{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weitere Schritte mit Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing & Cleaning\n",
    "\n",
    "Lade deinen Datensatz von letzter Woche relativ zu deinem Arbeitsverzeichnis.\n",
    "Formatiere deinen DataFrame anschließend so, dass die Column 'Date' als neuer Index fungiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/city-stats.csv')\n",
    "\n",
    "df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lösche die Column mit den wenigsten Seitenaufrufen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Berlin</th>\n",
       "      <th>München</th>\n",
       "      <th>Leipzig</th>\n",
       "      <th>Stuttgart</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>13436</td>\n",
       "      <td>5129</td>\n",
       "      <td>1287</td>\n",
       "      <td>1676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>15673</td>\n",
       "      <td>3629</td>\n",
       "      <td>1172</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>12500</td>\n",
       "      <td>3334</td>\n",
       "      <td>1104</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>12368</td>\n",
       "      <td>4476</td>\n",
       "      <td>1462</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>20432</td>\n",
       "      <td>5712</td>\n",
       "      <td>1933</td>\n",
       "      <td>3559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>22500</td>\n",
       "      <td>6668</td>\n",
       "      <td>1947</td>\n",
       "      <td>3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04</th>\n",
       "      <td>13439</td>\n",
       "      <td>4014</td>\n",
       "      <td>1629</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>5668</td>\n",
       "      <td>1834</td>\n",
       "      <td>1241</td>\n",
       "      <td>2144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>9887</td>\n",
       "      <td>2697</td>\n",
       "      <td>1683</td>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>6211</td>\n",
       "      <td>2137</td>\n",
       "      <td>808</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>10759</td>\n",
       "      <td>2133</td>\n",
       "      <td>1235</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>5556</td>\n",
       "      <td>1711</td>\n",
       "      <td>986</td>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>6053</td>\n",
       "      <td>3683</td>\n",
       "      <td>1279</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>7455</td>\n",
       "      <td>2020</td>\n",
       "      <td>1153</td>\n",
       "      <td>1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>8865</td>\n",
       "      <td>2872</td>\n",
       "      <td>2118</td>\n",
       "      <td>1619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>12223</td>\n",
       "      <td>3872</td>\n",
       "      <td>2554</td>\n",
       "      <td>1820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>3620</td>\n",
       "      <td>1648</td>\n",
       "      <td>1161</td>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03</th>\n",
       "      <td>5006</td>\n",
       "      <td>2333</td>\n",
       "      <td>1131</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04</th>\n",
       "      <td>7528</td>\n",
       "      <td>2144</td>\n",
       "      <td>1167</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05</th>\n",
       "      <td>8110</td>\n",
       "      <td>20529</td>\n",
       "      <td>1326</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>9112</td>\n",
       "      <td>21624</td>\n",
       "      <td>1103</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>10268</td>\n",
       "      <td>4255</td>\n",
       "      <td>3931</td>\n",
       "      <td>3061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>17969</td>\n",
       "      <td>10034</td>\n",
       "      <td>6358</td>\n",
       "      <td>5666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>20162</td>\n",
       "      <td>10399</td>\n",
       "      <td>6607</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>20790</td>\n",
       "      <td>7277</td>\n",
       "      <td>7148</td>\n",
       "      <td>7093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Berlin  München  Leipzig  Stuttgart\n",
       "Date                                        \n",
       "2020-10   13436     5129     1287       1676\n",
       "2020-11   15673     3629     1172       1870\n",
       "2020-12   12500     3334     1104       1589\n",
       "2021-01   12368     4476     1462       1979\n",
       "2021-02   20432     5712     1933       3559\n",
       "2021-03   22500     6668     1947       3238\n",
       "2021-04   13439     4014     1629       1817\n",
       "2021-05    5668     1834     1241       2144\n",
       "2021-06    9887     2697     1683       1079\n",
       "2021-07    6211     2137      808        981\n",
       "2021-08   10759     2133     1235       1373\n",
       "2021-09    5556     1711      986       1055\n",
       "2021-10    6053     3683     1279       1225\n",
       "2021-11    7455     2020     1153       1273\n",
       "2021-12    8865     2872     2118       1619\n",
       "2022-01   12223     3872     2554       1820\n",
       "2022-02    3620     1648     1161       1172\n",
       "2022-03    5006     2333     1131       1101\n",
       "2022-04    7528     2144     1167       1225\n",
       "2022-05    8110    20529     1326       1229\n",
       "2022-06    9112    21624     1103        906\n",
       "2022-07   10268     4255     3931       3061\n",
       "2022-08   17969    10034     6358       5666\n",
       "2022-09   20162    10399     6607       7169\n",
       "2022-10   20790     7277     7148       7093"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.pop(df.sum().idxmin())\n",
    "\n",
    "df.drop(df.sum().idxmin(), axis=1)\n",
    "\n",
    "#del df[df.sum().idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Füge dem DataFrame eine weitere Zeile hinzu, in der die durchschnittlichen Aufrufzahlen der jeweiligen Seite angezeigt werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berlin</th>\n",
       "      <th>München</th>\n",
       "      <th>Leipzig</th>\n",
       "      <th>Stuttgart</th>\n",
       "      <th>Köln</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>13436.0</td>\n",
       "      <td>5129.00</td>\n",
       "      <td>1287.00</td>\n",
       "      <td>1676.00</td>\n",
       "      <td>1911.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>15673.0</td>\n",
       "      <td>3629.00</td>\n",
       "      <td>1172.00</td>\n",
       "      <td>1870.00</td>\n",
       "      <td>2713.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>12500.0</td>\n",
       "      <td>3334.00</td>\n",
       "      <td>1104.00</td>\n",
       "      <td>1589.00</td>\n",
       "      <td>1854.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>12368.0</td>\n",
       "      <td>4476.00</td>\n",
       "      <td>1462.00</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>2766.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>20432.0</td>\n",
       "      <td>5712.00</td>\n",
       "      <td>1933.00</td>\n",
       "      <td>3559.00</td>\n",
       "      <td>3191.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>22500.0</td>\n",
       "      <td>6668.00</td>\n",
       "      <td>1947.00</td>\n",
       "      <td>3238.00</td>\n",
       "      <td>3598.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04</th>\n",
       "      <td>13439.0</td>\n",
       "      <td>4014.00</td>\n",
       "      <td>1629.00</td>\n",
       "      <td>1817.00</td>\n",
       "      <td>1991.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>5668.0</td>\n",
       "      <td>1834.00</td>\n",
       "      <td>1241.00</td>\n",
       "      <td>2144.00</td>\n",
       "      <td>1534.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>9887.0</td>\n",
       "      <td>2697.00</td>\n",
       "      <td>1683.00</td>\n",
       "      <td>1079.00</td>\n",
       "      <td>1726.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>6211.0</td>\n",
       "      <td>2137.00</td>\n",
       "      <td>808.00</td>\n",
       "      <td>981.00</td>\n",
       "      <td>1097.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>10759.0</td>\n",
       "      <td>2133.00</td>\n",
       "      <td>1235.00</td>\n",
       "      <td>1373.00</td>\n",
       "      <td>1535.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>5556.0</td>\n",
       "      <td>1711.00</td>\n",
       "      <td>986.00</td>\n",
       "      <td>1055.00</td>\n",
       "      <td>1134.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>6053.0</td>\n",
       "      <td>3683.00</td>\n",
       "      <td>1279.00</td>\n",
       "      <td>1225.00</td>\n",
       "      <td>1458.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>7455.0</td>\n",
       "      <td>2020.00</td>\n",
       "      <td>1153.00</td>\n",
       "      <td>1273.00</td>\n",
       "      <td>1891.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>8865.0</td>\n",
       "      <td>2872.00</td>\n",
       "      <td>2118.00</td>\n",
       "      <td>1619.00</td>\n",
       "      <td>1727.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>12223.0</td>\n",
       "      <td>3872.00</td>\n",
       "      <td>2554.00</td>\n",
       "      <td>1820.00</td>\n",
       "      <td>2504.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>3620.0</td>\n",
       "      <td>1648.00</td>\n",
       "      <td>1161.00</td>\n",
       "      <td>1172.00</td>\n",
       "      <td>1184.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03</th>\n",
       "      <td>5006.0</td>\n",
       "      <td>2333.00</td>\n",
       "      <td>1131.00</td>\n",
       "      <td>1101.00</td>\n",
       "      <td>1223.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04</th>\n",
       "      <td>7528.0</td>\n",
       "      <td>2144.00</td>\n",
       "      <td>1167.00</td>\n",
       "      <td>1225.00</td>\n",
       "      <td>1680.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05</th>\n",
       "      <td>8110.0</td>\n",
       "      <td>20529.00</td>\n",
       "      <td>1326.00</td>\n",
       "      <td>1229.00</td>\n",
       "      <td>1509.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>9112.0</td>\n",
       "      <td>21624.00</td>\n",
       "      <td>1103.00</td>\n",
       "      <td>906.00</td>\n",
       "      <td>1019.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>10268.0</td>\n",
       "      <td>4255.00</td>\n",
       "      <td>3931.00</td>\n",
       "      <td>3061.00</td>\n",
       "      <td>1757.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>17969.0</td>\n",
       "      <td>10034.00</td>\n",
       "      <td>6358.00</td>\n",
       "      <td>5666.00</td>\n",
       "      <td>3177.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>20162.0</td>\n",
       "      <td>10399.00</td>\n",
       "      <td>6607.00</td>\n",
       "      <td>7169.00</td>\n",
       "      <td>3887.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>20790.0</td>\n",
       "      <td>7277.00</td>\n",
       "      <td>7148.00</td>\n",
       "      <td>7093.00</td>\n",
       "      <td>3481.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durchschnitt</th>\n",
       "      <td>11423.6</td>\n",
       "      <td>5446.56</td>\n",
       "      <td>2140.92</td>\n",
       "      <td>2276.76</td>\n",
       "      <td>2061.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Berlin   München  Leipzig  Stuttgart     Köln\n",
       "Date                                                        \n",
       "2020-10       13436.0   5129.00  1287.00    1676.00  1911.00\n",
       "2020-11       15673.0   3629.00  1172.00    1870.00  2713.00\n",
       "2020-12       12500.0   3334.00  1104.00    1589.00  1854.00\n",
       "2021-01       12368.0   4476.00  1462.00    1979.00  2766.00\n",
       "2021-02       20432.0   5712.00  1933.00    3559.00  3191.00\n",
       "2021-03       22500.0   6668.00  1947.00    3238.00  3598.00\n",
       "2021-04       13439.0   4014.00  1629.00    1817.00  1991.00\n",
       "2021-05        5668.0   1834.00  1241.00    2144.00  1534.00\n",
       "2021-06        9887.0   2697.00  1683.00    1079.00  1726.00\n",
       "2021-07        6211.0   2137.00   808.00     981.00  1097.00\n",
       "2021-08       10759.0   2133.00  1235.00    1373.00  1535.00\n",
       "2021-09        5556.0   1711.00   986.00    1055.00  1134.00\n",
       "2021-10        6053.0   3683.00  1279.00    1225.00  1458.00\n",
       "2021-11        7455.0   2020.00  1153.00    1273.00  1891.00\n",
       "2021-12        8865.0   2872.00  2118.00    1619.00  1727.00\n",
       "2022-01       12223.0   3872.00  2554.00    1820.00  2504.00\n",
       "2022-02        3620.0   1648.00  1161.00    1172.00  1184.00\n",
       "2022-03        5006.0   2333.00  1131.00    1101.00  1223.00\n",
       "2022-04        7528.0   2144.00  1167.00    1225.00  1680.00\n",
       "2022-05        8110.0  20529.00  1326.00    1229.00  1509.00\n",
       "2022-06        9112.0  21624.00  1103.00     906.00  1019.00\n",
       "2022-07       10268.0   4255.00  3931.00    3061.00  1757.00\n",
       "2022-08       17969.0  10034.00  6358.00    5666.00  3177.00\n",
       "2022-09       20162.0  10399.00  6607.00    7169.00  3887.00\n",
       "2022-10       20790.0   7277.00  7148.00    7093.00  3481.00\n",
       "Durchschnitt  11423.6   5446.56  2140.92    2276.76  2061.88"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Um neue Zeilen und Spalten hinzuzufügen, werden unterschiedliche Schreibweisen verwendet.\n",
    "Neue Zeilen müssen mit hilfe von loc zugewiesen werden, neue Spalten mit der bekannten Zuweisung in eckigen Klammern.\n",
    "Beachte in diesem Fall, dass sich der Parameter 'axis' genau umgekehrt zu drop verhält.\n",
    "Default wird die Funktion auf jede Spalte angewandt, um den Durchschnitt einer Zeile zu errechnen, muss 'axis' auf 1 gesetzt werden.\n",
    "'''\n",
    "data.loc['Durchschnitt'] = data.mean()\n",
    "data['Tagesdurchschnitt'] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]\n",
    "data['Tagesdurchschnitt'] = {'2022-10-26':1, '2022-10-27': 5}\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisierung\n",
    "\n",
    "Visualisiere die Seitenaufrufe deiner gewählten Seiten, aber ohne den Durchschnittswert auszugeben.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IRo0axdVXX90qWwTtpMYEX/8JDn4p3578Eox6oPX7KAnJwqPTgBO5Zby+/jg/HpQHnuq0Gm4c2pmHrmpbrqWj0EgdOAhcWlqKwWDAaDQ2+2YpcD4rd2bx5KoDhPj58Mtj44gK9mv3ng+v2Mu3v5/jqam9uW9st4s/oYNRXV1Neno6Xbp0wd/fX21zBG1A/Bu2gupSeTBn+ibQ+spenIE3tW2v7/4sD/oc/zSMfdyhZnoimYUVvPnTCb7edxZJkqPg1wxKYP6EnnSJck44vDWf38KjI1AdY5WFV9bKYYD5E3s6ROQAJEeKyiuBQIBcBr5kBmT/Dr5BcPNS6H5V2/cTTQMBOFdSxds/n+Cz3WfscwUn94vlkYm96BUXorJ1dQihI1CdN9Yfp6jCTPeYYO4YleywfUWJuUAgAODAF7LICYyE276ATkPbt18HFzr5ZSbe2XiS5duzMFvl6sKxPaNZMKknAzuHqWtcIwihI1CV00WVLN2eCcBzM/riq3NcfnySEDoCgQCgPFf+2ffa9osc6LBCp6TSzHub0liyNYMqi9wVemSXCB6d3ItLUty3n5AQOgJV2Xu6BKtNYlBiGJf3iHbo3orQOVtcRY3Vho8DRZRAIPAglH43ykDO9tLBhE5ZtYWPNmfwwW9plJnk0SeDEsN4dFJPLuse1eoRPa5GCB2BqqTnyw39esW2fszDxYgL9Uev02K22sg2VttDWQKBoIOhlIHbxze0kw4idKrMVj7elsF7m05RXCk32uwdF8KCSb2Y0CfG7QWOghA6AlVJKygHoGu044WOVquhc0QAafkVnC6qFEJHIOioVBvln8Kj0yJMNVZW7jzNwl9Okl9mAqBrVBB/mdiTaQPi29XjTA1a5ct/+eWXueSSSwgJCSEmJoaZM2de0DRLkiSef/55EhISCAgIYNy4cRw6dKjBGpPJxEMPPURUVBRBQUFcc801nDlzpsGa4uJi5syZg8FgwGAwMGfOHEpKShqsycrKYsaMGQQFBREVFcXDDz9s75Iq8AzSC2SPjrNKEJXwVabI0xEIOi5K6MpRHh1FMFWVyF2WvYgcYzWT3viV5749RH6Zic7hAfzrxoGs+8sVzBiU4HEiB1opdDZt2sQDDzzA9u3bWb9+PTU1NUyaNImKirp5Qq+88gqvv/46CxcuZNeuXcTFxTFx4kTKysrsa+bPn89XX33FypUr2bx5M+Xl5UyfPh2rtW7k/ezZs9m3bx9r1qxhzZo17Nu3jzlz5tgft1qtTJs2jYqKCjZv3szKlSv58ssvWbBgQXt+HwIXIkkSabWhq27RzhE6ySIhWSAQKKErR3t0bBYwe9c8vVfWHiWzsJLoED9enNmfnxeM46bhiZ6d4yi1g7y8PAmQNm3aJEmSJNlsNikuLk76xz/+YV9TXV0tGQwG6b333pMkSZJKSkokX19faeXKlfY1Z8+elbRarbRmzRpJkiTp8OHDEiBt377dvmbbtm0SIB09elSSJElavXq1pNVqpbNnz9rXrFixQvLz85OMRmOL7DcajRLQ4vUCx5JbWiUlP/G91OXJ76VqS41Tznj/11NS8hPfS/cvT3XK/p5MVVWVdPjwYamqqkptUwRtRPwbtpB/9ZSk50Il6dw+x+xns0nS36LlPYuzHLOnG3DgTImU8uT3UvIT30v7sorVNqdZWvP53S6JZjTKcc+ICLmsLD09nZycHCZNmmRf4+fnx9ixY9m6dSsAqampWCyWBmsSEhLo37+/fc22bdswGAyMHDnSvubSSy/FYDA0WNO/f38SEhLsayZPnozJZCI1NbVRe00mE6WlpQ0uAvVQvDmJEYH4+bRu/lBLqZtiLjw6Ahg3bhzz58932H5z585l5syZDttP4CQc7dHRaLwuT0eSJF5afQRJkrsaO2IMj7vQ5mRkSZJ45JFHuOyyy+jfvz8AOTnyfIvY2NgGa2NjY8nMzLSv0ev1hIeHX7BGeX5OTg4xMTEXnBkTE9NgzfnnhIeHo9fr7WvO5+WXX+aFF15o7UsVOAln5+dA3RRzEbryHubOnUtJSQlff/11q5+7atWqNk0gb4q33noLyctyNLwOSzXUVMvXHZWjA7LQKc/xGqGz8Vg+W08VotdpeWxyL7XNcSht9ug8+OCD7N+/nxUrVlzw2PklZ5IkXbQM7fw1ja1vy5r6PPXUUxiNRvvl9OnTzdokcC5p+bUVV1GOr7hSUDw6xioLxtrySEHHJSIigpAQx7WmNxgMhIWFOWw/gRNQvDkaLegdOJbAizw6NVYbL60+AsBdY1K8rkK1TULnoYce4ttvv+WXX36hc+fO9vvj4uIALvCo5OXl2b0vcXFxmM1miouLm12Tm5t7wbn5+fkN1px/TnFxMRaL5QJPj4Kfnx+hoaENLgL1sHt0nJSIDBCo97HPzjpdLLw63s7hw4e5+uqrCQ4OJjY2ljlz5lBQUGB//PzQVUpKCi+++CKzZ88mODiYhIQE3n77bfvjixcvRqPRXHB5/vnngQtDV2VlZdx2220EBQURHx/PG2+84fBwmaCVKBVXfqGgdWBCrRcJnc9Tz3Air5ywQF/uH6/upHFn0Kp/dUmSePDBB1m1ahU///wzXbp0afB4ly5diIuLY/369fb7zGYzmzZtYvTo0QAMGzYMX1/fBmuys7M5ePCgfc2oUaMwGo3s3LnTvmbHjh0YjcYGaw4ePEh2drZ9zbp16/Dz82PYsGGteVkClbBXXDkxdAWQFBEAQKbI02kWSZKotFSqcnFE+Cc7O5uxY8cyePBgdu/ezZo1a8jNzWXWrFnNPu9f//oXAwcOZM+ePTz11FP85S9/sb8/3XzzzWRnZ9svK1aswMfHhzFjxjS61yOPPMKWLVv49ttvWb9+Pb/99ht79uxp92sTtANHNwtU8BKhU2Gq4bV1xwF4+MoeGAIcF9p1F1qVo/PAAw/wySef8M033xASEmL3qBgMBgICAtBoNMyfP5+XXnqJHj160KNHD1566SUCAwOZPXu2fe0999zDggULiIyMJCIigkcffZQBAwYwYcIEAPr06cOUKVOYN28e//3vfwG49957mT59Or16ybHDSZMm0bdvX+bMmcO//vUvioqKePTRR5k3b57w1HgAFqvNnjfjTI8OyOGrPVklIk/nIlTVVDHyk5EXX+gEdszeQaBv+9zl7777LkOHDuWll16y3/fRRx+RmJjI8ePH6dmzZ6PPGzNmDE8++SQAPXv2ZMuWLbzxxhtMnDiRgIAAAgJkoXzq1CkefPBBXnrpJSZOnHjBPmVlZSxZsoRPPvmEq66SJ2MvWrSoQcGEQAUcPf5BQRFOipDyUP73axoF5SaSIwO5/VLHDVV2J1oldN59911Adv/WZ9GiRcydOxeAxx9/nKqqKu6//36Ki4sZOXIk69ataxAXf+ONN/Dx8WHWrFlUVVVx1VVXsXjxYnS6usqb5cuX8/DDD9urs6655hoWLlxof1yn0/HDDz9w//33M2bMGAICApg9ezavvvpqq34BAnU4XVRJjU0iwFdHXKi/U89KipSFlBA63k1qaiq//PILwcEX5nydOnWqSaEzatSoC26/+eabDe4zGo1Mnz6dqVOn8thjjzW6T1paGhaLhREjRtjvMxgM9i9nApVQuiI73KNTu58He3RyS6v5369pADwxpTd6Hw/uldMMrRI6LXEvK/FrJYbdGP7+/rz99tsNYuHnExERwbJly5o9Kykpie+///6iNgncj/oVV86el6IkJJ8WQqdZAnwC2DF7h2pntxebzcaMGTP45z//ecFj8fHxrdqr/v9Jq9XKzTffTGhoKO+//36Tz1HeHxsrxhCoiKNLyxW8IHT1+rrjVFmsDE0KY2r/OLXNcRpi1pVAFZT8nK5ODltB/TEQ3tXB1NFoNJp2h4/UZOjQoXz55ZekpKTg49Pyt7bt27dfcLt3797223/5y184cOAAu3btwt+/ae9jt27d8PX1ZefOnSQmJgJQWlrKiRMnGDt2bCtfjcBhOHr8g4Jd6JQ4dl8XcTSnlM9T5crjp6f18ZgBnW1BCB2BKqTVenS6OjkRGSC5tpfOuZJqLFYbvp7cylwAyKGkffv2Nbjvvvvu4/333+fWW2/lscceIyoqipMnT7Jy5Uref//9BqHx+mzZsoVXXnmFmTNnsn79ej7//HN++OEHQA7Lv/POO3z11VdotVp7XmJwcPAFIbKQkBDuvPNOHnvsMSIiIoiJieG5555Dq9V69YeI2yM8Oo3y8uqj2CS4ekAcw5Ij1DbHqQihI1AFew8dJ0wtP5/oYD/8fLSYamxkl1TbmwgKPJeNGzcyZMiQBvfdeeedbNmyhSeeeMLeJT05OZkpU6agbaaseMGCBaSmpvLCCy8QEhLCa6+9xuTJkwF5vp/VauWaa65p8Jznnnuu0fD866+/zh//+EemT59OaGgojz/+OKdPn27WEyRwMk736Hie0PntRD6bjufjq9Pw+OTeF3+ChyOEjkAVXNEVWUGr1ZAYEcjJvHIyiyqE0PFwFi9ezOLFi5t8fNWqVU0+tnHjxgvuCw0N5dNPP23TWec/FhISwvLly+23KyoqeOGFF7j33nub3EPgZIRHpwFWm8RLq48CcPulyaS44D1YbYTQEbicsmoLeWUmwPml5QpJtUJHVF4JnMnevXs5evQoI0aMwGg08re//Q2Aa6+9VmXLOjD28nKDY/dVhI6lEmpM4OPn2P2dxFd7z3Iku5QQfx8evrKH2ua4BCF0BC4no0AWG1HBfoT6u6Y5lX24pxA6Aifz6quvcuzYMfR6PcOGDeO3334jKipKbbM6Ls5qGKgPkcdKSDZZTIU03pHfnagyW3l17TEAHhzfnfAgvcoWuQYhdAQuJ61Ayc9xnctUlJgLGiMjI8Oh+w0ZMoTU1FSH7iloJ85qGKjVyntWFcnhKw8QOh9uTiOntJpOYQHcOTpFbXNchig/Ebgce2m5C2PD9hJzMQZCIOhYOKthIHhUnk5+mYl3N54C4PEpvfD3bbwK0RsRQkfgcuyl5a706NQmIGcVOmaukkAg8ACsFrDU9s9ytEcHPErovLXhOBVmKwM7G5gxsGONJRFCR+By0mtDV12inF9arpAYLgudMlMNxiqLy84VCAQqUr+Zn6OTkcFjhM7JvHJW7JSbA/716j5otR2rr5MQOgKXIkkS6S7siqwQoNcREyJXRYiEZIGgg6AkIvsZQOuEUI2HCJ1//HgUq01iQp9YLu0aqbY5LkcIHYFLySszUWG2otNq7F4WVyHydASCDoa9WaATvDngEUJne1ohPx3JRafV8ORU728O2BhC6AhcyqnajshJEYEun5Rrz9MRHh2BoGPgrGaBCm4udGw2iZdWHwHg1hGJdI9xXbqAOyGEjsCluLIj8vmIEnOBoIPhrGaBCkoll5sKne/2n2P/GSPBfj7Mn9BTbXNUQwgdgUtRo7RcQYSuvIe8vDzuu+8+kpKS8PPzIy4ujsmTJ7Nt2zZAnsT+9ddft3rfcePGMX/+/Ab3bdy4EY1GQ0lJSfsNbyHPP/88gwcPdtl5XouzmgUqKB4d5Rw3otpi5ZU1cnPAP47tSlSwZ3RudgaiYaDApdg9Oi5MRFYQ3ZG9hxtuuAGLxcKSJUvo2rUrubm5bNiwgaKiIrVNaxeSJGG1WtU2w3twVrNABTcOXS3ZmsHZkiriQv2557KuapujKsKjI3Ap9qnlLiwtV1BydLKNVZhrbC4/X+AYSkpK2Lx5M//85z8ZP348ycnJjBgxgqeeeopp06aRkpICwHXXXYdGo7Hfnjt3LjNnzmyw1/z58xk3bpz98U2bNvHWW2+h0WjQaDRkZGQwfvx4AMLDw9FoNMydOxeAsrIybrvtNoKCgoiPj+eNN964wCO0bNkyhg8fTkhICHFxccyePZu8vDz744q3aO3atQwfPhw/Pz+WLl3KCy+8wO+//263o7nBooJmcJVHx82ETnGFmYW/nATg0cm9CNB3nOaAjSE8OgKXYa6xcbq4CnBtablCdLAf/r5aqi02zpVUdYipva1BkiSkqipVztYEBKDRtKy3R3BwMMHBwXz99ddceuml+Pk1dMnv2rWLmJgYFi1axJQpU9DpWvYm/9Zbb3H8+HH69+9vH8YZHR3Nl19+yQ033MCxY8cIDQ0lICAAgEceeYQtW7bw7bffEhsby7PPPsuePXsahJzMZjMvvvgivXr1Ii8vj7/85S/MnTuX1atXNzj78ccf59VXX6Vr1674+/uzYMEC1qxZw08//QSAweCkHBNvp4MmI//75xOUVdfQJz6U64Z0Utsc1RFCR+Aysooqsdokgur1tHElGo2GpIhAjueWk1lUKYTOeUhVVRwbOkyVs3vtSUUT2LJ2Az4+PixevJh58+bx3nvvMXToUMaOHcstt9zCwIEDiY6OBiAsLIy4uLgW22AwGNDr9QQGBjZ4XkREBAAxMTGEhYUBsjdnyZIlfPLJJ1x11VUALFq0iISEhh1n7777bvv1rl278u9//5sRI0ZQXl5OcHCdV/Nvf/sbEydOtN8ODg7Gx8enVfYLGsFeXh7mnP3tOTpGsFmd06unlWQUVLBseyYAT1/dB10Haw7YGCJ0JXAZ9fNzWvrt3dGIPB3v4IYbbuDcuXN8++23TJ48mY0bNzJ06FCXhXjS0tKwWCyMGDHCfp/BYKBXr14N1u3du5drr72W5ORkQkJC7GGyrKysBuuGDx/udJs7JMqcK2d5dOrvq5ylMq+sPYrFKjG2ZzSX9YhS2xy3QHh0BC5DzfwchaQI2YsjSswvRBMQQK896kze1tSGg1qDv78/EydOZOLEiTz77LP84Q9/4LnnnrPn0JyPVqu9YM6ZxdK2cSDKPucL9vr7V1RUMGnSJCZNmsSyZcuIjo4mKyuLyZMnYzabGzwvKEh4F52Csz06Oh/wCwVTqRy+CoxwzjktJDWziNUHctBq5FEPAhkhdAQuQyktV6OHjkJShPyBmiVKzC9Ao9G0OHzkjvTt29deUu7r63tB9VJ0dDQHDx5scN++ffvw9fW139br9Rc8T6/XAzS4v1u3bvj6+rJz504SExMBKC0t5cSJE4wdOxaAo0ePUlBQwD/+8Q/7mt27d7fotTRmh6AN2HN0wp13hn9YndBREUmS+L8f5OaAs4Yn0isuRFV73AkRuhK4jHQVppafj1J5lSk8Oh5LYWEhV155JcuWLWP//v2kp6fz+eef88orr3DttdcCkJKSwoYNG8jJyaG4WP4AuvLKK9m9ezcff/wxJ06c4LnnnrtA+KSkpLBjxw4yMjIoKCjAZrORnJyMRqPh+++/Jz8/n/LyckJCQrjzzjt57LHH+OWXXzh06BB33303Wq3W7uVJSkpCr9fz9ttvk5aWxrfffsuLL77YoteYkpJCeno6+/bto6CgAJPJ5MDfYAfC2Q0DoV7TwBLnndECfjyYw56sEgJ8dTwyseM2B2wMIXQELiOtwL1CV+eHMQSeQXBwMCNHjuSNN97giiuuoH///jzzzDPMmzePhQsXAvDaa6+xfv16EhMTGTJkCACTJ0/mmWee4fHHH+eSSy6hrKyMO+64o8Hejz76KDqdjr59+9pDTZ06deKFF17gySefJDY2lgcffBCA119/nVGjRjF9+nQmTJjAmDFj6NOnD/7+/oDsQVq8eDGff/45ffv25R//+Aevvvpqi17jDTfcwJQpUxg/fjzR0dGsWLHCUb++joO1Bsxl8nVnha7ALSqvbDaJV9YcBeDeK7oSE+qvmi3uiEbqwO/2paWlGAwGjEYjoaGhapvj1RirLAx6YR0AB1+YTLCfOlHTaouV3s+sAWDPMxOJCNKrYoc7UF1dTXp6Ol26dLF/OAvaTkVFBZ06deK1117jnnvuccmZ4t+wGSoK4V+1jfKeKQCdb/Pr28pnd8Lhr2Hqv2Dkvc454yKkF1Qw/tWN+Plo2fPMRIJUen91Ja35/Pb+34bALVDCVjEhfqqJHAB/Xx1xof7klFaTWVjRoYWOoH3s3buXo0ePMmLECIxGo733jhI+E6iMkp+jD3aeyAG38OicypO95d2igzuEyGktInQlcAnpSthKxfwcBVFiLnAUr776KoMGDWLChAlUVFTw22+/ERUlSnrdAmc3C1RwA6FzsraitVsHnU5+MYT0E7iEuoor9f8QkyID2ZlRJErMBe1iyJAhpKaqU44vaAHOLi1XcAOhU+fRUf+LpDsiPDoCl5BWG7pyhz9E4dERCDoAHcijc6rWo9NdeHQaRQgdgUtwhx46CorQyRS9dAQC78VlHp3a/VUSOpIkcbJejo7gQoTQETgdm00iw95DR/0/xMRaoSNCVwKBF+Nqj45ynospKDdTWl2DRuMeXyTdESF0BE4np7SaKosVH62GzuGtb/XvaJJrmwZml1ZjqhHdZwUCr8QVzQJB9dCVErZKDA/E31f9oaLuiBA6AqejlJYnRQbiq1P/v1xkkJ5AvQ5JgjPFVWqbIxAInIHiYXFlMrIKbelOikTki6L+p47A66kb5ukef4gajUYkJAsE3o7doxPm3HMUoWOrAXO5c89qBMWjI/JzmkYIHYHTSXOj/ByFJJGn47UsXryYsLAwAMaNG8f8+fMbPJ6SksKbb77pcrsELqbaKP90tkfHNwB8artSqxC+OlVb6CEqrppGCB2B03GniisFu0dHVF55HHPnzmXmzJkN7vviiy/w9/fnlVde4eabb+b48eMArFq1qsWDNAVehquSkUHVPB17Dx0hdJpENAwUOB371HJ3EjpiirnX8MEHH/DAAw/wn//8hz/84Q8ABATISe8RERFqmiZQE1eVl4MsdMqyXS50Ks01nC2R8wxF6KpphEdH4FRMNVbOFMtioosbJcuJEnPv4JVXXuHBBx/kk08+sYucL7/8kn79+uHn50dKSgqvvfZas3toNBo++OADrrvuOgIDA+nRowfffvutK8wXOBNXenSUM1wsdBRveUSQXsztawYhdAROJauwEpsEIX4+RAf7qW2OneR6yciSCpUS7ogkSVhMVlUubfk3ePLJJ3nxxRf5/vvvueGGGwBITU1l1qxZ3HLLLRw4cIDnn3+eZ555hsWLFze71wsvvMCsWbPYv38/V199NbfddhtFRUVt+TUK3AGbDapL5euu8uiAy4VOXSKy+3yJdEdE6ErgVJREuS7RQWg0GpWtqaNTeAAaDVSarRRWmIlyIxGmFjVmG//78yZVzr73rbH4+rW8B8iPP/7IN998w4YNG7jyyivt97/++utcddVVPPPMMwD07NmTw4cP869//Yu5c+c2ud/cuXO59dZbAXjppZd4++232blzJ1OmTGnbCxKoi8kI1IpnZ/fRgXpCp8T5Z9XjlOiI3CKER0fgVNwxPwfAz0dHfKhcKSFGQXgeAwcOJCUlhWeffZaysjL7/UeOHGHMmDEN1o4ZM4YTJ05gtTbdHHLgwIH260FBQYSEhJCXl+d4wwWuQREcPgHg44IvMSqNgVC+SAqh0zzCoyNwKkoPHXeYWn4+SZGBnDNWc7qokmHJ4Wqbozo+ei33vjVWtbNbQ6dOnfjyyy8ZP348U6ZMYc2aNYSEhCBJ0gWew5aExXx9fRvc1mg02Gy2VtkkcCNc1SxQQeXQlSgtbx4hdAROxe7RccMYclJEINvTikTTwFo0Gk2rwkdqk5SUxKZNmxg/fjyTJk1i7dq19O3bl82bNzdYt3XrVnr27IlO5zmvTdBOXNUsUEEFoWO1SfYeZcKj0zwidCVwKsofojv10FEQU8w9n86dO7Nx40YKCwuZNGkS9957Lxs2bODFF1/k+PHjLFmyhIULF/Loo4+qbarAlbiqWaCCCjk6Z4orMdfY0Pto6eQGMwTdGSF0BE6jpNJMUYUZcE+hI0rMvYNOnTqxadMmSkpKeOyxx/jss89YuXIl/fv359lnn+Vvf/tbs4nIAi/ElaXloIpH51S90To6rfsUergjInQlcBqKNycu1J8gP/f7r5YcKYsvEbryLBorFY+Pj+fo0aP220q5eWNkZGQ0uN1YDk9JSUlbzRO4A65sFlj/HBcKnZOiI3KLER4dgdNIz3ff/ByoC13llFZTbWm6IkcgEHgYHcGjkyfyc1qKEDoCp5FWoFRcuafQCQ/0JbjW06R0bxYIBF6Ayz06tUKnpgos1S45UlRctRwhdAROI90Np5bXR6PR2PN0RPhKIPAiXO3R8QsFja7h2U5EkiROiq7ILUYIHYHTUOawuFuzwPokiynmAoH34WqPjkbj0jydogozJZUWNBro6oY9ytwNIXQETsFmk9y6h46CMsU8q6hKZUvUQ8z68lzEv10T2D06Lhj/oODCPB2lI3KnsAAC9KI/1MUQQkfgFM4ZqzDV2PDVaegU5r49HupCVxUqW+J6lAZ6ZrNZZUsEbaWyUvZEnt/ZucPj6oaB4FKhc1LMuGoV7lfzK/AKFG9OcmQQPjr31dPJHThHx8fHh8DAQPLz8/H19UWrdd9/J0FDJEmisrKSvLw8wsLCRNfn83H1CAhwsUdHCJ3WIISOwCko+TnuWnGlkFRP6DQ2J8mb0Wg0xMfHk56eTmZmptrmCNpAWFgYcXFxapvhXkhSXWdkV3p0lLNcKHRExVXLEEJH4BQ8IT8HICEsAK0Gqi028stMxNRONO8o6PV6evToIcJXHoivr6/w5DSGqQyk2oGsXurRqQtduff7q7sghI7AKdRvT+7O6H20xBsCOFtSRVZRZYcTOgBarRZ//473ugVeihK20vmBrwvzA10076rKbOVsiVw8IboitwwRlBc4BXfvoVOf5MiOm6cjEHgdri4tV3CRRye9oAJJgrBAXyKD9E49y1sQQkfgcKotdd843D1HBxrm6QgEAg/H1c0CFVwkdOonIneknML2IISOm1NtsfLLsTysNs/pl5FZWIkkQai/j0d840gUTQMFAu/Byz06Ij+n9Qih4+Y8+vnv3LVoFx/8lqa2KS0mrfYbRxcP+cYhPDoCgRehRrNAUMWjI2gZQui4MftOl/D9/mwAPtmZ5TFdUNNq83O6eUDYCkSOjkDgVajRLBBcloysdEUWpeUtRwgdN0WSJF5efcR+O7Owkp3pRSpa1HI8pYeOguLRySszUWW2qmyNQCBoF2o0C6x/nskI1hqnHGG1SXaPufDotBwhdNyUX47lsSO9CL2PlvG9ogH4bPcZla1qGekFtaXlHvKHaAjwJcRf7rRwulh4dQQCj0aNZoHnn6fY4GDOlcijdfQ6LZ3D3Xe0jrshhI4bYrVJ/PPHYwDcNSaFB6/sAcDqA9mUVVvUNK1FpHlIs0AFjUZTF74SCckCgWejVjKyzgf8QmttcE6ezkkl/zHKvUfruBviN+WGfLnnDMdyyzAE+HL/2O4MTQqjW3QQVRYrP9Tm7LgrxRVmSiplMZYS6RlCB0RCskDgNahVXg514kqxwcGcUiquYjznvdUdEELHzai2WHlj/XEAHhjfDUOgLxqNhlnDEwH4bPdpNc27KGm1YatOYQEE6D2nPX2iEDoCgXeglkcHnF55JSqu2karhc6vv/7KjBkzSEhIQKPR8PXXXzd4fO7cuWg0mgaXSy+9tMEak8nEQw89RFRUFEFBQVxzzTWcOdMw/6S4uJg5c+ZgMBgwGAzMmTOHkpKSBmuysrKYMWMGQUFBREVF8fDDD3v8zJ7FWzPINlbTKSyAO0al2O+/bmgndFoNe7JKOJlXpp6BF8HTEpEVhEdHIPASVPXoOFno5ImKq7bQaqFTUVHBoEGDWLhwYZNrpkyZQnZ2tv2yevXqBo/Pnz+fr776ipUrV7J582bKy8uZPn06Vmtdxcvs2bPZt28fa9asYc2aNezbt485c+bYH7darUybNo2Kigo2b97MypUr+fLLL1mwYEFrX5LbUFJp5p1fTgLwyMSe+PvWeURiQvztScmfp7pvUrKn5ecoJEfI9gqhIxB4OF7s0TkpPDptotVDPadOncrUqVObXePn50dcXFyjjxmNRj788EOWLl3KhAkTAFi2bBmJiYn89NNPTJ48mSNHjrBmzRq2b9/OyJEjAXj//fcZNWoUx44do1evXqxbt47Dhw9z+vRpEhISAHjttdeYO3cu//d//0doaGhrX5rq/OeXk5RW19A7LoSZQzpd8PhNwxP56UgeX6ae5dFJvfB1w2S0dA/36JwuqsRmk9Bq3b/RoUAgOA9JUq9hIDhV6BRVmCmqkCMWnvZFUm2c8km5ceNGYmJi6NmzJ/PmzSMvL8/+WGpqKhaLhUmTJtnvS0hIoH///mzduhWAbdu2YTAY7CIH4NJLL8VgMDRY079/f7vIAZg8eTImk4nU1NRG7TKZTJSWlja4uAtniitZsjUTgCem9kbXyAftlb1jiArWU1BuYtOxfFeb2CLSPKy0XCE+zB+dVoOpxkZemUltcwQCQVswV4CttoeNGqEr5UwnCB2lf06nsAAC9a32UXRoHC50pk6dyvLly/n555957bXX2LVrF1deeSUmk/zhkZOTg16vJzw8vMHzYmNjycnJsa+JiYm5YO+YmJgGa2JjYxs8Hh4ejl6vt685n5dfftme82MwGEhMTGz363UUr68/jtlqY1TXSMb1jG50ja9Oy3W1nh53TEq22iQyasuzu3qYR8dXpyUhzB8Q4SuBwGNRvDlaH9Cr8B7kRI+OMuNKeHNaj8OFzs0338y0adPo378/M2bM4Mcff+T48eP88MMPzT5PkqQGc5Eam5HUljX1eeqppzAajfbL6dPuIRYOnyvlq71nAXjq6t7Nzoe6qbb66uejeeS7mefhXEkV5hobeh8tCWGe18xK5OkIBB5O/WaBaszZc6LQERVXbcfpSR7x8fEkJydz4sQJAOLi4jCbzRQXN/yPkJeXZ/fQxMXFkZube8Fe+fn5Ddac77kpLi7GYrFc4OlR8PPzIzQ0tMHFHfjnmqNIEkwfGM/AzmHNru0ZG8LgxDBqbBJf14ojd0FJRE6JDGw09ObuiBJzgcDDUTMRGZw670rMuGo7Thc6hYWFnD59mvj4eACGDRuGr68v69evt6/Jzs7m4MGDjB49GoBRo0ZhNBrZuXOnfc2OHTswGo0N1hw8eJDs7LoGeuvWrcPPz49hw4Y5+2U5jK0nC9h0PB8frYbHJvdq0XPq99Rxp0GfSgy5a5Rn/iHaS8wLK1S2RCAQtAk1S8vBJaEr4dFpPa0WOuXl5ezbt499+/YBkJ6ezr59+8jKyqK8vJxHH32Ubdu2kZGRwcaNG5kxYwZRUVFcd911ABgMBu655x4WLFjAhg0b2Lt3L7fffjsDBgywV2H16dOHKVOmMG/ePLZv38727duZN28e06dPp1cvWQxMmjSJvn37MmfOHPbu3cuGDRt49NFHmTdvntt4ai6GzSbx8o9HAbhtZBLJLewkPH1QPP6+Wk7klfP7GefMVGkL9h46HhpDFlPMBQIPx208Oo4VOtUWq30On+iK3HpaLXR2797NkCFDGDJkCACPPPIIQ4YM4dlnn0Wn03HgwAGuvfZaevbsyZ133knPnj3Ztm0bISEh9j3eeOMNZs6cyaxZsxgzZgyBgYF899136HR1fWOWL1/OgAEDmDRpEpMmTWLgwIEsXbrU/rhOp+OHH37A39+fMWPGMGvWLGbOnMmrr77ant+HS/nhQDYHzhoJ0ut46KoeLX5eqL8vU/vLHjJ3SkpOV3roeFgiskJd08AqlS0RCARtwp08Og70tmcUViBJEOrvQ3Swn8P27Si0ukZt3LhxzYZL1q5de9E9/P39efvtt3n77bebXBMREcGyZcua3ScpKYnvv//+oue5I+YaG/9aKw/uvG9sN6Ja+Z/3puGd+WrvWb7bd45npvV1i3EL9tCVh3p0lBydgnITFaYagvxECadA4FGo7tGpPVeygqkM/B0TXVA6IneLCW62WEXQOO7Xca6D8MmOTLKKKokO8eMPl3dp9fMv7RJJYkQAZaYa1hxSf9BnldnKOWM14Lk5OoYAXwwBvgB2N7FAIPAg1GwWCOAbAD5ymwpHhq9Efk77EEJHBcqqLfz7Z3nUw/wJPdrU/Emr1XDTsNqk5F3qj4RQwlZhgb6EB+lVtqbt2PN0CoXQEQg8DsWjo1boCpySp6OUlouKq7YhhI4K/O/XNIoqzHSNCrJXULWFG4Z1RqOBbWmFqn8we3p+joIoMRcIPBjFo6NW6AqcKnSER6dtCKHjYvJKq/ngt3QAHp/SvnlVncICuKx7FABfpKqblKzk53Tx0LCVgphiLhB4MPUbBqqFInQU0dVObDapntDx7C+SaiGEjot5c8MJqixWhiaFMblf44NPW4PSKfmL1DNYber11En30Knl5yOEjkDgwaidjAwO9+icM1ZRbbHhq9PY358ErUMIHRdyKr+cT3fJnpenru7jkOz5SX1jCfX34Zyxmq2nCtq9X1s55SWhq2QhdAQCz0Xt8nKoE1kOEjpKR+SUyCB82hEB6MiI35oLeWXNUaw2iQl9YrkkJcIhe/r76phpH/SpTlKyJEmk53vm1PLzUXJ0zhRVYVPRQyYQCNqAF3p0RMVV+xFCx0WkZhax9lAuWg08MaVlox5aipLQvPZQDiWVZofu3RIKK8yUVteg0dRVLXkq8QZ/fLQazFYbOaXVapsjEAhaiqUKrLWDjt0hR8dhHp1aoSM6IrcZIXRcgCRJvLxaHvVw07BEesSGXOQZraNfQih94kMx19j49vdzDt27JSj5OZ3CAvD3Vb9xYXvw0WnpHC5PXhfhK4HAg1C8ORot+Dn2PbZVKCLLQYM9T+WJ0vL2IoSOC/jpSB67M4vx99Xyl4k9Hb6/RqNh1vDOgDojIeoqrrzjG4coMRcIPJD6zQLV7B7sLI+OCF21GSF0nEyN1cY/18jenLvHdCHO4O+Uc2YO7oRep+Xg2VIOnXPtoM+0Wo+Ot/wh1k0xF0JHIPAY3KFZIDhU6JRUmikol9MRPD3/UU2E0HEyX6Se4WReOWGBvvxxXDennRMepGdi31gAPndxUrJ9armXeHREiblA4IG4Q7NAcKjQUSqu4g3+BIvZe21GCB0nUmW28sZPxwF4cHx3Qv19nXrejbXhq6/3ncVUY3XqWfXxlh46CvYxEELoCASegzs0C4R6Qqek3VudEhVXDkEIHSfy0ZZ0cktNdA4PYM6oZKefd0WPaOJC/SmptLDhSJ7TzwM5NJdZ6F0eHSVH57QQOgKB5+AOpeVQJ3RqquRKsHYgOiI7BiF0nERRhZn3Np4C4NFJvfDzcX41kk6r4YZhSk8d1yQlny2pwmKV8PPRkmAIcMmZzkYROoUVZspNNSpbIxAIWoQ7NAsEueJLU/t+306vjhjm6RiE0HESC38+SZmphn4JoVwzKMFl5yoTzX89nk+2sX3fJlpC/fwcrVbFSgcHEurvS3igHGYUCckCgYfgLh4djcZheTqiWaBjEELHCZwuqmTp9gwAnpza26UCICUqiBFdIrBJsGrPWaefl+Zl+TkKSZHy6xF5OgKBh+AuHh1wyBgIU43V/v7TTXh02oUQOk7goy3pWKwSl3WP4vIe0S4/X+mU/Nnu00iSc8cYeFsPHQVl5lVGbf6RQCBwc9zFowMO8ehkFlZikyDEz4eYED8HGdYxEULHCTw1tQ/Pz+jLk1N7q3L+1QPiCNLryCysZGd6kVPPsldcRXnXNw7FVawIOYFA4ObUbxioNg4QOkrYqmtMsEMGQHdkhNBxAnofLXPHdKF/J3X+4AL1PkwfKOcFOXvQpz1Hx8tCV8pcGaWPhUAgcHPcpWEgOETo1JWWe9d7qxoIoeOlzLpE7qmz+kC20yqHKkw19sGXXb0sdKV4qE7mlTs9/CcQCByAuzQMhDqho9jUBkTFleMQQsdLGZoUTtfoIKosVn7Y75xBn0rYKiJIT1ig3ilnqEWXqCA0GjBWWSiqcP1EeIFA0Eq8zaOT712jddRECB0vRR70qSQlOyd8VZef413eHIAAvY5OYXJfIBG+EgjcnBqT3KAP3Muj00ahY7NJYpinAxFCx4u5fkgndFoNqZnF9sQ2R+JtM67OR3mDOSUSkgUC90YZ/4AG/Dw/GTmntJpKsxUfrcY+kkbQdoTQ8WJiQv0Z30sub/881fGdktMLaqsCvPQbh13oOEEkCgQCB2IPW4WC1g0+1pTwWRuFjvLlKjkyEF+dG7weD0f8Br2cm2rDV1+mnsVitTl0b6VZoNd6dOyVV0LoCARujTs1C4R2e3RER2THIoSOl3Nl7xgig/QUlJvYdCzfYftKkkS6PVnOS4WOPXQlcnQEArfGnZoFQrsnmNvzc0TFlUMQQsfL8dVpuW6I4wd95pebKDPVoNVAkpfGkBWhc7q4kmqLVWVrBAJBk7hTs0CoEzqmUrC2vr3HqTz5y1V34dFxCELodACU8NXPR/MoKDc5ZE/Fm9M5PNAlk9nVICpYT6i/D5IkRkEIBG6NO5WWQ0PBZU+UbjknhUfHoQih0wHoFRfCoMQwamwSX+91zKBPb8/PAblEX3mjUb5hCQQCN8SdmgUC6Hzqqr9amadjrLKQXyZ/IfW2YclqIYROB2HWcLlT8qe7TpNXVo3N1r5uv+leOrX8fESJuUDgAbibRwfaPMFcma8XG+pHqL+vg43qmPiobYDANcwYlMDfvjvMibxyRvzfBnx1GmJC/Ik3+BNnUH4GEBdadzsmxA+fJkoblT9Gb2wWWB8hdAQCD0AJD7mLRwfkPJ2SzFYLHVFx5XiE0OkghPr7smBSTz74LZ38chMWq8TZkirOllQ1+RytBqJD/IgzBBBfK4AUEXQkuwzw3h46CkpFmRA6AoEb427l5dBmj44Y/eB4hNDpQNx7RTfuvaIbFquN/DIT2cZqcozVZBuryDFWk1Oq3K4mt7SaGptEbqmJ3FITvzexpzfn6AANcnRsNgmtVqOyRQKB4ALcrbwc2txLRwzzdDxC6HRAfHVaEsICSKid5dQYNptEQYVJFkC1Iuh8YTQkKZx4g78LLXc9SRGB+Gg1VFms5JRWN/s7EwgEKuGWHp02Ch0RunI4QugIGkWrlXN4YkL8GdhZbWvUw1enJTkykFP5FZzKLxdCR+A2bDqez//9cJhXbxrEwM5hapujLl7i0THX2MgsqgTqOrML2o+ouhIILoKYeSVwR15bd4zjueWs2uOYlhEejZd4dLKKKrDaJIL0OuJCvdtb7kqE0BEILoI9T0eMghC4CSfzytl/Rq40yuzozSytFjDXfglxR6GjiLAWYK+4iglGoxH5gI5CCB2B4CKIEnOBu1G/8WdmYaWKlrgB9TsPu8sICGiTR0dUXDkHIXQEgosgSswF7oTNJvH1vjqhc7q4khqrTUWLVEbJz9GHyB2J3YW2CJ08UXHlDITQEQgugtIrKLfURFm1RWVrBB2d3ZnFnCmuItjPB72PFotVIttYrbZZ6uGOzQKhLozWKo+OUnElEpEdiRA6AsFFMAT4Eh3iB9SNvhAI1OKr2rDVlP5xJEcEAh186Gx1rZBwp/wcaOjRsV3c4yZJkghdOQkhdASCFqCMuhDhK4GaVFus/LD/HADXD+lEcqT8/zKjI+fpuGNpOdTZI9nAXHbR5bmlJspNNei0Gvu/q8AxCKEjELQAMcVc4A5sPJZHaXUNcaH+jOwaSUqk7NHJ7MieRntpuRslIgP4BoBPbd+tFoSvlC9RyRGB6H3ER7MjEb9NgaAFiMorgTughK2uHZwgf/OPUjw6HVjouKtHB1qVkKyUlnv7/EA1EEJHIGgBovJKoDYllWZ+PpoHwHVDOwHYPTodOnTljs0CFexCp+SiS+2JyKIjssMRQkcgaAGKRyejoIOX8gpU44cD2VisEr3jQugdFwpASm0uR1ZhJVabpKZ56qGICLcWOi0PXXUXHh2HI4SOQNACOoUF4OejxWy1caa4Sm1zBB2Qr2pHPVxf680BiDf446vTYLbayCntoCXmikfHLUNXYfLPVoSuuokeOg5HCB2BoAVotRp77FyErwSuJquwkt2ZxWg0cM2gOqHjo9OSGN7BE5Ld2qMTJv+8iNApq7aQW2oCRGm5MxBCRyBoISJPR6AWSifkMd2iiDM0HPaYEtXBS8zdtWEgtDh0lVbbPyc6xA9DgK+zrepwCKEjELSQuinmHfSbs0AVJEmyz7aaOaTTBY8nKyXmHbXyyguSke1hK9ER2SkIoSMQtJC6KebCoyNwHb+fMZJWUIG/r5Yp/eMueDwlsoOXmFd5vkenbvSDCFs5AyF0BIIWIkJXAjVQvDmT+sYR7Hfh0Mo6j04HDF3ZrGCqFTpu7dFpmdARwzydgxA6AkEL6RolvwkVV1ooqjCrbI2gI2Cx2vjud3nkw3VDLwxbQUOPjiR1sBJzJT8H3Nujo4TXmqAudCWEjjMQQkcgaCEBeh2dwuSW7sKrI3AFv53Ip7DCTFSwnsu7RzW6plN4ADqthmqLjbwyk4stVBlFQPgGgc4Nk3hb4NGxWG12b5woLXcOQugIBK2gbuaVEDoC57OqtnfOjEEJ+Ogaf7v21WnpHC4L8PSOVmJuLy13szlXCi0QOllFldTYJAL1OuJD/ZtcJ2g7QugIBK1A5OkIXEVZtYX1h3MBuK6Raqv6KNOuO1zllTs3C4S6vKGaarA03mi0bsZVEFqtxkWGdSyE0BEIWkHdcM8O9oHSDJIkcSq/HFtHHUHgJNYczMFUY6NbdBADOjXvseiwM6/cuVkggF8IaHTy9Sa8OqLiyvkIoSMQtAIxxfxClu/I4qrXNrFoa4bapngVyqTy64Z0QqNp/pt+x/XouHFpOYBGc9HwldKXS8y4ch5C6AgErUCZLHy6qJJqi1Vla9wDpSpo7aEclS3xHrKNVWxLKwTg2sHNh60AukTVenQKOphHx52bBSpcTOjkixlXzkYIHYGgFUQH+xHi74NN6qB9S86j0lzDniz5Dfz30yWYaoT4cwTf7DuHJMGIlAgSIwIvur6+R6dDlZgroSt39ehAs0JHkiR7YYMIXTkPIXQEglag0WhE+KoeO9OLsFjlD1ZTjY2DZ0tVtsg7UJoENtU753w6hweg1UCF2UpBeQfq8eThHp38MhNlphq0GkiJurigFbQNIXQEglZSN/NKCJ3NJwoa3E7NLFLJEu/h8LlSjuaUoddpubp/fIue4+ejI6G2x1OHytPxKI9OyQUPnaz9spQUEYifj86FRnUshNARCFqJkqcjPDqw+aQsdAZ2lquCdmU03+pecHGUSeVX9YnBENjyJnh1HZI7UEjVwz06bhe2qiqG/Z/Dd/PhxE9qW+MwLhycIhAImkV5U0rraM3ZziO/zMTRnDIAHr6yB3/4eDepmcVIknTRKiFB41htEt/sa3pSeXMkRway+WQH9ei4a8NAqPM2NSZ0attUqDrjqvAUHPtRvmRtA6k2z+7Id/DIEfDRq2ebgxBCRyBoJfamgXnlHfpDfesp2ZvTNz6UK3pG4+ejpajCTFpBhft8Q/Uwtp0qJLfUhCHAl3G9olv13A7t0fGI0NWFQudEnvxFwaV/L9YaOL0Djv8Ix9ZA4YmGj0f3gbJsqCyQ1/S91nW2OQkhdASCVpIUEYROq6HCbCW31EScoWO2bVfycy7vEYXeR8ugxDB2phexO6NICJ02smrvGQCmD4xvdc6GMsU8oyN5Gt29YSA0KXQ++C2NLSflFgJ9E0Kda0O1EU7+JAubE+saDhnV+kLKGOg5FXpOhogu8NPzsPkN2LPUK4ROq3N0fv31V2bMmEFCQgIajYavv/66weOSJPH888+TkJBAQEAA48aN49ChQw3WmEwmHnroIaKioggKCuKaa67hzJkzDdYUFxczZ84cDAYDBoOBOXPmUFJS0mBNVlYWM2bMICgoiKioKB5++GHM5g5UcSBQBb2PluTakt+OmqcjSRJbavNzxtQOm7wkRX5DF3k6baPSXMPag3IvoouNfGiMlKgONsXcZgNTbZWfB3l0JEnirZ9O8PcfjgDwp3Hd6H+RztdtoigNtr0DS2bAK13hi7vhwGeyyAkIh4E3w02L4fFTcMc3cOkfZZEDMGSO/PPUBjCeaeoEj6HVQqeiooJBgwaxcOHCRh9/5ZVXeP3111m4cCG7du0iLi6OiRMnUlZWZl8zf/58vvrqK1auXMnmzZspLy9n+vTpWK11PThmz57Nvn37WLNmDWvWrGHfvn3MmTPH/rjVamXatGlUVFSwefNmVq5cyZdffsmCBQta+5IEglbTtYOXmKcXVHDOWI1ep+WSlAgAhtf+TM0UQqctrD+cS4XZSmJEAMOSw1v9/KRa8V1WXUNxpcXR5rkf5jKQbPJ1j/DolCBJEv/48Shv/HQcgEcn9eTxyb0cc47NCpnbYP2zsHAE/HsIrH0K0n8FWw1E9YTRD8NdP8KjJ+H6/0G/6xrPb4rsBsmXyb/ffZ84xj4VaXXoaurUqUydOrXRxyRJ4s033+Tpp5/m+uuvB2DJkiXExsbyySefcN9992E0Gvnwww9ZunQpEyZMAGDZsmUkJiby008/MXnyZI4cOcKaNWvYvn07I0eOBOD9999n1KhRHDt2jF69erFu3ToOHz7M6dOnSUhIAOC1115j7ty5/N///R+hoU52BQo6NN1igvjpSMctMVeqrYanhBOgl0MsQ5PC0WhkEZRfZiI6xE9NEz0O+8iHwRcf+dAY/r464g3+ZBurySisICLI85NIm0UJW/n4g68bh49rhY5UVcwz3xxk2fYsAJ6Z3pd7LuvimDOsNfC/sZB7sO4+rQ8kjYJeU6HnFFm8tIahcyBzM+xdCpc/ClrPLdJ2qOXp6enk5OQwadIk+31+fn6MHTuWrVu3ApCamorFYmmwJiEhgf79+9vXbNu2DYPBYBc5AJdeeikGg6HBmv79+9tFDsDkyZMxmUykpqY2ap/JZKK0tLTBRSBoCx19uKeSn6OErQAMAb70ig0BRD+d1pJfZuK32t9pa6ut6pPSkWZeeUJpOdiFjsZcxsrtaWg08PL1AxwncgDyDskiR6eHATfBDR/CY6dg7vcw6oHWixyAPteAnwFKsiB9k+NsVQGHCp2cHDm+HBsb2+D+2NhY+2M5OTno9XrCw8ObXRMTE3PB/jExMQ3WnH9OeHg4er3evuZ8Xn75ZXvOj8FgIDExsQ2vUiBQZ7inJEm8vu4YH/yW5rIzG6PGarPPYbqsntAB2cMDIk+ntXz3+zmsNolBiWH2sGhbSOlIM688oVkgYPYJsV8P11bxxqzB3DoiybGHnK39cp88Bm74AAbc2P7fiz5Q3gdkr44H4xRf1Plu15aU4J6/prH1bVlTn6eeegqj0Wi/nD59ulmbBIKmUErMs43VlJtqXHLmwbOl/Pvnk/z9hyOcK6lyyZmNceCskbLqGgwBvhckUQ5PlvN0dos8nVahNAm8vh3eHOhgU8w9wKNTbbFy3/K9lEqyAH19RlK7PHZNogidTsMcu+/QO+SfR76DSs/10jpU6MTFxQFc4FHJy8uze1/i4uIwm80UFxc3uyY3N/eC/fPz8xusOf+c4uJiLBbLBZ4eBT8/P0JDQxtcBIK2EBaoJypYzoFId1H46pdjefbrPx5Ub1K4ErYa3S0SnbbhlwrFo3PorJFKs2sEoKdzMq+c/WeM+Gg1TB/YspEPTZGilJh3hF46bt4ssMJUw12LdvHLsXyMyF66yzs7qaPL2b3yT0cLnYTBEDcArGbY/5lj93YhDhU6Xbp0IS4ujvXr19vvM5vNbNq0idGjRwMwbNgwfH19G6zJzs7m4MGD9jWjRo3CaDSyc+dO+5odO3ZgNBobrDl48CDZ2dn2NevWrcPPz49hwxz8jy0QNIKrK6821hM6qw9kN7PSuWw+eWF+jkKnsADiDf7U2CT2nS5xsWWeiTLAc2zPaCKD25fA3SE9Om4YujJWWZjz4Q62pRUSpNcRHlWbitFI08B2YyqHfLlUnU5DHb//0Dvln3s+Bg9tW9BqoVNeXs6+ffvYt28fICcg79u3j6ysLDQaDfPnz+ell17iq6++4uDBg8ydO5fAwEBmz54NgMFg4J577mHBggVs2LCBvXv3cvvttzNgwAB7FVafPn2YMmUK8+bNY/v27Wzfvp158+Yxffp0evWSS/EmTZpE3759mTNnDnv37mXDhg08+uijzJs3T3hqBC7BlXk6RRVm9tYTDqmZxeQYq51+7vlUmmvYkyW/WZ+fnwNyOFkpjU4VeToXxWaT7NVWjghpKE0DiystGL29xNxNmwUWlpu49X/b2ZNVgiHAl+XzLiXYUNvl2hlCJ/t3uQw8tBOExDl+/wE3gs5PTng+t8fx+7uAVgud3bt3M2TIEIYMGQLAI488wpAhQ3j22WcBePzxx5k/fz73338/w4cP5+zZs6xbt46QkLqErDfeeIOZM2cya9YsxowZQ2BgIN999x06XV0n0OXLlzNgwAAmTZrEpEmTGDhwIEuX1iVE6XQ6fvjhB/z9/RkzZgyzZs1i5syZvPrqq23+ZQgErcE+CsIFQue3E/lIEvSOC2F4rZD48aDrvTo704uwWCU6hwfYP1TPR+mrs0vk6VyU3ZnFnC2pItjPh4l9Gw+5t4ZAvQ8xtWX9mUVe7tVxQ49Obmk1N/9vO4ezS4kK1rPy3ksZnBjW7BiIdmPPz3GCNwdk25XuyHs+ds4ZTqbVAcNx48Y123VTo9Hw/PPP8/zzzze5xt/fn7fffpu33367yTUREREsW7asWVuSkpL4/vvvL2qzQOAMutUO4juV5/wPlI3H8gEY1yuGmBA/dmcW8+OBHO4a48AS1Rag5Odc1j2qyaR/JU9nT2YxVpt0QR6PoI6vakc+TO0fh79v60Y+NEVKZBB5ZSbSCyoY2DnMIXu6JdVG+aebeHROF1Vy2wc7yCqqJC7Un+XzRtaNQnGm0FG8LAlOEjog99Q58Bkc+BImvwT6IOed5QQ8twOQQKAy3WvfxNILKrDanBe7ttkkNh1XhE40UwfI7uldmUXklbo2fNVcfo5C77hQgv18KDfVcCynrMl1HZ1qi5Xv98teueuGOq4SR/G0ZXp7QrIblZen5Zcz67/byCqqJDEigM//OKrhvDeXeHScmJuafBmEd5G7UR/+xnnnOAkhdASCNpIQFoCfjxaz1caZYud9qOw/a6SowkyInw/DksOJNwQwNCkMSYI1h1xXfZVfZuJorXAZ3S2yyXU6rYYhSWEA7BaNA5vkl6N5lFXXEG/w59IuTf8+W0v9mVdejZuUlx/NKWXWf7eTbaymW3QQn983msSI88K69cZAOJSKArmhHxq5QspZaLUw5Hb5ugeGr4TQEQjaiE6roUuU8/N0fjkqV1td1iMKX538J3v1ALkM+Yf9rsvT2XpK9ub0Swi9aHWQPU9HJCQ3iZKEfO3gTmgdGN6r644sPDrO5vfTJdz83+0UlJvoEx/Kp/eNIs7QyDgKxUZHe3TO1oatono6v8x+8G2g0ULWNig44dyzHIwQOgJBO3BFno5SVj6+V1238Km1QmdnRhH5ZSannV2f+vk5F2O4vfJKeHQao7jCbO+L1JZJ5c1RF7oSHh1nsjO9iNs+2IGxysLgxDBWzruUqKa+ADgrdOXsROT6hMZDj9rRTR7m1RFCRyBoB84uMS8oN7H/rJx0ObZXtP3+TmEBDEp0XfhKkiS2tCA/R2FwUhg6rYZzxmrOqtjF2V354UA2FqtE3/hQesWFXPwJrUAROgXlZsqqvbTEXJJUbRj46/F87vhoB+WmGkZ2iWDZH0ZiCPRt+glOFzou6h2ndEr+fQVYPef/lhA6AkE7cHaJ+a/H5bLyvvGhxIY2dIlPq01K/tEFzQPTCyo4Z6xGr9Paw1LNEaj3oX+C3M9qt/DqXIB9UrkTxgGE+Pvau3Z7bfjKXA6SVb7u4tDV6gPZ/GHJbqotNsb2jGbxXSMI9rtIAbMzhI4kudajA7JHJygGKvLh+BrXnOkAhNARCNqBs6eYK2Xl43tHX/DY1P5y+Gp7WiEF5c4NXynVVsNTwgnQt6wMepgy90rk6TQgq7CS1MxitBq4ZnCCU85I9vY8HcWbo/UF38b7OTkaSZJ466cT3L98D2arjSn94vjfHcNa9vegCJ3qErDZHGNQSSZUFckTy2P7O2bPi6HzhcG3ytf3eM6gTyF0BIJ20LXWo1NUYaaowuzQva0NyspjLng8MSKQgZ0N2CRYd+jC2XCORMnPaUnYSuES+yRz4dGpjzLAc0z3qAu8dI4i2T7zykvzdOo3C7zIwGhHUGW28uCKvbzx03EA5o5OYeHsIfj5tLD3kZJHJNnAVOoYoxRvTmx/8Gnf6JBWMaQ2fHVyPZSec9257UAIHYGgHQTqfegUFgDIvTQcyb7TxRirLIT6+zAkMazRNYpXx5mzr2qsNralFQItS0RWGFYrdI7llmGs8px4vjORpHojHwY7YYp1LSnePvPKhc0Cs41V3PTfrfywPxsfrYaXrx/A89f0w0fXio9PX/86z5OjwldKxZWr8nMUorpD0mhZtO1b7tqz24gQOgJBO1G8OmkODl8pYasrekY3+aZ6dW2ezra0Qod7lBQOnDVSVl2DIcCX/p1anvgZE+JPcmQgkoR9PlZHZ1taIekFFQT46pjS3wlziWpJ9vYp5i4qLd+TVcw1C7dw8GwpEUF6lv9hJLeOSGrbZvXDV47A1YnI9VGSkvcsdVwozokIoSMQtBNnVV4p5ceNha0UkiOD6JcQitUmsc5J1VdK2Gp0t8hWj3MYXpunIwZ8yiMCHl6xF4BrBiUQdLEE1nageHQyCrzVo1Mi/3SiR2fVnjPc8r/t5JeZ6BUbwjcPjGFk13Y0dlRsdYRHx1ojD/ME1yUi16fvteAXKucJZfzm+vNbiRA6AkE7cUblVV5ZNQfPyrH8sT0vTESuj9I8cPVBJwmdVpSVn4/I05ExVlm4e/EuCsrN9I0P5ZkZfZ16niJ08spMVJprnHqWKjjRo2O1Sbz84xEe+ex3zDU2JvSJ5cv7G+l23FocWXmVfxQslaAPgcge7d+vtegD5anmAHvdPylZCB2BoJ04o/JqU23YakAnA9EhzScaTq0NgWw9WUBJpWPDV5XmGnvYqTX5OQrDa0vR950uwVzj/i5uZ2Cx2nhg+R5O5JUTF+rPR3MvuXg5cjsxBPoSVtvXxSsrr5zk0SmrtnDvx7v576Y0AO4f143/zRnmmH8vR3ZHtoethsjjGdRgyBz55+FvnTPDy4EIoSMQtBOlO3JWUSWmGqtD9txYW201vlfz3hyArtHB9I4LocYmse6wY6uvdqQXYbFKdA4PsOd9tIZu0UGEB/piqrFx6JzRobZ5ApIk8f++OsjmkwUE6nV8OHd44yMCnIBXJyQ7waOTVVjJDe9uZcPRPPQ+Wt66ZTCPT+ntuPEcjvTonFMpEbk+CUMgdgBYTbD/c/XsaAFC6AgE7SQmxI9gPx+sNoksB3x7rrHa+FUpK+/ddH5OfaYNcE711ZZ6Yx80bSjj1Wg0HbqfzrubTvHp7tNoNbBw9hD6Jbiui2+KNyck2z06jvl9bjtVyLX/2czx3HJiQvz47L5RXOvoqjhHDvZUPDoJKuTnKGg0MLTWq7PnY7mBoZsihI5A0E40Go1D83T2ZJVQVl1DeKAvgzqHteg5yuyrLScLMFY6rpS7Pfk5Ch01T+f7/ed4Zc0xAJ6/ph9X9o516fnJHcGj44DQ1fIdmcz5cAfFlRYGdjbw7YOXMbiJdg7twlEeHXMl5B6Wr6vp0QEYcBPo/CD3AGTvU9eWZhBCRyBwAI7M01GGeF7RM7rFVU7dY4LpFRuCxSqx/ohjwlf5ZSaO5pQB7RM6w2uFTmpmMZIbf+tzJKmZxTzymVwVc/eYLtwxKsXlNqRE1Xp0CrzYo9OO0JXFauPZbw7y9FcHqbFJzBiUwGdNTR93BI4SOjn75fEXwXEQ6pzO2i0mMAL6zJCvu/GgTyF0BAIHUDfFvP0enV+OKd2QL56fU5+pDp59tfWU7M3plxBKRJC+zfv072RA76OlsMJMureWO9cjs7CCeR/vtlfsPD2tjyp2eLVHp50NA0sqzcxdtJOPt2UC8Oiknvz7lsH4+7aw03FbcFToqn6jQBd0hb4oSk+dA1/I3iY3RAgdgcABOCp0lWOs5kh2KRoNXNGjdUJHydP57UQBpQ6YWr25Xn5Oe/Dz0TG4NgTn7Xk6JZVm7lq8i6IKMwM6Gfj3rYNb3XvIUSjJyOeM1VRbHJMk7za0Ixn5ZF4ZM/+zhS0nCwnU63jv9mE8eGWPNuWgtQpHVV3Vr7hyB1Iuh7BkebTF4W/UtqZRhNARCBxA/dBVe8Izm47LYatBncOIDG7d/JoesSF0jwnGbLWxoZ3hK0mS2OKA/BwFZRzE7kzvzdMx19i4b2kqafkVJBj8+fDO4QTqnVtG3hzhgb6E+Mvnny5yz2/abUKS2lxe/suxPK77z1YyCivpFBbAl38a7dQO1Q1wVOhKzY7IjaHV1pWau2lPHSF0BAIHkBQZiE6rodxUQ15Z2yeJ/3K0bWErBXvzwAPtax6YXlDBOWM1eh8tI7pEtGsvqEtI9laPjiRJPLlqPzvSiwj28+Gjuy4hxkkDO1uKRqOp65DsTZVXliqw1vaLaqFHR5IkPvgtjXsW76LMVMMlKeF88+AY+sSHOs/O86kvdNr6ZaiyCIrT5esJbuLRARg8GzRayNwCBSfVtuYChNARCByAn4+OpNrOqW3N07FYbfYqp/HNjH1oDmX21abj+ZS1I3yl2DE8OdwheQvDkmSxlFZQQUF524Wgu/LvDSdZtecsOq2G/9w2lN5xLvwAbQb7zCtvyo1SvDkaHeiDm11qs0msPpDN9Lc38/cfjmCT4ObhiSz/w6VEtdJj2m4UoWM1yWKtLSj9cyK71+3nDhg6QfcJ8nU39OoIoSMQOIj25unsziim3FRDZJCeAa0YnlmfXrEhdI0Owlxj4+ejeW3aA+rycxwRtgK5U2/PWPlDKTXTu7w6X+09wxs/HQfgxWv7X3Rkhyup8+h4kdCpn5/TRF6NxWrjy9QzTHxjE/cv38Ohc6UE6nU8N6Mv/7hhAHofFT769MGgrQ1ltjV8pSQiq9k/pymUpOTfV4DVcS0uHIEQOgKBg2hvifnG2vycsT2j29yNVaPRcHX/9jUPrLHa2JZWCMDlPRwjdKBuHMRuL+qnsyOtkCe+OADAfVd0ZfbINk62dhIpUUrllReFrpppFlhtsbJ0eybjX93Igs9/51R+BaH+Pjx8ZXe2PHEld43p4vyk46bQaNqfp+Nu+Tn16TkFgqKhPBdOrFPbmgYIoSMQOIj2TjHfeLR13ZCbQsnT2XgsnwpT6wc67j9rpKy6BkOAr0M7+dY1DvQOj05afjn3LUvFbLUxtX8cT0zprbZJF1DXHdkLPTr1EpErTDW8/2saV7zyC898fZAzxVVEBet5Ykpvtjx5JY9M6kV4O1okOIz2CB1Jcm+ho/OFQbfK1/e4V/hKvZIAgcDL6BZTG7pqQ47OuZIqjuWWodXAFe30ovSJDyElMpCMwkp+PprHjEGtayqmjH0Y3S3SoaXRw2tHQRw8a6TKbCVA78SeJU6mqMLM3Yt3UVJpYXBiGG/cPNhxM5EciNJL51xJFaYaK34+nvs7t1OvWaCx0sKSbRl8tCWdktqO4PEGf+67ois3X5Lkfv/H2iN0jGegIl8Of8UNcKxdjmLIHNj6bzixFkqzITRebYsA4dERCBxG1yjZo3POWN1qT8rG2iaBQ5LCCQts3zdPjUZj9+r8eLD14StHjH1ojM7hAcSG+lFjk/j9TIlD93Yl1RYr9368m4zCSjqHB/D+HcOd22iuHUQF6wnS67BJcKa4jQmw7kZts8AjJVrG/PNnXl9/nJJKCymRgfzzhgFsemw8c8d0cT+RA3VeKEWstQbFmxPbD3zVrehrkuiekDQKJBv8/ona1tgRQkcgcBDhQXoia93jre0A/Evt2IdxDkpkVYTOz0fzqDS3XHRVmmvYkyV/23Rkfg7IAszT83RsNonHvtjP7sxiQvx9WDT3EqJDXFy90wo0Go1XdUg+V1LFxn1y4ndqrkS5qYbecSH8+9YhbFgwjpsvSVIn0biltMej485hq/ooPXX2LAWbTV1banHj/xECgefRljwdU42VrUpZeTvzcxT6JYSSGBFAtcVm9xa1hB3pRVisEp3DA+zl8o5keLJn5+m88dNxvvv9HD5aDe/dPowesSFqm3RRvGHmVUZBBU98sZ+x//qF9DNnAfALieT9O4az+uHLuWZQgmodqFtFe4TOub3yT3cXOv1mgj5E7veTuUVtawAhdAQCh9KWPJ3dGcVUmK1Eh/jR10ENzOqHr35oRfXVlnpjH5xRnXJJrUdnT1YxVptnDfj8fPdp3v5Zbob20nUDHB7acxae7NE5nlvGwyv2cuVrG/l092ksVoluIbKH8sYx/ZnYN9Ytc6OapK1Cx2atEzruWFpeH30QDLhBvu4mgz6F0BEIHEhbSsx/Odr+svLGUMrMfzmaR5W5ZbOOnJWfo9A7LoQgvY6y6hqO55Y55QxnsPVkAU+tksvIHxjfjVmXJKpsUcupq7zyLI/OsZwyZry9mW9/P4dNgit7x/Dln0ZzRaIvAJp2TC5XjbYKnYLjYC4H3yCI7uV4uxyN0lPnyLftH3nhAITQEQgcSFtCVxuPy6GltnZDboqBnQ10Cgug0my1z9BqjvwyE0dzZPHhLKHjo9MyJEkZB+EZeToncsu4b1kqNTaJ6QPjWTDRAz5o6uGpHp23fz6BqcbG0KQwfnj4Mj6aewnDksPbNdBTddoqdJT8nIQhoHXDJOvzSRgKMf2gplqeaq4yQugIBA6ka2135LSCihaFZk4XVXIyrxydVsNlTkj+VUZCtGT21dZTsjenX0IoEU7sOTLcPuBT/W96FyO/zMRdi3dRVl3DsORwXr1pkGeFSqjrjny6uAqL1T2SQy9GWn65PeT695kDGvZzaqZhoNvTZqFT2xG5k5uHrRQ0mjqvjhuEr4TQEQgcSOfwQPQ6LeYaG+dKLl7Oq3hzhiWFYwjwdbg9Sp7OhiO5VFuaD19trpef40wusVdeubfQqTJb+cOSXZwpriIlMtCty8ibIybED39fLVabxFkPKTF/d+MpJAmu6h1D34Tz8tYaaRjoMdiFTknrnmevuPIQoQMwcBbo9JCzH87tU9UUIXQEAgei02roUtt2/2QLwlcba/NzxvV2znykwYlhJBj8qTBb+fV409VXkiSxpTY/x9GepcZs0mk1nC2papEYVAOrTeLPK/fy+xkjYYG+LLprhFO9XM5Eq9V41MyrM8WVfLVXrqx64MruFy6o1zDQ41Bsbo3QsVRD7kH5urtXXNUnMAJ6T5evqzzoU3RGFggcTLeYII7llnEqr7zZvJtqi5UtteGicT0dm5+joNFomDogng83p7P6QDaT+sU1ui6toIJzxmr0Plq7x8VZBPn50Dc+lANnjezOLOaasACnntcWXlp9hHWHc9HrtLx/x3C7ePVUkiMDOZpT5hEzr/73axo1Nokx3SMZmnTehG5LtZz3AZ7t0TGXyYMvdS3w4uYcAFuNPEfK4PwkeFtlJflv/RtrRTm6kFB0oSFoQ0PRhYaiDQlBFxqKLqT2vpAQNAEBTVZoVg26he8K9zAiNJYUp1veNELoCAQOpqWVVzvTi6i22IgN9aNPvPP6sVw9II4PN6fz05G8JscAKN6c4cnhLgnPDE8Jl4VORhHXtHJEhbNZsjWDDzenA/DqrEFOF36uwFM8Onml1azcdRqAB8Y35s0x1l7RgJ9jWjG4lPp5RVUlENwCT+65ehPLnTyQVJIkzv31acrWrGn5k3x8aoVPSK0wCqUmyI80ay4Hq9Mp8q2hRJfBvZc7z+6Lmqje0QKBd9LSyqu6bsgxTp2oPCQxnLhQf3JKq/nteAET+sZesEbJz3FVb5jhyREs2pLhdo0DfzqcywvfHQLgscm93E6EtZW6yiv39uh8sDkdc22l1aiukRcuqJ+IrPXAzAutTra92ignJLdE6LiwI3Lh/96XRY6vL5F3341ksWArK8VaWmb/aS0rxVZahrW0FKxWqKnBWlyMtbgYS729OtVeAAr9S51ue3MIoSMQOBhF6KRdROhsqu1YPN5J+TkKWq2GKf3jWLw1g9UHsy8QOjVWG9vSCgHHj31oCqXy6lhOKaXVFkL9HZ+I3VoOnDHy0Iq92CS4eXgi94/rprZJDsMTppgXV5hZtj0TgAev7N64+Pfk0nKFgPA6odMSXCR0yjdtIv/NNwGI+3//j/CbZzW7XpIkpKoqaoxG9pz6jTUHviT97EGCTBBYDd184hgW2JtOhJEwYoRTbb8YQugIBA5GKTEvKDdTUmludEhnRkEFaQUV+Gg1LvGiTBsYz+KtGaw/nHtB+Gr/WSNl1TUYAnwblvE6kdhQf5IiAskqqmRvVgljHTTjq62cLani7iW7qLJYubxHFH+/rr9TvWyuJrk2x+h0USVWm+SW4xIWbc2g0mylb3xo07ltdo9OmKvMcjwB4VCc0TKhU1UChXI3bmdWXJnS0zn76GMgSYTdcvNFRQ6A2Wbmh7Nr+PjQx5wynoJA0Pb0YULSBG7vO4fBMYOdZm9rEUJHIHAwQX4+xBv8yTZWcyq/gmHJFwqdjbVhq+Ep4YS4wJsxLCmcmBA/8spMbD1Z2GCmljL2YXS3SJd+AA5PDierqJLdGUWqCp3Sagt3LdpJfpmJXrEh/Oe2ofjqPDAs0gzxof7oferaHiQ6YY5ZeyirtrB4i5wX9cD4Jrw54D0eHWiZ0FHGPoSnyFVMTsBaXs6ZBx7EVlZGwLBhxP31r82uL6wq5LNjn7Hy2EqKquWmn0G+QVzf43pu63MbnYI7Nft8NRBCRyBwAt2ig2uFTrnczfU8nNUNuSm0Wg1T+8exZFsmPxzIbiB0NruorPx8hqdEsGrvWVX76VisNu5ftofjueXEhPjx0V2XuEUYzdFotRqSIgI5mVdOZmGl2wmdZduzKK2uoWt0EFP6N14ZCHh2s0CF1ggdJ4etJJuNc489jjktDZ/YWDq/9SYafeNtFE6VnGLp4aV8d+o7zDYzAHFBcdze53au73E9IXr3HXArhI5A4AS6RQex+WRBownJ1RYr207JOTHjXCR0AKYOiGfJtkzWHcrBfN0A9D5aKs017MmS33Cd3SjwfC6pzdPZe7oYi9Xmci+KJEk8/dUBNp8sIFCv46O5l9DJDUvdHUVKpCx0MgorXC5qm6PaYuXDzWkA3D+ue/NeRU9uFqig2K6Ituawd0R2jtApWLiQ8l9+QaPX03nhQnyiGv6/kCSJ7dnb+fjwx2w+u9l+f//I/tzZ704mJE/AR+v+MsL9LRQIWsrhb2DrQhj7OPSYqKop3WJqK6/yLkz+3JZWiKnGRoLBn56xwS6z6ZKUCKKC/SgoN7H1VAHjesWwI70Ii1Wic3gASS7+lt8tOhhDgC/GKguHzpUyODHMpee/s/EUn+0+g1YDb986hP6dPNhL0AKUyquMAvdKSF65M4uCcjOdwwO4dvBFqtw8uVmgQqtCV84TOqXr1lHwzrsAxP3tBQIG9Lc/ZraaWZ2+mo8Pf8yJ4hMAaNBwZdKV3NnvTgZHD/aoHDYhdASej7UGfv4bbHlLvv3DI/DQXtCp99+7ucqrum7Izi0rPx+dVsOU/rEs257FjwdyGNcrxp6fc1n3KJe/cWm1GoYnh7PhaB67M4pcKnS+2XeWf609BsDz1/Tjqj4Xltx7GylRSi8d9ykxN9fY+O+vsjfnj2O7Xdyr5w0enZYKndJzUJYNGh3EDXSoCdXHj3PuyacAiLjzDmomX8am05s4WHiQQwWH2F+wH6NJ7lkU4BPAdd2v4/Y+t5MY6vyGhc5ACB2BZ1NRAF/cDemb5Ns6PyjJgsNfw4AbVTNLETqZRZWYa2zofeQ3cEmS+KW2rHycCgm4V/ePZ9n2LNYezuHv1v6q5ecoDE+JqBU6xfzBRQ3FdqYX8djn+wH4w2VduGNUimsOVhmlxNydppiv2nOGbGM1MSF+3Dis88WfoDQM7AgeHSU/J6Yv6B3nbS3Oy+LsfX9AV1nJmV4R/LnLT2R/9skF62ICYpjdZzY39rwRg59nezuF0BF4Lmf3wKdzoPQM+AbBtQuh4ARsfAm2/hv63+D0TqJNERvqR5BeR4XZSlZRBd1j5ES99IIKsooq8dW5pqz8fEZ0iSAySE9hhZnv95/jaE4ZAKO7qSV0lEnmRUiS5HSvUlp+Ofcu3Y3ZamNyv1j+enUfp57nTijdkTOLKrHZJNWnsNdYbby76RQA917RtWUdub2lvBxaLnTaUVZeYangcOFhDhUc4lDhIQ7nHmDOR1kMypbIM8BzU4yUmUrRoKFbWDf6Rvalf1R/+kX2o09kH3y13pGYL4SOwDPZsxR+WABWE0R0g1uWQ0wfqCiEzW9A9u+Q/it0HauKeRqNhm4xwew/Y+RkXp3QUbw5I7pEEOTn+j8/H52WSf3iWLEzi5dXHwWgX0KoagMrB3QyoNdpKSg3k1FY6dSZUoXlJu5avIuSSguDEsN48+Yhqn/Yu5J4gz++Og3mGhs5pdUkqJx4/cOBbDILKwkP9GX2yKSWPakjlZe3MhHZbDVzpOgIBwsO2oVNujEdCcm+5vafrQzKkDD7atj60OXcN2Q0/aP60yeiD4G+7lWJ50iE0BF4FjUm+PEJSF0k3+51NVz3Xl25aVAkDLkddr0ve3VUEjogh6/2nzE2qLxS+ue4qqy8MaYNiGfFzizyykyAemErAH9fHQM7G9idWczujCKnCZ1qi5V5H+8ms7CSzuEBfHDHcAL0zp/p5U746LQkhgeSVlBBRmGFqkLHZpP4zy9yI7y7x3QhUN/Cj6KO4tGx2ep66LTAo1NpqeS21bdxsuTkBY/FBcXRL7If447o6LVjNQBdXnmNp6ZObbXpnooQOgLPwXgWPrsDzu4GNDD+abh8wYUzb0Y9ALs/hJM/Qe4hiO2nirndajskK0Kn0lzDjjS5wZYry8rP59KuEYQH+lJcKU+mcXVZ+fkMSwmvFTrF3DTc8cmONpvEgs9+Z09WCaH+Piy+6xKiQ/wcfo4nkBwpC53MwkpGqzjhYv2RXI7nlhPi58Mdo1Na/kSv8uiUyIKmsZldhSfBVAo+ARB98fDqiqMrOFlykkCfQIbFDqN/VH/6R/Wnb2RfogKiqDp0iMz5tyEBkffeS2gHEjkA3tX+U+C9pP8G/xsrixz/MLjtcxj7WONvEhFdoM818vWtb7vUzPqcP8V826lCzFYbncMD7CJIDXx0Wib3k5uy6X20qk/nviRZPn9XZpFT9n9l7TF+OJCNr07Df+cMt4cROyLJbjDFXJLqvDlzRiVjCGhhHojVApZauz3aoxNWe0UCk7HxNfaJ5YMvWj1aaalk8aHFADx96dO8M+Ed7h98P1d0voKogChqCgs58+BDSCYTQWOvIPrPDzviVXgUQugI3BtJgm3/gY+vhYp8iB0A9268eJ+cMbV/zAc+lz1BKqD00knLK6+ttqoLW6ndg+LGYZ3RauCq3jEtSwJ1Ikrn6LT8CgrLTQ7d+5MdWbxXm/D6zxsGMqpbIxOxOxD2yqsC9UrMfztRwP4zRvx9tdxzWZeWP1Hx5oBnd0b28QMlH6b+a6qPkoiccPGw1YqjKygxlZAUksTVXa5u8JhksXD2z/Opyc5Gn5JCp3/9C42uY4VsQQgdgTtjroAv74G1fwXJCgNvhnvWyR6bi9FpGCRfBrYa2PGu821thOTIQLQaKDPVkFdm4pejtWXlvdQdYAlyWffPC8bx6k2D1DaF8CA9PWpFYWqm48ZBrDuUwzPfHARg/oQeXD+0BeXLXk5ylPoenYW13pzZI5KJDG5FCFHJz/ELBa2Hf1hfLE+nhRVX9b059w2674Iuxbkv/4PK3bvRBgXR+T8L0YWGtsdqj0UIHYF7UngKPpgAB78ErQ9MfQWu+2/r+kkoXp3di+v6b7gQPx+dvdvwukM5nC2pQu+jdRuvQkpUkCqVX41RV2bePqFTaa7h011ZzHh7M/cuTcVqk7h+aCf+fFUPR5jp8dhLzAsrkSTpIqsdz870InamF6HXabn3iq6te7I3NAtUaE7o1Jgh54B8/SIVV815c0q++ILiT+T+OAn/egW/biomZamMe7zLCQT1ObYGVt0rx6+DY+GmJZA8qvX7dJ8I0b0h/yikLoYxf3a4qRejW3QwGYWVfLQlA4BLu0a2vMKkAzE8OYIVO0+zO6NteTon88pYtj2LL/ecoay6BgC9TssNwzrzwjX9VA8VugudwwPQaTVUWazklZmIDfV36fmKN+eGYZ2JM7TybHuzQA8OWyk0J3RyD4LVDAER8tTyJmjOm1O1bx85L/wNgKiHHyLkyisdZblHIt5xBe6DzQab/gmb/iHfThwpi5zQ+Lbtp9XC6Ifgmwdg+3sw8k/g49p+MV2jg9hwVG4UCOp0Q/YElIToA2eNVFusLcobMtfYWHsoh2XbM9mRXieQkiICuW1kEjcO69y60EgHwFenpXN4AJmFlWQUVLhU6Ow/U8Kvx/PRaTX8aWwbvAveUFquoCQkNyZ06oetmhHoTXlzLLl5nHnoYSSLhZCJE4j64x8daLhnIoSOwD2oKpa9OCfWybcvmQeTX2q/MBlwE2x4EcrOwcEvYPDs9tvaCpTKK4XxvdUrK3dnEiMCiA7xI7/MxO+nSxjZtenw3pniSlbszOLTXWcoqE1e1mrgqj6x3H5pMpd3j+pQjQBbS3JkEJmFlWQWVjb7e3Y0SqXVNYMSSIpsQ3M6RRR4cmm5Qv0S8/NpQaPAprw5NrOZMw8/RE1+Pn49uhP/8j/QNFaZ2sEQQkegPjkH4dPboDgDfPxh+psw+FbH7O3jB5f+EX56Xi41H3SrS8dCKJVXICcnO7Pzryej0Wi4JCWc1Qdy2J1ZfMEHsNUm8evxfJZtz+SXY3nYatNLYkL8uOWSRG4ZkaR6p19PISUykF9xbULy8dwy1h7KBeD+cW3MFfEqj04zoSu7R6dpobPy2MoLvDmSJJHzwgtU/74fbWgonRcuRBcs3m9ACB2B2qRtghW3gKUSwpLg5mUQ7+BKoGF3wa+vQt5huYngxUrTHUh9j46a3ZA9geHJEbLQqZenU1Bu4rPdp/lkRxZniqvs94/pHsntI5OZ0Df24hOvBQ1IrpeQ7CreqfXmTOkXR4/YNvYx8oZmgQqKWDtf6FSXQsFx+XoTpeWVlkoWH1wMNPTmFH/yCcYvV4FWS6fXXkOfnOwEwz0TIXQE6pGxpU7kdBkLNy2GQCc0rwsIg2FzYdtC2PKWS4VORJCeqGA9BeVmxrpBWbk7U7/yakdaIct3ZPHjwWwsVtl9Ywjw5cZhnZk9MumCkKCg5Si9dFzl0cksrODb388B8MD47m3fyO7R8aJkZOU1KWTvAyQwJEFw4+8XK4+tpNhU3MCbYzl3jtx//BOAmAWPEHz5Zc6x20MRQkegDlnbYflNssjpdhXc8gn4OjEx8tI/wY73IOM3eYZMwhDnnXUeL18/kKPZpYztIYROc/SNDyVQr6Osuoab/7fdfv+gxDBuH5nEjEEJqjc39AaSzysxd3ZF2nubTmGT5P5RAzq3Q6R0hPLyi/TPacqbU7h4MVgsBF5yCRF33+0Egz0bIXQEruf0Llh2o9zOves4efK4M0UOgKEz9L8B9n8KW/4NNy1y7nn1mNg3lol9Y112nqfio9MyulskPx3JI8BXx8whCdw2Mpn+nbzgG7wbkRgRgEYD5aYaCivMRDmxMu1cSRVfpJ4B4MH2eHOgXnl5eDutcgOaFDrNJyI35s2pKS6m5PMvAHmOlWilcCFC6Ahcy9lUWHY9mMsg5XK4ZQX4uiiJdPRDstA5/DUUP9dsjwqBOrx8/UBuyipmVLdIQv1bOANJ0Cr8fHQkGAI4W1JFZmGFU4XO/35Nw2KVGNklguHtnanWITw6itC50KPTXG6OVFWFX58+BF02xlkWezQii0/gOs7tg6XXyVN5k0bD7E9b1+m4vcQNgG5XgmSDbe+47lxBi4kO8WNyvzghcpxMSlRtno4TZ14VlJtYuSsLgAevbKc3B+p5dMLav5fa1Bc6SofqshwoPQMaLcQPvuApjXlzbJWVFC9dBkDkH+4R3pwmEEJH4BpyDsDSmfKbVeJIuO0z0KtQ+ji6dizE3qVQ6Zxp2QKBu1OXp+O8hOQPN6dTbbExqLOBy7pHtX9Dbywvt5rlPEWo8+ZE9wa/hsn29b059w681+7NKflyFdaSEnw7dyZ08mRXWO6RCKEjcD65h+Xp41XF0Gk43PYF+LWxxLS9dB0ne3YslbDrQ3VsEAhUpkut0El3Uom5sdLC0m2ZgFxp1W5Pg80qe4LBOzw6+iDQ1notlfDVuabDVoo3JzEkkWldpwHyZPKiRXKuYcTdd6HxEZkoTeFwofP888+j0WgaXOLi4uyPS5LE888/T0JCAgEBAYwbN45Dhw412MNkMvHQQw8RFRVFUFAQ11xzDWfOnGmwpri4mDlz5mAwGDAYDMyZM4eSkhJHv5y2cyZVHs7W0ck/Bh9fA5WFcqXT7V+Cv4oTdDUaGF0782rnf8FSrZ4tAoFKJNeWmDvLo7NkWwblphp6xYYwoY8DEvHrD+X1hvJyjebCMRBKxdV5/XMa5OYMrMvNKV2zBsu5c+giIgi7/noXGO25OMWj069fP7Kzs+2XAwcO2B975ZVXeP3111m4cCG7du0iLi6OiRMnUlZWZl8zf/58vvrqK1auXMnmzZspLy9n+vTpWK1W+5rZs2ezb98+1qxZw5o1a9i3bx9z5sxxxstpPbs/gg8nwtqn1LZEXQpOwJIZUJEve1FuX+Ue38b6zQRDomzX7yvUtkYgcDkptR260wsqHD7FvMJUw0db0gG4f3w3x4zjUMSAPhh0XpK/VX8MhCQ12RG5UW+OJFH4geyRjrhjDlp/1w5n9TScInR8fHyIi4uzX6Kj5f4hkiTx5ptv8vTTT3P99dfTv39/lixZQmVlJZ/UjpM3Go18+OGHvPbaa0yYMIEhQ4awbNkyDhw4wE8//QTAkSNHWLNmDR988AGjRo1i1KhRvP/++3z//fccO3bMGS+pdYTEywmvuz6A1CVqW6MOhadkkVOeCzH94I5vndMMsC3ofOHS++Xr2xbKw0QFgg5EUoTs0SmrrqGk0uLQvT/ZkUVJpYUuUUFMH5jgmE29KT9HoX5CclGa7LXS+UFsP/uSprw5Fb/9hunYMbSBgYTf6qBxOV6MU4TOiRMnSEhIoEuXLtxyyy2kpaUBkJ6eTk5ODpMmTbKv9fPzY+zYsWzduhWA1NRULBZLgzUJCQn079/fvmbbtm0YDAZGjhxpX3PppZdiMBjsaxrDZDJRWlra4OIUek2F8U/L139YAFk7nHOOu1KcAUuugbJsiO4Dd7qRyFEYeofsAi88CcdWq22NQOBS/H11xBtkL4AjOySfK6nivU2nAPjT2G7oHDVc1V5a7gVhK4X6QkdJRI4f1MBj1Zg3B6Dwf+8DEDZrFjqDF/1OnITDhc7IkSP5+OOPWbt2Le+//z45OTmMHj2awsJCcnJyAIiNbRizjY2NtT+Wk5ODXq8nPDy82TUxMRfODYqJibGvaYyXX37ZntNjMBhITExs12ttlisehb7Xgs0Cn82B0nPOO8udKMmCxTPkMsmonrLICXJAxYWj8QuG4ffI17f+W11bBAIVqMvTcUxCcmm1hbsW7aKwwkyv2BBmDunkkH2BOo+OO4S+HUUDoXNhR+SmvDlV+/ZRuXs3+PoSMfdOV1rssThc6EydOpUbbriBAQMGMGHCBH744QcAliypC+Gcn4Hfkjbk569pbP3F9nnqqacwGo32y+nTp1v0mtqERgPXviOHbcpzYeVt3p/4ajwrh6uMWRDRDe78DoLdeJDlyPtAp4fTOzqe103Q4UmprbxyhEfHXGPjT8tSOZZbRkyIHx/ddQl6Hwd+vHhTs0CFRoVOXX5OU96cgg8+AMAwfTq+9Qp9BE3j9PLyoKAgBgwYwIkTJ+zVV+d7XfLy8uxenri4OMxmM8XFxc2uyc3NveCs/Pz8C7xF9fHz8yM0NLTBxan4BcOtn8j/oc/tge/n1zWH8jZKs2HJdDlsFZ4ii5wQN/8jDImDgTfL14VXR9DBcNQUc0mSeHLVfracLCRQr+OjuZfQKczB3c69qVmggiJ0KvIhZ798vVboNOXNMaWlUb7hZ0BuEChoGU4XOiaTiSNHjhAfH0+XLl2Ii4tj/fr19sfNZjObNm1i9OjRAAwbNgxfX98Ga7Kzszl48KB9zahRozAajezcudO+ZseOHRiNRvsatyE8RZ7KrdHJFT7b31XbIsdTlit7corSICwJ7vweDA50WzuT0Q/JP4/+AAUn1bVFIHAhjppi/sZPJ1i15yw6rYb/3DbUObPJvDkZOXML1FTL+UcRXQH49NinjefmfPghSBLBV12FX7dualjtkThc6Dz66KNs2rSJ9PR0duzYwY033khpaSl33nknGo2G+fPn89JLL/HVV19x8OBB5s6dS2BgILNnzwbAYDBwzz33sGDBAjZs2MDevXu5/fbb7aEwgD59+jBlyhTmzZvH9u3b2b59O/PmzWP69On06tXL0S+p/XQdB5P+Ll9f9/8gbaOa1jiW8nxZ5BSekEu27/wewpyY++RoontBz6mABNveVtsagcBlOMKj89mu0/x7wwkA/j6zP+N7OSlUrYSuvMmjo4i24gz5Z8JQ0GiotFSy6KDcCLC+N8eSm4vx2+8A4c1pLQ4XOmfOnOHWW2+lV69eXH/99ej1erZv305ycjIAjz/+OPPnz+f+++9n+PDhnD17lnXr1hESUtcp94033mDmzJnMmjWLMWPGEBgYyHfffYdOp7OvWb58OQMGDGDSpElMmjSJgQMHsnTpUke/HMdx6Z9g0GyQrPD5XChKV9ui9lNRKHc8LjgGIQly4nF4stpWtZ4xtWMh9q2A8jx1bREIXISSjFxUYcZY1foS803H83nqK7lH2oPju3PriCSH2tcAb/boKNSGrZry5hQtXgIWCwHDhxE4ZIgrLfV4NJKju0V5EKWlpRgMBoxGo/PzdUBORl40Vc7XiekH96y7YKaJx1BZJHc8zjkAwXFw12qI9FBXqiTBBxPg7G644jG48v+pbZFA4BJG/N9P5JWZ+PbBMQzsHNbi5x06Z2TWe9uoMFu5bkgnXp81yLkDJZdcA+mb4Pr3YeAs553jSs6kwgdX1t2+ZQWV3cYx5cspFJuK+fuYv3Nt92sBsBqNnBx/JbbKSjq/9y4h48apY7Mb0ZrPbzHrypX4+sPNyyAoBvIOwTf3e2ZyckWhPKAz54D8Wu78znNFDsgVcopXZ9cHYHbeoEOBwJ2oq7xqefjqXEkVdy/eRYXZyqiukfzzhoHOn5rtlR6dsIa3Ow1t0ptTvGIltspK/Hr0IHjsWNfa6QUIoeNqDJ1ksaP1hcPfwG+vqm1R6yg9B4uvhuzfITBKFjnRPdW2qv30ni4nAlYVw95lalsjELgEey+dgpaJe6VXTm6piZ6xwbw3Z5hjy8ibwpsbBgKEdqLSP7TR3BxbdTVFtWkZkfP+4HxR6YUIoaMGSSNhWq3A+fn/4Ngade1pKUXp8NEUyD8q5+Tc9SPE9FbbKseg1cGoB+Tr2xaCtUZdewQCF6DMvGqJR+f8XjmL7hqBIcBFc6e8sWGgvwGoFS3NeHOMX3+NtbAQn4R4QqdOVcdWD0cIHbUYNre2M68EX/4B8o+rbVHz5B2RRU5JJoR3gbvXeIcnpz6Db4PASLm785Fv1LZGIHA6LZ1iXr9XTpCzeuU0hc0G1bXjerwpdKXV2T1UlfED7d6cewfea/fmSDU1FH74EQCRc+9C4+slA01djBA6ajLlH5A0GsxlsPLWOvesu3F2Dyy6GspzIKavLHI8sbrqYvgGwIh75etb/u2Z+VMCQStoaY6OS3rlNIWpFKj9W/Qmjw7Ym6p+qim3e3Omd51uf7hs3Tosp0+jCwsj7MYb1LLS4xFCR0189DDrYwjtLA+XXDUPbFa1rWpIxha54qGqSO7zMPcH9+943B4umQc+AZC9DzJ+U9sagcCpJNV6dArKTZSbGg/X1u+V838z+zPOWb1ymkIJW/kEgI+fa892NjPeonLS31l0diNwnjdHkuzjHsJvvx1tYKBaVno8QuioTXA03LIMfPzhxDr4+e9qW1THifWw7HrZ45R8mXtOIXc0QZEw5Db5+hYxFkLg3YT6+xIZpAcaD1/V75Xz0JXducWZvXKawhubBSokXcqnocGNenMqtm7FdPgImoAAwm+braKRno8QOu5AwhC4ZqF8ffPrcHCVuvYAHPoKVtwqtybvMRlu/wL8Qi7+PG9g1AOg0cLJ9ZB7SG1rBAKn0tQU80PnjNy/LBWrTeL6IZ14ZKJKOXneWFpeS/0uyPW9OQCFtd6csBtvxCc8vNHnC1qGEDruwsCb6uYuffOA3KNGLfYshS/uBpsF+t8AtyyX81c6ChFdoc818nV38rAJBE6gsSnm5/fK+YcreuU0hRd7dOpXWtX35lQdOEjltu2g0xE5904VLfQOhNBxJya8AN2uBEslrJgtN+ZzNdvegW8fBMkGQ++UO5HqOmCm//in5UGsx1ZDusjVEbghBSdh8xty76d2oJSYZxbIHh3VeuU0hZd6dCxWC0sOLQFg3oB5jXpzQqddjW8n1w5IPneimNXv7mfD4sNkHCjAWmNz6fnOwOfiSwQuQ6uDGz+C/42H4nT4/E6Y85VrhIYkwaZ/wsaX5dujH4KJL8pdgzsi0T1h+N2w631Y9zTM2wha8b1A4CYcXwdf3iNXJJ3bB7OWtHkrJXSVXlihbq+cpvDGZoHA2sy1FFYXEhMQw/Rudd4cc0YGZevWARB5zx9cZk9ZUTXbVp3kxO66eX9Ht+fgF+hDl0FRdB8WS+fe4ejUFL1tRAgddyMgHG5dIc9eyvhNnnY+9Z/OPVOSYO3TsP0/8u3x/w+ueLTjihyFcU/C/k/lLtAHPoNBt6htkaCjI0mw9W1Y/yz2kuvDX8OZ3dB5eJu2tIeuCirU65XTHN7YLBD45MgnAMzqNQtfbZ2YLPxoEUgSwWPH4t/L+XlRNRYr+9ZnkbomkxqzDTTQ97IEdDotp/bkUVlq5ui2HI5uk0VP18HRdBsWI4senWeIHiF03JGYPnDdf+HT22DHexA3AIbc7pyzbFb47s+wt3by+5R/wqV/dM5ZnkZQFFz+CPz0PGz4G/S9tmPlKnk6v68E4xm47BHv8MZZquH7v8Dv8gckQ++UiwX2fwrrnpEH67bhy4kidPLKTOr1ymkOu0cnTE0rHMr+/P0cKDiAr9aXG3veaL/fkpeH8auvAHncgzORJIn03wvY8sUJSguqAYjvbuDym3sSnSgXnlw2qwc5p0o4uTuPk3vzqSo1c2RrNke2ZuMXJIue7kNj6OTmokcIHXelz3QY+yRs+of85nY2FZLHyJfQeMecUWOGr+6VK6w0WrjmbecJKhdgqrSQm1FKQvcwfPQ6x2w68o+w60MwnoZt/5E9XQL352wqfPVHQJJDwpf9RW2L2kdZrvzF58wuOXdsystyc8vSc/LMvKytcHwN9Gr9iABDoC9hgb6UVFoAlXrlNIcXenSWH1kOwNQuU4kMiLTfX7x0KZLFQsDgwQQMG+a084uyK9j82XFOH5Hzu4LC/BhzQ3e6D49pkHSu1WpI6BFOQo9wLru5J9knSziZmsepPXlUlVk4siWbI1tk0dOt1tPTqZf7iR4hdNyZsU9A7kE4+j3s/ki+gDyCIXkMpIyB5NEQltz6b3LmSvjsDrmEWusLN3wA/WY6/CVcDGt5Obn/+AdavZ7AUaMIGjECnaHl3yTNVTWk7y/gZGoeWYcLsdVIJPYJZ/pDg9FqHRB68w2Aq56VmzlufgOG3gHBbvQhILgQm1X+cqCEdn7+O6Rc3ubQjuqc2wcrZ0PpWTlP5aYl0G28/JihE1z6J/n/5vrnoPtE0LX+bb1fQihbThaq1yunOaqN8k8v8ejkV+azLlPOwZndp64/jrWsjOIVKwHnDe80VVrY9X0GBzaewWaT0PpoGDIxiWFTUvD1a/7LoVaroVPPcDr1DOfym3uSfaJW9OyVRc/hLdkc3pKNf5AvXYdE031YDJ16hqF1A9GjkaSO2+e+tLQUg8GA0WgkNDRUbXMax1oDJ9ZCxmbI3CKXnUvnZcGHdqr19oyWf0b1aF74VJfCilvk/XwC5GnqPSY493U0giRJnJ3/F8rWrq27U6vFv18/gkaNImj0KAKGDEHr17AbamPixo4GkGDEjC5cMq2LYwy12eCDK+HcXjlBefobjtlX4Bx2/Bd+fBz8DJA8SvZ0hCXDH3/zvITWQ1/BV3+CmiqI6gm3roTIbg3XVBvhrcFy9/IZb8lz9FpJtrGK47nlXNEjyv2mY/9vPJzbI7/2Nnis3I139r3Du7+/y5CYIXw89WP7/YUffEDeq6+h79aNrt99i8aB4VbJJnFkWzbbvz5FVZnsuesyKIoxN3bHEN2+jss2m8S5WtGTVit6FPyDZdHT85JYOvV0bC+g1nx+C6Hj7kLnfKqNkLVDFimZW+U3ANt5rdsDo2TRk3KZ/DOmX12OQkUhLL9B/tD2C4XZn8prVKDo44/Jfell8PXFcO01VO3ZizktrcEajZ8fgcOG4TtiFIUxg8nK9SXrUHGDksfwuEC6DYuh+7AY8jPL2LDkCBoNXDN/CJ17OeiPK2MLLL5aDhv8aav3TG33NkqzYeElcjfvaa/DgBvhvcvlYbT9b4AbPvSMJHubTQ5bb6otROg+Qa7IbEqobXsH1j4FwXHw8B7QB7nOVmfz7yFQlAZ3rZGFqwdjtpqZ9MUkCqsL+dcV/2JKlykA2MxmTl01gZr8fOJfeomw669z2Jk5aUZ++/Q4eZllgPx+edmsHiT1jbzIM1uPzWrj7IkSTqXmcWpvPtXlsujpPjyGyX/o79CzWvP5LUJXnoa/AXpOki8A5go5bp+5Vb6c2QWVBXDkW/miPCdplCxo9n0C+UflKd23r4KEwaq8jMo9e8l95V8AxD7+OBFz5NwgS24uFdu2UbltG8btqeTYYskz9qUwtTM2Xan9+SEBNXQfGk3P8d2J7BRs/xYamRDM2RMlHN2azboPD3Hz05cQZHDAfJyUMdB7uhxG/Ok5WSAK3I+1f5VFTqdhMOwuWeDf+BF8NBkOfgldx8PQOWpb2TzmCvjqPjjynXx71IMw8W9yrlFTXHKPXLhQkimLnrGPucZWV+BFDQPXZtSVlF+VfJX9fuM331CTn49PXByG6dMcclaF0cS2Vac4tiMHAL2/jkumd2HA+M5Oy6HR6rQk9o4gsXcEV9zSk7PHSzi5J4+ug6Kdcl5LEULH09EHQddx8gWgxiRPG1c8Pqd3yF6g42vkC0BIPNzxDUT3UsXkmqIizv7lL1BTQ8jUKYTfflvdg2FR5Hcexamu3cisnEqNpc5zE1BdQEzuLmLz9hBUcQ7Nj1CyKAHz6FEEXTqKoFGX4hMZyRW39CQvo5SicxWs/+gQ1/x5iGPydSa8UPd7TNsEXce2f0+B4zi5AQ6tkhPrp79R58XsPFxuALnhBTmklThCtf/7F6UkS24WmnsAdHqY/mbd7LXm8PGTc8m+vAe21IavgtX9cHEIkuRVOTpKSfnNvW+2l5RLVitFH3wIQMSdd6LR69t1htVi4/efT7N7dQYWkzwkus/oeC6d2Y3A0Pbt3Rq0Oi2JfSJI7KP+fEQRuvK00FVrsdZAzu+y6MnYIsf6Z7wF4SmqmCNZrZyeN4+KrdvQd+lCyuefY9P7k3mgkJOpeWQeKGggbkKjA+heG5aKjPOn+uBBKrdvp2LrNir37QOLpcH+fr17E/vE45i7DOSzl3dTY7IyfFoKI2d0dcwLWP047PyvXPJ/76/eUbbsDViq4d1Rcohj5J9g6j8aPm6zwbLrIG0jxA6AP/wEvv6qmNokWdth5W2yRzYoGm5eDkkjW/58mw3eHw/Z++SKrKv/5TRTXUZ1KfwjUb7+12zQe+4E7/35+7lt9W3otXrW37SeCH9ZAJSuXcfZP/8ZbWgo3X/+GV1w28OOGQcK2Pz5CYx5VQDEdgnl8lk9ie3ifZ9vIkenhXQIoeNm5P/7bQreeQcpMAjflz8i/YyO9P0F1NR+8wAIjfKn+7BYug+LISoxuMnkSFtlJZWpqVRs207Ftm2YjhwBQBMYSMryZWSWhvPTosOggRkPDXJMTLqiEP49WO5IO/NdGCymCrsFv7ws57SExMMDO8G/kb/nshx4d4wsJEbcB1e/4no7m2LPUrlSzGaRRfQtKyAssfX7pP8KS2aA1kf+PZyfuOxplGTBmwNk79b/y/OM/KomeOLXJ1idvppru13L3y+TZ+hJkkTGrJupPnCAyD/9kZg//7lNe1stNtZ/dIhTe/MBCAjVM/r6bvQaEYfGEd5sN0Tk6AjckrJff+XEJ+vI7XkLBcmXYf6qwP5YSKS/3XMTnRTSosoPbWAgwZdfTvDllwO1IbEFC6jctp3Tf/wTXT/7lL6XJ3D4t3P8tOgwNz89gqCwdubrBEXC5QvkPJ0NL0LfmR79LdMrKDgJm1+Xr095uXGRAxASB9e9B8tvlL1yXcdB76tdZmajWGtg/TOw/R35dt9rZQHd1mTiLldAj0lwYp3c5LIdoyHcgvrNAj1Y5ORV5rEu48KS8sodO6k+cACNnx8Rt7eth5kkSfyy/Cin9uaj1WkYdGUiw69OQR8gPt4VhN9d4FQkSSI/q4zfluzj08UF7B38F84lXI7ZoiEgVM/A8Z254YlhzPn7KEZf352Y5NA2l7f6RETQ+a230HftSk1uLqf/9CfGTOtEZOdgqsosrP3gIDarAwbUjfwjGJKg7JzcRFCgHpIEqxeA1SxXJvWd2fz6HhPl5F6Ab+4H41mnm9gkVSXwyaw6kTPuKbhxcfsrpiY8D2jqRkN4Ml7SLPDz459TI9UwJGYIfSP72u8v/N9/AQi74Xp8ItvmcU79MYNj23PQaDVMu38go2/oLkTOeQihI3AKJbmV7PohnRUv7OCzl3axf1sRJn0YPjYTvUfGcM2fBzP35dFcfnNP4roYHNa7QxcaSuJ/30MXEYHp8BFy//okk+/pg6+/juyTRnZ8l97+Q3z9YcJz8vXNb8hdawXqcPBLOe/Gx1/OSWnJ/6OrnoP4wfLU71X3yg0GXU3BSfjgKji1AXwDYdbH8mw1R+R8xfaDwbUJzOuflcWgp+IFichmq5nPjn0GNPTmlP/2GxVbt4GvLxF3392mvY/vymHHt/J72hW39CSpn+NLxr0BIXQEDqOixMS+n7L4/OVdLH9uOzu/S6c4pxItNqLz9jAwbRlz/tqfq+7qT2KfCKd1zNQnJtJ54UI0ej3lP/+Meck7jL9d7nuzZ00mmQcL239I/xvkEmZLBWx8qf37CVpPVYlcTg5w+aMQ0cKEcx+9XHKuD4bMzfDba04zsVFOboD3r4TCkxDaGe5eK4esHMn4v8ri7/+3d97hUVXpH/9MySSZlEnvPaH30EF6FVDB7roqru7KWlBR+e2uu+qurri2ddW1rqK7q2sBVFRAEKSH3pJQAiEV0ntmJpl2fn9cMiGSQDpJOJ/nmWdm7px77pl3yv3ec96StaM+2rI70gNCy3/I/IHSmlKC9EFMi1JCyoXNRuGLio+Y3+23o4uIaHG/eekVbPr4OABDpkcycGJ4+w26hyGFjqRN1BitHN1+lq//fpCPfr+DHStOUZhVhUqtIqq/H2OHWLhq2xMMOvoBQ/9wJ/qYVjhYtgJ94jBClykCpPSjjwg49RODJil/BBuWp1JVWtO2A6hUMPOvyuMD/4bCY23rT9JyNj0H1QXg3wvGL27Zvv7xMPecwNm8DLKS2n98P8duVZymP7kRaisgcjT85icIHdz+x6orDQFKaQi77eLtuyp1S1fddEZHCOGsa3VLn/qQ8vIVK6k9eQqNwUDAb1teRLmiyMyat49gtzmIHRLAuOsT2nXcPQ0pdCStIiu1hDVvH2H50u389N/jnDlRBgJC4pTqtwtfGM/MOZ7o338Srb0G/0X34TV5cqeO0TB3LoEPKyfA/GefY0hoIYFRXtQabaz/Vwr2tvrrRI+FftcoJTk2PNUOI5Y0mzMHYO+/lMdzX1HyyLSUIbfC4FuVz2/lvWAqbd8xnk9JOnw4W4kMEw6leO5d33Zs3bSrHgV3Pyg+AYc+6bjjdCTdfEbnSPERUktS0al1zirl9upqil5/HYCABx9sUW0/UC4uv//nYWqqrQRGeTHjVwPaJ09YD0YKnS5OXnoF21ecpLzAdLmHAoDNamfL/07w3RuHyThcjMMu8A/3YMz8OO54biw3LB3O4CkRuGms5D78CMJkQj9mDIEPPXRZxuu/aBGG664Du538xx5l8nQPdO5a8k9Xsuvr05fu4FJM/7MSyntyPaT/1Pb+LkXOHsjd3/HH6cqcX7Rz0M1tS9w492VlyasyF1Y/1P7+LELA/o/gnavgzD6l/tYNH8B1/2ydOGsJbgaYeC5D8k/PKxmXuxvdfEbn/CrldXlzSt59D3tpKbqYGHxvvaVF/dltDta9l0JZvglPX1fm3j/4ksU4JVLodFmstXa2fZHGqpf3c/jHHD7/6x5St53hcqY9qigyseqlA6RsUSJVBk0K59Y/jeLWP41m+OwYvAPcAWW6Nu+pp7Gkp6MNCiL8lZdRaS7Pj1GlUhHy7F/QjxiBw2ik4ncPMnl+GACHNmSTcbiobQfwj4eR9yqP1/+x4xxbLSbl5P7BDKXA6Acz4fj3SpK4K429HyhJ8VwNMOuvbevL1Uvx11G7KOU99n3YLkMEwFisVB3/9mGwmpQK6vfvVOpvdRYj71EKmlbnK6UhuhvO8PJuVowVJaR8Q+YGoN4J2ZJ7htKPlZD/oKVLUbm4NLs/IQRb/neCMyfKcHHVMPeBwW1Pl3GFIIVOF+RMWhmfPbeHI5tyQYAhyB2bxcHmT06w9p1kzNWWTh/Tqf2FfPHXvRRlV+Hm4cK8B4cw8bY++Id7XtC27H//o/L770GjIfzvr7Y6bLK9UOt0hL/xOrroaGxn89D+80kGTQwBYOPHx6gsNrftAJP+TznpFqTA4f+1w4h/Rt5heHdi/UlYo1NKe3z2C/jnKMVHyFbb/sftilTlw6ZnlcfTn2qfpZ+wYedCslGcmwuOtr3PtPXw1lg4sUb5vGY+B3euBkPLnU6bwiEcFJuLL96orjQEKKUhqtso7DubbhxeXhdSnhiU6AwpL3r1VYTFgn7MGDynTG5RfwfXZ3NsR57iHnjvAAIivNp/0D0UKXS6EJYaG1v/d4KvXz1IZZEZT19X5j00hNufGcO4GxJQa1VkHC7ms7/sITu1HSKHmoHd6mDrZ2n88H4Klho7ofEGbvnjSKIHNi5ezEeOULBMSb8f9Pjj6IcP75RxXgqtr68Sdm4wUHPkCDG73icoxotak40f/pXaoBp6i9H7wcTHlcebnmu/JQKHA3a8Du9Pg5KTStbfO76GR5LhqiWKuCo5qSy5vDYYtr9WH47bU/nhD0pW6rqine3FmPshYQbYamDF3coMWmuwmOC7JfDpTWAshMB+8OtNMO6hdisXcqrsFK/tf415n13Hwnce4p/73r74DgOuV8LpLVWwtQtlg24O5ycM7EacH1J+W7/bADAdPEjlmjWgUhH8f0tblFLj1P5Ckr5KB+Cqm3sRMyig/Qfdg5FCp4uQc7yUz57dQ/K5ZaH+E8K47anRRA/wR6VWMWxGFDf9bgS+oR6YKi18+8Zhtn2ehs3acTlAKorMrHxpP8mbcwFInBXFdUuG4enbeI0gW1kZuY88AlYrXjNm4Lfwrg4bW2vQxcQQ8eYb4OKCcf06RrALV72WwsxKdq461bbOR98HPlFQlQc732z7YCvPwn+uU7LmOqxK5fTf7oT4KUqG3+lPw5JUZabAK0xZmvjxaXh1AKz/E1TmtX0MXY30TUreHGfRznZcDlWrlYzEnsFQdBx++H3L+zhz4NzMm1KgkTH3w282KyUd2kihqZCPUz/mpm9vYsE3C9i8eT9Tdt7L1Sd+TfYKO6uOf9X0zmo1zDw3C7bvQ8UxurvQTWd0fh5SLoSg4AXlAtBw/QLc+vVrdl8FGZX8+JEyyzhoSgSDp3RO5GpPQta6usy1rixmGztXnSJ121kAvPzcmHJH3yYrvtosdnauSneKD78wD2beM6DRJaS2kH6gkE3/Poalxo6rh5bpC/tf9CpCOBzkLFqEces2XKKjiF2xAo1X15xaLf/6a/J+p5zI7I/8jS2HFNvNvm8g8cPasBSSshJW/ApcPGDxAUWQtIaj38DqxcqfvIseZr8AiXc2nQzPZoGUFcrSRJGSVwO1Cwy5BcYt7rqVulvCpYp2thfpP8F/FgACbvoYBsy/9D4Ou5I4cvMycNiUmbf5byuitA0YrUY2Zm/ku/Tv2J2/G4dw4FFrYGLGLUSXDWjQ9lTAAW5cNJbxEeOb7vCTmxSn+f7zu09piJcSwFgEi3ZAyMDLPZpmIYTg1u9v5WjJURYPW8yvB/+aiu+/5+xjj6PS64lfuxaX4Ob9z1SWmFnxt/2YKy1ED/Jnzm8Hywirc8iins3kcgud7KMl/PSf41SXKf4VAyeFM3ZBPDq3S6fvzkopYeO/j2GutKDWqhg7P54hUyPbXMDNbnWwc9UpjvykCKmQOAMz7x2Al9/FKz0Xv/02Rf94HZWrKzGff4Zb375tGkdHU/T66xS/9TZotRTc9xapqVZ07lpu/sMIDIGtrF0lBPxruhJdk3gnXPtGy/avrYZ1v4OD/1Gehw5VInQCmpkjw+GAUxuUJazsnfXb+8yB8Y+0rBJ2V6M5RTvbix+fUYSLmwEWbVdm6pqiLBNW3Qc5u5Tn/a+Dea8py5mtwOqwknQ2ie9Of8dP2T9RYz+X70momGW6hbjjoxEWNWqNiuGzowmI9GLNu4dRCTXHw5J44IEb6OvfxG+vIFUpaoqAezdCxIhWjfGiVBcqgrssEwJ6KTmOAnorj1s6KyMEPBuozGg+mtqu/k0dyaHCQ9yx9g5nlXIf9KTPmYPtbB4Bix8i8P77m9VPrdnGqpf2U3rWiH+EJ9c/ntisc8OVghQ6zeRyCZ1as40dK05ybIeyvOAd4MbUO/oR3se3Rf2YKi389N/jZB5RHBIj+/ky7a7+rfbEryw288P7KRRmVQEwbEYUo+fHoblEBmPjzp1k33MvCEHo88/jc/2CVh2/MxFCcPaxx5U1c28fUq55mcIztQRGeXH9E4loXVq5LJK9Cz6cpSyvLNqupONvDmf2w8pfQ2k6oFJyoEz+vZLFtzXk7FFOOMe/B879xCPHwPiHoffsdvMX6RRK0uGtMUo9q5s+ggEd/P2yW5WcN2f2KUn9Fq4Bzc9OMEIojudrliq+LzovpQTFkFtbXHxSCEFKcQrfnf6OdZnrKK2pz+cT4x3DXN8FGHb1pTRTET3Bsd5MuaMv/mHKTOSx3WfYuPw4KlQcjd3Cnx78LSEeTcwmfv0AHPovRI+Hhd+3X6FMa41Ss2vbq4o9GsMjqF70BPSuf2yIbPz7aDHC80qEJL8/A67tO2vdUSzdupS1GWuZnzCfZ8c/S/F771P06qtoQ0KIX7sGtbv7Jftw2B18988j5BwtRW/QcdPvRjTpMnClIoVOM7kcQiczuZjNn5zAWF4LKhg8OYIx8+NbnQtBCEHqtrPs+PIkNqsDNw8XpvyyL3HDAlvUz+mDRWz89zEsZpuyVHVXf2IGX9rhzZqfT8aC67GXleFz042EPvtsq97H5cBRW0v2XQsxHzqEPaY/u/svpsZkZ+CkcCbd1oblns/vgGOrlSKTv1x5iUHYYcdrSp4Th00pCXD9uxBzVeuPfz7FJ2Hn63D4M0UogHKCGbcYBt/c8blc2ooQ8J/5Sj2r+GmKPTujinVZJrwzQXF8nvA4TPtT/WumUvjuEWWJERQBef274BvTokPkVObwXcZ3fH/6e7Iqs5zb/dz8mBM7hzlRc6nZ78G+tZk4bAIXVw1j5sczcFL4BcsXezacZO/KHABODNjM8/ctxVPXiDCoOANvJCpO17d9Bn2ubtGYL0AISP1K8Q8rz1a2hQ1TSqSUZUJxmvIdrLqIz5jW7dzMT6/zRFAvZQn4zeFKnqo/FXeL6uWFpkJmrZiFTdj4Yt4X9BKBpM+ajcNoJOzFv2G49tpL9qGEkaeRuvUMWp2aBY8lEhR9eVwrujJS6DSTzhQ6NUYr2788yYld+YASMj71zn6EJfi0S/9l+UY2fHiUomzlaqr/+FDG39TrklOddpuDpFXpHN6k/EmGxHkz896Bl1yqAhBWK1l33oX54EFc+/Uj5n+fonbrXlcdttJSMm++BWtuLlWjrmWvfhaghG/2GhHcuk5L0uGfo7FUODBGL8Z4shThcBDy1FMN1+bLc+Cr+5R6RKDMVMz7O7i3bGavWVTlw+53YO+HSvkBUJaBQs6VH1CpAFX9vXMbjWxrpJ3GFWInQt+57buslLwCVt6j9H9/kpK3qLOo87lCBXetVt5f+ib4+n7lxK3WKjWlxj/SbMdoq8PKVye/YnX6ag4XHXZud9e6MzVqKvPi5jEmdAzFmUZ++u9xSs8qEXzRA/2Z9Is+F/1d/rjyMCc2lCBwkDNqN88tXOosOdCw4TPK0lxAH8XB/eezVc0ld7/itJ2zW3nuFaY4yQ+6+cIZmppKJUKwuO52TgCVptcL8KbQB8DS7uFA/ebBN3n3yLskBiXy8dUfk/f0M5R//jluAwcS88XnqJoxk3rox2x2rDgFKrj6vkHEDW3ZReuVghQ6zaSzhE7G4SI2f3ICU6UFVDBkWiSjr43DRde+SfTsNgd7vj3NgfXZSv6dQHdm/GoAwbGNv7fKYjM//CuVwsxKAIbOiGJMM5aq6ihY9gKlH3+M2suL2JUr0EVdxJehC1Obnk7mrbfhqKoid8YS0qzxuLhpuPn3I/EJbr6/jr28HOOu3Rh37sS48TusJQ1DlLVhoUS9+y6uvXopJ9FvH1VEh87z3LLHbR1/1VpTCQc+VpLHVZ1t//61btBrppIUr9cspdJ7a6mpgDdHKvWspjwJk5a23zibyzcPKj5TXqFKuY897ynb/XvBDe8rsxfNpNhczONbHmd/gZLZWq1SMzZ0LHPj5jItahp6Fz2WGhu7vznNkc1KDi13Lxcm3NybhBFBlwxHFkLw9Ue7OLvbjF1lo2raMf5ww+IL96upgH8MBXMpXPM6DG9hdGR5Dmz8MyR/qTx30Stib9yDoPNoWV92G5RnnSd+0pRip8VpYDqXQiN2olIuo4tjsVuYsWIGpTWlvDzpZSZZYsmYvwAcDqL/+x/0Iy7tE3X6UBFr300GAeNvTGDo9O75n9oZSKHTTDpa6NRUW9n6eRon9xYA4BOsZ9pd/QiJ69gsn2dOlPHjR0epLqtFpVYxal4MibNjGkx3nz5UxKZ/H6PWZMNVr2Xawv7ENmOpqo7KNWs4s+QxACL++SZe06a1+/voTIxJSWT/+jc47A5Sr36BIpMH/hGezHtgCJ6+jS/vOCwWzAcPKcJm505qUlIalhBQC/T+FvSTplO5PxdLRgZqT08ibovHo2qt0iZ8hHLCbG7l7fbCZlEcl531ncS5sf/8noaPm3rNVKIs5ZScrD+Gzgv6zYOBN0Lc5JbPHKx5QhEW/gnKzMPlWGazGOG9ycqJt46R98KMZ0HXfBGcXJTMI5sfodBUiIeLB4sGL2Ju3FwC9fVX61kpJWz+9DjVpUpwQt8xIYy/sRduns3PnutwCD59czMVRwVWdS3uC4q4b8adFzZMekuZjfEMUSIEmyNQaquVZdadbyhLX6hg6C9g6h/BO6zZY2w2xhJl+Suwt5LBuouzOn01T25/kmB9MGtvWEveb+7HuH07XjNnEvH6Py65f2FWJV+9cgCbxcGAieFMuq13i3LtXGlIodNMOlLopB8sZMunJzBXWVGplNmSUfNi0bbzLE5T1BitbPnfCU7tKwQgNN7A9LsVR+Wkr9I5vFFZqgqO9WbmvQPw9r+4g5wQgtrjx6nasIHK9euxnFKmkv3vvYegxx9v9Thzq3JZl7mOEnMJDuHALuw4hKPB4/O3OYQDu8Pe6Ot2YUeFir5+fRkTOoYRISPwcGn+FWb5ihXk/fFP1OoM7Jv8V2otKlQqiOzvR9+xocQM9seeedopbEx79yHMDbMq6xLi8Rg3Trk5DqDe+mfwDMF2x0Zy778fc+pJVGpB6OgKDHc+pNQi0jT/RNalEQLyjyjLTSmrlPpRdej9laW5gTcqDr6XmsI/cwDenwoIuPMbRShdLvKT4cOrldmp696C3jNbtPuqk6t4btdzWB1WYrxj+MfUfxBnqBe25ioL27446bwg8g5wY/Iv+hLZv3WRW3abgw9e/AFrtis1WiNxd2pYMGpOw0a2WmW2rDxLESp1NbEaw2FXioLWVYsHxZl51vMQNrRVY+xpnB9S/nDiw9xa3oecX/8GXFyI//67S852V5XWsOJv+zBVWIjs78fcBwY3e2b9SkUKnWbSUUJn9+rT7FuTCYBvqAfT7uzX5PJRYwghsOXlUZt+GsvpdGzFxbj27oM+cRjasLBmq3whBGm789nyWRrWGjs6Nw3ege4U51QDMGR6JGPnx6PRNv6DEg4H5sOHqdrwI1UbNmDNyal/UavFMG8eoc89i0rbsiv1Wnstm7I3serkKnbl7WrRvi1Bo9IwMGAgo0NHMyZ0DEMCh6DTXDyKqfCVVyh5/19U+caRPXMpBQX1CRm1NjPBBXsIzd+FV1U2KkATEIDH2LHnxM1YXILP8+ux1SolGsoyIXIMjqy9nE3ypipHEZWBS5bg/+t7e+ZVm8Oh+G6krIDUr8F0XqkC7wgYeL2yvBUy+MLlOoddETl5h2DQTXDDvzpz5A2oKq0hbU8+6fvysNtVeAfqMQS6Ywh0xztAuffyd2v0N2S1W3lhzwt8kaZkyJ0SOYXnr3re6SRc9/vc/uUpaozKBdGQaZGMuiauzYUaLTU23v3rWtRFHlTryhl/fzhX9f1ZeoE6/yedFyw+CJ6N+IJkbFWyUecnK899Y5Xkg33ndQvn4M6iQUj5gnWU3Xo3llPp+N19N8H/d/ElV0uNjVUvH6Aktxq/MA+uf2I4ru4yjPxSSKHTTDpK6BRlV7Hypf0MnRbJyLmxaFyaEBJWK5acHGrT07Gkn6b29Ln7jAyEqfEU9NqQEPSJw3Afloh+eCKuvXtfUmhUFpvZ8OFR8k8rTqiuei1T7+zXqJObsNkw7dtH1foNVP34I7bCQudrKldXPCZchfeMGXhOnozG0LIluBOlJ/jq1Fd8m/4tlRbFL0iFijGhY+jv3x+1So1apUaj0ij3ak2D5w1eO2+b87lajcVu4WDhQXbn7SanKqfB8d00biQGJzI6dDSjQ0fT17cvmp85kQqHgzOPPErV+vXg4oJJ60NeyGjyg8dQ61bvJGxwt9BnRAD95w7Ew+cifiipX8GXC+v7H3AThWlRlP5HqYnlc+sthPzxjy0Wi90Kuw0yNkPySjj2bcPwY/9eiuAZeGN9vqDd78HaJ5QSFw/uBa9WOoW3khqjlfQDhaTtKeDsyfJLtlepwMPXVRFAAe54B7qj8rbyXuY/2WveiVVbw/1D7+c3g3+DWqX8F1QWm9n86QlyjipLh/4Rnky9o+9Fo2uc/xenTlF76hSWU+nYykoxzJuH4brrLvgOmapqeffZdegqvahwL2LBY4n0i+hV38DhgPenKIJy1G8UP7E6ik8pWblPrFGeuxoUH6lRv2l9yoMezNItS1mbqYSUL8nuT/6f/4LGx4f49T+guci5xWF3sOadZLKSS3D3cuHG34245Oy6REEKnWbSkUtXpkoLem/lD8FhMlF7OgPL6fRzszSnqT19GktWFthsjXeg1aKLjsY1Lg5NgD81qUepOXr0gvZqvR73oUOcwsdt8BA0nhcu1zjsDg79mENxbjVjrotzVhoHxdfEuHMnVRs2UL1xE/by8vr+PTzwnDwZrxkz8Jw4AbW+Zcn0qixVrM1Yy6qTq0gtSXVuD/EIYX7CfOYnzCfcM7xFfTaXM9Vn2J23m115u9iTt4eSmob1wbx13owKGeUUPjHeMahUKhxmM1l3LaTmyBFQqXDr3x/92HFUxo0mo8SL00dKsVuV2lgqtYroAXVLWwEXXtkLoRTfzNmtZDgefDMApf/+DwXLloEQeE6eTPirr1zUtsIhqCqtobzAhKefG36hLXT67CpYa5TsvCkrIO2Hc74e5wgdoiTc2/6aEtY952UY9etOGZbNaifzSAlpe/LJSi3BYav/Wwzv7UPvUSF4+LpSWWSmotis3BeZqSw2Y7NcvE6axg38g73wrhNBKji8MQebxYFGq2bkvBiGzohyLlUIqxVLdja1J09Rm14vamozM8FqbfQYuvh4gh59BM9p0xrMEJYUV/LxXzfjavak3DuPX/1+OqG+5wnHjK3w8TVKBNkDe5SIvy0vwt73lXQHKo1SAX3S78Dj8hbn7aoUGAuYvXK2ElI+eTmaWxdjLysj+I9/xO+Xtze5X43RyrbP00jbU4DGRc38JcMIie1+VdovF1LoNJOOEjqmAwep+mEdtedmaWxnm84hodLrcY2LwzU+Dl1sHLr4OFzj49FFRqJyaei74TCbMR9JxnxgP6YDBzEfOoSj6mfJudRqXPv2QZ84XJn5SUzEJeTC5GEOo5HqbdsVcbN5Mw5jfSFKjY8PntOm4j1zJvqxY1HrWnYFJ4TgQOEBVp1cxfrM9c7srlq1limRU7i+1/WMDR17wWxKRyKE4FT5KXbn7WZ33m72FuzFaG1YfDNIH8SY0DGMDh3NKO9BeB7PxW3gQLS+DcO9a802Tu0r4HhSHvmnK53b3Txc6DUqmH5jQwmI9Kw/4TgcymX/z6b6Kzds4OzjTyBqa3EbOJDId94Ggy8VhWbK8k2U5Rud9+X5JmzW+hNq7LAAxlwb330FDygRYCfWKEso6ZtAnFe3LSwR7v2xfetZ/QzhEJw5WU7a7nzSDxZhMddfRPiHe9J7VDC9RgZfNKRbCIGp0kJlcQ0VRSaSju/jcPoxvGr88LUE41rb9OcTlmBg/ER33ErrRE06tadOYsls+gLI+X+RkIBrQjzCZqd0+XLsFcpsrfuQIQQ+tgSPUaOc+2Rm5fHVy/vRWfWU+59h8ZML8NKfl2OnrjREyGAlF05dfales5RlqstQQkQIQVF2Faf2FSJQIkgNQcpSoaevW5cqg/DGwTd478h7JAYl8lLqIEr+9QG6uDjivvn6gv9wUN7bid357Fx5CnOVFVQw696BJAxvQ/mZKxApdJpJRwmd0k8/peAvDRPnafz8cI2LQxcfr4iaOOVeGxLSah8NYbdTe+oU5gMHFOGzfz/WsxeGDLuEheGemIh74jDU7nqqNv6Icdt2RG2ts402KAiv6dPxmjkT/YjhrVpKKTYXszp9NV+d/IrMykzn9nhDPAt6LeCa+Gvwc2udg2V7Y3PYSClOUYRP/m4OFR7C6mh4tRxriGVK5BSmR01nYMDARj+nsnwjx5PyObErD2NFfT4Q/3BP+o0LpfeoYNy9LhSKNUYrZfkmCvYcI/fLH6hW+2DyjqDG1ZemfpF2lY0q1xIMNYGoUIMK+owOYeTcWAyB3Xy621gCR79Wwu4rcuDW/3VIbSMhBCVnqjmxu4CTewuUxJ3n8PR1pfeoYHqPCmlx7bhaey3P736eVSdXATAjegbPjX8OF4crlcXK7E9ZRiElqVlU55XhX3SEwKNrUDUhaNR6PbqEBFzj452iRhefgEtY6AW5WOxVVZR88AGlH//b6RzvMXECQUuWOEuxHElNY9Nb6bjYXakMP8Pjv78Nl7rf+PmlIQCC+sOsv0L81BbZoD0wVVo4sTuf40l5zhxCP0etVWE45x9lCNQ7BZAhSI+XnyvqTnTiPT+k/LU+fyD818sQVisR77yN1+TJF7QvOVPN1s/SnEuiviF6Jt7Wh4gWZsWXSKHTbDpK6NQcP07FV1/Xz87ExV0wK9BRWAsKFOGz/wDmAweoOX5cmVFoBJfISLxmzMB75gzcBg9uVjKrn2Nz2NhxZgcrT65ka+5W7Oeuyt217lwdezXX97qewQGDu7zDrdlmdvr27M7bzdGSowjqfxrB+mCmRU1jevR0hgUNQ6tuKAQddgc5x8s4vjOP04eLnEsfarWK6EH+hMb7UFFkcs7QmKsaX4IAcHEBAmycdckgnaOUuRdQ7l6I1dPEVRHjOXbyNP1PTyK2bLDzGP3GhzJiTsxlSRNvrKil4HQlAVGeXda/oM6pOG1PQYMTqKteS3xiEL1HBROW4NOqWnH5xnyWbF5CcnEyapWaxcMW86uBvwKbDdOBgxi3baV66zZq09Iu2Fft4YEuIR7X+ASnoHFNSEAbGtri34y1sJDit9+m/MsVyoyQSoX3vHkELn4IXWQkm5P2cuTfZWiEltreBTzyyC2o637zm19QIuXG/BaG3dH6JIKtwG5zkJVcwrGkPLJSShAO5bejcVETNyQAd28dFUVmKgqVpUKHvelTllqtwivAraEACnTHJ0jfpNN4Wzg/pHz5joFUr/sBj3Fjifzggwafn6XGxr7vMzm8MQeHQ6B1UTNibgxDp0e1+5iuFKTQaSaXu6hnZ+AwGjEfOeIUPvaqKjwnXIXXzJm49unTagGSUZHB6vTVfHPqG4rMRc7tQwKHcH2v65kVM6tFod1djYraCnae3cnG7I1szd2K2VYfRu7r6suUqClMi5rGmNAxF0Ry1RitnNyrLG3V1Q1rDE9fV3xD9PiEeODpaaPq49cwHD8M9kr+eY2aXf3UqFAxOnQ018Rfw/So6ehd9JytPsvvtv2O3NMljMyeQ1RFPwA0WjUDJ4WTOCva6R/WUVhqbGQcLubE7nxyj5U6Z6GCYrxJSAwiPjGwgR/Y5cBcbeH0waILnIo1WjUxg/zpPSqE6IH+TQYLNId9+ft4bMtjlNaU4q3z5uV+v6dvmgnjtm0YdyY1WBJGpcJ98GA8JkzAfcgQXBPi2zSj2xSWrCyK/vEPKtecy9Xk4oLvzTcT8NtFrN5/kLNfqVChxmVEBb+59/LVpSvKqeJ4Uh5pewqoqa4X/sGx3vQdG0qvEUG46n+2fO8QVJfWKMKnyExFoYmKIjPlhYrflN3WtL+USq0iaoAfAyaEEz3Qv83LX+eHlP/R40YG//EzUKmI/for3Pr0cbY5fbCI7V+edBZvjh0SwFU39+qyFwXdBSl0msmVIHTaC6vdyv7C/WzJ2cLW3K1kV2U7X/N19eWa+Gu4vtf1xPt0Yor+TqLGVsOuvF38mPUjm3M3U1FXQgHwcPFgYvhEpkVPY0L4BPQuDR2KS85Uc2JXPpUlZnyC9PiGeijiJliPi6uGlOIUvj39Lesy1mGsLuXhbxyMPClwqCD7zskMfegpQj1DLxiTzWHj/SPv886RdwiuiOWqMwvwL48EQOuqYciUCIbOiMLNo/1y9DjsDnKPl3FiTz6nDxVjq633qTEEuVNRZOa8STCCor2ITwwiPjGoU5bWbBY7eekV5B4vJedYGUU5VfXjUdU7FccPC7zgBNpShBB8evxT/r7rJRJyrEw948ukXC8c6ZkN2mn8/PCccBUeEybiMX5cp83sAphTUin6+98x7lBKjKj0evwXLmS1byA1OxR/kMBpDm6+aXrnjanKQtqeAo4l5VGSW+3crjfo6DsmhD5jQlvtdyYcgury2gYCqG4mqKLI1MBp3NPXlf5XhdFvXFiTCUEvRV1IuavKhc9Wx2JNOdqg3l95oYltn6eRnapE1nn5uzHxlt7NqiEouTRS6DQTKXQuTrG5mO1ntrM1dys7z+5s4LyrVWsZEzqGBQkLmBI5BZeekvTuEtgcNvYX7OfHrB/ZlL2JQnN9+L1OrWNc+DimR01ncuRkDK6NR1CcrT7Ld6e/49v0bxv4Mvm7+TMnZjbXfFMIq5Srcd877iD4d/+HStO4U+7BwoP839b/I686j+iKAcwpuRNRqCxfueq1DJ0exeCpEZesedYUQgiKc6o5sTufk3sLlDIm5zAEuhETaiU4dyea1N2IuP6URI0htyaQ/GxzA1+jwCgv4hMDiU8MwieoZZF7TY7NISjKqSL3eBk5x0rJS69wRsPVERDpSa+RwfQeGdxuy3rVOZl8+e8/oN59iEGZAvfzSzXVzdpMnIDnxEm4DejfqiXh9sS4axeFr7xKTbKSC0fj68v2q+ZjrlJKEvS63oOZM0dfrIs2Ybc7yE4p4djOPLKSS3CcW5pSa1XEDg6k37hQIvv5dqhvjcVmITM7j4L9NaTtKqTGqMwgqdQqYgb5M2BiOFH9/Fq0dPnElidYl7mOR0qGM+693aj0ehJ+WAc+fhxYl8WBH7Kx2xyotSoSZ0YzfHZ0pyWMvRKQQqeZdJTQSSlO4fvT3xPgHkCAewCB7oH4u/sT4B6Ar5uvM5dGV0MIwbHSY2zJ3cK23G0kFyc3eN3fzZ+JEROZGDGRsWFju/XSVHvgEA6Si5PZmLWRH7N/bJC3R6PSMDJkJNOjpjM1aipuWjc2ZG3g2/Rv2Vewz9nOTePGlKgpXBt/LWNCx6BVaxFCUPrhcgpfUvKaeM2YTthLLzVZMLXSUslfkv7CD5k/gICZ4gaGZs2gMl+ZKnfzdGH47GgGTgxv9h9tnU/Lid0FlOXVC1w3Dy3RoTZC8naj3fY1orKy0f0d0b0pHzSbfLcECko0DURPQKQn8cOCSBge1KJaYqDkn8k5pszYnDlR5jxh1eHh40pkX1/8e7lR5JtJtUsFQfogAvWBBOuD8dZ5N7pUJIRAWCw4TCZETQ0Ocw2ixoyjpgaH2awsAR86TPnmTThOZzbY93LO2jQXIQRVP6yn6LXXsGRmIoDUATdTGDgJB3bG3B3ByNH92vWYJWeqOZaUR9ru/AY+aUHRXsrS1MjgdptxFEJQUlNCblUuZ6rP1N9X55JblUuBqQCHcKBVa0nw7MXgqqsIyEzAcbb+N+Ud4Oac5bnU0m9dSLnKYuWT//hBQRGBjzyMccJNbP0sjcoiZak7sp8vE2/t0+LvueTSSKHTTDpK6Hxx4gue3fVso69pVBr83fydwidQH4i/W/3jAPcAAtwC8Hf3v2AZpCMwWU3sytvF1tytbMvd1mCGAqC/f38mRUxiYsREZ1I/yYUIIUgrS2Nj9kY2Zm8krayh46mL2sUZ1aVCxciQkU6/m7pMuT+ncs0azv7f7xBWK+5DhhDxzttNnkSFEHx96muW7VmG2WbGV+fLoz7PYNx5bkkJRQSMmBNDv3GhjTpA1pptSqK83fmcSSt3btdoVUQE2Qgp3o/HjlVgrPc7Uvn5UjAihkNRdqKL1USnleN2Igfs9ctaFhdPygfMpDBkBEW1hgaixz/ck/jEQBKGB+EbcqFwrqm2knuijJzjpeQeK6WyuKbB6y5uGsJ7++Ib70KxXxZpJdvJTduHNTuHkDKBwQiuVnC1gc4K7lYVHnYt7nY1rjYVOotAa7WjrrWhauZfoUMFGeFaImZcS585t3WJWZvmImw2yletovjNf2ItLOJo3zspCBmFQ2Vl5oP96DMgCrvdgd2q3Gw/u7fb7Ngs557bHNgsyr3Sxq5stzjIPVFGUXb998Tdy4U+o0PoOza0yYg2h8mErbAQa2EhtsIibAUF2IqKcNQqn7nNbsVoqcZoMWK01t0bMVmNmKwmHA4bCFABqvM+StW5bUKlIiMYjsSoyAkEVCp8TSH0LxhHn+LR6GznRI9aEDLAgxFTEojq69/oLE9dSPkDh0OYtCYXW2Qfcq9/htOHlWUqD4OOq27uTXxiYJcPxOiuSKHTTDpK6BwqPMSmnE2UmEsoNhdTZC6ixFxCaU3ppXc+Dw8XDwLcA/B388dL54WHiweeLp546jzxdPHEw8Wj0e11902VO8itymVr7la25m5lT/6eBmHV7lp3xoWNY2LERCaET2hQdFDSfLIrs9mYrcz0HCk6AkCcIY5r4q9hbuzcRv1uGsO0dy85DzyIo7ISl+goot57D110dJPtMysyWbp1KcdKjwFwS69bucb2Sw6tzXU6Q3oHuDFyXiy9R4UgHILso6Wc2JVP5pHiBs6cwX42QssOYdi1Eo25fuZGExJM0ah41sdU8rX7cRw/O8e71QoS89y5Kt+b3um1eGfXJ2q0uHhQHDSMkpirKHaJQFB/EvAL8yA+MYigKC/y0ivIOVba0M8GJaomMMINg3clFvNhzGd3Y8vJwrvISEgZDZeRWolVAxYXFTadBuHqgnDV4dBpSfEo42Ac1A7vy7I5bzb7M+yKOMxmSv/7Xwrf+xdHYn5Bif8gEOc++3a6mFFpwL+vK4ED1ei9ShGlRTgKixFFJVBShrq4HE1pBdqSSnSl1WjN7fDhNROLQU9WHx/2R1rZHFJKpYeO+JJh9CsYR0h1rLOdyaMce98Swod70C+sN338+qDT6Ji5Yib24hLe/kDHGf/xZPWej82uQqVWMXhqBKPmxbZ6uVjSPKTQaSad7aNjdVgpqylzCp9iczFFpiKKzcWU1JQ4Hxebi51J9tqCi9rlAiFUYi7hdMXpBu0iPCOYFKnM2owIHnHJelCSllFoKqTaWk2sd2yrru5q09PJ+fVvsJ49i8bXl5Cn/oRLZBRaP180fn4XLGlZ7Vb+ceAffHz0YwASfBJYNu4FLCl69q3NwnzOz8YQ6E6tydZg+cegtxFalYLfga9xM9ZH02mjIikd05sfY6tZqTmMVdTnfxkaOJTJkZMpNBWSUpLCidIT1Nrr89N4GwUDsgQjcnUMzgLDuVkZq1ZPUcAQikKGU2rog2jiBOvtYsJgycKj4CB+mQdwqzE32g5AqMAe5Id7dCwesQlog4NQu+tRu7th12mpUluoUNVQholSUU2xqKLAUU6BvYw8exlnrEWYRG2T/V8bfy1/GvMn3LSdH8bfEdgrKkh7/VX2p4ZQ4XNhYkCVw4rGYUXtsKF2WJWb/dy9w4ZKWAErDqzYVTbsait2tRWNtYSgon0ElhvxbvrjuoAaFyj1glJPFWWeUO4JNef+jgQqdFpXvF29z90MeLsaMOgMeLt5460zoNZolN+YSgWcl6RTpULU1mDafwDTvguL8TqiwigdGM6JeDcOemhRZ4UTVzgMnV35nO0qG6f9D5EavANLYDmltaU8tLEPOjEfo4dSuT003sDE2/oQENGyHEyS1iGFTjPpqs7IQghMNlMDEWS0GqmyVGG0Gqm2VlNtqabaWq08P/e4brvJ1nidrDo0Kg2JwYlMDJ/IxMiJrT4BSzoPW1EROfctUsqA/AyVXo/WxweNnx8aP1+0vr5ofP3I1VaxqnA9Z12MmD1duGnkPcwZfDvHDlZyYH02tUZFrLi52AkzHcP/yPd4nitWCqBLSKBibD82xplY6diL8bzvVYJPAnPj5jI7ZjYRXhENxmN1WEkvTye1OJWUkhRSi1M5WXYS2zlxFFguGJglGJQpGJylwtvowKp1pzhgCIWBwzC5B2KozMS37Dh+ZSdwtVQ06N+ugiIfFcYgL7RREfgnDCCq/xh84vviEhHR4kze5yOEoNJSSZGpiEJTIYXmQgpNhRSbixkSOIQ5sXN65G/l9Kn97P1+JRir0ZiNaMxG1CYjWrMFrakWrcmCi9mKzmxDZ7bhWmNDffHKFw2waVRUeWupNugw+bhh9nGnxldPra8HVl9P7P7e2P0NqD09cdW4otPo0Kl16DQ6/N39ifCMINwrHG9d2/+nHRYL5kOHMCYlYdqZhDk5uWGuMbUat0GDqBk2kOOesZzJM6AqqXcjKHXPx6LOJ8Q4FABXNxXjb+5L3zEhrcrDJGkdUug0k64qdNqK3WHHZDM1EEN1IslF7cLI0JHt8och6VwcRiMFL76E+fBh7KWl2MrKmqx9dFG0Ghx+IRQHDkFzNgPf8hOozy1buA0YgHH8IDbH17CiJqlBfbBQj1Cujr2aObFz6O3bu0Un/Fp7LSdKT5BakkpKcQpHS46SXp6OEA4ii2BQpmBQlqBftkBvUZaPCnygwFdFvi8U+7vgEZNARN8RDBgwiUGhw3DXyjwklwshBMJsxl5VjaO6CkdVlfOxvaoKlVqNNjgYbVAQ2qAgND4+XVYg2isrMe3Zg3FnEsakJCwZGQ1eV7m7YxkxgzMh48kq88ReN5kpHMRos5n24h3tmsZB0jyk0GkmPVXoSK4MhBA4jEbspaVO4WMvLcNedt7j0lJsZaVUFOTgKCu/0IdFpcJ92DAsExLZEl/Dqqrt5FbnOl/2cfVhVsws5sTOYWjQ0HZ1RjdZTRwrPUZKcQqpJamkFqdypjwLbxPYfbwYGprI8ODhJAYlMsB/wBWTwkByebHm5WFM2oUxSRE+9uJi52s2jRuFMZOoUhsILd7PsC/eRhcZeRlHe+UihU4zkUJHciVxovQEf9j4OEUFGRhMKm4InolLXCxfl2/heOlxZzt3rTtTo6YyJ3YOY8PG4qLuPIFRUVtBSU0J0V7RnVr0VSJpDCEEtWknMSbtVJa69u5DmJQlXL97fkXwE09c5hFeuUih00yk0JFcaZhtZl7a+xJfpn3ZYLtWpWV8+HjmxM5hcuTkTkltIJF0N4TFgvnIESy5uRjmzEHVBn8wSduQQqeZSKEjuVLZmLWRF/e+SIhHCHPj5jIzeiY+bj6Xe1gSiUTSLFpy/paB/hLJFci06GlMi552uYchkUgkHU73SOcpkUgkEolE0gqk0JFIJBKJRNJjkUJHIpFIJBJJj0UKHYlEIpFIJD0WKXQkEolEIpH0WKTQkUgkEolE0mORQkcikUgkEkmPpdsLnbfeeovY2Fjc3NwYPnw427Ztu9xDkkgkEolE0kXo1kLn888/55FHHuHJJ5/k4MGDTJgwgauvvprs7OzLPTSJRCKRSCRdgG5dAmL06NEkJiby9ttvO7f169eP+fPns2zZsgva19bWUltb63xeWVlJZGSkLAEhkUgkEkk3oiUlILrtjI7FYmH//v3MnDmzwfaZM2eyc+fORvdZtmwZBoPBeYuMjOyMoUokEolEIrlMdFuhU1xcjN1uJzg4uMH24OBg8vPzG93n97//PRUVFc5bTk5OZwxVIpFIJBLJZaLbF/VUqVQNngshLthWh6urK66urp0xLIlEIpFIJF2AbjujExAQgEajuWD2prCw8IJZHolEIpFIJFcm3XZGR6fTMXz4cDZs2MCCBQuc2zds2MB1113XrD7q/LArKys7ZIwSiUQikUjan7rzdnPiqbqt0AFYsmQJd9xxByNGjGDs2LG89957ZGdns2jRombtX1VVBSCdkiUSiUQi6YZUVVVhMBgu2qZbC51bbrmFkpIS/vKXv5CXl8fAgQNZs2YN0dHRzdo/LCyMnJwcvLy8mvTraS11oes5OTkydL0TkXa/PEi7dz7S5pcHaffLw8/tLoSgqqqKsLCwS+7brfPodGVaEuMvaT+k3S8P0u6dj7T55UHa/fLQFrt3W2dkiUQikUgkkkshhY5EIpFIJJIeixQ6HYSrqytPP/20zNvTyUi7Xx6k3TsfafPLg7T75aEtdpc+OhKJRCKRSHosckZHIpFIJBJJj0UKHYlEIpFIJD0WKXQkEolEIpH0WKTQkUgkEolE0mO5ooTOsmXLGDlyJF5eXgQFBTF//nxOnDjRoI0QgmeeeYawsDDc3d2ZPHkyqampztdLS0t56KGH6NOnD3q9nqioKBYvXkxFRUWDfsrKyrjjjjswGAwYDAbuuOMOysvLLzq+mpoaFi5cyKBBg9BqtcyfP7/Rdlu2bGH48OG4ubkRFxfHO++80yp7dBadafe//vWvjBs3Dr1ej4+PT7PHmJyczKRJk3B3dyc8PJy//OUvDWqobN++nfHjx+Pv74+7uzt9+/bl73//e+sM0gn0BJsDfPLJJwwZMgS9Xk9oaCh33303JSUlLTdIJ9FZds/MzOSee+4hNjYWd3d34uPjefrpp7FYLJcc46XsnpeXxy9+8Qv69OmDWq3mkUceabthOpieYHeA2tpannzySaKjo3F1dSU+Pp4PP/ywjdaRIK4gZs2aJZYvXy5SUlLEoUOHxNy5c0VUVJSorq52tnnhhReEl5eXWLlypUhOTha33HKLCA0NFZWVlUIIIZKTk8X1118vVq9eLU6dOiU2btwoevXqJW644YYGx5o9e7YYOHCg2Llzp9i5c6cYOHCgmDdv3kXHV11dLRYtWiTee+89MWvWLHHddddd0Ob06dNCr9eLhx9+WBw9elS8//77wsXFRaxYsaLtBuogOtPuTz31lHj11VfFkiVLhMFgaNb4KioqRHBwsLj11ltFcnKyWLlypfDy8hIvv/yys82BAwfEp59+KlJSUkRGRob4z3/+I/R6vXj33XfbbqAOoCfYfNu2bUKtVot//OMf4vTp02Lbtm1iwIABYv78+W03UAfRWXZfu3atWLhwofjhhx9Eenq6+Oabb0RQUJB47LHHLjq+5tg9IyNDLF68WHz88cdi6NCh4uGHH25fI3UAPcHuQghx7bXXitGjR4sNGzaIjIwMsXv3brFjx452tNSVyRUldH5OYWGhAMSWLVuEEEI4HA4REhIiXnjhBWebmpoaYTAYxDvvvNNkP1988YXQ6XTCarUKIYQ4evSoAMSuXbucbZKSkgQgjh8/3qyx3XXXXY0KnaVLl4q+ffs22HbfffeJMWPGNKvfrkBH2f18li9f3uyT7ltvvSUMBoOoqalxblu2bJkICwsTDoejyf0WLFggfvnLXzbrGJeb7mjzl156ScTFxTXY7/XXXxcRERHNOkZXoDPsXseLL74oYmNjLzqeln7XJ02a1C2Ezs/pjnZfu3atMBgMoqSkpFnvUdJ8rqilq59TNyXp5+cHQEZGBvn5+cycOdPZxtXVlUmTJrFz586L9uPt7Y1Wq9RITUpKwmAwMHr0aGebMWPGYDAYLtpPc0hKSmowPoBZs2axb98+rFZrm/ruLDrK7q0lKSmJSZMmNUhENWvWLM6ePUtmZmaj+xw8eJCdO3cyadKkNh27s+iONh83bhy5ubmsWbMGIQQFBQWsWLGCuXPntunYnUln2r2iosJ5nKZozXe9O9Id7b569WpGjBjBiy++SHh4OL179+bxxx/HbDZf8v1KLs4VK3SEECxZsoSrrrqKgQMHApCfnw9AcHBwg7bBwcHO135OSUkJzz77LPfdd59zW35+PkFBQRe0DQoKarKf5pKfn9/o+Gw2G8XFxW3quzPoSLu3lqZsev7Y6oiIiMDV1ZURI0bwwAMPcO+997b5+B1Nd7X5uHHj+OSTT7jlllvQ6XSEhITg4+PDG2+80ebjdwadaff09HTeeOMNFi1adNExteS73l3prnY/ffo027dvJyUlha+++orXXnuNFStW8MADD1y0b8mluWKFzoMPPsiRI0f43//+d8FrKpWqwXMhxAXbQKmmOnfuXPr378/TTz990T5+3s+AAQPw9PTE09OTq6++ukVjb2x8TR2zq9HRdr8UTdm9uTbdtm0b+/bt45133uG1115r9H10NbqrzY8ePcrixYt56qmn2L9/P+vWrSMjI+OSJ5WuQmfZ/ezZs8yePZubbrqpgfBu63e9u9Jd7e5wOFCpVHzyySeMGjWKOXPm8Oqrr/LRRx/JWZ020rb5527KQw89xOrVq9m6dSsRERHO7SEhIYCisENDQ53bCwsLL1DjVVVVzJ49G09PT7766itcXFwa9FNQUHDBcYuKipz9rFmzxrnU5O7u3uyxh4SEXHAFUlhYiFarxd/fv9n9XA462u7NoTG7N2VTuPAKMDY2FoBBgwZRUFDAM888w2233daiMXQm3dnmy5YtY/z48TzxxBMADB48GA8PDyZMmMBzzz3XYNxdjc6y+9mzZ5kyZQpjx47lvffea/BaW7/r3ZHubPfQ0FDCw8MxGAzONv369UMIQW5uLr169WqZMSROrqgZHSEEDz74IKtWrWLTpk3Ok1YdsbGxhISEsGHDBuc2i8XCli1bGDdunHNbZWUlM2fORKfTsXr1atzc3Br0M3bsWCoqKtizZ49z2+7du6moqHD2Ex0dTUJCAgkJCYSHhzf7PYwdO7bB+ADWr1/PiBEjWnwC6iw6y+7NoTG7jx07lq1btzYIEV2/fj1hYWHExMRc9H3V1ta2eAydQU+wuclkQq1u+Bel0Wic768r0pl2P3PmDJMnTyYxMZHly5dfYKv2/K53dXqC3cePH8/Zs2eprq52tklLS0OtVjcQbZJW0Gluz12A3/72t8JgMIjNmzeLvLw8581kMjnbvPDCC8JgMIhVq1aJ5ORkcdtttzUIQaysrBSjR48WgwYNEqdOnWrQj81mc/Yze/ZsMXjwYJGUlCSSkpLEoEGDLhleLoQQqamp4uDBg+Kaa64RkydPFgcPHhQHDx50vl4XXv7oo4+Ko0ePig8++KDLh5d3pt2zsrLEwYMHxZ///Gfh6enptF9VVVWT4ysvLxfBwcHitttuE8nJyWLVqlXC29u7Qejnm2++KVavXi3S0tJEWlqa+PDDD4W3t7d48sknO8Bibacn2Hz58uVCq9WKt956S6Snp4vt27eLESNGiFGjRnWAxdqHzrL7mTNnREJCgpg6darIzc1t0OZiNMfuQgjnZzh8+HDxi1/8Qhw8eFCkpqa2s7Xaj55g96qqKhERESFuvPFGkZqaKrZs2SJ69eol7r333g6w2JXFFSV0gEZvy5cvd7ZxOBzi6aefFiEhIcLV1VVMnDhRJCcnO1//6aefmuwnIyPD2a6kpETcfvvtwsvLS3h5eYnbb79dlJWVXXKM0dHRjfZ9Pps3bxbDhg0TOp1OxMTEiLfffrutpulQOtPud911V6Ntfvrpp4uO8ciRI2LChAnC1dVVhISEiGeeeaZBuO3rr78uBgwYIPR6vfD29hbDhg0Tb731lrDb7e1lpnalJ9hcCMXu/fv3F+7u7iI0NFTcfvvtIjc3tz1M1CF0lt2XL1/eZJtL0Ry7N9ZvdHR0e5ioQ+gpdj927JiYPn26cHd3FxEREWLJkiUNxJqkdaiE6KJzwBKJRCKRSCRt5Iry0ZFIJBKJRHJlIYWORCKRSCSSHosUOhKJRCKRSHosUuhIJBKJRCLpsUihI5FIJBKJpMcihY5EIpFIJJIeixQ6EolEIpFIeixS6EgkEolEIumxSKEjkUgkEomkxyKFjkQi6dIsXLgQlUqFSqXCxcWF4OBgZsyYwYcffojD4Wh2Px999BE+Pj4dN1CJRNIlkUJHIpF0eWbPnk1eXh6ZmZmsXbuWKVOm8PDDDzNv3jxsNtvlHp5EIunCSKEjkUi6PK6uroSEhBAeHk5iYiJ/+MMf+Oabb1i7di0fffQRAK+++iqDBg3Cw8ODyMhI7r//fqqrqwHYvHkzd999NxUVFc7ZoWeeeQYAi8XC0qVLCQ8Px8PDg9GjR7N58+bL80YlEkm7I4WORCLplkydOpUhQ4awatUqANRqNa+//jopKSl8/PHHbNq0iaVLlwIwbtw4XnvtNby9vcnLyyMvL4/HH38cgLvvvpsdO3bw2WefceTIEW666SZmz57NyZMnL9t7k0gk7YesXi6RSLo0CxcupLy8nK+//vqC12699VaOHDnC0aNHL3jtyy+/5Le//S3FxcWA4qPzyCOPUF5e7myTnp5Or169yM3NJSwszLl9+vTpjBo1iueff77d349EIulctJd7ABKJRNJahBCoVCoAfvrpJ55//nmOHj1KZWUlNpuNmpoajEYjHh4eje5/4MABhBD07t27wfba2lr8/f07fPwSiaTjkUJHIpF0W44dO0ZsbCxZWVnMmTOHRYsW8eyzz+Ln58f27du55557sFqtTe7vcDjQaDTs378fjUbT4DVPT8+OHr5EIukEpNCRSCTdkk2bNpGcnMyjjz7Kvn37sNlsvPLKK6jViuvhF1980aC9TqfDbrc32DZs2DDsdjuFhYVMmDCh08YukUg6Dyl0JBJJl6e2tpb8/HzsdjsFBQWsW7eOZcuWMW/ePO68806Sk5Ox2Wy88cYbXHPNNezYsYN33nmnQR8xMTFUV1ezceNGhgwZgl6vp3fv3tx+++3ceeedvPLKKwwbNozi4mI2bdrEoEGDmDNnzmV6xxKJpL2QUVcSiaTLs27dOkJDQ4mJiWH27Nn89NNPvP7663zzzTdoNBqGDh3Kq6++yt/+9jcGDhzIJ598wrJlyxr0MW7cOBYtWsQtt9xCYGAgL774IgDLly/nzjvv5LHHHqNPnz5ce+217N69m8jIyMvxViUSSTsjo64kEolEIpH0WOSMjkQikUgkkh6LFDoSiUQikUh6LFLoSCQSiUQi6bFIoSORSCQSiaTHIoWORCKRSCSSHosUOhKJRCKRSHosUuhIJBKJRCLpsUihI5FIJBKJpMcihY5EIpFIJJIeixQ6EolEIpFIeixS6EgkEolEIumx/D+4y7mI4KoSAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "fig = df.drop('Durchschnitt').plot(kind='line').get_figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Speichere deinen DataFrame in einer Markdown-Datei und auch das Ergebnis der Visualisierung als png-Datei."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "Cannot save file into a non-existent directory: 'out'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout/Stadt-Daten.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout/Stadt-graph.png\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/core/generic.py:3720\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3709\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m   3711\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[1;32m   3712\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[1;32m   3713\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3717\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[1;32m   3718\u001b[0m )\n\u001b[0;32m-> 3720\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3723\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3724\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3725\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3726\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3727\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3728\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3729\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3731\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3732\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3733\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3734\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3735\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3737\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/io/formats/format.py:1189\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m   1168\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1170\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[1;32m   1171\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[1;32m   1172\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1187\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[1;32m   1188\u001b[0m )\n\u001b[0;32m-> 1189\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[1;32m   1192\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/io/formats/csvs.py:241\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[1;32m    239\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    240\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 241\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    242\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    243\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    244\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    245\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    249\u001b[0m \n\u001b[1;32m    250\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[1;32m    251\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[1;32m    252\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m    253\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    258\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[1;32m    259\u001b[0m     )\n\u001b[1;32m    261\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/io/common.py:734\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    732\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    733\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 734\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    736\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    737\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    738\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/rise-environment/lib/python3.10/site-packages/pandas/io/common.py:597\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    595\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    596\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 597\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: 'out'"
     ]
    }
   ],
   "source": [
    "df.to_csv('out/Stadt-Daten.csv')\n",
    "fig.savefig('out/Stadt-graph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rechercheaufgabe\n",
    "## Input/Output in Python\n",
    "Recherchiere zuerst was **JSON**, **TXT** und **PICKLE** ist.\n",
    "Schreibe deine Daten (Name, E-Mail-Adresse, Traumberuf) in ein dictionary.\n",
    "Speichere dieses Dictionary anschließend in drei Versionen: 'visitenkarte.txt', 'visitenkarte.json' und 'visitenkarte.pkl' relativ zu deinem Arbeitsverzeichnis in dem Ordner 'meine_visitenkarten'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "visitenkarte = {'Name': 'Max Mustermann', 'E-Mail-Adresse': 'max@muster.de', 'Traumberuf': 'Passbild-Model'}\n",
    "\n",
    "vdf = pd.DataFrame(visitenkarte.items())\n",
    "\n",
    "vdf.to_csv('out/visitenkarte.csv')\n",
    "\n",
    "vdf.to_json('out/visitenkarte.json')\n",
    "\n",
    "vdf.to_pickle('out/visitenkarte.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lese die Datei 'visitenkarte.json' jetzt ein und gib in der Konsole den Traumberuf aus.\n",
    "Versuche das Gleiche mit den anderen Dateitypen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[\"{'Name': 'Jonas', 'Mail': 'jr74xaqo', 'Traumberuf': 'Feuerwehr'}\"]"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "skip-execution",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "pickleDF = pd.read_pickle('out/visitenkarte.pkl')\n",
    "\n",
    "jsonDF = pd.read_json('out/visitenkarte.json')\n",
    "\n",
    "csvDF = pd.read_csv('out/visitenkarte.csv')\n",
    "\n",
    "jsonDF"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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