python_pandas-ders5

ders5.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:d647485e753c2c218054b02b9dee005c32cebe9db2b46b81848689ed65919145"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "\u00d6ZETLE"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Bu b\u00f6l\u00fcmde Kanada'n\u0131n 2012 y\u0131l\u0131ndaki t\u00fcm hava verilerini indirece\u011fiz ve csv dosyas\u0131na kaydedece\u011fiz.  \n",
      "her seferinde bir ay indirip ve ard\u0131ndan indirdi\u011fimiz ayl\u0131k verileri birle\u015ftirerek bu yapaca\u011f\u0131z.  \n",
      "2012 y\u0131l\u0131 i\u00e7in s\u0131cakl\u0131k her saatin s\u0131cakl\u0131k verileri burada ki gibidir."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012_final = pd.read_csv('dosya/weather_2012.csv', index_col='Date/Time')\n",
      "weather_2012_final['Temp (C)'].plot(figsize=(15, 6))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7facb1ba5490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAF/CAYAAAALuOdGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYHFW5/7/dPVsmIYSQsC8JqyAgy0UQEQeQzQ0VF1BE\nRcX1qteVnxuguMJ1v4LLVRG8CgqyyAUBYUDEC4JGJOwQwppASAKZJLP08vvjnW/OW6dPVVev093z\nfp5nnuruqTp16lR19fnWuwGGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiG\nYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiG0dXkAPwDwBWT7+cCuBbA/QCuATBnivpl\nGIZhGIZhGIbRkWSnugN18FEAdwMoTb4/FSIQdwHwp8n3hmEYhmEYhmEYRpezDYDrABwKZ0G8F8Dm\nk6+3mHxvGIZhGIZhGIZhdDm/BbAPgJfDCcRV6v8Z771hGIZhGIZhGIZRgU50MX01gKch8YeZmHVK\ncK6nhmEYhmEYhmEYRgp6proDNXAQgNcCeCWAAQCzAZwPYDnEtXQZgC0hIrKMrbbaqvTkk0+2pqeG\nYRiGYRiGYRjtx0MAdgr9oxMtiJ8FsC2AhQCOB3A9gLcDuBzAOybXeQeAS0MbP/nkkyiVSvbXwr/T\nTjttyvsw3f5szG3Mp8OfjbmN+XT4szG3MZ8Ofzbmrf8DsGOc2OpEgehDV9KvAzgCUubisMn3Rhvw\nyCOPTHUXph025q3Hxrz12Ji3Hhvz1mNj3npszFuPjXl70YkuppobJ/8AYCWAV0xhXwzDMAzDMAzD\nMDqa3FR3YAo4/fTTT5/qPkwr5syZgwULFkx1N6YVNuatx8a89diYtx4b89ZjY956bMxbj4156znj\njDMA4IzQ/+KygHYzpUm/W8MwDMMwDMMwjGlHJpMBYrRgN8QgGm3O8PDwVHdh2mFj3npszFuPjXnr\nsTFvPTbmrcfGvPXYmLcXJhANwzAMwzAMwzAMAOZiahiGYRiGYRiGMa0wF1PDMAzDMAzDMAyjIiYQ\njaZjfuWtx8a89diYtx4b89ZjY956bMxbj41567Exby9MIBqGYRiGYRiGYRgALAbRMAzDMAzDMAxj\nWmExiIZhGIZhGIZhGEZFTCAaTcf8yluPjXnrsTFvPTbmrcfGvPXYmLceG/PWY2PeXphANAzDMAzD\nMAzDMABYDKJhGIZhGIZhGMa0wmIQDcMwDMMwDMMwjIqYQDSajvmVtx4b89ZjY956bMxbj41567Ex\nbz025q3Hxry9MIFoGIZhGIZhGIZhALAYRMMwDMMwDMMwjGmFxSAahmEYhmEYhmEYFTGBaDQd8ytv\nPTbmrcfGvPXYmLceG/PWY2PeemzMW4+NeXthAtEwDMMwDMMwDMMAYDGIhmEYhmEYhmEY0wqLQTQM\nwzAMwzAMwzAqYgLRaDrmV956bMxbj41567Exbz025q3Hxrz12Ji3Hhvz9sIEomEYhmEYhmEYhgHA\nYhANwzAMwzAMwzCmFRaDaBiGYRiGYRiGYVTEBKLRdMyvvPXYmLceG/PWY2PeemzMW4+NeeuxMW89\nNubthQlEwzAMwzAMwzAMA4DFIBqGYRiGYRiGYUwrLAbRMAzDmPaccw4we/ZU96JxZDLAmjVT3Yva\nsOe0hmEY7YsJRKPpmF9567Exbz025q2n2jG/6abOFVQ+ExOyXL68tfttxHX+6KNA1mYfqbF7S+ux\nMW89Nubthd2iDcMwjK6mVAJGRoBCYap70jjWr5dlJwre8XFZdtP5MAzD6CYsBtEwDMPoas4+G/jU\np4Djjwd+85vucG9cvhzYYgvgr38FDjxwqntTHQ89BOy0E/Dkk8CWW051bwzDMKYnFoNoGIZhTFtu\nuEGWudzU9qMR3HGHiF1aEGmN6yTyeVk+++zU9sMwDMMIYwLRaDrmV956bMxbj41560k75hQk3SAQ\nL7hALKJTJRAbcZ3TtZRxlEYydm9pPTbmrcfGvL0wgWgYhmF0NcWiLLtBIG6yiSwprjpRZFGwc2kY\nhmG0FyYQjaYzNDQ01V2YdtiYtx4b89aTdsxpseqGzJkUiBRXrbYgNuI65/kwgZgOu7e0Hhvz1mNj\n3l50wc+lYRiGQf71L4vt8ukmC+KsWbKkuDILomEYhtFoTCAaTcf8yluPjXnraZcx32sv4AMfmOpe\ntIa0Y95ogfhf/wUcfHBj2qqVsTFZdnIMYj4PzJwJ3Hxz3U22jFtvBXbZpbX7bJd7y3TCxrz12Ji3\nF50qEAcA3ApgEYC7AXxt8vO5AK4FcD+AawDMmZLeGYZhTCFr1051D9qLRruYXnwx8Je/NKattNx5\nJ3Dffe5YeI47OYtpPg+sWyeiq1O4+27ggQemuheGYRjNpVMF4iiAQwHsDWCvydcHAzgVIhB3AfCn\nyffGFGN+5a3Hxrz1tNOYT5facmnHnBZELust0M52WsmLXiR/FFfr1slyYqJxdR0zmcouq42MQeS+\n6j0frSQzBdWj2+neMl2wMW89NubtRacKRACY/HlEH4AcgFUAXgvgvMnPzwPwuinol2EYhtFG+IKE\n7pm1Uq8gy2SAX/+6um1mzpR+UyBqC2I2CyxfXl+f2O7ISH3tVLOvTo9F/OY3O9OCaxiGUYlOFohZ\niIvpcgA3AFgMYPPJ95hcbj41XTM05lfeemzMW4+NeeNYvz6daKg2BpECsd5JfSMsXvfeW936TE4T\n52L61FO192XNGldXsZJAbMR17gvDTrIgkrEx4DOfAZYsaf6+7N7SemzMW4+NeXvRyQKxCHEx3QbA\nIRA3U01p8s8wDGNaMRVucI1kcBD4+Mcb1x4FIgVJvRZE3d7q1bW1UW3CHK7vWxApemu1at53HzB7\nthuTNWtqaweIz5571VXARRe5936Zi04SiPxuPfaYLAcGpq4vhmEYzaJnqjvQAJ4DcCWA/SBWwy0A\nLAOwJYCnQxu8853vxIIFCwAAc+bMwd57773B95lPMOx9Y9+TdumPvbf3jX4/NDTUNv0plaZ2//W+\nB4Zw992V1+dnldorFOT9E0/I+/Hx+vpXLMr7d71rGBdcUP14A0NYv766/WezADCM++6T7SUG0b3v\n6anteB55RLYXS+Qw/vxnYPfdK/UfZf//05+AV7xiGDfcUL7+8ccP4fnngc02k/f5vPz/n/9045G2\nv1P9/p57pL8izIdx883A9tu3T//sfffdz6fLe37WLv3pxveLFi3C6smnmo/Izb/rmAeXoXQGgJsA\nHA7gmwA+M/n5qQC+Hti2ZBiG0a0ApdK73z3VvagPoFQ67LDGtbfHHtLm618vywcfrK+9Aw+Udt76\nVllWi9j7qttmwQLZ5swzZfnZz8ry85+X5V13Vd+PUqlUuvde2X7JEln+7W+1tXPRRfHH5B/vJZfI\n+3e9S5annVbbPqeC886TPv/977K8556p7pFhGEZtIMHTMts4zdZStgRwPSQG8VYAV0Cyln4dwBGQ\nMheHISwQjRbDpxhG67Axbz3tNOad7mKalrRj7scg1uti2ojx3Xnn6tb3XUwZMxhyCf3ud4EzzkjX\nLseGLquVXFXjxryngj9Sb697zWOIc0ntBHgttSLBTjvdW6YLNuatx8a8vehUF9N/Adg38PlKAK9o\ncV8MwzCMBtOo0g1AeRbT9euBO+4A9tuvtvYoEOsRioceCjzxhLSx1VaV1/cF4uioLB9/XJZ6vD7/\neUk2c9ppldulQBRX1eby+98Dv/gFcMIJ8r5SSY12hNcSx78Tj8EwDKMSnWpBNDoI7V9utAYb89bT\nbmO+ZAmwdOlU96J20gjEtGPuWxCvvRb4t3+rrV8AJuMB68tgWSoBe+4J7LVXuvXjLIish6jHq5o6\njRQ8q1aVtxMibsy53XPPAYsWhbe95BLg8ss7u7yFL9BbIRDb7d4yHbAxbz025u2FCUTDMIwuo1QC\ndtkFOOCAqe5J7TTTgljvpJ4CsZ6Mn6US8Pzz6d0s4yyIFIgAcNZZwGteU5tArHdMeL4+9Slgn32c\ngNUw46efxVTz+OPAv/5VX1+aiS/QzYJoGEY3YgLRaDrmV956bMxbT7uNeT7fWJHVatKUPqglBnHG\njMYUugeAo46qflvuu1SqrtQF1+W4rF8P9PU5oVIqARdeCPzhD9UdH9tj/cNaYxA5xrTMhuopUlj7\nFkS9z2OPTW9VnQr8ONYkK2ixKFbTemm3e8t0wMa89diYtxcmEA3DMLoETtI54WZx9U6kke6HWiDO\nnOmKy9e6DwrEkJi6+GJg5Ur3/r//W4Qc0VbMaoRcyII4Y0bUxZRisRoL4ooVsmSSmlqh0JwxQ5Yc\nYw3HLcnFlMJr3TrgV7+qr0/NoBoL4oMPAscd1/w+xZHJAL/97dTt3zCMzsUEotF0zK+89diYt552\nGHNO0rnMxtzhf/xjSZDSzqRx3as05j/4AbB8uRuPdetEIHJyX2s2U45rSIi98Y3A97/v3t9yS/RY\n6Bo6OlqdkAvFIA4ORl1MH344vl9xUBjSXbbWGEQKQo5tSCD+6Eey9K/TEFdeCZx4YnJfpoJqYhDT\nWIhLpfBYAXL8++5b372lnd1125l2uJ9PN2zM2wsTiIZhtDVjY/ETKCNKmok3ALzvfcD55ze/P/VQ\njciJ49//HdhiCzeJv/9+sapSVIXi5NJAS1jcOOt2fSsZRenYWDoL4tq1sh/f+jY6GhWIpZITLdVY\nJtmfkEtoGsbHo99R9mF8XPoeOo9JFkT2nZbIduH22+U74wvEJCt0pesEAC69FOjvD/9veBj4xz+q\n7moEXV7EMAwjLSYQjaZjfuWtp5vGfJ99gGOOmepeVKYdxtyfeCeVYWj3+MQ0AjHtmI+PA5tuKhN1\nbUHkJL9akiyI+v9AuTigIEtrQZw1C/jiF917bUH0XUxJNeKawq7WOohDQ8DBB5dbEAsF6ft3vlPe\nRlJinLvvlmUjHhA0kte+FjjppOosiGnqbiYlOuJ1VM+9xQRibbTD/Xy6YWPeXphANAyjrbnnHuDW\nW6e6F51BNeUDGlHsvZmkSVLjE3KbPfBAmahvtJG81xbEWgUiRVScOMjlgJtuAk4/vfxc8H017q1/\n+Uv59r4FkTDeMe35rdeC+Pe/i3XNtyBS4D30UPQ9kO46TRKIF1wg9RRbiZ95tRqBmHSdbbpp/P+q\nSWIUx8yZ9bdhGMb0wwSi0XTMr7z1dNuYd0K9tHYY87QupoC4tZ1xBvCtbzW3T7WSxoKkx/ymm4Bt\ntilfp69PxAsF4syZYRfTY491YiaOlSvlj+MbJ/JyOeDlL5fx9c9FGtHgw0QygHwXentdDKKfCZTu\nirUKxGpjELm/kAVRt9fX57K+FgoyRiFxRcGUdA2//e3AyScn97NZVCMQ/TEJQREYGveeHllWc28Z\nHxePC/ZrYAB4//uBG29M3YSB9rifTzdszNsLE4iGYbQ9nSAQ24E0LqZ6wn766cAnPtGSrlVNtS6G\nq1eHPx8fl8kyM7pqC6IWeJdfDlx1VfI+dt4ZOPLIylZAbfmJsyBWEoiLF7vXxaI7b+PjMulnFlPC\n/9OlMC5BkQ9FTK0WxEoCkedxcBB45BF5nc/LdqHv9aGHRrePI611LZ8XL4RaWbxYxjbOgjgyAmy7\nbXjbNA8DeN70WDz+uFzPtVgQV68Grr4aWLVK3heLkhzopz+tvi3DMKYvJhCNpmN+5a2n28a8FnfD\nVtMOY57Gglhtwp/77weee672PlXDokWuf9XWQYyzfI2OiiWGYkpbEH2BUkkkrVwJPPZY5dgyPbH3\njyOfF/HGbUNCrlgE9tjD9S+Xi1otBwacBdGHLqaVBOKDDwI77FC9BdG/zuMEIoUhj6FQcH0qFOQY\nKBg1tJpVOv9p7wnf+haw++7p1g2xxx6SKIbn3BeIq1aJoAuNm+92G4LHob+X224r2XBriUHk+Xz+\n+Wh/7SFbdbTD/Xy6YWPeXphANAyjrWn3WLl2IqkAOaE4SjvB3nVX4IMfrL9vadhnH+AnP5HX1VoQ\nn3oq/Pm6dSKaKGSSBGJSwhBSKqWzIM6ZE95HPi99oGgIXd/choljcrnoPmfMiLcgUpxW+t7ccQew\nZIkTPn4sY4hSCfjnP+X1nnsC113nLLe+GPItbnSN5WuduVNfp+x/pfNfKgEPPOBch+PQNSlrJZOJ\n1q8EykulhOpI+uuGCAlEAHj66dr6qutI6vZNIBqGUQ0mEI2mY37lraebxpwWhXanHcbct9qEqFYg\nAvUXUa8G30UxiaGhIfz+9yJiP/CB8DpJArFQABYsAK65Rt6nPc5KbqLZrLPk+TFq+by4uXIinyQQ\nf/Mb9xnHg8lpCoWoBZEii9dAWhdT9i/NsS9bBnzsY0MAgLvuAm6+2f0vzsWUxzIxEXWnHBgI74MC\nsdL5z2aBa6+tbPVtxAOmvr54CyKvpdDDBX3O4ogTiLpECO8t554LvPSlyX31YyPNglgb7XA/n27Y\nmLcXJhANwzC6BN/FNGSFibOeJZFWbNQDxQNFT1oB+8c/ihus3w4ZG5MJPgWJLnORzwNLl0q9OSA5\n4YgmTiD6VjygfOJfjQXx2mvD+5w9W15rgaiFGFD5nJ1zjtuuvz+di6lf3uPuu11f4rKYjo46l1q2\nTRfTEGkFYi4HfPKTyes0ilBMqS8Q6dKpqUcgPvxw+bm4/HLglluS+8oHD2zPBKJhGLVgAtFoOuZX\n3nq6aczTTtqnmqke84sucrGCnAyGBGI1FjqyaFF9fUsD+0yXxbR1EDlBZxKahx+Oiq7xcXFtpHuj\nLg3BfdLal9ZarWsRhj4vleJLYdCCSNEQEnJsZ9ddXXshgahdTPl/v65hHIzLy+fD5TJCyDUzjO9/\nX97/9rduXOMsiOPjMr69vVHRovseolKsbFqBWE89RW6rx98XiMwwW6tA9M8befGL3TXEe0ua76xv\nQTQX09qY6vv5dMTGvL0wgWgYRtvTiHpg3c5b3gL8z//I6yQLok4akpYlS+rrWxp0Vkggff+43Tve\nIctnnon+f/16ESi8hvr7ywUi36e5zigWBgbKRZVvtfFfn322uCJWcjH9299kue++7jMtNhh3py2I\nfowc+xrH3Lmuf4ODbt2kbdj2bbe5zyoJxLExEd79/VHLbVx9Pm5XSeBms8DGG5d/rpMc5fO1CcTR\nUeCrX3Xnbs893f/8zKTPPivLUDwqjyUpBpHt+QJxiy3K+64t5XGwH76Laac8aDMMoz0wgWg0HfMr\nbw6jo/E/+t025pyEtjPtMOZ+YpTQ5NiPU9xqq8rttiJRECfcnOCmrYNIQePHVmpXOy0QWRcRKBfL\naS2IExMiqthnXYJCt6f3AQCf+hTw17+KuOQ2IQviJpvEtzM2Fm9BHByMCo00Int8PJwNNYT0YWiD\ntXbvvd2YcbxDFsSeHjlmxumNjkYFohalcRY1n1wufI309wNXXAGceKIkPapFIN55J/C5zwGPPuqO\nZa+95DWPj+c+ySKvRf0XvhAtxfLXvwI//7k7zieekLqZpFh0fd9iiyH88Ifp+u4LRLMg1kY73M+n\nGzbm7YUJRMPoUHbdVVKhTwc6QSC2A3SVTBKIfpxiJVc/APjYx+rvWyV8gZjWgujXkaMFknXgAOC+\n+6IWROLH61XjYqrdMpcvF0GirTZJLqYUTEBYfPuT+mzWZQGNi0GcmJA248Spj05OE0p2k7TNy14m\ny7lz3f60+AOiYrm3V64zrjM2FhWI+jpNGzOXzUYT85x0kvvfkiXAxRdLIp1aSuTQQvvww9H9Ae6c\nj47KNaUfTJx2WrTmohaIZ54JXH+9+99BBwEnn+zG9Pe/l7qkelse31lnAR/6ELDffpX77o+/WRAN\nw6gFE4hG0zG/8ubw6KPRLIKabhvzThCIUznmnIj6VoPQZJ+Tca6TZmzTiMh64cSWyzQWDz3mvlDx\nywRwgk8RDZRbK9O4mFKsDQ46N8ibbgJ+9Svg0kujfQHCArG31wnVpCQ12m2RiV2SXEx1rKXfDx8K\niJGR9BZEOZbhDeNVLMYLRFrWaEGcMSNqVawkEDlucRZAbUG8+27g/POjiXYqba+54ALgZz9z7/m9\nYemUbNb1fd06OWfr18sxaQvil77kyrToffu1ITVxdT+1QFy+fBgAsMsulY+F7fkCMW4cjjuudXVO\nO4lu+w3tBGzM2wsTiIbRwdSTgKGT0JN6oxwdF9Xbmzw59q1TaaxmU+FiWsnF8AtfkAckvgWRQuX5\n552wPfBAJxC1BVGLmLSEBCJj4Sgo9OTej02jBTGNQNTWKcY9xgnEfF5Ek/6uJIls9ssXiEkWRN8l\nVwtExnr6LphaIOp9002V7fj74DX8l7+E+5LLuX0/8og7Fr3MZKJtP/hguK2TTwbe/e7yPrCG4pw5\nUYHIOpQ6DjX0UMZPUnPdde5/O+4YPU4/TlFfQ2lrW+p9+g+L4n4rLrkEuP32yu0ahjG9MIFoNB3z\nK28ecansu2XMH3hAlp1gQZzKMdcJQnSMXVJcFCeQaSadrRCIFCxjY3JdT0wA994bL3LOPBO4/PKh\nSOkEwAnEtWvddbN6ddiC6Bc7TxJHGgpEQpHNTJY6OQoFBN9PTMj67FvoO+wnQikWXTmK8XHnYqpL\nRfjtsh9xpLEg/upXEjcZ7ddQxE1ZiyBdQoRL9ksLRN+CGCrXwf7FCRteI4AbD57Hz39eln19bvs7\n7gB23jnqehw9rvI+8Bje9KaoQBwYcBZE//wCYpF7/PFygXjVVeX75nH6116h4Nrcfvuh8g1j0NeZ\nPpakazspic50pVt+QzsJG/P2wgSiYXQwrZi4TyWc5JgFMRldg66/vzxRhUbXpwPSiaJWWxD7+2Vi\nu9tuUhw8jvHxcgsiRdq6dU4sFQrhGETfrTWtRd4v03DeebKcN8/tz68PqJO49PQ4UZnJSMzcVVfJ\nsdx2W7lIohCjIKQF0ReDPT3R70roAcGVVwLHHOP6E4pBvPNO+f8PfyiZV0nIKqXFlBaIFE6+BbGn\nJxqDuHBh1LXXz+oZ5/aby7lj4PnzPdQyGddXXhePPeb+v3hxuLyHb8HVltK1a51raZwF8YQTgG23\nLc9i+r73Rfumj5fv9QMPXo++lT/pO1utBVGvaxiGQUwgGk3H/MqbR5wFsVvGnBPgTrAgTuWYa/HR\n15csEH2rRqkEfOQjzuoSohUCkX1lWQROiv1YQs1DDw3HJqnRFkSdNEYLKF8gphHLxaLLYkrOPz+6\nfaFQLnRoPVq7NjrhLxSAHXYAXvlKEYoHHBAdC+6TLqaAE4i6nVNOkfd6vEIWxFe/Grj66mQL4ote\nBHz/++Wxp4xB1O0WCnIfogVRP6zgMWiBSCso91ksilDz+xwq26HJ5crFN8/9/vvLslQqdx/WY7bH\nHsCXvyyvKej1PnV8YaEg1866dS6DbW9vWCCy9IX/XSsWJV5y+XK3L9/aH0p09OSTwxu21+uGqMWC\naAKxnG75De0kbMzbCxOIhtHBxAnEbqGTBOJUoiflFIi9vWGBwAmkdqv8/veBr3wlvv1WCERdnkLH\n0iXte8cdo6Ism3UupiMj0ayuHCN+NmdObQKRdRC1qNphB7dP7s8XeVtuKUsKRNaWnJgQa5NeN5QJ\nlC6mgIt51GJndLTc0lQoOMukjxaIm24aPT7+X7uw8rjYLuAsdMyuqt1GKa6WLo0KxIGBqIvp0qXA\nb35Tvg+OxfBwVLyRbLZcIPKc8Hxogch2/euJpSf0574FMZ+X46RL6YwZ8tndd7tttGjjeQgJxBe+\nUB4GEN+dmHGtExPlMYhJyaf8fvjHbbVkDcOohi6fXhrtgPmVN49uj0HkpK0TJjf1jvntt7tkG9Xi\nxyDS/S2tBbESzRSIpRJw4YXRiW0u5x4KJCXRWbhwKGJBHByMtyC+6EUiNCiyBgfLYxDTuJjS9U+L\np9e/3u2T68S5+PkWxHze1djjNswqqUt+FIuu7xRM/oMTf6xWrRIxwmQrALDZZtG2164VN09C4aoT\n6RDGIPoWxL4+uZ4oAmlRJLmcGy+6QG+9tcQ4/uQnEuOnxwNw1/T11zvRrwlZELnU/fPrM/rfCV9I\n6dfaOqgFoi+cuQ4g1zPvV8WijKOfxXTpUufq6ltceQ09+6xbf4cdhsr2EQe38S2TSQ8Tuz1UoRa6\n5Te0k7Axby9MIBpGB9PtP+yc7KRNHtLJ7L8/8KpX1batnmTOmuWsTbrYNqlVID7+OHDNNbX1L4ln\nngGOP9655dGCSPGT9HAgk4laEHXSEF8gvv/9IrxoQezvr82CGMpCyvg2XyCGSmqwX8ccE20PcOeR\nAlG7qWazbr2QiylQPlbf/KYs9TXgWyfXrhVrKqFw+cxn4gRiVLjm8y4xEgWidnPmduxbT497CPDW\nt4pllePG8dD949j62WB1DGKcQCyVgL//XV7HZaz985/dNosXS9bUJIFYKrnj/OAHy/utBWKhIN9H\n7apKfDdwnhfuc2zMXY9+vGI1MYi6lqZhGEZa7JZhNB3zK28e3R6DqOuttTuNGHPfUnL22dFJZVw5\nBm1B5GQ/l5PJuO9mWmuSmuOOA446qvK61cIJ8aOPukm/djFNEogrVgxveM3EMRQbWiDqMaTo6e8v\nL8mQdJ3pcdPZQkslV/vOdzHVliYtAHp6gPvuKz8+XxRpgajjMuMEov+e7pP6utEupCw1oQWivqf4\n7cm1NBwRYnQx1QJRi2/uk8fJc8y2Z82KXve+QGSxej/TZsjF1LcGlkrAJpvI6xtukKVvQWSm5Hxe\n4jMPPjhZIAJOsO20k2tH1+/ULqY9PcBFF7n3gDwM4TnkgwTfgjg+7q65Bx8cBuCur6TrtBqB2En3\n11bTLb+hnYSNeXthAtEwOowvf9kloshmxU2rW+tYTScLIhAVc+vXS4kB7R7Y3w/84x/l2+mJMien\npVK0JiKp1YLYrFT4Ou6OCUzSuphWsiDqGETCz+69t1wgpknkQbEWyqyrLUUTE1ELnLYy9vQ44cOs\nnnofWoDR4qYFop88hvT0ACed5N77ljUgGoNKV1Uu9T50fzguvrVLJ2/R/dKZdLk94xyXLJF6hBSM\nG23khA+7YF89AAAgAElEQVSPGYjGSOp9E+1i6tfQDLmL/uhH0XbJNtu4Y6GLd1ySGh7fPffIUseh\ncl3Wo+R2eh3ue7fdgH32ceuzriIg4rGnJxqD6GdDTeNi6rs4hx60+DGlhmEYxASi0XTMr7yxfPGL\nwKWXyutcDjjxRODjH4+u0y1jzolQJzzhbsSY6+Oka5w/oaUrpobrTEw4SwEFom9B5GRwZETWTSsQ\nmyXS2ffxcREWTz0l/UoSiBynefOiMYhxFkQtLkJlLtLEILJ/7FMocZLvxqjXeeYZWdKCSJjgRW+n\nzyetc7QIc/9AuVWopwc47DB5PTAgcZf6+ICoBZFWLC04tVigqIjG7w1VFIi+i2mpVO6u6lsQ162T\nhyG+QCT+dawtiH7tRV1b0Bc/vtDcd99y8RRnQaRFmIJaj1ucQDz2WLcOx2T+/Oi9TVtcly8Httoq\nWsJl882HALhkPrW4mIbCEbhuUr3M6Uq3/IZ2Ejbm7UXCs1nDMNoVXUMN6N74kulmQdQCxZ/8kpDl\nSgsKTk5LJWeJ8PeRzUqc28yZst5OO4lVJ45MpnEifWREJqbMxKndA1nEfN68eBfTTAa49VZ5XSiU\nWxAZv0eBeNZZ0euH7b7whdVbEGfMkP5mMskWRLqh6r7TjdJPUkMLIcdAj0k+L8fw/POyby0Qd9wR\nWLAgun+dLXTzzcVtl+1mMuJtwGMcG3PnYMYMGQ9tzXzBC6LnZnDQCQkmssnn5bqgEOa+SyXgiSeA\noSFXm9C/R+m6lGNjwHveI/UX/SymJGRB5Dq0znKMKeyYcVbjC8++PjluLSR9gXjZZSJkdS1HfbwA\ncNddrp/axVQLY/ZXu5QWi+UupjNmSD95jfl9TuMKnSZJjVkQDcOIo0unlUY7YX7ltfP3vwNve1v5\n574FwZ8EdcuYd5JAbMSYhwSiPzGuJBB9C2LIxXRwUOLTmHQjDbVOIh96KPr+6KOBXXYp77ueBFdK\nUvOlL8ly+XJXk48CzrcgfvKT4qpLOGF/xSuqS1JDsQa4ST0AvOENbp3166W/69fLunpSrrOraqHX\n21vuYupbEAERzxRCvb0i6BlfR3p6olYuiha2/5OfRAUEy2sMDsq68+a5fR99dPk1yDqIl1/u2shm\n3Tnivp94Arjxxqh7pX8eOQa8Rv/4R0kSo62mOmmTf4/TLqZ8KECBqB+q+NetL7YymXIrtb6GdDs6\nSyv7Ts47z7XP9vgwhvA8MIEU12F9RcBlSaXVEgCWLh2O9K+aMhdcJlkQTSCW0y2/oZ2EjXl7YQLR\nMNqYiy8G/ud/yj/nZEuLgW6kWJRj7QQX00YQSkjjT4yTBCJdEUnIxbRYFMvhyIj8P008U6lU+znY\naSdnyQIkxb8u5q6tVIcfLq/jBKI/oWUWTW4fF4Oo6e8HjjwS2GKL6spcaMuQtprtuacsGafJeDJf\nIFJs0LILuJi/ShZEEmeNO+QQWeZybrxmzXKxcmx3v/2iAnHHHeW1FnI6btEXrqESEbmc648fG6kT\n+cRZEJlIiSJPZ0V94Qvd+v6+6WLa2+vidEMCkRl9jz463I7uy/HHy5Ju3HECkYRE17bbRl1M9fXL\n8dTb0cVUi1I+OPCvT5LmO7tihexHu9v68LtjLqaGYfiYQDSajvmV104l11H+/957o593y5hTIHaC\nAG70mIcyMgLh2Lc4C6LvYrrrrpK1ceZMl30yrUCsZxIZKmPg911nwdQCkcfzt785Cxpj6zbeeCgi\nEAcH3b4ogH0yGbFW6di/tC6mOisqX7Pg+4wZ5QJRiwOdiEVbz2gt02OhhVncMWhokdVurYwvBKJj\nrgUiC8pTIGYyUcHuWxClnaHIuj095QKRlk0e57p1Us5C41sQdTwcBeLcueFjAJwFcZNNJOHPFluE\nXUwLBenn1Ve749JkMm48d9tNlitWiMDW50wLxKQHCf395QKRiYO0W6mOQfQtiEyCxDGZPXsoso80\nFsQLL4zGNob6bBbEeLrlN7STsDFvL0wgGkYbU6lAPCc2OtNlN8H0+NPFgqjLIsRZb5JcxfwYRD+b\n5P33R13/MhmJJ4urp6nd4HwrRhr8LIwAsGxZdB1tsaL7Zy7n+kTr5YtfLAlsADeZvu66eAviunVh\ncUV6eqpLUqPFWrHo+sc+UyAODDgXUz2RHxlxoq23F7jkEuB3v4sWUveT1fgWxDhYriJOIHL8dSxp\nPg/svrvrO0lrQRwYcCVJuE+/PiD7/thjwMknR8tZaIGoRZsWiMx86u8bcAJx/nxJALTJJuI2resO\nhh5sTExI7UN9TfrJsJ59Vlx04yyIe+0V3U6jk9TwNcuIsD3GbnKf/f2un9qCCESvaZImBhGIilyz\nIBqGUQ0mEI2mY37ltVNJIE6HOog9PZ1hQWzEmOvz6VsQmQUzhBaI2oI4OFie5Cafd1avSpYD/r9U\nqk0g8hj0pHX77cPrjI87gawFYrHoSnv4BemB8hhEwLnsVRKI69fLury+KsUg6uQjhAJxYMAlFqEF\nUYuakZFoOYnXv15cXXt7o6IWcGN91VXpElDpuoiVLIhatFNYchu6JFKg8dy85CXyP8Yg8rjpYnrH\nHbIeHzxwqRO0ZLNuf3qfWiCzr9y/jrEMZTEdG3MCcd484LbbZAwrxSAecohYkQmvTx7HmjXR8SO8\nPs85p/x/up86yY32gNAC0c9iSmhB5Hnv6wOeemo4so9KFsS+PrEOz5rlvjMhUWlJauLplt/QTsLG\nvL0wgWgYbUycZYcThG7NXkosBtFN4pJqoCUJRO1ux/YoEP19+dRrQWShdj25Z905ol0aQxbEYtEd\nH9vjJHrevHILIiDZOVevTmdB1JPztC6m+npMEoh6XH2BSLRA9Ms0AFGh8sY3hvtGQaYtyFqM+aUO\n+J1asAD48Y+jbY2NyT61BXHFCncMut9+zCsFFM9DXL1GIGpB1NBqOjYW3X5iIprBVlsQ161z1sYP\nfzhdkhpmzNXn/A9/kOWaNe5c0coKuJhWP7b1da+L9p8PBtauDQvEiYlyCyIZGZGx4bnyBbTfZ59i\n0bk5a5drczE1DKMaunx6abQD5ldeO5UsZ3ECsVvGfLrFIIYEIiebSRlddeZCPWEfHJTMlZmMSwLC\nz3WbceJPT2LjRGQSnIRrgei349dBBKI1/3S8FgXKunVyDNmsi0FctcoJinnzxHKS1oJI0rqYhiyI\nG28s45hkQdSWPt2POAsiEE0Cc8QR5f363/8FPvQheX3ZZa5t7quvL5okiG0WCrLue98b/d/4uIhL\nWhC1SJPkQkMb+s02yJVXypLb8DoL3af8RFtEWxD1+eN5vvNOWdLaOX++vJ83T5Ybb+zaZmkOFqWf\nM8edE34fdKkUogWijp2kMPSznm6xhXs9MeH2MTYWrTXK8xtyMdX79h8g5HJDkYQ9oXvArbfKmNAV\nltd2UnytuZjG0y2/oZ2EjXl7YQLRMNqYOGE03SyInSAQG4EWHjqTJVBZINIF0LcgXnNNtJ1SyVkQ\n6e7JfT37LPDDH7p2681iSvc29u+HP3QTVv84tbjVmT619ZKWlLVr5dgmJqKTWy0QgeoFYtx1NjFR\nLlgIt990U1kvJBBf/vJkCyLRFsR99gE22yxqQQ+5nB9zTNTK6FsQZ8wod80dGHDfLQ1Fl7YgauuV\njiEMZZmlWOP/+vuBU06JitATToiOgfaSmDHDCUSdNRYoLwHChxa+QOzvjwrTQgF405vk/cYbu+sv\nSSCOjblzpa8PXyCWSsBf/gJ89atuHW1BHB2N3r+0QOR3nQ8V2N9Vq6R9ZlLlAwQ9zqHvIuuY0oK4\nbp0lqZlqQhlzDaNT6PLppdEOmF957aQViDrbH9A9Y95JSWpCY37JJc4lLg2crI6MlLuYxglEFp6n\nINEiq6/PTeq12xsF4oIF4vLJiesvf+msUXqfxWJt58AXuf/4R7m1MlSiQ4sXbb3UljaJrxyOTMKq\nFYjFYjQxUNwxDg4Cn/501PWPUEBoix2T1LDfm20m5T1CAlG/1gJRx/iRSg+EdtstLBD9gvK0IPrt\n3XyzZPvUFkTNyAhw0EHDG46TbW23nbx+97uj64+NAT/6EXDuue6zn/3MbafJZuVPu5jqsdGuxICL\ni6VA5LK/310HLIPC631wEHjySXlNsRwSiKOjyedKj9tBB0msJNfXMYgUiLyutIsp13n4YeDaa+V1\nsSiC03+A8Pzzw5FrOfS7QCFYKLgEQlzGbWMxiPE04je0r8+5Z8eh3banO90yb+kWOlUgbgvgBgCL\nAdwF4COTn88FcC2A+wFcA2DOlPTOMBpEWhfTuFjFTqdSkho+ZW9XjjtOLChpKRRkQr3RRlFR9Mgj\nYYG4ZImsq8su+GUu/In1unVuwpzLRTOd+q5mnDh+7Wu1CcRQqY40AlFbEEulcoFIC6KugwhUJxAp\nlrRAjLvO8nkps+GXrgCcQKRA6OuTY9QWRE7UK1kQtYsp3Sy1GKkkEO+5pzxJjc6CuX69E8YhCyLX\nowXRt4CsWeNKjGgL4gEHRI/r0ENl+fjj5X3kdr6bZi7nXDJZnkWPzZ//LMs4CyLdPDOZaCIeCiZA\nrnturwUir+3XvtZ9H3iu9JiznUwG2Hprl80UcJlKtYspBSIJZTHt6SkXCT09Lt6UdTL1eIWuUz5c\noAURqOw+rUuLtCurVnXGA8I4tGt/iI02kiy/htFudKpAnADwHwBeCOBAAB8CsBuAUyECcRcAf5p8\nb0wx5ldeO2kFor9et4x5pSQ18+ZJ2YZ2IG7Mq3EDLhREDAJuIvvmNwMLF0YzipLf/laW69dHLTpc\nTwtETlp1+QeWwtD719Q7MfOzmK5aFbVmrVkTdjHlgwG+DlkQpZbjUFAgbryxLJME4u23yzJtkhqK\nPvaR/ea4awsi961FAADcdVd5v6qxIA4NAfvvH99HoFwg6kQl69a5+nohCyKhBTGfj4qgNWuA3XYb\nihxDTw9w8cXR46KLZ8g6EpeZOZdzwkknaHnLW6S9M8+Uz5ioiA8OmJyG4m1kxJ3H8XG57nmOBwej\nZTyA6DXPDKBxAnHvvd3rxx8Hdtyx/DiSXEx1zJ9OLKXHmMfNz8QSPRS5ZpYsKd8vvxt+Nl/SqVlM\n584FvvOd1u+3Ub+hacR3uz/obBXdMm/pFjpVIC4DsGjy9QiAewBsDeC1AM6b/Pw8AK8r39QwOoe4\nCTonHN1qOSRpYhArPaGdavyMh0mwsDZQ7t4XsiDutJMs16yJxjFxPSYS0e3peKZ8PjqJ9N3RGi0Q\n77jDucLtu6+458W5mIYEIgUU46v4PyYhoTChe2WSQKQVNW2SGiBaRuJVrxLxTmHCfWqBqLcDgIMP\njr7X6zGxCOAsiPl8VKBsv72Uckgi5GKqLYgUiKVSskAcG5P9U9QCIr7YX21B5Lj5cYlbbx3fT3+s\nQ660PT3Ab34jcZaEAvGWW2TJUhj6AYl/v+D/Zs6MjjEQdTHl92F01AlsWqOBaGbYOJIEol6HAn1s\nDPjyl4FjjwW+8hV33NyGFt93vMNtT5dUDY9n7VonECtZx7UF8Sc/mRohlgY+NOtEkuIQOf6dbCE1\nupdOFYiaBQD2AXArgM0BLJ/8fPnke2OKMb/y5hHnYtotY55GIPqualNF3JinKXROtED0XTFDApEi\nRye60BZEPenWFkReN75ApOWNT7T9iYt/nR17rKtRGMIXiEND7rgeekjER5wFMS4GcWDAWUFzueFI\nohmOhy5IH0eoVl+pJJNknXRE09vrxm6vvYALL3QWLN+C6E/yAWC//aLv9Wst5PyMrmn47nel7+wf\nx4LjBTgLIpMZxT1gogVxYgL4xjfks/nz5UHEQw8NbxgLv3/aqvj008D3vhffX//BScgVk+3Rmgc4\n12BArg3t2guERS/bmTmzvKSIzpJbKDiBGGpvs83ij0eLLZbkoMusXxaFZS7ozjpjBnDppcCBB8o6\n+tqRvg9veLgAIJLRlNBSpQViNRbEj38c+I//iD++qWQqXGAb9RtaqWYlUJ64a7rSLfOWbqHTBeIs\nABcD+CiANd7/SpN/htGx+D8ufkHvOBfTbiFNkpp2t6KmneADUWFEUcRMoyGBqK1vfgwiEM4Cmc+7\nfWj3u0zGrZ/W9ezyy4ELLoj/v04owmPxn6iHLIiMQwOiAvGee+R/69fLurlctJYhLTxpBCKtK3qd\nYhE49VTgc58LbxOKQWQZBIoHX6gD7lgoSkMWxIGBqItpkuAJ8ZGPAB/9qHuvaxH6FkTfMunDGMR8\n3mUd3WQTV9dP90u3owXi/PlRC5bmmWfKxZb+Hi9Y4NoBgJ//3P3P9xjwBWLogRLb0QKR14q2IBaL\n5Qly9Hdg++1Z6qMcijbGIA4Ohi2IAwPOgsjYVI4hPewymXL3ZC432SQsKEICUcdMJglE/0FRPcyZ\n03iXyXaOkayECUSjU2mTZ+810QsRh+cDuHTys+UAtoC4oG4JIHgrf+c734kFk79Ac+bMwd57773B\n95lPMOx9Y9+TdulPp7xfskTef+97Q/jzn4EPfEDeF4vy/+efl/esTTbV/W3U+wMOGMLgIHD22cMY\nHQVKpfD6wDAWLQKOOWbq+z80NBTsn0xoK28vyTWG8dBDsv74OJDJDE+KhqHJCd4wbr8d2H9/2X7R\nItl+YmJocoI8jKeekvVLJWDZsuHJfgxNiix5n83K9suWDU9OvobQ3w8sXuzaA4C//GUYm20G7LLL\nEG6+Wfp3ww3AoYcOTU58hrF4cfzx3XmnvF+9Wt4vWTI8eRxufB5+2PXv0Udd/2RCLOMxZ46sv2rV\nMDbfHFiyZGhDfcDR0WHceKP8/557ZPtZs+T9o48OY3g4fryB4cksg248xKLp1pfyAfJ+5crhyUld\ntL3nnhvCZZdJeytXyv9FSMn/czlZ/8EH5X1Pj9ue6w8OAsuXy//HxoYmJ+zDky6V4fENvWd/RYwO\nY+1a+f5kMsCzzw5PnjfpX+h65fiJQByerDs4hDlzpP9RgTg8ae2T7W+7rfz4KvVXGMLMmUBf3zCe\neQaYO1f+f/vtcj2fc84QPvAB2Z/UdHT9XbTIbQ8M45FH3P2Cx8P+rF4d/X7wfBUK8v8VK+T7MDbm\nrj9JIFL5eK64AvjmN4dx2WXy/RkclOv1gQei/clkgHxe3mcyMn78Pt50k7SXzzO21o0v+yMPRcr3\nL9flMJ54AthzT/n/M8/I/3t75Xj9/t9xh7zX11vc9yXt++eeA554Ygibbtq4+yvHa6rv59W85/V1\n223AXnslrx86n9PxPT9rl/504/tFixZh9aSf/iOd7LudQAbALwF82/v8mwA+M/n6VABfD2xbMoxO\n4dRTSyWgVNpnH1mOj8vyxz+W5dCQLDfddKp72lhWrpTjuvrqUmnvveV1Pl++HlAqXXFF6/uXFqBU\nevnL063b0yPrf+ITsnzb20qlWbNKpW23lfe33CLLW29121x0kXz23veWSnvuKa8/9jFZ9veXSh/9\nqLwGSqXhYff6tNNkefTRpdJrXiOvZ88ulT77WXn98MPS/sMPl0oLFpRKL3uZfJ7NlkoTE/K/FSvk\ns9e9zvXnNa8plb78Zff+3HOj1+vnP18qZTKuH0Cp9J73yGdz55ZKZ54pn82YUSqdfLK8nj/frfuK\nV5RKhxwir1/1qlJp442jbf3jH+6aAEqlb30rfrwvvNCdHx7b8ce7tsgvfynv58yRczJvXvT/5H/+\nRz4/5RRZclyBUumTn4yeg+uuc9udcIJ8tueepdLuu8t5y2RcO696VXmfknjwQXcOATmml7ykVJo5\nU96/4AVujH24n29/W9bPZkulYlE+O/xwWf7617Lkedhjj1LpyCPl9bJlsiwW0/VV73fBglJp4UJ5\n/f73y3LpUvk/jwUolfbaK3rO2T+uc845cq6AUumAA2R51VXuPPB78va3R9fJZmWc9thD3v/kJ9H1\n0oz/ddeVSocdVirtsEOptMsupdImm5RK551XKj30UKn0i19IG1ttJdf6llvKMWezcl3osTjhBPc9\nPvRQWT76qCx32qlU+sEPyvfN+8aBB7rv/cc/LsuZM+Xa8vnjH+X/73iHG/t6AUqlu+6qvx3d3kkn\nNa69VpHPS9/vuCN+neefl3V+97vW9cswNEjwtMw2Qq1NAS8FcCKAQwH8Y/LvaIggPAJS5uIwhAWi\n0WL4FMOoHt+VVNel05/7biydPuY8Pp0FUSxV8etONXFjntb9ly5kq1bJcmxMXOJ0fBQALFvmtqG7\nJgu0A+FSEUA06Q3X0e6Z/f1u32zXL4XA+nmAc7XT7lFXXAH8+tfl+9S1HP3xGBsTdzztYrp+vdvP\nM89E26PbnLgeD0fa4v/iXBs1b36zLHmN9faGXdnoirh6dTQG0Yfjr2PzBgflHPA8hNz46FrZ1yfH\nPTgoY+Sfz7TsuKOMM91Z6WLKuL5QLT+fYlHW7+11/WOs5b33Dpdt/+pXy1K7NFZLJuOOleOk3SrJ\n888Du+8u34NLL5XtfvpTqcV4wQXA297mEtmwH6EYRN+1OZeLfo98d+s0sDxHsSjZN1etknZ32MEl\nmRELonMxLRbLz0WhAHzmM5IdlpZD9mdgIOySyO/L2rXOxZrjGeemz210FmTy7LPADTfI69/9Lv19\nTO+3WkZH5R4S189W8b//C1x99XBdbXC8k/puLqZROn3e0m10qkC8GdL3vSEJavYBcDWAlQBeASlz\ncSSA1VPVQcNoBGkFYrfBSdn69S5WifFZJM0PcLtx003xk2dOqvh/pqvncXJM3v9+t40uYcEJnn5o\nEIpB1Os8+mg0xkkLup/8xGVaZJ+0QOT58GMK9fHpSbheasbH5Ti1QHzlK4Gvfx3YaqvouhMTUYHI\n43jZy2RJYUFBkObhAfvPCb0PhRbbjZv8ct/6PCxaJMXnOcbclmILcOPZ0yPixU8wUst3PJMJxyBy\nP3F8+MOy5Pr6YQPrDPrXaSbjXm+8cXVCwscX0nzP5EmAxCD29ACbby5JkgDg3e+Wfr3tbU4c6T6G\nYhB9gZjN1i8Q+ZCBCWh0O2T9ercOr2X/HHO83/CG8rqRcQJRxyD6DxfiEn1xm9HR8ocqX/86cNhh\nst2b3pQcV1goyFjzIUStAvGaa6QWZaj9VvKqVwF//GN9bbDPSX3n/ckEotGOdOn00mgntH+5UR38\nUedEx0+LHTd57PQx1wKxpwc44ojyyTsn9iHRMRWkGfN//Sv+f/651FkOAXe8WihrgRjKKhknEHM5\nsWi89rXRZCMcy4kJ4JRTZDIeEohf+pLrhz+BThKIfmZWfuYLxGJRJsj+QwFfIPb2DgEADjlEPvMT\nz6SZWHLdefPCk2AKLUDGM+47R0Gw+eaufzvvLEXktQi/4gpXbB5wY0OByGOo1YLo93vGDEzGuVVu\nj+eS/dXnkv3ae+8hANEMyo1KFMVz4Qv9TEbE31veIhbEtJmLkwSinz2XFsT/+7/oNhMT0SyqSbDu\naJJAXLvWWRBpnQ4JRN2mi2l1ZS98tED07wVJFkQKTt+6zfPN+0aopiV54AFZ+pbbtBSLsh8+DGRt\nW7Y3FUlqdtxxqK7t/Ye5Sev497npSqfPW7oNE4iG0cbEWRA58W33DJ61ogViLieTXP8pq18Avt0I\nPUFOsgbxXGuRxpT7+vM4gRiyIOqn4LTUATKmv/gF8O1vhwUi2x0fjwrEbFZKKZx2mohHpuyPwxeI\no6PlYzA+7qx0WiACwDnnRNf1BSLh63oE4vz5mEwY46xlAHDrrdF13/te4H3vK2+H4x+a9GsL4qtf\nHf3ealG2fr2UkgDqsyCyr0cd5UQR3TSTxBX74j+QAtzYhlxUG3EfymTKBaLu6wUXAJ/9rPSpkkCk\nhZZ91AKRQofCcHzcCX99LWuBeNRR6Y4hJBB9QU431CSBqEXF1VdH+1PJXTQkEHXZDE0+L2Py8MPl\n9TW1GzrXjYPu5nyI98Mfxq8b4t//XTLX8hhYyuSSS2Q5FWEE9Votq7EgJolvw5gqTCAaTcf8ymvH\ntxT6TyVDLnFA54+5dn3K5dzEK7ROu1gQ/TEPWc3STBboppXPi+hIssLpMQi5lfHJPhAtaaEnpFog\nsm2KFF8g5nLOwnLppbJ+kospj1f33bfC0YKo+86xYC0/4ovc8fHhyHa+QEwzsaQ43XRTJxB5DgDg\n0592r3t7RRyfe255O74g0BNyPVGPg9c4Yy6rrYMY4uqrpZ21a8X6Vqk9nsskgXjXXcMAog8iGvWg\n6u9/l6VvQSS8HioJxBUrov3SdTJ5TKwdSqE4MhL9vlAYzZqVXqDoGMQ4CyL7lM+7a88XiFowMPsw\nS4fstVeyuyhrhAKoaHWkBVEy30ZhnzgOjz8e/S5ofO+Ge+4JrxfHLbcATz7pjkHHHbOfrea++4br\n2r6aGETed6Y7nT5v6TZMIBpGB8Afa9/F1H/q2y1oC2I2KxMc/4e23S2IIUGXNNEsFGTCSnFCq6Au\ncO+jk9TQHUtbTXx3z5D1jdCaAgCvf73ruy8QWYeOQjHJshASiDp2kH33+8XjCglEva5vYa8lBpH7\nmD3bjX3cpC5JmPiCTu/bj0EM4f9Pi/lttonfrhJ0nQzVYPThefLvN0D5OeI6K1Y03pMhTlzxGJLq\nW2rYVz5U0NfT+Lhz4Q6Nyfg48MgjwI9/nD6uUscgxrkIMzZ4bCzeShyyKOVyYqnbYotkCyJrOep9\nx1kd8/n4hE46YRQAXHcdcNZZ4XV53fAeVW0cqr7nAeUPPqfCxbSeWFognUD048sNo50wgWg0HfMr\nr504F9MvfSl5u04fc9/FtBMsiP6YUxhWIxB1sXS/ePU3vlG+jRbJc+bI62wWOPts4D//U9xBAZlY\nx7lnEu1iSrFJQacFIifTRx4JHH54uUDXEyJfxGuByFi90GSaVgudzIXt6GNgfbu44u1pHh5o91a2\nE3dNJQkT38U3JBArWRBD7eVywC67xG9XCbbr909bSQlda1/5SlnqY+C4/9u/DUXaGRtrvECME1dM\nQGka7/YAACAASURBVJM2BpHnKyQQfQsioRgfHwe2314eHFSTibhSDCIFYqFQjRvx0IZ2sllp/8gj\nxepG8nl3nL5AXLo03uroZy/lev53MWkMkh5ipcGPZ+b9JxR33Spe/OKhDa+/+115UFAN1biYdlKi\ntWbS6fOWbsMEomG0MXECkVacbkW7S3HiHicQ292CqMVGSCA+8ICsw0klJ1n5fFSQ+G5XXAeQMdhz\nT3mdywGf+ITEyb3jHa5kRJyLqb7G/LH0YwZzOeDQQ+X12JiIhrjzAsRbEDMZJ5wLhfIJLf+3YEG0\nbW1BzGbLvx9kzhzgYx8DjjsOFdHZKjk+OjGNJkkgxsVR6n2EhDmPwRcS2iLZiMygfvbREF/7GrB8\nuZSR8PsaZ0EcH3div15OOCG5rxR4oWRHIXyBqDPS0oJIgchSHb//vfs/qUYg+i6mIQsi+xWXxTSE\nfvhRLALXXgucfrqcrxUr5PplTGMoYVWcBdG/1nncvgUx6T7rC7lar1feO+ji7ru8N5JnnnGxkxqO\nkz7/H/uYy/CblmpcTE0gGu2ICUSj6Zhfee3wRyYUE5REp495NTGI7eKeE4pB7OurHIO4yy7AV7/q\n0t77MYhk111lSfdPrsN9xVkjOHaVXEwZg6gzNlIgagsiJ4Fsz584phGI2WzUauBPaGm1mD3bubIC\n5RbEiYnhsuN5+mlgyy0lAY/OFhoHj43JSgBg//0l5mrZMik1QP70p/h20gjEaiyI+nzutlvyMSTh\nJ5XJZJy12ae/32WSjHN5/ec/hyPtTUxIuYnQA4xqiRPLhOfqscfStecnvaEYouWQS12rklZKfV3X\n4mIaZ0EsFsvLeVQWiMORRFG8ts4/X+pevvzl8l3jd7cagei7mPpu1nyf5KnhWxBrFYjaZR5w985m\nPBDdZx+J5/Th/Yuxtn7f0mIWxOrp9HlLt2EC0TA6gJDbWjdTTQziVAvEfB74y1/KPy8WZUKaxsX0\n+utl8tvX5yZZ2uIHuOLp2jVQT6jixB9LU/jWN90u1xsfdxkEARFbWiA+/rgrBUELYjVJaq6/3iWp\n0WUGOKHNZsVquPferg0tZvzj9N3hAEnkUQ0hd9SJCWDhQuDgg4EXvEBKLABRce7jJxzR51rHgsXh\nW/q0m+V3vxt2CU1DyIKYxmLlCxuOuxYqgIxVJiNlQuohk3FjVikxz9Kl6drkdTVrFvChD7l2GXvI\nMR4djY7TmWe6pD5AfS6mcTGIQLnbLwD8/OfiHk7OOCO6vRaIp5wi18WqVbJfXvshF93QMejvE+F3\nlt8H3g/SZCvmvYsuytXi39NpMW6GQHzqKbG++vj3LKC2RFHVxCC28nd98eJy133DCGEC0Wg65lde\nO3Eupm98Y/J2nT7maWIQOWGZaoF48cUiJPwxDwlE9tWfENBS2tsbdTHlRG/GDJmobbRRuMxFkgWR\ngkAXww5NeBYvliySOlPhb34TFYgAcOWVbp8hF9Mkgch9s4+04GiLx/33S0kDPTbEz2Kayw0BCFsC\n0nDeecAXvuD6rUVPPi8TU1o9AXE1i8PPxKqFdpIF0Re5POf6fPb0RN0jqyHk3ppGIMZZEPfddwi/\n+pVYvbVYaQQcCz8uTrP99unb0w8efvCDaDKjsbHo/7Wl9XOfiyYGamQMIvcBhAXiO98JHHige/+y\nlwHAUGRb3lN22snto1BwIr0eC6IvbHgdc59xQhNw965aBYhvQdxmG7m3MiaxkcS5WvO4t956aMNn\naZMihdpJUwexlRbEO+9s36ypnT5v6TZMIBpGG1OpzAXptnqInRSDqIWAplBwBeCJX2eQbLednOM4\ngbjZZjJJ0oW+dTuVEtCwxl7SOkRbgg44IJqk5uijga23dsdSrQWR++b1yxgwPaHt7Y32b/fd3Wtt\nVdXtHH54bZOsk05yk+xMxk1+6dZKAbvXXsCPfpTclo5lBICbbnL/Syp677tVciyStqkGLYJImjb9\ndbRoe+tbxbJay8Q5CZ5PxtKF+OMfo2ObhN8/jsHAQHmMYUhU6f+nQccgJmUxZT9C58bHv9dns+4e\nwO9CJiPXne9iqttNG4PIY/UFovZs8PEFYq2Cx7cgFgoiNovF9HGnaYkbc/ZdP5iq5Tpv1xhEXjNf\n+Urr9ml0JiYQjaZjfuW1wx9rPwbRjwcpFIAlS4AbbpD3nT7mWiDy6X67xiCecoos/TFnTCEnjEC8\nQNxvv3ILIstcACIQ16yR96EsodqCGJrAMXYwTcyTdukcGYlaEGfNck+f4yyImjiByPc9PfKacW+h\nfvX2AtdcAxxzjCsFwnXz+eEN69VaUD7UX73v8XGx3vE8VyKU+ILtJlkQfbdDxsLVe1yNcjHlJPmO\nO4Y3fJZk6asFfk90HKzPrrvSqlaZagRimkRCafaXxsWU36c0AlH2PRw5BoolfvcoEHk+QmLXP4Zi\nsToLIl2cda1I4iep0d+jUin9+PFBl/YO6e2Ve1Kj3UwrCcQHHhje8FnarLmaalxMp0Ignn566/aZ\nlk6ft3QbJhANo40J/agD5QKxWJQJ7GGHtaZfzYY/mCMjzsW03WIQOREM8eUvS6xeLhcuds9j0U/r\nKRD5WT7vJmXz5slrfxy0BZGTwxtvLO9PkgXRv8a0QFy7NjqRmjnT1SijNc8XpDq7JY/LF4jchhYX\niqE4y9YRRzjLZqW4qnrwj2XpUpfMJC2/+lX5Z3/4gyzTWBAbLRB9EZJWIPp9DVlRWBajEQwOuuvz\noIOA//3f+tvcZJPoe31M69a5a10njgmNTT0upr4A0xbEpH2SkAUxJBD1wxO2q635xaK4VH/wgyLy\ncjmxkuk4Z/YPiLcgnnSSW/feeyXrqx+DqO9RJ5wAHHJI/PFpaNHM5+X++b3vybHMnFl7DG4ccRbJ\nUPmkWgRiknVw9Wrgpz+dmhhEXiP779+6fRqdiQlEo+mYX3nt8Meak3L+kIRqz+nJUKePuU533q4x\niHvsAbzrXcDrXifvOealEvDFLwJ33SV9Z7wTUG5B1AkhstnoZC2fdyn3582TyVxvb3TCoWN2KiVA\n0QIxNOHZbjtZapczCkRONLVApNUyzsW0v1/OX6EAXHVVtC/6tRZgSRNlv7yCfDYUv0GVZDJRtzJS\njUD81rekoDgQddVlXGdo3LlP/o/jTytave7jjbYg7rff0IbPbrhBREK93HsvcPXVEie4aJH09Zhj\n6m/39NOBv/7VvedxP/igLGmVWrEi2YKYViCEylz4saPFYi0upkORY/AFoljTy79HvHa2206+i+98\nJ3DOOZKgBZDvMtddsADYaqvKFkSOHSDJm97whmQX08suA26+Of74NBSIExMS/3zzzTKmfjboZsK+\nb7rp0IbPGm1BPPdc4L3vnRoXU/4G7LNP6/aZlk6ft3QbJhANo43hj8y228pSW6K23NKtp2vJdSKl\nksuOCZRbEEMxiP4kqdXcd5/EQvlWCt13ij7fgugLRSZCodWI/+PERCef0E+btQUxSSDSxTRkQXz7\n26XoNt32enqc+1FIIBLfxVRbWfiZdrdkcpFsFth4Y7cv7R6bdAycyDbTghiiGoH4H//hrgct7PhZ\n6Pje/nbgxBPd/97/fmDnnSWLKlD/MfruhrVaEH0LJyDxqCy/Ug+77iriZO7cdKVJ0jJzZjThi58c\nRo9tkjXvZz8Lu1b6cMzyefcgJRTjV41A9M9/NuvuHxRmvotpKMGQtojxocToqNtm/vxobVHexyii\nKRB1zLWf8ZQCUd+jqnnAQRfTfD6a+dfvfzPhMWlBWstDmiTxx/HieWilQOQ1N9WhGUb7YwLRaDrm\nV147/OGgQNDxabmcm0QWi9EfsU4b85//3B3jk09GLYhxZS78SdJUsPnmLuX+H/4wjLe8xfVn7Vrn\nYupbEHWtQ8AJRB17NTHhHgLQCuFbELU7FCdUvmAFpA8jI2EL4lFHSeIPbt/TI5YGwAn0kECkW2uh\nIJNK7fLLOnU64+pLX+pe8zjpfppGIIYsiIXCcPwGVRI3CVy5sv5YO11H0ufkk6WeHc/J+94nmVy5\nTb3uZyHhU4sFke9Xrx6ur0NTCI+b3yc9SaZLa+gcbbqpS2ZUCY4Ta2ZqgTh/viRd4rlN72I6vOF9\nyIIIyPcwLsbYf3ioBSI9Bw4/PJqVlvcZxhxrgXz22ZLN+OCD5X0jk9TQgviBD8hnPT1RL4xGEjJY\nse+PPTZcV9tp4gtPPrnyOs3ixz9u/T4r0Wnzlm7HBKJhtDH84dCF4wFX3Fn/CDUiScdUcf/9siwW\nxSpBIZXkYhqX8KWVzJvnJntPPw1cdFFUDNKCGCcQObGiwKMFsb9fjos10PbYQ5YUiDfeCHzyk2EL\noh9TBMiEeOVKNzEOuUwxlXxPj1j7vvMdZwX98IflT2eX5D55brRgf/xxeR2XCVD3Q8dPpnExbbYF\nkdZNcvvttQlELTj5OskSEScW2sWCuHAhsO++nZ0xmcfNBxT6AQYf9NSbNVY/aAGiMYiLF0vN1Gos\niNqrgOtWcjHV5/pTn4qWrgHc9qz/WCoBX/taNJMvf3tWrJDl+vVi3d16a2nzs5916ybFIFbDxISL\nQSTNFIghQjGItXwH07iP3nNP5XUaTSu8LozuoIOnlEanYH7ltePHHPqCQgtEPbHptDHncVBQ6FIO\nvb1hgeiL51bCH9mBAfeasVmcUPEcJQlEbUHMZt3ElQJx113FGrfzzvI5XUzPPVeKaYcsiCGL08yZ\nIhBpzQgJxMsvlyXbmTHDuZgefTTw/e87gUg3NwrEiYmoQOR+9ASck+FSyVkiOTmtxoKo18lkhuI3\nqIGzzhJhrHnuufotiAsXOqtqHM0SiKHYujQiyL9GNtoIuOOOzru3aPxs0H62TaD+B23+91CP4/z5\nkhinGoH4spcBjz02tOF9miQ1bG90FPjmN4Hjjou2qS2I+sFNyIL4zDPu/xMT4gYMSD09//epkRZE\nsmxZ82IQQ98t9n3mzKHE9SqRlIDGb++yy6pvv1aKRTnn++7bun2mpZPvLd2ICUTDaGN8EaRj2LRA\nBDr7yT5/MJk8QWesmzUrHIPIY2+1QPzgB4ELL5TXpVJ53SyKIoq+UAyib0H0XUwZh5PNSrFobXEr\nFCSBg94eqCwQn33WtRMSCJ/6lNs34ASivq7o8trX5yyIvb3yWj/E8NPeA9GJKAWib/Ws1sW0kU/D\nMxmxyr7hDeX/q9eCCEQLr4dotgWx3iym3QBFRiheO8kNuBq4PV03Q/flalxMM5notaNjEPn9iotB\nHBmRpX8NcRzGxqICNmRB1AJxjz3CiZySBGLS79Kdd0bb01lM9fattCCG6iDWwlSUsEhDqSS/A/Ue\nn9H9mEA0mo75ldeOLxDjLIj+BKPTxpyTEsa+aYE4MBCOQYzL6NpszjlHrGmATIjYr9tuG97wGeDO\n0T//6Yqsc2LnWwC4LtPu04Lox0zFxSACbt2QQJw1S5JmJFkQGQNKsUaBqCfMOlEJM6+y9hvrBaYR\niH4sZBoX0+99T5bsT6kEFIvD8RtUiV+b7uGHgV/+Ul43spxDHCEBDNQ/MW5UFlPSafcWzeaby5IW\neU2jLIjcnjF0SeuksSAC0THXFkS6hfsC0b8X+JlU9cMsfZ5DFkSKzGOPFTdULSw4ZhMT0s5tt7lt\nH30UeOSR5ON60YuiBdvpfaDv6fQwaLUF8ZlnhhPXq8RUZChNQ6kkY9yOArGT7y3diAlEw2hj4gQi\nrTf8Me/0p/38AaYw1C6mTFLTLhZEwE2a1q8vnwj4FkTA1cbTIgqICn7tYkoBxe0p7OhiyhTlOtMp\nr4HQhITCMykGkZNVbUH0Y1t1FsiQi2lagejHaaWxIPp98F/Xy5o1suQke7vtJMPomjW1Zeqs1qLP\nrJf+MV10UfX71pgF0cFz+5rXuM9e/3pZHn64LOs9bo7tC18InHlm8jppBaK/rY4hBOSc0nUQKPcm\nyOWAz3zGtRHnYhqyIE5MAL/4BXDxxWL5DwlEfvdJoQDstx+wyy7xx8H2H3wwWt/XFy/5fOUsppkM\ncMst8f/3Yb/jBGI2Wy5SqyWNBZFJflpJOwtEo70wgWg0HfMrrx1fBMW5mPqTmk4bc/4AcwKnLYh/\n+lPYxZRZ+6ZCIFL4rFvnJhK77z604TMgGhe4997us5kzyxPsUPBzkuVnI9Sf63jTkFtZaELC2MEk\ngfjnP8tSC0Tdrn6tBaJ2MU0jEEul+gSiXqe3d6jyBim59FLXvh5jnZinmdBC6ouFAw6or92QBbGW\nGETSafeWELvv7l5zvBkH1igX0/5+4HOfC6/jW6srCUQ95logcsnfAt3er38dtWLqbKpxLqa+BZH/\nmz1b9jEwEC7/wO8+IA+jikV5sJLk3aGTAvE+uH699JMPawB3j6tU2F4/VKyE/8DV/9/MmfXXWE2y\nIHLctt5axLf+rNm0s0DshntLN2EC0TDamLgYRN+CqOtXdWKWMh7Hm98sy7VrndVrq63iLYh9fVMr\nENevd+ckLgbxIx9xcW3j4yI4ODHxBSLFme8qxsldSCBqq8GNNwLf/W55f9ke2wlNRhYvlqUvEKOF\n6d06PD6em5BABFwGxZAFUQtOf19xVJtspRYaYZmsdcLnb1dLkW4NrzHtmvvPf1berhstiIAcP62F\ngHs4wWW9E/U0145fyL6a6+2551ysNu89fX1yP9QPA44/XspzEG3hi3MxzWQkEdE//xmtrct7wcBA\nVFhogch1DjwwnVsl7/n9/dHSQAMDzkMDcBbET3wC2HPP8nZqqSWoPTd8CoV4S6kmkwGuuy5+H1po\nH3GE1Df127vwwnL332bD66Te8jlG92MC0Wg65ldeO34hYi6ff77cgsiJYKHQuWPOCcnYmBQPnz9f\nXMDiYhCn0oKYzcpTaz7ZvuOOYQDlFkTGzxSLEqMzY0Z5DUcWhubEQVt79HuJu3Pv/SQ1hxwSjpfj\n5JeTkYceKl+HwjBJILLfPCZtQRwfl20zmejT/h13lKVO9OIn6Yir36b53e+i6zQ6BrHRVDvxC8XF\nAfXHMPF65LgxprcScQKxU+8tIUoleaDyf//n3CtbIRAJrWjVxCCec44sMxknpDjhT7JI6gc02sXU\ntyC+/e3i8aCzom61lSzpOQBIwhr+/qxb5673/feXbXlviruOeN9+9lm3Lmu1ag8SxmKvWAHcdVd5\nO7zXaG+FSujkYD4UiGvWDG/4LO6hK8szheD3dmxMhCTjM/X/gHDCpGZCD452FIjddG/pBkwgGkYb\nE2dBBMoFop8ps5MIxb3kclJb8Jhjki2IrU5SA8jEavbsqED0M/nRwsZJ1fPPu/+HXEy1FZgTPH+i\nVyyWWxCfe05eJ1l8fAtiSFRrKyXgJny6D5yEaYHI45uYkMlOb2/U3Yvt6YmQXwKA/UsSQ74rrd+3\nduL++8WaWw06WYem3oncggWyzGSAl7wEeNvbymvrhajXctkpzJtXvxuvJs01eeedskyTxdTnhBNk\nWSoB118vr4vFcguijxa+2sXUj0Ek+rvIhxfMKMp9cp1165wHAmOX4xKoEd4vL7vMCd2LL5b2/RjE\npPHR7qlpYftJAjHN72iS+zmP/7TTZEmRDUTvv1MhEPVvjWHE0aY/r0Y3YX7ltePHIE5MAP/2b/Ja\nu4nooPrx8c4b85BA1BPUuDIXU2VBHBuTguorVrhJxsKFQwDcRMW3IPKp+O67l1uEKYhf+Urgb3+r\nLBA5kfMfGMThi74Xvah8HV3CAgjHILK//f3uSfSsWcCppwL/+ldYILI9HYNI92FtGQWSrTehOMW+\nvqH4DargJS8Bvv71hjQFQCbUW29d3TZxx16vBZH9KBYlkcfrXy/lPD7+8eTt4q6nTru3tJpqXHNr\niUH88IfL/5/Ph5PUaLiP7baLJrnxLYikUHAPn+hV4L/mvVdb/Eoll3Cpt7eyBdHvI9tnvUU+PIuj\n0QKRMYgTE0MAxMIZJ6aS+uU/2NExoPqhplkQHXZvaS+myTNCw+hMQllM+QPquyT6boudRCgZj19e\noZ1iEPN5YJNNxEWM8UCcdGgXU9ZBfP55eUq+886S2c+3INLFtLdXHgDElTygpYDXxdq1wEEHycQ/\nabJCkUaBGMrK+b3vSaIWCpWQiykngtrykc8DV14pae3nzpXrkxNLIGxB/K//As44A/jQh6L9S8J3\nQy2VgC9+MRqvVCvVZEBsFnHnr1ETOd3OZz9bef33vx/YdtvG7Hs6UY01sJYspsx0rJmYkPPL+0qo\nPX5WKERr/fkxiCSulqH+focSxOjEPL29yRZEHdP4spdJaRn+vu24I7BypYQZJI2Pzh7961/L/eC9\n741fH5D+b7ppvAVxYEDuL/m8K/+j4Xcp6bvpP9ihizwg7Z51ltSe1XUmWwEFolkQjUqYBdFoOuZX\nXjv8kdHuo9qSEudi2mlj7seE+BbEuBjEqRKIQPSJ8Ny5wN13DwMoT1JDF8yREbG29faWi3n/KXkl\nCyK3Gx2VWE0gnQUxJPrItttGY2pC6zItu04y8/e/Y8Nx9/bKxGvZsvLkH5wMZzLi4rhwoTvHCxfG\n990/Bp2t9UUvGo7NFNlpPPlk+PNmCMQ0HHcc8LOflX/eafeWVsPahGnwY43j0GPOdXWpDgrExx6T\n95UEIq8F38XUtyCG4PcQCFsQ3/Me97qSBZFWQsD9hul71fLlUiIojUBcswZ461uBU06JX5csXSoP\n6uIEojysG44Vb0lZUEdG5KFd0veNycXe+EZg330r97eRMIa9HS2Idm9pL0wgGkYbwx+/22+XJeO8\nALnJ6wQUnRyDyB9cPk1udwsiUF74neMel6Rm7VpxXdJxk34WU3LTTbL0J46c3GmByP1WE4MYt65O\nlBLKeMr/a4HIJ/4PPugE4lNPufgc38VUU00Mlm9BnKrz3iziYhanSiBOB5phRalGIB5yiCxrjffk\nd4/3BX4vQwmSjjkGOOoo545K4iyIcd8v7cGiPRm47TveIQIJkO9+kkDUWVZZvoPtz5kDbLaZuPKn\ncTGtxhK3bl2yBZGx1R/7WPI+QwJxxQrgr391/2PNWn/73l7gt78FdttN/lqFxSAaaTGBaDQd8yuv\nHf8prhZO+gbvu5h22phzwkKBeM016WIQpypJDVBetmHLLYcAAN/6lnxGCyKLPNOCqDMB+i6mPv7E\niO5hWlCnEVlch5OvalLxhyaKG2/s1jniCPf5VVeJdfDCCyV2BwhbEEmcK20I34I4MNB513kSb3tb\n+WcDA42bPDZKIHbTmE81W23lJuxJhMa8UHBCkALxqKPkfSh5yoIF4oKZz0d/V5JiEFmeR8N7SG9v\n1MV0r71cDDI/1y6muZyIJzIxIW76et/MhOwfQxoLorZiVqJYlAdgY2PlQon1HzfaaAgXXRTeXj/g\nO+EEGVe/P+vWiXsq73v9/VLz8AMfkO1CDwVaIdosBtFIiwlEw2hj/Ju4Foj6f52exVTHkZA0FsRm\nJ6n54x9dUiAg+gPe2ysiaGRE+ucXHua5YpHnp56SJ+JpXEyJ/9l++7m2AWn3yCPL+xZHnOtqEv5x\nPfdc1ILo1wKbOVOWLLcRikEkfumKJLRA/M//lPqS3YQu3k6efRY499zGtN+OE8Lpyn//t5SDqAeK\nHL4uFiXR1HPPxcf08uFUGgtioSAxwsy+TOIE4k9/KtZTfa/u749mPeW9CnD1DZkwK5OJCkTeR4B0\nArGaWGQm9GEMtYYP67QrLeE9VlsQf/Mb4Oc/d+vQKrl2rcSLrlzpfp/f9S75PvsPBNPENDYKsyAa\naTGBaDSdbvcrHxmpv3ZWHL4FUQsJfYP3s5h22pjzh1ELRD8G8ZprJMun3qbZLqbXXy+Fo4m2Vvb0\nSAzNzJny+qGHhiPbagvi2JjU8Npjj3QupkRPjEZHgS98QdbTaepDFuVG4rthzZ4t6egBl1gHALbZ\nRpac2L3pTbL0XUzjshdWgu1ks5KBc8cdO+86T2LhQuBLX4p+NjjYuCyHjbo+umXMr7wSsRaiZnPy\nydG6eJUIjXmpVO5ims2Gk9gQCsS4Ony+BVEsadE2QgJxYkLE6uCgbMP727x5cj/4/Ofl/T/+4dqh\nmyWtiNmsHAPb126yaVxMfSGbBMeK92YNXUzz+WEA0aQ7S5bIb72fhVp/t7RAnDtXXO91aSKuo7PB\ncvtWCcR2tSB2y72lWzCBaBh1smZNuvWuvTaa3TENhYJYnYgWiLzBb755e2YxXbpUClAn8cADUhNM\nZ9UjvkAExIWRtEIg6h9xIGqd9V1MfcuttiCOj0sZCArEkAWxkkDs75fJCWN1/O2SBAD/NzgIfP/7\n8euFCMXp6P7x+qRLGAUi3VB9C2Lo+gw99PAJlbnoJnp75QFAswjFpU1nXvlKKW/SqRSL5S6mlR60\n0LoX52LqWxBD3zWun8lE7718gKMtiJmMtMOyNprRUflO+y7y/J4304JYKEibM2dGXVMzGannmMu5\nY9fW2LvvlqUfg6jvWzoWXX/nBgbc9bZmTdRC6Zd5aiaMVTULolEJE4hG0+l2v3IdR5bEkUcC3/52\ndW0XCu6HhJan0GTadzFthzE/+eTKE7CDDhIXo5BA1JOT0ESlUGh+DCLHmk+ntQjU4lGsiUMYHHQT\nG21BvOIK4E9/EoGoXUzzeTfRShODCMh6o6PlheOTfvD5MCGTCddRSyLksvzf/+32/Z73yHERHj8t\nGWkF4u9+B7z4xfH9oAClpRJoj+u8E/j976X+YSOwMW89cTGIvAfQxbSSQMxknMs7vTGSYhBD9ySK\nyFIpbInUlkX2TWd8fvZZ4PLLXUy2LxBrdTFlfOOhh8avq/uUzcpDLP+h7WOPsb7rEADgsMPk80zG\n3WP1AzpAPn/iCbFKawuiPu7+fvcw+emnp9aC2K4upnZvaS9MIBpGnXAC7cdqhaj2B4BWMsBZnkIx\niL6LaTuQJquc/8NYyYKoaUUMIrnmGlnqsdUTGMYgbrSReyI9OirnSk8SttkmKmonJuQY4mIQrU5b\n4wAAIABJREFUQ67LuZy44vLpdBrrWz2TgZAFUVvzPvMZsY6yP75A9CeAoeszm5WyCknZHAcH5ThC\nsUFGMq97nY1bt1EsuvtDWgsiIPef9evdfUl7QqSxIHKdYjF672U7PT3uO04RqcXQWWcBxx7rsjrr\nOEqgOhfTs85y96fly2W53XbhdTW0ommBSKFJq+Zdd8l71kEcHHRWypUrZakF4uc+B7z61e7YR0ai\n935dP3LVqrAFsZUCUe+3UWQywHe+Ixlim11+qF08pboZE4hG0+l2v/JasqhV07YWiJUsiIyPaIcx\nr8YVME2SGp9WCES2TdGjf5S0QMzlgMcfH94wIejrcxM2Wr6+9S05P74Fkec1rQUxmxXxyQmUThgT\nRz0xspUEIvnDH4B77nH9ooupX+tNC8RvfKO+/rXDdT7dsDFvPaEx14IwrQURkPvN6KgTL7VaEH2B\nqF1M+aCPWZe1QOT9ZM0auYfyPsHwAQpNvU3ccX3608Djj8vrp592+6xEyILI8kRPPcXjHgbgxmDG\nDCcQuS/+HuixT2NBXL16ai2ImUzUItpIFi0SAX3LLdVvW829RYdqGM3BBKJh1Alvss2IH9IWRAoL\nv/wAg87Hx+UHqV1umnGZ9DR6sgFEBaKeFMQJxGbHILJt7b5LwaefcPOpue8elcu514zD8ctc8Bj0\nZOyCC2QZ52Lq7+PWWyXNfBzve180QUQ1hASiThhDttwSeMELwmMAhAXiC18Y/Z9hGOnQoqQaC2Jv\nb9SCWG0MorZaxlkQKRBZc1GLIf7v1FPD5TjoeaKPJXR/0CJUr5fm9yAkEHmfu+SS6HGzHzNmOIH3\n5JOy1BZEbsP729q1MiYvfSnwta9FBeKqVdExoZWylRbETKY5++M4NPN3mf1uxkN5w2EC0Wg63exX\nftJJwKOPyutGZRvUJLmYamvWs8+6J5btUgexGoEYikEM1cvTn9UrEJcsAd773uR12DbrXI2Pu/IN\nvoVzcHBow6SL4khbEJmtb3xcCiQD8S6m22/vtvfxi2HnchK7lySyBgaAvfdOPtY4Qq7TOi7Wh+fy\nwAOj/fWfsOv/1Uo7XOfTDRvz1hMa83y+doHIh4lA7RbEv/1N1vn0p127XGqB6FsQdczfjBnl9y3u\nUyd/Cx0X29Eiob+/OgviwEA0I7Tuwyc/OQTAPQDu63MWxB/9SJZaIIYsiNkscPPNIobZt9mz3X2f\n0HraCoFI1+RmxSHy/NWScCftvaWWxERG9ZhANIw6OP98F5+mi/42imLRFRxfuzYqJLRAfOyxzrQg\nklAMov7x4iRGi3AKxFpjEa64Qmp3JcG2L7xQluPjTpg99ZRbj4ljQtYzXyDef7/bjhZErks4aUqa\n8IW2awahLL2c3ITi2vjj7VsQs1nJoPqd77h1uzUjqTF9+fd/b81+/IzWaQViT4+ImHpjEAG5f225\npbzmvYDt8/+FQtTVcmQE+NSn5PXgoLv3v/WtsjziCODNb5Z4PpIkEHW20NHR6gSilLOQz3yBuOmm\n8loLRN4LFy2Spf7t8R/cUiAS/jbQk0SL5rlzZX+tyGKqXUybIUj5W93MYzGB2BpMIBpNp9tjVniT\njRNmixe7H5lqXekKBeDhh922elJAa9YLXuDWHxhobh3ERx5Jf1OmgAk9pdxySzkW34KoXUzTCsRa\nLYhpnp7qtvfaS8aWx6UnPT09wIoVw2UCkanUAWd5pDsREH2SHBKIoeuF1xvHpNkiiwkZNJyQhQTi\n/PnR/7F/W24pGVQ/+lG3bpoJbRLdfm9pR2zMW48/5rkcsMMOUQsiJ/6VoDt8vRZEQO6Pr3iFeNGw\nHd1eyMV08WLJDPr003I/OOUUqZP4//6fa//CC4Fddw33i6xeLUstEE84oTqBqEty6N/vTAbYb79h\nPPWU+5245x5XN/OQQ9zxAVELIj9bty46frwfUiD6985W1SbkddKOFsS09xa2nbbEmFEbnSoQfwZg\nOYB/qc/mArgWwP0ArgEQqLxjGI2HN/W4enF77OECtqu9IesfDLoG6SeVDzwAXH21S1lOF9NmsXCh\ne9KblpBwXrZMxKCOQWTyFbrv6LEKuTQWizImfrr1evrlk8+7sgr/+pds09srk5Ozz3brsTi0X8OL\nheRPPBHYaSf5jGUmmOQhFM/H16GJkS8Q6xVZSVx0kVjJfTi5DAnEE0+UlO96ovD008Buu5Wva7GH\nhlE9K1fK95LfHz5sq1cg6gdwaQViTw+w7bbR9vX/fRfThx+W9efPl7YOOgj48pfld5LJX3x4j9P7\n1i6mFIjbbFO9QOT6Y2PuOFavlrHYYovobzCFMIWJdjHlfVwnrfPr2AIuKY9fYzebbX2SmmbsTz+0\naBYU4T/4QfP2YXSuQPw5gKO9z06FCMRdAPxp8r3RBnR7zApvhEmCg08707J0qVjrfIHoWxBnzZIf\n+q22ks/oYtrMMX/iiXTr6XTfleAkYnQ0mh2UsGSCb0HUheirRVsr48jno8loaEHceOOoOMrlgGx2\nqEwg8lydf355ttEf/rCyBTFJ/LXCgvimNwFveUv555wMhQRiT4+7Hj/4QRkrWhV96hWI3X5vaUds\nzJNpxvfRH/PZs+W7pzNap31QlORiWq1ALBTKE4j5bfgCEYi/H8R9zmPL5cQyd/vt7neF5TIAKTNU\nqwVxbAzYfHO3Dsecvz26j75A7O11oQRaIFZjQWylQMxmm18LsRbPnmpjEJctq34fRno6VSD+GcAq\n77PXAjhv8vV5AF7X0h4Z05bHHpNlkuUuVLswiYMPFhcivX4uFx+DyB8bupg2k7Q/KnECkduXSq52\nFScR69e7CYfez0YbyfL/s3fecXJV5f//7GzPbrLpIRAgIST03gVh8YsKSBUQCyBfFVGKFEFABZWv\nKEUQFOn+pEhV+QKKXxGVpYceekslIYHU3Wzf2dn7++PZh/PcM+feuXfK3dnN83699nX3ztx6Zuae\n8zlPq6oC5s83s592ooE4RBWIUrCm0+7YSs7cx+/Z8XcuTj/df7yoApHfe+yx3OcoFXxOe+Bn87vf\nhdc2VAuiMtI4/XTg979P5ly5Mn26CLMgymdbkECUuMrz8DqXIJIupgcdRMvx46NdKyMF4hVXALvt\n5hd2fLwxYwqLQeR+VLblhhvSs51dXvv7swViVZVpOy6XEdeCuGwZsG5d7msvFE5SUyoLIh/z3XeL\nf2yGP+NHHy3dOZThKxBdTAG5nWJwOSVkWyVBRnrMygMP0DJMONmxdrmorKTjyQd4KuV3MZVCUMbF\nlUsdRJnuW8LtJNuCBxFyUOKKQaypoRThfPxUijpeV6bNXEQRiOm0P9awtzdYIHZ0tHwyCLCL2AfR\n2hrfgmgPBEvpYhoEfx5DXXy9HL7n6xva5uFsthnwjW8U95hBbS5/+1En7tiCyAJFHiOuBTGdzp4A\n4vfZOpfJGFH04x+TeA6bNHIhM4Ryf8LirKfHJJRpaopmucpk3C6mUiDKNq+t9dd35UlPaUHk/2VS\nOdl+3G/wZKerH+HyRqWk1DGI3J759A1Rny2lLKGhGGL+TIcN3uCfkxNPPBHTp08HAIwdOxY77rjj\nJ6Zt/oLqevHW586dW1bXU8x1LqYLNGNgIPv9//yH1gcGaH3Bgha0tOQ+flMTrXd3m+NXVgKtrS2D\nNZiafUKwpoa27+howTvv0Axrqe6XZjlzb08dZguefRbYemvzPj3cmwc7Eto+k2keHLC04O236X3Z\nSTc3N+PNN4EDD2zBSy/R+8uWAWvWtMDzgN7e+PdDojL881i2rGVwO1p/+umWwdlj//aVlc3o66Pr\nAYDGRnp/yRL38Xn/115rGbR+NiOVMu/z/o8/nn391G7Ng8dpweOPJ//932wz0x4VFfkfb9kyf3vE\n3X/uYDrBcnkerA/rI/l5Xq7rjP3+SSe14NBDge99L/rxOjuBvr5mbLopcPbZ/ucHP7/4+TxnTgvG\nj/fvT7F/tN7Z2YLnnwc23ti+3uZBrwp6XlVX0/vPP98yWG4n3v2nUs3cAli4kPbn/mXxYmDKFHp/\n+fKWQYFG/fETT2Qf76mngAsvbMZ559H2/Pzt7aX+EwAqKrKvhwQP9V/r1tH7H31E71dWmut57TU6\nXmcn8OGH5vmfSpnrtZ/3zc3NOOssYO3aaOODQtbffJPur6KC2qexsbjjg6VL6f46OoC7727B1KnF\nf55vvrk5X6nba6Stz507F62DMU+LFi3CSGU6/Elq3gEwmCcQUwfXXXiKUixoDo7+Tjgh+/2uLnrv\ngQdoedZZ0Y67xx60/eTJnrf99vT/ySd73mabed6ZZ9J6TY3Z/sIL6bWvfMXzLrqoOPfmAvC87baL\ntu0223heKuV5r7zif72nh46zfDktp06l+91qK1p/8EHP+9e/qO0k773neTNnet6cObTdUUd53mWX\ned7mm3veG29Ev4cJEzzvhz/0vOOOo+OE8cUvet5hh5nP+OKLaT+bL3+Z3j/lFFqefTYtgz4LPt5e\ne5njf/az5v3nnw++tokT6b399899/aViwYKhO7eiKH5Sqei/x332oW3nzMl+7xvf8PdpK1dmb/Px\nx5539930/ujRtG4DeN748Z7X0OB59fWe19lJr82bF+++mKuvNtd0+um0vPlmWh5xhOd97Wu03eOP\n0/3dcou/PTo6aL2tzRznhz/0vB/8wPN++Uva5v77Pe/ww+k9Pp5k99395wc874ADzDWcey79f+SR\n1DcDnvf975v9v/Qleo2f3zYXXuh5P/tZfu0ThzvuoPsbO9bz1qwp7rEBzzvpJNM+TzxR3OMzixaZ\ncyiFgRBjWqoYSq1MeAjA1wf//zqAB4bwWpT1EJc/v12EN6prBLs1DgyQn/1jj5lkLJWVFOjOQfFA\ntotpKYnqJptOG7dRCbfTW2/RsqLCn8igshL4r//yu3YCxmWJ70+6mG67rSk+n4vVq6nIc5TEQXYM\n4pIl7rg76QILGPeaVI4n7LJlZttVq3JfjzzmfvtF274UaOygopQPU2IE1IRlP+bnF+NyMZ08Gfjy\nl01CtCB3UelimkrRcH7mzOjXKZHPG9u9s6fHnwAskwH+8x///mzYfP994Mgj6f9UKjtJDT+LXX05\n92OHHkrLnXYyoQ1cggoA/vd/TWIb2X4PP0zLjTZy32NjYzJ1/ZKKQQRK5wra3w9sson/81OKz3AV\niHcDeAbAFgCWAPhvAJcC+CyozMVnBteVMsB2kxkp2P77Ln9+7jRYIHJ8Qi5YhHCmzuZminPo7aWO\n7Y03gFdfNdtLgSjrIHZ25hefF0YmA5x/PqX8lnz96/64yL6+cIH4wx/6X+N7DhpwcJyHrFvFAhHw\nt0cuctWc6uqiGMX+fr8Q/+ADt0CkgYCJQXTFFbpYvNgdixIGD+yGMv5v002BOXOG7vzMSH22lDPa\n5smTq83jPAvCsh/LGMSgbZjubuqPgp7XNTW0v4ybzxd+Vk+caOqycj/Q0+O/JxakEl5/5RXgkEPo\nf46fdMUg3n13dpvzNfDzn7Nu83tyYpYForyOBQuAO+808ZI2SQnEpGIQgfjiLeqzJZOh71ddnUkK\npBSf4SoQvwJgQwA1ADYGlb1YA+AAUJmLzwGIWVhAUeJhP1zDZh1ZON11V7Rjy5lM7mT4YZhK0Swk\n1+cDgPvvN9tIkbbddiZzXLHIZIDLLqNOVHL77SYrKWASvHR3+61j3CayM7QtiC6qq90WRN4vjuWU\nkwAB7s9tjz2A/ff3C8TRo4Enngi3ILLVk0VfLgsi4LY2Rkl4FFdYFpOKCmojRVGGnjihRGEWxDgC\nMdc2lZXAzjtHP04UxowhoQX4J1/5+Cz47HvjZ+bkyeZ539YWbEF0ceaZlKGWn/8yORoLRI7956Q8\n8r4nT6YawqecAnzrW9nHT1ogltKCyO1fKuseJ1Di8YVSGoarQFSGESaAeWQRZBlzbRO3DAMP/uXD\ntraWBKKrs6XENcbFlNt84ULg2WfjnTsXfJ8dHZTue6+9zHtS2LCL6dlnU+2ogQF/bUcWstxR5bK6\ncTF63o+tqbxfmEDs7QV23dWsr1hhPhuXy+wbb9Bsczptss7tvz91RkG1/4Bm3+Ah7F4AYM89acmf\ntXSjiiIQhzqDaDkwUp8t5Yy2efIUs83DLIh2HxZF2IVlJH3qKVoW6pLOfWB7O7B8Of0f5GLa358t\nEPkZ395u7nHdOn+Zi/5+cy9f+EJ2m59wAvCb3xiBWFNj+nXPo+vhcAQWiC4RftRRwM03Z78+UiyI\nAwOmHeMKxKjfc/6suDSWUhpUICpKntidaTEFIs/kytnQ2trggsjsrskupkwqlV+NwDCkBXDRInI1\n5Ndkh8Aupi+/TOv33APMmGHaiV1D7GLKYRbEdDrbgjhuHK2HCcS2NuCll0yH+MIL/oGBi8ZGvwUx\nbJDD18wWxFwxiEuWkBVWbht1EMXHHEoLoqIow5MwC2I+AjFoG/k8K1Qgcv+ycqXxRpEC0XYxvfVW\n9/4dHf4Jyqoq6ofee8+EcyxZAvz1r8HXIicBuW998023QIxjOW1oyC4JVQp4DFEqC2ImY/q0JCyI\nxQ6hUQwqEJWSM1JjVvIRiLNnRzu2rJvInUyY2+I++9CSXUwLafPeXuD114Pf54d+R4e5Tunus3Qp\n8NFHxoLIRYw5doTbhAViX5+/U4nqYrp4sV8ghnVGLAxlB5xLII4e7ReIBx5Iy0suyd6WBigtkS2I\n06aZdlGBmD8j9dlSzmibJ08x2zzMgignF4O2sYkiEAvlyCOBc88l6xsjYxDtJDU2HPfX0WHe33ln\naoubbgK22MJMxk6bll0HUeKKQVy4MLoFMQi2IL70EvWfpUImqSmVBZHDX6ImtGPixCCqi2npUYGo\nKHkSx8WUO147M2cQfKx02nQybFV0dcgyBk5a0vKZIfztb4Httw9+nzvmW24x18b319NDne2nP02v\n1deT1RDIjvnjB3s67U9Sk8vFlO9v5UraltslzILI13fVVeY1/myC9qut9WcxnTUL2HJL4O9/d18b\nEN2CCJhEBq5tw+6lHGIQFUUpHw47LPq2YRZEO+FHFIETJASLKRCnTwcuv5wyVzLcj7gsiAwLLe5T\nzjmH9vv614HzzvMnK2MLYi5cMYgAPbNHjaL/CxGIu+4KXHBB9P3iIl1Mi2VBrKszrr8DA9Qnb7NN\nca17CxeaxHjSxVQtiKVDBaJSckZqzIr9cHXNxtkWRHuGNggWYdLFNMyCKAVKX19hbe5yc5kzx1gA\nXfcgy3l0dVGymnTa34naApGzyKXTdJ8srsLSpmcy/vNXVpp2iSIQZRIdbmO+LxfSgjhqFPD22+6k\nPzSwiBeDaCe0key8s1/MZp9LYxCBkftsKWe0zZMnV5sfcUT0Y4UJRFnSp1DiiKOoyCQ63I8sX+6f\nPJUCcepUWsrXBgZIwHGZC+4b7OQ2QW0eJhC5/VxlLnLR2Ai89hr9b7vIFhOZpKZYFsTeXuobAWrf\ngQFqi7jiLex7vvfeNEELqAUxKVQgKkqeRHExlRnSOOV3FKTbYz4CsRBcM7977QX86lf0v4xptK2B\n/B4n06muNu/ZApFFYV9ftCQ1FRUmZoSJKxB33NG81t9P+69c6d6HEw+wQOTBhgtZZgSIZkHkbfl+\nZdmIUaOAs85y78eftQpERVHiEuZi+tOfAg8UqYJ0KkVWpGIiJw+l6OPXKyqA+fOz97MFIj+X5fHy\nsSDKvrYYLqZJwAJx3LjotXejwG3JOQVGjSqudS+dNmMJFohqQSwtKhCVkjNSY1biupiOGhW9FIM8\ndlyBmE4X1uZBs4o8S8udYl2dPx4RMA/rTIa2T6WMRVIKQ6amxqQIz+ViCtB9yhnDVCqaQGRrnBS3\n/f0k+lasMK9NnQpcfTX973m0zYYbAtdfT7X/gpg4EQBasgRiFAtinPIc8pibbx5vv5HISH22lDPa\n5slTihhEVz8yeTJw+OHFOU9FBWUDLSYuC6J8nZ+lsgQUQH3O2LEUOsETg0C24JRtEtTmvA/3O9xv\n/fvfxsWUl3FcOCdPjr5tIXCSmsbG4lrfuE0LsSCGfc+lp00mQ5+DWhBLiwpERcmTuElq6uvJh/6a\na3IfW1oQ7Zgzl+iQWTQLtSAGCUTuCKX7K3fIbNXr7jadIxdKZrdOV91BtgDKGlRhadOrq7MtiDLj\naxC33ELLtjZznkyGEsW0t5vtPvoIeOYZc729vXQ/3/lOeEyNTFwARLMg8me2007B24Ttt8UWpUky\noCjK8CJOMpAwC6Jku+3yvx7AuDEWEykQL788+3Xum+wwiYEB6mf6+ymkgEsX2aIjigWR74knFmUd\nXrYg8nHiiBfZVltsEX2/uHCSGk76VijcB3FfNzBQGguihEW+CsTSogJRKTkjNWYlH4EIUMHdXLgE\nIneCYRZEzmJqt7nL7SaIMIEo3RoHBsx1smh7802zf3W1v8OVyQDkdVdXU0diiywX7GIqBzlxitKv\nW2e25/hCuxPj43genStKYiE6ZrOvRhZfXxjTp8d3wypW0emRwEh9tpQz2ubJk6vN4wz0X3qJlrme\nl5wdOl9KLRBdr2+7LS3XrvW/n8nQM3n+fODSS41AlM92rqvL5Grzu+7KPgZPjvIz2k76k4uNN6Zl\nKUUPu5hWVRVHINqTE+xiykne4hD12aIupsmgAlFR8iSui2nUDKaA+8EaJoT4tbo6v8vilCm0bG2N\nfu6oApFj9ADTEXJGUr5eKWZ4Vle2G1sAe3rMscMEIsc0yvg9HoREqffY1mb27eujGV97P1sgcqcf\nhu1uFMWCCFBmts02o//POy/3eYBwC6uiKOsfcdzUFy2iZdhE07x5wF/+UtAlfZIps5iwENx3X1py\nch5+PUiQsmDhZz0LRNnXcJ6AqHB9X3kM7m/5GR03xm/xYuChh+ILyyi0tgKnnOIXiHHDG1zY5aIy\nGb/FtljI8ZW6mCaDCkSl5IzUmJW4WUwLFYhhMXrSyijrIPI1xun4bOHLLquplL/DlxZEfkj/7Gfm\nnLYFkV1Nly0zr7EFsL/fiKqwdqquJqEpBSLHP4ZlI2XWrTP3197utiDyYMPz/C6zYZBAbPkkOUGU\neErJu+9SgogoqAXRMFKfLeWMtnny5Grz5mbgU5+KdiwWSWHibeZMjqvOn1JYEFl48XOW+4EgyyIj\n6+zK7W0Lony2hrW55wE77JB9bra68nGWLg2/LpuKCuAznymNQHz5ZYqlZ4FYLBdTWyD29+dvQQxr\nc7uGsVoQS48KREUJYMGC8PfjWBBtgegqJRF2bMCIDlfHzp1UTQ0JurfeIlfWfASiPasoLX+ZjCk6\nLLeVHRq/xklqGB4sPP64eU3GEEaxILKLqRSIHEP4zDP++Ev+/GQn3dZG21RXk6BsasruYHgQ0tdH\nnWmuwQdgLIjcNlEtiMzs2eH37bo+RVEUgOIFn3462rZywq+UlNLFNB+BKOMN7bq1QLaLaS6kpwnH\na8rkZBdcAHz3u9GPx7BVrNjx5XxtnKSm2C6m3O+zF1E+AjGIK6/05wrQMhfJoAJRKTnDNWZl5kxT\nmNVF3BhE2Ykdd1z4ucMsiK5ObPRoutaaGnpAz5nTjGuuMeeP0/HZ4pQf/DwzKGtl8Ww0P6R33928\nZ7uYsuVQPtClQOQOPKx8g8vFVHYc/Pry5fT5tbX5Be+6dbQ+dizdS5hATKejiUO+DqA5y/pZikGY\nWhANw/XZMpzRNk+eYrZ5PpOG+VBKgTh2LC25T5STZr/8JS3vuYcm7Lbbzrg8AsBWWwHHHkv/h1kQ\no7b5wICpzyev5xe/AL74xWj3JUmlsmssFgOZRCYJC2I+LqZBbX7OObTkz5BdTKXbsFJ8VCAqw5re\nXuD110t3/LCMoHEE4gsv+DuxDz8MP29cF1OAAtzZxfSOO+g1tpbFGQzwffAMphSI/f1+l0tZ97Cp\niTK7scBjd1CGxbYUdDLJDBMmqjiLKXfsqZT/eAMDdA1sNbRnhVkw8gz0mDHZHQy3lZ32PA5Rylzk\niwpERVEKZThbELkmrcuCuOGGtJw1C1i9GnjjDb8FcdasYBfTfNpEZj+1a9vmy6hRxXcz5f5cxiDK\n2rtR+de/gM9+1qzzWIXHCT09xbcgMjw5zS6m7DGllAYViErJKWXMym9+Q7WNig0/TMM6uDgupm1t\n8TqNuBZEpqaGxWcLAHp4brJJvHpMfO7WVuCVV7JnBt95h9Zl/F93N4mu5ctNHEYqRR00QHWpuFNe\ntcrEt6RS/pi/XHAdRNkRX3ghcMMNZpuDDjKda08P8Nxz5r3WVmM5BMjyGpTFtK8v7oChBQB91pts\nQq8Ue4AEADvvrG6mjMbDJY+2efIUs83/8AdaDkcLIvcRXFTeJRBZREjxJ2MQpQdMvjGIkp4es1+x\nJgZZID75ZPFElksg/upX/jrAUbjrLhKJnkfhIrYFkeP82ZspDrnanD93FuW1tSoQS4kKRGVYs25d\naY7LVqU4FsSwJDVAYYligOgCUe7LyV/iCER+qF9wAYkRGVsgj11dbQRiVxe5/fT2klUOoFTjLBCX\nLjVCraPDCJx58+IJRFeSmh12AE4+2WzT1kZCFSAxye5EgClzwQLVVauJr6OnJ78ZZb5/oDQC8Yor\ncsewKoqiuNh/f1qW2oJoJzUrBitX0nLGDFryRB/3M0C2eAT8ApEzmALZWUzzud6uLtO3x01OFgQL\nxH33paymxYD77v5+6pc4Rj+uAOX+csECSo4kPYwA6mNLZUFk7yWum8w1lJXSoAJRKTmljFmJUyA4\nDvwQDHv4SNH1m9+4RZh8QEqrD6fIDiIfF1PAiK2JE5sxa5bZPk478QOfa0nxel+fX/DU1Bihwi6m\ngOmYe3tN3CG/NmoUuYTyPfT2GtefKLON7GJq15uSzJ9vrqutzf8eJ6nh/RsazGfNwpA/x/7+uAOG\n5qxXSjFLn0plu+Wur2g8XPJomydPMducn2lR46uDuOEG4Kqrgt8vhQWRJyTZCjhpEi2T4YMTAAAg\nAElEQVTlc5b7GhnLLl1MuZ+S2wJ+SyAQvyafPGehHh6NjabvWrassJIj3/secOSR/oneVAp49lla\nf+QR935//nP4pC0fjz8TjtlnC2Ip6iDy9bS3k9BXF9PSsl4KxDfeoJgwZfgzlAJRnnuXXcJdTAF/\n55PrugtxMQXoQc0DgMrKeBZEfuByG3BHwC443/62OYe0IPLMLZ93YIBcOmtqqP4SQO6n69aZDrSm\nxmzPgf5huFxMmeefp+UxxxgXU55xnjYNePVV/3EAOg5/xvIzlzGOhRCWcEdRFGWoKHTy6uSTgbPO\nCn6/FAKRn9Xcz/E9yPPIGHiGLVqA38ND9iNx6yAynZ1mP+5XCu03Nt7Y9Fennw4cfXR0d01b1N13\nH/DAA9kTvVdeSetPPZV9jJ4e6ke5f3fBYxTua/v7jQdTsS2Iu+1GoSN8D93dpi6zCsTSsV4KxEMP\n9WdbHK4Uo8hpEpQyZqVUDwcWC1FdTINEWL4upq4HK3dwbNlzwZ1if3/LJ51UKpWfBZE7Y1sgcidY\nXW0SxHR0ZHfa3FHV1xuXzilTKAaRjyHLSOyzT243UztJjWzT3XYDbr+d2oktiGvWUGe7ZIk/VlW6\nBPFnzZ1hT4+xMEYd4MyeDXAMon29SunQeLjk0TZPnmK2OfcFpXB/l9TXF79Uw3nnAU88kV1GyPZs\nAfzP3nTa9Bk8kSm34WyYUtjFaXPuT4rlMTJjhj92HvDXDw4jlQJee82s8z1KC2JFhenj9tmHamh+\n+ctmHw7dcSXK4c+UxyhyIrmqisZBxY5BTKdpYlomxFEX09KzXqY6GAlZAD3PpBEeCfeTL6V6OMR1\nMQ0SYUECcfz48PO7BCJ3Xlxw3gWfo6fHdJpRLYhdXXQOmY1MXktPD3UA3OFUVxux2tZmXpfptHk7\nFoibbEJZZzmGRLrnRBmwuLKYShoaSBxKgSgHBAwLVCkQeZ+eHn/NqChssQXw2GPZr5c6zkdRFCUO\nxRZtLt58k0pMjBoFHHhg8Y47dizw6U8b65qcBGXY/VS6eUr3UdekXSEWRIBq8MrrKDQEYMaMbPfd\n6dOjf3arV1M/WVdn+lUprlIpfzzfs88C779v9ueJ8Y4OmtR1YQvE/n6/QBw1qngT+H199LlKK2h1\ntbqYlhoViMMUHnz39PizcpUjpYxZKbUFMZeLaVMTtX9VVW6BKB/uuVwvwix+UcRee3uzr/OMss9h\nh5GY4nMHuZhKC2JrK/3f1gZssIE5H0D3e9ppZMFjgbh8uZlp5HuJ83sMczEFjEDkmc+1a90C0WVB\nlB0eDyLiWF5d3/NSz9Kv72g8XPJomydPMdt8o43yK28Qh623Nv/vtlvxj29nC5UCcdYsuj8pEKX4\ns2vV1tVRCIJtQYzS5jvtRJm+udQWn6NQgbhuHbB4MZXz4IRrcWloAI4/PrtcVTpN9+wqY8XwuCos\nGZqs8czH4fvv76d+N24ytaA2lwYRvr6GBnUxLTXr5fz2SEgRzwP+YtfKGW6Uys02qgVx9mwqK1FV\n5RZ9mQzw1a/S//L9fGIQ49DQgJwuprbLyr//DTz8sDk3F7TnYHnuZKUFsb2d7r2tzfyuWBQNDAC/\n/S3wgx8YgchuM/I3GMfKVl1N9xIkENlVSFoQXRMoMqkAf8ZSGOcjEG1mz/YPlBRFUcqBPfYY6iso\nDBZgLgtiRQXdX5AFUdbxBaifmzw5Pwvirrv614tlQTzxRFpyXH5TE4VGxeWOO4yAspPU7LKL//XJ\nk81+LoH4//4fcPfdwS6mMqmb51ESmWKNTwcG/C6r6TStq4tpaVkvBeJIsCDywJUH8eVMKWNWip1G\nmYkqEPmBWFkZLBDlLOcPf2hed3HyyZRAKey+oriZzJjR4rs224L4wgs0kwyYdNcAndd+8EuBWFWV\nHYM4frw/8QwLRHmdLBBZLMvfYFwLorw2e1/uMIIsiMcfT52my4IoBSKfJ873y/6ev/uuKeislAaN\nh0sebfPk0Tb3Y1sQufauRIpGFoif+QzF3NlUV+cXg2j3P8USiCzWKLadyl1EmQznPkyOL7j/li6m\nFRXAttsC55xjXpdjHeliynzzm+QRxLhiEGX7NTbGF4hBbc5Jb9JpGq/09RmBqBbE0qECcZjCP/rh\nIBBLSamzmOZyseAHYpgFUQpEFk927b233qLlTTcBZ5xRuPCVNahcLqZcN8rzgJkzKaidO137wS+F\nU2Ul8JWvACedZARiUxO1k8uCyEybRstvfYuWcnY3zu9RWi/53iTSglhTQwJRWhBvvx047rjcAtEk\n+4l+bYqiKErpkUlqlixxW9ekez+Lv3//2/RFkn/9i1w5444Nv/lNyjLKcH8kS2nkAwtMvtYJE8Iz\nijIyRm+zzeh/O9kcC0SA+rkwgWiPf9asMf+HWRABU8uxGLAFsauLxiscg6gupqVlvRSIc+cO9RUU\nzn//Ny2Hg0AcjnUQuV05m5cLGT8XFoMoBeLxx2dvs2IFsM02Zv3ZZwsXJuPHN3/SCbhcTO1ENPPn\nm07J7jBkrEFlJblN3nRTtkC040FkB93YSMJs+nRaL5ZAtDsH7jC4LmNQkhrpYmrHILKlVL4WBY3N\nSh5t8+TRNk8ebXM/MmP2tGm5Y73tGodBxI1B3HVXqoG8995m/xUrTBK2QuGSHAMDJmN4GFIgjh7t\nfk8KRDmxHUUgTpsWzcUU8E++RiWozWWZEgC4+Wb6PF0upitWmHwISmGslwKx3Dj+eOCKK+Lt8+GH\ntBxJMYhHHgl85zvx9im1BTHsoRzXglhRQdkuXdsA/v3DhH8UF9PKSnNcl4uprCcE+DtQVxbTqiq/\ncAKoA1i7ljqxzk4j2ioqgObm7FndO+4wrkCFupjyPnbnwBbE7m7KeJdLINbVUTKAn/3M77ZdXa0Z\nSBVFUcoRu8xFLqLGF+brXXbcceb/SZPyO4YLFpqpFJWHqqgIHxuwsOvry+4bbRdTwD9ukWLO5WIK\n0OQwjz9cyewKFYhBDAxk5w5pbXVbEN99NzzTuxIdHQKVAX/8I3DLLfH24R/FcLAgRo2feOAB4MYb\ngcsuix6QXeoYxDgWxCgupoydYQ3IfhgfdZT7vFEEYltbyyffEZeLqS0QV6zITofNy0zGWNpkB7rh\nhvRaUxMdXwrE//s/4A9/yL4udvfMVyDarqV2ByRjEJuasl1MGVnmAgCuuSY7BjFuMiuNE0oebfPk\n0TZPHm1zP7KvCePyy8nVMqoFUW4Tp81LUTqku9uMg7bf3hgFfvSj4H2kBdEWiHaSGoD6OLvuMe8P\nZFsQXZnY588367ZAjJtAJqjNPS97MoAT1dgCUV1Oi4cKxDIhbjr8d96h5T33FP9aclFRQdmsSsUf\n/wj87W/Rti2lBbGiIrdAzGVBZOsbYLZ9912qB8iwSGNr5YQJtIybIlpSUWEezlFcTNva/AKxrs4f\n8M4Pe9mBsgsLu8HIGMS6One9KX6Nk7fMmhU/iylgZlbthAP5WBABU7sJoH2rqrSjURRFKUfsWrtB\nnHsuxcLlIxCHGu6bMhmK+Wc23TR4H2lBtCdPpXi0LYj2JDIfx/ZQcwnEG280x5d9uQzfKBQ51pLX\n6HIx5fWPPgKefjr+ubbZBrj++vyuc6SxXgvEkTAAjGt5LBacjTIKceInttoKeOON6MfmQX2xZ/B6\neqhALCcDCjp3HBfTP/6RlvbMGj9E16whAcQi6MYbgaee8h/vkUeACy7Iff2Vlc2fnCOKi2l3t7kX\nFoi8j7QgSqsaC6+xY2kZlDjGxfTplJntiCPyczHdfnv6zLkTZaRAbGoy9ZiCYFelTMbvapNPKRyN\nE0oebfPk0TZPHm1zN1E8iFwZSoOQfUWcNi+FBZFJpfxeMGETx1Ig2t5l559PS1eSGhnfJ49jt6/n\nZccgMhyCssMOtM59cZy2CYtBdAlEl4spT7SfeKI7Y20u3npraAwv5ch6KRDHjqXB43CN3wsTLaUm\n14+90LqEb78db3tZ46eYNRF7esjKxYXgXcR1MWXshxoLxFWr6D0WWptsYoLfmc99jspK5CKTCbYg\nXnGFeYhKgRhkQeT4wr4+/71wp8UB4XYW01zX9/jj5AKUj4tp0D62QJTXKWHxy51OR4e7zIWiKIpS\nnkTp86uqolsQ7cQuUSmlQAT8fSpnIHchrYRB4UeuGMQggZjJANdea8YL8j5tr6TeXuqfX32V1lOp\n4tUpdAnEvfZyu5jy+Pijj/I/36pV+e87klgvBWJtbXFS8Pb1AQ89RFadQonjYrp4MQULp9P00Cv1\nw0kS5jKwbJm7/k8p4yf44fOpT9G5n3yyOMdlgbh2bfA2AwPuYG9JkECUD01+kK9eTfdQqJvLYYcB\ne+3V4rQgvv02Fa5/5hlal0IxSCA+9BA9+Pn7xrDwmjKFli6X0iDkdzYfF9MgAVdTQ59da6uxbLos\niOwWK5EdXj4CUeOEkkfbPHm0zZNH29xNHAtilH5V9gtx2nzp0sib5s2uu9JSipf33weuu86sy9wU\nvb3ALrtkHydMIL72GnDbbX6BePrplFMAAP7zH3Mcu+3b27P7zcbGaNlXmbA6iHzNv/wljR8OOcQt\nQFkgslC1j8O1qMP44IPo1zySWS8FYl0dDW4LifECyLpz+OHAF75Q+DXFEXmLFpGLXlUV3Uuu+jiZ\nTLglLA5hMXlxzyFr6uSKJQiCH2QvvUTLxYvzO45NTw9ZxsIEogycjiIQv/99WrLYkucCgJUr6UFd\naPbMBx+kjLDf/z5w9tn++AL+/HhyhNddLqbMs8/SgzhIILIQ487hn//MfY3y+15IFlPX+7W1JkmN\nvE7JeeeZ7wwj71stiIqiKOWN69luU11degviTTflt18ceHwhLYg/+xlw6qlUPP5nPzPjoXXrgPp6\nyiZu40pSw/3eFVeQa6YUiIBfhAW5mN57Lx1vq63Ma+PGFWfsKcda0pjicjENs1h2dJDADPLC4/vd\nfXf3+/39uT34nnjCxGUOd9ZbgThvHgUwFwLPULisZqVE1nkZOzb3D3DyZFNeoFDWraNZNk6kIgkS\nuUF+5RMmkKD77nfpRxX1eABlyJSJWJhiJa3p6aF2C5v9km4PlZW5BeLMmWbbTIaK9l56qRGIzzwT\nrcOLQnNzM370I+DKK/0uptJaCZj76+ryWxDr6+n/r32NluxiKoUTd6Z2DOJOO8W71nxcTMME3EYb\n+a/PZS2srQV23tn/GifjyXX8IDROKHm0zZNH2zx5tM2zmTs32uR8vgIxTpsnkc+CPXWkBfHOO2l5\n663AT39qJp7b26mPc41Nw2IQeTxjC0SX55hrvFNV5U+iM3Zs+CS7TZQYRCkQ+TOV1xJmbPj612np\nGtetWGFeDxpT//CHZrwTxJe/HL9cW7myXgpE/jE8+GBhx+EvZzEEYhwX00zGDGCbmnLPaEhLXaG0\ntdEDoLU1W8DxDzOONfStt4AbbgB+/GPz2rHHkgtwmGX0G9+gZVCtn0Lp6aHOIux48qGVStF92w8n\nKRBlcd9MBvj5zynhDN/DXXcVTyBKpIspC0R+aK9dS23tcjEFjLhyuZhyLCRPPvB3Moq4yteCGEUg\n8r3yNlGL5nZ0mGdDVRWw3XbRr0tRFEVJjh12iDZuqqggg0AUz5x8LYiTJ+e3Xxzuu48m0levpol1\nKUq5D5XZR7mQvE1QHUTACMEwCyLjGhtVV/vHQA0Nxcn1ESQQgew4RHsM9uKLtPQ8KqUGuAXilClk\nhQWCrZDPPht8jf395Jq6fHnwNsON9VogFgp/YYt1vKhIc3sUC2IxaW8nYeBybZUZtCRhvvy33UZL\nGVBcW0ui4x//IFdJFz/9KS3tma1iWhAbGqILxIoKetja5w8TiPZsHUBirRjINpcuprKsBUCzkePH\nB7uY8vWwQJTCjK3ItgUxiuDLNwYxl4spYO6VH/Ibbxzt2CyW+fhh6cRdaJxQ8mibJ4+2efJom+fP\no4/SWKWUdRBbWigPQikZM4ZyT6xaRSFGMpaOhS2PJdhiGlUg8riFhRMbIXj8I0UX992usVZVlX/b\nJ54A9t8//L7uu4/iHIHwGMQggWi7mdoCcbfdgIUL/dsEGR/+/W9aBglEO6u85Npr448Zyp31UiCy\n+1yh8MxI0vFK8scyblxxLYS5aG+nh5HLdcCefYrCvffSkoutAvRQGz+eSnjwjI8NW7bsH3oxLYiN\njeGC086s5YpDlAKRrzmVooccB3z39QFHH03/NzTEr4mZC5eLKQvElSuNQOTzdnQYgchLVxZTthxy\nrJ9d7zEqceJPo1gQuQNjdxu7FIbNihVUV3HNGvNsqKsr/uegKIqiDA1hApH7sHzZeOP8au7FZdw4\n03fLfpYFIgsbnvCNIhDTaTOWYYE4MEB9LY8bXH1hfz/wxS/6X3OV1MrFNdeQuAojlwVRCjp5fhan\na9b4jQlBAvH116nvDxrDhnkVhWWXHa6stwLxJz8p3vGWLSv8GHEGozKj05ZbAm++Wfj5o9LRQcJp\nzJhsMz3/SOfMoevjgXrc+IklS8g6xZmkXOmaWYitWWMycsprKBQWiFEtiEC4QDz3XH/AuLSg9fUZ\nd81iWRBlm1dWAj/6Ebnxcltygqb586nTkQKxt9eIKhZMLuvgjBmmELHcJopAlPcfpzxJFAvi1Knx\njjtpEnWwUiBWVsYXuhonlDza5smjbZ482uaFE9ZncCyfpBzbPJXKnpCVcHKU7m63BbGykgSmnVwv\nk6FJ/yCBKJFJamzvuVde8Qu03/8eOOCA8HuS5wiLQeTxiT1WXrnSH2aVyQBHHUWJg3jbtja/QAwq\nAQJQOwSNIzlc5fXXgdmz6Zhz5gQfa7iz3grEQw5xpwCOw377Fed64iJdTHfeGXj55eTO/bWvURFR\nu1QDYH6Ar7xCyw8/jHZMDr4GgDvuIPE+YYJxnXX5i8vB/7Rp5v/OThIthfi9z59PpR3iuJgC4QLx\n8suDZyn7+oonDF2kUiS2L7kk+8H49tvZFkQgWyByRyM7pfp6ui9+j/ePKxC33DL6BAlff5gFkQcC\ncWJhR4/2u5g++mjh2WQVRVGU8iDseT6cnvXsudPTQ7WSATPuePhh855LIG6+Obmo2klqBgZovMPj\nqiAL4gYb+AUijxP23decd7fdzDXOmJHbqyuK15cc886alf2+tEAODNA1nHSSOXZrq18ghpVrGzcu\nWCDy+OOhh6jEyMUXUz1GF1HHv+XMMPpZFI/6evoLm0WIAg/4Dz648GuKgxQmO+xAtWuSoqGB0gS7\nBCKvc905NuO7/Mp5lmnPPf0phY87jn5wEycaS5CrtAY/yE44wZwvlaKZol/9iq7z3Xfj3x9Aogmg\nY2QywUIjqkDM5YLc12ce5B0dxXFttGMQGflg3HVXqt80YQL9FuTMHz/4OWkOlw9xzcLy9XJnEreO\n41lnRXcN/p//oWVYm/LkwLe+RRMOUbAtiE1N8QcNGieUPNrmyaNtnjza5oUT1i81NwM33+x/rVzb\nnL2NenqCS04EuZhuvjktXTGI0rUykzFhJYDbbVSWx+CxdG8vcNllxt1y9Ojw0mh8LgB47rncMYiZ\nDHDoodnvy/hPOS7jcWJcC2KQiylbKv/2N1ryMdliK3njjeBzDBdGokA8EMA7AN4HcJ5rAxaIYbMI\nUeDBczEK1efrYjphgjuN8KWXUnyfvLZ//5uCdQth2jQqxB4mEE89lZZhyXPkA80VIzZhgrFEhlkQ\nR40yAnHCBH/B2kceCT5/GHxto0b5E7zYRBGI/f25BVM6bR7kxSpHIpHnT6eNlWz77Wk5eTI94OS1\ns+sIt+2CBdnHsuF2iiKs5DYVFdHFGM8Uhl0HC8SNNqIJhyiwQOTEO+PGDa9ZZUVRFCWYsD6jvp4m\nFIcDPEaQ4y8WWdOm0bjUtiByv8nWN5lxnC2I9fWm7+zpMeVBADPeqqjwZzblcQJbECsqzB9AoUhR\nBeLcucHb8FjL1Sd/9av+MZocl33pS7RsbfWPb8IE4urVwRbElStpyW6lDz1Ey1WrsietCzVAlQMj\nbQhUCeBakEjcGsBXAGxlb1RXR39xP8CKiux6K7W1uZNgFBv5Axg92i2gLriA6rGsW0fXWF1NvuAn\nnljYuTs76QHkythpz7qwcHX5lfO+HR30YLrjDsoExkycGHxcwHwOfG8A/Xg5bhEAzjgj5+044WPX\n1ZmMoy7iJqmRyPpNvb30IN9lF+DAA/O7ZhvZ5vIaZdzADjvQcr/96Lcg3Xa5TXkSZLfdaBklOUwu\nYfXkk8BFF4VvE8Rmm+W+jmOOAY44It5xWSBOmkTrlZXxLbnlGLMy0tE2Tx5t8+TRNi+cuJ4t5drm\nPMErDRw87li7liZ1bQviIYfQcpttaCmTvaXTxoIo6yJLgchjIM/zJ7yzJ5Jd1xoU7jNnDuWPYO+k\nmppodRBtGhr8SWfktmeeSWONtjZ3OQ8Xvb3uMd/HH/uz7e+6K4UjjR9PJfM6OymMp6uLJpqLUd5j\nqBlpAnF3APMALAKQBnAPgMPtjfJxMeUvjPziZDI0GI2TZKMYSH/sUaNIQAVdw9ix9GBk17kXXijs\n3FIg2mLIvoYoFkTOmHnccf6YTuln7nI/5HNJF4oxY/wWxHyRAtF1n4z90KqsjC4Q//Y3k7Rm5Uq6\njxdfpFnMYmfPlOeXAnHrrWm5zz7ZApEFGD/4OdC8GBbEffbJXWw2iChJai65BPjf/4133I8+IpcQ\n7jgBtSAqiqKMFOIKxHKFxzy2QBwzhsZnLBClBZH7NZ6A5n5UxiDW1xuh1dVF29gCkc8F0DnYOBI0\nYVtXF2yN23dfYO+9jSjNNbYIGhc1NgYLRMCUgpP3EDb252zztucYl8AAKBZzq0HT06mnkgXxyisp\nI3p9PRkA4mTzj0pYbfBSMNKGQBsBWCLWlw6+5qO+nr64cVxMefBs/1Dq6oojEPN1Ma2ocFsRZSmP\nri7zQ95nn/yvsa+PZqgaGtxiyF5nC6LLr9wWiDYyoYurfW2B6HlUU1GmGg6qoZiLfAVie3v2QyFI\nIALAY49RgpaFC93pqAshKAZRCsTRo6nd2JW2u5sszwDw1lu0ZIHI++R6iB95JHD88cW5BxdBqa4L\nhdOES4EY9xzlGrMyktE2Tx5t8+TRNi+ckRZTbscg8phJWhC5P5s6lYq8c14HFj92DCIfr7vbbUGU\nLqY9PdkC0e4zw8bZvI+8hyh1EG1yCUR2c83lYsrhJamU23NszBiTb+Tmmylj7Isv0n6rV9OY9E9/\nove5bnQxmTePxilJWiYTruBXciJFAz788IlIpaajpwf49a/HYqeddvzEtM1fUHt9l11o/bHHWtDQ\nQO9nMsCqVS2Dfsnh++daj7P/vHnA+PFmvboaaG9vxvjxZvuamubBHwG9X19P22+6aQtaWuJfX3Nz\nM1atAiorW/Dcc0BVFd2/fL+/H5g1qwXvv0/309pK78+dOzfreNtuS+ttbS1YsSL7/idMaB5slxa8\n+GL29aTTtL50qbmf+nqgs9O0Z09Pfp/H66/T/nV1wMBACx5/HDj00OztBwaAjz8251+zBrjyyhZ8\n9avmeB9+aNrDdb5Ro1rwxBPAYYeZ98mFJPr1utaZlpaWwTIs9P6CBS2DD0pqL/l96eoCliyh9Uce\noe3feIPW6+poffVq9/cHoPb43vfMeiHXH7T+8sulOf4FFzTj3nvp8wKADTZoHuzs8v+96Hrp1+cO\nBq6Uy/WsD+uu57mul3adKZfrGW7rQDMqK8vnegpZ5/FSTw/Q00Pv9/c3o6mJ+u9MBujupvvl/rui\nohl77mmOt2IFHW/u3JZBr7LmQaMCvd/V1YzqamDFClq/8ELavre3BatWmfN/8IE5PkDjJdlfPvdc\ny+A4NPt+SBi2YLPNgAULaPzoep7TdtQfu9rj44+Bpiaz/sEHwJZbmvWFC6k9SPDR/j092ddDYrcF\nHR1AZSVt//TT5v3eXmDduhbccgvwhS/Q9bS30+exZk0zGhuB88+n+6+ubkY6XdzPnz73Ftx5J3DS\nSfkfb+7cuWgddO9btGgR1if2BPAPsX4BshPVeFdd5Xme53l1dZ7X3e1FYs0azwM8b/Vq89pBB3ne\nued63r77RjtGEIDnbbNN9O1/8QvPu+ACs7711p73+uv+bSZOpOMCnjdunOfNnk3//+IX+V/nvHme\nN2MG/X/EEZ73l7/43//1rz3v8MPpPFVVnnfOOcHHWr7cXN9PfpL9/uLF5v2//z37/W99i9675BLz\n2uOP02u77+55d93leQccEPsWPc/zvJtuouO0tlI7rljh3u7GGz3vpJPM+owZ2dd63HGed9ttwef6\nzGfoXNdcY15ra/O8jz7K79pdnHmmacuzz/a8bbel/997z2zT1ESvXXSR2RbwvPnzaXnDDbQ84QT3\nOQDP+81vinfNQbz4Ip2r2PB9Xnyx5334oed1dHje8ceX5lyKoihKMhx3HD3H7713qK+kOEyfTvfz\nqU953rRp9P+ZZ3re3nvT/5//PI2/dtzR8/7xj+w+DPC8X/2K/n/1VdPXf/nL5v+DD/a83XYzYwX+\nmzrV8/bZh/4/8kgatwCed+mltKytzb5ewPPS6ezX6+rMcaZM8bxrr3XfbyYT3g//7nee993vmvWT\nT/a8664z6/ffT+PSJ5+kNvrlLz3vBz/IPs6UKXSeHXf0vPp6GgNI7ryT2sjm73+nNgc8b+lSeu17\n3/O8q68OvuZ8eOYZOscHHxT3uAgxrKVKINKGkhcBzAIwHUANgGMBPGRvxO6XcRLVsLlYmqnZLD+U\nLqYAmb5loVDA77JYX2/M+YVca3e3aTtXkpr+fvN+f394DKLctxAX05T4BvO5p06lv3x9wGW5jjgu\npltvHT0GkXFd45gx/tqQhSKvsb/ffDdku/P3Q7roAsb1krcNuxdO8FJKiu1aykgX0w03JDfqUp1L\nURRFSQYuizBSYhDZ6CNdN19+2YyZxoyhfj6VcrtlLl1KyVuA7LrGTFcXjRNs9y1H5AMAACAASURB\nVNDly/1Jamw30SD++c/s12R5rNra4HFWf78/9MOmsZFiL5mBAf9nzflGOKN8UOJBPn9FBW1jxyD2\n9JhQG8m4cSb3xUaDAW2yREix4OvhMidJMNIEYj+A0wA8AuAtAPcCeNveiH8IcRLVuAQiC6KhEIjy\nhz9xokm/y9g/EN6+mALRlaRGPmSixCAC5FttwwP2GTOCBeKmmwJHH21ek9dWU5P/vcpEKHGymFZX\nU2mRY481r+USiD//OdUNOuWU/K41CNnmQQJRflb8/fv4Y/9xZLazMFas8N/3cIO/b/I+XQV5w3B9\nz5XSom2ePNrmyaNtnj/ct7nEUhjl2ubcL8kYxCeeMAKRl5WV7nveaCMzJpHCS04Yd3b6YxAl5OoI\n/OMf2eOCoHGsHGu89lp2iQ42tNx4Y0vWvv394eOPXDGIPM7nmtRBk/7ytVQqe9zX2+s2ZowfT6Jd\nTuqXIgYxnaYstJxZPglGmkAEgP8DsAWAzQH80rUBf1k5g1MUbIG4ejXwn//QFybqMcKIM7sls5gC\n9IWxha78ckqBWMishhSIQUlq+AfU2Oiuzyi3ZV56Kfv9VIruc7fdgn/Ml11mCr8C/s+1kBmcTAb4\n7nfp/zgWxOpq4M47gfvu8x8r7LPdbz/gBz/ILcAKwc5iyjNR8qHN2KKIOxA+RlAdzUmTkrG4xe3k\no8Izg3Im9IIL3CVkFEVRlOHBhhsO9RUUF1mUXsKZwfn9IAuiRI4TeezGRo8ggSjHmlHGLZ/9rH8s\ntsMOwOWXm76WLYhLlwLf+U72/rkE4ujR5EH3q18B//3f2eMy9hTMZUFMp4HrrgN++1v3NrLuo2Tc\nOBLUcnK9FAKxry/57/JIFIg54WxFYYN/G96OvzTstlCsL0IcgWi7mLquoa/PbNPbW3yB6HIxTadJ\nKFx8MfCvfxkXUw6QleRyMWWC2jedzn5oyGsr5HORDwLXfTIugWiTSyCWCtnmtgXx+efpf9nu/F35\n+c/p/mfMoHW+J35/zZrSXG9UttqKvl/FRv5WmMrK4PpOLlzfc6W0aJsnj7Z58mib58/ee9PSdhnM\nRbm2+ac+BUyeTOJN9u08ucv3GWRBlMycaf7n8QC7ey5disGENH7kGDKKiylnEZXY5eKMoaU563PK\nJRC5jMXNNwO33gr84Q/ZFsR33qEyVlVV4S6m3/gGfV/iCkSbmpriu5j29RU/230u1kuBuOWWtIwj\nELmumqwBA4TXIIxDXIEofwCuL6N093z/fbN9ISlyo1gQa2uBCy8kER7VgrjnnsHbcSFXG57hktgW\nxNdeCz5uGPJB4LpPJqpALKV1MAryodLfD3z72/S/6zuXStH27MbA9/Tmm7QsZmxkPvD3q1TEKX2j\nKIqiDA9GSkz5PfeQ2Onp8Y9NeMwi8zPkEohS8LBArKmh41L282ykBZHHEHYODMno0dkCkUM6AON5\nxn2vyxsubAw1bhyNNeXnawvEjg7gnHPoesNcTGV4UVSByPucdJJ5rVQWRBWICcBm2jgCkYNgefvD\nDqNlT8/QC0TXl5GteQz/eGQwb1yCYhCXL6cgZPlDHjXK/NBdvvzyxxdWN4/dgG+/3Z9AJUwgVlUZ\nwZzPZ2NbEKMKRPnj9TyayfrrX43FLklkm8vrSqfd3zW78zzsMGCPPfz1Nl3bjTQKEYjlGrMyktE2\nTx5t8+TRNk+ecm3zigoa6/T0+MdRVVUU9vSTn9B6ZSWw116mNl8ueJzAFsRdd3VvJ40MPN676irg\n1Vfd4UIuC6Icg7AFkY7bkjVGjeJiumCBv++W4zIqG2au1yX+Bgb847k4ApGR76lAHMbwIDeOQNxx\nR1ry9vwF32qr4C/Cyy9HH1DHjUGUx3VZEPv63Jmj7rwz+nlsglxMzz4b+Pzn/T/kiopwtwPZ7ty2\nLviH9vWvAw8+SMVen33WndlKzqCxm0LYzFYQt90GfPQR/Z9LINquvkx7O7krAMB778W/hmIiH1zp\nNLmoPPxw+D6XXALMmWPW+fulAlFRFEUZboykvqu21lgQb72VXquqAvbfH9h4Y7NeVeVP5OfiiCNo\nyeJjyRJKHHjIIe7t5biOx3tXXglsvz1lcrcZM4bGQ5de6g/hkDGIsqKA7eWWK4spC8DFi81rUiBK\nF1C2ILoy8FdVme9IoQJRXUxHAEHuiy74w2ax0NsL/M//kHtkkID44IPo11JMC2ImQz8+9knfcUf3\nzE5cglxM+Ycuf8icZAZw+/L391O2rcbGcDcI+RllMsCnP00Cx+V2wD/ujg5gp53o/3wE4qJFwE03\n0f+jRgVbXe1kQfIhJt1bzzor/jUUimxz+VDp7aXP8OCDox/r+ecpYQuQO531cKcQgViuMSsjGW3z\n5NE2Tx5t88LYZhtg9ux4+5Rzm1dVUV/c3W1cQ3k8xMswUSXhMYwtPqLsX1VFwuiznw3epqmJXEAv\nuMCIODmO4CQ1JAybY1sQpYWQkeNp+T4nqXGFR8n7jSsQt9oKOPBAs16qLKYqEBMkjgXRFois5sNE\nZpwZqzjZGXPFIMrZkOOPp4yc++0X/fhBdHWZH5ucheEHlBRtFRXhQeGZDMWC5soSKTPNep75Ebtc\nTBn2vd9pp/BajFFw1ZhkwmIQ77rL/B+3Yyo28rq6utztFvZd3W03agfAbZUeKXzucyb5lKIoijIy\neOMNEokjhYoKKq9ge3XJZVQxESQQoxgtKitpUpXzergYM8bEM3KYkBwb2hbEuAKxstIvzoDg8Ux1\ntVv82eewt/E84Nprg9v0rbeAAw7wn6cUFsSoor9YqECMKRD5S9PXR7MJYTMFcQRiIS6m9jXIL/vt\nt1NSkgcfBJ57zmRwzYd164xQkLMwLBDlLIx0MQ2qgxjlnqUAl7NOuQQiYLJb5cMZZ9Cyri47nTQT\nJhCvv978X0ib54tsc76u+np6+LoetiPJ/SZfHnkEOPzw/Pcv15iVkYy2efJomyePtnnylHubc4Zt\nHn/xeKpYFkQ5tuGkMqed5t8mSgI+KRC5XqHt8SZjEO3xdC6BCGSPsex7l4aNoAz8YQKRaz9GtQrK\nHBzFQl1ME6YQC2JvL31YYQIxjlWwmFlMXZkzm5rIDF6IC11bmynCKtvOZUGULqYuomb3lO0r7zvs\noZGPQBwYINdSANhiC1OPp7Y2P4FYTvB1jRlDD+ihzqqqKIqiKEr+yIlfwD/2AqKPKYMEomvS2Hbn\nLEQgytAkIxCzx+S5spgC2TWd7Wt/4w1zvdK4kcmQ22suF1O2akZN8jh6dPFrKKtATJhiuJgOhQXR\nJRBtC6LreGFix2b58uwyFevWmZkkl4upbUFkN4KgGMSoApEfHD09pk3DLIhPPUXLsWNzxyC+/TZt\nc//9pvZfZ6d5EOYjECdNGnoR5mrzUaPooVXItZ17bv77jnTKOWZlpKJtnjza5smjbZ485d7mS5bQ\n0o5B5DFS1HwBPIaxx1NybMP/c/krJsq4tamJxpOAEYhy3G3HINpj8ijCyK6lbZfokCJair+77gKm\nTyeRaFsQpRssj0GjCsTGRnOvxUIFYsLEEYg8AyEFYm2tOUahyTviWBtdLqYsYG+4ATj5ZLcIqK6m\nH0ZQ4XfJJpv4faoB+nGwW4OcheHA3d7e6BbEOC6mPBPT3R1NIHJyoCgWxK23Bs47z38tnZ3mQcjZ\nwlwECcTPfIbar9yoqyvcgjiS4jgURVEUZTjC4xIed+Tbr/MYxk5kKMc2PO6yLYgffpj7+GPGGHdL\nHsul0+aYixf7YxDt8Wl7u79uogsWgJyl3zZuyBrZ0rjB5Teef97ffqmU/zr42qLWgVYL4gggjkC8\n+WZa2i6mnBDFJbqiCDHeptAkNWxBvPpq4M9/Dq51F9WK2N8PvP++/7WgJDXM/fe7LYhBdRCjCMTq\nardADEt9fMIJtHzhBeD73899jnfeMT+83l66TxaI+cQgjh5d/NmjuMg2Z6FeXx9sQYxi7W5rM22r\nZFPuMSsjEW3z5NE2Tx5t8+QZLm3O4w57PBTXgsj5JezXp00zr9kWxChjSXlcVwxiRweVouAYRHtM\n3tFhDBNBsAWRS6bZY0vbgsjn2GILWvb1hccgdndT0sNTTgm/DkYtiCOAOAKRsyTZLqZAsJupLM8Q\nxI030jKOBdIlTOyMSUGzSXHcTO0ZECkQ5Y9MZhnNVQdx3jwjHqMKRP6hdXebY7a2Bt/jXnvR8rnn\nch8foM+JZ4jWrKF1torm42LKsX7f+lbxA5ULob4++0HIRBGIY8ZoMhtFURRFKRfsWETmz3+Otn9Q\nzCInCFy4MNiCGMVTSgrE886jpe11t+mmZqzkEoi5LIgsEKurgd/9jupmS4LiC/lcFRXhMYhdXRQ6\nFNWQ09AQ3R01KioQEyaOQOzqolkBO4spECwQWViEZT667DJaSn/nHXeklLpB2AXapQWRf3RB4itI\n8Bx0EHD++Wadf5DSvbK7229BtAUivw4E10Fk909bXAVRW2vcBbq7zUMkShbTqCUL+vrMcZcvp3vk\n9g1qr7vvBn7+82xLLkAPxK4uemjZvvFJ4YqfsGMVlOJS7jErIxFt8+TRNk8ebfPkGS5tHiQQo3L/\n/bTcdtvs43LWTx4PsQWRx1Zh9Q8Z2zIJZI+Ja2t5bN2cZVBpa3MfQyJdSE85BZg61f++DE2S3m98\nHbbBwWVBdNVbDCIfC+Jf/hJuTAob85aK9Vogyhp7ufj4Y2DjjbNdTMOOw+IqTCB+7WtU+F0KxFdf\nBU4/PXgfO8FLHAtiXZ07pu4f/6BSGAz7Wst6fkEupvJL7XIxlfC2tsgNorHR1M7p7jb79/YG/1j4\nQXfJJdFqEPb1mSDkDz7wu1EECcQ5c2jJgeKA38UUSH62JwgWsXa2M0VRFEVRhh88RuP+PI6AkXAc\n3he+4LfqyXHCmjW05LHNn/8c3euNjRXSaGGPieW57LF0ayu5oIYRdfKbc1/Yxo2LLw4XiF1d8QQ4\nWxCjtlF3N3D00VRPMQi1ICaMq8j9woXAPfdkb9vTQz8Ol4upfRzPAy6/PJoFsaeHvkxhReVt7Pg7\nO4spEN+CCJAJnWGR9M1vmtdyuZgCbgui9OWXls4oFkQpEHt7jT98UNFQzyPBDQSLYd5fXhNbEOfP\n9z9og9qL75NdhHlbwDxEr7oq+L5KjWzzXXahZZhAVNfRwhkuMSsjCW3z5NE2Tx5t8+Qp9zaXYTBA\n/hbEIFzjKxZq+Uwyb721+V+OGY85Rp4rOwZx7VpKOBiGdDENI532iz85bg5zMY1rQayqonF51LJy\n771Hy6VLg7dRgZgwLhfTiy4CvvIV4Ne/9r/e3Z0tEINcTDs6yNearVK2dc8+bpBADDI3uyyIHDvH\noiyfGEQ5S+P6ItoWRCkQL7rIXAsQbEHktojqYtrQAKxaRf/39gIzZ5r3cj2kwu61tha47TZzTZyQ\nZ968aBZE12fDopwfVuecE359SbH11vS9UBdTRVEURRn+8KQu16bO5YYZF9c4gc8VJ6kiI3Na9PWZ\nseCNN4ZbENeujW5BjCIQpfeby7gBuAViXAHe0BDdzZQttP/v/wVvowIxYVwCkb8UZ5/tf50tfUEu\nplIgsjXq7rtpmcuCOGqUW0wF7WcLxJoa4D//IUHC+wRZEMOycspMUa4fmm1BlD8y3teVpEb68kur\nalQLIu/T2+v/vHI9DMIsiADw2GO0nD+f0jXX1JgYRCaOQOQHNl/XrFnh11dKXPETYRbEqK7WSjDD\nJWZlJKFtnjza5smjbZ485d7mPH6qrQWefhqYMMH/fr75D774RVq6xleFiFA53vvjH83//gQx2TGI\nUSyIMgYxF0Heb3LMbIeNxXUxBeLFIXK97rDEQioQEyZMINr09NAHHiWLKWcv4roy+QrEIMujy4II\nAC+/bL6QbL20cdX1YyEnLWd8bwcfbF6zLYgrVhjRxvvytQTVQZRW1agxiHw9fX3BLgEuwsSwzYMP\nArvvDqxcGc2CyMl2jjnGvMb3w20XJUtrkoTNskV1hVAURVEUZWiRgovDaiT5egrZsY2SCROAiRPz\nO65LYL36Kok/OSZxxSAWy8W0sjI7Sc2WW9L/duJHOf6O62IK0Ng1ai3EtjbyUNx11+BtVCAmjEsg\nuiwpmQx9kUaNCs5iKvdjd0smTCCGuZjGsSACwFNPmXizoPIKLsHDQcpc6xEw4oZ/1Ok0XaMsyvrg\ng8Cpp9L18A+Uf2RBdRBZIEa1ILJYGzuWrlu2Sa79+V5ZqH7wAV0Xz9akUsBmm5ntt9zSLRBdgvqh\nh+j/M880r/O1SZE8VLjiJ8IsiCoQC6fcY1ZGItrmyaNtnjza5slT7m2ea4I9al6B3/7Wv87jNim2\neCK8pobGSHE56yxT4kKy/fa0NGOSlqxxb2dn9DqIuQQiZ2blc8h8HtKgYQvEfCyITU1mrJmLtjYa\na774YvA2KhATxiUQXUKtt5e+gFIIShdTO0mNNJ9PmxYeg5iPiyn7UTOu2ZcgS6hL8HASGMCIR96f\nBR3PoPBDhwXkggW0rS2KgiyIfPy+vugupgD5oNsCMReplP+zefppWs6bZ96XD9HNN6dYRCkQXVZI\n6TYg3Tj22w/48pfN92IoBaKLMIG4xRb+IHJFURRFUcqTXOOLqALRFl88FpXjhEIT4Fx1FXDkkcHv\n8/hxgw2yx+Q9PbndZdlYE9YmZ5wBfPvbdK6uLhoHyrG0HIMXw4I4frwp0ZaLZ54BjjjCnMuFlrlI\nmKgCsaODPjS5fZiLqWTixNwWRFsg7r03LcMsiPKLwpk95T5BP5Ta2uzjPvKI+fJfeaX/ON3dlGHJ\nnkHhHxUn7uF1Fo65YhCjCkS+rnwEIuAXxPzA5FTCqZT/83/ySVrmcjGVglqK7WnTKO6UP5uhdDGN\nWwfxmWeA558v7TWNdMo9ZmUkom2ePNrmyaNtnjzl3ubFEoif/zxw8slmfdNNaSnHmMXIkBo2HuIx\nycSJzVkGlZ6e3OeX484grr6aXDirqqhawckn+8fSUpi5BGLcNhg/3iSfycW995Jldtq0YAutWhAT\nxiUQXVavf/2Llu3twCuv0P9hWUwnTAB2243EVph4BNwWROnG6sJ2Md1kE1OXb/58WsqSFRJXzcRT\nTvFbCvkchx8OtLSQdSmd9n85+Qc5Zoy5nu99zyRm4R+q3Z587qgxiNzG+QpEaQHk6z/hBFqmUn5L\n6yWX0FI+yGprgYcfBg47zLwmHyR77ZV9znJwMXURZkFsaPALY0VRFEVRypPddiOLm4s//cldrs3F\n1KnADTeY9TPOoKUcJ/A4rBBsgfjDH5r/ecxUX589IR/Fghgn3pK37ez0WxDnzjXbFMPFdNy46AIR\nAPbYgwxKnLXfRgViwjQ2mvg7xiUQx40j98Fbb6U/IDyL6axZJA7PPtstQiWuOoiuGi0SWyAC9OXa\naCMjXjbayL2vq2ai/T6fe/Jk87rt1sr/c+Keqirgmmv8P2S2IkpffikQowgoWXy+UAvi1Kn+99iC\nWF0NnHYasNVW9Pq77/r3B4C//tW8Jh8cLpFbDklqXPETWuaitJR7zMpIRNs8ebTNk0fbPHnKvc3/\n8AeTLM/m6KOBAw/M77g8brHrAhYKH+P662kpva94TNLT0+K0IOYSiHGuj8/V1eUeSwPJu5hWVJAw\nb2w0SS5tVCAmzNix2UGkLhfT9nayyHEBdMD/YUnRNTBALowczyUzJrlwuZjmIxAB+kKuWwdcfLF/\nRkhiWxBZEN97r7lePof8UdpurSyM6uvpPdcP1FULMa6LKW/z/PNGILILRBTsz8a+vv5+esj+5jfm\n85SfFwvEL33JvMbtx21mMxwtiIqiKIqiDA8qK0sTk8bjFjlOKMZYho/B42hXRvra2mwPtyjundKY\nkQs+V2dn9riWKYYFMaqLaX8/tU19PY25g2IQVSAmjMua5rIgchalRx+lUggsIPgHJL9MLDi5Jo2s\nueIiSCBWVER3MWU4ic6hhwaLKPueu7vpS3nooeYYAG0j3Qrsc3I7ZTLB18OJaqQvf1wLIsPJftJp\n4Pzzo+8nBbEt1DMZ+qupMYJ3hx2AL3zBbMNtINuCjxc0q1WuMYj8gCu38hsjhXKPWRmJaJsnj7Z5\n8mibJ8/62uY8Liu2BZHHWLJWNsOvTZvWnGXA6OjIHf4ydap77O5CWhDTaQrRsimGBTGqi2l7O4nm\nigoao5WTQFyvbQmu+MCgJDUNDfQhrlzpdy8F/F+m9nZ/MdFcFsTOTtreFoh1ddGzmMr74XMGwYJp\nYIB+UOvW0fntLFArVvj92+1zHnIIBTen03S9rnO6LIhxYxCZxkZjfTzqKPeP2oX8bOzPIZ3Otn5K\nP3SAfuSA/2HJx+P6Oa5zAoUVlS0FdXX0+ZabZVNRFEVRlKHHFnJAcZLUMC6LnYxBlMKsp4fGU8X0\nepICcWAAmDGD1uWYslhJaqK4mLa2mrFiXZ0/S76kr0+zmCaKSyC6ZiFWraIvx+jRlP3o0kuDBSIL\nLiaXBbGzk44rhVRbW7hADLLY8WthP6aaGvoCHnkk1Uxcu5auMZWiOjW//S3dY2+v36XWNsVXVwNf\n/KIRWUEC0Y5BjOtiyjQ0GBfT+nrg4IOj7RfmYtrXFyxuGbbEys+7rw/43OeA2bPd+/BMU1NTtGss\nBUF1ENW9tHSUe8zKSETbPHm0zZNH2zx51tc253GZ9JA691yTCLFQeHzssiC2trb4ktSwda2YSIHo\neXS/775LdcSZJF1MV640LrJ/+hNw3HHu7dTFNGHsLwFgrEyyNszFFwO/+pX5oj72mN/l0LYgyi90\nWJIaFlf19Ua8DAwAS5bQjEWYi2nYLEzYLEN1NWWPeugh4NVX6Ye/fDm9V1cHLFpE8Yv9/f57dFkt\nWWDncjGVcLbUOALxgAOAr33NCMQ4syhc2PXUU6NZEG0qKoBrr/U/zHL9UFkYlqMFUQWioiiKoigu\neKwzfrx5raGBEiEWiue53UWDYhA7OrLrNBYKn6u/n8aUqRRN9m+8sdmmWC6ma9cCzz4L3HJL8HYc\nwpYLFYgJ47Ig8rrLksjCj+PWmHxcTFevptmFxkYSKCwQP/qIlrNmlc6CKJk2zdTA4RmjG27IvkfX\nOXMJRHYxlb787F8dRyA++ihZPFOp/ATi008D113nFoi5LIiAie1kcv1Q+cde7JmvOATFIKpALB3r\na8zKUKJtnjza5smjbZ4862ubs+dXqUJR+LguC+KsWf4YxFJaEPn4rvsspgXxRz8CTjrJ/x4bZfjY\nUcRnPm6uhaIC0RJh/KWQgmDTTYF//MNYmjwv3MVUfqGDXEwnTgS++U2aTUmljEBsb6fZjE02KY1A\ntMXV2rUAPwdZIK5bl21BdFktpUB0WeHCLIg8cxMHvq84+0VxMc0VgG1bgXMJxFQK2HZb/wxcOVBf\nn7wPu6IoiqIoCuAev/HYrq6u9BZEef6OjmgCMR8LIldJ4Jrk0nV2ww2BpUvp/6gCMUq5j2Kz3gtE\n243TJRDr600A6z330JcqjotpUJKauXPpyy8FIlvIwuoVhmUx5XMGYQubjz82/s/yy2eL4CAX076+\n3BZE6cvf1UX3GzdJDZDfjJa0/tmfQ08PHTPXdcQViADw+uvJ/5glQXUQ1YJYOtbXmJWhRNs8ebTN\nk0fbPHm0zUvDFlvQUo67+P8lS1pKbkGUxBGIca13VVVkAOJxYGsr8Pbb5n02lkjr5LPPZtfrBowW\n0CQ1CeISYS+/TMtMxli/ZOKZxkZyDy1GkpoPP6SC9lIgsvhwiVcmKItpPhbExx93C8TKSr9lrasr\ne19uv7Aspi4LYlMT7RdX8OUjEOVn3N8PHH64KSDb0xMtfXM+ArEcmToVOO20ob4KRVEURVHWR8Jy\nM1RU+CfyS2FBlEQRiIsXxw9tYsaPp7weAHD88aY+OmDGxu3t5h6nTHGPLYfCegis5wLRdjGVX0wp\n7Nra/AJx1Sr/h5WvBZGP57IgutxfmWLGIAImODdMIHZ25p+khn35PY8S44wZEz+LKR8vLjU1dO0A\n8Oab/rbu7o5mUauqIjHPDEW64bi44ifq6oAf/zj5a1lfWF9jVoYSbfPk0TZPHm3z5NE2T56ZM5t9\n4+WhtCA++ijVxZ4+nV6L6/EGUD30BQvo/0cfpSXrCtYMra2mnFpdHYlBm6GIPwRUIGYFojJsNcpk\n6MPhzEsNDSSK5CxIIWUuRo2iLyj/KKQF0RaI//wnWRyDspjOm2euPQhbIM6aZbJuyveqqvzH6erK\nP0kNw4G5TU35xSDmU6y1poY+EwD44AM6Brd1V1e0Y44aRZMETND9KoqiKIqiKOG4BFcq5R8vd3bG\nj/2LQ5hAXLYMeO21wo4/e7aJNTz6aFqyVmAhuHYtxSsCJBBlrCLT06MCMXFsF1O2NAEmdo2TzvCX\niE3BQQIxrgWRBaJ0MeUYRNvFtKWFvrRBAuWVV8y1B2G/J+u0yB9sFBfTKAJR1kFctw6YOZOS/uQT\ng5ivQGxvp/9vucUvWt9+O7goqWTDDf3XGiXz6VCj8RPJo22ePNrmyaNtnjza5smjbZ48Cxe2+MbL\nXV3ushjF4q23ggViMZg50/y/cCEtWQByRv+ODqMZwiyI6mKaMLaVjsXCr39tLIhr1xrzL2AKp0u/\n6DCBGMWCKK1a6XSwBZGFTZAg+8UvaBnHgrh6tfn/oIOMCEylsgVimAUxShbTdeuoLXm/pFxM2YLI\n1/CZz8T7sdliPVftREVRFEVRFMWNy0AwaRKyBGIpLYhAaQXi7rub/196iZY8lmSDlIwvrK0lAel5\nwJ13Gu2gFsQhwHYxlRZEFoitrcb8C5gPSYqlMBfTKBZEKRDZgugSiLxNkEDkawsTUrYV8Cc/Mf/X\n1VG9QYB+vFFdTIMsauw6y778LJ6rqpKLQayuNhZEgGZifvxjY22NAv9omeHgYqrxE8mjbZ482ubJ\no22ePNrmyaNtniyeBxx0EMUgptO0XiqBKA0XuQTiUUcBhxyS33k+9ansNlUplQAAIABJREFU13gs\nyePS3l4jEFMpGlum08Bxx1HeDEAtiEOC7WIq3Q37+ui9tja/QAw6TiEWRHkdnKTGzpwJmC91kECJ\nMuthbyPLdQDGnF9REc+CGHQ9sn1ZPLNATKLMhW1BZCtsnCQztkAcDi6miqIoiqIo5Yhr/MfGkpoa\n4LbbysOC+MADwL775nfsCROARx7xv8b5LHhcamcolW6mbBTSJDXROQbAmwAyAHa23rsAwPsA3gHw\nuVwHsq10UgSsWwdcd122RZCRVsFCLIiNjX5LJiegCdsvqLh7FIFoCyN7VuLqq2lpC8Tu7uxz5hKI\nfF/syy8tiL298d008xGI9fX+OEs+ZyECcTi4mGr8RPJomyePtnnyaJsnj7Z58mibl4499jDlxiSv\nvtryiWFk0aLyEIhB4+2ofM5SIttvT0uXBRGg8WZrK/2/yy7A889rmYs4vA7gSABPWK9vDeDYweWB\nAK5DjvuzXUztpDBz5wan2ZXWvUIsiBMnZtfq4wQxQftVVblnX2TG0CDsL7ptQbTFLUDt5KoZGMWC\nKNvUtiAm4WI6aZLfxZTvIY6PuVoQFUVRFEVRCmfOHOC//iv7dZnRv6GhtALx+uvNOW3k+LC2tnCD\nwAMPZL8mLYhyHF5XR6X0mO9/Xy2IcXgHwHuO1w8HcDeANIBFAOYB2N2x3SfYFkT+n105H344Pwti\nnCymdXV+ocriI2y/IHESRSC6zu9CWhDr690WRL7vXC6m7MvPbcn3m4RAtH9UhVgQFy8GjjhieFgQ\nNX4iebTNk0fbPHm0zZNH2zx5tM2TZ489mj8ZC3d1lVYYzZhBSy4PJ+FzTpqUbeHLB5fIbWujYy9Y\n4D9+YyPw0Udm/dhj1YJYDDYEsFSsLwWwUdgOdowcfzFlQGpcgdjenm2FCyp4D5jZCc+jY7LYclkQ\n2WoYJBBl4G0uFi8253dRUWFE1KhRuV1Mg1xepQVxKJLU8A/9Rz/yHyOOQOT7eOwx4MEH1YKoKIqi\nKIpSTCorjWVt7drSWhDZjZML2Ev4nBtvTMupUws7l+seWluBM8+k+txyPLrBBqYkBkBjT7Ug+nkU\n5Epq/x0a8zihksnlYnrssVQ8nrEtggCVkzjnHLPOAiKTIaUv67ZEsSCyGJPumqW2IG6yCS2DRJfM\nYsoWxKAkNX19bpdNbhdZB5FdTHt74wu+fKx2POsyaRJwxRXARRfFP1ZlJV2r7QZczmj8RPJomyeP\ntnnyaJsnj7Z58mibJ89LL7V8IhBXry6tQGRR5hrL8Rh+2jRacnm7fHHdQ18fcOON9L/UHJtsQvUZ\nme7uobMglqsd5LN57PMhgI3F+rTB17I48cQTMX36dHgekE6PxWOP7Yj9929GOg2sWdOClhbgllua\n8fe/A2+/3TI4i9AMgB4ae+0F7L67WX/7baCvrxnt7UBdXQsef9y4J3zwQctgQUyzPUHrb7/dMpjJ\nlM7/1lst+PhjYNasZvT3m+2Nu0PLoBD0H6+5uXnwdbp+3t7e/7XX/Of/6CP39n19zYM/oBYsXAgs\nXNiM7bbzH6+6GujqaoHnAZWV2eerqQGee64FwFw0N1P7LF3aguXL6fipVPb1ha3TDzn8/uz1efNo\nvba2GWecQe+3tAD77mvaM8rxamubB/3CW7B4MTBjRrTzD9U6Uy7Xo+u6Xor1uXPnltX1rA/rc+fO\nLavrWR/WmXK5Hl3X9VKsv/fe3MFyc81Yswb4+OMWvPUWsPfexT9fKgUALVi5ks4n399rL1qvraX1\nrbcu7HxTp9I60DK4pPG959F6U5PZvrMT6Ogw27//PnDbbc3YddfiPb9bB82nixYtwkjlMQC7iPWt\nAcwFUANgBoD5AFyFFDxJZaXnpdP0/803e943vkH///WvnnfwwZ53wgmed+utXigtLZ63776e98EH\nnrfRRv73LrvM8845J3sfcgj1vCefNOvbbed511zjeaed5nlXXul5Z57p3+ecc2i7DTZwX8fVV9P7\nYTz8sNmmu9u9DV/bsmXmf8DzzjvPv11nJ71eU+M+zgEHeN7ll3vehx/S+uGHe97993ve+efTfr/8\nZfi12myzTe77s3n0UdrnD3/Ifo/vKwpjx3reJZfQ9ied5HnXXhvvOhRFURRFURQ38+ebcdmee3re\n7Nme9847pTnXiy/SefbbL/u9gQF6b84cz1u9uvBzffCBfywNeN748e4x6Hnned7++9PfRRd53gUX\nxBurxgUhnpapIgq2pDgSwBIAewJ4GMD/Db7+FoD7Bpf/B+AU5HAxBfxupum0cZVsbKS6iC4X06Bj\nuLatrg7PYipjAF9/PTxJDR876HhRYhDZtRTIbbJmE/wxx1A9F1cMIhCcEbS6GvjBD4BDBx2DuX3Y\nVTUV89sXFC8ZBvtt57OvpKaG3B0ACi7WGERFURRFUZTiIMeYq1YB771XOhfTXQbNS7NnZ7/H+T4q\nK4Hx4ws/lww7Y9asAaZMMflAGC5z0dhI42XWJ5qkJhr/C3IlrQewAYCDxHu/ALA5gC0BPJK9azay\nhEFfnxE9o0dTUpWgJDWSmhra105QA7gL3tvnl4QlqWEBGFRgPiwZDrPtttGT2XBb1NfT/dkCkUVS\nkFhi4bh6dQsA0z75CsR8fiDFEojV1Rh0faDg6XIXiOxaoCSHtnnyaJsnj7Z58mibJ4+2efI8/3wL\nAGDDDU120VInZwkbDxeanIZh485jj/lf7+oCmpr8r9XW0hizvt5facElMktNmQ9zS48tEG0LYmVl\nNIGYTrstiFGymErCktSECU2+/mLCIojbyBaILFSDBCu3JR9HJqkB4id6GTs23vaAEZWFzr7U1BiB\nuGZN+SepURRFURRFGS6w0WDKFGDZMvq/1MIoSCDGqQqQCza22EaR9vbs+6urIwtiXZ2/EkCpLKlh\nqEAUAlG6mI4eTV/Q99/PLRDZxZTLONjvFcuCmEsgFtuqxV/q2lq3BTEX3JYTJjQDMO0T9GPJxW23\nmYdGVIppQezooP87OsrfgshByUpyaJsnj7Z58mibJ4+2efJomycPJw+sriaR+PHHpbcgJlk+YuZM\nWqZSpuqAPZasraWxck0NtQOX41AX0yFAKnTpYtrYaCxGuWIQ2YJ49NHZ5mKXi6ksR8Ef+n330UwC\nl1Bw7dfeHn4dZ5wBvPJK+DZxkBZEIH+ByG1qWxDjCsSJE4Htt4+3TyksiGxZVhRFURRFUQqHx1W5\nDCvF4vXXqWxdEngesNFGwEknAaecErxdXR15D1ZX098999DrWgdxCAiyIEqzb5QkNVTKIrsWocvF\nVH7xWXwdcIBxK7VdTO+6C7jjjtwCsa4O2HHH8G3iwALOdhWNCu/X2dmCTIb8rRsa8heI+VCKGMTO\nzvK3IGr8RPJomyePtnnyaJsnj7Z58mibJ88zz7QA8I+pS8m22+Ye3xebm24CvvMd+t9l8OCxak2N\nPwGkWhCHgKAkNdJCFCUGkcXb2rX+91wzIS6ByJZMl4vp174GnHhidkxiErz7rhHLQVazoBhLKSw7\nOsgqm0rl72KaDywQXT8uNvdHQS2IiqIoiqIopUFaEDlr/EgkKPM/AHBpwupq/3abbVbSS3KiAjEg\nSU0cZHzahhv633O5iroEIl9HUJKaykqzHpQUppjwfcyebX60QaKI792GheCUKc24/34jojkzVBIC\n0XZzlcQR3NKCyJ9ROaPxE8mjbZ482ubJo22ePNrmyaNtnjzc5q7x2khi1qzg9yZNomV1NfDyy+b1\n3/2utNfkosyHuaUnyMWU+cpXch+D96mvB37zG/97uVxMWXSx4OjtdSep6e9P1oI4YYL5P9+so1Kc\nyXbhbExJCMSKCuDkk7OFO5DtDhyGtCACakFUFEVRFEUpFnbei113HbprSQKXsYfvubran0m1sTGZ\na5KoBTHAxZS5++7cxzCWsuxYt1wuphIWIZykRgpCz0tWIMovZi4LYhAsEFtbW3DggeZ1bqMkBCIA\n3HCD2888rgWxo8P8oMvdgqjxE8mjbZ482ubJo22ePNrmyaNtnjxPPtkCAHjhBVov93FWKZButttu\na14fCquqCsQcFsRTT819DFkOwiaXi6l9LV1dxsWUt2MhlZRAnD4d2Gsvs16oBbGqiuI4zz+f1jke\nMCmBGESc9mTxzvGo6+ODS1EURVEUpRTwmHDpUuBPfwKuu25or6fUrFiR/Rq3QXU1cMwxwGmn0XoS\noWU26/0wN5cFcbvtch+DhZMrftHlYhqW1IUzZEoX06oqujYWNK4vVTGZP9//ZeT2iSsQuS033rgZ\n3d1GGCZtQQwijospxyBuvDHQ1lb+LqYaP5E82ubJo22ePNrmyaNtnjza5skj2/zoo4fuOpJi+fLs\n16QFsaLCH+6VNGpBzGFBjIPLBOyyIHLdRde1dHTQcaSLKX9h4giaQkil/ALx1ltpGddqxvdZUQH0\n9GTXJBxqgRjn/Py9YFdVtSAqiqIoiqIoxUJaEIGhNUaoQMxhQYyDSzS4YhCDLIgsEGtq/BZE/oIM\nRZkLwIjFuF9UTury4YctPoHIImuorXBPPw28+Wa0bW2BONTXnguNn0gebfPk0TZPHm3z5NE2Tx5t\n8+TRNleBWFYUo8wF47JIuVxMgyyINTVuC6KMQbzqKrdZupTkm6SG69g88gi1AX/hOaOozAo6FGy2\nGbD11tG25WvXGERFURRFURSl2EgXU7k+FKhArDWCrVAXU1fyGZeLaZgFsbOTvhjSgshCMZMBtt8e\n2GCD/K8xH+xSHFExaXmbfeKbLZJB7VCODDcLosZPJI+2efJomyePtnnyaJsnj7Z58jQ3N2PiROCe\ne4b6SoYOtSCWEblcTONkDnIJHpeLaVAsoR2D2N9PhTLZ0pbJDM2Xhb+wcQXiD35AWZgAvwWR4XqI\nwwG1ICqKoiiKopSOlSuBY48d6qtIBs7sL+Ex/pw5tNx3X+D445O7JokKxCImqXEJRJcFMSiWkLOY\nShfTm2/27zcUAtE2eUvuuAO48EL3fk1NXMeFYhCHs0Dk7wULxHK3IKovf/JomyePtnnyaJsnj7Z5\n8mibJ8/61uazZmW/xgaZdetoufvuwO23J3dNkvXeDsJWO6A0FkRXDGKQBfH118018H5STA4MDE3m\nz54eWrqsZscdF74vtx8LX+bII2lmZLjA165ZTBVFURRFUZR8OeYY4IADsl+3XUyHErUghiSp+c53\ngIMOin4sV/KZmprs14MEYmsrLaurab902ojLVGroLIjf/S4t8zn3V78KAM2fuM4y99+ffCxlIfD3\ngj+jcheIGj+RPNrmyaNtnjza5smjbZ482ubJsz61+X33AZtskv16vjk/SkEZXMLQUltL5Q66u7Nd\nTK+/PvpxZs8GttvOfXwWoEyueoY1NfT33nvAVlvRa5WVQycQ+b48L/6+M2cCkyeb8h3DFRa3y5bR\nstxdTBVFURRFUZThg1oQy4iqKuC554Df/76wOohvvOHOvOQSiJkMMGkScNdd7mOxBREAHnzQXOdQ\nCcQJE2iZbx3GgYGWLAvicIM/j3PPpWW538v65stfDmibJ4+2efJomyePtnnyaJsnj7a5GeOfeurQ\nXgegAvETF86GhsKS1HDcoI0sowFQTN68ecAOOwBf+UrwsWpr/a8NpUDcYgta5rJ8BlFbSwG35S6q\nwuBrZ4vucEqwoyiKoiiKopQ3bEHcdNOhvQ5ABeJglk0a8BdiQQyipoYsiNI9c8GC8GQz0oLIDKVA\nZPIViJMmNaO1dXgLRP48WBiWu0Bcn3z5ywVt8+TRNk8ebfPk0TZPHm3z5NE2z7+sXCkog0sYWvbf\nH/j614GuruwkNcWgspI+8P5+I5B6e/MTiEOVxZTJVyA2NMBZ5mI4wT/W6ur8YjEVRVEURVEUJYiw\nsnJJs95bEAGyBrmS1Pz/9u496I66vuP4OyFXQIhUyyUCkYsEqoDiBa88VCVoiw6lFbUCUadUGYvj\npa1opzJVKGJFEAcZxRq1paiUMmIxEFEaVC6O8AQEQVAyBgy3SiTcDAH6x3e3u895dvec58lzfuf2\nfs1kzvW3Z8/nPPme89v9/XZnSus8xE2bmvcE9uMexLlzYenS6bV9/PErgeE4SM1UTnvSS47lT8/M\n0zPz9Mw8PTNPz8zTM/NCL3cG/f869HoF+sHWWxd7ELvRa2891cUTTzR/+PPmxeMHHVTct359bzuI\nmzYVcxGnasGCuOyHLSLTNcidW0mSJPW3fCdEP+yMsINIdBAfeWTiMNCZ1LoH8aGH2g8xBTjkkIn3\n93oO4nQtXjwGFB3FQbRoUa/XYGocy5+emadn5umZeXpmnp6Zp2fmMSVrzZper0Wwg0h0EH/3uxjG\n2Y1ee2sHcd265g5i3glsPZLpoHYQV66Myx126O16bIlly2B8vNdrIUmSpGG1//69XoNgB5HoIG7Y\n0L1hhLNnw9VXF+cRfPTR5o5efkCUvIO4enVcPvHEYHYQH3roSgC2376367El5syJU5MMCsfyp2fm\n6Zl5emaenpmnZ+bpmXl/sYNIsQexWx3EtWvh7W8vzrm4Zk31HsQLLojLbbaJy3x98stNm/pj4up0\nDfK6S5IkSaPAn+zEeQkvuqj7B1HJO4hQ3VlqnaOX70nM9xq2O/ppv7rnnjE+8IFer8VocSx/emae\nnpmnZ+bpmXl6Zp6emfcXO4hEBxG6f6TKdh3EOvl59x55ZDA7iDvuCGec0eu1kCRJktSOHUSKzku3\nT4C+eXNxvaqjV/f65fsHsYPouPL0zDw9M0/PzNMz8/TMPD0zT8/M+4sdRGCXXeJy48buvs5jjxXD\nSKezBxEGs4MoSZIkaTDYQSx5+OHuL3/bbeN6VQdxWPcgOq48PTNPz8zTM/P0zDw9M0/PzNMz8/5i\nB7GkW0NMX/3quNy4seggVp00fr/9JnYc8/UZ9A6iJEmSpMFgBzHTelL6mbR6Ney8c3QQ847hwoWT\nn7fPPsW5EqGYs1juIA7iqSIcV56emadn5umZeXpmnp6Zp2fm6Zl5fxnA7kZ35Oce7JYFC2KIaX4q\njU6OmFq1t9E9iJIkSZK6ZVavV6AHnn66Yizp178O990HH/pQd150333hpJPgrLPg+uvhqKPgwgub\n2zzxBKxbB3vsAe99L5x7bvePtCpJkiRpuM2aNQtq+oJz0q5K/zrmmO4uv3UP4ne+077N3LnROQQ4\n4IDurZskSZIkwWAOMf008HNgDXARsH3psZOA24FbgcPSr1q9+fOjgzgn65IvXTq19t0+wmo3Oa48\nPTNPz8zTM/P0zDw9M0/PzNMz8/4yiB3Ey4E/Ag4AfkF0CgH2A47OLg8HzqGP3l95D+J73gNXXDG1\n9t0+R6MkSZIkDfocxCOBo4B3EB3Fp4BPZY+tBE4GrmlpUzkHsdsOPzzmId58M1x++dTbr1gB73yn\ncxAlSZIkbZmmOYh9s4dtmt4FXJpd3wW4q/TYXcDi5GtUIx9ims9BnKrjjpt4CgxJkiRJmmn92kFc\nBdxU8e+I0nM+BmwCzm9YTt/sb/v2t+G886bfQZw1azDPgQiOK+8FM0/PzNMz8/TMPD0zT8/M0zPz\n/tKvRzF9fZvHlwNvBF5buu9uYNfS7edk901uvHw5S5YsAWDRokUceOCBjI2NAcUf6EzfXrZsjOuu\ngwcfvJIrr5z55ffz7fHx8b5an1G4neuX9fG2t7txe3x8vK/WZxRuW8+t5972djduW8+7f3t8fJwN\nGzYAsHbtWpoM4hzEw4HPAIcAD5Tu34/Ym/hSYmjp94C9mLwXsSdzEE8/Hc48E8bG4Pzzk7+8JEmS\nJAHDdx7Es4F5xDBUgKuBE4BbgG9ml5uz+/pmiOm8ebB+fXGaC0mSJEnqN7N7vQLTsDewO/DC7N8J\npcdOJfYaLgUuS79q9e65Jy6nOwdxkOW7uZWOmadn5umZeXpmnp6Zp2fm6Zl5fxnEDuJA2rw5Lt2D\nKEmSJKlfDeIcxC3VkzmIe+4Jv/oVHHssfPWryV9ekiRJkoDhPg/iwDjzzLh8/PHerockSZIk1bGD\nmMgR2Rkct9qqt+vRC44rT8/M0zPz9Mw8PTNPz8zTM/P0zLy/2EFMbBQ7iJIkSZIGg3MQE5o1C445\nBr72tZ68vCRJkiQ5B7GfzDZxSZIkSX3K7kpiozjE1HHl6Zl5emaenpmnZ+bpmXl6Zp6emfcXO4iJ\nzRrFQb2SJEmSBsIodld6OgcxVqAnLy9JkiRJzkGUJEmSJLVnBzGxgw/u9Rqk57jy9Mw8PTNPz8zT\nM/P0zDw9M0/PzPvLnF6vwCi57z7Ydtter4UkSZIkVXMOoiRJkiSNEOcgSpIkSZLasoOornNceXpm\nnp6Zp2fm6Zl5emaenpmnZ+b9xQ6iJEmSJAlwDqIkSZIkjRTnIEqSJEmS2rKDqK5zXHl6Zp6emadn\n5umZeXpmnp6Zp2fm/cUOoiRJkiQJcA6iJEmSJI0U5yBKkiRJktqyg6iuc1x5emaenpmnZ+bpmXl6\nZp6emadn5v3FDqIkSZIkCXAOoiRJkiSNFOcgSpIkSZLasoOornNceXpmnp6Zp2fm6Zl5emaenpmn\nZ+b9xQ6iJEmSJAlwDqIkSZIkjRTnIEqSJEmS2rKDqK5zXHl6Zp6emadn5umZeXpmnp6Zp2fm/cUO\noiRJkiQJcA6iJEmSJI0U5yBKkiRJktqyg6iuc1x5emaenpmnZ+bpmXl6Zp6emadn5v3FDqIkSZIk\nCXAOoiRJkiSNFOcgSpIkSZLaGsQO4ieANcA4cAWwa+mxk4DbgVuBw9Kvmqo4rjw9M0/PzNMz8/TM\nPD0zT8/M0zPz/jKIHcTTgQOAA4GLgY9n9+8HHJ1dHg6cw2C+v6EzPj7e61UYOWaenpmnZ+bpmXl6\nZp6emadn5v1lEDtQG0vXtwUeyK6/GfgP4AlgLXAH8NKka6ZKGzZs6PUqjBwzT8/M0zPz9Mw8PTNP\nz8zTM/P+MqfXKzBNpwDHAI9RdAJ3Aa4pPecuYHHi9ZIkSZKkgdWvexBXATdV/Dsie/xjwG7AV4Az\nG5bj4Ur7wNq1a3u9CiPHzNMz8/TMPD0zT8/M0zPz9My8vwz6aS52Ay4Fng98JLvvtOxyJTE/8dqW\nNncAeyZZO0mSJEnqP78E9ur1SsyUvUvX/wb4enZ9P+LIpvOA5xJvetA7wJIkSZKkBhcSw03Hgf8E\n/rD02EeJPYS3AsvSr5okSZIkSZIkSZI05HYFfgDcDPwMOLH02A7EwWR+AVwOLCrd/wPidBRnl56/\nEPhv4OfZsv654XUPIvYS3g6cVbr/NcD1xKksjmpoPx/4Rtb+GmD30mMrgQeBSxra1703gJOy5d4K\nHNaF9rsCVwEPA7/PnlfO9nZgE/AIcGTp/m5l/kHi818DfI+Y91ml6bPpJPO/yF7nSeBFLY/tD1yd\nvYcbic+31TBl/p7sfd6Qve8Datq3+2y2I47mezbV6jJbQJwy5kbgFor5vZ22h8HLfDlwP5H5DcC7\nato31ZYnS+0vrmlvbZnoLRTfL/9e076ptnSSubWlcAZFXrcRdblKXW05tNT+BuJI4m+qaG9tKewO\nXEFk+QPqj65eV1u2NHMY3tpyCvBrJp7+DJrrdFlTbfkc8X/gFibXrdwo1pa6zDv9fd70u+VTFAel\nfEtN+17WlploPzR2Ik5ID3HOwduApdnt04G/y67/PcXBYbYGXgn8NZP/AA/Jrs8FVhMntK9yHcXp\nKy4tPW934AXAV2n+AzwBOCe7fjRwQemxPwb+lObOSt17y+c5zgWWEMNZq44EuyXtdwJWZO23JX60\nfjF77MvAb7L2pxFf7rPpbuZjxH88iI7LBVRr+mw6yXwp8DyieJUL7RyikLwgu/1Mhj/zZ5SecwRR\nRKuM0fzZnEX86K7rINZltpwotBDv506qNwwMU+bHET8I2mmqLa1fmFWsLcXz9iZ+UGyf3X5WTfum\n2tJJ5taW6ue9Dzivpv0Y7ev+M4H/LT2vzNpSPO9bxGm5IDp7X6tp31RbctPJfJhry0uz126tA51k\nCfW1ZQz4IXEci9nAj0vrVDaKtaUu805/n49RXVv+hOhw5et5HRN/C+V6WVtmov3Quhh4bXb9VmDH\n7PpO2e2y5dT/MIU4PcW7K+7fmdiKkXsrcG7Lc75C8x/gSuBl2fU5xH+csjGaOyt17+0k4g+i/DoH\nd7n9d4mtNQD3AZ8oLffhlvbL6V7mAC8kimaTus9mjObMc62F9o0UByJqMqyZv436L7ey1s/mIKJY\nHtewfnWZLQO+DWxF/Gi/jYlbydq1H8TM2y0z11RbOumsWFuKzE+nfk9tlara0knmOWvLRD+m+D5v\nUlf3j6c+P2tLkfnPKPYazgJ+V7Psdr9bYHqZD2ttKWutA51kWdZaW/YlOigLgG2AnwD7NLQfldpS\nVld72/0+LyvXlg8D/1B67DxiD22rfqot02k/MKbSu11CfJj5aSN2BO7Nrt9LEViu6RyEi4g9I1dU\nPLaYGBaXu5upn/B+MbAuu76ZKMg7TKF93XvbpWXd7sruA/gSRYGYqfZLiK0S+VaU7Sj+GO8ltlSU\ns+l25u8mtoymtDfxvlYCPwX+tvTYMGd+ArEF6gyi8LRT/mxmA/8CfKhNm7rMLgMeAtYDa4FPAxuy\nx4Y186eJL7UbiS3+z6lZflNtWUD8jV4NvLmmvbWlWO7exI+uHxKZTefAYp1kXmdUawvE1v4lwPcb\nXiNXV/ffSrHFvpW1pVjuGoofzEdm6/LMmmW0+90yncyHtbY02dLfgD8n9matJz7LlUSHo1PDWltm\nWrm2rCH2Wi4kOniHUv093C+1pZP2U+279JU5HT5vW+Looe8ntkS0eprOT0o/hyhwZxEfYr/r9L39\n1Qy3L2f+rw3L7WTZM5H5O4j/IB+YZvvpmgu8CngxMffiCqLgfp/hzvyc7N/bsnU5tOG5rZ/NCUTR\n/Q2dn+ql/L7eQRTpnYkv1auI3O9keDO/BDifmD9xPDFMppO9K2WRUCjHAAAGnElEQVS7EV9OzyX+\nPm8CftXw/FGvLXOJ8y8dQsynWU0MUarbw1Jlqpm3vv4o1haIjsa3Oni9urq/M3H+4cs6eK1Rry0f\nBj5P7LVZTXQ4npziMmD6mTcZ1sy31GuI79zFxHfoKiL3diOocqNcWzrVWltWAS8hRjbcT2z0e6rN\nMnpVWzpt32m/qC91sgdxLnE6iX9j4kEA7iV2r0J8IPd1+JpfJLbE5PN9tiLG7d4AnEz0ustbDZ5D\nFNRW5eA/mbW/Prt9N8XY4znEHJff1rStUvfe7iZ+yLRbt5lofwmR+bWl9g8RQx/y5W6uad9qSzN/\nHXEKkTcRP6BhcuZlVflO9z/KOuJL9bdEob2UyZPBYfgyz32D4v2ewuTMqz6bg4n5RXcSW9GOBU6t\nWHZdZq8A/ov4EXM/8CPii67T9oOY+W8p8vsyMUQXJmfeVFvWZ5d3AlcSIy5aWVuKrazrsnV5kvgx\n8gtiy/tUaksnmdcZ5dpyNBP3RFVlXlVbcm8BLqK+o2NtKZa7ntiD+CKKIXQPMbXaAtPPfJhqS36g\nnpPbtK3Lsuo7NFeuLQcTQzYfJQ7y8l3g5R2sc26YakunmZc1/T6H+tpyKlHDDyM65lV7bfuhtmxJ\n+6Exi5hQ/dmKx06nGG/7EYqJmrnlTB7j/EliS0e7vRrXEuPHZ1E9wX4F7Q9S84Xs+luZPIdrjPYH\nqal6b/kk1HnEFutfUv1etqT9LGLOwlUV7fOJx/OIoz092NJ+OTOf+QuJoY57tmmfW8GWz0E8qHR7\nEbHlbSFR6FcBb6hoN0yZ71V6zhGl9WrVyWfTNAexLrMTKbZEbkMccez5U2g/iJnvVHrOkcRWzCp1\ntWURxVHqnkV0dpYymbWlyHwZUS8gMvs11UPvciuYWFs6zTxnbQlLiQ51k3a15RqqD9qRs7YUmf8B\nxcb4U6j/od3ud8t0Mx/m2pKrOkhNU5atVjCxtryJqAdbETtJvkccRKXOKNWWXN0cxBU0/z6vqy2z\nif8rEEeAvYn6g8T0qrbMRPuh8SpiF2++pewGiqK3A/GfpupQr2uJI21tJLakLCV6008RH1q7Q8nn\nh4u+g4lHFnxJtryHgQey51SZD3yT4hDHS0qPXUX0+B/NlvX6ivZN7+2j2XrdysQ5M1+iKBBb0j7P\nfCPwOLElKP/PtkPWNj908Z+V2q+lO5mvIraAtjuUfNNn00nmR2aPPQbcQ2yxy/0lUQhvYuKGiGHN\n/Mzs/d6QvZe9JjcFOvtsmo7OWZfZfGKL5E3Z+yjPZRzWzE8lMh8nhqU8r6Z9XW15OTF/cTy7fGdN\ne2vLRJ/JlnEj9Yc1r6str6CzzK0tE32c6hEFZU21ZQnF/K461pbCUdn7uI3YQzO3pn3T75YlTD9z\nGN7acnrWbnN2+Y/Z/U1ZljX9bvksURtuJub0VxnF2lKXeae/z+tqy4Ls9W8mNtDuX9O+l7Vluu0l\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZI0/J4kzs+Vnxfzg7Q/4fHuwNum\n8BrfBb6fvc7twIbs+vXEOTV/NLVVliRJkiR1w8bS9WcTJ3Y+uU2bMeCSDpe/ELi2dPuQKbSVJEmS\nJCW0seX2c4EHsutLgNXAT7N/L8/uv4ZiL+D7gdnAp4HrgDXA8aXlvQE4rXR7jMkdxIdLj/0PcDHw\ny6zdMdlybwT2yJ73bODC7P7rgFd08kYlSZIkSc1aO4gADxKdsIXA/Oy+vYGfZNdb9wIeD3wsuz4/\ne96S7PbniI5fbozJHcSNpcceBHYE5gF3U+zNPBH4bHb9fOCV2fXdgFsq3oMkSR2Z0+sVkCRpQMwD\nPg8cQMxV3Du7v3WO4mHAC4A/z25vB+wFrCX27n1wCq/5E+De7PodwGXZ9Z8Bh2bXXwfsW2rzDGBr\n4NEpvI4kSYAdREmSmuxBdAbvJ/berSeGeW4FPN7Q7n3E/MXWZa0DNk/h9X9fuv5U6fZTFN/hs4CX\nAZumsFxJkirN7vUKSJLUp54NnAucnd3eDrgnu34s0UmEGBL6jFK7y4ATKDpwzyP26L2BOILpTLuc\nGHKaO7ALryFJGhF2ECVJKiykOM3FKmAl8E/ZY+cAxxGnv9iH4mAya4i9jOPEQWrOI+YBXg/cBHyB\n6Cwuy5ZX9nT2r/W+qut17U4EXpytx81MPCiOJEmSJKnPzCeOMCpJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0lD4P0GrK1w/c5SkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7facd037b9d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Bir ayl\u0131k hava durumu verilerini indirme"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Burada Montreal verilerini almak i\u00e7in kullanabilece\u011finiz bir URL var."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mart 2012 ye ait datalar\u0131 alabilmek i\u00e7in month=3, year=2012."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = url_template.format(month=3, year=2012)\n",
      "weather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "read_csv fonksiyonunu dosya ad\u0131 k\u0131sm\u0131na url vererekte kullanabiliriz.\n",
      "  \n",
      "sonu\u00e7 olarak dataframimiz a\u015fa\u011f\u0131daki gibidir."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Month</th>\n",
        "      <th>Day</th>\n",
        "      <th>Time</th>\n",
        "      <th>Data Quality</th>\n",
        "      <th>Temp (\u00c2\u00b0C)</th>\n",
        "      <th>Temp Flag</th>\n",
        "      <th>Dew Point Temp (\u00c2\u00b0C)</th>\n",
        "      <th>Dew Point Temp Flag</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Rel Hum Flag</th>\n",
        "      <th>Wind Dir (10s deg)</th>\n",
        "      <th>Wind Dir Flag</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Wind Spd Flag</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Visibility Flag</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Stn Press Flag</th>\n",
        "      <th>Hmdx</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-03-01 00:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 00:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-9.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 72</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 24</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 4.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 100.97</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 01:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 01:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-8.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 79</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 6</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 26</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2.4</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 100.87</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 02:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 02:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.4</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-8.3</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 80</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 28</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 4.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 100.80</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 03:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 03:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-4.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-7.7</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 79</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 28</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 4.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 100.69</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 04:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 04:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.4</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-7.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 83</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 35</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1.6</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 100.62</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 24 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                     Year  Month  Day   Time Data Quality  Temp (\u00c2\u00b0C)  \\\n",
        "Date/Time                                                               \n",
        "2012-03-01 00:00:00  2012      3    1  00:00                     -5.5   \n",
        "2012-03-01 01:00:00  2012      3    1  01:00                     -5.7   \n",
        "2012-03-01 02:00:00  2012      3    1  02:00                     -5.4   \n",
        "2012-03-01 03:00:00  2012      3    1  03:00                     -4.7   \n",
        "2012-03-01 04:00:00  2012      3    1  04:00                     -5.4   \n",
        "\n",
        "                     Temp Flag  Dew Point Temp (\u00c2\u00b0C)  Dew Point Temp Flag  \\\n",
        "Date/Time                                                                   \n",
        "2012-03-01 00:00:00        NaN                  -9.7                  NaN   \n",
        "2012-03-01 01:00:00        NaN                  -8.7                  NaN   \n",
        "2012-03-01 02:00:00        NaN                  -8.3                  NaN   \n",
        "2012-03-01 03:00:00        NaN                  -7.7                  NaN   \n",
        "2012-03-01 04:00:00        NaN                  -7.8                  NaN   \n",
        "\n",
        "                     Rel Hum (%)  Rel Hum Flag  Wind Dir (10s deg)  \\\n",
        "Date/Time                                                            \n",
        "2012-03-01 00:00:00           72           NaN                   5   \n",
        "2012-03-01 01:00:00           79           NaN                   6   \n",
        "2012-03-01 02:00:00           80           NaN                   5   \n",
        "2012-03-01 03:00:00           79           NaN                   5   \n",
        "2012-03-01 04:00:00           83           NaN                   5   \n",
        "\n",
        "                     Wind Dir Flag  Wind Spd (km/h) Wind Spd Flag  \\\n",
        "Date/Time                                                           \n",
        "2012-03-01 00:00:00            NaN               24           NaN   \n",
        "2012-03-01 01:00:00            NaN               26           NaN   \n",
        "2012-03-01 02:00:00            NaN               28           NaN   \n",
        "2012-03-01 03:00:00            NaN               28           NaN   \n",
        "2012-03-01 04:00:00            NaN               35           NaN   \n",
        "\n",
        "                     Visibility (km)  Visibility Flag  Stn Press (kPa)  \\\n",
        "Date/Time                                                                \n",
        "2012-03-01 00:00:00              4.0              NaN           100.97   \n",
        "2012-03-01 01:00:00              2.4              NaN           100.87   \n",
        "2012-03-01 02:00:00              4.8              NaN           100.80   \n",
        "2012-03-01 03:00:00              4.0              NaN           100.69   \n",
        "2012-03-01 04:00:00              1.6              NaN           100.62   \n",
        "\n",
        "                     Stn Press Flag  Hmdx      \n",
        "Date/Time                                      \n",
        "2012-03-01 00:00:00             NaN   NaN ...  \n",
        "2012-03-01 01:00:00             NaN   NaN ...  \n",
        "2012-03-01 02:00:00             NaN   NaN ...  \n",
        "2012-03-01 03:00:00             NaN   NaN ...  \n",
        "2012-03-01 04:00:00             NaN   NaN ...  \n",
        "\n",
        "[5 rows x 24 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "grafi\u011fini \u00e7izdirmek i\u00e7in:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#weather_mar2012[u\"Temp (\u00c2\u00b0C)\"].plot(figsize=(15, 5))\n",
      "weather_mar2012[u\"Temp (\u00c2\\xb0C)\"].plot(figsize=(15, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7facb2185510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAFqCAYAAABrtKkiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7facb22d5d50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " \u00b0 karakteri yerine '\\xb0' kulland\u0131k.    \n",
      "Yaz\u0131l\u0131m\u0131n\u0131 daha da kolayla\u015ft\u0131rmak i\u00e7in '\\xb0' karakteri yerine '' koyal\u0131m"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012.columns = [s.replace(u'\\xb0', '') for s in weather_mar2012.columns]\n",
      "weather_mar2012[u\"Temp (\u00c2C)\"].plot(figsize=(15, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7facb2224510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAFqCAYAAABrtKkiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7facb228fb50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dataframe de birka\u00e7 s\u00fctunun ya tamamen bo\u015f oldu\u011funu veya onlardan sadece birka\u00e7 tanesinin de\u011ferlere sahip oldu\u011funu fark edeceksiniz.Bu bo\u015f de\u011ferlerden dropna ile kurtulal\u0131m.\n",
      "\n",
      "dropna fonksiyonunda \"axis=1\" arguman\u0131 \"kolonlar\u0131 d\u00fc\u015f\u00fcr sat\u0131rlar\u0131 de\u011fil\" demektir.  \n",
      "and \"how='any'\" arguman\u0131 ise \"herhangi bir de\u011fer null ise s\u00fctun b\u0131rak\u0131n\" demektir."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')\n",
      "weather_mar2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Month</th>\n",
        "      <th>Day</th>\n",
        "      <th>Time</th>\n",
        "      <th>Data Quality</th>\n",
        "      <th>Temp (\u00c2C)</th>\n",
        "      <th>Dew Point Temp (\u00c2C)</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Weather</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-03-01 00:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 00:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.5</td>\n",
        "      <td>-9.7</td>\n",
        "      <td> 72</td>\n",
        "      <td> 24</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 100.97</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 01:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 01:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.7</td>\n",
        "      <td>-8.7</td>\n",
        "      <td> 79</td>\n",
        "      <td> 26</td>\n",
        "      <td> 2.4</td>\n",
        "      <td> 100.87</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 02:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 02:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.4</td>\n",
        "      <td>-8.3</td>\n",
        "      <td> 80</td>\n",
        "      <td> 28</td>\n",
        "      <td> 4.8</td>\n",
        "      <td> 100.80</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 03:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 03:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-4.7</td>\n",
        "      <td>-7.7</td>\n",
        "      <td> 79</td>\n",
        "      <td> 28</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 100.69</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 04:00:00</th>\n",
        "      <td> 2012</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 04:00</td>\n",
        "      <td>  </td>\n",
        "      <td>-5.4</td>\n",
        "      <td>-7.8</td>\n",
        "      <td> 83</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> 100.62</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                     Year  Month  Day   Time Data Quality  Temp (\u00c2C)  \\\n",
        "Date/Time                                                              \n",
        "2012-03-01 00:00:00  2012      3    1  00:00                    -5.5   \n",
        "2012-03-01 01:00:00  2012      3    1  01:00                    -5.7   \n",
        "2012-03-01 02:00:00  2012      3    1  02:00                    -5.4   \n",
        "2012-03-01 03:00:00  2012      3    1  03:00                    -4.7   \n",
        "2012-03-01 04:00:00  2012      3    1  04:00                    -5.4   \n",
        "\n",
        "                     Dew Point Temp (\u00c2C)  Rel Hum (%)  Wind Spd (km/h)  \\\n",
        "Date/Time                                                                \n",
        "2012-03-01 00:00:00                 -9.7           72               24   \n",
        "2012-03-01 01:00:00                 -8.7           79               26   \n",
        "2012-03-01 02:00:00                 -8.3           80               28   \n",
        "2012-03-01 03:00:00                 -7.7           79               28   \n",
        "2012-03-01 04:00:00                 -7.8           83               35   \n",
        "\n",
        "                     Visibility (km)  Stn Press (kPa) Weather  \n",
        "Date/Time                                                      \n",
        "2012-03-01 00:00:00              4.0           100.97    Snow  \n",
        "2012-03-01 01:00:00              2.4           100.87    Snow  \n",
        "2012-03-01 02:00:00              4.8           100.80    Snow  \n",
        "2012-03-01 03:00:00              4.0           100.69    Snow  \n",
        "2012-03-01 04:00:00              1.6           100.62    Snow  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Year/Month/Day/Time kolanlar\u0131 tekrar eden kolonlar ve data quality kolonu kullan\u0131\u015fl\u0131 g\u00f6z\u00fckm\u00fcyor o y\u00fczden bu kolonlar\u0131 silelim.  \n",
      "axis=1 arguman\u0131 \"Drop columns\" demektir.  \n",
      "dropna ve drop i\u015flemleri default olarak sat\u0131rlar \u00fczerinde i\u015flem yapar."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012 = weather_mar2012.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)\n",
      "weather_mar2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Temp (\u00c2C)</th>\n",
        "      <th>Dew Point Temp (\u00c2C)</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Weather</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-03-01 00:00:00</th>\n",
        "      <td>-5.5</td>\n",
        "      <td>-9.7</td>\n",
        "      <td> 72</td>\n",
        "      <td> 24</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 100.97</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 01:00:00</th>\n",
        "      <td>-5.7</td>\n",
        "      <td>-8.7</td>\n",
        "      <td> 79</td>\n",
        "      <td> 26</td>\n",
        "      <td> 2.4</td>\n",
        "      <td> 100.87</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 02:00:00</th>\n",
        "      <td>-5.4</td>\n",
        "      <td>-8.3</td>\n",
        "      <td> 80</td>\n",
        "      <td> 28</td>\n",
        "      <td> 4.8</td>\n",
        "      <td> 100.80</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 03:00:00</th>\n",
        "      <td>-4.7</td>\n",
        "      <td>-7.7</td>\n",
        "      <td> 79</td>\n",
        "      <td> 28</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 100.69</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-01 04:00:00</th>\n",
        "      <td>-5.4</td>\n",
        "      <td>-7.8</td>\n",
        "      <td> 83</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> 100.62</td>\n",
        "      <td> Snow</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                     Temp (\u00c2C)  Dew Point Temp (\u00c2C)  Rel Hum (%)  \\\n",
        "Date/Time                                                          \n",
        "2012-03-01 00:00:00       -5.5                 -9.7           72   \n",
        "2012-03-01 01:00:00       -5.7                 -8.7           79   \n",
        "2012-03-01 02:00:00       -5.4                 -8.3           80   \n",
        "2012-03-01 03:00:00       -4.7                 -7.7           79   \n",
        "2012-03-01 04:00:00       -5.4                 -7.8           83   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather  \n",
        "Date/Time                                                                       \n",
        "2012-03-01 00:00:00               24              4.0           100.97    Snow  \n",
        "2012-03-01 01:00:00               26              2.4           100.87    Snow  \n",
        "2012-03-01 02:00:00               28              4.8           100.80    Snow  \n",
        "2012-03-01 03:00:00               28              4.0           100.69    Snow  \n",
        "2012-03-01 04:00:00               35              1.6           100.62    Snow  \n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2.3 G\u00fcn\u00fcn saatine g\u00f6re s\u0131cakl\u0131k \u00e7izimi"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "groupby ve aggregate i kullanal\u0131m. g\u00fcn\u00fcn her saatindeki s\u0131cakl\u0131k de\u011fi\u015fimini g\u00f6zlemleyelim.   \n",
      "Gecelerin so\u011fuk oldu\u011funu grafikle g\u00f6sterelim."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temperatures = weather_mar2012[[u'Temp (\u00c2C)']]\n",
      "temperatures['Hour'] = weather_mar2012.index.hour\n",
      "#temperatures\n",
      "temperatures.groupby('Hour').aggregate(np.median).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7facb20dea10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEPCAYAAACwWiQoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiIiIiLjF/wGdE3h4KGyulAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7facb20d5d50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "\n",
      "5.3 T\u00fcm y\u0131l\u0131n verilerini alma"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "b\u00fct\u00fcn y\u0131l\u0131n datas\u0131n\u0131 indirmek istersek;  \n",
      "\n",
      "\u00d6ncelikle, belirli bir ay i\u00e7in hava durumunu alan bir fonksiyon yazal\u0131m\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def download_weather_month(year, month):\n",
      "    url = url_template.format(year=year, month=month)\n",
      "    weather_data = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True)\n",
      "    weather_data = weather_data.dropna(axis=1)\n",
      "    weather_data.columns = [col.replace('\\xb0', '') for col in weather_data.columns]\n",
      "    weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)\n",
      "    return weather_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "fonksiyon \u00e7al\u0131\u015f\u0131yor mu kontrol edelim."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "download_weather_month(2012, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Temp (C)</th>\n",
        "      <th>Dew Point Temp (C)</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Weather</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01 00:00:00</th>\n",
        "      <td> -1.8</td>\n",
        "      <td> -3.9</td>\n",
        "      <td> 86</td>\n",
        "      <td>  4</td>\n",
        "      <td>  8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 01:00:00</th>\n",
        "      <td> -1.8</td>\n",
        "      <td> -3.7</td>\n",
        "      <td> 87</td>\n",
        "      <td>  4</td>\n",
        "      <td>  8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 02:00:00</th>\n",
        "      <td> -1.8</td>\n",
        "      <td> -3.4</td>\n",
        "      <td> 89</td>\n",
        "      <td>  7</td>\n",
        "      <td>  4.0</td>\n",
        "      <td> 101.26</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 03:00:00</th>\n",
        "      <td> -1.5</td>\n",
        "      <td> -3.2</td>\n",
        "      <td> 88</td>\n",
        "      <td>  6</td>\n",
        "      <td>  4.0</td>\n",
        "      <td> 101.27</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 04:00:00</th>\n",
        "      <td> -1.5</td>\n",
        "      <td> -3.3</td>\n",
        "      <td> 88</td>\n",
        "      <td>  7</td>\n",
        "      <td>  4.8</td>\n",
        "      <td> 101.23</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 05:00:00</th>\n",
        "      <td> -1.4</td>\n",
        "      <td> -3.3</td>\n",
        "      <td> 87</td>\n",
        "      <td>  9</td>\n",
        "      <td>  6.4</td>\n",
        "      <td> 101.27</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 06:00:00</th>\n",
        "      <td> -1.5</td>\n",
        "      <td> -3.1</td>\n",
        "      <td> 89</td>\n",
        "      <td>  7</td>\n",
        "      <td>  6.4</td>\n",
        "      <td> 101.29</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 07:00:00</th>\n",
        "      <td> -1.4</td>\n",
        "      <td> -3.6</td>\n",
        "      <td> 85</td>\n",
        "      <td>  7</td>\n",
        "      <td>  8.0</td>\n",
        "      <td> 101.26</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 08:00:00</th>\n",
        "      <td> -1.4</td>\n",
        "      <td> -3.6</td>\n",
        "      <td> 85</td>\n",
        "      <td>  9</td>\n",
        "      <td>  8.0</td>\n",
        "      <td> 101.23</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 09:00:00</th>\n",
        "      <td> -1.3</td>\n",
        "      <td> -3.1</td>\n",
        "      <td> 88</td>\n",
        "      <td> 15</td>\n",
        "      <td>  4.0</td>\n",
        "      <td> 101.20</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 10:00:00</th>\n",
        "      <td> -1.0</td>\n",
        "      <td> -2.3</td>\n",
        "      <td> 91</td>\n",
        "      <td>  9</td>\n",
        "      <td>  1.2</td>\n",
        "      <td> 101.15</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 11:00:00</th>\n",
        "      <td> -0.5</td>\n",
        "      <td> -2.1</td>\n",
        "      <td> 89</td>\n",
        "      <td>  7</td>\n",
        "      <td>  4.0</td>\n",
        "      <td> 100.98</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 12:00:00</th>\n",
        "      <td> -0.2</td>\n",
        "      <td> -2.0</td>\n",
        "      <td> 88</td>\n",
        "      <td>  9</td>\n",
        "      <td>  4.8</td>\n",
        "      <td> 100.79</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 13:00:00</th>\n",
        "      <td>  0.2</td>\n",
        "      <td> -1.7</td>\n",
        "      <td> 87</td>\n",
        "      <td> 13</td>\n",
        "      <td>  4.8</td>\n",
        "      <td> 100.58</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 14:00:00</th>\n",
        "      <td>  0.8</td>\n",
        "      <td> -1.1</td>\n",
        "      <td> 87</td>\n",
        "      <td> 20</td>\n",
        "      <td>  4.8</td>\n",
        "      <td> 100.31</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 15:00:00</th>\n",
        "      <td>  1.8</td>\n",
        "      <td> -0.4</td>\n",
        "      <td> 85</td>\n",
        "      <td> 22</td>\n",
        "      <td>  6.4</td>\n",
        "      <td> 100.07</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 16:00:00</th>\n",
        "      <td>  2.6</td>\n",
        "      <td> -0.2</td>\n",
        "      <td> 82</td>\n",
        "      <td> 13</td>\n",
        "      <td> 12.9</td>\n",
        "      <td>  99.93</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 17:00:00</th>\n",
        "      <td>  3.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 81</td>\n",
        "      <td> 13</td>\n",
        "      <td> 16.1</td>\n",
        "      <td>  99.81</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 18:00:00</th>\n",
        "      <td>  3.8</td>\n",
        "      <td>  1.0</td>\n",
        "      <td> 82</td>\n",
        "      <td> 15</td>\n",
        "      <td> 12.9</td>\n",
        "      <td>  99.74</td>\n",
        "      <td>                 Rain</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 19:00:00</th>\n",
        "      <td>  3.1</td>\n",
        "      <td>  1.3</td>\n",
        "      <td> 88</td>\n",
        "      <td> 15</td>\n",
        "      <td> 12.9</td>\n",
        "      <td>  99.68</td>\n",
        "      <td>                 Rain</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 20:00:00</th>\n",
        "      <td>  3.2</td>\n",
        "      <td>  1.3</td>\n",
        "      <td> 87</td>\n",
        "      <td> 19</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.50</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 21:00:00</th>\n",
        "      <td>  4.0</td>\n",
        "      <td>  1.7</td>\n",
        "      <td> 85</td>\n",
        "      <td> 20</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.39</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 22:00:00</th>\n",
        "      <td>  4.4</td>\n",
        "      <td>  1.9</td>\n",
        "      <td> 84</td>\n",
        "      <td> 24</td>\n",
        "      <td> 19.3</td>\n",
        "      <td>  99.32</td>\n",
        "      <td>         Rain Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 23:00:00</th>\n",
        "      <td>  5.3</td>\n",
        "      <td>  2.0</td>\n",
        "      <td> 79</td>\n",
        "      <td> 30</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.31</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 00:00:00</th>\n",
        "      <td>  5.2</td>\n",
        "      <td>  1.5</td>\n",
        "      <td> 77</td>\n",
        "      <td> 35</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.26</td>\n",
        "      <td>         Rain Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 01:00:00</th>\n",
        "      <td>  4.6</td>\n",
        "      <td>  0.0</td>\n",
        "      <td> 72</td>\n",
        "      <td> 39</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.26</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 02:00:00</th>\n",
        "      <td>  3.9</td>\n",
        "      <td> -0.9</td>\n",
        "      <td> 71</td>\n",
        "      <td> 32</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.26</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 03:00:00</th>\n",
        "      <td>  3.7</td>\n",
        "      <td> -1.5</td>\n",
        "      <td> 69</td>\n",
        "      <td> 33</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.30</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 04:00:00</th>\n",
        "      <td>  2.9</td>\n",
        "      <td> -2.3</td>\n",
        "      <td> 69</td>\n",
        "      <td> 32</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.26</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 05:00:00</th>\n",
        "      <td>  2.6</td>\n",
        "      <td> -2.3</td>\n",
        "      <td> 70</td>\n",
        "      <td> 32</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.21</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 06:00:00</th>\n",
        "      <td>  2.3</td>\n",
        "      <td> -2.6</td>\n",
        "      <td> 70</td>\n",
        "      <td> 26</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.18</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 07:00:00</th>\n",
        "      <td>  2.0</td>\n",
        "      <td> -2.9</td>\n",
        "      <td> 70</td>\n",
        "      <td> 33</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.14</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 08:00:00</th>\n",
        "      <td>  1.9</td>\n",
        "      <td> -3.3</td>\n",
        "      <td> 68</td>\n",
        "      <td> 39</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.14</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 09:00:00</th>\n",
        "      <td>  1.8</td>\n",
        "      <td> -3.7</td>\n",
        "      <td> 67</td>\n",
        "      <td> 44</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.14</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 10:00:00</th>\n",
        "      <td>  1.5</td>\n",
        "      <td> -4.1</td>\n",
        "      <td> 66</td>\n",
        "      <td> 43</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.18</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 11:00:00</th>\n",
        "      <td>  2.2</td>\n",
        "      <td> -3.5</td>\n",
        "      <td> 66</td>\n",
        "      <td> 30</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.19</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 12:00:00</th>\n",
        "      <td>  1.7</td>\n",
        "      <td> -6.2</td>\n",
        "      <td> 56</td>\n",
        "      <td> 48</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.21</td>\n",
        "      <td>         Mainly Clear</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 13:00:00</th>\n",
        "      <td>  1.1</td>\n",
        "      <td> -6.5</td>\n",
        "      <td> 57</td>\n",
        "      <td> 37</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.27</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 14:00:00</th>\n",
        "      <td>  1.1</td>\n",
        "      <td> -6.8</td>\n",
        "      <td> 56</td>\n",
        "      <td> 33</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.33</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 15:00:00</th>\n",
        "      <td>  0.0</td>\n",
        "      <td> -7.0</td>\n",
        "      <td> 59</td>\n",
        "      <td> 33</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.41</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 16:00:00</th>\n",
        "      <td> -0.7</td>\n",
        "      <td> -8.7</td>\n",
        "      <td> 55</td>\n",
        "      <td> 24</td>\n",
        "      <td> 24.1</td>\n",
        "      <td>  99.50</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 17:00:00</th>\n",
        "      <td> -2.1</td>\n",
        "      <td> -9.5</td>\n",
        "      <td> 57</td>\n",
        "      <td> 22</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.66</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 18:00:00</th>\n",
        "      <td> -4.1</td>\n",
        "      <td>-11.4</td>\n",
        "      <td> 57</td>\n",
        "      <td> 28</td>\n",
        "      <td> 25.0</td>\n",
        "      <td>  99.86</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 19:00:00</th>\n",
        "      <td> -4.8</td>\n",
        "      <td>-12.1</td>\n",
        "      <td> 57</td>\n",
        "      <td> 24</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.00</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 20:00:00</th>\n",
        "      <td> -5.6</td>\n",
        "      <td>-13.4</td>\n",
        "      <td> 54</td>\n",
        "      <td> 24</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.07</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 21:00:00</th>\n",
        "      <td> -5.8</td>\n",
        "      <td>-12.8</td>\n",
        "      <td> 58</td>\n",
        "      <td> 26</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.15</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 22:00:00</th>\n",
        "      <td> -7.0</td>\n",
        "      <td>-14.7</td>\n",
        "      <td> 54</td>\n",
        "      <td> 20</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.26</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02 23:00:00</th>\n",
        "      <td> -7.4</td>\n",
        "      <td>-14.1</td>\n",
        "      <td> 59</td>\n",
        "      <td> 17</td>\n",
        "      <td> 19.3</td>\n",
        "      <td> 100.27</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 00:00:00</th>\n",
        "      <td> -9.0</td>\n",
        "      <td>-16.0</td>\n",
        "      <td> 57</td>\n",
        "      <td> 28</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.35</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 01:00:00</th>\n",
        "      <td> -9.7</td>\n",
        "      <td>-17.2</td>\n",
        "      <td> 54</td>\n",
        "      <td> 20</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.43</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 02:00:00</th>\n",
        "      <td>-10.5</td>\n",
        "      <td>-15.8</td>\n",
        "      <td> 65</td>\n",
        "      <td> 22</td>\n",
        "      <td> 12.9</td>\n",
        "      <td> 100.53</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 03:00:00</th>\n",
        "      <td>-11.3</td>\n",
        "      <td>-18.7</td>\n",
        "      <td> 54</td>\n",
        "      <td> 33</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.61</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 04:00:00</th>\n",
        "      <td>-12.6</td>\n",
        "      <td>-20.1</td>\n",
        "      <td> 53</td>\n",
        "      <td> 24</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.68</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 05:00:00</th>\n",
        "      <td>-12.9</td>\n",
        "      <td>-19.1</td>\n",
        "      <td> 60</td>\n",
        "      <td> 22</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.76</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 06:00:00</th>\n",
        "      <td>-13.3</td>\n",
        "      <td>-19.3</td>\n",
        "      <td> 61</td>\n",
        "      <td> 19</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.85</td>\n",
        "      <td>         Snow Showers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 07:00:00</th>\n",
        "      <td>-14.0</td>\n",
        "      <td>-19.5</td>\n",
        "      <td> 63</td>\n",
        "      <td> 19</td>\n",
        "      <td> 25.0</td>\n",
        "      <td> 100.95</td>\n",
        "      <td>                 Snow</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 08:00:00</th>\n",
        "      <td>-14.8</td>\n",
        "      <td>-21.3</td>\n",
        "      <td> 58</td>\n",
        "      <td> 26</td>\n",
        "      <td> 24.1</td>\n",
        "      <td> 101.07</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 09:00:00</th>\n",
        "      <td>-15.0</td>\n",
        "      <td>-21.9</td>\n",
        "      <td> 56</td>\n",
        "      <td> 19</td>\n",
        "      <td> 24.1</td>\n",
        "      <td> 101.20</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 10:00:00</th>\n",
        "      <td>-15.3</td>\n",
        "      <td>-22.2</td>\n",
        "      <td> 56</td>\n",
        "      <td> 24</td>\n",
        "      <td> 24.1</td>\n",
        "      <td> 101.29</td>\n",
        "      <td>               Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03 11:00:00</th>\n",
        "      <td>-14.9</td>\n",
        "      <td>-22.2</td>\n",
        "      <td> 54</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24.1</td>\n",
        "      <td> 101.33</td>\n",
        "      <td>        Mostly Cloudy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>744 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
        "Date/Time                                                        \n",
        "2012-01-01 00:00:00      -1.8                -3.9           86   \n",
        "2012-01-01 01:00:00      -1.8                -3.7           87   \n",
        "2012-01-01 02:00:00      -1.8                -3.4           89   \n",
        "2012-01-01 03:00:00      -1.5                -3.2           88   \n",
        "2012-01-01 04:00:00      -1.5                -3.3           88   \n",
        "2012-01-01 05:00:00      -1.4                -3.3           87   \n",
        "2012-01-01 06:00:00      -1.5                -3.1           89   \n",
        "2012-01-01 07:00:00      -1.4                -3.6           85   \n",
        "2012-01-01 08:00:00      -1.4                -3.6           85   \n",
        "2012-01-01 09:00:00      -1.3                -3.1           88   \n",
        "2012-01-01 10:00:00      -1.0                -2.3           91   \n",
        "2012-01-01 11:00:00      -0.5                -2.1           89   \n",
        "2012-01-01 12:00:00      -0.2                -2.0           88   \n",
        "2012-01-01 13:00:00       0.2                -1.7           87   \n",
        "2012-01-01 14:00:00       0.8                -1.1           87   \n",
        "2012-01-01 15:00:00       1.8                -0.4           85   \n",
        "2012-01-01 16:00:00       2.6                -0.2           82   \n",
        "2012-01-01 17:00:00       3.0                 0.0           81   \n",
        "2012-01-01 18:00:00       3.8                 1.0           82   \n",
        "2012-01-01 19:00:00       3.1                 1.3           88   \n",
        "2012-01-01 20:00:00       3.2                 1.3           87   \n",
        "2012-01-01 21:00:00       4.0                 1.7           85   \n",
        "2012-01-01 22:00:00       4.4                 1.9           84   \n",
        "2012-01-01 23:00:00       5.3                 2.0           79   \n",
        "2012-01-02 00:00:00       5.2                 1.5           77   \n",
        "2012-01-02 01:00:00       4.6                 0.0           72   \n",
        "2012-01-02 02:00:00       3.9                -0.9           71   \n",
        "2012-01-02 03:00:00       3.7                -1.5           69   \n",
        "2012-01-02 04:00:00       2.9                -2.3           69   \n",
        "2012-01-02 05:00:00       2.6                -2.3           70   \n",
        "2012-01-02 06:00:00       2.3                -2.6           70   \n",
        "2012-01-02 07:00:00       2.0                -2.9           70   \n",
        "2012-01-02 08:00:00       1.9                -3.3           68   \n",
        "2012-01-02 09:00:00       1.8                -3.7           67   \n",
        "2012-01-02 10:00:00       1.5                -4.1           66   \n",
        "2012-01-02 11:00:00       2.2                -3.5           66   \n",
        "2012-01-02 12:00:00       1.7                -6.2           56   \n",
        "2012-01-02 13:00:00       1.1                -6.5           57   \n",
        "2012-01-02 14:00:00       1.1                -6.8           56   \n",
        "2012-01-02 15:00:00       0.0                -7.0           59   \n",
        "2012-01-02 16:00:00      -0.7                -8.7           55   \n",
        "2012-01-02 17:00:00      -2.1                -9.5           57   \n",
        "2012-01-02 18:00:00      -4.1               -11.4           57   \n",
        "2012-01-02 19:00:00      -4.8               -12.1           57   \n",
        "2012-01-02 20:00:00      -5.6               -13.4           54   \n",
        "2012-01-02 21:00:00      -5.8               -12.8           58   \n",
        "2012-01-02 22:00:00      -7.0               -14.7           54   \n",
        "2012-01-02 23:00:00      -7.4               -14.1           59   \n",
        "2012-01-03 00:00:00      -9.0               -16.0           57   \n",
        "2012-01-03 01:00:00      -9.7               -17.2           54   \n",
        "2012-01-03 02:00:00     -10.5               -15.8           65   \n",
        "2012-01-03 03:00:00     -11.3               -18.7           54   \n",
        "2012-01-03 04:00:00     -12.6               -20.1           53   \n",
        "2012-01-03 05:00:00     -12.9               -19.1           60   \n",
        "2012-01-03 06:00:00     -13.3               -19.3           61   \n",
        "2012-01-03 07:00:00     -14.0               -19.5           63   \n",
        "2012-01-03 08:00:00     -14.8               -21.3           58   \n",
        "2012-01-03 09:00:00     -15.0               -21.9           56   \n",
        "2012-01-03 10:00:00     -15.3               -22.2           56   \n",
        "2012-01-03 11:00:00     -14.9               -22.2           54   \n",
        "                          ...                 ...          ...   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
        "Date/Time                                                                \n",
        "2012-01-01 00:00:00                4              8.0           101.24   \n",
        "2012-01-01 01:00:00                4              8.0           101.24   \n",
        "2012-01-01 02:00:00                7              4.0           101.26   \n",
        "2012-01-01 03:00:00                6              4.0           101.27   \n",
        "2012-01-01 04:00:00                7              4.8           101.23   \n",
        "2012-01-01 05:00:00                9              6.4           101.27   \n",
        "2012-01-01 06:00:00                7              6.4           101.29   \n",
        "2012-01-01 07:00:00                7              8.0           101.26   \n",
        "2012-01-01 08:00:00                9              8.0           101.23   \n",
        "2012-01-01 09:00:00               15              4.0           101.20   \n",
        "2012-01-01 10:00:00                9              1.2           101.15   \n",
        "2012-01-01 11:00:00                7              4.0           100.98   \n",
        "2012-01-01 12:00:00                9              4.8           100.79   \n",
        "2012-01-01 13:00:00               13              4.8           100.58   \n",
        "2012-01-01 14:00:00               20              4.8           100.31   \n",
        "2012-01-01 15:00:00               22              6.4           100.07   \n",
        "2012-01-01 16:00:00               13             12.9            99.93   \n",
        "2012-01-01 17:00:00               13             16.1            99.81   \n",
        "2012-01-01 18:00:00               15             12.9            99.74   \n",
        "2012-01-01 19:00:00               15             12.9            99.68   \n",
        "2012-01-01 20:00:00               19             25.0            99.50   \n",
        "2012-01-01 21:00:00               20             25.0            99.39   \n",
        "2012-01-01 22:00:00               24             19.3            99.32   \n",
        "2012-01-01 23:00:00               30             25.0            99.31   \n",
        "2012-01-02 00:00:00               35             25.0            99.26   \n",
        "2012-01-02 01:00:00               39             25.0            99.26   \n",
        "2012-01-02 02:00:00               32             25.0            99.26   \n",
        "2012-01-02 03:00:00               33             25.0            99.30   \n",
        "2012-01-02 04:00:00               32             25.0            99.26   \n",
        "2012-01-02 05:00:00               32             25.0            99.21   \n",
        "2012-01-02 06:00:00               26             25.0            99.18   \n",
        "2012-01-02 07:00:00               33             25.0            99.14   \n",
        "2012-01-02 08:00:00               39             24.1            99.14   \n",
        "2012-01-02 09:00:00               44             24.1            99.14   \n",
        "2012-01-02 10:00:00               43             24.1            99.18   \n",
        "2012-01-02 11:00:00               30             24.1            99.19   \n",
        "2012-01-02 12:00:00               48             24.1            99.21   \n",
        "2012-01-02 13:00:00               37             24.1            99.27   \n",
        "2012-01-02 14:00:00               33             24.1            99.33   \n",
        "2012-01-02 15:00:00               33             24.1            99.41   \n",
        "2012-01-02 16:00:00               24             24.1            99.50   \n",
        "2012-01-02 17:00:00               22             25.0            99.66   \n",
        "2012-01-02 18:00:00               28             25.0            99.86   \n",
        "2012-01-02 19:00:00               24             25.0           100.00   \n",
        "2012-01-02 20:00:00               24             25.0           100.07   \n",
        "2012-01-02 21:00:00               26             25.0           100.15   \n",
        "2012-01-02 22:00:00               20             25.0           100.26   \n",
        "2012-01-02 23:00:00               17             19.3           100.27   \n",
        "2012-01-03 00:00:00               28             25.0           100.35   \n",
        "2012-01-03 01:00:00               20             25.0           100.43   \n",
        "2012-01-03 02:00:00               22             12.9           100.53   \n",
        "2012-01-03 03:00:00               33             25.0           100.61   \n",
        "2012-01-03 04:00:00               24             25.0           100.68   \n",
        "2012-01-03 05:00:00               22             25.0           100.76   \n",
        "2012-01-03 06:00:00               19             25.0           100.85   \n",
        "2012-01-03 07:00:00               19             25.0           100.95   \n",
        "2012-01-03 08:00:00               26             24.1           101.07   \n",
        "2012-01-03 09:00:00               19             24.1           101.20   \n",
        "2012-01-03 10:00:00               24             24.1           101.29   \n",
        "2012-01-03 11:00:00               22             24.1           101.33   \n",
        "                                 ...              ...              ...   \n",
        "\n",
        "                                  Weather  \n",
        "Date/Time                                  \n",
        "2012-01-01 00:00:00                   Fog  \n",
        "2012-01-01 01:00:00                   Fog  \n",
        "2012-01-01 02:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 03:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 04:00:00                   Fog  \n",
        "2012-01-01 05:00:00                   Fog  \n",
        "2012-01-01 06:00:00                   Fog  \n",
        "2012-01-01 07:00:00                   Fog  \n",
        "2012-01-01 08:00:00                   Fog  \n",
        "2012-01-01 09:00:00                   Fog  \n",
        "2012-01-01 10:00:00                   Fog  \n",
        "2012-01-01 11:00:00                   Fog  \n",
        "2012-01-01 12:00:00                   Fog  \n",
        "2012-01-01 13:00:00                   Fog  \n",
        "2012-01-01 14:00:00                   Fog  \n",
        "2012-01-01 15:00:00                   Fog  \n",
        "2012-01-01 16:00:00         Mostly Cloudy  \n",
        "2012-01-01 17:00:00                Cloudy  \n",
        "2012-01-01 18:00:00                  Rain  \n",
        "2012-01-01 19:00:00                  Rain  \n",
        "2012-01-01 20:00:00                Cloudy  \n",
        "2012-01-01 21:00:00                Cloudy  \n",
        "2012-01-01 22:00:00          Rain Showers  \n",
        "2012-01-01 23:00:00                Cloudy  \n",
        "2012-01-02 00:00:00          Rain Showers  \n",
        "2012-01-02 01:00:00                Cloudy  \n",
        "2012-01-02 02:00:00         Mostly Cloudy  \n",
        "2012-01-02 03:00:00         Mostly Cloudy  \n",
        "2012-01-02 04:00:00         Mostly Cloudy  \n",
        "2012-01-02 05:00:00         Mostly Cloudy  \n",
        "2012-01-02 06:00:00         Mostly Cloudy  \n",
        "2012-01-02 07:00:00         Mostly Cloudy  \n",
        "2012-01-02 08:00:00         Mostly Cloudy  \n",
        "2012-01-02 09:00:00         Mostly Cloudy  \n",
        "2012-01-02 10:00:00         Mostly Cloudy  \n",
        "2012-01-02 11:00:00         Mostly Cloudy  \n",
        "2012-01-02 12:00:00          Mainly Clear  \n",
        "2012-01-02 13:00:00         Mostly Cloudy  \n",
        "2012-01-02 14:00:00         Mostly Cloudy  \n",
        "2012-01-02 15:00:00         Mostly Cloudy  \n",
        "2012-01-02 16:00:00         Mostly Cloudy  \n",
        "2012-01-02 17:00:00          Snow Showers  \n",
        "2012-01-02 18:00:00         Mostly Cloudy  \n",
        "2012-01-02 19:00:00         Mostly Cloudy  \n",
        "2012-01-02 20:00:00          Snow Showers  \n",
        "2012-01-02 21:00:00          Snow Showers  \n",
        "2012-01-02 22:00:00                Cloudy  \n",
        "2012-01-02 23:00:00          Snow Showers  \n",
        "2012-01-03 00:00:00          Snow Showers  \n",
        "2012-01-03 01:00:00                Cloudy  \n",
        "2012-01-03 02:00:00          Snow Showers  \n",
        "2012-01-03 03:00:00          Snow Showers  \n",
        "2012-01-03 04:00:00                Cloudy  \n",
        "2012-01-03 05:00:00          Snow Showers  \n",
        "2012-01-03 06:00:00          Snow Showers  \n",
        "2012-01-03 07:00:00                  Snow  \n",
        "2012-01-03 08:00:00         Mostly Cloudy  \n",
        "2012-01-03 09:00:00         Mostly Cloudy  \n",
        "2012-01-03 10:00:00                Cloudy  \n",
        "2012-01-03 11:00:00         Mostly Cloudy  \n",
        "                                      ...  \n",
        "\n",
        "[744 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ayl\u0131k verilerini indirebiliyoruz.bunlar\u0131 birle\u015ftirirsek y\u0131l\u0131n b\u00fct\u00fcn verisini elde etmi\u015f oluruz."
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "\u015eimdi bir kerede t\u00fcm aylar\u0131 alabilirsiniz. Bu \u00e7al\u0131\u015ft\u0131rmak biraz zaman alacakt\u0131r."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Bunu elde etti\u011fimizde, t\u00fcm dataframeleri pd.concat kullanarak b\u00fcy\u00fck bir dataframe i\u00e7inde birle\u015ftirmek kolay ve b\u00f6ylece b\u00fct\u00fcn y\u0131l\u0131n datas\u0131n\u0131 elde ediyoruz. :)\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012 = pd.concat(data_by_month)\n",
      "weather_2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Temp (C)</th>\n",
        "      <th>Dew Point Temp (C)</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Weather</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01 00:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.9</td>\n",
        "      <td> 86</td>\n",
        "      <td> 4</td>\n",
        "      <td> 8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 01:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.7</td>\n",
        "      <td> 87</td>\n",
        "      <td> 4</td>\n",
        "      <td> 8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 02:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.4</td>\n",
        "      <td> 89</td>\n",
        "      <td> 7</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 101.26</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 03:00:00</th>\n",
        "      <td>-1.5</td>\n",
        "      <td>-3.2</td>\n",
        "      <td> 88</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 101.27</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 04:00:00</th>\n",
        "      <td>-1.5</td>\n",
        "      <td>-3.3</td>\n",
        "      <td> 88</td>\n",
        "      <td> 7</td>\n",
        "      <td> 4.8</td>\n",
        "      <td> 101.23</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
        "Date/Time                                                        \n",
        "2012-01-01 00:00:00      -1.8                -3.9           86   \n",
        "2012-01-01 01:00:00      -1.8                -3.7           87   \n",
        "2012-01-01 02:00:00      -1.8                -3.4           89   \n",
        "2012-01-01 03:00:00      -1.5                -3.2           88   \n",
        "2012-01-01 04:00:00      -1.5                -3.3           88   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
        "Date/Time                                                                \n",
        "2012-01-01 00:00:00                4              8.0           101.24   \n",
        "2012-01-01 01:00:00                4              8.0           101.24   \n",
        "2012-01-01 02:00:00                7              4.0           101.26   \n",
        "2012-01-01 03:00:00                6              4.0           101.27   \n",
        "2012-01-01 04:00:00                7              4.8           101.23   \n",
        "\n",
        "                                  Weather  \n",
        "Date/Time                                  \n",
        "2012-01-01 00:00:00                   Fog  \n",
        "2012-01-01 01:00:00                   Fog  \n",
        "2012-01-01 02:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 03:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 04:00:00                   Fog  \n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "5.4 Saving to a CSV"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Her zaman verilerini indirmek yava\u015f ve gereksiz, bu y\u00fczden bir kere indirip csv olarak kaydedelim;"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012.to_csv('dosya/weather_2012--.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}