{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEPRECATED\n",
    "\n",
    "This notebook is in the process of being deprecated with code being moved to several other notebooks, one per chart type.\n",
    "\n",
    "Updates to chart code will be made in the new notebooks. This notebook will become a matter of historical interest only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>itinerary_event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>itinerary_legs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>itinerary_sections</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>itinerary_stages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>itinerary_controls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>startlists</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>startlist_classes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>retirements</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>stagewinners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>stage_overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>split_times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>stage_times_stage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>stage_times_overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>championship_lookup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>championship_results</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>championship_entries_codrivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>championship_entries_manufacturers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>championship_rounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>championship_events</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>championship_entries_drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name\n",
       "0                      itinerary_event\n",
       "1                       itinerary_legs\n",
       "2                   itinerary_sections\n",
       "3                     itinerary_stages\n",
       "4                   itinerary_controls\n",
       "5                           startlists\n",
       "6                    startlist_classes\n",
       "7                            penalties\n",
       "8                          retirements\n",
       "9                         stagewinners\n",
       "10                       stage_overall\n",
       "11                         split_times\n",
       "12                   stage_times_stage\n",
       "13                 stage_times_overall\n",
       "14                 championship_lookup\n",
       "15                championship_results\n",
       "16      championship_entries_codrivers\n",
       "17  championship_entries_manufacturers\n",
       "18                 championship_rounds\n",
       "19                 championship_events\n",
       "20        championship_entries_drivers"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbname='wrc18.db'\n",
    "conn = sqlite3.connect(dbname)\n",
    "\n",
    "q=\"SELECT name FROM sqlite_master WHERE type = 'table';\"\n",
    "pd.read_sql(q,conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2018\n",
    "rc='RC2'\n",
    "ss='SS4'\n",
    "rally='Sweden'\n",
    "\n",
    "typ='stage_times_stage' #stage_times_stage stage_times_overall\n",
    "typ='stage_times_overall'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diffFirst</th>\n",
       "      <th>diffFirstMs</th>\n",
       "      <th>diffPrev</th>\n",
       "      <th>diffPrevMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>penaltyTime</th>\n",
       "      <th>penaltyTimeMs</th>\n",
       "      <th>position</th>\n",
       "      <th>stageTime</th>\n",
       "      <th>stageTimeMs</th>\n",
       "      <th>...</th>\n",
       "      <th>totalTimeMs</th>\n",
       "      <th>stageId</th>\n",
       "      <th>class</th>\n",
       "      <th>code</th>\n",
       "      <th>distance</th>\n",
       "      <th>name</th>\n",
       "      <th>snum</th>\n",
       "      <th>driver.code</th>\n",
       "      <th>entrant.name</th>\n",
       "      <th>classrank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT4.1S</td>\n",
       "      <td>4100</td>\n",
       "      <td>PT0.6S</td>\n",
       "      <td>600</td>\n",
       "      <td>1589</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>PT1M36.8S</td>\n",
       "      <td>96800</td>\n",
       "      <td>...</td>\n",
       "      <td>96800</td>\n",
       "      <td>317</td>\n",
       "      <td>RC2</td>\n",
       "      <td>SS1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>SS1 SSS Karlstad 1</td>\n",
       "      <td>1</td>\n",
       "      <td>TID</td>\n",
       "      <td>Skoda Motorsport</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT4.2S</td>\n",
       "      <td>4200</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1590</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>PT1M36.9S</td>\n",
       "      <td>96900</td>\n",
       "      <td>...</td>\n",
       "      <td>96900</td>\n",
       "      <td>317</td>\n",
       "      <td>RC2</td>\n",
       "      <td>SS1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>SS1 SSS Karlstad 1</td>\n",
       "      <td>1</td>\n",
       "      <td>VEI</td>\n",
       "      <td>Skoda Motorsport</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT4.5S</td>\n",
       "      <td>4500</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1597</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>PT1M37.2S</td>\n",
       "      <td>97200</td>\n",
       "      <td>...</td>\n",
       "      <td>97200</td>\n",
       "      <td>317</td>\n",
       "      <td>RC2</td>\n",
       "      <td>SS1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>SS1 SSS Karlstad 1</td>\n",
       "      <td>1</td>\n",
       "      <td>ABB</td>\n",
       "      <td>Kevin Abbring</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diffFirst  diffFirstMs diffPrev  diffPrevMs  entryId penaltyTime  \\\n",
       "0    PT4.1S         4100   PT0.6S         600     1589        PT0S   \n",
       "1    PT4.2S         4200   PT0.1S         100     1590        PT0S   \n",
       "2    PT4.5S         4500   PT0.3S         300     1597        PT0S   \n",
       "\n",
       "   penaltyTimeMs  position  stageTime  stageTimeMs    ...    totalTimeMs  \\\n",
       "0              0        13  PT1M36.8S        96800    ...          96800   \n",
       "1              0        14  PT1M36.9S        96900    ...          96900   \n",
       "2              0        15  PT1M37.2S        97200    ...          97200   \n",
       "\n",
       "   stageId  class code distance                name snum  driver.code  \\\n",
       "0      317    RC2  SS1      1.9  SS1 SSS Karlstad 1    1          TID   \n",
       "1      317    RC2  SS1      1.9  SS1 SSS Karlstad 1    1          VEI   \n",
       "2      317    RC2  SS1      1.9  SS1 SSS Karlstad 1    1          ABB   \n",
       "\n",
       "       entrant.name classrank  \n",
       "0  Skoda Motorsport         1  \n",
       "1  Skoda Motorsport         2  \n",
       "2     Kevin Abbring         3  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q='''\n",
    "SELECT st.*, sc.name as class, i.code, i.distance, i.name, CAST(REPLACE(code,'SS','') AS INTEGER) snum,\n",
    "sl.`driver.code` drivercode, sl.`entrant.name`\n",
    "FROM {typ} st INNER JOIN itinerary_stages i ON st.stageId = i.stageId\n",
    "INNER JOIN startlist_classes sc ON sc.entryid = st.entryId \n",
    "INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "WHERE sc.name=\"{rc}\" AND ce.`country.name`=\"{rally}\" ORDER BY snum\n",
    "'''.format(rc=rc,rally=rally, typ=typ)\n",
    "stagerank=pd.read_sql(q,conn)\n",
    "\n",
    "stagerank['classrank'] = stagerank.groupby(['snum'])['position'].rank(method='dense').astype(int)\n",
    "\n",
    "stagerank.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get differences in position and time to leader cf previous stage\n",
    "stagerank['gainedPos'] = stagerank.groupby(['drivercode'])['position'].diff()<0 \n",
    "stagerank['gainedTime'] = stagerank.groupby(['drivercode'])['diffFirstMs'].diff()<0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S+9LIXlFaZ7uVum2oVAhYo7W+KvV+jdZ6RCrIfA94HxMNrwZu0FrfmsXOv4Hva61XK6UmYuYKj8JE82uAbbXWq1LpfffQWucUpA4okehE4L/AsL42zZGr8PvOsWSrK5Hop4CVLFTJpI7dcN/qPW3Y6s5Jh4/47dAhnhNt2NJat77yz9ad3/6gbYUNe+kcecBw//gxnnqlVG5doL2gtV6zYlXy/xY80fioDW3plJRA9Y9HfWnLXlu7fvwvD64+1Za9dKp/PPKJkhI13YYtrfWq+feu3saGre789JARpw8fps61UdpJa73m4886Zj/9avNHNrSlc9Dew7bzTi59yNJShTbgXPy+6yzY6kkkuhoY0ed2ufEqfp8r7ROR6ELgEBumtNZ6/r2rrffOAxwTGH5k5UjP7zszGzpBa71q8dLEzx5+tukVG9rS2WX7IeN22b7870opK8MDza3J+lseXvNbG7a6c9qckW94PGqyDVta6+b59662PnIM1n3o6k+/7jgqHGn+tw176Vj2oYmmZn3FbY+uud6GtnRs+9BEQr9Tf//qA23ZS8eyD00ueKJxsxWrkh027KVj2Yeu+nZFsuaBvzVan29v2YcC1OP3nZbLhp2xVur/G2Oyy54JnKe13j1tuxLgC2AnrfXXqdjsca319lnsbgrcqbXeJ/X+MMxg4ThgJbBIa31g6rvrgb9prf9ayB+bTs4nWmsd6vZ+ROrfGPkFW7dgFr/epLVeDGRztvOAge++zo027BV71Vgaxs5Cmy1DHo9qrqmv+sqWvS5EokuxVJRYKeXZa6ehn+111l6rnQvrRiS6mOydoHmhlEqOqSxZ4soxjUQVkMDSdVpepla4eO6tnSelVKuLOr/FHFPHDlMpxfc2Lfvye8ft4ca5r7RoLYHFNiQDNme2tFq01R1ryz6UUh0uXqNL6FrerGCUUnrSuNKvXGqfWrCXY0IPG+L51sVjas0/K6XaisSH4t2o7KuaelfaJ5s+NDG8Qi0rBh9aUqIai8SH6nmBEV/i91mfXmvZh+rxY0oWu3TubfpQyOMZvzPWSv3/c2Cz1NtHu22XwKzT7HwfAzIGnKnvP1VKLVNKfU9r/ZHW+hHgke7bKaWGYJLAnpmr5t5wK5FQb9xAHw8DSqlyYKHW+oP+keSYFdh7WGoCPrRkKxPvYW5ypySAdy3YycYH2Au+m/D77Aecho/Ib51yb5Sm7NnH79OArca4FXjbkq1M/Buw0auqgY8t2MnGR9i778uBzyzZ6k4Me9doAreuUaAlob9M5jj7pg/agX/ZMJSFf2Ev+HbrvIM5V7b8/FDcu5/WRx+qgf9ZsJMNmz50CO5ptelD2ygOH9qBu+2TLR8KsNiVgNNg04cOwT3fFMPeNdpE5pw3A0GQtEA1C5sAwc7SKk7p96BTa92itb6zj23atNZ39Jcmx5jG6BicN/DNwN8BN3PA/xJYjjOnmQCWkuP63AK5B3gDO8f0GOdysuD3fQH8FtOQOKERuBHjLNziJ6nfcUIr8B9cWouS4mLMNBEnzkgDceBkK4oy8zfgKZyf+ybgZPw+d0YQ/b5m4DSc30tNmHpdzznW1A1vMDzUGwxfeNhry7/X2KHRua77yEw7JpC71JK8TFyPWVLidDS1ETjOuZysvAXchvP7vgm4EL/Pnars658P1ZilSFamv2bBlg9tAs7A71vjXFIG7PnQJuAZ4AnHmrJjw4d2YILXi5zLyYoNHwrmmP7EuZys2PShtfh9nzqXlAF7PrQZU6ak1xiov9Baf6C1frGPbT7SWr9g6zdzXtMp5EAkehDwG8yU4S4BfUJrz7K25PhhHtU0ssyzKsPeq4AXMI27m9PXIBLdHJiPKShbDtDSqjfQWpcMG+pZ2sfebZgRzmrXbvC1Oodi5pj7gXzn0ieBz4Hf4Pf9zba0HkSiZ2BSWk8oYO+lwL3A71MPX+4Rie4F1AJe8p8m1Ih5oKt27QGkk0h0AiawnU7+PYztmB7PX+D3/ce2tC5EoiXAlcAPgdF57t3Zc34lft99tqX1IBKdh+komkSWKXeJhC5radMblnhoG1KulqdlFVwBhDHrOa1M1+zEGwwfiAnitgAeqJtReWNg0tALgKlAWaZ9klqrFW3JsRrUBuWeZSVKdQYAzcAi4FT8vlzTwRdGJDqqqSV5f3sHB5SX0THUHK9c798OTO/5Ofh9f3dPJJ3TAi/EJJ4YW4CFxcCN+H03WNWViV58aA4MqA/Ng3ZMDogz8fsW2ZbWBWc+VANfA9fh97n/kOzMh8Yxwcuv8fusrz3sgjMf2oLpWD4Fv2+xZWVdceZDE5iOuyB+X69BiWOc+VAw5TxudS3PQDo5+NBeWA28BNTg961TVTnyQYLOfsQbDN8DHARMjtUGrJfvcEJddcP/gNdq6qvmDbQWQRAGjrrqhmOBO4AbauqrTnfrd7zB8MbAtZg07x8BP4/VBp7OY/+tMZ0gMWCvWG3A3Y6QbtRVN4wH/oF58Nylpr7KerkbQRAEQVhXGIg1nesz84FKTH2dQUNddcMITK+du6NCgiAMemrqq+7E9DyfVlfdkFOGvXzwBsNl3mD4HMy6lgAmzfsO+QScALHawPvA0cAOwO3eYLjf/FlddUM58BAwHjhUAk5BEARB6B0JOvuXVzC1bk73BsOOM8pZZOvUv24mBhIEoXg4DzOd9k911Q372TLqDYb3xUx/vQJoALaN1QYujdUGClobGasNmCl1cDhmWqbr1FU3KEwH4l7A8TX1VdH++F1BEARBKGYk6OxHYrUBjXlY2RHYdYDlpLNt6l8JOgVBoKa+KoEpW/Uh8GBddcNUJ/a8wfBEbzB8F/A8UAEcHKsNHByrDdjIiHkdplj2Rd5g+McW7PXFLzBrz35XU1/l/lpcQRAEQVgHkKCz/7kLWIPJhDVY2BaTIOiTgRYiCMLgoKa+ahVwcOrtY3XVDXkXb/cGw6XeYPgMTPmGo4DfAdvFagOOi0x3kurMOw14FbjNGwzvZMt2d+qqGw4ErsHUM+uXkVVBEARBWBeQoLOfidUGVmPSJR/tDYY3HGg9KbYDPqipr3I365sgCEVFTX3Vx5hgcSvgnrrqhpyzNXqD4T0wiXb+iEn4s0OsNnBhrDbgND1+D1LTcw/HZIJ+1BsMT7T9G3XVDVsB92GWSBxXU19lNYuvIAiCIKzLSNA5MNyAKWR7/ADr6GRbZGqtIAgZqKmvasBMKQ0Av+9re28wPNYbDN+EGXkcB/wYmBWrDXzops5YbeAbzMjsGOARbzBsq5g3ddUNGwCPYcpdHFJTX9WvmXIFQRAEodiRoHMAiNUG/g28DJzWnxkXM1FX3VABbIYEnYIgZKGmvmo+UA/8X6qkSg+8wbDHGwyfgplK+1PgKmCbWG3ggdQUWNeJ1QYWAccBuwN/tpGwra66oRRTR3cz4PCa+qqYU5uCIAiCsL4hdTptEomOBw4DNqGPosHR5W3b/GNF+8F7jy2/f/vKsv9hiq+/5nohXqOzlNSUuZWrE5v+9/OO4zcaX7Jw4tjSD3LYuwVT6uAh14swA0Si+2IeIPMtGpwAPgUexu9baltWDyLRLYFZwEYF7L0YeA6/7992RWUgEq0EjsQ8QJfmufcq4E38vr9Z19UdU9D+MMzU7+F57t2KWZ98f78UYY5EdwP2AfKdLp8EvgAew+/7wrqu7kSimwA/AiaTf4fj0pZW/fLND6++DNgD8NfUV73e+aU3GN4ZkyRtV+BF4PRYbaCwEkyR6DDM6OjmmBkh+bAGeGezJ7+ZpuFi4JxYbeCqgnSkqKtuuBY4E/hZTX3Vzd20HoRJDDcyT7PtwP+AB/H7VjnRlxOR6DRgf2BCAXt/CTyJ3/exXVEZyMOHZmBAfCiQ74i6+NBMiA+1i/hQ+zj0oUAEv+8f1nUVERJ02iIS3Qh4HXPTDCvAQnvqdRZ+3402pXXBOMtHgP0wOvO9cZJAM/A3YI6rTjMSrcGUVign/8ZdY5z7t8Bu+H2LLatbSyS6MyYrZxn5P4CAaeATQAC/7wWLyroSiW6AKduzKSaDaL50YBJOXY7fd4lNaV0wzvImYA7meOb78Kkx1+ibwAGuOs1I9CjgNsz1WV6AhWagEdgdv8+9RF7mge41zHkv5BptAzpWrk6cdvfjjSFM27HLlaOb12CSA50OLMGUL7m74JHNSLQCeAaYkfqNfEcqE0CL1vrOzZ9asqE2D4ezY7WBJwqRU1fdcBLmWvxDTX3Vmd20Xgr8EhMYF9I+NQP/BfbC74sXoi8nItH9MVODS8g/iAfTjrYBfvy+RTaldUF8qH3Eh9pFfKh91jMfCvwEv+8Rm9KKCZlea4/ngYkU5izBNLYVwLVEovtYU9WTK4EqTM9XIeffk9p3FnCpRV1diUR/gHGWFeTvLME8rA4DJgHPWlTWlUh0FKbe4EgKa4jAPAhWAI+nHrzc4lHM6FEhzhLMeagAziUSPdKaqp6cjXGWw8nfWYI59xWAD/iLRV1diUSnY5xlBYU5SzDX6AbAy6kHBftEoiWY6fyjKfwaLQcqRo8sueH7M4acqdEjGpV+qUzzASZzbB2wdaw2cJfDqbS3Ajthjmkhx6MEGK6UOu6dmeNex9QEvdcbDG/bx349qKtu2Auz/v4ZTDC9lkh0Hmb0cziFt08VwPeAhwrYPzdMz/yjqd8qJOAEc82MBF4gEs13xCQfxIfaRHyoG4gPtcl66EOBu4hEt7MlrdiQoNMGpnd+cwpr2LtTjpm+5hb7U3iDmU5FypZb7EnhD0nplAFbE4nasJWJbTG9gjboAKZbstUV0xjvip1jWgHsa8FONmaS/3SgTAwD/BbsZGM3S3ZKgErMw50bbIw5Zzba+6SeqHYIV7R/WqHxHtxYXuLR7BKrDZwRqw2stGB/Hwp36ulUDC/1+IFDgCbgsXyyhddVN2wKPIyZAjsnQ2bvKuy0o0MwUx/dYkdMu+IUlXptacFWT8SHuoH4UJuID3WD9dKHArtYsFOUSNBph1GYaT02KAXGWrKViUqLtvJdI5IPG1JYD10mOoARlmx1x+bx9ODuMbX50DDeoq3ubGDRVr5r7fJhNIX3znanA/fO/WjM1DPHdCT1sMcXt17yXnli0selyYc27yjZ8Oz4sINs2E5h8z7dMFYb+Byzrmlj4H5vMFzW10511Q0jMNNRy4GDa+qrVmTYzGYbXejIXi5UYq8d1di9N9MRH2of8aH2ER9ql/XOh2L+Xjev0UFNr0GnUuoFpdSB3T47Uyk1XynlVUo1K6UWpb2OS20TU0r1aPSVoUEpNUoptbFS6nml1HtKqf8opX6Ztt1VSqkqW3/kQHHi5Zcw/tAD2P74OT2+u+reO1H77sLSlTYGB5yxHujs14XLxXI8oXi0rgc6i+IaTWhKpgzzvA5s+b2OkqOAu4Df1lU3HDbYtHYSqw28BpyCGZ28trffqqtu8AB3ANtjRjhzSa5mRWd/USw6oXi0ZtJ54c03MO3Eucw4aR4H/PrnfLX02wFUaHCgc8Dbp8F4PKF4tK4HOte5a1QptWFafLVYKfVl2vum1Dadsdg/U/HUG0qpn/Zic0el1E2p/x+jlPpX6vWqUmp66vNypdSLSikbM0+60NdI5wLg6G6fHZ36HOATrfWMtNcdfdg7CHhba70K0ytxttZ6G8z0ohqlVOe6mz8BwZz/ikHK8bNm89QVf+zx+edLFvPMm2+wyQTr9csLQnTapVh0QvFoFZ12KVRnmYf2E73D74/VBpbV1Fdp4GTgDeDOuuqGaYNJazqx2sDtmBIuNd5guLqXTUOYkdGza+qr8sowua6f+4GgWLRm0nnO0cfyr1sWsOjme5i9x15ccvtNA6RuLaLTPsWiVXTapT90aq2XdcZXmJJl16a9T6Zt+onWesdUPHU0cJZS6oQsZs/HxFhglo/4tdbTgN8CN6Z+tw14DrMu2Cp9BZ0PArOVUkPARNSYdNYvF/h7x2AWYqO1/lpr/Vbq/6sxKcQnp95/CmyolBocHqVA9pm+E2NGjurx+VnXX8sVp56BKihPhn1Ep12KRScUj1bRaZdCdXqU6tKbXFNf1QIcCsSBx+qqG6xPGbN4TIPAE8CfvMHwvt2/rKtumANcCNwC/GEAdbpKseiE4tGaSec5b6dXAAAgAElEQVSo4Wtnoza2NKPUwGsVnfYpFq2i0y6DVafW+r/Ar4BfdP9OKTUSmKa1fju17ata687lI38HpqRtvhATs1ml16BTa70M04s9K/XR0cB9em2dlandptfu3cfv7YlJwdyFVDC7IyZdeidvpbZfp3jslQiTx41j+hbu5GOwhei0S7HohOLRKjrtUqjOmvqqrzEJeyYAD9ZVN9hao5OVQrTGagMJYB7wEfCQNxjevPO7uuqGnTFZFF8BTk+N4g6IzoGgWHRCcWm94Kb5bHxUgLufeYpLTjx1oOVkRXTap1i0ik67DBKdbwFbZ/jcB7yTZZ+TgCfT3r+DCwmPckkklD7FNn1qLfScXvtSH7bGpEY1v0MpNQKTMv7M1LTbTpZQWJHgQUtTSwuX3nUrl5zQ2+yugUd02qVYdELxaBWddnGqs6a+KgqcAOwNzK+rbnCti9eJ1lhtIA4cjMnE+pg3GB5VV90wCTMDZwlweE19VetA6+xPikUnFJdWgEt/djqfPxDmmJmzuP6R+wdaTlZEp32KRavotMsg0ZnN/07C1N3turFS+2GCznM7P9NaJ4C21OioNXIJOhcC+yuldgKGdU6JLZAOpdR3v6mUKsMEnHdrrR/utu1QTNHXdYZPvvqC/339FdNPmod3zsF88e0SdjrlJyxetnSgpXVBdNqlWHRC8WgVnXaxobOmvupeTN3Bk8gwtWewaI3VBj4GjgK2LtPco9GPYLI9HlJTX7VksOjsL4pFJxSX1nTm7T+LhyINAy2jT0SnfYpFq+i0ywDr3BGzZLE7zXQrSaaUmgbcBBySmt2azhCgxaawPjMTaa3XKKVewKxzWdDH5n3xAaYW18fKTHa+GXhPa31Nhm23BB5w+HuDih0234IlC5/+7r13zsFE/3wHY0d3zZ6svwzPU6H93Zla/P0HJjBkjBWdtCyZTKjyVTdksk3Qy8SZvW6Ss85k+xBem/skz6+wUa+uK+P2Gc3W54ygNHvZtpx1DgJy1Rr5trXqp8GwK+f+6b023GbLkb03TbnqXNORHLm9SzrP2XLERqduVlFS6sk+qJerzqaO5PA5r6+4+99Phq13tO08uqzijl1GDx9emr2P0eI1ehGwHXBNXXXDezX1VU/3tUO+5Kx19cc7ENo/47mPDYWvkmM+e2nV6YGl7EzVqD99sE1Fw3xCOYqYcdXWbLCjHZ0DTB730oiz3l71x2eeDK/qbsMpGw31lD2/z9ghQ0p6HyDPVeu9nzfNCwbDrvjQN/YbO2H80L4rkXz0xWd8b8omADz26otsvYm3xzZfNycm7+FS+3TNtFHewyf3XY0nF51tST1kz+eXPvntk2HrPvSgiUNGX7nDqBG9tU+56mzqSFZc+v6ai+9+MnyGbZ0K+N8PJ+S0bS5aB9qHQm46B9qH5qpzoH1orjrdJrVc8SrWJgtK5z3g7LRtN8HUoz5Wa/1hNzsbAt9qrW2VsgJyL8S8ICWseybbqUqpRWnvb9Fa90w1t5YwpiDux5j1mscC/06zcb7W+onUCOgWQDRHfYOSuZdcwAuL3mRpfCVTjgxw8QmncFLgkD73a6VMDYU1rojqlgjEiU6UR+OaTk+PC71gnaBBNWKvDlyazpIh0PWYOtDZ7xSqtUTRgUvn3qO6ZGVzpFOZNOqu6CxVtNnSCeBRNAGNtnV6FEp1Syfv1jVaU1+VrKtuOBZ4Fbivrrpht5r6qg/72q83HLRPCXo597E1gfalLTszvuI5Rg17u623bXvi6fHwXSz3vZN7qUTRggv3k1KZa/U5aJ/a3dAJ4FE9SzNk0vnE66/wwWef4vF42HTCROp/dV4PW0q51z6ljoEVnYBWCld8aIliCDm0TzkdT2PPlWs0G4VqHQw+NMdjOuA+NMdrdMB9aK46XWCqUuqfmFHM1cCftNa3dt9Ia/2+UqpSKTUytdTxIkw93/mppEcdWmtfavP9MIn3rKLW5gRyH6XUJOAOrXWvQ1hKqcOAnbTWF/aPModEohOB/2KpyPdjXzW/e/DcvbezYasHkeinwCaWrH2C37eFJVtdiUT/CNjqrWwFJuP3dZ864JxI9EDgPuwUuF4NnIrf53RGQU8iUQU9HZEDHsDv+7FFe2uJRN/A3gL2lfh97hS0j0T/DzOd1EYtq9XA7vh971qw1ZVIdAYQAXqmAc2fViCI33ddbxvVVTd4gX8Ay4HdauqrciumGImuxl4R+lfx+zKOdtVVNwSAvybRD15d2TIexe6AP1YbeD3T9hl0LsQkT7JBEr+v76GzQohEjwPqsHNM48Dh+H3254xZ9qHAVfh951iy1RXxoXYRH2of8aF2GQAf6hZKqbOA1VrrXuu5KKUeBs7TWudcqzoXclnTaQ2t9dfAX5RSfZ24UuDqfpBkizhk7qnNl46kTr67umMrbzA8zoa9DKzoe5MBsdWdb4GEJVulmAbJDWweg6Rle92xOe3kG4u2umPzwcb6VMA0lmMciA3KcO/cL8eOUwcz0tGnzpr6qhhwBGY5xb111Q25/r7N+zTjQr+66obtMLN3/ulBHY/iSOAr4BFvMDw5R9vW1n4CTRZtdWdFKiGEDRTmWnIDaz4Uc426WeVefKhdxIfaR3yoXfrdh7rIDfRxzJVS5cBC2wEn9HPQCaC1vr9bltpM2zygtc6tZ3ww4Pc1Y+ZKO5564lG0vbG8vQSTCdINnsLOQ04TXdMr2+ZF7DRGbcC/8Pt6TOOwxHuYaRc2pgyUAIv63KoQ/D6NmfJoY1F4E/CsBTvZeAI7U2Tc1vl3smeJy4cOzAPiYgu2MvEFsBI7D6Aeupa2ykpNfdWLwGnAgcAVfe4Qqixn1fuLSVjJW9BEhqlBddUNGwKPpb4/tKa+qilWG1iKyWg7EljoDYZzGW17GjvtaAvQV9b3grnwP6tWtyatjHJqzHXqaKp0Viz60JSNQmuJ54L4ULuID7WP+FC7DIgPdQOtdYvW+s4+tmnTWt/hxu/3e9C5DrM/8Dlm7nsHpsctn1cz0ORR6uS3VrZHgGpvMOzGlKsLgL+2d+hkIqnz1ZjEOKBGzBrfkAv6DH5fBKjBNHrNBejsSOn8FDjARZ2rMWUiVqV+L9GnNq2T6KRGJ0AnINnRitZrgJn4fW41mgCHAe8ntW5sT2oSWqN1XtdAC+Z8XIjf96iLOq/HJBlrxDw05XvuO9dzvYQJetzB73sHmIM5Jk3kcu4zX6NLgb1SDzVu6Exi1tAvI9drtOsrkfY3HoHf936uP11TX3UT8EfgrLrqhhOzbhiqrALe5q0zd6Tp82/RySbMecz33Lem/sb5wI3pP1FX3VCGSU43BRNwft75Xaw28A6mEPbOwC3eYLj3ByG/70Hgt5hj0pKTNnPfd97zGp1sxtRCO6rX3yoAbzA8whsMX37nZ83P/DS6oqk9qVu11oWe+0bMSOTe+H1ujspa8aGY6ZXuJLgzXAD8FXNc2grQKT60q878fWjma7TffCiFX6PiQ9MRH7re0a9rOtd5ItENgIOAjck/oI8Dr+P3Rb3B8I8xaxwCsdqA9YW8z13+0pCWNt242eTSF7adWt6Q5+6tmCzET6RuRHeJRHfFrE3Id71HEvgMo9P9UfNIdFOgClMHKTeavhjL0tcOpumzqax69z80xk4iFHe1B+yuO18c80684/Ux5WrTQzYa9uetRpZ+ncfua4C38PvcHEUwmPUzMzGZUPNd59WOSVYWdrF3fi2R6DTg+0DvaaF7ojE9qH/D77M5XTMzZt3cAcBk8u9dXga8knpIyIvU1NonMEnkqmrqq9ZeP6HKjTBLKY4GPgHOYL/nngNmA1MxU6byoQkTyD3X/QGkrrqhDjgd+GlNfVXGXlxvMBwELgMuiNUGft/nr0Wi+wAzyHXNpNawPDqd+L9/SNMXI1jx5s10rDmXUNzKtNVUsHw4cC3GD90KnBv74YQNgH2A8QWY/QpzPD/vc0unWPKh1nV1JxItwejcElNWIB/Eh2aiEB+6liVABL/vI7uiMhCJDse0T17MyGo+iA/NhPjQ9QYJOgch3mC4HNPY/yNWG/iRbft11Q3bAO8Cx9bUV91l276QB6FKBfwYuAbjbP8CnE8obj1hQ+qB9HZM1ugjYrWB7rVxBcEV6qobNsBMKRoN7FIz8bAvgZ8Dl2DW8l0GXE4obrUmWNrvn4YZ/byypr7q/7Jtl7pH7sSMeh4aqw24MxoRqqwELsYkelkO/B9wO6F4wUGINxjeApMmfxbwL+D0WG3gFQtqBUEQBMExMr12EBKrDbRhgo+ANxj2uvATnZlx/+OCbSEfQnFNKH4fsDVmdOIk4ANClScRqrR9f/4aE3D+RgJOoT+pqa9agVk3WV6mmp9tTQ77J+Z6fxnYjlD8YhcDzv0wwVgY6DWHfaw2oIGTMZl37/YGw9Pc0EQoHicUPxPYCbNO8hbgRUKV0/M15Q2Gh3mD4YsxI7x7AmcCO0vAKQiCIAwmJOgcvNyImTpwigu2t03Ztp6ZSiiQUHw1ofjZwI6YxAo3AS8Tqpxhw7w3GA4AlwP3Y9aiCUK/UjPxsOX7jbr+jQ5dvsUz8bOmJnTpEUCAUPwTt36zrrphKvAgJrCbV1Nf1WciiFhtoBk4FDNd8zEXM4lDKP42Zj3bicBWwJuEKq8lVJlTan5vMHwQJti8CLNGcOtYbeAPsdpAjzqigiAIgjCQyPTaQYw3GF6Imee+caw2YCutNHXVDfcCu9TUV021ZVOwiJlyexxwJaZw7/XARYTi8ULMeYPh7YDXgI+AvWO1ATeTgQhCV0KVJZjOs98Dw5+PV7/4bvOB+wOX1NRX/catn62rbhiFue4nAbvW1Fd9nM/+3mB4F0wG0H8AP0jNQHGPUOUYzDE6BZOF8WzgXkLxHk7aGwxvClyHCY7fB2pitYF81+cLgiAIQr8hI52Dm/nAOEy9O5tsi1nTKQxGzJTb2zEjH/WYdV/vE6o8JhWQ5ow3GO5SIkICTqFfCVXugkmLPx/4JzD93eYDZ2IS3FxUV91gPXMrQF11QwlwN+YeOjLfgBMgVhv4B2a6+97A9X1mtHVKKL6cULwa2A2TvOce4FlCldt0buINhsu9wfB5mNkQBwBBYLoEnIIgCMJgR4LOwc2zmIyO1lJWpzJJboWs5xz8hOIrCMVrgF0xpQTuAhoIVW6by+7eYLhLiYhYbcD97JOCAGbULlR5AyZ50GRgHrA/ofh7NfVVGtOmvQrcXlfdsJMLCi7FZJj8RU19VcEBWaw2cA9m9PFkTOIj9wnF/4EJPE/HrPl8m1Bl7b7n3RTAJAj6PaZW5Dax2sDlro/ACoIgCIIFJOgcxMRqA0ngBmAvbzC8gyWzUzHZImWks1gIxaPAHkA1MB3zEHo5ocq+yjRcB+wHnByrDfzdZZWCAKFKD6HKEzDrxU8G/gBsTSi+IH2aaE19VSumtMdS4NG66oaJtiTUVTf8BDgXqK+pr5pvweSFwKPAtd5geKYFe30TiicIxW8Atlqlhz0MnHtX+WWPH+x5pbKExEGx2sDhsdrAZ/2iRRAEQRAsIEHn4Oc2TF0vW6OdnaNkEnQWE+Yh9M+YUeo7MCUW3iNUeUSmKbfeYLgaM1JyZaw2kLEmoSBYxWRefRGTifVDYGdC8bMIxVdl2rymvuobTEbbMcAjddUNQ51KqKtu2A2ThOsF4BdO7cF3nX/HYqa03u8Nhre0YbcvvMFwqbflnmOmtd580FGtF7WV0/HNH8vrJn4y9NgzCFXKenxBEAShqJBEQjaJRI/E1J2bSP4BfTNmzdNx+H1L07/wBsO3YdZ1bhSrDax2IrGuuuGC0SM9v5t70PDXPR61OWbUMx86gBjwc/w+d0fPTBHmW4C9gOF57q2Bb4Hr8PtsjHZkxxRhPg+TgXJsARZWYaZSV+dciDlU+X3MKPg04G/AGYTiHwF4g+F9gWdSnx8Sqw2szdgZif4AuBoz5TbfwtatmM6Kn+L3uTvKEolOwQTX25F/8fUE8CXwa/y+p21L60IkWoZZr3ggkFPG0W4sxdRO/R1+n7uNcSR6KvArYDz5F7ZuxEyHPQG/b02Xb0ym1c6akyvIs+ZkXXXDEZgMs3cAx9fMHeXDXNteoDRXgVpr1dqmR65qTLY1t1C16dzdX81131zwBsObAW9ginzvHvvhBA+mpudO5F98PYlJFnQxft99GX5rL8x1tQPwJHBGbOi8z1hb27QMqAVq+yw1E4l6gCswPmSDPHUCrAQeAc7G7yu4jmhOuORDrROJbo3xTVuQvw9tw3TKnIDf95FtaV1w5kOTmLXF/w+/b6FtaV1w7kOXYzKzn9cP7agTH9oEvAkci9+30ra0LjjzoR3A/4BT8fvesi2tC858qAa+Aa7E77vZtrQeOPOhq4AnMM/O6212cQk6bRGJHo1p3PN9+EinDfgM2B2/b1nnh95geFfM2qjTY7WBG5zIfPCCyOM/2rfih0PKlSL/myadNcBM1wLPSLQCaMBMJ3UyAtIEXITfd7UVXZmIRC/DPHDn69TTacLULPxRHoFnKVCDKYEyBKg9tPWSexfpLV7GNMS7x2oDa0eZItEDMA+PFQ50JjDB/K74fe6sETXO8g1MEq2cg44MNAFH4Pc9ZUVXd4yzfBTw4+yYNgHz8fvOsaIrE5HoL4DLcKazBVOeY1/8vsbUCPvRmAewicCfgQsIxZfna7iuuuE3QGgrb9kffrDHsJOAvqaOZ0VrrZVScWBP/D6rMzq8wbAfeHZcuSfyRtXYjZVSXvIPOtJpBk7G77s7ZX88prTR8Zh13L8EFqbqhxpClRthjvnRmDX/ZxCKP5nRugk4bwGOxHn79AgmoHMn8HTRh1rFBJyvAqMp3IcmMR00e7gWeNrzoc2Y8/6gFV2ZsONDG4F7MfeTOw+2dnxoK+a+3dO1wNOeD12Nae/dCTzt+dBmTCezewMMdnxoE/AccPj6GnjK9FobmF46p84SzMPLppjGN51/AG8BpzvNoLjH9CF7l5fhwVnACeah8M8ObfTGMZhefqdT7iqAy4hERzqXlIFIdFNMMXYnzhKMzu8DB+W8RyjeQSj+B8yU2weBi+aXX/f2TE+0HDi4S8BpuBVnDSaYnt1xmJEtt7gI05PoxFmC+VtvcS4nK7MwmU2dHtMK4OdEops5l5QB8/B5Fc51DsX0mv8klVH1WUyG1a+A3QjFTysk4EzxW+CB6VuX/xIHASeAUkoBlcC1TuxkIlYbiAA1R288bP+ELmiUqzvDgJuOvuLpEm8wfBpmLewxmFHMbWK1gUe6BJwAofhXhOJzgR9gRiSeIFT5MKHKTTLY9+E84ARz7RyKSWpmH/d9qE2uwVnACebZawPMCLRb2PKhw3CzHbXnQ4cDczFBtlvY8KFDMCPkbtRg78SWDx2JmXXiFrZ86DDgOiLRfEd0c8OeD63A5NnY37GmIkWCTjuMIf9pFtkow0yZ/I7UQ8d8YHvMNJmCqKtuKBlR4Rlpnsms4LVlKANb4vwBpJNmwJ0HepOYyVYN1XKMM8qPUPxrb8s9x53W9svXGvXQsr+UXzMiNnTeVYQqN/1uG/NQZytZSwkm+HCL7bB3P00gErVlqztTMferDVop5NznhhczSmmDYSz9+0nA25hppadjAs5/ODFaU1+VBI6vHOFJ9LlxbihMG2KdWG3gxn3HlX9U6lFW/GdSa0+sKRHFtPFvYUqgnBerDTT2umMo/hzmAft8zMPbe4Qqg4Qq0wPhqZhRNStSce8addWHWmZLnHfagnn+2tqCnWzY9KHDiEQLWT6QCzZ9aAduXaN2fWg5pkPALWz60M0t2cmEbR+aqePNBl7s+dAS3GtHBz0SdNqhDDPt0BaZemvuBeI4Syi0mcdjxVl24rQXrTeGYMexg5n3b6th645NuyUO7F36ZHK3PQ5ru+SXmNp9MzEPoecTquy8nmze724dTzdsu9XOlWPPsYN7x7Qccw/YoXXJLnTWwAzFbyAUt9L21dRXNZWVWnPs4OI1uuPosvdt2WpJUO6BSZhRmh/EagPv5bxzKN5KKH4ZsA3wNGb619uEKvdLbVGGvXbUg/OR3Wz0hw+1hc3rys121KYP7aA4fKiybK87Nn1JsVyjbj7n2fShSYrDhzp5zit6crqBlFKHKaW0UmrrtM+8SqlmpdQipdTbSqlXlVJbpb7bVykVT333L6XUs0qp8Vls76iUuin1/0NS2y9SSkWVUnulPh+nlHJnXZaLnHj5JYw/9AC2P37Od5+Fbr2RyUcexIyT5jHjpHk88fdXcrKV6vW+DTjSGwxPKFBSxvqONnW6iejMjDcY/q5ExBoq/kQofjnmIfRJTL3Ct3l+ZsbpHHJM7bJe6Nxg54WE4icQii+xrUsp1cOxD8Zj6lGqx+hhoTpLPXSEth25b6w2cG+PqbS5Eop/Sih+GKY26RBMPd97aPqi0pbOgaBYtGbS2clV996J2ncXlq50N29MLohO+xSLVtFpl/7QqZR6QSl1YLfPzlRKze8Wg3W+jkttE1NK9UjIpQwNSqlRSqmhSqk3UvHbf5RSF6dtd69S6nuOxGch116buZgkJ0d3+/wTrfUMrfV0TAbG89O+eyn13TTMmsSaLLbPB/6U+v9zwHSt9QxMFrObALTW3wJfK6X2zFHvoOD4WbN56oo/9vj8rCPnsujme1h08z0ctHtef9INmB6SEwuUlHFKpAs6XUF09sQbDHcpEfHdQ2so/hmh+BGYNaKlkHxmoLU6QXTaxZHOismLXZbXhXX9mJZ7VNsBE4au6fFFIYTiYUw7fwlwOJ/ecwXJ9i696sVyPKF4tGbT+fmSxTzz5htsMsFaGVpHiE77FItW0WmXftK5gJ5x19Gpz2FtDNb56qs83kHA21rrVZjpyFWp+G0GMEsptXtquxswWeit02fQqZQaAewJnETPPz6dUZhsbN33V5jFyJm+GwlM01q/DaC1XqPXptMdTtfh7IWYhfFFwz7Td2LMSHvLIGK1gQ8w2eiqvcFwIVMStlWq5xQm2zrdQnR2xRsMT8HcF18BR8VqA+09NjJZLbcHFcpkQ46pXUSnfYpF66DRGYo3E4r/BtiespEfkmzvMo1v0OjMgWLRmk3nWddfyxWnnoGyuqqlcESnfYpFq+i0Sz/pfBCYrZQaAmaGKbARZhCwEI7BZAtGGzo7O8tSr86Y6yXgB0op61OrcxnpPBR4Smv9IbBcKbVT2ndTU0O6n2Bq11yT9t3eSqlFmPTlPyBz9jMfJgX/d6Sm8r4PhOk6ohfFZLkqeq5/5AGmnTiXEy+/hBWrM9ZN7435mMXSuWc5Xcu2Hg89A5MsONTZb6yPOr3BcAUm4BwB/ChWG8hely4Ub2G/Zy8ZKK1uIjrtUiw6oXi0DpjOUPxjpp56NZ7ynNbJFsvxhOLQ+tgrESaPG8f0LVzJZ2UN0WmfYtEqOu1iW6fWehmm9M2s1EdHA/elDc51xmCdr75ipD0xdWIBUEqVpOK0JcAzWuvXU7+bBD7GhUzQuQSdczFJbEj9Ozftu86h3amYlNc3pn3XOb12Y0ya6UypwSdhav59h9b6Ea311phg97dpXy3BRPhFzWmHHMEn9zzCopvuZtKGYzl7/nX5mngM+Jo8EwrVVTd4gG08Kreg04LOfmF91Jkqm3MzJnvovFht4D+2dML6eUzdRHTap1i0DrhO5QFPaZ/14AZcZx4Ug9amlhYuvetWLjmheqCl9IrotE+xaBWddnFRZ/oU2/SptdBzeu1Lfdgao7Ve3flGa51ILWecAuyqlNo+bVtXYq5eh06VUhsCVcD2qQQPJYBWSmWa6/sYJrjMxGPAQxk+byZLDSmt9YtKqalKqbFa66Wp7Zp701sMTBiz4Xf/PzlwKLPPO6vHNh26aXikYWrGrpKbD4Aroz9/aEXL6Jrzbj2x6gebRr7I5XfHz9h/yoqP9x3mKdk8nktfQy46BwPrqc7zMY3PebHawF8dSutBTloTrUMJVbrT7bhPeCglfZeWy0mn1vDavO/x/BL7hZh3u30swyZDHyWIctOZ8LBi0WRC+9s/pludvQmTZnnoo8JHsdxLUDxa1ymdOulhzScTXLlGN/rRWLb8herrGoUctXY0DnetffL/rRRP77POPvnqC/739VdMP2keAF98u4SdTvkJb9xwGxM3TMvvkWwvc03n9+/fgCEb9rpJzjq19vDu76fyfEPvBgthWu1kxuzca/uUu86kh1XvT3TlGlWlsO/f+twsZ63tq0YOpA/N45iWuKYzBx+a17n/cuGmhPa37+tz8KE568yfhcA1qVmmw7TWbzmw1aGU8qRGMr9Da71SKfUCZkS1c/apKzFXX/N1jwTu0Fqf2vmBUiqCqRX5ebdt9wI+yWIn23fvAWen2d4CE7nr1AEuB5alvt6SblNxi5Gvly1lUuoCfOTlF9h+s6k9tlmp3/djioRn5Bzf9Z3/fS7X3x2z5XOM2fI5Ep7rK2GcFZ2DgfVNpzcYPhT4HaZkxeXWBKaRk9amz7ahl2vUEY0xGNV3+brcjqn20LbM6kjwWgFPwGYnmoeR3jbLRWdH80g+f/DGnl9Y4MuFMH4fKB3R62bFci9B8Whdp3QmWofxxcLfA7+3LmDpy7BFNbl0NuWkNf5Orz7UEW3LYGjvCeR32HwLlix8+rv33jkHE/3zHYwdPbrrhi1LNsEtnd++CFMO63WTnHUm28tZHn3dDZl88SCM2gbKsrdPOetMNA/ni4evoetSLzvo3GKZnLWueGsWA+hDc9bZ0TgSt3Tm4EPzOPcj+PLxJ1zRmYMPzVlnnmit16QCwlvoOspZCB9g6q5+rJQaB7SnAs5hmGWQ6c+UWwLWn536CjrnArXdPnsImIcRNzU1H1gBbcDP0rbbO+27eLfvANBav6+UqlRKjUwN+R4BHKeUasdE2HPS5i7vh1nnWTTMveQCXlj0JkvjK5lyZICLTziFFxa9yaKPP0QphXfiJP589vk99orvlOIAACAASURBVBuupixCc2Vvtu/94LAzG9sqtpm3zYM/H1ba2ueU2WXvHzi7deXGc8u+P3I5phC3Y539zfqu0xsMTwPuwszx/1nB5RVsaB0y/lO6Zqu2x9AJv6VbQerCj6nSeIb8lESTzRqAhtHTZqM8PyatzljBOkuGNDF6+vUsf+Nt6zpHbrUpniEXklYovljuJSgereu8Tk9ZKxvMuJ3FT0Wsixo6qRJP2XV0qwNasNaKTRdB7z60YMpG/RHoMuKXSedJgUP6tlU++htMPgz7jNrmp8ABVnR6StoZOu7nrFllJ8tyOpXTplEy5AygwrnOIc1sMONmljz/mnWdZobY3d0/LVjriKmvAz3Tn9ogRx+ak86SoU3Aya7ozNGH5nzuR27xG5o+/dK6zhx9aE46C2MB8DA9k7l2xmCd3KK17u2aCgP7YtZrTgJuV0qVYC7u+7XWjwMopSYAzVrrry3p/w61NqYbGJRSZwGrtdY39bHdi8AhWuseWXAHnEh0PPApWaYKF8Dj+H0/6m0DbzC8P/AscGysNnBXXwbrqhtuA2bWzB3VBnhtiATW4PeNtGSrK5HotZh1wjZYBeyL3/dPS/bWEonOBB4AetTDK4AO4P/h9/UYwfQGw+MwpYfKAF+sNpBfYxCJKkzxdVup397E7/NZstWVSPTvwG6WrGmgHL/P/pSbSPTXmFEfG4We48DR+H326xFHotMw2ehspQG9Hr/vDEu2uhKJxrGn83P8vk0s2epKJPow0PswUu40A1vg931lyd5aItGfYJLP2Win1wBn4PfdZsFWVwbAhxZMJPo/7PnQj/D73Jm6aNeHtgET8fvsP3/Z9aGrgZPx++6zYKsr9n3ovfh9c/verADs+tAV+H1j+t6sAOz60NXArvh971uw1RW7PrQFOBe/z50Oh15QSk3CzF6d2cd2ZwGrtNY329aQa51ON7kBUy8mK6lh4GsGZcBpWEpaT41DNJDLTdMAfAicnqPd7YB3MdmEbfGNRVvd+V8ymWyzZGsoPaeD2+Jz7DSYYBqjHjq9wXA5ZobBBOCQvANOAL9Pk6FskQP+a9FWd7JN0y+EuCsBp+Ez+mi78qAU967Rz0jroXVIG+6ee5s1QN06nmCmKdnqsS3DJG1wA5vHQGPXf6QzED60UGweg08t2urO/7DXPgE4q3SfHZs+VOHWfW/XhyYwz29uYdOH2u8MW4tNHzoMyCnHSQHY9KHtuOubspIaufyLUqqv4HklcLsbGgY86NRat2it7+xjm2+11gv7S1Pe+H1J4Fqg0aEljRmV67N3ITW18gZgD28wPKO3bTsz12LmZ/8OO4uDm4GQBTs9uHrO7PG33POnPZuaG8uTScezIhuBh/D7spcUccaHwCtAk0M77ZgHzyfTP0xlqq3DlAs6MVYbiDr4jUtwrhPMMb3Kgp1s2LiXwPytv+1zq8J5GtPx4rRzpAl4HbcelP2+lZjM407PfQLjjO53rCk7v8HONdqMu+f+VszotNPAsxH4g4sdI69j2qicyqb0QgvmQfZVx4oyMQA+1AG2fGgTbqyPXcuDmCDJqRNtBK5IBV1uYMuHtmCecZz4yL6w4UM1pu24w7mcrNjyoc2YNtktbPnQRuB2/D7707/Bpg9twwTxOedjsY3W+n6tda91prTWt2qd4yLmPBnwoHMdIogJDjowN2pTnq/OoGOvPKYH3J76rb7Kp2wMDAfexe97BrN2thFzA3TRobVuSuhWndBtySw6WzGN+2n4fX1O682Hq+fMLrl6zuzTgQ9WrFx22MIn7pmvlCeGOaY9tGitm9vb2+no6Ehqrbt/34xxto8Ax9rU2QXzsDQbeA6d1CRaINnR47gCTSQ72lPft3f7rrPXc48M05fOwKyHvjRWG3C2iNzv+wNwIea8t2TU2PurDRN0HIjf94YjLb3rjAIzMQ9MmY9l768WzP0Uwu+zn1Birc6VwO6YYDFRgM7Oa/QF4CD8PvvrTtdyPCZYLLR96sD0zu6G32d/zUwnft+9wKmYc9hagM42TNv2Y1emKq/V+SEmQd5izLVWyLnvAOqBc1zU2YJZwxOlsGu0s316C9gnZc8tBsKH5k8fPjSHVxtmKuBh+H3Pu6jzK8wUy0/J4kP7eLWk9rsOuMhFnWt9qLnWCjn3CUwHy/74fbZmSGXS6tSHtmNK3n0fv8/maGR3nU59aOdz3gn4fZkqT9jSacOHdgD3AKe4ptNwPM58aALTKfJ9/L7BWVy4HxjwNZ3rHJFoCSY9bL4BfSN+Xzzfn/MGwzdjFhdvFKsNZNy/rrrhh8ATwN419VUvp3QqTEKhId23fz1x7hltxIPj1a77bOU5sXvD2A4std3refWc2btgRm53xkwdrjn7vsffT2kdQZa59Hc9cOPP1jSuvnj82InzDp99THqCCw186+IIQldClT9Dlf6Fisk3sustF2fcJtEK/zh5PomWgxm+2fHMuPzZ1Dcr8fuaum/uDYZnAk9hSg4dEasNJLtvUxCRqAdzjeY7na3ZlTU9vRGJbkD+01oSmHNv53jlQiRaARSSpm6pqw9J3YlESzHnPt91Satc60XOhGmfxpL/tLtWYLmLozI9iURHAb2nBu5JEnONutnR0JVIdAjdEuDkyHKXg82u9LMPLZhefGgfDMQ1mtWH9kISWNLP7Wg55r7Pl2X4fTanEvdO4T50Tb8HHIX5UFee83qlMB/av8954MSHrsDvK/qyj06RoLPI8QbDO2N6sc+I1Qauz7RNXXXD2ZjpkBvW1Fct78vmcw1TJ2BGNer2r/rE1cJyV8+ZPQYzxegUzKjBr4D7zr7v8ZwuzKvnzC7HlN5pBHY8+77H++8hrpNQ5T6YpE4NwGxC8ewNYKiyAngRk456D0LxjCmpvcHwlpie28+B78dqA/33wC8IgiAIgiAIFpGgcx3AGwy/gelp3y5TGY266oZbgB/W1FdNytXmcw1TF2AKxU7ev+qTHqNwTrl6zmwPZrrC5cAGmNThobPvezzvXsCr58z+MXAfcOLZ9z1+q02dfRKq9GKyyi4DdicU7zvZQqhySmqfJmBXQvFl6V97g+HRwN8xoxK7xGoDMbuiBUEQBEEQBKH/kDWd6wbzMYmC/Fm+3xaTuTZfm6PpWRfIMVfPmT0dk376ZkwWyJ3Ovu/xXxUScKZ4AFO78ndXz5ld0dfG1ghVjsRMfS0FDs4p4AQIxb/AlFuYDP+fvfMObKraH/jnpoMyw0YUsYpCAw7QuEe0dTcKrhe3uBHccdSnPq8+f1pH9DkY7vnUqM/BSx0oxYjP5wiKiiYONDwFlWkEWmibnN8f3xQ60jbjpiVwP/9Ak3PP/eaO8z3fc76Dl9GtG1wIiyuq8pGA9RGIS23YYKlNTExMTExMTExMOhXT6Nw88CIB460SCk2dVK2RntH5ARL0PGV29QhD6lJ5XE6rx+X8B5KUYifgbOAgt9f3ZSb9xl1xrwK2xri6ZO2jWy3AM0gpGhd6JLX053rkI6Tg8sHAfU2+uRM4Apgcrix/3xBZTUxMTExMTExMTLoQ0+jcDAhXltciKfyPL66oaulCuw1SGDwlo7OsdKFCdjt3B/bMRD6Py6l5XM5TkQxllwIPAaPcXt+Tbq/PkCQFbq9vLvA6UOFxOQcb0WcH3AKMB65Ej8xKqwc98gxwF3ARuvWi4oqqc4ArgPvDleWPGCapiYmJiYmJiYmJSRdiGp2bDzMQN89zW3w+Jv5vqjudAM8iCXo6KsnSJh6X04akQv8nkhRnL7fXN9nt9WUjC2oF0INspncH0K0nA9cDjyKxqJlwHVClFA/sa/n6IeAdwJ1hnyYmJiYmJiYmJiabDKbRuZkQriz/HjFYLozHBTYyOv5vwiyp7VFWuvBPxIX05NnVI/qncqzH5ezlcTnvAL4ExiKG675ury9rhZvjJVYeAS70uJwjs3IS3WpHdpXnAlPQI5ll4tIj0cl1l13zoxrKjIJ7LY8V3HVtuLK889J/m5iYmJiYmJiYmGSZ/I6bmCSN1G7aCdiWdGqMQQiHfUWHLdtmGvAqUI64moIYncumzChd3kLWreOytltjbBfLlfMXx94t6qVtdxv+wCtI/aYfcdgXJWrvcTk1JEnOP5Dr8ARwrdvrW5bWL/IHrEiSpKRqjJ1+0oVzPvh49llF3bo/jj9wIfCtYTWcdOvWyHX9HTgBPbKxvqLUFysBtkqly19ro0XLrQfcdfHq7Wt8eVdGy/I+fxbdui96JDu1vKS23A5AMenU6YTvcNh/N1qshPgDg4FRpFencxGwsFPqjEkNPBuShTlVfkOe0ezXmJP6YqOQ2OfU63RCsNPqIPoD2yHPaap1OuuA73HYFxsvVAL8gf7Ive+Z4pEx4Bfkfcp+HUR/oDsyPg1K4+jlyL3Pfo25rtehyZOkDk1AuzrUcFLUoU1QwBKM1KHtkaYOjbMKeUazX1rM1KHGY+rQLQazZIpRyIA5EzgQUSrpUAichMNelc7B8R3On4BvwpXlRwBMnVT9H6B+yozSg5vIeiVSGzOpgt8NqrYXKC2P7qs1TQNRsjOAK5sOSB6Xc0fgAaTUypfAZLfX9590fktczn2Q3duUam82NNR3a4hGiwoLCmstFksQOBiHfXXacgDo1u7Ae4i78n7okY3Jj/yBAcCHwFBkMpkUSilqo6pHg6Kge562pmDhjMv45eWHgbeA8egRY2uOyoTuSeBEZHKeDkXABTjsTxslVkL8gdMR9+V0i9IXAq8Bp2d1Uu8PjEaSbqXrNWIBlgL74rCntzCTDP5AT2AOotjTfa7ygCNw2D80TK6WyITuDuASIN1JRDfgJhz2Ow2TKxH+wJHAK6T/LhUg40Y5Dnu6fXSMPzAc+C9SVisdha8h5Z32yaqhtAno0KRJUYcmIKEONZw0dWgT8pEM85nr0PZIU4e2IAYchMO+wDC5WmLqUOMxdegWhWl0GoU/8DpwKBJTmAm1yEM5N52DiyuqbkSS3Iy8+o/uPyArgM9NmVE6OS7neUi21EzlXAvcg8P+N4/L2R24FomprANuBKa6vb70V0dlIPoYmSiljVJqnaZpC4C90lbuulVD3IxPA45Hj7zaRM5C4Ctk1bMwE1GBP/ns8ruIfHUrcCd65NoM+muNPzADOJ3Ud2VaUgucisP+WuZCJcAfOAbJyJzq6mxLaoDncNjPz1yoBPgDw5DFlb6kvurZlDok3nlMVlZrxZD7CNgVmfBkwlrE+MjOxM4fuBEZSzJ9RmsANw77jMyFSoA/sD8wi8zH0RpgDg67M3OhEuAP9AWCwEAy82yKIjueNhz2bMTjbzI6tEOyoEMzFyoBBulQxGjJTIe2h9E6FHbFYf+fAZK1xtShxmLq0C0OM6bTCPyBAsSl1Ygakd2Bv2Rw/KNAA3AhsmpopXk85/kYI2dPYKLH5TwaUUg3Iav+JW6v776MDE6hnMwUEACaphUBOyPXIl2uQQzOG5sZnIINcbXIVFYNyGf3fyxGVsCvQbeekWGfLTmVzJUlyDN6jgH9tMXZZK4sQZ5zw+vMNuEQZLcq05JChcBgNsZfG81gYDcyV5YgspYb0E9bnIsxz2gP4DwD+mmLkzBmHO0BHBmfeGeDveLnyDSUJg95J/fOWKJEbFo6tCMM1aEG9NMWhuhQZNzIVIe2h7E6FEozlqhtTB1qLKYO3cIwjU5jGED6rhaJ2CHdA8OV5b8icZ3nrNXU2PjHTTPXphMvkZCGhoatgSrkt5e5vb5T3V7fEoO63xZjFCZKqTrSVZi69RjgduAF4P8StNiK9F0tWtINkfNSxJX3EXSrMZM8WanrbUhfwjAD+8pm3z3jE9psMBRjlBCIa1i2JnVDSd9VtSUFwHYG9ZWIAQb2NcTAvlpSbGBf65FJTTbYCuP0fB5iHGSDTUaHJoFhOhRIKUFfihimQ5FnNFvjUzZ0qPGYOjQbmDp0C8M0OrPIOXfcwuAJh7PzRFezzx94xcuoM05gzMS/cM2MTCtuJGQ60O+X/Nip8b/bLZeSSE7Xzdcx9txTGXvuqRS7jmXsuae2Ok6pWB7iUrub2+urNk785OWEjq9nNBZNT/Hq1p2B54DPgHOTzVSbwX2X1T49Uo/EjCwGXkO3Zk05deEzmhIZyNmp8QNbwPWEzFelUyLj96mTyOV7n8x43xXk8jWF3HhG9SceZpsTj95w/9/4KP00DEaRgZydej1hi7imXa5DN7PrCV3wnG4qdOhyo2naGqVUr/j/j0ZiGcoQ14DzgWXIatrflVLPx9s9CfiUUi8n6O8fwCtKqfebfPYAcHaT81wMrFVKPZHZz+taJh7p5OLj/sKZt9204bM5nwd4/QM/Xz72PN0KC1m6amU2Tv0eEIqhDgNWIkHWKcnpven2Df93T7sXa8/WYSH5+QW1bq/vDoNk7pB0r2e0oaHbypXLeqW0laBbByJJLdYgSX1qsi1n8/NHVqBbj0USgLyGbj0oFRk6VdZOwJTTWHJFTsgdWXNZzmTG+64gl69prsgJcMWJp3DVyUZHc6RPrsgJuSOrKaexdKacmqYdh4Sv2ZRSofhnxUi8fgjZLV4NTFVKPRX/fiJgV0pdnKC/CcCuSqlbmnx2IvASsKdSKqBp2i6AWyk10cjfkvROp6ZpZcQzkyqlGoO071VKjQXGAw9pmtbuFrymaf2BfVoYnHYkiLgpjyMuhjnNQbvtTv/ezbOUT3/9X1ScehbdCmXzbXA/471rwpXlCpjWO6YNXodaNGVGabsrVYnkbEQpxYtz3uWUsiNafadpWqeugGVwPbX/Bt6fmPSJdGsB8vJtDUxAj6RUfsGw+65HvkZiSHYHHo8nNDKUrnpGU8WU01hyRU7IHVlzWc5G2hvvu4Jcvqa5IuemSK7ICbkjqymnsXSynKcgGX5bxtcuVEqNU0rZ4t9doWna2Un0dw1SYhEATdN6IzbXx42fKaW+AoZpmjY8U+GbkpTRqWnagcAjQLlSamHL75VS3yNZrjqqsXMiUg6isd884C7kAjTtrwYIa5q2VzLy5RLf/byIuV/NZ++LJuK47AI+DX3d8UFp0C3G0wNjFhbnxzIKJp/75ecM6TeAnYYZ+twZRjLX02Kx1K9eEznF43Jum2S39wMHA+ehRz7uoK1hciZEj/iA6wAXcL0RsnREZz2jmWLKaSy5Iifkjqy5Imcjm/p4D7lzTXNFToAHX32JXc85hXPuuIVVq7NTItoIckVOyB1ZTTmNxWg5NU3rBeyPJNlrM6mTUupH4Eo62LDTNG0ksF4ptbzJx38H7qR1eZ1/t3fOdEjG6OwGvA5MaNzWbYmmabsD3yul2nXjRC7cvCZ/XwzMVEr9mqBtAKnXtVnREI2yavVqPpr2BHdNuoy/6H8lG2VrLv2ze1GR0liUHx1RXFGVTsFdAJ6fPYtTyg43UjRDSeZ65ufnr0N86G/tsEPdOhmYhJQsebYz5WyHO4Fngb+jW48zSqa26KxnNFNMOY0lV+SE3JE1V+RsZFMf7yF3rmmuyHnR+BNY+NyrzH/0nwwdMBD3tH90tUgJyRU5IXdkNeU0lizJOQF4Syn1HbAybm+1xWdASQf97R9vB4CmaeOAbZVSvgRtDbfDkjE665Givecm+O4KTdO+RbZk9ST6GorEgKJp2tZI2vkH2mi7lOxlyusyhg0azPEHHoKmaexlG4PForE88kc2TjUaYHmeKgDOTKeDhoYGXpk7B9chhxkqmJEkcz01zRLr3avPC8AZHpdzbOKeAN1aiuxy+oC/dracbcsVUUj89CfAM+jWXY2UrSWd+IxmhCmnseSKnJA7suaKnJAb4z3kzjXNFTmH9B9AXl4eFouF88sn8Elw09yRzRU5IXdkNeU0lizJeQpSPYH4v6e00zaZEKymdpgFuBdwt9HWcDssmdpdMaTm1buapv1VKXVbk+/uVUrdrWna8cDTmqaNUEq13J5tSi0b0yOPA3YEftA0DaCHpmk/KKV2jH9fFG+/WTHhgIOp/vxTDh63B9/9vIi6+noGWluGtBrCGIBVFjUPuKi4our+eKxn0rw77xNKhm/HsMGJKw8opbo8BVey13PvPQ56/IefQk5k17D1Ur5u3RF4GQnKPg09YlQK95TkbBM9sg7dOgH4FJiJbt0LPdKRZ0HXyNpJbE5yxurqipa++uJZqy484wCjz99r13GDt7ng4m6WgvaTOCd7PdcEF+z184Vn3Jagi4wZ9eDjhZaCjrPzJyNrtKamz3cltqzIuf31fx9dNLzYEDk3FToa76PRhsLPvvz4+Pen6TsmbJAB/fsN6nnmXyYV5OXlddg2V65pMnLWxVTByIqqrDyjT9n77usY1K3Ddr+uWM7QAQMBePWD99h5+xGt2qyLqqLLv4hMeevNqt+MlvPM4d23/2tJ76KivPZnE8nIGVPK8uqSdYe7K6qMLG0CyGz+p6OSq8KUjKy/1ka33jdL9/6DgwcOG9a943cpyWuqjZ619O/r3qyKGS3nnbv02eeEbYoseVrm935dVBXd8e2as554s8pwHXro4G6DHxxr7WbEM5oKmqYNQOrO7hzPoZIHKE3TrmnjkHFIcqH2qAWs8f/3Rmrwvhe3w7YCZmqadqxSKkAW7LCkCkYrpWo0TXMCczVN+10p9ViL71/RNO0s4CzgoXa6CiKG5ntKqSqa1LuKZ8ltqsxGAl2fFzkDTrnlet6bP4/lkT8YdmI5N599AeccfSzn3HELO090UVhQwFPX6WgdvHBpMhr4Y7VF3Qc8jRThTVjWJJGc55aP54XqWZxS2nZCCUUsvzONzkyu51aDt16DuNfe43E5j3B7fW9v+FK3WpFMtQo4Fj2SkSN+1u67Hvk1bnjOBf6Fbi1Dj2RU266Ln1FTzjiqoaFb/dLfjicL6enrV63UVENDPk2MzkyuZ/3S38cCuxgtJ4CKNhTQwuhMV9bY+nW9gauyIWf9qpV5LY3OXH5Gkxnvo7FYwco/lh9JokW7DFm/vpaYiuXn0XyinMvXNBk5G2KqgCw9o+G10TzHoI7lfG/+POb/8B2aplG81VAecrd28qmLqW6La6NnkIXxaVFNVKuLqfymE/p05VSg/bgmeiDiQmgobf3wdGVdWR8bTJbu/cq6WH5LozOTa9qguDIbcv64tsESU1ia2nKZPKPhmoas6NAltVGt3qBnNEVOBJ5WSl3Y+IGmaX7gAODnpg3j2Wzvpm3v0UaCwOkASqkIMLBJH+8BV8UNThA7bEEmP6AlWkcxBi1KpmwLvA9cjljUa5RSd8e/2wOpaWhDss+2KpkST0h0oVLq9PbOE//7M+DwFsGumyb+wFbAj0BGSXua8AYOe3kmHUydVP0eUHBX39oy4BfgvXBl+Yn4A4sAQ7JERFVd7L3YGfllpQuND1TxB+4HLjGotwhQ5pmmL0BeuDXAOLfXF0W35iEG5+HA4eiROSnKeQTgZePKUSZEgRtx2G9vt5VudSFuFo8B5ydVP1QKWxu5Uvk5Dnt7sQXp4w98AuxpUG8xoAiHvd6g/jbiD1wD/B9JLt51QAQ4FYf9DQP6ao4/MBbwA0al2puBw36RQX01xx9YDRhVr2MxDnt2atz6A68hWduNoAYYhcP+i0H9bcQfOBOYijHXdC1wKQ774wb01ZxNUIe2iYE6FKjBYe9pUF/NyYIOxWGf12HLVOkKHZoOpg41dWgn69C4EViplGqagPVSxM66g9YlU6Y3lppsq2SKpmk9EI+5nVULA7Cl0alp2oPA20qpf6f3M1vTYUxnU0NQKfWzUmp7pdTrSim90eCMfzdPKTVKKRVTSk1MVKNTKTUXKNY0rZUvTAuDcxzwdU4YnEItxrw0jaw2oI/RwDfhyvJ1yCLAhOKKqm0wcKs8Rp0F2Nuo/lqwBuMGeAtQ6/b61iOZYHcBGgsp3Q4cDVyassEpGOl6UJ9Uf3rEiwzU55JaaaGG9MRKiOE1Q7PUdwyZiGSDGoy9ptkKJ6ghhfJYHaAwZnxqi/UG9pXN8Iw1BvaVT/ZkrcW4cTRG9t77TVGHtoWR98rI570lhutQg/pqSefr0PQxdaixmDq0vYZKHdzU4Ix/dr9S6iKlVFgp1b2xZIpSaq9GgzPe7slENTrj1UHeBcraOF+jwdkNsANvpvTrOsCoi5gKbjpeJRwI3NgJshiDwx4BjMrhvA5ZhUibqZOqBwGDgG/iHz2E+IKfB3yODMwZoZSK1vBrFJicaV9t8BXGDSAFyCo6wItIQp5b193Y/zzgamA6emR6mn1/y8Y45UyJAslGnv8NeA24B93asbubw66i69b9pmKGzEHWk+Ez2gEBjJmMKWARDrvhsSgAdcuX/hhrqO84cCY5uiErltkgjHET+hrgS4P6SsQ3GDMJqQe+MKCftghg3PhUA6w0qK+WfIOMf0aQT/LjU2psYjq0PZavj4brY4akoY3ScfxVJmRLhxpNV+nQ1HDYFXINjLj3pg4VvsY4g9bUoclzG9CjgzbDgQqllJGLAp1vdCqlPlZKtXvBlVLvKKXCnSSSUZST+ep3LVIAtiOf7I4YHf/3a4BwZflCpD7qBQsi9ZcjL1AmhmdU07Tlodij/wRcs6tHDOzwiNTxAv8i8xW7WuAEHPZ1AG6vTwFXDy36c5tCS3QGMAe4LO3eHfbfgXPIXLmvBR5GVqA6Ro/EkN3aBYAX3TqyrabBEtu2wRLby+HbbxoWW78u1tKlIhWUUuvj58zmotBNyKCcidJUiLuNUS6QGwiW2LRgic218Hr3Iytnv10Qq6/PdFCuAS7AYU9UOipzHPY6JO16pu9SDeKK/lzGMrXNaUhmvUwmIvXA/5DSR9liGuJuZcR7f3R8Qms8DvvXSH1fI+7933DYv8pcqDbZlHRoK4orqvoUV1Tde+jcFYcuqY3GYkpl8oxGgeXAqQaJl4is6FDD6Sodmh4TgD/IzPDc4nVoE95D3P+NGJ9MHZokSqnflVIzO2jzvVLqPaPP3WFMp0kK+AN747kh5QAAIABJREFUIPGuO9LCoF+2dvXIhlis+9De1rZW31cjpWlujT/gaTN1UvVFyKRo2ykzSn8BKK6oOhapt3pC+KghfuBvSqmxy1b8vhfAoAFDvopnx+qIOmT1/P9mR129kcHzmrLShXdlInNC/AELsjNeqpTqv37xzzsDdNtm2wVtJY5Q0Whe/aqV20RX/zmkfuWK+trwjw+snFV1jS0U3LhSp1u3XRfN/35dNL9wwR9DbAdM+/LbTEX9/e2PT/zmz4bHBnSz9NipV/433fO0VBT0cqAKmJ7y5FO3FiMrpiuBfdAjqxq/CpbYCpDn8Sbkebx1xK2emYWDBl8JjCLJlbtobU2v+hXLi6Nr1/aoX75sUeSjuRO2m/nq/JTkTBV/oOeS337+Z0NDw/jevfos72vt/2PTe77yj+Uj6urq+g4euNV8i8XScvIXRVbP78RhN3QnIVhiGwU8CBwKfIbFcpFt+lO7A07EuyBVfgaexmFvVwEYgj/gQAyx7UkutXpTViELNHdlcdVb8AeGIYbSzkD7KXdbsw6ZbN2Mw57d8Ax/oBCRcz9axPpEa2p61/3+q61g0ODv83v1XpXg6BjwA3AfDnsgwfeG8sfMWZNW/rH8np49eucN7D/o67y8/FQWHX8FXsZhN6xucZu0o0NXrFo2MtrQ0H3woKFZ16FNKa6o0pAC6R4k+eFD147sdfdFI3peCuxG6nGoG3QoDvv/jJIzIU10KNA/xaMV8BPwMA57OqEnqeEPlAMTSS9eNn0dmir+wCjgWlLQoU2oQXYhb8ZhN9JFvzX+QE/EI2pPINW44azp0FZIrOyFmDp0i8A0OjsJXdcvRlZfd9R1fWE2zzV1UvUDSCZh65QZpQqguKIqD3EN+T5cWX4ogMflnARMBya4vb7X0znX7OoRfmAYsFNZ6cKsvUjBEtu+yITiQlso+HAS7ccghvdBSB3ZybZQ8DN0aw9gbkwx6ukfdy9aUddzutvryyjZQnFFlQV4FVmpPyJcWT47k/5SRrceCMxGBrRy9EhDsMR2MLKCOBpZWbvMFgqG0z1FsMSWB1yExJIWAXcBt9lCwazEpXhczt2RHYvPgdJ4PG7T7w9Asvie7/b6Hs2GDE0Jlth6ADcg2QZrEGNjhi0UzFasi0kOEyyxFQIrgGdsoWC2QhCSwuNydkPGh92BA9xe32cdHLJJ4nE5L0HqKO/o9vqyqkMbKa6osiHj6CHAPOCicGV5Nt0iTUxMTDZbuiKmc0tlVvxfw9PNJ2A08E2jwQkQriyPIrGdZcUVVaM8Lmdv4GZk4p7JCtE0YAey/7smIzE/Sbkl2ELBr4GDgTORValPgyUlD8aiPAuMs2i4VtT1fASY5HE523RNTZJbgWOByzvd4ATQI3OR63N4dL02NVhiexYxQHsAx9hCwfGZGJwAtlAwagsFH0RWd19EjK6vgyW2YzITvjUel3MrZFd+GXB8S4Mzzn+QmKXJHpcza/US4q6045GdieuA54FRtlBwqmlwmrSFLRSsQ97BtmuQdALxd2M6UkLirFw1OON0mg4trqjqWVxRVYnEBY9Dxte9TYPTxMTEJH1Mo7Pz+B6JpewMo3MMiYPpH0PinSYhCXQGA1fH4xzT5VXgd7KXUIhgiW0g8BfgaVsomLRLii0UVLZQ8BnEUJo2cMyayZY8jlu9uNuLwRe2fgPQkZiHtNOrF1dUnYYYIw8jK+JdQvCFrZ9cu7TQn9dNXWDdYa0L+Dsw2hYK+ow8jy0U/M0WCp6BGPQ1wMxgiW1msMS2vRH9e1zOIuSZ6g+Md3t9vydqF39mpyETwr2MOHdLgiW2HYB/IwmbVgMH2ULBs2yhYEKZTExa8DawQ7DEllmF8My4HDgbuMXt9b3UhXIYwXfAIrKoQ4srqrTiiqrjkEWma4F/AqPCleXT4wu3JiYmJiZpYhqdnYSu6wpZqS3Vdd2obIKtmDqpegAwhI2ZazcQriz/HfhXz4Y1ZyuJ83jR7fV9nMn5ykoX1gGPAs7Z1SO2y6SvdjgHie1KK8OsLRT8w3bykjmDdlmtrf6laPkvc/u7gPeO/mLhIOBO4HiPy5lyMeniiqq9EEPeD1wSrizvEl/1uOvxp/+bM8BRu6JgxdA9I8p28pJZtlAwa6njbaGgHxiLLF6UAt8ES2w3BEts3dLtM74r8xCwD3CG2+vrKG70n0jiEUMXPIIltqJgie1vyMKNA3Gp3d0WCs418jwmmz2d6d3SCo/LeQRSLPxfiFdLThNfaJoFlHpcTsN1aHFF1QgkLvAVJIHKgeHK8rPDleVLjT6XiYmJyZaIaXR2Lm8jCSeyVdsSpGgsJDA640zfe1XAiqSXvs6gcz6MJB24wKD+NhAssVmQnVm/LRRs6ze1j24dCzwDfFzQIzoctPORJCXzD13wU3+U+g24KxU3zXjN09eAJcCJ4cpywxJXJEuwxDYwWGJ7FIl1HYTSTirs07CTphEGXkG3ZmsRAABbKFhvCwXvBkoAH7K7+lWwxJbuJNuNuEPf5Pb6Xumwsde3GngacHlczgFpnrMZwRLbkYjb7s2I23mJLRT02EJB4wtjm2zu/IAkYul0F1uPyzkKyV76FeJWu7kkrjBchxZXVHUvrqjSkUWmA4Argd3DleUfGHUOExMTExPT6OxsqpGshdlc+W4sl5LQQJv804wVtjUhvulVssLt9RlSd6usdOH/EKPjvNnVI1LNNtkRRyAxmdPSOlq3DkbiA1cBxxXdv7zWFgo+irjcPlEYjV02ZvHybsC+KHV8Ml0WV1R1RwzO3sCx4cry7GbJbEGwxGYJltguQLLLnYXsZthsoeDLef/3xyrgGGRn+HV0a69sy2MLBX+xhYInAUfGP3o7WGJ7MVhiG5ZsHx6X82hk1/klxHhNlunIAsrZKRzTisayMkgh5BhwmC0UdNlCwcWZ9Guy5WILBTfszMUzSXcKHpezH+IWXoe4qK/trHN3Aobq0OKKqqORDOw3IW79JeHK8nvDleWG1qYzMTExMTGNzk5F1/U/kEyq2TQ6xyD1qn5O9GUe6k6FVvth/30HF1dU7WngeachMaJJGW4pMBn4DTHyUkO3dkNcpQYB49EjG2o42ULB5bZQ8Hxg321X/BnuVVtHUX306cCuu4xpr8t4+vzHgD2A08KV5QtSlisDgiW2PYD/Im6oC4CxtlDwalsouHpDIz3yLeACdgGeRrd2yntuCwXfjp/zRsTwDQVLbFd1NOH2uJyjkQQ984GJqcQYu72+BUgyrEkelzPl3xkssRUGS2zXIEXaj0YSJO1qCwWzWevNZMthFrI4tU9nnMzjcuYjO5zFwHFur29RZ5y3s3B7fauAT8hw97i4omp4cUXVK4g7bT1waLiy/JRwZfkSA8Q0MTExMUmAaXR2Pm8De+q6nmrNrGRpzFzbyp3K43KWAkc3aPm3r8srWouxsXDvAAuN7DNYYitGypA8Gs8GmTy6VUMM4f2Bs9Ej8xI1s4WCH1lgz6GRNdPXFeb3WNqn5/xgie3WeImMRFQApwDXhyvLs18XKk6wxNY3WGJ7EKnJuR1wBnBwPEtva/TI20gs4nFIwqROwRYKrreFgrciz6HUpILPgyW2gxK1j7vFzkQKg493e33plGCZBowADkvloHhZmfnAHUhJidG2UPA2WyiYSUFtE5OmVCM17zorrvNu5D2Y5Pb6/tNJ5+xs3gb29LicKevQ4oqqwuKKqgpkkekIJMRk1y7JOm5iYmKyhWHW6TQSKRp8LjASyEvUZE3d+n6L//xj/yE9e8/r273Hr02+Wo5MUJ7NpLjx1EnVi4F3pswondj08/gu0KfAwMMPPnb3Bb1Lqqz5FnvZ4G7vdsvTUolXq0Em6vfjsDdz25pdPeIqxMjYpax0YcY7gEHb6Nus++xfMeQvp/8rr0fPopQOXruomJWf7kxkwaNM8Z3fUXOPy6lZYrH3NcWepd8s6lYQi4WR2pYbDMviiqrxyI7rc8DpzRIH+QMHILuLxSnJKfwOvI3D3iq7ZLDEpiEG5l3AQCRD7t9soeAfHfYqhvdjiOupCz3yIv7AtkiMrA1I1eVvDVLY+kEc9qQMs2CJ7Viktt52wLPA1bZQ8DeAeDKQt5A4qoPdXt9/NxzoDxQCFyOFrdt1EY7FYpZFv/xYVlhQ+Mc2Q4d/iuxchIAZiYqvB0tsWyGT89OQjNKXZJTl1x84EXEtHpLG0WHgJRz299M+f7L4A+OQmNkdSH3BcQWSMOvJTii+PhC59zsjrtOpsA4Znx7AYf/TaNGa4Q80xpvvB1jbarb2u+D+KKX1HDW6aYxgPRICMR2H3RAXbo/LeR7wCHCv2+u7soWsRwLjkZrKqfIj4MVh/zBjITsiCR26tmZNv9+WLt5/YP8h86x9+iatQ4srqkqR8bMEGccvD1eWp7cT7A/0Ai4FdkNKU6VCmzrUcPwBDXnnDwFSjXuPIff+WRz2hIu2hpIlHWo4naxD0yYFHZqAdnWo4Zg6dIvBNDqNwh8YgxSztwLp1g2sAR4ArkvnoZw6qbovErt47ZQZpXc2/c7jcp4GPNu7l/W8C868YnJMqZ0tmpZu/OV6pH5ZaVOlObt6xABgMfBYWenCKWn2DcAP+x/Yrf9hR6/se8DB3SwFBQknHx2iYoC2Fk07Eoe9w6QQHpdzDyDQu3b9Pw/87pdxyG6dD7j0qAl390LcWr8BHOHK8o2ZYf2Bo4GXge5pySnUALfisG8o3xIsse2M7OIdiLhlX2QLBT9PqVdxMZbC8FsfeyKjLnscMV7Tu6Yyqf8vcFQKhmcP4K/ANciO5vXA9Dd2G3EfMAVJdPL0hgNEWVYhu9TpXtMosBLYC4c9HJcjH9mJ/ztQhOxw3p5Rll9/4GpkJznViWdTaoCTcdj/nUEf7eMP7Ie4evYg/fFpLfA4cFnWlKYYnB8Bw0l9QtfIemQny5E1w1MMzmcRQy7dex9FJst747D/kok4HpfzQOQ9rwacbq9vY0yiP3AmEvuc6TN6HA77rA5bpkuWdGhxRdXWgAc4GTGiLglXlr+RgZy9kMnjGFJfFGkkoQ41FDE4PUiCv55p9qKQa5qUDk2bLOlQw/EHtkN0cafq0JTJkg7NCqYO3aJIylLXNO08TdO+0jQto2QdmznvkJmyBHmYL0ZcStMhYRKheO3D24DPzj/j8gOBMRkYnCCKdlfgnqYflpUuXIHEE505u3pE7wz6p/9hR91u3ffAHmkbnACaBTStJ/AW/kCHkwO31zcP+Ofq7t1OmFMy3ImUAzlEwTdnfvOmvyBaHwEmtDA4B5C5sgS59zfgDxwULLH1DpbY7kZWw8cA5wP7pWxwAuiR9Uic7TKGTXgNpTJRliDG2j6kUILBFgrW2ELBG5B4z0+AB34cZP0JMTjvbmZwCn8jM2UJ8hv7I4p3Q1kZ4D7EqNnZFgr+LUODcz8yV5bEj/fiDwzKsJ/E+AMFiEtiTzIbn3oi5YtONEKsNniOzAxOkPHJRrrJx5LjQjIzOEGe0cGkE6/eBI/LWYzErv8InNzC4BwJzMCYZ/Q1/IE2d3QNwFAdWlxRlV9cUXU5smNzHDJm7ZyRwSk8SGYGJ7ShQw3mODIzOEHuRdI6NC2yoEMzF6pN/k1mBiekoUPTwHAdmhVMHbrFkez28AlILb6TsihL7uIP9EYGokwexkaKkIL36dBodLaM87sEmchdrWnaXmSmLBspInHa+mmIK8dpGfWuWU6wFBqWCFeRvMvO9YCltlvBTbZQ8O7/9Rq8y2eDR64+5bvZ/V6qurHhzdeu2rlFexuSJTJzIZWyrJ4/byIySXIDTwCjbKHgo7ZQMP2SB3pkKVreeLpvXYCmZaIsG+kO7JvqQbZQ8Fvg8O+H9Lv526EDth3051qO/GJhv2CJraXb135kPgEByFNKjQzaxjxGY1kZGcOOtIWC3xvQ/1iMeedBniFbh63Sw8jSOd1Jf3xKhrFkZnA20g1xK8sW+5D5RAkgn43jdsp4XM5eSHbufOBYt9fX0u1+F8RVzgjqkKzfxmOwDl0QqT8WmAfcC/wHMTb1ZguG6ZNtHWoU4zBmHIXUdGiqGKZDkTntbgb11RzZOR5FZgZnI2np0BQwTIcCI+OeHdnA1KFbGMk+SB8DS+P/bkDTtDUt/p6oadqD8f/rmqZd1V6nmlCtaVqf+N/h+I7qfE3TAk3a3a1pWmmSsnYFPQGjUqznIXXI0mE04r64IU4lnqjleuBNt9dXTWarni1JFCfwCfA5MHl29Yi0BpNgiW0XYrHhmsWwcS5KkjEN8WyP9wNn3uU6ZrcLD73muhv2u2DQ8yPLKrvFGtYj5UBealIOxLjrqVRR7U8Lz0Zc7va1hYLn20JBY8qxHDzrCyyGVm1Iayf7jd1GbP/9Vv0vVvDtLj8ve8ACE4HvgiW28+I1WSH1+JO2USofi3YmEhNbYgsFX46XsjCCXhhjIDXtLxv0QuKzjMAC9DOor0QYNUkGY8e6lhi545eWAROP038GiX11ub2+7xI064mxCQOz9YwaqkM/XFF3PvKcngAcHa4s/8GgviH7OtQorBh375PWoWlg5PUsILvX1MiScBl5g3WAkdfAgjGGdiJMHbqFkZ9ku15IWQKjXWuOBr5QSjWNuzlEKdVyov0AkiCh2uDzZ5Vz7rgF338/YHDffix40gvA/O+/ZdI9layrW09+Xj7TrriWvWztVulIhdFAsEXm2uuRwe2azpCzrHShml09Yhpyv/ZDVplT5SIsWgMtns9Ecrpuvo5v/yc29h9r1tC3Vy/mP/ZcGqdsxm3AuX/m934BSTpx+y0zH/xrsMSmIy631wNHBUts+qj7HvnGUtQ8x1H6ciqKirefCRxvCwWjmf6IZOjEa4rH5eyDZKq1oGnOcQsW/BAssT2CJPd4BDg3WGKbbHvoGUPlzO/dx77TRx9+kfEPSILOvJ6ZkCtyQu7I2gXj/S3ABOByt9eXdKxlF8iZNunKun3PvE+A0nBleafUKM2Va5pIToAHXvHy4Ksvkp+XR/k+B3DnpEu7UMrckRNye3yCTe+amnJuRNO0AUisPsBWyOLPMsTrYIlSanSTtjqwRil1t6ZpGjJPPQvxUlgMXKyUSljtQNO0l4FrlFI/apr2HjAU2cACOFwptVTTtIuBtUqpJ9L+QSSxCqZpmgWJDTgTOE4zxj2vkdMQ16B2UUotAgZomraVgefOOhOPdPLWnfc3++yahx7gponnMf+x57jlnAu5Zsb9bRydFmNoEs/pcTl3QOJbnojXM+wsOZ8HIqRRPiVYYusNnJFv7dfK/TGRnN6bbmf+Y88x/7HnOMFxCMcfdEiqp2yF2+tb9UvR1i/0bfizZOSa7/4L3ACtyoFUA3ctfmLGNBWNNjOO05ZTs0T7jNvzo84yOKHzrqnH5cxDEq+UACe5vb4fAGyh4FeAAxkcdwACdcuWtnJlSf+aarGd7rj/m9ZfZIfOup6ZkityQu7I2pnjvcflPBmZVDyKeGZsknJmSrqyHjak6P3OMjgzkbOzSSTnnM8DvP6Bny8fe56vn3yRq1ynd5F0G8kVOSG3x6dN8Zqacm5EKbVCKTVWKTUWic2/N/7/sbS/8zoF2fTZTSk1ErgdmKlpWqsqEJqmjUFCkX5s8vFpjedVSi2Nf/Y4krE7I5JxvSgFvlRKhWnMtraR7nFX2Pmaps1HVl5TYX8k7qIRBczSNG2epmkXtGj7Wbx9znDQbrvTv3dzT1lN0/hzrejCyNo1bD3QmLjnqZOq+yDp8JuuZNyGuCz9rTPlLCtduBZ4CjhpdvWIwUkfKJwO9Coatu1XycjZiFKKF+e8yyllGdUMB6C4omqnmVuVn7I6r2f9Ycuqe1/y0/RmbsK2UPAnWyh4LDCeWKwotn5dM/egdOWUxanOpbOuKXArcAxwmdvra1YTzxYKKlso+DQSLzM9unZNq2cmV65pJ17PjMgVOSF3ZO2s8d7jctqReO+5wBS315eSu3hn6qVMyRVZc1nO6a//i4pTz6JbPIfC4H7ZKiGePLkiJ+T2+LQpXlNTTkO4FrhEKVUDoJSaheS1SJRrJdnNvxogHM8LkzbJuNeehuxcEf/3NCTLHEBt3OoGJKYTsKdw/v5KqdVN/t5fKbVE07TBwDuapoWUUo21d5YCW6fQ9ybJPy6+kiOuvoSrpt9HTCk+fPAxo7puDKD+BsDjcu6F1Ly61e31LekCOWcgqyLnAJXJHBCvSTkZ+Kxg4ODfUznZ3C8/Z0i/Aew0bHiqcjajuKLKCsyMavkNtXnd3b2ja+9H6mQ+2bKtLRSc2TDzXbT8ghdJMi7LKDk7A6NkjZfrqQAeop2MovHaoxdH3/7gEFJIrpIr19SU03g2dVmNHu89LudQJNvt78AJbq/PkAQsWdRLhpMrsuaKnN/9vIi5X83n+semU1RYyN0XXcaeJV3vWt2SXJGzKZv6+JQr19SUM3niOXJ6KqUWtvgqgHhDtmR/Ntp4jTyhaVoU+Bdwq9pYWzOAlPD7JF352t3p1DStO5IW/m5N08JIMo7x8c+NoCHuvguAUmpJ/N+lwKtIlrhGitjoY5yzTH/9X9w75Up+fqmKe6dcwbl3/t2orjeUS/G4nBpyr5YCd7Z9SPbkLCtdGATmAJNmV49I1iV7fyQpxrRUd6ienz2LU8oOT+mYlhRXVOUBLwA7AicOrlv+IFJm41aPy5kwU2W+te96S2Hhus6Us7MwQtb44sdjSF27S5LZlckrKkrJLS5Xrqkpp/Fs6rIaOd7HS1+9BvRFMtUu2xTlzDa5ImuuyNkQjbJq9Wo+mvYEd026jL/of2VTrN+eK3I2ZVMfn3LlmppytqKtTts7mdbG90ORONFGTlNK7YIYlwcimy6NZLz515F77bHAm0qp4UqpYqXUcKRO0TGZnLQJ3yJxXGia1lPTtN6N/wcOB5rGIY5s8XdO8tTbvg3+/ScdfCifhAwLNxuNFJz+Cbk/BwG62+tb3e5R2ZVzGpJq+sgk209GYkFbrrq0S0NDA6/MnYPrkMNSFK8VdyCyTglXlvvjBtJVwDbAZZl2bqCcWccIWT0u5zbIJHkJcKLb6zOqdMMGOpJzU1FMuXLvc0VOyA1ZjRrv4wuJjyALsWe4vb4vDROSrOolw8kVWXNFzmGDBnP8gYegaRp72cZgsWgsj7SsvNP15IqcjeTC+JQr19SUsxUraJ0Btz+wPJ6Yda2maTu0+H53muR8aUItsqkHgFJqcfzf1UjdbEM3/zpyrz0NaFm4/VXgbODFJPq/QdO0yxv/UEoNa/F9FXAw8AMwBHg1vsOVDzynlHoLQNO0AmT3KUCOs/WAQfjnf8bB4/ag+rNP2WnYtkZ1PQYIrVt1j4YYT98iSSa6Us7XgV8RY7LdAsPBEttgpGDuNFsoWIM/+Vv97rxPKBm+HcMGD0lHRgCKK6omIrUxHwxXlj/c+Lnb63vf43LOBK7zuJyPZrK7YIScnUWmsnpczu6IwdkbONzt9RlT+qUFScjZ+YGyCehQzoa1fQhWPsOcD9cbfvLeI/MZe3dv8juuTJDUfV/y5pnoZeMNlHAjB/l6kpecI02Hsq5fMRTdmnJoQVLsels/BnRcYjGpcVTFLB3JecnIvF51sbzehZbo6sK86FR069Sk5Bx6dHd2mtzhNc2iXjKcXJE1KTlrfyvO2jM68oo+bOPssNmEAw6m+vNPOXjcHnz38yLq6usZaO3bvFHD2j58cc1bzAkZvnDIgH26MfqvfToan5KSU0XzCD97HXPKLjFcToBDZnfcJk6H49OaH3dGL8vOvbc/NJDeO3bYLLlrGrPgP2oRc4yqaNSE7U7vSfEZ+VjaN0WSfka7WIcmJacBKKXWaJr2q6ZpZUqp2Zqm9Uc2TO6LN7kLuF/TtJOUUrWaph0KHABcmKC7IGJfhTVNywf6KqWWx+0uJ/Buk7YjSa8ixQbavdNKqWMTfPYqYniilOrV4rsnice+KaV0QO/g/I8iRu2j8cxJbRX1dQIvK6Wy8NRnj1NuuZ735s9jeeQPhp1Yzs1nX8AjV13PZQ96aIhGKSos5GH3X4063WjkYTgPyRA6IdmdpWzJWVa6sH529YhHgBtnV4/Yvqx04U/tND8Xqdc0PRU5zy0fzwvVszilNP1A/eKKqv2QeMPZwBUJmlyL7LL/DehQmWVLzmyQDVnjuzKPA3sgz2HGHgoZyNmpRmfacmp59RT2/wT42XChCvoMQMs/hib10DK674X9FgEfGC4nAHkTaVG3LW1ZLYW1gC8rYhb0OZwWBcPTH0c12pNz+foew5fU9DmiZ37dwh16rUytbFhh/50gb386uPdZ1EsZkSuypi1nXre1ZOsZLey3Hy1iuBLJec7Rx3LOHbew80QXhQUFPHWd3joBm5ZfT4F1LmD84mFhv2FoeWU0qYGZtpxoisIBIWC+4XLKe3p+y0/THp/yevxJtu59fo8JQLPMVelfU0CzVKHadd1Mj279d0PT9qSJnk7/Ge16HZr09TSGM4GpmqZ54n/f3CSO8wFkJ/SreGzmb8B4pVSiXcrGzb93kdwkb8cNzrz4Z480abs/cHMmQmtd7X6madpfgLda1Ops2eYk4B2l1Ka3nw7gD2wF/Ihxhc3vxmG/OtnGUydV9wJWq9iaW9ZHHp4EfAcclDB+zh9YBBgV1b4Qh73d5bTZ1SOGAWHg7rLShRWJ2gRLbHnAQmChLRQsi8t5P0kYeEkSAcpw2Oe1/KK4omo4Erf5J7B3uLJ8ZaIOPC7nDMQwHtOsCLs/cATgxZgatlHgRhz22w3oqzn+gIZxxY0BPsdh3z3RFx6X86/A/wHXub2+pJJINcMf+ATYMzPxBKUUz7708NZnTH3kVyP6a4Y/cA2SldeI4tYR4FQc9jcM6Ks5/sBYJKY2cYrF1JmOw55yOaSk8AdWY1yB719w2LOzDeYPvIbkOzCCGA57wrh3j8s5BvgI8Vw5yO311aTUsz9wJlIH14jJBodOAAAgAElEQVRrGgGOx2E3vl52F+vQlOhkHZo2naRDM8bUocZjoA5F4gC74bAbv8tt6tAuJ56jZw6SyLXNcn2apo0DrlRKndFWm2RIpmRKVlFKvdiewRlv89Ima3AKK2mySpch65EYuFQoAahbW1UCDAauaidhi2HJJ5Cg4nYpK134CzATOHd29Yi2MrwehewaNM1sugQwapArJIGsxRVVPREX4CLgmLYMzjg6cm9aKrNlGLebto4krmlaOOwKMLJu3W+JPvS4nOMRg/OfiJu3YX2nQ0NDPUuX/3qmUf21YClKGZI9FHmGsnPvpV+jxqcG4BeD+krEKgP7yopLdxwjr0HCuHuPyzkAGTvXAONTNjiFZchE3AgsSNbcbLBSKdWVOjQVOlWHZkDWdahBmDrUeIzsuzYrBqewFHlfjcDUoWkQ3/28Ccld0h4DgRszPV+XG52bBQ57HVIzLekspu0QRdw8U2GMiq1BNSw+BnjR7fV93E7b54B0Ji8tqYn3lQzTkAf2xDa+n4zEfs5s8tksjFGYdcAiYHHTD4srqiyIK/iuwMnhyvJQe524vb7fkEzAx3tczv2afBVEJo1GuH4rZDUtW/ybLN57j8u5C2Jsfgqcn2r9wCY8j0FyLv7152XAJI/LmWwG5eTQrRpf3dSd6LqEWY1TpAGZzGQr08ivyC6SEQZyHfJuZosXMWZiV0OKCclS5DWMyaZeE++rGR6XswB4GZkITHB7fYtbtkmSTxGdkukOTT2yIPB9hv0kpPjN37f9+s+GyLqoIRtJ6ejQ5NCtvVjs+5OoEao+JR2aDlnVoQZi6lDjMUyH0kE+jgx5n/YzriaLqUMzQCn1tlLqfx20eUcpFc70XMnU6TRJjnKgGomhiNFi5a5BkV8L3YtgXYHWXBHUNTT0BGKF+fl1gBOHPdXMhKMbaj+MIfezowCXexED8DLkRe1whTGmlGV9VBVFIU+DaPc8rcGiaXfjsD+YpHzVyGRlMmKUbCBYYtsBCYC+xRYKbrwuDvtn+AMTUOo1YusLULECLIW1WPJTUUwKWZk/AIe95WzmRsQIvipcWf5mkv15gIuAuz0u5/5ur0/hsNfiD+wNfIwkzUlnxTaG+M8fhsP+QxrHJ8uZyAT3IBI8owlR0TyidUWgLGhaA5aiKJp2Aw77s02beVzOQciiQQSZJKc/IXfYn8cfGIjslNaR+uKYih/zge+dl55GlPsRgDFuN7p1J+ABln9wBF9e9z3jPNug5dWnISfIfagB9sZhN2KS0BqHXeEPHIgUh0433XkMcYE6AYc9mwndrkGy8P0FMR5SfZ8UMg5ORZIpZAeH/V38gbOBJ0jxGVUNDQWx+roiS7eiWs1ieQeJw2/JfUiczRkdLCJ2JOdy/IH9kHufl5ScSoFqKCRWL54plvz1aAXL0LR94gushlFcUVWE1PCtOOmjlfXVjoGLtrKoAZqmJTc+xamrrxMdWlCYrg5tH92qIfriXr67dxvyir5gSNlINK0+FTnjKORdSkWHpk6jDpUxP93xqT0dagybuw5tTuO9b6VDDcVAHQqcbrB0G3HYf8QfKEMWiWKYOnSzp8tjOjcr/IFCwI7EezR7eRoU2jXKclcBrLnDEtObfjfnp29PXbI6cvh+2+5Qsv2EI39M9bQPnPfsnLrV3oNB3ef2+i7v8ACJTRiDZKIq6qA1ADGleHnxur3nLl9/Wmh1w4Dv10QfAyrCleVJubDNrh5xBXAPMLasdOEXjZ8HS2x3IFljt7OFgq1XUj173kpB3+vpu8vLbHfqq8mcK45CXBjmtRyIiiuqTkJ2VJ4EzglXlif9EnhczvOBh5ESIP/a8IU/0AdJSZ3OgLQSie/IluvaRvyBPCTBTzHJLjpF1+ex+PXD+fObE1kbtlDz823AHeiRdQAel7MQCTjfE4k7+9QgWUciCbJS3U2MInHEAc803QL8D5jn9vo6TuXYHrq1O/EJMuISdCMwlUNmDwDGAgPS6PVX5BltN8TAEPyBHsi9H0bqk6UI8AUOe/bdgmR8GguMIHWXpvVITPuCuDtcdvEHtkdqC/dO9pD1v/3ad9nMl6dq+fkPbHPORVfgsDdzf/W4nJMRo/lOt9d3rUFy9gfGIVnik2NtuD9L3jyNNd/tw+qFC4munYIeedsQeYDiiqqjkIQXI5CYPnf4qCHLaEOHtseHn8w57bdlSw6377bfqOGnnZSyDm0X3ToSeBA4DElQM5lDZn9Eijq0CeuAb3HYvzZUzrbwB7ZBkjSmmkazTR2aFTZnHbqRWuAbHPZvjRYrIQbo0JbjU1bwB4Zg6tAtAtPo7ES2mjP/cmSncdxvh4zdkF1N1/XDgbeBo3RdfyvVfu89bfLaWMOSAmgY6vb6VhgncWuKK6p6I1lcr0BeogrgsXBleburoLOrR/RH3HOeKitdOAkgWGIrQjKNvW8LBU9odZBudSK7Zy8BJ6NHMn5YiyuqxiFZfucDh4Qry1OKJ/C4nPnAF8hkeIzb6zN05X+TRrdug+z2upDET5d4gge+hWT+PR84ze31ZdNlKC08LuctwA3ADm6vL5xWJ7q1HJkgb4+4Ll2FHslm3JjJZkywxPYlsNQWCh7a9HOPy1mKuF69hcRxZn/C1xG69XDE6NoJ+BdwBXok7QyR8eRt/wCOQxIkTQlXlmfkDutxOY9ArtlRbq8vZR2aEN3aA7gO2X1fhywyTUOP5FQWfRMTE5NNBTOms3N5ClnpuqjF543xoCnXqPCc7Doy1vC/HpbCneZk2+AECFeWrw5Xll+NrEp9jez6fVhcUdVuFray0oUrkcn66bOrRzRmADsRcfWd1uoA3ToGcYv8HDjbIINzCJI4aDlwfKoGJ4Db62tAJiE7AhdkKlNOoUcWo0dORlb8G4A3ztph3ue989edD9y2KRqccR5BVu0T1ahqH926Hbr1NSS1/XqgDD1yqmlwmmTI28CBwRLbhsJvHpdzBLLA9i1w6iZhcALokVnALojRVQ4E0a1Xo1tTyjhZXFFVWFxRVYHE8B2JhILslqnBGed95P00piaVbj0G0W83IPdkFHrkftPgNDExMUkf0+jsRH47ZOwqxPA6bas58zekB9d1vRZRmoen0p/H5bSg6u7B0puC7gc9Yay07ROuLF8AOJAYh+2BT4srqh4srqhqz4VnOtATaEy5PBlxhWuegl+3NmZtXAuMR49k7NpTXFHVDakvOwAYH64szyS72xtIiumbPC6nEWnecws98i6w25Ka3o9bC9btds6IQPSKkrmr0a1GZXczFLfX9zPyPJ3ncTnbyqDcHN3aDd36V2SCfBiyo78besT4chEmWyKzEG+JgwA8LmcfJEkJwDFury/7rmKpoEfWo0duRVz1qpGkavPRrY5kDi+uqDoE8RC5Hfnto8OV5bens/CXiHgMeco6tBW6dXt060xkvKgBDkGPnI4eyWamURMTE5MtAtPo7HxaGl6NzAJG67o+LIW+ToZ6W0HR/miWnl903NxYwpXlKlxZ/gwwCtmtvAj4trii6oziiqpW/u5lpQs/BQLA5G/GlIwF9gWm20LBjbuYsnremLXxOPRIxv7vcVlmxM93Vriy/PNM+otnZb0a2aU1JuYqx/AEDxz+/KKxxz/907hvNY23LBq3I5PQQ7patjaYjtyv1m7cLdGtZcgE+f8Qlz0beuQO9MiW40ptkm0+IO7dEs+s/Bzivnqi2+szNibRSPTIT+iRY4FjkTix99Ctz6Jbt0rUvLiiamhxRdVziKHaDXCGK8uPC1eWh7Mg3SxgtMflTL02qywyXY9kvyxFvFnGoUfeM1RCExMTky0Y0+jsZH47ZGwASWM/eas585saZo0plJNaqfW4nEXAbWi9frcU2hqAbGZsa5dwZfkf4cryS5BEMmHgaeC94oqqnRM0nwaMrh0XuxlxNX6qxfeNWRvPR498ZJCIVwATgZvDleUvG9Gh2+ubh0wUr0hrkpPDeFzOvsiuTMMfdT2Oyrv5DyfgRBJqVKNbn0O3Du1SIVvzLvKOtF2QWbdujW59Id42HzgaPXI8eqTdVOImJqliCwVrkdIOhwO3IW6rl7q9vjldKliy6JF/I4l0bgVOAr5Ft16Cbs0HKK6oyi+uqLoccRU+AbgFGBOuLM9m+YXGJEeHpXSUbj0M+BL5LVXIItNd5iKTiYmJibGYRmfXMB2wEXetirMAycKVrHvQxcB2BT0cP2ia9t2UGaXZKt6bNOHK8s+Q3cQLkIyO84srqu6OJx9qxIvij3VjY0cDz9tCwY3F4HXrRchu6V3okWeMkCmeIfEuJAHGLUb02YTrkXfI6H43WeK7Ms8jGSdPdHt9PwGgR6qQSegtyCTzW3TrZY2T0K7G7fXFkPduf4/LuWuzL3VrAbr1SmSCPAHQgZ3RI8mW0jExSYdZv/TrZUN21aa7vb7pXS1QSuiRGvTIjUi858fA/cCnD99wygXAPCRp3n8QY/OmcGW5EXVN2yM1Hapbh6FbX0QWfC3AkeiREzNJkmRiYmJi0jam0dk1eJFC2xt2XXRdV4jyO0zX9XYL2Xtczv6IwfNmXuGoIWSvIG7KhCvLY+HK8kcQl9snkHIoweKKqpOKK6q0stKFNYXfa5+v21Xl1+4R9W44UNwyH0BWmq8zQpbiiiob8AKyin1WRxl2UyWeCfV+4CyPy7mbkX1vwtyJJAGZ4vb6mhfh1iO16JGbkAWHD5EMlQF0636dLmVinkRcGjcm8tKtByATZA+S0GsMeuTmxnIwJibZ4ottB/26YNhgiurqQ0jd5NxEj3wHHBGODTnvD9Vz5AX5bzx0T8G0EbtqCycCR4cryzvFCyce9jALOCy+ONaGvNYCdOtVQAg4BsnGvouR5WBMTExMTFqzSexCbHb4AwW0Y9D/ZiE6Ipb3dD1MOWbO58P/bYn+jhRvfhs4C6lV1V6twxuAPpql743AJ8A/05QzHymonApRHPYOM/jF63eeX1xR9TjiUvsi8M6OV79+8SuBvOHLbmxg1bnRPYFZ6NYRSBznd8Cp6JHmWRv9AQtSVDdpjvvvSiuSDGIdkjhobSrHp8BtwLmIMXYE/kByiWqaU9cpNQWb0sEzmoh7Z9xyKnAl8IDb63u4zYZ65Ht061HA8Yjh+R906xPAteiRZSnKmUfq41TCZ9Tt9a30uJwvAGd8fflOd43pu/RvyPv2c1zW19LOkiy1JVNNpKRw2DvfhU/qCadaY6w+a8Xh2yKL45OhpDE+PeWdvvXyfr3v7V7fEN3nh8Wh3b9akH1PlfSeUehgfCquqMqD584HbutJbeG9BdM+Oc7ywR7Hd/vgHqAbOo+iR1J7dtIYnxAdOov2dKhuPQjRR2OQBc5L0SPpx9Buxs8oEMNh73wPqs1Yh9I142g6OrTz732645PDbkgyspTIFR26CWLW6TQSf8CNpJXvjZRoaBOl0GJg0UBZNGKAiqnYTw989N5Oq9bV3KDr+v8lOs7jcm6PuAE+U9TvyvuRepOuKTNKX0xBzgOQHcCEyR86QANWAOfjsL+ezAHFFVX5yO7Srbss+6H7nf+ZUfDr3+uDaoDqtXdg1a69aqL/icuyN3pk46q4PzAYmRjsRgqDu1IKBXlrGhTfr2n4xx4T9rsi+Z+XOveeMt59qMN595hRu623WCwFdHDvW6Ahqf7fAE7NuhHiD5wF3IMUCk9aTqWUFlMxy+rVf67Jy8vbtXd56U9JHahbe7GxrutqZBf70VYLC63l3AUpbzM8WRmboCFlcc7CYW9Ws++BU47cx2Zd+t+DB/9Ym29R+cgO563okfQWJfyB3nE590cUe6r3fi2yw39DVidNotArEe+KHqQmJ0AM+Ao4OusF2P0BJ/A4kmk6nWvyO3AKDvv7hsrVEn+gGHlvdyKFCUhc5+bV1dex/L9zF/z53FPbAINsoWB2SqTIJN6LlBMpJPVntA54B/gLDnszD4Diiio74rZuR2JUJ4cry79Bt+6MGHcHIq63k9EjnyUha9I6NAEqFov+9PhzD+4U+XPVjW6v79YN3+jWIUiYxRnAImRneWYGi0ydqkPTJk0dGkdDxuybcNjvM1q0Zohh9AhSA7qIzVCHxlHI/O1oHPbs5grIXIdGADcOe3YrI2SuQ9chmxbn4LBnr8xULunQTRjT6DQKf+BqJBasRwa9xGrr62KPffbhZxdfc9XeiRp4XM7ngfHATkX9rjwISWazy5QZpf/P3nnHN1W1D/x70gEFpGwKokQK2qBoleCW2FZBTcWBrxVcuBnuOuKrP73uKNb1iqCv8rpetU7QxoG2te5Rpc4UoRJFpIKMQgcdyfn9cW4xLWmbcdPxku/nw6ck995zn9xxnvOc84wfgpTzMNQAom9Hu3ZAHZCDzfpmh3vqmB2ulHs+evTzvbauG/3grOwNF6Q/N/SILzeX9q73HQhMaVGOoqR0MGqmehShz9D6U4sa0D8QQRttU1IqfD5fvtfrPS0hISHUmS9/6lAZLU+I2iy4UpaPEsEzKqX0CiE2AFZs1rVBH6jqri5Aldn5CjUILW1Dzn1RsWD9CX020Z9a4BRs1mW6DJOkZKEQTFxbu1tNSlL1pLhbt7jDbr2ktK8u5z6ogVK41AALsFmjlwm5pPQBVKx1JP1TI/AHMAmbNbQV62ApKT0BVRcxEjlB3fup2KwfRy5UAEpK90Rl4h5E6CtdO5Beb/2GN17ttfGdNw+xlLu/NEy+ZtRqzLvAoUBSBC3VobxqjsVmbTQ7XANR2Z1nA+tRYRTPe5x2/0zkAjgLuA+VOXohcBNa1ZY2ZDVEh27fXud74bUnvznvsacOQUuOQ5/w1NudD9wZURmuLtShIdGDdCgqT8CJRHbve4QOBbxA6Do0FIzToXXAvKgZnsbp0FrUpMPpUZu87Sk6tJsTi+k0AuW60qzUIsHUOz4hLj1llFXTtP6tN+blZE8CzgDycvML1qJchLzAyhDOcQeRK0tQA5j7Qjng7SXXsP/GX3bf1Lv/S5//Zd2w+yovvet91grfiFsD1D88AxhBZMoS1D25S1ds0WCsyWTKjtDgBHU9DwcmGiBTW8wnwmdUCBGHGkBeENKBWtWPQAZqELon8CVa8qNoyQMD7H0dkStLaB5kasmD0JIXAV8Iwcjlm0Y89uKvB/R9sPzIIRG2fxwqoVIkyhLU+3g1JaWR9h+BKSntD8wj8v4pAbWyMyNimdrmPiKXE72NgN4iBnERERqcACIurtfQaaeCyTTVGLF24hBUVvFIDE704yc2+OQRZodrFmq15hLUKv0+Hqf9vy0MTgCtSuoJ4VqU1EJLPkc3SP/GQB3aq1fvuH3T0q1/3TAiA2Uo/wtlfE1Aq7rRgLrPXaZDQ6TH6FD+LsETCT1Ch6L6jNB1aGgYpUOj/YwapUP7ACeg+hrj6Vk6tFsTMzqNYTBq6TxihBBiSJ9+JlTZkB3k5WQL1Mu/HhU/CKpQ98p5izJD8Wnfywg5dUIti3EhED962583/ZR4/vy0dVv4fURvZsQ5bjQ7XDeZHS7/WA4jOqJmTECyQW21ZjTK/cwIvHp7xqMGDJEaWc3EA2khH6UGof9FKYZ/oQatK9CSz201CN2byJWlwteQihogX4gqx5NW9OfYq0Fsob3yKcFhJrwYuUDUAdEqvTNab98IeqHezWgx0sC2zAa21RoLERqczUiflAkDB9mNaCsAownPRXknvD5puru8+nFUgrhVwESP036Fx2mvavdArWoLWpV/Sa2nUfU9/UtqGapD90lJMg1OrC1EDfDOQHnSrDCifbpWh4ZCTIcaSXfQocFjnA6FgXrscjQwY5wObSRa975n6dBuTczoNIY4Aij28++5jWEnT2G/WTk7vvt21c8cNvd8Jpx3BifecBVba6p3bsxk8qLib/zJRpVY0XLzC7bp340n9My1Ow2UwpUzUFtt4U6zxKNcE96znPHHoN6icZHXxKcrxvT1Tkt9+1fgduB7s8PVnO5+p04ukJxlK1dw6JzzSL9gJtaLz+FL94+BTu8LRdYQCep6BimniKKcze23IAJZw1dCWlUVWtUVqDiwClRW2Q/Rkifoexh3Tb31fVFG50S0qqvQqrbm5hfU6uecnpeTPTzs36HkbNGHRnA9JV38jAb5zoNxg4RAdEn/FAZB9U/ByCmEaJJen9WdZtnJu8UA4mj13ocrZ71P9qn3yRGoCZwjPU57WUiSqJjOFiW10JLvQ0veDYN1aP/EJlZuG/wtkIZWlR927GZg/qee0ZgODYnuoUM7JqhrmnPrDaRfMJP0C2ZizplG+gUzA7UlMc6ADSRnhzo0SDmb24uWnC2IUM5o6tBuTYdGpxCiutXnWUKIR/T/PyWEOK31/kKICUKIMv3fJiHEav3/7wdoP0kIUaK77SGE2FMIsUwI4RZC/CSEMOvfvyiEGBf+T+18Zh2XzTv3Ptziuwvn34Hz4nl8/58XOeWoDOa/uHM5yt7x8RvxqzWWl5Mdj1rd/Bl4AmDB7KJeKLeUiMulhCtniNiBPfqmbM8HlgB/xPk4iTjx+mEjS4cM6r15Gqpje9fscL20ucHXLxg5r3vsX9wy60LKnnye286/hOsWPdz6sE6np8gJXSyrVrUclTzgQtTK0XK05PuR3p06+LDljOtdC0xGq/q21ZZFKFcXQ12cesq976R33hB6iqxhy2kyeUHGAZndWc44IZouH9v3nx6n/cmwy09pVT60qp1KavHNFScSIL9EuLJu8A7d9Oba8buhVW3baWMU6MnPaE/pn7qjnNBzZA0kZ/4td1P25POUPfk8020ZnDo5o4uk+5uYnC3xt8GEECcIIVbqdpImhFir21Y/CSFm+O23k23mt+1BIcRk/f9CCHGnEOJn3ea6XP8+Wwhxa8TCByAqK51Syu+llOlSynRU2Ypr9c/HBNj9fOA1KWVz1qlngPlSSgtwMMqdFFQSguuiIW+0mHzAQQzareXk9Yo1vzH5gIMAONZ6MK9+WLzTcUkJiX8BYzVNG6N/dQHKFeP63PyC5jTWe6NmXyI2OsOVM0Tmijjf2j1sm+YA/YAT0ar+QsX6DJo/+ZaBqCLjNwMnvrlu+1nByCmEYGuNSjxaVVPNyCFDI5UzYnqKnNANZFWD0CdRg9AngSupXj2h9W5hy2lKaAhUriE3v2AFUAhc0m5NvxDp8usZJJ30zhtCT5E1bDmF8CFEDX4TjdEkXDl7xYn6lN5xxpSe0qr+Qqu6CBWDt4G6tY8jG3cqlRGurPG9+lcCqXk52Z3ixtaTn9Ge0j91Rzmh58gaSM5mpJS8VPw+M7KiFVoePDE5AyOEyEKFJR0npWzOfPyAbmedBDwmhGg3flsIMQg4VErZnNl9FiqsJ023uV7Uv3cB04QQhueZ6A7utWei0iUjhBgPxEsp3wOQUlZLKZuD/j8CjhFC9OjaovvtNYY3PlH3++UPClmzfufMyf0SezVntZqSl5O9G3ArKiubf3r18frfgH4bnSFnsLjTLGNBThmdtXGTEBwEzNATywB8gCrSPdfjtG/3OO23A/sOTBS/B9P2g5dezbWLHmaPf9i5ZuFD3H3RvLDljCY9RU7oIlm1qo1oVZcAhyHigqoPZoCcC1FJjU4I9cBQ6Cn33sh3Ptr0FFmDkVMIgSkh8VM6yegMRJddT63qM2ASiYNvRMqgxiPByNo3qV+zDj3WKFFDpac8oz2lf+opckLPkhXgo++WM3zgYMaNCqe6Suexq8ophDgKVVLILqWsaL1dSrkSlcE3UGJGf04D/MvIzQFuk1L69HbW638lamyeHbHwrQimk0/yc5UtA24z6uRCiERgjJTSo3+1N7BFCPGaEGK5EGJ+s9utflFWoepN9VgWX3czC5a8zMSLz2ZbbS2JCTtPTPSKi69B1RKbgnI/Gg5ck5tf4O9/NB4VZ/FzV8kZArOH7FvtSxrUOAG4Aa2qoHlDVmaFRA3+DyksSj0IwOO0/3LiiKSCNtpqwcKlr/LAvKtZ87KLB+ZdxQX33h6JnFGjp8gJXSyrVvUFfc1Brd4bIOcbqPTlkSYUapeecu8NfuejSk+RNVg54/r1+xBIdadZuiTBRJdeT62qiUmPLcaUuL3jnYOTNTGxVw3wGzvnRug0esoz2lP6p54iJ/QsWQFeKFzGjKwum/MKml1Uzl6oBaeTpZTlgXYQQhwErGw2GtvhCOBrv8+pQI4QolQI8XarEMZSVJ1lQwnG6KxrdpXVl3Fv9tsWKDg/lID9IYB/za541I+8BpXpbgxq+beZ9Rib3bDTSRttZtl9j/D1488yI2sKqSN332kfIQTAMqQ8RiKuBV7OzS/4otVu44GKeYsyg1LU0ZAzGNxplqTd9qi7ZOiEbSbgOf7OvOvPM6hZmjmhtv/0uwU7/Ob/cfQxfFkesbdxVOgpckI3kFWIoPqQSOXUXdUfB6ZG0w2vy69nkBj1zncGPUXWYOVMGrv3B/p/u2RE1S2uZ5DvfQg69F0gMy8nu0usvW5xTYOgp/RPPUVO6FmyNjU18dpHxeRkdJlTQFDswnI2Ap8SOP/EVUKIFcAXqBrHHTECVSO2mV7AdimlFbWSuthvW1TsrUhdVTfit5yr+wv/FcLxdbRM6f07sFxK+Yve3hJUUesn9e29MS5tcZewfvMmhg0chM/n445nFzN72vS2dn0XIS7yJvVtiq+rviHA9nAy10ZDznZJNtdenTKxqp+3QbjjEuVFgbIIZmVWbCksSn0emFlYlHptVmZF4OLhARg5eCglZd9w9IETKfrmK8aNilbVicgIRk6fzxv3c4V7f9ej2iajzy+E4Oo5txgma3fAIDmfAP4PVb4lKjHjQd176Y3/WT51xNqiHMMv9p4ie0yqmBlvEu2Hrgb7ztfI30d9XpQTlRWkTNPzcaIDOcG4/inaBCvn0GnTPZuWveVBrcwt7EwZoedcTwhJ1mWoeqoHowrQdyo95ZoG1z/JuMXffJrxe3GB4YmZThi338SJI2vFU4UAACAASURBVPeMjzO1vwYSjJxeny/OvWHd/q9oBcbrUAS3ZARX2SgYWbdsr015UNOi0o9ef+SU5KSE4BKkvv/1l6TtOZpRwwIncpdSins+Xnbs9uICb8AdImDWgYftPTp5kNAniSKS0+vzxS9ft+agAs14OQ/fY8yYrDFpHT6jwcgZBj7gdOB9IcQ/pZR3+W17QEp5nxDiVOAZIUSqlLK9hahANter+v9fRyV3ayYq9lakRucHwJVCiKellA2oVcmgo+WllJuFEHFCiN76hfoKGCiEGCql3IDK5lfqd8jeRCmGMRrMuO1GPij7mr+qtjDqNDu3nncx1XW1LFjyCgCnHnU05x1/4k7HNUgSktas/KNu1FgaBw79+vqnXmzhw71gdlEi6los3engTpSzQ7TkYUMPMN3s84omU7wvC62qvZdhISqL6TlAwFRvgeT89zU3csUjeTR5vfROTOTx3H+GLqfBhCtnY1NTn4rV5Q7AYbRMMkBmyEhk7WyiJWdufsHavJzsJcD5eTnZN+fmF0TkORCunD7q+2yRPz4eybnbYpP8nr1EPSa/utaRvPOb5U/ZRCHWA8BLA/Ektfguav2TwUQip0kNEN8FZrrTLAmWcndQcc2dLWdnE6GshagB21SibHT2lGsabv/k9fkSNtZWvxwNmVZs/JMJw3cnyW9AH66cTT5vn/K//oyODm3DiS9cWddu3XIULePrDGNTXS27tzI6A8l5gf0kXixaxozMtm1fCaLB2+SKhpwrN65nj/4DifMzOsOVs9Hr7bvirz+j4sf8y+a/mOzzEtfBMxqMnOEgpawVQmQDHwkh/pRSPtlq+2tCiHOBc4HH2mnKjap68YH+eQnKzloM2GgZrrc38IMxv+BvRFuD0R07CFEtpezn93kWYJVSXqp/vgUVnOpF1dybrRuMzfs/BRRIKV9po/0ngReklO/rn48F8lDlM74GLpZSNgghhgNvSikPDvO3Ro+S0hTgF2g1WgqTCslPSxZqq2vMacf5evcp0269zeq/fcHsovEo4/useYsy/xuirL+ikqcYQS02a9+AW7TkRG+D+FyY5IGVpQPmj3zn1w5XkQqLUj9HFaAenxWX/xBwmUFybgdGYbNuNKi9vykpnQrkY0DhbJ/PV+v5bdU9r7/1/HuRC9YSIUxcPefmTw1s8mVs1tMNbO9vSkq/RLnXG8EWbNZ2g+vzcrKzgPeBc3LzC4Kva1BSeh1wJwbUW5PSW1sh8y/8VS71RNpWa8aI08eNFic/ahJxgd/VEKmVf77+me/y+Ua01ZpM0wvvC2EyKmPeWmzWUQa11ZKS0iWojIFGUAvs477k7INRs85HWcrdHxvScknpOcACVMbwSKkBLsdmXdzhnqFisA4F3sJmteflZH8GiNz8gkMNarfzdGiklJQ+jEE6VErZ8My3X0xbvfmvrUa0588plvRDJgwfebtJmCJ+Rn1S1q7auP6e57//ynAdahKCm4+2G6ZDt9VvL8r79P2bjGrPH8dRU5/sHZ9gMaItKaWc/8l7k2sbGwxfQbxo4pFnjtwtebYIxr2lA7w+X/WPG9bd8tpPyz8zQjZ/Mvbae9xRo8c+ahImo97VRdisQYWT+dtgQog9gA+BK4EDgWop5X36tonA86jyc4sJYHfpCYkukVKepX8eAPwX1Z9Vo+y3b/VtBcANUsrvI/2x/nQ4WPI3OPXPT6EKqzd/vhWVXbWt42d1cIpHgKtRgz70zLX7B9hvJu1b8F1JEwZmAv7NJ/eSMN7U1Pi+T5gyNU0bpGktXC6bM9eG417bZICIzQTuhLRkATwalygPXPvZgPqtv/a5M0jH8EeBp4GjgQZjRARUaRkjf7c/hrVrMpmaxpj3dufmFxjeaVJSKlCz/kY9p0ben9ZE/xltSRGwApVQKJRiek2oaxoxQsT5xoqZX4+13W98YrCS0m0Edx2Coo8Y/ltWZoXxzyhASamRK3zReudBxdkYhUDJWoS6T1NR2cqNoInQ8iy0hyS6/aiR2fSb788y4Ka8nOxBufmGuVx2dv8ULob10UIIcW76oZ9js1YZ1eYOSkr7YNB1MAnRtPeQ4W5N07q9Dt2tV+91UZEToKQ06BCljhBCyOuOnPIZNqvxz2pJ6WGoex+x0RlnMnn3H777D/ufflI07r2hOhSoD3ZHfxtMSrkG2Ev/uLTVfl+jSs9By1w4/vt8JIS4WwgxQEq5RUq5BdjJZ1xf5Esy2uCEblAyRUq5HCgOYqZjC8og6Y4YtoLmk/i+NZmSVu8xbr23d987Ufcoq9Vu41EDgBVhnOKXSGX0o62yJpcDF/z1U7+mrb/2ecZS7g5WUb0EbEIN/iswzp+8ETB8hlbHAwQXPNExccBqg9pqic0qaRlAHgmNRDGeGPVcGzVQ/rWjHfSs0AuBQ/Nysg8Moe3VPuk1xOhExU/81uFe4fErxq0gbQdWGtRWIIIqlRQkRvZ1LfD5fG6f9Bl17+OA9ZZy9xZUQggjkwl5UEatUUTrmhrpheLlb7ewdwmsQ8OmSXbcp4SAkc97C6SUFT7pM8rwjOnQXVeHboyKwalYjXGTI4lE6973LB3aEbl07Kmxp76f4XS50QkgpVwspWz3oZZS/kdKGc2Z6/BRndFVKDepSGhqamrc+vz2eoqOsP8qExI/BqrYeRCyL7B63qLMcM53PWBEMoBalHHZEi15CnB/fVX89xu+2y2eEJJiZGVWbEe5BZz8k29hEcZ0SHXAFfo9iga/oNwTIi2aXosaIH0TsURtcymRG/KNqDIjT0QuTpvciZp8iHRQX4t6L4PhadS1CcrlpbAoda9i7zkXbmVVb69sjPTZqgVuxWaNSiZqbNZtgJPI+6cGlPJ9PmKZ2uYyIpcTlKtQVBJD5eVkH/zEcw+dVFNTbfL6vEbc+1xs1uZnfRkwyZ1mGRRhu818gYrfifSa1qLcuj6PVKCAGKhDgT9RXjMAXxJYh4ZMSnGZSCkuO3majLNUG6NNAutQA9A0bd97P14286/amsQmny9SaWM69G92NR1ah/rN0eJt4DuUIRYJNagwp1URSxSInqVD20VK+YWU8rsO9vlKSlkWjfN3C6PzfwKbdSFKaa5DKT5voH9SSp/0epE+H7qh3fyvRkpf2dMvPlI9sOKH9ZuTBx+0yHbyMFQyhCmapvnPVjfHdIYj5zdABipguF5K6W3ySbwquNdfTq9XSrxStpazATUzeTI26/st2taS9wbypeRHz3tDeoH43FLuXh6ihI8B8evkBznAkSg3s2bXhlD+NaE69nnYrNHr3JUivhiVbvqvMOT0opRDPpDjN/iMhqyvAGejZtcbw5CzBpXY61Bs1o7qQUUi5yrgcNRqRV0YcjagBjInYLN+GMwpc/MLtqAUwZl5OdltxucWFqX2KixK/T/gJx/1k7/z3XeDibgC1MA2nGf0T5TBeUdQ1yZcbNabgbtQadDb7J/a+VeNStt+ODbr5ijKWQycyN8TTh3KJqVU/ar04pUNNMiqzbXyj2xs1tLAJwmPvJzsQXk52Y8Bn2+rrhr61vuvzjUJ01eo9yKce78OuAab9RG/07yLWpk8xhCh1QrFKV4pX9vW5Gtq8snmmtehyLoZeA3V50dv4jdIHdrOvxpgOXAINusfALn5BU0oHTo1Lyc77BXflOKyMUAB8Po3iA2PIy5E16FhyNm2Do0QTdP6aZo2Hyira2ocn/996ZVxQnxATIcaJeuuokMb9d94LjbrS1GUcztwLKrfC0eHelFeEouBC6I4MdJzdGg3p8NEQjGMx51mmYVKTfyIpdy9I9A/Lyc7F7jv+30OPPudjOnPAtrskiXrUIaYRdO08gWzi+JRsy33z1uUGXGGNrPDdRGqVuGRHqf9k1bbLKiZ7V/07W3PRGrJA1Cz6oPWfjbg8q2/9nkeOMdS7g4lRg6AwqLUd4D9AHNWZkX3XN2O8T9HXk72RNSA4PLc/IJ/td5eWJQ6FRWDPhZ4Gbg6K7Miau5xMUKjsCh1KHAPcB6wBmXAvJaVWRGRksvLyTahYmTuQZUIexjQcvMLDHc3dKdZ4lEufK9ayt0XGtWu2eF6FLWKf7bHaX/OqHZ7Ank52ZcAiwBLbn5BwOLqbZFSXNYbtWL+T9RA/GbgkcqM9KhlFw4HfVJ6OvAAMApVZs6haVooJexixIgRI6rEVjq7AEu5+yngfuBSd5rlElCz6MBNwDtP3Xb7c6hU2hdv65XUXIKm2T0oFUjAgHgAs8MlUPGT36FmYFrgcdrdwBmoxE5Pmx2uwM+LlhwPvAiMAaZv/bXPdNTsU7gp1h8FdketeMSI0Snk5hd8jXLHm+O/KlJYlDqqsCj1ZdQ7KYGpWZkVp8cMzu5FVmbFhqzMivNRHhKbgVeAtwqLUseG22ZeTnY6ytviSVSc1EG5+QVXR8PgBLCUu3eszLnTLIbEYpodrrkog/PeXc3g1Fmm/w2pjkFKcdlxqJWiW1GlBdIqM9If6IYG5zhU3/QyaqXwcE3TLowZnDFixOhuxIzOruM6lKJ4xJ1mORq4EejP33FIC4GR/z106gRU0HGzwtxX/2tEEPohQDrwqMdpD7ga4HHa39Zlmo6a5Q3Evbp8c9wvjlwFnAwstpS7w/XTd6FWKuaGeXyMGOHyKCrluK2wKDWhsCj1WqAcVZfyJmBCVmbFsvYaiNG1ZGVWfAJMRKWVPwL4obAo9dbCotSgE0Hk5WQn5+VkP4Qq2zUWtXo6OTe/oN1YGIN4F7ValRZpQ2aHKxO1MutCrdbtcuTmF6xG6dCg4jpTisv2SCkuexUVb9YEHFuZkX5GZUb62iiKGTKapiVpmnY7yjA+FBUfOilqGVFjxIgRI0JiRmcXYSl3e1GriKtqEuNfQ9U9fSo3v6A5RbELlc1yLmoQcrSmab34u1yK2wAx5qJiPTqq9Xk/qkzOLWaH6x8ttmjJ56Pc2B5Gq3oCuAiVQS7s8jZZmRVe/fhjCotS9w63nRgxwuAlYHPyXltvAcpQEypFwPiszIo7szIrgk51HqPryMqsaMrKrHgIlUL+VdSE2Q+FRak7pYf3Jy8nW+TlZJ+Jmmi4DNUP7ZObX/BUbn5B9GLFWtJcYzCi5DdmhysVtfq1Apjpcdq9kQrWg1kGHJ2Xk92rrR1SissSU4rLrkfd++NRRvoBlRnphsZdGoGmaSeiJp5vQt3jfTRN+5emabFwlBgxYnRbYkZnF6KXEpm2YsTgviYpE4ZV1dzTvK0yI92LirXM+n3AkB+BPqjA8PGAZ96izIgyvZkdriFADvCMx2mvbm9ffRV0NvAZys1WlZXQko9Excq8B+S60ywJqIQA71jK3RWRyIdyZ2vSzxsjRqeQfok7eew0z7q9pqw9WvrYDZiWlVkxLSuzIlqp2GNEkazMinVZmRVnokpmNAAFhUWpSwqLUke33jcvJ3s8aoLhOVQSjUNy8wvm5uYXdGrSB0u524NKUhOSO6g/ZoerP/Cm/nGax2mPVrmLnsIylA49ItDGlOKyDNQkkxOlz8ZXZqTfXZmR3q0mmTRNM2uathR4A5XbIUPTtLM0TavsYtFixIgRo0NiRmcX89YBqcmVA/ol7rWhCquncr47zeJ/T54EGgstk/ZDGWBTUUanEa6156HqGgVV0sTjtNcDp6JiNZe+cNPJE1EZDT1ADlpVEyoGcyR/p6sPm6zMikrUCsWswqLUPpG2FyNGexQWpcYXFqVeBqzom1I3rvLrwfzwzLinsjIr3uzw4BjdnqzMiiLgAMCBypboLixKvaGwKDUxLye7X15O9j3At/o+c4BDc/MLvuo6iZV3izvN0ubKXFuYHa44VCbmccBpHqc90gnA/wWKUTq0xepxSnHZiJTisv+iJht6A9mVGeknV2akezpfxLbRNK2Xpmk3onR/FirkJV3TtA+6VLAYMWLECIGY0dmF6MlK7gM27Llx6/XANGBH6YTKjPRK4NW6xF5n+hCfIZmCivOJyOjUEwLNBj70OO1Bl17xOO2VwLQ+1A05xFT+oZQkAtPQqppXAuaiXILfikQ+PxaiskXmGNRejBg7UViUeijwFSr27XMh2K+ydNgyb338+Xk52fFdLF4Mg8jKrGjIyqy4BxWz+zZwl7fB9Mtue1RXoAbxz6JcaRfl5hd0tSvqMlQx8iPDOPYuwA5c7nHaizvaeVcgN79gGypZ3lSAlOKy+JTisitQrrSnAbcB+1ZmpLu6TsrAaJp2LCrZ3x0o3WrRNG2+pmndKqFRjBgxYnREbEDVtWQDNmBeUmPTQtTM9A3uNMsPlnJ3c/HYhcAZawYN2zB6059H+UwNmHyJka50TkFlmg05sYSn98xvf/UN+26U2HDI9U0XvfeSN2OFB3CnWfZBzcDeqMerGsGHKAN7LqrETIwYhlFYlDoE5U53AbAWOB14JSuzQpY9lr0QeB31ji7pOiljGE1WZsVveTnZ1w8cW7VninWDNfWENTRUxxcm9mu6KSuzYkNXy6fzAapExxRUNtugMDtcZ6MM6IUepz0oL5ZdiHeBOyc+v9TOiNF3ola13wEuq8xIj05R+QjQNG13VD6F01FF74/TNO3drpUqRowYMcInZnQaSUlpH2AysAcdrCJ7vV7TxAMOvVlK/jzqsGNkfFx8TuPGvx5c9c+r9gEWu9Msqyzl7i+Bj4Afl+8xLm30pj9pSNxM7+3Dg16dDISAuZMGJmx6etLAfpSUXhLSwakXHz96w0eH5G8a/+5L3oypqCRC96NWThtRLsGGkJVZIcuKTnslXiTdvOWD5+4aINJ+DeFwH2rV9UNs1jqjZGqTktIhqHihlDCO3gx8hc0a/bjBktJE1DNqRiV8CoU64Cds1lKjxQpISelEVLbmoLOO6niBX4ESbNaG1hsLi1JNKEPTicoYfR9wW1ZmxTa/3QpQcX1z6cjoLCk1AwejVuVDQaKM3Y+wWaMfc1dSmgwchXKBD7UcxybgC2zW3wyXqzUlpXEoOcegykOFQi3wIzbrN4E25uVkJ6FcbK/fvCq5Yeuaftfsd87P/RP7NV0PlBcWpd4C/Cvo+sAlpRNQxkvfEOVsQoUmfIjNutOKlaXcXe1Os3yCMjqvD6ZBs8N1KPAEypX0ilZyjkRlOB0aopwA64BPsFk3hnFsaISgQwNQBXyNzboy0MZVo9O+XGW2sHbE6OZ3ezrwemVGeuh1XEtKBXAYsDcQqgt0PWqF9YtAxew1TUtAZaLVUGO0m4H5mqaFlw2+pHRvVDbn/iEeKYE/gI+xWbeEde5QiOlQ4wlfhzYCFSjdFP0EajEdussgpIyobnaMZkpK+6MMxL0IoiPy+bzxXq83MS4uvt5kMnlRA+W4+nV/XPiLdv1dKEU2yVLuXptSXDYPKR+5+MOC7Ul1g3v3r0rrP29R5rb2zxCYff7pGn1j2m6rz9gjqSnRJOoJRbH7vPH46hMxxTdJkfDImHfW7yHhlAHbt01/4Z1b/4NKIDQjHLkCUlJ6kpTyBS/bk0zEN5lEwk4GRDtIlOG5EjgamzWs6xUUJaVjgM9R9yyciZwm1DOTjc36gYGStaSktDdqZn8iqsMMtdP0oZ6Xe7FZbzVYupaUlN4E3OB3zlCQ+r/lwBRs1h2DtcKi1INQMceHoFbS52ZlVgScxMnLyb4JuB3YOze/IOBAlpLSyahM0z7Cu/deYD1wGDZr9FbZSkqHo57RwYQ+UAL1jJqAqdisO9X0NQxlcL6Iyh4KoT+jXtTvuxWb9V7/DXk52XaUC/UY4AXgmtz8gj8ACotSU/VtJwDfo56LjzuQ9QL9mOZzhkLzM/oFcEKgyRF3muUGlKtsiqXc/Wd7jZkdrlFAKVADHOxx2v82EEtKDwBKUNcy3P6pDjgEmzWUib/QCFGHBqD5PpyOzbrDTTaluMwEXISUd5ukb+A+FT/86B53wKGVGentJtBrR04B/AuYhbqHofZPPtS9+Ddwtb/hqWnaZGABsB+qX7lc07RfwpJTyXoSKr43nGcU/bjNwKHYrOvClqMjYjrUeCLXoaASVp0VVcMzpkN3KWJGp1GUlH4GHEjos56tqd1U/N55f774zJOo2dDJGQtfSAD+OLX0Mzl866Y+Mq4xXtO0sG7c689/VDx1eK+j+8RHHM5bU9Pku3vf9zacFu9r2mdB0f1Je1avt1nK3R9G2jAAJaWHoJI7RJpEaDtqBnRy5EIFQM3MrwaGEHmMdC0wEZu1PGK5AlFSugS1chLqrGdraoErsVn/HblQASgpnYUaeEV67+uAQmzWEwuLUgegYqLmoAqoXwM8l5VZ0eZ7lJeTnYKqF/twbn5BbgA59wa+IfRVrtY0oJ4hS6CVj4gpKTWhsqHuSegrh62pBg7AZg1/INweJaWPAucS+b2vBS7BZn0uLyd7NPAQcBKqT52Xm19Q1PqAwqJUoe/zEOpaPQ1cl5VZsT6AnCegSlUY8YwWYLOe3nqDO81yEKpO6NmWcvdzbTVgdrj6oIy1ccBhLeL01UBpBWqVK9QBsj/NA7u9sFmjk9HVQB0KHIXN+k1KcdlEVIjKJOCDGUserx1V+dtEYERufkF471pJ6Y2owXyk730NanJkvqZpw4H5wNkoL40rgDfC1fO6nEbp0EbUatJYbFbjY51jOtR4jNOhtcDj2KxXRSxTIGI6dJcjlkjICEpKe6GUWqTKEiB+UMaxewBnoWbTniyeM2Mb8Jx75Mi+Mq7RBEwIp2Gzw5WYtlv8kQYYnAB9+8absoGTkprqE/7v8AsbTsm+8wcjGtbJRGXXjZTewGGUlEbLldyin8OIiypRrhvR4hgiV5agFNl0A9ppi+lEriwBkqSUWYVFY89GDbznoFY598nKrHi2PYMTIDe/oBKVofk83S2zNQHLL4RBIjCa8Fwfg2EEyh0oUmUJ6hk93IB22uJEjLn3fXw+3/S8nOwbUDWNj0W51R4QyOAE5c6flVmxBJUh3AnMBFYUFqXOKSxKbT2znW2QnEm0XRqlDNjQznbMDpcAFqOMtZkBEsNN1P9GYnCCmtnvh3InNR6DdegmyQkpxWULUAnC9gTOBDJHVf72CjCcMHWozqlEPkgG6CulnK5p2jxU/3QGamV7vKZpSyMyOBVG6dAEYBgwyoC2AhHTocZjlA7tg5qIixYxHbqLETM6jWEgaqbFCBJR7lRLUYWfZwCOxEb52G+DhzUPHMItGn5q/wSTkcbX0LeXXDP8ls//E78haUDc9vhe+WaHy6j2R2BczLEXSDaordYM4W9XlEjprbdnPMolzIiBUjPDDGyrNcMNbCtJYHoGNQs6KSuz4rKszIpQ4pPay6A8FGMGdaBWE6Jz75WcRmW6TCR6cgIMMKqh9X9VTkUN4t8GLLn5Bffk5hd02E9nZVbUZGVW3ADsj5qFfxT4vLAodZLfbiONkhNlzO2EpdztQ9WMPLZVKS1/bkQ9mw6P014QYPsQwnMFC4SP6A3qDNWhryJuQOUZ+BewT2VG+vN67OYyfZ+wa6Bi4PO/tX57OvAIyjieoGnajZqm1RrUvJE6NJr9U0yHGo+ROjTUOMtQiOnQXYyY0Rkl1qyvJOPK2VjO+Qf7zjqdh155AYBNW6s4Nnce4848lWNz57F5W7uxz3ejYo/ufOb2+dZBW+PZktSvSYavMOfGCVokyDBAzrn7bvLU9G+ovQw1C3h/mLJ1SASydqoP+fn33Mawk6ew36y/7ZRvV/3MYXPPZ8J5Z3DiDVextSa8cCKj6Smyhi+nxETCbODwrMyKgMllOqAEtVI2t43tLVaQesozGoGcka6YdZaczbKekJtfMD03vyDk5A1ZmRXlqD5tBrA78EVhUeqiwqLUQVGQtS2W0cbKnNnhOgUVc/wsyjUzKKIkp+FEImcf2AJMrMxIv6IyI72q+fvc/IK1wI+EP3FrqJw+KeNRK5xTNE1bYZRMbfG/3993Pj1F1kBylq1cwaFzziP9gplYLz6HL90R5agMlxY6JQI5u3ycF6ScnapDuxtBGZ1CiFOEEFIIkeb3nVkIUSeEKBNC/CSEeEYIkdDquIeEEGuFEG2eRwhxoBDiCf3/1+rtlQkhfhBCeIUQg4QQiUKID4UQPSbbbnxcPHlzr8T9zMt8/uh/WLDkFX7y/ILz+afJOmgSK//7GlkHTcL5/NNttmEpd0tUls2vTb6mhyZW1PPr4OHxgE3TtJBcJ8wO137AUX3jRIveLxI5pc9nQinMZ799IGchyuC8zOxwXRyKbMFixDXtDGYdl8079z7c4rsL59+B8+J5fP+fFznlqAzmv/hsF0nXkp4ia/hyCt/RcU8vzsqsCCsRgh739SgwKS8n29rR/j3lGd0V5Bw2JKUsN7/g7UjOr7vcvoiqj/wgcCGwolau29NIWdvhPf1vi4lGs8N1AMrY/AK42OO0Bz3g2hXu/QzkC5UZ6WVtNL0MOCovJ9sI98OI5EzunfSbpmn5BrjSBsX/fn/f+fQUWQPJed1j/+KWWRdS9uTz3Hb+JVy36OE2ju48YnK2RIiWY3YhxCwhxCP6/58SQpzWen8hxAQ/W2qTEGK1/v/3A7SfJIQoEULECSEy/I4rE0JsF0KcrO/3ohBiXKS/J9iVzhnAxygDw58KKWU6ahZ2FKqeVPMPMQGnoBJxtJfE5Z8oFxiklPOllOl6mzcAJVLKTVLKBlStskAubt2SEYOHcNDeykbfrU9fLKPNrP1rA0s/KeHc47IBOPe4bJZ8/EG77VjK3XXAydX9RjVZ1jTwR/KQbUL5lYeaGGcOUN8nvuUDHImcvrq6/ih3luZ6cNehMrstMDtcthDl6xCjrmm0mXzAQQzarWV2+hVrfmPyAQcBcKz1YF79sHvUbO8psoYrpxCGTCo+i0qoMKejHXvKM7oryGkymQzLuJiVWbE1K7PiauAgYEWN/P1AI2VtC0u5+w9UNt0dK3Nmh2sYKqvkFuAUj9MeUimNXeLei3ZXPd5FxY4aklwuMjlF9MtR+PG/3t93BT1FjtvvogAAIABJREFU1kByCiHYWlMDQFVNNSOHRMt7PnhickaOlPJ7P1vqDeBa/fMxAXY/H3hNSumVUhb7HZeJGvc0hyQsRI3xI6JDo1MI0Q8V7HsBOxudAEgpvcCXKBekZjKAH3RBA5bREELsBuwvpfw2wOYZKNfSZpagkgH0ODzr/mD5yhUcYtmXPzdtYsRg5c49YvAQ1m/e3OHxlnL32j9GHPFR37ot7P/Tj1VNwkRdQuKpwZ7f7HDthsqKlx/XjpILVU7v9rr+wCeWcvd3AB6n3Yu6bxXAq2aHa69gZQyVSK9pZ7PfXmN44xOV2PflDwpZs77dCghdSk+RtbPkzM0vqAKeA2bk5WQHHd/SU57RmJzBk5VZ8R0wua8Ytby9/QyWdRlwlDvN0tfscCUCr6JijE7yOO0RlbHoDtc0GAyW8yNUrUzDXGyb6SnX059Yf288PUXWBy+9mmsXPcwe/7BzzcKHuPuieV0tUkBickaVM4GlAb4/DXhbStkcY/4RcEykHqfBrHSeDLwjpfwZ2CSEOKj1DkKI3qi6d+/4fd1sNL4OZLd2vdWxogzT1u31AY5DKddmfkBlt+tRVNfWMv2W63nw0qvp3zdgvoigqO89cBjS9/2MZW+MWpc8GIla8g6SM4Hd+HtF0hg5vd6E1m16nPYtqAyUccAbusFrKEZd085k8XU3s2DJy0y8+Gy21daSmGBEErTo0FNk7WQ5F6KyFp4bzM495RmNyRk6WZkVvj5iRJsxolGQ9V0g0SvEZJSr95HAeR6n/etIGu1O17Q9jJYzN7+gFlWjN5JkQjvRU65na2L9vfH0FFkXLn2VB+ZdzZqXXTww7youuPf2rhYpILu4nEn+Lq/AbUY0CiCESATGSCk9ATafgd/Cn5TSB6wCDojknMEYnTNQxbrR//qvWqbqF2Ej8JuU8jvY8UNOAJZIKbei4k4CzSqOQKWEb82JwCdSyk3NX+irqQ366miPoLGpiem3XM+ZxxzHqZMzARg+aBDrNv4FwLqNfzFsYMcLJwtmF5kAS33vQUUjNm5wmuob6dNYP/Qq573mjo7VU+rPBZaj7oNxcpqED3il9dcep30lytXaAjxrdrgMS1hl1DXtbNJGm1l23yN8/fizzMiaQurI3Ts+qIvoKbJ2ppy5+QVlwGfAnLyc7HZ9dnvKMxqT03iiJOvHwPbHJpx0Hcrj6A6P057fDeU0nCjKuQwYn5eTbUgZkJ5yPQMR6++Np6fI+vS7BZw6OQOAfxx9DF+W/9TFEgVmF5ezrtnlVXd7vdlvW6AwglDiw4egwjRaIIQYgQqbfLfVpvVEmLm93WVSIcRglF/vfkIIiVq9kkKIZr/eCillui7gB0KIaVLKN1CrlMnA93pMVR+Ub7Cr1SnqUDGBrWlhYfvRCwgpfqWrkFJywb23Y9nTzNWn/+0VPO3wyTz9TgGOM2fx9DsFnHREUKGPe6JSdf8EPLF3xaoTakYO2d/bJJsLnrfH4aiH5yKP0y4pKTVMTlOvpK2WcnfAQuEep/09s8N1NarQ+u2o1P4RYfA17VTWb97EsIGD8Pl83PHsYmZP27lEl1dK03O/1Z1xi8O1v9HnF8Dq44PLoh6MrN2BLpBzIfAMqk8sDLRDT3lGY3IaT7iySilNRUWpgfSd4lH46et9aws2Hn702AEVv18/6eGxhUWXt72/H6PFSea9xGm948TfVQl6yjWNspzLUBl/p6DqnHZXOaNOMP2or76+z++LHrqz5pLvDfcRTj70yJSUmbP6mHq1X6I1GDmlz2faXFJ4xp+XnG24DgWwPBZcQqBgZG3YsN5ckWYJ6j0OlTG3zU/tNTylw/1GDh5KSdk3HH3gRIq++Ypxo/bYaR9ffX3fFVGSc/jpZ1kGZhwTJ0ztV3UKSs6G+j6/PXjP/LpLVhqedrvPPuMH7HHp1UmmxPaf0WDkNJiN+JW0EUIMAv4K4fi2bLDTgdellK3LxPTWjwmbjnxzTwOekVJe0vyFEKIE5d6zpvk7KeU6IYQDlfznDdRq6IVSyhf0Y/oCq4UQffz8g0GVIsj1P6EQIhmwAWe1+n4wsCHAReiWfPL9tzy77C0mjBlL+gUzAbjronk4Zp7L6bfewJNvvcGew4fzsuYMprnx+t+fLOVun3uqffIXe47c4ouPP9GdZhlnKXevbOfYuUAVgY34iOSMS0qq2unLlvwL2A/4p9nh+sHjtEfUcRl8TaPGjNtu5IOyr/mragujTrNz63kXU11Xy4IlalH41KOO5rzjTwx0qNja6BtJ4E6gu8naqXQTOV8GHkC9UwGNzp7yjMbkNJ7wZZWgEhQF5PdtIxIe3XLOwFG913HlAYsaTUK2uW9rmqjpD74WniY95ZpGWc7vgUoMMDp7yvWE8PtRKX1xvu11aahYWEPx1tX2kT5fC6sjEh3qq63pETpUNjb0oZ33PhL0tjuU89/X3MgVj+TR5PXSOzGRx3P/uXNb6t5ERU5vbc1AZMsSIuHLKU2++vrxGFfvdwe++u29gnlGg5HTYD4ArhRCPK0nXJ0FBJ21Skq5Wc9a21tK6b+gNwNlz7Vmb1TJqbARUra9EiuE+ABwSinf8fvucpTb5D1AgZRyP/17AZQBl6PiOM26a23zca8B+VLKFm5BQojvgcOllNv0z7OA46SUZ7Ta7zTgMCllCyO1W1BSmgL8gor5MoL7sFmvbf6wYHbRNahZ2cHzFmVuArj03vu/79dQt9/hxR/+Mm7tbwdZyt07GYB6lsM1wCKP036FLuuvqJVTI6jAZh3b3g564ov3gIOByR6n/augWi4pfRi4LGIJFduBUdisGw1q729KSqcC+aiV/UjxAv+HzXq3AW21RBW2NjJT4nJs1qgoIkpKv8S4+G0f0Bub1ZDJqryc7HtQE2Wjc+dqZwJ3YkwB9q3AYdisxvsNlZSmo+qN9u9o1yBoAK7HZn3QgLZ2pqR0G2BUQNyn2KxHGNRWS0pKl9Cxl0mw+LBZA071mx2ugcDnJp936OL3nQOH126+wFLuDt5IKik9B1iAMde0CjgVm7XIgLZaEmUdGoi8nOyngWxgWG5+gTfoljtZh4aNsTq0CsjCZo0ojjggMR1qPMbq0C3YrNHxDS8pvY6YDm0XIUS1lLKf3+dZgFVKean++RbUAqEXlcRztpRyg9/+T6FstZ1C4fTtTwIvSCnf1z+bgU+APfQ4zub9hgNvSikPDvqXBqDdWDsp5dH+Bqf+3cNSyjlSSk+zwal/L6WUB0gpS6SUg/wNTn37qa0NTp3F+JVCkVI+1drg1JkJPB7Mj+oCqlFlTIygCWjtwjIeqGw2OAESfE0Lejc18t4RR5uBF91plkCDlvOBRGCR33fbDJIzqLY8TnsD6oWoBJaaHa5g/cE3Y1wHnwDUGNRWa4y8no0Gt9caI2cAo1k53si2vah3yigeQ/WbF6HkNMrzIp7o3futqNAII4j2M1rb8S5B05EnRiQY6WYYcPXI7HDFo/Io7IUQJw2v3fwHoSe/2YZx/aggeu99tHVoIJYBgwh9FadTdWgEbPYfNEZIHNGTNaZDjcfItqM1doKYDu0Qf4NT//xUs8Gpf75VSjlBj/mc7m9w6ttntWVw6jyCX4JE3bbbPUDfMRM1/okIwxK8RMBCOnDZ0BMTLZFSrugckULEZq0G/sAY5d4AlLb6bjwqnnMHydtrXwNYbR7tq0vsdRxq5XkHZocrDpgNFHucdveODTWeNXgbIi5ELaWsQyW46BCP074BtSrQH1hidriCmc3+igh9x3W8wK/YrFGJBX58dc3aBp/sa1BzPsD4mWQAm1WiXMqM6OCbsz9Gha3yl1+9MvJnFDX4/FH/7YaQm1/wCypL98Xv/rGtst4rEzs6Jggk6r1fa0BbgfgNdS2MuA4C+MaAdtriK4xx4atFzUxHi48xxkBuBAKVDIO/Yw7n/HLPiR+hjKRj2phgbItvMc6YiwOiooNTfPENWyXVXmPe1EA6NBDv6X9DKp2yvmbbmiavIZIGrUPDocb9wx+yqdGovs8HeAxqqzVujHtGYzpUUYIx/VM9bSSgNIhvMEYvxXRomEgplwPFQoiO9MoW4OlIz9flRqeUcruUst3obCllg5Tymc6SKUyygE2Ev6oiUZ3EXdiszcVYWTC7SBDA6NQ0bX1DXPyKkVUb45894ZRiINedZvEv53AcMJrmkiZashkteQmlc46jZvV2pC/sgZ30NjUKIb4A2nVf8sfjtH+HitOdBDyhZ9VtG5u1ALgPNcsW1osupWxC3ZNjwzm+PcwOlzA7XGfeVV79xTlfbY5r8MmmCGaVm+/99disnxkoZmtORK04h6009ZjsIgxM291MYVHq8MKi1GdKfTeev5kf63zSG4nx0Qj8CdgNEm8HdaZe/y5NPnDE7LLqF+8o3+Zt9MlG2V6cQjvoWbm3ATZs1ugUirdZm1B1k6v184VD8zN6OTZruzUqI+QsYKWUMpJ7Xwu8heo/osViVO3WSFYBmgdJO7npmh2u84ErgYc8TvuT+tfNK3MTgz6DzfoLqkZzLeEPmHz68cdjsxq+kpBSXHY0UDZFxg2ohQYpjdWhbZGbX7AeldU9qNVjTdNGa5q25PHSj477s2bbdl9kz2gdajAftA4NFneapY87zXLXbw/e869N773dKL1NDeH2T6hJ220o11rDY+UAsFk3o/rpWsKfuO8xOhQlZ1R0qB93oyZVIjE864Fy4DxDJAqEzfolKlwlkv6pU3Wofr5w6CwdGjJSysUdjQ2klP/Rx9QRYYQfdQwAm3UVJaWTUEvQ4wj92m4APsRmXdLq+1GoGps7Be8meJteT9m6yfFU5vGDL1zyYpEJHnenWVZayt2fopKdVN4Rv/gttJn/BG4CJL6G66muWET/fc5BJfkJepWu5me3teHPynH9JqT/X8KAgQ+FunrocdqXmB2um4A7UDOG7WdWsFk1Skq/QyWuGhrMOaT0Uc2ve1fLNem1rI3b6PvupW38UplFRSiitovZ4RqPipM6Gvjy802N9kSTAJgG7AW0b1C3EhllHBVhs75tmJCBsFnX6c/ouUAaQc4uVzX6+i7f0jhpTa13lKfWW/XS73UPfn/HCYa5rBYWpcYBc1DPRR+J787ffe/cMyTuwDOAA1HPfyg0olZknsZmrTRKTgCzw5XJnufdhRDsXrd267O/7T7p9n37W1AZbVMI4t5LKfm52rtX+bbGiauqvb0+39Tw5lebG3/zRDO5pc26/Nely7LWVG1yJfdO6j920NC3kxISq1vvVt/U1HvlpvUnAHLcoGFv94qP3456Rv8ACrFZ32t9jMFybqlf9vGRP5QvX5aQkHjw7iP2/HDwwKFt1sQMQBXKkHgKmzXcwUHH2KySktLZqJWqSfhlEAySBtSA7ils1hbuUGaH60hUSMR7wDV+m95D3YspwJchyPoqJaXrURORexB6//QLsNTogVJKcVkKamLgTGC1B3HiboIfMF6Htse7wDV5Odn9c/MLArokaprWCzUwvgmQTT7f9ZXVWxft3n9AyDpUpwal/5400gPHnWYRqAmMh1Axp89sWPrKdUNOOOkIQtChfvhQMWKvYrNGlDykQ2zWDygpncz/qA71Yxsq98li3ZCJDjZrEyWlp6EMxnB06HbUmHMxNms03YDBZl1ESamHEHSoH81xjC9hs0bXE9JmXU5J6SHAP4BUQlu06zwd2s1pN5FQjK5nweyiqSh3Ptu8RZkt3DE0TcsAit7e9xB61dRNfcx546NAv0UTTjp1aepRH58dt+y52xOeOgSVcepV4Gq0qlAGcDtwp1n6ol6aNy3l7rM62r8t9BXO51FxvCd5nPY3w22rPQqLUocB96KUw2/AFcDSrMyKsB94s8PVD1Uj6SqU8nAAT3ic9ujMrnUzzA5XNiojsRl1D6/xOO3rImmzsCj1UFTB+wNRg+pLszIrfo5QVMMxO1wjgDxUVrdfDt/0+UcTq5afC1hy8wvKw2xzAKqc0FxUmvNrgWc9TrvhnbKmaXGozOJTgGM1TfugnX0PQhlTZUCGpmmGZ6xsj7yc7KtR1/q23PyCWzrz3F2N2eEajXIx3gwc6nHaW8QmutMspUCtpdw9uSvkM4KU4rJ41DN/OyrL6D3A3ZUZ6UaEU4REXk52BmrV6aTc/II3Wm/XNO0Y1ARjsw69StO0Na3362rcaZYxwMOoFcMfgLmWcvdHXStVjBgxYrSky91rY3TIjnIpAbZ9KqFmz01/Nv48esxZKNePpN0SapY8Ev8Qtyc8dTYqDud4tKrTwjU4dWaiYjIXRtAG+oD6fFTcxfNmh2u/Dg4Ji6zMivVZmRWzgMmoIPDXgTcLi1LHhNqW7ko7HRV7ci3Kr31vj9P++K5icAJ4nPYC1PN4Oyo5VLnZ4bpCT3gSEoVFqYMLi1IfBz4DhqPqQk3tbgan2eGKNztcV6JWTk8FbgX2m1i1/DrUiurscNv2OO1bPE77ZaiVstWo5+qDKL0TTuAE4LL2DE4ATdO+QaVePwxYpGlaKDPPEZGXk308KpbxVdS13mXQJ7WWopK/TWttcOosAw5zp1mMyKLY6aQUlx2GMqofQr37+1VmpN/cFQanzqcol7cWLraapu2uadqLqImwOOB4TdNO624GpzvN0tudZrkZtSplQ63IHhQzOGPEiNEdia10dnMWzC56Apg2b1HmsEDbNU0rqE3odeQzhx/f+4Bt5eb8t6+6M2GY9/wEmkjAqwnBPWhVEbnw6G4736AmKdIt5e6IHxqzw7U7fycNmeRx2kMpaBsShUWpCajU8beiXLbuBu7Nyqzo8LqYHa5xqNW9qaikHHM9Tvun0ZK1p6Bfl0dQK2ffAnM8TnuHsTSFRakm1KSDExgAPAjcmpVZEc2Mg2FhdriOQK3C7o/yNrjM47Svat6el5P9PMqQ2z03vyCiDH9mh6v5utyDKh3wIHCrx2mP+LpomnYu8BTwqKZp80I47jbg/4BcTdPuj1SOjsjLyU5DxbmtBo6I9Jr2JPT7/wrKPfIEj9P+bqD93GkWG6o228mWcvfSzpMwMlKKy4agnu3zgd9R8aqvVWakd/kAJC8nuwDYJze/YJymaQF1haZpUUlEFwnuNMtxKN00FlVyJNdS7o5WIpUYMWLEiJjYSmf3ZzztF2N9t09jffJRf5b2eu57x/IBw2vP/9w3nrc/nUh5/kgiNTh1DgHSgYVGGJwAHqd9LXAyMAJ4Ra/nGRWyMisaszIr7kfFYCxFDSi+LyxKPa6tY8wOV5LZ4boN5ap0GMo91xozOBUep30lKkbsH8AQ4FOzw/Wk2eEa0tYxhUWpB6LqP/0btXKfnpVZcU13MzjNDtdQs8P1H5SL6UBgOsoQWNVq14UoAzFQiaeQ8DjtPo/T/gTKjW8xasWi3Oxwnd5h0q120DTtMFSpqWLUQD+kw1EeAvM1TTs+XBmCIS8neyDK/bce5eq4yxicOhpwCpDblsGp8xkqmUWopVO6hJTiMlNKcdnFKE+Bc1Cr2JbKjPRXu4PBqbMMGHt77pWnoyZX81DZP/fVNO227mZwutMse7jTLK8Cb6PiLo+1lLvPiBmcMWLE6O7EVjq7MXrm2s3Af+ctygy4QvGSNvPwn9j7k2zeZ/fEdU33Vv9j1RLvEXFvLr3+szjkOcBplnL3q5HI4U6zPIMyEHe3lLsNNRDMDteZqCyQj6FWy6L+QBYWpR6DWqXbB3gNuDIrs2KH25Qeu/gwKqHBf4FrI41d/F8mQKzrDcC/m12PC4tSk1EuufNQsYvXAM9FEl8bDfQyQxcBd6ESL+QBt3uc9oAGUF5OtgC+QyWFsebmFxj2e8wO106xrh6nPSTXY03T9kB5E1QDh2iatjFUOTRN64cyvvfS2wgrfrU98nKy41EDaBuQkZtf8InR5+jOmB2u01ErVYuBCzvqA91pljeA8ZZy99jOkC9cUorLJqKe4YNRRty8yoz06CakCQPnxece1tRnt0+bBgwBFf9/OfCGpmndqn9yp1kSURNHN6MWDO4A8izl7k6NuY4RI0aMcIkZnd2YBbOLRqJS6l86b1HmghYbteQ44BIJdz3IBcmmOO+6h/adPsL35TaA3LeXXPMoanVjf+AIS7m7LBwZ3GmWISh3qCcs5e5LO9o/HMwOlxO4HjWwXtDR/kZQWJTqn5HQB9x2w0f/9/r6uqHzUS5ubmCex2kv7gx5/hcwO1z7ogaZk4EvTcI799/HXmVBZagciloZvCkrs2JLF4oZELPDZUXJZ0W5L87zOO2B4qhbkJeTPReVaOSQ3PyC4DOKBieTf1bfJNQq0V0ep73DNPiapvUBPkJlAT1U07QOf0s7bTUnt6lCGZ6bwm0rEHk52Q+hBvoX5OYXLDay7e6O2eFqTtr0DZDlcdo7NCDcaZZL0d0qLeVu49JyG0RKcdlA1DM7B1iP6mef70Yrm8CO5FqXIOWdIAfEV21a0TRgyERN07rdKrs7zXI0qm+1oLx1rrSUuz1dKVOMGDFihErMvbZ7s6/+t+WAUUs+GBX7tEDA117iXtrkHdi38ffGOinwAU9Zyt3bUe5am4A33GmW4WHKcB7QiwgTCHXAjUAB8JDZ4cqK4nl2kJVZUZ+VWXEXML7em1Dk+uXYezbXD1gh8E1FGcDpMYMzNDxO+4+oMjJnm4Q31SdNpc+5//Hs1oZ+vwOTsjIrLu1uBqfZ4RpodrgWokpQjEKVb8gMxuDUeQ61kjjXaNk8TrvX47Q3r8jno96Tn8wO17T2jtMT/zyFWiU9IxKDU2/vV1RfMhrI1zTNsFJbeTnZF6EMzgd2QYMzBWVAbABODcbg1GmuQTklKoKFSUpxmUgpLjsH5Uo7G2UY71OZkf7fbmhw7tChCPF10q8/v5y0zjNiN3dpdGpRhok7zTLCnWb5L2oCuTdwoqXcfXLM4IwRI0ZPJFans3vTMnOtljwY5fp3EapA8Qwgv5q+0wWcnlK5oWndiOGiacLA3YBNlnJ3pTvNchJqJv01d5olMxRXHHeaxYQaPHxoKXdHzS3K47R7dTfbT4GXzQ7XwQHi56LCBcseHosa1LP/kB9qzx7/Ut9BvbccAAyGmEttqDw55fK+wAG1jUnJr648cXvxmiN7Fa85ck8QE1jmWt4Z7tPBoCduORdVVmcQyp36Fo/TXhVKO7n5BVvzcrKfBc7Py8nOzc0vCNmFtSM8TvufwDlmh+sJ1GrHUrPDVQBc7nHaVwc45P9QsbbXaZr2lhEyaJr2iaZps4EngftRhmJE5OVkT0b9nneB6yJtrydhdrh6o+JlBwFHeJz29SEcvhLwoIzOaE4GBk1Kcdl+qHt5FPA5MLUyI71bFUAH0DRtECo5ULMOPQN4Kb6uejrqnTkYFXfepbjTLK3LytwO3G0pd3dVlt8YMWLEiJiYe62RlJT+P3vnHR5Vlf7xz5kUQq9qQIQoKhlBjTqIZWEM2BPd1VWz2HXVVbDHkrWsZ1fXjSX2gq7dnyX21aDCKnFEXV1nNSq7EwsYFSEUQVogbc7vj3ciKZNkZu6ZFL2f5/ExZO49881t733PecuvkN6NWcS/irwRMdaX4/fVANx99vz7gN8eMuSmrXfMeO80pPrfEOQFWaPXrgPQWg81hh8+aRylPvDlhM3g9OLq3JwrmwYOZXuPBZ5BVj9O9973eCbysj0RWcWMSuPmTQMa1qzeLnXwkO9T+vVvahBcj8xkX4nf92Wcf2OHZBXN2QFZcVoB7Ft12DYbkaI/05CCLfFgkLDgO/H7yqJ817bIy/NxSHPhcx88+PwAcv6KkObIfwLunj5tUedNnAPBGcAZSGGkeFkDzAH+ht+X3BsyENwdKVqyI9IKIB42IC9kRfh9LSYv3pw/TiEFd25FVgwfBIp+P++O7ZAX48nI5MfMquK8z2LQOQS5RvdCQkvjoR4Jj/4jfl8bpyyraM5uyAvy/khhlplVxXkJhZ8TCKofVq+4ZXPt5guHDhm+ol/f/vE6ncuQBtxPxLJxVtGcNKSolUbO31+Bm5pWybTWv0WqoD4OnNIiLy0QPDyy72jia8AN0nao/Nq3Xu3baMIXAGdprf8e5xg/UVKQn4WE7P4A7FNYWrZlBTwQHIc4BuOJv/l6HVL86zL8vqWJ6ouJQLAvUoV5P6B/rLsZY1i2OTyycn3DkE/W1l990RkHXBfvV4eyvfchk47DvZWh+hi0noP0Ro5aBb0TViLN19ukPmSWVwxErsULgB+RKJGHq3NzEmslZdmGNqG19iBtgG5AioPdIb/W6+CnQlargOti6g0bCI4kBhvaDh3a0FC2d1/k+ZSDTMic560MJWZrA8FUErehYaSS9PX4fZ1WJneMMxu6HHgKv+9+u6KikCQbap0k2lCrBIIKeW7kI5Nw8WCA74Bb8fs6KsBmB2c29AekWN7NSX/P68G4TqctAsEDEMehn4NRNiO5PQfh99Xcffb8BZlpoX6/HX5FLVJB9V1gJnrtp813yiqao45I/+9GhQk/k3vIfOQFf7vq3JyfQoVC2d4/A3/K2G6s3v6q604HRpH4SncYcZT2w++z2lcxq2jOAcA/UxVvfHHI1ps9Sh2Ms2O6CTgFv+/ZyPhRS+JXFef9VKHwzfnjWrdJOWf6tEXtG91A8A+IA+tEZw3wNHBG0h5IgeAeSEGPAcT/wGxiExKWdmiT0Ywcr6b2KRXAzObHq1k7kKY2KbcDut12IGIsFyCVXBOtahxGHvKTm4xmVtGcQch5Pw+5fi8HHkm416oYy3uBk3B+7i+L9lLfHllFc0Yj19yxyMrXuadmfLgCeUZ8CuS2qLoZCB6NOKKOdBpjyq8NvJoWNmYaMF1r/Xa8g5QU5A+M6NwOyYXd8gwJBHdEJgKGkXj6RwPyErp30hxPcTjnITnAGQ5GqgGOxO97M56dQtneo5FeplO8laF3Otw4ENRI8a6YHeMo1CAvdleBhNIi196tiJPwd+CK6tycxFf6k2BDAbRi7FMLAAAgAElEQVTWOYgTty+RiS+tdZuJr5KC/H8BqrC0bJ9OdI5EnFurNjRSP6FNW5mEK8aLw/ks8lx2+nw6FL8veb0/7dnQ6/H7/mpHVBSSZEOtkyQbah17NnQT8Dv8vpet6IqGHRu6EXgYOP+X6ni6OZ32eAlnFyPIy8tewPm1fxo5eOqg+yYdPezKPZAZtdOAqa0dzghTvgsP6TtUbeo3Yv2ax5DZ7KNbbfNn4Pnhh//6GmPMtjgLrfYgs8WPOxgjKlXFeW8B5x0xMuPQBkMezo9pX+BxAsF+WUVzptCqJH5Vcd5fmjucANOnLfoSOAw4BhgOvPfm/HEPvjl/XNt2IPICcrsFnf2QlYh227hY4DmcGUuQ4zkZOPvN+eP6vjl/3LXIytI+yAzgpNYOerN2IOORCp0XI+1ACtppB6JxZixBrtFhwMNZRXNUVtGcGUBlROMDwPiq4ryHEnY4hQOBE7Fz7ksIBLeNdYeq4rwlVcV5xxFpnZFB/dxNJvXdsGEdcHQrhzMDeNKGTqXUARftO/0fwGLgea11VjwDlBTke5DnxgSgoIXDKTyGM4cT5Nm2DeJoJItZOHc4Qc7JS5GXr3iYj7wUdpzXGQjuBlyKM4cTROdFBIJ7ZJZXjEcc7lLEud+3OjfnD44cTsGqDdVaD9Za3w78B7GhpwJTozmcEeYBk0oK8jtbbbkTZw4nNLOhoWyvJ5TtbdNWxlsZet5hi7IZwEHYeT696HCM9rFrQ68kEPQ6F9UuVm2oFUXR0Vi0oTYEtYMtG9oXeCYy0WIfeza0P/Iu73esqZfiOp02kFklpxdjE32o+e43aWrTFxP7zu1TXT/+XWA8eu0j6LXtvSDPXNo4aL1SqKM/elshL4TnNN/AWxkKA6dkjM2qU0rFGxISDQ/yULNOVXHe7GlbpX+UquIOrYtK2Jj6495f/STwNjAIaf9yRFVx3uL29pk+bZGZPm3R80i1wBuRl4Ev3pw/7g9vzh/X/L7ZGZldt0Eq0kvUPvJSOxZnxrKJvhvN0iOQXOOrkNn08dOnLbqjo1DkquK8H6qK885CVhyWIyu787KK5oxvtekeODOWTaQ0hM1E4A3EYCwFJlcV551dVZxnowKrl/jDq9pjM5Hc4nioKs6bl5P6/V556f/7No1wvzl1uwx+ZPOkEyMr+k1kIWGnNug3sE/G9sARyPX6stZ6YBz7/wWpDn1xYWnZvCifj8eOXUpFQh+TxZ44dzibSEMmt2LGWxn6EVkt6axfZzay8usYY2gsMepPwGfAJOBcYFJ1bs4Hjge3bENX1Wz4DTLJdB4wGxivtX60kzYo85Brr7Nidrthpx6GxzQ2epGV/fuQ45rjrQxd5q0MbbAw/i7YO6aDCQSdTly0h00bWk8vsaFI+HSysGZDSdbxFGza0AZgjKWxWpOFPRuqSO4x7dG4TqcdMrBk2AHYtHRyvem3+tkfbuSF1X/T6LVr2ts0UgHx6FWm/4PAOg/mIMTITo0Ud/gJb2VoY9qQYTYLndh4qEXlsMyMf3mUjWc7bGwwAzY1mjwklHaXquK8f8Ra0Gb6tEUbpk9bdDmwOxK2OBv415vzx+0V2cTWiyfIi4zN8Vpj6+HOBvPNdCScKXf6tEUnTp+2qDrWfauK895HXlrPi/z/s6yiOX/NKprT9IIUb/5Ju2xsNMMRB2Em4nB+aGts5FzZnFmN+9xrrVVO6tLbBnrqxvxoMs79wfR/A5kk+TiraE7TbGoGkvtiAwUM0Fp/gazMTwAej+TMdUhJQf4MpALvA0hOXTRsPlOSeS/ZepkHaCQxrXOBSaFsb0crcxlYsvObYMASo34DPIVUpb27Ojen0cbYWLahqzdtnIzkeu2ttZ6ltW7XhjbjAyR3ubOqwPHmcLaLaWgYiDgyJwK5lgv29ceOgwRybpJ1P9kcV1kerzXWbCjOow86wpoNxeL1HgWbNjTR52gs2LShyX7P69F0aIyUUsOVUhWR/6qVUt9Hfv5RKfW/VttqpdQlkZ+VUuoqpdSXSqkvlFLlSqkJ0b8FlFLPKaV2iPw8Qyn1mVLqU6XU60qpEZHf36yUmub8T+4avltRTe6FZ+M9+VgmnHoctz/3FADPvvUGE049Dk/u3gQr2+lk0Hf0wodWPHzPyoYdoXW7lLb8HkgL47kXeBM4JLWh4WGgllarnQAqJaWFYXekM4mkelSbVd1EtXoUDSeO6fe7quK8K6qK8xLqwTZ92qL/AbnIy8FY4MM354+7u8YsG2BLZ1fTns7V69ZyUOEsdjrhaA4qnMWa9eva7NtPjfwU2GP6tEVvJfLdrdqBPA1cgbQD+XW0PPNEtaZ5VB0SSntvVXGerRfkdumGc38BkgN27V1/uezuquK8I4EjkReat7KK5jxevqK2zSqaDZ1a63lIqPSvkeqa7VJSkD8JCa1eAMwqLC2LyYD3lnsJukXrPOQl+8Cu0KnAHKHMDdW5OadU5+Yst/EHJEMnwPC+AxYC+2qtg7F+X2FpWQNiQw8uKciP2VlzdN5TPA3AeG9l6AmHobSOtMbyzO9KXJ326S1aXZ1tUUodpZQySqnsZr/LUkptivhk/1NKPaaUSmu13+0Rv61dX08ptYdS6oHIz4OVUq8opT5RSv1XKXVa5PdbKaVed/yH0InTaYz5wRiTY4zJQVZ4bo38nIPkk7THLKSi3+7GmJ2RFaaXlVJtvPuIM5pijFmslEpFYvtzjTG7IStL50Y2vROpatcrSE1JpWTmhYQee5b373mYu196jv9VLWbi9uN44S83MnW3Pdrfud+234ZJ8yLVANtdQYo0j/8D8EZVcd4XyMz3mDPeLRuOVKs9KVJdMDk6u5hEtfZLURsLtutb5fT7IyG3TyCO0p3A2V+EH30wbOpbrM70lmPans7iJx9l+p6T+PKJF5i+5ySKn3y0zb4DVdbn06ctchxuUlWct7yqOO9kJMdhA/DS4o2NbcK2E9XaL0XVxNmOwhFdee611ocg+ckvIjk8AFQV572CrEBeBxxX8uWGl+rCJlnX6B1IpeIrtNYzom1QUpA/CsnXWw78trC0LObrprfcS9AtWj9EbERc/ToT1ZkBm6YpY7VwXDJ0Agzv1/9brXUik0xzkRC9mEPdnej0pKbVeStDcbVpcoKTZ35X4ur85Wp1dUZlBlIE7Xetfr8o4pPtilTUPa7pg4ijeRQS8TG1g7GvQN5nQXy3/xljdkd6rpcopdKNMSuBZUqp/Z3+IckKr70cOM8YUwNgjJmH9GA8Icq2JyANskFmbRXQXymlkPy7pZExvgGGK6Uyk6TZKiOHj2DPnWVSYmC//njHZvH9qpV4x27P+DFZsQwxAfjfrNnTOpr9zEMqQDb1amvKkToEKaIxkOjH3KbOLiNRrcpSmG4T06ctWjt92qILAF8K6UsbqWsRytJbjml7Ov/xboBTDs0H4JRD83npnbeSrqWqOO9tJA/l0ppG02aipCdp7YiuOvda6/FIIZeFwMla6xaTgFXFeTVVxXlXA7v2TVELNzeaFhN+tnRGcuRmIiuYD2mtJzX/vKQgvy/iFA8GjiwsLVsZz9/ZW+4l6Hqt3srQTytzoWxvzA+5xJ+jDgXHSTed+yYbGrMj/3O4RnvLc9TVmTi9RaursyVKqQFIe7ff09bpBMAY04i0G2xejDAXeT+4F3Fao409ENjNGPNJ01DAwIj/NQBYzZa0h5foxJ+IBetOp1JqENDfGLOo1UdBxJFqzf5IdTmMMfVISOhniLO5CzKL3sRHke17FVXLlvLxl58z2dtuhHE0dgE6y++YiRynlwG01l8j7RMORvJTKoBzIuXtk6WzW+gJWqdPW/TxBM/5F6eQ3m7D7p6gMxaa61y+ejUjh0uh3pHDR7BiTSzpUBY0FOfVVxXn3bzzgNQO+3j2BK2xkKxzr7UeCryCFDY4UmvdbtGRquK8L57ae+g5fVNUTbvbONSpta5DerQuB17SWo8CiIQo/h3YGzipsLQsWuXtmOkt9xJ0qdZ5yMRjQoUpessx7SqdhaVlXwNf0XmBpqj0luMJvfM56uq0Q2/R6uoEpPDl68aYL4DVSqk9W28QiSKdDDQPgZ2B5N+/COS3Dr2N4EMc0ybuQoo7LUX8sAuMMU0T2kFgisO/JWGns73Vt45W5VQ7n49Emk8TOSjnICseo5Dw2j8223ZF5Pe9hg01Nfz2msu57dyLGdS/TfpfVBoaTTowgg7yObOK5oxDDOP9VcV5zfM05wG5ZwdeSkdWO3dDQp2t6+wuepJWj0o1HpUWNVywJ+nsiJ6ms0+Kajf8sqdpbY9k6dRapyIrnFlIa5RvOtsn1aNI86ioRVps6dRar0RySQcjjmdf4DJkZvTqwtIyRy0Xest5hy7XGvfKXBO95Zh2g865wAElBflxFVDpLccTeo9WV6d9eotWV+dPzEDqXhD5f/NVy3FKqQqkn+q3xphPAZRS6cDhwEvGmHXIIlQ0G/GT/xXhEGSxahSSRnlXZCERLPlfiVaN+gHpMdWcYcDXxph1SqmNSqkdjDHNW1LsifRGbM0mtlRyygFoWiVVSj1DyzzOjMj2vYL6hgZ+e83lnHDgoRw9NfYaSHV1punK7ajqxNlIta6/t/r9PLbk1D4J3Iw48u/a1tkd9Batieg0xmCoTymfP856tbgMtmL/lLti0rnNsGEs+2EVI4ePYNkPq9h6aOtbvXvoLVqTfI3ejPTeO0Nr/Y6TgRLVWR82np2K5kS5Rid9fnyf/5yWrsLPUFf7ej2eKSjPs09s+7ub7oy6fVu+PnTrNuHwieo0xrB9jN8bL4sO3TolJUq8aVc/n7yVoapQtvcL5IXi9lj3+zk/Ry3Q3IaWx7JDbzmekNhz1Bijvltalf5sgbZ+P806/fK0Pn0yYrrvY9BJfX1d6p1xThjEwqCBQzjzpAvb/D5RuxQOh1NuTYJOgIvPucYTLa0o0XN/S5J0XnDWVSkpKSm2zr1at2Ft2gNJ0HrWyRenDeg/0IrOeFBKDQemAROVUgapnmyUUpdFNllkjMlRSo0E3lJKHWmMeRnp9z4Y+CyiuR/SbWBOq69o7n+B9BAtNlLN8Sul1NdIFM2/seR/JbTSaYzZgCSVTgdQSg1D/siml6CbgDuUUn0jnx8I/ApxgloTQho3A3wP7KKU2iry74MinzexMy2Xgnssxhh+f+O1eMdkcfFx8YVB1zX8lNMW1enMKprTF6lY+WJVcd7SVh+XIzHYB1fn5mwEHgWOzSyv2Nq2zq6mt2hNXGc4ZbF5/lqkZ5nV/zazsk0ftPZ0HrnfVB59vQyAR18v49f7d38f496iNZnXqNb6DKRa7W1a6wc7274jnOgs/W7TWbRznT1Zu9czH9VvC+l9pr6745FqdtaZx65PGxjzdbqx0bSYKnais3pzeNtYvzfe/95cUZvf+vu68fk0DzgglO2N6WXr5/8cdcxbiA2NKcS2txxPSPw52tjY0OeV159ZShLupbJ/PvdKbd3mwTZ01tXXDfznW688lgyd69b/aNWGfrk4dHQydAKbl69YuhetSFRr5NwkRed7/y6/PhwOt1j8Svzc1w58oeyJimTofHHOk/+uq6sd1Pz7uuid5BjgMWPMWGNMljFmO+BrxJ9qrmUZskDXFBk6Azgjsk8WsD1wsFKqdauv5v4XwLdE+hQrpbZBCqo1LR5a8b+c9Mc5GbhbKVUS+fefm+Vx3omshH6mlGpEKrD+2hgTzUueg1RJesMYs1Qp9WfgbaVUPfANcCr8FHq7IxJX3ON597NPeHzeq+y6w47k/P54AK4/cxa19XWcd/vNrFy7hrw/XkTOjjsz96Y7W+xbX88ApF/Y9+0Mfyyysnxv6w+01uu11u8hBvOPkW3OQ5zUYps6u5reojVxncoMYMw8okcEOEQBXB+LzqLjT+G4P/+RB199mTHbbMOzus1l0+X0Fq3Juka11lOQcPm5wKXdqXPHAakfsKX4WwsyGjf167Pm+1nrhjBwz4wlqWAe+6xxVGWsutI9StOsV6cTnQNS1TqiPPNsMLZfyolI3v1PdOPzaS5S5X1/YH5nG//8n6POKCwtW1dSkP8esnpc1FN1JkKiz1GPJ6UhI6Nv8ebaTe3mhifKiGFb75SakjqDZisuiepMSUmpHTFimxf5aqGj/PHo2LWhQwYP+wzJubNO//4DZiLVTB1rTU1J3Qz8JRk6txqxzVTlUQfTbAEs8XOfWjt40JB7V69Zab1a/ZDBQ0empKSeiYVrNE5m0NaGPQ8cD9zQ6vcvAVop5Ufe///Q9IExZqNS6h3gCCQ9p+n3lZE2KQONMeuR1mePKKU+Qy74y40xqyKb59J2pTRuVLSeeF1JZDW0HNg/UoGpve2OAvY0xlzdZeJiJRDMRGYDrDTkXbqi4YcX36z5ctbsaftG+zyraM6/EKfeW1Wc1+YEaq2vAP4KbKO1XpFZXlGO5IDtWO1pWIT0mbTBRvy+5ATbB4K3A+dbGm0tMA2/7yNL420hEDwEuYkHd7ZpDDQAV+P32X9RDgQVHbc5ipdn8PsKLI63hUDw38CkTreLjTX4fcMsjdWSQPBS5D6LlqAfL2uBGfh9r7X+QGudhbTHWA1M1lr/GNfIgeDuwNtINXAb3IPfN6v1L0sK8tOQVbd9G9MzDq4ZN/EWZKZ0X611bDOkgeA6pOq2DZbg921naayWBIIvIf1JbVAD7Izf194kY4eEsr1NVQZv9VaGLm/xYSB4MnA3UonQKRuB8/D7HrYwVkss21BgDn5fm9XoWCkpyL8SaTmUWVha1rInaSBYxS/PhtYCo/D7Vlsabwt2beh64Cz8vqc73TJeXBtqH7s2dD0wGb8v1OmW8WLXhtYCl+P3xZwOkUyUUhcB640xD3Sy3dvI4qGjykjJapkSM5HVz2toWeo3GqlIT7qeyCrs3DQArF7b2Jf2Q2v3BPYB7o3mcEZoKi7R1DT8HsTpPBRYZksn8ncni2+R6pw26EOk9U4SWIrE2dtgM8nS6fcZxLGxgQFaV6e2ybcWx0pmj86liAGxQQpRzr3WegCyqpiGVKqNz+EUliL3gA3qgKp2PrsDiVo547LHn3sbcco2AC9rrUfEOL7NZ4rNZ11rFtNx4bx46IOD69RbGdqA5OxHKxSxNGys6Wyk/egbp6wyxlizoUgFWifMjfz/wCif/RJtqAfpCZsMbNpQg2tD4ZdpQzNI7nueTRuaLJ2JcC+dnINIyuMtTh1O6AFOJ4AxZq4xpsObxBjzrDEmWQ89Z/h9DcAzyIy1I4wxm0KL6/vRfhGhc5Bk3o46zn6MFHtqykl5CQlxPgd5MbRRjKkGcWaTxcuIE+b0hWkTkgS9vLMNE6QSeQF1+uBsRP7eNxwrap8HkNUKp9Qg13uyuA971+jdFsZpjzeRc+Z09rsWceRazNBqrT3A48BE4Dit9ecJjr8KcUqcHlOD/L0vt/6gpCB/JlLc7MbC0rL/A9Baf4+Uex8FPKe1Tm+9XxTuwsJzNDJG28pZ9ngSezqfx++rdzjOPCAnlO3dpukXmeUVnr3CKdnLoH+dc7ezHnE63nc8UhR0edk+n69avqGuMWqR5XipIXr9iHhosqEtHPlQtnf08mefUOE6K+/JvcWGyvH0+2yu8jXHlg2tR551/3GsqH1cG2oXWzZ0EzAfv8/WpEBrbNnQxsgYbzkVZAtjzGZjzOOdbLPSGPOSje/r9vDanw2BYAoSn38kchPFiwdI+eyLuqK3/7P5DuDwWbOntQi1yyqaMwSZaX6yqjjvzI4G01o/haw6jNJam8zyir8AVwHjqj0N0xBjl4hBUsiM0vX4fX+Oc9/4kJCGAHJsEnkopSM5wIfg9yWv6nEgOAgJvdgJMXzxkoI8iPbD73M6Q98+Eh50J3AmiT08PZH/Dsbve8+mtDYEgr9GQq5qSfwavRq/70bb0loQCO4AvAf0RwxKhxhjCIcb0xoaGvoaUGmpqQ1Keb7weDz74/e1mFTTWl+L3LMXaa1vc6gzA3gN6eWVyOqHBzkPB+D3fdz8g5KC/OnIytDrwK8LS8taHAet9YmI83wfcI7WuuPzGQheCfwJZ8+nC/D7Zse5b3wEglORY9pIYs+nPpH9j4tMXCZMKNu7F/KsO8lbGfq/zPKKPZFn/ORRmH+9rxq3S1cMZUuj73hIRV669sXvs7p6rLXeGrgROEWhvj1vnwOWDM3ot5dSKmEbCuTh973lVFtJQf7TgB8Ydfgni1KBC5GoLM82BSe+PjT34LyIzh5tQ40xgYaGhgGN4caU9LS0DR5PSqfPqWZkAC8AJ+H3xbNffDi3oanICvR++H0rO9s4YVwbap84bWgU+iBFTPPx+2ytmrbFuQ1NQUKA98Pvq7KorFfhOp02kQfSSKRZd7yryBuARXc/te4kYDYwdtbsaS1Wf7OK5pyPlMXfq6o4r8P8RK31acBDwG5a688yyytGI6spN1fn5hQRCA4GxhF/yEA98DV+3w9x7pcYgWAfRGe8+R4GcdC/T+IM7RYCQQ8wBjn/8bIGWIzfZysUqmMCwa2RcOt4Q5o2AF8l1YFvTiA4ACkeFm+eVz1yPO3nH0UjEExHqsPFnPeyfOXSgR/8Z8HZy1cu+836DWuXG2MuBp4uLC0zAFrrAqQn14PAmZ06arHpVEhxiW2JVMWIg3XAIvy+Fs5ASUH+jkgkwTJg38LSsnXRdtZa34D07TxXa935zHkgOBw5pvGGXNYi575romICwX7I8ynevLww8B2wLBK254hQttcDLP9h0OA3jrlh9g9IVMtK4BLgiWpPQwqShxi1inknrASqnDrGzdFapwBnIYVZ+iOpM9fp3PwaHNpQ/D4rxW5KCvJPAx4av+yH08et+PESpHDUy8AF3spQVW+xoXecePSdAwcMPnfM6O2vnz7l8LI4dg0jYZrVNq7RTnFmQ1cg12jyHOPmuDbULgnY0AgGWIK853XFNerEhv6A3PdOo1p6Na7T2cO4++z5tyOVZgfNmj3tp5OTVTRHISG3a6uK8/bpbByt9WjkpeZSrfXNAJnlFS8ipZZHV+fmJG9GyMXFJSZKCvInITkVeyEF1Wat9/r6AwuQlavpWuuumYyIk5KC/MHAv4BtgEmFpWWL29s24mT8A8krP0Rr/WbXqPxlkFleoc588al3nzkwb/LaAQNBqXuAq6tzc3pcSorWehKyCutDqu3O0lrHXOG4q3h+2q/2qNpqyEfZS1exw8q1VcD53srQK92tKx5KCvLPQHp531ZYWnZRd+txcXH5ZdMjcjpdWrALEGrucEY4AGnS2qZNSjS01kuA/9IyJ+UeYATS+8fFxaWbKSwt+xAJ15kJ7BFOTfuUxob5GLMS+G0PdjhTkLy5nYBjOnI4AbTWjUiZ98+BZ7XWO3a0vUvsZJZXTATe+vtRM/YdtWq55+jyucdX5+ac19McTq31MK31bOADZKVgBnBgT3M4Q9ne1FC297xdlv7w1oDNdea7YYO+Bib0QofTapslFxcXF6e4TmfPYwLRiwjNRMrix5N8Pg+YqrVuagj7JlLV7xxHCl1cXKxRWFrWWFhadm9D/0G71Ywd/yNKDexXFUobGAr+qqQgP94Qnq7ib8DhwPmFpWXlseygtV6H9AkzSEVbGy0SfrFkllcMzCyvuBkpejNx1Mrll9x10zWc9+yjY7pbW3O01p5IusfnwBnAbUC21vppK2HjFglle/dB2hPdAbxfn+J5ZGNG+qhXdx/Xzcrio6QgPwvJxfwa+F1haZm10GgXFxeXRHGdzh7E3WfPH4rkM/y3+e+ziuaMQqpAPlxVnBdPLsBcJN9kCkB1bk4YWSndP7O8Yjcrol1cXByjtVabxux8vUnPGJG6Ye2VKZtrViJNoOdE8iZ7DCUF+ScjKyf3FpaWxRR50YTWejESabET8GQk7NYlDjLLK1RmecWxSLXjQuARYPxHxx1S4jFmIVuqlnc7WuvdkVDxhxCnc0+t9cWRCYgeQyjbOyKU7X0ACRffCjgWOLQ2LfUZxIZO7U598VBSkN+izVJhaVmPWvF2cXH55eI6nT0Lb+T/rVc6z0Cqs8VbjXEBUlijeYjtI0hVSHe108Wl53ApcCLwp6tuueN6YE/gYiQHe2FJQb4uKciPtxiEdUoK8vdFcsTKgQsSGUNrXY40rT8cKLan7udPZnnFzshk4jNI8ZR9q3NzzqzOzWnq9zgXmBLK9vZrb4yuQGs9KFJx+T/IBMNpSNTNp92pqzWhbK8nlO09E3GITwFuBrzeytBz3sqQQSqqtrahPZaSgvwWbZYKS8sSbbPk4uLiYh3X6exZ7BL5/09OZ1bRnFSkyt/cquK8uNppaK1rEMfzp5nv6tyc1UhVzJMyyysGOVbs4uLiCK11PuJ8lQLXARSWljUUlpbdiuRxv4i0alhYUpB/eHfpLCnI3y6iZQlwbGFpWcJV+LTW9yJRF5dorU+xJPFnS2Z5Rb/M8orrgM+QHODzgEnVuTmt+2bOQ1pF+btYIiAr9lrrGYgTdz5wPzBea/2I1jr5VcTjIJTt3RNp1XA/sBDI8VaGLvVWhtY3bVNYWtZkQ3uF0wn8GYmKuriwtGxed4txcXFxaY7rdPYsJiC9n75p9rsjkKILcYWxNWMuMEFrvW2z392DlKk/KcExXVxcLKC1noAU5PkIOL11jlthadnSwtKyGcCBSBn7OSUF+S+WFOSP7UqdJQX5/YCXgH5IyJ6Ndg8XICum92ut97Uw3s+SzPKKI5CUiyuRFc7x1bk5d1Xn5kRrD7EAiWTpcidJa+1F6gY8iUxMTNZaz9Rar+lqLR0RyvYOCWV770JyN7MQO3iAtzL033Z2mQtMKCnIH91FEhOipCD/d0hf3weRnFQXFxeXHkVqdwv42SF927Ylfod+o4JdjFSubT4jfA7S+mROgormATchLyEPA1Tn5nw4uvzj/4yB8+vfCr6RFl+pknpgSRf2lFRAJhDvqqxBeuCt72G+RxkAACAASURBVHRLW0jftswE9lwDrOySPlPQ1OR4NPH3GNuE9MPqql5oKci9FG9YaSNyjSbSYD5+5BrdChgaz25f/LB8SEZq2tObG+o3Ar+JRCZEpbC07M2SgvzdkJDbq4FQSUH+X4BbCkvLYr8XpW/bKOLoMdbY2MiQQcNu/HHd6j2AIwpLy9p7OY8LrXW91vpYpM/ni1rrSVrr7yI605FrNN4+nXXAdzZ7SnaI9BYchUzixUMYuZfaPeeZ5RXbI32Zj0CiXw6ozs0JdDSotzK0KZTtfZtoTmcgOAy5TuNlVUc9JbXW/ZFr8mKkD+E5wN8jFYvjx4ENBZa215c5lO1ViIN5E1LF/W7gT97KUGc5j0029CAiNjSiMxW5RhPp02nVhpYU5Psi2t4BZjb1+43odG2obVwbapcEbWiEavy+tZYVtU8CNjTCOrqq520Pxu3TaQu5ae4Ffo/MNMd7YFPr6k3GvHc3vZZ/zdQ8gKyiOTsjYUpXVxXnXZeILK21ApYCb0XCniAQPKbR8NRmSO0DNamKeB6AHsTIXozfd2cimmImENwJMaKDEEMdDwrIAF4FjklqQ95AsC/wGrAfcu7jJQ1YDvwKv2+JTWltCAT/AvyRxK7RFOS4/hq/75+2pbUgEJwOvIJojNdAK8TIFgNXJ/UhHwiOQq7RkcRxjRpjqA839ktRnpS6xsZP+qal7RdrU/vIKudtSBhdJTCrsLRsfic6U5Hw3SOJ89w3NDakY0wfY8zGtLT0vfD7rOaJaa13Ad4HvgSm6Nz8U5CVmlrEOYsHD3I/nYzfV2pTZxsCwb2Qlb10IF4nt+n59ChwVnNHKbO8og9wCbJq1Qho4Pbq3JyYrq9QtrcQyU0c460MfUcgOBR4CwnVTqQ/cx/gC8DfvFl8xLYchVyL2yFOz+Va65UJfIcVG4rcgwfi933Y/INQtnci4mRORVq2nOOtDH0cy6CRCtJLgUBhadnvIlqPAZ6IfF8i16g1G1pSkD8SWbVtRPrlrvjpQ9eG2se1oXZJ0IY2IwO5pw+J1YYmhAMbGiENcTyn2rahvQnX6bRFIHgPcDLxz3i3oLHR1KakqL3x+z7NKppzC5K7s11VcV51omNqrR8F8oCtdW5+HnLjOC1KUgMU4vfFW9woNgLBsUgRiqE4CwOvQRqQH5mUB6fMJAaQwi9OjmkDUhgkB78vsZe2zggErwaKkBBJJ9QAh+H3ve1cVBQCwV8hIW02dN6E36cda4pGIDgcqEBm5p1EjWwCPgGmxLNCF8nvvBPYAcnTLiwsLVvajtYXkdWvhI+pMSaslPoR8OH3fZ3oONHQWucBr0wdu+N/crcfv4tSyum53wScgN/3ogV5bQkEJyL5gAMdjrQReBK/7yyAzPKKg4C7gJ2B54CLqnNz4nqJjjhYnwG/9973+NOIQzKO+FfkmlMHLEbO/Uat9Tjk2jsM+BSYqbV+18H41mwosB5xPj4NZXsHIvnQFwJrgcuBh7yVobgcxZKC/EeBfGDrwpn6cHqIDS0pyM9A7M8EYP/C0rJPfvrQtaH2cW2oXbrZhsaFBRuKTFAlxYb2FtycThvIQ/NMnBtLPB76AKdkFc3pB5wKPO/E4YwwDxiOPNQvx7mxBLnxktlw+ihgAM6v0X5IWNQIx4qiMx7IwfkxTUVmo5OZi3URzo0QkTHOtzBOe5yLPZ0XWhinPQ4ChuA8TaEvUm0yO56dCkvLXo3s92fkfqksKci/sKQgv6UeCas8HIfHVCnlQe7Jo5yMEw2t9RygaPfM7XwWHE6QY3qZhXHa42TkWDilP3D6pPKPx2aWVzyDPKsVcEh1bs6x8TqcEf4LLEMKyE1GViGdOJwgq7nbbqirnaq11pHv+BXyTNnLgsNpzYYCA40xp4Syvcexpa3MQ8DO3srQA/E6nBHmAcOAPeghNjSyAvt3YG/gpBYOp+DaUPu4NtQu3WpDY8aSDUXuxaTY0N6C63TaYQTxh1dFRSkFMstdgMxQJlpAqDlNYRwHI/H9ttjG4litycL5i1ITtciLVzIYjaVzj4SJJEenhK4NsTji9hbHSubYgyJhMckgkXyu9mgkgXNfWFq2qbC0TCMG913gVuA/JQX5+zfbbDSJhaxFIx1ZWU0GNw3qk2EzhM/ms641OxJ/Tk9U6g3hBsnZPALJjdy1Ojcn4cqjkVYf84ADTUODtedJYzic9tbXXzyKrBy+AGRrrW/TWtt4/lmzoQCbvvr8JGQ1cgWwr7cydJa3MuSk+FWTDT2EnmNDm9osXV1YWhZtRT8L14baw7WhyaDbbWiM9BYb2uNxnU47KFrFd3+3oprcC8/Ge/KxTDj1OG5/7ikALr33drJPOobdTp/BUVddyo/ro+boe5BiDP9D+oQ5Qmu9AvgYcTpbnHOHOq28dLVDm2vTgVaTRK1txnWgUyVRZxutPfjc2zymyTz3ntZjd9cxLSwt+wqZif0tMln1TklB/sMlBflbY/degviLZ8SE1tqkejwtcg4tPEeThbVjWg9pfSVUd5fq3JzrqnNzEsm7bM1cYFjNl59vj6VrtDEczvAo1QBM11ofr7WOHsqdGFZtaLiubiiRtjLeylDrtjJxE8mT7DE2tKQg/wi2tFn6azubxXyNPvvWG0w49Tg8uXsTrGzdJhzoITY0Bp09wobGoLPNWMnU6VBrj7Ch3XxMbd5LEIcNVUptUEplKaUWRv59gFJqrVLqY6VUpVLq5g723UMp9UDk56FKqReVUp8qpf6tlJoY+X26UuptpVSXFJbt1CgrpYxSqqTZvy9RSunIz1opdUnk50eUUsdEfh4WOSCnRRmvr1IqoJRKifz7BqXUwsh/Bc22e1optZPjv7CbSE1JpWTmhYQee5b373mYu196jv9VLeYg32QWPvw0nz70FDtvN4a/PflIm31X14UHA5OAe6uK82zlUMwD9jfGtLgpnejsanqLVlfnL1drd+osLC0zhaVlLwBe4AZkFeTzV9944djWufs99XhGIj16vM5oJKo1HWrf8zSeVp2bYzPH5w3AbP6ualdbOlM8nrqDxnmv0Vp3XLTKEk7Ofb+dsgPeytBd3sqQzSqh84D9jDEt3pu6+hotKchv0WapRaXaTmhP68Ttx/HCX25k6m57WNHoFFenfXqLVldnTCwwxuyBhPvnK6X2b2e7K5Dc+6afK4wxuyHpIbcDGGPqkIJ4BVFHsEwsM8G1wNFKqZji+ZVSg5FZ1vuNMQ9H2eR04AVjTKNSKg/JM8xBck8uVUo1lfW+l+Tm5CSVkcNHsOfOEl4+sF9/vGOz+H7VSg6etA+pqTKhsM8uE1mycnmbfZdsahyLFJh43KKkuUBqfbgxw5bOrqa3aHV12qe3aO0JOgtLyzYWlpYVAbsBH6/6YcUV9fV1LXLleoLOWOgtOiFxrXFWD48Jb2VoJfBR/ZrVE23pTPF46tNSUrqm1YMDnQCe9HQbq8WtmQukNjTUtwgH7MprtKQgfzjwMtKa5jeFpWVxVetsT6t37PaMH5PlWJ8tXJ326S1aXZ2xY4zZhBRiahPyr5QaCOxmjGnK9d4FcS4xxlQCWUqppvD+l4ATkq84NqezAbgfSaDujAFI2esnjTHt5SKeAPwj8vMuQMAY02CM2YhUnzo08tkC4MCuWvJNJlXLlvLxl58z2Tuhxe8fevVlDtt7vzbbr64LjwKeqCrOs9l76D2gpiEcbjdZP16d3Ulv0erqtE9v0drdOgtLy0LA9MxtRl1hOnjWd7fOWOktOqHHaJ0XrqnZsaMK9T1EZ6f0EJ1iQxsbusWGlhTkpyEVjbcFjiosLXPUGqQ9rT0NV6d9eotWV2fHKKWGAjsRPQ3PByxs9u9PgKMj++0NjEVyVYlsNyl5SrcQa87L3cAJkVXMjrgFeMcYc2u0D5VS6cAOxpiqyK8+AQ5TSvWLrKTmEkkENsaEga+A3WPU2CPZUFPDb6+5nNvOvZhB/bcUO/zr4w+RmpLKCQcd1mYfY/Bgp4DQT2ita4HyhnA4I9rniejsLnqLVlenfXqL1p6is7C0zBx8wJGvpaelR00o6yk6O6O36IQepXUemBTC4agTtz1IZ4f0FJ2FpWW1wFuNjQ3dZUNvBw4AziwsLXOUp9qe1p6Gq9M+vUWrq7NDpiilPgWqgTJjTLQOFyOB5q2DioGhSqkKJN/9YyIFvIwxjUBdZHU0qcS0imiMWaeUegwp8bypg03nA79WSt1sjFkR5fMRSI+apnHnKaUmITOIK4F/0bKK2QpgFNJrqtdR39DAb6+5nBMOPJSjp0776fePvl5G2b/e4c1b7mmTwwTQP1WtqSrOq0iCpHlhE86zpbM76C1aXZ326S1ae6LOaN/XE3VGo7fohB6n9T2Up9aEwykqpWXdih6ms116oM654XD48K7WWVKQPxMpLnhTYWmZo7Sb9rT2NFyd9uktWl2dnbLAGJOvlNoZeEcp9aIxprXPsAmp5gyIHwecBqDkYfR15L8m+mCvQm+7xBO6ehuSuB4tT7OJp4F3gFeVUrnGmNaz6y0OAoAx5q9Eqq8ppZ4Evmz2cQYdO7k9FmMMv7/xWrxjsrj4uC2h0q9/8B43PPUYgdvvo19G1AlTRmZ4vk2SrDZl+J3o7GoS1doQbkwv+3zh0RXlZT7bmqaO3WmXqVk7pad6tgQNJKrTGKM+X7V876e1/oNtnQq4Jje/9fclfO431NWOuDkJOgEK9ztw64F9Wn6vk2P6twWvn1FXXpZIb74OKZi41+TxIzI9nmYvk73lfnJ12qenafVWhuqWn3dxiHC4RbRQT9PZHj1Up1UbasIm9fuiBR0+R5fVfD1+3MCcC/qk9PtswpD9Fi/pZPsmttonfWKfoS0nG9rT2hkmbNLXLKw/uua1BdZt6MBxqbsM2jE1XXk6f452qtMYtXlFeO8fYjxG8TL6sJaR1YnqBGisNSOWJUnnyGkZW6f0aTnJ4eSYLv3n5jPMawus29Dhe6RPztjG41Ex2NBOdTaa9PWLGw5b99oC621T+o1KGT101zQr16hNjDFfKKX+hvQOntHq46YexQAopYYANZHCQWcAb0ccUZRSw4GVxhibLcuiojrK94iI2WCMGRD5+Ubgd8BDxhgdqWK7wRhzs1LqEWSZ9zml1A3AXsDhkT+w+XjfATsZYzZHKtgOMcb8oJTaDanIlmOMaYhs+xlwsDFmmc0/2jqBYCawmGbNjd/5tIIp55/JrjvsSNNL6fVnzuL8O26mtr6O4YMkUnmfXXZlduEfWwzXaMzrKQdMsh4zpLVWF+87vX5QRt+fLJETnUANfp+NZt5tCQTvQEIAfiJRrZsb6nn04/dZtsFmiqwwbthWHLPLnvRNS3OsM2zCzF/8Oe98u8i6TgDdyul0cu6XrV/LfcEFSdF55l6/YttBLduhJX5MDdcFXiXcyXMuEfYfM45p248npdmEg4NjuhY4Hr/vVetCA8EcIIA0TneqE2A2ft851nWK1vVIbQAbOr/H7xvd+peWdL4E/Lr5rxxorQHG4/c5ytGLxmr9t8cG7/urk1IytrwsO9C5ETgfv+8h2zpt21DgVfy+NlE9TikpyFdnnXxx/cABg6zY0HCDYek/k7O4MNibysCstBa/a09rbX0d591+MyvXrmHIgIHk7Lgzc2+686f9wvWGlf+upX6d/edonxEehuek40nb8kKfqE5jDOu+aGD9YmstX1vQ2ulMVCdA3bowK95NRr0r2HrfPqQPaZk95+SYfj93c6uGRnYYsH0qg3dOpbkz5+QaXf1JHZtXWveNSRuo2GpyHyvXaISYbGikps1yxJ8qM8ZMVEodAFxijMmPbNMXSUX8lTHm61b7f4ZU3F6vlNoXeAzpZ/o/4PfGmDWR7Y4B9jXGFJJk4nU6t0GWY2/syOmMbPsw0A+YEcnPbBrvQeApY8wbSqkMZPUUYB1wdtMSceS7XjHG7G3vz00SUQymQ5JiMAFq3nhvfb+0dFuB513qdCaKMWb9V6tXHvvEp//+1MZ4zTlrr18dMHLg4NnNqi4njDGmcUNd7Y0l773R5gnllFSPh6v8h1vrq9cYDi+8NvDqwbbGa85V/sNfS/V4rORyG2PC93wYyFq5cYP1t5CL9ztw5sD0PkWWip11qdPpkC5zOh3SpU6nA5LmdG569JnL0jNH3tDc6XRAlzqdDkmaDd30+tvr+/btZ+UaNcZs+v71zeOiffbthsoBFavnzwEzYrv+2YfvMXz6N/GMPerAjOs8aep0SzrX164KH7sqWGfdhm69X58D0gYpazY0XMuNy8o3W7ehygPbHtLXmg01YbPw+7mbk2JDtz0k4zXlUdZs6PJ3arMaNhjrNnRkbsZMTx+s2FBjzLr6dWbmivdqrbd0Gr5X+oSMrTwvWMx5jNXp3B34e6J+kFLqImC9MeaBTrZ7AfijMebzRL4nHjo90U0OZ+Tn5Ygj2fRv3eznU1vt16ZHZ4S7gIuBN4wxm5EKttE4HrivM309hDrsNkxPWkhxilIbsPdSl8yl+E1YakqslGKn4Vt/r7W2v2IeCFZjaQ5QKdU4sE/GqiTpVMgMl5XrNMXj2ZAUndDkeFhBKWVm7X3AUvw++60eAsFVyDG1VWE7OdPeMq7NxLe42jTEic1nSrKOJ9jNffGQJK0ZY7K+DdfV2WruHiZ5x7TX2FBPSoo1G6qUqhtdPKXNc7SkID8F+DuwA3DwEX/X8RcOkueTNRuasVXK99G0OsayDU3JYFWSdFq1ocqjNiRFJ1i3oZlTMnq8DVVKmfTBqjpJ535I5xvFRac2VCl1NlJH50IH33MvcGwn35MOvNQVDifEXr3WGsaYj4HySGhtR/wIPNoFkmywBnsvS3VI352kkOLxVITDVsIPDDIznRTqGhsWNYbDtmbW+tAyYdomX9IqT9kB9bTMabaH32cAW7O0DcBnlsaKxmfYu5+qk2IshS9pWfjMCX2QEJlkUIW9a7QGyRVJFoux8wIaBiotjBOVjYZQg7HWX9MAqyyN1YJGzFekptoKjEsBvrA0VmvWmHDY1vFMqg31kBLqAht6PZAHnF9YWpboys3n2HO+XRvq2tBk4NrQdjDGzDbG7GKMaZNHHivGmM3GmA4Ljxlj6owxjyX6HfHS5U4ngDHmoUiJ3o62ebgpt7PHIw+jGThcAQhLEu/ngPXQkCZSPSnn1DY2NITD4YRfRIzBAOuJVMKyjdb64BvfmXfpt2tXpzWEG50+7DYBs/D7NtrQ1gYJifszzld/apDm4/bDK7dwPBIi54R64Hvgaudy2kUDS3BuNDcif3OymAvMwc65vx6/L67wuZjx+zYBf8D5C+gmIAj8n2NN7XMakmrhxFEKA6uRWWKrZJZXqMzyimP2MClnVkJKvXPHcxMwI2JDrKK13u26wGu3BZd956lvcPz+uRGZBA46V9aSULZ3eOgPJ92/5N7b+kZWZZ1QS5Js6JKiBalLihZcuOq9hkmmHhw6nu3a0JKC/JOAy4DZhaVl9zj4jieAD7Bz37s2VHBtqF1cG/oLo9OcTpc4CAQPA/6E9BqN6tA3hhtTN2+uGaGUp7FvRr9V9Q0MWNcYHrApTMO2fT3PeZQ6G7/PfsWbZjxwy223+bN2vmDcsK2+9SiV1vkeUGdIXweDN0Pqali9EHXK8QfsVWZTl9Z6NHArcAzwZf/0Phdduv9BRwJ+4s9Ha5qVvA2/7wmbOqMSCM4Cfg9kJrD3j8DrwKVJnFEUAsH9gBuALOIPE9oMfAr8Ab9vuWVlLQkEtwZmI316483zagS+AYrw+96xLa0FgWAKcjwPA4YmMMJy4GH8vjus6opGIDgDSW0YRfwhd+uRBtTn4fclt6x6ILgrUi19RyCm51NDuLHP5oaGwQ2NjSkb6+uql67/8ZhJxx/zrk1ZmeUVOyHpIQcDFbtjLpnraTwd2BuIN7c9jLwUXovfN8emTq31IOQl/jxgTd9Nm645rV7dNdQ/fUXakKGJeEqrgOciWq29MISyvR7gdOT+GQzcuuPfbvtX2rDhl9KBDe2AjcgLnXUbuqRowf7APcBuwGsVtXOXT9hzwqljR4/71uPxxHSNNqMBWZG5CL/vk+YflBTk7wO8hbSPO7iwtMyZ0xAI9gHuwLWh9nBtqF1cG/qLwnU6u4GSgvxDkdmdf6wZfv7g/xtUPw24oqo4729d8f1a6/2R1jbHaK2fj3W/zPKKVGAWcC2QjjwoiqtzcxzN/mit05C49WuQh/hfgZu01snMyXJxcfkZobXOAC4FrkBemjRwu9ba0Yt7ZnlFX+CPSFn6zcgKxT3VuTk9KhJHa62Q6vIlyIv7/cAVWuvVoWzve4DHWxnapzs1NhHK9u6BOHH7AAuAmd7K0MLuVdWWJUULtkbs3KnAd8AFwEulX9+wH2JDjy0sLXvOxneVFOSPRpzmjcDehaVlP9gY18XFxaWn0C3htb90CkvLZEYOjlpf++5+yhAGHuxCCf9GQtgOiWen6tychurcnNuBbOAFZFV3YWZ5RcJVArXWfuBj4EZgPrCL1vo61+F0cXGJB631Zq31tcAEZLXoJuBjrfXURMfMLK/IB/6LOJrPAtnVuTl39ECH0wu8gbQdWwpM1lqfrbVeHdlkHjAplO0d1l0aAULZ3iGhbO+diHO1A3Ay4O9pDueSogUpS4oWnIOE6p6IOJ7e0cVTXhxdPMWQoA1tj5KC/H7AP5BCjUe6DqeLi8vPEXels5soKchXjSr1iRTTMOPDobnfPT27cExXfr/W+gVgT2B7rXVCF0FmecU04G7ECf0HcEF1bk5MMfVa60zkpfBEJEn7fK31K4nocHFxcWlOZNXvSOB2YCzwOHCp1jqmkLbM8oqsyL5HIj3NZlXn5ryVFLEO0Fr3B65CmoBvRFZ579datwgxDGV79wXeA47zVoae7WqdoWyvAk4Abga2QlY5r/ZWhn7sai2dsaRowSREnw8oB2aNLp7SpvBHSUH+C0j/vKzC0rKEX6RKCvIV8BRwHOJwWk1bcXFxcekp2Cr17xInhaVlZurF/1g8adUr+NYEMksKyvcsLC37qPM9rTEPOArYGZnNjZvq3Jz5meUVuwMXIaueoczyimuBkurcnLpo+2itU4FzgOuQamDXAX/TWiezDYOLi8sviMhE2j+01v8ErkQiS47UWl8JzG7tlDWRWV7RB7gkso9BQmpva+951l1EnOrfILmvY4BHgMu11iva2eVDpBfsIciKbZcRyvZOQCYn/cgK4eHeylBX2rqYWFK0YBiS2vEHJE/seODpyMpmNBzb0AhXAgVAketwuri4/JxxVzq7iayiOapvmMXDGzZlHfX9Y9UQbgAmFZaWVXfF92utt0fKtZ+vtXZc6S+zvGIM8gJ0FGKAZ1Xn5rzZ6jv3QfoG5QD/BM7VWierBL+Li4sLAFrrbKQI0HTgI2Cm1vqD5ttkllcciDhHOwPPAxdV5+Z819VaO0NrPQ6pznoY0hphpta602IfoWzv88AkYKy3MpR0wx/K9g5A8vQvREJRi4AHvZUhK/1GbLGkaIEHOAVJ8RiKHNtrRhdPWdfRfiUF+TsAi4ALCkvLEipiUlKQfxSSqvJ/wMlOVkxdXFxcejqu09lNZBXNmQy8f2BNWti74oHJmJoAUtUst7C0rEsqW2mtvwQqtdZH2Bozs7ziMMRojwNKgYvPDrxUCxQDZyClwi8Cnks0rNfFxcUlXiKrg8cBtwAjgQeAP872/yYj8rvjkKqi51bn5sztNqHtECmUdDlS1KgeiS65K9ZCSaFs71nAfYDXWxlKWg/TSCjtMUgl8m2JHGdvZSgpvUidsKRowe7IRMP+SPjxzNHFUz7peK8tlBTkfwl8Xlhalh/vd5cU5O8OvIvkDPu7yu67uLi4dBduIaHu45xUQ4O3LuWrwqefCSIFFfYB7o/keHQF84BcrXW6rQGrc3NeAyYCGmN+s8vSrxc1eDzfGulHdjPg1Vo/6zqcLi4uXYnW2mitS5Ec9FsNnN7g8XzjXfr1Iow5EnHidu2hDudhwEKkIu+LwHit9a1xVuZtajJupfhNNELZ3p2Q1hXPACuB/byVoTN7msO5pGjBoCVFC24F/gOMR1q3TInH4YwwD8gtKcjvE89OJQX5WwMvI60+fuM6nC4uLr8E3JzObiCraM5w4Hfj61M2ZKAWAhSWlj1fUpCvkZeKz5AiO8lmLjAT2A+p9miF6tyczVrrVxo8nt+khsM5SwcP571xE79YNXDoP6pzc9bb+h4XFxeXeNFar88sr3hpq/Vrjtjvq4U7+b/8hP0XffZxajj8ita6R738a61bpy0cqLV+s+O9ouOtDFWFsr1fIH1Gb7enEkLZ3n7ICuxlSFuZ84F7vZWhHlXld0nRgtZtZe4DrhxdPGV1hzu2T3MbWh7LDiUF+elI+PbWwJTC0rJlCX63i4uLS6/CDa+1SSC4DfJyMIYOHPrAytpJlesbDphYl2JGDUh5f/vRae8AqxsbG9+/7b5rZwG/BY4oLC2z2jS8mc404NjahoZdg0u/uWx43/4fZm+V+XaMe28CKoFn8fvavFBorYcgfTxnAqsMXHL/1F9vMErdjjT8fhS4rDo3p72CF6215iIrwPE2DW5Amhu/gN+3Ms594ycQHA8cSvxNgw1QDbyJ3/dpMqS1IBAcgoS+bQ/E29R8LfARft9r1nW1JhD0IPfSRKB/nHvXImGSz+D3OeohGxOB4D7AVGA48Z37MNL772X8vuTnDgaCY4EjgNHEF+VigFVAAL/v38mQ1oJAsB8S6joOiGsFCdiArAi+iN/Xxrhllldsg+TunQx8o0z4grPefnmwkiiM4Uio5dVa67Ux6FTA4cAewKA4ddYBXyPP0Ta5g5Hok6YCbQp5pt6ScCupQDAHmLbuP/8+oX71ql2H5R58p0pNjVpMqRUGSYl4Db/vy2gbhLK9RwB3AFlIbuKl3spQYrUJYrSh7bAa+Bd+XyDah0uKFniR85uL2minOQAAIABJREFUrHCeM7p4yocJ6kwDjq2rq931k/8GLxs6ZPiHO26f3akNNcbw388rDvzm20V7LKr6/Pjzn3jxqYS+Pz6tidrQMLAEKMPvq7Itqw2uDbWLMxtaj3QVeA6/b41lZW1J3IYapEXUXPy+pKUM/ETiNhRgDfBv/L6EJg1/LrhOpy0CwW2RynxDgb4JjFAP1NfWbf7jXQ8Un4a8cO1TWFr2P4sqm4zli8ABiM54b5ww4ni+ARzT5HhG8qVORF7eRhApia+1/hEgs7wiamn/6tyc9l98AsHzkFzQNOJ/uBPRuQqYjN+XvNnkQNCH9BhNJ/6XZBAnqRHIx++LabY8IQLBYUgO0Rjk3Mcbxt2AaL0Zv0/bFdcMMZYPII5HBpAS5wgGqEH6vx6cVMczECwAHkJekBMJU9+MOEr74vd9ZVNaC+SF7j2kD2BGAiPUIef/ZPy+521Ka4E4nG8Cu5HYNdqIHNMngLObHM/M8ooU4GykOmk/JJLkr9W5OTUAWuuhSCXtc4AVyHPqyXbTAMThvA64ALnn43WQDPJ8WgxMwe/7qXWI1joXcY68wEvARVrrqjjHb671oMg4KST2fNqMnP8D8Ps+bvplKNubRau2Mt7K0FsOdFqxoUAhft/spl8uKVrQH+mzejHNbM/o4imxON3RdDqyoeFwGGPC9Skpqa/SzIYmBec2dDNyne6P39embYw1XBtqFzs2dDMyKboffl/y+sY6t6G1yH1/IH7fB51tnDDObWg98hy9Cr/vNpvSehOu02mLQPALZObLachyzTffLT71uVceuxN5GZ1stVF0IHgbcCZy4zihBrgbv+8yrfVE5CVpKvABUk0xakn8zPIKL1JFchrSIHxmdW5O29nmQPBAtjTLdkI98CV+3wSH40QnEByMPJgHWhhtI7Azft9SC2O1JRBcgFSvTMSoN6cGOBW/LzmtFwLBS5Ewc6fnfhOy0n2iY03RkNWjd3GusxGZHBkZbXXOMYFgCrISMAznefw1yCTOQse6ohEIPgPkk5jT0Zwa4Ar8vtszyysmI5NgeyIO7bnVuTlRZ8W11ntFtt0bSTmYpbVuO/EXCJ4A3I/zc18LvIvfN11rPRIJ+5yBrIKep7V2Fu0SCI4BQhZ0GmA9MCr0h5Ma2NJWJgz8GbjNWxmKJ7+0LRZtKHD4ktc2vU2UtjKji6fEFmXTHkmwoQ7HiY49G2qQVeTMpDjIrg21jz0bWoeszO7rWFM07NlQkPfl7ZpP4FnDvg09Gr+vx9UO6ArcQkI2kNl5G8YSIH3sdjuMQ8IitgOeKSnIT2SGsj2mYecG7xc24YO01jcjK0oTgbOA/dpzOAGqc3NCwIHIi9W2wAeZ5RWzM8srhrXadH+cP9hBZnfH/z975x0eVbX14XfPpNEEqUGjRrFkgCjieK1xBMUGKoqKYEHlKtZriSXqVY/1xoK9oV650XtVbCgGO4Qx6mcZNRo1WNAAUSJVWkLa7O+PPYHJZCaZmbPPZEbO+zw+kjPn7PnNKXudtffaa+H16WgrHC7Ui5cOmlHlZPSjZmZ0GEtQ98+hGtqJxOHouUe7oQZCrOJvmtpxosIzt9PUXig7EF9UQzj8qPvIKgow73ACdN8kOSa7rGIG8H+o9XunAmMiOZwAhmF8ARyAqtW4F/C1YRh3GYbRM2TXUei5RzOllPsbhnEpas3mBOAWYJhph1OxN2rgzSwCYPnsF89EZVm/DZgL5LkWVt2tweHUakOb6/xjA/peRYU0FuQUF5xt2uFUaLOhqBI+VqHLhgrUzE6uhrbCYdtQ/eiyoRnAyMBvtwJdNhTU4K01kwt6bWgW6tncKrETCelhG5Rh13E+04B+hbNK/2/6xHHnoUZn7wMu1tA2QG9N7bB206Z8VAf/FHCtYRhRZSisHTVCAi9kl1W8iRolvwSYkF1WcTVQUjtqhB8V2x9rSEgkmoGeqFkF3Wg7n6gOTWd7oeh0vAdobCuUWNcedYSO0fNI9CG+cKBwNKOu/W+a2gumD8og6yADa+/RUOcubr5XzoFA9Z9GtEnMDMPwA08YhjEbFZp4FTDJMIzLgFcDIbf9delEvRzej8r6eolhGDrDrHujqR/1NzR0q/910aOo9dJHuRZW6Ryp12pD6/9ouQIV6XAF8FBOcYHOGTqd97+Vz5JuG2qVVtuG6kenDU1HPZc6Bq9C0WlDwbprr9OGOrD22ic1UXXwQojWAsYuKeXCwLZcVNjOQpTnvh54REpZEvj8LMAtpWznLAkhxgN7SilvEUIcgjK4ewKnSilfDtpvCmodIMBtQW2/D5wspbR+gXOcLF1ey5l3GNSuXoXDIThv3AlcetIkbvj3Y7z+0Qc4hGDgtn35T9FNbNc//P1XOKu0ZPrEcflA4fSJ474tnFX6eNgdu0inQ4gW4GDDMD6J57trR41YB1yeXVbxH1R47tPA37PLKi6sDTOeZEJrQmPIdVx7W2dqarXv0eTRmanCmA6uHTWiMp7vNgxjBTDVMIx/o0JuXwbeMQzjEmNU+7KMJs/pBGB2IkpJxatTtrSkZ2YPnln3w/cXuhZWWZ7l18z5dGaKn4DDcooLrAmz1KQz0UTSasx8gifnvsaA3n0AuOPcizhm/66bjImks5V7XniWqx5/kBWvvUf/Pn1snVEQSetVjz3AGx+Xk5GezpDtcph5zY306dVmrDYpbFMUOhNKonQKIa4HJqOcXj8wTUr5qRAiA5UU79jA9u+Bi6SUNYHjNkgpOxzE7QpfLKo1nUKIF1HFtOdJKY3AtlygVEo5PPD3LijH9AEp5cxOnM6PgeOklCsD7WyDWiMyp/WHCiH6otb8uVE3/RfAPlLKNYETkCOlvD3aH2opXl82KiHE5rCwZatWsmzVSkbunsf6uo3sc96ZvHbb3eQMGMg2PdR98OArL/B99a88XnhtaIv34HFfBTB94jgn8AYwBhhTOKt0gUmti1HrW0zrlFIuEofuu6spPQGyyyocqIySdwPb/lc0Vx4u2obJmNC6CcixZDG813ckMIugETYTOtcD0/C49Wc0VOExbUKYTN6jL+Fxn6Jdp9L6GSEhnCa0/onHrXPUN1jn1ajENJsH70zoXIdKJqQ3cZjSOQLwEpRh1YTORuAayxIheH3rCZrtNNc/8bE41K3lzdkwjDRURu7bgMzz3QW/ZvfqvUfwPvFqlVJKcei+1ix18frORA3omT6n0u9fJxyOE/C451ug0zIbaoFWbTYUWITHrcWGhtH5ICqKaDORtL5Y9j49u3XjylPPiNTaWuAwPO4vLNAZtQ0dmrsLS5fX8ve7b2fhkmq+mPFsqDOXFDY0Cp2QJDa0ZsVyRu/tJi0tjWtmPATAndPa3DZ+IAuPW/9MZww2NAqda4HJeNxvWqAzahsahU6Ax/G4L+jsa4UQBwD3AodKKRuEEP2BDCnl70KIe1Az2udJKVuEEGejEuHtJ6WUUTqdCffFOjV0QoieqPjjqag1MWGRUv6CCmX5Ryft7Q40SClXBo6rllJ+Q/uY/iOB96SUqwNe9HuodNqgiipPIokZ3K8/I3fPA6BX9x64dsrlt5UrNhshgI2b6hGi41D5wlmlLajf+jPwyvSJ43ZJFp2daY+F2lEj/LWjRvwHVaj7ycWIdusydJ1Tq7F16idVtNo69WKuf9KnwzCMZsMwHkT1Ty//ual+j9B94tUqhEjoTELcOh2OlNCZaFJFJ0TWmmx0pPPyh+/jrmmXIGJOHKufVNEJkbUese/+pKUpn2//ocOpWfFHV8q0dYZ8DbBSStkAIKVcGXA4uwNnA5dLKVsCn81ELSEbHU3DXeWLRRNeOx54W0r5oxBitRBipJQyUqKYL4G8Tto7KLBfZ2yPymjWSk1gGwEPO1MI0U9KaV0qZ01UL/udr376gf1cao3z9U89yjPvzKV3j56U3d95xGzhrNK10yeOOxaVTn7O9InjDiycVdqutltX69RB7agRq4ELFi/w9UWl+05ardFg69RPqmi1deolGXQahrEMOG39ux/uRAfJIJJBazTYOvWSKjqhrdaPKr/m4dkv8cy7b+Lew8X0Cy9j216xlp61hmCdcz7ysv2AAey16+5dLasdqaIT2t+nrTz95hwmjhrTRaraY+vkXeBGIcSPqDKFs6SUXmBXYImUMtQP8KGSKUVTC7RLfLFoQnomAS8E/v0CHXu10QzpDAaiGVoL11bwSOtyrMv2qI0NdXVMuOka7r/4is0jn7f//UKWvjSX08YcxcOzX4yqncJZpT8DJ6Oc+v8Gwm6TTqcudhJEHB5KNq2RsHXqJ1W02jr1kmw6e2VmRUyalmxaI2Hr1Euq6IT2Wi84fgKLnptNxVP/Y3C//hQ+mhxlBIN1pjnTuP2/M7nl7PO7WlY7UkUnhL9PAW5/9mnSnGmcNuboLlS3BVsnSCk3APugKkOsAGYFli4Kwq+1jbQ9HF3ii3XodAoh+qGmap8SQlSjsvhNFJHjRPZGJRfqiHqiK6xag0pT3EoOEJwUICvQVtLS1NzMhJuu4bTDj+LEQ9rPeE8+7Che8Ua/PKZwVuk84DLUwuHbklWnlaSKVlunflJFq61TL6miE1JHq61TL6miE8JrHdS3H06nE4fDwbljx/NZ1XddrLK9zkW/1/Drst/Za+pkciceR82K5Yw873RqV0WVNH+r1xlOayslb5dS+n8f8r9/3poUYeC2zi1IKVuklAuklDehqlhMQC2320kIEZqhaCQqoVA0dIkv1ll47UnAM1LKaa0bhBBe4GDaTre2Jha6B3iokzargGiKtb8D3CGEaE0CcgRwbeC7BKruWnUU7XQJUkqm3nUrrh1zueKU0zZv/6lmCbvlqBwEcz7+gLwdc2Nt+hEgHyiaPnHcd4WzSv+bpDq1E6/WRknmPtL59oqyCu2Frcfh6H2/8PfsGdSvxKuzTtLdkI6bnymr6HBddDwInCxztM34bebal0lGTyqr+D/dOgEWCIbmhfTT8WrdIOm1q0U6rxViu4uQzjQN136jpMcJ0vncN2UV2gfS9sXZ/QXR0qOHBp2NkvQHEZfeU1YxUbdOgF8E3btr0NkVpIpWW6deUkUnRNa6bNVKBvdTVYBmf7iA4TsPaXNcQ3Nzz+e++Wzm4rLSjbo1DR0wuPfxeXv1zEzb8joaTmf+Lruy/LV3N++TO/E4fDOeaZOgp7Glufu7P39/s6+sVLsNBQjNWh2vToCfVy0f/V/DsMQ2XbivZ+jAnm39kUjX/u1PP+bO55/B+8AMume190GklI7bvG+Wt5SVal/Tfdgue2x30I5DnA6xZf4rXp0Nzc095/zwzfTvykpv0K1zh2227X76Xvv16OwejUZnLAgh9gD8UsqfAptGAIullBuFECXAvUKI8wOJhM5Eld+KdnSrS3yxzpzOSag6ZcG8gkrfeycwRAjxFVtKpjwUWMzaER8A04UQIpBhaV9gNioL07FCiJullMOklKuFELcCnweOu0VKuTrw732AT6SU2p0IXXxU+TXPvvsm+bvsyoipkwGVhvzfb77OD0sW43A42GlQNo9f0S6bXYcUziqV0yeOuwQVZvvU9InjfiqcVfppsum0AhNapUPdn9qzr6VBuggJZzBzTtPUiJH29brhMKPTqeq2WaLTEaYeVrxaA9fGEp1p0FejTpywAdD+UudU50DLPSpApqlkBZacU53PUqJJFa22zq1TJ0TW+vy8d6j4+UeEEORmD2ZG4XVtjhMCKYTYiAXPvUOIqG1oNGVcHMLR5TY0Gp1CCMtsqBDR29B/PHgPDU2NjCm8CID9h+aHybIs1kEUpS5ixCEcUdvQznQKkE4h6rDgnAohpBDR3aPRnc+o6Qk8JITog3rn+hkVagvK8bsH+FEI4UeVrzxBbilJ0l0IURPU1r1SynuD/u4SXyyqkim6EUI8ALwhpXzfxPFzpJTRLJa1njDp3k3Sabr36RPH9UfdBJnAvoWzSqMrKh+S7t0kCU33boIGYPtElUwxQULTvZskoeneTWB1yZTbUMWzzbIe2D9RJVNM0AAUJapkikk+xqOnZEo7vL7XgOM1tebH49a6Rn8zYUqmmGAtcGKiSqaYJGElU0ySKjY0oSVTTGDbUNBtQyWQaWHJFF02NKElU0zyGB73hZraipuu8MWsqQ3WOXegpoHj5dukcTgVa4EMTW01EcXi3sJZpStRazt7Aa9NnzguWmMddRHXBLcVynLUyI4O0lDGyArWEF0CrWjwY+051Rm6aWW+cp2DA1aOeK9G3+x5OtZd+9VEl6k8Gpqw9h7V+ZxauYhK5/1fp7GtUNYQJnIgTgTqXrKChNtQE2ytNtQqrbYN1Y9OG9qIvvsoFJ02FFLDhragns1kIOG+WJc4nVLKP6SUc0wc/6ROPabxuOtRi3cbNbTWBJRHs2PhrNJvgdNQU9xPT584LpqO+y30vORsBPSPKG3hA/Scz0bgazxuHW2F43uUodMRMuAEvtLQTns8bgl8BGzS0FodqlaTVcxFT5ip1Tp1rcdpRr0k12pqL5Qa4E/0OB8O4BMN7UTiPfS82NWh7iOreBc9/egmVF9nFV+i+hWzSNR9+oOGttpR81Z9t+Z6/1rp1xJ5FbUNjZOtzYa2oAaDlmhoKxy2DdWPLhvagIoYsSokUpcNbV1CYlUGLJ02tAE1a9rldIUv1lUznX9FDkMlV9qAMnot4f6TUkq/Mqyhn9WjOqJz8bijfhALZ5XOAa4HTiWwuLcTrgdeR3VIjZF0dvBfQ+DYV4Cbo9UZMx73B8AFqHNSH4fOJtS1qEYtfLZK5wZUYq21qPPSHIfWjQGth+NxWzn6eSLKwHd4j3bwX+s9ej0ed9wdVRQ8AjyJOi+b4tDZGPiNrfeQNXjc36FqydYF/otVZ3NA5wrgIMsMu8ftBw5EzfzFe4+2/sYT8bgtcTwCnAt8jDov8fRPmwK/8WHUPWQNHndr/1dHfPdoU0DnN3RQj1iDzt9Qxb1b1wvHe4/+ibpHtSa6qikqd9QUlZ8N/LD844Y+/mbWSik3Yq5/ismGxkFK2lD16uEnsOYrlmu/GjgQj9ua2S7bhlqBWRva+v60EBhvmUrzNrQloHMd4MHjtiaySY8Nbb32l+Bxl1miMwXokjWdf1m8vj7A0aj1HmEdet93Df9Ic7J2RF5mSchHfwKf4nFHU6y1DYEZzv+iEjydUDir9LVOdDoCOncnupTJwWxCjXa/HXgQrcXrc6PWJvTpbNcQWlAjs2/hca/VrisUr29HYBTx1Y79A/DicS/SKyoMXl8P4Bggl9jDRdYDX+Jxf6xbVjvU+pnDUIWOYw3/aEQtuH/TknUooXh9w1EGqV+MR0rUQNW7eNxWhgMqvL5BwBhUyvNYw9lWoka8ra+l4PVloO7RIcQecrkR+BYos3B0fgte38GojIKhqes7oxn4FZir25ELi9e3G1AADIrxSIlKjz8fj7ums51joaaofE/gUVSR8v8DLsw5uls1ndjQDojbhsZMCtrQj5f8cvmm5qacA3bY+e5u6RnRzNZJ1AzP+3jcVkVhbMG2oXoxZ0NbUAP2c/G4tSe3a0f8NhRgGaq/X6xXVBjM2dC1wOd43J93uudfGNvpTCCPnD8/DTUq8+BFj4++WmfbgTWdXmAocGDhrNJvdLZvY2NjY2Njlpqi8m1QM3yXoNZgXQP8J6e4wHoHbCvFMIwjUKUPCg3DuLez/W1sbGyswA6vTSw7o7LNas9QWTirtB4VBrEWmDN94rgBur/DxsbGxsYmHmqKykVNUfkkVMjepcBTwB45xQVP2w6ndRiG4QTuRs2sP9LFcmxsbLZibKczsQwN/F9/WQSgcFbp7yjHcxDwyvSJ43RlA7SxsbGxsYmLmqJyF/A+8BwqHG7/nOKC83OKC6zKhmuzhdOBPYHrDMNo6GoxNjY2Wy+205lYhgX+X2XVFxTOKv0cmIpav/NwlBltbWxsbGxstFJTVN6jpqj8X8DXwEjgIuBvOcUFn3Wtsq0DwzC6oeogfo6qhWljY2PTZeiqO2MTHUOBJRc9PtqqmpEAFM4qfW76xHHDUdlsK4GHrPw+GxsbGxubVmqKygUq6uZ+VFKgEuDqnOKCZKlPt7VwGSrpyemGYdgJPGxsbLoUe6YzsQzFotDaMPwTmAPcP33iuDEJ+k4bGxsbm62YmqLyIag6ga+iShkcklNccJbtcCYWwzAGoAae3zAMIynqAtrY2Gzd2E5ngnjk/PlOwEWCnM7CWaV+1FqO74EXp08ct3sivtfGxsbGZuujpqg8q6ao/CZUgfYCoBAYmVNcUN61yrZabkSVyrimq4XY2NjYgO10JpJcVD2vRM10UjirdD1wHKom3JzpE8fFWuvSxsbGxsamQ2qKyo9G1Uc1gNeAvJzigntzigusr5Nr0w7DMHYDzgeeMgzDshwSNjY2NrFg1+nUidd3Cqr+2GBCCsf6/TKtuYXuaU42OhyiJczRG4By4Bw87jqdsqZPHOdBZQ58HxhXeKGxGzAT2A1Ij7G5RuAn4Cw87h916myHKsI8EzgY6BHj0X5UlsTr8bhn65bWBlWE+TrgHKB/HC2sBl4CrrG8oL3XNwaYjlrn44zx6DrgC+AMPO41uqW1wevbAXgGlXwrM8ajm1HlAabhcX+hW1obvL504HHgCGCbOFpYifqdtyTg2p+Pmn0aQOyFrdcBbwEX4nE365bWBq/vb6hzuhOx5x1oQDk/p+Nx/65bWhu8vn7As6gEOd1iPLoFWApchsddpltaG7w+B6pkxgRg2zhaWAk8gcd9Z+gHNUXlO6LWbZ4A/ABcnFNc8L4JrRFtaBRYZkPb4fXlkcQ21DCMl4GjXAOy8ycOd99JfDZUAiuA+/G4rS21Yt6GrkO930zD427UKa0d5mxoA2ri4Uw87iW6pbXBnA31AzXAlXjc7+iW1oatx4ZuBD5GPfcbdEtLFWynUxde32RU3bFYXz6C2QR8CRyOx12vRVeA6RPHnQfMyNkud+bE8WeNB/oQ+0PTih9V1PsAPO6fdGlsg3I456NSvWeZaKkemILH/ZIWXeHw+oqBi4ndqAezEZVd8O+WdZxe35GodVbdTbTSCCwCDsTj/lOLrlC8vhzgM1THbibZ2XpglGWOpzKWc4BDMHdONwIz8LgLtegKh9d3GXA75nTWoZ7JEyxzPJXDOQ/oaaKVZuAP4G+WOZ7K4fwYFcFipjRVHXAsHvd8HbLaoRzOmSiH00z/VAfci8d9A0BNUXkGcAVwA8qO3AZMzykuiL8kR5Lb0M0oh/NjktSGGoZxIPBR9/SM268++IgxmLehdcBNeNz3aBEYDj02tB74EBhnmeOpx4a2oJz5/SxzPPXZ0HpgAh73W1p0hbL12dBNqOUHh26tjqcdXqsDNUpn1liCMgwjgFNMawqhcFbpE8DD++590NlSSjPGEtR9sy1q9NwqJgP5mDOWoK7J0+blRMDr2wlV6NyMsSRw/Kmo628VT2OuwwT1gj0EmGZeTkRuBAZiPrt2L+BR83IichRq7ZrZc9oDuBCvbxfzksLg9XUH7sK8zu7AocDhZiV1wAzMOZyg7ptsVBIVq7gQ2BlzDieoc2pd/wRuzDucoHRehdc3uKaofBSqBMq/gPeAoTnFBXeYdDiT3oYGcS/mHE6wyIYahiGAe4DaS/Y7tBY9NrQ7cAdeXy+z+sKiz4Z2Aw4AxprWFBkdNtSJcgZvNi8nIrpsqLXvT1ufDc1CJRQ93bSmFMV2OvXQF33nshuwq6a2Qrl8QL9B9UIIHbU7HcAeGtqJxO6YfwFpJROvL56wjWgYggqZ0UFzoD39qJe6bE2tZQDDNbUVjmHEHrYUCWuMkGII+spONWDVtVezcZs0teXEOp2gtOrAibX36HBiD6uMxPaa2gnHENSsmmmklA0rv2x4BjXbnQmMyykuGJ9TXFCtoflUsaGgbFOy2tATUI7Xjd3SM3ZCnw2tRw2yWIFOG9o6KKofvTbUiXI+rEKnDR2I16errVC2RhvaDWvfnZMa2+nUQzqaDDvKmJkdmQxL4azS5h7de67S2KSul65wxLoGoSNasE6rznaF5vZC0fm867w+oeg8B1bWIs5An2EH6659Bmp9lg6cWHuP6rxeZmchO0Ln/W+lHU5Hj4OEbKaXbMYD3AIMyykumKuj3QApYUMD6Lz/tbVlGEY6UIxaMzgTvfeoJDVsqNX9k85n1Uqdutu2qo/aGm0oWNs/JTWd3khCiA1B/z5GCPGTEGLHoG1fCyGeD/r7ESFEhRDieyFEfeDfFUKIk8K0fZkQ4szAv08WQnwnhPALIdxB+/QTQpQJITYIIR4OOf59IUQ8iREsZ+nyWkZddj6uM09m2Fmn8MDLz7f5/J4XnkUcui8r/7RmWVwkHA5HG8OerDrDkSpabZ36SRWtkXQaM59g+5OOYcTUyYyYOpk3P/koKXW2kuzns5Vk0QmpozVunYKWnrlpU3OKC27KKS6wZr2kDp0JJol0TkMlNrraMIywa6470vrQq7PY44wJDDvrFK5+/EGrtXZIqvejyaYTUkerrbM9QogThBBSCJEXtC03sO2SoG0PCyHOCvz7P0KIXwM+2o9CiGeEEBGjaoQQLwshdgn8+3YhxNJg/y+w/RAhxJdCiOZgn04IMUAI8XY0vyXqUWUhxGHAQ8ARUsolgW0ulON6iBCih5Ryo5TyosBnuUCplDLsGjUhRBoqU9nIwKZvgRNRa3qC2YRKVjCc9uFSz6LW1twe7e9IFGnONKZfeBkjd89jfd1G9jnvTMa492No7i4sXV7Le198xo6DdEVq/PV1QupotXXqJ1W0RtIJcPlJk7jy1DO6WKEi1c9nsumE1NEar07hZGO3gc7fkl1nokkGnYZh9AZuAhYAb8aq9Y81q3n9Qy/f/Pt5MjMyWL5mtaV6OyPV+1FILp2QOlptnWGZhEqSdSqqLFUry4FLhRAzpJThkmddJaV8ObCk7jKgTAgxPHQDymShAAAgAElEQVRfIcQwwCml/CWw6Q3gYVSW7WCWAGcBVwZvlFKuEEIsE0IcJKXs0MuOaspcCFEAPAmMlVIuCvpoMsrxexdVDzIWRgNfSimbA6KrpJQ/hO4UcGQ/JHw89RzUxUg6Bvfrz8jd1aBEr+49cO2Uy28rVwBw+cP3cde0SxB6op5MkSo6IXW02jr1kypaO9KZTPwVzmcy6YTU0RqvTj2pAKzXmWiSROc1qFIjVxqGETEMMJLWx15/haLJU8jMUBHpA7fta7XeDvkr9KPJRqpotXW2RQjREzgImIpyOoNZgcr2PqWjNqTiPqAWODrMLqcBrwft/4mUclmYdqqllN8QfinEa4F2OiQapzMzIGa8lHJhyGcTUWUenid25+8gVM2/uJFSrgEyhRD9zLRjNdXLfuern35gP9cw5nzkZfsBA9hr1927WlY7UkUnpI5WW6d+UkVrsE6Ah2e/xJ7nTOKcO29hzfp1XaxuC6l4PpNZJ6SOVlunXrpCp2EYOcDlwHOGYUT9ThWs9celiymvrGC/C87Cc+l5fL7wO+sEx0gq9qOQvDohdbTaOgEYD7wtpfwRWC2EGBnyeTFQKISIZm3sl0BemO2m/THAh8pE3CHROJ1NqJpUU4M3CiH2BVZIKRejPO2RMa6vHIzy0s2yHNhOQzuWsKGujgk3XcP9F19BmjON2/87k1vOPr+rZbUjVXRC6mi1deonVbQG69ymR08uOH4Ci56bTcVT/2Nwv/4UPnp/V0sEUvN8JrNOSB2ttk69dKHOW1HvctdHe0Bo/9Tc0sKa9ev55NGZ3H3+pZxiXEcy1HBPxX40mXVC6mi1dW5mEvBC4N8vEDLBJ6X8FVWTdXIUbUUKudDhj0Xli0WzptOPqnn1vhDiOinlHYHtk4A8IUR14O9tUPXAnopSYD16MjhlBdpKOpqam5lw0zWcdvhRnHjIaCp/+Zlfl/3OXlPVvVGzYjkjzzudzx77D9n9+ts6/0Ja49EppXQ0/unfbkVRebiRKHMIyDmqffb8eM9nS6PcZpkVOoHtjsjKcjjb941xnlPnbxbpHFSQOSCth2gXdhiqE2BQ3y3BGOeOHc+4ay9vc4yU0tGw0p+z0gKtfYal5/bYwenoTGeU55PmDXLgHxad0+2PytKiE8DfLLv9btU9enhWL0e6tntUWHWPDjwgc3B6b6HtnCaaeHU2rV7V9+c8lyXnNO+Rmekire2rU1edT8Mw9kKF1k03DKM6mmPC9U85AwZyYsEohBD8zTUMh0Owcu2fDOij5hFapHS+XLMp95qiuRu1iQ8wc58+OYcMyHA49fSj4te6lgGji+Zqv/bpAn46alC77fHoBKhvkVkuC3QCVB0xMKtbFDY0Gq1SSg5csHKPZW/NDZucygzzCvoN2KWHU4sNbZHS8eHKxpwpFpzT24f1yj11h25a7tFYCERxjgaGCyEkKtOvFEJcHbLrHcDLwAedNLk3apIwFB3+WFS+WFSJhKSUdUKIcUC5EOIPVDruk4E9pZS/AQghRgH/JHqnswqTtbQCi2OzgWoz7ViBlJKpd92Ka8dcrjhFhTnn77Iry197d/M+uROPwzfjGfr36dNVMlNGJ6SO1nh1ymZ6bFjccg+quLdmUfp0AjSsajkS9Qxrp3m9JKNP287dxDntZZXOjTUt9N49rc3YYTidAMtWrWRw4GVz9ocLGL5z23JispleGxY3hyZR06NzSTPdBzsRQcnk4772fjI21jRfC1xrhVbZAiLIKpm5R5vW+/fGomvfsNpPt0Fto5lMaBVW6dywuJk+w9K1ndNEYkbnOt+n56ASFWqnee2fpAc5jl18Pu8C/kS9dHZKpP5p/MGHMv+rzzl07334celiGpua6N97i9aNzbLns0vqXtYtHmDm4jr22TadbYIGceLtR1vA8XJN/eWocGOtNEVpQ6PRCfDLxmYXFj33P65vZq8+bauHxKvVD44VDf5KK3S+WFPPlbv3JF2DDd3YLHvNXFxniQ3935J6jh2cpeUejZGTgGeklNNaNwghvMDBwNIgLQuFEN8D41Cznm0I+EqXoGY0w2WZbfXHqk1o3R2VELZDos5eK6VcLYQ4CuVJrwR+a3U4A3wADBVCDA63ADUMb6GSEAEqJTAqO+4AYK4QokJKeWTgs2rUTGqGEGI8KoPu98A+wCetyYiSiY8qv+bZd98kf5ddGREY7bzj3Is4Zv+DulhZW1JFJ6SO1nh1Cif1Wf0cT9Uva/nYImltcvmbOZ/p2zg+AR6wQqSzm7iNkCLPJs5pHSFLA3SR2c9xLIKJBNUZi6Tz+XnvUPHzjwghyM0ezIzC69rpzOzreGjTCn+Fbp0ZfRw7CSc3EVQoPu5r76A5s6/zuQ3VLW/p1gkgHMwkaMTVzD2a1t3xIyqjp3bSe4lCwB28zYRWSXShUTGT2c9RIBycg6ZzmkjM6Oy2626lwP+s0OXs2fMhVNIe0zrNYBjGEcARQKFhGGuiOSaS1nOOOY5z7ryF4WdNJCM9nZJrjTazT92com5kn/Tbvl3X/Kvu37F/34w9s5ziUqB7Zzo760cd4N+vb8aLj/5S9zqaCaxFi8qGdqYTYLssZzUWDd7ldHNGbUOjOKcyyyHO2NAiW3Tr3L9fxrFOTTa0m1PUHdA346EFKxq129C9+qTv1M0porKh0Vz7GJiEWrMZzCsoe3FnyPbbga9Ctt0thLgB9Wx9AoyKkOV2LnAo8D6AEOKuwHd0F0LUAE9JKY3AssrZwLbAsUKIm6WUwwJtjAq00yGiK+P2hRCzgaullKFpeaM9/gFgjpQy3HRx4vD6BgKL0Vfw9U487iJNbbXF6/sVyNXU2k943NZkSfD67kOleNZBI5CNxx2VYY4Jr28M8BLQW0Nr64Dz8LhnaWirLV6fAFrQVCgeeAGP25rM0V7fJ8B+mlpbjcdtTaIxr+9K1AyDjoLUa4FT8bijqnUVE17fnkA5auDOLJuAa/C4rSnk5/WtRY9OgHI87kM0tdUWr+9V4ARNrbXgcUc9ABwTXt/pwKNALw2trQXG43Ev0NBWW2wbGhOGYThRiT+2AVyGYTS020mvDV0HHIrHHfpSax69NrQJuAGPO/SF3Dz6begXeNzuzneLA702VAIZeNz6J3e2ThsK8DAe9yWd75YYhBDdgDLgICnjG1wQQnwAHB9I8BqRqEqmWEgRaro3Xr7tcodTsZKgkRqT1GNtuPASjW0t1thWKL/6pb9JU1sSFYJkBUvR02GCeh6XdrpXPHjcEtDldLcAP2pqKxyLOt8laqKJuoiXJUD7l734SMOqa690tl/QGx9NWKcTVEp3Xei8j0L5gfBp4+NhlaZ2wqHzWmWg134Es1JKadvQ6Dkd2Au4LqzDqfjVL6UuG5qFdc+9ThvaQGrYUIBfOt8lbnT2fWstcTgVW6MNbcDaax8zUsp6VFTQ9vEcL4QYANzbmcMJXex0Sil/kFJ2tvC1o+Of1KknbjxuPzAdMLvIvhnVqc02rSkytwJ1GtqpQ03naye/JH/Q2JqiglUt69KbzUd01AF3BQyGFfyIGgEze07rge9Qaaet4hbM6/SjRhRLzMuJyH3ABg3t1AM3amgnEu+gnKRw4SqxUIcKfbFkfQ8e95+osDCz/VMD8BuBEByLuBE9ieE2ANbMxipmomZ/zPYrdbQt9q2bT1AOcrg617FQh0pAoT28sirPJaqmnTF59ftvN/obzMr869tQwzC6AbehbMWL4fbJLZo76FDvyoKVDf70Zr9p07cReBmPe6XZhiKgy4Y2ojJoWhL6H0CHDQV1TvXnbtiCLhtah/rNVrG12dAW1PtT2Oe2K5FSviOljGtATUq5Qkr5WjT7dml47V8KFXpxB3AlyvDFemIzUKMfo/C4f+tsZ1N4fUehbvpM1EMAgN/vd7a0tGSkpTkbhHBEGsV3ol5gTsLjfk+nrPySfCdwAcqgdh+akTvj+cE3HOMQjh1RMyyAWsC9STZ2SxPO5nSRFmkkVwS03gncaKHTCV5fBvCSlPJY6UcIQaNwiFi85QyU0R2Lx63DoEXG67sctUZAEvtMTTrKQByOx/2Dbmlt8Pr2B0pR4SyxjrI6A8ecjcdtbefu9fVHOWHDic9wZqAW9k/A49Y14tser88BPAmcSfz909fAGDzu1ZrVtcXrOw2l1UlQ/xQlaSijfgwe9+e6pbXB6xsKvIfKQxDrPdo64FuIx/2IVl2heH09gTeBA4n/Hn0dFbqma+YMgKo81zDgEcADfLbLLXd/nzko+3RS1IZKKZFNTd2Ew9Es0iLaJlM21DCMIuBfwKGGYXiDP8stmtvGhuZvk/b46wf2HesQoo0NjRKBep7+B0zF49a+pm8zARsKHEPsOkFd+++Bw/C4dZTii4w5G5oGrAeOxeO2KneDwpwNFbSW4fG4rXSOtyYbmo6aMT0Mj7tar7jUwXY6daNuzP7EPou8znKHIxSvb1uU0QTgjXde2qvm98Vv99u2/5RTxp/1boSjGqxYG5lfkr8/au3R3qgO6OLKKZU/BHT2IGRN0mFLL/dlioyP3sy589IITfqBlYFZaMupKSo/D8GMtO7i8exDsm6O8fDVeNxmR/qiRw2Q9Cf2kPANeNw6Rk+jx+vrTexhLU2oc5q4zs3rywLiSU25SveLfId4fU7UtY91XdKfeNymp6GiRt2jfYk97K4OjzuxVcO9vl5AjxiPakZd+0TeoxmocxorK3WH11XluXqiZrUvR72EFwFPuRZW+VPZhgL8VHRZKYKG3f51/4QIR8VtQw3DGAD8DHgNwzgu+LPcorntbGh18diINjQKJOraW+dshuL1pQPxrMFfi8eduNJ58dvQejzutRYoikx8NrQFde1tGxpK/DZ0PR639pJDqYbtdNpsZvrEcdmoNXAXFc4qfTQR35lfkt8PNWp7LvA76iXkpcoplR3emPkl+R8CTZVTKkdZr7JjaorKPSgj/z4wLqe4IHFG2sbGxiZFqMpzCVQ97/uAHODfQJFrYZVVoZsJpyrP9Tiqtnk/18IqrS9YhmE8CFwI5BuGUQWQWzQ3rA2tLh5rv9zZ2NgkFdZkzLNJVZajZolyrP6i/JJ8B6qOWjFqdOtewKicUrk+yiaWoC87W9zUFJXvjEphvQiYZDucNjY2Nu2pynPtBjyMKvNRAZziWlj1f12ryhK+Bqah7Ki2xCaGYeyGCp19yjCMqtyiuaE2dDpwc3Xx2GhtqI2NjU1C6erstTZJROGsUj8qUcgOVn5Pfkn+3sBHqPj474G9K6dUFsbgcIJyOncIOK9dQk1ReS9gDirE5tic4gKrMuTa2NjYpCRVea5uVXmuW1CFw/cHLgX2/Ys6nKCcTlDZZXXyL1QyLyO3aG47G1pdPPZK2+G0sbFJZuyZTptQarBopjO/JL83KvPfRagyM1OAZzsLpY3AYtR6r0FYWxYjLDVF5Q7gv4ALODqnuCCuWrM2NjY2f1Wq8lzjUFmEd0YlpLnKtbAq4f11gqkM/H8vVCIX0xiGcQAwYaNM/9dLDSOuI8SG2qG0NjY2qYDtdNqEUgNoLVicX5IvgNNQKcIHopId/LNySqWZmcHW1M470gVOJyo74HHAP3KKC7Rm8bWxsbFJZaryXLnAA6g+8ntglGth1YKu1JQoXAur1lfluRahaabTMAwhJff83NL/z4+ac6eikpg8BvyzunisHV1jY2OTMthOp00oNcD46RPHicJZpaZHT/NL8tukxAfGVk6p/MJsu7R1Oj/V0F7U1BSVTwauRYU2PZzI77axsbFJVqryXJmosmHXo7KfXgPc71pYlbjs3MnB12hyOn9s7v+PRS39DvxDbgPKhh5dXTz2Sx1t29jY2CQS2+m0CaUGyEKl1l8VbyP5JfmhKfGnAU9VTqnUVcKk1encSVN7UVFTVL4vKuPiB8DFOcUFdliTjY3NVk9Vnutw1ADj7qjkape7FlZpS6STYnwDnFCV5+rhWlgVV5mE3KK5PQXyZsi9Ip2WFgf+i/04nqguHpuQMmA2NjY2urGdTp2o+mJ7oBLxxJrgZiNQhcedmNTxXl8OsBshNcYOOeCI/ktqfmHPYfucgtf3a5gjm4Bf8LjDfdYaStsuJX7llMr4fpfX1wcYiipwvJnK3Jlc+Md9ddlpfQ/G6/s2zJESlT5+oa4aTjVF5duhiqTXAiflFBdsGb33+jJR6zuz42h6DfA9Hrf1SSBUfbFdgVziqTEGP+Bx1+qWFRavLxv1PMVTY2wx8FNC6oypOo1DgW3jOLoW9dxbV9S6Fa8vDXWPbkfsNcbWoXRqr9EbFq9vZ2AXYq/T2Yi67olxdlRhcxex1+n0owb4FiaklrDX1x2lc0AcR69E9U9ha2BW5bm2R2UfPwVVQ/Io18Kqd+LUmfI2FGDgyZMdG7+vFL33O/jveH0/hHzcoQ3NLZq72YZKRM5uzhXs5lxx5qO3XPVcnDrD2tAo0G5DO8S2ofqxbaheUsmGJil2nU5dqMLbpcBBKKMSDxnAKXjcWpIPRMTrK0StSWz3kPr9fmdjU2PP9LT0jU6nM1JB8ExgBnB5cIeUX5IfnBL/a+DCyimVH5vQuT/wHqoDbMdGf30vgaOluyMzUkFwJ/AT4DFrjGqKyrsBXlSHc2BOcUHl5g/Vi+fHKGMZzwukCBxXgMcdzoHWg3qhewY4EfVyHg9ZwDQ87hJtusLh9Z2BCl/eFGcLGagBgtMtLW7u9Q0FPiT+TOAOVKmiA/C4V2jTFYrX1xNYgHoBifd8pAFH4nF/pEtWO9QL3d2oRCnxvkRkATfhcd+pTVc4vL6jUTN68T5LacAnwFhLX5i8vh0D39MD5UjEigDqUPdodevGqjxXOvAPwED9ljuAu10Lq+J7Zv8iNhRA+v0O/6b6XiI9o96Rnh7u/ghrQ3OL5m62oQL5zVEZC3cY5NjwNTDaMIzYr10nNjQKtNnQDrFtqH5sG6qXVLGhSY7tdOrC65sDHE7sI0qh1ANH4XF/YF5UGLy+c4H7ge4mW9oI3IfHfUN+SX431BrHa1Ad3A3Ao5VTKiM5rdHoHIZ6UeppUucm4Dtg33hH7GqKygUqU+0k4ISc4oLXg3RmoEoB7ITqpONFokbC9sLjXmyinch4fTNQCZ1inZUJpR44DY97tnlRYfD6jgNewPyzVAe8gMc91byoMKiZjkqgN7GPegbThKrnNwyPO94XhMgoR+4TYE/UC48ZNqKMe2Wne8aD13cTcBXm79E64Eo87sfMiwqD13cQ8C7m+9F6oAyPe6x5UWFQs1xVqBnOWGdlgmlBLbdw4XGvrspzFaASwg0H5gL/cC2s+sWk1q3WhuYWze0GXAdcTcCGnpHpy3YKeS3gNgwj9jwISWRDO8S2ofqxbaheUsmGJjl2nU4deH3pwDGYf8AJtHGyhnYi8XfMG0tQne6U/JL8cSiDdAPwEpBXOaXyQVMOp+IYzBmgVrKAYcBgE21cA0wG/tnG4VS0hgOZ1SpQo2CjTLbTEZMwbyxB3aNna2gnEmeh51nqjgr5s4pRqGtmxliCCiEdgLqXrGAgKqmJWWMJ6j4/RkM7kTgbPfdod8CaFyXFyejpR7sBRwZCC63gbyidZhxOAsdnbVq6+IiqPFcJak17L2A8cKwGh3OrtaG5RXNbbeg/UTZ0j7OyPn/VKeTlwHNxOZyKZLKhHWHbUP2chW1DdZJKNjSpsZ1OPfQj/lCLcOyisa1Q4lkvEZYG2bQd8AZqJHR05ZTK0yunVOoqX7IDegwmqGsTl8GsKSo/DhU29jyqOHco8YYDhSMLqwy7GqnrpbFFS2q5WtB2j8ALrRUMRo8RAnUPWfVSN5j4Q1VDScfa5F39NLY1SGNboeRqbKuB+NZaRkM2muy8v7k5c82C959GvXjfAQx1Lax63bWwSsfs19ZpQ1tkqw2tB0ZVF489vbp4bC2qnrUDlQU4XpLChkaBbUP1Y9tQvaSSDU1q7ERCFrF0eS1n3mFQu3oVDofgvHEncOlJkwB46NVZPDz7RdKcTsbufzB3nf+PpNNpzHyCJ+e+xoDefQC449yLOGb/g9oc65d+J4GU+JVTKi1PiW9Ca7xhtfmoguZfAFOjzVSb6tc+2XSa1JrQ9QOpck5N6jQ7Km1aZzT9UyJ1atCa/DpbmjMd3btXASe6FlYt7CqdE2++lh+WqOjJPzdsoE/PnlT8O74cO12ts0XiRIXUPlBdPLYRwDCMPYEpwL2GYVQniVa7H01hnSa12tdev86E2qZkolOnUwixQUrZM+jvswC3lPLiwN/nAVcEPl4HXCGl/DDw2QLgSimlr4P2BwNPSinHCSH6AS8D+wL/CfqO7qiwkyGotSVvSCmLAp9dDGyUUs6M5YdbTZozjekXXsbI3fNYX7eRfc47kzHu/fhjzWpe/9DLN/9+nsyMDJavWZ2UOgEuP2kSV556RsRjM0VGfeWUyrtSQWus1BSV9wfmoMq9jM8pLqg3qzNVrn2y6UwlrbbOxOgE/c+8WVJFa7w6RVp6w8ATJ94z6MHpljucHemcddOWgJPCR++jdw+zSxbNYUZnlpP66uKxd4dsvgv4E7g9mbQmklTvn5JNZypptXW2RQhxPWp5VwtqNnealPLTwGcDUBmmL5ZSzgg6phr4Qko5IfD3ScA4KeVZYdrfG7hISvl3IUQeMBMYCVwvpbwnsM8OqARarVEJT0gpHwh8dg/wppRyfjS/x9RMpxBiHKr+4sFSypVCiJHAa0KIv0kpo00JfQUqwxZsSUIzPPBfMPdIKcuEEBnAPCHE0VLKt4CngY9QJyppGNyvP4P79QegV/ceuHbK5beVK3iy9DWKJk8hM0NFvQzctm9XyoyoMxocQiR0BMyM1lioKSrPQA1+DAYOySku+E2HzlS59smmE1JHq61TL4l65nWQKlrj1SkiZzO3hEg6h+aqyFkpJS+Wvc/8+6zJFRUtZnSG2lDDMMYARwKFhmFoL62Q6uc0VfqnZNMJqaPV1rkFIcQBwDhgpJSyQQjRn7Yh8yejEhxNQmXDDsYthBgmpfyuk6+5DpWJG2A1KiP5+JB9moFCKeWXQohewBdCiPeklN8DD6F8uKicTrNrPa4BrpJSrgSQUn4JlKDS3UfLBODtwPEbA7OkbbJPSSnrpJRlgX83Al8SiFmXUtYB1UKIv5n8LZZRvex3vvrpB/ZzDePHpYspr6xgvwvOwnPpeXy+sLP7IXEE6wR4ePZL7HnOJM658xbWrF/XxeraYpXWQKbaBwEPcE5OccFnunSmyrVPZp2QOlptnXqx+yf9pKpOgPJvvmLQtv3YLWfHLlTWFjM6DcNwosoFVQOPWKkTUvOcpkr/lMw6IXW02joZDKyUUjYASClXSil/D/p8ElAI5Aghtg859h6UQxmRgAO5p5Ty60D7y6WUnxNSskpKuSzg3yGlXI/KiL594O/FQD8hRFRr3aNxOrsJISpa/wNuCfpsGGq9WzC+wPZOEULsDKxpPaFRHtMHOBaYF/KdBdG2kUg21NUx4aZruP/iK9imR0+aW1pYs349nzw6k7vPv5RTjOtIhrI1oTovOH4Ci56bTcVT/2Nwv/4UPnp/V0vcjMVaL0TN3hfnFBeYWiiUqtc+WXVC6mi1dVqrcyvun7SRqjpbeX7eu0w67IguVNYWDTpPQ2XIvM4wDEsL3afqOU2V/ilZdULqaLV1Aqos1w5CiB+FEI8KITytHwRCXrOllJ8BLwITQ459ERgphNi1g/bdqHJFUSOEyAX2Bj4N2vwlqr5yp0TjdNZLKUe0/gfc2Jkmol94PBiIOv5ICJGGyiL6oJQyOEX7cmC7aNtJFE3NzUy46RpOO/woTjxkNAA5AwZyYsEohBD8zTUMh0Owcu2fSadzUN9+OJ1OHA4H544dz2dVyTGqZKXWmqLyw4AHUNkEzWQNTOlrn4w6IXW02jqt17k19k86SWWdAM3NzbxaXsbEUWO6UN0WzOo0DKMbKsTNB8xKZq2JIpX7p2TUCamj1dapkFJuAPYBzkP5SrMCeXUATkU5lqBqsk4KObwFFTlxbQdfEasP1hN4BbhMShkcChO1D2Y2e+33qBMSHMs7MrA9GuqJLV3yE8BPUsrQIdisQFtJg5SSqXfdimvHXK445bTN28cffCjzv/qcQ/fehx+XLqaxqYn+gWyByaRz2aqVm+PVZ3+4gOE7D+kqiZuJV+smSdYF0nHxW2UVf0Rqe1C9f9vD98g8s8nBmjnbpf+8KU3cQVlFp5rOxrHzTcKflRWUiyzea++XOF5CHHlpWUXvzs9GbAicLHO0tNmWKvcoxK9VSil+uGTq7bKpSVdK/s0MPvPv+/U+oMAhHFvG7uK+9o2NWctnzzprzbQzDtGts+eeew/c/ryLMx3pW5aCmLn2G77/dr+l084o1q0TYI+Hn85wpG/Jzm+mf2qpq9vmxzyXJTp3vv7WoVk75rbZFq9Wf3NzevW/brq2YdqS9bp1Djj+5OF9xxydqeucJpJIOgHe/+Iz8nbciZyB4aviVK1r2uPoormWXPuvDx/Qp3d65898NDqDuBRV4uQMwzC091WtxKt1k78xq2jljIvnlXwZ0YbGy6m9Ru9cuO3ErCyH+f7JL/2ONzZ+fOQ/S862wIYKvsl9us22rcWGLvCfebt/fqP2+9Ilzt9vsPA4hDBvQ1tkY9Yi+dxZS+dP1G5D+7PPwOGOyzKdQo8NjQUpZQuwAFgghKhEZbb+D8rJHCSEaP3y7YQQu0kpfwo6/FmU0xlpBDFqH0wIkY5yOP8npXw15OOofTCzTuddwJ1CiKOklKuEECNQRWn3i/L4H4my3pkQ4jagN6owcyi7o5IJJQ0fVX7Ns+++Sf4uuzJi6mRApaA/55jjOOfOWxh+1kQy0tMpudZAiK7LnhxJ5/Pz3qHi5x8RQpCbPZgZhR2GhieEeLU2QubviMlEmoGXktWZIuPFHdJFkwMHQlwYraZqcDRAevBTG++194P4RYqDgIxAlwoAACAASURBVP2j/f5oCffDU+UeNaVVSiH9fkvyqjf+UetE+h3BASPx6pTNzZlNK5aPR1+9us00rVktZHNzBkFOp5lr37Ry+Z7AUN06AWRLcwZBDpKZ/snfsKkXcJkVOpvWrE4PdTrj1trSkt6yccNUK3Q2Lq91yJbmdF3nNJFE0nnM/gfxwvx3mTT6yIjH/tHg3xmLrv3GZpnRO6hqoRmdAIZhDEC9GL5hGIbXCs1mtTbRnFnbvDqyDTXB0uYVjiaa07Mw3z9JpFjc9IdFNrT9T98qbChSSFossaF1LHNK/A6hwYb6acqsk7WW2NBNrBKS5gw03KOxIITYA/AHOZIjgMWB7T2klNsH7Xszavbz1tZtUsomIcR9QBHhE/1UodaEdqZDAP8GqqSU94bZZXdUhZHOf1NnscZRlEy5ANW5S1R5iUIp5QeBzxYALrYsSv0/KeXJIe3PQ6UA/jnwdzWwDerq/gkcgSrFshRYyJYCrQ9LKZ8KHPMlcERrQqOE4/VlA78A3TS1+CYe91hNbbXF61sM6MoSUIfH3UNTW23x+h4ELtHU2lrgMDzu0PXH1BSVO1HhtGOAMTnFBQtiatnrOxIVDqVjZLUFuAGP+1+d7hkrqrC1zs74KzzukRrb24LX9xmqbJIO/EAWHndTp3vGitd3NaqsgY56x2uByXjcb2poqy1e3wjAi+pXdfA4HvcFmtpqi9e3HtBVr+E3PG5rCrB7fa8Bx2tqrQ7YA4+7RlN7W/D6zkQlpdFxTjcC/8DjfrrTPWNlK7ahRlnpv1HJF/MNw4g2SqxzEmRDTWPbUP3YNlQvXWRDhRD7oLLD9kFlkP0ZFWp7MZDVWjoysO+ewAtSyqEBP8odqCqSCfwKvBuhZEolcKCUcn0gGZAP9Tv9wAbUAPOeQDlQyZZn4Dop5ZuBGdBvgHwpZacZzju90MEOZ+Dv/6Cmdlv/fgwIm19bSnloZ+0DD6NmR/8ZOCY3wn5hhwoCNWa+6zKHU1GPnoemFe1hVkHoDEO2MtnBBtRAho7hQQeRf3cxcDRwfswOp0Ln+WzS3F4ozei7T+s0tWN1237Ui4gV1KH3nFp17eswn6m8FYkaBLSKBvQ5nVY+Sxs0tpWGdVrr0fei7Me6536rtKF+9ZJ2AfCUVodTkSgbahbbhurHtqF66RIbKqX8AjgwzEdGmH2/IRCBFOxHBRK1drTe8mlUEqKnAqUuww3UfkjkfmQc8HI0DifoO4lxI6WcjUoRHi/9UbU9uw6Pey36XsTqAVNlOjrhK6BRQzstRL92Nx6+QV/HmY4aRW9DTVH5FOBK4JGc4oLQGkfRspDY1iV3RAsxZhKLGo9bAovQEx7VAHyuoZ2wNK1Z9bO/WcsaTAlU43FbtUbqW/QZ40zUvWQF1eh9UfpGU1vh+A71EmKWRqDzRdnx8zn6XnDqUPXRrOA7VP+ngzQirw0yRe5bf6xraJGbOt8zKlLGhq7cuKEJ1Z/epKG9UCy3oZqwbahmNslVP/ulbUM1Uk3q2NBYeQxzE0hpwPRod+5ypxOgNUw2zmPfk1JWa5QTL8dgfvS7HjWi8LB5ORG5CPUAmQmVaEFlvJqsQ1AEXgRexrzRrANOxONu80JTU1R+ICox1Xzg8rhb97iXo2bqzb6AbgQep20pIN2MR4WsmzGaDagQC+0DPVV5ru5Vea5bF914zRkNNTVStrSYcT4kKtxGVwhkOLyoZ9XsPVoPnIfHvcy8pDB43I2o86DjWXodlUHcKk5D9S1mXkSagCWoGSSreBR1/c0+9xuAowMvtPrxuL9HrRfUce1vwOOuNC+qLblFc/cA3j3l09W96luk2ZfblLGhLX7/n//75tN+wN2GYdRqUdYWS22oNmwbqo1584d0nzd/yK3/57/8jA0skVLaNlQLqWVDY0JKuUlK+ayJ41+SUkadnrfTNZ02MeD1jURloduVCA59gyRjIQwXIPPg2wyx2XCtA/4PuCNwg1upsy+q9M1eDbJpmx8al+zd17HNspz0AUtad1m3fu12Gzau2zF74PafOxxt0p42omY478DjXmqxTkelZPoKuCQb6vKgyiHah4pVNvyyb29HjxU7pg+qDtqsRujgCTzuBcH71xSV74AaZdwA/C2nuMD8LIPXNxY4k/jW+qwE5gIzLHv5bMXr2x24GtiDoJG73yTbLYMdR8DnaSLiC38dKt7/VjxuneGFVOW5jgUeRCUW+2+vvd035px/6TRUHalY1w03o5KU3Y3HbdXIp0Kt8zkXFWIyII4WlgLP4nG/oVVXOLy+Q4DzUec41pC7P1EDNNMtHPVWeH05qKLWwwjO3BAdm1CjyLficVu75MLrS0fpPJDY1/r4UetzHrRknVwoXt8k4CTiKy22DHgFj/t/OiXlFs3tjlpWcyWqb7n+uzEDPu2R5riEDmxoB3SJDaWTWbqWTfU9Gn7/LT9jwMCf0nptswpolFJ+/9CnZe7V9XU5wG6GYWjtT4N0OqoaFk9f5V93yUBnn7pd07evcghHtM9vRBtqCSluQwEWN9Xuss5f1294xs4+IUQ4HZbZ0Hnzh7SxoQP42417OgttG6qTVLGhSYztdHYB2WUVbtSiXB9wWO2oEdYayA7IL8k3UMZzt8oplYtat0+fOO5U1GjMsMJZpVaG0UYku6wiG+UcSmDf2lEjwqZrzy/J/wb4tXJKZacjcjVF5T1Q534IsH9OcUGVRskpS3ZZRev1Hl47akTCCvRV5bl2RtVGPRY1mHGRa2HVgkR9v42NTWLJLZorgONQz/1OwDPA1dXFY7WX40gGqvJcWagBzn+5FlbdAGAYxgnAq8A0wzCesOq780vy29jQyimVf8lznCzkl+SfhMrieVDllMqPE/Gd8+YPaWdDDxu9aEEivtvGJlaSIrx2a6N21AgfKpzkYODR7LKKLsmlnV+Sn47KhPV2sMMZoDWTojXZHzshu6wiC5gN9AWOi+RwBlhCFKOjNUXlApiJSjs9yXY425DQ612V58qsynNdjzKSo1EjxyNsh9PG5q9LbtHcXVDZwl9DJfs5pLp47JS/qsMJ4FpYtQm11mwvAMMw0oE7UeUK9GcCDpBfkt/GhtoOZ0KYj4piOMLqL5o3f0jmvPlD2tlQ2+G0SWZ0ZouziYHaUSNmZZdV5APXo+L7H+gCGccBg4FpYT5rDZ3dIXFyFAEnfAaq1tZJtaNGdJYQZDFwQBRN3wCcDFyVU1ygP612apMwp7MqzzUGtY5jd9Sao8tdC6v0l4uwsbFJCnKL5mahXoqvRYXuXQk8WF08Vn8ZhuTka9QgM6iB3t2AYw3D0JE0qx35JfltbGjllEork2rZBKicUrk6vyT/c+BIwmQY1cW8+UPa2dDDRi+ybahN0mM7nV3Ljah1S/dml1UsrB014p0Ef/+FqFnCcA7YMlRITlfMdBai1nbcVDtqxCtR7L8E6Jtfkt+zckpl2HUSNUXlE4CbUaFcUWfa2or4HYuvd1WeKwe4F+X4/wwc5VpYleh73sbGJoHkFs09ClVrbldUcpsrqovH/ta1qhLO18DkV8efsCMj9roJWIBag2gVm21o5ZTKaGyojT7eAf6ZX5K/beWUyjU6G543f0g7G3rY6EW2DbVJGezw2i6kdtQIP3AGKm30rOyyij0S9d35Jfl5qJCMGZVTKtsljimcVdoI/EGCnc7ssopjgLtQ6yJujfKw1gRIYWdla4rKR6CczU+AaTnFBfZC5hAC64otud5Vea70qjzXlagQs2NRgy35tsNpY/PXJbdo7g65RXNfBt5ChRyOqS4eO3ErdDhBOZ0s2WnH21DJUq4yDMMSO5Rfkh+PDbXRx7uod+vRuhqcN39I+rz5Q9rZUNvhtEk17JnOLqZ21IgN2WUVx6MW+8/JLqvYv3bUCK2jYxE4H5Xy/d8d7FNDAp3O7LKKocALqPp6Z9WOGhGtUW51OndCrZPZTE1R+SBgDqoO3gk5xQXWpH3/a1CD5nDqqjzXIajyEsOAUuAfroVVv+r8Dhsbm+Qht2huBnAZ6sXYgVpCMr26eKyZWnCpztd13bqxtnfvU4HnDcPwWfEl+SX5bWxo5ZRKe4A18XyKyqR8JGB6lnne/CHtbOhhoxfZNtQmJbFnOpOA2lEjqoETgZ2BF7LLKiwdDMgvye+BSmT0cifJBRLmdGaXVfRDOYd1wPG1o0bEUg+p1elsk0yopqg8E5UhsD9wfE5xgRW10P5KaLveVXmuQVV5rmdQdbh6Ase7FlYdazucNjZ/XXKL5h6KcnjuRNVLHFpdPPaOrdzhxLWwqvabvfbcBDhRZXa0k1+S38aGVk6pNFtT0CYOKqdUNqPu/SMCa2vjYt78IYPmzR/SxoYeNnrRsbbDaZPK2DOdSULtqBHl2WUVFwJPAncDl1v4dacCvYHHOtmvBjjUQh0AZJdVpKPW+uwAHFo7akSs9T+XoYrJb3Y6A5lqH0PV0Dslp7jgS01y/8rUAKPMNFCV53ICFwC3Ad2BO4DbXQur7BcgG5u/KLlFc7OBe4DTULUdj60uHlvapaKSCMMw9mSnnbJ2+eWXP8589tlq3e0HMtFvtqGVUyqtraFt0xnvAiegEv38EMuB8+YPCWtDDxu9yLahNimPXadTJ15fHvB3VNHgeApbf5zjd+7WjLgE+HvtqBEdhb7GRX5Jvujl6Pbl1G3GDT6n99EfCiG6Rdp31ZoVQ9auW7PHTjlD3nU6/5+9Mw9vqtr68LuTtszzYNGqlYo0SrRIBBVLaIsKVlHRq+JUFEVGZzHOURziPIMTauR+Ko7otQ5oW2KdrkYtRm29WK1YtTIPpaU0yf7+2CmkbdImOSctxb7P00dpztlZPcP+7bX32msZG7Ls1aH2oD6G1bJGD5uSi0oeA+agQmqdOz9QRYOnofZG9G+pjS9qf8jqaei+YUSXA1YC1Ff7U7ev8x28Y6N8ZsDlR1+sh50toooGn4kqGhwta4D3sVqW6mpTKFzu/VCCdjBNJp1WSdJ+Qwy3IpcnCkJlVawGvkYVtG8WplyabjoSFQY0EvgImGsqK41KcIPs7ALMA45AzfJGQ8Mz+jBWy/qYvj8aXO4zgEnA4CjP9APlwPNYLfHPLulyj0IlF0kj+sLWq4HXsFoKdLerKS73INS9HwF0ifLsraitCo9itcS3/rHL3TA4HAv0jvLselSpg4VYLfHPOulynwCcAuwTw9m/Ai9jtXzS9INUW34CKiHdAqAraoXzrgpHbm2MdmrWUNQ1bZanQFdc7l7AZajyW2E1tIHVmzeM3lFf33/wmjX0Sj9kmTAYvgEewmrZpoc5Zqd5p4Z68jwxaWgIfCiH6Wmslv/pYWeLaNPQ34E3sVriv8exBQ1tYJt/e/evt/80fr/EvX5MTUyuCPqoRQ0tKExrpqE52eWdGtoyvwGvYLWs0NWmUGjT0L+BfKyWf3Rir06nUy9c7hHAJ6jBR6whFTV+yXv7SmNvH2I8kF2VldFM6LVw5ksnjbcPmFZ0UNK+XqMwxLrS7UWtih2J1aKp9ldyUcklwBPA/VVZGVfv/ECJ5YPAxajZvpiQUtYIISZhtXysxc4WcblPRM0ytzr4aIEa4C6sltv1MSoELncqar/JAFSYVyxsRyVkmtQgmqXppgHAXah79Sdqlf5VU1lpbJ2LEst81GC+a4x21qNWXI6Kq2i63Nei9q7F/IyiBiLHYrV8oY9RIXC5x6KyKvbQ0EotcF5cRVM5nF+gohZi7Z9qgY+ByXFzPJXD+X+opB6x3nsfasJpDFZL/FamXO5pqIGs1v7pNKyW9xt+kWrLPyrQbgZqZWduhSN3lQY7ddFQVDb2s+LmeCqH04XaY5cUYyt1qFJp47U6nmaneaeGevI8emuoBDYDY7FaftRiZ4voo6G1wAysln/rY1QI4qShBYVpITU0J7u8U0MjowY4G6vlLX2MCoE+GloD3ITV8oA+RnU8Ovd06seHaBNLgO4GwaTvhO9D1Mv+RnJRyf56GNfAZf1Oe3po4t5ocDhBDQb3AV7UYktyUcl4VK2p94Brm3x8KmrGW0tHhBCiO/BuoBPWH5d7INrFEtTfeV1gtjdevI02sQQlYGOAW0vTTYbSdNNFqNnwC1Gp3NNNZaWvxOxwKm5ChUXHKpYAiajEUs7WDowZJUJaxRLULPSHAUdGf1zuRLSLJahnfAku997ajQrL/6FCBLX0T92AcYBNF4tCMwNtDieo93AQsEwXi0Lhch+EdocT1N/5Bi53n1Rb/sBUW/4zqFXFQajyDRM1OZwKXTQUOAG1EhUvHkWtcsXqcIJawR+Bcgpjxuw0jye+GipQW3Heb+3AmNFPQ7sBT+NyH6DdqLDoqqEFhWmGgsK0Zhqak13+SswOp+KfpqHdgZdxuWNZKW0d/TS0O7AAl3u0dqM6Jp1Opx6omc8BaBPLBroPEIwEJqNE7e3kopJoQyNCYnaaByQb+6d1MSTq0VwicFisJycXlQxFFTVeBUytyspoOis9Eu0i1IAktpCdSEgH9FpNMaDhmraImvVOR5tYNtDNV1szAfgUtQf5R2Ckqaz0KlNZ6VYd2j8afe59EnC4Du2E4zD0eedB3ftYQh8jQc+Jqx2oAXe8yED1LVrpBhylQzvhOArtAyVQzrVJh3bCYUatWGhGSrnjuu+3XIsaIOehcg+kVzhyX6tw5GoLmdJZQ1F9SLwYTfRh36HoGmgrJsxO81BUdtRVwNQQpc/00lAB7I3LrZceN0VPDa1DOfP6o7OGemVzDc3JLr8qJ7u8U0Njow51f+KBnhoqgUN1bK9D0el06kMPCLkHLlb6VGVllKH2N4wAXkguKtHjXk3rauii1wsOMXZqyUUlvVEzhgZgclVWxuYQh/VBv+fTR/R7GiJF68xXMInEz86G9nWhfu3aw4GhqMGn1VRW6tGrbfS9BvEaKIGyU69r6iV+974n6h3QA0F8n1E971e0+yzbq+34RGEoeqBTP7rNJ3tWbPNehwoLzahw5M6vcORW69E2cdBQHdtqip59fkzvktlpbtBQAUz25HniraH16DPJEgo9r6eBDqKhtfzdSENzsss7NVSf9uKBnhoa73Hebk2rIUxCiBuAs1EX3A9cIqX8rxAiEZU84DTUDEMNcIuU8r3AeSOBb4CJUsqQm7uFEAKVWvoUKeUWIcSzwInAGinliKDjFgAnB75/DTBNSvmnEOJE4Agp5S2x/fnx4/c1VZx/p52qDesxGAQzTjyVy06fypm3XsdPq38DYFN1NX179qRkcego1aqsjA+Si0quRoVc2FFhCDFhdpoNwKwkEuoIGuSEsxPg0TeW8tibr5BgNJJ75DHcM/PSWL9+J8lFJUbg36gZqeOrsjJ+jvRcPa5pW/BPsNPQo8c6YLiprHRTW9i6va6OcZfNoK6+Hq/Py+nWHG694BIee+MVHnrtJcr/rGTtsg8Z2LdvW5izx9r5619/cNZtN7BhyxYOP2g4S66/jaREPccE0RHuGS1Z9RMzH3CwfUcdCcYEFl5xLaNNh7SbnR3J1ljtlBLDEf2T7vx8Q/2Nmlc2Ndi58uf/MfMBB9W1NaQmD+H/blxA7x7tN4Zr6/tudpobaagnz6NZQ/eUZ3R3sTMSDU2g5zpgeE52eaeGBtH53jcmlA+G2jpyAMp5HYRK9gYqqduLgEVKuS5w/njgainliSHaHgnMkVJeJIRIB55DrWzfIKW8L3BMV1SOhC4on/G1Bp9LCPEycJOUMuKtFS06nUKIo1BO4OFSyjohxEB27WNYAAwBRgQ+2wuwBp0+FZUUYCoqFjoUJwArpZRbAv9+HrU/4YUmx90rpbwpYNOlKOdrJmqj9AIhxN1Syt0qnXSCMYH7Z1/O4Qels7VmG6NmnM+xljEsveWuncdctfBB+rT+0jyECpG6Kbmo5IeqrIxYM5xOANK6G7qsJ8jpDGfn3xs38NYnLr5b/BJdkpJYs3FDjF/bjNtRe6HmVWVlRJUJU8drGlf+CXYmDRj4e1s5nABdkpIofGARPbt3p97r5Zh5FzFp9NGMNR/GiUcdw/jLZ7aVKS3S0e184NUXueL0szkr5zhm3n8Xi999i1knn95udoZ7Ruc/+Si3TLuISWPG8u4XnzL/iUdY8fCT7WZnR7I1Vjt7JIgtVw7rWXDlRePbJPtgODsvuvd27pt1GdaMUTz77tvc+/ISFkyP5xbO2OyM433fqaGePI8uGrqnPKO7i52RaGg3Mej3tnI4oeNoU+d7v4twPpiU8tTA5+Np4lCqtbyIuR7VnwBsAC5FZTkPpg7IllJWBxYbPxFCvCel/AJVlnA+KgFWRLQWejEEWCelrAOQUq4LrDB2D3zJvKDP/pZSvgI7VzBPR6XqPi7gKYfiHGBntikp5ceoP7wRQU4pqFAMGfi9BFagbspuxZABAzn8IBVe3qt7D0z7p/LHurU7P5dS8krRR0zNOb7FdqqyMiQqKcJnwPPJRSWjYjRpNrC2m+jSyDkPZ+eit17HdnYeXZLUHMPgftFmW29OclHJOagZmqeAx6M9X69rGm867dQfIQQ9u6vornqvl3qvFyEEI4cNJ3VIPPPaREdHt7Pwm6843ZoNQN7EXJZ94mpPM8M+o0IItmxTST83b6tm74GD2tNMoOPYGqudhugGM3Gz86ffVzPuMLXN7FjLaF7/uKhN7WpKW953s9McFw3dU57RtqZTQ/Wn871v/DWE8ME0mg6AEKIXcKiUcmWg7TVSyq9okgNAKhq2USQGfhomHouBCUKIiBP/tXbgcuBmIcT/UDWDlkopXcCBwOomzmAwY4FfpZTlQogVqBXNN8Icd0kkhgoh7kDVx9lM4wL2biATlf1st6Tirz/5dtVPjAlaYi/+7lv26jeAYSn7tXp+VVZGXXJRyRRU/bm3kotKjqjKyvgr0u83O837omZG7xFCnB2JndcsephiTwk3LF5E16Qk7pt1GUekxx4ikFxUMhpYjEo1Py/gTMeM1mvaVnTaqR8+n49RM87j5z8qmXPqvxhzcHzyRWilo9qZtncKfXv2IiFByULKoMH8sVaXUry6EPyMPjT3So6/Zh5XL3oYv5R89pjuJY010VFs7Yh2jjhgKG9/+jEnH2Pl1RUF/L5GU9UuXYnn9TQ7zY001JPn0U1DO8q97yh2NtCpodrofO/D+mB6YEHVYm0VIYQRVV/2QOBxKeV/AaSUfiHEz6iEUF9H0laLK50B73YUKj38WmCpEGJaBO1OBV4O/P/LgX+Hor+UMqJMXVLKG6SU+6JS6s8N+mgNsPtM0zShuqaG0265lofmXtko/vylguVMzTku4naqsjL+RmW07Qe8mVxUEk0q7BmohANh1/ib2un1+di4dStfLHyOe2dexhn264m1pmtyUck+qLIAfwKnV2VlaMpUp9c1jTedduqL0WikZPGLVL6az5elP/D9LxFvZWpTOqqdpat/bXZMlKE6caPpM7rordd5cM6V/P5qPg/OuYLp9yxobxN30lFs7ah2Pjv/Zh5f9iqjZpzH1pqadt1zHEw8r6fZaW6koZ48j64a2lHufUexs4FODY2dzvc+Zh8s1EA91O+GBNqMxA6flDIDSAFGCyGCZyui8sFazWwW+LIVgY2jc1GJg34G9gsszzYi4BGfhvLOK1A1rSaFOhbwCiGiza72YqD9BrqiigLvdtR7vZx2y7WcM2EiU8Zl7/y91+vljeIizsw6Nqr2qrIySoDzUDWenkouKml1RGh2mpNQtbryPXmeikjtTBk0mCmZWQghGG06BINBsG5z9NsPkotKuqHEshcqU+26qBtpxVaI/ZrGi04740ffXr0YnzGK97/8vL1NaZGOZucXP37PpuqteL0qiWjl2jXtHr4GoZ9R5wfvMGWcCnj51/gJfFkWv7r10dBRbO3Idqbvn8ry+x7j66eWMDXnONL2jlelociJ5/U0O82NNNST59FdQzvKve8odkKnhmqh873fRRgfrCXWoxanGugPhOozaomyjquUchNqS+PEoF9H5YO1lkhoOOAPykyUAfwmpawRQiwGHhFCXCKl3CGEGALkoDznlVLK44PacaI2py5p8hU/oVJGtzjdIoQYFmTDZKAs6OODiHCJuC2RUjL9ngWY9kvlyjPOafTZR19/Sfp++5MyeK+Q525f58tZZyuuCvWZG1h4YFL1s2ldzpu1qu6Uyg+KW0ygtEw81HWbobZPT3/3sZW24qrkrK6DErru8lXD2XnKMeMp/PYrxo8cxf9+/40d9fUM7BNdVrOAU/wsaqbmlKqsDE33Scs1bUtitVP6fcZ1+W9dv+6S8y6Lh12mJxu/fh3legKs3bSRRGMCfXv1orZuOx99/SXXTj2/vc1qRqx2+r2y15rP61ze94r1Ssu+k8TeImHQ6C69DIm73vtwdmaNtPCaq5Czco7D+X4+J48dp7c5URHuGd17wCBcJd8wfuQoCr/5imEp+zY7t2RT/RGn2PJD9qNaeW5U375ZgxtXOonVVr+UhqFxsvPMlK5dbzb16tEjYdfcrpZr2paEs3PNxg0M7tcfv9/P7UueZebk5mOw8g1rc5bY7XG5plcelTOod9ddVSTieT3NTnMjDfXkeeKioa0/o/VdP/HP+rG+cKvuSaQGcHjSCMO83gliV0WWWO30ytqeP8lnF1UVfvyg3nYC5Bgb53H8J2joVn9Nn6Od5ri8SzP6nNRjVt+TExLErtKnsb731f6a3tevfebfRc5v9ar5upODk1ITFifP79XT0DbvfQPhfLBWTluBWpy6ObAIeC5q0qoppcBVEdgwCKiXUm4SQnRDJSW9O+iQg4AfWmungdb2dPYEHhVC9EXV0PoZtcwLcCMq69GPQojtwDZUVtmpwJtN2nkdlQynqdOZD4wPtIsQ4qXAvwcKISpRJVgWA46Gi4+64MFptrKA6yL4W9uUTz0rWbL8XcxDDyRjutpGeefFczjhyLG8XLicqdnhN5YbEsXfwHvhPp/5KGxrvwAAIABJREFU8w6+7m88dtGBSUP71MtPTv+9fnW4Y7/o6Zm8w1BvyNk8+lVACgPnEFQjKJydF54wmQvvvo0R084kKTER53X2WELtrgPOAq6vysp4O9qTm6LlmrYlsdspZELffj8C3+pulLp3jfZPd5TrCfDX+nXk3WXH5/fj9/s5I2sCJx6dySOvv8w9Ly2hasN6Dp0+lRPGjOWZ+Td2PDsFXkOSKAS5UW+bDEliAAYmsyvzeFg7D049gLNuu4EbFy9i5LDhTD/hZL3NiYpwz+jTV9/AZY/dj9fno2tSEk9ddX2zc3snig2EFlvN9E8SxwOpetkaLzv36mo8KEGIYwiqhafRzjYjnJ2rKlfz+LLXAJiSOZ4LJp3U7NxuiUktaqgWEozGiDRUp+u5U0M9eZ64aWjrtgqfgS7vwtY6rTY0pYvomyIwHktQ/xSrnQKjtwv93LSykBEb/0wNTcBYT5z6p8HGvocJxGiCoi5jfe8TSKgfmNDnCyDseDhW+hl6DkjA2EhD26gfbckHC8cCYJEQYiXqoX0fVWKpEVLKMiFEHyFELynlViFEMmpdqzfgF0JcDhyMCsN1BhxYA/CKlPIdgEDVklopZcQ5ZkSs+/T0ILA6+oKUMqb4g8Af/KKUMkdfy6LE5U4GfkG/IrrvYrXktnRAclFJd1RJmgOBo6qyMprNNJid5oNRMxA2T57n7oCtvwF67WqvwWoJWdg5uajkZFRH9SJwbtSJg1zuR4B5mi1UbAZysFoi2ugcFS738cBS9ClG7gNuwmq5q9Ujo8XlFqhJG734FqvlcB3b24XL/SVwhE6tbcJq6df6YTHgcs9HTbzpsblkK3AkVov+8WIudwYq+UhvHVrbDEzDaonLIASXeyv6Fc7+DKtlrE5tNcblXoaqHa0HfqwWY+uHxYDLfT4qy6ke13QzMAWrpVCHthrTDhoaM/pqaDlWy4GhPjA7zY00NOrEQfpqaB2wD1bLep3a24W+GroVuASr5SUd2mpMp4bqj74auhk4G6vlXR3aaoy+GloH2LBaHtKhLU0IIa4Atkopn9Fw/pbA4mBERLufUlcC3vHTQohYb+R+RLA83AZsIGgGRAdaWz6nKiujBjXoqQHeTi4qGRDisFnADlR4TgN6pqMMWbwzuajEjEr49BVwUYyZav+gSepmDSQB8Up1tkZKqdd7tJ142Wm1SKC61eMiJ+KZrXZuW/9B0i7+RgmIHsTzGf0bffuneKYN1HOlt1LHtpryu45thcsCrwdrUJNZemAA4hJmB2yQUraphmpATw0N+S6ZneZGGhpjplo9NdQIxKum5Br0G4v66dRQvdvuKBoq6BgauoP4amg0LELb9d8EOKM5oV2dTgAp5SstlF5p7dyvpJQletsUNVbLDuBj9EloVEvz8OSQVGVl/A6cisoo9WpyUcnO2SKz09wTVWLmVU+eJzhD1YsoR1Ur24DXmv4yuahkEPA2atbplKqsjFivyXL0EcwdQAUq65/u/Hz9Fb19WzZ39QeSr2hEombT4sXb6HPva1DPke6k2vK7PvpzdV2tT5cIjBriW0rpY0JnhYuWOtQ+9ZCTODpQBZSjXdx9qP7Jo9miEKTa8gfk/7V9Y41Xl8WEGtTqSbxYhn7vUnxWjRVfoe6b1otaj3o+45Lu0l70zn5/VW/eXO/TxT+OWENjRBcN9dfv8BOiHzU7zY001JPnaW8NrQUKsVp0328eoBT1t2oVUR/q79V/e8oudnsNNTvNXZ/c9HZdrV8XX66jaKgXNSFQqkNbodBLQ2Xg51PNFumAlHK7lLLptsdozn9OShnVe9uu4bV7FC53d1QdHTNK4KPdAOkHugAXRhsaklxUcj5qtmFhVVbGHACz0zwDVSJlrCfP81kTW+8ArkB10CHtlH5plD66Y8Dr83uRSENSYtI21AuTALwasHXnYCa5qCQJ+BAYDYyrysr4Kpq/oxkud46U8m3pIxFJojBSKwwi7AMupWSb3N4rUSTs6CISGzqHv4CjsFp0HdCXppsGojZTX5jQr/9fabfekySSkhID+15jufcGYAJWy5d62tkIlzsRVS83CyXQIe2slXTzg6GHYFsIO7sA12K1PKK3eam2/InAY0Da/IN6fDNraI+DhRB1RDA55pdS1Pnp6vXLBCHwdzOKeqMQzwGzAzPU8cHltgCFfp9Mwk8XYWS7MIhIB3oSdQ9+A47BatkcRzv7AZ8BIVP+Sb9MlD66CiM1wiBCDS79qJX4MVgtuq4ipdryDcCFgAPo++LovqVH9U8aKoTwEns/ejlWS9gSUbrgcp8BPI+a2Ip2AleiVo+WA2dgtegyaxUSl/sg4PPA98ViJ6gZ7TFYLbqudNrt9m6ADbg20WDcMXfM+PW9u3QdKIRoUw2Nmgg0tAX80uvtuX55/va1b73Wy1RWurN/CmSb/wgVGjnOk+fRRUP97EiU+BINJNUaREKkz5pEPS/fAsdhtWzXZEvLdu4N/Jdd4Yux3Pt6lNbHrw5IhBoailp/XXc/ftHD0G0HcdJQs9O8U0Mv63v6N9P7nBCxhjahYZz3Am2koewaB0WLH+Ucj8ZqiV90SysaGgENfdp4rJZ4Tozs1nQ6nXqiOiQLsC/RvzzVwPdYLRWxfHVyUcm9wNXArEGrz3uSXbN9I0OG5rjch6CyTnVp9lmArRXeCTs2+aevqv7qtw3b/+53woQpl6A69l+AkuCOKJCp9kngYuCcqqwMXWbx/r7zk9uNSeKGpP6Gpb3TEltdEbhx7TOP9jX2LL26/1mPo8KLvsFq0WNmEoDSdJMBVYLmLpRAPgjcZnpyiQAOR226jpaNqOsZ/5ALl9uIsjMVNQhtxu1+w/QqGPOYwd90w3otUIrV8j89TUq15e8LPARMQWW0nlvhyP0Il3sYaiN7xPu8/vPndnPB2roLftrqHVK21fuahCsqHLnxDLPk7zs/mWtIEo8m9TZ80Gd44vNRnCpRIaBfx3VA14DL3Q2VBXMfmgyW/F6ZsOmH+scNifzU9+CkB0KcXYXag6SrY5xqyx8JLASOBIqB2RWT9voBVWw6jej3+tQAP8Z14BmMy50KjCD6PZNeVATGN8ETd3FDDZhGAoNjOPtPlJ16hhZit9tPQJVUGwq8BFxtzzpxLe2koVETgYaGoe73hQ9Zqld+fT2wt6ms9C/Ymal2p4Z68jy6aOiXhRNuT6LPDX3E8FcOMEyJZgXYj0rK8jVWi15huuFxuXsRu4auQT2j8QoB3kUEGhqKxZvzT1y5vfyc7O6HZ55y4uWf6GmS2WlupqGePE9MGhqgHhXV8F1cHc4GXO7BqMys/WM4uwp17+O5TUHRgoZGwHrUOC+i2ph7Kp1O5x5CclGJERX6cVzX6qLLe2149jFgpifPo2m2v9JW/PgPGz+b/f2mYoBuVy19J+TgOLmoZB7wCHBXVVaGLmkPK23FJwFvocLkzk5xZLb6sJqdZheAJ89j1cOGYErTTaNQA+TRqDDYOaay0ohTRXcUkotKbkRlQOtWlZURN2co1ZafhFotuBnVgS8AHqhw5GoKYUm15XdBTcDciJqNtgMPVzhydR84VdqKjwaKUA7TxBRHZvxWrOJMpa34LmA+kJriyNRzz2IzUm35fVD3ew6qhtjVwL8rHLmdgrSHY7fb90cNkE9BhZXPsdvt+icn2o0pTTdZCdS7M5WVfgBgdpp3aqgnz6OLhhYUpjVo6CvA1Jzs8s73q50wO82HAiuBCzx5nud1ajOkhnryPLpnGe6kEz1orWRKJx2EqqwMX3JRydnA53Xdj3qg25b86gTv3/+nQ9OXGw0JWYBp1IDjclHlbxqRXFQyAbXi9zZqoK+ZSlvxCNSeh2+A6ZE4nAFWA8foYUMDpemmfqgMa7NQM6rnAi8Gh0XtYTSsDO6D2segO6m2/CxUVk0TalB0eYUjt0KPtgNO6x2ptvwXgYeBe4Fpqbb8ORWOXN32zFbaivdD7R1bDZzRkR3OAE8C16JWWm6Oxxek2vIFcA5wHzAIlcjgxgpHbvxXKDppV+x2exIq8d9NqFV+G/Cg3W7Xva5eB+C7wH8PAz4wO826a2hBYdoh7NLQCzsdznbHg0ogcxwqLF8TZqe5mYZ68jwVWtvtpJN40rnSuYexX/7TR9R3MX0p5I4N/oT+B1RlZWgOOXj63LxTttSvf9OafOaG5G6pGcGrIMlFJcOAL1GOytFVWRlbtX5fpa14YKDNboAlxZH5R6Tnmp3mO1CrNV09eR5NiQ9K000CVWT3PmAAqoO/2VRWukcPkAOTCB8C46uyMnRNbJRqyx+Cup5nA78C8yocufl6fkeI7zwJtYKQiqpXdU2FI1fT3rRKW3EPVMmiocCYFEdmmVY7dwcqbcXvoMKH9ktxZOq6Mpxqyz8E9Q5ZUe/3rApH7jd6fkcnuyd2uz0Hde+HoyZqLrfb7brX0+tIlKabfgM+OeO6BDtBGurJ82jW0ILCtIGoPZLdgSNyssvjusWgk8gwO80vAJOAvTx5npjC6s1OczMN9eR54qqhnXSiF50rnXsYfdY9krWjy8FsHmzrA/xfclHJKVVZGZqcry3160sBar3V3YC3Km3FmSmOzG3JRSV9UDOzPmCyTg5nIipJ0d6ANRqHM8Bq1HM9BA0lE0rTTSNQobSZwBfA8aay0n/K5u+GSYUUvRpMteUnoEIpbwO6Bv7rqHDk6pHxuUUqHLn/SbXlF6AKrc8HJqfa8m8AFlU4cqN+NyptxQbUTPWhwIl7isMZYCGQjwp9fFWPBlNt+T2BW4DLUeVBZgCLKxy58d/L2Em7Yrfb9wbuB85CRU2cYLfb32tfq3YbVm7txkjgPwQ0VCeHs0FD9wGsnQ7nbsUHqInskUBUdcPNTnNIDdWQ3biTTtqcTqdzD8LsNBuAmUl1P7oQ4lVUFrM7UGFMWvgD4Nfq714+oNeIacBzJ7zunkr/hJeBA4EJVVkZv2r8jgYeAcYD56U4Mv8bw/kNs+f7EYPTWZpu6oXaA3gZKmPjRcBzprLSf9IAucHR18XpTLXlH41yZg5Die68CkfuKj3ajpQKR24NcFOqLX8JKnnJo8CFqbb82RWO3C+ibO4m4HTg6hRH5p42gP4AleBmNhqdzkAo7WmosMEUYDFgq3DkrtNoYye7OXa7PQGYB9yKqm9nB+622+3xT5jVQfAa+O6RkwwnIqUPISZ48jy6a2hOdnksGtpJ/Pgw8N/jiMLpNDvNzTTUk+dpUw3tpBM9aPc6nZ3oyvHAAah9UgsJ7NFKLio5V0ujVy19pxrYtHb779tQe77+1dXHx8BEYI5eIZiVtuLZwEzg7hRH5r9jbCbY6YyY0nSTKE03nYFKbHEF8Cww3FRWuvgf5nBSlZVRjXK499XSTqotf1CqLX8xqibVAJSjNqmtHc5gKhy5/0M9t2egMnl+nmrLfzrVlj8gkvMrbcWnoQbQzwOhsrx2aFIcmT7gCWB8pa3YFGs7qbb8YcD7KMd1HXB0hSP3ok6Hc8/Hbrcfg9pH+AAqBP0Qu91+a6fD2ZibzjMeujLNIA76g7s9eR5dNLSgMK1BQ+/JyS6PVUM7iROePM8aVGWB4yM53uw0DzI7zc00tNPh7KSj0rnSuWcxG7VR/c2qrAwZyCibDjyTXFSyqiorQ8usZyVqteLSZ4YmnfLZoISjh2/xvec6edRTOthNpa04GzVD+w5wg4amonY6S9NNw1GrwhNQgjDFVFb6T58hbrjfUROou3gxqqxML+AeYEGFI1fXkguxEsiQ+mqqLf99VMKcy4Epqbb8a4Fnw4V9VtqKM1B1yz4HZkaR3Kqj8SwqdGsWcGk0J6ba8rsB16PCmLcHzl9U4cjt6EmWOmkFu90+GFW7eBoqRH8KsMxut++p70nMmJ3maewtTpro9nPhh/5yTYoXoKAwrUFD81HvYCe7J8uBK81Oc69w4dSBqLVmGurJ8+wWGtpJJ7HSmUgoHrjcSUS/irxDS702s9Ociqqfeacnz7Mz+11yUUlwUp4jqrIydoWcutwJRDjx8PBTd7wFDHjyopuv2CD5aMRmf+0zX9YkJkjGpjgyS2K1G6DSVnxgwMY/gaNTHJmNkx+53AZUiFZEWH6b8adAvPzV+V/Pbum40nRTd5SDew2qvt8NwBOmstLY9sC63ILoa7YB1LVJLaxgWnlGh/qNb0kY+KvBNzbo1/VYLS1em1Rb/ijUSvsRqLIysyscuT9qsDPiZzQIL1ZLxE5OIMHNQmAcav/u7ApHbqP9u5W24r2AhkLto1McmY0TEcV27yVWS9untne5u9BKjbE/ltc+KyW5PfYxDu03ImkbETyjTRI2/R9wtaaETaruccR18AK0+ozqjqrbF209UT9WS9tmbY3TM2q3242ofbp3Aj1Qezhvt9vt22KyE2LV0PboR6Pun8atnnfERn/1R0hZ/NLdviONkmdMZaWXazGjoDAtDaWhVcBROdnlmjQ0gK9N6nMG07H60Vie0XpzxQVWoAC1h/c/TQ8wO83NNNST59mtNVQXOta9b1VDQ9D2/dNuSKfTqScu93xUuvMeqJTw0SBRq2wnYLVEHYJmdprvRIW+HuDJ8zTKCphcVDICtTrzEzCuyuAdhap9GXGhcL/0G5AIKQxsBd/mGv9lxo/rbKgC0kekODLXRGszQKWtuE/Atr0C7fyy80NVMPg9VMKWiF9wn/QbBWAQhi2AHavlwabHlKabJqMGyPsDS4BrTGWlf8fyNwQGnYtRIZtdiP7ee4F3gbPiPhB1uS9Ahb31pgU7fRKDBJEgCB7AS+B/qGf0t+DjU235TcvKXAW8GHPdRZf7UFQa+FhDfP8EpmC1uCM5OLD/8FxUVsCBKCf0pgpH7qZKW3EXVC3Ow4BjUhyZuxxSl7s3KpnWUUTvIAmgFrW/9Pq4CpIS9HtRoXfdaOUZlVIKJAYEfiGERCU6+QQ4Gaul0Wx7qi0/FVWaZjLwIzCnwpG7QoOtJ6FWW/u3ZmcYVgMnYbXEt4auyz0Utap0INEPQASwGbgUqyW+YZAud1dUf3880Q8+BWpC7j6sllubfmi3249AvSsWoBCYa7fbSzXYqkVDfaj3dApWS03MNkSCy51JlBoKIKVEIo1b/bW+Lf5t06rnXjUH2G4qK82K1ZSCwrTeqMmyvYDROdnlu8pcxaihAQRqm8VMrBZdkoqFRZuGClRUxfPAPC2T9xERoYaGQfqlXDXpj/mpf3rXLfbkeeY1fGB2mkNqqCfP0yE0NGb2UA0NQVgN/SfR6XTqhcttQyUY6a6hlR2osKTRWC0bIj3J7DR3QQ20vvDkeU4OdUxyUclkYNlE/AXPCf9RQtBDg50AtbV/+65f/82OO1H7d3JSHJlRzThV2oqNqM7mOOC4FEdm0c4PXe6BqJWlfYh+FSGYGuAmrJYHAErTTQegnM0TgR+A2aay0o9jbl11REuBE0DTNa0BPgMmxW2G0eWehhogdtPQig+1R8+C1VIZcNbOR3XGA1BhyjdXOHI3a7BzBKpz7k30A6VgtgJWrJaIsw6n2vL7ogpszwbWCrjaRa8cA2Ia8K8UR+ZrQXb2QO21SSe2Fe4GtgFPYLVcraGNlnG5Hwamo+0Z3Y5yKjOxWmpSbfldgKtREQJ+VNKYhyocubGvjrjcuah9oFqeUYly6I7GaondAWoJl3t/VP/Un+gHSsHUADOwWvSoqdwctVq8HBiNNm2qAe7HarkZwG6390clqbsEtbp2JbBUUyitPhpaC5QAWXFbAXG5x6KSuWjW0KqXnB9vXPHRaGBALHWfCwrTjCjH4njguJzs8nhp6PlYLc1qdOuCfhq6DXgZq+UiXewKhU4autm3zXvGX/Y/PjinKM3sNIfUUE+ep0NqaFT8wzVUF7s6GJ2JhPRAha7cijaxBBX+sjcq42M0TEHNuC4Md0BVVsbbwPUXCf8EHRxOgG7d9jLOQu3fGQssqrQVR9u53YUSmksbOZyKs4BktIklqHtyx6qjxnYtTTfdhHrhx6MGyyM1OZyKYUAu2gcg3YEjUTUS48W9aBNLUAPsAajMr2bgY9QMczlgqXDkXqbJ4VTMR7tYgtoLE1Wh9QpH7qYKR+48VGhThYQll1Iz7XPqFzZyOBWTgDS0iSWoZ+cyXG6t/Udo1EzyLLQ/o12Bg4AJqbb8Y1EF7m9HrdKbKhy592pyOBX3of0ZFUAflCMULy5Gu8MJ6r2/V7s5YTkStQqp9dnqDly36o13k+x2+wWoqJmLUSvc6Xa7/WWNDqdeGtoNMKNC5ePFArS/SwDdBk0+bSTQj9gzhd+F0p9LGzmcCj01NJ7PqF4a2gM4D5d7L+0mhUUXDe1l6JZ4Uo+jh5qd5uNpoqGePM9lmhxORbtpaJT8YzVUs00dlM5EQvowADXTrwddUQXno2EWqsP6sJXj7h6+K3RJD4akODJfqbQVj0DNUHtQ5RFapdJWfD5qL+WiFEfmohCHDEVdC81Ivz/Bv6Puh0CbrwBXmcpK9apdth+g174XPyrcV/8kRmo2OaIMrRGQ4N64YyoqCc/OsjI61l0chnaxbODAWE6qcOR+84vt49vep/6dx9hefw21M66x5dcAtwYlRNqf6PdJhaMWFQb1k07tBbN/oH2tg0/8UiY+8UuNHVVn7mdgYoUj9wOt7QYxRKd2BErc40U62h3OBqIK0YyS/YgtRLkZfinr31/1w2eoibFPgTl2u32lHm2jr4YK1L7ieKFb24Zu3XoG/vcwdtVHjoiCwrSdGpqTXR5XDUU5r/FCTw2tRfV3sW2VaQkdNdQgDIYDEoeAmrDbSEBDPXmePUZDI+Qfp6Eov2t/HdrpkHQ6nfpgpImw/76mivPvtFO1YT0Gg2DGiady2elTKVn1EzMfcLB9Rx0JxgQWXnEto02HBJ8qiOK+mJ1mM5AJXNNah1WVlSH9K9wbCHI6w9l55q3X8dNqtW1vU3U1fXv2pGTxi6H+blAlJEYA91Xain9McWS2OAittBUfCTyN2n9zWZjDml2DWK+p9HkThMEogeNMZaWtOebR0mzgub2ujnGXzaCuvh6vz8vp1hxuveASzrn9Rtw/lZJoTGC06RCevOp6EhMa/ZkiVHs60kiENNjJ39v96cBTwPUVjtz1OtsZ8TWdfs8C3D+VIqXkoJT9eN52Cz27d2+xrUiotBWnJyFemkzSyq/wnlyE9ybU6vjUVFv+FcBrFZP2MtIkWkTDNZWx2hoBEV/Px954hYdee4nyPytZu+xDBvbt2+i8ej9d1u/wj0BNONxb4cjVuwxGI1tjtTNAPPWtWdsabNVrcBgKY9P2w9n5619/cNZtN7BhyxYOP2g4S66/jaTEXWOsep+vm0QeAFwAvGC32/XcO9dMQ2O1E/VOxrMfbdR2OF1a+fP/mPmAg+raGlKTh/B/Ny6gd4+ejVsSO7uPw1CZ2yOioDAtJg3V0I/GMypOz/4+ZHs6opuGJggjwG/AEZ48T7tpaAPzHr6X5977D9XvNwv+ive7FJGGTrvLjmvlt/TpoYavz9tuIWPY8OBTdwsNjcDOePdPuzWtirIQ4gbgbNReLj9qD4cNVQ+yJzAIaChqPBt4EbBIKdcFzh8PXC2lPDFE2yOBOVLKi4QQ6cBzwOHADVLK+5ocawTcwB8NbQkhXgZuklLudjWLEowJ3D/7cg4/KJ2tNdsYNeN8jrWMYf6Tj3LLtIuYNGYs737xKfOfeIQVDz+p5atmAXWoa9cqBtFY2MPZufSWu3Yec9XCB+nTVCyDSHFk+gMrl58ASyttxWNSHJkhZ5sqbcX7AstQs7r/SnFkRjzDGes1FQajd5+L52T2mHH+X5F+lxa6JCVR+MAienbvTr3XyzHzLmLS6KM5Z8Ik/n3DAgDOXnAjz+QvY9bJp7eFSbrbeUS/xIIKR+4lodptS1sfnHPFzoHclY8/yGNvvoLtnGmavqvSVtwf+A9q/8XJzzmOXw1cHKg5uhC1Wr78l23elUN7NO5CO/q9H2s+jBOPOobxl88MeZ5B4D93v2533zQza8HubGd70FFsDWfnA6++yBWnn81ZOccx8/67WPzuW42eUaNB1B+bdvAE0+knxmd/l052tjXhdOmie2/nvlmXYc0YxbPvvs29Ly9hwfRZjc4VQoDKOn9opN9XUJiWgtLQSuBfOdnlEWtoW/ajWujodkbS3/cz9PwTFaKvNZRWk61HHmLGXfYjm6pDVm9pc8LZCXDvzEs5fXxOO1uoaCs7hRDVUsqeQf+ehvKx5gb+PYNdW0m2AFdKKT8JfLYCFT1UG/j8Zylls85SCHEKcKiU8jYhxDjgIVSfdJaU8rUmx/YGSoE3g2z4CPiXlHJja39Pi7NXQoijUAlXDpdSHoqKQ/5dSnmqlDIDFRJQLKXMCPx81toXNuF6VNYpgA2omm73hTn2MtQfGswiVOz6bseQAQM5/KB0AHp174Fp/1T+WLcWIQRbtqlM8pu3VbP3wEExf4fZae4FnAcsjXWmLJydDUgpeaXoI6bmtFzLOMWRWQ2cjEqG9J9KW3G/psdU2oq7o8SyOzA5xZEZlc2xXlNhNHp7DD+4zcoTCCF2zr7We73Ue70IITjhyLEIIRBCMNp0CJVrY0r4u1vYObirMeJEV/G0tWEAIqWktq6uYRAXM5W24gRUUov9gFNTHJk7M0FXOHK/QO31nAcc+Upl7ZU+KRv1oR393o8cNpzUIXuHPS9B4D2gR4Les/K629kedBRbw9lZ+M1XnG7NBiBvYi7LPnE1Oi/BYKwzDUpukwGyFjvbmnC69NPvqxl32OEAHGsZzesfN91yuZOVqJXOVikoTOuOShzUHZick10e1bvYVv2oVjq6nZH093sl9P8Ntff8iPa01efzcc0Tj3DPzKhKMseNcHbubuwOdgohTkQtBB4jpUxHZdZ9UQgRHA5/TpCPFm52bj67csKsRuVqaRbaGGABqpRPMEtQi46t0lrIxBBgnZSyDkBKuU5K+WfF1/T2AAAgAElEQVQkDbeGEKIXyrNeGWh7jZTyK0LE9gshUlAbzZ9p8lExMEEIsVuHCVf89SffrvqJMaZDeGjulVzzxCPs+69crl70MHddPEdL0+eiVpvDJhCK1c4Gir/7lr36DWBYyn6tnp/iyPwNOBW152VpYAAPQCDJ0HOovWBTUxyZsdedIq7XVBd8Ph8Z089m8CnHcaxlDGMOHrHzs3qvlyXL32Xi6KPa0UJFR7ETwtt6geNWkqdMpGx1BfOmnKn1ax5ATa5dkuLIbDaJVuHI9VU4ch8Dhid3Ma70y+Z9aEe5pi3ZGY72EP9Y7GwvOoqtTe1M2zuFvj17kRAIAUwZNJg/2nliBDqOnQ0E69KIA4by9qcqVPHVFQX8vibsNsOVwLBA3eiwFBSmBWvo2TnZ5TGVBGqjflQzHd1OaLm/38vYbw0qerDlGf042/rYm68weew4hgwY2FZmtEq4a3rD4oUceuFUrnjsAep2tG2Z41DsBnZeC1zTEFkqpfwGcAIRD4KFEAcBdUFtVEgpvyPEHnshxChUaablTT56G5gayfe15nQuB/YVQvxPCLFQCGGNpNEIsQDfR3jsQyhPvNFFkFL6UcksIpolbA+qa2o47ZZreWjulfTu0ZNFb73Og3Ou5PdX83lwzhVMvye2KLVAmu1ZqHIlX+ptZwMvFSxnas5xEbeT4sj8FDXbciyqSHgDN6JqcNlSHJn5etqq1zXVE6PRSMniF6l8NZ8vS3/g+19+3vnZ7AcdjDt0JJmHjmxHCxUdxU4Ib+tztlv487V3Me2fytKipn1h5FTaii9GrWI+kOLIfL6lYyscuVXTUrsvNTauY9qinbB7XdOW7Nyd6Ch2Qsextamdpat/bXbM7rC60FHshOa69Oz8m3l82auMmnEeW2tqmu47DWYlaizW2gzFTg3NyS6PeP9nU+Ldj+pFR7cTWu7vuxqSdqBK2kQ+wNLZ1o9XfsOrKwqYd+oZbWVCRIS6pnfNmEvZC6/x1RNONmzdwt0vOdvbzLays5sQoqThB7gt6LNDgK+bHO8O/L6B/ws6P1QW6rEoP6JFhBAG1Jj+mqafBcJquwghWk201aLTKaWsRmWqmwGsBZYG4olbPC3C3w0JtNkigeXjNVLKphe2gTWoMiO7HfVeL6fdci3nTJjIlHEqHMj5wTtMGafqQP9r/AS+LIt5wW8sKjX8opiLB7dgJ4DX6+WN4iLOzDo2qvZSHJnPoiYKLq20FV9UaSuegnpRlqAx9Xqcr6nu9O3Vi/EZo3j/y88BuPX5p1m7aRMPzLminS1rTEexE5rbCqrzPzPrWF53hQ1ha5FKW/E4VMTA+0QRsm8QIuy711GuaajrGY72rOscjZ3tTUextcHOL378nk3VW/F6VYngyrVrNG390Jvd3c5QupS+fyrL73uMr59awtSc40jbe59wpzdkAA47eV5QmKabhjYQj340HnRUOyPs75cDY8xOc8hMaPGiwdaib7/m5z9+58BzppB65mRq6rZz4NmntqUpLRJ8TYcMGIgQgi5JSVww8SS+LN09x3lxsLM2KDw2A5XEryUEjX2u4PDaZg4jEfpiqPDZd6WU4bJsR+SLtRqWKqX0ASuAFUIID5CHqikUjvWoulPrAv/uH/T/wdQSWTrvscBkIcQJgeN7CyH+LaU8N/B5V3Ztkt1tkFIy/Z4FmPZL5cozztn5+70HDMJV8g3jR46i8JuvGJayb6xfMRu1Af2leNgJ8NHXX5K+3/6kDI6p7NU1gAm179aLKgMyI8WRGfOotQ2uqS6s3bSRRGMCfXv1orZuOx99/SXXTj2fZ95ZxgdffU7BAwsxGJrP93j90vhuVd2hl9ryW92MHS1GAeUTG9/HWO1sD0LZOn/qefxc+TsHpuyLlJL/fFZM+n7RZyKvtBUfALyOKjs0NcWR2Wz1UoudEd77BHvp1mP+/V5+63HsUXJRavehtuE9ExIMu1aEwtnZKhJjXHOtNiFmO9uBWG2VUopb7faJ8bDpjENGmYcP3MtoDHrmwtmZNdLCa65Czso5Duf7+Zw8Np6lLlsnVjt90m/4tNZz8BznBXG5pl/t92S3roZdVR7C6dKajRsY3K8/fr+f25c8y8zJYctvV6ASgIR0OgsK0w5DOZv/BWbkZJfHrKGx9qN+6Tcc5jTH5Xpe1/+cUWf0ykoIZHPVZKdX+hKe3vzOmIXOC/robacBAytTFzf6nQ4a+gGq3FwOSoPiRjhbq97cVWyg58Rx/Pzim43O89XW9vxfuiku936/q64/qPuwdBEcrRDOzr/Wr2PIgIFIKVn2yQpGHNC4sqD0+RKqXn7hmE2XnKe7hvafMGno4ClnJghjy89oJHbqzI+ohcHCoN8dHvh9pNSi9ha3xlFAphBiNmpbX1IgyZEt8HlEvliLTqcQYjjgD8oOm4FK8dwSK1DJbW4OZJw9F5U8pimlwFWtGSilvA64LmDPeFQm3HODDjkIiGlvQzz51LOSJcvfxTz0QDKmnw3AnRfP4emrb+Cyx+7H6/PRNSmJp666Puq2zU7zYOB01CrntnjYecKRY3m5cDlTs2PbbpDiyPRW2ornou5zEjAvxZGpqbxCPK+pnvy1fh15d9nx+f34/X7OyJrAiUdnkpB9JPsnJ3PU7AsBmDIui5vzLt55Xq1Pdv9oTZ0NlR1aV3whhimx2tkehLI198hjyLz0YrZs24aUksMOHMaiK6K7dJW24l6o/QhG4KQUR+Ymve2M9N5/sWGHpjTW4fhs/Q5qfJLeQU5nODsfef1l7nlpCVUb1nPo9KmcMGYsz8wPqg0uMUqfNLaV3xmzne1ArLZKNTP9Xjxs+mldFWn9BxLsdIaz8+DUAzjrthu4cfEiRg4bzvQTTo6HSRETq507ZH2XFbUl81Ch8rqzwb+VvQ27osjC6dKqytU8vkwlfpySOZ4LJp0Usj1TWaksTTd9Rwins6AwbTCqf9oInJqTXa5JQ2PtR734EonTM+qqWclJPY6ml3HXltZY7ayRdT0+rfVEVCs8WvwhysjqoKFfoiYcjiPOTmc4W1vDu3H9/sTp3ld7VtI9bRgYd7ki4ezMvmIWazdtREpJxoEH8cSV1zVqy79jR/eaVWVx0dBtZT/g31GHsVvLz2gkdurMPcDdQoiJUsr1QogMVBKgMVG0UYry01pESrlzRi0og64t8G+BquVb0Vo7oqVwqcCm0UeBvqjVqp+BGS2VQxFC9EGtbh2CEtP3AVtg/2XT9j3A0VLKrYFsS26gN2rvZjVwsJRyS9Dxjb5PCLEX8B8p5ejW/tC44nIno9Ked9OpxfuwWkItgwNgdpqvA+4ETJ48T1lULbvcv6Eyc+pBDVZLj1AfVNqKuwAFqFmYelRZnbGBLLeR2PkI+g0atgMpWC36Z910uY9HZTvVPLPqk7LGtXaH48KvN+ldS7RhpVPPWL9XsVrisxHE5f4S/TL6/YDVEnKvVKWt2AC8gcrQPTHFkflRVC273POBO9ChHqTPL7fdt6r6okW/1FRobaspVw7rMWz20B6LEgwi5LsaDdIn2b7O/0K3M8bk6WFbM1zurahZVD34DKtlrE5tNcblXobK1q0ZKaW8dUX+0Xq01ZRzDxszaWjfAdcYDAY9tKkaOAyr5Rcd2mqMjhrql/66b+pWPXxBlePN1o+OHvf+Ty3rIhJjCv8JQQ1WS4/SdNNjqMn6vqayUglQUJiWhNJQC5CZk13ujqplHTXUL2X9Yb9dGJel7zsHXjxmUo8xtycIo+b33it91S9vLbzi7g0vRpovJGKMGChJXay7hpqd5jdRyaEO0LpVaic6aqi/ru7Xny696Gw92mpK6nW3ntt1/wNmBhaoNCF9vm1r3379ovXv/6dCB9MaMWjyacMGTDxpkTAaNWsoqsLDtVgtD0VycAQlU2YBl6NCarcCV0kpPw58toLGJVPWSSknNGm/O2pv8QgppRRCHAG8iYpY3Q5USSkPaXJOUxsswHVSyrDhHA20OFgK7KMMK4ZSyhWolc3g321G1fWMhGeBM4FnpJRVQEor9jT9vrOBuMxsRIkX/YonS9RDGRKz02xEpUguitrhVHhjNSwEIcMQA5lqF6JCo89CzdK+ByyptBWfluLIjKSouJ4pv4zo+3cHo1u7RiG82YO7lAVKc+iLyy1Qkzl6PafxTMmm571qqa0FKMfh0qgdzl1tR/Ist4rRIOS1w3t9c+2MrP/p0V4jXO5qwryr0SJB7tjsH6vX7FoIdLEzQMT1C9uzbSGEz2636//OA7jcQ9HvfTLo2FZTdNNQgzB4LV2Hr/LkeeJ1TTVFFzWh4Xlfico4mQr8GshUuxA4BjgraodToVsfbRDCG8fr2QOd3vsEYfSd2/vYleeedN1XerTXiPhp6AfAKcAwQK/+X7f31NClyxZTWWm87v3RqHuv2ekURqMcfOoZ3wx+6J7dWkMD7UR8f4IdzsC/nydoi6OUchFqoS/UueMjaL8mUGczB/goUEWkNV+skQ2oCbOIqmi094atRUCdhvM3odIDtzd6rqDVovaWhWMSsD+xl0nRc5a6MszvLwMuBG5PcWQuTXFkLkcVrz2Fxpm3WqIc/fbq1qNCWOJBBdBFp7aMqBVh/bFaJJFtFo+EeqLbMxAtPxE6+Vi0yEBbzai0FU9F1Ql+GngsxvZ/Rb+BXVdUfax4UIFOURhCUF+/xZ9WaSuOSy0QKX26lORCDQ6b1nXWDb+Upf4Q0TsxUqVTO6GoAN124SYBet2fpqyXUuplp0RfnWtKPDT0u8B/G0JsLwWmA7fnZJcvjbFtPTX0D53aCUUF+mloNyII8YuJ+GloQwpe3bLYSp9vldQn61tYDdWJf5yGopzO+IzzYudOVO3fWPleSlkQyYHt6nRKKbdLKZdoOP85KWW8Zl4jR3VGl6O9g69DCcWrLRwzC/gLVSA6FuajluC1UkOI0J1KW/HxqLTKbwK3BH30CLAYuKHSVnxWBO2/iHoxtUxK4JXeemBe4B7Fg1+AFwCts981qFD0VlNXa2A22p/RetQApGnNXD25HdiAtlVEiUq0dUvTDyptxUegoiw+BuZqSG71HmqFQus1rQHsWC2a9muFxWqpRolKjcaWagH39vX+OlQ/pBsFhWmioDAt71v/XUN8UvNl8KMmAu/Rbllz7Hb7mIc+Lzh5a912g8/v19qv1BJhUe0Y+S8qyYTWe18DzMdq0V1vzU5zF3PFBTfcsWGJ2O7X1N2Dup6foWp4x4t4aOj3qD7rsILCtONR9YKbami06KKhKDvnamyjJfTU0IexWvRyDEOhu4Z68jy/oLau6VKvszTdNOGX2647xretWkht/VNYDdWRf6KGfg3EElkVN6SUf0sp39Zw/tORHtveK517DlbLE6jZyT9RnYo3+EdK6fNKH37p9zf9LPCzBXABx2C1hBQ0s9M8FLXS+bQnzxNbeJfV8i0wHihDvQChbAn74/N55baa6m3AZKyWRjMblbbi4aj9jd8D5weH0QYG9rNRg4HnKm3Fllbs3AyM9dfX/9e3vVZKn68hk3Kktu5Y493of2bzu8VYLc/GcqkiQjmzM1Fh3mullD6vX+JTs4yR2roelYX4TKwWvVZPQtn6BnAOajZwRyS2eSX4JA1/SzUq8cGRWC3xq8hutZQDR/lk3SqfrENKH4HJpRZ/pJQ+KX34ZB21cu2Wv+XnJ2O1NApBr7QV741KbFYFnJ7iyIx9llUJ3LGo8KhNrdkX5qcKuAWr5Y6Y7YjMVjvKmf87Rjs3AvnCIHLwsxQ4P5CESTMFhWlm1ATA8xvxlK6VX12MmnyrC2ePlNLnl76GZyO4X6gBSlDPqK6zyXa7fYDdbn8K+HxL3faBr//47UyDEP9FvRfRXs8dqKR8Z2K1/EdPOxthtfiA04BXUP1MtHbWoyY5r8Vq0T1Bi9lpPg7wAAuWbi1662/fpvmE0dAIfjai3u2TsFriF1qtUUNRz/UvBGmoqax0G7Bqx/7+sQRpaE52eex6ENDQOln/323+7dLb/F2JxM5fgdOwWj4I9RW60ERDo7Av+Odv1GT3tXGzU9katYY2+QmnocuBLLPTnESMlKab9ilNN70MfLij6i+55rWXpguDYSWqT4zWzlpUgs6xTTVUV/6BGgpMxGrRPLvWUWkxkVAn+mJ2mt9GZZXa15PniXqwa3aa70Zl/E315HnChbbGlfvPPPEjoPtVS99ptNe30lbcDzWr3g84IsWRWRHq/Epb8SDUpuWEwHF/hfuu0nRTEvAh6pqNM5WVfhmNrWan+Vug0pPnCZ02MA6k2vKXo5InpVU4cjVlQt0dSC4q+QjoUZWVcVRbfm9BYVrDc5IIWHKyy8M+J03O64YaeNhQg4KbgMdzssu9lbbibqiJHRNwdIoj0xMX4/dwKm3FRwKfA7NSHJlPxNpOQWFaL+BW1GTdJtQK0vORDrQLCtOORO3x/ww4Pie7PC6Oht1uN6C2CzhQSfUeVr+267Ha9Y/E7DSnoFbz/gWsAuZ68jzLWz5rz+b70elvrruqPtc3iM3AETnZ5RVa2ww4MTs11JPniUpDO2kbzE7zyagJkyxPnmdFNOeWppsSUSvmt6L08k7gHlNZaXxW/TrpRAOdK51ty0JgMDAl2hPNTnNX1MDn7fZyOANU0mSTcaWtOAE1O5sKTAnncAKkODLXApNRg7dllbbikLVaS9NNArXXbhxwYbQOZ4DV6Jept1VSbfnHo2btbt8THM4Aze53vAlkbXwd2As4OVKHEyAnu7w2J7vcDowAPgUeAtwFBcOORoUzWYBzOx1OTfwX+BaYHUgaFhWBUNqzUCtFl6Puy/Cc7PJno1nZycku/wK4GMhC3WfdsdvtGajn6GnUHtGRdrv9qk6HMzbMTnOi2Wm+GnXvT0JNCpn/6Q5nQWFawvrL60f4+pFo/JtzdXI4G2lop8O5W1OEWg2Lal9nabopExWueT8qWuQQU1npbZ0OZye7K51OZ9uyHBVWE8t+qNOBgcSeQEgvKoG97z/zxOBsY/ehnK1ZKY7MVvfSpDgyv0NluxoNPB1m4DoXNaC8y1RW+mKMtq5GJV2KO6m2fCNwLyocqb3vkZ5UAkOSi0o0lwWJhEDWxseBTGBajFkbycku/xk4ARVa2B/h//SvQxafXdfjzztTHJmx7ofuhJ2h8gsBMy1kNw9FQWHacNTKy0uosKgjc7LLZ+Zkl8eUjC0nu3wJau/m7ILCNN32mdrt9j52u/0R1IBuKJAHjLPb7Z2TFTFidprHoSYr7kUNsg/25Hlu9+R5/rGhZkHc603mwD4vG9nr1iS9JjR2aqgnzxOrhnbSBnjyPFtQ0SMROZ2l6abBpemm51GOZh/gVOBEU1lpS0koO+mk3el0OtsQT57HDzwBjDM7zdFmf5yFSqddqLth0fE7KstqMkClrXg6KlvtwymOzMWRNpLiyHwTNct9LtCoJmlpumkC8CCqKPaNGmxdDfQxO82aa2hGwHmoQfh1FY7cPWkQ1XC/9apL1xrzgIuAOzRkbQQgJ7tc5mSXv3FA8b1X9v/1BLbs/amsOPr62QWFaZcUFKZpTtH+D+clVJKJiJLgFBSmdS8oTLsTtX/PgioPMTonu1yP1ZfrUXtlHi0oTMvS0pDdbhd2u/1cVMbGuagM68PtdvsLdru9cy9KDJid5r3MTvMLqND2nsDJnjzPSZ48z+6WwbFdKChMmw5cbtjK4h6fGWFXBtuYMTvNemloJ23HB8DhZqd5ULgDStNNxtJ002xU/3Q2cBdwsKmsdFlDfddOOtmd6XQ6257nUBv0Z0Z6gtlpzkCtKDwRcFzbk4bQ3pRKW3EmalD2IXB1DG3dgQrLdVTaik8CKE03DUNl7y0FzjWVlWr5exvSZ++roY1WSbXld0dtMP8KlbBjT2Ln/Y73FxUUph2LGigtA27Wo81KW/GIpNpBzw1adYY7sWbwKAQrURM/nxcUprWczKqTsKQ4MrehylWdXmkrHhzuuEAo7Smo9/k6VEbNg3KyyxfmZJfrUvcs0M7ZqIHYawWFaWmxtGO32w9BrcAtQSX5OcJut8+12+17Sqh8m2J2mo1mp3ku6r6chervD/bkeWLOkrinUVCYdgwBDf1/9s47vMmy++OfO2lLWYKKCFq1WsVGjKJGRbEEUvUFwYW+Iq7gZrgVrTuvi7pw/ERQUYn6qogDBw400Vq3UcGoKa9WqpThYENL2zT374+TYkfSrCel1X6vy0tNnufOt8+4z33uc873mNeoCYjYSEpOp9VtbWJD28GaoRPxYQHS3ujISF8G8i2HIKUN0xGV+/0sZYHrwiJUnehEh0Cn09nG8Dv9fyKOyVlWtzVe9ceJiJrY7HTxSgCVADt12/MA4GUknXRsTnFBMNGBwml65yAT6LMV5zxyKLIzWw8cZykLpJpm9Ev43+mu67wM2Bm4qqJ41N9tt7FNnE6PN28A8l58D5yZkmpjGJVFpX2A15H2BicMPfbjbwAHoj64C/CFx5v3sMebt22qv/UPxUykd+M5kb70ePP2QK7/K4g699BCR/n4Qke54crHhY7y9UitOMBrHm/eNvGe63K5erhcrrsQ1dv9gAuBw1wu11dG8/ynwOq2DkY24f4PUevc1+/03+B3+lNtOfC3gcebtxuNbOjQ8T8FkfYRSTud4ayeLTbU7/R31h53HHyNtApr0jolkG/ZLpBveQT4DNgJGAccaSkLpE9VthOdSBM6nc6tg4eBnsjit1WEjcjpwPN+p39NuonFgcoMlcnBfUbciCilHZdTXJA0r5zigirgeB0Kbaz931vvA3sCJ1nKAkakXjVEOtPmdOYWze+LKKW+VlE86sN0/c5WRIPTmbZosceb1xtZKAWB4wod5RtTHbOyqDQLeBHoD5yQU1ywDLak3D4L5CO9Yy8EFnu8ec5wPWkn4kROcUEAiQxOqCwq3ZKu7PHmZXu8eTchGwh2RHH7wEJHeTp7J1LoKC9H1FD3Bv4bK4U6nEp7MhIRmoJEbge4XK5HXS5XZ3QoCVjd1u2tbuujSH3ajsApwL/8Tv//ti6z9gWPN68HMudlIXNegw1dBFgD+ZaE12ZWt9UMPE/YhnamL3cs+J3+eiRr7Gir26oC+RZTIN9yDpIpcC4ilpZvKQs835lK24mOijYRB+lEC3yO7KpPtLqtj/id/tYmkDOB7rQTcZrD+x6/xoQ51MXUrT8wMqe4YHGqY+YUFywrsw3z6I2/nZ61zwn/6zLgmE8MoAoiVFJHeiOdNwLdSHd/sK2H1cBm0hTp9HjzMpCFUh5QaIRqY1iY6kHE4Tk9p7igRd1goaN8HXCZx5s3G3m3ZgPnebx5kwsd5d+myuEfhBlIhHoEMN/jzRuBRLf2RFLnryx0lC9rKzKFjnKvx5t3CZKCdgdR3kuXy7UXoux5NLLQH+tyuYyad/5xsLqtzdvK3Av8pzPS1hIeb54JeApR2D6m0FHe2IYuQux9HtJKJhHcibyHE/xOf4kRXDvR5lgAjD17Qf0Y4AqkrOpjYJKlLNBplzrR4dHpdBqJEl93RJ58F1qJIvtzn+S1jR9/t7DmpzNGdDvkLkp85cAm4FvstkUNx4UlzycCPr/Tn5SKZxSeCpnMBgBdEjl13wP3Pab6t5Dp24pPvz5y5lWGNIwO5FvGA6ebeue+22XAMUchffAmhbnujfS9jDtdLgztz31y+RG/XrR8XWhTWhRsc4vmD0Bqcx+rGLnjKkp8JxAWWEoQy4GPsNtWG0owEkp8WYgzlosIBLWKlSa4OWTa0EtpOyW+U4EvsduMVMi7C0knOr/QUd40UlziOxjYB+iayIC9B2YeXlUZPLN2nS7OKS5oVbWx0FG+MFxXNR5ZtH3t8eY9CLjCKZuxUeLbHVFiTjRNtx6JxpeEm2SnFyW+3ogq8E5I7VAi+A34BLvtt2afzwNW1nZbcYXHm3cO0g5qMXBUoaP8vSR5mpF5dA8kmyIRVBWa53zuqR87A7ja4837LqxwC4DL5eqKCA9djWymXAo87HK5Ei4PCHPdD0mH7J7gmUGgArn3aekv2gQlvp2BwUBUkZJWsAL4GLvtz0hfWt3WA5CNm8FAKTDJ7/R/lyTPuGxoFKwDvsJuS39UNQUbmqdOG/Gb/vj4jfxyRaGjvLkNbbD/+5OA02l1W8cjGQXT/U7/I824JmtDQ8j89CF2W/rTokt8OwBDSM6G/g58it0Wd3utpJGgDW2GjcBC7LaI70fBd6FP81ZoRnyl5wJ/AmcDTyWtbZGkDUU268uBUuw2Q2rvW0XyNlQDy5BnND57nQpSs6Grgc+x236JeeTfGErrzii9ISjx9UIMbi4yEbX6QGqtqdG1XU3KXJ+lMmqRRYgZuAq7bQaA1W21I83Pz/E7/U8axFMhC4QzkRc27klTh7RZh8jSaF3207crBuYPysFuS+kBCuRbDkdS9EqBET1PePR2ZEE4OWdk1+XAf5HFeaIbJBoIrqpf1+W05bcufOeMDwanwjMScovmvwT8a9p+2zjG7Nz1DWTxkegiWSP3fhNwCHZb+nqwlviykZ3UA5DnM65FXY2miwayFZuQ5+VE7LaU++qFnZTHgQcLHeWXNuN6ExKlCpHIM6q1SdfTRSnqMXG3Uuq6eJ9RjzdvOyQ6dgESJb8CmFPoKI9+fonPjiinNjyjiRgiHT7vB6AQuy19ghAlvn5ITdC2JMcziHA9HLttSy2Rx5uXtc3yIfM39PUdqc01m1HcCtxb6ChPTsFZHM4XkAhk3M9oI9QDppAO3v5+6PQjEcfAXugo/9zlco1GIuC7I/PKFJfLlfwitcR3PpLylsz8FEKu6xfAMdht6VO8LvENQpRjIfF7D7IA3Qwcit1W0fBhuPTjVkSJ+E9ETO6ZGJk7rfFMyIZGQIMNHYvd9kZSHOJBCjY0pOvNIWqzTGQEFRlXqmEHP9j4+0C+JRtxTKZaygI3xjOm1W1tbENH+p3+vzYxZCM0FQ77lgoAACAASURBVBtajzgfQ7Hb0he1LvHlISnZqdhQDRRgt6WvrVGSNrQRgsh9mITdNrvhw3Bf8nHAvSHo91m+Wnp4md7fUhZIvqQqSRsaRoOT+wZwelodT2Ns6B/AYdhthusFbIExNtQEjMRu+8h4gh0DnU6nUSjxfY7sTia06xkBVcA47LbXrG7r88jiK8cwAYYS343IRJToznwT1NXV6szMrCLstruSHSOQb9kVEZtYDxxqKQusDteGvZrVW43YYXCXoFIqpesZ1PWhP+vX6X4Z23UxcuLMLZo/BPho20x12zdH9r0Q2J7UaqSDiKOzR9oiHyW+V5F+qonuejZHFTA4FePu8eYNQRZKHyApZn9Fmkp85yApmt1So8kmoAi77aEEuR2CLCoPAjzARYWO8paiDSW+AYj4Q0rvErKgL8VuS6gxeNwo8ZmQqMkuJL6ga4wQsBZ5Rtd5vHnDkOtk6fH7AXq7n499ZK/rnKn1yizxzQDOIvV7X7VZr7ry49CkKVqrnl/5jv2murrX0Uj95mSXy/V+ijxHIc5xyjyBN7Hb/p3iOJFR4tsRabXVk8SduMZoWNjtbq04uwbRGbgHiZzOAG7wO/2pqfwaa0OHYrelRwjKIBuK8DwLu+2lxh8G8i3fA+WWssBxkU/7C1a3tYkN9Tv9f2XLlPgGI/NXqs/oZsCH3VaQ4jiRUeLrhkT9U7WhGrkOe0aLyqcMY23oidhtCwL5ln2QVP/hgO+eMaYfv9jbdCKwnd/pr06Sp5E2dBZ222UpjhMZxtnQOkSQKz/VQEhEGGdDQTaVBhmcMdZh0CkkZARk9+sgUjeWIJPEaKvb2g9pbD/bYMW/E0n9BSczM0tprccke34g39IdeBXIRpRqVwPkFBfUA6dl9zWvQqd+PTOU2dTL1N38e3CNYSm2uUXzFdLgfMXLh233DnLfU32XMpAdtNwUx2kNR5K6sQRZvA5N9mSPN29XRLWxAhjbxOEUjCF1YwnynCf8jIZ7Rx6KpHgfBHzr8ebd4fHmNX9vjkidIiDvgD0cQUkHdkIElVI1libAvEYHhnm8ef9FNg2ygdE7L7x0Xtf1e5xcWVSa6jt7LMbc+25ZbPuv778b/kZ9vXmH/PyPjszI3HwDMChlh1MwGoN4EmdD+CRhQxbjqT5bZqD70+veGUGztjJ+p/8iAxxOI21oBjDMgHGiwRAbitz74yN8HpeCrdVtbWJDmzicAgciVJQqsoHBlPjSVY61D8bY0IZn/IAUx2kNRtnQrqHa2uMC+ZY7kfs9CCmlGvzF3qankGueipNvpA2NufmRAoyyoZmIdkcy5QPxwCgbCjIfH2bAOB0SnU6nMegN1Bo43s7AeYjxnGnguCC7iYZAa71jMueFlflmI4Z1nKUsEGj8fU5xwfruORlvKZMxa/Ag9fhrluQbMphgDDJp3LR794zuyCRiBIJAH4PGagpxaIwwQiBGNymeYcftNWSR0Vi1sTGi9n1MAkmNVegory90lM9AlFCfRXpM/uDx5p3QSOW2D8Ys6kDmYiMWM5HQB9kJThkhXddlaeitZ4GTkdTKgYWO8vlItKtP+PNU0CvF87dg5Yb1I1avzrnk5/KDP+7eY7U67LC51oKhTxuVRdDfoHEAehg4VnNsT+KpdRGxOVSbVVK96EXAiqSgH+Z3+r82YmyMtaFZJFcXGC8Ms6FEfo4WAbsG8i1R69vCwk2zCdtQv9MfiHBYP4zT7ajHwHezGbbHOBtqomPYULXpB//ZSCnR08DelrLATEtZoB74EHkXUtmMMtKGprOlmJE2tI503XsDbSjy96aLZ7tHp5BQmrD095WcdYeLlatXYTIpLhh9IpeePI5FP/2PCdOK2VhdRW6//vz3hlvZpnvTNUdIhxRi1N9Lt9R8NJ4Lf1zMhGnFbK6tIcOcwcOXX8MhloFNeYbqM5LctbgRWZxOsZQF3ox0gLmLalEUnso1La9b1q8wOa5NkFs0PwtRaPweMfotht1cU8PQSy+gpq6OYH2Qk+2F/OfsC7d8f/EDd/PkW6+z8e2t22GlrXmGVRvdyKJ1VMSU1XbCtQHhnpLjPd68x5FU0leANz3evIsLzXOgWQTp737v66nLzlDdvkEzttBR3ljoxIOkH01CasjSyvP0227AtzhApjmDQywDeeTK68jMiGjORk6c+MzbHm/eNch7+x1wm1H8DObaJojG86GXX+D+F5+jfHklf8x7lz69ezc5r1bXddnB3PtNwBnuN90uebY1ovFcsmIZp95yPavXr+fAAXvz9HW3kJUZV6CkQUxoP/6qxW2OLTbU7/RHtKGJcNVac8PjM5j7gQezycTE40/ikpNObXxqm9ZhpcCzTRGN5/ipLkoWfUOv7hIQn110M4P22rvpyWZzHXCEpSzwceOP/U5/ldVtLUUE9q5KN9d2cE3jsqF/o2f0H92aLablU0rVA37kQtUDF2mtP1FK5SL1MY3lvqdprZ9SSlUgDdk1sAY4S2vdQrFJKaWQxcoJWuv1SqknkLSl37XW+zY6zgWcj9SUAFyntX5TKWUFrtRaj0/kj24LZJgzuHfSZRw4IJ8NVZs46IKzOMp2KOfdfRv3TLwU+6CDeOLN17j7+ae59dymZVC/16/ti+SOXxpx8DbgefUj/8fN489j5KFDePOzj7l65oN88EBTUbxQKJTwyimQb/k34EIk4+81gmt813SNUZGJC5F2EKMrikcFKWkpKtwlKwvvtBn06NaNumCQIy4+j5GHHM7ggVZ8ZT+wdmP76CKwFXjejKSMX1HoKH+7nXNtgkJHeanHm3cgcDHwH+CHFaEPS/qpAmQaax8840WyPM1kVVnUBbfsM3xWE2XNnOKCUGVR6Uzg3sqi0v1zigsWRRzAIJ6nHzmSZ66/FYDTbr2BWfPnMfH4pkHWfj17LXS5XA3P2V1Ii4pbPd687wsd5a8Ywc8orm2JaDyHWPdn9GFHMOyyCRHP62bK3jh1hwvunXryo2l3OFPh2daIxnPa3Ge5/OTTOLXwaCbcO5XH33w13vveWMG2hdNpdVuTtqHRuAZ+XcLS33+j7Km5mEwmfl+TfiH1vzNPgLsnXMLJw6JvdfcYuN+7zR3ORngHuMvqtu7kd/qXp5NrR7mm/1SeSikNPKO1PjP8/xmIovjnWuvR4c9OAG5Boqh1wI1a63nh72YDb2itX2zlN7oCbwMOrXW9UuptRI38o4bfaDSWHVEKBxivtV6olBoNHKy1vjnW3xNPoKpaaz1Ia70/kmI2tdF35eHvGv55qtF3w7XW+yFCITdEGfsYYJHWuiGqNRvpMxUJ9zX6nTcBtNZ+IEcplc4+jEmh//Z9OHCAZHT27NYdy265LPvzDxYv/ZWh+x8IwFG2Q3jpw5blRSuDq3ZDZKBf31o8lVKs3yRimus2bWSnPi1T5UM6MaczkG85AIl0fQpcmGiD41Su6fpQVcrpVrlF83shjtP7QNTdZaUUPbpJFk5dMEhdMIhSivr6eqbMfJC7JlySKhVD0JY8Pd68fwM3AU8iip/tlms0FDrK6wod5dOAfGDeRpYerWn6DrQHnvEgWZ4mlRFUKqrZmI2IjqQmJhQHz2MGD0EphVKKQywDqfyjpWihSaktbQbCCsTnIz2Sn/Z48/YziqMRXNsS0XgesNfe5PbfKep5Gcqc/tYJjZAsz7ZGNJ7er7/kZLsDAOeIUcz7KO7WmSuRDfYWz2i4Pc0WG5qoUnA0rjNefYmbzjoPk0ne7b7bbpfIsIajo/OM61yTqbV716AOf1RqDBv9Xge/pv9gnpuAfcOOIcgzsaX3tVJqf0TU7XitdT5Sg3uPUioRG3cO8LLWumGOvxtR546EKY18sYXhz+YDxymlYqafJ5oduQ0SuUwEnyI1ipFwOlIID4DW+kOkl00ieB3YevkVcaBixXK++XExh1oGsu/ue/Dax5KyNvcDD0t/b972DtaHqvoAj/qd/uR6xxnA8/6LrmDKzAfZ5d+juGrGA0w9f3KL4xOJdAbyLTsi93oVMMZSFkipL2Ei11Sh9KbQZiNqfK5B6k+mVBSPatXY19fXM+jc0+h7wtEcZTuUQ/fZl4deeYHjhgyl//btJ52/LXiGI4Ru4BNgYqstSLYy13hQ6ChfVugoP3UHdeBj7ZlnLBjNM6e4YDXwHHBGZVFpoj0BE+LZgLpgkKcXvMmIQ2LrMhQ6yjcjIjBrgdc83jwj654M5ZputMazPaGj8szbKYfePXqSEU6jztmhL8vi3GwIb8a2EBOyuq1NbKjf6U/Khka6puXLlzHn/XexXXAWI6++hB8rf01maEPRkXkCXP/4w+x3zjguf2gaNbUJlyt/i/RG/le6uXaUa/oP5/kWMCr83+MQO9uAq4A7tNZLAML/ngpMSWD85r6YB8lWjQta2qB8gGSqtop4nM6uSqmFSqkyYBYiHtGAvPB3Df9EUtsagTQQj4QhQLyy5hcppb5VSj2hlGpc2OwjNZWvtGJjVRUn3XwN9190Bdt078ETV9/E9HlzOeiCM9lQVRWxxkOhNHKttxrPGa++xH2Tr2Dp3PncN/lyzr3r1hbn6DidzkC+pQtSC9cHUapdaSTXWNdUoUI1ujYlpzO3aP4uwOXAfyuKR8V8Zs1mMwsff5bKufP5IvA9Hy76mrkfeLj4xFNSoWE40s3T483rh0xmfwJjku7f2AZcE0VvZflJYWqxMdTeeEZDmnjOQBQPo+2SJozmPL/7+act3026r5ih+x1AwX7xCVYWOspXACcAOwIvebx5RolYGM41nWiNZ3tCR+UZ+HVJi2PijYCFsQjYN5BvyQCwuq1NbKjf6U/ahka6pjW1tWRnZeF79CnOH30C59zZ0t63NToyz6kXXETZUy/y5Uw3qzes587n3AmNGY5gLwCOCotGpY1rR7mm/3CezwOnKqWykQyIzxt9N5CWfpQv/HlMKKWygD201hVxcrk97Ivd16ylYVy+WCLptfmIA/mU+mv2bJ5eW9rovPeVUr8jEtPPRhl7O611PN70DCAPkZVeQdM6ht8ROeN2h7pgkJNuvobTjxzBmKGSZpO/Wy4L7nmIrx59mnGFR5O3U8sgcC9T99+MyuNPlqf7nTcYM3Q4AP8ediRflP3Q4ryQ1jGdznDT45mI2qvTUhb4xmiusa6pCaVrdV2qkc5bkffl+kRO6t2zJ8MGHcT733zFT8uWsufpY8gdexxVNZvZ87QTU6RkHNLB0+PN64K0RtkOUaptGdZvJ1yTRWsLyfbEszUYyTOnuOBLxPhMqiwqNVQwoYHn2198CsB/Zj/GH2vXMm3y5QmNU+go9wHjEbn+hxupEbc7rulGc57tFR2N52c/fMfajRsIBmVPqvKP3yOWqLSCRUjbjL2sbmsTG+p3+lOyoc25vv3Fp+Ts0JeTwvb0xILhfPvzjzHObjt0RJ79t++DUoouWVmcPeJYvgi0XD/FgQXIJoPhu1Qd8Zr+k3lqrb9F2umNo2Vpl6KliFKkz6KhD5L9Ew+uRcqLDkbWddc0+i4uXyyhmjyt9adKqT7E1wtnOJKLPBspcL0iwjFBpZRJax2K8F3j392yWFVKPQa80ejrbCC5BrpphNaac++6FcuuuVxxyulbPv99zWr6brsdoVCI255+ggnHndTi3J0y+rQQXWprnjttvwMlC79m2AEH4f36S/bK2aXFuXGm116OLPBusZQF5qaDa6xrqlChOl2ftNOZWzR/f6Rh/b0VxaNi3ps/1q4h05xB7549qa7ZzHtffcE1485i5SvvbDmmx4ih/PRsUy2T6lBNt6v/mHnLB+6Fq5LlGg0K+Db3SUN4xoPwQv5RZKF0cqGjfGGMU1pFOrkaiWR5al1vLgmd6673VhteN7ctA3vvb7qmq7nRpmSar+cM4HFk1zMlmd5oPGe9MY93vvwUz7SHt9TOJIJCR/kcjzfPimwi+YEHUuGZCteQ1qb93dbnU/39SDi31zG5E3odn51t+iugG41ne0MqPFeGPhr9vXdsS6NlAApMj/XJUn9lj0fjOfwAGy+WeDm18Gjcb8/n+CEJtTZuLCY0krAN9Tv9KdnQaFxPOMKO9xsf5/Q/jpKFXzMgp6lMhg4Gs366/orHgmvXGNkaDoBeg4/o1++08d1MXWLPT7F4hmpruv7x6ksXr77wzEj9T1OG5ZGnm/x/NJ4rVv1J/+37oLVm3kcfsO/ueyTzc++G/3008WcERkWy17QuqLs/OsGblvnpiAO75Fv3yjKbGrXHS5ZnMKi7vfp+1T0rn/O26HyQKnbe0dx71NBuXTMzUueZAl5DajeH0bRt0/dIP+ZvG312IBDvTkc14kfFhNZ6Rfg/a5RST9JUXTkuXywhp1MplY/0/FpFHP2KtNbVSqnLAL9S6jatdfN6zcXAHkCrOTNKqf6N/tgTEdn7Bgxo9v/tAh/7F/H0gjex7rEng849DYA7zp/Mj5W/Mn2eiEiNKRjG2SOPbXFupsowfGJPlOdjV13PpQ/dS7C+nuysLB698roW52odyrh37Gh15Zw3Iu6oBPItI5GC5JcQxc+0cI11TRUqFCLUw+q29vI7/etaDBwbdyE7QXfEc/CKVX/inOqiPhQiFApxyvAjGX147AzwekLmTXrzXohysaGIdIOS5RknrkQcdVeho/ylVAdLM1fDkCxPDQpC+5EG2fd6arpoQk16Nab5ej6PZKNMIkWnMxrPDMdgduvXj8MmnQPAmKHDucl5fqLD34SkIE3zePMChY7yBbFOSA9XDZLFYzjWh6q2CaGbeLrReD740vPc9dzTrFy9iv3OHccxhw5h1tXRNADTj1R4BqnqS5quqSbYJCU7Gs99cnfn1Fuu54bHZ3DAXntz7jEJ+UFlQF15P05EWqMYYkOjcT3COojTb7+R++Y+S4+u3Zg1pen11ForXV+/L9BqgCAZ1FdXddeh+OanmDxD2lRfXbUbEoVJO6LxdFw+kT/WrkFrzaA9BzDzimsTHtvv9P9mdVsXIk7n1FjHJ8s19r3HTJrepZpatqNZC5FkeYY0pmBQ52Ncv98tqAvSJXwdUuaZAp4A1mmt/UqpYY0+vweYq5Tyaq0rwp1FriPOntla6zVKKbNSKltr3WqdeIMvFs52PYEkfDEl9Z+t/khDyxSQh+M6rfX8KC1TntBaPxhumWLTWv8ZHuP/kDYoTZKblVI3Aiu01rPC//8c4sX3QYqob9ZaP66Uehp56DVQAVzY4IQqpR4C3tFap13pNSpKfP2AnzGoufsPNRW/7nP0ybsZMVYLlPh+AQzZelmzbjVP/PfBvlfOeeOP5t8F8i0W4DNgCTDEUhbYlCDPB5H2FCmjTgerzlhxW7cfan/Zz+/0+2Of8Rdyi+YfjciXX1FRPOq+CDz/BczBmMbZ64CR2G3G55BJY2sjFwwu7LaIiyCPN+8YJBvhReDUQkd5Yr9b4vsCSd8wAt9jt6VHfaTEdzVwO8b0Ow4CvbDbqgwYqylKfIOQ1gtGiPusB5zYbdHq9AGoLCqdBlwE7JpTXBB//VmJbwPQI+Zx8eET7LYhrR3g8eb1AD4GdgMOLXSUL27t+C0o8c0DjIqmhLDbzLEPSwIlvrOA6RhzTdcBY7DbvAaM1RQG21DgHuy2RIQ04oeBNhR4D7stokLpwn0tgcAuas87TjV/DwzxO/1bzYYiqtQ52G2GZ+EYbEM3ABditz0X88hEYbwNnYvd1moBvdVtLUayBLfzO/0b4x7ZWBu6Frtt29iHJQFjbeh64DDstqRymVuFsTa0FrgGuy0uFX+l1EatdY9mnw0DrmrUMmUMsimVibRMuVlr/XL4u9nAsfwVhVyqtT6s2XiPA89prd8L/38pkkbbAwkynqu1fkcp5UUyXRWwEJigtd4YPucN4NpwV5GoiHmjtdYRjWG46DSigdBa5zb7/2gT3yyk19Ss8HHjoowXUZQiXMRqAy6LMn5bYSNysw3BkrqVO411W7fxO/2GpwmQgCJVLNTV1oBE5Zo4nYF8y3ZIKsBmRDgoMWMpWINM8CkX0ZswqU2hzSALhbidztyi+WYkUrsEeDjKYUY2XjQZPF5z1CJ9nFJFDVF4erx5FkRZbSEwPmGHU2Dkc5+Od6jx2HUYYzBBrms6sB4wyqnRxPeMzkRS689FFhXxogrjnM6YdSqFjvKNHm/e8cCXiKLt4EJHeTwK7YmquLeGdN13kHtl1EJZkb73yUgbGsTY+9McRs7REXla3dbtLttT7WxZqk2IcNBWtaHIvUmGQzww8nqGDB6vOYyyoRBfp4YFSN3cMJqWlcWCke9puu47GGtDM0nfvTfShtaRmDJsC3uotf4AUYtt+P+XEf2MSOePj+NnHkI2N94LnxMx7Ulr7Yj0uVJqR6BrLIcTjJmMkkY4WvmYUirZ3YNdgSKtdZu2FmkBu20j0jcnZeMe0qHNX24OZABnpMwrMj5CnMGUENKhmsoVvwLkNP48rLY3B4kcjLGUBZLVi/4Cg2p1Fap+WfBPSHx3+kxEKey6iuJR0RaGAQzccADKDRzrL9htGsn5N+JdqUfEYprA483bDmlhVA0cX+goTzZq9wHifKSKaiI0VzcQX2FMOmwQWIzdlq4+iL+Gf8MIrpnEkUKTU1zwP8SAXVhZVBqXsXa5XGpN9aZlwXpDLkMVcd77Qkd5BTAG2B143uPNi7kA8m1e/OvmUK0RzlwdskGTLizCuPnJTNPMJsPgqR9bW6c3bQxpQ+59DRHmJwNhiA0lyjNqdVszgDk/7aS6bbsJ0wtTg8nOhUbZ0HqgArvNiL85Eoy0oVk0rW0zDsba0E1IhkUsfIw8J0cnMvhvq4K/BIMx0hjjQw2SsZYuGGVDNfJOLot1YJIw0oYq4GsDxjEMWutvEPHXZB3rXZGSqpjYqk4ngNb6Ba11UrsyWusfwx5/e0AhsnOV9IQU0qHNJmV65uWNH34FTAqr1hmNy4AvtdapGKPqUH2978NPFkAzpxOYhigWX2gpC8QzqUaG3TYfqaWsIvkXvR7YUEdwWJD6OhJwOnOL5ncDbkMiIHNa4bkGOAYxIskuQuuRnf4jsdvSKYp1LLCcJGseQhrqNLXAtdhtTWr1PN68TOAFJPJ9YqGjfGkKPIuBt0nN8axCnNebUhijddhtXyLRvFSe0TokU2CEUbRawG4LIjvlG5BnLRmEkL/zROy2eFWIZyDPw6hYB7pcrn0A78wvSw9YV7O5WmudSl1OFRIVuDfWgQ0odJSXIjWoRyPZDRFhdVu3tbqtD5+9svjGtzZ9VhPU9alEKWuBSqQ2Jj2w235GerBVk9r8tAkYgd1meCTB481zAIu+DF3bO0RNbQqbyBq597djt6VUnxsDlyF2IZW5ehMwF3lHmmMacGSXOhrKOfaPcExsNLKhOlYdVXTUIeuaI5M8PzaMsaEh5H6MxW5LZwPHlGwo8oxuQoTWnol1sN/pr0HsWFz9OqdP8PabPsH7zEvvVp1T+VuwOhTSqcxPm5Ha4rNTGKN1GGND65FIpB27zfCaY8BoG3oRdpshCtRGQmv9hNbJ7fpprb/UWse1eRqzprMTCaDEtxsiaTyAOEPxwQ3rey1f4h/V/Y+NGbW/rViytvSDsadco6zIpGT3O/0pCXFE4Zm9oSL4mlIcldXb9HlWL1Miu9ebAP/ylZVPPvfyrA3A3VfOeeM6gEC+5QLgEeA+S1kgklpxMlxPQFobtFBMrl0XGlC3ITS4y/amtzK6mprXlWpEoOol7LaA1W0tBz7zO/2nNx8nEnKL5l+LCAfZK4pHxb4HJb4DgOMW1/569OLapYcPzNr9w7ysnSri+CmNRDdfwW5LvyBWia8vIvCTT7PdZa21WlDlOyETc7Wj+4FvN/4upGEG6oyVWr146/ADxzYf1uPN+z+khm98oaM8saZkkXlmAE5gUCio+1b/Vn+yOVv9L3t78xdxnL0RifA8id1WlzKXWCjxHY2odSfauimIGPVnsNtWxDo4ZZT48hFxgT1pJt4QB5YAb2K3xXP9AagsKs0AfgH8OcUFEZ1ql8vVA9kYuBwx6NdOPsT+wg7de54F7EOcqnqNsA74Bngqmcixx5v3AHAJcG6ho/yJhs/DG4BnIQ7p9sBDPU1db/pk14dHI7VTidY81SERHjd225+J8kwYJb4jEIc6Ua0AjdRavordtijWwYnA483rj2wMjEOer4sLzXO+I0Eb2gi/A6XYba8ZyTMiSnzZwDnAvkhf2kSwFonwPNN8kWx1W7fY0BemBqcif9OVlrLAtGSprp06zVW/aeNNXfrvvLT7wP1KlFLxLvqCwP+AZ7HbUtlEjA9hG4pkHCQ6P/0CvJUWPYTmaMWGxoHVSI/FOeHIaUxY3dZLgfuB3f1Of0WkY6ZP8GYgm2a3InPmXbv2z7jr2GHdTkH0UBLNJtyMqKI+mY6NphZI3oaGgB+BF7Hb/mc4r+ZIzYYuAzzYbR7DeXUgdDqdWxlWt9WB1p5z3wk9/K9v9IlAvzozT1x4kfnkjd3UW36nP2KdayqoLCo9E6mlfQSYmFNckNRDcO/Y0RXAh1fOeeOsQL7FjqTTeYDRlrJA2lOeK4tKeyIv8ryc4oJWtfStbuv7QKbf6T8i1ri5RfN3QBzB9yuKR8UtFmJ1W7sjRnopcFi4wXOHgtVtvRJRQ9vf7/Q3SVPq9/7CCqB05fBBTWqsPd68C5EavnsLHeWNJbQNQWVR6TVI9HNgTnGB8SIBnUgbKotKbwZcwJ45xQVbUsddLpdCUlrvR7IlngCKXC5XC1GytkQ4tfZNZFfbUego/8jqtlqRmu4jkFSzSUb1SvwnInyNL0JaqXVB3u3iQkd5u2t91pawuq1NbKjf6Q8G8i3LgXctZQFnMmMG8i17I07OL4igX/xiNJ1oF7C6rRak/cWFfqf/0ebfT5/gPQyZnwYhNaAXT57pSL8D1olOJIGtnl7bCSai1OrHR5ivRHbOHsisZ/z0h+uzhi8K/fvwGfsm3V8yC6Eu8gAAIABJREFUEiqLSg8FHkNqSS5O1uFsGA7ICeRbdkck3cuBU9vC4QTIKS7YADwNnFJZVNonxuG/En967U1IS6CiBCldjuzUXdURHc4wZiO7nBMjfFdJs3RqjzdvGFKE/hZNGwUbgnA94IVASafD2SHxGJKONKHhA5fLtRfyvLyI7PwPcblc525thxOg0FEeBMYCFdUhXjns6X0fQyKnFuA8REW00+FMEh5v3hAkyncfUq82sNBRfnOnw2ltYkP9Tn+DDV1Ekum1gXzLtoigXx1wfKfD2WFRhmxkN6nrnD7B22f6BO/jwCdIJtgpwIhOh7MT7RmdTudWhNVt3QnpO/qE3+nfbCkLrLeUBS4HDlQQmPhmyDx1dv3ngXxLcjUdzVBZVJoDzENqEk7OKS5INfVwKVrvghg2M6JUG1Mx0mDMQHbLY9Ud/ArsHBZpiIrcovkDkAXyrIriUYF4SVjd1r6I0zXP7/R/FO957Q1+p38V0mfxTKvb2jwlp4nT6fHm7YE4Dj8C4wod5ekQwvkXkm4VTT24E+0YOcUFy4FXgHNevWH2ti6X6xZEiOhwpDbuIJfL9cnW5Ngcly3ttvb+37rMuGNF1+03hjgvA+0G9vY7/Y/7nf701Az9zeHx5u3g8eY9iYjwbAecBBxT6ChvtUf3PwFWt7UnjWyo3+lvbEMXAfsE8i0JKaaGBf2eR+bOMZayQIVBdDvRxghvYC8ACq1ua8b0CV7T9AneCxBRr7OQzCTL5JmOuZNnOjrqZncn/iHodDq3Ls5DDM3Mxh9aygLfZtdx8FMO0w9da8jR8HUg33J/IN+SdI+gyqLSbsCrSDuCY3OKC1KvI9K6Umn20BIFOMVSFmjzHbac4oLvkAb0EyqLSlt7nn9BnvdYNQN3IIptrgSp3Iy0EEo0OtoeMQOpU2quoLwUyOn3/kLl8eb1RJ4nE3BcoaN8XZq4TER69rbaF7IT7RozfjX9ud135qWLgRuRjYq9XS7XAy6Xa+sqjzeD1W3dG3i3otY8Lagpv6xvTeieXaq3vX+XqnS24PjbwuPNM3u8eROQBfIZiLCNpdBR/nKho/wfv0C2uq0mJFvHApzid/qb29AG9eG9Exz6HiQyNslSFihNmWgntjbeAXof8svoM4BPkdKo74BBk2c6pkye6Uh/3WUnOmEAOp3OrQSr25qJpA2+43f6W7TJsJQFQm8carrxkglm09I+vIOIWywO5FvGBfItCRUwVxaVKqRm6gDgtJzigu8N+BPYZdX6/bVJmTZ1ybzBUhZ414gxk8QMYA9alxVvULSLmmKbWzT/cGQH/q6K4lFxN7QPL1QvBB71O/1paSnQxvgSSYFrrqBcCWTvoH/rA/wXWSj9O13Risqi0lxE+fSxnOKCVNRMO7GV4HK5cmd18VyyIGsR2TqzB+BwuVynu1yu9AsnJQCr29rd6rbegfTxtQEXVWuVn9sldBWSjeLamvw6IjzePBtSAzuDcJpooaP8mkJHeWea51+4FTgeuMLv9EeyoQ3iTXFnOwXyLecClwIPWsoCs1Kn2ImtDdvSEb4jfj6ZA5Yf+QSQi0Q4h02e6TBkLdeJTrQVjGpq3onEcSwSdYtUO9eA1zZ1VcuvOj9DvTA1eCiSYvgscH4g3zLZUhaIN/3zeqRO6dqc4oLXU2IdRiDfclqfXt2PWtqnF6V77/LWwUYMmjxeRlT+JiGtNiKhVaczt2i+QnaHV5JAq4UwihG59v8keF67hN/p11a3dQYwCxFPadgprwQ4i8eLkef34kJHeTqV2C5A1DNbiCd0on3D5XJ1Qfp23YBC717f9/VhdQOPNWNKV0Q8KYQ3VY4HHkDmhqeAq/1O/28AHm/e/YAVuNHjzfuu0FH+wlYj20Hg8eZti2SMXIjMy6cDz3VGNpvC6raeBlyHzLP/F+Ww/yGZN/sTR4uNQL7lCMTJf5c4++Z1ov1i+gSvAs60MfIeTYgf+/h+G/DnwZbJMx1tXcbUiU4Ygs5I59bDRCRdcX60A8JiAo8CI065NmM1MDh83gHAt4F8S3Eg39KqXHtlUemJyG7qf4E7jSAeyLccDDyeWR9aCKCVat6rs00RjoLNAkZVFpVGawnQIPke7fsxwGHATRXFozbF+9tWt7UA6bN3Z8NC9W+C55D2E5MafVYZ/vc5yHM5PV0/XllU2gVJP389p7gg/XL9nTAMLpfrSKSJ+u2IYJClsM56lhlTNa1vsrUprG7rHsDrSM3pBqRFlbPxexx2lCYiYh2zPd68g7YK2Q4AjzfP5PHmjUdSaS9AHKm9Cx3lz3Y6nE1hdVsPRtqilQKTownPhUX5viOOSGcg37IbsgFbAYxtK0G/TqQH0yd490UEH93AzwsGPPmYd69n+s487NKtzKwTnUgenU7nVoDVbR2ANFt+xO/0xxJfaVB/vNBSFqi3lAVmIvUdzyDCNYFAvmVMpJTbyqLS/ZB6kS+A81NUqgUgkG/ZCanl+21lr+4NbUq2qtMZRkM07IJIX/qd/k3AKiJEOnOL5mch0cofgCfj/cFwlORuRJgp6T5q7RF+p78KUbI9yeq27ggwUT/QB2Apu/2IRDnTuZA8CVHk6xQQ6iBwuVw7u1yuOUiUxQyMdLlcJ7lcrl9zigvWIhtfp1UWlfbemjytbmu21W29CelDZweuAg6I1hO50FFeg2xK/Qm8Gu4v2YlG8Hjz9kNq659E+iMfVOgovzSNtd4dFmEBwVeRWvWT/E5/rNKBmAq2gXxLD0SMKAs41lIW6KxB7qCYPsHbc/oE773AQmAgsqY5fMn23z6FrNkdW5NfJzqRCjr7dBqJEl8BcC2Scx/Vof+zfl3fjaHq3jkZO/ycocwNu5F/ILuU9zdvGmx1W19E+sbl+J3+zQ2fh1NppgP7IRGFSyxlgZ8AKotK+yLOZiZgyyku+KuGqsTXH3GWrIjya1zQoZCq/W1FTtWPizPXlr5f8PK2Zh+S+jP1yjlv3BjvOHGjxJeB9HIrBHrFOjxYHdpZh+ia0U2VK6VqkdSkG7DbAgBWt/VrYIXf6R/V+LzcovkXIbvyoyuKR0WNPDeH1W39N/ACcK4/98lq4HwSb24MktL7HHbbI0mcmxhKfIMI900kRnp9ra7LWhb8c/feph5/9jJ1XbeRyl1f0LmZ/6c3P7DIccRl6aS5/MbST3rtkzmwW39zuVKqW4Kn1yIy89dgty1JA72/UOJTiHjUsYgqZyLQSNr3NOy2d4ym1gIlvmMQxdhdSLyx9SpkoXx38/nJ5XJlAhcj6eWZwFTgTpfLtbnxcZVFpQcitcKX5hQXPNgKzz3DYyTTfH094vxch93WYjFvdVtHIC1+8pB39wq/078snoE93rxBSJsPPzCs0DxHIcI4hyECbYmgFnF6r8ZuS38kv8Q3CTgV6JvE2cuAR7Hb5jT/wuPN2wa57xcDa4GrgdmFjvLkVH7jtKFRsBbpcXkTdls6lLT/QpI2NKRDakndypyva/6X+eKGDwrmjHvj81jnBPItlyDp3/0tZYEWWgOBfIsJEec6HjjGUhb4ay5J0IY2Qz0SNb0duy39CtMlvnH8zWxoBGxA5pAi7LYm82M4lfbfSDuhnZDsrWsnz3T8CVt0QP4Envc7/RfGwbM3UjJ0INICLhF0FBtaD/wM3ILd9qXR1FogNRu6AngKuy3uwMbfEZ1Op1Eo8Q0H3iDxl7sxqhAn8prGCzur2+pAjOlZfqf/6cYnhKXRGxptZwHFmblDp2UPOmM+IohRkFNc4GvEsz/SLLo/Sdb06lCoTplMi4Bh9z7sCgDeK+e8MT6ZsaJCjOWLwFEkf001shA5ArvtB6vb+gqwp9/ptzYckFs0vxeyM+8HCiuKR8X1Qljd1iwkMlr9zW6zHs5Q5ntS4Aly76dit92Wwhito8R3IPAB0DPZITZrWAIrLYrdmxtNo/DHPR8f3js/8+OM7qpemZQ5yWFCiKM0GLvtZwPp/QUxlo8gNWup3PtqYBx226uG8IqEEt9JSNZD1xRG2YREvy9umJ9cLlcBEo3eF3gTuMTlcrUQRmtAZVHpZ8jid5+ImRficH4GbEvymTjViON5XIPjaXVbdwHuRyKWPyIpjQmLn3m8eWOAl8x0fdZuenI3pdRBQHaSPOuRmsdD0+p4lvj+g0RzU31GL8ZuexzA481TiE7ANKAfkpFzXaGjfFUKPI2yoW8Cp6bN8TTAhtbrUJ1ZiQ3Fbmu1nCOQb7Ej8/aIJg7lX9/fgqhCX2EpC9zXiKcRNhTkvT8Guy1iJoAhKPFNQLQU/vY2FHmXPgdGNtjQ6RO8eyObYUcifYEnTZ7p+Kz5ieE1zAHA7q32AheH8yNgL2RdmAw6kg3dCByF3dbimhkGY2xoFXAjdtvfKjMuEXSm1xqHV0jtpSF8/mTgkGafv4/UyUxqfoKlLBC0lAXuR6ICrwA3m7ffqxIRgDmnicMpeIgUjCWAMpkykR3eq2jWu9FAjEMm4FSuqQJ6I+JLIFGl5um11wB9gCnxOpxhTATyDs8eODVDme9LkSfh86+jxGdJcZzW8CKpGUuyFewh0ZLYO61Joseu5pkZ3RUpOJwgc9v2iGpzunAUcBqp3/uuwBxKfIlG9eJDiS8bSW1NxViCtNEZD9hdLteOLpfLjTh32yAKr6NbczjDmIHMVcOifP8UqTmcIH9nATDe6rZmWd3Wq4EAMBK4AbAm43ACFDrKXwZu3lkdeZomdCjJO5wgKch9SadQVolvf1J3OEGu6XRKfH083rx84D2k7nsFMLjQUX5hSg6nwCgbOhI4OcVxWkPKNtSsmtjQWPg2/O8WKbaBfMtYxOF8EtlUaQwjbCjIe/9yimNEhzjx9/MPsaHIuzQYuHD6BG+36RO8tyOb3ocgGQMHR3I4w1iA6FLsFeM3/kNqDid0LBvaA3gpdTpRYJwN7QbcTolv99RJdUx0Op1GQHaVUn0YGxBCFmVbEN7RmgEMtrqtB0Q6yVIWWG4pC4zrOuSK6Zm7HNqzZvGbbJh3wSlhcYHGsGKManEXJG0jXU7nPqQ+EYE4nnnh//4V2MbqtvYCyC2avwtwOfBsRfGor+Id0Oq29kYMvefhHa9YBhgV8auj2b03DLKjGLVdTCLoojABg4wYqzkqi0p7mjLVPsqUaOZKRJhI1/UU5COOgxEIIik76UAuki6VMrTW6psVSxsak49DaqH3cblc81wuVzybNi8Aq4mwgRbG3hhjl7otqV0xAqmLuhPJFNnH7/Tf7nf6a1Ic+9bt1f6/mpTZiHnUjNRtpQt7I89WytBa13wXevBexAk6iPAGaaGj/IuUBzfWhnZF2jmlC0bb0FYRrs9cSjOnM5BvOQjJPPgEmGgpCzR//4yyoQC9KPElmkIeLwbwD7OhQPaa9fXHItlS1wFzgL0nz3Q8NHmmo7UIfUOku7X2cCD2ORWHswEdyYb2S9vGrYE2FClJS7Tv7t8GnU6nMchGUqWMgJnIxteNpGVEVX+sLCo9OmOH/Ilah16rLXvtOmRiCgTyLUWBfEvDBBR3/Ukc6EbY6bx37GhDvIRG6E7iOfPR0DARNbRNaXDEb0HegesTHK8I2QGcYlamVCIdzaFILXISC0ZN7mhNq6rJKeCMFCOczWHk894c2RjXdqqe9N37bCTVPGXUa931t43rxyG1mfu5XK5rXS5X3GrPOcUF1UhU5oTKotJItVtGLJQAWFTz04nIXHqc3+k/3u/0VxgxbqGjXPdmn29jHxk3DPubIyAbg+x8PdU9avXas5AI596FjvKHCx3lRtk9I22oCdI2P4HxNjQeNBETCuRb+iM11r8DYyxlgUgbKUba0CDpm0v/kTZ01dpQIZK6PGzyTMeZk2c6YvYG9zv9PwPlxHY6jdrAgY5jQ0MkrgEQLwyzoaT/GW3XiHmzlVL1SOg/A0lRcmqtq5RSG7XWPZRSueHPFzc6bZrW+qkIY70IXK21/lkpdTvS4HZbrXWPZsedghRqa2CR1vo0pdQOwNNa6xFJ/J1tjs01NQy99AJq6uoI1gc52V7If86+kCUrlnHqLdezev16DhywN09fdwtZmbHfE7/Tv9bqtj4LnG51W6f4nf4mqoCVRaUDkN2y75UynWEJfL8hkG95FklbmQo4A/mWyZZHmpSEGsGzEjFuvZD6ybQhGtcGXPzA3Tz51utsfDtq6cmWXp3hvpxO4N6K4lEV8XKwuq27IoXkz/id/m8o8f0rXp7jp7ooWfQNvbrLemh20c0M2mvrbXilwrMGsoyeNSuLShUwSZmpotli7J9wTTsGT61yt93+qc8ql4yPM7IZCTORHoLnIRs/CfM8965b8S0OoLVmQM6uzC66mR7dmq7fd8nccRFweFiJ2VCYlLmFg5QK17ZENJ6n33YDvsUBMs0ZHGIZyCNXXkdmRuMlgqKvOuyyA4e/9sDW5Km15obHZzD3Aw9mk4mJx5/EJSed2haUEuJp8H1fBIwM5Fsapt1XkDT0IZayQNytuqJxLbj4fDZUyd7R72vXcEj+QObdfk+yXFNGR+cZzzPau6fpW8A2eaajLsGfXQCcaXVbs+JQPo7JtaNcU89XXzBl5oOEQiF6dO3G7KKb2TMnXclB7Yfn380Hi2eHoVprPShM5L/ABFq2hyhvOCYalFIDAbPWuqEg+XWkNuLHZsfthajXDdFar1FK9QXQWv+hlFqhlBqitf44Dt5bFV2ysvBOm0GPbt2oCwY54uLzGHnI4Uyb+yyXn3wapxYezYR7p/L4m68y8fi4y08eBs5FHpQtzaQri0q3Ra5nEDgup7hgA4ClLPALcGIg33JM+HhPcMP6qoye2xjJs6F3Yw5pdjqjcR080Iqv7AfWbtwQa4gtTid/qS3ekSCNBpGCGxLlCXD3hEs4eVhhgj+ZHqTCcxN0TcNW3RHAvqYsVYGksxjCtS3xd+dpVqba/D79vknB4SSnuOCnyqLSd4ALKotK78gpLoia/hmN532TL2eb7mInr5h+Hw+98gJFp49vcu5B2QO+SYfDmQ6ubYloPE8/ciTPXH8rAKfdegOz5s9rMuebyd6QYzrKv7V5Bn5dwtLff6PsqbmYTCZ+X7O6rSglxNPg+74IibLtg2x6HgqcZCkLLDSCa+n/PbblmJNuuprjh9iT5WkIOjrPeJ7RPtuaFyfhcIKk2E5EVLNLUuXaUa7pxPvu5NXb78Gy2+48PG8utz39OLOvdf0TeP6tfLBE025KEZnoZHA6kg4CgNb6M631igjHnQ9M11qvCR/3e6Pv5oXHafdQSm3Z1awLBqkLBlFK4f36S062S5sl54hRzPso7jkDv9P/NdIGZWK4RySVRaUZwPPAHsBJOcUFFc3Ps5QF3kTUJf+j6+qapF0YwLNBdTHtvTqjca2vr2fKzAe5a8IlsYb4DaitXT14KJKecltF8ai4+5mF62nPAB7wO/2/JMqzvSEVnlXGpu80YCKwzpRFCwv9T7imbYlkeRr4t8wAdkZk8lv9vUg8GxbzWmuqa2raxTXuKFyj8Txm8BCUUiilOMQykMo/fm9xXnvgOePVl7jprPMwmWT50nfbRLssGIs2uu+Lwv++BjgTuNlSFkhY3CfWe7+hahPer32ccMTWdTw6Os80P6PvIwGGWCm2cXFtQHu/pkrB+k0SkV23aSM79dlha9LcWjw7vA8Wt9OplMpAVOEi7XTmKaUWNvqnIMIxQ5A6oFgYAAxQSn2slPpMKdU4lOtDVAk7BOrr6xl07mn0PeFojrIdSt5OOfTu0ZOMcMpSzg59WdbMsMeBhxGRhIaZ4W5k8pmUU1wQNa/UUhaotpQFXBm9erV4yFLk2RDpbJM8h+ZcD91nXx565QWOGzKU/tv3afVcv9Mf0lotrV099F/AEqQ9TVwIO/l3ISIoU5PhCXD94w+z3znjuPyhadTUGlWXnjyS5VllnEAFAJVFpTsiipOzw+kkhnFta3TyjAvzkQ2rqDXqsXieXfwf+o0ZQdmvFVw8Zmy6eCaEjsI1Gk+QBdTTC95kxCGHbUWGgkg8y5cvY87772K74CxGXn0JP1b+GnugrcATDL3v5YgAySnAXOBWo7kCvFL6AYUHHrzFYd6a6Mg80/mM+p3+9cCnQIvSnmS4NqC9X9NZU27gmKLLyDl5FE8veIui05xbm2ab8vy7+GDxOJ1dlVILwz/2K/B4hGPKtdaDGv1TGuGY/sAfcfxeBiL1PAxRR5yllOod/u53kmscvFVgNptZ+PizVM6dzxeB7wn82rLHbhK7ny8Aa4BJlUWl5yCpNv+XU1zwWOunhX/PnNEilS1FniuRAu60RzqhJdcPF33N3A88XHziKXGdX7d6SK2u2643cF1F8ahEVCz/hcjP3+p3+mOmETfn+d3PPzH1gosoe+pFvpzpZvWG9dz5nDuBn08PkuW52fhI5zmICMAMo7m2NTp5xkY4pfZR4KhwPXpCPAGeLLqZ5S++iWW3XOa8vyAtPBNFR+EajSfApPuKGbrfARTsF1EovU0RiWdNbS3ZWVn4Hn2K80efwDl3Ju1/pZUnGHrf85G10UZgfASl2pS5AjzneYdxhQn7MmlBR+bZBs/oAuBAq9uaUBitI1/T++Y+y5vF91P54nzOHnksV0xv3iHob8vzb+WDJVTTmSKqiU+xqRL4TGtdByxRSi1GLsCX4fOrDeDSpujdsyfDBh3EZz98x9qNGwgGg2RkZFD5x+8Jh979Tn+11W19YmBV3mUafYJCvQdcsbV4Xjnnjbp7x45eSRs5nc25vv/NV/y0bCl7nj4GgKqazex52on89OwrW47VWiuvN6/rvJ+Oya5dNXw3c/ayuhsPePo1j/eSuBynnzabTFmqyz0alozpXfukx5u35bwC06NZmWwT1Slv4Pn2F59y1alnAlILcPaIY7lnzjNNjtVaE6Qq88NG4xuFLHpTYH4k6veJ8ARjnc7KolIzUqfgzSkuWExJ89ayqXHdWujkGROzgJuRex9zDmvMc989JMPIbDYzdvhR3P38M5w98rh0ck0IiXBtmJ/SwcNuejLTTNe45qd999iT/8x+jD/WruWRW69LB52k0Zhnzg59OWmolH6cWDCcs+9sqUVVq+syDnJb03JNF+32hDLFeT0h9Wc0kG/ZHngNadkQxKA1UHOuq9at5YuyH3jl1rtbHKu1VhXLgtlvTvAafk3PObFHVnYXFfczGoMntXVkzkoDz27ZirNPjN6iM9FnNEUsQKLdRyJq0gkh0Xu/rKg0Le/STkdnZyhT9KBGA8+3vviUReU/bonMjh1+FCOujllO1WZIM8+/lQ/Wli1TAsSXizwPGA6glOqDhHobCl8HAN+lhZ3B+GPtGtZuEGGb6prNvPfVF1h2zWX4ATZeLPEC4H57PscPGZrw2OP+GPn6DZUXmNebN60HTmlNiKONeKarV2dMrgcNyGflK+9QMec1Kua8Rrcu2U0cTgBNMAuoyjTVrdb13btdue9Lmbv0XL4JqIrnnz2zQxvvyqkeeHdO9e6H9ahf1/i770PTXwtS1SsWz/xdc1mx6k/hozXzPvqAfXffownPeqp7Ltaz3PHySuSfWta2EFRJlifAZmNbEoxExJ0ejnZAKlzbEp0840dOccFKpOn8+Mqi0ojp2pF47r3rbvxUuXQLz9c/KSV/1+btiNseyXKtZV1f0vDOA1WL9ZOz6tncJF8u2r2f9cY83vnyU5676bYttWhbE9F4nnCEHe83sjFVsvBrBuS0bJ347Pr3LiNN13Rl/eompSTpfEYD+ZZMJJ02BxH96I3UQieFaNcUYO4HHkYfdgTZXVp2yKgP0cXz+eZK0nA93/20+rXaOh2XDY3Fsy6oe5b4qtNiQ6s267htaDzPaIr4Cin1ibuuM9lrqoP0Ik3v0vofg7ejmwa/oq1J123cyP+WipTGu77PseyWG++fnhZ0FJ6N0C58MKP64+SFw78NeEJr/WCzY+Yj4dr3AJRSdwGnAd2UUpXALK21C1HmOlop9QPSt2uK1npVeIzh4XHaPVas+hPnVBf1oRChUIhThh/J6MML2Cd3d0695XpueHwGB+y1N+cec3xC41YWlfY4i2MfqFY1wctz7wr+kr1igz9iineb8qwkvQ2EW+UaE9rE8g39b3utfOSU/j2XrP6f6Zf+OwRV8XYZOmaabE2ITM+GzCmZSq87smdwevMNuR5ql71MZJ5Oox2kaDwdl0/kj7Vr0FozaM8BzLzi2iZjmcis6UHuy7/xySIMhwIobvxJsjwBqnX9Ni0+TB6TgBXIbn5EpMK1LdHJM2E8jNSpjUX6d8bkOWrwERRccj7rN21Ca83+e+7FjMuL0s0zJpLlaiZ7I3+pYhuKnux2oImME2jUCzTavc9wDGa3fv04bNI5AIwZOpybnOeng1ZciMbzCOsgTr/9Ru6b+yw9unZj1pSWQuJ7ZeV8CLyZDl49TV2LEOcvKk8Dn9H7kXWPE6nrnIL066xs7aRoaM2GPu9dELX+zKQIZndRd9TUtnS8UsX2vc17mc0qLhsai6fZpGr69Da//OMvwTTYUCBOGxrPM5oK/E5/vdVtfQ842uq2Kr/THzPdOtlrqkxsRtpnGI6sbZQdxb9oFACLxvOxKddz0k3XYDKZ2LZHT5645sZ0UIob7Yxnh/HBlNZG9TuN8UNKdUVUt4ZorZNqAq2U+hA4vkFVqd2gxLcjIkxjRApCFXAFdluLXMjKolITsut5wivbeW59dMeXbgZO9Dv98xLgWgEYFRZ4F7vt6HvHjr4fOPvKOW/0inlGvCjx3Q9casRQul5z5II/Xy8ndEyXfvMuyNr2s8eBoX6nP1LeexNY3dbrkQVh5ONLfEcjdbZG/O0bgAuw2543YKymKPEppPbWELwaCukbfj3XHI+xaw2VRaV7AD8Bt+QUF7gAKPF9DhySMknBGuy29MhblvimALdjTEPqDcCh2G0BA8ZqihLf/sCHgBEbBTXANdhthvRqDPdm/Q7YlFNccAjRSpNVAAAgAElEQVQlvvVA9By2xPAEdtu5Bo3VFCW+V4ATDBrtN+y2fgaN1RQlvjMRx94IdZB1wInYbe8bMFZTGGtDAe7CbrvGoLGaIg02NNIXgXzLROTe3WMpC0wJ5Ft6AuuB6y1lgditvgy0och7vxN2m/H9af6hNhR4AbstaXUpq9t6DlLfZ/U7/U2jT/9MG1oP9MRuM74Ez1gbug5wYre9GvNIA9FefLA2y6HRWlcj9TtJpYaEG5NOa3cOp2AVxrw0IDUby6J85wLGAFMe3fGl25DdzkkJjh9JIjlZNITcK4Ft7h072sjo11KkhiVl1AV1aAmh0cCsrG0/a+gvFDPfxeq29kUk6l9txUFdgfRPMwIaWG7QWE1ht2lksksZIa2pUGYVzNx1pAHDXYgY8sZCWEujHJsMEpaHTgDLQzpklLxrNum69zJuy7yp5FCLgTxzigs0Ih51cGVRqU1r/adBQwf5a35KB5Yg76sRWGnQOJGwAuMWyhlEt02pYpXW2igbuhlj55DmSIcNbYJAvmU4/8/emYdHUWVt/FedhRD2RURsFUUlrRaitiijoSG4jII7I6LOtI6jIugoo2irqNe99RO3kcUFpd1xG0dxl2gmo46YUbBGOy4oSqMIyE4gW9/vj1OBTtJJeqkORPI+jw+mu+rW6VtV99yzvUd6a78OBAA85eH19vEHJji2YzoU2Stmqg/3DqdDETkXpTnGO/a/8ZwWbUaHIg4NJ1CFvPuZwE9aa6d0qIvM6fomsb3YYK1auKG1fktrnRJ3tNZ6hdY68Yhea8LnrQHmABvTHKkG8dQ1arwaCZSeDlwHzAbusfxWDfAgcLQZMvdJ4hr34wwRQQXweJ149r9O1nW+gsiZ7sau4qXIpl87gHExHWaxdTFOpMjiBqQ1SHMe83K20tmngyqEWSwRSutU8QjpP6PUQuUr2oU2OqRVIR8JlOYB5wH/dAcLlwIopfr8I7ygS1VtSo64hqhANm6OwwyZOactvX7gmuiG/NrUnIaxqACK8Xmd2tA0xEpkTUn3va9FZH0/XYEa4Alg45cdvws8vHZudUXUkT3IJuAfLR6VOp5G5iJdVCBrcqbwMVL7lUoT+lhsZuta5zjMxef+rrjisw0V0bT3jBqRNZNlOJnQoVsQLvAMAF4AvgbO9JSHYxeYhSRudDqmQ4Gn8HmdjPLFYofTocicPpfOAJbfWoLU6dWjnI0ESvus+ryqS7TWEZ9YxnSojXnIM5quEt0IzLYdA47jwMXnHfpZ5TfRTenrpkrEcfd5+lIlj+3BBnOqprMdUnPxNHASqXlbspHNwVB83nqehEig9GDE2PwQGG9HB6A+++PlCV3F532GkrJ8JG1nM8krJJct60n4vB/WiWj/6wa+THK8puT8hpKyYUAJ4gWN1urKjlFqc7PpuM4wjJbkNoC8r9ZXPx/4eoP/XHKjZ9DhtCv81idmyFxJC+lRZsgciEThHrL81lfNyFkdI+e+pLa5y0Y8X0fg8zqh0JrCZCTidQGp33vXLFxXfIExo5sr7ygzZO5i+a1UPf9jgF7AdKVUli3XbQuXRTp3ysl96egBnlGGYVQmIqfWmppoNK82Gu2Agc7Nyo4CN7qGH5pwL9ZEYYZMHzD96+ol+52/7P/mPddPHYhEKltUnFprqqnNrdJVeVpj5Llya4GPc4zszFGv+ryakrJRwOtRHT2yqrY2J8twVeVkZSWzcc5CUvt+h8+bCO16wjjOM6F6xNpDvy7t+ulpNatr1w3I6fdqUf7BxxqGkeozClCEz+vMWhQPPm8ZJWXHA28g9z3ZDbmBPDNX4fM+6rR4W+Dzrqek7HBEd+yMODaTRS6yrg/H53XEG1QHM2TujPSa/uPfVkz7Ye6ut/8v35V3KKnp0Cxk/S3E523c98spZEaHAhAu8HRFjEUDONFTHm7oiFoInBQu8OR7ysPNOz1sHaq1LqmppUs0SlZONhtcrvh9kJtAHkIskpk0ddghdShwDD7vgpYOTgBvAePNkNnxjfD0Kluu2yqW1nZy5RovdhuYPTpRHdoAdeuTwud1XIdugc+7LGZ96kSCOhRNrq4lDzCMLGq15kVXlnGx0+KZIXMPpK765At+ueurt91TN3WkQwGpOUhygMXAkfi8TkV32xxaraZzh4Dk/O8M7EbyUeSVwA921HQLIoHSvghVsQYOdQcLf4n93gyZc4CjgV0tv5X4JrKkrCuwF8mn3W0EvsPn3aLwpo4d3R9JNzvv8jlznd1AlZTlAnut09+5v4o+OjeXbm8cmDU52OJ5ULOhJvr9Ae+seBXY63W6fNIVYyjgPs4z4UPgF8tvHd/UyWbIfAmZ170tv/VLU8fFyOlCjO5dEvlZDbAc+NHpDV2TKCnrDfQn+ZSmDcCivtHsvsD3nX99hI4bS663/FZKTcgigdIPgV6zOsw7WxtMB7xIzcFEpVSYkrJOwACSqPP67Ocley74eckVv2xcd8jmmpr5wASllCOebzNk9kU2yGcjyuOvlt96lZKyHGQ+E657+bJycZcH1756oVX53Wkratf8AlwBPJ1ujWxzuOnGG4/slNuheKf8zp+NPeCQSR2yc5J53n4FFjdcn9KFGTJPRCJHe4xcM4RTVx11/bAbx91MSVkPYE+SL1tYDyxqNaVeUtYRWUeTrZmsQtbRTEW366OkLAvJ7uiTwtk/AUudjHSZIbOuTdKtSDbJ/wG3Wv0f20TqOnQtMqdOpZQ2Dwd1KEC4wJOFGHjHAcd4ysPFDU8MF3hOQRifD/OUh+cncrEHJxY/0DnfNdHdN+sWn7fj3CTkjCJ6qWX95wR2IB2Kz+tIGqgZMo8DXj9zxXEX/3HlCecgOrQYmOgOFpanokNtVCPPaKZSqusjBR1atTbaZd231RdUrYmOiVaxRYfGBGVShhkyc5FAznXI3vsm4B6r/2PVSJ/KXbHZGZPAMmBJBjMG2gTajc7tGHb64XvAIOAId7CwkWfMjrq8D5xr+a3ZrSqgjaljR+cinp8bLp8z1/GGVADzigdcinicDh5ZtOizRM7pH3jtVOBF4IJ/03Ux0tvq7OM8E8YA+1h+64B455kh80igFEjZoPoto+97C3KByrz1by/qsvqJDsCedrp3wogESgdvpvqzd3IXfviLa+1Q4BekV+OzSqm0FiWllIGwsk1FNtkzgClKqZRqEcyQmQ1chBBK5QF3Ardbfivt9EozZHqRiMmhyHs80fJbjkfolFJ7IM6rNcBhqc6FUzBD5l7AfcBo4Atgwhvh6bcDOwEF7mDhDq2Yf8swQ+ZhyDN/MMKkeHGz2SQ7CMIFniBSynGxpzwcN7oULvDshaSiXuApDz8c75hYTBtffD7wEHDPxJlFjvTzbsf2g6duvWfXjVmbfzxuzREuA+NnRIfOccLwaiuIBEob6VB3sDBlHWqGzJHANGAg4uCZZPmtlFJS29EY7Ubndgqb1XE28CdgjDtY+GK848yQuYX90fJbTrGVJY2pY0cvA165fM7cC5wee17xAAOpXVg9smjR0ETO6R94LQfZzFYDB/6brlGkdmT5cZ4JZUi6UNeGkSV7Pj9E0m/3sfxWJlN12iz6vrdgWVZV5POey64+miQZlJVSroNq9iz+Miviq6S6FoO/AzcopdY5KaNSqhviobwYidRNBh5Pxqg1Q+bhiNE6GCFuuNjyW187Kacd9fkLcDvC3Ho3cLPltzY4Mb5SqjPwb8STfLhSqtyJcVOBGTLzkPtwDZJKpYD7LL9VHQmUno3Udx7tDha+u61kbEdmYIbMXsgz/heEPOZvwHOZjO63FYQLPHXP/oPARZ7ycNw5CRd46gh9HveUh5tNJ5w2vtiHGPXvAidMnFnkaJZCO7Yd7E4GfuDOWqK93un+0a+/X3PEAHew0FEd2lYQCZTG1aHuYGHCOtQMmf0QR/UZiGPnEstvvZEBcXdobPsO0O1oClcgBqdqyuAEsBX2DOBQO2qyrbAESYnKBEYgXqfpSZxzIbAPcNXi4KgaO3IyEzjiyHUHVSPpcPEo2scAhwPXtRuczSJSm9MvSpIMykqpAw1tfPhZ9ve+PJ2zHINDlFKTnDY47WutVUpdChyCtGWZDfxLKWW2dK4ZMnubIfMR4CMk+nY6cKzTBidIzzXLbz2IPOOPA1cCYTNknmY7QVKGUsplj2kCZ2xjg/NYwEIcAa8CBZbfusvyW3U1XC8gZQYXbSMR25EBmCHTZYbM84CvgD8D9yD3fk67wQnhAs9hCD9DCXBJUwYngKc8HEVISJolE5o2vnhPJMtnETCu3eD87SASKD0QycR6FPjqtl0fufe+XZ7qfZxnQqdtLNo2gztYWOsOFjbSoZFA6Wl2AKdJmCEz2wyZk5CgxCmII/SAdoMzM2g3OrdDRAKlo4A7kE1YIumdTyAsY9tysxbBWfbaWExAIlXPJ3Jw/8BrXRGCpfepz2I4G9g8buVxg+y/6zHY2nn8tyOR49npCLwDYAmGa1ckdatFBmWlVFel1L3Af10Y+w+r9nBK1ZDRSqlMNfGOvfYC4EjEE+oBPlNKTVVKNeoHaW+Qz0c2yH7gLsBj+a3nM71BtvzWCstvnQccgZCKvQC8bobMvdMYViGKdLJS6s30pUweZsjczQyZLwBvIvUxx1p+63TLb9VrcO8OFm5GNlInRQKlmVpL2tGKMEPmYCTK/giSrXKQ5bcut/zW+m0r2faBcIHHjdRx/gSM8ZSHEyHRWQgMChd44m6mp40v7oKQEWUhEc7WqctrR0YRCZR2jQRK70XYefcFzgWGfdh1Qcg+JG6/1x0J7mDhCnewsJEOjQRK4+pQu5TqUyQyWgrsb/mtGy2/lanWKzs82o3O7QyRQOl+wDPAAuCcRGqbLL+1FngSGGeGzB4ZFrEpZMTonFc8YFekCfujI4sWJboQXAX0Bq5YHBy1xVBwBwtXAc/2r+xXmF+bB40ZbMcjRfdXWn6rdQgJ2i7q7vcjCCPm+HgHKaUMpdQ4xIj7K5qHz6g8Yum+tf0+6R8c/klrCauUiiqlZiGe0FnAJKBcKTXWrgHFDJkHI6nVDyGOh8GW35rc2htky299iERnL0OU5//MkHmjGTKTIoNQStW1WXoMiS61KsyQmWOGzMmIsTEKmII0Mn+7mdMeRPTS+a0gYjsyBDNkdjND5v3IBnkA4sAZZvkta9tKtv0gXODJRwzOzsAJnvJwoj1qFyJN6hsxsE8bX+xCnNAe4PSJM4u+cUjcdmwjRAKlRiRQulWHSk/rge5g4Wx7f2ghnAg7vNFZB3ewsJEOjQRKb4wESjuC9GA3Q+ZsxNDshjhmR1t+KyMtodqxFe1G53aESKC0F+KhrABOcgcLk0nvnIEwlPkzIVsCiADdp44dnSyLY0v4C+KxfTCRg/sHXnMjtUJPLw6OisdaOt2Fq2PR2iEQE+k0Q2Z34HqE+W2bRITaGCJA9xW7P7Ee6Yd4bkOjSCnlQfpwPY30pjrsL5Ujn+1IbrKp0o5BKfWrUupCJIV6GfBspauy+NBZhz6JEO3siaS1D7f81hfbQkYAy2/VWH7rPsRIfhF5Nr8wQ+aoRM5XSsW2WbooXXKmZGETnC1ASJeKgf0sv3Wr5beaZZV1Bwu/Q96/8yOB0mSZa9uxjWGGTMMMmWchG+SLEb000PJbj7en0m6FHaWchZApnekpDyez1tRlh8RLsb0Zadv2t4kzi95JT8p2bGtEAqWxOjQCHOYOFl5kO9ABsPxWFOEbOMYMme17ehvuYGGNO1hYT4dq9BfBuwLTkPXpTCCI6KaX29en1kH7A7qdwN5gPYfURZ7iDhYuSeZ8y28tQOrPLkq3DixF1KXK7erUgPOKB+QgfafeHFm0KFEP1M3Ic31tvC/dwcJPNPq/o1cP04Y2YtNrA0jPyMnti09CiO3NOh3oAYwFUEp1UkoFkc3RQUh69GFKqU/s/18NzGl1iWOglJq/Im/FkM96ffb4O+53fJuzNp+1c8XOn+2+fvfBlt96Ynt5Biy/9bPlt84CipC+cHPNkPmy3T8sLpRSfYF/IvWRpyqlWq0nmBky+5oh80kktT0fONHyWydafiuZvonTkbYJJ2VAxHZkCGbI3A9xMDwJ/AAcavmtiy2/1Z7e2RjXIIQl13jKw68mee7/kDT1ekbntPHF4+xxHwH+7oSQ7dg2iARKO0UCpbE69CLgcHewsKnsoLeQ7K7BrSRim4E7WPizO1h41tvdPrpoWc7KXc5eOWrCHT9cVvOHlcf83vJbV7dzd7Qusre1AL85SF8kN0ka9NmdjMk1G3URklL7UYpXn46k1hQh3rHm5MxBDNxke4xVIv2wGhITxBohTtHfnwD068mB11BS5mnp4Pu/3TBw705Z/p06uGY/c1jPDpSU5TfshQZgYEzfo6rfrCPWDz4EwAyZuyNpGE9afuvTlKWV3oJ9UzhzObAKn7d1DB3pLbgbqfUYq+vXF3u/5wFhNBfZhED32uPPBq5SSi2HLT1nTwXudwcLW+4pK70FdyO1HmM/NtevzwyZB7AL04BhLu369Ihfjvip76a+o4GPbPKhVxKODm7tz5tsartG5rPF9F3Lb71n18dNQqKeYTNk3gxMtfzWlt+plMpDIs89gSOUUvV77ElvwX4k32NsFbC8qWc0TluZW0i9rcwbuPghp5PxN0rKko02rwd+arVeaFt7CyZL4lGF9GxrrZ6SBuJU2ymFs39url+fGTI7I8/kJGT+LwQesSMwySNFHYrU/a9oxXU0JR26ZNrdR2IYt6D1Uwh3Q1LwlIc3hgs83xBjdE4bX3woUg9dCkycOLNo6xzIve+HpOQmgxrkGW29+rbUdehPrdbzFtLRoWuAZU09ozbpzSk00KHuYOHyFsati2ofg9Qo1smZMR3qKDKoQ82Q2RO4jX5ckB3NWnZDZPz7h2zc76RBFfvOjQRKbwamuoOFif/ODOnQHQXtLVOcgrw0MxF2vpY31DHQUZ2jNXlE2ejKMQ7E500pr9xuRxABSiy/dVozsv4B8UZXIw2gk4ELadh+Bj7vP+o+nDp2dF3/sHMunzM31NTJyeDj4pEfHuSaclgOnSsNw2iRfW9TbbRjrSarU5axwTAMkIX2IeDi2Bc9EijNr3BtXvu/jt+uOfW6i3YyQ2YIidINtPzWD0kLWlKWj6QEHo5Eo5JFB+BLYHjGFWdJ2c3A1cgzmuzLn404HUb0jWavRxhhz102YvDsoQ8PvXFD7obri5YW0aOqhwVMUEr9O/bkSKB0ChKJ3tcdLGy+1qikbCTCcBol+WfUQOb0Ynzeh2K/MENmF4Rk6jKkmfxVwKOW34oqpQoRx80BCAHVX5VS37UgZz/gA2SjlAgJSEN0ROq6xsVx5MSF7SS5BzHgv0J6e86za1NDwB+BMUqprazXJWXZCBHXKFJ7RnORteVIfN5lDeSJbSvzNkI1nzrLb0nZBK3133UtLiOLjYZhJHP/s5Fnexg+b2bTo0vKvIjDJRtp/5IMXMicnofP+4TTotWDbORLkDSzVKLeeQgZ0PGxBoidUXMqskF2I+miActvJVqb2FDOlHWojVzge6AQnzc1GRJFijpUR6MuXV3dCYNo7caNI3NOPub9VC4fLvA8BxzsKQ/vPW18cT+gDLm3QybOLFoRI+e+yL3rjBiRycBA7v3V+Lx3pSJnwkhfh+Yh7+JJGTeU0tOhOUgGypH4vPX2GpFA6QAkQn0cwlA80R0s/HfjIeLDDJmfAWssvzXCljMjOtRxZEiH2qnGfqTEowdwP6Asv7UuEihtpEPdwcKWAjUZ06E7EtqNTqdQUjYD2eylTFuttY4ahrEKOAifN9LiCXFghsw7gMuBPSy/tTSOnCcCz5K856shNgFj8HlfB5g6dnSe/dl1l8+Ze0uaY/Pte5eP2MMYXZxNJ20YrnTShTcCj+Lz/jX2w2duve+LoesH7Xfjbg8eVdb5i3eAuyy/dWXSo4sn8V9ICkw6c1oJfAMcmjHPcknZ9YiRlZ/GKBpY/71m2FCdvSCrtvbG8//9KlVGVeD13V/v0K2q28cjfh5xpFKq3gYnEijNRjaEYXewsHnCg5KyQmQDko6cIM/jRfi8IXuD/AeEpW5XJAXt6oYbZKVUDkLWoBBj4nbgTqVU43tSUtYbqVvcmfSyRiqA1/F5/5DMSWbI/D3wAELUMqdoadE3Pap6TJGfoW5sIOvLwNGkN6fVSH/Fwfi8q82Q2RupiTkPqdmdBLyQVmpySdn5iBGT7jO6FnmXvk1jnKZRUmYiG6VGDMhJYhNwLj5vZtLNZTNfBuxF8lktsdiE1DyPwOeN2mzVfweORVIAJ9gEWOnImrYOpS6CDAfj82amZ6FzOnQj4MPnjcc90CzCBZ5rgVsi/Yb1+Xrfsa8B+wG/mziz6PMYOfdAol7dSa+UqgK4Bp/3vjTGaBrO6dAK5J08NmPRJGd0aC1ieA7G511mk9tchZT4VCFZAw+4g4VJOQnsvd8koKfV/7GDcFiHpjlOfGRIh5oh80DEgfw75JmYYPmtzxueFAmU1tOhwN/cwcKfmpDVcR2axjhtFu01nU5AFs2/kJ6yxDAMF+KRPDGNYVpif7yK9JUl9hhX1P1x+Zy5m4EVOMRgm0evG7LII02DE+SeXGh70bfg9R6l72WRxc85Kx5EagxvS3H8AiTNKd057YCwEWay1+ok0ldCBtB5T4Mxrmjt2gErlk4CbsjVuf9waddTq/JWDXpxzxfjbcRHIc/GjASucbEDcoLck6vMkLkvUvMyB0llHmr5rfPjRWSUUtVKqanIfX0FuBGwlFK/jzP+0QjzXbplCvnASXbaTsKw/NabSFT2BjSn/muXf035vMfnC/7T5z/1n+WSsl6I9zzdOc0BetTo2pFmyLyAzLSVSXdDB/YzijgZMoU/2ddIF3UbzkzhcOS9S8fgBJHT+3XVEo8ZMm9C6gqHApcCXgcMTkd0KBJN6IO0SMoUnNKhnRCHTSpYqIEfdj/mSeBQ4Ox6BqfgVPsa6e7z8onR9RmAUzo0HyjEQV6JOHBCh2Yha8fxkUDp8ci7dANSFlHgDhbem6zBaeNtZI0ejsM61IFxmoKjOvTBf16/qxky70WcLftgt5WJZ3ACuIOFdTpUIV0SyiOB0km2k3wrHNahSAncDol2o9MZ9Cb51JWmkEccKvREYfmtOvbHC8yQGY/90ckFebcGfzvSNmVe8YB8jT7cZThKXlmvVmBBp68+e67n2yztsHyAS7tuSYPsYlecu/eaTPU6FaO7u0Ojub5btfKcnhvXd9vYIS8LOEopNa46q/oummZQnoBEwxIhzejvkJxsjlbtiVDKDwEuQchN/tPSeUqppUqpsYhSjAJvKKVeUErFPvNObOa3iEoK76bltzaf9v1pLxwbObaye2X3dd90/2bw0k5L/2v3H4uV05HoeVRHOzy45pW7EOdWJtrK9HFonGzEe50pDCD5mp6msItD48SDY+t9ta7Rd69+7h2kDc/zQIHlt+63/JYT65+TOrSuji1TcFKHpvqMLvxh96OpzOtxDDBl4syil+McswfOrU+p1AInCid1aCVtQIdqrfM3/FBzBVLGUQ0c5Q4WjmsyypYY/o1EJo/BQR1KajW2icIxHVqta2rf3Dh/AZKp9CBSLjW7pdpyd7BwsztYeCOwP1ITfTfwaSRQmhEdihieO2wf6oS8C4ZhbEC8AXO11gcYhjEcYUf8DtlkztVax/WEGYZxEDBRa/0XwzAKkJ5xBwPXaq3vso8ZSH02y72A67XW9xqGcRfwuta6OKVf2DowaJDbv7mykmGXXkBldTU1tTWM8Y3kxnMv5IGXnuPeF55h0U8RVrz8Dr27x13D0nUGTEc29yciVNENZXVKzoabrggxbUjSwBkQbbQQNSXrObcrShZ+RrdO4iSfHbiBwfsMjD01Gk/W53q/Rd+q3tz3/VXJ13FuRaONZ1Nyaq2ZMmsGz78/jyyXi4tOOo2/nnZGw7FajXk4DTnZXFO9S21Ofnhp951qlFLzQBiUzZD5H4RB+b66qJfdmPkY4PoEPbiOzWmVrs5DyLUmW37rl4bjtgSl1LtKqUFIyvoU4PdKqZuAe9WI0Y3uVxpzquP97gTk6wW80rmm88Yjfzny0Jf7v3wIcB9QatcqX2n1f8yx+aymNrdSV/dE0iCfygDLb0LzOe+/85k8836i0SidO+YzO3ADe7sb2RnJknwkg0ZrdBqyZvKdT/gZLbzkfNZXCJHj8jWrGVKwPy/furWUb1O0spML10qgyPJb72VAzoR0aILzmek5dUJOSFHXF/v+Phig25pvv13bfe+msnScfEYziYTXpwTl3OY6tCU5DcMwotV6b6Q29O6kiGyagOW3Ks2Q+T6iaxulljcla/Gnn3DFjPuoqq7mkIEeZk2eQnZ2PdNgu1ifWpKzUlfn5RjZ3wLHW34r6R7g7mDhokigdDTCmn4fUBoJlIaAK93HdUz4GU1gPl0Nf/OOhHRC2qVa69GGYXQEPjMM4x9a6w/iHHcNwmQIwt70VySMvQVa66+wqZ4Nw8hCIiJ1JDV/R5rhbs9GZyN0yM2l+O4ZdM7Pp7qmhiMv+QvHDfkdR5gHMnrokQy/bHwmL/8GQlk/gcZGZybljCA59OliQhZ5qxAGzhZlBfi/8X9lzPCRyVzjoIqszVz4y5iNXaOdzgdeckDuZuUM//g9S5b/Qvnjz+NyuVi+elXLg2UQ6ci5V8/eb652dfwRu01KDKYDj1OfQXk84sl+pLVlzXflbbD81p9SvS6A3XLkNqXU04gyugM4Z8naVf/arVu9R7RV771df1rXZmn4zTfcvORmbl5ihsx3kJZBVwAnBVY8NP323udjk2ulJWcWrto/dzv+9r+dcueTaf+ABNCUnBfdcwf/vPUuPHvsyfSXn+eWJ2Yx+2rVGiK1eVmbkrP07w9vOea066/kpCN89c8zcjcHe19wQdcin9MGZ1JytpX5zJSc00g29k4AACAASURBVMYXH4Dhejp/47INB1rTV5lfLEjY8dM+p9uPnPnu7Ae6njc06LBIbwH3VkarP+/gqp8pFk/WYw89HP/tinl3T2ff3fbg+kdnEnrrNc4bte26VaUqZwcjZ/Ojfa8a13lE4f9SvbY7WKiBlyOB0ncQJ/PlwEmrFlZN7zEop0Udmon5NAyjFsnWquPF+KPWeo393f6IjeRGDNnHgVu01towjHOQIN9RWut59vGnIHvdP2itX4hzrXuBl7TW/zIM42KEcHEAsJPWeqV9zGTgLPuUbMCDZEJsAN4FirTWTQYX0k6v1VpvQgqBG6WcGIbRBRiktV5oH7tca/0JzTNUjQQWaa1/sM/5AehlGEYmQ/yOwzAMOudL+nd1TQ3VNTUYhsFB+wyk/y79Mnpty2/VIukFRWbILGhFOZcAvaaOHZ1ybca84gGHAofkG7s0ysFvStZkYYbMfCTVko7RvP8Av7eZ4xxBU3LO+OeLXP+nv+ByyWvXp0fP5obJONKRMy87pwJxMvTs+96C2DqH55HWBRcB2CQJ5wL/cAcLf25tWbONLKfStlBKLVZKnYS08ulYvvKXC6Na14umtfK9vwcx7i9QSm1ps2T5rY2W37oGGAR8+m115JpNurJerVwa81nbPatzKsyiKaEpOQ0D1m2UqNzajRvo1zuT2X+Joa3I2tI6ur5iI8WflnHykQ2MTldOZdesTo69T6nK2VbmMxNyThtf3BupNV9/wJePzMmurdw/XOBJeB/XPqfOIh05s/OMTDDVvw2wPlrRiB8gnqxZriw65OSy725S0XW09zBe/Ne2je+kKmeOkV3V2dXRkVZZ7mDhRnew8GpsHVq9PnqNrqVFHZqh+dyktR6stT4ACdxNtK/fEVkLglrrfZG66N8hwaY6WMC4mL/PQIjfGsEwjJ7A4Vrrf9kffQAchQSwtkBr/X+2PIORSH2J1nqV1roKCTQ0DETUQ9pGp2EYPZCC3X/F+dqL1P0kgzOAZxp89ilwRPLSbVvU1tYy+Lwz6XPyMRztPYzD9jugNS8/CzHuWwxVOihnbO/GVDEB2NiFPeP2+mxK1mtnTWfQn8cx6YG7qaxqMVNlEtJnac0TO736E8Imd2EaMick56KfljLnvXfwXvAnjrvyr3wT+dHJS24LOevu9xaHk+W3NiP94k42Q+auwOlIxDoRAqFMyuoYlFJzgf12yu88T2vdyOvRGnIqpcYjymeqUious6Dlt8qBo8zcvQJRdKO1fnuZz5YQT85HJk/h+MBluMeM4om33yBwZrwy4tZHW5G1uTX/H6XvM/LgQ+nayQmepPTQlufTaTmnjS/OBV5AdNfJnTf+/CFCFJSUw7R9Tn/TcpYDkQ16U7dEZB3i2Z/q2hrKyr8E4IWSeSxZnnQViuPYXuR0BwvLgaNyu7sC6Mb20jaQ8yO27rfOBD7QWr8NoLWuQAikAjHHlwJDDMPIMQyjM7A3EiSMhzEIHwz2eJ9prRe3IM846ttrL7M1ChoX6RidhYZhfA4sQ2o64/Wd2QVhNE0IhmHkInWIzzf4ajmy0LYpZGVlsWDW00Sef4354S/433eZYe+PB8tvLUcU1DlmyGyWEdBBOdMyOucVD+iJOB2eyDI6xLUc48l6+wUXU/74C3wyM8Sq9eu445mm2b3NkNkHYWP7J7BoSYdfdkJelPPsqJwjiCdnZVUVebm5lD30OOePPpk/33GzU5fbVnIusf9teL9jGZQnIIrw/W0sq6NQSm0avMtub7vi9I/NtJxKqeFISs0btMAsaPktfUPvc97qZOQ1IvrZnuazOcST857nn+b14L1EXniNc487gb9Nu3dbiwm0HVmbW/OfmfcW40Yeuw2l24q2PJ9OyjltfLGB9Bn0AedNnFk0n60RiwO3J1mdQrucycOusX+rIro5LhN6Q1m/+H4Rz15/K5Om3cOQ8X66dOxEdlYmS+ETw/YkpztYqHsckPuWkU2LOjSTctqlhyOR6CYI8VG9dkta60VAZ8Mw6u6/RlJej0VqVV+haRzRcLwW5MkHfk/9Er7/IWzaTSIdo7NUaz0IMIGLDMMYHOeYTQgba6I4DvhUa93QNVDXA7JNonuXLgwffAhvzv+o5YOdxQyEjvqMlg4ER+RMN9J5DnKvW4yKxcq6S6/eGIZBh9xczv39CcwPf9ncqdcjtNcB4EeE+Gg6Eo1zvMVCrJzunfpw2jBhyj6lcASff/eN05dLGSnKGfd+W35rEfBWts6aUEPtEGCGXSuxLWXNCJpL786EnEqpvZBF/htgnFKqdnuUMxOok/ON+R+xcNE3W6JzY0cczYdfxGXE32ZoK7I2XPN/XbuG+eVfMurw7SuxqK3NZwbknIBk49wxcWbRU/ZnXyBZOoO2M1kdRbucSePtKNFmLZ3Y937o/oMo/fvDzJ8ZYtiBB7GP2wkuSGewPcmZqA7NgJwdDcNYgJQt9QTeqROJBuRrMYj9/FnEBoiXRRqLpIKESJnRB1rrLcQPWutaoMourYyLdHvjoLX+2jCM2xGP+7gGX4eRQtxE0TBUW4d9aRz93K6xYs1qcrKy6d6lC5sqN/Puf+dz1bi0+ExSwb8Rz8MEM2Q+Go9p0mE5l9r/Jm10zise4ELqAD8YWbToc0rKGh3TlKw//7qSXXr1RmvNy/9+nwP23CvuNex+jRcCD1t+q9wMmT8CRz/Z+7X3zl456iv7+o8nK3uicp58pI/iz8r48y4nUrLgU/ZtsBjVRqM5nyxdfNSbaq5TrU22wABuGDHaETljUHe/49EcTq8xal/5sMuCymHrD9lmc9rayKScSqmuiKfSAE5USqVcE9TW53Pthg18veQH9t1tD94p+xjPHv23qZzpyLopWpk/JGRelAmZru35x8NO6zIsJ8fYquqbW/Off38eo4ceSV4HpzptpI5U51PX1mat/fiDYT9f+MeMMETue+9DnbM6bk2KyfQzOm188UiEwGwuQhAGgKc8vDlc4PmKJCKdKc+p1llLA6UZeUa7DMj2dN07Oze2JXfKckZ17ppw9Skb3yiNFwRJG+7j6idDbafr07x45KhNybp89Sr69OhJZVUVdzwT4tqz/1zvvCpd3eGQDK1Pd+808fCR+Qe7XMbW+FeqcrY2WlHOTVrrwYZhdEPWgIlI1sMXwLDYAw3D2AvYoLVeX2cka63nG4ZxgD3O180Yz8kGCZsyYjvQTHuZFo1OwzCykd5HzWEmcIVhGHtqrb+v+1BrXW4YRjfDMLrYk9AXKAO6AlHDMC4D9tNar7NDtUfToLbOMIwcJA+5sRWyHePnX1fiv11RG40SjUY5fcRRjP5dIfe/+Cx3PvMEy1b9yqDzxnH8YUfwyJVTMiKD5be0GTKnI5G8Q4H5mZTz8jlzK6aOHb2K1CKdI5H7fENTBzQla9Gki1ixZjVaawbvvS8z/3Z1U0MEkZdB2X//AHR+aqfXup+9ctQM4N5IoPQgd7DwsxTkb1HOI83BnHXrddzz/NN07pjPI5Prz2dNtDYvsm7NnwHHV9N47rBU5azDshGDK/q+tyDu/X5w0fUfTNn97/qZ3m+sPPPay1LtgeqYrK2FTMmplMoCnkQaqR+rlEorV7+tz+fDk6/ltOuvwuVy0aNzFx696rptKmc6slbozV2QNdpxfF65iBM6DyXW6GxKToBni9/eLurkIPX51DU1uZu+XzSOxk5wRxCt2Eis0ZnJZ3Ta+OK9EYd7OXDWxJlFDTMbFpIEY3zKskbJJkPPaNXaKLoWYuyO1O99LR2r1+nJmZAzHrbH9cnyW79++87LG6E+8U1Tsk6ecR9zP/o3UR3lohNPo+jg+tmRm6NVHcnQvbcqv2N4/mBcMUmXqcrZ2mhtObXWaw3D+CvwT8MwZgBPAdcYhnGU1vpdm1jofuDOOKdfTct9RsPIHvz9lmSxDWAfcHaDz3sBK7TWTZLFGlo3n/VmGMaBwMNa6yEtCdLE+ZOA9VrrlNol2BS/B2utt/2uoimUlPVla89SJ3AXPq8jC6cZMrsAPwEvWn7rHErKfsCZXpoAi/B59479YOrY0QuBxZfPmZsUR/S84gH/QHLKdxtZtKiSkrL7sRlmHcDm85bdeer8zeHXgestv3UzgBkyT0PqXge/EZ7+AzJPT7qDhRckPHJJ2bFIj9m4hfvJQGu9YdWmisl///i9f7R8dHLIcWVxre+4eHXXqeJ5fN7T+763YCHww7IRg0+M/TISKL302V5v3hvq8wpIE/m4xFBxUVI2nxbqApLAGnzeHg6NVR8lZVcCt+JAxgjSV20oPm/c3HCl1O1ISvglSqkHkhq5pGwwUII4+9JFFXAVPm9mipRKytYDTjHYzMbnPdehseqjpOxlpEYmbUR1dMWBP5xnOjFWQ7y+6x1/2DW7d9BluJqt608Qa4FT8Xmdp7d0UIdqrTfXrF51/bdXX5Z2hkU8FMwI/ddwuRqx9aeId/F5j473xbTxxd0Q4pA+wJCJM4u+a3hMuMBzFeJM7eEpD9d37jmoQ7XWVUvf3JyRtIc+R3QYntPFeCimDi1laK3XVa2OjlvxcVXCtWmJwsiCXY/p6KQOVfi8Nzo43hb8PO+tpbtk93KEByWq9boDf/jzvk6M1RDF7nsm9s7qdrUd3EoXzerQtLANdahhGBu01p1j/n4VeE5r/YRhGCbC77AL0pf6CeCmmJYpXq31xQ3Gm43w8LzQ4PNC4EKt9dn2338FrgT6Irw6r2ut/2J/dw7we631GQ3GGAMM1Vo3meHa7I02DGM80lfzsuaOawEzSK9WLhuYmsb5rYEqnGtErnGwftXyW+vNkPkEcK4ZMi+3+j+WdhPiGMSLgC8hyUjnvOIBboRA6s6RRYvqxtyEzEXaKVJa66xw1Q83AD8Dd8d8VUfNubs7WLgwEih9GjgrEii90h0sTDQ659h8GoYR7ZXfablSynlatpIyA6n/ceo5rbD/jdDgfkcCpQZw0ci1h/031OeVQQiD8qQkxm4psyIZNNeeKV1UInPqhMJ00cTvVkqdjRicDwHTUhi7EueaUdfg7P1pCCfv10YHx2qIlrzGCcNluDZbfiszVIwlZSsBR9oI2MjUvXdMhxqGUZPTs9dKT3k4U3PqJL9E3LGmjS/OQlLX9gGOjmdw2qgjExpE4w4CjulQwzCq3cHCTM3ncpquTUsKhmHQoWfWsozI6qwOzeg6mm/k/YpD5Jsuw6jM4Pr0K62gQx3ANtOhsQan/fcJMf9vAcObOG82MDvO5+c0cXypYRi3G4bRXWu9Rmt9PxI5TXhshFG3yVRDaIFISGs9U2u9Xx0lbyrQWm/WWj+RxvnP1zVC3Y6xGueMjwog8ahQYpiB5GqfU1Vb8100GnViga8F4nmUIsSv8WsOFyAv9IMxn5XjkPFdQ231+mjFYcB1lt+K3YjWGZ172P9OR0iGkilq/Ybk8uCbQ7Y9nvPweTVbazDTxWak/xPEdzKMAAbuVNPjfoT05tyWGJRjsTG6+etaXevERlmTqfkUfI1zRlIuW9mAt0ApdRjwCOJlvUQplcq7uxjnntEomZ3TRTizAd1E/PXJEWyMbvqyRtcmROLUAqLIc5QpfI1zjqY8MnfvV+to1In5BNElmZzTLxH9ly6qaKJnHhK9PA64ZOLMovebGaM5BlvHdCiyhmQKTurQPCRi7jyc1aGVZHAd7ezK+6BaO9JSt83rUIewmLajQ9PB5aSYCWl3H3lZa92s/ZJ2n852ULcYjSP9Bb4CIf9xlDTJ8luWS7s+KFhTcPW0+SVHVNRUG1Gt09nU1yIsV/FC6BFgp6ljRyf0gs4rHpCDtNZ4fWTRosUxXz0N/Ic051RrXXHFiulrkaLr2Q2+XoEs/rsDuIOFnyJ1rxfZ0bqW4fNGkDrUipYObQEViNGbbF/bZHAW6Ud/NiMyzrT/jgA79X1vQez9noA0MX4O+U0JMSibIdNlhsy/jIpcdcrPNatcaRqeGliDPFuZwtvAa6R/7zcB5+Hz1nNcKaV2Bf6BpH2PUUql5tjyeTchjp1016eNSHuhTHYPPxdJk0rH8NwMfAY85ohEMTBDpmGGzNNHLvnb+G+rIlnVuiYd4yOKOCwzQtJh4zPEaZHue18BXIPPuzx9keojXODpHb7wj49Ept/TMVpVla7DYSMSIfzQAdGawqUIk2Q6974K+JY4WVzTxhf7gSuA6RNnFs1s+H0DLEP0WDyj0xEdCqwnOUdscnBOh24CJuHzZjJI4YQOrQDmIW3bMoIsI+uGNbUbKtI0PNu8DnUMbUuHpgyt9cda65SolrXWVVrrFksaWqzpbEcSKCn7PdKSw03yBv164D3gMqdfHKVUYZWr6qncaO5um7I2fTxq98GTh/XfJwB4EO/QFmyoquxjQLRTboeVTQxXhXh6J+DzNuocP3Xs6HOQzd6Ay+fMbdHjOK94wB8Q42T0yKJFr9X7sqSsw6+V0UfX1kTHdsk2dK9c1wqXYSRqiESBJXevem7BY+vemACMsvzW6w0PMkPmN8B/Lb91BkAkUOpHjNMid7DwvQSvBSVlFwF/qYnW7l5RXdW7Q1b2ug7ZOY36OjWBlchG6U7bgZE5lJT9Drgd2JPkn9GNyCZmAj7vRoC+7y04B/t+Lxsx+LtIoLQfEkG+xx0snGyGTAOJilYC3ngMygBmyDwIMVAPB0qP73T4lDt2uvBSZDOVrIexBvGgXorPG07y3ORQUpbF1qhEQqzDUa1dlTXV3apqa/M3VldV/bRuzbS5X1uXx0YxlVIdkXS5AmCoUip9Z0RJ2RnA35D6j2RThVZTx57p8zqZrtkYJWUHAPcgqYX10q50rc6rraaXK4dVriwj3gZgA/ABcLG9UXAMNvv1Awjh3Wee3D2ueK6f+jMwBMmQSAbVSEbLJfi8mfV6S1rgNUiZS+8URlgGPITP+5CTYoULPHW9fG8HugD37B2878OcHj2vJDUdugrZ0N3QCutofyTVvZEOTQCbEGLEixoaSNPGFw9FiDw+AI6dOLOoxShQuMDzDtDdUx5uXAdfUtahtlI/Gq3RY13ZhnblssJIXIfWIobxZHzeTxM8J3XYOhTYOckzNfKMPoDP23STbqeQng5di7S8uAKf15FQZFMY8/Txky/sfuKdBbm709nVcXN3V+dVhmEk+l5s1zo0Bhpxyt6Fz5v5DhdtRYdux2g3On/DUErtjDBZ/Umjf/i4z8fdl+Yvfd86xzq5mXMeBMYCPZVSSb8YU8eOPgpZVH2Xz5nbsMakEeYVD3gP6A/sPbJoUT3Pcf/Aa10Rj3U/YMji4KikGDvNkNkNSddbCBwVz+AxQ+a7QL7lt34HEAmUdkSid/PcwcLTk7meUsoFfIwUXu+rlGqzvWUTRd/3FtTd7+HLRgwuiQRKb0DYgfd2BwsXAZghcwKyQTvM8lv1GJTNkNkduBmJjq5EPPxPNmWc/pZgp87OAA5C5vASpdRXSikDiVKMBU5WSjXX0HmHQiRQmgP8Asx1BwtbpQeVGTLzEaNtMmIwTAFmWH7LqXTQHQ7hAs8hiJNpCJI6PtFTHv5i20q1bTFtfPFuwCeI4+SwiTOLfk3kvHCBZyqyfnbxlIfrGTKRQGk9HeoOFqbFet2OtgMzZI5AImo/I2R+67axSO1oR3t67W8RSqkspdQExJM+DrjNwNhvaael0zE4wQyZzdVcfoKkQ+7dzDHNIWL/2yKZ0LziAR6kCHpmHIMztkXEmGQNThsBoBcwuRkj5kdictjdwcJNSOTulEigdJckr3c64AWm7AgGp40t99s2CC4A3qwzOG08iURJt6QR2mmKZyN1RxMQ42ug5bee2BEMTgCl1McIS+/FyObbUkrdgmRLnAFc225w1oc7WFiNRLNOigRKM95M0gyZJyKZHdciLNUDLb/1QLvBmRrCBZ4e4QLPNETP7IFQ7o9oNziL85HnOh84MVGD08ZCJBtkn9gPI4HSLKStQgHwh3aDc8eBGTK7IlkZUeDLdoOzHdsL2o3O3xiUUkOQaNs0JIVnkFLqWqVUBcJ+aSCGQVP4xP431ZYVCRudiBFSBTwa57tbgBOASxcHRyWd/24b1pcBT1l+q7nUoB+BfmbIzIn5bCaS1veXRK+nlOqApNwsRIysHQV1xApu5H71QwzILbAV3hPAGWbI7GmGzP2RVPInkF6ph1p+62LLb23vhGGOQylVq5SaBgwEnkWMG4VEf4LbULTtGS8g1PVHZeoCZsjc0wyZryJ1VxsAn+W3/pQxFsffOMIFHiNc4PkT4ggdj9D8D/SUh5/ylId3CCdTU5g2vthASjoOAsZNnFmULAFWU2RCtwKjgUvdwcJ5aQnZjjYDM2RmIZky+wKvAoeZITPZFPB2tCMjaDc6fyNQSvWyU2P/g+SbnwEcrZQqrzvG8luLkaLt85tZhL5AUsi8qchx+Zy5G5Di82YZbOcVD+gE+IHnRxYtWhH7Xf/Aa2chUcqZpN6U+BbEwL62heN+tI/bYiTbHuG3gQsjgdJEqbwnImnCk5VSO0wUZNmIweuROhU34kT4EXnGGqKOQflFYAFgIs6PoS04BXYI2G1y7kLevU1I4+VXlFJ7blPBtk+8izxz6bTiigszZHYwQ+YUJLo5AkmpPcjyWy2WCrQjPsIFHhNxooSQcgevpzx8qac8vHbbSrbd4DrkWb5q4syieGtnSwgjNcJbjM5IoPRs4CqEET5VHdqOtonbgFFIu8PZSN/jodtSoHa0ow7tRmcbh1LKpZQ6D/Egn4cQcBQopeY00V5hBlKoH7euUylVgzAephrphDi9G+PgTCRaUS8q1j/w2hBgFkKm8NfFwVFJe8HNkDkY+CNwv+W3fmjh8C29Oht8Ph3YFYneNQulVA+kzustpdQ7SYr7W8CSLtW6AIk8PeQOFtYzum0yoYFIVHs4svkcaPmthy2/tcMW1MdCKbUT8ApCNlCA1LYOB75USl1nR9LbAbiDhVVIBPKkSKDUMQ++GTKPQUivbkYiBAWW37rL8luZ7PX6m0W4wNPFrjf8DNgPyRw5wlMe/mzbSrb9YNr44tOAG5Gsj7tSGcNTHq5CDM8DASKB0nptltzBwh06krwjwQyZfwSuRGrOZyA1nbXAMdtUsHa0w0a70dmGoZQajLRYeQRROgcppS5XSjXHmvoW8D1SR9cUyoCDlVKpNuxt1uicVzzAsK//OTHU9v0Dr+2K1LX8BPxhcXBUqpu9O5HN+20JHNuU0fka8jsSaWdwLcK6dmWiAv7GEOlcrU3E2z4r9gszZO4DvIkwFP9sf/yc5beaYkfe4aCUykUiwDsjxEE/KqWmIsyYrwI3IfWe7RuHrXgeeeeK0h3IDJluM2Q+j6yNAMdafut0y29FmjuvHfFhp9KejtRrT0LWhIGe8vAsT3m43clkY9r44sHA40h20gUTZxalYxwuBAZFAqX12izZNdDt2AFghszDgIeR0pVLYUtpy0e0G53t2E7QbnS2QSiluiml7gP+ixD+nAMMU0pZLZ1rE2DMBHxmyNyvicM+AToi3ulU0FKk8zBgMDB9ZNEiDdA/8FpHxODsApywODgqJaPEDJnHIi0NbkmwRrCumXA9o9MdLKxBUpOOjgRK92l0lg2lVH/gEiCklEqpv1FbR05U/7w5iz7AS+5g4TIQxk8zZN6M9PQ8HEn12Q/pKZfJvoRtCjZT7QNAIXCeUuqTmO8iSqnTgWPtj95SSj2vlEqkXvq3jneQfp4pp9iaITPHDJmTEeNoNJKtYFp+621nRNzxEC7wDERKE+YgbSyGesrDF3rKw8kQ4/zmMW18cR8kWr8aOHXizKLNaQ65EFfOrjpa+yqiQ090BwvbHXs7CMyQ6Ub2T0uBPzTIzngLOMQMmam0S2pHOxxFu9HZhqCUMpRSZyGbpEsQ43GgUirURCptU3gMSXVsavPvBJnQzlPHjm4q9W0C0pf0KYD+gdcMhEzoEODMxcFRKTEZ2gX0/4dEchOqY7H81iZgOY0jnSAR5BqE+KIp3Iqkr1yXlLC/IXhX1fZc3cFl/JprPARghswTkNrgKUhEaqDlt/5u+a0KJOpxYgsMyjsSLsbuVaiUejreAUqpt5Ea2CmIcVSulJqslMqJd/yOAHewsBJJRz7ZZk1OCmbI9CFpn3ciKWj7WX7rVstvVTor6Y6BcIEnP1zguRVJT97CyOwpD3+8bSXb/jBtfHEH4CVgJ+CkiTOLfm7hlJZhuBbmHewHwzUYOMsdLEy/r2872gTslk4vA52AEy2/1dDB8zbCW5Ex4rV2tCNRpJo+2Y54KCnrC5yKUMEnO7e/Ah/h874X70ul1H4II+1wxCg8QSlVloqYVv/H1tyz6vlPurjyz9/83oc6z5VbLwXnhuGj+DjyfVXXDh0voaSsYbSzAjF65zTT3DiCLHK7IOykWzCveEBvpLXIIyOLFm2wP74aIT66enFw1Kv1RiopG4lEynq29LtCfa/e36r8zjwob5+5gzoMuImSssXAi/i8y1s4tV7blDq4g4XLIoHSl4BzI4HSKXY7lS1QSnmR2tTb1IjRnSkpm4TUgSbTNFgjaafv4vMubOngtFFS1gOJDu0FJLtZXwv8F5+3HtnF4StrDvyodzZjBs+tygtd9E+grs3ECMtvvd9gjAcRgovzkdYgTcnpAk4DDkCIEJLBZqSp+bP4vJlvXSPNwocBvUni3v+8fu2u3n57/OGrlcveWF9VOaW5Y5VSlcCttmF6H2Is+ZVSE5VSJQnK2R+5N7uRnMNRIxHqEnze/yRxXmooKctH1oMBCPlUXPT5XW6XylXRnh16ZT1DSVndOrMa+ASf961455ghsy/imDobWIxs0l6Nd2wCchqIE+BgpD49GVQB3wHP4fNmnlCnpOwgYCTSQzjZ9SkCvI7P+3W8A8IFnhOB+xG99zhwpac8nBrLbwZ1qKMoKctB+ugORNqcJAStNSMPyxu5ZFnNgd8trRl34QNF/3VCnE6//78Rrg5dIC5GsQAAIABJREFUqF5a9vqeT0yq32YpCR3aADXIO5KIDk0fJWUFwHHsgDo0Vdh8CY8ia9AJlt/a6rC3deiCPWaZz66ft7l3VrcplJQlGkhoEzoUaQnzI/BPfN4fWzo4bbQVHbodw9C6vcbcEZSUuYH5QA+a2Sg1gxpkIzIZn3dLlE4p1RmJov0NiQ5eDTySMkOqKMt/1uroCAPyXEbSwW6NGJ7FwKnxDM+pY0cfg6R0HHn5nLkfxH43r3jAZGTDfMDIokVf9A+8dhLipXsK+GM94qCSsr8ibUhySc1BsgnZiByGz/tTUweZIfNFwGP5rUbpxJFAqQ8hNTrXHSycXfe5nRZZDBww4VDf6X06d3kFUUCpEL5UIff/RHzezFHbl5T1RGpod0ee0WQWd5CI7mbgHnze6wAigdJDS3tF51+7z/vkr32x0kDXIMQY9zZFwGKGzLlIVHv3uMeIsnwUGIOkeSf7kGrk3i8Ajsqo0iwpOwOJ3uaQ/AaE6tpane1y/WgYxpBkNnZKqROQjX5/pEXPZKXUsmbkLEDufUdSW5+q7f/8+LwvpHB+Yigp6wTMQyK7HUn+Ga0BKoFb8Xlvr/vQDJnZSGbHLcjvvxO43Y6+pyKngaxNF9vjZSU5gkbepe+BI/B5M9cuqKTsGKTOLxtZS5NFJXUkYD7vFqbpcIFnL8QBMhrJbJjgKQ+nzvKbIR3qOGwdimyS80nyGdVaE41SnZVlvEkTOjQZRAKlJwP/qF5atmnzJw895ykPnxMja6vo0LRRUjYEee93OB2aDmym7ZuBqyy/dWeMnDuMDkXmcyMwFJ/3GydFq4e2okO3c7Sn1zqH95B0mVQeRhCFkA/8HyVlw+1U2tMQgqArEXa7gUqpB9NsyXEX4MsyXKkYnCCLbCfEa357E8fUEXDUS6GcVzzAhaSq/ss2OAchxuZ84PwGBufR9vj5pB6R74iQs7zbwnE/ArvbXsOG+BcStWtIvHQ8MLx7Xsc7+nTu8k8kGpcqw2gu8jtfoaRs1xTHSASvAHuS2mYeZGPdCfgbJWWnA7zT7aObH+5xk+609gVqs/ssQIz3/2uB8XM6EnGJy6CMMLeOsa+VykNqIPN5EJIinRlI9GiWfa2UUl1zsrIMwzD6AW8kc55S6lVgf8SIOh34Sil1SVzyr5KyLKAUId5JdX3KQX5niJKyA1IcIxHMRlg4k97M28hGnptrKSk7EcAMmYcj2SH3Iz2MTctvXZeywSk4Cylx6ETyBifIb+uIRHNfSkOO5lFStgdicOaTmsEJsq51Ad6jpKxTuMCTFy7wXIcYmsOR9/WgtAxOgaM6NE1ZmsNdSEujTqTwjBqGQVaWkUPzOjQhRAKlgxCn0yebPw2VEturs3V1aOooKetmj79D6tBUYYbMUxCD8wkkeyMWO4wORdaLHsC/bWeg82hbOnS7RrvR6QQkHaw/zqQr56yq2DgK2YS+AKwCjlRK/VkptaL5UxPCCJJIB2oG+TTNHFlndDYkPDkGSUmZ0T/wWl2LiLXAyYuDoxp60n5H6gooFjnAvpSUNTfWj8ji3KPhFzbd/Azg0Eig1Atgb+zvBL650DvsIyTFwwnU67XmKGQx9pL6xjMW+WtrNx5/yGMH/+Pufk8cW2VsXr9mp8ms7nfXC5bfWtLy6byFpG01xaA8Erkf6aIjQtCTKaTTVigWOcAgW7ElDKVUhVLqOiQq+B/EqPpEKXV4g0N3I/VNUkNEce53x8ORpK7UY5G/PlpxlBkyH0bYG3dCjPNjLb8VN000SQzHmXW0AzDEgXGawmBkXXEEy1+acw5St3kTsn4XeMrDUz3l4fSu4bAORdJJM4XW0KEtIhIoradDqa38DNgvXOCp27y3pg5NBx52QB2KOC5SghkyByHG5sfABZbfapiyuKPpUBfiGOvn0HgN0ZZ06HaNdqPTGXTFOcWeU75y2SREYVwGHKKU+qCFc5JBt0yPdfmcueuQVOCGRucEYPnL3x73KltbRJy0ODgqHpFCL1KLIMRDNc3XBdbVg8UjEwJZ3CvYSrx0LsLEGuiYk5NsvWFzcOHs/WkIxzYN8zeHz6oxakf/ccVo7vph0lHVHQfFu99xEcOgPLwJBuVGxn8a6OLgWA3RHWc2IHVI6f4opb4Gfo8YVTsBHymlHlZK1bEVdkfSupxALpl9Rp16n4z3KxZcgLyrdyER+OfjbM5ShZNMkB0dHKshuuHQOhqt3Nxx0+LvHkBS747xlIfHesrDS50YG4d1KKI/MoWM69CWYPen3dJmyR0s/Alpm5KL9PmF1tWh6cDJ+WwzOhRZq5OGGTL7IM6GNcAplt+Kx3y8I+rQajJ379uSDt2u0aJX0TCMaxGylFrEQr9Qa/2xYRi5SLTnBPvzL4GJWuuIfd4GrXWzi5RhGCcDg7TWNxmGMQy4FxgEnKG1fiHmuDuBUciC8g5wqdZaG4bxLvAHrfXqZH94prG5spJhl15AZXU1NbU1jPGN5MZzL+SsW6ZQ9lWYnKxshnj258HLryEnu/5t6NIh72tgpFIqfVa7DMrZAuq1TZlXPGAPYFRUG7e/+t1x9yDeszMXB0clTIbUlKyFl5zP+oqNACxfs5ohBfvz8q1J9dmO7dW5oOGX7mDh2kig9Engj29d+/QN5HAT8AGSttao/1UG5XQU6cjZ1ZW/4sHvrqt0V+38kztY+AnvLYjQIJ26BTyKREvGI+1UWkRtbS3eC//Err37MDd4Dw+89Bz3vvAMi36KsOLld+jdvXsSl88MmprTef+dz+SZ9xONRuncMZ/ZgRvY2+0cga/NXv28UupNhKDpMuBUpVTg2mHHleVk1d97tpVntA4N7/33Py/ljJuuZdW6dRy870CeuOYmcnPqZ2flGx1WA0dbfqtVmDybmtPiTz/hihn3UVVdzSEDPcyaPIXs5NbS7UJOXRvN6bBLv0crvvpygqc8nHGW39/6fKaKSKA0ts3SWe5gYR3bfB2JzoFINDou2sI6Woe2ImtryWmGzFwkA25noNDyW0ntEdvn03lkWtZmbLD3EbLOuizBb7XWYwzDUMAGrXWTitswDAOpoT5Za73OMIxHkdr85VrrA2KOG4wECPKQmukJWuv5hmGMBg7VWt+Q7O9pNtJpGMZQW5CDtdaDEMrluvS52xAPyL5a630QMpiX7B+TKK5ka2uLH5F+k/XaBhiG8TvgCMQYPQAJS9elJTxB0yl62xQdcnMpvnsGC2c9zYJHnubN+R/xny8szjrqOMoffwHrsWfZVFnJI6+93Ohcc+ddX2sNgzNdOVtAw16dFwBc8+/rqhDm0tsWB0c944SspX9/mAWznmbBrKcZur/JqcNGJCtrrNHZFGYAHZe6Vj2M1CNObqpNTQbldBTpyHkIBd+4q3buz9b3t6XerPVg+a0VSDsVvxkyE/Kg3/fis3j22HPL30eYB/Lu1GnssfMuiV4242hqTi+65w6emnIzC2Y9zZlHHcstT8zKyPWVUuuVUpORtMr/AQ89vuA/oaiO1lvr28ozWoeG9/6qBx9g0pgz+eapl+jRuSuzXv9no3OK8g9+s7UMTog/px/+byH+2xXPXn8r/5s9hz127kvoLUeIK1tdTlde3rq+4/xPtYbBmY6crY1tIGddm6Xb3MHC2P3S1wjpU7PppW1hHa1DW5G1NeS0OSemI86Gcyy/lXT3gvb5dB6ZlLUFGwzgLK31YPu/MUkMfTywUGu9zv57NpIt1RB3AjdqrQcjzuw6sqrXgBMNw0i6zKCl9NpdgJVa60oArfVKrfVP9oXOBSZprWvt7x5DFryEahQMw9gXqNRar7TPX6y1/pzGuf0asbJzkdSGHKCOjv0VYFwi12ttGIZB53y5H9U1NVT/P3tnHt5UsTbw33Sh7AVBBI2CKy0aRI2CYozUDQFFQMW9Ki5IXS9Xzb2ijntcuV5Fq+LV4oqK+im4U4xxxahg1OKCIEZBNim00CXJfH9MAl3SJjk56QL5PU8faHLOnLdzzpl33pl3CQQQQjBq2HCEEAghOCx/f/xrUp+JvJXk/J2wETK/dO8OwEULVx78+ZotvW9CZ/5LOHNbU7JG2LS5ktKvvZxyZMKhEmvQz27/pg6wuOyLKqjy/pmx/gQUc6SUn7WCnKaSjJyBzWpvYC161RUSNDrDPIp2q4v5DvtX/8W8zz/motFjt3520L4DGdAvVSEcxmiqT4WAjZV6B7G8soJdexvyrIobKWUkyct5ARXarSYYrGfYt5dnFBrfe6UUpV9/yakOrWoKR47m9Y8bV41JbP0zeaL1aWZGJjnZHdhvdz20HGcbypyPSltUroYYlVNkZLRoqvvtvT+N4Hd6jkN7hDXSoflLygLo5E5NGp3tZRyF9iNrC8p5JTAJuN1X6Jud6Mnp/jSfFpA1qg1mQrtno8cQwu1+hM4f0xDFtnJgucCf4eMVuqrDmEQvHMvofA/YXQjxkxDiESFEZAayD7CijpUcwYvOqBgPw4GvYx2klPoMndVuZfjnXaVUWfi7v4EcIUQq4zcMEwwGGTLpLPqccjzH2YYydNC2hFW1gQDPvPcWIw87vBUl1KRITj/Q7/6JY7KA8X9V7txn5nfnDkZn4z13uWu0ocQBzcn6mudDjjn4ULp3SSz0JBznFbVWZ13e7/DtxiBK2AN5Mev6pULOVGBUzlCN2hV40uKyR3Y9/EC/vgsWJZKB7lPgW2BKE5mDt3L1ww9wz6VXYjDjcosSrU9nXjuNUc6rsZw6mmfeexvnWYUpl0NKqaSUz4ze74BTMkVGTTxyRmhLz2jDe7+uvJweXbttdVW07NyHP1p58S5Cwz49LH9/aoMBvEt+AOAV93x+X22shKWZpOU0l5aQ0+/07Ae8hA5lOtfiskfToYtpxuhsT+Noe5G1JeS0lliPBx5Ah/Qk7NII6f5MBS0ga1M2WITnhBCLwj8NMxg3x3AgnhrBVwP3CiF+R+dG+Fed77wYSDLVbE8ppSrQ9fQuQe8GzRZCnI/O4BRt1bOpz6PRL9xmswgh9kFnN7OgiwYXhOM/I6wmdRmrkiIzM5NFTz6P/+V5LCz7nu9+/WXrd1Omuzhq8EHYBx/UihJqUiSnH/189dtU0+WK6V9Prg2qjCrg5OWu0ZtSIesL89/lzGNOMNp0s0anlDJ/ndjkGBjctWpgcLeTW1FOU0lCTgE8Vuf338Of9Y332mFj/xG0K+jQpo6b+6mHPj17csjA/HibblWi9en0l5/nLdd/8L8yjwtOPIl/zPhPi8lj6d5zU3ZmZqNkE+3hGY1271UUFdPSu5pN0bBPv1+2lBdvuoNrZkznsMmFdOvUhaxMs3K7pOXcUeT0Oz090J5dAeBki8velA5dDPQpy8tvNA63p3G0vcjaEnJaS6z7AbPR4RLn+Qp9CS/Yp/vTfFpC1mZssAh13WuvTaDpnZRS8czDL0N7tO4OXIMubxPBkO0V0zxXSgWVUh+GA0YvByYAvwD9hRANs1odjF6Fi4ctxJcafxzwuVKqInwD3qZ+OvSObAukbZP06NaNo4ccwjsLtUfmLU8/wZoNG3ig6JpWlqw+JsvpB9hpyAbHTN+5R6zd0isTxITlrtHLUiHruvINLFzyA6OHDTfa5G80v9N5N4JKa3CPJ4CxfqcnrlpgKZAzJSQqZ0a2WGlx2evey6bK5MTiOXSm4yZjsz/5bjFvfOJhwMSTOePWf1P6zZecc3vSdbVTTqRP3174GYuX/rx1J3HiiOP49PtvW1m6bbTlZzTavb/64fvZULGJQCAAgH/N6pS7KydK3T49fP/BeB56goXFJRx14EHsa2nWoaJFSctpLqmQ0+/0ZAIvoGu6ntpg3G1IJJnQ4IZftKdxtL3Immo5rSXWHsCb6MWGsb5CX0VblNMs2ouc0HKyNmGDJUtAiLi2ZwvZVkf6ZeqX9zJkezWbSk0IMRAIKaV+Dn80BPhNKVUphCgBHhBCTFZKBYUQ56FrD8UbuFAGnBPHcSuAi4UQd6F3UhzomIZIBqa+6Jp/bYo1G/4mOzOLHt26saW6ig++Wsj1Z57HzLmv8+6XnzH/gUfIyGh994Fk5FwVWG85rsT6RbTveh/RodOYT/vxZrZ95ndrB9F9l7l+tdPH91pLnHHJdUfvi/qf3LX+pLcpWQFe/nA+Yw4/ko45hjOarwD6WUusHXyFvnquiFJKBzpL879zVefZbEvkIKM1ZFTOzaHqzvetf/G2l0suuNroH9EUAvh2wFOmyAmQ1VksbfBRxOjcHV0XMS58hb4Ka4l1FnCxtcT6D1+hb23DY+665HLuuuRyAD785ivum/0sz067La72qwOBbndJGfUZTZaCPQf2O3KPvTPrvh9N9Wl5RQU//f4b++3en/e9X5Dff0AqRIqbFL9LphHt3j837XZOu9nJK+5SzjjmeEremcfY4UfFaCn1NNWnq/9eT5+eO1FdU8PdL5RwwzkX1jtPqVDGkrz8lDyjPUcc17vPuImdM+rcS6NytjTJyPn9LzVnf/hCaUoeivNP6dq3S6fY77yJ/XkPOsnHpRaXvXHwcn3qZrCtRzLjKKHaHD478z0W/B2IW+p42fmoXPKu7UrWtpwkhmUNbOnM0uLbWDDXdB0KwIj59X5Nqk9jYC2xZgEvAnsCx/gKfcuNtpWMnJWhLd2GNTHPS5Yreozvd2HuqMwskZm0nJtDVV0uWOV68YeS30zfhDowZ5/Oj+8ytUvnjG37ZKm89xGassFMaPpHYC/0BmJz/Im2uT5E5+v5uc53+6F33xMiVv7ursBDQoge6JWWXwhnIUX79t4H/CSECAFLgHHhAFOAzkIIf522HlBKPVDn94+A+4UQIlz+5FC0v3pP4CQhxC1Kqf3RiUoK0CnAFfCOUioSU3cIehfU/IEwSVauW0vhXZJgKEQoFOL0Eccy5gg7WQXD6N+3L4dP0Qpo/FEjuKnw4nYpZwYiSPTgYyo6BbKW5O5r/XrtkTnWvp9WLO/5cbw74ABkkblLvLICvFj6XrJxcivQttluwNaVZCllBnAv2qj6j8Vl3+J3et4BLvE7PXdYTmxcYs+onAJUtsisoIk+NZtk+jOzk1jV4COjO52gEwoVoZOTxR2X8N85L3LPC8+wav06Bk86k1FDhzPzumn1jhF6zEhJf2ZmZOQ2LBXdVJ8+ce0NTLjpejIyMujZtRv/u751V29T/C6lnLsvvZwzbr2BaU8+ykH7DmTSqLGxT0oxTfXptY8+yNzPPiakQlx28gQKDm5YF1xAip5RkZnVEVHfH9m4nC1LMnJmZFBFqvpU1E92mMr+9Ds9FwD/AB6yuOyPxzo+f0nZ32V5+b+jjc64/v54xlFAQcbfmFdLdRsiKwNEXGFZMWUVQiGyUqRD43fhj7NPY3EPcAJwka/Q50n05HiIT4eKlOnQbJGVG0+vxitnJpkbgEqz5cwio9a0ZzQxmrPBQMd0RozstUqpY8P/nyaE2LrwopRqOC+bh040+AuAEOKF8O+9w3bbzUqpJ9GbKw8KIbKAqgbXHkH9GM+4ENtsxJZHCPEg8KZS6oMkzn9DKTU/5sGpxO3tC/yKeUW+78NhS8Q/O37c3lhupImwFIdtn2hf7Hn9m0dkiNAnA3f6hckHPnXkySd890lCLbu9/wWuMENIdGba3XDY1jV1gLXEegzwATDCV+j7MPK5lPIMtGvT+VLKEgC/0zMG7fJyquXEThXoeAsziv1uAi7FYUuolExcuL2Cxpmhk+FlHLbTI7/0XbBIABXAY6tGDPlHoo1ZS6xutMG6r2/AU5+jSyOZwQYcNjMLZW/D7b0OuB2dUTtZAkAuDttmE9qqj9s7BHCzLQtdMlQDThy21ASlur2bMK8I/VM4bKnZrnN7XwfMsnRDOGypCUx0e88DZmBOn5YD43HYzE8Xm9ahjfA7PcPRSRTdwIkWlz2uxfWyvPw3gQH5jz2zgBbUoYZxe08grUPrYS2xXoiOn3vQV+hLfNfW7V3IjqdDNwHDcNgS2uCIi/akQ+NACNEPmKWUOs7g+bsAzyuljkn03Nb277wT7ZJrlO9a3eDUlKNLuphBLTpAN1X8bWJbUVe/Bjjn7aGEeK1nzgZ1/oDnyrtkb/nUQNur0RNxM8gCGmZabkijWp1SyhzgLrTL0rN1jn0b7eIwBfhbKWVWFpMQqd3lNNPtpF4d2VUjhiiMlU2J8Cja3eN4wMyJTbmJbTVkPeat/Cv0SmIqWE9sr5Z4qSW1z6jhJGN1CXvcNNyNNxMzU6aavjJfh7/RRcXNQGDuu1mXtA6tg9/p2QMdS/UbMDFegzPMYiAvFKhdR8vqUKP8TSLbiM3TbnVoBGuJ9UigGHgf+KfBtndEHZpN6u59e9KhMVFKrQSeEEIYNaL3AKYaObFVjU6l1F9KqTeSOP8JM+UxjMO2BV0fq1FZAgPUAh+b0E5TvAWYsZtSGW6rHgOc87oAb2QQ6nK1rVhU/ZKz9piCpUa20z/CnP6sARbhsMUa2CIFd+uuYBcBA4BrpZRbJ24Wlz2Iztxa8ITv3T6bVXWnkDJlATQDWGRGQ41w2BT6uTLDsNmM3hVuyNbarAZ4FT1RnALMxZyJ+Ga04k4VcceuxqAa+AKHzcxV9Lr4Mc/4yABSEt9jLbHmLK5aumpLqDr2wbGpRsehpIr3MGccrUKPdaniK8CMXVSFNmB+MqGtRsx4YWOnTZWhDcGgKZ5X7VqH+p2eLuhMtR2Bkywue6IT1MVA1saFn/lpWR1qlB/QY5MZN79d61BribU/WhcuAyb6Cn1GFw12NB0aQGd3TVX9pHahQxNBKfVSlLKX8Z77pVLK0HvW2jud2xPHoCfdFWilF0jwZ3P4ZxIOm1kvYjSmoYvCVqInZjFlCymlgqEQ4d+rwue+Atxat+EBznkZQAlgPW//FxfukrOmdt33PY1lI3HYPgIms61fEu3PWvS9WIbePWsWX6GvCj1g7QEgpeyJ7qv3pJSNBt2vu5Q9+2zvecEZXV9+/dyVt9fWEtwSzq5s5N5XhH+OxWFLZdG58WgFX4GeSMQto1IqFAoqlFJVwL9w2KLVKjW80xlO3vQEMPqQ3y6eCzyulKoMhIKhYCiEUiqYgLw14b/RTTNZcZPGYfseOI3kn9GfMVBkOQE5Q+i6XGvD14vnfgdCSoWCoRDhn1ql1GZgHA7bj2aLaC2xHgd8e/6quw5aVrvyr5C+VkLPaFjuLaGgomJZwIPD9p7Zcm7FYZsDSKXU5lAgEFTBoNFndDEwMYVy/omOC6tAj9tGn9ENwPDwAqtpzJhcmjFjcumFwI8vvVvZo6ZWlSulKtnOdGiDnyZ1qN/p2apDgTMsLvsSA/ItBlhZ8kSQiA4N1QYIBUCFQgnImZAONYzDVoGu91eO8flT29ShSgVQoRChIIQCEKoNoNQWouhQa4m1K/q56gCc7Cv0JbOj/gjwOPo5q4opZ/vXoWuAI8ILA6mQM2EdGuWnMvw3pkSHthfM2i5O47Ctxe21obPM7U7iBn05erfjG9Nlq4vDFsLtPQct535ATKPwhzUrj1y5qXzMMMued3fL6bgWnfnq3Sg7MzcBE3I7lN9s3+2LGzau6LIoFMg4+P6JYzKnzp6b+AqRw/YMbu8P6NiEROM9QmiX2Xdw2OJ1D6lbq/PfQA+gUVyQtcQ6kj14CMgcvnFIbWVm1YE5u2VXowOr+yUoJ+gdvo9w2BpmhDUXh60Ct9cOjELv4Ma1A6KUYtPSwJUqCN33yzq1mQmdH9i174JFmatGDDGyIvg48K8aFbgEmPrmj77dsjMyTh/Qs9dr+Tv3S2RlsBYdIP92ClfnNQ7bW7i9Q4HDgZ0SPDuIdp97OzzxSh0O22+4vYOB49DJspp1ZxNCIIDy6qruZWtWnrS6cpP1j03lf/5VsRHpsJkmlrXEakEXPT8N+DlAcOSgnAELgBPRJSISivURQmxa+3X1mdVrQwPLnR5hcdlTl7TAYbt31UWX9RdZWUWd9tzn/dxhwxMJ9Qigs66/ZbYh1wiH7VPc3oPQE/s+Blr4E1iAw+aPeWQCzJhceiB6cnwE8ElVtZrSqWPGCrZDHdqAamLoUOCfFpf9bYMSLkVPbg/EYbuGx8/eE7iFrvt8zm4nvZ5AO0Z0qDEctu/D49P2pUOFAAQEK3JY/dFxVPxyBBVLqyn/roIFZCDLQwDWEmvdxYZRvkJfckaJw6Zwe6eiE8bsT+Kx0u1Fh0bCet7HYVtjumx1SVCHRmEd8GlKYk7bEa2aSChN+0BKeSTgAcZKKaO6Qw9wzjsNeAl4+onjrvoxQ6i7lr272y3ly7vfDOw2dfbcP1tQZENYS6yvAIMmLJswCj0peF5KeUGd73cHpqMnBT+d8PcRj1696pzpwEUWl/3JqI1uB/idnmFoN5jLLC57cVPH9V2waDI6NtOyasSQP4xcy1pi/T9g2NjlY/+VpbKeBB6WUpqVDCONQaSUxwMPA/ui63X9Q0pp2AixllizgavQZYcygTuAe32FvqR9a/1Oz3noCdxQi8u+MNn2mqIsL38U2o3tFWBi/pKytDKNgxmTS7ujd/iuQE/ErgNmFRUXpMq9vF3gd3q26lDgwmQWTMry8j8HNuef8ee/0btVnwAnIMtTa0CkaR6ZOxi90DIc+BSYgixfbC2x3oJecLjGV+hrtQQzadKkmrR7bZp4+Aa96hl1i2OAc95B6Enep4N7fz8lQ6hLgQXly7t7w4dYWkbMpFkB9FeoO9B/742gJ8jWEuu16Nqyo4AbgMFXrzrnQXSdoiK/02NWIoS2yGVol5LnYhyXTNmUCI/uVLVTn0yV+RgwH7gmibbSmISU8j30KvyN6Jq1S6SU/5RSJpx10FpiPQo9ptyLzs45yFfou90MgzPMG+iV+tNMaq8RZXn5g9A19BYB56cNztjMmFwqZkwuPRO9oHclOi5+YFFxwdNpg9NzMGEdCkw2YYf+26zOgYOU4nV02M9paYOzDSDLvwWOQpcH2w/4+rvUUOr0AAAgAElEQVQ7d57XJRS6Cfgf8GBripcmTapJG51pYiKlrEQnSmqUgnuAc15fdBzCWmD8VQc/VoB2O3mUbcl52o3R2aO6R2eBOAuYLqX0W0usR6MnlvegjaBBvkLfnb5CX3V4YvAIcBBwWKtJnUL8Tk8vdMzZLIvLHiuzaNJG58nLT/7hiL+OCG7J3BIATpdSBoy2lcZcpJTVUsrbgUFoY/Fe4Bsp5VHxnG8tse5iLbHOQu+8dAXG+gp9J/kKfctinJoQFpd9Azr5xampWAwqy8vvhTZsNwNj85eUmV/mZjtjxuTSfPT4+Tx6nBhaVFwwpai4wMxMsO0Sv9NTT4daXPakF18yc4I/WI78uwfQBTgZWZ6qrMNpEkWWh5DlTwMD/8zMfHVQTc2ot3//s/bz5b9/6Fu2ItbZadK0a9JGZ5p48QKHSim3TuIGOOfloDOt9QLGLneN/gsddL4KeJ1tRsjuLSyrIYQSK6zrrYQIbfhy5y+fspZYn0VPrjsDJ/kKfWN9hb7lDU57Fr0LeFkLi9tSXICOWXo0jmOTWmSQUnbJVtmvZ4Wyaj/u+3HHOXvO2dVIO2lSi5RymZTyJHSdyq6AW0o5S0q5S7TjrSXWTGuJ9XL0DtcZaFfaQb5Cn+HM5XHwCnrx6xAzGy3Ly89Gu0DuDozLX1L2e4xTdmhmTC7tMmNyqQv4FhiCHieHFRUXfNm6krUN/E5PRIfuBIy1uOzJJ8GRuWLPE9ac1LFnLRt+6Xw/snyHjiFrq1j33KPDCXvsNqyw3y6ruoZC33dRahYwH5mb39qypUmTKtJGZ5p4+RJtXA4AGOCcJ9DuUYcDhctdo7+ZX7r3nugEIE8cU7A0Uouoinay0zl09dDde1f15vM+n3+/ousKL9o97zb0BHlutHPCu3/PAGeEdwW3G8KZFCcDHovL/l0cpxi+31LKDHQs05DK7MoLNnXYVM32a8hvF4TjuwehjcgzgB+llJdLKbcm17CWWIehx46Hwv9afYW+ab5CX6p3B/8PnaznVJPbnQ4UAJfkLylLZYbUdk3YlXY8OiThevQYObCouKC4qLjArLqh7ZrwLvxWHWpx2c1KgDQtu3OoYPXibqz6qocZJVPSmIy1xNoReA3YaVHHnBOzdejSZWivqW+RuS5kbpdWFTJNmhSQNjrTxEtkZTriYvsPoBC4Zblr9Cvhzy5Fx0I+DjB19txIZrE2b3RKKbOyQlnXfLDbB6zssnI48DlwgK/Qd5Ov0Bcru+Sj6N3A81MtZwtzHDqD6CPxHLxqxJBk7veNaAPhugdueOBFYDZwrrXE2s1AW2laCCnlZinlNHS851bj8jLXZcdbS6yPoxNQ7YJ20T4+6ayMcRKubTgfOM0sF9uyvPxL0bV778tfUlZiRpvbIzMml+6Dzpo5B13b7sii4oILi4oLUptdsv2xVYdaXPZXYh0cFzJ3PDpJ0zPrl3RdBhxoSrtpTMNaYo0sNgwDzvUV+hYhy4PI8mJgINp76nqgDJk7Hpm7PeeLSLODkTY608TLt+jaTbYBznknomMct9YZm1+6dw4wCXjjmIKldbNatnmj01pi7T1/1/kLPu73cf/KrErQ8VojfYW+n+M53+Ky+9BFoy8L7w5uL0xBp6J/NYFz/CToTi2lnIDOYloC3B/++FGgG3B2Im2laR2klD8Cx4cInbG029I9F/ZZ+C6Ki7JCWTOAPF+h7yVfoa+lk+28DOyFdutMirK8/KPR2XvfApzJtrc9MmNyaacZk0slOrnakcDVwCFFxQWftKpgbRC/0xPRoXNoUKvTMDJ3CHpH+QvgEhCLgcGmtJ3GTKYC5wE3+wp99XWrLF+NLL8A/f78jX4+5iFz92lxKdOkSQHb0wQ5TQqRUtYAi9eGOjvQWRu/Bc5f7hodyTp4KtCbxrF/bdbotJZYM6wl1ktQ/FjeofzIARsH/BESoZ+BagMT5EfQu4LHmi9py+N3evYAxgAzLS57Ii5aCd1vKeUQYBZ6R+xSKWWk379AZzidEl4ZTtPGmbPnnCGv7fna1Yt6L+rRIdTBf+wfx4bG/jb2jAnLJkwMu0+3NP+HroWaVBbbsrz8vdCTv5+Bs/KXlKXdQxswY3LpKLSxeTN6kSqvqLjgwaLignQisAb4nZ58tunQQovLnnzmXpnbB/28/w2MQ5ZXAYuB/cry8hOt0ZgmRVhLrKPQiw0voUN3oiPLP0HHo1+DNkC/Q+ZKZG76XqZp16TrdJqJ2zsRuAXoi7HC1m8CV+CwpXZS4/YOAp4C9iGB4uu1wWDHTUGyf9oUqCldU3P4vy8d8XXku/mle3+MLjyed0zB0q1K9P6JY+4E/gl0nDp7bmLK1e3tgt79Go7OwpcIQXRim8tx2D5q+KW1xHoI2lA8rEttl+VH/HXEgO613YfP2XPOLUA3X6FvWAJyChVSMlitpmV0EKGMTJFosfe/0bsy10UpFm4ubu8J6N1EC808oyqocpSig8ikUggRkWkL8BVwNg5b1KyTfRcsugt9v3NWjRjS7N8STjzzJbrI8qFSylV15NxjVWDd+zmiw37dMzpvzhSZibwTAWA5cAkOmzfGscnh9nZAu0odj96ZTQSF3km+F4ftcbNFa4TbOwXt0rcziRe23gi8DUxpWCzcWmLNRU+gitAZOK8FnpmwbMIBwAzAjnZXnyKljB23pguFFwP9gawE5awCfkA/o3/4nZ730XHo+xkpQ1GWl98dXcZiV+Cw/CVlv9SRszfaFe5goGOCTdcCy4BJOGyLE5UrIdzeDPQ7PwHokeDZCvgLuB2HbVbDL2dMLu0P/Ac4BVgCFBUVF5QmIet2rUOVUiJUS6faTaFg1ZrQ4T0uPfzr2GfFQObmoF3JDwbsyPKvAMry8seJDjmv7nPH/aVZ3XMHkbgODQErgWk4bHOSlrM53F4BTAMuROeOSJQ2p0MbElKhjA2his6/1q7c9Fdgff7o0VfEV7tc5u4K3AecCfwKXIEsfysOOfdA73wPQof/JEJ70aEh4E/gehy2N80WrRHJ6dD16LJz03DYdljDK210moXbezbwBJDMStRm9MRuYsqUptubjy4U3YPEXxoAQkoFM4TwA0Nx2P6aX7r3geiyIlOPKVj6QN1j7584Zgp64tlv6uy5q6I015ScXYAPgQNIfEJXl0pgVMTwtJZYewC3E3Yd7Vrb9dbj/cffIxDvSClPtZZYnwRO9BX64s+c6vbeE24vmcD/SvTq56SUDUhu70j0jk3nJFqpQSu+I6IZnn0XLCpCuyH2WzViSJP3W0qZA5SiEycMr2eMuL27AwuVUr2FEIkaHXXZBBSkTGm6vdnoSa6d5Pp0M1ppPmyKXNFwe/+BNgyTlXMBcAoOWyC8A302ekK0M9rLYZqv0LchckI42/W56PIqvdFjwY1SyvIm5BwKfIDOimuUANqYP8z/9pbR6AnNgRaX/dtEGinLy89EJ/sYBZyQv6Rsfh05e6F35/sDHQzKqdDPqB2HLSHZ4kYbnCXAOJIbnzYDV+KwPQkwY3JpB/Tk6yb033ErML2ouMB44podSIcqpQJCiD8I61DDsuh4v5loY+0MZPnsyFe/nTx+/z4Tzviu4+79a0VmZsL1dOuwBTgfh+2lJNponh1IhyqlaoQQTerQJpG5BejxMw9dHeBqZPlvTci5O7AQPebuCDp0C3AmDtv/mSJXNMzRoZXo8fjyHdXwTLvXmoFepUtWWYJ+mEei3SlSxf0koSwBMoTIBHZDu36AzrpWhc4+2hCjtRvPQq/QJWNwglZij1tLrMJaYj0PXbbhMrRhlHeC/4TBApED/Ct8/G9AP2uJNb6VQbe3P3AFySnLiJwTMSH+rBmeJLkBE/QEey/gkia+j1k2JWyIPAocARRG2f26Edg5SYMT9KppXEmQDDISvQufbJ92Bh7A7U10NTo+3N7OgAtz5HQAx1pLrPujDdBn0O/Mob5C3+V1DU4AKaWSUs5CJ8h4FL0b+qOU8py65Zfq8BjJGZygJ1m7oGMvX0evhhtxsb0dOAm4qp7BqZmC3kE1anCCHoO7oZMvpQobyRucoO/9I7i9mTMmlxagXTfvAt4B8ouKC+5O0uDcoXRoeGyrq0ONchXa4Ly9rsEJsMc1zuE5u+5GkgYn6HvyZJJtNM0OpkOFELF0aHRkeSk6MdS/0LuCZchcJzI32hh0I3ohcEfRoal+Rs3SoV3QZegGJi1TOyVtdJrDTpjbl3ub2FZD9iMJZVmHLMA6v3Tv7sA5wAvHFCxdH+U4o0bnfiT/ggMQUqH+6IL0JehdOpuv0HflhGUT+gEXAcVSykjSoEh15t3ibH5vIOli3mECpOre60ldX5Na64DegY5GPPf7GvTAe6uU8uUo3+8PZEb53Ah7mtRONPYmAde6GFQDe5jUVkMGoBeFkkYplfXcxvenoj0brOiM1Yf7Cn3NuglKKTdIKS9HZ7/+DW2sLpBS7t/g0P5myIl+fg6wuOyr0R4TCWWxLcvLPxtttD5G9EnXAZhz7wWpHe/3RhvdSaOUCj43r2IO2pUzGxhVVFwwvqi4wIyK9jusDjV8tsyNuHm+ho6lrYfIyNg3o4Np61g5uL3dzWqsAWkdGi+yvAZZ7gLy0Qs+dwGLw7ugddkRdWjPsKtuKhiASToU7S2WyvGpTZM2Os0hG5MUO/qepOrFAfNecNBynotevWmYQCiCUaPTNG0ZINgRvWt6ETDcV+iL7Ky50O4OdbMHRiZQ8RoAZvanMLm9hpj5vjd1f5q931LKE9GulnPQsVvRMLMPkl3pbY4OmKfYQ6Tu3ndAu0AmTS3Bjv7AmmPRCzgDfYW+x32FvrjHPinl1+i6hJeiM2suklLeK6WM7G6aeb8i4+gr6JXlhgZuVMry8g9Dr5q7gSvyl5RF6zszd6VT+YxmY46BRCBAp1BInYjONH1AUXHB22a0G2ZH1qGJI3MHostKfQechyyP1ndmPqNBUjc+pXVoosjyFcjy8cBo9DM0H5n7PDK3X/iIHVWHpkpW03QoqX9G2zRxvUBCiHFCCCWEyKvz2YDwZ1fU+exhIcT54f8/LYRYJoRYLIT4SQgxSwjR5O6REOIVIcRe4f/fIYT4XQhR0eCY/kKI+UKIb4UQHwohLOHPdxZCvJPQX55iqqqrOWxyIQdOOov9zz+dm596DIBJ99zGgZPOYvCFZ3LqTddTsTnVNdKNyTn/q4UcfPE5DJl0FkdefhG/+H9vdG44HvgywHtMwdIvGx2gWYte2Uk6g61RWTPICKEnyE9GJshSyqOAkwGXlLJu/biI0Zn0jkswGOSgi85mjFN7UJ19+zQGnjuBA86fyIV330ptoG0kdmwo58OvvsQ+Z41DHH0oazdsiHF2IyL3u1HZFCllHnWyNkopE5pkttf+bKtyQuKyClAjOx/2oK/Qd5Gv0LfWyDWllCEp5ePoHaMSdOKpJVLK05rKMZDkM/oqesJwaqwDy/Lyd0O75P4JnJq/pKw2xinNyrls5R8Mvex89j17PBNv+Rc1tQk1l1ISlVUIgoP27nBCUXHBLUXFBWat+psuZ2vRYnLK3J7oOLga4GRkeUWMM5qVc3san1qLVpNTJxQ6AL2AOx74EZl7NUpFXWhqr/3Z1ubNdWkoa4QrHryXriOPMuUazdhg30U5dpgQ4gshxCIhRJkQQjbR5kFCiJnh/+cJIT4TQlQLIf7Z4LirhBDfCSG+F0JcXefz+4QQDXfYYxLvqs2Z6DqEZzT4fDVwVdhHPRrXKqUORK8yfwMsiHasEGJ/IFMp9Wv4ozeBw6K0dx8wSyk1GL07dReAUmoNsFIIMTzOvyfl5HToQOkDj7L4yedZNPN53ln4GZ9/72N60TUsfvJ5vv3fC+yxS18efi11sfnJyHnZ9Lt5btptLHryec469gRuf6axu3wtm3qidw+a2uUknLH2D0wwOo3KmiUya32FvnWR38PlG+5D78r9p8FlIjt1Sbs6PjjnRfL7b/NMOfvYE1ky6xV8T73IlupqZs57PdlLmEJDOYdbD+SD+2fQf5d+zZwVnXDG2kb3W0rZE13/tAoYK6WsTFbO9tKfbVVOSFzWLDJrD+y4z3Izri2lXCulvAgd27sGeCkQCkWN6UvmGbW47H8BHxEjrjNcWuJ1dBzTyflLyhI2qhvKef1jD3PNqWfx83Ov0rNrd558K3V5LhIlUVkzM9lyyKAcM1xpUypna9EicsrcLPQO5wBgfJOJZBKQc3san1qLVpVTlm9Blku08fkxMJ3KZVG9Otprf7a1eXNdGsoK4F3yAxsqNpl5maZssGiUAJcopYagn4mmOuvfbMsnsB64Ej0v3ooQ4gDgYrQ9diAwRgixb/jrhzBQszqm0SmE6IoO9J1E4z94DTq+o7C5NpRmOrAKODHKIWeja0xFjv9cKbUyynGDwtcDncRibJ3vXqcNFZIXQtC1sw5JrA0EqA0EEELQvYv2IlNKsaW6GiFatwRhU3IKARsrtV1QXlnBrr13bnRuFesswAb07lVz/I4JRmcysjbgdHRs2Y1SynrlTXyFvip0iYCkjE7/6r+Y9/nHXDR62yM6atjwsLyCw/L3x79mdTKXMIVoch6070AG9Is/eW+0Zqlzv6WUWeiBbwAwXkqZ8MS1PfdnW5QTjMmaivFKSvkZ+n28KqRUI1crk57Rl4FBfqdnULQvy/LyBfA/dG28s/OXlDVaQY5FQzmVUpR+/SWnOvRicOHI0bz+sTvRZlOCEVlbQ1e1lz5tQTnvA44DLkOWf5ysnLB9jU+tQZuRU5b/gna3HY8KxTWOtpf+bGvz5gjRZA0Gg1xb/F/umXylKdeIYYNFow+61BFKqaBS6ocobXYDBiulFoePW62U+hJdyqsu+cDnSqnNSqkAOuRkXPic34BeQoiE4pzj2ek8BXhHKfUTsF4IcXCD713AVKEzmsbia3S654YMR9f/i8VidK0x0H94NyFEpKaTF512uc0QDAYZMuks+pxyPMfZhjJ0kI4bv8B1C33Hj2TJiuVcMX5iK0sZXc6Z105jlPNqLKeO5pn33sZ5VuN1hQAVfYCnjylYGsvXwU8Ud8uWlDVCuFTHnWgXz2eaOOw3kjQ6r374Ae659EoyRONXrDYQ4Jn33mLkYYcncwlTaE7OJGi4yHA/cCxwqZTyEyMNbg/92ZbkhLYlq5QyIKX8b4fMzEY74CY9o7FcbP+FVuj/zl9S9oaRCzSUc115OT26diMrS4cZWXbuwx9tYFIH7UfWtJx1kLmT0NlqH0SWG8rU2Zbe+Vi0F1nblJyyXCHLX6Prno0WzdqUnM3QlJxtbd4M0WV9+LWXOHn4UfTr1dusy8SywRoyHfhRCPGaEOJSIUS0ChA2dDx4LL4DjhJC9BJCdEaXD6s7l/8abb/FTTxa/Ey27WS9GP59K0qpZeh6QGfF0VZTyxP90Lumsfgn4BBCfINO3f8HOlsZaFffpLZnzCYzM5NFTz6P/+V5LCz7nu9+1XXFn3LezJ+vvEV+/wHMXvBeK0sZXc7pLz/PW67/4H9lHheceBL/mNHQCxUUSqCLuMfCD1junzgm6eUpo7LWYQo6G9u1Usqm6ritIAmjc+6nHvr07MkhA/OjCzDdxVGDD8I++CCjlzCFWHImgR+w9F2wSEgpL0a7bUyXUj5lpLHtpT/bipzQdmUVQtQL6jTrGbW47CvR7kmNjM6yvPyxwB3owt13G2k/mpwqSt6JtrBC315kTctZB5l7JDqM5X30PChh2uo7H432ImublVNk1pvbtFk5G9CcnG1t3hxN1j/XruHlD+dzxbjTzbxUszZYQ5RSt6KNyvfQdlm0fDdx2VxKqTK0Tnw/3M5ittlcYMDuajbTU3gXsQA4IDwZyASUEOK6Bofeic4Q+FGM6x3ENvfYumwhjnqMSqk/0cHSkS3nCUqpSJHxjuF22hw9unXj6CGH8M7Czzhgr30AbTxNHHEc9774LBecePLWY0NKibJNgb6jnfNMtwQAfj6hT3Z2RnTlF5Hz7YWfsXjpz1t3ZieOOI6R1zV2FchSndcfU7D0xzgu60dn/+pNfIsLMUlUVtgaV3gj8J6UsrlRawUwylpiFb5CX8IZyz75bjFvfOLhrc8/paqmmo2bKznn9ht5dtpt3PL0E6zZsIHHbvt3o/OUUhk1G0K7rnF6zL/3Aiwj64fLNSdnkviBDqMXfzIGXW7iXaDhmBE3RvsTIKhCmUNKrCl5l/5v1zv67Jndb+tkMhk5QyqU+czG9/a6r+QC0wtGT9vp3D1P7XZ0Rmad1dhkZG1JTH5GXwEe9Ds9Ay0u+48AZXn5VrSx+SVwcROZag3JefXD97OhYhOBQICsrCz8a1bH4/qfctqLrEblVEqJ2k2q7+pUjKPAbid0zBZ1dGjK+1Pm9kfv1C8DJiLLDWV7SW58UhnXrnl03/dKvuxj7I9omof7XL37kZ2spoxPIRXKWFy9dNfzSi4w/d5nk8nXA2bW+2x7GEdjyqmCmcjclLxLHPZUHzrvDnHoUGh63qzlVIKvpgxkwU/mJznb76o92XVMBjGe0f3Pn0hOdjb7nD0egM3VVexz1jh+ef41Q5dNwAarh1JqKfCoEOIJYI0QopdSal2dQ+KyucJtPUm4BqoQ4k625T0BA3ZXrPTCp6IT91wa+UAI4UYXXt6aIlQptUQI8QMwBr3rWQ+hZ2VXoK3raFZ3GbAPsLw5YYQQvYH1SqkQ2h3qf3W+3o/4totbhDUb/iY7M4se3bqxpbqKD75ayHVnnssv/t/Zx7I7Sine/NRD3h71k6RWh1TH51dsuZkodbfMYHV1iN06bfOEjibn9WeeR3lFBT/9/hv77d6f971fkN9/QKO2MkK50epyRqNuGQ3DRmcysob5N7qodywDaAW6RmgvdDbWhLjrksu565LLAfjwm6+4b/azPDvtNmbOfZ13v/yM+Q88QkZGYycDFaBLxW/B+2gQzG0KUabSTclpAn6AnGDtLGApcIaU0nBaPKP9CVAZ2tINaBTTYAavV3zMFT3Hkx0eRpORc7Oq7vJqhefNVMj5csWHjOo6jG5iW9nbZGRtSUx+RucAD6L12h1lefk7o5NblQOn5C8pM7xoGU3O56bdzmk3O3nFXcoZxxxPyTvzGDvcnGyGydBeZDUqpwrRsXJFIGU6NFityOq0zehMaX/K3K7oZ7QDOlPt30blTuadr1W1Hb6o+uEzo9dujuc3fsDBOfvSLTP58Wmzqu7ywqb5KdGhtTR2jGrv42hccgY2p0yHsuod2PNCEE3r0GduuDXmvBnQxnHlb83WizbMn3NhlwLI6rr1o2iyznVNr3da15FHGTY4w8Rlg9VFCDEaeEvpVPD7ossdNUzxXgZMjUcAIUQfpdRqIcQe6E2/uj7Y+6HzJcRNLKPzTHTMZl3moLdsG7oh3YHOUFuXe4UQN6In8J8DI5RSNVGuMw84GvgAQAhxT/ganYUQfmCmUkqGj7krbPF/BBTVaWNEuJ02wcp1aym8SxIMhQiFQpw+4lhGDzsS+5UXs7GyEqUUB+6zL49eUz/5U7YQNUf06jDrud+3fJAKuXpmi4fRO45NyjnmCDtPXHsDE266noyMDHp27cb/rr+xUVu1tZ3jLXlR1+hs+IzETTKySikHoN08Z0kpF8e4VN1anYbKQkRj8gMu+vfty+FTLgRg/FEjuKnw4q3fi0y2dOyV8cSWlcFPzbpmA2IlfALgv3Ne5J4XnmHV+nUMnnQmo4YOZ+Z10+K+SI/Kjes3dOlORU6n7D6bNpwkpUy47ko8xOpPgE4ZOZuBC1Nx/UM75p2UScYZxKgzFo+cOSJ7y/4dBkz7tfbPP8yWc3CHvft3FB0kEDUzbKKytgWMPKMWl/0Pv9PzKXBaWV5+pFZsX+Co/CVlf6ZCzrsvvZwzbr2BaU8+ykH7DmTSqLGxT2ol2ousseQUgpqcXpmzKn8PpkSHZjTQoUbljInMzUDnHTgAOBFZHo9XUcLE885niazaflm9LiuvqUyoPEs8HNoxb3DHjA5Xo+eJScmaI7K3DO046Im3K78wXYdm6OiwuHRoexlH45Izs2PKdCg9hpyEaF6HKqUodMlm580AiIwgWd0uoKY6mo2RHN3z+5ORI4lDh5pMLBtsYNhGinANOu/NdCHEZrQr7NlKqXorJuGNwlwhRDel1KZwMiAv0B0IhUujDFJKbQTmhHdca4EipdTfAEKIbPRmoTeRP0g0VRetJRFCdEJnox3esHMSaOMjYGykQ1oUt7cPOvlMXNvVMdgMXIPD9rgJbTXG7V2GziKaNH9u2vDnrmOObbL2aoT7J47pi86mNWXq7LlNlleph9s7Hbg65nHxUSUXzJ2Dfhn3lVL6mzvYWmI9BP0ijfMV+prPIe72Hode6ck1Qc6NwCU4bLNNaKs+bq9Ar3iZFQD1Ig5bo9gCKWVGecfO814YevxIy/q/HvJOOCHxFG5u7+fAUDOEBNbjsPWKfZgB3N5/ohfbzChEvwk4DIdtiQlt1cftHQx40AolWaqA63HY/mtCW41xe8sxR04ADw5bve0lv9NztVJqeuXb/5ytajZNRGeqfd6AnHMIh3qYwCoctsTrE8WD23sO2sW9mwmtVQIH4LAtN6Gt+uygOhR4F4dtZNRvZO5twDTgGmR5s4kKomKuDq0B+uKwmT+/SutQ89kxdWgQ6IrDZr57rbk6tBw4D4fNUMI6sxBCXANsUkrNjHlw9PPHAQcrpRrv8jRD6+/7A0qpLWhXmJgGTDSEEDsDD7SKwalZS4zdjgQIopVvqjClzppSilWbNsa76rMaveKSSAbbZUB1woJFIRAKbkCX05key+AME+n/eJIJ/Q5kG5WtARmYdH8a4bApwKz3Iwg0tep+W7eqzSNRKuTfaRejq+JLDZ4XjZTsYIVZgZ6MmUEnmnCXMYEVmLdCGyBVz6hmlYlt/RLlszm1vy4gbHDeacjg1PwExOvpEYt4xiSj/K6UMhV5I7IAACAASURBVEvP55C692mtilIuxyDtQoeG+SnqpzJ3Itrg/B/aJdwIpulQdIBGSjxWSOvQVLAj6tDNKTE4NWbq0CxSq0Pj5VGSGx+y0JUJEqJNGJ0ASql3lVKGboRSao1SqvWq2jpsIXQMQcIF7xtQjVYUnqRlappb0SvBhlFKEVSh6s9+/7WHlDLmys/U2XND6EzDidTqfBldsNZwLCDoBY0Pli5Zj14YiDcz5Tp0cHQ8RudPaFfvpPoUvYPkI77SQUaRJC9nCL1SN6tR41KeCfw7A2aSXG3WBwAz3Li2ADeZ0E5TvIvewU9WaVYC/8NhS3b8iI7DtgGdLCfZe1+Nvq/RksGZxY2YkxBuE9BoN3bT65cMrPbNJnPn/PLwtYzyP/R7kKyrUGTB1XRmTC7t8vjLG0evWR/qHAgk7dG0GXgYh8101zW/0yP8b285u2JZoCaUvJztQoein5tyomV/l7mHAE8DnwBTkOVGO8UUHYr+W11hoysVpHWo+exoOnQzesc0NZinQ7egk9b5kpYpSZRSVUqppsoGxnP+y0qphBei2ozRuR1wA3qSU4se/LYk+BNE17yx47Al+2A3jcM2H+0WtjEsa9wyKhWsCakAisDf7y9dcs2azRUCiFUzKIKfRIwQh20lMDSkQr8FVZCqUA0hpaoTkLcGqFqydtXjn/uXDQJulVKWR7tUQ8IZa+Mrm6IXHE5Gp6cOYPzefwocm4pJXR1ZH0InU6oO/yQqZwC9eHA4DtuvdZuWUh6Knoh7gCJ0nIExo9Nh+wpd13MdCT6jbLv3m4FCHLakovhjyFmODqr/Hn0PE5WzCt2ns4DJKZNTMwl4geSeUR8wHIdtU8qkdNheAi5C378aA3LWopOVFeCwLarbdFle/r7Ay6JD11WdDrsst9spjw9IQs6f0eEgq1RIoYIKpVSz/aqU2qJCqkYFFeFzagObQ5Nw2N4yLEcUZkwuFTMml44DfqgNcO0bCyqfE4JPSe4ZfRz4h5lyAvidngOAD4FZ5T8GvgtWqf+xHetQto2jfwFH4bDVT9Aic/sB/4f2DhqPLDe+E1FXh4aChGpqCIVCibxT1eG/7z4cNmlYjthypnWo+XLuKDq0OizrbThshspdJUAyOrQq/Pd9CJyIw2YojHB7oE3EdG5XuL0Z6KyniRr0G3DYzHKFiQ+3twfaZSouPg5OmRdgS/cgmwd6Pjq3F3pyd52U8t5Y594/ccyLwMFTZ8/dL97rWUusGcBLHUWHcYNz9jrnyb7Xl8Z7LhB4zOspX7mpfDE6XmB/KWXcyshaYn0PyPUV+obGfUW3Nwt97xNlPQ5brYHzjKFjU3qRuEt4RbTdOCnlrujVu1rgUCnlmr4LFr0IHLJqxJB9k5S1O4m7tdQCf6dwZb4xbm9HEo9JUsC6FlVAbm8m+t4nGpfUsuOTfkZ7krjb3RYcto0NPyzLy++BTmbXK3uf48d1POBUD3C9xWW/x6iIfqcnG3hPZHJ4Tq+Mcb0PyYkrc2KlP9Bt48+Bfwar1MXoSeG1wCyLy5708zpjcunewEPAiehFgilFxQUfA+D2dkD3aSIoYG3YMDANv9PTFb3DezXaeHMCT1pc9tD2rEPDbI66cCNzO6InpVbgCGR5rIR3MYno0Ly/ssfd8XJ2Roc+fScNuO7GeBMuhtDjk6n3vll2UB2aUrZvHRpEP6MtKadRHdqyz2gbJW10pomL+aV7H4x2W7n6mIKlDwJIKZcBX0opY1bCvX/imPuAKUCXqbPnxvXQWUusEj0x+aev0Jew77iU8iLgCeBUKeWcRM61llhnAqN9hb7UJPjYTpBSdkJPlPYHjpBSfgvQd8Gi+9DZpTuvGjEkPcikaTXK8vIzgbnolf9j85eUuf1Oz0IAi8t+mNF2/U7Po+gd6nMtLvuzBs4fgk7yczjwMTDF4rIbcruaMbm0I3A9upRYLdol7qGi4oJkXStNxe/0CHQZgOnoHA4zgX9ZXHbTsoS3S2SuQHs8nIPe4TRldymiQ7tsUc6n/hO8Ax3HnEp3yTRp0qRpkrR7bZp4uQztJlBS5zMvcGic5/+OXm2La6XdWmI9DW1wPo2OT0gIKWUXdOzNp+jC2omyAuhrLbEmuoq9wyClFGij/jDgnIjBGcaPzkS5U2vIliZNHe4BRgJT8peUucOfvQIc6nd6Bhhp0O/0TEEbnPcYMTgBLC77InS9tYuAfOAbv9PzgN/pSSjT7IzJpSeia1RL4DVgYFFxwfQ2aHDuh47hegntPnq4xWW/eIc3ODXXog3Om0w0OLfq0MpO4h504poDzWg7TZo0aYyQNjrTxGR+6d490HWBnj+mYGndwOEvgQFSyp3jaKZurc5msZZYD0Ibt58Bk8MxlokyFegHXCulNHJ+JPuh0WQ4OwLXo7MCT5NSNkzkFff9TpMmVZTl5Z+PjkV8KH9J2RN1vnol/O+ERNv0Oz0F6Pj9eej4LsNYXPaQxWV/EhgIPIl2N13id3omhncFm2TG5NI9ZkwufRV4Cx1ndGxRccGZRcUFqcw2mTB+p6ez3+m5De3uOxS4AjjU4rJ/3rqStRFk7hh0Lb6XgNvNaLIJHfotaaMzTZo0rUja6EwTD+ehCzc/0uDzL8P/HhJHGxEjpNmyKdYSa190IoV1wHhfoS/hGB0pZV/gOuBVKaXRItGRTMrxZLDd4ZBSngTciS6WfWeUQyLlP9JGZ5pWoSwv/wjgMeADGiTBsbjsv6KTzpyWSJt+p2dvdFbQH4GzLC67KfG4Fpd9ncVlvxQYhi4b8yLwnt/pGdjw2BmTSzvMmFzqBMrQO7j/BgYXFRekMquwIfxOz0noJCHT0EbVQIvL/rBZ/dbukbn7A88D3wAXJJGpdivWEusuRNehi4H+4fjmNGnSpGlx0kZnmmaZX7q3QLvWfnFMwdKGiTK+QieaiMfFNubOV9iV9VV0kPbJvkKf0Zp9N6OTOzgNng9po7NJpJQHoCdKXwOTmthJTu90pmk1yvLy90C7mq4AJuYvKYvmavoyMNTv9MT1jvudnu7Am+FfT7K47I0SFiWLxWVfiHZXvxw9rvr8Ts+dfqenM8CMyaUj0MbDXehsn/lFxQV3FRUXpC5jpwH8Ts+efqfnDeANdBbMoy0u+7kWl93MOqztG5nbi239cwqyPOmMu2Ed+hrQm8Y6NJKYaHCy10mTJk0aI6SNzjSxOBrIQxeSrYeUciN6xT8eo3MVOtNYVCPEWmIV6F2Jw4FCX6HvGyPCSinzgIuBYinlz0baCBMxmvon0cZ2h5SyN3qiVAGMlVI2NVFq9n6nSZMqyvLyu6B3ejoCJ+UvKVvfxKFxu9j6nZ5MdLr8fYFTwzulKcHisgctLvsMtMvtC8C/Qkr9OO/KD91AKTob9+ii4oJxRcUFvzXXVkvjd3py/E7PDcAPQAE6VnGIxWV3N3/mDobMzUYveljQBufvMc6ISRw6NGJ0pl1s06RJ0yqkjc40sbgMXWD6pSa+96LLZDQbfzR19twgughwU0bIP4BC4BZfoe+VJo6Jh7vRK8e3JtEGYZekVaR3OrcipYxMlHYFTpFS/tHUsatGDInc72bdqdOkMZOyvPwMdCzbYOCM/CVlS5o61uKy/wIsQmdTjcVdwCjgSovLvsAMWWNhcdn/+r8NtZMWbw4+VBFi1wM7Zx41olvWz0d3yxpTVFxgam1PM/A7Pceh4wZvR2cLzrO47PdZXPYdvkxAFP4DjAAuRpabFdtaV4e+HOX7lcBa0kZnmjRpWoms1hZgu0LXF8tDT7QTNejXAd+3WA0nt3d39Kp9k9lZN6qlO/ViyPhOYpf/G5hx4dG4vaCL8f6EwxbZCfwSnXVvN7btDjaFnyhGp7XEeiI6w+QcGhqLbm9PYBDQPdaf9M3K3w/YZ6edT+7TpVvJ8fsMGoLbuwxYmkQNpxXEa3S6vTlhOfsauM6fwA8tUsNJ1xfbFxhAAjXGQkqRv3PfS35dv/bo6mDgHCnlF3GcFvV+x43b2w+925NojbEq4EcctpZJqOL2dkOXjEm0BmIQ/Yz92CJ1xtzebHSW1F1JvMbYX+hntMp0uaLh9u4F7EWCdTp7HHn06Zu+/WZCcGP51PwlZW/HccorwO1+p8dicdmjjl9+p+c89I7doxaXvb7Hh9u7M7pPuyQiJzrxz3Lgl6bu/YzJpcOBR5bXhAavqAm9O6Jb1tfdM8UVwNd+p+cudObc+O6H29sZPT7Fk/StISuBsqZqYPqdHgs6w/hpwM/ASIvL/q6B62x3OjQqPz9yHNk9plC74V5k+SwzRIlHh+Y/9kz3P2Y+4lehoB2398Q4mg2xbXxKfa3O5HToavT4tMVcoaJgUIeGqUC/Sy2Tsdm4Dq1Fz52WmS9UFIzrUAX8ge7T1GfrTk6HrkePTxWmy9WOSNfpNAtdeHsucAR6QmGEWuBwHLZfTJMrGm7vtcBt6Ml5kwRVTccQtTlZdNokREZdpaPjJR22B6WUh6PLkoyLksG0HvdPHPMSYJ06e25+5DNriTUfXbT9V+BIX6Fv24TB7T0CHbcUsz+VUtQEg10VKiMnM2ujEAK0G9os4DIjk3prifUlYLCv0JfX7IFub290lsBd0Io6UTLRE1A7DtuGGMcaR0/ongXGop+1uAmEgh0CoVCnTJFRk52ZOQyHLab7c98Fi14GrKtGDGm+/6LLeh7aVcxosfeOwFU4bI8ZPD8+3N79AQ/GvUay0WUkTk+p0nR7u6LrqQ5EG7uJIoANwDActpUmSlYfPaG7H+1hkdC9DwUC2aqmunNGh5wgmZnniqMPfSHWOeEyHj8CV1lc9v9G+X4Y4AY+AU6ot2vn9o5G7/wbjafsgPYgubDupH7G5NKd0R4bF6ATcl0NvFZUXKD8Ts9uaAPvdOAX4AqLy/5Os1dxe/ujx9jO6ElaomSgF8aOwGHb6qrsd3qygavQpVoygTuAey0uu7F3djvUoY0IBbIIVnUhMycETGTE4cl49QCJ6dBQTU1HFQjkZHTqVB7WkbHIAj4CxqZ0UTR5HSrQBt0wHLakXZWbJAkdWocOwCk4bO+ZJlc0ktehOcCDwL9SuiiavA7NJOLOn8oFp+R1KOHzhuOwNemBs72TNjrNwu19Ex3D0jmJVoLA38AgHLY1psjVELf3EnRh7mTkBO3CerlcMPdFYBNwt5TyhuZOuH/imAeAS4BuU2fPVdYS607AF+hdzEN9hb4VWw/WA9EXJL6D0JBK4FEctmsTPdFaYr0PmAJ0abJsi16d/Q69I9ohCTn1DjIchMOWmsyObu/j6BInhu+9UkoJITah5Ww2rq3vgkXT0fG13VaNGBL/QOP2jkUnKjLjGZ2Ew/Ziku1ER+90fAvkkviqZ102A6/hsJ1jilwN0YbcQuAAtDFulFr0rlc+DlvSSU+i4vZK4J8k/95vAU7DYZsX60C/0/MtUG5x2e0NPt8d7clRCRxmcdnX1ZHzSPRiQbLPaCXwNA7b5TMml2ai35c7gW5o4/u2ouKCRhOpsCvrw8B+6N2taywue+PJtt7lKkMnlkl0V6YuNcAywIrDVut3eo5CZzPfH20oXmlx2ZPbFdkxdejJOGyGsw63kA7dDLyLwzY+yXaiY54ODQBrgDwcNtOTfAGm6NAwm4FjcNhSUzbIXB16Bw5btAz1yWOeDt0CLMJhO8IUuRping5V6MXbA1rME6uNkY7pNAO95X4iyb/gmehB9/CkZWqaSSQvJ+E2LpBSbkErjHgz2HYBcq0l1mz0Kv8ewLh6ylIziuQUUIQuaPdfI6xAu6X0auaYPPTqbLKy5gB7k9oYyDNI8t4LvTyeBRwbx+G/E77fCV6mEPOe0fNNaKcpjkb3RTLKErScE8KKLRX0AawkpyxB78r2CLeVKs4n+Uky6Pc23vf+ZWC43+nZNfJBOFvs6+h7c1I9g1NzKuY8o12AM2dMLrWhd6seRSd8ObCouMAZzeAEsLjs76PjVm9Aj5VlfqfnOr/T03AcOhR935MxOEGPb7ttXhU8zO/0zELv/nYFxlpc9pNMMDh3VB16htGT6+jQ/ujSKKnSoZ2Bk3B7k32GmsIsHZqFfp8OTlqipklah4bpBIwzoZ2m2NF0aCfgENzenZKWKDpm6dDI/Mke68DtlbTRaQ69MO5i1ZBstL94qjASL9EUETm/BGyxkglRv4zGA8AxwKW+Ql+0Wpq7k2A8VzMkGicQIZ6yKX0x5g4UjVqgn0lt1UcbNN1Maq0z8clptGyKmRlvU5k9tx/JK6EI2SQedxMv/TDuYtVUe6miuQWeRIk3Cdgr6MnAOAC/0yOAp4CDgDMtLvsPUc4ZYIaAAEqpndCr6Bb0LkpBUXFBtGvWw+KyV1tc9jvRcXDz0S65i/xOz9F1DutL8ganljOkMitXBN5FT7zvAAZZXPY3zGibHVeHDkji3IgOvcRX6Pskyvdm6tAgesEpFZipQwXtQ4cKYE+T2oqGmXrPSBx4vJipQ6sx992si5k6tAOp1aFtmrTRmSKqqqs5bHIhB046i/3PP52bn6ofWnbFg/fSdeRRrSTdNpqSs/TrLzn44nM44PyJFN4lCQSaDbH5Em3Y7RXjcn6ADw5ZfTG6Dt0DvkLf04nIGwwGOeiisxnjvAaASffcxoGTzmLwhWdy6k3XU7HZVK+/iNFpqGxKC8tqmBTKGTE6Tdm93c77s1XiHNprn559+zQGnjuBA86fyIV330pt8+NTs1hc9jLge3QiHIBp6JhJp8Vlj+me25ycD7/6EvucNQ5x9KGs3dBsuPZ/gbyi4oLni4oLEnoWLC77covLPhY4Cb1wscDv9Dznd3qandgkKqsK0ikjiyWA1eKyT7O47C3ycLTXZzSVclpLrJdgkg5VSnHDzEfY75wJ5J93Gv+dEzUaoU2MT3HK2uKk5TSX7fwZTZVHU7sgptEphLhBCPG9EOJbIcQiIcTQOt/tLISoFUJc2uCc5UKIOXV+P1UI8XQT7R8khJgZ/v/Z4et8K4T4VAhxYJ3jRgohfhRC/CKEcNb5/EUh/p+9845vqnr/+Pukkz2F8iV+wZ+ojRoEjThDpC5QRHGhoBYXVuvmi8Z93XEgrmpVUOtG9OsCt4UYJ0RFo6Z+XahRy1J2Z3J+f5wU0pCdmw7M+/Xqq5B778mndz3nOec5zyN2SuqvbgMK8vOpvutBvpzzDEtnP8Obiz/mk288ALhrvmXNhvXtrFARSedHX39J6a0az117M18/PpchA4uoeitm32tJ8He8EFtfbd96fAPqyoE3gcuS1XvPi89hGrJlgHBW+SV8OecZvnr0Wf49sIj7X4pW2SUlEpnpjEoba02ZDOpMdaYzItnzqT+dRWu4zimHjKPmiRfwPPYcdQ0NzF4QM4dZIrwAjPbZXaejsn8+CdyRrs4DzHvw7swKhgyM6f/J8sqSi8srS9Ym+32hGB3W+ahZzxtR4b81G5Y1Hxotb0OyWkUOm/qOzL/M6LB+l47OZOms92imdJqrzDagAp1s6ONvvsZvK5ZT88Q8vE/M46SSw3TRqQedRWtWp750Fp2Qea1CCH/Q9/pSCPG5EGL/4OdDhRB1wW0tP6cFty0TQniCvpRTCBFx4kQoqoUQPYUQ2wshFgohvEGf76KQ/foKId4RQnwf/N0n+Pl4IcT1yfw9MZ1OIcR+wHhgTynlcNQ6rtBkBSeg1qGcHOFwixBitwQ0XAncF/z3z4At+F03Ag8HdeSgXrLjUEb1ZCHErsFjHiSFF2+mEULQvasKqW9qbqapuRkhBH6/nxmV93J72YXtrFARSWeOIYeCvHx23l7dp4da9uHF96tjNfM1KvQgptM5b4yvYOGeKylsNKwBTvaUepJKmONbsZwFn3zAWUcevfmznt26A2p0qa6hgQQz8iXKatRC+qSdznbQmhIZ1vknavQxbaczez71p7NojaTziH0PQAiBEIJRpt3wrVyR7te0hNhWopKvTDM6rEmNnEfSOXKnXRg6KHakpxBCtxF6o8NaZ3RYr0UlvPikcW3gFOnfem1XKlqFQTS19f3Qme/RTOg0V5l3QCWO+hGdbOiDr7zItaedhcGguoMD+mRqWVxydBatWZ360ll0QptprZNSjpBS7gFcgaoZ3cKPwW0tP6ElmMYEfalFqOidSBwBfCmlXIdKxDVdSmkC9gXKQ/wsO/CelHIn1FKOlom/BcAEIUTC64fjzXQOAlZJKRsApJSrpJShGZdOBqYDRiHE4LBj70Q5lFERQvQAhkspvwy2/5GU8u/g5k/Y0lkdBfwgpfxJStkIPIdKWQ0q1fIhQogOV3PU7/cz4szJDDjmMA617MM+u+7O/S89z4QDRjOoX//2lreZcJ2jTLvR5G/GXaOWFb3gfI/fViyPerymaU2oIutRnU5zlbnHxi7+/waElAd/NqDaU+pJujTIxfffxe3nXIhBtL5tT3dcT9GxY6n5dRkXHDsp2WajEsxYm3itzhDaWmuqZFJn7ZgRTUAtOjid2fOpP51FazSdoAbKnnz7dcaOSjtvzAqU0ZXAxITrXyaos60xOqzfA2ML+hvuJ0I4V0fSGovOfo/qqdNcZe4BvIpao3uUXjb0xz9+Z+7Cd7BMO41xl13I977wfETtQ2fRmtWpL51FJ7SL1p6o7NzJ8DEQ7p+1MAV4BUBK+aeU8vPgv9ejsp63HHc0UBX8dxVwTHA/iXJqxycqJp7FeRvYXgjxPyHEA0IIW8sGIcT2QJGUcjEqg1r4G/V5YE8hxLAY7VtQs2SROBNoKe49mNYzrL7gZ0gpA6h6ZXvQwcjJyWHpnGfwzVvAYu83vP/l58xb9B4XTDyxvaW1IlznNz//yHPX3swlFbMYVVZKjy7dyM2Jm4tiCbCnpmlb7WiuMrfUtjId+FX/77ZbU5BsNlPmf+RiQJ8+7LWLaattj9mv448XXsc0ZChzF+pe+ippp7MdtSZFG+n8jTSdzuz51J/OojWWToDzZjkYPXwk1uEjU/4On91VAPw3+N88UqjBFk9ne2B0WGW3wblLRA6tMuB2RK2R2BbuUb10htpQ4ERPqed7vXQ2NDZSmJ+P++EnOHv8MZxx240p69SLzqI1q1NfOotOaFOtXYKhszXAbFQUaAs7hoXXRsqKOxaViT0SBwCfhX8ohBiKSqT3afCjgVLKP0E5p6hsvi24SSIbb0ynU0q5AdgLVVtxJTBXCDE1uPkklGMJauYxPMTWj1oTc0WMrxgUbLcVQogxKKfz8paPIskL+fcKMputLi169+jBQSP2YuEXn/HD778xbMqxDJ00gU0N9QybnMms2cnRovPNxR+z327Dcd33CIsrqxi9x0h2Msb1u5ag0pUXR9h2EzABuGTI8q7fkoIT8uHXX/Lqhy6GTprASTdcSfUXSzjlpms2b8/JyWHSmEN50bkw2abjkbTT2Y5ak6KNdPpI0+nMnk/96SxaY+m8/vFHWLlmDXeVX5Jy+8FMtQ+gjO+VKJt4jJ4625vwsM6OrDWUbeEeBd10ttjQSz2lnnf01GncbgDHjS4BYKJ1DF/9lLQ/qzudRWtW5z9TJ7Sp1pbw2mKUA/mE2PJSDw+vdYUct1AIsQK1LPKZKG33Dc5qbkYI0R0Vwn9xMOw2Hkn5X3FDUqWUftT06SIhhAdV/+dxlJM5UAgxJbjrv4QQO0kpQ8/wkyin85sozdcRli5ZCDEc5c2Pk1K21Ebz0ToDphEIDfMtDLbVYVi55m/ycnLp3aMHdQ31vPvZYi4/+TRqX3pr8z7dx47mh2deanVcQDbn+eRbE7+vnpSRdNqjDXN654nucXWu+PsvBvTpS0NjI7c9W8VVp5wRr+nQZEKbr7e5yjwZdQ88gipmPozEajy24tZp53PrtPMBWPTFZ9w59ymevOoGfvD9xjDj9kgpee0jF8X/TinRbCx+BYrMVeZCT6knoZC7dtSaFG2k00cK17sddKZNZ9EJqWsNNDUWrHz1xdP+OufUjNRB3OX+R/MNeVuqPETS+dTVNzJ7/su8teRj3rvrgc1rZ1LkIuAMVKf+TuBsVK6Ch5NpJJrOjkhn0Zry8xRozuP3VyaiHZyZkhQHvtybvC2VMzL93IfZ0Pvi7B6VaNfd/tB9VH/h5oxBE3Au/ZydIwwwS78/78drZlzbtHpl0mHn8ehjO2SHAcefXGjI31KmM1WtgaamgtVvvnbyqnNOHaG3ToTAVPlEq4/SOad1P/84fNk5p96mu05gx1vu2j6/35ZKJ+noDDQ2dvmu2JQRnYNOO2tUr/2sBhF8h6els6mp0Fcx89KN53wTXlM5bboPHzlg8LTzCwx56d+j6SCl/FgI0Z/EytiMATai/LUbgEsj7NMshDAEI0YRQuShHM6npZT/DdlvuRBikJTyTyHEIJSj2UJS/ldMp1MIsQsQCHEkRwC/BD/vJqUcHLLv9ajZz83WS0rZJISYhVp0GikTjRe1JrSljX+jwpxOlVL+L2S/JcBOQogdgN+D3zM5ZPvORHds24U/V6+i9FYNfyBAIBDgxDGHMH7/+DPQEn/uJmrHABmpp+KnviCPLU5nNJ0zHryH+R9/QEAGOHfCcZTsGS8xLd8BG1BO5+MA5irz3sAc4H3gfE+pR858fbwP6Dlz0vie0+fOT2QUJSpSSkodGus2bkRKyR7DduLBS+zxD0yOloB8IyqMOyXaSGvaZECnD+hZtHBpz9oxI9K63qH8g89nxkhEq2xuLmhaseJI9Kur17p9vz+fvPilBcvucjCkqIj9zlODYceOHsO1pWcn9V0+u+twYCbwEnCd0WGVPrtrHnC5z+7qb3RYVyX9B4Rx74vPcfuzT1L712qGn3kyR+xzALMvi5bToX3pDFoTep5kcy6bfBmzofjrCkKdzpR1JkAkG5qS5hjYJ09lys3XMGveM3Tv0pXZM7a+5jIQyAnU103T+7sBGletMMjm5nxCnM5UtUp/c37jitqxwKG6C42SBToSiZzTpr9XDwMyklHSv2F946lXWAAAIABJREFUAf3i+yUJXfvmpgIypLNxeW0uMmCIt9IvMZ3N+U1r1pxCBkqnNP39l5DNzQXkpX+PpoMQohi1pns1bJ0gLhwpZZ0Q4mLAI4S4SUr5V9gu36FKHf4QnD2dA3illHeF7fcqasLREfz9Ssi2nYm+THLrvyFaOnUAIcReqJG13qgkCz+gQm3PBwqllKGlS4YDz0kpdxVCLAMsUspVQogCVFbat6WUUyN8hwfYX0q5Plg65Tjgl+DmZimlJbjfEcDdqBP+qJTy5uDnA4HXpJSjEv2jdcfpLgJ+Qp/i7nXApdgslTq0tTVO9y+kWAIkAj9is2xes6tp2iKgq6Zpo8xV5sGowYIGYJSn1LMSYOak8Sejpvp3nT53vjeGznuBC3TS2YDNklIBYnOV+SBgIXCwp9Sz9cCJ0304MBdIep1qBNYC47BZPtahrdaowtZ6OgoaNkvcVNlFC5e2XO/daseMiFvwHgCnezHxy+8kyjfYLLvr1FZrnO7LgJtJIGIkAZqBXtgs+hcddLpHAE5UEoJ0WQeUYrOkXaMkIk73eggZFUuPj7BZDoi0wWd37YJar/ILcIDRYd0Q/Hwk8DlwttFhnR1D58tsSWaXLgFslriL5lPC6T4Nlfldj3O6FjgWmyVmKvOU+Ofa0HexWSI6SOYq879Q66Va2dCE0deG1gNGbBbdZ5F0tqHrgXOwWZ7Voa3W6G9D52GzZCbBh742dA02Sx+d2mqNvjZ0HbAfNkti/Yxk0NeGNgKXY7PcncjOQgg/4Gn5L3CllHJBcN2lF+U4tvColPLeUB8s2MZ9wAopZauQFiHENcCfUsrZQogDUYlZPWy5z6+UUr4uhOiHWk75b9REzAktDqwQYj5whZTSQwLEvNBSys+A/SNs0iLs+xWqnAlSyqEhnzcQO973UVQSotlSyrOAs6JoeR14PcKmycBDMdpvC+rQ56EBtRZ2Y9y9UkfPMOTwDvIS4MKj7j+qJz14GfWA7h9mLFsSQhlRD0w01qNGrPTIh9+YxrHxanXq6SQIndsLpxl97tMmEtcZWqszUWOg5/2fyWdpE/qdU0F692ksNpFATeYEkWT2Hq1HP6dzQ6QPfXZXH9TIbSMwocXhDLIUVY7ieNQyj2joWWi5Sce2wtmEvh3lTD1P/1QbGvE+MleZu0BUG5pM23rZ0Fwyt4RJz/dJQOf2wtHrfQ/KScoUet7/uodUh6CnDc0hc9deTxvaTBI6pZQRBySllMuIMkgX6oMF/x9t8Gk28ATK//qAKO+K4FLHg8M/D076dUnU4QT9TmI6PIgayUuVNWxJ5ds+2Cxr0e8FYiCzocJfoE/ntpGts14tkcj82i61c1EJqKZ4Sj1fhe3T4oRsT2w86PMC8RPbuY3H7yjDHdHpbFof+FEGZLc02g+lANXh1R+bRQbb1qMD2kji4RShTmdCNNcFfpQBqYfOBtRMQab4mhQynUZAAr9hszTr0FYklqFfR6mA9J6neHyLMsrpUs+Wdeab8dlduahZlR2AY40O6y+h24O1OV8ADvbZXbEKrLnRpxMeAP4Xd6/U+RaVkVcPCsmU1oUHryPQqFfntrPY0HpgcfiH5ipzS5jb3sApEWxoouhlQwFWZSQKQ/Ed6r2iB/kkPriZHPra0DoiZA3VETf6OIvNbJllywR62VBQz/1vcfdKjWXoZ0OhgywHDGaifUQIkeoM7r8JWSKZCO3udEop66WUT6Zx/GNSykx11pJhHOmPfm8CbsBm+VwHPdEoR4U7pzO63gh8D4SnjVzyXa/vqM+tHwtc7Sn1vLL1oZsTQMVzQp4P/qRj6AKo2PeUC6R5Sj0NqFqTWzmdPrvLtvyDhnf++rIpVwZkui/OOuB4bJaIszM6cTSwRsaKqY/PJtTo2JsJ7v978Hdcp9Nnd3X12V03L3+/4bSmdVLKQOrPdTDCwsuWIsaZwAncS3r3qEQNWB2li6JI2CyNqMyX6XYa64BzsVkyZdhBRa4sJ72OSD2qQ3dzhG13otZ7nWt0WD+Icvw8VAcjVvjsA6iw+3QcT4mquXZsGm3ERoWaXY4+134KNkuyNeLio/UyAe/y+cU98Nen26HvLDa0DvgImBVhmx2VqPFqT6knnTB2PWwoqH7NkWm2ER2bZQUwlfQHceqA6dgsmUxtejRqoiMdG1qHKkf4iC6KInMd8CXpTeg0A3+i1vBlCj1sKMHjj8Rm0cuBbY1+NnQTcCc2y4fpi9IHKeXzCWapjXTsEinl0mSOibmmM0uSON0jUQuuh6Gm+pOhFvgvNstTuusKx+nuC1yNqm2a7BqaOlQI2o3YLK2KU5sfN08AXulf3/+nVV1WDYuW9GDmpPG1wKvT586PnZjA6Tb8sW7NnRsaGy7qWdhl44BuPb41CJFop6QRNeJ5GzbLL/F2joW5yvwJsN5T6jkUwGd3FaHKAZ2CGgG70DiuSzNwGpBQikIpJYFGtmuuk0OaNwVyG1YFXtn0u/90o8OadMHvZPAvXLzTj3+tfK0gN3eXfl27f989vyCZZCkrUc5mZXDUNyGKFi6tBV6tHTMi4vUOlq2YANyDOn9PFg4wXNt/r4JpqFq+CYVbyoDMa94kh/rrZf+mDYG1G3/1Tyu65sDn4x+ZBmqdz5mo4sgDvm/07dogm7rslj/0cyGEXNm8Zrvfm1cN2yGv6NteOd3Xhh3tR43y35WRdShhNM+vLqn76Yfncrr36Fcw2Ph1TmGXZEKwfgOexmZ5NVP6NuN0D0Zl69yN5GdANqBmj27GZmnVifXZXWeiBkzuMTqsF0drIHg//gR4jQ7rETF05tWuan6ksYnTuncVa/r0NNSElyeJQTNqUOR2bJbMRDeE4nSfiMrKG61IeDQkat3r49gs+hbE1Hp1A65BZVXcCFyJdf5icrucz7ZtQ9cBnwC3YrO0cgrMVeYJqLDaucDktBMHOd0GVIbmg4FYM/eR8KMGl+/GZkl1tjVxnO5xJGFDw/gdeA6b5UV9RUXA6d4JmIEqD5fs7Nda1Lq52zMY2aJwurui7tGEbWgIDagZzluxWf7UW1orwmxokke3zD5XYrN8pLe0rXC6rai8Nv9H8mHry4FXsVke011XJyLrdGbRBXOV2Qx81KOxR6Dkj5Jfb7ruJnO0fWdOGu8GVkyfOz96hw7QNG0QKkTOD+ytadqKWPtnCnOV+Xlgjze8D+wGnIsqr1AI3A7canRYUx798tldvYPtnQusAv4DPBUM89MdTdMuAe4CbtQ07dpMfEc4RQuXuoGVtWNGjAvf5rO7/g810nkkKuTkPKPD+n463+ezuw5CJU/ZFbV27yKjw7osnTYTwVxl3gkVfnitp9RzY/CzQlSI8SJPqef4TGuIhrfYJIDHUKPWJ5pqvPPaS0t74LO7DkRlUF8EHGF0WGN2+Hx21+3AxcBAo8MacXavoqx6Z1QyIh+wf3lliZ7rPLddtF4CVQv1blQESRVwGdradnm/dxRabChQA4z2lHo6VBm4LFmyZEmXdg+vzdL5MVeZt0N17tePWjnqsVyZu6umabHWOfqIE26paVohqpRBb2BCezmcQX4xbdphiEQuQTlInwJmo8N6TToOJ4DRYV1jdFjPR63fWYZa1L3IZ3fpnnFV07TDUeGFLxEhGVgG2ep6++yuQp/ddQ3K0TwINXI8Ml2HE8DosC4CRgKXoUb4v/XZXVf57C691g1Foww1g7U5AU2wtuujwDHBbJTtxaUoh/OGf6DDOQRViutnYFI8hzPIC6i1kBHDnivKqnuj3nnNwISsw5kgWq8dgQWo67EWGI22dmrW4TT3J2hDgWOyDmeWLFm2RbJOZ5a0MFeZ81EdtEHAMb0be1ej7qtYxZl/I4bTqWmaQBVn3wc4VdO0L/VTnBw+u6v/rJ9nHHDXLzMKJHIgcCJwuNFh1TWhhtFh/RzYDxW6sTvwhc/uusNnd+mSyVPTtF1QIVtfA6dpmpaROotRaOV0BusjelAFi18Dio0O651Gh1W3LJ5Gh7XR6LDeAZhQWa9vAr7y2V36128DzFXmrsDpwH89pZ7wcKSHUM9EcoUkdcJbbBqHmpV/EYhb5mZbIvj8vIpKMDIh2qxlBJYQTA0fvqGirDoHeBbYETi+vLLkZ53kbrtovQrRemmoQSYrahBkL7S1rnbV1QEIt6GeUs/vcQ7JkiVLlk5J1unMkjLBLHv3oYpwn+kp9SxmS8bIWDWifECfmZPGR5sNnQGcClyradpLeulNBp/dZfDZXWcD3+1SP3SfF/q+wyVD75hkdFjnZSr01eiwBowO6yPALqiQs/8ANT6764TgOrOU0DStD8q5a0TNGmcyWVEkfEDvF+/4aBef3fUCal2oRDnvJxodVl/sw1PH6LD+ZnRYjwfGot53b/vsrrk+uyvZtW3xmAT0QWXjboWn1PMj8BYwzVxl1iuTaEJ4i00m4DmUk19qqvG25WBDu+KzuwzAk6hBnBONDut3cQ7ZTEgW28N8dld4/cDbUffTeeWVJU699G6zaL3GoQa7rkOtVyxGWzsLbW0mS8V0CkJsqI0tNjRLlixZtkmyTmeWdChHzcw5PKWepwE0TfsTtag/ntMJEWY7NU0bDzhQWfdu0lVtgvjsrj1Ra2seBr6Z2++tSXMGvsT/uvzSvy2+3+iwrjI6rGehauSuRJ2Lt3x2187JtqVpWkuJiKHAsZqmpZVUKRW6Nss/Tv25kb1X+z9Hrd28BhWerG9ikhgYHda3ADNwLSppUY3P7prus7v0cgLPRSWGieaEPICqV5y5LLVheItNfVGzfPXA0aYabyZrF3ZErketHZye4r02DzVDuvmaVZRVT0XN0t1XXlmSyeyTnR+t17/Rev0XFWnQDByKtvYktLXZmbwttNjQ21psaJYsWbJsq2SdziwpYa4yH4xKBPEacFXYZjcpOJ2apu0GPIOqg3a6pmltmuXKZ3f19tld96Nma3dAZdKzVQ14dWFwl1Sy6qWM0WH9GHUeL0KFGnt8dteNPruraxLNbC4RoWlatBIRGcNnd9neXLTh+ov+18DqAvElsKvRYb3J6LCmk8o9JYwOa73RYb0RlRXViTo3X/jsrtHptGuuMltQ1+nBGNkmX0eFa56XznclirfY1DLYMAQ41lTjbfPBhvbEZ3dNQmVufBSVFTkVFqPeVccDVJRV748KlX4X5XhmiYTWKx+tlx01CDMWuBLYA23tu+0rrGMRx4ZmyZIlyzZH1unMkjTBLJ3zUFn2pnhKPeEhe0uAnTRN6x2lia2cTk3T+qFmZTYCR2ualqlC1FuLsbuEz+46FVW+4lzUrNQuRof1yWCY3V+o+kpb1erMNEaHtdnosN6LCrmdh+pIf+Ozu+LOmGmadhbKYb1H07Q5mVXaGp/dVeSzu54CFuUFyL1kZBfGHdT9IaPD2u7r34wO60+o2atjUKnknT676wmf3TUwxSbPRd0fT0TbwVPq8aMcloPNVeZdUvyeZLgLOAQ4x1Tj7TA1wdoCn921F/A48CEqG3JKg1dGhzWAWgc79oXzF+6KSsD1KzCpvLKkI9SG7nhovcag6gPeCrwD7Iq29la0tW0+yNSRiWBDM1NfMEuWLFk6EFmnM0tSmKvMvVDOYQCY4Cn1RMra2LKuc68ozbSEVxkBNE3LQxngwcBETdMytsYvnGCW2EUoh2EZsLfRYb0gtF5mcPbqF9rB6WzB6LDWGh3WU4AxKAfnVZ/d9arP7toh0v6apllRzvPbqLWhbYLP7sr12V0XoBz4E4CbXtg+b7hrQC7A9m2lIx5Gh1UaHdZXUGVVbgFOAr7z2V3lPrsr4fqA5ipzH2Ay8JSn1BNehzOcOaiC8mUpyk4Ib7FpGnABMMtU4/1H1QTz2V2DgFeAFcCxOsyoz2uWsuAvv3wdVSbpqPLKkr/S1bnNofUahNbrGVRZmgJgPNraY9DWLmtfYR2PBG1olixZsmxzJFvYNkssnG4TKkPlLiRX2FoCfwKvYLO8kglprXC6ewCXoDLMJhyqKaVkzsDLRiyu9273zqbPxr8y+a2fouzqDv7eG3gvfOP0ufPrZ04av5ItM513o5yp0zRN+yREp0BlBC0Bkl1P2YxyfB7CZtkq06zP7uqBSmxxEapY9zRgTnB2IxK/EsvpdLptKMdlKMkVDQ6gCtE/hc3ySbydjQ7rIp/dNTKo+zpUOZCbgDtbOtiapg0lpESEpmlbZmWc7iGoEM9dUSUhkmEF8BY2S8S1Rz67a19UIp0RqFmO840O6/9mADMXLl1FnDI5rXC6C1B/495AjyR1bgA+QxU1j1l6IFjy5iqf3fUkcH/w5wyf3XWe0WH9NIHvmnp4170LL+87eRBO9xvEuPaeoY/xTcPPq9YFNpU3L/p0t1yR04gKQXwQmyXas5QU3mKTDVWj9C1UyZgtON0WVNmUHUluwFGi7v952CyZD5F0urdDXXszyoFJCCmlod9e+XvWr/R33/ibfx/jrda0y3DU1Pk/WeuX9U2SIcAR5ZUlNSE6c1DP0gGo0k7JUI+aEZyFzbIm3s5p43QfCUxEDewl837yA98Dc7BZPFtt1XrlAuejMlEXBH870NamVvIjdRsKav37e0AVNktml2akYUMfHXj5iE/rv41nQ/UhgzZUd1K3oRKVEf+/2CxvZkBZa9KzoX8DH6De+ZlN6paeDa1HJZ+7G5tltd7StsLpPhE4AhhI8v2nH4DHsFm+yIS0VqRnQ5cD87FZXsiEtM6CkLJNl81tuzjdZsAF9CS5hyaUTcAMbJYHdNMVjjKWLlQpifxUmvDLQHOOMCwFxmCzRMyEqmnaj8BSTdOOi7R95qTxnwO/rzdZXkfNyN2hadqWTrIylncDZ5GEUQ9DomrBHYDN8i2oUFrUGq1ZqA7YbOAKo8O6KlZD5irzw6hR6aKtNjrd41Hr51LVCeraT8Bm2cpJj4bP7toe9XccB/wPOH924Xsfo8IKhwD7aJq2JWOn0z0UtU6tH6lHOWwC7sJmuSZER39U8qczUbPYlwAvhIY1Fi1c+gXwe+2YEePjfoMylm+gSsgUpqizHvgcOCSe49lC8N44ERWaOogt90ZEo2uuMhvO7z1x+dSeY/sWGPJTPZ8BVEdkP2yW71NsAwBvsWkHVJTBKmBfU413izPjdB+Iyhocq35uPOqA0zJqNJ3uAcAnqFnxlAZFZUA2CIN4Cjg7XeejoqxaA67brdDQPKwwp4/RYd0Q1JmDWn8+ntSf+0ZUh+mAjDqeTvfpqMGUdN5P64GDsFk+3/yJ1usA1Lt7OGqQ4wK0tanfw/rZ0AdRdjQznZs2sqFpkyEbmhH0saF1wDnYLE/qIyoC+tnQl1Dv0sw4nvrY0CZUhNd+2Cwx+0dp4XRfgVo2lM613wAcmsjAfcroY0M3Addis8zUR1TnIxteqx9vk56xBPXQ3YHTPVwfSRG5HygmRWMJkCMMuahZiFkxdltCnGRC0mAwodLFLwCuCNt+LMqJSedFJIBeqA4Rweyvb6Gywa4E9jc6rGfHcziD/AoMNFeZW7/Ane7+wfbS0Unw+NdwuhN+oUUqB3JE48iabrJgd+DEVg6n4jWgL+k9912BS3C6Dw0tKwNMRSXmMUUpKxOzNmsY1wL7krqxJHjsnqi1ZQkRDLmdi3o+ZgFnoEJuzwqW32jFRb2Pv+C0nof3T8PhBHUt+qASDaWMt9jUAxWylwMcFeZw5pG+sQToAjyJ0613uZlQnkbdJylH4QiDKEDNmExJR0hFWfUJwHVdDbyxY4EhF5V5uYVzSM/hBPUOHoYa3MgMTvfOqJnvdN9PPYD3cLoFWq/t0Ho9ipqx6YsaxBuXlsOp0MuGlqGuTaZoKxuaLrrb0Iygnw3tAjyE0/1/6YuKil42dCLq2mQKPWxoHmrwukoXRZFwug8gfYcTVG6Gd4KDgfqjnw3tCtyA071P+qI6J1mnUw/UyGc/0jOWLQRQI8eZYm+SCFmLQQEwKsb2JcD2mqZFTM4SyMldB+L/UDN0kzVNC0+kMAJlRNJFSCkH/X61y4EKF9kHuBC1dvPjJNr5Nfg7fF1iMWrGQg+aUWEbSdFSDuRHQ61zQKDX4BMb9m8+q/7g4a3KgahR71RC1iKR17gucAQhZWWAEUaHdYbRYY22PslH4k7nfuhz7QtRhjcpjA7reqPDOh0YiQp/fQT40Gd3jQjdrxn/VKHLI48B2AGnOyVHy1tsaqlHaQJONNV4wzv/Q1AzFnrQgAoryxR7kHzIWiS6En1NeVwqyqpHojpbH/UwiOOEELUEs9gG2Zf0O0qgHJeUdSaAGTVjkT5SdmHJtP+gBplORdUrNaGtfRFtbXr3l742tBBlPzJFW9nQdNHNhgKDcLr1aCsSetrQRlRdXv3R14Z2JbPXXi8bmoeyg5liD/R55kHZ0cSX8CSHnjZUktk+focm63TqQzeUw6AHOahRm0yR7khNom21JBOyhG/QNK1nU+/+h4qAXxjqNp6gadq6CMf3Qq/7M4BBCC5HjabuYnRY7zM6rMlerxanM3xdp57nM5Bqe7ML3zt6Yf43ttfyP5tnQLwD3MHW5UD0qkmZX7fcfxEhZWWMDuvXcY7xAf2KFi5NxBDqef+n3JbRYfUAo1GzuDsCn/nsrnt9dlcvc5V5cF2gfnieyNUrPEqS+szJjcDRwKWmGu87EbZ3R91beiDQ954PR6+OrSD5dZYAVJRVF6GSEa0Cjj3+/jF1qDXSR/rsrpa/vacuKhWZ6syDulY6vUcb8mjedDvwFaoEyuVoa/UKDdXbhup5fcJpKxuaLvrZUDVwoccgSyT0PAcGndsLRy8bCur6ZAo9bWgm30/d0e+cNpO5a6+nDc0js/dohybuyLoQYoOUsnvI/6cCFinl+cH/T2NLzbJ1wKVSyg+C2xah1ka1rKn6QUoZOlrc0uYxwHAp5Q1CiEtRaxCaUSGQZ0gpfwnu9yZqhPkDKeX4kOOfA66RUqYb3qMb9Q0NjL5oGg1NTTT7mznedjDXn34O7322mBmV9xIIBOjepSuP269jmLH9k3r6/X4s55zG4P4DmO+YxZSbrsb9nZe8nFxGmXbjoelXkpeb1ETM56iO9N6o8FkANE3LAZ4O5Bf2B+i2zJtUdslwnVJKrp7zIPMWvUeOwcC5Rx/Hhced1PogAbndDUcPuPyAV5P5rjBa6hwmnME2XGsLF9xzB4+98Rob3nw/DTlb0DTNgioR8cFqw/pTBaIRmICqT+j02V1PihxmDD4ssu1IRWduV7EEODw0y28cQsvkpPScttX5DCUYJlzls7teBW5CJU05ceqKoz+SPRuFAeEnrGPXljq9xabJqDqIs1Gh6knRHuc0FcJ1nnn7jbi/8yKlZGfjv3ncfh3du6bfJ64oqy5AOZj9gAPLK0uWBzfNQyUPGQdEXdPaVjr1IHmtUtBtyCXU/3lP2jObaei0XnA26zdtBGDFmr8ZVbwbL998Z1vJiUq4zqm3aji//IJe3VT/8nH7dYzYqS2qJcWms5xP6Dxaszr1pbPohMxr1cEH+4+U0k0UhBCDgEeklOOFEIei8nPko6IGZkgpq4P73YyaZOgTpud8YKOUMqFM+WllrxVCjEetbTlQSrlKCLEn8LIQYpSUsja425RYf3CQy1CdZIAvUCd0kxDiXFQIz6TgtjtQo23nhB3/YLCNs9P5e/SkID+f6rsepHvXrjQ1N3PgBWcxbtT+nDvrNl65+U5MQ3bggZfncdOTc3j8Cq295XLPi89hGrID6zaqh2XKIeN46qobAZh849XMXvAy5x691XhBVDRN26Bpmpet13XeDIwX/uZ7UJnVjKhkGinpfPzN1/htxXJqnpiHwWBgxd8RqhkIGgfsW5BurcLfUU50wk5nuFYAd823rNmgX4Z8TdMGAS+jMssep2laixP/is/uegdVdHyG9G9+vnTR2W1w7uvdJu+bTPKTtJ3Otjif0TA6rH8D5T676zGJfHDS6sMnNvbbuIk+W89OtpVOb7Fpb1QZFhdQbqrxJu0ItOc5TYZwnbPKL6FnN2X3Lq2Yxf0vPY99ytS0vqOirFqgaqnuB5xQXlkSmg3RhXrGTiCG09kWOvUiaa2Gwk0Mv/nVjGeGjaPTdd8jm7cdd+1lHH2ArS3lRCXSs3RH2YUcf9DB7ahqazrL+YTOozWrU186i05oX60J+mDxuBS1hAhUdM9RUso/hBC7o9Zyt+RweA21nj28//YoKnllQk5nuqEXl6M84VUAUsrPUetgyhNtQAixM9AQ0sZCKeWm4OZPCInRllK+h8qiF44LOEQI0WFKwAghNo8SNzU309TcjBACIdh8c67duIF/9d+uPWUC4FuxnAWffMBZRx69+bMj9j0gqFcwyrQbvpUpVR9YAuytaZoA0DTtFNQ9U1m4wlcR3CfhGPxIOh985UWuPe0sDAZ1Kw/o03er44QQaXeSPKWeRlRZm4Sczkha/X4/Myrv5fayC9OVA4CmaYUoh7M3MEHTtFYXyeiwbjI6rFeh1g9EHPhpC50tX9UiK6WD205nTIwOq/uYXS6+496iZ+jd3CMH2Xrgrq10eotN/0KFgC4HjjPVeJNeE9VRzmk8IulscY6klNQ1NCCELsuCLkWlw9fKK0taOZZGh9XPlhDbiFOVbagzbVLSqsN7NFki6Wxh/aaNVH/u5pgD278DGktnR6KznE/oPFqzOvWls+iEDqE1bR8MVf3gzeDxX0gp/wh+/g1QKIQoCG77REr5Z/jBQX9tmRAioTXKiTidXYQQS1t+UHW4WtgNVQ8vFHfw8xaeDjn+jgjtH4AKxYzEmai0zzGRUrbU6tkj3r5tid/vZ8SZkxlwzGEcatmHfXbdndkzruYI+8UYjz+SJ99+A/vk0vaWycX338Xt51yIQWx9OzQ1N/Pk268zdtR+qTS9BNgOlVBoH1QI4CJUIp/fg/skHFscSeePf/zO3IXvYJl2GuMuu5Dvfb/GaCFtYtfqDCGS1vtfep4JB4zRC2MjAAAgAElEQVRmUL9ky6VtTdCRfwSVjOBUTdO+jLav0WGt6Tks99D20BlCWk5nG+qMS6Oh6Zw3+nzwa4+hubcjWicXaAud3mJTF9RgQ09ggqnGuzKVdjrSOY1FtPfT6Y7rKTp2LDW/LuOCYydFOToxKsqqx6Gial5ArZGNxAuotTiHt5dOvegsWmPZppdcizh4z703O8vtSTSdV815gOFnnMwl999FQ6NeuXJSp7OcT+g8WrM69aWz6IQ205quDxYVIcQOwN9SykjL3I4DvoiyLRw3YE3kOxNxOuuklCNaflCpmGMhaJ3laUrI8TMi7D8ItXazdSNCnIJKQhPJUY3ECuBfCe7bJuTk5LB0zjP45i1gsfcbvv7pB2bNe4bXHXfje2EBp487iksr7m5XjfM/cjGgTx/22sUUcft5sxyMHj4S6/CUEpi1JBM6HNVJ/gM4QdO0pulz528C/iJBJySazobGRgrz83E//ARnjz+GM26L1l/UhV9RWcxiEknrH6tWMm/Re1ww8US9tMwATgGu1TTtpXg799xp67X6baQTgNoxI5K63qG0pc54mKvMxahC6w/lFeRsEEJszrrcFjq9xSaBGrzZGzjFVOP9KpV2OtI5jUWs99Nj9uv444XXMQ0ZytyFb6f8HRVl1SbgOVSCnKnllSXREkY4UeFHJ7SHTr3oLFrj2aZn33uLkw+O6P+3KdF03jrtfGqeeIEllVX8tX4dtz2bucoTidBZzid0Hq1ZnfrSWXRCm2pN1weLRTT/azfgNrZeyhiNhP2vdMNRv0Wle68O+WzP4OeJUkdYFi8hxCGotWi2BL1sUGnSEyoC39b07tGDg0bsxRuLP+bLH79nn11VRu9JYw5l7GXtG8b24ddf8uqHLl7/5CPqGxtYt2kjp9x0DU9dfSPXP/4IK9es4aEbr0y1+S9RWe9uQGX/OkTTtNCamAmX0Yim07jdAI4bXQLAROsYTr/thjgtpcWvwNHmKrPwlHqiPtSRtO42dRIFeXkMm3IsAJsa6hk2eSI/PBPXX9wKTdPGoxZ7P49KbpMSmdYZgWTKprSnzliUoe7pOahQTF10SikTDb28HJgMXG2q8b6c6h/Rwc5pUjpb3k+gBvYmjTmUO557itPHRV22HJWKsuq+qPqm9cDR5ZUlG6Pta3RYm31210vAyTIgFwnDluuVaZ160lm0xtK5eu0aFtd8y0s3Jjom3T46QeV3OH3sUdw596kOq7MjnU/oPFqzOv+ZOqHDaE3XB6sjrJarEMIIvAScJqX8McF2Eva/hJSxHeJYmZOEEBOAa4CxUsrVQogRKAO+j5TyzwQzJ40FTpFSnhL8/0hUGNPYSNlohRAHBdscH/a5BzgsUsxxxnG6i4CfCEktvXLN3+Tl5NK7Rw/qGuo57D8XcPnJpzHVcT0fVcxh5+2HMGfBK7z+6Ye8eMPtoa3VAZdis1RmSOsvRAkRXfTFZ9w59ynmO2Yxe/7LPPrGq7x31wN0KYhaX/hHbJZh0TYGQ0BXA31Qaw5fC90+c9L4BUDR9Lnzt65T53TfC1wQT6f9ofvYefshnHHEBBZ98RkzKu9hyUNPhB9SDxixWVZH05oI5irz+agMoQM9pZ4VQZ2HA3OJkv48VGso3ceOjpQZdC0wDpslav1QTdN2Q611/h9g1TRtU7R9W6FqjEVN+Z2kTgANm+X6hL47SNHCpQuAotoxI2LXJXS6F7N1AqpUdX6DzaJL3TZzlbkbKiz8dU+pZzJO92WoxFhbDd4lq1MGJH+8W79ShlerDd3H35Qvm+t6CUNeA7mF6xJdH5jXU+RuN6qgtyEv8gHJaA00Sfn3143r6moDGYkV/Nehhf0NubF1vnbrXfz4u49hxu2RUjLjwXsAuPO8i8MPeRyb5fRo31VRVp2HWr5hBcaUV5Z8FE+fz+46FHi76KCCxbldDBHXsCSts3FNgA+PS+vdFJVB4wrZqbw7OV30OKcbgD2wWX7SXWcEGxpJZ8s9WvnKi3z8rYeqKEn4nlr3zqbb/nom6gBCOrxjnNmvKLdvxCixUJ1/rl7FoH79kVJyyf13UZifj+OcrUxaTBuaFgnaUIh/PhskjJQ5q/5C/3W9hxDIf0AEevaMUvQ4Ga0bJPIKaVg/D0NSWfETQ1Jr8EdNwpHsOX1DiobTZU6kcnFp845o7mMWkSeVktW5TiJ3lrmrIm5Mk4sJdJ0uAt3yIlz5ZHVukMijZM7fXkQMK5oaw5G5Lwp/7x463KOorLCXY7MkFOaYSR9MCNEN+EZKOTT4/96oiJ4bpJQvJqIn+Nl9wIdSyufi/T1pzXRKKV8VQgwGPgoma1mPciBDHb+nhRAtHvAqKeUhYc28D8wUQgipPOA7ULNi84Kdql+llBOCf5gLVUi4uxDCB5wppXxLCDEQNQXd9g5nFP5cvYrSWzX8gQCBQIATxxzC+P2tPDLjKo679nIMBgN9uvfg0cuvaW+pESm7y8GQoiL2O+8MAI4dPYZrS5NODnwlyuGsJ6RsSgg+ItTxTAb75KlMufkaZs17hu5dujJ7xtXpNBeP0FqdKWVWSgdN0/qhXigbgKMTdjg7Dj6iOJOdhJNRgwsP6t2wROIX/lcN5ER05gJ1f/Vtrv36WJGbvzL3X3u9LIRIuKahIV/0w8AxpF4HNKQxmnIKxMfAz2m3FQEhOIs4dduklJQ6NNZt3IiUkj2G7cSDl9hT+bq7gIOB0xNxOIMsAv7y18t/5capXpewTkNeAzEy4qZFwXY7Q85o2u6ctgnPVb8dMx/CgJze/wOiDt6lQ6HIP5UE6iBOuekaVq75GyklI4btTOWlV2RCji7EO58G8HdRtkd3Z24gbJ8Hh5Hg+ymW1lxoHgSfkkRG/EQJehvnJrp/vHPaH+kDMhLD3lOtxxuQyL7xdOapyJ6MvJ8GCTnCAPuQwFK/eDpzobkP8m0Qf+upEaAfsl8uidvQeFr1IkEfbIEQoin474+llCeEHL9RCPGjEGKYlPIHVEm4YcA1QogW5+QwKeUKIcTtqCirrkH/a7aUUgvucwCQ0CRE3JnOtkAIcQ/wmpTy3RSPvwRYJ6Wco6+yBIkzSpskm4Dp7THTmQJRR2k1TZuIyvb4MaoEwS6apv0vdJ+Zk8ZfjUraUTh97vzWxizGKG0KNACDdZjpHIEq6XOcp9TzXyDuTGeSRJ3p1DQtD2Wg9gMO0jTtk6RajjPTmQKpzHRegwq17lI7ZkR91B1jzHSmgC4zneYqs0At2M8DhntKPTI403kTOhS3bpZ+xvpmnPLuKe8/Hb7NW2zqj1ofXQDsbarx/r5VA7FwukegRi97pqsTdY9OxWZJObQ3Jk73evQrbP4YNssZkTZUlFVPQ5VHmVleWfKfZBr12V2z++2VX9plQI5e2dKXY7MU6dRWa5zu04AK9Dmn7TbTmQJ3YrNEyiGRPm1kQ9OmA9rQiOhrQ9cD52CzPKtDW63R34bOw2bJzAJ6fW3oGmyWPjq11RodbSjq2u+LzZLM8r7E0NeGNgD2RGc6M40QYiKwl5QypRmbYHTqpVLKUxPZP92SKXpxC6r+ZqqsQaUJbi/+Qo9ZBEUzkGh9nVTQc4ZueaQPNU0bDjwJLEZlqoXIL8CWjKaRFiD/jhph04Mc1D2SLqEznS2sQL/nKJco5xS4BzgIODtphxMI1tbbkLKy1mwius5YxLreoegZsaBXW6OAkcCDIet5l6PTyH8AyQr/32eFf+4tNuWjRpkHAcck7XAqlqPf+6mlvUyh1yh1gC3PaysqyqptKEfsDdQa2WR5wb9J6lmeKyOha0FWAHqFmxWSuWv/l5RSr3u0gS3Z0TNBxm2oTnREGxoJPW1ogEydU31tqAR+0amtSOhpQzMT+q/QzYaiBmUz9TzpaUMbyexznxRSypeAZWk00R8V4psQHcLplFIul1K+msbxj0kpEw430x2bpRE1CpJuIiOJemkm71QkztMopyFdNgXbaoWmaQNQYThrUeEIS1HnJZbTGalsytvoYzDrgPewWfToeP0NbKR1Blsv6m9N9/5rRGX33coQaZp2Liqs5w5N055M4zteQZ9rHwAWpnDcb8Hf8ZIJPUMG79EUORfV4QjNBvI+iWeJi8WmZU1/fiPhIHOVeXMqvGCm2nsBG3Cmqca7OMX2a1HhZukadz/qefKk2U4snkM9Y+lST4RyWxVl1TsAL6LOx8nllSWpvBeq61b6N8hArBW4CbMRdb9niiWod1O6MzT1wBJsloyskzQvO33IN43L1tYHdOl/+oH39GgoChm1oTrSEW1oJPSyoQHU3/tF2oqio5cNrQdei7tX6uhlQzei3smZQi8b2oBKnvOXDm1FQi8bKoM/H6StSEeklLPTOPYdKeWyRPfXc7T2n85RwDvAcCIYeOn35wYaG7oa8vLrRG5uJEPQcjMeiM2SuZlOm+VunO7+qELojQ2yqbAp0FTQxVC4MUcYEjEqEjXicxs2ywOhGzRNa5mVGYhKcvNn8PMviO10bu2E2Cxf4HRPkFK+KgPkESBP5FAnDCIZI2pAGaBjkjgmKp5SjzRXmVvX6rRZ6nG69wE+9ftlv2Y/XXIMNObmiqjho1JKAgHy/QEKpETk5NCYY+B3IcT+4YZd07QSVPKiBUC6i4KmokKYxpDa7IdEjXgfhs3yXQrHJ1ar02aZG7xH7wAaA81NBbKpqdCQX7BJ5ORE7JjIQMAgm5sKpN+fByJgKChoFgbDddgsj6egsxXmKnM/4CTgUU+pZ0viB5vlZ5zuMajMcRIiJxmIgwH4+IbVVVNR6yTPZUt0wHmolOW3mWq8qXdObRaJ0z0aZei2JzUjL1Gdz32xWfQa7Y/E5UBf1PrZVDqhLe+nE7BZPg3dUFFW3QM1IGYAJpRXlqxNRaDRYW302V0v/PVV04l998gziEB9LjKQS07hJkTk+zOKzjxUiO+tqehICJtlNU73/sBHKHufyj0qUInLDtNTGoC5ytwFtfb/stNrHfWvDb7l54EifzshRLJOckvoYy5wJDZL5gZGwmwoyZ/TqDZUVzbbUF6tgzw/5BXCpjyR1HOlqw2NSIgNBXqQ2j0qUQ7nvtgsuq/pC2Eq6dvQfGAyNkvEzHe6EGZDSe2c5qIi1jKXeEQfGypQM3UHBWej9Uc/GyoAGzbLb/F23lbpEGs6txmc7jxUuuLtCZtFloEAfz4xe5bIydk46NQzI8VOrwKWYrNkaqSmNU636duGZfs9tu6Nyv45vT69vO/k+xI8sgH4DpulJvTDYKbah4GzgJM1TXsuZNvdwDSgp6Zpmw3ezEnjewDrgMunz53fKoVvC8tv/uAWQ4G4oqCP4bmew/KSqd3QEl73OTaLbrPg5irzG0B/T6mnlRP93OWLDi7IF2/06WX47sCRhbfk5sbPoLZuQ6C75/vGk2pX+Q9Z9bd/ebOfS4C55ZUlEkDTtB1RIcrLgX01TUs/053TbUCFiQ5FOZDJ8DfwJTZLSuFlRQuXbr7etWNGRLzerXC6d2xavcqy4uXnHzEUdvml6OTSG4UhdnDG2iUfmzcs/ez0ul+XDW5asfy/wMWmGm9aL3hzlXk6cCdqLefWnVmneztgD5SzlAx+1Mz2F9gsfnOV+SnU4NW/nr+1eV/gLdRs3TGmGm/6swxOdxfUtR9M8sa9FvV+yki2xVaotVNmYEeSX+uzHvgKm6VVeGVFWbUBlQb+SGBseWVJSvkDWvDZXUcC8wf0vuXt/K7Nh9F370q2PzaZ2f9G4Hvg24x1lEJxunsDI0gwuUgIEhWh8Hkwokc3zFXm8aiZ/B1QM37/8Qx9bDVRbGgCrEW9nzK5PGULTrcJ2BkV1pcMEW1opjh44Re3bIe8Yh8hn7tEyHa3oVFxuruj3k+DUjh6Beo9mtJAUlKkZ0M3AJ42czqc7h2BXUl+rXQTambv6zZ6P6VqQyVqMPsLbJboeSL0Ij0buhr1fsrkcooOT9bpbEO8xaaLgLuBPU013kyGgCSEucr8GCob1S6eUs+ydNrSNO1C1LrDmzVNuzps2xRUWOIemqa1KmY/c9L4NcAT0+fO36pgqc/umgC8jEoyMNnosLb7zWquMj8EHOMp9Qxs+ayirPrfqDC29cCo8sqSpAYOKsqqR6Eyou6JGvErX1n0/h+oMOuBwD6apumeja89KFq4dA3wZO2YEQkluPAWm24F7MBephrv5wkeU4CahbgG1XG6AbjbVONNutNsrjIbULM8f3pKPdZkj0/yuw4APhj5Q+CqK+YF/oMKt97fVOPNvKO3jVNRVn0LKlLgwvLKkkQH2KLis7sKuuW88XefvIouwD1oa7eq15IlMuYq81CUrZiACokr95R6FrWnpm2VooVLW2zoc8CU2jEj2t2GZsmS5Z9Lh1jT+Q+iChWilnDa7UxhrjIPRxW3v08Hh/MwYBbKuF0bYZclwd/RQmy3Crf02V1m1Oj3Z8AZHcHhDPIrMCAYFkZFWXU31DqPQuCoZB1OgPLKksWoRDXlwF4S+VX3tTt/TsCwM3DCtuJwBol4vSPhLTZtD1wMPJWowwlgqvE2mGq8t6JGeN8FbgOWeotNByUvl0NRM266l0mJwEfd6+TXZ7wTuFYqZ3lC1uFMn4qy6skoh/Nh4H492jQWjt+7d+6DBfX+4c1rms7suLVFOhDmKnOBucp8FcrRPBi4DBiZdTgzQ9HCpaE29Mysw5klS5b2Jut0tiGmGu8a1ALvKd5ikx7pwdPhdlQ2upvTaUTTtJ1RM5HfAKdqmhZpLc4PqNCnSE7nb4Q5IT67qz9q7dV64Bijw5pugiY9acmIaQyG7D2OCgs5ubyyxJtqo+WVJf7yypIHgJ2b8tZ+16WuaMd+K/ddu13t6J4VZdWprHPoqCTsdKLK6QggpVTephrvMlON9xhgPGpQYKG32PS0t9iUTPjWucBKVPKZjPL8rc2GW6r89FtHwXt7iGtNNV79S1P8w6goq94bmINKWHFBS+h6Wmi9hgD/lRT8ubrpytwN/om2tNvcxjFXmQ8BvkKVR3gdMHlKPXd4Sj26huxmURQtXNrKhtaOGdGRbGiWLFn+oWSdzrbnAVR5mNPaS4C5ynwocDhwk6fUk/Kie03TeqMysDUDEzRNi5hgJOiIfkb0mc7N2Wt9dlerEhFGhzWTqe9TocXpHIIK3zweuKy8suR1PRpfWfT+4Wv7fbX7ul7eVwwy9w/UOrT5FWXVO+rRfgcgIafTW2zaA/WM3Guq8aaVWt5U410A7IYKsz0eqPEWmy7yFptiJlIzV5n/jVpjOdtT6tG9KHoEHEV/s3vVIYb6h4/I2bcNvm+bpqKs+l+oKIRa4LjyypL0HRytV3dUZz7fL/uMk3Rfj7qnskTAXGUebK4yz0Ul2TMAYz2lnuM9pZ5/bCKNTFO0cGkrG1o7ZkRHs6FZsmT5h5J1OtuYYJjgYuDcYEmENsVcZc5BZTRbhqpVlxKapuWi1on8H3CcpmnL4hyyBDBrmlYY9rkPGDhz0vh8n90lUJlabcCZRoc11RIRmeRXgP2XTZwIaMATwEw9GtY0bV/gEWBRQ5eVJ6DWeE4HRgPfVJRVX1dRVh1+/jobvwEDgx2jWNyBSlx0ix5faqrx1plqvNcBuwMfo9ZWu73Fpv1iHHY2aqb1YT00xMJbbCoF/gM88NZehjnAieYqc/9Mf++2SkVZdRdUuH8PVKba9JM3aL0MqOd9d+DEvBu8HpQDOtFnd+lR3HybwVxlzgsm4KpBrd28FjB7Sj1vta+ybZuihUtblVmqHTOiI9rQLFmy/EPJOp3twwOACWUY2popqHDQK9OcvbkdNVt6nqZpiaT+XoLKRDk87HMfqmM/CLWmcRpwm9FhzWT9snTw9d84WO5Wa52GSvRzjh4he5qmGVGd5D9Q6zibyitLmsorS+4CioPbNODrirLqcel+XzsSer0j4i02HY5aS3ljMCRdN0w13u+BcajZqX7AR95i0xxvsamVg2euMuejnM4F6a55jkfQ8X0YVfv0YtT60QLg9Ex+77ZKMBx9NmABTimvLNGrfIYGTASmo619O/jZC6j7KBtiG8RcZbYCn6MyPjuB3Tylnhs9pZ7MZ5fM0lJmyVE7ZkRHtaFZsmT5h5J1OtuH51GzOOe15ZcGk9/cBLhR6zBTQtO0M4BLgHs1TXskwcOiJRPyAVj6jT0aNfs0H7gqVW2Zpuzje/qOq5kWaMqpbwAmlleWpN2R0jStKyoMsBtwlKZprWZlyitLfi+vLDkJ5Yg1A69XlFW/GMya29loqdW5faSN3mJTDmpA4yfU4IzumGq80lTjfRE18HM7Koz3O2+xaZq32NTyTpyIyhyc0QRCwWRJL6FmgE8w1XibPKWeb1BrEMuC2XOzJIcdlZX76vLKkld0aVHrNQkVTv8oKvNqC2+hyiCcoMv3dGLMVeaB5ipzFere7YGq7XiUp9STXZvcBhQtXHow6t58jQ5sQ7NkyfLPJeaapiwp4nQXEsOhNz30JN9dUvaEbG4q/+34k/5v+wv+UwvUY7MkWxA7WS5CdfZP9ZR6AsG6okmFhTlcb1qAStQanelJHPobKiHLVk5n99w+DO2x+y2oUKwpRoe1dU1CpzuH5GuhNetdV66irLoAeKmgqZtYYKr85u0LX2pdD07VF0yqHtaGxgZQHdmRwNGapn0Tbd/yypJ3K8qq90Cd96uBsRVl1TcAs5Jer+Z0F5B8jbEGbJZ060W2OJ3R1nWeipoNP8lU423E6c5FFdNOhoSuvanGuwG43FtsqkI5uA8BZ3qLTedxRe65wM8opyI+qnZbUqHPy198tgtqsKErUGKq8a4O2fwg8CxwGPBmMu3GRdUaSza0v65N6rWF4nTnk6SNenjeusNQydGeBW7VRYfWay9UwrAPgfPQ1m4+D0aHtc53hWuBMDCx/oVP/1O4XU6iz4fu76e4pPB+AgLx6t8Fl2yUoc57V1RI/M2eUs+mlHRCXBsahbawoa1JwYYCjXrXvSxauHQnYB5BG1o7ZkTr85CaDY177XUntXsU2uf91F42NDkyaEN1JQUbCvixWdoi30JrOosN7YBk63TqidN9OWqEsSuqaG1UpJSCQCAHIQLCYGgxEF8C47BZVuotzVxl3g6VRdbpGfrYnaiZzgGo0gwJIaVEQu66hrqm1Zs2HrDjxHFL4h+1BU3TFgBDNE3bveWzd8ruMA7uutNvXXN7bsw15A03OqxbRsWd7oHAG6hC8cliQGXMLcNmeT6F41sRDNl7FJj6wdAXP/p60PvbeUo9Owd15gS3nYjqgCT8UAWkNARkwLB608ZvBnbvuVeiL9CKsuqhqJnhowEvUF5eWRK/OL3TfQZqDWpPkrj2IfwAjMVmSSm5T9HCpb1QWZNn1I4ZcWfoNm+xqSuqJubvxQ88Pk3k5LyCck6TfUkZgL+AqdgsCxI5ILi+ejIwU8KAt/cU4rVRhuvfu/hrLeaBTndP1MzCfiRhhKSUIGWObGoSDX/+Ma/L0B0mhRqkYHjvb8CnnlLPhETbjaFToMIdy1Cdz2TPqQQ+AI7GZlmftp5YON0TUM9TH5J7PwkpydlULxub/XL/3uP3+SxtLVqvQagoDT+wN9raFSE6/w94XUq5ExIDAr8QItHzagBWAVOwWd5NW2cslAM3DzWAkawjJ4B64E5sFi18o7nKvA9qwGZPVHmi8z2lnu/S0JqwDY1CxmxoK5zu0aRgQ4MIVE6FI7FZUj9XQYLv1E+A7YBRtWNG6GVDBbAJ0LBZ7kpXZ0zSsKFB/Ki/86SMOyHtbEMTxuneAzWo2SY2NGVStKFBDKhIkyuwWTISGbWZzmRDOzDZ0C29cLqvQCVL6IEa/cqN9SOEyBE5OQiDwRDy+XDgY5zuvhlQeDXQ3dF/2lxUyvoi1PWPqTNMc65BCHoVdMnZse92b+J0D0tSwxLApGladwCf3ZVj6r3vw93zevPZ6rffCHM4+6MM6e7JaAz5MaA6rY/jdOuRXfISYCpww9eD3v8Q+Le5ymwIvoieRa0RLCSBax/6YxDCkGvIYUC3Hv8HLAiOSsalvLJkWXllSWg5kOqKsupnKsqqo5cDcbpPRyVq6k2S1z7kZyfgU5zuRMuehLMOZSQiHX8xMLjf4UdWiJyc94F/k+T5ZMu17w/Mw+kem4ioYMjt08Aunw0TXx36heTeSn+5t9h0WtSEX053d5QR2QfVUUrqWRIGgzAUFNBl6A5HoIzZZoKlJB4BjjRXmYck8jfE4R6UsexKauc0D9UpWITT3VUHPZFxusejEpT1I/n3U47BIOjWReT17pHz3v+zd+bxTRXr/39PurJWFqFoEVSERo1UDeJWYuuKVlRQQVzqbrHq1YtI/ap4FL3WBbdrpSqoFUVw14v7taVWUaFqMWiqqKAEqSBggZZuyfz+mFNI0+w5acvv9vN69QXNOWfy6cyceeaZZ6O80hwVFy0lGeX63A+Y4KVwDkOtTwcJIUzCJBBChNOvJpTC8jbllVlR8QwEZYn7AFUXs9VyHM5PHMrtfwbllbNbm7WUWAZYSixPoZJypQJTgFOiVDjDkqF+fmIpQ1t5HkeEMpTdfbo/sCwCGdoGqWVVcSj5MwI410vhjFaGxqHGYjblleF4NYWHKGWo/pOEyjERsgyNkGtXkKGh8DwU5ebeYTI0Qp4Ry1APnn2BBymvjHW42p4hQ7s4upVOI6DcAjTUZIwGCcA+wKRoKXnCUmIZgYofnXdG72OuQG0iIoYQolWhyw/z0RWoOXeE/nshMP67LeU1a3es8t7YT0EJ9WizQvZAZUKNGHringdRtRrvQmWwTUKdLB8EnEGUYy+E6IFaeI8M57n84mzPciCTgB+L8kr/UZRX6kvwPhAtT9RiOwC4PJKH9QLl7WqzOtLNg1CxeG8PmjjlZNRmJ9rszj2A+8N54Pxb410PnBd3wL2TTe+aYDVQApQ70s2+LAWnoTaP4bqteaMXcIMPQdSaNffqqFpXJ8mtwtUsXTYAACAASURBVDIaJAMjgZOibCcQHiQy97pdEEIIIAX4Z8SNaCmtWYvHAheh1a70uuNqoD/Ry9CeGOUG7BtHo9aUqPoUxbPgs/efS7SUWK4AfgSuAB4B0u259sX2XHvkblNdXIZ6YTZRylB2H4qGK0O9UYhKjHZ9TVbGUq9rRsnQnsA9unIYCxgiQ1FzPGwZGiY6XYaGiJl0kgwNE0bJ0Niuo3uWDO3S6FY6jcEAInOz8IVkVBkSI3Ef0AjcCQw3qE2ByqoaDir1f8c4Cyp2lYj4cdsKO+0tXwcQvn+/PwyO9MGivFIzyvJiB3Lzi7PdQKtbzH76T3PUDBXcqPqfYSG/OHtnfnF2azmQZejlQIrySo/ddZPaMAwwiGc84Y+9J5y0H+870De2qFN7ozY44Z4mTwX62Pc3/Qs4HrgSOBj41pFunuNIN/fxuHcY4cfK+MNOvJIr2XPtv6MSa12pu9tGimF6+0YgngjmaBjwb6kPH6OiePZmVHzxLLTaN/20HW48lz/EMiHYfkTmptoObuluLtyy8AtUZmAHcLg91z7dnms3wlWsq8tQTww3qJ1IZOgupJZVtcrQopqsjGIftxgpQ02og5xYoNNlaEjoWjI0GDpThoYDI2Vobz2UIBbYk2Rol0a30mkM4vAS7A2NjRyVl8voK6ZyyKXnc+dzTwFQ+s0KjrjqIg69dDK592m0tLTLJyBQk9IQWEosx6DcVh6059pr8LNRcrlcHH7lheQU3NTm8+sfe5Dep43z13xYPDVN+xNYt59r4Gm0LRHhSwnx2bY3z0++Xs4RV11ExhVTOf66K/nZ6bPmeETzvCivtD+qDl8DcFZ+cXadful3/d9W1xWf8OYqpeS2eU8y8qJJmC85j8dfX+T9iAjUXjDkF2d7lwP5vCiv9NmivNK9PdoPyvPS+zT2n3IWGVdMJeOKqVSt9ukxF80cbTPejnTzSNQp4jPmakc1Ic7REPoTwhh7S4lFoCwPK4EvzNUOt7naMR+lYMxHuVhXO9LN5+sut3G+2o+wP6Wfv3suyg1zYqh/hw+EPEdD4GoK1J4BCGnsQ+zTyHhqKTmo0/1XUNm+fSGk9akVQdbRWMrhOEJ874OtpTtlUw+JexiQC4yz59qNKkXTytOncuzNs6NlqB+uQXmGKJsi4plaVnUsSoaWotalkNuOoD9BKXOxeu9DXp9CHPtYrk8hvUsh9mmnz9EQecZ6fQoqQ0N8l9wx5GrkHI21DO3SCDpAQggphFjg8Xu8EGKTEGKJx2dnCyG+E0JUCyHsQoizPa49L4QIGFMnhOghhCgXQsQJITKEEF8IIb7X25zscd9LQogfhRCrhBDPCiES9M9zhBB3hfvHxxJJiYmUPjyXlfMXUjVvIR8s/4Jlq1aSe5/Goln3sur5xQwbnErJh7GL0dY30Q8BNajAd7947PVFmIft3+azyuof+HuHsfHOKe6eqzKbzVnoJSLSCjObUUrIkDmTc4Iuwt48pz1yPy/dPpuq+QuZetKp3LNgviE8dffUxagTqYn5xdmeQf+eSmfIXJ//4D+s2/gn1S+8iuOFV5mSfYohXD2RX5wt84uzPcuBXAz8+OTL2/y6aPoa+wfzbqBq/kKq5i8k46BoDEY+4QSGpJZVtY73fSjFXgv0UAf05zGomLC5nq6C5mrHZnO14xr9eg1qXnzUWLNhb1+NGNyfH6HKx0wL6y8JEZ0w9hGhw3hqKYcAC4Fvgcs8M9VGyjMW66gRCHctjSeueUb/KSfZc+0vROVKGwVPt9vdoTI0HHSUbEotqxoKvIEuQ2uyMsKyEu4p/Ql7Dtdunsaio94lIxDrPtV1sDkev98shND0/2tCiJv1/+/StYQQ/YUQ3woh2tX67mx9K5RTgTrgUD3mDFStwPUexEajFJuzpJTpwATgISHEYWHwuBx4Q0rpQmVLu0RKeQjK3/tRIcRe+n0voVwSLChf8yv1z98FJgghukxwrhCC3j0VneaWFppbWogzxZGUkMjIocqyfrJ1LK9/WhpLGmcDxwJ32nPtO/zd5Nz4J+9++RlXnnHWrs9cLhczih/ngbwbDCPjLKjodXrTEaPjMMWtN225OK0ws7VExDrUXEwN+LwPnkLAtjplgKyt28E+A33qAZHgYZTf/TX5xdmfe137G5UMx6/S6Yvr3LdfZ9YlV6JyR8GgfrHLdZFfnL0jvzh7JjAa+E6qMjch8ewAOFHjPcSRbj4OZcV7wFzt+NPvAx3Tn9OA7ah1ph3M1Y6vgKOA64Axtcs+vUm63W3WUKP7055rd6OsneMsJZZDg90fDjpp7MNGh/HUUgagPBvqgbPRasMq+dFR66gRiGQtTTIlNJ7Q8/Btnclz87bajpahIaGjZFNqWVUvdpdZmlCTlbElGp5dtT9hz+HazdNYdPA+Lyp0UJ82AhOFEANDuVkIkYIq9fa0lPI5H7d0qr4Vqin6fVSgN8AFqExjrbgZ+JeUcg2A/u99wIwweFyIWkiRUv4kpVyt//8PYCMqYQtSyvekDmA5uoue/vtSVCbPLgOXy0XGFVMZdPYpnGwdy1HmQ2h2tVBZ/QMAr5V/wrqNfvfZUcFSYklAuYg5UKnI/eLGJx7mgWtuwCR2T4cn3nyFCceNY8iAkOZ5UDgLKgTwXE8Sh5QmrOL9xG89Y+Oc+r8BYwd88Zw343ZOL7iRtHPPYMFH71MwNTdqrkV5pVcB16PqX7Z7afVT/t8J4Jfvi+svf6xncdnHWK++hPG33MBq5+/+HjcM+cXZPwBZAi4KlSfAbfOf5LDLL+CmJx6mscnwcl1OgDiXKw2VOGYDSsn3i1j3p15S6HygJNABjbna4TJXO4qAUfH9+lUh2yqdMerP51CCx1BrZyeNfdjoEJ5aSgKqrMi+KIXTp/9WuDyNXkeNQketpdHCm+fAlL06TIaGg47oz9SyKoFaCzKAKTVZGT9Ey7Or9ifsOVy7eRqLPWVtgg7r0xaUK70/N3pP9EbpawullHP93NOp+laoSuciYIoQIhnlfvaVx7VDAO96aJX650EhhEgEDpBSrvVx7ShUkPEvXp8noNwGPYumVwKZoXxnRyEuLo6q+Qtxvvouyx3f8/2aX1g0615uKnqEo/Jy6dOjF/FxMXPtvhqVFe4We67dbyHqJcsqGNSvH0eO2l1d4I+/NvHq0k+4/pzzjeRzB3BeEy13OuM2A4zxuNaqdA5t91QAngCPvLqQ9wofxfnau1w2/kz+WfRoVCSL8krHoerOfQjcEuDW3/Fj6fTHtbGpieTERCqffoGrcs7m8vtn+3rccOQXZ8trL+i7MFSe9119HdUvvMaK4hK2bN/G/S+XGE3JCXD94ucnoVxWZ5mrHXX+bu6g/rwMtdb4tAh7w1zt+LN/1imvYIrbVeg7Vv1pz7VvRrn0XmwpsfQJdn8o6MSxDwsdyPNRIAu4Cq32SyN4xmgdjRodtZZGC188hRAdKUNDQgf25x3AecDMmqyM94zg2RX7E/Ycrt08jcWesjZBh/dpEXChbsUMhIeBz6SUj/i62BX0rZACmaWU3wkhhqOsnN6LnaB9AgBfn/nDQJS7YtsGhBgCLABypZTeWe2eBD6VUlZ4fLYRlSq9y2GvPn04IeNIPlj+BTdPuZiKfz8DwEcrvuSnGFi7LCWWvqhMtUtRpnC/+HzVSt75vIL3vlxGQ1Mj2+rrOOTSySQlJDDiQpW7pL6xgRFTz+Hnhb6SOAaHs6BiEqrUyIIkEu5BTWBfSqdfS6cvnmcU3Ej172sZe7DyOpycdTKn3RK5G1tRXun+qLIovwBT8ouz/SrrqAy2PlOz++J60T13kLb3ICaNywbgnMwsLrv/7oi5GgF/PF+8XSlvSYmJXHbamTy0+EWjv3pdfEsLWV9/kQt8jzq9D5unUf1pKbHEoRIZldtz7d+H86wQYtc6F+P+nAtcgjqlDEkxDoROHPuux1NLmYYqKfUgWu2CYLeHytPoddQodMRaGiuerWMfaxkaLU+j+zO1rKpVhr6AV03faHh2xf6EPYdrN8/Y8+yKaxN0bJ9KKbcJIV4AbiBwFt1S4CwhxENSyo0+rne6vhVO9qx3UIvdCbRNG/09YAW+8/jsCCBU14+deKX1FkL0RSlLt0spv/S6difK/HuNVzvJGJfSOGps+nsrCXHx7NWnDzsbG/jv18uZecElbNy6hUH9+tPY1MT9L5dw20UxKdV0C6qPZgRL+HDf1ddx39XXAbD02695aPGLLClse0jS+7Rx0SicGShB+SVwdVphpkT7ZAVg87jtb5RvuV+l0xfPt+55kNSJp/HTut8YOXQYH1d+hXnY8Ih4FuWV9kHN8XhgQn5xdrsX0wu/A3tvcW1L7B/XNyjXF2+fTcFT/6b020ouHzKB8qpvGJkWy0oJweGP54bNfzFkwECklLz12VIO3b999YFtO9xDFuSVnh7J994GOPv+2NKUPHzgj/se+u/1+447tTRvd9zDFRP7pCQn7U4S2AH9eSqqVtitkTYQiGco/RkCvkIlt5lmKbE8FW0il0i5uqUU62tc5nciHPtgmDa5T5zJFHzsDepT0FKyUMXe3yWK8Y/1OmokYr2WxpLni7fPDkmGNtW6D9hYUBGTObrPKck9THGB52io/elqkAM3BOH50rCEA0yjkh7q00z1omV1b+zdKMc7P6wI9AgAg45PGp7YZ7czWzT92dGIlKvb7Yr/8efvM957Uqs1mpMQgn9Ou9MQngC127YOmTc5JyZz9NrLZ6b0SN5dmjcani6XK+HRGPE876zcUUP3Ga6XVo7uXZJSinkvPnrqtidrG43meeToow/IPObk+DjTbqtlJ7xPjwLfEPiQfhHwGfCeECJLSumdwa7T9a1wlM5ngVoppV0IcYLH5w8BrwohSqWUa3WL6P+hyjYEhZRyq55FKVlK2aCbf98EXpBSvup5rxDiStQG8UQf2vhIYFUYf09MsWHzX+Tep+Fyu3G73ZyfdRI5x2YyY+5jLPniM9zSzbQJk8g+Yky7Z10NO5MjNchbSiz7ooqiv2zPtVcGuz+WcBZUDEYpcluAc9IKMxv0SyuAqZqmDdE0bcP0xUvknMk5vsqmBER8fDzPzLiNSbNmYjKZ6Ne7D8/OvCNsnkV5pSbUKY8ZGJ9fnP1TCI/9DuBo+m3v43pYQvqegqmXcuG9d/DIqwvp3aMn82bcHjbXjsCF99zBpr+3IqUkY8RIiv/Zfi++cYvreIJY0f3BBOy3bRTfHTYK1Ol9G9TucJOcFPwNMLA/pwF/otYdwxFKfwaDPdcuLSWWuajYjmMB7+RWhiAYV7ebhLUbWq5Gue8bjhYXJIYQ9GFEn6KlHAi8BvwETEWrdQV54v9bGLWWdgQeXLQgqAxt2OyaSHRlhvzC3SQx9QhcAjHU/mze4c4gwDq6OVGwcFgiezdISr6sTx/YJN8KlWfjZheeSqc/hNKfXQXBuDa3tPT4de1PtxA4NCYiqFC20BBKn9Zs/CNiGRoMtbVb6JG8b9D7QuHZ3NLUixjxXPvbz+ybuh9xAVxQQ32X3NIdV7+z/o1Y8PzduZaW5mbiQtiXxOp9klJuEUK8AlxBgDwtUspHdcvlm0KI06WUTR7XOl3fEsFeJCHEDillb6/PTgBullLm6L9PRG0eE1BFfu+UUr6hX3seOJPdWvE6KeUxXu3NB16WUv5XCHERSpP3dHO7VEpZJYRoQbk1tmrvb0gp79bbWALcKqU0snZYaCivTEWVNegR7NZQsMOxqqx33qXZkTxrKbE8i3LBG2XPta9td0N55W8YV4z8v9isJ/u64CyoSEKZ+g8Hjk8rzPym9ZqmacehTmPO0jTtHYA5k3M+AZKnL15ynM7zcVQyHyPQiM3qt2hwUV7pvaiDkn/kF2c/HkqDlhLLOKD89v4X3zq5b3YBxhTO3g5cg836ctA7w4UqbG1U8XXqdro/ef6tHf8XybOHfD9/Wo+GzZc+MuWq1Zmr+7VLcHTVuX2eS0wQB0fPEoC/sVn7+btoKbEMR72799pz7eHtsssrbwHuxZh6a9uAY7BZ/XqIWEosvYA/gCX2XPuFIbdcXpkBlAN9g90aDFLK5o1b3E+89lGdz6Ko0WLa5D5lJpNhWciXYbMe5/OKltIX+AKVMXssWu3PYbVcXvkWYFQ63T+xWQNm7o4Y5ZWXoOKBege7NQTsAEZjs/5qQFttYbAMba5zv/Tnp40hreXhYt9Tkt8WccKQ8XI1yeUbPmnwKedqkkXClGN7PVkXz6iL1zRddcPqJp/FaP0h1ZY0Pb6nyaiA4gYgDZt1c9A7w0V55amomPWoZajb7a7/bd0v973x7ksfRU+sLUzCxE3TZn0V/M7QUFe3/ZPikjkRydBguO7KW59LSkwyRIa63e4djxTffaIRbXnjovOuvnjQwCHThBBRBz5KKV3PLyrK2rL1L8MtncePzT5ozOHHP2UymXoZ0FwTMBObNaTAVE8dTAgxGFgDPCCl1PTSKTuklA/putYSKeVr+r3PobJcX+CpNHa2vhV0s+StcOqfLUXFC7b+/gaqbpSv5y8NgccTKOvcf6WULwI+A3SklD756gPRo1MUToUWDCpKK6Wkcb3zsHXpZmGudoTlQmcpsRwGXAo87FPhVAgUpxgufNYH0zPVzkVZZCZ7Kpw6vkUpQGNQllBQZVOyPO4xMm2mX2WrKK/0ApTCOQ/lZhcqfgeocW01Mi2lxNjx8YZhxZN79TDV5BdnLw/3OUe6eSgw5ZtRh/y69IgDkxdNz2jfRnmlka5RwfrzalS/Px1h20Yp8iaCcLXn2ussJZbngTxLieUme67dV8yGL7Tgp6h5uBBCtAweEPdrJGMfEsorw6o5GAS+29JS4lDp4EcBp4StcAZqOzLE0sLaQuj5FYLBhLF/tycMk6GATOhlWptWmBmrORpWKZ1AiEsUm33x1DPVzkclbpz8wBVjXwm78fLKsDMwB0AcsZNNhrVrMpla9h920I/TFy8xfux3H9waI0N79amJCU8wVIaaTKaGGPI8HrX+Ra10CiHEZRdctxyb1XClU3/njZL1LsKY8546mJTyT5Qi2fq75vH/S72ea1ejU0en6ltGLfJRQUr5LVAWxWnHfsB0AymFi80YJdjd7qbG9esGAJFYOu8HaoF/+bvB5XavDcdNJABctD0d8cSNqGygs9MKM9sJS03T6vVnvZMJ7TNnck7rHPgZ42J01/v6sCivdAzKTaECyM8vzg6nY9YD7q8bfkxAZfwyAnGoUyzjYbNKVPC3EWgm9Jhtb8wGxDNnT3kP2Ce1rMrXO+/AuAXebxS/pcSShKo99R97rj2SDdqvGHc4kkwArh4oRs23cAJF1kopDbEgod772MxRBaM2ym7UPPKFe1Hp3m9Aq420iNr3GLdZjmV/rnFJlxGWeFDz7g+D2vLGZoPkEii5Ybw1djd+xhh5H5IMrcnKCF/hVDBShjZjsxoeJ6ljjZRuv55IYeJ/QYaGgg6RoQbASBlaGxOFU2EtXrGQUSDWMjQgOlvf6hJKJ4CU8lm9WGkkz66QUlYZzSlkqMXoRlQynGjQiBA/bv9m+RZUNsWQYSmxnIQq7nqPPdfermC0pmlC07RJ87/5/JBGV0tY8Qk+0IJafB/zvuAsqDgNFef7JqAFaGMFYNU0rdUC40RZ3gfrv7+MyiIb7SJSD1zn/WFRXuk+wFuoWL5J+cXZYS189lx7M/BHVePqvkAJ4LfkRxg830cFiscK+UQ/R5tRCvcz4T7oSDePRmVgfbx6+IjvURuEwT5u/RcqDjhaoVkH/CPA9YmoIHl/9ayC4QOgiug3dvXAndisDcFutOfaHUAZcI2edTcgivJK+xS9vE1bsarR1NwS9T65HpUq/eNoGwqA64h+jrpQB4H3t7uipVwEzASK0WqfjOI7ilH1ZaNVPHegTp0Nh6XEsm/G2ituLK9f2WOnuzHawa8HZmCzGm6VdRZUJDvf33nH3z80m9yuqOdoI7AaVXM1VpiJcjmLhqxfGZpaVnUqocnQYDBKhu7EuFCXNpgzOWfAnCe1gu++/yapubnJiDn6/7UMDQMdJUOjhVEydCc+9nmGwWbdgTqsjHbsO0KGBkVn6ltdRunc42GzPoV6OdejTm6aPX+klC0ut8StfKubffzUAkuFyXS8u6FhHnCWI90cPBIcsJRYTMCDqNOYJ7yva5p2EGoxfu2P7bU1XznXXCqEcKBeVF9cAv3sQC3qY7FZnZ7f4yyoSEfFZtiBS9IKMwMteCtQWZCHtz6u/6uSCalT1UzcTV/SUi9xt4CULUH5SdmCu0XiaoLGTQ1s+uyf2Kwfen5xUV5pD5TCmYLKVLspAM9A+F2qU59rUZvQjWqc3YHG2dfPX8BCYAo2q2Fxl+1gs74BXITy028Mg1/rz3ZUFtWx2KyR9NmDwFaUQPRfJsdm/QVVv3MlSuiFxE9K6XK7JS0uSV29e8f6jS0XYbN+FoDPtahNWWQCQCmJp6Dera2h8vT4aUIpLrOwWf16J/jAXNR7c5q/G4rySkVRXul5qBPv6StWNT3b0iJnAzX4WJ9C+NmCSiYxHpvVSNf3trBZy4EzUFaahgh41qE2MUdjs65t07aWMhblRr8UlXo+Gp41wFjUOrY9VH5SyhbpllK6JC073XX1G1y52KyGuq5ZSiwJlhLLP4FqF+6zZmyae1eCiFuAWmcimaN/EEYMUjjQDyntwF11v7vecDfIGfiRoSH81KLGdhw2q3fWRuNgs1ahsvhXY7AMTS2raiNDa7IyIpcHrTIUPkX1TXhz1C1xNcqmxq2ue7FZ/SYuiQRzJueY5kzOuQL4Ebj8v58ueRgV3rIxVJ5eP/8rMjRUnhHJUI+fBtThzfggMjRantHK0EaUJfZybNZ2tcgN5noXcDddXYZ2cQRNJNQN41CUV/o2cDQwNJBlzZFuPgC16brbXO3QgrVrKbFcgrK2TbXn2nclodE0rQeqDMBM1Mt5O/CkpmmGx2Y4Cyr6oRbTvYAxaYWZvwW6X9O0I1EnPpM1TXtlzuScDFSs56Tpi5eo+GAtJRH4L8oNNxOtNrRsvFqKCRXbej/QD3gc0NBqtxXllQqUD/tU4Jz84uyQswF6w1JieRmw2nPtB+l/kwA+QpUMOlDTtGBlV/5n4Eg3nwJ8CNxkrnY8mlpWtWu8a7IyDMs4V5RXmoya77eiFvo7gX/nF2c3e95nKbFYUGWeZthz7RHVvessWEosCShB+7U9157jfb0or3Qk6vDpZFQfX5tfnP2l933/U9BS0lAK4k7gKLTavzqLirOgIh51Kn83ymX1PuB+j+zeEcNSYjkeVVfNgqqpfYM91/5L4Kc6Hs6CiqHAI8AkVPbg/LTCzP92LqvORWpZVRsZWpOVEVCGxhLOgopWGfqAzudxQEsrzNwWbdu6rH8SpRR9Blw7ffGSzsrH0Y1udKMD0W3p7FjMBQYRJJW7udrxK8rt4GpHujkh0L2WEksP4B6UAre49XNN03JQ8SJ3oMoCjNI07fEYKZzxwCso68s5wRROHXbUaVFrXGdby5eWIlBZFzOBy0NWOAG0Wjda7bOoRCHzUK7P1WgpU0DORCmct0ejcOr4HRiqW5pBndidBMzuVjh3w5FujkNZOdew25W1dbyHGvld+cXZDfnF2XcBh6BO+OcAXxfllR7vdes01EFMoJpXXRK6a/czwOmWEsv+rZ8X5ZX2LMorvQf1bh2Fcokb061wpvREeTb0Bs7sTIUTIK0wsyWtMPNRIB2VgE8DVjkLKsZH2qalxDJITzJVgfLgOAfI6WoKp7OgItFZUHELygJ/Oqps72HdCmdVGxnamQonQFphpjutMLOdDHUWVEzREwWGjTmTc1LmTM55HPgaOBDIBcZ1K5zd6Mb/DoxKMNCN0PARKnD6WlQR10CYi8rsOgF4PcB9N6A27pfYc+1uTdOGoeJEzkIJ9mxN08qiJR4Ec1DK1hVphZkh1Q/UNK1J07SVgFX/aDOtadkVrkclefkXWm1kJUS02i1AHlrKs8DcNQ3Wl0GSLLa/2yD7huPO6A+/A0nAIE3TNqEUq19Rp7jd2I2LUVkYp5irHa3xRZtRSl9YtVlDRX5x9i9FeaU5qPfgMaCiKK+0BLil+Jh/7NQ5LbLn2o0vAdAxeBqVdfkaoKAor/RMlDViOKrm7Iz84uw/O49eF4E6vJqP8j6YgFbrL3FLhyOtMPMPYKqzoGIe6oDtPWdBxZvAjWmFmSEl79Djeq9BxRv1QllN77Xn2qONMTcczoKKE1Broxl4G/V3ru1MTl0Iu2RoTVZGTGrwRoK0wszNQJ6zoOJZ1J7kZeBKZ0FFflphZkglXOZMzhGog945qEP3J4Hbpy9e0n0w241u/I+h2722g1GUV3ozSjmx5Bdn+y2uqluHfgV+Nlc7fNZIspRYBqJi0j6dtGbSuaiMUrejEhzcBTyqaVpMfcedBRVXoqwuj6YVZt4UzrOaphWhNv97aZrmnjM5ZzWwYrq54jmUpfc/wES02qhjNJ6etuQwl0xc3j/+9/iJA251J4imh4B70Woj3pxZSixnog4Gxk5aM+kQVCbcyZqmRZpt8P87ONLNPVHuc+uBoz3LAKWWVf0MrKjJyrgglhyK8kp7od6L6UDdjwOXf7B0xMIpUsij7bl2w+qudTQsJZY3+jYMtE359rYvTJjOQGVDvDa/OLu8s7l1GWgpt6E8QQrQatsnFuoicBZUJKISCrXWip0NPJxWmOl3/baUWMagFIEjUTWR8+259upYcw0XzoKKVFRinAtReQeuTyvMXNKppLoQUsuqdsnQmqyMsGRoR8JZUNF6wPEvVNmGh4B70woz/crQOZNzDkYdqJwALEe50n4de7bd6EY3uiK63Ws7Hs+hLDzTAt1krna4UMlpsh3p5nQ/t90B9B69efSbqPi0e1EB2WZN0x7oAIVzHOrU8kNgRgRNrAD6oFx4AJx7J+0YiXIz+h64yAiFVCX8egAAIABJREFUsyivdGCz7PmWm/gtQxKrj0gQTQtRMX8/oKWcrVtDIsFvAD1aeoxAbWyXE9usiXsibgT2BWb4qDvrJEaWTk/kF2fX5Rdn3wocJpHfjPrrqCnnrZxZf/UXD8cu4USMUZRXmnS2/ca/z6+a2R/kSaj3L6Nb4fSAlnI26r18ERWb1mWRVpjZlFaYWQgcjPKIuQ9Y6SyoyPK+11Ji6W8psRSj4v/2AS4ATupqCqezoCLeWVBxPSpZzHkoRfrgboVzN1LLqjKJToZ2GNIKM11phZlPAiNRSXtuBX5wFlSc7e1yO2dyTu85k3MeQCWyGY1SVo/pVji70Y3/bXRbOjsBupvfOcC++cXZfrPsOdLNg1Ab8yfN1Y4bPa9ZSiwjkDiG1A/57diNxx6Isnhep2naB7Hk3gpnQcVwlNK4GTg6rTAzbFcZTdMOAVYBuZqmvTB36imLLhi+cuJeiQ21wBi02rXR8izKK01EbeKOBsbtKmqvpbQK+0NRGcVuQKsNq7abpcSyF7D16D+Pfn/f+n3HA+M0TauIlvP/L3Ckm/dGzcsyc7XjLO/rqWVVC4Dja7Iy9m/3cIxw2POjMw/YPPrTE365oDbBndQX3U01vzi7XZmhroqivNKTUYmCRq7tt2rHsuFvOj6/puyozubVpaClHAYsQx1e2dBqo07S05FwFlScjsrmeQDKpXH6ePO1f+IjQZo91x51chej4SyoOAa1vmagFKrr0wozV3cuq66F1LKq4SgZugUYW5OVsUe5mzoLKtrJ0MVr7l+DylnxKOpAcT5QMH3xkk6No+5GN7rRNdBt6ewczEVZ+C4MdJO52rERlQToUke6uVfr55qmJaQ0prwWJ+PiD998eBoqQ+ehHahw9kG5lcYDEyJROHVUo9J5j0FLiT9vP/uYPgmNCS4pzjVI4RSojZkNuGKXwgmg1Vag4rym69e/R0uZhZYSTgHg2iRX0o7UnaknAm93K5ztMAvlhjXTz3UnsG9qWVWHrUNSuKf9MvDb2upBX6WjNkZXAj8W5ZVeXpRX2qXXw6K80rSivNJXUIcoAjj1g/Rn7tyW/NcYS4lldCfT6zrQUgah1qda4Ow9TeEESCvMfA+1mb8LmOjGvfrCTWf8bJKm+SjL4RH2XPs/u5rC6SyoGKjHqC5D1cA9DxjfrXC2RWpZlacMPXNPUzgB0goz28jQ7c1bfuibMOAn1J5lM3Dc9MVLruxWOLvRjW60otvS2QnQlaGvgTiUS5zfQXCkm1vrbF1prnbM1zQtc2PyxucqhlQceMC2A1Yfvvnw8ZqmdViGQj2V+hvAmajNxEfRtKdp2qdAgsYjXwA3fbRhBPa/h6ROX7wk6iQoRXml+SiL0P35xdkF/kmk7ItKcjAZ3WKMVhuSAn/pQ5duHrZjWD+BMGuaFlJihf8FONLNI1FWpnnmaodPV/LUsqrW8RlSk5VRE2tOlhLLYGAd8KQ9134jQFFe6WGo0/rjUBvla/OLs1fGmks4KMorTUDVANZQa8a9wIP5xdmNlhJLf1S8bIk9157XeSy7CCIts9RFYSmx9DXX7//Y1L9Ov9Radwi1cTvW9XQnT93/vqzY1c6LALpcuBLlFtwXVQ7l7rTCzB2dSqwLQj9k2yVDa7IyopKhnY05k3N6JMf1+leja+cN8SLeZN7rmL9G9D08d/8HTnqvs7l1oxvd6FroVjqNRHllL1TA/FCCWJF/XNt0XM0m19QRwxLm7Dso/lfUyeAX2KxtshY60s0C+M4thPvV88+rkoJLyvYpa6xNqK2Pl/H7fX351+EL9fJKARyPis1ICufR7Wuax++scec0/e2+Ma0w87Gwv9sLmqbNOQL79RP4b8LGhl5LFqw5Igew7or9KK9MR2W47RtOu79vaDnou5+abvhjY8uHzS2cmV+c7QpOJuUklBI0CrUpuBGtdl0A7iPduKudvZx/PXvUtYei+jQ1HJ46NgLLsFn/iODZ8FBemYSao8NRCkw4+BtYjs36c7AbHenm11ElZEaYqx0+DxBSy6rOQpWyGFOTldFeOSivPApV+qRHmDybUYcH5disu8bdUmK5FZUEw+wZ/6ZbOC9Bxf0NQLk1zsovzg7NilReeQAwFuXyGA4kSmEs1wu5t0NRXmlr3PQhqMRa/8gvzl7jeY+lxPIscD6wT0DLV3nlXiir/j4oS2k42AJ8ic26Nsznwkd5ZRxqjh4ABCwZ1QbSDWsXnM/mr2xs/3EKWu3i4A9FifLK0SgX0l7BbvWCG2XpX4rN2m4Nt5RYBCpWcw4wWEiKn/tl9heDmwfci5IvzwEz0wozQyswX16ZhqqLuHeYPAE2AJ/5K2bvLKg4EjVHjwLKUTU3I8sSHIYM9YFaoBKbNfaHf1HI0KekOP1jKc6oRPxzbdbhj8SEnycilKGoOfo7ao7W+7phzuScHJQ30f7AS5mDz31nn54H3o2HDE0rzPQrQ714DiJyGboVJZtifwAfnQzdCfyAzdoxCewil6EuVM6KMmzWxmA3R40YylBDsafI0C6MbqXTKJRXpqAKHQ8jhFI0UkpcLnoIgSsuTjShNsom4ERs1l1uoJqmxR315Vcv7r927ZSPTz6p5YsD/np7Vf9Vk4Br7Ln2pyPgKVAbhIv1T0IW7NIt46SbRCFowcRcIcQ/sFmjmkAfa2fMymbZXU0kfPXsT4df3+BKWA6cPX3xkrcprzwHlQTERRjlfaSUphYXSYA7zsT3JpM4Hps1NAVCS0lidxZgN6qA+6Note2SMmma9oZLuHK+3/+bHW8Mu8uFKvQe+iZ5N5pRi+c4bNbvIng+NJRXJqNcMw9HjXu4i2YLSsieg83q93TekW4+FvgcmGWudsz2d19qWdWRqPqy59RkZbStmVpeeSdwC2oMwhXsrQmC3gWmYrO69NISvwI/23PtPrNBF+WV9kNZEfOAP1Hz4OVAngiUV54ALNG/M9wSVBI1t/8CjsZm3ejBZTAqy/XFKOF/Q35x9ju+GtGzmC4HrrPn2ov88ExFJZ7pFwFPUGMvgPHYrLGzspVXttYrPFn/vtAVD3dLPO6mBEwJzZgSNGxWI8oi+Ud55TXAw0Q2R0H16TrgOGzWXe6VlhKLGZXxMwsV83etPddeCeAsqOiFSiA3HdiOKpvzTFphpv9DtfLKw4Gl+m+Rrk+NwFGeGyZnQcVeqERN16IOzqYDC9MKMyOTCWHKUB9oXZ+mYLP+JyIOoSAKGdoiiWuExHhoSYT3hWAiNqvhtbN3IUIZqqN1ffoVJZt2ydA5k3OGo8ITWkuz5U9fvKQMwFlQ4VOGBsrETHnlgcCXRC5DW8d+AjbrJxE8Hxqil6Eu/bmHsVnvCHZzVIhOhkr9ue+Ak7BZdxrMbjdiJEMNx54iQ7s4upVOo1Be+RUqS1tYp54+sAM4GJt1naZpRwFPxjc3H3nWW2+7mhLil1x6k8uMeskOs+fawxdW5ZV3oGLswj2Z90YdMBubNfIyBFrKMDfi662kDFjIWTc1OX5diNrsXzf9Wu1r4BNUTGA0aAC+xmY9Pkxuw2krVK9Fq12667KmHQd8trnPxtK7Dj83u5+pj0sIEcnGsxUS2AaMwGaNTQxMeeU7qFpw4Z56eqMetcC3K+qtW+Y/R50CH2SudvhNp59aVjUYqAGur8nKeMKD5+Wo0/No52g9MB+b9QZLiSUHZSk8z55rfy3QQ0V5pWNQm0orUAbk5xdnO9rdWF45EvjGAJ7NqFISo4pe3mZCZba+BzVODwL/yi/O9mlpaIWlxLJCv99iz7W3XdTLK03Az6jEHpFs6DyxA8iImUWhvLIYtZmP9r2vB/KwWRdET8oHyitzgMVEz7MRWIXNarWUWHwqlPZcezuF0llQ4amYVgLT0gozfXkLDEaVLOpD+BtkT7Ru7IY739/ZiBqjB4GBOo9ZUcT2t3I1SobWAzZs1ti4VhsnQ+uBhdisV0VPygfKK4/GYBk6Z3KOp0K5qzTb9MVL2imUerLBNjI0rTBzqQ+evYA1QH8iO7zxhMoRYbO2X6+NgLEy9CZs1vANB6HAOBnaAJRis54RPSkfiIEMjdYQ4hN7kgzt4ujSiTP2GKjTryOJXlgCuLfsrMvWNO0p1MnfPi0JCRfEuVxPJzU2ntG7Xo4EZkakcCqcQ/QvOHob50T8tJbSG3hbIOMXMaF2M/0PQW1qmlCuVdmoU89okQyM1S0oYfCrXYtWezaQo7dRhpbyElrKEE3TBGrDtWHIfkmfJ4h4olQ4YfeG8PAo2wmEE4leWILiOs7PtYkoN75ZgRROHZtQ4+1dNmUSxszRnqjxA2WN2YAqSh8Q+cXZK1DZjqehXCe/K8orLdTrfXoivIMM/0gAhr77af2pKIvlv/V/LfnF2bcHUzh1zEW5UWX6uLYPymUtWmEJaqN5jAHt+EMO0W+S0ds424B2/OEMjOGZJKXMOOnFcVNQG/OZwAJglD3XXuxL4QRIK8x0oN7nC1Hvz3JnQcWTzoIKb/c0K2rMolE4QSkDPbevaR6PspqWoCxg1rTCzBsMUDiNlKHxKBe4WMEoGdoTNY9iBUNl6L8vmnQKu0uzvQekT1+85AFfCidAWmHm2rTCzDYy1FlQ8ZKzoGKI161m1LhHK0NBzXOj1mVfMEqG9kTJyljBKBmaDGTp1v1YwFAZSmThA6FgT5KhXRrdSqcx2Au1eY4aLrc7+ct1a4qAK1CnhOmapi36NZUX4iTxOcvdv6BcESLFACN46hgY0VNaigm1abEImLKJActRcZxulH9+GuoFj8SFwRdcQEpkXGvfRW3mZwPnAtUX8NZ8E+5jgDvMPfarNdBZwESkfRoMSmgYsUkGJXTb8XSkmxOBQlQCoeeCNVKTleE53p4wUnD0s5RYDgBOA56259qbQ3kovzjblV+cXYyKTVqAUgYcRXmlE/VEYKD6wIhNHS0tMq52u/s91Lw/Hzg1vzj7pzCaWISKufWVtGkg6iTYCCQSqzmqENl76huRxIaFCu+Nc8Roki2gyqJsBY6359ovt+fag8ZqphVmyrTCzIVAOsqqcQ3wo7OgItejbuIAjNnM43bJxIZN7tdQ6+GVwHFphZnfGtE2BspQ1ByN5dgbKUONnO/eMEyGulwuk8lk+hA1l8ZPX7zk3OmLl4QUq5lWmNlOhjoLKv7hLKho5TYAtRE3AknsGTIUYJCBbXnDSBmajHF7MW8YJkNRMi5WsmlPkqFdGrGaSN3Q4XK5sF5zCfsOHMSSwkdYs2E9U+6+jS3btnHEyFEs+L+7SUzYfXjilu7ExLi4dcBETdN2xffdell8zl0LWshZLpMmftEiyDVskfbJM/P6q9herwxVG//eylHph/DWvQ8Z9XV3ok75pqPVfoCmZQIzNU3r0Ucl10hDBV23w/DJE+jTsydxJhPxcfFUPv0CW7bVMvmu/2NtzQaGpw7hFe0++vVpkzMhur7SancCs9BSFrgRT4xizWXTWNCQwrZqe8LjB/g7AuzgPo0Y3jw/+Xo5M4ofx+1207tHT54vuJMRaUNDaeoaYARwhrnaETxxk0LreIfNs/SbFdw89zGamps5cpSZ+TNuJz7e55J2DSpe5JkQOe1CfnH2JuDyorzS+SiX29eB94vySm/Iv6Av+LAgRTLubklCrx7i+a3buCFQ7V5/sOfa6y0llueBfEuJJdWeaw8pG3AEXGN14m00z06Br/Xp1aX/RXv+aRy/rWX53Oexph/c5hk37rjB8f3v/NO19V+ReLCkFWbWAjc6CyqeQ83R54ErnQUV+WnjfRtlIlpH3STFJYl3gdy0wszN4fKMBL54tuKhRQuYUfw4m976mIF77dURdPwiknHvLEQy9lJKU3JScmFD4867pi9eEnYJorTCzJ3ALGdBxQKUJ8ejwGXOgopp4czRGXMf4z/LKkhMSODAfdJ4buYs9urTJ7KOMAh7+tiH0KexjsFrJ1M6ZZ8XASLk2SkytKsgqKVTCHGbEOJ7IcR3QogqIcRY/fOlQgirx33DhRCrvJ59TAixXgjh93uEEIcLIebp/79Q/57vhBDLhBCj9c+ThRDLhRArdS53eTy/SAhxUPh/esfgsdcXYR62/67fZz71BDedO5XVL71Bv959mf9eW2+/OGFqyj4gfY6nwmkpsewL/LPyINOyRBdpqJiCmPKs+PczVM1fSNX8hRxziIWJ47KM+SIt5TxU/cbnUWn1QSXLiEO5MgZVQsoeKaZq/sJdG5DChSWceMQYVr/0BiceMYbChSXGcG3HvXb1bG54dzE57MW2HYm0fHb4mncu8nd7h/VplPDmOe2R+3np9tlUzV/I1JNO5Z4F84O24Ug3p6DGtRR4P4yvd6LcYsLi6Xa7yb1PY9Gse1n1/GKGDU6l5MN32z0jpRQor4G37bn29WHwaoP84uzPUe5/N6JcglZVr2k62VdMfCTjHh9P3VnZvR6MROH0QDHK/eeKUB/YU+doV+UJ7denQ/c/kDfufoBxh/n2nE8WiTtfGnL7s1GETACQVpi5EuVefQXK+vnNtp+bL/CXtyHcdVTEsaP/6MSHO0rh9McTYN3GGj7+ejn7DY6lQTM8hDvunYlwxz4uLq7xiov+8VAkCqcn9Hqt41EWzwHAsi32ppv0dTooz5OtY1n13CK+e/ZlRg7dj/sWPh8NHcOwJ4/9ntKnHbbPCxOx5hlAB0sQQhQKIVYLIVbpOtJ4j+cOF0JIIcSpAdoWQohSIURfIcRQIUSZEMKhf98/PO6b7fH9Hwkh9tE/z/HUx0JBQKVTCHEMyh//CCnlYShlJySXCl3RPEe/31/8F6hkCf/W/78GsOnfNRtoDbJuBLKllK3p6U8TQhytX5uLytDV5eDc+CfvfvkZV55xFqAy1pZ+s4JzbdkA5J52Bm99Vt7mGZPJ5DIJ4b1LuBuIW3mAuBwVB3dtLHl6Ynt9HaXfVHL28QaEyGgpR6DcapcBeWi1rX/nCv3fMaj5khZOgqu3Py8n9zQVupd7Wg5vfbY0eq4+oGlaisQ0y8FBpc3E7w88aGrafGIv6W53b4f1aZTwxVMI2FanrEi1dTvYZ2BInjozUS4jt5irHeGcODqBtNSyqoCnf948N2+rJSkhkZFDhwFKcL7+aWm755pxJaA2N3PD4OQT+cXZLfnF2Y+hNvRvbKl1nyRlW2+RSMfdJESolmG/sOfaf0TVqLxGz9YbEHvyHG1FV+LpD+Zh+zNqv+F+r4v2633ESCvMdKcVZj6Lcguf11InT5Wu0GK7gq2jwhT9HDUKNz3xCA9ccz2iCxsNgo17V0LQsTd2jsq0wszXUbGcD7oa5EmyhZDMlaeMOXqXN8vRBx+Kc1PU5bxjgj1p7PeUPu2ofV60MJJnEB1sNirM41Ap5aGour+e79EFqGzgFwT4itOBlVLKbajsutOllGZUTot8IUSrif5BKeVhUsoMVHjfLP3zd4EJQoiQ3c6DWTqHAH9JKRsBpJR/SSlDrSWYBaxCbfZ8/tFCiD7AYVLKlXr7y6SUW/XLX6JbvKRCay2zBP2ndRGsAE4SQnQ5V+Ebn3iYB665AZNu6N1cW8tevfvsesHT9h7E+k2BMzxbSiwW4DLgiXdnrPoRmAec6Ug37xcrnp54s2IpJx4xhr69ekf3JVpKKiqJy1/ARLRaz9pPf6CymFpRSkhSU3NTsq9mhBCcMuM6jrz6Yp7+zxsA/LllC0MGKBf5IQMGsnHrVl+PGoEClAIzo6f25w602lvYd8K1kvYCuUP61AD44jlvxu2cXnAjaeeewYKP3qdgam7ANhzp5qHATcBL5mrH12FScBJCjIM3z4Epe9HsaqGy+gcAXiv/hHUb2wvLZtmchMre2V4jjRD5xdl/5BdnT91/3/h2mQe7wLjPRVmOgyYo6QJcQ8KewhN8r0+dgbTCzC1phZl5PfeNuwvR3u2sk9fRkOGL5zufl7Pv3nszesTITma3G11l3ENBVxn7tMLMHWmFmbf03i/+WgTtDjSC9emz773D+KOOjTnPYNjTx94TXblP95T1yWCePnUwXcm7Crje49qfUspXdF4C5U1wKXCKEMLnfhqViO5t/fkNUspv9P9vRyW221f/3bPkYC90/Usq69BSdidsDIpgitpHwCwhxE+oE/TFUkpP09xLQojW+j2J7K6PB0rRfFn/g/4lhEiQUnoH4lpRiqkvXIGHm56eHfRrVMxYkZTyKwAppVsI8TMq1Xq4G96YYcmyCgb168eRo8ws/VbRkj5cztXc2A0ppfdnD6CKXt+r//40Svm5CpVe33Cennj5kw+58owok0BqKcnAm6iU6Meh1bbRDjRNk5qmrUBZOt8EaGjY2ScpsX0iw8+fmMc+A/dm49YtnHzzdaR30GmipmlDUa6VL2qa9s2uCwOP/a3J3djSwyOrWYf0qQHwx/ORVxfyXuGjjD34UB5ctIB/Fj3KvFtuD9TUbFScwm0R0Gg9tUtDWfFD4imEYNGse7mp6BEam5s4xXo08XHtjXsu6Y4Diu259vbm6CgxZO/4X6SULejraBcZ93dQhzjT9P/7RBfhGhR7Cs9W+Fqfxo0+otP4JA+MWyOlrAfaaOWdtY6GC188733xOT568IngD3cgutq4B0JXG/seg+N+k1LW4ZVUKVCf3rvgWeLj4rnw5PG+muxQ7Oljv6f0aVdEB/D0p4ONAH73UgY9cRywRkr5ixBiKcqi6etE5DhUzos2EEIMR1VS+Mrjs3uBS1D6iGc8SyUqrOOVUP6ggJZO3bp4JHA1akO4WAhxqcctF0opM3ST6+ke5BL139/SO+Ur4BQfXzEEHxtNIUQWSumc6cHFpX9PGnCUEOJQj0c2olIadxl8vmol73xewfDJE5hy9/9R+u0KbnxiDn/v2E5LiwrdcW7a2M51UcrdmQYtJZaTUFk377Hn2rcAmKsda1Em7av0bKGG87zoHqXLbq79m+XVP3DG0cdF/gVaigCeQpnrL0GrrfJz5wpglCu511aAxqYGn6aL1v4a1K8/5xx/Assd3zO4f382bFalLTds/otB/byrBhiCe1CKVTvtSyDaKDQx71OD4IvnGQU3svKX1Yw9WL1ek7NOZtn33/ltw5FuHo1aiB43Vzt+i4CGU//Xbxyvv/485pDDqPj3MywvLmHc6MM5KK298V93v3s+Al4hwfOAqCuMux4X+DRwmqXEcqC/+7oC11Cwp/Bsha/1qbPhfbAJnbqOhgVvnuUrv2HNhj8YfcVUhk+egHPTRo64+iJqNsemtHGkPLvCuPtDVxz7UOcoQMkHS1jyxWe8dPtsn891NPb0sYc9o087e476Qqx5hqCD+cMFqIz26P/6c7Htr1s1d0EI0RuVMPFGT6VWSnmblHIo8BJwnccjYelfQV1SpZQulPl0qRDCDuQSfBN3GurUyq5P4J6oYrjemT52otIx74IQ4jCUC+l4KWW7hAVSyr91zf00dltJk/W2ugzuu/o67rtajcvSb7/mocUv8tLt93DenQW8Vl7KlBNPoeSDdznruLbhrm5JvAmwlFhMqFqQawHvY925KHP22YR4uhAOzxdvnw3Aq0s/IeeY40lOiqp02nSUUqKh1b4e4L4VgGgYPHRAr9+qaW5uaqd01u3ciVu66dOzF3U7d/JR5ZfMuuRKJhw7jpIPllBw4aWUfLCEs44zNr5L07QMVDH0hzRNa6dYmURbpTPiPnU19ODX526g7LXI65/6hYCs/7b5xBfPt+55kNSJp/HTut8YOXQYH1d+hXnY8PbNrf/PeWgnHpLcb+C4xu3xzSPO2HgQWkrYc/EXU4/kTwaMZWTdbxrlay4GYOzzB9Jzd24hf/25cesWBvXrT2NTE/e/XMJtF13erv14Eddkz7V3iB9OB7xLoeIZlBdEHjDDUK7ulnicr+WinRgbH6xx7yUTt/t7u1CfBoW/9amrobPWUaN4bnzro133DJ88gcqnXmiXvfZ1Kc7ML6saFgteqwQDB3rsy/eUcYc9f+w/+GoZ97/8AuWPPUXPZH8eg53PMxTUbnft/2JeaVT7N3+48IxeB+7Vt63nz57ep8HmqNsle/61vOnhpvcr/Fn/IkZSf9NeA6yJPUxxu1/8jnqX/OhgrwD7CSH6+FAa41B1WicIIW5DGUsG+LoXaBFCmKRUiUmEEAkohfMlKaU/X/GFKF3uTv33sPSvgEqnEGIU4JZSrtY/ygBCsWZcAFwppXxZb6cXsEYI0VN39WmFA6WUtH7ffigT8MVSyp88Pt8baNYVzh6oYNr7PdoZiaoN2OVx/zXXMeXu27h9/lwOP2gUV5zeNjmGlJj+3OzaF+VrnQFcaM+1N3o18yEq6dK1RKl0BsKi0o+CxvMFhJZyOso9+DWUC2YgVAK4e/QaAbQ0+VA6/9y6mXPuUDmjWlwtTD3xNE4beyxj0g/m/LtuZf5777Df4MG8qhVGztk3HkDV0PuXr4vels5ACNinUppw1Q0lJrXbQssDER8fzzMzbmPSrJmYTCb69e7DszN9eHG76gdtdyb3adiaOHhvy7Y/43u4R0XCqqd7J+l1a9iredt+tBbddjeFFJT+4KIFLPniM9zSzbQJk8g+Yky7exJFvPe70ymI+l0KA/Zc+x+WEstbwOWWEsss+/CgJVPbIDBXKWipH4KqhxcDuEOuHd2RfRoK/K1Pb1aUcf1jD7Gpditn3HoTGSNG8uGD/w7SWsfz7IB11BCeoWC7ihE/NOiNEaDFq0D8njLugbjuKWM/Yuo5NDY3cfL0fACOPthC8fRbuxzPUMbe5SaZWM1RV/t6ont6nwadoxKTdMkRGFfvd3fTLpmEbFvvuCPeJX86mJSyXggxH3hcCHGNlLJJCDEEOBFlEV0ppTzVo50SlJFqgddX/AgcAPysx4HOBxxSyoe9eBzkwWECUO1xeST+wyTb/02BsoQKIY5EZZbdC5XZ6GfgainlX7q18WYpZaV+73BUVqOjUC5zwz1Ns0KIN1D+yIu9vsMOHCvG843+AAAgAElEQVSl3K6XTpnEbsW2RUpp1a2fJaiyGibgFSnl3frzg4H/SCmPCvWPNhzllanAr7RumqNAc4tk5U8Nn1/W89r9UGbro3zFoznSzTOBQuBQc3UY/hzllb8BRiUh+gWbdYTPK1qKGZUM6hcgE622LlhjmqatAVb0cVQeNeG0yTsPOsCcbhDPBiANmzXsVP+app0KfADcpGnao+1uKK881SXdr8YJkxHFwrYD12CzvmxAW22hClsbFtco3e67qqflnoPKlmY2VzsiVu5Sy6p+A8prsjIuAaC8cjkqvtcI/I3NGhs/nPLKW1Cx1kYkMdsGHIPN+oMBbWEpsWQDnwC59uHPfQeUA30DPxUSmoCZ2Kzt3wUjUF65Ha/4wyiwDJs1Nr635ZVvAe3T6UaGemAUNqsz6J3horzyEqAIY/q0FpiIzWpYUq5dMFCG6ngIm9WnlT9qGCtD67FZQ8ouHDbKKx8HrjeotYhlaFCUV54KLMaYw1YXcAc2630GtNUWBstQ4Fts1tgEfxorQ91AMjard06W6NGFZWgblFdm0AkyNIgOlogK+5qIej/rUFllLwC+lFIWe7QzAZgmpRzv1f4dwAYp5TwhxPGoxKx2ds/z/5NSvieEeB2VEd2N0s/ypJTr9TaWALdKKe2h/PEBB1pK+TXg82hRSnmC1+9r2X1q09/H/RP9fM2zwGRgnpTySqCdb4KU8jtUUKsvTEXFDHYmduB1+hkpBDRvrnWNTUhMim+Ob7wkQAKUZ1GlVPIIT7BEUwcwtLa0lP7Af1Am97NCUTh1tCYTcu6o274PaoKHbPkIgATUCxkWNE2LQ7k4r8F/yY3tJh/ZayOEG2PHxxtNqIRf0aJx+8pvRgGHAVOiUTh1eNdmrY2yPU+EPe5hYBuqT40QmAkYO/ZlqFPMaSiviaAlVEJEM7Gdo+2S3kSBvw1qxxeMdNk2euw9sR3aZwaNEAI152MBw2QoanMWS5d6I8eqPvgtEWOrnmix02RoiDCyP5sMbs9X+0bIUIjduwTGylAX6p2KBbqyDPXENjpBhgbRwZpQ5SK9S0Z+6OPed/CdWHAe8AJK//oMfNegklJO8vW5bvTrEarCCcZs6KPFXFQdzkjxN8oK2nmwWXcA6zHgFMwUh3vzFuJHb8heZc+1L/V3n7nasQnlWpvrSDeHs0n7DGPiX3cCn7b7VEtJ0HkNBc5Bqw2prquOFcBwd3zipnXr1yQZxNMFrMVmjaSo9SWABbhV0zR/c9Sh+9AbgUTAf9aeaGCzSr3tqE8rpZSurWUfn4gaLyPcu72VznIM2IzpqcS/jLadADAqW7ZEnVSuN6g97Ll2iVpbj7509VX74GqMx0c92QggiG2W8OVEJw9aUY+aRzHBiobqdQ3uJiM61AX8ic1q5CbREysxbpNsQh1kGI5Ud3xTrWSHy5jjuyZ2136OBYySoY3EkOea31ZvcLkM0RWikaGhwIExSgeotbTSoLbaoOjlbaa6ne4al9uQSVqPitOLFQyRoaj9wip9/2A46mtcf0iXNCJUw3AZ6oXfUX1hRD/EWoaGDCnlBuAZIUSkFtz98AiRDAWdrnRKKRuklN5+xuE8/5xetqCzcSKwmcg39W6g/om/3yj90bWWjPUnJhfllQZLIzYX5d44NYzvuREl6KIRmvXAMtqfsAA8guqLq9Fqvwiz3RUALX1S3Kt/dfSTUt6vf1ekL3ozakxOCvdBTdN6ouJQlxNIsbJZtwKnN7ib3C4ZsTRyo8bjfGzW3yNsIxSciSqnEWnMgxuo/3vZp5/U//jD3sDN5mqHEYvwOiAttayqdb7fh3JpjlhotsgWlxDiB1SN29jAZl0B/IPo5mgL6hR1HDaroWVdervdJSfV1Tc98sfa9/n6uiTczc1EvlZK1N+Zj83qLwO1EbgIVVc1mg1uHcrTYo4hjDxgKbH0s5RY5l5ec//t79V92dAiXY0yUIxKYDSjDk2zgt0YMWzWX1HyoZ7ID0VdqD49DZvVcEtCalnVicB3p8i4veqgScqIrSqtc/RubNaPjWPYDkbI0AaUq1w4sjskzJmc03vO5Jz733j3pX8v//bzRpfb1RTlHI1IhoYMXYai5lhEVnn976sHbsZmNfygsSivdCyw/JUP6vZraJQNupUpUtQDH6NcI2OFqGWo/jfWEELN53DhLKhIchZU3Lbl26ZFfzuaW6RbNkcxR2MmQ3fBZm0BTkBZKLu6DA0LUspXApReCfbsCillWH+LUadL3bBZf6G80orypx5BeH0rgfXl9St/eKZ2SYm1n+vTQXX7jUPVvmlvTdyNL1An2dc60s3PhKQA2KwNegzFZdIlM+prXBeZ4qhLHhz3boipsutQQcPPYbO2tUhoKdcA+cActNpIrM/fANLVq28Ptm7q8fDcu4qmX6t9Bxz32x/N5+5skEMPGJrwemKCCEXYtwCrgYURxkrdiCqMe4GmaYH71Wb9dNric5fZeow+4NKU8R/ix0XBDyRK6foAmzVcJT082Kw1lFcehcrEO4rw3Nkk8GfjeufKmhfmPQ28Y652BJqb4cCJiuXqB2zBZnVRXnk+KkvbaEKNo3A3x238u/LkATs3pZrq17WwqWIhjZt2YouVEQmwWefpMV5ZQCrhjb0LFfP8Gjbr6mA3hwUt5cAvVCxI4uqEhITNTevGj4hLWoOKmT+Q8A4cJbABKMVm/cRQnt6wWWsprzweVdTajFd28xBQC1QBC7BZjXIrbc0mnotKKtYPeGzO1sV3TuwzLgdVbzrcuGEXan1ahM0aSamh0GGzvkV55SmosmVDCX99WgO8g8260khaqWVV+6AOBqYAv/yGOL2v4Hsik6GgEmhUYLP+x0ie7eAhQ4FDUMXSw0EDKhHH80ZauOdMzhGo+K5HUOP87BcrlhYcO+aEY1H1+PYO9LwPRCtDQ4fN+in/j73zjmuzWh/49yRA6ZLWDqmltc4WFUWN6ypG4tXrwLovjp/iNjV1YhX368aBO23c4sZdrVtDI+p1REW5SvV6vQ5qsVZbOqCM5Pz+eEJlBTLeMGq+n08/H0re9+ThvOe8z3nOeYbPn4dsjE4jwjHa1hjcqKUhuHtbox7TukrXNC0JvJFlYuJdt9M7DkkgeBpQ37hWH52WqiqVUrHoUBCj5QugPGTIJIYYdWggoK2//h7YbsWqYE7DqmDwpyVtnmUrgr+5TOzTupKqfZGKDFsBz635OXDe2G3TZjDYdGhX7LYvQuunwa1DBzm9JhJK0r/klOc8DRw4dfnW2x246IxPgTdcHsfRvd1TOyP7DMAD/C17UW1URktdSdWlyG5bXlZp3nuxyg2AkbE3snv3FnAwRkNMCz7DMGpVS/PqUf+tsQHbF1cs+DK0y/ghcLXL47iyjybixjCMicgutNcwjIgqz+eU58wDjqopqhmfUOEGmNoZ2XchcYLbZi+qXdTX9ZGQWVl9JPAMsH19fm7M7sU55TkpwP9mNLf8+Mwv9Y3AvogBMgujIZFutoMHIyMdqW98MdD6dVrqPcdtnFncptQ5NUU1dw6wdEOSnPKc7YC5yML9A+DMmqIaUw2wvxKZldUpSB6CqxDX3xuAG+vzcxPlvrneU1ZYsAWyybQ/EkYxq7hiwQcDK1X/UFdSlQqcDRjIRsX1wM1ZpXkxjye302tBNhVuRJK43AkYLo8jkXGYgwa307sVYhiu06EujyMuHVpXUjUZuBX4J7K+mp1Vmtct/jDJ+s2Au9cmEXLKc3YDjgJueeXciu+RWqiHu53ezD5ufRzZPTszmu+rK6maiCxOXzTB4NwMKYvyHXBMrAZniE90auqmoa2QrJCL8c1IJt9b4pIzcq5AasuWRHHPT8C4nPKcxGQiHATUzsjeCklcdZ9ZBmeI9l30Kb1e1TcHAVmLhqWVAf9AlNsE4F8YGfdhZKzXGwIYGQcgXggG8AIwfetLll3QptRHwKyc8pyBr/o9hMgpz9kgpzznNsQDYzpwMpCXNDhjJ7Oyek+kP29FMiVuU5+fe1XS4IyNssKC4WWFBVch834PxENnp7+KwQmQVZrXmlWaVwbMQJKlXA3U1JVU/aP3O3vG7fTmInG79yPxpju4PI7z/yoGJ4DL4/iWLjrU7fTeFzr5jYq6kqrUupKqYuR0fyayvspJGpx/TZLutYOA0GLwZuBX/jSsPIgCOQVJKd0j2YtqV9fOyC4HTq+dkX1+KMFQJFxJ9IZVd4yM0cB8ZAPjYIyGeN2FPkFZjtcpqai21izkJZWH7LQlMisdAIZhbAWcAdxnGEY0hlW7i9wUOtcwWp+4Hkl+cZXJ7bYbnVm9XtU3ZyKJBF7GaNDAMxgZryNK7jzgcIyMEuABjIbExH4MBEbGVOB24DAk0cvfMRo6uvDMQzax9kay2ibphdD7+GjE9TMTyY5+aU1RzR8DKtgQJrOyeiLimlyEhBMcDrxYn5+bdLWKkbLCgoOQE7jNkILtFxRXLFgysFINHFmleYuBwrqSqvuRMkGv15VUPQecl1Wa12dCQ7fTm4EYrLORONYTgUdcHsdfcoyG/u5n3E5vJx3qdnpLgAdcHkefOrSupGov5FlsC7wCnJ1Vmvd9AsVOMshJnnQODg4B9gSurCmqWQ3g8ji+Ad4GznA7vX1lR/UgbkoRJU2pK6majhhW92SV5sWejdDIsCInrdnAURgN38Xc1p98AhAYPjIIKVMR95ZFyK5jf3ADEnNjRHlfewIgs+q3DSpqZ2T/DYlluCl7UW29yc3XI0lOYjY6c8pztkRi1u6pKar5M1bGaFiF0TAHKar8b+Be4AOMjMTUR+tPjIw0jIyLkN34/YFLgO27GJwgibD+QNyik/RCTnlONvLefQJJurVrTVHNrKTBGRuZldXWzMrqWchmyLFIbens+vzcF5IGZ2yUFRZsUlZY8CJSF70ZcBRXLDjur2xwdiSrNO8tJOv8ZUiCotq6kqo5ITfcbridXuV2eo9D1hlnIeup6S6Po/yvanB2xOVxrHJ5HN10qNvpDatD60qqNqorqXoEyaI7GjgUODhpcCZJGp0DTE55TipiWNUCD3T5eB5yctZr9rDsRbVfIZPbWTsjO5JnWopk0Yr3xOpaJPD/3B4WurHyBdAWGD5qTUr6bg7Ere0il8eR8AzFhmHsgezA32QYxq9R3r7eGp21M7IVcgK/hARkBK3Pz21DFvjxnHSegSS+6Hlzwmj4N3LSdwKwKfAJRsbdGBlj4vjOgcPIyEdibUqBN4FsjIYbMBq6lRupKappAh4CDsspz5nUv4IODXLKc0bmlOfcgLx/dkROzXetKapJZOmN9ZrMyuqdkVj8uYhL7Xb1+bkX1+fnJrJ27npLWWFBWllhwcXIWmFfxEspt7hiQdJ7oQtZpXnNWaV51wFbA+8gp+zVdSVVnVLiuJ3erQEv8BhyAr+Ly+NwuTyORNZ5HZK4PI5uOtTt9N7ldnrX6dC6kiprXUmVC9lkOhrxjto6qzRvflZp3l/egE+SdK8dDJyGZPGa2emERngJWYzPoufCrh2ZBzyF+OG/Fu6iupKqPZFdp8uySvOWxio0RsZxiNK7F3GfMAXDMJoMw/h3MD1jU2vzdjshcT+JzUYo39vu4rwEiTeKll+Q07r1zuhEDPHdgdOyF9UmasHYtVZnxOSU5wxH4u1eqCmqCb/bLy63j2JkvIyUwzkTOAoj4wLgsdDngxsjYxJi+B+DZBUtwGh4JYI7PUg9rVORvz0J61xpD0Xck6cibsgX1RTVxP5u/IuTWVm9IbLYPB3xYjgGqEiebMZOWWHBPoienQ48D5xXXLEgkeW11guySvN+AA6pK6k6GHFFXlhXUvV4XUvwik8bA2cA5yM5MZzA/S6Pw7SM1+sjoZPfR91Obycd6nZ65xyckfKtRam5yKbd20iioITU9U0ydEmedA4gOeU5oxE3Th/iKtOJ0OnevcD+bqd38z6aewGJCQ2bUKiupKrdsPoFSaseG0bGLsip7LvAWQlYrH8yLDB9tLKkpwEX9JOLyxGIYXWFYRhRG1ahDYPFwCZmCzaQ1M7ITkNO075CFuSJImajE0l2MBbZeOkbo2EFRsNZwM6I4fYIsBAjY9sYvz/xGBkpGBnnIDvIhyOxR9tEaHBSU1TzHXIiekYoy+9fnpzynM2ROKPnkVIreTVFNSclDc7YyKystmRWVp+EjNFTgTuAGfX5uU8lDc7YKCss2LissOApZBGfAhxQXLHgiKTBGR1ZpXkvA9tora9d3BIs/Kop8B1SZ/xRxJX2nqTBGTkuj2OFy+M4C9hZie5+5IPVgQ8b2nQWcsK5X9LgTNITSaNzYLkQyQw2p6aoJpxSvg+pReTsraHsRbUtiGvhQbUzsqeFuexIYDfg8qzSvNiKBhsZk4EXkRPBIzAa4imS3CMpLaO/HdE0xdLW9l1g7fJbE+7eZhhGe9r+rxA3xFj5ifXvpPN0pGbeRdmLahPp4lwHTMmsrI4lw+qZSDzOwqjuMho+A/6G/I3bAtUYGbeEkmMNHoyMvwF+5DTufWBbjIYrMRqiLU4/F6k9e7DJEg4pcspz0nPKc65E5nsectqxY01RTXxZvP/CZFZWb494pTyIGJ071ufnnlefn/uXyfhpJmWFBSllhQXnIe+1Q5HN6W2LKxa8PqCCDWHmr2id/FJD287+xkCKVbE6b5SVQ8ak7nDImNS+NvST9EBdSZXlkDGpOxyckTJt++HW4IqAXrtwdduG81e02uavaF1vs/gniY9knU4z8fn3QpJ5bEIfBn2bDqQsbvtt0+Fq2OqJKWOXIDvtbwOXdy1q7nZ6nwEcQJbL4wi70KydkT0VObm5MXtR7SUdP6srqUoDvgYaJznSD7QOUzchC+1hEf99OqhorJtMQ00qS17bk/M//Djie6PgzjNfe94STD2sMfgC5x517B1pacP2IMLixh2lRU4e78Bu69U12TCMsxDXm4MMw3g1JqF9/uO+bfn51uFq2NgpqRP/F+Xdy4FXgWux2xKbVdXn3wGJ5d0C6DVBlQ4GLa3LfttUpaS0pIzd8Gel1GqkTuEc7DZTSxxkVlYXI3GjY+vzc1fg849F3Eh3BIaHu29tsCW9PvDH1LGWUb9lWEe1x+G0Iou1C7HbIktcIOVUbkBOaBYjhsgzfZ7i+/wKmfMFwIYRfdefBJD5ehV2W/e5ZGRMQOK9T0KM8nOAF2L1LGhe+K+Z/27+/umNU8YHJqWMi7bY+x9IluobsdsSqzR8/i2R0/VYiq83IvGD52O3dcuknVOecwBSz3BzJByhuKao5pcY5RyBeI7sDkS7yAoim1Q3YLctjOn7o8Hnnw0UAhNjuHsZkozqzq7PPrOyegP+zPi5HJgDPFKfnxvbeywKHdoDa5AY0guw22LbVI0Un39j5H0VnQ4VmoEaoBi7rVtStrLCgj2RDaIcRC+cXVyx4L8xypmCZL93kEAdago+/3FIuFEscee/Ak9it3XzdnE7vcORUKASpO8vz7Di3nt0artb/WTEc6skqzRvWQRyRqxDeyBhOrQbEerQMPSqQ+tKqnKRMbo7Ul7mzPkrWpfQWYeeBzzbp6dafDpUI7G4t2K3hQ0rMw2fvwCpKpEFRLtB3n86dBCTNDrNwuffB4m7HBFHK43A68A/OxqebqfXgQTDF7k8jkd6a6B2RvaLyOnNlOxFteuSitSVVJ0N3JE+3nLc+J2H3YiUAojNzU4HWlHWL4G9sdtWx9RGGNxO77Ya/cXakYuZaRtp2WLSlLUWZUmPo8km4CTstoqePjQMIwP4L5JA5O+GYUQ/IXz+M5HFZzzPfg3wDHBKwgxPn38npGRGPCd5a4GPgX+YqTQzK6sLESMgp97SthhRZFsgWZljIYi85HfDbot8wWZk7IYo0x2At4DZGA3f9nitKMv7kJi1eJ79amA/7LZ/hWSwIor7BuRZ3Qpcg9EQ+1zz+Y9E3IijXXx0ZE2oDVfClKYYnP9C3KVj9cRpQU7b8toNz5zynK5lZVw1RTWxJz8Tg/MtZEEXz/upETgUu+2tONroHZ//WmQBGK9uugO4FLtNhzwSjkbG5kaEysrU5+fGnuXXHB26FnmXOxJmeIrB+RHx6FBJerYYeT/VA5QVFnQsK/MTssk0v7hiQWxzTQzO54F9iK9Pe9WhpmCODm1EFvRXt//C7fQehGwybUqorIzL41gX919XUjUaKQdyLrASuBi4P6s0r2cdPIh1aCfE4DRdh9aVVGUgsZwuZDNqDvBoxyRBbqe3mw4N1f3sSU6zdGgTcCx224txtNE7Q0WHDnKS7rXm8TzxTRpC9/8DOKrL7yuRhVIkJQ/mIS67h7f/oq6kagzyYn1nnC3tCOJTlqCsqcgO7wUxtxGeGxVqVfYWgfppEycTp8EJ8oJ4JLRI7IkSYBxwYYwG58bIwiveZz8See77x9lObzxDfMoSZIG9C5It1kw61uo0iE9ZgrzbNkTc/SLHaPgQifU8C9gVqMHIuBYjo6fnuy+y8I732Y8CnpXvz7AhRpcHWTxvj9FwUZwGZzpS2igeZQkyRk9AMhgmikeIz+AEGTfTgctyynPScspzupaV2S4ug1NwEb/BCTJ2ng8tvszH59+e+A1OQvefA+yQWVmdjWyCPoHM213r83NnxWVwCmbo0HSktMM5cbbTG3cRrw6VeycDt5UVFljLCgu6lpXZurhiwYsxG5zCscgJZ7x92pcOjQ/zdKjUHff5t3Y7vZu4nd72sjJrAYfL4ziuo8EJkFWatyqrNK9jOZB7gA/qSqrClQMZzDq0IwYm6tC6kipVV1J1HDJGZyPrzOlZpXmPdM1K6/I4uulQt9N7jdvpTaQOHQ48hc8frWdMZAwtHTqoSRqdZuDzjyH+xUc7w4EZHX8Rck+YB+zmdnp36OP+t5CTu44JhUqQl8ccpVQO5mQtHobsZJlG6ET3QOC6KWPHLEuxRuu5EpZmZKezE4ZhTEF2OB83DOPTGNveElFqZpCCLJbNRxa1ZsWbti/szKS9eHdWqO14lGU7FmLpT6MhgNFwd+jep4FLga8wMmZ2uXIG0btX9YzWmVw9wYPsgE8FjgMcGA1fm9D6NGQOmIEiUWNU2Apz9FLaH4GVDrqUlakpqrmhpqjGjDj0HTDvnZ+KbHwlgunIqVrcaE3gFq2uRDZDdkA2QXerz8+NP+7eXB1qum7qglk6NKW1tWUXupSVKa5YcHFxxQIzsoRnE/9ivp0edahJmKZDtdatH3259hK6lJVxeRy9lpXJKs37ii7lQOpKqu4ObdgLg1+HdsQ0HaqDehv+LCvzI7BzVmne7KzSvBXhbnJ5HAGXx9FRh14GfOV2ervmEzBPh8p7bopJbXVlGkNHhw5qkkanOaQj8VlmYKHn+KByxIWg19PO7EW1QeSUZM/aGdk5dSVVUxHD6rGs0rzPiT7+pDdM2/l0O70WJEbmR+CusSNGLrUo0zb/g/S8oLkGeQFcGkfbZi2UQBYyZrbXFdOseKKPYeuLJUh8Rhbx7yZ2JPbxbjTUYzQcjyxGGoH5GBkvY2S0L77SMavslA5YQJ2GnKJMx2h4wsSs0OlI35pBoseoGQslAH5s/XVH5PkX1BTVHFZTVPOjWW1j4rsP0R2J6tN0TNLzTTBqsVYzkR3/6fX5uZ76/Fyz9J6ZOhTMfz91xDQdurpx9WbIiecxwN+LKxYsMqttpA/MUqLhdKgZmNZuaxujGlYHj0PKxmW7PI4bXR5HRJtMWaV5Oqs071HEIJiLrLW+qSupOiGU+R8Gtw7tiGk6VAcYB2yPnMzunlWaF/EGvcvjqHd5HB116Etup/clt9Nrvg5N/Ht0qOjQQU2fD1spdSniphFAXjxnaK0/UkotRAK+2xPbfKe1PlIpZQCrtda39NKmQtxzDgUyEJeqzFD792qt7+hy/QWIv/8ErfUypVQBsLPW+spo/tiBIBAIYDvjBCaPn8iC0ts47trL8H9TS6o1hV2yt+Ge4ktITel7zrk8jhVup/cJ4Di303uhy+MIu8uEZGC9Fnlpjgr97rJo5Hzn04+Z47mTYDDIqOEjeLjkSrbIStQmEiBjbAfg/1wex9qVb77XLcECwLTCmYweMQKrxUKKNQX/vRLietfzFdz9wtOkWK0ctNue3OQ8u9cvMwxje2RX8xbDMMxcjIaV85mFb2M8fC+1P/7Ax/MexjZja7O/Nia6PnvvZ59wwbw7aGltZafp2Tww5zJSIhij8VCfn9uaWVldTy87lT316R8rGyi86hJ+qF/CtMxJPG3cwNjR0ebL6AOjwYeR0e6yZwBfY2Rcx16vBbB2t5FimktKaYZvvAdzqj80V/jwhJtLALc89ShzPHfy24tvMX7MmF5aSTw9yXn5A/OY//67WJRi4tgNebjkSjYeP6HTfROsY34Gtq0pqok2y6+pskL076fBKKcCfajSpbftnXtJuHYHg5wDQaxzaVjasAZgRnHFgn7L8tuTrMZD93LfKy8yIUPku/40Fwfutkd/iRSxnO301qebTk4t3e/ivS6O9XtDJ3hn1ZVUPYQYn+XAKXWvN7my9u9uy8X6fhoIYpLVQguwVURJlsLg8jh8bqe3kw51O73XnvHP0cEUa/d9kaHSp/0hp1JqtdZ6VJffGYTsLKXUw8ip/mZa62al1HjAr7We1kNbw5G8MQ7EY2MekmAsAFynta7ocv1dwEnt36+Umg2s0VrHXOWh15WkUmp3JKPUjh3+mI6rrOO01v4YvvdA4Aut9Uql1EigWGv9mVJqNPCpUuotrfXXIRmmIB3asS7VK8A1SqkbtdaJzVIXJ3c89xTZm2zKyjXiLXPc3w/gsUulNvux11zG/a+8yKxDjoy0ubnAKYixdGe4i7IX1f5eOyP7KUvG1CKt9XCl1E1ZpXm91vXqKues225k/nW3kL3Jpsx98RmuffQBHr7YiFTOqHA7velIhr3PgCcBRqcNC2tUV97m6aRoKj/3M/89H18+8CTD0lz08uwAACAASURBVNJYujyi0KKbgRVIspaE0FXObTfdnOevvokzyhL2lTHR8dkHg0GKbjB459a5bDVlE6540EP5G69wykGH9Icofdbq7NqnpU+Us8+OO1Ny3ImUPv4wpU+Uc+MZZ5kvmdHQCtyCkfEUUuP2Gn56chnTjleozgdJsc0lS4Bd7v/SfMF7p2t/Avy8tJ63Pv2YqRtl9rc4Yekq55yjj+eaU8Tp487nnuLq8vvxFHdeZ2alTvi5Pw3Odkx6PyWcaOVMhya70t8NdjkHiljm0ojhI5f1p8HZTk+ynnfkMVxw9PH9LUqvRNunqSms2XKTVFPeo1mleZ/VlVT9DTgZuBFNdaRyRvJ+GiiildViVY3xGJztuDyOVuAWt9PbrkOv/ezrlt923jYtdA4Vn5wDxSCRM4CM075qlZ8MPK+1DiilGoETtNb/UUptjNheb2itVwAopWxA1x3nB5GybTEbnX253UwClmmtmwG01su01rGll+/McUjqYLTWS7TWn4V+XoX44k/ucO1tSD3LdUfbWlLuLkQM4kFL3dJfeeXD9zi1w4L9wN32QCmFUopdsreh7rfI65C7PI7PkKx5s9xOb+9uM8oyb9g2h48g2NqIxDRFJadSrFs0N6xZnegdpbOQWIk5Lo8jKN+vInZlmDf/OUqOLWJYmuyHTBzbe9ZtwzD+gWxkXGMYxvJeLzaR7E02ZfrUaf31dRHR9dn/vrKBYalpbDVlEwD2te3Kc+96+00c+jA6uzL/fR9F+8troGj/Al58b2ECxOqA0VCH0fBnwicd7ORuFfNcMs+VPG7Ou/s2bjrjLJRpnnnms8HIPzd+16xtoqdFy2Ah2vfTQNGXnIOli4dKf8LQmEtDjd761Oz3QFZpXjCrNO9+xOU24oX2UHo/9besLo+jzuVxtOtQFdSRuSwPlT4dIDlvB85TSvXlktbR9vpWa/2f0M+/AEuRJKQopazIwcyFHW8OHfL9oJTaJVZB+zI63wSmKKW+VUrNVUrZu3z+uFKqOvTv5ii+dw+gm1+4Umoa4mL5Uej/M4HFWusvemjDjxT2HrSce/et3HTG2VhU925ubWvj0TdfZf9ddo+22XlI8PXevV00aubcsSkTt6blm1dXrnrx9G716vqS8/45l3FgyblkHXkQj775GiXHFkUrZ0S4nd5xSEzlqy6Po0/rRinFfnNms9Ppx3Pvy88D8O3PP1JVU82us07Efs7pfLLoq7D3G4ZhRdLS/w85OU4IPck5GOn67MdnjKE10IZ/keSvedb3Dj8v/bW/xOnV6OypT3/94w8mjRsPwKRx41m6vJ/2EIyGN5h6zK0oS6fU+gM5l6Klp/586X0fkydMYPstthpg6f4k3Fy69P65TDnqIB5/63WuPjmRiSAjJ973U3+RlNNchspcgvDz6e4XnmG7k4/h5BuvZvmqfj987cZg6tOs0rxlWQcMPz1SOWHovJ9gYGR1eRxv7Lh12q1K0a08zWCSszcGkZw/ISVywroqKKXSEBfcH3r4bBfEi7W9vNxs4CWt9ZKu1xKn7dWrVay1Xq2U2in0BflAhVKqRGv9cOiSWN1rNwydaq5DKTUKeA44N+R2OwIxRvYL08ZSYOMYvrtfWPBBFRPHjmWn6dks/Lx73PWZt5Wy13Y7kLdd1En2nkbSi89CSql0o66kyqqU5Wbd2vhby3/emIQY+e9FI+dtzzzBq6W3s+vW23LzU49yvvt27r+w17DQWLkMSUF+YV8XArx/9/1sPH4CS5f/wb4XzGbG1Gm0BQIsX7WKD+c+xCeLvuafxiV8/+SL4XaYjge2A442DMOsbGQRybnX9uGysA8MPT17pRRPXXEd57lvo7m1hf1su2FiFuG++BnYoE1jTenh0fXUpwOKNa09zt0Cg2IuRUVP/XndYw/x5s13D7RonQg3l6479UyuO/VMbnj8Ie5+4WmuOmngFyEmvJ+Scq4ncg7GuQQ9yzrrkCO4/IRTUEpx+YMeiufezoMXXTHo5ByMfTrU308DKWuKVXXSoYNVznAMMjmvR+ocvxLm8/FISFknlFKTgEeBIq11MORqexThD7aW0qXCRjT0mR1Eax1AXFkXKqVqkMLFD8f6hSHalFIWrXW7K2UqYnA+rrVu3y7YHEld/UVImWQBnymldtFa1yPZn/o9XidS3v/3F7z0fhWvfvgBa1uaWdm4hv+79nIeu+warnr4Pn5bsYJ7rok+H4PL42hyO70PAee4nd6NXR5HT+7OJwDb6mDgeHTgbqR8So9GZ09yHlRyLot++oFdt94WgML8fdn/QvMTNbid3s2RencPujyOiLar210TJ47dkMP23JuPa78ia8JEDs/LX+eybLEoljWsYMKYsQBorVXrsqXpLx1xxDhrdvZ1SutPD1rwysu1T1XEnYFyy5vuGmbdIKPbgqcnOfsyOrXW6JaW1G9mZJteD806egO2usXd6Xe9jdGqu+4D4M1PPuTbul7Dgc2kDqAF0np6MfXUpxttuCFLfl/GpHHjWfL7MiaOHdtfsnZjIOdSLHTtT98Xn/G/Jb+w/SnHAlD321J2PP3/+Hjew2SGTpO11jTq5tTdynMSUrPvy00ejHouHbvP/hxUcu6AL0Ag1vcTfATph1ZWm96n3yjSNqC7a2wscg4EscoZDAastxUWJGSMnj/rym4xaLHMpYGir/l02kGHUnDxeZ3u0Vqrlat1+mM911qMi5MOHTVseLrqc94P1j6N9f2kg9q6uKQqIWN08v7plp42YYbKuzQWObXWavnKYPqTCRijxxWMTM8YZRnUuklr/Z1Sqhr4Z5hLmuiSNVcptQFipF6mtW5PYrgDUuP1u9DfO0Ip9Z3WeovQ53HZXr261yqlpiultuzwq1ykpEW8fANsFvoOBTwA1Gqtb22/QGtdo7WeqLWeFsrCVIckNGrParoVUsx3UHLD6bOpe/YVfqh4iaeuuB7HDjvz2GXXcP+CF3njk3/x5BXXYrHEnMneg2wYnNr1gzp5iV0LfGQZNvpxJPPakbUzsjeKVM75195Cw+rVfPuzPOq3/B+Rvcm0WGXtjeuAViCiLMRrmppY1bhm3c9v+j9k200359A998b7uZSK+/bnH2lpbWV8xp/xz8Gmpg0W33v3e9t89fWyI599buMjnnt+p/Tm5jVA3P9+efje+cGmxoxI5OyTYNC67NX5V5khV9d/gVUru9V9CzdG2xNzNLe0cOOT5ThnHt637Oawzujs+kG4Pp35t70of30BAOWvL+CQPbpGAPQfAzyXoqKn/tx5+tYsffFNfqh4iR8qXiJrwkQ+u/exTgu6FtrS7l7x/E0kYIwCaxr12k5Z+sI99/902Ah56YN3B/7Um9jfT00wYpa2/ocE9Odl2nLfmj8zmMclZ38Tj5w//Pzff5CgMbpqdUOn1NOxzqWBIFyfLvn9zzwxL7y3sJu+amllgzfeb3yPBPTn2x82zW9p1X3q0MHYp/G8n5p+DRxOgsZoa4PeyUxZ+5NY5WxtY/Tr7zV9SgL68433mv7V0kqntPiDtD+vAy7o6QOt9XLAqpRKh3Xuti8Aj2itn+lw3Sta68wOtldjB4MT4rS9+jrpHAXcpZQagxRe/Q7o6Nf+uFKq3eJdprX+e+jny5RS53b4I7rGab2CHN1+h7h+Hg/UhKx0gEu01q/2IVs+MPCpq6LEeWspm2RmsvuZJwNw+F75XFF0WlRtuDyO79xO75vA6W6n93qXx9Gx+Pd5iNtxYVZpnq59kXnA2UjWqojSpqakpHDfnEs54oqLsFgsjB01mgcvujwqGfvC7fTuAhQC14Q5re3Gr8t/57DLxQu3LdDGsfvsz/67/o2W1lZOvvFqtj2xkLTUVMovNjrtSKnU1LVM2vjRL8eMPmFYc/N/p3/zbblZf8ewyVO2Uqmpx9FhBymcnC9UVXLWHbfwW8NyDrr4PHK32Io3br7rz8YsSqdPmfoGYdym40L648ZILr35qUdZ8K/3COogs2YegWPHnU0XJwxhjc5wfbrzjK3551UX88CrLzF1o414xug1Z1a/0x9zKRbC9WdfWLEEZqRNfZUwnhPxkqpSr6HD8w8n5xFXXMg3P/2IxWJhk40y8Zw/8Kog1vdTKrRuBDcvgV5j72NhW6V3TIPDiKBP+5Kzv4lHzowNxi4ijgyLvTEsLf1iOmR1jHUuDQThZD3+uiuo/u5blFJMy5zEPcWdvbCsVtaO2cBy72/Lg4vNlmncGOtWVquKSIcONuJ5P6WMtHyJ1Lw1HUs6Z9ElP8JQeZfGKqfVQvMGoyx3L18ZjDw7Z4RkjLZMslpxEsEYTUB/jlBK1XX4/63hLtRaf6WU+gwI51b3JrAn8DZyIroXME4pdWLo8xO11mGzNIfYA7gqEsF7Qkki2P4l5EP8iNZ63xjv3wh4Qmu9j7mSxYjPvxGSmMasgrw3Ybdd1NsFbqf3EOBF4HCXx/ECQF1J1UTEkH8nqzTvsPZra2dkv4Mcl2+Wfc+j/wU2MUnOt7DbwsXchiWUedeHZITbwuVxrOp2kc9/O1LTyQwanvjy49e//X3pkcC2hmGYV4Db598PibPN6OvSCGgDLsduM99y8vkVdA/Yj4OnsdsKTWyPzMrqYcBav2pbnKU6ZbCOh+XYbYlJb+nzz0F2FlNNaC0AjMZuMz9kwOffHngXOu/UxkgzcBF22x19XhkLPv9KJMbbDN7HbtvTpLY64/O/gNSZNoNGYCvsNtMX9Pj8xyMJ00b1dWkErAHOwm4z35gzX4e+gt2WmOz2Pv8PmKdDv8Nu27Lvy2LAZB0KOLDbPjOpvT8xV4euAk7HbnvKhLY6MwR06Dp8/o+AmDOMdmGo6NBVwK7YbbUmtNWZoaRDe0EptQNwvtY6ptpI8d4PfWevTQihjEj3hfyJY2EqUGyiSPHyO+ZMGoC1SEKVvngldN2sDr+7EhgBlHS5dh7SZwcAZpS8aef7GO+biSSnurJHg1P4SWvdGmP7ndBaD/9lVcMRwH2mGpzCLxBZyu8IWIu5z+dP7DaNeScpmj+znJlGfX5uM7D0F3kpm4Xpu54dWIx5srYgzz8RLAaGmdRWC4kao8JvJrb1g4ltdeV7E/drh5G4cfoL5i2UA8hYSgS/a63N0qEgm6+Jwszxn6j+BBN1KHLCk6h5b6YO1fyFdWgHzEzEMFR0aCLH6FDSoWHRWn8OVIZKosTCeCAuV60BMToBtNZPa61jysuttf4kgiPg/sNuawOeQnas40EjC89X+row5FJ7L7Cv2+ndsq6kajpwBnBPVmneN10unw8sQRIK3Yk5CZgakXjRqHA7vSmIm+c3SCxvN2pnZI/44eZrNw+ubUrVwbjXS02/rl7ZsLqluZk4XAJ64RtkgROvwRAMtfFW3BKF5x7ktCJeGoEKE9rpjm6rv6tl6QZNQVP0UCMy3hPFO8hcCsTZzhrggdCiJhH8DlQR/7wPILIujFegXriL+N+jhNq434R2upFZWZ1xQtCysRlCInI+jd1mlnHQlY+R5x9v+63AcuDDvi6MhbK5xt+++1/t6tbWFjOaW0OC3BZDmKVDm5Dxbjpup3fE82+v2byllVQTvNmagH8BiaqdZZYObUEMpO7lAsxj8OtQ4R7Me48OBR3aBLyN3WZ6iEKIoaRDe0Vr/WAoQWws977VU8mVaBgQ99r1Ep/fCjyGxM/E8vK0Ii/NPOy2ryO5we30ZiKnnXceMiZ1U2BfYIus0rxuyqF2RvZVyA7FFtn3PGpHkhGtJbA2Hd2WhnXEqq41B8NgQWKBD8Zueyeiv6yzzE7k5PVQl8cxvwc5ZyIvuU1GbJX98qSzig/QFmuKUrSmWVOifYkOa2xtqb31g7d3aAsGrzQM4+po5Y0In3804i48A2hpDK4dBegRlvRIlVMKsBrYA7stcbuf4h50O+BExmi0k98CKGBf7DZTF5855TlWwNkw4fzbA9bx1vMafc8Wjy2cqZRqIfqTGoW46V2M3VZmppzd8PmnIQuy0Yh7dDQoZHe2HDgjgUYn+PzDkM2sPYhtZzkFSbf+N+y2xKYz9vlLgKsRBR/LGE0FDsdue81MsTIrqxVSXPsWYOKhBF+Yp4L7K0V72v9oSQdeBo7Gbot30RUen38i8AEwidiMz1TE4Ngdu81Uw6OssCATKUL+f0qpH08+9qyfxmRsuAux6VALokcPwG5710w5u+Hzn0S7Do1+jLbP+1Ox2x41WzS30zsTuAOYtvFE68uHOkbsFTrZiGWMDUM2Gg7AbkuUJ0Y3HRpDC6lIToA9sNuW9XVxzAxiHdoNn/9g4BmkP9dXHQoylxYCM7HbTNm16pGhpEMHMUmj00zkhTQRmEL0p8gNwP+inTRup7dinFUdsOfolNHA5Vmledf2dF3tjOwsxN2sLHtR7UX4/KNZ/JKDJW88x7Dxz5FzVaQvljXA97HEnrmd3tHIjuY3gN3lcawbfLUzsjdFjM0C4CvAlb2o1nfHcYfNTd0i59T0MePrT97xb0dG8XW6NRBYfN27rz0LTAO2NAzDjB3KnvH5LcBkYNKZv9529epgU84jky45rK/bQiwHfkjgaUdnfP5xSJ9E62KxGnn2pi4+cspzdkXiznZcOe7MuuYRu46ud+w4Bp9/BFI6Kdo4r9aQnIna9eyMz5+C9Ge0cS8B4MeELpI6Iu+nScg4jTZDzO+IrLEsCqLH5x+DlMyK1uWyEXn2Jh1ECpmV1dsAbsCOnB6eWZ+f+yk+fzqSiT3amMkgsmG4NKGbDe3IpugURD9Fy2/AT2YaxmWFBVYkNOQ6ZNF4M3B98ZlGE7Hr0IS8n8IihtJmRO9214zIGS60JCbcTm83HeryOHz4/GnIXIo2ZlIjboVL+mmMrtOhMdy9FPg5oZs3HRlkOjQs678O1chmQ30/jdGho0MHKUmjc4gz1+m17znKunADq1qRotTkrNK8sIut2hnZzyPZqrKyF9WuxciYj2QB3hyjwcxYqh5xO71XI6etu7o8jo9DMg0D5gCXIotwA7gje1FtK0BZYUFJ87jMG1omZgGMNQyjW3HbcBiGcQTwLHCaYRgJcbXriZzynOuRtNXDa4pq+kcJDkFyynPGIQWNTwPqgfN+m/LIpih1AzCqPj83cZsESZJEQGZl9SjgCiQr+CokXv7++vxcMxOK/KUoKyzYDdlk2gEJJ5hdXLHg24GVaujidnp71KEuj6N/NjGTJEmSJEL6KpmSZJBzcEbKBKUUNU2B5QfcsXdfu/tzEfffIzEyfkIS+lzaTwbnxkjyp6c7GJz7AXcDWyJuIOdnL6qt63Lrz9a16/4sG5LquU8Mw0gDSpEd34fjlT9KfkJOaDZigALGBzM55TkW4CQktncM4q5k1BTVrMysrP6/0GVZyIl4kiT9TsiV9gjgNmQsPgCU1Ofn9s+p9HpIWWHBOOSdfCryXvwn8GxxxYLkzneMuJ3ebjrU5XF01aFJkiRJMihIGp1DmLqSqjSlVGlzUP/yfXNwU7fTa3N5HP5ebvEC34I+E3ELWYws+PuDqxBD7OKQq+9twJHAf4B/ZC+qfTPMfXXWpnUHXhEbnUhSpS2AAsMw+tudod1ffypJo7MTOeU5ucjmx+5IrUdXTVHNlx0uaV8wJY3OJANCZmX1lshCfj/gC6CwPj/3g4GVauhSVlhgQepE34i4eJYBVxVXLDDVvfSvhNvp7aZDXR5HOB2aJEmSJIOCpNE5tHECm7dpjkSSkcwCTgl3cfai2mDtjGwPqFvXLk8hfWzbSRgNpsY+9YTb6d0GOFnpwN35vrOPQEq7WBFX25uzF9X2FpRdp4IBCASWYrXuHMn3GYaRgbjEVQKvxil+LHQ0OhObLGCIkFOek4Ekh5mNxDacCDxSU1TT9ZSjo9GZJEm/kVlZLYkz4CIkScg5wNz6/Ny/dAxOPJQVFuyAbDLthmR/PLO4YsG/B1aqoYvb6U1FxqVBBx3q8jjMLDOVJEmSJAkhaXQOUepKqsYghtU7I63qeWRX/gS303uBy+NYHu6+kZlrn2hcOqzs90Wjlk/efYXpmfPCcJMKBhr3+Nel+yHZ6V4GzsleVPu/CO5dDGBpWVsfHD4yIqMTWTSOB+YYhjEQrlvtRqdZBcSHLDnlOQo4Fsn4uRGS8fHSmqKacGM0aXQm6XcyK6sLkCQsmyLlNubU5+cuGViphi5lhQVjgGuQMl3LgBOAx5KutLHjdnrtSDKrbQjpUJfHEYkOTZIkSZJBQdLoHLpchGT6mpNVmqdxeucBpyMnSLeFu2nq3n8c+8tHY1TDD8NHrvxxxKhsw7Rixz3y6LEPHsEG0w7c7H8vkda6Kh2Ymb2o9uVI7y+uWLC2rLBgmbVp9erg8JHbGYaxkWEYYdP2G4YxBUn68bhhGIms1xWWmqKalTnlOQ3ISedflpzynK2RRdLewCfAwTVFNb25f1Ofn7s2s7J6GUmjM0k/kFlZPQ0pLzETqAUc9fm5lQMq1BCmrLCgY1mZCUh5rMuKKxZEnAAuSWdCpdFuBv4PyUA/0+VxRKxDkyRJkmSwEG1K8iSDgLqSqinAucBjWaV5nwO4PI5qpNbRLLfT2/NzNTLGApdnTGv8CK2GAccnSsbaGdkpX2Vvc3ZQWZ8etvYPJv/y3g3ANtEYnB2os65ZF/7T12nnNci4viyG7zGTH/mLGp055TmjcspzbkTi4bZH3MB378vg7EAdSaMzSQLJrKwelllZfQnwNfB3ZBMvN2lwxk5ZYcE2SEjDo8j7b+fiigWzkwZnbLidXqvb6Z2NxLb/E7gW2CZpcCZJkmSokjzpNBuffxSyYI7WoP+dyGu2XYvUCLq8y+/nIgrfQc8Jdy4GxozcqOUMZU25P3XChHO090OvskY1DFqRelhh60zVzsjeDZi3dMJOuatHT2H0yh/m5Hz1+S3RfMk6fH7L2Ixxy1KGDZuyasSoYEb68APw+b/v6dJ3f/jP9AkjRp0walj6w0W5uw3H58/otzpTIuuGQCbAdsM2+6NVB7bC5986gjuX0191pqC9dtcUYqsxVofd1mO5iJAr7eFIcqos4CHgopqimmizI9cBU0K1u6YQW42xn7Db+ifOSWp3ZQJjo7xT6uDZbSvNFyoMPn8GsDHR1xhbCfwS7tmbjhTinkr0dTpbkGcftt5xZmX135ET+K2A54Dz6vNzf45RTisy1kdGeWcQefb9k0xHxuh45PQxWpYBv4V7P5UVFoxC4vTPRcbJ6cADxRULYhsrsevQBqSmZH+N0VRkjEZbp7NPHep2eruVlXF5HMmyMkmSJBnSJOt0moUUNr4HcW9tQhaUkaKQxdVPwJ7YbWEX6XUlVbnAZ8DNWaV5F3X8zO30piMFx991eRxHdLrRyNgE2TF9ivx3XtPB4GPBlpYUS2pqo7Jao0mUYUGU7IXYbZ0y39bOyB4P3ACcGrCkLq7a48ZhQUvaTyi1s8vjiH4h4PNPB6oCgcDYQKDNisWqUQTTrCk9Jj9qCbSN1Fpb0qwpq5VSIEXH3wQO720RGjdixL2JnMI2A7o52DK8jUDKSMvwvhaVCtn8WQbsgd0W2+I3MjkVsmFxIZIoJdpnkoIs6h3YbZ93/CCnPGdL4C7gH8CXwJk1RTXvxyJmZmX1PAfBY5+wBFNDMkZb67R9jJ6N3eaJRYaI8fknAx8gi/k2op/36cBLwNEJLRotC+RngQOQZx/tiz8VWAPshd1Wa7J0nfH5ZwO3InMp2jFqAdKAk7HbHu/4QWZl9eRQu/8EvgPOqs/PfT0OOXdGNvdSkGcfDQrZTHkMOCWhhpJshvmQshotRD9G04D/Is/+9/YPQq60RyLhHJMJlZUprlgQW1mZ+HQoyBhdiejQ72KSIVJ8/kLgEaQ/YxmjPepQt9PbsazMYiRU5FmXx5FcqCVJkmTIkzQ6zcLn9yAxF9HueHdEdkBhh55OP+pKqhRi3OwAbJFVmtfNbcnt9N6I1MPcxOVxLF73gZHxGHAEtnmzGb3VXUR/etSVRuAC7LZ5tTOyLUjW3FIkJf5tvj1vaQikDL8G2MflcXijbt3nnwZ8itRxjMcNvBFYCBQk5CRRTjreQ9xI4+nTNsTw3B67bakZonXD578SMThHxNGKBlYBu2O3fZ1TnjMcKAn9a0ZO3901RTUxG1AXVH5239UqeOqIaM/iutMIzMZueyjulnrC5x+PuBBPJD6vkUbgNey2I02Rqyd8/vmIG2m8z34FYMNu69HbIG58/tMRIyYeOUGMlhOw257NrKxOBc5CyjalANcDN9fn54Y9aYpAzu2QeT86TjnXAE9ht50aZzs94/OPBPxIgqRoT+Q60oLEE+6E3ba6rLCgY1mZaiQr7b/ilNUMHRpAxugOCdvA8/kPAZ7ERB0aConpWFbmNuBql8eRLCuTJEmS9YZkTKcZiOFxCvEpS5Cd2gnAnmE+3w9ZOF7Tk8EZ4h7kuZ627jdGxo5IcofbGb3VycSvLEEWhXNqZ2TviJz03At8BeR693aXBlKGXwC8FpPBKRyG9Ge8Y3QEsA+xuZVFwgwgh/j7NAUYhTzjRHEu8S/mFSLnMTnlOQchz/wK5BRtek1RzR3xGJwAZ6jgdiYYnBAao6a01DP7AhsQf5jCCGAmPv8G8YvUAz7/OGB/zHn2I5G5mSji3RRpZzhwQWZldR7iGVKGnPZtU5+fe01cBqdwPDIP4mUkUBRyJU8EuyKnkPEYnCCnnZNWr1llLyssuAb4N1IG5WwkdjNeg9MsHWoNtXFwnO30xoWYqEPdTu8OwPvAfYR0qMvjmJM0OJMkSbK+kTQ6zWE80btXhcOKxLF1oq6kyopksPseyQjYIy6P43vgdeB0t9ObipGhQvctQ04iJ5skJ8HW1ilIVtJNkZT49uxFtf8GLkVOAC6Mo/lNiH+h1E4ziUtMMxnznn06iZJTXGvHmNSa5cOmr08CFiB9u09NUc1xNUU1ppSYmCCGnFlkmthWV7Iwb4yuxcS52YWsUPtmwmokdAAAIABJREFUkIbM90Qx0ayG/tDkAO8i76JDgYPr83PNOqHdnOjjYsPRgpQTSgTmve+DgdQP/b6HkSRtTwPTiysW3FVcscCM95+ZOjSdxJarMq1PAwE9BTmJ3oyQDnV5HMk6pkmSJFkvSSYSMgdFmPiTQCCA7YwTmDx+IgtK/6xkctYdN/PQay+z+vV3e2qrp8XMCciJWmFWaV5f8YlzkTpehyAuPA7gbIyGhpDx0YlphTMZPWIEVouFFGsK/nsfofCqi/nmpx8BWLF6NWNGjaL6gSc63xgMpiAuVpdnL6pdAeB2ejcDZgMPxak8e9wQiVFWjXkLxK702G5PchoP3ct9r7zIhAyx/a4/zcWBu+3Rta1EydkjPcl5+QPzmP/+u1iUYuLYDXm45Eo2Ht/5oHhVsDETcam9raaoxtR42WHiZh6RrNX/+QbnraWsbWkmxZrC3PMuYpfsbTrelsj+7PF5xSjnoBijEc376BNQxSVrrHI2y0nS9cB19fm5PcaBx4GZ7ydI7LM3pU/bAoF0i8XyB5BfXLFgYQLk7KZDY5xLkNgN9Yj6E+Cu5yu4+4WnSbFaOWi3PbnJeXan+4KadTrU5XEks/wmSZJkvaZPo1MptVprParL7wxgtdb6FqXUw4ib2WZa62al1HjAr7We1kNbw5FTOAdiQM1DTjUCwHVa64rQdQ8DdlhXQ/JErXW1UqoA2FlrfWUMf+uAcMdzT5G9yaasXLNm3e/8i75mxerIPWfqSqpGIAlgPgKeieCW14AfQZ+JnBx8h7jdhqXyNg/jx/x5EFZx5Q3rfi6eexsZI7t7kqnU1LXZi2rP6vLr65Ed6ysikDMmYpF1IOgqJ8B5Rx7DBUcnrFJNTHSVc87Rx3PNKbMAuPO5p7i6/H48xRd3umf34du8XlNUc2Mi5EkLY3T2JOuF99zFlSeeygG77sGrH77PhZ47WXhHr0O9Xxiqcg6VuRSJnOPh1/r83Ev7RcAOrK99arVYW/bafT/DcdkVC/tLRhi6c6nycz/z3/Px5QNPMiwtjaXL/+h2T4qVtS6Po6sOTZIkSZL1ErN2AwNIEHxfnAw8r7UOICdwJ2itt0HijW5XSnVcoc/RWueG/lWHfvcKMFMpZUbMT8KpW/orr3z4HqcedMi63wUCAeZ47uy249kH5yJlDi7IKs3rMxmOy+MIAPeAyl/eNnkb4GKMhphOo7TWPF35Nsfs849unymLpVPWPrfTuwtQCJS5PI5fYvm+eOhN1iSRs0GHxeaatU2EMgF3YpRluNknR+uwqsiz1Sql1m3oNKxZ3e1EdrAwVORsZ6jMpd7kTFWmuWuawlDvU6vV2pqSkhJtJmnTGSpzad785yg5tohhaWkATBy7YbdrlFL9U94lSZIkSQYBZhmdtwPnKaX6Ojk9DpgPoLX+Vmv9n9DPvwBL6SPZi5ZUuwuBgngF7g/OvftWbjrjbCzqz26++4WnmbnHXkwaNz6iNupKqiYibowvZpXmvRfpd2867KMnLbTx2ZrDlyC16MKilGK/ObPZ6fTjuffl5zt9VvXl52w0dhxbZk3t9fvcTq8CbkGe402RyhktZsjaH4ST8+4XnmG7k4/h5BuvZvmq/ivPGI5wcl56/1ymHHUQj7/1OleffMYASvgnPcl6++zzmeO5kylHHcQF8+7ghtNcAyzl0JaznaEwl2BwyQlDR9ahLOdQmUvf/vwjVTXV7DrrROznnM4ni74aYCmTJEmSZGAxK6bzJyR9/PFILGE3lFJpiAvuDz18tgt/1gJr5zql1BXAO0CJ1rq90LsfyEMSGQxaFnxQxcSxY9lpejYLP/8UgF+W/cYzC99h4e1RlQ68AolNKonmpgPHlh7/5orz+KbJPnpRk2OES1Lz98j7d9/PxuMnsHT5H+x7wWxmTJ3GXtvvCMCT77zJMftElFD1YOS5nJnIrHsmyZpwepJz1iFHcPkJp6CU4vIHPRTPvZ0HL0qYF3LMcu61/Y5cd+qZXHfqmdzw+EPc/cLTXHXSwBuePcn6rM/Lba7zOcLu4OnKtzjlpmt4+9a5STljlHOozKXBKCcMHVmHspxDZS61BQIsX7WKD+c+xCeLvuafxiV8/+SLPXqOJEmSJMlfATOD7a9HyhOEa3M8Uj+rE0qpScCjwEla63ZXk4uRUhQ7AxsCF3W4ZSniajqoef/fX/DS+1VMK5zJ0VdfgvfzT9jmxEK+W/wzWxx3ONMKZ9LYvJYtjg1ffaCupGor4Azg3qzSvG8i/nIjYyPgwi3S3/dprKOAo3u7vN09aeLYDTlsz735uFZ2ZNva2ni+qpLC/H17/Tq305uC1Bf7Brg/YjljIF5Z+4ue5Nxow3FYrVYsFgunHXToOtkHknD92c6x++zPc75Yq96YS0+ylr+xgMP3ygfgqL3/zseLvh5IEYGhLScMjbkEg09OGDqyDmU5h8pcypowkcPz8lFKsUv2NlgsimUNyVxBSZIk+etiWvZarfV3Sqlq4J9hLmlCUpmvQym1ARKneZnW+sMObbWXXmhWSj0EXNDhtvRQW4OaG06fzQ2nzwZg4eefckvFY52y1wKM2n8vvnvihd6aKUVKHVwV5dcbQPomwz47HXGtdbmd3gddHke3eNA1TU0EdZDRI0aypqmJN/0fcsUJUqf87U8/ZsbUTcia2Gc2/1ORTYJDXR5H2CQw8WKSrAknnJxLfl+2zq36hfcWsu2mm3e6L6i1qmz8bNdzy08602yZFIovpz0YkZz/qftpnWvdSx+8y4yp08wWJ2rCybrxuAn4qj9j7x12wvvZJ2yZ1bnaUFugbdgdhQWm9yfAwf/45+5bbjbDojq4z8cqZ38Tz1zya7YpqKxOSJ/+rEhN7XAQNFTmPMQuayAYSHn6xYdP+GWuYbpFsk/egbvmbL1TqtX6Z8LhodKn8cylul/bcuc7vQkZo6cdOXp0WodBGk7OUcNH4P38E/beYSe+/flHWlpbGZ9hVtWqJEmSJBl6mF0y5TrEiOyG1nq5UsqqlErXWq8Nudu+ADyite6UkVUpNUlrvUSJH8qhSCHqdrbq8v/1krqSqj2QIuyXZ5Xm/RrxjUbGDOA0wGO96vdvcXrnIiVUdkGy33bi1+W/c9jlUk6zLdDGsfvsz/67/g2Ap7xvcoyj96QXbqd3NGIUvwe8FLGcMRCvrP1FODmPv+4Kqr/7FqUU0zIncU/xJZ3u0wQtNS3/OwQpdWMquoeKPuHkPOKKC/nmpx+xWCxsslEmnvMv7nZvfxNO1lHDR3DO3WW0BQKkp6Vxb5c+bWtrHQ64EyFT/a+L2XzadDqs52OWs7+JZy79G5WHuNKbTjOQapKc/U2ssgYDgbSVqxquS4RMS5YuZuvp29PR6BwqfRrPXPrtj8Dfgb8nQq7mFk1HozOcnC2trZx849Vse2IhaamplF9sJF1rkyRJ8pdGSW6eXi6Q7GodM5HeipQ56VgyZYHW+tnQ9c8DO4YpmfIA8KTW+m2l1P8BDwEd/fnaS6N4kaRCCqgGnFrr1aE2FgAXa61rYvmDE4LPnwl8Dww3obUmrfX5i19fewJS4HrLrNK8yDOFGhkvIiVpNsdo+C1kFP4CPOfyOE7E5/8RMCtDRKP7yZW3IHGnu7k8jm5Gbcz4/HcCZqWSbwD2wW771KT2/sTn/wdQAWTE25TWOvBHcFXp3j+fc2f8gnVmuErj403uiXzzom+ewW4L59UQHz7/x4hrfdxorVfeOu+qLc1oqyvOEy9wjRg+8pIIEqhFwkpgd+w2830Fff5cwIe8t+NmlebhLXXKRX1fGT2/qLbvLYqRJjW3GLsty6S2OuPzv4hJm0Na68YvvvL/7Z13X1nS99XRcer/nXPUBqPH3KiUMqNP1wBnY7c92OeV0WKuDmVti577wHOrovUQiohZhaM/s1jUZJOaa8RuM2u8J0mSJMmgps/Fkta617hPrfWJXf5/eC+X3w2cD7yttX4MeCxMm46efq+U2ggYPqgMTqEF8wqmB5qWBHKA3YFTozQ485CF0KUYDb8BuDyOVW6n91HgZLfTW+w6ZoOYSqf0hNa6DXF9ftpUg1NoQoqFm7E1bEEOURKBaf2plAqMs27wR01RzVKz2lyHz6+Q0kZmjdOElUzBxGellGourlhgfn8C+Px/IH1qhtFpJXFjtBlz5hGAHq34vX7v3ET1aQuYZnQmqj9Bwh5MQSllzd12519yXbN+M6vNdfj8vwNmleUIktj3qFnvJp2epn53eRyJGqNmhvckLBwlSZIkSQYbZiYS6hOt9edApVIqVuUyFSg2USSzWI5JxofWWq35OXAI4kL8cMQ3GhntJUsWIyVsOjIPGAacCHyNOYsQvWqNbkW84RLhK7gI82J3hwH/M6mtrvwn1L4ZtIbaMx+7TSNjwwyagYRt/DS3NH8bCAZNGaMkqj+FbzFv0ZiKZAFPBD/QJZ4+DpqQuZko/gs9+IJHT4AEytncsrY2GAyYVbMyACwzqa2ufIt5xpw11F4iWB4MarP6sxFJapcoTNOhyOlukiRJkvwl6FejE0Br/aDWsSkXrfUnWutqs2WKG1nQH038RtKalhXB6uY/gpOBC7NK86Lpp6OQuM3LMRo6nUK5PI4aJOZyVkurPgf4DVnoxIoOar3m1arGscBcl8fx3z7viJ7HgX8Rf582AbOw28KWjIkLu60OcS+O9+SvEXg19C9RHEMvpXMiZC3wBRBV3Z9IKCsssJQVFpz+4ON3HrZq1QpLSx+u/32gkWzZp5gjXY+8gZSIivfZNwEnYbcl5tTDbmtCkn2ZMZc+RrKNJ4oTEXf4eB5+APgDmG2GQB0pKyxQZYUFR99bfpvzt9+XWgOBuA3PJuDokA5JBJ8D9xL/vF8DPICULDMVt9M7wf3kygdfrWoa3tYW36RH5mIViS2pdjYm6FDEpf5EMwRKkiRJkqFAnzGdSaLA598PMUCmEsagDwZ1SuNaPdGiaBuern5TSmlEAf0ebNWv/fL22tOQONZ9s0rzIns4RsYwZPd1DbADRkM3Zeh2eo8BngD2dx2zwSLE1Xlroj+lawX+89xba9LrlwW2AzZ3eRy/R9lGZPj8aciprR3IWNO4eiOLsrQOHz7ijwjubj/ZuwO77cmEyNcRn/8MZGGfSXSujO3G0etACXabWbv9PePz7wbcAGxK9G6hq5GNgNlmG/FlhQU78WfCq3enb7Htpen7HvnKtmDNVN1LLfVBC3Iicy52WyJP5cDntyDlog4ExhLBsw/qoKWluXmDltaWkY1Nq1vrf1189ztVrxYXVyxI6Ms4sNBf+F+YNwrGbgjL0lVUrpIrgXeBc7DbEum2Cj7/NkjugC2R+s3R0AzUAmdht5nq3VBWWDADeW/uA3y20YRJxf931BknI2N2VJTNBYGfgWuw2143U85uiGt9CbIx2Z4rIVI0cgr7LHC9mcax2+m1IknvrgdGA2UnHjLq/ZEjLBfRiw7thQbAC5yfsA2cdnz+TYhThwLF2G2DLVQoSZIkSRJG0ugcANxO737Aa8gpyeEujyMIUFdSdQOyONgpqzTvs4gbNDLOBW4D9sdoeCPMdw5DFjkfuDyOQ+OU3wG8A1zk8jhuiqetaCgrLHgd2LC4YsEu/fWdSRJLWWHBWOBaYBZSg/cC4PHiigU6s7L6dWDD+vzc9fJ5lxUW7IK4vu+ILJZdxRULEmYkZ1ZWX4/UQD6nPj/X9GRV6ytlhQUjgUuRsdkY+tlTXLEgsRtE6zFup9eGjH0bsBBwuTyOgS+4mSRJkiRJEka/u9cmAZfH8SYSm3oIcDVAXUnVFOBc4LEoDc4xwOXAW+EMztB3NgP3Awe7nd6Ys9e6nV4LcDMSf9bfC9c6IDHZKJP0KyE3xROQ2CsnUtZkRnHFgsc6nPjVAQNb0DKBFFcs+Bg5JXMBOwFflhUWXB8yckwls7L6WMTgvA+4y+z210dCY/RQxIvkYuBJYHpxxQJ30uCMDbfTO9bt9M5D3LSzgOMAR9LgTJIkSZL1H7PrdCaJnDuAbYFL3U7vvw8Zk3oA4vZ0WZTtXIy49V0YwbX3ICeppyGGaiwcg5zMHO/yOEzL4BghP/P/7N13fNPV/sfx1+kCimyEoiw3ImrVoDgugeCAay+K4q2K6ypqNI6rda9b1xVH9TqCcYv+BKteZ1UcpEZUvBq1chXEdRVREAQsq9CR8/vjfNHSQTNOmgQ/z8ejD0qS7+m7Wc3nexYUlBUX5ZaUV8iqfxmqrLhod0yR+SfM3rFjS8orWjrRsgjoW1BZlbdkdKG1VYLTiVO8TC0rLvo3cAvm9TyprLjofOAFG0NuCyqrhmPm470NnLNkdKEMb2lDWXHR9pji/M+YRd1GlpRXzE5tqszlnKw8GfMc74k5YfkPX8BTndJgQggh2o30dKaIL+DRwNnAO92yeVRrfSJwZ/8pf/o+6kZKuw0Czgcep7S6zQWWfAHP98DLwOl+bzDWuVL4vcGOmPk3n2Dmh7a3RZjCvF8KfrZIUFlxUZey4qIyzPNnN8zJjwNaKTjhD/R4l5RX/FxSXnEyMBIzf/I5oMIpfuJWUFm1LfACsASYuKUW77aUFRd1LCsuugbTuzkSM6R2byk44+f3BvfAnPB4GDOXcR9fwPN3KTiFEOKPRXo6U8gX8NT6vcGjh3XK/r5Oo+fV1D8S49jRGzALPcTSOzoVs0LqkcS+wt85mAUeTt04D7WdLXL+7U/ytpcQlpUVFynMIiZ3ANtghnlfXlJe0dY2EY0f7+hPxmSwkvKK2WXFRXsD5wLXAvPKiov+CdxSUl4R08iCgsqqTsDzQFfggCWjC+3vA7kFKSsuGotZHGYHzHvjhSXlFba2GfrD8XuDXTHP4XMx24qdBjyaor8dQgghUkyKzhQ7onvuXkDHz2oaar+v1dP83uAoX8DT9tYGpd32Bk4AplBa/UMMP/I1zJ6VZxND0en3BntiFtB41RfwzIrh59m0sQjZYuf5bWnKiot2wXyQPxjTw3l0SXnF+1EevvF5/Yeax+sMHb+9rLioHCjDfHA/qay46JyS8oqoVjotqKxSmCG1LmDCktGFc5MWOMOVFRcNwKySfRRm1eNDSsor3kxtqszl9wYVZguxMsxq3vcDV/gCnmhWHRdCCLGFkuG1KbTostnZmEV5vl1YGzkBs6jIA84f7daVdlPOccuBKbH8TOcscwBw+73B3WI49CpMj0k0c0eTpXHPl0hjZcVF+WXFRTcC/wWGY3o7hsdQcMIf/PEuKa/4saS84ljgEMyegK+WFRf92ymS2nIZZv71VUtGFz6fzJyZqqy4KK+suOhS4AtgHOak2h5ScMbP7w3uCryJmX6xGBjhC3i8UnAKIYSQojO1TgR2By4/417P05g9PifRdmE3FvAA11FaHc+8mIcx+9l5o7mx3xvcHjO09lFfwPNZHD/PllWYvSL/kEVIpigrLhqPmRN3BfAkZsXPe+JY8VMeb8ApgvbAFEXjgC/KiosuLSsuanFedkFl1XjgRsx9/892C5pByoqLRmH2Q56CKZKGlpRX/LOkvCK5e5BuofzeYGe/N3gT8ClmJWYfsK8v4PkgtcmEEEKkC9mn06ZQuAA4GtiONoYu6wads3ZRw8kqizX5/bPLlVLLIlrPuffJ1WcAfwWO8AU8LzU7sLRbNubDUkdgN0qrY18YJBTOnfPp+soOuWr4HjvnPZiToza7EuxX39eOW7deb7/z4NxHO3XI+hnTM/Akbld9zD879qwHA/sDvQDmzvvoxLzcDsuH7DTslTaOjGDmAb6E2/VtklNCKLwrpiAYQOybry8FZuN2vZOMaJsIhXtgnl87ALEuJrUas5Ln07hdzeZlOYve3AkUYYrOs0vKK0Jx5swCJga08hfAyiOVbuvxbqwO+AZ4Crcr+T0sofCBmEVn+hD7Y/8DMBO3K6otI8qKiwZj7uPxwHzM3p6VG68vqKzaHXgP8xoduWR04e9D9UPhwZhtmgYR+wnH5cB/cLtej/G42IXCnTHDM3fCvM/FYhWmd/0Z3K5mf9zKiov6AbcBxwPfAeeWlFdUxJlTAX/BrObdPcaj652f/2/crsVx/fxYhMJ7A2Mwc6pjfY4uBmbhdn3U9EpnVM6RmOHJA4FpwCW+gGdpwpmFEEJsUaTotCUUHoDZ/qEHsX9QAvMhpLa2Tl/1wDOrJwG7APs361ks7XYaZiGWYyitfiaOnLnAi5GIHgV0zMqK5fMHYD6ErAMqgQlJLTxD4b9jemw6ANlxtLABqAEOwu363Ga0TYTC+wKzMEVczKsC4zz2gBe363Gb0TYRCvfCFCQDgE5xtLDxsX8BOHFj4VlWXNQRuBjTs9kAlAJ3xr2tjSk4HwGOjmg6x/4UBczj/hMwArerrQWL4hcKH4/Z+zKP+ObI1zpfh+J2zYn2oLLioiLMlh6DMUMZL7rVe0M9Zv/DDsDwJaMLf18EJxQegnns853rY9WAeT39A7frtjiOj44pOIOY7aQ6EVuBBOZkUw1QDkzeWHiWFRflYHrfrsO8P98M3FRSXtH2/PmWcypML6nPyRnPqKH1wK/AfrhdyVsYLRQ+DHgWyHW+YlXnfE3E7Xp144V+b3AHzHNwHOZk1Nm+gEdW+RVCCNEiGV5rTxDYmvgKTjAfWPPzctUNR47JvxkzrPBFvzfY+7dblHbrDFwPzAH+HefPuQ0YmZWl4ik4wXwI7IwZ3ntTnBnaFgofiik484mv4ATz4bob8LZTbNsXCnfDFJxbEV/BCc5jDwQIhV22orXgBUyREk/BCb8/9kdi9pOkrLjoMEzP0nXAS8CQkvKK2xLcR/UiYCLEXXCC+R0HAPH1YkUjFN4LU3DmE/+ibHmY587rTi90VJzeuaGY94OJDVnZX3RdvXIOWvcDjmxScGYDszG9cfEUnGBeg/lAKaHwuDjbiMY0zFDifGIvOMH8TdvYU3o+QFlx0f5AGNMb9z4wrKS84pq4C05jEmbKQWfi/zvaEfM3460EcmxeKDwIU3DmE1/BiXNcPvAMofB2fm+wo98b/AfwOWav3RJgbyk4hRBCbI4UnTaYs/ODsbMacN62fXJ2ACZghkI97fcGN35YuACzZ+HFlFbH20U9GvMBIlH5mMIzWfYn/g/IjSnMB/tBFtpqya6YXiAbFGYxKftMz4yL+AvjxvLr6ur+XFZc9AwwE9MDelhJecVfS8orFrVxbDTGYOc5mgfs5fzuyTDcYlsNmCIyaiXlFTUl5RXX1GdlD6sYc8yqVV167HDI7Bd/vjhwVdP3oY092zbuh06Y12ayHEj8J+4ay6+vrxtXVlz0EKaHtzdm256xJeUVX1lofxR2nqPZQH9C4S4W2mpJIaaX0ob6+d/Wnorp1SzFbMczxBfw3O4LeGz9DCGEEFso2TLFji6YP+w27s8coJcv4Hnf7w2eDjwG3EVpt1LgUuA5SqvfTaD9bhYyJqOtpnoRfw9nUw0kL6vNdvMst9eUjSIegJXVy0dghoVeDdxqeQGWqHv8opDjfCXjQ3F37BTxG8X12N9xxrWHAf23WbLw+cJ5Hw4H3i0rLnoYuKykvGKZk9PWiZEsTO9csmxlq6Fly3/eeFLsVuC6kvKKNbbaxhSxttRhHvvVFtvcqBuW3kfrG3TnxcsargIWAIf4Ah5Z5VcIIUTU2iySlFINmOFzOZj9HU/UWv/qXLcbZk5Hf8xZ9MeAG7TWWil1CmZe1sFa61nO7Sdghvoco7VuNh9RKfUv4Fmt9dtKqScwPTN1mHlKZ2qt65zbjcIMlcoFftFau5VSeZhVCD1a6+QvcBOFwcXj6ZKfT3ZWFjnZOYTvf4wVq6opvvYKvluymMEF/Xiq9CZ6dOna4vG+gOdxvzc4DLhkce0uQ/vlLeiI2Qoh6TmrvlqA9/YprK/dQE52DlMvuJR9d41lh5XkaCnrp19/iff2KaypWcfggn48cdX1dO1s7bOr5GwhZ25O7hqgsKS84n/pnrW9tWfOgsqqMZj3wpd+Khg4EdMTeQ1mVMSEsuKiy88/48oPcnKaj6zM5PszmvennOycdcABJeUVyZvPHUXWTLlP28qpI2T36p71LHC8L+CRVX6FEELEJJqeuRqtdSGAUmoaZuGEG5VSnYAXgbO01q8rpfIx8wzPBvzOsf/F7BU3y/n/sZgl1ZtRSvUERmit/+5c9ARwgvP9dGAycK9SqjswFRirtV6olOoDoLWuVUrNAoqdY9NC5R0Benf/fWHDKdOnMWbv4Vw26RSmPPEoU6ZP4+Yzz91cE1f0yf1q3765X41aWb/t8z1umPdle+S85L67+ccpkxm334G88v67XBK4i7fuvC8ZPzpmTbNOvvUGbjvrfNyF+/DwKy9y65OPc/1pZ6UwobEl5+zRvdc37VlwJpI1FdojZ0Fl1U7A05iVaictGV3YwOiKNcAlZcVF0zDvw4GnX5j2efGEU7OysprPpsjU+zOa96fevfp+1p4FZ2tZM+U+bStnTg7r9tylw8t7nnGgFJxCCCFiFuuczjnAts73xwPvaq1fB9Bar8MsrNC4J242sK9SKlcptRWwI2a7j5ZMxMwPw2nvFe3A9HRu3KvveExv6ELndo2XZn8es8BD2nrh3RAnjy0C4OSxRTz/zlubvb0v4Gk4qucVaxp0XuS5Fde7nRUDk04pxaq1awGoXruGbXonc0RdYhb8sJCRe+4NwCGuffn325VtHJEaktO+TMlqO2dBZVU3zEm/CDB+yejCTYZmOsXWaOCEhkhDQV1dbVRda5lyf0bz/qRUsqbyxiZT7tO2ciqlZKl7IYQQcYu66FRKZWMW+HjRuWg3YJN9u7TW3wBbKaU2jhfVmCGvh2H2h3uR1h3YtD3n5+YCJ/J7Qboz0EMp9ZZS6iOl1EmNbv4Zdhf3SIhSikMvPod9zjiR+196FoCfV6ygXy8zHahfr94sXbly842UdvtTtqovqol0/VdNpIcGXvJ7gy2Px7WY81/nXMjFgbs1ClsvAAAgAElEQVQYcMzhXHTvndx0us/mj4xbS1mHbbc9L777NgBPvzWLH5b+nMqIgORMhkzJmuycBZVV2cAMzEm8iUtGF7a4D21JeYUuKa94YszIwydkZ2c32883k+9PeX9KTKbkFEIIseWIZnhtJ6VUFWZ11o+AN5zLFaaobEnjy58EzsMsaFCC2cuvJf2AZS1cPhV4W2u9cTn2HGAfTAHcCZijlHpfa/2l1rpBKVWrlOqitU7GogwxefeeB9mm99YsXbmCQy46hyEDB8fWQGk3hVkE48euOUuvBl4GXgdm+L3B8b6Ax8riIC3lfCYU5A7fhRzt9vBU5Rucdsv1vHn7VBs/znrWhy+5hvPuvo3rHnuQ8QeMJC83ObujSM7UypSs7ZDzZszeiGctGV34Vls37te3/2rMnpCbLHqUyfenvD/9MXIKIYTYckTT07lxTucgzIeWjaeUP8cs9PMbpdT2wJrGBZ/W+gPMRt+9tdabm49YQ5Ol8pVS/8CslHhho4sXATO11mu11r8AbwN7Nrq+A+YDVsptHPLVp0dPJhw0ig/mf07fnj1ZvNzsVb94+S/06bHZhTqPAfYDrqa0ep0v4AliCvg/Y3GPzJZyTnutgqNGjjYhRh3MB1/Ms/XjEtJS1iGDBvP6bffw0f2Pc9yYQ9lhm23baCX5JKd9mZI1mTkLKqtOxpy8m7pkdGEgXXPaJO9P9mVKTiGEEFuOqLf40FpXK6XOA15QSt2LWaznCqXUwVrrN52Fhe4Cbmnh8MtpuxCcjxku9haAUmoyZljuGK11pNHtXgDuUUrlYIrg/YA7nGN6Acs2rnKbSmtraojoCF3yO7O2pobXw+9zzUmTGX/ASKbNrOCySacwbWYFRxzobrmB0m4dMIXlfzGrAgPgC3imOivaXuz3Bj/zBTyPtdxAYjm36bU1oaqPGbXXPgQ//pCd+g9I5MdY0VrWpStX0KdHTyKRCDc8/jDe8Udvclxt7Yatnn35icd+nFq6znamnbbftetYz5Fb5eX9vhtJvDkjkUh2uOpd3+yppUfZzglQcnbpJv+PN2cqxJtVa50VeGr1nMiMoPX5aPvt0aFg713zsrOyfp87mMz7tKCy6gDgfiAI/L2Nm29WIjnnfTn36FenlrpaaDZh551+RX5u7u8dsn+E96f6et1xxitrKlbNCFr/u7X7Trm99i/smJ+b0z7PUSGEEKI1Me0rqbX+RCn1KXCs1vpxpdQRwN1KKT9mL7DHgXtaOO7VKJp/GTgTeND5fwD4HjN8FsziQddprecrpWYCczGLaDyotf7MOWY08Eosv1Oy/LxyOROuvgSA+oZ6jh8zlrH7HcDwIUP567WX89ArLzKwb1+eLp3SWhNeYHtgLKXVTYfRng8MAR7we4Nf+gKe923n3KpTPuffU0Z9QwMd8/K4v6S1UdHtp7Wsdz4zA//zZgeeo/40ir+N+8smxymldFZW9gpgle1M2dk5EZossBFvTkBnZ+esAZY2vSIZEsjZ7hLMupTWpwLELTur+Z6SybpPCyqrBmC2m/oBOGbJ6MKECpREcmZnZ68jWc/RKF9LW9L7EwqtstRy0NZH6GRnq1zV5LmfSa97IYQQWw5lFodND0qpd4CijfuAxnH8s8DlWusFdpO1IRQuAL7FzDFNXP26u5n9l0nAx8ChlFY3e5D83mAvzKq+nQGXL+BZFGXW74GBVnLCN7hdO1pqa1Oh8F3AZveSiUE1MAa3q9lCVQkLhQ8DyjFzlhPVAFyN22Vt6PRvQmGFOUljyye4XXtbbO93ofAH2FsQTAMdcLvsj34IhS8BbsDsF5yoauB43K5mJ80KKqs6Y1YC3xEYsWR0YWxjSUPhQiAE2FqA7F7crrMttbWpUHg1NC/m4/QebteBltraVCj8PGZxPBvWAbvgdkX3Hh6LUPgkzNY5Nu7TtcB5uF0PW2hLCCHEH0ysW6YkWwlxFkRKqTzg+XYvOI1qmizSkYBalr61O9ADuLilghPAF/AsB8YD+cALfm8wP8r221guNyYrLLbV1FKg3lJbOSQvq837cwPJvU9rLLb1i8W2mlpusa1a7D2PmlrhtG+zvU0UVFZlAY8ChcCxMRecv7cb06iWzWgAkrmsqc0F4JI5YsDmfZCH3feRxlZiHjMb6rH72hRCCPEHklZFp9b6P1rruXEeW6u1Tmh+Y9zcrhrMdi2JfwDVkQYWv3oA8Dil1a3taQqAL+D5HLNv6V7AI35vMJqN6V7GnLFO1FqnrWQJYecDfT1mWO1CC221ZB6mB9HWkIH3LLWzKbdLY3rLbAzhWwu8ZKGdZvzeYNaX39X+UldvZQjGeuAd53dPhvcwq3gnSjtfLRWUV2P2ML50yejCeKcOLMIUnjaKjw2Y12ayvI7p+UvUOpL0/uT3BtVnX9VW19t5jtYCX+B22XhPbslHmKkvNmTTwrZmQgghRDTSqujMcAdj5qCuwXyQqIvhqx7zQX4d39z/IavmaeCqaH6oL+CpwCzU9Ncoj7kaeN7JuT7GnHXOMWuAp4Dro8kYF7drNmaO7zrnK9acdU7OFcABuF22zvY3zbkGOAj41fl58eRc6/yOxbhd/01KTuNozMmReJ6jdZie0rXAA7QwdztRfm9wT2D2G3PWn/DV93U/aa1rgBqtqavT0KBpiDJnrfM7zgMm2M75G7drHuY+XYe5X9rMprWui0R0pCGiaYhoIhG9Xmu9CvgTbtcmc44LKquOBkoxC4ndlkDOCGYf5GXE/xzd+Do8G7frrbiztO0MzMmRNZgCN97n6L+Ah2yH83uDOwGvhsLrL/54/obFEa3XOz8z1pwbn6M/AB7bOX/jdv0EHOL8rHgf+43HHpaUIcBCCCH+ENJqTmfGC4W7YVbcHUDsBX01C5+q5pv7ZgC3UFp9WbQHOj2cjwEnAEf7Ap5n28iZBRwK7IzZYiYWG4AFwBvOh9nkCoX3wszvi3XOpAZ+BGbhdiV/YZ5QuD8wCrPfbKxWAnNwuz63mqkloXA+MBaz726sPSA1mEKu0mbvod8b7Apch5nDuxy4ZIcBOY+NPSjfDewW0XS6UWf9c3ul35mkdDS9fQ2YE0Cv4nZZX7G4mVB4KLA/0DPaQ35e3jBo4eL6Cb+ujmyzdHlD6NfVkdN9Ac9XG68vqKwqBN7FLJg2esnowsR7qEPhrTEnx7Yl9h7aVUA4KfOimwqFczHP0R2Ifb7sOsyJlbctP0c7AZc5XxswJ/im+o7rOhwz9DnWOZMRTMH5Om5XXGsYxCQU3gFzcqxPHEcvw4wY+NpuKCGEEH8kUnSmi9JuCngD8wFmB0qrq2M53O8NdsRsN7M7cIAv4PnUekYhLHJOlhwHlAF9MStWX+kLeJrNbyuorPoa+GDJ6MLj2zdl8vi9wRzgbMxiRB2Am4Gbri/u2RX4EFMYDl8yunBJ6lIKvzd4OHA3sB0wHbjIF/AsTm0qIYQQIrNI0ZkuSruNw2z3cj6l1XfF04TfG+yH+bDaAAz3BTztsvWGELHye4O7YlbVHA2EgbN9Ac+Hrd2+oLLqLSB7yejCP7VPwvbjvG5vA46vz+J/d/6l+/p1HbMGAwctGV34cWrT/XH5vcFBwJ2YVWrnAz5fwFOZ2lRCCCFEZpKiMx2UdssGqoCOwG6UVse9gI7fG3Rh5kSFgTG+gMfm6ppCJMTvDXbGzCsuwaxUegXwgC/g2eyc24LKqv8DDlwyunC75KdMjbvPCo5+bkTnZ+cN7NB97EdrPxz+9YZjfAHP96nO9Ufj9wY7YJ6fV2GG6V8L/EveS4UQQoj4SdGZDkq7nYpZ9OIYSqufSbQ5vzdYDDwJPAxM9gU88iCLlHKG0k7ALPAyAHgEuNQX8CyL5viCyqopwIVAxyWjC5M/lzgFCiqrLgBu3/nH2lnF76zZHzO89gagzBfwbEhtuj8Gvzc4BtMDvwvwLHCBL+BJ1srXQgghxB+Grb3bRLxKu3XGrAL7PvBvG036Ap5yvze4O3Al8F/MB30hUsLvDe4I3AWMwyyMc5wv4Hk3xmYWYRaV2Zrk7hOZEgWVVeMwQ2yf/XLbvGOA/sDtwI3ASX5v8BxfwPNmKjNuyfze4LaYucXFwDfAOF/AMzO1qYQQQogthxSdqXcBsA3wV0qrbfZIXgPsBpT5vcH5voDnNYttC9EmZ8XPSzErftZinuv3+AKe+jia+8H5tz9bWNFZUFk1BDMy4b/ASUtGF0YYzUJgot8bHIvZnuYNvzdYDpT4Ap4fUxh3i+L3BnMxqyZfizmp8Q/gFl/AY2M/WyGEEEI4ZHhtKpV26wt8DbxBafVRtpv3e4NbYbZdGATs5wt4Ftj+GUK0xO8N/hmz4uf2wAzMip8/xdteQWXVPph5ykcuGV34gp2UqVdQWdUD+A/QHbNSbbM5nM7K1Jdg5r/WYQqju30BT117Zt3S+L3Bg4CpmBW/XwHO8wU836Q2lRBCCLFlkqIzlUq7+YEzgaGUVn+ZjB/h9wYHY1a0XQGMaGk7CiFscVb8/BdwJPAFZsXPYKLtFlRW9QWWAOcuGV14T6LtpYOCyqoc4FXAjdmLc7NDjv3e4PaYYcqHY3pFfb6AZ3bSg25h/N5gH+AW4GRgIXA+8ILMfRdCCCGSR4pOm0LhYzGb3BfQ1ubrOpJFw4Z8srLryMrbAKzHrGB7PG5XVIurRMvvDf4JmAVUAof7juu6M/AosCOxb75eB3wFnITbldye01B4K2AacCDQOcajI8Bi4CrcroQXZ9qsUFhhhjP/DegVRwsrgaeBi3G7krtITih8GGau4LZAdoxH1wAfAZNwu1Y0vsLvDeZhFvq5BrPi5/XA7XGv+BkKDwT+DxgKdNAa1sFWOVDXQdHWojr1wHfAGbhdrW7DYkUonAfcDxwKdInl0A2aDosh90PUY8eM2ufkaI5xFmQajyk+B2JeH5dEtT1SKOzDPEZ9YsnpWAXMBLy4XcntYQ2FRwD3YUZoxPoc3QB8jnmOLmp8hd8bzMac5LsR835yG3CjL+BZm3BmIYQQQmyWFJ22hMInYD58dkqglTrM3LURSSg8JwMPbNs3+7EjPZ3HA91oqzBuXQT4FTggaYVnKNwZCGHmpXZMoKUa4G+4XeVWcrUkFL4N8BJ7YdzYWkzheSpuV3JelKHwOOAZID+BVmqB/wH743atBPB7gx7Mip9DgOeAvye04qcpOP+DWTQo1qKjsTWAJ2mFpyk4K4CDSOx1vw64DLfr7mgPcLaeuRK4CPPcuRK4r9WtZ0LhEswJsUQe+3XAW8CRSSs8TcH5Jom9luqBpcC+uF0/Avi9weHAvcA+QBDTS/xFgmmFEEIIEaWsVAfYIpiergdI7IMnmF7HgZiFLazyBTwPAnfuNaTDSVrrRApOMM+b7pghaskyCdPLlUjBCeYxeTDxOK0IhQcDPhL7kIxz/F+BvRKNtBkPkVjRAZAHbAec4fcGt/F7gzMwveh5wOG+gOcoC1tMXE3iBSfAVphiOFnGAvuT+Os+H7idULhDtAf4Ap61voDnCmAPTO+zH/iP3xvct9mNzQmcm0j8sc8HRgIHJ9jO5gRI/LWUA/QFLvd7gz393mAAcxJjG+A44GApOIUQQoj2JUWnHT1JrIhrLAcYZqmtpi7q1T2rRillI2sWsLOFdlqzE4l/mN+oA6FwV0ttNbU9tDncM1r1Tnv2mRMjfS21lrd0RcNEzJzNCZiVP4f5Ap5XLLU/lMQLzo22s9ROS3Yg9uHprVmPOeEUE6d4OgQ4FlNUve/3Bu/ze4M9G91ssNO+Ddkk6zlqDLLUTvbqtZGDgQXAZMw84yG+gOdJmbsphBBCtD/ZMsWOXMyQU1ui7vGIhS/gqY+89eFyzLYTNtj6wN0Sm/dBA8nLarNdZbm9pqydZFq1JuLCzPE71xfwfG2rXYfN+yCZ73G52CuOI8T5eztFVLnfG3wVKAXOA47ye4OXAo/6juuai5lna0N2vDmjZO3xWrMusgtm9e6zfQHPXFvtCiGEECJ2UX0IVUpNUEpppdSQRpcNVkp91sJtRyil/qOUqlJKzVdKlbbS5l5KqQed7ycppeY6X+8ppfZ0Lt/FaWfj1yql1N+d625TSnni+J3bzeDi8ez+t2MpPO14XGecBMDF997JkBMnssepxzHhqov5dfXqds2UpVSz4vjX1auZeM2lDDlxIruedAxzPp/LilXVHFLiY6dJR3FIiY+Vq1e1a87WZErWLT3n1j2y5wB/TkLBaT1re0tVTl/As8oX8FwI7I3p4XsImF31xYZd0ilnrOLN2XWrrK+BkVJwCiGEEKkXbc/HccA7mCFcbZkGnKG1LsQME32qldtdgdnHD8zCJG6t9R6YVS/vB9BaL9BaFzpt7YNZyOI555i7MZvOp7XKOwJUPTSd8P2PAXCIaz8+e+RJ5j48g50HDOSm6Y+mNiBw/j1ljN13f754/Bk+fWg6uw7cjinTpzFm7+F89cSzjNl7OFOmT0t1TCBzsm7pObt1yVrU3sMUt/T71BanyBqJWU15pwX/q5teX6+bzY1Odc5oxZuzc6espb6AJ7mrQQshhBAiKm0WnUqprTBbVpxGdEVnH8xWFWitG7TW81poswuwh9b6U+d272mtN+4f+T4tD/8cA3yjtf7eOeZ7oJdSqiCKTGnj0OEjyMkxI8hGDB3GomU/pzTPqrVrePvTTzjt8CMAyMvNpXuXLrzwboiTxxYBcPLYIp5/560UpjQyJavktC9TsqZLTl/AE/EFPI8Cu+R3Us80RMhLx5xtyZScQgghhNi8aHo6jwRmaq2/BFYopfZu4/Z3AAuUUs8ppc5USrW0+qgLaDY013EaZsP0po4FZjS57GNMQZyWlFIcevE57HPGidz/0rPNrn/4lRcZt+8BKUj2u29/+pGtu3fnb1OuZa/Jk5h8yw2sranh5xUr6NerNwD9evVm6cqVbbSUfJmSVXLalylZ0y2nL+BZ+ZdRnW/KzWGTvSjTLWdrMiWnEEIIITYvmqLzOOBJ5/snnf+3Smt9HaaofB04HrPYSFP9gGb7UCqlRmOKzkubXJ6H2RD96SaHLMWs2JiW3r3nQT5+4P949eY78T//DG9/+vFv1934+MPkZOcw6ZBxKUwI9Q0NfPzlAs46YiKfPPgEnTt1ZEoaDPltSaZklZz2ZUrWdM2ZlaU22b8zXXM2lSk5hRBCCLF5m10pUCnVC/AAw5RSGrNyoVZKXbK547TW3wD3KqUeAJYppXpprZc3ukkNTfZfVErtgdlPcVyT2wKMAz7WWjcdi9rRaSstbdN7awD69OjJhING8cH8zxm5595Mm1lBxZx3mHX7VOzsXhK//lv3of/WfdhvqNmlZaJ7DFOmT6Nvz54sXv4L/Xr1ZvHyX+jTo0dKc0LmZI03Z0TrrCXLGvo/5w3uZjtTVhacVbzprjGZcn9C/FkbNNnbVlZZvz8BQoq+OwONX8KZcp/Gm1NrzfLqSEF5Ep6jAGcf2yWr8XtiptyfQgghhNi8tno6JwKPaa0Haa0Ha60HYBb9Oai1A5RShzfaB3InzHYVvza52Xxgx0bHDASeBU50hvE2dRzNh9aC2SeytWG6KbW2pobV69b+9v3r4fcZtt0OzPzPe9w84zFe/GcZ+R1bGnncvgp69WZAn74sWPgdALM++pChg7Zj/AEjmTazAoBpMys44kB3ClMamZI13pz19brzZ1/X3oJ5Tlv9ikSav04y5f6E+LOugS4k4f4EPntKZ11Y3+TEXabcp/HmbIiQN//buktJ0n1aV0++jZxCCCGESC9t7Yl2HDClyWX/xgybvRnYRSm1qNF1FwBHA3copdZhNrufpLXeZGiX1voLpVQ3pVQXrfVq4BqgFzDVqVfrtdYuAKVUPmbz8zMbt6GUysUUruFof9n29PPK5Uy42nQI1zfUc/yYsYzd7wB2PH4CG+pqOaTEB8CIobsTKLk8lVG5+7yLmHTDNdTW17F9v2155LJriEQi/PXay3nolRcZ2LcvT5c2fRqkRqZkjSdndraq6d835/6vvq9/13Ye5zRQs5WkM+X+hPiydjIrXp+SjDwHKT0+27xHbrJXZ6bcp/HkzMqivn/f7P+bu4BXkpEpJ5vHaDIKJlPuTyGEEEK0Tmndrrse/P6DlboAWK21fjDO4ycAe2utr7abLA6hcB/ge5p8WErAi7hdR1hqa1Oh8P+AwZZa+wq3a2dLbW0qFL4D+Lul1mqBAtwu+6uNhMKHYOYad7PQ2irgDNyucgttbSoUVphRB7bGcz+J27XZ+d1xC4XfB/az1NoK3K5eltraVCh8EXAjbLoybJxWA/vidn1hoa1NhcJ7ALOBrm3dNArrgUtxu+6y0FZzoXA1dnICzMbtGmmpLSGEEEIkINp9OpPhXmBDAsfnAGWWsiTqF5r0diRAYzZ2T5aFFtv6zmJbTf0vEonUWmpL03yIty0/aK1tFB1gXo82H5/fuV0asFV0N5Dc5+g3Ftv6yWJbTS3EnNCwoRPwg6W2mlrotG9DHcl6jhpLLLb1lcW2hBBCCJGAlBWdWuv1WuvHEzj+aa11sgqJ2LhdEeAW2HRbgjhooBqzoFKyXIsZcpiodcANFtpppqy4qN+D/3ene13N2ryGSEOiXfHrgJucossqvzfYYeqTqyYuXFzfoa4+4eZrgLkkd7j4NST+2EcwBfy0xOO0qgxYY6GdGiCZIyFmYoraRAvPtcBDuF2Jvn+0zO36FXicxN+fNmAK41kJZ2rdldh5f1oNJKc3VgghhBAxS9nw2i2OGb54PXAxpniM9Y7NAX4GDsXtmmc53aZC4UMx8/vyMb1Wm6W1RmtyIpocpUBr1tfX6wkdD923pe1w4lZWXJQD+IDrgI59evfzn3DMGUVKqe0xPSxtikQiWZFIJBdQWVkqUrO+5o7O40ZdZDMngN8bPBjwAztnZ/HsqUd1yc/LVQdj5jHHKgcIAeNxu2x84G5dKHwu5gRJFqaAjEUO8CNwCG5XcnuRQuF9gQqgB7Hfp9mYAulvuF3P2I62iVC4J/AGsCdRPkcbUZjH4QHgnGScGPlNKJwFBIC/YV7zsf6sXMy+yIclZZh6Y6HwsZgTb7nE9xxdDhyO2/WR7WhCCCGEiI8UnbaZ4rMnsfcir0taT0drQuGuQIdob/7Ox+u3WfBd3bXrN+gjgC8Bny/gedNGlLLiogOAqZgP768B55aUV3zl5OyIWYU0Kv9b+FXHWW+/cl71qpXnYeagXQP4S8or4ikIN+H3BrcFbgf+CnwNnOMLeF5zcmZjHvtYrcTtSjhb1MxztAexDwlfm/SiuKlQeCtiHxpa5/TutZ9QuAOxz0XUmDmnsRZW8TPFZ09in9tbjdtlayhx28xztBum8IxFDW6XjV5yIYQQQlgkRaeImd8bPAy4B7N68FPAhb6A58d42iorLtoas0LyqcAizOJBz5aUVyT8xCwrLtoJuBs4DPgUOLukvOK9eNrye4O5wHlAKaY35Z/Arb6AZ32iOYUQQgghhNiSSdEp4uL3BjtihhJfgRn+WArc5Qt4ohpiWFZclAWcDtyE6cW8Hbi+pLzCai9FWXGRAo4C/gX0Bx4GLispr1gWbRt+b/BPmF7YYcDLwHm+gOdbmzmFEEIIIYTYUknRKRLi9wa3xyzYcThmg/ezfQHP7M0dU1ZctA9m9eLhmLmMZ5eUVyR1HmtZcdFWmGG2F2AWGbkceLCkvKLVOa1+b7AvZv7jSZgtcc4HXvQFPPKiEUIIIYQQIkpSdIqE+b1BBYwH7gQGAY8Bl/gCnp8b366suKgHZsXbs4ClQAkw3cZQ2miVFRfthlkAyA18CJxVUl6xyYIjfm8wG/Bi9mDMB24FbvQFPO07n1EIIYQQQogtgBSdwhq/N5iP2fLgYsy2B1cCgfUrb49gegtvBXph5oNeU1JeUZ2KnM6Q2+MxW3P0wfS6XlVSXrHS7w3uhxlKuzfwJmahoGTuSSmEEEIIIcQWTYpOYZ3fG9wFU1geHKlfMq92zb/r0Bv2BOZghtJWpTahUVZc1A2zPcs5wIqcjvt/lt1xhFsptRi4EHhKhtIKIYQQQgiRGCk6RVKUHXt0F5XVY4Zu+PlwVAeyO+wZyulQOPGc+//yS6qzNeb3BrPqN3z2j4YNn16hG37OUVndf0TlHn3h9Ef+k+psQgghhBBCbAmk6LTJ7NO4KzCA2PfpXAvMw+1aaj1XS0LhQcBOxLBPp6MO+Bb4pqXN7J2hq3/FrEbbD7Ifzet6Sk1WdrczgV+BS4FHfAFPdHsThsI9gd2Ibw/En4DPcbtaXFHX7w0WYobS7q915J3a1U++oRuWnAd0xyyO9I+S8orVUebs6OQsiDEnwEonZ/KHG5v9D3cBBhP7Pp01wALcrri2x4lZKLwNMITY9+lswCz89EVLz1HrzH63wzB7n8ZqCeaxT/7WO6FwLuY5ug2x79O5AvgMtyu610OiQuEdge2JfZ/OdZjHfbH9UEIIIYSIlxSdtpjN4V8GRmC2EIlHB6AYt+tFa7laEgpfAlwLbIizhTzgIeC8xh/qy4qLfhtWC3yMWaTnAwC/N7g7psA7CGeYrS/g2fww21D4AOB14r8/s4FvgJG4Xas2Xuj3BhsPq10OXAQ87gt4dFlxUS/MHpynA4sxix2Vb3axo1B4a+d36gNEV0y3bCRu19wEjt88c1Lk/zCLPkW1tU0LOgA+3K6HreVqSSh8MhAg/udoLub1eBxuV6srFCcsFB4GzCb2Im6jLOAXYERSTziFwl2At4CdMUV5PCLAgbhd823FasacFLkdOBOojbOVDsBk3K4nrOUSQgghREKk6LQlFK4ARmNWO01EDTAOtyuUeKgWhMJnYj7UJZpzLXAnbteVZcVF+cBVmOLttwWEmm5H4qxyeyJmQaHeOAsK+QKe5j185sP8+0DnBHNuAOYDe/tnrAKzgNBtQF+cBYR8Ac/KpgeVFRft61y/NzAL8JWUVzRfUMicbPgcGEjsve0B11wAABcKSURBVDKNaWAVsCdu1/cJtNO6UPgBzO9v4zl6Im7XvxMP1YJQ+AhgOonnXAc8hdv1t8RDtSAUHgDMBboRf9EJ5gTAImBoUno8TSH3AaY3tmMCLWnMaIVhuF0/2YjWTCh8LeZET6Kv+xrgGNyulxMPJYQQQohExToEVLTEDFs7jMQ/JIMZSniMhXZacyp2cnbWWp9YVlx0BDAPs+/lDGCXkvIKf0v7X/oCHu0LeB7DDO+8FzgX+MLvDU5yCtLGxmF6VBPVAdglFK5xA0FMT99CYLgv4PG1VHACOD20+2J6Q13Af8uKi/7pFNiNDcH0cCZScIIpWrIBT4LtbE4x9p6jp1hopzUnYydnPsl9LY0Cckis4ATz3OkFDE00UCv6kHjBCeb3zAFGJpyodSeTeMEJ5jl6ooV2hBBCCGGBFJ129CL+4Yot2c5iW03FM+ewRQ0NDdsAzwOrgZEl5RUnl5RX/NzGYfgCnl99Ac85wHBMAfh/QNDvDTb+0D2AxAs5AOobdNbPyxveAPbEDNvb3xfwfNTGYZSUVzSUlFf4MUXyDExhPb+suOhIZ+4qmPszkSG1jXXC4uOzCdPb1cVii9tabKup/hbbyndOCiVDP2KfE90aTbIee5Mz3qGqTeWRvJxg3kttGWCxLSGEEEIkICfVAbZkv65ezeRbb+Cz/32DUoqHL72a1z54nwdefp6tu3UH4J+n+/jziAPTLuezb1fy0nuzycvNZYdt+vPIpdfQvcumNYvWkWzMkNq7SsorYi66fQHPR35vcH9gMjAF+NTvDd4OXO87ruV1g+LJ2tBAh04dsl6AyOm+gGdZrDmdQvrksuKiBzHzUp8DXi4rLjqv5OzSqHMuWraU0kfvZ/733/HBvY/iGpKsjq3oZUpOiDtrsucPNOvlzJT7tKWcr7z/Li+8+zZZStGnR08evewfbNN768aHJdqr2145hRBCCJFGNtvTqZR6Syl1WJPL/q6UmqqUGqyU+sy5bJRSqlop9YlSaoFS6m2lVNFm2j1SKXWN8/2FSql5Sqm5SqlZSqlBjW53i1Lqc6XUfKXUXUop5Vz+plIqnpUi29X595Qxdt/9+eLxZ/j0oensOtB0YF4w8TiqHppO1UPTU15wQss5D3Htx2ePPMnch2ew84CB3DT90WbH5eTk1pSUV5TFU3Bu5At4Ir6A537MAifTgEuA+ctWNOxgK2tuDmv/Mir/+ngKzsZKyitmY+Z4lgBu4PPKd2ZOamledEs5h223A89edwsj99grkRhWZUpOyJysmZzz4mNPZO7DM6h6aDpF+x/EddMeTHXMjMkphBBCiNa1Nbx2BnBsk8uOdS5varbWei+t9S7AecA9SqkxrbR7CabHCOATwKW13gN4BrgFQCl1AHAgsAdmPtJwzAd9gMeBs9vInlKr1q7h7U8/4bTDjwAgLze3WU9hOmgt56HDR5CTYzrCRwwdxqJlzUfNKqWs9SL5Ap5ffAHPZMxjvnzxsvo/28qalaXiXf22mZLyirqS8orbMXM5X1i+ctmJtXUbNnlgW8u566Dt2GXgYFtREpYpOSFzsmZ6zq6dt/rtNmvX1+Cc50uZTMkphBBCiM1rq+h8BihSSnUAUEoNxuzx9s7mDtJaV/H7lhSbUErtDGzQWv/i3LZSa73Oufp9fp/PpTELX+Rh5k3lAhuriReB49rInlLf/vQjW3fvzt+mXMtekycx+ZYbWFtTA8A9zz3NHqcex6k3X8fK1avaaCl1OTd6+JUXGbfvAe2SxxfwvAe4evXIfjuds5aUV/xYUl5x7I7bDbk8npzpIFNyQuZk3RJyXvngVAYcczhPvDGT6049U3IKIYQQImGbLTq11ssxS+2PdS46FijX0e2z8jGmN6ipA53rWnIa8Krzs+cAlZi9EhcDr2mt5zvXrQQ6KKVsLjphVX1DAx9/uYCzjpjIJw8+QedOHZky/VHOOuJovpn+HFUPPkG/Xr0pmfqvtMy50Y2PP0xOdg6TDhnXbpl8AU/9tn1yPs2ErIXDhn+Sl9thdSw500Wm5ITMybol5Lxx8tn88PTLTDpkLPc895TkFEIIIUTColm9tvEQ29aG1raktfFO/YBmc+uUUidgtqa41fn/jsCumJ7PbQGPUqrxUv1LMb2uaan/1n3ov3Uf9hs6DICJ7jF8/NUC+vbsRXZ2NllZWZx++JF8MP/ztMwJMG1mBRVz3uGJq65Pi+Fr6Zq16c/bXM50kik5IXOybkk5jx8zln+HgqmI95tMySmEEEKIzYtm9drngduVUnsDnbTWrfVSNrUXML+Fy2swm6n/Ril1MHAl4NZab3AungC8r7Ve49zmVWAEsHHYZUenrbRU0Ks3A/r0ZcHC79hl4GBmffQhQwdtx+Llv9CvV28AnnvnLYZt13y9nGW/LBnyWHHRrcnI5Tv10u4dO3ZqM+fM/7zHzTMeI3TnfeR3THR7PzsyJWtrOduitc76fEHVuNemlvZMRq6mK+3GmxNg1erqbR9I0nN08gl/H9Cta3crWbXW6q77b5xSP7Xe1rY2vzl01Pjhuw0pzMrK+v3cXbw56+rrOs6e88apn0wtHW075/aDdt76L4cd0zEn5/edY1rL+dWihezUfyAAL773NkOazENtiDRk/3fex+NnTS1NypY5559xVYeN87MTySmEEEKI9NJm0am1XqOUegt4mCh7OZVSewBXY7bCaGo+cEKj2+4F3AeM1VovbXS7hcDpSqmbML2mbuBfzjEKs1fcd9HkSZW7z7uISTdcQ219Hdv325ZHLruG8+66jaqvv0QpxeCCftxXckWz49asWz2IJC2UVFtX26lx0dlazuFnnsyGuloOKfEBMGLo7gRKmk1fbHeZkrWlnM/NruTcO29jWfVKDr/8Agp33JnXbr37t2O01urX6hUjgH3SOSfA+g01vUnSc7Rm/bqOTYvOeLNqtIroiDcZOX9dtSJXo5uNFoknZ6ShoUP1ql+LgAbbOdesXZXV0NCQ17jobC3n5FtvYMHC78nKymJQ3wICF276OtIRnf1r9YoDgf1s5wSIRBpym/5ZiienEEIIIdKLimZ6plJqAvAssKvW+gvnssFAhdZ6mFJqFPAC8C2Qjxn6eovW+qUW2soHPgSGaa21UupNYHfMvE2AhVrr8UqpbMwKtyMxiwrN1Fpf6LThAi7XWh8d7y9uVShcgPndO7V10yi9gtt1uKW2NhUKfw8MtNTaOtyuzpba2lQofBdwrqXWqoExuF0fWWrvd6HwYUA5TXrv49QAXI3bdZOFtjYVCivAZm/fJ7hde1ts73eh8AeY1aptiAAdcbvi3tanVaHwJcCN2NnvuBo4HrfrFQttbSoULgRCQMub38amFrgUtys5k9FD4dXAVm3eLjrv4Xalfk8qIYQQQkT3YUlr/RxN5mhqrb/DbGWC1votovzQrbVe5xSaY4A3tdYHt3K7BqC1JQlP5PctV9LBOux88NwomUva2hySvN5iW02txpxssDFBMwvzGCWDzXbrLLfXVD32nqdrLbWT7LYjJKH30LEO85jZuk+T+RyNZv5+NOpJ7nN0PfaKzjWW2hFCCCFEgmx9EInVPzE9ovH6TGs9y1aYhLldqzA9FTbUYFYMTpaPML0ViaoH5llopzVzsffhNhf4n6W2mlqA2dLHhgbgM0ttbcrt0sDX2Ont3ACELbTTmg+wc0JDA//D7bI+n9PxX+z1HncAvrDUVlPfAdkW20vOc9T4HPPekqj1mBE1QgghhEgDKSk6tdY/a61fTOD4B2zmsWQcpncuETWYYXD3JB6nVedghgInUng2YFYgPt5KopY9jRm2mmjhWQMciduVnF5Zt2spcDKJ9yCvA/xAMpfhPBL4FVOMxWsD8ClwlZVELbsWqCKxwlNjftfxVhK1xO0KAXdi5zl6Km7XksRDtcDtqsXcD4nmXAfcitv1XuKhWjUJsx9zIr3T6zEnRW60kkgIIYQQCYtqTqeIkpk7dS6wI7H3LKwC5gBTkjL/rLFQuAdmteBCzCrAsajF9EbcjNu1yHa0TYTCWcB5gAeIdUVX08sFD+J2vd3WjRMWCo8FTgIGEPuQ4GXAK5isyX1BhsI7ASWYPXRjHRa6FvNh/p+4XckcXguhcD5wBWZuZ6zzhusxPdBluF1f2o7WTCh8GnA40CeOo38AHk/KXM6mQuGDgDOA7Yj9Ofoz8BJu16O2YzUTCm8DXIaZvpEX49FrMD3lN+F2pe3q5kIIIcQfjRSdQgghhBBCCCGSJlVzOoUQQgghhBBC/AFI0SmEEEIIIYQQImmk6BRCCCGEEEIIkTRSdAohhBBCCCGESBopOoUQQgghhBBCJI0UnUIIIYQQQgghkkaKTiGEEEIIIYQQSSNFpxBCCCGEEEKIpJGiUwghhBBCCCFE0kjRKYQQQgghhBAiaaToFEIIIYQQQgiRNFJ0CiGEEEIIIYRIGik6hRBCCCGEEEIkjRSdQgghhBBCCCGSRopOIYQQQgghhBBJI0WnEEIIIYQQQoikkaJTCCGEEEIIIUTSSNEphBBCCCGEECJppOgUQgghhBBCCJE0UnQKIYQQQgghhEgaKTqFEEIIIYQQQiSNFJ1CCCGEEEIIIZJGik4hhBBCCCGEEEkjRacQQgghhBBCiKSRolMIIYQQQgghRNJI0SmEEEIIIYQQImmk6BRCCCGEEEIIkTRSdAohhBBCCCGESBopOtuJUuqMVGeIhuS0K1NyQuZklZz2ZUpWyWlXpuSEzMkqOe3KlJyQOVklp32ZlDWVpOhsP5nyhJScdmVKTsicrJLTvkzJKjntypSckDlZJaddmZITMier5LQvk7KmjBSdQgghhBBCCCGSRopOIYQQQgghhBBJI0Vn+7k/1QGiJDntypSckDlZJad9mZJVctqVKTkhc7JKTrsyJSdkTlbJaV8mZU0ZpbVOdQYhhBBCCCGEEFso6ekUQgghhBBCCJE0UnQKIYQQQgghhEgaKTotUUpdqZT6XCk1VylVpZTaTylVpJT6RCn1qVJqnlLqTOe2I5VSHyul6pVSE9M454XO/+cqpWYppQalaU6vUuq/zu3eUUoNba+csWZtdMxEpZRWSrnSMadS6hSl1DLndlVKqcnpmNO5/V+dyz5XSk1Px5xKqTsa3ZdfKqV+ba+ccWQdqJSqdK6bq5T6c5rmHOS8L81VSr2llOqfRtlafY9XSp2slPrK+To5jXPOVEr9qpSqsJ3RZlalVKFSak6jdorTNOcgpdRHzvGfK6W86ZizUVtdlVI/KqXuSdecSqkG9fv76ou2c1rOOlAp9bpSar5zzOB0y6mUGt3o/qxSSq1XSh2Zbjmd625x2pmvlLpLKaVs5rSc9Wal1GfOl/X3p4yitZavBL+A/YE5QAfn/72BQcBPQH/nsg7ALs73g4E9gMeAiWmcczSQ73x/FlCepjm7Njp2PDAzXe9T5/9dgLeB9wFXOuYETgHuaa/7MYGcOwGfAD2c//dJx5xNjj0XeDiN79P7gbOc74cC36VpzqeBk53vPcDjaZRtMC28xwM9gW+df3s43/dIt5zOdWOAvwAVaX6f7gzs5Hy/DbAY6J6GOfMatbEV8B2wTbrlbNTencB0LP8dsPwcXZOM52aSsr4FHNLo8c9Px5yN2uwJrEjHnMABwLtAtvM1BxiVjo89cDjwBpADdAbCNPrM+kf7ykHY0A/4RWu9AUBr/YtSKoJ5ki13LtsALHC+/w7AuU0656xsdOz7wAlpmnNVo2M7A+25OlZMWR3XA7cAF6V5zlSINefpgF9rvdK5bmma5mzsOOAf7ZQTYs+qga7O990wf2TTMedQ4ALn+0rg+XTJtpn3+MOAN7TWK5zr3wDGAjPSLCda61lKqVGWciUtq9b6y0bf/6SUWgpsDdgaTWArZ22j/3bA/kgza4+9UmofoC8wE7A9GidTPi9Zy6rM6KscrfUbzu3WpGPOJiYCr2qt16VhTg10xJzIUUAu8LPFnDazDgVCWut6oF4p9SnmPf8py3kzggyvteN1YIAyw+amKqXczoeKF4HvlVIzlFKTlFKpvr8TyXka8Gq65lRK+ZRS32CKufPaKWfMWZVSewEDtNZJG7JmI6fjaGdYyTNKqQFpmnNnYGel1LtKqfeVUmPTNCdghtgB2wHBdsoZT9ZS4ASl1CLgFUzPbDrm/BQ42vl+AtBFKdUrTbK1Zlvgh0b/X+Rclm4524P1rEqpfTEfRL9Jx5xKqQFKqbmY58DNWmubJ3Ss5HSuLwMutpjNek5HR6VU2HnvtzoM1HLWnYFflVLPKjM081alVHYa5mzsWOydDLOaU2s9B3OicbHz9ZrWen46ZsX8nRqnlMpXSvXGjCBsr89TaScd/vBkPOes1T7AGcAyoFwpdYrWejJmiNIHmF6th1OXMv6cSqkTMGc7b03XnFprv9Z6B+BS4Kr2yBlrVufN6Q6gpL3yxZPTOeQlYLDWeg/gTWBamubMwQyxHYXpQXxQKdU9DXNudCzwjNa6IdkZE8h6HPCo1ro/8Gfg8fYoUuLIeRHgVkp9AriBH4H6NMnWmpbmHVkbmZEpf4vAflalVD/gceBvWmtrvWI2c2qtf3DeU3cETlZK9U3DnGcDr2itf2jjdqnOCTBQa+0Cjgf+pZTaIU2z5gB/cm47HNgeM4Ul3XICv72Wdgdes5XRZk6l1I7ArkB/zEk7j1JqZDpm1Vq/jjl5+x6miJ9Dkv5OZQSdBmN8t7QvzLCEl5pc1htY3eSyR2nHOZ3x5AQOBubTTnPlErk/ncuzgOp0zIoZqvgLZi7Pd8B6zNDFdpnXmcB9mp2q+7StnEAAOKXRdbOA4emWs9FlnwAHpOK+jOE+/RzTG7/xum9T8fqP8Tm6FbAo3bI1fY/HFPT3Nfr/fcBx6Zaz0eWjSNKcTptZMcPBPwaOSeecTa5/ZHPXpyon8ASw0Pkb9QuwCpiSbjlbaGez16f4Ph0BvNXo/ydipoWkVc5Gl58P3J/M+zLB+/Ni4OpG/78GuCQds7bQznTgz8m+b9P1S3o6LVBK7aKU2qnRRYXAz2rT+TCFwPftGqyJWHMqMxT0PmC8br+5cvHkbHzbw4Gvkh7SEUtWrXW11rq31nqw1nowZp7seK11OJ1yOrfv1+jy8ZgTD0kXx2vpecxwFZyhKztjiqR0y4lSahfM4jFzkp2vsTiyLsScyUUptStm7syydMuplOrdqAf2cpLYe2fxPf414FClVA+lVA/gUCz2JmTK3yKwl1UplQc8BzymtX46jXP2V0p1cr7vARyIxTn0tnJqrSdprQc6f6Muwtyvl6VbTuc11MH5vjfm/pxnK+f/t3P3Ko0FYRzGn/ELWwt7S1FYLLSxXFBvYAvt1AWvw85OsBS8BgsLi8VCEGxsEsNaiJX3IBY2Fu+7CKLdDI7L84PhkOSc8GeSc04m81EzK3ADzJRSZvPxTypmbXDeb1F/aG3NnI/EKJeJUsokMdKl6m+Uit/T8ZLTPkopP4jFhv7UzPqtfHWr938oRBf8NXERuQVOiTHb58RNZUCstLWc+68Qc3meiAnJfzvNeUFMzh5kOes05xHROzMgxvkv9vrZvzv28qPne8gJHGSdDrNO5zvNWYDD3H8EbPaYM4/Zp2GPQcU6XcjHw3xtvdOcv4g/mO6BE3KVwU6yfXqNB3aBhyw7Hee8Iv5seM59NnrMSixw98LbfWoALHWYcy2PH+Z2r8f6fPee29RfvbZWfa4S1/xhbn/XzNngfPr3+Y+I3rCpTnPOEVMVxnqtT2L01THR0LwDDjvOOp3vcUd0NFS7Nn3HUrJSJEmSJEmqzuG1kiRJkqRmbHRKkiRJkpqx0SlJkiRJasZGpyRJkiSpGRudkiRJkqRmbHRKkiRJkpqx0SlJkiRJauYVYJFZ7eqN9boAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11402a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group properties\n",
    "gc = stagerank\n",
    "gcSize = len(gc['entryId'].unique()) #number of entries in class\n",
    "\n",
    "rankpos='position'\n",
    "#rankpos='classrank'\n",
    "\n",
    "# Create plot\n",
    "fig, ax = plt.subplots(figsize=(15,gcSize/2))\n",
    "\n",
    "#Set blank axes\n",
    "ax.xaxis.label.set_visible(False)\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "ax.set_yticks([]) \n",
    "ax.set_xticks([]) \n",
    "\n",
    "lhoffset=-1.95\n",
    "rhoffset=+0.5\n",
    "\n",
    "#labeler\n",
    "addlabels = True\n",
    "pinklabel= lambda x: int(x['position'])\n",
    "#pinklabel= lambda x: int(x['classrank'])\n",
    "#pinklabel= lambda x: x['drivercode']\n",
    "\n",
    "ylabel = lambda x: x['drivercode']\n",
    "ylabel = lambda x: int(x['position'])\n",
    "ylabel = lambda x: int(x['classrank'])\n",
    "ylabel = lambda x: '{} ({})'.format(x['drivercode'], int(x['classrank']))\n",
    "\n",
    "gcSize=10\n",
    "\n",
    "# Rally properties\n",
    "smin = gc['snum'].min() # min stage number\n",
    "smax = gc['snum'].max() # max stage number\n",
    "\n",
    "# Define a dummy rank to provide compact ranking display\n",
    "# This is primarily for RC1 which we expect to hold the top ranking positions overall\n",
    "gc['xrank']= (gc[rankpos]>gcSize)\n",
    "gc['xrank']=gc.groupby('snum')['xrank'].cumsum()\n",
    "gc['xrank']=gc.apply(lambda row: row[rankpos] if row[rankpos]<=gcSize  else row['xrank'] +gcSize, axis=1)\n",
    "    \n",
    "#Base plot - line chart showing position of each driver against stages\n",
    "gc.groupby('entryId').plot(x='snum',y='xrank',ax=ax,legend=None)\n",
    "\n",
    "#Label cars ranked outside the group count - primarily for RC1\n",
    "if addlabels:\n",
    "    for i,d in gc[gc['xrank']>gcSize].iterrows():\n",
    "        ax.text(d['snum']-0.1, d['xrank'], pinklabel(d),\n",
    "                bbox=dict(  boxstyle='round,pad=0.3',color='pink')) #facecolor='none',edgecolor='black',\n",
    "\n",
    "#Label driver names at left hand edge\n",
    "for i,d in gc[gc['snum']==1].iterrows():\n",
    "    ax.text(smin+lhoffset, d['xrank'], ylabel(d))\n",
    "    \n",
    "#Label driver names at right hand edge\n",
    "for i,d in gc[gc['snum']==smax].iterrows():\n",
    "    ax.text(smax+rhoffset, d['xrank'], ylabel(d))\n",
    "\n",
    "# Label x-axis stage numbers\n",
    "ax.set_xticks(stagerank['snum'].unique()) # choose which x locations to have ticks\n",
    "ax.set_xticklabels(stagerank['code'].unique())\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "#Hide outer box\n",
    "plt.box(on=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#need a view that shows start number on stage versus final position on stage?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make  Functions For That..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stage_times_stage, stage_times_overall\n",
    "\n",
    "def stageClassEnrichers(stagerank):\n",
    "    stagerank['classrank'] = stagerank.groupby(['snum'])['position'].rank(method='dense').astype(int)\n",
    "    stagerank['gainedClassPos'] = stagerank.groupby(['drivercode'])['classrank'].diff()<0 \n",
    "    stagerank['gainedClassLead'] = (stagerank.groupby(['drivercode'])['classrank'].diff()<0 ) & (stagerank['classrank']==1)\n",
    "    stagerank['classPosDiff'] = stagerank.groupby(['drivercode'])['classrank'].diff().fillna(0)\n",
    "    stagerank['lostClassLead'] = (stagerank['classrank']!=1) & (stagerank.groupby(['drivercode'])['classrank'].diff() == stagerank['classrank']-1)\n",
    "    stagerank['retainedClassLead'] = ((stagerank['classrank'] ==1) & (~stagerank['gainedClassLead']) & (stagerank['snum']!=1))\n",
    "    return stagerank\n",
    "\n",
    "#https://stackoverflow.com/a/35428677/454773\n",
    "def winningstreak(x):\n",
    "    x['winsinarow'] = x.groupby( (x['position'] != 1).cumsum()).cumcount() +  ( (x['position'] != 1).cumsum() == 0).astype(int) \n",
    "    return x\n",
    "\n",
    "def _debug_winningstreak(x):\n",
    "    x['notwin'] = (x['position'] != 1)\n",
    "    #The notwincumcount creates a new group for each not win\n",
    "    #So if we have a streak of wins, the notwincumcount groups those together\n",
    "    x['notwincumcount'] = x['notwin'].cumsum()\n",
    "    x['startwithawin'] = (x['notwincumcount'] == 0).astype(int)\n",
    "    #groupby.cumcount  - number each item in each group from 0 to the length of that group - 1.\n",
    "    x['streakgroupwincount'] = x.groupby( 'notwincumcount' ).cumcount()\n",
    "\n",
    "    x['winstreak'] = x.groupby( 'notwincumcount' ).cumcount() + x['startwithawin']\n",
    "    return x\n",
    "\n",
    "def stageOverallEnrichers(stagerank):\n",
    "    stagerank['gainedOverallPos'] = stagerank.groupby(['drivercode'])['position'].diff()<0 \n",
    "    stagerank['gainedOverallLead'] = (stagerank.groupby(['drivercode'])['position'].diff()<0 ) & (stagerank['position']==1)\n",
    "    stagerank['overallPosDiff'] = stagerank.groupby(['drivercode'])['position'].diff().fillna(0)\n",
    "    stagerank['lostOverallLead'] = (stagerank['position']!=1) & (stagerank.groupby(['drivercode'])['position'].diff() == stagerank['position']-1)\n",
    "    stagerank['retainedOverallLead'] = ((stagerank['position'] ==1) & (~stagerank['gainedOverallLead']) & (stagerank['snum']!=1))\n",
    "\n",
    "    stagerank['stagewin'] = stagerank['position']==1\n",
    "    stagerank['stagewincount'] = stagerank.groupby(['drivercode'])['stagewin'].cumsum()\n",
    "\n",
    "    stagerank = stagerank.groupby('drivercode').apply(winningstreak) \n",
    "\n",
    "\n",
    "    return stagerank\n",
    "    \n",
    "def stageTimeEnrichers(stagerank):\n",
    "    stagerank['gainedTime'] = stagerank.groupby(['drivercode'])['diffFirstMs'].diff()<=0     \n",
    "    return stagerank\n",
    "    \n",
    "def getStageRank(conn,rally,rc='RC1',typ='stage_times_overall'):\n",
    "    q='''\n",
    "    SELECT st.*, sc.name as class, i.code, i.distance, i.name, CAST(REPLACE(code,'SS','') AS INTEGER) snum,\n",
    "    sl.`driver.code` drivercode, sl.`entrant.name`\n",
    "    FROM {typ} st INNER JOIN itinerary_stages i ON st.stageId = i.stageId\n",
    "    INNER JOIN startlist_classes sc ON sc.entryid = st.entryId \n",
    "    INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "    INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "    WHERE sc.name=\"{rc}\" AND ce.`country.name`=\"{rally}\" ORDER BY snum\n",
    "    '''.format(rc=rc,rally=rally, typ=typ)\n",
    "    stagerank=pd.read_sql(q,conn)\n",
    "    \n",
    "    return stagerank\n",
    "\n",
    "\n",
    "#THERE MAY BE BAD ENCODINGS HERE - NEED TO CHECK THE CORREXT RESULTS DATAFRAME IS PASSED IN\n",
    "#CONTEXT OF STAGE OR OVERALL POSITION MATTERS!\n",
    "def getEnrichedStageRank(conn,rally,rc='RC1',typ='stage_times_overall'):\n",
    "    stagerank = getStageRank(conn,rally,rc,typ)\n",
    "    stagerank = stageClassEnrichers(stagerank)\n",
    "    stagerank = stageOverallEnrichers(stagerank)\n",
    "    stagerank = stageTimeEnrichers(stagerank)\n",
    "    return stagerank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotStageProgressionChart(gc, lhoffset=-0.5,rhoffset=0.5,linecolor=None,\n",
    "                              deltalabels=False, progress=False, winstreak ='winsinarow'):\n",
    "    entrySize = len(gc['entryId'].unique()) #number of entries in class\n",
    "\n",
    "    progress = progress and 'leggainedpos' in gc.columns and 'leggainedtime' in gc.columns\n",
    "    rankpos='position'\n",
    "    #rankpos='classrank'\n",
    "\n",
    "    # Rally properties\n",
    "    smin = gc['snum'].min() # min stage number\n",
    "    smax = gc['snum'].max() # max stage number\n",
    "    \n",
    "    # Create plot\n",
    "    fig, ax = plt.subplots(figsize=(smax-smin,entrySize/2))\n",
    "\n",
    "    #Set blank axes\n",
    "    ax.xaxis.label.set_visible(False)\n",
    "    ax.get_yaxis().set_ticklabels([])\n",
    "    ax.set_yticks([]) \n",
    "    ax.set_xticks([]) \n",
    "\n",
    "    #labeler\n",
    "    addlabels = True\n",
    "    pinklabel= lambda x: int(x['position'])\n",
    "    #pinklabel= lambda x: int(x['classrank'])\n",
    "    #pinklabel= lambda x: x['drivercode']\n",
    "\n",
    "    ylabel = lambda x: x['drivercode']\n",
    "    ylabel = lambda x: int(x['position'])\n",
    "    ylabel = lambda x: int(x['classrank'])\n",
    "    ylabel = lambda x: '{} ({})'.format(x['drivercode'], int(x['classrank'])) \n",
    "     \n",
    "    def elaboratelabel(x):\n",
    "        if x['snum']==1:\n",
    "            return '{} ({})'.format(x['drivercode'], int(x['classrank']))\n",
    "        elif x['snum']==smin or not progress:\n",
    "            txt='{} ({}: {})'\n",
    "        elif x['leggainedpos'] and x['leggainedtime']:\n",
    "            txt='{} ($\\mathbf{{{}}}$: $\\mathbf{{{}}}$)' \n",
    "        elif x['leggainedpos']:\n",
    "            txt='{} ($\\mathbf{{{}}}$: {})'\n",
    "        elif x['leggainedtime']:\n",
    "            txt='{} ({}: $\\mathbf{{{}}}$)'\n",
    "        else:  txt='{} ({}: {})'\n",
    "        return txt.format(x['drivercode'], int(x['classrank']),x['diffFirst'].replace('PT','+').replace('S',''))\n",
    "    ylabel= lambda x:elaboratelabel(x)\n",
    "    gcSize=10\n",
    "    \n",
    "    ax.set_xlim(smin-0.2,smax+0.2)\n",
    "\n",
    "    # Define a dummy rank to provide compact ranking display\n",
    "    # This is primarily for RC1 which we expect to hold the top ranking positions overall\n",
    "    gc['xrank'] = (gc[rankpos]>gcSize)\n",
    "    gc['xrank'] = gc.groupby('snum')['xrank'].cumsum()\n",
    "    gc['xrank'] = gc.apply(lambda row: row[rankpos] if row[rankpos]<=gcSize  else row['xrank'] +gcSize, axis=1)\n",
    "\n",
    "    #Base plot - line chart showing position of each driver against stages\n",
    "    gc.groupby('entryId').plot(x='snum',y='xrank',ax=ax,legend=None, color=linecolor)\n",
    "\n",
    "    #test - lead data\n",
    "    #for i,d in gc.iterrows():\n",
    "    for i,d in gc[gc['position']==1].iterrows():\n",
    "        resp=d[winstreak]#d['lostClassLead']#'1' if d['gainedLead'] else '0'\n",
    "        ax.text(d['snum'], d['xrank'], resp,\n",
    "               bbox=dict(  boxstyle='round,pad=0.3',color='lightgrey'),\n",
    "               horizontalalignment='center')\n",
    "    \n",
    "    #Label cars ranked outside the group count - primarily for RC1\n",
    "    if addlabels:\n",
    "        for i,d in gc[gc['xrank']>gcSize].iterrows():\n",
    "            if deltalabels: col='lightgreen' if d['gainedTime'] else 'pink'\n",
    "            else: col ='pink'\n",
    "            ax.text(d['snum'], d['xrank'], pinklabel(d),\n",
    "                    bbox=dict(  boxstyle='round,pad=0.3',color=col),\n",
    "                    horizontalalignment='center') #facecolor='none',edgecolor='black',\n",
    "\n",
    "    #Stage time gain / loss indicator\n",
    "    if deltalabels:\n",
    "        for i,d in gc.iterrows():\n",
    "            col='green' if d['gainedTime'] else 'red'\n",
    "            ax.plot(d['snum'], d['xrank'], 'o', color=col)\n",
    "    \n",
    "    \n",
    "    #Label driver names at left hand edge\n",
    "    for i,d in gc[gc['snum']==smin].iterrows():\n",
    "        ax.text(smin+lhoffset, d['xrank'], ylabel(d), horizontalalignment='right')\n",
    "\n",
    "    #Label driver names at right hand edge\n",
    "    for i,d in gc[gc['snum']==smax].iterrows():\n",
    "        ax.text(smax+rhoffset, d['xrank'], ylabel(d), horizontalalignment='left')\n",
    "\n",
    "    # Label x-axis stage numbers\n",
    "    ax.set_xticks(gc['snum'].unique()) # choose which x locations to have ticks\n",
    "    ax.set_xticklabels(gc['code'].unique())\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    #Hide outer box\n",
    "    plt.box(on=None)\n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diffFirst</th>\n",
       "      <th>diffFirstMs</th>\n",
       "      <th>diffPrev</th>\n",
       "      <th>diffPrevMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>penaltyTime</th>\n",
       "      <th>penaltyTimeMs</th>\n",
       "      <th>position</th>\n",
       "      <th>stageTime</th>\n",
       "      <th>stageTimeMs</th>\n",
       "      <th>...</th>\n",
       "      <th>retainedClassLead</th>\n",
       "      <th>gainedOverallPos</th>\n",
       "      <th>gainedOverallLead</th>\n",
       "      <th>overallPosDiff</th>\n",
       "      <th>lostOverallLead</th>\n",
       "      <th>retainedOverallLead</th>\n",
       "      <th>stagewin</th>\n",
       "      <th>stagewincount</th>\n",
       "      <th>winsinarow</th>\n",
       "      <th>gainedTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PT1S</td>\n",
       "      <td>1000</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>PT1M33.7S</td>\n",
       "      <td>93700</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PT7.5S</td>\n",
       "      <td>7500</td>\n",
       "      <td>PT0.5S</td>\n",
       "      <td>500</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PT12M12.9S</td>\n",
       "      <td>732900</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT25M29.3S</td>\n",
       "      <td>1529300</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT35M49.7S</td>\n",
       "      <td>2149700</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT46M16.7S</td>\n",
       "      <td>2776700</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT59M38.1S</td>\n",
       "      <td>3578100</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H9M46.4S</td>\n",
       "      <td>4186400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H16M13.1S</td>\n",
       "      <td>4573100</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H26M22.9S</td>\n",
       "      <td>5182900</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H39M30.4S</td>\n",
       "      <td>5970400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H48M6.3S</td>\n",
       "      <td>6486300</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1H58M2.9S</td>\n",
       "      <td>7082900</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H10M47.4S</td>\n",
       "      <td>7847400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H19M15.6S</td>\n",
       "      <td>8355600</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H20M51.1S</td>\n",
       "      <td>8451100</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H23M23.8S</td>\n",
       "      <td>8603800</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H34M51.8S</td>\n",
       "      <td>9291800</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H46M8.7S</td>\n",
       "      <td>9968700</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2H52M13.1S</td>\n",
       "      <td>10333100</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diffFirst  diffFirstMs diffPrev  diffPrevMs  entryId penaltyTime  \\\n",
       "5        PT1S         1000   PT0.1S         100     1578        PT0S   \n",
       "17     PT7.5S         7500   PT0.5S         500     1578        PT0S   \n",
       "28       PT0S            0     PT0S           0     1578        PT0S   \n",
       "42       PT0S            0     PT0S           0     1578        PT0S   \n",
       "56       PT0S            0     PT0S           0     1578        PT0S   \n",
       "70       PT0S            0     PT0S           0     1578        PT0S   \n",
       "84       PT0S            0     PT0S           0     1578        PT0S   \n",
       "98       PT0S            0     PT0S           0     1578        PT0S   \n",
       "112      PT0S            0     PT0S           0     1578        PT0S   \n",
       "126      PT0S            0     PT0S           0     1578        PT0S   \n",
       "140      PT0S            0     PT0S           0     1578        PT0S   \n",
       "154      PT0S            0     PT0S           0     1578        PT0S   \n",
       "168      PT0S            0     PT0S           0     1578        PT0S   \n",
       "182      PT0S            0     PT0S           0     1578        PT0S   \n",
       "196      PT0S            0     PT0S           0     1578        PT0S   \n",
       "210      PT0S            0     PT0S           0     1578        PT0S   \n",
       "224      PT0S            0     PT0S           0     1578        PT0S   \n",
       "238      PT0S            0     PT0S           0     1578        PT0S   \n",
       "252      PT0S            0     PT0S           0     1578        PT0S   \n",
       "\n",
       "     penaltyTimeMs  position     stageTime  stageTimeMs    ...      \\\n",
       "5                0         6     PT1M33.7S        93700    ...       \n",
       "17               0         4    PT12M12.9S       732900    ...       \n",
       "28               0         1    PT25M29.3S      1529300    ...       \n",
       "42               0         1    PT35M49.7S      2149700    ...       \n",
       "56               0         1    PT46M16.7S      2776700    ...       \n",
       "70               0         1    PT59M38.1S      3578100    ...       \n",
       "84               0         1   PT1H9M46.4S      4186400    ...       \n",
       "98               0         1  PT1H16M13.1S      4573100    ...       \n",
       "112              0         1  PT1H26M22.9S      5182900    ...       \n",
       "126              0         1  PT1H39M30.4S      5970400    ...       \n",
       "140              0         1   PT1H48M6.3S      6486300    ...       \n",
       "154              0         1   PT1H58M2.9S      7082900    ...       \n",
       "168              0         1  PT2H10M47.4S      7847400    ...       \n",
       "182              0         1  PT2H19M15.6S      8355600    ...       \n",
       "196              0         1  PT2H20M51.1S      8451100    ...       \n",
       "210              0         1  PT2H23M23.8S      8603800    ...       \n",
       "224              0         1  PT2H34M51.8S      9291800    ...       \n",
       "238              0         1   PT2H46M8.7S      9968700    ...       \n",
       "252              0         1  PT2H52M13.1S     10333100    ...       \n",
       "\n",
       "    retainedClassLead  gainedOverallPos  gainedOverallLead overallPosDiff  \\\n",
       "5               False             False              False            0.0   \n",
       "17              False              True              False           -2.0   \n",
       "28              False              True               True           -3.0   \n",
       "42               True             False              False            0.0   \n",
       "56               True             False              False            0.0   \n",
       "70               True             False              False            0.0   \n",
       "84               True             False              False            0.0   \n",
       "98               True             False              False            0.0   \n",
       "112              True             False              False            0.0   \n",
       "126              True             False              False            0.0   \n",
       "140              True             False              False            0.0   \n",
       "154              True             False              False            0.0   \n",
       "168              True             False              False            0.0   \n",
       "182              True             False              False            0.0   \n",
       "196              True             False              False            0.0   \n",
       "210              True             False              False            0.0   \n",
       "224              True             False              False            0.0   \n",
       "238              True             False              False            0.0   \n",
       "252              True             False              False            0.0   \n",
       "\n",
       "    lostOverallLead  retainedOverallLead stagewin  stagewincount winsinarow  \\\n",
       "5             False                False    False            0.0          0   \n",
       "17            False                False    False            0.0          0   \n",
       "28            False                False     True            1.0          1   \n",
       "42            False                 True     True            2.0          2   \n",
       "56            False                 True     True            3.0          3   \n",
       "70            False                 True     True            4.0          4   \n",
       "84            False                 True     True            5.0          5   \n",
       "98            False                 True     True            6.0          6   \n",
       "112           False                 True     True            7.0          7   \n",
       "126           False                 True     True            8.0          8   \n",
       "140           False                 True     True            9.0          9   \n",
       "154           False                 True     True           10.0         10   \n",
       "168           False                 True     True           11.0         11   \n",
       "182           False                 True     True           12.0         12   \n",
       "196           False                 True     True           13.0         13   \n",
       "210           False                 True     True           14.0         14   \n",
       "224           False                 True     True           15.0         15   \n",
       "238           False                 True     True           16.0         16   \n",
       "252           False                 True     True           17.0         17   \n",
       "\n",
       "    gainedTime  \n",
       "5        False  \n",
       "17       False  \n",
       "28        True  \n",
       "42        True  \n",
       "56        True  \n",
       "70        True  \n",
       "84        True  \n",
       "98        True  \n",
       "112       True  \n",
       "126       True  \n",
       "140       True  \n",
       "154       True  \n",
       "168       True  \n",
       "182       True  \n",
       "196       True  \n",
       "210       True  \n",
       "224       True  \n",
       "238       True  \n",
       "252       True  \n",
       "\n",
       "[19 rows x 35 columns]"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stagerank[stagerank['drivercode']=='NEU']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diffFirst</th>\n",
       "      <th>diffFirstMs</th>\n",
       "      <th>diffPrev</th>\n",
       "      <th>diffPrevMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>penaltyTime</th>\n",
       "      <th>penaltyTimeMs</th>\n",
       "      <th>position</th>\n",
       "      <th>stageTime</th>\n",
       "      <th>stageTimeMs</th>\n",
       "      <th>...</th>\n",
       "      <th>retainedClassLead</th>\n",
       "      <th>gainedOverallPos</th>\n",
       "      <th>gainedOverallLead</th>\n",
       "      <th>overallPosDiff</th>\n",
       "      <th>lostOverallLead</th>\n",
       "      <th>retainedOverallLead</th>\n",
       "      <th>stagewin</th>\n",
       "      <th>stagewincount</th>\n",
       "      <th>winsinarow</th>\n",
       "      <th>gainedTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1581</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1M32.7S</td>\n",
       "      <td>92700</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1580</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PT1M33S</td>\n",
       "      <td>93000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT0.6S</td>\n",
       "      <td>600</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1585</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>PT1M33.3S</td>\n",
       "      <td>93300</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT0.9S</td>\n",
       "      <td>900</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1583</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PT1M33.6S</td>\n",
       "      <td>93600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PT0.9S</td>\n",
       "      <td>900</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1577</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>PT1M33.6S</td>\n",
       "      <td>93600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PT1S</td>\n",
       "      <td>1000</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>PT1M33.7S</td>\n",
       "      <td>93700</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PT2.1S</td>\n",
       "      <td>2100</td>\n",
       "      <td>PT1.1S</td>\n",
       "      <td>1100</td>\n",
       "      <td>1579</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>PT1M34.8S</td>\n",
       "      <td>94800</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PT2.2S</td>\n",
       "      <td>2200</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1582</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>PT1M34.9S</td>\n",
       "      <td>94900</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PT2.5S</td>\n",
       "      <td>2500</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1574</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>PT1M35.2S</td>\n",
       "      <td>95200</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PT2.9S</td>\n",
       "      <td>2900</td>\n",
       "      <td>PT0.4S</td>\n",
       "      <td>400</td>\n",
       "      <td>1584</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>PT1M35.6S</td>\n",
       "      <td>95600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PT3.1S</td>\n",
       "      <td>3100</td>\n",
       "      <td>PT0.2S</td>\n",
       "      <td>200</td>\n",
       "      <td>1576</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>PT1M35.8S</td>\n",
       "      <td>95800</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PT3.5S</td>\n",
       "      <td>3500</td>\n",
       "      <td>PT0.4S</td>\n",
       "      <td>400</td>\n",
       "      <td>1575</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>PT1M36.2S</td>\n",
       "      <td>96200</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PT5S</td>\n",
       "      <td>5000</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1586</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>PT1M37.7S</td>\n",
       "      <td>97700</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PT7.8S</td>\n",
       "      <td>7800</td>\n",
       "      <td>PT0.5S</td>\n",
       "      <td>500</td>\n",
       "      <td>1588</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>PT1M40.5S</td>\n",
       "      <td>100500</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1581</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT12M5.4S</td>\n",
       "      <td>725400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PT6.8S</td>\n",
       "      <td>6800</td>\n",
       "      <td>PT6.8S</td>\n",
       "      <td>6800</td>\n",
       "      <td>1585</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PT12M12.2S</td>\n",
       "      <td>732200</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PT7S</td>\n",
       "      <td>7000</td>\n",
       "      <td>PT0.2S</td>\n",
       "      <td>200</td>\n",
       "      <td>1577</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>PT12M12.4S</td>\n",
       "      <td>732400</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PT7.5S</td>\n",
       "      <td>7500</td>\n",
       "      <td>PT0.5S</td>\n",
       "      <td>500</td>\n",
       "      <td>1578</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PT12M12.9S</td>\n",
       "      <td>732900</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PT9.8S</td>\n",
       "      <td>9800</td>\n",
       "      <td>PT2.3S</td>\n",
       "      <td>2300</td>\n",
       "      <td>1580</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>PT12M15.2S</td>\n",
       "      <td>735200</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PT10.2S</td>\n",
       "      <td>10200</td>\n",
       "      <td>PT0.4S</td>\n",
       "      <td>400</td>\n",
       "      <td>1582</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>PT12M15.6S</td>\n",
       "      <td>735600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diffFirst  diffFirstMs diffPrev  diffPrevMs  entryId penaltyTime  \\\n",
       "0       PT0S            0     PT0S           0     1581        PT0S   \n",
       "1     PT0.3S          300   PT0.3S         300     1580        PT0S   \n",
       "2     PT0.6S          600   PT0.3S         300     1585        PT0S   \n",
       "3     PT0.9S          900   PT0.3S         300     1583        PT0S   \n",
       "4     PT0.9S          900     PT0S           0     1577        PT0S   \n",
       "5       PT1S         1000   PT0.1S         100     1578        PT0S   \n",
       "6     PT2.1S         2100   PT1.1S        1100     1579        PT0S   \n",
       "7     PT2.2S         2200   PT0.1S         100     1582        PT0S   \n",
       "8     PT2.5S         2500   PT0.3S         300     1574        PT0S   \n",
       "9     PT2.9S         2900   PT0.4S         400     1584        PT0S   \n",
       "10    PT3.1S         3100   PT0.2S         200     1576        PT0S   \n",
       "11    PT3.5S         3500   PT0.4S         400     1575        PT0S   \n",
       "12      PT5S         5000   PT0.1S         100     1586        PT0S   \n",
       "13    PT7.8S         7800   PT0.5S         500     1588        PT0S   \n",
       "14      PT0S            0     PT0S           0     1581        PT0S   \n",
       "15    PT6.8S         6800   PT6.8S        6800     1585        PT0S   \n",
       "16      PT7S         7000   PT0.2S         200     1577        PT0S   \n",
       "17    PT7.5S         7500   PT0.5S         500     1578        PT0S   \n",
       "18    PT9.8S         9800   PT2.3S        2300     1580        PT0S   \n",
       "19   PT10.2S        10200   PT0.4S         400     1582        PT0S   \n",
       "\n",
       "    penaltyTimeMs  position   stageTime  stageTimeMs    ...      \\\n",
       "0               0         1   PT1M32.7S        92700    ...       \n",
       "1               0         2     PT1M33S        93000    ...       \n",
       "2               0         3   PT1M33.3S        93300    ...       \n",
       "3               0         4   PT1M33.6S        93600    ...       \n",
       "4               0         5   PT1M33.6S        93600    ...       \n",
       "5               0         6   PT1M33.7S        93700    ...       \n",
       "6               0         7   PT1M34.8S        94800    ...       \n",
       "7               0         8   PT1M34.9S        94900    ...       \n",
       "8               0         9   PT1M35.2S        95200    ...       \n",
       "9               0        10   PT1M35.6S        95600    ...       \n",
       "10              0        11   PT1M35.8S        95800    ...       \n",
       "11              0        12   PT1M36.2S        96200    ...       \n",
       "12              0        17   PT1M37.7S        97700    ...       \n",
       "13              0        25   PT1M40.5S       100500    ...       \n",
       "14              0         1   PT12M5.4S       725400    ...       \n",
       "15              0         2  PT12M12.2S       732200    ...       \n",
       "16              0         3  PT12M12.4S       732400    ...       \n",
       "17              0         4  PT12M12.9S       732900    ...       \n",
       "18              0         5  PT12M15.2S       735200    ...       \n",
       "19              0         6  PT12M15.6S       735600    ...       \n",
       "\n",
       "   retainedClassLead  gainedOverallPos  gainedOverallLead overallPosDiff  \\\n",
       "0              False             False              False            0.0   \n",
       "1              False             False              False            0.0   \n",
       "2              False             False              False            0.0   \n",
       "3              False             False              False            0.0   \n",
       "4              False             False              False            0.0   \n",
       "5              False             False              False            0.0   \n",
       "6              False             False              False            0.0   \n",
       "7              False             False              False            0.0   \n",
       "8              False             False              False            0.0   \n",
       "9              False             False              False            0.0   \n",
       "10             False             False              False            0.0   \n",
       "11             False             False              False            0.0   \n",
       "12             False             False              False            0.0   \n",
       "13             False             False              False            0.0   \n",
       "14              True             False              False            0.0   \n",
       "15             False              True              False           -1.0   \n",
       "16             False              True              False           -2.0   \n",
       "17             False              True              False           -2.0   \n",
       "18             False             False              False            3.0   \n",
       "19             False              True              False           -2.0   \n",
       "\n",
       "   lostOverallLead  retainedOverallLead stagewin  stagewincount winsinarow  \\\n",
       "0            False                False     True            1.0          1   \n",
       "1            False                False    False            0.0          0   \n",
       "2            False                False    False            0.0          0   \n",
       "3            False                False    False            0.0          0   \n",
       "4            False                False    False            0.0          0   \n",
       "5            False                False    False            0.0          0   \n",
       "6            False                False    False            0.0          0   \n",
       "7            False                False    False            0.0          0   \n",
       "8            False                False    False            0.0          0   \n",
       "9            False                False    False            0.0          0   \n",
       "10           False                False    False            0.0          0   \n",
       "11           False                False    False            0.0          0   \n",
       "12           False                False    False            0.0          0   \n",
       "13           False                False    False            0.0          0   \n",
       "14           False                 True     True            2.0          2   \n",
       "15           False                False    False            0.0          0   \n",
       "16           False                False    False            0.0          0   \n",
       "17           False                False    False            0.0          0   \n",
       "18           False                False    False            0.0          0   \n",
       "19           False                False    False            0.0          0   \n",
       "\n",
       "   gainedTime  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  \n",
       "5       False  \n",
       "6       False  \n",
       "7       False  \n",
       "8       False  \n",
       "9       False  \n",
       "10      False  \n",
       "11      False  \n",
       "12      False  \n",
       "13      False  \n",
       "14       True  \n",
       "15      False  \n",
       "16      False  \n",
       "17      False  \n",
       "18      False  \n",
       "19      False  \n",
       "\n",
       "[20 rows x 35 columns]"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stagerank = getEnrichedStageRank(conn,rally)\n",
    "stagerank.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssdemo = stagerank[stagerank['code'].isin(['SS2','SS3','SS4'])].copy()\n",
    "lgp=(ssdemo.groupby('drivercode')['classrank'].agg('first')-ssdemo.groupby('drivercode')['classrank'].agg('last'))>0\n",
    "ssdemo['leggainedpos']=ssdemo['drivercode'].map(lgp.to_dict())\n",
    "\n",
    "tgp=(ssdemo.groupby('drivercode')['diffFirstMs'].agg('first')-ssdemo.groupby('drivercode')['diffFirstMs'].agg('last'))>=0\n",
    "ssdemo['leggainedtime']=ssdemo['drivercode'].map(tgp.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stageLegEnrichers(legStagerank):\n",
    "    lgp=(legStagerank.groupby('drivercode')['classrank'].agg('first')-legStagerank.groupby('drivercode')['classrank'].agg('last'))>0\n",
    "    legStagerank['leggainedpos']=ssdemo['drivercode'].map(lgp.to_dict())\n",
    "\n",
    "    tgp=(legStagerank.groupby('drivercode')['diffFirstMs'].agg('first')-legStagerank.groupby('drivercode')['diffFirstMs'].agg('last'))>=0\n",
    "    legStagerank['leggainedtime']=legStagerank['drivercode'].map(tgp.to_dict())\n",
    "    \n",
    "    legStagerank['legstagewincount'] = legStagerank.groupby(['drivercode'])['stagewin'].cumsum()\n",
    "\n",
    "    legStagerank = legStagerank.groupby('drivercode').apply(winningstreak) \n",
    "    return legStagerank\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssdemo = stagerank[stagerank['code'].isin(['SS2','SS3','SS4'])].copy()\n",
    "ssdemo = stageLegEnrichers(ssdemo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125094080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plotStageProgressionChart( ssdemo,linecolor='lightgrey' , deltalabels=True,progress=True);\n",
    "\n",
    "#fig, ax = plotStageProgressionChart( stagerank,deltalabels=True );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>legDate</th>\n",
       "      <th>date</th>\n",
       "      <th>startListId</th>\n",
       "      <th>status</th>\n",
       "      <th>itineraryLegId</th>\n",
       "      <th>itinerarySectionId</th>\n",
       "      <th>section</th>\n",
       "      <th>order</th>\n",
       "      <th>code</th>\n",
       "      <th>distance</th>\n",
       "      <th>eventId</th>\n",
       "      <th>name</th>\n",
       "      <th>number</th>\n",
       "      <th>stageId</th>\n",
       "      <th>stageType</th>\n",
       "      <th>status</th>\n",
       "      <th>timingPrecision</th>\n",
       "      <th>itineraryLegId</th>\n",
       "      <th>itinerarySections.itinerarySectionId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-15</td>\n",
       "      <td>Thursday 15 February</td>\n",
       "      <td>74</td>\n",
       "      <td>Completed</td>\n",
       "      <td>53</td>\n",
       "      <td>117</td>\n",
       "      <td>Section 1</td>\n",
       "      <td>1</td>\n",
       "      <td>SS1</td>\n",
       "      <td>1.90</td>\n",
       "      <td>27</td>\n",
       "      <td>SS1 SSS Karlstad 1</td>\n",
       "      <td>1</td>\n",
       "      <td>317</td>\n",
       "      <td>HeadToHeadSuperSpecialStage</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Tenth</td>\n",
       "      <td>53</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-16</td>\n",
       "      <td>Friday 16 February</td>\n",
       "      <td>78</td>\n",
       "      <td>Completed</td>\n",
       "      <td>54</td>\n",
       "      <td>118</td>\n",
       "      <td>Section 2</td>\n",
       "      <td>2</td>\n",
       "      <td>SS2</td>\n",
       "      <td>21.26</td>\n",
       "      <td>27</td>\n",
       "      <td>SS2 Hof-Finnskog 1</td>\n",
       "      <td>2</td>\n",
       "      <td>299</td>\n",
       "      <td>SpecialStage</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Tenth</td>\n",
       "      <td>54</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-16</td>\n",
       "      <td>Friday 16 February</td>\n",
       "      <td>78</td>\n",
       "      <td>Completed</td>\n",
       "      <td>54</td>\n",
       "      <td>118</td>\n",
       "      <td>Section 2</td>\n",
       "      <td>2</td>\n",
       "      <td>SS3</td>\n",
       "      <td>24.88</td>\n",
       "      <td>27</td>\n",
       "      <td>SS3 Svullrya 1</td>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>SpecialStage</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Tenth</td>\n",
       "      <td>54</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      legDate                  date  startListId     status  itineraryLegId  \\\n",
       "0  2018-02-15  Thursday 15 February           74  Completed              53   \n",
       "1  2018-02-16    Friday 16 February           78  Completed              54   \n",
       "2  2018-02-16    Friday 16 February           78  Completed              54   \n",
       "\n",
       "   itinerarySectionId    section  order code  distance  eventId  \\\n",
       "0                 117  Section 1      1  SS1      1.90       27   \n",
       "1                 118  Section 2      2  SS2     21.26       27   \n",
       "2                 118  Section 2      2  SS3     24.88       27   \n",
       "\n",
       "                 name  number  stageId                    stageType  \\\n",
       "0  SS1 SSS Karlstad 1       1      317  HeadToHeadSuperSpecialStage   \n",
       "1  SS2 Hof-Finnskog 1       2      299                 SpecialStage   \n",
       "2      SS3 Svullrya 1       3      300                 SpecialStage   \n",
       "\n",
       "      status timingPrecision  itineraryLegId  \\\n",
       "0  Completed           Tenth              53   \n",
       "1  Completed           Tenth              54   \n",
       "2  Completed           Tenth              54   \n",
       "\n",
       "   itinerarySections.itinerarySectionId  \n",
       "0                                   117  \n",
       "1                                   118  \n",
       "2                                   118  "
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q='''\n",
    "SELECT il.legDate, il.name AS date, il.startListId, il.status,\n",
    "    isc.itineraryLegId, isc.itinerarySectionId, isc.name AS section, isc.`order`,\n",
    "    ist.* FROM championship_events ce \n",
    "JOIN itinerary_event ie ON ce.eventId = ie.eventId \n",
    "JOIN itinerary_legs il ON ie.itineraryId=il.itineraryId\n",
    "JOIN itinerary_sections isc ON il.itineraryLegId=isc.itineraryLegId\n",
    "JOIN itinerary_stages ist ON ist.`itinerarySections.itinerarySectionId`=isc.itinerarySectionId\n",
    "WHERE ce.`country.name`=\"{rally}\" ORDER BY isc.`order`\n",
    "'''.format(rally=rally)\n",
    "rally_stages = pd.read_sql(q,conn)\n",
    "rally_stages.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "section\n",
       "Section 1     1\n",
       "Section 2     4\n",
       "Section 3     8\n",
       "Section 4    11\n",
       "Section 5    14\n",
       "Section 6    16\n",
       "Section 7    17\n",
       "Section 8    18\n",
       "Section 9    19\n",
       "Name: number, dtype: int64"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rally_stages.groupby('section')['number'].max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for a in ax.get_xticklabels(): print(a.get_text(), a.get_position())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124edf358>"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll=len(stagerank['drivercode'].unique())\n",
    "for i,d in rally_stages.iterrows():\n",
    "    #Show stage distance\n",
    "    ax.text(i+1,2.5+ll,'{}km'.format(d['distance']),\n",
    "            horizontalalignment='center',fontsize=10, rotation=45)\n",
    "    #Show stage name\n",
    "    ax.text(i+1,-2,'{}'.format(d['name']),\n",
    "            horizontalalignment='center',fontsize=10, rotation=45)\n",
    "#Separate out the separate sections\n",
    "for i in rally_stages.groupby('section')['number'].max()[:-1]:\n",
    "    ax.plot((i+0.5,i+0.5), (0.5,ll+0.5), color='lightgrey', linestyle='dashed')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rally Delta Charts\n",
    "\n",
    "Macroscope intended to show gaps between cars on each stage of a rally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 92700,\n",
       " 2: 725400,\n",
       " 3: 1529300,\n",
       " 4: 2149700,\n",
       " 5: 2776700,\n",
       " 6: 3578100,\n",
       " 7: 4186400,\n",
       " 8: 4573100,\n",
       " 9: 5182900,\n",
       " 10: 5970400,\n",
       " 11: 6486300,\n",
       " 12: 7082900,\n",
       " 13: 7847400,\n",
       " 14: 8355600,\n",
       " 15: 8451100,\n",
       " 16: 8603800,\n",
       " 17: 9291800,\n",
       " 18: 9968700,\n",
       " 19: 10333100}"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebase= stagerank[stagerank['position']==1][['snum','totalTimeMs']].set_index('snum').to_dict(orient='dict')['totalTimeMs']\n",
    "rebase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diffFirst</th>\n",
       "      <th>diffFirstMs</th>\n",
       "      <th>diffPrev</th>\n",
       "      <th>diffPrevMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>penaltyTime</th>\n",
       "      <th>penaltyTimeMs</th>\n",
       "      <th>position</th>\n",
       "      <th>stageTime</th>\n",
       "      <th>stageTimeMs</th>\n",
       "      <th>...</th>\n",
       "      <th>retainedClassLead</th>\n",
       "      <th>gainedOverallPos</th>\n",
       "      <th>gainedOverallLead</th>\n",
       "      <th>overallPosDiff</th>\n",
       "      <th>lostOverallLead</th>\n",
       "      <th>retainedOverallLead</th>\n",
       "      <th>stagewin</th>\n",
       "      <th>stagewincount</th>\n",
       "      <th>winsinarow</th>\n",
       "      <th>gainedTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1581</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT1M32.7S</td>\n",
       "      <td>92700</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1580</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PT1M33S</td>\n",
       "      <td>93000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT0.6S</td>\n",
       "      <td>600</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1585</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>PT1M33.3S</td>\n",
       "      <td>93300</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT0.9S</td>\n",
       "      <td>900</td>\n",
       "      <td>PT0.3S</td>\n",
       "      <td>300</td>\n",
       "      <td>1583</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PT1M33.6S</td>\n",
       "      <td>93600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PT0.9S</td>\n",
       "      <td>900</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1577</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>PT1M33.6S</td>\n",
       "      <td>93600</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diffFirst  diffFirstMs diffPrev  diffPrevMs  entryId penaltyTime  \\\n",
       "0      PT0S            0     PT0S           0     1581        PT0S   \n",
       "1    PT0.3S          300   PT0.3S         300     1580        PT0S   \n",
       "2    PT0.6S          600   PT0.3S         300     1585        PT0S   \n",
       "3    PT0.9S          900   PT0.3S         300     1583        PT0S   \n",
       "4    PT0.9S          900     PT0S           0     1577        PT0S   \n",
       "\n",
       "   penaltyTimeMs  position  stageTime  stageTimeMs    ...      \\\n",
       "0              0         1  PT1M32.7S        92700    ...       \n",
       "1              0         2    PT1M33S        93000    ...       \n",
       "2              0         3  PT1M33.3S        93300    ...       \n",
       "3              0         4  PT1M33.6S        93600    ...       \n",
       "4              0         5  PT1M33.6S        93600    ...       \n",
       "\n",
       "  retainedClassLead  gainedOverallPos  gainedOverallLead overallPosDiff  \\\n",
       "0             False             False              False            0.0   \n",
       "1             False             False              False            0.0   \n",
       "2             False             False              False            0.0   \n",
       "3             False             False              False            0.0   \n",
       "4             False             False              False            0.0   \n",
       "\n",
       "  lostOverallLead  retainedOverallLead stagewin  stagewincount winsinarow  \\\n",
       "0           False                False     True            1.0          1   \n",
       "1           False                False    False            0.0          0   \n",
       "2           False                False    False            0.0          0   \n",
       "3           False                False    False            0.0          0   \n",
       "4           False                False    False            0.0          0   \n",
       "\n",
       "  gainedTime  \n",
       "0      False  \n",
       "1      False  \n",
       "2      False  \n",
       "3      False  \n",
       "4      False  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stagerank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124906320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot Stage Times\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "ax.yaxis.set_major_locator(MultipleLocator(1))\n",
    "plt.xscale('symlog')\n",
    "plt.box(on=None)\n",
    "stagerank['rebaseOverallTime']  = ((stagerank['totalTimeMs'] - stagerank['snum'].map(rebase))/1000).round(1)\n",
    "stagerank.plot(kind='scatter',y='snum',x='rebaseOverallTime',xlim=(-0.1,10000),s=1,ax=ax )\n",
    "\n",
    "for i, row in stagerank.iterrows():\n",
    "    ax.annotate(row['drivercode'], (stagerank.iloc[i]['rebaseOverallTime'],stagerank.iloc[i]['snum']), rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 93300,\n",
       " 2: 732200,\n",
       " 3: 1536400,\n",
       " 4: 2157100,\n",
       " 5: 2783000,\n",
       " 6: 3585100,\n",
       " 7: 4196800,\n",
       " 8: 4586300,\n",
       " 9: 5200100,\n",
       " 10: 5987000,\n",
       " 11: 6507800,\n",
       " 12: 7115900,\n",
       " 13: 7895400,\n",
       " 14: 8412100,\n",
       " 15: 8507100,\n",
       " 16: 8660600,\n",
       " 17: 9348200,\n",
       " 18: 10037300,\n",
       " 19: 10408400}"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show deltas for target driver\n",
    "drivercode='OST'\n",
    "rebase= stagerank[stagerank['drivercode']==drivercode][['snum','totalTimeMs']].set_index('snum').to_dict(orient='dict')['totalTimeMs']\n",
    "rebase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotDeltaLabels(stagerank, rebaseType='position',drivercode=None, logscale=True,xlim=None, ylim=None, title=None):\n",
    "    ''' rebaseType: driver | position '''\n",
    "    if rebaseType=='driver' and drivercode is not None:\n",
    "        rebase= stagerank[stagerank['drivercode']==drivercode][['snum','totalTimeMs']].set_index('snum').to_dict(orient='dict')['totalTimeMs']\n",
    "    else:\n",
    "        rebase= stagerank[stagerank['position']==1][['snum','totalTimeMs']].set_index('snum').to_dict(orient='dict')['totalTimeMs']\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "    plt.box(on=None)\n",
    "    \n",
    "    ax.yaxis.label.set_visible(False)\n",
    "    ax.tick_params(axis='y', which='both',length=0)\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(1))\n",
    "    \n",
    "    if logscale:\n",
    "        plt.xscale('symlog')\n",
    "    \n",
    "    plt.axvline(x=0,linestyle='dashed',color='grey')\n",
    "    \n",
    "    title = 'Overall Deltas by Stage' if title is None else title\n",
    "    title = '{} (relative to {})'.format(title, drivercode) if drivercode else title\n",
    "    \n",
    "    stagerank['rebaseOverallTime']  = ((stagerank['totalTimeMs'] - stagerank['snum'].map(rebase))/1000).round(1)\n",
    "    stagerank.plot(kind='scatter',y='snum',x='rebaseOverallTime',s=1,ax=ax, xlim=xlim, ylim=ylim ,\n",
    "                   title = title)\n",
    "\n",
    "    for i, row in stagerank.iterrows():\n",
    "        if xlim is not None:\n",
    "            if stagerank.iloc[i]['rebaseOverallTime']>=xlim[0] and stagerank.iloc[i]['rebaseOverallTime']<=xlim[1]:\n",
    "                if row['drivercode'] != drivercode:\n",
    "                    col='g' if row['rebaseOverallTime']<0 else 'r'\n",
    "                    txt = '{} [{}]'.format(row['drivercode'],row['position'])\n",
    "                else:\n",
    "                    col='black'\n",
    "                    txt = row['position']\n",
    "                \n",
    "                ax.annotate(txt,\n",
    "                            (row['rebaseOverallTime'],row['snum']),color=col, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotDeltaLabels(stagerank, 'driver','PAD',logscale=False,xlim=(-20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split times on a Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsedDuration</th>\n",
       "      <th>elapsedDurationMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>splitDateTime</th>\n",
       "      <th>splitDateTimeLocal</th>\n",
       "      <th>splitPointId</th>\n",
       "      <th>splitPointTimeId</th>\n",
       "      <th>stageTimeDuration</th>\n",
       "      <th>stageTimeDurationMs</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>startDateTimeLocal</th>\n",
       "      <th>stageId</th>\n",
       "      <th>class</th>\n",
       "      <th>code</th>\n",
       "      <th>distance</th>\n",
       "      <th>name</th>\n",
       "      <th>drivercode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT1M28.059S</td>\n",
       "      <td>88059</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T08:55:28.059Z</td>\n",
       "      <td>2018-02-16T09:55:28.059+01:00</td>\n",
       "      <td>661</td>\n",
       "      <td>16671</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT4M11.143S</td>\n",
       "      <td>251143</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T08:58:11.143Z</td>\n",
       "      <td>2018-02-16T09:58:11.143+01:00</td>\n",
       "      <td>683</td>\n",
       "      <td>16503</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT6M49.659S</td>\n",
       "      <td>409659</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T09:00:49.659Z</td>\n",
       "      <td>2018-02-16T10:00:49.659+01:00</td>\n",
       "      <td>688</td>\n",
       "      <td>16683</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT9M5.5S</td>\n",
       "      <td>545500</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T09:03:05.5Z</td>\n",
       "      <td>2018-02-16T10:03:05.5+01:00</td>\n",
       "      <td>690</td>\n",
       "      <td>16690</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PT1M27.079S</td>\n",
       "      <td>87079</td>\n",
       "      <td>1581</td>\n",
       "      <td>2018-02-16T08:56:27.079Z</td>\n",
       "      <td>2018-02-16T09:56:27.079+01:00</td>\n",
       "      <td>661</td>\n",
       "      <td>16509</td>\n",
       "      <td>00:10:37.1000000</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>2018-02-16T09:55:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>TÄN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  elapsedDuration  elapsedDurationMs  entryId             splitDateTime  \\\n",
       "0     PT1M28.059S              88059     1574  2018-02-16T08:55:28.059Z   \n",
       "1     PT4M11.143S             251143     1574  2018-02-16T08:58:11.143Z   \n",
       "2     PT6M49.659S             409659     1574  2018-02-16T09:00:49.659Z   \n",
       "3        PT9M5.5S             545500     1574    2018-02-16T09:03:05.5Z   \n",
       "4     PT1M27.079S              87079     1581  2018-02-16T08:56:27.079Z   \n",
       "\n",
       "              splitDateTimeLocal  splitPointId  splitPointTimeId  \\\n",
       "0  2018-02-16T09:55:28.059+01:00           661             16671   \n",
       "1  2018-02-16T09:58:11.143+01:00           683             16503   \n",
       "2  2018-02-16T10:00:49.659+01:00           688             16683   \n",
       "3    2018-02-16T10:03:05.5+01:00           690             16690   \n",
       "4  2018-02-16T09:56:27.079+01:00           661             16509   \n",
       "\n",
       "  stageTimeDuration  stageTimeDurationMs        startDateTime  \\\n",
       "0  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "1  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "2  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "3  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "4  00:10:37.1000000             637100.0  2018-02-16T08:55:00   \n",
       "\n",
       "          startDateTimeLocal  stageId class code  distance          name  \\\n",
       "0  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "1  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "2  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "3  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "4  2018-02-16T09:55:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "\n",
       "  drivercode  \n",
       "0        OGI  \n",
       "1        OGI  \n",
       "2        OGI  \n",
       "3        OGI  \n",
       "4        TÄN  "
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc='RC1'\n",
    "\n",
    "q='''\n",
    "SELECT st.*, sc.name as class, i.code, i.distance, i.name, sl.`driver.code` drivercode\n",
    "FROM split_times st INNER JOIN itinerary_stages i ON st.stageid = i.stageid\n",
    "INNER JOIN startlist_classes sc ON sc.entryid = st.entryid \n",
    "INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "WHERE i.code=\"{ss}\" AND sc.name=\"{rc}\" AND ce.`country.name`=\"{rally}\" ORDER BY startDateTime,entryId, splitDateTime\n",
    "'''.format(rc=rc,ss=ss, rally=rally)\n",
    "splits=pd.read_sql(q,conn)\n",
    "splits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deltas within a particular sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsedDuration</th>\n",
       "      <th>elapsedDurationMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>splitDateTime</th>\n",
       "      <th>splitDateTimeLocal</th>\n",
       "      <th>splitPointId</th>\n",
       "      <th>splitPointTimeId</th>\n",
       "      <th>stageTimeDuration</th>\n",
       "      <th>stageTimeDurationMs</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>startDateTimeLocal</th>\n",
       "      <th>stageId</th>\n",
       "      <th>class</th>\n",
       "      <th>code</th>\n",
       "      <th>distance</th>\n",
       "      <th>name</th>\n",
       "      <th>drivercode</th>\n",
       "      <th>splitDurationMs</th>\n",
       "      <th>splitDurationS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT1M28.059S</td>\n",
       "      <td>88059</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T08:55:28.059Z</td>\n",
       "      <td>2018-02-16T09:55:28.059+01:00</td>\n",
       "      <td>661</td>\n",
       "      <td>16671</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "      <td>88059.0</td>\n",
       "      <td>88.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT4M11.143S</td>\n",
       "      <td>251143</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T08:58:11.143Z</td>\n",
       "      <td>2018-02-16T09:58:11.143+01:00</td>\n",
       "      <td>683</td>\n",
       "      <td>16503</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "      <td>163084.0</td>\n",
       "      <td>163.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT6M49.659S</td>\n",
       "      <td>409659</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T09:00:49.659Z</td>\n",
       "      <td>2018-02-16T10:00:49.659+01:00</td>\n",
       "      <td>688</td>\n",
       "      <td>16683</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "      <td>158516.0</td>\n",
       "      <td>158.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT9M5.5S</td>\n",
       "      <td>545500</td>\n",
       "      <td>1574</td>\n",
       "      <td>2018-02-16T09:03:05.5Z</td>\n",
       "      <td>2018-02-16T10:03:05.5+01:00</td>\n",
       "      <td>690</td>\n",
       "      <td>16690</td>\n",
       "      <td>00:10:40.5000000</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2018-02-16T09:54:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>OGI</td>\n",
       "      <td>135841.0</td>\n",
       "      <td>135.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PT1M27.079S</td>\n",
       "      <td>87079</td>\n",
       "      <td>1581</td>\n",
       "      <td>2018-02-16T08:56:27.079Z</td>\n",
       "      <td>2018-02-16T09:56:27.079+01:00</td>\n",
       "      <td>661</td>\n",
       "      <td>16509</td>\n",
       "      <td>00:10:37.1000000</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>2018-02-16T09:55:00+01:00</td>\n",
       "      <td>301</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>TÄN</td>\n",
       "      <td>87079.0</td>\n",
       "      <td>87.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  elapsedDuration  elapsedDurationMs  entryId             splitDateTime  \\\n",
       "0     PT1M28.059S              88059     1574  2018-02-16T08:55:28.059Z   \n",
       "1     PT4M11.143S             251143     1574  2018-02-16T08:58:11.143Z   \n",
       "2     PT6M49.659S             409659     1574  2018-02-16T09:00:49.659Z   \n",
       "3        PT9M5.5S             545500     1574    2018-02-16T09:03:05.5Z   \n",
       "4     PT1M27.079S              87079     1581  2018-02-16T08:56:27.079Z   \n",
       "\n",
       "              splitDateTimeLocal  splitPointId  splitPointTimeId  \\\n",
       "0  2018-02-16T09:55:28.059+01:00           661             16671   \n",
       "1  2018-02-16T09:58:11.143+01:00           683             16503   \n",
       "2  2018-02-16T10:00:49.659+01:00           688             16683   \n",
       "3    2018-02-16T10:03:05.5+01:00           690             16690   \n",
       "4  2018-02-16T09:56:27.079+01:00           661             16509   \n",
       "\n",
       "  stageTimeDuration  stageTimeDurationMs        startDateTime  \\\n",
       "0  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "1  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "2  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "3  00:10:40.5000000             640500.0  2018-02-16T08:54:00   \n",
       "4  00:10:37.1000000             637100.0  2018-02-16T08:55:00   \n",
       "\n",
       "          startDateTimeLocal  stageId class code  distance          name  \\\n",
       "0  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "1  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "2  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "3  2018-02-16T09:54:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "4  2018-02-16T09:55:00+01:00      301   RC1  SS4     19.13  SS4 Röjden 1   \n",
       "\n",
       "  drivercode  splitDurationMs  splitDurationS  \n",
       "0        OGI          88059.0            88.1  \n",
       "1        OGI         163084.0           163.1  \n",
       "2        OGI         158516.0           158.5  \n",
       "3        OGI         135841.0           135.8  \n",
       "4        TÄN          87079.0            87.1  "
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits['splitDurationMs']=splits.groupby('drivercode')['elapsedDurationMs'].transform(pd.Series.diff)\n",
    "#splits.loc[0, 'splitDurationMs'] = splits['elapsedDurationMs'].iloc[0]\n",
    "#Set the value of the first difference to be the original split time\n",
    "splits.loc[splits.groupby('drivercode',as_index=False).head(1).index,'splitDurationMs'] = splits.loc[splits.groupby('drivercode',as_index=False).head(1).index,'elapsedDurationMs']\n",
    "splits['splitDurationS']=(splits['splitDurationMs']/1000).round(1)\n",
    "#splits['splitnum'] = splits.groupby('drivercode').cumcount()+1\n",
    "splits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drivercode</th>\n",
       "      <th>stageTimeDurationMs</th>\n",
       "      <th>elapsedDurationMs</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>splitDurationS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>687600.0</td>\n",
       "      <td>594275</td>\n",
       "      <td>2018-02-16T09:20:00</td>\n",
       "      <td>93.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRE</td>\n",
       "      <td>621900.0</td>\n",
       "      <td>533370</td>\n",
       "      <td>2018-02-16T09:10:00</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EVA</td>\n",
       "      <td>689800.0</td>\n",
       "      <td>577005</td>\n",
       "      <td>2018-02-16T09:03:00</td>\n",
       "      <td>112.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAP</td>\n",
       "      <td>621400.0</td>\n",
       "      <td>532025</td>\n",
       "      <td>2018-02-16T09:06:00</td>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAT</td>\n",
       "      <td>625900.0</td>\n",
       "      <td>533383</td>\n",
       "      <td>2018-02-16T08:58:00</td>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MEE</td>\n",
       "      <td>633900.0</td>\n",
       "      <td>540520</td>\n",
       "      <td>2018-02-16T09:00:00</td>\n",
       "      <td>93.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MIK</td>\n",
       "      <td>618500.0</td>\n",
       "      <td>529534</td>\n",
       "      <td>2018-02-16T09:08:00</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NEU</td>\n",
       "      <td>620400.0</td>\n",
       "      <td>530569</td>\n",
       "      <td>2018-02-16T09:02:00</td>\n",
       "      <td>89.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OGI</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>545500</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OST</td>\n",
       "      <td>620700.0</td>\n",
       "      <td>532727</td>\n",
       "      <td>2018-02-16T09:16:00</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PAD</td>\n",
       "      <td>622600.0</td>\n",
       "      <td>533966</td>\n",
       "      <td>2018-02-16T09:14:00</td>\n",
       "      <td>88.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SOL</td>\n",
       "      <td>665100.0</td>\n",
       "      <td>569300</td>\n",
       "      <td>2018-02-16T09:18:00</td>\n",
       "      <td>95.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SUN</td>\n",
       "      <td>622900.0</td>\n",
       "      <td>534497</td>\n",
       "      <td>2018-02-16T09:12:00</td>\n",
       "      <td>88.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>539156</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>97.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  stageTimeDurationMs  elapsedDurationMs        startDateTime  \\\n",
       "0         AL              687600.0             594275  2018-02-16T09:20:00   \n",
       "1         BRE             621900.0             533370  2018-02-16T09:10:00   \n",
       "2         EVA             689800.0             577005  2018-02-16T09:03:00   \n",
       "3         LAP             621400.0             532025  2018-02-16T09:06:00   \n",
       "4         LAT             625900.0             533383  2018-02-16T08:58:00   \n",
       "5         MEE             633900.0             540520  2018-02-16T09:00:00   \n",
       "6         MIK             618500.0             529534  2018-02-16T09:08:00   \n",
       "7         NEU             620400.0             530569  2018-02-16T09:02:00   \n",
       "8         OGI             640500.0             545500  2018-02-16T08:54:00   \n",
       "9         OST             620700.0             532727  2018-02-16T09:16:00   \n",
       "10        PAD             622600.0             533966  2018-02-16T09:14:00   \n",
       "11        SOL             665100.0             569300  2018-02-16T09:18:00   \n",
       "12        SUN             622900.0             534497  2018-02-16T09:12:00   \n",
       "13        TÄN             637100.0             539156  2018-02-16T08:55:00   \n",
       "\n",
       "    splitDurationS  \n",
       "0             93.3  \n",
       "1             88.5  \n",
       "2            112.8  \n",
       "3             89.4  \n",
       "4             92.5  \n",
       "5             93.4  \n",
       "6             89.0  \n",
       "7             89.8  \n",
       "8             95.0  \n",
       "9             88.0  \n",
       "10            88.6  \n",
       "11            95.8  \n",
       "12            88.4  \n",
       "13            97.9  "
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the time for the last section - i.e. diff between stage time and final split time\n",
    "tmp = splits.groupby('drivercode', as_index=False).last()[['drivercode','stageTimeDurationMs','elapsedDurationMs', 'startDateTime' ]]\n",
    "tmp['splitDurationS']=((tmp['stageTimeDurationMs']-tmp['elapsedDurationMs'])/1000).round(1)\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drivercode</th>\n",
       "      <th>splitDurationS</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>stageTimeDurationMs</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OGI</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGI</td>\n",
       "      <td>163.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGI</td>\n",
       "      <td>158.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>135.8</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "\n",
       "    section  \n",
       "0         1  \n",
       "1         2  \n",
       "2         3  \n",
       "3         4  \n",
       "63        5  "
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['drivercode','splitDurationS', 'startDateTime','stageTimeDurationMs']\n",
    "splitdurations = pd.concat([splits[cols],tmp[cols]]).reset_index(drop=True)\n",
    "splitdurations['section'] = splitdurations.groupby('drivercode').cumcount()+1\n",
    "splitdurations = splitdurations.sort_values(['startDateTime','drivercode','section'])\n",
    "splitdurations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make That a Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSplits(conn,rally,ss,rc='RC1'):\n",
    "    q='''\n",
    "    SELECT st.*, sc.name as class, i.code, i.distance, i.name, sl.`driver.code` drivercode\n",
    "    FROM split_times st INNER JOIN itinerary_stages i ON st.stageid = i.stageid\n",
    "    INNER JOIN startlist_classes sc ON sc.entryid = st.entryid \n",
    "    INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "    INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "    WHERE i.code=\"{ss}\" AND sc.name=\"{rc}\" AND ce.`country.name`=\"{rally}\" ORDER BY startDateTime,entryId, splitDateTime\n",
    "    '''.format(rc=rc,ss=ss, rally=rally)\n",
    "    splits=pd.read_sql(q,conn)\n",
    "    return splits\n",
    "\n",
    "def _getSplitDurationsFromSplits(splits):\n",
    "        \n",
    "    splits['splitDurationMs']=splits.groupby('drivercode')['elapsedDurationMs'].transform(pd.Series.diff)\n",
    "    #Set the value of the first difference to be the original split time\n",
    "    splits.loc[splits.groupby('drivercode',as_index=False).head(1).index,'splitDurationMs'] = splits.loc[splits.groupby('drivercode',as_index=False).head(1).index,'elapsedDurationMs']\n",
    "    splits['splitDurationS']=(splits['splitDurationMs']/1000).round(1)\n",
    "\n",
    "    #Find the time for the last section - i.e. diff between stage time and final split time\n",
    "    tmp = splits.groupby('drivercode', as_index=False).last()[['drivercode','stageTimeDurationMs','elapsedDurationMs', 'startDateTime' ]]\n",
    "    tmp['splitDurationS']=((tmp['stageTimeDurationMs']-tmp['elapsedDurationMs'])/1000).round(1)\n",
    "\n",
    "    cols = ['drivercode','splitDurationS', 'startDateTime','stageTimeDurationMs']\n",
    "    splitdurations = pd.concat([splits[cols],tmp[cols]]).reset_index(drop=True)\n",
    "    splitdurations['section'] = splitdurations.groupby('drivercode').cumcount()+1\n",
    "    splitdurations = splitdurations.sort_values(['startDateTime','drivercode','section'])\n",
    "\n",
    "    return splitdurations\n",
    "\n",
    "def getSplitDurationsFromSplits(conn,rally,ss,rc='RC1'):\n",
    "    splits = getSplits(conn,rally,ss,rc='RC1')\n",
    "    return _getSplitDurationsFromSplits(splits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rebasing\n",
    "\n",
    "The aim of rebasing is to derive time deltas for one car relative to times recorded by an arbitrary other car.\n",
    "\n",
    "The first step is to capture the base times for the target vehicle, relative to which rebased times will be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 88.1, 2: 163.1, 3: 158.5, 4: 135.8, 5: 95.0}"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drivercode = 'OGI'\n",
    "\n",
    "rebase = splitdurations[splitdurations['drivercode']==drivercode][['section','splitDurationS']].set_index('section').to_dict(orient='dict')['splitDurationS']\n",
    "rebase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The base times are then subtracted from the corresponding times for each other car."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [],
   "source": [
    "splitdurations['rebased'] = splitdurations['splitDurationS'] - splitdurations['section'].map(rebase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drivercode</th>\n",
       "      <th>splitDurationS</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>stageTimeDurationMs</th>\n",
       "      <th>section</th>\n",
       "      <th>rebased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OGI</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGI</td>\n",
       "      <td>163.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGI</td>\n",
       "      <td>158.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>135.8</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "\n",
       "    section  rebased  \n",
       "0         1      0.0  \n",
       "1         2      0.0  \n",
       "2         3      0.0  \n",
       "3         4      0.0  \n",
       "63        5      0.0  "
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitdurations['section'] = splitdurations['section']#.astype(str)\n",
    "splitdurations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make That a Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebaseSplitDurations(splitdurations, drivercode=None):\n",
    "    if drivercode is None: return splitdurations\n",
    "    \n",
    "    rebase = splitdurations[splitdurations['drivercode']==drivercode][['section','splitDurationS']].set_index('section').to_dict(orient='dict')['splitDurationS']\n",
    "    splitdurations['rebased'] = splitdurations['splitDurationS'] - splitdurations['section'].map(rebase)\n",
    "    return splitdurations\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebaseSplitDurationsBest(splitdurations):\n",
    "    \n",
    "    #Note this ranking may include undesired drivers w/ bad data\n",
    "    if 'sectorrank' not in splitdurations:\n",
    "        splitdurations['sectorrank']=splitdurations.groupby(['section'])['splitDurationS'].rank(method='dense').astype(int)\n",
    "\n",
    "    rebase = splitdurations[splitdurations['sectorrank']==1][['section','splitDurationS']].set_index('section').to_dict(orient='dict')['splitDurationS']\n",
    "    splitdurations['rebased'] = splitdurations['splitDurationS'] - splitdurations['section'].map(rebase)\n",
    "\n",
    "    sectorPurpleDriver = splitdurations[splitdurations['sectorrank']==1][['section','drivercode']].set_index('section').to_dict(orient='dict')['drivercode']\n",
    "    splitdurations['sectorBestDriver'] = splitdurations['section'].map(sectorPurpleDriver)\n",
    "    return splitdurations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a sort order for the display of times grouped by drivers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Split Derived Sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = splitdurations.sort_values(['stageTimeDurationMs','drivercode'])['drivercode'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rebaseSplitDurations(splitdurations, drivercode=None).pivot('drivercode','section','rebased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>section</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drivercode</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>AL</th>\n",
       "      <td>10.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVA</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>22.3</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>158.4</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-29.3</td>\n",
       "      <td>-46.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAT</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEE</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-4.7</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIK</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OGI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAD</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>-6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOL</th>\n",
       "      <td>4.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TÄN</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "section         1     2     3     4     5\n",
       "drivercode                               \n",
       "AL           10.2  19.2  16.2   3.2  -1.7\n",
       "BRE          -0.4  -0.7  -2.8  -8.3  -6.5\n",
       "EVA           1.0   0.5   7.7  22.3  17.8\n",
       "LAP         158.4  -6.8 -29.3 -46.4   NaN\n",
       "LAT          -0.8  -2.0  -3.3  -6.0  -2.5\n",
       "MEE           0.2   0.3  -0.8  -4.7  -1.6\n",
       "MIK          -1.5  -3.0  -2.8  -8.6  -6.0\n",
       "NEU          -2.5  -1.3  -3.3  -7.8  -5.2\n",
       "OGI           0.0   0.0   0.0   0.0   0.0\n",
       "OST          -2.2  -0.4  -2.0  -8.2  -7.0\n",
       "PAD          -0.2  -1.3  -2.8  -7.2  -6.4\n",
       "SOL           4.8   9.9   7.8   1.3   0.8\n",
       "SUN          -1.3  -1.4  -0.8  -7.5  -6.6\n",
       "TÄN          -1.0  -1.4  -2.5  -1.4   2.9"
      ]
     },
     "execution_count": 637,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitdurations.pivot('drivercode','section','rebased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1268dd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "zz=splitdurations[splitdurations['rebased'].abs()<25].pivot('drivercode','section','rebased')\n",
    "#zz['stagediff']=zz.sum(axis=1)\n",
    "#zz = zz.sort_values('stagediff',ascending=True).drop(['stagediff'], axis=1)\n",
    "zz=zz.reindex(order)\n",
    "\n",
    "\n",
    "#Define a color map for the bars\n",
    "#This is based on generating a list of lists.\n",
    "#The outer list represents the cars; each inner list represents the bar colours grouped for each car.\n",
    "#In this case, we define colours based on positive or negative deltas relative to the target car.\n",
    "cmap=[]\n",
    "for col in zz.columns:\n",
    "    cmap.append( ['r' if x else 'g' for x in zz[col]>0 ] )\n",
    "\n",
    "zz.plot(kind='barh',title='delta by stage section',color=cmap,ax=ax, legend=False).invert_yaxis()\n",
    "\n",
    "#Hide outer box\n",
    "plt.box(on=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above chart, we see times relative to OGI. The drivers are ordered based on overall stage time. The grouped bars show the delta to the target driver within each \"sector\" of the stage, with the first sector at the top of the grouping and the last sector at the bottom.\n",
    "\n",
    "In this case, we see OGI lost time on each sector relative to 7 of the top 8 cars (in this particular example, the times for LAP appear to be corrupt).\n",
    "\n",
    "The color selection reflects the perspective with which we read the chart. In the above case, the red colour indicates that the target drive lost time compared to the drivers ahead on the stage. The negative x-axis time shoud be read as indicating that the the driver lost that amount of time relative those cars (i.e. the target river is negatively affected by those times).\n",
    "\n",
    "The green colouring suggests that the target driver *gained* time against the lesser ranked cars, making up \"positive\" seconds against them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SS1</th>\n",
       "      <th>SS2</th>\n",
       "      <th>SS3</th>\n",
       "      <th>SS4</th>\n",
       "      <th>SS5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drivercode</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>MIK</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SS1  SS2  SS3  SS4  SS5\n",
       "drivercode                         \n",
       "MIK        -1.5 -3.0 -2.8 -8.6 -6.0\n",
       "NEU        -2.5 -1.3 -3.3 -7.8 -5.2\n",
       "OST        -2.2 -0.4 -2.0 -8.2 -7.0\n",
       "LAP         NaN -6.8  NaN  NaN  NaN\n",
       "BRE        -0.4 -0.7 -2.8 -8.3 -6.5"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz.columns=['SS{}'.format(c) for c in zz.columns]\n",
    "zz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SS1</th>\n",
       "      <th>SS2</th>\n",
       "      <th>SS3</th>\n",
       "      <th>SS4</th>\n",
       "      <th>SS5</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drivercode</th>\n",
       "      <th></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>MIK</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>-20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-18.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SS1  SS2  SS3  SS4  SS5  total\n",
       "drivercode                                \n",
       "MIK        -1.5 -3.0 -2.8 -8.6 -6.0  -22.0\n",
       "NEU        -2.5 -1.3 -3.3 -7.8 -5.2  -20.1\n",
       "OST        -2.2 -0.4 -2.0 -8.2 -7.0  -19.8\n",
       "LAP         NaN -6.8  NaN  NaN  NaN  -19.1\n",
       "BRE        -0.4 -0.7 -2.8 -8.3 -6.5  -18.6"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This requires all the split times to be in\n",
    "#zz['total']=zz.sum(axis=1)\n",
    "#Better to use the actual stage delta time?\n",
    "zz['total']=(splitdurations[['drivercode','stageTimeDurationMs']].drop_duplicates().set_index('drivercode')/1000).round(1)\n",
    "rebase = ((splitdurations[splitdurations['drivercode']==drivercode][['section','stageTimeDurationMs']].iloc[0]['stageTimeDurationMs'])/1000).round(1)\n",
    "zz['total'] = zz['total'] - rebase \n",
    "zz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a25320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "#Define a color map for the bars\n",
    "#This is based on generating a list of lists.\n",
    "#The outer list represents the cars; each inner list represents the bar colours grouped for each car.\n",
    "#In this case, we define colours based on positive or negative deltas relative to the target car.\n",
    "cmap=[]\n",
    "\n",
    "cols=[c for c in zz.columns if c.startswith('SS')]\n",
    "for col in cols:\n",
    "    cmap.append( ['g' if x else 'r' for x in zz[col]>0 ] )\n",
    "\n",
    "zz['total'].plot(kind='barh',ax=ax, color='lightgrey',legend=False)\n",
    "\n",
    "zz[cols].plot(kind='barh',color=cmap,ax=ax, legend=False).invert_yaxis()\n",
    "\n",
    "#Hide outer box\n",
    "plt.box(on=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Functions of Those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSplitOrderByStageTimeDuration(splitdurations):\n",
    "    return splitdurations.sort_values(['stageTimeDurationMs','drivercode'])['drivercode'].unique().tolist()\n",
    "\n",
    "def plotSplitSectionDelta(splitdurations, drivercode=None, order=None,\n",
    "                          fig=None,ax=None, alpha=1, pos='r', neg='g', total=False,invert_yaxis=True,\n",
    "                          ignoresplitsfordrivercodes=None):\n",
    "\n",
    "    splitdurations = rebaseSplitDurations(splitdurations, drivercode)\n",
    "    if 'rebased' not in splitdurations: return\n",
    "    \n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(15,8))\n",
    "        \n",
    "        ax.yaxis.label.set_visible(False)\n",
    "        ax.tick_params(axis='y', which='both',length=0)\n",
    "        #Hide outer box\n",
    "        plt.box(on=None)\n",
    "\n",
    "    #zz=splitdurations[splitdurations['rebased'].abs()<25].pivot('drivercode','section','rebased')\n",
    "    zz=splitdurations.pivot('drivercode','section','rebased')\n",
    "    if order is None:\n",
    "        order = getSplitOrderByStageTimeDuration(splitdurations)\n",
    "    zz=zz.reindex(order)\n",
    "    \n",
    "    if total:\n",
    "        #zz.sum(axis=1).plot(kind='barh',ax=ax, color='lightgrey',legend=False)\n",
    "        _total=(splitdurations[['drivercode','stageTimeDurationMs']].drop_duplicates().set_index('drivercode')/1000).round(1)\n",
    "        rebase = ((splitdurations[splitdurations['drivercode']==drivercode][['section','stageTimeDurationMs']].iloc[0]['stageTimeDurationMs'])/1000).round(1)\n",
    "        _total = _total - rebase \n",
    "        _total.reindex(order).plot(kind='barh',ax=ax, color='lightgrey',legend=False)\n",
    "\n",
    "    if ignoresplitsfordrivercodes is not None:\n",
    "        ignoresplitsfordrivercodes = [ignoresplitsfordrivercodes] if not isinstance(ignoresplitsfordrivercodes,list) else ignoresplitsfordrivercodes\n",
    "        zz = zz[~zz.index.isin(ignoresplitsfordrivercodes)]\n",
    "        \n",
    "    cmap=[]\n",
    "    \n",
    "    #Need to set the column order to plot splits correctly\n",
    "    cols=zz.columns[::-1] if not invert_yaxis else zz.columns\n",
    "    \n",
    "    for col in cols:\n",
    "        cmap.append( [pos if x else neg for x in zz[col]>0 ] )\n",
    "    \n",
    "    tmp = zz[cols].plot(kind='barh',color=cmap,legend=False,alpha=alpha,ax=ax)\n",
    "    if invert_yaxis: tmp.invert_yaxis()\n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAMAArHkDAAAYgJE3AACAAQhvAAAAAxDeAAAABiC8AQAADEB4AwAAGIDwBgAAMADhDQAAYADCGwAAwACENwAAgAEIbwAAAAMQ3gAAAAYgvAEAAAxAeAMAABiA8AYAADAA4Q0AAGAAexddAPOxurrai66BI9t/4f4tvb7P6ZpTKQAADMrIGwAAwACENwAAgAEIbwAAAAOw5g220VbXugEAwCEzjbxV1bOq6iNV9eGqurSq7lVVn6iq205dc1pVvWny/IlV9c2quvvU+dWq2jfvHwAAAGAZHHXkraruk+ThSU7t7usnge24Gd776iTPSnLm1koEAABglmmTJyX5QndfnyTd/YUkqTrqzuVvSvKAqrpTd1++pSphUAfPOLjoEgAAOEbMMm3yj5OcXFVXVNWvVdUDZ3zvbyb5H0meuenqAAAASDJDeOvua5P8YJKfS/L5JK+tqicmOdxNode3vSbJvavqe7dYJwAAwFKbabfJ7r4hySVJLqmqg0mekOSLSW6V5AuTy2499fzQ675RVc9P8svzKhgWaVG7R/bK4X5XAgDAMjnqyFtV3amqTplqOpDkr7MW5n56cs2eJD+V5O2HeYtXJXlwktttsVYAAIClNcvI24lJXlpVt0zyjSRXZm0K5T8k+fWq+lCSSvKWJL+7/sXd/fWqekmSF8+tagAAgCVT3aZjHQtWV1d15A5Y2LTJc/qo27sCAHBsm2nNG4xkUQELAAC20yy3CgAAAGDBhDcAAIABWPMGAAAwACNvAAAAAxDeAAAABiC8AQAADEB4AwAAGIDwBgAAMADhDQAAYADCGwAAwACENwAAgAEIbwAAAAMQ3gAAAAYgvAEAAAxAeAMAABiA8AYAADAA4Q0AAGAAwhsAAMAAhDcAAIAB7F10AcxmdXW1F10DO2f/hfv/v+M+p2tBpQAAsEsYeQMAABiA8AYAADAA0yZhm62fAgkAAJth5A0AAGAAM4W3qrqhqi6tqtWq+v2qutnUuUdXVVfVnafa9lXVdVX1waq6rKreW1VP2I4fAAAAYBnMOvJ2XXcf6O6VJF9P8m+nzp2d5F1Jzlr3mqu6+x7dfZfJuadU1c9suWIAAIAltJk1b+9McvckqaoTk9w3yYOSXJzk3MO9oLv/qqr+Y5LnJ3nlpiqFQVjjBgDAdtjQmreq2pvkR5McnDQ9KslbuvuKJF+qqlNv5OUfSHLnGzkPAADAEcwa3k6oqkuTvC/JJ5P89qT97CQXTJ5fMDk+EjcZBgAA2KRZp01e190Hphuq6jZJTk+yUlWdZE+SrqqnHeE97pHksk1XCruQKZIAAOyUrdwq4DFJzuvuO3T3vu4+OcnHk9xv/YVVtS/J85K8dAufBwAAsLS2Et7OTnLRurYLkzxu8vyOh24VkOR1SV7a3TYrAQAA2ISZpk1294mHaTvtMG0vmTo8YfNlwdaZ0ggAwLFkKyNvAAAA7BDhDQAAYADCGwAAwACquxddAwAAAEdh5A0AAGAAwhsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMQHgDAAAYgPAGAAAwAOENAABgAMIbAADAAIQ3AACAAQhvAAAAAxDeAAAABiC8AQAADEB4AwAAGMDeRRfAP7W6utqLroHF2H/h/sO29zldO1wKAAC7jJE3AACAAQhvAAAAAxDeAAAABmDNGyzQkda4AQDAejONvFXVDVV1aVV9qKo+UFU/PGnfV1XXTc59tKrOq6qbTs6dVlV/Nzl36PHg7fxhAAAAjlWzjrxd190HkqSqHpLkV5M8cHLuqu4+UFV7krw1yU8k+b3JuXd298PnWTAAAMAy2sy0yZsn+fL6xu6+oarem+R7tlwVDMxUSAAAtsOs4e2Eqro0yfFJTkpy+voLqur4JPdK8qSp5vtPXnfIGd191WaLBQAAWFabmTZ5nyTnVdXK5NwdJwHtlCSv7+4PT73OtEkAAIA52PC0ye7+86q6bZLbTZoOrXk7KcklVfWI7r54rlXCDjHlEQCA3WrD93mrqjsn2ZPki9Pt3f3ZJE9P8oz5lAYAAMAhs4a3Ew5t95/ktUme0N03HOa6P0hys6q6/+T4/utuFfCYeRQNAACwbGaaNtnde47Q/okkK1PHneQHpi65xVaKAwAAYM1mbhUAu561awAAHGs2vOYNAACAnSe8AQAADKDWlqkBAACwmxl5AwAAGIDwBgAAMADhDQAAYADCGwAAwACENwAAgAEIbwAAAAMQ3gAAAAYgvAEAAAxAeAMAABiA8AYAADAA4Q0AAGAAwhsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMYO+iC1hGq6urvegamJ/9F+7f9s/oc7q2/UMAANjVjLwBAAAMQHgDAAAYgGmTsEUHzzg4l/fZiemXAACMa+bwVlW3T/LyJHfN2ojdm5L8p8l7/K8kd09SSa5J8pNJ3jh56XcnuSHJ5yfH9+zur8+jeAAAgGUxU3irqkryhiS/3t2PrKo9SX4zyXOSfCnJ33b3/sm1d0ryN919YHJ8bpJru/t521A/AADAUph15O30JF/r7lcmSXffUFVPSfLxJBcmufzQhd19+eHfAkhMjwQAYHNm3bDkbkneP93Q3V9J8skkv5vkl6vqz6vqv1XVKXOuEQAAYOnNGt4qyeHuTVZJvpzk+5L8zyS3TvIXVXWX+ZQHAABAMvu0yY8kOWO6oapunuTkJFd19//N2pq4N1TVN5M8LMll8ywUAABgmc0a3t6W5LlV9fjuPm+yYcnzk7wqyT2q6qPd/eWqOi5ru1Fesi3Vwg6wJg0AgN1opmmT3d1JHp3ksVX1sSRXJPlakmcmuWOSP6uqg0k+mOR9WdvEBAAAgDmZ+T5v3f2pJP/6MKfOmzyO9LpzN14WAAAA02YOb7BbmeYIAMAymHW3SQAAABZIeAMAABiA8AYAADCAWttIEgAAgN3MyBsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMQHgDAAAYgPAGAAAwAOENAABgAMIbAADAAIQ3AACAAQhvAAAAAxDeAAAABiC8AQAADEB4AwAAGIDwBgAAMIC9iy5gWayurvaia2A+9l+4f8c/s8/p2vEPBQBgVzHyBgAAMADhDQAAYADCGwAAwACseYMNOnjGwQ1dv4g1cgAAHHuOOvJWVV1Vz586fmpVnTt5fm5VfbqqLp163LKqnlhVL1v3PpdU1Q/N/ScAAABYArNMm7w+yY9X1W2PcP6F3X1g6nHNHOsDAAAgs02b/EaS30zylCTP2t5yYCymRAIAsFNm3bDk5Ul+sqpucZhzT5maMvn2OdYGAADAxEwblnT3V6rqvCT/Icl1606/sLuft/4lR3qrDdYHAABANrbb5IuSfCDJK2e49otJbrWu7dZJvrCBz4NdwdRIAAB2g5nv89bdX0ryuiQ/O8Plf5HkvlX13Uky2WXy25J8ajNFAgAALLuN3uft+Ul+cV3bU6rqp6aOH9Xdn6iqJyX5w6q6SZJrk5zd3d/cQq0AAABLq7otQ9sJq6ur/qIHtRumTfY5XYuuAQCAxdroyBsc03ZDUAMAgMOZec0bAAAAiyO8AQAADMCaNwAAgAEYeQMAABiA8AYAADAA4Q0AAGAAwhsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMQHgDAAAYgPAGAAAwAOENAABgAMIbAADAAIQ3AACAAQhvAAAAAxDeAAAABiC8AQAADGDvogvYrVZXV3vRNTC+/Rfun8v79Dldc3kjAACGZeQNAABgAMIbAADAAEybhCOY15RHAACYhw2NvFVVV9XvTB3vrarPV9WbJsdPrKqXTZ6fW1VPnTw/vqreWlXnzLN4AACAZbHRaZN/n2Slqk6YHP9Ikk/f2Auq6rgkFyZ5f3c/e+MlAgAAsJlpk29O8mNJXp/k7CTnJ7n/jbz/BUk+1t1P31SFsCAHzzi4qdeZbgkAwHbYzIYlFyQ5q6qOT3L3JO+5kWufluQb3f3kzRQHAADAmg2Ht+7+cJJ9WRt1+8OjXP6uJPepqu/feGkAAAAcstlbBVyc5HlZmzJ5Y96R5MlJ3lxV/2yTnwUAALD0NnurgFck+bvuPlhVp93Yhd19YVXdLslbquoB3X3NJj8TtoU1agAAjGBTI2/dfXV3v3gD1/9GkjckuXiyVg4AAIANqO5edA270urqqr+YJTHCyFuf07XoGgAAWKzNTpuE4YwQ0gAA4Eg2u2EJAAAAO0h4AwAAGIA1bwAAAAMw8gYAADAA4Q0AAGAAwhsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMQHgDAAAYgPC2xKrq5xZdA7PRV+PQV+PQV2PQT+PQV+PQV+MS3pabf7jj0Ffj0Ffj0Fdj0E/j0Ffj0FeDEt4AAAAGILwBAAAMQHhbbr+56AKYmb4ah74ah74ag34ah74ah74aVHX3omsAAADgKIy8AQAADEB4W0JV9diq+khVfbOqfmjduWdU1ZVVdXlVPWRRNfItVfXQSX9cWVVPX3Q9fEtVvaKqPldVq1Ntt66qt1bVxyZ/3mqRNZJU1clV9faqumzy3fekSbu+2mWq6viqem9VfWjSV8+etH9vVb1n0levrarjFl0rSVXtqaoPVtWbJsf6aReqqk9U1cGqurSq3jdp8/03KOFtOa0m+fEk75hurKq7Jjkryd2SPDTJr1XVnp0vj0Mmf/8vT/KjSe6a5OxJP7E7vCpr/1amPT3J27r7lCRvmxyzWN9I8kvdfZck907yC5N/R/pq97k+yend/QNJDiR5aFXdO8l/T/LCSV99OcnPLrBGvuVJSS6bOtZPu9eDuvtAdx/6pb3vv0EJb0uouy/r7ssPc+qRSS7o7uu7++NJrkxyz52tjnXumeTK7v6r7v56kguy1k/sAt39jiRfWtf8yCSvnjx/dZJH7WhR/BPd/dnu/sDk+Vez9h+b3xN9tev0mmsnhzedPDrJ6UleP2nXV7tAVd0+yY8l+a3JcUU/jcT336CEN6Z9T5JPTR1fPWljcfTJeL6ruz+brIWGJN+54HqYUlX7ktwjyXuir3alyVS8S5N8Lslbk1yV5Jru/sbkEt+Du8OLkjwtyTcnx7eJftqtOskfV9X7q+rQzbl9/w1q76ILYHtU1Z8k+e7DnHpWd7/xSC87TJvtSBdLn8CcVNWJSS5M8uTu/sraQAG7TXffkORAVd0yyUVJ7nK4y3a2KqZV1cOTfK67319Vpx1qPsyl+ml3uG93f6aqvjPJW6vqLxddEJsnvB2juvvBm3jZ1UlOnjq+fZLPzKciNkmfjOdvq+qk7v5sVZ2UtdEDFqyqbpq14PZ73f2GSbO+2sW6+5qquiRr6xRvWVV7J6M6vgcX775JHlFVD0tyfJKbZ20kTj/tQt39mcmfn6uqi7K2JMP336BMm2TaxUnOqqpvq6rvTXJKkvcuuKZl9xdJTpns4HVc1jaUuXjBNXHjLk7yhMnzJyQ50kg3O2SyFue3k1zW3S+YOqWvdpmqut1kxC1VdUKSB2dtjeLbkzxmcpm+WrDufkZ3376792Xt/5f+tLt/Mvpp16mqb6+q7zj0PMm/ytrGdb7/BuUm3Uuoqh6d5KVJbpfkmiSXdvdDJueeleTfZG13tid395sXVihJkslvNl+UZE+SV3T3cxZcEhNVdX6S05LcNsnfJjknyR8keV2Sf57kk0ke293rNzVhB1XV/ZK8M8nBfGt9zjOztu5NX+0iVXX3rG2esCdrv2B+XXf/16r6vqxt2HTrJB9M8lPdff3iKuWQybTJp3b3w/XT7jPpk4smh3uTvKa7n1NVt4nvvyEJbwAAAAMwbRIAAGAAwhsAAMAAhDcAAIABCG8AAAADEN4AAAAGILwBAAAMQHgDAAAYgPAGAAAwgP8HOiO5EpCGb40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125186860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotSplitSectionDelta(splitdurations,'MEE',total=True,invert_yaxis=False, ignoresplitsfordrivercodes='LAP');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Time Deltas at each split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drivercode</th>\n",
       "      <th>elapsedDurationS</th>\n",
       "      <th>startDateTime</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OGI</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGI</td>\n",
       "      <td>251.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGI</td>\n",
       "      <td>409.7</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>545.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>640.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  elapsedDurationS        startDateTime  section\n",
       "0         OGI              88.1  2018-02-16T08:54:00        1\n",
       "1         OGI             251.1  2018-02-16T08:54:00        2\n",
       "2         OGI             409.7  2018-02-16T08:54:00        3\n",
       "3         OGI             545.5  2018-02-16T08:54:00        4\n",
       "63        OGI             640.5  2018-02-16T08:54:00        5"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits['elapsedDurationS'] = (splits['elapsedDurationMs']/1000).round(1)\n",
    "\n",
    "#Find the time for the last section - i.e. diff between stage time and final split time\n",
    "tmp = splits.groupby('drivercode', as_index=False).last()[['drivercode','stageTimeDurationMs','elapsedDurationMs', 'startDateTime' ]]\n",
    "tmp['elapsedDurationS']=((tmp['stageTimeDurationMs'])/1000).round(1)\n",
    "\n",
    "cols = ['drivercode','elapsedDurationS', 'startDateTime']\n",
    "elapseddurations = pd.concat([splits[cols],tmp[cols]]).reset_index(drop=True)\n",
    "elapseddurations['section'] = elapseddurations.groupby('drivercode').cumcount()+1\n",
    "elapseddurations = elapseddurations.sort_values(['startDateTime','drivercode','section'])\n",
    "elapseddurations = elapseddurations\n",
    "elapseddurations.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = splits.sort_values(['stageTimeDurationMs','drivercode'])['drivercode'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 88.1, 2: 251.1, 3: 409.7, 4: 545.5, 5: 640.5}"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drivercode = 'OGI'\n",
    "\n",
    "rebase = elapseddurations[elapseddurations['drivercode']==drivercode][['section','elapsedDurationS']].set_index('section').to_dict(orient='dict')['elapsedDurationS']\n",
    "rebase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [],
   "source": [
    "elapseddurations['rebased']=elapseddurations['elapsedDurationS']-elapseddurations['section'].map(rebase)\n",
    "elapseddurations['section']=elapseddurations['section']#.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MIK',\n",
       " 'NEU',\n",
       " 'OST',\n",
       " 'LAP',\n",
       " 'BRE',\n",
       " 'PAD',\n",
       " 'SUN',\n",
       " 'LAT',\n",
       " 'MEE',\n",
       " 'TÄN',\n",
       " 'OGI',\n",
       " 'SOL',\n",
       " 'AL ',\n",
       " 'EVA']"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e43c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "\n",
    "zzz=elapseddurations[elapseddurations['rebased'].abs()<25].pivot('drivercode','section','rebased')\n",
    "#zz['stagediff']=zz.sum(axis=1)\n",
    "#zz = zz.sort_values('stagediff',ascending=True).drop(['stagediff'], axis=1)\n",
    "\n",
    "\n",
    "cmap=[]\n",
    "\n",
    "    \n",
    "zzz=zzz.reindex(order)\n",
    "for col in zzz.columns:\n",
    "    cmap.append( ['r' if x else 'g' for x in zzz[col]>0 ] )\n",
    "\n",
    "zzz.plot(kind='barh',title='Overall delta at each split',color=cmap,legend=False,ax=ax).invert_yaxis()\n",
    "\n",
    "#Hide outer box\n",
    "plt.box(on=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MIK',\n",
       " 'NEU',\n",
       " 'OST',\n",
       " 'BRE',\n",
       " 'PAD',\n",
       " 'SUN',\n",
       " 'LAT',\n",
       " 'MEE',\n",
       " 'TÄN',\n",
       " 'OGI',\n",
       " 'SOL',\n",
       " 'AL ',\n",
       " 'EVA']"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order2=elapseddurations[elapseddurations['section']==elapseddurations['section'].max()].sort_values(['elapsedDurationS','drivercode'])['drivercode'].unique().tolist()\n",
    "order2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a Function of That"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getElapsedDurations(splits):\n",
    "\n",
    "    splits['elapsedDurationS'] = (splits['elapsedDurationMs']/1000).round(1)\n",
    "\n",
    "    #Find the time for the last section - i.e. diff between stage time and final split time\n",
    "    tmp = splits.groupby('drivercode', as_index=False).last()[['drivercode','stageTimeDurationMs','elapsedDurationMs', 'startDateTime' ]]\n",
    "    tmp['elapsedDurationS']=((tmp['stageTimeDurationMs'])/1000).round(1)\n",
    "\n",
    "    cols = ['drivercode','elapsedDurationS', 'startDateTime']\n",
    "    elapseddurations = pd.concat([splits[cols],tmp[cols]]).reset_index(drop=True)\n",
    "    elapseddurations['section'] = elapseddurations.groupby('drivercode').cumcount()+1\n",
    "    elapseddurations = elapseddurations.sort_values(['startDateTime','drivercode','section'])\n",
    "\n",
    "    return elapsedDurations\n",
    "\n",
    "def rebaseElapsedDurations(elapseddurations, drivercode=None):\n",
    "    if drivercode is None: return elapseddurations\n",
    "    rebase = elapseddurations[elapseddurations['drivercode']==drivercode][['section','elapsedDurationS']].set_index('section').to_dict(orient='dict')['elapsedDurationS']\n",
    "    elapseddurations['rebased']=elapseddurations['elapsedDurationS']-elapseddurations['section'].map(rebase)\n",
    "    return elapseddurations\n",
    "\n",
    "def plotSplitOverallDelta(elapseddurations,drivercode=None, order=None, fig=None, ax=None, alpha=1, pos='r', neg='g',invert_yaxis=True):\n",
    "\n",
    "    elapseddurations = rebaseElapsedDurations(elapseddurations, drivercode)\n",
    "    if 'rebased' not in elapseddurations: return\n",
    "        \n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(15,8))\n",
    "        \n",
    "        ax.yaxis.label.set_visible(False)\n",
    "        ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "    zz=elapseddurations[elapseddurations['rebased'].abs()<25].pivot('drivercode','section','rebased')\n",
    "\n",
    "    cmap=[]\n",
    "\n",
    "    if order is None:\n",
    "        order=elapseddurations[elapseddurations['section']==elapseddurations['section'].max()].sort_values(['elapsedDurationS','drivercode'])['drivercode'].unique().tolist()\n",
    "\n",
    "    zz=zz.reindex(order)\n",
    "    for col in zz.columns:\n",
    "        cmap.append( [pos if x else neg for x in zz[col]>0 ] )\n",
    "\n",
    "    tmp = zz.plot(kind='barh',title='Overall delta at each split',color=cmap,legend=False,ax=ax)\n",
    "\n",
    "    if invert_yaxis: tmp.invert_yaxis()\n",
    "        \n",
    "    #Hide outer box\n",
    "    plt.box(on=None)\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125f634e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotSplitOverallDelta(elapseddurations,'OGI');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage Progress\n",
    "\n",
    "The *stage progress chart* is a deltascope designd to reveal differences arising from, and associated with, a particular stage compared to the previous stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [],
   "source": [
    "def samesign(a,b):\n",
    "    return (a*b) >0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1 -0.5 -0.5 -0.5 True\n",
      "-0.5 -1 -0.5 -0.5 True\n",
      "-0.5 1 1 1 True\n",
      "-1 0.5 0.5 0.5 True\n",
      "0.5 1 0.5 0.5 True\n",
      "1 0.5 0.5 0.5 True\n"
     ]
    }
   ],
   "source": [
    "#want rebased delta if different sign else smaller\n",
    "#?? if same sign, then return smaller?\n",
    "def thing(overall,delta):\n",
    "    if overall<0 and delta<0:\n",
    "        return overall if overall>delta else delta\n",
    "    elif overall>0 and delta>0:\n",
    "        return overall if overall<delta else delta\n",
    "    return delta\n",
    "    #return min([a,b], key=abs)\n",
    "\n",
    "#Return thing closest to zero if samesign, else delta\n",
    "def thing2(overall,delta):\n",
    "    if samesign(overall,delta): return min([overall,delta], key=abs)\n",
    "    return delta\n",
    "\n",
    "thing(-2,-1.5),thing(1.5,2), thing(2,1.5)\n",
    "\n",
    "for a,b in [(-1,-0.5),(-0.5,-1),(-0.5,1),(-1,0.5),(0.5,1),(1,0.5)]:\n",
    "    print(a,b, thing(a,b), thing2(a,b), thing(a,b)==thing2(a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delta</th>\n",
       "      <th>deltax</th>\n",
       "      <th>overall</th>\n",
       "      <th>prev</th>\n",
       "      <th>deltaxx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>a</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>-4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-6</td>\n",
       "      <td>-4</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-12</td>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      delta  deltax  overall  prev  deltaxx\n",
       "name                                       \n",
       "a         2       2       11     9        2\n",
       "b        -2      -2        6     8       -2\n",
       "g        12       8        8    -4        8\n",
       "c         0       0        0     0        0\n",
       "d        -2      -2       -6    -4       -2\n",
       "e         2       2       -8   -10        2\n",
       "f       -12      -8       -8     4       -8"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#overall is rebased diff at end of current stage; negative means target is ahead\n",
    "#prev is rebased diff at end if revious stage; negative means target is ahead\n",
    "#delta is rebased_curr - rebased_prev; positive means target lost ground\n",
    "df=pd.DataFrame([{'name':'a','overall':11,'delta':2,'prev':9,'deltax':2},\n",
    "                {'name':'b','overall':6,'delta':-2,'prev':8,'deltax':-2},\n",
    "                {'name':'g','overall':8,'delta':12,'prev':-4,'deltax':8},\n",
    "                {'name':'c','overall':0,'delta':0,'prev':0,'deltax':0},\n",
    "                {'name':'d','overall':-6,'delta':-2,'prev':-4,'deltax':-2},\n",
    "                {'name':'e','overall':-8,'delta':2,'prev':-10,'deltax':2},\n",
    "                {'name':'f','overall':-8,'delta':-12,'prev':4,'deltax':-8},\n",
    "                ])\n",
    "\n",
    "df['deltaxx']=df.apply(lambda x: thing(x['overall'],x['delta']), axis=1)\n",
    "df = df.set_index('name')\n",
    "df\n",
    "#if overall and delta have same sign, then use the one closest to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e56f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "plt.axvline(x=0,linestyle='dashed',color='grey')\n",
    "\n",
    "df['prev'].plot(kind='barh',edgecolor='black',linestyle='dashed', fill=False, ax=ax)\n",
    "\n",
    "df['overall'].plot(kind='barh',color='lightgrey',edgecolor='black',ax=ax)\n",
    "df['deltaxx'].plot(kind='barh',color=['r' if x else 'g' for x in df['deltaxx']<0],edgecolor='black',ax=ax)\n",
    "\n",
    "\n",
    "\n",
    "ax.invert_xaxis()\n",
    "#Hide outer box\n",
    "plt.box(on=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to read the above chart:\n",
    "\n",
    "- chart is ordered from top down, fastest overall at top;\n",
    "- x-axis is time delta to other identified cars from a target car; negative bar means target is behind target car by that time;\n",
    "- green colour shows the target gained time on the stage relative to the identified car, reducing any lead it has over the target, or adding to the gap behind the target;\n",
    "- red colour shows time was lost on the stage by the target relative to the identified car, adding to any lead that car has over the target, or reducing the gap to a car behind the target;\n",
    "- a dashed bar shows the situation at the end of the previous stage;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Additional Colour Maps\n",
    "\n",
    "We can use colour to highlight information in different ways. For example, if a target driver loses time to another car, should we colour that other car's delta as red, from the perspective of the target driver that this is a bad thing and we need to be warned about it, or green, as it might be displayed on a \"global\" timing screen to show that the other car has made time on the target car?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2AAAAHSCAYAAACKD6jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAH6RJREFUeJzt3X+MpdV5H/DnmF122TZUMTYwY9YmWF5a0602M1DF2MW0WA1JZ0hME0hCW29SzUR1ojRVU9X5pZlXzo8qqltLVuvOoKbQmDZGMaln3MRNggSujWvvDCEsG9c0ceyaGYhjUgXVJARvn/7B9Q7XYpeB3XvOnXc+H+no7px9V/pe6eoOX855z1syMwAAABi9V7QOAAAAsFsoYAAAAJUoYAAAAJUoYAAAAJUoYAAAAJUoYAAAAJUoYAAw0HXdB7qu+0DrHAD0157WAQBgjFzWOgAA/WYFDAAAoBIFDAAAoBIFDAAAoBL3gAHAlk+2DgBAv5XMbJ0BAABgV7AFEQAAoBIFDAAGuq77UNd1H2qdA4D+cg8YAGy5qHUAAPrNChgAAEAlChgAAEAlChgAAEAl7gEDgC33tg4AQL95DhgAAEAltiACvfLag689WUrJzc3NXF1dzVLKqbG8vJwRMTQ3OzubEZGzs7ND8xGRy8vLQ3Orq6u5ubk5NDc/P58RkdPT06fmJicnMyJycXFx6Nr19fVcX18fmltcXMyIyMnJyVNz09PTGRE5Pz8/dK33VOc9XXDBBUM/G4Zh1B779+8/WfnXJxVZAQN6pZSSed+x1jHYoe56+NPx6596IN71rne1jgLsYocPH47MLK1zMBpWwIBemZubax2BHezZkydj7969rWMA0GMKGNAry8vLrSMAAJyWAgb0yvT0dOsIAACnpYABvfLggw+2jgAAcFoKGAAMHLro4nj00UdbxwCgxxQwoFcmJiZaR2AHu/a1r48HHnigdQwAekwBA3plc3OzdQQAgNNSwIBeWVxcbB2BHeyO3/5kHD16tHUMAHpMAQN6peu61hEAAE5LAQMAAKjkJRWwUsqPlFI+U0q5a1SBAAAA+mrPS7z+nRHxbZn5B6MIA3C21tbWIv5vto4BAPCCtr0CVkr5dxFxRUSslFL+yegiAUAbV108ESdOnGgdA4AeK5nb/z/FpZTPR8TVmfnlkSUCOAullMz7jrWOwQ5Wrr8mjh8/3joGsIsdPnw4MrO0zsFoOIQDAAaePXky9u7d2zoGAD2mgAHAwF0Pfzpuu+221jEA6DEFDOiVhYWF1hEAAE5LAQN6ZXFxsXUEAIDTekkFLDMvdwAHMM4mJydbRwAAOC0rYECvPP74460jAACclgIGAANHLr0sHnroodYxAOgxBQzolampqdYR2MGOTBxUwAAYKQUM6JX19fXWEdjBnv7zP48DBw60jgFAjylgQK/Mz8+3jsAOdveJ9bjllltaxwCgxxQwoFduv/321hEAAE5LAQMAAKhEAQMAAKhEAQN6ZWNjo3UEAIDTUsCAXnEKImfj6te8Lo4dO9Y6BgA9poABvXLTTTe1jsAO9lcvnowTJ060jgFAn2WmYRhGb8bByw6ejIhcWlrKzMyIODVmZmYyM3NmZmZoPjNzaWlpaG5lZSU3NjaG5ubm5jIzc2pq6tTcxMREZmYuLCwMXbu2tpZra2tDcwsLC5mZOTExcWpuamoqMzPn5uaGrt3Y2MiVlZWhOe9p9O/pwgsvzFe/+tVD1xiGYdQe+/btO9n696kxulEyMwCAiK7r7ouIWFhYuL5tEgD6yhZEAACAShQwAACAShQwAACAShQwAACASva0DgAAY+Q9rQMA0G9OQQQAAKjEFkQAGOi67squ665snQOA/rIFEQC2LA1er28ZAoD+sgIGAABQiQIGAABQiQIGAABQiQIGAABQiUM4AGDLz7QOAEC/eQ4YAABAJbYgAsBA13VHuq470joHAP1lCyIAbHnv4PX6liEA6C8rYAAAAJUoYAAAAJUoYAAAAJUoYAAAAJU4hAMAtvxE6wAA9JvngAEAAFRiCyIADHRdd23Xdde2zgFAf9mCCABbfm7wen3LEAD0lxUwAACAShQwAACAShQwAACAShQwAACAShzCAQBbfrR1AAB6LjMNwzCMXTJiTzwREWmcYewZgwyGYezqcckllwz9vLS0lJk5NDczM5OZmTMzM0PzmZlLS0tDcysrK7mxsTE0Nzc3l5mZU1NTp+YmJiYyM3NhYWHo2rW1tVxbWxuaW1hYyMzMiYmJU3NTU1OZmTk3Nzd07cbGRq6srJyz93Tw4MGTrX+fns3wIGaAXaSUkrHYOsX4uiKuiPiPER/+8IdbRwF2scOHD4f/Rj+91dXVmJ2dLa1zvFy2IALAwHVxXcR1rVMAcCbT09OtI5wVh3AAAAA7xmte85rWEc6KAgYAAFCJAgYAAFCJAgYAAOwYc3NzrSOcFacgAuwiTkE8s4viooj3Rdx3332towC7mFMQt2XHnoJoBQwABp6MJ+PJJ59sHQOAM9jppyA6hh4ABg7FoYhDrVMAcCYPPvhg6whnRQEDgIFr49qIa1unAKDPbEEEAAB2jImJidYRzooCBgAA7Bibm5utI5yVbRWwUsrfK6V8upTyUCllqZRy3qiDAQAAfL3FxcXWEc7KixawUspfiYhbI+LNmXkkIk5GxG2jDgYAAPD1uq5rHeGsbGcF7IaImI6IY6WUhwY/XzHSVADQwD1xT9xzzz2tYwDQY9s5BbFExJ2Z+eOjDgMALT0VT0U81ToFAH22nRWweyPiu0opF0dElFJeWUp53WhjAUB9V8VVcdVVV7WOAcAZrK2ttY5wVl50BSwzf7eU8lMR8RullFdExLMR8UMR8YVRhwOAmq6JayKuaZ0CgD7b1oOYM/ODEfHBEWcBAAA4o6uvvjoys3WMl81zwAAAACpRwAAAACpRwAAAgB1jYWGhdYSzUnby/kkAXppSSsZi6xTj60AciPiFiE996lOtowC72OHDh3f0PU6VlNYBXi4rYAAw8HQ8HU8//XTrGACcweTkZOsIZ2VbpyACwG5wJI5EHGmdAoAzefzxx1tHOCsKGAAMKGAAjJotiAAAwI4xNTXVOsJZUcAAAIAdY319vXWEs6KAAQAAO8b8/HzrCGdFAQMAAHaM22+/vXWEs5OZhmEYxi4ZsSeeiIg0Xnjs3bs39+7f2zyHYRi7e7zqVa/KzByam5mZyczMmZmZofnMzKWlpaG5lZWV3NjYGJqbm5vLzMypqalTcxMTE5mZubCwMHTt2tparq2tDc0tLCxkZubExMSpuampqczMnJubG7p2Y2MjV1ZWhuaWlpbO2Xs6ePDgyda/T89meBAzAABAJbYgAsBA13Xv7Lruna1zANBfChgAbLllMABgJBQwAACAShQwAACAShQwAACAShQwAACAShxDDwAAUIkVMAAAgEoUMAAY6Lrux7qu+7HWOQDoLwUMALbMDAYAjIQCBgAAUIkCBgAAUIkCBgAAUMme1gEAYIz8aesAAPSb54ABAABUYgsiAABAJQoYAAx0XffTXdf9dOscAPSXAgYAW24YDAAYCQUMAACgEgUMAACgEgUMAACgEs8BA4AtT7YOAEC/eQ4YAABAJbYgAgAAVKKAAcBA13U/33Xdz7fOAUB/uQcMALa8qXUAAPrNChgAAEAlChgAAEAlChgAAEAl7gEDgC2PPfLww7eVUjyj5TT2RcQzrUOwo+3bt+8P/+zP/uzS1jmgFc8BA4DnKaX4zXgGJSKOHz/eOgY72OHDhyMzS+sc0IotiAAAAJUoYAAw0HXde2+88cbWMQDoMQUMALYcufRSt6YAMDoKGAAAQCUKGAAAQCUKGAAAQCUKGABsefTJJ59snQGAHvMcMAB4Hs8BOzPPAeNseQ4Yu50VMAAAgEoUMAAY6LpueXZ2tnUMAHrsJRewUspiKeXHRhEGABo7dNFFF7XOAECPWQEDAACoZFsFrJTyk6WUz5ZSfisirhxxJgAAgF7a82IXlFKmI+J7IuKbB9c/GBHrI84FAADQO9tZAfsbEfGrmfl0Zj4VESsjzgQArTz0xBNPtM4AQI+96ArYgEeiANB7CwsLP1pK+cetcwDQX9tZAftYRLy9lHJBKeUbIsL5vAAAAC/DixawzHwwIj4YEQ9FxIci4r+POhQAtNB13Qduvvnm1jEA6LFtbUHMzJ+NiJ8dcRYAaO2yCy+8sHUGAHrMc8AAAAAqUcAAAAAqUcAAAAAqUcAAYMsnv/jFL7bOAECPlUyP+AKAryml+M14BiUijh8/3joGO9jhw4cjM0vrHNCKFTAAAIBKFDAAGOi67kO33HJL6xgA9JgCBgBbLjpw4EDrDAD0mAIGAABQiQIGAABQyZ7WAQBgnLwinjvpjxe2L547xQ5ern379v1h6wzQkgIGAFvufe3ll9+bme9uHQSAfvIcMAAAgErcAwYAAFCJAgYAA13X/XrXdb/eOgcA/eUeMADYckHrAAD0mxUwAACAShQwAACAShQwAACAStwDBgBbPtI6AAD95jlgAAAAldiCCAAAUIkCBgADXdfd13Xdfa1zANBfChgAAEAlChgAAEAlChgAAEAlChgAAEAlngMGAFvubh0AgH7zHDAAAIBKbEEEgIGu6w50XXegdQ4A+ssWRADY8muD1+tbhgCgv6yAAQAAVKKAAQAAVKKAAQAAVKKAAQAAVOIQDgDYckfrAAD0m+eAAQAAVGILIgAMdF33qq7rXtU6BwD9ZQsiAGz5lcHr9S1DANBfVsAAAAAqUcAAAAAqUcAA4Hn+6I/+6FtaZwCgvxQwAHier3zlK/taZwCgvxQwANjy/mPHjrXOAECPeQ4YADxPKSUzs7TOAUA/WQEDgIGu6w5eeOGFrWMA0GMKGABs+aWbb765dQYAekwBAwAAqEQBAwAAqEQBAwAAqEQBAwAAqEQBA4At73nggQdaZwCgxzwHDACex3PAABglK2AAMNB13ZUXXXRR6xgA9JgCBgBblmZnZ1tnAKDHtl3ASin/oJTycCnld0opvzTKUAAAAH20ZzsXlVKuioifjIg3Z+aXSymvHG0sAACA/tnuCtjfiohfycwvR0Rk5h+PLhIAAEA/bbeAlYhwXCIAAMBZ2G4BuzcibimlXBQRYQsiAD31Mx/72MdaZwCgx7b9HLBSyjsi4p9FxMmI+O3MPDrCXADQhOeAATBK2zqEIyIiM++MiDtHmAUAmuq67sill17aOgYAPeY5YACw5b033nhj6wwA9JgCBgAAUIkCBgAAUIkCBgAAUIkCBgAAUIkCBgBbfuLee+9tnQGAHtv2c8AAYDfwHDAARskKGAAMdF137cGDB1vHAKDHFDAA2PJzN9xwQ+sMAPSYAgYAAFCJAgYAAFCJAgYAz3P55Zff3zoDAP2lgAEAAFSyp3UAABgjP9o6AAD95jlgAAAAldiCCAADXde9reu6t7XOAUB/2YIIAFt+avD6W01TANBbVsAAAAAqUcAAAAAqUcAAAAAqUcAAAAAqcQgHAGz5wdYBAOg3zwEDAACoxBZEABjoum6267rZ1jkA6C9bEAFgyz8dvK42TQFAb1kBAwAAqEQBAwAAqEQBAwAAqEQBAwAAqMQhHACw5e+3DgBAv3kOGAAAQCW2IALAQNd1t3Zdd2vrHAD0ly2IALDlHw1eP9g0BQC9ZQUMAACgEgUMAACgEgUMAACgEgUMAACgEodwAMCW72odAIB+8xwwAACASmxBBICBruuOdl13tHUOAPpLAQOALUcHAwBGQgFjrOzfv/9kKSUNwzBajM9//vNv/cIXvvDWr/28uLiYEZGTk5Onrpmens6IyPn5+aF/u7m5maurq0Nzy8vLGRFDc7OzsxkROTs7OzQfEbm8vDw0t7q6mpubm0Nz8/PzGRE5PT19am5ycjIjIhcXF4euXV9fz/X19aE572n07+ngaw+erPzrE9hB3APGWCml5PHjx1vHAHap+++/P9bW1uLIvzrSOgo72Il/fiKWl5dL6xzAeLICBgBwDi0vL7eOAIwxBQwA4Byanp5uHQEYYwoYAAy8+c1vjrvuuqt1DHa4Bx98sHUEYIwpYAAwsGfPnnj22WdbxwCgxxQwABj4/d///bjmmmtax2CHm5iYaB0BGGMKGAAMPPbYY3HVVVe1jsEOt7m52ToCMMYUMACAc2hxcbF1BGCMKWAAAOdQ13WtIwBjTAEDAACoRAEDAACoRAEDgIG3vvWtcccdd7SOwQ63trbWOgIwxhQwAACASvZs56JSyk9HxG0R8cWI+HJErGfmvxxlMACo7dFHH41rr722dQx2uKuvvjoys3UMYEy9aAErpVwdEX83Ir55cP2DEbE+4lwAUN3jjz8ehw4dah0DgB7bzgrYWyLiw5n5pxERpZTV0UYCAADop+3cA1ZGngIAoCcWFhZaRwDG2HYK2McjYraUsr+U8hcj4u+MOBMAwI61uLjYOgIwxl60gGXmsYhYiYjfiYh7ImItIv5kxLkAoLrzzjsvnn322dYx2OEmJydbRwDG2HaPof+XmXllRHxnRFwZDuEAoIfe8pa3xF133dU6Bjvc448/3joCMMa2dQx9RCyXUt4YEfsj4s7MfHCEmQAAAHppWwUsM79v1EEAoLXPfOYzcd1117WOwQ43NTXVOgIwxra7BREAeu9LX/pSXHHFFa1jsMOtr7tTAzg9BQwA4Byan59vHQEYYwoYAMA5dPvtt7eOAIwxBQwAAKASBQwABs4///x4+umnW8cAoMcUMAAYeNOb3hR333136xjscBsbG60jAGNMAQMAOIecggiciQIGAAOPPPJI3HDDDa1jsMPddNNNrSMA4ywzDWNsxr59+05GRBqGYbQYR48eze///u8fmltbW8u1tbWhuYWFhczMnJiYODU3NTWVmZlzc3ND125sbOTKysrQ3NLSUmbm0NzMzExmZs7MzAzNZ2YuLS0Nza2srOTGxsbQ3NzcXGZmTk1NnZqbmJjIzMyFhQXvqeJ7uuzgZSdb/z41DGN8R8nMAAAiuq67LyJiYWHh+rZJAOgrWxABAAAqUcAAAAAq2dM6AACMkcdaBwCg39wDBgAAUIktiAAAAJUoYAAw0HXde7uue2/rHAD0l3vAAGDLkdYBAOg3K2AAAACVKGAAAACVKGAAAACVuAcMALY82joAAP3mOWAAAACV2IIIAABQiQIGAANd1y13XbfcOgcA/eUeMADYcqh1AAD6zQoYAABAJQoYAABAJQoYAABAJe4BA4AtD7UOAEC/eQ4YAABAJbYgAgAAVKKAAcBA13Uf6LruA61zANBf7gEDgC2XtQ4AQL9ZAQMAAKhEAQMAAKhEAQMAAKjEPWAAsOWTrQMA0G+eAwYAAFCJLYgAAACVKGCMlVJKrq+v5/r6epZSTo3FxcWMiJycnDw1Nz09nRGR8/PzQ9dubm7m6urq0Nzy8nJGxNDc7OxsRkTOzs4OzUdELi8vD82trq7m5ubm0Nz8/HxGRE5PT5+am5yczIjIxcXFoWvP1Xvat2/f0LxhGOd23Hrrrfl9t97aPMc4jwtKeaLZLwmAHrAFkbFSSkmfydMrpcTx48dbx4Deuv/++2NtbS3+wx13tI4ytkpEZGZpnQNgp7ICBgAAUIkCxlhZWFhoHQEAAEZGAWOsLC4uto4AAAAjo4AxViYnJ1tHAHaxiy++OD73uc+1jgFAjzmEg7HiEI4zcwgHjN7hw4fDt9DpOYQD4OxYAQMAAKhEAWOsTE1NtY4A7GIf//jH47bbbmsdA4AeU8AYK+vr660jALvYyZMnY+/eva1jANBjChhjZX5+vnUEAAAYGQWMsXL77be3jgAAACPzogWslHJ5KeWRGmEAAAD6zAoYAAxMTEzEo48+2joGAD223QK2p5RyZynl4VLKr5RSDow0FbvWxsZG6wjALnbo0KF44IEHWscAoMe2W8CujIjlzPxrEfFURLxzdJHYzZyCCABAn223gH0xMz8x+PMHIuItI8rDLnfTTTe1jgDsYvfff38cPXq0dQwAemy7BSxf5GcAAABexHYL2GtLKW8a/Pl7I+LjI8oDAADQW9stYJ+JiHeUUh6OiFdGxPtHF4ndbGlpqXUEAAAYmT0vdkFmfj4i3jj6KBAxPz/fOgIAAIyM54AxVkoprSMAu9hll10WJ06caB0DgB5TwABg4PWvf30cO3asdQwAekwBA4CBr371q7F3797WMQDoMQWMsTIzM9M6ArCLfeITn4jbbrutdQwAekwBY6ysrq62jgAAACOjgDFWZmdnW0cAAICRUcAYKx/5yEdaRwAAgJFRwAAAACpRwABg4HWve1089NBDrWMA0GMKGGMlM1tHAHaxyy+/XAEDYLQy0zDGZrzvfe/7k4jIr42VlZXc2NjI58/Nzc1lZubU1NSpuYmJiczMXFhYGLp2bW0t19bWhuYWFhYyM3NiYuLU3NTUVGZmzs3NDV27sbGRKysrQ3NLS0uZmUNzMzMzmZk5MzMzNJ+ZubS0lOfqPZ1//vlD/84wjHM7Dhw4kN944EDzHOM89kc80fp3hWEYxk4eJTMDAIjouu6+iIiFhYXr2yYBoK9sQQQAAKhEAQMAAKhEAQMAAKhEAQMAAKhkT+sAADBG3t86AAD95hREAACASmxBBICBrusOdl13sHUOAPrLFkQA2PJLg9frW4YAoL+sgAEAAFSigAEAAFSigAEAAFSigAEAAFTiEA4A2PKe1gEA6DfPAQMAAKjEFkQAGOi67squ665snQOA/rIFEQC2LA1er28ZAoD+sgIGAABQiQIGAABQiQIGAABQiQIGAABQiUM4AGDLz7QOAEC/eQ4YAABAJbYgAsBA13VHuq470joHAP1lCyIAbHnv4PX6liEA6C8rYAAAAJUoYAAAAJUoYAAAAJUoYAAAAJU4hAMAtvxE6wAA9FuvnwO2f//+J5555plLWucAGBvnRcTJ1iHG2yvOf0WcfOZkaZ0DgH7q9QrYM888c8nx48dbxwAYG4cPH45YbJ1ifB2MgxH/vnUKAPqs1wUMAF6KG+KGiBtapwCgzxzCAQAAUIkCBgAAUIkCBgAAUIkCBgAAUIlDOABg4KPx0YiPtk4BQJ8pYAAw8EQ8EfFE6xQA9JkCBgADV8QVEVe0TgFAnylgADBwXVwXcV3rFAD0mUM4AAAAKlHAAAAAKlHAAAAAKtlWASul/JdSynop5UQpZX7UoQAAAPpou4dw/EBm/nEp5YKIOFZK+VBmPjnKYABQ22qsRqy2TgFAn223gP1IKeXtgz8fjIg3RIQCBkCvPBlP+u0GwEi9aAErpVwfEW+LiDdl5tOllPsiYv+IcwFAdYfiUMSh1ikA6LPtrID9pYj4P4Py9Zcj4ltGnAkAmrg2ro24tnUKAPpsO4dwfDQi9pRSHo6Id0fE/xhtJAAAgH560RWwzHwmIr6tQhYAAIBe8xwwAACAShQwAACAShQwABi4J+6Je+65p3UMAHpsu88BA4DeeyqeiniqdQoA+kwBA4CBq+KqiKtapwCgzxQwABi4Jq6JuKZ1CgD6zD1gAAAAlShgAAAAlShgAAAAlShgAAAAlZTMbJ1hZPbv3//EM888c0nrHABj47yIONk6xPg6cOBAxHkRX3nqK6V1FgD6qdcFDAAAYJzYgggAA13XHe267mjrHAD0lwIGAFuODgYAjIQCBgAAUIkCBgAAUIkCBgAAUIkCBgAAUMme1gEAYIx8e+sAAPSb54ABAABUYgsiAAx0XffOruve2ToHAP2lgAHAllsGAwBGQgEDAACoRAEDAACoRAEDAACoRAEDAACoxDH0FZVS5jNzuXUOOJd8rukbn2n6xmeavtnpn2krYHXNtw4AI+BzTd/4TNM3PtP0zY7+TCtgAAAAlShgAAAAlShgde3YvapwBj7X9I3PNH3jM03f7OjPtEM4AAAAKrECBgAAUIkCNiKllF8spXyplPLI8+ZeWUr5zVLK/xq8fmPLjHA2SimfL6UcL6U8VEpZa50HzlYp5cZSymdLKb9XSnlX6zxwLpVSFkspG4Pv7IdKKd/eOhO8VKWU7y6lnCil/L9SytVf93c/Pvj+/mwp5VtbZdwOBWx07oiIG79u7l0RcW9mviEi7h38DDvZ38zMI5l59YtfCuOrlHJeRPybiPi2iHhjRHxvKeWNbVPBOfevB9/ZRzLz11qHgZfhkYi4OSI+9vzJwff190TEVfHcf3//28H3+lhSwEYkMz8WEX/8ddPfERF3Dv58Z0R8Z9VQAJzOX4+I38vMz2Xmn0fEL8dz39kAjInM/ExmfvYF/uo7IuKXM/OZzPyDiPi9eO57fSwpYHVdkpmPR0QMXi9unAfORkbEb5RS1kspO/qBiBARr4mILz7v58cGc9AnP1xKeXhwm4TbIOiTHfUdvqd1AGDHenNmbpZSLo6I3yyl/M/Byi/sROUF5hwTzI5SSvmtiLj0Bf7qJyPi/RHx7njuc/3uiHhPRPxAvXSwPWf6HGfmh0/3z15gbmy/wxWwuv6wlDKRmY+XUiYi4kutA8HLlZmbg9cvlVJ+NZ5b6lfA2Kkei4iDz/v5sojYbJQFXpbMfNt2riul3B4RHxlxHHhZtvs5/jo76jvcFsS6ViLiHYM/vyMiTtfiYayVUv5CKeUbvvbniPjb8dyNsbBTHYuIN5RSvqmUcn48dzP3SuNMcM4M/sfv17w9fGfTLysR8T2llH2llG+KiDdExKcbZzotK2AjUkr5zxFxfUS8qpTyWEQsRMS/iIi7Syn/MCL+d0R8d7uEcFYuiYhfLaVEPPc98p8y86NtI8HLl5lfLaX8cET8t4g4LyJ+MTNPNI4F59IvlFKOxHPbsj4fET/YNg68dKWUt0fE+yLi1RHxX0spD2Xmt2bmiVLK3RHxuxHx1Yj4ocw82TLrmZTMsd0eCQAA0Cu2IAIAAFSigAEAAFSigAEAAFSigAEAAFSigAEAAFSigAEAAFSigAEAAFSigAEAAFTy/wGRKy3T7KUzhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e5334a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.yaxis.label.set_visible(False)\n",
    "ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "plt.axvline(x=0,linestyle='dashed',color='grey')\n",
    "\n",
    "\n",
    "#Let's start trying to figure out color maps\n",
    "def colmap1(a,b):\n",
    "    if a==b: return 'lightgrey'\n",
    "    else: return 'r' if b<0 else 'g'\n",
    "        \n",
    "def colmap2(a,b):\n",
    "    if not samesign(a,b): return 'w'\n",
    "    else: return 'pink' if b<0 else 'lightgreen'\n",
    "\n",
    "df['prev'].plot(kind='barh', color=df.apply(lambda x: colmap2(x['overall'],x['deltaxx']),axis=1),edgecolor='w',ax=ax)\n",
    "df['prev'].plot(kind='barh',edgecolor='black',linestyle='dashed', fill=False, ax=ax)\n",
    "\n",
    "df['overall'].plot(kind='barh',color='lightgrey',edgecolor='black',ax=ax)\n",
    "#df['deltaxx'].plot(kind='barh',color=['r' if x else 'g' for x in df['deltaxx']<0],edgecolor='black',ax=ax)\n",
    "df['deltaxx'].plot(kind='barh',color=df.apply(lambda x: colmap1(x['overall'],x['deltaxx']),axis=1),edgecolor='black',ax=ax)\n",
    "\n",
    "\n",
    "ax.invert_xaxis()\n",
    "#Hide outer box\n",
    "plt.box(on=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what do we need for this sort of chart?\n",
    "\n",
    "- drivercode;\n",
    "- total accumulated time at end of previous stage;\n",
    "- total accumulated time at end of current stage;\n",
    "- rebased differences from each driver to a target driver for previous and current stage;\n",
    "- delta for each driver based on rebased difference times from previous and current stages.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalTimeMs</th>\n",
       "      <th>class</th>\n",
       "      <th>code</th>\n",
       "      <th>distance</th>\n",
       "      <th>name</th>\n",
       "      <th>snum</th>\n",
       "      <th>driver.code</th>\n",
       "      <th>entrant.name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2149700</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>curr</td>\n",
       "      <td>NEU</td>\n",
       "      <td>HYUNDAI SHELL MOBIS WRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2153400</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>curr</td>\n",
       "      <td>MIK</td>\n",
       "      <td>HYUNDAI SHELL MOBIS WRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2155000</td>\n",
       "      <td>RC1</td>\n",
       "      <td>SS4</td>\n",
       "      <td>19.13</td>\n",
       "      <td>SS4 Röjden 1</td>\n",
       "      <td>curr</td>\n",
       "      <td>LAP</td>\n",
       "      <td>TOYOTA GAZOO RACING WRT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalTimeMs class code  distance          name  snum driver.code  \\\n",
       "0      2149700   RC1  SS4     19.13  SS4 Röjden 1  curr         NEU   \n",
       "1      2153400   RC1  SS4     19.13  SS4 Röjden 1  curr         MIK   \n",
       "2      2155000   RC1  SS4     19.13  SS4 Röjden 1  curr         LAP   \n",
       "\n",
       "              entrant.name  \n",
       "0  HYUNDAI SHELL MOBIS WRT  \n",
       "1  HYUNDAI SHELL MOBIS WRT  \n",
       "2  TOYOTA GAZOO RACING WRT  "
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snum=4\n",
    "typ='stage_times_overall'\n",
    "\n",
    "#this represents a subset of dbGetStageRank in \"Charts - Stage Results.ipynb\"\n",
    "#use that function instead?\n",
    "def getStageResult( rally, typ, snum, rc):\n",
    "    q='''\n",
    "    SELECT st.totalTimeMs, sc.name as class, i.code, i.distance, i.name, CAST(REPLACE(code,'SS','') AS INTEGER) snum,\n",
    "    sl.`driver.code` drivercode, sl.`entrant.name`\n",
    "    FROM {typ} st INNER JOIN itinerary_stages i ON st.stageId = i.stageId\n",
    "    INNER JOIN startlist_classes sc ON sc.entryid = st.entryId \n",
    "    INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "    INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "    WHERE sc.name=\"{rc}\" AND ce.`country.name`=\"{rally}\" AND snum={stage} ORDER BY snum\n",
    "    '''.format(rc=rc,rally=rally, typ=typ, stage=snum)\n",
    "    return pd.read_sql(q,conn)\n",
    "\n",
    "curr = getStageResult( rally, typ, snum, rc)\n",
    "curr['snum'] = 'curr'\n",
    "prev = getStageResult( rally, typ, snum-1, rc)\n",
    "prev['snum'] = 'prev'\n",
    "\n",
    "stagerank= pd.concat([curr,prev]).reset_index(drop=True)\n",
    "stagerank.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>snum</th>\n",
       "      <th>curr</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver.code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2386400</td>\n",
       "      <td>1698800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>2164700</td>\n",
       "      <td>1542800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVA</th>\n",
       "      <td>2247400</td>\n",
       "      <td>1557600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>2155000</td>\n",
       "      <td>1533600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAT</th>\n",
       "      <td>2166700</td>\n",
       "      <td>1540800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEE</th>\n",
       "      <td>2188400</td>\n",
       "      <td>1554500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIK</th>\n",
       "      <td>2153400</td>\n",
       "      <td>1534900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>2149700</td>\n",
       "      <td>1529300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OGI</th>\n",
       "      <td>2197400</td>\n",
       "      <td>1556900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>2157100</td>\n",
       "      <td>1536400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAD</th>\n",
       "      <td>2169200</td>\n",
       "      <td>1546600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOL</th>\n",
       "      <td>2298400</td>\n",
       "      <td>1633300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>2170100</td>\n",
       "      <td>1547200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TÄN</th>\n",
       "      <td>2169100</td>\n",
       "      <td>1532000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "snum            curr     prev\n",
       "driver.code                  \n",
       "AL           2386400  1698800\n",
       "BRE          2164700  1542800\n",
       "EVA          2247400  1557600\n",
       "LAP          2155000  1533600\n",
       "LAT          2166700  1540800\n",
       "MEE          2188400  1554500\n",
       "MIK          2153400  1534900\n",
       "NEU          2149700  1529300\n",
       "OGI          2197400  1556900\n",
       "OST          2157100  1536400\n",
       "PAD          2169200  1546600\n",
       "SOL          2298400  1633300\n",
       "SUN          2170100  1547200\n",
       "TÄN          2169100  1532000"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx = stagerank.pivot('drivercode','snum','totalTimeMs')\n",
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [],
   "source": [
    "drivercode = 'TÄN'\n",
    "#drivercode = 'LAT'\n",
    "#drivercode = 'PAD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'curr': 2169100, 'prev': 1532000}"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebase = dfx.loc[drivercode].to_dict()\n",
    "rebase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rebased_curr is rebased diff at end of current stage; negative means target is ahead\n",
    "#rebased_prev is rebased diff at end if revious stage; negative means target is ahead\n",
    "#rebased_delta is rebased_curr - rebased_prev; positive means target lost ground"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>snum</th>\n",
       "      <th>curr</th>\n",
       "      <th>prev</th>\n",
       "      <th>rebased_curr</th>\n",
       "      <th>rebased_prev</th>\n",
       "      <th>rebased_delta</th>\n",
       "      <th>deltaxx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver.code</th>\n",
       "      <th></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>AL</th>\n",
       "      <td>2386400</td>\n",
       "      <td>1698800</td>\n",
       "      <td>217.3</td>\n",
       "      <td>166.8</td>\n",
       "      <td>50.5</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>2164700</td>\n",
       "      <td>1542800</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVA</th>\n",
       "      <td>2247400</td>\n",
       "      <td>1557600</td>\n",
       "      <td>78.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>52.7</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>2155000</td>\n",
       "      <td>1533600</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-15.7</td>\n",
       "      <td>-14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAT</th>\n",
       "      <td>2166700</td>\n",
       "      <td>1540800</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>-11.2</td>\n",
       "      <td>-2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEE</th>\n",
       "      <td>2188400</td>\n",
       "      <td>1554500</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIK</th>\n",
       "      <td>2153400</td>\n",
       "      <td>1534900</td>\n",
       "      <td>-15.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>-15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>2149700</td>\n",
       "      <td>1529300</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>-16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OGI</th>\n",
       "      <td>2197400</td>\n",
       "      <td>1556900</td>\n",
       "      <td>28.3</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>2157100</td>\n",
       "      <td>1536400</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAD</th>\n",
       "      <td>2169200</td>\n",
       "      <td>1546600</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>-14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOL</th>\n",
       "      <td>2298400</td>\n",
       "      <td>1633300</td>\n",
       "      <td>129.3</td>\n",
       "      <td>101.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>2170100</td>\n",
       "      <td>1547200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>-14.2</td>\n",
       "      <td>-14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TÄN</th>\n",
       "      <td>2169100</td>\n",
       "      <td>1532000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "snum            curr     prev  rebased_curr  rebased_prev  rebased_delta  \\\n",
       "driver.code                                                                \n",
       "AL           2386400  1698800         217.3         166.8           50.5   \n",
       "BRE          2164700  1542800          -4.4          10.8          -15.2   \n",
       "EVA          2247400  1557600          78.3          25.6           52.7   \n",
       "LAP          2155000  1533600         -14.1           1.6          -15.7   \n",
       "LAT          2166700  1540800          -2.4           8.8          -11.2   \n",
       "MEE          2188400  1554500          19.3          22.5           -3.2   \n",
       "MIK          2153400  1534900         -15.7           2.9          -18.6   \n",
       "NEU          2149700  1529300         -19.4          -2.7          -16.7   \n",
       "OGI          2197400  1556900          28.3          24.9            3.4   \n",
       "OST          2157100  1536400         -12.0           4.4          -16.4   \n",
       "PAD          2169200  1546600           0.1          14.6          -14.5   \n",
       "SOL          2298400  1633300         129.3         101.3           28.0   \n",
       "SUN          2170100  1547200           1.0          15.2          -14.2   \n",
       "TÄN          2169100  1532000           0.0           0.0            0.0   \n",
       "\n",
       "snum         deltaxx  \n",
       "driver.code           \n",
       "AL              50.5  \n",
       "BRE             -4.4  \n",
       "EVA             52.7  \n",
       "LAP            -14.1  \n",
       "LAT             -2.4  \n",
       "MEE             -3.2  \n",
       "MIK            -15.7  \n",
       "NEU            -16.7  \n",
       "OGI              3.4  \n",
       "OST            -12.0  \n",
       "PAD            -14.5  \n",
       "SOL             28.0  \n",
       "SUN            -14.2  \n",
       "TÄN              0.0  "
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx['rebased_curr'] = ((dfx['curr'] - rebase['curr'])/1000).round(1)\n",
    "dfx['rebased_prev'] = ((dfx['prev'] - rebase['prev'])/1000).round(1)\n",
    "dfx['rebased_delta'] = dfx['rebased_curr'] - dfx['rebased_prev']\n",
    "\n",
    "dfx['deltaxx']=dfx.apply(lambda x: thing(x['rebased_curr'],x['rebased_delta']), axis=1)\n",
    "\n",
    "dfx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a function of the original stage progress chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125d15a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def stageProgessBarOrig(df,prev,curr,delta,deltaxx,title=None):\n",
    "    df = df.sort_values(curr,ascending=False)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "    ax.yaxis.label.set_visible(False)\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "    plt.axvline(x=0,linestyle='dashed',color='grey')\n",
    "\n",
    "    \n",
    "    #Thre previous colour maps weren't quite correct\n",
    "    def colmap3(row):\n",
    "        if not samesign(row[deltaxx], row[curr]): return 'w'\n",
    "        return 'pink' if row[curr]<row[prev] else 'lightgreen'\n",
    "\n",
    "    def colmap4(row):\n",
    "        #if not samesign(row[delta], row[deltaxx]): return 'lightgrey'    \n",
    "        if not samesign(row[curr],row[delta]):\n",
    "            return 'pink' if (row[deltaxx]<0) else 'lightgreen'\n",
    "        return 'r' if (row[deltaxx]<0) else 'g'\n",
    "\n",
    "    df[prev].plot(kind='barh', color=df.apply(lambda x: colmap3(x),axis=1),edgecolor='w',ax=ax)\n",
    "    df[prev].plot(kind='barh',edgecolor='black',linestyle='dashed', fill=False, ax=ax)\n",
    "    #df[prev].plot(kind='barh',edgecolor='black', fill=False, ax=ax)\n",
    "    df[curr].plot(kind='barh',color='lightgrey',edgecolor='black',ax=ax)\n",
    "    \n",
    "    df[deltaxx].plot(kind='barh',color=df.apply(lambda x: colmap4(x),axis=1),edgecolor='black',ax=ax)\n",
    "    \n",
    "\n",
    "    ax.invert_xaxis()\n",
    "    #Hide outer box\n",
    "    plt.box(on=None)\n",
    "    if title: plt.title(title)\n",
    "\n",
    "#MEE needs to be pink,\n",
    "stageProgessBarOrig(dfx,'rebased_prev','rebased_curr','rebased_delta','deltaxx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Improved Stage Progress Chart\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebaseStageRankProgress(stagerank, drivercode):   \n",
    "    dfx = stagerank.pivot('drivercode','snum','totalTimeMs')\n",
    "\n",
    "    rebase = dfx.loc[drivercode].to_dict()\n",
    "    dfx['rebased_curr'] = ((dfx['curr'] - rebase['curr'])/1000).round(1)\n",
    "    dfx['rebased_prev'] = ((dfx['prev'] - rebase['prev'])/1000).round(1)\n",
    "    dfx['rebased_delta'] = dfx['rebased_curr'] - dfx['rebased_prev']\n",
    "\n",
    "    dfx['deltaxx']=dfx.apply(lambda x: thing(x['rebased_curr'],x['rebased_delta']), axis=1)\n",
    "    return dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stageProgessBar(df,prev,curr,delta,deltaxx,title=None, neg='r',pos='g', lneg='pink',lpos='lightgreen'):\n",
    "    \n",
    "    #df = df.sort_values(curr,ascending=False)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "    ax.yaxis.label.set_visible(False)\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.tick_params(axis='y', which='both',length=0)\n",
    "\n",
    "    plt.axvline(x=0,linestyle='dashed',color='grey')\n",
    "\n",
    "    \n",
    "    #The previous colour maps weren't quite correct\n",
    "    def colmap3(row):\n",
    "        if not samesign(row[deltaxx], row[curr]): return 'w'\n",
    "        return lneg if row[curr]<row[prev] else lpos\n",
    "\n",
    "    def colmap4(row):\n",
    "        #if not samesign(row[delta], row[deltaxx]): return 'lightgrey'    \n",
    "        if not samesign(row[curr],row[delta]):\n",
    "            return lneg if (row[deltaxx]<0) else lpos\n",
    "        return neg if (row[deltaxx]<0) else pos\n",
    "    \n",
    "    def colmap5(row):\n",
    "        if not samesign(row[deltaxx], row[curr]): return 'w'\n",
    "        return neg if row[curr]<row[prev] else pos\n",
    "\n",
    "    df[prev].plot(kind='barh', color=df.apply(lambda x: colmap5(x),axis=1),edgecolor='w',ax=ax)\n",
    "    #df[prev].plot(kind='barh',edgecolor='black',linestyle='dashed', fill=False, ax=ax)\n",
    "    df[prev].plot(kind='barh',edgecolor='black', fill=False, ax=ax)\n",
    "    df[curr].plot(kind='barh',color='lightgrey',edgecolor='black',ax=ax)\n",
    "    \n",
    "    df[deltaxx].plot(kind='barh',color=df.apply(lambda x: colmap4(x),axis=1),edgecolor='black',ax=ax) \n",
    "\n",
    "    ax.invert_xaxis()\n",
    "    #Hide outer box\n",
    "    plt.box(on=None)\n",
    "    if title: plt.title(title)\n",
    "        \n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12504f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rebaseStageProgressBar(stagerank, drivercode, title=None,neg='green',pos='r', lneg='lightgreen',lpos='pink', overlay=False):   \n",
    "    dfx = rebaseStageRankProgress(stagerank, drivercode)\n",
    "    dfx = dfx.sort_values('rebased_curr',ascending=False)\n",
    "    \n",
    "    fig, ax = stageProgessBar(dfx,'rebased_prev','rebased_curr','rebased_delta','deltaxx',title=title, neg=neg,lneg=lneg,pos=pos,lpos=lpos)\n",
    "\n",
    "    if overlay:\n",
    "        fig,ax=plotSplitSectionDelta(splitdurations,drivercode,dfx.index,fig,ax,alpha=0.7,invert_yaxis=False);\n",
    "    \n",
    "    return fig, ax\n",
    "\n",
    "#rebaseStageProgressBar(stagerank,'TÄN', title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')))\n",
    "#rebaseStageProgressBar(stagerank,'PAD', title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')))\n",
    "#rebaseStageProgressBar(stagerank,'OST', title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')))\n",
    "fig, ax = rebaseStageProgressBar(stagerank,'TÄN', title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solid color shows we are consolidating a position. Pastel colour shows that there has been a gain or loss on leader/laggard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124e0d630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drivercode='MEE'#'TÄN'\n",
    "fig, ax = rebaseStageProgressBar(stagerank,drivercode, neg='lightgreen',pos='pink',\n",
    "                                 title='Stage Progress Chart for {dc} - {rally}, {year} - SS{ss}'.format(dc=drivercode,\n",
    "                                                                                                         year=year,\n",
    "                                                                                                         rally=rally,\n",
    "                                                                                                         ss=str(snum).replace('SS','')));\n",
    "\n",
    "dfx = rebaseStageRankProgress(stagerank, drivercode)\n",
    "dfx = dfx.sort_values('rebased_curr',ascending=False)\n",
    "order = dfx.index\n",
    "fig,ax=plotSplitSectionDelta(splitdurations,drivercode,order,fig,ax,alpha=0.7,invert_yaxis=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127072f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = rebaseStageProgressBar(stagerank,'TÄN', overlay=True,neg='lightgreen',pos='pink',\n",
    "                                 title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')));\n",
    "fig.savefig('test1.png')   # save the figure to file\n",
    "#plt.close(fig)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Outputter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TAN'"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!/Users/ajh59/anaconda3/bin/conda install -y unidecode\n",
    "#In some cases we may want to remove unicode characters and use a simple ascii equivalent\n",
    "import unidecode\n",
    "unidecode.unidecode('TÄN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt=''\n",
    "!mkdir -p testout/img\n",
    "path='testout/'\n",
    "with open('{}testfile1.md'.format(path), 'w') as file:\n",
    "    for dc in stagerank['drivercode'].unique():\n",
    "        fig, ax = rebaseStageProgressBar(stagerank,dc, overlay=True,neg='lightgreen',pos='pink',\n",
    "                                 title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,\n",
    "                                                                                                rally=rally,\n",
    "                                                                                                ss=str(snum).replace('SS','')));\n",
    "        img='img/test_{}_{}.png'.format(str(snum).replace('SS',''), unidecode.unidecode(dc))\n",
    "        fig.savefig('{}{}'.format(path,img))   # save the figure to file\n",
    "        plt.close(fig)  \n",
    "        file.write('# Stage Progress Chart - {rally}, {year} - {dc} - SS{ss}\\n\\n'.format(year=year,rally=rally,\n",
    "                                                                              dc=dc,\n",
    "                                                                              ss=str(snum).replace('SS','')))\n",
    "        file.write('![]({})\\n'.format(img))\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a test fig:\n",
    "\n",
    "![](test1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>section</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drivercode</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>AL</th>\n",
       "      <td>10.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRE</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVA</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAP</th>\n",
       "      <td>158.2</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>-28.5</td>\n",
       "      <td>-41.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAT</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIK</th>\n",
       "      <td>-1.7</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEU</th>\n",
       "      <td>-2.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OGI</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST</th>\n",
       "      <td>-2.4</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAD</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOL</th>\n",
       "      <td>4.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TÄN</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "section         1     2     3     4     5\n",
       "drivercode                               \n",
       "AL           10.0  18.9  17.0   7.9  -0.1\n",
       "BRE          -0.6  -1.0  -2.0  -3.6  -4.9\n",
       "EVA           0.8   0.2   8.5  27.0  19.4\n",
       "LAP         158.2  -7.1 -28.5 -41.7   NaN\n",
       "LAT          -1.0  -2.3  -2.5  -1.3  -0.9\n",
       "MEE           0.0   0.0   0.0   0.0   0.0\n",
       "MIK          -1.7  -3.3  -2.0  -3.9  -4.4\n",
       "NEU          -2.7  -1.6  -2.5  -3.1  -3.6\n",
       "OGI          -0.2  -0.3   0.8   4.7   1.6\n",
       "OST          -2.4  -0.7  -1.2  -3.5  -5.4\n",
       "PAD          -0.4  -1.6  -2.0  -2.5  -4.8\n",
       "SOL           4.6   9.6   8.6   6.0   2.4\n",
       "SUN          -1.5  -1.7   0.0  -2.8  -5.0\n",
       "TÄN          -1.2  -1.7  -1.7   3.3   4.5"
      ]
     },
     "execution_count": 672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebaseSplitDurations(splitdurations, drivercode).pivot('drivercode','section','rebased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
       "      <td>-0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGI</td>\n",
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       "      <td>640500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGI</td>\n",
       "      <td>158.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>135.8</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "\n",
       "    section  rebased  \n",
       "0         1     -0.2  \n",
       "1         2     -0.3  \n",
       "2         3      0.8  \n",
       "3         4      4.7  \n",
       "63        5      1.6  "
      ]
     },
     "execution_count": 673,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitdurations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>drivercode</th>\n",
       "      <th>splitDurationS</th>\n",
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       "      <th>stageTimeDurationMs</th>\n",
       "      <th>section</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>87.1</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>161.7</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>134.4</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>TÄN</td>\n",
       "      <td>97.9</td>\n",
       "      <td>2018-02-16T08:55:00</td>\n",
       "      <td>637100.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "4         TÄN            87.1  2018-02-16T08:55:00             637100.0   \n",
       "5         TÄN           161.7  2018-02-16T08:55:00             637100.0   \n",
       "6         TÄN           156.0  2018-02-16T08:55:00             637100.0   \n",
       "7         TÄN           134.4  2018-02-16T08:55:00             637100.0   \n",
       "68        TÄN            97.9  2018-02-16T08:55:00             637100.0   \n",
       "\n",
       "    section  \n",
       "0         1  \n",
       "1         2  \n",
       "2         3  \n",
       "3         4  \n",
       "63        5  \n",
       "4         1  \n",
       "5         2  \n",
       "6         3  \n",
       "7         4  \n",
       "68        5  "
      ]
     },
     "execution_count": 674,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitdurations = getSplitDurationsFromSplits(conn,rally,ss,rc='RC1')\n",
    "\n",
    "splitdurations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [],
   "source": [
    "ignoresplitsfordrivercodes=['LAP']\n",
    "if ignoresplitsfordrivercodes is not None:\n",
    "    ignoresplitsfordrivercodes = [ignoresplitsfordrivercodes] if not isinstance(ignoresplitsfordrivercodes,list) else ignoresplitsfordrivercodes\n",
    "    splitdurations = splitdurations[~splitdurations['drivercode'].isin(ignoresplitsfordrivercodes)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>stageTimeDurationMs</th>\n",
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       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>135.8</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "\n",
       "    section  sectorrank  \n",
       "0         1           9  \n",
       "1         2           7  \n",
       "2         3           6  \n",
       "3         4          10  \n",
       "63        5          10  "
      ]
     },
     "execution_count": 676,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitdurations['sectorrank']=splitdurations.groupby(['section'])['splitDurationS'].rank(method='dense').astype(int)\n",
    "splitdurations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = splits.sort_values(['stageTimeDurationMs','drivercode'])['drivercode'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGI</td>\n",
       "      <td>163.1</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>MIK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGI</td>\n",
       "      <td>158.5</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGI</td>\n",
       "      <td>135.8</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8.6</td>\n",
       "      <td>MIK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OGI</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2018-02-16T08:54:00</td>\n",
       "      <td>640500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>OST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drivercode  splitDurationS        startDateTime  stageTimeDurationMs  \\\n",
       "0         OGI            88.1  2018-02-16T08:54:00             640500.0   \n",
       "1         OGI           163.1  2018-02-16T08:54:00             640500.0   \n",
       "2         OGI           158.5  2018-02-16T08:54:00             640500.0   \n",
       "3         OGI           135.8  2018-02-16T08:54:00             640500.0   \n",
       "63        OGI            95.0  2018-02-16T08:54:00             640500.0   \n",
       "\n",
       "    section  sectorrank  rebased sectorBestDriver  \n",
       "0         1           9      2.5              NEU  \n",
       "1         2           7      3.0              MIK  \n",
       "2         3           6      3.3              NEU  \n",
       "3         4          10      8.6              MIK  \n",
       "63        5          10      7.0              OST  "
      ]
     },
     "execution_count": 678,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#splitdurations = rebaseSplitDurations(splitdurations, drivercode='MIK')\n",
    "splitdurations = rebaseSplitDurationsBest(splitdurations)\n",
    "splitdurations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1278aad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics import utils\n",
    "from numpy import arange\n",
    "\n",
    "order = getSplitOrderByStageTimeDuration(splitdurations)\n",
    "splitdurationsbydriver=splitdurations.groupby('drivercode')\n",
    "\n",
    "ydim='rebased'\n",
    "\n",
    "\n",
    "labdim = 'sectorrank'\n",
    "ylabel=None\n",
    "xticklabels=None\n",
    "label=True\n",
    "ylim=(-1,10)\n",
    "keyHighlight=None#'leader'#None\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "#fig, ax = utils.create_mpl_ax(ax)\n",
    "        \n",
    "start = 0\n",
    "ticks = []\n",
    "tmpxlabels = []\n",
    "for key in order:\n",
    "    df=splitdurationsbydriver.get_group(key)\n",
    "    nobs = len(df)\n",
    "    x_plot = arange(start, start + nobs)\n",
    "    ticks.append(x_plot.mean())\n",
    "    #Third parameter is color; 'k' is black\n",
    "    #ax.plot(x_plot, df[ydim], 'g', linestyle='--')\n",
    "\n",
    "    #named colors: http://matplotlib.org/examples/color/named_colors.html\n",
    "    highlightColors=['mistyrose','lightsalmon','salmon','tomato','orangered']\n",
    "    baseColors=['silver','lightblue','paleturquoise','lightcyan','lightgreen']\n",
    "    if key==keyHighlight:\n",
    "        colors=highlightColors\n",
    "    else:\n",
    "        colors=baseColors\n",
    "    for i in range(0,nobs):\n",
    "        if ylim is not None and (df[ydim].iloc[i]<=ylim[0] or df[ydim].iloc[i]>=ylim[1]): continue\n",
    "        if label:\n",
    "            if nobs<=len(colors):\n",
    "                if keyHighlight=='leader':\n",
    "                    if int(df[labdim].iloc[i])==1: color=highlightColors[i-nobs]\n",
    "                    else: color=baseColors[i-nobs]\n",
    "                else:\n",
    "                    color=colors[i-nobs]\n",
    "            else: color='pink'\n",
    "            ax.plot((x_plot[i],x_plot[i]), (0,df[ydim].iloc[i]), 'lightgrey')\n",
    "            ax.text(x_plot[i], df[ydim].iloc[i], int(df[labdim].iloc[i]),\n",
    "                    bbox=dict(  boxstyle='round,pad=0.3',color=color),horizontalalignment='center')\n",
    "        else:\n",
    "            pass\n",
    "            #ax.plot(x_plot[i], df[labdim].iloc[i], 'or')\n",
    "        \n",
    "    start += nobs+3\n",
    "    plt.axvline(x=start-2,linestyle='dashed',color='lightgrey')\n",
    "    tmpxlabels.append(key)\n",
    "    \n",
    "if xticklabels is None:\n",
    "    xticklabels=tmpxlabels\n",
    "elif isinstance(xticklabels, dict):\n",
    "    xticklabels=[xticklabels[x] if x in xticklabels else x for x in tmpxlabels]\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_xticklabels(xticklabels)\n",
    "if ylabel is not None: ax.set_ylabel(ylabel)\n",
    "    \n",
    "if ylim is not None: ax.set_ylim(ylim)\n",
    "ax.margins(.1, .05)\n",
    "\n",
    "plt.box(on=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126363198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import barh\n",
    "\n",
    "#swap x and y\n",
    "labdim = 'sectorrank'\n",
    "timelabel='Time (s)'\n",
    "driverlabels=None\n",
    "label=True\n",
    "timelim=(-0.1,100)\n",
    "keyHighlight=None#'leader'#None\n",
    "fig, ax = plt.subplots(figsize=(15,12))\n",
    "\n",
    "\n",
    "order = getSplitOrderByStageTimeDuration(splitdurations)\n",
    "splitdurationsbydriver=splitdurations.groupby('drivercode')\n",
    "\n",
    "#fig, ax = utils.create_mpl_ax(ax)\n",
    "\n",
    "timedim='rebased'\n",
    "\n",
    "splitdurations['stageTimeDurationS']=(splitdurations['stageTimeDurationMs']/1000).round(1)\n",
    "tmp=splitdurations[['stageTimeDurationS','drivercode']].drop_duplicates()#.plot(kind='barh',ax=ax)\n",
    "\n",
    "start = 0\n",
    "ticks = []\n",
    "tmpxlabels = []\n",
    "for key in order[::-1]:\n",
    "    df=splitdurationsbydriver.get_group(key)\n",
    "    nobs = len(df)\n",
    "    driver_plot = arange(start, start + nobs)\n",
    "    ticks.append(driver_plot.mean())\n",
    "    #Third parameter is color; 'k' is black\n",
    "    #ax.plot(x_plot, df[ydim], 'g', linestyle='--')\n",
    "    #?so how can we plot bars underneath this chart?\n",
    "    ax.barh(start+2, df['rebased'].sum(), len(df), color='lightgrey')\n",
    "    \n",
    "    #named colors: http://matplotlib.org/examples/color/named_colors.html\n",
    "    highlightColors=['mistyrose','lightsalmon','salmon','tomato','orangered']\n",
    "    baseColors=['silver','lightblue','paleturquoise','lightcyan','lightgreen']\n",
    "    if key==keyHighlight:\n",
    "        colors=highlightColors\n",
    "    else:\n",
    "        colors=baseColors\n",
    "    for i in range(0,nobs):\n",
    "        ax.plot( (0, df[ydim].iloc[i]),(driver_plot[i],driver_plot[i]), 'grey')\n",
    "        #if timelim is not None and (df[timedim].iloc[i]<=timelim[0] or df[timedim].iloc[i]>=timelim[1]): continue\n",
    "        if label:\n",
    "            if nobs<=len(colors):\n",
    "                if keyHighlight=='leader':\n",
    "                    if int(df[labdim].iloc[i])==1: color=highlightColors[i-nobs]\n",
    "                    else: color=baseColors[i-nobs]\n",
    "                else:\n",
    "                    color=colors[i-nobs]\n",
    "            else: color='pink'\n",
    "            if timelim is not None and df[timedim].iloc[i]>=timelim[1]:\n",
    "                timemax = timelim[1]\n",
    "            else:\n",
    "                timemax = df[timedim].iloc[i]\n",
    "            ax.text(timemax,driver_plot[i],  \n",
    "                    '{}: {} {}'.format(int(df[labdim].iloc[i]),df['sectorBestDriver'].iloc[i],df[timedim].iloc[i].round(1) ),\n",
    "                    bbox=dict(  boxstyle='round,pad=0.3',color=color, alpha=0.4), #circle | round\n",
    "                    verticalalignment='center', horizontalalignment='left')\n",
    "        else:\n",
    "            pass\n",
    "            #ax.plot(x_plot[i], df[labdim].iloc[i], 'or')\n",
    "        \n",
    "    start += nobs+3\n",
    "    plt.axhline(y=start-2,linestyle='dashed',color='lightgrey')\n",
    "    tmpxlabels.append(key)\n",
    "    \n",
    "if driverlabels is None:\n",
    "    driverlabels=tmpxlabels\n",
    "elif isinstance(driverlabels, dict):\n",
    "    driverlabels=[driverlabels[x] if x in driverlabels else x for x in tmpxlabels]\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_yticklabels(driverlabels)\n",
    "if timelabel is not None: ax.set_xlabel(timelabel)\n",
    "    \n",
    "if timelim is not None: ax.set_xlim(timelim)\n",
    "ax.margins(.1, .05)\n",
    "plt.box(on=None)\n",
    "plt.rc('font', size=10)\n",
    "plt.xscale('symlog')\n",
    "#This chart shows the time left on the table - the delta to the ultimate stage\n",
    "#in the label could add the name of the driver with best time\n",
    "#alternatively, pass in a target name of someone you are interested in?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MIK',\n",
       " 'NEU',\n",
       " 'OST',\n",
       " 'BRE',\n",
       " 'PAD',\n",
       " 'SUN',\n",
       " 'LAT',\n",
       " 'MEE',\n",
       " 'TÄN',\n",
       " 'OGI',\n",
       " 'SOL',\n",
       " 'AL ',\n",
       " 'EVA']"
      ]
     },
     "execution_count": 681,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbname='mexico18.db'\n",
    "rally='Mexico'\n",
    "snum=3\n",
    "rc='RC1'\n",
    "typ='stage_times_overall'\n",
    "conn = sqlite3.connect(dbname)\n",
    "currm = getStageResult( rally, typ, snum, rc)\n",
    "currm['snum'] = 'curr'\n",
    "prevm = getStageResult( rally, typ, snum-1, rc)\n",
    "prevm['snum'] = 'prev'\n",
    "\n",
    "stagerankm= pd.concat([currm,prevm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "rebaseStageProgressBar() got an unexpected keyword argument 'lg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-683-60f6320048ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrebaseStageProgressBar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstagerankm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TÄN'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pink'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightgreen'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'g'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Stage Progress Chart - {rally}, {year} - SS{ss}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrally\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrally\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'SS'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: rebaseStageProgressBar() got an unexpected keyword argument 'lg'"
     ]
    }
   ],
   "source": [
    "fig, ax = rebaseStageProgressBar(stagerankm,'TÄN',lg='pink',p='lightgreen',r='g',g='r', title='Stage Progress Chart - {rally}, {year} - SS{ss}'.format(year=year,rally=rally,ss=str(snum).replace('SS','')));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diffFirst</th>\n",
       "      <th>diffFirstMs</th>\n",
       "      <th>diffPrev</th>\n",
       "      <th>diffPrevMs</th>\n",
       "      <th>entryId</th>\n",
       "      <th>penaltyTime</th>\n",
       "      <th>penaltyTimeMs</th>\n",
       "      <th>position</th>\n",
       "      <th>stageTime</th>\n",
       "      <th>stageTimeMs</th>\n",
       "      <th>...</th>\n",
       "      <th>manufacturer.name</th>\n",
       "      <th>manufacturerId</th>\n",
       "      <th>priority</th>\n",
       "      <th>status</th>\n",
       "      <th>tag</th>\n",
       "      <th>tag.name</th>\n",
       "      <th>tag.tagId</th>\n",
       "      <th>tagId</th>\n",
       "      <th>tyreManufacturer</th>\n",
       "      <th>vehicleModel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1761</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PT2M6.7S</td>\n",
       "      <td>126700</td>\n",
       "      <td>...</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>33</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelin</td>\n",
       "      <td>i20 Coupe WRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT1.9S</td>\n",
       "      <td>1900</td>\n",
       "      <td>PT1.9S</td>\n",
       "      <td>1900</td>\n",
       "      <td>1764</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PT2M8.6S</td>\n",
       "      <td>128600</td>\n",
       "      <td>...</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>84</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelin</td>\n",
       "      <td>Yaris WRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT2S</td>\n",
       "      <td>2000</td>\n",
       "      <td>PT0.1S</td>\n",
       "      <td>100</td>\n",
       "      <td>1757</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>PT2M8.7S</td>\n",
       "      <td>128700</td>\n",
       "      <td>...</td>\n",
       "      <td>Ford</td>\n",
       "      <td>26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelin</td>\n",
       "      <td>Fiesta WRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT2.5S</td>\n",
       "      <td>2500</td>\n",
       "      <td>PT0.5S</td>\n",
       "      <td>500</td>\n",
       "      <td>1763</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PT2M9.2S</td>\n",
       "      <td>129200</td>\n",
       "      <td>...</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>84</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelin</td>\n",
       "      <td>Yaris WRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PT2.9S</td>\n",
       "      <td>2900</td>\n",
       "      <td>PT0.4S</td>\n",
       "      <td>400</td>\n",
       "      <td>1765</td>\n",
       "      <td>PT0S</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>PT2M9.6S</td>\n",
       "      <td>129600</td>\n",
       "      <td>...</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>84</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelin</td>\n",
       "      <td>Yaris WRC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diffFirst  diffFirstMs diffPrev  diffPrevMs  entryId penaltyTime  \\\n",
       "0      PT0S            0     PT0S           0     1761        PT0S   \n",
       "1    PT1.9S         1900   PT1.9S        1900     1764        PT0S   \n",
       "2      PT2S         2000   PT0.1S         100     1757        PT0S   \n",
       "3    PT2.5S         2500   PT0.5S         500     1763        PT0S   \n",
       "4    PT2.9S         2900   PT0.4S         400     1765        PT0S   \n",
       "\n",
       "   penaltyTimeMs  position stageTime  stageTimeMs      ...        \\\n",
       "0              0         1  PT2M6.7S       126700      ...         \n",
       "1              0         2  PT2M8.6S       128600      ...         \n",
       "2              0         3  PT2M8.7S       128700      ...         \n",
       "3              0         4  PT2M9.2S       129200      ...         \n",
       "4              0         5  PT2M9.6S       129600      ...         \n",
       "\n",
       "  manufacturer.name  manufacturerId  priority status   tag  tag.name  \\\n",
       "0           Hyundai              33        P1  Entry  None      None   \n",
       "1            Toyota              84        P1  Entry  None      None   \n",
       "2              Ford              26        P1  Entry  None      None   \n",
       "3            Toyota              84        P1  Entry  None      None   \n",
       "4            Toyota              84        P1  Entry  None      None   \n",
       "\n",
       "  tag.tagId  tagId  tyreManufacturer   vehicleModel  \n",
       "0       NaN    NaN          Michelin  i20 Coupe WRC  \n",
       "1       NaN    NaN          Michelin      Yaris WRC  \n",
       "2       NaN    NaN          Michelin     Fiesta WRC  \n",
       "3       NaN    NaN          Michelin      Yaris WRC  \n",
       "4       NaN    NaN          Michelin      Yaris WRC  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q='''\n",
    "SELECT *\n",
    "FROM {typ} st INNER JOIN itinerary_stages i ON st.stageId = i.stageId\n",
    "INNER JOIN startlist_classes sc ON sc.entryid = st.entryId \n",
    "INNER JOIN championship_events ce ON i.eventId=ce.eventId\n",
    "INNER JOIN startlists sl ON sl.entryId=sc.entryId\n",
    "\n",
    "'''.format(rc=rc,rally=rally, typ=typ, stage=snum)\n",
    "aa=pd.read_sql(q,conn)\n",
    "aa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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