PandemicVisualizationCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b68cb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "# from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d6f3c17",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/owid-covid-data.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d52ad5e5ca32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcovid_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/owid-covid-data.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mglobal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/WHO.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1176\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\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[1;32m   1177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1179\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2008\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2009\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/owid-covid-data.csv'"
     ]
    }
   ],
   "source": [
    "covid_data = pd.read_csv('/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/owid-covid-data.csv')\n",
    "global_data = pd.read_csv('/Users/shotomorisaki/Desktop/Pandemic Visualization/Data/WHO.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057203ff",
   "metadata": {},
   "source": [
    "# Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16b97158",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2020-02-24          5.0        5.0   \n",
       "1      AFG      Asia  Afghanistan  2020-02-25          5.0        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-02-26          5.0        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-02-27          5.0        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-02-28          5.0        0.0   \n",
       "5      AFG      Asia  Afghanistan  2020-02-29          5.0        0.0   \n",
       "6      AFG      Asia  Afghanistan  2020-03-01          5.0        0.0   \n",
       "7      AFG      Asia  Afghanistan  2020-03-02          5.0        0.0   \n",
       "8      AFG      Asia  Afghanistan  2020-03-03          5.0        0.0   \n",
       "9      AFG      Asia  Afghanistan  2020-03-04          5.0        0.0   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           NaN         NaN                  NaN  ...   \n",
       "1                 NaN           NaN         NaN                  NaN  ...   \n",
       "2                 NaN           NaN         NaN                  NaN  ...   \n",
       "3                 NaN           NaN         NaN                  NaN  ...   \n",
       "4                 NaN           NaN         NaN                  NaN  ...   \n",
       "5               0.714           NaN         NaN                  0.0  ...   \n",
       "6               0.714           NaN         NaN                  0.0  ...   \n",
       "7               0.000           NaN         NaN                  0.0  ...   \n",
       "8               0.000           NaN         NaN                  0.0  ...   \n",
       "9               0.000           NaN         NaN                  0.0  ...   \n",
       "\n",
       "   female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0             NaN           NaN                  37.746   \n",
       "1             NaN           NaN                  37.746   \n",
       "2             NaN           NaN                  37.746   \n",
       "3             NaN           NaN                  37.746   \n",
       "4             NaN           NaN                  37.746   \n",
       "5             NaN           NaN                  37.746   \n",
       "6             NaN           NaN                  37.746   \n",
       "7             NaN           NaN                  37.746   \n",
       "8             NaN           NaN                  37.746   \n",
       "9             NaN           NaN                  37.746   \n",
       "\n",
       "   hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                         0.5            64.83                    0.511   \n",
       "1                         0.5            64.83                    0.511   \n",
       "2                         0.5            64.83                    0.511   \n",
       "3                         0.5            64.83                    0.511   \n",
       "4                         0.5            64.83                    0.511   \n",
       "5                         0.5            64.83                    0.511   \n",
       "6                         0.5            64.83                    0.511   \n",
       "7                         0.5            64.83                    0.511   \n",
       "8                         0.5            64.83                    0.511   \n",
       "9                         0.5            64.83                    0.511   \n",
       "\n",
       "   excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                   NaN                          NaN   \n",
       "1                                   NaN                          NaN   \n",
       "2                                   NaN                          NaN   \n",
       "3                                   NaN                          NaN   \n",
       "4                                   NaN                          NaN   \n",
       "5                                   NaN                          NaN   \n",
       "6                                   NaN                          NaN   \n",
       "7                                   NaN                          NaN   \n",
       "8                                   NaN                          NaN   \n",
       "9                                   NaN                          NaN   \n",
       "\n",
       "   excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0               NaN                                      NaN  \n",
       "1               NaN                                      NaN  \n",
       "2               NaN                                      NaN  \n",
       "3               NaN                                      NaN  \n",
       "4               NaN                                      NaN  \n",
       "5               NaN                                      NaN  \n",
       "6               NaN                                      NaN  \n",
       "7               NaN                                      NaN  \n",
       "8               NaN                                      NaN  \n",
       "9               NaN                                      NaN  \n",
       "\n",
       "[10 rows x 65 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top 10 of file\n",
    "covid_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d514e520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8238d5c610>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oYNnSlQQas//b1q/GgcPnsOvgDJW1+og65rpzTOVBMmi4pJz9fQAvSykvAYAQ4lkAPwXgr4QQN0spXxdC3Azge83lXwNwi+/370QjRe215t+Dn/t/85oQYhjACgCXm59/IPCbr7nuHCGEkP5C2dhsEtegNaUxlUtFHJu4k4ZyxjBNJNgEA3ieSB5xUTm7AGCLEGKkWdfy9wB8G8BzAJTq2KcA/Ofm358D8PGmctntAN4N4BvNtLQfCCG2NNfz84HfqHV9BMARKaUEcBjAXUKIlc1I0V3NzwghhGQcysZmC3+K0WefOhVLASssjYnKWvmAggFk0AiN0EgpXxRCPA3gzwHMAZhGI73rJgBPCSF+AQ2n56PN5c80ldD+orn8LzUVzgDgFwH8BwBFNMQAvtL8/HcA/MemgMBlNFTSIKW8LIT4PIBvNpd7SAkEEEIIyTacBc4OwcjJvNRnZ4cZtLY0JtvvaShnCwoGkEHDSeVMSrkXDflkP2+hEa3RLf8FAF/QfH4CwN/VfH4NTYdI893vAvhdl3ESQgjpD7rUMhq32cGlmB9wM2ht9VA0lPMBlQfJoOHUWJMQQggxYUotK4142uWDxi3VltLHxYlMwqANS0kj2YDKg2TQcO1DQwghhGgxpZYtHR5C0StYZ4FZRN4bTJGTghBYkDIxwYawlDSSHag8SAYJOjSEEEK6wjT7/0atjkd3brIat6yz6Q2mFKM0ZuVpKBNCeg0dGkIIIV1hq5sIM25ZZ9Mb+hk5oXQ3ISRt6NAQQgjpCtcCY51hWxrxcGW23rFOFpEnTz8iJ0wpJIT0AooCEEII6QqXAmOdcMDuL5/CG7VOZ8YrCBaRDwjsS0MI6QWM0BBCCOmasNl/nWFbX9D3Qlm+ZJiz9wmQhVQvphQSQnoBIzSEEEJSJ4oBq4vakGiYpLR7LYltSh1kSiEhJEno0BBCCEmdKAYsjd3uyUqqF/vS5BP2hiJ5gw4NIYSQ1NEZtjpEc1nSHVlJ9WIDx/yRlegeIVFgDQ0hhJAWSdZdBNe1Y3MZR89ewsWmoaRDgupXSWCT0u4lWajjIdHY//yZSL2heI5JFqBDQwghBEB8iV2dQQOgY13PnKy0Zue3Th7RGtxlppslgquUdppQsjl/TE1XtDLqgD66x3NMsgJTzgghhACIV3dhSk+xzfICrK1ImyykemWljifv9LKeZf/zZ4zf6aJ7PMckKzBCQwghBFPTFW3EBLDXXZgMmuBnwXX1s3P9YqEfjTT9mK6bSrWGqekKz7UDSUZN1fKm72zRGQDYtn41tk4eaftdVmq1CKFDQwghixxlNJmw1V1ENVz86+q3wU3sdFsbYarjAcC0JEdsERDTsbM5QUBnKqj6zhZVKXpDeOZkpeN3pRFP6wRRqZD0GqacEULIIkdnNCnC0sBMhkup6DGlLMckoXRlU7ZjWpIbcSIgNifI9p1tncu8gvZ3UoL3OckEdGgIIWSRYzdk7K8JUy3Mvns39L2Gg8QnidoIVcdjwhS9ITeI05jU5gTZvrNNTlQNqWjVWh07Npd5n5O+w5QzQghZ5NhSg67M1q3pQWG1MDRs0iNNudykaiPGR8s4cPic9voSAGtpQoijVhcm2a37bkXRM25r370bjOcQQJt6ISH9ghEaQghZ5IQ1vQybmR8fLePYxJ14efJuHJu4k4ZND0i7+WGcyICJ3dvXQWg+l7DXbZB4anW6+1mgUdS/e/s6eEOdZ+Pq9TkAMG6L6YMk6zBCQwghiwzdzP4j9220zsJStShbxCkWj0KSfWzGR8t44OCM9jteV+FEFc8YHy3jxPnLeOL4hVYDW4lGJGXs1lW4adlwRyF/fV7iwOFzxgkJ9RnPI8kqjNAQQsgiwjSzDwDHJu40NrakalG2SFsuN+k+NryuesvRs5dazoxCObymephKtWbtdTM+WuZ5JJmFDg0hhCwiwoq92fAyHySZEmYiyVRCXle9JU7xP4DQ9EWeR5JV6NAQQsgiImxmv1cd5nvZ/XwQyZth2avrijSwObxhNXOAuS6G55FkFdbQEELIgGFTvwpTQALSb3jp0v08TQWvQSBMXS4KvTrWbKTaO2w1UMFrJ5iapjBNfvA8kiwipDRdyvlkbGxMnjhxot/DIISQvhB0FoCGIaNmUcO+t603KaN36+QRrVNVLhVxbOLO2GMkemznjsc6G6ThVLquM+x+JCQrCCFOSinHdN8xQkMIIQOEqUZm33Nn2mZWoxhPLhGVKISlvaWt4LWYCDt3PNb9p5v7y+a0uEZSdNEcJfMctg1CsgIdGkIIySk6Q8PkLFRr9VYTw6gpI0kbvWFpb2kreA0CYVEX9d2QEJgPZGL4zx1luvtP3PsrqYkGm8wzmn8mNZlBSFrQoSGEkBxiMmZKI15HjwlFXAckaQcjrMeJS53PYsZmyAJo+y7ozCguVmuYmq5AANoaCnWsXR0nztzHJ+79FdURmpquYN9zZ1CtNZ4PK0c87L1nA8ZHy0aZ5ydffNXqEBOSFejQEEJIDjEZM0uHzeKVyoiNaoSaHIwVRS/W2MPS3pJs6jiIhElvB7/TsaZUxIHD57TOjEDjHERxnDhzH5+4DnwUR2hquoLdXz6F+sKNM35lto7dT5+yrsvkEFeqNWydPEInlmQGyjYTQkgOMRkgb9TqWDmidzRWFD1tU80wyeTd29fBGxIdn1+9PhdbbtnW44TSsHZshqxL1Ew5h6ZlJcLra/Y/f8bqVBF34kpwlwz3uc4ROnD4XJszo6jPSxw4fM7oPBVE532vcH1+ENIL6NAQQkiGMfVrsfWZ2HvPBq2BVJ9fiGWEjo+WcdOyzoC+MobSIMmmjoOG7dzbDNOgc2hathxSy1Sp1oxpjVHTENmPKJ4DPzVdwZvX5jo+9wpC6wjZzsvFas3oVH3i/bdYe9bQiSVZgSlnhBCSUWwpP1H6TKwpFbFt/Wo8fvyCdjsuRmg1IQOWdE9YSp5OsWpeSpQjpvaZUqEKGqEBRZQ6p6TV8/JMHKEOXcRl+ZJh7XpM51J9Z0sDHbt1FQ4cPkcBCZJp6NAQQkhGsaX8qP4QrpKtWyePGLfjYoTa6mi2Th5hYXgPcZHeVgaov+g/6DDErWWy1ehEqXOiZHR8bCmnOnZvX9dRQwO0R3RMTpX63NSvhmIdJAvQoSGEkIwSVvQbZVbXNovqYoTqjFtvSODq9bmWatJinmHvNbZzbzNAgw5D2HqATofHNFtfKnqRzjvlueMTVUhAnReTypkLFOsgWYYODSGEZJQk5YtN63I1QnXG7ez1uY5aiqRn2CkNHJ8kHAaTw6MzbPfduyHS+CjPHZ84zkXUtDbd74FoTXkJ6RV0aAghpM+YjPZt61e3NbsDwo0W07pMBpCLERpc56M7N2F8tIzbJw5pl09qhn3P1Om2/WcEKBppOQxJGbac8Q/HdD+bzgGAVFNAu3WKCEkLOjSEENIngo3ugBtG+4nzl/HMyUqbMyMA7NhsNijC+oYsHR5qfeeabmJbZ5oz7FPTlQ5nDlh8NRbdRKjSdBiSMGw5428nTDQheA4oskAWM3RoCCGkDwSNDz+mDt0SwNGzl4zrNBVZP3BwpqMj/LX6QttYTEalrXA7TYPZ1PQRWDw1Ft0aqHlwGDjjbyaqaIJp+X3PneExJgMPHRpCCOkBQadh9vqcVS3KJIsb1k/ChCnSAdg7vtvqMJI0mIPHxyQRCyyeGoskVMDoMOSXqDLJps+rtTqmpiu8DshAQ4eGEEJSRjfTHoap14fNmA9zBIJcrNZCjeawtLIkDGbd8QlGlBQC0aSB84ytsWXWoZhDd0xNV4z3gK2xqunaWExpmmRxMtTvARBCyKCjcxrC0HXoVulcpu7qum7fNoaECJ0FNnUQT9Kp0B0fiYbz4kcAuH/L2kVjmJkMVwG0znkWUQ5qpVqDxI2oX5bHnDVMKZcCwLb1q433v4lepGmankuE9AI6NIQQkjJRjYlS0cPD4xvxyH0bUS4VIQCUS0U8ct9GADAai+Oj5dZvXDCltQHtERjdOJJ0KkzHRza3p7b76M5NeHh8Y2LbzTrb1q/Wfi6BVrpgFo1IW9SPuGG7J545WTHe/ytHPO3v0k7T1Dmxuw7OYM/U6dDfEpIETDkjhJCUMaWCFL0hXKsvdMgyKyllXTrX1skj1hQx9Z9OdEClsJjS2fxj8M/2pl2HYTo+5VIRxybuTG27WWZquoJnTpqdk4vVWmZVrdgws3tM90RBCOv9v/eeDX2Rwt7//BltlPWJ4xcwduuqRRNVJf2DERpCCEkZU9rWI/e9F4/u3KSNfphm3l2NRV1k5dGdm/DK5N1YsDgzaURgwuhFWlveCEtTXFMqZjYSYqvx0F3XWYwy9RvTPREmFtKLiGqQqelKR4NdhT+aSEiaMEJDCCEpE6YGFjQ2kur9YoqsZC0ikgd54V5ji2YoZ2/XwZnIv+0FJjnvbetXd1zXu798ChBAfV62Ptt1cAYnzl9eVOmFQUz3xIHD50Lv/14r24U5LP2+HsnigA4NIYT0gChGRtq9X1zW0WuVKsoLt2NLOVIz7i7GbT+wGePB67q+0BlxYKpSA9M90Y+UMhthDku/r0eyOKBDQwghGSNu7xdXJyQsIpJ2bQYlfcMxOZ3+9KE0G5t2S/AaMzlfJlSqEq+LdvoVzbTdsza56Kxcj2TwEdKSS51HxsbG5IkTJ/o9DEIIic3WySORU8J0IgBBAzjN7buS5DgHHRfHz9U57LUTaROlcEUAeHny7qSHRiISds/qvgeAlSMe9t6zgfc1SQwhxEkp5ZjuO0ZoCCEkY2xbvxpPHL/QoX5mm+lMoqu8Ik2VqqTGuRiiPC5peC7L9EMNzdZbyNWpWcypSlm6vsPuWVPUSH226+BM3/eBDD50aAghJEMouV6/0ScA7NhsN1yTdEKiCA9EJYlxZlWuOKsk6ey6EtZbKCz9bDGnKmXt+na5Z4OOddb2gQw+lG0mhJAMYZrZPnr2kvV3NqncqHQjoxwmwZvEOLMqV5wFdMe/H31hTOdTpS0+tnNTxzUmfMss5hTErF3fce7ZrO0DGXycHBohREkI8bQQ4qwQ4ttCiJ8UQqwSQnxVCPGd5p8rfcs/KIT4rhDinBBiu+/zzUKI083v/o0QQjQ/XyqEONj8/EUhxG2+33yquY3vCCE+leC+E0JIT3HptxHX+Eyyl0vcXha6buGqi3mS4zTN7i92eVjT8S/1oXt82Hm29Uk6NnHnonVmgOw1Jo1zz2ZtH8jg45py9lsA/kRK+REhxBIAIwB+BcB/kVJOCiEmAEwA+JwQ4j0APg5gA4A1AP5UCPFjUsp5AP8OwKcBHAfwxwA+COArAH4BwBUp5Y8KIT4O4DcA7BRCrAKwF8AYGpOUJ4UQz0kprySy94QQ0gOmpivY99wZVGs3ms8FUzBUzrypviDM+Exa/SiOjLJpVnb/82faxrVjcxlHz14yjtNWPzA1XTHWYSzmmgvAfPyXDg+h6BV6qobmcj0uNqlu17qYNFM+4xDn2ZK1fSCDT6hDI4R4G4D/CcA/AgAp5XUA14UQHwbwgeZivwfgawA+B+DDAP5ASvkWgJeFEN8F8D4hxCsA3ial/Hpzvb8PYBwNh+bDAPY11/U0gN9uRm+2A/iqlPJy8zdfRcMJejL+LhNCSO8wKQAB7SkYpmUAd+Oz3waiafb1ymy91Um8Uq3hmZMVY8QnLPfe5PQJYNHWXChMx/+NWh2P7tzU8yLzfl+PWSJKTUkW5bijnsss7gMZbFwiND8C4BKA/0sIcQeAkwB+GcDfklK+DgBSyteFEO9oLl9GIwKjeK35Wb359+Dn6jevNtc1J4R4A8Db/Z9rftNCCPFpNCI/WLt2rcMuEUJIb9DNmvtRPTpMy5Q1EYqsqB8FsfWj8GMrSA8rYLcVmy92bLPidC76SxRhhn71mkkS3T5sW7+aqmckNVwcmmEAPw7gf5VSviiE+C000stMCM1n0vJ53N/c+EDKLwH4EtDoQ2MZGyGE9BSXLtqmZQSAYxN3tmpvKtVaW7pVpVrDAwdn8JmnZrAgO52fXqOblTVRqdawdfJIx3jDcu9tTtNiV1HirHh2iVpTMggOqH8fqHpG0sZFFOA1AK9JKV9s/vtpNBycvxJC3AwAzT+/51v+Ft/v3wngYvPzd2o+b/uNEGIYwAoAly3rIoSQXGDLGVfGpk1FyF/oDegjEQvND3VF+L1EV+hdKuoL0gH9eMMUlXQFyopBVlFyEZSIK+ZA0idJFcI8QtUzkjahDo2U8r8DeFUIoaZ4/h6AvwDwHAClOvYpAP+5+ffnAHy8qVx2O4B3A/hGMz3tB0KILc36mJ8P/Eat6yMAjkgpJYDDAO4SQqxsqqjd1fyMEEJygckAXznitYxNm4pQWMpakH4bCeOjZRybuBMvN9Wq9t27weiAAJ3jdVXHMjGIKkou6nGK4PGnM5MNklQhzCNUPSNp46py9r8CeKKpcPaXAP4xGs7QU0KIXwBwAcBHAUBKeUYI8RQaTs8cgF9qKpwBwC8C+A8AimiIAXyl+fnvAPiPTQGBy2iopEFKeVkI8XkA32wu95ASCCCEkDzgqvZkWmbXwZnI28ySkeDfNxe5ZdfjZVrfIM5496MxZj/Icn1YtyRVF5PXY0TVM5I2ohEIGRzGxsbkiRMn+j0MQghJBFU7EwXVvDBrmPYlznh16nFFrzCQKVa3TxwyKru9PHl3r4eTCovpfMYlz8doz9RpPHH8Qtt1nJexk+wghDgppRzTfefUWJMQQogdlxqHONhqRnRkOY0lbtqN7tgupnqRxVB/Meg1Fkk8H/J6jKamK3jmZKXNmREAdmzOv/AByQ6uKWeEEEIMpKngY0pVUZ9VqjUUhMC8lH1XObOhUmVq9flI4w07tlnc16TRqZcJmJXi8ogpTbJSreH2iUO5Sq8KktTzIa91KPufP9PhiEkAR89e6s+AyEBCh4YQQrok7RoHk+GeF+MuaNDNS9mKzITtw2KpH7ERrEMKSncPgvytTY7bL4QA5G8/w65h17qYPNahTE1XWk11g2TdESP5gilnhBDSJWGzy0mmoOWRblJl8jorHZWwlCSlXlYuFTvqafKQdhSGS2plXvfTdg1HUbDLo1Ka7Xxl2REj+YMRGkII6ZI4s8t5VSuyYdqnbpySPM5KRyVKStKgOnjB1EqTXFEe99N2DevSsUwRyKSU0nqJ7Xxl2REj+YMRGkII6RLX2eV9z50BEK2vSF6w7VM3Re15nJWOSpQIlumYrSh6qYhS9BJ/D52yZT/zhuka3rZ+deR0rLz1GTJdr6Wil/mxk3xBh4YQQhJg6XD447Raq7cVx/vJazqNwrRPDxycwez1OXhDou07V6ckqGZWKnpY5g1h18GZ3BruQaJEXXTGsTckcPX63EA5yLu3r+u4ZgDg6vW53O2XSZHPVhQfNQKZlspit5icuX33bujTiMigwpQzQgjpAl1vCH/RdhCVLqIjj+k0CtvYr8zW4RUESkUPb9TqkVNllChCmmpy/UA5t6ZrRWfU6tKOZq/Pdcz0Z1k4wSXdcny0jP3Pn+nYr/q8zOx+2dAJe9ia5kaJQKZ1XySVFrt0eKg1tpUjHvbes0G7nkFMwyW9gw4NIYR0gS4yYWtXrF7Wg1YXYqsjAhqG6PKlw5jZe1fsbQyS4pnOEfZji2AFjePbJw5pl8uigxzF+K4OuDqW6Z6Jmo6Vxn2RhJOku8av1RdS2x5Z3DDljBBCuiCqcaVmHvNSF+KayuJSR9StITpIkS2dEaqI2iQ0T403k6gXyuJ+2TDdQ6bnwIfuuDlS+lga90USabFh6/Afl88+dWrg0nBJb2GEhhBCusA2y/rW3ELbSzrYeyXr6RVRZk2DvVJ0dGuIDlJkK0z9Kcq1oGu8mVUH2SZxHiRP+2XC5R7yPwe2rV+NZ05WIkUqurkv0lAmDFvWL1ft701lWwfT0UgYjNAQQkgX2IpedYXA6iXcK7WiboqFo87Sqn16bOemVCJQeYpshWEzNqPOSpuKzrNo8Jn2WwDa3jt52S8TYfdQ8Dlw9OylyJGKuPdFWsqEYcuuKRWtEcrgsoOoCkmShxEaQgjpgrBoS1TjK8mZSJfZYdv2TJGWsFnatCJQUdeb5Vnd3dvX4QFDUXjUVKEs72eQ3dvXYdfBmY46Mwm01XwE9+nRnZsyu082okY64kRGwu4L0/Vhc7aSiI7Z1mETRAguO0i1cyQ96NAQQkiX6BSM4pB0YWyYIbBn6jSeOH6hZVz6tweY1dpcZmnjHBNX9SuX9Wa9yNik4gVEmwXP+n4GGR8thzpyedsnG7Z0MN31Hjd9zHRf2I6lzXlKYlLCtg5baqpCReNMzk8ea+dIetChIYSQjBDmgESdiQ/LYfc7M8HtAXpnRiCdDt+9duaywN57NnQ9C56l/XS9PsshRnuW9qlbTFGKbetXa6/3HZvLbTU0avm495ztWIY5T0lM1JjWoTsufsqlYut3Waudy1NEdDHBGhpCCMkILkW0UfLIw3LYTfLSF6s141gkwh2MOHU7STYbnZquxE6X6yVJ1IhkRfktyvUZVvORlX1KAltTTd31fvTspUTrhmzHsp81aeq4lIpex3fBMWSpdo71PNmFERpCCMkItpnIOLPWcXPYlSOkG0s5ZFY0bqQlrhEbnC1VKlEmsqaI1u0seK9nr02z0/ufP+N8fYalM2VtRr4bTMfLlkaVVAorYD+W/VZb9DfMtY2h3+NUTE1X8NmnTnUosuU1ejho0KEhhJCMEMcBcSkW3vfcGVRrjVqNZV4jMG8ydPwpZXHSoeKmC8UxYnXOky6NLsr4s4JrWksvpY1NzuqJ85e1tUCA+fq0Ge2DINcM2J37XjltYccySecpLi5j6Pc41bkMk5cm/YMODSGEdEGY4Rkl3zpOEa2LAfTW3I3u3Fdm68ZcfQHg/i1r28YXdVY0bqRFZ3gJANvWrzb+Ruc8mZwZALmR/I3b/yft2WtTFObJF181/iaOgZ6VGfluSVtFzIW8HMs4dSlp1LJEUYTzs0KTOkd6i5AGbzOvjI2NyRMnTvR7GISQRUDQ8AQaRokynMO+T3JbJrZOHjGmjilJ1KgGge6lD9ibavq3adpGUHUtbB9vnzhkdWCC2z42cafj0v3Fds76tQ97pk7j8eMXIv/usZzKLcfFf2+Yrk0B4OXJuyMZ5INciB7n2Zbks9VlnTqpcT9eQeDAR+4YmHOSVYQQJ6WUY7rvGKEhhJCYhKVXJanWFLfXRJg0a7eOVaVaw+6nTwESqC/Y3YuwepqjZy8ZVdd0y9vS5oJOUZ7SlbJWFK8U8UwUhNCm4giR5qiyh84g1hFVRWyQZKx1xHlOpqGEF0cRTlGfl6yj6TNUOSOEkJiEGZ5JG6bjo+1dxf3OTJodv/3oXvr1eRnqzChsymVRj5dJ/ej+LWtz3V0+6XPWLTZFPAD4xPtv6TgPACAlFpUCVFhaEhDPuU5SATCLhKk76hQT03D6Tb+tVGvYtn619hpPatukexihIYQQR4JRkNKIZ22MaJrVW1H0sHXySGLpI73M1U/ipW1aR9RC6bzUB7iirq9KtZapKJPtnAsBjN26CmO3rjIqQO177kxX5yQv6VbW4wTEHnvWInZJY3tO9lJUwRaFeeZkBTs2l3H07CXjMnlU4RskGKEhhBAHdFGQN6/NwSu059X4DU9dBMEbErh6fS7RPgZhaWVJ9rWI8tIuGHKOTOuI02/CFLXKG/7rC2g4M+ro9TvKZDvnKgoDAAuGmtxqrR77+s5T3w/TcSqXitqoqmuvpqxF7JLGdN8LAetETdK9aXTr9G/36NlLODZxJx7buSkzfXHIDejQEEKIA9pUqwWJ5UuGjc6Czpm4adkw6vP6PgZxCTN4kjT6tU5aQcAb6nTstvzISgRdGtuLP2nnK0+YFNuUEEA/j4HN0APa6wxMxL2+85Ru5WpkR3XSstRYMg1M933VIgWexrNCrdOEmjhazM+pLMOUM0IIccAUBXmjVsfM3ruMvwsW/t4+cSjS+hW2tJtt61dr1cHSMHj8aV6Vag0FIVCfl1g54kHKxvHwN7j0j0kA2LG5/Xjo9suk5JWX1CMdYWPPUlqRrlnp0uEha33IxWoN929Za1RCi7sfYfUVWboeXFMgoxa0D1pqpQ6dQEKYVH0avWlcJfL921bX4a6DMwN5bvICHRpCCHHAlF8t0ZDZdX2JJdVAUqX5AHByHJJErdc/piuzdRS9Ah5tyvRunTyijTgcPXvJab+CY8+z0pPL2JOoCUjCwNeN1VWu2bZc3PQo03FZ5g21Selm5XpwMbLjOK/9bizZD+L0poqC6X6JUneY5+fSoMGUM0IIccCWdhMlr3/b+tWR0rAA+4yuKVXJ7zikQVgqkE0xSNUN/OofdkrcmtKJ8pR6FMRl7N2mFSVVa6JrnumCTQWtm2ih6b6r1ReMEt9ZJ25NTJS6m0FgfLSMHZvLbc9LicYETrf7brtfoqSU5fm5NGgwQkMIIQ4EU62CKDUn2wz51HQlVjQlzoxuWqlKfiUu23ZtikHKgDCh+y5LKVlRcRl7t2lFSfTlmJquaFX7uqWb+oLx0TJOnL/ckVJpop/Xg2uELI7yYNYiAWmn+9meM932mwHC7xfXiFien0uDBh0aQghxRL3kTB3qq7U6qrWGQagzOMKiKSYjISwdKWn5UhMujQOHhMDUdEVrtLkimtvyGxRpyLT2ijB5b0U3aUVJGFZRZpWDstImyqVipH3S3QO6hqsm+nU9RHE44jivaTSSjEvazpXLc8Z/XcdxrpJyRPL8XBo0mHJGCCEO+NM9hhxboAdTD8IKnE0pELZ0pF4qILk0DpyXsmXc+NM2oiDRaVznVelparqCN6/NdXzuFUSiY09C2tcWNQvi4mBEPT+me8B1XALo2/XgmnqkniO7Ds4AAB7duamlYmdLKctSJCDtNCuX54y6ruOmWiYlhZ3X59IgwggNIYSEEJwxDDYPtOE3OGyzeTYjQal+2WYhe6GA5Go8+cetxrF18kgkgzm4rbwqPe1//gzqC53Xy/Ilw7EK9k37320D1anpinPUxYXlSwr4ws9FSzUz3QMFIULvOQHg/i1r+3Y9uDgcYeIetqhHliIBSTpXums6bD3+6zpO5GpquoKrb3VOMsRxRPL6XBpE6NAQQkgILjOGJvwGh83oVDO2Qfy9D0wvyV4pINnqYoIEjZKoKWg6Qy3KfmZB0tdWk1Kt1bF18ojz+MLSfJKowUnKmQGAq9fn8cDBGex//gz23rOhq3qEeSlR9Apt1443JHDTsmFUZ+uZMCJdHI6wyIbNMO/WYU2SpJwr0zVtStEEGimM/nMd1bkypbOtHPGcr9Mgi1GBLosw5YwQQkLoJq1j9vpcK/3Bpp6Th27guvQKUzqZrj4kuO+f3LIWpaLX8dtuDbWsdJe3peAIINL4XNJ8ummgmlbq0pXZOnY/fcrp2JuudXWfqGunVPQy5cwAbuqFNuM7zDDvtpljkgppSaVZma5pKaFd/2O+9DyF6ZpZUfS0+2uanBqJETEl2YIRGkIICSFKZCLIldl6x0x6UspHvUYXBVANNF3Grdv3h8c3Jh5NyUoBtc1JMEkOm8Znk8FOgm6u8TDq89Lp2NvuAXXtZE3tC3BXL+xW3CNuJCDpY5ZUmpWtWfGjOzfFVozzhgSuXp/TCrS4pgb2O7pLokOHhhBCQoiSLqXL93cxpvOSi60zqsZuXdXVuJNM2ZiaroRKSveKqE6CbXymdekU4eLQjSqdCy7HweUeyIqz6se1F1TYpEVaExppHLMk7lmbg+e6ft01M3t9riNlrVafx2efOmVMZ1vRjBRn0WEmbtChIYSQENSLbN9zZ1qzfjqCef5+XIzpLORiT01X2vbTJbe8F+N2mTVVxoiJNNP3dONLom5IoeqsgpEdpQjX7fEPGoYrih6uz81jtr4AABgSgEbbIBK3TRzqqIEIG4dKqYtbM9ELXB1oV4ct6QmNXh+zNPvx6Ag+f26fOKRdbl5KvHltTnstX22mBmfRYSZu0KEhhBAHxkfLOHD4nNGhUYaaqRlclmphTExNV7D7y6faVLlUDQTQ35Qel1lTm3hDErPdJkNNN75dB2dw/5a1eOS+jdZGpK7jGx8t44EQ4YhuCDNC90yddm5uaSNsxjvsXLsWpPcqbcimDhdV2CKtiYFeKqQl2Y/Hdr/Zzq0tMlpfkNCp7qu0yCw6zMQNOjSEEBKCrWs10Ej7UdLKQHqpI2kyNV3BZ586pZXHda2BiLo9V4PTddbUZnR0060e6DTo/YaaKeXoieMXMHbrKhybuNPYjBXoVG4yUU7JMA0zQnU1IiZKRc8axQTsM95h59plVr+XaUMmdbi4PXHScMTCjpltm1HHEzXCYXLiTOfwxPnLbTV7unMbFhk1KYCrfczrhNRihw4NIYRYcOlaHSzcBbJfC+NH7aOt14frDGWU1DBXg9N11tSUHx/sVh/VSJuarmijE7X6vDUN0Z8OZjKUyqVimzNsIy3hiDAFNZOjG6ToFbDv3g1OEamoM+F+tS815n7U2QRTMk1IRHee0nLEbMesm944OpKKcJjO4ZMvvhpao6j+3PXUjNF50bGi6CXWn4b0Hjo0hBBiIawHje5ll4VamChE6cxto5vUMJvB6TJrOjVdwZvXOo0RryC6nr239WgJM2yVIZeEM5KWs2xTUAtzdBWlood9996otYoyCRD8vFu1r26M6rBoRTAl00Q5xox+t46YbeymY9ZNbxzdNk2TClEjHLaeRK7LDwuBuqNHo5TR6vPty3fTn4b0FvahIYQQCzYjKGoviKwSZuh5BYFt61eH9rFw6ZVi257pc5e+FwcOn9MamssD/SX2P3/GaYwu43JBGXLjo931EVGMj8bvNRM2xiAFIUIdXdVPaPnSYew6OIOtk0cANFL8CrpiBdjTsZLocRK3p1NY/yLTNRZEjTdq75duHbE4vZe66Y2zZ+o0dh2cadvmm9fm4BXaz3ucCIftmnRZ3vVcqXXetGy4w5kB2J8mTzBCQwghFpJIFco6tiLalSMe7n7vzR1567u/fAr7nz/T1tzQ1SCLmqfuEpmw9bRQTE1XjB3I40gmhxE05Fwid/3ogWGKHoVFJh+5byMAfVrSI/dtxIJhdtxmZiYRhYobDQuLkIQ5FgJojRcwp2uZ9q+b+o240Z24vXFMaZj1BYlS0cPypcNdXcOmc7hjc7mj75VqUrt18kjr2Ee5XxekjPVcINmCDg0hhFjIQ8PLbjHto4ogbJ080mEs1RduGAHKWFthKAgPGmSuhd1Bo8/mQLoYg7YojOpDoSNOj5aCEJEjMP3qgWFyIky1MAUhsGNz2fi9MqRtjqBtv7pN2YzrFIU55Lb9CU5w6O4ZVXP11tyC9hx386yJG92J2xvHlob5Rq2Omb13hY7Zhu0cqr5XlWqtTWFOTbRAH8QxsqLo4Y1a3apUx2ab2YcODSGEWMhjkX9UwvbRZZayVp/HMm+oY2bfVGNk214cw97FGLTN2qo+FCYDG9D3IfKGBCDQlq7idwaj0M8eGH4nwq/qF5QkFgC2/MjKjlnyIBerNTy6c5PREUx7v+I4RWFO8e7t67Q1NLo6LdO1pnP41bFQDlGcZ03c6E7c3ji7DBLiLtt0xXQO1edbJ4907LMtzcx0rwqhjxqq1Eg228wHdGgIISSEtIv8ezn7Z2ucadqma8rVldk6PrllLY6evRSqcpa0SpWLk2TqFwKES1Or46Mbu227UchCD4yg8aZr5PlnL10OlXBW3d4BpNo/J0lMkbjZgLNrazwb1tzVhF/FLc610010x7ZN03e2Z8K29asdR90dkZXTPnpH48/AvWpyzpRS3ehDL7DZZg6gQ0MIIX2kl7N/cRtnmmamdTxzsmKNTrjsb1zD3maY2VJkXNdv20ZSxfn97oHhongXdhyLXgHb1q/G6EMvGGsTgGT2y+ZgVqo1FITAvJROvX5Mkbgrs/W2a9S2jrDmrsu8oURUwIL0OpJsS8N85mQFY7euSn0SaKh5bl1QqnO642NKnSw3a4VYX5MPqHJGCCF9JEwZLKpSUti2dE6Jik6YGB8t46ZlbvNfYYphLkpocVWqbLgYH/1unqdT+BJId8Y7eH3FET/ws3LEw47NZRz85qtWZ8YbEk7RAxs6Za/dXz6F3U+fau2HMnhdVb/GR8tYvrTzWg+7rhVhzV333rOhaxU3E2ko4Nm2ZVKycz1WcbH1zfKGhFZlbdv61UYVOJuynm0/+v28IO0wQkMIIT56XfxpMiAvVmuJR29sxlaYwV+1GKdJbMf/uWvqTxTC0uZcO6eH4f/tiqIHIdCmBBcWIThx/nKbepREejPeuuurW67M1vH48Quhy920rHs5XJ1zbIsiuqYJdZP6Z1NFDKtLyRu2Wpo0oxemKFhBCGNamW0ixVa7ZKsVUlL2eT+PgwIdGkIIaZJm+pcpNcZU17GmVLT2TEnaqA+bbYwiXWxbl2vjRAB48NlvoVZfaH0eTP2Jgs5JUsfen47UzTUQ/K0/bcl1PUfPXuq4HtLK19ddXy6ESTq7EMVBNhHHaO7GKXGZkXepZUm7Jq+X9CNN0nQOF6RsHdfg8Q1zvKLWChW9oQ4pewoF9BemnBFCSJM4TRddMDW92//8GaO6zrb1qxPP3d69fV1D6SdAUKXJ9NtgWoYpvcO2riiNE6/5nBlF3POhUmT8jS0f3bkJrwTSc7q5BsLqT1zW0ythAFttQBjqOHZDEgZvnHX4ZXhNqZzb1q/uUP6NUmCfRAPVvJBEI9SoxElJjZvGatq/ZRqnPu1UO2LHOUIjhCgAOAGgIqX8kBBiFYCDAG4D8AqAj0kprzSXfRDALwCYB/AvpJSHm59vBvAfABQB/DGAX5ZSSiHEUgC/D2AzgL8BsFNK+UrzN58CsKc5jIellL/Xxf4SQoiWpIs//REZCCCY7l2rzxuNX4nGTL2JuMagi0qTDrUvtfp8R5E1EC19xrV42VbEH9e4D5sZ7/YaSGKZXs14xzW8Vo54RklnV1wcaBd00RCdNK9CGdq2KBzQSPELSlXv2OweVRmkCEwY/ZC1j6PoFlcFzrR//Ui1I3aipJz9MoBvA3hb898TAP6LlHJSCDHR/PfnhBDvAfBxABsArAHwp0KIH5NSzgP4dwA+DeA4Gg7NBwF8BQ3n54qU8keFEB8H8BsAdjadpr0AxtB4bp4UQjynHCdCCEmKJIs/gwZTVKuvXCpaX4zdGINRja3gvsxL2TIETOkdSYzBtv9xzoeLwdXtNeCSlucyI9yLRq5xDC+vIPDmtbmW0ydhTpm04Reh6Mbw1TnoNy0bxt3vvRlHz17SqpwBwGefOtVRUO6fXQ9ONIRNMOSVpOoFe+3AxXGiunG8dPtnUkajUED/cHJohBDvBHA3gC8A+Ezz4w8D+EDz778H4GsAPtf8/A+klG8BeFkI8V0A7xNCvALgbVLKrzfX+fsAxtFwaD4MYF9zXU8D+G0hhACwHcBXpZSXm7/5KhpO0JNxdpYQQkwk6UC4SN+aUM3cTC/MUtFzquWIGoUx0a9mjybnQB0fV6LUxNicEZdt2qRsge5mhJM+1jbnSzkppYCowdW35joaQ6rlrr415yTrrUiy5uCtufY6K5N0uE0dCwgXs8hqt/g448p7s8g4TlSSjlevJh6IO64RmscA/EsAP+T77G9JKV8HACnl60KIdzQ/L6MRgVG81vys3vx78HP1m1eb65oTQrwB4O3+zzW/IYSQxDAZeC4ORJBu0g5UMzcA2hfmvns3WH8fp9eMzSDqV7NHUxH//VvWRjofrg6ZrfGm6zUQdEaiqpz515O2UWlzvpRQglJ/Utw+cUi7rmqtjsd2bjI20DQR1THWXadRHO6wiQY1u657Dqwoepl0AOI6Jv2aqBgU+pFqR+yEOjRCiA8B+J6U8qQQ4gMO6+ysOL0RmdZ9Hvc3/jF+Go1UNqxdu9ZhiIQQ0o5pxu1Dd9wcWZoziiJYENXMzVSzErbtsF4zpllrk0HUr2aPSRkMrg6ZqWZHAB3XwLb1q3H07CXtuPJUP7F0eMho4FeaEQn/vtiiZkDj2o163bs6xqbr1DR+3Xpt2/LX9eieA0J0pqJlwQGI65j0a6JikMjTvb4YcInQbAVwrxDiZwEsA/A2IcTjAP5KCHFzMzpzM4DvNZd/DcAtvt+/E8DF5ufv1Hzu/81rQohhACsAXG5+/oHAb74WHKCU8ksAvgQAY2NjcWoUCSGLHJ0Bfdvbi209QVxnP8NSj0z4G8D5a1aU6pnLy9NmkFSqtQ7nLMwgSjq1whQNMn3uss+2CJOrQ2Y6bqoPjN+Q9vdZycpMfRQ6arwMBPdr2/rV2h4zEo37Zvf2dR3RwTBcHWPTdVowdIvXrdc20bB8SXtfnLwUgcd1TPo1UZE2WU0LJOkTKtsspXxQSvlOKeVtaBT7H5FSfhLAcwA+1VzsUwD+c/PvzwH4uBBiqRDidgDvBvCNZnraD4QQW5r1MT8f+I1a10ea25AADgO4SwixUgixEsBdzc8IISRRgi/CbetX489eumzsCWIjKN1aKnrQqCUDaDSD88u7Hj17SVuU/MTxC6FdzgG7QSKADuloW2NP3b50I0Nrkq/eM3Xa2MU77jrVb11lZU3HrSBEqOGfN7lW1xqvWn0e+547g62TR3DbxCE8YWmYWanWsOvgDJYvHcaI594RYtv61U7LmQx0JVLhx+Rw25zwN3y1QeOjZRybuBMv+yS948r+pk3ScsRp14DY5LKTWHfc5wjJP9001pwE8JQQ4hcAXADwUQCQUp4RQjwF4C8AzAH4pabCGQD8Im7INn+l+R8A/A6A/9gUELiMhuMEKeVlIcTnAXyzudxDSiCAEEKSQpfO4o/MBHGZlQ1GF0z1BwtS4uXJu1v/Ns0Eq1nwYN1HcDbSNkuuc85cZriTSq0wzbI/+eKrRtUplxS7sJQbf2qVSSDBFIlyjbL1e6Y+ClHGWq3VW0IAYXEX2Vy+6BWwcsRz6nPzzMkKxm5dFTuNs1T0sO/eDU6z8uOjZex//ox2XGEOwLb1qzueCVkoAg+LoIZFPl2OW1JRj7SFCJKuC2K0J19EcmiklF9DM+VLSvk3AP6eYbkvoKGIFvz8BIC/q/n8GpoOkea73wXwu1HGSQghUdC9CG3GW9yGfi4pHra0GL8hajIOHrlvIw589I42lbMRbwizmiaVwI2UtqQNNZ0xYJtl19FNXxelShU09vzNOoNj3LG53FEbY1KbC9LvmfoodFPj5YKtx5JuWReD0+SoX70+BwAdAgYm9t6zIXIK5dR0paM3DQAMicYEhEq364exa3NMwhwIl4mKJJ2QtIUIkqwLyrsK3GKkmwgNIYQMBFFeeFFlgxWutSgqX19n4vuNZptxoNJkFFsnj2DWso/+fiKu4gM2TMZAyTBrH6UOQreMyVG0HSMAHWP0y/0qZ8elgWQWZuqjoIs22BpSpo1rxFMXXTGJXSj8TqtSnTOJbZhm5E0pelevZ8PYNTkmURyIKPse1wlJW4ggybogqsDlDzo0hJBFj+uMdVTZYJcIQHBd46NlnDh/2ZreMjVdMY5XV/jvYjCYpHrjYDIGlg4PdaRxFb0CdmwutxXeq89NTkKwz04Q9VtbIXcUZyfo8NlUztT4spqqoos2CAA733cLxm5d1Tbu2etzTmljOkpFD2/NLThFalwNzqphLKbrO+hY+6+XYINY24y8y/1Tq8/js0/dkEbPwjXg6kDE2XedCl4YaQsRJClgQhW4/EGHhhCy6HFRJSsIgS9+7A6nmWAlKhBUxzI1/NP9/v4ta7VGszI+TKjCf7VNU7RHRzClLa5BZnrpv1Gr49Gdm7TrDRrTtnx+m5JWOTC7bDKgbAaLKQXRxeHLeqqKad/+6NTrHdcb0Clh7IKSO/+jU687/dbV4HQxiP3X7ZAh8qfwz7ibHNzPPnXK+f6ZlxIPPnsaJ85f7rj3+3ENuDoQcff9gYMz2P/8GWPT3rBnIpBsdDPJ3jCDqgI3yAhpudnzyNjYmDxx4kS/h0EIyShhssG27un+4v3gOnVNIHVPV51RrPt90StonZ+tk0dCO7zHRY0tynh0jD70gnZm3zUCZHOmbPsfXL9tP0znutx0dkw9aUzXgMI0vqSiX91y+8Qhp2tEHScAxkJ6HWWD4Wpi+ZICSiNLnAzQsOvSVY7ajzqnrsfFBVMKZa+vAdf7uNt9161zz9RpbZQ5LEqdhciWGkc3z0CSDkKIk1LKMd13jNAQQhYNutnzXQdncOL8ZTw83nhRmQxS0yzwmlIRV9+acxYV0EUGbDOkQPusri3lwdUoKRU9XL0+11Yv4Z8pjZs/bksF8zcutBEW4Qjrs+MnbMbWlJ5ii+yEEZaq0m+DzTW90l+PBcCa4qcQaBTnb5084i4KcH0eV6/fiCjaIhlh59NVjtqPOqdRhBJ094+fbkQuksQ1YtGtSETw2TA1XdGqRNbq8zh69pLRqctSdDPJaA/pDXRoCCGLBlO6jWoW+PD4RicZ1OBLNwo6o9im/hV8oZuMj3JzvWHjEQBm9t5lNazj5I+HzY4HGxeaCHOmbMaXaI7Dvx1TwXRcZycMW6pKFgw2nSCACZNSnCkSuKLotX7nSlB7L8xxtilzRXUY/OfUtRmu//757FOntM5LNyIXSeOiZBa3EbAf/7E/cPhc6ISO7vnTj0J823MwKbl60hvo0BBCckmcmW6bwfPE8Qtt/TCSmAV2lUO2GenBF3pYP4www0QZVbaXdZz88bDj8kbI7L4izJkK67MTxfiJ6+zYsDnE/VZOMgkCjCwptBS7/JRGPK3RbjJWr16fw9R0pesZ/7iRDNN2C0JgQcqWyll1tq41XoEb59xUf+O/fwC94xtV5KJXuPakMe27LaXV/2ywnT+bY296fqQV2crCBANJDjo0hJDcEfdFZJINBtqN4SRmgcPyxYNysl5BGFNYlHKZTkJYANixuX28JrlhV6PKNGM72zRYdccm7Li4zk6HOVNq2w9YFMySIO7srM0ZsqmuJY3rDLgE4BWGUPTQ9l1hSERWOFMSyt3O+MeNZJicSVvdg62mLixKZzvXriIXvSJKTxrTvu/YXMYfnXq9I/UweFxM97CSvDc59r2ObPV7goEkCx0aQkjuiPMimpqu4M1rc9b1uhiWppf1yhEPI0uG2xR9bM5MUE7WGxIYEoBOvMuvXBb8WgI4evZSa73KiHKRFzahlgnWTVyZrRsdR9usfJTZaRfp1TAFszgkWdticoZMx0ilaiVF1BnwoPqcKWLjwsVqzXj9uLJt/epY244aWXOZGAlbly3KlyWjOMoz07bvD49vDL1XdPewX/Le5NgrKe3g7+JeD2FQmnmwoENDCMkdcV5EBw6fM0r9KlyMYd3L2isISAmrZLPfUNIZF/UFqe3d4aJcpqt1CJOJDkONM2iQmowg06z8yhHPKOtqYunwUGs9pt8n2XOiV6kntm73UXt62IgzA64M8KnpitHgVBS9ApZ5Q9oIjj+Sprt+XHjCV9MWlSiORJiRnzWnpBuiPjNt+277Tjk7pualgL0OMJhSKwE8c7KCsVtXAUi2SJ/SzIPFUL8HQAghUTG9cGwvorBZN1djeHy0jEfu24hyqQiBhsEN2YiySDSM4SeOX7A2bbT1afGvu1wqOhVvrykVQxtFxiGKERQ8LuVSEY/t3ITpX7vL2ehQjoXfCL5WD5aNm7cX13lL49jpGB8t46ZlnfOIKlUrKWwiE0Wv0PF5pVrDbROHMPrQC9j33BnrNVcQAo/ctxF779nQsa7gPRR3plui4dRMTVdi/d6VxTRDH+WZOTVdwdbJI7h94hC2Th5xPg/q/lVOQrB5qWL39nXGa+fo2UtadbR9z51prVs9Zx989nRX14htHCR/MEJDCMkdcWbnbSlRwRnEMPwzlFsnj3TMVIcp/NhmBoOzn7a+K8CN/U6jPiPqDKbLzK1tdtXkWOx77oyxmDmJGfReGrZRu93HwTYDbpOlDquZEUBHc1nbOe1GHCCqwEMcFtMMveszs5topWtaW5w6M12kr9t6l27EP0j2oENDCMkdri+isML7JBqlRTFElaEUxSEz5aOrzvVqv5OuKYk6ThuuRpLpWFZr9ZZBk0Y6WC8N215sy3Tetq1f3bofTOlnNlQNhCLMoexWHCDtSEmSaYtZx/WZ2U2hfNSIbpQ6s6jb9JOmNHO/+0qRG9ChIYTkkuALWqXshBXerxzxtLKtUVEvMpNJGKYw5lInottP07jjGmdhL3vdtoFG5Mj1Je5qJEVp+qhrOhqXXhq2vdiW7rwF67qiOjOf3LI2Vk2L/zqPStqRkjgTI0EltDwZsy7Gu+n+60YwJcp5NN0fYTVbrmp1SU6IUPY5WwgZ8aGWdcbGxuSJEyf6PQxCSEJElVVVUsk2wzhqipluTLaZZ5tks2ncLpGiMAMqqoEVZyxxfnP7xCGt4ycAvDx5d+vfe6ZOOzd9dNluFHppnPbDEA5LXbRRKnqY2XtXpN+E3SNhJHluu8H2nNH1msnCmOOiBCF091+5VMSxiTtDfx/32RZcj24SxbRu23emyLXL/oRhuqeSWDfRI4Q4KaUc035Hh4YQ0k9sxp3tBWl6UbmogvnXE8f4sBmHJmdJ7aftd7aXYFLGgp+wF7Kpl0nUl7jLi9/UkT5MQpjGgxsmp9IVAURyvrpxoADgsZ2bujaCk3AsTPthStnL8/Vo2lcB4NHm+TA5G+qz0ogHKRsCJ0k766ZzbHu+XGyKCOj2yT+ZEgfXiRqSHDaHhilnhJC+ERayt6UqmVIgXI02f8pTVGPItG0BaI0Zl9nqsJSONJrA2dJLovYyqTR/oxvLbW/vTEUJpllFafrosg+knW4K9AG0KUsB4Sk13da/BFNIbeiu1V0HZ3Di/OVYaXJ+bIpxUZbPA7Znqil9a/eXTwECrdrEK7N1FL1CywFKElPKnK12p9s0ONu7YTGJSuQByjYTQvpGmFxu2IuqW/yGe1AOdM/UaaN0aVTZ6P3PnwlNvQnbn6SVuKamKxCWsdh6mZjQyajumTqNYy9d7lj2x9eucBIEUFLWpu2K5r7oiCs/m9Tvs4ROotZ8Js24yll3e39GkeU1OcNJSD+b9sN0PXbT2NV0rfXqOjSNvdz83NQ/yy+0AjSukf3Pn0lljDpsz+NupJlN7wZ1/E33VFqNQIkdOjSEkNQxvZDDjPTSiL6DuulFFdVAsxnuTxy/EOlFZnpJTk1XQuVwXV6wcXrv2DAJGgg09i9qLxPghsSy/1w/8eIF7bLH//JK279t+zc+WsYXP3aH9vwqed8gYcZIGN3+PgmSNGT9fXsA99RMHS5OtO4eiYqr82SLLHTb38d0r3/i/bck1sPEdq2lcR1OTVewaf8LuG3iUKv/0NR0BdvWr+64x/z7FGXy5MpsvWf3is2xUNd9qXjjXbLMazd9TfdZ2ITb+GgZOzaX246ZagSa58mPvEKHhhCSKrYXss2InZqu4M1rcx3feQXRCvsHGyvev2Wt87jUi9o1dS34InNt6hhmULk2hEy6CVxYeolttlYV4uqo1upt59pUphlM2Qnbv/HRcmh/Hz8mY+SBgzNOzoFrs820Zs/DDNk42x0fLePYxJ3ODVttETyXbfnvkVJTNj0qrupa3fw+jKXDN0yllSMeHrlvIx4e39iTxq5JN32dmq5g95dPtfV1uTJbx2eemsHBb77adl0IADs2l9tSrKLQq3vFxbF4a+5Gg94rs3Unh9ElKm5qBJp0U14SDmtoCCGpYnsh2yRsDxw+h/pCp9m1fMmwtYfAoW+9ro2IlIoeli8d7siFthXqB/G/yNS2VY71roMzrX1ySaUCohU+J90EztZ8EbDLC0c9bjqCKTsu+1eOkLNuO+4utSAuxkyasq1hhmyUmpFgHYDreZMIlx+3Ebw/o6rYAW5GtGosq1tvmKyvDV3t27X6DcO4n41dTd+F7afpuboggYVA6phEw2BX6J4J3pDQri84xqTuFdP+hTkWtnvJ9J1LjUwvm/ISO3RoCCFtJK0WZHvg2/qcmIyuNzQdo/3svWeD1hDfd6++z4utcWWQoHHl8pI2vRRLRS/ycY1rQOnOaVg/lDAHo9umiVt+ZGXHZ3EaNZq6nQ+FNI4ME1QIM2ampivY9dRMRwSqW6EGhe2+sdWMjN26qkPKO3iNRkE1cE3ieaAzOm2o9McwxkfLOHH+coezpK6NoCMVvE9Nz7w0hDh0hF1rrk687lw/cHAG+58/0+pzFdXQDk7iAJ3PhH3PnWmL+ChW+NK8kjiWtudtkk6h+u7RnZu074ZKtYatk0ewe/s6CgNkCDo0hJAWacw42x74tn4DtvXZMDUVVFEUl+aRwSaEgN5wNr2k/U0fTYZ/tVbH6EMvGBtqhuHqeJrO6SP3bWzJX5vWYXMwdMdt9vqcNjrmDQG+iW0AwJ9feMOoimbCJYqj9telcaTNoLE5Typtx7QJJTbRzcSA7b4JqxkJzsi7OJ0mJz5JGeKoxvT9W9Y6H7OHxzdi7NZV2ueJLipki3Z1YyTHIcxRd23EajrXKsUKiK52F3zemp4Ju798qiNSc/X6XOseT+JY2pyibpxC03f+502lWmu7R9R1YupHlEZTXmKHfWgIIS3SaBQWtTGdqSO0+t41T93f90WXNhO2HheD1Nbbw7+NqemKcRZTUW46UrpmnLqx6ZyklSNeh4M0+tAL2uOZRr8MZej7DRtvSOCmZcM9GcPUdAWffeqUkzOjtq/SG019kKL0vVCUih7emlvoqmeQ6RwvX1LAkmHzPRLsgeHSf2bliIf33PxD+LOXLke+T6Lg2ptGCEDK7hvghm1T9daxpV72qnFiWD8ul4a6Yce2IAQ+8f5bcPAbr3Y4H0MCKAyJNsWyKOc/7Dlj6+ezIKWT02/r+6KLprg03rR95x+L7d1oe4aQZGEfGkKIE2nMSJpm1U2zbbbZ5GBhvomgMWianbWtQzcTGTQsSiOe0bBUBeiqrmb50mGrQ1Op1vD48Qtt/zZFx1xmYpUjZRpfajnewbpvAeMYkuwhEyUyAzSMlm3rV1sjklH7XiiEMOflR6mZOnH+cts1AQBXr9vvEf+MukvqHQC8+dYcvvHyFWtBeBKYopXKEW+ljTUHokuZiortXNmiXZVqDY8ZjOQ0Zt/DoqGm71x6XCnmpcQzJyvY+b5b8EenXm97Hq0oerj7vTcbJ1TCnKpqyHPGdO7VtemSDWCLwrhEb+N+p8ano1Kt4cT5y7ltpjpI0KEhZACJm+5iMtC7zQfWvZB3HZyJtS6X/HeXNBv1ou0mdcsbEvAKoqMPQ3C8poLlMExGsM1I8//GprSTRo73gcPnOo5FfV62ZtyDqB4y3RjNrrPTOlGIuHn9trSdUtELNe5c8Rdk+1mQQNEbwrX6grFgP4qDp7t+gwXhSWAzOqemK0bBgCuz9diNMk3nStXnmK4d5ZeHpWX2G9eUQkWtPo8nX3wV81K2Ra6vzNbxzMmKNiITVoMEmI/zkBC4feIQ1pSK2LG53HKYdI522L3nUvcXxyl0qU0sWCYGHj9+AS9fehNP/NOftK6DpAsdGkIGCF1qk2sdTJhMctLYohthhOW/u7zg15SKTi9qhampXKno4QfX5qyGYzeJvTojOCwPXv0mrEYkSaamK8YxmQ6NruYj6jZdzrdJFMLkVAfVmXSCCrqaAa8gsO/eDUYjOaoTaTt/1+oLeHTnJqOxHdXQjbr9uJiMR1NfJIVJ9CAMk+iHvz5HN+Ggrs1jE3dmyoEJEuccqWeVS+Ta5GgGl3WJwPgdptsnDkXeH5coTFqETQwce+ly15MzpDvo0BCSQ4KOy8qRRrpAsCZF4X/56H67954NTjLJLmPxrxMwz8bqnKcoVKo1bb2EixGn0o1cXtQK04u2Wquj6A2hVk+nHlFnBIcpjA0J0erzk5TCmg3lWMShmwJ6F6O9IISxDsB0fJQ6k01Q4cBH79DeR2o7SaQq2RxXlWZjOk5JpPP1UqnJxTCP4wCHGcHjo2U84ODYZgnXqGQclIJXpVqzRiUAuwJaWAQmrjqYSzQlDUyS8X6SVr8j0aBDQ0jO2DN1uiOv/spsveOzIMpwDM4sX5mtY/fTp4xpUzaZZN361Do/89RMW5Gpv1fG0bOXjL0LouBaLwHcUHHyF3FGadRoMy5rQQmvhDAZweqlaRIamJfSqsCz794NiY5z//NnYkcDVhS92Mp6YQZnWFGzKdKi1JlsKWm2mfukZpJtkSCbcxS1YaFXEIBE23bSVGrSObCu6ltxnIwwIzhKf6N+E6VmJg5KlhgIf77aFNDCIjCuEuxZwUWmPqsO8GKBDg0hOUKF/+OwplTE/ufPaB2J+rw0zsaZ5JVVjYbJMTE1aovaWC8JdGpJthqeOFGRqOM5NnGn1jn14zfGdedgZu9dRmWvWn0eR89eSr0GwCY8EEbRK3RVQG8zgl0UssZHy9j//JmO8dfnZeuY6XAxXJKYSdY5rjoluyD7nz8TaTs7f+IWreRxGrPNuqiXSvkySUf7ScPJyJNx7ZpKOCQaz+AouBx//7L+ei1XB1Wdv36mj9nwR7/UO1E9Sx65byMefPZbxgmsLDrAiwnKNhOSI1xlT4OonPGwKI5O3lg3y6/WF9c5CUtlCKIKuuPuu1/GVmE6lkoC1CQM0G2qh1cQOPCRO1rrN8md+uVMTX1x/PnoJjlT3b6HESUFLO41OeIN4dfve69RMMFl7CZJcCXH6rIPtmNnk/TNsqrRbYbZcROqAL4XRmXc6wW48dyJKgzgQtINhZMaU9CZdZ08aEiHzztHj11SqvwsX1LA7PV5rCh6uHp9rkPuWffeUOMyNTnu9zmwRb/8z9v7//3Xceyly23fB5/rJB1sss1DvR4MISQ+cUPaLopFQWdGybYePXvJ2JW8NOIhDvNSNtJcHFApUru3r+tQBHbBNGu2e/s6FL1C22fBYuEg46NlHJu4s9WjIg7BmqS992zoGAfQOEYSN+ScTVEMwLyPcWYM1Uu9Uq21tv/gs6eNaUzxr8nG2TRdQ8GxT01XsHXyCG6fOIStk0daBbiP3LcR5VIRAg2jbMfmMvY9dwYPHJxx2gfbsdNdI1mdue8G2fwv7FwnQTdpORLAMycrqYxP3dsvT96dCSGAPVOn8cDBmbaU0iiR0Gqt7uzMFITAtvWrURDhT9hS0YNXELh6fR6yuZ1gurI/OrwycH9Xa3XtNaZ77uw6OIPbfPd72tiiX/7n7UfH1sIbChyrwYoN5BI6NITkCJuB6hUEli/pNIyBhqEXZkjoVH6UxKZp+epsHcHnujOyMeMo0HhJ6v5eLhVbs2JhKkgAIhmfOmP40Z2bjLO/foN69np8QYNgTVJwHC5GhcKfjx5l33XOgcJWN6IjbppFrT6Pfc+dcVLWszlZfkN09/Z1eOZkRVtXZNoH27HTXSNJNppMg24NP9u5ToJu03LSHl8WCEstjvvINTEvJR4/fiE0aq4i5TaZesXFag3jo2WMLOmsbFD3vh/dcyeoPpm2UxP2jlTf61Kt6wty4K/LrMMaGkJyhKmOY/mSAr7wc+aux9Z+C4YeIQBaoX+jHC+AYSGwbFhgNmJhfH1BYmTJMKZ/7a7WZ7aUg7CXTZyOza51DsFUBNtMaanoWRto6gw6l2Ja27qi5KOblLuUWIPpXAePfxLpd6bjFIxiufaLCasv0F1DLipYWXZggiRhVKVZ3JxELdqgF1+7SFhHTRFLgjdqdatIjB/1bLKpQ/pljsPOadTGtHEIE6YI26dBvy6zDh0aQnKEq+Fq+l7Xj8E2Kad+b2sMWV+QeMfblmElokvFBvt92NSubC8b/4x68FgkkZdtM5SD6mnjo2VrfU5YupKr2lMwAhPcdxWFCe63TpGsVp8PrYcKdqBPU2kpaDS5GhBhBoUpOpA3p8VP8PpOwshVstVpMD5axonzl0Pr+WwMevG1y+SNv4ZLJ52fBhJu9Y/q2TQ1XdHKNyv8DorLtZu2wxDmbG9bvxqAeayDfl1mHTo0hOQMv/GljJldB2fajFadceZ3hirVWqiijd/4XuYNWfOx475o/C+AsFl408vGpvoU5iS5Yts/9ZK/WK21ZsddmvmZCHMgFbbUJ1sUxhRdcr0WAHelpYIAHLJTOggaBq4GhIvTmyf8zsqKogchGmmeSijij0693tFENwmUbHVaTl5YPV8YyrAcVMKMe9UrRj3vT5y/7Bw56Radc+INCdy0bLh1bar77MFnTzv3sXGJ3KXtMKjrXacaCdy4bvOkireYYA0NITklavG23zgqCBFqMKvvGw9uezrZmlIx8ssm+AJwmYVfOnzjkbVyxMNjOze1UtY27X8Bt00cwm0ThzD60AuhfUSiELZv/gJ+5TBFqc/xMz5aDj035WZTRROm/Y47Ky6BNifa1XCO48wAnQara42Qbjmgca1ktfbFVM8UvL+rtTquzNbbhCLSmpGvz0s8cHAmtWLsbmfa0xIGAOz1Zb3CdB37Uc+aPVOn+yKFr1i+pIADH70D0792V6uO7cDhc3jg4EzopIf/ueqvVwM664S8IYHZ63M9OS8mJ0xdt2qsJV8kc5lHc7rfULaZkIwSliplkz8tN2dwVVG/TlozjCjSyo/t3ASgM6UtyMoRr20Wz2V/VCqXTZ7X1HzQtL9R5YyjpljpZH2D59N/foLHw3Zuw5pFAtFle8NQEsu/+oencfV6OmlmflyOn61GKGy5fsvDKkzG6MoRD1Kaa4x6icv1FpVupJsVaUhn22TAe319uNaoRZXAT4NPNqW0ozwnw45rMDoZfH/pUn27JWz8/msuS9fKYsIm20yHhpAM4vKwNPXP6DWlooeZvY0oianJY3A5HbZ9Nr3Y1Wye6aVvetnHMYai5KkHHSaXF73//JqWN6XXBV/+UQ3hojcEQFjHF6XpXhI8tnNTKk5HVgyRqemKU2phFkjaeUiiBitujyUbtkmVtPsOmZzsrDznbajeXa4iIQUh8MWPufdsCXOAk7p/o0wkmZaNum8kGuxDQ0jOMKUM+aUuk84nViH+KLLB3pCAEGilAQDAFz92hzY9aN+9G0LXF0wpUy8QWzqaLX1lXsrE+oiMj5Yxs/cuPLZzU6jEcvDc6Arxg9Tq8/jsU6dw+8QhHDh8Djs2l9tS1lR6nc6ZCaYmRUUACEtC7LVR5dpHJiomUYReS666yJBnhaTVtILpRXFIo56iX+pVtvThXheaj3hDkWWhJW4I0biwIGViNYxA/PvXn164af8L1ut8mTeEXb40TNOY5qXsicQ06YQODSEZJEzqEnDLsw6jIERbjccrk3djISRqq152paIHCLTl9ZvqR4KzZ8E89T1Tp/Hgs6fbjPFrvrodW/ND2wtfbbvbPiL+8R44fA67t6/Dy5N3G523YA8V14Z4/lqcZ05WWtuxNfpzLdC3MVtfiCy77UJH87kuSMLpsJ2LXkuu5kniVaD7/jZBVP+gVybvxie3rDUa0bpLSCAdYYAkG9RGwTaBdeXqW6luO0itvoD7LefDhIosuRD1eLosH/V+ijIRJND5nrMpAS6GXklZhCpnhGQQm8rNvufOtCmZxe0FYgrT27YdlCYOvgTUg9xmgOsUuHR1BGEKZ37HwVRDY1N9c8VFKc2WGhX3xaYiNv7t+MfUC5nWblDXin+cK0c83P3em/HEixescuEmunUCbOciLaliUyqRi0zt8iWFntQshSHReO6kVXekxDJ0z4EFzXUi0RAGGLt1VaKpPf1Sr7JNYPUaCcQSD1lR9HD1rfCGwwI3VNpUHWGlKVQzL6W2JiZpBTRbarRuvLp30zJvCEWvYBxTniYsBgU6NIT0GZ3Bs3v7OjxwcEa7fLVWx20Th1oP/mMTdzrlpOukNXXGgK0A37983PQMW0do07pcHIeg4WySco6KafbU72youhcloa2iOLZ0ORdU+oLaDtC4XnQOXNbQOZNq7HFLN7udKbedizCp4qAT6XKN2ZzhMCOt6BXwcz9e7qpfS5JUa/XWvseVP7dx9OylSCl4aTRajNKgNklKI55zFDeLDAFa0Rk1gaGcFr9zoJT6FMq5qFRr2HVwBifOX245umEtB3ROp2kiQd2TrkIKpqWqs3U8unOT0TFiT5reQ1EAQvqIqUB5x+ay0yx2sJBcp6IVNvtlGlfYSz1OAe3UdMXoqOnoRTFuGLaiXOUkXpmtd7xkVd8ZdQ66wX8cXBSihgAkn0DmjhKACF5HV9+a62rWOY6j6h+DrckfYL7ebE6kUnjSYWywKoBHP7YJwA3jOdhnRsnf9robfBSSvD/jFL+nIQyg6KUS3qb9L2Q62hqXqM8tP0poII6SoU1UBUAizqPat6yIjCwWbKIAjNAQ0kdMs/+ufQX8ubq22ShTupTpxeCSphU1PUONxYTLrFs/sKUG1Rdk6+UYPF8SjRSan3rXKlxs5mmb8IYEIGCUmfZHFsKMAqH+18e5qg/dcbP22ouCVxCAlPCX9lyZrUeKDATHEDYra4rgHDh8zhgRe6I5y+xvcqkcL9P6pAR2P30KBz5yR0fHd3U/Zt2ZAZJNq3FJweugKUiStMORVEPe4DpNRnivmmL2mkq11op6Rr1WlNAAoH+36Woy1TKz1+e0kc+komD+d1O/onqkEzo0hPQR0ws8ii2qXra6l29YY8ng74Kh/jCWDg+1fh82e24rXldRKVNfln7ikr9tQgL4s5cuh57P+oJs9ejRLavSF6amK6G+igRip3QlxePHL3SVKlUqevjQHTdr16FLNTIZi1EFE0xpIjZjTFdzcGW2jgcOzlhrYOrzsm0/dEZ0n/3SUJJMq4lzn6nrPOkUONtzM876wxykWM5cTlD7GWcfbe82NSEXrCVM+zjqshy6rdMkyUCHhpA+4WKculAQnf1D1MvXVudiqmV54viF0GJbXZhdqZKZjEubUZjl8Lwal2sRaRDXX1yZrTejEmiLBhS9AratX51IM8K8sO/eDfjVPzRH8/zHwWYs2q65YEGvLSIY1+AMK+i/2JzBNkVjJDqDbQLAT71rFc5c/EHLkOuH46NEN5JifLSME+cvx+56H9XhsEVMbM/NOKloJgdp//NnjHUhg4I6L3EcVtu7DQhv5JwGlWoNn33qVKSJP9IbWENDSIrYXn5xDFRdWpbpgS5gNsTKpaI1DSosN9409lLRw1tzC5GaY4Y13Own/vM30iPFqZUjHkaWDLeumdveXnSK8gwSXkEY0++ARg3KmhVFa11MWNPVkqZmxR8tCdajHfzGq7GEGIQwR8yWNPczbK3l5n3sr4Xz18glRVSj2lY/FIdunXZ/TY2tKFynEOivexh96AVtepLt+WZzam6bOOQ8fp0Tm3fUedHdV4e+9br2WMd9t/WSpK9/Eg4baxLSB2zN0oDoofGVIx4kbjRzVD1VVKFjEPUSN/VJsaWLqJlIf68Yfx8Km8yoaUZt9/Z12r4kSl0qawTPX6/kc6uzdRybuBMvT96N3dvXLTpnBjDXEimkROu8mKJmF6s1a6+maq2Oa/UFPLpzU5vM+J6p09gVaOr5zMkKdr7vFhS9zldmWL8OKc39eK47ODMFIVrPCr8S1OPHLyRm0Ak0jLOoPHH8QqL3brc1Of7UTN2zV9fvSqGeU1PTFbx5rVN+2Cs0mgiHNTzW4dqsWKLpADst3VtWjnh4bOemWL3P1HlRvYdUb62xW1e19Rvzb8vWeHVF0Qu9VkpFr9ErLUWefPHVVNdPokGHhpCUsDVL2zNlTqcxoWax5qVsS48xvXzVjKSpseTu7euMxtiKomd1xqLmzl+s1jA+WsZNyzqzXFUtQdZIomFlHPz9UKJ2k+cD/QZrSsXQjvTBBnhT0xVjT6SjZy/h25//GTy2c1Pb/XT/lrXWBqLlUhEHPnoHHG3aDuKkOUZFoiGbHPW+Vv1pkqLbmhzVbNP07H3yxVet97RKxdVF4pYvaUje66jW6hh96IVWx3n1dzURFOUcZjVppjpbj/1MNKUmmtZ3pbmtbetXGyfBSoaJPKAxgSBEQ2yhVPSMk37dMi+lcdKP9B7W0BCSErYoRre9JZRjtHzpsPHlq2acgyosyoAz5awXvYJ1JnJ8tIxt61drf7fMG9KmDyhDxWQQZKkJma2moRdUa3XsmTqNh8c3ho7Bnza1oujhB2/N6TsRLjKCKkTjo2WjLLD/2tv//BmnnkjB9KKxW1fhwWe/hVpgtlmNY3y0jF0R5Mr7QaVawye3rMUzJyuRjNZqrW7t3xOGPw2pNOLBGxKxeywd/OarGLt1lfF5EuZYrCh6xnvujVrdmuaknnvBAvXPPDXjMPLuSTsNa00zTTkOJ85fbj1T/WmTtrEG+9T4qc9LSKlPS1tSEJBoPx9Fr4BPblkbWp8VlupqGqf6M+neTCQanNAjJCXSbqxVrdWtL1+FLfXt4fGNeDQw4/zIfRutM5F7pk7jmZOVjkLlHZvL2HvPBmOKG2A+JllpQuZPN+onjx+/gPf8q69Yl1k54qFaq+P7tTlIAD+4Nod5OjNtUUg/phldf5qSTdbVdo2Oj5a10Rv/OLJyjdt45mQFOzZHN8ZcI6zBNFaVAqaeTVdm64AIT+MzoaK9ttl7G1evmzvdqxTeqPTqlrx/y1ocm7gTn9yyNvbxs3H1LXtUxMYTvvRIf9pkN+N8o1bHjs3ljnXU52WHU6Kic7ZT8djOTTjwkTva7t9PbllrjO7qCEZ8SW8JjdAIIW4B8PsA/jYavdq+JKX8LSHEKgAHAdwG4BUAH5NSXmn+5kEAvwBgHsC/kFIebn6+GcB/AFAE8McAfllKKYUQS5vb2AzgbwDslFK+0vzNpwDsaQ7nYSnl73W914T0gN3b10VqIulHFYWWm5r6JkOrYCiI9htPYRKkuhlnW4TiyRdf7dimSllRBZImIYSovWt6iSndyIURbwhLvQKqs3WURjy8eW3OOMvsL/q3bWtWk1uuKAyJthRE/5/9ZsQbso49bXSqU7a6CHXt2QwRAXPajB+bfGs38t+9QqXWhc2eB3GZudep0enut6gz5EEq1Zo1BdCGadv+SNv+588k1s8kCQpC4BPvvwUPj2/E1HSlY7IpKbpp/GkaTzfjXFMq4ujZS9r+Xzpsz8flSwodGQ1B9kydbr37TO9dIFvZBouNUJUzIcTNAG6WUv65EOKHAJwEMA7gHwG4LKWcFEJMAFgppfycEOI9AJ4E8D4AawD8KYAfk1LOCyG+AeCXARxHw6H5N1LKrwgh/hcA75VS/jMhxMcB/JyUcmfTaToBYAyN6/QkgM3KcdJBlTOSJUxqOTaCOvdT0xWrY6STn/XPDJtSbWxdtsO2qcO1a3cvO3BHoRuFpaAqnIuSUrfbJHr85yIsfbDoDWHV8qWhzmVQzch2Dbt8l+Vzrjq0R3G+SkUPy5cOd6QUdavqmCVUeueV2XpmVMiCioi2ya80EaJxfHq1bfUc3XVwJpHzoNzfKO8j0/UcphBKusOmchYaoZFSvg7g9ebffyCE+DaAMoAPA/hAc7HfA/A1AJ9rfv4HUsq3ALwshPgugPcJIV4B8DYp5debg/p9NByjrzR/s6+5rqcB/LYQQgDYDuCrUsrLzd98FcAH0XCYCMk8e+/Z4OQYqN4Sr/xNraPOxTYrqIwGm4NgyqsOS6ExbdMlKmQjq03IuplZq1RrHR3LlUSs7dx0E8UjetR51PVKClKrLzjVKY3duqrViXxF0cPV63Ot2Xx/7jzQ2az2gYMzrXNcKnrYd++GTDs1SkwBsEdq/fzgrbmW8+5PKfIfl6zuL2Cv/1P4Jyey4Mx4BYE3r91wYPp5fKVEz5yZghCtSaGk7iN1Pk11MLrnuC7iKnBDmIL0nkh9aIQQtwH4rwD+LoALUsqS77srUsqVQojfBnBcSvl48/PfQcNpeQXApJTy7zc//x8BfE5K+SEhxH8D8EEp5WvN714C8H40okDLpJQPNz//VwBqUsp/HRjXpwF8GgDWrl27+fz58xEPAyHdYzJew6I0qqdEsBhXoJEXrVIJdKlaLg0p4/7W9Lsdm8sdY3UdS5ZJagY56rHY8Gt/0jNJ6MWA6muUxPk0Xe86wvre+BnxhlBf6Mz17zdeQWDnT9yCo2cvJTbr35C67myQmCVWjniZiryEIZoDzcNYk8QbEjjw0TvaIp5ppHEWhMAXP3ZHa1LK9P40ierk/V2YZRLpQyOEuAnAMwAekFJ+37ao5jNp+Tzub258IOWXpJRjUsqx1avpHZPeYyu833vPBuPvBIBjE3fi6NlLHQ9liRt9Hmzyy2HE/a3pdw+Pb4w9lixj61kShaiFobN0ZhJFiMb92G2DxlLRwzJvCI8fv+BkMF2s1pyjfLP1BczPS8Qs9UgPKVsF3Oo51u3Me62+0HNnJsphFbgRXejWQejV6ZRdODMrRzxtP6WsUC4V8crk3Xhs56Y2ueVS0Ws5M1PTFYw+9AIeODiDWn3e+bh7BeFUXzUvZev9batB1dXwUBigfzjJNgshPDScmSeklM82P/4rIcTNUsrXm3U232t+/hqAW3w/fyeAi83P36n53P+b14QQwwBWALjc/PwDgd98zWnPCOkhtofesYk7jelbqueIyRCSzXWbivddiftb0++ymjbWLUuHh1rnceWIh7vfezMOfvPVyDPprkXSUfvMZImszmZfma23pTqZEELf80OlcUad+V0TIUIDNBR2snYAbVoOWT3fOpSgilLSMo076X2SaBje3RTQp833M66GONtstGx6x0xNV7D76VNtz2SJRvTmpmXDVgGdnT9xC8ZuXdXKpBiyFPer97fpWW57xlMYoD+EuunNWpbfAfBtKeVv+r56DsCnmn//FID/7Pv840KIpUKI2wG8G8A3mrU4PxBCbGmu8+cDv1Hr+giAI7KRC3cYwF1CiJVCiJUA7mp+RkhfCMqOqkZaYQ+9vfdsMDYIm5quWOtP+HBMHxVh8xsi1dlGv6DlS4axfMmNyI1Lg8SweiJ/RC+vZNUkKojw9KaiV8D9719rlBiP2kBQ/S6pKF8WUcZ6cP/iKoqlSanoYff2dSgIYbxOy6Vi4tewQHdqYL0gy84McGNCItikUr17Hzg4o51gqi/c6E2jY74ZfTxx/jKOTdyJlyfvxhc/dof1flVplzrWlIqZb0Ow2HCJO24F8A8B3CmEmGn+97MAJgH8tBDiOwB+uvlvSCnPAHgKwF8A+BMAvySlVG+GXwTwfwL4LoCX0KitARoO09ubAgKfATDRXNdlAJ8H8M3mfw8pgQBCeo0trcyozy8aKmMHDp/DkuHO2031Tdi9fZ0xbM6HY/roDFj1yqzW6liQjT4Fj+3chBXL7L0YXGSo43bcJnaKXiFUvtoldTJsEsEbElg54nX8zp+mOYi8Uavjkfs2olS8cQ/ctGwYSzXPtn7y/Wt17H76lPFaUKm+Uc5T2ERGniJYWUJ3XGv1eXz2qVMtp8Z1Akhdn6bz6k/jBm6kVRcMJ1fVwpomPmzfkd4TSRQgD1C2maSFqci46A11dAiPgpI73jN1mgWGfcIkbe1n5YiHa5p6AL8h4+9HY5MAddkeic5jOzcZlY/C5FT9oh62VJSgHLH6bVCmW6Usmrqd55G46XhZQ10LrkXlpv329wvLc7Q1q6jj6+osrhzxMP1rdwEAbps4ZFwu+CwwvXt3bC7j0Ldeb0tjGxKNZqnqmgDMfddI8nQl20wIaWCate3GmQFuRGAeHt/Ylt/Lh2PvMElb+zHlZvtfgrP1hVZTSZMEqOv2SHR+5dlvaQ2fsFnToGGrc2a8gsDwkGhJMe9//kxL8GP3l091NFK9MlvHwW++iuVLCgOhYhc3Hc+G6l8TfN6l6fD7rwW/PPXFak3bFNffVNO/bPD5HKfnWBjKeE6bojeEa/WFzE2yyMCfYbwxW29FX2xOkP9drmtGKgD8+NoV2vpJdT7Uc2DliIe992zgezoDMEJDiCNpNIZjBCYbpCX/CegjA7oZwSyRZvpMr1NzgjOqunvNdG8XhMCClCiNeHhjto7g1IVXELhpqbkQeVDwS8gn5WzYnn1pNeEMyvHqnJM4jX/3TJ3OdSRO9Ufa9dSMVigjT/ibvNrw93Az3fth6asKvsd7ByM0hCRAEqkWKvdcpaYsy7B85mIiaiPBKAQje7oZwbSI4zwUhMAn3n9Lag6XRDRjoVv8M6q7v3wK+58/05ESaIq+LkiJlyfvxtbJI1qnpT4vB96ZARrn7OjZSwCSiS6GzWqnkdZWGBL44kfvAABs2v9CW3pgMJoaxTCdmq7giT47M91OElRrjZqjvDszQGNf3nAQZlDPg2BkVRHl+aQU0ejQ9BdGaAiJgGuevY5BbkqZN8JmYZNMH/HPCie97jDe/Y7l+M73rkb+XS8cjiwUUat7z+TIus72Lhbi1oqoRpClEQ/X6vOtNF1VZ+Rv5KnuxTSimFvftQp/fuENo6MUVmelXWdK0aRIY3jXKhx7iXpJCtUotZeoWliSLrYIDR0aQiKijOEoLzFl1MYtWCbJYRNfANBR3G2jMCScZFD963/g4EzUIccmDcekVPTwg2tzPYuwpI2KFgQjAt6QAAQi9yBKgxFvqFWblTf8175tRlyhHN1eRvH821ZGqWtKWr+dGTrdnXhD9p5KaWFLayXJQIeGkIToptbilcm7jfnnnN3pDVPTFew6OKM9Bys1BcEuqAaN5VIR29avxpMvvmo0xPphpJFwVOG+Oj+mppv9wNVpzhJ+9a9t61fj6NlLuTG41ZiDkfSs8tjOTcZnGkkWF/EEZlyki82hYQI/IREIU/gxtSoQgLWBJnvN9IYDh88ZX0ZXZuuRnRmgYfh6QwK7t6/Dw+MbsWCxhOnMZBOlQqbOT79Ok7+/iyIvzkxBiFZfnkd3bsIrk3dj9/Z1eOZkJTfODNCorXji+IVcODNCACfOX3Zq9ku6o1wq4tuf/5nQ5VQ9Dek9dGgIiYCt4V65VMT9W9ZqnRoJtBposhFX/whrmBiX+oJsvcTonJI4rBzx8NZcftPKvvixO/Dy5N04NnFnm8hGHhyDIFl0Ib1C55tFSuDx4xd6Iu282Lnt7UVs2v+C07WR1nuG2KFDQ4gDU9MVbJ08YnyYqRqYh8c3WrXv/Z3Eg13GSfqk6Wyol5jOaV3MeEMCK0cakQdTR+7FjlcQkBK5NP4LQhifYTTskmP5kmHeP33k2EuXnWsrSyOdkVaSPpRtJsSCrgN4kGCExaQEpIzpqLKgJDnS7HLuP79AOhLQecArCCxfMow3anWtgpytjmmxoo5XHvnE+29pnV+/YEpBiNBzvHR4KLdRqV7zRq3OeyYnvHltDlPTFb7newwdGjKwRG2QFlzepTB0SLTnzI6PlrVGM9PKssPS4aHEHRpVQ6NQ19nup09lQiUrDnFllevzEsuXDmNm710d301NV7D7y6domAWo1up9kZpNgieOX8Djxy+gVPRw9fpc63p3qRejM+MO75n8oFKQ6dD0Fjo0ZCAJqpEFG6cFnZfb3l7En710ufXSqFRrTp2f/U37/OsHELnbNEmXbhTqbKgu28Hze+Dwudw6M0B3BlSlWsPWySMd1/3+58/EEl5YDLyRQ2cGuHGduKbjELIYYLpl76FDQwYSXTFqrT6Pfc+dwYPPfqvV2A1oGF9JpAb5uwUzraw7gqkr81J2rfGfdIFymNT2Yn+h6SYR8hiB6BVJxCqGBCBE/mSeCRk0VmgUC0m60KEhA4nJmEx7FrHfRmzUNLt+E6xRUp3D/al+KnWlUq1h18EZnDh/GQ+Pbwzd17Qb4IUJDGSh6V5cvCGBm5YNd+2A1OrzeODgDPY/fwZVOjOpsnxJQ4hCSVATQtJjSMCqLkf9ht5Dh4YMJP0yJnsh2atzAvbeswEArGl2WWPP1OmOtL4rs3Vrqp9EQ6b0xb/8G3zne1dbn1eqNex++lTr30Ehh0q1ZqwJ8Xfadm2s6FITtW39aqe0xayhUuhOnL+c2PgZmbmBSTSkWxZyqpK2WPEKItcpqYPCiDeE2Xr0+GhYEJQTOL2HDg3RkreZ/iDb1q/GE8cvJFpIKdCQYzQZZwJIvfBfFVX76xCuzNax++lTuGnpsDbNzl+c6BLV0DlLSZ/7qekKnujCWPY7M4r6vMQDB2eMhoLuWvCGBIRoRNb8KW23Txwybts19e3o2Uuh+5El/B2up6YreOZkpd9DGjhWjnipObp0ZrJBQQj8yOoR7TPKT31etiZQ1J+k94iYoZSwCM0yj11Reg0dGtJBWEF91tkzdTpxZwYA7t+yFg+Pb8TtE4e065ZI//gcOHxOW1Rdn5dGR6tSrWFqumGcBs9rMIXL5CwByeybvzYmLaLMetYXbhw3/3VuivCpfkN+TE5g0umHYS/QbvE7v3ltiJh1wiKQJP/MS4nXrlzDJ7esxR/+ecWaAjgvJbwhgffdvhLHXrqc6rjiqhYOOnFTNBdkY0LMJHJCBb/eQ4eGdGAqqM+DDKGa+U/jwf3MyQrGbl1lNXbTJq6R/OCzp7HM65QrVilcQCOiYHKWbOfeJZqXlpOZNLX6vLVHyqUfXMNtzeiNqvc5+I1XO5zABw7OJDquRqNO2SZmkQbq+up3LVgUvCGBuQXZ92vLG2qYjCmfIitpO729YsQbwlKvgOpsHaURD29em8uVOl6tPo9D33rd6VzUF2TXzkxY9gBAZyYNbHWGObpcBwbGxEgHJmMmD0bOgcPnUntwK6dO1wm+V31m4tbo1Orz1pfdE8cvWKMmSob39olD2Dp5pBXxUdG8SrUGiRtRDv/37/6VQ3g8B86MwjbO6/Ptjsvjxy8kamitHPFajrHqCl4uFbFjczl1Zwa4cX2ZrrNS0UOWal2L3hDed/vKTFxb9QWJBdnfozMoRtRsfQHX6gt4dOcm7L1nA5Yvzd/c65XZes+inBKsU+sHtmNeoCpAzxFywPI2x8bG5IkTJ/o9jFyzdfKIc7pNvzCl+STRgdwWmldSva41RknXIunSwnqB6ZiYcr9VnUk/xppn/FLQwWu8l4x4Q6gvyLb0PVVjEzf6VPSGcK2+0Gpae/TsJatYgwu9TqPJQhQmSbJemF4qenhrbqHNMRgCgAGJRJHB5ZPNFHWSLEKIk1LKMd13+Zv2IKmT9U73plqPBw7OYOnwUNe5q4/u3GSs81Az1y59ZtKoRVK/2/XUjFWFS0ep6OGNWj2WAWj6jamQtVKtJZ52tRhQ11e/HFfFbH0BQ6IxUVCdrbeccSC+E1GrL2DliIerb83hieMXsKZUxMqQNJkwen106gtyYORYy6Uirr41l+mGmLqxLQDOJ16g4UjHUbEadApC4G1FfcpUmMojsbP1XavozPQBppyRDsZHy9ixudwKmRaEwI7N2WkUaes23q0zUyp6GB8tJ5JWZqtFcmFquqJN8xofLePRj21qzha7U43hzAyK8ZYXtq1fDcAs/hCVctNpiMOCbEwUlEa8VmSx25TOK7P11nVYqdZymSYzKIbetvWrM+3MdIsQjcmpX7/vvf0eSiZZkBJ3v/dm7XdSNqJ3JBoFIfDYzk144p/+ZL+HsihhhIZ0oCRb1ez7vJStgvh+OzV7pk6nagRdvT6HqelKaz+7SRdzqUWK21NmfLSM/c+fSd0glJLqOL3k8eMXElPBEkAianIq+hkWcSt6BSzzhnLppCxGupFNzwNSAifOX8bYrav6PZS+oerxdM+BFUXPKi2/fEnDPNQ5vYMiPpE0lN7uL6yhIR2YamgKQmBByp71pUmzhqBcKqI6e10r2ZhUrZDpOJaKHmb23mVMK/IKAksKQ9qxqd8CaKltEdJvhgTwD96/Fn906vWBnvUn+WOxp089tnOT8T1jq58SaDg9uvu5ZPictPfzIsnDGhoSCVNkQc0+JNWXxt+TRBWXl325+mnVEHgFgd3b12GXYcbZpuYWpcjfVBRfrdVx/7//Oo7/5RXtjE59XqI+r1fHqdbq2PBrfxJbO58QoPHSTVKBaenwEJ45WWHvmgEhbvf0LJJ1Z6YgBD7x/ls65N+TQCWN6eSF/Y09dZicGaDxHoobuX/3O5aHNh3NM3lpcTGIMEJDWkRteuiPFris1+8EAOgQHlD0InXFFoo3RWiCRf5qrLbZmNGHXmAKDskUAo0msWzwSEg2SDOKVDb0TVPoIjXekLD2WLE5QjaK3hC+/fmfwbse/OOBT896palWSZKFERoCQJ/CpWo2Tpy/HLnxYbVWbxWq6+pAxkfLHQ0VVXRH1+RRUavPpz7Te7Faw6M7N0VScwtrOKpz3Kp0ZkjG8DdTJSQpWGsXnzRte5szozIi/PWYAg01P9tEXFxn5Foz6pe2M+MNCRz46B04cf4ynnzx1b44T/5aXNIbGKFZJNhkYLsp8Ct6Q8aGf1vftQp/9tLlvr7kbH1Sjk3cGSmF7PaJQ8Z90R0HFkkTQhYT3cpwk95TdsiaiELRK2Dp8JA2XU29d031pUmi62HUS7LUt2+QYISGWKWOu0nbtXUvP/bS5fgrTohPvP+Wjtx+fxTGpZ+MYo0ldK87Do1tSs5cEkIWBXRm8odL1oQrJpVOoP29a6ovjdro1fZu7bdoga0Wl6QD+9AsAqamKwP7orH12SgVPTw8vhGP3LcR5VIRorl8XAUSXW+aMGr1BfzUuxavbCghhJBsU6vPd2UjFHwNy/Y9dwa7Ds5gmTeEUtHreO+qrIhgk9qVIx4OfOSOVn2rC1meKFwTYT9IMjBCswhwbeSYNwRgDJUXvQL23duYKYoShbExPlpuy8l1jbxkIVJF8g/TeQgh/cIkLiBwoybG/3y6MltH0Svg0Z2bWu/foLCOlHphnbQUTntF1CbcJBno0AwwUVXL8oZEu2x0N00wTfhrbEojHt68Ntd6eOf3cUvyRtEbwt57NiSS404IIVG5cvWtjpSwsEm9oIRxmLBO24pzSrlHffpIJ3RoBpSgutgg4g9NdxuFCSrArRzxcPd7b26rv+HsOOkXb80tYNfBGZRGPFybm898bw1CyGAxW1+ANyTaehS5PIYq1VpLDdU0uaqWGR8tN9LRItTRZIlPblmLh8c39nsYixY6NAPInqnTuZNlXb6koG0WqXpm2Ar7TZj63wQdl/fc/EMdaWFXZuu5O4YkOyTdmFBlX9CpJiQ9SkUP1+fmB6apaNLUF2SsVLDdT58K9X5Us+48Z5Q8cfwCxm5dxehMn6Bs84AxNV3BroMzmYzMvPsdy/Hd711tG5tyWMZuXdWRTqO+e3h8YyR5ZaApU/30qdzO9JD8sqQg8LGfuGXgI6SEDBpUpOwvpaKHN2r1XJ8DyjWnC2WbFxEHDp/L5MOg6A3hu9+7ihVFD0IA1dm61jExOS1RU8p+9Q9P05khfeH6vMTBb76KH33Hcnzne1f7PRxCiCN8Y/SXfkstJ0GeI0x5hw7NgJG1m2l4SGBuQbb6tFRrnconiqTUyPZMndamrxHSK+rzks4MIYQsQvZMnWYtTR9gH5oBQhXeZYk5Tb6tUjUBGmPeOnkEt08cwtbJI13vQx7rhwghhBAyGDxx/EIm7bFBhzU0A8TWySOZi9DY2PquVfizly5ra2r8dTOVag0FITAvZUsSEQAefPZbrcgPAHhDAGs5CSGEEJImYfVWrKVJB9bQLAKmpiu5cmYAfcNJCeDx4xfw1DdfxXVfDYzq/VKp1vDAwRnt+ujMEEIIISRtwkIBF3Nmjw0CTDkbAKamK/jMUzP9HkaiXGdBPyGEEEJyyBpfnzzSG+jQDAC/+oenEUManhBCCCGEJMy29av7PYRFBx2aHDM1XcHf+VdfoaIXIYQQQkhGOHr2Ur+HsOigQ5NT9kydxgMHZ9qK4gmJQqno4ZNb1vZ7GIQQQshAkbea5kGADk0OmZquUJqYhOINCawc8bTfieafvI4IIYSQZBEifBmSLHRocsj+58/0ewgk4ywpCBz46B3Ye88G7U0uMRhdmQkhhJCsMWAdUXIBZZtzyJVZGqLEzvV5aZS3JoQQQggZJOjQZBjVWPJitYYVRQ9C0JkhhBBCCCHED1POMorqLVOp1lrpQXRmCCGEEEKyz0//5tf6PYRFBSM0GWRqusJ0IUIIIYSQnPKd713t9xAWFXRo+szUdAX7njvDAm1CCCGEEJI6P/2bXwt1uN79juX46mc+0JsBJYCQOZBiEEJ8EMBvASgA+D+llJOmZcfGxuSJEyd6NjYd7937J/j+W2x2SQghhBBC9CTtNLg4Kt3QbydHCHFSSjmm+y7zNTRCiAKAfwvgZwC8B8AnhBDv6e+ozNCZIYQQQgghYXzne1cTqbV5794/wW0Th1JPc0tqvGmQeYcGwPsAfFdK+ZdSyusA/gDAh/s8JiN0ZgghhBBCiAvdOiG9nkjPam1QHhyaMoBXff9+rfkZIYQQQgghixZOpDfIg0MjNJ+1Ff4IIT4thDghhDhx6dKlHg2LEEIIIYQQ0m/y4NC8BuAW37/fCeCifwEp5ZeklGNSyrHVq1f3dHCEEEIIIYSQ/pEHh+abAN4thLhdCLEEwMcBPNfnMRFCCCGEENJX3ra00O8hZILMOzRSyjkA/xzAYQDfBvCUlPJMf0dl5pXJu/s9BEIIIYQQkgO6tRu/tf+DPXVqsmrn5qIPTRSy0Icm62ydPIJKtab9rlwqYvf2dRgfddddMK2vXCri2MSdsceZNFPTFRw4fA4XqzWsibGfpHumpit48NnTqNVvFDEWvQIeuW8jzwUhCcNnHiFkkLD1oRnu9WBI/7locGYEEMsB2b19ndZI3b19XdwhpsL4aJkv8z6jjj+NLELSh888QshigQ7NImRNqaiNqKwpFWOtj0YqiQKNLEIIIYQkCR2aRUgaERUaqYQQQgghpB/QoVmEMKJCCCGEEEIGBTo0ixRGVAghhBBCyCBAh4YQQlKESlOEEEJIutChIYSQlAjKVFeqNTz47GkAoFNDCCGEJETmG2sSQkheOXD4XJv4BgDU6vM4cPhcn0ZECCGEDB50aAghJCVMPZ9MnxNCCCEkOkw5I4SQlEi65xMhZHBhvR0h8WGEhhBCUmL39nUoeoW2z7rt+UQIGTxUvV2lWoPEjXq7qelKv4dGSC6gQ0MIISkxPlrGI/dtRLlUhABQLhXxyH0bOetKCGmD9XaEdAdTzgghJEXY84kQEgbr7QjpDkZoCCGEEEL6iKmujvV2hLhBh4YQQgiJydR0BVsnj+D2iUPYOnmENQ8kFqy3I6Q7mHJGCCGExICNU0lSqOuFKmeExIMODSGEEBIDWyE3DVESFdbbERIfppwRQgghMWAhNyGEZAM6NIQQQkgMWMhNCCHZgA4NIYQQEgMWchNCSDZgDQ0hhBASAxZyE0JINqBDQwghhMSEhdyEENJ/mHJGCCGEEEIIyS10aAghhBBCCCG5hQ4NIYQQQgghJLfQoSGEEEIIIYTkFjo0hBBCCCGEkNxCh4YQQgghhBCSW+jQEEIIIYQQQnILHRpCCCGEEEJIbqFDQwghhBBCCMktdGgIIYQQQgghuUVIKfs9hkQRQlwCcL7f42jywymt969TXHfacOz9gWPvPXkdN8Cx9wuOvT9w7P2BY+8Pccf+10kPJAa3SilX674YOIcmSwghTqSxXinlWFrrThuOvT9w7L0nr+MGOPZ+wbH3B469P3Ds/SHu2KWUY2mMJymYckYIIYQQQgjJLXRoCCGEEEIIIblluN8DGHC+lNN1pw3H3h849t6T13EDHHu/4Nj7A8feHzj2/pDnsWthDQ0hhBBCCCEktzDljBBCCCGEEJJbFlXKmRDiFgAHAWwCsCz4dc8HRAghhBBCyOJBotPmvgbgOwC+IKU8GGeliyrlTAhxM4CNzX8WAfxbAB6A5c0/gUbUSkWuggecTg8hhBBCCFnM1HHDbvY7EgLAQvNPAeA6Gr0h3938/gcA3gDwGoD3AXgVDXv8hwBsBnAEwN+RUlajDmhRRWiklK8DeF39WwjxTwCsBjCKxgkAGiem0PvREUIIIYQQkkmU4yLQbifbJv8LaNjZiusARgAsAfAygCqAqwDeg0b21Peay1ejDm5ROTR+hBC3ARhD48AvDX7d8wERQgghhBCSXZR9bKvBl77vJYAVvu9WouHU/DgaUZ7nAHy8udwWNBydl2INbDGlnCmEEDcB+H9w4yDfGliEYgmEEEIIIWSxoatxibqc+k5lP82jkaL2BhopZsMA3mwu91007HAJ4F4p5fE4g150hrsQwgPwLBoH9Hrzvzk0DvY8Ggdf+v4jhBBCCCFkMeCapWRbzh/JEbhRb/M2NGzti2jUz/wmGlGbEoD/FNeZARZZhEYIIQD8HhqpZn7egYaDAzQOuj83cNE5fYQQQgghhBh4s/nnTb7P5tGwmVV0RqARMJgG8CMA3o6GKMByACcBvAvAK7gh1vW3pZSX4w5osTk0/wMaqWaEEEIIIYSQ/jCHG7X8svnvVwHskFLORF3ZonJoCCGEEEIIIYMF06kIIYQQQgghuYUODSGEEEIIISS30KEhhBBCCCGE5BY6NIQQQgghhJDcQoeGEEIIIYQQklvo0BBCCCGEEEJyCx0aQgghhBBCSG6hQ0MIIYQQQgjJLf8vg/Uc+cMg0g0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time Line\n",
    "fig = plt.figure(figsize=(30,6))\n",
    "x='date'\n",
    "y='new_cases'\n",
    "ax = fig.add_subplot(122)\n",
    "\n",
    "plt.title('Covid Cases Time Line')\n",
    "plt.plot_date(x,y,data=covid_data)\n",
    "\n",
    "# lable x cordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87eca851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Covid Cases in Continent order'}, xlabel='count', ylabel='continent'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Country Order\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.title('Covid Cases in Continent order')\n",
    "sns.countplot(y='continent',data=covid_data,palette='pastel', order=covid_data['continent'].value_counts().iloc[:12].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60bf2c66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}