16_DL_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6139ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40a47141",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f09056c",
   "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>date</th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>5.827</td>\n",
       "      <td>2.009</td>\n",
       "      <td>1.599</td>\n",
       "      <td>0.462</td>\n",
       "      <td>4.203</td>\n",
       "      <td>1.340</td>\n",
       "      <td>30.531000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>5.693</td>\n",
       "      <td>2.076</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.426</td>\n",
       "      <td>4.142</td>\n",
       "      <td>1.371</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>5.157</td>\n",
       "      <td>1.741</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.355</td>\n",
       "      <td>3.777</td>\n",
       "      <td>1.218</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>5.090</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.391</td>\n",
       "      <td>3.807</td>\n",
       "      <td>1.279</td>\n",
       "      <td>25.044001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>5.358</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.868</td>\n",
       "      <td>1.279</td>\n",
       "      <td>21.948000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date   HUFL   HULL   MUFL   MULL   LUFL   LULL         OT\n",
       "0  2016-07-01 00:00:00  5.827  2.009  1.599  0.462  4.203  1.340  30.531000\n",
       "1  2016-07-01 01:00:00  5.693  2.076  1.492  0.426  4.142  1.371  27.787001\n",
       "2  2016-07-01 02:00:00  5.157  1.741  1.279  0.355  3.777  1.218  27.787001\n",
       "3  2016-07-01 03:00:00  5.090  1.942  1.279  0.391  3.807  1.279  25.044001\n",
       "4  2016-07-01 04:00:00  5.358  1.942  1.492  0.462  3.868  1.279  21.948000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57476b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ERFRrMciLA1e8MxcAsHTrAc3jidiP9/6MjbEuAhERERFRrcbhmnGstLwy1kWwbev+Q7EuAhERERFRrcYgL46t210U6yLYMrBdI6QGuEgeEREREVEsMcgjz8zduA8pXAmdiIiIiCimGORRzMzZsBffLdoW62IQERERESUVJl6hmDnnlRkAgG652eiWlx3j0hARERERJQf25JGnnAzW3Lq/2PNyEBERERHVVgzyYuyVyWtjXYSYCzgKDYmIiIiISIRBXow9/vNKT493++iujve96Ii2uOuE7h6WhoiIiIiIoo1z8pLIKxcNwNheeY73P29gW6zbfdBdIdgpR0REREQUU+zJSyJ1M1Jd7R8IAIriUWFsnTgG5yQiIiIiSlIM8pJI0IMIrVerHFf7i+bXKRblYoxHREREROQdBnlJxG2MFwgAnZvXx9fXHYm/H93Bm0LBulyBAMM8IiIiIiKvMMhLIl705AFA/7aN0LJhHU+OBQCiUu0sLAn/u7S80rNzERERERHVdgzykkhl0LsJdU7jRVGnnCj4LK9UVM87OxcREREREUVikJdEUlwOe1TPp1uVX+jwGJFEAWNWWs1Xr1l2hqNzERERERFRJAZ5SaRVI3dDLNUx4qdzN7ssTQ1RT556Hl5mmrusoEREREREVINBXhI5rEUDV/s77Qgc27Nqbb6cOunS+1hl3CQiIiIiImcY5JHQ8K7NpLetU70+X8GhcvyweHvE86KePIZ4RERERET+YJBHYeo5ebnZmdL7qecCbtpbHPG8qNNO/dgX87ZIn4uIiIiIiMwxyKvF7jnpMM3f6uGaeTlZ0sexGuYp7MlTPfbO9A0IMsUmEREREZEnGOTVYvpATh2rNbfVk2f+vCh8e3v6Bs3fa3cdlD4fEREREREZY5CXwF67eICr/QPCBQ/ss1q6QQlq/66oDOLlSWs1jy3ZWuBJWYiIiIiIaru0WBegNvrfxDX4bcUOXDiknavjBFyui6fXqVl99cE9K4d6uObjP69ERmrk9m7X+CMiIiIioioM8mJg055iLNi0H8d2b+7qOG7DInVc9cRZvZFiNe7SgOxwze0Fh/DK5LXCbRjjERERERF5I6bDNV9++WX06dMHDRo0QIMGDTB06FD8/PPP4ecVRcG4cePQsmVL1KlTByNHjsSyZctiWGJveBXQuD2OevdzB7bxrRxBRcGBknIcOFRhuA178oiIiIiIvBHTIK9169Z4/PHHMXfuXMydOxfHHHMMTjvttHAg9+STT+KZZ57Biy++iDlz5iAvLw+jR49GYWFhLIvtGbfrgXsZF+mHXI7oIr9OntXrOFhSgaOfmIjjn5ticn7p0xERERERkYmYBnmnnHIKTjzxRHTt2hVdu3bFI488gvr162PmzJlQFAXPPfcc7r77bpx55pno1asX3n33XRQXF+Ojjz6KZbFdCwU0bhcN8CpxikjbJnXx96M7SG1r9TpWbD+AgkPlptukMsojIiIiIvJE3GTXrKysxCeffIKioiIMHToU69evR35+PsaMGRPeJjMzEyNGjMD06dMNj1NaWooDBw5o/os/Xo3XdLm7xf5tG9eVOo5VT55MMNu4XobUuYiIiIiIyFzMg7wlS5agfv36yMzMxLXXXouvv/4aPXr0QH5+PgAgNzdXs31ubm74OZHHHnsMOTk54f/atHE+18xvrodrelMMQ2mpsl8P8xci8zoLSyqwZifXyiMiIiIicivmQV63bt2wcOFCzJw5E//4xz9w6aWXYvny5eHn9XPFFEUxTdl/1113oaCgIPzf5s2bfSu7U94lXnGdX9P02TTJbJvBoMXzElHeVe/NxXHPTOai6ERERERELsU8yMvIyEDnzp0xcOBAPPbYY+jbty+ef/555OXlAUBEr93OnTsjevfUMjMzw9k6Q//Fm1DopNiYlXfW4a0Nj+O4HBYHSJfsybN6HXY6LGev3wsA2HmgBPM27rOxJxERERERAXEQ5OkpioLS0lJ06NABeXl5+PXXX8PPlZWVYfLkyTjyyCNjWELv2BmuOaZnLto0rqN5zO9lB9IEi5aLWM7Js/FCQ2c88vE/cNbL0zFv417pfYmIiIiIKMaLof/73//GCSecgDZt2qCwsBCffPIJJk2ahPHjxyMQCOCWW27Bo48+ii5duqBLly549NFHUbduXVxwwQWxLLZrodhMZhhjiCig8zshpfRwTYuXYed1hl5TRfVBJ6/ahQHtGkvvT0RERERU28U0yNuxYwcuvvhibN++HTk5OejTpw/Gjx+P0aNHAwDuvPNOHDp0CNdddx327duHIUOGYMKECcjOzo5lsT3z6/Id0tuK4i1/Z+R511NoNWfPjNtlJoiIiIiIapuYBnlvvvmm6fOBQADjxo3DuHHjolOgKAmtb7cyX35R95SUQOS6eH735EkP1zQPxWz15EGfaEd6VyIiIiIiQhzOyasNnHSQNc/OjEhw4nYxdKvsnKkpcl8PqyDOVqCmK5KdAJGIiIiIiBjkJYxuuZFDVN2OprTaPdXgBJcd2V7zt1UY5iZQY4hHRERERGQPg7wYcBKbCROvuCyHVQBl1JF3/mDtAvNWMZxVYhY1/WtiTx4RERERkT0M8mLAySLmKYLMK24XQ7eaS5dmEOW1b1JPexyL89jLrql7TYzxiIiIiIhsiWniFXLH7XBNq/hJvxb6rH8fi3qZachKT7V3Hp+Ga1ZUBrF02wH0atkAaZILtxMRERERJTvWjBOY6+GaFsGXfohoVloq6mdGtgtYHafSxnhN/Wsy2/fRn1bi9P9Nw33fLZM+PhERERFRsmOQFwNOe+D0sZTrnjyL2CtVN0Q04PDbMu775dLb2nlNb01bDwD4aNYmu0UiIiIiIkpaHK6Z0NxFeaO6N0erhnXQp3WO8PmIIM/gOE3rZ7oqh5mKShcrqRMRERER1ULsyYsBp+vb6Xu51u486KocWempmHLnKLx04eHC51s3rKs7f2S5R3VrhhN7t3BVDu05tH+/O2Mj8gtKPDs+EREREVGyY5AXAzJDEhvXy7DcpqiswnVZUlMChlk6c+qm47WLB4T/VnfszbzrWHx/w1F4+/LBECT+dEwUAIeGZRaVun+9RERERETJjkFenPr2+mHhf79/5eCYlaN7XoPwv9UBWF5OFnpXD/N0OzdQTXSs8sogHvlxOXre/wumrdnt3cmIiIiIiJIQg7wYkImJ1MFOr5biOXPRoC6HYrCggZ3FzmW8PmWdtgwI4PU/q3rzHv1phXCf816dgXkb93pbECKiGAgGFdz48QI8++vqWBeFiIgSFIO8OKVOemIUQ3nYgWYoI63mK2KUjfPP1bs8PecjukBOE2galGHW+r046+UZnpaDiCgW5mzYi+8XbcPzv/8V66IQEVGCYnbNGJAZ3piq2ihYHdlELqHgf5iX2yALZx3eGhlpAdQTrJEHAKUeZsCMxmsiIopnJRXMKkxERO6wJy8GZAKZgCDI0zume3PPymTm6XP74rEz+xg+nxrFwMzuyFBFUfDq5LWY+hfn8hERERFR7cAgL05phmsaRDZ5OVlRKo05/Xp6bnhxpHW7DmL4kxPx6ZxNmLR6Fx77eSUuenOWcNui0gqMfW4KHv95pQdnJiIiIiKKPQZ5MSATyGRnpaFVwzrIbZAZXmxc32GWGgjgodN7AQD++7f+HpdSXrNs7xZDF3UKqh9SjCJelbu/XopNe4vxf18uwfb95mvsfT53M1bmF+KVyWttlpSIyB8ctE5ERG5xTl4sSNzBUwIBTP7nSCgw7ilLSQng4iPa4ZwBrZGVnuptGW04tW9L3PftMt+OX1hib328MtUcwX9/vcR02wqvU4MSEREREcUYg7w4FQCQmirX0RrLAA8AMtO8O79oMfRP5262dQwvF2cnIiIiIko0HK4ZA6JAJmKbBApU0lI9nJPn8lCVQQVzNuzzpjBERERERAmIQV4MyAQyogycEtPRYiLNw64zt6/xc5u9fkRE8SaRGvmIiCg+Mcgj17xc206xWCTBKgicbHNhdq7LR0TxTCbZFBERkZ6jIO/PP//ERRddhKFDh2Lr1q0AgPfffx9Tp071tHDJimGFMav6zKodhdEpCBFRjOw5WBb+N2M8IiJywnaQ9+WXX+L4449HnTp1sGDBApSWlgIACgsL8eijj3pewGTEziNjPyzeFtXzVagycar/TUQUC5v3FuOWTxeG/w6qorwfFm/Dr8t3xKBURESUaGwHeQ8//DBeeeUVvP7660hPTw8/fuSRR2L+/PmeFo60akNw+MsydxUYu63e+4rLw/8+WGpvqQYiIq/9tkJ7DQxd0gqKy3HDRwvw9/fmorSiMvoFIyKihGI7yFu1ahWGDx8e8XiDBg2wf/9+L8qU9GSyayaa326L/E74aeOeIk+OUxsCZyJKXKGePHVgd6iMQR4REZmzHeS1aNECa9asiXh86tSp6NixoyeFIrF4npvRuXl2VM935kvTo3o+IqJo0Lc7ha776sv/2l3eNHIREVHysh3kXXPNNbj55psxa9YsBAIBbNu2DR9++CHuuOMOXHfddX6UMemw98i9PUVlwsetsnMSUXJL9GyU+tKHgzzVE2e9PB17DpZGrUxERJR4bAd5d955J04//XSMGjUKBw8exPDhw3HVVVfhmmuuwQ033OBHGZNOssd4Tepl4Mt/DHW0r5dr7llZs/Mglm4tCP+d4HVDolrv0zmb0O/BX7Fg075YF8UzQYML08a9xVEuCRERJRJbQV5lZSUmT56M22+/Hbt378bs2bMxc+ZM7Nq1Cw899JBfZaQEk5mWggHtGjvatyJoHml1zzMfFlpWIZ8h87hnJuPPv3aH/2aMR5TY/u/LJSg4VI4bP14Q66J4xijI+3r+Vtz37VIELa6ZRERUO6XZ2Tg1NRXHH388VqxYgcaNG2PgwIF+lSu5cbymYw2y0k2fn7jK3mLoRJT4dh4owewNe8N/J9MlVgn/rzaYe3/mRgDAUZ2bYkzPvCiXioiI4p3t4Zq9e/fGunXr/ChLrZFE9Q+hgI81LDdz7hJ9rg4RiR3/3BTc8FFy9N7pL1NK9eAEow67q9+fh0r25hERkY7tIO+RRx7BHXfcgR9++AHbt2/HgQMHNP+Rf5KpddopN3Gael9RwMcgkCgxqde7BJJrmZrQcE2zYZm/r+AC6UREpGVruCYAjB07FgBw6qmnanpsFEVBIBBAZSXX77HiNFhjDOJu3lxQUZBSXfkT1ZeKyyrRxMXxiZLJXV8tRkoggEfO6B3rotQaOw6UoCKonVcsc807WFrhT4GIiChh2Q7yJk6c6Ec5apVkamUWsRvENsvOxK5CuXTgRkkIZKj3FB2n1EbSFqJkVlhSjo9nbwYA3HJcVzTLzoxxiWrMWLsH63cX4YIhbQ23ScRRD8u2FeCkF6ZGPB7uyWMrHxER2WA7yBsxYoQf5aBazE59zE09R11JElWYWIkiilReGV+NH397fSYAoGtufQxs7yyLb7xQFAV/7TyIdk3q4tuF2wy2qfpfs2l3G/dwOQUiItKyHeRNmTLF9Pnhw4c7LkxtkYitzHaEXt9lR7bHO9M3SG8vw00Ypp2TF/k8gzyiKorBv/1QUl6J/01cg2MPy0W/Ng0jnv9y3hZUBhWcO6iN5vFNe4tNg7yt+w/hg5kbccnQdmiRU8frYnvipyX5uP6j+RjQrhEGtGsk3EaR6Ml7/ve/cErflujcvL4v5SQiosRjO8gbOXJkxGPquXmck2fNKqbp1apBVMrht3+M7CQX5Nnpy3MzXFO1q7AnL746LIhiRvZn9urktfhw1iZ8ds1Q5OVkhR//dfkO3P/tUjx3fn8M7mDe2/balHX47x9r8N8/1mDD4ydpnispr8Ttny8CAIzpmYuGdTMiynigRJt0Bai6xl785iys21WEKat34cebjpZ7QVH28exNAIB5G/fh8LYNhduEevCsPpOpf+1ikEdERGG2s2vu27dP89/OnTsxfvx4DBo0CBMmTPCjjLVOoncohYI22R66lGj15Kn2Zk8ekTHZTLOP/bwSm/YW4z8TVmke//t7c7GtoAQXvznL8hirdhQaPlemGip6qFzbgBgq4Sn/jZzHBgDrdhUBAJZtS+ysz69OWYsPZm60/EwquIwCERGp2O7Jy8nJiXhs9OjRyMzMxK233op58+Z5UrBkluzDNUNSJF+onXX13M3JU/9btISC82MTJROr5Ub0SsrFIzjcBh7qK4O+GKHfsGg+mp9rdTpRVhHE1DW7MLhDE9TPFN92jd7mt6dtAAD8ZNEb+fCPK3DW4a3RqF6G6XZERFQ72O7JM9KsWTOsWrXKekNy7MULDo91EWzxo5rl1WLoorone/KIqtj9JRj9dOz00otolumJOKm7Y0fT0xNW4Yp35uLKd+Y4PobM9emDmRsdH5+IiJKL7Z68xYsXa/5WFAXbt2/H448/jr59+3pWsGRm1cpsdC/v16YhRnVrhomrdvlQKu+EXp58T578sb3qyRP1TjDII6ri1W9B9hpgxGxvswaf+OrHAz6dW7Ucxaz1ex0fQ+Yz4RWMiIhCbAd5/fr1QyAQiKgkH3HEEXjrrbc8KxiJxdswJJFQCf0I8lyN/tIM1/T42ERJxCoTrSy3QZ4my6euIKblivJl8psFW/Hjku147rx+qGcwHNMtzrkjIiI7bN+N1q9fr/k7JSUFzZo1Q1ZWlsEeVFsFJAcDb957SPqYsgkhRKzWyXNzbKJkou4lc9OrJzNc07S3zuTc8RTz3PLpQgDAq1PW4bbRXX05xy9L8y234SWMiIhCbM/Jmzx5MvLy8tCuXTu0a9cObdq0QVZWFsrKyvDee+/5UcakkwCdcZ5w24rvNXX9RxjkRa8oGvuKylAoSANPFCte9eQVlblbUkcdyBWWVGB/cZnqufgbrrnnYKmt7dWXyDemrjfeEFUBpBU3c5aJiCi52A7yLr/8chQUFEQ8XlhYiMsvv9yTQiU7o3XhTuvXEgBwwzGdo1kcz4WGlJq14ndxuJ6Tuzl5FksoxKBroKi0Av0f+hW9x3H5EYofVmtKRvArqlKd+oTn/0S/B38VPRU3jC4hRm+P1z1v7MkjIqIQ20GeoijCeWFbtmwRLq9A8p49tx9m3nUsTuzdItZF8YRRT97g9o1xzGHNAQAN66bjgyuHSB9z1Y5Cx8MqLRdDj0EFadPemvTvHC5K8UKzpmQUz7t5bzH2FtX01jntmYrV3OVYNBQRERGJSM/J69+/PwKBAAKBAI499likpdXsWllZifXr12Ps2LG+FDLZGNU/UlICyMtJ/LmNoZdn/DqBW4/rinaN62Fkt2Zo2bAO/nVCdzz+80qp4381f6ujclktoRCLICtV1d0ZVIDU+BrhSrWUVSZavxz95EQAwIbHT6o+t/G28fhTiXVyFDYUERFRiHSQd/rppwMAFi5ciOOPPx7169cMt8vIyED79u1x1llneV7AZBSPlRM/GPXkpQQCyEpPxQVD2oYfS7OxoNYHs5ytBaWZkyeojMWifqZ+jyqDiiboI4oVqwaRqJXD5Lk4m/ILwH6QFY+vgYiIkoN0kHf//fcDANq3b4/zzjuP2TTJklGQ9+8TD5Petl2Tuti4p1jz2IJN+x2Vx2puUSzWyVPHdFynj+KFTOKVyau9Wa/TbGil056pWMVOXy3YisfP6oOMNNszITzBKwgREYXYvhNdeumlDPBcSvrW24DmfzQy01LQq1Xk3E2j96R7XrZnxbKekxfb4ZqVnM9DcUL9U1iZf0C4zaVvzfa/HCbPGSWwirUPZkaONIjWHEE2FBERUYjtIK+yshL/+c9/MHjwYOTl5aFx48aa/8iam8pJIs25ENVrjIZlmg3t1BvRtZmj8gQt5+Q5Oqwr6tcX6/k8RCHqhCc3f7Iw+uev/jGazskzuYzGsiFt6375dT+JiIj8YjvIe+CBB/DMM8/g3HPPRUFBAW677TaceeaZSElJwbhx43woIiWazLRUAOLWa6M6m9FUNFGQ53TeWrz35DEzH8ULuz8FpzGVoij4cfE2w/PPXLfH83PGgjpjqPrfXvt+0XbDhsCCQ+X4ct4WrslJRFRL2A7yPvzwQ7z++uu44447kJaWhr/97W944403cN9992HmzJl+lDHpJOtwzefO64d2TeriufP6GW5jFEgZDWcSPey8Qqn+d3wkXglwTh7FoWh9F8cvzRf+7oKKggMl5bjx4wWG+8bqOvroTyvw8A/LHe9/yVuzPCyN1qa9xRi/NF/43HUfzsPtny/CnV8s9u38REQUP2wHefn5+ejduzcAoH79+uGF0U8++WT8+OOP3paOEsrp/Vth8j9HoZvJPLpg0N4xRb12Tit3VsM1Yx1ksSOP4kW0vorzNu4zPP+BQ857nPyar1dwqByvTVmHN6auxz6DHjmry8jSreI5jl4xek+nranqFf3ZIAgkIqLkYjvIa926NbZv3w4A6Ny5MyZMmAAAmDNnDjIzM70tXZJyU3lJdHYCqZuP7SIcruk0iYE2yIssR6znO8b6/EQhsf4qBhXFVbISq1237j+Es1+ejh8Xb7d13IrKmlaqSoM36a1p67G/uCYAnGKShTRaCVkmrtoZlfMQEVH8sB3knXHGGfj9998BADfffDPuvfdedOnSBZdccgmuuOIKzwuYjF74Y02sixAzRnVHUV3n1tFdPR2uqe4pE/UoxronLdbnJwrRNzhEuwFCUax/52566+7/dhnmbtyH6z+a7/gYZmd/4feaa/wlJllI/QjxRJ/U5W/P0fz9/owNPpyZiIjiifQ6eSGPP/54+N9nn3022rRpg2nTpqFz58449dRTPS0cJR+7QyLFPXnOzq1Y9OTd+PECnNi7hbODO2SVDIYoFvTfREWJ7hw4qfO5KE/BIfvJT279dCE27CkK/33Rm7Px441HIUUwpLyotELqmH68pzKXkWd+XY2Lh7b3/uRERBQ3bPXklZeX4/LLL8e6devCjw0ZMgS33XYbAzySYlQBGdMjT/i4KJGm0xZ8dU+ZqByVQUW6cuYHBnkUL/RfRatvptfDDhUolr/zaOZdKasI4usFW7Fg0/7wYyu2H8CCzeL5b7JvR6xycBktWUNERMnDVpCXnp6Or7/+2q+yUC3WLFs8n1OUeGX8MmeJA6zm5Jk97hf12RjjUbzQ/w6iPVwzKNlz+MyEVcLH1UGnF/GM0XWh0mYiKb1YBVvRmgtIRESx42hO3jfffONDUai2E/baeVgZkQvyPDsdUcLS/zyWbvMnI6TRz1smqAwEAobzm1dsNy+v3dEAdht/rC5boTUxYxVrMcYjIkp+tufkde7cGQ899BCmT5+OAQMGoF69eprnb7rpJs8KR8nnoiPaGj4XCAQiapcO1z0X0qyTZ7hNtBNMMKqk+KPofiELNu1DvzYNo3b+oFTiFTn6n1gwqGD2hr22ylNp0Poza90eDO7Q2NaxAOCXZfk4oXcL+DFgU//ZiTDGIyJKfraDvDfeeAMNGzbEvHnzMG/ePM1zgUCAQR6ZuvfkHra2Fw1nGtS+EeZsEM+FMaOuqBkFV0aVOfV+JeVB1MlItX1+K4z3KF7ov4tpXra2SJ3fOspz2hv19YKttvcxuiw8/etqnNy3peAZ88KFgrxY9ahxTh4RUfKzHeStX7/ej3KQpESOAwIBIDPNXnAkqozYPUaI1WLoALBg036kpgYwqltz4fP3fLMUH87ahJ9uOho9WjZwVA41BnYUj/Tfy9IKl5PPXJ7fS5NM1q0zEjRp/FmVX2j7eN8s3Ibnzu8fw8Qrzvedua5qUfUjOjYJP7ZmZyFW5hfipN4tON+PiChO2J6TF1JWVoZVq1ahoiJ22QgpsVhV3ERVA1GQ17hehqPza9fJExfmqvfm4vK352DTnmLh8x/O2gQA+N9E79c6lBlmRRQN+u9i/Uzz9kCn1XqjgCCoKHHVomW08DkgHhUgG+eY9aj19KARyYjTQOxQWSXOf20mzn9tJorLau79xz0zBTd8tACTHQTQRETkD9tBXnFxMa688krUrVsXPXv2xKZNVZXem266SbOGHpEX1C3O14zoCMB5BkxFoicvZMv+yCDPap09t+wecvqa3fh+0TbPy0Gk/320bVJXet99RfJr0BkNm1Ygs2yD9GlcM+vJm7cxcui4zG+5tKLS9DX8/eiOMkWL4GaReCuHyivD/y4qrYx4funWAt/OTURE9tgO8u666y4sWrQIkyZNQlZWVvjx4447Dp9++qmnhaPEd/OxXaS3FVV41AsNh+YFOY2vtOvk2T+IupXaixgvGFRw9JMTHe9/wRuzcOPHC/DXDvvDxYiM7CosxYFD5doHbXzfr/9ovusyBBUFuw+Wmm7jZzCjZ9aT98ZU0RQG6zds+/4S0yDv9P6tJErmTIrDMTzq4opGHnCoJhFR/LB9qf/mm2/w4osv4qijjtJc0Hv06IG1a9d6WjhKfLeO7upqf3WdoaI6SvtxyXZHx5KZk2dmzc6DwnI5tWDzfs3fZkWauW4PXp+yThicvv7nOveFIQJwoKQcgx75DZe8NVvzuJ2fy/S1e6S3NQoKikorcdILUy32tVEo9X4O9rFKyOTEyvzCqAaqap4kXomj4bRERBTJdpC3a9cuNG8emZSiqKiIrXjkOXVl5NXJ7oKZP/+q6YlzMtzS6wWWy3TJLMx6F89/bSYe+WkFfl4auRB8SXl0k2JQ8lq3q0j4+P7icqzZGb0e4w17xOXwwnLdGnpfzttiuY8fiWAmLMv3ZS0DP2/D6mMzxiMiim+2g7xBgwbhxx9/DP8dqvi+/vrrGDp0qHclI4J2Tt7Ynnm29z+tX0168/9NrOlpNht+JcOLFngniVaWbYuc88K2FfKKUdbF6z+aj+OemYLVURoa7Of6keoeeQC4/fNFlvvY7cmTKf5XC7ZiV6H5kFS/OO3JU1/3RK+R1yIiovhhewmFxx57DGPHjsXy5ctRUVGB559/HsuWLcOMGTMwefJkP8pIBAD425C2GL8ssifLzL0n98C3C7XJScoqgrj87Tm2z6+ueHoy2klXSZKpRlZUKhFlIfKKVePF61PW4alz+vpeDpmvt52RI7+v2IFjD8t1XB63jUJGZq+3tyi7DP27UlIemSDF6eWLGYCJiBKH7Z68I488EtOmTUNxcTE6deqECRMmIDc3FzNmzMCAAQP8KCPVElYVTLsLMp87sDWa1s+MeHza2t2W+4rqdOrHNhossSBLURQ8OX6l5Tkj9hNsy3iPvGIVN30+bws27I4cSun1/Divv9NXvjvX1f5m2TVF4uk3eeunCyMfdPh5qd8G0WceqzmGREQUyXZPHgD07t0b7777rtdlodpOVT/43wWHVz9U82CqzSCv0mCqmkyFTTRnT/3YEpepwudu3IdFW+wfI9SD51fPAtVuMsHaiu0H0L5pPV/LIfPtjmY4Yff35uecQiv6z1A0j9dxTx6vO0RECcNRkFdZWYmvv/4aK1asQCAQwGGHHYbTTjsNaWmODkcU4aQ+LQBo5wjZ7ckzSq4i0ygvmoOjf2j2+r14d/oG3HtyD+TlZEVsb0a8lphM8Bn6X1a2yHuyDSl+V/Zljh+t+V9PjF+JTTZ77g+WVlhvFEOBQAC7Cksx7vtluHBIWxzZqanUfrzqEBElDttR2dKlS3HaaachPz8f3bp1AwCsXr0azZo1w3fffYfevXt7Xshkc1LvFo6XAaht1Ovk2U1+YLT9eEHLtl5o7puaPrA699UZAIDC0gq8d8VgW2UTlUym3hwqg2a4pq0zExlLlYicAgHg5k8WenK+XQZr4cXLd3rb/kN4eZL9pYGWbavK4PmFROZOr8nMVUwJAPd9uxQ/L83Hj4u3Y8PjJ0kdW33dmbhyJ84f3FZ3bltFJSIiH9mek3fVVVehZ8+e2LJlC+bPn4/58+dj8+bN6NOnD66++mo/yph0zhnYOtZFiEui+oG60rll3yFbxxP1dq3YfgBfzreueFXYCCg373U3P8+O0EvyY90uItlkJt8t2ma9kYRyQWMKINmT53DQYW6DyHm6IsGggn98MM/ROULukMjcGdIsW65cXgggYPt6CmgTr/zrqyXYsi961z4iIrLHdpC3aNEiPPbYY2jUqFH4sUaNGuGRRx7BwoULvSxb0vJkIdpaQt2Tl5bqfrimKGmEiHC4pkFgpa6QlpRX4q2p67He4jyiVyITtoXOpX5tGam2f8ZEQjKXpsmrrRMXyTIK5uSya9o71+7qXsM2jepKbT9r/V5H82adOrxtQ0+O88HMjZZBstNbkP6w+QUl2uM6OywREfnAdu2wW7du2LFjR8TjO3fuROfOnT0pFBlL5qlYouBX/ZDd4FgUqKVLBkSigFKm8+zFP9bgwR+WY9R/Jplu53S4ZmgTdVlGdGtmvSORRz6evSniMa+vSzK/NbsBxdXv2cuwKVp6wE+D2jf25DjFZZX4YbH1dAAnyyEk8/2HiCjZ2A7yHn30Udx000344osvsGXLFmzZsgVffPEFbrnlFjzxxBM4cOBA+D8SY0eemCjng3q45oB2jSI3MCHKrlknI1Vq34y0yJ+GUbIT9RC3Wev3SB3fbkp2fRnU+9tNSEPkNafDN42Gh/qRPXb+pv32dojyz+qyI9t7diyrReudBrBWgSHvbURE8cN24pWTTz4ZAHDuueeGb9ChoSGnnHJK+O9AIIDKyui2hFJiE/XUqbP9Na6XYet4bjJQVgrmChkdTT00SvaUhwSVLKMKVGFJecTxgw7OSWQlXuroFUbrn6jsKy633MaJsoogzn9tBvYf8uf4IteP6oS01BQEAtrfc48WDbB8u/0GU6tRD0ZzIa3orzX603CdPCKi+GE7yJs4caIf5ahVeCMUE9VL1JUVu+vk3XRsFwDAxUe0w/szN+KM/q2k9xUlXpHpfZOtOokO9fqU9Xj63L4Rjx/79OSI/dT7czkFshJqeEsUouy2ev/+eonp82ce3gpfzd9q+9wTV+203+vnUuie0LR+JnYV1mQc7d0qx5cgb+v+Q2hUL932cXmtISJKHLaHa44YMUL6PyuPPfYYBg0ahOzsbDRv3hynn346Vq1apdlGURSMGzcOLVu2RJ06dTBy5EgsW7bMbrEpAaQIgjh1YCeT3j3k4iPaoV+bhgCAVo3qVB0/EJDu9Vq8ZX/EY0YVHPWjsuuHiY715fwtwmFWOwvVaeYjE6+w2kVmVuYfwKBHfseHszZabuvnd0lRFExfuxv7i6vWiMwvKMH3BsM8yyR68qz0r/79R5TDYj+vMtcW6dbKe/xM4+WFjC5tTuNymanH6kvQoTK5UTf6y9ZDP6zQ/J1A7QhEREnPUVq+kpISzJ49Gz/88AO+++47zX92TJ48Gddffz1mzpyJX3/9FRUVFRgzZgyKimoyEz755JN45pln8OKLL2LOnDnIy8vD6NGjUVhoPueA4sfz5/dDemoAr108wHQ7UeuzOvATBYEhd47tpvk7UzWnLrSbUv1/Ml6atBbbC7QpxqWyX0od3dhug3XD9LTDNRnmkbF/fr4Yuw+W4u6vl1pu68dXaceBqgyMXy/Yigten4Uxz04BADz/+1+G+5RVuA/yzK4XZryKUyqCiiboMRuJUDP1Qfu4qDGoXRPr7KB2e21v/mSB8PFgUMG475bh24XiHtGFm/e7Oi8REfnH9nDN8ePH45JLLsHu3ZFptO3Owxs/frzm77fffhvNmzfHvHnzMHz4cCiKgueeew533303zjzzTADAu+++i9zcXHz00Ue45ppr7BY/LtS2++Bp/VrhpN4tkGbRvCyqA8nW0/TJR9SN8aGhUEYV2CEdGmPW+r0Rjw997A/8fPPROKxFg+pjSgzXlK0kG2xXWi5XuVW/vp0H5AJDqp1ivabiHZ8vwvtXDsFPS6oyPu4sLMWnczaZNk540ZNnlJDIr+UF9FJTAkgJBMJJZMyGUBo9Ixo23rheBjbuMV+fzu7Q9gnLIzNmA8D4Zfl4Z/oGvDO96joueuv+Uo0+qGW3NiKiuGa7J++GG27AOeecg+3btyMYDGr+c5topaCgak2ixo2rUkmvX78e+fn5GDNmTHibzMxMjBgxAtOnTxceo7S0VJPhk1k+44NVgAeIW4Fl5uYAkRUodUAWekpRFGElxaxCdP93qqHBBkVRH1N+Tp54S6sKeTjximq7R35aYbA1kV3OAsKjuzQ1fG7NzoMAtA0T//flEtNeHy8C09QUp+tHeheqqANKs+LUvBXa1y16H544q4/leWWGtss0SOlHFoiuW6Ore2aB2teASUQUz2zfBXfu3InbbrsNubm5nhZEURTcdtttOOqoo9CrVy8AQH5+PgBEnCs3Nzf8nN5jjz2GnJyc8H9t2rTxtJxe4H1QTBRrlVbINRzoAzVtkBeofkxchTUL8tSVNKPAbNNeVau69Jw8g/NZ7FdaPYyNCRDIiRdMhkgCzodr/vmX9QLp+h40s84mp0uMqDldWsSrQEVRFOGIAuE5q5+7enhHAMAJvfIAiNf17Jqbjf4WC6c7HaqqpigKvl2onTNp9anw3kZEFD9sB3lnn302Jk2a5HlBbrjhBixevBgff/xxxHP6Fl+zTHF33XUXCgoKwv9t3rzZ87KSP0SVoN6tGkrtaxrkGTxutK9aqWpukEy9U7ZqarQOmLp8L09ai2vfn6d5PpSYJcYj8ChBPfPratPn/fxa6b+zZsMXvfh+iwKdJVsKohao6M8T6tEUnrP6pH8/uiN+uPEovPC3/gCAO8Z0Q9fc+uHtQkPHrbJnerF05p9/7ca8jfs0j1kPdWWYR0QUL2zPyXvxxRdxzjnn4M8//0Tv3r2Rnq5Nw3zTTTfZLsSNN96I7777DlOmTEHr1q3Dj+flVbVm5ufno0WLFuHHd+7cadiTmJmZiczMTNtloNgTVUyO6NgY407pgS652ab71s3QfpVFlURR9SQ7K02TpEVv8ZaC8L+37T9kuF3NeeVqp0aVJXXl7YnxKwX7Vf1vrOdZUXLys4NY/9sw7cnzoCCiIYvnvjoD3VuYX0u8ClTGL9GONsmvTkAjsr96zb9AIIBerXLCj+flZGHCrSOws7AEH8/ajPMHV41MsQrirILA7nnZlsHuyvzIqQ5W+3gRXAJVPbkfzNqIw9s20rwfREQkz3aQ99FHH+GXX35BnTp1MGnSJM0NMRAI2AryFEXBjTfeiK+//hqTJk1Chw4dNM936NABeXl5+PXXX9G/f1XLZllZGSZPnownnnjCbtHjBxs7hUSVq0AggMuGdRBsDVw7ohPqZ6Zi1vq9OLVvS9zx+aLwc+rhXuo5efpayvtXDsGCTfvwyzJx4gG1n5eKhwirydZNm9UXN0S0yMky3S9U+WVGTYoVpwt065kNKfQiyBPFOYfKrYd/e3V5vvPLxZq/62WkGm47f9M+w+cAoHl2Fm4+rkv4b6tA1Is4VRQoWl53PAqQv1u0Dfd9WzUfesPjJ3lyTCKi2sZ2kHfPPffgwQcfxL/+9S+kOJ7YXuX666/HRx99hG+//RbZ2dnheXY5OTmoU6cOAoEAbrnlFjz66KPo0qULunTpgkcffRR169bFBRdc4OrcsSSbTEQkmav2dusHjeqm45oRnXCD4Dl1T1fosAqABnW0Pc9Z6Sm4+Ih2eOD75fZObkC2h61lwzrCx2XrtuzIIz/ILDFyQq88W0Fe6Dut/25vMskQ6UmQZ1Eew/18aoSrm2l8u82QWdhOxarHzI+e/o9mbcIrk9eabuNVT94qVcbOKat3YXjXZt4cmIioFrEd5JWVleG8885zHeABwMsvvwwAGDlypObxt99+G5dddhkA4M4778ShQ4dw3XXXYd++fRgyZAgmTJiA7GzzITfxrNyD9OBkXhnTJDwId+VFVn4yUlOkMn/KWpkvt36jURXMqnIberoiyO8QeU8mtjL73f35166Ix0IVf/13+/eVOw2Ps7eozLogHiopr0RWelVPm19BnlkimPQ0eye1Go5p1ZBY9VEYb1NUWoGHf9Rm7f3310ssy2WWXMYO9VDbS96azd48IiIHbNduL730Unz66aeenLwqpX3kf6EAD6iqoI8bNw7bt29HSUkJJk+eHM6+magY5Im5bbz/8aajVMeKHK4JVM3JURNlr4sGo2FPVm9BqKflzanrPS4RJSs7Pyu5IM+4In/xm7MNt7fz+/54dnQTZl2iKrdXgYqe2eu325P3z+O7mT6fnuruNXw219n7z7wrRETxw3ZPXmVlJZ588kn88ssv6NOnT0TilWeeecazwiWrchfDNZOZ3Xlm+spYz5Y1E/RFPWKioWihzJpnHd4aX87fEvF8XgPzOXJes+rJC3VEfjV/axRKQ7WNzHBNuxX5gEFPXqyISjF7w96aP2IQqNhtbOrfthFO6t0CP1YvMK/Xtkld0/0VKKbBrNMpBYzxiIjih+1ujCVLlqB///5ISUnB0qVLsWDBgvB/Cxcu9KGIySdeKjvxxu40ElFl87qRnZCdmYZbjutas131/4re9tBDRhXX/AMlpnOHAKBjs3qGvbOvTl6LF/+IXJvM6KVafTUaZNlul4lQURnEPd8swfeLtllvTKRjt6erJvGRD4XxQwzKmW6S4dfIHcd3Q4OsNNx4TGfcNrqr9Q5RYKcBYPWOQtz26UJs3FPkX4GIiGox2zXGiRMn+lGOWiVhKjtRJtOLYOXOsd1x+5hu2rXvJIaLmdVNhj81EZ9dM9Tw+XW7itDtnp/x5T+O1Dx+qKwSj/1ctQzC+YPboqkqo6ZRWax6M/u0bmj6vIwfFm/HBzM34YOZm3BK35auj0fJw+2cPJHQ/DEvft92GJXTqvh+NcIpUPDlP4birJdnRDy360Cp7eN1aFoPC+4bg9SUQMQi925fgtNhl3YaAM58aToOllZgweb9mHjHSGcnJCIiQ44nJK1Zswa//PILDh2qWjuMKd3lRbuykyi8+grpFzevya5pfAKrSo1+Lp9eUAHu/Xap5jF1cpSyCn1Pn9Fi6Obl8MK+4ugmtaDkYpQ/xKjH26wnPRaMyh9adsWv32BqIIAB7RpjxYNjI57TDBe1c8zqF2M3JlMU48+jqLTCeaBroyAHSysAAOt3R/bk8R5JROSe7SBvz549OPbYY9G1a1eceOKJ2L69ak7AVVddhdtvv93zAiYjJkYUsz9cU65GIbOZF8kWzI6hL8OybeIU9FaNJV40psQq2QzFP6mePIPv+dpdB8XbV3/542WYulFmykvfrkq+4sfyAwAwukceAKCOyXp5Th1zWHPN307f6i37itHz/l/w6E8rHe3POXlERPHDdm3v1ltvRXp6OjZt2oS6dWsmd5933nkYP368p4VLVvFR1YlH/r4zpsM1JWondjPgmb2a0EK/elb1Sy/eIXXmvbHPTfHgiJQsZHpQjLbZuv+Q8HHVCiZxQd/TH/LnX7sB+DMqJTUlgN6tc6w3dKhnyxycdXhr6e2NXqHbhE5WSzsQEVH02A7yJkyYgCeeeAKtW2tvKF26dMHGjRs9K1gyE7Vo3x4nE+djyW7dSrY6Eep5EB1etkJXJz0VbRqLFzAPn8ekQOrej50HSgy3syqPJ4tEqwq6Mr8QJeWVro9JtYfRV/DhH5cLHw9926IxFFmGUZAX4qacRksbdGle33S/xvUynJ+0WmfVOZZuK8Db09aHh6CKiIJ1t5cXD5bPJSIij9hOvFJUVKTpwQvZvXs3MjMzBXuQnr4i/+edo9C6kXkAURvssbkAsmyjcU12P+dz8ioVJbxYsuExdH8bna7CtOJlzotOBn1re5yMoqM4IPNdCCUT0ispF49Dr1knL9pfNPGP2qq3qdJmORfcOxrXvD8PZw1ohfMGtcVTv6yKLInFOVs2dL9Ui3qgwXO/VSViqZdZc4tv27gu/n1id1z7wfyqz8KHXjevPmL9cUorKpGZ5v0wVyKiZGa73W348OF47733wn8HAgEEg0E89dRTGDVqlKeFS1b6G1ibxnWl55eRfaF31myuzdpd5mm8K4OK5VydxVsLtA+oNpf9eK166ryoQ+mLYrdSS4nFTnDlxzch9H1bvKXAdDsvdWxWz/C5FIuePLvBaKN6Gfjs2qE4b1Bb43Na/P6drkunPUfkSVZuLwz/OzsrDY3qVvUYWl3vnLJ669bvLsJTv6zEXpsNeue9OtNFqYiIaifbPXlPPfUURo4ciblz56KsrAx33nknli1bhr1792LatGl+lDHpxMuwpUQnPVwzvBhz5HOhSsns9ebZ7SqDSnjb7Kw0FJZUCI/VNbc+Vu+oSkChHg4lW1bLOXkGz8/buA/92jS0HIoGRA6pipeEGBR7fvS2xaL9asItw/Hbip3C51ItyuPH78Gq9/C4w3J9P0dKIKBJ+CReN9Tda7fa+6QX/kRxWSWWbhUnngKAXYWleGnSWs1jCzfvd1UuIqLayHZPXo8ePbB48WIMHjwYo0ePRlFREc4880wsWLAAnTp18qOMSYfpob0hnV0T1tn9WjW0Hi4b6vFqYjJ/pm5GTbuJ5nSSFV3rSrb4+bNeno5nf10tdQ59ZTByeQeqrfy4MsWiDSEtNcXwt2Q5XNOHn4NZ72H7JnVx47GdXZ+jaXbkdAn1S00JAHuKatbjMxs27pTV9au4rGr+7+TVuwy30a/5R0REztgO8jZt2oTc3Fw88MAD+OGHH/DTTz/h4YcfRosWLbBp0yY/yph03NxbuR6hc2ZBXoXEuhahJAY3H9dF6nxOPimrj9esmK//uc5034rKICav3hXRC/nu9A2SpSOyL1ZXrAnLdwgfNwu4KiqDuOPzRdLn+Oq6I6W2M+tgP6N/a0/mm53Uu4Xp84FAAGmqbvzQOnVqbm8vXnzWbHQiIvKG7SCvQ4cO2LUrshVuz5496NChgyeFSnqqO2nPlg1iWJDEJj0MLDRc06TusONAqfGT1UI9eW0bRyYeEnESkFv18po9b3W2N6eux6VvzcY932gXbf9y3hbZ4lGS86MNKVYNUzsLxVlsU00uHEsN1q80cnjbRlLbmfUeSoywlpKaEsDAdsblSQlol0/ZVWh9zbONbZBERHHDdpCnKIpwmNzBgweRleU+Q1htoO7Ju30Ml07Qk86aKXu86v91m2Ak1BMouxaUpsdW8tRWHYpuXsK3C7cJH2/bRC5opdogcWvp/ds21PxttGi7WZp/v6YPmgVyVolgvCtDAKkWaxy4/fS9mIrA6QxERN6QTrxy2223Aaga8nHvvfdqllGorKzErFmz0K9fP88LmIzUwwbVc7ioitcL6oYaJayyY1rZvLdqseeUQADT/3UMNuwuwgVvzNKdq+bf6sqK7JlD3w2j3g83r6BuhnhIWE6ddBdHpXhSWFKO7Czzz/O9GRtw0ZB2wuBi90F7WQ/VstJThMsoRKvKfmKvFliwaX/4b6PLiNn1xa8kRGbn9PJypz/Wm1PXa8qQbpJ1JpZTAQpLylE/M41ZpomIPCTdk7dgwQIsWLAAiqJgyZIl4b8XLFiAlStXom/fvnjnnXd8LGry4LQ6cw2yjAPfY7o3r/lDskKwZV8xgKoMlF5ITQmgZcM66N4icqjtUtUyCurPWfYzD23mx3fEqMfgl2XiuUuUWN6bsQG9x03ABzM3mm5337fL8Pf35gqfu+b9eZq/29vo5TVaJy9aUZ5MZlkrfmU+rmPQwAIA8z26LlkJBMzfo792HnR94flpSb7w8W37D+GjWcZz9nuPm4B7v60aRs77IxGRN6S7kSZOnAgAuPzyy/H888+jQQPOJXOqfdOaihPbLWu8fdkgPPrTCvznnL6G2wzu0Bh/rKxKjS773n01f6sHpasRapUXnb9ctd6VJsiTrOmGWtONtm4myKBHBFQFbwBwzzdLcdER7Uy3/X1l5PICOw9EzmFLT7U9oj9m0nS9VEa9QuZBRPR78oyWenDCaIhqqAxpJkGeFwlPjLJmjn1uCg4Ilp1R+2DmJjx8em/XZSAioiq2xwq+/fbbfpSjVhnVraY3KiMtcSpRfhvVvTlGqXvqBJw01usrf2pOWo1DreFWHYlOhn7N37QfY3u1MB6u6aIOygYFMrProA+JOABs2X/Il+Pq6XupjK4VZg0uybyGaUpK1dISIX1b52CRaoH6hnX9G7ZtFeCpJfFHQEQUVYwwYiAQCODaEZ1wUu8W6NemYayLk1DULeKy0ze8nuMXqjyatZoD2sqKbHD22pSqZRCMKpt25s3sLy7DqS9OxVvV83K8eBvemroep744FfuLnc/dokhb9x9CwaHyWBfDF2UVQZT7sficjj5rptHX3ewnFDSJ8g7XJXaxI0q5VUxbcgIIaMqxakdhxDbxEGBxuCZRcntn2nqc8t+p2FfEeoTfGOTFyL9O6I7/XXg4J5rbpAnyJPumvK5gyWbDUwdkdhNtGvU2KABmr98rdf6XJ6/F4i0FePCH5QDk3y8zD/6wHIu3FOCVyeZr8pG8HQdKMOzxP9D3gQmxLopvQo0XfpL9XYZ62Pu0zol4ziwDb5pFZkoz8RC46G81+jmU8VDGZyaswoLN0ZmjSESxMe775ViytQD//WNNrIuS9BjkUUJxErCZ9eQ5Sdcd7jGwKIs28UrNHzPW7rE8h1GFS1EUnPvqDIMntX8Wl1Zq/vayPWHjniL8JegJIPsWbd4fk/MqioJFm/eHexBFjQBe1ft/WrLdoyMZ0883s5qTZ7V4uJ4XiV1ELhlqPn/SK1YjGhQl9oHeC3+swbpdRbEtBBFFxfaC6Azlr80Y5CWY1o3qxLoIMaVurY9VJ2g48YqtIK/m39tdzFEyHWqme/J9iyyLbvy8NB+jn50iTNZBiWHS6l047X/TMPqZyb6f65S+LX0/hz4IMxyuWf2/oqDH7PeVlhpAh6b1HJXN7FrRqqF313SzXn6rGNWv5SP8oigK+oz7BWe8NC3WRSGqVYrLKjDuu2WYtc66wVrvy3lbwv/+eak4Gy95RyrxynfffSd9wFNPPdVxYcja/43tjpLyIM48vFWsixITTubXeT0nLzXVOLummlGlSWq4plFPnoPzhfgRFP+18yCaN8iS2jYYVDBt7W70aNEATeonXpbQg6UVmLN+L4Z1buppwqRYVa0nVC+dsbOwKuGKn40mjXxM6hGi77kz7slTqp+PfK7MZO5gakoAb102CKP+M8lxGWMpJRAwHbKtwP1C5OcNbONqfzs63PUTAGDBpv3YV1SGRvUyonZuotrsv3+swTvTN+Cd6Ruw4fGTbO17++eLNH/vLSpDY/52fSMV5J1++ulSBwsEAqisrLTekBxrWDcDz57XL9bFiBntnDzJfUzq404ar9PC2TWdJV6ROadhgGjak2d+TLtz8l784y8oCnDjsV1s7Wfk6wVbcfvni9CwbjoW3jfGk2NG01XvzsHMdXtxzYiOuOuEw2JdHNfM0ulH0zHdm4eXRXEjIvGKUXZNJfR85AZmywjce3IPdGhaDx9cOQQXvTnLcTkB4NS+LfHdom2m5fRaIBAwDeIURXG9jEKT+rGprBWXV6JRTM5MVPus23XQs2Md/tCvtgNFkicV5AWD/mdGI5KhrpfGLrumvQQPgLaFXKa13GiLQ+XyqcjdKCwpx38mrAYAXDK0PXI86In5dXlVz9H+4sTMIjlzXdVQuFcnr0uKIC+aw529SPpjRZ9IxShTZuh3KYpxzYKcTs3qAwCGdW6C60d1Qvc852vF7inyZ7kKM1Yx/S/LduD1P9e7OkeslqCorEysoaZERNHAOXmUUJwEbF5nMA335FlsZ5w8xfocRkslVDqsRSmKgm02JjmrF3UvN2nksfPOJnIi2f9NTKwsYDLJfaL6cZiczKtGmDaN62L8LUdj1r+PBQCUG/xWzObkySz1EAgE8M/ju7uaZ6j+eUcjAAaqFrY3O9cT41e6PoedJV6cKDhUjsqgYnqeN/5ch9s/W2QY5P/51y5c8c4cJn0gciha1yxyT6on74UXXsDVV1+NrKwsvPDCC6bb3nTTTZ4UjEhEk3glRksohOfkWSZeUfXe2az7GG3+2dwtBs+Yu+jNWbay1qnLXlRagaYJOIfOS0/9sirWRbBlydb9GNqpiek2kXPY/CuP2aG9+H1+ce1QAND0rhlV8lfnV2WFFc7Jczlc0VjsK0XRaGTxM8Tbsq8YRz0xEYPbN8YnVx9huN3DP64AAJzWryWGd20W8fzFb84GAPzryyV494rB/hSWKIm5nbtL0SMV5D377LO48MILkZWVhWeffdZwu0AgwCCPfKWpEEpWWsw2c3KpCs39sbUYus1zKh7XNaetsZcFS13GJVsL0K6Js6yCZM2Pzo9ESpToRfAxsH3jiMcqDHqgtxVUZYT9YVHksg5miVfcMf5AErmHW89sMXm3flhc9XnN3rA3YlSC6D0sKjUf2r5lX7FnZSMiikdSQd769euF/yaKtrjIrpki25On/nfNH30FizBH7OtBS9nIbs0wadUu18fJSks1fjKJKqixsmxbgefHdPLtEQWGXg2/Mxsy7fXvM6R1o7oAjJcU2Lg3sme7nHO7XPFzTp46sc7eojLNc8LvrsXxKmI1gZCIKEocz8nbvXs39uyxv0YGkRva4ZqS+5hUInPqVCUUsVPPlM1KqE28UqOhRBITL+rWpsGZjfN3al7fZEP5YyZTj4WXvpq/1fNjynx/9J+Hn+ukrdh+wPC5FJ+yfGalm9/ebh/dLeKxCt968mrHl1/0HdIHZF6ol2ndPm31dd64hz15RJTcbAV5+/fvx/XXX4+mTZsiNzcXzZs3R9OmTXHDDTdg//79PhWRqIY2u6Zcxclos7cuGxhen8VO/Va/6LIR9aKfdpdQiKc2ZrPenFemrItiSUiWk55gP4d4vjnVeASIX+GP1etpmh2Z7p+9O94rLPEmm676Op6uWxcn9H0/oDrX/kPeB5dERIlEargmAOzduxdDhw7F1q1bceGFF+Kwww6DoihYsWIF3nnnHfz++++YPn06GjXiajXkHy+Hax7TPddRGULBpVVR3pm+QfWXgvW7i5AaCEguhu6+summ50y75IOxKavdDwetzUrKK7F1v/dZ/qR68nThVazm8fl1WqvjpgoW0Ey0BDt2ROPjFfXkLdnq/XBk/XlC1/jP5mwOPzbuu2W4cEg70+McLK1AfYleQSKiRCTdk/fggw8iIyMDa9euxauvvopbbrkFt956K1577TWsWbMG6enpePDBB/0sK5G2J09yH5lgx0lAZJV4Rd0rUFRaiVH/mYThT02UStMe8w4Fmz2PMph2OdL4pfmav39eEpkMxC8ywzW9+OiP7tLUg6PYZ/W91S+eHiteL/ESS6Lv0A0fLfD8PJW68wQVBcGggh0HSsKPycyv/GLuZsttiMian0mXyDnpIO+bb77Bf/7zH+TmRvZ+5OXl4cknn8TXX3/taeGI9NS9cn4thv7hVUNwWr+WaN+krul2dg6rrnzIpGnXD7fr2dL+wst26457DpZi4qqdCAb1Z+fF22vBoIJJq3Zi90Htotj/+HC+J8dX9wTLBhFezsnr2bJBeCh0dpa7npIfbzoKqx8+AUd1thcsjuwWmT5fzWzY9SiLfUksWvU8/UiH2z9bhGFP/IGiskrT/fS/N+bZIXLvQEk5hj7+O/7vi8Wm29322cLoFIjCpIO87du3o2fPnobP9+rVC/n5+YbPE3nByXDNDk2t0/+rj9qpWX08f35/dGxmknDEptfszl3TVT6uH9XZ9jnt9pwd/9wUXP72HHw+b7PtOYRkz9cLtuKyt+eE1/Ty2n8mrMa6XQdNt/lJ12voZQX92hGdcNXRHQDAOsOryXnH9MhFz5Y5yEhLQadm9pbxGNMjFx9eNQTf3TBM+LxZkJfpImmRXbHux8s2Ga54+bD2to7l57VCfWz9d3Xuxn3YXlCCCcvM6yDHPztF87c+6CMia/q6xVfztmDHgVJ8atEz7keSMTInHeQ1bdoUGzZsMHx+/fr1aNLEfPFdIrfU02hk472/H93R0bkqLWq9dipnczfuC/9bak6e6t/nDmwtndHTjd0HqxIVfLtwm/ScPFtiXZuNI7+t2OH7OS57e47p89sLSjR/W80D/dvgttLnDgSA5377CwBQbNG7Ipsk5o7ju+GyI9vbKEMAwzo3RW6DLOHzZj+pTIvMnG7FouHE6OU2qGOc7deoUe3b68WBs1dLbuilp2rLYdTrnJ5q/rnt0WX6jJchu0REfpC+k40dOxZ33303ysoiM1aVlpbi3nvvxdixYz0tHJGeeuiZbE9VVoazCpvV8DWnc2lkKkLqcz98em9H55GlH043TxWQAl7OyUs85ZVBz7IDRtumvfZSxAs/ZoeffQABqWHJoW1lypSdlY5xpxqPJjE8vsHhzX6+GRbBgpeiFWcoQMSPsEm9DAjyz4QZBcLd8rKFj/u1DIe+vc1gnXvb1yq/lu8gSmZsG0kc0pMlHnjgAQwcOBBdunTB9ddfj+7duwMAli9fjpdeegmlpaV4//33fSsoEeCs5VVmiGcgELBdQ3B6ndNXUC47sr0uE2dNUTJSU5CRloIpf/mXxfKIjpE98JrhmrV4Tt4xT0/C5r3i7JffLtyKto3ron/b5MgobDVx3k4mVVt1Z58rDEa/f7OXq0/s4VbcVIp0L+vukw4L97iK2B0e79ecPH3waBRMHtYiG/kHSoTPibAnj8g+/mwSh3SQ17p1a8yYMQPXXXcd7rrrrnBvRCAQwOjRo/Hiiy+iTZs2vhWUCHCWeMVxMGbZk+fNce87uUdkkBc+SdX/WA15EymTXNhZ9DIVi+edcDKfMtaMArz5m/bh5k8WAgA2PH5SFEvkH6sKup2lHux81GabevHdM/remWW53VWonavVPDvTfUEMxPJXcai8Uirbr57R5+vXMFRF0TY2GV2bG9WLXPvQDDvyiOzjPP3EYSvtWYcOHfDzzz9j3759+Ouvqta/zp07o3Hjxr4UjkjPyU3ZaXBhNCQoxOlwTX1lWjRkKNyIEjqXg6pghXTlTZA6X3UV92y4puolBINKVIZKTVq1EzsOlOC8QfLzyWRM+2u3q/2jdZMsLqvAiu0HpLYVDSN2XkztZ3vuwNb4bO4WB8dx/0YZfc0y04zHKerndn113ZGuyxEXdO9FeUUwYm6m2rS19r7nfs3Jqzp2zb+N5ktzuCaR/6zyFdiRZzBnmrzhaOJBo0aNMHjwYAwePJgBHkXVzPV7be8jtU6e4DH/5pdEHve1iweE/32wtAKn/HcqgJoA1c/gVl8c/chVP4ZrfrMwOlm2Lnt7Dv7vyyVYmS8X6Mh6+tfVps8HgwqWbCnAos37NcF2MKhg6dYCz4cDGvnPL+blVPNyqJ3+q5dmMsfNrLHEi7fJ6Pg9W+YY7qPPvNm6kflyKnbFagh05+bajMFWDVVLt4p/N0aNTn5dMwHtEFqj89g9fzMXPbSVQQUXvzkLd3+9xPExiBKRl0Fek/r2et/JHncLGBFF2YFDNUkwZHvSnCdIcbSbJVFFpF+bhgCqKsefzN6EfcXl4b/V/+sHq5fpR+KVv3aap/f32vaCEnTPs7fW4GdznC+U/Pzvf+H536tGO5w/qA0eP6sPAODJX1bhlclrHR/Xrnmb9llvVM3LwEOfmv6jWZsMt/W7L8WogcTsN5Wsw5EO92j+qNF75+c6eerPxGihc7ufm5sgb/6mffizukf/kTP8TY5FFM+c1rEA64y45A7fXUoo5wxsHf637GXF6YicaGWKAxB+MYqircCEiu5kyKls+RVFwVrVmmp+VXDVN4Joj5KyO4zsYGkF7vzSfGFXM//9oyaZxSeqYDGaAZ5dou/l+t1Fjo4VT0GSk9+On8MOq47v6+GlOX2dRu/od4u2OS+MBXVioAqDIM9uD7mbz9moDEQkr0UOh2v6iUEeJZQ66fYXKXY+dy7yJt6qYR1Hx9IcV1CbVg9/StWsBeg8GrJTBSksqdDsp37pE1fudFwGNfUrURT/K9Jqdk9VWm4/0Y2am88tVvTf97oZzhcEtxNY+f1WGZXFbJ6rn8MO40kivUr1ZbPcYMK03WuK1bxrp7wczkZE3lAUxTKLdLJhkEcJRT1XRrZy6EWv0ZmHtwIA/HTT0a6PJapAql+LulJ6sLQq+BrS0f7cV9n6jgLte1QZVDRD937zKMhT16lfmrQWHe76CaUV7oIpWXbr7LXrNlBFX0F2k2wkEABO6dvSbZE8+RzM1oEzkoydNE4ug0afoVkjhl+VKHUvXaVHwzXdBPNGw5tPfXEqOv37J0xc5dF1kyjOePkLj1Z72ortB9D3gQkY/exkRxmFExWDPEoomiBPstriRer+Z87thw2Pn4Scuumuj2VVBxKVd3CHyLXsvFLVq1bzd+9WOZq/00yi5EHt3c3xefD75a72J++oP/OcOulScxg/vfoIHNkp8ruZEgAuHdpO6rx2f53dDRbiNmLYk2dx4lcuOhyN6qbjgyuH2Dqf8Fy6v2MRQ/7f2O6297n3pMOEj5u9dXaW2rBD3QhRYdAFZ7cHzY/PYfGWAgDA5W/PwcY9zoY7E8Wb/IIS3PDRfMzdYD/5nZmSKDT0BoMKTnj+TxwoqcDaXUVYvs3bRGzxjEEeJZQ0J83yEq48qkPEY35VxIQ9eRbnddIbKT0nT3fOhZv3a/42O3dOHfnMWKLK9ocmCTm8FK3Rk7sPluLrBVsScrjWz0vzw/+W/e4M6dhEmAI7EAiYZtTUb2tENPzuluO6Sh03xEkjTwDA2F4tMP/e0TiqS1Pb+8eb4w5rjjaN7WcIzc4SN2qZvaV+9c4HNUGeUXZNe8d0NWRcYtfVO6KbYIrIL//6ajF+WLwdZ78yQ/N4icupDZNW7cLiLftdHcOKfq5uAs6mcIxBHiWUFM18Ncl9JDa8bmRnhyWyT12xyM6sSnCrruiKKh5+LiRuVdExn18Wf8GMoii4/bNF+N/ENS6OYf58W4MK83mvzsCtny6KeLyotAJXvTvHcXmi4Yt5NevY2RpyJ/h6BACkevCd9aLBw81wba/mVnZvoesVjfLPxus5ombHKyn3ZyhUqeq4xklPjN9Y0XXO77aY2jK3k5Lfpr3F4X+rf0v/+WWVVF3MrJ7x8A8rXJXN+ty+Hj6ucQkFSijqnjzZG6hMJS8gaO7w68KgHg5+y+iqXgl9UhI9J3U0V43Uqp1HdG3m/ECaY3pyGEuz1+/Fl/OdLLwtr3WjOpqbXsjaXeLhWa9OXovfViTOHJ2iMvnWWdGw6UAgID0XzuyrPWnVLuGx7TDaPpqNudeN7BTFs0X62+A2jvZzct3xK7BR9zSHli6IPLfx/jd9slCwvZs5eRLb1ObaJSUVdaPdRNV1efyyfDSUmMai/20+ckYv3P31UgDAbI+HgEaeW9eTF9Wrf2yxJ48SinpOnuyQOD8zHTrJtqm+4IQyGKqLKB7O6WdPnvbvk/u00FRgOjatBwB4a+p6y33jwaodhbEuQoTdRWWxLoJvRD+vlEDkguLGB7B3Pv1h7zrB/lwzK+mp3v7esnRZgbvm1TfY0h/HdM8VPu7H79evocr1MmveQ6NGHLNzfy9Y3sFNUXcWllhuU4vyO5CPNuwuwhkvTcMvy/INt9lVWIrCknLD590yup5v2XcIS7daz3GLp15tDtckilNmSUCMiHbRZ41Tt1JJV04dEvWSqYM4r+bk2VncWl2mQCAgrPw9+ENkkhQ7l20vF9s2c9+3yyIei/VFPd7TNrvpcRC9tYGAeLjmM+f2tZ04RXRstWtGeN9LliuYZ+ilE3u18PX4seRlZa65arFymc/E7pllv/eKouDd6RswfU1VD+KuwlLNsGyjync8VWwpcd333TIs2LQf17w/T5g0ZH9xGQY98ht6j5vgWxnc1ovMetPaNHa/NJWdc9cmDPIooTi50Oh78hrXy8AL5/fTPFYvMw1XDOuAi49oh2bVFQv/Eq9U/W/n5vXRMtQTaNGTJ5qT9/KFh5ueR34JBW34VVXxUVTP1z5eB6TxnohFNCxSlrgnL4AUwW81EAC+v/Eo7WM2u/K86NW2qlTcMaab63OYadXI30qNH+plpGJoxyb44tqhptt5+VVvlp2Jdk2q5r/KLD5utzFF9ho5c91e3P/dMlzwxiwAwIJN+zTPX/P+PIM5f/H9u6fEUFxas47tiS/8GfH88u3+Z4t021Cq/ymoj9enVUN3B7egvyxc9vbsuG949QqDPEoo6iDP6f1zb1GZcAjnfaf0wEOn93JaNGmhG3/juuLMlLKv64Te5r0B0kGeEtmirf7T7Di2eoBqxzVVyM5LL6uI/hivhZv3O97XaE6eUeKVdMmsmyYndO24w3JNh3E3qiefNdaJdk3q+Xp8r6jfouFdm+Hjq4/AwPbma3Z63aARKoLM2lZ2gyrZ7bfploUQfXdECWcY5JEX/Ey8prZ6RyEe/WkF9gmmF7j9Kpv9FoyWRJFRXhnEU7+sxMx1ewy30ddTdh8sw8r8+JvW4QcGeZRQ1BVHv2+gY3pUzWMRpYgPcTLMLVwHUl23/biGt5ScL6go5q3vXvVqJVN1x+7npc5caSUa6wbpbdCt55VTR349SFErcmogIKyYiANC6VMB8KbCk5YS8H1YdrKRfd+9biHfsKcqwdHcjfsstqzKYmuHbFHTdHM0Re/EIUEqecZ45EZlUMH0tbtRVGbve+3UmGen4LUp6/CvrxZ7fmz9b039G/pl2Q7Hx/1w5kb8b+JanP/aTADAos37kV+gnS8r+p27CSwTCbNrUkJJ8aAnT9bVwzuiY9N6li3XdoV6atQXOfW/vaok5eVkWm+EqiBOf07t8E2zfeXEerii7SF+FsWdtsa41TAR6T8eUeNFt9xsYVKbPQdLIx5ToAiza3rRmOFFbGa0hl9GagoeOr2n+xMkCKtfpfp3I/vZ6dekcsPu9yXHYHSEEdlGOn3Pnahc7LUjr70yeS2e+mVV1M+7aHOB430VRRH2dPv1+wg1AgHAqvxCnPa/aVWPP36SqlCR+xmttZls2JNHZCA9NQUn9G4RnqPnlSfGrwSgrSioL4qlguF6Ti5HdoZrRlbyvTvHki0F6HHfeHy9YKvcQX0QraQvXoiHuqKoCEM7NRFuK7qhK4rzifpnD2ht+rwXc/JEQ0l7tGiA5Q8ej/MGtXV9/GQkm6XYy7qT3bUWy20OdZb9rem/ynaDvJ2FJZiwLD/mjV2UWD6evUn4uL6nymtu7pdGPwN9Q7Ifo5c+n7tZfG7RfNnq8mzcU4Q//3I+Jz3eMcijhJXot0tNK7nq8RcFi3jLpHSvl6FN0/7SpLXSZVEPR8pIS9Fc5GXXgzpksLba3d8sEQaucS2GI/m8XFvrkOQwH/3LlUlyYUaBfAVdv1WvljULhzcSrL/kJHY8rZ82m64oKYwC4x6+2kr9Ef6l68X922BxMByqPBldD+yd396HPcNkXo6IbO+CvmFB1NBgdqjjn52Cq9+fZ1hpJxIx+voPe+KP8L8PlVVqbtJe3D/crNVr9JvSt2+I5rA6oR5GXnBIvISEqEShnrwRT03CxW/OxnxdMqVkwTsaJaxEX2hWPZzN6gKanZWOPq1zIh5vrcrSp3439tpYl00BkJVWEyA2yErXJV5RsMggMUdos39/vQSH3TceS7ZEDvPwc51C38Twq+Xl13pvkdy6SfqPyM46caKPd2C7RgbZNa2Pqx5GM6pbc0fH0Hv+/P7aY9g+AukTFZgtZ/Pk+JU47L7xmL5WvGi5rDq69QW9JtuxFvGVM+jJM7on7Suu+h2+M32DfOGIDIR6hDftKcZh943HdR/NDz/nRWex6Biy9yX1vrPX78XUv3ZXP+5/T55+7mxNmawLv9hF8rF4xiCPElZih3j6njzrK95FR7SLeOyViwaEK+SOx7wrkcMz9IcKjXOP2LV6w49nVw2T+O8ff0VsEw/5LewGBrH8bnk5d0EmI6HIW5cNkt5W/9b+eNNRaNmwjrAnb+3Og5b7q4O8+0+JnB/nV5tBojcaqXm17pTZW62vUPVv2xBA1ecXGkXwyI8rHJ87My0Fj57Z2/H+MmSHpf281HgR6pCgYl0RXiP4/hMZ2XPQvLH2w9kbAQD7i2sa87y4f7i5FoZ+U+WVQZz76gxc9OYs7C8uiyiX/hROG4TUv+GjuzQL//ujWTW95kl0abeNQR4lrjj44bopgkxlVd1aLqo092qVg4l3jKwqi6ow39iY/6ZAiei50zxv40UarZkWa3ZLEMubgpfTdiokgzz9+2Mn2ZC+gaJTs/oAxEMiN+0tjnhMv796XlWOYLimX9+mq4d39OnI0Wd3LpsTTetr5yqHzlmpyloXCIgbeS47sr3psY/o2BirHj4BHZr6u9SE7G9t4WbtUC7RuytKmKW/jrTMsV7QnZLbvI378KVEtuUv521BscWQZ1FvupMgb8u+Yrw9bb2rY4SEdlUP+S84VB7xW9DXM96fsdHxOUPUy/P8++slhudSlzPZMcijhBUPyTS86nUxqpMNUlW2jRJZhHqp1CV58Ifl0ueuSryinYOnnZMn/xqFKfKl93bPKDOp/Z682H23vDy3bKbDto3rCh9XD9s0egv1j4dOKfq+ynwKXXLrmz7vxfBf0SHOPNw84UsiUQBcIxG0ummxv2iIdmRB6PNW98QGEIgIpPq3bYj7T+lheuxhnZo6Lpcdsq9f3VAVDIqzByqKdaPfoA7eZmqmxHPWy9Nx++eLMM9iSRCZe7ioAdXJT/qkF6bige9rzuemobG4rBKKouAG1RDSquuA+UG9mLdvdGeQeTkJOa1EAoM8Sjjtm1RVSEd0jZyvE236jpKm9eVTeC/ctN9yG/V1R9QzAqgubDYvzF2rK9OKbldFN+zok9nijFWhbTVliXFP3v3fLfPkOLFs5Zux1rvlGY7qLFdZ7tkqcr4nIDeMWL9FZlrVbUXYmyR6SPfY8T3z8NDpvfDdDcPE5/Pg61QbWnG9CYaNj6HvZQ0N35TJIGlVtu0H/M0eGCK7XI1+fVZhT55gTp7+6LXhe0dyNurWJvWKPpiauHInPpxl3kumT1jipvH6yfErMX/TPvy+cqfumNrt9Gc4uouzhh2ZoopejwIlPF8wmTHIo4Qz4dYRWHjfaOTFwdCXMT1zNX/baQEr1w1rElE/bjQEK/Sw0x4gRdEN19QdZ9b6vdLHEi0ILFovzS/vzxTfzOz2WDh5J//7e+R8RCdu/mShJ8cBgN9W7LTeCO7WZtRX2EONEcJ18qqrx2+r5vzpv9aBQAAXH9EOfVo3FJ/PcUlrl2g3TKdWf+DqIE/2mvTP47tp/o7WPF4niVc27i02XEJBf7h1uw5GbEMEeBPwixsbtH9f/s4c3P31UizfdkD+wC7K9smczfjn59rF1AOByHuM/vV3bm4+ggMAZq3bg2P+MwnT1oiDszemrhM+vl2w5MQFr8/CRW/OMj3fr8t34Jj/TMLiLfstyxavGORRwslIS0FDm4ve+uWekw7Dk2f1Cf9t5yau3lSmx8Qou3toX7s3De322p1FwZrwGNC2Xk9aFbneTDzMybPLyTC2p39d7UNJosPN8ByjT1fUKBF6aFT32PfCR8uIrs2sN4pTdn65oZG96uGasj+jC4dol2OI1jVD9np9lmrtxvu/FY8WEP2G9MvYMMSjkB+XbDd9XuoeZGPR8R02esfdNkas2x3ZS6k+5K+3Dnf0WzjvtZlYt7sIF74hDs5mrhM3SJ/50nTLY4suOX9/by7W7S7CVe/OtVXOeMIgj8iFuhlpOHdQm/DfdnpENEGeRJ3GqOKTEu7Js0dR/a+62IoCvPhH5Fp9RowurCHxEOPZfm9qWW1Mdu6ekOrzffqcvuF/yy+GHp0vSFZ6bG53VklG/BKNd1Uz0kDQkye7+He6rgUrI87WK2yZU5OtdOqa3Zr5SyH6ERFCtey6Qsb+WCk3ysKM6DeuGExtszPSx8vkXwCwbNsBbKgenpqdmYYuudkR2/hxzw0GjZc1scMqAU48S4t1AYiSiZ3ribpiLVMhM6w0Vz/stPVNPwdPAbBat/Cx2b5WcwvitSevMqjgkR9XYEC7RjipT4tYFyemDG+EEh+depPD2zWqeVzwuXsxDC9Ov05xx4v3yeoYqYEAKqq/O6FMf5UWPXkdmkRmzNRf2+QbCNyRuWYqioJHf9IuBSFaCiGoRFak9deVeEgWRsnDaNiwyLjvlmNE1+ZSvy2vhxVf+8G88L/D00sszlFRGUSaSWPPzHV78O3CrZi/cb/hNpWKgoDkSyksqTB8LpGX14mv5jKiBGenR0S2lTvEOPGKs+GaIRv2FGkuYvqgz4yiAAdKzBfcjousVYLX89OS7Xhr2npcr8oCFt48wa7pJQbDa2UXNTe6qcvsbeetEmZfjYOvh69i8PoUBTiqs//DRNVDM1MFiVfU36uP/j4E5w9qg3tPjsysqa94Rmvumswl+LcVO7GzsFTiWJEH+3Hxds1vM9GuK+Sv8Uu3Sy9zI8vot7Npb7HlENEQ0RG8+u4a1WPUh39r6nr0uP8XTQbS35bv0Gx//msz8fHszVhl0iBdGVSkeyXfnb5BbsMEwyCPyEN2A7cQo0BIXSm2SrxiZYCqlwWoaY3eX1yuuxAqtlqcrS7+Zg2H+rW2/CJ6PbtMKm6J1uL+wPfieULlqrWKzD4HN/WMdbtqenKtWjwlE25aqNnjrhO62967thjaqQm+uHYo5tx9nOE2Vr9dOw00aYIlFNSNXoPbN8bjZ/VBo3qR86n117Y2Bkt66HUTDPuyw+r1Fxwqx9/fk5uPEzQYrqmuqDLII7VrP5iPd1wEF6JGM7MqiNk9T83PniujkT3qcz74w3KUVQTxzy8WhR+7SvJ3qFYV5Mm9llHdknOeOIM8Ig85bYF2M1xT/ajZxdm8kq/tyZOt9Cuwbikzf22xqfXkF5Tgs7nyS0OYiYehHB+bLHMRclq/VobPRavnxOshhI1sJGASvcTD2zZ0X6A4MrJbVe/dpdXzAAe2b4xm2dFpSBEthq6+rpgNE1O37jeqm46/DW5ruK1a8wZyr21noTjphNX3/qWJ8nOTjQ6lfnz8snyUebAeGCWPCboeqhCZK7LoeurF/Uh0T7eTuMVMKMiTKWZpubvfSqXMPNlq+iVhkgWDPCIPOZ2wbFT5rZeZGv63ugXs70d3UO1b87jZBc2sVV69jk7VcE25F1IVEJpfiM3m5HkRWyiKYtlCqT/PKS9Oxcp842Ee93yz1H3B4sg1IzpiTI9cw+eNPm+7QVn9LKtp3vE1XLN/20bWGyWQVy8egO9uGIYrhrWPyvmm/HMUAOCh03rir+qRAeplO0JBVCAg3yt407FdIhKxGJGd73vOKzOEj1td5/YXmw9FVzMKGPWjAv5nI3Ck5Od1I6FZHUT+vq7dbt2ug9hTVCZdBrMG5ZpEcdpziEpWVt3avDLfxvIPKkEbPXlm703sm3GdY5BH5CGvh2s2rFPTU6FuCb98mCrIU21vdnb9hVe9+OiMdTULcFf1ztmZW2j+vFk9zIuL533fLsOgR37Dtwu3Gp9HdyKzoHDLvmJMNViHR+bY8ahHiwamz7sZrtm0fs13tHm2+dqV4uGaNQ/K9Kxpv+8Okw1V/2/djFTT7RJNZloq+rRuKB1Quc042rZJXWx4/CRcPLQ9lmwtAADMVq2rGWr/kQnGrjqqA7rm1reVjVS2gWDjnmLh41aX61TJOa2hY4muBfrHJq5yn1WRkofh/UPi0iZeJ089KsfZ9VH/u7jx4wW29u/bpqHhc4a/WUFRQ/MVL3trjq3zh1QGFXeZo5MAgzyiOKa+IKqDNHXAp97G7KKuHy5VL6Om1yWoG66pPopZ5kkFQJ82OYbPV5XPrCfP/QU4tAD6U7+scn0sQDuPTYbft5CS8kps3ltTSa0MKli366Ct907/merJBPVGaznambdltaXcECVnXX+iY/99eEcMbt8YD57W09ExE905A9tYb+TC1v2HABjPJ1a75+QemHDrCFufr9vhXF7W//YXlyFfYkhbaXkQa23+fil5uck+bZVdU/8Vc/qVk0k8pGbWE59WvdSKTFlC83tlflcilYpiuKSEnll5EnkJBQZ5RAkixSCw0wzXNNtfd0dQ/6lZJw/aC546sYaIZbINs31N97THrBh+Vqf8rqyd/N+pOPrJiVi0eT8A4JZPF+KYpyfjo9mbpI9h1eOlfg0y2esfPaM3frzpKADuk0fareN4ObqzQVY6Prt2KC4Z2t6zY/5yy3A8fHqv8N/xljy0kWruSVa6tifzibN6+3LOFJ9qGm4TJJk1blRUBvHRLPnf2GVvz8Go/0yKeFx/hlU7CnHs05PxyuR10sem5OX1cHX17An999vO70WdFVamkUZzHpPfVSjjs34LYdlc3lqDQfn55sna5MIgjyiOaXvyxJk2tcGa8aXKbB0YfeufumdvxXaT8fCKdc+X+RwB010Tgt8vIZQF9btF2wAA31f/70sT10ofw2pZDPVn9M31w8L/VvfeqbOzXjCkLXq2NO/BFfF6zURb358ofde65WXjoiPaRedkHjtvkFzCE7vsVhJllVj05JVbjEM2q4x+NneLozLJnuPpCd6MPKDE8dKkyPmYhtkmJY4n+mqp7+X6e6+d6+UnqkZEUb1ibM88w33N7vlGa9+JylZYWoHl2w44DoQrFfmpJ9FKPhZtDPKIYkRuPoz46qYZrql63Ow6VarL6mYUHNqZk6dAwWdzzDM7mh1LphdMtM2Hszbig5kbBVs7P0+I3fuJH/cGUXmN1lMyyhwYcUyTakNoLunwrs3Qp3VD4TYn9s7Df//WHxPvGCl1PhEvllDQDE92XBKKJq+D+xCr+XtT/zKfW2tWGV27K3LBcyfMvqMHSyvw8A/Lw730lNyeHB8Z2Lvp5f6vIImPNsjTfvv2FssnT1lcPccWEA/XHNvLOMgzy02wfrf5yCC96z+a73g0RNDDOXn5Bd5kF402BnlEMZIucXXXDMvUPB4Q/jtElIJc34Cm7qXRXJMt5m/p/b7SPJGAaZBncexnJqzC0Mf+wE7VmPyDpRW4++uluOebpSg4VJP9zk12LDdDLv1YU++kF6biHx/M0zxWrrtxhj72K96xnpRu9fLCWRBNtgkEAjilb0t0aFrP8nyGxxA8Zne+hdHcQIpfRgsguzWqu/naVpY9eSa/3TSPymx0bVEADHv8D7wxdT1O+980T85FiUdm3TgjouU4zObkvTp5XcRvQn0PVbPqfW9osuSAWdlDAaB+npvRLsVlFY4biSqD8ksoWL3ea3X340TBII8oRg6zyHgIAJ2a1Rc+btWTJ1obK+JiZ5CwRYGNcewKcIUq06eIacZRi9O88Mca5B8owUuTqoYmFpVW4LZPF4afn7uhJpOfbKi1ekfk0gnxNlJj+fYD+HlpvuYxfU9e6J60dKt1emnrIFd7TP05vCJqkPhlmXidKK+pK/R5DcyzgCYzp4lrrBxjEnCZrZHnhtVLscqeafb8Xhsp482YNUqoK9hcP692mqO6h3lB/Z0W3cfnb9yn+ftcg+VF0iwyy5r9pmWSjH+ny4ZttEtQcX4fsjNc0+oatTBBe9sZ5BHFyAt/62/6/PCuzXCBwaLARnPywhXZ6gvbBUNq9lcU4JKhVXOFTu/XUlP1UAdiS1XDNKwo0CZysEt/+Z2zYa/w/KHy/W/iGs3isVe+O1fuPKoTiYZGqW8Edm8o0QoQ3cwRUiwWhQ0NaYlIzuP4jPHtnIGtY12EmJHtobIb8P9jZCfD5/ya72L1SqzOa/b85n3iZRfskg1w35m+3pPzUWLJTHO2jMtOgxEQ6nu56PutH764StDoCVgPsTbLOCmzlJTsalPBoOK4YaoyqEifx6/Gr1hjkEfkAbsN1XXSU5GXY96b8OgZvVBHtY6XNqOm6t+qqk7o+h26rqXpMnLec1IPfHTVEDx+Vh/NRU19Ifxr50HpC+O8jfvw9K+rNY/d9dVivDdjQ0SZRNQ9iHsOluKcV2bg5P9OjdgudLP6w2RoqGw9UnTzcrqIfSzZGbaoAGgu6N0NefznldXHdFAOGzuJtu1oc/indskQW7uGyS62nYxevXgAmmdn4vnz+3l63EHtGxs+J1pUPDszTbClPVYVM6sgz+zpOunerKEoO9TssZ9XclmFWmh412bCx62+Ccc/NwWDBb85s8QrgPx9w6pxwjyRl8Rce/1i6Ab7BBXFcWNjZVDRJJEzk6y3hCR9WUTRlVPHXm+WTOuuWeXAcJ280P+Ght/pjpeRloIjOzdFVnqq6YVT9sKol5GWgo9nb8Z93y4LP2bWqqd+Rr3gqv6CH/prZb641bFqG7Mym/fUuelp8LNe9txvq603kqEAgzs0xj+P72a6md+NmeoKxi3HdQEAfH/jUY6PZ2c+JOvPVfq3bYRZ/z4Wp/VrpXlcf01y8lXo1cp6CHpI/SwPgjyL5yuDSsQwyMuHtUfT+hkAzOcOTVy1y23xbFGUqmUYqHaxGBVpaF9xObq3yI54XL2EwhZBb7TsNT5U/5iwLD/iuXZN6po2bDu51hrtUhlUXM3Jk723+5UcKtZiGuRNmTIFp5xyClq2bIlAIIBvvvlG87yiKBg3bhxatmyJOnXqYOTIkVi2bJn4YEQx9M7lg9GpWT28eelAXCyRPl3memIa5FksoRCq/AYMtgOALs3F8/2AmkVI7RIFdLIX2elr94T/vWFPMS54fWb4b7ct3OrdRe+r+nm7iT38SLwS8txvfxk+t2mv/HAyBVVDXq4f1dl0u2gOWbnluK7Y8PhJqKfq0bH7MSdC4GbVYx8LTj5nmT0GtG1kvZGHrF7G7oNlERXdi49oF14EPt568Cevjm5gSdGjXndOzTjxivUxxaNSanb8ct7WiOdlg5kDJVW97/d8szTiudP6tbKYkyfRkyc7XFOxP1Iq5Kv5WzlcM5YnLyoqQt++ffHiiy8Kn3/yySfxzDPP4MUXX8ScOXOQl5eH0aNHo7DQuDWfKBb6tmmI328fiWMPy8VDqoWQjZitWRdidmGzHK4puLCt3alNCX5qv5aGx7c7hC7EbpBn9NStny7UBH1BD3MS+NGTZ2ceo59Ew3cA+Ruq/q2RufHZCYq9yL/h9F4c7fr8O5cPwmNn9kb3PPnerVhy8tnrpfq16jmA86oDMzWr797nczejTJewSF1pjMbaWHbfRqOlUiixDXjoV+HjXmeeVc+5E32/Zb+PX83fiiVbjO9rZseRWbZAv4nRLm568t6atl76N77nYOQyEckgpkHeCSecgIcffhhnnnlmxHOKouC5557D3XffjTPPPBO9evXCu+++i+LiYnz00UcxKC0lMnWr0ysXDYhhSeTpK1nqjJmGPXS6OXnq54p0E6VDF07Ren1erC1z8n//xJ9/7TJtSTtk0Lq5df8hzd8yvWWmc/8s9nUV5AHCeYR+sOrRdFvH9n24pgfHb1a/5ndg51OLdjvtyG7NhUuZJDOrjHxuCNdYtDjdyvxCvD11g+axqjk+VTua/ZyuGd7RZgnF7F5a7v46sueEEp/+/htilbrfjLBR1aLrKsPG5LNTXpwqvMZaldjZ7VS8U6WiuLp4y44C+nDWJuuNElDczslbv3498vPzMWbMmPBjmZmZGDFiBKZPnx7DklEiUl8jzBbxjCf6Br4WOXXwykUD8OFVQzSPa2O86uGa4Tl51ldH0TWwotL8wihzX1q69QAufnM2+rdpaLqdaBjLLt3iq26HVWmGY1okXonnURv6m/q3ujTURkNoZN++RJiX0Nzh8gdOhyD7pXte1Xya0T3j83rk5JtgZ6mEf594GICqOXJ+WrB5n+bvikolfG1VFAVFpRV4ZsIqrNiuXYqkcb0MT85vdzj3p3M3e3JeSgxGDYwyDY+ia5rVEgp2GR3CbeIVWcGggkMmmTyt2OkYn7ZmN575dbXh0NpE5H7ms0/y86sme+bm5moez83NxcaNGw33Ky0tRWlpTQXxwAHrNaQo+XnRMxUPRAGqOmjRv0yzOnvoOdE7o+9J03vw1J6491u5+bGZgp5Ctf3F5cjLMc9kJ/PxyQcyouO7Ga4Zve+W/qZ+8ycLNX+7md8BCNLmO9jHjOeLYifw7/qHG49CUWklclwsQRJNMsM3jdb1FDmlb0sM69zU1RIsMt89fcNISkrNawkqwFO/rMI70zfghT/WYMPjJ4W38+qbZdS2IJNm3khJeSXOenk6juzUBHef1MPxcSj2jEazyFzaKgXzGNTfK9Ex7Adg9r+nTr7bRsVy2zh38ZuzpLe98I2qbYNBBXdYJClLFHHbkxeiv7EoivmaGY899hhycnLC/7VpEzmOn2qfeKsL9m2dY7lNeprcz1MwWlOq9ThgFuVZKLWxcO//Jq41fV5uKKbb4Zo1T3q9hEI0v1pWN8+TercQPi7bm2A36YxdboYmiTSwmdU2nqSlpsRFgHffyVVBwvPn99c87iTgP61fS1s9YI3rZUjP/RMtASLzfdX/ZDo2rR++BgQVxfdFjv2Y9/fj4u1Ytu0AXv9zvefHpugy+v7LfG9EvVTaJRScJ0ILcdKTZ/ZcAw+y6tqxs9D+XLslcTLH3gtxG+Tl5VX1WIR69EJ27twZ0bundtddd6GgoCD83+bNHPpA8UemYtMgS64CqF03TDspT32WVg3raPcL7eMgTBGte+UnLytKonc+XpdQ0Fu3q8j0+XZNxAlzpMsYkX1Dcj9JVgtxyxbzqbP74PxBbXByH+PkQSTniqM6YOVDY3FSH3EDgR3pqSk4Z4C3C82/dvEAnNG/Fa41WWzdzPrdNb+ZiXeMREZaSviaGVSMG5C8+l07XY7GTLQblqas3oWCQ9G95ieimev2YIdgkfJtJiNjRPee8spgRA/W6YJEaaKePPXhRF89r76OZocxu59+8Y8jbR/PS2dLXJ+ikZApWuI2yOvQoQPy8vLw6681GYnKysowefJkHHmk+EsCVM3ba9CggeY/onjj5ag1/aLmxWUVeHXKuurnaraLWAcrPC/Fu7I44d1QTLksnvpse4D2om57oewovn8P/bDc9Pl4n5PnVZrqcwa2weNn9bE1B4yMZQkW/nbaq+tmGKLImJ55ePa8fqibEdkDYPfrlFE9OqLma6Ng877ICnhBcTmeGL/SZknFbv98kSfHUYvm1/7taetxyVuzcc4rzIVgZvqa3Tj/tZkY8ujvEc8d+fgfhvvVF3yvX/g9cumcm47tEvGYaOq8ttHWfmIWL5SbzOnvmls1FzliLdwo3UcfPr2XcFSAWiAQiOo0DD/FNMg7ePAgFi5ciIULFwKoSraycOFCbNq0CYFAALfccgseffRRfP3111i6dCkuu+wy1K1bFxdccEEsi03kmteV6ZqRlwpenbxO9bjxedwMzfOy+DKtZjL3JdnsmsL3xEXiFT/XydMr93AticHtG0dkVp25bo/B1t5IhMQuJCb70UVz/rPdb1PjulVDScNz8oJAvczIAPeMl6e5LVqYH6Meovkz+m7RNgDA6h0HLbas3dRL/tjRJTdyHuuncyJHoIkatEQ9eVb3Srsx3p6isojHrNenjN/lCLLSU3FcD+PRgCGb95rnJUgUMQ3y5s6di/79+6N//6q5ALfddhv69++P++67DwBw55134pZbbsF1112HgQMHYuvWrZgwYQKys7NjWWwi1zwP8kL/UIDtBYciHzfhpErWplFdB3tV6demIX666Whb+3jZqrZZsIi4qzl5UWzwc9pLIpoLFwhEBvr6rKZSiVfslCNux45QBIeXKD97CvTrQNrtGa6TURXQqefk6Xvu523cZzksWm1kt2bCxwe1b2SrbHb4PXeW7PNyVIFoHpmozmC1Lq3o3hS6l363aBuu/3C+o/J1bl7f9T1Zv3c0G0srLbKHA8DwpyZGoST+i+ktd+TIkVAUJeK/d955B0DVBXzcuHHYvn07SkpKMHnyZPTqZb3QNJHebaO7AgDuHBsfGZO8bokN3QAiLl2q80QkUlClEbfLTfkHtW8UrmxVnd96H7dDOtWv8alfVkU8b3VjjBdWS1ukpgAvX3h4xONGc+Gi3bHm1XBN8l92pnYYmWxg4edSFUd0aqL52863aaxqqQr1nDy95dvkky60a1IX7Q3mwfrZa82fUfzx+zMR9+QJx2vW/NNkTt5NHy/Aj0u22y7HpUPb4YRe7ufv6oXTCfh4A/7yH0MBAN/olh6SIZoTmQjYrkq1wnUjO+H7G47CNcOdTd73ml/DNYOKol0TzqQaVJN4xcn5nJdffw1XFAgnq6vJDOl0c3Nwuxh6tFidq3+bRsJlNkRLF1T15BFpvX3ZIHRpXh9vXz7I0f7RXI7QzmWoc/OaYXHhdfIEvyg7c3Irg4rhdSdaQ5Mf/mE5/u+Lxb4dn9cIf6i/jzLqCObN/rZiZ8Rj6ntZi4aRa4raudfpGwfPG9gGD5zWC6kpAd/ue14k+DlNEJA1yErDgHZVIwHsZAcPEc1bTgQM8qhWSEtNQe/WOXGTqMHrcoSCOUUBPp+3peZxs9O4SLzipvSn9Wul+fv6j+YLJ6uredW49+dfuyyPr19g3Eo0J2ibneu0fi2RkhJAIBCISMxQN0N8gyqyWGRWJpj3tHcunrtRa4lR3Zvj19tGoE/rho729zWxg+77Yee7p/5NhEc+KJHXsnIb5Tf7ukbrXvPG1PX4dO5mbNoTOQyd4levlvJJAU/u0wL1JZceUH8ne7bMiXjezpxZ/bqXbZvUTNNwe6mOaOwFcKisEv0e/FW4PQC8fslAqWOLpiccKKmwU7wIaanxUXe0i0EeUQxkSK6BJyt0TRMFMc2qM0mN7KqdOxLteR0XDmmL6f86Br11awTKrEkTan3ULwOhZj5cs+p/L35ztunxt+wrxmM/28uqF82wZKsgE2DIWYfXpIbW11PHSEw0j4bueZxPnbAkLxd+Dtd0RVXxC6jm5OkDxed/Wy19yMqg8UwiUe+5jC4SPTyi4FaUNZjin0ywNKZnnnTPsNU6eW4aJbvlqq/f7n7n+pejKAo27DGfCzu6Ry7uHNsNI7qK58GGWDXUWDXAiJ5tnh3ZK5oIGOQRxYDVemF2hS6Ya3UJAwIAvr1+GB46rSf+74Tuwn3cnM+OjLQUtDQJ0syELtlmrWluWhZD94Q9ByMziVmJZudTYan91sgGWWlIEwxBkwnyveyke+rsPjime/PonZA8JfvJxGvq8YDg36K64G4b1wCjoW9je+YJr/GDOzQWbK0l09PibqEUigd2eqGHdWpiumxG71Y5OLxtQwDab4FwTp6LtoAhHa2/v7JEZZNJLHbdyM5494rBptuUlJuPUDmshXljo+h3nVNHbt3ieMMgjygG0r3uyQsP19QPZwJaNqyDi4e2j1hnyk112sl8E3VQEUplLiv0spz2BFll7gpd1J3EGG6zgh2pSybhlFEp4iXZyTkD28RNWcg+2W95NJdQsEPdeh/654JN+1zO5RU/3rl5fWFvwRCJIE9muCt/RrXHixf0R5P6mebLIQVqrvPa77N59k0r+nub7TVkbfLq0iGantC0fs3aeNmZ9gK2Y7o3x3mD2rguVywwyCOKgUyPL5ZGC5v7NSTTbSUjp669i6yiKBi/NB+/LNvh6HxWNw9XvQ8ub0z/GOlNMiAvPmmvhxFT8iiTTFYQr8M11b+P0FDKLfsORYx+sOOps/uIzxUQj9aQmacXr0Ey+cOqkVCmBykAVTIh1eFEP0U7P88Nu7XzPNXfX6+/put3F7lKgKZm1ZNnNb9O36P4xiUDmXiFiORFqzJtFox5mSHTSVn0C3GbmbR6F679YJ5FmcwLZfZ8LOulXrWOGvWu2vmYW+Zo5x1EvcOAFdyEF6/DNdVz5Iyufb8sy7d1zFHdmwu/soFAQBjQiRJC6FkNp5u3ca9woWY/3vaKyiAWbZFfUoLkyV5bZe5NI7o2Czfoqrc3WydPhn6ep5fTTI7q0lTz93O//eVZA0dJufmPSP/bbJ6dqflbH2w6nV8bDxjkEcVAl1x/ElBs0GVYk0iuGTX689npZZQZq2+2iaKIn29aP6N636onf1tuv6fQ7W3Jq1TrRvchO0dP5JsZxQeZ36pTXh3Z6Ft+zfvmDUkiop6YAMQVYplh+mbv35qdB3HWyzPwxPjI5FB23pspq3fhtk8XWqarf2nSWhtHTW7qnuyyimDUGjPMLsmh58b0zNMsoxQi+m66+XmqG0fcvvrjDoucn+3Ve1paIerJqzl2V139S38LLrbIOp1IGOQRRdHblw3C5cPa45Kh7Xw5/m8rdEGKxTj+aDJajN0rlUHFNLGHqPIUzrJXff9+4Y81ts/r9r4UCABXHtXB3UEA21G76P1niEdu+Znk0c2yBOrGFC/XsBP35IkbTERrnemZ9WYs337AVjmMXPLWbHy1YCuembDKdLuv5m8xfb62+POvXeh6z89448912FVYin4PTsCtny7UbOP0PmC1n1ljaDNVD1R4WRDV8+sFQ5GdDolsoRvl4UeM61X70CGLnrwbj+ms+Vv/Hi9Oot5rBnlEUTSqe3Pcf0pP4RA9N2nujW4D5j150a3S64dIeX32Q+WV2LZfvMSAAvHNLUXQ+inLaEHlAe0aYfa/j5U+TgDAv07ojhcv6G+7DNrjGA3XlH+n9dsOaNcIAJDJuXq1Xl/d0idGdhWW+FaGy4903hiiXSfPg8JUE1VMUwIBYU+ezDB9s8QrZsVev/ug5bH1thpcL8Pn010Pftc3InrkQIn7BbD9dOuniwAAD/+4Ap/O2YTiskp8s3Cbu4NKfgfNLt+h21ZV4pXQYzXfn6d/rVkOJHQtdxrk/fdv7u5PMpyssXlYiwaYcdcxmHDr8PBj5YL5w+pDZ2dp5zmavcdXD+9ou0zxhHduotrKZkUntDaN0wpSxHBNH7oSV+YXCh9XFEW4jpR6UWS7Qr0K+n2//MeRaN5Afk2dQKBqXt7g9u7SUxu9nUbvslnQG/Lk2X3xj5Gd8NPNRxue99oRVTfB43vGx1p85I9OEuu3AfB1Dpdswqa+bRpGPBbQBHleXnvEwzVTUyKrV24Tr5gVOxSI+OnKd+d6fsw3p65Hn3ET8OGsjZ4f2yvqj83uvUJmCOL0Nbvxx0pxAC3zVQ0gYHkvC/UiOw3y9PdrJ1mlv/zHkeojRjzvpCfvzuO7oUVOHc0QzDqC7JpOh5FffIQ/o66ihUEeUZyIdroCu/Wcly48HH/eOQoL7x/jzfk9OYq8t6duiHgsdGN0cuMLCIbHOBPQ/I9T6gqkekFlo8955rq9EY/pK7+N62Xg/8Z2R6dmxhX8i45oh19uGY4XLzjcZokpkUS759+NZ87tG/GY5rvt8KUcrUsWAYgTpRhl15RJXGFWGTX7DA5ZZBQUsR4qaG5/cRnOf20GPp+72fa5Qx76YTkA4O6vlzo+ht/U3x3R9bSsIojnf/9LuK/o47xKNTw/qAAXvDELV7wzF3sOlgLQNlJkptUELPef0kNzHPWhRXPyNK8hRTs1wS43Q6WBquQmod5EAGglWDO3uMx4HdirdFMapv3rGHxw5RCMUk3R+O/f+qN7XjaeOCsy662+lzA7q2ZJKbNXlujz1BnkESUBo14x0+yaNs+RmhJAm8Z10SAr3dnacJGZV6JGATBz3Z6Ix0ON7U7SvoeKrygKerRoAAD4v7HdjXcwkBKO8dy9IbG6FwUCAXTLy/YkS2h85mUkILHWZssQfBfV10gnPXn/PL4bnj8/csiasEc8xSC7psSPtLCkAi9PWotCwRDGaH8GotEPas/99hdmrtuLf36xOEolig2rnrzvFhkP3RR9P+45uYcqG2bN80u3HYg4X9fcmga2y4d1wEBVoKQdrmnekyc7NeG20V1N99efW5b+u9u7dQ4eP7O35rEPZ20y3L+pLgNmq4Z1IjJ0ntK3JcbfMhydm9ePWCtP/7pD92wrMhlx4xmDPKIk5mXru9trXeQaftGzo6BEOAwqFJhUVAZtD+dQvx/1M6taBds1qWu7bF4NWzU+jvM5eUQhifTNaN0ospdAXX67DSItcrJw/ajOaFwvI+I5/VWjZ8sGOO6wXMfr5AHAE+NX4v7vlkU87nVDjtUVb8s+8zl7m/fWZHO2WpsskVlllTTrRbUzSmTSqp1V+1Tfi54+p2/E/LGXLxoQ/vfu6p6/QKDm+23Ykyc5NcEoeZnbIc6iusj5g9tq/j5gku3V7vIN95yk7fXU3/+75dUM7zS79bvtwYw1BnlEScxsQrvdCr36Iu1kWL8+iIrmMIinf12NwpLIoSChG0dFUMHs9ZHDF2VULc9Q9drUL+mk3i2k9g/t4ja+MhpSZOe4CX4/Ix+M7ZkHALhsWPvYFsQG0bVNm3jF3hf9ccHwrxB9pfrHm45G19xspAoWXLbTK/DV/K2CR433b6iarzhx5U68PGmt72n+1fcXq16/eDVv4148PWGVZokEPXWCGvVbOn3NbgDmDSBWwyPVn1Hon6FbZeP6kY0K6QYLedckAhNzk2QMcH9/ktnfrGx2gy39+URDOEPM5sEyyCMiKfrhA9Hw5tT1hs/ZvXS5vdbpg7xoXzq37CuOeCxU2dt9sBTlNisp6qC35uZU89iLF/THP4/vZn0cb6bkeRKgeZuQgpLByxcdjsXjxqBnS7nsmvFKuxi6/H5N6mWEk06JGNUPRQGd2wqjWbkvUPWKXP7OHDwxfiWmrYkcou4l9TXdiyvHhW/MjNr6cyFnvTwD//1jDd6Yus72vhe8MQuA+ediFLjUZMM03kd0PRY1YAQQUA3XNDpfaHiocVntUB/mgiFtDbcLn1/imH72qJ3Wr5Xm7yM6Nqk5r8mJGeQRUYSW1WvK5NSpaV21une5ubc5uQzZrc+7Hcqnv/lEe2igaDhmKBvnzZ8sFLaAnj+ojeHx1Dfp0L7q+0EgEDBtHQ5vV/3puX0/jAI0o6NmpYvmLbkqgmttGtsf7kr+CgQCaJAll9USANo0rhkqKbNkgFOHSc6pCVF/te381uplpmn+1jfWXT+qk3A/N8M1jZg1wqQJ5iFuLzAfbunEuO+WYVp1D5Z6LvPBUuOkGbKmrdmDzXu9L7OMiSt3Sm0nmo9u1kNr1XMmejb0toq+LqLHAgFVT54S+l/dyBmXPXlmuz16Rm/jJ6s9cbZxT1qI2ZSJk/u0tNxfzeqXdkKvPLxcnUyOPXlEZMv7Vw3Baf1a4vNrh4Yfc5SsxEd25+u57snTB3nuDmebVXIVUQuo2TCtcOIVKOGbcsRagBIv0ruevJojrN5Rs2aWURka1okcChSr29mnVx+BM/q3wgOn9oxRCcgr6utKhyb1fDuPaN6dGW3iFfn99JVi/d+dm2dDyGZP3tuXDTJ8rjKo4JlfV+NNk94m0fXL6o5j1WvWsWnk5/fO9A24sLoHq6KyZv8nx5svrC4rVg1Nczbsk9pO9JaZTT2w6jkTHU8x6ckzDvS1PXUR0yMkMknfe3IPw+fcOrqLcW94yJKtBYbPiebDuhEIBHBC7xZo07gu9hcbT2tJ9MQradabEJFdnZrVF2ZiMxPta0m0e/L0N7uoZ4qT6FWzQ5PNrPrGqX9JMoF06H1wkuFTTZ0SWoao0SFWiVeGdGyCIarhM5S4otWYZfeb6nSdvExdb2SfVg0xe8Ne1LMYfv/BzI0Rj5kFeWbD+Z/8ZSVenWw+nDBUdz/6yT9UD5ruYvlJNa6XgXW7iwyfr1BNOFu3y/5i7CKJmLLetCdPd12f/q9jAJgnSgk9Jjqs6DsUQGRPnf52EnpfzXrLrjyqA5YaBFqJFuuoy3vTsV0cH4c9eUTkCfW1/qOrhnhyTNFaNLES2Woc3YunvrKmZ7dqWtOTV3NDdXJDaN3QmyGKRkMdjQJN0b1eNlmMXdcM7+jLcSn+qH/mflYM7R47xWFPXqtG2t/V83/rh4uOaItvrh9mut/eorKIx8yuD/phoWpWAR5QVbmvqAxqhju6DbitguFSjxvOAOCnxdtREcdJXETvqNnnqg/iWuruyaLjhQIx8Zy8yO0DAaCgOjPlobJK4XnTQ0nGKs2/E0YdfYm0TqbeFy7WcWSQR0RS7AyFP7Jz5KK7pgyuQzcc09l4FxfXLi+ya0a7ZdCy9d5plAfjllerU/5++wjkVGfFc1Mh69TMeFicURlEQ7WOPUycPtut5g2yfDku1V5uhpuXlMsHEfohxC1y6uDh03ujS67BME0TZj0+gYBx5kQZQSWy4Ub/Ew8GlXAwIHo+slDmT2/cE5nMyq1HflqBd2dsREFxedSTsEgxWBfRyFvTjJOf6Y/XpF4GNu8txtpdVb2nogBDfB8LYFZ1duhHfloBIPJ+m1q9KKzTESNW9zKrRtRoU18f9hZHNrjISvAYj0EeUbTE4nYVT61Q+sbZaJfMKnvmzZ8sED5+3kDj5CtAVbBUM1FeNyfPokydmtUsdOvmC2I2zNLoGdG9nuvkkVvxWC8HtJW+ojK5JCHd87LRQTAvTcaQDo01fx/bvTnSTIK4lEAA5Qa9LDLBn1L9f9rHtK58dw76PjDB8lghdq4GoeDxz7924Y+VO2zsGemhH5aj74MTME6wVmAsqD9LYU+eyXXzfxPXCh8PJ+5SPdaxWX3c9tnC8N/ixCtyn0pET15qaLim+X2wXVODESG608ZlAK6iXtLDTqOOXqLfExnkEUWJ13PCZJhdnqI9/CLWNwWrFswDgnX0AOBfJ3RH71Y5uP8U7aR09XBNo4nydu4Pbt4dJ5+kaC6IX9+IxL5NUqLo1aoB/nVCd+Fzq3YUhv8tW1FOF2SslHXx0Haav58+t2+4N0WvZU4WuucZ9wymGeynpiiRAbb+74mrdlkeR83O3MUNe4pRGVRw8ZuzccU7c4XDVe16d0bkvMZYyEo3n3/ppi1V/RmVlFdi2/6S8N9W6z3WbBf5mDqWS0sJhBt8jRoSLq9eB7NBVjqePa+v4LyJdRWP7xA0ehjkEcWJK6ovsiO7WWeh8kK0r9n67Jo7C0ujWwCHGtXLwPc3HoXLh3XQPK5OvGI2UT5kdI9c0/NE+yYqirm9LkL7JlWtwscdZv7aKXmoG3Oi3Qr+w41H49oR4iUNNu2tGVq456DctSe3QabjsugDs7TUFGGPz9kDWmP6XceavlcyPXnBoBLxm/5invO5SID964F6iKDse5wI1IGV+LqpfaPsNGiqe19v/3yRZuF1+XXyaoSuuer77YRbh4cbLIwSr9x/Ss2w5JY5kXP57f6SP7n6CDQVLOYeNXHe0xgtDPKI4sSRnZti1r+PxZuXGqfSNuKkKhXtdjmvFmGNpcPbNgz/O3SvXbJ1f3jJgsg5eTUPmC24CgDNssUVyln/PhZPSawxZOTsAa2FjztdL8mOCbeOwNx7jkPbJlz/rraI15+5+us+WzJd/oOn9XJ8Pv1Q+ZQAkJ4WedU9z2QtzpC+bRpablOVAEr77s/ftN9yHzN2gzz1+f1IyuK1Savk1sZTB1syc6dlLq2hkTRmtwU7PYQXH1HVc3xK35bVx605cIem9cLfx417redRioYVW30XxvbK025veRZ/JUN9wwsM8ojiSG6DLFfz6FrkaBNcmM7VinIrezSCCr+9cvEAAMCAdo3CN7FbP10Ufl7f8npyn5pslRVBxXRIlsj3NxyF3AZZlsPGzN7bawx6NoQt0h7fmjPSUtC0vvPeEEo8muyafp7HdjhZs32GRM9Yr1YNIjIh2qG/jAcQEP6OZS6Lo7pZJ0QKKt4vXmH3eqB+LWap+uPFZW/PkdrOelkH87mQZhZv2W98Xsl7dCBQMxwzvIRCOENn1fOhwO37Rduw3yIRiei8mWnmQ1YfOaO3ZlH0WM9li/X0kHjBII8oCYTmk20vKNE8bj4nL7qserKi7f/GiufumGmenYVlDxyPz64ZajBfQvtYniroDioKfrzpaFvn6906B4B1Ah2zt9ZoX+GcvFg3vxL5pubLLTPXbpjdDMc6ovm5GYLzygRDMg1/6mHjsqwqwmbXg9WqOY5A1UgEP9ZIjIfKunqYrWgJAv1HaOdzmLZmj+FzstlLA6j5voXyjeiX9ZldnX0TABZvKTA9nmgOqNESPSH1M9NwwZC2NWUKxHbEZJxVN2KGQR5RLeWmQu9k33hr2XW6Jly9zDSkpgSEiQX0dTF95UBdWbt+lLiHTWRl/gH7Ba1mtKxDN5u9ikQy1BV9PxsN7FYg1WWRCfLO6N/KZon0J4w8vyjNvEwQIxfkRc7JcyrUIGfWG7O/uFzztzrLsJeKqtd9k/Hsr6vxucWaaE4aG9Uxz6tTItcs1Ad1UsM1JX4bxZJZYAEg9JUOfZ9Cc/JCn+EC1dDdAyXaz05PH+OFhoLaEev2Qi++iqf1a+nBUWKLQR5REotWz8xhLRpYbhNnMR52FJZYb2STvlKkbs0f3lWbUKduhvHix3rqVlgRs5ZjfY/Ct9cPw+n9WuJ/Fxwuff5k8LfBVXOfTuydZ7ElueF3633z6rmrY3o6/xxbNbIehpnmcvkZ/e8uJRBAhijIU/371erh4HoyQV5QEQeMdgIFAJi4cid6jfsFPy7eblpR18fJQQc9iTIOlVXit+U7LF/Hki0FeP73v/DPLxYbbrMqvxB9H5yAVyZHLmtg9hZbDT006slz2wtZL1PuHhEI1Hzf9MM1Rcl+ii0CZ31P3lkG87qtyhRL6vf+6C7OeuWfO6+fR6WJHQZ5RLWUl2PmW0tUmrysAPxjpHwvmJEeEoGpXfqKQkpKAD1bVp3n6uEdHR/XupJh/N7qb/J9WjfEc+f3R8uGdTDjrmN053FcxLg37tSeeOfyQXj6nH6xLkpSU38T/egtHn/LcHxw5RCcabOnTf0TaVQ33XJ7t9dH/d4BiHsQ1cHg8QaB65tTLRbURtU1QNSQ9uty+TXrVu8oxOXvzEFxWSWu/2i+ReKPyCcVH3KtnPriVFz13lzc/tki0+32SSx4fd+3S1FYUoHHf14Z8ZzZ/DezdfBKKyoNgzm3DZtGybj0AgiEv6+h4Zqh74zoMywtNw/y9I0K/SQS/4hKFct2XfVH4nTUTqznFXqBQR5REouna5SXQZ6b9atC6maYTyR3QnRT+OHGo7D+sRMjbpx2PhvLaf8mb63ZeXKz5RP1JLrMtFSM7NYcdXz43KnGZUe2BwCM6tYM95/cE5cd2R7f3TDMs+M3rpeBo7o0lUiGoaUeoiaTUMTtkib6nsCUQEA4XFPmZazZedByG/V6nWprdxXhP7+sksokec4rMzR/Hyw17j1bvl07hFxRFBy02WsoIzTP/Oel+abbydxdnF4nzT6jvg9MCC8EHxK615lNUbD6emVnpmFgu0bmG6mOFbolhs79zvQNAMTDXetnmfcQys4BtSrTlUd1AOBsCZ2bjulsex819bDxge0bm2yZ3OTHCxFR0nE6OTpinpfEPl4O15TJjmfFj4BGvFCt+DxeZrI0r7zInyd5QzyKln+M6IShnZqgR4sGyEpPxbhTe1rvFAXa9fust3c5WhPpuoAuEKgaBvfnnaMQVBSMeGpSVblMjrHzQAnu/NJ4+KGa0Zy4F37/y2Qf7d/6YGWOyVIT936zVHssADPWGicR8ZvM0MjZG8yHvRsxex9KyoN4e9oGXVmq/tdNw+ato7vaunaHGiWM3odLhrbDe9WLy6szQou4Haoccq3qWmBXo3ru1thTvw2dm9d3daxExp48oiRmFUg4vZQ7CVC8zK7pRU+eH+y0/tuJMa3mZjitTCRxxx3FSEpKAIe3bYSs9Oj0mF5XPXT7qupeAyN2Ez953ZMXqrC3aVwX7ZrUkzrG/d8tw6RVu6S2DQadLCvhneMOy03oZXLKKxW8PW29sPdSvUC5yPrdRZq/w/PiXLwfdpdSCg/XNDinne+zzLlF80v150uN8rVALd5yAMQKe/KIarFAdVdep2b1sHZXkfUO1fTplGVuCl5m14zXIM/LIZhqVwzrgD9WGg+38qpuxaCPEs0/j++GMw9vhY5NzVvr7V5+3P4WRGno7Rq/zHyIopoC+9k1vQwKu+dla3qRol3H9uJ8D3y/HLsKS3Gnankdq0yUZmVZsd0sK7L5F0x2OPLIbs3QqmGd8LxBowQ8Rga1bxQxnFHmfj66Ry6GdmyC/m0bCp9v4rInDqhquHlDYj6qSCI3OHgpPmtKRBQVoUt56HrYXDXR2yjTG1C1GPj9p/QI/92kvvUF3aiFUa+5xGTzdA+Ga8qeyw4ve/LUC6nXyzRvCfXqhub1YuhEfgsEAujcPNuyUtw1tyYIzLaYkwS478nr1Ny8t65e9dzQHi2Nh7LZ+VlXVe7lt7d7fJljqQNp2WtS9zhbyuXzeVs0f/cZN8H2MUIvXXadOxGzZC8hPVo0wDuXD0ZKSiA8vFhRFHy/eHvEtucNaiM8xufXHhmxZqxMkJeemoKPrz5CExADwLtXDMbz5/ezXFdPxr9O6I77Tu6Bn2+2t74s1WCQR5TErO4ToedDt+Mbj+2CYZ2b4Olz+uL4nnk4pa/xOjGXD+uAZ8/ri2Gdm+D20d0syyLbuig1X0Z1E2ookSkvWmwFeRYB1aXVCSwA6wqT07paMidaIVJTL1lyRn/rlPBufxp1M9LQq5VxADfv3tFYdN8YNMjSXr/uGNPV0fmqsmvGrvciqChoVr+m0Uz2ei+7TIAfmgoaJ0tUmSfNEs+YqVqzUMFtuoygax45QfoYMktfqN/h0D2xqLQSN328IGLbw1o0QMucrIjHRdzMyRvRtRlO6+dyjclQOVJTcMVRHaSWaNLzcnpIImOQR0ThG3LDOun48KojwuviiG6Camf0b40PrzoCjepl4LfbRphuK3vNtVtPcbM0gdfs3BstA3DVvystUpOLsvY5wZiPkpV6+JjVfCKvdMs1rpxmpaciR9BAdXKfqoa1bLvBj2K/scfTnjwAzRvUBHmy13vPLjkOXktmWuQIidLymovtul3WWU2FRVGA/AOR67CmqaYZWF1rQ9kxZZVWVJV7jklymeYN5II8u5lr/eD2u8kQrwqDPKJaLNSbFLoh6288R3RsIn0sqwxWsnPyZDZT3wDcDDH0OqixczyrXj91L5vVe9fFo+xhsb+1E/njqqOj3xj02Jm9AQDdcu0PSbRbSQ0qiu3eC9kh9DI+nLURp744Lfy32aHv+moxxjw7WdNr5pZofuH93y7FsU9Pws7CEsxeHxn8iHoby1Qtaku2Fjgqy22fLXQ9B13mvVFfr//7R1UW1dCSEyHqqQ2y77dX2TVlfHL1ERjdw/4SC1Y4J68Kgzyi2qz6Wh66IOoDpjE9cvH6JQPx552jHB3+ybP6hP8te9Ht1Mw685z6SLHqfRLNJbGX8tr8efWhvExaY37S6JyGKNrsro9op/Ho9UsGAgD+c05fzeMZaSnY8PhJ+OXW4fLnVc2tMpozLFrj08klwsvryua92gyUZtf7j2dvxuodBzFh+Q5felxCwdu7MzZi7a4iDH7kd5z7as0agKFe0gqD13/PN0tQUl7paJggAExctct1T5TZZ/OvE7ojLSWAh8/oFX5M5nwr8wulzm03s6cbR3RsEv79qLn9XjDGq8IgjyiJqYeeiOgTr0Q8HwhgdI9cx5Ooz1VN9pYN8p47vx/OPLyVaeIXTRl1f7984eF46uw+wm291EnQe2ZnTp7VkBjNcE3esYji1ugeufjrkRNw9gDruX5WQsGlAqCFwRwqUVKO7xZtwyM/rrB1rgqrceAuyFzvFZvzCHcKhkDWHEv8b6Fw46b46Q9mbsIrk9e6Sr5juVi4xf5jeuQZPnftiE5Y8dBYHN62ZrH0fm0ayhfOgkzSl3hnJ8NoMmOQR5TEJq4yTrsPaFuN1X/7QbY+0SKnDp45tx+O75mH724YJpwXaLawcWpKAOcMFGcS09txoNTwuQ5NzXsURTdCOw2guRbzI9QVDKthWF7dzphdk8gZr5Z1Cf3sD5VXGl6Q37xskPBxO0suAEDDuu7T3BuRqWPf/MlC7CgwDtz0QvPOrM4nez00+8g27C5CZdB5ECyak2fHaf2Nk54Bkd+3G4/pXP24+2t4NHvy/MIQrwqDPKJaLGJOno/nctKy1qd1QzTPjgyG1ENZ9IFJaMjkC3/r7yrz5ntXDDZ9XnQjlGn5fe3iAbhhVGeMsZiHoD5UtOYXJEEDLpFth7dtiH8er80QHOvfgqIAizbvj3i8QVYaBndoHLmDA35eV0SH3l9chi90SxRssxHkyQYfsq/LLAFPUJFvmBTZuEd+3VkRu72IdaoXHNcHf+WV9j/jZMi6zDl5VRjkEdViNUso+H9BdHrRLSzVLkZ7+bD2miAv12BI06l9W+KGUZ0dnROIXPBdT3QflLk3jumZhzuO72Z5I1UHr9G6XyX+rZ3IvtP7t8L1Lq4V0eRlBdzPub6i6/0178/DHZ8vEmwtx6y06ufW75YLsMw66oKK4ur9KbOIEO1kV5aRXh2wmvV2JhK3wy25gkIVBnlEScwyg2P1/xpl1/SS/oZ5Zn+5tXTOPlw79PK+k3tgVPfm4b8HtGukeV79Evxskfxq/taIx9wuoKxmpyePjZZEzol+tbFafzOanSjRDvJmCTJc2jqmZHmv/WCe1HYVJlGeorh7f8qjHGyFMmKaldmLoZyJgvfEKgzyiHx2RMeqoTVeToyWJnlNr7kg+ncT0N97ZNfiGdheF8QFAujUrD6m/t8oLH/weFvz4PSMsqdlOJxf47aCpp4HqD6W1f1KPQHfjWQYpkNkV6Fg0WvRGmrxwMufaCgQKymvxNb9hwy3G9jO/vXFjzq2UTZMQNvzs25XEW7/zLjHMPQWmnW2BRXFVcIrs7LKsHstlpkT+ugZvW2X48Texglg4pmd0UlvXz4IDeum4/sbjvKxRLHBII/IZy9dOAB3n3iYME1wrIVuJH4mXvnb4LYAgNvHdNU8LpvBy2iz1o3qom5GWsRx7LyGT/5+hPDxX2+TT3mu5rYnb0zPmnl6doZrXjvSmzXAGOJRbZQnuUh0NFhV7u8/pYdn5wr1+pz4wp8Y9vgfhtvN3bjP9rF/WWovCYwMs0Qo+kvkl/O3CLcDgAMlFVi8ZT92HzROvBUImJ/Pin645tie2mDJ6yRXRvML59x9XPjfdTPSbB83YRv+bMTYo7o1x8L7xqB36xz/yhMjDPKIfNa4Xgb+PrwjmhmseeQn2eGafo5sePSMXph997E4uY82W9ggXfKAo7s0xWuCZROsgkGzm5DV7SnHYEhWuybWa/WJy+JoN8tjWc1PiNdeB6J49fm1Q8P/rl+9bpp+6HcsmF1CZt99LM7o736ZhpBQTo51u8znsB3dpantY38yZ7OTIpkyugyu23UQBYfKxU8aUC/cLhIIBFwlXtEnzXnpwsNt7b9gk73A2qgnT13v6CxY9sdKtJYiePuyQTiqs/3vmRG3iVdO6t3Co5LEFoM8oiRwxbAOwsfTLFfcrvqfmsXQvRcIBIQZMvVz8t6/cgjG9IwcGtKndcPwv487rHnE8/qMa1v21Qw7OrmPfxdq0Zwdtz156tZd9bGiNb0gURttiexq16QmsVLotxYPqeONfoPdcrOF11E3ZOe4xUtPp6i463YdxDFPT8adXyz29Fw/Lt6OL+cZ9wZa+WXZDs3fstMTQsyW9xGRmW/XLS/b1jEB8+Q0XhrVvTk+uGqIZ8dzG5u2blzHm4LEmP2+WyKKO0b3D6tKSzjxSjA0XDN6lRzZm16djFSsfGgsyiqDyM6MvGSZHaa5j5WTK4d1wNO/rtaVJTaJV/SGd22GKat32T8nB2xSLaFpUEmA5u63LxevjeeGbGKReMlhIZpn5TaZixm76w7aYXWrKC6LnCdqxuk8ciuJuhSB25xCXt7LYykBLm1EZMXoemTVuhcK6vzMsuaFrPRUNMhKFwah+seM3ou+bRp6eiMUBaledgSoD2X3Put4iE1y3NeILKmvE/E070jU0JKRmoKWDY17Fpo7nAqwfPsBFAmSzuiFFtqONVGvUvx8ct7KSrc3BF8m8YoTsaoauA2y3AanyfK9YpBHlASMKimWc/Kqny4qq6z629NSRYe+t9Lo2h5A1Rp7gHjYp12i99ZtT9h81TyMgIvhmona+koULQHBv+Ph+ie6ZFtleVQPPQ2RzYr49ITVlts4maMcyirtJau15/x0/qA21ht5qFMze+95usnC7kbOHmA9vzNac/L03DaYui13HLX7uMIgjygJGF2QmtTL8OQ48Ux/Myg3qAgEAsAdx3fD+1cOxgt/6+/6vKKG04DLK+oBVfIA2cQr950cmW3Pac9sIn7+RE6oG1HCWYZjVRgV4Zp9dczX7BP93n9aIjfUcO2ug6bPOx0memLvFqgMKigpr8ShskpHle5zB2qDkP9NXBOxTTSuWSO6NsPjZ/VxvL/oGu11sS3n3wv8+8TDLLdxs4yEG3k57ubEcbhmFc7JI0oChj1Iktk1E5nsxTiAqiEtR3dp5tt53d4Y1L2SAYPH+7VpiIXVmdvqpKfiiqMik+44vcElw/eBSIZ6IewdB0piWBJrvVqZp3afv2m/5u9zB7bGZ3PlkoZMNpm7e/aA1hjVLXLUQ5vGdbB5r/G6ekDViIrRz04OZ+60kznx6uEdcUz35vh24TbN478u3xGxbTTmETsJoNRE12grdi/hTqYiZGdZhwBlUV7U/Y1LBmLRlv04XrWckBNOR7NkZ6WhsKQCx3R3P9onHrAnjygJOL0H6Yd5ejk3786x3Tw7lhl9YBWtDHmi87g9tfqY6tc1ukfNDU+mwuF0qEo8zU0i8pP6J2LU+x8Tgp+g3QrrIw4WvRZpkSNOXCWTcTGoKJqlGaau2S193n+feBiO6NgEUqFOFC5ZdjNjesHuJTwlJYCTbGaTlpnHN33tHnsFcem4Hrm4fUw31/cip1WZqf93DH65ZTj6t439cipeYJBHlAQMe5As7hT6vbycyzXcox4zK/pgy+i98DqAEQV5aS7T9GmHkNU8brQOnijbHACUlMtXWr0YukoUz967YrDp8/E0NEvUM2V1XdYHY14l4TB6V2TuE160F8rcjqLxybntyRPZXuB973GPFg08P2aictrQmVMn3dFSE/GKQR5REnBaR9HvV+HyznxTDLKw6e+/RvfjIR28TQQgqhhmOJj8rqZNBiHTYyd+fMnWAulz9lYNBYufqi6Rd4Z3jWxwUgcqoWvG4PbeJwvxgqjnbGD1wu1DOzZBp2b2F7mWYnBjkRnxEa2EHdEYfeBHT95vKyKHnqo5ef/iqK0i5ph8rAqDPKIkYHhtt7zqeztc86Zju7ja34lAIIB3VMkB9Df9SXeMxH0n9/C8bH4MCy1VzX/YVmA+58WMzMK4IerKBCsJlCzuP6Uq2cXto7sKn29Sr2bZgboZVXOTbjimM+47uQcm3jHS9/IZEf0GRRXWVy4egHtOOgz/u/DwqGedlBneGq1KdjQuWak+XBjTLHpb6wnWhLWib3js26ah7WPodWhqP7NqPIinEdixxCCPKBk4vAl5fe+K1ZyukarkAPoitG9aD1cc1UF63aHPrhkqtZ0fN371Ed0E3BNuHYEeLRrgw6uG2Dw/ozxKDpcP64C59xyHGw0ad9S97qEGm6zqREaxrNiKfoGigKlp/UxcdXRHNK6XEfU5hfuKa7IAP3JGL+E2XhRJarhmFC5ZfgzX1B9Rv0TDaf1a2T6mupjNsjPxxbVy9zKR58/vh16tGuCb64Y5PkYsxWrph3jDII8oCThOvOJtMbTHjlG84DZQGSw5rNOPITzqKX0yLeFGm3RoWg8/3Xw0hklktGOyFUpWTevLLRIeT0O7RL9Hq/Yeu0FeqJfTC0bXWy/eU6M5x5rzR+HyVaBa2sYv/zqhu+ZvJ0P/1T15jeqmC+dmfnfDMBzRsTEW3T/G9Fin9WuFH248Gjl1zZfviFdXj+gIADjzcPvBcjLhEgpEScCrlka39+V4CBd6W6Qb94pHuQ00Yt1yzXiPaqM4ivGErAKmtTuLTJ/XO7KT3HIGMpcD9TWjW242Vu0oBABUVHoQ5EklXvH/ovX7yp2eH1P/2rwY/q9uIDBKJtSndUN8crXzHr5E0T2vAZY/eDzqSI7gSVbsySNKAk57Y/S7ybSc2jleNE28YyTev3IwereOTpDnd0a+Xi2tX8eo7tHJYEpE0SEermm+z6HyyvC/m9bPiHj+gVN7av6WrfjmGSyhYOSTq49Aw+qen0qZdRYSlBdz3fTzKL24n6inYnOERtVc29r+PjDII0piVo2D+lZQL1u07bSwepH6uUPTep4tdC7DjyBvZX5h+N9pEslT6mVwMAaRW32i1DAkQ3RZsbyOq54X9dI01A25S7W4tozs1gyXDm2Hcwa0tiyf+s9G9TJwQq+qtdqcZmoWBalmvLgMy46Eee+Kwfjb4LZ4V5Xoyyte9OSlaNZZdX04SgIM8oiS0LUjOqFN4zq47Mj2pttF9OS5Ha7p8I7bpnEddyeOAT+ya6qTrRi9l34OT6rljZ5Uy0z9v1H4/oaj0K5J/GYQzKmTjsfONF/cXH3d7ty8ajmFf5/YXbjt+FuOtkwaNaxTUzxwWi9hBkj9PSJX19sXuiy6zdQMSC2F7onD28ktfD28azM8dmZv6SRednhx7ZUZrkm1C5uBiZKA/nr+rxO64//GdrMMuvTPenlTtXOPMVrsOxrOGdAan8/bYns/3kSJElvrRnXRWq5+HzX6Rpx59xxnmW5f5OrhnRBUgFYN62iu63kNsiyXXDBrwEpLCWh66UZ2bYZbj+uKw1pULSAdui467cmrk1FzL6iQSCjjxXC8F//WH69OWYc3p64XPv/U2X1055Q/tj6hipHUQACn9m2J7xZtw9Fd5OZM6qk/NjtrpVLyYk8eURIQ9e7I3PxiPV794dN7oWtufekboR8etWglN6KvCD14Wk+DLZ1hCElUC+l++G5GDFw7ohNO6dtSd/iAaU9e97xsYUbCj64ago7N6uED3bIsgUAANx/XBWN65gGoCTTsZvx85aIB6NSsHl6+cED4sXKJ5C1epMpv3iAL955snHH0nIHa5Q3sjKbIbSCX4TUlEMAz5/bFdzcMwzuXD5Y+vv4YRGrsySNKAl5d271cW6Zt47qW21x0RDtcdEQ7z87phCjNtAz9brLp2p0a1a0ZJq7ahSuOao/ZG/b6ei4iio2IOW8eXNzVRwikAKkmQcr4W4YLHz+yc1P8cftIy3NNXbMbAPDejI12ioixvfIwtlee5rG6Gf6P8HCSmdrOR7Jm50HLbe49uQdSUgJIQQB9Wje0XZ4QzsMjPQZ5RElg8qpdjvaLzK7p3qL7x6CiMoh6mcl9eYny+sN4/ZKB2F5QgjYSwbMdOXVqkjL4scA7ETnz7hXOenT01D/rAIzX+PxQ10vnxNpd9pZzMPPPsd2wbncR5m3cF35MURRN4JtfUOLqHE/qhmLKsHOV/N/Etfjn8eYjVU7q3cJ2GURiPTKH4k9y18KIaok1u6xbC0Ui7gkeRHnqoCGZzdH1pvl9e01LTfE8wAOAxvUy8OrFA5CZluJo7g8ReUd9HWmQ5U0VTT280GhI37Hdm2NYZ3tzwfzuOWqenYUv/3Ek2v/rx/Bjuw+WoVl21aiJwpJyPPbzSlfnaNfE/jXVTjAls25rVro3110O1yQ93tGJkoDTS3taCi8BTnXPy9b8PX3tHtfHFM2FiYbje+ZhZLfmMTk3EdVQBxB+9MwEAuJ5furlW6wc3rYhAOBU3Xw/pz69+ghH+23cU2xr+wGSWTSt2PlUjujY2HKbuh4thcPhmqTHGh5REnDagqe/KbhdDL020a/JN7iD9c3cit3PkZ8WUfLyqs6uvqykBALC68zW/Yekj/fmpYPw1Nl98MgZzpJW6dlZkqBCtci6ncvlER0b461LRevb1RzkmO5yDV12zntaP+uGu4w0b6riXq5zS8mBQR5REnDagqfvyeNNQp6+NbxlwyyDLeXZ/Rj9WKuPiGLHj1+0/phurxuN6mXgnIFtXM27Vi+0bhXk1NMsq1Bzk5q9Xjtk/uQ+LdAiR3wd/uTqocipm446EQFlzfHevHSgVbEB2OthbS6ZXdML6gCYCGCQR5QUnA7r0U/Ar60x3mEtGtjeJzJJifvqmd2evPYO5pMQUfzSJEmRvBy8ffkgZKWn4Pnz+1luW9WT56xsXunZsgEePqNX+O+Gdc3ncU+8Y2T43+qlGfTXyxcvONwyI6fZe+rH8NhorgFrZ2kHqh2YeIUoCTidWqfPs1Fbe/Kc3BoDuvfOi/qB7DHeu2IwfluxA1cd3dH9SYkobqgr6rKV9lHdmmPZA2MNe+i0wzVjm4Uxr0EWvr/hKKSkBHD/KT1QVhFEi5w6pvs0b5CFJvUysKeoTLPI+v3fLYvYtqTcvDcrIteY7p532ZHt8c70DabHsCOaiciYd4X0GOQRJQGnLXip+uGatbQvz8nN0Y9MZrKVr+Fdm2F412bWGxJRwrJziTEfgulvMhc7UgI1I0guH9ZBer+01Kp9yirMgziruYUD2jfGlNXGSw4N7tDY0yAvmphdk/Q4XJMoCajv7yf1kV9zJ9bDdhKZ94M1dcfjZ0NU6/jxu9f35MWS0yBzx4FSAECZywVKnzm3L64ZbjwCYmzPPMPn1K4d0clVOfzAewbpMcgjSgLquXX/Pb+//H66uwJbAuXp3ypvWshrZ08qEflH3fsVuk7Nvec43HPSYVEvi9tVez6YuRFA1aLoTjStn4m7TjR+3UYLxevdeXw3/HDjUY7K4BfevkmPwzWJkoA6CYjsTQqIbNWNdStvrCzbdsD2Pn5MqFcPueIkeqLazatK+6D2Vcu7tGpYM/etaf1MtGtSL/z3UTYXQnfK7bzvWeuqMmrKrJHXt3UOzhrQGl2aZxtu0yXX+DkzKSkB9FItdD60YxPMWCe/Vmp6agDlld426vGeQXoM8oiSgPN18rT7dWpW34vi1AqpKQG8fOHh+MeH8wF4M1xTnYCgjkWWOCJKPprsmh5V2vNysjDzrmPRoI62yqc++s3HdfHkXFbcBnnd8rJx48cLsGWf3ELolwxtL3x86QPHo6S8MqqJUdQ+u2YoHvxhOe45qYdnx2RPHukxyCNKAoe3a4hVOwpt76e/4Q5s735B79rkeNX8DS9usA1iVOEgovjgV29MnmD9OPXQyWgN1Xe6llurhnWwdf8h9GvTEM/8ujri+Zl3HRvxWLNs47VL62emob6Ldf7c6t+2Eb6+bljMzk+1A4M8oiTQIMthcMCWP1e8rhedO7A1pq/ZjaO7MHMmUW3kZJ085+eqOUFalMbqBx325PVo2QBb9x/SrJOnJgpiHz69l2BLotqDiVeIksBFR7QDAJxsI7MmAFQ6veNSBC9a4DPTUvHyRQNwwZC2HpSIiBKZ70Ge6t/mSzDIG9nNvIEq3eF50quXUKiwuGcNbNco/G9R4JfMYr08BsWfhAjyXnrpJXTo0AFZWVkYMGAA/vzzz1gXiSiutGlcFysfGov//k0+syYAzNu4z6cS1Q7qmyrvr0TkVjQvI+ohml4FeXUt5hJXOpyUl55aVV21apj0Yo7d25cPcn2MWOAtiPTiPsj79NNPccstt+Duu+/+//buPCrKen/g+GcYCEEFYlcB0VAgJFHLDLOV41KWZtqimetVE7H0XG91fx5L763s3q5Zmp4wt8Q0r5XVTb1Zlgu3ssU2d9HS3BBTcQOB+fz+sHkElzQdeOaZ5/06x3OYZ2YePsPH78x8nu8ma9eulXbt2kmnTp1kx44dZocGeJVaAU6u5AGAhVW5cFTNX9uro8i7UMyXOnrE/7cJhOfaDH347acXjfHEZ+CtydGXfQ4z8PGPM3l9kTdhwgQZMGCADBw4UFJTU2XixIkSHx8vU6dONTs0AACAalH9c/JO/+yxhVcucJoLDbc8H/dwzdLyirPui6obaPxs122ARNhCAWfz6iLv5MmT8vXXX0v79u2rHG/fvr3873//MykqADgbV1EBXC7HeX6ult9V6Rd4auGVC52l4hL3hnMP1zxWenaRV3ljdE/1SAK+wKtX1ywqKpKKigqJiYmpcjwmJkb27t17zueUlpZKaWmpcbu4+I9vcgwAfxRfLgBcriq9a9X8nnK8UsHkqfevWgG/PyfvSGn5JZ332G/Pe++73WfdF+h/ur+itonbIpit8v+doAvkAfbg1T15bmeOsVbV8467fu655yQ0NNT4Fx8fXxMhApZUuRld4bTE20G1aBJ96ZvA339tvNyWEi3JMXU9GBEAO3I4HNK9VZxkpcZI48ja1fq7vt15yPjZU0Xen9o19sh5zrR8U+F57+uUfnpV6VEdkiW1Xshlb58wpvOpTco7pMVc4JGnNQgLuqzfebkqZ/C/j91kWhzwHl59ySMyMlKcTudZvXaFhYVn9e65PfnkkzJy5EjjdnFxMYUecB6B/n5SUnZqIruKfbdTaJlwpWwpPHpJz32++zUejgaAnb3Qo3mN/J7KdZ2nirwrgy9/dctzyYgPk0837T/nfZX3iY0JqSVLHm132b+v/42NpP+NjS768Q6HSP4Tt4mISOITH1z2779cCRHBZocAL+DVl+6vuOIKadWqlSxbtqzK8WXLlklmZuY5nxMYGCghISFV/gE4NyZqn8J8OgC2U+mNz1MLr1TXEFOPLQxTTS5xZwiP8vI/EUzg1T15IiIjR46U3r17y7XXXis33HCD5Obmyo4dO2TIkCFmhwZYXuUPBW/4kDILH44AbKfSm76nFl5xVtObKW/RF4O/Eqry+iLv/vvvlwMHDsi4ceNkz5490qxZM1m8eLE0bNjQ7NAAy+MjwY2/BAB7qXxdz1M9cE5nNRV5XnolLqSWvxSXlMutKVFmh8LFSpzF64s8EZGhQ4fK0KFDzQ4D8Gk27siTeqG1zA4BAGpUh7RYmbR8q4h4f0+et1o28mZZs/1X6dgs1uxQuFSJs1iiyANQPSpfHVUbj9f8U7vG8vOB439oJTUAsLK6tU5/BfTUwisXOk9IrUv72umttWNMSC25q3l9s8MQEe/t7YR5KPIAG6v8kWDfEk8k6Aqn/Ou+mlnRDgC8QfyVwZLeIFTqBPpX2WvuclxogZS0+qGXdF7KF+CPo8gD7IyFVwDAlvz8HPJudltxODzXC3RmT17mVRHSNKaudG3RQBZ+vVNybmtySef19tU1Kxt2a5JM/mSrZKXW7MiQq+uzmjyqosgDbMw6H5sAAE/z9JYHZ56ubVKkZN+aJCKn9rq75PN69YZfVT2W1URuahol18RdWq/lpWoQFiRLH2snoUHVs1chrIciD7AxxvADADzF4XCIn0PE9dvIEE/N9bMSf6eftG4UbsrvTomlNw+nWejaCABPs+HnLwCgGvlX6nbz1KqdLpdHTgPYCkUeYGP05AEAPKny0EpPzaW7/7p4j5wHsBOKPMDGKPEAAJ5Uea88Tw3XPN/8ttgQ9jgFzociD7AxOvIAAJ5UeTEXTxV55+sRXJTd1iPnB3wRRR5ga1R5AADPcVZDkXe+C5KxofTkAedDkQfYGD15AABPqo7hmgD+OIo8wMb4+AUAeFJJWYXxs5MriYBpKPIAG+PzFwDgScdOni7ydh864ZFzqnrkNICtUOQBNuagLw8AUE32HSnxyHlqB/p75DyAnVDkATZGTx4AoLqE1ArwyHmu8OfrKvBH0WoAG6PGAwBUFy4kAuahyANszMEnMAAAgM+hyANsjBoPAFBdumQ0MDsEwLYo8gAbo8gDAFSXpjF1zQ4BsC2KPMDGWF0TAOBJf+uSJiIiA25sZHIkgL2xJi1gY/TkAQA8qfcNidKlRQOPrawJ4NLQkwfYGBvMAgA8jQIPMB9FHmBjLqo8AIAFDL6psdkhAJZCkQfY2NX1QswOAQCAC3qiU4qM6pBsdhiAZVDkATb2XLd0s0MAAOCCHA6HZN+aJAFOJpMDF4MiD7CxiDqBEujP2wAAwBpczDIALgrf7gCb8/fjqigAwBqYSw5cHIo8wOacFHkAAIugxgMuDkUeYHMBTt4GAADWMO63zdZzbksyORLAu7EZOmBz9OQBAKzi4RsSpWNarETVDTQ7FMCrUeQBNlfBLHYAgIVEh9QyOwTA6zFOC7C5e1o0EBGRNo3DTY4EAAAAnkBPHmBzf+6QLBkJYdKuSZTZoQAAAMADKPIAm6sV4JTO19Q3OwwAAAB4CMM1AQAAAMCHUOQBAAAAgA+hyAMAAAAAH0KRBwAAAAA+hCIPAAAAAHwIRR4AAAAA+BCKPAAAAADwIRR5AAAAAOBDKPIAAAAAwIdQ5AEAAACAD6HIAwAAAAAfQpEHAAAAAD6EIg8AAAAAfAhFHgAAAAD4EIo8AAAAAPAhFHkAAAAA4EMo8gAAAADAh1DkAQAAAIAPocgDAAAAAB/ib3YA1U1VRUSkuLjY5EgAAAAA4NK5axp3jXM+Pl/kHTlyRERE4uPjTY4EAAAAAC7fkSNHJDQ09Lz3O/RCZaDFuVwu2b17t9StW1ccDofZ4YjIqQo8Pj5edu7cKSEhIWaHgwsgX9ZBrqyFfFkHubIOcmUt5Ms6vCVXqipHjhyR+vXri5/f+Wfe+XxPnp+fn8TFxZkdxjmFhITQoC2EfFkHubIW8mUd5Mo6yJW1kC/r8IZc/V4PnhsLrwAAAACAD6HIAwAAAAAfQpFngsDAQHnqqackMDDQ7FBwEciXdZArayFf1kGurINcWQv5sg6r5crnF14BAAAAADuhJw8AAAAAfAhFHgAAAAD4EIo8AAAAAPAhFHkAAAAA4EMo8mB7rD0EAAAAX0KRVw1Wr14tO3bsMDsMXITCwkI5cuSIcZuCzxrIk3WQK2sgT9ZBrqyDXFmLr+WLIs+Dli9fLo0bN5aHHnpI2rRpIw8//LCsXbvW7LBwDuXl5TJgwABp3bq1ZGVlSa9evaSoqEgcDofZoeEMJ0+elOeff14mT54sK1asEBEhT16KXFnT/v37xeVyGbd97YuOLyFX1kGurMUX80WR5yG//PKLjB49Wnr27CkrV66U3Nxc+eGHH+Qvf/mLbN261ezwUEl5ebn07dtX1q9fL7Nnz5YHH3xQvv/+e+nWrZts2LDB7PBQyZIlS6R+/fqyaNEimTlzptxzzz0yevRoKSkpMTs0nIFcWc/Ro0elZ8+ecsstt0hWVpaMHTtWRCjMvRG5sg5yZS2+nC+KPA/ZsGGDrF27Vvr27SsJCQnSuXNnGT9+vFRUVMjo0aPNDg+V7NmzR9asWSPZ2dly8803y4gRI2TZsmWybds2mTp1quzbt8/sEPGbmTNnSvfu3eWzzz6Tjz76SF599VV54YUX5NVXX5UTJ06YHR4qIVfWcvjwYencubMUFhbKiy++KGlpaZKbmyv9+vWTY8eOmR0eKiFX1kGurMXn86XwiPnz52tGRoZu2LDBOFZRUaGvvPKKJiYm6tKlS02MDpWtXbtWg4KCdMuWLaqqWlJSoqqqkydP1uTkZF2wYIGZ4eE327Zt0wYNGmheXl6V4zk5OdqqVStdtmyZSZHhTOTKevLz8zUxMVG/+OIL49iKFSvU6XTqK6+8oqWlpSZGh8rIlXWQK2vx9XzRk+chaWlpsmHDBtm4caNxzM/PT26//XbJyMiQefPmmRidfT377LPy1FNPyfz5841jqampEh0dLXl5eSJyKk8iItnZ2VK3bl1ZsmSJlJaWmhKvnW3atEnKy8uN2w0bNpSysjJjYRx3b9CYMWPk2LFjsnjxYjl69KgpsdodubK+vXv3SmFhobRu3VpETg1jv+mmm+SRRx6RCRMmyObNm02OEG7kyjrIlbX4er4o8jykWbNmctttt8nEiROrrNaYnJwsCQkJsm/fPoYs1aA1a9ZIw4YN5a233pIvv/xSBg0aJN27d5dt27ZJYGCg9OjRQ+bNmyeFhYUSEBBgzBvKycmRd955xycm3FrFjBkzJC4uTrp27SrXXnutzJ49W8rKysTPz0+6du0qU6dOFRGRoKAgKSsrk8jISOnbt68sXLhQKioqTI7eXsiV9ZxrMRwRkcaNG0tYWJi88cYbInJ6kYEJEyZIUVGRLF68uMpxVD9yZR3kylpsmy/T+hB90Lfffqv+/v46depUYwigqurf/vY3TUhI0LKyMhOjs5eRI0fqnXfeqaqnhs3+8MMP2rBhQx0yZIgeOnRIP//8c23ZsqUOHTpUVVVdLpeqqn7yyScaHR2t3333nWmx28nbb7+tiYmJOmvWLP3444911KhRGhwcrJMmTdKKigr94IMPtGnTpjpx4kRVPT20dv/+/RoUFKSrVq0yM3xbIVfWs3jxYo2IiNA2bdpoy5Yt9corr9Qnn3xSXS6X7tu3T7t166ZdunTREydOqOrpnI0aNUqvvvpqM0O3HXJlHeTKWuycL3ryPKh58+by+OOPy7hx4+T111+Xo0ePSnFxsaxZs0Z69eol/v7+Zofos9zL3paXl8vhw4dlzZo1kpqaatzfrFkzefzxx+WLL76Q+fPny/XXXy+9e/eWWbNmyTvvvCNlZWUiIpKfny9XX321pKenm/I67MKdr1WrVklcXJz06dNHbrvtNvnHP/4h/fv3l7y8PFm8eLFkZWVJhw4d5F//+pfs2rVLAgMDRURk7dq1EhkZKXXr1jXzZdiCe1gmubKeMxfDyc3NlQkTJshLL70k0dHR0qlTJ9m5c6dMnz5dREQCAgJERKRBgwYSEBDAIlQ1iFxZB7myFlvny+wq0xcNHTpUY2Ji9LrrrtOGDRtqamqq/vDDD2aH5bOeeOIJo0fO7brrrtPBgwer6umrMidPntRu3brp3Xffrbt27dKTJ0/qqFGjtG7dunrzzTdrjx49NCgoSF955RVVPd27h+rTvn17HTRokKqeyo+q6q5duzQrK0t79uypxcXFumXLFs3MzNSMjAzNy8vTLVu26AMPPKBZWVl67NgxM8P3acuXL69yu0OHDuTKQs5cDMf9fpaTk6MZGRman5+vxcXFOnjwYE1KStJ169YZzx0wYID27NnTlLjtwp0Pl8tFriyEXHk/2tZpFHnVoKSkRL/55hudMWPGWavNwXPmzJmj4eHhmpKSovn5+ap6amimquqkSZO0Tp06xhdL9wpJb731lsbFxRmPV1X997//rU899ZQOGTKkyuqo8Jw333xTe/furc8//7x+++23xvG///3vGhsba9wuLy9XVdUpU6boNddcox999JGqqu7du1c7duyoqamp2qBBA23btq1u3769Rl+DXcyZM0cjIiL0zjvv1OPHjxs5efbZZ8mVF9u4cWOVKQEVFRUaHR2tU6ZMUVXV48ePq6pqUVGRpqSk6KOPPqoul0s3btyod999t4aGhupDDz2k99xzj4aGhurbb7+tqlzsqg5PPPGEvvTSS8Ztl8ulMTEx5MoL0a6shbZVFUUeLGffvn16xx13qNPp1OnTp5/zMT/++KM2bdrU6M1z9zyoqkZERJz3efAcl8ulR48e1d69e2tERIT26dNHMzIyNDw83Pj7r1u3TsPCwvTFF19U1dO9ruXl5RoTE2P0qrrv27NnD73i1WT9+vXaokULjYiIMPJR2dq1azU8PJxceZnp06drgwYNNCUlRZs3b66zZs0y3u8GDRqk11xzjfFY9/Hx48drgwYN9PDhw8Z9L774og4dOlT79eunP//8c82+CJuYOnWq1qlTR1u0aKHr1683jpeXl+uQIUPIlRehXVkLbevcKPJgOfn5+ZqWlqYDBw5U1VNfKHNzc/W9997Tzz77TFVP9dxNmTJFnU6nrlixwnhuQUGBXnXVVfrWW2+ZErvdfPXVV9q4cWP95ptvjGN9+/bVG264Qd98801VVR0xYoSGhobqoUOHVPV0b2ybNm102LBhNR+0DW3evFkbNmyoTZo0MY6VlJTo8ePHjSuYBw8e1D//+c/kyoucazGc2rVrG4vh/Oc//2ExHC+wZcsWbd26tYaEhOi8efPO+ZiFCxdqSkoKufICtCvroG39Poo8WNIzzzyjmZmZ2rNnT23UqJFmZmZqYmKiBgQE6Msvv6zHjh3T0tJSfeihhzQ2NlbHjh2ra9eu1cGDB2t6erru2rXL7JfgsxYtWmT8PHv2bG3UqJH+9NNPxrFt27bpfffdp7fffrsePHhQd+7cqc2aNdP27dsbV842bNigKSkp+vHHH9d4/HYyY8YMnTRpkrpcLs3OztY77rhDf/zxR83NzdUbb7xRb775Zr3zzjt106ZNqqq6detWTU9PJ1cmcxfXI0aM0BtvvLHKfcOGDdPrr79e33//fS0tLdWcnByNj4/XX375xXjMhx9+qPHx8VWGTqP6vPHGGxoeHm4MIzt48KDm5eXpypUrjba1b98+HT58OLkykXtYJu3K+7kvPubl5dG2fgdFHrzahx9+qDk5OTpx4kT94osvjOObN2/W9u3ba0ZGhs6dO1cPHDighw4d0nHjxmlSUpIuXLjQeKx7gm1SUpK2bNlSv//+ezNeis9btGiR1q9fX5OSkrSoqEhVVefNm6dRUVFGked+Y37jjTe0devWxhC/DRs2aEJCgiYkJGiPHj00MjJS77zzTj1w4IA5L8bHLVq0SOPi4tThcGjv3r1VVfXTTz/Vtm3bamhoqF5zzTX6yiuv6NixY7Vly5aakZGhq1evVlVy5U1YuMi7VZ7H07NnT+3cubP+6U9/0vj4eG3Tpo1GRUVpvXr1jPms69ev17Zt25KrGsYiU9Yyffr0KheTaVvnR5EHr7R7927t3LmzRkdHa69evTQ9PV1DQ0OrFHrvvfee/ve//zUWgHBr1qyZDh8+3LhdUVGhx44d040bN9ZY/Hby888/a2ZmpoaEhOgzzzxT5b6SkhKNjIzU8ePHq+rpxToOHTqknTp10oEDB+qRI0dU9dSbcF5eno4YMUIXLFhQsy/CJjZu3KiZmZlau3Ztfemll3T06NGalpZm3D9+/HgdMWJElZ7XgwcPanJyso4cOdL4MCRXNYuFi6zj/fff19GjR+vrr79epR2tWLFCGzdurJmZmfrOO+/o7t27devWrfrggw9qUlKSsegXuao5LDJlLXPmzNHo6Gh1OBw6adIk4zht6/wo8uB1jh07pn369NH7779ft23bZhy/7rrrtF+/fsbtM4s791CLrKwsbd++fc0Ea3NHjhzR9u3ba1BQkLGCaVlZme7fv9+4AvrUU09paGioFhYWqurpq9ujR4/W9PR0cwK3oe3bt6vD4dD+/fvrr7/+qqqqL7zwgqalpRkXQAoLC7WgoMB4jruNDR8+XDMyMmo+aBtj4SJr2bp1q950000aFxenXbt21bi4OE1NTdWjR48aj8nNzT2r16i0tFRr166t06ZNM46Rq+rFIlPW8s0332jLli01LCxMJ06cqN27d9cePXpUeQxt69zYnRteJzg4WAIDA+WBBx6QRo0aSXl5ufj7+0vnzp1lyZIlxuOcTmeV5/n7+8uPP/4ohYWFMm7cuJoO25bq1KkjvXr1kv3798vy5culuLhY/vnPf4q/v7+UlJTICy+8IL169ZL3339fhg4dKjNmzDA2xS4uLpaGDRtKWVmZsfkoqk9iYqJs27ZNEhMTjWNpaWlSUFAgYWFhIiISFRUlUVFRxv1+fn5y4sQJKSgokKuuukpcLpf4+fnVcOT25HA4ZOPGjZKfny/Lli2TFi1aiIhIv3795LXXXpM6derIfffdJ/369ZOnn35a+vXrJ6GhoeJyucTpdEqjRo1kw4YNxvkCAwMlNjZWYmNjzXpJPmv79u2SnZ0tTZo0kQULFkhERIQUFhZK8+bNZdq0afLYY4+JiEifPn3kiiuuMJ6nquLn5yf16tWTdevWGcfJVfX58ccfpUuXLuJ0OqWoqEhEREpLS8XlckmtWrXE4XBIYmKi9O/fn3blBV5//XXp27evDB8+XFatWiXBwcHy1VdfyeHDh+Xo0aMSFBQkTqeTtnUefFrDK02ePFk6duwoIqeLuS1btkh6erqInGrAbnv27JHvvvtOZs+eLXfffbekpKRI27Ztaz5om+revbukpKTIXXfdJWPHjpX+/ftL3759JS4uTnJyciQ/P19mzJghS5Yskb59+8rcuXNl1qxZsmDBAunatSsFXg1KTEys0naSk5MlJCREPvnkExGp2q4qKirE4XDIvHnz5KeffpKBAwdS4NUQdx7WrVsnqirh4eHGfWPGjJH4+HjJzc2VQ4cOyciRIyU+Pl7uu+8+2bFjh/j5+cnGjRvl0KFDcs8995j1EmwlLCxMVFUGDx4sMTEx4nQ6pV69enLLLbfI+vXrjcdV/hIqcqqQX7FihQQEBEjv3r1rOmxbSklJkU6dOkmTJk1k3bp1Mm3aNMnKypJOnTrJXXfdJZs3b5awsDAZMmSIJCQk0K5M9sADD8jmzZtl4sSJEhwcLCIiLVq0kDVr1kidOnWM74e0rfMwrxMR+GPatWunM2fOVNWqE9qXLl2qycnJ2rBhQ506dapJ0dnbihUrdOTIkWctatO1a1e94447VPXUXJXu3btrRkaGJiYm6quvvmpGqKikoKBAr776amOjWLcvv/xSc3JytG3bthoeHq5z5841KUL7WLFihS5durTKxsssXOSdzpWrgwcPGj+783T99dfrhAkTznr+7t27dfPmzTp16lRNSEjQ7OxsPXr0qGU3XPZm58rV6tWrWWTKS52ZL3ebqNw2PvjgA61Xr56uWbPmrOfTtqqiyIMlFBQUaExMjH711VfGscobnC9dutSMsPCbioqKKhOY3Uu85+bmalRUlJ44ccK4b8eOHTUdHn5HRkaGZmdnq+rpOXjFxcX64IMP6pgxY8wMzRb279+vDz/8sDocDm3evHmVdsTCRd7lzFxVnjOuWvWL6IEDBzQpKckoGNxKS0t11qxZ2qRJE23UqJHOmTOnRmK3m99rV6qqzz//PItMeZEL5auyVatWaXR0tLEvshtt62wUefBq7g/N2bNn61VXXWUcf/rpp3XIkCG6Z88es0LD73C5XOpyufSxxx7T1q1ba3FxsVH4wTu421Z2dra2a9fOuGjizpN7IR1Un7KyMp0yZYp26NBB58+fr8HBwfrcc89V+duPHTtWw8LCWLjIZBeTq8qWLVumCQkJunfvXuOYu20VFhbqu+++WyNx29HF5Gr//v0sMuUl/mjbKi0t1aioKGMESuXvFrStqlh4BV7N4XCIiMiaNWvk3nvvlWXLlsmgQYPk+PHjMmfOHNtMnrUS90IqCxculA8//FBGjBhhLLYC7+FuW8HBwXLixAlxuVwiIsa8uzPnOMDz/P39pWXLlhIfHy+dO3eWTZs2yYQJE6Rjx46SkZEhIiIDBw6UefPmSU5OjkybNo2Fi0xyMbkSOTWX1el0yvLly6Vp06YSExMjJ0+elL/+9a8SGxsrw4cPl6ioKLn77rvNezE+7mJyFRkZKZGRkcZzWGTKPBfbttyOHDkiGRkZxnzXynmibZ3B7CoTuJATJ05oUlKSOhwODQwMNIYuwfv8+uuvOmzYML311lu1bt26Z831gvdw9wjNmjVLIyMj9fDhwyZHZE9nzhWpX7++Dho0qEo+8vPztXbt2tqtWzfNy8vTmTNnamxsrL722ms1Ha6tnS9XxcXFZz22Q4cOOm3aNH333Xe1fv36Wq9ePf38889rKlTbu1CuKt/v7sWbPn26pqWl6ZIlS2ouUKjqH2tbqqfal3sbBUYJnR9FHiwhKytLH3nkkSpzu+Cdnn32Wf2///s/cmURRUVFVea3whzuoUkLFixQf39//fDDD6vc/+677+q9997LwkVe4Pdy5XK5dNOmTRoYGKgBAQEaHByskydPNitU27tQu/ryyy912LBhLDLlJS6UL3dB/thjj2nz5s1rOjzLcahWWjMb8FLuITDwfqpqDAUE8MdlZmZK7dq1Ze7cuRIdHS379+839i/csWOHJCQkmBwh3M7MVVFRkaiqpKenS79+/eS5554zO0T85lztKigoSB555BFp1KgR++t6mTPztXfvXmOKzubNm6Vp06YmR+j9KPIAAPAC5eXl4u/vL+vWrZPmzZvLhAkTpKCgQD777DOZMmWKXHvttWaHiN+cL1f5+fkyffp0SUpKktq1a5sdJuT8uVq9erXk5eVJkyZNxN+fJSq8xe+9D06dOlVatWpldoiWQZEHAICXad26tXz11VeSkJAgr776qnTo0MHskHAe7ly5N6knV96LdmUt5OvysHQQAABeoqCgQNLT02XdunUybdo0+emnn/hi46XOzNXPP/9MrrwU7cpayJdnUOQBAOAlnE6n3HvvvVJUVCQDBgwwOxz8DnJlHeTKWsiXZzBcEwAAAAB8CD15AAAAAOBDKPIAAAAAwIdQ5AEAAACAD6HIAwAAAAAfQpEHAAAAAD6EIg8AAAAAfAhFHgAAAAD4EIo8AAAuwtNPPy0ZGRlmhwEAwAWxGToAwPYcDsfv3t+nTx+ZPHmylJaWSkRERA1FBQDApaHIAwDY3t69e42f33zzTRkzZoxs2rTJOBYUFCShoaFmhAYAwB/GcE0AgO3FxsYa/0JDQ8XhcJx17Mzhmn379pWuXbvKs88+KzExMRIWFiZjx46V8vJyGTVqlISHh0tcXJzMmDGjyu/atWuX3H///XLllVdKRESEdOnSRX766aeafcEAAJ9GkQcAwCVavny57N69W1auXCkTJkyQp59+Wjp37ixXXnmlfPHFFzJkyBAZMmSI7Ny5U0REjh8/LrfeeqvUqVNHVq5cKatXr5Y6depIx44d5eTJkya/GgCAr6DIAwDgEoWHh8vLL78sycnJ0r9/f0lOTpbjx4/LX//6V2nSpIk8+eSTcsUVV0h+fr6IiMyfP1/8/Pzktddek/T0dElNTZWZM2fKjh075NNPPzX3xQAAfIa/2QEAAGBVaWlp4ud3+nppTEyMNGvWzLjtdDolIiJCCgsLRUTk66+/lq1bt0rdunWrnKekpEQKCgpqJmgAgM+jyAMA4BIFBARUue1wOM55zOVyiYiIy+WSVq1aydy5c886V1RUVPUFCgCwFYo8AABqSMuWLeXNN9+U6OhoCQkJMTscAICPYk4eAAA1pFevXhIZGSldunSRVatWyfbt22XFihXy6KOPyi+//GJ2eAAAH0GRBwBADQkODpaVK1dKQkKCdOvWTVJTU6V///5y4sQJevYAAB7DZugAAAAA4EPoyQMAAAAAH0KRBwAAAAA+hCIPAAAAAHwIRR4AAAAA+BCKPAAAAADwIRR5AAAAAOBDKPIAAAAAwIdQ5AEAAACAD6HIAwAAAAAfQpEHAAAAAD6EIg8AAAAAfAhFHgAAAAD4kP8HtIDiWLAQ/CUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(df['OT'])\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Oil temperature')\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "522a80b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uWaxk8+wWY8CuvqVqkPf41hosuncjfvCPw2E/f1u/E26vrDz36D6HP5OX2J68TUdbtbfbGeTRGJ3tGDpZMxoM8oiIKCoii1eaY4PZaNBegJp7nKPq4SGi+GruEZM1hy/VFEQW7niUmbwDIUo1heuXTUFVSRY+vGwKJMm/5mD9nOKAoG/rybawn79Rfe4pybbBZPSfwoo1Conelbf5eIv2dkcfgzwaG3+5ZvSTNQHuySMioiiJ6XaT1XKpPLsZ2VYTep0e1HUOYFZJ9nAfTkTjrCnCoSuCyOSdaBk5kyfLSh9ahsWI/epS84VThgZ5BZkWvH7nuiG3l+TYsP3uS7CzpgOfeHQ7nJ7wF4pEP155XuD3kWNTTmcTWa7Z0uPAwXr/EvmRyjUP1ndj++l2fO6CShi5249CEOWao83kMcgjIqKoiCBP9MRIkoTSXBt6W/rQ0uNkkEeUZCJZn6BXpf4Nn+0YwKDLiwxL6BJPr0/Gt58/gGd21QbcHiqTNxyDQUKmRTkldXp8Ye8nqggm5WYE3J4Mg1c2H1dKNS1GA1xeHzoGhg/yvvPiQeyt7cLs0mysnV08HodIKeRkSx/q1ddafQ9rNFiuSUREUanrHDrdriDTAoB9KETJqCXCyZpCUZYF+XYzZBlhJ2x6fTK+9tc9eGZXLQwSkKH24BkNEuaWRX+hx2pWTkmHC/K0TF5ucCYv8eWaW9Qg7+K5JQBGzuSJQVVn2MtMIfz3xmOQZWDD/FIUZ0f2dxuMQR4REUXFX67pP9EqylKDvD5nQo6JiJSr/68fbh6yWNzfkxdZJk+SJFRpfXmhSzZ/8fpxvLy/EWajhF/fsAyv37kWH142Bd+6Yk5Ew12CWU1qkDdMX6/I5JXnBWbyRE9ez2DiBq+IFRBXnVMOAOgccMHrC73g3eH2ahfEGtXnUyJhX20XXjnYBEkCvnnZnFF/HpZrEhFRVBpC7KkSmTyODSdKjAGXB5/8/Xa09jrx3Q/MxxfWVGrvEz15JdmRBXkAMLs0CzurO0IOX3ntUBMe2XQSAPDAR87BlYuUwOa/P7Z41MdvNSmB4bDlmuK5JziTpy5DT1S5ZkuvA/Vdg5AkYF2VUnopy0DXgAuFWUOzMM26xe+N3ZEtgaeJ4+cbjwEAPrR0MuaMIisuMJNHRERR0fpidEFeYaZyItPGII8oIR7fWoPWXiVjd9+/juDdU+2QZRl/212Ho03KQJBIyzUB/4TNI409+MuOM3h651l4vD5sO9WGrz+zFwDw+Qsq8aGlU2Jy/CKT5/HJ8Hj9gZ7H68Ojb53C0aYebbpmcCYv0Xvy9tUqA2eqSrKQazdrmcVwF730gV0DM3mk0z3gxtsnlAmzd1wye0yfi5k8IiKK2KDLq524TM7XBXlquSbHhhONv85+F363+RQAJdA40dKHT/3vDpiNkrYEfdnUvKgGoojhK1uOt2r9Zn/cWoPq9n64PD6snV2Mu6+aG7PvQfTkAYDL69NWJGw53or7/nUUj75VjY5+JYgNzuT5yzUTFeR1AQCWqDv/CjMt6B50o73fhaoQ92/SB3ndDPLIb399FwBloubUwtFN1RSYySMiooiJE5Jsq0m7eg6wXJMokX731in0Oj2YW5aNl267AOdOz4fXJ8Ph9sFqMuBbV8zFM18+P2C33Ehmq2sUAGVNSm6GGceae+Hy+LBhfike/fRymKP4fCOx6D6X0+3P5IlS07Y+J3wyYDZKKAoqgUzUMnSRcdxX1wUAWKwGeSM9H+ozeU3dDvjC9O7RxCN6O8UFg7FgJo+IiCIWvD5B8JdrcvAK0Xj714FGAMAdl1bBbjHh2S+fr03Bzc+0IMsa/eleYZYVd1xaha4BN26/eBZkAL968wTsVhPu3DA7pgEeAJiMBpgMEjw+OaAvL3hiZmmODYagvXJiT16f0wOPLgsYT49vrcYDrx7DLz+5FHvVTN7iKXkARp423KTL3rm9Mtr6nVH1S1L6Cr5gMBYM8oiIKGL12vqEwBMSrVyTmTyiceXx+rQ+2aVT8wEo0zErRrlAWe+OSwN7gr5/7cIxf87hWE0GeFzegIXo3QNKkGcxGeDy+DAtRAmbyOQBQK/Dg3w1yBoLn09GS68TpTlWSNLQZeX/PtqCQbcXX39mL/qcHtjMBm1Ixkjl68HDVhq7HAzyCLIsY6/a37mkIrpdk6GwXJOIiCIWPpOnnNR0Dbjh9oafjkdEsdXY7YDXJ8NiMqA4xCTHVGI1D52wKTJ5X1hTiTs3zMbdV84b8nFmowF2dWF7rCZsPr6tBufd/yb+sb8x5PtFX12fUykRXTgpV8tu5tvFRa/QlQ1NPUFBHvvyEupUax9+8frxhE1nFeq7BtHW54TJIGHBJAZ5REQ0jupDTNYEgDy7BeJid+cAs3lE46W2U1mmPSUvY0gZY6rx78obGuSV59rw1UuqsDDM8Bj/hM3Y9OW9opbA/vtIc8j3NwVl4/TldVpP3kDooEFk8qaq2VaRiaXx8dqhJlz888043KBMnf3pK0fx8Jsn8OKe+oQel5jSOrc8e1S7JoMxyCMiooiJTN6U/MAgz2iQUGBnySbReKvrUP8mY1CemWhakKcv11SDvFxdSWYoke7Ka+p24P/93z6cah26/09werzYX6+ccB9u7NFuF0vmex1u9KoZvBtXTYVBAq5YWKbdz1++PjST5/L40Nan3L58mlJeO56ZvN+/dRqPb60et6+XjJ55rxan2/rx7K5aAP4+uLPtAwk8Kl0/ntrbOVYM8oiIKGKizKgsZ2j/iDZsgGsUiMZNncjkBV14SUWhFqJ3DUQW5In3Bw9qCfboW6fxf7vr8PPXjoW9z6GGHrjUYzjV2g+H24u/7DiDqntewfbT7doy8xybCT/+0CIc/sEVOHd6gfbxBeogqlDPhS29DsiyMk10waQcAEDDOC1Er2nrx4//dQTf/8dhtPRO3OzhaTXA31vbhZZeB5p7lKA70YvpYzlZE2CQR0REUWhXr0AXhuj9EVevw02UI6LYq1WHIVXkp0EmzzyGTF6EC9H31nYCAN493R52dcHumk7tba9PxonmPjy14yw8PhkbDzVrwUB5rhJYB5fWFQ6zQkGUeZbl2jBZLXtvHKeF6O+cbNPe3q+WBk40Lo9P+5s53NCDPWpgBSR2Z6GSPVaOhUEeERGNK7fXp+2hKggxva5Qu3rNNQpE4yW9MnnKaalLl8nribhcU+zKCx/kuTw+HFT7sLoG3AGlmHq7z3QG/Pudk204pH5cdVsfGrv8gVoo4vmxc8CllXgKjbogr1wEeeOUQdqqC/LE2oeJ5mxHP7xqcO/y+vB/askmAO33mgg7qzvgcPtQkm3FrJKskT8gAgzyiIgoIp3qVWmDBOSFOOHiGgWaqJq6Hdh6si0hk2Vr1Z68WKxMSLTgck2P16f1vuXZh1+LIILA4QavHGvqDQgg9ZktQZZl7D6rBHlLp+YBAJ7YVqO9/3Rbvy6TN3yQ5/bKQxa0i/678lwbJqkf39zj0Barx4vXJ2PbqXbt36L/a6I51dof8O9/H23R3m7pjf/vIZxNR1sBABfNKQm5smM0GOQREVFEOtSpmfl2S8gpfuLEpi2FevJcHt+QK+1Ew+kecKOp24GjTT349aaTuPaRd3De/W/ixsd24M/bz4zrsTg9XjSrvVUVaZTJE9M19QGSWHgejnj/cD15e9XARpxDbw0R5NV1DqK11wmzUcLHV1QACFx5UNsxgDMdSqAQLpNnMxu1lQ7BF730mbyiLCvMRgk+GWjujW8FxOGGHnQPurXvfV9tV9hy1XR2OijI0/8IxuP3EM7mY0qwedHc4ph9TgZ5REQUEbHYN1SpJuDv0wu3GyrZ1HcNYukPNuK2p/cw0KOI/GNfAxb/YCPOu/9NXPHQ2/jZa8ewr87f26Tv7xkPDV3KEI8MszHs32UqCe7JEwFbltUEk3H4U1ZRrtk1TJC3Ty1RvGKBMglTKZHzBtxHlGounJyrLZfX88nAjtMdAIBJueEDa22NQtDzoejJK8+xwWCQUKoOsYp3X57IWq6bXQyryYAehwc17f0jfFT6qW5Thq7oV15IElCkVqKMV3+kXk1bP0639cNkkHDBrKKYfV4GeUREFBExUCVskDfMsIFktKumA/0uL/65vxEv7W1I9OFQChB7tIwGCRlmI9bNLsZ9H1qEH123EADG/aS5tkPpx6soyIhZiVciBZdrdqnVAyP14wH+ctXjTb1h7yOCvA8vm4KSbCucHh/ePxvYf7ejWilpXD41HzOKM2FRs4smg4SZxZkAlAtEQPhMHuB/PgyesNmgfawSIObZle9NlKXGyn3/OoJvPbdf6z8TWcv1s4u1XYMTsWRTZPKuXzpZu62yKBMzipU+uPoEBHkii3fu9AJk20Z+rEeKQR4REUVEBG+i9y5YuJOaZKUfdvCDlw+nTHBKieH2+rD9tBIAvHTrBTjywyvwxOdX4oZVU7Xx+dVt/eOaFa5Lo8magH5PnhLkRTpZEwCWqVm34y29IUs2ex1unFRH5y+ZmqdlTN7V9anJsozNx5TeqDVVRTAbDZhTmq19/uBF7OF68gD/3sIjjf6gU5ZlnG5TgozpRcr7zWqG0u2JXS9YR78Lj751Gs/sqsX7Zzsx4PLgvRol+7imqkjbw7ZvAk7YFD//5dPyUVGgBNqLJudq/ZGJWKOwSX3MxbJUE2CQR0REERoxk5diKxSadC/mHf0ufPR32/DwGycm9P4oCm9fbRf6XV7k282YX54T8L5phcoJe6/DM64XC2rTaLImoO/JCyzXjCTIK862YlqhHbIM7AnKzgHAgbpuyLLysyrKsmo76sRJPwAca+5FY7cDNrMB580oBOBfWL5+bjFmFAVOPRwuk7e2SgkiNx3zD/Zo73eh1+GBJAHTC5WsoBbkeWN3ceBok39q6GsHm/D64WY4PT5MLbBjZnEWFlcoweqeCTZhs2vApf19VhZl4rxK5Xe8YnqBf9LpOGfymrod2HZKybJeNKckpp+bQR4REUVE9JYUhJlyJxYAdw+6EzJlMFpiyt1Hlk9BltWEU639+MUbx/HVp/ck+MgoGYmeptWzioYMHrKZjVomYDxLNv3lmmmSyTMHlmuKIE+UNI5kuZrNe//M0CBP9E6KLJYIjOs7/Sf1YsLh6plF2u67r2+YjYc+vgRfvHAGZqjlmoDSJzhcad169YR9X12XtlZGlApOzsvQPr9FDfI8vtg9Zx7Tlay+eqhJK0e/bskkSJKk7WE70tCjlXNOBGKyZlmODZlWE7591Tw8/Ikl+OS5Fdrf73gspn/2vVp876WDcLi9+OO2Gri9MlZOL0CVmjWOFQZ5REQUkY4RMnl5GWaIc9/OFMjmiUze5QvK8NZdF+H+6xcBAHZUd6A1QRPWKHltO6mU9V0wM/RghOlFSgBQ3TYwbsckljqnXSZPBHkDkWfyAGD5dCXI2x0ik3eoQQnyRMnl5DwlMNb3YIms20Vz/GVzuRlmXLd0MsxGQ0CQN1wWDwBKc2yYX54DWQa2HFeCx9Nquajo/wIAs1F50nTFsFxTH+TVdQ5qawKuWaL0oU1Ss1Yur0/re5wIqtWsrfg95mdacO2SyTAZDdpi+8ZxWIh+3ytH8OS7Z/CN/9uHv+xQJvJ+ae2MmH8dBnlERBQR0WtXoE7RDKafFPf3fck/yES/66og04JPrpyKxVNyIcvA64ebE3x0lEz6nR5tQMeaMNPvRJBX0zY+mTyXx4ej6jLvWSWxzQAkij/Ii75cE/CXVu492zVk35lYfD5fLdMUgXFrrxMOtxfdg25tsub6MGVzlUX+IG+4fjxB9FiJnitRGjpD93lMcSjXPKIGeVlW/9qJhZNztCXbZqNB+5lOpF5kEWTrf49CeZ6YchrfTF6f04Mu9eLFP/c3otfhwYziTFw8N7almgCDPCIiipA2eGWYUe3/sW4mAOD+V44GDDRINm6vD61qCZX+ivzlC5XR6q8eakrIcVFy2lndAY9PRkVBBqYWhi6NrFR7rKrjWK65+0wHfvXmCXi8Phxu7IHT40Oe3axNfUx12nRNd2C5Zk6EQV5VSTayrSb0u7w41uzPZg24PFoWR/RT5tnN2i67hq5BvHOiDV6fjFklWWHLX+0WkxbcleVEEOSpweJbx1vh8fp0mTz/78uiBXmxyeT5fDJOqN/7Taunabdfu3hywP20QVkTKMjbq/YgzikbelFErMNo73cNWasRS6F6/r544YyQu2fHikEeERFFpHNg+HJNAPjM+dNw/dLJ8Ppk3PbU+1q5VbJp6XVClpUTLH2P4eXq/qx3T7Whx5Gcx07jTwxGCFeqCcQ/k+fx+nDrX/bgv18/jhf3NmCXOi1x+dT8tFifAOj35KkrFKLsyTMaJCyZmgcgsC/vWFMvZFkZzlKcrVQiSJKEyWrZYn3XoPY7Xjd7+AmHIkCLJJO3pCIPuRlmdA+6sae2S5fJG1quGasgr7ZzAAMuLywmA76wZgZsZgPMRgkfXDwp4H4FKbbyZqwcbi92qY+J1SH+jvPsZtjUx19THPvyRM/fnNJsfGX9TFyzeBI+tHTyCB81OgzyiIhoRD6fjE41YBsukydJEu67fhFmFmeivd+F5/fUBbzf7fUlxVAWcTW1NNcacAV1ZnEWZpVkwe2VseloS7gPpwlmR7USUImJi6FUqiPxa0axRuGf+xtxy593Y/eZjrD32XysFU09ygniqwebtPJR0YeWDsZargn4Szbfq/EHeVqpZtBU1Mm64SviPvol2aFcNr8MVpMB5w8T8Asmo0Erw3tpbz3Otiv9mvpMXqynax5VSzWrSrJQkGnBX790Pv76pfOG9BBOtCBvV00nXB4fynJsITPfkiRp2byGOPblidee8jwb7rpiLn75yaXaEJ5YY5BHREQj6h50a1PY8ocJ8gBl0uBnV08HADy986x2wuvzyfjIb7dh/c82oy/Gi3+jpfXj5QwdWHGFms377osHceeze3GyJfxyZUp/vQ43DtYrQztWzSgIe7+KAjsMEtDv8mqlwJF48t0a3PrU+3jlYBM+/Nt38e0XDmDQNbRc7OmdZ7W33z7Rip3V/kxeugheht4ziiBvpbqzcGd1h/bcc7ghsB9PEH15ZzsGcFTdZxccCAa7afV0HPr+5Th/ZviAX+8aNYP27K46eHwybGZDQKmn2RTbck3xfcwtU76PJRV5WD5t6ONWrLyZKEGemI57wayisJlv0ZfXEMe+vAatFzz+w5IY5BER0YhE30a2zaRdeR7OtUsnw2Y24Hhzn5Zx2FfXhX113ajvGtQGHCSKKMcJNSHvI8unoCTbih6HB8+/X4+7nz8w3odHSWT3mU74ZGBqgX3YEzOryahNLaxRJ2xuPdmGD/92W9gLBX/efgbfe+kQAH8G6akdZ3HTH3aiV1cu3Ng9qE1+LMi0wOnxoa3PBZNBwjnqSoB04N+Tp5ZrqtUDeRnDX1jSWzo1H2ajhKYeB2o7lKxJ2EyeOmFz66l2DLq9sJkNIYdyBDNF8BworKkqQkGmRZueWVmUFVA9YDbEtlzzWLPyvc4N0Xeml29PvyDP55NxOMxaiK1qkLemKnxwPlXtxTwbx75akcmbFEG571gxyCMiohFFMnRFL8dmxgfOUa5gP72zFgDw2iH/xMpEB3n6yZrBphdlYtt/Xow/fu5cAMqxTqQx4xRIlGqurAyfxRMqg/ry/rz9DHaf6cQfttYMuW9nvws/feUoAOC2i2bhxa+sxl9uXoVsmwk7azpw42M7tL7QZ9+rg08GVlUW4CPLp2ifY8GkHGRY4lPqlQj+nrzRl2tmWIxa4Lu9uh1en6xlt+aFKdfcpw3kyIExxgMwzEYDrl5Urv17RlCpoLho5hpjkPfinnp8/Zm92KYOvAo1XESvIM0GrzjcXnzxyV246pdv49G3Tge8r7PfhYPqCo3h+mpFr+SpOE7I1V578pjJIyKiJKAtQo8wyAOAT66cCgB4eX8D2vqc2KibWBlqWfF4aupRrqaG23VlMhqwfk4J5pRmwycDb51oG8/DoySy47Ry0rwqgiBvujphUyxEFzvYRBZB73dbTqHX6cG88hzcuWE2JEnCBbOK8PQXz0NBpgX767px/7+OoKXHgcfeUU5ab1g1FZcvKNU+x7Jp6VOqCQSWazo9XgyqUw6jCfIA/+9qx+kO1LT3h83SBe8XHKlUc7SuXeIfejIz6Bi0ck3P6HvyugZc+Ob/7cMLe+rRNeCGySANKU0N5i/XTP2doP1ODz73+Ht4U+2jfiGoF/zd0+2QZWB2aRZKhpmKKh4fYml9PIh+v0l5zOQR0TjaVdOBr/xlN+o6x2+Zb6zJsoz7XzmCh944nuhDSSvt2iL00DvyQlk2NQ+LJufC4fbhP/60W5ssBwB7znaGLKkZL8Nl8vTWq3uuNnMIy4Q06PJif52SARhu6IowTV2vcLZDeQ6tV5eVn2kfQG2H/3m1qduBP26rAQDcdfmcgPK9hZNz8btPLQegZMG/8MQu9Do8OGdKLq5eVI6lFfkoUSdErgjRa5XK9MvQRRZPkpQy8WiIrOvOmnatn3JuiCzdlKBsykiB0Wgtn5avBZQzS7IC3mcOs0Lhz9vP4K7n9g3Z9xfKG0da4PHJmFZox3eunocnP78SRWH2mQriuVzsP01lT757Bu+ebkeW1QSTQcLx5j5tZYYsy3hutxL0hZqqqSeyrDVt/fDF4fVJlmVtD98k9uQR0Xh69K3T+NeBJvzrQGOiD2XUajsG8T9bTuOhN04E9LSMF7fXhyffrdH2IaWLjr7oyjUBZVrZj65bCIMEbXT1utnFyBJ7rJoSN9DE35M3/Aut2HO1+XhrXF70Kbm9f7YTHp+MSbm2IVmfUMR+tdqOAQy6vAGlcPps3m82n4TT48O50/Oxfs7Qkf0rKwu0TPiB+m4YDRJ+cv05MBkNMBgk/PfHFuOW9TMDsnrpwL8nz6sNXcmxmaPeIbZiegGMBgm1HYO4719HAPinbuoVZVm1PXVA/DJ5kiThvz+6GF+8sBJXLiwPeJ9FXaHg8fmDuX/sa8B3XjyIZ3fVYY9aSjqcVw8qVRLXLZmMmy+cgdWzRp78WZhG0zWPNSl9iLesn6kNxHlNrRz514Em/PtoC8xGCZ86b+qwn6eiwA6TQcKg26tNso2l7kG3lp0OV0USSwzyiEhzUg1Meh2JnXwo/PDlw/jc4zsjupIp7K/v0t4+0z7+Gcl/H23B9146hB/988i4f+140jJ5WZEHeYAyTOKzqyu1f1+1qAxL1T1Wu88mpmTT4/WhpVcpURqp+X35tHxkW03o6Hdhv5oRSDYOt1dbfkyxJXanrawsiGgXnTa4oWMA9V2Bzz9iul+/04O/qZmFOy6dHfbz/ueVc7WM3RcvnBGQZbqwqhjfumJuVANAUoF+T95o+vGELKsJC9WfV3OPE1ML7Lj94llD7mcwSFpfniSNPKxkLFbNKMQ9V8+HxRT4OxO/Q5darnmksQd3Pbdfe3/nCEFYv9ODt0+0AgCuWFgW8fGI0vvOAVfUKz+STa2aMZ9WaMdl6nTkVw82oXvAjf/6uzLY6Jb1szCrZPjfr9lowFQ1Gx+Pkk0xtbMw0xK3tQl66fXsQESj5vL4tKBovIK8zcda8Lstp0K+wPh8Mp7YVoNNx1pxLIoT2AO6E/FEBHmiJKuhK357dhJBLEKPJpMnfOOy2ZhRnIl8uxkb5pdhmTryfXdN+J1g8dTW54LXJ8NkkFA4QkmT2WjAhbOVq+LJujfvruf2Y8Mv3sKW462JPpS0s/mY8jNdFyLbForI5HUOuHGsSbloJpZdbzvVDp9Pxsv7G9Dv8mJ6oR2rhxnDn5thxh8/txLfuXoevr6haizfRsrQl2uKyZqjCfIAf8lmhtmI//n0cuTZQz93iYXolYWZyLRGVxYaC8Hlmt//xyEt2wP4F8KHs+V4K5weH6YW2KMKUkWQ5/bK6E3wSpuxEi0mFfl2XDZfyW7vre3Ch367FW19TswozsStF82M6HPNEH15bbGvxmnQ7cgbDwzyiAgAcKa9X+uRGq8dZnc9tx8/eeWoNr1Or2vQDY96PKKvJRIH9UFeR/yap8NpU8sa02VimSBKevLDnCgNJ9Nqwsu3r8GWuy5CQaYFK9TlzYnK5InG99IcW0ST9NarJZuv6QbHJIumbgde3t8AQCnxothp7nHgUEMPJAlYWxVZkJdlNWknz++eVjJ3588sQqbFiI5+Fw439uApddrsJ1dOHTE7OH9SDm6+cIZWxpju/INXvP71CfbRBXmfPm861s0uxm8+tWzIVE09EeTNi1M/3khEuaYI8sSwHpEV7h4YPsgTz0tXLCyLKNss2MxGZKqTWTtSuC/P4faiuUepzKgosKM0x4ZlarXI6dZ+FGVZ8IuPLYn4b2hGcZb2sbHWqL72jMeOPIBBHhGpTrb4r1r1jUMmr2vApZXM7TnbNeT9bbplwnURBnmyLONgfY/27zNt45/Ja1W/p87+1C+B0RPBa7TlmoLdYkKOTTlZW1KRB0lS+idbeuO3dDYccTU10p6Iy+aXwmyUcLSpN6F9hKE8u6sWolVw8zH2DcbSFjWLt3hK3ogZXz2RzXtXHWU/vdCOVerQli888R721XbBbJTwYd0qBFJYtcXg8pguLAHA1EI7nvj8Sq2vNpyL5pYgw2zEVUG9cuMlOJMndgSKheldg+EDMFmWtQqD0fRn5qfBGgXxfG63GJGvXhD40toZyLKa8IlzK/Dmneu1HZSR8Gfy4lCu2S2GrjCTR0Tj6JRuUEi/K/5Bnj6o3Beisbyt1x/k1UdY+ljbMaj1cQCJyuQpx+3xyegZTO0SGL1mtQm9bJjx05HKtplRpU6Y21c7/n1uIjMcySANAMizW7Rs3kt76+N2XNHy+mQ8816t9u+2PicONfQM8xEUDbF8fKQgIZjIwJxSMwFT8jPwzcvmYHJehpZxuGx+2YjTDyci0ZMHQBt8kT/KTF6krlhYhoPfvxxXn5PoIE+5QCP25ZXkKI+PrmEyeY3dDvQ4PDAZJCyanBf1106H4SuiH68i365lMq9YWI6D378cP/nwOciN8vHjz+TFvlyzUSvXZCaPKKWdbu1LyHTH0dIHXePRk6cPKvfVdQ15f6sukxdpuaboxxNXgxPRk6fPQLanwf4hQCmHEScBI60ciNQS9cpqqAA/3sRFg8lRvNBet2QyAOClvQ1Jky17+0Qr6rsGkZthxrrZSjmhCExobNxeH95WdyNeNDeyUk1hakHg42pynh3zJ+Vg49fX4ktrZ2Dh5Bx89ZKJ0WMXLf2kSzEBN3eUmbxoxHoBejS0PXlqcOfyqEFetprJGybIO672q08vyhwy0CUSBVqQl7qvVaIfL9KLdiMRu/Lquwbh0PVGxkJDhKt7YoVBHlEcnGnvx6UPbsGHfrMt5k8S8XJSF3SNR0+ePqhs7HZomSKhVZfJq+uKLFgTQZ648t7Y7Rj3n78+yEvlq6N64ndjMxtGPQQhmCifCRXgx1udlsmzR/wxl8wrQZbVhPquQbyfoF7CYCKLd/2yybhSnarHIC82dtV0os/pQVGWBQsn5Ub1sSKTJ4jpjZlWE7591Ty8fPuFmBPHKY6pzGQ0wKQGXOOVyUs0syGwJ08EeaUikzdMueaJZuV1dHZpVtj7DEfblZfCr1W1HWomryDy5/PhFGVZkG0zQZbHfqH41YNN+NlrR+H2+uDzyahRS0AnMZNHlLqONvXCJyuBzK/+fSLRhzMin0/GqRZ/aeN49OTpgzxAmYSl16ZrBI80kyeGrqydXYxsdUqafgFxvPl8csBxp/ILp55/cXhGVI39w1k8JQ+Akskb78yYeDxNjuLKr81sxOXqaO6X9iZ+wEn3oBtvqr04H1k+RSsn3VvblTYXFxJJ7LRbW1Uc9Y624JPNWGUYJgpRiSEyeaPtyUsVolzT5ZXh9cnawLFIyjVFJq9qhNUA4RSqPdapPHilNsaZPEmStJLNP28/M+oLxSdbevHVp/fg15tO4W+767DtVDtaep3ItpqivnA0WgzyiOKgXfeE+T9bTuNIY3L3yTT2OAJGNo9LJk/NHIrSiOCyPX1GrHPAjf4RjkmWZS2Tt2hyrrbrZjxLNrsG3dqEUmDkTF6qDGfRFofHoB9PmFOWDavJgB6HBzXt49c7KcvyqMo1AWjZsnd0S60T5bWDTXB5fKgqycL88hyU5dowrzwHsgx8+/kDcHpSo4IgWe0+o2RrxRj+aOgzeTazYVRrRyYyq7o/TAxlGu10zVShlWt6fFoWD4isXPNEi8jkjS7I08o1B1I3yBtNZcZIPqj2Z/5p+xlc+fDbOBPla5TPJ+Pu5w9o/ZW/f/s0ntp5BgBw3dLJyLCMz7RcBnlEcaAPUDw+GT955WgCj2ZkIqsmnvD7nJ64Zlccbq/2xPzhZUqvU3DZnr5cExh5+EpTjwPdg24YDRJml2VhmgjyxjGTp/+9A8MHeX/bXYflP3odj/z7ZLwPa8wa49BHYDYasGiycjUzOIsbT92Dbu0iRrRXfs+tLIAkAdVt/WjpGf+poHov7VMGwFy3dLKWXf3PK+fCYjTg1UNNuPmJXQz0Rsnj9WmPyeXT8qP++PLcDK3kcFJe7LLfE4V+wiYwETJ5yuPD4wsO8pRMXneYPXmyLGuv3aMv10z9wSt16mt8RUHsMuZfWFOJ331qGUpzrKhu68eX/7QbA1EMpPvre7V4r6YTdosR2VYTTrX2418HlFUXn1w5NWbHORIGeURx0K6e7J83Q7kKHFyamGzE8S2e4i8hGNBl9hxuLw7Wd8cs63S6tR+yrFyhvWiuUma2v7Y7ILAMDphGKtkU38O0QjusJiOmFSoZwlBX4Gra+uMyur+tN7Igr6XHgXv/cQg+GdiTgMEj0Wrqjs8C18UJGL4iLi4UZVlgM0d3NTU3w4z56r6tULsdx0tzjwPb1PH81yyepN2+bnYx/vDZc5FhNuLtE23407tnEnWIKe1oUy8G3V7k2EyYWRz9ybPRIGkXEGKZXZgorEEDRNI9yLPopms6vcrrriRBW9vR5/Ro/Xp6Dd0O9DmVyZrT1YqYaBXYUzvIG3B5tLaIWP6tSZKEKxaW4++3rUFxthVHm3px13P7w54DdQ+6tfU6Xp+Mh988DgD4xmVzcMN5/qBucUUe5o/jPkYGeURxIPqyRJ18T5grccniuPrkNH9SjnYFWt+X96N/HsYHfvUONqt7o8ZKlGrOLM7CnNJsZJiN6HV6AvbSiCBPlAjWjZDJO6UGebPUk7JpBaHLNbedasOlD27Bh3+7LebZytYIM3n/9fdD2gTTzhQokxGZvLIYL3AVQd7euvFbozDaUk1BlO/tqG6P2TFF6x/7GiDLSpYpuP9rTVURvvuB+QCAx7fWhDw5pOHtqlEC+GXT8qPuxxPE72W0j7OJLHhpdV5mepdrmkRPnq5c02oKHHIVKpsn+vEqizK1vr5oib2n7SF68nw+OenbCcRFuxybKWZDwfRKc2z4zY3LYDJIeHl/I948MnSwlSzL+Pwf38MVD7+FrSfbsKO6Hc09TuTYTPjUeVPxudWVWrb2hpUVMT/G4TDII4oDcbI/s1i5utbr9AT0aiUTWZbx9gkleFs+LR9ZNmVgSZ/T/6JyvEkJoM7GqPTxpC4gMxkNqFJLTU6oL1r6ASZi1L4Ykxz2c4rAUd2/FiqTV9c5gNue2gOPT0Ztx6DWwxfO5mMt2jSsSIgSU1GdFWrwyjsn2vDKwSbt38P1WyQLMeWuPIY9eQCwRB2+cqShJ6pSmLGoH2P/xqpKZan1zgRm8t440gwgMIund/2yySjKsqC+axD/OtA4noeWFnaf7QIArBhFqaYwT834zhllGd1Ept+VZzJI2hCtdCUCALfXB6ca5FmMBhgNEnLU1+NQrxMnm8fWjwf49+S19jm1peKAkj1c9/NNuO7XW+NS9RIrtVqpZvwy5udOL9BKLENNL95+ugO7z3RCloHfbD6Jl/Yog7muPqccVpMRZbk2/NcHF+BjK6bgWnUVz3hhkEcUB6Jcc4au1CdZs3nHm/vQ0O2A1WTA+TOKkKW+oOp35Ymm7FitIxA78mapAZnIvonb9QNMRLYn0nJN8bmmFylP+rWdg2jrc8Lt9eGWP78fkF0bbtz8wfpufPbx93Dtr7fibITDW0RgOl0NMEPtHtp4WAnwzp2er94nlTJ5sQ3yKgoyMK3QDpfXh7/trovp5w6nbhSTNfVEJu94c9+4/e52nG7HzU+8h/quQTg9XuxRg5A1VUUh728zG/GZ86cDUBr+k/1qfLJ5Xx26smwMQd7tF8/Co59ejk+MY/9NutCXa+bZzWnf0+gv1/Rn8ixqNjNPLafs1q1RcLi9aOtz+idrjuFCwuS8DMwozoTL48NHf/cuqtWLmu+caENtxyD21XXj4/+zfcSe+ETxD12Jb8Zc7CHdGmLo1qNvndLe3nqyHS/uVfqlr1nsD+g+dd40PPCRxVG3CIwVgzyiOBAn+2W5NtjVKUrhmqcTTQQ6588sRIbFqAV5+gmb4mTW6YlN6Ze4AjmzJFP9v/IiJQI1UaqZZzdjujpAZaQXmVOtyouTCBzLcmxYXJEHr0/Gk++ewbO7anGgvht5drO2iHjTMOWnomSre9CNL/1pV0SZJpHJE03wocZSiwzQNeoVvR6HO2mzvIBSQiR+H7Fe4CpJEr6wphIA8Ng71ePyc6hXdy6OtoyuINOi/X7HK5v3yKaTeONIC/5nyynsr+uG0+NDUZYVM4bpw/nUedNgMxtwsL4H755KXGlpqmnsHkR91yCMBklb8zEa2TYzLltQNu4ndelAX66Zl+b9eIB/hYLHKweUawL+yaL6TN6nHtuBFT96QwsmxpLJMxkN+PMXVmFGUSbquwbxmT/sgMfrCwhmqtv68anHdiTlzl8RlIrKnXhZNaMARoOEmvYB1HYM4FBDNx759wm8tLcem461QpL8F26dHh/KcmxYNYrJvLHGII8oRk619qGtzwmXx6cFdEVZVq1OPGmDPHXX1sXqABQR5ImVBV6frPWNxeJJvnvQjeMtyhVIsStGBGai5FIMMCnKsmoZl7phMnndg24twJqhlshKkoQvXTgDAPCnd2vw8BvKvsI7LqnCjauUq+v767qGDHgRDtT7114cberF9/9+eMTvTXyuOeqLbnvQioTOfheOqv2Pl80vBQDIcvI+NgBlyIcsK1ebC+IwCv4jy6cgz27GmfYBvH64aeQPGCNxsWAsV35FyeZ49OXJsqztf9x4qBnb1YBtVWXBsBmOgkwLPrZC6f949O3TcT/OsTjZ0ofmBE8rFUTgPq88G5lpXiaYrPSZvHRfhA74Vyi4vD5t5L5FvU2cP3SqQV5LjwO71EyzmD4qhkGN1qS8DDz7H+cjz25Gbccg3j3djq2nlCDv+9csQFmODdVt/XjojeTb+Xu0SXmdHkugG4lsm1kbTLfpWAtufmIXfr7xOL72170AgMvnl+G/PrhAu/81SyaNup83lhjkEcXAqdY+XPnQ2/jik7u0rJfRICEvw5zUQV6Pw629YKyfrQZ5tsByza4BF0Sc4nCPPZO3q6YDsqw0i5eoPV5igt2pln74fLLW01icZdV6p1p7nWGDTJEBLMuxIdvmPym4YmEZKgoy0DngRkuvE1PyM/DJVVNRmmPDgknKTrG3jofO5okTa5FpemFP/Yj7A0WQN7tMecFxenwYcPmP+T01OzirJAulOTat3yKZSzZFP15Zri0uZVN2iwmfPm8aAOB3W+JTWuj0ePHfG4/hpb31Yy7XBIDVM5Ugb9PRlriXQtZ3DWoneE09DvxpuzIxc9WMka8Sf2FNJSQJ2HysVZv8lkw6+l34xrP7cOmDW3DtI1sx6EpspsDh9uLB15WpeGurihN6LBOZvidvQmTyDLqePHdwJk/5/rvUC63vn1Ver2cWZ+LnH12M331q+agna+oVZVlx9SJlN9zvtpzC6dZ+GCRlRcsPr1sIQCn9PtQwfkOyRiLLsnbRdG5ZfIM8AFgzSymPf+DVY2jsdiDHZkK2zQSryYDbLp6FhZNzceXCMtgtRnz83PEdsBIOgzyiGHjtUBNcXh/21XZpmYKCTAsMBgk5SRzkvXOiDV6fjJnFmdry8OByTX0A4ojB3i0xel5fyjCt0A6TQcKg24vGHoeWlSvKtiLfbtauagbvzhOCe/wEo0HCzWtmaP/++qWztVKgi+YoQW2oks1Blxcn1GzjFy+cgRlFmXB5fdg8TA8f4A/yphVkases//mJ7130deVnBr6AJ6N49ePpfeb86bCYDNhb24W/72uI+ed/ctsZ/OrfJ/G1v+7Vyp7GMvVw7exiWEwG1LQP4FhzL9xeH17aWx+XYP1g0HCgFvVvIJIl3dMKM3HFAmWB+++TLJvncHtx/W+24m/vK72YTT0O7e1EefjNEzjTPoCyHBtuWT8zoccykenLNSdEJk8t1/TJwKB6IVO8fojvX5w/7FYvyp43oxAfWT4FVywsi9lxXLdUaSHYelKpFjhnSh5yM8zYML8UVy4sg9cn44cvj1zRMl5aep3oGlB24wa/9sfDBWqQJ86NvnP1fLz/3Q3Y/d0NWKjufP3VJ5di93c2jGr1SjwwyCOKgc1HlUDBJ/t7uYrUHTfJnMnbppZkrFOzeACQLaZrqpk8/YRIZwwyeVqQp8tEmI0G7WrkyZY+raexKMsCSZKQqfY1DobJ5GnrE0I80X90xRQsmpyLC6uKtBcxALhornKlftPRFvQ6An83R5p64JOV32FpjhWXqSfKrx4MX06onwhanG3VppbpT/x3BgW4+Smwo0jbkRfHIK8424rbLpoFAPjBPw6jM4Y/jx6HG7/eHLhwPjfDHJDxjVam1aRlel492IQHX1fKdr774sExHWsoYgKseD4BlD6d2SWRXbn+4lrlIseLe+pxxUNv4aY/7ESPI/HPRYcbe1DTPoAcm0krn37s7dMJ60893tyLR99SAuEfXLtgTI8PGpvAcs0JkMnTfb+i91sMY8nLCOzJE0He8jEMBQpn+dT8gItfInMFALdfrPSxH2lMnooAkcWbXmgfl97XpVPzkaF+nZnFmbh+2WSYjQbtwjig9DhmWJKnD5dBHtEYdQ+4sVstoQD8QUyRun8mmYO8mjZlCIV+OWemJX6ZvD6nR8tMrFT7mgQxFVMJ8vw9eYBS0gcgbDmXKNcUKyv07BYT/nH7GvzpC6tg1NXIL63Ix4ziTPQ5PXjmvdqAjxHHuGhyDiRJwuULlP65TUdbwpaM6ieCFmZZtP418fPrcbi1UhfR05Ufoqk+2YxHJg8A/mPdTMwuzUJ7vws/+ueRmH3ex946ja4BN2YWZ+I7V88DAJyj9laMhXhMPP9+PR7fWg0AeP1wc8z/zkVv6M0XVmonfiunF0Tc77Fsaj5WzyyEx6eUNm053opXD8S/93EkYl3K4oo8fPuqecjNMKOmfQCvH25OyPG8frgZXp+MdbOLtYs6lBiB0zUnQJBn9P8ti9ddrSdPlGsOuuFwe3FQfT6IR5BnMEi4Zol/LcsFuiAvV32tSnRJtd7RRuVnMbdsfJaLW0wGXLagFJIE/OeV87T9hsks+Y+QKMm9daI14Orze9WhM3nJuEJB7L2bqtsx49+TFyqTN7Yn+PfPdMLrkzElP2NIuZzIwp1q7dPKMouzlZ+huDI2EOYF5lTQjrxIGAwSvqgOZgleGn2gTgR5SjCweEoeynJs6Hd5texnMP1EULNuSIn4+b1X3QGfrJSmioBJXKXWL0R//2wn1v9sE95I0MlusKbu+OzIC2YxGXD/9ecAAJ7fUzemYOmNw8249MEtmP+9V/GrTUoW7/9dPgc3XzgDW/7fevzuU8vHfLyXziuF0SDhbMeA1qvq8vrw2jDZ3mjph66cN6MQa9Ux3uFWJ4Tz6GdW4KkvrsKHl00B4L8QlUjHm/3Z90yrsjQYULJ5iSD2bS2dmpeQr09+VvMEK9c0+E/F+4OCPH8mz4VDDd1weX0oyrIEvGbH0nXq1OdsqwnLpuVpt9vV34nL64PHG5sp22Ml+oznjEM/nvCT68/Bpm+sxwZ1cFqyY5BHNEZiBYG2X059kk72TJ7H69P6BwOCvOCePN0aAHEyu+N0O+5+fn/UZV9iGmGofiKxTkGfyStWA2VRIjHoHjr4xOnxasHqrCjr4D+0NPTSaFEiJ+rsDQYJl6mZm3Alm2IiqDhmf7mmE7Is43+2KCev62f7BzqInrwOXZD35pFm1LQPaPv0Eq1WXUJflhvfPUSAcnW6LMcGWfZnZ6Ph9cm44697cPOTu3CypQ8DLi9kWSmPvVzNzkwrzIzJ1MT8TEtAX+kl6nRaMdY8Fhq6Hejod8FkkDC3LBv3Xb8QP7l+EW6IcvdaltWE1TOLtKv04zEVdCRix5eYinfT+dNhkIBdZzpR1xnZXspYCnXBixJjomXyDAYJJjUzL9okglcodA+6tVLNZVPz47Y7cE5ZNh7/3Ll48gsrA3oj9SWIA0mySmE8h64IGRZjTAbdjBcGeXHmS+L9VzR2Pp+MLergjk8ETVNK9p68xm4HvD4ZFpMBJdn+fp/gnjz9Qm+nWq75682n8PTOWrx+KLpsk+hJOy+oVBMAZhUrT9RHGnpQo+6+ET9D8QIz6Bp6BbGxywGfDNjMBi3zFymb2Yib1KXRf9haA0AZCHFCDTAW6cr6rlyoTB77x75GtIQY935aPWaRpSvIVI6lvd+FzcdbsbOmAxaTAf+hG+iglWv2+x8bHerbsZhkOlY9DjcONyglMYtiUOIYCS2jO4ogb9PRFry4twFGg4Qvr5uBzd9cj7fvugh/uXlVXE6KrlWDpkvmluDea5Tx2e+ebo/ZOgCRUa4qzYbNbERJtg2fWDl11GVCy6flw2iQUNc5mPDlxiKIFzsHS3JsWDFdCZo3Rvm8EgsM8pLHRFuhAAAmtWSzzykGr4hl6P6S/nj24+ldNKcES6cGfg2ryQBRIZ4MJZser097Dhmvcs1UNCGDvN/+9rc455xzkJOTg5ycHJx//vl45ZVXtPfLsox7770XkyZNQkZGBtavX49Dhw6N6msl24k9xdaWE61o73chy2oaMjK3MMmDPHFSU5GfEdDfk2VVjldkJDt0/WIi8BCDStr7Q0+7DEU/7viciqEBg8jk9To96Hd5MaskC7PLlBNAkckLtZDcv/fMPqoT+euXKyVsB+u74fb6cKypF16fjMJMC8p0JYrnzSjA0ql5GHR78at/n8Tp1j586rEdeGrHWQD+AHaZ+uJYqGZyT7X042evHgMAfHb1dJTrMmJ5Ico1u8LsJJRlGbUdA3Ef2a+3/VQ7fDIwoyhzTNMooyH6KkUJbjREFu2m86fj7ivnYXpRJioK7HHrnfjYigo8+fmV+NUNS1FRYMfyafmQZeAfMZoQqu8NjYUsqwkL1f7bnQnM5vU43Fqv5yzdABkxCfTVQ+ObxXZ7fWgIUdVAiREwXTMOuzmTkZiwqZVrGsWePOX7b+11Ypu6IzPeQV4okiRpvfHh2ibGU3VbP1xeH+wW45h2nqa7CRnkTZkyBT/5yU+wa9cu7Nq1CxdffDGuvfZaLZB74IEH8OCDD+KRRx7Be++9h7KyMmzYsAG9vdFPFepI4tHoNDY+n6ydvH9yZQUqizK1J2Yg+cs1w125zrQqL7ChMnki8BBX8jr6I/+eOvpd2u696YWhB6QsmJQDSVKCoRe+slp7sbermbxQQ09Eaddog5DyHBtsZgO8Phl1nYNacFFVmhUQNEqShLsunwsAeHrnWXz0d+/inZNt+NlrR+Hx+vyTM9WpoaIn740jzTjc2INsqwm3rAscyy7uow/yxKAWhycwk/eHrTW48IFN+ME4jrDeelLpP9Q34MebyORFW67Z5/TgjSNKBui6pZNGuHdsSJKEtbOLtZOfD5yjZHs3h1jLES1ZlrWS3WVTY3dSt2qGusj9dOL68k40+/daiudHAFpJ9K6aDjT3OPDY26fD7rGMpYauQfhkJVsRbTUAxV7AnryMiZHJE+cOfa6gnjwx8MTtRa/Dgzml2UOybOPF3xs//L7Y8SAuGM8uzU6KpePJakIGeR/84Adx1VVXYfbs2Zg9ezZ+/OMfIysrC9u3b4csy3jooYdwzz334Prrr8fChQvxxBNPYGBgAE899VTUXyuZ91/R2Lx8oFE7ef/K+lkwGQ2o1NVqi1JDsScvGcaW64UL8kS5Zr/6RN7eN3S6plhlEM2o+5p2pZxxUq4t7Ljjp790Ht751sW495rAEeb+TN7QIK9+jMutDQZJCzpr2vpRrZZdzgjR33f+TGX4hccnawNVOgfceG53HZp6HDAbJSytUF6AL5pTgiUVeZiSn4HphXZ874Pzh1yVFi/gnbpsqZi0qQ9ovT4Zf3hHmeD4+NYa/N+uwGmg8fJOAoI8MTznZJSZvI2HmuBw+zCjKFMbmDPeVs9Ufk67z3QGDPIZjffPduJ4cx9sZgOuUoPHWBB9hDsTOHxFTNasKg38G5uSb8fCyTnwycD1v9mGH/3zCG79y/tamXi81Hb4s3jx6nWiyE20njxgaCZP/Axyg4Lcb14+J2BK9Hiya20Tic3kOdxebe/nwhhVOaSrCRnk6Xm9Xvz1r39Ff38/zj//fFRXV6OpqQmXXXaZdh+r1Yp169Zh27ZtYT+P0+lET09PwH9AdJkOSh0erw8PblSyeF9cO0M7edfvaRvSk5dkY/K1cs2gIE+Ua/ozeUP35IlgK5pMdbW6rmG4puUcmzlkRi5jmD15dVq55uhLNkSQV93Wj9OtapAX5jjvvnIu8uxmrJ5ZiCvVRbQ/33gcgDKFUxxrWa4NL956Ad751sXY/P8uwkdXVAz5XAUhlqGLrJ5+kunbJ1pR3zUIcf55z4sHhyzJjrXG7kGcau2HQQLOnzG0hzJexN9QbcdA2HUVoby0VymRvGbJpISdqFeVZCHfbsag26sN74nG4YYe3PnsXhxp7MHTO5VA/gPnTEJODHe2rZheAElSekg//b878K3n9kf1c44FMVlTDF3REyWbogy71+nRytTihf14yUVUcGRajFpGK92ZTcpzVnCQp9/DtmxqHi6dVxL6E4yD4S62jhdZlvG9lw5if1038uxmfHntzJE/aAKbGH89IRw4cABZWVmwWq34j//4D7zwwguYP38+mpqU8pjS0sDxqKWlpdr7Qrn//vuRm5ur/VdRoZzQdTKTl5ZOtPShpn0AWVYTvrCmUrtdP8JfnMCLIK/X6UmqQTy14YI8m39KqCzLAY/h4HLNqDJ5aoZsNJOphruCWCcyeWPoGRPHVNPer5Vrzgixcw8A5pXnYOe3L8VTXzwPH1H7+cQ0UP2C90j4Vyi4IctywM9bP3jl6Z1K399N50/HJXNL4PL4cO/fD8W1P2/rSeXEetGUPG1H0ngozrIi22aCT/Znf0fS0e/Sso7XLpk8wr3jx2CQcK46PGQ05ZD/89YpPP9+PT72P+/i5f1K0PrJKCdpjiQ3w6yVf759og3P7KrVHl/C0aYe/Pto/IafnGhRM3khVp5ctagcRoOETIsRK9WfZSzXUni8Pjy14yx+vekk/mfLKTR0DYa94EWJ4Z8sOTGyeIB/jYJ/8Ir/9HxmSRYkCfjWFXMTmmm2j7DKaDy8frgZz+6qg0ECfvXJpfybHcGEDfLmzJmDvXv3Yvv27bjllltw00034fBhf69L8B+SLMvD/nHdfffd6O7u1v6rrVWuwkZzEkypQ5yIl+faAsaxi6ERuRlm/zJTNciTZWg9ackg3NVrcdXQ5fGhvd8Ft9cfSDg8PsiyrGXUosrkqSfslSH68UbiX6EQvlxzLJm8yiLlZ3C6tV8LLGYUhV/HIH63F8wqQqZutHTwgveRiHJNr09Gj0MZOCN+3qI0tqXHgTeOKGs6blg1FT/+0CJYTQbsOtOpre+IB9GPt2bW+GXxAOW5N9q+vPdqOuD1yZhdmhVQMp0IWs/bKAabiEmmvQ4PHG4f5pRmY1kc9rb99sZlePgTS/DZ1dMBAP/7TrW2+2rQ5cUNv9+Bz/9xF/bWdsX8awP+9QlVITJ5M4qz8Pwtq/HqHWtx+yWzAPgXlcfCi3sb8O0XDuBnrx3D/a8cxbf+tj/sBS9KDNHiUJozcfojww1eAYBHP70c/7htjfbckiii9zjUKqPxsvusMmH0o8srcGFV8Qj3pgkb5FksFsyaNQsrVqzA/fffj8WLF+Phhx9GWZlSKhKctWtpaRmS3dOzWq3atE7xH8DBK+lKlF7mBWU4lk/Lh9koYcEkf524xWTQgpRkGb7SPejWer+CT2z0QYsIBAWXxwenx6edcHVFUYI6lkxeRpipXh6vD03quPrJeaM/QRPlmrvPdMLh9sFslCIKGm1mI9bPUcpnjAYp6qlnVpNR+3l39rsCLgqJrOXf9zXA65OxYlo+ZpdmoyzXpp2cP/Dqsbhkh30+GW+fUAZejGc/niD2HUYa5InR4mIEfyKJnrddNZ1RBSYOt1fLIq9QH0efXzM9LlfuS3JsuHbJZPznlXNRkGlBXecgXlGzZf+3u1Yr0X5FtzsyEi/trccf3qketoeurc+J5h4l8x3ckycsrshDRYEd580oRI7NhPZ+l/Y7Hitx8eIcdSXI1pNtWjDLcs3ksLKyAN/YMBvf+cD8RB/KuAku19Rn8kpzbNrO1kTKSIJMXov63FEZptKGAk3YIC+YLMtwOp2orKxEWVkZXn/9de19LpcLW7ZswerVq6P+vMzkpY9DDd3aFd8uNVgT442FKfl2vHXXRfjDZ88NuD3REza3nmzD6vvfxGf+sBNP7zyrnUwWZlq0zJ1gMvqD0rPtyvdboBsY0hUwJMQV0YmsLMtakCeyZtHIUKetBZdrNvUou/7MRilg11+0RPZHZAqnRjF2/2p1KMayqXlDfpaR0K9RCFUaK0bNL5/uDyBvWT8T2TYTjjb14l8HozsRj8TBhm609SmrQVZMG//ASduV1xpZuaa2PypBU+f05pXnINtmQp/To2XmInGsqRc+WZnK++yXz8eb31iHj4Xo44wlm9mIz5w/DQDw6Fun4fXJeOztau39rx5qirgkeNDlxTee3YcfvHwYVz78NnbV+MtVTzT3as85L7yvrLk4Z0ruiL2GZqMBl85TLq7+fV99TMqTxcCZuy6fi2VT8+CT/f1/DPKSg9lowO2XVMV0qmyyE5m8vhBBXrJIhsErYgepfr0RhZd8j6Jx8O1vfxtvv/02ampqcODAAdxzzz3YvHkzbrzxRkiShDvuuAP33XcfXnjhBRw8eBCf/exnYbfbccMNN0T9tTqTbNjGRODzyTGfxtba68R1v96KGx7bDsAf6ARn8gCgPDdjyPTInAzl5D9RQd7GQ01o6HbgreOtuPv5A/jSk7sBhC9PEn15Z9QgrzzX/4SqD0R8MtATwffU2udEv8sLgzS6kih/mUjg71WUak7KyxjTGOXibGtABjPUZM1wrlxYhl/fsAwPfmzJqL62f/iKO+D5QqxQEN+z3ewPIPPsFty4Sjk533Q09iPmxedcM6soIScbM0fI5LX0OPD9fxzCn96tgdPj1ZaGJ2J/VDCjvi8vipLNw41KQDivPAcGg4SZxVnj0n/z6fOmwWoy4EB9N656+G2c7RhAnt0Mq8mAM+0DONYc2eqgmvZ+eNQLPqdb+3HjYztQ1zmA2o4BfOBX7+ADv3wHZ9r7tf6/SHsNL1MHsfx5+1ms/sm/8ex7o58sW9sxgPquQZgMEpZNyxvSv1lRwH1blBgiyHOqz/v6XYHJQgR5/c7EBXmicqdkApXyjsWEDPKam5vx6U9/GnPmzMEll1yCHTt24NVXX8WGDRsAAHfddRfuuOMOfOUrX8GKFStQX1+PjRs3Ijt7aP/ASDqYyRt3Nzy2HRf+dFNMd7kcauiG2yujtmMQLo9Pm4YY6Q6fRGfyxGLzVZUFyLKatEEh4a5cZ6sZqTMdSialJNsKkxpEBQ8TiqQkuUadrDkpL2NUL17h9vOIK/BjXdQtSRKm6XoFww1dCfexV59TPup+HnGhoCOoXNPl8cHnk7WrphmWwKfrlZVKQLO3NjZlbHqi1++iuYnpeRCZvNOtfXDp9gX6fDL+vP0MLnlwCx7fWoPvvnQIL+1tgMvrQ2GmBdMKkyMTc546gGdLFDveRNZv/qTxHQlemGXF969ZAIvJoAV0nzlvmtbv8mqEQ09Epn5OaTZWTMuH0+PDQ2+cwC9ePw6nx4dBtxef+cNOnG7rR6bFiA8ujmyX4SXzSnD9ssnIMBvR2O3AT149GlFGz+eT8eyuWi2DCAA71Czeoim5sFtMuPqccm0cfVGWVbuYRDTezMbACzrJmMnLUC80DiSwJ0+Ua5YykxeR5HsUjYP//d//RU1NDZxOJ1paWvDGG29oAR6gnLTde++9aGxshMPhwJYtW7Bw4cJRfS3uyRtfLo8P2093oKXXqQUWsaAvG+vodw2byQsl0UGeqPP/wOJJeOqLq7TjDjekQgyTEeWpBZlWLTvZGbQWJJKSZH+p5ujq6P2DVwJ3j9XFYOiKoD+2cOsT4iE/TLkmoFzVFYFtRtAJ6OIpeQCUx2YsdzC29zmxr64LALBudmLGdVcU2FGaY4XT48Prh5Upj8eaevGR323Dd148iF6HRzsJ+uE/lIFZy6flJ82OM1Fi+O6p9ohXp4hM3vzy8d/79ImVU/Hq1y7ExXNLsHhKLm5aPR1XqOtBXjsU2ZRNMVhpbnk2vqv2Uj3/fh1e2KuUZ1qMBq0y4JolkyMubTYbDXjwY0uw855LYDRI6Oh3aT19jd2D2t9HZ78Ln/nDTvx5+xkAyh7Tu57bj+se2aqVaO44rWRWV6kDkoqyrLiwSuk5ncosHiWQOag9IBmDvESXa/Y5PVo5K4O8yCTfoyjNdKij0Wl8iHptILYBlb5srL3fia5B5WQ8N8IRzzkJD/KUJ+UsqxHnTMnD87esxlcvqcKn1X6cYOIETAxeKcyywKb2xQVn7iLJVosTwOmjmKwJ6F9cgjJ52vqEsWdwput6BaMp1xyrgHLNoJ+lw+3VAlt7UAlwYZZVKy8T5Yqx8NaJVsiyUjZYlpuYF1KjQdL60Z7eeRZn2vvxod9sxftnu5BpMeLeD87Hk59fCcCfpU6GUk1hRnEWZpdmweOT8e9jIwdJPp+MIwkM8gDlmP/w2XPx0m1rUJhlxaXzSmA0SDjS2IPTESym1wYrFWZicUUerlhQBp+sTBW+elE5vr5htnbfG0axFiLbZtamFx9u7MbJll6sfWATbn5iFwDgr+/V4q3jrfjJK0fhcHvxr/1Kr2qv04PP/GEHXjnQiJ1qn6AYjgMAnzl/GiQJOC/BkwtpYhsS5EXYEz6eEj14pUU9v8uymkbV/z4RJd+jKM24PT70J7BJdaJpCgjyYpdFPaUP8vp0mbwUKdcUV78y1WzQjOIs3LlhtrawPZjoyRNXzAsyLVqZZVdQIBLJhM2xZvJsYZaha+WaMcjk6QPQ8czkaeWaA64hPbwOj1cLbO2WoWWuIpsXy1H3oh/vojmJHU/9sRUVkCTgnZNtuPWp9zHg8mLp1Dy88Y11+OwFlVhVWYAlFXna/ZMpyAOAy9VeMn25Y7/Tg7dPtGrrCoSzHQMYcHlhNRkSvgJCyLNbsFbNcj2za+Q+OFE5IY7/m5fPhtEgwWiQcOdls3HzhZX4yPIp+PwFlVg0ZXSTAkUAfLihB28eaYHbK2PbqXYcrO/GS2rGsM/pwb8ONGqlsosm58Lh9uGWv7yPM+0DMEiBQ4wunluK7Xdfgjt1QSjReAsO6qxJmMnLTHAmj/140Uu+R1Ea6uhjyeZ4EZMIgdgGVPq+jo5+l/a5U61cM9KrX+frrmpbTAacO70AVjWTFxyIRNKTJ8q0po9isiYQvkykrlP5vLEo1xTZuzy7OWCaaLyJoTa1HQNDyjUdbp921dQWIsgTQU5wkDeWflQxFXFN1fivTtCrKLBrfWEH63tgNRnwi48tQXmu8ruWJAlfXjsDgHKClAwjxvVEkLfleCsGXV68eaQZGx7cgk//7078fOPxgPuKUs25ZdkRT3UdD2I4yt921wX0RoaiZevVIG9WSTaeunkVnrp5FWYWZ8FsNODnH12M731w9GPxRb/ikcZerb8OAH748mEcbfIPiPnxP49g0O3F5LwM/O2W1bhl/Uytp3j+pJwhUz1Lc2xJ9XOniSe4Jy8Zgzy7tsooMT15Wj9eNks1I8V85zho73diapIMBEh3Td2D2tuxCqg6+11o12Wv2vqcukxeZMGACPIimUQZDyLIy4wwyPv8mkp8eNkUuLw+2C1GZFpNsKmZvOBAJJKevPZ+5cm5ZJRPzqInT18m4vPJaOgSO/LGHuQtrcjDzWsqsXBy7rj2ds0p85+4zikLLBNVyjXFdM3hgzxZluHxyfjW3/bjhT31+N+bVuDiueF3e4bS0uNAQ7cDBsmfJUykG1ZW4C01I/O1S6uG7Fi8bEEZvnpJFaYV2IdMtE20BZNyMDkvA/Vdg7jwgU3asCMAeG53Hf7f5XNgNEio7RjA41uVtQXjPXRlJBfPLUFJthUtvU68caQZVy0qD3m/PqcHrb3q/ipdRjzWy5vnlyuB/IH67oDnIRHwVZVk4URLn/Z8ffmCMlhMBnzrirm4dskkPPZ2NT60dPLQT0yUYKnQk5fock1tfUKC2ghSEYO8ccAJm+NHn8mLZlH3cE4G9aN09Lu0nrxUyeT1RRnkAUBu0Pem9eT1R9eTJ8uyNqwlf5QZsgxduaYsy5AkCc29Dri8PpgMUsCKh9EyGKSELN+dXZoFSVIuHphaAoNLh9urZS9DTf5bMCkXRoOE1l4nTrb04aevHsUbR5TJmBsPNUcd5ImMYFVJdlSPlXi5ZF4pLqwqgsVowBcvnDHk/UaDlLRldpIk4cqFZXjsnWq09TlhMRnw2dXT8X+7atHW58S7p9rh8flwy5/fx6DbC4vRgA8vm5Loww5gMhrwsRUVeGTTSTz0xnE8teMscjJMeOjjSwNOQkU5dr7dPOR5I5bmlSsTrkWvcJbVhMIsi1YpcMels/Hwm8dxvFl5zhbDYwBgblkOfv7RxXE7NqKxCM4kJ2OQZw/TNjFeRPsIyzUjl/hX8QmgnUHeuGmKQ7nmqaBdXQ1dg3CowzBSIciTZVnrC822jf5PXuvJC87kjVCu2e/ywqX2IBVEOKgmmAhwZFmZOGkzG7UeoMn5GSldamW3mDC1wI4z7QMBPaVAYLlm8AoF5TYj5pRm43BjDy576C3oZzyNpk9PTNVcXJEcpY9mowF/+sKqRB/GqN1+SRWybCZUlWRj3ZxiZFlN6Hd68JcdZ/Gn7TV4/2wXBt1erJxegPs/vEjbD5hMPn5uBX69+SSON/dpwdMNKzsCynlrgko146Uwy4qyHJv2d7Jiej5WVRbip68eRZbVhEvmlaCmvR8/e+0YCjMtSdenSRSOJRVWKCRJJo/lmpFLvkdRGoqknI1iIx49eWKypmg6Pq1etTYapIh73MIt8x4PDrcPXnVJ8ViyM7agnjzRtzZSJk88/m1mg/YiEa0MXSmeyGydVXf4TRvlxM5kMqc0cAenuCjg8PjLNYNXKAhiKqAsK0MvHv7EEgDA8ebeqHsn9tUqUzqXVPDkOBZyM8y449LZuPqccu25Qizgfu1QM1p7nZhRnIknv7AyKQM8QOmNvPPS2biwqgiL1L7Hd062BdxHG6w0Dn+L+pLWlZUFuGHVVFyxoAz3XD0PNrMRN66aisvml+Keq+dpO/CIkl0qTNfUzmPiEOTJsoxNx1q08zavT8abR5oDzuNYrhk9ZvLGAcs1x088MnmiXHP59AK8dbwVp9WdeXkZ5oh7t8TQEqdn/IM8UaoJhO7ripR/T57yeJ6UZ1MWeI9QFise/6PN4gFKQG0xGeDy+DDg9iIf/mEu00a5hDyZzC3PwcbD/lH75bk2dA+6MeD0agMvwv3u7thQhYWTc7BgUq5a+inhJ68cRWO3AwfquiPui/L5ZOxTs3/JkslLRyum5Wu9egDwk+vPSbp+wmC3X1IFAHhhTx2+/sw+bA0K8qrbxGClcQjyynPw76NKSfKqykLkZpjxu08v196fZ7fg0c+siPtxEMWSOShzZ03C5wS7lsmL/eCVfx5oxG1P7cGc0my8cOtq3PevI/jz9rNYP6cYf/ycsi6nuVfN5LFcM2LJd6kgDbFcc3x4vD609MahXFMN8sRuJRE0RdN7IiZlOd3DT6iLB23oisUIwxiubIsTUbGXTAw7GalcU0zfHG0/nqAtRFevImpBXhoMNZpb5s/kmQySttpC/7MNlwXNsZlx/bIpmFOWrV10EENT9tV1obnHgSffrRlxOuLptn70Oj2wmQ1DMosUOwaDhI+uUHrvblw1FSt1O9uS3QUzlRLNgw3dARUq41WuCfgzeTazQcssEqW6VMjkhRqAFiviwtGx5l5c/5tt+PP2swCAzcda8e6pdsiy7O/JY7lmxJLvUZSGmMkbH619Tvh0PUmxCPIcbi/q1IXbwSdjke7IA/z9bM4RTrTjYTRDV0IJHuk8SQ3yugfdQ/Z+6YkevrGuJQheo3Amnco1dUFefqZ/8bw4kZak6EZqL9ZN3fzyn3bjey8dwhPbaob9GJHFWzQ5N6V7HFPBbRfNwjNfOg8/uHZhog8lKiU5NswuzYIsA++ebgeglFmNZ7nmhVVFWDEtH19eOzMp+5aIRsNsSP6ePPEa7PT4W0BiZfeZTu1tsQ6lokA5x3jgtaPoGnBrFyo5eCVyyfcoSkPM5I0PfT8eEJsgb/vpdsgyUJJtxeySwOxGXhTlh1ombxTlmr0ON/6+rwE/evkwjjf3jvwBQaLdkRdOcEmZmGgpy8P/rDv6xU7BGGXy1AmbZ9rSJ5M3vTBTe4zk281aqY7IgtrNxqjWOohyy42HmrUBLG8caR7mI/yDWpJhdUK6MxkNWDWjMCV7xi6YpWTzxJX3fXXdaO93wWIyYEZx/IO8bJsZz92yGl9P0qmqRKMxpFwzKYM8/zlELOcLdA+6taFOd26YDYMEXLWoDH/7j9XIMBux52wX/qhepCzItGgXzWlkyfcoSkP1nQN4eudZnA4axU/R+eHLh3Hpg1sCSjL1RD/eVLVHq3vQDd8Yrza9dkg5Md4wvxQ5GSZtoS4QZSZP68nzQZYjP6aD9d1Ydd+b+OrTe/DYO9X45ZsnIv5Yod8Vo0yeOfDpIttm1gaEDFey2an15I1ttHqGrh+gc8CtlY1OTYOePKNBwmy1RDLPbvHvJFR/duGGroRzzpQ8SBLg0T3+d53pRI8jfDB+TL2AsGgKS+AovDVBQd7TO5SyqqsXlSfF2g2iVJQK5Zo2swHiWmMs+/LeP6tk8SqLMvHVS6rw3j2X4tc3LENJjg2fXzMdAPCweu5Tks0sXjSS71GUhtr6XLj7+QO46fGdiT6UlPbCnnqcbOnD0ztqQ75fZPJE6Zss+/vHRsPrk/G6Ogzj8gVlkCQpoOQwmp48kQWTZWjrBCKx/XQ7Blxe7Yq/WDgcjT6ncsUt0zq2q1+2oKtndotRN2FzmExejHry9OWaZ9QeoLIcW9IPrYiUeNwW2P3lmh3qUJtQ6xOGk2U1oapEmdaYZzejoiADXp+Md060hf2YNvWxVZbDfgcKb9WMQpgMEmraB/D8+3X4+74GAMAnV05N8JERpS79CgWzURpT/3y8SJI0pDc+Ft5XSzWXTVWmOhdmWbXKldsvrsL6OcXafUv5+hQVBnlxduv6WbhIfYDWdgzCHcUJPvk53F6tt/GZ986GrAdv6lZ656YV2LWT5J4xlGy+f7YTbX1O5NhM2pj6wiz/VaT8UZRrAtH15bWoJ96z1PHqoylB7XOIcs2xZdKCM3kZZiPy1UC3oz988Kll8sYY5Nl05ZrpNHRFED2fM0syte+1SyvXjD5DsrZKed756sVVuGy+shR6kzqVMJTWPuV3qH+MEwXLsppw0+rpAIA7n92HQbcXM4szce50rt0gGi19Ji8Zs3iCPQ678kQ/Xqi9ljazEY9+egWuXlQOAJhXnjPkPhRe8j6S0sQtF83EYzedq6W4u0YYN0+h6fvtGrodeOt4a9j7lOdlIC9DCSjG8vN+7WATAOCSeaVaE3ShLlCJdBE6EPikHc2ETZG5m1U6+iDP35MX20xehsWoTYFs7QtfrimC82iC4lD0Ly7pGOR9eNkUvPCV1fjaJbP9mTytXDP63903L5+Dl29fg89dMB0XzSkBAGw+3hqyhNnp8aJXvRhQzCCPRvCfV84NGET1yZVTo+oZJaJA+mFXyTh0RYj1QnSP16f1g4cK8gDl5/GrTy7Fi7degK9vqIrJ150okveRlEaMBkk7weWkzdFpUHdKCU/tPDvkPlqQl2vTesVGO3xFlmW8ekgJ8i5fUKbdHlCuGUVPniRJoxq+IvoPRendqDJ5MZquGVwWabcYtdKJlp7QfZKAP9Aea5AnykQcbn+5ZjpM1hSMBglLp+bDYjIM7ckbRUmqzWzEwsm5kCQJ51bmw24xorXXicONPUPu264G6WajhJwM9lXR8MxGA359wzJMyc9AYaYFH142JdGHRJTSzLpyzWQO8kRVSazKNY829WLA5UW2zd9iEIrBIGFJRR6HrkQpeR9JaUYEB+3DlLVReCLIEyN1/320ZcgAFjF4pSwGQd7ptn7UdQ7CYjJg3Wx/PXhhlj6TF13Q4g/yoijXVPfCVKmTPQdc3hH3nQWL3XTN4HJNk7aUtKk7fJDn78kb6+AV5fgHXF6c6Ui/TJ6eCKj71RdS+ygyeXpWk1EbmPGqmqHWaxOlmplWZmQoIsXZVmz8+lps/n/rx9xvSzTR6at9kjmQyYjxQvTndtcBAFZMy0/KPsRUxyBvnPgHVDCTNxoiS3f+jEJMLbDD65O1kj1B28dmtyBHDfK6Bkf38xY14kum5AWUygWUa0aRyQOgjcWPqlxTPfmeWZKplfxGG7jGbLpmiHJNkclrDjMQRpblmPXkZYTqyStIn0ye3pCAeoxBHgBcfY7S0/D3fQ1DJryKTF5RNk/WKXJ2iwnZtrFdvCGioJ68ZM7kWfyvw2NV2zGAv+w4AwD4wpoZY/58NFTyPpLSTAHLNcekUR2qUp6boWWk9DXhsixjwO3Peoh+udFm8rRpT0E14vqhFNH05AHR78pzerxaqWNZjg3Z6vcd7ffkn64Z20xeJOWavU6PNsY/Vj15rb1OLfM0NU0zedag8szRlGsG2zC/FHaLEWc7BrBH7YEQWnWZPCIiGl/6PXkTZfDKQ2+cgNsr44JZhVhTVTTmz0dDJe8jKYS3334bn/rUp3D++eejvr4eAPCnP/0J77zzToKPbGQFaplf+zADKii8hi4liJiUZ/M/yejWIyj755S3MyzGMZdr7lKDvBVBQV5BQCYvuqDFpvWURZbJE0NXLEYDcjPM2sqGqDN5sRq8Ehx46DN5YYI8kcWzW4xjXnUgslmHGpSesqIsa1R9kakkVP/jWNktJlw2vxQA8NKe+oD3iaC5iENXiIjGXar05OnbJsbiWFMvnt+jlGredfncMR8XhZa8j6Qgf/vb33D55ZcjIyMDe/bsgdOpnJT09vbivvvuS/DRjayQ5Zpjos/khZrupG8CzjDrgrxRTNfsGnDhZIuyuD44k1ekBuuSBGTbosuMRZvJE0FecbbSJyWCyu4oS1D7nLFZoTC0J8+o9eR1DrjhCFG+EavJmuLrAcAJdWn3jKL0LNUEAJspuFwzNsNQrl06GQDw8v5GeHTrXFiuSUSUOClTrqntyRtbT97PNx6DLANXLizD4oq8GBwZhZK8j6QgP/rRj/C73/0Ov//972E2+09WV69ejffffz+BRxYZ9uSNTWOoTJ4uqBBvW4wGmIyGMZVr7jnbBUAJIoL7yCry7TBIwJT8jKibhKMdvNKiC/IAjDo7KfbkjXUZur4nz2yUYFYzjOL7CrWovTNGQ1cAfzZLlH/OKE7jIC8OmTwAWDOrCAWZFrT3u/CmbmeelsljuSYR0bgzBwxeSd5T81isUNh9phOvH26GQQK+cdmcWB0ahZC8j6Qgx44dw9q1a4fcnpOTg66urvE/oChxuubo9Trc6FWzUeW5Gci0iBG+/itJ4m3xBDSWcs3dYfrxAKAkx4anvngeHv/suVF/XhEkjTnIizI7KQavxHK6psiqSZI0bMlmZ39s1icAQ4ePTKQgLxY9eYByIvHR5cq4+x/987A2IU0L8pjJIyIad/pyzWQO8sbakyfLMn766lEAwEeXV2DWMGsTaOyS95EUpLy8HCdPnhxy+zvvvIMZM5J/Ko8YaMBMXvTEZM3cDDMyrSbtZL/fqS/XVAIn8QSkTdfUBURbT7bhtUNDx8cHE0FeuMWc580oxCx1pUE0rGqQ5IxwKpXIjJWoQV6OFrhGVybRH6M9efpMnl1XPqitUQgV5A3EZrImMDTQmVGUvi8O8ZiuKdx+SRUm5dpQ2zGIh984AcBfrsnBK0RE48+SKuWaYrrmKIO8ndUd2FndAYvJgK9dysXm8Za8j6QgX/7yl/G1r30NO3bsgCRJaGhowF/+8hd885vfxFe+8pVEH96I/OWaoxsEMpGJHXnluUrGKNQI34GgTF5eUCbP4/Xhi0/uwpf/tBsH6rrDfi2P14e96uTBcEHeaEVbrtmq7gEsyVa+b1GCGu1aiL6Y7cnzBxr6oMOfyRuapY5pT94EyuQFB7SxKtcElMfBD69bCAB47J1qHGvq5eAVIqIECujJS+LpmtrglVGuUHjtUDMA4JrFkzApLyNmx0Whxaabfxzcdddd6O7uxkUXXQSHw4G1a9fCarXim9/8Jm677bZEH96IxBLtzgEXfD6ZSx+jIDJ54gnBrk138me0xBOOODkWpY09apDX2ufUygt+//Zp/PKTS0N+raYeBwbdXliMBswqjm2mKOpyzZ6x9+R5vD5tmueYM3khyjUBDLtGIZaZPH2gYzJIqChIz/UJQPzKNYVL5pXiojnF2HSsFa8cbNSCcZZrEhGNP1OKTNf0Z/JGN3hl8zGlF/zSeSUxOyYKL3kfSTperxdbtmzBN77xDbS1tWHnzp3Yvn07Wltb8cMf/jDRhxcRkYXx+mT0OJjNi0a4TN6ArlzT4fIGvE8ERL1ODzxeH5q6/QHIPw80oq4zcJG60KFb3B3rQFyU4IWaQhmK2F1WEhTk9UQR5PXrSirGOnjFFlCuqQ/ywpdr+jN5Yx+8kmH2B6lTC+wBVz7TjTWO5ZrCutnFAIA3j7TAJysTYwtikHElIqLoWAIGr8T++T5WxtKTV9PWj9Nt/TAZJFwwi3vxxkNKnCUZjUZcfvnl6O7uht1ux4oVK7By5UpkZaVOT47VZNSWWbezLy8q/h15IpM39ElGvC1KCfT703ocnoAgz+uT8Yd3akJ+rfb+2GWego02k1eiBlF5IfoMRyL68cxGacwvHGajBBH3hi7XdMDl8eFIYw8ONXTj1YON2k67/Fj05Om+ZjqXagKhpmvGvuhixfQCAMCBeqV8Od9ugSmNA2ciomSVKisURFXJaII8kcU7d3oBsm3pueM22aRMueaiRYtw+vRpVFZWJvpQRq0gy4Jepwcd/S7MLE700aQO/448JZgIVRPuL9dUnhxN6nj/7kE3WnudWpapONuK1l4n/vreWdx1xZwhJ9OdcQ3yIt+T5/PJWp/UWMo1Y9WPByiTNG1mIwZc3jDlmk7c9tT72Hi4ecjHxmJIij0gyEudCzyjYTPFt1wTAOaWZSPDbNR6Wwvj8JgnIqKRmU2pEeTZtenm0Qd5m461AgAumssT4PGSvI+kID/+8Y/xzW9+Ey+//DIaGxvR09MT8F8q0NYo9DGTF6nuQbeWaRA9WJkhasLF2/qMR0WBkvmr7RjQgryrF5Ujy2rCgMuLus7BIV+vI55BnjZdc+RMXseACx6fDEnyD8PIGUOQN9Z+PEEEqvYQmbwzHQPYeLgZkqSUcM4qycLNayrx0q0XYP6knDF/bX1Ans6L0IHArCkQn3JNk9GAJboltBy6QkSUGPoVCsk9eEXsKY6uJ2/Q5cW7p9sBABfNYT/eeEmZTN4VV1wBALjmmmsgSf4/BlmWIUkSvN7RL2YcL4VciB61R986hV6HB7NLs7BsqjLtcrgVCvqT4akFdhys78HZjgE0q+Wa5bk2TM7LwLHmXjR0DQ7Z0ZIs5ZpifUKB3aKVcfina0ZfrhmLTB4gAi23lk0F/D15XnVJ+VWLyvHrG5bF5Ovp6QPLyjQP8vRZUyC20zX1lk/L1154i7IZ5BERJYLZkBqZPPF639jlwIDLE3Erwc6aDrg8PkzOy+BuvHGUMkHepk2bEn0IY+Zfo8CF6JFo6XFovXPfvGwOjGpqQysXCCjXVDN5umyPyPyd1WXyynJtmJRn04K8YB3avrDElmsGL0IH/OWaLo8PDrd3SKlpKLHakSeIr6kPOuwWE7JtJvQ6lK/1pQvjs7fSbDSgKMuKHocbs0uj31OYasYlyJvuXxPCck0iosQwGCQYDRK8Pjmpl6FPzstAea4Njd0OvH+mC2uqIhugcqK5FwCwuCI3IFFD8ZUyQd66desSfQhjVqAuGubglcj8etNJDLq9WDo1Dxvml2q3+wev6Ms1xeCVwEweANR1Dmg73EpzbNoAl1BBnpbJy4pnkDdyJq8tRJCXZTVpLwLdg+4Rg7zmHgf61GxnrMs1g3vESnNs6HX0YWVlARbrSgBj7a9fOg8DLk9MBrkkO5vuhT6SgH40llX4g7xiZvKIiBLGbEz+IE+SJKyqLMCLexuws7o94iCvpr0fADC9ML2rcJJNygR5b7311rDvX7t27TgdyegVZCqZGJZrRmbrKaWM7JZ1MwOu/IRaoRAqyKvIV4K8M+0D2vCWMl2QV69O7fzHvgYAwAcXT9KyrHHJ5Kkn6s4IViiIvrsc3ZRQSZKQYzOhc8CN7kG31gsXyt921+Eb/7dPO3HPGuP6BEF8D8E9Yksq8nC6tQ+3XzwrJl8nnIlU5iECO4OEuL3o59rNqCrJwomWPmbyiIgSyGw0wOH2JXW5JgCsmlGIF/c2YHt1R8QfU9OmrK2anuatFskmZYK89evXD7lNf+KfCj15IpPHIC8yotSwPDcj4Ha7brqm6MkU0zX15Zoik3e6rV/rFytTe/IAJZPXNeDC1/66B5Ik4aK5JbrBK7HPaog9eZFk8sTAlBxb4J9ont2CzgH3iGsUxOoC0duXGaMR/LYQg1cA4EfXLcTXLqlK6wXl482qlcaa4lre8qW1M/DUzrNYN4cTz4iIEkUMXEn2IG9lpbJ+Z29tV8StI9VtSiYv3fvpk01yP5J0Ojs7A/5raWnBq6++inPPPRcbN25M9OFFhINXojMQIjsHAHY1K+X1yXB5lYApVCZvUl4GDJJ/IEie3Qyb2egv1+wexJHGXvhk5T61HQPjM3glgumavQ4liAveJRPphE3x8cEfN1ai7DO4/NNmNjLAizFxUSBepZrCR1dU4IWvXDDkYgoREY0fMWTNYkzeZeiAMt26KMsKl8eHfbVdI97f4faiQa2mYrnm+EqZTF5ubu6Q2zZs2ACr1Yqvf/3r2L17dwKOKjoFDPKiInruMoNKDfXZukGXF1aTUbuvfuqjxWRAeW4G6rv8pZoAMDlfOZlt7HLgSKN//cap1j5teEiiB6+I48gOCqYi3ZXXowZ5H142BS6vDx8/tyLq4w3l5jWVyLaZcOm80pHvTGMiduXFa+gKERElD5O6RiGZe/IAtS9vRgH+ub8RO6o7sGpG4bD3r+0YgCwrcwWK4jDvgMJLmSAvnOLiYhw7dizRhxEREeR1DjDIG4nL44Pbq2Tggkf0mowGWIwGuLw+9Lu8yLMDg2p2zB6U9ZhaYNeCPNHDVppthUECXF4ftp5s0+67v07Zx2eQ/MFULEWzQkEL8oLLNdXj6hrhMSQ+fu3sIly7ZHLUxxrO6llFWD0rskZrGhuRyWOQR0SU/lKlXBMAVlWKIK8dQNWw9xWlmtOL7JysOc5SJsjbv39/wL9lWUZjYyN+8pOfYPHixQk6qujkqnvOHO7IR+BPVPrJmaFOcjMsRrgGfdoS9EEtkzc0yBN7wEQmz2Q0oCzHhoZuB7ae8gd5e892AQDy7RYYDLF/IrJG0ZMnMnFZQeWaIvjsGbFcU/T0xT5YpfEhnh/4PEFElP4qizJR3d6fEiWNCycr1XVioMpwOFkzcVImyFuyZAkkSYIsywG3n3feefjDH/6QoKOKTrZuBH7XgBtluTx5C6df7bGzmAxanbpepsWI7kG31rcXrn9vaqG/T6w01z+NclJeBhq6HXDo+uMO1CuZvHj04wG6cs0IpmuKwSvBmbxoyzWDP55SR6idhERElJ5+feMytPe7tOFwyUwMc3NEcD5TrQaCHLoy/lLmDLC6ujrg3waDAcXFxbDZwo+RTzaSJCEvw4z2fhc6B1woy02dYx9vA2KJd5gTXBHM9atrFMQTTfAJsX4YSFlOYJCHM50B9xXL1eMX5MWgXFPNBotl6SN/PDN5qYrlmkREE4fNbEyJAA+Ibu9vTRszeYmS/IW/qi1btqCsrAzTpk3DtGnTUFFRAZvNBpfLhSeffDLRhxcxcZI+0gj8iU5k8oL78QRx+6BbCWZEJs9uDrx/Rb7/CbMs178WYbLu9nx7YCBUGKfGYHHSHsmVLzEdM7jccpFaIrGjukObGhpMlmX/x2ekzHUcCiIuCmTEaP0FERFRLIhKk0gGyWnlmszkjbuUCfI+97nPobu7e8jtvb29+NznPpeAIxqdfLsSQIw0OGOi0zJ5YZZ4awvRXcquPJGFs1kCH9JTdZm80uBMnmrt7GKYdD14yZzJWzYtH9k2Ezr6Xdhf1xXyYx1u/9AaZvJSl3gRzTCnzNM0ERFNACKT5/bKYS84A8oE9MZuBwCWayZCypw9iKXXwerq6kKuV0hWWiZvhJ6qiW7kTJ4a5Dm9cLh9EK2awfcvyLRgeqEd2TZTQKnA5Dx/wDe/PCcgsxePReiA/0nR45Ph8YYP9Dxen5aZDA7SzEYD1lYpS6s3HWsN+fEii2eQwpe7UvKrKslS/5+d4CMhIiLys+ouPg6XzTvToWTxcmymIVVTFH9JXwe0dOlSSJIESZJwySWXwGTyH7LX60V1dTWuuOKKBB5hdPLsXKMQiXA78gQRzA24PFoWDwAygiYRSpKEv9++Bi6PL2CBtz6TN6csG1ML7DjTrjQHx2NHHhD4pOjy+mAKMVAG8PcZAspemWDr5xTjnwcasflYC+7cMHvI+3t0/XgcV5y6rl82GedOL0BFQWr0aBAR0cQgKpMAwOn2wR7mtOmsel41vSiT5yMJkPRB3nXXXQcA2Lt3Ly6//HJkZWVp77NYLJg+fTo+/OEPJ+joouffc8ZM3nBEoDNiJs/t1QJCi8kAY4jVB6HWCOiDvHnlOQEDWvLjFORZdEHdcE+KYjKm1WQIuS9n3Rwlk7e/rhstvQ6UZNtCfjwna6Y2SZICpsMSERElA6NBgtkowe2V4Rgmk9fUo5RqlnPQYEIk/Vngf/3XfwEApk+fjo9//OMpNU0zFBFAsCdveFomL0y5oQjyBl1eDLpCT9YcTo7NjP+8ci68PhmlObaA3r14ZfJMRgNMBgkenzxsX95IkzFLsm1YNDkXB+q7seVYKz66oiKqjyciIiIaC6vJCLfXA6c7/PlMk9qPp59uTuMnZXrybrrpppQP8AD/nrNOZvKGpWXyQpQrAv6Jg/1Or1auaY9yafR/rJuJWy+aBSBwQEu8Bq8A+rHD4a98+Sdrhr8Gc5Gazdscoi8vko8nIiIiGq1I1iiITF5ZLtsOEiFlgjyv14uf//znWLlyJcrKylBQUBDwX6oQ0zW7GeQNK+JMntsTdhF6NMYjkwcAVjUQdQxz5SvcZE299XNLAABvnWiFO2iIS88gM3lEREQUPzbtfGaYck2RycuNz0A7Gl7KBHnf//738eCDD+JjH/sYuru7ceedd+L666+HwWDAvffem+jDi5iYrsnBK8PrV4O8EXvydOWaYwnyphXaYTEZkGkxxq0nDwBskWTynKKnLnyQtnhKHgoyLeh1ePB+0FJ3ZvKIiIgonqLJ5JWyXDMhUibI+8tf/oLf//73+OY3vwmTyYRPfvKTeOyxx/C9730P27dvT/ThRYwrFCIzoJZrjjRds9/pDbsIPRrZNjOe/PxKPPH5lTCHmXoZC1bzyLvy+tRMXqjJmoLRIGHd7NCrFHq0RejM5BEREVHsWSK4aN3MnryESpkgr6mpCYsWLQIAZGVlaYvRP/CBD+Cf//xnIg8tKnm6ZeiyHH6B5EQXaSZv0O1foTCWTB4AnDejECumx7f0V7vyNUy5Zk8E5ZqAskoBADYfawm4PZJyTyIiIqLREuWa4c5neh1ubedxGadrJkTKBHlTpkxBY2MjAGDWrFnYuHEjAOC9996D1Zo6tb5iGaTbK2sZKL36rkH0Oz3jfVhJR/xswmfy9OWays8reEdeMops8EpkPXVrq4phkICjTb1o6BoM8fEM8oiIiCj2xPlMuBUKzWqpZrbNFPaCPcVXygR5H/rQh/Dmm28CAL72ta/hu9/9LqqqqvCZz3wGn//85xN8dJHLMBu1fWnBfXnbT7dj/c824T/+vDsRh5ZURKAbPpOn3D7o0pVrjjGTNx7EAtHhVyhEtucuP9OCpVPzAQRO2ewZFD15LNckIiKi2LOOkMlr6nYCYKlmIqVMaP2Tn/xEe/sjH/kIKioqsHXrVsyaNQvXXHNNAo8sOpIkIc9uRkuvE10DbkxRztHhcHtx9/MH4PbK2FndAZ9PhiHEYu+JQsvkhQnyRGlmvyt25ZrjwWqOJpM38p/nxXNLsPtMJ17e34AbVk0N+ngGeURERBR7thEGr/jXJzDIS5SUyOS53W587nOfw+nTp7XbVq1ahTvvvDOlAjxBG76iW6Pw600nUd3WD0D5g2noHgz5sROF1pM3QrnmaJehJ4pW3jBMT16fM/Ig79olkyBJwLZT7ahRHz/+wSspcw2HiIiIUoh1hBUKTep5LDN5iZMSQZ7ZbMYLL7yQ6MOIGW34yqBSrtnYPYjfbj4FwB8EnG7tT8zBJQltumaYTJ64fUBXrpkSPXlaecPIy9AjycRNybdrUzb/+l6t+vHM5BEREVH8jLRCgZm8xEuJIA9QevJefPHFRB9GTORliF15ysn8gbpueHwy5pZlY616wn66tS9hx5cM/NM1QwduGbrBK+K+GSnQ2BvJXploB6d84lylTPO53bVweXxaJo+DV4iIiCgebCO0n4iePO7IS5yUOQucNWsWfvjDH2Lbtm1Yvnw5MjMzA97/1a9+NUFHFr18NZPXrQ5eOdsxAACYVZKFyfkZAIDTbRM3k+f1yVo5Y2aYXXH64K+z3zXktmQV2eCV6DJxl8wrQXG2Fa29Trx+uFkr9+TgFSIiIooHcT4jztdkWYYk+WdJiOmaLNdMnJQJ8h577DHk5eVh9+7d2L07cPqkJEkpFeSJnjyRyatpVwK6aYV2TCtQgteJXK454PKvkAibydOVZrarQV5KlGtGsEIh2kyc2WjAR5ZPwW83n8Jf3zsLsX6RmTwiIiKKB/35TGe/C1f/8m1csbAc3/vgfAAs10wGKXMWWF1dnehDiBn/QnTlZP5Mu5LJm1aQiRnFSpBXnQKZPFmWMej2xnz/ieixMxok7UkkmMEgIcNsxKDbi/Y+NchLhUye2b8MfcDlQYbZGHDlS5blqAavCJfOK8FvN5/Cu6faAQAWo0FbVEpEREQUS9oydI8PB+q70dDtwJtHm/G9D86H2+tDWx/LNRMtZXryBJfLhWPHjsHjSd2F4f7pmkpwogV5hXZUFilBXn3XoDY1Mln9cVsN5n/vNWw53jrynaPg35EXGAAFE4vSW3ud2v2TnShv2F/XjeU/fAPfe+lQwPv7XV5/Js4aebnlOVPykGU1weNTPpiTNYmIiChe/NPCvdp5mzhvbel1QpYBs1FCYaYlYcc40aVMkDcwMIAvfOELsNvtWLBgAc6ePQtA6cXT79BLBfkiyBt0w+31ob5LGTM7rTATBZkW5KqDWZI9m/fsrjoAwDsnYhPk9Tk9qG7rH3FHnnBhlTKkxuVV6sFTqVxzZ00HBt1e7KhuD3i/mKxpMkhaU3MkzEYDzptRoP2bkzWJiIgoXvSD5PqCgrymbqVUsyTbNqF3PidaygR5d999N/bt24fNmzfDZvOnfi+99FI888wzCTyy6OVmKFc1OgdcaOgahNcnw2Y2oCTbCkmStJLN023JO2Gzrc+JI409AIAG9Y95rL729B5c8t+b8V5NB4DwO/KE71+7ADOK/AN4UqFcM7iEsqPfHfBv/WTN4bKYoVwwq0h7m/14REREFC/+lVA+fyZPXQ8lBuIVZjGLl0gpE+S9+OKLeOSRR7BmzZqAk9/58+fj1KlTCTyy6BVnWwEADV2DON6sBHJTC+za1Y4ZRVkAknv4yrZT/gxUY9fYF7e39jrx72Mt8MnAv4+2ABg5k5djM+PRzyxHpsUISUqNuu/gHsPOARd8aoklEN2OvGBrdEEeJ2sSERFRvOhXKPSrGTyPT4bb68OAGuylQhtNOkuZy/2tra0oKSkZcnt/f3/UGY9Em1mciZJsK1p6nXhut7LAelqhPyOVCsNXtp5o095ujEEm740jzVov2r7aLgCRPTnMKsnG329fg+ZuByblZYz5OOItOMjz+mT0OjzIVUt4e6Lckac3qyRLe1wxk0dERETxoq2EcvvLNQElmzeo7TrmuUgipUwm79xzz8U///lP7d8isPv973+P888/P1GHNSqSJGH9HKWf7PXDzQCAaQV27f2iBDFZF6LLsox3TvqDvOYeBzze8HvfIvHaoSbtbRHohNuRF2xmcRZW67JYycwaom+wQx3AAwB96veeFeH3ridJkpbNY5BHRERE8aJfodCvD/JcXm22Qiq00aSzlDkTvP/++3HFFVfg8OHD8Hg8ePjhh3Ho0CG8++672LJlS6IPL2oXzSnBs7vqICr1phX6g7wStexQf/KfTM52DKC+axBmowRZVtLzzb1OTB5lJq3H4cZWXdAopGOaXzwpmgwScjLM6Oh3oaPfqU1V7RpUyjXFBNZo3XjeNByo78bV50yKzQETERERBdGvUOgLE+TZU2AgXjpLmUze6tWrsXXrVgwMDGDmzJnYuHEjSktL8e6772L58uWJPryoXVBVBJNu4pC+XFNkYfqdyblCQWTxlk7N15ZcjqUvb9PRFri9MqYV2mHU/UxG6slLRfPLc2A2Srh2yWRU5CtBsX74Srca2OdljK5Zefm0fLx+5zqsm1089oMlIiIiCiHUCgVAlGuyJy8ZpNRZ9KJFi/DEE08k+jBiIsdmxorp+dh+Wpkkqc/kiTJFUbqXbN5QS0zXzCrCOyfaUNc5OKYJmxvVz3f1onK8eqhJGzgz0nTNVFRRYMf+/7ocJqOELz25C4B/ChUAdA2ombxMDk4hIiKi5KT15Hl8AUmJQbe+XDOlwoy0k1I/fa/XixdeeAFHjhyBJEmYN28err32WphMKfVtaC6aU4LtpztgMkgBpY6iH8vl9cHp8Wp/SOOprc+JoizrkNvb+5x4Sx26ctWicpxS+wYbuwbh88noGnSjIMrFl7vUlQnrZhfjdGu/FuSlYyYP8Neo56s/p3ZdkNcpgrxRZvKIiIiI4s0/XXNoueagWwxeSb+L9akkZco1Dx48iNmzZ+Omm27CCy+8gOeffx433XQTqqqqcODAgUQf3qhcvqAMVpMBS6fmwWT0/yr0QzcSUbL57K5arPjRG/jT9jND3vevA43w+mQsnJyDWSVZKM9VgtPGbgd++e8TWP6j1/HqwcaIv1ZLrwPNPU5IErBwci5ml2Zp70vHTJ5egd2/L1HoHlTezh9lTx4RERFRvIkExJByTX1PHoO8hEqZIO/mm2/GggULUFdXh/fffx/vv/8+amtrcc455+BLX/pSog9vVKYXZeKNO9fhsc+cG3C70SAhQ21WTUTJ5jtqpu7dU0OHoby0twEAcN2SyQCASXlKT15D1yBe3FMPWQZ+8foJyLJ/99vfdtfh8l+8hZoQKyEO1ncDUCaKZlpNqCrN1t6Xrpk8oUBdEtreFyKTxyCPiIiIkpRVl8kL7snjdM3kkDJB3r59+3D//fcjPz9fuy0/Px8//vGPsXfv3sQd2BhVFNi1HWl6WerwFX0KfLwcb+4FAJxsCVzhUNsxgF1nOiFJwAfU6Y0ik7entgs17QMAgGPNvdhyvFX7uL+9X4djzb3YeFhZk7D7TAd+/M/DGHB5cKCuBwCwaHIuAGC2LshL9ytAoTJ5XWLwip3lmkRERJScbGomz+uT0T3oHyA36OLglWSRMkHenDlz0NzcPOT2lpYWzJo1KwFHFF+iZDMWQZ7L48M/9jWgvc854n09Xp/WE1fd1h+w/+4f+5Us3vkzCrWpmuXq/1t7Az/3798+rb0tMlXVbUoQeP+/juL3b1fjiW1ncLBByeQtVIO86UX+CZuR7slLVaJ3sSPU4BVm8oiIiChJiUweAPS7ggevKOeuGeb0Po9LdikT5N1333346le/iueeew51dXWoq6vDc889hzvuuAM//elP0dPTo/2XDvxBnnuEe47sxb31uP3pPXjg1WMj3vdMxwBcamDn9sqo7fSvRnjlgJKJ+4BuB9ukoN14H142BUaDhK0n23FIDeDa+5UAsKatH7Isa5nCl/bWa+WaIpNnNRkxs1hZJ5Gbkd6BTnCQJ8uyticvn5k8IiIiSlIWY+gQQl+uyUxeYqVMiP2BD3wAAPCxj30MkqRkekTf1wc/+EHt35IkwetNzv1y0chUh470xWDwyuEGJfA92do3wj2BE2oAJpxs6UNlUSbquwZxoL4bkgRctqBUe3++3QyryQCnRwkMP7ZiCnocbrx+uBlbT7ZhblmOFsTUtPejrc+FHrXP8GiT8rUkCVigBnkAcO81C7D1ZBtWVhaM4btOfmK6plih0Ov0wOtTHtPpHuASERFR6jIYJFhMBrg8voDblemaDPKSQcoEeZs2bYrJ57n//vvx/PPP4+jRo8jIyMDq1avx05/+FHPmzNHuI8syvv/97+PRRx9FZ2cnVq1ahV//+tdYsGBBTI4hEllW5SQ/FoNXatqV8stIFpYfbw4MBE+29GHD/FK8dlDJ4p07rSBgtYIkSZiUl4Hqtn5kmI1YOjUflUUtAICWHie6BlxQ4xY0dju08ky9yqLMgImiq2cWYfXMoui+yRRUqAZ5vU4PXB4futVSzQyzETYznxiJiIgoeVlDBXkcvJI0UibIW7duXUw+z5YtW3Drrbfi3HPPhcfjwT333IPLLrsMhw8fRmamUib4wAMP4MEHH8Qf//hHzJ49Gz/60Y+wYcMGHDt2DNnZ2SN8hdjIVgev9MegJ09MtWzudcLj9QWsawh2Qh22kpthRvegWxu+8tohJci7fGHZkI8pz7Whuq0fq2YUwGIyoCRbCQJbep0BO+AA4N9HlADQbjFqTwKLdFm8iSTHZoZBAnyyMnylUxu6wiweERERJTeryYheBJ6nBg5eSZkwIy2l1E/f4XBg//79aGlpgc8XeOXgmmuuiehzvPrqqwH/fvzxx1FSUoLdu3dj7dq1kGUZDz30EO655x5cf/31AIAnnngCpaWleOqpp/DlL385Nt/MCES5Zq8uyHN6vHj1YBOWVuRjaqE9os/j9vq0vjqvT0ZLr3NIH52eKNfcML8Uz+2uw6nWPrT3OfGeurD8cl2pprBgUg62nWrHhvnK+0pylGEszT0OtAUNe3nziDI85yPLp+Bvu+vQ7/JO2CDPYJCQb7egvd+Fjn6XbugK+/GIiIgoudnMQ5MGAy7/4BWWayZWygR5r776Kj7zmc+grW3o7rax9OF1dyvlgwUFSv9XdXU1mpqacNlll2n3sVqtWLduHbZt2zZuQZ4o1xSZvJ3VHfj2CwdwsqUPy6fl42+3rI7o89R1Dmp9XgDQ2D0YNsjTT9a8alGZEuS19GHj4Wb4ZGDh5BxMyR8aXH7t0tlYN7sEF8wqBAAtk9fa6wzYAQcADd0OAMDCSbkozbHh6Z1ncfmCodnBiaIgUwnyOvt1mTz24xEREVGSs5qGBnndg26tTYflmomVMtM1b7vtNnz0ox9FY2MjfD5fwH+jDfBkWcadd96JNWvWYOHChQCApialLLG0NDBjVVpaqr0vFKfTGTDhc6xTPrOs/mXoNW39uPGx7Vrp5N7arohXKwQvIG/ocoS/b7syWdNuMWL1zCIYJCWT+LPXlKmcVy+aFPLjsqwmrKkq0gbiiCCvuccRdm3DzJJM3HrRLLzzrYtRURBZVjIdieEr7f0ubc9MfiaDPCIiIkpuVtPQIK6j33/eZ+d8gYRKmSCvpaUFd95555Dgayxuu+027N+/H08//fSQ94mARRCTO8O5//77kZubq/1XUVExpmPT78nbV9cFt1fG7NIsTM7LgNcnY5daPjmS6qAgr7E7/PAVUapZVZIFm9mIaYVKj2JHvwtTC+z47OrpEX1NUa7Z7/LibIfy9YKnRc4szoroc6U7MXylc8CFzn4lyMvNYLkmERERJTdriHJNMYvBYjQMOwOC4i9lfvof+chHsHnz5ph9vttvvx1///vfsWnTJkyZMkW7vaxMKR0Mztq1tLQMG2Defffd6O7u1v6rra0d0/Fl6oK8NrXkcXZpNs6boZRE7qyOLMgTkzVFfDpcJk9kCmeVKMNl9IHYfR9aFHHaPctqQqZ63yONSkZz+bR87f1FWRb2nam0TF6fC12Dyu85n4NXiIiIKMnZdJm8DDVr16Ges7JUM/FSpifvkUcewUc/+lG8/fbbWLRoEczmwBPhr371qxF9HlmWcfvtt+OFF17A5s2bUVlZGfD+yspKlJWV4fXXX8fSpUsBAC6XC1u2bMFPf/rTsJ/XarXCarWGfX+0xHRNJchTUt9FWVbMn5SDv71fhx26IM/nk/HSvnqsqiwc0m8nMnnzy3NwqKFn2EzemY4BAEBlkVI+uXRqHt440owPL5uCNVXRrTQozbHhdFs/DuuCvH8fVSZrMovnV2D3Z/J61XUZnK5JREREyU6fySvKtqC2Y1AbGMihK4mXMkHeU089hddeew0ZGRnYvHlzQOmkJEkRB3m33nornnrqKbz00kvIzs7WMna5ubnIyMiAJEm44447cN9996GqqgpVVVW47777YLfbccMNN8TlewtFP3ilXQvyLFilLgjfX9eFQZcXGRYj/nWwEV9/Zh8um1+KRz+zAi09Dtz57D7csGqqlslbPbNQDfLCZ/LqOpUgT/TIfWFNJRZOzsXqmYVRH39xthWn2/q1PrOZxZnIt5vROeDGrBIGeUKBmsnr6HdpQ3aY5SQiIqJkpx+8UpRlRW2HP5HATF7ipUyQ953vfAc/+MEP8J//+Z8wGEZfZfrb3/4WALB+/fqA2x9//HF89rOfBQDcddddGBwcxFe+8hVtGfrGjRvHbUceoFuh4PCXaxZlWTG1wI6yHBuaehzYc7YTq2cV4d1T7QD8pZn/OtCId062YfeZTjg9ylCa1TOL8Pu3q4ct1xR/nFPylWygzWzEutnFozr+UrUvTyjMsmJ6USY6z3Yxk6cjgrzWXiec6kJRTtckIiKiZGfTDVYpzgqsZmMmL/FSJshzuVz4+Mc/PqYAD1DKNUciSRLuvfde3HvvvWP6WmOhLUN3BZZrSpKEVTMK8NLeBmyv7sDqWUXYfaYTgLJ8HFCWngPAoFsJ8DItRpwzRdlF19bnhNPjHTIRyeP1oalHCQArQqxJiJaYsCkUZlrw2dXTYZTO4MpFE3dlQrDZpcqFgwP13chXM3iiT4+IiIgoWQVk8oLO++zmlAkx0lbKDF656aab8MwzzyT6MMaNNnjF4UGbGrQVZikn/yvVks2tJ9vQ43DjmDoVs2vADYfbi+agksxphZkoyLRof4zN3UPXGjR2O+D1ybCYDCjKGntvYUlOUJCXZcW1SybjuVtWozw3/DL2iWZuWTYKMy0YcHlR36VkUpnJIyIiomSnTxgEnzuyXDPxUibM9nq9eOCBB/Daa6/hnHPOGTJ45cEHH0zQkcWHWKHg8claZk78AV08twSSBOw+04l/7m+EPjnZ2utEc68S5M0rz8GRxh4srsiFJEkoz7Whpn0ADd2DmFoYmK2rVYeuTMnPgMEQflVEpPTlmmajhBxbyjzUxpXBIGH1rCL8Y1+Ddht78oiIiCjZ2XSDV4qDM3kM8hIuZc68Dxw4oE27PHjwYMD7httfl6oyLf5fjdenRHEiyCvPzcCqygJsP92B/954LODjWnqdaO5RgsLvXD0PRoOE+ZNytI+raR8IOWGzrlO5LRalmkDgH3thpjUtf0exsmZWYUCQF7xTkIiIiCjZ6DN5xVmBF6iZyUu8lAnyNm3alOhDGFcGg4RMixH9Ln9fnf4P5tolk7H9dIc2lEVo6XGgWe2tK82xBUyyLM9Tsmuhhq/UdvozebGgz+QVZjEzNZwLZvnXU2RZTbCYUqaKmoiIiCYofU9eboYFJoMEj5qYYCYv8VLubPLkyZN47bXXMDioZJ4iGaSSqrJ0JY7BDa1XLSyH2ejPjk1Xyy9r2ge0fWtluYETLiepvXANXcNk8gpik8nTD14pjEGPXzqbkm/Xfn/M4hEREVEq0E/XzLKatIXoAGC3pEweKW2lTJDX3t6OSy65BLNnz8ZVV12FxsZGAMDNN9+Mb3zjGwk+uvgQw1cAZTqlXq7djPVzSpT7WYzasvID9V3abVnWwD+wmSWZ6n26h3wtfU9eLGRZTdpVnCJOixyRyOblZzLIIyIiouSnX4aeaTXCpsve6QM+SoyUCfK+/vWvw2w24+zZs7Db/dmmj3/843j11VcTeGTxk60L0kJNvPzEuRUAgLWzi7WJlSKAC95TByi78sR9ugYCyzxFuWasevIkSdKyeSzXHNml80sBANMLMxN8JEREREQj05drZuou7gMs10wGKZNL3bhxI1577TVMmTIl4PaqqiqcOXMmQUcVX/pMXnC5JgBcMq8UL916AaYXZmLj4SYA/oXmwSsMACXwqyrJwomWPrx7qh1XLioHADg9Xm1YS6wyeQBQkq1M82S55sjWzy7GM186D3PLcxJ9KEREREQj0pdrZg4p12SQl2gpk8nr7+8PyOAJbW1tsFrTM4jQl1uGK3lcXJGHXLt5SOauLEQmD/CXBb5zsk27rV7tx7NbjCiIYWnl4gplAfvCSbkx+5zpSllyX8iePCIiIkoJ+kye3WwMCPoy2JOXcCkT5K1duxZPPvmk9m9JkuDz+fCzn/0MF110UQKPLH6yRsjk6QVn7kKVawLAGjXI26oL8vTrE2K56uDuK+dh239erPULEhEREVF6ECsUMi1GGAwSyzWTTMqE2T/72c+wfv167Nq1Cy6XC3fddRcOHTqEjo4ObN26NdGHFxf66ZqFmSMEedmBQV1JmCBv1YwCGA0SatoH8Ox7tXjlYCPOxnjoimAwSJiUF9vPSURERESJJzJ5or0oIyCTxyAv0VImkzd//nzs378fK1euxIYNG9Df34/rr78ee/bswcyZMxN9eHERkMkbYXhJvt0csFKhNERPHgBk28xYUpEHALjrb/ux6VgrTrX2AwAWTGI/GBERERGNbLKaHJimroHST9e0c7pmwqVMJu/s2bOoqKjA97///ZDvmzp1agKOKr5GGryip0yztKFe3YEXricPUEo2d5/phCQBN50/HedOL4DNbAhYyk1EREREFM60wky8fPsalKt7me3ck5dUUuY3UFlZicbGRpSUlATc3t7ejsrKSni93gQdWfxk65ehj1CuCSh9eSLIC9eTBwCfv6ASAHDJvBKcMyVvbAdJRERERBPSwsn+4Xr6Ek2WayZeygR5siyHHArS19cHmy18QJPKMtWrIGajhJyMkX9VJbpsX/Ewmb9cuxlf3zB77AdIRERERARwhUKSSfog78477wSglCN+97vfDVij4PV6sWPHDixZsiRBRxdfYvBKYaY1oqmXInuXZzcHjLElIiIiIoonG4O8pJL0Qd6ePXsAKJm8AwcOwGLxDyCxWCxYvHgxvvnNbybq8OJqemEmAKCqNCui+4tM3nD9eEREREREsWZnuWZSSfogb9OmTQCAz33uc3j44YeRkzNxJkDOKcvGy7eviXi1QUWBkuWM9SoEIiIiIqLhiMDOaJBgMabMAP+0lfRBnvD4448n+hASQt/QOpLLF5ThW1fMxaXzSka+MxERERFRjIhyTbvZGFGbEcVXygR5NDKb2Yhb1qfnzkAiIiIiSl6iXJOlmsmBuVQiIiIiIhoTMV2TQ1eSA4M8IiIiIiIak6nqbIip6uBASiyWaxIRERER0ZhUlWbjla9diEl5HACYDJI6yPv73/8e8X2vueaaOB4JERERERENZ175xJmCn+ySOsi77rrrIrqfJEnwer3xPRgiIiIiIqIUkNRBns/nS/QhEBERERERpRQOXiEiIiIiIkojSZ3J++Uvf4kvfelLsNls+OUvfznsfb/61a+O01ERERERERElL0mWZTnRBxFOZWUldu3ahcLCQlRWVoa9nyRJOH369Dge2ch6enqQm5uL7u5u5OSwCZWIiIiIiMYm0hgjqTN51dXVId8mIiIiIiKi0FKuJ6+trQ3t7e2JPgwiIiIiIqKklBJBXldXF2699VYUFRWhtLQUJSUlKCoqwm233Yaurq5EHx4REREREVHSSOpyTQDo6OjA+eefj/r6etx4442YN28eZFnGkSNH8Mc//hFvvvkmtm3bhvz8/EQfKhERERERUcIlfZD3gx/8ABaLBadOnUJpaemQ91122WX4wQ9+gF/84hcJOkIiIiIiIqLkkfTlmi+++CJ+/vOfDwnwAKCsrAwPPPAAXnjhhQQcGRERERERUfJJ+iCvsbERCxYsCPv+hQsXoqmpaRyPiIiIiIiIKHklfZBXVFSEmpqasO+vrq5GYWHh+B0QERERERFREkv6IO+KK67APffcA5fLNeR9TqcT3/3ud3HFFVck4MiIiIiIiIiSjyTLspzogxhOXV0dVqxYAavViltvvRVz584FABw+fBi/+c1v4HQ6sWvXLlRUVCT4SANFuo2eiIiIiIgoEpHGGEk/XXPKlCl499138ZWvfAV33303REwqSRI2bNiARx55JOkCPCIiIiIiokRJ+iAPACorK/HKK6+gs7MTJ06cAADMmjULBQUFCT4yIiIiIiKi5JISQZ6Qn5+PlStXJvowiIiIiIiIklbSD14hIiIiIiKiyDHIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0wiCPiIiIiIgojTDIIyIiIiIiSiMM8oiIiIiIiNIIgzwiIiIiIqI0MiGDvLfeegsf/OAHMWnSJEiShBdffDHg/bIs495778WkSZOQkZGB9evX49ChQ4k5WCIiIiIioihMyCCvv78fixcvxiOPPBLy/Q888AAefPBBPPLII3jvvfdQVlaGDRs2oLe3d5yPlIiIiIiIKDqmRB9AIlx55ZW48sorQ75PlmU89NBDuOeee3D99dcDAJ544gmUlpbiqaeewpe//OXxPFQiIiIiIqKoTMhM3nCqq6vR1NSEyy67TLvNarVi3bp12LZtW9iPczqd6OnpCfiPiIiIiIhovDHIC9LU1AQAKC0tDbi9tLRUe18o999/P3Jzc7X/Kioq4nqcREREREREoTDIC0OSpIB/y7I85Da9u+++G93d3dp/tbW18T5EIiIiIiKiISZkT95wysrKACgZvfLycu32lpaWIdk9PavVCqvVGvfj+//t3Xt4VPWdx/HvJAGEkkSgEBcN1LYBud+sgiIpukZE2oBWECOFItAAKqy4Kbi1SLFA+yhFpFCpVYo3cLd1a0Ur9LEIqJVbFasIC8hNAimgCSSQC/PZP+gMGQjkYjJnfifv1/P41Ewm9O3Jj9/km3NmBgAAAAAuhDN5Z7n88svtkksusVWrVoVvKykpsbfeesuuueYaD8sAAAAAoHL18kze8ePHbceOHeGPP/30U3v//fetefPm1qZNG5s8ebLNmjXL0tLSLC0tzWbNmmVNmjSxO++808NqAAAAAKhcvRzyNm7caP379w9/fP/995uZ2ciRI23JkiWWk5NjJ06csAkTJtjnn39uV199ta1cudISExO9SgYAAACAKglIktcRflRQUGDJycmWn59vSUlJXucAAAAAcFxVZwyekwcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpAHAAAAAD7CkAcAAAAAPsKQBwAAAAA+wpB3AQsXLrTLL7/cLrroIuvVq5etXbvW6yQAAAAAuCCGvPNYvny5TZ482f7rv/7L/v73v9t1111nN998s+3du9frNAAAAAA4r4AkeR0Ri66++mrr2bOnLVq0KHxbhw4dbPDgwTZ79uxKv76goMCSk5MtPz/fkpKS6jIVAAAAQD1Q1RmDM3kVKCkpsU2bNllGRkbE7RkZGfbOO+94VAUAAAAAlUvwOiAWHT582E6dOmUpKSkRt6ekpNjBgwcr/Jri4mIrLi4Of1xQUFCnjQAAAABQEc7kXUAgEIj4WNI5t4XMnj3bkpOTw/+kpqZGIxEAAAAAIjDkVeCrX/2qxcfHn3PWLi8v75yzeyHTpk2z/Pz88D/79u2LRioAAAAARGDIq0DDhg2tV69etmrVqojbV61aZddcc02FX9OoUSNLSkqK+AcAAAAAoo3n5J3H/fffbyNGjLArr7zS+vTpY4sXL7a9e/dadna212kAAAAAcF4MeecxbNgwO3LkiP30pz+13Nxc69y5s7322mvWtm1br9MAAAAA4Lx4n7w6wvvkAQAAAKhNvE8eAAAAANRDDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwx5AAAAAOAjDHkAAAAA4CMMeQAAAADgIwleB/iVJDMzKygo8LgEAAAAgB+EZovQrHE+DHl15MiRI2Zmlpqa6nEJAAAAAD85duyYJScnn/fzDHl1pHnz5mZmtnfv3gt+A2JVQUGBpaam2r59+ywpKcnrnGpxud3M7X6X283c7ne53Yx+L7ncbuZ2v8vtZvR7yeV2M7f7vW6XZMeOHbPWrVtf8H4MeXUkLu700x2Tk5OdW7zlJSUlOdvvcruZ2/0ut5u53e9yuxn9XnK53cztfpfbzej3ksvtZm73e9lelRNIvPAKAAAAAPgIQx4AAAAA+AhDXh1p1KiRTZ8+3Ro1auR1So243O9yu5nb/S63m7nd73K7Gf1ecrndzO1+l9vN6PeSy+1mbve70h5QZa+/CQAAAABwBmfyAAAAAMBHGPIAAAAAwEcY8gAAAADARxjyAAAAAMBHGPIAAAAAwEcY8oAoysvLs1OnTnmdUWObN2+2Y8eOeZ1RI8ePH/c6AUA9w57vnQ8++MB27tzpdUaNbdiwwebNm2cFBQVep1Qbj7exgSGvGg4dOmQrVqwwV9914uDBg/bTn/7UFi5caK+99prXOdWWm5tr9913n/3oRz+y+fPne51TZZKspKTExo0bZzfddJO9++67XidV24EDBywjI8P69+9v77//vtc51ZKbm2tZWVk2fPhwu/vuu23z5s1eJ1VLbm6u3XPPPTZr1ixbunSp1znVlpubaxMmTLCHHnrIlixZ4nVOtbDne8fV/d6MPd9rhw4dsu9+97v27//+7/bGG2/YiRMnvE6qlgMHDtjAgQPt6quvtvnz51tSUpIzexCPt7GFIa+KFixYYK1bt7bvfOc79tFHH3mdU20zZ860b37zm7Z+/XpbsmSJDRkyxF544QUzMyc2j4cfftjS0tJsz549lpeXZ5MnT7aZM2eaWez3BwIBy8vLs1deecX++c9/2ptvvmn5+flmFvvtZmY5OTnWtm1ba9KkiW3dutWuu+46r5Oq7LnnnrPOnTtbSUmJ3Xzzzfbmm2/az3/+czt48KDXaVXy9NNPW6dOnWzPnj22a9cuy87OtokTJ9qOHTu8TquSl19+2a644grbvXu3ffLJJzZ69GjLzs62bdu2eZ1WKfZ877i835ux53tp3759NmjQIAsEAvbOO+/YiBEjrHHjxl5nVdkDDzxgqamp1rRpU1u8eLEVFxfbtm3bLBAIeJ1WKR5vY5BwQcFgUCtWrNANN9ygRx99VD179tT3vvc9nTp1yuu0KikrK9OcOXPUu3dvrVixQpJUUFCgqVOnqk2bNh7XVa60tFRz5sxRenq6Xn/99fDtDz30kL7+9a97WFY9u3bt0t13362pU6cqLS1Nf/7zn71OqlRJSYnuueceBQIBLVu2LHz7oUOHPKyqurKyMt1444165JFHwrctX75cKSkp+uKLLzwsq5rjx48rPT1dCxYsCN/2+uuvKzExUePHj1cwGPSwrmoyMzM1adKk8McrVqzQlVdeqTFjxsRsP3u+d/yy30vs+V759a9/rZtuuin88Ycffqg9e/aotLTUw6rKFRQU6KKLLlKXLl20bt06SdJf/vIXtWnTRqtXr/a4rnI83sYmzuRVIhAIWEpKio0YMcJ++MMf2i9/+Uv7/e9/b2+88YbXaVUSHx9vJSUldv3119uAAQPMzCwxMdHS09MtISEh5q9XT0hIsN69e9v06dMtIyMjfHtpaallZ2c7cxnG/v37bcuWLTZ79mz7yle+Yi+++GL4N7uxqkGDBnbddddZv3797PDhw/bJJ5/YkCFD7LbbbrP09HRbvHixlZSUeJ15Xlu2bLFdu3ZZ69atw7cVFRXZbbfdFvPH3sxszZo19tFHH1n//v0tGAxaMBi0m266yS6++GL7wx/+EPOXkuTm5trOnTstNTU1fNvAgQPtjjvusM2bN8fspZvs+d5JSEiwPn36OL/fm7m350tyfs83O73vt27d2o4ePWr9+/e3oUOHWp8+fSwrK8vWrFnjdV6FgsGgJSYm2urVq23Lli127bXXmpnZ1VdfbXl5eXb48OHw/WLVhx9+yONtLPJ6yow1+fn5evfdd7V///7z3mfo0KHq0aOHCgoKolhWNRX1FxUVhf899NuI559/Xh07dox6X2UqO/75+fnKzMxUIBBQz549lZaWpv/+7/9WYWFhlEsrbjtf+wsvvKCRI0dKkpYtW6bLL79cixYt0tixY3XgwIEol1asov7S0lJNnDhRl1xyiVq0aKFJkybp8ccf17hx49SoUSM99thjEevLKxW1FxcX66qrrtK1116rJ598UrfffrsCgYD69Omj5ORkTZo0KWZ+Q11R/549exQfH68333wzfNv69evVr18/3Xzzzbr99ttjZg8qLi4Or4PQHlNWVqbU1FT94he/kKTwb9L379+voUOHavDgwfr888896S2vfPv5ztbF8p5fUf+JEyfCn4/lPb+yYx/L+70U2X82F/b88v1lZWWSTv89veeee2J+z6/o2AeDQQ0fPlz33nuvxo4dq7Fjx+rjjz/WSy+9pAEDBqhv377Kzc31qDjShdZ+MBhUMBhUfn6++vbtq3vvvdeLxPOqqN2lx9uK+l16vK0OhrxyZs2apaSkJHXu3FlJSUmaN29e+IeusrKy8GLYuXOnGjdurPnz53uZe44L9Z86dSpiI7nnnnuUlZUl6fQlGrGgsv6SkhL99re/1cCBA7Vu3Tpt2bJFEyZMUMeOHcOXJcViu3T6EpLMzMzwx2lpaWrQoIF69Oih3Nxczy8FqKh/z549kqQ1a9Zo5MiReuWVVyK+5r777lO3bt304YcfepEcVlH77t27JUlr167V448/roEDB6pnz57asGGD8vPz9cwzz6hPnz6aPn26p+3Suf2//OUvw2tn9OjRSk5OVk5OjiZPnqy4uDg99thjmjFjhjp27KjPPvvM43ppzpw5ateuXcTlaKGB7v7779c3vvGN8A+QoXW+YMECdevWTevXr49+cDkVtZcX63t+Vful2NvzK2uP5f1eOn9/6JjH+p5fUX/o7+m7774b03v+hdqfeOIJBQIBtWnTRn//+9/Dn1+xYoX69OmjefPmRTv3HJWt/fL69eun8ePHS5Lna0a68H7/7rvvxvzj7YWO/bhx42L+8ba6GPL+5bXXXlOHDh308ssva9euXfrZz36mTp06afTo0eH7lP8L9uMf/1gpKSnat2+fJKmwsFDHjx+PendIVfqlMw/u3/rWtzR37tyIz3m5gVS1v6Lf4DZr1kwvvPBCtFLPUZX2qVOnauHChVq1apUuvfRSXXbZZWrevLkeffRRz58rcL7+H/zgB+H7vP/++zp58qSkMz/EHDx4UIFAQO+9954n3VLV2iXphhtuiLjWXpIyMjKUnZ3t6Q+8FfV37NhRd999d/g+DzzwgG655Ralp6eHH5j27Nmjxo0ba+/evV6l68iRI8rOzlbXrl2VlJSkW2+9Vf/85z8j7rN161YlJSXpsccek3Rm7RQUFCguLk5/+9vfot4tVa39bLG051enP9b2/Oq0x+J+X9X+Bx98MCb3/Av1l18PW7Zsibk9/0LtocaTJ0+qU6dOSkxM1AcffBD+2pKSEnXt2lWPP/64J+1S9dZ+aGh94IEHYuIMfFXXjRSbj7dVPfY5OTkx+XhbUwx5/3LfffepR48eEbc98cQTat++vRYvXizpzF866fSTNNu2bav77rtPS5cuVd++ffXSSy9Ftbm8qvSHHlj27t2rli1bhs92vP766xo+fLg+/fTTqDaXV9Xjf/ZmsmHDBrVp0ybiSfrRdqH2RYsWSTo95AUCASUmJmrGjBnhr+vSpYv++te/Rjs5woX6f/3rX0uKPCMQ+h68+OKLatWqVcQDabRVZd3s2LFDbdu21YYNG8L3KSwsVO/evTVr1qyo9p7tQv0LFy6UdHrdn/3D7iOPPKLOnTvr6NGjnr0gyK5du5STk6MVK1Zo7dq1CgQCevHFFyN6Tp06pUceeURNmzbVxo0bw7dv2LBBbdu29eyHxaq0ny2W9vzq9sfSnl+d9ljc7yvrD/3vQw89FJN7fmX9FQ3+sbLnV/XYL1u2TIFAQHPmzAm3Hzt2TN26ddOzzz7rSXtV+iuycOFCderUSdu3b49i6bmq2r579+6YfLytrL/8L15i8fG2phjydHpjGD9+vO64447wb64k6cCBA/rhD3+obt266dixY+H7hkyfPl2BQEANGzbUtGnTot4dUp1+SXruued0ww03aN++fbr55puVkJCgKVOmeJEuqfr9oU1727ZtGjRokG677baIz0dTVdqLior0l7/8RbNmzYrYqPPy8tShQwe99dZbXqRLqlp/+bMVoWO/detWZWRkaOzYsVFvDqmsvWvXruFr6Lt166b09HQ9++yz2rx5swYNGqROnTp5OqDWZN2Xlpbqo48+Ur9+/TRz5kwvssPKysrCl/RKp5+31rVr13MGh2AwqP79+6t79+7KycnR22+/reuvv1433HCDZ8+tqmr72WJlz69ufyzt+dVtj6X9Xqp6/yuvvKKf//znMbfn1/T4x8KeX532ESNGqEOHDrr99tv1pz/9Sbfccot69ep1wddbqGvV6Q8d9xUrVigxMdHTbql67T169Ii5x9uarPtYerytqXo/5IX+Is2ePVupqakVbtRXXnll+KyAdPo3uhMnTlQgENDdd9/t6YsHVLX/N7/5Tfj+3//+9xUIBNSgQQPdcsstOnz4cLSzw6rbf/z4cc2ePVtjxoxR06ZNNXz4cOXn50c7W1LV2nv06KElS5ZE3F86c1a4uLg4OrEVqMmxnzFjhkaNGqUmTZooKyvLsyciV7U9dCZ169at6tWrl9q3b6+vf/3rGjp0qI4ePRrt7LDqHvvS0lL96U9/Cr/4wZ133unp5eHlhf5bjhw5ogYNGmj27NnnXOZ1+PBhPfjgg+revbvatWunW2+9NSZedOVC7eXF0p5fXmX9oc/H0p4fUpVjX1hYGDP7/dkq6y9/5U/5j73c88ur6vGPlT2/vAu1h87I5Ofna+nSperXr5+uuuoqDRs2zNM9v7yq7juStH37diUkJITfVsFrF2oPrfFt27bF1ONteVU59mVlZTH7eFtd9WbI27BhQ/gVxyr6YfvEiRNKSkqKeI8P6fRzR6666irNnj07fNvu3bs1derUqP6lq83+MWPGqEuXLhFPSq5rtdn/1FNP6a677oq4/Ksu1Wa7F2qz/1e/+pW+973vRVyKUZdqoz30dfn5+dqxY0dUL1GrzWO/ceNGzZgxI2rHXjp//9mXdIV+sHr44YfVsmXLiL2l/A+7+fn5UXtlwdpoD913586dMbPn1+TY/+AHP4jqnl8b7aFfEDz55JNR3e+l2l07XjzvsTb758+fHxN7fk33nOLi4qgOGLV57KXTvyALXWZd12qjPfS5zz//PGYeb2uy76xfvz7qj7d1wfdD3s6dO/Xd735XgUBAv/vd7yI+d/aTnx999FElJiae803t3r27JkyYUOetFamL/mhe6lJb/aFXl5Ki96DJ2jn32EfrenSOvRv9oR+myv9Qdemll2rcuHE6evSoVq5cqaVLl0Yn+l9qs/3sr4+GuuiP1p5fW+1vvPFG+OujOSS5vO6l2j3+oStQYm3P9/uxZ8+sPtf765Jv3wxdkk2YMMHS0tLMzCw5OdmaNm1qZmfeUDIhIcEk2Y9+9CN77rnnbMqUKdauXTubOnWqvfbaa2ZmtnnzZpNkgwcP9k1/6M9xqX/IkCHhPzsQCDjV7vraKX/s4+Lqdsvg2LvVv2zZMgsGgxYfH2+nTp0yM7MnnnjCfvvb31p6errddNNNduzYMWfbjx8/HpX2uuoPHfu63vNru33AgAHhY1/X+31d9Edz3ddF/4ABA6ywsNDMYm/P9/uxZ8+sP/1RUWfjo4defvllNW7cWFdddVX4Jbr79OmjMWPGSDrzm8ElS5aoefPm6t69uzZv3ixJ+vjjjzVkyBA1bNhQGRkZatKkie64446ovkAA/d71u9zuer/L7fW1f8uWLRF/xv79+5WTk6NAIKA77rgj/HYDtPu33+V2+r3td7nd9X6X2/3QHy2+GfLKX9KxePFi/f73vw9/XFRUpLvuuku33npr+Iem48ePa+bMmVq0aNE5b9Sbn5+vlStXasGCBVF7Dgb93vW73O56v8vt9Ee+sERxcbEmT56s5s2bR+Ul4l1ud73f5Xb6WTv1td/ldj/0e8EXQ15RUVHEq+OUv4Y89I2dPHmyunbtKunMQomV97ug3zsut0tu97vcLtFfkYMHD9ZF6jlcbpfc7ne5XaK/IqydqnG53+V2yf1+rzj/nLxp06ZZ3759bdCgQTZ//nw7duyYxcXFha/HDV3Pf+ONN9ru3btt79694dvq+lrzqqDfOy63m7nd73K7Gf3nk5KSQnslXO53ud2M/vNh7VTO5X6X283c7/eS9z9t1FBJSYndfvvt9sorr1hOTo61bt3annzySRs+fLiZnfnGhv63rKzMWrRoYfv27fOsuTz6veNyu5nb/S63m9HvJZfbzdzud7ndjH4vudxu5na/y+1m7vfHBK9PJdbUxx9/rLS0NK1cuTJ827p169S4cWP94he/OOdU7ZEjR9SwYUO9+uqrEbd7hX7v+l1ul9zud7ldop+1U3Mu97vcLtHP2qk5l/tdbpfc748Fzg55mzZtUiAQ0JEjRySduf529uzZatasmbZv3x5x/y+++EL9+vXTlClTot5aEfq943K75Ha/y+0S/V5yuV1yu9/ldol+L7ncLrnd73K75H5/LHD2cs24uDjr2LGjvfDCCxG3T5kyxS6++GJ78sknzez06Vuz0+8TlJuba4WFhVZaWhr13rPR7x2X283c7ne53Yx+L7ncbuZ2v8vtZvR7yeV2M7f7XW43c78/Jng9ZdbU0aNHNXjwYA0bNkwHDhyQdOad7R977DG1bt06fKo29Mo7S5cu1bZt27wJPgv93nG5XXK73+V2iX4vudwuud3vcrtEv5dcbpfc7ne5XXK/PxbE5Jm87du3W3Z2tq1du/acz4Um9mbNmtl3vvMd++STT+yll14ys9PvbG92+l3vmzVrFn7yZXx8vJmZjRgxwtq1a0e/j/tdbne93+V2+lk79bXf5Xb6WTv1td/ldj/0uyKmhrxgMGj/8R//Yd27d7fCwkI7duxYxOfMTn+DT548acuWLbPRo0db9+7dbfny5fbXv/41fN/9+/dby5YtrW3btvTXk36X213vd7mdftZOfe13uZ1+1k597Xe53Q/9zvH6VGJ5r776qq699lqtXr36vPd5/PHH1bx5c2VmZkqSPvjgA2VlZalhw4YaP368xo0bp8TERC1atEjSmSdqRgP93vW73C653e9yu0Q/a6fmXO53uV2in7VTcy73u9wuud/vmpga8gYPHqyJEydKklavXq0f//jHeuaZZ7Rnzx5J0m9+8xu1adNGzz//fMRLowaDQc2aNUtjx47VwIED9fbbb9Nfz/pdbne93+V2+lk79bXf5Xb6WTv1td/ldj/0uyZmhryCggL17dtXf/jDH/TII4/okksuUWZmplJTU/W1r30t/D4ZhYWFEV8XKxM8/d5xuV1yu9/ldol+L7ncLrnd73K7RL+XXG6X3O53uV1yv99Fngx5K1askHTuN653794aOHCgRowYoXfeeUelpaUKBoPKyMhQZmamPv74Yy9yz0G/d1xul9zud7ldot9LLrdLbve73C7R7yWX2yW3+11ul9zv94uoDnmvvvqqLr30UgUCgfCp1mAwGF4ETz/9tAKBgNq1a6e8vLzw161du1b/9m//pnfeeSeaueeg37t+l9slt/tdbpfoZ+3UnMv9LrdL9LN2as7lfpfbJff7/SZqr665bt06W7BggQ0ZMsQGDBhgkyZNMjOzQCBggUDAzMx69+5t6enplpCQYKdOnQp/ba9evaygoMA+++yzaOWeg37v+l1uN3O73+V2M/pZOzXncr/L7Wb0s3ZqzuV+l9vN3O/3pbqeIkPT+/bt2zV37lzt2rVLGzduVJMmTfTUU09JOvMmhmVlZfrf//1fNWrUSNOnT9f+/fslScuXL1efPn106NChus6lP4b6XW53vd/ldvpZO/W13+V2+lk79bXf5XY/9PtZnQ15mzZt0hdffBFxW+ibXFpaqilTpqhly5Y6efKkJEW8is78+fPVunVrtW/fXkOGDNFXvvIV/exnP6ur1ArR712/y+2S2/0ut0v0s3ZqzuV+l9sl+lk7Nedyv8vtkvv99UGtD3n/8z//o8suu0zf+MY31KZNG/3kJz9Rbm6upMjrcnft2qXU1FRNmTJFUuQ3X5L+9re/aeHChZo2bZq2bdtW25n0x2C/y+2u97vcTr+3/S63u97vcjv93va73O56v8vtfuivT2p1yNuwYYOuuOIKzZs3Tx988IEWLlyoli1bavz48Tpy5IikM1N+MBjUwoULlZCQoF27dkmSiouLVVBQUJtJ9DvS73K76/0ut9PP2qmv/S6308/aqa/9Lrf7ob++qZUhLzS1L1q0SJdddpny8/PDn1uwYIF69+6tmTNnnvN1R44c0TXXXKPMzExt2rRJGRkZevbZZ895ydW6Rr93/S63S273u9wu0c/aqTmX+11ul+hn7dScy/0ut0vu99dXtXomLycnR9dff33EGxkeP35cEydO1DXXXKN//OMfks5M+ZL0zDPPKBAIKC4uToMGDVJRUVFtJlUL/d71u9wuud3vcrtEP2un5lzud7ldop+1U3Mu97vcLrnfX9/UaMhbuXKl7r33Xs2bN0/vvfde+PY//vGPuuiii7Rz505JZ77JK1eu1LXXXqu5c+eG71tcXKxf/epXiouLU3p6enhhRAP93vW73O56v8vt9Hvb73K76/0ut9Pvbb/L7a73u9zuh36cVq0h78CBAxo0aJBatWqlrKwsdenSRcnJyeEFcOLECV1xxRUaN26cpMgnWV533XWaMGFC+OODBw9q0qRJ+t3vflcb/x30x3i/y+2u97vcTj9rp772u9xOP2unvva73O6HfkSq8pBXWFiokSNHatiwYeEnUErSt771LY0aNUrS6Yl+6dKliouLC7/TfUhWVpb69+9fS9nVR793/S63S273u9wu0c/aqTmX+11ul+hn7dScy/0ut0vu9+NccVV90/QmTZpYo0aNbNSoUXb55ZdbWVmZmZkNGjTItm7damZm8fHxNnToUMvMzLQxY8bYW2+9ZZLs4MGD9n//93+WlZVVN+/oTn9M97vc7nq/y+30s3bqa7/L7fSzduprv8vtfuhHBaozEZaUlIT/PfTKOHfddZfGjh0bcduJEyf07W9/W61atVJGRoZat26t3r17a+/evTWcRWsH/d71u9wuud3vcrtEP2un5lzud7ldop+1U3Mu97vcLrnfj0gBSfoyQ2K/fv1s9OjRNmrUKJNkwWDQ4uPj7dChQ7ZlyxbbsGGDfe1rX7M777yztubSWkW/d1xuN3O73+V2M/q95HK7mdv9Lreb0e8ll9vN3O53ud3M/f567ctMiDt37lRKSoo2btwYvq24uPjL/JFRRb93XG6X3O53uV2i30sut0tu97vcLtHvJZfbJbf7XW6X3O+v76r8nLyzBkMzM1u3bp01bdrUevXqZWZmM2bMsEmTJlleXl7tTaF1gH7vuNxu5na/y+1m9HvJ5XYzt/tdbjej30sut5u53e9yu5n7/TgtoSZfFAgEzMxs/fr1dtttt9mqVats3LhxVlRUZM8++6y1atWqViNrG/3ecbndzO1+l9vN6PeSy+1mbve73G5Gv5dcbjdzu9/ldjP3+/EvNT0FeOLECX3zm99UIBBQo0aNNGfOnC9xQjH66PeOy+2S2/0ut0v0e8nldsntfpfbJfq95HK75Ha/y+2S+/34ki+8cuONN1paWprNnTvXLrrootqcPaOCfu+43G7mdr/L7Wb0e8nldjO3+11uN6PfSy63m7nd73K7mfv99d2XGvJOnTpl8fHxtdkTVfR7x+V2M7f7XW43o99LLrebud3vcrsZ/V5yud3M7X6X283c76/vvvRbKAAAAAAAYkeNXl0TAAAAABCbGPIAAAAAwEcY8gAAAADARxjyAAAAAMBHGPIAAAAAwEcY8gAAAADARxjyAACoYw8//LB1797d6wwAQD3B++QBAPAlBAKBC35+5MiRtmDBAisuLrYWLVpEqQoAUJ8x5AEA8CUcPHgw/O/Lly+3n/zkJ7Zt27bwbY0bN7bk5GQv0gAA9RSXawIA8CVccskl4X+Sk5MtEAicc9vZl2uOGjXKBg8ebLNmzbKUlBS7+OKLbcaMGVZWVmb/+Z//ac2bN7fLLrvMnn766Yj/r88++8yGDRtmzZo1sxYtWlhmZqbt3r07uv/BAICYx5AHAIAH3nzzTTtw4ICtWbPG5s6daw8//LANGjTImjVrZu+9955lZ2dbdna27du3z8zMioqKrH///ta0aVNbs2aNrVu3zpo2bWoDBgywkpISj/9rAACxhCEPAAAPNG/e3ObPn2/t27e30aNHW/v27a2oqMgefPBBS0tLs2nTplnDhg3t7bffNjOzZcuWWVxcnD311FPWpUsX69Chgz3zzDO2d+9eW716tbf/MQCAmJLgdQAAAPVRp06dLC7uzO9aU1JSrHPnzuGP4+PjrUWLFpaXl2dmZps2bbIdO3ZYYmJixJ9z8uRJ27lzZ3SiAQBOYMgDAMADDRo0iPg4EAhUeFswGDQzs2AwaL169bLnn3/+nD+rZcuWdRcKAHAOQx4AAA7o2bOnLV++3Fq1amVJSUle5wAAYhjPyQMAwAFZWVn21a9+1TIzM23t2rX26aef2ltvvWWTJk2y/fv3e50HAIghDHkAADigSZMmtmbNGmvTpo3deuut1qFDBxs9erSdOHGCM3sAgAi8GToAAAAA+Ahn8gAAAADARxjyAAAAAMBHGPIAAAAAwEcY8gAAAADARxjyAAAAAMBHGPIAAAAAwEcY8gAAAADARxjyAAAAAMBHGPIAAAAAwEcY8gAAAADARxjyAAAAAMBHGPIAAAAAwEf+HyVHF+CZtIubAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(df['OT'])\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Oil temperature')\n",
    "\n",
    "plt.xticks(\n",
    "    np.arange(0, 400, 24), \n",
    "    ['2016-07-01', '2016-07-02', '2016-07-03', '2016-07-04', '2016-07-05', '2016-07-08', '2016-07-09', '2016-07-10', '2016-07-11', '2016-07-12', '2016-07-13', '2016-07-14', '2016-07-15', '2016-07-16', '2016-07-17', '2016-07-18', '2016-07-19']\n",
    ")\n",
    "plt.xlim(0, 400)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0808ed38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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84dKkhTt0PNdV3U2pMnYvm/7YrYm6XhFe3U0BAAAALjmEvEvMdzvS9N+Eg9XdjCqXX1hMyAMAAADOAyHvElPkdkuSWtcL0R+7NanexlSBzQcz9eHaJLmK3NXdFAAAAOCSRMi7RNWvXUtDOjWs7mZUukBfb324Nqm6mwEAAABcsph4BQAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZSJSHv0KFDuu+++xQWFqaAgABdc801SkhIMPcbhqFnn31W0dHR8vf3V8+ePbVt2zaPcxQUFOixxx5TeHi4AgMDNXjwYB08eNCjJiMjQ/Hx8XI4HHI4HIqPj1dmZqZHTVJSkgYNGqTAwECFh4drzJgxcrlcVXHbAAAAAFDtKj3kZWRkqFu3bvLx8dHXX3+t7du3a8aMGapdu7ZZM3XqVM2cOVOzZ8/Whg0bFBUVpT59+ig7O9usGTt2rBYsWKB58+Zp1apVysnJ0cCBA1VcXGzWxMXFKTExUYsXL9bixYuVmJio+Ph4c39xcbEGDBig3NxcrVq1SvPmzdP8+fM1fvz4yr5tAAAAAKgRvCv7hC+++KIaNmyod99919zWpEkT88+GYeill17SX//6V91xxx2SpPfee0+RkZH66KOP9PDDD8vpdOrtt9/WBx98oN69e0uSPvzwQzVs2FDffvut+vXrpx07dmjx4sVau3atunTpIkl66623FBsbq127dqlVq1ZasmSJtm/fruTkZEVHR0uSZsyYoWHDhmnSpEkKCQmp7NsHAAAAgGpV6T15X375pTp16qS7775bERER6tChg9566y1z//79+5Wamqq+ffua2/z8/NSjRw+tXr1akpSQkKDCwkKPmujoaLVt29asWbNmjRwOhxnwJOm6666Tw+HwqGnbtq0Z8CSpX79+Kigo8Bg+CgAAAABWUekh75dfftFrr72mFi1a6JtvvtEjjzyiMWPG6P3335ckpaamSpIiIyM9jouMjDT3paamytfXV6GhoWetiYiIKHX9iIgIj5rTrxMaGipfX1+z5nQFBQXKysry+AIAAACAS0WlD9d0u93q1KmTJk+eLEnq0KGDtm3bptdee03333+/WWez2TyOMwyj1LbTnV5TVv351JxqypQpeu65587aDgAAAACoqSq9J69evXpq06aNx7bWrVsrKSlJkhQVFSVJpXrS0tLSzF63qKgouVwuZWRknLXmyJEjpa5/9OhRj5rTr5ORkaHCwsJSPXwnTZw4UU6n0/xKTk4u130DAAAAQE1Q6SGvW7du2rVrl8e23bt3q3HjxpKkpk2bKioqSkuXLjX3u1wurVixQl27dpUkxcTEyMfHx6MmJSVFW7duNWtiY2PldDq1fv16s2bdunVyOp0eNVu3blVKSopZs2TJEvn5+SkmJqbM9vv5+SkkJMTjCwAAAAAuFZU+XPPPf/6zunbtqsmTJ2vIkCFav3693nzzTb355puSSoZPjh07VpMnT1aLFi3UokULTZ48WQEBAYqLi5MkORwOjRgxQuPHj1dYWJjq1KmjCRMmqF27duZsm61bt1b//v01cuRIvfHGG5Kkhx56SAMHDlSrVq0kSX379lWbNm0UHx+vadOmKT09XRMmTNDIkSMJbwAAAAAsqdJDXufOnbVgwQJNnDhRzz//vJo2baqXXnpJQ4cONWuefPJJ5eXladSoUcrIyFCXLl20ZMkSBQcHmzWzZs2St7e3hgwZory8PPXq1Utz5syR3W43a+bOnasxY8aYs3AOHjxYs2fPNvfb7XYtXLhQo0aNUrdu3eTv76+4uDhNnz69sm8bAAAAAGoEm2EYRnU3oibLysqSw+GQ0+msEb1/n2xI0lPzt6h36wj954HO1d2cSrdwc4pGf7RRXZrW0ScPx1Z3cwAAAIAao7zZpNKfyQMAAAAAVB9CHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIVUecibMmWKbDabxo4da24zDEPPPvusoqOj5e/vr549e2rbtm0exxUUFOixxx5TeHi4AgMDNXjwYB08eNCjJiMjQ/Hx8XI4HHI4HIqPj1dmZqZHTVJSkgYNGqTAwECFh4drzJgxcrlcVXW7AAAAAFCtqjTkbdiwQW+++aauvvpqj+1Tp07VzJkzNXv2bG3YsEFRUVHq06ePsrOzzZqxY8dqwYIFmjdvnlatWqWcnBwNHDhQxcXFZk1cXJwSExO1ePFiLV68WImJiYqPjzf3FxcXa8CAAcrNzdWqVas0b948zZ8/X+PHj6/K2wYAAACAalNlIS8nJ0dDhw7VW2+9pdDQUHO7YRh66aWX9Ne//lV33HGH2rZtq/fee08nTpzQRx99JElyOp16++23NWPGDPXu3VsdOnTQhx9+qC1btujbb7+VJO3YsUOLFy/Wf/7zH8XGxio2NlZvvfWWvvrqK+3atUuStGTJEm3fvl0ffvihOnTooN69e2vGjBl66623lJWVVVW3DgAAAADVpspC3ujRozVgwAD17t3bY/v+/fuVmpqqvn37mtv8/PzUo0cPrV69WpKUkJCgwsJCj5ro6Gi1bdvWrFmzZo0cDoe6dOli1lx33XVyOBweNW3btlV0dLRZ069fPxUUFCghIaHMdhcUFCgrK8vjCwAAAAAuFd5VcdJ58+YpISFBP/30U6l9qampkqTIyEiP7ZGRkfr111/NGl9fX48ewJM1J49PTU1VREREqfNHRER41Jx+ndDQUPn6+po1p5syZYqee+658twmAAAAANQ4ld6Tl5ycrMcff1xz585VrVq1zlhns9k8XhuGUWrb6U6vKav+fGpONXHiRDmdTvMrOTn5rG0CAAAAgJqk0kNeQkKC0tLSFBMTI29vb3l7e2vFihV6+eWX5e3tbfasnd6TlpaWZu6LioqSy+VSRkbGWWuOHDlS6vpHjx71qDn9OhkZGSosLCzVw3eSn5+fQkJCPL4AAAAA4FJR6SGvV69e2rJlixITE82vTp06aejQoUpMTFSzZs0UFRWlpUuXmse4XC6tWLFCXbt2lSTFxMTIx8fHoyYlJUVbt241a2JjY+V0OrV+/XqzZt26dXI6nR41W7duVUpKilmzZMkS+fn5KSYmprJvHQAAAACqXaU/kxccHKy2bdt6bAsMDFRYWJi5fezYsZo8ebJatGihFi1aaPLkyQoICFBcXJwkyeFwaMSIERo/frzCwsJUp04dTZgwQe3atTMncmndurX69++vkSNH6o033pAkPfTQQxo4cKBatWolSerbt6/atGmj+Ph4TZs2Tenp6ZowYYJGjhxJDx0AAAAAS6qSiVfO5cknn1ReXp5GjRqljIwMdenSRUuWLFFwcLBZM2vWLHl7e2vIkCHKy8tTr169NGfOHNntdrNm7ty5GjNmjDkL5+DBgzV79mxzv91u18KFCzVq1Ch169ZN/v7+iouL0/Tp0y/ezQIAAADARWQzDMOo7kbUZFlZWXI4HHI6nTWi9++TDUl6av4W9W4dof880Lm6m1PpFm5O0eiPNqpL0zr65OHY6m4OAAAAUGOUN5tU2Tp5AAAAAICLr1qGa16Kth7KVFCWu7qboYMZedXdBAAAAAA1GCGvnP7w5jp5+QVUdzNOcfY1BQEAAABcngh55VTPUUvetfyruxmSJB+7TXd2rF/dzQAAAABQAxHyymnpuB41YuIVAAAAADgbJl4BAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhXhXdwOAsjjzCrVqz7Fqu36gn13tG9SWl5et2toAAAAAnA9CHmqUk5lqZ2q27nt7XbW25R+3XqX42CbV2gYAAACgogh5qFFirwhTj5Z1dSQrv9rakJZdoPRclw5m5FVbGwAAAIDzRchDjVI7wFfvDb+2WtswedEOvbnyl2ptAwAAAHC+mHgFAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFlLpIW/KlCnq3LmzgoODFRERodtuu027du3yqDEMQ88++6yio6Pl7++vnj17atu2bR41BQUFeuyxxxQeHq7AwEANHjxYBw8e9KjJyMhQfHy8HA6HHA6H4uPjlZmZ6VGTlJSkQYMGKTAwUOHh4RozZoxcLldl3zYAAAAA1AiVHvJWrFih0aNHa+3atVq6dKmKiorUt29f5ebmmjVTp07VzJkzNXv2bG3YsEFRUVHq06ePsrOzzZqxY8dqwYIFmjdvnlatWqWcnBwNHDhQxcXFZk1cXJwSExO1ePFiLV68WImJiYqPjzf3FxcXa8CAAcrNzdWqVas0b948zZ8/X+PHj6/s2wYAAACAGsFmGIZRlRc4evSoIiIitGLFCnXv3l2GYSg6Olpjx47VU089Jamk1y4yMlIvvviiHn74YTmdTtWtW1cffPCB7rnnHknS4cOH1bBhQy1atEj9+vXTjh071KZNG61du1ZdunSRJK1du1axsbHauXOnWrVqpa+//loDBw5UcnKyoqOjJUnz5s3TsGHDlJaWppCQkHO2PysrSw6HQ06ns1z1uPRNXrRDb678RQ93b6aJt7Su7uYAAAAAksqfTar8mTyn0ylJqlOnjiRp//79Sk1NVd++fc0aPz8/9ejRQ6tXr5YkJSQkqLCw0KMmOjpabdu2NWvWrFkjh8NhBjxJuu666+RwODxq2rZtawY8SerXr58KCgqUkJBQZnsLCgqUlZXl8QUAAAAAl4oqDXmGYWjcuHG6/vrr1bZtW0lSamqqJCkyMtKjNjIy0tyXmpoqX19fhYaGnrUmIiKi1DUjIiI8ak6/TmhoqHx9fc2a002ZMsV8xs/hcKhhw4YVvW0AAAAAqDZVGvIeffRRbd68WR9//HGpfTabzeO1YRiltp3u9Jqy6s+n5lQTJ06U0+k0v5KTk8/aJgAAAACoSaos5D322GP68ssvtWzZMjVo0MDcHhUVJUmletLS0tLMXreoqCi5XC5lZGSctebIkSOlrnv06FGPmtOvk5GRocLCwlI9fCf5+fkpJCTE4wsAAAAALhWVHvIMw9Cjjz6qzz77TN9//72aNm3qsb9p06aKiorS0qVLzW0ul0srVqxQ165dJUkxMTHy8fHxqElJSdHWrVvNmtjYWDmdTq1fv96sWbdunZxOp0fN1q1blZKSYtYsWbJEfn5+iomJqexbBwAAAIBq513ZJxw9erQ++ugjffHFFwoODjZ70hwOh/z9/WWz2TR27FhNnjxZLVq0UIsWLTR58mQFBAQoLi7OrB0xYoTGjx+vsLAw1alTRxMmTFC7du3Uu3dvSVLr1q3Vv39/jRw5Um+88YYk6aGHHtLAgQPVqlUrSVLfvn3Vpk0bxcfHa9q0aUpPT9eECRM0cuRIeugAAAAAWFKlh7zXXntNktSzZ0+P7e+++66GDRsmSXryySeVl5enUaNGKSMjQ126dNGSJUsUHBxs1s+aNUve3t4aMmSI8vLy1KtXL82ZM0d2u92smTt3rsaMGWPOwjl48GDNnj3b3G+327Vw4UKNGjVK3bp1k7+/v+Li4jR9+vTKvm0AAAAAqBGqfJ28Sx3r5F1+WCcPAAAANVF5s0ml9+QBOLtUZ75Ss/Iv+Dw2Sa3rhcjXu8qXuwQAAMAlhJAHXEQHjuXqphnL5a6k/vMro4L19rDOFT6ulreXwoL8KqcRAAAAqFEIecBF9Gv6CbkNycduU2RIrfM+z8GMPEnSztRsdfvn9+d1jn/e0U5/uLbRebcBAAAANRMhD6gGLSODtXDMDed9fE5Bke58dbUOHM+t8LFFbkPFbkObDzn1h/NuAQAAAGoqQh5wBt/uOKIU54U/O3eqtOzKOV+Qn7e++XP38zr25e/2aObS3ZXSDgAAANQ8hDzgNLUDfCRJ+47mat/RiveUlUdogG+VnBcAAAAg5AGneSC2icID/ZRTUFQl57d72dSrdUSVnBsAAAAg5AGnCfTz1pDODau7GQAAAMB5YYEtAAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAAC/Gu7gYAqPmKit06kl1QrlqbpHqOWrLZbFXbKAAAAJSJkAfgrAzD0KDZP2pHSla5jxncPlov39uhClsFAACAMyHkATirwmLDDHi+3l46W/+c2zBUWGzo54OZF6VtAAAAKI2QB6DcNvy1txz+Pmfcn/Brhu58bfVFbBEAAABOx8QrAAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFuJd3Q0AcPlZuDlFU7/ZqcIi91nr7uncSI/3bnGRWgUAAGANhDwAF92CTQf16/ET56z7cN2vhDwAAIAKIuQBuOgMo+S/Y25qrj5tokrt3388V2M+3mTWAQAAoPwIecBlauOvGXrhq+3nrCuuwqTVIDRA7Ro4Sm33ttuq7JoAAABWR8gDLjOBfiX/2+9MzdbO1OxyH+dr95KvnbmaAAAAajpCHnCZuSumgfILi5WVX1ih4zo3riN/X3sVtQoAAACVhZAHXGYc/j4afWPz6m4GAAAAqghjrwAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyE2TUBXHKy8wv10bokOfPKXgbCz9uuuzs1UHRt/4vcMgAAgOpHyANwyVmw6ZCmfL3zrDVHsvM1+fZ2F6lFAHBpKix2y8fOwC7Aagh5AC452flFkqQWEUG6oUVdj33bDju1bn+6cn6rAQCUbdo3O/Xujwc098Eu6tAotLqbA6ASEfIAXLI6NKqtZwa18dj2zqr9Wrc/vZpaBACXjn8v2ydJmv39Xr09rHM1twZAZaJ/HgAA4DKWV1hc3U0AUMkIeQAAAABgIQzXBFBjuYqKtXrfsVLbk46fqIbWAAAAXBoIeQBqHC+bTZKUlV+kuLfWnbMOAAAAvyPkAahxmkcEaUC7etp9JPuMNbV87Lq9Q/2L2CoAAIBLAyEPQI1j97Lp30M7Vncz8Jtdqdk6cDxXktQ4LEBXRoVUc4sAAMDZEPIAAGd0KDNP/f+1UoZR8tpmk1Y9dZPq1/av3oYBAIAzIuQBuGy8/N0eLdycUua+axrW1j/vbCcbz/l5SMvKl2FIvvaSyZhdxW6lZeUT8gAAqMEIeQAuG6+v2KcTrrLXg9p1JFt/7tNSUY5aF7lVl4aIED/ZbFJyel51NwUAAJwDIQ/AZcP925jDf/3hGtUN8jO33//OehW5DXM/AADApYyQB+CyE9M4VA1CA8zXXl42yU3AAwAA1kDIA1DpDmbkqeuU7864/3iu6yK2BgAA4PJCyANQaRqG+svX7iVXsVuHnfnnrG9aN/AitAoomUDm/nfWKy27QDe2itCMIe2ru0kAAFQZQh6AShMRUkurJ96klMxzB7w6Qb7M0IiLZv2BdO1MzZYkzd94UFPuaCdfb69qbhUAAFWDkAegUoUH+Sn8lElNAAAAcHHxa0wAAAAAsBB68gAANUZ6rkuPfJigtKx8hQb66l/3dFCjsIBzHwgAAEyEPACoZHvTsvX2qv0qKHSb22w2m4Z0aqAuzcKqsWXSC19t14YD6ZIku5dNf+rZXH3aRFZrm0617pfjWr+/pH0Hjp/Qsl1peqBrk+ptFAAAlxhCHgBUsjdX/qJPfzpYavsvx3K0YFS3amhRCeeJQv1n1X6Pbe/+uP+ihTxXkVujP9qoA8dy5W330tjeLdTvqiiPmtNXKzRYoF6ZJ1zy9fZSgC8/sgEA5cNPDACoZAVFJT14vVtH6NqmdfTL0VzN25Cs/FN69qpD8SmB6eHuzfTGyl9UfBEXgd922Kml24+Yrz9en6R+V0Xpx73HNGf1Abndhn7Yc6zSrrcxKUOHM/PUIiK40s55sS3akqLRH22Uv49d347roWhmpAUAlAMhD4Al7Tuao3dO67UqKr64vUKxV4RrxPVNtXL3Uc3bkFzu4zJyXZq+ZJcyTvy+aHyD0AA92a+VvO2VM1/WVfUdlXKeijg9T558/cr3e7T2l/RynSO/sFiSVMvHfta6HSlZuuPV1ZIkX7uX/j64TcUaW0NsPuiUYUgnXMXak5ZDyAMAlAshD4Cl+PmUhKBth7O07fD2Mmtq+vpo32xL1dx1SaW292kTqc5N6lRDi6pW4W/hu1GdAHVpWkfedi/9uPeYktJPeNQ988VWvb/mV0nS471a6M99Wp7xnEeyfl+r0VXsVuaJwipoeeX6eH2S3lz5i7o0raN/3nl1dTcHAHAJI+QBsJRb2tbT7tRsZZzhQ/3VDRyKCK5Vqdd0FbmV5yr2eH1B5ysuOb5NvRDde21DvfL9XqVlF5zzvPuO5uiH3UfN143DA3Vjq4hyXTMtO98MW1EhtWT3sp1n68/f/93SWv3bljyjN/qjjUpKP6G3ftivtOwCPdn/So+hnKv2HjtryLsUvbf6gPYfy9X+Y7n664DWCq7lU91NAgBcogh5ACwlNNBXz93a9qJdLy07X/1mrTxjqDyTH/Yc1fbDWR7beraKUKuo358faxIeoPjYJpq7Lklp2QXnPOefPkzQ7iM5Htu+H99DzeoGnfW4fy/bq2nf7DJfX9ukjj59JLY8t1Fl6oWUBPFDmXl6dfk+jbqxebW250KlZedr9d7jahkZrDbRIWXWuE95ZpLpZgAAF4KQBwC/+XHvMYUF+Zqvfexe6tykzlmf/9qdmlNmwAuu5a3OTULLPCYj16Vh724oNenJfxMO6ttxPc6z9VJ6bkk7uresq58OpOuEq9jjub4z+Tk5U5LkZSt5Ti7xYOZ5t6GyPNG/lbo1D9cf52yQ5BmAqsuu1GztTM1S+wa11SQ8sELHjvl4k/nc4fg+LXVbh/pqWIf1/wAAVYOQB+CyZ7eVDE184v9tLrUv/rrG+sdt5+4ZvDIqWF89dr352stmk9cZhjzmFBSp2G3I7mXTbdfUlzPPpW93pFXac2P/d8uVeuSDBB04XvJM2/9+Pqz//XzYHAZ6JqN6NtfsZXsrfD3DMPTpT8n65ViubLLJZpNskmKvCNMNLeqezy3Iz9uubs3Dz3nd73emKTUrX03CAs9ZfyFyC4o04OUfVOQ2FBboqw1/7X3Gv9+yHD2lJ3bG0t1a88txfTTyOklScvoJDZ+zQXvScs50OAAAFULIA3DZG9OrhRZvTfHYdizHpUOZeUpx5pX7PBWd+dLX7qUZQ9prR0qWvt2RVqFjK2LSwh1KPWUiktAAH501nhglQdTHXr4QsyctR0/N31Jq+5zVB7T12X4VCkMV8dOvGRrx3k/m6+UTep6xh23FrqNlbj9VWna+Vu4+ptr+PrrxygiP5xLTc10q+q3n9XiuS6+t2KfV+46pS9MwjenVosJtP7X3d93+dAIeAKBSEfIAXPb+1PMK/annFR7b5q1P0l8+26I9aTn659c7Pfb1aROhmMbVM8tlfmGxPljzq47l/N4z1LV5uHq0PHOPWeFvPXiP92qhyJBaimkcqj1p2WesdxW71fbv38jfx65x5ZjcJKegSJIU7OetIZ0bKr+wWHPXJemEq7hKny07nuM5FPV4ruuMIW/9Ac8lGvIKi7V0+xEVFrvVs1Vd1Q7w1fhPfzYnd3ltaEfd3K6eTriKdPu/V2vXEc/v18lnGH/ce1zDujVRyCmTpLjdhgrdbvl5n32Zh7PJyS+Sj1fNngW2PAqL3fKy2aplIh8AuJwR8gCgDAF+Jf88/nr8hF5fsc9j36ItKVr55I3V0Sx9vzNNkxbt8Nj2wdpfte25fuc8dsDV9dQysmRil5MhLyn9hPx+W1IiIsRPV0YFa2dqyb68wmJtTMood9tCA3319MA2yjzhKnMJiNP9nJypW/71g/YePXcv1k3Tl+t47rmfLyyvt3/4RS9/XzI09fYO9fXs4Ks8Zu88GaK3Hc4qFfBO9/cvtqnfVZHq37aenCcK1f9fK5XizNfY3i00tvf5zQDa9Z/fK8DXrutPGYI6aeF21a8do+YRZ59IpyK+33lEs7/fq1uvqa8HujaptPNKJcNQB76ySg5/Hy35c/dzrm0IAKg8l/6vCQGgCvRtE6mn+l+pB69van7d0bG+JCnzhEsbkzK0MSnjrD1iVeFkr1nDOv6Kv66xpJKFsisq8LcQm+LMN5/dc/j7aNGYG7TzH/116zXRZzw2z1VsLkp+IZx5hdqekmUuDdE47MwTkRzLcelcc6/8nJwp9+krrp/i0VNm6Dx6Si/gsZwC3f7vH8/Z3o1P9ylz+4JNh/TIhxtVUFSs3WnZSnGWDI39bOMhHcw4UeYxZYltFubx+oSrWDtSf5+BdfeRHM1aurvc5yuPKYt2amNSpv7+5Ta5itwePcQXaushp5x5hUpKP2GueXjCVVRp50flWb3veHU3AUAloycPAMpQy8deagjn7iPZ+mzjIWXlF+mOV1d77LPZzj0c7WDGCY2eu7FSPui2iAjWn/u01Adrfz2v469vHq6nB7ZR2m/P6oUF+arfVVHy8rKplpf9rMPr4v6zTn7eXnr7gc6KvSJMRcUXNijz1muiNaFvq3PONtmjZV0Nah+tCf/9ucz9z3+1XcdzC/REvysr3IbTF16XStY7fPm7PZKkZuGB5xxyePpsqUnpJ3T9i8v0j1uvUupvwa9T41D99Gv5e0dPt3BLiv4vM0/1a/uX2pecfkL5hcVqERlcxpFly8z7/dnA/i+t1C/HcjVzSHvd0bHBebexLGt/Oa75Gw/qjRW/aHyflnrsPJ5jBACUHyEPAMqpWXigereOKDV8z8tmU1yXRmc8rk5gybIM2flFWrglpdT2qvTBml+VW0ao9LF7acT1TSt0rh4t6+rDtSXDMAuK3Prkp2Q9+vHGs84Kmpx+QvVD/eVzlklpokJqlWs5gaBa3gquVfJjK+HXDCX8mlCq5tfj5es5yzzD0hIdG9XWxqRMPf3FNv3ru71mz1awf9kLk8/5Y2cNe7dkmYf9x3JVUFh6BtOnv9hm/vm6ZmEXFPIkadnONN33Wy/uSfuO5qjXjBWSpLfu76RUZ572Hc3VQ92baVNSpvakZWtol8aqG+x3xvP+cixXkrQxKaNUyDMMQ499vElbDzn18r0ddHWD2hVq8zOnfA9mLN1NyAOAKkbIA4By8rZ76T8PdK7wcVdFh+jtBzrpUKbnTJ3XnTY8rzIF+Jb88/554uFTtl3YM1Ev3NZOTw9so79/sU3zNiTrfz8f9tjf9YrS99Nz+nJdFR2iN+JjtKkCz/edScvIYNXy8VL+KWGqU+NQDbi6np7733Zz27mGsH69NfWc1zp16OK0u65WkJ+3moUHmmGoR8u6Hn+Hg2f/WKo373RXlbEQ+sme3fLOs2KUMW41+ZSeyLdX/WKuyVdY7Dafj3zp2z2a0Lel0rILNKZXC4UH+alOgK/H8g5n8siHCfpm2xFJ0jfbUrUpKVPFbkMPdG2iIrdbj3yQoIIit16Pj1GQr7dW7T2m7SlZ5zgrULUyT7i09pd09WodcdZfNAFWRMgDgCpms9nUq3XkRb3mpNvbauHmFHN2y1aRwWoQWvHFt0//oO7nbVfEab1BfdtEasaQ9gr+bYZJh7+PbroyQmv2HVdeYbG2Hc7S9S8uq/C1few29W4doWW7jsrX7qVeV0aoaXigfvpbH935aukZL0+au+5X/XXB1lLbg2t5a0C7eh69qZI8JlwpS+cmoeaENYvHdldadr4iQ2qZHxof6XGFXl+x75wBb/rd7c1nIU/Kzi80e7m8vbzUu3Wkvt1xxNyfnP77LwaCa3krO//cQ31PBjxJyjitx3L6kpJn+hqHBWrE9U0VEeJX6vv44dok3dgqwnzPFhW7zYAnSZsPOvXDnpLJiNrWd8jbbtOy35aomLJohz5en3zONp7uo3VJ2pmapfF9WumwM09rfzmuOzo0kCOg7B5UXDhnXuWsy1mTDXljjXYfydGYXi3KNVMwYCWEPACoIXIKCvXMF1u1M+XCJ3Pp0ChUHRqFnvfxwafMLipJQaeEk4d7XKEm4YHKKyyWj5eX+rSJNAOeVBJq3xnWWSdcRWr/3BIVnvLMXtv6IWocFqiFm38PWmcaQmiz2crsOQ3y85aP95mfj9uwP73M7Tab9O+hHbX/Xz+cdy+Tr7dXqbD8SI9mpWZgLa+Tk7RIJbOfNqjt7xHyTnrw+qY67MzToi0lPZC5BUW6+/U1OpZToFfu7eBxnvLYfDBThmGU6l0+6dn/bdOV9ULKfPbv1FA87Zuduj+2ifm6vAFv39EcXVE3SCnOPK3ee1z/t6BkncWWkcGatXS3jue6lJyep2cGtanAXaEi1uw7+y83rGD3kZKZe7/6+TAhD5cdQh4AVLOTASq/0K331/w+kUqwX/X1Yoy6sbnCg/yUX1Qsm2zqd1WUuS/Qz7tcE3ME+HrrPw90Nodp3tCirmIah8rtNhR3bSMdzS5QoJ/3Wdf4q6ivNqeYSwxMvPlKvfL9XnNG0pom6fgJvbnyF0klz2cO6dRQq/ee+4P3019s83jO7543156xdv+xsp9R/CLxsAJ8vfXL0dwy9yen56n71GWKv66x5qw+cMbzbziQoQ0HKj4Mt9eMFRp4dT3tTcsxl+yQSp4HPLlUxtZDTrndhib8v59VUOTWrCHXyNe7YkPuDMNQkdtgqF4ZzjVbrZW4L6ebBX5DyAOAatawToBevreD9p4ybM7X20t3xpw++YVUpauLnyIypFalTI7Ro2XdUiHOy8umbqes/1YZTv0Qvzet5Lf3Z5oN89RnE++PbWwGay+b5H+Bzy1K0uv3xeivC7acdV2/HSlZ6j7t9yGs5xo6d75rzO04S49lWT2Gpyp2G2cNeBfqq80ppbZ9tvGQ+ef1B9LV8m9fq+i3YbDf70jT56O7qVVUsI5k5Ssrr/CMM4kmJmfqx73HtHrfMSUmZWreQ7Fq18Ch/cdy9fmmQ7orpoEa1gnQ/mO5MgxDT/y/zbLbbJo9tIMigmudsc2GYZx1Jt21vxzX/ISDevSm5mocFljeb0W1KDzH8GIruYxuFTAR8gCgBhjc/szr0p1039vrLkJLLk03t43Sxl8z9NmmQ+es/dvANlqw8aAC/bw14vqmimkcqp2p2WrfwKEvT5tMpjxq+djlZSv5IFnPUUv920apT5tIzVy6S/9e5jmM80z54OTzfBEhZQ9dHXpdI037ZleF23Y25ZlwpboVnfLpPK+wWP1eWqk34mP0+LxNHpPvPNyjmRIOZOgvN1+px+cllhqG+kXiIbVr4NC4TxO1KSlT//puj2bHddCjH23yqHtt+T79fdBVkqT0XJdOuIoU4u+jmUt2638/H9bxXJdaRgbpm7HdlZVfpNdX7JOP3Ut/6nGFjucW6A+/9ar+fDBT4UF+uqNjA9Xy8VJkSC11blKnwvd/wlVkTqJ0LknHT+hQZp5iy5gAqSyn9xqv2H1Uy3am6f7YxmpWN6jCba3JqvP5w5+TM7Vq7zE91L0ZPcq4qAh5AFDDXdu0jseQNkmlPjDOWLJbuTV0WGJVaB0Voq2HSnqpWtcLUViQn6bd3V6Lt6WaM2uG1Cp7uOs1DWvrmoa1zde3XlNft/72531Hc83n3sqrlo9dXz56vbYccurapiV/L3Yvm3ztpXvfOjYKVWyzMB3JzldWXlGpxcebRwRr/p+6Kj3XpZHv/ySp5JnFeg5/jerZXAVFbi3cnKJaPl56fnBbHXbm6aVv93ico26wn1xFbj3QtYm5zt/Z1A7wOesyGCf1u6pkIpZTJ2GpDg9/UHrpjDdWlAx7ffD9n8q8l/+s2q+P1id5zLp6esCTpHd/PKDWUSHqe1WkOv5jaZnX330kR00nLvLYFlLLW/mFxR41u4/klFpk/JZ2UerdOlJ703LUrr5Df5q7UZL0w5M3msuIbD6Yqbd+2G/OXjv1zqvl72vXsZwCbT2Updb1gvXgDc2UecKlYe9uUGJypqIdtXT4t+cye10ZodfjY+TtZdPbq/ZrR0q2/titiVrXCzF7t+/7zzqtOi3kPfDOeknSnNUH9N7wa9XtijB5nxJK3G5DXqf0jn+0LkltokM8/l+qqUKrcQKfW//9oyTJ38eu4RVctga4EDajrLmYLebVV1/VtGnTlJKSoquuukovvfSSbrjhhnIdm5WVJYfDIafTqZCQ0lNfA8DFkHnCZQ45snvZ5PD3UU5BycQmp8/quPKJG9UorOIzaV5KDMPQgeMnZJPUOCzAHEK3et8xrfslXaEBPrqncyN1nvSt+Uze/91ypR7qfsVZzlricGae5q771eyF69wkVP99pGuF2zhvfZL+8tkW8/X0u9vrrtOG4Db5y0Lzzwf+OaDMfS0igrR0XA9zu9ttyG0Y8rZ76acD6brr9TXmPn8fu3b8o7/5etwniefs3WwZGSRfby8zNEvSe8Ov1fhPE9WwToA2JWVKKlm0/rZr6uuPczac69Y9XNOwtv7YrYken5dYoeMuN5Nub6vFW1PPOdtrebRv4FCfNpHmbKonxTQOVUIF1mkc1D5aB47lasshpyTp0Rubq1frCN3+6mqzZmiXRnqoezOt2Xdcc9clqesVYRp+fVNFhvw+7NUwDB3MyFNhsVvN6gbJ7TaUX1RcZi9lnqtYBUXFqh3w+zqiR7MLNGXRDjWPDNLIG8ruETMMQ8dzXQoP+r03/Gz/f51LfmGxfO1eHsH2fJxsw5BODTT1rvYXdC5AKn82sXxP3ieffKKxY8fq1VdfVbdu3fTGG2/o5ptv1vbt29Wo0ZkXLwaAmuTUDzwnBfl5a+aQ9tp4yoe2llHBlg94UsnMm03DSz/z1PWKcHW94vfn/VpFBZsfas/0/Nbpomv7n/W5rPK6u1NDvbZinzlDaUU/Kv7rD9do5e5juruTZzD08rLJ6wxn87Z7bn/+trbqe1WkFm1JPeNQ1Ns61NefelyhyYt26K0f9kuSrq7v0E9/6yNJmrFkl1buOaYHujaRfzmfDezRsq52H8lW3zaReu7WtjqS9fvsn1PvvFpPzt9crvNcTspa9uN8/XzQqZ8POkttr0jAk1RqLczZy/Zq9rK9Htvmrksy12KUpC2HnHrjtwmFzmVC35Y6cPyE9qbl6MDxXI3u2VyTFu2QJI3v01L7j+Wq71WReuTDjeYxUxfv0twHu8jX20uLtqTo0w3J+svNV5qTEXVpWkebkjIVFuT5b2aTvyzU6/d1VLG7ZDjtX/pfqWM5BTrszFe7+g49Pm+TAnztevHOq3U0p0DXTvpO7Rs49MWj15vnKCouGSLsNqQ9adm6KtpxxnszDEPHcn5/LvfTnw56hLxzPd9ZHjtTs/TU/C16d1hn1Qn01bGcAoUF+l7weatacvoJRTlqMXy1ilm+J69Lly7q2LGjXnvtNXNb69atddttt2nKlCnnPJ6ePAC4dBUWu3XgWK5C/H08ehbO5dS19mKbhenjh647r+uv2nNMf/t8i4JqeevN+E6KPm1Jgs6TvtXR7AJdUTdQ343vWeHzn96T9+feLfV477NPmLM3LVt/mb9F0bX99dI915g9Fd/tOKI/fbhRLaOC9OXo68vswdiRkqWb//WDpJIP6Kf3FJ30l5uv1CM9PHtNP1j7q45mF2hUzyvUedK35VrzD7C6Mb1aeAyrrl/bv8ylTTo2qq2Nv/Wqn8u1Teoov6hYuQVF6tkqQm+vKvnlzZBODbQjJVtbDjn11v2dZPeS9hzJ0eaDTi3ckqKrokN0VXSIrm9RV+t+Oa6brozQdc3C9I+vtutodoFeva+jth/O0tHsAl13RZj8vL20YtdRfbU5RU8PbKP0XJeia9fSrtRs1Q7wVS0fLwX7+ei/CcmKctTS9c3DtXrfcY2au1HhQX5qGRmk7i3r6qEbmin9hEuuIrcc/j7Kzi9SQVGxit2G+r20Uk8PbKO4axtp/7FcNawTID9vLxW7DeUXueXtZdPS7UfUPCJIob9dM8DXW3Yvm7xsJROWGZLW7T+uxmGBCvLzlsPfR4ZhyG2UhG27l01uo2TyrZMB+WQIL3YbchW55W231ZhQWt5sYumQ53K5FBAQoP/+97+6/fbbze2PP/64EhMTtWLFinOeg5AHAJefo9kFemHhdmXlFeqP3ZqqeyUu83CqVGe+EpMzFNO4zhnXCzybjFyXes9coaz8Qr0Z30k3XhlxQe051xC1U0PejLvba+bS3WV+IH37gU7mYupl+XRD8hl785b8ubv6zlppvg4L9NWGv/bW2v3HFffW75MPdW4Sel7LN5wqNMBH98c20f5juec16Q6Ay8NT/a/Un3qee7j/xUDIk3T48GHVr19fP/74o7p2/f15ismTJ+u9997Trl2lZyorKChQQcHvD8I7nU41atRIycnJhDwAQI3jKnKryO0u9yyMF+KIM1+9Zpb8gvTV+zrqmoa1lXz8hNyGoRaRwSoocivjhEtNyrl8wKItKSp2u3VVfYfufG2NQv199PXYG5SdV6Se05dLkhY/foMa/DYpidttaOGWw2pTL0SNwgJ1ODNPjX7b92XiYf31c88hj+FBvooKqaXo2rW0ZHuaPnzwWtUN8tPyXWnKzi9WfGxjBf62TqUzr1DOPJc+2ZCs91b/qrJ0bxmuV4fG6LsdR/TDnqP6fwmezzvGXhGmNadNtiJJA6+uV+aSEUO7NPIY6nhSozr+SkovHZ5vaRulRVsrNjEQgMqx9bl+1d0ESSUhr2HDhsrMzJTDceYhw5dFyFu9erViY2PN7ZMmTdIHH3ygnTt3ljrm2Wef1XPPPXcxmwkAAAAA5ZacnKwGDRqccb+lJ14JDw+X3W5Xaqrnb73S0tIUGVn2MJKJEydq3Lhx5mu326309HSFhYVd8IOsnTt31oYNFZuVrLLPU9FjK1Jf3tqz1Z387YSVe04r631QU9twKb7PK3JMZddZVU24/6puQ2W/1yv6719Vvs/LW1sT/p6rW3V/Dy7k+uV5z12K/6bzPq98NeF7wGeXEoZhKDs7W9HRZ19f19Ihz9fXVzExMVq6dKnHM3lLly7VrbfeWuYxfn5+8vPzfC6idu3aldIeu91eKcHlQs5T0WMrUl/e2vLUhYSEWDbkVdb7oKa24VJ8n1fkmMqus6qacP9V3Yaqeq+X99+/qnyfl7e2Jvw9V7fq/h5UxvXP9p67FP9N531e+WrC94DPLr872zDNkywd8iRp3Lhxio+PV6dOnRQbG6s333xTSUlJeuSRRy56W0aPHl3t56nosRWpL29tZX0fLlU14f6rsg2X4vu8IsfwPi+fmnD/Vd2G6n6vV+X7vLy1NeHvubpV9/fA6u/z8zmW93nlqwnfAz67VIyln8k76dVXX9XUqVOVkpKitm3batasWerevXt1NwtlYDZTAJcr/v3DxcZ7DrAuy/fkSdKoUaM0atSo6m4GysHPz09///vfSw2ZBQCr498/XGy85wDruix68gAAAADgclEzlm4HAAAAAFQKQh4AAAAAWAghDwAA4DK0fPly2Ww2ZWZmVndTAFQyQh4uyMqVKzVo0CBFR0fLZrPp888/L7OuZ8+eev311y9u4wDgMtSkSRO99NJL1d0MVDF+/gI4G0IeLkhubq7at2+v2bNnn7EmPT1dq1ev1qBBgy5iyzy5XK5quzYA6+CDNWqKS+XnL4DqQcjDBbn55pv1wgsv6I477jhjzcKFC9W+fXtFR0erefPmmj59usf+rVu3ysvLS/v27ZMkOZ1OPfTQQ4qIiFBISIhuuukm/fzzz2b9vn37dOuttyoyMlJBQUHq3Lmzvv32W49zNmnSRC+88IKGDRsmh8OhkSNHVuJdA7hc8cEaNUVFfv7Wr19fkrRo0SK1bNlS/v7+uvHGG3XgwAGP+uPHj+vee+9VgwYNFBAQoHbt2unjjz8297///vsKCwtTQUGBx3F33nmn7r///sq7OQAXjJCHKvfll1/q1ltvlc1m0/Dhw/Xuu+967H/nnXd0ww036IorrpBhGBowYIBSU1O1aNEiJSQkqGPHjurVq5fS09MlSTk5Obrlllv07bffatOmTerXr58GDRqkpKQkj/NOmzZNbdu2VUJCgp5++umLdr8ArKuiH6xjYmI0Y8YMc99tt90mb29vZWVlSZJSU1Nls9m0a9cuSSWjDp588knVr19fgYGB6tKli5YvX+5x/tWrV6t79+7y9/dXw4YNNWbMGOXm5p6xPe+++64cDoeWLl16AXeOS9HJn7+SlJycrDvuuEO33HKLEhMT9eCDD+ovf/mLR31+fr5iYmL01VdfaevWrXrooYcUHx+vdevWSZLuvvtuFRcX68svvzSPOXbsmL766iv98Y9/vHg3BuDcDKCSSDIWLFjgsS0/P98IDg42Nm/ebBiGYRw+fNiw2+3GunXrDMMwDJfLZdStW9eYM2eOYRiG8d133xkhISFGfn6+x3muuOIK44033jjjtdu0aWO88sor5uvGjRsbt912W2XcFgCUqax/8wzDMO666y7jH//4h2EYhjFu3Dhj4MCBhmEYhtvtNurUqWOEh4cbCxcuNAzDMD766CMjKirKPDYuLs7o2rWrsXLlSmPv3r3GtGnTDD8/P2P37t2GYRjG5s2bjaCgIGPWrFnG7t27jR9//NHo0KGDMWzYMPMcjRs3NmbNmmUYhmFMmzbNqFOnjrFmzZqq+BaghijPz9+JEycarVu3Ntxut1nz1FNPGZKMjIyMM577lltuMcaPH2++/tOf/mTcfPPN5uuXXnrJaNasmcd5AVQ/evJQpb7//nuFhYWpXbt2kqR69eppwIABeueddyRJX331lfLz83X33XdLkhISEpSTk6OwsDAFBQWZX/v37zeHc+bm5urJJ59UmzZtVLt2bQUFBWnnzp2levI6dep0Ee8UAKSCggJ98803Zu9Jz5499cMPP8jtdmvz5s2y2+2Kj483e+eWL1+uHj16SCoZiv7xxx/rv//9rzm6YcKECbr++uvNERDTpk1TXFycxo4dqxYtWqhr1656+eWX9f777ys/P9+jLRMnTtTMmTO1fPlyXXfddRfvm4Aa4fSfvzt27NB1110nm81m1sTGxnocU1xcrEmTJunqq682fw4vWbLE4+fryJEjtWTJEh06dEhSSU/xsGHDPM4LoPp5V3cDYG2nDhU56cEHH1R8fLxmzZqld999V/fcc48CAgIkSW63W/Xq1Ss1PEmSateuLUl64okn9M0332j69Olq3ry5/P39ddddd5WaXCUwMLBK7gkAzuT0D9bdu3dXdna2Nm3apB9//FE9evTQjTfeqBdeeEFSScgbO3asJGnjxo0yDEMtW7b0OGdBQYHCwsIklfwibO/evZo7d6653zAMud1u7d+/X61bt5YkzZgxQ7m5ufrpp5/UrFmzqr5t1ECn//w1DOOcx8yYMUOzZs3SSy+9pHbt2ikwMFBjx471+PnaoUMHtW/fXu+//7769eunLVu26H//+1+V3AOA80fIQ5UxDEP/+9//9P7773tsv+WWWxQYGKjXXntNX3/9tVauXGnu69ixo1JTU+Xt7a0mTZqUed4ffvhBw4YN0+233y6p5Bm90x8eB4DqcPoHa4fDoWuuuUbLly/X6tWrddNNN+mGG25QYmKi9uzZo927d6tnz56SSn7JZbfblZCQILvd7nHeoKAgs+bhhx/WmDFjSl27UaNG5p9vuOEGLVy4UJ9++mmp565gfWX9/G3Tpk2p2WDXrl3r8fqHH37Qrbfeqvvuu09Syfttz5495i8PTnrwwQc1a9YsHTp0SL1791bDhg2r5kYAnDdCHi5ITk6O9u7da77ev3+/EhMTVadOHaWlpSk3N1fdu3f3OMZut2vYsGGaOHGimjdv7jFcpHfv3oqNjdVtt92mF198Ua1atdLhw4e1aNEi3XbbberUqZOaN2+uzz77TIMGDZLNZtPTTz8tt9t90e4ZAMpypl9s9ezZU8uWLdO6dev0/PPPq3bt2mrTpo1eeOEFRUREmB+gO3TooOLiYqWlpemGG24o8xodO3bUtm3b1Lx587O25dprr9Vjjz2mfv36yW6364knnqicm0SNUdGfv4888ohmzJihcePG6eGHH1ZCQoLmzJnjcc7mzZtr/vz5Wr16tUJDQzVz5kylpqaWCnlDhw7VhAkT9NZbb5V6vwOoGXgmDxfkp59+UocOHdShQwdJ0rhx49ShQwc988wz+uKLLzRgwAB5e5f+XcKIESPkcrk0fPhwj+02m02LFi1S9+7dNXz4cLVs2VJ/+MMfdODAAUVGRkqSZs2apdDQUHXt2lWDBg1Sv3791LFjx6q/WQCXvZycHCUmJioxMVHS7x+sk5KSlJCQUOYvtnr27KnFixfLZrOpTZs25ra5c+eaz+NJUsuWLTV06FDdf//9+uyzz7R//35t2LBBL774ohYtWiRJeuqpp7RmzRqNHj3a7A388ssv9dhjj5Vqa2xsrL7++ms9//zzmjVrVhV9R1BdKvrzt1GjRpo/f77+97//qX379nr99dc1efJkj3M+/fTT6tixo/r166eePXsqKipKt912W6lrh4SE6M4771RQUFCZ+wHUANU56wusrV27dsYnn3xS5r5Vq1YZ3t7eRmpq6kVuFQCcv2XLlhmSSn098MADxt/+9jdj6NChpY7JzMw07Ha7cdddd5nbFixYYEgyZs+e7VHrcrmMZ555xmjSpInh4+NjREVFGbfffrs5Q6JhGMb69euNPn36GEFBQUZgYKBx9dVXG5MmTTL3nzq7pmEYxooVK4zAwEDjX//6VyV+J1CTne3nb2Xp3bu38dhjj1XpNQCcP5thlONJXKCCXC6XpkyZonHjxik4ONjcXlBQoOTkZD300EOqV6+ex+QBAHApu/rqq/W3v/1NQ4YMqe6m4DJ2pp+/lSU9PV1LlizR0KFDtX37drVq1arSrwHgwhHycFHNmTNHI0aM0DXXXKMvv/xS9evXr+4mAcAFq+oP1kBN0aRJE2VkZOjpp5/WhAkTqrs5AM6AkAcAAAAAFsLEKwAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAsbdiwYbLZbKW+9u7dW91NAwCgSnhXdwMAAKhq/fv317vvvuuxrW7duh6vXS6XfH19L2azAACoEvTkAQAsz8/PT1FRUR5fvXr10qOPPqpx48YpPDxcffr0kSRt375dt9xyi4KCghQZGan4+HgdO3bMPFdubq7uv/9+BQUFqV69epoxY4Z69uypsWPHmjU2m02ff/65Rxtq166tOXPmmK8PHTqke+65R6GhoQoLC9Ott96qAwcOmPuHDRum2267TdOnT1e9evUUFham0aNHq7Cw0KwpKCjQk08+qYYNG8rPz08tWrTQ22+/LcMw1Lx5c02fPt2jDVu3bpWXl5f27dt34d9UAECNRcgDAFy23nvvPXl7e+vHH3/UG2+8oZSUFPXo0UPXXHONfvrpJy1evFhHjhzRkCFDzGOeeOIJLVu2TAsWLNCSJUu0fPlyJSQkVOi6J06c0I033qigoCCtXLlSq1atUlBQkPr37y+Xy2XWLVu2TPv27dOyZcv03nvvac6cOR5B8f7779e8efP08ssva8eOHXr99dcVFBQkm82m4cOHl+q9fOedd3TDDTfoiiuuOL9vGADgksBwTQCA5X311VcKCgoyX998882SpObNm2vq1Knm9meeeUYdO3bU5MmTzW3vvPOOGjZsqN27dys6Olpvv/223n//fbPn77333lODBg0q1J558+bJy8tL//nPf2Sz2SRJ7777rmrXrq3ly5erb9++kqTQ0FDNnj1bdrtdV155pQYMGKDvvvtOI0eO1O7du/Xpp59q6dKl6t27tySpWbNm5jX++Mc/6plnntH69et17bXXqrCwUB9++KGmTZtWobYCAC49hDwAgOXdeOONeu2118zXgYGBuvfee9WpUyePuoSEBC1btswjEJ60b98+5eXlyeVyKTY21txep04dtWrVqkLtSUhI0N69exUcHOyxPT8/32Mo5VVXXSW73W6+rlevnrZs2SJJSkxMlN1uV48ePcq8Rr169TRgwAC98847uvbaa/XVV18pPz9fd999d4XaCgC49BDyAACWFxgYqObNm5e5/VRut1uDBg3Siy++WKq2Xr162rNnT7muZ7PZZBiGx7ZTn6Vzu92KiYnR3LlzSx176oQwPj4+pc7rdrslSf7+/udsx4MPPqj4+HjNmjVL7777ru655x4FBASU6x4AAJcuQh4AAL/p2LGj5s+fryZNmsjbu/SPyObNm8vHx0dr165Vo0aNJEkZGRnavXu3R49a3bp1lZKSYr7es2ePTpw44XGdTz75RBEREQoJCTmvtrZr105ut1srVqwwh2ue7pZbblFgYKBee+01ff3111q5cuV5XQsAcGlh4hUAAH4zevRopaen695779X69ev1yy+/aMmSJRo+fLiKi4sVFBSkESNG6IknntB3332nrVu3atiwYfLy8vxxetNNN2n27NnauHGjfvrpJz3yyCMevXJDhw5VeHi4br31Vv3www/av3+/VqxYoccff1wHDx4sV1ubNGmiBx54QMOHD9fnn3+u/fv3a/ny5fr000/NGrvdrmHDhmnixIlq3ry5xzBTAIB1EfIAAPhNdHS0fvzxRxUXF6tfv35q27atHn/8cTkcDjPITZs2Td27d9fgwYPVu3dvXX/99YqJifE4z4wZM9SwYUN1795dcXFxmjBhgscwyYCAAK1cuVKNGjXSHXfcodatW2v48OHKy8urUM/ea6+9prvuukujRo3SlVdeqZEjRyo3N9ejZsSIEXK5XBo+fPgFfGcAAJcSm3H6QwMAAKBCevbsqWuuuUYvvfRSdTellB9//FE9e/bUwYMHFRkZWd3NAQBcBDyTBwCABRUUFCg5OVlPP/20hgwZQsADgMsIwzUBALCgjz/+WK1atZLT6fRYCxAAYH0M1wQAAAAAC6EnDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAs5P8DIcDkJA6hMqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.signal import rfft\n",
    "\n",
    "fft = rfft(df['OT'])\n",
    "f_per_dataset = np.arange(0, len(fft))\n",
    "\n",
    "n_sample_h = len(df['OT'])\n",
    "hours_per_week = 24 * 7\n",
    "hours_per_year = 24*365.2524\n",
    "weeks_per_dataset = n_sample_h / hours_per_week\n",
    "years_per_dataset = n_sample_h / hours_per_year\n",
    "\n",
    "f_per_year = f_per_dataset / years_per_dataset\n",
    "\n",
    "plt.step(f_per_year, np.abs(fft))\n",
    "plt.xscale('log')\n",
    "plt.xticks([1, 52, 365.2524], ['1/year', '1/week', '1/day'])\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylim(0, 100000)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69257932",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "timestamp_s = pd.to_datetime(df['date']).map(datetime.datetime.timestamp)\n",
    "\n",
    "day = 24*60*60\n",
    "year = (365.2425)*day\n",
    "\n",
    "df['day_sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
    "df['day_cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
    "df['year_sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
    "df['year_cos'] = np.cos(timestamp_s * (2 * np.pi / year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7e008cf",
   "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>date</th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>year_sin</th>\n",
       "      <th>year_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>5.827</td>\n",
       "      <td>2.009</td>\n",
       "      <td>1.599</td>\n",
       "      <td>0.462</td>\n",
       "      <td>4.203</td>\n",
       "      <td>1.340</td>\n",
       "      <td>30.531000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.010486</td>\n",
       "      <td>-0.999945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>5.693</td>\n",
       "      <td>2.076</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.426</td>\n",
       "      <td>4.142</td>\n",
       "      <td>1.371</td>\n",
       "      <td>27.787001</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.009770</td>\n",
       "      <td>-0.999952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>5.157</td>\n",
       "      <td>1.741</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.355</td>\n",
       "      <td>3.777</td>\n",
       "      <td>1.218</td>\n",
       "      <td>27.787001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.440628e-12</td>\n",
       "      <td>0.009053</td>\n",
       "      <td>-0.999959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>5.090</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.391</td>\n",
       "      <td>3.807</td>\n",
       "      <td>1.279</td>\n",
       "      <td>25.044001</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.008336</td>\n",
       "      <td>-0.999965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>5.358</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.868</td>\n",
       "      <td>1.279</td>\n",
       "      <td>21.948000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0.007619</td>\n",
       "      <td>-0.999971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date   HUFL   HULL   MUFL   MULL   LUFL   LULL         OT  \\\n",
       "0  2016-07-01 00:00:00  5.827  2.009  1.599  0.462  4.203  1.340  30.531000   \n",
       "1  2016-07-01 01:00:00  5.693  2.076  1.492  0.426  4.142  1.371  27.787001   \n",
       "2  2016-07-01 02:00:00  5.157  1.741  1.279  0.355  3.777  1.218  27.787001   \n",
       "3  2016-07-01 03:00:00  5.090  1.942  1.279  0.391  3.807  1.279  25.044001   \n",
       "4  2016-07-01 04:00:00  5.358  1.942  1.492  0.462  3.868  1.279  21.948000   \n",
       "\n",
       "    day_sin       day_cos  year_sin  year_cos  \n",
       "0  0.866025  5.000000e-01  0.010486 -0.999945  \n",
       "1  0.965926  2.588190e-01  0.009770 -0.999952  \n",
       "2  1.000000  7.440628e-12  0.009053 -0.999959  \n",
       "3  0.965926 -2.588190e-01  0.008336 -0.999965  \n",
       "4  0.866025 -5.000000e-01  0.007619 -0.999971  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1a3c19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjBhRp31K0qxZszRz5sy6lO81tuzPr3L95sw83XNl90aqBgDQHKQdOFrl+i3783XrwIhGqsYOXnNGLTg4WD4+PuXOdOXm5pY7I1aVIUOGKDPzf88WhYWF1Xqf06dPV1FRkWs5dKjpJfxh3YKrXD88KqSRKgEANBexXau+/7m6301NkdcENX9/f8XExGjt2rVu7WvXrtXQoUNrvJ/09HSFh4e7XsfGxpbb50cffVTlPgMCAhQUFOS2NDX3XRVV5XrOpgEA6lv8oM7ybVHxFS3fFo5mdzZN8rJLn9OmTVNCQoIGDhyo2NhYvfLKK8rKytLUqVMlnT3T9cMPP+iNN96QJD377LPq0qWLevfurdLSUr311ltavny5li9f7trnAw88oBEjRmjOnDm64YYb9MEHH+jjjz/Wp59+6pEx2iQ5cUiFDw4kJw7xQDUAgOZgxb3DNG7+lgqf+myOvCqoxcfH6+jRo3riiSeUnZ2tPn36aNWqVYqMjJQkZWdnu82pVlpaqoceekg//PCDWrVqpd69e2vlypW69tprXX2GDh2qZcuW6U9/+pMee+wxdevWTcnJyRo8eHCjj882g7u118HZ12nB+n3anJmn4VEhnEkDADSoXr9yat+T1+qd7Ye0ZX++hnULbpZn0s7xqnnUbNVU51EDAAANo8nNowYAANDcENQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALCUr6cLwIX7Lu+4vv/XSXVp31pdg1t7uhwAAKy3cW+uMg4XakDnthoeFeLpcipFUPNihSdLdf/SDG3KzHO1jYgK0bzb+ssZ6OfBygAAsNP3R0/oxvlbVHCyzNXWNtBPK+69XBHtAz1YWcW87tLnggUL1LVrV7Vs2VIxMTHavHlzpX3fe+89XX311QoJCVFQUJBiY2P14YcfuvVZvHixHA5HueXnn39u6KFcsPuXZmjLvny3ti378vWHpekeqggAALudH9IkqeBkmcbN/9RDFVXNq4JacnKykpKS9Oijjyo9PV3Dhw/XNddco6ysrAr7b9q0SVdffbVWrVqlHTt26Morr9T111+v9HT3IBMUFKTs7Gy3pWXLlo0xpDr7Lu+4NmXm6bQxbu2njdGmzDwdyD/hocoAALDTxr255ULaOQUny7T5F1eobOFVlz7nzp2rKVOm6Pe//70k6dlnn9WHH36oF198UbNmzSrX/9lnn3V7/eSTT+qDDz7QP//5T/Xv39/V7nA4FBYW1qC117fv/3WyyvUHj57gfjUAAH4h43Bhlet3ZhVYd7+a15xRKy0t1Y4dOxQXF+fWHhcXp9TU1Brt48yZMzp27JjatWvn1n78+HFFRkaqU6dOGjt2bLkzbucrKSlRcXGx29LYIttVfR29S3tCGgAAv/TrThdXuX5A57aNU0gteE1Qy8/P1+nTpxUaGurWHhoaqpycnBrt4+mnn9aJEyc0fvx4V1uPHj20ePFirVixQkuXLlXLli01bNgwZWZmVrqfWbNmyel0upaIiIi6DeoCXBJykUZEhcjH4XBr93E4NCIqhLNpAACc54roDmpbycN2bQP9rDubJnlRUDvHcV4wMcaUa6vI0qVLNWPGDCUnJ6tDhw6u9iFDhuiOO+7QZZddpuHDh+sf//iHLr30Us2bN6/SfU2fPl1FRUWu5dChQ3Uf0AWYd1t/Dese7NY2rHuw5t3Wv5ItAABo3lbce3m5sHbuqU8bec09asHBwfLx8Sl39iw3N7fcWbbzJScna8qUKXrnnXc0atSoKvu2aNFCv/nNb6o8oxYQEKCAgICaF99AnIF+emPKIB3IP6GDR08wjxoAANWIaB+o9D/HaXNmnnZmFVg/j5rXnFHz9/dXTEyM1q5d69a+du1aDR06tNLtli5dqsmTJ+vtt9/WddddV+37GGOUkZGh8PDwC665sXQNbq0rozsQ0gAAqKHhUSF64KpLrQ5pkhedUZOkadOmKSEhQQMHDlRsbKxeeeUVZWVlaerUqZLOXpL84Ycf9MYbb0g6G9LuvPNOPffccxoyZIjrbFyrVq3kdDolSTNnztSQIUMUFRWl4uJiPf/888rIyND8+fM9M0gAAID/z6uCWnx8vI4ePaonnnhC2dnZ6tOnj1atWqXIyEhJUnZ2ttucai+//LJOnTqle++9V/fee6+rfdKkSVq8eLEkqbCwUHfffbdycnLkdDrVv39/bdq0SYMGDWrUsQEAAJzPYcx5M6ai1oqLi+V0OlVUVKSgoCBPlwMAACxX0+zgNfeoAQAANDcENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxVL0GtuLhY77//vvbs2VMfuwMAAIDqGNTGjx+vF154QZL0008/aeDAgRo/frz69eun5cuX12uBAAAAzVWdgtqmTZs0fPhwSVJKSoqMMSosLNTzzz+v//qv/6rXAgEAAJqrOgW1oqIitWvXTpK0Zs0a3XLLLQoMDNR1112nzMzMei0QAACguapTUIuIiFBaWppOnDihNWvWKC4uTpJUUFCgli1b1muBAAAAzZVvXTZKSkrSxIkTddFFFykyMlIjR46UdPaSaN++feuzPgAAgGarTkHtnnvu0aBBg3To0CFdffXVatHi7Im5Sy65hHvUAAAA6onDGGMuZAfnNnc4HPVSkDcqLi6W0+lUUVGRgoKCPF0OAACwXE2zQ53nUXvjjTfUt29ftWrVSq1atVK/fv305ptv1nV3AAAAOE+dLn3OnTtXjz32mO677z4NGzZMxhht2bJFU6dOVX5+vv74xz/Wd50AAADNTp0ufXbt2lUzZ87UnXfe6db++uuva8aMGTpw4EC9FegNuPQJAABqo0EvfWZnZ2vo0KHl2ocOHars7Oy67BIAAADnqVNQ6969u/7xj3+Ua09OTlZUVNQFFwUAAIA63qM2c+ZMxcfHa9OmTRo2bJgcDoc+/fRTrVu3rsIABwAAgNqr0xm1W265RZ999pmCg4P1/vvv67333lNwcLC2bdumm266qb5rBAAAaJYueB418DABAAConQZ9mGDVqlX68MMPy7V/+OGHWr16dV12CQAAgPPUKag98sgjOn36dLl2Y4weeeSRCy4KAAAAdQxqmZmZ6tWrV7n2Hj16aN++fRdcFAAAAOoY1JxOp7777rty7fv27VPr1q0vuCgAAADUMaiNGzdOSUlJ2r9/v6tt3759evDBBzVu3Lh6Kw4AAKA5q1NQe+qpp9S6dWv16NFDXbt2VdeuXdWzZ0+1b99ef//73+u7RgAAgGapzpc+U1NTtXLlSt1zzz168MEHtW7dOn3yySe6+OKL67lEdwsWLFDXrl3VsmVLxcTEaPPmzVX237hxo2JiYtSyZUtdcskleumll8r1Wb58uXr16qWAgAD16tVLKSkpDVV+nWzcm6vn1n2rzZl5ni4FAIBm4bu841q/N1cH8k94tI46/WUCSXI4HIqLi1NcXFylffr27atVq1YpIiKirm/jJjk5WUlJSVqwYIGGDRuml19+Wddcc42+/vprde7cuVz/AwcO6Nprr1ViYqLeeustbdmyRffcc49CQkJ0yy23SJLS0tIUHx+vv/zlL7rpppuUkpKi8ePH69NPP9XgwYPrpe66+v7oCd04f4sKTpa52toG+mnFvZcron2gBysDAKBpKjxZqvuXZmjTL06OjIgK0bzb+ssZ6Nfo9TTohLdt2rTRrl27dMkll9TL/gYPHqwBAwboxRdfdLX17NlTN954o2bNmlWu/8MPP6wVK1Zoz549rrapU6dq165dSktLkyTFx8eruLjYbf63MWPGqG3btlq6dGmN6mqoCW/7P/GRW0g7p22gn9L/XHlABgAAdXPnwm3asi9fp38Rj3wcDg3rHqw3pgyqt/dp0AlvPaG0tFQ7duwodwYvLi5OqampFW6TlpZWrv/o0aO1fft2lZWVVdmnsn1KUklJiYqLi92W+rZxb26FIU2SCk6WcRkUAIB69l3ecW3KzHMLaZJ02hhtyszzyGVQrwlq+fn5On36tEJDQ93aQ0NDlZOTU+E2OTk5FfY/deqU8vPzq+xT2T4ladasWXI6na6lvi7t/lLG4cIq1+/MKqj39wQAoDn7/l8nq1x/8ChBrVoOh8PttTGmXFt1/c9vr+0+p0+frqKiItdy6NChGtdfU7/udHGV6wd0blvv7wkAQHMW2a7q+7+7tG/8uWLr/DBBYwsODpaPj0+5M125ubnlzoidExYWVmF/X19ftW/fvso+le1TkgICAhQQEFCXYdTYFdEd1DbQr9J71IZHhTTo+wMA0NxcEnKRRkSFVHqPWtfgxg9qXnNGzd/fXzExMVq7dq1b+9q1azV06NAKt4mNjS3X/6OPPtLAgQPl5+dXZZ/K9tmYVtx7udqe94TJuac+AQBA/Zt3W38N6x7s1jase7Dm3dbfMwWZOvjuu+9q1G/JkiXm+PHjdXmLCi1btsz4+fmZhQsXmq+//tokJSWZ1q1bm4MHDxpjjHnkkUdMQkKCW52BgYHmj3/8o/n666/NwoULjZ+fn3n33XddfbZs2WJ8fHzM7NmzzZ49e8zs2bONr6+v2bp1a43rKioqMpJMUVFRvY31lzZ9m2ue/Xiv2fRtboPsHwAAuPsu77j55JsfzXd59Zdjfqmm2aFOQa1FixZm5MiR5s033zQ//fRTnQqsq/nz55vIyEjj7+9vBgwYYDZu3OhaN2nSJHPFFVe49d+wYYPp37+/8ff3N126dDEvvvhiuX2+8847Jjo62vj5+ZkePXqY5cuX16qmhg5qAACgaalpdqjTPGq7d+/WokWLtGTJEpWUlCg+Pl5TpkzRoEH1N7+IN2moedQAAEDT1KDzqPXp00dz587VDz/8oNdee005OTm6/PLL1bt3b82dO1d5eczxBQAAcKEu6GECX19f3XTTTfrHP/6hOXPmaP/+/XrooYfUqVMn3XnnncrOzq6vOgEAAJqdCwpq27dv1z333KPw8HDNnTtXDz30kPbv369PPvlEP/zwg2644Yb6qhMAAKDZqdM8anPnztVrr72mvXv36tprr9Ubb7yha6+9Vi1anM19Xbt21csvv6wePXrUa7EAAADNSZ2C2osvvqjf/e53uuuuuxQWFlZhn86dO2vhwoUXVBwAAEBzVqenPuGOpz4BAEBt1DQ7XNCfkDp58qSysrJUWlrq1t6vX78L2S0AAABUx6CWl5enyZMna82aNRWuP3369AUVBQAAgDo+9ZmUlKTCwkJt3bpVrVq10po1a/T6668rKipKK1asqO8aAQAAmqU6nVH75JNP9MEHH+g3v/mNWrRoocjISF199dUKCgrSrFmzdN1119V3nQAAAM1Onc6onThxQh06dJAktWvXzvWXCPr27audO3fWX3UAAADNWJ2CWnR0tPbu3StJ+vWvf62XX35ZP/zwg1566SWFh4fXa4EAAADNVZ0ufSYlJbn+PNTjjz+u0aNH66233pK/v79ef/31ei0QAACguaqXedROnjypb775Rp07d1ZwcHB91OVVmEcNAADURr3PozZt2rQav/ncuXNr3BcAAAAVq3FQS09Pd3u9Y8cOnT59WtHR0ZKkb7/9Vj4+PoqJianfCgEAAJqpGge19evXu/577ty5atOmjV5//XW1bdtWklRQUKC77rpLw4cPr/8qAQAAmqE63aP2q1/9Sh999JF69+7t1r57927FxcXpyJEj9VagN+AeNQAAUBs1zQ51mp6juLhYP/74Y7n23NxcHTt2rC67BAAAwHnqFNRuuukm3XXXXXr33Xd1+PBhHT58WO+++66mTJmim2++ub5rBAAAaJbqNI/aSy+9pIceekh33HGHysrKzu7I11dTpkzRU089Va8FAgAANFcXNI/aiRMntH//fhlj1L17d7Vu3bo+a/Ma3KMGAABqo97nUatI69at1a9fvwvZBQAAACpRp3vUAAAA0PAIagAAAJYiqAEAAFiKoAYAAGApghoAAIClCGoAAACWIqgBAABYiqAGAABgKYIaAACApQhqAAAAliKoAQAAWIqgBgAAYCmCGgAAgKUIagAAAJYiqAEAAFiKoAYAAGApghoAAIClCGoAAACWIqgBAABYiqAGAABgKYIaAACApQhqAAAAliKoAQAAWIqgBgAAYCmCGgAAgKUIagAAAJbymqBWUFCghIQEOZ1OOZ1OJSQkqLCwsNL+ZWVlevjhh9W3b1+1bt1aHTt21J133qkjR4649Rs5cqQcDofbMmHChAYeDQAAQPW8JqjdfvvtysjI0Jo1a7RmzRplZGQoISGh0v4nT57Uzp079dhjj2nnzp1677339O2332rcuHHl+iYmJio7O9u1vPzyyw05FAAAgBrx9XQBNbFnzx6tWbNGW7du1eDBgyVJr776qmJjY7V3715FR0eX28bpdGrt2rVubfPmzdOgQYOUlZWlzp07u9oDAwMVFhbWsIMAAADW2Lg3VxmHCzWgc1sNjwrxdDmV8oqglpaWJqfT6QppkjRkyBA5nU6lpqZWGNQqUlRUJIfDoYsvvtitfcmSJXrrrbcUGhqqa665Ro8//rjatGlT6X5KSkpUUlLiel1cXFy7AQEAAI/4/ugJ3Th/iwpOlrna2gb6acW9lyuifaAHK6uYV1z6zMnJUYcOHcq1d+jQQTk5OTXax88//6xHHnlEt99+u4KCglztEydO1NKlS7VhwwY99thjWr58uW6++eYq9zVr1izXvXJOp1MRERG1GxAAAPCI80OaJBWcLNO4+Z96qKKqeTSozZgxo9yN/Ocv27dvlyQ5HI5y2xtjKmw/X1lZmSZMmKAzZ85owYIFbusSExM1atQo9enTRxMmTNC7776rjz/+WDt37qx0f9OnT1dRUZFrOXToUC1HDgAAGtvGvbnlQto5BSfLtDkzr5Erqp5HL33ed9991T5h2aVLF33xxRf68ccfy63Ly8tTaGholduXlZVp/PjxOnDggD755BO3s2kVGTBggPz8/JSZmakBAwZU2CcgIEABAQFV7gcAANgl43Bhlet3ZhVYd7+aR4NacHCwgoODq+0XGxuroqIibdu2TYMGDZIkffbZZyoqKtLQoUMr3e5cSMvMzNT69evVvn37at/rq6++UllZmcLDw2s+EAAAYL1fd7q4yvUDOrdtnEJqwSvuUevZs6fGjBmjxMREbd26VVu3blViYqLGjh3r9iBBjx49lJKSIkk6deqU/s//+T/avn27lixZotOnTysnJ0c5OTkqLS2VJO3fv19PPPGEtm/froMHD2rVqlW69dZb1b9/fw0bNswjYwUAAA3jiugOahvoV+G6toF+1p1Nk7wkqElnn8zs27ev4uLiFBcXp379+unNN99067N3714VFRVJkg4fPqwVK1bo8OHD+vWvf63w8HDXkpqaKkny9/fXunXrNHr0aEVHR+v+++9XXFycPv74Y/n4+DT6GAEAQMNace/l5cLauac+beQwxhhPF+HtiouL5XQ6VVRUVO09cAAAwPM2Z+ZpZ1aBx+ZRq2l28Ip51AAAAOrT8KgQKy91ns9rLn0CAAA0NwQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAs5TVBraCgQAkJCXI6nXI6nUpISFBhYWGV20yePFkOh8NtGTJkiFufkpIS/eEPf1BwcLBat26tcePG6fDhww04EgAAgJrxmqB2++23KyMjQ2vWrNGaNWuUkZGhhISEarcbM2aMsrOzXcuqVavc1iclJSklJUXLli3Tp59+quPHj2vs2LE6ffp0Qw0FAABU44V1mbrtlTQtWL/P06V4lMMYYzxdRHX27NmjXr16aevWrRo8eLAkaevWrYqNjdU333yj6OjoCrebPHmyCgsL9f7771e4vqioSCEhIXrzzTcVHx8vSTpy5IgiIiK0atUqjR49ukb1FRcXy+l0qqioSEFBQbUfIAAAkCSl7svT7f+9rVx7cuIQDe7W3gMVNYyaZgevOKOWlpYmp9PpCmmSNGTIEDmdTqWmpla57YYNG9ShQwddeumlSkxMVG5urmvdjh07VFZWpri4OFdbx44d1adPn2r3CwAA6l9FIU2S4l/d2siV2MErglpOTo46dOhQrr1Dhw7KycmpdLtrrrlGS5Ys0SeffKKnn35an3/+uX7729+qpKTEtV9/f3+1bdvWbbvQ0NAq91tSUqLi4mK3BQAAXJgX1mVWub45Xgb1aFCbMWNGuZv9z1+2b98uSXI4HOW2N8ZU2H5OfHy8rrvuOvXp00fXX3+9Vq9erW+//VYrV66ssq7q9jtr1izXQw1Op1MRERE1HDEAAKjMlv35Va7fnJnXSJXYw9eTb37fffdpwoQJVfbp0qWLvvjiC/3444/l1uXl5Sk0NLTG7xceHq7IyEhlZp5N7GFhYSotLVVBQYHbWbXc3FwNHTq00v1Mnz5d06ZNc70uLi4mrAEAcIGGdQtW2nf/qnT98KiQRqzGDh4NasHBwQoODq62X2xsrIqKirRt2zYNGjRIkvTZZ5+pqKioykB1vqNHj+rQoUMKDw+XJMXExMjPz09r167V+PHjJUnZ2dnavXu3/va3v1W6n4CAAAUEBNT4fQEAQPXuuypKf1/7baXr77myeyNWYwevuEetZ8+eGjNmjBITE7V161Zt3bpViYmJGjt2rNsTnz169FBKSook6fjx43rooYeUlpamgwcPasOGDbr++usVHBysm266SZLkdDo1ZcoUPfjgg1q3bp3S09N1xx13qG/fvho1apRHxgoAQHOWnDikVu1NnUfPqNXGkiVLdP/997ue0Bw3bpxeeOEFtz579+5VUVGRJMnHx0dffvml3njjDRUWFio8PFxXXnmlkpOT1aZNG9c2zzzzjHx9fTV+/Hj99NNPuuqqq7R48WL5+Pg03uAAAIAkaXC39jo4+zotWL9PmzPzNDwqpFmeSTvHK+ZRsx3zqAEAgNpoUvOoAQAANEcENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALCUr6cLAOoqeVuW0g4c1bBuwbp1YISnywEAr/TCukxt2Z+v4VEhuufK7p4uB+dxGGOMp4vwdsXFxXI6nSoqKlJQUJCny2nyvjxcqJsWpOrUmf/90fVt4dCKe4ep16+cHqwMALxH6r483f7f28q1JycO0eBu7T1QUfNS0+zApU94nfNDmiSdOmM0bv4WD1UEAN6nopAmSfGvbm3kSlAVghq8SvK2rHIh7ZxTZ4ze2X6okSsCAO/zwrrMKtcvWL+vkSpBdQhq8CppB45WuX7L/vxGqgQAvFd135WbM/MaqRJUh6AGrxLbter7JoZ1C26kSgDAe1X3XTk8KqSRKkF1CGrwKvGDOsu3haPCdb4tHDz9CQA1cN9VUVWu5+lPexDU4HVW3DusXFg799QnAKBmkhOH1KodnsH0HPWA6Tk8453th7Rlfz7zqAHABViwfp82Z+Yxj1oja3LTcxQUFCghIUFOp1NOp1MJCQkqLCyschuHw1Hh8tRTT7n6jBw5stz6CRMmNPBoUB9uHRihZ+P7E9IA4ALcc2V3Lb07lpBmKa/5ywS33367Dh8+rDVr1kiS7r77biUkJOif//xnpdtkZ2e7vV69erWmTJmiW265xa09MTFRTzzxhOt1q1at6rFyAACAuvGKoLZnzx6tWbNGW7du1eDBgyVJr776qmJjY7V3715FR0dXuF1YWJjb6w8++EBXXnmlLrnkErf2wMDAcn0BAAA8zSsufaalpcnpdLpCmiQNGTJETqdTqampNdrHjz/+qJUrV2rKlCnl1i1ZskTBwcHq3bu3HnroIR07dqzeagcAAKgrrzijlpOTow4dOpRr79Chg3Jycmq0j9dff11t2rTRzTff7NY+ceJEde3aVWFhYdq9e7emT5+uXbt2ae3atZXuq6SkRCUlJa7XxcXFNRwJAABAzXn0jNqMGTMqveH/3LJ9+3ZJZx8MOJ8xpsL2iixatEgTJ05Uy5Yt3doTExM1atQo9enTRxMmTNC7776rjz/+WDt37qx0X7NmzXI91OB0OhURwc3sAACg/nn0jNp9991X7ROWXbp00RdffKEff/yx3Lq8vDyFhoZW+z6bN2/W3r17lZycXG3fAQMGyM/PT5mZmRowYECFfaZPn65p06a5XhcXFxPWAABAvfNoUAsODlZwcPV/8ic2NlZFRUXatm2bBg0aJEn67LPPVFRUpKFDh1a7/cKFCxUTE6PLLrus2r5fffWVysrKFB4eXmmfgIAABQQEVLsvAACAC+EVDxP07NlTY8aMUWJiorZu3aqtW7cqMTFRY8eOdXvis0ePHkpJSXHbtri4WO+8845+//vfl9vv/v379cQTT2j79u06ePCgVq1apVtvvVX9+/fXsGHMcg8AADzLK4KadPbJzL59+youLk5xcXHq16+f3nzzTbc+e/fuVVFRkVvbsmXLZIzRbbfdVm6f/v7+WrdunUaPHq3o6Gjdf//9iouL08cffywfH58GHQ8AAEB1+BNS9YA/IQUAAGqjyf0JKQAAgOaGoAYAAGApghoAAIClCGoAAACWIqgBAABYiqAGAABgKYIaAACApQhqAAAAliKoAQAAWIqgBgAAYCmCGgAAgKUIagAAAJYiqAEAAFiKoAYAAGApghoAAIClCGoAAACWIqgBAABYiqAGAABgKV9PFwA0d9/lHdf3/zqpLu1bq2twa0+XA6CG+OyiMRDUAA8pPFmq+5dmaFNmnqttRFSI5t3WX85APw9WBqAqfHbRmLj0CXjI/UsztGVfvlvbln35+sPSdA9VBKAm+OyiMRHUAA/4Lu+4NmXm6bQxbu2njdGmzDwdyD/hocoAVIXPLhobQQ3wgO//dbLK9QeP8mUP2IjPLhobQQ3wgMh2gVWu79KeG5MBG/HZRWMjqAEecEnIRRoRFSIfh8Ot3cfh0IioEJ4gAyzFZxeNjaAGeMi82/prWPdgt7Zh3YM177b+HqoIQE3w2UVjchhz3h2RqLXi4mI5nU4VFRUpKCjI0+XAyxzIP6GDR08wFxPgZfjs4kLUNDswjxrgYV2D+ZIHvBGfXTQGLn0CAABYiqAGAABgKYIaAACApQhqAAAAliKoAQAAWIqgBgAAYCmCGgAAgKUIagAAAJYiqAEAAFiKoAYAAGApghoAAIClCGoAAACWIqgBAABYytfTBQBo+jbuzVXG4UIN6NxWw6NCPF0OGhjHG6g/BDUADeb7oyd04/wtKjhZ5mprG+inFfderoj2gR6sDA2B4w3UPy59Amgw5//SlqSCk2UaN/9TD1WEhsTxBuofQQ1Ag9i4N7fcL+1zCk6WaXNmXiNXhIbE8QYaBkENQIPIOFxY5fqdWQWNUwgaBccbaBgENQAN4tedLq5y/YDObRunEDQKjjfQMAhqABrEFdEd1DbQr8J1bQP9eBqwieF4Aw2DoAagway49/Jyv7zPPQWIpofjDdQ/hzHGeLqImvjrX/+qlStXKiMjQ/7+/iosLKx2G2OMZs6cqVdeeUUFBQUaPHiw5s+fr969e7v6lJSU6KGHHtLSpUv1008/6aqrrtKCBQvUqVOnGtdWXFwsp9OpoqIiBQUF1WV4QJO2OTNPO7MKmFermeB4A9WraXbwmjNqpaWluvXWW/Xv//7vNd7mb3/7m+bOnasXXnhBn3/+ucLCwnT11Vfr2LFjrj5JSUlKSUnRsmXL9Omnn+r48eMaO3asTp8+3RDDAJql4VEheuCqS5vdL+2Ne3P13Lpvm90Tj831eAMNwWvOqJ2zePFiJSUlVXtGzRijjh07KikpSQ8//LCks2fPQkNDNWfOHP3bv/2bioqKFBISojfffFPx8fGSpCNHjigiIkKrVq3S6NGja1QTZ9QA/BITvwKoTpM7o1ZbBw4cUE5OjuLi4lxtAQEBuuKKK5SamipJ2rFjh8rKytz6dOzYUX369HH1AYDaYuJXAPWlyQa1nJwcSVJoaKhbe2hoqGtdTk6O/P391bZt20r7VKSkpETFxcVuCwBITPwKoH55NKjNmDFDDoejymX79u0X9B4Oh8PttTGmXNv5qusza9YsOZ1O1xIREXFBNQJoOpj4FUB98ugfZb/vvvs0YcKEKvt06dKlTvsOCwuTdPasWXh4uKs9NzfXdZYtLCxMpaWlKigocDurlpubq6FDh1a67+nTp2vatGmu18XFxYQ1AJKY+BVA/fJoUAsODlZwcHCD7Ltr164KCwvT2rVr1b9/f0lnnxzduHGj5syZI0mKiYmRn5+f1q5dq/Hjx0uSsrOztXv3bv3tb3+rdN8BAQEKCAhokLoBeLdzE79WdPmTiV8B1JbX3KOWlZWljIwMZWVl6fTp08rIyFBGRoaOHz/u6tOjRw+lpKRIOnvJMykpSU8++aRSUlK0e/duTZ48WYGBgbr99tslSU6nU1OmTNGDDz6odevWKT09XXfccYf69u2rUaNGeWScALwfE78CqC8ePaNWG3/+85/1+uuvu16fO0u2fv16jRw5UpK0d+9eFRUVufr853/+p3766Sfdc889rglvP/roI7Vp08bV55lnnpGvr6/Gjx/vmvB28eLF8vHxaZyBAWhyItoHKv3PcUz8CuCCed08ajZiHjUAAFAbzX4eNQAAAG9HUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABLEdQAAAAs5evpApoCY4wkqbi42MOVAAAAb3AuM5zLEJUhqNWDY8eOSZIiIiI8XAkAAPAmx44dk9PprHS9w1QX5VCtM2fO6MiRI2rTpo0cDke977+4uFgRERE6dOiQgoKC6n3/tmG8TVdzGqvEeJs6xtu0NfR4jTE6duyYOnbsqBYtKr8TjTNq9aBFixbq1KlTg79PUFBQs/hwnMN4m67mNFaJ8TZ1jLdpa8jxVnUm7RweJgAAALAUQQ0AAMBSBDUvEBAQoMcff1wBAQGeLqVRMN6mqzmNVWK8TR3jbdpsGS8PEwAAAFiKM2oAAACWIqgBAABYiqAGAABgKYKaBf76179q6NChCgwM1MUXX1yjbYwxmjFjhjp27KhWrVpp5MiR+uqrr9z6lJSU6A9/+IOCg4PVunVrjRs3TocPH26AEdROQUGBEhIS5HQ65XQ6lZCQoMLCwiq3cTgcFS5PPfWUq8/IkSPLrZ8wYUIDj6Z6dRnv5MmTy41lyJAhbn2ayvEtKyvTww8/rL59+6p169bq2LGj7rzzTh05csStny3Hd8GCBeratatatmypmJgYbd68ucr+GzduVExMjFq2bKlLLrlEL730Urk+y5cvV69evRQQEKBevXopJSWlocqvtdqM97333tPVV1+tkJAQBQUFKTY2Vh9++KFbn8WLF1f4Wf75558beijVqs1YN2zYUOE4vvnmG7d+TeXYVvSd5HA41Lt3b1cfm4/tpk2bdP3116tjx45yOBx6//33q93Gms+ugcf9+c9/NnPnzjXTpk0zTqezRtvMnj3btGnTxixfvtx8+eWXJj4+3oSHh5vi4mJXn6lTp5pf/epXZu3atWbnzp3myiuvNJdddpk5depUA42kZsaMGWP69OljUlNTTWpqqunTp48ZO3ZsldtkZ2e7LYsWLTIOh8Ps37/f1eeKK64wiYmJbv0KCwsbejjVqst4J02aZMaMGeM2lqNHj7r1aSrHt7Cw0IwaNcokJyebb775xqSlpZnBgwebmJgYt342HN9ly5YZPz8/8+qrr5qvv/7aPPDAA6Z169bm+++/r7D/d999ZwIDA80DDzxgvv76a/Pqq68aPz8/8+6777r6pKamGh8fH/Pkk0+aPXv2mCeffNL4+vqarVu3NtawKlXb8T7wwANmzpw5Ztu2bebbb78106dPN35+fmbnzp2uPq+99poJCgoq95n2tNqOdf369UaS2bt3r9s4fvn5a0rHtrCw0G2chw4dMu3atTOPP/64q4+tx9YYY1atWmUeffRRs3z5ciPJpKSkVNnfps8uQc0ir732Wo2C2pkzZ0xYWJiZPXu2q+3nn382TqfTvPTSS8aYsx8qPz8/s2zZMlefH374wbRo0cKsWbOm3muvqa+//tpIcvtBTktLM5LMN998U+P93HDDDea3v/2tW9sVV1xhHnjggfoqtV7UdbyTJk0yN9xwQ6Xrm/rx3bZtm5Hk9kvDhuM7aNAgM3XqVLe2Hj16mEceeaTC/v/5n/9pevTo4db2b//2b2bIkCGu1+PHjzdjxoxx6zN69GgzYcKEeqq67mo73or06tXLzJw50/W6pt9zja22Yz0X1AoKCirdZ1M+tikpKcbhcJiDBw+62mw9tuerSVCz6bPLpU8vdODAAeXk5CguLs7VFhAQoCuuuEKpqamSpB07dqisrMytT8eOHdWnTx9XH09IS0uT0+nU4MGDXW1DhgyR0+mscV0//vijVq5cqSlTppRbt2TJEgUHB6t379566KGHdOzYsXqrvS4uZLwbNmxQhw4ddOmllyoxMVG5ubmudU35+EpSUVGRHA5HuVsBPHl8S0tLtWPHDrf/55IUFxdX6djS0tLK9R89erS2b9+usrKyKvt48jhKdRvv+c6cOaNjx46pXbt2bu3Hjx9XZGSkOnXqpLFjxyo9Pb3e6q6LCxlr//79FR4erquuukrr1693W9eUj+3ChQs1atQoRUZGurXbdmzryqbPLn/r0wvl5ORIkkJDQ93aQ0ND9f3337v6+Pv7q23btuX6nNveE3JyctShQ4dy7R06dKhxXa+//rratGmjm2++2a194sSJ6tq1q8LCwrR7925Nnz5du3bt0tq1a+ul9rqo63ivueYa3XrrrYqMjNSBAwf02GOP6be//a127NihgICAJn18f/75Zz3yyCO6/fbb3f6+nqePb35+vk6fPl3h566yseXk5FTY/9SpU8rPz1d4eHilfTx5HKW6jfd8Tz/9tE6cOKHx48e72nr06KHFixerb9++Ki4u1nPPPadhw4Zp165dioqKqtcx1FRdxhoeHq5XXnlFMTExKikp0ZtvvqmrrrpKGzZs0IgRIyRVfvy9/dhmZ2dr9erVevvtt93abTy2dWXTZ5eg1kBmzJihmTNnVtnn888/18CBA+v8Hg6Hw+21MaZc2/lq0qcuajpeqXzdta1r0aJFmjhxolq2bOnWnpiY6PrvPn36KCoqSgMHDtTOnTs1YMCAGu27php6vPHx8a7/7tOnjwYOHKjIyEitXLmyXECtzX7rqrGOb1lZmSZMmKAzZ85owYIFbusa8/hWpbafu4r6n99el89yY6lrbUuXLtWMGTP0wQcfuIX3IUOGuD0YM2zYMA0YMEDz5s3T888/X3+F10FtxhodHa3o6GjX69jYWB06dEh///vfXUGttvtsbHWtbfHixbr44ot14403urXbfGzrwpbPLkGtgdx3333VPpHWpUuXOu07LCxM0tnEHx4e7mrPzc11pfuwsDCVlpaqoKDA7axLbm6uhg4dWqf3rUpNx/vFF1/oxx9/LLcuLy+v3L9MKrJ582bt3btXycnJ1fYdMGCA/Pz8lJmZWe+/yBtrvOeEh4crMjJSmZmZkprm8S0rK9P48eN14MABffLJJ25n0yrSkMe3IsHBwfLx8Sn3r+Vffu7OFxYWVmF/X19ftW/fvso+tfn5aAh1Ge85ycnJmjJlit555x2NGjWqyr4tWrTQb37zG9fPtidcyFh/aciQIXrrrbdcr5visTXGaNGiRUpISJC/v3+VfW04tnVl1We3Xu94wwWp7cMEc+bMcbWVlJRU+DBBcnKyq8+RI0esudn8s88+c7Vt3bq1xjebT5o0qdzTgJX58ssvjSSzcePGOtd7oS50vOfk5+ebgIAA8/rrrxtjmt7xLS0tNTfeeKPp3bu3yc3NrdF7eeL4Dho0yPz7v/+7W1vPnj2rfJigZ8+ebm1Tp04td0PyNddc49ZnzJgx1txwXpvxGmPM22+/bVq2bFntzdrnnDlzxgwcONDcddddF1LqBavLWM93yy23mCuvvNL1uqkdW2P+9yGKL7/8str3sOXYnk81fJjAls8uQc0C33//vUlPTzczZ840F110kUlPTzfp6enm2LFjrj7R0dHmvffec72ePXu2cTqd5r333jNffvmlue222yqcnqNTp07m448/Njt37jS//e1vrZm+oV+/fiYtLc2kpaWZvn37lpu+4fzxGmNMUVGRCQwMNC+++GK5fe7bt8/MnDnTfP755+bAgQNm5cqVpkePHqZ///5eN95jx46ZBx980KSmppoDBw6Y9evXm9jYWPOrX/2qSR7fsrIyM27cONOpUyeTkZHh9lh/SUmJMcae43tuSoOFCxear7/+2iQlJZnWrVu7nnx75JFHTEJCgqv/uUf8//jHP5qvv/7aLFy4sNwj/lu2bDE+Pj5m9uzZZs+ePWb27NnWTeFQ0/G+/fbbxtfX18yfP7/SaVRmzJhh1qxZY/bv32/S09PNXXfdZXx9fd3CvSfUdqzPPPOMSUlJMd9++63ZvXu3eeSRR4wks3z5clefpnRsz7njjjvM4MGDK9ynrcfWmLPfq+d+t0oyc+fONenp6a4ny23+7BLULDBp0iQjqdyyfv16Vx9J5rXXXnO9PnPmjHn88cdNWFiYCQgIMCNGjCj3L5yffvrJ3HfffaZdu3amVatWZuzYsSYrK6uRRlW5o0ePmokTJ5o2bdqYNm3amIkTJ5Z7xP388RpjzMsvv2xatWpV4dxZWVlZZsSIEaZdu3bG39/fdOvWzdx///3l5h7zhNqO9+TJkyYuLs6EhIQYPz8/07lzZzNp0qRyx66pHN8DBw5U+PP/y8+ATcd3/vz5JjIy0vj7+5sBAwa4ndGbNGmSueKKK9z6b9iwwfTv39/4+/ubLl26VPgPjXfeecdER0cbPz8/06NHD7df9p5Wm/FeccUVFR7HSZMmufokJSWZzp07G39/fxMSEmLi4uJMampqI46ocrUZ65w5c0y3bt1My5YtTdu2bc3ll19uVq5cWW6fTeXYGnP2TH6rVq3MK6+8UuH+bD62584EVvazafNn12HM/787DgAAAFZhHjUAAABLEdQAAAAsRVADAACwFEENAADAUgQ1AAAASxHUAAAALEVQAwAAsBRBDQAAwFIENQA4z8iRI5WUlOTpMtSlSxc9++yzni4DgAf5eroAAEDFPv/8c7Vu3drTZQDwIIIaAFgqJCTE0yUA8DAufQJo1k6cOKE777xTF110kcLDw/X000+7rX/rrbc0cOBAtWnTRmFhYbr99tuVm5srSTLGqHv37vr73//uts3u3bvVokUL7d+/v9r3nzFjhjp37qyAgAB17NhR999/v2vd+Zc+HQ6H/vu//1s33XSTAgMDFRUVpRUrVlzA6AHYjqAGoFn7j//4D61fv14pKSn66KOPtGHDBu3YscO1vrS0VH/5y1+0a9cuvf/++zpw4IAmT54s6Wxw+t3vfqfXXnvNbZ+LFi3S8OHD1a1btyrf+91339Uzzzyjl19+WZmZmXr//ffVt2/fKreZOXOmxo8fry+++ELXXnutJk6cqH/96191GzwA+xkAaKaOHTtm/P39zbJly1xtR48eNa1atTIPPPBAhdts27bNSDLHjh0zxhhz5MgR4+PjYz777DNjjDGlpaUmJCTELF68uNr3f/rpp82ll15qSktLK1wfGRlpnnnmGddrSeZPf/qT6/Xx48eNw+Ewq1evrva9AHgnzqgBaLb279+v0tJSxcbGutratWun6Oho1+v09HTdcMMNioyMVJs2bTRy5EhJUlZWliQpPDxc1113nRYtWiRJ+p//+R/9/PPPuvXWW6t9/1tvvVU//fSTLrnkEiUmJiolJUWnTp2qcpt+/fq5/rt169Zq06aN61IsgKaHoAag2TLGVLn+xIkTiouL00UXXaS33npLn3/+uVJSUiSdvSR6zu9//3stW7ZMP/30k1577TXFx8crMDCw2vePiIjQ3r17NX/+fLVq1Ur33HOPRowYobKyskq38fPzc3vtcDh05syZat8LgHciqAFotrp37y4/Pz9t3brV1VZQUKBvv/1WkvTNN98oPz9fs2fP1vDhw9WjR48Kz15de+21at26tV588UWtXr1av/vd72pcQ6tWrTRu3Dg9//zz2rBhg9LS0vTll19e+OAANAlMzwGg2brooos0ZcoU/cd//Ifat2+v0NBQPfroo2rR4uy/YTt37ix/f3/NmzdPU6dO1e7du/WXv/yl3H58fHw0efJkTZ8+Xd27d3e7lFqVxYsX6/Tp0xo8eLACAwP15ptvqlWrVoqMjKzXcQLwXpxRA9CsPfXUUxoxYoTGjRunUaNG6fLLL1dMTIyks/OYLV68WO+884569eql2bNnl5uK45wpU6aotLS0VmfTLr74Yr366qsaNmyY+vXrp3Xr1umf//yn2rdvXy9jA+D9HKa6mzQAANXasmWLRo4cqcOHDys0NNTT5QBoIghqAHABSkpKdOjQId19990KDw/XkiVLPF0SgCaES58AcAGWLl2q6OhoFRUV6W9/+5vbuiVLluiiiy6qcOndu7eHKgbgTTijBgAN5NixY/rxxx8rXOfn58dDAwCqRVADAACwFJc+AQAALEVQAwAAsBRBDQAAwFIENQAAAEsR1AAAACxFUAMAALAUQQ0AAMBSBDUAAABL/T//Y2VVG0QevQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sample(50).plot.scatter('day_sin','day_cos').set_aspect('equal');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c22aff54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12194, 11), (3484, 11), (1742, 11))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['date'], axis=1)\n",
    "\n",
    "n = len(df)\n",
    "\n",
    "# Split 70:20:10 (train:validation:test)\n",
    "train_df = df[0:int(n*0.7)]\n",
    "val_df = df[int(n*0.7):int(n*0.9)]\n",
    "test_df = df[int(n*0.9):]\n",
    "\n",
    "train_df.shape, val_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "040e1a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.568999767303468 -5.934000015258789 4.690999984741211 -3.3399999141693115\n",
      "46.00699996948242 -4.079999923706056 17.165000915527347 3.025000095367432\n"
     ]
    }
   ],
   "source": [
    "MULL_max_train = np.max(train_df['MULL'])\n",
    "MULL_min_train = np.min(train_df['MULL'])\n",
    "MULL_max_test = np.max(test_df['MULL'])\n",
    "MULL_min_test = np.min(test_df['MULL'])\n",
    "\n",
    "OT_max_train = np.max(train_df['OT'])\n",
    "OT_min_train = np.min(train_df['OT'])\n",
    "OT_max_test = np.max(test_df['OT'])\n",
    "OT_min_test = np.min(test_df['OT'])\n",
    "\n",
    "print(MULL_max_train, MULL_min_train, MULL_max_test, MULL_min_test)\n",
    "print(OT_max_train, OT_min_train, OT_max_test, OT_min_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ec9ecff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "train_scaler = MinMaxScaler()\n",
    "test_scaler = MinMaxScaler()\n",
    "\n",
    "train_scaler.fit(train_df[train_df.columns[:-4]])\n",
    "test_scaler.fit(test_df[test_df.columns[:-4]])\n",
    "\n",
    "\n",
    "train_df[train_df.columns[:-4]] = train_scaler.transform(train_df[train_df.columns[:-4]])\n",
    "val_df[val_df.columns[:-4]] = train_scaler.transform(val_df[val_df.columns[:-4]])\n",
    "test_df[test_df.columns[:-4]] = test_scaler.transform(test_df[test_df.columns[:-4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbca011f",
   "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>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>year_sin</th>\n",
       "      <th>year_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.588227</td>\n",
       "      <td>0.454943</td>\n",
       "      <td>0.599461</td>\n",
       "      <td>0.473673</td>\n",
       "      <td>0.556576</td>\n",
       "      <td>0.613765</td>\n",
       "      <td>0.691018</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.010486</td>\n",
       "      <td>-0.999945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.585130</td>\n",
       "      <td>0.459449</td>\n",
       "      <td>0.596738</td>\n",
       "      <td>0.471006</td>\n",
       "      <td>0.550279</td>\n",
       "      <td>0.620783</td>\n",
       "      <td>0.636233</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.009770</td>\n",
       "      <td>-0.999952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.572743</td>\n",
       "      <td>0.436920</td>\n",
       "      <td>0.591319</td>\n",
       "      <td>0.465748</td>\n",
       "      <td>0.512595</td>\n",
       "      <td>0.586144</td>\n",
       "      <td>0.636233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.440628e-12</td>\n",
       "      <td>0.009053</td>\n",
       "      <td>-0.999959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.571194</td>\n",
       "      <td>0.450437</td>\n",
       "      <td>0.591319</td>\n",
       "      <td>0.468414</td>\n",
       "      <td>0.515693</td>\n",
       "      <td>0.599955</td>\n",
       "      <td>0.581468</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.008336</td>\n",
       "      <td>-0.999965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.577388</td>\n",
       "      <td>0.450437</td>\n",
       "      <td>0.596738</td>\n",
       "      <td>0.473673</td>\n",
       "      <td>0.521990</td>\n",
       "      <td>0.599955</td>\n",
       "      <td>0.519656</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0.007619</td>\n",
       "      <td>-0.999971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HUFL      HULL      MUFL      MULL      LUFL      LULL        OT  \\\n",
       "0  0.588227  0.454943  0.599461  0.473673  0.556576  0.613765  0.691018   \n",
       "1  0.585130  0.459449  0.596738  0.471006  0.550279  0.620783  0.636233   \n",
       "2  0.572743  0.436920  0.591319  0.465748  0.512595  0.586144  0.636233   \n",
       "3  0.571194  0.450437  0.591319  0.468414  0.515693  0.599955  0.581468   \n",
       "4  0.577388  0.450437  0.596738  0.473673  0.521990  0.599955  0.519656   \n",
       "\n",
       "    day_sin       day_cos  year_sin  year_cos  \n",
       "0  0.866025  5.000000e-01  0.010486 -0.999945  \n",
       "1  0.965926  2.588190e-01  0.009770 -0.999952  \n",
       "2  1.000000  7.440628e-12  0.009053 -0.999959  \n",
       "3  0.965926 -2.588190e-01  0.008336 -0.999965  \n",
       "4  0.866025 -5.000000e-01  0.007619 -0.999971  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18c7377a",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HUFL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.625619</td>\n",
       "      <td>0.146785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.591324</td>\n",
       "      <td>6.485937e-01</td>\n",
       "      <td>0.710532</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HULL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.451445</td>\n",
       "      <td>0.142104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.342367</td>\n",
       "      <td>4.414257e-01</td>\n",
       "      <td>0.549563</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUFL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.674532</td>\n",
       "      <td>0.156663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.650094</td>\n",
       "      <td>7.070633e-01</td>\n",
       "      <td>0.768536</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MULL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.490824</td>\n",
       "      <td>0.142757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386803</td>\n",
       "      <td>4.921129e-01</td>\n",
       "      <td>0.600015</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUFL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.423712</td>\n",
       "      <td>0.122714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.345963</td>\n",
       "      <td>3.993393e-01</td>\n",
       "      <td>0.468511</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LULL</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.487091</td>\n",
       "      <td>0.149976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.455286</td>\n",
       "      <td>5.241114e-01</td>\n",
       "      <td>0.579352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OT</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.406786</td>\n",
       "      <td>0.166686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.282309</td>\n",
       "      <td>3.834328e-01</td>\n",
       "      <td>0.494380</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_sin</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.707171</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-1.910140e-14</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_cos</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.707100</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-2.548329e-14</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_sin</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>-0.202106</td>\n",
       "      <td>0.697644</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.854478</td>\n",
       "      <td>-3.946346e-01</td>\n",
       "      <td>0.460379</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_cos</th>\n",
       "      <td>12194.0</td>\n",
       "      <td>-0.074526</td>\n",
       "      <td>0.683352</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.748535</td>\n",
       "      <td>-1.173138e-01</td>\n",
       "      <td>0.573425</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean       std  min       25%           50%       75%  \\\n",
       "HUFL      12194.0  0.625619  0.146785  0.0  0.591324  6.485937e-01  0.710532   \n",
       "HULL      12194.0  0.451445  0.142104  0.0  0.342367  4.414257e-01  0.549563   \n",
       "MUFL      12194.0  0.674532  0.156663  0.0  0.650094  7.070633e-01  0.768536   \n",
       "MULL      12194.0  0.490824  0.142757  0.0  0.386803  4.921129e-01  0.600015   \n",
       "LUFL      12194.0  0.423712  0.122714  0.0  0.345963  3.993393e-01  0.468511   \n",
       "LULL      12194.0  0.487091  0.149976  0.0  0.455286  5.241114e-01  0.579352   \n",
       "OT        12194.0  0.406786  0.166686  0.0  0.282309  3.834328e-01  0.494380   \n",
       "day_sin   12194.0  0.000147  0.707171 -1.0 -0.707107 -1.910140e-14  0.707107   \n",
       "day_cos   12194.0  0.000041  0.707100 -1.0 -0.707107 -2.548329e-14  0.707107   \n",
       "year_sin  12194.0 -0.202106  0.697644 -1.0 -0.854478 -3.946346e-01  0.460379   \n",
       "year_cos  12194.0 -0.074526  0.683352 -1.0 -0.748535 -1.173138e-01  0.573425   \n",
       "\n",
       "          max  \n",
       "HUFL      1.0  \n",
       "HULL      1.0  \n",
       "MUFL      1.0  \n",
       "MULL      1.0  \n",
       "LUFL      1.0  \n",
       "LULL      1.0  \n",
       "OT        1.0  \n",
       "day_sin   1.0  \n",
       "day_cos   1.0  \n",
       "year_sin  1.0  \n",
       "year_cos  1.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "85e4be81",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('data/train.csv', header=True, index=False)\n",
    "val_df.to_csv('data/val.csv', header=True, index=False)\n",
    "test_df.to_csv('data/test.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48748dab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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