analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wisdom of Polarized Crowds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Workbench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "import glob\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pylab as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import NullFormatter,MaxNLocator\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max.column',None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_align=pickle.load(open('Data/user_alignments.pkl','rb'), encoding='latin1')\n",
    "user_align=pd.DataFrame(list(user_align.items()),columns=['user','align'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/bill10/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py:3449: UserWarning: Attempted to set non-positive ylimits for log-scale axis; invalid limits will be ignored.\n",
      "  'Attempted to set non-positive ylimits for log-scale axis; '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\", palette=\"muted\", color_codes=True)\n",
    "sns.distplot(user_align['align'], bins=30, norm_hist=False, hist_kws={'log':True, 'edgecolor':\"w\", 'lw':1},kde_kws={'bw':0.02});\n",
    "plt.plot([user_align['align'].mean()]*2,[1e-2,10],'--', color='grey')\n",
    "plt.xlim([-1,1]);\n",
    "plt.ylim([1e-2,20]);\n",
    "plt.xlabel('Editor Alignment', fontsize=18)\n",
    "plt.ylabel('Distribution', fontsize=18);\n",
    "sns.despine(offset=10,trim=True);\n",
    "plt.tight_layout();\n",
    "# plt.savefig('alignment_distribution.pdf');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Previous Edits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_info2=pd.read_csv(\"Data/user_edits.tsv\",sep='\\t')\n",
    "df=pd.merge(user_align,user_info2, left_on='user',right_on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jzZg5ABGMMYQlVds3iEcGTTCoap+ZKDbWPJ6zs8gtwu0S4C12wucPozeswiEwY9btgBZJ9FVAgiTxhDWZBstQouG1hD5NoLDEMa6kAG0BCf6ADEnmKHQLcEWUQWtANdpSVM3voiuUVE1HFVM8KPE4oxIX9bDfdPwUVkYn5bo9/2CEFEWK5FpsdyphrvEGhsE+pPEGcGfEvGFuV2BASOZo8oeN0hZqZJKuTdY1E4+o8EjZAx410000IIYljhsvmoS6+gB+t64BscO3Q2R9CXWDGNkZQ8pmqpSvCUAUGHjkwgVOEU6RoXRMn4msoTlonMMYizKX6RFiQHo2/JDEo8xwOun4KYZrdFKuPae5AimKFMi12O5YeTVzjza6mAftTDow411LMy/1mTi6g5rJRx/o9MijWDg0H0KBk8HpYFF9PZAfpWKKB5MPGYEGXxCSrEJgDCrUIQ/yQ32/EXJrviYiJrKIGamzV8aYYofhrA6FVa1UOThcTk1ZiIwBTHOAx4Ysp0omHMbDMTopneeUFEp6kKJIgeE6e0pETZ0/KjJGiTiJzbH7gR4FDEgrHj8e+gPX1BoCR3T2cpFbRInHaZiEJJlj7GgnDrRLccNSY9FnzN1BBf/79D6MKXaiwMnQHVT6rY5mV3gNWdoiBf9KPE74O2UoEZ9FNknUOueaU9sJraCUOWpL/940haqZmgQBEJjmfB7sdqUD+aLWb/XhhXdajXDcs08qwaIzyqPOjadsuoMKunsV/OC3HwIAJpW7095GdigDeKrP6WAmfvmuWEhRpMBwnD0lY/eXvVEzeYbIQBlx6OqvadnBg18dmR+44sjqQXdQ6wO5eaC46v/qwTmHX5SNCKdkM3VdSWjyMowe6TCimzQHrKYQJpW7se3fbXj704CRfe1yMPg7ZYTCKhgDHAJLSTllC0nhEBiiTXImYRUOCNBWWKOLtATAwZLIFwUAP/3jp/iiOWSUNmkNqPhbbTMAGMqirj6Atk4pKowZAJrbwobMDMB/9nbjgeca8bNzDhl0Eub2XZ0YVeTAEeNHDDg4p/qcpjvxS1exDFWpDEelRIoiBTIV223XDRCWou05ghCJkomUqDDv56CTriOzps6PD3Z19ltFBHoUdPYomDyhsN9MN1E/xkP3N+tRUozpu86pmq0ewCFjC9AdVLBrfy8+3NMNBs0XoVV31ZzEosiMSCoHEL8W0zBBNZnkYjWaqgIyVCgqQ6si4frVu8Cg+RsGcy/F+qL0wXDvgVBfMcTIP7LKsemfB7DojPKoQVN3ircGZIhCtGLTzGpaOPHLkYF5oHvfPICbJzQd3XJKE5pUn1OfPxy14tYd+maFMlgfX6KQcG+kNliiz663t/vLXnR0y8YzNVzM3KQoUmCwsd3mmy12ZzTzDQAM/BClg8vZP8ZUEICxo1wodIuG89RMqqsj84MQjkQwmVcReoLYjRdN6vdevR8512bPyfIORhZqn0ELd40Eh0bkDkuaz+GL5mBUxVYObWbOVETqK2kvqKrmNBYEpk3NcxRFRcSZzbFzbzcEgUXVjAIGP8PVB+lYn5GuLDq6FSx/vAFtppBnczXUPb5gXJnDkordX/biy9b+K4UCpwCBaffrEeNHYPeXvSgs0L53cwa5OREx2YTm6+VubN/V2S+aK/Y5LXAyo1SKfn1zzs5QfHzRZl9tUqMoqpFpH2/gN7fX0S1H7beit5dtM7dlimLZsmXYtm0bSkpKsHnzZgDAXXfdha1bt8LpdOKwww7D8uXL4fEMfzvfYGK7Y282fXc2IPpme2Jbc1R5a/Nye0yxA109cpSJJRWb7xHjR4BzHlUIz1MoaseBuLMul5Nh+eMNAyor86zPnCBmjm5KtNLSr3fPk19oPpIkkUSFBZrC8RQ6tIdH4eAxqwG9LlMssddkTLP3c87iOpWHK3rlWvPncQiArAAKAKZy+NrCcDkEcB6dpZ1ohprInJPKRKGpNYQGXy9EUWvPvDJVTf6UWEKSCjVyP4YkFYqiJQ72RJIMtc/I4e+U0Rr5XZJ5ZEMpFlVqJJ6cdfUBPLGtGfWNPZHIMBil4+M9pwN9/7GmKf0+N9/j3UFtNXLV/9VHVRx47z8BBCXViGhT1EhIczj65jUP/Ob2zErR3F62zdyWKYoFCxbgkksuwQ033GAcmzlzJq699lo4HA7cc889ePDBB3H99ddbJUJGSSVsNNlyNXZnNJ0GXxDlkYJwelVTBm0m1tIWgqzAsMN83NCNP2zci18uPGzAkg1NraG4Dl8A/VZHelKXrrASLXfr6gNGhrDTwVDgZEaCmLk0RLKVVsUUDw4tdWNXY0/SneFa2iWMcAlQef/yF2ZSGfS1HA2tUmwuEU+Jymr0PhmcaxFioqDdW3o1Wn0nP/MMNRhWjeTEWHOObrYxK38zDiEStaZqSYfaYKyVQnE6hD6fiu7cgPY7EwB/pxT1HejRbmYl39YpQ1G0wAtdMcgq4ACPKjUSr0TMmpomNEUGUs61UjBixAT5wjutmDyhMOo+DkvclEvSN5HSc3ZiB2U9OVS/x3WzWInHCc55VMUBPdueI7qOmKQAu5t6jX5oM+2waG7P3P/mZyrbJUwsUxQnnngiGhsbo46dcsopxu/Tp0/Hiy++aFXztjPQcjV2ZzQz+o2nv6bXBjJXMWWsb0/nJ7Y1Y9f+noSRKamsgMyv+Tulfpv26OfELo91CUNhjp4gjySL9T1gEyOlIMwz2rCkGuaFqgovwpJWXC9kelhi4RxoaA7BIQ4tO06flesDVK6TaAWmqIAr8jS/8E4rRo/U/oidoYoCTDW1NMXwxLZmfHNyMbbv6oza3S+qXQBfdUhRmplzTTkokYABQzbTOYLAoEb6XlZ5lCIxtxSSIptBMW2DKJVrKw+HKCQNA9ZNPT3BvoKL4DAUTnuXjDvXfxG1gtKUIo9aHei+teWPN8DlZAiF+6SLLY4pyTwqh0XvW8Ck/Hj/SYxu1pMVjq8CEn76x09x+dkTovbTFiJ5Reas+3if226y5qPYuHEjzjrrrJTOXbFiBVauXGmxREMjdrnKmDZz8fnDKHSLcDm02bd5GQ1o5iTdvhsvzj4WDmBnYw/qG3uMY4EeGeu3RkemJFsBxb521f/VG7+bnXxftASx6HcfIihpU8UitwhPoYjmNqXPvxAZHZyiFvkUCqvG7ApAVPRVMKxg+67OvqquKSSxDabUhhl9K1MgskHRkK42vNFXTJ09CkaP1PIxFLUvPFrlAAMz1dTSTDz/2duN1oAET6EYmTD073MeWdnp28IO1Jf6XW7Om9EnO+YnwFxtV5K5MTEQGAMTgbCsosEXhCBoW9b+79P7oiYdsRF+hr+Kwyh3okQUSUe3jM/292B2ZMUNIG4UoLkkjE6RW8TiqnGomOIxovfMaLN/hgKXAETyX5LBADR+FcYDzzUiGO5bragm+RVFGz8mDYM6XFlRFH/6058giiLOOeeclM6vrq5GdXV11LHGxkZUVlZmTKahRiTFVjBVzMvOSBROkVvEhK8VRMW/A4hkEkcnZSWy92rbanK4TApHVTkUFXj0FS3+XX+QEm0EFItudugOKlqOg9r3cIdNYUIhScYIF0vohNbNauYS3/ogxZi2GnI6GAQWMQPZNMUXhYjJ5mBYUiRBc8pK4OBo8AUNxcFNXR1rGnGIDByaKUQU+8xzzPgHxvk6enkUfSDW2zDfsxx9CtpMsvWhbmaUFEWbWUdMWYKozbI7exX0hlQIkfpYTa0hdPXKkQKQ/b9fs2z6LoOtARXbd3VicdU4vFznx/ZdnXGjALuD2goD6PMNAlreUbycIX327ykU0SqrWkBF5PPGW6Tp/a+vRMymMFHsi+oDNNNitiOfbFcUzzzzDLZt24ZHHnnEqHGTbTKReW0ebFvapb4HLvLECRGHaihOkpSeSawvt/VwTn2MNveSKGh7M0ty9EOq/aLVM2IAAt3SgBsB6VRVePGHjXvhD8gDjt294fhnyAogRMwcIUmNWgnE2qRVzlHgFIzoJSsRBaDAJYJJalJfx8EAB9DWJWPkCAGdPQOvnRS1bxUiR/I4zNdiER+DORKKMc0soqr99Xyi3tUVCANQOEKErHDju4+7auZ9wWl6IEKfzFrioZE82jtwuXj9s+irl48auvHN/T248aJJ/VYH5hXGYaXaTD4YVqP8PsWFIg60S/AFFYgiQ4FTy9txu/rMZPoYUOAUEIoUqoxHKKxAEISoCLKmSHJqLNmMfBqgVmdmef311/HQQw/hT3/6E0aMGGFn00lJloCTKlUVXuMmM9/YIgOK3AJUzhEMq8ZMaE1NE37/1Be47M6PsXNfD4JhFcUjRK1stkuEIAgoG+2Ey8GM5b7bKaC4UIwUz9OIN9ByAJ292s1pDjNM9Jl27e9BoHtgJZEMDu0h3tsSgixzw9HKdQdn339wOgR4ix1wOhicIosaoDINg2aPP9iVhI5uchlMlxpbwOrXQrSS0O3nnKeZ6c76VhejixxwitomWC6nFhAhsPirD6DPD6LfR2pkwO/qVbDH15vyniJmaTkH1m9txvqtvn5OYv15MfsHAM3vE++KSkSjuV0CZld4Mb6kAMUjHDjqsEKM87pwyNgCjC6KPx9nTFvpSrKKpkhuB4CIg71/h2Qz8smyFcU111yDd999F21tbTjttNNQXV2N1atXIxwOY8mSJQCA448/Hr/97W+tEiFlBsroTGSWis2TaO/SKn7qdnFHJLxPdyKab77WgITdX/ZGTDHaA9DeJcNb7ETF5GJjxVFXH+jneGZMS2SKS6yZISb5Lt5nfeGd1kS7eKaFHnGSaKap3/r6kl2PwrEyCS6dzYkOJjJdxNCcRa/nrgzUhsD6zF5up4AjJ4zA3Usn41erd2GPL9gXvBHruIhDvGbkJPeNKfAqLmGZY/3WZpw6bbQxg3c6GEKSCoFFb+oE9Pl9AE2Z6OVUAAaXg6GlXcKjL/tQ4nEagSX6syswbQyI9bfpfacnieq5E+Zsd7PP0FMopl0KPlNYpijuu+++fscuuOACq5obEskyOhOZpcxL0e6ggtZmCWE5sgcC9BmXdrsqKodoKgkNAO3dsmGT1WOunZHVg74ntb6vgx6jvX5bMz5p6AYi56s8euOdeH4NJbIpjr6s1fMlzNFIX3VIGXEXKCqgJLtSZEaqx9N3BxW4HCzu9qHE4AlbUEod6L+KjfVLxKKXi/cWa6XmL/xuGerqA2gNSAiFY0xPA1wrbQbQFDyyMnnt320oLtQjxDSZikaI/ULLC5zMqAwQlrlhS2PgaOvss6vGBpbog/pld36MQI/mT1FUbkzMRAEYO7qv7Lskcyw8dSze/LgjZrdG9CuQaSeUmY3kmdeJzFLmEERzaJwelqfvgaDNOKJLQncHlaglPUdfFdH2bjluCQBzDLxu5nEIDKOLRbR3aUon3jMhmuy5/k4J3UEVitINxchWti8BTe+frl4FXb3aMVISmYcb/2T4miaYabUQO8DrEdO6ydTlYNjfGsItj3wODhimxn4rEqaZatPdKTDutSJyxfpYYpEUbdJWPsYFQIS/U0Z7l4xwZAav1xsLK9q+5VGYnjnGo81nL7zTaqwqaur8aO+StcAOFt1X+mcdF8ml6uxR8NYnARS4BLS0a6v/WGd7NnwVpCiQPO9g7cu+uO9p75LRG1YRCvc3nehx0ADDYaUFmFjmxtufBtAa0KJ+9CVobMy1HpHS4AsaG/cAmmPMcDYygEduLily8xa4hP43MbS4epdTgCxzuF0CuoOaj0RRNVOXqnAtxdcmcizfjUiAHmyhW1Jixnrtf6bNgBWVY9+BUNTgb3ZUO0U91wLGRlOAdo/GW2EkiwY0mzgHWmmbUdW+/csFQYvK0ydmnhEimBDZOjfJdcwKENDGh1+t3oX6xh44HQJEUbMAxFOCB9olhIpUdPVqyYuca3lKwbDaL18DyI6vghRFhER5B4nKKXNoEQvx7KSyyuFgDKOLRHznmFF48+MOeApFI/xNkrX9BcJS/GWApHDIvVrJZvPLRuariZCkJnQEygowscyF8SUFkfDB3r7rEMQQSDRL11czHAOvFjmP+BkiOTUOUcCYYodWGj6sxh2UEw7UvP856ayszAEoKudwito2ugoHurqUvnALsXMpAAAgAElEQVThJNdTud4vHBzMyI/St+eN12f6KrujW/Njms3TTofQr5IDkJ0sbVujnnKRqjgZkYEeBaOKRChxHLeAdtM6BIarFhyKbf9uwx5fEE3+MIJhFUVu0ZjlJ9sfOo5OiIIxbQZT4BQSLtVVrjnN/7W7E2982I6wrA45gY0gEt33g0G/zxnTym4AgDyYCIQh+mTUSESVrGj+RJ1QJBhEm2glvwaPvF9RgVFFYlSIqyBEyygwbWWmm8xkhUNVtbwK3cTsKRTjhslmI0s7r1cUqSTZxTNL9YRUFBYICHQrmvkmDsGwivuf2actaU1lJNq65L4knCE+baLABgxT7OxRIAh9y3HSE8RwROUwkkgHc49mNh9H87zog3667yweIcJb7ERI0qKp4l1HX+3o1gDdXyjJKg60K/BHCi+6XQLcLmHQm1RlirxVFOkk2cWapfTszAKXACWoxDXlMAYciEQTMXP4H89MGQm3U1ump2KvlGSelrnJiv2hCSIZnCcJ+bZTDkQSXQc5oxrhElDg7MvS9vnjjw9GxJfu51F5lBlbUiI+Ea6ivrEHUw7pv7+LneStohgoyS7ZSkOPkvJEahvFW1WIgnbDGWbNDA683mKHUTt/ICST4zxlEUhJEMSgMNe2AlL3ByZKnJUULbpxjy+Y1TIeeasoYmfiemJLgy+Iul2dSXeY0v9/Ylsz2jrluFtsDibELxWKRwhGsTAAkSShge/GdMZ+0hMEkZy44bgRq0Fnr4wClxCVA5HqNeIhKxxSr4LPvuzF79btwaIzyvrtYW41easozNFM5sQWDiB2h6nuoIL/fXofxhQ7oxLg9n8V0rJU41w/alvLDNITUsHA4C4QjAQdgiBSRxggtyIV4q4AIk5pB1hkp76+dXw8pZCKkog9R88oB2CrsshbRWFOsjPXQzIHT+gZxG2dWh2k3rCKrl5tw5dAt4y2Ltl2W74SCb/rDSkQGKNQV4KIIZW8Cavg0MLjGw+EIJvMCpkYJ/RrqGpfQp9d5K2iMEcz7WsJGdmPgR4FobAKReUIS1oGsV4m27zSaG5Pvqy0mgHLZRBEnpL1p4IDQQsrI8sKR3u3vY7/vFUUQF80kx7FBGhhrd290eYkc9alqnJj20WCIPKDdIJBMh28Yvf145HXigLQwmTbOiXsbQnC6YjskRDnPC3rklta6ZQgiOFJ1lcpMdi9lU9eZ2bruRR6TRWgb7vH2KxpzkFKgiCIYYGeq2EXea0ozLkURW4R47yuPhMTt6RSM0EQxJARBG2ia1t7trU0zKirD+CDXZ3Y2xI0dpfqDmoZcsbubBa1beWObgRBHPwEehTc/lgD1m+NX90601imKJYtW4YZM2Zg3rx5xrH29nYsWbIEVVVVWLJkCTo6OqxqPim6yUlXBJKsorktjCZ/2NLQOR0KaSUIYihwroXrP7mtxZaVhWWKYsGCBfjzn/8cdWz16tWYMWMGampqMGPGDKxevdqq5pOim5w8hSJUVfNLKCrVNyIIIrcIhlWj7JCVWKYoTjzxRIwaNSrqWG1tLc477zwAwHnnnYdXXnnFquaTElu+gxQEQRC5CIc9GxnZGh7b2tqK0tJSAEBpaSn8/tQ04YoVK7By5cqMyaGX7whESnDbucsbQRBEJrFjI6OccGZXV1dj586dUT+1tbWDvp5eq0mStc1CCIIgchEGezYyslVRlJSUoKWlBQDQ0tICr9f+nZoALSN7cdU4eApFy6q8EgRBWM2IAsGWsuO2KopZs2Zh06ZNAIBNmzahsrLSzuajqJjiwVULDoXTwShfgiCInKSwQBz4pAyQkqLYvXt3SsfMXHPNNVi0aBH27NmD0047DU899RSWLl2Kf/7zn6iqqsI///lPLF26dHBSZ4iKKR5MOaQQokjKgiCI3KM3bI+DNSVn9nXXXYdnnnlmwGNm7rvvvrjH16xZk4Z41vPNycXYtb8XMm0mTRBEjtETUlFXH7Dc/JRUUfj9fvj9foRCIezevRs8Ekfa2dmJnp4eSwWzmrr6AB56YT/2tYQoAY4giJzl5Tp/dhXF888/jzVr1qClpQU//vGPjePFxcW4/PLLLRXMSurqA3jguUb4/OFhVxWSIAgiVRgbBnkUixcvxuLFi7Fq1SpcccUVlgtjFzV1fgR6lKzUdScIgsgUosBsyaNIyUdxxRVXoLe3Fz6fD4rS5zw58sgjLRPMSnz+MCRZBQNA0bEEQeQqduVRpKQoHnvsMdx7770YNWoUBEELlGKMDSnpLVvoGxWFZU6lOwiCIFIgJUXx8MMPY/PmzZgwYYLV8liKXjVWtbKGOEEQhMUwAA5R81HY4cxOKY9i7NixOa8kgL6qsSGJ276VIEEQRMZggCAwOB1C9p3ZOt/5zndw9913Y+7cuSgoKDCO55qPQu/Q3pBCIbEEQeQsnGu16orcw8iZrZfdePHFF41jueij0KvGkpIgCCLXEQWG7qCCSeVuy9tKSVG8+uqrVsthC1UVXqypaSL/BEEQOU+BS4CnUESDL2h5Wykpis8++yzu8VwzPekOn9+t24OwxElfEASRs4yLmJyGjY/CXLwvHA7jq6++wvjx43NypVExxYNFZ5Thb7XNkKi+E0EQOcq+AyF4ix2YPKHQ8rYGZXp666238Prrr1sikB1MnlCI4kIRbV0y5VIQBJGThMIqDrSHh+/GRTNmzMAHH3yQaVls44ltzQjLnEqLEwSRs3AADlEYnj4KVVXx4YcforOz0zKhrKSuPoD6xh6oKij6iSCInEblfHj6KBwOBw477DDceeedlgllJTV1fjgdAnqC9mz4QRAEYRVOhzB88igy7bR+5JFH8NRTT4ExhilTpmD58uVRiXxW4vOH4XIwdNFqgiCIHIYxwFMoDp+igADwxhtv4M033wRjDDNnzsTMmTMH1WBzczPWrl2LF154AW63G1dddRW2bNmCBQsWDOp66eJyakkqDJROQRBE7iIywOtx2tJWSs7shx56CHfddRc8Hg+Ki4tx55134i9/+cugG1UUBcFgELIsIxgMorS0dNDXShfW7xeCIIjcggEQRQZ/QMKamibU1QcsbS+lFcVzzz2H9evXY+TIkQCASy+9FBdddBF+9KMfpd1gWVkZfvjDH+KMM85AQUEBZs6ciVNOOSXt6wyWkMRR4nHS7nYEQeQukYluoEdBkVu0vIJsyuGxupKI/T1dOjo6UFtbi9raWrzxxhvo7e3Fs88+m/Q9K1aswNSpU6N+KisrB9V+udeFIreIQrcIp0jLCoIgcg995JJkbbprdeRTSori2GOPxbJly7B9+3Z88MEHuOmmm3DssccOqsE333wThxxyCLxeL5xOJ6qqqgbMyaiursbOnTujfgZbkLAq4vjxFIoQBpVFQhAEkV1UDqgqh9OhqQyrI59SMj3dfPPNeOCBB3DbbbcB0MqO/+xnPxtUg+PHj8e///1v9Pb2wu1246233hq00hkM+vLs5To/BNaL/a0hys4mCCLnkFWgOKIorI58SklRFBYW4rrrrstIg8cffzzmzJmD73//+3A4HDjqqKNw4YUXZuTaqVIxxYOKKR7U1Qdw+2MN6A3TztkEQeQWDpFBFBgWV40bHjvc3XbbbWhvbzf+bmtrw+233z7oRq+88kq8+OKL2Lx5M+655x64XNYnjMSib4sqkp+CIIgcZOwoJ7zFTsuVBJCionj//fcxevRo4+8xY8bgvffes0woO9C3RXU5SFEQBJF7+DtlW7KygRQVhaL0L3chy3LGhbETPUogLJODgiCI3CMkqbZkZQMpKopp06bhtttuQ3NzM3w+H2677TZMmzbNatksRdfEkkz+CYIgcg+BwRazE5CiorjpppvQ3d2N8847DwsWLEBPTw9uuukmq2WzFD1MljEyPREEkXu4HPbF96dc62n58uVRf3d1dWVcGDupmOLBrv09eGJbS7ZFIQiCSJuyMfbUeQJSXFFceumlKR3LJerqA3jq9RYEKTSWIIgcxNcmYf1Wny1tJV1RyLIMSZKgqiqCwSB4JDOts7MTvb29tghoBXX1Afxh4170BElJEASRm4QkFU9sa8HkCYWW+yqSKopVq1Zh5cqVYIxh+vTpxvGRI0diyZIllgpmJTV1fnR008ZFBEHkLpzryqI5u4riF7/4BX7xi1/gt7/9LW655RZLBbETnz8MhfZBJQgix2GALXtmp+SjOJiUBKCFxooCRTsRBJHb2DWOJV1RLF68GGvWrMHJJ58cFUbKOQdjDG+99ZblAlpBVYUXHzd0oTWQ20mDBEHkLwyAIACTyt2Wt5VUUdxzzz0AgI0bN1ouiJ1UTPHglwsPw/+s/RwSuSoIgshBGANKPE5c+N0yy9tKqij0LUonTJhguSDZgNPO2QRB5CglHid+ds4htmRnJ1UUsSanWHLV9FRXH8ADzzVCVkhJEASRm1y14FDbSngkVRS6yWnDhg1ob2/HhRdeCM45Nm7ciLIy65c7VlFT50egh2xOBEHkJkeMd9umJIABFIVucnrvvfewbt064/hvfvMbXHLJJfjxj39srXQW4fOHjb1mCYIgcgmBAYd8zXoHdlSbqZzU0tICv99v/O33+3HgwAHLhLKacq8LTgcDBcgSBJFrFDgFY5sEu0ipKODixYtx7rnn4owzzgAAvPbaa1i6dOmgGw0EAvjNb36D+vp6MMZwxx134IQTThj09dKlqsKLz/b3oDekQKEqHgRB5BCywuFy2jvNTUlRXHzxxZg6dSreeecdFBcX47/+67/wjW98Y9CN3n777Tj11FNx//33IxwOIxi0PrPQTMUUD2ZXePHkNq0oIBmhCILIFQT7qov3tZnKSa+99hquvfZabNiwAZdddhkkScIVV1wxqAa7urrw3nvv4fzzzwcAuFwueDz2OWV09viCGFPssF0zEwRBDIUit4iwZO/0NqUVxf33348NGzYYzutp06Zh7969g2pw37598Hq9WLZsGf7zn//gmGOOwa9//WsUFhYmfM+KFSuwcuXKQbWXCJ8/nPORT5QFQhD5R29ItW2vbJ2UFzFjx46N+tvlGpygsizjk08+wUUXXYRNmzZhxIgRWL16ddL3VFdXY+fOnVE/tbW1g2pfp9zryvltUElJEET+0RtW4e+UUFcfsK3NlBRFUVERvvrqKyP5TvdVDIby8nKUl5fj+OOPBwB873vfwyeffDKoaw2FqgovnJGtBKk+IEEQuQJjQDCsYk1Nk23KIiVFcd111+HHP/4xGhsbcemll+K6667DDTfcMKgGx44di/Lycnz++ecAtOzuI444YlDXGgoVUzxYeOpYOB0CRJE0BUEQuUOTP4zuoIKX6/wDn5wBUvJRHHfccVi7di22b98OADjhhBOG5IC++eabcd1110GSJBx66KH99uO2g7r6APb4ghhV5EBYUtHWKSHHLVEEQeQJobCKVlmFwOzZaTQlRQEAxcXFOP300zPS6FFHHYWnn346I9caDHX1AaypaQKglUxv65JJSRAEkTPIKocDDCHJnoErZUVxMFETWa51BxX42sJQSUkQBJEj8EgUi6xyFDjtSarIS0Wx+8tedHTL6AkqoB1RCYLISTgwptieITwLOX7Zpa4+gI5uGaGwSkqCIIicxc7hK+8URU2dHy4Hg0xagiCIHMeuDO28UxQ+fxhhmcMhgKrHEgSR09iVoZ13ikLPyBYEBpeTwSlmWyKCIIj0YQyYXeG1pa28UxTmjGwA5KcgCCInmX7ESNt2ucs7RWHOyFY5aD8KgiByDsaAhaeW2tZe3ikKAFh0RjluXDQRTirdQRBEDsKA4Vfr6WCkYooHYdo3myCIHESvTzesaj0drCjkoCAIIgeRFY7WgASB2WMVydsVBQA4qL44QRA5COdAe5ds2w6dea0o3AV5/fEJgshh7DSI5O1IWVcfAOeAmLc9QBBELiMwysy2nJo6PzyFolGJkSAIIpfgsC8zO+ed2ete9sHjBbipRJZ57GdAlDLQf/337k6AU8IdQRC5CefA+/UBLLrto3iv9jtXR+o5kHZbWVMUiqJg4cKFKCsrw4MPPjjo67zygR/OopzXdwRBEGnTHVQBpJc1LHXLabeTtRF27dq1OOKII9DV1ZUtEQiCIHKWMcUOTCx1I9UIWf283o4e7EuzrawoCp/Ph23btuGKK67AI488MqRrPXDlVIyfMAEAwJL0mPml3/x1N3bv74GkgHwUBEHkHALTwvuXX35k2u9tbHRjy6r03pMVRXHHHXfg+uuvR3d3d0rnr1ixAitXroz7WqFbxMgR6X2MhqYgKQmCIHIWlQOtAcm29myPetq6dSu8Xi+OPfbYlN9TXV2NnTt3Rv3U1tYOqv26+gBCkkpKgiCInEblsK3Wk+0riu3bt+PVV1/F66+/jlAohK6uLlx33XW49957bWm/ps4Ph8iozhNBEDnPy3V+W0qN264orr32Wlx77bUAgHfeeQcPP/ywbUoC0Ha4IxVBEESu4xAZfP6wLW3lXcKdy8kg0WqCIIiDALtqPWU1AeGkk07CSSedZGubXb2Kre0RBEFYgaJy7P8qhLr6gOXmp7xbUbR22BcpQBAEYRUOAQjL3JY9KfJOUQBIOUGFIAhiOKIPYZKs2uKnyDtF4fU4KDSWIIichgOQVS3J2I7CgHmnKDyFVBeKIIiDAK6tLGZXeC1vKu8URcim+u0EQRBWwhhQ4nHakkeRd4rCrvrtBEEQVqJywCHa01beKYoqG5ZpBEEQdvBla9iWMh55pygIgiAOFiSF44ltzZa3k3eKosaGmGOCIAi7aPAFLW8j7xSFXbVRCIIgLMem2Jy8UxTlXhcESrgjCOIggAOYVO62vJ28UxRVFV4Uum0KFSAIgrCYb04utryNvFMUFVM8WHjq2GyLQRAEMWQYIx+FZSw6oxxOWlQQBHEQQLWeLKKuPgCZqo0TBJHjcG5PEnFeKoqaOj/tckcQxEHBQenMbmpqwqWXXoqzzjoLc+fOxZo1a+wWAbu/7LW9TYIgCCvYvqvT8jZsL6UqiiJuvPFGHHPMMejq6sLChQsxc+ZMHHnkkba0X1cfwFcdlEtBEMTBwUHpzC4tLcUxxxwDABg5ciQOP/xwNDdbn4IOaEpiTU0TVJUMTwRBEKmS1c0ZGhsb8emnn+L4449Pet6KFSuwcuXKIbenl+8QRQGSog75egRBENmEMXt8FFlTFN3d3bjyyitx0003YeTIkUnPra6uRnV1ddSxxsZGVFZWptWmzx9Gd1CBotCKgiCI3MfpAC78bpnl7WQl6kmSJFx55ZWYP38+qqqqbGu33OtCoEeBTIqCIIiDAK6yg3PjIs45fv3rX+Pwww/HkiVLbG27qsKL7l6FQmMJgjgokGya9NquKOrq6vDss8/i7bffxrnnnotzzz0Xr732mi1tV0zxkJIgCIJIE9t9FN/61rewc+dOu5slCIIgBkneZWYzKjFOEASRFnmnKEYVUjVAgiAODuya+OadoigdUwBaVBAEQaRO3imKcq8LRSNoVUEQRO7jctgz7c07RVFV4UWBk9YUBEHkPqdOG21LO3mnKCqmeDCuhMxPBEHkPtdeMNGWdvJOUQBAWOIgTUEQBJEaeakoyr0uiAJpCoIgiFTIS0VRVeGFp1CkRQVBEDmLwID1W332tGVLK8OMiikeXHP+YSgd7cy2KARBEINCFID1W5ttURZ5qSgATVlMPawo22IQBEEMCn1LnRfeabW8rbxVFIC2PwVBEEQuohc47exRLG8rrxVFudeVbREIgiAGhe5jLbahLFFeK4qqCi9clHxHEEQOIkZG77NPKrG8rbxWFBVTPLjlkq+jyD30bnA5GCaVFWBSWcGQ0upFAXCa3i8wQEhDPIFp1yD1RxCZQ2BAUYGAwUTVCyyzzyNjgFNkGD3SiUVnlGHRGeUZvHp8srZn9nChYooHG/7fcdkWgyAIYtiS1ysKgiAIYmCysqJ4/fXXcfvtt0NVVVxwwQVYunRpNsQYkLr6AGrq/PD5wyj3uvD1cjf2+ILG31UVXks2Nrez3di2qiq8AICaOj92f9mLsKTC5RRwxPgRKckR73rxZK2rD+CJbc34bH8vJIXDKTIcOWEEvjm5OGkb67f68MI7rQj0KPAUijj7pBJj6a1fs8EXBABMLHfDKTJ88kU3wjKHy8Fw6rTR/erjmGUucDJwaGVeYvvj44ZutHdJRlhiqggMOHpSERZ9t0z7DNua8UVExknlblz43bKUv0/98/s7JXAOMMbgLXZE9UMmSHZfZOL7J3ILxjm3dRtpRVEwZ84c/PWvf0VZWRnOP/983HfffTjyyCPTuk5jYyMqKytRW1uLQw45JONy1tUHsKamyfi7O6igNSChxONEkbsvymBx1biMPgh2thvblt6eTmtAMn4fOUJEV6+SVI5414sna119AA8814gD7RJkte/2ExggMIaxo+O3sX6rD+u3Nve7/qIzyjB5QiEeeK4xSmZJ4VAjg7p5g5fKE8YYysIss97XAIzPqfdHSFLRGpD7tZ0O3mKH0Y6ZEo8TPzvnkAG/T/3zywqPUlaiADhEljF7dbL7wvy9AIP7/onsMpix03bT044dOzBx4kQceuihcLlcmDt3Lmpra+0WY0Bq6vxRfwciscqBmJjll2POy6V2Y9vS29F/zHR0DyxHvOvFnqOfF+hRoKjRcxRFBRSVJ2wjUWLRC++0Gtc0oyaY+b/xYXtcmc3vN/d7oEcxPv9QiNev+vFUvk/988euaDKdeJXsvohlMN8/kXvYbnpqbm5GeXnfrKesrAw7duxI+p4VK1Zg5cqVVosWRWwyniSrkf950vNyqd1419Da6b/IVFQOUWCGPPGukUim2OM+fxiSzOO0orWc6LPGG6gALeFIv2YqhE3nmWUzv7+v31UArJ9SGwz6NWILUkoyT+n7TPT5dTKVeJXOfTGY75/IPWxfUcSzdLEBNn6trq7Gzp07o36sXoXEJuM5HULkf5b0vFxqN941nA4Gp0Mw2tXRB7fY4+ZrJJIp9ni51wWng8UNGWRI/Fk9CRKLigtF45qpYA5fNstmfn9fvwtwOlhGqg2LQvzrOB0spe8z0efXyVTiVbL7Itm5qX7/RO5hu6IoLy+Hz9dXxKq5uRmlpaV2izEguvNOR39IYx/W2THn5VK7sW3p7eg/ZkYVDSxHvOvFnqOf5ykU+w2aoqANponaSJRYdPZJJcY1zSTKPzHvCmaW2fx+c797CkXj8w+FeP2qH0/l+9Q/vxjzuTKdeJXsvohlMN8/kXvYbnqaNm0aGhoasG/fPpSVlWHLli34/e9/b7cYA6I7316ORHBMnlCI2RVuNJgicmZbENFhZ7uxbenX1o8JrBchSUVBJOppUnlyORJdL1bWiike/OycQ7Sopy97IcnRUU+J2tAdtS+804rOHgXFMVFP+jX1qKcph7jhGCDqKVbmEo9WUViPejL3x8cN3WgbZNTTMZOKcGEk6sksYzpRT+bPr0c9CYxhTIajnga6L4b6/RO5h+1RTwDw2muv4Y477oCiKFi4cCF++tOfpn0Nq6OeCIIgDkYGM3ZmJY/i9NNPx+mnn56NpgmCIIg0ocxsgiAIIimkKAiCIIikkKIgCIIgkpKz1WMVRUsuMofaEgRBEMnRx0x9DE2FnFUUBw4cAABcfPHFWZaEIAgi9zhw4AAmTpw48InIUnhsJggGg/joo48wduxYiGL6yVB6eNhwg+RKD5IrfYarbCRXegxWLkVRcODAARx77LFwu90pvSdnVxRutxvf+ta3hnSN4Zp/QXKlB8mVPsNVNpIrPQYrV6orCR1yZhMEQRBJIUVBEARBJIUUBUEQBJEU8dZbb70120Jki5NOOinbIsSF5EoPkit9hqtsJFd62CVXzkY9EQRBEPZApieCIAgiKaQoCIIgiKSQoiAIgiCSQoqCIAiCSAopCoIgCCIppCgIgiCIpBzUiuLvf/875s6di2984xv48MMPE573+uuvY86cOZg9ezZWr15tHN+3bx8uuOACVFVV4eqrr0Y4HM6IXO3t7ViyZAmqqqqwZMkSdHR09Dvn7bffxrnnnmv8TJs2Da+88goA4MYbb8SsWbOM1z799FPb5AKAo446ymj7iiuuMI5ns78+/fRTXHjhhZg7dy7mz5+PF154wXgt0/2V6H7RCYfDuPrqqzF79mxccMEFaGxsNF578MEHMXv2bMyZMwdvvPHGkORIV66//vWvOPvsszF//nwsXrwY+/fvN15L9J3aIdfTTz+Nk08+2Wj/qaeeMl575plnUFVVhaqqKjzzzDO2ynXHHXcYMs2ZMyeqtpyV/bVs2TLMmDED8+bNi/s65xy33XYbZs+ejfnz5+Pjjz82XrOsv/hBzGeffcZ3797NL7nkEr5jx46458iyzCsrK/nevXt5KBTi8+fP57t27eKcc37llVfyzZs3c845v/nmm/ljjz2WEbnuuusu/uCDD3LOOX/wwQf53XffnfT8trY2fuKJJ/Kenh7OOec33HAD//vf/54RWQYj1/Tp0+Mez2Z/ff7553zPnj2cc859Ph+fOXMm7+jo4Jxntr+S3S8669at4zfffDPnnPPNmzfzq666inPO+a5du/j8+fN5KBTie/fu5ZWVlVyWZdvkeuutt4x76LHHHjPk4jzxd2qHXBs3buT/8z//0++9bW1tfNasWbytrY23t7fzWbNm8fb2dtvkMrN27Vp+4403Gn9b1V+cc/7uu+/yjz76iM+dOzfu69u2beM/+tGPuKqq/IMPPuDnn38+59za/jqoVxRHHHEEDj/88KTn7NixAxMnTsShhx4Kl8uFuXPnora2FpxzvP3225gzZw4A4Pvf/37GSg3X1tbivPPOAwCcd955xkohES+99BJOPfVUjBgxIiPtZ0ouM9nur69//euYNGkSAKCsrAxerxd+vz8j7ZtJdL+YefXVV/H9738fADBnzhy89dZb4JyjtrYWc+zWPt8AAAd+SURBVOfOhcvlwqGHHoqJEydix44dtsl18sknG/fQ9OnTbdn0KxW5EvGPf/wDM2fOxOjRozFq1CjMnDkzY6uwdOXasmVLwhl+pjnxxBMxatSohK/rzwNjDNOnT0cgEEBLS4ul/XVQK4pUaG5uRnl5ufF3WVkZmpub0dbWBo/HA4dDq8ReXl6O5ubmjLTZ2tqK0tJSAEBpaemAA1q8m/QPf/gD5s+fjzvuuCNjJp5U5QqFQliwYAF+8IMfGIP2cOqvHTt2QJIkHHbYYcaxTPVXovsl9pxx48YBABwOB4qLi9HW1pbSe62Uy8yGDRtw2mmnGX/H+07tlKumpgbz58/HlVdeiaamprTea6VcALB//340Njbi5JNPNo5Z1V+pECu7/qxZ2V85ux+Fzn//93/jq6++6nf86quvxplnnjng+3mcCiaMsbjnJjqerlzp0NLSgvr6epxyyinGsWuuuQZjx46FJEm4+eabsXr1avziF7+wTa6tW7eirKwM+/btw+LFizFlyhSMHDmy33nZ6q/rr78ed911FwRBmwcNpb9iSeV+SXROOveaFXLpPPvss/joo4+wbt0641i879SsaK2U64wzzsC8efPgcrnw+OOP44YbbsDatWuHTX9t2bIFc+bMidogzar+SoVs3F85rygeeeSRIb2/vLw8agne3NyM0tJSjBkzBoFAALIsw+FwwOfzGbPaocpVUlKClpYWlJaWoqWlBV6vN+G5f//73zF79mw4nU7jmC6Hy+XCggUL8PDDD9sqV1lZGQDg0EMPxbe//W188sknmDNnTtb7q6urCz/5yU9w9dVXY/r06cbxofRXLInul9hzmpqaUF5eDlmW0dnZidGjR6f0XivlAoA333wTq1atwrp16+ByuYzj8b7TTAx8qcg1ZswY4/cf/OAHuPfee433vvvuu1Hv/fa3vz1kmVKVS+eFF17ALbfcEnXMqv5KhVjZ9WfNyv7Ke9PTtGnT0NDQgH379iEcDmPLli2YNWsWGGM46aST8NJLLwHQoglmzZqVkTZnzZqFTZs2AQA2bdqEysrKhOdu2bIFc+fOjTrW0tICQJtZvPLKK5g8ebJtcnV0dBimG7/fj+3bt+PII4/Men+Fw2H8/Oc/x7nnnouzzjor6rVM9lei+yVWXj3i5KWXXsLJJ58MxhhmzZqFLVu2IBwOY9++fWhoaMBxxx03aFnSleuTTz7BLbfcgj/96U8oKSkxjif6Tu2SS/9+AM2/c8QRRwAATjnlFPzjH/9AR0cHOjo68I9//CNqZW21XADw+eefIxAI4IQTTjCOWdlfqaA/D5xz/Otf/0JxcTFKS0st7a+DOuqppqaGn3rqqfyYY47hM2bM4D/84Q8551pUzOWXX26ct23bNl5VVcUrKyv5Aw88YBzfu3cvX7hwIT/zzDN5dXU1D4VCGZHL7/fzyy67jM+ePZtfdtllvK2tjXPO+Y4dO/hNN91knLdv3z5+yimncEVRot5/6aWX8nnz5vG5c+fya6+9lnd1ddkmV11dHZ83bx6fP38+nzdvHn/yySeN92ezvzZt2sSPPvpofs455xg/n3zyCec88/0V73754x//yF955RXOOefBYJBXV1fzM888ky9cuJDv3bvXeO8DDzzAKysreVVVFd+2bduQ5EhXrsWLF/MZM2YY/fOTn/yEc578O7VDrnvvvZefffbZfP78+fySSy7hn332mfHep556ip955pn8zDPP5Bs2bLBVLs45v//++/k999wT9T6r++uXv/wlnzlzJj/66KP5qaeeyp988kn+t7/9jf/tb3/jnHOuqiq/9dZbeWVlJZ83b15URKdV/UVlxgmCIIik5L3piSAIgkgOKQqCIAgiKaQoCIIgiKSQoiAIgiCSQoqCIAiCSAopCoIYJO+88w4WLFgAQEtuuvTSS7MsEUFYAykKgsgAZWVlePTRR7MtBkFYAikKgkiBa6+9FgsWLMD8+fPx85//vN+eGI2NjTjppJOMv1966SV873vfw3nnnYdVq1Zh6tSp6O7uBgBMnToVq1atwsKFC1FZWWlksxPEcIUUBUGkwK9//Ws8/fTTeP7553HkkUfioYceSnhua2srbrnlFqxatQqbNm2C2+3ud87IkSOxceNG3H333bjtttusFJ0ghgwpCoJIgWeffdZYUWzevDnpLnn/+te/cPTRRxv7YyxcuLDfOWeffTYAbV+IlpYWhEIhS+QmiEyQ89VjCcJq3n//fTz++ONYv349vF4vnn/+eTz55JMJz+ecD1jeuaCgAACM0tWyLBvHCGK4QSsKghiAQCCAkSNHYvTo0QiHw9i4cWPS86dPn46PP/4YX3zxBQBtT2iCyGVIURDEAJx22mk47LDDcNZZZ+Hyyy/H0UcfnfT8r33ta7j11luxdOlSLFq0CKFQCE6n0/KtbAnCKqh6LEFYQFdXl7Hr38aNG7FhwwY8/vjjWZaKIAYH+SgIwgIeffRRvPjii1AUBaNGjaLIJiKnoRUFQRAEkRTyURAEQRBJIUVBEARBJIUUBUEQBJEUUhQEQRBEUkhREARBEEn5/yAC8L2HfpM7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df['align'],np.log(df['editcount']+1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0011774645327912252, 0.6461219837427921)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(df['align'],np.log(df['editcount']+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liberal Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "liberal_pages=pd.read_csv('Data/liberal_pages.tsv',sep='\\t',parse_dates=[1])\n",
    "liberal_pages['time']=pd.to_datetime(liberal_pages['time'])\n",
    "liberal_pages=liberal_pages.sort_values('time')\n",
    "\n",
    "liberal_page_stats=liberal_pages.groupby('title').agg({'title':len, 'user':lambda x: x.nunique(), 'byte':'last'})\n",
    "liberal_page_stats.columns=['edits', 'editors', 'length']\n",
    "liberal_page_stats=liberal_page_stats.reset_index()\n",
    "liberal_page_stats['short']=liberal_page_stats['title'].str.lower().str.replace(' ','')\n",
    "liberal_page_stats=liberal_page_stats.sort_values('length').drop_duplicates(subset='short', keep='last')\n",
    "\n",
    "liberal_pages['byte']=liberal_pages[['title','byte']].groupby('title').diff()['byte'].fillna(liberal_pages['byte'])\n",
    "liberal_pages=liberal_pages[liberal_pages['byte']>0]\n",
    "liberal_pages=liberal_pages.groupby(['title','user']).sum().reset_index()\n",
    "\n",
    "liberal_pages=liberal_pages.merge(user_align, how ='inner', on='user')\n",
    "liberal_pages=liberal_pages.merge(user_info2, how='inner', left_on='user', right_on='name')\n",
    "liberal_pages=liberal_pages.dropna()\n",
    "liberal_pages=liberal_pages.groupby('title').agg({'align':[np.mean, lambda x: np.var(x)], 'user':lambda x: x.nunique(), 'title':len, 'editcount':np.mean})\n",
    "liberal_pages.columns=['ave_align','var_align','peditors','pedits', 'editscount']\n",
    "liberal_pages=liberal_pages.reset_index()\n",
    "\n",
    "liberal_pages=liberal_pages.merge(liberal_page_stats, how='inner', on='title')\n",
    "\n",
    "liberal_quality=pd.read_csv(\"Data/liberal_quality.tsv\",sep='\\t',header=None, names=['title','quality'])\n",
    "liberal_quality['short']=liberal_quality.title.str.replace(' ','')\n",
    "liberal_pages=liberal_pages.merge(liberal_quality, how='inner', on='short')\n",
    "\n",
    "liberal_pages=liberal_pages.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10038, 5), (6167, 12))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liberal_page_stats.shape, liberal_pages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start    2545\n",
       "C        1630\n",
       "Stub     1557\n",
       "GA        377\n",
       "B         212\n",
       "FA         91\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liberal_quality['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>edits</th>\n",
       "      <th>editors</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10038.000000</td>\n",
       "      <td>10038.000000</td>\n",
       "      <td>10038.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.751046</td>\n",
       "      <td>74.784917</td>\n",
       "      <td>8644.557482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>686.060364</td>\n",
       "      <td>281.532331</td>\n",
       "      <td>14279.521972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2839.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4708.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>8519.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24489.000000</td>\n",
       "      <td>6566.000000</td>\n",
       "      <td>222398.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              edits       editors         length\n",
       "count  10038.000000  10038.000000   10038.000000\n",
       "mean     154.751046     74.784917    8644.557482\n",
       "std      686.060364    281.532331   14279.521972\n",
       "min        1.000000      1.000000      21.000000\n",
       "25%       16.000000      9.000000    2839.000000\n",
       "50%       34.000000     22.000000    4708.500000\n",
       "75%       79.000000     44.000000    8519.750000\n",
       "max    24489.000000   6566.000000  222398.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liberal_page_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='var_align', y='quality',data=liberal_pages, order=['Stub','Start','C','B','GA','FA'][-1::-1], orient='h');\n",
    "plt.xlabel('Polarization', fontsize=18);\n",
    "plt.ylabel('Quality', fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + scale(log(editors)) + \n",
       "    scale(log(length)) + scale(log(edits)) + scale(log(editscount)), \n",
       "    data = liberal_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                           Value Std. Error t value\n",
       "abs(ave_align)         -1.577916    0.37609 -4.1956\n",
       "var_align               2.755338    0.71916  3.8313\n",
       "scale(log(editors))    -0.814590    0.12035 -6.7683\n",
       "scale(log(length))      3.408269    0.07332 46.4859\n",
       "scale(log(edits))       1.173300    0.13611  8.6202\n",
       "scale(log(editscount))  0.007256    0.03123  0.2323\n",
       "\n",
       "Intercepts:\n",
       "           Value    Std. Error t value \n",
       "Stub|Start  -2.9617   0.0834   -35.5100\n",
       "Start|C      1.2238   0.0748    16.3705\n",
       "C|B          5.9281   0.1278    46.3851\n",
       "B|GA         6.8956   0.1386    49.7593\n",
       "GA|FA       10.2832   0.2181    47.1567\n",
       "\n",
       "Residual Deviance: 8867.297 \n",
       "AIC: 8889.297 \n",
       "                              Value Std. Error     t value       p value\n",
       "abs(ave_align)         -1.577915601 0.37608878  -4.1955934  2.721581e-05\n",
       "var_align               2.755338047 0.71916060   3.8313251  1.274550e-04\n",
       "scale(log(editors))    -0.814589928 0.12035411  -6.7682768  1.303252e-11\n",
       "scale(log(length))      3.408269263 0.07331833  46.4859086  0.000000e+00\n",
       "scale(log(edits))       1.173299584 0.13611110   8.6201612  6.685993e-18\n",
       "scale(log(editscount))  0.007256112 0.03123371   0.2323167  8.162920e-01\n",
       "Stub|Start             -2.961656796 0.08340351 -35.5099760 3.448334e-276\n",
       "Start|C                 1.223828450 0.07475822  16.3704861  3.107220e-60\n",
       "C|B                     5.928107034 0.12780196  46.3851026  0.000000e+00\n",
       "B|GA                    6.895627348 0.13857979  49.7592555  0.000000e+00\n",
       "GA|FA                  10.283239809 0.21806523  47.1567137  0.000000e+00\n",
       "                               OR\n",
       "abs(ave_align)          0.2064049\n",
       "var_align              15.7263562\n",
       "scale(log(editors))     0.4428209\n",
       "scale(log(length))     30.2129084\n",
       "scale(log(edits))       3.2326414\n",
       "scale(log(editscount))  1.0072825\n",
       "Stub|Start              0.2064049\n",
       "Start|C                15.7263562\n",
       "C|B                     0.4428209\n",
       "B|GA                   30.2129084\n",
       "GA|FA                   3.2326414\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i liberal_pages\n",
    "library(MASS)\n",
    "\n",
    "liberal_pages$quality=factor(liberal_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = liberal_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "\n",
    "ctable <- cbind(ctable, \"p value\" = p, \"OR\" = exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conservative Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "conservative_pages=pd.read_csv('Data/conservative_pages.tsv',sep='\\t',parse_dates=[1])\n",
    "conservative_pages['time']=pd.to_datetime(conservative_pages['time'])\n",
    "conservative_pages=conservative_pages.sort_values('time')\n",
    "\n",
    "conservative_page_stats=conservative_pages.groupby('title').agg({'title':len, 'user':lambda x: x.nunique(), 'byte':'last'})\n",
    "conservative_page_stats.columns=['edits','editors', 'length']\n",
    "conservative_page_stats=conservative_page_stats.reset_index()\n",
    "conservative_page_stats['short']=conservative_page_stats['title'].str.lower().str.replace(' ','')\n",
    "conservative_page_stats=conservative_page_stats.sort_values('length').drop_duplicates(subset='short', keep='last')\n",
    "\n",
    "conservative_pages['byte']=conservative_pages[['title','byte']].groupby('title').diff()['byte'].fillna(conservative_pages['byte'])\n",
    "conservative_pages=conservative_pages[conservative_pages['byte']>0]\n",
    "conservative_pages=conservative_pages.groupby(['title','user']).sum().reset_index()\n",
    "\n",
    "conservative_pages=conservative_pages.merge(user_align,on='user')\n",
    "conservative_pages=conservative_pages.merge(user_info2, how='inner', left_on='user', right_on='name')\n",
    "conservative_pages=conservative_pages.dropna()\n",
    "conservative_pages=conservative_pages.groupby('title').agg({'align':[np.mean, lambda x: np.var(x)], 'user':lambda x: x.nunique(), 'title':len, 'editcount':np.mean})\n",
    "conservative_pages.columns=['ave_align','var_align','peditors','pedits', 'editscount']\n",
    "conservative_pages=conservative_pages.reset_index()\n",
    "\n",
    "conservative_pages=conservative_pages.merge(conservative_page_stats, how='inner', on='title')\n",
    "\n",
    "conservative_quality=pd.read_csv('Data/conservative_quality.tsv',sep='\\t',header=None, names=['title','quality'])\n",
    "conservative_quality['short']=conservative_quality.title.str.replace(' ','')\n",
    "conservative_pages=conservative_pages.merge(conservative_quality, how='inner', on='short')\n",
    "\n",
    "conservative_pages=conservative_pages.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10909, 5), (6403, 12))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conservative_page_stats.shape, conservative_pages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start    2464\n",
       "C        1911\n",
       "Stub     1393\n",
       "GA        494\n",
       "B         273\n",
       "FA        124\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conservative_quality['quality'].value_counts()"
   ]
  },
  {
   "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>edits</th>\n",
       "      <th>editors</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10909.000000</td>\n",
       "      <td>10909.000000</td>\n",
       "      <td>10909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>177.182418</td>\n",
       "      <td>79.580255</td>\n",
       "      <td>9448.811440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>808.058741</td>\n",
       "      <td>294.429830</td>\n",
       "      <td>15012.707384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>9705.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45741.000000</td>\n",
       "      <td>14417.000000</td>\n",
       "      <td>256674.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              edits       editors         length\n",
       "count  10909.000000  10909.000000   10909.000000\n",
       "mean     177.182418     79.580255    9448.811440\n",
       "std      808.058741    294.429830   15012.707384\n",
       "min        1.000000      1.000000      45.000000\n",
       "25%       15.000000      8.000000    3006.000000\n",
       "50%       37.000000     23.000000    5181.000000\n",
       "75%       93.000000     49.000000    9705.000000\n",
       "max    45741.000000  14417.000000  256674.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conservative_page_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='var_align', y='quality',data=conservative_pages, order=['Stub','Start','C','B','GA','FA'][-1::-1], orient='h');\n",
    "plt.xlabel('Polarization', fontsize=18);\n",
    "plt.ylabel('Quality', fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + scale(log(editors)) + \n",
       "    scale(log(length)) + scale(log(edits)) + scale(log(editscount)), \n",
       "    data = conservative_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                          Value Std. Error t value\n",
       "abs(ave_align)         -0.64634    0.40953  -1.578\n",
       "var_align               2.91860    0.66453   4.392\n",
       "scale(log(editors))    -0.96836    0.12591  -7.691\n",
       "scale(log(length))      3.59318    0.07198  49.919\n",
       "scale(log(edits))       1.19607    0.14045   8.516\n",
       "scale(log(editscount))  0.08746    0.03015   2.901\n",
       "\n",
       "Intercepts:\n",
       "           Value    Std. Error t value \n",
       "Stub|Start  -3.3049   0.0822   -40.2175\n",
       "Start|C      0.8150   0.0698    11.6710\n",
       "C|B          5.3448   0.1113    48.0319\n",
       "B|GA         6.2714   0.1201    52.2310\n",
       "GA|FA        9.5122   0.1884    50.4997\n",
       "\n",
       "Residual Deviance: 9558.07 \n",
       "AIC: 9580.07 \n",
       "                             Value Std. Error    t value      p value\n",
       "abs(ave_align)         -0.64633925 0.40953470  -1.578228 1.145132e-01\n",
       "var_align               2.91859639 0.66453258   4.391954 1.123366e-05\n",
       "scale(log(editors))    -0.96836073 0.12590575  -7.691156 1.458119e-14\n",
       "scale(log(length))      3.59318180 0.07198029  49.918969 0.000000e+00\n",
       "scale(log(edits))       1.19607286 0.14044520   8.516296 1.647380e-17\n",
       "scale(log(editscount))  0.08745762 0.03014638   2.901098 3.718573e-03\n",
       "Stub|Start             -3.30487396 0.08217503 -40.217498 0.000000e+00\n",
       "Start|C                 0.81499723 0.06983078  11.671031 1.792384e-31\n",
       "C|B                     5.34479258 0.11127579  48.031943 0.000000e+00\n",
       "B|GA                    6.27144582 0.12007125  52.231036 0.000000e+00\n",
       "GA|FA                   9.51216784 0.18836091  50.499691 0.000000e+00\n",
       "                               OR\n",
       "abs(ave_align)          0.5239604\n",
       "var_align              18.5152810\n",
       "scale(log(editors))     0.3797050\n",
       "scale(log(length))     36.3495491\n",
       "scale(log(edits))       3.3071039\n",
       "scale(log(editscount))  1.0913960\n",
       "Stub|Start              0.5239604\n",
       "Start|C                18.5152810\n",
       "C|B                     0.3797050\n",
       "B|GA                   36.3495491\n",
       "GA|FA                   3.3071039\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i conservative_pages\n",
    "library(MASS)\n",
    "\n",
    "conservative_pages$quality=factor(conservative_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = conservative_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "\n",
    "ctable <- cbind(ctable, \"p value\" = p, \"OR\" = exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Science Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sci_pages=pd.read_csv('Data/science_pages.tsv',sep='\\t',parse_dates=[1])\n",
    "sci_pages=sci_pages.sort_values('time')\n",
    "\n",
    "sci_page_stats=sci_pages.groupby('title').agg({'title':len, 'user':lambda x: x.nunique(), 'byte':sum})\n",
    "\n",
    "sci_page_stats.columns=['edits','editors', 'length']\n",
    "sci_page_stats=sci_page_stats.reset_index()\n",
    "sci_page_stats['short']=sci_page_stats['title'].str.lower().str.replace(' ','')\n",
    "sci_page_stats=sci_page_stats.sort_values('length').drop_duplicates(subset='short', keep='last')\n",
    "\n",
    "sci_pages=sci_pages[sci_pages['byte']>0]\n",
    "sci_pages=sci_pages.groupby(['title','user']).sum().reset_index()\n",
    "\n",
    "sci_pages=sci_pages.merge(user_align,on='user')\n",
    "\n",
    "sci_pages=sci_pages.merge(user_info2, how='inner', left_on='user', right_on='name')\n",
    "\n",
    "sci_pages=sci_pages.dropna()\n",
    "sci_pages=sci_pages.groupby('title').agg({'align':[np.mean,lambda x:np.var(x)], 'user':lambda x: x.nunique(), 'title':len, 'editcount':np.mean})\n",
    "sci_pages.columns=['ave_align','var_align','peditors','pedits','editscount']\n",
    "sci_pages=sci_pages.reset_index()\n",
    "\n",
    "sci_pages=sci_pages.merge(sci_page_stats, how='inner', on='title')\n",
    "\n",
    "sci_quality=pd.read_csv('Data/science_quality.tsv',sep='\\t',header=None, names=['title','quality'])\n",
    "sci_quality=sci_quality.dropna()\n",
    "sci_pages=sci_pages.merge(sci_quality,how='inner', on='title')\n",
    "\n",
    "sci_pages=sci_pages.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((49998, 5), (49533, 11))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_page_stats.shape, sci_pages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start    16901\n",
       "C        12455\n",
       "Stub     12192\n",
       "B         5323\n",
       "GA        1696\n",
       "FA         966\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_pages['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>edits</th>\n",
       "      <th>editors</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49998.000000</td>\n",
       "      <td>49998.000000</td>\n",
       "      <td>49998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>209.908216</td>\n",
       "      <td>102.556502</td>\n",
       "      <td>11192.224649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>632.017444</td>\n",
       "      <td>283.634577</td>\n",
       "      <td>17296.576007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5331.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>12493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17387.000000</td>\n",
       "      <td>5814.000000</td>\n",
       "      <td>301365.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              edits       editors         length\n",
       "count  49998.000000  49998.000000   49998.000000\n",
       "mean     209.908216    102.556502   11192.224649\n",
       "std      632.017444    283.634577   17296.576007\n",
       "min        1.000000      1.000000      16.000000\n",
       "25%       23.000000     13.000000    2366.000000\n",
       "50%       51.000000     26.000000    5331.500000\n",
       "75%      140.000000     70.000000   12493.000000\n",
       "max    17387.000000   5814.000000  301365.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_page_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='var_align', y='quality',data=sci_pages, order=['Stub','Start','C','B','GA','FA'][-1::-1], orient='h');\n",
    "plt.xlabel('Polarization', fontsize=18);\n",
    "plt.ylabel('Quality', fontsize=18);\n",
    "plt.xlim([-0.001,0.23]);\n",
    "plt.grid(True)\n",
    "sns.despine(offset=10,trim=True);\n",
    "plt.tight_layout();\n",
    "# plt.savefig('science_quality.pdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + scale(log(editors)) + \n",
       "    scale(log(length)) + scale(log(edits)) + scale(log(editscount)), \n",
       "    data = sci_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                          Value Std. Error t value\n",
       "abs(ave_align)         -0.42715    0.13835  -3.087\n",
       "var_align               0.58265    0.21063   2.766\n",
       "scale(log(editors))    -0.57339    0.03524 -16.272\n",
       "scale(log(length))      4.03471    0.02671 151.072\n",
       "scale(log(edits))       0.52314    0.03982  13.136\n",
       "scale(log(editscount))  0.01097    0.01065   1.031\n",
       "\n",
       "Intercepts:\n",
       "           Value     Std. Error t value  \n",
       "Stub|Start   -3.1004    0.0273  -113.5018\n",
       "Start|C       1.0904    0.0235    46.4794\n",
       "C|B           4.6849    0.0340   137.7326\n",
       "B|GA          7.0614    0.0450   156.8531\n",
       "GA|FA         8.5424    0.0552   154.6919\n",
       "\n",
       "Residual Deviance: 75939.22 \n",
       "AIC: 75961.22 \n",
       "                             Value Std. Error     t value      p value\n",
       "abs(ave_align)         -0.42715094 0.13834886   -3.087492 2.018534e-03\n",
       "var_align               0.58264951 0.21063208    2.766196 5.671452e-03\n",
       "scale(log(editors))    -0.57339477 0.03523793  -16.272092 1.557448e-59\n",
       "scale(log(length))      4.03471059 0.02670727  151.071635 0.000000e+00\n",
       "scale(log(edits))       0.52313588 0.03982486   13.135914 2.050022e-39\n",
       "scale(log(editscount))  0.01097441 0.01064902    1.030556 3.027492e-01\n",
       "Stub|Start             -3.10043737 0.02731620 -113.501793 0.000000e+00\n",
       "Start|C                 1.09040950 0.02346005   46.479412 0.000000e+00\n",
       "C|B                     4.68492506 0.03401465  137.732559 0.000000e+00\n",
       "B|GA                    7.06142345 0.04501935  156.853089 0.000000e+00\n",
       "GA|FA                   8.54239198 0.05522197  154.691900 0.000000e+00\n",
       "                               OR\n",
       "abs(ave_align)          0.6523651\n",
       "var_align               1.7907768\n",
       "scale(log(editors))     0.5636089\n",
       "scale(log(length))     56.5265583\n",
       "scale(log(edits))       1.6873106\n",
       "scale(log(editscount))  1.0110348\n",
       "Stub|Start              0.6523651\n",
       "Start|C                 1.7907768\n",
       "C|B                     0.5636089\n",
       "B|GA                   56.5265583\n",
       "GA|FA                   1.6873106\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i sci_pages\n",
    "library(MASS)\n",
    "\n",
    "sci_pages$quality=factor(sci_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = sci_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "\n",
    "ctable <- cbind(ctable, \"p value\" = p, 'OR'=exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + I(var_align^2) + \n",
       "    scale(log(editors)) + scale(log(length)) + scale(log(edits)) + \n",
       "    scale(log(editscount)), data = sci_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                           Value Std. Error  t value\n",
       "abs(ave_align)         -0.467668    0.13905  -3.3633\n",
       "var_align               1.874715    0.45316   4.1370\n",
       "I(var_align^2)         -5.953430    1.85005  -3.2180\n",
       "scale(log(editors))    -0.591123    0.03567 -16.5707\n",
       "scale(log(length))      4.037092    0.02673 151.0498\n",
       "scale(log(edits))       0.525171    0.03983  13.1844\n",
       "scale(log(editscount))  0.008498    0.01068   0.7957\n",
       "\n",
       "Intercepts:\n",
       "           Value     Std. Error t value  \n",
       "Stub|Start   -3.0587    0.0302  -101.2868\n",
       "Start|C       1.1340    0.0271    41.7833\n",
       "C|B           4.7287    0.0367   128.7944\n",
       "B|GA          7.1044    0.0470   151.0208\n",
       "GA|FA         8.5852    0.0569   150.9712\n",
       "\n",
       "Residual Deviance: 75928.83 \n",
       "AIC: 75952.83 \n",
       "                              Value Std. Error      t value      p value\n",
       "abs(ave_align)         -0.467668451 0.13904845   -3.3633489 7.700298e-04\n",
       "var_align               1.874715396 0.45315771    4.1370043 3.518695e-05\n",
       "I(var_align^2)         -5.953430339 1.85004577   -3.2179908 1.290919e-03\n",
       "scale(log(editors))    -0.591123103 0.03567278  -16.5707050 1.134875e-61\n",
       "scale(log(length))      4.037092391 0.02672690  151.0497800 0.000000e+00\n",
       "scale(log(edits))       0.525171369 0.03983287   13.1843712 1.079503e-39\n",
       "scale(log(editscount))  0.008498211 0.01067999    0.7957137 4.261985e-01\n",
       "Stub|Start             -3.058674494 0.03019814 -101.2868461 0.000000e+00\n",
       "Start|C                 1.134049875 0.02714124   41.7832793 0.000000e+00\n",
       "C|B                     4.728653310 0.03671475  128.7943676 0.000000e+00\n",
       "B|GA                    7.104394662 0.04704250  151.0207734 0.000000e+00\n",
       "GA|FA                   8.585179981 0.05686636  150.9711591 0.000000e+00\n",
       "                                 OR\n",
       "abs(ave_align)          0.626461192\n",
       "var_align               6.518963534\n",
       "I(var_align^2)          0.002596917\n",
       "scale(log(editors))     0.553705067\n",
       "scale(log(length))     56.661353989\n",
       "scale(log(edits))       1.690748566\n",
       "scale(log(editscount))  1.008534424\n",
       "Stub|Start              0.626461192\n",
       "Start|C                 6.518963534\n",
       "C|B                     0.002596917\n",
       "B|GA                    0.553705067\n",
       "GA|FA                  56.661353989\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i sci_pages\n",
    "library(MASS)\n",
    "\n",
    "sci_pages$quality=factor(sci_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + I(var_align^2) + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = sci_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "\n",
    "ctable <- cbind(ctable, \"p value\" = p, 'OR'=exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Social Issue Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_pages=pd.read_csv('Data/social_issue_pages.tsv',sep='\\t',parse_dates=[1])\n",
    "social_pages['time']=pd.to_datetime(social_pages['time'])\n",
    "social_pages=social_pages.sort_values('time')\n",
    "\n",
    "social_page_stats=social_pages.groupby('title').agg({'title':len, 'user':lambda x: x.nunique(), 'page_len':'last'})\n",
    "\n",
    "social_page_stats.columns=['edits', 'editors', 'length']\n",
    "social_page_stats=social_page_stats.reset_index()\n",
    "social_page_stats['short']=social_page_stats['title'].str.replace(' ','').str.lower()\n",
    "social_page_stats=social_page_stats.sort_values('length').drop_duplicates(subset='short', keep='last')\n",
    "\n",
    "social_pages['byte']=social_pages[['title','page_len']].groupby('title').diff()['page_len'].fillna(social_pages['page_len'])\n",
    "social_pages=social_pages[social_pages['byte']>0]\n",
    "social_pages=social_pages.groupby(['title','user']).sum().reset_index()\n",
    "\n",
    "social_pages=social_pages.merge(user_align,on='user')\n",
    "social_pages=social_pages.merge(user_info2, how='inner', left_on='user', right_on='name')\n",
    "social_pages=social_pages.dropna()\n",
    "social_pages=social_pages.groupby('title').agg({'align':[np.mean,lambda x: np.var(x)], 'user':lambda x: x.nunique(), 'title':len, 'editcount':np.mean})\n",
    "social_pages.columns=['ave_align','var_align','peditors','pedits','editscount']\n",
    "social_pages=social_pages.reset_index()\n",
    "\n",
    "social_pages=social_pages.merge(social_page_stats, how='inner', on='title')\n",
    "\n",
    "social_quality=pd.read_csv('Data/social_issue_quality.tsv',sep='\\t')\n",
    "social_pages=social_pages.merge(social_quality,how='inner', on='title')\n",
    "\n",
    "social_pages=social_pages.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((162085, 5), (161055, 11))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_page_stats.shape, social_pages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start    55884\n",
       "C        48292\n",
       "Stub     30853\n",
       "B        14050\n",
       "GA       10108\n",
       "FA        3814\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_quality['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>edits</th>\n",
       "      <th>editors</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>162085.000000</td>\n",
       "      <td>162085.000000</td>\n",
       "      <td>162085.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>264.668433</td>\n",
       "      <td>121.797440</td>\n",
       "      <td>13152.758602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>818.542165</td>\n",
       "      <td>336.536286</td>\n",
       "      <td>20847.338808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6447.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>176.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36613.000000</td>\n",
       "      <td>13305.000000</td>\n",
       "      <td>538466.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               edits        editors         length\n",
       "count  162085.000000  162085.000000  162085.000000\n",
       "mean      264.668433     121.797440   13152.758602\n",
       "std       818.542165     336.536286   20847.338808\n",
       "min         1.000000       1.000000     124.000000\n",
       "25%        27.000000      15.000000    3129.000000\n",
       "50%        63.000000      32.000000    6447.000000\n",
       "75%       176.000000      86.000000   14088.000000\n",
       "max     36613.000000   13305.000000  538466.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_page_stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='var_align', y='quality',data=social_pages, order=['Stub','Start','C','B','GA','FA'][-1::-1], orient='h');\n",
    "plt.xlabel('Polarization', fontsize=18);\n",
    "plt.ylabel('Quality', fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + scale(log(editors)) + \n",
       "    scale(log(length)) + scale(log(edits)) + scale(log(editscount)), \n",
       "    data = social_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                          Value Std. Error t value\n",
       "abs(ave_align)         -0.69825   0.082165  -8.498\n",
       "var_align               0.73133   0.121673   6.011\n",
       "scale(log(editors))    -0.66943   0.020470 -32.702\n",
       "scale(log(length))      3.86699   0.014134 273.603\n",
       "scale(log(edits))       0.53482   0.022977  23.276\n",
       "scale(log(editscount))  0.05633   0.005746   9.804\n",
       "\n",
       "Intercepts:\n",
       "           Value     Std. Error t value  \n",
       "Stub|Start   -3.8643    0.0166  -232.1487\n",
       "Start|C       0.2631    0.0127    20.6827\n",
       "C|B           4.3135    0.0185   233.2757\n",
       "B|GA          5.9667    0.0219   272.7497\n",
       "GA|FA         8.0753    0.0291   277.2197\n",
       "\n",
       "Residual Deviance: 256435.23 \n",
       "AIC: 256457.23 \n",
       "                             Value  Std. Error     t value       p value\n",
       "abs(ave_align)         -0.69825371 0.082165486   -8.498139  1.926545e-17\n",
       "var_align               0.73133143 0.121673203    6.010620  1.848148e-09\n",
       "scale(log(editors))    -0.66943022 0.020470332  -32.702460 1.440931e-234\n",
       "scale(log(length))      3.86699351 0.014133580  273.603264  0.000000e+00\n",
       "scale(log(edits))       0.53481987 0.022977310   23.276000 7.760649e-120\n",
       "scale(log(editscount))  0.05632936 0.005745514    9.804060  1.081494e-22\n",
       "Stub|Start             -3.86427766 0.016645700 -232.148702  0.000000e+00\n",
       "Start|C                 0.26314518 0.012722974   20.682679  4.960796e-95\n",
       "C|B                     4.31352515 0.018491101  233.275732  0.000000e+00\n",
       "B|GA                    5.96667463 0.021876006  272.749723  0.000000e+00\n",
       "GA|FA                   8.07532017 0.029129678  277.219689  0.000000e+00\n",
       "                               OR\n",
       "abs(ave_align)          0.4974532\n",
       "var_align               2.0778453\n",
       "scale(log(editors))     0.5120002\n",
       "scale(log(length))     47.7984642\n",
       "scale(log(edits))       1.7071407\n",
       "scale(log(editscount))  1.0579461\n",
       "Stub|Start              0.4974532\n",
       "Start|C                 2.0778453\n",
       "C|B                     0.5120002\n",
       "B|GA                   47.7984642\n",
       "GA|FA                   1.7071407\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i social_pages\n",
    "library(MASS)\n",
    "\n",
    "social_pages$quality=factor(social_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = social_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "ctable <- cbind(ctable, \"p value\" = p, 'OR' = exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + I(var_align^2) + \n",
       "    scale(log(editors)) + scale(log(length)) + scale(log(edits)) + \n",
       "    scale(log(editscount)), data = social_pages, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                           Value Std. Error t value\n",
       "abs(ave_align)          -0.75855   0.082480  -9.197\n",
       "var_align                3.44725   0.297662  11.581\n",
       "I(var_align^2)         -13.21482   1.335772  -9.893\n",
       "scale(log(editors))     -0.70124   0.020719 -33.846\n",
       "scale(log(length))       3.87071   0.014147 273.606\n",
       "scale(log(edits))        0.53834   0.022988  23.418\n",
       "scale(log(editscount))   0.05121   0.005771   8.874\n",
       "\n",
       "Intercepts:\n",
       "           Value     Std. Error t value  \n",
       "Stub|Start   -3.7749    0.0188  -200.6292\n",
       "Start|C       0.3569    0.0158    22.6025\n",
       "C|B           4.4085    0.0208   211.5649\n",
       "B|GA          6.0611    0.0239   253.8425\n",
       "GA|FA         8.1685    0.0306   266.7462\n",
       "\n",
       "Residual Deviance: 256328.61 \n",
       "AIC: 256352.61 \n",
       "                             Value  Std. Error     t value       p value\n",
       "abs(ave_align)          -0.7585546 0.082480007   -9.196829  3.686673e-20\n",
       "var_align                3.4472493 0.297661732   11.581097  5.138437e-31\n",
       "I(var_align^2)         -13.2148201 1.335771573   -9.893024  4.463380e-23\n",
       "scale(log(editors))     -0.7012419 0.020718595  -33.846018 4.153283e-251\n",
       "scale(log(length))       3.8707083 0.014146996  273.606381  0.000000e+00\n",
       "scale(log(edits))        0.5383424 0.022988497   23.417904 2.808286e-121\n",
       "scale(log(editscount))   0.0512086 0.005770767    8.873795  7.069461e-19\n",
       "Stub|Start              -3.7749452 0.018815531 -200.629219  0.000000e+00\n",
       "Start|C                  0.3568957 0.015790069   22.602543 4.091187e-113\n",
       "C|B                      4.4085039 0.020837595  211.564911  0.000000e+00\n",
       "B|GA                     6.0611021 0.023877409  253.842540  0.000000e+00\n",
       "GA|FA                    8.1685290 0.030622849  266.746214  0.000000e+00\n",
       "                                 OR\n",
       "abs(ave_align)         4.683429e-01\n",
       "var_align              3.141386e+01\n",
       "I(var_align^2)         1.823377e-06\n",
       "scale(log(editors))    4.959690e-01\n",
       "scale(log(length))     4.797635e+01\n",
       "scale(log(edits))      1.713165e+00\n",
       "scale(log(editscount)) 1.052542e+00\n",
       "Stub|Start             4.683429e-01\n",
       "Start|C                3.141386e+01\n",
       "C|B                    1.823377e-06\n",
       "B|GA                   4.959690e-01\n",
       "GA|FA                  4.797635e+01\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i social_pages\n",
    "library(MASS)\n",
    "\n",
    "social_pages$quality=factor(social_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~ abs(ave_align) + var_align + I(var_align^2) + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = social_pages, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "ctable <- cbind(ctable, \"p value\" = p, 'OR' = exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Articles Combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All 3 corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sci_pages['cat']='science'\n",
    "sci_pages['cat2']=''\n",
    "liberal_pages['cat']='politics'\n",
    "liberal_pages['cat2']='liberal'\n",
    "conservative_pages['cat']='politics'\n",
    "conservative_pages['cat2']='conserv'\n",
    "social_pages['cat']='social issue'\n",
    "social_pages['cat2']=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "liberal_pages['title']=liberal_pages['title_x']\n",
    "del liberal_pages['title_x']\n",
    "del liberal_pages['title_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "conservative_pages['title']=conservative_pages['title_x']\n",
    "del conservative_pages['title_x']\n",
    "del conservative_pages['title_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=pd.concat([sci_pages,liberal_pages,conservative_pages,social_pages], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.07426896515993779, 2.1365957785369076e-270)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(df_all['var_align'], np.log(df_all['editscount']+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def colorify(y):\n",
    "    if y<-0.96875: color='#001AE5'\n",
    "    elif y<-0.9375: color='#0012E4'\n",
    "    elif y<-0.90625: color='#000BE3'\n",
    "    elif y<-0.875: color='#0004E3'\n",
    "    elif y<-0.84375: color='#0200E2'\n",
    "    elif y<-0.8125: color='#0900E1'\n",
    "    elif y<-0.78125: color='#1100E1'\n",
    "    elif y<-0.75: color='#1800E0'\n",
    "    elif y<-0.71875: color='#1F00E0'\n",
    "    elif y<-0.6875: color='#2600DF'\n",
    "    elif y<-0.65625: color='#2D00DE'\n",
    "    elif y<-0.625: color='#3300DE'\n",
    "    elif y<-0.59375: color='#3A00DD'\n",
    "    elif y<-0.5625: color='#4100DD'\n",
    "    elif y<-0.53125: color='#4800DC'\n",
    "    elif y<-0.5: color='#4F00DB'\n",
    "    elif y<-0.46875: color='#5500DB'\n",
    "    elif y<-0.4375: color='#5C00DA'\n",
    "    elif y<-0.40625: color='#6300DA'\n",
    "    elif y<-0.375: color='#6900D9'\n",
    "    elif y<-0.34375: color='#7000D8'\n",
    "    elif y<-0.3125: color='#7600D8'\n",
    "    elif y<-0.28125: color='#7D00D7'\n",
    "    elif y<-0.25: color='#8300D7'\n",
    "    elif y<-0.21875: color='#8A00D6'\n",
    "    elif y<-0.1875: color='#9000D5'\n",
    "    elif y<-0.15625: color='#9600D5'\n",
    "    elif y<-0.125: color='#9D00D4'\n",
    "    elif y<-0.09375: color='#A300D4'\n",
    "    elif y<-0.0625: color='#A900D3'\n",
    "    elif y<-0.03125: color='#AF00D2'\n",
    "    elif y<0.: color='#B500D2'\n",
    "    elif y<0.03125: color='#BB00D1'\n",
    "    elif y<0.0625: color='#C100D1'\n",
    "    elif y<0.09375: color='#C800Cf'\n",
    "    elif y<0.125: color='#CF00CA'\n",
    "    elif y<0.15625: color='#CE00C3'\n",
    "    elif y<0.1875: color='#CE00BC'\n",
    "    elif y<0.21875: color='#CD00B5'\n",
    "    elif y<0.25: color='#CC00AE'\n",
    "    elif y<0.28125: color='#CC00A7'\n",
    "    elif y<0.3125: color='#CB00A0'\n",
    "    elif y<0.34375: color='#CB0099'\n",
    "    elif y<0.375: color='#CA0092'\n",
    "    elif y<0.40625: color='#C9008C'\n",
    "    elif y<0.4375: color='#C90085'\n",
    "    elif y<0.46875: color='#C8007E'\n",
    "    elif y<0.5: color='#C80077'\n",
    "    elif y<0.53125: color='#C70071'\n",
    "    elif y<0.5625: color='#C6006A'\n",
    "    elif y<0.59375: color='#C60063'\n",
    "    elif y<0.625: color='#C5005D'\n",
    "    elif y<0.65625: color='#C50056'\n",
    "    elif y<0.6875: color='#C40050'\n",
    "    elif y<0.71875: color='#C3004A'\n",
    "    elif y<0.75: color='#C30043'\n",
    "    elif y<0.78125: color='#C2003D'\n",
    "    elif y<0.8125: color='#C20036'\n",
    "    elif y<0.84375: color='#C10030'\n",
    "    elif y<0.875: color='#C0002A'\n",
    "    elif y<0.90625: color='#C00024'\n",
    "    elif y<0.9375: color='#BF001E'\n",
    "    else: color='#BF0018'\n",
    " \n",
    "    return color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def downsample2d(x,y,logx=False,logy=False,width=500,height=500,weights=None):\n",
    "    if logx:\n",
    "        binx=np.logspace(np.log10(np.min(x)),np.log10(np.max(x)),width)\n",
    "    else:\n",
    "        binx=width\n",
    "    if logy:\n",
    "        biny=np.logspace(np.log10(np.min(y)),np.log10(np.max(y)),height)\n",
    "    else:\n",
    "        biny=height\n",
    "\n",
    "    z,binx2,biny2=np.histogram2d(x,y,bins=[binx, biny])\n",
    "    xi,yi=z.nonzero()\n",
    "    binx2=(binx2[:-1] + binx2[1:])/2\n",
    "    biny2=(biny2[:-1] + biny2[1:])/2\n",
    "    \n",
    "    if weights is not None:\n",
    "        z2,_,_=np.histogram2d(x,y,bins=[binx, biny],weights=weights)\n",
    "        return binx2[xi],biny2[yi],z2[xi,yi]/z[xi,yi]\n",
    "    \n",
    "    return binx2[xi],biny2[yi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x,y=downsample2d(df_all['editors'],df_all['ave_align'],logx=True,width=500,height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=map(colorify,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation (0.042151521016916584, 2.0501021388378951e-153)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pT6fTyWuvvcaDDz7YGFUW/iREQONj3bp13HHHHXTo0MHv2cyZM4mLi2P9+vWsWLGCnTt3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\nQ0XoEtS9OFJETQ+JvmU0tpzS6jw0OMt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EU6prkiZDlhtX1v+/27FKw2oQ/XduofL8RRQt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\nNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225f7e2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\", palette=\"muted\", color_codes=True)\n",
    "gs = gridspec.GridSpec(4, 4)\n",
    "histax=plt.subplot(gs[-1,:-1])\n",
    "mainax=plt.subplot(gs[0:-1,:-1])\n",
    "histax2=plt.subplot(gs[0:-1,-1])\n",
    "gs.update(hspace=0, wspace=0)\n",
    "\n",
    "mainax.scatter(x,y,s=10,c=c);\n",
    "mainax.set_xscale('log')\n",
    "mainax.set_xlim([10**(-0.1),10**4.2])\n",
    "mainax.set_xlabel('# editors per article', fontsize=18);\n",
    "mainax.set_ylabel('Average alignment', fontsize=18);\n",
    "mainax.xaxis.set_tick_params(which='major', labelsize=14)\n",
    "mainax.yaxis.set_tick_params(which='major', labelsize=14)\n",
    "mainax.yaxis.set_major_locator(MaxNLocator(5))\n",
    "mainax.xaxis.set_label_position('top')\n",
    "mainax.xaxis.tick_top()\n",
    "\n",
    "sns.distplot(np.log10(df_all.editors), bins=10, norm_hist=False, color='grey',hist_kws={'log':False, 'edgecolor':\"w\", 'lw':1},kde_kws={'bw':0.2},ax=histax);\n",
    "histax.set_xlim([-0.1,4.2])\n",
    "histax.xaxis.set_major_formatter(NullFormatter());\n",
    "sns.despine(left=True,bottom=True,ax=histax);\n",
    "histax.set_yticks([]);\n",
    "histax.set_xticks([]);\n",
    "histax.set_xlabel('');\n",
    "histax.invert_yaxis()\n",
    "\n",
    "sns.distplot(df_all.ave_align, bins=10, norm_hist=False, color='grey', hist_kws={'edgecolor':\"w\", 'lw':1},kde_kws={'bw':.1},ax=histax2,vertical=True);\n",
    "histax2.yaxis.set_major_formatter(NullFormatter());\n",
    "sns.despine(bottom=True,ax=histax2);\n",
    "histax2.set_yticks([]);\n",
    "histax2.set_xticks([]);\n",
    "histax2.set_ylabel('');\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('article_align3.pdf');\n",
    "print \"Correlation {}\".format(pearsonr(np.log(df_all.editors),df_all.ave_align))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d2022d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\",rc={\"lines.linewidth\": 1})\n",
    "plt.figure(figsize=[8.2,5.3])\n",
    "ax=sns.pointplot(x='quality', y='var_align', hue='cat', data=df_all,\n",
    "              order=['Stub','Start','C','B','GA','FA'], ci=95,\n",
    "              join=True, capsize=0.2, markers=['o','s','^'],\n",
    "              palette={\"politics\": \"purple\", \"science\": \"green\", \"social issue\":'orange'});\n",
    "plt.setp(ax.collections, sizes=[100])\n",
    "plt.setp(ax.collections, facecolor='none')\n",
    "plt.ylim([0,0.11])\n",
    "leg=plt.legend()\n",
    "leg.texts[0].set_text('Science');\n",
    "leg.texts[0].set_fontsize(18)\n",
    "leg.texts[1].set_text('Politics');\n",
    "leg.texts[1].set_fontsize(18);\n",
    "leg.texts[2].set_text('Social Issues');\n",
    "leg.texts[2].set_fontsize(18);\n",
    "plt.xlabel('Quality', fontsize=18);\n",
    "plt.ylabel('Polarization', fontsize=18);\n",
    "plt.tick_params(which='major', labelsize=14)\n",
    "sns.despine(offset=0,trim=True);\n",
    "plt.tight_layout();\n",
    "#plt.savefig('mean_quality4.pdf');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Politics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_poli=df_all[df_all['cat']=='politics']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IzIArr7yS5cuXRx3JsMbz7zJT5h6XQshs38rLy+np6fl2QVdADj0oMzvVzN5gZm8IJ81KvR/wWEhwGaTnCh1krtz9EYIe0uvzrSuRSDB58vH8XV5eTiKR6DffrH++LisrG1WZ8vLynGIauKyISFQy27eysjLIsvcqX7ns4rsR+E34cOCrGe8zH48TnPu0qdBB5srM3gRMAvIeSVBRUUFdXV36fV1dHbNmzeo3v7a2FjOjqqoKM6O2tnZUZTLXM2PG8UsappYZbFkRkahktm+1tbVs3br1xUKvI5ddfK3hswENBEPMdwwo48BB4Gfu/p8Fiy4308xse/jagIS7HytExYlEgm3btvG6170ua88lkUjQ0dFBfX09TU1NeZX51a9+xXPPPUdjYyN33nknAJ/97GdpamoaclkRkaik2rexapuGTVDhcPKfAJjZucAmd//5mEQzCu4+aazqrqio4Prrrx90329FRQUbN24ESD+PtkwqKQEsWrQo/Xq4ZUVEopLZvo2FEQ2ScPdPjVUgIiIimYZMUKmBEe7+28z3w0mVl7FRXV0ddQgiUmJKsd0YrgfVAfSZ2XR3Pxq+9yGXCIzZbjcp/HBRERn/SrHdGC5BNRIkpN4B70VERMbUkAnK3W8Z6r2IiMhYKcQNC0VERApOCUpERGJpuFF8fYz8mJO7+0iv8SdS+rqhrLVI//PVZayrO3gq2rpz0R11ADIeDJdI7kaDIkSGVewhvNOnTOet57wVgE7vBKDynMqixjCkc+Ckk06KOgopccMNklhRpDhESlqxh/C2trbG/krhra2tUYcgJS5G+wRERESOU4ISEZFYGnGCMrO3m9n9ZvaymfWa2bEBj2LfsFBERMahESUoM7sEeBj4E+Dn4fIPA48R3OriKeBbBY5RREQmoJEOB/8CsBdYRDC67yXgNnf/sZktA34AfKawIcpE1tTURHt7e9HWt3jx4hEPeOjsDEfRVRZvFN1o4qyuri7J67HJxDXSBHUh8GV3f9nMTg+nlQG4+xYz+xbBbX/fVcAYZQJrb2/n19u3M7tI6zt23nl0b98+fMEMB8Ln7q6uwgc0iJHGWfBbnYoUwUgT1EnA8+Hr18LnUzLmbwc+kW9QIplmA9diRVnXgVGs65vhqYLFihFGHuc3dTqjlKCRDpLYC1QCuPshgvPF35wxv5LjVz4XEREZtZH2oB4D3p7xfgtwo5ntIUh2f04weEJERCQvI+1BfRPoMrNp4fubgSPAXcD/JNjt99cFi05ERCasEfWg3P1B4MGM97vNrAZ4N3AM+Km7v1LYEEVEZCLK+6rj4bGo+woQi4iISJoudTSEpqYmmpubow5DRCRnTU1NNDU1RR1GQYyoB2Vmu3Mo5u7+xlHGEyvt7e10d+vGNiJSOop5YvtYG+kuvt9y4v2hJgNzgLOBdo6fJyUiIjJqIx0ksWSweWZ2DfAPwKo8YxIREcl/kESKu3/XzN5BkKSuLFS9wzGz2cBXgcUEw9w7gL90913FikFEJE56enpYvXo1n/zkJ2loaGDjxo309fWxevVq3J3bbruNZDLJLbfcgrvzxS9+kU9+8pOsW7eOyspKrr/+etauXYu7c9ZZZ1FeXp6uu7e3l71793LGGWfw0ksvcdZZZzFp0iTa2tr+c+vWrX9ayM9RsAQVKuqljszMgB8CSXe/Opy2EDgTUIISkQnpxRdfZP/+/axfv55Dhw7R2NgIwJEjRwBoaGjg0KFDJJNJ3J0dO3awfv16Dh8+zK5du1i/fn267G9+85us69izZw8Au3enhyZcVOjPUegEtRDoK3CdQ7kU6HH3TakJ7j6yK32KiIwjPT097N+/H3fn4MGDAHR0dPQrk5re3NyMu/crmzk/aiMdxXfJILNOBy4D/gzYnG9QI/Bm4PGxqryzs5MDBw6UxC0Kuru72by5mJt+5EYTY1tbm86FKIB9wMttbUX9LZfCbxJKI86RxPjMM8/gntvFgXt6evIJa8yNtAfVyomj+ID0ZZUfAlbnE9BYMLPrgOvCtxVRxiIiMpZ6e3O/XneuiSwqI01Qn8oyzYH9wK4IBiY8DXx4uELufgdwB4CZbcu18srKSrq7u0vipLfW1laWLFkSdRhDGk2M9fX1I74/k5xoFjBz3ryi/pZL4TcJpRHnSGK86qqr2LdvX05lg8P48U1UIx1mnhyrQEbpx8BtZvZn7v5PAGa2GJju7j+JNjQRkeKbPXt2+hjUcMrLy3H32O7qG9XufTObbmZnmtmkQgc0Eh58A1cBS83sWTN7GrgFeCHKuEREolJeXs7pp5+OmTFjxgwAqqqqqKqqSpeZMWMGZkZdXR11dXX9yqbmx0HOPSgzqwD+CvgQwZUjAPrCe0F9C7jd3V8ufIhDc/cXgI8Ue70iInE1e/ZsKisr0+dBNTQ09DsPqrGxkWQySSKRwN3p6OgoxHlQjxT6c+SUoMzsQuBegvOLegiO/bwCnAr8EbAeWGlm73P3J8Nlrnf3bxQ6YBERGVp5eXn6eGNLS0t6+o9+9KP060WLFqVfb9y4EYB///d/z1o2RwU9SRdy2MVnZq8H7gemAjcAM919gbu/w93PB04DPgOcDNxvZqea2Y3A7YUOVkREJo5celA3Aa8DLnL3JwbOdPcjwCYzexT4T+CnwHlAy8Cypaa6uprOzs6owxARyVl1dXXUIRRMLgnqSuBb2ZJTJnd/wsy+Dfw34F+BjxYgvkjV19fT2toadRgiIjkrhQsL5CqXUXxVwM9yrO/nBOdFfdjd4zluUURESkIuCeoYUD5sqcBk4JC7F/N6fCIiMg7lkqDaCC7KmoslYXkREZG85JKg/hX4oJldMVQhM7sc+CDFvVisiIiMU7kMkvgq4cAHM/sycKe7p28AYmZzgZXAjQS3hP/aWAQqE9eLwDezXqO48N4FfH+E69obPhcrRhh5nC8CM8csGpGxMWyCcvcDYe/o34DPA58zswMcP1H3VIKrme8C3ufu8biRiIwLxR4yO2n6dGYuXDiiZQ6GpyLMrKwci5CyGmmcMxlfw49lYsjpShLuviu8U+1KgquHnwecBfwB+N/AvxD0rI6MVaAyMRV7yGxrayvLly8v6jpHo1TiFMlHztfiC5PPxvAhIiIypnSzUhERiSUlKBERiSUlKBERiaWR3vJdYqypqYn29vaowxjU4sWL04MeUhfhrSziyLdcZMaYr+rq6nF1XTSRYlOCGkfa29t5+pe/Yub0M6IOJavzF/Ty/LP7AHjl8AEA7LV9UYZ0gswY89F9+KUCRCMysSlBjTMzp5/BpW+6Ouowsjp1al86tod//T2A2MWaGWM+Up9PREZPx6BERCSWlKBERCSWlKBERCSWlKBERCSWNEhiCE1NTXR2drJkyZKoQxGRCaipqQmABQsWRBxJNNSDGkJ7ezsvvvhi1GGIyATV3t4e63Mbx5oSlIiIxJISlIiIxJKOQYmIlIhHH32Um266CYDZs2fzhz/8gU9/+tN85Stf4cYbb2TTpk1s3LiRmTNnsnbtWg4dOsSePXuYMmUKZoaZcfvtt7N//35uuukmpk6dyg033JBe/utf/zrujpnxhje8gc997nN86UtforOzk7POOovJkyfT29vL3r17+du//VvuvPNOent76e3tZffu3X/YunXrqYX8vJEmKDP7AvAx4BjQB1wPXATc4e6HR1jXCmCLu79Q6DhFROLglltuSb9OHR//8pe/jLunnxsbGzn//PPZuXNnuuzRo0fTrxsbG+nq6gLg1VdfPWH5lF27drFhwwY6OjoA2L17d79YGhoaOHiw3w3UTynMpzwusgRlZhcBVwJvc/fXzKwCmALcA3wbyDlBmdkkYAXwFKAEJSLjzqOPPjowIQCkk0rquaOjg+eee27QelIJZ7DlhyqbKVsshRZlD+osoMvdXwNw9y4zqwfOBh42sy53v9TM/hFYDEwDfuDu6wHMrAP4n8AyYBOwCPhnMzsCXFSI2893dnZy4MCBkrgidXd3Ny+//DJ9Ry3qUAQ4+OrvaWvbP2a/ne7ubjZv3jwmdRdKKcQI8Y6zra2NadOmAf17T8M5duzYGEVUXFEOktgC/Bcz22Vmt5vZO929iaAHdKm7XxqW+4K7LwIWAO80s8wTAl5194vd/dvANuDj7r5wYHIys+vMbJuZbQMqxv6jiYgUVjF6LHETWQ/K3Q+a2QXAO4BLgXvM7PNZin7EzK4jiPUsYD6wI5x3T47rugO4AyBMUjmprKyku7s7fbJcnLW2trJ58+aC3CpC8jdj6mmc88ZZY/bbaW1tjf0J5KUQI8Q7zswe+IwZMyZckop0mLm7H3P31nC33Z8DH8qcb2ZzgJuAd7v7AuABYGpGkUNFC1ZEJEIj2cU3adKksQukiCJLUGb2R2Y2L2PSQmAPcIDjo0FOJUhCr5jZmUDtEFVmLiciMq5ceOGFzJgx44TpZtbvuaqqiiuvvHLQeqqqqvrVM3D5gWUHky2WQouyBzUDSJrZTjPbQbDr7haCXXEtZvawu/8CeBJ4mmBAxP8Zor67gE1mtt3Mpo1p5CIiEcjsRc2ePZvp06ezZs0aysrKWLNmDSeffDINDQ0kEgnmz5/PueeeC8CUKVM46aSTmDp1Kg0NDel6pk6d2m/5qVOnpsvV1NSwbt065s2bx7Rp05g7dy41NTXMnTuXadOm0djYyPz589PTCDoJBRXlMajHgT/NMmtj+EiVWzHI8lUD3v8L8C+Fi1BEJF4uvPBCtm7desL097///f2eATZt2jRkXZn1ZFs+5Zvf/OagdSxatCjzbUFP0gVd6khERGJKlzoaQnV1NZ2dnVGHISITVHV1ddQhREoJagj19fW0trZGHYaITFCpYeYTtR3SLj4REYklJSgREYklJSgREYklJSgREYklJSgREYkljeIbZ7oPv8TDv/5e1GFkVffHl/Hwrx8CgjiB2MWaGWM+ug+/xDnMKkBEIhOXEtQ4EvdzJqacNJlz3hg02t4Z3BHlnMp4NeKZMebjHGbF/vsQiTslqHEk7jdWbG1tZfny5VGHMaRSiFFkotAxKBERiSUlKBERiSUlKBERiSUlKBERiSUNkshTU1MT7e3tRV1n6grrlZWV6WmLFy+O/SCJsY6xuro69ttARHKnBJWn9vZ2nvrFLzhlSvE25YGjvQAcO/BKetr5b3kze371dNFiGI2xjDG1TURk/FCCKoBTpkzmwjNPK9r6Hv3d7wH6rfPkycWNYTTGMsbUNhGR8UPHoEREJJaUoEREJJaUoEREJJaUoEREJJaUoIbQ1NREc3Nz1GGISBE1NTXR1NQUdRiCRvENqb29ne7u7qjDEJEiKvZ5jTI49aBERCSWlKBERCSWlKBERLLo6upi1apVJBIJrrjiCtrb2+nq6mL16tU89thjXHHFFaxcuZK2tjZWr17NQw89xJIlS7j33nupra1l27ZtXHvttVxxxRVs27aNVatWsWrVKtra2k6YvnLlSlauXEkikWDZsmUsXbqUFStWcO2117Jy5Upuv/12VqxYweWXX057ezu7du2itra23+7Irq4uVqxYwSWXXMJll12WrmPVqlXs27cvXS617LZt21i9ejVtbW0kEgmWLFnC448/fsI2WL16dXr51DZJfY7UvK6uLi655JKfFPo7iPwYlJl9AfgYcAzoA64HLgLucPfDwyx7F3C/u/9grOMUkYklmUyyc+fO9PvGxkbOP/98duzYwfr16zl8+DC7du1iw4YN7Nmzh6effpq+vj6+/OUv4+40NDRw8OBBgH6vN2zYQEdHxwnTB9q9e3fW6Y2NjQAcOnSIxsZG7r777nS8qWWOHj3ar45kMsmaNWsAuPXWWzl06BANDQ0cOnSoXzzr1q3rNzAsmUyyY8eO9PKZ2yT1uZPJJO4OcHHOGzdHkSYoM7sIuBJ4m7u/ZmYVwBTgHuDbwJAJSkRkLPT09NDS0tJvWkdHB88//zzu3i+ppBr33t7gepBhY92vTLbyA6fnKnP5jo4O2tvbmTlzJg888MCgyzQ3N5NIJNi3b196+dS6B8bz+OOPc8EFF9DV1UVLSwvuTktLC+9973v7bZPUcs3NzanPXPA9clH3oM4Cutz9NQB37zKzeuBs4GEz63L3S83soLuB9zqUAAAQLklEQVTPADCzDwNXuvuKsI7LzOwvgDOBNe5+f6GC6+zs5MCBA0NeIbutrY3e3mOFWqWM0uHeY7S1teV9NfPu7m42b95coKjGTinEWQoxwolxBn/TvfT09JxQNtu0qKV6dqkEmU1PTw/JZJLt27cPW1+qF5XRM6Kvr48NGzYUfZtEnaC2AA1mtgt4CLjH3ZvMbA1wqbt35VBHFfBO4I0ESa3a3V/NLGBm1wHXhW+nFix6ERmXjh49mm6c466jo4OXXnppyDLuzpYtWzh8ePidUqme1YMPPphOPj09Pf16WgPrHiuRJih3P2hmFwDvAC4F7jGzz4+wmu+7ex/QZma7gTcB/f5NcPc7gDtGGl9lZSXd3d1DnrRXX18f+9tcTATTJ0/i3Hnz8j7BsrW1lSVLlhQmqDFUCnGWQoxwYpz19fU899xz7N+/vySSVFVVFeeffz733nvvoGXMjGXLlrF9+/ZBE03KjBkzAFi6dCnNzc309PRQXl7OOeecw549e07YJmYGjE2iinwUn7sfc/dWd18P/DnwoWzFMl4P7AEN3Crx/0WJSKzNnj2b8vLyE6Znmxa1hoYGEokEkycP3t8oLy8nkUiwdu3aYevbsGEDAIlEIp18ysrKWLdu3aDbZKh15yPSBGVmf2Rm8zImLQT2AAeAUzKm/87M/tjMyoCrBlTz/5hZmZm9EZgLPDOmQYvIuFdeXk5tbW2/aVVVVdTV1WFm6V5GarqZpRvpVKOeWWZg+WzTc1VVVZWuo6qqiurqaioqKnjPe94z6DJ1dXXMmjWLmpqa9LIzZszAzE6I54ILLgCgoqKC2tpazIza2lrmzZvXb5ukPnddXR11dXUQjMIuqKh7UDOApJntNLMdwHzgFoLdcS1m9nBY7vPA/cCPgb0D6ngG+AnQAqwaePxJRGQ0EokE8+fPZ86cOUyfPj3dU1mwYAFf/OIXmT59OjU1Naxbt44FCxZw8803U1ZWxpo1azj55JNpbGxk3rx5TJ8+ncbGRubPn8/8+fNZt27dCdNramqoqalhzpw5TJ06lZNOOom5c+cyb948ampqmD17NnPnzmXatGk0NDSwdu1aTj75ZBoaGvrFO3fuXACmTJmSrmP+/PkkEol0udSyjY2NLFiwgHXr1jFnzhzKysrSvafMOhcsWJBePrVNUp8jNS+c/9NCfwdWCvtYI+L19fV0d3enzzPIJnUMKuo76r7x3XU8+x/xvrDtWMb46O9+z7l/fJ6OQcVIKcQI2Y9BAbG6YGyJbEsrdIVR96BERESyinqYeaxVV1fT2dkZdRgiUkTV1dVRhyAhJagh1NfX09raGnUYIlJE+Z7sLYWjXXwiIhJLSlAiIhJLSlAiIhJLSlAiIhJLGiRRAAeO9qbPTSrW+oB+65zdW9wYRmMsY0xtExEZP5Sg8hTFkNTU0PfKysr0tClTp3HuH59X9FhGYqxj1PBgkfFFCSpPcRmS2trayvLly6MOY0ilEKOIxIeOQYmISCwpQYmISCzpYrEiIhJL6kGJiEgsKUGJiEgsKUGJiEgsKUGJiEgsKUGJiEgsKUGJiEgsKUGJiEgsKUGJiEgsKUGJiEgs6WKxwzCzp4BXo44jBxVAV9RBDEMxFk4pxFkKMUJpxFkKMU519zcXskIlqOG96u6Log5iOGa2Le5xKsbCKYU4SyFGKI04SyXGQtepXXwiIhJLSlAiIhJLSlDDuyPqAHJUCnEqxsIphThLIUYojTgnZIy63YaIiMSSelAiIhJLSlAiIhJLEy5BmdkVZvaMmbWb2eezzD/JzO4J5//czKoy5v1NOP0ZM7s81zqLFaOZLTWzx83sl+HzuzKWaQ3r3B4+zogoxiozO5IRx6aMZS4IY283syYzs3xizDPOj2fEuN3M+sxsYTiv2NvyEjN7wsx6zezDA+YlzKwtfCQyphd0W442RjNbaGaPmNnTZrbDzD6aMe8uM/tNxnZcmE+M+cQZzjuWEct9GdPnhL+NtvC3MiWKGM3s0gG/yVfN7APhvCi25Roz2xl+r/9hZudmzCvM79LdJ8wDmAQ8C8wFpgC/AOYPKPMZYFP4+mrgnvD1/LD8ScCcsJ5JudRZxBjfCpwdvn4z8HzGMq3AohhsxyrgqUHqfRS4CDCgBaiNKs4BZd4C7I5wW1YBC4C7gQ9nTD8d2B0+nxa+Pq3Q2zLPGGuAeeHrs4G9wMzw/V2ZZaPcluG8g4PU+33g6vD1JuDTUcU44LvfD0yPcFtemrH+T3P8b7xgv8uJ1oO6EGh3993ufhT4HvD+AWXeDyTD1z8A3h1m+fcD33P319z9N0B7WF8udRYlRnd/0t1fCKc/DUw1s5PyiKXgMQ5WoZmdBZzq7o948Eu+G/hATOK8BvhunrGMOkZ373D3HUDfgGUvBx509/3u/nvgQeCKMdiWo47R3Xe5e1v4+gXgJeD1ecQyJnEOJvwtvIvgtwHBbyWSbTnAh4EWdz+cRyz5xvlwxvp/BlSGrwv2u5xoCeoc4LmM953htKxl3L0XeAWYNcSyudRZrBgzfQh40t1fy5j2v8Lu/7o8d/nkG+McM3vSzH5iZu/IKN85TJ3FjjPlo5yYoIq5LUe6bKG3ZUF+42Z2IcF/489mTP7bcBfRVwrwz1S+cU41s21m9rPUrjOC30J3+NsYTZ2FjjHlak78TUa5La8l6BENteyIf5cTLUFla0gGjrMfrMxIp49WPjEGM83OA/47cH3G/I+7+1uAd4SP5RHFuBd4g7u/FVgDfMfMTs2xzpEqxLb8E+Cwuz+VMb/Y23Kky0bxmxy6guC/528Bn3L3VM/gb4A3AYsJdgd9Lo8YIf843+DB5YQ+BnzVzN5YgDoHKtS2fAvwo4zJkW1LM/sEsAj4+2GWHfFnn2gJqhP4LxnvK4EXBitjZpOB1xHs6x1s2VzqLFaMmFkl8EPgk+6e/k/V3Z8Pnw8A3yHowhc9xnAX6b4wlscJ/puuCctXZiyf73bMK86M+Sf8pxrBthzpsoXelnn9xsN/QB4A1rr7z1LT3X2vB14D/hf5bce840ztHnf33QTHGd9KcIHWmeFvY8R1FjrG0EeAH7p7T2pCVNvSzC4DvgC8L2NvTeF+l4U6qFYKD4KL4+4mGOSQOvB33oAyN9D/oPn3w9fn0X+QxG6CA4nD1lnEGGeG5T+Upc6K8HU5wf70VRHF+HpgUvh6LvA8cHr4/jHgv3L8AGpdVN93+L6M4I9qbpTbMqPsXZw4SOI3BAeiTwtfF3xb5hnjFOA/gL/MUvas8NmArwJfGuvve4g4TwNOCl9XAG2EgwKA/5/+gyQ+E0WMGdN/Blwa9bYkSODPEg6CGYvf5ag/QKk+gDpgV7hhvxBOayT4DwBgaviDbCcYcZLZOH0hXO4ZMkafZKszihiBtcAhYHvG4wzgZOBxYAfB4ImvESaJCGL8UBjDL4AngPdm1LkIeCqs8/8jvNJJhN/3EuBnA+qLYlsuJkiUh4B9wNMZy/63MPZ2gt1nY7ItRxsj8AmgZ8BvcmE478fAL8M4vw3MKML3PVicfxrG8ovw+dqMOueGv4328LdyUoTfdxXBP3VlA+qMYls+BPwu43u9r9C/S13qSEREYmmiHYMSEZESoQQlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlMkpmtsLM3MyWjFH9d5lZpMNsw893V5QxyMSlBCUTgpktCRvbzMdBC25L8hdmNinqGKNgZjPN7JaxSrIi+Zg8fBGRceW7QDPBmexnAysIzrw/D7guurCy+jNg1RivYyawPnzdmmX+NODYGMcgkpUSlEw0T7j7t1NvzOwfgV8BK81snbv/LrrQ0rd3ONndD3pwrbWe4ZYZS+7+apTrl4lNu/hkQnP3PwCPEPSo5kL6rr/fMrPfmdlrZvasmd1mZtOHq8/MTjGzW8M7sHaFy7eb2ZcGLp+x23GFmd1gZjuBV4Gbwvn9jkENspsy87EiLFdmZl8ws61m9qKZHTWz35rZP5rZrMz6CK6TBrA+o56OjDJZj0GZ2UoL7vp6xMxeMbMtZnZxlnIefo6LwturHAq3y51mNmO47SkTm3pQMqGFPZbq8G2XBbetfpTgqub/SHAtsiUEtzN4u5m924/fGyibc4CVwL8QXOm8F3gn8NcEF9e8PMsyf0lw36F/Al6k/710Mv2K7Lf2uAk4n+C6aBBc3POvwhjuJbim22KCe/ZcbGYXeHATul8BNwJfIbgC/uZw+YNDfD7M7L+Hn+dR4GbgFILdow+b2fvdvXnAIguB+wmusv0dgu15LcEN+eK2W1XiJN8LCuqhRyk8CBpFBxoIrlb9eoLbav9TOP2RsNw/h+/rBiz/9+H0zIuIrginLcmYNgUoz7L+DWHZC7PEtB84I8sydwV/okN+rk+HdXw1Y5oB07KUvTYs+5GMaVXhtFsGqd+BuzLe/xFBYvkpMCVj+tlAN9BBxsVzw+X7gP86oN4HCHZf5n1RUz3G70O7+GSi+SLwMsGtx39BcNXl+4APmFkZ8D6COxEP7AX8HUFDe9VQlbv7UQ/v02Nmk83sNDOrILjyM8CfZFnsbnd/aaQfxMwuBzYSNPZrMmJwdz8SlpkUjtSrILji9WAx5Or9BAnwf3jQC0ut8wWChHouQU8x0yOecR+o0I8J9uBU5RGLjHPaxScTzR0Et0xwgl1fu9w9dbPHM4EZBLfR6Mfd95vZXsLjVEMxs88QjL47jxOP856WZZFdI/kA4TreAnyf4NYFV/vxu9Sm5n8E+CxBsijPIYZczQmfT9hGYSwQbKNtGdN3Zym7L3yelWWeCKAEJRNPm7s/NMi8bLekHhEzWwP8A7AFaCK4Y+hRgmNTd5F9YNLhEa5jNsExnUME99M6OGD+B4F7CI4R/QXBMa1XCW6w+e+DxJDz6kexzFDD1PPe5jJ+KUGJHPcScICg59OPmZ0GnEVwY7ahLCc4DlOb2asxsysKEWA4EvA+guNo73T3bAMqlhMkpEvd/XDGsm/KUnakV6p4Nnw+L+N1yvzwOVuPSWTEdAxKJBQmlH8D3poloXye4O/lh8NUc4yg0U/3DMxscrh8XsIRh98iuCvpcnffNkjRVAxlA5Zdm6Vsqvd1eo5h3BfW/Vdmlt51aGZnAZ8C9gBP5liXyJDUgxLp72ZgKfCvZnY7wS2rLwE+CmwFksMs/wOCARUtZrYZOBX4GIU54XYV8EHgJ8B0M/vEgPn/6e67wxg+BPzYzO4mOAb1AeCE87jcfZ+ZtQNXm9mzBEPVD7n7v2ULwN2fMbO/JxhmvtXM7uH4MPMZwMfdXVeekIJQghLJ4O57zOxPgEbgEwSXAuokSDq3+tDnQEEwHN0IhnR/jeC8pnsIzgHamWd4Z4bP7wwfA30K2O3u3zOzUwjOcfp/gd8T9Aw/z/HBCZk+TnAu1G0ESWxPWD4rd/9cmNQ+A3yJ4Bjbz4GPufv/HsXnEsnK3CO9WLKIiEhWOgYlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlIiKxpAQlIiKx9H8B4KJ/9tAEDQYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='var_align', y='quality',data=df_poli, order=['Stub','Start','C','B','GA','FA'][-1::-1], orient='h');\n",
    "plt.xlabel('Polarization', fontsize=18);\n",
    "plt.ylabel('Quality', fontsize=18);\n",
    "plt.xlim([-0.001,0.2]);\n",
    "plt.grid(axis='x')\n",
    "sns.despine(offset=10,trim=True);\n",
    "plt.tight_layout();\n",
    "# plt.savefig('political_quality.pdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_poli.loc[df_poli.cat2=='liberal','editors'], df_poli.loc[df_poli.cat2=='liberal','ave_align'], '.', color='blue', alpha=0.5);\n",
    "plt.plot(df_poli.loc[df_poli.cat2=='conserv','editors'], df_poli.loc[df_poli.cat2=='conserv','ave_align'], '.', color='red', alpha=0.5);\n",
    "plt.xscale('log')\n",
    "plt.xlabel('# editors per article',fontsize=18);\n",
    "plt.ylabel('Average alignment',fontsize=18);\n",
    "plt.tight_layout();\n",
    "#plt.savefig('political_article_align.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + scale(log(editors)) + \n",
       "    scale(log(length)) + scale(log(edits)) + scale(log(editscount)), \n",
       "    data = df_poli, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                          Value Std. Error t value\n",
       "abs(ave_align)         -1.18548    0.27243  -4.352\n",
       "var_align               2.93448    0.48742   6.020\n",
       "scale(log(editors))    -0.89666    0.08705 -10.300\n",
       "scale(log(length))      3.51284    0.05258  66.815\n",
       "scale(log(edits))       1.18210    0.09844  12.009\n",
       "scale(log(editscount))  0.04878    0.02176   2.242\n",
       "\n",
       "Intercepts:\n",
       "           Value    Std. Error t value \n",
       "Stub|Start  -3.1319   0.0570   -54.9857\n",
       "Start|C      1.0169   0.0518    19.6407\n",
       "C|B          5.6195   0.0869    64.6627\n",
       "B|GA         6.5620   0.0938    69.9832\n",
       "GA|FA        9.8632   0.1461    67.4993\n",
       "\n",
       "Residual Deviance: 18440.50 \n",
       "AIC: 18462.50 \n",
       "                             Value Std. Error    t value      p value\n",
       "abs(ave_align)         -1.18547799 0.27242620  -4.351556 1.351745e-05\n",
       "var_align               2.93448438 0.48742498   6.020382 1.740063e-09\n",
       "scale(log(editors))    -0.89665780 0.08705306 -10.300129 7.036664e-25\n",
       "scale(log(length))      3.51283945 0.05257549  66.815154 0.000000e+00\n",
       "scale(log(edits))       1.18209987 0.09843568  12.008856 3.192317e-33\n",
       "scale(log(editscount))  0.04877882 0.02175662   2.242022 2.495992e-02\n",
       "Stub|Start             -3.13188074 0.05695813 -54.985670 0.000000e+00\n",
       "Start|C                 1.01692588 0.05177636  19.640736 6.938495e-86\n",
       "C|B                     5.61946796 0.08690437  64.662660 0.000000e+00\n",
       "B|GA                    6.56202089 0.09376569  69.983176 0.000000e+00\n",
       "GA|FA                   9.86317456 0.14612259  67.499312 0.000000e+00\n",
       "                               OR\n",
       "abs(ave_align)          0.3056001\n",
       "var_align              18.8118009\n",
       "scale(log(editors))     0.4079308\n",
       "scale(log(length))     33.5433775\n",
       "scale(log(edits))       3.2612152\n",
       "scale(log(editscount))  1.0499881\n",
       "Stub|Start              0.3056001\n",
       "Start|C                18.8118009\n",
       "C|B                     0.4079308\n",
       "B|GA                   33.5433775\n",
       "GA|FA                   3.2612152\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df_poli\n",
    "library(MASS)\n",
    "\n",
    "df_poli$quality=factor(df_poli$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~  abs(ave_align) + var_align + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = df_poli, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "ctable <- cbind(ctable, \"p value\" = p, \"OR\"=exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "polr(formula = quality ~ abs(ave_align) + var_align + I(var_align^2) + \n",
       "    scale(log(editors)) + scale(log(length)) + scale(log(edits)) + \n",
       "    scale(log(editscount)), data = df_poli, Hess = TRUE)\n",
       "\n",
       "Coefficients:\n",
       "                           Value Std. Error t value\n",
       "abs(ave_align)          -1.10646    0.27202  -4.068\n",
       "var_align                9.80206    1.29166   7.589\n",
       "I(var_align^2)         -35.21436    6.20485  -5.675\n",
       "scale(log(editors))     -0.94588    0.08759 -10.799\n",
       "scale(log(length))       3.52848    0.05171  68.239\n",
       "scale(log(edits))        1.15130    0.09808  11.738\n",
       "scale(log(editscount))   0.03671    0.02176   1.687\n",
       "\n",
       "Intercepts:\n",
       "           Value    Std. Error t value \n",
       "Stub|Start  -2.9138   0.0696   -41.8365\n",
       "Start|C      1.2593   0.0661    19.0473\n",
       "C|B          5.8568   0.0934    62.6994\n",
       "B|GA         6.7954   0.0992    68.4779\n",
       "GA|FA       10.0805   0.1466    68.7594\n",
       "\n",
       "Residual Deviance: 18408.94 \n",
       "AIC: 18432.94 \n",
       "                             Value Std. Error    t value      p value\n",
       "abs(ave_align)          -1.1064589 0.27201842  -4.067588 4.750221e-05\n",
       "var_align                9.8020641 1.29166488   7.588705 3.231177e-14\n",
       "I(var_align^2)         -35.2143602 6.20485320  -5.675293 1.384514e-08\n",
       "scale(log(editors))     -0.9458759 0.08758575 -10.799426 3.463615e-27\n",
       "scale(log(length))       3.5284816 0.05170786  68.238787 0.000000e+00\n",
       "scale(log(edits))        1.1513028 0.09808332  11.738008 8.138046e-32\n",
       "scale(log(editscount))   0.0367130 0.02176363   1.686897 9.162317e-02\n",
       "Stub|Start              -2.9137719 0.06964657 -41.836547 0.000000e+00\n",
       "Start|C                  1.2593148 0.06611508  19.047315 6.915952e-81\n",
       "C|B                      5.8568432 0.09341143  62.699427 0.000000e+00\n",
       "B|GA                     6.7953793 0.09923468  68.477869 0.000000e+00\n",
       "GA|FA                   10.0804992 0.14660535  68.759424 0.000000e+00\n",
       "                                 OR\n",
       "abs(ave_align)         3.307280e-01\n",
       "var_align              1.807101e+04\n",
       "I(var_align^2)         5.088593e-16\n",
       "scale(log(editors))    3.883393e-01\n",
       "scale(log(length))     3.407219e+01\n",
       "scale(log(edits))      3.162310e+00\n",
       "scale(log(editscount)) 1.037395e+00\n",
       "Stub|Start             3.307280e-01\n",
       "Start|C                1.807101e+04\n",
       "C|B                    5.088593e-16\n",
       "B|GA                   3.883393e-01\n",
       "GA|FA                  3.407219e+01\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df_poli\n",
    "library(MASS)\n",
    "\n",
    "df_poli$quality=factor(df_poli$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "m <- polr(quality ~  abs(ave_align) + var_align + I(var_align^2) + scale(log(editors)) + scale(log(length)) + scale(log(edits)) + scale(log(editscount)), data = df_poli, Hess=TRUE)\n",
    "\n",
    "ctable <- coef(summary(m))\n",
    "p <- pnorm(abs(ctable[, \"t value\"]), lower.tail = FALSE) * 2\n",
    "ctable <- cbind(ctable, \"p value\" = p, \"OR\"=exp(coef(m)))\n",
    "print(summary(m))\n",
    "print(ctable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk Pages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of edits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pages=pd.read_csv('Data/talk_pages.tsv',sep='\\t',parse_dates=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pages=talk_pages.sort_values('time')\n",
    "talk_page_stats=talk_pages.groupby('title').agg({'title':len, 'user':lambda x: x.nunique(), 'page_len':'last'})\n",
    "talk_page_stats.columns=['edits', 'editors', 'length']\n",
    "\n",
    "talk_page_stats=talk_page_stats.reset_index()\n",
    "talk_page_stats['short']=talk_page_stats['title'].str.replace(' ','').str.lower()\n",
    "talk_page_stats=talk_page_stats.sort_values('length').drop_duplicates(subset='short', keep='last')\n",
    "\n",
    "talk_pages=talk_page_stats.merge(df_all,on='title',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((319514, 5), (206818, 17))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_page_stats.shape, talk_pages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.log(edits_x)</td> <th>  R-squared:         </th> <td>   0.952</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.952</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.018e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Mar 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:54</td>     <th>  Log-Likelihood:    </th> <td> -53815.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>206818</td>      <th>  AIC:               </th> <td>1.076e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>206813</td>      <th>  BIC:               </th> <td>1.077e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -0.7937</td> <td>    0.006</td> <td> -125.744</td> <td> 0.000</td> <td>   -0.806</td> <td>   -0.781</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>    0.1075</td> <td>    0.015</td> <td>    7.326</td> <td> 0.000</td> <td>    0.079</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>    1.0330</td> <td>    0.001</td> <td>  983.495</td> <td> 0.000</td> <td>    1.031</td> <td>    1.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>    0.0913</td> <td>    0.001</td> <td>  154.033</td> <td> 0.000</td> <td>    0.090</td> <td>    0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>    0.0489</td> <td>    0.001</td> <td>   60.847</td> <td> 0.000</td> <td>    0.047</td> <td>    0.051</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>92777.183</td> <th>  Durbin-Watson:     </th>  <td>   1.606</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>629444.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.049</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>10.500</td>   <th>  Cond. No.          </th>  <td>    242.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:        np.log(edits_x)   R-squared:                       0.952\n",
       "Model:                            OLS   Adj. R-squared:                  0.952\n",
       "Method:                 Least Squares   F-statistic:                 1.018e+06\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:45:54   Log-Likelihood:                -53815.\n",
       "No. Observations:              206818   AIC:                         1.076e+05\n",
       "Df Residuals:                  206813   BIC:                         1.077e+05\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -0.7937      0.006   -125.744      0.000      -0.806      -0.781\n",
       "var_align                0.1075      0.015      7.326      0.000       0.079       0.136\n",
       "np.log(editors_x)        1.0330      0.001    983.495      0.000       1.031       1.035\n",
       "np.log(length_x + 1)     0.0913      0.001    154.033      0.000       0.090       0.092\n",
       "np.log(length_y)         0.0489      0.001     60.847      0.000       0.047       0.051\n",
       "==============================================================================\n",
       "Omnibus:                    92777.183   Durbin-Watson:                   1.606\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           629444.850\n",
       "Skew:                           2.049   Prob(JB):                         0.00\n",
       "Kurtosis:                      10.500   Cond. No.                         242.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(edits_x) ~  var_align + np.log(editors_x) + np.log(length_x+1) + np.log(length_y)', data = talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TF-IDF score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf=pd.read_csv('Data/talk_page_tfidf.tsv', sep='\\t')\n",
    "talk_pages=talk_pages.merge(tfidf, on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf=pd.read_csv('Data/article_tfidf.tsv', sep='\\t')\n",
    "del tfidf['id']\n",
    "tfidf.columns=['article_tfidf','title']\n",
    "talk_pages=talk_pages.merge(tfidf, on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(tfidf)</td>  <th>  R-squared:         </th>  <td>   0.681</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.681</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.100e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Mar 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:50:38</td>     <th>  Log-Likelihood:    </th> <td>-3.4776e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>206256</td>      <th>  AIC:               </th>  <td>6.955e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>206251</td>      <th>  BIC:               </th>  <td>6.956e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                        <td>    2.7665</td> <td>    0.017</td> <td>  158.630</td> <td> 0.000</td> <td>    2.732</td> <td>    2.801</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>                        <td>    1.4009</td> <td>    0.061</td> <td>   22.866</td> <td> 0.000</td> <td>    1.281</td> <td>    1.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>                <td>   -0.0902</td> <td>    0.010</td> <td>   -8.650</td> <td> 0.000</td> <td>   -0.111</td> <td>   -0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>                  <td>    1.3932</td> <td>    0.008</td> <td>  164.063</td> <td> 0.000</td> <td>    1.377</td> <td>    1.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(article_tfidf / length_y)</th> <td>    0.1127</td> <td>    0.017</td> <td>    6.809</td> <td> 0.000</td> <td>    0.080</td> <td>    0.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5573.280</td> <th>  Durbin-Watson:     </th> <td>   1.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>6353.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.374</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 3.422</td>  <th>  Cond. No.          </th> <td>    73.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(tfidf)   R-squared:                       0.681\n",
       "Model:                            OLS   Adj. R-squared:                  0.681\n",
       "Method:                 Least Squares   F-statistic:                 1.100e+05\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:50:38   Log-Likelihood:            -3.4776e+05\n",
       "No. Observations:              206256   AIC:                         6.955e+05\n",
       "Df Residuals:                  206251   BIC:                         6.956e+05\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================================\n",
       "                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Intercept                            2.7665      0.017    158.630      0.000       2.732       2.801\n",
       "var_align                            1.4009      0.061     22.866      0.000       1.281       1.521\n",
       "np.log(editors_x)                   -0.0902      0.010     -8.650      0.000      -0.111      -0.070\n",
       "np.log(edits_x)                      1.3932      0.008    164.063      0.000       1.377       1.410\n",
       "np.log(article_tfidf / length_y)     0.1127      0.017      6.809      0.000       0.080       0.145\n",
       "==============================================================================\n",
       "Omnibus:                     5573.280   Durbin-Watson:                   1.108\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6353.241\n",
       "Skew:                          -0.374   Prob(JB):                         0.00\n",
       "Kurtosis:                       3.422   Cond. No.                         73.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = sm.ols('np.log(tfidf)-np.log(length_x+1) ~ var_align + np.log(editors_x) + np.log(edits_x) + np.log(article_tfidf/length_y)', data = talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Word Radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedr=pd.read_csv('Data/talk_page_radius.tsv', sep='\\t')\n",
    "talk_pages=talk_pages.merge(embedr[['radius','title']], on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedr=pd.read_csv('Data/article_radius.tsv', sep='\\t')\n",
    "embedr.columns=[u'title', u'words', u'article_radius']\n",
    "talk_pages=talk_pages.merge(embedr[['article_radius','title']], on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>radius</td>      <th>  R-squared:         </th> <td>   0.210</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.210</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.368e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Mar 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:52:51</td>     <th>  Log-Likelihood:    </th> <td> -83286.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205744</td>      <th>  AIC:               </th> <td>1.666e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205739</td>      <th>  BIC:               </th> <td>1.666e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    2.4838</td> <td>    0.010</td> <td>  238.080</td> <td> 0.000</td> <td>    2.463</td> <td>    2.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>         <td>   -0.4431</td> <td>    0.017</td> <td>  -25.997</td> <td> 0.000</td> <td>   -0.476</td> <td>   -0.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th> <td>    0.1510</td> <td>    0.003</td> <td>   52.145</td> <td> 0.000</td> <td>    0.145</td> <td>    0.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>   <td>   -0.2125</td> <td>    0.002</td> <td>  -90.069</td> <td> 0.000</td> <td>   -0.217</td> <td>   -0.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>article_radius</th>    <td>    0.3415</td> <td>    0.003</td> <td>  126.225</td> <td> 0.000</td> <td>    0.336</td> <td>    0.347</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>62295.642</td> <th>  Durbin-Watson:     </th>  <td>   1.397</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>780481.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-1.102</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>12.283</td>   <th>  Cond. No.          </th>  <td>    104.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 radius   R-squared:                       0.210\n",
       "Model:                            OLS   Adj. R-squared:                  0.210\n",
       "Method:                 Least Squares   F-statistic:                 1.368e+04\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:52:51   Log-Likelihood:                -83286.\n",
       "No. Observations:              205744   AIC:                         1.666e+05\n",
       "Df Residuals:                  205739   BIC:                         1.666e+05\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             2.4838      0.010    238.080      0.000       2.463       2.504\n",
       "var_align            -0.4431      0.017    -25.997      0.000      -0.476      -0.410\n",
       "np.log(editors_x)     0.1510      0.003     52.145      0.000       0.145       0.157\n",
       "np.log(edits_x)      -0.2125      0.002    -90.069      0.000      -0.217      -0.208\n",
       "article_radius        0.3415      0.003    126.225      0.000       0.336       0.347\n",
       "==============================================================================\n",
       "Omnibus:                    62295.642   Durbin-Watson:                   1.397\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           780481.499\n",
       "Skew:                          -1.102   Prob(JB):                         0.00\n",
       "Kurtosis:                      12.283   Cond. No.                         104.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = sm.ols('radius ~ var_align + np.log(editors_x) + np.log(edits_x) + article_radius', data = talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Agressiveness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "attack=pd.read_csv('Data/talk_page_attack.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pages=talk_pages.merge(attack, on='title', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.log(attack)</td>  <th>  R-squared:         </th>  <td>   0.167</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.167</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   8223.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Mar 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:53:43</td>     <th>  Log-Likelihood:    </th> <td>-2.2363e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205744</td>      <th>  AIC:               </th>  <td>4.473e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205738</td>      <th>  BIC:               </th>  <td>4.473e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -3.6980</td> <td>    0.015</td> <td> -244.088</td> <td> 0.000</td> <td>   -3.728</td> <td>   -3.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>   -0.1727</td> <td>    0.034</td> <td>   -5.121</td> <td> 0.000</td> <td>   -0.239</td> <td>   -0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>    0.2458</td> <td>    0.006</td> <td>   42.879</td> <td> 0.000</td> <td>    0.235</td> <td>    0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>      <td>   -0.0285</td> <td>    0.005</td> <td>   -5.646</td> <td> 0.000</td> <td>   -0.038</td> <td>   -0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>   -0.2078</td> <td>    0.001</td> <td> -141.486</td> <td> 0.000</td> <td>   -0.211</td> <td>   -0.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>   -0.0894</td> <td>    0.002</td> <td>  -47.691</td> <td> 0.000</td> <td>   -0.093</td> <td>   -0.086</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24419.925</td> <th>  Durbin-Watson:     </th>  <td>   1.825</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>133756.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.447</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.848</td>   <th>  Cond. No.          </th>  <td>    248.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         np.log(attack)   R-squared:                       0.167\n",
       "Model:                            OLS   Adj. R-squared:                  0.167\n",
       "Method:                 Least Squares   F-statistic:                     8223.\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:53:43   Log-Likelihood:            -2.2363e+05\n",
       "No. Observations:              205744   AIC:                         4.473e+05\n",
       "Df Residuals:                  205738   BIC:                         4.473e+05\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -3.6980      0.015   -244.088      0.000      -3.728      -3.668\n",
       "var_align               -0.1727      0.034     -5.121      0.000      -0.239      -0.107\n",
       "np.log(editors_x)        0.2458      0.006     42.879      0.000       0.235       0.257\n",
       "np.log(edits_x)         -0.0285      0.005     -5.646      0.000      -0.038      -0.019\n",
       "np.log(length_x + 1)    -0.2078      0.001   -141.486      0.000      -0.211      -0.205\n",
       "np.log(length_y)        -0.0894      0.002    -47.691      0.000      -0.093      -0.086\n",
       "==============================================================================\n",
       "Omnibus:                    24419.925   Durbin-Watson:                   1.825\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           133756.670\n",
       "Skew:                           0.447   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.848   Cond. No.                         248.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(attack) ~  var_align + np.log(editors_x) + np.log(edits_x) + np.log(length_x+1) + np.log(length_y)', data=talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.log(attack)</td>  <th>  R-squared:         </th>  <td>   0.167</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.167</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   6855.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Mar 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:24:42</td>     <th>  Log-Likelihood:    </th> <td>-2.2362e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205744</td>      <th>  AIC:               </th>  <td>4.473e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205737</td>      <th>  BIC:               </th>  <td>4.473e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -3.7105</td> <td>    0.016</td> <td> -238.959</td> <td> 0.000</td> <td>   -3.741</td> <td>   -3.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.abs(ave_align)</th>    <td>    0.0821</td> <td>    0.022</td> <td>    3.671</td> <td> 0.000</td> <td>    0.038</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>   -0.1889</td> <td>    0.034</td> <td>   -5.554</td> <td> 0.000</td> <td>   -0.256</td> <td>   -0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>    0.2482</td> <td>    0.006</td> <td>   43.021</td> <td> 0.000</td> <td>    0.237</td> <td>    0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>      <td>   -0.0295</td> <td>    0.005</td> <td>   -5.840</td> <td> 0.000</td> <td>   -0.039</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>   -0.2076</td> <td>    0.001</td> <td> -141.238</td> <td> 0.000</td> <td>   -0.211</td> <td>   -0.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>   -0.0889</td> <td>    0.002</td> <td>  -47.304</td> <td> 0.000</td> <td>   -0.093</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24415.459</td> <th>  Durbin-Watson:     </th>  <td>   1.825</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>133773.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.446</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.848</td>   <th>  Cond. No.          </th>  <td>    251.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         np.log(attack)   R-squared:                       0.167\n",
       "Model:                            OLS   Adj. R-squared:                  0.167\n",
       "Method:                 Least Squares   F-statistic:                     6855.\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        21:24:42   Log-Likelihood:            -2.2362e+05\n",
       "No. Observations:              205744   AIC:                         4.473e+05\n",
       "Df Residuals:                  205737   BIC:                         4.473e+05\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -3.7105      0.016   -238.959      0.000      -3.741      -3.680\n",
       "np.abs(ave_align)        0.0821      0.022      3.671      0.000       0.038       0.126\n",
       "var_align               -0.1889      0.034     -5.554      0.000      -0.256      -0.122\n",
       "np.log(editors_x)        0.2482      0.006     43.021      0.000       0.237       0.259\n",
       "np.log(edits_x)         -0.0295      0.005     -5.840      0.000      -0.039      -0.020\n",
       "np.log(length_x + 1)    -0.2076      0.001   -141.238      0.000      -0.211      -0.205\n",
       "np.log(length_y)        -0.0889      0.002    -47.304      0.000      -0.093      -0.085\n",
       "==============================================================================\n",
       "Omnibus:                    24415.459   Durbin-Watson:                   1.825\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           133773.024\n",
       "Skew:                           0.446   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.848   Cond. No.                         251.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(attack) ~  np.abs(ave_align) + var_align + np.log(editors_x) + np.log(edits_x) + np.log(length_x+1) + np.log(length_y)', data=talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Policy Mention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "policies=pd.read_csv('Data/talk_page_policy.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=policies.groupby('title').sum()\n",
    "df=df.reset_index()\n",
    "df.columns=['title','all_policy']\n",
    "talk_pages=talk_pages.merge(df, on='title', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.log(all_policy)</td> <th>  R-squared:         </th> <td>   0.287</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.286</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   2163.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Sun, 03 Mar 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>21:26:10</td>      <th>  Log-Likelihood:    </th> <td> -32042.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 26938</td>       <th>  AIC:               </th> <td>6.410e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 26932</td>       <th>  BIC:               </th> <td>6.415e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     5</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -1.7721</td> <td>    0.063</td> <td>  -27.994</td> <td> 0.000</td> <td>   -1.896</td> <td>   -1.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>    0.5658</td> <td>    0.143</td> <td>    3.965</td> <td> 0.000</td> <td>    0.286</td> <td>    0.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>   -0.3566</td> <td>    0.013</td> <td>  -28.078</td> <td> 0.000</td> <td>   -0.382</td> <td>   -0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>      <td>    0.2959</td> <td>    0.011</td> <td>   26.459</td> <td> 0.000</td> <td>    0.274</td> <td>    0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>    0.3669</td> <td>    0.006</td> <td>   59.043</td> <td> 0.000</td> <td>    0.355</td> <td>    0.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>   -0.0673</td> <td>    0.006</td> <td>  -10.873</td> <td> 0.000</td> <td>   -0.079</td> <td>   -0.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>312.867</td> <th>  Durbin-Watson:     </th> <td>   1.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 235.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.137</td>  <th>  Prob(JB):          </th> <td>8.15e-52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.633</td>  <th>  Cond. No.          </th> <td>    433.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     np.log(all_policy)   R-squared:                       0.287\n",
       "Model:                            OLS   Adj. R-squared:                  0.286\n",
       "Method:                 Least Squares   F-statistic:                     2163.\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        21:26:10   Log-Likelihood:                -32042.\n",
       "No. Observations:               26938   AIC:                         6.410e+04\n",
       "Df Residuals:                   26932   BIC:                         6.415e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -1.7721      0.063    -27.994      0.000      -1.896      -1.648\n",
       "var_align                0.5658      0.143      3.965      0.000       0.286       0.845\n",
       "np.log(editors_x)       -0.3566      0.013    -28.078      0.000      -0.382      -0.332\n",
       "np.log(edits_x)          0.2959      0.011     26.459      0.000       0.274       0.318\n",
       "np.log(length_x + 1)     0.3669      0.006     59.043      0.000       0.355       0.379\n",
       "np.log(length_y)        -0.0673      0.006    -10.873      0.000      -0.079      -0.055\n",
       "==============================================================================\n",
       "Omnibus:                      312.867   Durbin-Watson:                   1.744\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              235.273\n",
       "Skew:                           0.137   Prob(JB):                     8.15e-52\n",
       "Kurtosis:                       2.633   Cond. No.                         433.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(all_policy) ~  var_align + np.log(editors_x) + np.log(edits_x) + np.log(length_x+1) + np.log(length_y)', data=talk_pages).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "x='''WP:ADMIN, WP:SYSOP, WP:MOP\n",
    "WP:AAB, WP:APPEAL\n",
    "WP:AT, WP:TITLE\n",
    "WP:BAN\n",
    "WP:BP, WP:BLOCK\n",
    "WP:BOTPOL\n",
    "WP:CON, WP:CONS\n",
    "WP:CSD\n",
    "WP:CIVIL\n",
    "WP:C, WP:COPY\n",
    "WP:DP, WP:DEL, WP:DELETE\n",
    "WP:DR\n",
    "WP:EP\n",
    "WP:EW, WP:WAR\n",
    "WP:HA\n",
    "WP:IAR\n",
    "WP:EXEMPT, WP:IPEXEMPT, WP:IPBE\n",
    "WP:IUP\n",
    "WP:LOP\n",
    "WP:NPOV, WP:NEU\n",
    "WP:NLT, WP:LEGAL, WP:THREAT\n",
    "WP:NOR, WP:OR\n",
    "WP:NPA, WP:PA\n",
    "WP:OWN\n",
    "WP:POLICY, WP:RULES\n",
    "WP:PROD\n",
    "WP:SOCK\n",
    "WP:NIU, WP:NOU, WP:U, WP:UN\n",
    "WP:VAND, WP:VANDAL, WP:-(\n",
    "WP:V, WP:VERIFY\n",
    "WP:NOT, WP:WWIN, WP:WWPIN, WP:ISNOT, WP:ALSONOT\n",
    "WP:ENC\n",
    "WP:WINAD, WP:DICDEF'''\n",
    "\n",
    "x=[i.strip(',') for i in x.split()]\n",
    "\n",
    "df=policies[policies['policy'].isin(x)].groupby('title').sum()\n",
    "df=df.reset_index()\n",
    "df.columns=['title','policy']\n",
    "talk_pages=talk_pages.merge(df, on='title', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.log(policy + 1)</td> <th>  R-squared:         </th> <td>   0.272</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.272</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>1.535e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Sun, 03 Mar 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>21:26:53</td>      <th>  Log-Likelihood:    </th> <td> -75675.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>205744</td>       <th>  AIC:               </th> <td>1.514e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>205738</td>       <th>  BIC:               </th> <td>1.514e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     5</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -0.2845</td> <td>    0.007</td> <td>  -38.542</td> <td> 0.000</td> <td>   -0.299</td> <td>   -0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>    0.0529</td> <td>    0.016</td> <td>    3.220</td> <td> 0.001</td> <td>    0.021</td> <td>    0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>   -0.2813</td> <td>    0.003</td> <td> -100.739</td> <td> 0.000</td> <td>   -0.287</td> <td>   -0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>      <td>    0.2964</td> <td>    0.002</td> <td>  120.457</td> <td> 0.000</td> <td>    0.292</td> <td>    0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>    0.0527</td> <td>    0.001</td> <td>   73.616</td> <td> 0.000</td> <td>    0.051</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>   -0.0075</td> <td>    0.001</td> <td>   -8.158</td> <td> 0.000</td> <td>   -0.009</td> <td>   -0.006</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>124844.245</td> <th>  Durbin-Watson:     </th>  <td>   1.441</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1658738.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 2.719</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>15.803</td>   <th>  Cond. No.          </th>  <td>    248.</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     np.log(policy + 1)   R-squared:                       0.272\n",
       "Model:                            OLS   Adj. R-squared:                  0.272\n",
       "Method:                 Least Squares   F-statistic:                 1.535e+04\n",
       "Date:                Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        21:26:53   Log-Likelihood:                -75675.\n",
       "No. Observations:              205744   AIC:                         1.514e+05\n",
       "Df Residuals:                  205738   BIC:                         1.514e+05\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -0.2845      0.007    -38.542      0.000      -0.299      -0.270\n",
       "var_align                0.0529      0.016      3.220      0.001       0.021       0.085\n",
       "np.log(editors_x)       -0.2813      0.003   -100.739      0.000      -0.287      -0.276\n",
       "np.log(edits_x)          0.2964      0.002    120.457      0.000       0.292       0.301\n",
       "np.log(length_x + 1)     0.0527      0.001     73.616      0.000       0.051       0.054\n",
       "np.log(length_y)        -0.0075      0.001     -8.158      0.000      -0.009      -0.006\n",
       "==============================================================================\n",
       "Omnibus:                   124844.245   Durbin-Watson:                   1.441\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1658738.125\n",
       "Skew:                           2.719   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.803   Cond. No.                         248.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(policy+1) ~  var_align + np.log(editors_x) + np.log(edits_x) + np.log(length_x+1) + np.log(length_y)', data=talk_pages.fillna(0)).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x='''WP:LGL, WP:LOGL, WP:GUIDELINES\n",
    "WP:AGF, WP:ASG, WP:AFG, WP:GF, WP:FAITH\n",
    "WP:BB, WP:BBIUP, WP:BBIEP, WP:BOLD, WP:SOFIXIT\n",
    "WP:LIVING, WP:BLP\n",
    "WP:CLS, WP:CSL\n",
    "WP:CG, WP:CAT\n",
    "WP:CITE, WP:REF\n",
    "WP:CK, WP:COMKNOW\n",
    "WP:COI\n",
    "WP:DGFA\n",
    "WP:D, WP:DAB, WP:DISAM, WP:DISAMBIG\n",
    "WP:DE, WP:DISRUPT\n",
    "WP:POINT\n",
    "WP:EL, WP:EXT\n",
    "WP:LOGOS, WP:LOGO\n",
    "WP:L&P\n",
    "WP:READERS\n",
    "WP:MOS, WP:STYLE, MOS:\n",
    "WP:ABB, WP:ABBR, MOS:ABB\n",
    "WP:MOSBIO, WP:NAMES, MOS:BIO\n",
    "WP:MOS-ZH\n",
    "WP:MOSDATE, WP:MOSNUM, WP:DATE, WP:MOSNUM, MOS:DATE, MOS:NUM\n",
    "WP:MOSDAB, WP:MOSDP, MOS:DAB, MOS:DP\n",
    "WP:IMOS, WP:MOS-IR\n",
    "WP:MOS-JA, WP:MOS-JP, WP:MJ\n",
    "WP:MOS-KO\n",
    "WP:GTL, WP:LAY, WP:LAYOUT\n",
    "WP:LEAD, WP:LS\n",
    "WP:MOS-L, MOS:L\n",
    "WP:L, WP:LIST, WP:LISTS\n",
    "WP:MSM, WP:MOS-MATH, MOS:MATH\n",
    "WP:MOS-P, WP:MOSIPA, WP:PRON\n",
    "WP:SRTA, WP:SELF\n",
    "WP:HTUT\n",
    "WP:MOS-T, MOS:T\n",
    "WP:MOS-TM, WP:MOSTM, MOS:TM\n",
    "WP:WAF\n",
    "WP:NC-ZH\n",
    "WP:N\n",
    "WP:FICT\n",
    "WP:MUSIC, WP:MUS, WP:NMG\n",
    "WP:CORP, WP:ORG\n",
    "WP:WEB\n",
    "WP:ROWN\n",
    "WP:SIG, WP:SIGNATURE, WP:SIGN\n",
    "WP:NOSPAM, WP:SPAM\n",
    "WP:SK\n",
    "WP:SPOIL, WP:SPOILER, WP:SW\n",
    "WP:STUB\n",
    "WP:SP, WP:SUBP, WP:SUBPAGE, WP:SUBPAGES, WP:DNUS\n",
    "WP:DISCUSSION, WP:TALK, WP:TPG\n",
    "WP:TPT\n",
    "WP:3O, WP:30, WP:THIRD, WP:RF3O\n",
    "WP:SISTER, WP:SIS, WP:MOSSIS'''\n",
    "\n",
    "x=[i.strip(',') for i in x.split()]\n",
    "\n",
    "df=policies[policies['policy'].isin(x)].groupby('title').sum()\n",
    "df=df.reset_index()\n",
    "df.columns=['title','guideline']\n",
    "\n",
    "talk_pages=talk_pages.merge(df, on='title', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.log(guideline + 1)</td> <th>  R-squared:         </th> <td>   0.264</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.264</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>1.473e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sun, 03 Mar 2019</td>    <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>21:28:04</td>        <th>  Log-Likelihood:    </th> <td> -68463.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>205744</td>         <th>  AIC:               </th> <td>1.369e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>205738</td>         <th>  BIC:               </th> <td>1.370e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     5</td>         <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>   -0.3058</td> <td>    0.007</td> <td>  -42.914</td> <td> 0.000</td> <td>   -0.320</td> <td>   -0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>var_align</th>            <td>    0.0825</td> <td>    0.016</td> <td>    5.199</td> <td> 0.000</td> <td>    0.051</td> <td>    0.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(editors_x)</th>    <td>   -0.2473</td> <td>    0.003</td> <td>  -91.709</td> <td> 0.000</td> <td>   -0.253</td> <td>   -0.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(edits_x)</th>      <td>    0.2607</td> <td>    0.002</td> <td>  109.703</td> <td> 0.000</td> <td>    0.256</td> <td>    0.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_x + 1)</th> <td>    0.0529</td> <td>    0.001</td> <td>   76.609</td> <td> 0.000</td> <td>    0.052</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(length_y)</th>     <td>   -0.0040</td> <td>    0.001</td> <td>   -4.580</td> <td> 0.000</td> <td>   -0.006</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>124024.584</td> <th>  Durbin-Watson:     </th>  <td>   1.495</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1641685.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 2.696</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>15.745</td>   <th>  Cond. No.          </th>  <td>    248.</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                             \n",
       "=================================================================================\n",
       "Dep. Variable:     np.log(guideline + 1)   R-squared:                       0.264\n",
       "Model:                               OLS   Adj. R-squared:                  0.264\n",
       "Method:                    Least Squares   F-statistic:                 1.473e+04\n",
       "Date:                   Sun, 03 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                           21:28:04   Log-Likelihood:                -68463.\n",
       "No. Observations:                 205744   AIC:                         1.369e+05\n",
       "Df Residuals:                     205738   BIC:                         1.370e+05\n",
       "Df Model:                              5                                         \n",
       "Covariance Type:               nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept               -0.3058      0.007    -42.914      0.000      -0.320      -0.292\n",
       "var_align                0.0825      0.016      5.199      0.000       0.051       0.114\n",
       "np.log(editors_x)       -0.2473      0.003    -91.709      0.000      -0.253      -0.242\n",
       "np.log(edits_x)          0.2607      0.002    109.703      0.000       0.256       0.265\n",
       "np.log(length_x + 1)     0.0529      0.001     76.609      0.000       0.052       0.054\n",
       "np.log(length_y)        -0.0040      0.001     -4.580      0.000      -0.006      -0.002\n",
       "==============================================================================\n",
       "Omnibus:                   124024.584   Durbin-Watson:                   1.495\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1641685.722\n",
       "Skew:                           2.696   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.745   Cond. No.                         248.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=sm.ols('np.log(guideline+1) ~ var_align + np.log(editors_x) + np.log(edits_x) + np.log(length_x+1) + np.log(length_y)', data=talk_pages.fillna(0)).fit()\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>edits_x</th>\n",
       "      <th>editors_x</th>\n",
       "      <th>length_x</th>\n",
       "      <th>short_x</th>\n",
       "      <th>ave_align</th>\n",
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       "      <th>cat2</th>\n",
       "      <th>editors_y</th>\n",
       "      <th>edits_y</th>\n",
       "      <th>editscount</th>\n",
       "      <th>length_y</th>\n",
       "      <th>peditors</th>\n",
       "      <th>pedits</th>\n",
       "      <th>quality</th>\n",
       "      <th>short_y</th>\n",
       "      <th>var_align</th>\n",
       "      <th>tfidf</th>\n",
       "      <th>article_tfidf</th>\n",
       "      <th>radius</th>\n",
       "      <th>article_radius</th>\n",
       "      <th>attack</th>\n",
       "      <th>aggressive</th>\n",
       "      <th>all_policy</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205744</td>\n",
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       "      <td>205744.000000</td>\n",
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       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744</td>\n",
       "      <td>205744</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>205744.000000</td>\n",
       "      <td>26938.000000</td>\n",
       "      <td>19470.000000</td>\n",
       "      <td>20398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>194473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>194473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Paul Krugman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>virginiaabernethy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>social issue</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Start</td>\n",
       "      <td>virginiaabernethy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149216</td>\n",
       "      <td>193567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70187</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>42.607741</td>\n",
       "      <td>14.259711</td>\n",
       "      <td>4943.420873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.469579</td>\n",
       "      <td>267.254282</td>\n",
       "      <td>4.961613e+05</td>\n",
       "      <td>13088.262365</td>\n",
       "      <td>32.897713</td>\n",
       "      <td>32.898383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074130</td>\n",
       "      <td>2162.202250</td>\n",
       "      <td>5050.308942</td>\n",
       "      <td>3.556069</td>\n",
       "      <td>3.737795</td>\n",
       "      <td>0.006408</td>\n",
       "      <td>0.006717</td>\n",
       "      <td>5.101307</td>\n",
       "      <td>3.434874</td>\n",
       "      <td>3.010393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>368.847557</td>\n",
       "      <td>52.505261</td>\n",
       "      <td>13238.088739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.582261</td>\n",
       "      <td>809.068986</td>\n",
       "      <td>4.016236e+05</td>\n",
       "      <td>20315.121023</td>\n",
       "      <td>71.007106</td>\n",
       "      <td>71.009537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048733</td>\n",
       "      <td>5940.515027</td>\n",
       "      <td>8171.478887</td>\n",
       "      <td>0.408105</td>\n",
       "      <td>0.297807</td>\n",
       "      <td>0.016011</td>\n",
       "      <td>0.019650</td>\n",
       "      <td>7.628744</td>\n",
       "      <td>4.636773</td>\n",
       "      <td>4.108208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.962408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.799167</td>\n",
       "      <td>50.721086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557641</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.057973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.339372e+05</td>\n",
       "      <td>3114.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037398</td>\n",
       "      <td>33.577067</td>\n",
       "      <td>1149.115497</td>\n",
       "      <td>3.227211</td>\n",
       "      <td>3.539056</td>\n",
       "      <td>0.002515</td>\n",
       "      <td>0.002775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.997730e+05</td>\n",
       "      <td>6464.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071544</td>\n",
       "      <td>237.011592</td>\n",
       "      <td>2436.556834</td>\n",
       "      <td>3.595653</td>\n",
       "      <td>3.740243</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>0.004174</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3228.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>6.485338e+05</td>\n",
       "      <td>14220.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103207</td>\n",
       "      <td>1357.113460</td>\n",
       "      <td>5453.994526</td>\n",
       "      <td>3.889960</td>\n",
       "      <td>3.946466</td>\n",
       "      <td>0.006179</td>\n",
       "      <td>0.006432</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45068.000000</td>\n",
       "      <td>6357.000000</td>\n",
       "      <td>468866.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.966517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13305.000000</td>\n",
       "      <td>36613.000000</td>\n",
       "      <td>5.921042e+06</td>\n",
       "      <td>538466.000000</td>\n",
       "      <td>4011.000000</td>\n",
       "      <td>4011.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>217609.411261</td>\n",
       "      <td>446390.623959</td>\n",
       "      <td>5.203868</td>\n",
       "      <td>5.139440</td>\n",
       "      <td>0.987165</td>\n",
       "      <td>0.991649</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               title        edits_x      editors_x       length_x  \\\n",
       "count         205744  205744.000000  205744.000000  205744.000000   \n",
       "unique        194473            NaN            NaN            NaN   \n",
       "top     Paul Krugman            NaN            NaN            NaN   \n",
       "freq               3            NaN            NaN            NaN   \n",
       "mean             NaN      42.607741      14.259711    4943.420873   \n",
       "std              NaN     368.847557      52.505261   13238.088739   \n",
       "min              NaN       1.000000       1.000000       2.000000   \n",
       "25%              NaN       3.000000       3.000000     134.000000   \n",
       "50%              NaN       6.000000       5.000000     623.000000   \n",
       "75%              NaN      15.000000      11.000000    3228.000000   \n",
       "max              NaN   45068.000000    6357.000000  468866.000000   \n",
       "\n",
       "                  short_x      ave_align           cat    cat2      editors_y  \\\n",
       "count              205744  205744.000000        205744  205744  205744.000000   \n",
       "unique             194473            NaN             3       3            NaN   \n",
       "top     virginiaabernethy            NaN  social issue                    NaN   \n",
       "freq                    3            NaN        149216  193567            NaN   \n",
       "mean                  NaN      -0.006196           NaN     NaN     124.469579   \n",
       "std                   NaN       0.103358           NaN     NaN     336.582261   \n",
       "min                   NaN      -0.962408           NaN     NaN       1.000000   \n",
       "25%                   NaN      -0.057973           NaN     NaN      16.000000   \n",
       "50%                   NaN      -0.006318           NaN     NaN      34.000000   \n",
       "75%                   NaN       0.042968           NaN     NaN      89.000000   \n",
       "max                   NaN       0.966517           NaN     NaN   13305.000000   \n",
       "\n",
       "              edits_y    editscount       length_y       peditors  \\\n",
       "count   205744.000000  2.057440e+05  205744.000000  205744.000000   \n",
       "unique            NaN           NaN            NaN            NaN   \n",
       "top               NaN           NaN            NaN            NaN   \n",
       "freq              NaN           NaN            NaN            NaN   \n",
       "mean       267.254282  4.961613e+05   13088.262365      32.897713   \n",
       "std        809.068986  4.016236e+05   20315.121023      71.007106   \n",
       "min          1.000000  4.200000e+01      21.000000       1.000000   \n",
       "25%         29.000000  2.339372e+05    3114.000000       7.000000   \n",
       "50%         65.000000  3.997730e+05    6464.000000      14.000000   \n",
       "75%        181.000000  6.485338e+05   14220.000000      30.000000   \n",
       "max      36613.000000  5.921042e+06  538466.000000    4011.000000   \n",
       "\n",
       "               pedits quality            short_y      var_align  \\\n",
       "count   205744.000000  205744             205744  205744.000000   \n",
       "unique            NaN       6             194473            NaN   \n",
       "top               NaN   Start  virginiaabernethy            NaN   \n",
       "freq              NaN   70187                  3            NaN   \n",
       "mean        32.898383     NaN                NaN       0.074130   \n",
       "std         71.009537     NaN                NaN       0.048733   \n",
       "min          1.000000     NaN                NaN       0.000000   \n",
       "25%          7.000000     NaN                NaN       0.037398   \n",
       "50%         14.000000     NaN                NaN       0.071544   \n",
       "75%         30.000000     NaN                NaN       0.103207   \n",
       "max       4011.000000     NaN                NaN       0.822493   \n",
       "\n",
       "                tfidf  article_tfidf         radius  article_radius  \\\n",
       "count   205744.000000  205744.000000  205744.000000   205744.000000   \n",
       "unique            NaN            NaN            NaN             NaN   \n",
       "top               NaN            NaN            NaN             NaN   \n",
       "freq              NaN            NaN            NaN             NaN   \n",
       "mean      2162.202250    5050.308942       3.556069        3.737795   \n",
       "std       5940.515027    8171.478887       0.408105        0.297807   \n",
       "min          0.799167      50.721086       0.000000        0.557641   \n",
       "25%         33.577067    1149.115497       3.227211        3.539056   \n",
       "50%        237.011592    2436.556834       3.595653        3.740243   \n",
       "75%       1357.113460    5453.994526       3.889960        3.946466   \n",
       "max     217609.411261  446390.623959       5.203868        5.139440   \n",
       "\n",
       "               attack     aggressive    all_policy        policy     guideline  \n",
       "count   205744.000000  205744.000000  26938.000000  19470.000000  20398.000000  \n",
       "unique            NaN            NaN           NaN           NaN           NaN  \n",
       "top               NaN            NaN           NaN           NaN           NaN  \n",
       "freq              NaN            NaN           NaN           NaN           NaN  \n",
       "mean         0.006408       0.006717      5.101307      3.434874      3.010393  \n",
       "std          0.016011       0.019650      7.628744      4.636773      4.108208  \n",
       "min          0.000026       0.000045      1.000000      1.000000      1.000000  \n",
       "25%          0.002515       0.002775      1.000000      1.000000      1.000000  \n",
       "50%          0.003996       0.004174      3.000000      2.000000      2.000000  \n",
       "75%          0.006179       0.006432      5.000000      4.000000      3.000000  \n",
       "max          0.987165       0.991649    143.000000     86.000000    101.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_pages.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=talk_pages[['var_align','tfidf','radius','policy','guideline']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['polarization','lexical div.','semantic div.','policy','guideline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'quality']=pd.Categorical(talk_pages['quality'], categories=[\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'], ordered=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'quality']=df['quality'].cat.codes;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'talk length']=talk_pages['length_x'];\n",
    "df.loc[:,'talk editors']=talk_pages['editors_x'];\n",
    "df.loc[:,'talk edits']=talk_pages['edits_x'];\n",
    "df.loc[:,'article length']=talk_pages['length_y'];\n",
    "df.loc[:,'article editors']=talk_pages['editors_y'];\n",
    "df.loc[:,'article edits']=talk_pages['edits_y'];\n",
    "df.loc[:,'temperature']=talk_pages['attack'];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'lexical div.']=np.log(df['lexical div.'])-np.log(df['talk length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['polarization', 'lexical div.','semantic div.', u'policy', u'guideline','temperature', u'talk length', u'talk editors',\n",
    "       u'talk edits', u'article length', u'article editors', u'article edits','quality']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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h+/sU4jw1sLekIbbPsb1hMib/Lamf7ddLFrlTaXb9M81H9khnhlJcVBe8yhMRRadut32oojDV6UTrgt3SuZcQLZG2LU34MSB5lN8phLC3EN6cq4BxCW/W4URF0UeB3wHP1TzwVSDp9BhR2XZrYpd/SKtzKmdUZUaNhhUJnI0owNWNCP39V83TV7XrnrwYhxEL8nMcVeVrn61PPK//IxZxLcCi6TdohKKHtdZPPYligP2AvkRl8sNt31M4d0VgReCgKust6XIi6mAHoqDYxcB0RLGxrySdR2xuzuYGKdZUIxmXjwH/sb1lOlaZiAL49UZeiv7aFVgD2Kq2AZI+Ox+Y3vbK6f0kwBLAa7Zf61rJR4803z5N1CV4hBh3exLrqJoH/g5gFmAuYEjVxuCR0ez6Z5qLbEhnhiPtFg9Oi86piRDfRYiF6d9snyzp94SndkKikuZEwOK2f6noovxWoqLt9GmBugLwd+AyIkf4g3TetkS+5UHAQGBa4Mt60zeFfE5v++HCsdWB7WyvWZ5kmbKQNCfhsbyMMC4FfAXsWFUDS9LpRJ2Gd4Arkhe2ZmQuRYQBT01U7R7UYEb01ETxnntsX6PIN92VMKoPs/2ApNMI3T8sfrc86UdOq43cYseHHsBtwAmOgnG18/8FfGB7w/R+StsflyB6pzAS/ecj2kz2T3Ps4YQB8iGRU1rXNSwKG3lTEXJ/RqwbuhHrito9e1/hO5VMD6shaUmilszC6X0fokDeckSqxSXp+LRuoP7uNZpd/0xzkUO7M8BwhajuJ4p+nEW0sloDuBz4mejLOtj2XxWtoNYgJsV/p4myqnlpmwA3As9JWsD2PyUdDNQ87lfafs32uen93cCHtr8oT+S2UeSO7kTsAheZCZimcF4t725q4Bfbn3WhmJkuIj3XAvYHzrZ9iKKdzCtEznDdGlataW0I2t5VUcthF2A7Sdi+PX38gaN/dO27VR2bhlIwomcnokr+ACwp6UfgduAMImrmAklvEV7qvWrfr/drXTAi57L9SgrZXpmYhz4jKpE/artWyPJMwtNVo65DfkfFSPR/H/gOOEvSpMCMRFTYqcD/AReUI/GoSYbw4LQZcCPwAVEI7hciuuBkYGfgSEXrpztgWJ/3er9nR0J3YHxJPWz/YvsTSRcSqRaHKLqanNXARmSz659pInLV7gwQE1eayNcDNgYeBo62/WiazG4C9gP2kLR/Ov2W9PngtOtcuYVqGui/IRZkPwCPJF2uJRYrKwMbSZqp9h3bD9VriFkKazzJ9i0KFkof3Q38qOivW8wh3ABYXRVu35UZMek5HQL0AO5Khx8EHrZ9sKRZCvdI3ZKeySGSZpW0s6RDJE3qyPs9D3gD2F7S2pLOIeVD1zYIqzg2tSbpPxNQa1e3KfAqYSyv7iiIeBywJ3GNZ0qbZZWp+CxpAPCSpOWB/wAr2R5I6DM70SGiVzp9NmDc5LFuiNz3NvRfzvYLROHPl4gQ70VtXwhcBExelqyjIj2zVuTJngv8xfZSRErGKsAaKdrrHOBFIjVhKFUxoiW15ZB6iLSpVTuQns+7iMrVt3SNdGOfZtc/k8mh3ZmhSBrX9kBJLwGzAqfYPrAQZjUO4QW5iAgTvqxMeceUgoenO3A+USF0B+BZYJH02QaEh/dxIrT9nfIkbpvC9WkBWjysOvdeRNTAkcTEdgrQh1iU3kD0Aj+KWJjlvo0NQlueHEkXA72JQnkvOtUzUFS0ftj237pe0vZRuL/nA+4hoi2mASYBtndU7p6dMCx/T7TeW9H2L6UJPZaQtAawie0BhWPnEeGxBxKh3j8VPqv7XNoUVbCF7cPS++OJwo4POuXJpuN/IbzsMxDj8QYMMzQrS3v1b/WdfYgUoyVs/7fLhB0FklYi0gkWLhzrDVxue+W0qfMkMQb9UVFx/zWiN/QnVdsMKYSttxAdLyYkQu7PI1LeLiU2+c4kWrVtDKziBqlO3ez6ZzKQPdIZhhaGIO38AywN/AbYVdKZtcnNUcX530T7pyvLkLUzKRgbNzMsX2sl4CcivL3F9jWEkT0/4bGuOwqLj8WSB6q7pEUIg/kFIvR1fsJofpHwYF1PGB7LZCO6cShsDs0kqb+k/umjQ4l6B9MXjOgLCa/eOSWJ2y6SET0VcAnwZ9vr2O5HGFRnSVo+3cOHE56u5Ry5pI2YutQLWCl5+WrsSmyQ7AIsmyJRamN6XRvRibmBlwvvPyKMqyUk9asdtL0vcAwxHr9I1OWotBGdGKX+tagCSbNJOoS45ivWkxGdeAaYUNKDhWN9gOkl9SU2dF+z/cf02fbAQrY/Ts95pXq7F4zIx4l6DDcRkRPnEkXxVk9/7gqsBmzaSEZks+ufyUD2SDc9hR3F6Ygdww+J/MK3kwfoUeBCYB+iVcULtk8pfrcs2TuDlHN2O7CX7ccLxx4EvgBWSL/PRG7VLqqeUBSAu4WoNn4E8IjtLVPY7paEB+9Y208qWphNBPyUwtozDUDBczsH8FR6zQLcZntHSYsRxub0REXrnsCqyeisu2e5MDZNSOR5H2Z7X0njEl7pxwgPyJKEB+/etNlX94W12kNB//GJZ7WWQ3sDkTd7oO3v0rELiDDfXkQ4dN174xVtFXvY/ii97wZsnsKWkXQssQBf1fZD6Vhf4I2qX1sYI/3nAp6pV4MkbfL8E/jB9pLp2LlEXv/VtjdKxy4nCnYuX+XrKWlZYHfba6f3VxObfEt6WMG4FmA823W5GT8mNLv+mUz2SDcxabFZKwTyKBEaeALwF0lzpt3+fsC6REhlP6KgDVAZb8eo+IHYPFg8LdAhqhnfSXjmH0jHvut60TrEPURBqQuJXthbAth+Oh37ANhHUn/bP9r+JBvR1adwz9Y8t72Jxfdutpclcv/XlnSe7X/bXhXYjiik9/ua57benuW0KTBY0vxEruh4wInp478Dr9velShgNDGwpgt9z6u8MIdfjc13A5dKOiZ9fDZhgNwkaT1J1xNG9NpEhMHmpQjdASRNQEQY7JNCmwGWSu9PBbB9IHGt75C0gqIA5KXEZkGlGU39DyFCZh+qVyMawFGEcwWi2NRj6fCORMuyJSUdK+kmYA5i02eIql2jY3rieUTSRcRGx9JAi6R1JI1je0gDG5HNrv9wVC2qIjPmVHnwyowhaQL7DeHJPMn2IkQI6OLA6ZLms/0S0RpqH2DuKoZMSppC0R7mV6Rw9qeIRegSkiZOnp+PiQXppum8ugzdqA3aHlZQ6h1gVknr1D4vGNM/AgMU/WczFUfShsC+hbDPXkQ0wmpEYSJsP0MsaldX9HvH9mO2364tYF2HhbjSpkAvovjhybY/tf1x8s5OCVyRTl2NSFXYqSRRO51CeP60RMeEfxGhzMtIutj2XcSm2RtEzuFPwMZpDHieiKSpa2x/T9TaWAT4o6Jl38PE/DNvwZjcl9i8PZrIid7V9pelCN2JjKb+6wP7VEH/ZEyvCIwn6WHbg9Lm7pHEfXszkYpU28ir8sbXvcB7kh4B5rU9b9rU25uouTJ+qdKNfZpdf2A4A7p7ep/tqyYhh3Y3IRq+R+UyRGjV4en9s0RF2O+ASYH9bT9f+G6lQiYlTUQUuviCKJ72duGzYt/Oc4gCaz8SPS43B35r+9Wul7p9aFgLq+F6bUraCTgY2Nv2VenY5ESxqa9qoYSZaiNpaSJ88qk0iXcnwid3I4yvnT2s8NycRB7mYbb/XJbM7SHp0ouoSP0B0ev6sULo+l8Jo+olInR9NjdIn+gakqYk2gL1tL1PijyYi8hn/z/bm6bzuheu8W5EVNHvbL9RkuijJF3fbumarUnkdj9OdEn4gihouTORRrRH+s6MxNj1VSlCdyLNpH8K874P+L4W5t3q87pLKekokiYGDiA2488gUuB2J57FFdwYefwjpNn1L6Lol30ZUTzwg7LlyXQNlfIsZsYcDcu76wtM5qh4+1767HbgJdubSToRWBjYhggVBaoXMmn7W0Xhk/5Er9lzbb+VPnPt97C9naRaaOQkwMJ1bkS31IwH4Ly0GfowcJPtv6WF94mSTGyIHATM5ZRTmakuinz4//PweZMDiKrs5wIDicX4EZL+ZHug7f+kENK6DQmtkTaFvlLkiJ4ELAQ8VtgsOomo/Dst0D89B5VfkLdifeKa/iipj6MP6wuEh+c0SffZXj7pPiVRB2E7og1W3RrRCXlYW66FiMi4A4nw/VOIgkUQ7czOtb1tbcxuEBpW/9abWba/ULTyulvSa7b7Fs+v6jNb3Ly2/U3a3PuUGHfXSaet2KhGZLPrPxImBH4mNng/aKTN3cyIyR7pJqJgRM9FeKz+R+SyDCSMrQuAQ22/IOlsYrF6fr2GNY+KVh7nTYgw0VeAc4sLk9aDXWsPb71RuI4ijOcPgK+JFiLvAUelhfdOwL5Ezvf2tp8oTehMpyBpPCIkdGFgadsfSFoKuJXwBhwD/EJEVKxOeG2Pdp23RNLwhbV+LkTM7AacDGxg+4aRfbcLxe102lpwSdqSSC35B3CB7c+T8bUoYTjvUPuOpHmAL22/38WijzaS/k2E+f4JWJ6o4HwL0UbnCyLqYACwre2Py5JzbFF1/YvPrO0fR3HuFMBfgG2q9qwWoz5G8HnriLAJgYbJCW52/UfGCMbtC4HpbK9UkliZLiYb0k1CYdKbD7ifKKI1AeHRsSKH+HbCMOtLGGWLVD1ksii7pM2I3dJfGdOtvlPXhjQMDQ9clwjL3ykdWwfYEPgcONKRUzolMMj25+VJWz5VuKbtJT2rRxLG9Oq231FUTj0fuIZYmP8CbAZsAVxmu25bXGlYTvA8wKnAl8TG3va230jG9InAhrZvLFPWsUFhbJ6ZMJJ7ATemzbDtiXaDDxObml+0GtNGusitV5JhdRWwru2v07E1ibDQS4gw5w+BCVzH3RJGl6rrP4JndmIiDeN/o/huZTa+NHyf5PMIp8PkwJ6tN60aaY6p0ez6j4zCMzATscZ6Nx2fiCj+erKjfWqmwcnJ8A2MpK0l7QhD+/3NDjxHeJ3XJRZsk0vqkQaBo4kq1q8DixZCJitnRCsVRGsVZnYpUeV3bmBbRd7Zr6jIZLAOYTQtpyhUQ/LYXUtc1xMlTe3oz9m0RnTacIAoxNYQFTXTs3oY8Cxwu6QZbD9ApGFsABxC6HspUYX/vJJEbRdpMTIDsfh4ANiPKPb3mKRZbZ+Wjl2vqOnQEKjQ6zkZJP8mPJNbE+HbO9k+m/hNFgf2ULThK45plTCi23juDCxAeFxri/BbiArlOxNF1Frq0YgcHRpJ/yRrW8/sZ8AjkmYd2fcrZETXOgeIeDZ7EWH3PwDPS/pt8fyKrBvaTbPr35raM5yigmrz1iTARsCLkg6QtGp6Zh8l1pkNsebIjJxsSDc2HxPVXmv0BrZ05NBOAvQBZvKwnqP/sr2P7R1dp21x2kMy/gdJapF0qqTTJB0PYPsS4DpikNtK0iylCttO1KoCpO3riQX3hMDSKZwK29cBdxA7xw09sbWHFG3RGzhf0rRVnuyLE7Lt94jKzS8AtyVj+n7CmF6PCKNssX2bq9FeZlHgTtt/tv0mkf98m+3XAWyfCmxC9I+uNLUxJ12XbpLGAY4FTrW9HeGBXpnU5sn2KcDTwDjUfxu+X5HmkVqKTXdJk6bNvaOAtRQt+WrP5X+JDgNXVmWTYFQ0gv6SlpDUP82tNVnbembvrD2z6XuVMiIkTSNpuVZ6LgV8ZHtdR8X8z4g+7sMVYS1B3E6n2fUfGWktMS5wYfqNZiBSp84i5t1ewDmSDiPG6b0lLVzlNUemfeRiYw2M7dsAFDnRS9s+C3g4eWsHEoZ2LazsSiJkZ+VaiE49TeQdoRCK9DQx6N9HeKBnBv5o+zJJQ4jB72dJx9WzripU5yaKWAy2/abtCyVNSoT5StINtr+zfbmkW+rRm1ESMwK/ISa691XBVIVCiN2UxAbYQNuvStqBqOR8m6TVbN+vCIXemih6AtRnkcBWoYA9gT7pfr6PKKa2VfJurWv7eNtXpu9VMpwZIOlzjaSLbJ9W8Ph0JyIIIDbC/mH7WEmzEbnPR9d+r1a/W12j4Ysi3kykHMwi6SgixWgaoijeH4haDlsDC6aNosrTCPonD9zuwETAIEWhu8FEatiIntn5ZFvJAAAgAElEQVQ/2D6xKvdpgUOJ1LbuBT17AtOk5/Ri4LdEWs24kray/bd6HF9Hk2bXv03S2vFDIuXoVaJux3TA4Y5Wb9cB10m6mKhLA/G7ra8oEvlLBZ+FTDtp+F2kZkXDestOCkwG/E3SrhDhgI7iIG8Bk0i6gPDQrpY+b4QH/mzgddsr2T4WeJDIKb5V0ri2ryB2Ei+q50V50btO9Gu8AbhE0a4L2ycTRaYOBjaWNEE63rRGdBve+yeJHfTT0vtKTfrJcBosaX4ijPIsos/7tek670psGt0iaWbb/7C9fs3oKlH0NqmNTQw//7xIVC1+jOgcMCAdPxSYv5U3vm6f13bwDWEwr6dhaTeDiOiRfRQFqF62vXE6/3CiH2/NI1IZIxqGet1FGI3fEovMo4gUlKmIXOCjiDlqPKJdzjslidvpNIL+yZjaGvgE2BFYMY2xrxBREm09swvW49jTDvYlCnbW9OxGFIR7n9jgmtf2fI4+ydsDGypaSzYKza7/r5DUjzCcezpaWt0FzE+EuD+TzmlRpEj+l0jNOJCoHbAKMFGVxuzMaGA7vxrsxbAicvMQu2czEz3+fgF2r51D5LsMIQaDHul497LlH02du7V6vz7RfgHgCqIC+VSEB/6fwLhly9yR60mEtF6adFiGqNR9aeGcg4mc2V5ly1sPLyLMcJbC+2mICXC52m9atowd1GcKwljeNr2fJz27G6X3UxHFAi9N71vKlnkEerSkP+cC/k5sbmwBdAP2IhZsGwHLpvv9+cLYVKlrNpLfoDfRO/hRYNd0bGUiTP/FwnkXEbmJlRyTC3r0Be4uvD+BMMJaCte2WxmyZf3bJX9NxgkIj+RNRJFLiFoN7zbCM9tKz0uSniukY4cTldQ3JKLC9iCi3eYvW+6sf5f8NrOmPycCpiY6YuxJbCKtOZLv3Ux4rUvXIb/G3iuHdjcgti1pMsKTcYajiub/JG1AhBXK9qmSLgcGE9VwB1U1ZFLDV5bc1vbZtq9VsBYwK7Ck7Z8l3UDkdk0J1NXO/0hYEfjQ9mYAkk4gqqTOKekS25s7Qj//7lQBthnRsCqa4xOhiDtJ2o/wljwk6Qvit7zfdt3uEKcwz/WBTQtyTgp8Z/vcdJ9fSBjNV0rqa/s1SZuSUjVch173wnM6PbEAOYtYlKxTeH1DtOSbjKg+v7CjXkNlKv2OCtufSro6vd1E0g+2z5d0MuGpfpZoTdgH+J2r3yd7EBECPAORhrIAsEB6VmvjVlXG4tGhsvqn++4XSVMTz+YORLTX7pIG2T4qjauVfmbT2ucXRdpbzdt6FrCbpF9sHynpJ6A/4a39itiof37E/2p1aHb920LRBWVp23vYfl3SNEQEyU3AZcTm9WTAfuk3ulPRNvZ04JU0B38CjFuSCpmuomxLPr86/0Xsmn0M/AdYKh3rnv5cG/gJ2D+9V/Hzqr0K8tdyom8FZi58vinwUPr7dsRO6wRlyz0KnVpavZ8EmC/9/a+E57kncDzhlby8+Fs044vk0QFmIrx7kwF/AM5N98XhwFbEQm+hsuUdhS4zpmf0vML9PTfwEGFcPQFcVTj/VGJh3ub9U08vwpuxJXBw7boR3vXrgVNaX8/Wf2+kF8M8048DW6Rj3YG1iNDB2j1dybG5oOcURErK48BzheO7pLFs2rJlzPq3KXctemQ+ogjaxun9BITn+RbC0Kid36Pw98o8s4XnrJY+tQvR9WBEek4MjF+23Fn/sf67LJvWV38qHDsX+AfRHaM7sRF8JPBmmp/fZJhnfzoiyqgpvPbN/Mo50g2II29yf6LAUr90eHDy2N1IGBSrtSpeUxlPtKQetb87jVjELvkLttfw8H0s3yQKZzwJ/Bk4zfb3XSdtx0g7w7UesTMA2P7K9gvJKzA/4XX/Afge2JbIxyn+Fk1FIYd4PiJNYU5gHNs3AXsT1Z77Ab8nPLsrpO/V3fiXrv9bwAzEptfZSb+XiUn9I2IxXmubczlhiA71DLgOvdEF9iD6XS8gaRyHx+oVwpBeONV0IB0fem1Lk7YTGFGuqO1PgasJ78b2inZXg2zfbPv5dE+3VGVsHtHzZPszYlOoD3CtpAEpUuQIoovE+219r2o0mv4e1uLqTuBs21ek+/F7wjP9FdGSbc2a5xqq98x6WMG/R4FPibWEC3p+TXjgV096fuOoMdMQNLv+I8LRUnIZYK8UBYjtbQkH1XZEBNWnRKrGDsRz0tfDojHeIxxZDeu1zwQ1b0emwhRCJocLpZK0JbFo3dL2xWmwVHGhXbXiNYrezzcCy9j+Jh1rIRbil9u+rvg7KFrLzALMRhjab5YieAdIBT7uJzwZLxHX8B6i1dX1hGdjELATsHirjYOmQNFC5svie6IQ1wW2/9rG+T0IT+8uRNj0/MmQqSsKz3J3YHNiAf4327tI6knshg8mFj1902vRNHnXXTXytmSSdBrwR+IZfi4d603ksK9v+42ul7TzSc/xkLRZOR3wRdoAa31eb+Ke3BPYL212VoZWeoqoP/FTG+f9nuiHPTdR4+E82y+2Pq9qNIr+kpYHJrV9fWGTfS2iAveWhdSZ2mfjExtBL9s+sFzp24+k3xBRSTcWdOkHHGJ79cJ5xVShy4niUtvX80Z8e2h2/dtL4bdZmjCSz7S9X/rsFGID+xyi5dt3he8NNx5UaX2dGT1yjnTFKSy85wR2ljQesdj+p6M9kogeuti+mFa9hav2kNt+S9JRNSM6MQEwPuFthCjORTI8VnX0Vv5P10raMVoZHEcRFdW3ILwWmwOT2L5a0n1E+4mZgFWa1Ihegago+ifgp/S7TUrk8J2TzinmzU/giNJ4jdhZn5oomnJGORqMmCTzXMBTxLW/HNhcUi9HjvzSkvYkNlLeI8It67K+QeEazEB42H+2/bjt3RSVXu+StD8RRbAnsVCr+42uUSFpJeB5258o+gaPS2yA7Q083Pp8R8709US0wS1dK+3ok67rd7Y/Txs/g4hq1LcSRalq59XaKd5FbJYMvTfKkLuzaCT907phZaLAXXFdMCWwVBp/vlZUJv5F0kxE8an1CL0rQTJydiT1oy/o2QdYRFKf9NzWxq4+RJrNxsBkVTcim13/9lBbi9V+G0d9lf7AnemzfWzvKelE4CCiXs29te8Xn+uqra8zo0fdhTZm2k8thErSPMQC7WeiHcWGwEGSJrd9ARH+e6GkVUsUd4zRsLY5Nyu4VlFo6VuiNcNpkhYsGBRbAMcqeu/WLckIGpJ0+g0xMB+fjOTdiYIV60ta3faxtjckqqY+W6bcJdINuCR592ph/j8Qi75aq6DaZDYxsISkcTUs9LIbkUNdOpKWkrRk+rvSPb41kYJwQjKeFwBWVbSpw/Yptk+3/XcPK0RVd4vZNDbNTRjKJwInS/pL+mwTosr4BcABxD2+fHoOKjsvJaN5F+BBSVOk69KNiCJ4J21s/grbH9u+If1m3do6p55IMh4GPCWpd20zh5h/XiqeW1xM1vSvJyNydGg0/ZMH/RDb/5U0vaR100f3EZvQ/RVtI39Jx48EBtj+uUrPbPrdj7J9WxpvV0ofPUrUnlhR0niF6/NHIvJroKP1UaVpdv1HRZpLh0iaRtJKkuZURL89BKwK7FiYw/Yh5q/7ypQ5Uz7ZI10xFOFhL9j+MIWO9CJ63J3sqNzcjaj42hcYR9IRyTP9CXB3iaJ3Gh6WQzwN4dVa3vZpimrAD0m6mzCsVgT62/64RHFHStoMqfWJfgr4Dvgd8JWk12x/KelI4BBg17SYub4YStQsSFoFeNb23en99MTEdp3tZyQ9APxe0ge2n05fOwv4wfZdkrqle2ZxwutfGmlBPRmwGZFjVVtwD1b0Au9dO9f2G5K2Ba5LhtrmrXa962pRruGjKzYh7t3zCY/XEZLOsL2L7a0k/ULkmi3vqKrfo7BYrxSSprH9gaQdiDH5bkmr2v5I0iDgs/Z4KOrterZG0gS2v5d0KFH08B+S1ki6j0eMYW3SCB6aRtG/1XNKYTNuG6CfIorteknPEUU7F5P0IOGF/i1Ra6X23bpKKSmS5tihv7uH5ffuBKwraRJHl49niPFqBkkPAwsRbSV/V0/XraM0u/7tJT0PtVortxCFSVuA+xSV9R9UOKNuVURobGf79MJ36/YZyIxdKrGLmBmOI4CHJU2V3vcg+iQfr8gDfRx4kAhb3YDwAk1q+/bCrnklSYNci6Qj0vsliSJLD0qayfa+RAjSvcQO6+/q2WvbaoLbHvg/YABRKXM3YuHSw/ZXwNGEof14KcLWB4cDjxXu/TmARYBNFTmoJxIF9o6UdIeka9M526fzh6Rd9TldcgEQB58Dezq11pD0u/TxPwBLWrbwlVeBKwkDu24XNYUd/ZkUIfjdiM2Pn4n+7QcCiyvypLG9PRFNcoekBSpsRC8EnCFpHdsfEqHqrwO3KEL1vwdmUkRGjJ++M3l5Eo8eilDPUyStn56lPYE3iCihqYkCVFMVPZSSJixH2s6nkfRPz+l4kvYCkDSXpF2JTaBXgI0UUVCHEmPPFESU149El4BBqkD0RG2OlTSgdkzSFkQY+/PABknPw4komYWI32BlYFnbL7X+N6tEs+vfXjysuN7tRETYQsAxRNHPwyTNYbu2kTSLErXvliZ4pnRysbEKIukeouz+isnbUctrOR6Yw/ZakqYlJr87gBMa5UFPu4VXAIvUdlYl3UyEvy7tqHhcKSTdT3gxjrT9VDp2DrAYsDPwRPLWNX3hisK9v4LtjxW5S9sS+cLHEvl6vwWWJPqEX1LYQBqcojhK/x1rMqSFaA+ihdVkxGbAK0Su9xCi3/IVwGnA67aPTN+vux3wgk5zE5t57wOzExtDu9v+IXnsliRySE+xfVL67g1EEaZ5k9FdKdIC7AjiWl5r++a04fNXomL89MTvsHg651PgGds7lCPx6JE2rI5Lb4t6nk60SeoNXAMsCgwEPiNSjgZUdZOkSKPpr4jyOQJ4mYgMOdr2iWnD4GCihc8ltm9O549Tez5Vh3UZRoSimNbpxLi0EvC27TVHouckRP2NXxWNqyLNrn97SEbxqkQf9No8+xRRtfw7oibAiY4OGkO/U/ZaIlM+2ZCuEMWJSxHG2pswKD5Kx04DPrR9rKIx/C/ArmlxW3cL7/bQWm5F1e4ngCVtv1Y4fgMRyj2v7be7Ws4xQdJRRPjrdrbPKxz/G7AGsIHtx8qSrx5o497vQ+yWf5LCrbYB3gXOsv2fVt+tq8I+MJzROU7aJOlHFIEZApxEGKF7AasQHuifgeUchX7qdvJWpJocBzxl+3xJOwLLE97ZY2x/mzyycxOe6mKXgWlcwTy8wrWcnliU9iL6fN+cNjQPJUImlyKu6yxEHYTX6+2+HBmj0HMaImJkG2B1woCchlh8fmz71bLk7iwaVX9JewN/IQqUrlQ43pvQcyrgHtvnFz6r2zFoRCSP7PnA/2zPWzheMyb7AHfbvrAkEccqza5/W7QxD/cBJif6pt8NfGR7M0nnAssBF9v+UxXv/8zYIxvSFaGtB1fSQ4QXq+aZPpBYtD1BTH7zuk7b4nSEtFN4MtHw/jNi0D+k5r0tnHc5cLjt17teyvbR2qgrDOSHEcb0yo7+hbXPTyXCjJquOneNkdz7vYkWSjVjekti5/joer4HYOg93YcoEriJ7SckLUyEiv4CnGr7ueTBnQZ4K4We1a0XKHkxLiYMxX1t36loP7c50cP7NeA4FyruJ4/80F6mVV2caFirmN8QlVxbG1knE5XL17L9SeF7dbfJMzIK49WI9DyJ8L6v7TpsLzemNIr+BT1mIYzor4jx6D+OFKnaeVMS4a0f2T64HGlHn+LaRxHlNTtRP+U5F1p2paiCPwHjArt4+K4glaXZ9R8VrebhjW0/mY5PQ9RX2cFR++AMIr3q9KrOUZmxRzakK4CGtSKYkuglPLntJ9Jn9xGhrks72qisROS+3+s2ektXicJkvzBR2ddE/s6MhFH9KVExcRzC2Kzrm1nDt2Q6jQj9Gx/YP3nqasb0So5cnKannfd+zZheh7g/Dq3KxpGkK4md7v62n033+h5Ey5FrnAqrpXPrfkNMkWO5MxHe+re0wVfrib0pEQ779zJl7ExaLVRr49XMwH7AJMCVHhb+eznwjqMnb6U2DdqaR0ai58XEgnwFUj/VLhe4k2kk/QubPlMSbZAOtX2lpA2AHYAnbe+fzl3e9n1Vu19hOD0F3E/UFzmMCN/dndCz1hd4dmKjb4riRleVaXb9O0Kah5cl5uHnJPUlHFJnA/MSxna/2vqt3ufhTNeSDek6p7A4m49YnL5HhEXeD2xt+0dFqOsUhAH2YeG7lTSiRya3okDPkUSRqYuJ/MN5CQ/AW10m5GiSJrUngbeJohYrEOHb89h+R9IhREXppWw/Up6k5dOBe39ywpNfvPfrbrIr6DOeC7lnyVOwLhFZ8qyicNVRwL9sHzeif69sCpscPYFxbX+Zjm9JhLheCVxv+8NkTPcHbq+36zK6FPTvSxSk6QlcZ/slRc70gYTH8krbt0iaDPiqavrXoiDSBuDGRI73I7ZfVaTaHEAYk5fbvlURHtnD9vulCd2JNKL+isJoRxOe5oPSsQkJI2snooBaC5Eu9Zs0blXRmBYRpdfb9q7pWE3P7YgCny8BZwJTNZoR2ez6t8Uo5uF1iHX0s5I2ISKpfiE805WP7syMHbIhXQEkTQo8APwduIQI274ZeNX2uumcl4mcw03LkrMzaOW1PYyodivbJxTOGQCsY3uD9L4yE7ykNYlc6NXT+9OIHNL5Cp6tA4Cb3SrXtxnpwL3/nKM3cV1SmLwnJzaATvTwIfxnA2sCq9h+XtIchI51OWkXvB3zEYXRfiaq+Z7u6FG6HeGBvhq4qWhUNMJipHA95yWqkT9OLLhWA9a0fbci/Hd/YFbgT7YfTt+tjP6F69xCbABCFN9ZnNDznrRpsC8wE3CG7TtLErfTaUT9ky7rESHdH9lerPBZT2AJorVVC7CZ67wuw8iQtDLwN6Jw1jyF4z0JD+RORIrQDq7jDh+jS7Pr35p2zsNrAb9P83Ali+tluhjb+VWnL4ZtdPQFngF6FT6bhAht3rNwrFvZMneSvi3As4TH9tD094eACdPnaxKVjVvKlrkdOrW0ev8H4M7098uAF4jQ9HGBrYHuZctcD69GuvcLukwBrE9U5r6fKJhXO2ci4BPCIO07ovunHl4FfaYnimftC4xH5If+j6h6ChEm+l9g3bJlHku/w5SEN2eXwrGvgG+ICBmAmdPvU3fXsYO63kFU9a29f4komFbTcyYiD3zasmXN+rcpv1q9n5Twrr9GdIsY2XcrMye1Md9ORhT6+x9w1Ai+02tsy5X1L//VwXl4YHEezq/8Gtmrsj2FG5lCaPM4xAP9BfFwrw+cl3bVvpJ0GbGABYYW7KlUOHcKg5Ptj23Xdry3ICpL1jyOMxHtrX5In/+HWMBPRhQfq0sKIYEivFJvENdyAknXEz2PF0jXbS9ih/h6YjHelDTava/hc7wPJcJ/byYiLY6TtJ/txxw58qcReeBDC8u5zjyXBQ/duER0wF22/5I+Wwq43/ZTkiawfZakT4GbypR5TNHwedA1/ccDviU2+86V1IPo834J8DlwgaIH/DWE56/uPdFFr2PhvhWxyfcmw/S4jBijriP0bLF9fbqXK+uxaVT9C7r0IWpKfOlII7qe6PO+jaTDPazlT7FDgupRp7ZoJff0hBf2U0nXEZvzrfXsYfsX21+XKHan0ez6j4xW8/AhwAQMPw/vb/vREc3DmczIaClbgMzwpIlrsKR5gKsVlaiPIna/Z5PUv2BwzkpMhEOpN0NiZCgq+t5MDGRTFT7qSQxwtUrcCxF9OXsp+l7+D9jUdj0b0d08LK/uEWAjYDLbDwEvEjmVewNTSNqFYZXIm9mIbqh7v6DPfMC5RC7/ukRe/H+Be4nNgZ0lXQMsZvuA2qZAeZL/GknjK7oC1MKZTyH6QfeTNLmkJ4E3bW+dFuw7Sepp+/p61KcjJMO5tf6nE8UPj7Q9ML1/2fZuwL+BD4l2ZsP9O10seocoGJGbAeOmsetYYtG5v+230obfXMQ9fCXhfT9TkXdZV89fR2lE/ZORXxuDHieKJz0jaXtinr2GGJuWk3QSQNFwLoy3dU1xvpV0P3FtXlHUaxif0PM8YFlJJwC4Dvt6jy7Nrv/IaDUPnw3MT2zMF+fhc6swD2fqk2xI1xFp0rOi9+jdwNOE91XEg78YsL6kx9Mu4wxEz9ZK4sg92RFYGDg46Q3h0ZlA0iXAnMCCadDfhsi7HMf2x2XI3F4K3oxngHdtH1Uz/G3vTLSa2Bu4gsirXMH2C6UJXDKNeO8nfaYgwkLvIwr37EhEIixKLGz/SuQrQhQ6qX233hbl0xPVxC8l5H7F9qnA80SY66u2N0znnkzkWf5Y+3Id6tNRivo/Brxg+0fbP6TnfHLgX+ncDYnWSMuXIukYoMjrPhk4gXj+prX9ue3v0il9iQJyA4ke50cS9R2+q4rRNTIaTf+0CTQTcCfwd9v9iE3bw4ixaALgWmIeGj/dy5WjMN8+SqT9rEbUJziUyAOekDAmLwfmSeNyw9Ds+o+Mwjx8O1FvZUUi7WgGqjcPZ+qQXGyszlBUAV0DmMH2PunYhMDhwMpEhdA5iJ3wi9MuZN2FtHaE5IG8DriHGPi/S39fBpjDUR11Z2LRsqztl0oTtgNIWhHY3fYa6X2tVcpAwgjsAXQHBtv+tjRB64RGvPfTxsAlRGGiWpTFqkSY6KNEz+u3CufXbUGTFA1yE/CE7aXTwm1HItriceKZ3ZLY/FrEFS5S1Bat9U/HRERGHAZsQKQi9AbmrHmI6t0T3ZrkuXkE+IAwEgfWrqOkfYlF6BNEUZ5+jbYB2Ej6p/tza2BG24ek948QG5S9CAP6HOCLVuHclXtmFe0P/2B78/T+DCIC6HPCQ3s2UShufDdgn+Rm139kKPpCX0bMw9+lY6sSedKPAMfYfrNwft3Ow5n6I3uk649piN2xZRT5HBBhztcROXmv2j7F9vlVMCTaQzKM1wNWIkLpuqW//wM4SdEveFOiLUEljOjER8Biko5T9CncnAgHXYOY8L6z/VU2oofSiPf+YCK//w8w1PN+BxGpsDCwfS0SIy1g63nyfo3YzJhU0plEjYJziQ2ubsQz/D7DjOhuVVyQj4Si/qdLmo7YjB4EXER4rK9mmBHdrWpGdOJL4AJgEJF2M33hOl4HHEG0olukno3IMaBh9E9y30jU3oAo2vm27cWBl4l7dsUqhnO3wYNEJeZa9eWlbU9NeCEPJDb8hjSwEdns+o+MIcBv+fU8/BzRSnV3Sb9Nn9X7PJypM3KxsTrD9qOS+hHhVstJutX295KeIMJzpiCKVtXOr3dDol04eq+uRyxU/grsDqxOhMG2EP1XvyhRxA6TdNqFCPF8yfZGAJJmIfKWMgUa8d63/ZGiN/jekr62fVv66Bsi3HsF4GHg/XpfwNp+AzhV0r3Ec2rgINv3KvKin3Vq2VaRTY4O0Yb+Q4BjgI+J6Jn7bf8Dqq2/7XeJheXsRA2LbpKOs/0Bsdn1hO1LSxVyLNJo+tv+HPhc0lzAd7V5CHibiIy5qjThOhHbn0u6L41FMxJ96wHeJTa6bqzqM9keml3/kdFqHv6qMA9/TjhsZgb+mMbtp0sTNFNJcmh3nSJpGaI4xLVECOhahGdrsUYeDFuFeR9t+6OSRepUJO1AhCovbfu1suWpRxrt3pc0EbALsCfRbqMPMLnt+SRdSGwSbFDvhnSR9JzWrs9sROGiRat4fUaHgv6PEPpPQAPqX9DzQWLjYB0inPmtMuXqKhpJf0mzEcWVDgfmJoytJRx51JXd+GlNCuN9BTie6PSxD7C47fdKFayLaHb9R0RhHt6L8NJPAUxiewFJSxO1A54FDnfUQMhk2kU2pOsYSb8jDMr7iBCUQ1KeVsNMem0haW5C54sIj1fldVW0o9iJyCFd1fYzJYtU1zTava+oANyP8Fx+D5xj+6eUx/aj7X1LFXA0kDQHUQiuJ3BYCueuXE7w6NIs+idP5k5EfYeTbD9bskhdSiPpL+mPwAAiV3azBr5ntwJ2JuqR7Gb7qZJF6lKaXf8RUZiHlyY2Gc6uGc2SlgNea/YNh0zHyYZ0nSNpCSJf6yDbN5QtT1chaU7g5xROWXkUfXeXAd5oFJ3GNo1876cJfb/0Wrpiuf9DKRYmasYCLc2iv6TuxHqhKVrmtKaR9Jc0jqNjRqPfsxMR16wZc4KbXv/2UnweMpnRIRvSFSCFuv4NOBq4Lj/0mWahEe99SeMRBee2AQ7M0QmZTKarqWp17kwmk6knsiFdERStlI4Flneu8pxpIhrx3pfUE+ievQWZTCaTyWQy1SQb0hVCUk/bP5QtRybT1eR7P5PJZDKZTCZTT2RDOpPJZDKZTCaTyWQymQ7QUrYAmUwmk8lkMplMJpPJVIlsSGcymUwmk8lkMplMprJIukDSJ5La7ISi4DRJr0t6QdKCY/p/ZkO6iZG0XdkydCVZ38almXSF5tK3mXSF5tK3mXSFrG8j00y6QnPp20y6NgAXAauM5PP+QN/02g74+5j+h9mQbm6abXDI+jYuzaQrNJe+zaQrNJe+zaQrZH0bmWbSFZpL32bStdLYfgj4YiSnrAVc4uBxYBJJU4/J/5kN6Uwmk8lkMplMJpPJNDLTAu8W3r+Xjo02uWp3ndBL3dyHHl36f37NYHrRrUv/zxotc83V5f/nV19+xiSTTtH1/29JnY9//O4zxp+w6/WdeEJ1+f/59Zef0auEa1sW33z5GROXoO9PP3f9tf3um0+ZcOLeXf7/Akww7pAu/z/LGqeGUMZz+ym9Ji3n2g4Z0vX6fvPVp0w8STn6dmvp+nu5rHG5RV2/rv3yi8+ZdLLJu/z/BbC7/l4ua5wqw2Ip6z7+6IN3+PrLz7r+4nYSC7VM4G88uFtcdkUAACAASURBVNP+vdcZ+DLwU+HQObbPaX2epBmB22zP08ZntwPH2n44vf8nsJ/tp0dXru6j+8VM59KHHpzSbYayxegyel71SNkidBm3PdT1C5gyWW6xccsWocvo3q25ru3LbzXXlLHobD+WLUKX8dOg5rq23w9sLn0n6zmwbBG6jHG6DSpbhC5l0JDmCS4d5ObRdYcNf1e2CGPENx7Mqd07z65ZfdCrP9leeAz/mfeA6QvvpwM+GJN/sLlmkkwmk8lkMplMJpPJjD0E6tGJDvXO2R+7BdhF0lXAYsDXtj8ck38wG9KZTCaTyWQymUwmk+kUJNHSvWsj0yVdCSwLTCHpPeBwiLxZ22cBdwCrAq8DPwBbjun/mQ3pTCaTyWQymUwmk8lUFtsbjeJzAzt35v+ZDelMJpPJZDKZTCaTyXQOAvVo/Jz2bEhnMplMJpPJZDKZTKZzEF0e2l0Gjb9VkMlkMplMJpPJZDKZTCeSPdKZTCaTyWQymUwmk+kcOrtqd52SPdKZTCaTyWQymUwmk8l0gGxINxi3DfmSPQe/zdqDX+OUIR+N9NybhnzJZoPfYMPBr/PXIR/xi4cM/exj/8JBg99l3cGvscPgN3nO349t0UeLb77+gsP22JDVFp2CjX4/O/+8/eo2z3v2iQfZa+tVWHOJqdh4lTl+9fnGq8xB/0UmY7XFerPaYr3Zb/s1xrboo80z953B2QfOzJn7TMPdl+3IoF8Gtnne4EE/c+t5m3D+YXNxyi4T8u6rD/3qnI/ffY5rTlmZM/aakrMPnIln7j9zbIvfbr79+guO2Xc91l+qF1uvMQsP/uPKNs974akHOHiHFRmw7ORss+asv/r84w/e4uAdVmS9303MjuvNw3P//ufYFr3DfPv1F/xp7/VZe4lJ+eOqfbn/zqvaPO/5Jx/ggO1WZr2le7PFarP96vNL/nYEO26wIKsv0pPLzvrT2BZ7jHno1lM5cqtpOHTTSbnmjK1HeC8DvPbCPzlh17k4aMCEnHXYCnz5ydtDP7vq9C05YIPxOHjjiYe+hgwe3BUqjJJvvv6Cg3cbwO8X6cMGK83JPbdf0+Z5zzzxILtv2Z9V+03DhivPNdxnH3/4LqssMuVwr2XmmZCrLzqtK1ToEN98/QVH7rkBa/abjM369+W+O9q+l5978gH23WZl1v5dHzbv/+t7ed9tVmaD5aZj7SV7s8MGi/Do/beObdE7zLdff8Fx+63LRstMzHZrzcxDd7U9RtnmkjMOYPOV+rD5Sn245PT9ieKxwYtP3cfemy/CJstNyo5r9+XuG8/tKhU6xDdff8Ehuw9glUV7s+HKc3DvSObbPbbqz2qLT82Gv5+zzXOuu+xMBqwyF6ss2pvN11yQd996bWyKPlp889UXHLDrJiy/0NSsvcI83H3btW2e9/S/H2KXLVZnpUWnZ50V5/3V5y8++2+23nA5Vlx4Wjb7wxI8//RjY1v0DhPj1EasvMiUrL/SXCMZpx5i9y1XpX+/adlg5bmH++zjD9/l94tMNdxr6Xkm4qo6HacO3X0Aqy7amwErzzHyteNW/Vlj8anZaAT38vWXncnGq8zFqov2Zos6vZfLoNb+qrNe9UpdhXZLMtDX9uuj8d3fAK8AvWx32gpK0lLAebZn76x/c2wymbqzgSbnWX/PQDzC857x91zvL/hzy3RMTneOHvIBl/M5W6g3AH8Z8iFzaDwO17Q8xfccN+RDzm6ZkV6qq1uG047ekx49xuG6B97i9f++wMG7rMMss8/LjLMOvxAdf/ye9P/D5gzsvz5XnPeXNv+tP59+HQv1W74rxB5t3nrlXp685yTW3e12Juw1NbeesxGP3XE0S611VJvnTzvLEiy43M7cdv5mv/rsx+8+48Yz/8Ay6x5P39/+gSGDf+bbr94f2yq0m7NO2I3u3cfhkrve581Xn+OoPdZipr7z8ZtZhp+8xxu/JyuuuQVLrbwh1110/K/+nRMP2ZQ55u3HYafeytOP3snxB2zIWTf85//Zu+/wKIo+gOPfSc+l0SGEFBJCaKH33nsoIlURBaQjYgF8bSigYAELiIogKCCiIL13hFACoXdISAgQSiDlcunz/nEhhbsAIZdLkPk8zz3P7c7s3vzuZsvczs7iUrSkuUJ5rB+mj8PKyoal28K5cv44H4/rgXfF6nj6ZK/HdvYOtOs2iBYd+vDngi8M1lPW3YfB4z5j49+F80Q8q/PBm9m5cgbDP9mGc7GyLJrRiy3LJtN54OcGebUxd/jtixd5cdTPVKkbwOY/PmLx1/0ZO2N/Rp6WPd6l44DC9+fBrKlvYW1twz+7r3Dp3AkmjXqRCn7VKG+wj3Kgc89XSOysY8m8r7KllXZ1Z9PhyIzpG9dCGdC5Os3bdTdLDLkx5/NxWFnb8OeOMC6fP86HY3viXbG6wT7Zzs6BDj0G0SqhD8vmG9blkRO+xtO7MpZWVpw7eYhJwzszf/VJipd0NVcojzXvy7FYWduwYON1Qi8cY9pb3fDyrY6Hd/Z91JZ/5nFo9xpmLj6KEIJPxnaktJs3HV4YTkpKMjMmvMjAMdNp3/N1Lp0N4uNRbfGtWp/yFWsUUGTGfZN+vF25K4RL507w3uhe+Pj5G9RlO3sHOvccSGKn3iw2crxdt2IhG1YuYvqcFXh6V+L6tRCcnIuYK4wn9tXUd7C2tmbdnotcPHeSd0b2oYJfNbx9szeo7O0d6PLCy7Tt3Ivffp6ZLS3mfhQTRvfj3Y9m0qJdN7au/5sJo/vy1+bjOLsUNWc4jzRr6ttYWduwavdlLp07wcRRvang50/5CtljtbPX0LnnQNp0fpHF877Ollba1Z3NhzMv4Fy/FsqAzjVoUQj3U9+m1+UV6XX5f6N74Z1DXe7YcyCtO/VmiZG6vH7FQjauXMRnhbwuFwjVtfvZIqUMk1I65rURLYSQQoiMy1pSyr3PSiMaoLFwopFwxAnLR+bbLmNoJ1zwFLY4Ckv6WRRnu4wBIEImcZlEBogS2AoLmggnPLFlv4wzRwhPTBevZe+2Vbw6+iPsNY74125Mo5Zd2LrO8KpAJf96tAsYgGu58gVQUtM5c3AJVRsNooRrFew0RWnQaSJnDiw2mtfSyobarUbj5tMYCwvD+nBkx2w8K7elcr2+WFnbYmPnRPEyhlfrC0KCTkvgjpW8NGIy9hpHqtRsSv3mXdm5YYlB3opV69Oq88uUcfM2SIu4eoHL54LpP+xjbO3sadz6BTwrVGP/jpXmCOOJJOi07Nv+DwNHfYy9xpGqtZrQoHlXdqw3jNWvWj3adH2JMjnU47YBA6nXpCP2Gqf8LnaeBe36jfptBlPGoyoax6K07f0+h3cuMpr35IGVlHavSo3GvbG2saN934+5fvU4t66dM3Opc0cXr2XP1tUMGfshGo0j1Ws3pnHLzmxZa3iVtrJ/XTp060/ZJ9hHbV6zlOp1muDq5pkfxX5qCTot/25bxaDR+rpcrVYTGrXowvb1Sw3yVvKvR9tH1GXviv5YWj3441aQkpLM7ZvX8rH0uZOg03Jg50oGDP8Ee40jlWs2pV6zAHZvNNwf79rwG90GjKdE6XIUL+VGt5fGs2Odvq7HRUcRr42hZeeXEULgW6Uebl6VuRZy1twhPdKDujx4zMN12fB4W9m/Lu0DBuBazssgLS0tjUVzP2P0hBl4+VRGCIGbuzfOLsXMEMWT08Vr2bVlDa+/8QEaB0dq1GlE01ad2GRk261SvQ6duvXDzUi8J48doljxUrTu2BNLS0s6dutLkaIl2L2t8PSw0MVr2b11NUPHfpDx2zZp2ZnNRn7bKrnaT/1BjUK4n9LFa9m7dTWvjfkwy7ljZ7Y+RV3+be5njCrkdVnJX/+JhrQQhewy6TMgTCZSHtuMaS9suU8qMTKVMBIpgzUakVk9ygsbwkgqiKLm6NrVi1hYWuLu5Zsxz6eiP6GXzjzV+j6bNJgXWngwYXgAl8+fMFUxTeruzbOUdKuWMV3SzZ/42Fvo4u7mel03Qw9h51CUZV+34cdJXqz6sTcxUeGmLO5Tiwi7gIWlJW6emV0+y/vWIOxK7n7bsCtnKOPmjcYhs2Hp5Vs91+vJTxHp9bhclli9K/pz9XLhKWN+iAw7g6tX9Yzpsl41iLsfiTbWsC5HhmfPa2PnQPHSPtwMP50xb/+muXz0Sgm+eaceJwJX5G/hn1D41UsG+6gKfv6EXMpbI2nzmj/o2P2lvBbP5K5l1OXMeMtXrP7UdfnDsT3pWt+FcQObUb1ucypWrWOqoubZ9fR9VFmPzO3W07c64Ub2LeFXzuDlm1l/vXyrEx6iz1ekeGmate/HjrULSU1N5fzJQG7fvErlmk3yP4hcMHq89fMn9HLu6vLtyAhuR0YQcukMvdtWpF/HKvw6ZyppaWmPX9iMwkL1266HV+btQr5+1Qi5lLs/76SU2brxP5h35WLh+aPE2H7Kx68aoSbZTw3Ia/FMLj/qct+2FRnQsQoLC2FdLjDpj7/6r3ftzpeGtBAiVAjxnhDijBDinhDiVyGEXXra60KIS0KIKCHEGiFE2RzW0UUIESyEiBFChAshJmdJ80q/cjxECBEG7Mgyz0oI0UgIEZfllSCECE1ftr4QIlAIcV8IcUMIMVsIYZOe9uAm0uPpy/UVQrQUQlzL8tmVhRC70pc/LYToliVtoRBijhBivRAiVghxUAjhY+rv1xQSkNkayg7pVUFHGjopM6Yf0GBJPIVr56CL1+Lg6JxtnoOjM7r43F85/9/nv7J001mWbjpHzXrNmTiiG3Ex901VVJNJTtRia++SMW2T/j4pMfcxx967ztmDS2n54hcMnXIOl+KebFj4msnKmhcJ8Vo0Di7Z5mkcndHFx+ZuPbo4NAZ1xAWdNnfryU+6+DgcHLPH6uDo8lT1+FmSmBCHnSYz7gfvE3WGv01iQhz2muzfkZ3GJSNv085jmTjnPB//epMO/T9h+feDCTm7Lx9L/2R08XE4Plz/nJzzVP+OH9nHvbu3aNG+R16LZ3LG6/LTxzvl+39Yte8OU2evpm6jdlhYFJ7//hPi4wz2Ufrt1jBW/X4oM6/GwYWE+LiMBlbT9n1ZPn8qfZtpeH94S14aMYUSpd3zN4BcMna8dXR0IV6bu/3U7Uj97UNB+7ezYOUhZs3fyPaNf7FhpfHeKAVFF681uu3mNl7/Wg24c/smW9b/TUpyMhtWLSUiPIQEXbwpi5snxvZTjk8Ra1aFez9l7NzRBV0e6vL8lYeYOX8jOwphXS4oAhCWwmSvwio/j0ovAR0AH6Ai8IEQojXwOdAHcAWuAsZHIgEt8ApQBOgCjBRCPLxFtgAqp39OBillYHo3b0egKHAAeNBnIxUYD5QAGgFtgFHpyzVPz1Mjfflsow8IIayBtcAWoBQwFlgihMja9bs/8En6514CpuUQH0KIYUKIICFEUDTmHRjHDkF8lsHFHjSS7bHAXgiDRrOONDSFrAODvcaB+IdO0LTaWOw1jrleV7VajbC1s8fOXsOAoe/i6FSEE0cL/kT87OE/mf1WaWa/VZp/fuiJta0DiQkxGelJOv17G9vcx2xlY4dP9QDKeNbBytqOhp3e48aVAyTqok1W/qdlp3EgXhuTbV68NjbXXZbt7B0NTuLjtTHYOxSers/2GkcjscY8VT0uzI7uXpIxENgvUzpja+dIoi4z7oT097b2hr+NrZ0jCfHZv6NEXUxG3nI+tXFwKo6lpRWV63SmVvMBnDrwTz5G82TsNY5oH65/cbF5qn+bVy+lebvuaAph/TBel/MWr5W1NfWadiAocCuBu9bltYgmY5fjdmsYq34/lJlXp43BTuOIEIJroef4+v0BvDH5V5b/q+PbP06w6vevCPp3fb7HkBvGj7cxaBxyVw9tbe0A6PfaeJyci+Dq5klA78Ec2LvZZGU1BXuNg8G2q42LzXW8LkWKMWP2UpYtmk2XZhU48O826jZqSakybqYsbp4Y2089TaxZbVq9lObtuhXS/ZRhXdafFzx9XXZ0LkIZN0+69h7MwUJWl5X8lZ8to9lSynApZRT6xmR/9I3rBVLKo1LKROA9oJEQwuvhhaWUu6SUJ6WUaVLKE+gbwi0eyjZZSqmVUuoeUY7v0DfK309f7xEp5QEpZYqUMhT4ych6c9IQcASmSymTpJQ7gHXpsT2wUkp5SEqZAiwBaua0Minlz1LKulLKui6PuafZ1DyELSFkjpAbQiJFsMRZWOKBLTdJztbQDpGJeGBj1jI+TjlPX1JTUrh2NXNsuivnTxoMavM0hBAgcx6szVwq1+vLmJmRjJkZSc9R/1C8TGXuXDuZkX474iQap1LYOxbP9bpLlK2mjzPdg/cPd0MrCG4eFUlLTeF6WObol6EXj+Phnbvf1sO7CjcjrmQ7aIZcPJHr9eQnt/R6HJEl1isXThoMNPasq93iJaYtjWHa0hiGfriB0h5VuB6aeQvFjdDjOBYpjYOTYV0u7Z49b1KClrs3L1PGvapBXgCEQD5isEVzcfesYLCPunT+pMEAPk8qMUHHri3/FMrukpC5T47Iuk++cMIkdTk1NYUb4VfyvB5TKWt0H3UCdyP7FnfvKoRePJE9X3l9vrDLp3Hz9KNWww5YWFjg5ulH7SadCQ4sXCfjxo63l8+fxMsnd3XZ3asi1tY22Y49hZGHl37bDQ+9nDHv0vlTlK+Q+3FEatVryoLlu9h84CofTf+ZsJBLVPavbcri5smD/VR4tt/2FF552k+tKpS3n4Dp6zKFvC4XGAEWlsJkr8IqPxvSWW+2vAqUTX9lPLNEShkH3AUM/poTQjQQQuwUQtwWQkQDI9BfRc7pMwwIIYYDLYEBUupbhUKIikKIdUKIm0KIGOAzI+vNSVkg/MG6ssSWtfxZnzkVj77hbTapUpIk00hDkob+faqRhlFr4cxWGU2YTCROprI87S5thL6ri5uwwRtb/pB3SZJpBMpYQkmksShc/yzaaxxo2rY7C+dMQRev5VRwIPt3raNd1/4GedPS0khKTCAlORkpJUmJCSQn6+/5jrwRzqngQJKTk0hKTODPX2cRff8uVWs1MndIj1WlwQBOBf7G3RtnSYi/x6HNX1Cl4cs55k9JTiQlOQGAtNRkUpITMhrKVRu+zKXja7l17QSpqckc2DSDsj6NsNMU/IiTdvYONGrVkyU/fUKCTsuZ4/s4uHstrTobHpgf/LapKYa/rZtnRcpXrMGyeVNISkwgcOcqrl48SePWL5g7pBzZ2TvQuHUPFs/9lASdltPH9nNg91pad8k51hQjsQKkJCeTlJhAmkwjNTVF/70UksdAPaxOi4Ec2r6AyPAzxMfdY9tfn1Gv1SCjeas16Elk+ClOBK4gOSmBrcun4OpZnVLl9Ce1J/b/TaIujrS0NM4f20Lw7iVUrVfwj7Cz1zjQvG035s+eii5ey8mjgezbuZ72Af0M8qalpZGY5bdNfOi3BdizfS2OTi7Uqv+k//2al529A03a9OC3ufrt9nTwfgJ3raNNF8OGf+Z2m4Ike10OCznP4X83k5igIyU5me3rl3LqyL/4121m7pByZGfvQIOWPVn282QSdFrOHt/H4T1raNHJcH/csvNA1vzxDXdvRRB1+zprls6idVd9Xff2q8mN8IucDNqBlJKb1y5zZN/6bPdUFwb2Ggeate3OgvTj7cngB3XZ+PE2MX2fzEN12c5eQ6uOvfjj11nEa2O5dTOCdX8vpFGLTuYO6ZHsNQ60aBfAvNnT0MVrOXH0AHt3bKDjI7fdlMxtNylz2z1/5jgpyclo42KY/eUHlCpTloZN25oznEd6sJ9akB7ryaOB/LtzPR0e8ds+yX6qdv3mBssXBg/q8q9Zzx13rqddDvHmdMy1s9fQsmMv/kyvy7dvRrC+ENblgiMQFqZ7FVYiP64+pd+PPF1K+WP6dCdgNrALuCulnJA+3wG4j/6RV6FZH38lhLicvsxcKWWCEOIboISU8uX0K9ghgHX6lV8enpf+2KoVQFMp5YUsZdsOBAOfSCljhRBvAi9KKZump2d7BJcQoiWwWEpZLn2dfwFlszTMlwIXpJSThRALgWtSyg8eXvZx35mvsJOzLPM+suHStDv8IaOyzesvitFWuDA6LZQ5Fl6UEtaA/jnSK2QUiUgaC0dGi1JYp983HSmT+SbtJhdIoCRWjLAoRU3hkOfyPaAJPmaS9cRER/HlRyM4GrgD5yLFGDpuCm269OXEkX28N6oH6w/eBuDY4T28PaRjtmVr1G3GzAWbCb10hqkTX+VG+BWsbe2o4Fed18dPwc9EA9us22Pae8uPbP+eoG0zSUlOoEKN7rTp9y1W1vqB4xZNrUv9Du9SuV5fAOZ/VIWYqLBsyw/+5DQuxfV17fjeeRzc9AUpSTrK+jSiTd9ZOBV9bHV9pFYNbB+f6QnERkfx3ZTXOXZwG04uxRk0ZhotOvbndPC/fDKuK8v36O9hP3lkN++PyH5SUq12cz77Sf+86MjroXz7yRAunD5EydIeDJ/wHTUbtDFJGa0sTfPbxkZHMeuTYQQf2I5zkeK8OnYqrTr149TRf/lobDdW7tNv0yeCdjNpWPtsy/rXac6MeVsBmPnxULat/T1b+vjJ82jX7RWTlPN0qGnHddy9Zha7/vmC5CQd/g1foNeIuRl1+atx/rR+YRK1W+j/ULhwfBurfnmDe7ev4uHbgL5jF1CslBcAP7zfghtXTyClpFip8rTuNZGaTQ1PeHOrfsVHdXZ6MjHRUcz4cBRBgTtwdinGsPGf0q5LH44f2cfEES9kPNYq+NAe3hzcOduyNes25duFmzKm3xnWncr+dRgy9qM8l+thCSmm+W1joqOY+fFwjh7YjnORYgx+YyqtO/fj5NF/+WB0d1YH6geTO354NxNez3ZXFtXrNOPL+VsJu3KOrz56nbArZ7GwsMTNowL9hk6gSWvTPUZHm5j3eGOjo5gzdSjHD+n3US+P/ozmHfpzJngvU8d3Zeku/W0yUkp+nz2JbWsWANC222AGjpmecVV237a/WD5/KrdvXkXj4ELzjv15edRnJr0nvJgm52e0Pyl9XR7JkQPpdfnNT2mbfrydMLInmw7dAiD48B7GD87emKhRtxnf/qqvy9q4GL76ZCwH9mzC0cmFrr1e45URk0x2ldrGMsUk64m5H8W0D8ZwOHAnLi7FGPnWZNp37c2xoP28PfxFth+5DsDRQ3sZ82rXbMvWqteUOYv03fM/emcwgXv0++iGTdsw/v0vKVbcdI9fTEnLez2JiY5i+oejCArcibNLMYaP/yRjPzVhRK+Mx1oFH9rLOCP7qe8WbsyYfntYDyr712Ho2A/zXK6HpUjTbBMx0VF8maUuv/7mpxnnjpNG9mRDel0+dngPbxmpy7Oy1OWZWepyl16vMdBEdXlE36acP3208LYgH6OSxkHO9zPdk2CaHjt6REpZ12QrNJH8bEjHAp3QX5VdDewFtqO/J7odcBb4AqhjrBErhLgFvCulXCSEqI++C/WWJ2lIo7//+iAwWEqZeRaiz3cofV1TAL/0st3OUoabwCtSyi3p0y3JbEjbpJd7HvA10AT9PdP1pJTnCkND+llhqob0s8DUDenCzlQN6WeBqRrSzwpTN6QLO1M0pJ8VpmpIPytM0ZB+lpiiIf2sMFVD+llhiob0s8JUDelnwTPfkHZwkAsqm+4WtSZHggplQzo/a+RS9INyXUl/TZVSbgc+RH+l+Ab6gchyumwwCvhUCBELfAQsz8VntwHKAH9nGbn7wXNS3gEGoG/ozwP+fGjZycCi9FG5+2RNkFImAd3Q/0FwB/gBfaO7cD/MVFEURVEURVEURTGZ/PxL9rCU8vOHZ6Z39/7R2AJSSpHl/d/A3znkC0U/snpO8xamv4wtuwd4uK/BR1nSjZWvXJb00+QwOJmU8tWHpndlXVZRFEVRFEVRFOW/TEChHiTMVJ6vvk2KoiiKoiiKoihK/hEU6kHCTOX5udlAURRFURRFURRFUUwgX65ISym98mO9iqIoiqIoiqIoSmFWuJ//bCqqa7eiKIqiKIqiKIpiEkKAeA4a0qprt6IoiqIoiqIoiqLkgroirSiKoiiKoiiKopiMsPjvX69VDWlFURRFURRFURTFNNSo3YqiKIqiKIqiKIqiPExdkS4kLKpUQbNsX0EXw2zia9Us6CKYzeGu8wu6CGZV279JQRfBjCwLugBmdfNGfEEXwazKVL9T0EUwmxu6kgVdBLOKlc/X6Y+r/e2CLoLZaFMdCroIZpVi8fwch7ySzxd0EczGHl1BFyGP1KjdiqIoiqIoiqIoivLEhOrarSiKoiiKoiiKoijKw9QVaUVRFEVRFEVRFMVknodRu//7ESqKoiiKoiiKoiiKCakr0oqiKIqiKIqiKIppPCf3SKuGtKIoiqIoiqIoimIiz8eo3aprt6IoiqIoiqIoiqLkgroirSiKoiiKoiiKopjE8/L4K9WQVhRFURRFURRFUUxGjdqtKIqiKIqiKIqiKEo2qiH9HxMTHcVHb/alS/0S9O/gx/b1fxrNF3xoN28N6Ui3xmUY0LGSQfqAjpXoVK8YXRqUpEuDkkwYHpDfRc+1dWn3GJ96lZ6pF5mVdvOReVel3WNg6mX6pl7i27SbJMu0jLRImcz/UsPplXqREakhHJPa/C56nkRc/pODm7oTuL4jF4I/Jy01yWi++JgQju0aSuCGTgRu6MTJfW8SHxOSkZ6SHMuFo9M4uDGAgxsDuHpugblCeCJxMVF88/4LDOngyJt9vNi/danRfFJKlv04kZEBJRgZUII/5k5ASmmQb++mRQxsYcGudb/kd9Fz7XmKNasjO2Yzd5I3379dlk2/jyQlOdFovtSUJNbMe4l5H1bh69GOhF/Yky19//ppzBpbhO/Gl8543b8TYnRd5nb//n1GjxxGTf/KtGrehLVrVhvN98u8n+jaqT21alSldcum/DLvp2zpZ8+cZkC/3tSp6U/zBoN2RQAAIABJREFUJg2Z/f235ih+rsVER/HBuH50rF+Svu0rse0Rx6A3B3eiSyNX+naobDTP34vn0K9jFTrWL8kr3WoTHnoxP4uea7HRUXwxsRcvtXRmRA9v9m7+w2g+KSW/z57Eq+1L8Wr7Uvz+/cRs2+3JoB28+0o9BrYuyqgXfNm6ap65QsiV560uR9+/x9ujB9G4piedW9Vi49oVRvMt+mU2vbs2o2ktL7q2rsOiX2ZnpEXdvc17bw2jfdNqNK/jzWv9OnPy+BFzhfDEou/f490xA2leqxzdWldn09q/jeb7ff539AtoTMvaHnRvU5Pf53+XLf3Hb6fRP6AJjaqW5Ofvp5uj6E/lXnQ0g8ZNxKt+K2q378mK9ZuN5vvp92XU7dgL74Zt8G8dwIczviElJSUj/eS5CwQMGoFPo7bUaNONr+bON1cIhV96125TvQors3btFkKEAkOllNvyuJ44oLqU8koe1rEQuCal/OAJ8noBIYC1lDJFCLERWCalXPS0n59fvps2HmtrG/7eFcqlcyd4f8wL+Pj541WhSrZ89vYaOvV4hcROvVn6y5dG1zX1+7+p07C1OYr9VIoJK/qI4gRLLYkYNiYeOCq1rJBRTLUoR3GsmJZ2nSXc5VVREoAv025QSdjxsXAjCC3T027wk4UXLqLw3flw79ZBrl1cQrXG32JrV4Izh/5H2LkFeFUdYZDXxr4ElepPwda+DJDGjSv/cO7IZGq30lfbKye/JzU1gbrt/iI56R6n9r2JnX1pSnt2MXNUxi2aNQYraxvm/HOTq5eO8fWkrnhUqEG58lWz5du59meO/LuaafOPgRDMeLs9pcp606Z75neijb3H2iXTcXto2cLieYr1gdAz2zi05Wt6v7EexyKurP65P/vXT6N5j0+N5nfzaUztVqNZN3+g0XS/Or3o/GrhO4n5dPKHWFtbs+9AEGfPnmH40MFUqlQZ34oVs+WTUjLjy5n4VapEWNhVhrz6Cq6urnTp2g2At8ePo137Dvy+ZBkR164xoN+LVK5chTZt2xVEWDn6Jv0YtHJXCJfOneC90b3w8fOn/EPHIDt7Bzr3HEhip94sNnIMWrdiIRtWLmL6nBV4elfi+rUQnJyLmCuMJ/LLV2OxsrLhlw3XCb1wjM/f7oaXb3XcvbNve1tXzePwnjV8vfgoIJjyRkdKuXnT4YXhpKQk8+XEF3l5zHTa9Xidy2eDmDy6Lb5V6+PlW6NgAsvB81aXp386EStra7btO835s6cYN3wAFStVxcc3+8UHKSWfzpiNr19VroWFMmpIb8q4utGhS0/i47VU9a/FW5M+pVjxkqz6ewnjhg1g3Y4jaBwcCygyQ19++i7W1tZs+vccF86dYvzwvvhWqoqPb/Y/uaSEydPnUsGvKhFhIYwd2ovSZdxo36UXAOU8vBn77mRWLltYAFE8uUnTvsbG2ppTu9Zz6txFXhr9NlX9fKlUwTtbvvYtm9KvexdcnJ24Fx3NkLfeZ96Svxg5qD8AIyd+TOc2LVi1YA5h128Q8MoIqlWqSMdWzQoirEKmcDeATeWZvCItpXTMSyPaBJ/fqTA2onXxWvZuW8Wroz/CXuOIf+3GNGrZha3rDP8lr+Rfj3YBA3AtV74ASmoajYUTjYQjTlg+Mt92GUM74YKnsMVRWNLPojjbZQwAETKJyyQyQJTAVljQRDjhiS37ZZw5Qsi1W2GbKO3RBQfn8ljZOOHhN4jI8I1G81pZO2GncUUIoT/6CQsStBEZ6VGR+ylXYQCWVnbYaVwp7dmFyLAN5grlkRJ0Wg7vWUGvIZ9ip3HEr3pTajfuxr4tvxvk3bvpNzr1eYtipcpRrKQbnfq+xd5N2TfP5T+/R/sXxuLkUsJcITyx5ynWrE4fXEK1RoMoUbYKdpqiNOw4kdMHFhvNa2llQ53WoylXoTHC4tHbe2ESHx/Pls2bGDf+bRwcHKhbtx6t27Rl9aqVBnlfHzaCqtWqYWVlhbe3D23atuPokcwrVxER1wjo1gNLS0s8PD2pXacely5eMGc4j6WL17Jn62oGj/kQjcaR6rUb07hlZ7asNTwGVfavS/uAAbiW8zJIS0tLY9Hczxg9YQZePpURQuDm7o2zSzEzRPFkEnRaDu5cSb/hn2CvcaRyzabUbRbA7o2GdXjXht8IGDCe4qXKUbyUGwEDxrNrvX67jYuOIl4bQ4tOLyOEoEKVerh5VeZayFlzh/RIz2Nd3r5lHaPGvYfGwZFadRvSvHVH1q9ebpD31dfHUrlqDaysrPDyrkDLNh05dvQgAOXcvXj5tZGULFUGS0tLevV9heTkJEJDLps7pBzp4rXs2LqW4W/8D42DIzXrNKR5605sXGMY6ytD36BSeqye3r40b92Z40cPZaR37dmfxs3bFao/CR6mjdexbutOJo0ZhqNGQ8PaNejQshl/rd1kkLe8ezlcnJ2A9NMoIQgJv5aRHn79Br26dMDS0pLy7uVoUKs65y8XWPNEKQDPZENaMe7a1YtYWFri7uWbMc+noj+hl8481fo+mzSYF1p4MGF4AJfPnzBVMc0uTCZSHtuMaS9suU8qMTKVMBIpgzUakbkplBc2hGG8u3RB08aG4OBSIWPawbkCyYlRJCdF57hM4PpO7FvXlisnv6Gc78NX82S2t9rYwnEAuBl+AQsLS1zdM690uFeozrUQw7ocEXoajwqZV248fGoQEXI6Y/ry2UOEnD9C6+6GV+0Lg+cp1qzu3jhLyXLVMqZLlvMnPvYWuri7T7W+yyc3MudddxZOqcuxPYWja2xoyBUsLCwoXz7zKkelypW5dPHRXZSllAQdPkQF38x9+aBXB7PqnxUkJydz5cpljgUfpVGTpvlW9qdh9Bjk50/o5dw1Cm9HRnA7MoKQS2fo3bYi/TpW4dc5U0lLS3v8wmZyPewCFpaWlPXI3G49fasTfsVwu7125QyeFapnz5e+fRcpXpqm7fuxY91CUlNTOX8ykNs3r1KpRpP8DyIXnre6fDX0MpYWlniW98mYV7FSVS5fOv/I5aSUBAcdwKeC4S1zAOfPniQ5ORl3z8JzESMsI9bMcwtfv6pcuXjukctJKTl2JBBvX+OxFlZXroZhaWmBj5dHxryqfhVybACvWL8Z74ZtqNSsI2cuXOKV3j0y0oa93JflazaSnJzCpZCrBB0/RfOG9fI9hmfF89C1u8Aa0kIICyHEJCHEZSHEXSHEciFEsfS0vkKIK0II5/TpTkKIm0Lo++IKIaQQokL6e3shxNdCiKtCiGghxL9CCPv0tL/Sl4sWQuwRQjxRX0chhKUQ4ishxB0hxBWgy0Ppu4QQQ4UQtkKI+0KIalnSSgohdEKIUib5onJBF6/FwdE52zwHR2d08bm/uvq/z39l6aazLN10jpr1mjNxRDfiYu6bqqhmlYDM1lB2SK/2OtLQSZkx/YAGS+IpPCdsWaWl6LCyyvyn19Ja/z41JT7HZRp12UijzhvxqT4eR5fMk5mipepz7eISUpLj0cVdIzJsPWmpxu9RNbdEXRwaR5ds8zQOLiToYg3yJuji0Dhk5tU4upCgi0NKSVpqKotmjWbgG99hUUhHj3yeYs0qKVGLrV1mLLb2Lunzc7+/8qv9Aq99eISRM0Jp99JsDmycztkgw6sp5hYfH4+Tk1O2eU6OTmi1j47x+29nkSbT6NWrd8a8lq3bsHnTRmpUq0Sn9m14sXcfqlcvXF1/jR2DHB1diH9MvA+7HanvORO0fzsLVh5i1vyNbN/4FxtWFp6OYA9vi6DfbnXxxrdbhyzbuIODCwnxcRn3STdt15e/50+lf3MNH45oSf/hUyhR2j1/A8il560ux8drcXwoXkcn58fW5R+//4K0NEm3Xv0N0uLiYvlwwmiGjXkHJydnI0sXjPh4LQ4PledJYp33/XTS0tIIeGFAfhbP5LTxOpwcs18xd3Z0JE5r/DyqV5cOXDmwnQPrljOodw9KFs/sGdOuRRPWbt2BR72WNO7WjwEvBFCrWhWj61H+mwrybOsNoAfQAigL3APmAEgp/wQCge+EEMWB+ejvrb5tZD1fAXWAxkAxYAJktII2Ar5AKeAosOQJy/Y60BWoBdQFXjSWSUqZCKwEsu4x+wC7pZS3HvchQohhQoggIUTQ/Xt3nrBoObPXOBCvzX4Q12pjsdfkvotNtVqNsLWzx85ew4Ch7+LoVIQTR/fluYwFwQ5BfJbBxR40ku2xwF4Ig0azjjQ0haSzxq3wLexf157969pzOvAdLKzsSUnJHAwtNf29pZXmkeuxtLKnjFd3LhydRlLiPQC8/d/EwtKWI9v7c/bQe5Qs1xZbu5L5F0wu2No7otPGZJuni4/Bzt7JIK+dvSO6+My8Om0MdvaOCCHYtuoH3L398a3WKN/L/LSel1jPHvozYyCwFXN6YmPrQFJCZixJOv17G9vc76+Ku1bGsYgrFhaWuHk3pFarUVwMXmWysj8tjUZDXFz2k9G4uDgcHtHtcfFvi1i1aiU/z/sVG1t9T5r79+8z9LVBjB77BidOn2f33kD+3buHJYsNu/8XJOPHoJhcd/O0tbUDoN9r43FyLoKrmycBvQdzYK/xAYEKgp29I/EPb7faGOw1xrfbrHnj42Ow0+i324jQc8z8YABjP/6VZXt1zFp6gtWLv+LIvvX5HkNuPG91WaNxQPtQvNq42EfW5WWLf2H9quV89/NSbGxss6UlJOh4c8RL+Neow+Dhb+ZLmZ+WPtaHttvHxLp88TzWr/6TWT8tM4i1sHPQ2BOnzT6obKxWi6PDo8+jvD3d8avgzcSp+jEd7kVH02/EeN4eMZjwoF0c27qaXfsPsmCZ8UHpnjf650hbmOxVWBVkyYYD70spr6U3SCcDLwqRMcLTaKA1sAtYK6Vc9/AKhBAWwGBgnJQyQkqZKqXcn74+pJQLpJSxWdZfQwjh8vB6jOgDfCOlDJdSRgGfPyLvUrI3pAekz3ssKeXPUsq6Usq6RYrm/X7Gcp6+pKakcO3qpYx5V86fNBho7Glk3Gf7DPIQtoSQeaU1hESKYImzsMQDW26SnK2hHSIT8cCmIIpqoJR7exp33ULjrluo2ugrHJzKo43O/H210Zewti2Gtc0TVGuZRlpqAkk6/f9R1jbO+NX5iAYdV1O79e9ImYZjUeOj55pbGfeKpKamcPNaZrfBsEsnKFfesC67eVUl7NLxLPmOZwy0dfroDoL2rmJMT1fG9HTl4qn9LP3hHRZ9Myb/g3hCz0uslev35Y1ZkbwxK5Jeo/+huGtlbkeczEi/HXESjVMp7B2L5/mzBMLoaObm5lXem9TUVEJDM0cQP3fubLZurln9/ddyfv5pLot+W0oZV9eM+eHh+q6IPXr2wsrKijKurnTuGsCe3TvzPYbcMHYMunz+JF4+uduvuHtVxNraRn/cKaTKelQkLTWFG2GZ223opRO4extut+W8qxB6MfP2qNCLJ3BP377DrpymrKcfNRt2wMLCAjdPP+o06UxwYOH50wCev7rs6eVDSmoKYaGZ9zJfOHcanwp+RvOv+nsJC3/+jh8XraB0mbLZ0pKSEnlr9CuUKu3K+59+na/lfhoeXj6kPhzr+dM5dtles2Ixv837hh8WrqJ0GTdzFdNkvD09SElJ5crV8Ix5p89fxM/H+xFL6aWkpBIaru8xc/XadSwtLOnbrTNWVlaULVOKHh3bsn3v/nwr+7PGwlKY7FVYFWRD2hP4J71r9H3gLJAKlAaQUt4H/gKqATnteUoAdoDBqA3p3bOnp3cdjwFCsyzzOGWB8CzTVx+RdwdgL4RoIITwBGoC/zzBZ5icvcaBpm27s3DOFHTxWk4FB7J/1zradTXsYpSWlkZSYgIpyclIKUlKTCA5WX9fcOSNcE4FB5KcnERSYgJ//jqL6Pt3qVqrcF3pSpWSJJlGGpI09O9TjZw8txbObJXRhMlE4mQqy9Pu0kZ/1wBuwgZvbPlD3iVJphEoYwklkcaicA6UUcq9I5Fh64mPCSElKZbwC79R2r2T0bz3bh0m7v4FpEwlJVnLlVOzsbJ2QuPkCYBOG0FyUjRSphIVeYCbV9fiXnGQOcPJkZ29A3Wbv8CK+R+ToNNy4eQ+ju5bTZP2hiM2N+0wkE3LZxF1O4J7d66zcflMmnXUxzFs0q/M+O0MU38JZuovwZT3q0vPQR/Re+g0c4eUo+cp1qyq1B/Ayf2/cffGWRLi73Fg0xdUbfhyjvlTkhNJSU4AIDU1mZTkhIzG8qXj60iIv4eUkhuhQQTvmkuF6l3NEsejaDQa2rXvwHffzCQ+Pp4jR4LYvm0r3Xu8YJB3zepVzPr6C35dtBh3D49saeW9yiOlZO2a1aSlpXH79i02rl9HpUqF44+vB+w1DjRr250F6cegk8GB7Nu5nvYBxo9BiYkJpKYkg5QkZjkG2dlraNWxF3/8Oot4bSy3bkaw7u+FNGphfF9XEOzsHajfsifL5k0mQafl3PF9BO1ZQ4tOhnW4RaeBrPvjG+7eiiDq9nXWLp1Fyy767bZ8xZrcDL/IyaAdSCm5ee0yR/atz3ZPdWHwPNbl1u26MPe7GejitRw7cpDd2zfSpXsfg7wb1vzNnFnT+OHXvynn7pUtLTk5mXffGIydrT2fzphTKG+7sdc40KpdV37+7nN08VqOHz3Anu0b6NTNMNZNa//ih1lT+X7BStweihUgJTmZxMQE0tLSSE1N1W/jqalmiOLJOWjs6dK2JTPmzEMbr+Ng8HE27dxL74COBnkXr1jD7btRAJy/HMJ383+jWYO6APh4eiCRrFi/mbS0NCLv3GXVpu1U9TP+55Ly3yTM+a991sdfCSHOA4OllEb7CwshagI70XfPLial7JglTaLvsn0F0AINpZTHH1p+IPA/oDP6RrQL+u7jvlLKS496/JUQYifwp5Tyx/TpdsAWMh9/tQtYLKX8JT39W/SjNkUCNaWUfXP73fhVrS3nLst71+mY6Ci+/GgERwN34FykGEPHTaFNl76cOLKP90b1YP1B/dXIY4f38PaQ7DuNGnWbMXPBZkIvnWHqxFe5EX4Fa1s7KvhV5/XxU/CrWifP5XsgvlbNPK9jadod/pBR2eb1F8VoK1wYnRbKHAsvSglrQP8c6RUyikQkjYUjo0UprNPvm46UyXyTdpMLJFASK0ZYlKKmcMhz+R6Y0dW0j+SJuLSMa5eWkpaaSHHXFlSo8Q4Wlvor6Ed3DKSc70BKubfnTsROrp77hUTdbSwsbXAqUhmvKsMzBiu7HbGDkJPfkZISh72DO15VR1C0VIM8l2/4W6YZJCcuJop5M4ZwKmgrTs7F6TPscxq3G8D543v5cmJnftmk74r24NnKu9frv+cWXYbQb8QMo1ezpo1rRZN2L9Gy61CTlNFUnpVYj53K+V78pxG0/XsOb51JSnICvjW707bft1hZ67sJLpxSlwYd3qVyff3udN6HVYiJCsu2/NBPT+NS3JN1C17l6rkdpCYn4li0LDWbvU7tVqPyXL5hHfJ+y839+/f536R32b/vX4oUKcrb704koFt3gg4f4vUhrxJ8Qj/oVOuWTYm8eRMbm8zeMAHde/DplM8ACAzcz1dfTCc0JAQ7O1tatW7L+x9+jL29fZ7LCHBDZ5rbOmKio5jx4UiOHNiBs0sxhr35KW3Tj0ETRvZk0yH9XU/Bh/cwfnD2hnGNus349lf9yLnauBi++mQsB/ZswtHJha69XuOVEZNMdpX6jjbv3VFjo6P4YdpQThzahpNLcV4a9RnNOvTnzLG9fDa+K4t36geBlFKyePYktq9dAECbgMG8PGZ6Riz7t/3FXwumcvvmVTQOLjTr0J+XRn1m0kaXf4lrj8/0GM9KXdammub4HX3/Hp/8bxwH9u+mSJGijH37QzoF9OJoUCBjX+/HvmD9NZauretwK/I61lni7RzQm/c//Yojh/bx+sAe2NnZZxsw6ft5y6hd1zQXJ1Jk3p9kEH3/HlPeH8uh/btwKVKU0W99TMeAFwkOCuTNYX3YfVR/bal7m5rciryerTt3x4DevPfJTAA+mTSa9auyj9L/0Wez6Wqi+6i9kh892NuTuhcdzbgPP2PPgUMUdXHhgzdH0qtLBw4cOUa/kW8RemgHAG98MJVte/cTr9NRvGgRAtq3ZtKYYdil36qw92AQU2b9wOWrYdjZ2tKhZVOmThyPxt4uz2Vs1/c1jp0+W3gvxT6Gf4kicnVX0w0i6LNo/REpZV2TrdBECrIhPR7oDgySUl5NH0issZRytRDCDghCf8/0gvT3c6WUP6SvR5LZIJ4DVAIGom/I1kd/P/QQYBjQHP2V7i+BkTxZQ3okMBZoj76h/jf6buY5NaQbAKuAu+i7q6/O7Xdjqob0s8IUDelnhakb0oWdqRrSSuFj6oZ0YWeKhvSzwlQN6WeFKRrSzxJTNKSfFaZqSD8rTNGQflaYqiH9LPgvNKTXdGtusvV5/7q2UDakC7KPybfAGmCLECIWOAA8uBz2OfpG7tz0+5tfBqYKIYz1l3gHOAkcBqKAGejj+g19l+wI4Ez6+p/UPGAzcBx9o9zwQYlZSCkPom9wl0V/BR0AIYSHECJOCOGR48KKoiiKoiiKoijKM8Xq8VlMR0rpleV9GjAz/fVwvvEPTR9HPyL3g2mR5b0OeDP99bDuD03/lmW5Vx9RzhRgfPrrgTlZ0lsaWaaCkXlhQOG82VZRFEVRFEVRFMXE9KN2P7MX1J+YWRvSiqIoiqIoiqIoyn/b89CQLnzDByqKoiiKoiiKoijKExJCdBRCnBdCXBJCTDKS7iGE2CmECBZCnBBCdM7rZ6or0oqiKIqiKIqiKIqJCIQZH/cmhLBEfxtuO+AacFgIsUZKeSZLtg+A5VLKuUKIKsAGwCsvn6uuSCuKoiiKoiiKoijPqvrAJSnlFSllErAMw7GyJOCc/t4FuJ7XD1VXpBVFURRFURRFURTTMP1gYyWEEEFZpn+WUv6cZdoNCM8yfY3Mp0E9MBn906LGAg5A27wWSjWkFUVRFEVRFEVRFBMxedfuO495jrSxVrt8aLo/sFBK+bUQohHwuxCiWvqTpJ6K6tqtKIqiKIqiKIqiPKuuAe5Zpsth2HV7CLAcQEoZCNgBJfLyoaohrSiKoiiKoiiKopiOEKZ7Pd5hwFcIUV4IYQP0A9Y8lCcMaKMvmqiMviF9Oy8hqq7dhcT9WFi356l7FjxzDnedX9BFMJuJ64YUdBHMak+nYwVdBLOR8uFeQ/9trRraFHQRzGrsJ/cKughmU69VmYIuglm5uPz3n2+a1eYdjgVdBLOxsX2+Tm2tbSwLughmE3GldEEXwWzC7jzb9ViY/h7pR5JSpgghxgCbAUtggZTytBDiUyBISrkGeBuYJ4QYj77b96syjydyz/avpCiKoiiKoiiKojzXpJQb0D/SKuu8j7K8PwM0MeVnqoa0oiiKoiiKoiiKYjLmfI50QVENaUVRFEVRFEVRFMU0hDBr1+6C8t//q0BRFEVRFEVRFEVRTEhdkVYURVEURVEURVFM5nno2v3fj1BRFEVRFEVRFEVRTEhdkVYURVEURVEURVFM5nm4R1o1pBVFURRFURRFURSTMPdzpAuK6tqtKIqiKIqiKIqiKLmgrkgriqIoiqIoiqIoJiLgORhsTDWk/6OO7pjN4a0zSUlOwLdmd1r3/QYra1uDfKkpSWxY+Bq3woKJiQrjxTc24F6xebY8keHH2P33BG6FH8faVkO99u9Qu9Voc4XyWBGX/+TaxaWkpSZSvGwLKlR/GwtLG4N88TEhXDg6DV18BACOLn74+I9D41wegJTkWK6c/I57kQcAKFO+J56VBpsvkMdYl3aP7TKGUJJoLpwYb1Emx7yr0u6xQkaRhKSxcGSUKIW10O/QImUy36bd5DwJlMSKERalqCkczBVGrgRt+56DW74mJSmBirW7067/dznW43XzX+Xm1aPERIXRd/wmPPyaG823cEp9khLjGDn9kjlCyJWg7d9zaMtMfby1utO2/7c5x7vgNSLT4+0zfiMeFY3Hu2hqA5IS4xjx+UVzhPBEYqOj+PbTYQQf2IZzkRIMGjOFlp36G+Q7cXgXf8ybxuVzwTg6F2XBuuwx/P7DxxzYtYbw0HP0HfIeLw3/yFwh5NrVM4sJOb2QtNRESnm0oUqD/xndT8Xdv8ypfR8SH3cNAOdilalUbwKORXyy5UtLTWb/uj6kpsTTotdms8SQGwe3fEfghq9JSdbhV6cHnQZ+n2NdXvXTIG6EHiH6bhgvT9iMZ6UWGemBG2dycv9iou+EYe9UnDqthtOo01vmDOWx9qz9hl2rviAlSUe1hi/wwrAfjMYKcPHEdlbNG8v9O2G4+zag75gFFC3lmZl+fBvrf5/E7evn0TgWo+ugL6nRpI+5QnkiJ/+dy/E935OSrKN8tQCadv8SSyvDeCPDgjiy7XPuRBxHCEtcvZvQuOtnaJz1x67rl/dydMdX3Ll+Alv7IvSfEGzuUB7r2O45HN35LanJCXj7B9DyxZlGY01NSWLrkqHcCg8m9l44PUauxa1CsyzpiexdNYkrJ9eRlpqMa/kGtHhxFo4uZc0ZzmMd3TGboG2zSElOoELNbrTuk/O548ZFg4kMCyY2Koxeb2zA3bdZtjy3wo+xe8XEbOeOtVqOMlcoj3Xh6HzOHf6J1JQEyvl2pHbrKUZ/27s3gjm1fyb3Ik8hLCwpVa4BNVt+jL1jKUD/2x7b9SkRl7eQlppCibJ1qNN2KvaOOZ+jPS+EUF27n3lCiP8JIX4xwXomCyEWp7/3EELECSEs815C0ws9s43DW7+m1xvrGPLpaaLvhBC4YVqO+d18GtNx0C9onEsbpOni7vDPnB74Nx3CiBlhvPbxCTwrt8nP4ufKvVsHuXZxCdUaf0O9dn+RoL1O2LkFRvPa2JegUv0pNOy0gYad1lG8TFPOHZmckX7l5PekpiZQt91f1GjxM7fDNxN5db2ZInnX4bZfAAAgAElEQVS8YsKKPhbFaSecH5nvqNSyQkYx1aIc8y3Kc1Mms0TezUj/Mu0G3sKWpRY+DLQowfS0G0TLlPwufq6FnN7Kwc1f0/fNDQyfdpbo26HsWzc1x/xuFRrTZfACHIzU4wcObZmFxqlkfhQ3z0LObOXQ5q/pM249w6ae4f6dUPY/It5yPo3o/Nr8R8Z7eOs3hTLeuTPewNrahsVbr/HO1EX88PlYrl4+bZDP1t6Bdt0HMfjN6UbXU9bdh9fGfU69pp3yu8h5cuf6fkJO/0rddj/RrOd6dHHXuHR8rtG8tppS1GjxFa367KZV752ULNeCE3vfM8gXemYRNnZF87voT+Xyqa0EbviKl97dyOgvznP/dgh7Vk3JMb+7b2O6v/4rDi7GTjwlAUPn8/bsm/Qfv5agHXM5fXB5/hU+l84Hb2bXPzMY9vFWJs29QlRkCFuWTTaaVxtzh9+/fJEO/T9h8qI7lPOpw5KZmX8gRYafYek3L9NxwBQ+/f0eb359lHI+dcwUyZMJv7CD47u/o/OQlfSfEExsVChHts0wmjdJd59K9V6h37vB9J8QjLWtI7tXvJGRbmXjgF/dl2jQabKZSp87Yee2c3THN3QfsZqB758gJiqUQ5s+zzG/a/mGtB3wMxonw33y8T0/cjP0EP3e2cerH5/Dxt6Fvf9MyM/i51ro2W0EbZtJr7HrGPzJKaLvhHLgEeeOZb0b0fGVeTmfO/7QE/8mgxk+/SqvfnQcz0qt87P4uXIzdA/nDv9Ii16L6TJkL9rocE4HfmM0b1JCNN7+/ekyZA9dhuzFysaBw1syf7uLwQu5eyOY9i9vIGDYAaxtnQneOdlMkSgF7T/VkBZCtBRCXMs6T0r5mZRyqCk/R0oZJqV0lFKmmnK9pnLm4BKqNhpECdcq2GmK0qDTRM4cWGw0r6WVDbVbjcbNpzEWFob/CxzZMRvPym2pXK8vVta22Ng5UbxMpfwO4YndCttEaY8uODiXx8rGCQ+/QUSGbzSa18raCTuNq/4fMilBWJCgjchIj4rcT7kKA7C0ssNO40ppzy5Ehm0wVyiP1Vg40Ug44sSj/7/ZLmNoJ1zwFLY4Ckv6WRRnu4wBIEImcZlEBogS2AoLmggnPLFlv4wzRwi5cvrAEvybvEKJslWwcyhKoy6TOBX4u9G8llY21G0zhnIVGiOM1GOA+3dCOXNoGQ06vpOfxX5qpwOX4N94UGa8nSdy6hHbbZ0njLd+h7fzs9i5lqDTsn/7P7w8cjL2Gkeq1mpCgxZd2bl+iUFev2r1aN3lZcq4lTe6rjYBr1C3SUfsNU75Xew8uX55LW4VeuBYxAdrW2e8/V/n+uW1RvNa2zhh71gWIQQSibCwJD42PFue+NgIblzZQPlqhafHTFYn9/1OjWaDKOlWBXuHojQNeI8T+3Leduu3H4t7xSZGj0GNOr2Nq2ctLCytKO5akYo1A7h2KTC/Q3hiR3b9Rr02gynjURWNY1HavPg+QbsWGc178sBKSrtXpXrj3ljb2NG+78dcv3qcW9fOAbD972k0bD+MSrU7YWlphYNTcYqX8TG6roJy8egy/Oq+RLHSlbC1L0KtVu9w4egyo3nd/dri7d8dGzsnrGw0VG04hMirBzPSS7nXxrdWH5yLeZmp9LlzLugPKjcYSPEylbHTFKFu2wmcDVpqNK+llQ01mo+irHcjo/vkmKirePi1QeNUCitrO3xr9iLq5rn8DiFXzh5cStWGr1DctbL+3LHjRM4cNNwvw+PPHY+mnztWynLuWKwQnTuGnllB+ap9cClRERs7Fyo3GEPomRVG87qWb4l7xc5Y2zphZW1PhRqvcPf6kYx0bUw4ZTybY+dQEksrWzz8uhJ9t/D0ACswQv8caVO9CqvCWzLlqd29eZaSbtUypku6+RMfewtd3N1HLGXczdBD2DkUZdnXbfhxkherfuxNTFT44xc0E21sCA4uFTKmHZwrkJwYRXJSdI7LBK7vxL51bbly8hvK+Q58KFVme6uNvWLiEue/MJlIeTK7J3lhy31SiZGphJFIGazRiMxNv7ywIYykgijqI925fpZS5apnTJcs5098zNPVY4Dtf75Fs+6TsbK2N1URTerujbOULOefMZ3XeHf8+bY+XpvCFW/E1QtYWFri5lkxY1553+pcvXKmAEuVv+KiL+NUNDNep6IVSUq4S1Li/RyX2bGsGduXNuTcoRl4P9RgPnd4BhVqjcHS0i7fypwXtyPOUto9c9st7V4dbUwk8U9Zlx+QUhJ+cR8lylbJaxFNJjL8DK5embGW9apB3P1ItLGGsUaGn8HVMzOvjZ0DxUv7EBmu740RdkHfyJw5vgZThrjxx7cDiY+NyucIcuferfMUc62aMV3ctSq6uFskxD++nDdCAylauvA0ph4n6uZZSrhmnkuVKFsNXewtErS5/02qNBjIjdCDaKNvkJwUz4Wjf+FRqa0pi5tnd2+cpYRblmOQWzX9uaM299vtjdDD2GmK8ufMNvz0XnlW/1S4zh1j7l6kSMnKGdNFSlYmMf4Oibp7j132dsQhnIv7ZkyXr9qHO9ePoIuLJCVZx9Vzq3H1avGINTwvBMLCdK/CKlcNaSHERCFEhBAiVghxXgjRJn2+hRBikhDishDirhBiuRCiWHqalxBCCiFeE0KECyHuCSFGCCHqCSFOCCHuCyFmZ/kMHyHEjvT13BFCLBH/Z+++w6Mo+gCOfye9kVBDSSAhEErovfcmSAfpERVBUKSolFdQQRAVCyJFQVFBKTY6KL1J7y10CCHUQCD1LrnczfvHHZdyFyDkkpwyn+fJ89zdzi7zY3Z3ZnZnZ4XIn2Z5uBDiHdO6MUKIX4UQbkIIT+AvoIRp2HW8EKJE2iHZpvUbCyH2mP7da0KIlzKJtbQQYocp1k1A4TTLHsbkJIToI4Q4lGHd0UKI1Vn5v7UlXVICru4+5u8ups/JSVm/6xh3/wZn9i+hec/pvDrlLD6FAlj/08s2y2t2GVI0ODl5mb87Ohs/61MSM12nwfN/0aDDX5SpOhovn9STYQHfukReWEyKLhFNfCS3I9Zh0CflXOZziBaZrqPsaTrMNRjQSGn+/pAHjiRiyNU8PonkpHhc3VOHsT/cp5O1Wd+Pzx9dhUGfQrkaXWyWP1tLTkqwHu9THLcXjq3GYEghuHpnm+XPVjSaBDy8fNL95uHlgybR/kZF2Ipep8HJOfU85eRiOk/pMj9Pteyzixa9d1Gh7njyFUztfNyO2IqUeoqWsp9hkhkZj93UMk49duOytd2dq6YgDQaqNX4xW9uxpSRtPO4eqbG6mT4naSxjTdbGm5enTf8wbUx0JEd2/ELomN8ZO/scuiQNKxeMsNhOXtIlJ+DilnqeevhZ95jz1L2bpzm69XPqPjcpJ7NnU7rkBFzSnJMffk5Oyvp+nL9IGfLl9+enDyvy3YSS3L99jjptx9ksr7agS05fB7lko86Nf3CdsANLaNZjOoM+PINPoUD+sqO2Y4ouEWfX1JFMzi7GzynJCY9c70HUGcL2zaJqk/Hm3/IVKI2Hd3HWfteAlXOqEhd9iZD69nXcKjnniScbE0KUB4YDdaSUN4QQgWAeYzoC6Ao0A6KAr4E5QNrZY+oBwUBTYDXwN9AacAaOCiF+l1LuAATwMbAT8Ab+BCYBo9JsqxfwHKAFdgMvSSm/FUK0B36RUvqnyXfaGEph7GwPAf4wbb9kJiEvAfYCbU15XwesspJuNfCdECJYSvlwLEc/4ItMtps2P0NMeSFfgcyy8XhnDv7KlqXGg9avbEOcXT1J0saalydrjJ9dXL2srv8oTi5ulKnaiWIBxue06rf/H9+ODyBJE5OuoZRb7lzbyMXjnwPgU6gqDk7upKSknvj0ps+OTh6P3I6jkzvFAruw/69O1Gz1Cy6uBQiqMorLJ7/i8Ja+OLt4U8S/NVGRm3MumBzihiBRGoxHEpg7ye444C5My9LQYMDDDganhO1fxsYlbwLgX7YhLq5e6Rqj5v3YLWv7cXJSAjuWT6Tn8BW2y6wNhB1YxqYlpuO2TENcXD1J0lqJN4vH7cN4ewxfbrvM2pC7uyea+Nh0vyUmxOLukfXzk726eXk9YfuNz7fn962Bo7M7KbrU89TDz47Ojz5POTm7U7JcT7b/1pKGnZfj6OTOhSNfUaPlrJzL/FM4tXcp6xcNB6BkcCPTsZtaxg/rIxe3px+Cf3DLN5zcs5gXx2/JdCKv3HBk52KWzxsGQOmKjXF180KbmBqr1hS3q7tlrC5u6f9fAJI0sea0zi7u1G75EkVKGEcvtOzxP76b3DZH4nhSF4/9zq6VxsdhigXWx9nFM90FkYedSudHnKdi7l3m74W9adBxGsVLN8jZDGfDucO/sf2P0YDx+d+MsepMn11cs74fb//jbVJStAyacgVnFw+ObJvJmu968sLILbbJ/FM4e/BXtiwbCUCJMg0ty1b7dHUuGM9dZat2NLcd67Ufz7zxgXnWdrx6ZiWHt0wEoIhfHZycPdJd/NElGz87uWQ+8Wr8g3B2rXiFGs3fp4h/XfPvR7a+hz4lmS7DjuDo5M65Q/PZteJlWvW1rzZHrhOoWbsz0AOuQIgQIkpKGZ5m2WvAcCllJBgn5gIihBBpx81OkVJqgY1CiARgqZTyjin9LqAGsENKeRF4OJ1ulBDiS+CDDHn5Wkp5w7TuGqD6E8bQH9gspVxq+n7P9JeOqcNdB2gtpUwCdpr+HQtSykQhxCqMFw0+FEIEAxUwdrAfSUo5H5gPULRUTfmY5JmqWKc3Fev0Nn9f/+PL3I08SfmaPQCIun4Sj3y+uHsVyvK2C5eonPFixMO8P212s8W3ZFt8S6Y2LM4dmkxCzEWK+BnvziTEXMTZtSDOLk9wopYGDHotyZooXFwL4OziTflaqbP+hofNw6tAxUdswD6VEq5cIYkmGCv7KySRH0e8hSOlpCu30JEoDea71ldkEs1E3j9jGlKvDyH1+pi/r13wElGRJ6hQ27gf34k8iYd31vfj+3cuEnvvKku+MA6jM6ToSNLEMGdsIAPG7sCncMBjtpAzQur2IaRuxnhPUqGW6bh9yngfmOJd+kUbIDXeueNK03/sdnwK5U28D/kFlEOvT+F6xAX8ShlHhFy5cIKAIPsZrptdxYM6UDyog/n7iV3/I+7+eYoFGs9dcdHncXErhItr/sw2YSalAb1eS5LmDiDQxN/k4IZBABgMOlJ08Wz/vTX12i/C3StvZgCu3KAvlRukXjdfOe9Fbl87QUjdngDcuXYCT++ieDxFHQRwbNdP7F3/OaHjNuFd0P/xK+Sgmk37U7Npf/P3JTP6czP8hHlm7Zvhx/HKXxTPfJaxFi0ZwuHtqc+KJ2sTuHfrEkVLGodKFwuoivkKqJ0oW/0FylZ/wfx967IhRN86TZmqXQG4d/MU7l6+uHkUtLp+3P1rrF/Qgxot3ia4hn3NPp5R+Vq9KF8rNY8bf3mVezdOEVy9GwB3b5zEPZ8vbp7WY32UezdPUa/9RNw8jBMEVm08hAN/T0MTf++p2ma2UKFObyqkaTv+9dMrRF0/Sbma3QG4G3nK2Hb0fIq2o19lSNt2JG/bjgEVuxJQsav5+771I3lw9wwlyz8PQEzUGVw9CuPqbn0Cx4TY6+z4M5SQesMJCOmWbtmDqLNUbvQ2Lm7G83nZ6gM5vXcGSZpoXN2zvq/8l9jzkGxbeeJLBaYO7iiMd4fvCCGWCSEe1toBwArTcOkHwBmMHe+0U/ndTvNZY+W7F4AQwte07etCiFjgF9IMqza5leZz4sN1n0BJ4NITpCsB3JdSph3jcfUR6ZeQeve9H7BSSpn5mL0cFlKvH6f2LuLezTNoE+9zYMN0QuoPyDR9ii6JFJ0WML5SJUWnNZ/sKtUfwMXja7gTeQK9Xse+vz+lRJkGuHk8vgGYG3xLPsftiHUkxl4hJTmOa+cXUbSk9Rl87985SPyD80ipJ0WXwOVTs3FyzodHPmPHQpNwHV1yDFLqib69j1tX11Cy3MDcDOeR9FKSLA0YkBgwftZbqZRaCm82yRgiZBLxUs9vhnu0Ms307SdcCMKVpfIeydLAXhlHOEk0FPZ3N7BS/X6c2LOQuzfOoE24z76/PqVyg4zPtKdKux/r9cnm/bhIiUq89vF5Bk7Yx8AJ+2gXOgcPb18GTthHvjxulKdVqX4/Tu5ZyN2bxnj3/vUplZ/0uE1JjbdwiUq8Nu0cA9/dy8B399J2gCned/eSr0Dex+vm7kmDll1Z/O1ktJoEwo7tYf/2NbR4vr9FWoPBQHKSlpQUHVJKkpO06HSpz/On6HQkJ2mRBgMGfQrJSVr0evubA7JEUEeuX1xJ/INL6JJiuXzye0qU6WQ17b0b+4iNPos06ElJjuf8oS9wdsmHp09pvPKXoWmPv2jQcRkNOi6jUoP3cXUrSIOOy3DzyHz29txWpeEAju9aSNT1M2gS7vPPmk+o2ugJj900+zIY73Zv//MD+r29jgK+QbmS/6yo1TyUA1t/4Pa1MBLj77Plj2nUbm693qhcrxu3r53i5N4/0SVr2fz7FIoHVMXX3zh0v06LgRza9hP3bl0mOSmR7SumU7HW87kZzmMF1+zNuUOLuX/7HEmaBxzd9iXlavaxmjYh5ibrFnQjpP4rhNSzHNYrDQZSdFoMeh0gSdFp0afYz3wd5Wv3IezAz0TfOos28QGHNn9Oxdr9Mk2vT0lbB6VvS/mWrMG5Q8tI0sSg1+s4uXsBnt7F86wTbU3Fun05vXcR926eRZt4n/0bphNSz/K8/FBmdRBApXoDuHR8rbntuP/v6ZQIsp+2Y2BId66c+o3YexdI1sYQdmAOgSE9rKbVxN9ixx/9KVstlDLVLP8/ChatwtWw5eiSYjHodVw68TNunkWf+U70s0I8zdUhIYQ3MA9IkVKGCiHOAa9IKXdbSRsIXAGcpTS+Y8c0s/YAKeV20/dfgLNSyqlCiAWAB8Y73PeEEF2B2Q+HawshwoFXpZSbTd8nAWWllAOEEM2AxRmGdqdd/j+grpQy/eUkyzwHYOxw+zzsTAshFgPStJ10MQkhnICbQBtgGTBaSml96uhMFC1VU/YftysrqzzS4S2zOLTZ+B7pstW60KpP6vtoF06tTd12Y8x3sRe8H0JsdES69V+ZfNp85+r4ru/Y/7fxHZklyjSgVe8Z2W6QH9xku/dFXr+4jMiLpvdIF29G2WrvmN/PemRrKP7BofiWbMvd69u4evZ7kjRRODi6kC9/RQJDXjNPVhZ1fStXTn5NSko87p4lCaw0lAK+9bKdv3FrB2V7GwBLDHdZKtNPctJXFKS18OENQzhzHALxFc5A6nukk0zvkX4jw3ukvzLc4nwOvUd655xjNtvWwc1fc2DDl6ToNJSr0YU2/VLfRfvD5FrUf26M+S72vHcrWOzHQ6aesbjjHHFuJ+t+fMUm75G29dX1Q5u/Nr5HWqcluEb692b/+GFt6j33jvku9vwJFS3iHTw1zOKOc8T5naz/cZBN3iPdvK5ztrcBpvdITx7M0f1b8PYpxMA3p9K8fV9OHf2HSW924o9/jBO+nDi0g3dfa5Nu3cq1mvLJfOMjFzM+GMSWtelngx71wfe07mybZ2i//srylVxPKzzsZ8JP/4Ren0TRUq0IqTfBfJ7avboHQZUHUTyoA7eubuLisbkkJd7GwdEVn0KVCK45It1kZQ9F3zrEyd0TbPIe6TotbDv6Zv+Gmez96wt0yRoq1OpK+xdnm/fleRNr0Oj5sea72LPHlCPmXvp9+Y3pZ8lfOJDZY8sTd/96uve7Vm7Qlw4vziY7fHxssy8D7Fw9g+0rp6NL1lClfne6v/aNOdYvRlahRY/x5rvYF45vZuX3I7h/9yqlguvRa/gPFPQNNG9r47JJ7N1gfDVa+ert6DxoJh5e2X/N2YVzj59E6Umd+GcuJ3bMIiVFQ+lKnWjc9XNz+fz+VSNqNB9F2eovcHjLdI5smW4xXPblScb7Ejcu/8O677umW1a8dEM6Ds7eFDMurlkZbPlox3bM5sjWmaTotJSp2onmPWeYY10yvT61Wr1lvou9aGoV4u6nn1ArdMJxvAsGoE2IZueKcURe2IY+JZlCxUJo1OUjipbK/uvNnF1s9ybWI1tnpb5HulpnWvZObTsu+qgOddu+Y76LveCDSsRlqINennQqTdvxew5seNh2rE/LXtlvO16/HJWt9dM6f/h7zh6ahz4lCf+y7ajZaqq5bDcsbEeFusMIqNiV03tnErZvpsWjON2HnwIgSXOfY9snc/vqbgyGZLwLlaN6s4kULFYtW/nbvLgz0bdP/mtv6dbw85VbX7d+ceJpFJz47WEpZW2bbdBGnrgjbXpG2g/jM8kS+BZwkFK+JIQYDXQBBkoprwohigANpZSrnqIj/RsQAwwFigG/AQFP2JGuABwFikkpY6wsLwWcBgYBywEfoKSU0qLlL4TYB/wDvAvUBdYDq611pE3pvwHKAtWAEg9/f1K27kjbO1t2pO2drTrS/xa27Ejbu7wappZXbNWR/rewZUfa3tm6I23vbNmR/jewZUfa3tmyI/1vYMuOtL2zZUfa3qmOdHr22pHOylPgrsAnwF2MQ6t9MXYyAWZifCZ4oxAiDtiHcYKupzEZqImxM70OY4f3iUgpzwJLgcumYeYlMiyPADoAbwPRwDGMHV9r+mGMIRrjM9qLHvPPL8E4edrvaTvRQoh3hRBZujutKIqiKIqiKIryryQAB2G7Pzv1xJftpJQnMN6ZtbbMAHxp+su4LJwMs2ekHXpt+j4gzefTQMaxLl+kWR6YYd1JGb6nf+Gm8ZnutMt38QSdfCnlZaBJJsvCsYxpV8bfTL9Pe9y/pSiKoiiKoiiK8l8hnoFZu//7ESqKoiiKoiiKoiiKDT1bD5IoiqIoiqIoiqIoOepZeP2V6kgriqIoiqIoiqIotiEEiP/+wOf/foSKoiiKoiiKoiiKYkPqjrSiKIqiKIqiKIpiM2pot6IoiqIoiqIoiqJkhZq1W1EURVEURVEURVGUtNQdaUVRFEVRFEVRFMUmhBAIoYZ2K7nE20vQop5rXmcj19Ss0iivs5BrdrY/ltdZyFVN36ie11nINc2+7JTXWchVcy59mtdZyFUrBxzJ6yzkmo0FK+d1FnKVwSDzOgu5qloZr7zOQq6J0z5bTVtXJ0NeZyHXODX2y+ss5Jpjf7vkdRaUJ/BsnW0URVEURVEURVGUnPUMPCOtOtKKoiiKoiiKoiiKzTwLs3b/9y8VKIqiKIqiKIqiKIoNqTvSiqIoiqIoiqIoim0IAeK/f79WdaQVRVEURVEURVEU21FDuxVFURRFURRFURRFSUvdkVYURVEURVEURVFsRqih3YqiKIqiKIqiKIryhARqaLeiKIqiKIqiKIqiKOmpO9KKoiiKoiiKoiiKjQiEw3//fu1/P8JsEEJIIURZ0+dvhRDv5XWeHicuJpppY3ryQhMfBnUqw46/l1pNd+LQdiYMbU2f5oV4tXNZi+W3b4QzYWhrejb2ZljPyhzbvyWns/5U4mOj+WpCdwa182JUr0D2bFpiNZ2UkmXfjmNYp8IM61SYpd+MRUppkW7X3wsJbebA9rXf53TWn9qhzbOYMzaQmaOK8dei10jRJVlNp09JZtW8fsx7twKfDfUg4tzOTNMt+KA634y33A/yylrDfUbrr9JNf4EZhluPTLvScJ9Q/SV66y8y03ALnTSYl92WOt7VX6OH/gJD9Vc4JhNyOutPJTpBQ7/vV1FszEwqTZrPb4fOZJr22LXbPPf1MoqP+ZoyE75h7vYj5mUnIu/QbuYy/MfNosL78/jk7725kf2n9s/ar/hosB+TBhbkj7mvZrovA1w8uYUvR1Xi/QH5+G5yK+5HXTUvm/FWVT4I9TH/TejjysJPuuRGCI8VHRtP78mzKdx5GOVDx/Dr1n1W0039eRXeHYZQpMvr5r8rN6MAuBsTR8vRH+PfcwTFuw+n+aiP2Hv6Qm6G8cTiYqL5eExPejfxYfAj6qCTh7YzcWhr+jUvxOBM6qCJQ1vTq7E3b/SszHE7rIPiYqL5ZGwP+jbzZkiXIHZusB6rlJJFs8fzYhtfXmzjy6JZ49LVPycPbeXtF+vQv0UBhnULZuOK73IrhCyJjYlm8uhedK5fkND2wWxdv8xqumMHtzPm1bZ0a+zLi+3LWSwf82pberXwp1ujIgztVYc929bkdNafSlxMNNPH9aB/c2+Gdg1i1yPK9+fZ43mprS8vtfXlZyvlO+bFOoS2LMDr3YPZtNL+yjcuJpqP3ulJz8b5eaVjWbY/ou347mtt6N2sMIM6BVssv30jnHdfa0OPRj4M7WG/bce4mGg+fOsFujYowMD2wWz7y/q+fPzgdsYNbkuPJkUY2CH9vvwg+g6fjA+lf5tAejQpwtsvNefsyQO5kX3FjqiO9BOSUg6VUk7J63w8zrfTR+Dk5MKiDdd5e8pCvvlkOBGXTlukc3P3oHXnl3hpxCdWt/P5xAEEla/O4s23CX39Qz4d35uY+1E5nf0sWzhjOE7OLsxZcYthE3/hpxmvE3nFMt5ta+Zz+J9VfLTgGB/9cJxje9exdfW8dGkS4u6zZvEn+JWulFvZz7Irpzexf8MX9B61ntc+OkNMVDi7107NNL1f2YY8/8oPeHoXzTTNgY0z8MhXJCey+9QKCid6ORSijfB+ZLojMoE/ZTRTHfxZ4FCaW1LHYnnPvPwzw02ChCtLHMoQ6lCYTww3iZEpOZ39LHv7jy24ODlwceowvg/twFu/b+bMzbsW6e7FJ9L92z95uWE1wj9+nWPvDaJVhQDz8kGL1tGojD9XP36D9SN6s2D3cdafvJiboTyx88c2sGPVdF59fyPj5lwi+s5lNv82yWrahNi7/PL5C7TpPZn3fojCL6gWS7/qa14++ssTTP45hsk/xzBp0QN8CpeiSoOeuRTJo42esxgXJz1aQKQAACAASURBVCfCf53Bj+OGMHLWL4SFX7eatmezOkStmmv+K13ceFx6ubvx7VsvE/HbV9z4cxZv9WpPz/e/JkWvz81Qnsh8Ux3004brjJ6ykHmZ1EGu7h606vwSAzOpg7401UE/b75Nfzutg7777E2cnF344a8bjJ68iPmfvkHEZctYN674jgM7VvPlL0eYsfgoh/5Zz8YV8wFISdHx6dietO06mF+2RvPWR0v4aeY7XDl/PLfDeaw5H4/EydmFX7dGMG7aT8yaNoLwi2EW6dzcPGnXdSCDR02zup1hY79g6aZwVuyOYtR7c5g+4WXuRd3M6exn2fefv4mTkwvfr7/ByEmL+G76G1yzUr6bVn7HwZ2r+eKXI3zxy1EO705fvp+N60mbboNZtCWat6YuYeHMdwi/YF/l++2nI3ByduHnjZG8PXUh33z8JlettR3dPGndeSAvj7R+3H42IZSg8tVZsuUWoa9/yCfj+tjdcQvGfdnZ2YWlW64xZtpPzJ72JlcvWdmX3T1p22Ugg0Z9bLFMkxhPuUq1mLVkL79tv0WrTgP4YERXNInxuRHCv4MQtvuzU6oj/R+i1SSwd+ty+g+dhLuHFyHVG1O3aUe2rV9skbZcpbq06DCAYn5BFsuuXz3PpbNH6TvkA1zd3GnYsjsBZSuzZ+vy3AjjiWk1CRzc+Sc9Bn2Im4cX5as2pmbDzuze+LNF2l1/L6J9r7co6OtPwSJ+tO/9Frv+XpguzW/z/0fb7m+Sz6dwboWQZaf3LaZKoxcpXCIEN88CNHh+PKf2WsYL4OjkQu1Ww/Ev2xDh4Gg1zYO74YQdWEa9597JyWxnWUORjwbCi3xYz/dDW2QsbYQPAcIVL+FIH4dCbJGxAFyXyVwiiX6iMK7CgUYiHwG4skfaVyWXkKRj9fELTOjQCC9XFxqU8ad95TIsO2hZqc/edphWFQLpXbsirk5O5HNzoXyxQublEdGx9KpdEUcHB4IK56dBkB9nbt2z2I49OLLjZ2q3eJmiJSvh7lWAlj0mcHj7IqtpTx9YQdGSIVRp0BNnFzdav/ABN8NPcOf6WYu0V87sJCHmDpXrdc/pEB4rQZvEyn8O8/7Arni5u9GwcjDPN6jG0i1ZGyng5uJMuZLFcHBwQEqJo4MD9+MTiY6zrxEWD+ugfmnqoDpNO7LdRnXQXjuqg7SaBPZtW06/1ybj7uFFxeqNqdOkEzv++sUi7fb1i+jcbzSFi/pTyNePzv1Hs3Wtsf6Jj4kmMSGW5h0GIIQgOKQOfoEVibyS+aiUvKDVJPDP5pUMfOMD3D28qFyjEQ2aPc+WdZajwCpUqUPrjv0p5l/a6raCylXB0enhk4WClBQdUbciczD3WafVJLB/23L6pCnf2o8o3079RlPI11i+nfqNZvu69OXbrL2xfMvaYflqNQns2bqCAabjtlL1Rpm3HSvXoeXzAyjmZ1m2D4/bfq+9j6ubO41amdqOW1bkRhhPTKtJYPeWFYS+nrov12/WkS1rLeMtX7kOrTr2p7iVeIv7B9E9dBQFixTH0dGRDj1eRadLJjL8fG6EYf8E4OBguz87Zb85syEhRLgQ4n9CiDAhxH0hxI9CCDfTssFCiItCiGghxGohRIlMtvGTEGJqmu9dhBDHhBCxQohLQojnhBAvCCEOZ1jvbSHEypyN0Oh6xHkcHB3xC0gdflI6uBoRly0b5I8ScTmMYn5BeHjmM/8WGFw1y9vJabeuncfBwZHiJVPjLVm2KpFXLPN5Pfw0pcpWM38vVaYa19Pcub505gBXzh2mZZehOZvpbLp74wy+/lXN34v4VyEx9g6a+KfrLG359S2adJmEk7O7rbKYqyJkEqVxNX8PxJUH6ImVeiJIohjOeKR5/UJp4UIEyXmR1UxdjIrG0cGBYN+C5t+q+BWx2gE+ePUmBTzcaD1jCUET5tJr/gquRceal7/erCZLD5xGp9dz4XY0B67coHm5AIvt2IPbkWEUD0w9JosHVCM+5jYJcZZx374WRvGA1LQubp4UKlaGO9csj/Uj23+mcv0euLh55kzGs+BC5C1j2foXM/9WpXRJwq7esJp+/b7j+PV4k1qD32P+mm0Wy+sO/YACnYbywgezeOm5Jvjmf/SIjdx2w0Z10DVTHeSepg4qbWd10MNYS5RKjTUguCrXrOTx2uUwAoNTz9uBwVW5Zqqn8hcqSpO2fdi65if0ej3nTu4l6tZVKlZvlPNBZEHk1Qs4ODriH5A6nLd0uapW7+I9iffe7EbHuj6MDG1C1dpNKVeplq2yahNZKd/Iy2EElK2aPl2a8m3ctg9b16Yv3wrV7Kd8r1+1ctyWy/rxZmw7lk7XdrS34xbS7stp463C1Wzm89K546TokilRskx2s6j8izxLk431B9oBCcAaYKIQYivwMdAWOA18DiwDmj5qQ0KIusAioCewBSgO5AOuAPOEEBWllA8vNw4AMh97a0PaxAQ8PH3S/ebh5Y0mMS5r29HE4+GVvoHm6eXDvTvWhyPmlSRNPB5eGeL19EGrsYxXq4lP93/j4eWDVhOPlBJpMLBwxhuEjvgaBzu+6gWQnBSPq3tq2bi6G2NK1sbj7lUos9WsOn90FQZ9CuVqdMn0+Wl7p0Wm6yh7mq4NajCgkdL8/SEPHLmHfQ3tTkjS4e3mku43bzdX4pMsO/zXH8RzPPIOK4f1pFKJwry3eievLFrHplHGYc7PVQ5iyC9/8/W2Q+gNknHt6lMroJjFduxBsjYeV4/UfdnNw7Qva+LwzFfIIq2nd/qRIm4e3iRp0x/ryUmJnNr/Jy+OtY87IAmaJLw901+k8vZ0J16jtUjbo2kdXunQlKL5fTh49jJ9p84lv5cHvVrUM6c58O1ktMk6Vu8+QnKKfe3HABob1UEaK3WQh53VQdrEeItYPb18rMaqzVBXeXj6oE001j9CCBq37c3cj15jwYzRALw2dg6Fi5bM2QCySJMYj6dXxni90SRkrWwfmjJrBSk6HUf3b+XalXN2V/dmbDOAsdwyK9+0/zeeGcu3TW++mfYaP5rKd/AY+ypfrSbBoi3l6eWDJiFro7es7SMeXt7ci7J+4TCvaK3uy1mPN62E+Fg+n/gy/YdMxDOfz+NXeCbY95BsW3mWOtKzpZTXAIQQHwGzMHaAf5BSHjH9/j/gvhAiUEoZ/ohtDTKtt8n03Vy7CyF+xdh5niCEqAQEAmutbUQIMQQYAlCkWKmnj8zEzcOTxITYdL8lJsTh7pEvkzUy2Y67l0XlmJgQm+7ugD1wdfdCkyFeTWIsbu6W+XRz90KTmJpWkxCLm7sXQgg2rZxLyaAqBFdukON5zqqw/cvYuORNAPzLNsTF1YukNBcKkjXGmFzcvLK03eSkBHYsn0jP4fbR4XhabggSpcE4hAhIxDjRmDsOuAvTsjQ0GPCws4E4nq7OxGnTd5rjtMl4ubpYpHV3dqJjlbLmzvH45xpQ+t25xGiS0BsMdP9mOZ/1bEmvWhW5HZdA6A9r8M3nyeAm1XMllkc5umsJK+cPAyCwYmNc3LxIStMo1T7cl60cvy5uXhYXyLSJcbi6pU97ev8K3L0KUjqkma2z/1Q83V2JS0zfaY5L1OLl7maRtmJA6mCo+pXK8kbX1qzYdShdRxqMw7x7tahHjVcnUjWoFFXL2E+D3N1GdZC7uxeJdl4HuXl4WYk11mqsbhnqKk1CLG4exvonMvwsX0zox7jpf1CtbhtuXrvAtLe6UKBwcWo3fj7H43hS7lbjjctWmTg5O1OncTtWLJlN8ZJBNGjeMbvZtBk3d8t4NY8o37RpExNTy/d6+Fm+nNiPsZ/+QVVT+X78dhcKFilOrUb2Ub5u7p4kxlvZlz2z1q6wto9oEuLwyOLxn9OsHrvxWY/3oSSthkkju1OhSj16Dxpriyz+Z6hZu/9brqX5fBUoYfozT/0qpYwH7gF+j9lWSeBSJssWAv2EEAIIBX6TUlqdilZKOV9KWVtKWdunQPafy/UrVQ6DPoUbEamzuYZfOE6poJAsbadUUAi3rl9O15C5cuFElreT04qVLIden8KtyNR4Iy6ewL+0ZT79AisRcfF4mnTHzZOKnT6ylUO7VjK8W3GGdyvOhVN7WDL3HRZ+NTzng3iMkHp9GDUzilEzo+j55ioKl6hIVOQJ8/I7kSfx8PbN8t3o+3cuEnvvKku+aM2csYGsmteXhJhbzBkbSMzdq4/fgJ0oJVy5QurhdYUk8uOIt3CkFK7cQpeuM31FJlEKyw5qXipbpCApBgMX79w3/3byRhQVi1mWaaUShdNd4H34UUpJ+L0YHB0E/epWwsnRAb/8+ehRszwbw67kcARPpkaTfuYJwV5+dx1F/UO4eTX1mLx19ThePkUt7kYDFC0Zwq00aZO1CUTfvoRvyfTH+pEdi6jZ1Pgsoj0I9i9Gil7Pxeu3zb+dvHyNkACrTxClIwDL9wqk0qXouXLLvibxKWGjOqhkUAi3r19Od0E33M7qIOuxnqCklTyWDAoh/MKJ9OlM9VTEpdP4BZSnRv12ODg44BdQnpqNOnB074acDyIL/AOC0aekcP1q6uSFl8+fIKBM9stEr0/h5rXL2d6OLT0s35tpy/ei9fL1f1T5Xj5NiYDyVE9TvrXsrHz9Aiz35Svns368GduOV+y+7Zi6L6eN9yQBT5HP5OQkPnyrJ4V9S/DmxDm2zKbyL/EsdaTTXrYvBdww/ZkfIBRCeAKFSHOHORPXAKsPQUgp9wHJQBOgH2B9Jqgc4ObuSYMW3Vg8bzJaTQJhx3ezf8caWnTob5HWYDCQnKRFn6JDSklykhadznhXzC+gHKXLVWPZd1NITtKyd9tKrl44ScOWeT95T1pu7p7UbtqdPxd8gFaTwPmTuzmyexWN2oZapG3cLpS/f5tBdNR17t+9wV+/fUmT5wYCMGT8j3y6KIyp3x9l6vdHKV2+Nt0Gvs8Lr36U2yE9VqX6/TixZyF3b5xBm3CffX99SuUGlvE+lKJLIkVnvCOm1yeTotMipaRIiUq89vF5Bk7Yx8AJ+2gXOgcPb18GTthHvoL+uRVOpvRSkiwNGJAYMH7WW3ldWUvhzSYZQ4RMIl7q+c1wj1ammb79hAtBuLJU3iNZGtgr4wgniYbi6a465xRPV2c6VQ3mo792k5CkY9/l66w/eZE+dSwr9QH1KrP2xEVORN5Bp9czfcM+GgT5kd/DjbK+BZDAb4fOYDBIbscmsPzoOar42deM7A/VaDaAQ1t/5HZkGJr4+2z9cxq1mr9oNW1I3a7cijjNqX3L0SVr2fLHFIoFVMHXr4I5Tcy9SC6f3k7NZta3kRc83Vzp0qgmUxatJEGbxN7TF1i79xh9W1mOflmz5yj34xKQUnLw7GXmrtpCxwbGkQQHzlxiz6kLJOtS0CQl88Wv67nzIIY6FaxP5pRX3Nw9qd+iG0tNddCZ47s5sGMNzR9RB6Wk6OAxddC+bSsJv3CSBnZUB7m5e1KveTeWzZ9kjvXgztU0az/AIm3zDqGsXvoV9+5cJzrqBquXzKBlR2P9E1S+OjevXeDkoa1IKbkVeYnDu9ele6baHri5e9KoVVcWfWMs29NH97B3+1paPd/PIm1q+yIFSfqyjbhyjoP/bCBJqyFFp2PLuiWcOvwPVWo3ye2QHsnN3ZO6zbux7Dtj+Z49vptDmZRvs/ahrE1TvmuWzKD588byLV2uOreslG/aZ6rzmrHt2JXF35rajsf2PLbtmJJJ2zGoXDWWfjfV3HYMv3CShq265XZIj+Tm7knDll35+ZsPjfvysT3s3bGGVh2zdp5K0en4aEwfXF3deWfKD3b3eEKeE4BwsN2fnRLW3qX7XyOECAfigPZAIrAK2IXx+eZlQBvgDDAdqCWlbGxaTwLBUsqLQoifgEgp5UTTM9IbgR7ANkzPSEspz5rWmwD0BryklJZTkloRHFJLfrlof7ZjjYuJ5uspgzm2fzP5fAoxcPhHNHuuL6eP/sPkkR35becDAE4e3sGEoa3TrVu5ZlOmzTO+8+/2jXBmTh7E+dMHKFK0FK+N/Zrq9VplO38PxSQ+ejbmJxUfG813nw7i1KFN5PMuRK8hH9OwTT/OHd/FZ+M68P3fxiujD98jvWPdAgCaPT+IPkM/tXrn6qORLWjUpj/NO75qkzyeOGP5PGR2HNz8NQc2fEmKTkO5Gl1o028WTs7GCbd+mFyL+s+NIaReHwDmvVuB2OiIdOsPmXoGn8LpJ6CKOLeTdT++wrBPsv+qpKZvZH8Y8RLDXZbK6HS/9RUFaS18eMMQzhyHQHyFM2B8j/SfMpokJA2FF28IX5xNJ93bUsdXhlucR0sRnBjq4Et1YbtJqJp92ckm24lO0PDG0g1sO3eVgh7uTOrUhF61K7LnUiQ9vl3Ozc9GmNN+/88xPtu4H02yjvpBfnz5Qiv8CxgvHuw4H8H7q3dyKeo+bs5OtK9chk+7t8DDxdkm+ZxT/FObbOehXWtnsHPVZ+iSNVSu152ug+ea9+UZb1Wlebfx1GhibKhfPLGZ1T+M5H7UVUoG1+WF13+ggG+geVvbV3zCuaN/8dqHO2yWv5E+Cx+f6DGiY+MZ+uWPbD0SRkFvL6a80oPeLeuz++R5uk78iqhVcwEY+PE8thw+TZIuBb/CBRjSqQWvdzWeo3edOMc7c5dw5VYUzo6OVCrtz/sDu9K4Svls5++hjQVtcwEiLiaaWVMGc9xUB4WmqYOmjOzIsjR10HsZ6qBKNZvyUZo66OvJg7hw+gCFTXVQNRvWQQZD9kctxMVEM2fqqxw/YIx1wBvTaNquL2FHdzF1dEeWbI8BUt8zvHn1DwC07vwKocM/Mdc/uzf/zm8LphJ16yoenj40fa4vA16fZtOGuadr9p+pj42J5ssPXuPIvi145y/IKyOm0rJDH04e+YeJb3Rh1V7jRIHHD+5g7OB26datWqsJny3YRMTls3z+/mAiLp/BwcERv1Jl6fPqWBq1tN173+O0tnlqMS4mmrkfvcoJU/n2f30aTdr1JezYLqaN7sgv21LL95fZ49myxli+rTq9woA05btn8+/8/kNq+TZp15f+NixfVyfD4xM9RlxMNDM/HMyx/VuMbcc3p9LcdNxOGtGJ33cZR0ydPLSDd4e2Sbdu5ZpN+Xj+ZsB43H416VVT27EkQ8fZtu3o5GibPktcTDQzJg0x7cuFeHnEVFq078OpI//w3vDOrNhjbH+cOLSDcYPbplu3Sq2mTP9+EycO7WTc4Da4urkj0nT0psxeTeWajbOdxxH9GnA+7LB9DK96CjWD/OXuqW/YbHse/d89LKWs/ag0QojngJmAI/C9lNLiPW1CiF7AJIyDvo5LKS2vBmbBs9SRnodxqHUJjB3pYVLKRCHEUGAMUADYAwyVUkaa1rPakTYt6wZMBkoDt4E3pJQbTMtKAeHAFCnlB0+SR1t1pP8tbNWR/jewdUfa3tmiI/1vYauO9L+FrTvS9s4WHel/C1t1pP8tbNGR/jexRUf638JWHel/C1t0pP8tbNWR/jdQHen0HteRFkI4Aucx3hyNBA4CfaWUYWnSBAO/AS2llPeFEL5SyjvZydezdLY5KKW0eKO6lPJb4FtrK0gpRZrPL2VYtgLIbKamKIyzg1u+cFBRFEVRFEVRFOU/SkC6O/W5oC5wUUp5GUAIsQzoAqR9r9lgYI6U8j5AdjvR8Gw9I52bhmHsuF94bEpFURRFURRFURQlM4WFEIfS/A3JsNyP9BNLR2I5eXQ5oJwQYrcQYp9pKHi2PEt3pHOFaRi5ALrmcVYURVEURVEURVFylwAcbDoy/e5jnpG29o9lfBbACQgGmgP+wC4hRGUp5YOnzdQz0ZGWUgb+F/8tRVEURVEURVEU+yJye7btSNK/ockf49uZMqbZJ6XUAVeEEOcwdqwPPu0/qoZ2K4qiKIqiKIqiKP9WB4FgIURpIYQL0AdYnSHNSqAFgBCiMMah3tl6if0zcUdaURRFURRFURRFySVWXjGbU6SUKUKI4cAGjK+/+kFKeVoI8SFwSEq52rSsrRAiDNADY6SU97Lz76qOtKIoiqIoiqIoimI7NnpX+pOSUq4H1mf47f00nyXwlunPJtTQbkVRFEVRFEVRFEXJAnVHWlEURVEURVEURbENkeuTjeUJ1ZFWFEVRFEVRFEVRbMe2r7+yS6ojbUecHA15nYVc5JjXGcg1xkcynh3NvuyU11nINTveWpPXWchVDr9+mtdZyFXSzTOvs6DkEMOzdVrmWaqGHB2eoWB59uJVFHuiOtKKoiiKoiiKoiiK7aih3YqiKIqiKIqiKIqSBbn4+qu88t+/VKAoiqIoiqIoiqIoNqTuSCuKoiiKoiiKoii2IUSuv0c6L/z3I1QURVEURVEURVEUG1J3pBVFURRFURRFURTbeQaekVYdaUVRFEVRFEVRFMV2noFZu//7ESqKoiiKoiiKoiiKDak70oqiKIqiKIqiKIptPCOTjamOtKIoiqIoiqIoimI7z8Az0v+pSwVCiG+FEO89YrkUQpR9gu00F0JEpvl+WgjR3EbZzFFxMdFMefsFujUswMAOwWz7a5nVdMcPbmf8kLb0bFqEl54vZ7F80dxJDOtVk451PPjl2yk5ne2nFh8bzVcTujOonRejegWyZ9MSq+mklCz7dhzDOhVmWKfCLP1mLFJKi3S7/l5IaDMHtq/9Pqez/tQObZnF3HGl+Xp0cf5eNJQUXZLVdPqUZFbN78/8CRX5fJgnEed3Zpruh0k1+PZ/wTmZ7SyLTtDQ7/tVFBszk0qT5vPboTOZpj127TbPfb2M4mO+psyEb5i7/Yh52YnIO7SbuQz/cbOo8P48Pvl7b25kP0vWGu4zWn+VbvoLzDDcemTalYb7hOov0Vt/kZmGW+ikwbzsttTxrv4aPfQXGKq/wjGZkNNZz5Zda75iyqt+vP9iQX6f82qm+zLAxRNb+HxEJSb2y8e8D1pxP+pquuUXTmxm5pg6TOzvzbTXAjm+5/eczv4TiY6Np8+ELyjSbiAVeg3n103/PDJ9si6FGqFvEdzz9XS/r999mNovvYPvcwNp+fp7nAmPzGQLeSsuJpqPx/SkdxMfBncqw46/l1pNd/LQdiYObU2/5oUY3NmyWr59I5yJQ1vTq7E3b/SszPH9W3I661kWFxPN9HE96N/cm6Fdg9i1wXqsUkp+nj2el9r68lJbX36eNS5d/XPy0FbGvFiH0JYFeL17MJtWfpdbIWRJXEw0H77Viy4NCvJi+0e3L8YObkv3Jr682MGyfTF2cFt6t/Cne+MiDOtVh73b1uR01p9KXEw0n4zpSZ+mPgzpXIadmezLUkoWzfofoa2LEtq6KAu/Hp+ufA/uWsuIPtXp2yw/4wc14drlsNwK4YnZou34IPoOn/4vlAFtA+nZtAhvv9ycsycP5Eb2s8y4L79A1wYFGPiYfXnc4Lb0aFKEgR0s4/1kfCj92wTSo0kR3n7JfuNVcs5/qiMtpRwqpbR5r09KWUlKud3W280Jcz8ZiZOTC0s2X2PsRz8x5+M3uXrJ8qTt5u5Jm84DGTTyY6vbKVGyDK+MnEbdxu1zOMfZs3DGcJycXZiz4hbDJv7CTzNeJ/LKaYt029bM5/A/q/howTE++uE4x/auY+vqeenSJMTdZ83iT/ArXSm3sp9lV8I2cWDDF/QauY4hU8N4cDecPWunZprev0wDOry8AE/vopmmObjpKzzyFcmJ7GbL239swcXJgYtTh/F9aAfe+n0zZ27etUh3Lz6R7t/+ycsNqxH+8esce28QrSoEmJcPWrSORmX8ufrxG6wf0ZsFu4+z/uTF3AzlsQoKJ3o5FKKN8H5kuiMygT9lNFMd/FngUJpbUsdiec+8/DPDTYKEK0scyhDqUJhPDDeJkSk5nf2ncu7YBravnM7gDzYyfu4lom9fZtOvk6ymTYi9y6LPX6Btn8l88FMU/mVqsfjLvublt6+FsfSrUNr1/ZDJi6IZ+flh/INq5lIkjzZ6xg+4ODtyZcU8fpg4nFEzFhB25Vqm6b9atoYi+dPvBxcjb/LK1Nl8/dar3Fj7A+0b1qLXu5+RkqLP6exn2fzpI3BycuGnDdcZPWUh8z4ZTsQly3Oyq7sHrTq/xMARn1jdzpcTBxBUvjo/b75N/9c/5NPxvYm5H5XT2c+S7z9/EycnF75ff4ORkxbx3fQ3uHbZMtZNK7/j4M7VfPHLEb745SiHd69n44r5AKSk6PhsXE/adBvMoi3RvDV1CQtnvkP4heO5Hc5jzf54JE7OLizbEsHYaT8xa9oIwjNpX7TtMpBXR02zup2hY75gyaZwlv8Txcj35jB94svci7qZ09nPsvmfjcDJ2YUf/77OqA8XMu9T6/vyxhXfsX/HamYsPsxXS45waPd6Niw3lu+NiAvMeP9Fho6fzS9b7lKnSUemvdMdfYp9nZdt0XbUJMYTXKkWXy/ey6/bbtG64wAmjeyKJjE+FyLImjkfj8TZ2YWlW64xZtpPzJ6Webxtuwxk0Cjr8ZarVItZS/by2/ZbtOo0gA9G2Ge8eUY42O7PTtlvzpQs02oS2L1lBaGvf4C7hxeVajSiXtOObF232CJt+cp1aNWxP8X8S1vdVutOodRp9BzuHvlyOttPTatJ4ODOP+kx6EPcPLwoX7UxNRt2ZvfGny3S7vp7Ee17vUVBX38KFvGjfe+32PX3wnRpfpv/P9p2f5N8PoVzK4QsO713MVUaDqRwiRDcPAvQoMM4Tu37xWpaRycXarUajn/ZhggHR6tpHtwNJ+zAMuq2ezsns51lCUk6Vh+/wIQOjfBydaFBGX/aVy7DsoOWFd3sbYdpVSGQ3rUr4urkRD43F8oXK2ReHhEdS6/aFXF0cCCocH4aBPlx5tY9i+3kpYYiHw2EF/mwXk4PbZGxtBE+BAhXvIQjfRwKsUXGAnBdJnOJJPqJwrgKBxqJfATgyh5pn5X6ke0/U6flyxQrWQkPrwK0EU6cpQAAIABJREFU6jmBQ9sXWU17av8KivqHULVhT5xd3GjT6wNuXj3BnetnAdj65zTqtRlMhZrtcXR0wjNfIQoVK5Ob4ViVoNGyaud+3hvUCy8PNxpWrUCHhrVYunGX1fThN++wbOMu3u7fJd3vmw8cp2HVCjSsWgEnJ0fe7teZG1HR7DpuX3e2tJoE9m5dTr+hk3D38CKkemPqNO3I9vWWdVC5SnVp0WEAxfyCLJZdv3qeS2eP0nfIB7i6udOwZXcCylZm79bluRHGE9FqEti/bTl9XpuMu4cXFas3pnaTTuz4y/J8vH39Ijr1G00hX38K+frRqd9otq8z1j/xMdEkJsTSrP0AhBCUDamDX2BFIq9kPgInLxjbFyt50dS+qFyjEfWbPc/WtZajwMpXrkPrjv0p5me9fRFUrgqOTsYnC4UQpKTouHvbvkZYaDUJ7Nu6nL6vZdiX/7Lcl7et+5ku/UdRuKixfLv0G8W2dcZz2bF9Gwmp3piQ6o1xdHKi24tjiI66zukj1keI5QVbtR2L+wfRfcAoChYpjqOjI+17vIpOl0xk+PncCOOJZYzXuC93ZMvazOMtbmVfLu4fRPfQ1Hg72Gm8eUcYh3bb6s9O2WVHWghRUwhxVAgRJ4T4XQjxqxBiqhDiJSHEPxnSmodrCyF+EkJMTbNsjBDiphDihhDilQzruQohPhdCRAghbpuGhbtnkp9wIURr0+dJQojfhBCLTPk7LYSonSZtCSHEn0KIKCHEFSHECFv+3zzK9asXcHB0xD8gdfhJULkqVq+y/RfcunYeBwdHipdMjbdk2apEXrGM93r4aUqVrWb+XqpMNa6nuXN96cwBrpw7TMsuQ3M209l07+YZivhXMX8v4l+FxNg7aOKfrmO49de3adJlEk4uVnf9PHMxKhpHBweCfQuaf6viV8RqB/jg1ZsU8HCj9YwlBE2YS6/5K7gWHWte/nqzmiw9cBqdXs+F29EcuHKD5uUCLLbzbxAhkyiNq/l7IK48QE+s1BNBEsVwxiPNldvSwoUIkvMiq491+1oYxQNTj8nigdWIf3CbhDjLMr59LYwSadK6uHlSqGgZbl8zHusR5/cDMOOt6kx91Z9lM18kMS46hyN4vAvXbhr345IlzL9VKRvAmSvWOw1vz/yRSYP74O7qku53KUk3VFRKkPDIO9t54UbEeRwcHfFLUweVDq5GRBaHsl67HEYxvyDcPVMv5JYOrprl7eSkh7GWKJUaa0BwVavDdiMvhxFQtmr6dKZ6Kn+hojRu24eta39Cr9dz7uReom5dpUK1RjkfRBZEmtsXqY8ABZWrytWnLJP3R3SjUz0fRoY2oWrtpgSH1LJVVm3C2r4cGFzNavleuxxGYHDVNOlS91UpZYZj1/j96uVTOZj7rMmptuOlc8dJ0SVTomTeX9RMK9JKvKXLVXnqffkhe41XyVl215EWQrgAK4CfgILAUqDbU2znOeAdoA0QDLTOkORToBxQHSgL+AHvP+HmOwPLgPzAamC26d90ANYAx03bawWMEkK0y2r+n4YmMR5PL590v3l6+fxnh5kkaeLxyBCvh6cPWk2cRVqtJh4Pz9S0Hl4+aDXxSCkx6PUsnPEGoSO+xsHOZxhMTkrA1T112Keru4/p96yX8YVjqzEYUgiu3tlm+bOVhCQd3m7pOxPebq7EJ1l2Cq8/iGfpwTA+7d6SsElDCCjkwyuL1pmXP1c5iJXHL+D7zkxqTfuR0PqVqRVQLMdjyAlaZLqOsqfpFK7BgEZK8/eHPHAkEQP2KEkbj5tH6r7s5mHcl5OsHL8Z0xrTe5vTxkRHcnTnYga88xtjZp1Fl6xh1YKROZj7J5Og0eLt5ZHuNx9PD+I1Gou0q3ceIEVvoHPTuhbLWtauwj/Hz7Dz6GmSdSl89ssKknUpJGrt6yKJJjEh3XkWwMPLG02iZZk+cjuaeDy80pe3h5cPmoSsbScnZaxTwFj/WItVq0lfN3t6+qBNjDd3sBq36c0fC6bSt6kH7w1tTt/XplC4aMmcDSCLtFbbF94kPmWZfPj1Clb8c5cps1ZRq0Ebu6t7tVnYl7UZ2iIeXqnlW61ea04f2cmpwzvQ6ZL586dPSNElk6y1PAfklZxoOybGx/L5ey/Tb8hEPPP5PH6FXGR9X/ZBk/D08SbEx/L5xJfpb4fx5hmBcdZuW/3ZKXuctbs+xnx9LY21zHIhxNM8vd8L+FFKeQqMd5KBvqbPAhgMVJVSRpt+mwYsAf73BNv+R0q53rTez8Ao0+91gCJSyg9N3y8LIb4D+gAbMm5ECDEEGALgW6zUU4SYnruHF4kJsel+S0yIxd3DK9vbtkeu7l5oMsSrSYzFzd1yOLqbuxeaxNS0moRY3Ny9EEKwaeVcSgZVIbhygxzPc1aFHVjGpiXGQQ1+ZRri4upJkja1Ik/WGGNycc1aGScnJbBj+UR6DLefoZJpebo6E5ehkxCnTcYrw506AHdnJzpWKWvuHI9/rgGl351LjCYJvcFA92+W81nPlvSqVZHbcQmE/rAG33yeDG5SPVdisSU3BInSYKygwNxJdscBd2FaloYGAx52cr306M4lLJ8/DIDACo1xdfNKd9FLa9qXXa0cvxnTgrHD/TCtk4s7tVoMpEgJ4x2GFt3H8/2HuXL98pE83d2IS0jfYI5N1ODlnn4ESIJGy8Rvl7B8+jir2ykf4Mf8/w3jrZk/cvveA3q3aUyFQD/8ihS0mj6vuHt4WqmD4rL8iJC7u5dFBy0xITbdHeq85uZuWd9qEmKtxpoxbWJiLG4exvrnevhZvpzYj7Gf/kHVum24ee0CH7/dhYJFilOr0fM5HseTcrPWvoiPwyMbZeLk7Eydxu1YuXQ2xf2DaNC8Y3azaTNuVvZlTSb7csby1SSklq9/YAVGfPAD8z8byf27N2nWvh/+pUMo5OuX4zE8KVu3HZO0GiaN6k6FKvXo/cpYW2TRpqzvy7G4e2Yj3pGmeAfZX7xKzrLHjnQJ4LpMP6Xy04xfKwEcTvM97RSvRQAP4LBIHXcv4DEPKKZKO61uIuAmhHACAoASQogHaZY7AlYfiJNSzgfmAwSH1LKcQjqL/AKC0aekcD3iAn6ljMOvLp8/SUCZkOxu2i4VK1kOvT6FW5EXKOZvjDfi4gn8S1vG6xdYiYiLxylTsa4p3XHzpGKnj2zl7LEdHN//F2CcCfzqhaNcvXiMgaNm51I01oXU7UNI3T7m72sXvERU5Ekq1OoBQFTkSTy8fXH3KpTZJqx6cOcisfeusvSLNgAYUnQkaWKYO640/cdux6dQ3g59LlukICkGAxfv3KesbwEATt6IomIxyzgrlSic7vGZhx+llITfi8HRQdCvrrGs/fLno0fN8mwMu/Kv7EiXEq5cIYkmGBtzV0giP454C0dKSVduoSNRGsx3ra/IJJoJ++h81GjajxpN+5m/L/1qADfDj1Ot4QsA3Aw/jlf+onjmsyzjoiVDOJzm+elkbQL3bl2iaEnjsV68VBWEHT5DFVyyOCl6PRcjb1LWvzgAJy9epWJp/3TpLkbe4uqtKNq8OQkAnS6FmIRESnd7je1zpxBQ3JduzevTrXl9AB7EJfDz+u3UqmBfQwhLlCqHQZ/CjYgLlDDVQeEXjlMqKGt1UMmgEG5fv2zsuJg6auEXTtC0XZ/HrJl7HsZ6M+ICxR/GevEEJa3E6h8Uwv/Zu++wKI4+gOPfAenYu4CAXbH3XrBFY2+xxiQmmqImxhSNiSVqovFVUWOaLbHX2E3svffeQIoFsaEIHEe5ef+48+DkKMbjuMT5PM89crtze/Nz53ZndmZnQ66fo7Sf/vwTcv0cXobzVNiNixTzLkvVuvoLPx7eZanRoC2nD2+1qYa057P6RWggHt76WdZvXDuH9wvuW3OSEhMJv3XjpbdjSWbL8rWzZvevl2H/ljHs3+Dr50zKfP3mXanfXH/Ojnn6mJ0bf6dUhZqptpNdLFl3TIjXMv7TbuQvVIwho2ZbOqsWkVyWr+NhuFUh+Nr5f1SW4+O1fPtpNwoUKsaQr20z3uwiAWmD52VLs42uClPhgIcwrRU9G+MUg74BDIAQIr3xmeEpPgeQssv3AaAB/KSUeQyv3FLKl+26vQkEp9hmHillTill25fcbqY4u7hR378Ti3/+ljhNDBfPHOLI3o34v94nVVqdTke8No7ExASklMRr40hISO4BTExIIF4bh07qSEpKJF4bR1KSbc0Q6+ziRs3GXVgzbwxxmhiunT/IqYPradCqX6q0DVv34++V03l0/zaRD+7w18ppNHqtPwADRyxg8sJLTJh7mglzT+Nbtiad+4+m+7sTrR1Shvzq9ub8oT94EH6ZuJhIDv81mYp1+6aZPjFBS2JCHAC6xHgSE+KQUlKgmB+DvrtK/68O0/+rw7TqOxvXXIXo/9Vhcub1THN71uLm5ED7yqWZ+NdBYrQJHLlxmy3nA+lZK/WJrm+dimw6F8i5W/dISErih61HqFfCgzyuzpQqlBcJrDxxGZ1OEhEVw5+nr1LJw7ZmKU+SknipQ4dEh/7vJDOPZ/MXudgunxAmtUTLJFbqHtLcMNO3h3CkBE4skw+JlzoOy6eEoKW+sM0RKdWb9OX4rgVE3LxEbHQku9Z8R82mb5pN61e7E3dvXuT8kT9JiI9jx6rxFPGuRCGPcgDUbPYWJ3b/wcOIG8RrY9mzbgrlamR/I8TNxZmOjWszft4qYjRxHD5/lc0HT9CrVSOTdH6+XlxdNZvDcydzeO5kZn8xiEJ583B47mQ8C+knPzx99QZJSTruP45i6NQ5tKlfnbLettOrBfpjct1mnVn26zjiNDFcPnuQY3s30rRt+ucgnjsHeXiXwbdMFZbPGU+8No4ju9cRcv089fy7WDukNDm7uFG7aWeWzxlLnCaGK2cPcmLfBpq0SX08btKmH5uWBfDw3m0e3b/DxqXTafq6/vzjW6Yqd29e5/yJXUgpuXsriJMHN5vcU20LnF3caODfiYU/jzPWLw7v3YR/u96p0j7bt0mJian27c3gqxw/sBVtnIbEhAR2bl7KhVMHqFSjUartZCdjWf4tRVnet5GmbVKX5aZt+7Jh6Qzj/t2wJIBmrycfy4IunyQpKYknkff5+fsPqdWwHZ4+5awZTrosVXdMTEhg4uc9cXR24bNv59vccP1nnsW7KEW8h/dupHm7FztOPYvXycmFz8bbbrzZR6hZu7PJYSAJGCyEyCGE6Ag8u2nsLOAnhKgqhHAGxqaznZXAW0KICkIIV2DMsxVSSh0wB5guhCgEIITwsMC9zMeAKCHEl0IIFyGEvRCiohCi1ktuN9M+GjkTrVZDr+ae/PDVm3w0chbeJStw4dQBujRIHgZ44dR+OtXLzZghHbl/N4xO9XLz9YfJFc+ZEz6gU73c7P17BSvmTaJTvdxmZ3DMbm8Nm018vIaPOhXmp29789awn/D09ePq2f28+1pyT5x/h0FUrd+Or96uzMi3KlGlblv8OwwCwC1nHvLkL2J85XBwxMUtV6r7r22Br18rarccxsrpbfjt6/Lkyl+c+u2+Nq5f8G1NLh1Lfh7i/LFVCRian+jHd1g9qyMBQ/MT9SgMO/scuOUuYnw5u+VFCDvcchfBLo0Zvq1tWvfmxCUkUvLrn3jnj81M696C8kULcCjoFkU/n2lM16RMcUa3a0j339ZSctTP3HjwmHlv6q9d5XJ2YvE7HZi95yTFR/5Igx8WUqFoAT5rVSe7wjJrhXxIV10gq2Uke+RTuuoCWSEfck8m0D3pOvdkAgA1hBtdRT5G6W7xji6YgsKBPiK5B/dzu6IEyjh66YL4Q/eAEXZFyS1sceARlK32Gk06fsZvY1sw6YMS5CnoTcs3xhrXT/2kMqf36WcEds9dkH6frWTr0m8Y+1YBbl4/Ru9hybMF12r+NtWb9GX2yPpM+qAEORyc6PBOgLVDMmv6sAHExcfj02kQb307k4BhA6jg68XBs5cpZLiYlyOHPUXy5zG+8uZ0w85OUCR/Huzt9afpz2f9QbHX36Fa32Hkdndj9ucDszOsNA36chZarYb+rYoxdVQ/Bo34keIl/bh4+gA9G+cxprt4ej89GuZk/CftuX83jB4NczJ2cPLjFodPXELg5ZP0bV6QhT+O4stJK8id17YugL33+Y/EazUMaFOUgNF9ee+L2XiV8OPSmf30bZZ8/mjVeSA1G77O8L5V+bRPFWrUb0Orzvr9V8SzJB+Omsv8acPo1zwvoz/wp07TzjTv8E5aX5ttBn81g3htHG/4ezFp5JsM+WomPob6Raf6yceh86f206FuHr4Z0pF7d2/SoW4evvpAX7+QUrL41wn0bO7FG/6erFs6m5GTF1O6fLXsCitNA7+YRXychrdaF2Pa1/0Y9KW+LF86fYBeTZLLcusuA6nV8HU+6V2Nj3tVpUaDNrTukvz7nDvtU/o2L8Dg7n645czNh6N+yY5w0mWJuuPlc4c5tn8Lp4/soHuTQnRpkI8uDfJx4dSBtL422wz+aibxWg09/T2ZPPJNBn+VHG/n+qbxdqybm9FDOnLvbhgd6+ZmlKEsXzp7mGP7tnDqyA66NS5E5/r56FzfNuNVso6QZno9spthFuy56CcB+wv98OjTUsrxQohRwDD0PcojgUVAaSlloBDid+CWlPJrw3ZGoL9/WQd8DcxLkdYZ/eRiPYECwG3gZynlTCFEU2CxlNLTsJ0Q4F0p5Q7DvdalpJR9Det8gGDAQUqZKIQoBkwFmgFOwFXgaynljvRiLl2hhpy55PBL/b/9mzx86pDdWbCas5dsZ1IRaxgdn9k5+/799n66MbuzYFWXVtjOrMnWMLjg6uzOgtVsd+2a3VmwqsSk//6Qw5TcnWzrucVZSZNgGxeDrcUph21OKJkVXoGRwkZDe9fj2qWT/9qIq5ctIQ/+armRna7Nep+UUtrOPREGNtlVIaU8gX42bQCEEEfRz4aNlHIikHLPLE7xubee284kYFKKRfNTrIsDvjK8nv/+PYBnivc+Kf4e+1zaEJJvy0RKeQfDpGaKoiiKoiiKoiivGnWPdDYRQjQRQhQxDO3uD1QG/s7ufCmKoiiKoiiKoiiKTfZIA2XR3+PsDgQB3aSU4dmbJUVRFEVRFEVRFCVdQtj0JGGWYpMN6ZSPhVIURVEURVEURVH+RdTQbkVRFEVRFEVRFEVRUrLJHmlFURRFURRFURTlX+oVeLb2fz9CRVEURVEURVEURbEg1SOtKIqiKIqiKIqiWIh4JR5/pRrSiqIoiqIoiqIoimUIXolZu//7ESqKoiiKoiiKoiiKBakeaRsRFy+4GPLq7I674bHZnQWraVbXMbuzYFWzgyZndxasxm7FqxMrQIU3KmR3FqwqYMnF7M6C1bi4vFrX1XPY//eHHKb0JMo+u7NgNU5Or1ZZfpX6xB49is/uLFjN0/9ANVm+Aj3Sr07LTVEURVEURVEURcliQj1HWlEURVEURVEURVEUU6pHWlEURVEURVEURbEYNbRbURRFURRFURRFUV6EGtqtKIqiKIqiKIqiKEpKqkdaURRFURRFURRFsQwh1HOkFUVRFEVRFEVRFEUxpXqkFUVRFEVRFEVRFIuQgHwF7pFWDWlFURRFURRFURTFctTQbkVRFEVRFEVRFEVRUlI90v9R+zYGsHvtDyTGa6hUtwtdBv1EDgcns2mvn9vJ2jlDeHw/jOJl6vDG4PnkLeQNwPJZb3Nm/zLsczga049fFImdvb1V4siMk7t+5Ni2aSQmxFG6akda9AwwG2tSYjybF7xNRNhpoh6F0ePjLXiVaWxcf2jzRI7+PQX7HMmffXPUEfIU8LVKHBl5+uQRM74dyOkjO8iVpwD9B4+naZteqdKdO76HZXMmEnTlNO658jJ/03WT9Yt+GsORPRu4GXKFNwaMpM+g0dYK4YUd2BTA3vVTSIjXULFOFzq9NzvNchx4ficb5g3l8YMwvErXptuH88lbUF+Op39amcf3Q41pExPiKFP1NfqPWG+VODJr/8YA9hjirVSnC50HphPvuZ2sm5scb4/ByfECXD+3gy2LRnL/zlVc3fPxev8pVKnf3VqhpGmTLpKdMooQ4mkscjLMrkiaadfpIlkjHxGPpL5w50NRCAfDFe4ImcAM3V2uEkdBcvC+XSGqCjdrhfHCDm4OYN+G/5EQr8GvTmc6Dkh73wad38WGBUN58iAMz1K16frBPOO+jY1+xIa5gwm6uAuA0pVb0mHAbJxdc1ktlszYuz6AXX/+QEK8hsr1utDtg7TPQdfO7uTPX4cQaTgH9fp4PvkKeZukiXn6iEkflqeQR1mGTNpnjRAybfe66exYrY+1Sv0u9PjoZxzSiPXqmZ2s+mUwkffD8C5Th77DFhhjXTfvM84f2UDU47vkzu9Bq+4jqd38TWuGkimHtgSwf6OhLNfuTPt30inLF3axacFQnjzUl+Uug+aRx1CWox7dZuOCIYReOYCDkytNOo2kdotB1gwlQ/s2BrBnnb4uVbFuF7oMTL8utW7OEMMx2bQuBXD97A42LxphPCa36z+FKg16WCuUTHmV4j22fSZH/55GYoKGMtU70brPzDTrjhvm9Cc89BRRD8Po9dlWvMsm1x2P75jFiZ0/oYl+iKOTG+VqdcO/2/fY2asmluS/P7T7pXukhRAhQogWlshMdhJCjBVCLM7ufFjC1dNb2f3nZAaN3c7IX27wMCKYbcvHmk0bE/WAhT90o3WvcYxb+ADPkjVYPNW0cda00+dMXBplfNlSIzrk0g6ObZtK96GbeG/8RZ48DObQ5olppvcoWZ82/efilquw2fVla3Rl6PQI48tWGtEAP08eioODI4u33+KzCX/w0/dDCA26mCqdk4sbLTv2551PJpndTjGvkrz98ffUatgmq7P8Uq6d2cre9T/w7uhtfDk7iEf3brBj5VizaWOiHrD4f91p+cY4vpl/H48SNVgWkFyOh007x7hFTxi36AljFz4md4HiVKrXzUqRZM7VM1vZs+4H3huzjRE/BfEo4gbbV4w1mzYm6gEL/9edVj3HMeb3+3iWrMGSacnxRty8xLKAfrTu9S3jFj7i4/+dxLNEdStFkr58Igc97PLTUqTf8DslY1gjHzHBzpN5dr7clQkskQ+N66fowikhnFhqV5J+dgWYpAvniUzM6uz/I9fPbmPvhim88/VWPp8VSGREMDtXjTObNibqAUumdadFj7GMmnsPjxI1WDGjt3H99hWj0cREMnzGNYbPuEr0k3vsWv2ttULJlCuntrJzzWQ+GL+dr+foz0F/Lx1rNm101AN+n9SN1/qMY8KSB3iVqsHCKakvEG76YwSFPctncc5f3OWTW9mxejKDJ+5g7PxgHt4N5q8lY8ymjX7ygHnfdeX1vt8yadlDipeuwYLJPY3rHZ3dGDh6A5NXPKbvsN9Z89sn3Lh8yFqhZMr1s9vYt2EKb43ayvAZgTy6F8yu1WmX5WXTu9O8+1hG/naPYr41WDEruSyv/qk/eQv68OXPt+n7+Xp2rPiGGxf3WCmSjF09vZU9ayczcMx2Rvx8g0cZ1KUWTdHXpcb+8cDsMXlpQF9e6z2ebxdF8snUU3iWrGGlSDLnVYr3xoXtHPlrKj0/3cIH31/h8f1gDmwYn2Z6z9L1aT9gPm65U1/4LVX5dd7+5jCfzrrHgHEnuXfzPCd2zs7K7P9LCKSws9jLVtluzixICJHll4Ws8R2ZdWLPQmo3f4cixf1wdc9Li+6jOL77D7Npzx/5k8JeflSp3x0HR2davTGGO6FnuXfripVz/c9cPLqEivX6U6BYBZxd81L3tS+5eMT89RD7HI7U8P8Iz1L1EXa2czEgM+I0MRzauZa+H4zFxdUdv2oNqNOkHbs3L0mVtmzFWvi/3pciHuYvAjRv/yY1G7yGi2vOrM72Szm1dxE1m71NYS8/XNzz4t91FCf3LDSb9uKxtRT2qkClet1wcHSmRfcxhIec497t1OU4+PI+Yp7co2KdLlkdwgs5tWcRtfzfpoiX/nfbvNsoTqQR74WjaynsWYHK9fXxtuwxhvDQ5Hh3rfmOOi3fo1z1Ntjb58AtZ37yFylpzXDSVF/kpJ5wJyfp/wZ3yihaitx4CyfchT097fKzU0YBcFvGE4SW3qIATsKOBiIn3jhxSEZbI4QXdmrvImo2TS7LzbqM4tTedMqyZwUq1dXv2+bdRhMeeo77hn0beS+E8rU64OyaC2fX3FSo1ZGIW5esGU6Gju9eSJ2Wyeeglj1GcXxXGuegw39SxMuPqg3056DWvcZwJ+QsESnOQSFXDnM37CK1m/e3VgiZdmzXQuq2fIei3vpYW/f8mqM7zMd69vCfFCnuR7WG+ljb9B7LneCzRNzUx9q2zzgKe5XDzs4On7J1KOnXiJDLh60ZToZO719EjaZvU9hTX5abdh7F6X3my/Kl42sp5FmBioay7N91NHcNZVkbF03wpb006TQS+xwOFPWugl/tLpzau8DKEaXt5J6F1EpRl9Ifk9OvS1VOoy61c/VE6rYaaJPH5GdepXgvHF5MlYb9KehRAWe3vDRoN5Lzh9KuO9ZqMQSv0g2wM9Ogy1uoBM6uefRvpETY2RF5/0ZWZl+xIS/VkBZCLAKKAxuFENFCiC+EEHWFEIeEEI+FEGeFEE1TpN8jhJhgWB8thNgohMgvhFgihIgSQhwXQvikSC+FEEOFEDeEEA+EEFOESC7FQoh3hBCXhRCRQoitQgjv5z77kRDiOnDdsGyGEOKm4btOCiEaGZa/BnwFvGHI11nDcpPe9pS91kIIH8N3DBBChAG7DMvTjN9aIsIuUdSnsvF9MZ8qRD+OIObpw9Rpb5qmdXR2I3/hkty9mdzTeejvnxn9ZgECPqvFucNrsjbzL+hh+GUKelY0vi/oWYnYp/fQRKeONTOCzv/F7M+9+H18Tc7sm2OpbL6026HXsLO3x8O7jHGZb+nKhN6wrQq0JUX+rM+cAAAgAElEQVTcukRRnyrG90W9qxD9JJ1y7J2c1tHZjfxFSnLvZur/n1N7FlGxblccnW1rGLD+t5gi3gx+t8V8nou3cEkiDPGGXTsKwPRPqzLhXU+Wz3iT2KePsjgCywqTWnxJHmbngxOPSSJKJhGGliI44JqiUuMrHAkjPjuymqF7ty5SxDv5OFvEuzLRTyKINbNv7926ZJLW0dmNfIVLGhvLdVt9wNVTW9BER6KJjuTisbWUqdo664N4AXfDLlEs5TnItwpPH0cQE5U63rthlyjmm5zWydmNAkVKcjdMfw7SJSWx5tchdBk4U/9cUhsTHnoRD9/k36JHerGGXsTDTKzhYalHFsVrNYRdP04Rb7+syfg/lKosF8+gLBdPXZbv3b4EUgIgDf8CSCQRN1P/X2SX5+tHGdalvFPXpZ7F8+yYPG1YFcYP8GDZjH42d0x+leK9f+cyhbwqGd8X8qxETFTEP647Xjy6nGlDCjFjmCf3bp6nauMBlsrqv5uws9wrM18nxGtCiKtCiEAhxIh00nUztOFqvmyIL9WQllL2A8KA9lJKd2AJsBmYAOQDPgPWCCEKpvhYT6Af4AGUBA4DCwzpLwPPj4nqDNQEqgMdgXcAhBCd0Dd+uwAFgf3Asuc+2wmoA1QwvD8OVDV811JglRDCWUr5N/AdsEJK6S6lrELmNQHKA62FEB6ZiN9ICDFQCHFCCHEiOur+C3xl+rRx0Ti75ja+f/a3VvPUbFqXFGmfpX+WtmHbIXw5+ypjFtylda9xrJz1DsGXD1osry8rXhuDk3Ny/p1cchuWv3jPVNnqXXj7m5N8MDmEln1+5Mhfk7h8YqXF8voyNJoYXN1N95Ore240sbbZA2cJ8XHROKW47/NZOY43U47j46JT3SPq7JoLbZxp2nhtLBeOrqFGU9u771D7XAwZ/W7NxmtI++TRLU7vW0Lfz1by+awrJMRrWD/v4yzMveXFIU0aym6G05UGHRopje+fccWeWHRWzWNmxcfFZPqYHP/c8VufPpex3BfzrUZSYjwT3yvMxPcKI+zsqdPqgyzM/YuLj4vG2S05BpcMy3La56D9m2bhXaY2XqVsZ1hoStrnYzX8HZfW+dbtuVjdcpv9f1k5+wOK+VahfHXbukiSZlmOM1OWtebLslbzFCeXnBQvU589ayeSEB/HneBTXDq2loR4TdYG8AKerx+9+O82t8kx+dTexfT7fBVf/HiVBK2GdfOGZmHuX9yrFG+CNtpYX4TkuqM27p/Vqfzq9OTTWfcYOOE81Zq8i1uuQhbJ57+a0D/+ylKvDL9OCHtgNtAGfbuvlxCigpl0OYGhwFFLhGnp4ch9gS1Syi2G99uFECeAtsCz8SELpJRBAEKIv4AKUsodhvergOdvUpgspXwEPBJCBAC9gLnAIOB7KeVlw2e/A74SQnhLKZ/NKvS94bMASClTjtuYKoT4GigLnH2JmMdKKWMMechM/EZSyt+A3wC8StWUz6/PrFN7l7DmV31Fyrd8Q5yc3dFqoozr4wx/O7mkHsrr5OxOXGyUyTKtJsqY1rNk8n2V5Wu0pVrj3lw4shbf8g3+aXZfyuVjK9i+TH8w9ihVH0cnN+LjkvMfb4jV0cn9hbedv2jy/XceJepSrdmHXD+9jvI1s39yDBcXNzTRpvspNiYKF9cXj9NWnd6/lHW/6cuxT/mGODq7o41NPoE/K8eOZsqxo7N7qoprXOxTnJxN0148uhYX93z4Vmhi6ey/sNP7lvLns3jL6X+3KWPI8Hf7XLzPKqcAORxdqNGsPwWL6UcwNOsygrnf2laFPCPOCGKljmdzlTxrJLtgh4swrEtBgw5XG7lb6cyBpayf8yEA3uUa4ujsZnJM1qazb/VlOfUx+Vm5XxbQkyLelen72Z9IJH8t/oJVP/an1yfPX0e2npN7lrDqZ31ZLlHh2W83xTkoNqOybBpvnOEc9OThHfZvmsWwacezMPcv5vjuJayY/T4AJf0apTqHPvvbOZPn27jYqFT/L+vmf86d0AsM+X4XIpt74c8eWMqGeaZlOc5cWXY2U5adTOsikLxvAbp/tJCNvw/lf0N8yVfIl8oNenH/9uWsCiVDp/Yt4c/n6lIm+zaD3+3zsaasSzk4ulDT/y3jMdm/60jmjGuVJXFk1qsU78Ujy/h78RAAvEo3wOG5svmsHunk/HJ1qnyFS1GgWHm2LfmYLh+ueKltKS+sNhAopbwBIIRYjr4D9vmhieOBH9B3dr40SzekvYHuQoj2KZY5ALtTvI9I8bfGzPvnS/HNFH+HAsVSfNcMIcTUFOsF+p7uUDOfRQgxHHjXsA0J5AIKpB9ShlJ+R2bit7jqTfpQvUkf4/sl0/twJ+SccXbE8JCzuOcpjFvO/Kk+W9irAid2LzK+j4+L4eHdIIp4pTGcTAgk/7jN/9LK136D8rXfML7fvOBt7t8+T9kaXQG4f/s8rjkL4eKeOtYXJRAmw86yk4d3GZKSErkddh2P4qUBCL5+Du8SqS62/WtVa9Sbao2SJ6JZPqMv4aFnqWyYafpu6Fncc6ddjlPecxofF8OjiCAKeZn+/5zau5Dqjftme+UUoFrj3lRrnBzvsoC+hIecNc6sndHvNuX94s9+t4UN8RYtXskmYnwZxYUTwWhphL5iFoyWPNiTS9hTXDpxlwRipc7Yax0stTQRtnHff9WGvanaMHnfrpjZj/DQc1SqZ9i3oedwz10YVzP7tpBnBU7vMz0mP4q4QWFP/b69G3qODgNmGW9NqN1iIHPGNM3CaDJWo2kfajRNPgctmtqHO8HnqNpQfw66E3KWnHkK45YrdbxFilfg+K7keLVxMTwMD6JIcT/Crh8jKjKcHwbrb99JiNeQEK9hTP9ijJl/M1smvqzVrA+1miXH+seUPtwJPkv1RvpYbwenE6u3H8d2Jv9utXExPLgbRNHiyefbLUvGcPnE3wydtAcXG5iJvUrD3lRJUZZX/tiPu6HnqFTXcFwOS78sn9lvWpYjI25QyENflvMU9Kbf58lPTlj5Yz88StbKqlAyVL1xH6o3Tt63S6f3IfwF6lIn96SuSxU21KX0w+Ft65j8KsXrV7cXfnWTJ0PbMKc/926dp3wt/aSjETfP45arsEXqjjpdEpH3g196O/920jDZmBV5YNomu4V+VLKREKIa4CWl3CSEsEhD2hIRpmxp3AQWSSnzpHi5SSnNTx+cOV4p/i4O3EnxXYOe+y4XKWXKKS6NeTPcD/0l0APIK6XMAzwh+ZdursUUA7imeG/uOS1ZHf8Lq9GkH8d2zifi5iVioyPZseo7ajUzP0lLxTqdibh5gXOH15AQH8f2leMp6l2ZQp7lADh3aDVaTTQ6nY6rZ7Zxeu8S/Gq1N7ut7FChdm/OH1rIw/DLxMVGcuTvH/Cr2zfN9IkJWhIT4gBISkogMSHO2FgOPLuJuNhIpJSEh5zg9J6fKVW5nVXiyIizixv1/Dux5JdxxGliuHTmEEf3bKTZ631SpdXpdMRr40hMTEBKSbw2joSE5HtHExMSiNfGIXU6dEmJxGvjSEpKsmY4mVKtSV9O7FpAxK1LaKIj2bXmuzSHZFeo3Ym7YRe5cORPEuLj2Ll6PEW8K1HIo5wxzZOHt7hxcQ/Vm9jesG6A6k36cnzXAuPvdtea76iZRrx+tTtx9+ZFzhvi3bHKNN6azd7ixO4/eBhxg3htLHvWTaFcjdetGU6akqQkXurQIdGh/zvJzAUrf5GL7fIJYVJLtExipe4hzQ0zfXsIR0rgxDL5kHip47B8Sgha6gvbHKFRrXFfTu5ewD1DWd6z9rs0y6Ff7U5E3LzIhaP6fbtrzQSKFK9EQcO+9ShZkxO75hsblcd3zjW5Z9UW1GzWj6M75nM3TF+Wt6/8jlr+5s9Blep25m7YBc4e0p+Dtq0YT1GfyhT2LEf5Gm34es4NhgecYnjAKV7rPQ4P32oMDzhlM0+PqOXfj8Pb5hNuiHXrionUaWE+1sr1OhMeeoEzB/Wx/r3sW4r5VKawl37fblv5PSf3LOPDCdvMNsRtQbVGfTm1x7QsV2ucxnG5lr4sXzymL8u7106gcIqyfO/2ZbSapyQmxnPmwBKCzm2nQdtPrBlOumo07cexXcl1qZ2rv6Nm0/TrUucNdakdq0zrUrWa9efE7t95eNdwTF77A+Vt5Jj8zKsUb8V6fTh34A8e3LlMXEwkhzZPolL9zNUddYnxJnXHs/sXEBN1D4AHdy5zZMsUfMo1zfIYXkEFnt0Oa3gNfG69uSs3KduBdsB0YLglMyVetsdNCHEEmC+l/E0I4YX+PuT+wA70vbF10Xe13xJC7AEWSynnGj47AfCUUr5leN8C+EVKWcrwXqKfxKsb+p7q7cA0w3d1Rt89/4aU8qIQIjfQSkq5KsVnS0spAw3v26IfEl4deASMQH8/dmsp5Q4hxPvoh6Y3llI/ZlAIsQT9jukPVAH+Bv6WUvY1TIoWDDhIqX/mSkbxp/f/6FWqpvx4yrEX+89Px94N09mzVv9cy0p1u9D1/Z+Nz8f738eV8O8ywtiLfe3sDtbNHUrk/VCKl67DG0Pmk6+QDwA/jWpCeOg5pJTkK+SLf9cvqdqwZ1pfm2l3wy13H9SJnbM4vj3lc6RnGGP9fXxN6rT+3NiLPeebCkQ9CjP5/LvfXiR3fm82zX+L0Cu7SErQ4p63GFUbvUf1Zh++dP6a1XXMOFEmPH3yiBnj3uP00Z3kyp2f/kMm0LRNLy6cPsDYIe1ZfSASgHMn9vLVoJYmn61YozGTftsBwPQxA9i5aZHJ+k/GzKVFB8s0MM8HWe4K5P5N09ln8hzp5GdaTv+0Mk07jzD2Ygee28GG+R8TeT8Ur9K16f7hfPIayjHAnrWTuHr6LwZ9u9di+bOz8MXWfRuns3edId7nnuE59ZPK+HcZYezFvn5uB+vnfkzkg1CKl6pN98HJv1uAbSvGcmTrLwCUqdqaDu8E4Oqe96XyV+GNlx8BsVT3gGXSdNKZXiIfLURuPtKFMNvOh0LCAUh+jrTW8Bzpj557jnSA7i7XsvA50ueWWG7iowObp7Nvw/9INDx7t+O7yft2xmdVaNLpS2MvduD5nWxc8DGP74fi9ew50oZ9++heMJt+H0bYtcMgJZ6latHurQAKFC39UvlzcbFsYd6zfjq71iQ/R7r7h8nnoMmDK9Gi2whjL/a1Mzv487ehPLofineZOvQaOp98hX1SbfPYzt85un2+RZ4jncPecr1lu9ZOY+eaH4jXaqjaoKvJc6S/+7AiLbuPNPZiXz2zg1W/DCHynj7WPsMWkN8Q69B2dtjncMQ+h4Nx2616jKRVj69eOo9Poix3sfTg5uns3/g/EhM0VKjVmQ4DksvyzM+r0KTjl8Ze7KDzO9n0+8c8fhCqf470+/PIW9AHgEN/zWDvukkkxMdS1Lsqbd+cikeJl577Bycny5XlfRums2ddcl2qy6Dkcjz140o06zrC2Kt7/Vld6oG+LtXj+WPy8rEc3vozAGWrtqbDgBkvfUy2NFuP99Ejy00oeWzbDI4YniNdtnonWvedZYx17ujq1Gv7ubEX+6cRZYl6aFp3fP/7K+Qp4M3mBQMJOr+VBG00LjkLUK5GFxp3GkMOB+eXyt/vExoQHnLSdrr1X1BVv3Jy1/K5Ftte/sqNTkop0zxACCHqob/dtrXh/UgAKeX3hve5gSDg2Y3wRdC3BztIKU/803xZoiHdEZiFfpj0BPSTfv0AVAKSgGPAB1LKsH/YkP4Y+ATIDfwOfCGlTDKs7wd8gX5I9RNgu5TynRSfTdmQtkd/P3J39D3N04EPgXcNDen8wHrADwiWUlYXQpRAP4GZH7AX/Q7Il1ZD2vA9ddKKP73/R0s3pG2dJRvSts5SDel/C0s2pG2dpRvSts4SDel/E0s2pG2dpRvSts6SDel/A0s2pG2dJRvSim2xZEPa1v0XGtI7V8y32PYKVGqQUUM6B3ANaA7cRt+x2VtKafZEbmiTfvYyjWiwwD3SUsr16BugKZmdyUdK2fS5918/934HUOq5j22RUs5MY3uLgEVprBPPvU8CBhhez/yQYv1DoOFzn7nBc+PrU6wLwcwwAinlUdKIX1EURVEURVEURbEcKWWiEGIwsBWwRz9a+qIQ4lvghJRyQ1Z8r6UnG1MURVEURVEURVFeWQJp5QnnDE9N2vLcstFppG1qie9UDWlFURRFURRFURTFYqw8a3e2sOmG9PPDsxVFURRFURRFURQlu9l0Q1pRFEVRFEVRFEX5FxGA+O/3h6qGtKIoiqIoiqIoimIhAsl/f2j3fz9CRVEURVEURVEURbEg1SOtKIqiKIqiKIqiWIQEpBrarSiKoiiKoiiKoiiZp2btVqzGzUlH7TKa7M6G1RSp/CC7s2A1Q8ZFZncWrGpd31PZnQWrkc5u2Z0FqwpYcjG7s2BVlfv4ZXcWrKbilY3ZnQWrikgslN1ZsKoKTw5kdxasJt4lT3ZnwbpegV6/ZxJyuGR3Fqxmi/ur0yb4N1MNaUVRFEVRFEVRFMViJP/9izz//T53RVEURVEURVEURbEg1SOtKIqiKIqiKIqiWIhQ90griqIoiqIoiqIoyot4FWbt/u9fKlAURVEURVEURVEUC1I90oqiKIqiKIqiKIpFSF6NycZUQ1pRFEVRFEVRFEWxDPFq3CP9349QURRFURRFURRFUSxI9UgriqIoiqIoiqIoFqOGdiuKoiiKoiiKoijKC1BDuxVFURRFURRFURRFMWHTDWkhxO9CiAmGv5sKIW5l8nNvCSEOZG3u0vxuY56zQ9STR4wa2pPWtQrRo2V5tm9eaTbdqWN7+fjtNrStW4w3WlUwWRcRfpPXahU2eTWp6M6K32daI4RMe/z4MR99MJCqlcrTrHEDNm5Ybzbd3Dm/0q5NK6pV8cO/aUPmzvnVZP3lSxfp3bM7NapWonGDuvw4a4Y1sv+PhV5azJ5VLdi1vBEXDo1FlxRvNl304yCObO7NrhWN2bWiMSe2DyL6cVCqdLqkBA6s78zeNa2zOusv5FFUNG+M+5ECHT6gbL/PWbHriNl0ExatJ1fbgRTs+KHxFRx+H4AHT57iP+x7PLsNpWiXwTT9ZCKHL163ZhiZ8igqmp6jplKwdX/K9RjMiu3pH77iExKp1u9TSnf70GT5loMnqfnWZxR6rT/+H37D5ZBMHTKzxcHNAXw/yJNv387Pml/eJTFBm2baoPO7mP5pRca+mYu537Yg8n6ocV1s9COWB/Rm4ntFmPheEVbO6kdcbJQ1QsiUTbpIhiWF0jnpOtN1d9NNu04XSb+kIN5ICmSG7i4JUmdcFyET+CrpJl2TrvN+UjBnZExWZ/0fefz4MYM+/IgKlavSoEkz1m/YaDbd/AW/07hZcypVrU6dBg0ZP/E7EhMTjeunTg/gtdfbU6pcBQJmzrJW9l/Yk8eRfDm4L02re9DJvxJbN60ym27xvJn0bl8P/xpedG5RhcXzTM+nd26H8WH/9jSpVow32tbm2KE9WZ/5F/ToyVN6fzmRIk274dfpHVZu3ZNu+viEBGq88T7l2r9lsjxX3fYUadqNos26U7RZdwZPtK26xTORT6J485OvKF6nJVVf68bqLdvTTR+fkEDdjn2o1LKLyfLzV67j33MAXnVa4N9zAOev2N45SB/rSIrXbkHV1l1ZvXmb2XS/LFpJjTbd8anXCr/mHRn1w0yT3+2xM+dp2fs9vOu2pHHX/hw5ddZaIbyQyMdPeHvIcErUaEDN5q/z56a/zKb7beFS6rTqQOlajanapDWjJ001iRdgzqKl1G7ZnhI1GtCoXVeCQkLNbutVJBEWe9kqqzakhRAhQogW1vzOrJSdDfa0TJ/wKQ4Ojqzde4OvJ89j+vhPCA68lCqdi4sbbTu/yfvDU7f5Cxf14u/jEcbXgrVHsbOzo3HLjtYIIdO+HfsNDg4OHDxyginTAhg7+muuX7uWKp2UkslTpnH85Fnmzv+DJYsWsnnTBuP64cM+plat2hw7eYbFS1ewfOlidu5I/4SZXR7cOUTwxQXUbPkrjTpvRhN9i8CzP5tN6+RaiCpN/kezHntp1n03BT2bcG7/yFTpQi79gaNz3qzO+gsbNnsJjjlyELJiOgu+HMjHsxZzKeS22bTdmtTi/vqfjC/fogUBcHdx5pdP3yZsZQB31szi0x5t6DZ6JolJSdYMJUPDps/H0cGe4LW/Mv/rwXwyfR6Xgm+mmT5g+UYK5sllsizwVjjvTPiRmZ++y51N82lTvwY9vppCYqJtxQpw/ew29m6Ywjtfb+XzWYFERgSzc9U4s2ljoh6wZFp3WvQYy6i59/AoUYMVM3ob129fMRpNTCTDZ1xj+IyrRD+5x67V31orlAzlEznoYZefliJXuulOyRjWyEdMsPNknp0vd2UCS+RD4/opunBKCCeW2pWkn10BJunCeSIT09li9hg99lscHBw4fvggAVOn8M2YsVy7nrrh0Nzfn03r13L+zCm2bt7E5StX+H3hIuN6H29vRnzxGc2aNrFm9l/Y/8Z/Rg4HR7bsv8rYKb/xw7jh3Lh+OVU6KSWjJ/3CtqPBBMxZzeolc9i+eY1x/ejh71KmfGW2Hg5i0Cdf89Un/Yl89MCaoWRo+P9+wTFHDgK3LGLu2OF8+sPPXL6RdqNhxuI/KZg3j9l1BxfNJHz3KsJ3r+LHUUOzKssv5YvvpuHg4MCl3ev55bvRfD5xKlcCg9NM/+PvyyiQz/RcGp+QQL9PRtL99VYE7t9Cz/Zt6PfJSOITErI6+y/ki4lT9bHu2cAv3z+L9UaqdK2bNmDXivmEHN7GgT8XcfFqIL8tXQ3oG+N9h45g8Fu9uHHwbwa/3Zs+Q77kcZTtXNh85qsJk3F0cOD8vu3M/mECI779nqvXU3c0tGraiG2rl3D9+D52r1/JpavXmLt4uXH9ktVrWbZmPYt+nkHQiQMs+imAfHnMl3nlv8mme6SVF6OJjWHf9vUMGPINrq7uVK5en/pN27Jt4/JUactXqknrDr0o5umb4Xa3blhK5RoNKOrhnRXZ/kdiY2PZtvVvPh42HDc3N2rWrIV/8xasX/dnqrTvDXwfv4oVyZEjByVKlKR5i5acOnnSuP727Vu079AJe3t7int7U71GLQKvp26Q24I7QRvxKNUJ9zwlcXDKRYlK73EnyHyPj4NjTlzciyGEQCIRdvbEPjVtnMU+vU34jS34VnzHGtnPtJg4LesOnGR0/064uzhTv2JpXq9XhWU7D7/QdpwdHSjjVQQ7OzuklNjb2REZHcujp7bTmxejiWP9vqN8M6AH7q7O1K9cjrb1a7Bs236z6UPC77F8236G9zG9sLXj2FnqVy5H/crlyJHDnuG9O3Dn/iP2n019IS27ndq7iJpN36awlx8u7nlp1mUUp/YuNJv24rG1FPasQKW63XBwdKZ5t9GEh57j/u0rAETeC6F8rQ44u+bC2TU3FWp1JOKW7cRcX+SknnAnJ/bpptspo2gpcuMtnHAX9vS0y89Oqa+A3pbxBKGltyiAk7CjgciJN04cktHWCCHTYmNj+XvbNj795GPc3NyoVbMmzZv7s3Zd6tFC3t7FyZVLf3FBSomdnR2hocmNsq5dOtO0SRPc3Nyslv8XpYmNYff2jQwa+hWubu5UrVGPRs3a8NeGFanS9nv3Y8r5VSFHjhx4+5amUfO2nD19FICw4ECuXjrLe0NG4Ozsgn+rDpQsU4Hd2zak2k52idHEsWH3IUYN6ou7qwv1qvrRplFtlv+122z6kDt3WfH3Hj59s5uVc2oZMbEaNu3Yy8iPBuDu6krd6pV5rUkDVm7aajZ96K07rNq8jU8G9DVZfvD4aRITk3i/bw+cHB0Z2KcbUkr2HztljTAyJTnWdw2xVuG1pg3Nxurr5UHuXDmBZ79bQXCYfuTTsTPnKZg/Lx1b+WNvb0+Pdq3Jny8Pm3bss2o8GYmN1bB5206+GPoBbm6u1KlRjVbNmrB64+ZUaX2KeyXHi8RO2BESpq9H6XQ6pv00h3FfDqdsqRIIIfAp7kXePLmtGo+tkugff2Wpl62yWs6EEIuA4sBGIUS0EOILw/JVQoi7QognQoh9Qgi/TG5vqBDikhDCMxNpywkhtgshHgkhrgoheqRY97sQYrYQYrMQ4qkQ4qgQomSK9a0Mn3kihPhJCLFXCPGuEKI88AtQzxDP4xRfmTet7WWlm6GB2Nnb4+VT2risVNlKBAemvjr+IrZuWMZrHfu8bPYsKiT4BnZ2dvj6ljAuK1e+PIFmej5SklJy4vgxSpVO/j/q/9Y7rFu7hoSEBG7cCOLM6VPUa9Awy/L+MqKfBJEzbxnj+5x5yxAf95B47eM0P7NreSN2Lq3LlWOTKfFcg/nK8cmUqjYYe3vnLMvzP3H91l3s7ewo7VnEuKySrxeXQu+YTb/lyFk8ug6hxnvf8NvG1BW72u+PIW/79+k+ZhZvvdaIQnnS7x20pus3w/WxehUzLqtUypvLweaHZQ+fsYCx7/XExcnRZLmU+vJt8h7S7dnOLvduXaSId2Xj+yLelYl+EkHs04dm0l4ySevo7Ea+wiWNjeW6rT7g6qktaKIj0URHcvHYWspUta3bFDIjTGrxxcn43gcnHpNElEwiDC1FcMA1RWXCVzgShvnbOrJLcHAIdnZ2lPBNvkBbvlw5rl0PNJt+/YaNVKpaneq163L58hV69expraxaRFhIEPZ29hT3LWVcVrpcRW4EXkn3c1JKzp48TIlS5QC4EXiFYl4+uLnlTN5O2Yy3Y02BYbext7ejdHEP47JKpX25fCPMbPrPp/7KmA/exMXJyez6Nh+MpFTbfvT58jtC70RkSZ5fRlDoTezt7SjlU9y4zK9sKa4Eme+RHjEpgK+HDMT5uXivBAXjV6YkQiQPTa1QumS6PdvWZjbWMmnncfXmbfjUa0WZxq9z8VoQ/bvrL+pKKUlxCjIsg8tmerazU1orTiwAACAASURBVFBIKPb29pT0Se4c8itbmqtp5PPPTX9RulZj/Oo35+LVa/Tr0RWAO3cjuHM3giuBgdTwb0vtlu2ZMusXdDqd2e28itTQbguSUvYDwoD2Ukp3KeUPhlV/AaWBQsApYElG2xJCfAO8BTSRUqZ7E6AQwg3YDiw1fEcv4KfnGuy9gHFAXiAQmGj4bAFgNTASyA9cBeob4rkMvA8cNsSTJ6PtmcnbQCHECSHEiceRLz+ESxMbjbu7aQPBLWcuNDFP//E2z548SOTDezRp1ells2dRsbGx5MyZ02RZTvecxMSk30Mza8Z0dFJH167djcua+jdn699/UaViOdq0ak637j2oXLlKluT7ZSUlaMjh4G58n8PR3bA8Ns3P+PfcT7M39lOu9ghy5itnXB4Rtgspkyhc3D/rMvwPxWi05HJzMVmWy82FaE1cqrRdG9fi1NzxhK2cwexP+vP90o2s3H3UJM2xX8YRsXY2v48YSP2KpVNtIzvFaOLI5e5qsiy3myvRGk2qtBv2HSMxSUeHxrVTrfOvWYkDZy+z7/RF4hMSmbJ4LfEJicTG2VZjCyA+LgZn1+Sr9s/+1mpSH6vi46JN0urT5yLekLaYbzWSEuOZ+F5hJr5XGGFnT51WH2Rh7rNGHNKkoexmOD1r0KGR0vj+GVfsicW2Kmwx5o7LOXMSE2N+BEjHDu05f+YUu7ZvpXevnhQskN8a2bQYTWw0bjmfO+e65yI2g/PQ3B8nodPpaNelj3E75s7dGW3HmmI0ceRyMz1O5XJzIzo29XFq457DJCbpaN+0ntlt/fXz91xYO5cTK36mSMF89PjsW5u7BSVGoyGXu7vJslzubkTHpj7Xbt65j6SkJF5v3jj1dmI15HQ3HVWRK6e72e1kl5hYc7Gmncdur7ci5PA2jm5cxlvdO1Iwfz4AaletxN37D1izZTsJCYksX/8XITdvo4lLfd7OTvp9YhpvzpzuRMeYj7dLuzZcP76Pg1vW8uYbXY3xhkfcA2DvwSPsWreCNb//yrotW1m6Zl3WBqDYlGzvK5dSzpdSPpVSaoGxQBUhRFrjIoQQYhrQGmgmpbyfia9oB4RIKRdIKROllKeANUDK8UZ/SimPSSkT0TfkqxqWtwUuSin/NKybCaQ/W0z62zMhpfxNSllTSlkzT94Cmdhs+lxc3Yl5rtEcG/0UF7ecaXwiY1vXL6Vxy464urpnnNiKXF1diY42rWRER0fj5pZ2Phcv/IN16/7ktzkLcDRcNX78+DHvvt2fj4YM5dzFq+zdf5gD+/exZPGiNLdjTeE3trBzWX12LqvPyZ0fYe/gQmJCcqX02d/2Dq5pbQKAHA4ueJXpxoWD36DVPCIxQcP1UwGUq/VFlub/n3JzceJprOnJ92lsHO4uqXvOy3sXo1j+vNjb21HXrxQfdWrB2v0nUqVzdnSgR7M6TF3xF+eCbKeX1s3FmacxppXRqFgN7i6mFxJiNHF8/ctSpn78ltntlPX24LeRH/DpjAWU7PI+D548pZyPBx4F82VV1jPtzIGljOufh3H98/D79+1wdHZDq0m+b+7Z304uqY9Vjs7uxGlM77HTaqJwNKRdFtCT/EVLM3pBJN8seES+wiVY9WP/LIwmazgjiE0xudizRrILdrgIkarRrEGHa/afwk24pXlcTn94tq+PD2VKl+abMebvk7dVLq7uxESbnnNjYp7ims55aNWS39iyfjnTflmBo6NT8naeO3fHRKe/HWvTH6dMGxpPY2Jxd019nPrmxwVM+XRQmttqUK0ijg4O5Mnpzg/D3iP0TgRXQ2znmAzg5uLC0+cuAD2NjsXd1fRcGxOrYWzAz3w/4hPz23F1SdVAexodk2o72cnN1UysMRnnsaS3F2VL+vLFxKkA5MuTm0UzvufnRSso36w9Ow8eoUndmhQrXCjL8v5P6ON9/jgVg7tb+vGW8ClO2VIlGTF+EoBx9MGHA/qTO1dOvDyK0a9HF3buO5g1Gf8XkkJY7GWrsvU50kIIe/S9td2BgmCsKRQAnpj5SB5gIPCGlNLcenO8gTrPDb3OAaRsKaVsHMcCz85exQDj0V1KKTM5c3ha28tSXt6lSEpM5FZoIJ7e+qFmgVfP41uq/D/anjZOw55ta5kwY6kls2kRPr4lSEpKIiQkGB8f/TDCK1cumwzZTmn1qpX89uvPLFm2kiJFixqX37wZhr29HZ0664fqFClalLbt2rNv72769O2X9YFkoGiJthQt0db4/tz+kTyNvEYRn1YAPH10DUfn/Dg6ZTy5hZQ6kpLi0GruAQJNdDjHtw4AQKdLIDEhmj2rWlCnzUJc3Iulv7EsVtqzCIlJSQTejqCUR2EAzt+4SQXvjPMl0A9pTktCYhLBd+9TuaSXZTL7kkp7FdXHeiucUp76snk+MJTyvqZ3rQTeukvo3fu0HDIWgISERJ7ExOLbeRB7fhqPd9FCdG5al85N6wLw+GkMi7bsoUY5q9xZkq6qDXtTtWHyBGErZvYjPPQclerpR4aEh57DPXdhXHOm7pEs5FmB0/uSD9fxcTE8irhBYU/90wbuhp6jw4BZODrrG2u1WwxkzpimWRhN1igunAhGSyP0FwiC0ZIHe3IJe4pLJ+6SQKzUGXutg6WWJuKfXyTNCr6+PiQlJREcEoKvjw8Al69coUzpUul9DIDEpERCb5ofJmyrivuUJCkpkbCQIIr76H9ngVcuGIdsP2/jmsUsnDODXxZtplCR5CHSJUqV487NEGJinhqHdwdeuUCrdrZzf3Gp4h4kJukIDLtDqeL64/D5wGDKlyhuki7o5h3Cwu/x2vtfAhCfmEhUdCyl2vZj59z/4V2scKptCyFMbkuxBSW9vUhMTCIo9CYlvfXniovXAilX0nRemRtht7h5J5z2bw8G9JOLRUXHUMG/I38v+oVyJX35aeEKpJTG4d2XrgcxoKfpzN7ZyWysVwMpVyrjOXQSk5IIuZk8CWiDmtXYsWyufl1iIjVff4MP37StWzZK+niTlJjEjZAwShiGs1+8ep2ypUpk8ElITEwi9Ka+GVDS1xtHBweTYfuKKSn/+/831r6c/fyRsjfQEWgB5AZ8DMvT+p+PRN/DvEAI0SCT33kT2CulzJPi5S6lzMzYv3DAWJsV+l9LytqtTR35XVzdaNyiA/N+nIAmNobzpw5zcPdmWrVPfRDT6XRotXEkJiYgpUSrjSMhwXQI6L6dG3HPmZtqtW1v1lRXV1datmrNzIBpxMbGcvLkCXbu2E7HTqlPThvWr2P61B9Y8MdivIqbnvR9fXyRUrJxw3p0Oh3379/jr82bKFfun118yGrFSrTjduA6oh8HkaCN4sb5uRQr2d5s2od3jhD16ApSl0RifDTXTkzFwTEnbrl9cc9TksZd/6Jeu+XUa7ccv3qjcXLOR712y3F2TV3RsTY3Zyc6NqjO+IXriInTcvjidTYdPkOv5qmHCm48dJrIpzFIKTl+5QY/rd9Ju3r6QSDHLgdx6MJ14hMS0WjjmbpiC/ceP6FWuYwrCNbi5uJMx8a1GT9vFTGaOA6fv8rmgyfo1aqRSTo/Xy+urprN4bmTOTx3MrO/GEShvHk4PHcynoX0I1pOX71BUpKO+4+jGDp1Dm3qV6est4e5r81W1Rr35eTuBdy7dQlNdCR71n5H9SZvmk3rV7sTETcvcuHonyTEx7FrzQSKFK9EQQ99Y8WjZE1O7JpPQryGhHgNx3fONbmnOrslSUm81KFDokP/d5KZRoO/yMV2+YQwqSVaJrFS95Dmhpm+PYQjJXBimXxIvNRxWD4lBC31he30WIL+uNy6VUumB8wkNjaWEydPsmPHTjp3Sv3Eh+UrV/Hgof6e+OvXA/n5l99oUC/5952QkIBWq0XqJImJiWi1WpJsbLZ9F1c3mrZox5xZ36GJjeHsqSPs27WFNh3eSJX2740r+TlgPDPn/YmHl4/JuuK+pShdrhLzZv+AVhvHnu2bCLx2kWatOlgpkoy5uTjTvmk9Js5ZQowmjiNnL7Fl31F6tmlmkq5CCW8ub1jAwUUzObhoJj+OHEKhfHk4uGgmnoULcPlGKOeu3SApKYnoWA1fzZxP0YL5KetrGxc2n3FzdeH15o2Z9NM8YmI1HD19jr/2HKBHO9P5F8qX8uXs1jXsXjmf3SvnEzDmSwrmz8vulfPxKFKIBrWqYW9vx29LV6ONj2fuMv1M7Y1qV8+OsMxyc3Xh9RZNmDR7brqxAixas5H7DyMBuBoUzIx5i2hUp6Zx/bnL10hISORpdAxjps6mWOFC+DeoY7VYMsPV1YW2Lf2Z8uMvxMZqOHbqDFt37aFb+9dTpV2yei0PHj4C4GrgDWbNWUDDuvpbq1xdXOjQphU/zfuD6JgY7tyNYPHqtbRs2ijVdpT/Lms3pCOAlJd8cgJa4CHgCnyX0QaklHuAPsBaIURmfp2bgDJCiH5CCAfDq5ZhsrCMbAYqCSE6CSFyAB8BRVKsjwA8hRCOZj+dDYZ9M514bdz/2bvv6KiKNoDDv0lPNo1QAgTSIKH3JiDSe5HeEaRJERQpgtIUVBBFLHwqClJFQJDeQTpID70nhN7SN8mm7Hx/bEhhk0BkkyxmnnNyzu7eN7PzJpPNnXLn0r6+D5+Oe5tRk+bgU7IsAScO0qJGSgcp4PgBmlUrwIdDO/Lg3i2aVSvAmEFp/2lvW7eM5u16mO1o25RPphMbG0udWtUY/f5Ipn46HT9/f44fO0qViin3xp7zzVeEhYXRuWM7qlQsS5WKZZk86SMAHJ2c+P5/P7Pwt/nUqFqJ9m1b4edfiiHD3s2ttDJVwKMu3uX6cnzHYPb91Qp7xyKUrJQyJnRwfSfu3dgMQHx8JGf2T2D3inrsX9uW6MhbVG08F0tLWywsrLC1L5D8ZW3jAsICW/sCCIvMdxfOKXPe7U2MLg6vru/T94t5fDuiN2W9PTh49goF30y5f/Kfe49S4e0JFGo/nEGz5jO6a0t6NzWMs+niExj1w1KKdRlJyZ6j2XbsLGumvUfR/OZ1u69vRg0gNi4O7/bv0O/T75gzagBlfYpzMOAihVoYlilbWVlSOL9r8lc+Jw0WFoLC+V2xtDR8lI/9fhFFW/enSu9RuDhqmDt2cG6mlSH/ys2p1240v05ryqwRJXAt4EnjLlOSj387phKnDxhWwmicC9Lzg5XsWDGZ6QMKcvvaUbqNTNlKo+OQXwh9dJOZw7yZOdSL0IeBdBo6P8dzysgK+YRO+mv8KUPZIyPppL/GCvmEhzKeLolXeSgNt8GpJjR0Em58rL9Nf30gBYU1vUTKDP1YiyJck7H00F9nkf4x4y2K4CJydVFZuqZNnUKsLpbqr9XhvVGjmfbJVPz9/Dh67DjlKlVJjjtx4iQtWrelbMXKvD1oMA3qv8HY0R8kH5/w8SRKl6/I+o0bmfvjT5QuXzHd3b9z29jJX6PTxdLydX8mjxnIuClf4+tXhtPHD9GwWsq4+8/ffkZ4WAj9uzamYbViNKxWjJlTRyUfnzZ7PhfPnaJZLR/+N/sTPp+ziHxuL3/JlynNHjuUWJ2OEi1703/yLGaPG0oZXy8OnT5PkYaG1SVWVpa458+X/JXP2QkLIXDPnw9LS0sehoTRb+KXeDTuRsVOgwi+94CVX03G2sr82vKsj0cTq9NRpmE7Bo//hFkfj6Z0SR8OnwzA6zXDqjArKyvcC+RP/nJ1ccZCWOBeID+WlpbYWFuz+JvPWbFhKyVeb8mytZtY/M3n2Fhb53J2aRlyjaNMg7YM/nBqUq6+HD4RgFetpslxR0+f5Y1Ob+FZswndh4+lyeu1mTgy5f/M978tw79+ayo268j9x09Y9M1zT+tzxReTxhMTG0v5ek0YOuYjZkyeQCm/Ehw5fooS1VI2mz12MoCG7bvhW60uvYeMpPEbdZnw/vDk459PHIfGwYHK9VvQtufbdGzdgh4dzetWsblHILEw2Ze5Ejm5nEYI8SbwPeAMTMew6/UyoBEQAkwCFgF+UsprQoiFwG0p5UQhRANgqZSyWFJZrYEFQCsp5Yln3qcfMFBK+XrS81LAbKAmhsGDAOADKeXp1O+RFPvs+7TAcG20e1JdqwD/k1IuSepA/wXUBvRSygLPKy8jpctVlfNWpn+7m/+iwnbmdX/M7DTik9DcrkKOWtvbfG7rkd2knfnemic7zLnXKberkKMq9nqhm0j8J5S/lP5t9P6rHiSY13Wb2a1s+IHcrkKOibPPY/fxNdPJjuwQb2X//KD/iOZdehNw7sIr+8stX6GiXLN2o8nKK1XS64SUsvrzI3NWjg4BSinXAc8OKT87dJN8M1EpZb9Uj/eQalm1lHIThs5teu+zEFiY6vllwHjNxjPvkcH7bAX8AYQQFsDtpC+klHHPlvu88hRFURRFURRFUZRXm/mtpTEzQojmwD9ADDAWw/XbR3K1UoqiKIqiKIqiKGbKnO//bCrmu+jcfNQGrgOPgbZAeyml8Y0TFUVRFEVRFEVRlDxBzUg/h5RyKob7WyuKoiiKoiiKoijPkRdmpFVHWlEURVEURVEURTERkSc60mppt6IoiqIoiqIoiqJkgZqRVhRFURRFURRFUUxGyv/+jLTqSCuKoiiKoiiKoigmIckb10irpd2KoiiKoiiKoiiKkgVqRtpM6BHEJuSdX8e9mIK5XYUcU6Nh4dyuQo7a7lY+t6ugZBN7+7w19lr+0obcrkKOOVe6bW5XIUdVHJS3PqesOrfP7SrkmJh1q3K7Cjkq8s6T3K5Cjomb9HNuVyHHJArr3K7CS8sLM9J5p+emKIqiKIqiKIqiZLu80JHOW9MLiqIoiqIoiqIoivKS1Iy0oiiKoiiKoiiKYiIiT+zarWakFUVRFEVRFEVRFCUL1Iy0oiiKoiiKoiiKYhISw0bK/3WqI60oiqIoiqIoiqKYjNpsTFEURVEURVEURVGUNNSMtKIoiqIoiqIoimIakjyx2ZjqSCuKoiiKoiiKoigmo5Z2K4qiKIqiKIqiKIqShpqR/o+JCA/hm6lDOHF4Jy758vP2iGk0atXdKO70sT0s+/lzrl06jZOTK4u3XElzfOzAZty8foH4OB3uHt68NXQydRq2zak0XlhEeAhfTh7G8cO7cHHNz6D3PqFJ625GcaeO7mXRTzO4evE0js6urNh20Sjmz6Vz+XPpXMJCHlGocHE++24Fxb39ciKNLPln+3cc3vw1CfExlKrWnpZ9vsfK2tYoLjEhjrU/9+Ve0AnCnwTTe9w2vErXTz5+eMtszh5aSvjjYOyd8lOt4TvUbvlBTqaSqcjwEH6YPpjTR3bg7FqA3sOnU79FD6O4s8f3sOLX6dy4dAqNcz5+WX8tzfEHd4P4/tOBXDl3lIKFPRk89lsq1WqcU2m8kLyUa2p7181h95oviY+LoWLtjnQe+r902zLAlYBdrPl5BKGPgvH0r0WP9xbgVsgrTYw2MoQZw8pQyKMUI2bsy4kUnissLIwPP/qY/QcOki9fPsaN/oA32xl/li74bSELFy8hNDQUB40DbVq1YsKH47CyMvyb/vqbOezYuYtr16/z7rChvD9yRE6n8kI26kPZJSMIIo43hBOjLApnGLtWH8pqGUIckjrCkWGiENbCML7/QMbzrf4+l4mlIFYMsShEZaHJqTReSFhsHOP2nGDf7Ye42dkwrlZ52vsVN4rTJSbyycEzbAu8S7xeT/XC+fm8XhUKO9pnqZzcFhIRxdCv57PrxFnyOzvx6YAudGtcxyhu+qI1fPn7BmytU04xj877DJ+ihQAYPnsBB85c4tqdB/w0ZiB9mtfLsRyyIjRGx6jNh9gTeI/89rZ81KAKncr5GsX1WLGTI7ceJj+PT9RTIr8zewe2A6D6/1bzSBuLhTDMztXwKMjKHk1zJokXFKaLY8KhMxy495h8tjaMqVKKdr4eRnG6xESmH7vA9uD7JOglVQvlY9prFSjsYAfAB/tPcfj+E6ITEiloZ8ug8r508/PM6XSeKywslEkfjeHQgX245nNj1OjxtGnXwShu/i8/su6vP7l79zb58rnRvedbDBg0NE3MkoW/snjRfEKePKZIEQ9++GkB3j7G7STvyfn7SAshWgDfApbAr1LKGc8c/wAYCCQAj4D+UsqbL/OeZtWRFkIsBG5LKScKIRoAS6WUxbLhffYklf2rEKIX0FdK2czU75Mb5n7xHlbWNqzYHcz1ywFMGtEBX/+KeJcsmybOzk5D8/Z9aRjblT/mf2lUztBxX+PlWwZLKysunT3K+HdaMX/dWfIXLJJTqbyQOZ+NwtrahjV7Arl26QwThneiRKkK+Dybr72GVh36oGvZhaW/zjIqZ+PqhWxes4gZc1fj5Vuau7cDcXJ2zak0Xtj1czs4vPkreo3diqNrEf78oSv71k6jUZfp6cYX96tDzabvsvrHXukclbQdOB/3YhUIfXiD32e3xtmtGOVqdc3eJF7QvC9HYmVlw8Jtdwi8cprp77+Jj19FPEuUSxNna+9A43b9qNesG38unGlUzuyJvSlV4TUmzdnAiUNbmDm+Gz+uuYhLvoI5lcpz5aVcn7p0chu7Vs9k2PSdOLsV5bcvOrH196m06fuFUWxUxGMWzuhM13fnUa5GW7Ysm8ziWT14f9ahNHEbF43HvVgZpNTnVBrPNXnqp1hbW3Ps8EEuXLzIgEHvUKZMafz90g7SNW7UiM6dOuLs7ExYWBjDRoxk4eIlDOz/NgDeXl6MHzeGZcv/yI00XpibsKKryM8pqUWHzDDupNSyWoYw3aIY+bHiM/1dlvGEfsLQVmfp71Fa2DFFeHAcLTP09/jZwhsXYT6nLZMOnMba0oITfVtz4XEYb285RNn8Lvi7OaeJ++3MdU4+CGFrl8Y42Vgzfu9JJh8MYF7z17JUTm4b9f1ibKwsCVr1A2eu3aTjx7OpUMKTst7Gp2mdG9RiwYQh6ZZToYQnnRvUYuKvK7K7yi9l/LZ/sLa04PzILpx7EEKvVbspV8iN0gXTnhss79YkzfMOy7bxulfaAaTFnRtS36dottf535r6z3msLSw40qUJF0MiGLj7GKXdnPF3dUoTt+hiEKcehbKp7Rs42Vjx0eGzfHr0HP9rUB2AIRVK8kWdithaWnI9PIpe245Qzs2F8vldciOtDE2fOhFraxv2HT7NpYvnGTqoL6XKlMXPr1TaQCmZMWsO/qXKcCv4JgPf7kmRIkVp1eZNAP5c+Tur//yDH+ctokRJP24F38TZxbxyzS2SnF3aLYSwBOYCTYHbwDEhxHop5YVUYaeA6lLKaCHEUOBLwHj2LQuydWm3ECJICNHk+ZG5R0q5LHUnWgghhRAlc7NO/1ZsjJYDO9fSd/gU7B0cKV+lLrXrt2bXpt+NYktXqEGTNr0oXMwn3bJ8/StgafX0hEWQkBDPo/u3s7H2WRcTrWXfjnX0f3cSDg6OVKxahzoNWrF9w3Kj2DIVqtOsbU+KFPM2OqbX61n04+cMHzcT7xJlEELgUdwXZxe3HMgia84eXEKlen0p6FEWe00+Xm87gTMHl6Qba2llQ81mIyjuXxcLC0uj47VbjqaIVxUsLK3IX8Qf/8ptuX3tcHan8EJiY7Qc3r2GnkOmYu/gSNnKr1PjjTbs2bzMKNa/XE0atupNYQ/jEeA7N69w/dIpegyegq2dPXUadcSrZHkO716TE2m8kLyUa2rH/l5Mrab9KexZDgfHfDTt+jHHdi9KN/bs4TUULl6OynW7YG1jR/MeU7gbFMCD25eSY4IuHeZ+8HlqNu6bUyk8V3R0NFu3b+eD999Do9FQo3p1GjduxF9r1xnFenl54uxs6DhJKbGwsODmzZSB8k4dO9Cgfn00GvOalX1WHeFEbeGIE8afOantkhE0FS54CVschSXdLfKzS0YAcEfGcR0dPUUBbIUFdYUTXthySEblRAovJDo+gS037jC6Rlk01lbUKFKAJl5FWHMl2Cj2VqSWN4oVoqCDHXZWlrQtWYyrIRFZLic3aWN0rN1/jMlvd8LR3o46FUrRuk4Vlu84mOWyhrzZhIZVy2FnbZ0NNTUNbVw8my4HM75eFTQ21tQq7k7zksVZde56pt8XHBbFkVsP6Vz+1ZmRjI5PYFvwPUZV8UdjbUV1dzcaF3dn7fU7RrG3o6KpV7QgBextsbW0pI13Ea6Gpfxd+rs6YWtp+NsXgBBwM1KbU6m8kOjoaLZv38zI98ei0WioVr0mDRs3ZcPa1UaxAwYPo2y5ClhZWeHjW4JGjZtx8uQxwHDu+L/vv+HDj6ZS0s8fIQSeXt64uubL6ZQUg5rANSnlDSllHPAH8GbqACnl31LK6KSnR4CXnqxV10ibkBC5O1R+++ZVLCwtKeaVMtPh41+Rm9cvZPJdGZs0ogNtarrwXp96VKz+Bv7lqpmqqibxNN/Uy69LlKpA0HXjZduZefTgDo8e3CHw2gW6NPGne4uy/DZ3Onq9+cxqPfXozkXci1dMfu5evCLaiAdERz15qXKllNy6epACRcs+PzgH3A2+goWlJR5e/smv+fhVIvhG1tryrRsXKOzhi70mZVTdx69ilsvJTnkp19TuB1+gqHdKWy7qU4nIsAdoI4zb8v3gCxT1SYm1tdNQoHAJ7gefB0CfmMjqn0fQcfB3hjM3MxEYGISFhQW+PikDlmVKl+bK1Wvpxq9bv4EKlatSteZrXLx4iR7djS/L+a8Iljp8SFnG740tYSQSIRMJRkdhrHEQKacoPsKGYOJyo6rpuhEehYUQ+KaasSuT34UroRFGsd1Ke3P8/hMeaGOIiU9g7dVbNPB0z3I5uenq7XtYWljgVyxlVVoFX08u3DTubAFsPnwKjw5DqTZgAvPW78qpaprMjZAILC0EJfKnrAoo556Py4/DM/2+Veeu81rxQng9M5M7bP0Bys5ZQdflOzj/ICRb6vxvBUZosRACH2fH5NfK5HPianikUWyXksU58TCUB9GxxCQksv7GXep7pF3xxI36MAAAIABJREFUNPnIWcov20KzdXspaG9LA49C2Z5DVgQF3sDSwiLN8utSpcty7eqVTL7LcJ504vhRSpY0zFrfv3+P+/fvce3KJRrVq0HThrX5/tuvzPLcMbdIKUz2BRQQQhxP9TX4mbfzAG6len476bWMDAC2vGyO2dbxE0IsATyBDUKIROBTKeWXQohVQD3AHggAhkopz79AeSOBIUAzKaXR1KgQoj8wFigMHAUGP133LoRoCnwPFAGWQMpaAyFEP2CglPJ1IcTTi+oChBASGCClXCGEGAR8CLgBB4AhUsq7Sd8vgXeB9wErIYQvMBvoBdgCN4GeUspzL/ij+9dioqPQOKZdUqJxdCZGa/xh+CKmff8XCfHxnPpnN7cCL2NhYV7jLjHRWjSOaZe+OTq6EK3N2qzFoweGE4Hjh3axYM1RoiLDGftOOwq6e9Cm89smq68pxOmisLVP+R0/fRwXG4mDY/5/Xe6+ddOQej2VXn/rpetoCjHRWhw0aduyg6MzMdFZa8sxMVE4PNNGHBxdePIw/ZO/3JCXck0tLjYKu1R52zsYHutiItE4p23LutgoHJ3TnqzZObigizH8jPZv/B4v/5oUL1mNezfPZnPNX5w2Ohonp7Qn1E5OTmi16c/QvNmuLW+2a0tgUBBr/lpLwQL//m/a3MUi03SUNUnj+jHoiZEy+flTDljyhIQcrWNmouMTcLZJO6PqbGONNs64jj6ujng4OVBzyRYshaCUmzPTXq+c5XJykzZWh7PGIc1rzhp7oqJjjWI7NahF/9YNcc/nwrFL1+nxyXe4OjrQtVHtnKruS9PGJ+Bkm/b34mRrjTYuPtPvW3n2BqPqVkjz2v/a1aOCuxsS+OXYRbqt2MnBwe1xsbMxdbX/leiERJyeWR3gaG2NNt64DXo7ayiqsafun7uwFAJ/Vyem1KqVJubT1yowpWZ5Tj0K5Z8HT7CxNK9zx+hoLY5Oaf9XGj6XMz93/OG7r5FST8dOhsvfHty/B8DBA/tYu2knkRERDHy7J4ULF6FLt/Qup8t7TDyk8FhKWT2T4+mNoqd7fZEQojdQHaif3vGsyLbWLaXsAwQDbaWUjlLKpxfibgH8gELAScB4/eIzhBCTgH5A/Qw60e2Bj4COQEFgP7A86VgBYDUwESgAXAfqZlDnN5IeVkqq8wohRCPgC6Arho74TQzLBVJrD9QCygLNgDcAf8AVw9r7dKcLhRCDn46shIc+et6P4bnsHRyJ1qYdxY7WRqaZocoqK2trarzenOOHd3B4z8aXraJJ2TtoiH5mkECrjcBB45jBd6TP1tawSUb3t0fh5OxKEQ8v2nbpz5H920xW13/r3OHlfDk0P18Ozc/y2e2wsXVEF5PyO9bFGh7b2P373/GxXT9y9tAyur3/V4YbPeU0w+82nbbskLU87e0djdpItDbipf4mTC2v5HpizzLGd3NmfDdn5n3SChs7R3TRKXnHJj22tTeur62dI7ExaX9GsTER2No7Ef7kLvs3fk/L3unvE5CbNA4OREWlPTmLiop67vJsH29v/P38mDTlk+ysXq6yQxCd6lr26KRTLnsssBci+flTMehxMKNFdA7WVkQ+09GIjE9AY2M8P/HxvtPoEhIJ6NeGiwPb0cK3KH03H8xyOblJY2dLZHRMmtcio2NxTNpkKrUyXh4ULZAPS0sLXivnx/AOzfhr37GcqqpJaKytiNKl7TRH6eLR2GS8HP2fWw94qI2hbem0myDWLFYIe2srHKyteK9OBVxsbThy60G21PvfcLCyJCr+mVzjE9BYG7fBKf+cQ5eYyPFuTTnTsznNvQozYJfx79bSQlDd3Y370bH8fvml9nIyOQcHDdqotP8rDZ/LGZ87LlvyG+vXrubHeYuwsTWcJz09dxwwaCjOzi54FCtO1+692bd3d/ZVXsnMbSD1Lo3FgLvPBiVdcvwx0E5KqXvZN83x/0pSygVSysikyk8FKgkhMroyXwghZgPNgYZSyox6m+8AX0gpL0opE4DPgcpCCC+gFXBBSvmnlDIemAPcz0KVewELpJQnk+o8AagthPBOFfOFlDJEShkDxANOQGlAJNXpXnoFSynnSSmrSymrm2IzoGJefiQmJHDnZsqywRtXzuBV4uWX6yYmJnDv1o2XLseUnuZ7O1W+1y+fxbtEmSyVU9zbH2trG4QZLQl9qnztHoz78QnjfnxCjw/WU9CjDA9unUk+/vDWGTTO7v96Nvr0/oWGzcvGbMHZzeT7+v1rRT390ScmcDf4avJrQVcD8PTNWlsu7luWB3dupFmVEXT1TJbLyU55JddqDXoxY0UEM1ZEMHjKZgp7luVuYEpbvhsUgJOru9FsNGAUq4vV8uTedQp7liP46lEiQu/x5bvlmdK3KGt/HUXw1aNM6VsUfWJijuSWER8fbxITEwkMCkp+7eKlS/j7PX8bjoTEBG7eMq/rZE3JU9gSSMo5TCA6XLHEWVjiiS33iU/T0Q6UOjwxjxk8AF8XRxL1egJTXR968Uk4/vmMNwi7+CSczqW8cLWzwdbSkn7lS3D6YSghMboslZOb/IoVISExkWu3U06fzl4PpqxXZisnDYQQyEw2njNHvm7OJOglN0JSBvDOPwylVIGMN5JacfY6rUt5ZtrZhqc/D/Ph46whUUqCIlJWylwKjcDPxXhQ82JoBB1LFMPV1tCW3yrtTcDjMEJi07/sIkEvCY6MTvdYbvH28SUhMZGgoJRz2suXLlDSzz/d+NWr/uDXn+eyYNEfFC6SsmGcj28Jsz13NBcmXtr9PMcAPyGEjxDCBugOrE8dIISoAvyMoRP9MJ0ysixHO9JCCEshxAwhxHUhRAQQlHSoQAbf4goMxtBRzezCFC/gWyFEmBAiDAjBMMXvARQl1Zp5KaUk7Rr65ymKYRb66fdHYZhhTv3fI3X5u4EfMOwc90AIMU8IkSP/Ee3sNdRt3J7FP35CbIyW86cOcXjPRhq37mkUq9fridPFkpiQgEQSp4slPt7wQRgceJljB7ahi40hIT6eXZt+59yJA1Sobl63qLB30FCvyZssmDuNmGgtZ08d5uDfm2jW1vi2QXq9Hp0ulsSEeJASXap87ewdaNiiE8t/+4ZobSQP799h458LqV2/ZU6n9FwV6vQmYP8iHt25SIw2lAMbZlCxbp8M4xPidSTEG5beJSbEkRAfi+FPwDDbvWf1FHqO3kS+Qua1MYqdvYbXGnZg+c+Gtnwx4CBH926gQSvj5VJP23JC0u82dVv28PLHx78Sf/wyjThdLEf+XkvQ1bPUbtQxp1PKUF7KNbXqDfvwz84F3A++QHRUKDtWfk6NRulvFFbhtQ7cDz5HwKHVxMfFsn3FNIp4V8S9WGnKVGvJxF9uMHrOSUbPOUmLnp/g4VOF0XNOYmGZ+YZX2c3BwYHmzZryzZzviI6O5viJE+zcuYsO7d80iv1j5SoePzEsXrp69Ro//jSPurVTlsLGx8ej0+mQeklCQgI6nY7EXB4oSE+ilMRJPXokegyPE6Vxt6GRcGaHDCdY6oiSiazUP6Fx0r9KD2GDL7Ysl0+Ik3oOy0iC0FFHZG21UXZysLaihY8Hs49fIDo+gWP3nrAj6C4d/Y1v9VOpkCurrwQToYsnPlHPkvM3cHeww83eNkvl5CaNvS1vvl6daYvWoI3RcfjcFTYeOkmPpsYL/DYcPEFopBYpJccuXed/f+2gTZ2qycfj4hOIjYtDAvEJhsfmdl2pxsaaVqU8mbnvNNq4eI7efsjWq7foUr5EuvEx8QlsuHSTbhXSHr8dHsXR2w+JS0wkNiGRuUfOERIdS00P87mTgoO1Fc08CzPn9BWi4xM48TCEnbce0L6E8SBJxfyurL1xh8i4eOL1epZevom7vS1udjY8idGxMfAu2vgEEvWSfXcesTHoLq8VNq9LVBwcHGjarCU/zPma6OhoTp44xu6d22nbvpNR7IZ1a5gzeya/LlxOcc+0Kw3s7e1p2bot83/5EW1UFPfv3eXPlb/ToKFZ77H8n5U0kfousA24CKyUUp4XQnwqhGiXFDYLcARWCSFOCyHWZ1DcCxMynX9wpiKECAQGSSl3Jj3vg2EJdisMnWgXIBTwk1JeS+/2V0BvYCXQQUqZ7vaQQohtwGIppdEycSFEXwzXYb+W9Fxg6PhOTbr9VT+SrpFOOi6f1ifp+XzgiZRyXNJzDRCWFBP0bPwz710oqe77pZSTMvtZ+ZerJn/4/VBmIS8kIjyE2VPe4eSRXTi7utF/5HQaterO2ZMHmDj8TdYdNpyoBRzby7hBzdN8b8Vq9Zg1fwfBNy7x1eRBBN+4iIWFJR6eJek+cBx1Gxmf/P1btpamOQGMCA9h5qShnDiyG2cXNwa//ylNWnfjzImDjBvaga1HDQNOp47tY1T/tB3jStXr8e1vWwHQRkXw1ScjOLJvK45OLrTp9DZvDRlvkpHGHcdMezL/z7ZvObzla+LjYihdrT0t3/oheUn2zxOrULf1OMrXNgwm/DDWn/AnaWe1hn95CdcC3vwwrhSRoXewtEpZzl2+dg9avfXDS9WvRgXT5BsZHsL30wYR8M9OnFzy0+fdz6jfogfnTx1g2ntt+GNfGABnT+xl0pC0/7jKVX2Dz342bHDz4G4Q330ygKvnj1LA3ZN3xn1ndvdWflVyvXHXtGOve9Z9w+7VKfeR7jLsx+S2PPPdCjTpPJ5qDQwDCldO72TNvJGEPLqJl38teoxcgJu7t1GZR3ct5J8dC0xyH+kOZdPfFCwrwsLCGDfhIw4cPEQ+V1fGjRnNm+3acvTYcd4eOIjzAacAGPvhBP7eu5fo6Gjc3Nxo1aI5o0e9j23SMsIx48az+q+/0pQ9a8YXdO5kmoGSc6WN7239b/yuf8xymXYzpR7CjSbCheH6IOZaeFNIGGbsnt5HWpd0H+nhz9xHeo7+Pley6T7SFQeVf+kywmLjGLvnBPtvPySfnQ0fJt3/+ei9x/TddJCLAw3/M0NjdUw5cIYDtx8Sr9fj7+bMpNoVqOzulmk5plSwc/uXLiMkIoohX/3K7pPncHNyZNrArnRrXIeDZy/TfsJXPNr4CwB9P/sfu46fRRefgEdBNwa3a8ywDil3GG3+wefsP3MpTdlbv5rAG5WztposI5G7dpqknNAYHe9vOsTeoHu42dvwcYOqdCrny5FbD+ixYheBY1ImKdacD+SzPSc5PqxjmvOGS4/CGLJuH0FhUdhZWlDO3Y1JDatSuUhG80dZF3nn5TYbBcN9pMcfOsPBe49xtbFmbNXStPP14NiDEAbsOsqZni0ACI2NY9qx8xy495j4RD3+rk58VKMslQq48iRWx7t7T3IpJAI94KGx563S3nQ34aBQ3KSfTVJOWFgoEyeM4fDBfbi45uODMRNo064Dx4/9wzsD+3AiwLDxWNOGtXlw/x7WNimrYdq268jUaYbbE0dFRjJl0ofs3bMLZydnOnftydB33zfJuWOXDq04dzbglZ3uLl2+qlywaq/Jyqtb1vnEc66RzhXZ3ZE+gmFZ9Lyk58MwzDC/ASRiGBkYSiYdaSllsaTNwpZhuN76n3TepwMwDeiWNPrggmFTslVJ10gHAn0xTPEPB77GsGFYeh3p+8BbUsrtSc8bY7gmuimGEY4vgWqZdLxrYJjpPwnYYLg++4iUcmpmPytTdaRfFabqSL8KTN2RNnem6kgr5sfUHWlzZ4qO9KvCVB3pV4UpOtKvElN0pF8VpupIvypM0ZF+VZiqI/0q+C90pOevfPkB7adeL+dklh3p7D4r+gKYmLTkegywGMMy6TvABQz38HouKeUO4G1gvRDC6B5MUsq/gJnAH0lLxs8BLZOOPQa6ADMwLMn2AzK78eFUYFFSnbtKKXcBkzB0iO8BJTCsu8+IM/ALhpn2m0nv+dWL5KkoiqIoiqIoiqKYv2zdFlJKuQ5Y98zLz64PXpwqvl+qx3tIdaNsKeUmwD2T91qC4dZW6R3bimEX7fSOLQQWpnr+E/DTMzFGr6U6Jp55vguomF6soiiKoiiKoijKf51M945U/y3mdX8FRVEURVEURVEU5dUlQW9O29Nnk7x1wZuiKIqiKIqiKIqivCQ1I60oiqIoiqIoiqKYhEQt7VYURVEURVEURVGULHlmG6n/JLW0W1EURVEURVEURVGyQM1IK4qiKIqiKIqiKCYj1WZjiqIoiqIoiqIoiqKkpmakFUVRFEVRFEVRFBMR6NVmY0pO0esFWl3e+XVEyryTq4vLf/+DJDV9XrhxYJI8lCoAVpZ5qy0/SCiU21XIMRUHlc/tKuSoM7+cy+0q5Kia736Y21XIMZrWmtyuQo5yCryY21XIMUdj8+V2FXJMvN4yt6vwUiRqszFFURRFURRFURRFUZ6Rd6YFFUVRFEVRFEVRlGyXFzYbUx1pRVEURVEURVEUxWRkHrhGWi3tVhRFURRFURRFUZQsUDPSiqIoiqIoiqIoimnIvLEhq+pIK4qiKIqiKIqiKCahdu1WFEVRFEVRFEVRFMWImpFWFEVRFEVRFEVRTCYv7NqtZqQVRVEURVEURVEUJQvUjLSiKIqiKIqiKIpiMnp1+yvlVRMZHsKMcZ3oUd+ZwW/6sm/b8nTjpJQs/mE8bzUtxFtNC7H4+w+RqdZgnD2+m9Fv1aBXw3wM7eDH9r9+yakUsiQyPIQvP+xErwbODGnvy/5M8l3yw3j6NStEv2aFWJJOvmPfqkGfRvkY1tGPHWvNM1+AfRvm8OmAokzuk4+VcweQEK/LMPbqmV3MGlGWj3s48tPkxoQ+vJn2eMBO5oypzsc9nfhssBcBB1dmd/VfWF5qy3mxHQP8vfYbPu5dhHFdXVk2pz/xmbTly6d3MX1IGUZ30vDdhEaEpGrLa+ePYdogf8Z2cWb6kDIc3bU4J6r/QsLDQvnw3d40qOpB+0YV2LZxVbpxS+d/R8+2tWlUrTgdmlRi6fzv0hy/eyeYYX3bUr9KUbq1qsnRQ3uyv/L/QlhsHIO3Hqb0r+uos3QLa6/eSjdOl5jIR/tOUW3RJir+toH+Ww5xPyomy+Xkpo36UEYl3qRD4lW+0d/PNHatPpQ+idfplniNb/X3iZf65GMPZDwfJd6iU+JVhiQGclpqs7vq/0poeDj9Ro7Du0Z9qjV9k9WbtqUb9/OS5dRo0YEStRpSsWFrJs38hoSEBABu37uPT40Gab7cy9fix4XLcjKVFxISEUn3CTMp1KQnZTq9w8rt+zONj4uPp0rPEfh3GJT82sGAC7g37ZXmy/H1Tqzdczi7q58lIVHRdJuzjAIDPqHU+7NYcSggw9hTQXdpOv0XCg78FO/hXzB32yEAHoZH0XfuCnxHzKTw4Gk0+nQeR6+Z398tQER4CBPf606LmgXp1qw0OzetSDfu1NG9vN+/Ja1rF6Fb8zLpxvy5dC7dW5SlRc2CvNWuKreCrmZn1V8pUpruy1y98jPSQoiFwG0p5UQhRANgqZSyWDa8z56ksn8VQvQC+kopm5n6fV7WL7NGYGVtw4Itdwm6cprPPmiHt19FPH3LpYnb/tcvHN27ntlLTyKE4JMRLXD38KV5x3dISIhn5rjO9Hl3Bs06DOLaxeNMGdYEv3I18fGvlEuZpe/Xr0ZgZWXDr5sN+X4x2pBv8Wfy3bH2F47tW8/XS08CgmkjW1AoVb6zPuxM73dn0LT9IK5fPM7U4YZ8vf3MK9/Lp7ax56+ZDJ66E2e3oiz+shPb/5hKqz5fGMVqIx6zZFZnOg+dR5nqbdm2fDLLZvfg3RmGf3oPbl3g9zm96TbiN/wqNSU2OpxYbVhOp5ShvNSW81o7Brh4Yhs7/5zJu5/twiV/UX6d3pEty6bQrt8Mo9io8MfM/7wTPUb+Qvmabdm0dBK/zezO6K8NJ6M2dhoGT15PQQ9/gq8e48fJLSlQtCS+ZerkdFpGvpo2BitrGzbvv8yVS2cZPaQbfqXK4+uX9qRMSsnkGT9RslQ57twK5L0BHXEv7EHT1p0AmDx6IOUr12D2zys5tG8HH73fl1VbT5DPrUBupJWhSQdOY21pwYm+rbnwOIy3txyibH4X/N2c08T9duY6Jx+EsLVLY5xsrBm/9ySTDwYwr/lrWSonN7kJK7qK/JySWnRkfKZ3UmpZLUOYblGM/Fjxmf4uy3hCP1EQgFn6e5QWdkwRHhxHywz9PX628MZFmNcp2vjps7C2tub83i2cu3SFXsM+oFwpP0qX9E0T17x+Pbq/2QYXZydCw8MZMGoCvy5byZC+PSlWpDCBx/Ykx968fZfXWnWiddOGOZzN833w9S/YWFtxY/18zlwNovO4zylf0ouyvp7pxs/5fR0F87mgjYlNfq1upbI82JEySLDv5Dm6fvgFTWtVyfb6Z8WoRRuwsbIkaO54zty8R8evl1DBszBli7mniXscqaX9l4uY2aslHWqWJy4hkTsh4QBodXFU8y3GjF6tKOSsYeGeE3T6egkXvxmNo51tbqSVoTmfjcLa2oY1ewK5dukME4Z3okSpCviULJsmzs5eQ6sOfdC17MLSX2cZlbNx9UI2r1nEjLmr8fItzd3bgTg5u+ZUGooZMLsZaSFEkBCiSW7XIzNSymWpO9FCCCmEKJmbdQKIjdFy5O819HznE+wdHClT+XVq1GvL3i1LjWL3bF5Mu56jKOBejPyFPGjXaxS7Ny4CICo8hGhtBA1a9UYIgV/ZGnh4l+F24MWcTilTsTFa/vl7Dd1T5Vs9k3zb9hxF/kKGfNv2HMWeTWnzrd/SkG9JM80X4MSexdRo3J/CnuVwcMxH484fc3zPonRjzx5Zg3vxclSs0wVrGzuadZvC3ZsBPLx9CYBdf37Ga80GU7pqSywtrdA45Sd/4RI5mU6G8lJbzovtGODo7sW81rQ/RbwMbbl594n8szP9thxweA2FPctR5XVDW27Zcyp3AwN4cMvQllv1+gT34qWxsLDAu1QtSpSrR9DF3J/xiYnW8veODbwz8iMcNI5Urlabeg1bsmW98exHn4HvUbpcJaysrPDy8aNe41YEnPoHgODAa1y+EMCgEeOxs7OnUbN2lPAvy9/b1+d0SpmKjk9gy407jK5RFo21FTWKFKCJVxHWXAk2ir0VqeWNYoUo6GCHnZUlbUsW42pIRJbLyU11hBO1hSNOWGYat0tG0FS44CVscRSWdLfIzy5pyPWOjOM6OnqKAtgKC+oKJ7yw5ZCMyokUXpg2OoZNO/5m/Ih30Dg4UKtqZZo3qMeqDVuMYr09i+Hi7AQYZpIsLCwIDL6dbrmr1m/mtWqV8fQomq31zyptTCzr9v7DpIE9cHSwp06lMrR6vTp/bNubbnzQ3Qf8sX0fY3p3zLTc37fuoX3D2mjs7bKj2v+KNjaOtccuMLlTExztbKlTypvWVUuz/OBpo9jvthykScWSdK9bGVtrK5zsbSntUQgAn0JujGxZlyKuTlhaWDCgUQ3iEhK5cu9xTqeUqZhoLft2rKP/u5NwcHCkYtU61GnQiu0bjFeClalQnWZte1KkmLfRMb1ez6IfP2f4uJl4lyiDEAKP4r44u7jlQBbmTyKQ0nRf5srsOtLKv3c3+AoWlpYU9fRPfs3LryK3blwwir114wLefhWTn3v7VeRWoCHONb879Zp1Z/eGhSQmJnL57GEe3b9Jmcp1sz+JLMhKvrdvXMCrZMW0canyfb1Zd3ZvTJtv6UrmlS8YZpGLeKfkUdS7ElFhD9BGPkk/1isl1sZOQ373Ejy4dR6A4CuGk/TZoyoxbYAHy7/tQ3RkSDZn8GLyUlvOi+0Y4N7N83j4pMyUe/hUIjLsAdoI47Z8/+Z5PHxS8ra101CgcAnuBZ83io3TxRB89RiFvcoZHctpwUHXsbSwxNMnZZzVr3R5bly7lOn3SSkJOHEY35KlAbhx7RJFi3uj0TillFPq+eXktBvhUVgIga9rSj3L5HfhSmiEUWy30t4cv/+EB9oYYuITWHv1Fg083bNczqsgWOrwIWVGzhtbwkgkQiYSjI7CWOMgUk7HfIQNwcTlRlUzdONmMJaWlpTwTpmNLVfKj8vXbqQbv3rTNkrUakiZ15tx4fJV3urSPt24les30+3N1tlS55dx7dZdLC0s8PNM6eBXKOHNxcD0lyqPmTOfqYN7YWdrk2GZ0bE61v59mJ4tGpi6ui/l6v3HWFoI/IqkrG6pULwIF24/NIo9du0W+TQONPzkZ7yGfUGnr5dw63H6K9kCbt4jLjGREu75s63u/8btm1exsLSkuLdf8mslSlUg6HrWBp0fPbjDowd3CLx2gS5N/Oneoiy/zZ2OXq9//jfnBRL0JvwyV2bVkRZCLAE8gQ1CiCghxLik11cJIe4LIcKFEPuEEC90hiSEGCmEuCCESHeptxCivxDiohAiVAixTQjhlepYUyHEpaT3/AFSrpgXQvQTQhxIerwv6eWApDp3E0IUEEJsFEKECSFChBD7hRDZ/rOOjY7CQeOS5jWNowsx0ZHGsTFRODimxDpoXIiNjkq+3vL1Zt1YOX863eo58PE7Deg1ZBoF3ItnbwJZFBtjnK+DJuN8Nany1Tybb9Nu/Dl/Oj3ecGDSkAb0eMf88gXQxUZh75CSh13SY12Mcc5xsVHJx1PHP40ND7nNyb1L6TN2FeN+uEy8Loa180dmY+1fXF5qy3mxHYOhLdulyts+6XFsOm1ZFxuVfPwpO41Luu1+5dyhFPWpRJmqzU1c46yLiY5C45R2KbLG0Zlobeazjb/+MAO9Xk+bjr2Sy3F0fKYcp+eXk9Oi4xNwtrFO85qzjTXauASjWB9XRzycHKi5ZAvlFmzgWmgk71Urk+VyXgWxyDQdZU3SqVcMemKkTH7+lAOWRGNeJ+Pa6GicHDVpXnNyckSrjU43vlPr5lz/528Ob1rFW107ULCAcWfqyIlTPHoSQttmjbKlzi8jKiYWZ0eHNK85OzoQGR1rFLt+7z8kJCTSrn6tTMtct+cw+V2dqVdlAYIPAAAgAElEQVQl9wf5UtPq4nB2SDtD7uxgS1Ss8Z4Vd0IjWHbgFLP6tObynDF4F8xH3/8Z760SERPLwJ/+5KP2DXFxMJ/ZdzDMSGue+Tx1dHTJ8ufpowd3ADh+aBcL1hzlm/lb2LVlFZvXpL+ySvlvMquOtJSyDxAMtJVSOkopv0w6tAXwAwoBJ4Hn7kohhJgE9APqSymN1hQJIdoDHwEdgYLAfmB50rECwGpgIlAAuA6kO60jpXwj6WGlpDqvAEYDt5PKdU96H6PxFCHEYCHEcSHE8YiwR89L6bnsHByJ1qYdsY/WRmDv4GQca+9ITKrYGG0Edg6OCCG4HXSJrz/uycipv7HyQAzfLj/D2iVfcfzAppeuoynZ2RvnG5NJvqljo6NT8r0TdInZE3syYspv/LE/hm9+P8O6pV9x4mDu53ty3zIm9nJmYi9n5k9vha2dI7HRKXnExhge29ob52xj54guJu3PRxcTkRxrbWNP9Ub9KFjUH1t7Rxp1msDlk8bL9HJDXmrLeaEdAxz7exljOjsxprMTP05Jpy0nPbZLpy0/G/s0/tl2v3bBWO7ePMfb41cgRO4vBbN3cEQblbazr9VG4qBxzPB7Vi2bx+Z1fzD7pxXY2NimlKN9ppyozMvJDQ7WVkTGp+3sRsYnoLExvtb3432n0SUkEtCvDRcHtqOFb1H6bj6Y5XJeBXYIolNtLva0k2yPBfZCGHWaY9DjYF6nZ2gcHIjSpt0ELSpKi0bjkMF3GPh6eVKqhC8fTvvS6NiKdZtp07QhGofMy8gNjvZ2RD4zSBCpjcHpmU6hNiaWST8u5qtRA55b5rIte+jRor5ZfDalprG1ITImbac5MkaX7nXN9tbWtK1Whuq+xbCzseajDo04cjWY8FQDDDFx8XT+eik1ShZjbLv62V7/rLJ30BD97OepNiLLn6e2toa20P3tUTg5u1LEw4u2XfpzZH/6m/DlRXlhszHz+qTOgJRygZQyUkqpA6YClYQQLhmECyHEbKA50FBKmVEP9R3gCynlRSllAvA5UDlpVroVcEFK+aeUMh6YA2S+JWda8UARwEtKGS+l3C+lcTOQUs6TUlaXUlZ3di2YheLTV9TTH31iAneDU3YMDLp6huK+ZY1ii/uWJejqmbRxPoa44Ovn8fAqRZXXmmNhYYGHVymq1m3FqcPm9eHwNN97qfO9ln6+xTLL98Z5inqVonKqfKuZSb5V3+jF9GURTF8WwYCJm3EvXpZ7QSl53AsKwNHVHY2T8Wi/e/Gy3E0VGxer5cn967gXN4yGF/aqCGZ6a4K81JbzQjsGqNGwF1/9GclXf0Yy9JPNFPEqx93AlJ1h7wQG4OTqjsbZuC0X9irHncCUvHWxWh7fv04Rz5SZnc3LpnDx+FaGT9uGvYN5bEjl6V2CxMQEgoOuJ7927dK55CXbz9qweimLf/mWH35bR6HCHsmv+5Yszd1bQWk605mVk1t8XRxJ1OsJDEuZ2bn4JBz/fMa/j4tPwulcygtXOxtsLS3pV74Epx+GEhKjy1I5rwJPYUsgKR2VQHS4YomzsMQTW+4Tn6ajHSh1eJLxEuHc4OvlSUJCIjduplynfv7yVUo9s9FYehITEwm6lXY+IyY2lg3bd9HVDJd1A5QsXpSERD3Xbt1Nfu3stSDK+KRd4XP99j1u3ntEs+GT8G03gF4fz+L+kzB82w3g5r2UpdG3Hzxm/+nzZresG8CvcAFDrvdTrmU+G3yfssUKGcWW93RPMxDw9OHTU1xdfALd5iyjqJszP7z9ZvZW/F8q5uVHYkICt29eS37t+uWzeJdIf1fujBT39sfa2sbsBkbMiUSY7MtcmX1HWghhKYSYIYS4LoSIAIKSDmW0VakrMBhDJzk8k6K9gG+Tll+HASEYehUeQFEg+UKYpE5wVvbwnwVcA7YLIW4IIcZn4Xv/NTt7DbUadOCPeVOJjdFyMeAgx/atp37L3kaxDVr1Yf3yOTx5eIeQR3dZ//s3NGrTFwDfUpW5d+sqZ4/vRkrJ/dvXOXFwU5rrUM2Bnb2Gmg068McvhnwvBRzkeAb51m/Zh42p8t3w+zc0aG3I18e/MvfTyTf1tajmolqDPhzdvYAHty4QHRXKrj8/p3qDvunGlq/VgQe3znH28Gri42LZuWoaRbwqUqiY4eS7RsO+HP97IU/u3yBOF82ev76kTDXzOKnJS205L7ZjgBqN+nB4+wLuBRva8rYVn1GrSfptuWLtDty7eY7TBw1teevyTynqXRH34oa2vH3lF5zYs5xh07en2xHPLfYOGho0acMv339OTLSWgJNH2Ld7My3bdTOK3bphJT/OmcZ389fgUdw7zTFPn5L4la7A/LlfotPFsmfHRq5dOU/DZu1yKJMX42BtRQsfD2Yfv0B0fALH7j1hR9BdOvob73JcqZArq68EE6GLJz5Rz5LzN3B3sMPN3jZL5eSmRCmJk3r0SPQYHiemM3XSSDizQ4YTLHVEyURW6p/QWBgGBTyEDb7Yslw+IU7qOSwjCUJHHWFeqw00Dva0atKAmT/MQxsdw9GTAWz9ex9d2rY0il365zoePTHst3H5+g2++3UR9V6rkSZm8669uDg58XrNajlS/6zS2NvRrn4tpv/6B9qYWA6fucSmA8fo3jztDGtZH08ur/mZw799xeHfvuKH8UMplM+Fw799RbFCKZ9Fy7ftpVb5Uvh6FM7pVJ5LY2fDm9XLMm31LrSxcRy+cpONJy/So25lo9g+9aqy4fgFAm7eIz4hkRlr91DH3wtXjT3xCYn0/G45dtbW/PpOJywszLOLYe+goV6TN1kwdxox0VrOnjrMwb830axtD6NYvV6PThdLYkI8SIlOF0t8vGH/Ajt7Bxq26MTy374hWhvJw/t32PjnQmrXN/6bUP67RDoTpblKCBEIDJJS7kx63gfD0uhWGDrRLkAo4CelvJbe7a+A3sBKoIOU8mAG77MNWCylNFomLoToCwyVUr6W9Fxg6EhPTbr9VT9goJTy9aTj8ml90imrHPA30ENKuSujvEuWqS5nLfrneT+e54oMD2Hu9IEEHN2Jk0t+eg//nDea9+DCqf1MH9WG3/cYxhae3o925/oFADRp158+785IHlk7uHMVK+dP59H9mzhoXHijRQ96D/vcZB+Mpto4IDI8hP99NpAzSfn2GvY59Zr34MLp/Xw+qg1L/07Jd+kP49m1wZBv47b96Z0q30M7V7FqQUq+9Zr3oJeJ8r1xx7QjafvWf8OetV8SHxdDhdc60vGdH7GyNizB+vq9CjTsNJ6qbxiurbwasJO1v44k9PFNPP1q0fXdBbgV8k4ua/sfUzm87UcASlVuTrsB3+LgmO+l6leymGl+ua9CW85L7Rjg7mPTnhjt/ms2u1Z/SZwuhsp1O9F1+I9YJ7Xlz4eVp2mXCdRoaGjLl0/vZNVPIwh9eBMv/1r0GvUb+d29ARjZxgJLKxssrVKuq23WdQLNun70UvWrWSKzsdgXEx4WymcT3+XooT24uOZj2AdTaN6mC6ePH2LUO135+4Rhpq5Dk0o8fHA3eTk3QIu2Xfhw6jeA4T7S0yYM48KZE7gXKcaYSbOoWafBS9fvqSLfPn9p6osIi41j7J4T7L/9kHx2NnxYqzzt/Ypz9N5j+m46yMWBhlmq0FgdUw6c4cDth8Tr9fi7OTOpdgUqu7tlWo6pnPnl3EuX8bv+Mctl2g0aewg3mggXhuuDmGvhTSFhaJNr9aGsliHokNQRjgwXhbBOum76gYxnjv4+V4ilIFYMsShEZaExer+XUTNgyUuXERoezvuTprP38FHcXFz4eNRwOrVuzpETp+gxZFTyba3em/gpO/cdQhsTQ4F8+WjbrBEfjngHO9uUtt1t8EiqVCjL+BFDXrpez9KEpb9DeFaFREQy7Iu57D52BjdnJz4d0puuzepxMOACHcd8lua2Vk/tO3mOgdO+5cpfv6R5vUrPEbzf8036tjH9TWksTHBnhpCoaIb88he7z13DzcmBaV2b0a1OJQ5eDqL9rMU8+nVycuy8nf8wc/0eYnTx1Pb34tt+bSmW35X9FwNp/vl87G2ssUg1S7t27FvULeX90nUEOFp5lEnKiQgPYeakoZw4shtnFzcGv/8pTVp348yJg4wb2oGtRw2rCU4d28eo/mk7xpWq1+Pb37YCoI2K4KtPRnBk31YcnVxo0+lt3hoy3iSz1IO7vc7l8yfNdyr2OUqUqS5nLjxqsvK6vGZ5QkpZ3WQFmog5dqSPAAuklPOSng/DMMP8BpCIYbZ3KJl0pKWUxYQQTTFcS91WSmnUQxVCdACmAd2klOeTloo3k1KuSrpGOhDoC6wHhgNfA0My6EjfB96SUm5Pet4GuITh2upiwFEMHek9GeVtqo70q8Kcd+AzNVN3pM2dqTrSr4K81I7B9B1pc2eKjvSrwlQd6VeFKTrSrxJTdKRfFabqSL8qTNGRflWYqiP9KlAd6bTMtSNtjmdFXwATk5ZcjwEWAzeBO8AF4MiLFCKl3AG8DawXQhitHZJS/gXMBP5IWjJ+DmiZdOwx0AWYATzBsNFZujPbSaYCi5Lq3DUpficQBRwG/pdZJ1pRFEVRFEVRFOW/Ii9sNmZ2W2BKKdcB6555+dkdCxaniu+X6vEeDDPAT59vwrBrdkbvtQRId5hWSrkV8M/g2EJgYarnPwE/PRP2TUbvqyiKoiiKoiiK8l9lzh1gUzHHGWlFURRFURRFURRFMVtmNyOtKIqiKIqiKIqivJqkBL18ZS/xfmGqI60oiqIoiqIoiqKYjFrarSiKoiiKoiiKoihKGmpGWlEURVEURVEURTGZvDAjrTrSiqL8n737Dm+yehs4/j3dbdpSNoXSSdlT9pK9997IUIYIigPhhyIKCgoKKjhB2RtBNiJ7yt6zQCebQkeadCTn/SOlbZoArXT5cj7X1Yv0ee6cnJu2yXOesxRFURRFURQlyxhfgoa0GtqtKIqiKIqiKIqiKJmgeqQVRVEURVEURVGULCEBqVbtVnKKrY2RAi7xuV2NHOPpfD+3q5Bjtu9yze0q5KgqAS9Pvi/D/J+0oqJtc7sKOap81IHcrkKOseveOberkKNqvfVhblchRx2tMiC3q5Bjmi0YmNtVyFGG6OjcrkKOqXV6Vm5XIcdodHdzuwpKBqiGtKIoiqIoiqIoipI15MvR2aAa0oqiKIqiKIqiKEqWUYuNKYqiKIqiKIqiKIpiRvVIK4qiKIqiKIqiKFnCtNhYbtci+6mGtKIoiqIoiqIoipJlXoaGtBrarSiKoiiKoiiKovxnCSFaCyGuCCGChBDjrZx3FEKsTD7/jxDC90VfUzWkFUVRFEVRFEVRlCxjlFn39TxCCFtgLtAGKA/0EUKUTxc2FHgkpSwFzAK+fNEcVUNaURRFURRFURRFyRrJ219l1VcG1AKCpJQ3pJQJwAqgU7qYTsDC5MdrgGZCCPEiaaqGtKIoiqIoiqIoipJXFRJCHE/zNSzd+RJAWJrvw5OPWY2RUiYBUUDBF6mUWmxMURRFURRFURRFyRISMBqztMgHUsoazzhvrWc5fV92RmIyRTWk/5+Jjorkq0lvcvzwTvJ5FOSNtz+lebteFnGnju5l4U/TuXbpNK7uHqzcfskiZs2SuaxZMpfHkfcpUqwkn3+3kpK+gTmRRoY8fvyYiRPGcfDAfvLnL8C774+jQ8f0ozhg3q8/s/6PtUTciiB//vz07TeA198YnnL+0sULTPlsMlcuX0aj0dCzdx/eGv12TqaSKecO/MiZfd+TlKjDr2IHGnSaga2do0Xc3dDjnPh7Gg8iziCELZ7+9anX/gtc3IsBcOv6fk7umsmDW2dxdPagz7hTOZ3KM0VHRTJr8ghOHP6bfPkLMnj0FJq27W0Rd/rYHpb+/AVBl0/j5ubBoq1Xzc5/8HpLQq5fJDEhnqIlfBk4chL1mnTIqTQyJCYqklmfJufqUZDBY6bQpI1lrmeO7WHpL6ZcXd08WLTFPNdxb7QkJOgiiYnxFC1uyrVuHss1rUNbZrN/40wSE3RUqNWFDkPmYmdv+bsMcP38Ljb9Poaoh6F4lapF1+Hz8SjsA0B0ZAQbfx9NyOUD2Du60KjzBGo1H261nJwWGRXDW198x65/TlHQw51PRg6kZ6vGT41PSEykbv/RaOP0XN64IOW4e50OuDg58mQUWrfmDZkzcUw21z7zIqNjGfn1fHaeOEdBdzc+G9qDXs3qWcRNXfgHXy3biKN96mXI0V8+x694EQBGffMbB85eJijiLj+9/zoDWjXMsRwy6lFUFGM//pw9h/+hoIcH/3vnTbq1a2UR9/Pi5cxbuorIR4/RuLjQqXVzPnlvNHZ2doTfvkPDjuZ/63E6HZPfH8PIQf1yKpUM2WR8xE4ZTTAJvCrcGGtT7Kmx642PWCsjSUBST7jypiiCvTANgrwrE/nWeIcr6CmMHSNsilBVaHIqjQyLjI1j5Lx17DwXREE3Fz7r2ZJe9apYjT0VfItxSzZzOvg2Gkd7PujYiFGtTL/3n675m00nLnH51n0+7NSIj7o2y8k0MiRSq2PU0m3suhRMQY0zkzu9Ss+a6aeXmpwOvcOHa3ZxJuwuLo72vN+qDm82SW3f/LD7OD/sPsH9mDi88ruxYnhXAosWyKlUMuRl+tnmphxetTscKJnmey/g1lNiwoUQdkA+IPJFXjRXGtJCiJ+ACCnllOfE7QGWSCnnZbJ8X+AmYJ/cdZ9jhBCDgNellA1y8nWfmP35WOztHfhjz02CLp9lwqhuBJSphF8p8zdEJ2cNbbsMIL5ND5bMm2FRzqa1C9jyx0Kmz12Lj39ZboXfxM3dI6fSyJDPJn+Mvb09B48c59Kliwx/fQhly5YjsHRpszgpJV/O+IYyZcsSGhrC0EED8fT0pF37jgC8N/ZtWrRsxeKlK4gID6dv7+6UK1eeZs1b5EZazxR2dRdn9n5H29fXoXEvxo4lAznx95fUaj3JIjZB95iyNQfi1bcpNja2HNw4nr1rx9Bm8CoA7Bw0lKnRj4DErpzeMzunU3muudPexs7egZW7Qrl+5Qwfj+6Cf+nK+Kb/XXbS0KrzazTR92TF/K8syhk57mt8/Mtha2fH5XNHGT+8LfP/PEfBwp45lcpzzUnOdcVOU66TxnTBr3RlfAMs/25bdnqNxq2t5zriA/NcJ4xoy7z1eSvXJ66d+Yt9G2Yw+KO/cPcozrJZ3dm15lNa9vnCIlYb/YDls3rQ+Y2fKfNKe3au/oSV3/dl+GcHAVjzw2sU865M77dXci/iIr9PbUEhzzL4V2icw1lZem/mTzjY2RG0ZTHnrt6gx3ufUSnQj3L+Plbjv13yB4Xze6CNu2Nx7uDi7wgoWTy7q/xCxn6/CAc7W4JXz+FsUAhdJ35DpQBvyvt6WcR2b1yb3yaMsFpOpQBvujeuzUfzVmZ3lf+18VNnYG9vz4W9Wzl/+Sr93nyXCmUCKVvK3yyuVaOG9O7UnnzubjyKimLo2AnMW7qKEa/1xcuzGDeP7UmJDQm/RZ223WjXokkOZ/N8BYQdPUVBTkkt8c/oxDkptayVkUy18aIgdnxuvMVSHjJIFAZghvE2ZYUTn4gSHEfLdONtfrbxJZ/IW307YxduNP0uzx3P2ZDbdP16MZW8i1Heq6hZ3IMYLZ2/WsiX/drQpVZFEpIMRERGpZwPKFqAqb1bMW/X0ZxOIcPeW7kDB1sbrk8bxdnwe/T4cQ2VShShXPFCZnEPYuPoMncN07s1oXO1MiQYDEQ8jk05v+DgGRYdOseakd0oU6wgNx88xsPFKafTea6X6Wf7EjkGBAoh/IAIoDfQN13MBuA14DDQHdgl5Ys197N9jrQQYpAQ4kDaY1LKEc9rRP8XCCF8hRAy+a5GrtPFadm340+GvPUxLi6uVH6lHvUat+WvjcstYstVqkHLDn3x9PK1OGc0Gln44xeMGvclvgHlEEJQoqQ/7vnyzh3FuLg4/tq+jbfHvodGo6FGjZo0bdacP9f/YRH7xrARVKhYETs7O/z9A2jWvAUnT5xIOR8REU6Hjp2xtbXF28eHV6rXJOjaVYty8oJrJ1dQpkY/ChQti6OzB9WavM/VkyusxpYs0xz/Sp1wcHLDzsGFCnWGcjfkn5TzRUq+QmC1nrgX8M2h2mecXqflwN/reW3UJzi7uFKxWn3qNmrHzs3LLGLLVqpJ8/b9KOblZ7Us/9KVsLV78icqSEpK5P6d8GysfebodVoO7lzPwDdTc63TqB27NlnmWqZicq4lnp+rEKZcH9zNO7mmdWr/Yqo3HkxRrwo4u+ancZeJnNq3yGrsxWPrKOJVnop1umPv4ETTbpO4E3KW+xGXidfHcvPiXhp1noCtnT2ePlWoUKsrJ/f+nsMZWdLq9GzYfYiJw/vj6uJM3aoVaNOwFiu27rYaH3zrDiu37eHdgd1zuKZZQ6uLZ/3+Y0wa3A1XZyfqVSpDu3rVWL7jYKbLGtGpOU1eqYCTvX021PTFaeN0bN6xm/Gjh6NxcaH2K1Vp1bghqzdutYj19fYin7sbYOqhsbGx4Wao9b/L1Ru2UKd6VbxL5L0bJvWEG3WFK27YPjNup4ymhciHj3DEVdjS26YgO2U0ABEygevE01cUwlHYUF+44YMjh2TsM8vMaVp9AuuPXWRSt+a4OjlSr4wv7V4py/KDpy1iv9t6kOaVS9G7flUc7e1wc3akbIkiKef7N3yFVlVK4+ZkfbRNbtPGJ/Dn6at81L4hrk4O1CvlRdtKpVh+9IJF7Jydx2lezpdetSqYcnVypGwx0xRTo1EyfcshpndrSlnPQggh8C+cnwIa55xO6Zlepp9tbsvJxcaSO07fArYDl4BVUsoLQojPhBAdk8PmAwWFEEHAu4DFFlmZla0N6bzSwHxZhIdcw8bW1mz4dUCZSgRftxy2/Sz370Zw/24EN4Mu0qN5aXq3Ls/vc6dizOLJDi8i+OYNbGxs8PNLvfNftlw5gq5de+bzpJQcP3aUUoGp/0evDRrC+nVrSUxM5MaN65w+dZK69XNlQMFzPbp3hQKeFVK+L+hZAV3sPfRxzx+Zcjv4MPmLls3O6mWZJ7/LXj6pPye/0pUJuX7xX5X38egutK+Vj7cHNKRyjVcpXaF6VlX1hVnL1b90ZUJu/LtcJ43pQofaqbkGls87uaZ1L/wCxXwqp3xfzLsysVF3iYt5aCX2IsW8U2MdnDQUKBrAvYiLKZ+waW8qSyR3wywvAnNaUGgEtrY2BHqnrndSKdCPSzdCrcZ/8PXPfDJyIM6O1i/K2oycQKm2A+j34ReE3LqbLXV+EdfCb2NrY0OgV+oIiEr+3lwMibAav+XwKUp0GUn1oRP4ZcPOnKpmlrgREoqtrS0Bvt4pxyqUCeRK0A2r8Ws3byegdhPKNWjJxSvXGNijs9W4VRu20KtTu2ypc04JlfH4kfo77IsjjzEQLQ2EEk8x7HERqZeffsKBUBJyo6pPde3OA2xtBIGeqT2ylUp6cjH8nkXssaAw8mtcaPLpz/i8OY1uXy8m7MHjnKzuCwm698j0d5tm+HVFryJcuv3AIvZY8C3ya5xpNnMJfh/OocePawmLTL5J8jiGiMcxXLx9n7ITf6TipJ/5fNMBjBnZuygHvUw/25eNlHKLlLK0lDJASvl58rFJUsoNyY/1UsoeUspSUspaUkrrb9iZkOmGtBBivBDiuhAiRghxUQjRJc25QUKIg0KIWUKISGAl8BNQVwgRK4R4nBy3QAgxNc3zOgkhTgshopPLbv2U1x4ihLgkhHgkhNguhLA+Ns7yefmEEPOFELeFEBFCiKnJ+42l9JgLIWYml3tTCNEmzXP9hBD7kvP9WwgxVwixJPn0vuR/HyfnVzfN86yWl510cVo0ru5mx1xd8xGnzdyd3vt3TRc9xw/t5Lc/jjJr/lZ2bl3Nlj8WPueZOScuLg43NzezY26ubmifk+v3387CKI1069Yj5Vjjps3Yvm0rVSqWpU3LZnTv0ZPKla3PlcltiQlaHJxSf8ZPHifGPzvvh7cvcGrXTGq1npyd1csyurhYNK75zI5pXN3RaWP+VXlTvl/H+oMPmDrnT2rUbYGNTd7ZsED/lFzj/mWun323jnUHHjDl+z+pnsdyTStBr8XJJTXvJ4/j9ZZ5J8THmsWa4t2J18Xg6OyGd+l67Fn3OYkJem7dPMnFo+tITNBlbwIZoNXpcde4mB1z12iIjbOs28Y9h0kyGOnQuK7FOYCtP07j/Lp5HF/5I8UKF6Dn+5+RlGTIlnr/W1p9vJV8nYmN01vEdmtcm5O/TSd0zVzmvjuEaUvWs2rX4Zyq6gvTxsXh5mo+r9fNzRWtNs5qfLd2rbj+z24Ob17NwJ5dKFzIcqHYIydOcf9hJB1aNs2WOucUPdKsoaxJvtTUYUQnZcr3T7hgSxx550Y9mHpp3dMNSXZ3cSRWH28RG/EomqUHTjFjQDuuzH4f38L5ee2HVTlV1RcWG5+Ae7oeVXdnR2LjLW9uRDyOYdk/5/mqRzMuTR2Bb8F8DP59Y8o5gJ2XgjkycTCb3+7N6hOXWHj4bPYnkQkv0882N8ks3EM6j92LMfNvrrCuAw0xTdD+FFgihEg7Aa82cAMoAvQHRgCHpZSuUkqLSbZCiFrAIuADwAN4FQi2EtcZ+B/QFSgM7AcsxyxbtxBIAkoB1YCWwOvp6nwFKAR8BcxPs6/YMuAopuXRJwMD0jzv1eR/PZLze3IV8Kzy0uY07Mky7lGPLO/8ZZazi8bi4lurjcZF45qpchwdTW8wvQePxc3dA88SPnToMYQj+7e/cB2ziouLC7Gx5o3H2NhYNM/Idcmihaxf/we//Po7Dsk9Po8fP+b1wa8xavQYzl64wt79hzmwfx9LlyzO1vpnVNDp1fw+2YffJ/uwdUEv7B00JKRpaCTEmx7bOz4976iHN9i2sBd123+Bp5/1i/S8xtnFlThttNmxOG0Mzhq3pyS75XEAACAASURBVDzj+ezs7anZoBXHD+/g8J5NL1rFLONkLdfYGFyyINcTeSjXMweWMWWwB1MGe7Doy/Y4OGnQ61Lzjk9+7OhkmbeDo2vK+Sf0umgcnU2xPUYt4tH9YGaO9mPjb29RuX4f8hVIv+tFztM4OxGTrmEVo43D1cV8qKNWp+fjOb8z492nL5BWv1pFHOzt8XBz5auxbxBy6y5XgsOeGp8bNE6OxKS7SRATp8fVyhzJcj4lKF4oP7a2NtSpEMioLi1Zt+9YTlX1hWlcXIjVas2OxcZq0aS7kZCev483ZQL8+XCK5RoHK//cQvsWTdC4PLuMvM4JQZxMbRg/aSQ7Y4OzEBaNZh1GXPLYbqwaRwdidOYNqxhdPK5WhvA629vToXo5avh74eRgz/+6NOXItVCirNxAyotcHR2I0VvJ1dHBItbZ3o72VQKp7uOJk70d49vW558bEUTp4nFOXjhwbItaeLg44VMwH0PqV+GvCy/c6ZelXqafbW6TUmbZV16V6XcuKeVqKeUtKaVRSrkSuIZpE+wnbkkpv5dSJkkpM9IlMBT4TUq5I7nMCCnlZStxw4FpUspLyePgvwCqPq9XWghRFGgDvCOl1Eop7wGzME1CfyJESvmrlNKAqdHtCRQVQngDNYFJUsoEKeUBTBPVn8dqeemDpJS/SClrSClr5MtfyKKQzPLyCcSQlER4SFDKsetXzuEbUC5T5ZT0LY29vQNW2v55hq+fPwaDgeDgmynHLl++ZDZkO601q1fxy88/snDRMop5pt73CQsLxdbWhs5dumFnZ0cxT0/atu/Avr3W5zDmtFJVezB4cgiDJ4fQZtBK8hcpQ+Sd1CGrD2+fx9m1CE4u1uevxzwKY8v8blRr8h6B1XrmVLVf2JPf5Yg0v8s3rp7FJ8D6KqKZYTAkcTss73ywPzVX/yzINSmJ2+F5I9cqDfry8e+P+fj3xwz8cBNFvCpwJyS1p+JO6Flc8xXFxc2yp66IV3nuhKbGJui1PLp7gyIlTP9HHoV9GPDBn0z4+TbDpxwiLjaSEgE1sz+p5yjlXYIkg5Gg0NSFQ88F3aScv7dZ3PWwW4TevkfrER+ahm5P+II7Dx9Rqu2Apw7hFkLkuYuLQC9PkgwGgsJTF0o7dz2U8j7Pv6khhEC+2C4kOcrfx5ukJAM3QlKH6V+4co0y6RYas8ZgMBAcZj5HWqfXs/GvnfT8jw/rBvAWjtwktaFyk3g8sMVd2OKNI3dINGto35TxeGPZaMtNgcUKmf5276R2cpwLvUN5ryIWsRW9i5pdLz15mNf+Pp+mVJH8JBmNBN1LnSJ2PuI+5Twtr0srFi+MwHqugUUL4GBni/UdhvKOl+lnq2S/fzO0e2DyMOzHyUO1K2LqeX0is7fIS2Lq5X4eH+DbNK8biemv9Xmf0D6APXA7zXN/xtRj/kTKp76U8kn3gStQHIhMcwwylt/TystWzi4aGjbvxG9zp6CL03Lu1GEO7t5Myw59LGKNRiPx8XoMSYkgJfHxehITTcN4nJxdaNK6G8t/n0WcNoZ7dyLYtGYBdRvlyAj1DHFxcaFFy1Z8N/sb4uLiOHHiODv/3kGnzl0tYjf8uZ5ZX3/F7wuXUNLb/ALWz9cPKSUbN/yJ0Wjk/v17bN28ibJlM3fzIacEvtKLK8eX8ujuFeJ1jzm1+xtKv2K5TRKANuo2m+d3oXydIZSvPdjivDQaSUrUYzQkApKkRD2GpLwxT83JWUP9Zp1Z9OOn6HVaLpw6xOE9m2jWLv0CjKbf5YR4PYakJCSShDS/y6E3r3DswHbi9TqSEhPZuXkZ508coFKNvLOVjpOzhvpN0+R6+hCH926iaftn54o0zzXMWq4nD1Cpet7JNa1qDftzcs/v3Au/iC72EXvWfUG1VwdajS1fszN3wy5w4egfJCbo2b1uKkW9K1G4hGnO/72IS8TrYkhKSuD0gaVcP7uD+m3fycl0rNI4O9GhcV0+/3UpWp2eI2cusmXfP/RuY74ic3l/Hy5t+J2Di7/j4OLvmDNhNEUKeHBw8Xd4FS3EpRshnL16A4PBQGycjv999xuehQtSxq/kU145d2icHenUoAZTFv6BVhfP4fNX2XToJH1a1LeI3XjwBI9itEgpOXb5Oj+s20H7eq+knE9ITEKfkIAEEpNMj/PSOh0aF2faNm/Ml3N+QRun4+jJM2zbvY8eHSw/J5es+ZP7D02NlCvXb/DdvIU0rGN+o2fLzr3kc3OjQa28uaYBgEFKEqQRIxIjpscGKw2KpsKdHTKKUBlPrDSwyviQZsI0DamEcMAfR5bLhyRII4dlDMHEU09k+yVSpmicHOhUozxT1u5Eq0/g8NUQNp28RJ/6VS1iBzR8hY3HL3Im5DaJSQamr99DvdI+eCQvspWYZECfkIhRSpIMRvQJiRjy0u+yowMdq5bm800H0MYncPh6OJvPXqNPrQoWsf3rVmLTmaucDbtLosHAV1sPUTfACw8XJ1wc7On2Sllm//0PMfp4Ih7FsODQWVpXDMiFrJ7uZfrZ5racXGwst4jM3FVJ7v29CjTDNFzbIIQ4DcyRUs6ztvWTEOI14I10xxYA4VLKj4QQPwNxUsqxVl5vD8nbXwkhtgOLpJRLM1BPX5K3v8I0DPwG4GZtK6yn1FkCgUAipka++5MG8ZP50VLK/sn/H8Gk2WbrWeVJKVO7nNIpU+EV+cvKA087nWHRUZF8+fFIThzZhXu+Agx75zOat+vF2RMHGTeyC9uOmhZTOHVsH2OHmH/gV6nRkG9/3waANjaamZ+O5si+bbi65aN9t8EMHDE+y3qpPZ3vv3AZjx8/5n/jP+DQwQN4eOTnvQ8+pEPHThw/dpQ3hg7i1FnTYk1NGzfg7p07ODik3vHu0Kkzn00xbbNz+PAhZn41neCbN3FycqRJ0+ZM/PgTnJ2zZqXJmauy9gLh7IEfOLv3e5KSdPhV6ECDzjNT9pFePbs+1Rq/Q6mqPTix8ytO7vwKOwfzeXyDJ4cAcOvGATbPM1/wxtOvHu3fyMigi6fr3jZr8o2OiuSbT4Zz8shO3D0KMGTMVJq27c25kwf4aFQn/jxsWpTqzLG9jHvDfO/WytUbMmP+DkJvXGbmpDcIvXEJGxtbSniXovfr46jf1HK/8X8jq97cY6Ii+Wayea5N2vTm/MkDfPRWJ9YfSs71+F4+TJdrpeoNmTHPlOvXn6TmWty7FL2HZl2uAMcuPXu13sw6uHkW+zfOJClRR/maXeg49IeUfaS/+6AKjTp9SJUGphsK18/tZNOCt3n8IMS0j/SI+eQv7AvAoa3fsnf9dBIT4vD0qUrbgV9Twr/G0142w9723/bCZURGxTDq82/ZffQ0BfK5MfnN1+jZqjGHTl+g29jJ3N692uI5+0+c443JX6fsI733+BnGfvUjt+49wMXZidqVyjLlrSGU8s66lZ3tYh9lSTmR0bGMmDmPXSfPU8DNlSmv96RXs3ocPHeFzhNmcn/TrwC89vkP7Dx+jvjEJEoULsCwjs14s0vLlHJavfsF+8+aD07bNnMCr1bNmpucMcXKvHAZj6KieOfjqew9fJQC+fIxcewourVrxZETp+gzYmzKtlZvf/QZf+87hFano1D+/HRo2ZQPRw/HKc2icr2GjaFapfKMH219O7AXdbTKgOcHPccy4wOWS/OFLfuIAjQX+RhlDGaujS9FhGmV9Sf7SMcn7yM9Kt0+0rONd7iaTftIN1tg/YZcZkXGxjHi13XsOh9EATcXpiTvNXzwSjCdZyzi/rzUbSd/+fsfvtywB118InVL+/DtoA54FTTNZhz281qWHDhlVvbPb3RlwKuvkBUM0dHPD3qOSK2ON5dsZfflEAponPi0UyN61izPwaAwus1dw51ZqZfo8/ad4qtth9ElJFInwItZvVvgld90oyRaF8+Y5dvZfv46+ZydGFS/Mh+2qZdl14627u7PD8qA/8LPtv6kHzh5IyJvd+8/g3dgDfneN1k3XeedjjYnpJQv/sGexTLbkC4PnASqAEHAQOBXYMQzGtKtMS04VlpKmZB8bAGpDelawF9AN2A3pmHQblLKy+ka0l2AKUCv5OXM8wEtpZQWVyHp95EWQvyJqcH7MRAL+AFeUsq9z2v4CiGOYFpU7COgOrAN2JjckHYBYoByUsqryc99ZnlP+7/Nqob0f0VWNKT/K7K6IZ3XZVVD+r8gL98lzQ5Z3ZDO67KiIf1fkVUN6f+KrGhI/5dkRUP6vyKrGtL/FVnRkP6vyKqG9H+Bakiby6sN6UwN7ZZSXgS+xrSR9V2gEvC8DSJ3AReAO0IIixW1pJRHgcGY5i1HAXsxDcdOH7cO+BJYIYSIBs5jmvucEQMBB+Ai8AhYg6nBnhH9gLrAQ2AqppXI45PrFAd8DhxMHjZeJ4NlKoqiKIqiKIqi/L+TlcO683KnRab3eZZSTgQmPuXcAmBBumMJQLt0xwal+34dsM5KeY3Tfb8YeO5yylLKYNKsdiCljAJGJn9lpM5pn/tklXIAhBArgctpzk8CJqV5+pFnlacoiqIoiqIoivL/WV7etiqr5K39BvIgIURNIUSAEMImeZh6J2B9btdLURRFURRFURRFyR2Z7pF+CRUD/sC0j3Q4MFJKeerZT1EURVEURVEURXk55eUh2VlFNaSfQ0q5EdiY2/VQFEVRFEVRFEVR8gbVkFYURVEURVEURVGyjHwJJkmrhrSiKIqiKIqiKIqSJaRUi40piqIoiqIoiqIoipKO6pFWFEVRFEVRFEVRsoxabExRFEVRFEVRFEVRMsH4EoztVg3pPMJGSBxsk3K7GjlGa9DkdhVyjIPjy/VnFqN/efK1tfn//yGRlqPjyzUbKMHZI7erkGN0f67O7SrkKE27l+czCKDZgoG5XYUcs3PQotyuQo5y9nLI7SrkmApbF+R2FXKMwWVpbldByYCX54pXURRFURRFURRFyVYSNbRbURRFURRFURRFUTJOvhwN6ZdrnJ6iKIqiKIqiKIqivCDVI60oiqIoiqIoiqJkEYnxJeiSVj3SiqIoiqIoiqIoipIJqkdaURRFURRFURRFyTLSmNs1yH6qIa0oiqIoiqIoiqJkCdOq3Wpot6IoiqIoiqIoiqIoaageaUVRFEVRFEVRFCVrSDCqod2KoiiKoiiKoiiKknFqaHcWEUL8JIT4OANxe4QQr+dAfSYLIZYkP/YWQsQKIWyz+3VzQvTjSMaP7kfT6p50aVaRvzatthp34p99vDWoPS1qlaRr80oW58+d+oehvZrQvEYJBnSux5kTh7O76v9K1ONHvDfqNepV9aFtk2ps3bjWatzCeXPo0b4hDar50r5pdRbOm5NyLvLhfSa8O4yWDSryanV/Bvduy7kzJ3IqhUw7vXcuv00uza8Tvdm5YhSGpHircYakBLYtHMiiqZWY+54HEUH7052PZ8+asfz2SSDzPvJl8/xexEbdyokUMiQmKpKvPuxGv8bujOjsz/7ty63GSSlZPGc8g1oWYVDLIiz+/kOzN+9zx3fxwcCaDGianze7BrJj/a85lUKGxURFMv2D7vR+NR/DOgawb9vTc130/QQGNC/KgOZFWfjdeLNcj+3fxJjeVenTyIPxQxsSduNiTqXwr+zbOJvPhhZn0oD8rJo7lKRE67/LANfO7mTG6PJM7OPKT5Oa8eheiPn5M38z+/0aTOzrxufDfDhzcFV2Vz9DHkVFM/Cd/+FduwVVW3dnzZYdz4xPSEykTqd+VGrR1ez4ucvXaNp7KCVrN6dp76Gcu3wtO6v9rz3SxTNo7W58Zy6j+ty1rL1ww2pcn5V/4zdzWcqX15dLaDRvQ8r5Gj+sxWfG0pTzPZc/+/8tt0RGx9B7wpcUad6Xct2Gs+qv/c+MT0hMpFrf0ZTu8kbKsYNnLlK0RT+zL9cG3Vi/J2997kbGxtFr9lIKDf2UMu/MYOWhM0+NPRV8ixZTf6Xw65/hO2oac7cfSjn36Zq/qTnhe9xem8TUP3bmRNUzbZPxEWMNIXQxXGOW8c4zY9cbHzHAcJ1ehiC+Nd4hMc0KS3dlIv8zhNHNcI0Rhpucltrsrvq/Em1I4pN7IbQPvUDf8Cvs1D62GhdrNPDlg3C6h12ie9glFj6+axHzR/QD+odfoX3oBYZEXCP8Ge/rueXR4ygGj34f/+oNqNGsPX9s2mY17pdFy6jdshOBNRtRtVFrJk3/mqSkJLOYXxcvp1aLjvhXb0DD9t25HhxitSzl/6cs75EWQgwCXpdSNnhyTEo5IqtfJ6tIKUMB1yffCyH2AEuklPNyrVIvYObU97G3t2fTvmtcu3yO90f2pFSZivgHljOLc3bW0K5rf5q37caiX74xOxf9OJJxo3rzwaRvaNSiIzs2r2HcqF6s3n4G93z5czKd55r+2YfY2dvz98ELXLl0nreH96V02QoEBJY1i5NS8tmXcwgsU4Hw0GDeHNqDYp4laNWuC3FxWipUqsa74z+jQMHCrF+zlLeH9WXTrhO4aFyf8sq5I/TyTk7umk2nkRvQuHuydUE/jm6bRt32k63Ge/rVoXLDkWxfNMji3Jl9P3En+Ci93z+Ig5M7u1ePYf+6cbQZtCR7k8igeTNHY2fnwLwttwi+eppp73XEN7AyJf0rmMXtWP8rx/Zt4OslJwHBlDGtKVLCn1Zdh5OUlMiMD7vT/63ptOj8BtcvHWfyqOYEVqiFb2CV3EnMil9mjMHO3oHft0Vw8+ppPh/bCd/AyngHmOf617pf+WfvBmYtPYEQgsmj21C0hB+tuw3nVug1Zk0ayEezNlCmYh3WL/maL97vypxV57G1y3uDj66c2s6edV8ybPLfuBcozqKvuvHXism0HTDNIlYb/YDFM7rTfeQvlKvRge3LJ7H0mz68Nd10cX437CLLZven1+jfCazSAn1cFPqnXAjmtHFffIO9vT0Xd//J+ctB9Bk9joqlS1G2lJ/V+DkLllOoQH60cbqUYwmJiQx4ZwLD+/VgSK8uLFy9gQHvTODoxuU42NvnVCoZMn77P9jb2nBhTA/O342k3+pdVChSgLKFPczilvdqbvZ9l6XbaeBTzOzYou5NaORXPNvr/CLe/fpXHOztuLFhPmevBdN93BdULOVDeX9vq/Gzl/1J4fz50Or0KcfqVynP3R1LU77fd/I8PT+cRova1bK9/pkxduFGHOxsCZ47nrMht+n69WIqeRejvFdRs7gHMVo6f7WQL/u1oUutiiQkGYiIjEo5H1C0AFN7t2LerqM5nUKGFRB29BQFOSW1xPP0XrWTUstaGclUGy8KYsfnxlss5SGDRGEAZhhvU1Y48YkowXG0TDfe5mcbX/KJvPWe/H3kbeyEYLVXWYIS9Ey8F0KAvRO+Dk5mcT9G3iZeGllSogyPjUl8cPcmRe0caO1qujbcEhPJ1thHfF7EB297R24nJeBqk/f6qf439Usc7O05t+8vzl++yoCRb1OhTCBlAgPM4lo2fpVenTuQz92NR4+jeGPsh8xbsoIRg/oDsHTNepav/ZPFP86mdIAfIWER5HN3y42U8hwJGP//d0hnbY+0EHnsnSGHCZNcW8BNF6dlz18beGPMR7hoXKlSvS4NmrRh28YVFrHlK1enTcfelPDytTh37vRRChQsQtPWXbC1taV1x1545C/E3r835kAWGaeL07Lzr028+fYEXDSuVKtRh1ebtmbzn5Y9UYPeGE25ClWws7PD178UjZu15vTJfwDwKulL/8EjKVykGLa2tnTrNZDExASCb17P6ZSe6/Lx5ZSrPYCCxcrh5OJBjebjuHR8mdVYWzsHqrz6JsX96yKsfJBFR4bgXaYZLm5FsLN3IrBqNyLvXM7uFDJEr9Pyz+4/6D38U5xdXClXtQE1GnZg71bLRv6eLYvo0HcsBYt4UbBICTr0HcuezQsBiI2KJE4bTaM2/RFCUKp8TUr4liP85qWcTump9DotR3b9QZ/hk3F2caV81QbUfLU9e7YutYjdvXkxnfq9Q6Giplw79X2H3ZsXAXD6yF+Ur9qA8lUbYGtnR5eBHxB5P4ILJ/fldEoZcmLPImo2G0Ix7wq4uOanWfeJHN+z0GrsuSN/ULRkBSrX64G9gxMte33CrZAz3As3/b7uXPM5dVoOo+wrbbC1tUPjVpCCxQKslpWTtHE6Nv29lwmjhuLq4kKdVyrTulF9Vm3abjU+JPwWqzf/xTtD+5sdP3jsFElJBkb074mjgwPD+nVHSsn+oydzIo0M0yYksvlKKOMbVkPjYE/tkkVpVaokq88/+7009HEsR8Lu0b2ifw7VNGtodXr+3PsPH7/eB1cXZ+pVKUfbBjVYsX2v1fjgW3dZ8dc+3u/f1er5J5Zt20PnJnXRODs9My4nafUJrD92kUndmuPq5Ei9Mr60e6Usyw+etoj9butBmlcuRe/6VXG0t8PN2ZGyJYqknO/f8BVaVSmNm5NjTqaQKfWEG3WFK248uxG4U0bTQuTDRzjiKmzpbVOQnTIagAiZwHXi6SsK4ShsqC/c8MGRQzI2J1LIMJ3RyP64aAZ7FMXZxpZKThrqubixw8rNyMO6GHq6F8LJxoZidg60cc3PtthHABilZHHUPUbm98THwQkhBMXtHXG3zVtNg7g4HZv/2sW4MSPQaFyoXb0qLZu8ypqNWyxifb29UhrGEomNsCE4NBwAo9HINz/8yqcfvkuZUv4IIfD19iK/R74czSfPkiCNMsu+8qrnNvqEEOOFENeFEDFCiItCiC5pzg0SQhwUQswSQkQCK4GfgLrJw6UfJ8ctEEJMTfO8TkKI00KI6OSyWz/ltYcIIS4JIR4JIbYLIXyeUc86QohDQojHQogzQojGac75CSH2JuewAyiU5pyvEEIKIeyEEJ8DDYE5yfWfkxxTTwhxTAgRlfxvvTTP3yOE+FwIcRCIA/yT/19uJL/eTSFEv+f9P2eF0OAgbGxt8fYtlXIssExFbgZlrnEkpbSY1yCl5Ma1vNP4AAgJvo6tjS0+fqkXzKXLVuB60JVnPk9KyanjRwgoVdbq+SuXzpGYmEhJH+s9Rrkp8s4lCnlWTPm+UPGK6GLuoddGZrqs8rUHcDv4H7RRt0lMiOPqydV4l23+/CfmgFuhV7GxtaW4d+mUYz6Bla0OVQ6/cRGfUpXN426a4jwKFqVBy97s2rQAg8HAlXOHuX8nhLJV6md/Ehn0JNcSPqm5+gZWsZpr2I2L+AZWThNXmdDkuPR/t0++D7lxPhtr/+/dDbuIp29qLsV9qxD7+C7amIfWY31SYx2cNBQsGsDdsAsAhF413RT7ZmwVpgwtwfJvBxAXk/m/iax2PSQMW1sbSvmm9k5WKFOKy9dvWo0fP302H40ehpOjeQPj8vWbVCgdgBAi5Vj5wAAuB1kvJ7fciIzG1kYQUNA95ViFovm58iDqGc+C1eevU6dkEXw8zHty3txwgPKzV9Jz+Q4u3M39n2d6QWG3sLWxIdA7tde8UoAvl26GWY1/f/Z8Jg/rh5Ojw1PLjNPHs373Yfq2bpzV1X0h1+48wNZGEOiZcvlEpZKeXAy/ZxF7LCiM/BoXmnz6Mz5vTqPb14sJe5A3RohktVAZjx+pf6++OPIYA9HSQCjxFMMelzT9K37CgVAScqOqTxWeFI+NAC/71Dz87Z0JycCQbAkEJ5pGV9w3JHLfkERwop4+4ZfpH36FhY/vYsxj82SvB4dga2tLgG9qk6JCmdJcCbI+DeWPTdsIrNmICvWac+HKVQb0NN0Iu3XnHrfu3OVy0HWqN21HrRYdmfH9zxhfhhW2lBQZ6T29jqlxmQ/4FFgihPBMc742cAMoAvQHRgCHpZSuUkqP9IUJIWoBi4APAA/gVSDYSlxn4H9AV6AwsB+wOnFQCFEC2AxMBQoA7wNrhUgeWwPLgBOYGtBTgNeslSOlnJj8Om8l1/8tIUSB5LK/AwoC3wCbhRAF0zx1ADAMcAPuJ8e2kVK6AfUAy1u22UAXp8XV1d3smMbNnTht5u5+VqpWmwf37/DX5jUkJSayZf0yIsJuotfFZWV1X1hcnBZXN/MLL9cM5PvT919hNEo6dutjcS42NoaPx41i2Fvv4+bmbuXZuSsxQYuDc2q9njxOiI/JdFkehQNw8/BiwWfl+HViSR7dvULNlh9mWV1fhF4Xi4vG/K6uiyYfujjLPPW6WDSuqbEaTT70cbEpjcoGLXqxZv5U+rzqwscjGtNn+BQKFS2ZvQlkgj5Oa5mrq/tTc3VJk6uLa2quVWo358LJfZw/sZfExATWLphOUmICCXqdRTl5Qbw+FmeX1Fyckh/H6yzzTtDHppxPG/8kNioynJN7lzDgg9WMm3OFxHgd6+ePycbaZ4xWp8Pd1Xx6iLurhtg4y/fSzTv3YTAYaNfsVcty4nS4uWrMy3FztVpObtImJuHmaD7U3M3RHm1C4jOft+rcDXpVMh9B8EPHhhx/syvHR3Wjvk8xeq38myh93mqAxOr0uLu6mB1zd3UhJk5vEbth7z8kJRno2Kj2M8v8c89hCnq407BahWfG5TRtfALuLuY95O4ujsTqLRtbEY+iWXrgFDMGtOPK7PfxLZyf137IG2sWZDU90qyhrEm+rNZhRCdlyvdPuGBLHHmroaU3GtGkWyZIY2NDnNFgEVvT2ZUVUQ+IMxqISIxnW+wj4pN7Cx8kzx0+ro/l1+KBzCzmxy5tFFuTe6zzCtP7qfn7spubK7Fa6/PXu7ZvzbVjezm45Q8G9upG4YIFALh91zQ/fO/BI+xav4K1C35i/ZbtLFv7Z/Ym8B8iZdZ95VXPbUhLKVdLKW9JKY1SypXANaBWmpBbUsrvpZRJUsqMXLENBX6TUu5ILjNCSmmty3Q4ME1KeUlKmQR8AVR9Sq90f2CLlHJLcpk7gONAWyGEN1AT+FhKGS+l3AdkZoxyO+CalHJxco7LgctAhzQxC6SUF5LrmQQYgYpCCGcp5W0p5QVrBQshhgkhjgshjj+KDdmi2wAAIABJREFUtOyFySxnFw1arflFqDY2JtPzfPN5FODLOctYsXAO7RqW4siBv6lRtzFFipV44TpmJRcXDdpY80bz8/JdsWQem9ev4rtfluHgYN7ro9freGdEPypVqc6Q4e9kS50z68qJVfw8oQQ/TyjBxl+7Y++gIUGf+jNOTH7s4Jj5OTl71rxHUpKeoVNuMnzaLfwrd2Djr92zrO4vwsnZlThttNkxnTYaZxfLPNPHxsVF4+TiihCCiODLfPNRX0Z/8jsr9uuYtewsfy6ZyYmDm7M9h4xyctFYyTUmQ7nqtKm5evmWZcwnv/HLjLcZ0qYk0Y8f4OVXnoJF8sbf7cl9S/monzsf9XNn/tS2ODq5oo9LzUWvMz12dLbM28HJlXid+f9RvC46JdbewZkaTQdRuHhpHJ1dadptAldObs3GbDJG4+xMTLqLs5jYOFxdzBtf2jgdk2f/yLTx1t93NC7OxGrNG80xsVqLcnKbxt6O2HjzRnNsfCIah6fP4/4n7C73tDo6lDX/aK/lVQRneztc7O14u14l8jk6cCTMcmGj3OTq7ERM+p+LVodbuganVqfn4x8XMXPs0OeWuXTrHvq0bmQ2+iAv0Dg6EKMzbzTH6OJxtTI829neng7Vy1HD3wsnB3v+16UpR66FEmXlBsN/nROCuDSLiz1pJDtjg7MQFo1mHUZccmad3wxzsrEhTpo3muOkERcrU8JG5ffEQQhei7jGpPuhNHHxoFDyGhwONqbf2V7uhXG1saWYnQPt3Qpw1MrN0dykcXEmJl2HS2ysFleN5inPMPH39aZMKX/GT/kSIGXk0JtDB5LP3Y2SJYozoGdXdu47mD0VV/KkjAztHpg8DPtx8lDtiqQZGg1YH8P0dCUx9XI/jw/wbZrXjQQEYO2q0Afo8SQ2Ob4B4AkUBx5JabZUYmaW1CtuJT4kXT1S/g+SX6cXpp7520KIzUIIq2OIpZS/SClrSClr5C9Q0FpIpnj7lsKQlERYcOp/b9CV8/g9ZQjzs1Sr2YDfVu1h+5EQJk3/hdCbQZSr9MoL1zEr+fgGkGRIIjRNvlcvXyCgVBmr8evXLGXBL9/x08K1FC1mvoBNQkI8744aSJGinkz87OtsrXdmlKnek+HTIhg+LYIOb6yhQLFyPLyVOlT3wa1zOLsVwUlTINNlP7x9nrI1++Lkkh9bO0cqNxjGvdAT6GJf/KbOiyruXRqjIYnboakrEwcHnaWkf3mLWC//8gRfO5sad+0sJf1McaE3LlDcpwxV67TCxsaGEj5lqF6/LacOW5+jmhue5Horba5Xz1jNtWS6XG9eO4t3mrh6zbrx3YrTLP77Ln2GfcL9OyGUKl8jexPIoFde7cfUpdFMXRrN0I+2ULRkeW4Hp+ZyO/gMrh5F0bhZvhcWLVmeW2liE/RaHt65TtGSpl67Yj6VMX085C0BPiVJSjJwPST1Y/LC1SDKBphPG7kRGk7Yrdt0GPwW5Zt2YtC7E7n74CHlm3YiNOI2ZQP8uHD1utnQ/YvXrj91wbLc4l/AnSSj5EZk6k2PC/ceUabQ0+cMrjx3nXZlvJ/Z2AYQQjxjyafcUapkcZIMRoLCUnc7OBcUTDk/8xEv18NvE3L7Pi1HfYx/x6H0mziDOw8f499xKCG3U4dGh999wP7TF/LcsG6AwGKFTLneeZBy7FzoHcp7FbGIrehd1OxGwJOH/x+3wvEWjtwk9QbDTeLxwBZ3YYs3jtwh0ayhfVPG483Th/bnBi87RwwSs9W1ryfo8bG3vEnibmvH/wqXZHXJsswvHohEUtbBdEOvpJ0j9og8+E5sLsDXB0OSgRvBoSnHLly5SplSz1+jISnJQEiYaY50gJ8vDvb2ee6mV15iNMos+8qrntmQTu79/RV4CyiYPFT7POZXLOmze162YUBGVoEJA4ZLKT3SfDlLKQ89JXZxuliNlHI6cBvIL4RIe6vJ+nKa1ut/C1NDPS1vIOJpz5FSbpdStsDUkL+M6f8w2zm7aGjUogO/zvkcXZyWsyePsH/XFlp36G0RazQaiY/Xk5SUhJSS+Hg9iQmpw+auXDxDUmIi2tho5sz4iCLFilOnQd6YP/uEs4uGpi3a8eN3X6KL03L6xD/s3bmVdp16WsRu2bCGubM+54ff1+BV0tfsXGJiIh+MGYKTozOffTkXG5u8dbc4rTI1enPx6GIi71xGH/eY43/PpFyNvk+NNyTFk5Q8f8lgSCQpUZ9yMVOkZDWuHF9BvC4KgyGRcwfno3H3xNn1xW/qvCgnZw21Gndhxa+T0eu0XD5zkOP7NtCoTX+L2EZtBrBp+Wwe3osg8v4tNi6bReN2ptkbfqWrcifsGueO70JKyZ3w65w4uNlsTnVuc3LWUKdJF5b/8il6nZZLZw5ydN9GGrexXFqhcdv+bFj2bUquG5bOpkm7gSnnr186gcFgIOrRfX6c9iY1G7THyzfzN9JyQvXGAzi66zfuhl0kLvYRO9d8QY3GVmfdULF2F+6Gnefc4bUkJuj5e/UUPH0qU8TLlFvNJq9xfPcCHt65QUJ8HHvWfUW56u1yMh2rNC7OtGv2KtN/mI82Tsc/p86ydc8BerZvZRZXrpQfZ7avZfeq39i96jdmf/IhhQvmZ/eq3yhRrAj1a1bD1taGX5atIT4hgXnLTdv8NayVt25uahzsaVvGmy/3nUabkMjR8HtsuxZGj4rWP/J1iUlsvBxiMaw7PCqWo+H3SDAY0CcZmHvkPJFxemqVKGy1nNyicXaiY6PaTJ23Aq1Oz+Gzl9l84Bi9WzUyiyvv582VP37m8O8zOfz7TOaMH0mR/Pk4/PtMvIqkvt8u376X2hXL4F+iWPqXynUaJwc61SjPlLU70eoTOHw1hE0nL9GnflWL2AENX2Hj8YucCblNYpKB6ev3UK+0Dx4aZwASkwzoExIxSkmSwYg+IRFDHptXapCSBGnEiMSI6bHByo2ApsKdHTKKUBlPrDSwyviQZsI05aqEcMAfR5bLhyRII4dlDMHEU0/krd1AnG1saODizsLH99AZjZzXazkUF00LjcXsTG4lxhNlSMIgJUd1MWyOfUS/fKa/SycbGxpp8rEy2jT0+35SIltiIqljZZRRbnJxcaZtiybMmPMTcXE6jp48zfZde+neoa1F7NI163nw0LQ+w5WgG3z/6wIa1KlpKsfZiY5tWvDD/EXEarXcunOXJWvW0aJxA4tyXkZP1mnJqq+86nktBg2mRuJ9ACHEYEw90s9yF/ASQjztltt8YLAQopkQwkYIUeIpPbY/AROEEBWSXzufEKLHU8pcAnQQQrQSQtgKIZyEEI2FEF5SyhBMw7w/FUI4CCEaYD4s21r9096W2gKUFkL0TV6QrBdQHthk7clCiKJCiI7JDfd4IBawnGiSTT74+Gvi9XraNSzFJ+8P5YNJ3+AfWI7Txw/RrHpqL+zp4wdpUq0o743ozt3bYTSpVpR33khZR46lv31Lm/r+dG5agYf37zDtO8sVhPOCCZ98RbxeT7N65fnfe8OZMHkGAYFlOXn8MPWrpd7/+GH2NKIeP2JA9xbUr+ZD/Wo+fD7pfQDOnjrK/t1/ceTgHhrVDEg5f/J43trDE8CnbHNeaTKG9T92YNHUSrjlL0mt1hNSzi/7qg5XTqTORVs6vQY/jy+GNuoWG3/pys/jixHzyHQXtn6HqdjaObF0enV+mxRA6KUdtBmcN7a+AnjjgzkkxOsY2saT2ZP688a4uZT0r8DF0/vp3yS1h6tll2HUaNCO9/pX5d1+Vaherw0tuwwDoJhXAG9OnMdv34xlQLP8TBrZlNqNu9Cs45DcSsuqYeO+J0GvY1Cr4nzz0QCGfzgH74AKXDx1gD6NUi9mWnUdRs0G7XinbzXe7lOV6vXb0KrrsJTz8755l/7NCvFWjwpo3PLx5sSfciOdDClTrTWNO33Az580Y9oIP/IX9qZl78kp579+uxIn95ned1zzFWbA+6vZtuxjPnmtIKHXjtL33dTV6ms2G0L1RgOYM6Eu00b4YWfvSMeh3+Z0SlbNmPge+vh4yjXpyLDxnzJj4nuULeXH4ZNn8KnTEgA7OzuKFiqY8uWRzx0bYUPRQgWxtbXFwd6eRbO+YOXGbQQ0aMPS9ZtZNOuLPLf1FcCXrWqjTzJQ4bvVjPhzH1+2qk3Zwh4cCbuL30zzHQa2Xg3D3dHBYtur2IQkxm07QulZK6n6/Wp23bjFsl7NKOCSd1axfmLWe2+gT0jAr8MQBk+exez3hlHe3ztlb2gAOztbihbMn/KV380VGxtB0YL5sbVNHT67bNse+rVpnEuZPN/sQR3QJSThM2oar/2wim8HdaS8V1EOXgmm8OufpcQ1rhDA5B4t6Pr1InxGTeP63YcseDP18m3U/PUUGPopqw6f5asNeykw9FOWHciRpWQybKV8SDdjEGvkI/bIGLoZg1gpH3JPJtLDcI170jSFobrQ0E0UYKIxnCHGmxQW9vRLs4TOBzaeBEk9fYzXWWh8wHgbzzy39RXAmAKexEsjPcIv8fmDcN4uWBxfByfO6bW0D01d+PJqgp5ht4PoGHaR+Y/uMqGQl9kWWaMLeOIsbOgVfoUxd67TVOORsjVWXjLt4/Ho9PFUbNiCke9PZPqkCZQJDODI8VMEVG+YEnfs5BmadO6Nf/UG9B/xNs1erc+Ed0alnP/io3FoXJyp2qgNHfoOoWu71vTp2ik3UlJyiXheKz95JeuRmOb9LgKqY+r9nWdtz+jkBvQ6oC5glFIWEkIsAMKllB8lx3TBtHCZH6aG6ygp5fb0ezgLIQYA4zD1CEcBO6SUVq+AhRC1ga+ASpgarkeBkVLKUCGEP7AQqAYcBq4AHlLK/kIIX+AmYC+lTBJC1E2OLZyc55jkxve3QCkgCHhbSnkg+XXT19kTWAFUxXQT4jTwppTScgneNMpVrCZ/W219y4z/j5xs89aiMdlp/qa8d7GbnZrUz1t327OTrU3evUuaHa6F5d3RGtlhSClrA6D+fzKsyJGBU3mGpt3LdbFrk4e2/MtuOwctyu0q5Chnr7w1VDw7Vdi6ILerkGNa9RjAmfMX/7Pjxov5vCIHTsi6+eIzRrqckFLmjblqaTz3tljyStYTn3JuAbAg3bEETAt0pT02KN336zA1ttOX1zjd94uBxc+rY3LsP/B/7N13fBTF+8Dxz6T3hE4gkBASepNepPdeIkUBwU5XbKCgWOAn4hcrRcUCAiIK0kGkCCi99w4JvUPKXerd/P64kMIlQOCSHOR5v1734u72ub15ws3uzs7sLI0zWXYKy8zjGS0LJ81Qda31ZqDMHTH/YTmBkNHn7yzzxczKIYQQQgghhBCPO3u79Vl2yFvdC0IIIYQQQgghxEOyvws1hBBCCCGEEEI8sux5kjBbkYa0EEIIIYQQQgib0Bq7vm2VrcjQbiGEEEIIIYQQIgukR1oIIYQQQgghhM3kgZHd0pAWQgghhBBCCGE7WoZ2CyGEEEIIIYQQIi3pkRZCCCGEEEIIYRNaa7mPtBBCCCGEEEIIIdKTHmk7obUiyZx3zmskOTjmdhFyjLNL3skVwNXJnNtFyDGODo//2db08s42CgClcrsEOSb6/PXcLkKO8j59OLeLkKNMUVG5XYQc4x7gkttFyFGx5xJyuwg5Rum8c3wBj/7xhVwjLYQQQgghhBBCZIE2a5s9HpZSKr9SapVS6njyv/kyiKmmlNqslDqolNqnlOp5r/VKQ1oIIYQQQgghxONqJLBGax0KrEl+fScj8KzWuiLQBvhSKeV3t5XK0G4hhBBCCCGEELahwc5GdncGmiQ/nwGsA0akDdBaH0vz/IJS6gpQCLiV2UqlIS2EEEIIIYQQwiY0Nr9GuqBSakea199rrb/PwueLaK0vAmitLyqlCt8tWClVG3ABTt4tThrSQgghhBBCCCHs1TWtdc27BSilVgNFM1g0KitfpJTyB2YC/bS++wx30pAWQgghhBBCCGEjGp3D95HWWrfIbJlS6rJSyj+5N9ofuJJJnA+wDBittd5yr++UycaEEEIIIYQQQtiGBrNZ2+xhA4uBfsnP+wGL7gxQSrkAC4BftNZ/3M9KpSEthBBCCCGEEOJxNR5oqZQ6DrRMfo1SqqZS6ofkmB5AI6C/UmpP8qPa3VYqQ7uFEEIIIYQQQthMTg/tvhut9XWgeQbv7wBeTH4+C5iVlfVKj7QQQgghhBBCCJEFdtkjrZT6Fjivtf74HnHrgFla6x/uFmeD8nwAhGit+yilSgKHAF+ttSk7v/dBREXe4NP3BrN981p8/Qrw8msf0LJ9D6u4Xds2MGPqeI4d3ou3jx+//30wZdnli2d5tlOtdPGxsQYGvTmOXv2HZXsOWRF56yZjRw9j68Z/8MuXn0HD36dNx6es4mb++DXLFv7GpfPn8M2Xn6eeeZ6+L6Tm8u1X41i/ejnhp47x3IA3eHloRvdptw+71k5ix+ovSEqMI6RaJ5r1+BInZ1erOFNSAitmPM/lM7uJvnGGsGHLKRHaMF3MlbN7WD9/BFfO7sXZ1YNard7kiSaDciqVu4qOvMHXH7/M7i2r8fEryLNDPqZJm6et4vbtWMdv08Zx8shuvHzy8eOS4+mWX74QzlcfvsTRA9soVLQEA97+imp1rE5K5qroyBt8+dEr7NpsybX/0I9p2raXVdze7euYM+3/OHFkN17e+Zi+LOWWh9y6cYXvPnuD/Tv/JS7OQGDpirz0+gTKVa6dk6lkyYYlX7Ju4QSSEmKpVLcb3V6ekuFvGeD4vjUsnDaUW9fOUCK0Dj2H/ES+woGpy/euZtnMkVy9cBQPr/x06PcZVRtYb/ty2s3IKF4d8wnrNm0nfz5fRg97hafat7KK+3bm70z79Q+u34rE08OdLq2b8+Hrg3Bysuymt+3Zz6gJX3PsVDiBxYsxYdTr1K1eNafTuadb8Qm8s2kf/128Rj5XF958oiydgotbxcWbTIzdfoi/z1wiyaypXjgfH9etTFEPNwBe/3c3my9dx5hkopCbKy9VCqZnaMmcTueubsQYGfjDAtbsP0EBbw8+6tGKnvUz/j/ZHX6Bt2ctY0/4RTxdnXmrU2MGt67PlcgY3pq1jH+PhGOMT6BCQBHGP9OW2iElcjibe7thiGXw7L9YezicAp7ufNC5ET1qVcgwds+ZS4yYt5a9Zy/j4erMm63rMqhp6oS7U/7ZwZR/dnI12khAPm9+e6UboUXy51Qq9yXKlMTE6+fZGReDj4MTL+QrQnNPP6u4GLOJyTcusj02GoCO3vnp51ckXcyfUdf4M+o6t8xJFHZ04aPCJQnIZFuXG5aab7JGRxFOAo2UN8MdMpr42GKh+Sbz9Q0S0NRXXgxShXFWln65yzqRr8yXOEochXBigENhqinPnErjvt28Fcnr749l3aat5PfzY9Rrg+jWoY1V3Pe/zOGH2XO5cdOyXe7cpgXvvzkMJycnzl24RKNOPdPFG2NjGfPWqwzs3zunUrFb2XD7K7uU6w1ppVR/4EWt9ZO339NaD8i9Et2d1voM4HX7dU415u/XF2PfwMnZhYXrT3LiyD5GDOpOSNnKlAopny7Ozd2Ddl370rzdU8yaNjHdsiL+JVi5/VLK6wvnwnmmXVUat+ycIzlkxWcfvYWzszN//XeEY0cOMPyVnoSWq0jp0PT5ag0fjJ9KSNmKnD9zmqEvhlGkaHFatQ8DIKBkMEPf+oA/f5ueC1ncv/DDq9mx+nPChi7D07coS6Y9w5bl43iy80cZxhcLrscTTQax7KdnrZbFxlxjwZSuNO42npBqXTCbEoi5dT67U7hv3346DCdnF2b+fY5Tx/by0audKRVahcDSFdPFubl50qJTPxq17skfP39qtZ7PRvWlXOW6jPlqMTs2rmD8iF58t+AQvvkK5VQq9zRl/Ks4Obnw6+qznDq6lzGvdiG4TBUCS6c/SHVz96Rlp340bt2DuT9NSLcs1hhDaMUavPT6BHzzF+bvhT/zwatd+HnpMdw9vLA3R3evZN2CT3n5g9X45C/GLxPC+Pu3D2jX9xOrWEPUNWZ+9hRPDfye8jU7snLO+8z+/GmGjN8EwOWzh/j1yz70HPozoVVbEmeMJM5wK6dTytDb4ybi7OzMoXWLOXDkOE8PeZtKZUMoFxKcLq51kwY83bktvj7e3IyM4rnXR/P9r/MY9GwvbkZG0WfYSD4b/QYdmjdm/orV9B46gp0rfsfPxyeXMsvYB1sP4uzgwJbuLTh8I4oX126nXH4fyvh5p4ubcTic3VdvsqxjI7xdnHh3834+2naAKU0sja0BlUP4pH4VXB0dORkZQ++VW6iY35dKBXxzI60MDZ+xBBcnR8Inj2RfxEW6TZxJ5ZJFqRCQvhF1LdpAlwkz+LR3W7rWrkRCkonzNyIBMMQnUCM4gPG921HYx5Pp63YSNnEmh794Ay83+2loAbwxdxUujg6c/GQw+85dofvUeVQuXpjyxQqmi7sWY6Tr5HmMD2tKlyfKkmAycf5WTMry6Rv38sum/cwbGEbZogU4fe0WfsknUOzJNzcu4qQUfwSU40RCHKOuRFDa2Y0gl/RlnXrjIvHazKziZbllTuKty6cp4uRCG698ACyPvsGKmJuMKxxISWdXLiYl4OXgmBspZSq/cqKHKsBubSCezBs/u7SB+foGYx0CKIAT48wXmM11+ivL/vQz80XKKTfGqOLswMB480W+cwjCV+V6cyOdd8Z+hrOzMwfW/8WBI8foM2g4FcqFUi6kdLq4Vk0a0rNLB8t2+VYkLw4fyQ+z5jKgf28CihXl1I71KbER585Tr20Y7Vs2zel07JPOGw3pXB3arZSd1axHXKzRwPpVi3hx6Gg8PLyoUr0+DZq0Y+WSOVaxFSrXpHWnpykWUOqe6125eA5VazTAv3jgPWNzUqzRwNpVS3hl2Lt4eHpRrUZdGjVry4rFv1vFPvviMMpVrIqTkxOBwaE0ataOvbu2pSzv0PVp6jdqiYen/TU40jq89Vcq1n2WAv7lcfPIR502Izi0dXaGsY5OLlRvOpjipevjkMFOe9faSQSWb0G5Wj1xcnbFxc2b/EXLZXcK9yUu1sCmtQvoM+AD3D28qFitAbUbdeCf5da5lqlUi2bt+1C0uPVv+XzEMU4e2c0zr7yPq5s7DZp3IzCkEpvWLMiJNO5LXKyBjWsW0HfQGEuuTzSgTqMOrF1mnWvZSrVo3qE3RTOot/4BwXTr8xr5C/nj6OhI27AXSUxM4Fz4MatYe7Bz3S/Uav48RUtWxMMrH82fGsWOdTMyjN2/5U+KlKhIlfrdcXZxo1XPMVyI2MuVc0cAWDNvHHVbvUy56m1xdHTC07sABYqWznBdOclgjGXp6vW8M/hFvDw8qFu9Km2aPMnvS1daxZYqURxfH0tjU2uNg4Pi9JlzgKU3ulCBfHRu1QxHR0d6dGhNgfx+LF29IUfzuRdjYhIrz1xk+BNl8HR2omaR/DQvUYSFJ61P0J2LMdKwWCEKurvi6uhIhyB/jqdpbJXx88bV0bLdUoBSEBFtyKlU7skQl8DC7Yd4P6wFXm6u1C8bRPvq5ZizcY9V7NcrNtKiSgi9GlTD1dkJb3dXyhUvDECpwvkZ1rYB/n7eODo48EKzWiQkmTh28VpOp3RXhvgEFu05xugODfFyc6F+SADtKocwZ9tBq9hJa3bQonwQPWtXtOTr5kq5ogUAyyy+45dvYnxYM8r5F0QpRXChfOT3dM/plO4q1mzmX2MUz/kVwd3BkcpuntT38GZVBifoNsdG08OnIG4ODhR1cqGtVz7+irkJgFlrZkZeYWA+fwJd3FBKUczZFR9H+zr8ra+8qae88ObuDfw1OoqWypdA5YqXcqSXQwHW6CgAzusEThLPM6ogrsqBBsqbQFzZpGPuus6cZjDGsmzVWkYMfQVPTw/q1KhG66aNmLd4hVVsUMmA1O0yGgcHh5Tt8p3+WLycujWeoGTxYtlafmFfbN6QVkqNVEqdVEpFK6UOKaW6plnWXym1USn1hVLqBjAX+Baop5SKUUrdSo6brpQam+ZznZNnTotKXrf1+AtL3PNKqcNKqZtKqZVKqUxbfkqpukqpTUqpW0qpvUqpJmmWlVJKrU/OYRVQMM2yIKWUVko5KaXGAQ2BScnln6QsvlBKXVFKRSql9imlKj3wHzQLzkacwMHRkRJBoSnvlS5bifAThx9qvSsXz6FN52cetng2dyb8JI4OjgSWCkl5L7RsRU4dP3LXz2mt2bNzM8Gh9tFozIrrFw9TsHjllNeFilfCGH2FWMP1LK/rYvh23DzyMffz5nz3TikWfdedqBtnbVncB3Y+4hgOjo4UDyyT8l6pMlU4c+pQltZz5tQhihYvhYdnam9YqdCsryc7nY84joOjIwFpcg0uU5mIkw9XxpNH95KUmECxErnfoMzI5bOH8A+qkvK6WFBVYm5dxhBt/Vu+fPYQ/oGpsS5unhQoUprLZy0H8WeObQXg8+FV+fiF4sz5qi/G6BvZnMG9nYw4i6OjAyFBqUOSK5YpzZETpzOMn7fsb4LqtaJMo/YcPHaSft0to4C01tw5Z4vWcPjEqWwr+4M4HWXAQSlK+aSekCyfz5vjkdFWsd1DSrDzyk0uG+OITTKx+NQFGhdPP0rk/S37qTR7Ba0WraeQuytNkhuf9uD4pWs4OihC/VN7YyuX8OfQOetbk24/cZZ8nh40/fA7Agd9QtjEmZy9lvGIib0RF0kwmShdpEC2lf1BnLhyE0cHh3TDrysFFOZwBg3+7eEXyOfpTvP/zaLUiEl0nzqfszeSG1u3ojl/K5pDF69SbtRUKr3/HeOW/mer29vYzLmkeBwU6YZfBzu7E5EYf8/PaiA8MQ6Aq6ZErpqSCE+M4+lzR+hz7igzbl3GbEeTMGXFGR1PKVL/JkG4cgsTUdrEGeIpijMeKrVpUUq5cIaE3Chqpk5FnMHR0ZHSQalNhAplQzmayfb0z6VBDIAMAAAgAElEQVR/EVK7KRUatOLg0eM826NrhnF/LF5Oj87ts6XMjyaNWdvuYa+yo0f6JJbGpS/wITAr+cbXt9UBTgGFgT7AAGCz1tpLa2118YlSqjbwC/AW4IdlWvLwDOK6AO8C3YBCwL+AdVesJbY4lpttjwXyA28C85VSt/fivwI7sTSgPyb1vmPpaK1HJX/PkOTyDwFaJZexTHJ5ewJZb+U8gFhjDF5e6Yf5eXn7YDQ8+NnAvTs3cvP6FRq36vKwxbM5o9GAp3fW8532zXjMZjMdu9nfyYF7SUww4OqemrOLu2WYY0Jc1v+PY26d59C2X2kcNoEXPjqMb4EgVkx/zmZlfRhxsQY8vNIP4fT08iU2i7/lWGMMnnesx8PLB6PR+sA+t2RURk8vX2KND15vjTFR/O+953jm5dF4etvPUNi04uNicPdILZtb8vP4WOv/m4S4mJTlaeNvx0beOMeu9bPo+9YfvD3pKInxsSz8MffnczAYY/HxSj/KxcfLixijMcP4p9q3Inzz32xdMof+3TtTqICl0VK7WmUuXb3G/OWrSExM4rdFKwg/e57YuLhszyErjEkmvJ2d073n5eyMITHJKjbIx5Ninu40mLeGanNWciIyhiFVQtPFfFS3MnufbsNvrevRumRRXBztZ35UQ3wCPncMR/bxcCUmzrqhdf5mFLP/281nfdtz9Ms3CSqUj35TrEdORcXG8eK383i3S1N87Wyoc0x8Aj53DDX3cXclJt66kXT+VjS/bj3AhO7NOTx2AEEFfHnu5yUpywDWHA5ny6jnWPZqL/7YeZgZm/dlfxJZEGc246nS9856OjhgNFtPjVPL3YvfIq9hNJs4nxjPXzE3iU8+MXAtyfLb3xEXw7RiofyvaCnWGiJZkdxj/aiJQ6drKHsmNyNiMROrdcrr2zxwxIg5R8t4LwajEW+v9Ndt32273K1DG05s+4dNy+fRr0e3lO1yWlt27ubqtRt0bNUsW8r8qNJmbbOHvbL5Xklr/YfW+oLW2qy1ngscB9LOdnNBa/2N1jpJax17H6t8AfhJa70qeZ3ntdYZdTm+AnyitT6stU4C/g+olkmvdB9gudZ6efI6VwE7gHbJk4nVAt7TWsdrrTcAS+7/L0Ai4A2UA1RyeS5mFKiUelkptUMptePWzYcfxuXu4YXBkP4g1BAT/VDDlf9a9CuNWnbCww6vsfTw8MQQk7V8f581jWWL5vLFd7/h4mJf159l5Mj2uUx+oyiT3yjKgindcHbxJCEuNeeEOMtZfhe3rP//ODm7E1KlA0UDa+Dk7EadtiO5eHor8bGRNiv/g3Jz98QYE5XuPaMhCvcs/pbdPbwwGtKvJ9YQjYeHdyafyHkZldFoiHrg65rj42L54LVulKtch57Pv22LItrErg2zGd3bh9G9ffhxbDtc3byIM6bmHRdree7qbv1/4+LmRXxs+r9RfGxUSqyzizs1m/WnULEyuLp70SzsHY7ush6ml9M8PdyJNqQfjhxtMODl4XHXz5UOLEHZ0qV4e5xl/or8fr7M/OoTps6cS/mmHVmzcQuN69akWBH76aEF8HByJCYxMd17MYlJeDpbD2Mds/UA8SYTO3q2ZN8zrWkdWJQX1my3inN0UNQskp9Lxjh+PRqRbWXPKk9XF6Jj0zeao2PjM7yu2d3ZmY41ylMzOAA3F2fe7dqMLcfPEGlMPRESm5DIUxNnUSskgLc6Nc728meVl6sL0XEZ5OvqYhXr7uxEh6qh1Aj0x83ZiZHtGrD11HkiY+NxT/4tDG9ZGz8PNwIL+PJ8g6r8fdC+Rle4OThgvGM+WaM245HBZVKD8/njohT9zh/n/atnaOrhR8HkSQJdHBQAPX0K4eXgSFEnFzp452dbBicMHwVuKIw6tWF8u5HsjgPuSlk1mmMx42FnNwjy9PAg5gG2y8GBJSkbEszIsROslv2+aBntWzbF0/Pu6xCPn+wY2v1s8jDsW8lDtSuRZmg0kNWxoyWw9HLfSyDwVZrvvYHl0irr6UItsd1vxybHPwn4A8WAm1rrtLXsvvfeWuu1wCRgMnBZKfW9UirD2WC01t9rrWtqrWv65SuYUUiWlAgMwZSUxNmIEynvnTx6gKA7Jhq7X/Fxsaz7eyFtOtvn7IMlg0pjMiVxJjz153Hs6MFMh2wvnj+LX6Z9yZTpCylSNKOfhf0pV6sngydeYvDES3Qd9CcF/Mtz9fz+lOXXzh3Aw7sw7p5ZHwZYsHgly4WHyRSW5/Zw37/igWUwm5K4cCZ1Bu7Tx/ZRMjjjGWIzUzK4ApfOn8aY5gTT6eNZX092Kh4YiikpifNpcj11bL/VRGP3IzEhno9ff4oChYsxdNRkWxbzoVVv1Juxs6MYOzuKF0Yvp0iJClwMT+2Fuhi+Fy+/Inh6W/+Wi5SowIU0sQlxBq5fOkmREpaJ54oGVgGU1edyW+nAEiQlmTgZkbrbO3j0BOVC7j03RZLJRPjZ1GuLG9R8gtVzfuDEfyuY+n/vcSL8DNUrPdi2PbuU8vHEpDXhUam7zyM3owj1tT45cvhmFN1KB+Dn6oKroyPPlgti77Vb3IjLeBhokllzJjrjHqPcEFq0IEkmMycupZ4E33/mEhUCrE9uVCpZBJV2W5v89Pa2Nj4xiZ5fzqZYfh8mPWd/k3oChBTOR5LZzIkrqZdMHDh/lfL+1sculYoVStmfQPp8Q4vkx8XJEXusr2kFOLli0nAuzVDukwlxBGYw07aPoxPvFirBHyXK8WOxUDSaci6WBlUJJ1ec0/01Hm0llSunSf2bnCYePxzxUY6UxJVLJKZraJ/W8ZTE+mRLbgoOLElSkolTEWdS3jt49Bhl75gAMiNJJhPhd1wjHRsXx5KVa+gpw7rT0dy+LMk2D3tl04Z0cu/vNGAIUCB5qPYB0m8x7/xr3Ouvcxa4n4v8zgKvaK390jzctdabMomdeUesp9Z6PHARyKdUuvn673bPDavya62/1lrXACpiGeL91n2U/6G5e3jSqEUnfpo0jlijgf27NvPfP8to3dH6lkFms5n4+DiSkhLRWhMfH0diYvoDmA1rluDl7Uv12o1yovhZ5u7hSdOWHfj+60+INRrYu2sLG9Ysp20n61ve/LXkD6Z8MZZvfvqT4iWCrJYnJSYSHx+H2WzGZDIRHx+HyWR3dzejfO2nObj5F65fPEKc8SZbV06gQp3MT3QkJcaTlHytljkpgaTEuJQNUsU6fTi5dylXzu3DZEpk618TKBZcDzcP69t75DQ3d0/qNe3C7G8/JC7WwKE9m9i6fglN21nnajabSUjzW05I81suHliG4DJVmTNtLAnxcWz+ZyHhx/dTv3nG1zjlBjd3T+o368KsqR8RF2vg4J5NbFm/hGbts5ZrUmIi497qhYubO29+9BMODvbVC3CnGk36sm3tT1w+ewhjzE3WzPs/ajbJ8CoaKtXpyuWzB9i/eT6JCXGs/uNj/AOrUDjActKsVtN+7PhnOtcvnSIh3si6BRMoXyP3D2o8Pdxp36Ix4yf/gMEYy9bd+1ix7j96dGhtFTtz/hKuXrcM9zx68jRf/TiThnVSbxe07/AxEhOTiI4xMGbiZIoVKUyzBnVyLJf74eHsRKuSRflyzzGMiUnsvHKD1Wcv06W09YnLKgX8WHjqPNEJiSSazcw6GkERd1fyu7lwPTaepacvYEhMwmTWbDh/laXhF6hb1H6uG/Z0c6FzzQp8PH8NhrgENh+LYOmuwzzdoJpVbN+G1Vmy4xB7Iy6SmGRi/MJ11C8TiJ+nO4lJJp75eg5uzs788EqY3dZbT1cXOlUrw7il/2GIT2DzyXMs23ecp2tXtIrtU68yS/ceY9/ZyySaTExYsYl6pQPw83DDw8WZsOrl+HL1VqLj4jl/M5rpm/bRppJ9zeXg7uDAkx4+zLh1hVizmQNxBjYZo2iZwe2vLiTGE2lKwqQ122KjWRZzk96+lisF3RwcaOzpy9woy9Dvq0mJLI++Qd0MRt7kJpPWJGgzZjRmLM9NGTRemikfVulIzuh4YrSJ383XaZ7cV1RcuRCMK3P0dRK0mc06mnDiqa/sa0Sjp4c77Vo2ZcI332MwxrJt115Wrt3AU53aWsXOnreQq9ctJ4+OnjjF19Om07Bu+tvDrli9Dh9vbxqk2V4LQFsmF7TVw17ZeovtiaVheRVAKfUclh7pu7kMBCilMjtl9SPwnFKquVLKQSlVXCmVUZfjt8A7SqmKyd/tq5Tqnsk6ZwEdlVKtlVKOSik3pVQTpVSA1joCyzDvD5VSLkqpJ4GO9yh/ymkspVQtpVQdpZQzYADigBxrkb3+3ufEx8fSuXEwH779PK+/9wWlQsqzd+dGWtdKvS/g3h0baVmjEG8PDOPyxbO0rFGIN15Kfyb8r0W/0rrT0+nOpNubt9//H3HxcbRuUJbRb7zEiDETKR1ant07NtO4eup9OKd+OY7IWzfo370FjauXoHH1Enwy5vWU5ePee42GVYvx97L5/PztRBpWLcaKRXNzI6W7CqrQkpotXmP+N+34aUxFfPKXoG67USnLfxlXiyPbU8s9Y2x1Jr1eiJhbF1gwpQuTXi9E1A3LWdgSZRtTv+MYFn37FN+/E8ytaydp2/+nHM8pMwNHfkN8fCx9Whbns1F9GfjONwSWrsjB3f/RvWG+lLiDu/4lrIEPH77aiauXzhDWwIf3B7dLWf7W/83ixKGdPN2sMDO+GcXIT3+zq1tfAQx+52vi42N5unkAE959lsHvfENg6Qoc2PUf3RqkXo91YNe/dKnny5ihnbl66Qxd6vkyepClwXh432a2/buc3VtW071xYbo1yE+3Bvk5sOu/3Errrso+0YYmnd/iuzHN+WRAKfIVKkmrXh+kLJ/4amV2bbDMXO7lW4i+b/7BX7++x5h+BThzfBvPvP5rSmyt5s9To3FfJr1Tj08GlMLJ2ZVOL3yV0yll6LNRbxAXn0D5Jh15ecQHfDbqDcqFBLN5514C67RMidu2Zz+Nwp6lZO0W9Br8Fi2erMfoYS+nLP/m59mUadyeKq26cenadWZ88X+5kc49fVinEnEmE3X+WM1rG3bzUZ1KlPHzZvvlG1T59a+UuJE1yuPq6EDzheuoPXcV689dYcrt+wwrmH0sgifnraH63L8Zv/Mwo2pWoGXJzO9tmxu+7N+R2IQkAgd/Qr8pv/NV/05UCCjCxqPhFHox9ZaETSqW5oPuLek28RcCB3/CycvXmT7Icniy5fgZVuw5ypoDJ/B/ZRyFXvyIQi9+xMaj4bmUVeY+79mS2MQkgkdO5vmfl/BFr1aUL1aQjSfOUnT4FylxjcsGMqZTI56aOp/gEZM4efUWPz3XIWX5/3q0wMvVhTLvTqH5/2bRo2Z5nq1XOaOvzFXD8vsTr810P3eYcdfO8WqBYgS5uLE/zkCHM6mTQR5LiOPliyfodPYQP968zDsFA9LdImtofn/clQM9zx1l2KWTNPP0S7k1lr2Yq68TZj7BPH2TdTqaMPMJ5urrXNGJdDcd54q2XLJRQ3kSpvIzynyO582nKaSc6a1ST3C95eDPCR3H0+aTzDBfY6SDv93d+gpg/Oi3iYuPo1Kj1gx8azSfvjeCciGl2bJzN8E1Uy+t2LZ7H027PkOpmo3oPXA4zRs14J3XBqVb1++LltGjUzu7PlYW2UfZurs8eSbrgYAZyyRhNbD0/v6Q0T2jkxvQC4B6gFlrXVApNR04p7UenRzTFcvEZaWwNFwHa61X3nkPZ6VUX+BtLEO3I4FVWuvnMylnHWACUBlLQ3cbMFBrfUYpFQzMAJ4ANgNHAT+tdR+lVBBwGnDWWicppeolxxYCZgKLgC+wNK7jgJVYesrvOnNQuYrV9bTf7etWJtnJzSnx3kGPiV9XOd876DHSvJ79X3tuK44O9nuWNDscOG1/B0TZ6fnQzbldhBxz63/W9+9+nBVrWS+3i5CjTFFR9w56TGydsDy3i5CjYs/Z16zY2anWvlm5XYQc06rHs+w9cPiRbZ0X8K+q2/S3XV38dXzATq213XX72/yoKHkm61GZLJsOTL/jvQSg/R3v9b/j9QIsje0719fkjtczsTRm76ecW4EMZ/TQWp/CMvN4RsvCSTNUXWu9Gcvw7bSqIIQQQgghhBDisZS3uheEEEIIIYQQQmQj+54kzFakIS2EEEIIIYQQwia0Bm22r3uIZwf7nB5SCCGEEEIIIYSwU9IjLYQQQgghhBDCZuz5tlW2Ig1pIYQQQgghhBA2kxeukZah3UIIIYQQQgghRBZIj7QQQgghhBBCCNvQGi1Du4UQQgghhBBCiPujIU80pGVotxBCCCGEEEIIkQXSI20nNJCk8855jaDEo7ldhBxz/lSR3C5CjnJ6snhuF0Fkkxs3EnK7CDkq0ck9t4uQYxLe+y63i5CjtsXly+0i5Kjae77I7SLkmIorpud2EXKU0o//vXpv216lT24XIccYTRG5XYSHZs4Dv82803ITQgghhBBCCCFsQHqkhRBCCCGEEELYhs4b10hLQ1oIIYQQQgghhE1o8sas3TK0WwghhBBCCCGEyALpkRZCCCGEEEIIYTNaP/490tKQFkIIIYQQQghhGxrMZpm1WwghhBBCCCGEEGlIj7QQQgghhBBCCJvJC5ONSUNaCCGEEEIIIYRNaDRay9BuIYQQQgghhBBCpCEN6XtQSgUppbRSyin59QqlVL/cLldmoiJv8N6rvWhXuxC9WpVjzbK5Gcbt3rae159vS8d6/jzdunyGMfNnTeaZNhVoV7sQ/TtV52z48ewsepbdjIyk36sjCKrdlOqtujJ/2coM476b+Rs124QRXLc5lZt15L1PvyQpKSll+f4jx+jYbwCl67WgavNO/G/qjzmVwgM5tutHFn9XmwWTq7D977cxJcVnGHf94m7Wz+/LwilPsOjbmmxeOpjYmCspy01J8excPYrF39Vi4ZQn+G/hi8TGXMqpNO4pOvIGH73enS718tGvbSj/rPgtw7i929cx4qVWhDUsRL92ZdItu3XjCuNH9qV3yyDCGhbijf5NOLJ/W04UP0vyUq63bVv1Nd+8EcQXw4qwbPorJCVm/Ds2JSWwYOrTTBlZlvEvuRNxdEO65dtXf8PUd8rz+dDCTHqzFKvnvoXZlJThunLDzVuRPDf0DYJrNKBm8/b8uXRFhnHf//IrdVp1IrRWI6o1bs374yem204BTJv5K7VbdiS4RgMadgjjZHhETqSQJbdu3WTooBeoUSWU5o3rsHTxggzjfpw2lU7tmlOzWllaNq3Hj9OmWsXMnP4DLZvWo0aVUDq0bkL46VPZXfwsiYq8wehXe9GmdiF6tirH6rvsb197vi3t6/nTM5P97bxZk+nVpgJtahfiWTvc3wLciDHS88vZFHzhQ8q+9hlzN+3NNHZ3+AVajp1GoRc/ImjwJ0xeuSll2YfzVlPrnW/w7vc+Y/9ckxNFfyCWuvsmwTWepGbzDvy59K8M4yx1tzOhtRpTrXGbTOruHGq37ERwjSdp2OEpu6u7N29F8tywtyhVsxE1WnS6S65zqN26CyG1m1K1STveH/95Sq7nLlwiuGbjdI+iFWszdfrsnEzlnpaabzLcFEFX03G+MN/9mGeh+SZ9TSfpaTrBV+ZLJKbpYb2sE3nXdJYw03EGmE6zRxuyu+iPFm0Z2m2rh72Sod1ZpLVue/u5Uqo/8KLW+sncK1F6X40bjrOzC/PXnebEkX28OziM4LKVKRVSIV2cm7snbbr2pVnb7sz+4TOr9SybP50Vf87g/ybPJzC4HBfOncbbxy+n0rgvI8dNxMXZmQPrlnHgyHF6D36DimVDKRcSnC6uVZMn6dW5Pb4+3tyMjOSF10cxbfYfDOz3NAADR4yhXfPGLPxpMmcuXKTjswOoVK4MbZo2zI207upS+AaObP+WxmGzcfcqwqYlAzi4+UuqNBxhFZsQF0lw5acp2qEhysGJ3f+MYfvfb9Oo23QAju+ezvWLu2nVZznOrj7sWPUOu//5gPodv83hrDI2+ZNXcXZ2Yc6as5w8upcxw7oQXKYKgaWtf8utOvejcZsezP1xQrplscYYylSswctvTMA3f2FWLvyZMcO6MH3ZMdw9vHIynbvKS7kCnDqwii0rJvL0Gyvw9vNn/pSe/Lf4Y5qEjc0wPiC0PjVbDGHhd32sloVUaU/l+n1x8/Aj1nCDBVOfYceaydRu9Wp2p3Ff3h37KS7OzuzfsIoDR47Sd+CrVCxbhrKhpdPFtWrSkJ5dOlq2U7cieWn42/ww6zcG9LfkPHveAubMX8TMqV9RpnQpIs6ew9fHJzdSuquxH4zG2dmFDZv3cOTwQQa+1I+y5SsQGlo2faDWjP/sS8qULc/ZMxG8+Nwz+PsXo12HzgDM+/1X5s/7janfz6B0SChnz0Tg4+ubCxll7svk/e2fyfvbdwaHUTqT/W27rn2Jb9udWRnsb5fOn87yP2cw3o73twDDZyzBxcmR8Mkj2RdxkW4TZ1K5ZFEqBBRJF3ct2kCXCTP4tHdbutauREKSifM3IlOWly6Sn7G9WvPDWvs90Qdp6+7fHDhyLLnuhmZQdxvdUXdH3FF3FybX3S+T6+55fH28cyOlTL0z9jOcnZ05sP4vDhw5Rp9Bw6lQLpRyIRltpzqk5Pri8JH8MGsuA/r3JqBYUU7tWJ8SG3HuPPXahtG+ZdOcTueu8isneqgC7NYG4sm8gbZLG5ivbzDWIYACODHOfIHZXKe/KgTAZ+aLlFNujFHF2YGB8eaLfOcQhK+SptVt9twAthXpkX6MxBoN/LtqEc8NeQ93Dy8qV69PvSbtWLVkjlVs+co1adXxGfwDgqyWmc1mfpn6fwx6+1OCSpdHKUXxEsH4+ObPgSzuj8EYy9JV/zByyMt4eXhQt3pVWjdpyB9LrM+ilioRkLLT0hqUUpw+ey5l+dkLFwlr3xpHR0dKlQigzhNVOHrSvno+bgs/NJ9SFXvgW7AMLm6+lK8zhPBD8zOM9S/VhBJl2uHs6o2TszshVZ/l+oWdKcsNUWcpGtgIN89CODq5UrJsByKv20cvSFysgY1rFtB30BjcPbyo9EQD6jbuwJql1me2y1aqRfMOvfEvXspqmX9AMN36vkb+Qv44OjrSLuxFEhMTOBd+LCfSuC95KdfbDmyeRdUn+1GoeAXcPPPRoMM77N80K8NYRycXarUYSonQBjgo611WvsLBuHkkNzq0Rjk4cPOqfdRfozGWZX+v4e1hA/H09KBOjSdo1bQx85Yss4oNKlkidTuFxkE5EH7mLGDZJn8+ZRofjniDsiHBKKUIKlmCfH721bA0Go38/fdyhr32Fp6entSoWZumzVuyZKH1NuqFlwdRoWJlnJycKBVcmmbNW7Fr13bAku+Ub75gxLsfEBJaBqUUJQOD8PPLl9MpZSrWaGDDqkU8P+Q9PDy8qFK9PvWbtOPvB9jfzpj6fwy24/0tgCEugYXbD/F+WAu83FypXzaI9tXLMWfjHqvYr1dspEWVEHo1qIarsxPe7q6UK144ZXmfhtVpXbUM3m6uOZlClljq7lreHjYgue5Wo1XTRsxbstwqNqhkQAZ113KMkVp3X09TdwPsqu4ajLEsW7WWEUNfScm1ddNGzFtsPXrGKlcHB06fOWcVB/DH4uXUrfEEJYsXy9byZ1V95U095YU3jneNW6OjaKl8CVSueClHejkUYI2OAuC8TuAk8TyjCuKqHGigvAnElU06JidSEHbksWpIK6WeUErtUkpFK6XmKqV+U0qNVUr1V0r9d0esVkqFJD9vr5TarZSKUkqdVUp9cJfvWKeUelEpVR74FqinlIpRSt1SStVSSl2+PQw8OT5MKWW9p8kG5yKO4+DoSImg0JT3SpetTPjJw1laz9XL57l6+TynTxyiZ4syPNOmAtMnj7Wr+8GdijiDo6MDpYNKprxXsWxIpg3g+ctWEly3OeUatuHQsRM8271LyrKX+/Tk98UrSExM4sTpCHbsPUCjurWyPYcHEXX9OH6FUocG+hUqT7zxGvGxN+/52avnt+FTIPW3UapiD65d2ElszGWSEmOJOLII/6DG2VLurLr9Ww4ITB2+XKpMZSJOHXqo9Z48upekxASKlSh97+Ackpdyve3qhcMULlE55XXhgMoYoi4TG3P9gdZ3cOtvfD60MF8ND+DK2f1Ua/SCrYr6UE6GR+Do6EjpoMCU9yqWDeXoiYy3U38uXUForUZUrN+cg0eP0bdHGAAXLl3mwqXLHDlxghrN2lG7ZUc+++Zbu9omA4SfPoWjgwNBpVJHBZUtV4ETx+9+Mkdrzc4d2wgJsfRaX7p0kUuXLnLi2BGaNaxFy6b1+Oar/9lVvtmxv+3eogy92lTgZzvb3wIcv3QNRwdFqH/BlPcql/Dn0LkrVrHbT5wln6cHTT/8jsBBnxA2cSZnr93KyeI+tIzrbpm71N2/CK3VmIr1WyTX3W4AXLh0JbnunqRGs/bUbtmJz775zq7+fy3HU+lzrXDX7dRfhNRuSoUGrTh49DjP9uiaYdwfi5fTo3P7bClzTjij4ylF6smeIFy5hYkobeIM8RTFGY80J3dLKRfOkJAbRbVTGrM22+xhrx6b8QdKKRdgIfAlMAnoDMwBPr2PjxuAZ4GDQCVglVJqj9Z6YWYf0FofVkoN4I6h3Uqp60BL4PapvD7AzEzK/DLwMkAR/xL3Ucy7izUa8PRKP9TP08uXWEPWzpBdvXwegB2b1vDjn9uIiY7k7Vc6UbBIcTo89dxDl9MWDMZYvL3SD1f18fIixmDMMD6sfWvC2rfmVMRZfl+8nEIFUs/2t2zcgCHvfsSUGb9iMpl4Y8DzPFGpQobryW1JiUacXVOHhDm7WJ4nJRhwdc+8t+bW1cMc2vINDTp9l/Ked75SePj4s3RaPZRyxLdgWao3+zD7Cp8FccYYPL3Sn7F/kN9yWoaYKP43+jl6vzwaT2/76Q3IS7nelhgfg6t7agujsyoAABBPSURBVLluP4+Pi8Hdq0CW11exTi8q1unFjcsnOLB5Np4+he/9oRyQ0XbK2zvz7VS3Dm3p1qEtp8LP8MfipSnbqYuXLY2V9Ru3sHbhXKKio+n14mD8ixamT/du2ZtEFhiNBry80++DvL29Mdzjtzzp64lobaZbWA8ALl+6CMDG/zawcNlqoqOiePG5Zyha1J/uPXtnT+GzKKP9rZeXL8aH2N/+lLy/feuVThSyo/0tgCE+AR8Pt3Tv+Xi4EhNnPbfB+ZtR7Im4yJIR/akUUIRRv62k35TfWfv+yzlV3IeWed3N+DrYbh3a0K1Dm+S6uyxN3b0M3K67vyXX3SHJdTfjBmhOMxiNeHt5pnvPx8uLGGNm26nkXCPO8Mei9MdTt23ZuZur127QsVWzbClzTohDp2soeyb3PcZiJlbrlNe3eeDIdexnfg6RMx6nHum6gDPwpdY6UWs9D9h+Px/UWq/TWu/XWpu11vuwNMAftGtuBpbGM0qp/EBr4NdMvvd7rXVNrXVN33wFMwrJEncPT4yG6HTvGQ1RuHtm7fpIV1fLzrLXc8Px8vGjaPFAOnR/nq3/ZjyZV27w9HC32qFFGwx4eXrc9XPBgSUoGxLMiLGW69RuRkbSa8Bw3hjwPGd3rGPPqkWs27SVn37LeLh0Tos4vJA/J1Xiz0mV+HfBczg5e5AYn3qglphgee7k4pnZKoi5Fc6/C57niSbvUyigdsr7u9a+hykpgc4Dd9F1yAGKh7Tm3wX2ceDm5uGF0RCV7j1jTNZ/y7fFx8XywavdKFe5Dj1feNsWRbSZvJDrwS1zmDikIBOHFOT3rzrj7OpFfGxqzglxlueubg93LXf+IiEULFaev2fbx/XRnh7uRN/RsIqJuY/tVFBJyoaUZuTH4wFwc7X0igx6oR++Pt6UKF6Mvj26sWbDxuwp+APy8PDEEJN+HxQTE4PnXX7Ls2f+zOKF85n6/QxckvO8vQ964aWB+Pj4UjygBD169WHD+rXZV/gsymh/azBE4fEQ+1tvHz/8iwfSsfvzbLGj/S2Ap6sL0bHpG83RsfF4ZTA8293ZmY41ylMzOAA3F2fe7dqMLcfPEGmMy6niPrTM627m+1q4XXeDGfmxpQ8nte4+a7d119PDI+PjKY97HU8l5zp2gtWy3xcto33LpnjeY1tnz9xQGNP0hBqxPHfHAXelUl7fFosZj8eqWfVwdB6ZbOxx+h8vBpzXWqf9a9/XtIhKqTpKqX+UUleVUpHAAOBBW7azgI5KKS+gB/Cv1vriA64rSwICQzElJXEu4kTKeyeP7ieodMazhGamRFAZnJ1dQClbF9FmggNLkpRk4lTE2ZT3Dh49TtnSwXf5lEVSkonws5ZegIhzF3B0cKRnp3Y4OTlRrGhhurRpwZp/N91jLTkjsHwXug05QLchB2jY9Wd8CoRy61rq0MHIq4dx9SiYaW+0Ieo86+f3pUKdIQRWSH/2+9bVIwRVDMPFzQ9HJ1dCqvXjxqW9xMfeyNac7sft3/L5iNRrtk8f209gcNZHCiQkxPPR609RsHAxho6ebMti2kReyLVi3ad5Y9I13ph0jR6vLqJQsfJcObc/Zfnls/vx9CnyQL3RdzKbTdy8evqh12MLpYMCMSWZOBV+JuW9g0ePUzbk/rZTEclzOZQuFYiLszPKjrfJAEGlgkkymQgPTx0SevTIIUJCy2QYP/+P3/jhu8n8NOM3ivqnXkdZKrg0zs4udp2vrfe39pwrQGjRgiSZzJy4dC3lvf1nLlEhwHr0R6WSRdLlc/tp+sMz+5Zx3T32AHU3yO7rburx1APkajKlXA9+W2xcHEtWrqHnIzysG6CkcuU0qSePThOPH474KEdK4solEtM1tE/reErikhtFtVvabLbZw149Tg3pi0BxlX5rdfsCWgOQclpMKVX0js/+CiwGSmitfbFc+3w/Wz2rvYLW+jywGegK9CWTYd3Zwd3Dk4YtOvPz5I+JNRo4sHszm/5ZRsuOT1vFms1mEuLjSEpKRGtNQnwciYmWazvc3D1o0iaMuT9/gdEQzdVL51k2bzr1Gre1Wk9u8fRwp32LJnw6eRoGYyxbd+/lr3/+pXvHNlaxs+Yv5up1S+Pw6MnTfP3jLzSsUxOA0oEl0WjmL1uJ2Wzm8rXrLPxrDRXLhlqtxx4EVejG6QO/E3X9OAlxkRzaNpmgCmEZxsbGXGL9vN6EVO1L6arWwyHzF6lMxKE/SYyPwmxK5OS+mbh5FsHVPfcnuXFz96R+sy7MnPoRcbEGDu7ZxOb1S2jewTqPtL9l7vgtJyUmMu6tXri6uvPmxz/h4GB/m7y8lOttler1Zt9/M7h24TBxhptsWjaeyvWtZ+S+LSkxnqRES2+WOSmBpMS4lIPyvf/+jCHKMvT52oXDbFn+GUHlmmR7DvfDw8Oddi2b8dmkbzEaY9m2aw8r167jqY7WB5iz5y3g2u3t1IlTfDPtZ56saxlB4uHuTqe2rZjy4wxiDAYuXLrMrHkLaNnEvu4s4OHhQctWbZn05USMRiO7dm5n7eq/6djFehu1ZNGffPn5p/wwfQ4lSgamW+bu7k7b9h35cdpUDDExXLp4gXm//0qTpi1yKpV7ur2//Sl5f7t/92Y2/rOMVpnsb+Pj4zAl19v4O/a3TduEMSd5f3vl0nmW2tn+FsDTzYXONSvw8fw1GOIS2HwsgqW7DvN0g2pWsX0bVmfJjkPsjbhIYpKJ8QvXUb9MIH6e7gAkJpmIS0jErDVJJjNxCYmY7OxA2VJ3m95Rd9fzVMd2VrGz5y28o+5O58nkeVY83N3o1LYlU3785Y66azc3e8EzOdcJ33yPwRjLtl17Wbl2A091sv4Nzp63MPV46sQpvp42nYZ3zCmzYvU6fLy9aZB8nGVvTFqToM2Y0ZixPDdlcJKnmfJhlY7kjI4nRpv43Xyd5spyOUdx5UIwrszR10nQZjbraMKJp76yrztkiOynHqUzhHeTfI30CWAiMBnoBMzFco30L8B+oA5wBMt11K8AoVrrE0qpK8BbWusZSqnawFLgb611H6VUEHAacNZaJyml1gGztNY/KKXaYGl0l9FaJ6QpS29gJBAI+Gt975vLla1YXX879797hd1TVOQNPntvIDu3rMXHNz8vvfYRzdv3ZN/OjYwc2JXl2ywHnHu2b+D159NvJKvWbMgXP1tmvTbERPH5h0PZsuEvvLx9aR/2HH0HjLTZWdVK7HvoddyMjOTV9/6PDVu2kc/Xl9GvDSSsfWu27NxDr4GvE77NMgxw2OixrP53E8bYWArk86Njq2aMHPJyypCrf7fu4OMvpnAy4gxurq60bvIkY0cMx8Pd7W5ff98G/1Tk3kFZcGznDxzZ8R2mpHgCQlpTvflYHJ0suayc0ZpytQcSWL4LBzd/xaEtX+HonH5oVbchBwCIj73JnnUfcjliI2ZzAj4FylCt8WjyF636UOV7/tniD/X526Ijb/DFBy+za8safPwK8NywsTRt24sDu/7jvSGdWLDJsjPft2M9I15qle6zlWs0YsIPq9i3YwMjXmqJq5s7Ks21Th9PWkyl6vZzIPOo5Lp+h+0Odrf9/RVb/vqcpMRYylbvQus+3+DkbPkd//B+deq1e4uKdS2NkikjyxJ1/Uy6zw/45Ah+BQNZ9vPLnNy/ksT4GNy9C1KuRjcadRmDk/PD199XGzzchG9guT/r8NEfsmHzVvL5+jLq9aF069CWLTt20/uVoZzcadnuv/buB6z5dyMGo5EC+fLRsXUL3h42MGU7FR0Tw1tjxrF6/X/4+njT+6kuDB/4ks22yTedbbOdunXrJqPfeZPNGzfg65eP1998hw6durJj+1ZeebEvO/daJh5r2bQely9dxNkltQenY6dufJA8nD0mOpox741g/bo1+Hj78FSPZxg45DWb5Xsl7uFnAI+KvMGnafa3L7/2ES2S97dvD+zKX8n7293bNzA8g/3tV2n2t/9Ls7/tEPYcz9pwfwtQe88XD72OGzFGBkxbwNoDJ8jv7cHHPVrRs35VNh4Np8tnv3D1h/dTYr9fvZVPF68jNj6RemUC+ap/RwIKWGbXf/m7+cz6b3e6dX/3Ujf6Nqr+0GUEiKxhfUL9QVjq7kd31N02yXV3GCd3/gvAa+9+mEHdHZBB3d2Ypu6+aLP/X2WDiZhu3opk+Hsfs37zNvL7+jJq+GBLrjt388wrr6Xc1urVUR+x5t9NaXJtzoihr6TkCtDrpaE8UbkiI4YNeOhy3Wl7lcxPuN6vX83XmKPTj7x7WuWnhfJlsDmcyQ5BFFbOgOU+0vP1DeLR1FdeDFaFcU7ev17WiXxpvsQx4iiEEwMcClNN3X3of1YMN0VwXMfZ71CGe/DJX0HXbv2Lzda35rdaO7XWdnd25rFpSAMopWoC04AQ4PY9Co5rrUcrpUYBw4FY4B0sPcW3G9JPYWmA5wfWA+GA3300pF2ABUA9wKy1LphcDg/gErBAa93vfspuq4b0o8IWDelHha0b0vbOVg1pYX9s2ZB+FNiiIf2osFVD+lFhi4b0o8QWDelHha0a0o8KWzSkHxW2aEg/Kh79hnR5XavVDJutb+3cOnbZkH5sZu0G0FrvAJ64/VopNT3NsnHAuDThs9IsmwfMy2Sd4aQZ5q21bpLmeQJgNUZPa21USl0lB4d1CyGEEEIIIYTIGY9VQ9peKKXCsFw/bT9TjAohhBBCCCFENtOA2Y5n27YVaUjbWPLQ7wpAX63z0HgbIYQQQgghhNDY9WzbtvJYN6S11v1z4Tub5PR3CiGEEEIIIYTIOY91Q1oIIYQQQgghRE7S6DwwtNt+bzQqhBBCCCGEEELYIemRFkIIIYQQQghhM3lhqijpkRZCCCGEEEIIYRsatFnb7PGwlFL5lVKrlFLHk//Nd5dYH6XUeaXUpHutVxrSQgghhBBCCCEeVyOBNVrrUGBN8uvMfAysv5+VSkNaCCGEEEIIIYRNaDTabLbZwwY6AzOSn88AumQUpJSqARQB/r6flSqtH/8Z1R4FSqmrQEQOf21B4FoOf2duknwfX3kpV8hb+ealXCFv5ZuXcgXJ93GWl3KFvJVvbuUaqLUulAvfaxNKqb+w/O1sxQ2IS/P6e63191kozy2ttV+a1ze11vnuiHEA1gJ9geZATa31kLutVyYbsxO5UVmUUju01jVz+ntzi+T7+MpLuULeyjcv5Qp5K9+8lCtIvo+zvJQr5K1881KutqS1bpPT36mUWg0UzWDRqPtcxSBgudb6rFLqvj4gDWkhhBBCCCGEEI8srXWLzJYppS4rpfy11heVUv7AlQzC6gENlVKDAC/ARSkVo7XO9HpqaUgLIYQQQgghhHhcLQb6AeOT/110Z4DWuvft50qp/liGdt9tUjKZbCyPu+9rCx4Tku/jKy/lCnkr37yUK+StfPNSriD5Ps7yUq6Qt/LNS7k+zsYDLZVSx4GWya9RStVUSv3woCuVycaEEEIIIYQQQogskB5pIYQQQgghhBAiC6QhLYQQQgghhBBCZIE0pIUQ4v/br2MBAAAAgEH+1mPYXxYBAMAg0gAAADCINAAAAAwiDQAAAINIAwAAwBARImmniV8/NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(15,7))\n",
    "p=ax.matshow(corr, cmap='coolwarm',aspect='auto',vmin=-0.4, vmax=1)\n",
    "plt.yticks(range(len(corr)), corr.index.tolist(),fontsize=12);\n",
    "plt.xticks(range(len(corr)), corr.index.tolist(),fontsize=12, rotation=45);\n",
    "plt.grid('off');\n",
    "cb=plt.colorbar(p);\n",
    "for (i, j), z in np.ndenumerate(corr):\n",
    "    ax.text(j, i, '{:0.2f}'.format(z), ha='center', va='center',fontsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('correlation.pdf');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structural equation modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill10/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: This is lavaan 0.5-23.1097\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/bill10/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: lavaan is BETA software! Please report any bugs.\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "%%R -i talk_pages\n",
    "\n",
    "library('lavaan')\n",
    "\n",
    "talk_pages[is.na(talk_pages)]=NA\n",
    "\n",
    "talk_pages$quality=factor(talk_pages$quality, levels=c(\"Stub\", \"Start\", \"C\", \"B\", \"GA\", 'FA'), ordered=T)\n",
    "talk_pages$ave_align=abs(talk_pages$ave_align)\n",
    "talk_pages$article_editors=log(talk_pages$editors_y)\n",
    "talk_pages$article_length=log(talk_pages$length_y)\n",
    "talk_pages$article_edits=log(talk_pages$edits_y)\n",
    "talk_pages$preedits=log(talk_pages$editscount)\n",
    "talk_pages$talk_edits=log(talk_pages$edits_x)\n",
    "talk_pages$talk_editors=log(talk_pages$editors_x)\n",
    "talk_pages$talk_length=log(talk_pages$length_x)\n",
    "talk_pages$talk_tfidf=log(talk_pages$tfidf)-log(talk_pages$length_x)\n",
    "talk_pages$article_tfidf=log(talk_pages$article_tfidf)-log(talk_pages$length_y)\n",
    "talk_pages$talk_radius=talk_pages$radius\n",
    "talk_pages$policy[is.na(talk_pages$policy)]=0\n",
    "talk_pages$policy=log(talk_pages$policy+1)\n",
    "talk_pages$guideline[is.na(talk_pages$guideline)]=0\n",
    "talk_pages$guideline=log(talk_pages$guideline+1)\n",
    "talk_pages$aggressive=log(talk_pages$attack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "model <- ' # latent variable definitions\n",
    "             volume =~ talk_edits + talk_length\n",
    "             institution =~ policy + guideline\n",
    "             article_activity =~ article_editors + article_length + article_edits\n",
    "           # direct effect\n",
    "             quality ~ ave_align + preedits + article_activity + d1*volume + d2*talk_tfidf + d25*talk_radius +d3*institution + d4*aggressive\n",
    "           # mediator\n",
    "             volume ~ m1*var_align + talk_editors\n",
    "             talk_tfidf ~ m2*var_align + talk_editors\n",
    "             talk_radius ~ m25*var_align + talk_editors\n",
    "             institution ~ m3*var_align + talk_editors\n",
    "             aggressive  ~  m4*var_align + talk_editors\n",
    "           # covariance\n",
    "           #  article_activity  ~ volume\n",
    "           # Effects\n",
    "             volumee:=d1*m1\n",
    "             lexi:=d2*m2\n",
    "             seman:=d25*m25\n",
    "             institutione:=d3*m3\n",
    "             heat:=d4*m4\n",
    "             totale:= volumee+lexi+seman+heat+institutione\n",
    "'\n",
    "\n",
    "fit <- sem(model, data = talk_pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lavaan (0.5-23.1097) converged normally after 123 iterations\n",
       "\n",
       "  Number of observations                        205744\n",
       "\n",
       "  Estimator                                       DWLS      Robust\n",
       "  Minimum Function Test Statistic           1434575.278  967954.785\n",
       "  Degrees of freedom                                74          74\n",
       "  P-value (Chi-square)                           0.000       0.000\n",
       "  Scaling correction factor                                  1.482\n",
       "  Shift parameter                                           26.179\n",
       "    for simple second-order correction (Mplus variant)\n",
       "\n",
       "Parameter Estimates:\n",
       "\n",
       "  Information                                 Expected\n",
       "  Standard Errors                           Robust.sem\n",
       "\n",
       "Latent Variables:\n",
       "                      Estimate  Std.Err  z-value  P(>|z|)\n",
       "  volume =~                                              \n",
       "    talk_edits           1.000                           \n",
       "    talk_length          1.152    0.002  605.543    0.000\n",
       "  institution =~                                         \n",
       "    policy               1.000                           \n",
       "    guideline            0.956    0.005  208.232    0.000\n",
       "  article_activity =~                                    \n",
       "    article_editrs       1.000                           \n",
       "    article_length       2.100    0.008  250.263    0.000\n",
       "    article_edits        1.484    0.003  438.912    0.000\n",
       "\n",
       "Regressions:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "  quality ~                                           \n",
       "    ave_algn         -1.182    0.030  -39.503    0.000\n",
       "    preedits         -0.053    0.003  -20.559    0.000\n",
       "    artcl_ct          2.141    0.007  286.072    0.000\n",
       "    volume    (d1)    0.332    0.002  160.221    0.000\n",
       "    tlk_tfdf  (d2)    0.496    0.006   87.265    0.000\n",
       "    talk_rds (d25)   -0.354    0.005  -71.008    0.000\n",
       "    instittn  (d3)    0.180    0.008   21.998    0.000\n",
       "    aggressv  (d4)   -0.227    0.003  -83.121    0.000\n",
       "  volume ~                                            \n",
       "    var_algn  (m1)    0.369    0.018   20.212    0.000\n",
       "    tlk_dtrs          1.191    0.001 1939.783    0.000\n",
       "  talk_tfidf ~                                        \n",
       "    var_algn  (m2)    0.201    0.014   13.851    0.000\n",
       "    tlk_dtrs          0.166    0.001  208.236    0.000\n",
       "  talk_radius ~                                       \n",
       "    var_algn (m25)   -0.646    0.017  -37.307    0.000\n",
       "    tlk_dtrs         -0.100    0.001 -114.771    0.000\n",
       "  institution ~                                       \n",
       "    var_algn  (m3)    0.190    0.023    8.204    0.000\n",
       "    tlk_dtrs          0.141    0.001  150.903    0.000\n",
       "  aggressive ~                                        \n",
       "    var_algn  (m4)   -0.593    0.032  -18.306    0.000\n",
       "    tlk_dtrs         -0.115    0.002  -60.431    0.000\n",
       "\n",
       "Intercepts:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "   .talk_edits        0.117    0.013    9.097    0.000\n",
       "   .talk_length       4.379    0.042  104.456    0.000\n",
       "   .policy            0.036    0.023    1.601    0.109\n",
       "   .guideline         0.037    0.021    1.739    0.082\n",
       "   .article_editrs    1.986    0.019  103.654    0.000\n",
       "   .article_length    8.708    0.028  312.752    0.000\n",
       "   .article_edits     2.735    0.021  129.536    0.000\n",
       "   .quality           0.000                           \n",
       "   .talk_tfidf       -1.666    0.010 -169.321    0.000\n",
       "   .talk_radius       3.458    0.012  292.463    0.000\n",
       "   .aggressive       -5.466    0.022 -251.780    0.000\n",
       "   .volume            0.000                           \n",
       "   .institution       0.000                           \n",
       "    article_actvty    0.000                           \n",
       "\n",
       "Thresholds:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "    quality|t1       -1.688    0.040  -41.767    0.000\n",
       "    quality|t2       -0.551    0.040  -13.609    0.000\n",
       "    quality|t3        0.503    0.040   12.452    0.000\n",
       "    quality|t4        1.042    0.040   25.753    0.000\n",
       "    quality|t5        1.754    0.041   42.991    0.000\n",
       "\n",
       "Variances:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "   .talk_edits       -0.018    0.001  -33.997    0.000\n",
       "   .talk_length       1.214    0.004  287.717    0.000\n",
       "   .policy            0.039    0.001   72.930    0.000\n",
       "   .guideline         0.037    0.000   75.432    0.000\n",
       "   .article_editrs    0.351    0.001  336.710    0.000\n",
       "   .article_length    0.118    0.001  102.234    0.000\n",
       "   .article_edits     0.276    0.001  335.350    0.000\n",
       "   .quality           0.232                           \n",
       "   .talk_tfidf        0.120    0.000  360.131    0.000\n",
       "   .talk_radius       0.147    0.000  564.891    0.000\n",
       "   .aggressive        0.592    0.001  534.591    0.000\n",
       "   .volume            0.133    0.001  225.549    0.000\n",
       "   .institution       0.101    0.001  190.923    0.000\n",
       "    article_actvty    0.147    0.001  138.182    0.000\n",
       "\n",
       "Scales y*:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "    quality           1.000                           \n",
       "\n",
       "Defined Parameters:\n",
       "                   Estimate  Std.Err  z-value  P(>|z|)\n",
       "    volumee           0.123    0.006   20.074    0.000\n",
       "    lexi              0.099    0.007   13.683    0.000\n",
       "    seman             0.229    0.007   33.117    0.000\n",
       "    institutione      0.034    0.004    7.692    0.000\n",
       "    heat              0.135    0.008   17.897    0.000\n",
       "    totale            0.620    0.020   31.651    0.000\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(fit)"
   ]
  }
 ],
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