAACCLhDoMusnfLv3gvGVJ/UyCs96uSX3vODZXbQaeP1dhVyyaDXC9EAdAe65nPQANS8wKqmRwNQ1coQFwIIIIAAAuoL/LPnvHSdvSs00ETx48rK7lUle5ok6gdOhAgggAACCDgp0H3Obpm/85wx+oMqPtInwE96zt0jc3ecNV535ouw2H87ia75MBqAmhdY1fRYgFStDHEhgAACCCBgDoE1hy/LwN0eAQAAIABJREFUgfO3pHbBjJIvY3JzBE2UCCCAAAIIOCkQ2Sf9uv+xW+bvCmsMdqrqI73r+0U5K/tvJ9E1H0YDUPMCq5oeC5CqlSEuBBBAAAEEEEAAAQQQQAABbwsM+me/TNsc9mUfLf2zy8imReWT2btk4Z7zRnidq+eRnnUL0AD0dsFMcH4agCYoko4h0gDUsarkhAACCCCAAAIIIIAAAggg4A6BkUsOyqR1x42pmpbIKmPeKi4fz9whi4PD7oPbraavfFY7Hw1Ad6BrPgcNQM0LrGp6NABVrQxxIYAAAggggAACCCCAAAIIeFtgzIoj8t2qo0YYDYpklgmtS8qr3w7co3Y++aSmLw1AbxfMBOenAWiCIukYIg1AHatKTggggAACCCCAAAIIIIAAAu4QmLAmREYvO2xMVcsvg0xpW0bem7ZNVh+6bLz+Rb0C8lG1PDQA3YGu+Rw0ADUvsKrp0QBUtTLEhQACCCCAAAIIIIAAAggg4G2BKRuOy7BFB40wKvumkxnvl5U2P2+VDUevGq/3DfCTjlV8aAB6u2AmOD8NQBMUyRMhVqtWTdatW+fU1M+fP3dqXHQG0QCMjhZjEUAAAQQQQAABBBBAAAEErCQwI/Ck9F+w30jZP3camdOpvLT6aYtsPnbNeH1go4LSvmJuGoBWujhczJUGoItwZj+MBqDZK0j8CCCAAAIIIIAAAggggAACugrM2XZGes3ba6RXLHsqWdC5orT4MVCCTl43Xh/auJC0KZ+LBqCuF4Ib86IB6EZMM031ogFYunRpmTp1apShFy5c2O2p8QlAt5MyIQIIIIAAAggggAACCCCAgCYCf+86J5/+sdvIpkCm5LL00yrS9IdNsvP0DeP1kU2LSEv/HDQANam7J9OgAehJXYXnftEArFq1qqxduzbWI6UBGOvknBABBBBAAAEEEEAAAQQQQMAkAkuCL8hHM3ca0fqkTyqre1ST17/fKHvP3jReH9W8qLQonZ0GoEnq6s0waQB6U9+L56YB6EV8To0AAggggAACCCCAAAIIIIBAFAKrD12S96ZtN0ZkTfWabPqyhgSM2yAHLtwyXh/7VjFpUiIbDUCuJocCNAAdEuk5gAagnnUlKwQQQAABBBBAAAEEEEAAAfMLbAq5Kq2nbDUSSZcskWzvV0vqjF0nRy7dMV7/rmUJeb1YFhqA5i+5xzOgAehxYjVPQANQzboQFQIIIIAAAggggAACCCCAAALbT16X5j8GGhDJE8eX4EF1pca3a+X4lbvG6xNbl5T6RTLTAOSScShAA9AhkZ4DXjQAM2bMKDlz5pTDhw/LgwcPJF26dFKqVClp1qyZtGzZUhIkSOARAO4B6BFWJkUAAQQQQAABBBBAAAEEENBAYO/ZG/L695uMTBLGjytHhtWXKqPWyOnr94zXJ7cpJXUKZaIBqEHNPZ0CDUBPCys6/4sGYFThFSxYUP7880/x8/OLdha2Bl9UjwsXLoi/v3/okDNnzki2bFHfsyDaAXAAAggggAACCCCAAAIIIIAAAiYVOHzxttT933q76E+MDJBKX6+RczfuG69PbVdGqhfIQAPQpHWOzbBpAMamtkLnqlGjhsSNG1cCAgKkWLFikjZtWrl9+7bs3LlTJk2aJAcPHgyN1vYJwaCgIMmRI+qvFX81tThx4jidLQ1Ap6kYiAACCCCAAAIIIIAAAgggYAGBk1fvSrVv1tplenhYPan89Rq5fPuh8fqv7/lLlXzpaQBa4JqIaYo0AGMqaNLjb9y4IalSpYow+sePH0vHjh1l+vTpof/epEkTmT9/frQypQEYLS4GI4AAAggggAACCCCAAAIIIGAIXLh5X8qPXG0nsndQHak+eq1cu/vIeH1Wx7JSIU86GoBcOw4FaAA6JPLegOg00SKLcurUqdKuXbtoJ/HkyRMpXLhw6L0BbQ/bn/RmzZrV6Xn4E2CnqRiIAAIIIIAAAggggAACCCCAgJ3AtTsPpdSwlXavbetbS2p+u1ZuPXhivD6nU3nxz52GBiDXj0MBGoAOibw3wJsNQFvWo0ePll69eoUCzJw5U1q1auU2DL4ExG2UTIQAAggggAACCCCAAAIIIKCZwJ2HT6TwwGV2WW38orrUHbte7j56arw+/+MKUjJHahqAmtXfE+nQAPSEqpvmPHToUIxnypw5s6RMmdKleRYtWiQNGzYMPXbUqFHSs2dPl+aJ6CAagG6jZCIEEEAAAQQQQAABBBBAAAHNBB4/fSa+fZfYZbW6R1WpN26DPHryzHj9ny4VpWi2iG/v9WIQ+2/NLg4X06EB6CKcFQ5bvHixNGjQgAagFYpNjggggAACCCCAAAIIIIAAAsoIPH/+XPL0WSzPnoeFtKRbZWk4fqM8fenFxV0rS8EsKaKMmwagMmX1aiA0AL3Kr/bJv/nmG+NTf7/99pu0bt3abQGzALmNkokQQAABBBBAAAEEEEAAAQQ0FPDrv1TuPw77c9+/Pq4gTX7YbJfp8s+qSL6MyWkAalh/d6dEA9DdoprMZ/sSkKJFi8rBgwdDMzp9+rRkz57dbdnRAHQbJRMhgAACCCCAAAIIIIAAAghoKFBs8HK5ef+xkdnMDmWl9ZStdpna/izYJ30yGoAa1t/dKdEAdLeoCeZbs2aNlChRQlKlivg+AY8fP5aOHTvK9OnTQ7Np1KiR/PPPP27NjAagWzmZDAEEEEAAAQQQQAABBBBAQDMB/+Er5fLth0ZWk9uUkg9m7LDLcn3P6pIjbRIagJrV3hPp0AD0hKric7Zr107mzZsnr7/+ulSrVk3y588vKVKkkDt37siOHTtk8uTJcuDAgdAsMmTIIFu2bJHcuXO7NSsagG7lZDIEEEAAAQQQQAABBBBAAAHNBCqPWi1nrt83svrfW8Xl0z9222W5+csakiXVazQANau9J9KhAegJVcXntDUAX3y6L6pQixQpIr///rsULFjQ7RnRAHQ7KRMigAACCCCAAAIIIIAAAghoJFBrzDoJuXzHyGjYG4Wl39/77DIM6lNTMqRITANQo7p7KhUagJ6SVXhe2339li1bJoGBgaGf9Lty5Ypcv35dEiVKJBkzZpTSpUtL8+bNpUmTJhIvXjyPZEID0COsTIoAAggggAACCCCAAAIIIKCJQMC4DXLgwi0jmz4BBWTE4kN22e3sX1vSJE1IA1CTmnsyDRqAntRl7kgFaABycSCAAAIIIIAAAggggAACCCAQuUCTHzbJrtM3jAHdavrKuFVH7Q7YM7COpHwtAQ1ALiSHAjQAHRIxwBMCNAA9ocqcCCCAAAIIIIAAAggggAACugi8PTlQthy/bqTTsXJu+WnDCbv0DgypK0kSxqcBqEvRPZgHDUAP4jJ15AI0ALk6EEAAAQQQQAABBBBAAAEEEIhcoO0vQbLuyBVjQEv/HDI76LTdAYeH1ZNE8aO+dRf7b64ymwANQK4DrwiwAHmFnZMigAACCCCAAAIIIIAAAgiYROCDX7fL8gOXjGgbF88iC3aft4v++IgAiRs3TpQZsf82ScE9HCYNQA8DM33EAixAXBkIIIAAAggggAACCCCAAAIIRC7QZdZO+XfvBWNALb+MsvJgWEMwThyREyMbOCRk/+2QyBIDaABaoszqJckCpF5NiAgBBBBAAAEEEEAAAQQQQEAdgR5z9si8nWeNgCrkSSubj10znieMF1eODK/vMGD23w6JLDGABqAlyqxekixA6tWEiBBAAAEEEEAAAQQQQAABBNQR6PNXsMzaGnbPv2LZUsqeszeNAF9LEE8ODq3nMGD23w6JLDGABqAlyqxekixA6tWEiBBAAAEEEEAAAQQQQAABBNQRGLxwv0zddNIIKG+GZBJy+Y7xPHni+BI8qK7DgNl/OySyxAAagJYos3pJsgCpVxMiQgABBBBAAAEEEEAAAQQQUEdg5JKDMmndcSOgzCkTy4WbD4znqZMkkF0D6jgMmP23QyJLDKABaIkyq5ckC5B6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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcedc629250>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "data.infl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-09-01    0.313532\n",
       "2019-09-02   -0.022779\n",
       "2019-09-03   -1.431959\n",
       "2019-09-04   -1.628216\n",
       "2019-09-05   -0.263348\n",
       "                ...   \n",
       "2019-12-15    1.949090\n",
       "2019-12-16    0.039795\n",
       "2019-12-17   -0.425080\n",
       "2019-12-18   -1.332584\n",
       "2019-12-19   -1.845546\n",
       "Freq: D, Length: 110, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2019-09-01', periods=110, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-09-30   -0.413720\n",
       "2019-10-31   -0.040300\n",
       "2019-11-30   -0.004320\n",
       "2019-12-31    0.269151\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-09   -3.115553\n",
       "2019-10   -2.315550\n",
       "2019-11   -1.479132\n",
       "2019-12   -1.845546\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M', kind='period').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01 00:00:00     0\n",
       "2019-01-01 00:01:00     1\n",
       "2019-01-01 00:02:00     2\n",
       "2019-01-01 00:03:00     3\n",
       "2019-01-01 00:04:00     4\n",
       "2019-01-01 00:05:00     5\n",
       "2019-01-01 00:06:00     6\n",
       "2019-01-01 00:07:00     7\n",
       "2019-01-01 00:08:00     8\n",
       "2019-01-01 00:09:00     9\n",
       "2019-01-01 00:10:00    10\n",
       "2019-01-01 00:11:00    11\n",
       "Freq: T, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2019-01-01', periods=12, freq='T')\n",
    "ts = pd.Series(np.arange(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01 00:00:00    10\n",
       "2019-01-01 00:05:00    35\n",
       "2019-01-01 00:10:00    21\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-18 21:54:00</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-18 21:59:00</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-18 22:04:00</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-18 22:09:00</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-18 22:14:00</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2019-12-19 09:58:00</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2019-12-19 10:19:00</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2019-12-19 10:20:00</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2019-12-19 10:22:00</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2019-12-19 10:23:00</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dt  temp\n",
       "0  2019-12-18 21:54:00  4.61\n",
       "1  2019-12-18 21:59:00  4.61\n",
       "2  2019-12-18 22:04:00  4.61\n",
       "3  2019-12-18 22:09:00  4.59\n",
       "4  2019-12-18 22:14:00  4.59\n",
       "..                 ...   ...\n",
       "56 2019-12-19 09:58:00  8.28\n",
       "57 2019-12-19 10:19:00  8.68\n",
       "58 2019-12-19 10:20:00  8.68\n",
       "59 2019-12-19 10:22:00  8.68\n",
       "60 2019-12-19 10:23:00  8.68\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "from FrozenJSON import FrozenJSON\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def loads(JSON):\n",
    "    with open(JSON) as fp:\n",
    "        return json.load(fp)\n",
    "\n",
    "def get_data():\n",
    "    files = [os.path.basename(x) for x in glob.glob(\"../data/weather/*\")]\n",
    "    data = {}\n",
    "    for file in files:\n",
    "        feed = FrozenJSON(loads('../data/weather/'+file))\n",
    "        temp = float(feed.main.temp) - 273.15\n",
    "        ctime = file[12:-5]\n",
    "        data[ctime] = '{: .2f}'.format(temp)\n",
    "    return data\n",
    "\n",
    "def format_data(data):\n",
    "    df = pd.DataFrame.from_dict(data, orient='index')\n",
    "    df = df.sort_index().reset_index()\n",
    "    df.columns = ['dt', 'temp']\n",
    "    df['dt'] = pd.to_datetime(df['dt'])\n",
    "    df['temp'] = pd.to_numeric(df['temp'])\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "data = get_data()\n",
    "df = format_data(data)\n",
    "df.head(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcedc708d30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "df.index = df.dt\n",
    "df.temp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.resample(\"T\").ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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Ls0p9VEagKlXuKD5uYAKWhhhESBAgAABAgQIECBAgEBSAt7fSZWz4mQ0ACumM7EaARdQNXrmEiBAgAABAgQIECBAgACB0gS8v0tzSn2UBmDqFS5ofi6gghZGWAQIECBAgAABAgQIECCQlID3d1LlrDgZDcCK6UysRsAFVI2euQQIECBAgAABAgQIECBAoDQB7+/SnFIfpQGYeoULmp8LqKCFERYBAgQIECBAgAABAgQIJCXg/Z1UOStORgOwYjoTqxFwAVWjZy4BAgQIECBAgAABAgQIEChNwPu7NKfUR2kApl7hgubnAipoYYRFgAABAgQIECBAgAABAkkJeH8nVc6Kk9EArJjOxGoEXEDV6JlLgAABAgQIECBAgAABAgRKE/D+Ls0p9VEagKlXuKD5uYAKWhhhESBAgAABAgQIECBAgEBSAt7fSZWz4mQ0ACumM7EaARdQNXrmEiBAgAABAgQIECBAgACB0gS8v0tzSn2UBmDqFS5ofi6gghZGWAQIECBAgAABAgQIECCQlID3d1LlrDgZDcCK6UysRsAFVI2euQQIECBAgAABAgQIECBAoDQB7+/SnFIfpQGYeoULmp8LqKCFERYBAgQIECBAgAABAgQIJCXg/Z1UOStORgOwYjoTqxFwAVWjZy4BAgQIECBAgAABAgQIEChNwPu7NKfUR2kApl7hgubnAipoYYRFgAABAgQIECBAgAABAkkJeH8nVc6Kk9EArJjOxGoEXEDV6JlLgAABAgQIECBAgAABAgT+W2Dm7Dnx9GvvxwefzMz+cdKbb8S+gzZq+37ChAnRu3dvdE0ooAHYhEUvQsoagEWoghgIECBAgAABAgQIECBAIBWBj2fMjv/5/bB46rX326U064PJ8fpFX9cATKXQFeahAVghnGnVCWgAVudnNgECBAgQIECAAAECBAgQ+E+BO8e8FQddOvy/UDQAnZNWAQ1A5yAXAQ3AXNhtSoAAAQIECBAgQIAAAQKJClz56Pg47vpRGoCJ1rfatDQAqxU0vyIBDcCK2EwiQIAAAQIECBAgQIAAAQLzFLh02Lg46YbR2b9169wpllq4W0x/f1KM/MV+bf/d3wBs3sOjAdi8tc81cw3AXPltToAAAQIECBAgQIAAAQKJCfzxwVfitH+NybLaarWl4vKDNg/v78QKXWE6GoAVwplWnYALqDo/swkQIECAAAECBAgQIECAwH8K/O6+l+LMW8Zm/2nQmkvHX76xmQagY9ImoAHoIOQioAGYC7tNCRAgQIAAAQIECBAgQCBRgd/c/UKcffvzWXZD1lk2/vDVTTQAE613uWlpAJYrZnxNBDQAa8JoEQIECBAgQIAAAQIECBAg0CZw3h3Px6/ueiHT2LnfcvHb/ftrADofbQIagA5CLgIagLmw25QAAQIECBAgQIAAAQIEEhX45W1j48J7Xsqy222DFeKC/TbSAEy03uWmpQFYrpjxNRHQAKwJo0UIECBAgAABAgQIECBAgECbwJk3Pxu/u//lTGPPjXvFuV/eUAPQ+WgT0AB0EHIR0ADMhd2mBAgQIECAAAECBAgQIJCowKn/HBN/euiVLLt9N1kxfrH3+hqAida73LQ0AMsVM74mAhqANWG0CAECBAgQIECAAAECBAgQaBM48R/PxGWPvJppHDCgT5y+Rz8NQOejTUAD0EHIRUADMBd2mxIgQIAAAQIECBAgQIBAogLHXvd0XPXYhCy7r2+5cpyy27oagInWu9y0NADLFTO+JgIagDVhtAgBAgQIECBAgAABAgQIEGgT+OE1T8W1I17LNA7eepU4fpd1NACdjzYBDUAHIRcBDcBc2G1KgAABAgQIECBAgAABAokKHHX1E/GPJ9/Isjt00Krx46FraQAmWu9y09IALFfM+JoIaADWhNEiBAgQIECAAAECBAgQIECgTeC7V46Mm56emGkcsd1qcfQOa2oAOh9tAhqADkIuAhqAubDblAABAgQIECBAgAABAgQSFfj2pcPj9jFvZdn9YMga8b3Bq2sAJlrvctPSACxXzPiaCGgA1oTRIgQIECBAgAABAgQIECBAoE3gm395PO4e+3am0frrv62/Buz97YC0CmgAOge5CLiAcmG3KQECBAgQIECAAAECBAgkKnDgHx+NB16YnGV3wi5rx0Fb99UATLTe5aalAViumPE1EdAArAmjRQgQIECAAAECBAgQIECAQJvAfr9/JIa9/E6mccqu68TXB66iAeh8tAloADoIuQhoAObCblMCBAgQIECAAAECBAgQSFRgn4sfjsfHvZdld/oe68UBA1bSAEy03uWmpQFYrpjxNRHQAKwJo0UIECBAgAABAgQIECBAgECbwO4XPhRPTZiSaZy11/rx5U1X1AB0PtoENAAdhFwENABzYbcpAQIECBAgQIAAAQIECCQqsMsFD8ToNz7Isjv3yxvEnhv31gBMtN7lpqUBWK6Y8TUR0ACsCaNFCBAgQIAAAQIECBAgQIBAm8CO590fz731YaZxwX4bxW4brKAB6Hy0CWgAOgi5CGgA5sJuUwIECBAgQIAAAQIECBBIVGC7c+6Nlyd9lGV30f4bx079ltcATLTe5aalAViumPE1EdAArAmjRQgQIECAAAECBAgQIECAQJvANmfdE+PfnZZp/OGrm8SQdZbVAHQ+2gQ0AB2EXAQ0AHNhtykBAgQIECBAgAABAgQIJCqwxZl3xcT3P8my+/M3No1t11xGAzDRepeblgZguWLG10RAA7AmjBYhQIAAAQIECBAgQIAAAQJtApucfmdMnjo907jioM1j4GpLaQA6H20CGoAOQi4CGoC5sNuUAAECBAgQIECAAAECBBIV2PDU22PKtJlZdn/79oDYvO+SGoCJ1rvctDQAyxUzviYCGoA1YbQIAQIECBAgQIAAAQIECBBoE1jv5Nti6vRZmcbfD90y+q+0uAag89EmoAHoIOQioAGYC7tNCRAgQIAAAQIECBAgQCBRgTVOuCVmzJqTZXfj4QNj/d6LaQAmWu9y09IALFfM+JoIaADWhNEiBAgQIECAAAECBAgQIECgTaDvsTfFnLmfYtx8xNaxzgo9NQCdjzYBDUAHIRcBDcBc2G1KgAABAgQIECBAgAABAgkKzJkzN/oed3O7zO48eptYbZlFNAATrHclKWkAVqJmTtUCGoBVE1qAAAECBAgQIECAAAECBAi0CUyfNTvWPOHWdhr3/nBQrLxUDw1AZ6RNQAPQQchFQAMwF3abEiBAgAABAgQIECBAgECCAh9NnxXrnnxbu8we/PG20XvxhTQAE6x3JSlpAFaiZk7VAhqAVRNagAABAgQIECBAgAABAgQItAlMmTYjNjz1jnYajx43OJbt2V0D0BlpE9AAdBByEdAAzIXdpgQIECBAgAABAgQIECCQoMCkD6fHpmfc2S6zESdsH0suvIAGYIL1riQlDcBK1MypWkADsGpCCxAgQIAAAQIECBAgQIAAgTaBie9/HFuceXc7jadP2SF6du+qAeiMtAloADoIuQhoAObCblMCBAgQIECAAAECBAgQSFBgwrvTYuuz7mmX2bOnDo0Fu3XWAEyw3pWkpAFYiZo5VQtoAFZNaAECBAgQIECAAAECBAgQINAm8PKkqbHdOfe103jhjJ2ia+dOGoDOSJuABqCDkIuABmAu7DYlQIAAAQIECBAgQIAAgQQFnnvzw9jx/PvbZfbKmTtHS0uLBmCC9a4kJQ3AStTMqVpAA7BqQgsQIECAAAECBAgQIECAAIE2gWdefz+++OsHM40unVrixZ/t3Pa997dD0iqgAegc5CLgAsqF3aYECBAgQIAAAQIECBAgkKDAkxOmxB4XPpRltmDXzvHsaUM1ABOsdaUpaQBWKmdeVQIagFXxmUyAAAECBAgQIECAAAECBDKB4ePejb0vHpZ9v0j3LjHqlB01AJ2RTEAD0GHIRUADMBd2mxIgQIAAAQIECBAgQIBAggLDXnon9vvDI1lmS/ToFiNPHKIBmGCtK01JA7BSOfOqEtAArIrPZAIECBAgQIAAAQIECBAgkAnc//yk+OqfHsu+X2aRBeKx47fXAHRGMgENQIchFwENwFzYbUqAAAECBAgQIECAAAECCQrcPfat+OZfhmeZ9VpswXjoJ9tpACZY60pT0gCsVM68qgQ0AKviM5kAAQIECBAgQIAAAQIECGQCt41+M75z2Yjs+5WWXCjuO2ZbDUBnJBPQAHQYchHQAMyF3aYECBAgQIAAAQIECBAgkKDATU9PjO9eOTLLbLVlFo47j/6CBmCCta40JQ3ASuXMq0pAA7AqPpMJECBAgAABAgQIECBAgEAmcMOTr8eRVz+Zfb/WcovErUdtowHojGQCGoAOQy4CGoC5sNuUAAECBAgQIECAAAECBBIUuGb4hDjm2qezzPr1WjT++b2tNAATrHWlKWkAVipnXlUCGoBV8ZlMgAABAgQIECBAgAABAgQygaseGx/HXjcq+36jPovF9YcN1AB0RjIBDUCHIRcBDcBc2G1KgAABAgQIECBAgAABAgkKXDZsXJx4w+gss81WXiL+95AtNAATrHWlKWkAVipnXlUCGoBV8ZlMgAABAgQIECBAgAABAgQygT89+Eqc+q8x2fcDV1syrjhogAagM5IJaAA6DLkIaADmwm5TAgQIECBAgAABAgQIEEhQ4Pf3vxQ/u3lsltkX1lg6/vrNzTQAE6x1pSlpAFYqZ15VAhqAVfGZTIAAAQIECBAgQIAAAQIEMoEL73kxfnnbc9n326+9TFzytU01AJ2RTEAD0GHIRUADMBd2mxIgQIAAAQIECBAgQIBAggLn3/l8nH/nC1lmQ9ddLi4+sL8GYIK1rjQlDcBK5cyrSkADsCo+kwkQIECAAAECBAgQIECAQCZw9m3PxW/ueTH7/ovrLx+/+crGGoDOSCagAegw5CKgAZgLu00JECBAgAABAgQIECBAIEGBM295Nn5338tZZntu1CvO3XdDDcAEa11pShqAlcqZV5WABmBVfCYTIECAAAECBAgQIECAAIFM4LR/jYk/PvhK9v2XN+kdZ+29gQagM5IJaAA6DLkIaADmwm5TAgQIECBAgAABAgQIEEhQ4KQbnolLh72aZfaVzfvEz77UTwMwwVpXmpIGYKVy5lUloAFYFZ/JBAgQIECAAAECBAgQIEAgEzj2ulFx1WPjs++/tsVK8dPd19MAdEYyAQ1AhyEXAQ3AXNhtSoAAAQIECBAgQIAAAQIJChxzzVNxzYjXssy+tdUqceIX19EATLDWlaakAVipnHlVCWgAVsVnMgECBAgQIECAAAECBAgQyAS+/7cn4/onXs++/84X+saxO62tAeiMZAIagA5DLgIagLmw25QAAQIECBAgQIAAAQIEOkjgk5mzY+7cDlp8Psv+4Jon4+ZRb2ajvrfdavGDHdbUAMynHIXcVQOwkGVJPygNwPRrLEMCBAgQIECAAAECBAg0g8DLk6bGYVeMjLFvfliYdL+//Rpx5ParawAWpiL5B6IBmH8NmjICDcCmLLukCRAgQIAAAQIECBAgkJzAIZeNiFtHf/rTd0VI8Jgd14zvbruaBmARilGQGDQAC1KIZgtDA7DZKi5fAgQIECBAgAABAgQIpCmw/bn3xYtvTy1Uchfst1HstsEKGoCFqkq+wWgA5uvftLtrADZt6SVOgAABAgQIECBAgACBpAS2OeueGP/utMLktO2aS8dFB/SP7l07awAWpir5B6IBmH8NmjICDcCmLLukCRAgQIAAAQIECBAgkJzAgJ/dFW9+8EmW19n7bBCbr7JELnn2WKBLLNGjW7u9vb9zKUXhNtUALFxJmiMgF1Bz1FmWBAgQIECAAAECBAgQSF1g49PuiHc/mpGleeXBm8eWqy5VmLS9vwtTilwD0QDMlb95N3cBNW/tZU6AAAECBAgQIECAAIGUBNY7+baYOn1WltLfD90i+q+Uz08AzsvV+zul01Z5LhqAlduZWYWAC6gKPFMJECBAgAABAgQIECBAoDACa+ImwHkAACAASURBVBx/S8yYPSeL55+HbxX9ei9amPi8vwtTilwD0QDMlb95N3cBNW/tZU6AAAECBAgQIECAAIFUBObMmRt9j7u5XTq3HbVNrLncIoVJ0fu7MKXINRANwFz5m3dzF1Dz1l7mBAgQIECAAAECBAgQSEXgk5mzY60Tb22Xzr0/HBQrL9WjMCl6fxemFLkGogGYK3/zbu4Cat7ay5wAAQIECBAgQIAAAQKpCHzwycxY/5Tb26Xz8E+2ixUWW7AwKXp/F6YUuQaiAZgrf/Nu7gJq3trLnAABAgQIECBAgAABAqkITJ46PTY5/c526Qw/YftYauEFCpOi93dhSpFrIBqAufI37+YuoOatvcwJECBAgAABAgQIECCQisAbUz6OLX9+d7t0nj5lh+jZvWthUvT+Lkwpcg1EAzBX/ubd3AXUvLWXOQECBAgQIECAAAECBFIRGDf5oxh09r3t0hl72tDo3rVzYVL0/i5MKXINRAMwV/7m3dwF1Ly1lzkBAgQIECBAgAABAgRSEXj+rQ9jh/Pub5fOK2fuHC0tLYVJ0fu7MKXINRANwFz5m3dzF1Dz1l7mBAgQIECAAAECBAgQSEVg1Gvvx66/eTBLp1vnTvH8GTsVKj3v70KVI7dgNABzo2/ujV1AzV1/2RMgQIAAAQIECBAgQCAFgRGvvht7XTQsS2XhBbrEMz/dsVCpeX8Xqhy5BaMBmBt9c2/sAmru+sueAAECBAgQIECAAAECKQg8/NLk+MofHs1SWaJHtxh54pBCpeb9Xahy5BaMBmBu9M29sQuouesvewIECBAgQIAAAQIECKQgcO9zb8fX//x4lspyPbvHI8cNLlRq3t+FKkduwWgA5kbf3Bu7gJq7/rInQIAAAQIECBAgQIBACgK3j34zvn3ZiCyVPkssFPf/aNtCpeb9Xahy5BaMBmBu9M29sQuouesvewIECBAgQIAAAQIECKQg8K+n34jDr3wiS2X1ZRaOO47+QqFS8/4uVDlyC0YDMDf65t7YBdTc9Zc9AQIECBAgQIAAAQIEUhD4+4jX4gfXPJWlsu4KPeOmI7YuVGre34UqR27BaADmRt/cG7uAmrv+sidAgAABAgQIECBAgEAKAlc9Nj6OvW5UlspGfRaL6w8bWKjUvL8LVY7cgtEAzI2+uTd2ATV3/WVPgAABAgQIECBAgACBFAT++vC4OPnG0Vkqm6+yRPztO1sUKjXv70KVI7dgNABzo2/ujV1AzV1/2RMgQIAAAQIECBAgQCAFgT/c/3KccfOzWSpbr75UXPatzQuVmvd3ocqRWzAagLnRN/fGLqDmrr/sCRAgQIAAAQIECBAgkILAhfe8GL+87bksle3XXjYu+domhUrN+7tQ5cgtGA3A3Oibe2MXUHPXX/YECBAgQIAAAQIECBBIQeDcO56PC+56IUtll37Lx4X7b1yo1Ly/C1WO3ILRAMyNvrk3dgE1d/1lT4AAAQIECBAgQIAAgRQEzrzl2fjdfS9nqXxpo15x3r4bFio17+9ClSO3YDQAc6Nv7o1dQM1df9kTIECAAAECBAgQIEAgBYGf/nN0/PmhcVkq+26yYvxi7/ULlZr3d6HKkVswGoC50Tf3xi6g5q6/7AkQIECAAAECBAgQIJCCwPHXj4orHh2fpXLggJXitD3WK1Rq3t+FKkduwWgA5kbf3Bu7gJq7/rInQIAAAQIECBAgQIBACgLHXPNUXDPitSyVb221Spz4xXUKlZr3d6HKkVswGoC50Tf3xi6g5q6/7AkQIECAAAECBAgQIJCCwJFXPxE3PPlGlsphg1aNHw1dq1CpeX8Xqhy5BaMBmBt9c2/sAmru+sueAAECBAgQIECAAAECKQgcevmIuOWZN7NUjtp+9Thq+zUKlZr3d6HKkVswGoC50Tf3xi6g5q6/7AkQIECAAAECBAgQIJCCwLf+8njcNfbtLJUfDV0zDhu0WqFS8/4uVDlyC0YDMDf65t7YBdTc9Zc9AQIECBAgQIAAAQIEUhA44JJH48EXJ2epnLDL2nHQ1n0LlZr3d6HKkVswGoC50Tf3xi6g5q6/7AkQIECAAAECBAgQIJCCwJcvHhaPjXs3S+W03deNA7dYuVCpeX8Xqhy5BaMBmBt9c2/sAmru+sueAAECBAgQIECAAAECKQjsfuFD8dSEKVkqv9irX+y7aZ9Cpeb9Xahy5BaMBmBu9M29sQuouesvewIECBAgQIAAAQIECKQgsNOvHohnJ36QpXL+vhvGHhv1KlRq3t+FKkduwWgA5kZfjI1nzJgRl156aVxzzTXx9NNPx7vvvhtdu3aNXr16xZZbbhkHH3xw2//W+ssFVGtR6xEgQIAAAQIECBAgQIBAvQUGn3NvvDTpo2zb3+6/cezcb/l6h/G5+3l/F6ocuQWjAZgbff4bv/rqq7HLLrvE6NGjPzeY733ve/GrX/0qWlpaaha0C6hmlBYiQIAAAQIECBAgQIAAgZwEtj7r7pjw7sfZ7pd8dZPYfp1lc4pm3tt6fxeqHLkFowGYG32+G8+cOTM22mijrPm3/vrrx9FHHx1rrrlmfPjhh/Hggw/GOeecEx999P//Pxlnnnlm/OQnP6lZ0C6gmlFaiAABAgQIECBAgAABAgRyEtjsjDvj7Q+nZ7tf+s3NYps1ls4pGg3AQsEXLBgNwIIVpF7hXHvttbHPPvu0bbfFFlvEAw88EJ07d263/YgRI9r+rbVZuNhii8WkSZOiS5cuNQlRA7AmjBYhQIAAAQIECBAgQIAAgRwFNjz19pgybWYWwdXfHhAD+i6ZY0T/vbX3d6HKkVswGoC50ee7cetP+5133nltQdx4442x6667zjOgPffcM66//vq2f2v9G4H9+vWrSeAuoJowWoQAAQIECBAgQIAAAQIEchRY56RbY9qM2VkE1x+2ZWzUZ/EcI9IALBR+gYLRACxQMeoZyuGHHx4XXnhh25bPPPNMrLvuuvPc/phjjomzzz677d+GDx8e/fv3r0mYGoA1YbQIAQIECBAgQIAAAQIECOQosNpxN8esOXOzCG46YqtYd4VFc4xIA7BQ+AUKRgOwQMWoZyi//vWv44gjjmjbspSfAGz9AJApU6ZEz549axKmBmBNGC1CgAABAgQIECBAgAABAjkJzJkzN/oed3O73e88eptYbZlFcopo3tt6fxeqHLkFowGYG32+G0+ePDlWXXXV+OCDD2LgwIFx3333/dffAHziiSdiwIABMWPGjNh///3j8ssvr1nQLqCaUVqIAAECBAgQIECAAAECBHIQ+GTm7FjrxFvb7Xz/MdtGnyUXyiGaz97S+7tQ5cgtGA3A3Ojz37j1J//222+/mDZtWtsnAh911FGxxhprxNSpU+Ohhx5q+xTg1k8E3njjjePmm2+OZZct/aPMWy+Yz/uaOHFibLbZZm1DJkyYEL17984fRAQECBAgQIAAAQIECBAgQKBEgfc/nhkb/PT2dqMfOXZwLLdo9xJXqM8wDcD6OBd9Fw3Aoleog+MbO3ZsW6Pvj3/8Y8yd++nfLWjdtrXhd+yxx8bBBx8cCy1U3v+D0forw6V+aQCWKmUcAQIECBAgQIAAAQIECHSUwD3PvR1XPzY+PvxkVklbzJw9Jx4f9167sSNPHBJL9OhW0vx6DdIArJd0sffRACx2fTo0utZf7T3llFPikksuiUmTJs1zr0022SROPPHE2G233cqKRQOwLC6DCRAgQIAAAQIECBAgQCBHgZcnTY0h590fs//jAz0qCWf0T3eMHgt0qWRqh83RAOww2oZaWAOwocpVu2A/+uij2GmnneKBBx5o+9t/P/jBD+Ib3/hG9O3bNz755JN49NFH49RTT40HH3wwWpt5rZ8EfPTRR5ccgF8BLpnKQAIECBAgQIAAAQIECBDIWeCqx8bHsdeNqiqK7l07xTOn7BhdOneqap1aT9YArLVoY66nAdiYdas66mOOOaatqdf69Ze//CW+9rWv/deas2bNih122CHuueee6NSpU4wcOTI22GCDqvduXcAFVBNGixAgQIAAAQIECBAgQIBADQQuvu+l+PktY6ta6atbrBSn7r5eVWt0xGTv745Qbbw1NQAbr2ZVR9z6t/6WWmqpePfdd9s+9OO55577zDVbPwxkq622avv31g8JOe+886reXwOwJoQWIUCAAAECBAgQIECAAIEaCfzytrFx4T0vZaut16tn7L1x6R9WufJSPWLr1ZeOzp1K/3v4NQp9vstoAM6XqCkGaAA2RZnbJ/nmm2/G8ssv3/Yf991337j66qs/U6H114EXXHDBtn8fOnRo3HLLLTURcwHVhNEiBAgQIECAAAECBAgQIFADgZNueCYuHfZqttJ+m60YZ+65fg1Wzn8J7+/8a1CECDQAi1CFOscwefLkWHrppdt23WuvveLaa6/9zAg+/PDD6NmzZ9u/f/GLX4x//vOfNYnWBVQTRosQIECAAAECBAgQIECAQA0Ejv7bk3HdE69nK317m75x3M5r12Dl/Jfw/s6/BkWIQAOwCFWocwxz5syJxRdfPD744INYYYUV4tVXX40uXeb9KUX/+te/Ytddd22L8Hvf+15ccMEFNYnWBVQTRosQIECAAAECBAgQIECAQA0EDvrr8Ljz2beylX4wZI343uDVa7By/kt4f+dfgyJEoAFYhCrkEMNXvvKVuOqqq9p2PuWUU+Lkk0/+ryjee++9tr//N2bMmLZ/u+2229o+FKQWXy6gWihagwABAgQIECBAgAABAgRqIbDv74bFo6+8my11yq7rxNcHrlKLpXNfw/s79xIUIgANwEKUof5BjB07Nvr37x/Tpk1r27z1p/xaPwm4b9++0fp3/x555JE4//zzY/z48W3/Pnjw4LjzzjtrFqgLqGaUFiJAgAABAgQIECBAgACBKgV2/tUDMWbiB9kqZ++zQezdv/QPAaly+w6d7v3dobwNs7gGYMOUqvaBtjb09ttvv2j9m4Cf97Xddtu1/Z3A1l8brtWXC6hWktYhQIAAAQIECBAgQIAAgWoFtj7r7pjw7sfZMr87sH/suO5y1S5biPne34UoQ+5BaADmXoJ8A3jnnXfij3/8Y9un+44ePTqmTJnS9vcAl1tuudh0002j9VeFd9ttt2hpqe1HmbuA8q273QkQIECAAAECBAgQIEDgU4ENT709pkybmf2HKw/ePLZcdakkiLy/kyhj1UloAFZNaIFKBFxAlaiZQ4AAAQIECBAgQIAAAQK1Fpg7d26sdvwtMXvO3Gzpf31vq1iv16K13iqX9by/c2Ev3KYagIUrSXME5AJqjjrLkgABAgQIECBAgAABAkUX+HjG7Fj7pFvbhXnfMYNipSV7FD30kuLz/i6JKflBGoDJl7iYCbqAilkXUREgQIAAAQIECBAgQKDZBN7+4JPY7Gd3tUt75IlDYoke3ZKg8P5OooxVJ6EBWDWhBSoRcAFVomYOAQIECBAgQIAAAQIECNRa4MW3p8b2597XbtnnT98punXpVOutclnP+zsX9sJtqgFYuJI0R0AuoOaosywJECBAgAABAgQIECBQdIEnxr8XX/rtw1mYC3TpFM+dvlPRwy45Pu/vkqmSHqgBmHR5i5ucC6i4tREZAQIECBAgQIAAAQIEmkng/ucnxVf/9FiW8lILLxDDT9g+GQLv72RKWVUiGoBV8ZlcqYALqFI58wgQIECAAAECBAgQIECglgI3PT0xvnvlyGzJvkv1iLt/OKiWW+S6lvd3rvyF2VwDsDClaK5AXEDNVW/ZEiBAgAABAgQIECBAoKgCVz82Pn5y3agsvA16Lxo3HL5VUcMtOy7v77LJkpygAZhkWYuflAuo+DUSIQECBAgQIECAAAECBJpB4JIHXo7Tb3o2S3Wr1ZaKyw/aPJnUvb+TKWVViWgAVsVncqUCLqBK5cwjQIAAAQIECBAgQIAAgVoKnHvH83HBXS9kS+603nJx0QH9a7lFrmt5f+fKX5jNNQALU4rmCsQF1Fz1li0BAgQIECBAgAABAgSKKvDTf46OPz80Lgtvn/6945f7bFDUcMuOy/u7bLIkJ2gAJlnW4iflAip+jURIgAABAgQIECBAgACBRhT4ZObsOPVfY+LesW/HjNlz55vCh5/MjOmz5mTjvjlwlThp13XmO69RBnh/N0qlOjZODcCO9bX6Zwi4gBwNAgQIECBAgAABAgQIEOgIgT/c/3KccfOnf9Ov3D2OHLx6fH/IGuVOK+x47+/ClqaugWkA1pXbZv8WcAE5CwQIECBAgAABAgQIECDQEQKHXj4ibnnmzYqXPmvv9ePLm6xY8fyiTfT+LlpF8olHAzAf96bf1QXU9EcAAAECBAgQIECAAAECBDpEYJ+LH47Hx71X0dprLrtI/O8hW8SiC3ataH4RJ3l/F7Eq9Y9JA7D+5naMCBeQY0CAAAECBAgQIECAAAECHSGw7dn3xiuTP8qWPmLw6rH5KkvMd6seC3SJdVfoGV07d5rv2EYa4P3dSNXquFg1ADvO1sqfI+ACcjwIECBAgAABAgQIECBAoCME+p18W3w4fVa29NXfHhAD+i7ZEVs1xJre3w1Rpg4PUgOww4ltMC8BF5BzQYAAAQIECBAgQIAAAQK1Fmj9BOC1Try13bJ3Hv2FWG2ZhWu9VcOs5/3dMKXq0EA1ADuU1+KfJeACcjYIECBAgAABAgQIECBAoNYCr703Lbb6xT3tln3qpB1i0YXS+Zt+5Zp5f5crluZ4DcA061r4rFxAhS+RAAkQIECAAAECBAgQINBwAk9OmBJ7XPhQFne3zp3iudOHRktLS8PlUquAvb9rJdnY62gANnb9GjZ6F1DDlk7gBAgQIECAAAECBAgQKKzAnWPeioMuHZ7Ft/yi3WPYsYMLG289AvP+rody8ffQACx+jZKM0AWUZFklRYAAAQIECBAgQIAAgVwFrn5sfPzkulFZDOv3XjRuPHyrXGPKe3Pv77wrUIz9NQCLUYemi8IF1HQllzABAgQIECBAgAABAgQ6XOA3d78QZ9/+fLbPdmstE3/6+qYdvm+RN/D+LnJ16hebBmD9rO30HwIuIMeBAAECBAgQIECAAAECBGotcMqNo+MvD4/Llv3yJr3jrL03qPU2DbWe93dDlavDgtUA7DBaC3+egAvI+SBAgAABAgQIECBAgACBWgt894qRcdOoidmyhw1aNX40dK1ab9NQ63l/N1S5OixYDcAOo7WwBqAzQIAAAQIECBAgQIAAAQK1Epg7d25MePfjmDT1k89c8uQbR8czr3+Q/ftJX1wnvrnVKrUKoSHX0QBsyLLVPGgNwJqTWrAUARdQKUrGECBAgAABAgQIECBAgMC/BY655qm4ZsRrZYFcsN9GsdsGK5Q1J7XB3t+pVbSyfDQAK3Mzq0oBF1CVgKYTIECAAAECBAgQIECgiQTGTf4oBp19b9kZX3nw5rHlqkuVPS+lCd7fKVWz8lw0ACu3M7MKARdQFXimEiBAgAABAgQIECBAoMkEHnhhUhz4x8fKyrpr55Z4/PjtY7GFupU1L7XB3t+pVbSyfDQAK3Mzq0oBF1CVgKYTIECAAAECBAgQIECgiQRuHjUxDrtiZJZxS0vEwt26fKbA0ossEEduv3rsvmGvJlKad6re301/BNoANACdg1wEXEC5sNuUAAECBAgQIECAAAECDSnwt8fHx4//PiqLff3ei8aNh2/VkLnUO2jv73qLF3M/DcBi1iX5qFxAyZdYggQIECBAgAABAgQIEKiZwCUPvByn3/Rstt7A1ZaMKw4aULP1U17I+zvl6paemwZg6VZG1lDABVRDTEsRIECAAAECBAgQIEAgcYFz73g+LrjrhSzLoesuFxcf2D/xrGuTnvd3bRwbfRUNwEavYIPG7wJq0MIJmwABAgQIECBAgAABAjkI/PSfo+PPD43Ldt6nf+/45T4b5BBJ423p/d14NeuIiDUAO0LVmvMVcAHNl8gAAgQIECBAgAABAgQIEPg/gWOueSquGfFa5vHNgavESbuuw6cEAe/vEpCaYIgGYBMUuYgpuoCKWBUxESBAgAABAgQIECBAoJgCh1w2Im4d/WYW3JGDV4/vD1mjmMEWLCrv74IVJKdwNABzgm/2bV1AzX4C5E+AAAECBAgQIECAAIHSBfa/5JF46MV3sgkn7LJ2HLR139IXaOKR3t9NXPz/SF0D0DnIRcAFlAu7TQkQIECAAAECBAgQINCQArv95sF4+rX3s9h/sVe/2HfTPg2ZS72D9v6ut3gx99MALGZdko/KBZR8iSVIgAABAgQIECBAgACBmglse/a98crkj7L1frv/xrFzv+Vrtn7KC3l/p1zd0nPTACzdysgaCriAaohpKQIECBAgQIAAAQIECCQusMnpd8TkqTOyLC/71max9epLJ551bdLz/q6NY6OvogHY6BVs0PhdQA1aOGETIECAAAECBAgQIEAgB4E1T7glps+ak+38j+8OjA1XXCyHSBpvS+/vxqtZR0SsAdgRqtacr4ALaL5EBhAgQIAAAQIECBAgQIBARMyYNSfWOOGWdhZ3/eALserSC/MpQcD7uwSkJhiiAdgERS5iii6gIlZFTAQIECBAgAABAgQIECiewDtTp0f/0+9sF9hjxw+OZRbpXrxgCxiR93cBi5JDSBqAOaDbMsIF5BQQIECAAAECBAgQIECAQCkC4yZ/FIPOvrfd0GdPHRoLdutcyvSmH+P93fRHoA1AA9A5yEXABZQLu00JECBAgAABAgQIECDQcAKjXns/dv3Ng1ncXTq1xAtn7BQtLS0Nl0seAXt/56FevD01AItXk6aIyAXUFGWWJAECBAgQIECAAAECBKoWePjFyfGVSx7N1ll8oa7xxEk7VL1usyzg/d0slf78PDUAnYNcBFxAubDblAABAgQIECBAgAABAg0ncOszb8Yhl4/I4u6zxEJx/4+2bbg88grY+zsv+WLtqwFYrHo0TTQuoKYptUQJECBAgAABAgQIECBQlcC1I16LH17zVLbGuiv0jJuO2LqqNZtpsvd3M1X7s3PVAHQOchFwAeXCblMCBAgQIECAAAECBAjkJjBr9px4+KV34o0pH5cVw7CX34kbnnwjmzOg7xJx9be3KGuNZh7s/d3M1f80dw1A5yAXARdQLuw2JUCAAAECBAgQIECAQG4CR1z1RNz41KeNvEoDGbLOsvGHr25S6fSmm+f93XQln2fCGoDOQS4CLqBc2G1KgAABAgQIECBAgACBXATe+2hGbHTaHTXZe8+Ne8W5X96wJms1wyLe381Q5fnnqAE4fyMjOkDABdQBqJYkQIAAAQIECBAgQIBAQQVemjQ1Bp9zX02iO2vv9ePLm6xYk7WaYRHv72ao8vxz1ACcv5ERHSDgAuoAVEsSIECAAAECBAgQIECgoAKj33g/drngwXbRbdRnsbKi7da5Uwxee5k4eOu+0dLSUtbcZh7s/d3M1f80dw1A5yAXARdQLuw2JUCAAAECBAgQIECAQC4CI159L/a66OFs7x7dOsfoU4fmEkuzber93WwVn3e+GoDOQS4CLqBc2G1KgAABAgQIECBAgACBXAQefnFyfOWSR7O9l+zRLUacOCSXWJptU+/vZqu4BqCKF0jABVSgYgiFAAECBAgQIECAAAECHSxw99i34pt/GZ7t0muxBeOhn2zXwbtavlXA+9s5aBXwE4DOQS4CLqBc2G1KgAABAgQIECBAgACBXARuHjUxDrtiZLZ336V7xN0/GJRLLM22qfd3s1V83vlqADoHuQi4gHJhtykBAgQIECBAgAABAgRyEbhu5Gtx9P8+le29zvI94+Yjt84llmbb1Pu72SquAajiBRJwARWoGEIhQIAAAQIECBAgQIBABwtc+ej4OO76UdkuG/dZLK47bGAH72r5VgHvb+egVcBPADoHuQi4gHJhtykBAgQIECBAgAABAgRyEfjTg6/Eqf8ak+295apLxpUHD8gllmbb1Pu72So+73w1AJ2DXARcQLmw25QAAQIECBAgQIAAAQK5CPz23hfjrFufy/bebq1l4k9f3zSXWJptU+/vZqu4BqCKF0jABVSgYgiFAAECBAgQIECAAAECHSxw7h3PxwV3vZDtsnO/5eK3+/fv4F0t3yrg/e0ctAr4CUDnIBcBF1Au7DYlQIAAAQIECBAgQIBALgJn3vxs/O7+l7O999yoV5y774a5xNJsm3p/N1vF552vBqBzkIuACygXdpsSIECAAAECBAgQIEAgF4GTb3gm/jrs1Wzv/TbrE2fu2S+XWJptU+/vZqu4BqCKF0jABVSgYgiFAAECBAgQIECAAAECHSzw42ufjr8Nn5Dt8o2BK8fJu67bwbtavlXA+9s5aBXwE4DOQS4CLqBc2G1KgAABAgQIECBAgACBXASOuOqJuPGpN7K9Dx20avx46Fq5xNJsm3p/N1vF552vBqBzkIuACygXdpsSIECAAAECBAgQIEAgF4FvXzo8bh/zVrb397dfI47cfvVcYmm2Tb2/m63iGoAqXiABF1CBiiEUAgQIECBAgAABAgQIdLDAV//0WNz//KRsl2N3Wiu+84VVO3hXy7cKeH87B60CfgLQOchFwAWUC7tNCRAgQIAAAQIECBAgkIvAl383LB575d1s75/utm58bcuVc4ml2Tb1/m62is87Xw1A5yAXARdQLuw2JUCAAAECBAgQIECAQC4Cu//mwXjqtfezvX+xV7/Yd9M+ucTSbJt6fzdbxTUAVbxAAi6gAhVDKAQIECBAgAABAgQIEOhggR3Puz+ee+vDbJdf/c+GsfuGvTp4V8u3Cnh/OwetAn4C0DnIRcAFlAu7TQkQIECAAAECBAgQaFKBtz/4JP4+8vWYPHV6RQILdu0cO667XPTrvWhF87/wy3vi1XemZXMvPqB/DF1vuYrWMqk8Ae/v8rxSHa0BmGplC56XC6jgBRIeAQIECBAgQIAAAQLJCMyaPScGPG/tMQAAIABJREFUn3tfuwZcJcl169wpbjh8YKy9fM+yp2/+szvjrQ8+bT7+5RubxqA1lyl7HRPKF/D+Lt8sxRkagClWtQFycgE1QJGESIAAAQIECBAgQIBAEgIjXn0v9rro4ZrkcuTg1eP7Q9Yoe60Nfnp7vP/xzGze1d8eEAP6Lln2OiaUL+D9Xb5ZijM0AFOsagPk5AJqgCIJkQABAgQIECBAgACBJARuHjUxDrtiZE1y+Z9NV4yf77V+2WutecItMX3WnGzeP747MDZccbGy1zGhfAHv7/LNUpyhAZhiVRsgJxdQAxRJiAQIECBAgAABAgQIJCFw6bBxcdINo7Ncll5kgdh2zaVLyu3ZiR/GqNc//fTe7ddeJi752qYlzf33oLlz58Yqx97cbs6tR20day1X/q8Sl7WxwW0C3t8OQquABqBzkIuACygXdpsSIECAAAECBAgQINCEAufe/lxccPeLWeZD110uLj6wf0kSVzz6ahx//TPZ2A1WXCxu+O7Akub+e9AnM2fHWife2m7OfccMipWW7FHWOgZXJuD9XZlbarM0AFOraIPk4wJqkEIJkwABAgQIECBAgACBhhc49rpRcdVj47M8DhjQJ07fo19Jed02+s34zmUjsrG9FlswHvrJdiXN/fegKdNmxIan3tFuzqPHDY5le3Yvax2DKxPw/q7MLbVZGoCpVbRB8nEBNUihhEmAAAECBAgQIECAQMMLHHzp8LhjzFtZHkdtv3octX1pH+Tx/36AyAJdOsXY04ZGS0tLyS5vvv9JDDjzrnbjnzpph1h0oa4lr2Fg5QLe35XbpTRTAzClajZQLi6gBiqWUAkQIECAAAECBAgQaGiBPS58KJ6cMCXL4fQ91osDBqxUUk7j35kW2/zynnZjR52yQyzSvfTm3bjJH8Wgs+9tt0ZrE7F7184lxWBQdQLe39X5pTJbAzCVSjZYHi6gBiuYcAkQIECAAAECBAgQaFiBrX5xd7z23sdZ/Bcf0D+GrrdcSflMmzEr1jnptnZj7/nhoFhlqdL/ft/YNz+Ioec/kK3R+sODL/9s57J+irCkYA2ap4D3t4PRKqAB6BzkIuACyoXdpgQIECBAgAABAgQINJlA6yfwrn3SrfHJzDlZ5n8/dIvov9ISJUusc9KtMW3G7Gz8NYdsEZuuXPr81p8+bP0pxH9/de/a+mvEO5W8v4HVCXh/V+eXymwNwFQq2WB5uIAarGDCJUCAAAECBAgQIECgIQWmTp8V653c/if4yv0E3m3OuifGvzsty/+i/TeOnfotX7LHIy+/E//z+0ey8Yst1DWePGmHkucbWJ2A93d1fqnM1gBMpZINlocLqMEKJlwCBAgQIECAAAECBBpSYF5/f2/0T3eMHgt0KTmfPX/7UIwc/+nfEDxt93XjwC1WLnn+vc+9HV//8+PZ+OV6do9Hjhtc8nwDqxPw/q7OL5XZGoCpVLLB8nABNVjBhEuAAAECBAgQIECAQEMKDB/3bux98bAs9gW7do5nTxtaVi7fvnR43P4fnyJ8xODV4+ghpX2KcOtGtz7zZhxy+Yhsz5WXXCjuPWbbsmIwuHIB7+/K7VKaqQGYUjUbKBcXUAMVS6gECBAgQIAAAQIECDSswK3PTIxDLh+Zxb/iEgvGAz/arqx8jrt+VFz56Phszn6b9Ykz9+xX8ho3PPl6HHn1k9n4tZZbJG49apuS5xtYnYD3d3V+qczWAEylkg2WhwuowQomXAIECBAgQIAAAQIECiHQ+qEeF933Ulw/8vV2H8zxWcG1forve9NmZv+8UZ/F4vrDBpaVy7l3PB8X3PVCNqf1pwiX6NGt5DX+3xg2WHGxuOG75cVQ8mYG/peA97dD0SqgAegc5CLgAsqF3aYECBAgQIAAAQIECDS4wD3PvR3f+I+/p1duOkPWWTb+8NVNypp22bBxceINo8ua83mDN19lifjbd7ao2XoW+nwB728nRAPQGchNwAWUG72NCRAgQIAAAQIECBBoYIFzbn8ufn33ixVncNBWq8QJX1ynrPn3Pz8pvvqnx8qa83mDd9tghbhgv41qtp6FNACdgfkL+AnA+RsZ0QECGoAdgGpJAgQIECBAgAABAgSSFzj2uqfjqscmVJTnkj26xf8eskWsuvTCZc2fOXtO/M/vH4kRr75X1rx5DV6oW+e47FubR/+VFq96LQuUJuD9XZpT6qM0AFOvcEHzcwEVtDDCIkCAAAECBAgQIECg0AIH/XV43PnsW1mMX9qoV+y6wfLzjblb586x/oqLRs/uXec7dl4DWpuAT7/2frz/8YyK5rdO6typU6zfa9FYvIy/H1jxZiZmAt7fDkOrgAagc5CLgAsoF3abEiBAgAABAgQIECDQ4AJ7XPhQPDlhSpbF6XusFwcMWKnBsxJ+Rwp4f3ekbuOsrQHYOLVKKlIXUFLllAwBAgQIECBAgAABAnUS2OoXd8dr732c7XbxAf1j6HrL1Wl32zSigPd3I1at9jFrANbe1IolCLiASkAyhAABAgQIECBAgAABAv8hMHfu3Fj7pFvjk5lzsv/690O3iP4rLcGJwGcKeH87HK0CGoDOQS4CLqBc2G1KgAABAgQIECBAgEADC0ydPivWO/m2dhncf8y20WfJhRo4K6F3tID3d0cLN8b6GoCNUafkonQBJVdSCREgQIAAAQIECBAg0MECr0z+KLY9+952u4w5dcdYqFuXDt7Z8o0s4P3dyNWrXewagLWztFIZAi6gMrAMJUCAAAECBAgQIECAQEQ8Pu7d2OfiYZnFQt06x5hTh7Ih8LkC3t8OSKuABqBzkIuACygXdpsSIECAAAECBAgQINDAAreMmhiHXjEyy6DPEgvF/T/atoEzEno9BLy/66Fc/D00AItfoyQjdAElWVZJESBAgAABAgQIECDQgQKXDRsXJ94wOtth4z6LxXWHDezAHS2dgoD3dwpVrD4HDcDqDa1QgYALqAI0UwgQIECAAAECBAgQaGqBc+94Pi6464XMYId1lo3ff3WTpjaR/PwFvL/nb9QMIzQAm6HKBczRBVTAogiJAAECBAgQIECAAIGyBFo/lXfc5I/KmlPN4IvufSluGjUxW+Irm/eJn32pXzVLmtsEAt7fTVDkElLUACwByZDaC7iAam9qRQIECBAgQIAAAQIE6ifQ+vf4jrj6iZg5e279Nv1/djpy8Orx/SFr5La/jRtDwPu7MerU0VFqAHa0sPXnKeACcjAIECBAgAABAgQIEGhkge3PvS9efHtqrimctsd6ceCAlXKNwebFF/D+Ln6N6hGhBmA9lO3xXwIuIIeCAAECBAgQIECAAIFGFlj3pFvjoxmzc03hH98dGBuuuFiuMdi8+ALe38WvUT0i1ACsh7I9NACdAQIECBAgQIAAAQIEkhJY4/hbYsbsOVlOXTq1RKeWlrrk2HPBLnHggJXjiMGrRUud9qxLYjbpEAENwA5hbbhFNQAbrmRpBOwCSqOOsiBAgAABAgQIECDQrAKrHHtTzP2PP/93y5Fbx9rL92xWDnkXWMD7u8DFqWNoGoB1xLbVpwIuIKeBAAECBAgQIECAAIFGFZg9Z26setzN7cK/8+htYrVlFmnUlMSdsID3d8LFLSM1DcAysAytnYALqHaWViJAgAABAgQIECBAoL4Cn8ycHWudeGu7Te/94aBYeake9Q3EbgRKEPD+LgGpCYZoADZBkYuYoguoiFUREwECBAgQIECAAAECpQhMnT4r1jv5tnZDH/zxttF78YVKmW4MgboKeH/Xlbuwm2kAFrY0aQfmAkq7vrIjQIAAAQIECBAgkLLAex/NiI1Ou6Ndio8dNziW6dk95bTl1qAC3t8NWrgah60BWGNQy5Um4AIqzckoAgQIECBAgAABAgSKJ/D2h5/EZmfc1S6wkScOiSV6dCtesCJqegHv76Y/Am0AGoDOQS4CLqBc2G1KgAABAgQIECBAgEANBN6Y8nFs+fO726309Ck7RM/uXWuwuiUI1FbA+7u2no26mgZgo1auweN2ATV4AYVPgAABAgQIECBAoIkFxr8zLbb55T3tBJ49dWgs2K1zE6tIvagC3t9FrUx949IArK+33f5PwAXkKBAgQIAAAQIECBAg0KgCL02aGoPPua9d+C+csVN07dypUVMSd8IC3t8JF7eM1DQAy8AytHYCLqDaWVqJAAECBAgQIECAAIH6Cox984MYev4D7TZ95cydo6Wlpb6B2I1ACQLe3yUgNcEQDcAmKHIRU3QBFbEqYiJAgAABAgQIECBAoBSBZ15/P7746wezoV07t8QLZ+xcylRjCNRdwPu77uSF3FADsJBlST8oF1D6NZYhAQIECBAgQIAAgVQFnhj/Xnzptw9n6S3YtXM8e9rQVNOVV4MLeH83eAFrFL4GYI0gLVOegAuoPC+jCRAgQIAAAQIECBAojsDj496NfS4elgW0SPcuMeqUHYsToEgI/IeA97fj0CqgAegc5CLgAsqF3aYECBAgQIAAAQIECNRA4OGXJsdX/vBottKSPbrFiBOH1GBlSxCovYD3d+1NG3FFDcBGrFoCMbuAEiiiFAgQIECAAAECBAg0qcB9z0+Kr/3psSz7ZXsuEI8et32Taki76ALe30WvUH3i0wCsj7Nd/h8BF5AjQYAAAQIECBAgQIBAowrc9exb8a2/Ds/C77XYgvHQT7Zr1HTEnbiA93fiBS4xPQ3AEqEMq62AC6i2nlYjQIAAAQIECBAgQKB+Arc+82YccvmIbMOVl1wo7j1m2/oFYCcCZQh4f5eBlfBQDcCEi1vk1FxARa6O2AgQIECAAAECBAgQ+DyBfz39Rhx+5RPZkNWWWTjuPPoL0AgUUsD7u5BlqXtQGoB1J7dhq4ALyDkgQIAAAQIECBAgQKBRBf7xxOtx1N+ezMJfe/meccuRWzdqOuJOXMD7O/ECl5ieBmCJUIbVVsAFVFtPqxEgQIAAAQIECBAgUD+B/x0+IX507dPZhuv3XjRuPHyr+gVgJwJlCHh/l4GV8FANwISLW+TUXEBFro7YCBAgQIAAAQIECBD4PIErHx0fx10/KhuycZ/F4rrDBkIjUEgB7+9ClqXuQWkA1p3chq0CLiDngAABAgQIECBAgACBRhW4dNi4OOmG0Vn4m62yRPzvd7Zo1HTEnbiA93fiBS4xPQ3AEqEMq62AC6i2nlYjQIAAAQIECBAgQKB+An988JU47V9jsg0HrrZkXHHQgPoFYCcCZQh4f5eBlfBQDcCEi1vk1FxARa6O2AgQIECAAAECBAgQ+DyB3933Upx5y9hsyKA1l46/fGMzaAQKKeD9Xciy1D0oDcC6k9uwVcAF5BwQIECAAAECBAgQINCoAr+5+4U4+/bns/C3X3vZuORrmzRqOuJOXMD7O/ECl5ieBmCJUIbVVsAFVFtPqxEgQIAAAQIECBAgUD+B8+54Pn511wvZhjutt1xcdED/+gVgJwJlCHh/l4GV8FANwISLW+TUXEBFro7YCBAgQIAAAQIECBD4PIFf3jY2LrznpWzIrhusEL/ebyNoBAop4P1dyLLUPSgNwLqT27BVwAXkHBAgQIAAAQIECBAg0KgCZ978bPzu/pez8PfcqFecu++GjZqOuBMX8P5OvMAlpqcBWCKUYbUVcAHV1tNqBAgQIECAAAECBAjUT+DUf46JPz30SrbhvpusGL/Ye/36BWAnAmUIeH+XgZXwUA3AhItb5NRcQEWujtgIECBAgAABAgQIEPg8gRP/8Uxc9sir2ZD9N+8TZ3ypHzQChRTw/i5kWeoelAZg3clt2CrgAnIOCBAgQIAAAQIECBBoVIFjr3s6rnpsQhb+17dcOU7Zbd1GTUfciQt4fyde4BLT0wAsEcqw2gq4gGrraTUCBAgQIECAAAECBOon8MNrnoprR7yWbXjQVqvECV9cp34B2IlAGQLe32VgJTxUAzDh4hY5NRdQkasjNgIECBAgQIAAAQIEPk/gqKufiH88+UY25JAvrBo/2WktaAQKKeD9Xciy1D0oDcC6k9uwVcAF5BwQIECAAAECBAgQINCoAt+9YmTcNGpiFv4R260WR++wZqOmI+7EBby/Ey9wielpAJYIZVhtBVxAtfW0GgECBAgQIECAAAEC9RP49qXD4/Yxb2UbHj1kjThi8Or1C8BOBMoQ8P4uAyvhoRqACRe3yKm5gIpcHbERIECAAAECBAgQIPB5At/8y+Nx99i3syE/GrpmHDZoNWgECing/V3IstQ9KA3AupPbsFXABeQcECBAgAABAgQIECDQqAIH/vHReOCFyVn4x++8dhy8Td9GTUfciQt4fyde4BLT0wAsESqlYYMGDYr77ruvrJTuueeeaJ1Xqy8XUK0krUOAAAECBAgQIECAQL0F9vv9IzHs5XeybU/edZ34xsBV6h2G/QiUJOD9XRJT8oM0AJMv8X8nWG4DsFOnTjF+/Pjo1atXzbRcQDWjtBABAgQIECBAgAABAnUW2Puih2P4q+9lu56+x3pxwICV6hyF7QiUJuD9XZpT6qM0AFOv8Dzye+WVV+Kjjz763MzHjBkT++67b9uYIUOGxO23315TKRdQTTktRoAAAQIECBAgQIBAHQV2v/CheGrClGzHX+zVL/bdtE8dI7AVgdIFvL9Lt0p5pAZgytWtIrcf//jHcdZZZ7WtcNlll8UBBxxQxWr/PdUFVFNOixEgQIAAAQIECBAgUEeBXS54IEa/8UG24zn7bBB79e9dxwhsRaB0Ae/v0q1SHqkBmHJ1K8xtzpw50adPn3j99ddj4YUXjrfeeisWWmihCleb9zQXUE05LUaAAAECBAgQIECAQB0Fdjzv/njurQ+zHX/1PxvG7hvW7k8m1TEVWzWBgPd3ExS5hBQ1AEtAarYhd9xxR+ywww5taX/961+PP//5zzUncAHVnNSCBAgQIECAAAECBAjUSWC7c+6Nlyd9+meVfrv/xrFzv+XrtLttCJQn4P1dnleqozUAU61sFXkdeOCBcfnll7etcPfdd8e2225bxWrznuoCqjmpBQkQIECAAAECBAgQqJPA1mfdHRPe/Tjb7Q9f3SSGrLNsnXa3DYHyBLy/y/NKdbQGYKqVrTCvqVOnxnLLLdf2ISErrbRStH5gSEtLS4WrffY0F1DNSS1IgAABAgQIECBAgECdBLY4866Y+P4n2W5//samse2ay9Rpd9sQKE/A+7s8r1RHawCmWtkK8/rrX//a9mu/rV/HH398nH766RWt1HrBfN7XxIkTY7PNNmsbMmHChOjd2x/MrQjaJAIECBAgQIAAAQIE6i6wyel3xuSp07N9L//W5rHV6kvVPQ4bEihFQAOwFKX0x2gApl/jsjIcPHhw26/9tn4999xzscYaa5Q1/9+Dy/mpQQ3AiohNIkCAAAECBAgQIEAgJ4ENT709pkybme1+9bcHxIC+S+YUjW0JfL6ABqAT0iqgAegcZAKtl0Lrr/22fgrwgAEDYtiwYRXraABWTGciAQIECBAgQIAAAQIFF1jv5Nti6vRZWZR/P3TL6L/S4gWPWnjNKqAB2KyVb5+3BqBzkAn8/Oc/j2OPPbbt+4suuigOOeSQinX8CnDFdCYSIECAAAECBAgQIFBwgTVOuCVmzJqTRXnj4QNj/d6LFTxq4TWrgAZgs1ZeA1DlP0Ng3XXXjTFjxsQCCywQrX+jb/HFO+7/wXIBOYYECBAgQIAAAQIECDSqQN9jb4o5cz+N/uYjto51VujZqOmIO3EB7+/EC1xien4CsESo1IcNHz48Nt1007Y099prr7j22ms7NGUXUIfyWpwAAQIECBAgQIAAgQ4SmDNnbvQ97uZ2q9/x/W1i9WUX6aAdLUugOgHv7+r8UpmtAZhKJavM44gjjohf//rXbavceOONseuuu1a54udPdwF1KK/FCRAgQIAAAQIECBDoIIHps2bHmifc2m71e344KFZZqkcH7WhZAtUJeH9X55fKbA3AVCpZRR4zZ86MXr16xaRJk2LppZeON954I7p06VLFivOf6gKav5ERBAgQIECAAAECBAgUT+Cj6bNi3ZNvaxfYgz/eNnovvlDxghURgYjw/nYMWgU0AJ2Dtp/423333dskjjzyyDj//PM7XMUF1OHENiBAgAABAgQIECBAoAMEpkybERueeke7lR89bnAs27N7B+xmSQLVC3h/V2+YwgoagClUscoc9t577/j73//etsqIESNi4403rnLF+U93Ac3fyAgCBAgQIECAAAECBIonMOnD6bHpGXe2C2zECdvHkgsvULxgRUTATwA6A/8noAHY5Efhvffei+WXXz6mT58e6623XowaNaouIhqAdWG2CQECBAgQIECAAAECNRaY+P7HscWZd7db9amTd4hFF+xa450sR6A2At7ftXFs9FU0ABu9glXGf/HFF8ehhx7atspZZ50VxxxzTJUrljbdBVSak1EECBAgQIAAAQIECBRLYMK702Lrs+5pF9SYU3eMhbp17N9RL5aCaBpJwPu7karVcbFqAHacbUOsPHDgwHj44Yejc+fOMX78+FhhhRXqErcLqC7MNiFAgAABAgQIECBAoMYCL0+aGtudc1+7VV84Y6fo2rlTjXeyHIHaCHh/18ax0VfRAGz0CjZo/C6gBi2csAkQIECAAAECBAg0ucBzb34YO55/fzuFV87cOVpaWppcRvpFFfD+Lmpl6huXBmB9ve32fwIuIEeBAAECBAgQIECAAIFGFHjm9ffji79+MAu9S6eWePFnOzdiKmJuEgHv7yYp9HzS1AB0DnIRcAHlwm5TAgQIECBAgAABAgSqFHhywpTY48KHslW6d+0UY0/bqcpVTSfQcQLe3x1n20grawA2UrUSitUFlFAxpUKAAAECBAgQIPCZAnc9+1acecvYeGPKx5QSEZg9Z25MnzUny2aRBbrEqJ/umEh20khRwPs7xaqWn5MGYPlmZtRAwAVUA0RLECBAgAABAgQIFFpgxqw5sfFpd8TU6bMKHafgqhNYoke3GHnikOoWMZtABwp4f3cgbgMtrQHYQMVKKVQXUErVlAsBAgQIECBAgMC8BF5956P4wi/vhZO4wDrL94ybj9w68Syl18gC3t+NXL3axa4BWDtLK5Uh4AIqA8tQAgQIECBAgACBhhR4adLUGHzOfQ0Zu6BLE+jauSUu/MrGscO6y5U2wSgCOQh4f+eAXsAtNQALWJRmCMkF1AxVliMBAgQIECBAoLkFXnjrwxhy3v3tEK47bMtoaW6WZLJvaWmJVZfuEYt075pMThJJU8D7O826lpuVBmC5YsbXRMAFVBNGixAgQIAAAQIECBRYYOybH8TQ8x/IImxpiXjlzF0KHLHQCBBIUcD7O8Wqlp+TBmD5ZmbUQMAFVANESxAgQIAAAQIECBRaYPQb78cuFzyYxdilU0u8+LOdCx2z4AgQSE/A+zu9mlaSkQZgJWrmVC3gAqqa0AIECBAgQIAAAQIFFxj12vux628+bQB269Ipnj99p4JHLTwCBFIT8P5OraKV5aMBWJmbWVUKuICqBDSdAAECBAgQIECg8AJPTZgSu1/4UBZn966dYuxpGoCFL5wACSQm4P2dWEErTEcDsEI406oTcAFV52c2AQIECBAgQIBA8QVGjn8v9vztw1mgPbp1jtGnDi1+4CIkQCApAe/vpMpZcTIagBXTmViNgAuoGj1zCfx/7N0JuFZV2TfwmxlEEWdxHlBxxAkQZxQV0dJyqiy1snLIsTTneSg1zSyzUjNNzSHNEVREEEdAEQdEUHBGBJxFxem9Dr09h0NHOec5D8/ez14/ruu93u+Lvfda/9+9vvv61t0BCBAgQIAAAQK1IDD6xbdi90seLm11oY5t46lTdqiFrdsjAQIFEnD/LlAxWxDFALAFeF4tX0ADKt/OmwQIECBAgAABArUh8OikGbHXXx4pbbbrAu3iiZO2r43N2yUBAoURcP8uTClbFMQAsEV8Xi5XQAMqV857BAgQIECAAAECtSLw0AvT43t/fbS03cU6t4/HTtyuVrZvnwQIFETA/bsghWxhDAPAFgJ6vTwBDag8N28RIECAAAECBAjUjsADE6fH9y+rHwAusVCHGHV8/9oJYKcECBRCwP27EGVscQgDwBYT+kA5AhpQOWreIUCAAAECBAgQqCWB4ROmxb6XjyxteekuHeOR47atpQj2SoBAAQTcvwtQxApEMACsAKJPNF9AA2q+mTcIECBAgAABAgRqS+C+8W/GD68YVdr0sl07xYPHbFNbIeyWAIGaF3D/rvkSViSAAWBFGH2kuQIaUHPFPE+AAAECBAgQIFBrAvc+OzV+/PfRpW0vv2inGHG0AWCt1dF+CdS6gPt3rVewMvs3AKyMo680U0ADaiaYxwkQIECAAAECBGpO4O5n3oifXvVYad8rLrZADD+qX83lsGECBGpbwP27tutXqd0bAFZK0neaJaABNYvLwwQIECBAgAABAjUoMPjpKXHAPx4v7XyVxTvH0F9uXYNJbJkAgVoWcP+u5epVbu8GgJWz9KVmCGhAzcDyKAECBAgQIECAQE0K3PHklDj4mvoBYPclF4whR25Vk1lsmgCB2hVw/67d2lVy5waAldT0rSYLaEBNpvIgAQIECBAgQIBAjQrcOvb1OPTaMaXdr7HUQnHXEVvWaBrbJkCgVgXcv2u1cpXdtwFgZT19rYkCGlAToTxGgAABAgQIECBQswL/HvNaHH7dE6X9r9mtSww6bIuazWPjBAjUpoD7d23WrdK7NgCstKjvNUlAA2oSk4cIECBAgAABAgRqWOBfj70av7hhbCnBOst2idsPMQCs4ZLaOoGaFHD/rsmyVXzTBoAVJ/XBpghoQE1R8gwBAgQIECBAgEAtC1w/+pU4+sYnSxF6Lrdw3PLzzWs5kr0TIFCDAu7fNVi0+bBlA8D5gOqT8xbQgOZt5AkCBAgQIECAAIHaFvjnyJfjmJueKoXYYIWucfPkMOkmAAAgAElEQVRBm9V2KLsnQKDmBNy/a65k82XDBoDzhdVH5yWgAc1LyO8TIECAAAECBAjUusA1j74cx91cPwDcaMVF4l8HblrrseyfAIEaE3D/rrGCzaftGgDOJ1if/XoBDcgJIUCAAAECBAgQKLrAVY+8FCf+++lSzN4rLRrXH9C36LHlI0AgZwLu3zkrSEbbMQDMCD71ZTWg1E+A/AQIECBAgACB4gv8/aEX4+RbnykF3WSVReOfPzUALH7lJSSQLwH373zVI6vdGABmJZ/4uhpQ4gdAfAIECBAgQIBAAgKXPzA5Trt9XCnpZt0Xi6v33ySB5CISIJAnAffvPFUju70YAGZnn/TKGlDS5ReeAAECBAgQIJCEwKUjJsUZdzxbyrrFaovHVT/uk0R2IQkQyI+A+3d+apHlTgwAs9RPeG0NKOHii06AAAECBAgQSETgz8NfiLMHjS+l3XqNJeKKH/ZOJL2YBAjkRcD9Oy+VyHYfBoDZ+ie7ugaUbOkFJ0CAAAECBAgkI3DxsOfjnMHPlfJu22PJuGy/XsnkF5QAgXwIuH/now5Z78IAMOsKJLq+BpRo4cUmQIAAAQIECCQk8IehE+O8uyeUEm+31lLx1302TkhAVAIE8iDg/p2HKmS/BwPA7GuQ5A40oCTLLjQBAgQIECBAICmBC4dMjAuG1A8AB6y9dFzyg42SMhCWAIHsBdy/s69BHnZgAJiHKiS4Bw0owaKLTIAAAQIECBBITOCCeybEhfdOLKUeuO7ScfHeBoCJHQNxCWQu4P6deQlysQEDwFyUIb1NaEDp1VxiAgQIECBAgEBqAr+9+7m4aOjzpdg7r9ct/vC9DVNjkJcAgYwF3L8zLkBOljcAzEkhUtuGBpRaxeUlQIAAAQIECKQncM7g8XHxsBdKwXdZf5m48DsbpAchMQECmQq4f2fKn5vFDQBzU4q0NqIBpVVvaQkQIECAAAECKQqcPejZ+PPwSaXo395g2Th/r/VTpJCZAIEMBdy/M8TP0dIGgDkqRkpb0YBSqrasBAgQIECAAIE0Bc68Y1z8dcTkUvjdN1ouztujZ5oYUhMgkJmA+3dm9Lla2AAwV+VIZzMaUDq1lpQAAQIECBAgkKrAabeNi8sfrB8A7rXx8vGb3ddLlUNuAgQyEnD/zgg+Z8saAOasIKlsRwNKpdJyEiBAgAABAgTSFTjl1mfiiodeLAF8t/cKcfa3100XRHICBDIRcP/OhD13ixoA5q4kaWxIA0qjzlISIECAAAECBFIWOPHfT8dVj7xUIvj+JivEGbsaAKZ8JmQnkIWA+3cW6vlb0wAwfzVJYkcaUBJlFpIAAQIECBAgkLTAcTc/Fdc8+nLJYN++K8apu6yTtInwBAhUX8D9u/rmeVzRADCPVUlgTxpQAkUWkQABAgQIECCQuMCxNz0Z1458paSw36YrxSnfXDtxFfEJEKi2gPt3tcXzuZ4BYD7rUvhdaUCFL7GABAgQIECAAIHkBY6+cWxcP/rVksOPN185Ttx5reRdABAgUF0B9+/qeud1NQPAvFam4PvSgApeYPEIECBAgAABAgTiF9ePjX89Xj8A/OmWq8RxA9ckQ4AAgaoKuH9XlTu3ixkA5rY0xd6YBlTs+kpHgAABAgQIECAQccR1T8TNY14rURyw1apxzI490BAgQKCqAu7fVeXO7WIGgLktTbE3pgEVu77SESBAgAABAgQIRBx67Zi4dezrJYqD+60aR+1gAOhsECBQXQH37+p653U1A8C8Vqbg+9KACl5g8QgQIECAAAECBOLgax6PO56cUpI4dJvuceT2a5AhQIBAVQXcv6vKndvFDABzW5pib0wDKnZ9pSNAgAABAgQIEIg48B+PxaCn3yhRHN5/tTi8/+poCBAgUFUB9++qcud2MQPA3Jam2BvTgIpdX+kIECBAgAABAgQifnrl6Lh73NQSxS+2Wz0O2XY1NAQIEKiqgPt3Vblzu5gBYG5LU+yNaUDFrq90BAgQIECAAAECEfv/fVQMefbNEsVRO6wRB/frjoYAAQJVFXD/rip3bhczAMxtaYq9MQ2o2PWVjgABAgQIECBAIOJHV4yKoePrB4C/GtAjDtx6VTQECBCoqoD7d1W5c7uYAWBuS1PsjWlAxa6vdAQIECBAgAABAhH7Xj4yhk+YVqI4bmCP+OmWBoDOBgEC1RVw/66ud15XMwDMa2UKvi8NqOAFFo8AAQIECBAgQCB+cNmjMWLi9JLECTutGftvsQoZAgQIVFXA/buq3LldzAAwt6Up9sY0oGLXVzoCBAgQIECAAIGI7/31kXjohRklipO/sVb8cLOV0RAgQKCqAu7fVeXO7WIGgLktTbE3pgEVu77SESBAgAABAgQIROz154fj0clvlShO22Xt2KfvSmgIECBQVQH376py53YxA8DclqbYG9OAil1f6QgQIECAAAECBCL2uOShGPXi2yWKM3ZdJ76/yYpoCBAgUFUB9++qcud2MQPA3Jam2BvTgIpdX+kIECBAgAABAgQivn3xg/H4y++UKM7+9rrx3d4roCFAgEBVBdy/q8qd28UMAHNbmmJvTAMqdn2lI0CAAAECBAgQiNjljw/G2FfqB4Dn7LZe7NlreTQECBCoqoD7d1W5c7uYAWBuS1PsjWlAxa6vdAQIECBAgAABAhHf/MMD8eSr75YoztujZ+y+0XJoCBAgUFUB9++qcud2MQPA3Jam2BvTgIpdX+kIECBAgAABAgQidvr9iHjm9fdKFOfv2TO+vaEBoLNBgEB1Bdy/q+ud19UMAPNamYLvSwMqeIHFI0CAAAECBAgQiAG/uz/Gv/F+SeLC76wfu6y/LBkCBAhUVcD9u6rcuV3MADC3pSn2xjSgYtdXOgIECBAgQIAAgYjtLxgeE6Z+UKK46LsbxDd6LoOGAAECVRVw/64qd24XMwDMbWmKvTENqNj1lY4AAQIECBAgQCBi298OixemfViiuHjvDWPgut3QECBAoKoC7t9V5c7tYgaAuS1NsTemARW7vtIRIECAAAECBAhE9DtvWEyeXj8AvOT7G8WAdZZGQ4AAgaoKuH9XlTu3ixkA5rY0xd6YBlTs+kpHgAABAgQIECAQseU598XLb80sUfx1n41ju7WWQkOAAIGqCrh/V5U7t4sZAOa2NMXemAZU7PpKR4AAAQIECBAgELHZr4fGa+98VKK4fL+NY5seBoDOBgEC1RVw/66ud15XMwDMa2UKvi8NqOAFFo8AAQIECBAgQCD6nn1vTHn345LEFT/sFVuvsSQZAgQIVFXA/buq3LldzAAwt6Up9sY0oGLXVzoCBAgQIECAAIGIPmcNianvfVKiuPJHvWPL1ZdAQ4AAgaoKuH9XlTu3ixkA5rY0xd6YBlTs+kpHgAABAgQIECAQsfEZQ2L6B/UDwKv37xObdV8cDQECBKoq4P5dVe7cLmYAmNvSFHtjGlCx6ysdAQIECBAgQIBAxIan3xNvfTirRHHtTzaJvqsuhoYAAQJVFXD/rip3bhczAMxtaYq9MQ2o2PWVjgABAgQIECBAIKLnqXfHux99WqK4/md9o/fKi6IhQIBAVQXcv6vKndvFDABzW5pib0wDKnZ9pSNAgAABAgQIEIhY9+S74v1PPitR/OvAvrHRigaAzgYBAtUVcP+urndeVzMAzGtlCr4vDajgBRaPAAECBAgQIEAg1jppcMyc9XlJ4uaDNo0NVliEDAECBKoq4P5dVe7cLmYAmNvSFHtjGlCx6ysdAQIECBAgQIBAxBonDIpPPvuiRHHrzzeL9ZbrioYAAQJVFXD/rip3bhczAMxtaYq9MQ2o2PWVjgABAgQIECBAIGK14++MTz//skRx+yGbxzrLLoyGAAECVRVw/64qd24XMwDMbWmKvTENqNj1lY4AAQIECBAgQCBilWPviC/q538x6LAtYs1uXdAQIECgqgLu31Xlzu1iBoC5LU2xN6YBFbu+0hEgQIAAAQIECESsdMwdDRjuOnzLWGPphdAQIECgqgLu31Xlzu1iBoC5LU2xN6YBFbu+0hEgQIAAAQIEUhf44osvY5Xj7mzAMOTILaP7kgaAqZ8N+QlUW8D9u9ri+VzPADCfdSn8rjSgwpdYQAIECBAgQIBA0gKffv5FrHb8oAYGQ3+xVayyxIJJuwhPgED1Bdy/q2+exxUNAPNYlQT2pAElUGQRCRAgQIAAAQIJC3z86efR48TBDQSGH7V1rLhY54RVRCdAIAsB9+8s1PO3pgFg/mqSxI40oCTKLCQBAgQIECBAIFmBmbM+i7VOuqtB/hFH94vlF10gWRPBCRDIRsD9Oxv3vK1qAJi3iiSyHw0okUKLSYAAAQIECBBIVOD9jz+NdU+5u0H6h47ZJpbp2ilREbEJEMhKwP07K/l8rWsAmK96JLMbDSiZUgtKgAABAgQIEEhS4N2Zn0bP0xoOAB89bttYqkvHJD2EJkAgOwH37+zs87SyAWCeqpHQXjSghIotKgECBAgQIEAgQYG3PpwVG55+T4Pko47vH0ss1CFBDZEJEMhSwP07S/38rG0AmJ9aJLUTDSipcgtLgAABAgQIEEhOYNr7n0SvM4c0yP34idvFop3bJ2chMAEC2Qq4f2frn5fVDQDzUonE9qEBJVZwcQkQIECAAAECiQm8+d7H0fusexukfuKk7aLrAgaAiR0FcQlkLuD+nXkJcrEBA8BclCG9TWhA6dVcYgIECBAgQIBASgJT3v0o+p49tEHkJ0/ZPrp0bJcSg6wECORAwP07B0XIwRYMAHNQhBS3oAGlWHWZCRAgQIAAAQLpCLz69szY/Df3NQj8zKk7ROcObdNBkJQAgVwIuH/nogyZb8IAMPMSpLkBDSjNuktNgAABAgQIEEhF4OUZM2PLcxsOAMefPiA6tmuTCoGcBAjkRMD9OyeFyHgbBoAZFyDV5TWgVCsvNwECBAgQIEAgDYHJ0z+MfucNaxB2whk7Rvu2rdMAkJIAgdwIuH/nphSZbsQAMFP+dBfXgNKtveQECBAgQIAAgRQEnn/zg+h//vAGUV84a2C0ad0qhfgyEiCQIwH37xwVI8OtGABmiJ/y0hpQytWXnQABAgQIECBQfIEJU9+P7S+4v0HQyWcPjFatDACLX30JCeRLwP07X/XIajcGgFnJJ76uBpT4ARCfAAECBAgQIFBwgWenvBc7XjiilLLuB/8mnb1TwVOLR4BAHgXcv/NYlervyQCw+uZWjAgNyDEgQIAAAQIECBAossAzr78bO/3+gVLEdm1axcQzBxY5smwECORUwP07p4Wp8rYMAKsMbrn/CGhATgIBAgQIECBAgECRBZ569d34xh/qB4B1//hH3T8C4hcBAgSqLeD+XW3xfK5nAJjPuhR+VxpQ4UssIAECBAgQIEAgaYEnXnkndv3jgyWDTu3axLOnD0jaRHgCBLIRcP/Oxj1vqxoA5q0iiexHA0qk0GISIECAAAECBBIVeOylt2O3Pz1USr9gh7bx9Kk7JKohNgECWQq4f2epn5+1DQDzU4ukdqIBJVVuYQkQIECAAAECyQmMevGt2OOSh0u5u3RsG0+eYgCY3EEQmEAOBNy/c1CEHGzBADAHRUhxCxpQilWXmQABAgQIECCQjsAjk2bEd/7ySCnwIgu0izEnbZ8OgKQECORGwP07N6XIdCMGgJnyp7u4BpRu7SUnQIAAAQIECKQg8NDz0+N7lz5airr4gu1j9AnbpRBdRgIEcibg/p2zgmS0HQPAjOBTX1YDSv0EyE+AAAECBAgQKLbAiInT4geXjSyFXHKhDjHy+P7FDi0dAQK5FHD/zmVZqr4pA8Cqk1uwTkADcg4IECBAgAABAgSKLDDsuTdjv7+NKkXstnDHePjYbYscWTYCBHIq4P6d08JUeVsGgFUGt9x/BDQgJ4EAAQIECBAgQKDIAveNfzN+eEX9AHDZrp3iwWO2KXJk2QgQyKmA+3dOC1PlbRkAVhnccgaAzgABAgQIECBAgEDxBYaMmxr7Xzm6FHT5RTvFiKMNAItfeQkJ5E/AADB/NcliRwaAWahb008AOgMECBAgQIAAAQKFFrjrmTfiZ1c9Vsq40mILxLCj+hU6s3AECORTwAAwn3Wp9q4MAKstbr3ZAhqQg0CAAAECBAgQIFBkgUFPTYkDr368FHGVJTrH0F9sXeTIshEgkFMB9++cFqbK2zIArDK45f4joAE5CQQIECBAgAABAkUWuP3J1+Pn14wpRVxtyQXjniO3KnJk2QgQyKmA+3dOC1PlbRkAVhnccgaAzgABAgQIECBAgEDxBW554rU47J9PlIL2WHqhGHz4lsUPLiEBArkTMADMXUky2ZABYCbsFtWAnAECBAgQIECAAIEiC9w85tU44rqxpYhrdesSdx62RZEjy0aAQE4F3L9zWpgqb8sAsMrglvuPgAbkJBAgQIAAAQIECBRZ4MbHXo1f3lA/AFx32YXjtkM2L3Jk2QgQyKmA+3dOC1PlbRkAVhnccgaAzgABAgQIECBAgEDxBa4f9Uoc/a8nS0F7Lt81bjl4s+IHl5AAgdwJGADmriSZbMgAMBN2i2pAzgABAgQIECBAgECRBf458uU45qanShE3WKFr3HyQAWCRay4bgbwKuH/ntTLV3ZcBYHW9rfb/AhqQo0CAAAECBAgQIFBkgasffSmOv/npUsSNV1wkbjxw0yJHlo0AgZwKuH/ntDBV3pYBYJXBLfcfAQ3ISSBAgAABAgQIECiywFUPvxgn3vJMKWLvlReN63/Wt8iRZSNAIKcC7t85LUyVt2UAWGVwyxkAOgMECBAgQIAAAQLFF7jiwclxym3jSkH7rrJYXPvTTYofXEICBHInYACYu5JksiEDwEzYLaoBOQMECBAgQIBANQU+/vTzuHXs6/HyjJnVXNZaCQuMffWdGDFxeklg8+6Lxz/275OwiOgECGQl4P6dlXy+1jUAzFc9ktmNBpRMqQUlQIAAAQK5EPjplaPj7nFTc7EXm0hTYMvVl4grf9Q7zfBSEyCQqYD7d6b8uVncADA3pUhrIxpQWvWWlgABAgQIZCnw0azPY+2TB8cXX2a5C2unLrDdWkvFX/fZOHUG+QkQyEDA/TsD9BwuaQCYw6KksCUNKIUqy0iAAAECBPIh8PaHs2KD0+/Jx2bsIlmBM7+1TuzdZ8Vk8wtOgEB2Au7f2dnnaWUDwDxVI6G9aEAJFVtUAgQIECCQscCMDz6Jjc4Y0mAXO6y9VHRo2ybjnVk+BYG2rVtF31UXi903Wi5atWqVQmQZCRDImYD7d84KktF2DAAzgk99WQ0o9RMgPwECBAgQqJ7AtPc/iV5nNhwAPn7idrFo5/bV24SVCBAgQIBARgLu3xnB52xZA8CcFSSV7WhAqVRaTgIECBAgkL3Am+9/HL3PvLfBRsacuF0sYgCYfXHsgAABAgTmu4D793wnrokFDABrokzF26QGVLyaSkSAAAECBPIqMPW9j6PPWQ0HgGNP2j4WXqBdXrdsXwQIECBAoGIC7t8Vo6zpDxkA1nT5anfzGlDt1s7OCRAgQIBArQlMefej6Hv20AbbfvKU7aNLRwPAWqul/RIgQIBA8wXcv5tvVsQ3DACLWNUayKQB1UCRbJEAAQIECBRE4PV3PopNf91wAPj0qTvEgh3aFiShGAQIECBA4KsF3L+djjoBA0DnIBMBDSgTdosSIECAAIEkBV59e2Zs/pv7GmR/5tQdorMBYJLnQWgCBAikJuD+nVrFG89rAOgcZCKgAWXCblECBAgQIJCkwCtvzYwtzmk4AHz2tAHRqX2bJD2EJkCAAIG0BNy/06r3V6U1AHQOMhHQgDJhtygBAgQIEEhS4OUZM2PLcxsOAMefPiA6tjMATPJACE2AAIHEBNy/Eyv4V8Q1AHQOMhHQgDJhtygBAgQIEEhS4MXpH8bW5w1rkP25MwZEh7YGgEkeCKEJECCQmID7d2IFNwBU8DwJaEB5qoa9ECBAgACBYgtMmvZBbPPb4Q1CTjxzx2jXpnWxg0tHgAABAgQiwv3bMagT8BOAzkEmAhpQJuwWJUCAAAECSQq8MO2D2HauAeALZw2MNq1bJekhNAECBAikJeD+nVa9vyqtAaBzkImABpQJu0UJECBAgECSAs+/+X70P//+BtknnTUwWhsAJnkehCZAgEBqAu7fqVW88bwGgM5BJgIaUCbsFiVAgAABAkkKTJj6fmx/QcMB4OSzB0arVn4CMMkDITQBAgQSE3D/TqzgXxHXANA5yERAA8qE3aIECBAgQCBJgfFvvBcDfjeilL1u7jf57J2StBCaAAECBNITcP9Or+aNJTYAdA4yEdCAMmG3KAECBAgQSFLg2SnvxY4X1g8A6/7k7yQDwCTPgtAECBBIUcD9O8Wq/29mA0DnIBMBDSgTdosSIECAAIEkBZ55/d3Y6fcPlLK3bd0qnj9rYJIWQhMgQIBAegLu3+nVvLHEBoDOQSYCGlAm7BYlQIAAAQJJCjz92rux80X1A8D2bVrHhDN3TNJCaAIECBBIT8D9O72aGwCq+VcKvPzyy3HZZZfFHXfcES+99FK8//77scQSS8RKK60U/fr1iz333DPWWWediglqQBWj9CECBAgQIEBgHgJPvfpufOMPcwwA27aOCWcYADo4BAgQIJCGgPt3GnWeV0o/ATgvoQR+/6KLLopjjz02Pvzww69Me9hhh8Xvfve7imloQBWj9CECBAgQIEBgHgJjX3kndvnjg6WnOrZrHeNPNwB0cAgQIEAgDQH37zTqPK+UBoDzEir4759xxhlx4oknzk65+uqrx09+8pPo1atXLLzwwjFjxowYM2ZM3HzzzdGnT584//zzK6ahAVWM0ocIECBAgACBeQiMefnt+NbFD5WeWqB9mxh32gBuBAgQIEAgCQH37yTKPM+QBoDzJCruA/fee2/0799/dsB99tknLr300mjXrl2jgWfNmhXt27evGIYGVDFKHyJAgAABAgTmIfD4y2/Ht+cYAC7YoW08feoO3AgQIECAQBIC7t9JlHmeIQ0A50lUzAe++OKL6NGjR0ycODF69uwZo0ePjrZt21YtrAZUNWoLESBAgACB5AUee+mt2O1PD5ccFurQNp4yAEz+XAAgQIBAKgLu36lU+utzGgAmeg4GDx4cO+74n7/75pprronvfve7VZXQgKrKbTECBAgQIJC0wKgX34o9LqkfAHbp2DaePMVPACZ9KIQnQIBAQgLu3wkV+2uiGgAmeg5+/OMfx+WXXx6tWrWKd999NxZaaKHZEm+99dbsv/tvscUWi0UXXXS+6WhA843WhwkQIECAAIG5BEZOfiv2/HP9ALDrAu3iiZO250SAAAECBJIQcP9OoszzDGkAOE+iYj6w9tprx7hx42LllVeOSZMmzf4pwLPPPjuefvrpUuD//qMghxxySHTo0KGiEBpQRTl9jAABAgQIEPgagUcmzYjv/OWR0hOLLNAuxhgAOjMECBAgkIiA+3cihZ5HTAPABM9B3d//V/ePfdT977p/8bdv377x+9///islNt1007jjjjuia9euTdaqazBf92vKlCnRu3fv2Y+88sorsdxyyzX52x4kQIAAAQIECDRH4KEXpsf3/vpo6ZXFOrePx07crjmf8CwBAgQIEKhZAQPAmi1dRTduAFhRztr42Ntvv136470dO3aMjz/+OLp16xbnnntuDBw4MOr+s1GjRsWvfvWreOSR//y35d/61rfipptuanLAuj9a3NRfBoBNlfIcAQIECBAgUI7AQ89Pj+9dWj8AXHzBDjH6hP7lfMo7BAgQIECg5gQMAGuuZPNlwwaA84U13x+d8//x1+10gQUWiMcffzzWWGONBhv/6KOPZv904NixY2f/53XDwD59+jQpnAFgk5g8RIAAAQIECFRB4IGJ0+P7l9UPAJdYqEOMOt4AsAr0liBAgACBHAgYAOagCDnYggFgDopQ7S1Mnz49llhiidKyhx56aFx44YWNbqPuj/7uvPPOs3/viCOOiPPPP79J2/VHgJvE5CECBAgQIECgCgL3T5gW+1w+srTSUl06xKPHGQBWgd4SBAgQIJADAQPAHBQhB1swAMxBEaq9hU8++WT2H/P976/bb789dtppp0a3UffHg+v+heDPPvssNt988xgxYkRFtqsBVYTRRwgQIECAAIEmCAx77s3Y72+jSk92W7hjPHzstk140yMECBAgQKD2Bdy/a7+GlUhgAFgJxRr8xpJLLhnTpk2bvfO6P+K73nrrfWWKur8f8I033pj9R4THjx9fkbQaUEUYfYQAAQIECBBogsB9z70ZP5xjALjMwh3jIQPAJsh5hAABAgSKIOD+XYQqtjyDAWDLDWvyC/369Ythw4bN3nvd3/+3wQYbfGWO/w4L11577Xj66acrklcDqgijjxAgQIAAAQJNEBg6fmr86IrRpSeX7dopHjxmmya86RECBAgQIFD7Au7ftV/DSiQwAKyEYg1+4+STT47TTjtt9s5vvPHG2G233RpN8d5770XXrl3jyy+/jO233z7uuuuuiqTVgCrC6CMECBAgQIBAEwSGjJsa+19ZPwBcftFOMeJoA8Am0HmEAAECBAog4P5dgCJWIIIBYAUQa/ETTz75ZPTs2XP21vfee+/4xz/+0WiMv//977HffvvN/r3TTz89TjjhhIrE1YAqwugjBAgQIECAQBME7hk3NX4yxwBwhUUXiPuP7teENz1CgAABAgRqX8D9u/ZrWIkEBoCVUKzRbwwcODAGDRoUrVu3jrvvvju23bbhX4Zd9/f+9erVK+qaRfv27WPSpEmx7LLLViStBlQRRh8hQIAAAQIEmiBw1zNvxM+ueqz05EqLLRDDjjIAbAKdRwgQIECgAALu3wUoYgUiGABWALFWPzFhwoTo06dPvPPOO7P/VeDDDz886oaCnTp1ipEjR8bZZ589e/hX9+s3v/lNHH300RWLqgFVjNKHCBAgQB8F5ScAACAASURBVIAAgXkIDH56Shzwj8dLT62yeOcY+sutuREgQIAAgSQE3L+TKPM8QxoAzpOo2A888MADsfvuu8fUqVMbDdqqVas4/vjjZ//x30r+0oAqqelbBAgQIECAwNcJDHpqShx4df0AcNUlOse9vzAAdGoIECBAIA0B9+806jyvlAaA8xJK4PdnzJgRF110Ufz73/+OyZMnx6xZs6Jbt26x9dZbxyGHHPK1/0JwuTwaULly3iNAgAABAgSaK3DHk1Pi4GvqB4Ddl1wwhhy5VXM/43kCBAgQIFCTAu7fNVm2im/aALDipD7YFAENqClKniFAgAABAgQqIXDb2NfjkGvHlD61+lILxt1HGABWwtY3CBAgQCD/Au7f+a9RNXZoAFgNZWv8j4AG5FAQIECAAAEC1RK4dezrcegcA8AeSy8Ugw/fslrLW4cAAQIECGQq4P6dKX9uFjcAzE0p0tqIBpRWvaUlQIAAAQJZCtzyxGtx2D+fKG3BADDLalibAAECBKot4P5dbfF8rmcAmM+6FH5XGlDhSywgAQIECBDIjcDNY16NI64bW9rPWt26xJ2HbZGb/dkIAQIECBCYnwLu3/NTt3a+bQBYO7Uq1E41oEKVUxgCBAgQIJBrgZsefzWOvL5+ALjOsl3i9kMMAHNdNJsjQIAAgYoJuH9XjLKmP2QAWNPlq93Na0C1Wzs7J0CAAAECtSZw42Ovxi9vqB8ArrvswnHbIZvXWgz7JUCAAAECZQm4f5fFVriXDAALV9LaCKQB1Uad7JIAAQIECBRB4PrRr8TRNz5ZitJzuYXjlp8bABahtjIQIECAwLwF3L/nbZTCEwaAKVQ5hxk1oBwWxZYIECBAgEBBBa4b9XL86l9PldKtv3zX+PfBmxU0rVgECBAgQKChgPu3E1EnYADoHGQioAFlwm5RAgQIECCQpMA/R74cx9xUPwDcYIWucfNBBoBJHgahCRAgkKCA+3eCRW8ksgGgc5CJgAaUCbtFCRAgQIBAkgLXPPpyHHdz/QBwoxUXiX8duGmSFkITIECAQHoC7t/p1byxxAaAzkEmAhpQJuwWJUCAAAECSQr845GX4oR/P13K3mulReKGAwwAkzwMQhMgQCBBAffvBIveSGQDQOcgEwENKBN2ixIgQIAAgSQFrnrkpThxjgFg75UWjesP6JukhdAECBAgkJ6A+3d6NW8ssQGgc5CJgAaUCbtFCRAgQIBAkgJXPvxinHTLM6XsfVZeNK77mQFgkodBaAIECCQo4P6dYNEbiWwA6BxkIqABZcJuUQIECBAgkKTAFQ9OjlNuG1fK3neVxeLan26SpIXQBAgQIJCegPt3ejVvLLEBoHOQiYAGlAm7RQkQIECAQJICf3twcpw6xwBws+6LxdX7GwAmeRiEJkCAQIIC7t8JFr2RyAaAzkEmAhpQJuwWJUCAAAECSQpc9sDkOP32+p8A3Lz74vGP/fskaSE0AQIECKQn4P6dXs0bS2wA6BxkIqABZcJuUQIECBAgkKTApSMmxRl3PFvKvsVqi8dVPzYATPIwCE2AAIEEBdy/Eyx6I5ENAJ2DTAQ0oEzYLUqAAAECBJIU+Ov9k+LMO+sHgFutvkT8/Ue9k7QQmgABAgTSE3D/Tq/mjSU2AHQOMhHQgDJhtygBAgQIEEhS4M/DX4izB40vZd96jSXiih8aACZ5GIQmQIBAggLu3wkWvZHIBoDOQSYCGlAm7BYlQIAAAQJJCvxp2Avxm8H1A8BteiwZl+/XK0kLoQkQIEAgPQH37/Rq3lhiA0DnIBMBDSgTdosSIECAAIEkBS4e9nycM/i5Uvb+ay4Zl+5rAJjkYRCaAAECCQq4fydY9EYiGwA6B5kIaECZsFuUAAECBAgkKfDH+56Pc++acwC4VFy678ZJWghNgAABAukJuH+nV/PGEhsAOgeZCGhAmbBblAABAgQIJClw0b0T47f3TChl336tpeIv+xgAJnkYhCZAgECCAu7fCRa9kcgGgM5BJgIaUCbsFiVAgAABAkkKXDhkYlwwpH4AOGDtpeOSH2yUpIXQBAgQIJCegPt3ejVvLLEBoHOQiYAGlAm7RQkQIECAQJICvxsyIX43ZGIp+47rLB1/+r4BYJKHQWgCBAgkKOD+nWDRG4lsAOgcZCKgAWXCblECBAgQIJCkwPn3TIjf31s/ANxp3W7xx703TNJCaAIECBBIT8D9O72aN5bYANA5yERAA8qE3aIECBAgQCBJgd/e/VxcNPT5Uvad1+sWf/ieAWCSh0FoAgQIJCjg/p1g0RuJbADoHGQioAFlwm5RAgQIECCQpMB5dz0Xf7ivfgD4jZ7LxEXf3SBJC6EJECBAID0B9+/0at5YYgNA5yATAQ0oE3aLEiBAgACBJAXOGTw+Lh72Qin7LusvExd+xwAwycMgNAECBBIUcP9OsOiNRDYAdA4yEdCAMmG3KAECBAgQSFLg14PGxyXD6weA39pg2bhgr/WTtBCaAAECBNITcP9Or+aNJTYAdA4yEdCAMmG3KAECBAgQSFLg7EHPxp+HTypl//aGy8b5exoAJnkYhCZAgECCAu7fCRa9kcgGgM5BJgIaUCbsFiVAgAABAkkKnHXns/GX++sHgLttuFz8ds+eSVoITYAAAQLpCbh/p1fzxhIbADoHmQhoQJmwW5QAAQIECCQpcMbt4+LSByaXsu+x0XJx7h4GgEkeBqEJECCQoID7d4JFbySyAaBzkImABpQJu0UJECBAgECSAqffPi4um2MAuNfGy8dvdl8vSQuhCRAgQCA9Affv9GreWGIDQOcgEwENKBN2ixIgQIAAgSQFTr3tmfjbgy+Wsn+n1/Lx690MAJM8DEITIEAgQQH37wSL3khkA0DnIBMBDSgTdosSIECAAIEkBU659Zm44qH6AeB3e68QZ3973SQthCZAgACB9ATcv9OreWOJDQCdg0wENKBM2C1KgAABAgSSFDj5lqfj7w+/VMq+d58V4sxvGQAmeRiEJkCAQIIC7t8JFr2RyAaAzkEmAhpQJuwWJUCAAAECSQqc+O+n46pH6geA399khThjVwPAJA+D0AQIEEhQwP07waIbACp6XgQ0oLxUwj4IECBAgEDxBY6/+am4+tGXS0H36btinLbLOsUPLiEBAgQIEIgI92/HoE7ATwA6B5kIaECZsFuUAAECBAgkKXDsTU/FtSPrB4D7bbpSnPLNtZO0EJoAAQIE0hNw/06v5o0lNgB0DjIR0IAyYbcoAQIECBBIUuDYm56Ma0e+UspuAJjkMRCaAAECyQq4fydb+gbBDQCdg0wENKBM2C1KgAABAgSSFPjVjU/GdaPrB4A/2mzlOOkbayVpITQBAgQIpCfg/p1ezRtLbADoHGQioAFlwm5RAgQIECCQpMBRN4yNGx57tZR9/81XjhN2NgBM8jAITYAAgQQF3L8TLHojkQ0AnYNMBDSgTNgtSoAAAQIEkhT45Q1j48Y5BoA/2WLlOH4nA8AkD4PQBAgQSFDA/TvBohsAKnpeBDSgvFTCPggQIECAQPEFjrz+ibjp8ddKQX+25Spx7MA1ix9cQgIECBAg4F8Bdgb+X8BPADoKmQgYAGbCblECBAgQIJCkwBHXPRE3j6kfAB6w1apxzI49krQQmgABAgTSE3D/Tq/mjSU2AHQOMhHQgDJhtygBAgQIEEhS4PB/jol/P/F6KftBW68aRw8wAEzyMAhNgACBBAXcvxMseiORDQCdg0wENKBM2C1KgAABAgSSFDj02jFx69j6AeDB/VaNo3YwAEzyMAhNgACBBAXcvxMsugGgoudFQAPKSyXsgwABAgQIFF/g59c8Hrc/OaUU9JBtuscvtl+j+MElJECAAAEC/g5AZ+D/BfwEoKOQiYABYCbsFiVAgAABAkkKHHzN43HHHAPAQ7ddLY7cbvUkLYQmQIAAgfQE3L/Tq3ljiQ0AnYNMBDSgTNgtSoAAAQIEkhQ46OrH4s6n3ihlP2zb1eIIA8Akz4LQBAgQSFHA/TvFqv9vZgNA5yATAQ0oE3aLEiBAgACBJAUOuOqxGPxM/QDwiP6rx2H9V0vSQmgCBAgQSE/A/Tu9mjeW2ADQOchEQAPKhN2iBAgQIEAgSYGfXTU67npmain7L7ZbPQ7Z1gAwycMgNAECBBIUcP9OsOiNRDYAdA4yEdCAMmG3KAECBAgQSFLgJ1eOjnvG1Q8Af7n96vHzbQwAkzwMQhMgQCBBAffvBItuAKjoeRHQgPJSCfsgQIAAAQLFF9j/76NiyLNvloIetcMacXC/7sUPLiEBAgQIEPCvADsD/y/gJwAdhUwEDAAzYbcoAQIECBBIUuBHV4yKoePrB4C/GtAjDtx61SQthCZAgACB9ATcv9OreWOJDQCdg0wENKBM2C1KgAABAgSSFPjh30bGfc9NK2U/ZsceccBWBoBJHgahCRAgkKCA+3eCRW8ksgGgc5CJgAaUCbtFCRAgQIBAkgL7Xj4yhk+oHwAeN7BH/HRLA8AkD4PQBAgQSFDA/TvBohsAKnpeBDSgvFTCPggQIECAQPEFfnDZozFi4vRS0BN2WjP232KV4geXkAABAgQI+DsAnYH/F/ATgI5CJgIGgJmwW5QAAQIECCQpYACYZNmFJkCAAIH/F3D/dhTqBAwAnYNMBDSgTNgtSoAAAQIEkhTY+9JH4sHnZ5Syn7TzWvGjzVdO0kJoAgQIEEhPwP07vZo3ltgA0DnIREADyoTdogQIECBAIEmB7/7lkXh4Uv0A8JRvrBX7bWYAmORhEJoAAQIJCrh/J1j0RiIbADoHmQhoQJmwW5QAAQIECCQp8J2/PByPTHqrlP3Ub64d+266UpIWQhMgQIBAegLu3+nVvLHEBoDOQSYCGlAm7BYlQIAAAQJJCuz554dj5OT6AeDpu6wdP+hrAJjkYRCaAAECCQq4fydY9EYiGwA6B5kIaECZsFuUAAECBAgkKbDHJQ/FqBffLmU/Y9d14vubrJikhdAECBAgkJ6A+3d6NW8ssQGgc5CJgAaUCbtFCRAgQIBAkgK7/+mhGP1S/QDwrG+tG9/rs0KSFkITIECAQHoC7t/p1dwAUM1zI6AB5aYUNkKAAAECBAov8O2LH4zHX36nlPPsb68b3+1tAFj4wgtIgAABArMF3L8dhDoBPwHoHGQioAFlwm5RAgQIECCQpMCuf3wwnnilfgD4m93Wjb16GQAmeRiEJkCAQIIC7t8JFr2RyAaAzkEmAhpQJuwWJUCAAAECSQrs8scHY+wcA8Bzdl8v9tx4+SQthCZAgACB9ATcv9OreWOJDQCdg0wENKBM2C1KgAABAgSSFPjmHx6IJ199t5T93N3Xiz0MAJM8C0ITIEAgRQH37xSr/r+ZDQCdg0wENKBM2C1KgAABAgSSFNj5ohHx9GvvlbL/do+esdtGyyVpITQBAgQIpCfg/p1ezRtLbADoHGQioAFlwm5RAgQIECCQpMDAC0fEuCn1A8AL9uoZ39rAADDJwyA0AQIEEhRw/06w6I1ENgB0DjIR0IAyYbcoAQIECBBIUmDHC0fEs3MMAH+31/qx6wbLJmkhNAECBAikJ+D+nV7NG0tsAOgcZCKgAWXCblECBAgQIJCkwIDf3R/j33i/lP3C76wfu6xvAJjkYRCaAAECCQq4fydY9EYiGwA6B5kIaECZsFuUAAECBAgkKbD9BcNjwtQPStkv+u4G8Y2eyyRpITQBAgQIpCfg/p1ezRtLbADoHGQioAFlwm5RAgQIECCQpMB25w+PiW/WDwD/8L0NYuf1DACTPAxCEyBAIEEB9+8Ei95IZANA5yATAQ0oE3aLEiBAgACBJAW2/e2weGHah6XsF++9YQxct1uSFkITIECAQHoC7t/p1byxxAaAzkEmAhpQJuwWJUCAAAECSQpsc96wmDS9fgB4yfc3jAHrGAAmeRiEJkCAQIIC7t8JFr2RyAaAzkEmAhpQJuwWJUCAAAECSQr0O29YTG4wANwoBqyzdJIWQhMgQIBAegLu3+nVvLHEBoDOQSYCGlAm7BYlQIAAAQJJCmx17n3x0oyZpex/+cFGsf3aBoBJHgahCRAgkKCA+3eCRW8ksgGgc5CJgAaUCbtFCRAgQIBAkgJbnDM0Xnnro1L2S/fZOPqvtVSSFkITIECAQHoC7t/p1byxxAaAzkEmAhpQJuwWJUCAAAECSQps/puh8erb9QPAy/fbOLbpYQCY5GEQmgABAgkKuH8nWPRGIhsAOgeZCGhAmbBblAABAgQIJCmw2a+Hxmvv1A8A/7Zfr+jXY8kkLYQmQIAAgfQE3L/Tq3ljiQ0AnYNMBDSgTNgtSoAAAQIEkhToe/a9MeXdj0vZr/hhr9h6DQPAJA+D0AQIEEhQwP07waI3EtkA0DnIREADyoTdogQIECBAIEmBTc66N954r34AeOWPeseWqy+RpIXQBAgQIJCegPt3ejVvLLEBoHOQiYAGlAm7RQkQIECAQJICvc8cEm++/0kp+1U/7h1brGYAmORhEJoAAQIJCrh/J1j0RiIbADoHmQhoQJmwW5QAAQIECCQpsPEZQ2L6B/UDwKv37xObdV88SQuhCRAgQCA9Affv9GreWGIDQOcgEwENKBN2ixIgQIAAgSQFNjr9npjx4axS9mt+0ic2XdUAMMnDIDQBAgQSFHD/TrDojUQ2AHQOMhHQgDJhtygBAgQIEEhSYMPT74m35hgAXvuTTaLvqoslaSE0AQIECKQn4P6dXs0bS2wA6BxkIqABZcJuUQIECBAgkKTA+qfdHe/M/LSU/bqfbhJ9VjEATPIwCE2AAIEEBdy/Eyx6I5ENAJ2DTAQ0oEzYLUqAAAECBJIUWO+Uu+K9jz8rZb/hgL7Ra6VFk7QQmgABAgTSE3D/Tq/mjSU2AHQOMhHQgDJhtygBAgQIEEhSYN1T7or35xgA3nhA39jYADDJsyA0AQIEUhRw/06x6v+b2QDQOchEQAPKhN2iBAgQIEAgSYF1Tr4rPvik/icA/3XgprHRioskaSE0AQIECKQn4P6dXs0bS2wA6BxkIqABZcJuUQIECBAgkKTAWicNjpmzPi9lv/mgTWODFQwAkzwMQhMgQCBBAffvBIveSGQDQOcgEwENKBN2ixIgQIAAgSQF1jxxcHz0af0A8N8HbxbrL981SQuhCRAgQCA9Affv9GreWGIDQOcgEwENKBN2ixIgQIAAgSQF1jhhUHzy2Rel7Lf+fLNYbzkDwCQPg9AECBBIUMD9O8GiNxLZANA5yERAA8qE3aIECBAgQCBJgdWPHxSzPq8fAN5+yOaxzrILJ2khNAECBAikJ+D+nV7NG0tsAOgcZCKgAWXCblECBAgQIJCkwGrH3xmffv5lKfsdh24eay9jAJjkYRCaAAECCQq4fydY9EYiGwA6B5kIaECZsFuUAAECBAgkKbDqcXfG51/UDwDvPHSLWGuZLklaCE2AAAEC6Qm4f6dX88YSGwA6B5kIaECZsFuUAAECBAgkKbDysXfEl/Xzvxh8+BbRY2kDwCQPg9AECBBIUMD9O8GiNxLZANA5yERAA8qE3aIECBAgQCBJgZWOuaNB7ruP2DJWX2qhJC2EJkCAAIH0BNy/06t5Y4kNAJ2DTAQ0oEzYLUqAAAECBJIT+PLLL2PlY+9skPueI7aM1QwAkzsLAhMgQCBVAffvVCvfMLcBoHOQiYAGlAm7RQkQIECAQHICdX/3X93fATjnryFHbhXdl1wwOQuBCRAgQCBNAffvNOs+d2oDQOcgEwENKBN2ixIgQIAAgeQEPv38i1jt+EENcg/9xVaxyhIGgMkdBoEJECCQqID7d6KFnyu2AaBzkImABpQJu0UJECBAgEByArM++yJWP6HhAPC+X24dKy/eOTkLgQkQIEAgTQH37zTrPndqA0DnIBMBDSgTdosSIECAAIHkBD7+9PPoceLgBrmHH7V1rLiYAWByh0FgAgQIJCrg/p1o4eeKbQDoHGQioAFlwm5RAgQIECCQnMBHsz6PNU9qOAAccXS/WH7RBZKzEJgAAQIE0hRw/06z7nOnNgB0DjIR0IAyYbcoAQIECBBITmDmrM9irZPuapDbADC5YyAwAQIEkhZw/066/KXwBoDOQSYCGlAm7BYlQIAAAQLJCXzwyWexzskNB4APHrNNLNu1U3IWAhMgQIBAmgLu32nWfe7UBoDOQSYCGlAm7BYlQIAAAQLJCbz38aex3il3N8j98LHbRLeFDQCTOwwCEyBAIFEB9+9ECz9XbANA5yATAQ0oE3aLEiBAgACB5ATe/ejT6HlqwwHgI8duG0sv3DE5C4EJECBAIE0B9+806z53agNA5yATAQ0oE3aLEiBAgACB5ATemTkr1j/tnga5Rx63bSzZxQAwucMgMAECBBIVcP9OtPBzxTYAdA4yEdCAMmG3KAECBAgQSE7grQ9nxYanNxwAjjq+fyyxUIfkLAQmQIAAgTQF3L/TrPvcqQ0AnYNMBDSgTNgtSoAAAQIEkhOY8cEnsdEZQxrkfuyE/rHYggaAyR0GgQkQIJCogPt3ooWfK7YBoHOQiYAGlAm7RQkQIECAQHIC097/JHqd2XAA+PiJ28WindsnZyEwAQIECKQp4P6dZt3nTm0A6BxkIqABZcJuUQIECBAgkJzAm+99HL3PurdB7idO2i66LmAAmNxhEJgAAQKJCrh/J1r4uWIbADoHmQhoQJmwW5QAAQIECCQnMPW9j6PPXAPAsSdvHwt3apechcAECBAgkKaA+3eadZ87tQGgc5CJgAaUCbtFCRAgQIBAcgJT3v0o+p49tEHuJ0/ZPrp0NABM7jAITIAAgUQF3L8TLfxcsQ0AnYNMBDSgTNgtSoAAAQIEkhN47Z2PYrNfNxwAPn3qDrFgh7bJWQhMgAABAmkKuH+nWfe5UxsAOgeZCGhAmbBblAABAgQIJCfwylszY4tz7muQe9xpO8QC7Q0AkzsMAhMgQCBRAffvRAs/V2wDQOcgEwENKBN2ixIgQIAAgeQEGhsAPnvagOjUvk1yFgITIECAQJoC7t9p1n3u1AaAzkEmAhpQJuwWJUCAAAECyQm8NOPD2OrcYQ1yjz99QHRsZwCY3GEQmAABAokKuH8nWvi5YhsAOgeZCGhAmbBblAABAgQIJCcwefqH0e+8hgPACWfsGO3btk7OQmACBAgQSFPA/TvNus+d2gDQOchEQAPKhN2iBAgQIEAgOYFJ0z6IbX47vEHuiWfuGO3aGAAmdxgEJkCAQKIC7t+JFn6u2AaAzkEmAhpQJuwWJUCAAAECyQk8/+YH0f/8hgPAF84aGG1at0rOQmACBAgQSFPA/TvNus+d2gDQOchEQAPKhN2iBAgQIEAgOYGJU9+P7S64v0HuyWcPjFatDACTOwwCEyBAIFEB9+9ECz9XbANA5yATAQ0oE3aLEiBAgACB5AQmTH0/tp9rAPjir3dKzkFgAgQIEEhXwP073drPmdwA0DnIREADyoTdogQIECBAIDmB8W+8FwN+N6KUu+4H/yafbQCY3EEQmAABAgkLuH8nXPw5ohsAOgeZCGhAmbBblAABAgQIJCcw7vX3YuDv6weAdX/3X93fAegXAQIECBBIRcD9O5VKf31OA0DnIBMBDSgTdosSIECAAIHkBJ55/d3Y6fcPlHK3a9MqJp5pAJjcQRCYAAECCQu4fydc/DmiGwA6B5kIaECZsFuUAAECBAgkJ/D0a+/GzhfVDwDbt2kdE87cMTkHgQkQIEAgXQH373RrP2dyA0DnIBMBDSgTdosSIECAAIHkBJ589Z345h8eLOXu0LZ1PHeGAWByB0FgAgQIJCzg/p1w8eeIbgDoHGQioAFlwm5RAgQIECCQnMDYV96JXf5YPwDs1K5NPHv6gOQcBCZAgACBdAXcv9Ot/ZzJDQCdg0wENKBM2C1KgAABAgSSExjz8tvxrYsfKuVeoH2bGHeaAWByB0FgAgQIJCzg/p1w8eeIbgDoHGQioAFlwm5RAgQIECCQnMBjL70du/2pfgC4YIe28fSpOyTnIDABAgQIpCvg/p1u7edMbgDoHGQioAFlwm5RAgQIECCQnMDoF9+K3S95uJR7oY5t46lTDACTOwgCEyBAIGEB9++Eiz9HdANA5yATAQ0oE3aLEiBAgACB5ARGvfhW7DHHALBLx7bxpAFgcudAYAIECKQs4P6dcvXrsxsAOgeZCGhAmbBblAABAgQIJCfw6KQZsddfHinl7rpAu3jipO2TcxCYAAECBNIVcP9Ot/ZzJjcAdA4yEdCAMmG3KAECBAgQSE7g4RdmxHf/Wj8AXLRz+3j8xO2ScxCYAAECBNIVcP9Ot/YGgGqfuYAGlHkJbIAAAQIECCQh8NAL0+N7f320lHWxzu3jMQPAJGovJAECBAj8R8D920moE/ATgM5BJgIaUCbsFiVAgAABAskJPPj89Nj70voB4OILdojRJ/RPzkFgAgQIEEhXwP073drPmdwA0DnIREADyoTdogQIECBAIDmBEROnxQ8uG1nKveRCHWLk8QaAyR0EgQkQIJCwgPt3wsWfI7oBYKLnoFWrVk1KvtVWW8WwYcOa9GxzHtKAmqPlWQIECBAgQKBcgfsnTIt9Lq8fAC7VpUM8epwBYLme3iNAgACB2hNw/669ms2PHRsAzg/VGvimAWANFMkWCRAgQIAAgRYLDHvuzdjvb6NK3+m2cMd4+NhtW/xdHyBAgAABArUiYABYK5Wav/s0AJy/vrn9+n8HgAceeGAcdNBBX7nPzp07x8orr1zxHBpQxUl9kAABAgQIEGhE4L7xb8YPr6gfAC7btVM8eMw2rAgQIECAQDIC7t/JlPprgxoAJnoO/jsAPPnkk+OUU06puoIGVHVyCxIgQIAAgSQFho6fGj+6YnQp+3KLdIoHfmUAmORhEJoAAQKJCrh/J1r4uWIbACZ6DgwAEy282AQIECBAIDGBIeOmxv5X1g8Al1+0U4w42gAwsWMgoljOFAAAIABJREFULgECBJIWMABMuvyl8AaAiZ4DA8BECy82AQIECBBITODuZ96In171WCn1iostEMOP6peYgrgECBAgkLKAAWDK1a/PbgCY6DkwAEy08GITIECAAIHEBO565o342RwDwJUX7xz3/XLrxBTEJUCAAIGUBQwAU66+AWDy1f/vAHCttdaKL7/8Ml588cVo06ZNLL300rHpppvGfvvtF/36zb//dlwDSv4IAiBAgAABAlURGPz0lDjgH4+X1lpl8c4x1ACwKvYWIUCAAIF8CLh/56MOWe/CTwBmXYGM1v/vAPDrlt91113jiiuuiIUXXrjZu6xrMF/3a8qUKdG7d+/Zj7zyyiux3HLLNXsNLxAgQIAAAQIE5iVw51NT4qCr6weAqy7ROe79hZ8AnJeb3ydAgACB4ggYABanli1JYgDYEr0afrdz587xzW9+M7bddtvo0aNHLLjggjFt2rQYPnx4XHLJJTFjxozZ6bbaaqu45557ol27ds1K25QB438/aADYLFoPEyBAgAABAs0QuP3J1+Pn14wpvbHakgvGPUdu1YwveJQAAQIECNS2gAFgbdevUrs3AKyUZI1955133omuXbs2uuupU6fGjjvuGGPG/Of/s3zhhRfGoYce2qyEBoDN4vIwAQIECBAgMJ8Ebhv7ehxybf0AcPWlFoy7jzAAnE/cPkuAAAECORQwAMxhUTLYkgFgBui1sOSkSZNm/2Tgp59+Gt27d4+JEyc2a9v+CHCzuDxMgAABAgQIzCeBW554LQ775xOlr/dYeqEYfPiW82k1nyVAgAABAvkTMADMX02y2JEBYBbqNbLmTjvtFHfeeefs3b722muxzDLLVGznGlDFKH2IAAECBAgQ+BqBf495LQ6/rn4AuGa3LjHosC2YESBAgACBZATcv5Mp9dcGNQB0Dr5S4Kijjorzzjtv9u+PHDkyevXqVTEtDahilD5EgAABAgQIfI3AzWNejSOuG1t6Yq1uXeJOA0BnhgABAgQSEnD/TqjYXxPVANA5+EqBo48+Os4991wDQGekWQJffPFl3P7UlBg/5b34sllvNv5wq4hYb7mFY4e1l47m/N2SFVjaJwgQIECgAAL/euzV+MUN9QPAdZbtErcf4icAC1BaEQgQIECgiQIGgE2EKvhjBoAFL3BL4u28885xxx13zP5EXcNYdtllW/K5Bu9qQBWjzN2Hzr1rfPzxvhcqvq8fb75ynLjzWhX/rg8SIECAQLEFbhj9Shx145OlkHX/pdKtP9+82KGlI0CAAAECcwi4fzsOdQIGgM5BowKTJ0+e/Y+AzJo1K1ZdddV4/vnnKyqlAVWUM1cf63XmkJj2/ifzbU9Ldekw377twwQIECBQPIGZsz6P9z/+rBSs53ILxy0GgMUrtEQECBAg8JUC7t8OhwFgomfgtttuix133DHatm3bqMDUqVNn//6YMWNm//5vf/vbOPLIIyuqpQFVlDM3H3tn5qxY/7R7crMfGyFAgAABAnMLrL981/j3wZuBIUCAAAECyQi4fydT6q8N6icAEzwHK620Unz66aex2267Rd++faPu/96pU6eYPn16DBs2LP785z/P/j/X/dp8881jyJAh0aFDZX/qSgMq5sEb/eJbsfslD5fCtW3dKnZer1vZYYdPmBZvz/y07Pe9SIAAAQIE5hbov+ZScem+G4MhQIAAAQLJCLh/J1NqA0ClbihQN/B76aWX5slSNyC89NJLo2vXrvN8trkPaEDNFauN568d+XIce9NTpc2utuSCcc+RW7Vo8xfcMyEuvHdii77hZQIECBAgUCfQoW3r+PuPescmqywGhAABAgQIJCPg/p1MqQ0AlbqhwPDhw6Pufx5++OGYNGnS7J/2e++992LBBReM5ZdfPjbddNPYd999Z/904Pz6pQHNL9lsv3v67ePisgcmlzYxcN2l4+K9N2rxpl6c/mG8/NbMFn/HBwgQIEAgXYE2rVvF2st0ia4LtE8XQXICBAgQSFLA/TvJsv9PaH8E2DnIREADyoR9vi+6z+Uj4/4J00rrHLrtanHkdqvP93UtQIAAAQIECBAgQIAAAQKNC7h/Oxl1AgaAzkEmAhpQJuxfuejk6R/GE6+8HZ9/0bJ9/XrQ+Jj+Qf2/APz7724Q3+y5TMs+6m0CBAgQIECAAAECBAgQKFvA/btsukK9aABYqHLWThgNKD+1um/8m/GTK0fHZ198WfFNDTpsi1izW5eKf9cHCRAgQIAAAQIECBAgQKBpAu7fTXMq+lMGgEWvcE7zaUD5Kcy+l4+Mun9tt9K/WreKGHfagOjYrk2lP+17BAgQIECAAAECBAgQINBEAffvJkIV/DEDwIIXOK/xNKD8VGarc++Ll2ZU/h/Y6LvKYnHtTzfJT1A7IUCAAAECBAgQIECAQIIC7t8JFr2RyAaAzkEmAhpQJuz/s+gXX3wZPU4cHLPm+Mv/ui+5YHRq4U/t1X3jmB17xFJdOuYjqF0QIECAAAECBAgQIEAgUQH370QLP1dsA0DnIBMBDSgT9v9ZdNr7n0SvM4c0+M8fOXbbWHphg7t8VMguCBAgQIAAAQIECBAg0DIB9++W+RXlbQPAolSyxnJoQPko2NhX3old/vhgaTNtW7eK587YMdrU/QV+fhEgQIAAAQIECBAgQIBAzQu4f9d8CSsSwACwIow+0lwBDai5YvPn+UFPTYkDr3689PHlFukUD/xqm/mzmK8SIECAAAECBAgQIECAQNUF3L+rTp7LBQ0Ac1mW4m9KA8pHjS97YHKcfvu40mZ6r7RoXH9A33xszi4IECBAgAABAgQIECBAoMUC7t8tJizEBwwAC1HG2guhAeWjZnXDv7oh4H9/7br+MvG772yQj83ZBQECBAgQIECAAAECBAi0WMD9u8WEhfiAAWAhylh7ITSgfNTswH88FoOefqO0mYO2XjWOHtAjH5uzCwIECBAgQIAAAQIECBBosYD7d4sJC/EBA8BClLH2QmhA+ahZ3T8AUvcPgfz31+m7rhM/2GTFfGzOLggQIECAAAECBAgQIECgxQLu3y0mLMQHDAALUcbaC6EBlV+zjz/9PI676akY+tybMeuzL8r/UETMnPV5g/cv32/j2KbHUi36ppcJECBAgAABAgQIECBAID8C7t/5qUWWOzEAzFI/4bU1oPKL/9f7J8WZdz5b/ge+5s1Bh20Ra3brMl++7aMECBAgQIAAAQIECBAgUH0B9+/qm+dxRQPAPFYlgT1pQOUX+cjrnoibxrxW/ge+4s32bVvHmBO3i84d2lb82z5IgAABAgQIECBAgAABAtkIuH9n4563VQ0A81aRRPajAZVf6IOveTzueHJK+R/4ijeP2mGNOLhf94p/1wcJECBAgAABAgQIECBAIDsB9+/s7PO0sgFgnqqR0F40oPKLvf/fR8eQZ6eWPrB3nxVi942WK/+DEbFs106xZJeOLfqGlwkQIECAAAECBAgQIEAgfwLu3/mrSRY7MgDMQt2aoQGVfwj2uXxk3D9hWukDxw9cM36y5Srlf9CbBAgQIECAAAECBAgQIFBYAffvwpa2WcEMAJvF5eFKCWhA5Uvu9eeH49HJb5U+cOo31459N12p/A96kwABAgQIECBAgAABAgQKK+D+XdjSNiuYAWCzuDxcKQENqHzJb138YIx5+Z3SB3797XXjO71XKP+D3iRAgAABAgQIECBAgACBwgq4fxe2tM0KZgDYLC4PV0pAAypfcuCFI2LclPdKHzh/z57x7Q1b9ncAlr8bbxIgQIAAAQIECBAgQIBAngXcv/NcnertzQCwetZWmkNAAyr/OPQ/f3g8/+YHpQ/84XsbxM7rLVP+B71JgAABAgQIECBAgAABAoUVcP8ubGmbFcwAsFlcHq6UgAZUvuSW59wXL781s/SBv+6zcWy31lLlf9CbBAgQIECAAAECBAgQIFBYAffvwpa2WcEMAJvF5eFKCWhA5Uv2OWtITH3vk9IH/v6j3rHV6kuU/0FvEiBAgAABAgQIECBAgEBhBdy/C1vaZgUzAGwWl4crJaABlS+5wWl3x9szPy194J8/3SQ2WWWx8j/oTQIECBAgQIAAAQIECBAorID7d2FL26xgBoDN4vJwpQQ0oPIl1zppcMyc9XnpAzcdtGlsuMIi5X/QmwQIECBAgAABAgQIECBQWAH378KWtlnBDACbxeXhSgloQOVLdj/uzvjsiy9LH7jj0M1j7WUWLv+D3iRAgAABAgQIECBAgACBwgq4fxe2tM0KZgDYLC4PV0pAAypP8vMvvoxVj7uzwctDjtwyui+5UHkf9BYBAgQIECBAgAABAgQIFFrA/bvQ5W1yOAPAJlN5sJICGlB5mh/N+jzWPGlwg5fvP6pfrLDYAuV90FsECBAgQIAAAQIECBAgUGgB9+9Cl7fJ4QwAm0zlwUoKaEDlab4789PoedrdDV5+9LhtY6kuHcv7oLcIECBAgAABAgQIECBAoNAC7t+FLm+TwxkANpnKg5UU0IDK03zzvY+j91n3Nnh5zInbxSKd25f3QW8RIECAAAECBAgQIECAQKEF3L8LXd4mhzMAbDKVByspoAGVp/nKWzNji3Pua/DyuNN2iAXaty3vg94iQIAAAQIECBAgQIAAgUILuH8XurxNDmcA2GQqD1ZSQAMqT/P5Nz+I/ucPb/Dy82fuGG3btC7vg94iQIAAAQIECBAgQIAAgUILuH8XurxNDmcA2GQqD1ZSQAMqT3Pc6+/FwN+PKL3cpnWreOGsgeV9zFsECBAgQIAAAQIECBAgUHgB9+/Cl7hJAQ0Am8TkoUoLaEDliT7xyjux6x8fLL3cqV2bePb0AeV9zFsECBAgQIAAAQIECBAgUHgB9+/Cl7hJAQ0Am8TkoUoLaEDliT46aUbs9ZdHSi8v3KldjD15+/I+5i0CBAgQIECAAAECBAgQKLyA+3fhS9ykgAaATWLyUKUFNKDyREdMnBY/uGxk6eUlF+oQI4/vX97HvEWAAAECBAgQIECAAAEChRdw/y58iZsU0ACwSUweqrSABlSe6JBxU2P/K0eXXl5ukU7xwK+2Ke9j3iJAgAABAgQIECBAgACBwgu4fxe+xE0KaADYJCYPVVpAAypP9M6npsRBVz9eennVJTrHvb/YuryPeYsAAQIECBAgQIAAAQIECi/g/l34EjcpoAFgk5g8VGkBDag80ZvHvBpHXDe29PKa3brEoMO2KO9j3iJAgAABAgQIECBAgACBwgu4fxe+xE0KaADYJCYPVVpAAypP9LpRL8ev/vVU6eWey3eNWw7erLyPeYsAAQIECBAgQIAAAQIECi/g/l34EjcpoAFgk5g8VGkBDag80asefjFOvOWZ0su9V140rv9Z3/I+5i0CBAgQIECAAAECBAgQKLyA+3fhS9ykgAaATWLyUKUFNKDyRC8dMSnOuOPZ0stbrLZ4XPXjPuV9zFsECBAgQIAAAQIECBAgUHgB9+/Cl7hJAQ0Am8TkoUoLaEDlif7xvufj3LueK73cf80l49J9e5X3MW8RIECAAAECBAgQIECAQOEF3L8LX+ImBTQAbBKThyotoAGVJ3r+PRPi9/dOLL08cN2l4+K9NyrvY94iQIAAAQIECBAgQIAAgcILuH8XvsRNCmgA2CQmD1VaQAMqT/TXg8bHJcNfKL38rQ2WjQv2Wr+8j3mLAAECBAgQIECAAAECBAov4P5d+BI3KaABYJOYPFRpgbw3oEcnzYhrR74c73/8WUWid+nULr7Ta/nos8piLfreqbc9E3978MXSN/baePn4ze7rteibXiZAgAABAgQIECBAgACB4grk/f5dXPl8JTMAzFc9ktlNnhvQa+98FFufe198+vmXFa1HuzatYvhR/WKZrp3K/u7xNz8VVz/6cun9H2yyYpy+6zplf8+LBAgQIECAAAECBAgQIFBsgTzfv4stn690BoD5qkcyu8lzA7ry4RfjpFuemS+1OGe39WLPXsuX/e2jbhgbNzz2aun9/TdfOU7Yea2yv+dFAgQIECBAgAABAgQIECi2QJ7v38WWz1c6A8B81SOZ3czZgM7514Ox6JLdWpS9fdvWsUX3xWOlxTu36Dt1L//yhrFx4xxDthZ/cI4PHLbtanHEdquX/clDrx0Tt459vfT+QVuvGkcP6FH297xIgAABAgQIECBAgAABAsUWMAAsdn2bms4AsKlSnquowJwNaNkDr4i2XRZv8ffrhoDX/6xvrL981xZ9a4cL7o/npr5f+saO6ywd6y1X3jeHjp8ao158u/StPTZaLs7do2fZ+zvgqsdi8DNvlN4/ov/qcVj/1cr+nhcJECBAgAABAgQIECBAoNgCBoDFrm9T0xkANlXKcxUVmB8DwLoNbt598fjH/n3K3uvMWZ/FOiffFV/M8df//evAvrHRiouW9c0/DXshfjN4fOndzbovFlfvv0lZ36p76Yd/Gxn3PTet9P6vBvSIA7detezveZEAAQIECBAgQIAAAQIEii1gAFjs+jY1nQFgU6U8V1GB+TUArNvkP37cJxZfqH1Z+33ujffjsH8+UXq3TetW8fQpO0Sn9m3K+t4tT7zW4HurLN45hv5y67K+VffS3pc+Eg8+P6P0/kk7rxU/2nzlsr/nRQIECBAgQIAAAQIECBAotoABYLHr29R0BoBNlfJcRQXmbEDfOufW6LzokmV///GX3o4PZ31e9vtf92KPpReKwYdvWfa3R7/4Vux+ycOl9zu0bR3jTx8QrVq1Kuube1zyUIM/UnzGruvE9zdZsaxveYkAAQIECBAgQIAAAQIEii9gAFj8GjcloQFgU5Q8U3GBSjagM+8YF38dMbnie6z74J4bLxfn7F7+39n3+jsfxaa/Htpgb4+d0D8WW7BDWfv95h8eiCdffbf07jm7rxd7blz+vypc1ia8RIAAAQIECBAgQIAAAQI1I1DJ+3fNhLbR/xEwAHQoMhGoZAOaPP3D2O784fHZnH9xX4VS/W6v9WPXDZYt+2ufff5FrH7CoAZ/p+BtP9881l1u4bK+OeB398f4N+r/gZILv7N+7LJ++fsraxNeIkCAAAECBAgQIECAAIGaEajk/btmQtuoAaAzkA+BSjegfz32apx/z4SY8u5HFQnYqV2b+EbPZeL0XdeJdm1at+ibm559b7z+7selb/z5BxvFDmsvXdY3+503LOoGnv/9dcn3N4wB63Qr61teIkCAAAECBAgQIECAAIHiC1T6/l18sWIm9BOAxaxr7lOl1IB2/9NDMfqlt0s1+cEmK8Y2a5b3dx4edcPYmP7BrNK3/rZfr+jXo7xv5f6Q2CABAgQIECBAgAABAgQItFggpft3i7EK/AEDwAIXN8/RUmpAh147Jm4d+/p8KcfV+/eJzbovPl++7aMECBAgQIAAAQIECBAgUPsCKd2/a79a8y+BAeD8s/XlrxFIqQGdPejZ+PPwSfPlPNx4QN/YeKVF58u3fZQAAQIECBAgQIAAAQIEal8gpft37Vdr/iUwAJx/tr5sADhbYMi4qbH/laMrfh7atWkVj524XXTp2K7i3/ZBAgQIECBAgAABAgQIECiGgAFgMerY0hQGgC0V9H5ZAik1oC+++DL+cN/zccsTr8VHsz4vy2vul5ZYqEMcss1q0X+tpSryPR8hQIAAAQIECBAgQIAAgWIKpHT/LmYFK5PKALAyjr7STAENqJlgHidAgAABAgQIECBAgAABAmUIuH+XgVbAVwwAC1jUWoikAdVCleyRAAECBAgQIECAAAECBGpdwP271itYmf0bAFbG0VeaKaABNRPM4wQIECBAgAABAgQIECBAoAwB9+8y0Ar4igFgAYtaC5E0oFqokj0SIECAAAECBAgQIECAQK0LuH/XegUrs38DwMo4+kozBTSgZoJ5nAABAgQIECBAgAABAgQIlCHg/l0GWgFfMQAsYFFrIZIGVAtVskcCBAgQIECAAAECBAgQqHUB9+9ar2Bl9m8AWBlHX2mmgAbUTDCPEyBAgAABAgQIECBAgACBMgTcv8tAK+ArBoAFLGotRNKAaqFK9kiAAAECBAgQIECAAAECtS7g/l3rFazM/g0AK+PoK80U0ICaCeZxAgQIECBAgAABAgQIECBQhoD7dxloBXzFALCARa2FSBpQLVTJHgkQIECAAAECBAgQIECg1gXcv2u9gpXZvwFgZRx9pZkCGlAzwTxOgAABAgQIECBAgAABAgTKEHD/LgOtgK8YABawqLUQSQOqhSrZIwECBAgQIECAAAECBAjUuoD7d61XsDL7NwCsjKOvNFNAA2ommMcJECBAgAABAgQIECBAgEAZAu7fZaAV8BUDwAIWtRYiaUC1UCV7JECAAAECBAgQIECAAIFaF3D/rvUKVmb/BoCVcfSVZgpoQM0E8zgBAgQIECBAgAABAgQIEChDwP27DLQCvmIAWMCi1kIkDagWqmSPBAgQIECAAAECBAgQIFDrAu7ftV7ByuzfALAyjr7STAENqJlgHidAgAABAgQIECBAgAABAmUIuH+XgVbAVwwAC1jUWoikAdVCleyRAAECBAgQIECAAAECBGpdwP271itYmf0bAFbG0VeaKaABNRPM4wQIECBAgAABAgQIECBAoAwB9+8y0Ar4igFgAYtaC5E0oFqokj0SIECAAAECBAgQIECAQK0LuH/XegUrs38DwMo4+kozBTSgZoJ5nAABAgQIECBAgAABAgQIlCHg/l0GWgFfMQAsYFFrIZIGVAtVskcCBAgQIECAAAECBAgQqHUB9+9ar2Bl9m8AWBlHX2mmgAbUTDCPEyBAgAABAgQIECBAgACBMgTcv8tAK+ArBoAFLGotRNKAaqFK9kiAAAECBAgQIECAAAECtS7g/l3rFazM/g0AK+PoK80U0ICaCeZxAgQIECBAgAABAgQIECBQhoD7dxloBXzFALCARa2FSBpQLVTJHgkQIECAAAECBAgQIECg1gXcv2u9gpXZvwFgZRx9pZkCGlAzwTxOgAABAgQIECBAgAABAgTKEHD/LgOtgK8YABawqLUQSQOqhSrZIwECBAgQIECAAAECBAjUuoD7d61XsDL7NwCsjKOvNFNAA2ommMcJECBAgAABAgQIECBAgEAZAu7fZaAV8BUDwAIWtRYivfjii7HyyivP3urIkSOjW7dutbBteyRAgAABAgQIECBAgAABAjUlMGXKlOjdu/fsPU+ePDlWWmmlmtq/zVZGwACwMo6+0kyBUaNGlRpQM1/1OAECBAgQIECAAAECBAgQIFCGQN0P4PTq1auMN71S6wIGgLVewRrd/+233x7f+MY3anT3tk2AAAECBAgQIECAAAECBGpP4Lbbboudd9659jZuxy0WMABsMaEPlCPw/PPPx2qrrTb71YceeiiWX375//nMNttsM/s/Gzp0aDlLeIdAWQJz/ni8P55eFqGXWiCg77UAz6stEnD2WsTn5RYIOHstwPNqiwScvRbxebkFAlmcvVdeeSU23XTT2bueOHFidO/evQUJvFqrAgaAtVq5Gt93U/4S0rXXXnt2ymeeeabG09p+LQk05WzWUh57rS0Bfa+26lWk3Tp7RapmbWVx9mqrXkXarbNXpGrWVpYszp47Tm2dkfm1WwPA+SXru18r0JQGlEVjVDYCTTmblAjMLwF9b37J+u68BJy9eQn5/fkl4OzNL1nfnZeAszcvIb8/vwSyOHvuOPOrmrX1XQPA2qpXYXbblAaURWMsDLAgZQs05WyW/XEvEpiHgL73f+2dCbxN5frHH0NUktzrRroht8SlRCgZk2YNKqQSmue63AbVjUqKG0WzJm6aB7pdbiWFlEaJJiEpJKWi4RYN/8/v/fzX+WzbXmevtdc65+y99vf5fHw4Z79rrff9vo+11/tbz/s8uEhFEcD3Koo818X38IGKIoDvVRR5rlsRvscaB78TAQRA/KBCCAS5AVXEjbFCYHDRvCIQxDfzqsN0JlEEuO8lajoLajD4XkFNV6I6i+8lajoLajD4XkFNV6I6WxG+xxonUS6U82AQAHNGx4FRCHADikKPY8uSAL5ZlnQ5NwQgAAEIQAACEIAABCBQ3gRY45Q38fy8HgJgfs5L4nvFDSjxU1ywA8Q3C3bq6DgEIAABCEAAAhCAAAQgkIEAaxzcQgQQAPGDCiHADahCsHPRAATwzQCQaAIBCEAAAhCAAAQgAAEIFAwB1jgFM1Vl2lEEwDLFy8n9CHADwjfylQC+ma8zQ78gAAEIQAACEIAABCAAgVwIsMbJhVryjkEATN6cMiIIQAACEIAABCAAAQhAAAIQgAAEIAABCJQQQADEGSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgkGACCIAJnlyGBgEIQAACEIAABCAAAQhAAAIQgAAEIAABBEB8AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACCSaAAJjgyWVoEIAABCAAAQhAAAIQgAAEIAABCEAAAhBAAMQHIAABCEAAAhCAAAQgAAEIQAACEIAABCCQYAIIgAme3LBDe/PNN23atGk2Z84ce//99+3LL7+0LbbYwurXr28dOnSwU045xTp27FjqaX/88Ud75plnbPr06abzLVmyxL7//nvbdtttrUmTJnbQQQfZmWeeafXq1QvbvVLb//e//7Xx48fbG2+84fr9pz/9ydq2bWunn366HXLIIYGu9csvv9jdd99tDzzwgH344Yeu3xp79+7d7fzzz7fmzZsHOg+NIACBwiSwfPlyGzdunE2dOtU+++wzq169uv3lL3+x3r172znnnGNbb711xoFt3LjRZsyYYc8++6y99tpr9tFHH9m6deusRo0a1rhxY9t///3trLPOcv+O01555RW77bbb7KWXXrIvvvjCtttuO2vZsqUNGDDA+vbtG/hSDz30kN133322YMEC+/bbb61u3brWqVMnN+b27dsHPg8NcyOQq9/5Xe23335z39mvvvpqSZNWoUdtAAAgAElEQVTff/89t85lOAq/iw1lhZ8oLt97/fXXbcKECfbiiy/aypUrTT6o+0jTpk3d/a9fv37uuSyq4XtRCebP8VF9b82aNXbHHXe4NYee2b/77jv3navvbPnc2WefbY0aNYptwPhebCgr5ETyF92n9EdrRf1Zu3at60v//v3d/SuMxbHuDHo9fC8oKdoFJYAAGJRUwtt17tzZLSKz2UknnWR33XWXVatWbbOmWjxq0SHhrDSTGCixrk+fPtkul/VzPWRK5Lvnnnt825566ql25513WuXKlX3bfPXVV3booYe6L4RMJiHglltuMZ0LgwAEkkfg6aefthNPPNHWr1+fcXB6gSFhcJdddtnkc71waNasWcmDpB8Z3TNHjRplF1xwQSzwhg0bZtdcc41baGeyww47zB5//HHbcsstfa/3v//9z4499lj34ieT6Z555ZVX2tChQ2PpMyfZnECuflcaS31XnXfeeZs0iUsAxO+S48Vx+N7PP/9s5557rnsGK83HJk+ebEcddVQkePheJHx5dXBU33vuuefsuOOOs2+++cZ3XFtttZV79pf4HNXwvagEK/74SpUq+XYijAAY17ozKBF8Lygp2oUhgAAYhlaC22pRu3TpUhfx1qtXLxf90aBBA/v1119t7ty5Nnr0aPdWV6bIkgcffHAzGooc1HEyCYE9evSwNm3a2B//+EcXlffkk0868VA3zypVqpgeAIJG5/mhHzJkiF1//fXu41atWtnFF1/s3v5pLFpsv/322+4ztRsxYkTG02iMXbt2dZGPsqOPPtpOO+00+8Mf/uCieYYPH256c6TF8H/+85/IfU6wGzE0CBQkAd0ndM+SILbNNtu4+8V+++3nfn744YfdfUsmEVCRzTVr1iwZ54oVK2ynnXZyP++555525JFH2t577+2iXxQFqLfEN998s/3000+ujRYkemkRxXQORVLLdL+77LLLbPfdd7dVq1bZ2LFjXRROafdq79q6l2t8Mo1X4qS+AxYuXOjul7qPxtXnKONN6rFR/M6Pib6nJUjrRZy+e/VySxaHAIjfJccT4/C9DRs2OFFP9zhZt27d7IQTTnBRf3rxoPuRIlf0IkLPY1EEQHwP3/MIfPzxx9aiRQv3/SzTd65EPq1Z5HNTpkyxiRMnunuenttnz57tvt9zNXwvV3L5dVyqAChf0X1KQrIsjAAYx7ozKBl8Lygp2oUlgAAYllhC20usU3TfMccc48S5dNMiQl+g2tommzVrlilqMNX0oKfFp6JF/vrXv2Yk9dRTT1nPnj3dF7MWrosXL7bS3sqUhlt90bZcbd2V0Kgveb3x80zbkbt06eIW7FWrVrUPPvhgs+gdtb333nvd9maZtgzceuutm1xW25j32msvFxkkoVTn0fkwCEAgGQS8CGj9v9Z9JH3b6z//+U/3ckGm+5veyHomwWXgwIF29dVX2z777JMRiF4keIJirVq13PbiVBExDMWvv/7abSWWuKiH2Lfeesvq1KlTcgq90NA9Vi9YZBID9YIj3V544QW3TUp2+OGHmyJ0Uu/9uufrvvfpp5+6rcVadNWuXTtMV2mbhUAUv/M7teZeC+CTTz7ZCbj6rpZFFQDxu2S5cxy+p+hgRSHrGU6pCLyXEplIKU2CUsrkYvheLtTy95iovqeIU+85/aKLLnLicrrppZtS98gUDa+X97kYvpcLtfw8Rs9uSg2lP3pB+8knn9jOO+/sOhtUAIxr3RmEEL4XhBJtciWAAJgruSI8Tl+gWijKtL1IubJyMW05e+KJJ9yhWry2bt06l9M4se722293xypKMdPiWzmQvMV8JnFPx0qslKiniD8tzDPl+VKUod76yB599FEXJYlBAAKFT0D5YBSxJzvjjDNcTqF0U9SyIg50n5AYpojgsIvZv//97y6SWqb7nyKNczEtdi655BJ3qHL3aRtUuikqUbmPJAYqtYG2Lqebfq/IHYmey5Ytsz//+c+btVF0oJdLUNfVYguLh0BZ+J2i7PUST4KwcmLp33EJgPhdPPOeD2eJw/f0QkARNBL2lCtU287LyvC9siJb/ueNw/e0ZlAEq4Rn5axVWqF00wsP3QcloujZ3sv1FnbE+F5YYoXTPhcBMK51ZxBK+F4QSrTJlQACYK7kivC4H374wW2Pk/ktKoNg0Zs7vcGT5Sqm6ctdC1aF++shVAtzP9PnixYtsh133NEJfKkRh3qbs9tuu7lD9fbaExTTz7V69WrbYYcd3K/9tkAHGTttshOImhhaV4grYW723pq9++67bovn888/7/xR/0fkc9oKpZyRQaNFyzOhcJBxFUsbbZ+97rrr3HD1wsATA9PHn/oSQMU+DjzwwFCIJMIp0loWRUzbd9993QsPLXqUWiFTPlZd4+CDD3ZFSZS/VO1SIw6VLF2LI23hUztvC1/6gPS5Evcr+lkvUvT/CouHQNx+pznS1l/dg1TQRYVgFPkZlwCI38Uz7/lwljh879JLL7WRI0e6LZaKEtbzVVkZvldWZMv/vHH4nl7Gvffee+47TN9tfqZIL+0A0jOZvvNyMXwvF2qFcUxYATCudWdQOvheUFK0y4UAAmAu1Ir0GL1JU04hmSIB//3vf+dEYsyYMTZ48GB3rF8kjLbYXXXVVa6Nt5hJvZjePmsLscwvasdrr89VdESm47yQb/2cuv3XL5rGO4+EQgmG2nYnkQqLn0DUxNDqURwJc4OOTLnhJGZLKMlk7dq1c9FXqVs009uVd0LhoGMrlnbediRVD1Q0gZ9gK9FND2QybX3z7k9BOXnRWWqvSMBBgwZtdqiq0Gk7sSx9q7F+Jz9ThLIi+1RRXdUP/UyiphZbMm331RZkz1K3/6qdFvN+pusoT464KK1C2MjHoHyKrV3cfudFJui8nugXVADE74rL++LwPT1/6XlK6Ve84mlaIOtlqXKzaYud7qnZDN/LRihZn8fhe4ps1vdpaRGAoqb1itYtSmUhITDd8L1k+VbY0YQVAONad6qf+F7Y2aJ93AQQAOMmmuDzKUeUt21N+bD09jcXU8JeTzx8//33XdRCumUTAFO3I99444124YUX+nZFn3uLbYkxil70LHVbnrYUKIm/n3n91kOH3iYGebjNhU+xHhNHUvK4EuYGmQNVTpUQLgFPi53LL7/cRY/pgVPCoB5QZR07drSZM2dmzK2pz8szoXCQcRVbG0W4Kd9dy5Ytbf78+b7DV7VBbSWSKQWAopfDmApseGkT5DuZCiBleyhUtKmKfch0vptuusm3C6n3a0VdSyDyLLVKbLbqnKn9VtSFX37XMCxoay6yMi6/kzitHL0SaeXD3hzFJQDid8ny2Ki+p6ir7bff3kHRCwulTdCLBP0tAVCmyEB9H+oZq7R0B9zzkuVb2UYT1fd0/unTp5dE4CsdhlcIMPXaqTuN9Dym3Rjphu9lm61kfx5WAIxr3Smq+F6yfasQRocAWAizlAd9lMihLWDK3yHT2zS9VQtr77zzjjtOESxayC5YsCDjKbIJgHrQPOuss9yxjz32mCmvoJ+pAp2Xs0/HKSLQM+XPeuSRR9yPeqgtLVIrNfGw8it5W4fDMqB9ZgJRE0PHlTA3yPwo75G2+eqNoLZizps3ryQi1TteeZGUGF2WKYpVvy/PhMJBxlVsbVSZ1yscFCRRuLYSKRWC8o1KdAlqn3/+ubtf6MWBFkDaMqcqmemW7aFQEX+ecKjCJFpc+5nu0doCJVOEn7fN2fvZe4Gj6B1F8fjZDTfcUJL7T9dXRCAWjUCcfqd7UatWrdyWuPRq93EJgPhdtPnOp6Pj8D1FmHqFhfQ9p1zOSp/gZ3pW874LueflkzeUb1/i8D2vx1dccYVde+217kcJzEq5ot05+q5VESR9l2rdouIO99xzT8YXsHzflu/859vVwgqAca07xQHfyzdvKL7+IAAW35znNGJtWfMWm/qy9Yp4hDnZzz//7KKhvFB8RQF6RUXSz5NNAEytyqn8Vcpj5Wf63Iv602LW236s9lr0KxpHpm0rmRbl3nn1ptGrNparABqGVzG1jSMxdFwJc4NwV/RXnz59XFO/LZTaLqk8lYocU0SOFujpVp4JhYOMq9japEayaD5V9KI0U6SnCoAoB9HChQsD4dK2OEUPe1V5FQWoIkqZLNtDoV529O7d2x2qfKWlVd1UXlQvEkwvL5Sn0rNUcVrtJGb7ma7jRQ/qZYq2X2HRCMTpd8OHD7d//OMfLrWF7jGeoK0exiUA4nfR5jufjo7D91Kji/XMJGFH6S4UiaWXI3rW03OXnhmVk1I2duzYkqqsqTy45+WTd5RtX+LwvdQeKu/yiBEjXKX7dNNLEe3KKO37Ct8r2/nO97OHFQDjWneKC76X796R/P4hACZ/jiOPUG97u3fvbr/88ovb9qGFr7f9I8zJTzvtNLv77rvdIUFLrvud/5prrnF5uGQzZsywbt26+XYlNd+VjtObQ8/2339/lx9LpqhEbVvxM11Px8teeuklJ2Zi8RCIIzF0HAlzg47m+OOPdxVYZXrjXK9evYyHSqDRtmSZCtE0adKkpF15JxQOOrZiaqeiQIoakPXr18/+9a9/lTp8tdUxyn+1ZMmSQKgUpeDdc5SHT4uW0u4zpZ30/vvvt5NOOsk1UVTDySef7Ns8NV/NKaecUnLv1QH6WflPZUuXLrXGjRv7nic1T6quf+KJJwYaN438CcTld4sXL7Y99tjDCTDp6S109aACYLa5wu+yESqcz+PwvUmTJrn7pWd6IfLaa6+5/KSpJv+UEKOoaaVP0LXT22Qjh+9lI1Q4n8fhe95oJSyrKr1Srej+l25VqlRx0fKKdM81bQW+Vzi+lUtPwwqAca07g/QV3wtCiTZRCCAARqFXBMcqoqBTp04uiklvelVVUls1w1pqQnptS9Mbuyg59OJ6E0MEYNiZLJv2URNDx1UcQaNTvj6vYIKfUO0JQdrWqe3gfiaRUGKhTEKKV+BBP8eZULhsZiX5Z407IiGd2AMPPOAWyhJ7FaGlKrp+YnEQ2kRiBaGU/23i8ju9+NJ3qaJcFJ2ZbnEJgPhd/vtU0B7G4XupaVV0XYkwPXv2zNgFiTTaeSF76qmn7IgjjgjaVdcO3wuFK68bx+F7GqCi1g844ABbuXKlK/QhYUa7iRShr1QwKlqll25KtaEULdpt1KVLl9Bs8L3QyArqgLACYFzrziCQ8L0glGgThQACYBR6CT922bJlLspNb9r0Nk3bfrWVLaylFmbQVjNFz5WWay/I+ePKxUAOwCC0y75N1MTQcSWp10izCYDff/+91axZ00HR/wflm/EzFTZp3bq1+1gLIW8LuX6OM6Fw2c9QMq8QZ06idEKKyNKiWDnaJPrNmTNnszyRYamSiy0ssfxsH4ffeZGZuhdpQbzjjjtuNti4BED8Lj/9KJdexeF7ehHspV1RUbR169aVfCem90kRzxJrZBJlvF0UQfuO7wUllf/t4vA9jVJBBErDo2hS/Z2pkKB2Zij69IsvvrD69eu7F67Vq1cPBQnfC4Wr4BqHFQDjWncGAYXvBaFEmygEEACj0EvwsRL9FPmnL0094Clfgbf1LMywFQGlLWNKxtuwYUO3CFZetKgWl3hCFeCoMxH9+DgeCuP6stRosgmAivjzHjiVS00VVf1MVT4lbsokNnvbhvVzeT5MRJ+l5J5BLyPWrl0baxVg+ZC2H8m3a9eu7XxKWzWjWlxCN1WAo85E9OOj+p2ij1VEqEePHi4Bfia7+uqrnTgo8+49irz3y73rNyr8Lvp859MZovre+++/b82bN3dD0v1NUVd+ptQXXo5RFWDT914Yw/fC0Mr/tlF9T4UE99xzTzdQpRUaP36876BT02/oRW3YAAZ8L//9KUoPwwqAca07g/QZ3wtCiTZRCCAARqGX0GMlWihcXg95Mi0WJXSENYXda2uScgfusMMOLvJPubPisLi2T6bmt9ICSSKNn3kLLm3/XL58eRzD4Bz/X33ZyymZayGGuMLlNSHZBEBVTVXCc5kKwyjxuZ+psIyX80gLda8QhNqX53YCHM2fQNTt5+lnVkEb5RZVpKiqBisCZu+9945lCuLa6p6aF9WviI3XYVX91ZaqqlWrmgrbbLHFFrGMpdhPEtXvGjVqlNP3kF7EaeETxvC7MLTyv21U31NUs4Rk/a0tlooA9LNUsTDbC7NM58D38t+fwvQwqu+pUFffvn3dJbMVwkp9MaznND2vhTF8LwytwmsbVgCMa90ZhBS+F4QSbaIQQACMQi+Bx+pBTnmF5s2b50aXy5emjlNhDuXXUzU45ehQIRHvjXEc2MIUUFC0lqK2tEVKSYgV0eiZIigk7MlUsEEPFJls9erVTsSU6eHjwQcfjGMYnMPMzUnUQgxxJcwNMiESsr08mKq+qSgbP1Pkq7bPyyQKSQzyrDwTCgcZV7G2iaMAjcduwYIFrvCClzNVlTD1c5wWR7Gb7777zqVh0EOmtvKpn5lMnyuCdf369da+fXuXwxCLh0BUvytPAVAjxu/imfd8OEtU39MY9JJ49uzZbjjK7eaX1kUvvby8f6rYOmTIkNAI8L3QyPL2gKi+l5p/UpXtVeHez1J9T3koBw8eHJoLvhcaWcEcEFYAjGvdGRQQvheUFO1yIYAAmAu1hB6j6I4DDzzQXn75ZTfCyy+/3IYPHx56tFok6jyq/FarVi0nBu61116hz5PtgLPPPrtEsJs7d67ts88+mx3y6quvuoWrTO1vvfXWzdqoQpi2SZVWpU5CqPfg+uijj1qvXr2ydY/PAxKIIzF0nBGA2bpNBGA2QoX1uSL2vAg9vy1qEnJV6VL3ie22287WrFmzWSScXiZIGFbOIUXJacvRoYceGjsM5ZH0Ihn8opZXrFhhEohU2Vx9UD7CdNPvJfwpsk/5XjOlZkiNttB1lccSi4dAXH5XWm/iygGoa+B38cx7PpwlDt8bN26cXXDBBW44d911l5166qkZh6bCV0ohI9PLM+WVDmv4Xlhi+ds+qu+99dZb1qZNGzfA3r172yOPPOI72NQUP8phfvTRR4cGg++FRlYwB4QVADWwuNadQSDhe0Eo0SZXAgiAuZJL2HGK9FBeIG31kunB7qabbgo9yvnz57sKqt9++63bIqJk0R06dAh9nmHDhtlVV13ljrvvvvtswIABm51DC26Jd1rk6oFAb6O32mqrknbafqkFuZIEa5GrrSi77rrrZudJ3QacaYvK0qVLXSEHRcHssssuTgTQ+bB4CORbDsBsoyIHYDZChfe5ty1J/691H/FeGngjSd2uPXToUNP9KdVUbVALW0WzKuJTwtmxxx4bGoQWyl6l6EzX0QmVb6tx48Zu2522c2pBpChrz3Q/VPERb7u5qsRmikJM3QasCB1V8vSiVXUupYLQixuNTaKntr8o3xcWH4GofpetJ0EFQPwuG8nkfR7V95TiQCld9DJERRb0Ykx/p5p2fijyXfckvUBRhHTqDgy1xfeS51vZRhTF9/QyTjtGVAFY31daY8jH0k27mHQdBSIoDYva63ss1fC9bDOV7M9zEQDjWnfie8n2rUIYHQJgIcxSOfRRufq0AJRpC7DEv/QHtdRuVKtWzZo0abJJzySUKWRZD4SyG2+80bp3715q75X7zcv/ltowiACo9orK83KwqeKXImP0UKq+jBw50lSF1Wun7SeZTA+n2s7iRT6KhZILa7Grt5XaqqkxVa5c2VVuVXJ/LF4CURNDx5UwN8iotH1SeY9kVAEOQiz/2+g+oRcVemmgvH3apqQXGfpZYp6XaFz3PL1Q8KpAa2QqICLBcPHixW6gF198sfXr16/UQevekqlqa5CHQp04tbK67neK1t59991dxXbduyX6ybKlK9DnGp9M473wwgvdIn7hwoWmBOq6j3rXO/300/N/Igush1H8LshQ4xQA8bsgxAunTRy+p+gr3UO0NU73DT2PKZpaqV+Uf23MmDHuHqoXK8qtm+llMPe8wvGZuHoa1fdSU74o2l6R+wpg0FpCL8gUyKDtwdrVJNMzvCpQpxu+F9eMFsZ5VIRyyZIlJZ3VS05vV4PuTelRzJkCT3RwHOtOfK8wfCbJvUQATPLshhhbaWJfptNkSiSeekMLemm/KJegAqDeBkqsUxSfn51yyiluAS8Bz8/0RaAtcXqLncmqV6/uiqH4bXMJOl7aZSYQNTF0XAlzg86P3kAr2kv5IxUR6Gfaonn88ce7j+WjXnSXfi7PhMJBx1XM7RQxp4rlivTNZBL/tJVWUcCpllo0Jii//v37l2yLSz0m6EOhjtG9UwsbLb4zme5n2va05ZZb+nZLi3NFKk6bNi1jG90zlecyPeIx6Dhpl51Arn6X/czmIj8VhSXz8xN9ht8FoZm8NnH4ntKqDBo0yOUTzWR6oTJp0iTfCqz4XvL8KsiIovqecvrpRZ0K0fiZ1jV6qTV69OiMAQ34XpCZSk4bCXoTJ04MPCC/78w41p34XuBpoGEZEUAALCOwhXbaQhUAPc5awErkk4AnMU8RZW3btnVvBoNG7KlasXLZqMCHtvlq64Deamt7gbZEx1nEpND8o6z7GzUxtPoXR8LcoOOUqCdxT/b5559bvXr1Mh6qwjKK1pItWrRok6jZ8k4oHHRsxdxO1b3Hjh3rhD7l0VOkswQ/5fxUsnGvonMqo4oSANUH5VvVAly5tZR7UFucWrZs6YRmr1JikPnUPU8PpO+8845L31C3bl3r1KmTG3P6dugg56NNOAK5+F2QK5SFAIjfBSFfOG3i8L333nvP3YemT59esjVTaQpUYEgCjFdALROVMAthfK9w/CpIT6P6np6p9NyvlxyK7NK2dH1HK0BBKTn0wr60/OP4XpBZSk6buATAONad+F5y/KpQR4IAWKgzR78hkCACURNDC0VcCXODYFUhmD59+rim1113nV166aWbHabtJyqsoIqwylWpRVK6lWdC4SDjog0EIAABCEAAAhCAAAQgAAEIJJMAAmAy55VRQaDgCERJDK3BxlUcITWiy2+rpradNG3a1G3jVT5AJZxWLrZUU0GZ2267zf2qrAvZFNxk02EIQAACEIAABCAAAQhAAAIQKFcCCIDlipuLQQACfgSiJobWeeMojhBEANS1tO1ciaeVD0RbJpVkul27di7iT1vJlX9Npq0oOmdqhdVUBnEkFMarIAABCEAAAhCAAAQgAAEIQAACpRFAAMQ/IACBvCEQNTG0BhK1OEJQAVDXktCnPGl+CdAlCCqfnHJS+lkcCYXzZgLpCAQgAAEIQAACEIAABCAAAQjkJQEEwLycFjoFgeIlEDUxtMhFKY4QRgDUtd59910bN26czZgxw1atWmU1atSwZs2a2QknnOCSUFetWjXQZMZRyCbQhWgEAQhAAAIQgAAEIAABCEAAAkVHAAGw6KacAUMAAhCAAAQgAAEIQAACEIAABCAAAQgUEwEEwGKabcYKAQhAAAIQgAAEIAABCEAAAhCAAAQgUHQEEACLbsoZMAQgAAEIQAACEIAABCAAAQhAAAIQgEAxEUAALKbZZqwQgAAEIAABCEAAAhCAAAQgAAEIQAACRUcAAbDoppwBQwACEIAABCAAAQhAAAIQgAAEIAABCBQTAQTAYpptxgoBCEAAAhCAAAQgAAEIQAACEIAABCBQdAQQAItuyhkwBCAAAQhAAAIQgAAEIAABCEAAAhCAQDERQAAsptlmrBCAAAQgAAEIQAACEIAABCAAAQhAAAJFRwABsOimnAFDAAIQgAAEIAABCEAAAhCAAAQgAAEIFBMBBMBimm3GCgEIQAACEIAABCAAAQhAAAIQgAAEIFB0BBAAi27KGTAEIAABCEAAAhCAAAQgAAEIQAACEIBAMRFAACym2WasEChnAmvWrLHXX3/d/XnjjTfcn7Vr17pe9O/f3yZMmBC4R8uWLbNx48bZ9OnTbfny5fbbb79Z/fr17YADDrBzzjnHmjdvHvhcpTVs1KiRO3+61ahRw2rVqmV16tSxli1bWtu2be3oo4+2HXfcMZbrchIIQAACEIAABCAAAQhAAAIQgEBZEUAALCuynBcCELBKlSr5UggjAI4fP97OO+8827BhQ8bzVatWzUaPHm3nnntuZOp+AmCmE1epUsWOOOIIGzNmjOk4DAIQgAAEIAABCEAAAhCAAAQgkI8EEADzcVboEwQSQiBVAGzQoIE1bdrUnnvuOTe6oALgww8/bH379nXHKAJv8ODB1q1bN6tevbq9/fbbNmrUKFuyZIkTG9W2d+/ekeh5AqCiC5999tmSc23cuNG++eYbFx34yiuv2GOPPWbr1q1zn2+77bY2adIkO/zwwyNdm4MhAAEIQAACEIAABCAAAQhAAAJlQQABsCyock4IQMARGDp0qNsqqz9169a1Tz75xHbeeefAAuCPP/7o2msr8TbbbGNz5861Fi1abEJ3/fr11rFjR1u4cKG7hsRAtc3VPAGwYcOGrr9+9v3337vxKfpPttVWW9ns2bOtTZs2uV6a4yAAAQhAAAIQgAAEIAABCEAAAmVCAAGwTLByUghAIBOBsALg448/br169XKnuvzyy2348OEZwT7//PMuF6Ds5ptvjrQVOKgA6HVk5MiRdumll7oflRtw/vz5TD4EIAABCEAAAhCAAAQgAAEIQCCvCCAA5tV00BkIJJtAWAFQwpoENtmcOXOsQ4cOGQH98ssvVrNmTfvpp5+sS5cuNnPmzJxBhhUAf//9d2vfvr299tpr7ppTp061Qw89NOP1f/31V7dVWNuH582b5wqiKFqxWbNmrqDIWWed5SIJSzMVP3nkkUdM4qiKqnz55Zeu+U477eQiLXv27GlHHnmkbbHFFjkz4EAIQAACEIAABCAAAQhAAAIQSBYBBMBkzSejgUBeEwgrAJ522ml29913uzEtXrzYdtllF9/xqRrvqlWrTAVBfvjhB6tatWpOLMIKgLrIQw89ZMcff7nKpSQAAA2QSURBVLy7nvqsoiXp9umnn7qCIe+8845vvzQ+CYhNmjTJ2Eb8JPBlizJ88cUXrWvXrjmNn4MgAAEIQAACEIAABCAAAQhAIHkEEACTN6eMCAJ5SyCsAPi3v/3NbrrpJjeeN9980/baa6+MY1MUngpxKC+f7IMPPnAFR3KxXARAReFtv/327nK6rq6faor0a9WqlX322WeueIlEQkUq6lrqswqjjB071pTzsHHjxi46UAVPUu2LL76w1q1bO5FTpkIoKqSi66kAyrJly+yFF15w0YVPPPEEAmAuk88xEIAABCAAAQhAAAIQgAAEEkoAATChE8uwIJCPBMIKgIqkO+OMM9xQRo8ebYMGDco4LAlmqeKgqvceeOCBOSHIRQDUhbQFd8WKFS7yUBWDU+2EE06wBx980FRYRNF5XiGU1DaqaNypUycXvXjZZZfZtddeu8k5tEV48uTJ7nfaFn3xxRdnHJ8ERV2/du3aOY2fgyAAAQhAAAIQgAAEIAABCEAgeQQQAJM3p4wIAnlLIKwAqIg5RcQpx5+2+Grra506dTYZn3LiHXbYYfbMM8+U/F758Y455picOOQqAO65554l23vXrVvnIhJlGrO29ir/39NPP209evTw7dcll1xio0aNsvr169vKlStL2i1atMjlCVSk41FHHVUiBOY0QA6CAAQgAAEIQAACEIAABCAAgaIjgABYdFPOgCFQcQTCCoDq6XnnnWe33HKL67Ry40kg22+//VyuPwmCw4YNM0X86ecNGza4dvfff7+deOKJOQ00VwGwY8eO9vLLL7trKhJQgqXs1ltvdVWJt956a1u/fr1VqVLFt1/K/+cJhMuXL7cGDRq4tmPGjLHBgwe7f8+aNcs6d+6c09g4CAIQgAAEIAABCEAAAhCAAASKkwACYHHOO6OGQIUQyEUA/Pnnn12F3GnTpvn2uU2bNq4C7u233+7aTJkyxVXClWlLrfLj+VmLFi02+ShXAbBly5a2YMECdy4JfapKLBs4cKBNmDAhNG9VFW7Xrp07rl+/fq56sCr7aouvxE4MAhCAAAQgAAEIQAACEIAABCAQlAACYFBStIMABCITyEUA1EW1zffee+910XSqoqutsDIV3lBBjSuuuMJUMOSOO+5wv0+Nkps5c6aLGPQz71ze57kKgF4V4vQcgNqeXJp46dev1Eq+hxxyiNviXLduXVu9enXkeeAEEIAABCAAAQhAAAIQgAAEIFBcBBAAi2u+GS0EKpRArgJgaqe/++47U0VcbamtV6+eVa5c2X3cvXt3mzFjhvu3qvJ6uQLLQwBcs2aNE+dkzZs3t3fffbekywcffLDboqz+SNQLaioUUqNGDdccATAoNdpBAAIQgAAEIAABCEAAAhCAQCYCCID4BQQgUG4E4hAAM3VWBTYkwK1du9YVDVm6dGnOY8olAvCBBx4oyTmoqsVeJKI64VUArl69utuOXFoOQL9OswU45+nkQAhAAAIQgAAEIAABCEAAAhAwMwRA3AACECg3AmUlAD7//PN2wAEHuHEMGTLERowYkfOYwgqA2kK8zz772Ouvv+6uqa26Bx10UMn1b7jhBrvooovcz6+++qrtvffeoft244032qBBg9xximjs0qVL6HNwAAQgAAEIQAACEIAABCAAAQgULwEEwOKde0YOgXInUBYCoAS4Tp06uQq8KpKxaNEi0/bZXC2sAHj99dc70VHWunVre+uttza5tPrTrFkzl7fwuOOOs4ceeih01z766CNr2rSpO8dRRx1lkydPDn0ODoAABCAAAQhAAAIQgAAEIACB4iWAAFi8c8/IIVDuBHIRALWtd5tttjFtoU03bf09//zz7bbbbnMfXXnllXbVVVdFGldQAVDVeIcNG2ajR49211NOwjlz5lirVq02u36fPn3s0Ucfdb9Xey+aL1NHVbFYkYJ9+/bd5ONjjjnGnnzySfe7UaNGlUQVpp9D24w3bNhgtWvXjsSBgyEAAQhAAAIQgAAEIAABCEAgOQQQAJMzl4wEAnlHQILYkiVLSvr11VdflQhXHTp0sFNPPXWTPg8YMGCzMTz++ON27rnnuug5bX1t0KCB/fTTT7ZgwQIbP368zZ8/3x2jQhlTpkyxatWqReLgCYD169d3xTs827hxo3377bcmEfOVV14x9Us/y2rVqmXKA6iKv5ns66+/trZt29rHH3/sPu7cubOddNJJrmCIhE2JnKpurO3DL7zwgvXs2dOdP9VU+EQRhqtWrXK/7tatm4mXIgMrVark+qUiIw8//LA98cQT1rVr10gcOBgCEIAABCAAAQhAAAIQgAAEkkMAATA5c8lIIJB3BCRQTZw4MXC/tMU13SSE9erVy/ccEr8GDhzoogAzRQkGvvj/N/QEwCDHVa1a1Y444ggbM2aMNWzYsNRDVq9ebb1797aXXnop66k1nnvvvXezdhIQjzzyyE2qDGc6mYRABMCsmGkAAQhAAAIQgAAEIAABCECgaAggABbNVDNQCJQ/gTgEQEW+3X///S4y7sMPPzT9XLlyZVOE3n777efEv1wKa/jR8BMAtcVXkX516tSxli1bWrt27UzbctWPMDZ16lSXB3Du3LkmUVCRhdttt53tuuuu1r59eycoKkLQz7TtedKkSfbYY4/ZvHnzTFGVEiJ32mknV4xEfVIkYi7VhsOMg7YQgAAEIAABCEAAAhCAAAQgUDgEEAALZ67oKQQgAAEIQAACEIAABCAAAQhAAAIQgAAEQhNAAAyNjAMgAAEIQAACEIAABCAAAQhAAAIQgAAEIFA4BBAAC2eu6CkEIAABCEAAAhCAAAQgAAEIQAACEIAABEITQAAMjYwDIAABCEAAAhCAAAQgAAEIQAACEIAABCBQOAQQAAtnrugpBCAAAQhAAAIQgAAEIAABCEAAAhCAAARCE0AADI2MAyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUDgEEAALZ67oKQQgAAEIQAACEIAABCAAAQhAAAIQgAAEQhNAAAyNjAMgAAEIQAACEIAABCAAAQhAAAIQgAAEIFA4BBAAC2eu6CkEIAABCEAAAhCAAAQgAAEIQAACEIAABEITQAAMjYwDIAABCEAAAhCAAAQgAAEIQAACEIAABCBQOAQQAAtnrugpBCAAAQhAAAIQgAAEIAABCEAAAhCAAARCE0AADI2MAyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUDgEEAALZ67oKQQgAAEIQAACEIAABCAAAQhAAAIQgAAEQhNAAAyNjAMgAAEIQAACEIAABCAAAQhAAAIQgAAEIFA4BBAAC2eu6CkEIAABCEAAAhCAAAQgAAEIQAACEIAABEITQAAMjYwDIAABCEAAAhCAAAQgAAEIQAACEIAABCBQOAQQAAtnrugpBCAAAQhAAAIQgAAEIAABCEAAAhCAAARCE0AADI2MAyAAAQhAAAIQgEBxEpgwYYINHDjQDX7ZsmXWqFGj4gTBqCEAAQhAAAIQgECBEUAALLAJo7sQgAAEIAABCECgogggAFYUea4LAQhAAAIQgAAEohFAAIzGj6MhAAEIQAACEIBA0RAoTQDs2rWrzZo1y7p06WIzZ84sGiYMFAIQgAAEIAABCBQCAQTAQpgl+ggBCEAAAhCAAATygAACYB5MAl2AAAQgAAEIQAACORBAAMwBGodAAAIQgAAEIACBYiSAAFiMs86YIQABCEAAAhBIAgEEwCTMImOAAAQgAAEIQAAC5UAAAbAcIHMJCEAAAhCAAAQgUAYEEADLACqnhAAEIAABCEAAAoVI4JtvvrGRI0falClTbPny5VazZk3bY4897IwzzrBevXpZJgFwwIABNnHixFKH27BhQ/vkk08KEQl9hgAEIAABCEAAAokggACYiGlkEBCAAAQgAAEIQCAagQ8++MC6d+9uq1atyniigQMHWufOnU1/y5YtW2aNGjUyBMBo3DkaAhCAAAQgAAEIlAcBBMDyoMw1IAABCEAAAhCAQB4TWL9+vbVo0cI+++wz18s+ffpY//79bfvtt7ePPvrIxowZY2+++aa1bdvW3njjjU0EwJUrV5oiByUMqk2bNm3svvvu22S01apVsyZNmuQxAboGAQhAAAIQgAAEkk0AATDZ88voIAABCEAAAhCAQFYCF110kd1www2u3YgRI2zIkCGbHLNx40br0aOHPffccyW/9yIAvV907drVZs2aZV26dLGZM2dmvSYNIAABCEAAAhCAAATKjwACYPmx5koQgAAEIAABCEAg7whs2LDB6tWr56L4lO9v/vz5VqlSpc36uWLFCmvcuLFJDJQhAObdVNIhCEAAAhCAAAQg4EsAARDngAAEIAABCEAAAkVMYO7cubbvvvs6AqNHj7ZBgwb50lAU4NSpUxEAi9hfGDoEIAABCEAAAoVJAAGwMOeNXkMAAhCAAAQgAIFYCIwfP95V+ZXNnj3bOnXq5Hveq6++2oYOHYoAGAt5TgIBCEAAAhCAAATKjwACYPmx5koQgAAEIAABCEAg7whcf/31JTn/PvzwQ9ttt918+3jnnXfamWeeiQCYd7NIhyAAAQhAAAIQgEDpBBAA8RAIQAACEIAABCBQxARSBcBFixaVWq0XAbCIHYWhQwACEIAABCBQ0AT+D0DcRPdSrCalAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcedc71f670>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "df.temp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcede3bd190>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "df.temp.plot()\n",
    "df.temp.rolling(window = 110).mean().plot(label = 'moving average')\n",
    "df.temp.ewm(span = 10).mean().plot(label = 'WE MA').legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "def update(curr):\n",
    "    \"\"\"does the plotting where curr is in the current frame\"\"\"\n",
    "    if curr == n:\n",
    "        a.event_source.stop() #stop when it is at the end of the interval\n",
    "    plt.cla()\n",
    "    axs=[ax1]\n",
    "    fig.subtitle('sampling weather with t {}'.format(df.dt[curr]))\n",
    "    axs[0].plot(df.dt[:curr], label='temp', color = 'red')\n",
    "    axs[0].plot(df.dt[:curr], df.temp.rolling(window = 10).mean()[:curr], label='moving average temp', color = 'blue')\n",
    "    axs[0].set_ylim([df.temp.min()-3, df.temp.max()+3])\n",
    "    plt.legend()\n",
    "\n",
    "n = len(df) -1\n",
    "fig, ((ax1)) = plt.subplots(1, 1, sharey=True)\n",
    "a = animation.FuncAnimation(fig, update, interval=60)\n",
    "\n",
    "    \n",
    "                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}