Dialogue Data Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dialogue Data Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>INSTRUCTIONS:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this data, create a forecast of the number of chats for the month of July.\n",
    "\n",
    "Please include the code used to generate the forecast and an explanation of why you chose this method, including visualizations and/or summary statistics.\n",
    "\n",
    "- Include an exploratory data analysis and report any finding on the dataset\n",
    "- Provide a recommendation on the number of nurses needed per day in July\n",
    "- How would you evaluate the accuracy of your model?\n",
    "- If the team starts using this model to staff the clinic, how would you evaluate the impact of this change?\n",
    "- What are the next steps to improve the model’s accuracy and its impact on costs and quality of service?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports and options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.layers import Dense, Dropout, SimpleRNN\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "import os\n",
    "os.chdir('C:/Users/Nicolas/Documents/data-challenge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "viridis = plt.cm.get_cmap('viridis', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_theme():\n",
    "    return \\\n",
    "        ax.set_facecolor(viridis(6)),\\\n",
    "        ax.minorticks_on(),\\\n",
    "        plt.grid(True, alpha=0.2, color='white', linewidth=2),\\\n",
    "        plt.grid(b=True, which='minor', color='white', alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading and inspecting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('chat_demand.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>paid_employees</th>\n",
       "      <th>chats</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>10000</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>10000</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats\n",
       "date                             \n",
       "2018-01-01           10000     61\n",
       "2018-01-02           10000    126\n",
       "2018-01-03           10000    119\n",
       "2018-01-04           10000    118\n",
       "2018-01-05           10000    117"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date of first observation: Monday 01 January 2018, date of last observation observation: Saturday 30 June 2018.\n"
     ]
    }
   ],
   "source": [
    "print(f'Date of first observation: {pd.to_datetime(df.index[0]).strftime(\"%A %d %B %Y\")}, '\\\n",
    "      f'date of last observation observation: {pd.to_datetime(df.index[-1]).strftime(\"%A %d %B %Y\")}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 181 rows and 2 columns.\n"
     ]
    }
   ],
   "source": [
    "print('We have %i rows and %i columns.' % (df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dates array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('1/1/2018', periods=30*6+1, freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
       "               '2018-01-09', '2018-01-10',\n",
       "               ...\n",
       "               '2018-06-21', '2018-06-22', '2018-06-23', '2018-06-24',\n",
       "               '2018-06-25', '2018-06-26', '2018-06-27', '2018-06-28',\n",
       "               '2018-06-29', '2018-06-30'],\n",
       "              dtype='datetime64[ns]', length=181, freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complete time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DGkkWQpwDnOOcVY/P6k6877W6AHCZbNgsyeYN1/aUN5Lc5vQBsH98KO39mjxXRSBwmq3UWBxp748dGMIiBdFgmN9dcq12rAApJfFYnBqLA6fZhsvrodauXWvcbQWgqakpMZ7d4cAU0o7v393F/d+5la5XthMJhonHYrQtX5Bmu6m5GZfZDoDT6UIEo9RYHIz1DdM8qzVxbEv7bP06HZgsxTXHMOxMJ06zDWDC+k4X+VyTJRJL+64Wg/H9nm5KsePR17zUay03M2HtKmWnGveoUutWSVuF2KnGPcmk1HWrMYX45vwnGI3ZuGjLRWWxM88+BsCRnnG6o4Wv3aG8R0udwwCsdIfYFynvnKvpd6ha9+WgOslSyruBu91L2j6QWWh6KBJgLB4mHJ2+xwtGC+Jim2JIJOOxSNr53Zt2MNI7wNwTD8dyy128549fo6a5Pq1RhT8aZDwWRiIT7xnl2AKxEBb9vZiME4qF8UeD3P3Vn9O8dC5nfPWD2NxOnvvdP9j64DNptsdiIcZiIfzRIFEZIxyP4o8GcTbU0tvVTZt+bE9nV2IeJorvIDfdzURK3Z9imLKZSFyWpfh6pYrsF2tnVF+HSs2zEKp97Splp1r3qJLzqbY9qtY9yaSU+c1yDWAWEp8lhD86SlTmfiCdrx2fRXOSmy0HGIrMLmpeh+Ie1ZjD1Fu1+frMA9My52r5HarWfVFyizIR8o+z49EXuPezP2b5WetoWjKXVW89iYev/x1jAyMA+HsH2PXEy0WNHw6MY3M7sbocDOzq4qU/P5T3uUveeAwv/N/9jPYMEBwJ8Myv7ylqDgqFQqF4fTPb5k/87DWHSh7PZYrgMmtBovmO0ZLHO5SYZ0+uxyzb2EGcyeuXGZW4V43ceeX3MJnNCCFoWDCLNRe/icPeppWQXv//LuTJm+/k95d/meCQn5omH4e9/VQ61q0u2M6J/3MRD3ztV2z4zb00LZ3HkjOOZe8zr+R17urzT2Zwdze/fff/YnM7OeqSs/I+V6FQKBSvPy5t3swbPD1cueMEQjL5xLHdnoz0eS1h+qPOkuw0WJJP7zocIyWNdaiR7iRXV4T19YJykkvg/Xd/d9LPLXYbJ3z0HZzw0XekyS0A5qxdzgfuvSHx2mQx84kNv047/52/SFbZaF+zjMv+8s10Ax++IPFj5rlnXpus02yymDn5kxdz8icvTrx3xIWnTzp3hUKhULx+eWvDDmbZx1jl7udZf3Pi/XZ7aiQ5XLKdBmvy38VW2zguU4SxuLXkcQ8F5qVE1ltnoJO8xDnIdxc8zp39K7m5e/7Bnk5RKLmFQqFQKBSKBAJJk3UcmBjdTZNbWMrrJGv2ql9yYSZObRmkJlORGklutgWxidi02ywnp/k6abIG+a/WZ7mwcevBnk5RKCdZoVAoFApFgjpLCKtJAjDfnu60psstSncUU+UWAB326pZczLOPcNuy+7l75T00Wsan15Z+wxDTtoLWGaZLXuEaTPx8ZfuLnFW3+yDOpjiUk6xQKBQKhSKBEUUGmJ8SSXaYojSmRH7LKbcYi5kn2Ks21td28cslD9HhGMVuirPYOTT1SUViJk67HrV/ZawemFm6ZIFkme4k/7lvBQCfm7uB9bVdB3NaBaOcZIVCoVAoFAmaU5zkVLnF7AwnrRyRZMPpfjHQqNurTrnFe5tf5boF/8FtjhKIaelc01lxYpY9gNUk2R92sTNYq9ubOU5yu92PxxzhQMTBzT1H88vuZViE5LNzNwDyYE8vb2ZUM5FyU8lmFZVovnGo2VHNRErjUGtUATNj7Splpxr3SDUTqb49ySSf6+lwJrWvDdYQ7Q4L/pidpa6+tOOarPGc15rvurVYIwBsGmtnXW0PCx3+gtdvuveo1TrKR2ZtIi7hlt41mJFc1vI8Hc7wtOy11+piuUOL0u8L+RiI1gG7mF9me9P5O7TW0wPAtvEmvFYXf+w/hnc1baPOEmaW3cZY3JZ2fLX+7qhmIlSuWYWyUxiqmUjpHGqNKqD6165Sdqp1j1Qzkerbk0ymml+NKV1GUG/upjPYiM/cD8D+sIs22xhO09ikY+WzDl6LdswTI7Vc1iJosY0yHh1JKzuXD9O5R/NsWkvojYEGfr5/AWf49gBQZx6etr1urNFuSLYHXewY16p91FuGym5vuuY/z74fgJcCtQxHxhiKjDEQtTPbHEXIQYYiNWnHV+vvjpJbKBQKhUKhSGDILcJxzUWYryfTGUl7r4zVAWXSJOuJe91hF52hGswC5tirS3LhMmnR7kBciyvuD2sR2LZplD8YlS12Bz10hbXo6kxqKLLCqemRje8KwFBUix7XlUGmUymUk6xQKBQKhSJBi+6MZeqEjRrJmwJaIlltiSXgzMTxWULEJQxF7ewKeoDqS95zmjT5yVjCSdac1rZpdFoNJ3lXKNVJnt4o6/raLq6b/zhu/aagWMzEWeLSnka8qicdAgxGNUmjcpIVk9L5/BZ+dcFncn7+j2t/zuM//nMFZ5SdJ352O/d9/qcHexoKhUKhqCBGdYunR7UmIkbynpG4Z1Rb8JXo7NRZQpiE5jzFMLFLT1CrtuQ9l1lzGsf1hL3+qINw3ES9NYRdRKfBomSevua7gx4GonaCcTNeSzgR1Z4OLm3ewnrvfk70llaBYoFzBIcpxt6Qm5FYUns8pDvJpX5vKonquFcCvzjnkwQGhjGZTFiddjqOP4xTPnUpNtfkiWbtRy7lsr9+qyibm+5+lPu/8gss9nTR+2V//RY1TXU5zjp0ueWCT3P+967Ef2CQJ39+B72bd+OodU/ohjjcdYB/fulmujfuwNPawCmfvpR5x65MfP7cbX9nw2/uJRIMs/jUtZx6zXux2FTXJ4VC8XpDJuQWT4+28FE2Mt8xgkXEabUFiEvYPK79W+MxhxFIJMXlDhnl3/qj2r+ZO0Oak1xtkWSXSXOEjU6AEkF32MVch5822xi79HmXC685hNcSwR+z6Gsj2B92Md8xyixbgG1BX1ntaUgWOocBmJ3SVbEYVjg1DXdqFBmSkeSZ5CSrSHKJnHf9J7ji0Zu4+LdfomfTTp76xV3TbrNt9SKuePSmtP9ejw7yUGcPMi6pm9eK1WFj5bknsv7j78x67H2f+wnNS+fxkQd/xPH//Tbu+cyNjA1qf4h3PfEyz/z6Hi752ed5/93fZXjfAZ742e2VvBSFQqGoCrzmMHZTnNGYlW3jXkJxE622cRY4hjEL6Im4CMYt+GMWzAI85uIjm426M94f0Z1kXW7RUWWaZLdZd5Jjybhid2T6dMntds1Z3R30gH4D0hXSJRf20u01W8fILMPWZhtLXGd7ide0XK+P/OpYul9iOMn1M8hJVpHkMlHTXE/H8YfRv70TgE13/ZsNv7mX0d5BnHUeTrj8PJaevx6AvRte5e9f+BkfuPcGAHo37+b+r/yCwb09zD/+MCjyrhzgB2ddwWHvOJVX732coc5elp7xBo7/6Nv555d+zr4XttK6agFnf/MKHLVuhrsO8Mtzr+K0z17GkzfdgZSStZeexVGXnJV17O2PPMdjP/oTgd4hWpd2cNLVl9IwfxYbfnMv+1/ezjnX/b/Esf/69q0Is4mTP3kxIf8Yj1z/e3Y+/iLCJFh5znrWfegCTGbtHm3jnf/m2VvvJdA/TOvKBZz+ucupbWtESsld37qZZ+7+F7FwlNq2Bs766kdoXNQOwM7HXmT+8YcD0LpqIa2rFrL7qU0T5j24u5vezbu54MZPYXHYWHza0Tz3+3+y9cENHP72U3nlb4+x8rwTaV40B380yLH/dS73ff5nrP9/Fxa9DwqFQjETabZpjmtv2EkME3tDHhY5hzm+VqtW0Kk7a0NROzXmKF5zKO2ReiE06M6S4STvDnqIS5jrGMVMnFiRcTyBZKVrgFfG6omX8O+pgVOPJBuJe5Bbl9xqDRDDxIGIs2h7cwwnOeRJvFcuXfJpvr18reMprus8ggeHD0+8v8gxnPi5vcBI8nzHMP0RByMxzQk2nORXMpxkJbeYZh6+ub/MI2Yf7+T/aih4pNHufnY+/iKLT1kLgLO+lvNu+ATe2c3se24Lt3/8u/iWzaFlWUfaebFIlLuu+j5r3nUGh7/zdLY//Dz3fe4nrH3vmwueg8HWhzZwwY8+jYzF+O3FX+DAlt288fPvp37BLO742Hd5/g//ZN0H35o4vnPDq1x++7cZ3tfLnz/8LRoXz02TIoDmaN77uZ9w7nc+TvvaZWz6/YPc+Ynv8d4/fYNlbz6OJ266neBoAIfHTTwaY8v9T/HWH3wSgH988ee46mt53x3XERkPccf/XI+npYHD3nYK2x5+lqdvuZvzvvcJ6ua08Myv/sa9n/sJF/3y82x5/Hl2bNjEZX/9FvYaFwO79mP3JOs67nz8Jda8+8wp16N/Ryfe2U3Y3Mk/Wk2L5zCwY5/++T4WnrQm+dmSuYz1DzM+5Mfpq5kwnkKhUBRKo2Wc2fZAIhmuWtGijNCrO3k7g5qTfILuJO8LaX8Th6M22u0BLXmvyPy9Bj2S3KfLLULSwv6wm9n2AO12P7uLlDG8o3EbV7a/yM3dy7m5e+XUJ0yBIbcYT3OSJ0aS7SLKr5Y+yHjcwgWvnFW0DGWOLTWSbNgrT4WLU31aIO/E2q40J9mQWkBhcot6S5Bblz7AYNTOh7aeTH/EyULnMDEJW8azR5KVk5wnB7uZSC7ybV4hENx91fcxmc04PC4WnXAkp3zgHVgtNg47+Q2J45YdewRL1h1B34s7WLhqGU6zDYGgxuJg94uvIGNx1r/nPIQQrHnTel783T+xmSxZ5+EwWdm/cTs/PvkjifdcPg9X/O0H2pwEvOFdZ9HS0gLAvDXLcdd7WbBqKQArTjuWnU9tpMbiIKo3zzj1I++kzuOlbpmXI847mR33P8PK44/CZrJgEWZqLA6ee/BZFq9fw8oTtJuAU9/3dp7+3X0MbtxNx9ErmXfUCvY89AJr3nYar/3nWdx1tSxcvRx//xC7/vMSn3r0FqwOG3jguEvP4bm/PMBx7zyLV27/N+vf/1bmLV6gjfvBd/DNX/2NaO8oLpuD8FiQ4N5+GlbV4dGPAYiMh+h9ZSfLjjkciyUZxXCarYm1NTCF4jg97rT3amprGe0doMbiIDYexuv1JpqJOL3ar4U1FJ+wB6qZyMFlJqxdpexU4x6pZiK59+QbHf9mpauXD247n86wt2zzK5SprmeeIw7AUKwWn9VNd6QB6GSlW4sO9kW1f6/H4i5gkNl2E/siE683n3WbZdeqRozHvYk12xf2MdseYJU7zHA8v+92pq1zG/YC8PbGHdw5cBSRlJrLxfze+KyGNMGdOG84pult5zqSDT5WOHvxWcL4CLO6JsqeUHHa4Q6H5nj3RZuy2AuW8DsvOapGCw6ucg/isyYDR8tdSWdfa/hhndDwIxuLnWNYhKTJGuRHix7j5u61WIRkZ9CHw+zFYU7uTwxtPRqtkQnXUI1/z2CGNRMpJsI7Ge0u7UvXOTaQ9n6+zSskknO+8/G0qGuIOKFokJ2Pv8iTP7+TwT3dEJdEgiE8C1rxR4OMx8JIJP5okL7uXtxNPgKx5J2Vu7WOcDyadR7BeIS2VQt55y/+N+ucpQSTz5U812bG6nMnXsesgvGxMfzRYMKmubEm8bmjxcf+rbvwR4OE41GiMoY/GmSw5wCu1rrkuBZwt9RzoLuHxuhClr55HS/85SGWnHc8z9/9CEvPWoc/GqR77z5i0RjfO/1DyXWTcWpa6rVxu3r5x7d/xf3fvTXlc0nv/m6OWXcsx73rzdzz9ZsZ7R5g4SlrOPHj78Je42T7E8/TetgiguY4pKzTeCySWFuDuN3EuH8s7b3A6CgmpxV/NIjZaWN4ZJixWEjbn2HtLjpiN03YA9VM5OBT7WtXKTvVukeqmcjE492mCCtcvZgENFh62Bg4uA9xJ7sej95IpDNoZSgS4NVAumxg+7j2fn9EczzNjOQcb6p185g07fHeoClx7Gvjbo7xQLP1AEOR/KPuxvlzbKMs0hPHfJYQR7m38PfBeYnjivm9saCd0xeJJ87bPq5df6Ml2VBkji9ZFWKerZOX/MUlf7dZtflvCtgSY2/V7TVZcq/3VHTYR/Dpdand5gi1pv3simhznGujT3moAAAgAElEQVTXnOdw3ITNFMdj6qUrNHWuUzxFP95m83N1+yMAbAz40uY5FAmwB+0GrNY8PuEaqvXv2YySW8wUouEIf/v0jZz5pQ+y8OQjMVss3HPVD5FyYr9yd6MPf+8gUkqE0G4IRrr78bY3V2y+oz391HfM0n7u7sfdOPEXw91UR9+2vYnXUkpGe/oTCYMLT17Dg9/8NX3bOtn52AuJBLqa1gbMNgsffuBGTFmeCnha6jnmfeew/Kzjss5t/aXnMv9tJzA2MMI9V/+IZ2+9l+M+8jZ2PZ7UI09Fw4J2hvcdIBwYT0guDmzdw9Iz1+mfz6bvtb2gS7EPbN2Dq8GrpBYKhaIsHObuw6zHe4zKEdVKQpOsyy12pehiATrDutxC1yF7S3h0Xp+obmFPvLd9XIuyr/N0c0vP8oLHPL1OkxMMRm3UWcJc0Lg9zUkuBleWxD1D/tCaIn8wqjoArHIPcPfA/IJt2USMFpufmIR94WRUNV2TLCkmd+momgNpr5c7D/CSfxY2EWOOXbP5rL+JdbU9zLYFJsglsmEk+73gb6DWHGGBU0uIz0zag0y5RXHXUGlUdYtpIB6JEotEcNV5MJnN7Hz8RXY8+VLWY9sOW4TJbOL5P9xPPBpj60Mb6Nm0s6Lzfermu4gEQ/Rt72TT3Y+y9IxjJhyz5PRj2PnYi+x5ehOxaJQnf/M3zDYrsw5fDIDFriXE3fe/P6F15QJqW7Wof02jj3nHruKRG35PyD+OjMcZ6uyh89nNABz2tlN45pa/0acnPIb8Y7z2wNMA7Hn5NXa/uIVYNIrVacdstyJM2ld2139eZv4JSSdZxuNEQ2Hi0ShSSqKhMLGI9stbN6+VpiVzefLndxANhdn2rw30be1k8WmadGTFW45n413/5sD2ToIjAZ7+xV2sOPuE6VhqhULxOiTVOWmqdic5Q5O8N1RDVCadmX164t6w3j3NV0LXvUY9qtmfkuT2yPAshqNWDqvp53B3X8FjnubTgjnf3ruG0ZiVw9wDLNG7vxVLsgRc0knuiziIxAUN1hB2oclGjIQ1gJWu4nKo5tpHMQtJV7gmTSbij9kYiVpxmmNFN+M4Uv8eGk1blrm01x2OESxCsjfkYYdeqzrf5D2jbnNX2M3Hd6ynK+QiLuE5f9OEY0PSwnjMjN0UT6xptaMiydOAze3k5Ksu4Z5rfkQsHGX++iNYctJRWY81Wy2cfd3HeOCrv+Q/P/kL848/jEWnZj/WYP/L27hx/QfT3nv7T6+mdeWCHGdMzuw1S7nl/E8jpeSoS85i3htWTzimvqONs77yIf513W/x9w7SuqyD867/H8zW5FdoxdknsPGORzjjC+9PO/dNX/4gj/3wj/zmwmsIB4J4Zzdx9HvfAsCiU9YSHgtx72d/wmh3H7YaF/OOWcmS048h6B/nrm/dTN/e/ZhtVjrWreaoS8+ib1snVqc94YgDdD63hT9/+JuJ1z88/gO0r1nGO266BoA3f/0j/PPam/nxqf9NbUsDb/nWFbjq9ML1xx3G2kvfzK0f+DKRUIhFp65l3YfeikKhUJSDI1Oc5KqPJFvTI8kRaWZfyM08h5+BiD1RK9iIJBffdU+mRJKTuR9jcSt/6VvI+1o3c2nzFl7cmb/kosM+wiLnCMNRK4+OzOKegT4uatrGBQ07+Gbn5P+uTkY2J1ki6I64mGMP0GoLMBB1MNfhJxQ3YUKywDGCyxRJrFe+LHFqcpfXxifq1veH3dRahphlCzAYzS93Kolkjf49vLV3KZ+fu4FlTu21Udli27g3kZiZr5OcLI9n5UDEyXteO51Wa+7a0UMxO07zGD5LiLFw9fciUE5yCWQ2rEjliAtP54gLT0+8rrE4EvrWOWuXJ8q/AbSumM8lv/tKXjZXnrOeleesz/n5x+67MU1He9ZXPpz2+erzT2b1+Senvbfq3BM57IJTJoyV6SguOmUti/TqHanXY+BpbcBit7Ho1KPT3rfXuDjtmss47Zrsc17xluNZ8ZbjJ7y/ZN3hXHXHDydoxnc+/iIdGVKLOWuX84kNv85uAPDOako4zNk46pI3cdJl5+etR1coFIp8cJkiLEuJMFZ3JDnZSKQ3nFJJKFjLPIefzlBSgjasPzovVm5RY47gMMUJxCxpVSMA/nhgMe9u3soJ3v0scAyzI5hfouPpdVoU+ZHh2USlib/2LeSipm2cWbeHG/evxl9kqTqj416q3AKgO+xmjj1Am22MFn3dto77tBJ07kFWugZ4xt9SkK3FepWJreMTk/66wm6WuoZos42xaaywHK0O+yj11hB9EQf3D87h6vZnmWcfwmWKJCpbbA966dRlHbPzLDXn1tfGKI/nj9nYNsk6D0TstNnGqLOE6ApXv6RRyS0UZUHG4zx3299Zesax2GuKrw+ZD7WzGll5bu4bBYVCoagWDtf1yIO6PKGanWSPOYLTHCMQs6TVBDbaRXemaGRH9OvxFim3aNClFgNZIqJDMTt393cAcEnzljxHlJyulzd7YHAOAHtCHp4ebcZpjvHmut1FzROSdZIzo8KpZeCWu4wuc3Vs1B3YVe70AE8+LE5EkrM5yZq9YmolG1Hk5/xNhKWZ18Z9mASscA2kRZI7C4wkG1H2QCy/qLBRK7lYyUilUU6yomQi4yF+dNKH2f3UxorIFJa+8Vga5s+adjsKhUJRKoZz8qDuuGmJcROTuKuBdKlFUof898G5bB33cu9AMgFuKJG4l5+TbEIyx5ashGC0pO6LZJcN/O7AEqJScEbdXlqtUzuFixzDdDhGGYjYeTZFD/uXvoUAXNz8Gg2WYm5QZFJSkBHxTm0oYuiRN43VszGgVc4qXJcsE07y1ixyCyN5b3YRXfeM7+Hzfk2+Yjjyq90DLNST7bYFvfSEXUSloMU2ntBaT4YRSc5cm1wMxWZWrWTlJL+O8c5q4hMbfp216kQhWJ12rnj0Jt77x2/gaS1vmT6FQqGYyRjOyWMjbQRiFhymWEmtnKeTRNJeOP1p4K5QLZdueSMbUqQDCblFnpHkcxt28qcV/+CChu1AMpLcn8NJ3h928+BgOxYheVfz1inHN6QWDw/PTuvU99hwG5sCdbTYxvn+wkdxmwqT1NlEHIuQhOMmojLdZUqNJK9IacWcHknO/4aoxTqO1xJhOGrP2rFvj55wN88+UtA1pOqRjYQ6w5E/obaLRmuQQMxCd9hFDFPWRim5cBcYSR6cYZHkqm4m4jBZsZkt01YlxGnW7oTzbR5SCkajCmUnfyq5P5DHNUkwmaKqmchBZCasXaXsVOMeqWYi6XviNEVY5hoiJgW7w3Ppj76M2zzMAifsDh2cfZvseuY50xuJTIpIRpJ9VheZ/1Bn2lmhN6u4pGUr/xpZzRy9aYk/7slp646BIzizfi/n1u/i173riOeI63mtLk7y9gDw5OiiCeN9ae8ZXNfxdxY5h7mq7W5u7H1n3r83XrPmVI/HrRPOGY1rzvBh7kGabeMEYlZG4q1IYCDioN4aZLk7zv5wfp0Dj/Rokec9oUZ81ol63f5YKwALHaNZ1zwX7bZh6q0hBiIOhuOt+KyCPeE5wNOJJjG7Q3V4dZs9YS9z7AGWuqIMTdHQxWvRbwJETc41Tf0uBOOao99ii6cdX41/z6DKm4n0hUepsdYQZuqQfzGMx7Q74Eolayk7hVHp/ZnKlhlBJBJUzUQOMtW+dpWyU617pJqJJI9f5unGLCQbA/XsD4XpCduZawenGGAoUlwSWTnIdT01RiORkDWPa5aE4ibsphjB2AjBLI/bU8ewCk3qMMvmZ6ljBy6hRUO7Quactp6P2OmLOGi0BrHST08kt4PfZNU0tM/7HYzGMv0JuGL7Cfxs0b+Yb9/P+xr+wkcG1hKWUz9FdQptrLH4xHm+NqY57S16xPXVMR+DES0a//JYPSd5u5hn6+TVQH51mtusmqO/ZdybdU2GIpLhqBWvJYxF9tMXzS//55RaTY/9nL+RIX1+Q5GkIw+wecyTsLkr6GStB3yWfoYi9ZOObRPa+QfC0Um/M8Zn+8OaY+8y+dOOr9a/Z1Utt9gS7MEUjmGj8q2pFYpUBOCKmdkZLLxup0KheH2S+YjbKKtWrcl7meXfJkckaiXnI7lIlZi8vXF7QpPcP0Ups3yS1ewiSo05SjhuYjTHY/8DEScf234iw1EXK117+fK8pzDrHeAmw63XAQ7EJt4E9EecaTWkUxtoGHKGVa78k/eW6FUmdgRzOaYiUeljgSN/yUXm99AYa/N48vX2YDLabSRottumTt5LVLdQcovKMxwP8szoLpY6WnCb7YmOdOXChKalsYWmP4nCEolhiys7hVDJ/YHJr0lKyc5QL92x0ayfKxQKRSZHZjgnB8LV7SS35NAk52IkZqeZIF5LaNIoLySdKYDjavezV6+ikEuTbLA/5OYw9wCz7AGeD0xsUAHg0xPyNIc7t5/QGa7huu7zuabtL5zs6+Kzc5/lq3vWIic5x5UjaQ8gjqA77KJdT6R7ZSzp3G7Sf17lzj95z0jay+0ka+X4jqzpY4FzmKfzLC9n1F5+KZCeM7R5vInjajUt97aURMFCaiUnqlvkWQ96KNF1r/gmNJWkqp1k0Bzlp8eKL90yGctqtQoJm0e6pjiydHxWd0UeIxxKdiq5P1C5tVMoFIc+TlOU5a5BolIknBMjGataG4oYLamzJY1lo5BIco3uJG8br2WRc4R5Ds0By1XdwqArUUEi99/met1JHohMnSvTGW7k+u5zuar1r7ylfjejMSs37DucXM51opFIjkjp/hQnOTWS/MpYPTEJi5zD2EWUkJzc3XKbIrTbA4TjJvaGvED274gR8S0kkmxUIMmM2m8eS950pNaiTkSS86ii4c5RQzoX6a2pq5+qllsoFAqFQjETmWsfxSIku4K1ifq6M0VuMVVU2GC4gDJwhpN8a++ytPez1UlOxXCSJ5Nb1BlOcp5d6LaH2vjMzuOIxEWiI18ujBrJmQ1PDIwycAMRO92R1AQ1C9vGfViETGtXnYtFiShybVp1jkx2FugkCyQe/SbGqG1tsGW8ke6wk5f8DYymNADp0iPJbbbAlJIUozxevpHkpNwiSLWWQkwlbydZCGEWQjwvhPib/nq+EOIpIcRWIcT/CaGlugoh7PrrbfrnHdMzdYVCoVAoqpN6y8Q6wAkn2VZ+J9kqYpTidLhMEdzmKOMxc05dbyaFRJINbe8TIy1sGdMaZUSlSDx+z0UhTvJU0o1Unva3cMO+IwA4wbs/53GTyS0gWQZOiyKnR6Nf1p8gHFEzdS6L0WkvWxORVHbosoj5jhHy2e8acwSzAH/MMsH5DkkLF20+kyu2n5jxvpnesBOLkImnC9kwE8dhihGXMB7PL3csGLcQjJtxmOI4TdNTlKGcFBJJ/jjwasrrbwHfk1IuBgaB9+vvvx8YlFIuAr6nH6dQKBQKxesGIzFpMMUJnC65xSLHEHeuuJfrFzxe9BjGnA5kNBKZDCOSXDvFo3OBTEvw+mvfAkBbm/gUtpJO8ljOY+os+SUBZrJZd0gne/RvOPe55ASPDM+mK+Tijv4FEz57PqA17jjSfWDKuSxJNBGZ3EkeitkZiNhxm6O0WnOviUFtjiiyQTBuyVrlw5BczJkkeS/9BiL/nLGZJLnIS0QihGgH3gJ8DbhSaBl0pwLv1g/5NXAt8BPgPP1ngD8DNwohhJQy5y2Pw2RN6E8BPBYno9HpfxzV4c6eBDAdVOqaDiU7ldwfOLTWrlQ7lV77fJkJa1cpO9W4R5Vat0raKsRO6p4srdGjk6aGxL9vAklUmvBZwqz2NhOZQqeaDy2WIT476168lhDHWnqmHDfX9SzV6xaPy9q0f48nw27pBrYw32WdcE6qHYcIYRYQiltYXNvOzlgLzwcG2RZsm9KWiTgxKWi0jrOqtploFrelw7kNAKulZcrxUveo3qJFgZut0ZznzXPt067BUpfjmFl8dt9yAJZllEMeFT7gKY6oGWBlbQuxSSp1ra7RHN6QaSGLPa2Tfue6o03UWzs5sd7KS+OTX+98u1ZWLkTNpHuUiT/eDPRxlNfCiCm7jQaLJvkIScek655pJyhrgDGO8HrwhrTaz5X+e/ZMnsflG0m+Afg0JMQpDcCQlDKqv+4EZus/zwb2AuifD+vHpyGE+KAQYoMQYkNoKL8e4QqFQqFQzARqzZrTMxJL6lQlgqGoFqHzmUtPEq4zj/KpttvxWjRbZiFps+ZfciyVGr0TXSCefzTWH3OmnZsLl0mLZo7FtQhiWFr5fs853DO8dkobcUwMRD2YBDRYs1cXqtXXcjhaWIOZUX1vPObcDqldaHMPyfwkKKmMxFzsC9dhN0WZb+/NeZyZGO1WrQrG3lDjlOPuC2su1Wzb1JUzakzatQVihTUA641qso5m63DOYxxCi7IH44XV/B7Rvze1k6x7tTDlbawQ4mygV0r5rBDiZOPtLIfKPD5LviHlTcBNAO4lbTK1gkGlqwyo6hbVawcOveoWM8lOpdY+X2bS2lXKTjXtUSX/LlTzHm0e6UL6NAfmtUAobY+6wlYarTAa2sXmHCXN8qHWHOLaxQ/TaB1lY6Ce4aiN473dmGPb2TwSzXlerutZYtEijp3BeN7fqQYC0AxCDk04J9XOAofmaA1FTUV9X3cH7TRZIRjeyebRiY6VtVVLjHtpZIzNY/mNr83DaIgSZZd/T9aGKOMeTQaxe2y8qLk/OVLH2xoHaRCv8LeR7A/UFziGsZpi7Au5eX64H5918qZVz1rNvNELNaKTzSNtk9qfa+oGoCs08W/FZN/t2aY4b6+HWtGV87qtLk1rPRiZ/O9Qpp1OHxzuAn94P5tH0p33avp7BvlFko8HzhVC7AL+gCazuAHwCSGMb1Q7YFxZJzAHQP/cCxR3a6tQKBQKxQzE0CRnliU7UKYKFxc2baPDMcq28Vqu3HECG8e06KJRJaFQvPp8R2L5RwWHdW1p7RSJe0ZlC3+eCYGZTJW8V59WJ7kQkomDuZpbTJW4NxXP6zWyj5wkec9I2tuaUqt4MnboFS4WOnJHeQ2MyiO5NMm5eCkl6TBXhQtjbbI1WpmMIX0uM6GhyJROspTyGillu5SyA7gIeEhKeTHwL+Dt+mHvBe7Uf75Lf43++UOT6ZEVCoVCoTjUqM+SuAfJRh2lOsmH600qbupeyUjMlmgGsSgPxykbUyV4ZSPfEnBG8luxTvL+SZ1kWXAJuFQSzS3M2R22qRL3psJoJHO4O7ezOd+uaXu3BQtzkjsco5imqHCR2NcC1/5AxMWuoAe3OcqKHF0D3QU2EjEYmkGJe6XUSf4MWhLfNjTN8S/0938BNOjvXwlcXdoUFQqFQqGYWRgVFzIdt0SFixLKwJmQCcdlk97+2HCwFjrzbzKRSq1FcwaHC4ok5+ck1xTYujiTZEORidUcaswRbKY4gZglq1xiKgamcNiMOsnFRpIHog52BT04zbGc9ZLr9Rbd+TZx8cds9IadOEyxSUvjAdTqezNcoCYZ4JnRZgCO9mTXUxfaSMTA+J04JCLJqUgpH5ZSnq3/vENKeYyUcpGU8h1SypD+flB/vUj/PHeVboVCoVAoDjkk9VbNAcisA1wOuUWHYwS3Ocr+sIv+qDZed9hFIGah0RrMGRWdDK9+TiGRZH/MRlyCxxyZtOmEp1S5RUhLsJuVpQNcQ46bkXwx9sfYr0wScosiI8mQjCavqcleCi7XU4fJMKLJ86doKuIt4gmBwdOjWtvrYzw9WT93FdhIxOCQklsoFAqFQqHIH485gkVI/LGJNWjL0XVvtUuTWmzUo8igVc4wWhYvdBYuuUhGHPN3puKIRKe2yaLJRsTRX6AzZTCZ3KJBj8L259GSOhtTyS0SbamLnDskneQjczjJiacOBTRD2ZnnXhezrwbP+ZuISsEq9wAuXXaSiiFFKVyT/PqQWygUCoVCocgg2UhkotNTjoYiK92a1MJI1jPYbuiSi3GSi4w4GpKLyZL3Sk3c6486CMW1+tKZzpoRhS08aU9jcJoT9wCe92tl3XLpko0o9kABkeTteUeSC39CYBCIW3llrA6LkFm7BibkFgXeQEy15tWEcpIVCoVCoSgjycjgRKenT3eSG61BRJFtpFfpeuTUSDKk6JKLSN5LJngV5kwNJSLJuR2eUp1kiUhEkzN1yfVFRGFTmar7WyKSXILcoj/qZFfQg8scY9kEXbJMXkMBjv7OtAoXub9HxWjNU3lmEsmFsTaFRpIHZ5AmufR2PyUghDgHOMc5qx6f1Z1432strCB4sXgs2kal2p4uKnVNh5KdSu4PHFprV6qdSq99vsyEtauUnWrco0qtWyVtFWLH2JMW3Rf0x91Z92coasdnCdHhNDMYzS9Zy8BtCrPAOUI4bqI3OhufNSnn6IloDs0y12jO70Wu6/HqzpQQdfis+bsGYzEXMMCRNWPsDmf/d7zOojlxkpqiv68HIrV0OEZZ7IrSH0uOMdseAyAQr81r7Mzfmwias9lsi2Y933AEbWYvPlPujnlTsWmsjQ7HKMd7h9kXmZsyfhi7KU4wbsFh9uIw5/edG9Cd3kXOEe5c8XdeCLTx9Gg7/xmdi0xpV+E1G/vqw2dNd8LzsfPq+FzgVd7g6ZuwPnVWfV/F5Ps60Y4kFDfjMMVosdkISWtV/j2Dg+wkSynvBu52L2n7QGZB60oUiR+NBitmS9kpnErvTyVtVbudg7H2+VLta1cpO9W6R6/n31djT+ZatUfgPWFL1nN7Iw58lhB2+hiK1E/4fDKW1GgRvS3jPg6E0zvdvejXoqJz7UOMRPzEs/b2mng9dhHFbooRipvoCQfJ3hMsO/cMzOINtZ1c1vIcjwz72JlSxsywYxOatKQ3HC96z/aE7BztgVrTAEORpMzEbdK68O0LmfIaO/P3pjNojDM24XyriGE1xYnExYS1LpQnR3y8pR6WOPYzFFmQeN9j0+bfH7Gn2Z/qWoaAX3Yv462NO2ixBTjTto0z67bxv7uO5YGhOYBWBaVGl1vsC0aIEZs4zhR2nhxxMxYz0+EYwiz7EomiAFa0NekLx6aeb8bng1EbrbZxTAwyFHFX7d8zJbdQKBQKhaKM1OnJZLmqFRxI1Eou3PFa6Z6YtGcwGrPRo5cGm2335z1mIrkraqMQBxng/qG53DMwD4cpxtc7nsRhmtjtr9TqFgBdIT15L6PCRSJxbxo0yeVI2jPYFdIi1q0ZyYfF6JENbupexZs3nsN7tpzGv4e1znvz7MnW3TXmCCYBI1ErsSLdvag0JRqiZJaCSzYTKXx9pkqYrBaUk6xQKBQKRRmpm6KkVynJe6v1pL1NGUl7BsU0FfEWqUc2uK7zSHYGPcx3jHLV7OcnfG4keI2W4iTnqHCRSNybBk1yOZL2DIwmMpl7XoweORWJ4LXxOp4YadXGT6m/nUjaK3JfDZ7xZ6+XbOxroIj1Sdyc5Ci9Vy0oJ1mhUCgUijJSn2hJnd3xSZSBy9IcY3IkK/Xyby8HcjjJiaYi+TvJRiS5WGcqGLfwuV1vIBg3c3bDbs7w7Un7PNFMpISIbNJJTl8zI5JcrJM5FrcQjptwmWPYRXoUvBxJewbDMRuhuIlaSyTRoARyty8vlN7EjVdyfdKfEBSPkbx3dE0PqUmC7sT6FL6vM6XChXKSFQqFQqEoI0Z1i1yR5D0hDwDrPOlOx1TMsfvxWiIciDjoydGdbXspkeQSnKkdQS8/7FoNwNkNu9I+K7W6BZBS3SKAsWYm5KSVRPJDJB79ZzpsRrm5ckSSQWStkV0/hTQnX3ojrgljF1uxJJPtwVqGo1aabUEaLUmJkKuESPLfBjr4yp61vKCXx6tWlJOsUCgUCkUZmUpu8ejwLAYidpa6hjimJnvL32ysSmtFnV07fDAiyQYv6NrVVEdNIFNKhRXvJI/ErPhjFtzmaMKp91lCmIWmby1Wcwu5JRfl6LaXyoEs0d7EU4ciI+EGhpyjJVVuUaZ9BUG3fpOSKudwl7Cvz/mbuWegg33hmhLnNr0oJ1mhUCgUijKSeISew0kOSTN/OLAYgEtbtuQ97iojaS+HHhlgd9BDJC6YYw+kPdafDCPiWOpj+QO6vKQhNdpoimISWi3dXNU28kNM0CUbet6hAsvoZZLr0b/h3I+XJZIMvWEt2puqSy61zrPBcMxGMG7CY44kIuDFNojJRm+Gjt4qYthMcaJSEJaHrit56F6ZQqFQKBQVxkKUWkuEqEy2bM7GX/sXEIhZONrTyzLnQF5j52oikkoMU6KSwjpPd17jlpq4ZzASsxHWdbeGvtejj11KFNnAqHDRrlfuSEoVSnOSc8otjOoNZahuAUlHMzXaWz/FDVX+CA5kSC5KaUmdSU/Cwdei4OlPB0q5+aluVDMRVDORarWjmokcPDvVWth9JqxdpexU4x6pZiIOfGatBNdw1IHXOtmjZDf3Di7lHY2beH/rdr7WOWfK8Q3naijWgs+a2zF8dGQhi53P8YV5zxDa7WPTWEvis2zX02TTWiVH8JT8fRqIumi1+ZnnlISkmza75gSOxe0lj70z1MLJdHGyr49nAsuZo/uV/nj+TUqy/d6MSV3vbJdp7zdYtThiTDrK8ns2GvcB0G6PJMZrsGqObJS6xHvFfrcHojXMsftZ4JQMx9006c0+IjL7vhZiZzSuyXjmOLSmK61W7TsTjFunXJt87FTj3zNQzUQqZkvZKRzVTOTg2anWwu5Q/WtXKTvVukev59/X0WiQepPWdrg/apvyvF93d3Be/ascX7sbj6mbvXpCXzYEEo9e0mtvMEqM3GPftH8+jZYBzm3YxbVzH+S/t53E1nFfzuuxCy062B0qfU17IzZabWBlgKFIDXGb1lhlJGoueex7+lu4vAXe4NnDWHQEp0nTXfeErQU3fEk9vjukddJziNG094XUfh6Mlue7tmtcs+M1jyTG81m0G5/dwTijsfybiWSjK2TjcDe4TIMMRbw4hAshIGYAACAASURBVDZGT1jmHC9fO7uD2txr9bk3mrV9HY1lb5hTqJ1q/Xum5BYKhUKhUJSJWrPmcA7mUW2hL+rk74NzMQn4VPvzXNq8mUubN3Nu/U7MxNOOdZmimHVt79RJaoJv7j2Kh4Zm4zFH+P6CR5ljG815dDLBq3RZQZ8uKWiwaOuQqGxRBslCZ7iGzWM+3OYo62q7E9rncsktfPo6GDjNZdYkG7pe/YmAXcRwm6OE46aSakhPGD9TbjENmuREjeQyJTVWK4f21SkUCoVCUUE8Zs2JGMyzWsFve5dydv0ujvH0ckxKs4aRmI2Hh2cnXhdagSKO4Iu7j8Fl+g9vqO3hstbNfGXP0VmPTSbulaqLTXGSrYaTrDmapZR/S+WBoTkscw1xum9vIhGwVCc5mbiX3gHRXcY6yTCxiUx6+bfSdb2ZJebKVQIOkl0ik05y+RqtVDMqkqxQKBQKRZnw6pHkfBOx9oQ8XLNrHb/pWcpvepayKVAHQEtG04xE97QCooIRaeaWnuUAdNhHch5XrhJwkKxwUa/LCGpMpddITuXBoXYATqjdT7tNS+ArPZKsXfd0J+4NRu1E4gKfJYxdxKYsFVgovRnJdYl9LWskeaxsZf1mAspJVigUCoWiTNQmIsn5Oz6PDM/mx/tX8+P9q3l8pA0AnzndYSvWke1KNOHI1d1PlqWZiEFSbpH+WL5cTvL+sJuNgXqc5hgr3Zr+e6DkSLKeNJYhtyhnxz3QWkgfSIn2ltqSOpPM6hnGvg7HSnfCQ9LCcNSG1SSps4RSWlIrJ1mhUCgUioJoswX4/oJ/s9rVd7CnUlE8iUhycY6PIXnwZjhstbpTUqi+tC/iIBQ3UW8NZa2b7DBp9W6DcTMhaS5qzpn2AOp1TbJRAq5cTjLAA3o02aBccovMZiLOMtdJhlRd8lhK+/LyRJJT5RwmJLWWCHFZvrXvSXHw3UY3wkNck6ycZIVCoVCUnQsatnNsbS9va9xxsKdSUYqJJKdi1LT1ZjhsxdYylgi69cfwbbaJlQPK2XACko6aoUk2tKvlfCz/YIqTrNWjLs3JDMQtROICtzmKXcQS7xttl8upuzXaRzdbxxOa5HJFkgejdsJxE15LmEZdOzwas5bYxCVJb4ouOSlFUU6yQqFQKBQFsabmAABz7LmrKhyKGNUtio0OGvpYwylOjFtCBYrMTnWpeMvYcAKgP5qhSS5jdQuDAxEXL/i1roMDETuyZCdQZI0ml1tuAenJdeVrJKKRKudYpLclHymD1MIg4eDbxhKR5ENdk6yaiaCaiVSrHdVM5ODZqdbC7jNh7Splpxr3yLgepynCMtcQAHPsY9Myx2rcI4/FgVePJMdEXVHXHRdaPeMGazTt/GRjiPwbZxj0R71ADwudUfZG069nlp7QNxZ3lmmfXITiZtzmCG12Gz6LVspOUvi8J+Px0YUcUdPPcMxV8B7BxN+bkZiLZoLMcZgIoX3mMWtRZbOptmxzH41pTTnmOiJ49GhsSHrL5gMNRN3MtgdY7da+h4FY7kYohdoZjWmdHOfao/h071EKt2omMl2oZiLKzmSoZiIHz061FnaH6l+7Stmp1j0aigRY5unGLDSnrtYSIh4fLEvlhGy2KkH+jSrGE5rkPeNxQrLw+e1Fc8zcpvE0u3aT0Rii8OveFdSjpOYBhiNjaeebpeYkD0RKb/ZhcCDioN0ewEI/DhHU5x0r63799UAbh7ta+ddw+4Rrmoxcvzf9EQuLnGBlkCFdV23Xo6U9oQhDsfLMfU8w2VDEKDHXGZw4n2LXqitsZ7Ub2m1aOcGB6OTNPgqxsyekuYy15pFEHe++cPyQbiZyaItJFAqFQlFxDKmFwRz7KJvGGg7SbCqHyxTGIuIEYpaik+BG0hpbSIz6uaXUvO0K6XIL+8QKF+VsOGHQpzvJjZZgSnWL8t4kBeJWrtp5AgC+Mjzxz9ZQJCG3KKPutidFk2w3aTdE5dIkQ7KecUJuUcZ9TdUkh6Wm1j3Um4koTbJCoVAoyorhJA/q/0DPsfsP5nQqhqfAGsnZCEkz4zEzNlMcpymZRFZKgl2XnriXTZNczoYTBkYZuEbrOJ4yl4CbLgZjRkMRTSdsEXFspjhRKRIOYTlIdTTLrUmGpOZ5rp4LUM59Ta3M4U4kNVb3vpaKcpIVCoVCUTZcpgjLXINEpeD+wbkAtFehk2wmzpvqdvPuptfQIral4020pC4tMmgk0aUmkZXS8CM9cS/9Wo0qGuWMJBsNRZqswbLXSZ4ujDbixpq7TKltl8tTHQK0qHFUCuqtIbyWMDFZnk6HBkZynVmfcjn3NbUyhzvRTOTQjiQf2lenUCgUioqy2t2PRUg2BurZMq4loc2xVZOTLDnR28VH2jYy36FF254cbWFH0FvyyEb5t1Ijg8NRO622cWrNYfbrSWSlRJJHYjYCMQtucxSPOcRQJHXOkcQx5aI/mhpxjBKX1d++eCiaHkl2TUONZNDahfdFHLTqDT+Go/aylWiDZLTXoJz7Oh63MBK1UmuJJMoJqmYiCoVCoVDkiSG1eM7fRGeoBqieSLJdRPnJokf49vwnEg4ylG9+HpMeSS7VSZ4QSZYJJ3m0KKdHJKLJrdb0azUiyeWVW2iR5Hn6I/+xuKUMZdqml0y5hVEHeGwaIuCpjmw59cgAPeF0J7mckWRIzt2lV/441CPJyklWKBQKRdlIdZL36k6ypkkuj6ShFJa5Bjmypg9/zMJ3Oo/gjr75QHatbjGU2kjEwIhqGrWSnaYYVpMsqStewknOiOobznd55RaaI9Wh34hUu9QCssktyp+0Z9AbTpZEK6ceGbQW21GZvCEpd1WZAxmRaqVJVigUCoVCZ31tF3euuIeVrv4JnzlMEZbreuSXAg0MRO0EYhZqLZEJzTEOBkYS2Qv+Rv7ct4g9IQ8As2zpVR/MxPnZon/xxblPFzS+26yVsSrVMTEcVsNhK0dXvK6Q5pi1WDOd5PLLLYxIcpu+ruWubDEdDGU0E5mObnsGqY7mQIn69UziKQ1FoPxOsqFLBgjGTUTLmNRYjahmIqhmItVqRzUTOXh2qrWw+0xYu0rZOVh7dKKvnxbbOG+q72VfZG7aZ0d7ND3y5rFGbGYfNjPsD9eyyDnACneUV8fryzaPYtauxab9gx6WLnxWN8MxbT5zHcG0dZxjG+Lwmn5WyQFu7cs/0ldj1qLlIo8GC5MRkloEvtmm7e9sh+a4BeK5G0NMxVDiWsfTxkiUPBM+fGX63kdIjy4G4/Zp/Z6Wo5lIXGhzrreE8FndNFk19ygiyz/30XhS/z4Wn9hkpdS/PwPR/8/em0dJct11vp+bmZFrZa1d1V3Vi7ql7lZLLdmSbAsbM96wwRiMGZZ5wODHbtYzMMzD2Jx585gZOMM6HgY4MBo8M5gBbAbGgwVmsUFmsy3bkmWpW2qpW+pWb9W1Z+WeGRlx3x8RkZlVlZmVS1RWVPXvc06frsqMvL+IeyujvvXL7/39UvU/UDRjvjUTAci6DUUAyna0q7mRZiJ9Is1EJE4npJnI7sUJamF3CP7cDSvObq2R0k7cY9GlLbHvir0MwOdzk/XnrpSTnEysMhZeJmP6+wuw12tXOFnUVVORMQtcLDnWhelIdsNY9yccy0hYaWJqiYzZncVBUXbHH6xxxkLV+bg8rvJkzAIq5tS8Xav13/DjxZLz634ynGkaQ5MOOwL8erlGtY/mJ63IoClZERKurzdTC+34z+mgzUQyaExbkQzXOBC+gdbOz8p6Tfl+7i+XGz9P89XWczNIzPlKjLOuLr1esch1aITSa5yr5YZszFnd/zzu1WYi+ztPLgiCIPiK1wDhVGKdzT7j+1O3AMeP7LHRl9w/yZDJq0cWmDG2NsTols01e9uVRvNqzALMGY2vtyOmnPFLdn++YY9MG7vFIKXC5lt4kpOhGkZIU7LCVPv0OrdGsdLU/rqwBzzJoPirjPPJyK/e9fecdtuq78jGverObdyDxuY6S/vvB2/edLgTcxM09ve2REEQBMFXPJE8aVQ4ECmz7Jb7MpTFqbjjU36mqbteryL5ZDzDw+nF+vepsMlDI0v10nKXSmN8x/Nv6+vcR+oVIpxf7nkrWi9pNRGpsOYKlmPxxrnOxXLAeFfjx5STOS0PuOPfq24x5lohBqmR7OH9QTBj5FFoNKohvnfAM7xaS3Ak5rS8zu+RzV2/cO0hZowSr0kv8q3Tl4Ad2rjX5OtdNf3duOeM77wnc1bU96oizSK5EPCyfn6w/69QEARB8I2YanSBO5XIsJxLuF+vEw3ZvFQa3bBRq1eR/HPHP8sd8a3HWtr5dzKxzkSkjKZ368aI+/F/s2ibr6YYjWSYixYaIrkpkzzbQyY56jagKA+YlfUyxmNbNu71LzZLdoS1WpSJSJUDRpklM+GL+G7Haq0hpnJ7JONY1WHee/nL+bW7/o77UquA/3WSwdnYaGsIqR3KJLvVM/xsSe2x0FSZ43bIJIvdQhAEQeiaWKhZJK/Xv77PrXZxrrhxc971qieSc2xfBk5zyN1w9OGlk/z+4ik+tHA37738Or763NfzlGvjOJtc7evcvUxy80fQXoZ1tqnCxQaRHO3FbuFPAwqvuoVXEWTMp9bRNyvN9pLGuH7X0gVYMfea3cKhZEf4iZe+gkslZ4Paks/VJwAsQtyoprD0RtHpF1fdP0wXTP/HLtpGvTbyfq+RDF1kkpVSceDvgJh7/B9prf8/pdT/AN4IeHfJ79JaP6WUUsCvAu8Aiu7jT+7EyQuCIAjDJRay61+fTmTqX3uZt/OFjSJ5rRYjb0UYCdcYD1fJWO0/Xk6ELGIhm7Id5j/deGDL888UJ3lVeon7kqucK/V+7p4nObdBJDtCwhOOqZDJlNFoBz0XzXY9fszLJFsDepKtTZnkyOAl4MDJmp9NrTEbLfClwoEdzSQ3e5L3Qp3kZrJWlB++9CZem77F364f3pEY77/8Og4Y5Y7vh365VB7nfZdfx8XS4F0kW7FoJjgRzu37bnvQnd2iArxFa51XShnAPyil/tx97ie11n+06fivAU65/74M+E33f0EQBGGPs9lu4dHIJE9teoXiWiXNPck1jsZyZIrtRYG3US3TRgyeLzhj35daga1lmrcl5YrYQotMsieSj7pZ5JuVJHOxIoeiDQ/vdjQ27g2WYSvbYcp2iHjIJqZqDbvFoJnkTdfqR/3ldqw2eVf3mkgGZ669jXw7waXyOJfKOzY8n9ohcQ/OxsMT8RxFySSD1loDnkHMcP91+szsXcCH3Nd9Vik1rpSa1VrPt3tBPGRwZnSu/n06kiBX6yNN0CPHU9PbH+QTw7qm/RRnmOsD+2vuBo0z7Lnvlr0wd8OKs1trNNL0W+NoLM8rxw4QD5nMxYqU7CjR6N2ciW508q3b08AaD48bmJE52nE8ugBAWY9s+J3gUQmPA5/mvlSG0+kZsrXKlmM6MWU4v7qmE0c4E5kAIByuAk9xMlnjzOgcrxtxMsc3zFlS4ZuMRUq8dmKUNSu97fhx14pybOQIB+zBMoRFO0E8VOBV4xMcijkCfSw223JeusUOrQDPc8+I5kxpjpOpGwBEwlNbxl38ksXK83aLUbrjUTXFJ9RdgFNPV+Nn9YyNZFBourMWPO/+kad1MO9x0Nv17Eac96qvJ6JqVLWB1tsL5U5x5h4OM3Y8NPT72ee7PK6rPwOUUmHgCeAk8Bta68eVUj8E/JxS6t8Afw28T2tdAQ4D15peft19bH7TmO8B3gOQOjjR5ekKgiAIu4nnu81aCUbDJY5EVxgLO37eq9VZdIutLgumUx3ioJHZ8lwzabetc95q7QPNWkkWzVFmjCyzxjLZ2vbCtZmkWxO42CRgl2qO93TayG44x1vmBOORAmOREjPGehciWRN1M8kVHzZ75a0Ek5ECI+ESqZCTcizYg/lj69cayXLQWOMVycstx9Vas/K8jdXb3yAbsAhRIbH9gb7Qfctzi/6F//AYVgv3/uIU6vnSwePYAV+Ort7JWmsLeEApNQ58VCl1H/B+4BYQBR4Bfgr4d9DyM6ktM6S1fsR9HanTs/pC9mb9uXEjNdSC0s2xd4phXdN+iwPDWR/Yf3PnR5xhzX237KW5G1acYa9RWDkf0T+VH+cNYyVi9iXGI45IfjI32vJ8vhjWvGsCksx3PN/jYSeXcqPS/rq+mB/jqyeypPRzXMge6eHMNXHlqL6nMitUtSOGLysLjsJkOMsL2RskJ5xzeCoHYR3lVBys2stcyHbe524oi5CyqekQ57MLPZxXaxamFcdikCnfIKac+f3S+jqLptn3mIVyGWbhRGyenzvyP4koTdkO8YkVgwvFxnzrUgirMgWGTfgr+9skOROL8run/xiA91x8Iy+XR7d5Rf+MGUnWze7qZ59KHwLgYu7Wjp3PoPRyPbsVJ4yN1WXth05xbkU0C022/6D9zunpz12tdUYp9Sng7VrrX3Yfriil/jvw/7jfXweONr3sCBCsqxYEQRD6wvMkP1OY4g1j85xKZLgj7vh4nyu2/sj0urvb/sg2ZeC8usCdmmacK0zx1RPXOJNYwvn10u1520RDNhU7tKFxRkWHWTbjHDDKHDBK9coWL5fTHHIbl2x33uBsOgSo+LSZyZuD0UjVtw1282YKSytiIRtLw5+snOC3b93D0qYqCDrrSAM1VkPF+ss2mkYEI1YjFa5RikT6HqcbQgaoUHfjR+JOHk9Vh5Wt7Z1erme34tgoVJeZ6GFdz07QTXWLacB0BXICeCvwC57P2K1m8Q3AOfclHwN+VCn1YZwNe+ud/MiCIAjC3iCExghpbA3PuqXeziQzHHebRlwoTQPWltdtrJWsaf2BI4yHO2/cAzhX8OIu93Turcq/edysJjlglDkcLdTrOV+tjNTF8ZHo9tn+WMixoVS78Gh2g9fgY8YoEg9ZVO0Q5QE7+dV0iI8s3894OMOHFs5wpdImu7vuXsNobaB4v33rXo7G8jtSRk0QhkE37+ZZ4HdcX3II+EOt9Z8qpf7GFdAKeAr4Qff4j+OUf7uEUwLuu/0/bUEQBGHYxOrZ0jAXS47P+J7kGuAI4awVB7YKynUrRqYWZTxSZdptZNGKbjLJF8vjVOwQx2LrpMNVcl1mV0c2taRuZr6S4hWpVe5PrZAK11ivRclasbq4341McsadA0+0O1nkwbunfWjxwW0tPnrdEeNqbDCR/AdLpwd6vSDsNt1Ut3gaeLDF429pc7wGfmTwUxMEQRCChGe1qOgwWSvKfDVZb8JxblN95M1cLo/y4MgyJ2LZDiLZzSR3EL41HeL54gSvGFnhbHKVz+YOdXXunUSyVxrttaOOT/VlVxzfcP8/HCvQKQMOkHAzyRW/MsluNv2YJ5J3oExbO/R6w24hCLcz0nFPEARB6IrmTDKwoVnB5k57m7lSdqpDHI+3b84x7loiMh0yyc2x7kt1Xyy50Uhkq9j0RPIr3PGuVZxzzVhRCpZBOmzWawq3I14XyT55kq3NmeTh1BrWNaAQBqUhvdU6Iwi3EyKSBUEQhK7YLJJfcC0X4Gyo68Rlt7rB8Xj7Ns9eJnm7NsnPuLF6aU/dTSY5opzNRVfLI+4zivmqI5iPbmO58GokV30o/waNOZiJOmXxdqIrXkuyEUDBaA0lCkG4zZG3gCAIgtAVdZHsVoe45Irksh3m0jYtcK+42dkTnTLJkV4zyatd77DvRiR7XK00aiJ7IvnwNpv36p5knzLJmzcvDstuUbdajEoWWRB2taegUuqdwDsTc5OMG42b1Jix851mANIRZ8dtc+ydYljXtJ/iDHN9YH/N3aBxhj333bIX5m5YcXZjjQ4YjlCs6SjjRorLlWOs157kC/nDpI10x+tZrTne4RPxXJtz1vW21Co0wbjRvpKDSYoVM8WUUeCX7/wcX8gf4Yv5WW6Z7Rt+TEe91ya3xK/qBJZWhN1M8po1Uz9mxZoEXuZkssrnCu3neso9X5u4L2ui1cYuCxWd8mXc7X7m8jmDMpCcDJEYIN6w3kO9xgrqva2ZvXD/8TtOUNdlV0Wy1vpR4NHU6dnv37zbdhiF/HO18tBiSZzeGfb6DDNW0OPsxtx3S9DnblhxdmONTNdyULCcuBkT3nHua9EobLeqRbvzyZiaghVxhLC9SsbamC1OhUwiyjlmqVre9lweWz/ONx84z+tHr/H6UafJ63+68Qo+3KaiQtg9vxWz9Tkumon6JsTnCmEq2jnmSsn55T0VWes417Z7fN4K+bImVWtjJnfJVL6tdadxamvOpwOlkSKVARqXbBfHb7qNFeR7WzNBv//4HSeo6yJ2C0EQBKErNnuSwWk/bHdVmkx13Lw33qUf2eO3F17FNz37dn7h2oN8KjMHwPcdepZUqLWwa2zca22HuFlxMljz1WTdTgJws+p4qY9Eu/Qk+1TdomhHMO3GvA7DbqE1kPPsFlLZQhBEJAuCIAhdEXMtAM0ishe8zXutfMnj9fJvnf3IDRQ3qiN8dOUu3nfly3kiN81IuMY3Hnix5dENT3Jrsen5kr3ayI3HHWG/Xa3kenULn+okg9owF0PZuJcPg6UgYaGie7NDmiD4iYhkQRAEoStaZZJ7wevw1qrCRaORSH9i8EOLdwPwrdMXiaqtm85GQp5Ibp3pve5mkl8ub/Q1r9SSVOwQU0alXgu5FY2Ne/65GJvnot956QWpjywIGxGRLAiCIHTFoCL5crl9hYtGS+puM8kbeTx3kOeL40wZFb5m8uUtz6e3yST/6epxPrJ0ko8sndrwuEbVs8yHO1guEr5nkjd2HhxGJtkTyYhIFgRARLIgCILQJZtLwPXKFa9WcqyV3cIr/9avGFT8rptN/o6ZFwhtKg03so0neaWW4AM3HuB6dWTLc152+bWjC22jx3z2JAOsNwnjodgtsuJHFoRmRCQLgiAIXVFvS91nJnm+mqJsh5iJlrdssKs3Eunak7yVxzKHuVFJcTSW501jNzY816lO8nZ8dOVOAN49c4GRNp33diKT3JxVF7uFIAyfXS0BJwiCIOwdBrVb2CiuVtKcTqxzPJ7lfLHRpa/bRiKdsAjxe4unee/RL/Lug8/zN+tH6s954rYfkfx47iBP5KZ5VXqJ75h5nt+av3/LMZubiWgb7CfS6Hz/v2YfMb6cP4q8Cg1kS2PQVRWRzqypEJZuI7grIYjYkLRbPy8ItxnSTARpJhLUONJMZPfiBLWw+16Yu2HF2Y01Gos4Hz4qlWgZt5vruVGZ5HRinXtTFW6YjTEOGE720tSjXV1Tu1j/kLuXf2E/wz3JNY7EIuTtGGFskmELSyui4TGi4e7FphfnQ0uv4VXpj/Ot05f4q8wrWK1tjD8SduwdYZVi3IhjroZYvxnvOk4rlkmzTPsGKf3gSPn2HyJHD9qMRne+aYmfSDORvR8nqOsizUSGFEvi9I40E9m9OEEt7A7Bn7thxdmVNdJuzJrVoWlI5/N5oZTgzeNw0FgmY87VH0+GSgDcrOiur6ndcdcrSU4msqRCS1yvTDDqbgrMWxEyZrGrsTfHedxM8qnMHG8av8k3Tz3BL15/aMMxEeVlwm0yZgE7GwPiqOkqobOdy8e14/XpeX5g7jw3Kyned+V1fY2xmXQkQa5Wavu8lbbIDNZDpE4Q791Bvrc1E/T7j99xgrouYrcQBEEQumJQuwW037zXqJM8uPd2vpriZCLLXLTA86WJbStbdMtvzd/HPxm7yddPXeYPFk9xrdrI8tbrJGsDqELRzdaO1lBjW0vSdYNOW9x9aIVqQaPW+htjMxFDo0x/xhKE/Y5s3BMEQRC6wg+R3GgosrFW8ljYq5PcvyfZwyvZNht1slKDbNpr5kpllD9bPU5Eab595oUNz9U9ybaTe9JFZ45Uon9/7zPFKc4VJvn46h19jyEIQv+ISBYEQdjHzBhFTifWfBlr0BJwANerI9S0YjZaIKac7KtCN6pb+FDFwRPJc1HHWuGXSAb45NpRYGsHPq+6RdXduFfPJCf7z9rmrSjfd/EtfHTlrr7HEAShf0QkC4Ig7GN+/sRn+OCpv6lnVQdh0BJwADUd4nplhJCCO9xscjpsElaQrRlYPvxauul2z5uLFerjQ/sayb3g1S4eDW807sbaZZIHEMmCIOwuIpIFQRD2KYayuDuxhhHSPDzSvhFGt/hhtwB4ybVcnIyvA/7USG5mvurspp+r2y36L/+2Ga+px2ikuV6ybtRJ1gZaAyV3jqScmiDsWUQkC4Ig7FOOxvJ41c4eGlkaeDw/7BYA5wuTANyfWgGaW1L70zDDs1scihYA3bBb+NDoI1vzMskNkRxTNiEFph1GE4KqAkuBYaMM3W4oQRACjohkQRCEfcqJeKOCxIMjy8Bggs0PuwXAlwoHAHilJ5Ij3qY9f0Ry0TZYr0WJh2ymImVfPclFO4KlIRWuEcbJEjcqW7gFo4peFlmsFoKwl5FmIkgzkaDGkWYiuxcnqIXd98LcDStON2t0T7Jc/3omWuKelM18dbTvmImwIwqj4ZG+m4kALNTiVO0QdyayHIlHmHVdFiU71fXP3HaxFsw0Y5EVTidtDrjauKZbn3evcfJWjLFIhSPxCOtWgmk3W2zqKOlInGQ1SQ6IphSj8h4KVKyg3tua2W9rJM1E+kSaiUicTkgzkd2LE9TC7hD8uRskTlRZ/PKd/8izhUl+69Z9HV/XzRodMpxMbdkOEQ/ZnIpd5bnCib7PNepWo1iuVvtuJuJxvjjJgyPL3BG9SlQ5G/gWzVBP897p2GuVOKcTMBpewVBOlYsls/tGJZ3irNcMxiIVtF4nY9qMh5zzL9sRcrUyhawJxDDj7edpN9lv76FeYgX53tbMflujvdpMROwWgiAIAeGe5CoPpxf51pmL9Y/yB+G4a7f464xTtmxQX7LnSS4PaLcA+FJhCnAsFw1Psj8b9wDmvQoX0YKvdgtoVMnwfMmb7RaNyhayaU8Q9jIikgVBEALCXa6o9YEETAAAIABJREFUjYcsTibWBxorjM0xt5bvR5fvBODBkSUG8SX75UkG+FLe8yUvMxbxr5GIR3NDkbQrZv0oAQdbK1w0Gom445cGr5EsCMLuIyJZEAQhIDRvtLsvuTrQWHOxAtGQzXw1yfniJJlalIPREof7rJccQhMN2dgaqnrwXx3nik4m+Z7kGjOGY4fwoyW1R6OhSIGUm+kt+CWSN1W4iNcbiUiNZEHYT4hIFgRBCAh3NWWPz7qVH/rFE9xXymk0ii/mp4H+LRdRN1ta1WFADXRuADkryqXSKNGQzStHloGdySTP7WQmuS6SvdJ4BlrrpuoWYrcQhL2MiGRBEISA0JxJvj81WCb5eMzZTHbFbdzxRdfe8GCfItlPq4XH024puHjIEZN+1UkGuOU2FJmJlup2jrxPmeqtdgvXk2wbWBWkRrIg7BNEJAuCIASAiUiZiUiVghWhbIc5Gssz5m5o6wdPcF92RfKTGzLJvYs3v7rtNePVS/bwc+NeVYdZrMaJKE0q7Nkt/CnolN3Umjpez7JHMAvu3CYkiywIex0RyYIgCAHgTlfUvlge5bniBABnB8gmH4+7meRK2h13jPValEPRUr1dcy/41W2vmafdChcAtnYsGH4yX23UXC1YESyffuV5nuT05uoWtkHV2SspfmRB2AdIMxGkmUhQ40gzkd2LE9TC7nth7vqNczbl1Am9UZ0iZ8V4cGSZV6dzPFs62fK1ndZIoTnhiuRV6xDjhpOhPVc8yOtHr/EVY1n+KnOwp3M9YDhZ7Zo22v5c9DpvJZ1kyUwybRTJWTFGjZGuX9tNrOXaGOB4u4t2tK+f51ZxbJw/PKYMi3EjxYThiG+t4oSKEcAiNhJiJGDvH9h/76FeYwX13tbMflsjaSbSJ9JMROJ0QpqJ7F6coBZ2h+DPXb9xZg3HK/xcIcmCmeSfHYCT8Vttz6PTGh0yCsRDNZbNONfLNcDJdD6eneT1o9e4O3GDP1ya6+k8TcNJkRatznPT67w9lZ/kbRNF1mrRnl+73fFXyg37RrYW6XtNN79uvupYKRKhEhmzgNIlAHI1RS7nWDCq8QoZs9RXvJ1mv72HeokV5HtbM/ttjaSZiCAIgtA3dzZ5iM8XJgE4m1wl1Id/uLmyRTOdfcm6xWMNdsJuAQ1f8rqPm/Y8bjbZLfxqJALNdgtHEMfqnmQDM+/OodgtBGHPIyJZEARh19HcmfA8yWMs1xLMV5OkwrV617xe8KwW3qY9D8eXbDAbLTIbLW547vsPPcsn7v8Ys238yo1ue/5+APm364e5Vhnhb9cP+zouwHy18TGvX+XfYGsJuERTx72qK5LFkywIex8RyYIgCLvMtFEmHTZZr0VZdSs8eNnkfpqKeMLa27TnoVE8VdhaLzmMzTcfeJF02OTVI4stx9yJEnAAS2aCb3nu7fz+0mlfxwW4WWneuLcDIjlSBXS9ukXZMqh/Wiw1kgVhzyMiWRAEYZc5EXeaiLxYHsVr1PGM25Huvj6aihzfVP6tmSdbNBV55UijNfQdbn3lzexECbidZtFMUtPOfPpZOaOmQxStMBGlSYZq9UxythTDrgERqZEsCPuBbUWyUiqulPqcUupLSqnzSql/6z5+Qin1uFLqolLqI0qpqPt4zP3+kvv88Z29BEEQhL3NXS1E7bkmX3Jv6C2NRJppJZLfMHaz/vXReL7lqDvlSd5JbFS9qYifnmTYaLnwMsnrWVeISxZZEPYF3WSSK8BbtNavBB4A3q6Uei3wC8AHtNangDXge93jvxdY01qfBD7gHicIgiC0wdto91JprP7YC6VxqnaIE/FsvR5vN0xFyoxGTNZrRt260cyLpTGydV9yAdC8YbQhko+1yyTvkN1ip/FqJedtf0Vyrsly4dVJzuSc+RY/siDsD7bdgaG11oCXWjDcfxp4C/Dt7uO/A/wM8JvAu9yvAf4I+HWllHLHaUk8ZHBmtFGOKB1JkKvtfOmc46npHY/hMaxr2k9xhrk+sL/mbtA4w577btkLc9dPnLMpJ1YtfGLDvfBy5RB3J27yX049zq8tvIM1q+ExbrdGp+M3AFiqTXFmtPVmuEuVIzwUucw7DtS4Wo0yFyuSs+Kkw2WOxgrcM3oIvSmHcjThCOlyYYb0+RnHVrCZUJiUPaQ6p13G+q0Lb+BPomtcLM2Sqm3NrPcb5z+/8AbGIkXyxaN88OkYyXCFKwvO+kyOJ5gd7b7m8zDZb++hXmMF9d7WzH5bo27iDHtdPt/lcV1tU1ZKhYEngJPAbwAvAhmttXebvA54d+PDwDUArXVNKbUOTAHLm8Z8D/AegNTBiS5PVxAEYb+hmYs6loob1akNz/zO8pv5l4ce5c74Aj9z+MP8+sLXcrHSub7xSMipN5q1Em2PuVA+wkOpy5xJXOdAxMliP1G4i/uTLzMVyXMgkmWpNr7hNYabLf3MU7OsXWqX82ilnHeK7mJ9iWm+hPcLuB+fcOs4n+FI/eur3LHhudi46iOOIAhBoyuRrLW2gAeUUuPAR4F7Wh3m/t/q7rDlzqS1fgR4BCB1elZfyDY+7hs3UkMtKN0ce6cY1jXttzgwnPWB/Td3fsQZ1tx3y16au27jxFkkETJZMWN8IbOG415zuAA8kXkjP3v8s7wmvcRPzf0xP/nSl/OZ3GzjmE1rdCpyC4CbZbvt+tlmlG+fgruiLzMTdmwDf7I0Tmpmkal0npr5IheaYgDkUxkAFtedDLO6s4ga3yggk5EYxVql94nog2HFahfnXVOXeWhkiY+tHuctY9cZCdf40PKbqIRj3EjfYrH3yn1DYb+9h/qNFbR7WzP7bY16iRO0demp4KXWOqOU+hTwWmBcKRVxs8lHAO/KrgNHgetKqQgwBvRew0gQBOE2wKuP/FJ5rOXz61aMH3/xn/CTR77INxy4zLumLm8QyZvxavdmO1RzuFQaJ2cZzMWKQJGiFeaJ/Az/ZGye16SXOBrL85lN1mRv416h6IwbmquipswNx8SNCGVzOCJ5WLHaxbljdpmvOXiRl27G+LqDL5AIW/yfy19JRYe4GVCBLAhCb3RT3WLazSCjlEoAbwWeAx4Dvtk97DuBP3G//pj7Pe7zf9PJjywIgnA743Xae6lFJQoPixAfXbkTgCOx1tUnPEYj24tkG8VT+QP17z+TO0RVh7lacXy0x1rE8ERyqeSOG7u9Kzisu/M7FqmSCDc67gmCsH/oprrFLPCYUuppHK/zJ7TWfwr8FPATSqlLOJ7jD7rHfxCYch//CeB9/p+2IAjC/mDGcDa0NLdQbsU1V8AejeVRHby19UzyNm2evVJwAH/ndrt72W1jfUd8a4WLWMhCayiXXCF4m4tk74+QaXf9ynYY3dJtKAjCXqWb6hZPAw+2ePwl4OEWj5eBb/Hl7ARBEPY5Y2Hno/z1bURt0TZYMuNMG2UObWop3YyXSV7fpnmGJ5JrWvHp7CEArrkd+o62KAMXUxbFqoFVC0NYQ+T2/oAw567XTJNIFgRhf9GTJ1kQBEHwF6/T3XYiGeBqOc20UeZoLEc722u3meQXSuP8weIpbpnJes3f+arToe5QtERM1ajoxq+IWMhiJZ90v7FRt3nSNOs2J/FEcsmWX6eCsN+QttSCIAi7SF0kW1sbf2ymk2fYYzTsbKbr5EkG0Ch+9eYr+cjSqfpjFiGuN9k6mtkskm93vPmdMZysvmSSBWH/ISJZEARhF+nWbgENO0RHkdzFxr1OtBPiMWWxUnBqL6u4iGQv+26EHNuJZJIFYf+xq+9qpdQ7gXcm5iYZNxqbVsaM5FDipyNxgA2xd4phXdN+ijPM9YH9NXeDxhn23HfLXpi7XuOMRdwyamqCcaOzsF2pORUp7koUSZdar9GYa7dQanzb8VqxaE4C85xOVniy2Bg7Gdb1THIsEWKkxc/GsOZtmLHaxVFqYyWLmo4G9n3TzG7P227HkjUKZpygrsuuimSt9aPAo6nTs9+/udD0MApc52rlocWSOL0z7PUZZqygx9mNue+WoM9dL0SURSpsUtOKG5UqYHY8/tmiI8xmjUzLNYoom2S4Rk0rbnYxXisuFh3bx7Sxumlssy6Sq9EqGbP15sHb5f2aQVPTiohyMsl5SwX6fdPMfnoP9RpL1iiYcYK6LmK3EARB2CXSGzbZbb8T7mYl5W6sK2Kore2SR3scrxVX65aOjRUu4uJJ3oTasDmyJJ5kQdh3iEgWBEHYJUbDTvZku3JtHhYhblZShBRMR9ZbjDeYHxmaPck5aKrHHFMWK/mE+42IZICc1bBclMWTLAj7DhHJgiAIu0S6h017Hp6InTXWtjzX2LTXf+e3lVqcghVhLGLW/c2wsbqFbNxzaP5jRKpbCML+Q0SyIAjCLlGvbNFF+TcPzw5xyMhsea7bGsmdUVssFwpNLGSL3WITG+0WkkkWhP2GiGRBEIRdIh3pJ5PsCNiD0Q4ieQC7hRPDtVzEnTJwUWVha+ol4EQkO+QkkywI+xoRyYIgCLvEqJtJ7iXz6wnYQx3tFoOJ5GsbfMmO1SJbimPZYTBslOhBYOM8lyzJJAvCfkNEsiAIwi4xWrdbdC9qr9XtFi1Esi92C6f9NTQaisRCFstitdhCs/e7rOUvB0HYb0gzEaSZSFDjSDOR3YsT1MLue2HuemHKsAAwdbrruTZJUrIijIbLHIxqbjW9bjqq3WNGBlq7jDUNwPF4kXEjxXTUYtWtbGEkFGNtxg5qA4mdimPqkfrXIZUK7PummSDM227GkjUKZpygros0ExlSLInTO9JMZPfiBLWwOwR/7nohrpwY89Xe4l2tjHB3MkNKLZAxG5ldb7yFymDn/6zrr52NZsmYeQ6Ec6yUnF90tajZcezb6f260Cj+wUrVIqeD+75pZrfnbTdjBfne1sx+WyNpJiIIgiD0xGgfJeCgvS/Zr417eSvKWi1KPGQxbZQ3lH8Tu0WDDZ5k2bgnCPsOEcmCIAi7hFfdIlPrvgQctC8DNxpx2lAPKpKhsXnvaCwnNZLb0Oz9rognWRD2HSKSBUEQdgmv416vovZqm817fm3cA7juieRonpiyWJbyb1to7rgn1S0EYf8hIlkQBGEXUGjSrqjt2W5R9uwWmzPJ/tgtoJFJPhLLE2+2W0gmuc5Gu4WIZEHYb4hIFgRB2AVGwiZhpclbEaweb8XXqo6APWhkUDgVLcLYpMMmtob8AG2p6zHcbPXRWH6j3UIyyXWaM/bSTEQQ9h8ikgVBEHaBsXoWuTc/Mjgb69ZrCWKhGtNGCXBENzgWABs18Pk1Z5JjSjbutcIiVLdcFO3B/zARBCFYyOdDgiAIu8BYpPdGIs3cMicYi5Q4FsuxaCZ9tVpAw5N8JFbAwCZTTKCUhpj2Zfz9wq/deAUHjBJZK8rcbp+MIAi+IplkQRCEXWC0Tz+yxy1zHGh0xfNz0x5AwTZYNWPEQxYTtpOlTsSrqMGT1PuKj62e4L8t3LvbpyEIwg4gHfeQjntBjSMd93YvTlC7H+2FueuWuZijNkt2sq95XnO74p1MlhnPppiLrbnjJXxbt3lzlEljiRm7BkAyWSPZYeygdlkbVpygvm+aCeK8DTOWrFEw4wR1XaTj3pBiSZzekY57uxcnqN2PIPhz1y0RlQNg2Qz3Fau5oUjGLBAmC8BKrb/xWnGlnORsEmJVJ0sdS1RYMK2Or7md369Bft80E7R5G2YsWaNgxgnquojdQhAEYRcY67Pbnse8OQE4zT7Af7sFNDbvlUrO5sJooubb2IIgCEFHRLIgCMIuMO5utMv0udFuyRzF1oq5aIGIsusb9/rdCNgKb/OeV9nCiItIFgTh9kFEsiAIwi4wOkAJOIAaEZZracIKDkfzjLol4HYik7ySd7rtRUQkC4JwGyEiWRAEYRfwSsANImpvuZaLY7F8w27hYyb52qZMcjje2Y8sCIKwnxCRLAiCsAuM+WCPaIjknO91ksFpkLFixqSRiCAItyXSTEQQBGEX8DrurZVj2C/HwOqtAPFK3OIv4ydYG6mxUJjkc5EE12IHeHFxBrsa9+08fyf3AFdXx5xv4iKSBUG4fRCRLAiCsAt4meTlFyawnx3t+fXz2MxziL/lkN+ntoHf5yEAQspGxzSILVkQhNsEaSaCNBMJahxpJrJ7cYJa2H0vzF03RFWNeMjCtEOEyu6muAMWkbHuM7VjRpJ4qMrDqXNU7AgKTTRk8Zdrp6jqsG/neiq+wpnkEvcfWeDR+BnGK9JMpB1Bfd80E8R5G2YsWaNgxgnqukgzkSHFkji9I81Edi9OUAu7Q/DnrhumjSIAWStGpWIBBvaxArWjle7HGE2iMPix458hHmpsqPvYUzNYPm43mR2b56dOfBaA//7cndvOy+38fg3y+6aZoM3bMGPJGgUzTlDXRTbuCYIgDJnx+ia7GJiuF9nQPY+jUfUKFAB5K+KrQAa4Vm2MX7H9y1ALgiAEHRHJgiAIQ8brtpe14mjTuQ0ro79NcVebRLKfNZI9rldEJAuCcHsiG/cEQRBaEMb2PSvr4W3ay9YGyyQDXCun61/7Wf7No2RHeCo/xZRRIbcD4wuCIAQVySQLgiBs4qvHL/LYK/4PD6SWdmR8r/xbbkC7BcDLlZ0VyQA/dOlNfNuFr8KmtzJ1giAIe5ltRbJS6qhS6jGl1HNKqfNKqR9zH/8ZpdQNpdRT7r93NL3m/UqpS0qp55VSX72TFyAIguA39yYXiYZsviy9sCPjjzV7kmvubTigdgtwvM81LTkVQRBuL7qxW9SAf6W1flIplQaeUEp9wn3uA1rrX24+WCl1L/CtwFlgDvikUuq01lr6mQqCsCdIhkwAjsbyOzL+qOtJXqvGnSYiSkOfdt9rQ8gkC4Ig3I5sK5K11vPAvPt1Tin1HHC4w0veBXxYa10BLiulLgEPA59p94J4yODM6Fz9+3QkQa5W6u4KBuB4anrHY3gM65r2U5xhrg/sr7kbNM6w575bhjV3B6KOreB0srzh3uQXxxLnAAjbB53/o4p7xnqL07xGOStOOlzGCE/uyPl2y7DWZ5ixeokT1PdNM0Gct2HGkjUKZpxhr8vnuzyup8/PlFLHgQeBx92HflQp9bRS6r8ppSbcxw4D15pedp0Wolop9R6l1BeUUl+oZHYmWyMIgtAPsZBjhzhoZFD05xXuxEjYqQm6VnaK7IcHTADfMp3bb8H2rx21IAjC7U7X1S2UUiPAHwM/rrXOKqV+E/j3gHb//xXge6Dlzo4tv2W01o8AjwCkTs/qC9mb9efGjdRQC0o3x94phnVN+y0ODGd9YP/NnR9xhjX33dLNNU1Gyvzzmef5/cXTrNQSfcVRc06zj1ioxkrpRRZNfztTRQ6uA3BxzbFdVMMmF7L9bRK8kL3JM6MJTsXhmZy5q2s2zPtCkN9HQXvfNBPkeRtmLFmjYMYJ2rp0JZKVUgaOQP49rfX/BtBaLzQ9/1+BP3W/vQ4cbXr5ESBYVy0Iwr7lPYfO8w0HLhNC86s3H+hrDM+TDHAslvNdJI96G/fKMQBUn5UtPB6ZP8uT+Wn+dr2TE04QBEHohW6qWyjgg8BzWuv/2PT4bNNh/xQ45379MeBblVIxpdQJ4BTwOf9OWRAEoTVhbN48fgOAs8m1vsfZKJL9t4ONhp3xCxXXZxEZTCRnrBifzByVChSCIAg+0k0m+fXAu4FnlFJPuY/9NPBtSqkHcKwUV4AfANBan1dK/SHwLE5ljB+RyhaCIAyD16QX6+XVTiczfTYE0STCDZF8NJbz8QwdPBFerhjOA32WfxMEQRB2jm6qW/wDrX3GH+/wmp8Dfm6A8xIEQeiZt4439gzHQxYn4lkulcd7GiOqbCKqkdn1O5NsKAsjpDFthVn1aiT7vzlQEARBGAz5bE4QhH2BoSzeOO5sf7hQdITxvX1YLlJNWWSAY3F/M8nJUA2Aom2g3W57g3qSBUEQBP8RkSwIwr7g4fQC6bDJxdIYf7l2DIB7kqs9j+OJ2MVqHEvDXLRARPlnh0iGPZEcQXt6XOwWgiAIgUNEsiAI+4K3jl8H4JNrR3i26NQN7ieT7InYrBVjvpoirOBw1D/LRcr1IxesCLrqOtkkkywIghA4uq6TvBMopd4JvDMxN8m4kao/Pmb4W26pHemIU3i/OfZOMaxr2k9xhrk+sL/mbtA4w577bml3TYayeMPYPACfy59mtZbA0oq7EutMR+OYuvuezzOuIK7oKGvVEY7ECtyTMlm3/ZmLg67HuapjhC3nFjwSjxI1ersdB3GNhvWzPcxYvcQJ4ppsJojzNsxYskbBjBPUddlVkay1fhR4NHV69vs3F5oeRoHrXK08tFgSp3eGvT7DjBX0OLsx993S6pzeOHaDVNjkueIEzxXDQJXL5TQnE1lmIjc4X5zqenytHQ9ythbiaiXBa9IwFV4mY3Y/RidsuzF+tWIBYQqhEkWz1tM4QV2j2/n9GtQ12UzQ5m2YsWSNghknqOsidgtBEPY4mq+dvALAX2eO1B99rjgJwNkefcmeJ7lgGVwtpwE4FvfPbrHRkyx2C0EQhKAiIlkQhD1LIlTjZ+94nDeMzWPaik+uNYtkx5d8T4++ZM8zXLQjXK2MAO1rJR+PZfn545/mTKJ7IV6vbmFF0FX3QRHJgiAIgUNEsiAIO86DqSX+x+lP8u3TzxNV/vQWmovm+a+nHuOtE9cpWBHef+V13DIbfrZn+xXJYS+THOFqxc0kt6iVnAyZ/OKJT/Om8Zt828zFrsdvZJIN7HomWapbCIIgBI1d9SQLgnB78NaJa5xJZjiTzPB/TV/ikVtn+fPVO7Bb9inantFwlQ+e/hsmIlVeLo/w3stfzsuV0Q3HXCqPU7VDHI/nSIVMCrbR1djJsJdJNlg0E5TtMAeMMsmQSbE+huanjjxZt2Hc14Olw+u2l60aYClQGrrfVygIgiAMCckkC4Kw44xHKgCs16IcjJb4f499gZ89/tm+xzubXGEiUuWl0ijf88JXbhHIADUd4mJpDIAzbjY5jM1ouLrl2GY22CFQXHMtF8eaLBdfP3mFr568RtEKU7bDHI4VmIyUuzr3lDv+etnZzY2hUf39rSAIgiDsICKSBUHYcSZckfyvr3wZ/+bKwxSsCG8Zv9HSy3s0miNMZ/vBiJvtfak82jFD3Lx576vGr/KRe/6Sj9/36AbBu5lGRzzng7ardZHsZI3viq/zE0e+CMAvXn+Ic4XeNgh6dov1UkMkC4IgCMFDRLIgCDvOpCuSV2px/ipzjI8u3wnAuw8+v+G4b5x6kf9171/yfx+80HE8TyTnrc4WiudKji/5PbPn+XfHP8eRWIGI0pxOZNq+xmtLXXDHvuZWuDiTXON7Dj7LI6ceIx6y+djKcf5i7Q7OuUL8vtRKx3Px8ER4thJ1HoiIH1kQBCGISDMRpJlIUONIM5Hdi+P33E9GHIuDxQTjRoKPZ17JP5u+xJvHbnA2VeNGdYypSIEfmTsHwIMj63x0dWts75oORB1/Qo1kx3O8WjkMfIGI0iyZSdZqCU4nVjgSs9u+biziZHaVGmHcSLFcc+ojf3vT5rzPZI/ywYXXM25EuFKZA57ngZH1ruZr3BXFtaqToTZiIcb6mOcgFt8PagOJYcUJ4ppsJojzNsxYskbBjBPUdZFmIkOKJXF6R5qJ7F4cP+c+jM1opIKt4VrZwqZAxoQ/X7uDd01d5p0TX+Lnr7+K9x7+TD2LO21k28bOmAUiOM+tmJ3PMWMa/NL1BwgBH1s5wbfNvMDpxArJUPvxDeVkvZfMGhmzwLlCrP7cucIkv3Hzfr5YmAYqQIXHs85N/VR8iZyZw9rmAzpv/JWS830tXOtrnoNafP92fr8GdU02E7R5G2YsWaNgxgnqukh1C0EQdpQxN4u8bkU3VLP4n4uneefkZd4x+TLPFSd4y/gNSlaYRNhiNloENLSpftGt3QLgj5dP1r9eNp1sxZTRfpNdczMRgAulCX7l+gPcqib5++zslnNaq8W5XklxJFbgRDzLpfJ4x/Opl4Dz7BZS/k0QBCGQiCdZEIQdxav6sOYKVI9rlTSfWj9MNGTz/mNPAvBbt+5jvWYQD1l1H3MrRtwyarkuRHIzK55I7lCJwstmFy0vh6D4X8sn+fvsHO1E+3l38979XfiSvRJw5bI7vmzcEwRBCCQikgVB2FG88m9rtdiW53534e76188Xx/mjpbuYrzr2hblo+4/dNm+u65aVmiOSD3STSe6yrjLAuaLjW74vtX2FCy+TXKk64ysRyYIgCIFERLIgCDvKRAeR/Fxpkr9fn6Vsh/j5aw9hEeJmFyK5F7tFM8tmAqBjTeNGM5Hu3WjP9FAGzhPh1aqXSRa7hSAIQhART7IgCDtKJ5EM8P4rryMZMslazvN1kRzrQiT3kO0FyNRiWBomjQph7C2b7MLYxEM2llaU7e7b4F0qj1N2u/uNhqtkrWjL4xS63va6VnXHl0yyIAhCIJFMsiAIO8rkNiK5pkN1gQwwX9m5TLKNYs21XEwaWz3PnoAt2RHa+Y9bUdMhni86NZnPJtv7khP1bn5htOmOLyJZEAQhkIhIFgRhR+nkSW7FzapTU3N2B0QyNG/eK215zttUV+oxQw3Um4qc7eBLbnTzM9Cmc/sVT7IgCEIwkWYiSDORoMaRZiK7F8fPuZ+Juh5cPdbVeDl7GoDDsdKW48eMJApdF5tGaIzxUG9/669bKSDDsTjcqm0c/2DMKVdXtmM9X/tL5cPARR7s0FTkkDsXZTtKuBbGAtKJGBGjtT2jE0Esvh/UBhLDihPENdlMEOdtmLFkjYIZJ6jrIs1EhhRL4vSONBPZvTh+zv1I2BnjekV3NV6x5tgQZowCWTO/obYygGmtE1JQsCKsmluzwdux4FaVSKgMGXNyw3NHjSwAeSvc87V/Luf8IjgVXyJj5mll15iNeOOHsKpOBjmniiiz9817QS2+fzu/X4O6JpsJ2rwNM5asUTDjBHVdxG4hCMIP3Q35AAAgAElEQVSO0ti4F9/mSIeqDrNkxokozYxR3PL8SJ/l3zw8u8VkizJwybonufexl8wEOctgNGK2rfFcbyRiR8C1WxARu4UgCEIQEZEsCMKOsl11i1bUN++1qHCRDjuWiH78yNC5VrJn4+hHJIPiankEgKOxXMsjvPFz1ShYCtAikgVBEAKKiGRBEHaMqLIYCdeoadVTdzyvDJzTnnojqXr5t/7cYssduu7VM719CvCrlTQAx2L5ls97575Sds5BRUF1X0RDEARBGCIikgVB2DE2VrboXg3OuxUuWpWBa1S26H2zG8Cqm0meapFJToW8RiL9imQnk3xsm0zyWtFpaiKVLQRBEIKLiGRBEHaMfqwWQMeueyNutrdfu4WXSW5lt0gN4EkGuOZmko+2ySR7mepM2ZkP1V8YQRAEYQiISBYEYceoi2TTR5Ec6r9GMjQ8yU5r6o2Z3OSAmeSXXZF8R7xdJtkZf73knENIMsmCIAiBRUSyIAg7hieSM31mkls1FEnVq1v050ku2xEKVoRYyCbtjuUxSHULgOuu3eJINE+IrQLYGz9f8TLJIpIFQRCCijQTQZqJBDWONBPZvTh+zf1czKn/W9AjPY1V0QksrZiJlpmOxjF1GHCu6YBrRa6R6vv81mpJUuEsx+OKa9XGGBP1O2L/Yy+ZSaaNIqeTmlvmyIbnvPGrruc6FosQ6zNOEIvvB7WBxLDiBHFNNhPEeRtmLFmjYMYJ6rpIM5EhxZI4vSPNRHYvjl9zH1eO7eBWpffmHAvVBHOxIgm1xFI1XX88glPxYtnsrjlJKxbNKEdiEFVrZMxG1jiinOYkK6bd99gvl1NMG0XGw4tcKG78sM4bf63kPG5GTEoBaPjiJ7fz+zWoa7KZoM3bMGPJGgUzTlDXRewWgiDsGP3aLaC9L3nQ6hYAq23KwHnVJ4p2/2M3ysBt9SXXxy87wlw8yYIgCMFFRLIgCDtGv9UtoBuR3P8HYcttGops6IjXJ40ycFsrXKRckVx2W2NLdQtBEITgIiJZEIQdY8JwRPJqHyJ5vp1IHrC6BbRvTe3VSS4NMHY9k9yiwoUnwisVR4SrqGSSBUEQgoqIZEHwiYfTt/jAnX/PkWjrGrm3I35kkg9t6ro3Uq9uMbhIPrDZblHPJPc/9rV6a+qtPwdeCTiz6opkySQLgiAElm1FslLqqFLqMaXUc0qp80qpH3Mfn1RKfUIpddH9f8J9XCml/rNS6pJS6mml1EM7fRGCsNs8NLLIL534NK8bXeAbD7y426cTEPRgnuSK23Uv1s5uMYBIbtN1z/MM91sCDhxxX9OK2WiRmLI2ju+KcLPqVOsQT7IgCEJw6cZ4VwP+ldb6SaVUGnhCKfUJ4LuAv9Za/7xS6n3A+4CfAr4GOOX++zLgN93/BWFfck9ilV868WliIafc2WvSi7t8RsEgEbKIhyzKdoiiHUGbCuzuX3+9lma1kCBRs9AVp6W1bUG1HGbVTJAtxtB2962um1kgxWohQaSi62MrNJWyQQWDQtFA12p9jV0jzPnVKY7GC8zqIpcro/XnyqUIq6EwliuSpU6yIAhCcNlWJGut54F59+ucUuo54DDwLuBN7mG/A3wKRyS/C/iQ1loDn1VKjSulZt1xWhIPGZwZnat/n44kyNVKfV1QLxxPTe94DI9hXdN+ijPM9YH+rmnOWOGn5z5NKlzj8fwpHkhe5lRinYcnxslajdqQEWpEQxZFO7Yn1siPuZ+OrAOQt1IcWp7l+j9a27xiI0vAV/3Zd214bBX4Bt498Lld4gBfxZ1bHv+qv2iO13+9zu/lW1o+/nV/8Z0bvr9zYppqfLTlsdsx7PdHNwzrZ3uYsXqJE8Q12UwQ522YsWSNghln2Ovy+S6P62kLt1LqOPAg8Dhw0BO+Wut5pdSMe9hh4FrTy667j20QyUqp9wDvAUgdnOjlNAQhMPzgzF8yEi7zVOE4jyx+FT9+6FHuT17l3vg1Plu4u37cDx38C+5NXOOnr30HNRJdjz9rrLJSS1PVe8+8mg47N8WslaCw4KSQQwaoHnZCjITLhNDk7Ri2DhFSMBIqoYGc1f08tmLUPb+clUADIaUZCZXRQN5O4vyd3x/xkElU1ajoCBXXuqGUJu2On7MSJA4ooilFtbe/HQRBEIQh0bVIVkqNAH8M/LjWOqtU2485Wz2x5beN1voR4BGA1OlZfSF7s/7cuJEaakHp5tg7xbCuab/FgeGsD/R3TbPHVwD4iRdfSdFe4LHYGPcn4XDkeS5knSoHc9E8r0q9BMCUepp/yN3XVZy74uv8hzs/wV+sHuNnrj7c49X4s0aDzP2BUee1t6ohMrkyEIMH11Gz1a7H+A93/R0Ppxf5ly++ns/kZjmd1PzO6T/mVjXBNzz7tX2fG8Dv3funzETLvOv8O1gwkxyLZfnIPX/Fy+URfuDFbxpo7t4+9RLvP/okf7Z6B//+6msAp8X2R+/9c+arSf7ps++gDFzKF3Z1jfxmmPeFIN/rgrQmmwnyvA0zlqxRMOMEbV26yukopQwcgfx7Wuv/7T68oJSadZ+fBTwj5nXgaNPLjwDBumpB8IEwNkZIY+tGXd3P5w8C8Jr0At7fhu+YfLn+mlemVroe36uO8MqRZZ/OeLg0V7bQZedWo2I9mJKBW2775lm3wkUq7AjsQTbteazUnEz0AaPkjj14ZQuPq+WtDUW88nLFAeo7C4IgCMNj27u1clLGHwSe01r/x6anPgZ8J/Dz7v9/0vT4jyqlPoyzYW+9kx9ZEPYqsZDzOXnZDuN9gHKpNMZaLcqhaIljsTzXKiN8zcTV+mtemepe8KbcKg6z0SKJUI3SAA0uWnHIKPCNB17CcCswaBSfWDvKc6VJX8Yf90SyGYOK+/d4vDeRPO+K5IOeSA45InmQ8m8eK6ZTcWPSPU+vPFvBBxHbqqGIH41KBEEQhOHRzd369cC7gWeUUk+5j/00jjj+Q6XU9wJXob5T5ePAO4BLQBH4bl/PWBACgieSKzpcf0yj+EJuhrdNXOc16QWmImUOxwosVBOkw1WOxfOMhUtkzO3H9zKPACfiWZ4t+iNePf75zAt8y/TGcnUPpxf5juff5sv4nvhcaRbJPWeSN9ZK9jLJuR3IJPspYldqcQpWhLFIldFwhawVa7SklkyyIAjCnqCb6hb/QGufMcBXtjheAz8y4HkJQuDxauCWN4mqz+UOOiJ5ZJF7EmsA/PnqHZxNrfKa9CJnk4u8XD6w7fheJhngzvi67yLZ6zb3Z6t3cLE0xg/OnuNkYp3xcMWX8T27xVIhCVpBxEaFt3nRJup2C8PLJA/eSMTDyyR7tZIbItaPTZKKa5URziQzHIvlOVdsEsk+2DkEQRCEnUc67glCn8S9TLK9Ufl9PucUenl1epE3j98A4M/W7uBLhSkAzia7q6M8skEkZwc+3y3ju4LzE2tH+fDSaZ5xz+/BkSVfxp9wu9kt5t1Saj1aLQBumY5I9jLJSc+T7IPQXDGdTPKUe57eHyV+2SFedn3J3tolfR5fEARB2FlEJAtCn8TaiORbZoprlRFGwjVS4RpP56e4VknzdN7JHncrklOhRjOLHRHJmzrXPZl36lQ+5JNIHo84gnYl79aL7tFqAbBYTWBpxxIRUXY9k+zHxr1rrm/4Fe5mSi/T64cnGeBieRyAU4nMhvHFbiEIgrA3EJEsCH3SLpMM8LncTP3rP1u7A4BzxUksDScTK8TU9t3cdjqT7FVz8ATnF12R/KBP1TS8TPJ60cnYqj4yyRYhls0EIQUzRpERH6tbPFmYZr1mcFciy4n4ev2PEr/sEC+UxoCGSPbm2w+riCAIgrDz7GpKQyn1TuCdiblJxo1Gd6sxI9n+RT6SjsQBNsTeKYZ1TfspzjDXB3q/pknD6ShXI7rlHJ8tHuObeImKHeYL+bsZN6IAvFSe5FRilS8bLfF08VDn84k0yovPREscjhkU7GjX57fd9aRd0RYOjTJupLhhxqnYYU4m1jkUtSnYyYHmfiziiHy77IwRS4QZ6WO8ZTPNwWiJkwnNeMQR2jYpX34uPpM7ztsnLvJ1kwuMuH/r2CR9+fleNJ0uoqcSWSaMJBOG21pbNeZ1kDjDfn90w7DuP8OM1UucIK7JZoI4b8OMJWsUzDhBXZddFcla60eBR1OnZ79/c6HpYRS4ztXKQ4slcXpn2OvTa6ya7RxbsLa+7pNr47xxbJan8tPcqJiAIxifzDsi+a74df5uPd1x/FjIqbpgaQgrmIrMc6Ow/Ya/ZjpdT9Itp3azUqPkXsszhUlenV7icOQlniie7Hvuo8oiFrIwbUWu6AjbqlEhY/beAvV6JcbZFIyEVjCU8/rFqvbl5+LjK4d4+8RFvmL0Jc4VHU/2sqlZN4sDj58xYcmMM22USapFwji+6hXT3jB2v3F24/3RDUF9vw4jTlDXZDNBm7dhxpI1CmacoK6L2C0EoU/aeZIBKjrCey+/nt9fOr3h8afdzXGv6KJesvfx/6WS42090cFycX9ymSPRfNvnNxPGJhG2qGlFqen8PcvF3YkbXY/VCs8qkrOiUHbH78OTDDDftHnPq5Psh90C4In8NGu1KHfE83Vvsp+e4Ut1y0WTnUPsFoIgCHsCEcmC0CdxV/SUW4jkdjztZoLvT60Q2tqtfQOe0PSE9V1tRPLZ5Ar/5dSn+KU7/7Hr8/AqOTib1BoVHr3Ne2fi/ojkvGU0NRLpfL3taK6VXD9vn3zDFiEeyxwBGh0O/aw+8UKpsXlPqlsIgiDsLUQkC0KftGomsh1LZoL5qlP54s74esdjPUHolWY70fJ4zY/MPUNIwYl4jtEuaxyP1AXbRrF5vjhJxQ5xLLZMyrV7HIiU+MFD57gv2X1L7XRT0w9d6a8ltcdC1dn4Nxst1i0ifmWSAT7pimQPPzPJF12RfDqRkeoWgiAIewwRyYLQJ3HV3Ja6e54tOpUvXtmhioRC10XVObeJSKtM8mvTCzzUNM7dbiWF7dhc/s2jqsOcd+PdHb/JeLjCr5/8O77r0AV++/Rj/MKJT3M8tn2ljZHmjG+5v5bUHvNNmWQ/q1t4PJWfZtmM17/3K0sNmzPJ0pZaEARhLyEiWRD6pJMnuRMXSo7l4nSifSY5EaoRUo4d4mY1RcGKMGlUGGvKFCs0Pzz3DADrNafqxZnkWlfn0E4kQ8Ny8UDqMh+46x84Hs9xs5KkZIV549hNfu/MX/HumQsdx0+742eqUagqQEO0P5HsNRQ5aBTrban9FMk2iscyh+vf+5npvV4ZoWSFORQtccjtGigl4ARBEPYGIpIFoU+8Osmb21Jvx5LpZEa9Tm+tGNnkGb5cHgU21kt+2/g1TifWWagmeGT+LNB9JjkVal+z1xPJb0g/yz3JNa5VUnzfxbfwzc+9nT9auouwgu8/9GxHT7V3/ov5EUBBTKP6vNuU7QiZWpRoyCYesrA0lHzOxn4yc7T+tZ9to20Ul8rO5r1Jo+KOL5lkQRCEvYCIZEHok348yQBrNbcdstFeJG/eoPaSJ5Ld7HNE2bxn9jwAv33r3nrL67uTg9ktAM4VpjDd7PhiNc6/ePENrNbirNQS/PKNB1k240RDNjNuZrQVnie53pK6Tz+yx61qo85mwTLQTZsN/eDpwhTnCxNcKo2S8znT6/mSPUQkC4Ig7A2kmQjSTCSocYLeTGQ04gi1kOqt6YaJc10HjErb1x2KOrUiy3aMcSPFvHkAuMKZZIkpI8EPzT7OkViBq5Ux/jF3Lwqo2iGOxvLMxQyKdrTj9UxHlXsuiZbn8HjhPu5JXOZfX30zJT3OeJNunK+OcsAoc0+qRrnQ+vwPuMdnC464NxKKsQHWcaU2Cjh/AJTsrc1b/OAnr3wdNooxQ/n68329OgO8VP8+GhpjPOTkJ6SZSPBjSaOKYMfpNZasUTDjBHVdpJnIkGJJnN4JejMRpZ2Pz9fMWk+vu6acm8FEpMS6mW+ZFdXxHADrtTAZs8D5vJN9Ph1f4N8e+wtenV7CtBW/cv1+Vt0GHRdLY5xNrXHIuMGT+ZmO1xPGeXzFbH3Mby+9EXgDF/LzwMbnr5ST3J+CifAyGXOs5fgR5WSZF/LOLaYWNQdax6vlKK939DZZKzKUnwm/YnzJXTuAkhWur9egcYJafD+o79dhxAnqmmwmaPM2zFiyRsGME9R1EbuFIPRJvxv3TB1mvRYlojRjri1hM43qEI7I9OwWdyczvDq9xLIZ50defCOP5xqtrZ8vTTjHdOFLbtRJ7mQtaG1puFYZARp1hTudf7bgCsQB7RYLZiMT4eemvWHwYnkM27Vvi9VCEARh7yAiWRD6pJ9mIh4rpme5aN2mORXaKGKXa3EybgWLc4VJvvuFr6w3JvF43vW+diOSR0LtPcnb4YnkIx1EslfdIleIAf3XSPbwGorA3qsOUbYjXK04Lcj93BQoCIIg7CyS1hCEPul34x7ASi3OnWSZNMrQYv/e1kyv4heuPcTxeJbfW7ybaouYF4pOJrmbMnCdNu5tR3eZZCdDXiw5wr7fGske800b9/zeWDcMLpbGOB7PSSMRQRCEPYTcsQWhT2KqP7sFNGWS25SBS7mNJ5pF7GPrR6BDk76XyqPUtOJYLEfCzXK3IzWASL5RdUTyXLRACI3dwpbhZZIrJXd8H6tb7DW7BTgVLt42cb1unxEEQRCCj9gtBKFP+vUkg2OfgPZl4DZ7krvB1GFeLI0RUk6Ht06MbCox1wslO8KSWwbuYLR1GThv/GrJOf9B7RbrVpSS5czzXrNbgFNiDmChOrwd/4IgCMJgiEgWhD7pt5kIwKrZWSSn+vQMe77kM4nOlguvmUi/WdmG5SLX8nkvk2yV3T8gBrRbgKp33svvQV/vU4VpfvjSG/jAjQd2+1QEQRCELhGRLAh9MognedkTydvYLXrNmj7v+pK3ayoyiCcZnHbLAEejW8v1GMoiHrLIVw2ohUBpMNp35+sWz3KxF+0WAE/mZ8ha0d0+DUEQBKFLpJkI0kwkqHGC3kwk4YrkWCjNuBHrKU4FJ+N7MGq2vL7xiJt5Vemerv+GOQt8kXuT2Y7X44nk/7+9N4+O7Lruc79za65CAYWhgQa6m0Sz2c3mTFESB0mkSUuRrIGS5diOlchhpDzTb8Vetvzi9SQrSmJHkgc5cWIpz36LjmzJcaTYsaxH0lEkUZQoSiEpNptsceqZPQMNNMYq1Dyc98e9t1AoVAE13CpcAPtbCwvAvbfOPvfsKmDXrn1+22v0EfOtDtzWW/uZwgBwln3hDLH4ymv6PKZix4W4KVtnBDX9/vZ9+OPkVbyhZ5YLubGOPyc2w+vIjeL7bm0g0S07bvRJNW5ct27aEh+5045b/SLNRLpkS+w0j9ubifitjXvTuSw5vfZGuWrOmQ+lz5OsadOvMtbYzTUqebHgp6hhT2CBdCHOQj676hoDTdhToKRhMptDk191zXprfzJlBtbD3vlV10QNswTjYtyUPSsFio748EtTV/PY3EFm82mqG5x0Are/jtwqvu/W12s37LjVJ9W4bd26aUt85E47bvWLlFsIQgsYaPxGiZKGnG7+ZTRbsHWS196412xpQVZ7OZPpxas01wbnal4Tqdi0V6vbXyOsJQNn1yNPxs1rVNv1yMsU5U+WIAiC0CXkP46wwWjujF4m5lmd8XQzK5Utmg80E0Uf2ZJBxFMoNyWppNWaZIDnrZbUb+k9V/N8WdmiDc1eO0ge8yfxsDIItjWSp5fMa9qVfxMEQRCEjUCCZGFDuTk8yx/v+yF/cd0T7KjTfc6NlLvttbBpz0QxV6i/ea+cSW5ByeGJ+d0A3Nt7DsXqDXOtKmdUktVepnMhfIZeJQNnZ5JnElYdmgTJgiAIwiZEgmRhQxnxm4HxqD/F5/c9Rd8mySi300jEZqaODJyHEkGjSEGrllpev5Ia5HIuxLA/yU3h2VXne1Z182uNi1ar6OqSC3v8uWQIaF8jWRAEQRA2AgmShQ3FzmoC7A0m+I/7fkjYWL2RzG20o5FsM1tHBi6yohyi+VIOjeKJBTOb/I7+i6vOtyv/ZnMha27MWxUkW/5bTJr3175GsiAIgiB0HwmShQ0lbNXePj6/m4vZCDeE5/nDvU+XM7VupZ1uezazdbru2fXIqTaaZnxnfg8APxm7iFFVctFOKUcldl3y7qog2S63SKTM+5NMsiAIgrAZ2VAJOGFzEzQKfObqH/FcYpi/mdnf0hh21vR8NsqfTN7Mw/u/xxujV/jM+LN8aXaMIq0HoZ3EDpL/8ru3Uzg60NRj55SipAP8Z8+9/KX3LhJFP4XCslbxZdXHB77zj8lrg0KLbYxfZoD3+cfwqBIqH6JQEcx/zxPlRe9+kkUfhUJtfefjVja4UKp/b39tvJnHfTeTKXko5EPl43/lvZOve27nyoK1cU8yyYIgCMImRJqJIM1EWrVzT+9Z3tY3yZuj0zy9dD1LxcYbatj0e81yghJh0nqYT517J384/i3u6Zsk4P0efzX73rb889boOX5x+AifuXAfF3N9a17bzNoN+hYplBSPv7AfmpSAs0PGBCEShFadz+PhUrL9zmyXU9Gax5N4SBJc87HLBS/136Sk8XCJ1T5fIIzd788IlYj1hlAOfWa1FV9HreJG8X23NpDolh03+qQaN65bN22Jj9xpx61+kWYiXbK1Fe3cOHAeMLOq90Rf46tXDjQ9hleZG/dm85qFfJKFvJ+Pvf5WvrDvKe6IvEa84OVTc/tppTYX4M7oacaDC7y971X+eOLWda9vdO0KpSTT8R5K2oBAEc89a7eBrqTXGyZeSPGmnml+a88LHF7awe9eeGP5vH38+cQOfu/iG9cYaW1uimT5nau+x0LBz0Mn76doreGHdxzng0Nn+KvpA3x99pqaj72mZwSA15em6o7vV0W+evBxClrxj4+9szz+J3cf5o3RK/zuhds5XBhisQg4WD2z1V5H0kzE/bakUYW77TRjS3zkTjtu9YvUJAstorkjuhxA/czQ6zXlxtbD3qRXqdn7WmqA//vMW8iXPLyj7yU+uecwvhZrlO3627t7L7f0+HoEjCITC1amNlJCNfHl6dGoSIlk0GD3QJyDQ7Mrzg/FUuweiBPqzTU1bvXXJV8fpXCJm4ZnuH3kcvn4roEEuwfieCLFuo8NRBWBqFpz/HxY4evJMz64yGh/onx87+ACuwfiFMOg3FktIwiCIAjrIkGy0BKjvgRjgRSLBR+TuTB7AksrguZGWd6ktvJDjcNLw/w/0+8mV/Lw/sGz/Mm132fI27yOsq20MB5MMOp37h1qwCgyaQXJKtxaAF9v416P4YxEGyi+Y6lc3Nc3sTy+Qxv3oHbnPXv8RNvzFwRBEISNQ4JkoSXe0DMJmMHs12fMj+z/4dDppscJrxEQHkldw2cnfo7LuRA3R+b40nVPcEO4dqvluuN7lqtr3xJ1LpscNArLmeQWg+Q5SwKu35tZoUARdqAjns2LSzsA2BdaLB9bloBrf/yLNYNks+OeBMmCIAjCZkaCZKEl3hAxg+TnEiM8NjdOvqR4a+8kO33NZWvrZZJtzuWG+ciJt/PC0hBDvgz/5qpDTY3fUxEk39072dRj1yKgKjPJrak3FDGYL/jxKIh5l5uoOKVjDKZqCMBVgcTy+A503LOplUmOluff/uZDQRAEQdgo1g2SlVJ/rpSaVkq9UnHst5VSl5RSR6yv91Sc+y2l1Cml1HGl1Ls6NXFh4zDQ3GYFyYcSw8wXgnx3YTceBT89eKapscJWe+e1SgvmC0F+7fS9pIsexoMJ+mu0ca5HZZD8pugV/DVqm4d9Kd43cIZf3vkcY/6lVedrETSKTC5ameRQ67vSZi3ptKGKltwRe00cKIeYzofIlAwGfdly45awQx33AC7kVmole1WJkMfsFphuQ0NaEARBEDaaRjLJXwJ+qsbx/6i1vs36+gaAUuoG4BeAG63H/IlSsnVnq3EgNE/Um2MiG+aS1Zr4b2f2AfD+wTNNbbKzdZLrZZJtCtrgaLofgBsbLLlQ6HIQfjLdR9Ao8oaeK+XzDwyc4a8PfpNHb/wGn7rqMB8cPGo1MimsO/bKmuTWdYBn86aEWmXXPSczyRrFxXJnvITj41dnkldmqVtTJBEEQRAEN7BuUaLW+iml1HiD430A+O9a6yxwRil1CrgDeGatBwUNHwd7x8q/R70hEoXmN2k1y3hkR8dt2HTrnrph573WJrAT2b0c7N0FQJ5Rzmdf4arADD87kuPF1L6GxrLLLXZHrmJEr3w6VvtnIn81tzPDT/TnmGGM9QipLIaCdMnHscx+9oee571DSyyqMW4MneNf7jyMoSBV8nMsvZvd/nn2heb59N4T/PnMO9Yce9h/kqlFM0C8bmQYw9N4QFjpo7waAKa5qTfIvDLvaSRgvq/sDwyveF00i21nvrgDWOTufi/4xuj1mm9ixiJXESvV1rZu9LXhU8OU9OOM+ZPc2DvCkNcMxLM61Nbc67GVXkft2unm369G6da6ddNWM3bc6JNq3Lhu3bQlPnKnnW77pdHCzXZqkn9VKfWSVY7Rbx3bBVyouOaidWwVSqmHlFLPK6Wezy409hG34A5uCJkufjW9p+Ko4tklUyf5jp6TDY3jpYBPlShog7xe/wOH09mdAOwLNrYBL2SYG8jSJT8/To0DcEvoLDHPEr88/G0MBY/M38Gvnn2Iz0+9j/8y80FyJQ/39r7G3T3H1hw7nfRQ1AaRSL6pALmaxaKZie/zpCrmnbXm3XxzllpczscAGPEtoNAElb0u7WeS89rLfLEHj9IMeROEPebcUw7NXRAEQRA2ila3t/8p8GlAW9//A/BRan++WlM8V2v9MPAwQOTAqD4WX5aoivkiXRWUrrTdKbp1T522E1BFrh2/REnDo9M+FovLa7eY6ePnB+HW0GnOJM6T1Ws/vfqsgCpZ9HIsXn9Tne2f6ZTi13fCuH+SE/FLlNb5OH9f0JMYo00AACAASURBVFR0WMh7+F9XSvzaiI+d/gV+Zfhr9HrSPJcY5g/O7aGEGXRfTkdQ+Vv5rate4BcHn+C7s3Au21tz7Ds9ZomFP5LlWLzxRiKw0kcnAgXeGwNdnC7fp9ppBsxHE4scT7f+Pta286IHHuiHMJOcXzqPocw1Pxpf/81GI6+N19MhBqNL5PKnSefM+c7kOvO62iqvIyftdOPvV6N082+3m33kJp9U4+Z166Yt8ZE77bjNLy39B9ZaT2mti1rrEvBnmCUVYGaOK9OLuwF33bHLiRj5mpvL1kaXA85Oc2tkhoBR4nRmgMWqNtSTuQivJvsJe4q8pYHmHY3WI9vMFYJMZMOEPUWuCS6ue/2yHrCXIgbPJcwucvtDi8zkg/z2uTtWBdqPzO3lW3N7CHuKfHLP4bpjLyVM+bZAJNfQ3Osxa8nADfg6U5MMKxUunB4b4EK55nlJNJIFQRCELUNLQbJSarTi1w8CtvLFo8AvKKUCSqm9wH7gufamuD3Y4UvxyT3P8+2bH+Gz48829diPjBzlGzc9xv19Fzs2tw/tOMEfXfND/mDv0wC8mKxdb/qdBfM90jv6L9Q8X0kjyhbVvJwaBODGyPqb9yJVKg5Px81yjZKGf3vuDuasZh4rUXzu4u1lG0adLoLxhPkGwRfJ1zzfKHaQPFSxca963u1yviKIjVoaxk6NDSu1kjsRhAuCIAjCRrBuCk8p9VXgPmBIKXUR+LfAfUqp2zBLKc4CvwygtX5VKfU3wGtAAfgVrXXr+ljbgIAq8Es7X+Nnd5wiaJgf4b+ld5KwkSfVYM3odaEFPAp+c/eLPHRqL819+L8+D+9/klH/cs3siXQf35q/tua1Tyzs5td3vcRbey8TMgqk18gSlzWSm2hq8WpygHf1X+Dm8CyPzF6z5rXVesDfW9zNvQsTPJPYyeGl4bqPS5Z8zOUDDPiyDPrSXMmHV12zmDCl2zyRIu2U9ttd95YzybocaCYbzLCvR7zoZ6HgJ+bNcXXQ3Fi35NDYsKxwsTuwxGTOXCsJkgVBEITNTiPqFh+qcfiLa1z/WeCz7UxqO/Evdx/h/YNnAfjO/G72BuPsC8W5NTLDM4nRtR9sEbaCzUFflo+MHOYz529xbH4eSoz6UxQ1fPr8mzmUGGa2ECLmiwCra4ym82FeWhrklp5Z3tY7weMLV9Wft9F8MPiKlUm+qYFMcnVWM13y8omzb2nIzmQuzIAvy05fqmaQPB83jxmhAtB6QDhjZZKHfWl8qogBeJUmW2psM2OjnM/2EPPOcX14HnA2k1wpA3csZe7hlXILQRAEYbMjHfc2kPFAnPcOnKWgFQ+dvI9PnbuLH8bNwPj2Cj3f9bCbRAC8p/8EN4VnAbPpxztiF/jg4GlUnbKB9eip6J72zfmrmS2E1n3MdxZ2A/CO/rXLP+xMcjMB24l0jGzJYDyYKJcO1B+/9bKFy1ZgvLMig17JrBUk04ZGMkCq5ONkuo+AUeKNPVccL7WwseuG7bbeTmZ6J3IRShpG/Un6rc6B0m1PEARB2OxIkLyB/J+jr+BR8OjsXl5KDgHwwpKpFdhUkGwFm99fHMNQ8Ik9L3BP7wR/ed3jfGb8R3x8z4v82thL1BEaWZNoCxuxvruwm5KGu6OXVwTwq+ZtNLdxD8ymIsetbOUN6zQVWd6410KQnKsfJOsSzCZM6bZisLU3H5V8f9Gs7763b2I5SHZAnq2S8xlL0zlkFuM4GSTntIepfBiPgv3W+JJJFgRBEDY7EiRvEDeGZ7kvNkG66OGLl68vH385OUhBK64LL5TLEdbD3gD3nyduZjLXw7WhRf7wmqe5NhTnci5EvqT40PBJPjJytOl59ljZ2maCqplCiCPJIfxGiXv76oubhMuZ5ObqY19JDQBwc2R2zeva2UR22eokOFork5wxKJYMhnqS5Iz2SyKeqgiSox3a+GZv3msle98I9ua9/SFTdURqkgVBEITNjnO7d1pAKfUA8EBobMCqcTXp862uAe0EUa9ZD1ppu1OsvCfNr+/6AQD/39wNFNUQsYqY4mR6kOvDM9zdl+TwUs1eLCuwA58iA/zF9E/w8V3fYKno57/P3Mzfzx3kzugFfmv3U/zy6GsU6eHRuevXGXGZ0YAZ9KRLwaZ89FxiL7f3zHBn7wLPLN1Q85pBnym/ViJc0wf1/HMmOwac5LbI4pq+67ee3ZqehnxceU+Jkpmt3h3IrnpsPmewCIzFEsQ9UWI01zijeu2mC2GmchFG/Enu6TNrhrOlQNvPy0o788WVGxULRNYcv9nXxlQ+BkzjtzafatXbkddVt/42bAY73fz71SjdWrdu2mrGjht9Uo0b162btsRH7rTjVr9saJCstX4MeCxyYPSXqoWmuyFwnShkumar0s5d0cvcEpliseDji5f3slRcaf9QwgySrwte4In52JpjKnT5I/rL2RxPlYY4FP8pFgoBS1kiw9/P7kDp2/lXVx3mX4w+x8mUr+FNgWhTDWGh4Fm1Tmut22tJe0PafN3rDMws7Wxe17ymnn9+FDdfRAdCV1jML6HrNBXxK7MN5nSu1LCP7etOp80M8ZA3seqxpXgACDIaS3AqCzldaGjsWnZsvr84ys/vOMX9fafM8wXDkeelPcbRwsqM90ydNbdp9rVxOr1STu9yttix11W3X69utdPtv1+N0s35uM1HbvVJNW5bt27aEh+5045b/SLlFhvAL+18FYAvTx2sucHpxSWzPrmRuuSQYcuoecqNMSZzkVXSa4/N7eXPLx8EKKtpNEIrNckAF3LLigf1KEvANVl/O50PMZ0L0uvNc3Ugscb4rUup2TXJI/4U1bXcyuqCN9qXIKedeQnZdcnjlkSb0zXJWe0py7OB8+UQtsKFjdQkC4IgCJsdCZK7TNST48bIPJmSwddmamsN/zg5REErDobny0FwPco1pg0EVY/N7QXgjugUHhpTZWi1rncqFyZXMtjhy9S9h7IEXJM1yaA4ljbLIa4N1e+8105NcqLoI1n0EvEU6PWsrA1XKfNls6Nvidqd2JvnyNIQi4XleTpdMwzLm/c6MX51kCw1yYIgCMJmR4LkLnN9yKw5PZmOka2jg5sq+TieiuFVmlsiM2uOFzEalwybzEV4Pd1LxFPg1p61x7WxN+41mxksoZiwNr/t8tfOJi9nkpvP9L6e7gNYsz11e93fVDnzutO/8uMfO5M80FtbHq4Vihg8HV8ugWn+jcP62Jv3wPkgdiIXoWgl3Iu6NZ8KgiAIgpuQILnLXG/Jlr1mKTTUo1EpuHCTgeYziRHAlGdrhGiFTnKzVDaZqEW4iQC/mtczvQBcE4zXvSZitKcUUU8GrpQy39z0Oxgkw7LKBXQmE1uZ7W1FFm8t8trDVLnbnh+nMuyCIAiCsFFIkNxlboiYmeSjltZvPewg+Q3rBMmRJksW7Gzl3b2NBck9LdYkw7IsWL0gua1M8rpBsm67MYctA7fTtxwMaw3FlDnf3p5Mzce1yrOJnWRL5kvS6Zpk6GwmGZaDcKlHFgRBELYCEiR3metDjWWSf5wcoqjhhnXqkpvd/Pbj5BDJopdrQ3GGfetnQtvR7V0/k9y6Zu+5bJSCVuwKLBFQxZpjexSkix6KLT7Np/Jmd8EVWskZA7RisCeJ9jibLU2XvDyb2AmYmxOd5lxFkNyJmme7q5/UIwuCIAhbASkc7CJD3jTD/gxLRe+qjU7VmO2KYxwML3AgNM+PkztqXtfs5reCNjiUGOa+2AR3917mkdlr1ry+uiZZL3ooHY+wiJdiae1GGo97b2AyspPFgp9isnfV+d979T58RonJ+AjFGioR57yW/nNh9WOLwK+8/D7CngJqqYdiVWCWNYr8xqvvJlsyKCZWP74Wi8bKe/p73y2cCu9hJh+kmDLH0AUzMB6LJcg6pGxRye9fuJ0nF3bxTHyn42NP5cIsFnx4lO5IzfNFK/MumWRBEARhKyDNROheM5Hrg2ZG8mR6iD7f2kEywERuwAySw3nO5WrPcchvBmoFHSLmizS0dkdSV3NfbIJ7+67w/fjNa8/ba2Zpleoj5ouw+FqA4rQHMzRfO0ieJ8APqB+gPjt11ZqPT5Sl12o36zg8tbvuY7PADybH1xy/mup7ukKAK/TVvPbAyCxF3VrDj7V8pInwzNIgfQ7EmbXsfPzsu/GqElFftMYjlmnltXE+uwt4iSv5WMdeU24Sv99oO24U33drA4lu2XGjT6px47p105b4yJ123OoXaSbSJVsAV/dOAvBysq8hm69nAvwkMOCZq3u90ubx+cLyfaw39hPz/XxsDG6LTJIsxMnXUdkACBtZACazBebjaYrTITA00TtzJEtr1+QaaH53/Fm8qsS/OnsnWb38dPNS4vf3PkNRKz5+9m5qbfTaHTJLUi6m52qO/87Yed7Zf4HvLuziG/PjK86NB+L86tjLnMtG+cLELWvO0ybiCZIsLt9Tr5Hj31x9iETRx++cv6N8/KbILL9x19M8n9rR8nNnowTcj+TtDZjOC7s/nY/w4PG3czYTJas7d39uEb/faDtuFd93YwOJbtlxq0+qcdu6ddOW+MiddtzqFym3qECh63Zvc4Lrw41t2rO5ZJVk7ArUf9KUdZKb+Ij7Sj7MqXQf14YWuTUyw/NLI3Wvrdy4py8EAIXamSGwq0Q6n1vX1t7xGfYGE+zlWk6kl+876sly73XnWCz4MTL5mo/t7TWz5Ea8tp1SX4F7957DWMzxzTNjK87tis5z775zPBsfwSitP0+AgM+34p6SaO7afwG/UeJz+ZvKQf5YbIGgr0BmnXKT7cjxdGPPbUEQBEFwO7Jxz+JPrn2SL1/3nZqbwJxBl4Pk9Tbt2VzMmh877F6za11rXeWeiVtScGuoXHgoEfEUTN3bopfSefPjEHVVtmE79TbvNStdVwtb4WJvDYUL+81DO5vINKq8ea9SBi5gmM+RejrXgiAIgiBsfiRIxgwGb++Z4UBokZ/bcaojNkZ9Cfq8OWbzgXLgtR6XrNbO9ZpxwLJCRKrJYNCWgrsrOlX3mkiFsoWe90HSC4EiakdjmVlYDpJ3V91DM01Q1ho7X1LsCiRXKYAsv3lor7h3ZXtqk6D1RiormWRBEARB2LJIkMxySQHAPx0+RtTTeBDYKNeFzA53R1MDNNpoYa4QIFn00ufN01tnTq1mku1s9tXBBEZ5g9xKKhuJlC5YWeQ9WVQTzxpbFqw6k1yWrmtDZaGIUZY1Gw+szCb3lBuJtFdRZDfIGPVVZpLNuUu5hSAIgiBsXSRIZmWQ3OvN8+Hh447bOBCaBeC1BuuRTRSX1mvt3KLWcFZ7uJIP4lW6rl6yvS5z2QD6oqkwYexproGGXTKyqtzCaC24r+b1jNWeOlQVJLfZSMTmcn51171yuYUEyYIgCIKwZZEgmcqyAjNg+0c7TjHkTTtq40A5k9zcxia7a129uuRwGxnZS1YAO1ZnY6CdUf/fJ66GggF9eVRvczXb62WS2w1iz5Q77y2uON7TRhOUSibtrnuV5RZWkCyZZEEQBEHYuoi6BcsB1cl0jIVCgPtjl/jozqN87uLtjozvocS1FZ32ShcD6Cn/Oo8y+eJrb+Z7wYOcTPdRzKzWtv2zk3cQ8RQ4szhKseQjYXgplhp77/P5Y29llz/JZHKAYm61bvNZn4d//dLbOXTeVI4wrmq+DfN0PkSmZDDgyxI28uXOgHZNcjsb9wBO12lP7XRNsmzcEwRBEITthTQTAQatBhJZHeQrV97IvX0TfGDwDIliL4eTY5xKD1JqI+k+HpgnaBS4nOsBNUDpSAiKjdUlH2eU44zWPf8UKzvmmboTjbn1JXbx0hrnLxPkf2EpcXg0sXEPhuWnZnx0OdfLeHCBGyJFTmViAAzaTVAI1RUPb0RcfKZgdqa7NpRYcV2/16yz1vQ0LE5e655SpUEAxvzp8ji9VjtqQ4UdbybiJFutUQVsjrXrlh03+sitDSS6ZceNPqnGjevWTVviI3facatfpJkIMOhZsmwavJz08fWZa/jZHad5cOQID3KEeMHH4aVhnksMcygxYrXfbVxPuT88DcCJdJSF+QwUwxAoYty4/j3uDcZ5cOQY5zI9/MXUDavOf3LP8/iNEr97/k3ktEHYEyBVbEyi7ZbIDD8z9DqvJAf425lrV52/O3qZdw2c55n4Tr5dHCNuFO22dEDjPjqXDTMeXCDmucJC3nwhGFYzi7l8/XEaEReP5w0yJYMdvhT54kI5cxwwzHKZqVypqedS9bXJApQ0DPpSJPIJihgo663IfL646ZqJNIpbhd3B/WvXLTtu9ZEbG0h0y45bfVKN29atm7bER+6041a/SLkFFZu8rI/+/+jSbTy/NMwd0SneHJ1iTyDJ/bFL3B+7BMCJdB8PnbyfTIOlArZKxEIhgF40H6P6Cxh71g9ms74C77nxJFfyQb786r4V5zyU+OnbjlPS8O9Kt2CgCPq8ZPKNBclGJMt79p/k6mQ/f3dyz6rzt+yc4D07T3Jx0oeaqt9wZD1qaSUvS9e19xQsoTiX6eW68ALXBBd5OTUELG9obLcmOa89zBaC7PBlGPJlmMqHpSZZEARBELYBEiSzepNXCcWTi7t4cnEXAKP+JHf0TPHm6DR3917mQGiRN/dM84P4WN0xV45vboBLFn3lIJm+whqPWGY6HyZXMtjhyxA0CisCc3vTnpk9bb5ToL1xr15Hv6hDm98u1Nh8WJaAa7NmGMymImaQHC8HyU6pW4BZl7zDl2HUn2QqH5aaZEEQBEHYBoi6BcubyOoFg5O5CI/MXcOnzt3Ff5s+AMAdazThqKaytTNxK5Pc21iQXEIxUZaBWxnMtpuNnS0EyZQMYt5ceQ1WzjtnzbuxTYb1KGeS/ZWZZDuIbf99mt15r1IGzil1C4DzlkLHvpCpoBEUnWRBEARB2PJIkExzcmSHEsMAvDk63fD4lRnZcrlFg5lkWM74VsvARdrOliomrAC2lgycU5lkW8ZuTzBRPhZxoC21zam0qZV8bVkGTi/L+jmQqba1rW+w2ooHpOOeIAiCIGx5JEimsiZ5/YDqtdQAyaKX8WCibhOOeuPPJEKQNcBbgnCp4fldrNOeOuJAQ45LdbLU4Fw2djofYqnopd+bY8BrFueHHWhLbXMybSpm7A8tAJqgUcSrNJmSQUG3/xQ/anUnvD5kBcnSTEQQBEEQtjwSJFMZDK4fbBYxOLy0A2g8m2yPf3nGzHjSV0A1UUK8nEmuKrdwoLXzWh39opVlIm2hyoHsgdACUDF3BzLJM4Ugc/kAvd48O32pcktqJwJwgJPpPvIlxXgwTtjIS5AsCIIgCNsACZJZvya5mkMJU+nhjp7G6pLL7Z1nzYC00Xpkm1ob36CiJXUbJQUTa2zes2uSnajrXZntbb2ddm0UJ62Si/2hRUfrkcFUuDiZiWEoOBieX1a3kI17giAIgrBlkWYiQK/XDHoM1duQkPWx9NXAEe7ovULMF2Y9ZYmYNX5qwdwAFhnwEGxCMDtRMjPXewKpFfPbYTfk0MHy8WbXbqFoNsu4OphZde9RK9trGDFivpWb95q1czE3DJzixsgSMV+kXJPsNXqJ+UI1H9OMuPj53A7uZJpbepLkMAPyTCnQlDD5Wvd0OjPMDeF5bu9JEjTMUpmAp5cYgYbHb8SOk2y1RhWwOdauW3bc6CO3NpDolh03+qQaN65bN22Jj9xpx61+kWYiQMgwdYUv5woN2V3Ie5nOBRn2Zxj0THI607fm9UFr/PicudzpnhSZfOPZ5GRBUdQw7FtiqZCoqLM15zpfUCvm3czanUiZcxr2xVc8zkOJiCdPScNkNodmtfpFM3Z+vGQGwuOBWRbyyXJN8mQ2T07Xrs9uRlz8lWSEnxuCPf4rHNLmm5HFoqfp51G9648sRXlgAMYDU/iV6bsr2SxZ3dynAuvZcZqt1qgC3L923bLjVh+5sYFEt+y41SfVuG3dumlLfOROO271i5RbsKy00PjH84pDS1bJRQNScFFPjnTOSyHhA6Uh2lxgldcepvNhPApGfctPoIgDMmqTOfMd3qg/iYFeHrtiTXQLGszVnMn0UtCKqwIJop4cfqNEQStyDmysA7PBC5g1zz0ONRKp5KilcHFjZI6AlUnOOjR3QRAEQRDcx7b/L6/Q5WCzmQ1wjUrBGWh6PAVOTw8ACqJFVAulrBdr1A6HHWjIkdVeruSDeJVeodYRcbiuN6c9nM1EMRTcHJkF7Hrk9gNwgPOZKNmSwVggxU5/qmJ8Zzib6SVV9DBqjW1qJDszd0EQBEEQ3Me2D5IDKoehIFX0UGxiOewg+Q2RK3hVfTm3sBVsvjxhZp6b3bRnc7FW1zqHGnLU6rwXdaiRSCX25r3bIjOAM8oWNkWMctnLbT1XgMbUShqlhOJ4ur/8uzQSEQRBEIStzbYPkkNGawoOs4UQp9O9hDxFbgrP1r3OllE7etncfNdME5FKLuXqt3ZuN2N6qazDvBwkO60QAauDZCczvZXj32qP70AjkUrskgsQ+TdBEARB2OqsGyQrpf5cKTWtlHql4tiAUupxpdRJ63u/dVwppT6vlDqllHpJKXV7JyfvBHaQ3EpA9ZyVTX7TGiUXdrB5aspUkaDVIDm7uumHvfmt3YysLQM3VhGAO6eRvIxdN3x9eA5oT9+5FrYMXJ/X+QAfzEYyNpJJFgRBEIStTSOZ5C8BP1V17BPAE1rr/cAT1u8A7wb2W18PAX/qzDQ7R9gOklsIqF5ODgHL7Ypr0WPkKZYU566YWc5Wyy3sph9jFUGyc5nk1QF4JzPJPsPcINhOp8C1xrdxOkhekUkWjWRBEARB2NKsG6VorZ9SSo1XHf4AcJ/185eBJ4GPW8f/UmutgWeVUjGl1KjWenItG0HDx8HesfLvUW+IRCHd4C20RnpWk345wEVPL184eidX8r3EkiNNjXHMiPGF05psyUcsPkytjVwpn59/f/KtZPNevGE4uGO0pfmG1CDwBLsDKQ72jgKKQSsG3BEa5aDHzGq3snZeL8Ah9oXyZT/si5iqHT5v/wrf2LTqo9lCD4NeM2PtMaI1x7YZj+xoamylBjGfiiaxwMia41ez/j1plopP0uPJoFSoqbGbs+MM7dhpdu27xWZYu27ZcaOPurVu3bTVjB03+qQaN65bN22Jj9xpp9t+OdTgda2m8kbswFdrPamUGraO7wIuVFx30Tq2KkhWSj2EmW0mMtJffbrjZBY1l44sAX6ex64Kqb8BrzY+vlx+rLa+VjJDH69yMwDhwdbVENI6QKIYJOrJ0OdJsViMELSy4JlSe5vrrhTMMoUdvsXysbCl7ZwqNt8sYy0uZIfKQXK6zXlXk9EBpvJ9jFj3kXJ4fFCcyQ5zc/g8eb2hEuOCIAiCIHQYp//T14oCV0eOgNb6YeBhgMiBUX0sPlE+F/NFOi4orf0eRt4wxN7AFG+KnOa1VD9PLuxqepz3D55hdyDJ/5y7mnOZ6KrzN0dmuadvkmPpIb4X6edYvNlAfJnzmRA3RjKksqc5lhrCp0zx7Zfjc8wXTGmy1tZOk9ljEPVkuLB0jmTJRzZq1g2fSWWo9I1Nqz56MRzkNquhzkQmV3Psahq5xubVZA8jMTNIPhqPcyzd+GMbuadD4TA3h2EuV2xqXs3acQIn7LR6j51iM61dt+y4yUfdWrdu2mrFjpt8Uo2b162btsRH7rTjNr+0GiRP2WUUSqlRwN65dhHYU3HdbsBdd2yheovs2O3hnr5J/tHgi3xlej9PTQys/8Aqxsem+fDwCfKTWb44dcOq83eOnOWfjb7GV6/czJOX1u7Mtx4TuQg3RubZFUjycmqIiOFMTTIoJrI9XBOKsyuwxIl0f7km2WmFiBMVdcNOjw3m5r2fjF0CYKkD4x9KjPDRncfKTVgEQRAEQdiatCoB9yjwoPXzg8AjFcf/qaVycRewuF498kbTqgSczfGUGfRdV2fz3vIGuPY/+q/cvOdTRUe71r2e6QWWG30s6yR3RqYNnFe3qB7faYk5gBeTO/jwsXfw+YlbHB9bEARBEAT30IgE3FeBZ4DrlFIXlVL/HPh94B8opU4C/8D6HeAbwOvAKeDPgH/RkVk7SLtB8jGrwcTB0ELN8+WMrANB8oTVUGRXIEl4RRa5/c5vT8d3AvATfWbivxPqFmAG+nbzk05kkisz1U7P3eZUJkZe1C0EQRAEYUvTiLrFh+qcenuNazXwK+1OqpuErA1qrQZsF7M9JItehv1pBrwZ5grBFeej5bIF5zLJu/xL5bbR7Xbbs/lhfJSihtt7rtDjyVXoJDu7+U2jOJ3u45ae2Y5kkqfzIZ6NmyolOQlkBUEQBEFokW2/RT/Uhk4ymEHfiXSMN/TMcCC0wLOJnSvOO1pukV0ut7AzySmHsrHxYoAjSzt4Y/QKb4le7lgmGeBrs/vwGiWOJDsh+aL42Ov3dGBcQRAEQRC2E9KWulxu0fr7hWNWXfLBGnXJTpZbTOfDFLRi2J9hwJexxnXufc5TcVP3996+iY7VJAN8a/4qPnri7VzJhxwfWxAEQRAEwQm2fZBs6wG3s8nruFWXfF2NuuQeT+ttr6spoZi0Si6uDS46Nq7NU4tmkHx372XCRoGS7szmN0EQBEEQBLez7YPkcia5jWDzuLVZ7LpQ/UyyE+UWsFxysT9kNcxwMJM8mYtwIt1HxFPAUGYArh3YFCgIgiAIgrDZ2NCaZKXUA8ADobEBYr5I+XifrzsatFFvkLBhBrEe1UfM19rH/4vFEJmSh7FAij1BD4mivXlPlzfAeT0xYr5s23OeKfQBU1wfjgOQJ+To2j2XGOdA6McApIr+FWNX0g0fRb3mOtabg9N063m3Gex0e+0bZTOsXbfsuNFH3Vq3btpqxo4bfVKNG9etm7bER+6041a/bGiQrLV+DHgscmD0l6q7sXSjC0yikCFomLW9k9k8Wd16N7wT6T5uicyx0zvBhYyprhA07NKFXAAADdBJREFUCniVJlMymM1lHbmnM2mzTfTugJlJXsivXqt27Hx7fogPW03GF4veNcfqtI8ShUxX7FTSLVtut7MRa98obl+7btlxq4+28+vVrT6pxm3r1k1b4iN32nGrX7Z1uYVPFfCpEvmSIttmQ47jKasuObxcl9xjOFtqAWbXPQCvMrt9J0vOvs85kY6Vu8l1SmdYEARBEATB7WzrIDmoKuuR26u9XW4qslyX3AkZtYu5nhW/O7+xTvEDawOf0xrJgiAIgiAIm4VtHSSH2+y2V8lye+rlTLIto+ZkkDyRXVmv04mGHI/M7mW+4OdHVlMOQRAEQRCE7ca2biZid9tLORDEns32UtQw5l/Cp4rktaecSXZSazhZ8rFQ8BPzOictV83pTB/vfuX9jo8rCIIgCIKwWdjWmeSQg5nkgjaYzEXwKNjlNwvPO9W17lJFNtnJZiKCIAiCIAiCiQTJtKeRXMnFrFkvvCewBFCWf3M6SLY374FzbakFQRAEQRCEZbZ1kLzcbc+ZbOwFK0jebQXJkU5lknOSSRYEQRAEQegk27qZSMw3B0Behx0RsJ4pDACn2RfKEPNFGLJi4wI9jt7TfGGw/LPH6NuQtZNmIlvbjluF3TfD2nXLjht95NYGEt2y40afVOPGdeumLfGRO+241S/bupmIoVMAzNVoyNEKJ1KmZNqIb4GFfBKfMsefyWkW8ynH7ulketltk9kc8WL3164bdqSZyMbZcauwO7h/7bplx60+2s6vV7f6pBq3rVs3bYmP3GnHrX7Z1uUWtrqFU+UQF6vKLTqhbgErZeCkJlkQBEEQBMF5tnVBq62T7JSM2kQuQkErRnwp/KpY1kl2Okiezoe5nAuRK3kotNkpUBAEQRAEQVjNtg6Sgw5KwAEUMZjMhdkTSDLmT5YzyU5rGZdQ/JNj70Q7OqogCIIgCIJgs63TkE6rW8BKGbhOlVuAGXhLqYUgCIIgCEJn2NZBcsjhcguAC9koAHsCiYpmIn7HxhcEQRAEQRA6zzYPkp3duAdw0dpUtyew1LFmIoIgCIIgCEJn2eZBsrM1ybDcUGRvME7QKFLQinTJ49j4giAIgiAIQufZ1s1EbHULr9FHzOdMScRCaRiA60KLACSLfmI+Z5uJrMVWsiPNRDbOjluF3TfD2nXLjht95NYGEt2y40afVOPGdeumLfGRO+241S/btpmIgSZo5ClpsyGHJu/IuIm8oqAVIU/B/L3oLd+LW0S7N4sdaSaycXbcKuwO7l+7btlxq4+28+vVrT6pxm3r1k1b4iN32nGrX7ZtuUXYqhfOaD8a5di4RYwVzT6kHlkQBEEQBGHzsW2D5B7DDJLTJeeVJy7meso/d0L+TRAEQRAEQegs2zdI9nQuSLY37wEkJUgWBEEQBEHYdGzbIDli1QynSwHHx64MkhOikSwIgiAIgrDp2MZBsplJTnU4kyw1yYIgCIIgCJuPbRsk2zXJmU7UJEuQLAiCIAiCsKnZtkFysuTldGaEyXy/42NfzoUpaFMxQzbuCYIgCIIgbD62bTORV9P7+U/TbwMg5ptyfPzLuSi7A3FK9BDzRVwl2r1Z7EgzkY2z41Zh982wdt2y40YfubWBRLfsuNEn1bhx3bppS3zkTjtu9cu2bSYCnRWvPpk2g+TzWSXNRFpEmolsnB23CruD+9euW3bc6qPt/Hp1q0+qcdu6ddOW+Middtzqlw0NkrcyX5i4mWfiOzmcGN7oqQiCIAiCIAhNIkFyh5jI9fDoXM/6FwqCIAiCIAiuY9tu3BMEQRAEQRCEekiQLAiCIAiCIAhVtFVuoZQ6CySAIlDQWr9JKTUA/DUwDpwFfl5rPd/eNAVBEARBEASheziRSb5fa32b1vpN1u+fAJ7QWu8HnrB+FwRBEARBEIRNQyc27n0AuM/6+cvAk8DH13pA0PBxsHes/HvUGyJRSHdgaisZj+zouA2bbt3TVrLTTf/A1lq7du10e+0bZTOsXbfsuNFH3Vq3btpqxo4bfVKNG9etm7bER+60022/HGrwunYzyRr4tlLqsFLqIevYiNZ6EsD6XlMDTSn1kFLqeaXU89mFpTanIQiCIAiCIAjO0W4m+a1a6wml1DDwuFLqWKMP1Fo/DDwMEDkwqo/FJ8rnYr5IVwWlK213im7d01azA93xD2y9tXPCTrfWvlE209p1y46bfNTNvwtu9pGbfFKNm9etm7bER+604za/tJVJ1lpPWN+nga8DdwBTSqlRAOv7dLuTFARBEARBEIRu0nKQrJSKKKWi9s/AO4FXgEeBB63LHgQeaXeSgiAIgiAIgtBN2im3GAG+rpSyx/mK1vqbSqlDwN8opf45cB74ufanKQiCIAiCIAjdo+UgWWv9OnBrjeOzwNvbmZQgCIIgCIIgbCTScU8QBEEQBEEQqpAgWRAEQRAEQRCqkCBZEARBEARBEKroRMe9hlFKPQA8EBobIOaLlI/3+cJdsR/1BgFW2O4U3bqnrWSnm/6BrbV27drp9to3ymZYu27ZcaOPurVu3bTVjB03+qQaN65bN22Jj9xpx61+UVrrjZ4DSqkrwLmKQ33AYpfMDwEzXbDTrXvaana65R/YemvXrp1urn2jbJa165Ydt/mom3+73eojt/mkGreuWzdtiY/caaebfrlaa71+L2ytteu+gIe7aOv5rXRPW9BOV/yzRdeuLTvdXPuttnbb1Udd/tvtSh+5zSebZd3ER1vXR43acaNf3FqT/NhGT6ADdOuetpqdbrLV1k58JHa6TTfvR3zUGltx3cRHYqcjuKLcYiNRSj2vtX7TRs9DqI34Z+OQtXc/4iP3IT5xP+Ijd+JGv7g1k9xNHt7oCQhrIv7ZOGTt3Y/4yH2IT9yP+MiduM4v2z6TLAiCIAiCIAjVSCZZEARBEARBEKqQIFkQBEEQBEEQqth0QbJSao9S6ntKqaNKqVeVUr9uHR9QSj2ulDppfe+3jh9USj2jlMoqpX6zaqzfsMZ4RSn1VaVUsI7NB61xTyqlHqw4/lml1AWl1FIn73kz4Rb/KKXCSqn/qZQ6Zo3x+52+943GLWtvHf+mUurH1hj/r1LK08l73yy4yUcV5x9VSr3SifvdDLjJJ0qpJ5VSx5VSR6yv4U7e+2bBZT7yK6UeVkqdsP6//MNO3rubcYtflFLRitfMEaXUjFLqPzlykxutQdeC3t4ocLv1cxQ4AdwAfA74hHX8E8AfWD8PA28GPgv8ZsU4u4AzQMj6/W+Af1bD3gDwuvW93/q53zp3lzWfpY1eF7d8ucU/QBi437rGD/wAePdGr892WHvrXK/1XQFfA35ho9fHDV9u8pF1/meArwCvbPTaiE80wJPAmzZ6Tdz25TIf/Q7wGetnAxja6PURv6y67jBwrxP3uOkyyVrrSa31C9bPCeAo5gJ/APiyddmXgZ+2rpnWWh8C8jWG8wIhpZQXM6iaqHHNu4DHtdZzWut54HHgp6yxn9VaTzp2c1sAt/hHa53SWn/PspEDXgB2O3SbrsQta2+NHa8Yxw/IDmHc5SOlVA/wfwGfcej2NiVu8olQG5f56KPA71l2SlprN3fu6ygu8wsASqn9mMH4D9q8PWATlltUopQaB94A/AgYsQNW6/uaH1NprS8B/x44D0wCi1rrb9e4dBdwoeL3i9YxYR3c4h+lVAx4AHiilfvYjLhh7ZVS3wKmgQTwty3eypbFBT76NPAfgFTLN7HFcIFPAP7C+sj4XyulVIu3smXZSB9Z/0sAPq2UekEp9T+UUiNt3M6WwSWvHYAPAX+trZRyu2zaINnKgnwN+FhF1qqZx/djvtvZC4wBEaXUh2tdWuOYZMXWwS3+sd6VfhX4vNb69WbnsRlxy9prrd+F+XFcAPjJZuexldloHymlbgOu1Vp/vVnbW5WN9on1/Z9orW8G7rG+frHZeWxlXOAjL+Ynkv9ba3078AxmcLetcYFfKvkFzP/5jrApg2SllA/TIf9Na/131uEppdSodX4UM4O1Fu8Azmitr2it88DfAW9RSt1ZUfz9fsx3KnsqHreb2h8DCBYu88/DwEmttTNF/C7HZWuP1joDPIr5B1DANT66G3ijUuos8EPggFLqSWfucPPhEp/YGTX7o+uvAHc4c4ebH5f4aBbzkxf7zeX/AG534PY2LS7xiz2XWwGv1vqwIzfHJgySrY+fvggc1Vr/UcWpRwF7B+qDwCPrDHUeuEuZKggKeLs15o+01rdZX48C3wLeqZTqt97tvNM6JtTATf5RSn0G6AM+5tT9uRm3rL1SqqfiD6QXeA9wzKn73My4xUda6z/VWo9prceBtwEntNb3OXWfmwm3+EQp5VVKDVlz8gHvA7at6kglbvGR9RH+Y8B91nhvB15z4BY3JW7xS8U4H8LBLDKwKdUt3oaZXn8JOGJ9vQcYxKw5PWl9H7Cu34n57iMOLFg/2zvvfwfzn/crwH8FAnVsfhQ4ZX19pOL456zxStb3397o9dnoL7f4B/MdpsbcSGDP4//Y6PXZJms/Ahyy5vEq8AXMd/cbvkYb/eUWH1WdH2d7q1u4widABHNXvv26+WPAs9Hr44Yvt/jIOn418JQ1lyeAqzZ6fcQv5XOvAwedvEdpSy0IgiAIgiAIVWy6cgtBEARBEARB6DQSJAuCIAiCIAhCFRIkC4IgCIIgCEIVEiQLgiAIgiAIQhUSJAuCIAiCIAhCFRIkC4IgCIIgCEIVEiQLgiAIgiAIQhX/P/9LQcVqJddBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.plot(dates, df['chats'], lw=2, color=viridis(9))\n",
    "ax.plot(dates, df['paid_employees']/100, lw=2, color=viridis(3))\n",
    "set_theme()\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in np.array(ax.get_yticks(), \n",
    "                                                       dtype=int)])\n",
    "plt.title('Time Series of Chat Demand')\n",
    "plt.legend(['Demand', 'Paid Employees/100'], framealpha=.3, fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see weekly seasonality (see below), and a growth month-over-month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series of the first two months of 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.plot(dates[:64], df['chats'][:64], lw=2, color=viridis(9), marker='o', markersize=8)\n",
    "ax.plot(dates[:64], df['paid_employees'][:64]/100, lw=1.5, color=viridis(3), marker='s', markersize=5)\n",
    "set_theme()\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in np.array(ax.get_yticks(), \n",
    "                                                       dtype=int)])\n",
    "[ax.axvspan(xmin=dates[i], \n",
    "            xmax=dates[i+7], alpha=0.15, \n",
    "            color='white') for i in range(0, 70, 14)]\n",
    "plt.title('Time Series of Chat Demand (Zoomed)')\n",
    "_ = plt.xticks(rotation=30)\n",
    "plt.legend(['Demand', 'Paid Employees/100'], framealpha=.3, fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see weekly seasonality. It's halfway on Monday, doubles during the weekend, and drops in the weekend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advanced visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "day_of_week = pd.date_range('2018-01-01', '2018-06-30', freq='D').to_series()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_dict = {\n",
    "    0: 'Monday',\n",
    "    1: 'Tuesday',\n",
    "    2: 'Wednesday',\n",
    "    3: 'Thursday',\n",
    "    4: 'Friday',\n",
    "    5: 'Saturday',\n",
    "    6: 'Sunday'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>paid_employees</th>\n",
       "      <th>chats</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>10000</td>\n",
       "      <td>61</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>10000</td>\n",
       "      <td>126</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000</td>\n",
       "      <td>119</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000</td>\n",
       "      <td>118</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000</td>\n",
       "      <td>117</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats    weekday\n",
       "date                                        \n",
       "2018-01-01           10000     61     Monday\n",
       "2018-01-02           10000    126    Tuesday\n",
       "2018-01-03           10000    119  Wednesday\n",
       "2018-01-04           10000    118   Thursday\n",
       "2018-01-05           10000    117     Friday"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = day_of_week.dt.dayofweek.map(weekday_dict)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the effect of the day of the week (mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_effect = df.groupby('weekday')['chats'].mean()\n",
    "weekday_effect.index=['Monday', 'Tuesday', 'Wednesday', 'Thursday', \n",
    "                       'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the effect of the day of the week (median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_effect_median = df.groupby('weekday')['chats'].median()\n",
    "weekday_effect_median.index=['Monday', 'Tuesday', 'Wednesday', 'Thursday', \n",
    "                       'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the effect of the day of the week (sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_effect_sum = df.groupby('weekday')['chats'].sum()\n",
    "weekday_effect_sum.index=['Monday', 'Tuesday', 'Wednesday', 'Thursday', \n",
    "                       'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.bar(weekday_effect_sum.index, weekday_effect_sum/22, color=viridis(4), width=.4,\n",
    "      linewidth=1, alpha=.5, hatch='x', edgecolor='k')\n",
    "ax.bar(weekday_effect.index, weekday_effect, color=viridis(8), width=.4,\n",
    "      edgecolor='k', linewidth=1, alpha=1, align='edge')\n",
    "ax.bar(weekday_effect_median.index, weekday_effect_median, color=viridis(9), width=-.4,\n",
    "      edgecolor='k', linewidth=1, alpha=1, align='edge')\n",
    "set_theme()\n",
    "ax.set_axisbelow(True)\n",
    "plt.title('Chats Per Weekday (sum is reduced, disregard actual values)')\n",
    "plt.legend(['Sum/22', 'Mean', 'Median'])\n",
    "plt.ylim(180, 360)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the sum, median, and mean, a pattern emerges: the chats are less frequent Tuesdays and Thursdays. <font color='red'>**Note:**</font> the sum is divided by 22, so the overall pattern can be compared to the mean and median. However, the y-axis doesn't represent actual values _for the sum_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "box = ax.boxplot(df.groupby('weekday')['chats'].apply(list), patch_artist=True)\n",
    "ax.set_facecolor(viridis(6))\n",
    "color=viridis(8)\n",
    "for patch in box['boxes']:\n",
    "    patch.set_facecolor(viridis(9))\n",
    "ax.grid(True, alpha=0.3, color='white')\n",
    "locs, labels = plt.xticks([1, 2, 3, 4, 5, 6, 7], \n",
    "                          ['Monday', 'Tuesday', 'Wednesday', 'Thursday', \n",
    "                           'Friday', 'Saturday', 'Sunday'])\n",
    "plt.title('Boxplot of Chats per Weekday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting shifted  values up to two weeks ago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 15):\n",
    "    df.loc[:, 't–%s'%i] = df.loc[:, 'chats'].shift(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>paid_employees</th>\n",
       "      <th>chats</th>\n",
       "      <th>weekday</th>\n",
       "      <th>t–1</th>\n",
       "      <th>t–2</th>\n",
       "      <th>t–3</th>\n",
       "      <th>t–4</th>\n",
       "      <th>t–5</th>\n",
       "      <th>t–6</th>\n",
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       "      <th>t–13</th>\n",
       "      <th>t–14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2018-01-01</th>\n",
       "      <td>10000</td>\n",
       "      <td>61</td>\n",
       "      <td>Monday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>10000</td>\n",
       "      <td>126</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000</td>\n",
       "      <td>119</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000</td>\n",
       "      <td>118</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000</td>\n",
       "      <td>117</td>\n",
       "      <td>Friday</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats    weekday    t–1    t–2    t–3   t–4  t–5  \\\n",
       "date                                                                           \n",
       "2018-01-01           10000     61     Monday    NaN    NaN    NaN   NaN  NaN   \n",
       "2018-01-02           10000    126    Tuesday   61.0    NaN    NaN   NaN  NaN   \n",
       "2018-01-03           10000    119  Wednesday  126.0   61.0    NaN   NaN  NaN   \n",
       "2018-01-04           10000    118   Thursday  119.0  126.0   61.0   NaN  NaN   \n",
       "2018-01-05           10000    117     Friday  118.0  119.0  126.0  61.0  NaN   \n",
       "\n",
       "            t–6  t–7  t–8  t–9  t–10  t–11  t–12  t–13  t–14  \n",
       "date                                                          \n",
       "2018-01-01  NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2018-01-02  NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2018-01-03  NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2018-01-04  NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2018-01-05  NaN  NaN  NaN  NaN   NaN   NaN   NaN   NaN   NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_col = df['weekday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('weekday', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2018-01-01</th>\n",
       "      <td>10000.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats    t–1    t–2    t–3   t–4   t–5   t–6  \\\n",
       "date                                                                       \n",
       "2018-01-01         10000.0   61.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-02         10000.0  126.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-03         10000.0  119.0  126.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-04         10000.0  118.0  119.0  126.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-05         10000.0  117.0  118.0  119.0  126.0  61.0  61.0  61.0   \n",
       "\n",
       "             t–7   t–8   t–9  t–10  t–11  t–12  t–13  t–14  \n",
       "date                                                        \n",
       "2018-01-01  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  \n",
       "2018-01-02  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  \n",
       "2018-01-03  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  \n",
       "2018-01-04  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  \n",
       "2018-01-05  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(method='ffill', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday'] = weekday_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Monday</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>61.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>119.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats    t–1    t–2    t–3   t–4   t–5   t–6  \\\n",
       "date                                                                       \n",
       "2018-01-01         10000.0   61.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-02         10000.0  126.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-03         10000.0  119.0  126.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-04         10000.0  118.0  119.0  126.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-05         10000.0  117.0  118.0  119.0  126.0  61.0  61.0  61.0   \n",
       "\n",
       "             t–7   t–8   t–9  t–10  t–11  t–12  t–13  t–14    weekday  \n",
       "date                                                                   \n",
       "2018-01-01  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0     Monday  \n",
       "2018-01-02  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0    Tuesday  \n",
       "2018-01-03  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0  Wednesday  \n",
       "2018-01-04  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0   Thursday  \n",
       "2018-01-05  61.0  61.0  61.0  61.0  61.0  61.0  61.0  61.0     Friday  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting dummy variables for the weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_dummies = pd.get_dummies(df.loc[:, 'weekday'].str.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, day_dummies], axis=1, sort=False)\n",
    "df.drop('weekday', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>paid_employees</th>\n",
       "      <th>chats</th>\n",
       "      <th>t–1</th>\n",
       "      <th>t–2</th>\n",
       "      <th>t–3</th>\n",
       "      <th>t–4</th>\n",
       "      <th>t–5</th>\n",
       "      <th>t–6</th>\n",
       "      <th>t–7</th>\n",
       "      <th>t–8</th>\n",
       "      <th>...</th>\n",
       "      <th>t–12</th>\n",
       "      <th>t–13</th>\n",
       "      <th>t–14</th>\n",
       "      <th>friday</th>\n",
       "      <th>monday</th>\n",
       "      <th>saturday</th>\n",
       "      <th>sunday</th>\n",
       "      <th>thursday</th>\n",
       "      <th>tuesday</th>\n",
       "      <th>wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2018-01-01</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\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>2018-01-02</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            paid_employees  chats    t–1    t–2    t–3   t–4   t–5   t–6  \\\n",
       "date                                                                       \n",
       "2018-01-01         10000.0   61.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-02         10000.0  126.0   61.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-03         10000.0  119.0  126.0   61.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-04         10000.0  118.0  119.0  126.0   61.0  61.0  61.0  61.0   \n",
       "2018-01-05         10000.0  117.0  118.0  119.0  126.0  61.0  61.0  61.0   \n",
       "\n",
       "             t–7   t–8    ...      t–12  t–13  t–14  friday  monday  saturday  \\\n",
       "date                      ...                                                   \n",
       "2018-01-01  61.0  61.0    ...      61.0  61.0  61.0       0       1         0   \n",
       "2018-01-02  61.0  61.0    ...      61.0  61.0  61.0       0       0         0   \n",
       "2018-01-03  61.0  61.0    ...      61.0  61.0  61.0       0       0         0   \n",
       "2018-01-04  61.0  61.0    ...      61.0  61.0  61.0       0       0         0   \n",
       "2018-01-05  61.0  61.0    ...      61.0  61.0  61.0       1       0         0   \n",
       "\n",
       "            sunday  thursday  tuesday  wednesday  \n",
       "date                                              \n",
       "2018-01-01       0         0        0          0  \n",
       "2018-01-02       0         0        1          0  \n",
       "2018-01-03       0         0        0          1  \n",
       "2018-01-04       0         1        0          0  \n",
       "2018-01-05       0         0        0          0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Casting dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the data into train and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing and scaling `X`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['chats', 'paid_employees'], axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['chats']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-06-01', '2018-06-02', '2018-06-03', '2018-06-04',\n",
       "               '2018-06-05', '2018-06-06', '2018-06-07', '2018-06-08',\n",
       "               '2018-06-09', '2018-06-10', '2018-06-11', '2018-06-12',\n",
       "               '2018-06-13', '2018-06-14', '2018-06-15', '2018-06-16',\n",
       "               '2018-06-17', '2018-06-18', '2018-06-19', '2018-06-20',\n",
       "               '2018-06-21', '2018-06-22', '2018-06-23', '2018-06-24',\n",
       "               '2018-06-25', '2018-06-26', '2018-06-27', '2018-06-28',\n",
       "               '2018-06-29', '2018-06-30'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[151:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test = X[:151], X[151:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train, y_test = y[:151], y[151:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the **mean absolute error** of chat number as the metric. We'll start by training a model, then we'll run it for the month of July. This is a good model because there is a strong seasonality, so values at time `t` are a function of values at time `t-h` (past values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Neural Network "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(X_train.shape[1],)),\n",
    "    Dense(24, activation='relu'),\n",
    "    Dense(48, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mae')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [EarlyStopping(monitor='val_loss', patience=20),\n",
    "             ModelCheckpoint('dense.h5', monitor='val_loss', save_best_only=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = model.fit(X_train, y_train,\n",
    "              epochs = 100, \n",
    "              batch_size = 8, \n",
    "              validation_data=[X_test, y_test],\n",
    "              callbacks = callbacks,\n",
    "              verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = load_model('dense.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.997186279296875"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.fill_between(dates[151:], 0, y_test, \n",
    "        lw=0, color=viridis(9), alpha=.4)\n",
    "ax.scatter(dates[151:], y_pred, \n",
    "        lw=2, color=viridis(9), alpha=1, s=50)\n",
    "ax.plot(dates[151:], y_pred, \n",
    "        lw=2, color=viridis(9), alpha=1)\n",
    "ax.set_axisbelow(True)\n",
    "plt.ylim(320, 470)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in np.array(ax.get_yticks(), \n",
    "                                                       dtype=int)])\n",
    "plt.xlim(dates[151], dates[-1])\n",
    "plt.xticks(rotation=30)\n",
    "set_theme()\n",
    "plt.title('Chat Demand, June 2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### July prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pddf = pd.DataFrame(y_test[-28:][::-1])\n",
    "for i in range(1, 15):\n",
    "    pddf.loc[:, 't–%s'%i] = pddf.shift(-i)\n",
    "pddf = np.hstack([pddf.values, pd.get_dummies(pd.to_datetime(pddf.index).dayofweek.map(weekday_dict))]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_test = pd.date_range('7/1/2018', periods=31, freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for i in range(31):\n",
    "    previous_pred = model.predict(scaler.transform(pddf[1, 1:].reshape(1, 21)))[0]\n",
    "    t_minus_14 = pddf[0, 0:-8]\n",
    "    dummies_day = pd.get_dummies(days_test.dayofweek.map(weekday_dict)).values[i, :]\n",
    "    new_row = np.hstack([previous_pred, t_minus_14, dummies_day])\n",
    "    pddf = np.vstack([new_row, pddf[:-1, :]])\n",
    "    predictions.append(pddf[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.plot(dates[60:], df['chats'][60:], lw=2, color=viridis(9))\n",
    "ax.plot(days_test, predictions, lw=2, color=viridis(9), linestyle='dashed', alpha=1)\n",
    "set_theme()\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in np.array(ax.get_yticks(), \n",
    "                                                       dtype=int)])\n",
    "plt.title('Time Series of Chat Demand')\n",
    "plt.legend(['Chats', 'Forecast'], framealpha=.3, fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizing the number of nurses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will aggregate the number of chats per month, along with the needed nurses. Then, I will make a simple model to extrapolate the relationship between the two to the forecasted values of the month of July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_of_week = pd.date_range('2018-01-01', '2018-06-30', freq='D').to_series()\n",
    "df['month'] = day_of_week.dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating the aggregated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.groupby('month').apply(lambda x: mode(x)[0][0][0]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.groupby('month').sum()['chats'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running a simple model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x.reshape(-1, 1), y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36000.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nurse_need = np.round(reg.predict(sum(predictions)), -3)\n",
    "nurse_need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the number of nurses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.plot(dates[60:], df['chats'][60:], lw=2, color=viridis(9))\n",
    "ax.plot(dates[60:], df['paid_employees'][60:]/100, lw=2, color=viridis(4))\n",
    "ax.plot(days_test, predictions, lw=2, color=viridis(9), linestyle='dashed', alpha=1)\n",
    "ax.plot(days_test, [nurse_need/100 for i in range(len(days_test))], lw=2, color=viridis(4), linestyle='dashed')\n",
    "set_theme()\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in np.array(ax.get_yticks(), \n",
    "                                                       dtype=int)])\n",
    "plt.title('Time Series of Chat Demand')\n",
    "plt.legend(['Chats', 'Nurses', 'Forecast', 'Nurse Need Forecast'], framealpha=.3, fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual values gave a number of nurses at the bottom of the lowest values at that month, multiplied by 100. The linear model replicated that relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please include the code used to generate the forecast and an explanation of why you chose this method, including visualizations and/or summary statistics.\n",
    "\n",
    "- Include an exploratory data analysis and report any finding on the dataset\n",
    ">There is a strong weekly seasonality. \n",
    "- Provide a recommendation on the number of nurses needed per day in July\n",
    ">36,000\n",
    "- How would you evaluate the accuracy of your model?\n",
    ">Here, with the MAE. In real life, I would like to obtain data on the waiting time of the clients (if the supply was adequate, etc)\n",
    "- If the team starts using this model to staff the clinic, how would you evaluate the impact of this change?\n",
    ">I would ideally look for improvement in customer satisfaction, but also waiting times, number of chats taken per nurse, the interval between chats, etc.\n",
    "- What are the next steps to improve the model’s accuracy and its impact on costs and quality of service?\n",
    ">This was based on a time series only. The next steps would be to use predictor variables, such as the number of customers. Also, we could take into account holidays, temperature, construction vacation weeks, and more. Alternatively, data that stretches further back in time would also help."
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