notebooks/hops.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hops\n",
    "\n",
    "What kind of hops are commonly used in certain beer styles? And how many do they use?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas\n",
    "\n",
    "json_data = json.load(open(\"../data/recipes.json\"))\n",
    "hops = pandas.json_normalize(json_data, \"hops\", [\"style\", \"uid\"], record_prefix=\"hop_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick styles to analyze:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\n",
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       "        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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hop_kind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cascade</th>\n",
       "      <td>4.816514</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.579787</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citra</th>\n",
       "      <td>4.816514</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.556312</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnum</th>\n",
       "      <td>6.557377</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.284281</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amarillo</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>44.472699</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simcoe</th>\n",
       "      <td>11.111111</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>47.477678</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perle</th>\n",
       "      <td>7.142857</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>41.402142</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chinook</th>\n",
       "      <td>9.090909</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.129876</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mandarina bavaria</th>\n",
       "      <td>20.202020</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>58.313395</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hüll melon</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>61.869781</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuggles</th>\n",
       "      <td>20.833333</td>\n",
       "      <td>54.782609</td>\n",
       "      <td>39.327606</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mosaic</th>\n",
       "      <td>14.285714</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>36.468415</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centennial</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>38.722228</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>galaxy</th>\n",
       "      <td>10.526316</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.073115</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>callista</th>\n",
       "      <td>38.144330</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>81.592204</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>northern brewer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.191781</td>\n",
       "      <td>31.622405</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golding</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>54.209638</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nelson sauvin</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>77.899259</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sorachi ace</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>49.248800</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>columbus/tomahawk/zeus, ctz</th>\n",
       "      <td>24.590164</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>51.472064</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>herkules</th>\n",
       "      <td>13.043478</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>36.033519</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   min         max       mean  count\n",
       "hop_kind                                                            \n",
       "cascade                       4.816514  100.000000  56.579787     81\n",
       "citra                         4.816514  100.000000  60.556312     49\n",
       "magnum                        6.557377  100.000000  27.284281     36\n",
       "amarillo                     20.000000  100.000000  44.472699     33\n",
       "simcoe                       11.111111  100.000000  47.477678     31\n",
       "perle                         7.142857  100.000000  41.402142     23\n",
       "chinook                       9.090909  100.000000  46.129876     16\n",
       "mandarina bavaria            20.202020  100.000000  58.313395     15\n",
       "hüll melon                   25.000000  100.000000  61.869781     12\n",
       "fuggles                      20.833333   54.782609  39.327606     10\n",
       "mosaic                       14.285714   50.000000  36.468415     10\n",
       "centennial                   16.666667  100.000000  38.722228      9\n",
       "galaxy                       10.526316  100.000000  50.073115      9\n",
       "callista                     38.144330  100.000000  81.592204      9\n",
       "northern brewer               0.000000   82.191781  31.622405      9\n",
       "golding                      30.000000  100.000000  54.209638      8\n",
       "nelson sauvin                25.000000  100.000000  77.899259      8\n",
       "sorachi ace                  20.000000  100.000000  49.248800      7\n",
       "columbus/tomahawk/zeus, ctz  24.590164  100.000000  51.472064      7\n",
       "herkules                     13.043478  100.000000  36.033519      7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick a style\n",
    "styles = [\"pale ale\"]\n",
    "\n",
    "# Hops are aggregated for each recipe\n",
    "style_hops = hops[hops[\"style\"].isin(styles)].groupby([\"uid\", \"hop_kind\"]).agg({\"hop_amount\": \"sum\", \"hop_amount_percent\": \"sum\"}).reset_index()\n",
    "style_hops[\"hop_kind\"].value_counts()[:10]\n",
    "\n",
    "hop_ratio = style_hops.groupby(\"hop_kind\")['hop_amount_percent'].agg(['min', 'max', 'mean', 'count'])\n",
    "hop_ratio = hop_ratio[hop_ratio['count'] > 3] # Filter low amount of data points\n",
    "hop_ratio.sort_values(by='count', ascending=0).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_hops = style_hops[\"hop_kind\"].value_counts()[:20].index.values\n",
    "style_top_hops = style_hops[style_hops['hop_kind'].isin(top_hops)] # Get only the values of the mostly used hops\n",
    "# style_top_hops.loc[:, [\"hop_kind\", \"hop_amount_percent\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hop_kind', ylabel='hop_amount_percent'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+oG3nzZsHwNixYwe0/aRJk5g4ceJ6xyZJkiRJktRp7BnVRt3d3XR3dxcdhiRJkiRJUmHsGTVIEydOHHDvpSlTpgAwderUVoYkSZIkSZLUsewZJUmSJEmSpLYxGSVJkiRJkqS2MRklSZIkSZKktmkoGRURO0bEhmtYNyIidmxOWJIkSZIkSaqiRntG3Qm8YA3rnldfL0mSJEmSJPWr0dn0hq1l3Ujg6UHEIkmSJEmSNGSdc845zJ49u+n7nTVrFgBTpkxp+r4Bxo8fz+TJkwe8/TqTURGxA9B7+N1zIqLv720E/AcwZ8B/WZIkSZIkSSvMnj2bO25J7Lj5lk3d72bDRgKw5L55Td0vwN3zH2n4dwbSM+o/gC8CtfrPef1sMwxYBnyk4QgkSZIkSZIEwI6bb8nn9nlH0WEM2Ff/cEnDvzOQZNT3gGvJCaergSOBW/pssxi4LaXU/BSbJEmShqwyDldodKiCJElDzTqTUSmlOdSH30XEAcDfUkoLWh2YJEmSNHv2bG669VaGjduiqfutjcgTRP/zkUebu9+5zd2fJElV1FAB85TS73oeR8RYcq2ovtvc34S4JEmSJACGjduCkQe/pegwBmTJ5T8vOgRJkjpeQ8moiNgcOB04FNh4DZttMNigJEmSJEmSVE0NJaOAs4E3At8F/kWuFSVJkiRJkiQNSKPJqIOAT6eUvt2KYCRJkiRJklRtjSajlgGzWhGIJEmSJKlzPDJvDj/99Veaus+FTz0GwOiNn9HU/T4ybw7jttq1qfuU1DqNJqMuJNeLmtGCWCRJkiRJHWD8+PEt2e9jsx4H4JlbbdnU/Y7bateWxSyp+RpNRt0KfDEiLgOuBLr7bpBSurgZgUmSJEmSijF58uSW7HfKlCkATJ06tSX7l1QOjSajLqj/uxPw+n7W1wCTUZIkSZIkSepXo8monVsShSRJkiRJkoaEhpJRKaU5rQpEkiRJkiRJ1ddozygAIuIg4JXAOOBrKaV7IuL/AXemlB5qZoCSJEmSJEmqjoaSURHxDOAy4OXAAmBT4DvAPcBRwKPAJ5oboiRJkiRJkqpieIPbn0quG/Vycq+oYb3WzQAObFJckiRJkiRJqqBGk1FvBI5NKf2RPHNeb3OAZzYlKkmSJEmSJFVSo8moTYAH17Bu9CBjkSRJkiRJUsU1moz6F3DIGtZNAv4+uHAkSZIkSZJUZY3OpncqcHFELAOm1ZftEhETgY8Cb2tmcJIkSZIkSaqWhpJRKaVLImIL4Kvk2fMALgGeAI5OKV3e5PgkSZLaZsaMGUyfPn1A286bNw+AsWPHDnj/kyZNYuLEiesVmyRJUlU02jOKlNJ/R8T3gJcBWwHzgOtTSguaHJskSVLH6u7uBhpLRkmSJGk9klEAKaUngd80ORZJkqRCTZw4ccA9l6ZMmQLA1KlTWxmSJElS5TRUwDwijoqIk9ew7uSIOLI5YUmSJEmSJKmKGp1NbzIwew3rbq+vlyRJkiRJkvrV6DC9ZwG3rWHdLGDnQUUjSRoSWlkk2gLRkiRJUmdrtGfUk8COa1i3E7BocOFIkrSq7u7uFYWiJUmSJJVfoz2jrgGOi4jLUkor7gwi4hnAZ+vrJUlaK4tES5IkSUNXo8mozwN/BmZFxC+A+4DtgTfV17+xeaFJkiRJkiSpahpKRqWUbouIFwFfAl4HjAMeBa4ATkgpram4uSRJUiHOOeccZs9u/iXKrFmzgJW995pt/PjxTJ7s3DCSJKl6BpyMioiR5NnyfptSek/rQpIkSWqe2bNnc9Ott8C4zZu74xHDALjpkfuau1+AufObv09JkqQOMeBkVEppSUScDBzUwngkSZKab9zmjDjklUVHMWBLf3Vd0SFIkiS1TKOz6f0T2L0VgUiSJEmSJKn6Gi1gfgzw3Yi4PaX0u1YEpPa4e/4TnHTdTU3d5/zFSwDYfNTIpu737vlPsOv2Td2lJEmSJEkqSKPJqO8AY4GrI+JJ4CGg1mt9LaUUzQpOrTF+/PiW7PfxeiHXLbffqan73XX71sUsSZIkSZLaq9Fk1PWsmnxSCbVqZp6e2YSmTp3akv1LkiRJkqTyaygZlVL6QIvikCRJkiRJ0hDQaM+oFSJiJNAFPJpSWta8kFb5G1sDXwXeADwDuBv4bErpZ722OaS+ze719SellC5oRTySJEmSJEkanEZn0yMiXhMRfwSeBO4DnldffnZEHNqswCJiM+D/gO2BtwIBfAiY02ublwI/B35Wj+MM4LyIeFOz4pAkSZIkSVLzNNQzqt4L6RfAb4Fjga/3Wn0/OVn0kybF9hlgA+CNKaUl9WV39dnmaOC6lNIJ9ee31hNUU4BLmxSHJEmSJEmSmqTRnlFfAi5IKU0Cvtln3U3Ac5oRVN2byAXTz4qIByPilog4ISI27LXNPsD0Pr93JfDiPttJkiRJkiSpAzRaM2oPcq8jWH1WvfnAuEFHtNIu9Z+LgdcDOwNnA5sCn65vsw3wUJ/fexDYENgCeGAgf+jmm29uQrjrtmDBAgBmzpzZlr/Xbp1wfAsWLGCDwv76+luwYEFl3xedoBPem73jKJNOeG92yuun8irjZw+K//x1ymevjK9f0a9dp8iv3XqXqC2Mr19rdcp3i9ZPJ7x+l19+OQ88MKBb/Yb17PeII45o+r633XZbDj744HVut2DBAkY1/a+3XqPfnY2eHbqBbdewbhdyIqhZhpETTYfXC6TPjIitgKkRMSWl1DcZtt723ntvRo1q/cs9bdo0ACZMmNDyv1WETji+adOmsfDxwv78ehszZkxl3xedoBPemz1xLHiy0BAa1gnvzU55/VRe06ZNg0XlOzkU/fnrlM9efv0WFxpDo4p+7TpFfu2eKjqMhvn6tVanfLdo/XTC6zdt2jQeuucBdtxsTamJ9de1waYAjJrf3P3e/fgDA/5umTZtGksen9fcANqg7/EtXrx4rR1/Gk1G/Rr4QkRczcrEUy0iNifXb/pVg/tbmweAO/rM1PdvYDS519Mj9Ri27vN7WwNLgUebGIskSZIkSeoAO262Lce99PCiwxiwr/35O0WH0HEarRl1LLnH0q3AZeShelPJSaIRwBebGNv1wK4R0TvG3cmz+PUkmv4ATOzze68B/pJSerqJsUiSJEmSJKkJGuoZlVJ6OCImAJ8EJgGzyDWcvgOcllJqZme2bwBvB86IiG+Ra0Z9EfhWryF6pwPXR8TxwCXAq4HDgLc1MQ5JkiRJkkrhnHPOYfbs2S3Z96xZswCYMmXKOrZs3Pjx45k8eXLT96vO1HBFwZTS48CX6z8tk1L6W0S8ATgJOBy4D/h2/XnPNn+OiLcCXwU+D9wDfDSldGkrY5PUGq06cbbypAmeOCVJktQ5Zs+eze233M6Om+7Y9H2PqY0BYPHdza3ld/cTdzd1f+p86zW9Rb1G1HPIxczvB25ucq8oAFJKVwFXrWObXwK/bPbfltR+s2fP5tZ/38S4ZzR3vyPqg30feeCm5u4YmPtY03cpSZIkDcqOm+7IsRM+U3QYA3bSzFOKDkFt1lAyql6/6UTg4+RC4sPqq56IiDOAL6aUljc3RElDybhnwMGvHrbO7TrF5b9t2sSekiRJkjQkNNoz6mTgv4AzgP8BHiLPXvd2cnHzkUB50q+SJEnqaN3d3dTmPsqSy39edCgDUpv7KN0jNig6DEmSOlqjyagPACemlL7Ua9ls4I8R8ThwJCajJGlIKmPNL+t9SZIkSe3XaDJqI+D6Nay7Hjh6cOFIkspq9uzZ/OvWm9hkbHP3u6x+prrr4ebW/HpyXlN3J6lFurq6uHfpMkYe/JaiQxmQJZf/nK6urqLDkCSpozWajPpf4BDgN/2sewPrKDYuSaq2TcbCXq8vOoqB+dcVRUcgSZIkDU2NJqMuAr4VEdsAPwMeBrYC3ga8FDgyIvbp2Til9IdmBSpJkiRJkqTyazQZdWn937fVf2qsnFGv9/ph9XVWb5QkSZIkSdIKjSajDmhJFJIkSZIkSRoSGkpGpZR+N9BtI2JYRBwPnJdSerDhyKT10N3dzSPza5x1/eKiQxmwe+fX2HJ0d9FhSJIkSZLUFsNbvO8vAtu18G9IkiRJkiSpRBodpteoYeveRGqerq4uRi28l6NePqroUAbsrOsXM9opoKVKmTFjBtOnTx/QtvPmzQNg7NixA9p+0qRJTJw4cb1jkyRJkorWyp5RkiRpHbq7u+nudqiuJEmSho5W94ySJGnImThx4oB7L02ZMgWAqVOntjIkSZIkqWPYM0qSJEmSJEltYzJKkiRJkiRJbWMySpIkSZIkSW3TUDIqInaMiA3XsG5EROzYa9Fy4PvAo4OIT5IkSZIkSRXSaAHzO4GXAX/pZ93z6ss3AEgp1YAPDio6SZIkSZIkVUqjw/SGrWXdSODpQcQiSZIkSZKkiltnz6iI2AHoPfzuORHR9/c2Av4DmNPE2CRJkiRJklQxAxmm9x/AF4Fa/ee8frYZBiwDPtK80CRJkiRJklQ1A0lGfQ+4lpxwuho4ErilzzaLgdtSSvOaGZwkSZIkSZKqZZ3JqJTSHOrD7yLiAOBvKaUFrQ5MkiRJkiRJ1dPQbHoppd/1PI6IseRaUX23ub8JcUmSJEmSJKmCGkpGRcTmwOnAocDGa9hsg8EGJUmSJEmSpGpqKBkFnA28Efgu8C9yrShJ0gB0d3fzWDf8fnrRkQzMY92w8ajuosOQJEmSVDGNJqMOAj6dUvp2K4KRJEmSJElStTWajFoGzGpFIJLW7ZxzzmH27NkD2ra7u5t581o3weXYsWPp6uoa0Lbjx49n8uTJLYulLLq6unhq8T3sO6noSAbm99MZ8GsM+T335Fz41xUtDKqJnpwL3Rva8wv8bpHKYsaMGUyfPrDutT2f07Fjxw5o+0mTJjFx4sT1jk1r18rXDnz9JJVPo8moC8n1oma0IBZJ6zB79mzSv29i683Xve2Ti2BpCwfSPtn9JMOeumed2z00v3UxSGqO2bNnc9Ot/4Jxm65744VL4OklLYvlyccf5Z6lj697w7lPtCwGqQq6u3OyvZGEhjqDr52koaDRZNStwBcj4jLgSmC1JuWU0sXNCKxIjbQQN2LWrNypbMqUKU3ft63DQ8fWm8O792/0o1uci65dWnQIapOuri7mP30Pe72+6EgG5l9XNNbzq8p6bnwGZPTI/NMBBhp3d3c3zJ3P0l9d1+KImmjufLpHjC46CnWYiRMnDrj3S8/15tSpU1sZkgbI106SVtXoHe0F9X93Avq73agBpU9GzZ49mztu+Tc7bt7c1ojNhuWJBpfc91BT93v3/NYNl5AkSZIkSWqmRpNRO7ckig604+Zj+fwry1HY5SvXlWRqLklSR+rq6uKepY+zwRufW3QoA7bslzcNuGdbPr6FjDjklS2OqnmW/uo6e+5JkqTKaigZlVKa06pAJEnqVK0avg0O4dbgDYXyArW5j7Lk8p83NYbawoUADBvd3OGQtbmPwpZbNHWfkiRVTUPJqIjYbl3bpJTuX/9wJEnqPLnA902wxfDm73zDGgA3PXpzc/f76PLm7k8dK78//82wcc0tL1AbkcsL/POR5pYXqM1trLzA+PHjm/r3e/Qk23ZpduJoyy1aFrMkSVXR6DC9e8l1odZmg/WMRZKkzrXFcIa/qTwFpZdfurDoENRGw8aNZcTBBxUdxoAsvfyqhrZvVe8+i0RLklScRpNRH2L1ZNQ44GByPakTmxGUJEmSJEmSqqnRmlHfW8Oq0yLih8Dug45IkiRJkiRJldXM4hc/Aj7YxP1JkiRJkiSpYpqZjNoK60VJkiRJkiRpLRqdTW/ffhaPBPYGjgUaq0gpSZIkSZKkIaXRAubXkguYD+uzfDHwU+ATTYhJkiRJkiRJFdVoMmrnfpYtSik91IxgJEmSJEmSVG2NzqY3p1WBSJIkSVJZnHPOOcyePbvp+501axYAU6ZMafq+AcaPH8/kyZNbsm9JGqhGe0YBEBGTgH2BLmAecF1KaXozA5MkSZKkTjV79mxuufUONh+3Y1P3O2zEZgDc98iSpu4XYP7cu5u+T0laH40WMB8DXEZORC0F5gLjgOMi4vfAG1JKTzQ9SkmSJEnqMJuP25GXv/G4osMYsOt/+bWiQ5AkAIY3uP3JwAuBw4CNU0rbAhvXn78QOKm54UmSJEmSJKlKGh2m9xbg2JTStJ4FKaVlwCURsSVwHHBUE+OTJEmSJElShTTaM6oLuH0N624Dxg4uHEmSJEmSJFVZo8moO4C3r2Hd21lzokqSJEmSJElqeJjeN4DvRsTWwI+BB4FtgHcCBwMfam54kiRJkiRJqpKGklEppQsjYiPgBHLyqQYMAx4Gjkwpfb/pEUqSJEmSJKkyGh2mR0rpHGBbYG9g3/q/26WUvt3k2CRJkiRJklQxjQ7TAyCltBy4pcmxSJIkSZIkqeIaTkZFxK7AW4AdgI36rK6llD7ajMAkSZIkSZJUPQ0loyLivcAF9acPA0v6bFJrRlCSJElNNXc+S391XXP3uXBx/nf0qObuF2DufNhy++bvV5IkqQM02jPqi8AvgMNTSvNbEI8kSVJTjR8/viX7nTVrFgC7tCJptOX2LYtbkiSpaI0mo7YCzjURJUmSymLy5Mkt2e+UKVMAmDp1akv2L0mSVFWNzqZ3NfC8VgQiSZIkSZKk6mu0Z9Rk4NKIWAbMAB7ru0FK6f4mxCVJkiRJkqQKajQZtYRcuPx01lysfINBRdSPiHgf8H3gqpTSa3otPwT4KrA7cDdwUkrpgv73IkmSJEmSpKI1moz6AbAP8A3gNlafTa/pIiKAk4Hf91n+UuDnwFeAacCBwHkRMS+ldGmr45IkSZIkSVLjGk1G7Q9MTin9oAWxrCYiRgGXAMeQk03b9Fp9NHBdSumE+vNb6wmqKcCl7YhPkiRJkiRJjWk0GfUgMLcVgazBacA/U0o/iogD+6zbBzinz7IrgQsjYsOU0tMD/SM333zzKs8XLFjAqPWJtkALFixg5syZhccAFBrHggULmj9OtA0G+vr1/B+Xjce3cruyaeS7pcrHV8ZjA4+vHX8fij3v9Y6jTIp+7XpigOJfv1bphOPLMTR6u1G8xr47y3bXUPznrxPem1W3YMECRjKy6DAa1shnr3yfPI+vr0bPDicD/xURv0kpLW7wdxsSEW8BDgJesIZNtgEe6rPsQWBDYAvggYH+rb333ptRo1a+3NOmTWPJ4wsbirdoY8aMYcKECYXGMG3aNIBC45g2bRoLHy/sz6+3gb5+06ZN47Hy3XM0dHyLnmhDQE3WyPEteLINATVRI98t06ZNY+5TLQ6oyRp57WjpWa81Gjq+RfPaEFFzFX3u64Tz3oo4Fnnd0qhOef1apROOL783S3ZioLHvzscXtbxqSdMV/fnrhPdm1U2bNo3F3eW7cGnks7dkfvluiho6vsfLf122ePHi1Tr+9NZoMuolwK7AXRHxf0B3n/W1lNJHG9znaiLimcC3gTeklMr3LpMkSZIkSVK/Gk1GHQgsBxYBL+pn/Zpm2GvUBGBL4PpcvxyA4QARsRR4KbkX1NZ9fm9rYCnwaJPikCRJkrQeuru7WTb3URb86ptFhzJgy+beS/eILYoOQ5Iqr6FkVEpp51YF0sdvgef0WfYV8vC7I4A7gD8AE4GTem3zGuAvjdSLkiRJkiRJUvt0ZEXB+tC8VQYXRsRjwEYppZvrz08n95w6njzj3quBw4C3tTdaSZIkSX11dXVx/9KNGHPIJ4sOZcAW/OqbdHVtXHQYklR565WMiojnA7sDG/Vdl1L6wSBjGpCU0p8j4q3AV4HPA/cAH00pXdqOvz9UzJgxg+nTpw9o21mzZgEwZcqUAW0/adIkJk6cuN6xSZIkSZKk8mkoGRURXcD/kguZ14Bh9VW9a0W1JBmVUvpAP8t+CfyyFX9Pjevq6io6BEmSJEmS1OEa7Rl1MjAGeBnwR+AQ4DHg/cABwKHNDK4o3d3dPDp/Hl+5bmA9goo2Z/48thg9siX7njhxor2XJEmSpD66u7uZP/dRrv/l14oOZcDmz53DaAu0S+oAjSajJgJfAG6oP38wpXQD8H8RcQZwDPDOJsYnSVLhuru74dHlLL90YdGhDNyjy+neoLvoKCRJkpqqu7ubRx9/hK/9+TtFhzJgcx5/gC26lxYdRkdpNBm1DTAnpbQsIhYC43qtuwz4n6ZFVqCuri42WbiEz79yUtGhDMhXrpvOSIfISZIkSW3T1dXFwqWb8PI3Hld0KAN2/S+/RldXa0ZUSFIjGk1G3QeMrT+eTe4pdVX9+fOBJc0JS5KkztHV1cU9y+5j+JtGFx3KgC2/dKG1/CRJUuV0dXWxyZMjOO6lhxcdyoB97c/fYWTXmKLD6CiNJqOuAfYDfgWcB5wZEXuSk1CvB77b3PAkSZIkSZJUJY0moz5PfWheSulbEbEB8HZgNHAq8OXmhidJkiRJkqQqaSgZlVJ6GHi41/MzgDPWtH1EvA+4LKVkBVVJ69Td3c3cx+Dy39aKDmXA5j4GIzbyK67Hk/PgX1c0d59Lnsr/jty4uft9ch6wVXP3KUmSJGndGu0ZNWD1XlMXAi8GvFOTpIobP358S/Y7a9YsAJ611S7N3fFWrYtZkiRJ0pq1LBlVN6zF+5dUIV1dXSxddA8Hv7o8Xx2X/7Zmkei6yZMnt2S/U6ZMAWDq1Kkt2b8kSZKk9hpedACSJEmSJEkaOkxGSZIkSZIkqW1MRkmSJEmSJKltTEZJkiRJkiSpbUxGSZIkSZIkqW1aOZteDfgdsKCFf0OSSmV+N/x+enP3ueip/O9GGzd3v/O7YbttmrtPdbC5T7Dslzc1f78Ll+R/R49s7n7nPgFbNneXkqSh45xzzmH27NkD2ra7u5t58+a1JI6xY8cOeGbm8ePHt2z2YnWO7u5uHp3/MF/9wyVFhzJgc+Y/zBajG5sRfb2SURGxO/ASYFvgAeDPKaXbe2+TUloOHLA++5ekKho/fnxL9jtr1iwAtttml6bud7ttWhdzKT26nOWXLmz+fhfW8r8NnsDX6dHlsMXANm3l69zz/txly52au+MtfX9Kktbf7Nmzue3fd7DtM3Zc57ZPLVrKsiXLWxLHU48tZcSiJevc7oHH7m7J35eK0lAyKiI2Ac4D3sGqQ/yWR8SPgY+mlFpwpS5J5deqlqwpU6YAMHXq1JbsX21K1mzR3GQiWww87la2svr+lCR1qm2fsSMf3f9zRYcxIOde+9WiQ1CbdHV1scnCGp/b5x1FhzJgX/3DJYwcYA+/Ho32jDoDeBNwNPA/wMPAVsDbgZOARcDhDe5TkqSOZrJGkiRJap5Gk1FvA45NKZ3Va9kDwJkRMRw4AZNRkiRJkiRJWoNGZ9NbDty6hnX/BpYNLhxJkiRJkiRVWaPJqJ8AH1jDug/U10uSJEmSJEn9anSY3g3A8RHxZ+DnrKwZ9VZgG+BLEXFYz8YppYubFagkSZJUJY1MLd+onskReurSNZPTy0uSBqvRZNR59X93AF7cz/rv9HpcA0xGSU3U3d3Nw4/BRdcuLTqUAXvoMaht3F10GJIkdZzZs2dz062J4eO2afq+ayM2AuDmR+Y3db/L5z7Y1P1JkoamRpNRO7ckCqmJ7ptf46zrFzd9v48vrgGw2ahhTd3vffNr7LZ9U3cpSZJKYvi4bdjoDe8rOowBW3TZDxraftnce1nwq282PY7lCx8HYPjozZq632Vz74Utd2vqPiVJq2soGZVSmtOqQKRmGD9+fMv2/UC9u/s22+/S1P3utv3A4+7q6mLYU/fw7v0bzSMX56Jrl/KMrq6iw5AkSW3WyuuyWbPuB2CXLbdu7o633K2lcUuSsvW6o42IScC+QBcwD/h9SmlGMwOT1kcr6xf01FyYOnVqy/6GJElSVXhdJklak4aSURExBriMnIhaCswFxgHHRcTvgTeklJ5oepSShoy5j8Hlv601dZ8LF+V/R2/U1N0COd4tt23+fiUVY8aMGUyfPn1A265PgehJkyYxceLE9YpNkiSpKhrtGXUy8ELgMOCnKaVlEbEB8DZycfOTgKOaG6KkoaJV3eJ7bhi33La5QyzzPls7DEFS5+pyCLIkSdJ6aTQZ9Rbg2JTStJ4FKaVlwCURsSVwHCajJK2nVnXntyu/pIGaOHGiPZckSZJarNFkVBdw+xrW3QaMHVw4kiRJklQO8+fezfW//FpT97l44XwARo3evKn7hRzv9lvu2vT9SlKjGk1G3QG8HeivmMLbWXOiSpIkSZIqo3XlBR4HYPstt2z6vrffclfLC0jqCI0mo74BfDcitgZ+DDwIbAO8EzgY+FBzw5MkSZKkzmN5gXLr7u7mkcce5dxrv1p0KAPywGNzWLrRFkWHITVNQ8molNKFEbERcAI5+dTjYeDIlNL3mxibJEmSJEmSKqbRnlGklM6JiHOBZ5NrSHUDt6aUljc7OEmSJEmSmq2rq4sRizbho/t/ruhQBuTca7/KmK6RRYchNU3DyaiIGE+eVW9HYKNeywFqKaWPNi26At09fx5fua6/0ljrb/7ipwDYfNTGTd3v3fPnsev2Wzd1n5IkSZIkSa3QUDIqIt4MXAIMJw/NW9xnk1qT4ipUq4r6PT5rFgBbNjlxtOv2W1uIUJIkSZIklUKjPaO+AvwGeF9K6dEWxNMRLEYoSZIkSZLUGo0mo54FfKLKiShJkiRJktS57n78Ab725+80fb/zFy8AYPNRY5q637sff4Bdae4+y67RZNRNwPatCESSJEmSJGltWlmi5vFZDwOw5Q7bNXW/uzLG0jp9NJqM+gRwQUTcnFKa2YqAJEmSJEmS+tOqsjpgaZ12WmcyKiJuZ9XC5FsAf4mIeUB3n81rKaVoYnySJEmSJEmqkIH0jLqeisySJ0mSJEmSpGKtMxmVUvpAG+KQJEmSJEnSENBozShJkiSpI82YMYPp06cPaNtZs2YBK+uDrMukSZOYOHHiescmVVlZP3sPPHY351771abuc8Gi+QCM2Wjzpu73gcfuZsy2uw5o2+7ubh5d8CgnzTylqTG00t0L7maL7i2KDkNtZDJKkiRJQ05XV1fRIUhDUqd89lo1s9nDsx4HYLttt2zqfsdsu6uzsalSTEZJktqurC2okjrbxIkT/fxLBSjjZ69VM7J1wmxsXV1djF4wmmMnfKawGBp10sxTGNU1qugw1EYmoyRJHa1TWlAlSZIkNYfJKElS25WxBVWSJElSc5iMkiRJkiRJ6hB3z3+Er/7hkqbuc/7iJwHYfNQmTd0v5Hh33X5sQ79jMkqSJEmSJKkDtKpQ/eOzugHYcvtnNn3fu24/tuG4TUZJkiRJkrQGTryidqpycf3eTEZJkiRJBeju7mb53IdZdNkPig5lwJbPfZDuEcuLDkPqWE68Ig2MyShJkiRJktbAiVek5jMZJUmSJBWgq6uL+5YOZ6M3vK/oUAZs0WU/oKtr86LDkCSVnMkoqWQemg8XXbu06ft9YlH+d9ONmrvfh+bDM7Zr7j4lSZIkSeVlMkoqkVbNrAAwt15scYftdmnqfp+xXWvjliRJkiSVi8koqURaNbMCdN7sCpIkSZKkahpedACSJEmSJEkaOjq2Z1REHAO8FXg2sAz4G/CFlNIf+2x3CPBVYHfgbuCklNIFbQ5XkqQVZsyYwfTp0we07az6ENme3onrMmnSJGf0kSRJUql1cs+o/YHzgFcCLycnmmZExK49G0TES4GfAz8DngecAZwXEW9qd7CSJK2Prq4uurq6ig5DkiRJapuO7RmVUnpd7+cR8WHgEOC1wFn1xUcD16WUTqg/v7WeoJoCXNqeSCVJWtXEiRPtvSRJDWikRynYq1SSyq5jk1H92AgYBXT3WrYPcE6f7a4ELoyIDVNKT7c6KIdiSJIkSe1lj1JJam0+AlqbkyhTMmoq8Bjwy17LtgEe6rPdg8CGwBbAAwPZ8c0337zeQd11110sWLBgQNtuvPHGAAPe/q677mLmzJnrHZuaq+d1q+prUuXjq/KxQfWPT+Xm+7P1Bnpd0UkWLFjge4JyvnbQmtdv7NixvPOd72zqPvsqy3vO700N1oIFCxjJyKLDaFgnnBvK9vlrZT6iZ/+t+r8oRTIqIj4PHAYcmFJq+ll77733ZtSoUev1uxMmTODwww9vckTqRNOmTQPya15FVT6+Kh8bVP/4VG6+P1tv2rRpsGhh0WE0ZMyYMb4n6Hnt5hcdRsN8/VrL700N1rRp01jcvbjoMBrWCd8tZfv8dXI+YvHixWvt+NPxyaiI+BLwcWBiSqlvSu5BYOs+y7YGlgKPtiE8SZIkSZIkNaCTZ9MjIk4BjiL3iLqhn03+APQdwPga4C/tqBclSZIkSZKkxnRsz6iIOAv4IPA24L6I2Ka+6qmUUk9/5tOB6yPieOAS4NXk4Xxva3e8kiRJkiRJWrdO7hn1MWAT4H/Jhch7fs7o2SCl9GfgrcChwD+BTwEfTSld2u5gJUmSJEmStG4d2zMqpTRsgNv9klVn2JMkSZIkSVKH6uSeUZIkSZIkSaoYk1GSJEmSJElqG5NRkiRJkiRJapuOrRklaXBmzJjB9OnTB7z9rFmzAJgyZcqAtp80aRITJ05cr9gkSZIkSUOXyShJAHR1dRUdgiRJkiRpCDAZJVXUxIkT7bkkSZIkSeo41oySJEmSJElS29gzSpIkqcS6u7upzZ3H0suvKjqUAanNnUf3iJFFhyFJkgpkzyhJkiRJkiS1jT2jJEmSSqyrq4t7ly5hxMEHFR3KgCy9/ConzZAkaYizZ5QkSZIkSZLaxp5RkiRJktRCM2bMYPr06QPadtasWQBMmTJlwPufNGmSsyhrFXc/cTcnzTyl6fudv2Q+AJuP3Lyp+737ibvZjd2auk91NpNRkiRJUkGWz32QRZf9oOn7rS18AoBhozdt6n6Xz30QtmzuTahW5TBWDdb48eNbtu8FsxYAsNWOWzV1v7uxW0vjVucxGSVJkiQVoJU3XrNmPQrALltu39wdb7m5N4zrYeLEifZcUttMnjy5Zfvu6bE3derUlv0NDQ0moyRJkqQCeMMoSRqqTEZJkiSVXG3uPJZeflVz97nwKQCGjd64ufudOw+23Lqp+5QkSeViMkqSJKnEGhky1d3dzbx58wa07VNP5WTUxkuXDWj7sWPHDqzWzZZbO8xLkqQhzmSUJElSiTUy1KuRGb16klZjx44d0PbO5iVJkgbKZJQkSdIQYRFlSZLUCYYXHYAkSZIkSZKGDpNRkiRJkiRJahuTUZIkSZIkSWoba0ZJkiRJkqTKaWTiDoBZs2YBMGXKlAFt7+Qd689klCRJkiRJGvK6urqKDmHIMBklSZIGzBZGSZJUFs4i27lMRkmSpJaxhVGSJEl9mYySJEkDZgujJEmSBsvZ9CRJkiRJktQ2JqMkSZIkSZLUNg7Tk1RKjRRRbrSAMlhEWZIkSZJaxWSUpMqzgLIkSZIkdQ6TUZJKySLKkiRJklRO1oySJEmSJElS25iMkiRJkiRJUtuYjJIkSZIkSVLbmIySJEmSJElS21jAXJIkSZKkIWrGjBlMnz59QNvOmjULgClTpgxo+0mTJjnpkPplMkqSJEmSJK1TV1dX0SGoIkxGSZIkSSVg7wVJrTBx4kQ//2o7k1GSJElSxdh7QZLUyUxGSZIkSSVg7wVJUlU4m54kSZIkSZLaxmSUJEmSJEmS2sZklCRJkiRJktrGmlEa0pyVRp3K96YkSZKkqjIZJQ2Qs9KoU/nelCRJklQmw2q1WtExFGbmzJnPAu7ce++9GTVqVNHhSJIkSZIkld7ixYu5+eabAXaeMGHCXX3XWzNKkiRJkiRJbWMySpIkSZIkSW1jMkqSJEmSJEltYzJKkiRJkiRJbWMyqiCHHHIIY8eOZeTIkeywww4cffTRLF26FIC//e1v7LnnnmyyySZssskmvOAFL+CXv/xlwRGvv1/84hc85znPYeONN2b33XfnJz/5SdEhDcp3v/td9thjDzbeeGPGjRvHIYccwr333gvAzJkz+X//7/+x0UYbMWzYME444YRig22Cqr1+n/zkJ3nmM5/JsGHDGDZs2Grrr7vuOl7xilew8cYbs/nmm3PggQcWEOXgvfnNb2bYsGGMGLFy0tRFixbxyU9+ku23357Ro0fz2te+ljlz5hQY5fq79tprV7yGvX/233//okNbb2t7b9ZqNU444QS23XZbNtpoI/bbb7+egpClVbXvlr7Wdq4os6H22fvud7/L85//fDbbbDOe8YxncPDBBzN79uyCIl0/a7vmBLj55pt5zWtew6abbsqmm27Ki1/8YhYsWFBgxI1Z2+v3ve99b7X36sEHH1xQpOtnKJ0bqvq92aNK573u7m4OOeQQtt9+ezbaaCN22WUXzjrrrBXrq3A/u7bvzrvvvpv99tuPTTfdlGHDhvGBD3yg2GAHqahzu8moguy1116cfPLJnH322YwZM4ZvfvOb/OhHPwJggw024NBDD+Xss8/ms5/9LP/85z95xzveweLFiwuOunE33XQThx56KE8++STf+ta32HTTTXnXu97FLbfcUnRo62XOnDl8+MMf5sEHH+TrX/86+++/P5dddhnHHnssAAsXLuTZz342++23X8GRNkfVXj+AZcuW8f73v5/hw1f/+ps1axYTJ07kX//6F8cffzxTp05lhx12KCDKwfne977HjBkzVlt+/PHHc8YZZ7D//vvzta99jd/+9re8853vLCDCwdtzzz358Y9/vOLnNa95DQAveclLCo5s/a3tvXnhhRfypS99iRe96EWccsop3HDDDbzxjW9c5YayTKr43dLbus4VZTbUPnt//vOfed7znsc3vvENJk6cyBVXXMH73//+AqJcf2u75nzsscd41atexe9+9zs+8YlPcMYZZ/Cc5zyHZcuWFRz1wK3t9evxhS98YcV79jOf+Uwboxu8oXJuqPL3JlTvvDd//nxuu+02jjzySKZOncpjjz3Gxz/+cX77298C1bifXdt35+LFi9luu+143eteV3CUzVHYub1Wqw3ZnxtuuOFZN9xwQ23RokW1Rtx55501oPbCF76w9q53vau2+eab15797GfXZs6cuWIboLbBBhusdT/z5s2rzZ49u/aa17ymBtS+//3vr1i3ZMmS2sMPP1y7/vrra6NHj66NHj269tRTTzUUZzMM9lhPO+20GlD73Oc+V6vVarVzzjmnBtSOPvrotsTf12CPZ9asWbVhw4bVnvWsZ9VuueWW2le+8pUaUPvEJz6xynYnnnhiDah98YtfbOHRrJuv35ptsMEGtfwVuNKHP/zhGlC74IILak899VRt+fLlTT+GtWnG8c2ZM6e22Wab1b797W+vtu1zn/vcGlC7/fbba7VarfbSl760BtRuvPHG1h3UGjTztVy0aFFt6623ro0YMaJ2zz33tDLsNWr1e3PChAk1oHbXXXfVarVa7b3vfW8NqF1++eXNPZABqtp3S1/tOlcUwc/emvX32et9jTh//vwaUNtss82adwDr0Oprzp735vHHH19btGhRbenSpS09nr5a/fpdeOGFNaA2ffr02sKFC5se/7oMpXNDlb83a7XqnfcGezxPP/10bdmyZSueH3300TWgdtppp61YVuT9bDvu12u1Wu2HP/xhDai9//3vb8VhDFinntsXLVpUu+GGG2o33HDDs2r95GPsGTUIf/vb33jmM5/JO97xDm699VaOO+64hn7/Oc95DuPHj+fKK6/kAx/4AIcddtiKdb/73e/YaqutePnLX86wYcO4+OKL2WijjZp9CAO2vse69dZbA/CHP/yBOXPm8Lvf/Q6AO+64o2WxDsT6Hs/48eP59re/zX333ceee+7J5z//eQ444ABOOeWUFkc8OL5+A3PjjTcCcNpppzF69Gg23XRTvvCFLzRl341Y3+Or1Wp84AMf4JWvfCUf/ehHV1vf83peddVV/Otf/+L2228Hin09m/Fa/vjHP+ahhx7i0EMPLbwnW6vemz2vUc/x7bTTTgArXsOiVO27pa8qnyv87A3MqFGjVjy+/PLLATjggAOasu9GtOqas+e897Of/YzRo0czevRoDj/88Lb3rGnV69fjoIMOYvTo0ey+++78+te/buq+B2IonRuq/L0J1Tvvre/xjBgxYkVvvUWLFvGb3/yG4cOHs++++67YphPuZ1t5v96JSndu7y9DNVR+Btszas8996zVarXa7Nmza0Btl112WbHN008/vc7Wpeuuu67205/+tPbyl7+8NmLEiNrPf/7zFevmzp1bu+qqq2rf+ta3aptttllt1113rXV3dzcUZzMM9liffvrp2mtf+9oasKI1EagdeuihbYm/r8Eez9y5c2s777xzbZtttqn95Cc/qR1xxBE1oPaZz3xmle06rWeUr9/q+mthfM5znlMDavvvv3/t0ksvre211141oPbrX/+6uQeyBoM9vgsuuKA2evTo2nXXXVe7/fbbV7SA3H777bWlS5fWbrjhhto222yz2uvZruPrrZmv5fOe97waULvhhhtaFu+6tPq9ufnmm9eAFb//uc99rgbUTj/99OYdRAOq9t3SV7vOFUXws7dm/X32elx22WW1jTfeuLbHHnvUHnrooeYEPwCtvuZ8wxveUANqe+21V+3SSy+t7b///jWgdvbZZ7fuoHpp9et33XXX1U477bTaZZddVjvllFNqI0aMqI0ePbpt19RD6dxQ5e/NWq16571mvTefeOKJFb2GzjjjjFXWFXk/24779Vqt83pGddq53Z5RLbTlllsCsOGGGwKs0oq0dOnSdbYqveIVr+Btb3sbxx13HEuXLuWCCy5YsW7s2LFMmjSJI488koMOOog77rhjxRjcIqzvsY4YMYJf//rX3Hrrrfzxj3/k1FNPBXKWuUjrezxXX301d955J6961at4+9vfzn/9138BcNlll7U44sHx9RuYXXbZBYB3vvOdvPGNb1xR5LTdLYzre3xz5sxh4cKFvPKVr2S33XYDcq2J3XbbjQceeIAJEyYwe/Zs/vKXv3Drrbfyohe9iGHDhrHXXnu1+IjWbLCv5dVXX82NN97Ivvvuy4QJE1oX6AC16r256667AnDPPfcAuXBm7+VFqdp3S19VPlf42Ru4733ve7z5zW9mjz324Nprr2WrrbYaXLDroVXXnD3nvTe84Q288Y1v5B3veAdQnvPeurziFa/g6KOP5uCDD+aYY47hBS94AQsXLmx7EfqhdG6o8vcmVO+8N5j35ty5c3n1q1/N9OnTOffcc/n4xz++yvpOuJ9t5f16JyrbuX3EujfR+th4443ZYIMN+n3B//CHP3DmmWey3377scEGG3DOOecA8NznPheA4447juXLl/PsZz+be++9l8svv5wNNtig0BvGtVnbsQIcddRRvPCFL+Shhx5i6tSpjBs3jiOPPLLNUQ7c2o6n5+R+5ZVXcs4556zoYttzInnggQe44oormDlzJpC7Sp5//vm8/vWvZ9ttt23TETRmKL1+AFdccQUPPPAAtVoNgPPPP59tt92W17/+9RxxxBFceumlnH/++Wy44Yb89Kc/ZYMNNuiogvRrO75DDz2Uvffee8Xzt7/97QwfPpxLLrmEcePGcd111/G73/2OHXbYgWuvvZarr76aD37wg+y4447tPIQBW9drCXD66acDrLho7WSDeW9OnjyZD3/4wxx11FEceOCB/OxnP2PnnXfmoIMOauchNKRq3y19DeZc0en87K387J1//vl85CMfYfPNN+eII47g6quvBnLyZpNNNmnbMazNYK45Dz/8cM4880x++tOfsttuu/Hd734XgFe/+tXtO4B1GOx356abbsqee+5JSomZM2cybtw4IqKdh7BWQ+ncUOXvTajeeW9tx/PEE0/wyle+kn//+98ceuihbLbZZkybNo2dd96Zl770paW4nx3Md+cTTzzBtGnT+NOf/gTkBP7555/Pfvvtt6JBuNN05Lm9v+5SZfrZfffdD9l9993/ufvuuy/efffdb999990/NNDfHewwvf32269Wq9Vq99xzTw2o7bTTTiu2YS0Fwm655ZbahAkTaptuumlt1KhRtfHjx9eOOeaY2uLFi2u1Wq32zW9+s/asZz2rNnLkyNoznvGM2ste9rLapZde2lCMzTLYY63VarV99tmnNnr06NrGG29ce/WrX137xz/+0eKo16wZx3PuuefWnv3sZ9c22mij2hZbbFE79NBDaw8++GCtVqvVrrnmmhXdb3v/XHPNNS08qjXz9Vvdfvvtt9rr07O/Wq1WO/PMM2s77rhjbaONNqo997nPrf3sZz9r0dGsrhnH11vfba+99traTjvtVNtwww1rW2+9de3jH/94IQVda7XmHOttt91WGzZsWG233XZbpYhmEVr93ly2bFntC1/4Qm3rrbeujRw5svaKV7yikMLzPar23dJXq88VRfKzt7q1ffbe//7393tev/POO1t3UL20+pqzVqvVLrnkktruu+9eGzlyZG233Xarffvb327lIa2i1a/f2WefXdt7771ro0ePrj3jGc+oHXjggW0dVjqUzg1V/t6s1Ro7vkcffbT2qU99qjZ37txV9tFJ573Bvl49v9/3p2e4WtH3s63+7lzT8V944YUtPrL+deq5fV3D9IbV6ln2MoqIlwLXA18BpgEHAt8E3pZSunRdvz9z5sxnAXfuvffeqxSolCRJkiSpUWeddRZXXHEFr3/96znqqKOKDkcqzOLFi7n55psBdp4wYcJdfdeXvWbU0cB1KaUTUkq3ppS+BfwYmFJwXJIkSZKkIWTu3LlMnz6dWq3G9OnTmTdvXtEhSR2r7MmofYDpfZZdCbw4IjYsIB5JkiRJ0hB08cUXs3z5cgCWL1/ORRddVHBEUucqewHzbYCH+ix7ENgQ2AJ4YCA7qXcdkyRJkiRpvcyYMWNFgeilS5cyY8YM9tlnn4KjkjpT2ZNRTWHNKEmSJEnSYEycOJErr7ySpUuXMmLECCZOnMiECROKDksqRK+aUf0q+zC9B4Gt+yzbGlgKPNr+cCRJkiRJQ9Fhhx3G8OH5Fnv48OG8+93vLjgiqXOVPRn1B2Bin2WvAf6SUnq6gHgkSZIkSUPQuHHjmDRpEsOGDWPSpEmMHTu26JCkjlX2YXqnA9dHxPHAJcCrgcOAtxUalSRJkiRpyDnssMOYM2eOvaKkdSh1z6iU0p+BtwKHAv8EPgV8NKV0aZFxSZIkSZKGnnHjxnHqqafaK0pah7L3jCKl9Evgl0XHIUmSJEmSpHUrdc8oSZIkSZIklYvJKEmSJEmSJLWNyShJkiRJkiS1jckoSZIkSZIktY3JKEmSJEmSJLWNyShJkiRJkiS1jckoSZIkSZIktY3JKEmSJEmSJLWNyShJkiRJkiS1zYiiAyjYBgBLliwpOg5JkiRJkqRK6JVn2aC/9UM9GbUtwG233VZ0HJIkSZIkSVWzLTCr78Khnoz6K/BK4AFgWcGxSJIkSZIkVcEG5ETUX/tbOaxWq7U3HEmSJEmSJA1ZFjCXJEmSJElS25iMkiRJkiRJUtuYjJIkSZIkSVLbmIySJEmSJElS25iMkiRJkiRJUtuYjJIkSZIkSVLbmIySJEmSJElS25iMkiRJkiRJUtuYjJIkdZSIeF9E7NHP8o0i4n1FxCRJktRXRGwYERdExM5FxyKVzbBarVZ0DENGRGwCkFJ6suhYpKqLiOEppeVrWLdZSunxdsekgYmI5cATwDtTSr/utXxr4P6U0gaFBad1ioifAuellGYUHYvUV0S8CzgQ2Jo+jbIppdcVEpQGLCL2Aw4DdgJG9l6XUnpVIUFpnSJiLHASa/7sjS4irmaJiMeB56WU7iw6FqmviNgLOALYFfhQSumBiHgTMCel9PciYxtR5B8fKiLiCOBYYIf683uAk1JK5xYaWBNFxLb0f2Hw+2Iiap5O/gA3KiKOGei2KaWvtzKWNjgP+HDfhRExBrgKeFnbI2qyiHg/a74oH19IUM3zTeDnETElpXRW0cE0U0Q8E6illO6tP38R8G7glpTSdwoNrjlqwGUR8QDwXeDClNJ9BcfUNGvpnVcDFgG3p5T+0b6IWi8iNk8pzS86jsGKiFOA/wKuAe4nv2YqiYh4D3AB8CvgAOByIMjX1z8uMDSt2/nAC8nXZlX87P0vcBDw7aID0fqJiIOAjwG7AAellO6JiMOB2Sml3xYb3fqLiFcDVwC/AV4FbFxftSvwPuAtBYUGmIxqufrN/xeBM4Df1RfvD5wWEWNSSqcWFVszRMQ25AuAfeuLhrHqCabUPRg6/QO8HiYPcLsaUPZk1P4R8ZWU0ud7FkTEpsCVwNPFhdUcEfFf5O+W7wD7AecCu5OTbKcVGFoz1IBvAX8ApkXEbsAnig2pqS4iJ2m+HxFbAb8FZgHvj4itUkpfLTS6QUopHRoR44D3Ax8CvhgRV5Hfq5etqcdiiZxLvn7agJXnu2HAsvq/wyLib8BrU0qPFhPi+qtft9ydUppWf34x8I6IuB94XUrpn4UGODjvB96dUvpJ0YG0Uv3ztw/990A5r5CgmuMY4BMppXMiYgEwBbiTnOB4pNDImiQiPgbMTyn9sM/y9wCbpZTOLiayQXsV+Tvxj0UH0iK/B74cEc8F/gqsMgqmKt859WuW95Lvg76QUno0Il5O7rVe2l5hEfEOcqL7QnLvvQ3rqzYgf8+UNhkFfAX4bErpm/XvzR7XAp8sJKJeTEa13mTgyJTS93otuyoibgOOB0qdjAJOJ39Qnw/8EXg9sA1wAnBUYVE1T0d/gBuVUhpK49lfA/xfRDyQUvrv+jDZ/yXfLL622NCa4iPAR1NK0yLio8AZKaXZEXEi+QakzIYBpJSurF/kXAaMp4SfuTXYG/hL/fHbgJRSeklEvJ7ccFHqZBRASmkuOSl6WkS8jNxLcRowLyIuBM4pcW+pQ4CvAZ8G/lRf9v/ICfzjgXnki9qpwAeLCHCQPgp8ACAiDgAOJje8vI18TK8pLLLBGwnMLDqIVoqItwA/AEYDC1m1gbBGTtyU1S7kBiWAJcAmKaVaRJxObjQ8vrDImucTwOH9LJ9DbsQoazLqsfpPVfX04D6i/tNbDSh9MioiXkBOytxPbvycCjwKTAR2I/fwLqvPAkeklH5YH3XQ40/ke9oy25s8iqKvR4Et2hzLaixg3nrbAf/Xz/LrgG3bHEsr7A9MqbeULidnxi8hD0v8YpGBNcnewC/7Wd4RH2CtWUrpDvJN1Fcj4oPk7vwjgddUpG7bjuQEMOShQWPqj38AvKOQiFogpfQv8o1+F3l4ZRVsxMpW01eTh5wA3ARsX0hELVIfFvtc4HnkBrCZ5GTw7IhYbRhtSZwOHJ1S+l1KaXH953fAp4BvpJT+Sh4K9upCo1x/2wF31R8fDPw0pfRLcpL0xUUF1SQ/At5UdBAtdiq5F+JmKaVNU0pjev1sVnRwg/QYsEn98QPkG2LIibcx/f1CCT2TlZ+/3u6pryurk4BjI6KSHSFSSsPX8lPqUSK9fINcD3JvYHGv5VcBLy8mpKYJcu+2vuYDz2hvKE23ANiyn+V7k79HC1XJL4QOcyf5Yu6bfZYfXF9XdpsCD9YfP0ZO0NwO/AOYUExITdXzAe77WnXEB3iwIqKL3MrdX82hLxcSVBOllG6IiLeTE1E3Aa+uUOHyR8gnyDnAveSb/RvJSe6yf7f/jtzqDUBK6eF6D43zqEYjym3AWyLiZ+QWxVPqy7emIi3H9R5tHyb3pplHbtF/Y09vqHoi6iRyHZGy2YV8TH1119dBfo3L2mDxOLAV+eb3QFa+P5fS5zxRQo8Ax9V76/2DXt8zUIlaiZDfd2ellJ4oOpAW+BO5LMTN5PP6NyLi+eQEY38Nv2X0CLAHqyek9iJ/x5TVm8gNS3dHxC2s/tlz8oDO90Jyr/y+7qf8PfIfBp5Fvqbu7aX9LCubXwAn1nvNAtTq5S9OoQN67JX9hqUMpgLfrndt7DlRvhJ4J7krfNndQb74ngPcArw7Iv5KPr4qjN/v6A/wYETEi8nD1pYDY8lJxW3I3frvBUqXjIqIX69h1TxynahpEQFU4sLn9+RimTcClwDfjIiJ5LoMV67tF0vg/eTWqBVSSkvqPdzK3DLc40vkIWvfAH6bUuoZsjcJKNWkCP2JiH+zcjjNO4H/7adO1KWUd7jQP4ETIuL9KaWnACJiNLk38E31bZ5FeRssrgK+ExF/Z9VhUXvRf4+NMvkQOdk2gdUbzKpQKxFyT8tXArOLDqQFPkVuBIX8PToGeCNwK3B0UUE12S+A0yPi7nrPYCJib/L54heFRjY4D5K/9yurXjLhKHJZgb3rpRM+Sy6AXep7hrqlrOyZ2NuaGmjK5Efk5Pah5HPBqIg4kHxOKOvQ2B6fIdc/fohc+/hP5EaLa+iAUUwmo1ospfTdiHiEXPzs4PriW4C3pJQuLy6yprmQfIF6NXAy+c3eM1a6CjWjOvoDPEhTgf8h1zV7nNzauIh8k3xmgXENxkNrWF725Ex/jgJG1R+fQi6e/ErgYuDEooJqkjvJPbwe7rN8bH1dqbu8p5R+ERE7kodD3dhr1TVU42L9EuD8ntkC+1Mv7F3WXm7/Se6VcX9E/Ku+bC/ysIXX15/vQHm/Rz9GHpK3I/C2lFLPTcaLKHkjzBCpm/ifwI/rjaD/pM+EHSmlHxQSVROklO7q9fgp4MjiommZ44AXADdFRM85cCvysPzPFhbVIKWUylg/b8Ai4ijg8+Rr697XYPeT36el/u6suxKYEhHvrT+v1UdYfJlc27PMTiCf824n1y29ub78+5S8kaLeS3a/+giDCeRrr5mdMkPgsFqtajNrqkj1KctfTJ7auswz7qyiUz/AgxERjwEvSyn9OyLmAy9NKd1aH75wQUppj2Ij1JrUay4cBlyVUlpTAq60ImI5sE1K6eE+y59JLvY9upjImi8itgYeqcAMcytExDNSSo+tYd2u9XpupVbvCfUe4Nn1Rf8GLkopLSwuKjWiPqkFFakhuEJEvBX4Ibk23RL6FDCv0vdnldV7Zbyg/vRvVbjurLL60MNjU0q/rE949Lx6z6i9gWtSSv3V7CmV+gzq15AbQp9JTnaPJ4+m2LdXw0VpRcR48nDE4eTPXemvVzqdPaM0KBHxPuCSlNJigJTSPcA9ETEyIt5X5ha43lJK15C/gKtkGStbTB8mt+TfSi7OvlNRQTVbROwM7Fl/ekuZp57tkVJaGhHnsvJGuBIiomcmpBrw6YjoXfNkA3KBzJtX+8WSiYgNyS2JHyP3uNydXND7FGBOiafu7nFjRLw7pbRKDZf6MMszgLIXUaaedCrrMMN1qur03QARcQR5kpUd6s/vAU5KKZ1baGDN8w1yLbbj15QULpOIWAjslFJ6JCKeYtXk2iqqlGhLKf2GPENgadUTNK9IKc2rD99e22u355rWlcTO5Dp0fS1i5dDSUkspPVjvcfkuVjbQ/ze5IWZRocE1ST2B+FD9cSUaKiLiK8B9KaVz+iw/AtgupVToLKQmo1pgXSfL3ipw4ryQ3G2z73CaMfV1pUtG9bohXqeSF/m+CXg+ue7Xn8iznNTIxQlTgXE1RX0Wr+8Ah/ZaXIuInwCHV6C469/JSbayF1bs7V31f4eRi50u67VuCXmI3qfbHFMrfB54K/BBchfwHn8lD+kuezLqf4DfRsTXyEm3TcmfxYPJNV9Krz7Mcj9y0dZVhhuWvQh2lafvjohjyEPszyBPlAB5VuDTImJMSunUomJroi2Ab1YhEVXXU0oAVpaBqJT6+/KslNJT9cdrVLLvl0vIyZiex1UejnMPebh232uyV5EntKiEetLpwvpPpVS4oeK99D/L9t/IQ39NRlXQZKr9hdvbMPo/1q3JM9GV0bv6PN+BPG1wz3CorclFvu+hhEW+e/kqK1trvkCujTWDXHj+bUUF1USnkYeMvoaV07XuR77R/wbln0DgRGBqPen2V2CVFpy+Q9zKoGdoaERcQ66rV+aZg9bmMOCjKaWrI6L3Bd3N5Jv9UkspfSoiZgDfIxdl35Z8PnhJSumWImNrhoh4F/nYlpK/L1cZBkXJ60uwcvruz9aHm/S4CvhAMSE1zWTgyJTS93otuyoibiNfkFchGfVr4GVUpIB5Sun7ABGxAblB4qYKJdp6TCbPOPpU/fGalOr7JaX0pV6PTygwlHY4Bziz3iEBYOeIeBW5nu5nigtrcOoFvQekzEXaK95QsTX919R9hDxxVaFMRrVAn4ucSoqIq+sPa8AvIqL3FK0bkIcP/X61XyyB3rWSIuI95Ja496WUZteXjSffiHynkACbpN4FvOfxXcBeETEW6E4pVSGZ+ibgHSmlq3stuyoiPkIu0l72ZNQV9X+nserNcE+CuLRFvlNKBxQdQ4vtwJpvFEetYXnZXEVuCT+KnLR5bRUSUXUnAqcDn0spLVvXxiVU5em7t2PlzMa9XUdOmlbBtcDXI+K55B7QfQuYl/KGMaW0rJ7kfjbwWMHhNFXvwvpVLbIfEWcB308p3VB0LK2QUjq9Xsz7cvLw+xnkXmEnp5TKfL8wbYDb1Sh3kfYqN1TcR26c7zvE/iXkWS4LZTJK66tnGNf+5GFeT/Vat4Q8/WwVunCeABzak4iCFeOJ/wv4KblIaGVUofhgL5uSb576up/+p6Ytm8ombCLiAnJ9r1P7LP8UsEdK6cPFRNY0t5EvAu7qs/wQckHQUouI7cizOj4beDP5vfrret2Cr1Qg2b0tuedQFRNRUO3pu+8kDxf9Zp/lB7P6hXpZfav+75R+1pX9hvHf5GR+VV6roWQCcGREJHIJj4tSSncXHFNTpZSOj4iTyMP1hgP/KnvdoZRSWWe9bVSVGyp+BJweEU+Sh+ADHEgeQVL4vbrJqBYYCjWjUkqTASLiQeDUsn/ZrsX29P9a1ihhC3FE/Bp4V0ppfv3xGqWUXtemsFplJnkK2sN7ZiqLiOHkmkMzC42sOe4E7ul7Yx8Rw8iznJTZa1l5Q9Xb1cB/tTmWVpgKfKs+m9cw4JUR8SHysZW2Hk8vN5FrETw/pfQg8MuImE6+6DmQPFy2zK4BnkdFhkH1o8rTd08Fvl2vi9Vz4/FK4J2Uv7csUPmbx0+Th6d/nDyzcSUTwvXP22vIk8mM7L2urLVKU0r7RMSu5FlIPwScGBHXkRt1f5pSKmtpDwAi4g3AtfXjqFzvr/rEK+cCJ5Z9Eos1qHJDxYnkxqTLWPWedhq500WhTEa1xpCpGdV7PDisKBr9KuC2lNK/i4mqqa4H/jsiDqsPZSMinkUeU3x9gXGtr4eA5b0eV9lnyDdVB0TEH+vLXkYu7npQYVE1z53k1pq+taHG1teVdpge0AXM72f548C4NsfSdCmlH0XESHJ9gtHkJM29wBEppV8UGlxzfB2Y2jtRmlL6dUQ8jzzEuewuJA+D2g64kdwbeIWU0l8Kiap5Pk1OuM0CNgJ+xsrpu48rMK5BSyl9NyIeIfcaOri++BZyjbrLi4tMA3QZsCHwR2B5RPQdgljKBt7eIuLFwP+Sr9XGkofRbEOuVXovJa5VmlK6g3zze0JE7ENOTH0dOJPyzzh3EbBxRPyd3HB2DXBdfebV0kspPR0RbyMnNqqosg0VKaWlwHsi4kvkiasA/l7/PBZuWK02JHImapH6zGT/l1I6MyJGkS/MdyOfRA8t+41VPfH0S3KX294FzP8FvKmirQOVERHbAB8DeuqA3QL8d723RqlFxHJgm76FyiPimUAq80V5fTro01JK5/dZ/hHgUymlKCay5ouILYDhZSw4P1TVP3trUksplTkRDEBEbMSq03fPpELTd1dZxWZjW0VEvH9t63uKnZdZRFwL3MrKWQSfR649NA04M6X0P8VF1zwREcD7yAmp7VJKGxYc0qBExAjy8Pv9yY3y+5AbBf8K/Dal9MXiomuOiLgEuCal9O2iY2mFiDiE3FCxZ33RLcApNlS0lskoDUpEPAC8LqX094h4B3nWiBeQu+AellJ6UaEBNklETGTVhMZvK1D3RCUUET1TsH6RPOvVE71WbwC8HNgspfSSdsfWLPUhGF8GPgf0FNqfCHwF+HJK6bSiYtPARcS29D/MpJSTW/SIiJ3Wtj6l1Hdqb6ltIqJvI9mG5F60TwEPp5TGtz8qDVREPAa8LKX074iYD7w0pXRrRLwMuKD3JDtlU298eRd5qvkJ5BqJPwQuTik9UGRszVaf7Og4csJtg4o0UhxJvvb8H/qfxbnM9egqrT5E9u30f032oUKCqnOYXgtExEJgp5TSI+uqH1Xm3gt1XeSpISFP4f2zlNJjEfEz4Etr/rVySSnNIM+MURn1lu+jyTVctia3fq+QUtqzv98rk4jYktwzqudYbgbOTik9subf6njvqv87jDxjYO+aGUvIQ/Q+3eaYmqre03IrcrfpntnllpB7S5UyERUR/2bgtQRL/dmr90j8MbBvfVHPDI89Sn1RXsVkU5Wn7x4KdTx76282tvpn8vv0X4uvlOrH1PemqgoFsZexcgbEh8kF228FHiXfSJZSRFxBvt6cS57g4sMppZuKjap5ImIcebKOV9V/dgL+TG5Eu3otv1omZ9X/PaL+01upJ0eIiL2AESmlG/ssfy6wtMyzAUfEQeRRPjcDzyXX9NyFnAf641p+tS1MRrVGT9daWP3DWjUPAHvXe0hNYuV00GPoM51wWVWxkGTdt4C3kmcFvI6K1TmLiP9Hnl6+m5Vfth8Ejo6ISWWt69LTKhoR15DrnHQXHFJLpJQ+X5+VZkV36ZJPlDDQ6ZGr4HRywun55M/e68k1T04AjiosqkGoJ2suTSktWVfipmzJmroqT989ZOp4rklK6cGI+Bx5VqXSFqGPiM3INTvfSZ/rsbpSJ7rrbiJ/d94B/Ak4NiJq5OvrtJbf63Td5BljZ/RMKlMxD5Mb5y8gN4L+X9WGNVd8coRzgXPI5WZ62wv4T3L9qLI6ETgppfSliFgAHEYuPXMxML3QyDAZ1RI9Y9YjYgNyL4WbUkqPFRpU61wAXALcT27N6Zky8iXklpxSq3IhSXKvmneklAr/ImqRU4FLgf+oF+/rGdN/AXl4W5lPLKSUDig6hlarJ5/+WnQczdB3soeK259cU++f9fpK96eUfhcRi8ld/H+z1t/uTNPI3/0Ps/bETRmTNZW+yUgpfa/oGDrEYvIMwWV2CvBi8nCTS4DDybPHfoxqzLQK8FVWFvP+AnAFuWf+I8DbigpqsFJK7yk6hha7lNwb+MPArsCuEXF1Sum2QqPSQD2X3JOtr78AZa+RtQc5AQWwFNg4pfRkRHwR+AXw34VFhsmolkopLYuIGcCzgccKDqclUkon1osN70iemrX3rEJTCwqrmaaSx0b39Hbbl16FJAuMqxmWUv7pStdmAnB4TyIK8owSEXEyJZ12NyLOBo5JKT1Rf7xGKaX/bFNYUl+bkhP3kM99WwC3A/8gfy5Lp3eypsqJm6GgPtnKweQbxnPrpQV2AbpTSvOKjW7wIqJvvcBhwHbkGWZLee7r5fXA+1JK19YT3X9JKV1c753/QXJP71JLKf2m1+O7gL0iYiz5/VnaHn5VLqwPkFJ6K0B91tgDgNcCX4uIJ4GrU0prLb5fFhHxUXIP5/HA3iml2RHxWWB2SXsF9zamn2WbUf4elwtZmfN5EHgWeSKup4GtCoppBZNRrfdv8njvyt70p5R+1s+yC4qIpQWeD0xOKdXqFz4j61+8nyH3sCnzrCZnk1sV13qBUGJPkGth/bvP8q3pU3SxRPZg5ff2ngzxoSdlVS8w3N9rVyMnu28Hzi/xDC53kOsRzCFP+PDuiPgreWhNmeu1DQkVHYYIrCgq/Btyvcsx5OTFY+RhGGNYWWqgzP5E/i4Z1mf59eRzfpmNA2bXHz8OPKP++DoKbt1vlYgYTu79tZxyN2xP7vN8lcL6QKmTUT1SSjfWE1BPkW/2DyHPGFj6ZFREHAV8ntxQf2KvVfcDR1LCXsG9/JGcZOtbzPsT9N9jqkxmsnLE0jXAV+o1994D/L3IwMBkVDt8Gphanx1qZkpp2bp+odM1UDujllIqeytVJQtJ1m0HHBoRryL3WOjdq60KPWt+BZxXb8W5vr7sFeTutpcWFdRg9B6al1Lav8BQNDjfBz5JvgjoqWf2MnLy+zxyjYJfRsSh/SX7S+BC8jFcTZ5h9QpW1k8sa82o9w1025TSD1oZSxusaRhiTwK1zDcc3wR+Tx5K07ve3q+A84sIqAX6FjBfDjxSkfo1c8jXYXeTk96vJ/f22pdVZ5YtrYj4JnBzSun8eiLqWvK1y5MR8fqyzkZa9cL69XPEq8i9onreo9cC/0FOAFTBZOAjKaVf1od49fgbufxFmX0euDYinsPKUgKvJl/LvKqwqJrjc+QeXgDHkz9zp5Nr0H2goJhWMBnVepeRs/9/BJZHxCpFvUs6c0sjtTPKnoyqaiFJgGBlob5d+qyrQo+bo4HvkeuY9RTLHEYeH1362hIRcQG5qPepfZZ/Cnh2SqnsLeBVtgNwet86UhHxBWDHlNLBEXECcCxQumRUSunMXo+vjYhnk+u83J5S+mdxkQ3KuX2ejyB33e/5rhzGysaLUiej+g5DrNfam0BuDf9MIUE1zz7AK+pDtnsvn0P56ykB1ZztsZefk2vS/YFcyPwnEfFh8jXp1wqMq5neQi4sDPA6cqmPF5B7MXyVkte77K0qhfXrTiYnnb4MXJNSmr2O7ctoZ3LjdV+LWFnnrJRSSn+tT3z0GXJvNshJttLP+phS+nuvx4+Sk/gdw2RU61VuNr0hVjujkoUkofoFsFNKjwNviYjdyBdzkJM3swoMq5leS/+tiVdTgWTbmkTEjsC9JZ+N523Ai/pZ/mNyK//h5OK8n2pnUK2SUroHuKfoOAYjpbRxz+OImES+8fgU+aYYcpJjKrkFslLqdff+XL9p/G/ghQWHNBgjWH34GuThQmUdvr2a+vfkfuRh6atcp5W5Nk9K6fO9Hv88IvYh9xpKKaUriousqbYiD3uCfJ7/SX3o1xNUYxhpX1UorE9KabuiY2iDe8g9hfomvF8FlL5Qe72xrOqF9juOyagW65lZT+VU1UKSQ0lK6XZyDZ6q6QLm97P8cXJdjaq6C/hXRPxnSum6ooNZT0vJN/R39Fn+wvo6yD1ullAS6ypO21uZb4brTiPXEuz9/rsmIv6LPAx4z2LCarmHyT1qy+xacrK3J2Ffq/f8Oo7c0FR6EfEucq/gpeSGs97XKjVKXJsnIrZKKT3c8zyl9BfybFdV8ii5wPC9wCRW1vXcsKiAmqHihfWB/P4E3kueHOELKaVHI+Ll5Bllq1A7+BzgzIh4qv5853qpj5Mpf69ZACJiDP0n8UufbOtUJqPaqD42emTvZSmluwsKZ72tq7hpb2UudAoQESOB4b1rLaSU5kXERhGxvM/sgaUSERuRh7IdSP9fvKW/oapflK/p+F5XSFDNM5tcm6BvT69Xs3qrVZV8iNxV/FTgpQXHsr4uBM6t99rrKYz5UnJPm+/Unx8AlGlIW9/itGtS6pvhul2Auf0sn8vq9XpKp35D1VvPTeOXyAXpy+wY4LqImACMIg/12gvYiNy7rQpOJNcD+VwV6pT2cV9ETCcPhb00pbS46IBa4GfARRFxG7lA+/T68udT7oa1KhfWJyJeQC4LcT+wO7mn7KPARGA34N3FRdccKaXTI6ILuBzYmJzAXwScnFL6zlp/ucNFxB7kJH7fXuvDyO/bss+o17FMRrVYRGxGvth5J30SUXVlfHOvrU5UbzXKXegUcs2r37N6Yb7J5IKZb257RM3zLeCt5GO8jmrUiVohIk4ht35fQ744qNTxkXtgnFqfprynB99E4CvkmgWVlFL6Xv3hF9e2XYf7DLmXySdZOSPNA+SaJ6fVn19KiepF9VectsJuBL4aEe9NKT0BEBGbkj97pa4tUfcgq39fDiMnuQfcGNWJUkq3RcRzyefwBeTr4IuBs3r3uCm5bYHzKpiIglzr5D3kYvPnRsTPgO+Xtaj3GnyKXPx6R+CYlFLP8NHtWdlYUUZVLqwP+T7hvJTSZyNiQa/lV9EBRaKbJaV0fERMJc/uXAP+3XMeLLnvAwuBN9L/OVAtYjKq9U4hF259O7kGyOHkKVo/RknrugyBOlG97UOeYaGvGcBn2xxLs70JeEdKafq6Niyp9wPvLnvvvDVJKZ1Z78EwldzCD3lY12kppdPW/JvlU59R6DnAnJTSYwWHM2j1eldTyTOtblZf9nifbR4oIjYNyH+S6wfeGxH/qi/bi9xCXPYel5B75fW2nJw8vaMKCY6U0kPACUXH0ULXAM8j956tlPr1yvSIGE1uTHsPcHVE3Av8MKX0hUIDbIJ6jbbVZiZLKZV6trKKF9aHPMy+v5pe95N755deRGxIbkD7GLlnFMBTEfHf5GGJpR0tQj6Hv8DheO1nMqr1Xg+8rz6j0HLgLymliyPiAeCDlH+2uarblFxcsa+nWTlNZlktBaowhn1NRgIziw6ilVJKn4+Ik1hZo+aWXq2opdXP1NbXkGcQKvXU1v3pm4SqinpX/tcAO7H68PRS99xLKf0tIsaTb4T3qC++ELg4pbSwuMiaZmFK6a/9rYiIw1JKF/e3rizqNaL2oP/h21VonLkQ+HpEbEfuxbfKDWK9zlKp1T9nPwR+WJ+t88fkul+lT0YBRMSW5Bv+nnP7zcDZKaVHiotq8OqfvRfT/3mh1LOQkq+pN+ln+S7AvDbH0ipnkpPAU8jDKwFeTk7ub8bAh+t3or+Rex9WIhlVL6lzaUppybrK6xTdaG8yqvXGsbJ16nHy+G/Iw6L+u4iABmso1YwC/k3usjm1z/I3Uv4vrLPJPfUGXHi4ZH5E7v1V6tbEdaknn/q9cSyxvlNb70GFpraOiDvpvwt4jdy75nbg/JTS5W0NrEki4sXA/5J71Iwld3nfhtwF/l4qMIw0pfQU5R4yszaXR8TLU0qrFNiPiHeSh0eVNhkVEa8gx789q9euqUpdkJ5GzrP6WVeJY6w3UryGXCz6EOAp4NxCg2qS+vTyVwHdwB/riz8IHB0Rk8qaTKzXSLyCnJyB/F4cDiwjN/CWPRl1JTAlIt5bf16rN8p8GbisuLCa6l3Ae/pcm9zU0zORciejPgqcHRGbk+t1Pt17ZQlrPE8jX3c9zNrL6xReUsdkVOvNAXYgj/++g9xT6gZyvaGyjrEdSjWjTgUurA+H6l2X52OUv+DidsCh9Zkw/sHqraf/WURQTfQIcFxEvIz+j6/sRZSrrOpTW3+fXC/q76y82XgZuUDteeTu4r+MiENTSqWpG9XLVOB/yBemj5PPd4vI544zC4xrvQ2xRphvk4dCvaw+pI2IeDtwAXn4c5mdC/yBXN+rqnVBKlu/LSJeRG6UeCe5cffX9eeXp5SeXsuvlsmp5JqB/1EfstfTo+gCcuNaWRtjTif38HoRcB95WNtYcv3STxcYV7NMAa4mTyqzEbnm43hyA8xxBcbVTEvpv4j+beSkYtl1AT9n1fNCKQuY9y6p0+nldUxGtd7Pgf3JFz9nAD+JiA+Ts5VfKzCu9dbpb+pmqg+pHA0cTy4qCfkkelRK6YfFRdYUQe7CDytbqnpU4QL9Q+Qb4Qn1n96qMKNXlVVyautedgBOTyl9qffCiPgCsGNK6eCIOAE4lhIVMe/l+cDklFKtPjx9ZEppdkR8hnxD9T+FRrd+hkwjTErpixGxLXBVROxLnqHzB8CHUkplLy2wE3BISqnvLKSVUfHaPH8hz0D6JeCSlFJVhj/1NgE4vCcRBbmOVEScTG7MLquXAgemlB6PiBp5puo/R8RnyYmqFxYb3uCklB6oz6j3LvJrOJw8AuaiChVpvwD4BLluYm8fIw8PLrMfkCe1eAvVbajoSCajWiyl9Plej38eES8nj69NKaUriotMA5VSOh84vz6Gn7KP2e+RUupbpLZShtjsXlVT1amte7yN1acPhlz35AZyr8tLWJkAL5ueYReQu4jvANxKTjLuVFRQgzGUGmHqjiA3pl1Hnpb8IymlHxcbUlP8Dtib3HuhkiLifWtYtWIYcErpH+2LqKkipVSFc8DaPEGuZ/bvPsu3BspcE3JDcgMh5HPB1kAilzJ5dlFBNUO9sPetwBtSShdS/sTMmmwOvCMiJpKTwgAvIfdmnxYRZ/dsWMLRFXsCL0wp3Vp0IK0QETsC+9F/rcRCG+dNRrVZSunPrPwAV0LFCxKukFJ6JCLGRMQbgdtSSn0vFEopIsYBuwL/SCn1V6xdareqTm3dYym5FfiOPstfWF8H+caxrDPT3EROHN4B/Ak4tt4S/hHyzYc6TES8pJ/F3yS3Fn8PSD3blLVmTd3hwPciYmf6rwtShckRziVf32/Aytb9YeQk8TBgWET8DXhtSunRYkJcP0MgEQXwK+C8iPgoK4tEv4I8fPbSooJqglvJN/x3kksn/GdE3EfuaXNPgXENWkrp6fooiioMVVub3cnlBSBfj0EeLXIfebRFjzL2KrqJnKipXDIqIt5FPo8vJZcw6f36FD5SxGRUG9Rv9veh/2zkeYUE1SRVL0gYET8B/i+ldGZEjCIXit4NWF6v5/KLYiNcfxGxKbkY7aHk1203YHZEnAvc33cIURnUW2WOSSk90buFpj8lbLUZMqo6tXUvFwLn1r8/exonXkpOwvUk2w4g3yyX0VfJM5FCnt3qCmAG+SLo7UUF1UwRsRd5+GjPbFf/AqamlP5VXFSD8ifyeaB3Ue+e50eQi7uWsnZGH7uSe0Yd2M+6sh9bj0PIZSA+TX5dAf4f+YbjePLMXheQa7t9sIgA11e9B8pxwGHkBtBVhm6nlKrw+h1NvnH8LXkSCMjX1T+nvL1lIZcq2aL++MvkIu23kWesfk9RQTXRhcCRwMeLDqRVKj6i4ovAN+rlEm5i9YaKhwuJqjlOJA+F/VxKqeMSpiajWiwi3kJOyIwmzyTUNxtZ6mQU1S9I+ErgpPrjNwGjyDMkfgj4HFDaZBT5uHYm3wRf02v55eQvrtIlo8izro3o9XhNythqM2SsoZfGCiXvmQHwGfLwtU+SP2sAD5BvIE+rP7+UctaLIqX0m16P7wL2ioixQHdKqfSfvYh4Pfn1+QsrJ7bYF/h7RLwxpfS/RcU2CENlWPNQKGB+OnBESun/ei37XUR8Cvh2SmnviPgv4LvFhDcoJwDvA04hJ9OOJb9330ZOfJdeSulx4C0RsSsrr2NuKXuds97DfFNK/4iIZ5GPb05KaW5hgTXPOOCdEfFq8nD7VYZU2gDa8XrO21dQgQLmfWwLnNeJiSgwGdUOp5Jbur+QUirr7HlrU+mChOSZFXpqRE0CfpZSeiwifkY5kzW9HQK8I6X01/pr1+Pf5BlASqd3q03FW3Cqbk29NHqU+aKAlNJy8o3U1IjYrL7s8T7bPFBEbM0QEdfQ/01+LSIWket+fa/EdWu+ApyWUvpM74UR8XVyr7DSJaN6il7Xe56cC5yYUrqz2KhaYkdyXZe+Q2SrZBdy76e+ulnZi/02VvZSKZN3khNtV0TESeRrslkRcSs5IfztYsNrjoh4Lbn33ooRFRF5FFRK6bDiImuelNJTETErpTS/6FiaZHfgb/XHO/ZZV8Wkd9VU+Z7hGuB55PpsHcdkVOttAZxV0UQUVLggYd0DwN4R8QA5GdUzrfwY+nThLKEtgYf6Wb4xqyYBpHbr20tjQ/LsNJ8jT59cGX2TUBVxG/AO8jmhpxfbi8nnw8uAV5HrhUxMKf2umBAHZQ/y8Oa+zqfkQzTqtU/exsoee1VzPfnapMrJqH8CJ0TE+1NKTwHU69l8kTz8BPJspWVMeG/LyuHLTwKb1R9fRvkbCAGIiK+Qe3z9nQr13ouIY4C7U0rT6s8vJhfDfoBcv6ysw9IBG0DLrqTXImvUZ4TBhcDXI2I78izqq9QjLXq0gcmo1vsVeahXR2Yjm6CyBQnrLiDPanU/uQ7Wb+vLX0L5i9zdRG5561sQ+t3k2lilVy/at0rrYo+U0usKCUrrtIapye+IiMfJCamr2hxSU0XEv1nLDUZKac81rSuJx8gzAx5Z7wVGRAwjD99+PKX0voj4NrkX0SsKi3L9zSMnpPoWU342/fdIKZv/BQ6iIr1M+vgucFpEPJP+64KUfQgw5GnXLwfuj4ieGmZ7kWvzvL7+fAfgzAJiG6z7yTN33Q3cRf7++Dv5OnTpmn+tVD4CvC+ldFHRgTTZR4EPAETEAcDBwFvIQyynAq8pLLImqk/q9Kz607vqNTDVgeoJm5kppWUVLA/R3wiDs/rZrvAhiCajWu8/gR9HxAvof+aWUhf4Zs0FCZeQkxqlllI6MSJuIXe5/WlKqXc2eWpBYTXLCcD/RMQO5C+id0XEHuQCwxOLDKwZIuIU4L/I3VPvpyKti0PcbeQeUmU3rc/zDclDml9GTtiU3QeBV/YkogBSSrWIOBP4P3Kr/9lAWVsiLybPdjWOfDyQG52+Rskn7aj7PfDliHguuWGib+2TnxQSVXP01K35737WFX5R3gwppRsiYjy5KHRPD/XvAxellBbWtylrouNq4I3kmjzfBc6KiEPJs3f+qMC4mmk5K3uUVsl25AQi5ETUT1NKv4yIxMpZA0urPsT5ROBjrBxhsDAi/ptcqqWss+NW2Z+Abcg1PPtL3vQo47mhNHUgTUa13kTyONTXkhM0fQuYl/rCdQgUJCSltFoR4ZTSBUXE0kwppSsj4k3A58kXP58DZpK7S1dheuv3A+8u+Y2T6iKii1z4+66CQxm0Nc1UGRHHknvxld0ocstw6rP8WcDI+uOFlHc48LHknpbnsPJ4lpATiZ8rKqgm6mk9PaL+01sNKPN3amku0AejnnQq+wQ5q0kpfaTey5KU0nci4jFyIngaudZZFZwN/Afw2aIDabLHyb3a7iH3WD+lvnwpK79Hy+xM4K3kUgI9ybWXkxt+NwMmFxOW1mJnVtYFrtS5YQ0jDDqSyajW+wa5jsTxKaXHCo6l6SLiAvIsH6dCLkgI/C0iPhURe6SUPlxshINX73L7YvI0wqucMMves60+69Vv1rlhOY0kJ9dUMhHxFKvPZjISWAC8q5Cg2uOnwJ/Js+yV2c+A8+sTWfy5vuyl5Bk8f1p//jJKOtQ5pfQ08F8R8XlWFoS+o6c+T9mllIave6tyKtMF+mBU/Lql1uvxT1n5nVIVJwJXRMSN9D+U9EOFRDV4VwHfiYi/k783r6wv34sKNDKRr03ek1K6vNeymyLiXuCHmIzqOH3OB+OAp3tql0XEG8gzp99CyevRRcTHgPkppR/2Wf4eYLOU0tnFRJaZjGq9LYBvVjERVfda+h9WcjV5iFSpRcRu5Gk+e244auQW8WXkC4TSXtRFxGzgxX17sEXEM4C/pZRKOaNeLz8C3kROCKtcJrNqMmo5uRv1X1JK3cWE1BbPo/wTIwAcSZ5J9rvkIYiQj+t84NP1538nt/6XzppmC6zPSlqF2QJVYlW+bgGIiC3IdZV6auvdAnwnpfTImn+rVL5Mrp90E3mimaqUGPgYuU7gjsDbUko99fVeRLl7W/ZYyup1BCGXF1jW5ljUuG+Ty6/8MyJ2Ir8nrySXnNkI+FSBsQ3WJ4DD+1k+h3ydZjKq4n5NbgGuagHzLqC/aVkfJ2eZy+504GbyyfI+cl2XseQE3KfX8ntl8Cz6HwM9Cti+vaE0R322lh4PA8dFxMvIxfX7zh7x9TaGpgaklL5XdAytFBF9T/zDyPU0JlGBotH1HkJH1j+PPTfEs1JKT/ba5uZCgmuOqs8WSER8FDgKGA/snVKaXe/pNtuhzx2vstctEbEf+TM2n5UTrUwGjo2Ig8v6eevjY8AHyt6Dra/6zLFH9bP8CwWE0woXkG/6/7PP8o+RZzNTZ9ud3EgGuaj+H1NKb65/5/yAciejnkn/vQ/vqa8rlMmo1ruWPJ3ic+m/u23ZL+pmk2tizeqz/NXkjGvZvRQ4MKX0eL3Ve3hK6c/1i/LTyRd5pRIR+/Z6+rKI6N3TZAPyDXFZZ0Ls2w36cXLB675Fr2uAyagOFhFbAe8FdiUX/3w0Il4O3J9SurPY6BoXEYcA/1sf4rVHn9U9Pb8+Ri40XAn15NNN69ywfB6jwrMFRsRR5FqCU8lDhnrcT+71Vvbrlqqr3HVLL2cB/wN8pGeWsvqQxHPr655bYGzN8jTwh6KD0Lr1aVgaTp4IaCIrh6e/hFwnq++kJeo8G7CyB9sB5M4kkO9ztyokouZ5hHzdeVef5XsBhY82MBnVej1D2Kb0s67shUAht+KfGhGjWFl7aCLwFXJX47LbkJzQgNwKvjW5KO9sVs5SUzbXkt97NeAX/ayfz+otO6WQUqpUAcKhqj776G/JN7+7k2+KHyV/t+xGOWfq/AWwLTnptC+wbUrp4WJD0nqq+myBk8k3+7+MiC/2Wv43HPZcBlW8bumxG3BoTyIKIKW0NCKmsrJXQ9l9h/wdU4XJEKqub8PS3+r/9owuuK/+s3vbItL6+idwRERcTu5Q0TOBwA7k79Ey+wVwekTcnVL6F0BE7E0+n/d3H9hWJqNarMqFQAFSSmfWezBMJQ/vgjwc6rSU0mnFRdY0t5LrEtxJHur1nxFxH7krbll7D21LHhZ0P7kVsXedhSVVqcnTt7h+r+WfAp6dUupv/LQ6wzeA81JKn42IBb2WXwV8oJiQBm0eecjaw+TPX1XqgAxFVZ8tcGfy+a6vRcCm7Q1F66GK1y09biYXZe87+cGzyLWjqmAL4NB6D5sbWX1ERSkbC6sopXRA0TGoaT4LXEoeyvz9XqUE3sDKIcFldRzwAnJB/Z5G0K2AP9IBs3aajGqD+pTkr2H1WU1qKaUT+/+t8kgpfT4iTqJXMcnetUFK7gzyhQHknl5XkeuFLKGcvTNIKT1Uf1jpRCkVL65fcS8kF6jt635yK38Z/Qy4JiIeICeiboiIfouaVmDygKqr9GyB5ITFXqw+1P5V5PNfqUTEvxlg8jeltOe6t+p4/V233A4spoTXLfUGzx5fBL4ZEV8F/lRf9v/IN1v9jUAoo91ZmQzetc86GzGkFkgp/T4itiTPLte7Uf67QKnvaVNKTwCvjIgDyUkpyBNV/bbAsFYwGdViEfFicjX+ZeQCkg8C25BbTe9l1XoMpVVPPpU9c7yalNKPez3+R0Q8i9wtd07fWejKKCJ2BPYj3+CvkpyqQIHvqhfXr7KlwCb9LN+F3MOojCaTaxDsTq5X9gPgiUIj0vqq9GyBwDnAmRHxVP35zhHxKuBk4DPFhbXehlS9lgpetzzIqkmYYeTvz1qv5wC/ov9JWUrF3jZSMVJKy+hTQyml1LcmcmmllH7DypI6HWNYrWaSvZUi4lpy6+hk8k3w88hd3acBZ6aU/qe46LQu6xjqtUdK6cPFRDZ4EfEu4HvkG/9HWPVir1b23hkRcQt5uOj5fZZ/BPhUSimKiUzrEhE/qj98L/l787nkotGXATenlI4oKLSmiIgLgY+nlBasc2N1rIjYhDXMFlh2EfFl8uxBG9cXLQJOTilVoRZk5UXEa4ED6b+h6bBCglpP9dmsBqQis+lVVv26c03vy9cVEpQ0BKxllBZFn9ftGdV6zwcm14ubLgdG1qdI/gx5GlCTUZ2tykO9TiTPrPO5emtA1VS9uH6VfRq4hjxL50bkYVHjyb1JjyswrqZIKX2w6Bg0eBWeLZCU0vH14fd7kW8a/1WlZFuVRcRXyEX0/87qvYpKxwRTNUTEKeTr5mvIQ+5L/b6UyqI+Sut/yTM39zdKy2RUxS1jZfHBh8lV+W8lV+bfqaigNGBVHuq1LblIdBUTUUOhuH5lpZQerM+o9y5gAvlm+L+Bi1JKiwoNTqq4iNgIOJo+PRgicmfSstVVGoI1oz4CvC+ldFHRgUi9vB94d0qp7LOIS2Uzldz5pWeU1r70GqVVYFyAyah2uIncO+oOcrHFYyOiRr5Y6DsTjzrPbOAAcg+N3l7N6sVdy+Ya8rDR2UUH0ioVL65fafWk04X1H0nt8y3greRi7NdR/h4MQ6pmFLn1+y9FByH1MRKYWXQQrVQfuv1C+h+GaBJORXk+HTxKy2RU632VlVMhfwG4AphBrtHztqKC0oBVeajXhcDXI2I78vTBS3qvTClV4mK2qsX1q6w+y9w1wFtSSo/3Wr41cH9KqfRFaqUO9ibgHSml6UUH0gwppS8VHUObnU0unl/4lN1SLz8if7d8o+A4WiIiXg1cQh4G1VcNMBmlonT0KC2TUS1Wr1zf8/guYK+IGAt0p5TK3tpYeRUf6tUzBflZ/ayrUYFZaVRaw4AtgT9FxOvq352910lqnaXAnUUHofV2InBFRNxI7p3/dO+VKaUPFRKVhrpHgOMi4mXAP1i9AbTsMzh/k9zh4NiU0v0FxyL11tGjtExGFSClVNapyYekCg/12rnoAKQ1qAFvAL4G/CUi3phS+mOvdZJa52zgcOCYogNptnXVj6pIzagvk2dNuomc1Pc7U53gQ+R6NRPqP73VgLIno3YB3mQiSh2oo0dpDavVPEdJkjpHfUz7NimlhyPiC+ThJv9BfRYeh+lJzRURZ/d6Ogx4B7me4D9YvQfDf7YvssGLiOOBU1NKCyPii31Wb0iu8fIy4FsppS+0PcAmi4hu4BMppR8UHUsrRMR+wGH0P0X5qwoJSkNeRFwHnJBS+m3RsUjr0kmjtOwZJQ1hEXEQ8DFyi85BKaV7IuJwYLYnVBVoxckxpXRiRCRyjbMLigtJqrQ9+jy/sf7vLn2WF37huh7eRS7KvnBN9aMi4lhy0eEqeBr4Q9FBtEJEvId8HvgVeXKZy4Eg10D5cYGhqQH1Qt9UZJRBjy8CU+vJ7/7qsD5cSFQa0iJiQ+Ap4AUppX/2LO+kUVomo6QhKiLeQb6ou5A8hfeG9VUbAFMAk1Eqyip1oVJKP4mIOcClxYQjVVtK6YCiY2iVlFLfRFt/fgr8Gfhka6Npi+8AHwQ+V3QgLXAMudfXORGxgHytcidwHnnIiTpYRBwBHEtOHhIR9wAnpZTOLTSw5uipEXwZqybth2EdVhUkpfR0RNxHBzckmYyShq7PAkeklH4YEe/vtfxPwAnFhKShLCJGp5QWkuuZPdp7XUrpzxHxfODZRcQmqZwi4mryzJyPrWWz59Gn0HeJbQEcGhETyT00+hYwL9Uwyz52Aa6sP14CbFKfrvx0cjLg+MIi01pFxDHk3kNnAL+rL94fOC0ixqSUTi0qtiapbEJfpfdN4PiIeG9KaXHRwfRlMkoaugL4fT/L5wPPaG8oEgCPRsQM4Bfk1sW5vVemlB4CHioiMEmllcgzBPatjQW518J2wCTg222Oq1V2J9f6Ati1z7qObR0foMeATeqPHyAf6z+B0cCYgmLSwEwGjkwpfa/Xsqsi4jZyErG0yaj6UKjjgckppduKjkfqYxLwcuC+iPgXsMrw2JTS6wqJqs5klDR0PQw8C5jTZ/lL+1kmtcMLgTeRL1rPjYg/khNTl6aUfE9KalhKaXKvp32H7C0nnws/Bny/bUG1UJWHXJJ7bu8L3EyuF/WNeo/ZNwH/V1xYGoDt6P81ug7Yts2xNFV9KNTzgWVFxyL140HgZ0UHsSYmo6Sh60fkC7lDya2loyLiQPL0un1bj6WWSyndCpwMnBwR2wNvBN5MLgr6L1Ympm4qMExJJVXxRM1Q8ClWTlH+JXJvqDcCtwJHFxWUBuRO4GDykKHeDq6vK7v/Ad4JfLXoQKTeUkofLDqGtRlWq5W9x66k9RERI8gFzN/NygKLkFuHP5xSWl5UbFJvEfEM8gXrm4GDyEP1PpVSurTAsCRJ0gBExH+Qh8JezMoeUq8kJ3A+mlK6sKjYmiEivgR8Avgj8BdWHwr19SLiknpExM7AnvWnt6SUOiIJbDJKGuLqX04TgOHA31JKdxQckoa4iBi+pmRoRGwDvAR4KqU0o72RSSqziNiI3IPmQGBr8nlvhZTSnv39njpLRAwDxrH66/dwMRFpICLiEPIMiCtuiIFTUkqXFxdVc0TE2m7sayml8W0LRuolIsaQZ1k9tNfiGvz/9u48yq6yzPf4t5JABIPNDI0McgEfQNvbl0loQAgizaB2BAzieIMCgqICUaDhqtAQaEBoEJDBRuUqnQ4yNAIqKohpZulmMs0jEQjYEJllMZNQ/cfeRQ4nlalyct59Ut/PWmfV3u/ep+qXtQKn8uz3fV6mAPtn5vNFgtVcpicNUxFxIVVl/FRapkhHxOHAJpn5uWLhNNydD8z196/+QL08M7fpfiRJS4GzgL2AS6h61fhEtodExHpUnw878uZ/wwzM7h5ZIJYWUmZeCVxZOseSkJnrl84gzcNpwJbArszZuGoHqpYs3wIOLJQLsBglDWe7Uf1i3u464LAuZ5Fa7RgRx2fmMQMDETGGakvvWeViSepx44B9MvPa0kE0JBcCKwETgEexmNgzImI5oC8zX6zP16Vaej9taZvlHBFrAE/Y7kINMY7qc++6lrGfR8QBwGQsRkkqZCXgz4OMP0c1/V0qZVfg3yPiscw8OyLeCvyU6un3rmWjSephs1g6miUPV1sB27qJRU+6gmpW1NkR8TbgVqpllitGxCGZeX7JcIsrIpYBjqPamXM54J3AAxHxj8CMzHRjIJUyhqp43+5R4K1dzjKXEQu+RdJS6gFgsJ2F3g/M6HIW6Q1137IPAidExASqLbyXBXbNzBfm+2ZJmrdzgP1Lh9CQ/TfVQwn1ns2AG+rjPYGngLcDn6Vq/N3rjqFaAjwBeKVl/HbgM0USSZU7gK9GxBt1n/p4Yn2tKGdGScPXucCpETEa+GU99gHgeKqnO1IxmfnbiPgoVSHqbuD9mflc4ViSekxEtM5I6AP2iYidgDuBV1vvzcyDuxhNi+4oYFJEfCIzny0dRotkDHNm438AuCIzZ0XEDcB55WJ1zMepdgW8LiJadwa8F9ioUCYJ4AiqNhdjI+LmemwbYFWqHaqLshglDVOZeWZErA6cAoyuh18FTsvM08ol03AUEdfM49LTwGvA5IgAIDN371YuST1vk7bzu+qvG7SN23+o+SYBawN/iohHmLuY6G6IzTUD2DYifgLsAuxdj68CvFgsVeesTbXiYDCj5zEuLXGZeWNEbES1hHTg8/BHwNmZObNcsorFKGkYy8xjIuJEWrbZdRmUCvnTPMZ/1tUUkpYqmTnYcnT1psmlA2jITgf+P/A88BDVbpYA7wPuKZSpk35P1dPsobbxD7N0/PnUw+qi0zELvLGAvv5+HwRJkiRJkpaMiNgCWAf4RWY+X499GHgmM6fO980NFxGfBE6jWhL1beALVMvzDgM+kZmXF4ynYSwiNgNey8x76vMPAfsB04BjM/PV+b1/SbMYJUlqlIg4BPhVZk4rnUWSJGlBImI/4OvAuvXQH4FjMvOicqk03EXEbcApmXlJRKwH3Ee16uD/AJdm5uEl81mMkiQ1SkTcR7Ut8uPAr4Hrgesy8/6SuSRJ3RURLwLrZeYTEfES8+ntlZnLdy+ZFiQivraw92bmyUsySzdFxKrAiMx8vHQWKSKeBbbIzOkRcTiwR2buFBE7ABdl5nol89kzSpLUKJm5cUSsBYytX18DzomIR6mKUm6TLEnDw0HAwE6qny8ZRIvsoIW8rx9YaopRmflk6QxSi5HA7Pp4LDCwYdADwOpFErVwZpQkqbEiYiSwNTAB+DQwMjNHlk0lSeqm+rNgW+DuzHy2cBzpTSJiZeBEYGdgDWBE63Vn7amUiLiJasOAq4BrgS0z896I2AaYkpnrlMznzChJUqPUzRZ3ql/bAU9TLdf7HHBduWSSpBIyc3ZE/ALYGHi2cByp3XeBzYDzgUeZz3JSqcuOBK4AJgI/yMx76/EPAbeXCjXAYpQkqWl+S9Uv6lTgS5k5vXAeSVJ5/wWsDTxYOogWXUQcCBwC/C/g3Zn5QEQcCTyQmVPKpltsOwG7ZebNpYNIrTLzNxGxGvC2zHym5dI/Ay8UivUGi1GSpKY5HdgRmASMj4jrqGZETc3Ml0oGkyQVMxE4JSK+BNyRmbMX9AY1Q71L7jHAKcA/tFx6FPgC0OvFqGdxxp4aqv5/5TNtY38oFOdNRiz4FkmSuiczD8/MzakaK04ClqP6BfbpiLihaDhJUik/AbYAbgZejogXW1+Fs2n+DgIOyMxTgVkt4/8BbFomUkedCBwVEU70kBaB/8FIkprqZeB54MX6eBlg86KJJEmluJte71ofuHOQ8ZeBMd2N0hkRcU3b0DbAwxExDXi19UJm7t61YFIPsRglSWqUiPg6Vf+F99ZDt1JtRTsRuKVULklSOZn5g9IZNGSPAO8CZrSN7wT8vvtxOuJPbedXlAgh9TKLUZKkptkVuJ6qr8SNmfly4TySpAaJiDWBZVvHMvPhQnG0YN8BzoyIgb6P60fETsBJwBHlYg1dZk4onUHqdX39/e48KUmSJKm5IuJtwBnAx2grRAFk5siuh9JCi4jjgMOp+kBCtUTvpMw8rlyqzqg3WtkzM59tG38bcEVm7lQkmNRwzoySJDVSRPwlsB5zP/3+TZlEkqSC/hHYEvgo8K/A/sA6wBeBwwrm0kLIzK9HxIlUy/VGAL/LzOJby3fIjgxSIAVGA9t3N4rUOyxGSZIapV5+MZk5v8D1Aa3TeH36LUnDzx7ApzPz1xHxOnBbZl4cEY8BE4BLysbTgmTmS8BvS+folIhYt+V07Yh4S8v5SGB3YGZ3U0m9w2KUJKlpTqd6avrXVFt47wGsCXwTOKRYKklSSasAD9THzwEr1sdTgbNLBNLCqYs0hwI7A2tQfca/ITM3LZGrAx6ieljWD9zedq0PmA18rcuZpJ5hMUqS1DQ7AuMy85766fejmXlDRLwCfAP4ZdF0kqQSZgBrAw8D06keVPwWeB/wfMFcWrCzgL2oZq9N5c2znXvZNlRFp5uo/j4+3XLtVeDhzHyqRDCpF1iMkiQ1zRjmTGt/FlgVuB+4E9i8TCRJUmGXUT2suImqkfmUiPgc1czZSQVzacHGAftk5rWlg3RSZt4aEcsAPwDuzcxHSmeSesmIBd8iSVJXTQc2qI+nAZ+IiFFUOyg9USyVJKmYzDwmMyfVx5cB21It6x6Xmd8oGk4LMgt4sHSIJSEzX6Nqqu+/q6VF5MwoSVLTfI9qt53rgJOAq4HP19fsGSVJIjNvBW4tnUML5Ryq3Q+X1v5JNwJbUy0llbSQ+vr7l5Ylu5KkpVFErEO1nff9mXlP6TySpO6IiE8v7L2ZedGSzKKhi4hzgfFUDejvpOqn9IbMPLhArI6JiH2pHp6dTdXI/IXW65l5W4lcUtM5M0qS1Gh1Dwb7MEjS8HNe2/koYCRzGmAP7Fj2GmAxqrkCuKs+3qDt2tIwM+JH9deTBrnWT/V3VlIbi1GSpMaJiG2Y9xbQPf0EVZK0cDJzuYHjiNiF6h/7h1M1MQf4G+AU4Ojup9PCysyxpTMsYeuXDiD1IotRkqRGiYjDgFOpGpk/ypufmi4NT1AlSYvuNOCgzJzaMnZ9/ZlxLrBpmVga7jLTXlHSEFiMkiQ1zVeAr2TmmaWDSJIaYwPgqUHGn8KZKSosIt5F1aB9oCj6O+DkzJxWLpXUbG5BKUlqmpWAq0qHkCQ1yl3ACRExZmCgPj4euLtYKg17EbEHVWP2DYFf1q+NgDsjYreC0aRGc2aUJKlp/g3YgWrXHUmSAA4Grgb+GBG/q8feBbwM7F4slVQVRE/LzCNaByPiZOAE4KdFUkkN19ffb/sNSVJZETG+5XQl4OvAxVRPwtu3gJ7SxWiSpIaIiOWATwKb1EPTgIsz88VyqTTcRcTLwF9l5v1t4+8E7s7Mt5RJJjWbM6MkSU0weZCxwwcZ6wcsRknSMJSZLwEXlM4htXmaqkB6f9v4xvU1SYOwGCVJKi4z7WEoSZqviFiXahn3GrT1vs3Mk4uEkqqZ3OdHxCrAv9dj2wOTgIuKpZIazmKUJEmSpEaLiH2B7wOzgCeoZsoO6AcsRqmUo6iKo98BlqnHXgPOAo4uFUpqOntGSZIaJyJWAnYF1gOWbb2WmccVCSVJKiYipgM/Bo7OzNml80jtImJ5YIP6dHq9rFTSPFiMkiQ1SkRsSbXzzOvAysBMYE3gReCPmblpwXiSpAIi4gWqJtHutCpJSwGX6UmSmuYUqqffBwHPAe+j2rp7MnBmwVySpHKuB/43YDFKjRIRbwEOBXZm8H5mPkSTBmExSpLUNH8NHJSZ/RHxOrBsZj4QEUcAF1IVqiRJw8v3gJMjYi3gLuDV1ouZeVuRVFLVG2ov4BJgKm/uZyZpHixGSZKaZjZV40+Ax4G1gfuAJ6l6SEmShp9L6q/fHuRaPzCyi1mkVuOAfTLz2tJBpF5iMUqS1DR3U82Omg7cAhwVEf3AAUAWzCVJKmf90gGkeZgFPFg6hNRrLEZJkprmBGBMffz/gKuBX1Bt5b13qVCSpHIyc0bpDNI8nAPsD3ytdBCpl7ibniSp8SJiZeCZzPRDS5KGqYgYBWxJtWR72dZrmXlRkVAa9iLiXGA8VXP9O5m7n9nBBWJJjefMKElS42Xm06UzSJLKiYiNqGbKblAP9VPtWjbQZ9BilEoJqqb6MOfv5wAfoknzYDFKktQo9ZPv/Zn3Fsl/UyKXJKmo04F7gS2A/wY2A1am2slsYsFcGuYyc2zpDFIvGrHgWyRJ6qqzgVOpnibeCdzR9pIkDT/vBY7NzOeoZ0Vl5q3AkVSFKklSD3FmlCSpacYDe2bmz0sHkSQ1xjLAc/Xxk1QzZ5OqT8/GpUJJkobGmVGSpKb5M+CuSZKkVvcBm9bHdwIHR8QGwKHAI6VCSZKGxmKUJKlpTgKOiohlSgeRJDXGGcCq9fFxwFjg98DngKNKhZIkDU1ff78N/iVJzVE3ML+SqkltUu2S9IbM3KlELklSc0TEcsAmwIzMfKp0HknSorFnlCSpac4GdgSuBWbitsiSpDaZ+RLwH6VzSJKGxmKUJKlpPg58NDOvLh1EktQcEbEbsDNV8/I3tRvJzI8XCSVJGhKLUZKkpnkWmF46hCSpOSLieKreUP+Js2YlqefZM0qS1CgR8WXgPcABmTm7dB5JUnkR8ThwaGb+qHQWSdLic2aUJKlpdgW2AXaNiGnM3cB89yKpJEklvQ7cVjqEJKkzLEZJkppmJnB56RCSpEY5B/gscGTpIJKkxecyPUmSJEmNFhF9wNXA24G7mXvW7H4lckmShsaZUZKkRoqI9YFN69NpmflgyTySpKKOo1rGfTewGjYwl6Se5swoSVKjRMQKwAXA+JbhfmAKsH9mPl8kmCSpmIh4BvhyZl5UOoskafGNKB1AkqQ2pwFbUj0BX75+7Q5sBXyrYC5JUjmvATeVDiFJ6gyX6UmSmmYcsE9mXtcy9vOIOACYDBxYJJUkqaQLgAnA0aWDSJIWn8UoSVLTjAEeHWT8UeCtXc4iSWqGVYHxEfEB4C7mbmB+cJFUkqQhcZmeJKlp7gC+GhFvfEbVxxPra5Kk4eedwJ3AC8CGwCYtr43LxZIkDYUNzCVJjRIR2wI/A54Abq6Ht6F6Kv63mXnzvN4rSZIkqfksRkmSGici1gS+SPXEG2AacHZmziyXSpIkSVInWIySJDVKRKwLPJKZc31ARcS6mflwgViSJEmSOsSeUZKkpnkQWK19MCJWqa9JkiRJ6mEWoyRJTdM3j/HlgVe6GUSSJElS540qHUCSJICI+Hp92A9MjIjnWy6PBLYF7u16MEmSJEkdZTFKktQU+9Zf+4BxwOyWa69SLdGb2OVMkiRJkjrMBuaSpEaJiOuBPTPzmdJZJEmSJHWeM6MkSY0QETcAlwMTLERJkiRJSy9nRkmSGiEiPk+1PG9H4D7gCuDyzLyrXCpJkiRJnWYxSpLUKBHxF8AeVIWpXYEngX+jmjU1NTP94JIkSZJ6mMUoSVJjRcRoYBfg74AP18NXAWdm5p2lckmSJEkaOotRkqSeEBEjgO2pClPTMvO7hSNJkiRJGgKLUZKkRouIFYCdgMzM+0rnkSRJkrR4RpQOIElSq4iYEhFfqo9HA7cDlwH3RMRHioaTJEmStNgsRkmSmmZ7YGp9PA4YDawCHAEcXSiTJEmSpA6xGCVJapqVgCfq412ASzPzWeBSIEqFkiRJktQZFqMkSU3zGPDuiBhJVYz6VT2+AvBasVSSJEmSOmJU6QCSJLW5EPhX4FFgNnOKUVsBNjCXJEmSepwzoyRJjZKZ/wDsB5wPbJeZr7ZcPqVMKkmSJEmd0tff3186gyRJkiRJkoYJl+lJkholIsbP73pmTulWFkmSJEmdZzFKktQ0k+cxPjCV12KUJEmS1MMsRkmSGiUz39TPMCJGAZtT9Ys6okgoSZIkSR1jzyhJUk+IiO2BMzJzs9JZJEmSJA2du+lJknrF40CUDiFJkiRp8bhMT5LUKBGxettQH7AWcCwwrfuJJEmSJHWSxShJUtPMZE6z8gF9wAxgvjvtSZIkSWo+i1GSpKYZ23b+OtUSvemZObtAHkmSJEkdZANzSZIkSZIkdY0zoyRJjRMRqwIHAJvWQ9OACzLziXKpJEmSJHWCM6MkSY0SETsAVwHPArfXw1sCKwIfzMwbyiSTJEmS1AnOjJIkNc23gUuAAzJzFkBEjALOq6+9p2A2SZIkSYtpROkAkiS12Qg4eaAQBVAfn1JfkyRJktTDLEZJkprmXmC9QcbfQdU7SpIkSVIPc5meJKm4iFi95fQbwD9FxAnALfXY1sDfA1/tdjZJkiRJnWUxSpLUBDOB1h01+oCLWsb66q9XAiO7mEuSJElSh1mMkiQ1wdjSASRJkiR1R19/f/+C75IkSZIkSZI6wJlRkqTGiYhRwCbAGrRttpGZ1xYJJUmSJKkjLEZJkholIrYDLgbezpxeUQP6sWeUJEmS1NMsRkmSmuY84CbgeOZubC5JkiSpx1mMkiQ1zXrAhzPzD6WDSJIkSeq8EQu+RZKkrroBeHfpEJIkSZKWDHfTkyQ1SkSsBXwfuAa4B3it9Xpm/qZALEmSJEkd4jI9SVLTbEg1M2rnQa7ZwFySJEnqcRajJElNYwNzSZIkaSlmMUqS1DTrAh/KzOmlg0iSJEnqPBuYS5Ka5kZg49IhJEmSJC0ZzoySJDXNPwOnRcQ6wN3M3cD8tiKpJEmSJHWExShJUtP8S/317EGu2cBckiRJ6nEWoyRJTbN+6QCSJEmSlpy+/n43KZIkSZIkSVJ3ODNKktQoETF+ftczc0q3skiSJEnqPItRkqSmmTyP8YGpvBajJEmSpB5mMUqS1CiZOaL1PCJGAZsDpwBHFAklSZIkqWPsGSVJ6gkRsT1wRmZuVjqLJEmSpKEbseBbJElqhMeBKB1CkiRJ0uJxmZ4kqVEiYvW2oT5gLeBYYFr3E0mSJEnqJItRkqSmmcmcZuUD+oAZwHx32pMkSZLUfBajJElNM7bt/HWqJXrTM3N2gTySJEmSOsgG5pIkSZIkSeoaZ0ZJkoqLiK0W9t7MvG1JZpEkSZK0ZFmMkiQ1wS1UfaL6FnBfPzByyceRJEmStKRYjJIkNcH6pQNIkiRJ6g57RkmSJEmSJKlrnBklSWqciFgN+CKwaT10L3BOZj5RLpUkSZKkThhROoAkSa0iYmtgOvAZYFb9mgDcvyiNziVJkiQ1kzOjJElNcypwBfDZzJwFEBGjgAuBbwHbl4smSZIkaXE5M0qS1DSbAycNFKIA6uOT6muSJEmSepjFKElS0zwPrDHI+BrAC13OIkmSJKnDXKYnSWqaK4HzI+JA4MZ6bDvgXKrle5IkSZJ6mDOjJElNcyjV7nm/Al6sX78A7gYOK5hLkiRJUgf09ff3l84gSdJcImJDYJP6dFpm/qFkHkmSJEmdYTFKkiRJkiRJXeMyPUlSo0TEhRExcZDxwyPiuyUySZIkSeoci1GSpKbZDbhukPHr6muSJEmSepjFKElS06wE/HmQ8eeAVbqcRZIkSVKHWYySJDXNA8DYQcbfD8zochZJkiRJHTaqdABJktqcC5waEaOBX9ZjHwCOB44rlkqSJElSR7ibniSpcSLieOAwYHQ99CpwWmYeXS6VJEmSpE6wGCVJaqSIeCuwaX06LTNfKJlHkiRJUmdYjJIkSZIkSVLX2MBckiRJkiRJXWMxSpIkSZIkSV1jMUqSJGkBIuL7ETG9dI4BEfF/I6I/Itaezz3vqO/5ZId+5nb199uxE99PkiQNX6NKB5AkSdIS8RiwDdCYIpokSRJYjJIkSVoqZeYrwC2lc0iSJLWzGCVJkrSQIuK9wBnAe4CHgGMy87KW6xOAicCGwDPAZcCRmflcff0dwIPAAcAWwHiqtgk/Br6cmc8vRrZdgUuBc+sM69U/61OZ+cP6nl8Ds4DTgRPrnP8FfCUzp7Z8r2WAk4DPAKOBa4CLh5pNkiSplT2jJEmSFs4qwPeA84BxwAxgSkRsCBARBwEXArcCfwecAHwKuCoi2n/n+gbwF8C+9fG+wAVDDRYRHwOuBCZl5uGZ2T+f2zcFJtWvvYHXgZ9ExIot95wAfBk4C9gLeAI4e6j5JEmSWjkzSpIkaeGsCPxtZt4GEBH/CcwE9oyIbwHHAtdk5n71/T+LiCepZhTtAvys5XvNzMyPtdz3OvBPEXFsZt63KKHqItiZwCGZee5CvGVlYOvMfLh+/0zgjjrjlIhYGfgicFpmfrN+z7URsRbwkUXJJkmSNBhnRkmSJC2cpwcKUQCZ+QTwOLAusDGwGnMvZbuEalnc+9rGL207/zHQB2y1iJn+nmrZ4KcWshAF8LuBQtTAef113frrXwHLAZcPklGSJGmxWYySJElaOM8MMvYK8BZgpfp8ZuvFzJwFPNVyfcDj8zj/y0XMNB64H/jpIrznTX+OutE5VH+O1gztGf+0iNkkSZIGZTFKkiRp8Q0UeNZsHYyIUVS9pp5uu3/1eZw/tog/9yPAGKqlfiss4nvnZSBDe8Y1OvT9JUnSMGcxSpIkafHdRzWT6GNt43tR9eicOsh4q72Bfqrm54viQWAssDZVQWrMIr5/MPcALzF3f6i9O/C9JUmSbGAuSZK0uDJzdkR8EzgnIr5L1StqI6od66YC17a9Zc2ImAx8n6rf1CRgcmbmEH72AxExFvg1cE1E7JaZLyzGn+XpiDgLOCwiXgBuBj4EbDnU7ylJktTKmVGSJEkdkJnfAfYDtgF+AhwD/BD4YGa+3nb7scCfgX8BjgMmAwcsxs+eTjVDagPgqohYfqjfq3Y01Q59X6Jqtr468IXF/J6SJEkA9PX395fOIEmSNCxExDuoltZ9KjN/WDiOJElSEc6MkiRJkiRJUtfYM0qSJKlBImIk0DefW2ZnplPbJUlSz7IYJUmS1CWZ+RDzLzQB/ArYYT7XJ1A1PpckSepJFqMkSZKa5UBghflcf7BbQSRJkpYEG5hLkiRJkiSpa2xgLkmSJEmSpK6xGCVJkiRJkqSusRglSZIkSZKkrrEYJUmSJEmSpK6xGCVJkiRJkqSu+R92jtCBkQVh4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 7))\n",
    "sns.set(style=\"whitegrid\", font_scale=1.3)\n",
    "\n",
    "style_top_hops = style_top_hops.sort_values([\"hop_kind\"]).reset_index(drop=True)\n",
    "\n",
    "# Calculate number of obs per group\n",
    "nobs = style_top_hops.groupby(\"hop_kind\")[\"hop_kind\"].agg([\"count\"]).sort_values([\"hop_kind\"]).reset_index(drop=True)['count'].values\n",
    "nobs = [str(x) for x in nobs]\n",
    "nobs = [\"n: \" + i for i in nobs]\n",
    "\n",
    "plot = sns.boxplot(x=style_top_hops[\"hop_kind\"], y=style_top_hops[\"hop_amount_percent\"])\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "pos = range(len(nobs))\n",
    "for tick, label in zip(pos, plot.get_xticklabels()):\n",
    "    plot.text(pos[tick], 1, nobs[tick],\n",
    "        horizontalalignment='center', size='x-small', color='black', weight='semibold')\n",
    "\n",
    "plot"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}