RFM Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sifarisci</th>\n",
       "      <th>tarix</th>\n",
       "      <th>Satis (azn)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>556227575</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>557660657</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>556724772</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>505244006</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>546450050</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sifarisci      tarix  Satis (azn)\n",
       "0  556227575 2019-05-18         15.0\n",
       "1  557660657 2019-05-18         30.0\n",
       "2  556724772 2019-05-18         23.0\n",
       "3  505244006 2019-05-18         28.0\n",
       "4  546450050 2019-05-18         26.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('sifarisler.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1999 entries, 0 to 1998\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   sifarisci    1990 non-null   object        \n",
      " 1   tarix        1999 non-null   datetime64[ns]\n",
      " 2   Satis (azn)  1990 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 47.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CcCuAN4nIGgAKwOMAfhsAlFKHReQLAB4AcBrA75S9R2Cv9+jKQ9Q+HXrVYu7rLsLrh4iolShD3EuRhoaG1Pj4eLuTQURERBRLRPYrpYZ0n3FKGyLqSRx/i4jywIIVEfUcjr9FRHnhXIFE1HM4/hYR5YUFKyLqORx/i4jywoIVEfUcjr9FRHlhwYqIeg7H3yKivDB4nYh6DsffIqK8sGBFRG3VrmEPhtcOsiBFRM6xYEVEbcNhD4io2zDGiojahsMeEFG3YcGKiNqGwx4QUbdhwYqI2obDHhBRt2HBiqjDjE3UsWHbvbhwdBc2bLsXYxP1dicpNQ57QETdhsHrRB2k24K9OewBEXUbFqyIOkhUsHenFkY47AERdRMWrChSu8YYIj0Ge/eGMlx3ZUgDxevW49TJ+WLBioy6rdmpGyztr6GuKUQx2Lt7lOG6K0MaKF63HqdOzxeD18mIYwy1ly5IncHe3a8M110Z0kDxuvU4dXq+WLAiIzY7tY//xlafbECh9Y1t66bVGOyvQQAM9tewddPqjniLIztluO7KkAaK163HqdPzxaZAMmKzU/tEvbHtGb2CBakuVobrrgxpoHjdepw6PV+ssepgeY9nxGan9un0NzZKrwzXXRnSQPG69Th1er5YY9Whigju4xhD7dPpb2yUXhmuuzKkgeJ163Hq9HyJUqrdacDQ0JAaHx9vdzI6yoZt92ofvIP9NewZvSLTuju5m2u3CBecgeYbG+OpiIjaT0T2K6WGdJ+xxqpD5dVU1OndXLtFp7+x9ZJ2vIjw5YeovFiw6hDhG2n/gipOnJya872sTUXdOLJ3p+KI5OWX9EXERYGILz9E5caCVQfQ3UirfYJqRTA1faYp10VwH4Om0+n0GoROT3+7JHkRcVUg4ssPUbmxV2AH0N1Ip2YUzjlrnvPxjEw1XgyaNjONOeW6l2ZeOj397ZTkRcTVoId8+SEqN9ZYdQDTDfO5xhQO3Hql022NbFylDZrulG6u7WBbg1DWWqEy1oCUdV+FJem9maRAFJV/9hglKjfWWHWAImuRhtcOcmTvhGwemGWuFSpbDUiZ91VYkvF2bK/juPx3+hg/RN2OBasOUPSNdHjtIPaMXoHvb7uao3xbsHlglnnuq7I1/5Z5X4UleRGxvY7j8s+XH6Jyi20KFJHPAPhVAM8qpX7eW7YYwA4AKwA8DuDdSqkTIiIA/hLAVQBOAvh1pdR38kl672DX+3KzaT4tW61QUNmaf8u8r3Rse2/aXsc2+WePUaLysomx+lsAfwXgc4FlowC+rpTaJiKj3r//AMBbAVzk/bcOwKe8/3ekTonzyMrPZ32ygYoIppXCYBfn1zWbB2aZ42LKVnAv877KyqZA1A35z+PeWeT9uBPu/Z2QRtc6Jc9WI6+LyAoA/xiosToC4E1KqWdEZAmAbyilVonI//b+vjP8vaj1l3Hk9TKNfJ1nWnTrdr0NKtf5VHa9vq86Pf95pL/IfdIJ+78T0uha2fIcNfJ62hir8wOFpR8AON/7exDA0cD3nvKWdZwyxXnkmRbdul1vgxgXk0Sv76tOz38e96si78dluvebdEIaXeukPGcebkEppUQk8YSDInITgJsAYPny5VmT4VyZ4jzyTEvcOsoa11IWSaqmGRdjr9f3VSfnP4/7VVH347GJurYZNo9tBbeZtHmrTM+nonRSntPWWP3QawKE9/9nveV1AMsC37vAWzaHUup2pdSQUmpoYGAgZTLyU6aeUnmmJW4dnRTXUbROGhaA7I1N1LFh2724cHQXNmy7N5fjWcQ22iWP+1UR92P/ek6aBhfbTHoPKdPzqSidlOe0Bau7Adzo/X0jgC8Hlv+aNK0H8FxcfFVZlWmsmDzTolu3bhvd/CBIq5OqpslOEYXlbi+Q53G/KuJ+HBUWkde9P+09pEzPp6J0Up5thlu4E8CbALxCRJ4CcCuAbQC+ICIfBPAEgHd7X/8qmkMtPILmcAsfyCHNhShTT6k80xJct6lXICd91eukqmmyU8Qo9GUc6d6lPO5XRdyPo67bvGLc0t5DyvR8Kkon5dmqV2DeytgrkM7YsO1ebdxBRQQzSpX6BM+Tab8M9tewZ/QK4++K6DLcKd2Sy+bC0V3Q3REFwPe3XV3YNuKOX5rjy3MiWtrrWcd2X0dtc2TjqtIdL55DZ+TRK5BKzHWznentaVqprmzKsJWmappNTeVWRBxH3Dbijl+a48tzIp6rpqYk+9q0zcsvHijd8eI5ZI8Fq5LJWiiyOfmTbsPmodKLsUVpusUXEZfF2K/0iojjiNtG3PFLc3x5TsRzNcxFkn1t2uZ9Dx0r3fHiOWQv83AL5I6LWKa4+I0029BNeaLTi7FFSbvF207YnKW6nbFf6RURxxG3jbjjl+b48pyw42KYi6T7WrfND+04kGgdReA5ZI8FqxJxEdQad/Kn2Ub4QdDnBbiHlbHba9nETVfionDdDVOimBQR41HEGFJR24g7fmmObzefE2XjYl+X8XiVMU1lxabAEnHxRhAXv5GlF8qe0Svw/W1X42PvvrRjur2WzeUX68ds85e7qG7vpG7JSfRKjEfc8UtzfLv1nCgjF/u6jMerjGkqK9ZYlYiLNwJds13w5HexjU7q9lo29z10LHK5i8J1tx6fdg1TUHRPqLjjl+b4dus5UUYu9nUZj1cZ01RWHG6hRFxNMhn1IIjaBsCLxlbah21cV3uXXb67TRFDIYSVZeJXdnMnKpeo4RZYY1Uirt4IouI3TNsAMCe25+YdB/CRrxzGrW+7pJQ38XY9bLLEQcXVGJo6Cpw8dRpjE/VSHoeitCPGowyDeSY930zXRZkKZ2VKCyVnc/zC37n84gHc99Cxnhi/jzVWBMA8UB2Q7A29qBPbdU1CknRnqVW6ZewQPr/3yTnLb1i/HLcNr55Ny5a7D2OyMdXynXbUlJRJO2qP2lFLFpbkfDPto2svG8Rd++ttr3mLSmMvn9udxOb46b4T5vqYF31escaKYkXF8Ni+oRc59Y3LmoSk6c4SBxUXY+Vvc/vuI3MKVu2IJ1pxXg17HzuBaaVQEcH165bNFgCLFlWjm6VAH/XbMvSESnK+ma4LXWHexfmUZr+XoRaQ0rM5flHzLpp+U0S6isJegQQg/kFhU2jI2qMtycClLsdUSZruLKNz26a7HWPG6Hrd7Xn0+OzQGtNK4fN7n8QtY4dyS0MaWXoLxv22DD2hkpxvSc+PLOdT2v3O8ZA6m83xsz2WLo95mc6rnq6ximsDLqJNOC5NRc0BFjcIqOnmfsvYIdy576h2XCufzYmtqzUa+eJBfOQrhzF5cirXmoSkF2Rcz0uTsYm61Rhgt4wd0jY/hb/nms1bJgDcsffJRNeFq3nvTDWL86t9qd9U495ys8Q9urguxybqeOGl03OWm84303VhkuV8SltD4LoWsCxxNb3C5vjZnodJj3nZa5d9PVtjpXvb+vzeJyP/nfeYOe2cA8yfWqG/Vp3zmekm7scLRRWqALsTW3eTnppROHFyKveahKQ1UGmmvvCPk2lf+cHpphgsIP+aEts3O/942JxvLue9Mz3IT5ycmvNdeOuKq/20KVQHx3DbM3qFdaEq63XpryPcJLxoQdV4vumuC5Os51PaGgKX126vjG1WJjbHz+Y8THrMO6F22dezBSvbt/OgvOdFavccYMNrB3Hg1ivxyevWWBUa7tx3NHadtie2zUM9mC9X83oB6S7IpA/buPPtxMkpbN55CH+3T1+oApB7cG/aN7uo883lvHdpqvTjHrZ5Tbrs4ro0nTMLzpoX2et366bVqIhoP6+IZL5efGn3nctrl/PXFc/m+Om+c8P65ZmOedyxdnleZdWzTYFp213zbK8tyxxgtlN6RNVUCZCoWt626jhck+Dioili4DvbgmOUvG8QtnNC6tQnG9rhIFzOe2c6R/prVbx0eiYy3aYmqrTNunFcXJdZZkkAoM2XywdNln3n6totU1xNL7E5fq6nhrKtXS5DM3DPFqySxiIEf5eGTRxAp80BVjHEC1VE8OjWqxKty/ah7ipfuuOR5wCcac+3oLzHsdIVMMO9AudX+/DCKf0x0vWkdDnvnelBvuWaS2bTHbWPdTfmJIXqJLE8Rc8Xp0vb1k2rM70sxOU3r16aSRR1/+v0OK4i0m87blXatJQphipOzxas0rydZ40DiOvOn+YNMK83bhvXr1umjQe6ft2yxOsK36QX1qp44dRpTE2fKbi5yleWYSHS3hRsz7datQ+NqRntZ3kNXREU98YXNT6NrlYo7vy8/OIB7Tmkm1PRphCUpANG+Fh+4ro1xrzbnDPB9fUvqKLaJ5iaSX/+2l7bprRt3bQ69cuC7TWiO1+KHHaliPtfkfnJQxHpH5uoY+RLB2fv136Mss/f5vgTx1vGU0uSlnY+65Lq6QFCi+oVmHSAv3b0Ckwr2CvQ9ThHeeUr7QCfWQegC+bHVHDcumk1xp84bgxgTzu1TdJ9GfX9qAB73cCZ/rrqk43ZWs5Bb52mWqY0+Ywa5BaYOwirrhC2aEFVO9NA3DmjW1+1IjjnrHl4rjG3V6stm+OWxzRIWdZZ9LRMed//On2aqSLSv/ajXzN2IgkytXLYpqVMNYccINSgqPbYJHEAadLUznbl24ZX5zZgZF75ShuXkXUAunB+TDeJ4bWDuGPvk9ohF9LEjiStbQkX+oLfB4C79pt7XOmq5XUxP/46TbVLafIZ95vgIKymwHC/E0Ew3VHr9pdre7VOK5xz9jwcuPXK2f37oR0HEj0QbK6BPOKMsqyz6LinvO9/nR7HVUT6bQpVgDku1zYtZYmhitPTBauidFocQJneCmwkTW/a45H2BhVVgMozRscXVyAMF7zC3fuD3/f/NnnmuQZWjO6arZEKNt/p0mB6g02Tz7g4NtsBDHWF5bjjEXVu5N0Uk8f9Jcs6OykWJkx3rXZyfoByHQ+X13uZ9exwC0VK0p0/yejj4d+5GM+l08aFSZPetOOdpOlennZ/uhyTJU1ti+n7cYVIP6QonE/T76aVcpbPuLFzwgMYRgmPgRV3PKLOjbyHBMh6rujuOVnWWabxhJIwXauXXzzQkfnxFXE8dOMfhtWqFVy/bllH70tbLFgVwHZ8jSyFGlc3704bFyZNetOOdzKycRWqldbxgaoVibwppEmf/9bs1+ggQRp14gqEttXwS/trid4sg/k0/c7Pl4uxZ/zjajPIrc0AhsFrEMBsOoHmm7efv7iCSN5NMVnG7zHdcwCkXmeZxhNKwnSt3vfQsY7Mj6+I47HlmktQ7Wu9N/ahGbMY3OZtw6s7el/a6ungdZdcNJ9lCTK8cHSXNiZHF0xcxHqKUmR6xybqGPniwZaeXtU+wfZ3XWo81knTl8cM7VHrBIAPf+Fg7Oj5we8n6U3r57PomedtrsexiTq23H1Y2/QZFhWkHtw3um2WOfi5zGkrWqfd+8qm00JIsmLwukFUb6Wk471kjaEYm6gb40Ns3mxN7eh9IonGP8qrPT6q92Da4xA3917UhZ7mJrB995GWQhXQnHYnKng96f50NUN7OH8XLJqPh599Yfbz1y9fCADGaXaqfYKXzZ+nnadx/InjsfNDhvNpM+ZRcD+ZzpEk8w3GFQz8GLfgb005imo29Y+PaQT+JN3Ei3445V2blrY3atZ7cpq0RF2rZS40tDNtSbZt6oWf5ViX9bj0bMEqXBjyHxJpCkVZH4Z+WkxsCjWmcZKmlYrsARY+GZOMK2Qr3D1/WqnZfw+9anGq4xA1916tWsHlFw8YC7uAvoeaaVu+NA+hpGOvuHjQ6Qr6YXsePY4DR5/T1jxV5EwtXLA32/bdR3D5xQO4a3/dqlAVzqfNmEe+qHMkfLyyvtgE02WqwfGvQdPLT1TQvM34W0B7xkvKM7A5aX5c3pPTpMV0rUbdS9r9EG/nGFtJtq37bviZkDT9ZR5frGdjrKICdpPGFKW52dqmJckUEaY5woL5iYvjCnZJDzItt2GaU/DOfUdTH4eo371++ULc99AxY2E3bRzZQkOApmk5kDy+IWmAvC7w2DYY3TSC+oxSLQWW4Llyx94njYWx4P9tYyfi0mo6R9LONxiUJmjbNAefvz6T4bWtc0sCsDpuecc35hnYnDQ/Lu/JadJiulaj7iXt1s6Y2CTbtr0nRa0jy/aL1rM1VnE1AFGfh2t8+uRMb6igqJuw7baCJ0rcQ2p47SA+tONA5DbiTsYszZEmptqNaaVSH4eo3+159LjxM9vu+LpaPdPhjDvMScZeSdpslGRsKFt+IU53rpjqqWaUwuMJ41Cimr99UedImvkGg01NgjP5CY5W7k8F4zdRBK+PqJq6rG/aLsf0itt+8Ny+9rJBJwMhhyW9duPyWZ9sYMO2e1MNbmu7Td21GndPbad2jrGVpEIhaXqyjJdWn9QP+VKkni1YxY15E1VDENfM4rNpLrFJS5IqzrTj7YSbyky/D7Np446aU/BnFs5PdRzi9lmU4ANVt62kD77Jk1PO2vptm4387yQZGypMAMyvVoyFuCQ3w6RNR3HN376oc8Tfpm1zVvi4hvdQMFYK0DcXL6j24aRhyiHb5v8ixvTS0QXr1ycbuGt/PZdOBAtrVW3HAFMNr801nbQ50f9+/4KqdhDLTh+Xq51pi7qv69KT5H694KzoHrs262xn02DPNgVGdbeOqgpPUqU5aHly23T9tq3iTDvejv9mrhNXY6JrVvSbWVaM7jI+5K9ftyzVcRibqOOFl05rfxP+vY5Cs1Bh2lbUg09nYa3qdOyvcLOR6aaQZGwonfetXx7ZTGl7c07TdGR7HZnOkeA2bZuzbLbp36hN50DjtL5Q5QvXeurGpCtiTK8w/1qNG/zVpaQ1vDb3QSB5c2JjahpKzb0fdMO4XO1MW1RLRJjtsfWdNIQpJF1nu5oGM9VYicjjAJ4HMA3gtFJqSEQWA9gBYAWAxwG8Wyl1Ilsy3QvWDCTplWD7Fp/k5A7XUsT1TIqqHYmr8TA1NUU9cExvs6ab2Ja7D+Ol0zPGdfYJ8N51y1umwrE9DuHJPk3ES/fNhmp8hWZBQtfcE/Xgq1akZdvVPsFPXpya0xRsU3uRpqdb8HPTW7g/350p70DrvHmmNJo6RARVRFLVdsRdR7p5J03Nc7a1fDbXrl94Nn03riIwrtbT/45piAN//kTXzXJxhco8mo4mDdOcmJab7sk6SZvAnmtM4RPXrUm1b5PUIhetnWkbjDiPbdJp6igFmEMOdOuMus8B7WmyddEUeLlS6keBf48C+LpSapuIjHr//gMH23Em/MDasHIx9j7WLPv94LkX8cXxJ+dc3OH/x3n98oWJH6rBoFbT0Am3jB2aMzv4zTsO4CNfOTxn8ljl5edmr0dXeIqR4EPKpL9Wne0VFrxobxk7ZKyGjRsXaMnCWssD0xSD5L/xB/fRR75yOLZQ5ec97oJ74aXT6JPWHik37ziAPjE/QKdnFBYtqGLy5BQW1qp4/qXT2vg6ID5OL5g+f9tAfE83oHn8TPNz+WmPmvA0bn7H8CClUW+nN+84MDsWlq5ArBtqI6pwoRsmwV9feJ/45/7Vr1sSmR/ArjliWimsGN1l/DxqX9jUet684wBqVX1DgU3PW1NhO64QHvdwcTWkQHAdaYZCGX/iOH7w3Iuz3z3nrIq2k8XS/lqi82ppf23O8Bof0twXo/KztL+GT1y3xnivcl24sR16Inz/vGXs0Oz1KGg2q508NW0c4iRq3VHnm67VwKZCwX8umQpVQGtzYlxFQniolrB2NNlmGiDUq7EaChasROQIgDcppZ4RkSUAvqGUitzTRQ4QaurenYdgrUA4DVEDTb7vr79pDMA2xQcBzZP62ssGWwpe4c/92gWb/VDtE0DQUpCpVSt4/fKFkQHicWwG3DMNxFjEcYuzaEEVE398ZeyM7v73dC76w13QhepU+4CH//RqY+G6v1aNrA30mfaVzaCcWa+R4DbCQ234LnrlOS3javnOOauCP3mHPn2mfRKXBp8pLbaizv1wuk2DTUbRHdu4a9Z0zYfzH7XvbNcRx+a8idqWad9W+gTTM3b3oA0rF+M7Tz6XeB9GzYQR992iBvW1WXfcOZ71GRH1e7+m3Cb2Lc6GlYtxx2+90WrfRuU5z0GIowYIzRpjpQB8TUT2i8hN3rLzlVLPeH//AMD5GbfhVJIYqazu3HdUG2ex5e7D2oEmt9x9GABma890om7Wjalp3LnvqFWX5bj9UJHmAJHh2qHG1HSmQhVg9wZheuMvA78wFTeje9Q7iyH+eXa5qYZhsjEVux9MtZAVEVx7WfMNL2ouyqzXSPA8Mw21oStUAc0hIDbvPIRbxg7NuW6SVOnrYiuyDBkCANdeNojHf6xPQ/+Cs+YMNpmU7tj6+RibqOPDXzio/Vx3zYfzb4pHWbSg6mxIAdN5UxGxGr7AdF+ZmVFz4gBN98i9j52IjBvMOkSA7rtZu/0nHTIlat2m6y342yzPiKjfLzhrnrEAk/Se4l9nNvvWdF2nDVNwIWtT4C8opeoi8koA94jIQ8EPlVJKRLSPF68gdhMALF++PGMy7BXZ3uoPzmndu8xrQrPtTWjaZpS4Luq+GaWMsRBZ2MaelaErc1bPWUyVYpK212NUrd60UnOakXW9Zlzse38dac7lxtQ07tj75JyhEEwxZXFpMP07KdNbvm7dNvFptvz8JwkWDqcpLhbHxZACpu/OKNVSQ23alokC5jQPm5r5p70x2Ey1JkmGk7GN48oy5EHaIVOi4kDj2DwjovZV3PmWZLiLqDQE/2/6POo7/nh87ZCpYKWUqnv/f1ZE/gHAGwD8UESWBJoCnzX89nYAtwPNpsAs6UgiSzf9NJLWulwYEd9hIy4GLK6Lui+vA6ILOtYxpU8i4p+KItK8eUR1vQea+/DVm3dhRumDsaOkeTDHxcvpPtcF2bu4RvpEImOV4uiGQngxYSElXGuUNV9R+1YBWDG6a85xjov/8NWqFcyv9hkLjmkKaOH8R8UYJZ0SS/fwtO36n/Q46Hrj2nT1D6ZxYa2KF06ZexLr8pk1P/45EdURRzdPp82xVmg2gYXvJ7YxwHHrjopPNW1DAXjzx7+Bx390crZFpj7ZwMgXDyZ+KYp7Ti2sVWfjb6Ni+doldVOgiJwjIuf6fwO4EsD9AO4GcKP3tRsBfDlrIl1K2u2zaFkuiVq1glcPLIj8PKqLuq2LXnlOqt/5bIYjMAXyvmYg27YrfXaDtkZRCi2B5FH8Fl9/ipZbxpq/M+1Df7k/CrStSl/8DdX0efjGpTs3qn2CasV+38Wl5aJXnpP4/Et6bZw8dbrlHCvi2g8eZ3/YjH7DuE3SDGGcba567ZJznaZFdw2Zhki5/OIB7b7xa92D+9G0jhXn6R9k4XSYvvfys/XHZv2rF81Zdv26Zdrv+svDaZxsTEV2etHl03Yog7jzSne/i5qSy1bwfuIz7RdXatUKrl+3zJjfh599QRvm8uLUtPW1F/ecqvYJXjh1evbYmvZhlmnYssoSY3U+gH8TkYMAvgVgl1Lq/wLYBuDNIvIwgP/k/bs0oqZ+8W1YuXjOOEcuCdw84MO2blqNx46dNH4ejNdI+uAOitqGLV2cQDDWwBQrkHbbFRHcsH45PvauS1P9PqwxNR1ZW2Xi5+vkKf1vT56amd0PH9pxwHr0/mlT10QL4W3opvbY/q5Lsf2dbvYd0Mxn2vPP1omTUy0PtGC+8vb5vU/ObnfLNZdob7ThAte/P5YtdjFMF3tiilm576FjVlNiRa3DFPcUTofpez95SV9To4tru214NW5Yv7xlKqVgZ6E0cYLhfNpOR2XzTLHZh2mE75Ph/SKYO2ZfFv4o/cn37UzLtRec+uqG9cuN+1h3DHSxvzpZYyqzyNQr0JUiewX64nobJG3G8E9e271Z7ZM5Jfss/G7qNunOkk/X/GlQ8uqtqeuBWIY8R/Uaa0fvR9vpaFzuu8e3XV3IsdAN4VBU7+D+WhW/eukS7Pj20ciHQV7HPNwMZTrv/Osk7vOodZiEr8E099a4XsRhaXplpt2W7Taz7MMocdeuq23Z9kg2STrllYltfrIcSxtRvQJ7ckob3Rg9WecVWlir4vkXT1tX7bosVAFnmj1s2tiDcTUu2uTTCr7h2b7BJU2vKUakneIeLEUXqioJ9pFpUMAy0wW3hsdzy8tkY6olEN8kr2Nen2xg5EsHATTzHBc3ZBNXZPpOVOxNMNYo6TWsm5oobgymtPF0wQFek44XZhO36sdFuYz1vWXsEO576Jhxf7jYVqVPIJL+PF20wDxRvYluXwMwxlSFmeLQitBzU9oE296BM9NIhAtVCwyD+Jk8/5J9oSoPfrOHLh5Bx8+/7ffzENxfthf++lcvShQno4udoFZJ9lHZYhSrfYJFC6rN5nVDU4wpiHV47WAh+Wl3m8DUtMJHvtIcyiXN9EBAa7yaaR1RsTfAmVijJPecakVaYpp09+/guqPSaOPyiweMMWS3jB2KnLrKZpt+XJQpziyNz+99MnJ/uDjHp2eUdfB5+CqsVgS3vu2SRNvTHYORLx7EyJfmBvtH0cWhFaEnaqziRgNuTE3jw184iJu9mJZppYzzWZlkiXFxpTE1jcd/3MAN65fPjkgcxzQujwtxb6bBWBfbt9jHf9zA1k2rW95kVpxXw97HThh/7x9fABh/wm0cS7cwTcETHqx2w8rFs/s/61vwLWOHMtWY9glw3RuaPfB0EwwDZwoNulG6h161WNsrqxv5D8W4YRf8/4f3pf/iFreOoVctjuxRFrxHWQ3WGjo0NuM7BYdbiBsFPuy+h44Zx9nS3VNN24y6NuLGmnLFT5vfDK6r5bv84oE5tV1ZbFi5GO8aWp55FHrdcU7bynPnvqOF11p1fYxVkSOtd5pPXrcmdtqXvIRHxE0SdxHVVh/V/t4HIHm4ee8IxySYZgDwb55lua4WVPswNaPmxDD5I0GPP3Fc+xAPj+jd7ZLEuJhGajdNORQUdy3755ntvTm4zSRxTEFJ4nKAZLWMpm22O5YTSBdnlCbdruKnfC5j0AD36QN6PMaqyJHWO4nAfsgA14IjgPtzEC6yHOckrpdcVDxBlkJV1FRC3SLcZGYaCXvPo8cjawizqIjg5bV5ica8MfXO9EeC9msrw5IUqtoZi+iCbsiHqDiiJANphsXtqz4RXDi6C0v7a7O9zGwmnwfi45hMzb62cUZJY2X9dev2ZdR+6BMY5xh1Kbw/dDW3WcfCytrLVhczlyQNcd+17VntUtfHWHXDCN55aVeB8+x5gh3fPtrSfv7TF09bjZM0rVRkm3leMTOd+0i10wdYjYjvy6uQMaMUbn3bJc15KjOqTzawYnRX5rTWqhVjXFDxt+zkqn2CLde0xrjExRGZ+J1BosSNpTSt1Ow279rfjAH6/rarjQ/oYOEg6vqOmtXB5r7gj4+U9HxZcV5Nuy+jYsmKKFSF94c/p56fv/DYer4kY2HZzqRhYoqZsz0GNrF9eY/tpdP1NVZFj7TeKWyvawFQixlhPCnduqZmFPpr1TkxMjp+s87QqxYb2/Lb1cTZqWbQjD9rd+/JPpFmHFyJSixRYzSZrqOy1HCaejtHzQMX9VDzOzoA5pkThl61GH+390mrGuLg+E7HX3hJ+53gQI/hOCbbXt3+clNMnT83apKaUp+u9jZxLFkOwmNumWK7wjFI/t+mdFdEMKNU6vipoCwtSoOh2L5wbFvS2S5cYowVRRIUVzhNsi0BMD809k+ZxufqZP4Nq12F07IUSrJqx3hkYabYkqwxLFGxVqb4rChR+8omrstW1DhdgNvzLm5ssCxsxi0MH/sk3wXixzxzoR3jjbkSFWPV9QUrwByES/Fsa5HKwn9LaddbIpHvhvXLZ3tclYV/fbhI1yevWwMg/7HAgsHuwRpqv0dbuMaa9/vmcX5061UtMVVJfrv+1Yti4yhNtYXBmKk8hXs16j7PWqMWpacLVn67MiVX7WtOZmYzfQARtfJrUIt4yCRVESDrZV3U/cF/QMa1PNSqFVywaD4efvaFXNPTDkmP14aVi3HhwMsKe/b55zqAUrUQhXufu9TTBauVm7/a0b15iNrJ9dRLREnUqhVce9lg4lqXXjfYX8MPnnux0H3mdz4o20uEy6bkoJ4eboEXI1EGJQoip97TmJq2mhKIWkUNX5HnNsuoHenq+uEW2jCEBVHXYDMwtRvPwOT6ROBgxJLE2+xPMSdg3kxjm+Wp6wtW81iwIiJK5aJXntPuJCRy/rln5bbul59dnjky40wrVchYWeFt2o5HWCSX8zLa6vqClcPhl4iIulKfNAOe/VGqKyLYsHIxTp7S30AH+2uF14jY+NFP8+vBfG7tLGxYudjJuvx95+/nbjE1o3DOWfMyj8bukmkMujx1fcGKiIiiKQW8a2g5fmbhfAiAc+fPw7cePzEnEDlYmLKtESlySpFgTG3Sh1tcKuuTDWfDOARDf599Xj8wap5uWL8cg/01CJqF5BvWL0/0+6iZEZ5rTGHP6BW4Yf3ytkwnE9aOOOuu7xXIgSKJiKItqPZBQRJ1k++WgVyBZuHvZxbOL12Ptk7kjy9VpmGOip6EmTVWRERdJE0TXWNqJvHYQwrFdhqt5Nj2eP26ZbnNM9pL/LkDTdPn9AoWrIg6nOvnTR7PrzI0CfSCG9Yvx8ffvWY2xsV2v6eteVLAbJNSf62aKnB5sL/WEt+lU+kTXP8G95PpVkRww/rluG24OYjk1k2rSxUf1Gn8wTh7fZijrh/Hqg+wmgzUBgdL7B6CZvfgLDeA/loVL51ufdOv9jUnc508OdUyzYZp/jR/VGnT5LBxgoPfpZmjLWzDysU4/PTzxmmMTM0/FcO+NA3O5yKtPt1xqFUrmF/tSzWpblGC08v4Y+1o52aT1pgck8H+2uyEs6aRpl2HRoSPb3DambjrK8m5MT2jcN9Dx4znWdZ0+4bXDs7uO5fnaC8Y7K/N7jtXx6lTdX2N1ce9+ayyWrSgiu3vujRxkF871aqVXLpL59ml2TXTW/DS/hquX2f3Bmx6j56ansHrly+c3YagWYg/cXIKCs1g1807D2Fsoq5tZqhVK7j84gFs3nko1U3Ir3b3uWjK2PPo8TmFKr8Ga7C/hvetX67Nx/pXL9Kub/LkKVw4ugsbtt2LsYm607T6295yzSWzNQ1+MO7WTatx69suybSNaiV5j62zKjKnyarSJ3OCfWvVCj727ktx2/Bq7Bm9At/fdrVxDKDavL7YfFQr0nIumPTXDNuoxm9D5/KLB4yfnTt/nrEGK3zuBpkGdHx6smF9zS6o9qXadlBU3npdeN+G96ntcSpCO3pddn3BanjtID553ZrUTRGD/TV88ro1mPjjKzG8dhC3Da9O3duh6OaQxtQ0Tp6acXZi+dXm+/7ozR3RRbhWreD6dcu0BYGRjausj2Wf4fMXTk1jz6PHZwtFCs0366DG1DS27z7S0swQfPjf99CxRLEtFZGW3wdrJvxtuD7Pliys4fFtV2PP6BW4bXi1Nh+P/1j/MHzh1PScQmYwrf56kiRZtw+G1w7OFlD2jF4xuyzYtBMcSgBoFjIWeYWZ8GeD/TVsf+eluOO33pjoep+emXsOTM8ovGz+vDn7LFyrNGmoXWtMzbTkQ5sSy3L5lmsumVPIq/YJtm56HbZuWm0seJnc99Cx2b/HJurYvPMQ6t6o35ONKUBhzj425d9nGtBxqVcjF3U8/Pv1A//jrdj+zkvnHPu4bZvyphM+Z9KoiOCiV55jvQ5/eIbw9/2efZHNqYF9EOwVmDT9FZHZfWs6n/3jlOVOtGHl4szNshtWLsYdv/XGTOtIo+t7BfouHN2VKI5AV1Ucnl097czZ/g3IpgnJxDY//szwadcT93tf2n2T9LjEqYhgRqmWNNimzXVafFH7MOk2bY6H7vzKwmabtvkwNcHY/j6PSVX986M+2Zhtwhg0nCdZmofieiZFNRcH95nt90zirofg/ojr+Rc8N7KmK7j98Pmb52S6JqZzMu56SHKOBM+J4HEx7fO093OX9yAgWS+7qPMt7T4ug56eK9C3tL+mPdlN8RnhquLwxe6/gQPmeAYT//v+ybawVsULp07PxoPYrNuUH9334j6PWo/tdADB2IQkbPNha0apRAXB4MPUFBeQNV5AoXmz1T2oTfk3bTN8PKJuWnGxLrb5sjkHbI+jqZkn6vo85+x5c/KX10uOvz+C1yCA2AdeHJtagZGNq7QFivC9KKqpzEbctRr+fGyijg/tOKDN+8JADVfWdAW3D8DJ8Q1KUoAGzOdk3PWgO442abP5TZ8IxibqiZ8LUWlOeg9KWosUdb5FpdfmGg9/5/KLB2ZjFl2dN2l0fVOgzxTjYorPCB+M7buPzDnp/WaeNIJNF+ecPW/OnGxx67aJUbGJJYhaj20sQhauuzjrCh7B5on6ZAMjXzyIkS8dnL2g/ZuH7iZiak5MKtwU5jOdl6aYpWDchy5v/jaC59fH3n2pdhs2+bI9B2yPo+kGH3V9hpv4ovKdlO669jWmprHl7sMt20rLpgBrai7WFcZ1TMvHJurYsO1ebaybjeG1g8b4r2B5MWm64rbpH/eRjauwffeR1OkHWq8VoLUAPfLFg9p1ms7JuOvBP45xzaq16pnHb9R5GDStVOS5nibNpt9EhVG4Ehd7GnWN6+4Dn9/7pJP7QlY9U7CKummF4zPGnziOlZu/ihWju7By81dxy9ghZ29jSdYRtW5dfsKj6dpUnZviUJLEImQRzkd/rZq6XV530etuWFMzKnJy4XAMjx9XZBN/Uu2T2ZiSMF1h2XRePvDM89p1/N2+J2dvFLaF/eG1g7j2ssGWmJBrLxvUxkulOYd0+dB1vY+6KdsWKpLk20bc9TvZmHLSpGr7lq+LFQtL8vB0VQg1xX8Fl6ctiERxlf6ogsvUjMKWuw/PWZ7knNT99sCtV+KT163R3jf6AGzd9LrZfyd5jkSd62nSbPqNKZ7S5TMhSexpON82hdEslR9Z9ExTIGCukgxWJ86v9qERmGBwWil8fu+TOOesCl44Nfcgupg5O22Vc9rmt7zW42r7uipgvwrfxFSln6bgO60UPnndmjmB4QAw8qWDLYWyPmk2h4Rj40yxA7r06PJvGiZgRmG2icq2QD42Ucdd++stNXN37a9j6FWLnR57m+OYpAnKxCbfttt23RSt4/otP0lTWVwh1Pb42Nyj8mjCi0p/kvXaFKB1sl4f/u/jzsek52HcS3ea8BTdb4p4Nui28aEdB7TfDebb9t7uovIjqZ4qWOmE27YbhlmbT56aRq1aiY1/SMM2tqJXmC7mNAGtaR+cI188OJsW3/bdR+bUdM0oYMFZ8zDxx1dabTeusOyfj1H8B4vtNlw9nJLK66Ycl+8k8ZBp4mF0gp0miojzyFoI9feJbcyo7T3K9TF31VJQRAE6Stx+Me1f01hsLl7oy8zm3uYqzjgPPdMUaGLbtq2A3KpFs1Q594q0+0jXPFHtk9gRonXNA0lu8mmbRWzPx6cnG9bbyLMZux3i8p2kqdA/r0zB5f21qlXsmN9pwh+SIq45ryimh0pF5s4L6LqJyQVXcVtxMYCm5vuimPavbiy2Xnjptrm3uYozzkPP11jZPlwqIrlWi7a7Oa4TpK3iBuY2T/jLot54ws0DSWqh0jaL2J6PS/tr1ttIW3tWVnH5TlqQ9H+nqzHYcs0lLdsy9bAs67401YSYCu+um5iyclWb76d7y92H51zX1Yrg1rddkj2xGUXtX9c9JMvO5t6m+05ZegXmNo6ViLwFwF8CqAD4G6XUNtN3ixjHysR2zBF/PinqPlHTfITHmcl7fB2b8zHpNssyLlBR0o6nZNu9u9P2ZZKYxaRjThXB1dAaea2PelPUOFa5FKxEpALgewDeDOApAN8GcL1S6gHd99tZsIobP6RPgPeuY6Gqm6396Ne0cQyLFlTnxE7lfVN2MXisab298jDJu/DTDfuyEwuIRGXSjoLVGwFsUUpt9P69GQCUUlt1329nwQpo3mRMk+CW8Q2O3BqbqM/p7VetNKdtaMdDphse3O3GfRiP+4govXYUrN4J4C1Kqd/0/v1+AOuUUr+r+367C1ZAZw+tT9nxIUNERLZKOaWNiNwE4CYAWL58ebuSMavbAnwpGXYeICIiF/IabqEOYFng3xd4y2YppW5XSg0ppYYGBgbQbnmMGkxERES9Ja8aq28DuEhELkSzQPUeAO/NaVtO5DFqMBEREfWWXApWSqnTIvK7AHajOdzCZ5RScydjKhk2BxEREVEWucVYKaW+CuCrea2fiIiIqGx6fkobIiIiIldYsCIiIiJyhAUrIiIiIkdYsCIiIiJyhAUrIiIiIkdYsCIiIiJyhAUrIiIiIkdymYQ5cSJEjgF4ot3pyNkrAPyo3Ylog17Nd1Cv74NezX+v5juoV/dBr+Y7qNv3wauUUtr5+EpRsOoFIjJumgm7m/VqvoN6fR/0av57Nd9BvboPejXfQb28D9gUSEREROQIC1ZEREREjrBgVZzb252ANunVfAf1+j7o1fz3ar6DenUf9Gq+g3p2HzDGioiIiMgR1lgREREROcKClYGILBOR+0TkARE5LCK/5y1fLCL3iMjD3v8XecsvFpFvishLIvL7oXX9nojc763n5ohtvkVEjojIIyIyGlguIvInIvI9EXlQRP5bTtkuW77/VUQOeP89LSJj+eR6TnrKtA9+RUS+4+2DfxOR1+SU7WBaypT/K7z83y8inxWReTllu135/oyIPCsi94eWa7eZt5Ltg3d5v50RkVx7l5Us39tF5CER+a6I/IOI9LvPsTY9SffB+7w0HhKRfxeRSwPr0l7Pmm3e6K33YRG5MbD8T0TkqIj8NM8850Ypxf80/wFYAuD13t/nAvgegNcC+HMAo97yUQB/5v39SgD/AcCfAPj9wHp+HsD9ABYAmAfgnwG8RrO9CoBHAbwawFkADgJ4rffZBwB8DkCfv61eyHfoe3cB+LUePPbfA/Bz3t//FcDf9kr+0XzxOwrgZ73vfRTAB7sl3953fwnA6wHcH1qu3Wa3HfuYffBzAFYB+AaAoR7K95UA5nl//1mJj/1/BLDI+/utAPZ5f9ve0xcDeMz7/yLvb3996730/LSIvLv+jzVWBkqpZ5RS3/H+fh7AgwAGAbwdwGe9r30WwLD3nWeVUt8GMBVa1c+hecKdVEqdBvD/AGzSbPINAB5RSj2mlDoF4O+9bQHAfwHwUaXUjL8tN7mcq2T5BgCIyMsBXAFgLHMGLZRsHygAL/f+Xgjg6ew5jFai/J8H4JRS6nve9+4BcK2bXM7VhnxDKfUvAI5rPtJuM29l2gdKqQeVUkcyZ8pCyfL9Ne+3ALAXwAUZsmYtxT74d6XUCU06Y+/pno0A7lFKHffWcw+At3jr3quUesZxFgvDgpUFEVkBYC2AfQDODxzwHwA4P+bn9wP4RRE5T0QWALgKwDLN9wbRfDv3PeUtA4CVAK4TkXER+ScRuShdTpIpQb59wwC+rpT6SaIMOFCCffCbAL4qIk8BeD+AbWnykVab8/8jAPMCzUDvNPzeuYLyHSXpNp0rwT5oi5Ll+zcA/FOG36eSYh98EGfSaXNPT/K9jpNbvEK3EJGXodkMdbNS6iciMvuZUkqJSGS3SqXUgyLyZwC+BuAFAAcATCdMxtkAXlRKDYnIJgCfAfCLCdeRSEny7bsewN+k/G1qJdkHHwJwlVJqn4iMAPg4moWt3LU7/9423gPgEyJytreetOeQtXbnW7O+2G26VrZ9UJQy5VtE/gjAaQB3pPl9Wkn3gYhcjmbB6heKTGeZscYqgohU0TzB7lBK7fQW/1BElnifLwEQ2yynlPq0UuoypdQvATgB4HteoKAfmP2fAdTR+mZzgbcMaJbk/e3/A4DXZc1blBLlGyLyCjSrlne5yJutMuwDERkAcKlSap+3fAeacQ25K0P+vd9/Uyn1i0qpNwD4FzTjPnJTcL6jJN6mKyXaB4UqU75F5NcB/CqA9ymlCitUJ90HIvI6NF96366U+rG32HQ/WxfYB9eYvpdHvorGGisDaRbTPw3gQaXUxwMf3Q3gRjSbZG4E8GWLdb1SKfWsiCxHs719vVJqEsCawHfmAbhIRC5E8+R6D4D3eh+PAbgcwPcB/DJyfLiULN9As/nnH5VSL2bJVxIl2gcnACwUkZ9VzTijN6MZ95CrEuU/+PuzAfwBmsHCuSg63zESb9OFku2DwpQp3yLyFgD/HcAvK6VOJslHFkn3gZe/nQDer87EQQLAt6G5npVSh9F63S8G8KdypsfrlQA255C14qkSRNCX8T80qzUVgO+iWZ17AM328vMAfB3Aw2j2+Fjsff9n0KxZ+gmASe/vl3uf/SuAB9DsHfErEdu8Cs1C06MA/iiwvB/NGptDAL6JZi1G1+fb++wbAN7Sw8f+Hd5xP+jti1f3WP63o1mYPIJm00S35ftOAM+gGQT9FLxej6ZtdumxN+2Dd3j/fgnADwHs7pF8P4Jm7JGfjv9V0mP/N2i+/PnfHY+7njXb/A0vv48A+EBg+Z97+2TG+/+WIvaBq/848joRERGRI4yxIiIiInKEBSsiIiIiR1iwIiIiInKEBSsiIiIiR1iwIiIiInKEBSsiIiIiR1iwIiIiInKEBSsiIiIiR/4/Ys5SipJ4DqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(df['tarix'],df['Satis (azn)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the figure above, we see sale transactions generally were in zero(0), 65 azn interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tarix</th>\n",
       "      <th>satis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>556227575</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>557660657</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>556724772</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>505244006</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>546450050</td>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id      tarix  satis\n",
       "0  556227575 2019-05-18   15.0\n",
       "1  557660657 2019-05-18   30.0\n",
       "2  556724772 2019-05-18   23.0\n",
       "3  505244006 2019-05-18   28.0\n",
       "4  546450050 2019-05-18   26.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'sifarisci':'id','Satis (azn)':'satis'},inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1981 entries, 0 to 1998\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   id      1981 non-null   object        \n",
      " 1   tarix   1981 non-null   datetime64[ns]\n",
      " 2   satis   1981 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 61.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1546"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.id.nunique() # looking number of unique customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "            uid\n",
       "0     556227575\n",
       "1     557660657\n",
       "2     556724772\n",
       "3     505244006\n",
       "4     546450050\n",
       "...         ...\n",
       "1541  556402456\n",
       "1542  555552227\n",
       "1543  552007006\n",
       "1544  552264462\n",
       "1545  552246004\n",
       "\n",
       "[1546 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cst=pd.DataFrame(df['id'].unique(),columns=['uid']).astype('str')\n",
    "cst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On recency**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>217</td>\n",
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       "      <th>1543</th>\n",
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       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>Emin</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>emin</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1546 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  recency\n",
       "0            224      167\n",
       "1       45002262      200\n",
       "2       50407770      219\n",
       "3       54272224      188\n",
       "4       55252426      188\n",
       "...          ...      ...\n",
       "1541  0776262626       44\n",
       "1542  0776262726      217\n",
       "1543  0777656656      205\n",
       "1544        Emin      240\n",
       "1545        emin      238\n",
       "\n",
       "[1546 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rcn=df.groupby('id')['tarix'].max().reset_index()\n",
    "df_rcn['recency']=(df_rcn['tarix'].max() - df_rcn['tarix']).dt.days\n",
    "df_rcn.drop(columns=['tarix'], inplace=True)\n",
    "df_rcn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On frequency**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1544</th>\n",
       "      <td>Emin</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>emin</td>\n",
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       "</table>\n",
       "<p>1546 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  frequency\n",
       "0            224          2\n",
       "1       45002262          1\n",
       "2       50407770          1\n",
       "3       54272224          1\n",
       "4       55252426          1\n",
       "...          ...        ...\n",
       "1541  0776262626          1\n",
       "1542  0776262726          1\n",
       "1543  0777656656          1\n",
       "1544        Emin          1\n",
       "1545        emin          2\n",
       "\n",
       "[1546 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frq=df.groupby('id')['tarix'].count().reset_index()\n",
    "df_frq['frequency']=df_frq['tarix']\n",
    "df_frq.drop(columns=['tarix'], inplace=True)\n",
    "df_frq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On Monetary**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually, Monetary is accepted as the sum of all purchase(or other types of) values, but in this case, due to my goal, it was considered and calculated as average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>45002262</td>\n",
       "      <td>36.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>50407770</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54272224</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55252426</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>0776262626</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>0776262726</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>0777656656</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>Emin</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>emin</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1546 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  mean_rev\n",
       "0            224      86.0\n",
       "1       45002262      36.0\n",
       "2       50407770      30.0\n",
       "3       54272224      14.0\n",
       "4       55252426      30.0\n",
       "...          ...       ...\n",
       "1541  0776262626      40.0\n",
       "1542  0776262726      30.0\n",
       "1543  0777656656      28.0\n",
       "1544        Emin       6.0\n",
       "1545        emin       8.0\n",
       "\n",
       "[1546 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on average sale(revenue) for each customer\n",
    "df_rvn=df.groupby('id')['satis'].mean().reset_index()\n",
    "df_rvn.rename(columns={'satis':'mean_rev'}, inplace=True)\n",
    "df_rvn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  recency  frequency  mean_rev\n",
       "0       224      167          2      86.0\n",
       "1  45002262      200          1      36.0\n",
       "2  50407770      219          1      30.0\n",
       "3  54272224      188          1      14.0\n",
       "4  55252426      188          1      30.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging datasets to RFM \n",
    "rfm=df_rcn.merge(df_frq, left_on='id', right_on='id').merge(df_rvn, left_on='id', right_on='id')\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Normalization(Scaling)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>mean_rev</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.188119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.514851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>45002262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.702970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>50407770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.396040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>54272224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.396040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>55252426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    recency  frequency  mean_rev        id\n",
       "0  0.188119        1.0     3.500       224\n",
       "1  0.514851        0.0     0.375  45002262\n",
       "2  0.702970        0.0     0.000  50407770\n",
       "3  0.396040        0.0    -1.000  54272224\n",
       "4  0.396040        0.0     0.000  55252426"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling\n",
    "md_rfm=rfm[['recency','frequency','mean_rev']]\n",
    "rb=RobustScaler()\n",
    "rb.fit(md_rfm)\n",
    "md_rfm=pd.DataFrame(rb.transform(md_rfm), columns=['recency','frequency','mean_rev'])\n",
    "md_rfm['id']=rfm.id\n",
    "md_rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I aim to divide customers into **four** segmentation: **Loyal customers, Can't lose them, Lost customers, New customers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modelling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segmented_group</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>mean_rev</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.188119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.514851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>45002262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.702970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>50407770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.396040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>54272224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.396040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>55252426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.029703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0776262626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>2</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0776262726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>2</td>\n",
       "      <td>0.564356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0777656656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>2</td>\n",
       "      <td>0.910891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>Emin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>2</td>\n",
       "      <td>0.891089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.375</td>\n",
       "      <td>emin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1546 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Segmented_group   recency  frequency  mean_rev          id\n",
       "0                   0  0.188119        1.0     3.500         224\n",
       "1                   2  0.514851        0.0     0.375    45002262\n",
       "2                   2  0.702970        0.0     0.000    50407770\n",
       "3                   2  0.396040        0.0    -1.000    54272224\n",
       "4                   2  0.396040        0.0     0.000    55252426\n",
       "...               ...       ...        ...       ...         ...\n",
       "1541                0 -1.029703        0.0     0.625  0776262626\n",
       "1542                2  0.683168        0.0     0.000  0776262726\n",
       "1543                2  0.564356        0.0    -0.125  0777656656\n",
       "1544                2  0.910891        0.0    -1.500        Emin\n",
       "1545                2  0.891089        1.0    -1.375        emin\n",
       "\n",
       "[1546 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kms=KMeans(n_clusters=4)\n",
    "kms.fit(md_rfm[['recency','frequency','mean_rev']])\n",
    "clustered=pd.DataFrame(kms.predict(md_rfm[['recency','frequency','mean_rev']]), columns=['Segmented_group'])\n",
    "clustered=clustered.merge(md_rfm, left_index=True, right_index=True)\n",
    "clustered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(clustered['recency'], clustered['mean_rev'], c=clustered['Segmented_group'], s=50, cmap='rainbow')\n",
    "plt.xlabel('Recency')\n",
    "plt.ylabel('Mean Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('Segmented_group', 'mean_rev',data=clustered)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can be seen, 1-st segmentation is a too wealthy group of customers, therefore **\"Can't lose them\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(clustered['frequency'], clustered['mean_rev'], c=clustered['Segmented_group'], s=50, cmap='rainbow')\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Mean Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('Segmented_group', 'frequency',data=clustered)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3-rd segmentation holds those customers who most frequently do purchase, so that is **\"Loyal Customers\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('Segmented_group', 'recency',data=clustered)\n",
    "plt.show()\n",
    "#sns.boxenplot('Segmented_group','recency',data=clustured)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Less period means they purchases recently, hence 0 segmentation are **\"New Customers\"** and other(2) segmentation displays, **\"Lost Customers\"**, unfortunately"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Loyal customers, 2- Lost customers, 1- Can't lose them, 0- New customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qiz53pt7yGIqY1mgNhgEKRaXPI7LYz+nzr6uUcgH/V2t9bi7baa0/Bz5P/N+mlHoXGAmkG8XTgAe01hp4XSlVqZQakdhWEPoNXV1RtjZ3cNcL73PZyUdy/bpNNLYEqa0KsGxWPXe98D7Pb96ZWh7sd/Hw659y1viRLHhow762MycypMzD9pYg162172P5rHruTO9z5kQaPmnm5DE1BMMxOsMxrlu7iZoKHz/+5he4es3bqb6WzKjjsKoAO/d0Zay//YLxPL6+kfMn1Wa1L/O6eOj1zyy/G1Hpp2lvKLV+3j+P5uwTarlidUOq3dKZE3n67e18Zewh3P/qxyz42jFEomZGX8tm1fPUW42c+sVDuebxt23HXuZ1UVnupS0YZX7aPhZPr+PlLTsy9j3vn0dzzgm1qXanjxvOlaeOyTjnqe3Gj+SK9N+i2zlPHsPZ40eilMrY95IZdQz2u/G4DfyeGMPBsTffpKym/z7LZtUzpro8a8y5tD2Q9oLQE6ap2bKjjQ927OELh1Wyqy2UoReTerCpLWypKwJeF0v/8AGXnXwk97/6MT/8l2M5eqjIYn+mz+4TWusYcIRSynugfSilRgMTgL90+2oksC1tuTGxThD6Fc3BMFesbmB6/aiUQQzQ2BJMrU9fjsRgxqTDU8ZZ6ruHNhCO6pTit+tjfvc+H9rAqeNGsL2li90dkdT286cenbpRJNtet3YTkajOWn/1mreZ85WjLNvv7ojYfheNkbF+xqTDU0ZRst2ChzYwY9LhXL9uE9PrR9HSEcnq64rVDcyYdDjXPP52j2Pf3REhEtUpozT53fXrNmXte8akwzPaTa8flXXOU9t1/y26nePkMexqD2ft+7q1m9jZFmZ7SxfhqKY5GM6DVBWGpKx2P1arMefS9kDaC0JPNHeEmfPAeiYcUU3j7mCWXkzqQTtd0dIRSenk6fWjmPegyGJ/J1+POx8BryilngQ6kiu11v+1vw2VUhXAOuD/aK33HugAlFJzgbkAhx9++IF2IwgFxU5Oo6amsSVIZcCTUsxJkuvTlw0FKGXZ1lD0qo/uy1pryryu1DJgOx67fbgM6zGVeV2233Xvy65dcn1y3Pvbv93Yy7yuXo+/+7Jdn3Zj7n6O424eLttxARgqLg/FZn+ymk5jS9ByzLm0PZD2gtDTvT8cjdHYEiRmatvrrid9UuZ1UYYrQzeLLPZv8hVo9yHwVKK/QWmfHlFKeYgbxA9prX9t0WQ7MCptuTaxLgut9b1a60la60k1NZL4QnAmdnLqNhS1VQFagxFqqwIZ2yTXpy+bGmKmtmxranrVR/dlpRSdCdeJ5PZ247Hbh92YOsOxXo/Xrl1yfWswkjFGu/3bjb0zHOv1+Lsv2/VpN+bu5zhmatuxJ8+9qePyUGz2J6vp1FYFLMecS9sDaS8IPd37vW4XtVUBXIayve5ag5EedUXyu+RfkcX+TV6MYq31f1h9etpGKaWAXwLv9jCj/CRwaSILxRRgj/gTC/2R6oCXZbPqWdewjcXT61IKOulTua5hW8ayxwVr13/G0pkTM9vOnIjXrVgyo+c+lnfvc+ZEXtz8OSOr/Awt96S2X/7Sh9x+wfiMvpbMqMPjVlnrb79gPCtf/siy/dByj+13bhcZ69eu/4xls+oz2i2dOZG16z9j8fQ61jVso6rck9XXsln1rF3/GbedP77HsQ8t9+BxK5Z328fi6XVZ+167/rOMdusatmWd89R23X+Lbuc4eQzDKrxZ+14yo47hg7yMrPLjdceDy5xKUla7H6vVmHNpeyDtBaEnqsu9rLx0Ehs/baZ2aCBLLyb1oJ2uqCr3pHTyuoZtrLhEZLG/o+IxbH3sRKnfEg9YT2cPsB5YobXustjmn4A/AX8FzMTqHwOHA2itlycM57uBM4FOYLbWev3+xjNp0iS9fv1+mwk9MPqGp3Nq/8nNZxVoJAWj6I/73eW0e/YJU2tcBc4+kcx+0JvsE9GYxrDIPmGaGo9F9on09iYQjpr4E9knomnZINKzT5imxt2L7BPJDA7RRAYHwyL7RDRm4u1F9olUFok8ZZ9I7iuP2SccKauSfUKwoKiyanXvt8o+YSavZYvsE0m95TEUptZxfSPZJ/obtnKaT5/iGuCRxPKFQBtwLLASuKT7BlrrP/c0sEQbDXw/T2MUBEfj97s5zE7hlve9/6o89JHOIH9++8sLuR6jTfshmW9SD/hYK8sObDun4/e7GdlL4yCXtgfSXhB6wjAUw3u6gPOsF4XSJl+a52St9Ylpy79VSr2ptT5RKfW3PO1DEARBEARBEApCvgLtKpRSqbDPxP8ViUXJXyIIgiAIgiA4mnzNFF8D/Fkp9SFxl4gjgQVKqXLg/jztQxAEQRAEQRAKQl6MYq3175RSY4AvJFZtSQuu+2+l1Ne11r/Px74EQRAEQRAEId/ky30CrXVIa/124tM928TifO1HEARBEARBEPJN3ozi/VD0lEKCIAiCIAiCYMfBMoqlLqIgCIIgCILgWA6WUSwIgiAIgiAIjuVgGcWfHKT9CIIgCIIgCELO9Cn7hFLqWz19r7X+deJvj+0EQRAEQRAEoZj0NSXbOYm/w4GTgRcTy18DXgV+3cf+BUEQBEEQBKHg9Mko1lrPBlBKPQ+M01p/nlgeAazq8+gEQRAEQRAE4SCQL5/iUUmDOMEO4HC7xoIgCIIgCILgJPJV5vkFpdRzwCOJ5QuB/81T34IgCIIgCIJQUPIyU6y1vhJYDoxPfO7VWv9gf9sppX6llNqplHrH5vupSqk9Sqm3Ep+f5mO8giAIgiAIgpBOvmaKATYAbVrr/1VKlSmlBmmt2/azzSrgbuCBHtr8SWt9dr4GKQiCIAiCIAjdyctMsVJqDrAWWJFYNRL4n/1tp7V+GdidjzEIgiAIgiAIwoGSr0C77wOnAHsBtNZbiadpywcnKaXeVko9o5Q6zq6RUmquUmq9Ump9U1NTnnYtCPlF5FQoFURWhVJA5FTIJ/kyikNa63ByQSnlBnQe+t0AHKG1Hg/cRQ+zz1rre7XWk7TWk2pqavKwa0HIPyKnQqkgsiqUAiKnQj7Jl1H8R6XUj4GAUurrwOPAb/vaqdZ6r9a6PfH/7wCPUmpYX/sVBEEQBEEQhHTyZRTfADQBfwXmAb/TWv+kr50qpQ5VSqnE/5OJj7e5r/0KgiAIgiAIQjr5yj7xA631HcDK5Aql1A8T62xRSj0CTAWGKaUagZ8BHgCt9XJgBnCFUioKBIGLtNb5cMsQBEEQBEEQhBT5MoovA7obwJdbrMtAa/3t/Xx/N/GUbYIgCIIgCIJQMPpkFCulvg1cDByplHoy7atBSKo1QRAEQRAEoUTo60zxq8DnwDDgtrT1bcCmPvYtCIIgCIIgCAeFPhnFWutPgU+Bk/IzHEEQBEEQBEE4+OSrot23lFJblVJ7lFJ7lVJtSqm9+ehbEARBEARBEApNvgLtbgHO0Vq/m6f+BEEQBEEQBOGgka88xTvEIBYEQRAEQRBKlXzNFK9XSj1GvAxzKLlSa/3rPPUvCIIgCIIgCAUjX0bxYKATOD1tnQbEKBYEQRAEQRAcT16MYq317Hz0IwiCIAiCIAjFIF/ZJ45VSr2glHonsVynlPr3fPQtCIIgCIIgCIUmX4F2K4F/AyIAWutNwEV56lsQBEEQBEEQCkq+jOIyrfUb3dZF89S3IAiCIAiCIBSUfBnFu5RSRxMPrkMpNYN4+WdBEARBEARBcDz5yj7xfeBe4AtKqe3Ax8CsPPUtCIIgCIIgCAUlX9knPgL+RSlVDhha67bebKeU+hVwNrBTa328xfcKuAP4JvGUb5drrTfkY8yC4FRMU9PcESYcjaGUwqXAMAyqy70Yhsr43ut2pdZ337b7dwey/2QfQE79RiIxdraHiJoat6Eo8xl0hsyM46kKeGgJRghHYwS8LqKmJhKNt/G6FKaGSMwkpjV+j4th5b6MfZqmZldHiK5IDJdSBLwuBvv29WkohaHARINWREwzo93uYDhj28qA/XlMH6vHbeB1KTpCsYyxAVnjSe+z+zluDYYJhuN9eAwDo9vv7GS6uqI0B8Op37c64MXvt76dhMNRmjr2ta0p9+L12t968iHDxehbEHIlHI7SEowQMTUxUxNwG0RMTdTUeN0GBhDTGjRETY1hKDyGwuM2snRLNGrGdW7MxGUo3IZK6RPYp7+TulabmpgGU2uMhF6OaSz1s9W1crCupQPZT1/GlhejWClVCVwKjAbccVsWtNZX7WfTVcDdwAM2338DGJP4fBlYlvgrCP0S09Rs2dHGnAfW09gSpLYqwOLpddz/6sdc/fWxjKmpYGtTe8b3Ky+dxNhDBgFkbZv8rrcKwWr/D3xnMqGo2et+I5EY7+1s54rVDan2S2dOZPVrn/LqR82p47nqtGO584X3aWoL86Mzx3Ld2k2p9stmTsTUmu8/vNFyn1bjvOfiCbgMg/lp+739gvH4PS6ueGhDxrqqci+X3/dmat2SGXUcMtjP6Opyy/O4fFY9d77wPs9v3snp44Zz5aljWJDW58pLJuHzGFz6qzcs++x+M/mkuYMde7syjvm288fzyz9/xNVfH5vTb3aw6eqKsrW5I+P3XTarnjHV5VmGcTgcZUtTdtuxNeWWhrHV75qrDNtRyL4FIVfC4SiftgbZ1RbiurWbqKnwZenBOy46Aa/LyNBfS2bUUeZ10eqPpHRLNGry3o62DN2Xft/wueO6KbmP+175mMtOPpLr123Kan/ZyUfy8pYdnHNCbUZ/+9O/hbiWDmQ/fR1bvnyKf0fcIP4r0JD26RGt9cvA7h6aTAMe0HFeByqVUiP6PlxBcCbNHeHUxQzQ2BLk+nWbmF4/ijkPrGdneyjr+zkPrKe5I2y5bfK7vuz/0+bOnPrd2R5KGUHJ9gse2sCcrxyVcTzzVzfE/049OnUjSLa/4qEN7O6I2O7Tapy7OyIpJZ5cd/Wat9nVHs5at213MGPddWs38Wlzp+15TI4VYHr9qJRBnBrbg+v5tLnTts/u5/jT5s6sY77m8bdTv3Muv9nBpjkYzvp9r1jdQHMwe8xNHdZtm2yOLx8ybDvuAvYtCLnS1BGmcXcwpQes9OAPH30rS39dt3YTuzsiGbplZ3soS/el3zeSuim5j+n1o1IGcff216/bxIxJh2f1tz/9W4hr6UD209ex5cun2K+1/tc89ZXOSGBb2nJjYl1WEJ9Sai4wF+Dwww8vwFAEoe/sT07D0VjqYk7S2BKkMuChsSVIJGZafh+OxlL/233XG6z2X+Z15dRv1NSW7V2Jp/T046kMeGzHXeZ12e4zl3Fa9WO3rqfzmBxrcuy59pkkHI3ZjjXZdy6/WaGwk1W73zdq6qw+cmkL9vKfj/NRyL6F4lGq9/6oqTP0QK56BUjJrt19Idlnsn263u2pvctQPV4rB+taOpD99HVs+ZopflApNUcpNUIpNTT5yVPfvUJrfa/WepLWelJNTc3B3LUg9Jr9yanX7aK2KpCxrrYqQGswQm1VAI/LsPze63bZbut1ZyrVnrDqozMcy6lft6Es28cShlD68bQGI6n/u7fvDMey1iX3mcs4rfqxW9fTeWwNRgByGm+yz3S8bpftWJN95/KbFQo7WbX7fd0WryZzaQv28p+P81HIvoXiUar3frehMvRArnolXbfY3ReSfSb7SNe7PbWPmbrHa+VgXUsHsp++ji1fRnEYWAK8xj7XifV56Hc7MCptuTaxThD6JdXlXlZeOil1USd9vdY1bGPlpZMYXuHL+n7lpZOoLvdabpv8ri/7P6K6LKd+h1f4WDarPqP90pkTWfnyRxnHs3xWffzvSx+yZEZdRvtlMycytNxju0+rcQ4t97C8235vv2A8wyq8WetGDQ1krFsyo44jqstsz2NyrADrGraxdObEzLFdMokjqsts++x+jo+oLss65tvOH5/6nXP5zQ421QFv1u+7bFY91YHsMdeUW7etsTm+fMiw7bgL2Lcg5EpNuZfaoYGUHrDSg3dcdEKW/loyo46h5Z4M3TK8wpel+9LvG0ndlNzHuoZtLJ5eZ9l+8fQ61q7/LKu//enfQlxLB7Kfvo5NaW39GisXlFIfAZO11rsOYNvRwFM22SfOAq4knn3iy8CdWuvJ++tz0qRJev36fNjkA5fRNzydU/tPbj6rQCMpGEWPrLGTU8k+0T37BPg9Rg/ZJ0xcih6yTwAam+wT+7Y9sOwT+8YGZI0nT9knHCerkn1CsKGoJ7PU7v3p2SdMU+NPZJ+ImRrPQco+obVO6eUBlH3CtoN8+RR/QDxlWk4opR4BpgLDlFKNwM8AD4DWejnxAL5vpvU/O0/jFQTHYhiKmkG+A/p+f9v2Zf+59OvxuBhZVZaxrttizn12xzAUwwf5D7hPq23T++7eT/flSovj6anP7v0PLfdBea+aOw6/381IGyO4O16vm5E9GMHdyYcMF6NvQcgVr9fNITlcGz3hdhscVhmw/f5A5P5A70P55ED205ex5cso7gDeUkr9AQglV+4vJZvW+tv7+V4TLwySFwbA7KcgCIIgCIJwAOTLKP6fxEcQBEEQBEEQSo58VbS7Px/9CIIgCIIgCEIxyFdFu78C3SP29hDPQPGfWuvmfOxHEARBEARBEApBvtwnngFiwMOJ5YuAMuAfxEs5n5On/QiCIAiCIAhC3smXUfwvWuuJact/VUpt0FpPVErNytM+BEEQBEEQBKEg5Kt4h0splcofrJQ6EUiWD4nmaR+CIAiCIAiCUBDyNVP8PeBXSqmKxHIb8D2lVDnw//K0D0EQBEEQBEEoCPnKPvEm8CWl1JDE8p60r9fkYx+CIAiCIAiCUCjy4j6hlDpEKfVL4FGt9R6l1Dil1Hfz0bcgCIIgCIIgFJp8+RSvAp4DDkssvw/8nzz1LQiCIAiCIAgFJV9G8TCt9RrABNBaR4mnaBMEQRAEQRAEx5Mvo7hDKVVNooCHUmoK8eIdgiAIgiAIguB48pV94l+BJ4GjlVKvADXAjDz1LQiCIAiCIAgFpU8zxUqpE5VSh2qtNwBfBX4MhIDngcY8jE8QBEEQBEEQCk5f3SdWAOHE/ycDPwHuAVqAe/e3sVLqTKXUFqXUB0qpGyy+v1wp1aSUeivx+V4fxysIgiAIgiAIWfTVfcKltd6d+P9C4F6t9TpgnVLqrZ42VEq5iBvQXyc+q/ymUupJrfXmbk0f01pf2cdxCoIgCIIgCIItfZ0pdimlkob1acCLad/tz+CeDHygtf5Iax0GHgWm9XE8giAIgiAIgpAzfTWKHwH+qJR6AggCfwJQSh3D/rNPjAS2pS03JtZ1Z7pSapNSaq1SapRdZ0qpuUqp9Uqp9U1NTTkdhCAcLEROhVJBZFUoBUROhXzSJ6NYa30TcA3x4h3/pLXWaf3+oG9DA+C3wGitdR3we+D+HsZyr9Z6ktZ6Uk1NTR52LQj5R+RUKBVEVoVSQORUyCd9TsmmtX7dYt37vdh0O5A+81ubWJfeT3Pa4i+AWw5kjIIgCIIgCILQE/kq3nEgvAmMUUodqZTyAhcRz3WcQik1Im3xXODdgzg+QRAEQRAEYYCQr+IdOaO1jiqlrgSeA1zAr7TWf1NK/RxYr7V+ErhKKXUuEAV2A5cXa7yCIAiCIAhC/6VoRjGA1vp3wO+6rftp2v//BvzbwR6XIAiCIAiCMLAopvuEIAiCIAiCIDgCMYoFQRAEQRCEAY8YxYIgCIIgCMKAR4xiQRAEQRAEYcAjRrEgCIIgCIIw4BGjWBAEQRAEQRjwiFEsCIIgCIIgDHjEKBYEQRAEQRAGPGIUC4IgCIIgCAMeMYoFQRAEQRCEAY8YxYIgCIIgCMKAR4xiQRAEQRAEYcAjRrEgCIIgCIIw4CmqUayUOlMptUUp9YFS6gaL731KqccS3/9FKTW6CMMUBEEQBEEQ+jnuYu1YKeUC7gG+DjQCbyqlntRab05r9l2gRWt9jFLqImAxcOHBH60gFB7T1OzpChGJaMKmxjQ1FX4XwbBJ1NS4DUXAa9AZNomZGo/LwO9RdIZNTFPjMhRKgdbgcxt0Rfet97gUURMiMROXofAaCpSiKxLD7TJwKeiKmvjdBiYQiZp4EuuDUROf28A0NdFEfxV+g86QBqUxUERMjUuBS8X/j5oav9sgpiGa2KeRGBuJdjFTE0kcV5nXIBKDcCxxbIai3GfQHtp37BV+g2g0Ps7kOq/bQJsaw1B0RfdtiwI0+LwGXYnz53cbmHrfOfB7DLoi+/ryewy0hlCif5eh8BgKv0fRHjJT59bUGpex75z53AYGEDY1MVPjdRl4XYpgcjwuhVsp/D5FMKSJaU00Ft+nYShCURO3ofB5DIaW+TAMVVxB7AVdXVGag+HUuasOePH7rW8nubQ9kPaCUAhMU9MeDtEejOsDI6EPIK6nDBXXUR2hTP3cFTFxK0VMx9sl9YgCfF5Fe1emznEZisF+b0lc9wOBYmqaycAHWuuPAJRSjwLTgHSjeBpwY+L/tcDdSimltdYHc6CCUGhMU7O9tZOuSIxd7WGuW7uJC+trmfrFQ7hidQONLUFOHzecH5x2bGq5tirAsln13PXC+zy/eSe1VQEWT6/j5S07OGv8SBY8tGHfdqeO4YrEcm1VgCUz6ijzurjxyc00tYdYMqOO32zYznkTR3Ld2k0Z7azWL5tVzydNexlVXUEwHOO+Vz7mB6eOoSsS4+o1b1NT4ePH3/wCV695O7XN4ul13P/qx8z9ytF4XIrvP7wx9d19s0+ktSOc0X75rHp++1YjK/70CbVVAR6a82X2dkYyjmPpzIkM8rto74plrL/t/PG8+/dW6o8cxhWrG6ip8PGjM8emjsHyXM6cSMDr4vL73sw4/mGDfLzy/k7GHDqE69dln5uZUw6nMxzL7Lvb+b7r2xMYWuFlV1uIHz76VkYftzy7hab2ELdfMJ695RFGD6tw9A2yqyvK1uaOLDkcU12eZbzm0vZA2gtCITBNTUswxOetIeanyWJ3vdld/y6bVU9VmZtPW4IZumzJjDpqBnnpatcZ/S2bOZEhZR7auqKMrCxz9HU/UCim+8RIYFvacmNinWUbrXUU2ANUH5TRCcJBpLkjTCiqaWzpShlX0ybWpowDgOn1ozKWG1uCXLG6gen1o1LL16/bxIxJh6cM4tR2acuNLUGuW7uJ3R0R5k89OrU85ytHpfad3s5q/RWrG5hwRDUtHRGuW7uJ6fWj2NW+z6idP/Xo1P/pY5teP4r/89hb7O6IZHzXuDuY1X7+6gZmTDo8tRyJ6qzjWPDQBtyGK2v9NY+/zanjRqTO1/ypR2ccg+W5fGgD23YHs46/cXeQU8eNSBnE3c/N7sQ56Ol8/+CRjUSiOmUQp/eR/A2uXvM2n+4O0twRzo9QFYjmYNhSDpuD2ePOpe2BtBeEQtDcEaYrbKYMWLDWm9317xWrGwCVpcuuW7sJMLL6u+KhDYSjmlBUO/66Hyj0m0A7pdRcpdR6pdT6pqamYg9HECyxk9NwNIahoMzrSilNU+vU/wCVAU/GMsQVa2XAk7HsMlSvtivzulLbWm23v/UxU6fGWxnwZIy9p7Em951O+rbd953EUFiPo9t5Sq7Xaeu7j6enc2K1rvtvkT6+7mO369tu/Om/QZnXRTgawwnYyWrUtD4XUTP7BV4ubQ+kvSAU4t4fjsZsZbG73uyuf2M229ld/4aK6zanXPcDnWK+j9oOjEpbrk2ss2rTqJRyA0OAZqvOtNb3AvcCTJo0STRoP2X0DU/n1P6Tm88q0EgODDs59bpdBCMmneEYtVWBhLJUqf8BWoORjGWA2qoArcFIxnLM1L3arjMcIxwzbbfb33qXoVLjbQ1G8LqMVLuexprcdzrpx91930lMjfU4up2n5HqVtr77eHo6J+kk13X/LdLH133sdn3bjT/5+yX35XVnGubFwk5W3Yb1uXBbvPrNpe2BtBeEQtz7vW4XRK11Une92V3/umxk2O76T6o4p1z3A51izhS/CYxRSh2plPICFwFPdmvzJHBZ4v8ZwIv9zZ949A1P5/wR+h/V5V58bkVtlZ8lM+qorQrwxIZGls2qp7YqAMC6hm0Zy0kftnUN21LLi6fXsXb9ZyydOTFzu7TlpI/b0HIPy1/6MLW88uWPUvtOb2e1ftmsejZ+2kxVuYclM+pY17CNYRVebr9gfNwf+KUPU/+nj21dwzb++8ITGFruyfiudmggq/3yWfWsXf9ZatnjVlnHsXTmRKJmLGv9beeP58XNn6fO1/KXPsw4BstzOXMio4YGso6/dmiAFzd/zuLp1udmaOIc9HS+7/r2BDxuxR0XnZDVR/I3uP2C8RwxNEB1uTc/QlUgqgNeSzmsDmSPO5e2B9JeEApBdbkXv9dgeTdZ7K43u+vfZbPqAZ2ly5bMqAPMrP6WzZyI163wuZXjr/uBgiqmjamU+ibw34AL+JXW+ial1M+B9VrrJ5VSfuBBYAKwG7goGZjXE5MmTdLr16/PWu/EWcYDMXKdOK6DNSObx3EVfeqpu5zmkn3CNDVui+wThorPqNpln4jGTIzeZJ+ImXiMA88+ETM1viJknzAT61NZOHrKPpH2XffsE7G0aPOesk+EoibetOwTZiIrSDL7RPx36jn7RDgaH08P2SccJ6uSfUKwoaiyanfvPxAk+0S/xvZkF1XTaK1/B/yu27qfpv3fBZx/sMclCMXAMBRVZf7sL8ozF4fuZ/lgMSRwEPZRlodOejo/vTx3eRkHMNji5y1F/H43I3tpqObS9kDaC0IhMAzFYL9/v9dsZY664WDoTeHA6TeBdoIgCIIgCIJwoBTVfaJQKKWagE8tvhoG7DrIwzkQSmGcpTBGsB/nLq31mQd7MOn0IKcHg1L5/XpDfzoWyD4eJ8tqfzv3Vsgx9p6iymoedepA+M3tGAjHbiun/dIotkMptV5rPanY49gfpTDOUhgjlM44Dzb96bz0p2OB0jqeUhrrgSLHOPAYyOdjIB87iPuEIAiCIAiCIIhRLAiCIAiCIAgDzSi+t9gD6CWlMM5SGCOUzjgPNv3pvPSnY4HSOp5SGuuBIsc48BjI52MgH/vA8ikWBEEQBEEQBCsG2kyxIAiCIAiCIGQhRrEgCIIgCIIw4BGjWBAEQRAEQRjwiFEsCIIgCIIgDHjEKBYEQRAEQRAGPGIUC4IgCIIgCAMeMYoFQRAEQRCEAY8YxYIgCIIgCMKAR4xiQRAEQRAEYcAjRrEgCIIgCIIw4BGjWBAEQRAEQRjwiFEsCIIgCIIgDHjEKBYEQRAEQRAGPGIUC4IgCIIgCAMeMYoFQRAEQRCEAU+/NIrPPPNMDchHPj19io7IqXx6+Sk6Iqvy6eWnqIicyqeXH1uKahQrpX6llNqplHrH5nullLpTKfWBUmqTUmpib/rdtWtXfgcqCAVA5FQoFURWhVJA5FToK8WeKV4FnNnD998AxiQ+c4FlB2FMgiAIgiAIwgCjqEax1vplYHcPTaYBD+g4rwOVSqkRB2d0giAIgiAIwkDBXewB7IeRwLa05cbEus+7N1RKzSU+m8zhhx9+UAaXb0xT09wRJhyN4XW7qC73Yhiq2MPKoKsrSnMwTNTUuA1FdcCL3+90MXIO/UFOhYFBT7IqekBwCn3RqaapaekMEYyYxEyNx1CU+w3au0wCXhdRUxOJmo69Hw8EDrZd1G+0mNb6XuBegEmTJmU4UpeCsWmami072pjzwHoaW4LUVgVYeekkxh4yyDFj7eqKsrMzRDiqMRREYpqdOsRwkBtiL+lJTgXBSdjJqugBwUkciE41TU1rMEzUNAmGTZraQjR3hFnXsI0fnnYsf2/t5JhDBrE3GKXM66I1GCEYjjJisJ/WrqijbYn+RDHsIqdrsO3AqLTl2sS6XmOamk92tfPp7iBlXhed4RhHDA0weliFo4S5uSPM7b/fwsKzx1EZ8NAajHD777dw03l11AzyFXt4AHTGonRFYmxv6Uqdy5FVfjpjUfyOFyVBEPJBZyyKywCf2yCmNR6lMAwtekBwNOmTYzFTg4K9wSjzVzekDK7F0+t48q1Gzj2hlkt++UbG+jv+931+cNqx3PXC+zy/eSe1VQEemD2ZioA7aza5GBNxpTD5lyvNHeGUQQzQ2BJkzgPr+c2CUwpmFzldgz0JXKmUehT4MrBHa53lOtETuzvjT4ALn3gnJeC3XzCewWUhhlX4CzLoA8E0Tb77T0dxzeNvp8Z52/njMU2z2ENLEYnGXyVlrjOJRGXCUygdRt/wdM7bfHLzWQUYSemyuyPKFWnGxLJZ9YysdFm27Y83a6F0SM4Kf97axR0vvM/0+lFUl3upGeTjt281Zhhc97/6Mf9+9nHs3NvFwrPHsfylD9m4rZXr121i4dnjuGJ1A/ddfiLf/aejMLWmuSPEpfe9kTGLOaamgq1N7Qd1dtNqRnXFrHpGVPqpDJTu9RaOxlK/T5LGliDhaKxg+yx2SrZHgNeAsUqpRqXUd5VS85VS8xNNfgd8BHwArAQW5LqPUMTk6jVvZwj+1WveJhRxjrEJYGpSBjHEx3nN429jOsze3NsVZeET73Dhva+z8Il32NsVLfaQBEE4iATDZsoghriuumJ1A8Fwtk5N3qzPW/oKpyz+A+ctfYUtO9ownabYhH5JUv7e3raHO154n8tOPpJFT21mxvLXmPmLv/DNupFMGFUJwIRRlVx28pFcvPJ1Zix/jUVPbebaM8YyYVQljS1BKgMeGluC7AlGuPDe1+mysC3mPLCene0hy9nN5o5wwY7TakZ13uoG3t62p6SvN6/bRW1VIGNdbVUAr9v6ATwfFDv7xLe11iO01h6tda3W+pda6+Va6+WJ77XW+vta66O11l/SWq/PdR9RU1s+acQcJiQR07QcZ8RJM8Wm5rq1mzIuvOvWbiLisHMpCELhsNOpUQs9sKvD2kDY1RE6KGMVBjZJY7HM62J6/SiuX5d5//r+wxuYP/VoAOZPPTrr++vXbWL+1KOprQrQGoxQWxVIGbdlXpf1dRCzvpcXcnbTbka1zOsquEFeSKrLvay8dFLKME7OuleXewu2T6e7T/QZl6E4fdxwptePSvnqrmvY5rjXCS6lqK0KZAh2bVUAl3LOOGMl8oAhCELhcBvWusptoVO7ItY36y6HvakT+idJY7E1GKG63Gspi0kDq6fv7754Al0Rk/u/M5nPW4NMGFWZMpKz7tk214cq4L08OaPafZ+twUjBDfJCYhiKsYcM4jcLTjlo7lfFLt5RcHxugytPHcOipzZz4b2vs+ipzVx56hh8bmcdesDrYsmMuownoiUz6gh4C/eaIFc8iYs9ndqqAB6HPWAIglA4yn0Gy2bVZ+iqZbPqKfdl69Tkw3468Yf9gzJUYYCTNBaXv/QhQ8u9lrI4YoifP143lRFD/Jbfj6oK4HEZXPv425x22x+54dd/5dozxvLC5h1Z9+yVl07C5zZYPD1z/eLpdQWVeasZ1cXT61j+0ocFdzcoNIahqBnkY2RVGTWDfAWf0Oz3M8WRqMmChzZkvBJZ8NAG1sydUuSRZVIZ8HLIYD+Lph2fyuxwyOC4k7xT8HkNls+qz4jWXT6rHp/XWQ8YgiAUjmBY89Rbjdx3+Ym4DEXM1Kxd/xmXn3IUlWWZbZMP+0m3Kyc+7Av9l6SxOOeB9Sx/6UOWzpyYsgdqqwKsuKQ+bmSZGgUsmzmRK9K+Xzy9jo+bO7m2W7zP9es2pe7Vj82dwud7uhha7sXjUpjA/a9+nJFJ6v5XP+am8+oKdpzJGdVfLziZzlCMj3d1cOtzW2hqDxXc3aC/0e+N4qi28X/TznrlbxiK0dXlDPJ7HBulHYtqFDrDcFdoYpJ9QhAGDBrNV8YewuxVb2YYD5CtByoD8dm5VbMnk7A98LmVox72hf5L99fvHpfBreePRwGHDy2jPRzl/OWv7cuiMnMij8z5Mjv2xrNW3frcFm74xhcsbYgjqsto64pgGIoZy1+jtirAreeP54ihZfz4m+P4eFcHNz/z3kEzTA1DMXyQH7NcU+5zc/fFExxpRzidfm8UGza+uoaDfHWTJF8TOJWwqXnyre3MmHR4xgzRJScfWeyhCYJwkNAaXt6yI2umeHS1tR5oD8Wy0lMJwsEi/b6aTA8454H1PDZ3CkuefY+bv/UlDh3ix6UUu9rDeFwGNz39Lhu3tQLY+g7HTM2NT27mvy86AYgbyjWDfJy/Yp+RXYy0aE63I5xOvzeKvS7D8vWd1yWv/HPF61KcNX5kxgzR0pkT8YqDoCAMGDxuaz3gcWfrgeaOMP+zYVuWAf29rxwjN27hoJOcOX7yylPoisSyagMsmVFHW1eEG88dx41PbmbjtlaWv/Rhlg2xeHodP1q7iab2EG5D8djcKXSGY+xuD2elRfvNglNkpraE6PdGMWhqBnkzXt+BidWrPqFnIjFt6Z/9mMP8swVBKByRaO/1gEJbGtBK9K9QJAxDETPjbzy61wa4bm3cVxjgqtPGMHvVmzS1hwh4Xdx6/ngOG+Lnw6Z9/rq3XzAew4BIzGSQ3011uZcL6mvZurOd+VOPpjIQd4c0TS2GcYkwAIxiCEY0V6zeV3Vm2ax6BjunmF0Kp1d+yiU/qSAI/ZNc9EDIJtC5pwdpp+tBofRJlnq2y+0LUF3h5bG5U2gNRviPxKzxH6+bSoXPzQ3f+AKd4RiDAm52tUe44dd/zXjoM7Xmyoc35lTRTuTeGfR7H4JITFtWX4rEnGXIlULlJ7dNSjar/KSCIPRPctEDuT5Il4IeFEofr9uFYSPHneEYneEYu9rDXHjv68x7sIGN21qprQpgas3uzvj62ave5PPWUJZ9seChDbR0RHKqaCdy7xz6/UxxqcxuNneEuf33WzLSuNz++y3cdF6dY3zvAl6D+2afSOPuYCr7RO3QAAFJySbYMPqGp3Nq/8nNZxVoJH2jvxxHPvB7rPWA35OtB3Ip9AHW5WrnPLCe3yw4xTF6UCh9qgIedneY3H7B+FSp5tqqAPdcPIEhAS8ajcdlcPq44Ty/eWfqDXM4GmN4mhzaVbWrLPOw4pL61L18+Usf9lhAI13uJ4yqZP7Uo+kIRfnH3i4OHeyXGeODSL83inNVysXCNE0uO/nIVJnJpDO/6aAyz6GISVswysIn3kmN8c6LJlAhOUcFYcAQiWnauzL1wF3fnsAgX/btxOs2snK/Lps5Ea9N8SS7crWlWpFLcB6mqdna1E5VmQev2+Ch732ZqKkp87rY3R5m1i//kmYkT+Rn5x7Hx00ddIVjDBrsI+BVTBhVycZtrfEHQgv7YkjAww8e2ec+sWRGHeU+F01tIUv3iKTcTxhVybVnjM2wA3rjeiHkj34/xZdL9aViEtNY1l13kpeHqeGqRzdmjPGqRzfisEl3QRAKiGnq1A0f4nrgB49stHzVG42ZeNyKVbMn8+I1X2XV7Ml43IpozPphP1mBLJ1Sr8glOIvkrGwoanLjk5sJR00u+9UbbGrcw7xurhDff3gDWsOQgIer17zF1CUvcfHK1/n5tON4fN5JHFbpY9nMiRn2xYpL6rn5mXcz+rnvlY/Z3tpl6x6RlPv5U4/OsgP253oh5Jd+P1PcGdbc9cL7GW4Jd73wPjeee3xW9aViom2KjGgHFRmxC0yIOdAqlqAFoZjk6m4BpeNykYtLmqnhe/c3ZM2k2QXapVcgS58py1fhA9ELQnJW1m0omtpD/GjtJhZPr8PnNmzeUpipNx0ANRU+drWHGTW0DJcCv4eUfdEZjjG03MPzm3dm9DO9fhTzHmzIMnaTbkFJue8IReVNSZHp90ZxJGby/OadWUL672c5xy0B9j0pdr95OGmGxO2ycUVxWJ7iZNBC9xurvIIShL7jsnFJc1lcW7k+SHevQJZPw1X0ggD77rVuQ3HHRSfww0ff4tbntrDk/PG2hb6S66zcG5bPqmf5Sx+min28cv3XsvqpLvf2aOwm5f4fe7scbwf0d/q9UWynwJ2mBAs9Q5IPXEpx17cnZPhK3fXtCbgcVh1QgnUGFgcyKyscOG7DWg9YxWm4XYbNg7S9+1qhKnKJXhAgfq99ZM6XcRmKUYkS5D63wu2Ku1oms0kkfYGVIiXDVu4N81c3sPDsccx7sIHaqgABryvrXl4zyGd5Hai0e6dhKA4d7He8HdDf6fdGcalUtDMMxTHDynls7hSipsZtKIZX+BxlvCsFFT4Xi6Ydn4o6r/C5cJhNLME6glBA3C7FIL87Qw8M8rst3xgNr/CxfFY989MMjeWz6hlecfCNUNELAsTfGOwJRln01GauOu1Y7nzh/VSQe02Fj0XTjmf0sHKa20Pc9PS7/HzacSyeXsf16zZRGfBYZ5sIeFKyDTCmpiLjbYdCp/pID6S3eslaXeHl4TlfxqUUHpeB1nGXH3H1OTj0e6PYZcSFLF2BV1d4cZhNTDRqsmVne9bN4wuHDMJtE6l9sInENEue28L0+lGU4SIcM1ny3BZ+ds5xxR5aBqXgiiIIpUooYnLLs+9l6IFbnn2PGy30gGEoyjyZD9JlHldRbu6iFwSAne0h5q9u4OZvfSk1y5s0iOdPPZoyr4tPdnUwZng5V502hq6Iya3PbWHRtOM5rDJgKUPDB8WN6bauCOfe3ZDlltPUFuL+Vz9m4dnjGD7IR4XPTThmEtOkqt1ZufcsmVHHLc/Gq+eJq8/BwRnWVgEJJgQ6nIh2Dsfiy8GIs3yKkxdq99cyO9tDRR7ZPgwFl518JIue2syF977Ooqc2c9nJR+K0azTpipIeESyvoAQhT9joAas3Rrs6Qlx63xvMXvVmquDBpfe9wa6O/Og109Q0tYXY3tJJU1uox2IHohcEiMcZNbYEOXSIPzXLW1Ph49ozxqZkeuET79DSGeGRNz6lzOfizm+fgMel+J8NjdxzcWa2ieWz6vl/z7yL121wy7NbLDNGVJd7ufrrY1nXsI3OcIzZq97krDv/zAUrXktlobBy77lu7SbmTz1aslAcRPr9THHM1DS1ZQpSU1vYcRkTkhdqOo0tQdvURcXAtEkb11PJ1mLhcxsZs1M+h8y2C0Kpo3PQA12RGCcfVc2crxyFy1DETM3Klz+iq4dJiWjUZGd7iEjMxOMyGF7hs3xblmvgXCGD+ITSwZPwc3epeLxRJGZy1WljsmT6ioc2pHyFbz1/PEopXtzSxItbmlh49jjGDK/g0+ZOhlV4mV4/iluf25IKtqup8BGKxvi0uSMlw2NqKvjpOcdx0b2vW/q127n3VAY8qf/F1afwFNUoVkqdCdwBuIBfaK1v7vb95cASYHti1d1a61/kso+Ax+BHZ47N8ikOWFRfKiaeAwhIOdiUSkq25o4wNz/zbsbr3ZufeddR1QEFoVTJRQ+Ue1xcctIRzF71ZkbxjnIb/RuNmry3o61XbmQHUgW0UEF8QumQ9HPf1R5myYw63IbBiErr7BBJH+JhFV4uv+/NlJG86KnNLJp2PH6PgQYWPbU5I0PFj84cmzJ+kzI8otLHP/Z02fq127n3tAYjqf97cvWRdIP5oWhGsVLKBdwDfB1oBN5USj2ptd7creljWusrD3Q/MZOUQQz7Xkk8Pu+kA+2yINSUe7MiX5fNqqfGQa/2cknFVExKoTqgIJQqueiBrqjJU29v577LT0zNFK9d/xmXnnykZd92bmRr5p3EYZWZRT3kOhcOBLfb4AuHDGJXRwhDaSKx+NsPK5mOxMyEMWqkZocfnTuFw4cG6IqYuAyFAdz/ncl81tzJnS9s5arTxmTZHPNXN/Do3Ck0d4Rt/dqtMlAlfYr35+oj6QbzRzFniicDH2itPwJQSj0KTAO6G8V9ImrauCU4THG2dkUti4zcdF4dNR5nBIL4PQZLZ05kQVrJ1qUzJ+J32Ky7XXXANQ57EBKEUsTvttEDFi4OHpfirPEjM2aKl86ciMcmt3kubmRynQsHittt4HUrtreGuWJ1AzUVPsssVW4jnr3KpVRqNrh7AFz6/ytm1TPI77aUYdPUrGvYlpWFYsUl9alZ3XT3Ho/bwG0o7r54wn5nfiXdYP4oplE8EtiWttwIfNmi3XSl1FeA94GrtdbbLNqglJoLzAU4/PDDU+sNZZOn2GF5xMLRmGWRkZ+d4xwfomDE5O4Xt2YY7ne/uJWfOiz7hJOrA9rJqSA4DTtZDUZ7rwciMZ0yniF+HS54aINtHEIubmROvs6Fg8eB6FTT1ATDZurNbGNLkFuejWeYGDU0wIdNHdzy7Bb++6ITuOfFD7jgxFFZb5uTrhTp/89b3cBjc6dYyrDHZXD118emXH6qy70MH+TjsCH76iZYuveU7/94JN1g/nDWFF82vwVGa63rgN8D99s11Frfq7WepLWeVFNTk1qvFCyeXpcRLbp4ep3jcut63S5OHzecFZfU89jcKay4pJ7Txw13VLqgmKl5fvNO5j3YwIX3vs68Bxt4fvNOx/kUJ32z0nFK6iU7ORUEp2Enq7nogVxKQgOUeeOz0On6eunMiZR5s29VTr7OhYNHrjrVNDWfNHdkyebGba3MXvUmze1h5j3YQFN7iI+aOvjGl0ZkZX3oHgA3ZngFKy6pp6bCh9tQLJ9Vn5WhoqbCx9hDBnHTeXUcf9hgjqgup7aqLOUrn0smle7ItZA/ijlTvB0YlbZcy76AOgC01s1pi78Absl1J1rD1n/s4eE5U9BaxyNIN3/O6OqyAxp0oagKeLjqtGOzAkyqEheeE/DY+BJ6HOazVArVAQWhVMlFD+SqMzpCMVa/9mmGD/LKlz/ih/8yhspuKluuc+FAaAmGaOuKMKzCuspcazCSehiLmSZDy318vqeLCaMqAZg/9Wiqy70MCXiYMKqSpvYQW3e2s+ipzYkKeIqxwyuyCnEljV8rd4a++gTLtZA/imkUvwmMUUodSdwYvgi4OL2BUmqE1vrzxOK5wLu57qTCZ3DKscP5cGd7Kj3XKccOp8LnrEnylmDEMsDEST5BgwKGZXWqQQFnnUtJvSQIhWOwjR4YbKEH/N7s0rnLZtXjt5j5hfiM16sfNbOmoTG1rrYqwHXuL2S1letcOBAiUU1nOEZM6yzZXD6rnnKfi5u/9SW01vzgkbdS391+wXg8boOlf/iA6fXx+bzbLhhPOBbj39a9k3Kr+PX8k/hgV0dOBm5ffYLlWsgfRTOKtdZRpdSVwHPEU7L9Smv9N6XUz4H1WusngauUUucCUWA3cHmu++mKaHa1hVj4xDsZTvKDfW6G5PF4+kop+ATtDZr89q1Gy0jyQf5ij04QhIPBnh70QEU3PdAZNnnKou0lJx/JUAtfyVxnvHJNsSZpqwY2pqmJmprr1m7i5m99iQ927M16izyiKi6YN/z6rxlG6tVr3ubW88dnZTxZPqueH505lluejecpDpva0sBdM+8kDh3st5S3fNz/Jd1gfihqnmKt9e+A33Vb99O0//8N+Le+7COSuAC6O8k/6rCCE6VQgjRqalb86RNW/OmTjPUXTxldlPHYIelpBKFw5KIHYjZtZ9rojELOeIleEJo7wqkMJx6XwY1PvceNT72X0SYZBGplpCbzFXd/o7to2vFce8ZY7n/1Y0ybANC/twbZE4xYylsp3P8HCs56710A7BLN5+LEfjAohRKk7oR/YDq1VQHcDruh2L2KkhKZgtB3ctEDXrdh2dbTQ4XJ5IzXyKoyagb58mawil4QwtEYps70HU4nud7uO5ehLO2JMq+L69dt4idnjcPvsQ56S8qflbyVwv1/oNDvyzy7bQI9nFZwohR8gsp91r6E5Q7zzy4FVxRBKFX8HoNlMydyRVqe4mU2+cr9HmWpM/yeg6/XRC8IXreL1mCExdPruP/Vj7n74gm0dERS8UYjq/zc+twWmtrCWXmLl8+qx+e2ThnYGozQ2BJEAUMD2S5Ai6fXcetzW2zlrRTu/wOFAWEU33HRCfzw0X0O83dcdILjZjdLgXBUMzjgZtXsyRgKTA1etyIcddasu7yKEoTCoTUMKfNk6QGr9MCmhphpsmja8SnDI2aa9PSirlB+v6IXhOpyL8FwlDv+932umHo0HsNg4RPvUFPh46rTxuBxGfzkrHEoBV6X4pE5UzC1JmZqKvxu2oIRVsyqZ17aQ17S4K2tChDTmp3tIYaWeXhs7hQ+39NFc0eYW5+L+xv3JG+l5hPcX/3z+71RHPAqRlT6eWTOFGJa41IKlyu+3kkkcyd+2tyZunkcUV3G6OpyBwmaYnvCJzs9aPHomopiDywDSU8jCIVDAzv2dHH1mrczIvNHD8uOnOsKm9yTiNYvw0U4Fl/+2TnHWRYlKKTfbyH1Qn81EPoLpqlpDYYJhmN43IofnnYsO9tCLHziLWoqfFx7xtiM4LlkpokrH95IY0uQ08cN5ydnjcMwFGU+F4/PP4lQxOTjXR3xmeX2EMtmTqS9K8olD71BY0uQef88mnNOqM2ohOek+1BfZLY/++f3e6M4EoVdbeGskqS+Ic5Kl9AaDLNjb1dWlozKMg9Dy53x9BiOmSURtAjgcxsZs1O+HnwYBUHoPeGomTKIYV9kvlWVOqXIitZfPL0Ou/tmIcvVFuoV9YEYCGJEHzySE0479nal7l9JI7exJcjCs8dllQu/es3bLJp2PI0tQSaMquSyk49k5i/+kmFDHDbEz+hhZdxx0QlETc2eYDiVwm3CqEq+MvYQ7nzhfdvqdcWkr0Ztfy4r3e8tha6oaVlmtCtqFnlkmQTDMUuDMxh2jr+bXdCi0yra7eoIcfMz7xKOxX/jcMzk5mfeZVdHqMgjE4TSx65KnZUe0Bruf/VjFp49jsfmTmHh2eMSEfrWfefq95trFbBCBPHlGsCXNEjOW/oKpyz+A+ctfYUtO9ocF/zdX2juCPNpc2fG/fX5zTv5qKmD2qoAlQGPbfAcxIt1dDeakzbE1279Ixf/4i98vqcLQxmpNsltkpUfZyx/jYt/8RdagpGDeOT29DXotD/75/f7meJcy4wWi5hNGpeYg4ZZKhXtYqZpOTvlNONdyGb0DU8XewjCfnDZ6AErA9NQsOBrx9DSETcGvC6DBV87xnamOBe/X6e8ws3VQOjPs2xOJByNUeZ1Zf1Gz/z1c5bNqmdXW8hS5joTE1J2RnPM1Ky4pJ7DhvgJeN14XYr7Lj+RO1/YaruNU4zGvhq1/dk/v9/PFJdKGjG7NC5WEd3FIlnRrntNd6dVtDNNsp7sr1+3SWZiBCEPeF0GS2bUZeiBJTPq8Lqy9YChFN01rUqst6Iq4LHUMVbl7p2SYi1pIKTTk4HQn2fZnIjXHXeh6/4bfeNLI7jrhfcZPtiXJXMrZtUzssrfY+o2t6HY8Ekze7uiXH7fG/zw0bcAuPWC8Qwb5OP0ccOztnGK0ZirzHanP6eQ6/czxX5PbmVGi8Wwch8rL5nEnAfTZj0umcQwh/gTA3SGNOU+IxWRayhF1IzRGdKOqmhnlzxdbGJB6DuGgppBvgyf/ZpBPsvZXw10hmNZsRJ2l2JLMJLyw6wMeGhNLN90Xl3WLKpTjMtcA/j68yybE6ku93JEdVlGirXTxw3nmOEV/ODUMQzyu3notU+4/zuTcRuKSExz7x8/pDUY5oHvTCbgyU5FumzmRAwDvv3l0cz65V8sg/WWzaoH4q4aB2I0FtLvvK9Bp/05hVy/N4q7IiZ3dVOyd73wvm30czHxeboFhzlolhjiN7i9wSi7O4KpMQ4t9xDwOEuMfB7rm47TzqcglCJRUxMzTUYNLUulZIuZMUuXtGRJ3d4G54ajMSoDXo4aVo7LUAwt91IZ8Foauk4xLnM1ECQ7zsHFMBSjq8upLPOwbv5JaGBXe5hvr3w9bheUefjK2EO47FdvpFKzzZt6NJ+3Brn5mXdZePZxrP94F4/MmUI0ZhJNlCqfcnQNo4aW2QbrXbG6gcfmTuFn5xyHx23gNhSf7wn2yoAstGtQPozaUksh11v6vZUQM3XK2f3Ce19n3oMNPL95p+P8S5s7wpbBYU6rtuT3GIwaGg9SGTW0zFHuHUmGlfssX+04adZdEAYCuVYULfe5mHXSEcxe9San3vZHZq96k1knHUG5L9vQddIr3FwC+NINkleu/xq/WXBKv0hl5WQMQ1EZ8LK7M8K7n7elZn0rAx6UUtz/6sfc/K0vcesF4wFY8dKHRGKaf/vmF3EZiglHDOXbK1/n/Z3tzF71Jl8/bgR3vrAVl6LHYL2YqVFK0RmO8fa2PVz58MaswEqrYNGD4RpUqMqRpY6zpvgKgF1FO6f5FJs2wWGm6ZwsGS5DEYpqFiTyMCZT00h1QEEYOOSiBzwu6wpgbgv/Y4DOsHW2oDXzTqKyLLNtKV/n/XWWzckkDc3bzh+fkq/WYIRRQwMZ997Txw3nylPHZKRxXT6rnpOPquYLhw7ikTlTMBT814Xj8bkNfnnZJP7e2mUdfKoUNz75TsqFIlnoIxlYWV3utZwRHlrm7EC9/ozzpvnyjM8b9ylOn01YNqsen8N8imPaOjjMSdknwjbp7cIOS28H8hQsCIUiFz3gNuC+2Sdy3+Un8tjcKdx3+YncN/tE7NKGR2KmdbagmLWOketc6C2mabLw7HEMH+RL2QPLX/oQ3e3eO71+VJZ8z1/dwNyvHk3M1Hyws51TFv+BS375Bp82d+IyFGMPrciyM+65eCJ7uiLc8I0vckF9beqePn/q0SkD125GOKbpUyDcgZ2f3NIb9lf6/UxxV7g0fIrtgsO0Ve3UIlEq6e0EQSgcuegBraG1I5wRaHf7BeMZ7LO+9eQ6sywIvcE0Nbs6wix6ajM1Fb5U0N3Gba20haIZ8mbnCuFxKYLhaCp/cdI/ftG04wnHTJa/9CGrZk+mIxRlkN/Nzc+8m5ohvufiiWzd2c7Gba1UBjwpA9cuWFTreLq3eQ82ZMwgF8o1yCnpDZ1Av9c0URufYqcZcoayTh2nbFIXFYNSSW8nCELhyEUPRExtWf0uYqN/h1dkp8daPque4RXiaiAcOM0d4ZSBuXFbK7c8u4VF047n5eumUu51Z8izXQo2l6EYUhafWEuSLPJRGfDQ1B7CbSj+sbeLS3/1Bs9v3plq8/2HNzB/6tHUVgXQkDJw7VKjxUzNHf8bn8xbO/8kHv7elxlTU1EwA9Up6Q2dQL83ikvFkFNKc/fFEzJeM9598QQcZBOn0tt1d0VxWno7QRAKh9/GJc1KD5g5Btq53QZfOGQQa+adxMvXTWXNvJP4wiGDcEuZdqEPdJ+R3bitlTtf2MreriiLnvpbxoPYuoZtLJ05MUO+F0+v4+e//Ru72iO8//neVD/JIh+1VQF+s+AUynxxv3Yrma8u97JkRh1HDStPzcBaBYuuuKSe/3x6c2oy76an3+XDpg7+vjdYMLcGp6Q3dAL93n2i3Gedp7jc5ywla6CIRM2s14xOGmVXxOSptxq57/ITcRmKWCI1zaUnH+koVxQobI5HQRjIdIV7rwfsqt/1FJzrdhscVhmw/X6gIbqs7yiVLYdXnTaG+asbOPmoavweg1WzJ9PaGaa5I8zq1z7lvstPZE8wQnNHmFuf28LGba1s/ryNVbMnc9v/bk3l3D5kkJ8RQ+IVHU1TE0wYyd1lfkjAw4/WbuLuiyekfj+rYFHTNFOzzBNGVWblPy6EW4NT0hs6gX5vFHeGNQ0f7+LhtIITL27+nOryw7KimYuJqbF8zfj4vJOKPLJ9xEzNij99woo/fZKxfuZJo4syHjvEP0oQCkfM1LzxSSsTR1en4jTe+KSVmVOyZ7CUgsXT67Ky6jjpDZiTEV2WH1wWcjh6WDzH8PypR3NpIkfxtWeMZdFTm2lsCXLBiaOYsfy1jH6SvsUv/OtX8XkM2rsiDCl3Zxi5hw32ZxX7WDy9jh+t3UTNIC9KKba3dGY84KRnImlKKzs9f+rRWQH4hSgJLrmz99HvjWLQfPGwSi5e+Xrqx77t/PEo25pKxcG+CptzxmnYzPrYlWwtFnb+UflWJIIwEPG6DX505thUUY5UmWcLFwdTw/2vfpwR6Hz/qx/z03OOs+1fZkb3IbosPxiGkSWHyTgel6FobAnS2BLk1ue2pNqMGOK3vN9FTc1p//VH1s4/CbdL0RU22R7eZ+R6PC6+cMggHp93El1Rk092dXDrc1uoGeTlqtOO5YIVr/X4gJNuoNoF/eXbraGU0xvmGye9nS8MGq55PHMG9prH38ZBtibQ91rkBwOPoVgyoy7D/2nJjDo8DrtwxD9KEAqHaVOlzsrX0Wsovv+1Y/Amskd4XUZ82UZnmKbmk+YO3tm+h8aWIO9s38MnzR0DNj2U6LL8UF3u5eqvx2eBL7z3ddY1bCMSM1kyoy4jyH3jtlbmPdjANY+/jdtQlr7z7V3xQLxIzCQYjnHhva9zyuI/ZBTlcLsNRlQGOGJoGcePHMLdF0/gxnOPT80eg30wW7qBWlsVOGh2Qff0hsCATNHWq5lipZRLa533q1ApdSZwB+ACfqG1vrnb9z7gAaAeaAYu1Fp/kss+IiWSRqwq4Ml65bJ8Vj1VAU+xh5ZCKUXNIC+rZk9OlXcF01EZMiD+gHH6uOFMrx+VmhVY17DNUQ8YglCqRG3easWsZhqM+CxdeqzEsln1YGMUtwbD7NjbldF+yYw6Kss8DLWoSNnfZ5XF1zM/pBuawUgM0Hy4s4Nhg3wYCu65eALff3hjRjxPOGbidSkenTuFaEwTiZlU+F1sb+ninosn0tYV4YZf/zXLyH183kkcMtiPYagM14jtLZ2W100wEstyp0huZ5q6KG4NA9ltp7fuE1uVUuuA+7TWm/OxY6WUC7gH+DrQCLyplHqyW//fBVq01scopS4CFgMX5rIfu0APp/2we0MRYqbJomnHU+Z10RmOETNN9oYiDHU74zWZoSAY0Vyx+o2MG5zTYmKqAh6uOu1YRz9gCEKp4jascwm7jOwXj5GoTgU5Q9wIuGJ1A4/NnWLZdzAcs5yFfmzulKwgvoFw4xZfz/yRNDR37Amysy2c9aB2+wUnYGqNBirLvXx75V9S399z8UTu+cNWvvNPRzGyMsBdL2xleqIgRzqNLUHCMZPGlk5GDPbT2hVNPbB53NbXzYeJ0tFW8lsst4aB7LbTW/eJ8cD7wC+UUq8rpeYqpQb3cd+TgQ+01h9prcPAo8C0bm2mAfcn/l8LnKZynJb0uw2WdUuvsmzmRPwOS/ETDMf4/sMbmb3qTS6893Vmr3qT7z+8kWDYOa/JuqKm5Q2uy2EV7VqCkazXVPNXN9CSll9SEIQDw6WwdKNyWWhmu0IfMZs3dTHbWejstgMht2q6UfTK9V/jNwtO6VdGfzGIabiiW8W6K1Y3UFkWnzSpLvcy+743M77//sMb+LdvfpFhFV5Wv/YxaxoaMZRKpVBdcUk9E0ZVUlsVwDQ1//n0Zt7b2c55S19JuVa0d0Wz0q8tmVHHnS9sTe3Hzp3iYFdtHMhuO72aKdZatwErgZVKqa8CDwO3K6XWAou01h8cwL5HAtvSlhuBL9u10VpHlVJ7gGpgV/fOlFJzgbkAhx9+eGq9aWoq/C4eScs+ETVjjvOPyeVmUCxiOeYcLRZOvqDt5FQQnIadrIaiJrc8uyUjaOmWZ7dwx0UnZPXhtnlTZ5eSzW/j+mQ1ieHk6zyfdM9OIGSSq07VNvfa5HSbUsrye7ehcCnFxVNGc8nJRxKMxFj5pw+ZXj+K6nIvt10wnkgsxj/2djG9flTWBNKlv3qDJ688JTXrC3DlwxvZuK01Yz9OkN+B7LbTa59i4CxgNjAauA14CPhn4HfAsQUaX6/RWt8L3AswadKklJWmDEVbV4wFD72ZehWydOZEKnzOepXu91gLod/jnBltr00JVo/DSrBa5aR0SnVAOzkVBKdhJ6tuQ9HUHmLegw2ptnYFkXxug19dPontLV0pt7CRVX58Nm/qvG7FlaeOYUFiJi+pr73u7L6dfJ0LB49cdaqdwbdtd5DZq97kvstPzPr+9HHD2ROMZrjk3XPxBL77T0elAvmTbnp+j8Ehgyus/YfDMUZWxXPBNrWFaGoPZbRxiuE5kN12emvNbCXuyrBEaz1Ba/1fWusdWuu1wLMHuO/twKi05drEOss2Sik3MIR4wF2vCUfNlIKFuGAueGgDYYe98h9W7st6tbLy0kkMswguKRYuQ3H7BeMzxnj7BeN7TMRfDJI5KbtXJLJ6vSsIQm4YNllorF7rGgZEYpqFT7zDhfe+zsIn3iES01i4HwPQEYpZ6uuOUPbs2YFc56apB2REvbCP6nIvKy6pz7qP+T0Gj82dgt9jZN3nfnLWuCyXvN0dkazMVvNXNzDI7+XvrcH9Zo2wqmbnFMNzILvt9DbQrk5r3W71hdb6qgPc95vAGKXUkcSN34uAi7u1eRK4DHgNmAG8qHVuydTsfNqcln2iFPIERk0Tj9vICAb0uA1iprMeMJShLHOj/ud5Xyr20ASh5MnFfSIU1cx7MNOYmPdgA4/Pty5KZJ+vPbutVe7Z+1/9mJvOq7Puu4CBef09C0Z/I+B2pe5jI6sC7GoPcXVa9om7L57AY3OnEDU1bkNZ2hFlXpelrLZ2hqnwu7nt/PEZs8grZtVnGLxOv+cPVLed3hrF9yilfqi1bgVQSlUBt2mtv3OgO074CF8JPEc8JduvtNZ/U0r9HFivtX4S+CXwoFLqA2A3ccM5J+x82qxe9RUbpwuh1nEfqO7n0i6SvFi4DJh9ypFZxQUc5uVhy+gbns55m09uPqsAIxGEbDw5uE9Eoqal4RCJWT9Iu2wzW2T3ncw929tXvIWKqB8IWTD6E7s6Qlx63xspOfjTj76WcV9rbAly5cMbue/yE5m96k3uuXgiwUh26eZOm3LOzR1hFj21mSUz6lh49jjGDK8AYGi5J0senH7PH4jkMlPcmlzQWrcopSb0deda698R90lOX/fTtP+7gPP7sg9fIvvEFWk+astmTrT1aRPsiZmamgpfxszM8pc+tI0kLxYdoRjrP96dUdr7iQ2NjKwqo7p8/9sLgmCPUmTNgt12/njL0s0uQ1kGzrls/H6V0r0uC20YijE1FayZdxKRmInHZTC8wj46P9fAvN7O/g7k9FWlSFckUw7CMdPyvtYeiqYyTyyZUceSGXUZEy3DKrxZtsXi6XXc+twWGluCGEqx6KnNLDx7HIue2syvF5xcxKMWektvjWJDKVWltW4BUEoNzWHbomJqzSFDfDw6dwoxU+MyFF63clT55CROfwWXS3nXYlLmcfHVLwzPKO29dOZEyhwUtCgIpUpMwy///FGGEfHLP39kWbrZ41L84NQxWZMSHhvHX9O0Lgv9M4u+TVOztam91zO0uUTU5zL7O1CyYPQXXN0CNPcGw5b3NYjbCEkD9+Zn3uPRuVMIRUwMBbvaw4ys8qXcLFRi8mXjtlZqqwJ0hmMZRnKkj3FMTrcP+gu9NWxvA15TSj0OKOL+vTcVbFR5xOdWfNYSSqVHSSbqPrzKWU/wpfAKTmt46b0d3Hf5ibgMRczUrF3/GaNPOarYQ8sgZBNc6TQ3D0EoRQJeg2vPGMv2li4gnpXm2jPGEvBmP3RGTbjrxa0ZRu5dL27lxnOPt+nbZen6FPBmG665ztDmElGfS98DOX1VKVLuc7F05sTUPSIU1dz3yscsmVHHoYP9xLRmV3uY4YP8TBhVSVN7iNZghKb2EDFTc9l9mcWr7nrhfZ7fvDM1+dIeinD2+JG0h6Lc8uyWlJHckzzsz+AtBfugv9DbPMUPKKUagK8lVn0rX5XtCk17yLrgxGNzpzCkrMiDS2NXR8hSCf96wckMH+Qv8ujiuAw4a/xIZq/KTG/nNF/dXAsGCILQe2JRzZ7OSEZFsNsvGE+l3yrNpeayk4/MdofA+lqsDHg5ZLA/I5j3kMF+KgPZhmuuM7S5BDbl0vdATl9VioRjmrsTD2rDB/k4ZLCPBV87hmA4xiW/yjR4f/zNLxLwurjzhfdZPquem57enGVLLDx7HM9v3pmafHlkzhSaO0IZD3Y9yUNvDF5x0Tl45GLOvAf8mnhGiHalVElUHiiV7BPd/ZwgPs6uiHMyO0Ri2nIGNuKkCiOAJ5FPOZ3aqgBup1nvglCChE3N1WsyU1FdveZtwhY6VWtSBnGy7fXrNllmk4C44Xp4VRljDx3EiCF+xh46iMOrynp0h0hnfzNyva0OlizJ271vj4Wr2EBOX1VqmKYmEjX57j/F327+/LebMTW0dESyyotfsbqBEUP8VJV5+I9px1PuNXh+886M/hpbglQGPBnL0ZjJ0j98wMPf+3Kv5KE3lRnFRefg0dviHT8AfgbsAGLEXSg0YJ37xkG4bQI9nJZ9orufEySirh00zFJ5wCjzGhmvx1I+xRavdwVByA0zh8qW9pU6rXWGaWo+a+nk0+bO1ExxV3WM0dXlWUZFIWdo3YlczN3dOOzuG5JFwPlYzcgunl5HJGbaplfb3hrkmsffZsmMOoaWezl93PAMw/j0ccMZWu7lsblTUraF121w03l1VAU8tAQjhKMxmjvCfXorkU8XHfFN7pne+hT/EBirtc6pcIYTKPMaXHXasRmVaJbPqnecgeT3GpZK2O+gcZZKeruOUIzVr32a4fu88uWP+OG/jKHSQS4zglCKuG0qW1q9ibF/2LfWGa3BMDv2dmW4ZiyZUUdlmYeh3QoZFTLPazAcs8zFfPfFE0Ay2JQkVi6K16/bxJq5U2zTq7UGIzS2BLlu7SZWzZ7MT84ax7cnH8GdL2ylZpCXH5x2bIY74fJZ9RgGhKIxdrbHuPHJv6X8je18gHtj8ObrAVB8k/dPby2ubcCeQg6kUAQjOqsSzfzVDQQjzprdVCiGlntYNXsyL17zVVbNnszQcg8K5wiq32OwbFZmJaBlibKWTsLrdvHqR818/faXOfW2P/L121/m1Y+aJfBFEPKAS2FZ0c7qrZZhU3XO7v4bDMeyXmNft3YTwbC9n3Bv3CFyxet2pXIxX3jv68x7sIGm9pDokBLFNDWdIesZWb/XoHZoIEum//vCE1j+0oepdq2dYb665CUWPvEOP592HDd844tZ8UrzVzewqXEvF937Op82d7Lga8cwYVSlpUtEkt5Wtquu8PLwnLhLxpNXHpiLTm9cNQY6vZ0p/gh4SSn1NJAq1q21/q+CjCqPRGLWyeOjNsnji0UkatLaGeWax/fNaN92/ngqA84ZZ1fE5Km3GrOyT1x68pHFHloGEvgiCIWjK4eKdqaGrf/Yw8NzpqB1PG3Vi5s/Z3S19SubXCraFRLRIf2L5o4wH+/qyJqRPX3ccNq6Yix59j1mn3IkD35ncir7RCRmsnFbK7CvKAck/I0f2sCq2SdaymplwJN6mFs07XjmTz06VdXRygd4f2887GZ3rYJP94f4Ju+f3hrFnyU+3sSnZHDZvPJ32quCmCarjvo1j7/NmnnW5VCLgak1K/70CSv+9EnG+lknjS7KeOxwevlMQShlXDYV7ayurzKfQf2RwzJyhi+bVU+Zz/rtkmHjbnGwL13RIf2LcDTGnS9sZfH0Ou5/9WOm14+iutzLoYP9NHeEeX7zzqwguie+fwqw7+3Grc9tSX3X2BLE77F2e2gNRlJtKss8hBP5iXvyAe7JJz0fmSeSfsTJcUj6QHt69d5ba/0fWuv/AJYk/08sOx6Pobj9gvEZryZuv2A8HocpN7sZEu2gIiPJG1Y68RuWs84lFO61qiAMdLyJILTu7hNei2us0yYlZmfI+g1Yru4WhUR0SP8h6Q6z4ZPd/Pib46gu99LcEWZPV4QKv9vyvlZdEQ+ge+A7k/G6DG74xhdYcUk9E0ZVUlsVQGudZVssnl6XcrmorQowyB9/k9KXNw19nd1NzjSft/QVrnx4Y9a1K29AMult9omTgF8CFcDhSqnxwDyt9YJCDi4feN2KynJvRt7LynIvXrezFJzdDIlykMFp2JR3deK9QiJsBaEwuFyKYRWZOnVYhReXhVNxrhlrTN37inYHguiFgUl1uZcHvjOZ9lCUWb/8S+r+dc/FE/H6leV9DTTrGhqZXl+b8V0yE8Xq1z7h7PEjufX88YwY4idmam5+5t1UsY7F0+vwexQn1A5hzbyT0Fr3mIXCjr5mnkifaW5sCXLLs1tYNO14jh5eQcAj10B3eus+8d/AGcRzFKO1flsp9ZVCDSqfBMMms+97M0ug1sydQpWDoohdiRmS7knunZSSzVCKyjI3q2ZPxlDxG1jMjDluptg0NVv+0cacB9N8sC6ZxNhDJcJWEPpKJKYxtWbU0LIMPWCVrzzXjDVKwXf/6aheP3jnYuSWauS9GPJ9xzAUQwJuPm3u5LbzxxOJmbgMRSRm4nEZ+D1GxkOe32PweWuIOV85KpVdAvYFft56/ni+WTcSw1BUl3txKbjpmXeZXj+K7/7TUamHuZ9PO57dnZE+yVxf/du7zzRv3NbK7FVv8sr1X5M0ghb01ihGa72t26xlSXhmR0okt65hGJYzJDed56xU0Ls7Ily3tiHjqflAHP4Lya72UMoghoQP1oPr+fUVJzN8sDOqAwpCKWOlB4ZY6IFkxpor0lJi9pSxxq2UpYFilcLNNDWfNHdk5DQ+orrMMqcxlEbV0O6UqiHvNExT8/meUFaqv5uefpf/unA83394Y9aD283f+hIuw2tpPyjg+w9vYOHZ41j01OZ4XvyvHcOViX6S/Wut++wP3Ff/dilDnhu9TsmmlDoZ0Eopj1LqWuDdAo4rbyQD7dJxYqBddbmXG77xRbyJXJ9xH6YvOsrXJ2Jqy3RJEYc9YARtqwOWxHOcIDiaqI0esJpo6IqYNHy8i4fnTOGP103l4TlTaPh4l22lzpiGe/7wAeFEdqBwzOSeP3yAVdHM1mCY5va4oXPhva+z8Il3aG4P0Rq0Ti9VClVDu+OkFFqmqWlqC7G9pZOmtpBlsRan0twRzkrNet3aTcyfejStnRHrVG0eFz6byobJ/MXJTBMLHtpAe1eUhWePY+38k7jv8hO55dktbG/tyskf2O4c98W/vbcp34Q4vZ0png/cAYwEtgPPA98v1KDyic9tXd3MZ1Gus9iEombGk+zKSycVe0gZxHKoZFVMSiXjiCCUIrnoAa/bOvuE10b/GgouO/nILDcyq0u3KxyzLDe9Zu4UywIbpVA1tDtOSaFV6jPWduexMuBhZ1vIUi6GVfiIap1VVOu288dz8zPvZWWa8LgMFj31VxZPr+NHazexcVsrzR3hXs/SFuocSyaV3OiVUay13gXMLPBYCkIkavL029uzcute5rDcus0dYW7/fWbuz9t/v4WbzqtzjN+PJ4dKVsUk1xKt/YHRNzydU/tPbj6rQCMR+jueHPyEIzFtmdv8slOOsuw7pkkZxJBWdcwiNWWurnEBr8tSLwS81q+RneDLqxwSgJ2PtGDFxM6FoDUYYflLH2bJxfJZ9Qzyu5h2z6ucfFR1Sn5NDR2hCE3toYw0bbVVAUZWBVg1ezL/2LNvH+satrHikvpUnuKeZmlbg2H+saeL284fnxpXvs6xlCHvPb3NPnE/8EOtdWtiuQq4TWv9nQKOLS8oBad+8dCMUoy3nT8eh8WGYZqm5QyJaTrn1V7Ao1g+qz6rZHbA46yT6TIU1d2i46srvLj6sVEsCAeLgNew1gOWJem1tf7F2nDVWlNT4cuYHFj+0oeWqSntgvjsrvPKgJdDBvsz9MIhg/2WMRFOmRl1SgC2U2asDxSrYLUlM+q45dktNLWHGFkV4KHvfZmmthDNHWHufOF9fnDqGC6sr+UrY4dnlXJ+dM6X+flTm1OZJpbNqufnv91X0jmZD/mHpx3L2OGZs7RVAU/WwxbA562Z5c2TRnepnOP+Qm/dJ+qSBjGA1rpFKTWhMEPKL9quKMbcKUUeWSa5zJAUi86wyZ0vvJ9xw7rzhff52TnHOSqTRyRqsm79NmZMOtzRbwcEoRTJSQ/kqH/9HoMfnTk2azbXKjAv4HVxz8UT2N0RSRm5Q8s9tjO/hqEYXV3OIL9nv7O/TpkZdUoAdqkHayVdCB6bO4VQ1OTvrUEGBzz8d1oVxpm/+EvG8W3+vI1H5kzh2wnXH9hXyvnh732Z6874At//2hiqK7wpgzjZ5vp1m1g1ezJDyz243UZKZuwetg4Z7GNeN5/n69fFK+KVyjnuL/TWKDaUUlVa6xYApdTQHLYtKhHTeubBadkntNacfFQ1c75yVMqQW/nyR44q3hE1tWXln38/a1yRRmSNYSi+MvaQjKf7xdPrxIdKEPJA1NQ0tWUGejW1hS11aq76NxS1DuKzmhwY7PPgMoyM2bXls+oZ7PPYjr23r5GdMjNaXe7l6q+PLXq56f5Q9towFF63i/d3tKdk5pkf/jMel0FrZ9jy947ETEv5/XxPFxfe+zoAL17z1Yx74oRRlcyfejQelyJqakxTp+49di4Sa+ZOydrHxm2tjB5WjmmaNLWFxA84B/ri+tRbw/Y24DWl1OOJ5fOBmw5grEDKqH4MGA18AlyQNLi7tYsBf00sfqa1PjfXffnc1jMPdoEexaLMZzDrpCMyDLmlMyfalkMtBi5Dcfq44UyvH5W6eNc1bHPchWqWwKy7IJQqfhud6rfQqX63wY3njmN3RzwgyeuKL9sFOkdiprWfcCzbjWx3MDujwPzVDXlJseaUmVGnBEk5ZRx9pbrcyxHVZdx+wXiuXvM29/35Y75/6jFEYib3XX4iZV5Xyihtag/hMpSlrJuJyaraqgAxU6dkZcKoSq49Y2yGu8uKS+oZOzzudmPnIhGKaRY9tTlj/f2vfsyOvV1cdO/rJRfYWEz66vrU2zLPDwDfAnYkPt/SWj/Yh3HfALygtR4DvJBYtiKotT4h8cnZIIb4CbKaeXBaxoTOkJnKkAGk0rzYlUMtBn63wbVnjM1IG3ftGWMtb4bFJGZa31hjDvvNBaEUiWksdapV2jQFdIZjGWnTOsMx7G5NST/hdOz8hAuZYs1JaaycUm7aKePoC0kXmiOqy7n1/PH84LQxeN0Kt6FY+MQ73PzMe3hdBrdeMJ5H5kyhvStqLesJQ3jpzIm8snUnKy6pp7YqwPypR2dNyMx7sIG/7wmyqyNk6SJx1Wlj+GRXR9b6G77xRRY/815qXbFS8ZUafU1jmIsLxFCgQ2t9n1KqRil1pNb649yHDMA0YGri//uBl4DrD7CvHsm1zGixKIVxxkxNc3s4KwH6EL/968piYFcyuwR1uKPINcOF0D+J2s3mWgQFh20mJR618Sk2DOvAMsPiubuQKdb6y8yoYE1nOMbiZ97jrotPIGYqrl7zNjUVvqxZ3ge/O9lS1murynjwO5P5f8+8y7+fNY4Rg/08/L0v297Hd7aFGD7YZ/nd6GFl/Otjb2et3xOMsHFba8Y6CbrbP311ferVFJ9S6mfEjdZ/S6zyAKt7P8wsDtFaf574/x/AITbt/Eqp9Uqp15VS/99+xjg30XZ9U1NTar3dzIPT0nOVwjhLpXhHMvVS+ixPT6mXDiZ2cioITsNOVu0KIllVncs1t7krLbDssYSf5f2vfozLwiou9HXeH2ZGBwK56tTmjjAf7+rgqtPG0BXRhKPxhzyrWd5PdnVayrpSsLcrwvObd6IUtHZFufgXf2HrznbL9s0d4dRDXPfvtIam9lDW+tbOSNY6CbrbP0nXp3RyOXe9fe99HnAu0AGgtf47MKinDZRS/6uUesfiMy29nY5HktlZVUdorScBFwP/rZQ62m5/Wut7tdaTtNaTampqUutdiZy13RWn09JzDa/wsWxWfcY4l82qZ3iFc3ILlkrxjvTUS4/NncKiacfbpl462NjJqSA4DTtZVYk0Yem6yq7ARi7uEADDyn1c/fWxLHpqMxfe+zqLntrM1V8fy7DybD3o5OtcOHjkolNNUxOOxqgs83DM8AqCiSqntVWBVHW6dO58YSvLu92XF0+v46anN1Ph96RyRidnJ5e/9KHltbGuYRsBryvLJWfx9DrLbVZeOokjqssc4b5TavTV9am37hNhrbVWSmkApdR+E3Bprf/F7jul1A6l1Ait9edKqRHATqt2Wuvtib8fKaVeAiYAH/ZyzAB0RU1ueTazKMYtz27JSMXiBDweF18YXsFjc6cQNTVuQzG8wofH45wnQ4/LJmm/w0pC5ZJ6SRCE3NAayzRhPzvnuKy2bkOlgpqSr6Rvv2C87Rsww1CMqalgzbyTiMRMPC6D4RXWs7RynQu5YBWA9cB3JhOMxLjt/PF0hmNZ97em9hBDyz0Zsn7rc1vYuK2VG77xRZbNnIjHUKkiKxu3tXLrc3F7o7rcy5CAhyXPvccN3/giUVMztMzDY3OnsKs9zD/2dqX6ag2GWTPvJLTWGbmLxX0nd/rq+rRfo1jFS+c8pZRaAVQqpeYA3wFW9mHcTwKXATcn/j5hsd8qoFNrHVJKDQNOAW7JdUcel0FTe4h5Dzak1tVWBfA4rAobxA3jkVVlxR6GLS5lfYOzem1abKSCT8+If7BwoLgNxexTjuxVxUivW1FZ5skomFFZ5sHrttYZpqnZ2tTe68hxuc6F3mKVDu3mZ97lJ2eN447/fZ+fnDWOZTMnckUi4D0p11FzX2aIJEkb4qdP/I2m9hCrZp+Yqly3cVsri57azIpL6hnsd3Pz9Dp27A1x6dJXM1IHrmvYlir+cfXXx3LoYH+WjItsHxh90Qv7NYoTM8TnA/8K7AXGAj/VWv/+gPYY52ZgjVLqu8CnwAUASqlJwHyt9feALwIrlFImcTePm7XWm3PdkddlXYXN67DZzVIgFDN5fH1mydaVL3/ElacdU+yhCYJwkAjHTH6zYXuWHviBhR7QKHweg2OGV2BqjaEUGhNtk3/CKUUzhP6FaWrbdGgBj8H3vzaGprYQNz39LgvPHsdhQ/z4PS7aQ1EUilWzT+Ty+zLTpd71wtZUINzl973Jkhl1LJp2PEcOK6fM52JYefwNR1NbKEum569uYM28k/jZOVpmgR1Gb90nNgCtWuvr8rFTrXUzcJrF+vXA9xL/vwp8qa/76gzHeODVT7INuVPFkMsVv9vFeRNHZuRSjucndY6LhyAIhcVnowd8FnogEjX5vDWUqmqXLPM8utpaZzilaIbQv2juCFumQ7v1/PF0hk3u+cNWfvzNL9LUHmL5Sx9y7RljM2aMV1xSz+PzTyIcNXEpxZ0vbGVNQ2Oq/8aWIIZSzF71JrVVAR6bO4XmjjDV5V5bmdZaO/rN8ECltz4EXyZevONDpdSm5KeQA8sXhqF49aNmvn77y5x62x/5+u0v8+pHzfJUdgBEY6Zl9gmrxPrFxjQ1TW0htrd00tQWclwwoCCUKrnogZiGX/75o4xsEr/880eWOY0BPG7DMjDPY5MLXa5zoTdYGaY1FT6OrinHbSh+ctY4PC6DFbMmctVpYyxzDXeEYuzc28W2lk5e/ag5o6/aqgCtwUiqfWNLkPOWvsKWHW0EvH3LhiAcXHprFJ8BHA2cCpyT9nE8HpvsEx4xinMmqq2zT8QcVIoa9gVUnLf0FU5Z/IeUcpIbpiD0nVz0gEvBZScfmZFN4rKTj7TNJey20ddW/sqFvs7F4O4/dE/TNWFUJTdPP57P93Rx0crX+eqSl7jw3tcxUYyuLrOUb0PB1WveJmZqywwTy1/6MLXcGoykXH+ipnZMIRhh//TKfUJr/WmhB1IoTDQBrysj0CPgdWHaZoET7LBLlm84LNBuV0e2D9ecB9bnpfyrIAx0ctEDsRxLrgfDMctsQXdfPAG65TwqpP9xX0vFCs4imaYr+XteddoYXIaLBQ+9kSE/V6xu4OE5UyzlO5mS1OMyuPmZ91h49jgOHeynsszDTU9vTgXNJX2Vk31GoqYUgikhnJeCId9oxdI/fEA48WovHDNZ+ocPQDtPIJ0+M2GXn9RhNnFBy78KwkAnFz2gtaamwseKS+p5bO4UVlxST02FD23zdsnrdqWyBV147+vMe7CBpvaQ5avmQvof51oq1um6e6CTTNP12NwpPDZ3CqOGxiucWsmPInsmePmsela+/BG1VQE6w7FUhon2UJSHX/+EG889npevm8qiacen0qwlt/W6XbaFYERunEcuZZ5LEo9LceO544iZipjWHFYZ4EvnjsPjsOwTpTAzoTW8vGVHRtDi2vWfMbr6yGIPLYNCln8VhIGO1rD1H3t4eM4UtNYopXhx8+eMrs4OGgp4XfzozLFZ6dvsqs5VBTyW2YKqAtml5JOvxLtf5/nw1czF4C4F3S3EDWOv28U1j7/NwrPHcXRNhU3efYMjhgZ4ZM4UTB2vGWBqzTe+NIK5Xz2Ktq4Iv73yFPweFzHTZMakw4nETD7f00VVuSdVnW5/bhIiN86k388UuwzY3RHl2ytfZ+qSl/j2ytfZ3RHFaWmKc52ZKAblPoPzTzycxpYgTW0hGluCnH/i4ZT7nHUyPS7D2o/caT+6IJQggwMGpxw7nA93tvOPPV18uLOdU44dzuBA9vUVjVmXho/aRNq1BCPc+cL7GYF5d77wPi3BSFbbvlau6olcSsWWgu4W4iRlZl3DNrQ2WTpzIrVVAS6or+V///WrPPS9LxOOmvz8qc388y1/YOYv/sJnuzu56pG3WPjEO3RFTEYMCVDuc9PcEaYrajJ71Zt8dclLXPv424QiJktm1LF2/kmsmXcSY2oqaO4IW84Ei9w4k34/UxwMm6lZB0jLETh3ClX7rct38CiFVEThaPxVT3quxyUz6hjsd5YYaW3tR273ylYQhN7TGdLsstIDPjfdXfa7bPRaV9TalSkcjfH85p08vzmzyOnPzsnWg32tXNUT3X1QezK4S0F3C3GSMnPTeXVETZPKMsX/LDiZz/eGuPy+NzJyGDe1hdm4rZXr1m5i4dnjmPdgA/NXN7Bo2vGp1GtLZtRRU+FLZZxIzkLPe7CBv/zbqT0WohG5cSb9fuosYlpHSkcc5ruTy8xEsQjbzPqE7fIrFQnDMCz9yA2j34u7IBSciGmtB6x0atKVKZ2eXJly1YN2vpp9Jd3gfuX6r/GbBafYvtYuBd0t7CMpMwbwwc52QlGTKyxyGM+fenRquTLhvtPYEmTU0AATRlWm5D7ZLr1tbVWAmKbHmWCRG2fS760Et2GtlK1S/BSTQr4KzBcxmweMmMMeMKrLvVz99bEZaaCu/vpYR51LQShV7PSAVZBQhd/Fsln1GXpt2ax6KvzWN34n6cHeGtxOGrOwf0xTs7Oti85IjK6ISdRGnpOGcHoO4tqqANt2B7n2jLEpw7gyzd+9tiqABlZeOgltk7owORMscuNMnPXeuwAk8152D/RwmlFsGIoxNRWsmXcS0ZiJ22UwvCJ/Mx/5wOMyLAMTnOarW8jXqoIw0PHmoAc6QiZ3JXyEkynW7nrhfW4893iGBLKal+S1W4pjHqh0D247fdxwfnbOcZby3BqMpOyFW57dkpFurak9xMKzx7Hoqc10hmOpbZbMqOOoYeUMq/DR3BHuMRC0FO75A5F+bxR3RU1+s2G748s8m6bu0f/ICbgNxe0XjOfqNftKtt5+wXjHPWDAvlkeQRDyi8tQ3HHRCfzw0bdSeuCOi07AZaEHIjHT0kf4J2fZp0csxWu3FMc8EEkPbpswqpJ/++YXCHiNrIwn91w8EbdLsfq7k/F5XNx2wfiU7ZBMt1Zd7uW288djas1jc6dwWGWAqx7ZGM+pTTzIf8Ws+lR56e4zwaVwzx+I9Huj2O82OG/iSGavejNjpthnUza0WBQyEX2+6IrE+L+/ey9j1uf//u497rjohGIPTRCEg0RXJMZ/PvVuhh74z6fetdQDSfe1rLRXctMXikB6cNvN04+nPWTyya69DB/sy5DnG5/8Gxu3tfLiNV/l/OWvZQTgbd3ZTlN7iCEBDz9auylVtOPB70ymqT2Ex22kZqNrKnwsmnY8Rw4rp8znYlj5vpngUrjnD0T6vVFsaiyDQh63qahULEohEtUwVCqxfpLaqoA81QrCACIXPeBxK5bNnMgVD21IGRbLZk7E47BJCWFgkJ7butzn4bv3vs5t54+nsSXIoqc2Zz28fdrcmRWAt2ja8VRXeFny3Hspg3jJjDp2tYdZeekk3IZKGbuNLcFUporfLDgl4xophXv+QKTfa6ZwzLTOPhFzVnWzUohETfpnd8//K7M+gjBwyEkPaIXXrVg1ezIvXvNVVs2ejNetwFmxucIAIT24LRkw2hqMsK5hW1YVuxWz6rnzha0Z2ze2BDmqppzqci9z/vloHps7hUXTjqdmkI/DhwYYU1NBMNw7Y7cU7vkDkX4/U5xLUEgxySUvZrFQQFm3/L9lXhdiEgvCwCEXPRCNmXz3/oYs/btm7pSDNl5BSNI9KLK2KsDylz7k2jPGcv+rH7Pw7HFUl3sZWu4lGI6lqtMlqa0K8N4/2lj01GaWzpxIVZmHD3Z2cN3jm2hqD7Hy0kkcMtjXq0qLpXDPH4g4yzIsAC6bWQ2roJBikktezGKhFFnlWQNeF8o5QxQEocC4XYoyX+Z8SpnPjdsi+XDUJi1VVGaKhSKRDIr0uBRLZ06kqT3Erc9t4duTj+DYQyo4ZLCfa9a8zb//zzss75ZOcPH0Opa/9CGNLUEWPLSBD3Z2MHvVmwAsPHscHaEoXRGTB74zeb+p1krhnj8Q6fczxcFIjFue3ZLhRH/Ls1scGRzm9AjmSEzz6tYmTh03AlNrDKV4cfPnfP24EcUemiAIB4lgxOS9v7cy4YhqYqbGZSg2ftrMhCOqs9r6PS7LWTO/p9/PxwgOxjQ1kZhmwyfNPDxnSsb97NRxI/j5tOPY1R5m+GAfj86dQjhqsnVnO7c+tyWVfaKxJUiZ18WEUZVce8ZYrl+3KWPG98krTyEY3peiD6CpLZSVts/J9/yBSL83il2GomZQ5hNazSDJIXkgDPIb1B85jItXvr4vaGZWPYP8coMThIGCz20wumYwF92bqQesMvoMDXhZcUk98x7cl5ZqxSX1DA3IK2KhOCRzFf/Phm2cPX5k5v1s5kTaghHKfG4C4Ri72kOUe91s3dluGYjXGY4xf+rRKYN4wqhK5k89OjFj7GHEkHgAavf8yJJ+zbn0e2vG7za48tQxGdXNrjx1DH6Jfs6Z9q7scphXrG6gvctZQYuCIBSOaExb6oGoRbn39nAEj0uxaNrxqaAkj0vRHo4c7GELArAvFdo/Hzs8lRUFEnL80AZ2toU47bY/cu3jb7OnM4LfbVgG4i2fVc/IKj/V5d6UQXztGfFKqjOWv8YFK15jy442TFPbpl9LlnwWnENRLEOl1PlKqb8ppUyl1KQe2p2plNqilPpAKXXDgewrHNMs6Cb4Cx7aQNhCgQs9E7EphxlxWJlnQRAKRy4ZfTpCMb6zaj2zV73Jhfe+zuxVb/KdVevpCEnaKaE4JFOhjRgSsJTjskTcTDJ9a0xrfnDasalAvLXzT+Kh732ZP7y7g1uf28KIIX5qqwIZM8bJ7ZOGr6RfKx2K5T7xDvAtYIVdA6WUC7gH+DrQCLyplHpSa705lx1FYyY1FZmJuZe/9CFRh6VkKwVcNon4nRa0KAhC4bDTA1avgWOmttS/MXmQFopEMhWa122dmWpkZYCXrp3K53uC3PLsFqIxzagqHz875ziiCR/6V7c2cdv/xtO1/ce041l56SQ6QlFbwze5z5oKH/OnHk1lwENnOJYVuC4Un6IYxVrrdwFUz2kLJgMfaK0/SrR9FJgG5GQU+z0ubjx3HLs74q/rvC6DG88dh98jwpgrg3wGq2afyLbdwVQqplFDAwzyOc8VJfnKqntQgyAIfcPrMlg1exJgYKh4gSQw8VqkufS7DX78zS9klYbvb+5rom9Kh+pyL4/Pn4KByirvvGRGHXe+sJXzJ42itqqMe2ZOQGto7Yzx8a4O7nxhK03tIZbOnMgF9bW8+lEz733exhHVZQwJBGxTsVWXe3ngO5PZsbcrVUws6VdcGRBZcRJODrQbCWxLW24EvmzXWCk1F5gLcPjhh+/7Qms6wzEWPvFOhuCjZaYiVyIx6IqYGedy+ax6Ig57A+TkoAZbORUEh2Enq143BCOaK1a/kRFoN6wiu4+oJmUQQ3zm7Oo1b7Omh4qipWZgOlnfDARy1anRaIxd7RGuWN2QKsM8elgZO/aG+HVDI5ecdATtoSiLnvobl518ZEZWicXT67j1uS0seGgDq2ZPZtqEkdz63Baa2kM8eeUptnmHDUNR4Xdz6a+y3SukrLOzKNjjulLqf5VS71h8phVif1rre7XWk7TWk2pqalLrw6a2LPMcltd3OROOmqmnaoify/mrGwhHneWK4uSgBjs5FQSnYSerHSHrgNuOULYeiORYUTRpYJ639BVOWfwHzlv6SipYyak4Wd8MBHLVqU0d4ZT8btzWyuxVb3LJL99gTzDCuSccxq72MNet3cT0+lFZPsLXr9vE/KlH09gSxONS3PrcFiCeo7itK8ohg338esHJlnmHI1Hra0H8ip1FwWaKtdb/0scutgOj0pZrE+tyImYTHOZkJetUSiXQToIaBKFwRG30QNRCD+Qah2BnYDp5Nk30TWlhJ7/V5V4qyzw0t4dpbAlSGfBYtqsMeKitCqA1jBlewbQJI7NyFFu9JUj6Fe+v0p1QXJzs2PUmMEYpdaRSygtcBDyZayeeRJnndGqrArgdVua5FHAZitPHDWfFJfU8NncKKy6p5/Rxwx0XaCc15QWhcLgThm46tVUB3BZ6wGtTUdRrozPC0Rg1Fb4MHVNT4cubgWmamqa2ENtbOmlqC+VlckT0TekQjZp4XIr7Lj8xJV8TRlVSWxWgusKLy1B0huPln1uDEcvftbrCx/3fmcw/9nZx9enHEo7Gg/mh57cEybLO+6t0JxSXovgUK6XOA+4CaoCnlVJvaa3PUEodBvxCa/1NrXVUKXUl8BzgAn6ltf5brvtyG7Bs5sRUPsJkgu5+FudxUCj3GvzgtGNTr56SvoTlXmedzKqAJyuAYvmseqoCnmIPTRBKngqfwbJZ9Vl6oMIi4FYDZV4Xi6YdnwrOLfO6sDNFA14XPzpzbEYw0pIZdXmJ0i+U72/S2LHyJRWcg2lqmtq72N4SzIiLWTpzIlVlHnbsDTGyys/Qcg+3XzCelX/6iMXT6zJmgZfNqueWZ9/l+c07U7bEI298yrVnjE1Vu+vpLYHPbWRcC1YFb4TionQ/DDibNGmSXr9+PQDbWzr5j9/+jen1o1IpgdY1bONn5xzHyKqyIo+0tPh7SycXJKpYJamtCrBm7hQOc9C5bGoL8ZPfbMr6zW86ry79FWzRp7fT5TSd0Tc8XYTRCMXmk5vPsvvKUbL695ZObrTQqTeec1yWHvi0uYP/8+hbqTRUyZRsd1x0AodXl2ftZ2dbF99a+mqWjvn1gpMZPsjfp2Noagtx3tJXsvrOh2tGqQUHFpCiHrSdToX4798einDJL9/IkoGFZ49j0VObue388Qwpc1PmdfPhzg6GVXjxe1y0h6IM8ntSBrHVtgvPHse8BxtsZcpOtp3sGtSPsZVTJ2efyAsxU/P85p0Zggzw72eNK9KI7HG6Yi0ln2Kr3/xn54iPnyD0lYiNTv2JhU51GYqm9hDzHmxIrbPLaQz2wUgRm2DeXHRmrr6/ufRtGEoMG4cTjsZsY4yS/sPXPP42N3/rSxw6xMXsVW9mtHts7pQsmU/fdvggH/ddfiJHDitHo1OuOUkZ0hpqKnwZ+8+X/An5o98bxUYOieaLSSmk9SmV4h1JP/Lu4/SIH7kg9Jlc9IDXUNxz8QR2d0RSr4yHlntsfYoNZaOvLXLa56ozcwl0KgV9LOSG1+1iW+K37C4DrcF4HYPGliB+jwuPK1sOk77GVtvWVgUYWu7lB49sTMnLA9+ZTChqZsjQkhl13PJs3M0iub3In7Po91aC11DcfsH4DOf22y8Yb6uUi0UppPXxugzroBkHGptW4xQEoe/kEjxnGApDKRY+8Q4X3vs6C594B0Mp+xlXZX3tWjXPVWcmYw3S+7aLNSgFfSzkRnW5l+GDfNxx0QkZMrB4eh3LX/owtVxZ5uWeFz/IksPDKv0s6yY/S2dOZMMnzay4pJ6bnt6cIS+fNndmydB1azdx1WljUtuvmFVv6Xsu8lc8BsRM8ZAyT4Zz+5Ayj+OetkolrY/VuXQawUiMW57dklFa9pZnt3DHRScUe2iCUPL4vHFXgXQ9UDPIh8+brVO7omYqyBkSOY0f2sCjc6dY9h2KmdbX7rcnZLXNVWe2BCPc+cL7GX3f+cL73WMNDqhvwfkYhmJ0dTkVPjeLph3PkcPKUQpuenozG7e1pibMmtq6mF5fi6k1t54/nkOH+FHE2zW1hVPFPprbw9z94lZ+ds5xuA2V5VpR5nVZytCooQEemzuFznCMEZV+S1tE5K949HujuCtqcutzW5heP4oyXIRj8eWfnnNcsYeWQSnkMIyaJgGPwTHDK4hpjUspomaMmOms4h25+jEKgtB7OrpM/vz+Tk4dNwJTawyleHHz55x+3AiGZGawyjlPvN/jsrx2/Z7st1G56sxcYg1KQR8LuWMYikjM5M4XtvJfF46nMxTj388+joVnj0MpRXsowvfu35dVZfH0upRBnB5Y+v9+9y7T60fx/Oad/PvZ4/Aa2fJi527xYVMHi57anCrxbIXIX/Fw3nvvPGMouOzkI1n01GYuvPd1Fj21mctOPhKXw+yjUshh6HUZtHXF+PbK15m65CW+vfJ12rpijvPVDXit3TwCDksdJwiliNulmDi6mosTeuDila8zcXQ1bgul6skhpzHAsHKfpR4cVp4dxJarzswln3Ap6GPhwEim/bvkl2/wzTv/zMUrX+ez3UF+/tu/0dIRycg5fP26TXhcytKGOGyIP+5LrxTV5V5WdHOtqCr3cNv5ma6bKy6p54TaIVnV7roj8lc8BkRKtgst0og9NneK41KyOT3atFTOpWlqPmnu4NPmztTr3SOqyxhdXZ5+Pot+YiUlm5BOqaRky0UPfL6nk8+a41H9ydm3284fz+HVZYzoPq2cIBc9mGvbXIKXnK6PHYpjU7IlsUuNlkytdt/lJ/KjtZtSwXB/vv5rXGQh76tmT6a5PcThQwOMqCxjd0eIt7ftoczrSqUerBnk5cZzj0drnbMMifwVlIGdks3q9V3MYWnESoFcyrsWE8NQjKoM4HMbRE2N21AMr/CJQhGEPJCLHjBN+OWfP8rw4/3lnz/iZz24r+WS3izXtmNqKlgz7yQiMROPyyiaXhCDp3jYpf1LplbbE4ykinE0tYdsbQiXEc+WohP2VWXAy6FD/BkPXf/3W1/i0MH7/IaTFRWdlOZPZDGTfm8UG4nSxN0TzTvtRy+FFCzJ8q7dn5jtXoUWi2jU5NOWTrbtDqZmioORGKOHluOWCkKC0Cdy0QOGgn89/VjchgtDQXWFj389/VjLbBIHQq4zxVub2nulYwupj0tB1/dn7Px1k6nVmjvCLHpqM4umHY83cb+wsiGiMc2ISh9uQ7G9pZOANy5/D8/5Mi6lCHhdVAa8GQax0353J46p2PR7C6EsUZo43R/oB6cdS5nD/EtLIQWLx6VYOnNiVkoaj8MctHd3hmlqC2WkgWpqC7G70znnUhBKFb/HyEpNtWxWvWUwnMdlEI3B5fe9wam3/ZHL73uDaIy8xCEkb+jnLX2FUxb/gfOWvsKWHW22QXy56NhC6uNS0PX9GSv/38XT61jXsC2Vnq2xJcgxw8txGQqvW/GDUzNtiCtPHcPa9Z/R2hnl3//nr1z58Ea2/KONby17la/c8hIX3vs6O/aGMvbrxN/diWMqNs6yDAtAV8TkitUNmSmBVjfQFXFWxoRSSMESiWmefns7911+Ii9e81Xuu/xEnn57O5GYs9wnwjGT69ZuysoPGY456zcXhFKkK2LS8PEuHp4zhT9eN5WH50yh4eNdljo1HDOZ303/zl/d0OO1mHzFvL2lk6a2UF6MXMhNxxZSH5eCru/PGIZiRKWfRdOO5+XrpvLInClUlnmYXj+KW5+LF9Y4fdxw9gSjXPv422xq3MsVD2XK8IKHNjBxdDXzVzcwvX4U86cenXXPmfPAelqCoZQsh6OxVBBfkmL/7iKL2fR794lIzKY0scMMuVJIweL3GFw85QgiietFKcXFU46wnCEqJjFTU1Phy/BjXP7Sh7Y3V0EQek/Aa/C1cYcSjpoYCkyt+dq4Qy2zu+Sqf3N5nZvrDd3rdlm+BrfSsYXUx6Wg6/s7lQEvxwwvx+0yaO+KEI2ZeF0GPz1nHIP8Hsq8Blv+0U5NhS/la5xO0ge5psLHscMr0MDCs8ex/KUPUwF6NRU+Pm8NpR4Kc6lod7AQWczGWdZMAUiWJE3HiaWJSyEFi9uAvV2xjFehe7tiOO36qfDFU+6kv+760ZljKfc5bKCCUIIooC0YzdADbcGoZTh3rvq3uSPM7b+PF+94bO4UFp49jtt/v8Vy9tfjNiz79tjEDVQFPFzVzZXuqtOOtaxoV0h9XAq6fiDgMgy6IjGGVnhxGwYLn3iH85a+yuX3vcHHuzp55I1PufaMsURipqWcHVYZ4PYLxxPT0BGK4nUZ/OzccUwYVQnAVaeNyXpL0r2iXbF/d5HFbPp9SraWji4aW0MpF4qk/1ttpY+qcn+RR5qJ06NASyUl2+etQc5f8VrWOB+fdxIjKlPKregnVlKyCemUSkq2v7cGucDi+loz7yQOq8w0Hprbuti+p4sFiap2yTiEkUP8VA/K1r879gT5oKmD69dtyiigcExNOYd0S+G2uyPEln+0pV5bJ2fixh46iKEWeY2b2kKct/SVrHH/ZsEpllH+hdTHTtf1fcDxKdnS30bUVPi489sT+PbK7PtaMkXbkhl1mJoMmVw6cyJ3v7iV5zfvTMno/a9+zOxTjgTgurWbePC7k/narX/M2v/L101N+Co743fvx7LYEwM3JVtn2OSubqU973rhfX52znFUlRd7dKVFqaRkC8esU+5ExKdYEPpMJIfrKxzT3P3i1gz9e/eLW/mPc4+37DuWZnwk+71+3SbWzDspq20wbF3O/e6LJ4CFbs/V3aKQKbEOVrotIRPT1Pxjb1fKIL72jLHs2NvVY4o2Qynue+UjHpkzhR17uxgS8LDkufdSlRGTMrrw7HFct3YTjybecBjKOkuLy1C9mkQ6WMaqyGIm/d4ojprasrTnT84aV6QRWdPLghNFpVRSsnlchvU4HVZ5TxBKEZeNHrByiQjHTBv9a/2AqrX1g7fVG02v27oktJ0/pPhPDmySM8QdoSiNLUEWnj0uZcz2lKJt+GAfl540mkjMZMby13hs7pQseU43omOmZvlLH3LbBeO5/zuT+ay5kztf2EpTeyhRWdWVMSYrw1dSpRWPfm8Ul4oh1xoMs2NvFwufeCfjVWBlmcfyVWAx8LgUv7p8EttbulKG+8gqv+NSsg2v8LF8Vn1GgMPyWfUMr3DGeRSEUsZrKO65eAL/f3v3HidFdeaP//NU36dnYIZhBhFQUBFFdxBmJKhZ12g0JmJcA5ooI97CRaNm3Wh0N2uSjdnvT0WXjUkEJBFF1KgQN4kmXqIxbtREGVSiKBoFBaLMMAw4l57pS53fH91V9KVqZnqme7q6+/N+veYFXV1ddaq66vTTp895zt7uiFkPjAl64LWoU7Otf7MJXI3+kOmBg11/yGzXp9JiZCsxgmBjoFzQ68oIXo3uEMvmN+BfH34Dr+3Yh1UXNaYEy3ZBNABc94VpWHjPKwe6bC6Yhd6IjtpKL6oD8eutv8DXLrOKXVcfyp2SD4oD3nhOzfQ+xVYjpQspFI5ZphF7ePEcy58CC0EpoLsvlhK4//iCmRjtzxyoUkiaJqit9OChRXOgKwVNBG4X+A2bKAfcboErMTAp+Uun2515f1X6NcsvqJV+6/q3NujF2stmZ/xiZhe4+twabj7nWHNdXz+T82iaYNq4Kjx25Uk5/0m6TPtlFhWj+8zK59/HrfMaoIng3790FK595I2Ua3NspRe6Uvju3GPQ1tVrvt543X0vbcOt8xoy+r3f99I2rGpuhM+tZXQBuuKBTXhkyQkps9v1F/gyVVrhlHxQ3Nkbw/Nv78aDi+ZAKQURwa827cQ5MydgjEOCTQCI2fxs6KTMcboCrn7otZSb+OqHXsOjFv39CmlfKIzte3oyBuD43C7HtLoTFatQ2Dr3sNUX+N6wQkzXUwLXmK6jN6yQNm7O1BfVUwLu1QubLNdr7w6brXGG/gbOAfnpP8mfuouD0cf3tR37cPtTW7H8q8eh+ed/ybiOH1z0GVy4+i8pAa+Rv/i+l7bhhi8ejd5IDL9YPAedvVG4NUE4puO7Zx+DH/zmLVz+2cNsuwAlXw/9Bb7s6lM4BWkuFZHzROQtEdFFxLrGi6+3XUT+KiKvi0j/Q0pteDTBwy07cfJtf8A/LXseJ9/2BzzcstNx3Sf8Hpdl2hcn5QAulgFsdq3uoTC/ZRMNl92A25jFgNuIrvCNB1/Dpfe+iq/e/Wdceu+r+MaDryGSgwk5nNKaxlnBioMmwLL5DWZgbHcdR6Iq5b28YcNmLD3lcEysCeDSk6ag9dNenHXnn/DJ/l58tLcHpy9/wXz89JbWlG4UBquA1gh8rdZjqrTCKVTE9SaArwB4YRDrfk4pdZxSyjZ47pcAd5w3I+XiuuO8GQ5IcpRqbNBneROMdVDLpl3OUae1hhRDqztRscqmHojZBB52E+lkE+j2F1SMJKcE59S/vphuZit5ePEc+D3Wea7TL+OdHSFMra/E/ZfNxm1PbjVbnNu7w6hO5Lg2HgMHulkMFND2F/gmd/V58YbP4bErT+IvDyOkIN0nlFJvA/EZ0fK/M+Dnf/ogJW3Pz//0Ab5/9jH533cW8tnfLVe8mmDZ/IaMbglWA2wKyWh1T//pyUmt7kTFyuvSrOsBi+wuXptMMB6bTDD5HGiXL/ypuzj4PanZSp645rOW1/GertQW/ok1AXzY3oNwTEdbVx96wjGzD/G8xkmYWBPAqosa8aPfvwsAZveMm885FofXVyLgsf4sH+gzn6nSCqOgk3eIyPMArlNKWXaNEJFtADoAKACrlFJ397OtxQAWA8AhhxzS+OGHHwIAwuEo3m3rzhjocWRdEF5vyXepzqne3ih2fBrCzr0hs3/gxDEBTBoVgN/vnHM5yD5+BYnk7a7TZJy8ozw5bfIOu2u1tzeKnZ+GsCOpHpg0JoCJFvVAb28U7+/txpL7D9S/qy5qxOFjgpZ1Rrb9c50wwI19ilOM+AEPpk4FMt+nM6bX49tnHpVxHQPAJWteTQmUA14X7vrD33D1qVPR1RfFmhe34ZufPxJjg15omoaagAfvtXXxGigetm9K3oJiEfk9gIMsnvqOUupXiXWeR/9B8QSl1C4RqQfwDICrlVIDdrlIn9UmHI6irTuMqK7g1gR1QS8D4iHq7Y2iPXTgXNYGvI4KiA2D+LAseE3FGe0omdOC4mTp12o29UC2dYYTAt1sFWOZ88TRM9qlv09VHhfaQ2EzS5EI4BKBDiAS1aFpAo8m0JUCIPB6BL1h3fI95jVQVEZ+Rjul1OdzsI1diX9bReQxALMxuH7IKbxeNyYwCM4Jv9+NCQ4MgtPxpyei/MmmHsi2zijGe7cYy1yOrN6nrD/PbLJW8RooDY7tZCkiQRGpMv4P4AzEB+gREREREeVUoVKynSsiOwGcAOAJEXkqsfxgEfltYrVxAP4kIm8AeAXAE0qpJwtRXiIiIiIqbQUdaJcvItIGwKq3/VgAe0a4OENRDOUshjIC9uXco5Q6c6QLk6yf63QkFMv7NxildCxA5vE4+VottXNvhcc4eAW9VnNYp5bDe26nHI7d9jotyaDYjohsHHK+4xFUDOUshjICxVPOkVZK56WUjgUoruMpprIOFY+x/JTz+SjnYwcc3KeYiIiIiGikMCgmIiIiorJXbkGx7eQfDlMM5SyGMgLFU86RVkrnpZSOBSiu4ymmsg4Vj7H8lPP5KOdjL68+xUREREREVsqtpZiIiIiIKAODYiIiIiIqewyKiYiIiKjsMSgmIiIiorLHoJiIiIiIyh6DYiIiIiIqewyKiYiIiKjsMSgmIiIiorLHoJiIiIiIyh6DYiIiIiIqewyKiYiIiKjsMSgmIiIiorLHoJiIiIiIyh6DYiIiIiIqewyKiYiIiKjslWRQfOaZZyoA/ONff38Fx+uUf4P8Kzheq/wb5F9B8Trl3yD/bJVkULxnz55CF4FoQLxOqVjwWqViwOuUhqskg2IiIiIiomwwKCYiIiKisucudAGI8kHXFdq7wwhHY/C6XagNeqFpUuhilTW+J0TkNKyXKBmDYio5uq6wdXcnFq3diJ0dIUysCWD1wiZMG1fFyq5A+J4QkdOwXqJ07D5BJae9O2xWcgCwsyOERWs3or07XOCSlS++J0TkNKyXKB1biqnkhKMxs5Iz7OwIIRyNFahExPekNE2+8YmsX7P9lrPyUBKi7LFeonRsKaaS43W7MLEmkLJsYk0AXrerQCUividE5DSslygdg2IqObVBL1YvbDIrO6OfWG3QW+CSlS++J0TkNKyXKB27T1DJ0TTBtHFVeOzKkzii2CH4nhCR07BeonQMiqkkaZqgrspX6GJQEr4nROQ0rJcoGbtPEBEREVHZY1BMRERERGWPQTERERERlT0GxURERERU9hwTFIvIPSLSKiJvJi0bIyLPiMh7iX9rCllGIiIiIipNjgmKAdwL4My0ZTcCeFYpNRXAs4nHREREREQ55ZigWCn1AoC9aYvPAXBf4v/3AfjnkSwTEREREZUHxwTFNsYppT5O/P8TAOPsVhSRxSKyUUQ2trW1jUzpiLLE65SKBa9VKga8TimXnB4Um5RSCoDq5/m7lVJNSqmmurq6ESwZ0eDxOqViwWuVigGvU8olpwfFu0VkPAAk/m0tcHmIiIiIqAQ5PSj+NYCLE/+/GMCvClgWIiIiIipRjgmKReQhAC8DmCYiO0XkcgC3ADhdRN4D8PnEYyIiIiKinHIXugAGpdQFNk+dNqIFISIiIqKy45iWYiIiIiKiQmFQTERERERlj0ExEREREZU9BsVEREREVPYYFBMRERFR2WNQTERERERlj0ExEREREZU9BsVEREREVPYYFBMRERFR2WNQTERERERlj0ExEREREZU9BsVEREREVPYYFBMRERFR2WNQTERERERlj0ExEREREZU9BsVEREREVPbchS7AYIjItQC+DkAB+CuAS5VSvYUtFTlZJBJDa1cforqCWxPUV/rg8bgKXayypusK+0JhhMIxxJSC3+PC2KAPmiY52357dxjhaAxetwu1QW/Otk1EpU3XFfZ096E3EoMmAo8m8Lg1VAdYj5QTxwfFIjIBwDUApiulQiLyCICvAbi3oAUjx4pEYnintQtXrGvBzo4QJtYEsKK5EUfVVzIwLhBdV9je3o3dn/bi+vWbzfdl9cImTBtXNewPHV1X2Lq7E4vWbsz5tomotFnVH8vmN6DC68I+fwSTa4OsR8pEsXSfcAMIiIgbQAWAvxe4PORgrV19ZkAMADs7QrhiXQtau/oKXLLy1d4dxoftPWZADMTfl0VrN6K9O5yT7RsfaLneNhGVNqv64/r1m7G3O4IP23tYj5QRxwfFSqldAG4H8BGAjwHsV0o9nb6eiCwWkY0isrGtrW2ki0kOEtWVWbkZdnaEENVVgUp0QLlep+FoDBVel+X7Eo7GcrL9fG27XJXrtUrFJRfXqV39UeF1ocLrYj1SRhwfFItIDYBzAEwBcDCAoIg0p6+nlLpbKdWklGqqq6sb6WKSg7g1wcSaQMqyiTUBuB3w81e5Xqdetws94Zjl++J1D79Li9ftytu2y1W5XqtUXHJxndrVHz3hGHrCMdYjZcTxQTGAzwPYppRqU0pFAPwSwIkFLhM5WH2lDyuaG81KzuhTXF/pK3DJyldt0ItDayuwbH5DyvuyemETaoPenGx/9cKmvGybiEqbVf2xbH4DxgQ9OLS2gvVIGXH8QDvEu03MEZEKACEApwHYWNgikZN5PC4cVV+JhxfPYfYJh9A0weTaIKorPHh48RzEFOD3aDnLPqFpgmnjqvDYlScx+wQRZcWoP3555YnMPlHmHB8UK6X+IiLrAWwCEAXwGoC7C1sqcjqPx4UJNRWFLgYl0TTBmKAPCOZv+3VV/DWAiLKnaYL6Kn+hi0EF5vigGACUUt8D8L1Cl4OIiIiISlMx9CkmIiIiIsorBsVEREREVPYYFBMRERFR2ct5n2IRaQFwD4AHlVIdud4+0WBEozpau/oQienwuDTUV/rgdpfGd0BdV2jvDjPLwjDwHBJRf/JRR7Decb58DLT7KoBLAbwqIhsBrAHwtFKq8NOJUVmIRnW8s7sTSxNTPU+sCWBlcyOOGldV9IGxrits3d1pTklq5OOdNq6Klesg8RwSUX/yUUew3ikOOY8QlFJ/U0p9B8CRAB5EvNX4QxH5TxEZk+v9EaVr7eozA2IgPl3n0nUtaO3qK3DJhq+9O2xWqkD82Bat3Yj27nCBS1Y8eA6JqD/5qCNY7xSHvDSbiUgDgDsALAOwAcB5AD4F8Fw+9keULBLTLeexj8b0ApUod8LRmOWxhaOxApWo+PAcElF/8lFHsN4pDjkPihN9ipcDeBVAg1LqGqXUX5RSdwD4INf7I0rncWmW89i7XcXddQIAvG6X5bF53Zytb7B4DomoP/moI1jvFId8RAnnKaVOU0o9qJRK+b1aKfWVPOyPKEV9pQ8rmxtT5rFf2dyI+srin+2sNujF6oVNKce2emETaoPeApesePAcElF/8lFHsN4pDvkYaPd1EblNKbUPAESkBsC3lFL/kYd9EWVwuzUcNa4Kjyw5AdGYDncJZZ/QNMG0cVV47MqTOIJ5iHgOiag/+agjWO8Uh3wExV9USv278UAp1SEiXwLAoJhGjNut4eDqwMArFiFNE9RVFX+rdyHxHBJRf/JRR7Decb58NJ25RMR810UkAIBXARERERE5Vj5aih8A8KyIrEk8vhTAfXnYDxERERFRTuQ8KFZK3SoimwGcllh0s1LqqVzvh4iIiIgoV/LRUgyl1O8A/C4f2yYiIiIiyrV85Cn+ioi8JyL7ReRTEekUkU9zvR8iIiIiolzJR0vxbQDOVkq9nasNikg1gJ8BOBaAAnCZUurlXG2fSk9fXxR7esKI6gpuTTC2wgufLy8/jOSUriu0d4dznrJnuNvNV7kKue9CHhMROUN6PVDtd6OtO4xITIdbEwR9Lozy9183sC4pHfmIEnbnMiBO+BGAJ5VS80XEC6Aix9unEtLXF8W7e7pxxboW7OwIYWJNACuaG3Hk2KCjA2NdV9i6uxOL1m40y716YROmjasaVgU73O3mq1wjUfaR3i4RFY/0emDJP07G3OMmpnx23LVgFmoqophQXWFZN7AuKS35SMm2UUQeFpELEl0pviIiQ57JTkRGAzgZwM8BQCkVNiYGIbKypydsVmpAfH75K9a1YE9PuMAl6197d9isWIF4uRet3Yj27uGVe7jbzVe5CrnvQh4TETlDej0wv+mQjM+OKx/YhL6osq0bWJeUlnw0m40C0APgjKRlCsAvh7i9KQDaAKwRkRkAWgB8UynVnbySiCwGsBgADjnkkCHuikpBVFdmBWXY2RFCVFcFKtEB/V2n4WjMstzhaGxY+xzudvNVrkLuu5DHVCxYp1IxGM51ml4PuDSxrBc0gW3dwLqktOS8pVgpdanF32XD2KQbwCwAK5RSMwF0A7jRYr93K6WalFJNdXV1w9gdFTu3Jub88oaJNQG4HfBTVn/Xqdftsiy31+0a1j6Hu918lauQ+y7kMRUL1qlUDIZznabXAzFdWdYLuoJt3cC6pLTkI/vEkSLyrIi8mXjcICLDmeJ5J4CdSqm/JB6vRzxIJrI0tsKLFc2NZkVl9CkeW+EtcMn6Vxv0YvXCppRyr17YhNrg8Mo93O3mq1yF3Hchj4mInCG9Hli/8aOMz467FsyCzy22dQPrktIiSuX2J2UR+SOA6wGsSrTsQkTeVEodO4xt/h+AryultorI9wEElVLX263f1NSkNm7cONTdUQkYRPaJgjcbW12nTs20wOwTBVXwQtnVqZNvfCLrbW2/5axcFImcqaDX6lA++5l9oizZvjn56FNcoZR6RSRln9FhbvNqAA8kMk98gPjU0US2fD43Jjg404QdTRPUVfkct918lauQ+y7kMRGRM1jVAwdXB2zWHvw2qDjlI2rYIyKHIz64DiIyH8DHw9mgUup1AE3DLxoRERERUaZ8BMXfAHA3gKNEZBeAbQCa87AfIiIiIqKcyHlQrJT6AMDnRSQIQFNKdeZ6H0REREREuZTzoFhEvpv2GACglPpBrvdFRERERJQL+eg+kTyphh/AXAC5nvaZqF+9vVG0hw5kn6gNeOH3F9/AOyv9jXTO9yjooWw/29ckrx/wuhDVFSJRnaO6iSjndF2hsy+Mrt6Y+XlRX+mDx2OdZzib+oxZKYpPPrpP3JH8WERuB/BUrvdDZKe3N4r32rtT5q9f0dyIqbXBog+MdV1h6+5Oc1pRIyfmtHFVAGD7XK5SmGW7/Wxfk7x+XaUP3z5zGq5fvzkvx0NE5U3XFXZ3hrCnK5LxeXFUfWVGYJxNfTaU+pIKL+eTd1ioADBxBPZDBABoD4Uz5q+/Yl0L2kPFPxd9e3fYrGSB+LEtWrsR7d3hfp/L975z9Zrk9ZeecrgZEOfjeIiovLV3hxGNwfLzorWrz3L9wdZn+a6PKT/y0af4r0ikYwPgAlAHgP2JacREdWU5F31Uz+1ENYUQjsYsjy0cjZn/t3su3/vOxWuS168OePJ6PERU3sLRGGJq8J8X2dRnQ6kvqfDy0VI8F8DZib8zAByslPpJHvZDZMmtieVc9O4S+MnK63ZZHpvX7er3uXzvO1evSV5/XyiS1+MhovLmdbvgksF/XmRTn+W7Pqb8yEdQ3Jn0FwIwSkTGGH952B9RitqAN2P++hXNjagNFP9c9LVBL1YvbEo5ttULm1Ab9Pb7XL73navXJK+/8vn3sWx+Q96Oh4jKW23QC7cLlp8X9ZWZM9RlU5/luz6m/BClcvuTsohsBzAJQAfi80tXA/go8bRSSh2W0x1aGMr851RaBpF9ouDNxkO9Tpl9ouBv3Ugr+AHbXauTb3wi621tv+WsXBSJnKmg1+pQ6lRmnyhLtm9CPobiPwPgMaXUbwFARL4I4J+VUkvysC8iS36/GxOKPNOEHU0T1FVltmIM9Fy+952r1+T7GIiIDJomGB3wYXRg4HWN9QdbP7EuKz756D4xxwiIAUAp9TsAJ+ZhP0REREREOZGPprS/i8h/AFiXeLwAwN/zsB8iIiIiopzIR0vxBYinYXsMwC8T/78gD/shIiIiIsqJfMxotxfAN0UkqJTqHvAFREREREQFlvOWYhE5UUS2AHg78XiGiNyVg+26ROQ1EXl82IUkIiIiIkqSjz7FywF8AcCvAUAp9YaInJyD7X4T8UB7VA625UhM35I7g0jJVlSSrw2PW4NbE4TCB64TACnXTk3Ag45QxJHXUjSqo7WrD5GYDo9LQ32lD253/9/PR+LeGIl0c0TkHMb9q+s6orpCTFdwaQKPS4vXT24NXpeguy8+853f48KYgNexdetQsA5LlZcoQSm1QyTlpA5rXkMRmQjgLAD/BeBfh7Mtp9J1ha27O8250o1E39PGVZX1BToUvb1RvNfebc5nbyRjn1obLMrA2OraWDa/Abc9uRVtXX1YvbAJPreGhfe8gp0dIZwxvR7XnHYkliYdv1OupWhUxzu7O1PKtrK5EUeNq7INjEfi3hjKPnjPEhUv4/5d/sxWXHziFNywYXNG/VpX5cVVp07FlQ9scnzdOhSswzLlY6DdDhE5EYASEY+IXIdEV4ph+B8A3wagD7dwTtXeHTYvTCA+R/qitRvR3h0ucMmKT3sobAbEQPxcXrGuBe2h4jyXVtfG9es3Y+kph5vXyYftPebz8xonmZW2sb5TrqXWrr6Msi1d14LWrj7b14zEvTGUffCeJSpexv07r3GSGRADqfXrvMZJZkAMOLtuHQrWYZnyERQvBfANABMA7AJwXOLxkIjIXACtSqmWAdZbLCIbRWRjW1vbUHdXMOFozLwwDTs7QghHh9XIXpaiurI8l1E9t7M3DsVQrlO7a6M64DH/X+E9MPtSdcDj2GspEtOt35uY/ffdkbg3hrKPUr9ni71OpfIw1OvUuH/t6svqgCfjOSfXrUNR6nXYUOQ0KBYRF4AfKaUWKKXGKaXqlVLNSqn2YWz2JABfTkwf/QsAp4rIuvSVlFJ3K6WalFJNdXV1w9hdYXjdLnOOdMPEmgC8buupJsmeWxPLc+l2wM9BQ7lO7a6NfaGI+f+e8IFKbF8o4thryePSrN8bl31VNBL3xlD2Uer3bLHXqVQehnqdGvevXX25LxTJeM7JdetQlHodNhQ5DYqVUjEAh4qIN4fb/Del1ESl1GQAXwPwnFKqOVfbd4raoBerFzaZF6jRt8cYREWDVxvwYkVzY8q5XNHciNpAcZ5Lq2tj2fwGrHz+ffM6ObS2wnx+Q8sOrEw7fqdcS/WVvoyyrWxuRH2l/VSoI3FvDGUfvGeJipdx/25o2YFb5zVY1q8bWnbgrgWziqJuHQrWYZlEqdz+pCwiawEcjXj2CTNPsVLqv3Ow7VMAXKeUmtvfek1NTWrjxo3D3d2I4yjQ3BlE9omCn9hsrtNSzD4RjelwM/vEYBT8jbO7Viff+ETW29p+y1m5KBI5U0Gv1Ww/+7PLPgH4PRqzT5QG2wPM2VB8EblfKXURgC8jnpZNA1CVq+0DgFLqeQDP53KbTqJpgroq+xYzGjy/340JRZhpwo7ltRFMfZj+vFOvJbdbw8HVgYFXTDIS98ZQ9sF7lqh4Dfb+ra5IfVxK9zzrsFS5jBoaReRgAB8B+HEOt0tERERElFe5DIpXAngWwBQAyb9fCAAF4LAc7ouIiIiIKGdyNtBOKXWnUupoAGuUUocl/U1RSjEgJiIiIiLHynmeYqXUFbneJhERERFRPpXOSCSiJIPIPuFIdiOBk0dJxxSglLIcKeyULA26rrAvFEYoHENMKfg9LowJePFpXwShcAxRXcGjCfxeF6oD9mW02heAQZ2L4ZS/v9eICFwCaJpWLiO1iUqWrit09vWhu08hGtMT2ScEkVg8G4XbFc/2E4npttl+WA+UDudHCURZ6u2N4r32bnOqZyNP8dTaoKMDY7t56KfWVeK9ti4sf2YrLj5xijklafo89SMxj/1g9qHrCtvbu7H7015cv/5AWVc2NyKm6/jGg6+Zy5afPwO1lT5Mrg1aBtZW+/K5Ndzyu7f7PRfDKf9gXnPrvAbc99I2XHv6tJyeXyIaObqusLszhPauiDl98xnT63HVqVPN6Z2NvMW3PbkVbV19Zh208J5X8lbPUuHkY5pnooJqD4XNgBiIT1t5xboWtIecPZ+73Tz0rV19WLR2I+Y1TjKDwOTnjXnqR2Ie+8Hso707jA/be8yA2Fhv6boW7O2OpCy79pE38GF7j2UZ7fb1YXvPgOdiOOUfzGtu2LAZ8xon5fz8EtHIae8OIxqDGRADwLzGSWZADMTv9+vXb8bSUw5PqYPyWc9S4Ti32YxoiKK6spzPParndqKaXLObhz4S07GzI4TqgKffeepHYh77wewjHI2hwuuyXK/C67JcZlVGu31VeF2ogPX2BzrWoZwju9cY70cuzy8RjZxwNN69K/n+tqtnqwMe8/9W9RjrgdLAlmIqOW5NLOdzdzv8py27eeg9Lg0TawLYF4r0O0/9SMxjP5h9eN0u9IRjluv1hGOWy6zKaLevnnBswHMxnPIP9jVGGXJ5folo5HjdLrgk9fPCrm7ZF4qY/7eqx1gPlAYGxVRyagNerEibn35FcyNqA86ez91uHvr6Sh9WL2zChpYduHVeg+089SMxj/1g9lEb9OLQ2gosm59a1pXNjRgT9KQsW37+DBxaW2FZRrt9HVpbMeC5GE75B/OaW+c1YEPLjpyfXyIaObVBL9wuYGXS58WGlh24a8GslPt92fwGrHz+/ZQ6KJ/1LBWOKOXsn5SHItv5z6n0DCL7RMGbja2u09LMPgH4PVpK9olY4n1h9olBceS1CgCTb3wi621tv+WsXBSJnKmg1+pQPvuZfaIs2b5Z7FNMJcnvd2OCgzNN2LGbh36w89OPxDz2g9mHpgnGBH1AMHX5GHfmsqHsazjHOJRzNBLnlYhGnqYJRgf8GB0YeN1krA9KE7tPEBEREVHZY1BMRERERGWPQTERERERlT0GxURERERU9hw/EklEJgFYC2AcAAXgbqXUj7LZxiAyETjCSGQOGK5iOZfFqr9sC4PJfBCN6mjt6gOgoFT8hvG646OnQ+GhX1fp5aoJeNARiiAcjSHgdSGqK0Siesr2o1Ede3vCCMd0xHQFr0uD1y3ojegQEWgCxHQFTQBN4uWJ6PER3x6XBr9HMzNTGNksIlEdEV1BT1x/FT4XwlGF3mgMLhEEbLJZGOclEtPh1gQ+twYFSTmOgbJ5BH0u9IR1RGI6PC4N9ZU+uN2Z7QrFcB8Tlav0+3O0z4XOviiiuoKRiyuaqGM8bg0uEYQiMfjcGnRdmZ99Y4NeuN0u23ox/d4fTr3AOmXkFEM0EwXwLaXUJhGpAtAiIs8opbYM5sW9vVG8195tTvtr5KydWht0VDCn6wpbd3ea08k6cT71YjmXxcruGvC5NSy85xVz2a3zGnDfS9tw7enTUq6PaFTHO7s7ceez7+LiE6eY0yAbeTZve3Ir2rr6sr6u0st1xvR6XHPakVi6rgV1lT58+8xp5pTORpmPGBvEhx09aOvsS3kuuRzGcVz5uSPg0QSf9kZT1v3R145DTUUEh9RU4KOOHnT2RtATjqWss2LBLPz4uffw9JZWc/vjRvkxuTaYcV6WJl23dy2YhU3b29E0ZWzK8uRzk3zcJx5Wi+YTDjWnfzXyLh81riolMC6G+5ioXFndnyubG1Hpd2F/T3xyjvQ6Ztn8Bjy2aRfOnTUhZfnK5kbUVnpw3so/Z9SL6fc+gCHXC6xTRpbju08opT5WSm1K/L8TwNsAJgz29e2hsBnEAfHpGK9Y14L2kLPmKW/vDpsXPeDM+dSL5VwWK7tr4MP2npRlN2zYjHmNkzKuj9auPixd14J5jZPMgNh4zfXrN2PpKYcP6bpKL9e8xklmxb/0lMPND4rkMrd29WHH3lDGc8nlMI6jozuC1s5wxrrf/MXr+LC9B61dffiwvQd7uyMZ61zxwCbMa5yUsv0P23ssz0vy6658YBNOnT4+Y3nyuUk+7kUnH2YGxMa6S9e1JFrlB34PnXQfE5Urq/tz6boWRGPA3u6IZR1z/frNWHTyYRnLjddZ1YvGOsa9P5x6gXXKyCqq5j0RmQxgJoC/WDy3GMBiADjkkEPM5VE9dV5zIH5RRXVnTVoSjsYsy+mk+dSL5Vw6md11CthfAxVeV8ay6oAn4/qIxPSU56xeY/w/m+sqvVzJ27fbV1RXqPC6BiyH8X/jsdWxR2K6eQ76217ya6zOS/rrlLK+no3XJh+3SxPr44zpKcuK4T4erP6uVSKnyOY6tbs/NUG/dYzd/a8nTX5mVxca9/5Q64VSqlOKgeNbig0iUglgA4B/UUp9mv68UupupVSTUqqprq7OXO7WUuc1B+LTMrod9rOD1+2yLKeT5lMvlnPpZHbXKWB/DfSEYxnL9oUiGdeHx6WlPGf1GuP/2VxX6eVK3r7dvtyaoCccG7Ac+0LxLhF26/aEY/C4tH7XMbaX/Bqr85L+OhHr69l4bfJxx3RlfZyu1Cq0GO7jwervWiVyimyuU7v7U1fot46xu/+NsRCAfV3odbuGVS+UUp1SDIoiKBYRD+IB8QNKqV9m89ragBcrkuY1N/rB1gacNU95bdCL1QubHD2ferGcy2Jldw0cWluRsuzWeQ3Y0LIj4/qor/RhZXMjNrTswK3zGlJes2x+A1Y+//6Qrqv0cm1o2YGVietg5fPvY9n8howy11f6MGlMIOO55HIYx1ET9KC+ypux7o++dhwOra1AfaUPh9ZWYEzQk7HOigWzsKFlR8r2D62tsDwvya+7a8EsPLfl44zlyecm+bhXv/AB7lowK2Xdlc2NqK9MndWqGO5jonJldX+ubG6E2wWMCXos65hl8xuw+oUPMpYbr7OqF411jHt/OPUC65SRJUo5+6dvEREA9wHYq5T6l8G8Jn3+82LJmFAMI0yL5VwOQsFPbPp1CuQu+4RAwejV4nFo9gmXxLvkpGef0HUFd9bZJ3S4BANmn4jGdLiGmX0iGtPhHtnsE468VgFg8o1PZL2t7beclYsikTMV9Fq1u06TDSX7RG8kBi+zT5QS25NXDNHMSQAuAvBXEXk9sezflVK/HewG/H43JhRB4KZp4vj51IvlXBYru2tgsNeF263h4OqA9ZPB3JZroDK53RrqR/mHvtO0/Y8JDv3e6O+89Hcc6cddXTHwvorhPiYqV1b3Z6136J9pg60Xh1MvsE4ZOY6PbpRSf4IDWkqIiIiIqHQVRZ9iIiIiIqJ8YlBMRERERGWPQTERERERlT0GxURERERU9hw/0C4XiiWNWDgcRVv3gXLWBb3wDmNUbD4Uy7ksRpFIDHu6w4gk0oaJxCeeiMR0RGIKHpfA59IQisZTnHlcAr9HQ09YRzw7j5iv9SfSB4V1hZiu4HdrEMB87HFpcAviqcw0QaXfhb6IQm80BrdIfFIKUYhE4ymI/G4NMQVEdR1+jwtjAl7sDYXRG4nBJQKPS4NSCn6voDccT20US6QvcmkCjyaIKQUoxIfNKsDn0dAb0eFP/GtcUz63hnBMh1JATCm4JH4uAMTTI0V1uDVBpV9DJAr0RQ+81uMSQAE64mmVjGMVKIgIlALCiXPk1gQCQIeCriOxD0HQJ+gJK4QT56bCq6E3HE8FZ+zDeD+iOsxcxa1dfYjqeuJ8CKKx+PFrieP3uOMp5gBgXyiMUDiGmFLwe1wYG/QxxRJRnum6wp7uPrPeqvRp6A4ruF2A0uMpIY30kX6PxFMw6gpBryulnjHqrFiifqvwatAB9IYPrOPSBK5E6kxdVynpKQMeF9wuQSSqI6YApVRGasuB0kXm49ww7VsZBMW9vVG8196NKxJzkhsTTkytDToqmAuHo9jallnOaXVBxwTGxXIui1EkEsPW1i4sTTq3P7lwJiJRHdc+8gZ2doRwxvR6XHXqVFz5wCZznZXNjdi4bQ+OPrga33r0jQPvy4JZ0JXCNx58DXWVPnz/y9PRE47h+vWbzXWWnz8D/++376CuyourTzsy5X39+cWNCEcVrnhgE+oqffj2mdNSXruyuRF3Pvsunt7Saia4r6vyobMP6O6LIpxUbuP5gNeFu/7wN1z+2cPw9t/3oXHKWLRs24PGKWNT9n3PJU34NBTFvzz8urns1nkNuO+lbbj0pCm47cmtaOvqw8rmRvg9Gi5Z82rK9eh1AXu7Iynl/Z+vHoeAR8OSdZtSylThdUEBuOsPf8PFJ07BC1t3Y+5xE83yLPnHySmPjYk/nnhjF+bOmIDH39iF844/BL0R3Xzvzphej6tPnYorHsjc1z5/BB5XfMrY5PKtXtiEaeOqyvJDiGgk6LrC1t2dWLR2Y0p90bJtD06aWoc9XeGMOu43r+9ER08UzSccmlLvrmhuxOOv78Sq/9turpteFxl11rWfn4ag34Vdaff8j752HFya4KpEHT1QHZvPOsLq3JRrnVTy3SfaQ2HzAw2Izxl+xboWtIfCBS5ZqrZu63K2dTunnMVyLotRa1efGVQB8XPb0R0xA0sAmNc4yayYjXWWrmvBqdPHmwGxsfyKBzZhb3ckvs4ph6cEicY61z7yBpaecjjmNU7KeF9dmssM6paecnjGa5eua8G8xknm4+vXbwYg2LE3hL1p5Tae7+iOYF7jJHzr0Tdw6vTxuCJR9vR97+roNQNiY9kNGzZjXuMkXL9+M5aecrhZhh17QxnXo0tzZZT3Xx5+Ha2d4Ywy7e2OmOW6YcNmzG86JKU86Y93doRw5QOb4ssT/+7YG0p57+Y1TjLPXfq+PmzvQV9UZZRv0dqNaHfQvU5Uatq7w2bQBxyoL06dPh47O3ot67j5TYdg0cmHZdS7VySeS143vS4y6qxF929E2OKe/+YvXkdHUh09UB2bzzrC6tyUa51U8s17UV2Zb7RhZ0cIUd1ZM/kVQzmLoYzFyurcVnhdKcuqAx7L86+U9ftS4XWZrzOWpa9j95wmB5bZ7dd4rfFYE5j7tCtPBeLHpCfKrFuUPf24k/eXXmZjf+nl6O98WC0zyuXSJOW16Y+N1xnL4z+dDu59MvZlV75wNAYiyo9wNGZbf9rVOa5EK2l/zxmPreoXoy4YqE4abB2brzrC7tyUY51U8i3Fbk3MOcMNE2sCcDvsJ4FiKGcxlLFYWZ3bnnAsZdm+UMTy/ItYvy894Zj5uvRtGevsC0Ust6srmMvs9rsvFEl5rKt4me321ROOmdvSEmXWLMreX1mT95t8jOnl6O98pC9LLldMVymvTX9svM5YHtPVoN8nY1925fO6Uz9UiSh3vG6Xbf1pV+fEEn2M7Z5LfmxVvxh1wUB10mDr2HzVEXbnphzrpJIPimsDXqxobjTfcKM/UG1iwItT1AWty1kXdE45i+VcFqP6Sh9Wpp3bmqAHy8+fYS7b0LIDdy2YlbLOyuZGPLflY9xx3ozU92XBLIwJeuLrPP8+xgQ9WDa/IWWd5efPwMrn38eGlh0Z72tMj2FFYl8rn38/47UrmxuxoWWH+XjZ/AYACpPGBDAmrdzG8zVBDza07MAd583Ac1s+xopE2dP3PaHGj//56nEpy26d14ANLTuwbH4DVj7/vlmGSWMCGddjTI9llPd/vnoc6qu8GWUaE/SY5bp1XgPWb/wopTzpj40+xes3foQViX8njQmkvHcbWnaY5y59X4fWVsDnlozyrV7YhFoH3etEpaY26MXqhU0Z9cVzWz7GxBq/ZR23fuNHWP3CBxn17orEc8nrptdFRp21+qImeC3u+R997TjUJNXRA9Wx+awjrM5NudZJolTp/fTd1NSkNm7caD4ulowJzD4xogrevJ1+naZnn9AEcLs0RGIK0ZgOt0uDzyWW2SfiSRcE0ZgOLS37hK4r+AadfUKHO7FfCBBJjLg+kH1Cwe/RkrJPxPdtl30ilpR9QVcKSsWzPKgss09o8aQScImgzyzzgewTxijw9OwTuq7gTss+EUmco4GyT0Si8fXym30C8Hu0gbJPOO5aNUy+8Ymst7X9lrNyUSRypoJeq3bXqeFA9ol4vWWVfUJP1I9DzT5hLLPLPqHr8YwzzD5RULYHVpTRTLb8fjcmFEHg5vW6McFhQXC6YjmXxcjjcWF8dWDgFdOMCeaoAFnuur7Kb7l8dPaH4DgZxzCIc3xwFu/dmKBvUNskotzRNMmot0ZX2K9fk+09arO+pgnqR1nXl/2pq/Jl/Zqh0jQZ0f05Vcl3nyAiIiIiGgiDYiIiIiIqewyKiYiIiKjsMSgmIiIiorJXFCOmRORMAD8C4ALwM6XULdm8vlgyJhRDOYuhjMWqtzeKjt4IIPFsCFFdwevS4E3JOKHB4xK4NCAUzszc4HEZg2oF4VjiNZog6NPQ1XdgvUq/hq7eA48D3ngWi4BHQzQWzx5hZG8wMk+EE1kxPJqgwifo6VOIJDJMeN1aIjNEDF6XhqiuzCwTulLQRCASzx6hJ23LyHHtcQliMWVmxzCzXSRl0+iL6vEsFCo+StyXyLARNf6f2K47UcZQVIfPrVlmnNA0gccFdCedE49bEI0BgaRR525NMDqg4dNQPPuESxME3Bp6kzJeiACC+LkPx3REYvHXBX2a5fY1iZfT73YhGjuQ1SLgjbdRVLjcvKeIcsTIOOFxAT2J+9Hr0qBJPONMJBavc3xuDTGlEIkps55Lfl7TBF5XPINNX0yHJoKqtHq1wqshFNHhdWkIRw/UGZ5EvePzSkq9W+HVEInBrKv9bg2xRJ3mSZQxlsjao1S83nIJoCWyWiRnh4hG9XgGnKS61VgPgJlZwshyoXRlZr6wyjYxUDaKkcpWMZT9DKdsjq95RcQF4KcATgewE8CrIvJrpdSWwby+tzeK99q7zalajRyDU2uDjvrgKYZyFkMZi1VvbxTv7+3Gj37/Li4+cQpu2LDZPMd3LZiFnzz3Hp7e0mrmr6wKuPGHLZ+gccrY1PdjwSxU+Fxo/bTPnDb0jOn1uPq0IzPet8df34lV/7fdfNzRFcL4miB6+qLoCcdw/frNqKv04T/mHo1v/uJ187XL5jfg4JoA9nT2pSxffv4MPLpxJ86dNQFrXtyWcRx3nDcDlT4XlqzblLKtMUEPNBHs6Qqb+/z2mdPM8hvn4Ik3duHUow/Ctx59I2Udq/WXzW/AY5t24bymieaU08byCq8LdaN8+OTTSMo5uWvBLGza3p5yTpf842ScfdxEcxrnM6bX46pTp5rTvhr5SF/YuhtzZ0zEFQ+knuMfP/uu+b4Zx3DKUePw2KZdOHfWhJQyr2huxJigG119MdQDvKeIhknXFbbu7sTfdu/H5LpRKff7j752HEb53bj03o2oq/Th3790VEZdUVvpxe1PbTXv4WXzGzDK78Z//O9baOvqy7jHVzQ3ojboxief9pl1hvG6uiovertUyvKVzY2I6Tq+8eBrlvXY8vNnwOPWcNWDr6XUN/e9tA3Xnj4N08ZVQdME0aiOd3Z3pmw7eT2fW8PCe14x92FVP69e2GRuzzhvxtTP2T6f6/cvm/0Mt2zF0H1iNoC/KaU+UEqFAfwCwDmDfXF7KGzeCMCBecvbQ86a07sYylkMZSxW7aEwltwfn+veqKiA+Dm+8oFNmNc4yXy8dF0LIlGFU6ePz3w/HtgEpcSsWAFgXuMky/dtftMhKY8Prx+FnXtD2NsdMV+/9JTDzcDXWPf69ZsRiaqM5dc+8gYWnXwYrl+/2fI4vvXoG2jtDGdsy6W5sLOjN2WfyeU3zsH8pkPwrUffyFjHav3r12/GopMPMz/kkpfv7Y4gFkPGObnygU0Z53R+0yHmB41xLo2A2HjdDRs2Y37TIWZAnHxOk9834xiMsqWX+Yp1LdB1QTiqeE8R5UB7dxiL1m7EzENrM+73eP3Va9YhVnXFro7elHv4+vWb0doZxtJTDre8x69Y1wJdSUqdYbwO0DKWL13Xgr3dEdt67NpH3kBH4nlj2Q0b4vXrorUb0d4drydau/oytp283oftPSn7sKqfk7dnnLehPp/r9y+b/Qy3bMUQFE8AsCPp8c7EshQislhENorIxra2NnN5VFeWc3pHdWdNWlIM5SyGMjrdQNdpdcBjeY6rA56Ux5rEuyVYrasJUpbbbdOV9K3ZeB8rvC5UeF3m+navTd9H8jb7O44KrytjmSYY1D6NbaevM5j108tgd+5U2vL0bWS7r/T3zVjPbv14VxM44p6yu1aJnKS/6zQcjWFnRwgxm88uoz7qr75Kv4eTl1nd43b7sqszB1OG9GXGuuFofJroSEy3rX+s9mG3L2N7xnkb6vO5MpT9DLdsxRAUD4pS6m6lVJNSqqmurs5c7tbEck5vdx76vgxHMZSzGMrodANdp/tCEctzvC8USXmsK0AT6/dDV0hZbrfNWFLgZbyPPeEYesIxc32716bvI3mb/R1HTziWsUxXGNQ+jW2nrzOY9dPLYHfuJG15+jay3Vf6+2asZ7e+luhz7YR7yu5aJXKS/q5Tr9uFiTUBuGw+u4z6qL/6Kv0eTl5mdY/b7cuuzhxMGdKXGet63fFg1+PSbOsfq33Y7cvYnnHehvp8rgxlP8MtWzEExbsATEp6PDGxbFBqA16saG7MmLe8NuCsOb2LoZzFUMZiVRvwYtVF8bnub53XkHKO71owCxtadpiPVzY3wuMWPLfl48z3Y8EsiCgsm39gGxtadli+b+s3fpTy+P3WTzFxTABjgh7z9Suffx8/+tpxKa9dNr8BHrdkLF9+/gysfuEDLJvfYHkcd5w3A/VV3oxtxfQYJtb4U/aZXH7jHKzf+BHuOG9GxjpW6y+b34DVL3yA5efPyFg+JuiBy4WMc3LXglkZ53T9xo+wMunxhpYduGvBrJTX3TqvAes3foQVCzLPcfL7ZhyDUbb0Mq9oboSmKXjdwnuKKAdqg16sXtiE1z5sz7jf4/WX36xDrOqKCTX+lHt42fwG1Fd5sfL59y3v8RXNjdBEpdQZxusAPWP5yuZGjAl6bOux5efPQE3ieWPZrfPi9evqhU3mILr6Sl/GtpPXO7S2ImUfVvVz8vaM8zbU53P9/mWzn+GWTZQq/M90/RERN4B3AZyGeDD8KoALlVJv2b0mff7zYsmYUAzlLIYyDlLBm+KsrtPk7BNGtgmnZJ8wMjikZ5/QdQWPRfaJeBcP6+wTydkggNTsE7o5Etw++0Q0kfEiPftEJClDRm9Uh3cEsk/ED+FA9oloYvR6DrNPOO5aNUy+8Ym873v7LWflfR+UMwW9Vq2u02yzT0Rjyqzn7LJPhGM6JMfZJ8x6j9knMt6/PGSfsN2A46MZpVRURK4C8BTiKdnu6S8gtuL3uzGhCAK3YihnMZSxWPn9bozP5twGs9v+6Iq0x6m/MGFMltur8me3vhNVV1gvr0k7F5VDPFa77RPRyNA0QX2isqrJw/2YXq/WDLR+YIAVhsjt1nBwtf3G66p8WW1P06Tf1wz0fK4MZT/DKVtRRDdKqd8C+G2hy0FEREREpakogmIiIipPI9FFYyjYrYOo9Di+T/FQiEgbgA8tnhoLYM8IF2coiqGcxVBGwL6ce5RSZ450YZL1c52OhGJ5/wajlI4FyDweJ1+rpXburfAYB6+g12oO69RyeM/tlMOx216nJRkU2xGRjUqppkKXYyDFUM5iKCNQPOUcaaV0XkrpWIDiOp5iKutQ8RjLTzmfj3I+dqA4UrIREREREeUVg2IiIiIiKnvlFhTfXegCDFIxlLMYyggUTzlHWimdl1I6FqC4jqeYyjpUPMbyU87no5yPvbz6FBMRERERWSm3lmIiIiIiogwMiomIiIio7DEoJiIiIqKyx6CYiIiIiMoeg2IiIiIiKnsMiomIiIio7DEoJiIiIqKyx6CYiIiIiMoeg2IiIiIiKnsMiomIiIio7DEoJiIiIqKyx6CYiIiIiMoeg2IiIiIiKnsMiomIiIio7DEoJiIiIqKyV5JB8ZlnnqkA8I9//f0VHK9T/g3yr+B4rfJvkH8FxeuUf4P8s1WSQfGePXsKXQSiAfE6pWLBa5WKAa9TGq6SDIqJiIiIiLLBoJiIiIiIyp670AWgA3Rdob07jHA0Bq/bhdqgF5omhS4WUdZ4LZc2vr9EVIoYFDuErits3d2JRWs3YmdHCBNrAli9sAnTxlXxw4aKCq/l0sb3l4hKFbtPOER7d9j8kAGAnR0hLFq7Ee3d4QKXjCg7vJZLG99fIipVbCl2iHA0Zn7IGHZ2hBCOxgpUIqKh4bVc2vj+UjGafOMTWa2//Zaz8lQScjK2FDuE1+3CxJpAyrKJNQF43a4ClYhoaHgtlza+v0RUqhgUO0Rt0IvVC5vMDxujn15t0FvgkhFlh9dyaeP7S0Slit0nHELTBNPGVeGxK0/iiG4qaryWSxvfXyIqVQyKHUTTBHVVvkIXg2jYeC2XNr6/RFSK2H2CiIiIiMoeg2IiIiIiKnsMiomIiIio7DEoJiIiIqKyx6CYiIiIiMoes084iK4rtHeHmeaIqIwU431fjGUmIhoIg2KH0HWFrbs7sWjtRuzsCJkJ8aeNq+KHDVGJKsb7vhjLTEQ0GOw+4RDt3WHzQwYAdnaEsGjtRrR3hwtcMiLKl2K874uxzEREg8Gg2CHC0Zj5IWPY2RFCOBorUImIKN+K8b4vxjITEQ0Gg2KH8LpdmFgTSFk2sSYAr9tVoBIRUb4V431fjGUmIhoMBsUOURv0YvXCJvPDxuinVxv0FrhkRJQvxXjfF2OZiYgGgwPtHELTBNPGVeGxK0/iiG6iMlGM930xlpmIaDAYFDuIpgnqqnyFLgYRjaBivO+LscxERANxTPcJEblHRFpF5M2kZd8XkV0i8nri70uFLCMRERERlSbHBMUA7gVwpsXy5Uqp4xJ/vx3hMhERERFRGXBMUKyUegHA3kKXg4iIiIjKj2OC4n5cJSKbE90rauxWEpHFIrJRRDa2tbWNZPmIBo3XKRULXqtUDHidUi45PSheAeBwAMcB+BjAHXYrKqXuVko1KaWa6urqRqh4RNnhdUrFgtcqFQNep5RLjg6KlVK7lVIxpZQOYDWA2YUuExERERGVHkcHxSIyPunhuQDetFuXiIiIiGioHJOnWEQeAnAKgLEishPA9wCcIiLHAVAAtgNYUqjyEREREVHpckxQrJS6wGLxz0e8IERERERUdhzdfYKIiIiIaCQwKCYiIiKissegmIiIiIjKHoNiIiIiIip7DIqJiIiIqOwxKCYiIiKissegmIiIiIjKHoNiIiIiIip7DIqJiIiIqOwxKCYiIiKissegmIiIiIjKHoNiIiIiIip77kIXgA7QdYX27jDC0Ri8bhdqg15omhS6WEQliffb0PHcEVEpYlDsELqusHV3Jxat3YidHSFMrAlg9cImTBtXxQ8bohzj/TZ0PHdEVKrYfcIh2rvD5ocMAOzsCGHR2o1o7w4XuGREpYf329Dx3BFRqWJQ7BDhaMz8kDHs7AghHI0VqEREpYv329Dx3BFRqWJQ7BBetwsTawIpyybWBOB1uwpUIqLSxftt6HjuiKhUMSh2iNqgF6sXNpkfNkY/vdqgt8AlIyo9vN+GjueOiEoVB9o5hKYJpo2rwmNXnsQR3UR5xvtt6HjuiKhU5TwoFpHfAHgIwK+UUt253n4p0zRBXZWv0MUgKgu834aO546ISlE+uk/cDuCzALaIyHoRmS8i/jzsh4iIiIgoJ3LeUqyU+iOAP4qIC8CpABYBuAfAqFzvq9QwIT5RdnjPFAbPOxGVorz0KRaRAICzAXwVwCwA9+VjP6WECfGJssN7pjB43omoVOW8+4SIPALgbcRbiX8C4HCl1NW53k+pYUJ8ouzwnikMnnciKlX5aCn+OYALlFLM5J4FJsQnyg7vmcLgeSeiUpWPgXb/B+DfRORuABCRqSIyd6AXicg9ItIqIm8mLRsjIs+IyHuJf2vyUF5HYEJ8ouwMdM/oukJbZx92dfSgrbMPuq4KUcySk21dxfeBiIpFPoLiNQDCAE5MPN4F4IeDeN29AM5MW3YjgGeVUlMBPJt4XJJqAh6sbG5MSYi/srkRNQFPgUtG5Ez9TSJh9Hs9964XcdKtf8C5d72Irbs7GZDlQDaTd/B9IKJiko/uE4crpb4qIhcAgFKqR0QGHH2hlHpBRCanLT4HwCmJ/98H4HkAN+SuqM7REYrgN6/vxJpLjodLE8R0hfUbP8K4k49gPlBKwZH/cf1NItHW2WfZ7/WxK0/i/TRM2UzeYdf/uFDvA+8dIupPPoLicCL7hAIAETkcQN8QtzVOKfVx4v+fABhnt6KILAawGAAOOeSQIe6ucHRdx8nTxuHSe181R3TfOq8Buq4XumiUQ8O9TjnyP5XdJBLs9zp8/V2rg528w0nvA++d0lTsn/3kLPnoPvE9AE8CmCQiDyDe7eHbw92oUkohEWjbPH+3UqpJKdVUV1c33N2NuJgC7ntpG26aOx0PL56Dm+ZOx30vbUOMvzKWlOFepxz5Pzjsbzx8/V2rgz1/ThorwXunNBX7Zz85S05bikVEA1AD4CsA5gAQAN9USu0Z4iZ3i8h4pdTHIjIeQGuOiuo4LgEuPnEKbtiwOaWl2MUGDEripJY3JzP6vaa3Cib3N2aL4dBkc/76ex9GGu8dIhpIToNipZQuIt9WSj0C4IkcbPLXAC4GcEvi31/lYJuOFFMwA2IgXlnfsGEzHllyQoFLRk5itLwlf7gzS0km9jfOn2z6CWfT/zjfeO8Q0UDy0X3i9yJynYhMSqRUGyMiYwZ6kYg8BOBlANNEZKeIXI54MHy6iLwH4POJxyVJKWXZihHvNUIUl83I/2KUy24NRr/XCTUVqKvymYEYWwyHJ9vzZ/c+jLRSv3eIaPjyMdDuq4l/v5G0TAE4rL8XKaUusHnqtFwUyuk8bs2yFcPjzsf3FipWTmp5y7WR6tbAFsPhKda6qpTvHSLKjZzXYkqpKRZ/ZkAsIqfnep+lwK0Jls1vSGnFWDa/AW5W2JTGKS1vuTZSA6HYYjg8xVxXleq9Q0S5kY+W4oHcCuCZAuzX0ULhGG57citumjsd1QEP9oUiuO3JrfjJhTOBYKFLR5R76TljR6pbA1sMh4d1FRGVqkIExfzkseBxa2jr6sOS+1vMZcXwkyTRUFh1lXjw658ZsW4Ng82zS5lYVxFRqSpELcaRYxaK+SdJomxZdZX44RNbsOqiRnZrcDjWVURUqgrRUkwW+JMklROrrhJPb2nFzeccy24NDse6isrB5Buzzyq7/Zaz8lASGkmFCIq3F2Cfjud1uyx/kuSIeCpFdhkgNE1jtwaHY11FRKUqL0GxiJwIYHLy9pVSaxP/fiUf+yx2tUEv1l42Gx+296DC60JPOIZDayv40zGVJCfNdFYI6YMMi6lFnHUVEZWqnAfFInI/gMMBvA7AGDauAKzN9b5KTV9Ux02/ejMlSCAqReWcAaIUpplmXUVEpSgfLcVNAKYrTsWWlfbuMJY/k9pPb/kzW/Ff5zbw52QqScWUASKXLbvGIMO6Sp95v3+yvxfjRvkwJuj888G6iohKVT6C4jcBHATg4zxsu2Tpuo6LT5yCGzZsNltfbp3XAF3XC100orKW65bdcDSGukofrvvCtJT7fVVzI6oDzm8tZ11FRKUqHynZxgLYIiJPicivjb887KekRHVlfsgA8RRVN2zYjKjOBneiQsr1THtetwvXnDY1435fsq4l57P35QPrKiIqVfloKf5+HrZZ8mK6spzNK8YPGqKCyvVMe7VBL6aMDY7I7H35wLqKiEpVzoNipdQfc73NcuB2aZYpqtwuzhJFVEh26eOGmoJM0wQVvtxucySxriKiUpXzWkxE5ojIqyLSJSJhEYmJyKe53k+pqa/0YWVz6mxeK5sbUV/JgStEhWSkj8vlTHtjg76cb3OksK4iolKVj+4TPwHwNQCPIp6JYiGAI/Own5Lidms4alwVHllyAqIxHW6XhvpKH9xutr4QFVI+0scVc0o61lVEVKryMnmHUupvIuJSSsUArBGR1wD8Wz72VUrcbg0HVwcKXQwiSpOP9HHFlJIuHesqIipF+QiKe0TEC+B1EbkN8dRsbEIgIiIiIsfKR1B8EeJB8FUArgUwCcC8POyn5BTz1K9Eucb7wbn43hBRKcpH9okPRSQAYLxS6j9zvf1SpesK29u78WF7Dyq8LvSEYzi0tgKTa4P8sKGSlx5k1QQ8eK+tq6inQi5V+Z6mmgE3ERVKzoNiETkbwO0AvACmiMhxAH6glPpyrvdVSvaFwujsjaQs6+yNYF8oXBRTvxINlVWQteqiRvzo9+9mTJjx2JUn9dsPlwFV/uVzmud8B9xERP3J1+QdswE8DwBKqddFZEoe9lNSIlEdPeEYbvrVm+aHwbL5DYhEOXUqlTarGeOW3N+Cm+ZOx9NbWs31BprcggHVyMjnNM92swcO9GWIiCgX8jEALqKU2p+2jFMdDSCiK1y/PnXq1OvXb0aEs0SVLV1XaOvsw66OHrR19kEfxLUwlNfkQzblsJsxLj1n70CTW+R6OmY7TjnHhRJTsJzmOZaD05Dr2QOJiLKRj5bit0TkQgAuEZkK4BoAL+VhPyVFt5k6tdw+cCluKK2eTmkpzbYcdjPG1Vf5zOWDmdzCLqAKRWKIRnV0hCLD7lbhlHNcSNnWVdl0acn17IFERNnIR0vx1QCOAdAH4EEA+wF8Mw/7KSluTcwZogwTawJwl8kHLaUaSqvnSLWUDiTbctjNGHfw6AAeu/IkvHjD5/DYlScNGHgaAVWyiTUB/H1fCO/s7sS5d72Ik279A86960Vs3d05pC+cTjnHhSQCy/MsFm+N8SVisOc+H7MHEhENVj6C4umJPzcAP4BzALyah/2UFgHuOG9GyofBHefNABgTl6Wh/IzslJ+esy1H8uxuyQGw262hrsqHCTUVqKvyDdgSaxVQ3TqvAUopLF3XkpNAtr9jK5duFSLArfMaMs6zVVCc7ZcIu2uhXFrhiaiw8tF94gEA1wF4E0BORomJyHYAnQBiAKJKqaZcbNdJlAKee/sTrLnkeLg0QUxXWL/xIyw8kWMUy9FQfkZ2yk/PduUIeF1o6+yz/Bk9F7O7GQHVw4vnYGdHCPtCEdz+1Fbc+MWjcvZlwTi2ukoflp5yOKoDHvSEYwj6XGXTrUIp4IWtuzPqqsm1mXXVUL6oFfNMf0RU3PIRFLcppX6Th+1+Tim1Jw/bdYSgT8PZx03Epfe+an6ormxuRNDHyQDLkdHqmR5k9fcz8lBekw/p5Thjej1umnsMPg1FsW1PN+589j20dfXlJWjUNIHX7cK3Hn3DDMb2hSJZf1mw6wdbG/Ri7WWzsfvTXnNgbH8p5H555Ymor/Ln7PicoNKvYW5aXbWiuRGV/sy6yut24Yzp9ZjXOMlM37ahZQf7CBORI+UjKP6eiPwMwLOI9ysGACilfpmHfZWMUDjzJ96l61rw6JITUF1R4MLRiEv+GXmwg8OG8pp8SC6HruvY0x3GBav/nJK+6/antuYt1VZ6UL6hZQdWNjea99dAXxYGGkxX6Xdj4T2p2RfsUsj19MWgB1VJtRZ39+q4Iq2uumJdCx5ZPAejU7saoybgwTWnHZly7lc2N6Im4ClAyYmI+pePoPhSAEcB8OBA9wkFYDhBsQLwtIgoAKuUUnenryAiiwEsBoBDDjlkGLsqjHBMt/yZMRJjnuJSks11OpSfkZ3203NfTEfrp32oq/RhZ0fITN9109zpWHJ/i+3P6MOZhCP9y0HA60I0pvDgos/AJYKA14XqgP32BsqVG4la36tWKeS27elG0Od21HsyWHbXasQm+4RV+siOUMTyyz7zDlOuFPtnPzlLPoLi45VS03K8zc8qpXaJSD2AZ0TkHaXUC8krJALluwGgqamp6Ea4GNkn0n/idZVQCxMV/3U6GFYtrUbr8Gs79mFnRwjVAY9tF4bBpj3rL3A2vhzYbas6kH1qNyOAt+szXZeWQs445p9cOHNY57NQ7K5Vj8u6rvK4Musqpwz+pNJVDnUqjZx8dFh9SUSm53KDSqldiX9bATyG+Ix5JcWtCZbNTx3RvWx+A1OylaFCZzGw2n82ZbJqab1hw2YsPeVwAPFruyccs+3CMJiMBYNN9TWUFGp2qd2MAN4ubdjogBs3n3MsHl48BzfNnY7bn9qKtq6+kus/6xLrusplkX7C49Ysz6XHzbESROQ8+WgpngPgdRHZhnifYgGglFINQ9mYiAQBaEqpzsT/zwDwg5yV1iH6ojpue3Irbpo73RyQctuTW/GjC44rdNFoBBV6cgir/a+9bDb6ovqgy2TXOmi0Dq9qbsT4ar9tF4bBtC4OdjrgobRUDjRg0a7vNgAcNNpf8IGO+WZbV33tuIx1jS/7yYMS+WWfiJwqH0HxmTne3jgAj0m8FcIN4EGl1JPZbCAa1dHa1YdITIfHpaG+0ge3w1oqXJqgrasPS+5vMZdNrAlYtr5Q6RpssDeS+/+wvQc3/erNQZVJ1xVEDvy8PnNSNZaecjhqg16MH+3Hr686qd/+vMDgUssNNtgdSpq6wQxYtOu77YSBjvlmW1dZHGcoHLMMoH9y4UwgOJKlJiIaWM6DYqXUhzne3gcAZgz19dGojnd2d2aMfj4qMTmAU2haPCH+DRsOtKjcOq8BmnOKWFSGM1CrkAYT7OXz2Kz2X+F1DSoANVqZlz+zFbfOa8B9L23DxSdOSbmmB+rPCwwutdxgg92hpqnrb8DiYPoyl7Js6iqPW7MMoMu9+0Sx1k9EpS4fLcWO0tbVh43b9uDBRXOgKwVNBM9t+TjeclUdGHgDI0QpwX0vbUtpUbnvpW34/pePLXTRik6huyAMx0DB3nAGoQEY8IPYav894digAtDkVubqgBf/MfcYtH7ai5vmTsfK59/Hazv2DarVezAttVa5kP/jrOkIR2No6+wz1891mrpoVMfW1k4suT81vdvUukp0hCJlEeTo+uAn72D3iUzFXD8RlbqSD4q9bkHjlLG4MClP6ormRng9zqp86oJey3yedSXWH3EkFLoLwnAM1LI50LHpusK+UBgf7+vFkqRrya5f8LhRPoTCBwI5q/0fWlsxqNZWo5V55qRqnDNzQso9l5x9YjCZBwZqcbXKhXzhz/5iGWTkqvVW1xX+vj9kBsRA/Pwvf2YrvnnakSnnu5SDnAqf9eQdFRYTDYXCMTy2aVdKAL36hQ/wzc9Pte0+UeqtqMVcPxGVupIPinsj1onmH148x1F92jweF6bVV+LhxXMQ1RXcmqC+0gePp7RGro+EYk4DNVDLZn/HZrRAfbK/N6MPsF2/4JvPORaX3vtqSiBnN4jMrkxGEBNTCmsuOR4el5g/rRv7MnIT3/z4lpxlYzCC3bbOvoxANf2LQi6CrPbuMFo7+zLO/7zGSWZAbLX/UtPTZ1+n1qRNNBTwunBeU2oAvfz8GQh4ra+BcmhFLeb6iajUlXzHrqhNovnoCKe5GgyPx4UJNRU4tDaICTUVDIiHaKCUWk5nBHsTaipQV+VLCQb6OzajBcqqD7Bdv+CKRHCSnKrMav92ZUpOjXbybc/jpl+9ifHVAct9Ga3QNQFPTlPODeaLwkCp2wa7n/bucMb5rw16yyrIyaZOjcYUrn3kjZQA+tpH3kA0Zn3+h5JCr9gUe/1EVMpKPig2JsVINrEmUNZ92kqdXR7ZYk6NpesKe7v7EInGcP/ls7HmkuMxc1J1yrGFozHUVfowJujF+qUnYNVFjZg5qRrAgX7BySbWBLAvFDEf2w2e6y+AtQpiPmrvsdzXwdUBTK2rxHttXTkJUg12QYaIYHdnCB6Xhh9fMBOrLmpEXaXPNsga6Fi9bhc2tOzArfNSc/SOCXrLKsjJpk4NRay/sPRGrL8wOKkVNV/5wkuxfiIqFSXffWJUQMPK5saMvrqjAiX/faCs+dwabj7nWFR4XegJx+Ar4tHuuq6wvb0buz/tTRmwlJ7vN+B14dtnTkv5qdrIAmHVL3jZ/Abc9uRWcz/pgdxgfsq2CmLufPY9rGpuzOhje9Aof7/9KWuD3kF3c0juEhHwujKObWVzI+578QOcPG1cRpaE25/aarYiJ29j96d9/R5rbdCLa0+fhuXPxFOMHVFXiY/29mDl8+9nZGMo5SAnmzrVZTNTp937OpQUevmQz24cuR78SUS5U/JBcahPwe8R3HvpbGgCxL/s6wj1KVT5C106yof27jAW3vNKxgdrsfTxTO8D69Jg2Sd4yboWPHblSeaHaVRXZtBsrHPDhs14ZMkJOGhU/GI3Pog9bg1dvVG0dfUBONBaZXRtCEdjEJEBBwRZBTFtXX04qNqHR5acgGhMhzuRG1zTpN+WQLsgBDiQNcPj1uB1CXZ1ZA4k/OWVJyIS1SEiuO/FDzC/6RDsD0VSsl/csGEzbj8v3qc1eX9rLjnePL9GbuXuvig++bQXB43yQ9MEuq5QHfDgO2dNh0sTBH0awjE/XvqgHe+1duH282Zg/Gg/3JrA79WwLxROGcSY3gdb13XEFKCUKqrAKJs61evSLLNPeF3WX1KHmkIv1/I9GK4cUvcRFaOSD4oVgLbOcEalXOXzFLpolCdO+gk2W1YtVKuaGzG2cuB+q5GobrmOUsoMtpI/iMcGVUprVU3Ag/fausx9r196woD7tAti9vdEccvv3sa8xkmoDXoRjek4eHTAtiXQLgD/5ZUnor0rnLL9tZfNzhjYtvCeV/DYlSdhQk0Fdu8P4eRp41KzIyyYha6+KG57civGjfIjGlMp+zP6XM+cVI3rvjAto9X3iLFBbG3tymgdnVZfmZIBY0FSBgyjJb6tqy8lwDdyOVvlcC6GAWXZ1KljKrzorQmkBNBet2BMhXWQ65RW1GKuQ4ho6Ir3N+VBili0nl2/fjMiDhxoR7lRzANZrFqolqxrQcDr7veYkmeSs1snmVVGho5QJGXfVoPKjO0Z/S0/3h9CbaUXv77qJLx4w+fw2JUnYdwoH2753du4+MQpuPnxLZi/8mVc+LO/YGtrJ2oCnsz+lBc1QSmFukofVl3UiIcXzzH7//ZG9JQy1VX6oCv0G7BEdZWR/eKKBzahN6Lj22dOw57OPvSmBT37QhFMrAlg6SmHZ7x20dqNaO3qMwNiY/nSdS1o6w4nBh5qGRkwrl+/GUtPOdzcxief9mJfKP7+zmucZLmfYhhQlk2dqmmC7t4YLlnzCk6944+4ZM0r6O6NOT7wL+Y6hIiGruSD4pjNSOkYg+KSVcwDWexaqFwasGx+6gCvVc2NqPa7zdbl7//6zYxBYFbHbZ+RIbWl2egrm769moAn5fVfuesl7P60D+NHB1Ab9CIUjuHfvnh0RtC35P4WtHX1weMS3HzOsXh48RwsP/84VPrdUFC484KZOGiUH/tCEWxo2YFvnzkNPpeY2zBacXfstR7IZwQsdvf82EoveiM66kb54BLBGdPrU4512fwG20wSthkXYnq/71t1wGP+/+/7Qvh4Xy/qKn2oDniKtiUymzq1vTuM5b+P98F+ePEc3DR3Opb/fqtt8J/LbCHDUcx1CBENXcl3n/C6NOuBGzZ92qj4aZpgal2lZZ9WJ0tu7U2/Xnd1hFDld+OBr38GbZ19aO8O40fPvotvfv5IjB/tx6K1G1FX6YMmwP2XzYau4l0CxiX6wyaz6y/5yJITUvb92o59uO+lbXhkyQlQKl42lwCtXX2Wr//1VSeZg9XuOG+GZeDUF9Nx25Pv4OktrWaQe/Pjb2V0Jbh1XgPWvLgN//nlY80yGa24dZW+jIFtqy5qRE0iAHVb3PNnTK+HrmD2GzYmnACAp7e0oq2rDwGvC6P8Hsvz77YZMOZO1CN23UKM7B4TawJo7w7j5se34OZzjjVbpgs9oGwoPDbnwmNxf+m6bvne6rpuuW2nTGyRbTeOUp9whKhclHxk6BJg+fkzUr7xLz9/Blysr0qWriu819aF81e9jJOXPY/zV72M99q6Rry1KRv9tfbeOq8Bdzz9Lj75tA8LfvYXzF/5Mpbc34Knt7Riyf0tCIXjqdiu+8I0XL9+Mz53xx9x8ZpXsNemNc6uVVMpldE6du3p01Bf6cOnvVGcv+plfOb/ew5/3xdKab1ddVEj7jhvBkLhmBnQGEFfsok1AWxr68bFJ07B+Y0Tcdv8eGBr1ZXAWK4rhZXNjfGUbqP9uGnudNz4xaOgCXDLV/4BDy+eg/svn41oTMdHHT3QdYX6Sp/5GmO/N37xaHzjwU2pXSrWteD6LxxltmD+56+34FuPvIFVaa9dvbDJcpsrmxvhEqCtsy+jW8gZ0+ux9rLZqK/yYc0lx+MnF87Eyuffx86OEKaMDVqmdiuWlkivW7M8F16LDC9WXVlu2LDZNk+8k/ry9pcvPJlTWreJaPhKvqU4qhQ8aem5PG4NUcUKq1Q5pbUpG8llbusM46a501Eb9GL8aD/+8zdv4bUd+2x/co8p4JrTpmZ2V1jXgl9eeSIEktKCZdeqGdMVptZVZrSOpZ9Po6+xEYgb+00emGeVpsxIh9bW1Ye1l83G3u6w2cXA6rhqg16ICO589l38+ILjoIng5se3pGzv53/6APMaJ+Hmx7fg9vNmoMrvQV2VD0eNq8KjS05Ab1TH9j3d2B+KWO6jJxzDV+/+c8p5GF/tt2whPGpclfnrgwLwX09swdNbWs2A1jh3xqA7IwPKxJoA7jhvhrl9r0vw/S8fC5fAbIUvptbFcEyHUiqlTlVKIRzLbP2163Zi133NKSnZslGM9Q0RWSv5oFgp4PUP9+LU6eOhKwVNBM9t+RinHzO+0EWjPHFSa9NgJZf5tR37sOT+FgDAX/7tVNw09xjc+MWj4XVreOKaz2JnR8hMMTaxJgC/R8OUsUHroK8vhuafH8iIYARvqy5qNAeGGQHmD5/Ygu9/+VgzfVt7dxgf749v88TDanHa9HGoDngQien4yYUz0dEdSQnEjWB5Z0cIr+3Yh9uf2or7L5+N1k/7sC8Uwe1PbcVrO/YBAPZ2h831jVblukoflp5yOKoDHnOykZiu8G9fPBoet4b7X9qGukofbpo73Vznu3Ono707jPsvmw1NE0RiOnRdwe3WML46AF1XCPpcCEd1y2CrusKDM6bXm8HtqosaMcrngaYJ9nT3IRSJ4pP9MbhdGiIxHV63C36Phi//5MWUIGj5M1vx/S8fa3YzSR90961H38Dt581AfZUPuzv7sK8ngiPqg/B5XIhEi+sLulJAy/b2QdWpdt1OXDbBv1NSsmWjGOsbIrJW8kGx36uhccpYXLj6zyl9CQPeku85UraKsbXJqsxnTK/Hnu5wRvC6oWUHrvvCNNz30jZcc9qRGBPwAginvH7mpGpcc9pURHWVkqfXaMEaG/SawWVywLr45BD2hyLwuTWzpfOM6fW46tSpuPKBTWY5lp8/A9MOqrQcmGcEym1dfXCJ4FuPvpFRruoKD3ojMfzkwpm46w9/w08unIlQOJaS5mtlcyPufPZdM2C955ImnH7MePzLw68f6Evc3Ai/R8NFSa2y6anN2rvCWP7MVty1YFbKMdw6rwH/9cQWfHfuMVh88uFwafF+2LsSXwSSW4KT06utao5nxtjZEU/h9u0zp6HS58b5q17OaDE37OwI4aBRfvMLyoFz+hfbcjuVXZ3qt6hTfW4NKxbMwhVJ533Fgln9TqZTbBPvFGN9Q0TWSj4o7g3ruCItldIV61rw8OI5QLDAhaO8qA16sfay2fiwvcf8YD20tsLRrU1GC9nyZ7aauX0PGu3HD37zVkrwet9L28w+uGsuOR7LnnoH/3VuA8YGfWYLW12lD98+c1pKgGl0XXhtxz6EwlG4XZrZFcFgDAb7l4dfx/Lzj8Oy+Q04aJQfbpeGCxIBEBC/h6595A1zYF5yC68mgkeXnAA90SXA6GtrV647zpuBS0+agpoKL6568C8p+1i6rgU3zZ2Op7e0YmdHCLs6ei0nMFl72WwzSE3/6Tr5p+1/Pf1Iyy8CN37xaLz8tz34p6PqcdHPX0k5Z22dYby2Yx+uX78ZN82djiX3t2DJuhbcfM6xuPPZ93DdF6ahN6Lj+vUH+isnt5gnn9vkrgTzGieZAbpxLMXyk3s2daoOhfpRXvxi8RzEdAWXJvC4AOthdtlPvOOEAW7F2LpNRNZKPii2TaXEQRClLf3tdfjbbWTM+ObnjzRbhp+45rOWI/dH+d3Y2RHC3u4wnt7Siu+dHcsYLf/Vu1OD2Bs2xIO6mx/fgrc/6cSGlh0ZU/UagXNdpQ/VFW7s6Qrjontesc0kEdMV1l46G7s7ezNaeI8aVwW3W4OuK4wb5cPDi+dAAfhaWrm+9egbuP/y2VCwzj1spDQDDkywkb7O3u4wlp5yuNnlJPmna13XzUC4wufBhpZ4y7NhYk0AugLOPm4Cmn/+l4zuGd8+cxpue3Irlp5yOKbWx7udrHz+fRxSW4FrTpuK+17ahn/74tG447wZ2BeKYOXz71v2p/7phbMQCkfN/RZzSrZs6lQNgo8/7ctoKR4/2no60Wy6IgxlKuZsgujBruuUCUeIaPhKPih2a/F8pPMaJ5ktRBtadsDNCqtkdYT6sLuzNyX91rL5DRgddKM26Ny5vTtCkZS+qF6XZjlyf80lx2NiTQBjgl4s+cfJAICP2rvhTQShUV1h2fwGxHQFj0szg7XaoNcMfAEgFI7h4cVz8PH+XrR3h82W0zWXHI+diVbZukofxgS9WL/0BLR3h1P6MkdiOnweLWMih6XrWrBh6QmI6Qp9MR17OsPoi8ZwcHXANrje09Vn2bo6JujFw4vnYF8oApVYZtW6nRw8G1kR2rt70dUXNdMvvt/ahevPPAoAzG4Rt85rQCgcRV9Uzxg4aAT4/9+8Y/H1+w58eVh+/gwEvS4cc3AVrjntyJSuGyubG1Hld0FE8NCieOuoCODS4sHkC9/+HFwaIMr6WDwO7yoAZFen9kV1MyAGDkyk8vDiOZbbzqYrQrYD3LIJorMNuDltM1FpKPmgOOjTcPVpR5o/9xn934I+53/40NCEwrrljFu/cHiXmXA0ltJS6U6auMKwsyOESEzHrfMasOypd3DNaUdiXyiCT0MRuDXBtY+8YXZTuPGXf01pnRtb5cPyp98FAFz3hWm49pHXUVfpw/e/PB1el4Ybv3gUesIxHFEfxN8Tk0xc94VpKdMl3zqvAfe9tA0XnzgFHpfg4/29GWWsq/ShtTPeOphcllu+8g9mwDNzUjWWnnJ44idmwbhRPvzoa8fhm794PeU+XfbUO2YAu+bS4zNat+84bwZ+/qcPcMHsQwHA/ALU1tUHv1tDR08k48vRD845FjecGYNLEzz0l+2YNbkWgHUGj6UW3TOufeTAoDmrWe4e+Ppn8MPHt6QE3i9s3Y25x03Ej599G1d+7ggI4pOxJLewLz9/BrpCUYwNKke3Mlba1KmVFnVqtpMn1Qa9eHTpHERjQEwpuETgdsGyK0K2A9yyCaKZUYKoPJV8UNzdZ9//rbqiwIWjvLD7IHZ63tCA14VvnzkNa16M9xs+uDpgOfhuVMCDvqiOeY2T8JvXd+L82Yei0udGJKawYsEsVPjcuDipX6bROnf7eTPiAZkAkZjCiYfFg0FNJHVSiwWzoIng3790NLr6oildA5L7Ml8w+1CEY3pKoPvtM6dhYk0F+qI6bvnKP8CliRn4uTTBsvnxSTnSu4Usm9+AyWMr8PDiOdjZEcKYoNcMiI1jWPbkO/HJPpIGYfk9Gq4/8yjs647g4cVzEEmkBYvGFCorPfj62sypl3+x6DP4aG8PjqgP4uITpyCixzMo6Mr6utnbHcZt8xuwtztsnof6KvvppqMxhUtPmmL2hTbO2aX3vopl8xswyu/B3u4wYokWfU0EPeEYorrCwjWvOD7w6rSpU3+xeA5Gp9WpLk2w5B8nY37TIXBpgpiusH7jR7bZJ2IxHe1dkZQvPiubGzG2wgdNS20tznaAWzZBNDNKEJWnkg+KdaVSWt+MDzWdeYpLlt0shh6Hz2IY1VVKwLhiwayUvqlGxgKjX+7EmgDuWjALt/7u7ZRWSbtgra7Kl5KebcWCWait9OL8VX/OCKAfXToH7V0RXPvIgZZbo+vF/lAEV586Fd/91VsAYLYeX/m5IxAKx8xBeWdMr8d3zpoez0zg1hDwaNje3oMfnHMsvvurN1PuyTUvbsN3z54OgcClCdyaoK0zdfKReY2TcNm9GzPe14cXz8Hpq17A+Y0TsWDOoeYkHb//13+ybMXeF4rioVc+xNWnTsWerrAZYB9RH7TtngEAX737z2YA7/do2PpJl+X6H+3twdhKL2ZOqsZrO/ZhZ0cIbk1QV+lDpd+Tkr/41nkNuO3Jd/Dajn3mFwKnB166Pvg61e/RMPe4iSm/NqxIZAyx0tYdtmx9f2TJCTi4OnUymGwH1GYTRDOjBFF5Kvmg2OvS8P0vT8fe7kjKY07zPDS9vVG0h8KI6gpuTVAb8MLvd9ZlVFvhwYrmxoyfd2srPAO/uIAiUR2XnjQFvREdd5w3A0GfGz9+7j0z+BgT9JrBBRAPGK58YFNKhobkPsfpLcxKIaXV94oHNuGhRXNS1jO6NYSjCm2dfWa3gbpKH8JRHcvOm4GAR0NnbwRtXX3Y2RHC7U9txW3zG7CzI2S2OM+cVI2LT5yCBT/7S0oAuPbl7fjmaUfiW2dMw+X3Heivecd5M9Ab0XHJmgPBk5EGzchtXBv02v4U/4vFc3DQaD+2tXWbOZU1AdZccjx+99ePzRzL9aN8uOjnr2DZ/AZ09UVTWsiXnz8D91zSZAbeyd1F5jVOMvd3/frNuP28Gbjz2ffw0wtnmUF4+gQlDy2agw/bu7H25e1wuTRcc9rUjBZW4/3aH4pgdCCeM9npgZffbV2n+i3q1N5IP5kqLERjuk3ru3W+ir6onvIerl7YZFvumoAno/vNyuYD04MnY0YJovLkrGgmT3rCsYx+hZS93t4odvf0IRJV0BI/we9WfRgHOCow3tMTwY+ffTelJevHz76L73/5WBzsdU450/m9Gip9bly/fpPZ1/fSk6aY3Q/s8t8mDzLb2RFCbySW0cJ89WlH4pI1r2QEb7pSKd0f0gea3TqvAb96bRfOmTkhYwDaI0vm4O/74oP0IjE9JTvE0lMOtxwkeNPc6WZKs+TnvvXoGxnLrl+/GTefcywuvfdVnDG9HgeN9lu23ikA1z0a70v97186Gov/6TDs2BtCW2cfAODrJ0+JB6qdYdx+fjyTxkGj/OYAOWN/1z7yBh74+mfw0KI52P1p/LiM/tPG4ERjXUF8kpUXtrbioUVz8Pd9oYwJSvZ09SESU/i3Lx0NTWA7wcr+UATzV75sfnmrdtC9ZGewdWq22X9cWUz2kW2/372hMO5MqxfufPZd/PDcf0B9VeoAXGaUICpPzq99hymiK/tBV5SVrkgUkaiOHXtD5s+Vk8YE0BWJOioojsT0jJ/e2zrDZn9Tp4rFgD1dYdxx3gyzVdj4mXpqfSU8Nt1C9oUiKY//vr8Xm7a346FFc8wZx9LzDN+wIR5w6kqZA77sAlmjP2z6T9p3LZiF0YkWbJ9bQ1Q/EGDbpRwzlld4XRnPVXhdZku1EbQcUR/Ek9/8LKI68IPfvJWR6mxlcyN6IzHcd9lseF0CBWBXUou10YXjO2dNh1sTaFq89djYZ3oZYrqCz6uZx/Xds4/B/S9tMwNd4xzXj/Jh1UWNCHpd+FtrV0r+ZGOduiof3vhoL8Ix3Zyy2+r9Gx3wmBk2fvzsu/iPs6ZjYk2FYwOwcBZ1qt2MdnbZfzwuyZhk5a4Fs+BxZa6fPjDV+AXErvtJbySGp7e0pqTkA4D/mGtdL2STUSLbfMlOyK9MRJmcE8nkSbEOunIiBaCtsy8z1ZnFz4+F5HdrGZNELJvfAL+D013pusKnvQeCW7cr3gfVmPJ53eWzsfbl7RlB4YoFjfjxc/GMEsmZDs6aMcEMhH/1jZMs74EpdUFoEs//e/M5x+LQ2grL9VxaZhaMukofNJGUvqIrmxux5pImXHrvRnPqZqsAfmJNAD3h1MBlYk0AmohlSrT6UV68tasTl3/2MOhK4Zav/AOq/B5UV3hSZp376YWzUBP0mO+7VReOlc2NmDQmnvrMqnw+t4Y9XeGUn9hXNDdiW3tP0n5mYldHCLVBL8aN8uPHz75n8b7Mwv0vbcPc4ybiinUtZst/esaJFQtmpWTYuHVeAzr7omjvDjt2sJ2eRZ3q92iWQa5dn+JITOGJN3ZhzSXHpwzMW3jilMxte23uc5vZSl1i0wo9zFhU1xW2t3dn9G2eXBu0DHSzTffGAJpo5BRFUCwiZwL4EQAXgJ8ppW4Z7GvtWtfc7FOctb5oP6nOHERXwJoXt2UM5PrPLx9b6KLZ2hcKW37hMPrUelwant7SirbOcMpx+T2CeY2TcMUpR6DK74HXLfjaZw41Z2YD4kGv1T0AFf+iE44qTK6tgLufAYrpeWlHBzyWA6LWXjYbN59zLMZWei0nB7nvpW1YsWAWYkndNow+xckDAZO3+cDXP5NyXm6d14DO3ojZl9dY9xsPbsIDX/9Mv104jO0BKiOQvXVeAzRBxnFdsa4Fjyyeg++efQyUUtgfiqaku1s2vwGPbdqVMulHV18UsybXmv1pb5o7Hd948LWMyUF6I3pKhg2jZd7Jg+2y6eLQ1RfDupc/TAlyV7/wAa469QjUVmZuWxPg5GnjMtIAWsWA4Yh1i/WjS06wLHfQ57IM0IO+4fXh3hcKozPpCy0AdPZGsC8Uxphg5hebbLp9DGWCEiIaOscHxSLiAvBTAKcD2AngVRH5tVJqy2Be73OL5eAKn5sVSrZiejyN16KTD0v5gLPLOVooCspyJjgnT2sXCscsP+CN6YRrK32WE2jce+lsHDzaj0q/O55WLPFLcHLAkt7H2DgfPk98/fGjffGBky5YBrL/u2knrj51asqsZPdfPtuytVAAHFEfxK59vTi42o97L52NUCQGv1tDbySGC2Yfiq6+KNa8uC0RnALvfNKJW373Dr579nTbLg03zZ1uHvcNGzZj3eWfsVxXJU2KYdeFIxpT8LlduO+lbRlTaH/nLOsyRHSFD9q64XEJ1r68PeML17996Wh8sr8XCsCUsRVwaRo07cD7YJRlZ0cIS+5vOZCnudJrzpJnZKroCcdQXeHcAV0iwM8uboRbc0GT+JfQqB6DWFSpLk3w0gfteKRlp7lsYk0A13x+quW2dQXLLjxWA/MiNoPy7LpJhWMKP0kauLovFMFPnnsPPzz3HwZ76JYiUd2yj3UkalOOLNK9tXeHsfyZrSllXv7MVvzXuQ2O/SWBqJg5PigGMBvA35RSHwCAiPwCwDkABhUUj/J50B2I4d5LZ5sVuNctGOVz1k/+xaDS60LzCYemtOLctWAWKr3OGi2vsvhgdYqYTY7cI+qD+ME5x2QMkrvvpW24+rQj8eirH+LUow9KCVjXXjY7pSXv7/t7saFlR0YA+N25x+AHj7+Fi0+cgvte2oZLT5qCxzbtws3nBkmaVgAAIspJREFUHItpB1VCV0A4quPcxokZ0zNv39Nj2Vq4a18oJTexOSlFb3x2vcPqggh4NPzwnGPRF1PQlTIzwbR2HpjVLnlyj5iusKFlB677wjRzIJuCstz/x/tDWNXciCXrWmy7cHy0twdjq+ITk6RnmhCxnmlub3d80N6UuiC+8bmpGRknjOwI1z36hrn8pxfOwhnT6/H0ltaUstgNaDSyVlRXeCwzIjiFz6UhElX4+gMHrskVC2bBZ/Hrm1sT/PziRriSAuiYHrPtU2yXK9oq3Vu2/ZUjUd2yT/H3zh7eWINsx61kk+5N13Vc+bkj0JGU6ePKzx0BXXf2+AiiYlUMfQgmANiR9HhnYtmgeDwujK/yI+DR4NIEAY+G8VV+eDzOCuSKQW9UN396BA6kBOu1aREplGxn0XICjxbvupDM6GdrpGV7ePEc3DR3Ou57aRu+d/YxGB1w4/zjD0VPOD7gCIgf5y2/ext3LZhlbm9Dyw5cdepU3Pz4Fnz17j/j5se34OITp0AhPsnEDRs24/ovHIU1L27DadPH4c2d+7C3O4Kv3f1nnHrHH7E/FDH3v+qiRsycVI07n30PK5L2YQR2yQGxUZ5rH3kDIoL5K1/Ggp/9BR09YYSiOnZ/2ot3PunEn99vw08XzMTRB1Vh3dc/g19fdRK+c9bRuPnxLZi/8mVceu+ruOa0IzFpTAB3XnAcfv+v/4QKjyvlGI3A7ODqAGorvbj5nGNx8Gg/fnph6jo/vXAWfvfXj3HFuhb0RnQ8tGgOnr/uFKy55Hi8sHU3lIrPNJf6mplwaRpu+tWbeHPXpxndNm7YsBkBrzvjuL/x4CZ8d+4xWL/0BAS9Lvz0wpmYWBOwHdB4zWlTsaq5EX94+xN0hFJ/jneSXpupm63qAa9bEIkBl6x5Bafe8UdcsuYVRGLx5VaMfr/J4v1+M9d3uwTLz5+R8l4tP38G3DadhD3ueDegVRc1mtfyGdPrbafWjkRi2NXRgw/bu7GroweRiHWXlmwCeeBAurfkctulexMRhBKt0F+9+8+46VdvIhSOQaya5Ylo2IqhpXhQRGQxgMUAcMghh6Q85/G4MKGG09cNV7bplQrF7bJpQRruiJocsLtOPS4xWziTWw+7+qKWXUEUFC5c/ZeMlsbXduzD01ta8a+nH4kHvv4Z7OuJYEzQi3UvZ3YVuGD2oWaasP2hCC4+cQrqq7w46qAqc3DazEnViMYUbn58S0ZLdVdf1MyM8V5rF25/aitu/OJRltdIXZUPMydVAwD6ogqX33eg7Pdc0oRdieB5Z0cIv1g8x2xxNV6/dF0Lbj9vBmK6MtOkvbB1Nx74+mcQ0xX8Hg17uyNY8LO/4I7zZuDSe18FAJzfOBFrL5uNvd1htHeH8dM/vIeLE4O2BDAHIxq/eoTCUdz2ZOrP1Z29B/oQ23XJCEetf8rvi+qI6QqRmIrnnb7gOIwOWOdbNlrRO3tjjuhTbHetZlMP9EWUZd9zu36/EFh29bGKAaO6gsetpcxw6HFrtl9+PS7gqlOnWmS2yFw3EonhndaujFznR9VXZjSo+G1afv02+aazSfcWiVmP4yjEr15OHfDX32c/UbaKISjeBWBS0uOJiWUplFJ3A7gbAJqampwVpZWIbH+uLBSXSMYo/2XzGyxbm0aa3XXaF9VRV+XFQ4vmIN4rWrA/FEGF1437Xtqa0ap476WzU7oZ+NwabpvfgG+v34y6Ki+CPo+Za/eFra348nETUwLuO86bAV0pSGKCC49L0NmrI+hz4+/7es39LT3lcMuW0Qe+/hmIAONiCm6Xhg0tO/Dajn32XRbae7D0lMMBIOPXhl0dvSkpzcZWWgeNYyu9uGTNq7hp7nQz5/GCn/0FN59zbLwFNnF8yWU4bfo4cwY5w5aPO3HvpbPNLinG9q9MTGYye3J1yr4n1ARSJlCxOr6Ybt2dI7kiUkrhoNEBfLI/ZLluOKrjv57Ygm+ediQq/YX/Ec/uWs2mHghn2e9XV7Ds6/3ds4+xKB9w1YOvZZTDLmDs7rP+pesXi+cgvc2ktavPdtKR9AYWo8Xa+FI3UIs1MPh0b3bdqmIj/Ann5AF//OynXCqGoPhVAFNFZAriwfDXAFxY2CKVJ79Hs5wpzi69UqH0RvWM1r7bntyK//nacYUumi1N4rmUf/Tsu5Ytw22dYTNX7s6OEDp7I5Z9U3964cyUvMQHujXAbFGLxHT4PJoZUBhdDz7c04nJYyvQ3h0ecLCaArAgqaV6RXMjAGDl8+9jxYJZKX2cjVbsG794lPn6ZMmTfgD9pc6SlDIl5zxOThu38vn3zdZGu/J7XJlp5nZ2hLD7017MPW4ifvzsu3h6Syu+P/coTKgOmC3lZ0yvz8hgcOu8Bqx+4YOMFs67FszCbU8emIJ72fwG7OuJxu8ji3O07Kl3cPGJU/CjZ9/F988+BqNTexE4Rjb1gF2mCrtAyq1JyoQ1xnmzCriz7SaVTSq5bFrDQ+EY/t9v30mpb/7fb9/BTy6cCQQtizJofo9NK/QI17nZTpRCVKwcHxQrpaIichWApxBPyXaPUuqtAherLPVGdDz++s5B5RAtJLcmaOvqw5L7W8xlTmzRTqYrYMm6FrMVNL1l9qa5083jmVgTwL6eCJaecnhGq1o4qnDtI69lvP5/vnpcPODtCmNM0J8xGccVD2zCg4vm4P3WLmxo2WEGeHYtv9vaui1b0T7e34tRAY8ZgBuzvLV19WFfKAKvRdq3nnAsZdknn/ZatvR/8mlv/NgTZdKVwppLjkdtpRfuRNq4p7e04rUd+3D7U1vj/YqrA1m17LZ3h3Hz41vMqbNPnT4eFyZNfGIM0jKOtb07bHZb2RcK495LZ6O9qw8TagL4wW/eSkm3dv36zbj/stm46J5X8MjiOZbb2PJxJ26aOx0Rh3VJSpZNPeDRrH+18djci4IDebONLhEVXhes1rZLIWiXbjObVHLZtIZ73S7L+iYX03WPDfosp5sea5HqLZ+yyZhBVMwcHxQDgFLqtwB+W+hylLuYrrDq/7Zj1f9tT1m+YM7kgpTHjsdtMyuWg9PwGa1edi2bxiCciTUBrGpuRFTX4XZpGa3KKxbMQl2lL2UbOztCGBP0mrmL7aaLVkrhzmffw3VfmGYG20YKw+Q0bauaG/Ef//tmxuujukIkFu8CcPlnD8O30jIx+D0aqvzujJ+ajZ+bjWVrXtyG6888KiUwCnhduOsPfzP7M//kwpmIRPWUNFhGa/XTW1rR1tWHuiofPBZp5pbNb0A4Fssoh9GibbwPgPUgqqe3tOLfvzQdkZie0tf6m6cdia6+CL716Bu479LZGVkO4j97x7dnBL3zV75s+V5bBWpOkU09IFq8O0zyezm20guxaeh0a4KA1wV0HxhoGPC6LIPRoM863WbQZ98KbdVf2WrblX6XZWt4pT8z0DUGzqUHrlYD57LllOmms8mYQVTMiiIoJmfIpqWloJRYzop1+T8eXuiS2TLOrV3LrDEVcE84hnGjfOgIReB3u7Dk/rR+jw9sMnMbG9Ml94RjaO8Km+sld49I3odIvIX99qe2mq/t7I3iiLogfrF4DnRdQdMEnkRLfDKjFa2uyocLZh+KCq8L918+Gy4RaJpAEyCmA+ev+rM5gUV9lQ9Vfg9G+V3weTSsuzw+aM7r1lDh1VDhcSGqK7g0gUuAG794NDwuwU1zj4EIMtLEGa3V3zlrOjQR7O3uwxXrXsO/f+lorGpuRKXfDZcmcGuCSEzH6IAH918+G+1d4ZTWWuN9AADNpiuH0V/0lq/8AzwuDT3hGKoCbvzw8S1Yc8nxtoM9P9nfa2YV0WzSv9VV+RBwWJekZNnUA9EYsH7jDsxvOiTlXrz4pMMstz3K70GfruOI+krElIJLBC5XfHm6Kp8PtZV6ynTmbld8uRVN0yz7K//XuQ0W2/ZibGXMYtuZgW6+A9dsppvOl3wG/kROwqCYBs3v0bDm0uOxc2/IbPWZOGbk+7cNZGylD/88c1JKPuXVFzVhbKVz+77VBeMzwN357LsZrVnLEgPo2rr6cOu8BvRGY6j0uRGxyXhwRH0wY/rbO86bgZmTqvHajn0pfW6TW5if2/Kx2cK+5P4Ws3Wssy+CT0NRuDUtEVjCsiVe0+KtiFPHVaK9K4xdHSGMCngwOuDBjn0hjK8OoK7SZwbcrZ19+MFvtuC/z5+BqK4QjukY7ffgB4+/lTLt8e1PbcV3z56Oc+96yTzOhxfPsTz2vT0RdPVGUo59bJUXn7v9j+agRCMgOni0Hz9+7j1cfdqRKS2+dy2YhZ889x4A4LktH1u2GEZisZR9rGxuhN/jwtWnTsWyp97BlZ87IqMl+o7zZuDnf/oAK5ob4XUDezojuP/y2di+pwd3Pvse2rr6sLK5EQGvhpoK516rHpdk9IlesWAWPBYDy6r8gvOOPwQ7kuqM844/BFV+m5RsLg37u6NYdP/GlHv3oKrMDtaaJhhXFRh0RoTaoBfXnj5tUIFdttt2QuCaT05psSbKN1E2uRSLWVNTk9q4cWOhi1Fydu8P4e/7e3H1QwcGaP34gpk4eLQf4xw2KmgQ6YMKXpsnX6dtnX34zmObsfjkwzF+tB9APCNFa2cfVKKlal8ogg0tO/D9s49BVCm839qdkrUBODD6/qtJrajG8uR+yWdMr8f3zj4GkZjCnq4+PPPWx1h44hR4XIJITJkttN19Edy44U20dfXh0SUnIBzT8dPn/oYrPnc4YjpSJsTZvb8P/++3b6Otqw93LZiFmgoP/tbajSq/G//y8OtYNj/eIpfev9RYduu8BlRXeHDWnX/KKDcAM3AFgFUXNaY8Nta9+ZxjMWlMAH2R+GBClybwujTL8/HQojkQAbwuQTim8Mn+XtQEvXjklQ8xa3ItqgMeRGI6DqmtgEskPuufJgj6NLy3uwdjK72Iqfjr1r68Hf8vMTNaX1SHSHzAYESPT1DiEoFIPGNChVfD3/f3ma38RpeU+lE+7A/FU+ilTQ/sqGt1V0cPnnnrY5w6fbzZivrclo9x+jHjMzIztHf34qP2UEadcUhtALVBf8Z+2jr7cO5dL2a8V7ka0OXUtGIlpKAns7/P/sk3PpH3/W+/5ay874NywvY6ZUsxDVpUV+aHGxBvmbv6odccOVNcsbXchKOxlNm2Hl48B7f87p2M7BKrmhtxUOILSKXPnZHbeNn8BrR29g3YL/nq047EM299jO8//o7ZYjZ+dDwrwK6OHvzTsuczyqgrhQqvGy990I73WrvwrTOOxPjqAHbsPdDSabTsXvnAJtx+3gwcXhc0+3j3hGMZ+YevX78Zt3zlH8wBgWsuOT6j3NUBD37+pw9SWqc3tOzIaK1e0dyI0QE3Hnh5O06eNg43rNtsZoxYsaARVzyQmgM6uQvGGdPrcfVpR+LW372d0U972fwGVHhdqPJ7MCkR9FX6w7jonldS1olnOXnHbOVee9lsVPrjLfpetwsuDfjyT17ETXOnpwT0OztCWLKuBWsvmw1NBNUBZ/8kHfRpaJwy1hyAaJz7oC/zF6PesG5fZ1hkZsj3gK5iqxeouGQbeDOIdh4GxTRodmmKnDxTXLFIH8iyLxQx+/cafSB7wjGMr/abLVtjgj5UB7zmT5oetwa3JhnZHIB4IFxd4cUfrz8Fbk0wtsKLg0b5cfox4zNazPobVJPct7A7HMPFaTmAkzNlHFwdwMTqeAAfCuuo8Lotrx9PIlvAzo4QesKpwY8xEO+H5/4DvC7Bw4vnIKYAv1uD1208VvBoGjQB+mI6Zk2uTcngYXzRWHPJ8djbHU6ZvCR9nZvmHoN9PWHcd9lsaIhnOPC4BB63oCbgM8/RtHFV+OWVJ6I3osMl8cFgo3we/Ne5Dfje2datkLqusHphE7r7otZdP7rDOKS2wvEtl119umUO318snoPqtHy/2ebZ5YAuIiokZ3UGJUczBtgk6y/nKA1e+tSvRkuokerpW4++gYNG+zNaEY2Wrwk1Faiv8mNM0IeDqvxY2dxobstoydQEmFRTgQk1FfD53Obr6qp8Ke9hf9PQJvctPOqgKsuApzrgwcSaAAIeV3yQnSaYXBtEhddlef0Yg9om1gRQ5Xdn7Hf86ADqq/yoroiX95AxFagflfw4iPHVAYwbHUDA40ZtMHPyj6e3tGJ/KGJOc33NaUfizmffy1hHVwpXPLAJp93xR1x0zyvo6ouivsqP2qA/5RxpmqC+yo9DxsTP55igD263ZntOjddMG1eFCYk0cennob07jIjDpky3Ypcf2Crfr5FnN1l/eXazmQKZiCjX2FJMg+Z1aZY5R702eUFp8IyAKbn1Mehz4ZdXnmj+/D7Y/o9ut4ajxlXh0SUnIBLToWkSH7wVyAzU+iuL3aAaIxBv67TOntATjmUEMpomOGiUP2ME+7L5Dbjtya1ml4aVz7+Pm+ZOR23Qi4OrAzholD+rL121QS9C4ahluQ6uDuDFGz4Hr9sFpZRlBg2PS8vrYCJNE/g8muUEIPe9tA2zDsnMhOA0VrmmjXOXLts8uxzQRUSFxKCYBs3tAuqqfCk5R+uqfOAvm7lhtD7mgtutYXz10Ac/DqbvpVWaplXNjRhfHW/RtmspTe/u8ZMLZyKmK/zwiS14eksrXvqgHasXNmUdEBv7OHh0AKsuakwZyJa+vWhUt8xxW18Zb/HNp1A4hnUvf4i1l83G3u54Orj7XtqGb552ZFG0iPo81vmBfZ7M92ooQS77/RJRoTAopkGL6YI/vRuf5UspBUmMOj9rxsRCF40KIGcBTzDe37a//rjZcLs1HH3QqH7LZbSmP7LkBERj8YlQRiIgBuL9Zo3BikaKuAtmH5rSX9zJYrpg47Y9eHDRnEHVAwxyiahYMCimQasNevGZw+tSRp2zv195y1XAk+vAaTDbc7s1HDyM1vShSm5hN/JBr17Y5PisEwbWA0RUqhgU06Cxvx/R8BX7fVTs5ScissOgmLLCn0KJhq/Y76NiLz+REwxlQhHmNs4vpg0gIiIiorLHlmIiIiKiIsDpqvNLlCq92chEpA3AhxZPjQWwZ4SLMxTFUM5iKCNgX849SqkzR7owyfq5TkdCsbx/g1FKxwJkHo+Tr9VSO/dWeIyDV9BrNYd1ajm853bK4dhtr9OSDIrtiMhGpVRTocsxkGIoZzGUESieco60UjovpXQsQHEdTzGVdah4jOWnnM9HOR87wD7FREREREQMiomIiIiIyi0ovrvQBRikYihnMZQRKJ5yjrRSOi+ldCxAcR1PMZV1qHiM5aecz0c5H3t59SkmIiIiIrJSbi3FREREREQZGBQTERERUdkri6BYRM4Uka0i8jcRubHQ5bEjIveISKuIvFnostgRkUki8gcR2SIib4nINwtdJisi4heRV0TkjUQ5/7PQZXISETkvcV50ESna9DvFcm8PRjHc/8lK6dzbEZHtIvJXEXldRDYWujy5YHWdicgYEXlGRN5L/FtTyDIWUjlc1wZeC5lKPigWEReAnwL4IoDpAC4QkemFLZWtewEUNEn/IEQBfEspNR3AHADfcOj57ANwqlJqBoDjAJwpInMKWyRHeRPAVwC8UOiCDFWR3duDcS+cf/8DKMlz35/PKaWOK6Hcrfci8zq7EcCzSqmpAJ5NPC47ZXZdA7wWMpR8UAxgNoC/KaU+UEqFAfwCwDkFLpMlpdQLAPYWuhz9UUp9rJTalPh/J4C3AUwobKkyqbiuxENP4o+jShOUUm8rpbYWuhzDVDT39mAUw/2fpKTOfTmxuc7OAXBf4v/3AfjnkSyTg5TVdc1rIVM5BMUTAOxIerwTDgziipGITAYwE8BfClwUSyLiEpHXAbQCeEYp5chy0pDx3i6ccjn3CsDTItIiIosLXZg8GqeU+jjx/08AjCtkYQqoXK7r/pT1teAudAGoOIlIJYANAP5FKfVpoctjRSkVA3CciFQDeExEjlVKFUV/zVwQkd8DOMjiqe8opX410uUhKkKfVUrtEpF6AM+IyDuJ1rWSpZRSIsJf1agsr4VyCIp3AZiU9HhiYhkNkYh4EA+IH1BK/bLQ5RmIUmqfiPwB8b5TZRMUK6U+X+gy5Bnv7cIpi3OvlNqV+LdVRB5D/Of1UgyKd4vIeKXUxyIyHvFf18pRWVzXAyjra6Ecuk+8CmCqiEwRES+ArwH4dYHLVLRERAD8HMDbSqn/LnR57IhIXaKFGCISAHA6gHcKWijKNd7bhVPy515EgiJSZfwfwBko3S/VvwZwceL/FwMo11+SSv66HoSyvhZKPihWSkUBXAXgKcQHhT2ilHqrsKWyJiIPAXgZwDQR2Skilxe6TBZOAnARgFMTaYpeF5EvFbpQFsYD+IOIbEa8ontGKfV4gcvkGCJyrojsBHACgCdE5KlClylbxXRvD0aR3P8ASu/c2xgH4E8i8gaAVwA8oZR6ssBlGjab6+wWAKeLyHsAPp94XHbK5Lo28VrIxGmeiYiIiKjslXxLMRERERHRQBgUExEREVHZY1BMRERERGWPQTERERERlT0GxURERERU9hgUExFRyRGRa0TkbRF5oNBlISomIuIqdBkKhUHxEInId0TkLRHZnMjV+5lCl8kgIpNF5MIhvO5eEZmfjzJReZA41ivkBFcCOF0ptcBYICLlMIsrFZnEZ/Y7ic/gd0XkARH5vIi8KCLvicjsxGQy94jIKyLymoick/Ta/xORTYm/ExPLTxGR50VkfWLbDyQm37Irw3YRuVVENgE4T0TOEJGXE9t8VEQqReRMEXk06TWniEhJ5f/nh9cQiMgJAOYCmKWUakA8wfWOwpYqxWQAWQfFucAPnfKTqJS3ishaxGf8uklEXk18YfzPpPUWJpa9ISL3J5bViciGxPqvishJieXfT3wAPC8iH4jINXbbEZEqEdkm8enHISKjkh9T+RGRlQAOA/A7EdmfuE5eBHB/P9dcrYg8nWjs+JmIfCgiYxPX95tJ275ORL6f+P/hIvKkiLQkApOjEsvvFZE7ReSlxPU7P+n1N4jIXxPX7y2JbWxKen5q8mMqG0cAuAPAUYm/CwF8FsB1AP4dwHcAPKeUmg3gcwCWSXymxVbEv/zNAvBVAHcmbXMmgH8BMB3x++GkAcrQntjO7wH8B4DPJx5vBPCvieWfSewXif39YhjH7DgMYIZmPIA9Sqk+AFBK7QEAEWkE8N8AKgHsAXBJYv7w4xGfGlkH8AyALyqljhWRSwD8M4AggKkAbgfgRXzGuD4AX1JK7RWRwwH8FEAdgB4Ai5RS74jIvQA+BdAE4CAA31ZKrUd8BpqjReR1APchfpPcAuAUAD4AP1VKrUp8a/wx4lMg7wAQ7u+gJT5z3X8D6AbwIoDDlFJzEx8QhyN+030kIv8G4B4AYwG0AbhUKfVRoryPJ8oIEelSSlWKyCkAfgCgE/GK4Q8ArlRK6YN4L8gZpiI+JegoAPMBzAYgAH4tIicDaEe8kj1RKbVHRMYkXvcjAMuVUn8SkUMQn0nq6MRzRyFe+VcB2CoiKwAcmb4dpVSniDwP4CwA/4v41Ky/VEpF8n3Q5ExKqaUicibi189VAM4G8FmlVEhEHoT1Nfc9AH9SSv1ARM4CMJgZBe8GsFQp9Z7Efy28C8CpiefGIx7UHIX41LnrReSLAM4B8BmlVE/i+t2bCNyPU0q9DuBSAGtycyaoiGxTSv0VAETkLQDPKqWUiPwV8YauiQC+LCLXJdb3AzgEwN8B/EREjgMQQ7yONLyilNqZ2Obrie38qZ8yPJz4dw7igfSLicZlL4CXlVJREXkSwNkish7xOvfbwzhmx2FQPDRPA/iuiLyL+DenhwG8hHiAeY5Sqk1EvgrgvwBchngFt0gp9bKIpE+ZeCzi3+b8AP4G4Aal1EwRWQ5gIYD/QZYVL4AbAVynlJoLACKyGMB+pdTxIuJD/EJ/OrHfaYhf/OMAbEE8mM0gIn4AqwCcrJTaJvHpIZNNx4EPnd8AuE8pdZ+IXIZ4UP7PA5zT2YltfAjgSQBfSRwLFYcPlVJ/FpHbAZwB4LXE8krEA+YZAB41vkAqpfYmnv88gOly4Fe9USJSmfj/E4kvnn0i0or4NXqqzXZ+hnjl/L+IBxWL8nKUVKx+rZQKJf5vd82djHi9A6XUEyLS0d8GE685EcCjSdvyJa3yv4kv9ltEZFzSvtcopXoS+0m+fi8VkX9FvPVt9tAOk4pYX9L/9aTHOuKxWgzAPKXU1uQXJRqldiNex2oAem22GcPAMV+3sVkAzyilLrBY5xeIf9HcC2CjUqpzgG0WFQbFQ6CU6kq0Cv8j4i0RDwP4IeIB7jOJCtIF4GMRqQZQpZR6OfHyBxHvemH4Q+Ki6hSR/QB+k1j+VwANQ6x4052R2JbxE95oxAOVkwE8pJSKAfi7iDzXz2EfBeADpdS2xOOHACxOej75Q+cEJD5cANwP4LZ+tmt4RSn1AWDOx/5ZMCguJsmV6f+nlFqV/KSIXG3zOg3AHKVUb9r6QBYVulLqxcTP3KcAcCml3rRbl8pSd9L/+7vmrESR2tXQn7SdfUqp42xel3z92m48YQPiLdXPAWhRSrUPsD6Vn6cAXC0iVydakGcqpV5D/PN8p1JKF5GLEY89huvPAH4qIkcopf6W6C4xQSn1LoA/It54tggl1nUCYJ/iIVNKxZRSzyulvof4t6Z5AN5SSh2X+PsHpdQZg9jUQN8OzYo36e9om9fbVbwC4Oqk109RSj09mOPMQvfAqxz4cJH4YCxv0nMqbd30x1QcngJwmdHaKyITRKQe8Q/780SkNrHc6D7xNAAzYE78BNgfu+0AwFrEv3Typ2fqj9019wISYzES3RxqEst3A6hP9Dn2IdGooZT6FMA2ETkv8RoRkRkD7PsZxFuEKxKvGZPYVi/i984K8PolazcD8ADYnOhecXNi+V0ALhaRNxBvvBrMZ3G/lFJtAC4B8JCIbAbwcmLbSDSiPQ7gi4l/SwqD4iEQkWkiMjVp0XEA3gZQJ/FBeBARj4gco5Tah3grsJGd4mvZ7GuIFW8n4v0wDU8BuEIODEQ6MvHN7wUAXxURl4iMR7zV285WAIeJyOTE46/2s+5LOHCcCwD8X+L/2wE0Jv7/ZcRvcMNsEZmSCJa/iv77PZFDJb5sPQjg5URfuPWI/1LyFuLdif6YqLz/O/GSawA0SXzg3BYASwfYvt12AOABxAOZ9K49RMnsrrn/BHByIuD4CoCPACDRN/0HAF5BPKh9J2lbCwBcnrgW30K8v7AtpdSTiHdz25jo43ld0tMPIN4YkusGC3I4pdR2pdSxSY8vMcbeGM8ppUJKqSWJBrdjjO6RSqn3lFINSqkZSqkblFKVieXPG+skHl+llLq3nzJMNrqlJR4/p5Q6PrHtBqXUr9O2VWl0AyolohQb5LKV6DrxYwDViLd+/g3xrgQTEe8/OxrxVt7/UUqtTgTEqxGv8P4IoEkpdZLEB9o1KaWuSmx3e+LxnuTnRGQK4i0I4xEPJH+RGAxyL6wHrnkQD4RrAdyL+GCmHyI+2EQQH/z2z4gP0jMG2n0EIALgHmN7Fsd9NoBliH8TfRXxYGdBok9Tl1Lq9sR6hyLe2pE+0G4cgF8BCCDeb/gbigPtKEcS3YPOUUpdVOiyUPFLro9HaH/XARitlLppJPZHRJkYFI8AEalUSnUl/n8jgPFKqW8WuFhZM45D4p3vfgrgPaXU8hxs9xQkDQwkypaI/Bjxn/O+lOj3RjQsIxkUi8hjiGfwOXWkgnAqT4lrbUra4huUUk8VojxOw4F2I+MsiacpcyOeXeGSwhZnyBYlOvJ7Ec8usGqA9YlGhFLKbiAf0ZAopSaP4L7OHal9UXnjtdY/thRTBn6TJCIionLDoJiIiIiIyh6zTxARERFR2WNQTERERERlj0ExEREREZU9BsVEREREVPb+fx+iYjtETBhIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(clustered)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}