Format: application/octet-stream
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   1  {
   2   "cells": [
   3    {
   4     "cell_type": "code",
   5     "execution_count": 1,
   6     "metadata": {
   7      "collapsed": true
   8     },
   9     "outputs": [],
  10     "source": [
  11      "%matplotlib notebook\n",
  12      "%matplotlib inline\n",
  13      "\n",
  14      "import matplotlib.pyplot as plt\n",
  15      "import matplotlib.dates as dates\n",
  16      "import matplotlib.ticker as ticker\n",
  17      "\n",
  18      "import pandas as pd\n",
  19      "import numpy as np"
  20     ]
  21    },
  22    {
  23     "cell_type": "code",
  24     "execution_count": 23,
  25     "metadata": {
  26      "collapsed": false
  27     },
  28     "outputs": [
  29      {
  30       "data": {
  31        "image/png": 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JW24h2NSE7OrC9VUQMhYrrIZW8bj2ja+qwlm8GK+sTMcO8mcOXl0dEhCp1DHF\nWTJMLZ5l4VVX4zU2oiIRrSZasmREOZlIIHftgo9+tLAAU61dq2eaJb53t6kJ95lntB2kKPKu4dgx\nM4ppjuzrm3SGtLHI2zeKs6fZ7e2kHnhAGyhdl8wjj5B5/PHCrMPt7MSrqyO9dCkDl1xCLhjE2bED\nAKezsxDbyIvFkFu3atfLaBSZzZLyPJI33IBz++1D1UtCkJs3D1lbO+EFaobTB7l1K15DA6k77ySz\nfj20tIy+DiSRQPb06HcikwHXxXv+eT0bKfG9y6oq7JkzSf/mNyYQ4QnC/MqmMUopGBgYjDKaz5A2\nET94H6+zs/BjU5aFrKjA9RPfKM8j+8QT2J2duH/4A9lvfpPsnj1Yb72Ft28fSkrU4cM6fpG/itlp\naSlEMvUOHcLzBYWKx/FSKbyKCm3YzmZR/f24TU2jdgrWwoWk3va243o+hqnB6+nBmz8fN5nE3rkT\niuJqFSPLysDzcBsaUACWhWtZOKOtki9GCJ0fo7cXe926E97+sxEjKKYxKpPRq2iHRRQt5REiU6nC\nzEFJSeqee8g9/bQ+aNt4FRWFhEDutm14VVW4ZWWor34V58gRUj/6EbkZM3TCmqNHtRqpyE3V9Wc1\n1oYNSNsuCAqZSOAqRea970UJoUeRq1ePfXMTcXM1nJbIzk49K/RnlowR8lxFIijPI/2Zz5C96Sa8\nhgacQ4dwJrKgLhgkc+mlIxIpFdqQTBay/hnGxwiKaYzs60MNW1gmYzHUKNnKQKuWkvfcQ/pXv9JZ\nzfr6UNEo1tq1Ore0ZemQ2uk0ynHIPvMM2fPOQx49iqyq0qGk02nsnh6cHTv0bGNYLCRZVYVKJsk+\n9RTJlSsLLpFufT1WJoNsboZ8PKdS/vyGMxZ10UXagO1n6qNood0QhCDX0MDA739PpqmJ5Kc+peNU\nTXDltVdZOXpo823bSH3rW+R+85tjvIOzDyMopjGyt7dgn8ijYjFkfz/J73+f3Isvkn7ggcIx65VX\nsGtqcI4exd22TYfXqKuDaFTPTiwLWVaGchxdtrwcp60N2damg+NlMtrgePHFOiFPdzeZSy8d2qhA\nALepiczllw8xSKqyMjK33qozslmW9rf/yEdO6vMxTBFf/SrZcFgPEpYvh69+dcyiuVtvxfviF0nF\nYqTPPVeroUaLWjsKKhZD5XIozxvc6UcnVgMDWBs24K5ejbNzJ8n//E9jzyiB8XqaJijLIvkf/4Eo\nL6f8Ix/QySOrAAAgAElEQVRBhMPIo0dxhwkKGYvhdXbqFJ7PPovww22DjsTqNDfj1tUR2baNUG0t\nbkUFgUwGlU6jLEsv0EokcHbs0PmhhUC2tCDb2pCBAPzgB9DaigP0+yk6h5NauXL0oHJtbTroXCKh\n3SHN+ojpSwnhMAI/bpSDDu8yYQIBSCRQySSiuprcmjUE1q8nrBQqGiW1ejXEYoVFgM5rr+F2dpK4\n5ZaRdW3bBvlB1R13lAx9Px0xgmKaIPv68FwXLxCAX/yC6NKlOHv3Flxj83iRCF5nJ7KxkeQ111D9\nwAMopRBC4A0MIJuaUHV1uC+8AJ6HV1WF6u9H+jMKVVGBiseRBw/iXXABoA3ReB5y9uwR3kmjUiLy\nqDdjBsJ1jZAwjM4ko/jm1+SIigqsNWsQkQjhaFQbybu66F+2DFpaSKxfT/bRR1GuS2z5cgLFKttt\n2+DiiweN7l/5CmzefFYJC6N6mibIgQFURQWpq64iVVdH9ne/Qx44MEKfq+JxvdAtkUBFo9rYnTde\nJ5PIREKnrrQsHYivsrJg11C2DeGwDt3teYMpRP0wEN6aNfD73x/XfbgVFcjTOWKr4YxClpXpVLa+\n44WUEvc739Gzja98Rce2isVwmppQUuLMno29adPQSl54ofAbAcBx4HvfQ6ZSuHv3ntobmiKMoJgm\nqIEBPD/tp3XBBXjV1Tqt5zB9rozFwLJ0WYB4XIfSUAp8QZF3L8zOm6djK0Wjg6qncFgLjmi0ULda\ntUobv/ftg3/5l+MSFvY555A7i0ZqhpOL68eLsnfuxJo1C7uyEue3v0X19cGXvwx++li7rY3Utdfq\n8DAHDugFn11d2r4xa5YeGBXHxfrFL3C2bCH73HNTc2OnGCMopgnewABeUda2zAUXkBslgma+c89n\neFN5LyjL0r7q/mjemTOH3MUXQyikDeD+jEKFw3pxXLFr6uOPQy43GKLjsceO+T5kZSXeBNKBGgwT\nQfrxorxDh/SC095encs7m9Uzg/Z2XTAcxp01S8+0Uymyzz1H8vvfx96wAQ4cQAUCJD/3ucH4Uj09\neEeOnDXBB42gmCbI/v4h6T3dlhashQtHlhsmKGQshspktOpqmOG7cE40ikylwBcUsqwMtziEwi23\n4KVSuoy/bTCcDsiyMp0ut7sbr7ZWu3LX1yMtS+fDGBY8MD9wkn19ePX1eP7iUllXB7EYVt6L74or\ndL0DA2eFt5QRFNMEOTBQMg90nsKMwvd2ktEoztatOvXnGOerWGyIoLDPPXdo5rRbbyW5cCFeczN8\n8Ytw663Hf0MGwwnAnTkTb+9evXgvGkWuX68zIObX6jz//JDyMh6HdBo5MIDT3Ix35Ag88gheczPe\n4cM4S5boMhs2IA8d0kEsR1urMc0wXk/TBDkwMGZHX4zyg6QVzyic9naQEm+MZC8yHkdlMgVBMWqc\nnXe9S/8ZDKcRKhLBmT2bgG+Mln4wS5k3Tp933tATwmEdGaCnB/eCC5B79kBVFW5zM9b+/QRjMYK3\n3UbZo4/qtUX19ToLn+/YMV0xM4rpQipVmCWUJBAgtWJFIWGNjEZBStLLlpEb/qPxUdEoKpXShj2T\nHMhwhpFdsoSMn/9bffrTKNfVizqDQbjrrpEnJBKQy+HOmKGdOH75S9zmZryeHjIbNmA3NJB517ug\nrAyvsrJgp/C6upCZzKm8tVOGERTTgLzX0qipP0fBOeecgj+6isW0Oumcc/Qq7NHqLysruMZO1o/d\nYJhqZGXloBG6rQ1VVYW84Qb40Y9GXa8j43E9kAqFUI6DrK5G+YKCt72N5Pz5pK+/nsySJXhlZXiH\nD6OUIvPII9r4PQ0xqqdpgNfZWUgANOlzq6ux584tHa47GMSeO5eI70poMJzJ2K2tyHPPhTEGRioe\nL0RY9vr6sK64QgfLtCzo7UW9+93k5w3hvXuxXnoJmc3idXRAJEJs+fJTdCenjimbUQghWoUQzwoh\n3hRCvCGE+Jy/v1YI8ZQQYrv/v+ZY6k/99KfHlXvhTMLr7MQZI+PXuOfW1ZGZQLhua/583GmuhzWc\nHWSvvHLM2TP4uVF8G56VTOJceilOvi8Zti7JmT2b/ttuw924EVVZiTxwYMyotM6WLWQff/zE3MQp\nZipVTy7wF0qpRcBVwP8QQiwCvgQ8o5SaDzzjb08KpRTe/v2403AEbLe342zfPmSf29FRCNd9svBq\na0ndeONJvYbBcDog4/HCmiTn5puRmcxgqtYPfnBEeVVejjtzJnZbG7K8HG+MfN3ekSM4fi6WM40p\nExRKqU6l1Ab/cxLYArQA7wHu9YvdC9w+6brTabDt40r7eTri7ttH5uGHyT77bGGfkhKvo2PMHMIG\ng2FyWAsWkM2nZr3ySpJLluCcfz58/es6QOEopJYvJ7t4MZmLLiL929/qxafDcn3LVEqvCB+GkpLc\nmjVDo9yOx7C6TzanhY1CCDEHuARYCzQqpTr9Q4eAiQWfL0IeOYIKBqed6sndtQtr4UKiu3fjdXUR\nbGzEeuklvETCJPExGE4QaliebblsGSxbVvocXyXltLVh79uH/cwzxD784UKubzZs0MmSfO9BUeQ9\naL34ItazzxJqbSU0LIjnqOSDFOa9EDdtOukBCqfc60kIUQ78Gvi8Umqg+JjSSx5HXfYohLhLCLFO\nCLGuu7t7yDHZ04M7axaqu3tarZqU6TQykcCaNw9r40aUbWO9+CKpq6823kgGw2mCNXcu9ubN2hMx\nk9HC4oUXkKkUQqnCam6lFEpKrJdewp49G3cMldUIHnhAR7K1bf2/KKfMyWJKBYUQIowWEj9TSuXT\nTXUJIZr9483A4dHOVUr9QCl1uVLq8hnDYgO5R47g1NXpcNhHjpzEOyhN5uGHcXftOmH1yXQaGYth\nzZuH096O8/rruI2NyGFZ7AwGw9ThNjfjhcMM/NVfMfBXf4UKBmHFCj2biMWQfX1kH32Uga9+FWv1\namQigT1vHs7u3Se9bdarr5I+hsx+U+n1JIB7gC1KqX8tOvQ74BP+508AD022bq+nB1lZidPYiHsK\nHv5YuAcPIgcGxi84QWQ6rV+0ykrcujoyjz1GdozV1AaDYYoIBOi/4w56V67Uazgee0yrhtJpvIYG\nZH8/zt695M4/H2v1at1PNTbidXRMrL+4/PKh201NY5ctsmXIvj6yjz+OmzfMT+aWJn3GiWMZ8DFg\npRBik//3TuBrwA1CiO3A9f72pFCZDDIWw25qwt6584Q12N68WS+6mWg7+vr0QrUThPJnFADJ666j\n/447cGfNOmH1GwyGE0hrK/bChbiOg3IclOviVFfrBXq9veQuukiHGGlsREUi5C64gPSDDyKTSdIP\nPYT9xhuj1/v97w/d/pM/Gd2onbdl/OmfwsUXI994Q7v35uNcTYKp9Hp6USkllFJLlFIX+3+PKqV6\nlFLvUErNV0pdr5Q6Oum6s1lUJILb3Izcu3dMv+bJ4GzfTubXv8adoJeByuX0F+I4x33tApnMYH6J\nQGCE0c1gMJxeOP5gVaVSkEjgNjVhb9iAqqxERSIkb7wRx18dnrvgArx9+3B37MDat09n3BvNE+rJ\nJ4due55OrjScYbYM9eCDOsxPLjfp+5hyY/ZJIZfT8YnicVQgoGPPHyf21q06ZPEEVUn5+C/yBM0o\nlJQ6UbwRDgbDGYPb1ITs7sY9eBDlCwoViRTc2b3a2sH4acEglJfjbNuG3dqKV1U1+sB0tBnBRDw8\nH3sMJYTOKzNJJ59pJyiUUgVBAehokEUPNr80f9L1ZjI6nn1//4TKy95efd4JmlGobBai0dKhNgwG\nw+mFH/4m9/jjOnqCEGSWLMEeIye8V12Nu3MnXlUVVmsr9kTXSfzbv43cN8x2oSIR5N69OmbbJNVP\n06/XcRydB9qX0qrooeRefJHUvfeWOntMZDarBcUkZhQqEDhhNgrlG7INBsOZRW7BAuy6OjKXXQaA\nfe65OGOsl3ArKsBxkFVVOt/3aLkuRtMqjDaAHaaOUpEIqrcXIhGtGp8E005QFEbe+e1IBGVZuPv3\nk3vpJZ0H9xiMOSqTwaupmVCSEru9ndwLL+BVVw9Nyn4MuPv2kf7lL5H9/YXQ4AaD4cxB1tSQuu46\nCI2/vtmrrET5//NpWUdw9dUj97kuPPHE0H3DPCJVNIqsqtJq+Umq46eFoCge5atsdogeX4XD2ovg\n/vtJXX01sr7+2GJAZbNaUKRS4+r33I4Ocuecg7Vo0RAbhbIsso8/PmH9oLIs0vffT+7oUTIPPljw\neDIYDNMTr7papyQOhXS483we+jxPPAFPPz3quc499xQ8pXIvvzwi5pSKRlEdHXrwfDbOKFQ2W+h8\nRxUUhw+j/OTpdkPDpNdWKKUgm9URJaNRnDffLBmXRVmWHhFEo0O8npytW7HXrsU7eHBC15U9Pch4\nnOQNN5BcsYK0P3U1GAzTE6++nuRNNwF+rphMZnBguW3biCySKhzG9e0d2YoK7FdfBcD6wx9wh4Ud\nkdGott+erYICQB7VXrR519g8KhxG9vUVRuNeTQ3uZFdrO45OjxgKocrKyP7qV3j79o1ZXFkWKhQq\nJD7JY7W349bWYre3T+yekkntzhYMam+JiWSwMxgMZy5CIMvL9edgUNsT8lnzXngBhrn625dcQvqj\nH8Wrq0O2tiL37dNeTb29Oi1A42CoPBWNaq+ns1VQqHC4MM1SuRyySFDIUAjZ21uYZahQaNI2CpXN\nFuLQ2zNnIisqkKPpDvPl/WxwKhgs2CiU6+Lt20dm2TI9I5mA+kkODOAZu4TBcNai4vFBO8WKFdpj\nqQjr0kuRjkP6wx/G3bdP2zcOH4ZQSEfPrq0tBMtTkYhOwRAOa1f7SXhkThtBkdu4Ua81yGaHCIrC\njMLfp8LhSRuYVdFCt+wll2C3tpY0aivLQoXDWij5X4bs6UFVVGg7RyAwoRhUsr8faQSFwXDWohKJ\nwUHpggXw0EMFo7hXU4OsqCD98ss4uRyptWtRgYBOO9Daiurtxbv7bpKf/7yeTUSjqOXLdZ+YzZJ7\n/vkJt2NaCAqiUVzPw163DpnJjLBRqHRa6+fy276gsF57rRC0L/PEEyR/+tNRZxvD65TxOF4pQWHb\nWiCFwwUbhTxyBFlZCULgNDdPKFigHBjQqieDwXBWImOxwRmFb6OQ4TCZW29FNjbi9fTg3nQTyYsu\n0qE9yspwtm7Fqa9HVVSQSiaR1dXIa66B+fO1oPC9ntzOztIXL+K0yEdx3AhBbv58Inv2IKLRId5B\nyp+qFWYZRTMKe8sWyOUonzsXZ8MGvPJynO3bkb29RJcvR/ihu1UmM3SWEo8XFtQV43V16WmfLyhU\nKFQQFN6RI4VUonZTE/aOHUSvvLLkbXkDA8hSAb8MBsO0xovFBrUX3/seuC7pO+/UOb/DYTzXhY99\nrFBebtmCt3s3zsUX48yahcjlKGtvR37nO6jHH9f9UiSCPHoUOYl8PdNjRgF4VVV4R47g5UfuPnlB\noYpmFAW7QTKJt3cv3v79qGAQa84cck8/jbVq1RDPJJXNFmYkoGcU+emgkhL3wAEAsi++iPXaa6MK\nCre7G6+qSn+eMQNZJM1lMoks8mv2Oju1Gs3MKAyGsxoZj+OlUno28e1vIxMJvNZWnF27cBYu1JFg\nfU8nQKdibW5GVlbqyLUNDXiJBGr7dujrQ3V34zY04O3YMakYeNNHUFRWQm8v6vBhHT/FpyAo/FlG\nXvWk/AQibksL1gsvIKurcWfORPX349bXY61fX6hDFqmuQH95eSmfffJJUvfcQ27NGtwtWwppWFU4\nDMEgyvN0utIjR3QbAVVWhnLdwqrLzKOPFpKuy0yG1D33kHn4YUgmC0neDQbD2YeKx3U/4Xs8ebNn\n43V14Rw9CrGYDin0y18WyudmzyaTT+Pq47ku3je+oQME/sVfIPv6tJdUXd2E2zFtBAXhsPY7jkRG\n2CigKL1hIKD/bBuVyWCdcw7ujh14lZV4NTXkFi0i/fa3427dWvBMksnkEKOyischnUZmMtgbNpC6\n+WbSW7fizJtXSHVIMKizzoVCqGRSG5by6UqFQNbX4x06pL0Q9uzR17Ms7PZ2nJkzyfb366iSw7wc\nDAbD2YPMC4oVKyAQwJk7F7urq5DuYHiQUrelBXeYulru2YOsqNCDZMeB9nYyixeTmzt3wu2YHjYK\nH1lVpdU9ReTXVBTPCAiH9bqLWEzPIkDbDwIBsldcUSgvu7oINjUhBwZQRbFZVDSKchxkVxeqpga3\nsZFkYyM4DpGf/xwikcHUpOEw1oYNuC0tQzp9p6YGr7OTQHk50k9G5Gzdiv3aa+QuvBB35syT85AM\nBsMZw5DV2YEAzuzZuOvWaUeXZFKvsfjgB8euYP9+5LPP4s2bp8ORSwlLlky6f5lWgsKprh4RhnvE\njAIgEsE7ckSrgGIxvBkz8GpqhpznNjbi7tlDsKlJe00Vu6kKoWPL79qFV5yGNG+XKDJ8Ewphb9hA\nbpjh2q2rw96xA5XL4TQ341VWYr/5ps73XbRIxmAwnL2oeFwH/Lv+eh35urYWr7cXrrqK/lQKvv51\nWLp07Ara2/Ui5IYGHRz1ox+FMSLXlmL6qJ7QaxxyF1wwZN9ogkKFw8ju7oL+P3njjSOma3ZjI7bv\nwqpSqRHrGbwZM7Db23W0x2Li8cI1wV/g53kjJLjT1oaby2Ft3Eh28WKcpia8bduQDQ2D8ekNZxyH\nnQD93qn5We2wQsjJpRUwnGGoTZv0uqzOTp3syLJ0AECAL32ptJAAWLJEO8pEo2TefBOuvfaY2jGt\nBAXB4Mh8DeEwyRtuGLJfhcPauJzv/EOhQVWRjztzJt7evchsVi+4GyYo7BkzUAMDBU+mQt2x2BAV\nkwqFsObOHdmuQIDkNdeQvP56vaimqgoVj2PPmHGMN284HdjlRNljjz9RtyV0OUHsY+zo+z3BukyM\nlBTjFzacuTz7rM5sWV6uDdD5cOIlQggNobUV9e1vk6ysxP7rvz6m2QRMQPUkhCgDskopKYRYACwE\nHlNKncAcnyeX4aN5FQohDx/GmzNnzHNULIbb0oK9dq3u+IeN8h1/BlLsigt6gYwocjuzWlvHjD2v\nysrw8u6vQpBdtAjH5MA+o8mqAJYSQOnV/9utMDudGHHhcUUiR2VAEhinz3cUZGSAioBkt63VmxkZ\noDI4doBKgJwUOAoqgloq2RIi02uIOH1paUF1duqwQfX1uHnjdT4e1ERoa8Mdow+aKBN5XVYDMSFE\nC/Ak8DHgx8d11SlGhcM6MuuwTn44uXnzsNauRY3ioiqrq7Fnzy64vBb2ex6quxv8UObWhRcih806\nxsJavBiZ94wynJFkPDjqBhgvlFdWBVjWnGBOdZxVyQSdzvjqxvXZGE8MJNjnhOhwQsyIBchOYEbx\nfDrBk8kydlshkp7g9wNl47bPcJpw8KDWapSX49XXD0aEOEEJ0SbKRASFUEplgPcB/66UugO4YJxz\nTmvyNgRvnE7ZbW7WNobR1jIIQfraa4cmI9m/H/n006gtW+CuuwrCwjC9kAqeT8ZGdLZKQcbTHXdG\nle7ALRWgLBzghtYKzquOkC0qf9QN8EJK19/pBNmcjaAUHHZDnF8Toc8NkJaCORURMqMIii25CNst\n/Y67CpKe4MPzq9iUi9HlBrGUIG1UVmcGK1fi+blw3KYmvLzq6fbbT2kzJiQohBBvAz4C/N7fd0Zb\nW2UopAPzjTd9CwSw5s2beATX9nZkNou0LN1rTDCcuOHMIqsEnW6InN+5d7sBtubCWApCAWiOB+l1\n9U9LKVh1OMojj+lxw1E3wJu5MDkVoDyky5SFg1h+x52TsCYT56gM0eUG2W5H6fLCpKQgIGBeZZRO\nN0RFSFAdDZJVQ3/C++wQ2+0IW6wIUkG/F6AmImguC9MQC7LN0iqrPXaIzdmx1+hIBa+ko6May3dZ\nITomMAMynACWLsX2PKwrrkDOmKEjwra1wa23ntJmTERQfB74G+C3Sqk3hBDnAM+e3GadXFQohKqq\nGmlgHoXshReSufzyiVW8ZAnWrl3au0AIGLZC0jA9yKt78qPybVaUvU6ErAxQHhK0VkQ44umZ5u4D\ncDgc5tF1Ie66Czb1RtjvRMhKQXk4LygCWP5PcX02zgW1cZY1JXg9F+WwEyDtCQ67QVrLw9TGgrrz\njwaoCAfIqcEOO+0JNmRjfPDcairCQTZmI+y3QzQmtECYWxVlwAvQFA/yZi7CPmfQjfuoGyDlCQ45\nQWwJfV6APXaYrBQctAevIRW8YUV50xolb7PhpOAqhQqFsPfs0WshpsDhZdyeUin1vFLq3Uqpr/vb\nu5RSd5/8pp08VDg8wltpTILBEWszxqS1VQfu+uxn4Qc/OGYPA8OpRyk44k7MwpuRulxKBshJQZcb\nJOkJUlJQEQnQVhGh2xcUm3cFkB7MXREhHIdutJ0g42kBAZAICSw1WNfbZ5axpC7O+fVl3Da7gpyE\nbi9Ea3mE2qjutOviYSojwYLqyVXwYibB1U0JmhIhbp5dQaK8gq1WhOYyLSjmVOj/S+piKAQpT+D5\nM4bXrRh77RAbczF22mEOu/o6B50Qr2Ri2BJ63QBdbpBEKEhWDnUD7nSCHLDNLOOk8MEPklqzhuzr\nrxe2TzUT8Xp6FhgxAVVKrTwpLToFuDNnDokHdUJpbTUC4gxkmxVmYzbC+6rSo3oEeUp3xtFA0YzC\nE3QTYFZZiAHbo9MJUVkepDkRot/VdoDW+YLfvxqi7SKXOW8LMSMcIh1wcRSEfTenRCiArfSsYVZZ\niJC/f3mz9oirOJiiww6yIhEiEhSUhwT1sRAV4UBBUBxygpRFQlzRoNWkzYkwzW1hmhMh5lXqmUNT\nIsR1MxMsqtEDn7VdGdJSUBZQHHYCRCMhkq5gNxHKA4qg0Go1Rwk25yL0eCFqgx6L62L05Fy6shZV\nQUnSE7ySiWNL+EA4RdCYP04sS5fifuELsGoVrFw5/tqJk8BEVmZ/sehzDHg/4J6c5pwaTpqQMJyR\nKAWv56LURwN0u0FaIkPdTS0Jz6TKSHmCa8szZJRW/WRUEDyoT4SIBwXb+iUrYrqjn18Z5vf9Zcyq\ndLhpRYh9RxUrPx3hguYIO/oUSWfQhToR0rOJw16IudUjZ69VkQADjmJGTP9cz6mM0JwIkQgJJFpw\n9XkBZlWGC6Hx81xcP2hfCwjBlY3aMePSGXG29+UY8ALkpEIAh5wAiRDUREP0WJJ5lSH2D+h27rLD\neEqQcgTdz0RoWQK2LxH22GEW10bZ2muRk4KyoHGpOuEsXTolAiLPuIJCKbV+2K41QohXRy1sMJyB\nWEoQFHB+TZTDfRYtDBUU7bkY82tihAKCwwM2ORVgVlmY7pSLQjArHmJGPETOU1zuj+jfc04VT+5P\nsumIYmVLkJn1ghcOecyuiNBreaiisVYiLLSgcIOsKB9pYK6OBkk6kojfMb9z9mA0gIXVEfZkLQZU\niHMSkwsgWRcLkUwH8JRgUU2U9qMWbfEgf7RAh7N56VCGbf0O5WFByoFyAvSkJX//J0EWrQxy198H\nUTHY44T5YF2cgymHrBKUjVRAGM5wxlXKCiFqi/7qhRA3ARNU8BsMpz85KSgLCWYXGaGPugH2WCGU\ngkNukMtmJJjt2x6yMsCs8jBJTzAgA9RGg1xYF+OOc6sJFI3oZ1dEkAiqotpuEQlAQzxIXSxERZF+\nKxEMkJVgS0FDfKSevyYaoikx+pjuovo4u+wIR90AjaOcW4q6eIikDNItwyysiRILaOGRp8K3oSyo\nilIdCSAPRtnXHiKdEvR1BujNBjjsBomHgjQm9D3l7TegV54bu8X0YCKqp/VoG4VAq5x2A585mY0y\nGE4GOSmwFVQOU41klfZAavJtC56CvU6YXi9IfchDIaiJBigPB+hxA4QEtJWHCQYC9DiKuujonWGr\nPzuoigSZEQvysQVakCypi3Je9aDHUTAgiAagrTw0QnUEcEl9DEeN7lAxMxGiIRFmb8qlZox2jMXc\nigjPHQzhKZhVFqYmGqQuXiQofGF2aX2My2fE6QwJvnZ3jEQC+g8FCZQJ9jhhltTpXC+VkSCplGCX\nFaIyKNliRYkEFFXBHCEB8YCZaZypTET1NPGg5QbDacwOK0SXF+Yd5Zkh+3NSUB4JEAoIqsKCfi9A\nt6u9k7p8t1QhBJEgLGuKk3QkVZEAS+pi/OFwlnhodOttIhRgZUsZtdEgQghm+J1wNBhgeJ+eCAnm\nVI4uDGKhALFRj4AQgltnV/JWvzVkNjMRqqNBGmJBPKWIBLX9ojExckZRHQ0SCghqz4PnHtc5dJYv\nF/wuCwfsEO+qHRQUr9phQsEgVk5hS6gKKdqtGEkvwMryNBFj6D4jGVNQCCFWKqVWCSHeN9pxpdRv\nTl6zDIYTT58M0e0E6PcEVUWziqwSlEd0z92YCNFlaXfXaBC2WxGurh/swN/WNJia9uL6OAJGnQXk\nWdowscWaF9XHmV8VGb/gKCTCAS6pn+Ci0GFc01JO2jesL6wZKqiqIkGuaogXvLAAFizQfyCo2Cyo\njQULbr4V4SBpGeDapjjNiRDdWZdnOzLYKkAiHGS/HWJe9Iz2gzlrKTWjuAZYBdw2yjEFGEFhOCPo\ncQO8nouSlEEWVIXZZ+mFZC1hl5aIh6UC1Ie1oGgui7CmP0JreYgAsCflsnAUTySA8nCAZc0nJqf5\nVY1Tk/J21ijG8zyhgODalrHvr6kszOLawblOXlU1tyJCYyLE7IoIr3RlsDzB0toYB3osJusw+dRA\nnOXlOaO2mmLGFBRKqS/7/z916ppjMJx4+rwAh5wgkQBcNiPOk3ttUp4gEoD96TD9MsB5/qi4pSxE\nKBjgprYKtvRaJMKBgreRYSi3zx0aELMqoleLFxvkayJatdWYCPFGdwiwJly/o6DHC9LnBYgHSkfI\nNZxcJuL19DkhRKXQ/FAIsUEIceOpaJzBUIxUsC03cgTc4QTpKbGqOi0D2uMoFqSlLEy/K7ClDrK3\nx5XMgNUAACAASURBVA7R6wYo8+0MM8vC/OniWqoiQa5siHNzW8WY9RqGUhkJ8tkLaoao4uriIZrK\nwjTGQ/S6YlJRa5P+yu+BU5QIyjA2E/kGPq2UGgBuBOrQYca/dlJbZTCMQk4JNmYjQwLVvXkwwOqB\nOJt6xw6zklFBVjSXcUNrBaGAoCURZEFVhKOuoDaqfwL5uEtAwSgshJi0gfhsJzjseV3VmODKxgTx\nUIBYkEklWsoLiKQ0gmKqmYh7bP6bfSfwEz8w4En/9Qghbga+iY5U+0OllBFOZzmWFCh0Ep6o0NFY\nf/p0CBWNMv8qi305aBslekpaatfXvEfPtbPKiQUDHEjZLK6NUhsLTdq11DAxqouea0M8RJ8XoMJP\ntKSUFv5RofR3GgBb6dFrSMCA1OFRkrYco3bDqWIionq9EOJJtKB4QghRAZzUb04IEQS+C9wCLAL+\nSAix6GRe03D6kw/rbfn/29uhrD7I1jVh7Kxg047RX+e0J6gqWuDWnNBrBi6qj7OgKsrC6qiZOZwC\nGhNh+rxgIRBhjxfgqWScvXaIlzLaa2tdJl7IpZFSQeZXRYzq6TRgIt/AZ4AvAVf4CYwiwMk2cC8F\ndviRam3gPuA9J/mahtOcfM4G2xcUS5ZA+YwAycNB9mwM07Rw5OvsKrDUUNVSnrfPLKM+PpFJteFE\n0JgIccQL8XC/jpvV5QTJyAAdbohuJ/h/2zvv8Diqc3G/38xsVS/uvRfJsmXjCsbYpptmMKGFFhMC\nuYRykxuSQEIg3IQbIBhTfgRCICaJCYReAgaMY0MA9w6427LcJBf1suX8/pjRaiWrW7Lk9XmfR492\np5xzZnf2fHO+SnFI2F1pUhw2KQ8LeQGTQUkegsou36ppPxqKoxhda1P/46BxqqIHEF0ebjcw/nh1\nrmlfdlXa0cL9avncV60kKqL03AmdhSN7DA5ssyg/22RlqcFATwCfoVhW6mWIp5IES9saOgJdfBb7\nA7bA3hc0yQ+7sAR2V5q4DPiq1Eu8SyhRBqvLPYxM95LqNUnxGBSGDdINLS3ai4Yepx5tYJ8C2jXN\nuIjcDNwM0KNHj/YciqaV2Re0KFPGUYKituppzXpwZ8OhvQaHdpscKDHJdwt+Q2GJYlelRUHIYHCq\nLrLTEUhy295n3fwu9lSGyA8YZKZ6WH2wgux0LxsOVXBx30Te2naEQizOdYII070mhZUG6ZYtKIpD\nQknYoIvLtnXsCZikmiG8WkPVZjQURzH1eA6kFrlAtFmyp7MtglLqWeBZgJEjR+ponBiiKGxy2Mm5\ndDhkkGyGsQQqlYHHEApDBpvKXQwaEeSTA4LPJxzKsQgnCUEl5IUsSsIGWWke1h6sYGRafQkwNMcT\nEeGCPgmkey2e/TpAdpqHvoludhYFOK1bHOM6+/GaQqkT41JlV0rzWhwoN8kLhulkhfmmwk1R2KSL\nq5S9AZMlxT7G+8vpq6O+24ymFC7yA/8N9FZK3Swig4AhSql323Bcy4BBItIPW0BcCVzdhv1pOgBK\n2V4ShSHBawpbKlysLfMwLq6cPu4gFcogzWuSU25RoYQJncMMiTeYOxdOPc3g7VKhq88gtwx6+E3O\n7hlPzziXtkN0IAY7Ue7XDU6im99WMSa7TVyGRAo5xVlE8mOBHYvx5X6LLeUWlySXkBNwEVZVdUS8\npHiMyCpT0zY0ZbH2AlAJTHLe5wIPttmIAKVUELgN+BD4GnhFKbWhLfvUtD87AxafFPtRwJhOXrYE\nvKT7zIjXS4US0rx2PqGgEjZVuBnQycXs2TB0iJDoEvonehie7Oac3nbMRJZeTXRIusfZiRYtQ2ok\nIgRbcHSPr857leYxCSghjPB1uZuuPhOfKewKWAQwGJbs0YKijWnKo9YApdQVInIVgFKq9HjEUSil\n3gfeb+t+NB2HfUGXXVfBazCxaxwTu8ax4VA5a/dVAlAetvXVYKslDlTCxVGCoHeCm94JLk7v3jr5\nlzTtw/A0L12iVoGpHpORaR7Kg2E2FyrO6OFlZ2Elq4o8jO3ixWcZ7NeCok1pyoqiUkR8OHWzRWQA\nzUnYojlpWF3qpqIZjikVYVhR6omoEfYH7KR9ad7qIK00r0VRyEApu+RnVWGdiV389I23ahTaOb9P\nAn0TWpaBVdNxyE730T2uOlWLaQjn9U6gR7ybgBL6J7rpk+Cma5yLCV3tqO9Ak6Yym6WlXrZVaHVk\nc2jKp3Uf8AHQS0T+BpwK3NCWg9KcmGyrdOEzFEO8gci2l14X1u0yGZEW5Nprax7/Ramf/QGDfu4A\npoDLFGb0SSAYJWxSPSYFIbvgkCGQ7LE9Z0ameRiZpr2ZTiZ6xFmkegxSPCajO3kZ3cmLiOCzpIbq\nKaTg23IXw332fXgoaFASFnq5Q3ZRqnKL7UGLYGUJg3tqP5im0KgYVkp9BFyKLRzmA6copRa17bA0\nJxphZQfC7Qi4I4nfXnoJVu+zGPVdL+9+ZvDSa0JOpUlQ2dHSBWGDkWkeDgRNDocMuvosPKYRqW8A\n4DYFnwn7gyZJLiHNY3L1oGREpME6EJrYo0ecixuG2PW8o79/n2nUiK0pDBmsLfcQcO7D3IDFmnIv\nSsHGvSY5Xxts/MzFnBdNcnKO6qZZbMuBdz6WY26no1OvoBCRc0RkFoBS6qBS6j3H0+kMETnruI1Q\nc0IQUOA2wGOZfF1hq39eew1S+1jsWmcx9Ew3e+K8rCj38225i/1Bk97xLvomuskLuygKGfV6J6V7\nTHIDFsmOJ0x99aM1sU9dKd99lkQERXlYIkkEj4QMysNCsbKjvvcHTdblmmz+0sPury2SexqsXdvy\nseTkwDPvu1hd7OHmm4lpYdHQiuJXwL/r2L4IeKBNRqM5YalUgs8ULhuYxIZyO8Or2w1pfQ1Wvusl\nqZtJYjeDM7rHsSPgZn/IRf9ED73jXeRVGhSFTVK9dSfm6xbvZk+lRYpHCwjN0fgsg/KwbfN6u8BP\nbsC+j9aUevi02EdRyGBMupelRV7KO7lZ8a6HfVssknpaZGW1vN+1a+0HIW+ybUM7FqHT0Wnol+dR\nSuXV3qiUyhcR7VZykrOlwiKohKGOPaJS2bEPCS6TOEvYGzC58DduVILwzWI3598hmC5wH/Tgc5Vy\nOGTQP8mF3zJIdBvkViim1pPBtYvPIoCQUo8g0ZzcuAzBENhe6SKMkFNp0dVvsc8pjW4A3Uv9vP0Z\nLP23Sd4OC0OE3sMNErsIbxf4CSgh3QrTz13JoZDJKF/j/jpZWbCnzCAUABGOSeh0dBoSFIkiYjkx\nDRFExAW0rECvJmY4EHJRFjYYii0oKhxBAZDqMdha4cJKN9nzjUFRvl2wpuQIfLFRuPF7KTVyL/VN\ncJFfEa5XEFT52Se7taDQ1I3PhC2VbgYmudlSUMnQZDcHy4J09ZscKAkxNtugrCw+cnzBPgPLB5+V\n+Dm1ezyZqR7+tPEwa8o82AHhjQuKTj0guUAIheGPz0KvOlLcxwoNqZ5eB56LXj2ISDzwDLpe9knP\n4aDBwYBEDIaVYcFrVadc2BcwKV7t5+VfJAFC/k6D/J0mkydzVIK+Polu/JbgNeu+HZPdBl5TdM0I\nTb34LINkj8U0J4YmK9XL1YOTGJDkIVRkEqpVSTUUFCZ5Ezm7TyKj0714TIOsdC9KhKKQEFRwIGBQ\nFKrfYeJwyKSb38BjQZcYTzfXkKC4F9gP7BSRFSKyAtgO5Dn7NCcRyvFUCisoCAnlYaGb3yAvaE/e\nlUrwRQmKMMLofi7yd9n7D2y3yOxrMXjw0W33S3BzYZ/6S46KCLOHJdcogqPRRHNO7wRmDUgixWMw\nq38ifpdBN7+LrDQvp3WKx6x165gmfPMfN4OSPBHvqbGdfVw2IIlkt1AQMthY6WVd+dGR/UrZRvPD\nQYNucW7iLaE0xqvw1Xt1SqmgUupn2Mn5bnD+eiulfqaUCtR3niZ2iK4BcChksKDIz7ZKiw8L/aR7\nDfol2q6tYFcmqxIUVUbpb5ZV/zq/+IePkk11aywtQ+iX2HCgXIJLCwlN/fSIc+G1DESEgUnV95Lf\nMpgwzMXq1XDnneBygddrC4rJk2u24TUNesS56OJ3cThkcDhosC9gUlKrfOuegMkHRX4Oh21bSKLb\noLQZJV5PRJoSR1GmlFrn/JUdj0Fp2p+QgvcL4zgctG+RQ0E78dqmCjf9Et0MT/HSJ8FNXtCOoA1g\nRARFF59FRoobgtW3V+7XLijSXkua9mHwYHjsMVi/Hp58ElaupM7VLUCveDfbKt24DOif6GJ/oOZD\nyvaAm/KwkFtpl9hNcJuUxngKkdheL2mahVKwsszD7kqTPQGTciUcchLyHQlbuA07mOnc3vGM7eKn\nu9+iMCRUhu0U4FXGbJ9lcGHfRC6/HHw+203W54PLL2/Pq9NobOEwe3b9QgJgWIqbopBBF59Fus+K\nxGWEFCwo8pMfNMlK9WCKkOoxSfFYFIeqhYlS8FmxN6aq8ulHPA0AewMm68q9lCuDwyETA+jqtzgU\nssgvMTkUNslO97GloCKiBjINobvfJC9oZ/f0WTWfqgYPhtWrYckSe5nf0I9To+koeEyD0eleEt0m\nfpfBLmXf73lBE5dl8YNByeSXhygOhBEResa72JBvUeUpVRwWdgcshocNUmOkKl+DgsKpRSFKqZLj\nNB5NO7GxwsOYrvFkpHh4duMhElwGZ3T388qWACCEgUldfYztXNPO0MlnUVRiOHEURy9QBw/WAkJz\n4jG1ZzxKKQ6UhSgMGeRWmuQGLQalePBZBr3iDXoNTAKgm9+iIGjnI3OLnW4GqisyxgIN1cy+HZgI\nKBFZppR67PgNS3M8KXBKS2anezFEuLBvIqleE69p1wA4r3c8LhE8pkFtx6Nkj8XOIjvpWoJLazI1\nsYOI7ZJdGBQ+D/oIA5OTjna6sAyhq88kP2jiEcVOx75RFkMG7oZWFNcDpwACLAe0oDjBqErO11Du\nPKVgVZmPsZ39kfiGaA+kM7r7GZ7iiVQfq02Sx6AwbFIehkS3FhSa2MJtCn5LGJTkZkiKh6715CPr\nk+jmwBGTvUEX3ePddDcNyktaVo1BKbumQz0/uXahoV/277ED614D5hyf4Whak80VLr4o9UUERl18\nvMXFtp0uCtbU7bo6oYu/XiEBkOQ2yQsISS45KpBOo4kFTunkY3wXP30T3PVmLO6d4GZ3wEV52F6R\nd/JalKuWPTjlhwwWFXes5BcNxVH8Qyk10/mbdzwHpWkZFWFYVVq9GigJG+yqNMkJ1B2D8J+VQo7L\nw5M3J3LeucKHHza/z0S3QRjb+0OjiUUmdPU3GuzZ3W9RFhZ6xtl5pOJdBhXNcCoNKfiqxINSUBAy\nOBQyCHegUhlaVxBDlIQNtlW6IiuIAAadvCb5obqXy+8tMti32eTwHvtH8Mgjze/Taxp4DOpNEa7R\nnAxYjgdgb6fCYpzLoLwJ0dpfl7soCwv5QZPtlS5KlURqwhd3IBuH/nXHECEgoGzvC4/YaTV6xLs4\nUHR0IH1ODhw4InQ6VH0zl7TQty3JbZDq1beS5uRmRp/EiIt4vMugrBHP2LKwsLbMg1cUhY5QKQwZ\nlCoTU+xcUolmsOFGjhN6RXGCUxiSyAoi6LjjlTg3XaUSesRZFIZsr6Topezbb4MvSSg+WH0LrFwJ\nmzY1fwzjuvjpl+Bq/ECNJoZJ9ph4HBfxOMtO66GUXf2xdhoQgB2VFqbAkZDJvqCLrj6TgpBBSdig\nT4KdRqSj0HFGomkR/y72cdC5oYKOIKhaslYq6OyzKA/DwiI/myuqJ/N9+8CbKBQfqta9mqYdHNdc\nMlO9JOgU4BpNBLcpuMSOpVhX7ubzkqON0wdCLkale8kNWlQoYUSal6KwSUnI9rIqCnecVboWFCcw\nYQWlYYNDToBP7RVFRVjwu2yPpAA161lfeCF4Ew2KD9vHejy2G23tRGkajaZldPKZ5FRabK5wU1GH\nB1RxSBia7KE4JAxL8dDJa3EoZFKp7CSHda1C2ouOI7I0zaY0LCigIGwCAULYQS8lYbs0Y2XYNjZ3\n8bvIjrP4an8ZBSGDZCvMuHGwv7tBYoXwg/Pt9nSaDY2m9ejqd7HhoIt+CS6217IThp20/V39Filu\ng8w0LylukyAGKW4h2W1S5KiVO4LXuRYUJzAlYQO3AYfD1SuKZLdtDAth32AuQ5jRNwEDyCsLsr/M\nJC9kMMAdxJ8s/Oy/DXrGN9iNRqNpAV39FsvyDIakeNhaGCCkYF/ApEwJ3V0hvKbtLTV7WAqWE6v0\nw8xUgsr+3VaprnzSOn6ypWHh63IXY/yVzT5Xq55OUPYFTPKDBn3iXRQGhZCybRRpXpPSsFCpBI/z\n7ZoiiAh9Ej1sqnCzotTLkZBdkN5v6VtAo2kLqkr49klw4TFt55IDQYtvK2x1U6KT8saKCmgVkUiA\na6LbaFX1096A7YKrnOJjecGm//ZjbpbYngPvvm+7f8Yy6ys8bCx308ln4THsmtUhhFSvRUlI7NKk\nZs2brHe8rfd0G3AgaFLu2DA0Gk3rk+YxubhvAgkuO29apYJSZZdXzQlYJDUSxJfsNiP2xtbgYMiK\nxGd8XuLnsxJ/k8+NKUGRkwMvLfXwr1UWN98cu8IirOBI0CCEkOQxcZt27eoQ9lOKwnbH89ZK+x3v\nMhiT7mVK9zj2h1yEFHg6UkIZjSaGELGN1IAjKOxgumHJbrZWuBqtAZ/iNSluoGZ3c8kPmfhMWFvm\npnOcmwldTjJBUaXBW7sWXH5BXLYRaO3adh1Wm1EcFnym0NlrkuoxcRtCQAlBJbhMId6ya/7Wlfb7\nrF7xDE32sD9gkOCSenPXaDSa1sNr2qv84pAwvWc8Xf1Wo9kMkj0mpap13M5LwkJFWOif6GZ3wGJo\nioeJXZsuKGLCmF0ctCe7rCzYsEnwxtmG3Kysdh5YG3EkZNDZZ3Jp/yQMAY9pC4oQtn4z3mVwOGCQ\n5Kv7OSDOZfCjzNQOlZ1So4llfJZBcYVBGIizhO8OTqaxn1+qx6QoXL+gKA8LK8s8jPWXU6VB3lFh\nEQb6e2pGdO+osBia7CbFa6EOV9I3oeEa9bWJCUFRtaLo1Quy/EJpOtxxkf0+FjkSMukS78J0Znq3\nIQTDtteTy7CNYJvKLIbF1R8t7dNGbI3muOGzDA6WGiQ6q/imPKOlemwX2WiUgl0Bi16uIPsCJrkB\nC7PMy3h/OQB7Qy5yKkzSrBKSTHtm3FxhsS3gZla6j+JgmDSPQXwza8fElKAAsDzCgMFCr46Vpbde\nSkJ2LES8qTgYNEg0wzRkX1YK9gRdjIp6IvBYBoEKIYTtVpfoNgkqoVe8Tquh0XQE/C6TLSGD1HpW\n+XUR7zIIOvFQVaVevq5ws7bMTUpiCXkhixGpXnYUlEXOKQ3b2oY9AYsk03bJXVXq5exetrorjJ2t\nobnExGNltKAIOUbdE4HikPDvEj+bKuxJf3WZh9zK+r/EXTnw3Bsm+w4YbPxP9XFeM8pGYQgJbhOv\nCelenVZDo+kIeE2hIGTSI77pKh8RIdktFDmeT0rBtxVukl1CWdggL2gyONlNZZQLbXFIGJDkjqis\nSsNCnCWMSvciIpgiJLUg3U7MCYqgglAdtWobKt7THmytsFhQHIdlmQScr6FMGZGboooqP+qcHHjy\nX25cY3y896S/Rv0It2kQcOIoXIYd7ZmV6tWGao2mg+AzDQxgVHrzVB1pXjupJ9i2SZ8pdPFbFIQM\nypXQM85FhZOlNqjs/G59EqoFRUnYIKkVKk+2i6AQkYdF5BsRWSsib4hIctS+n4vIFhH5VkTOaW7b\nIceoG822HHhln58dHcRdVinYXOnh0v6JTOsRT6WyvbRKQ0JxlJdDSVj4uNC+sdashUFnuPjj7GSW\nv2lvmz/fPs5rCkGEoLLtFT3iXEzT4dYaTYehi9/k1K6+ZtsGOvksDofsOeFA0KR3gosEl0Fe0CDJ\nJbhNAbGFRElYSLCEdK9JQdCeU0rC0mjRpabQXiuKj4BMpVQWsAn4OYCIDAeuBDKAc4GnRaTRq4xe\nLQQVRKeBz8mBn//aQHkNHn7J6hCxFYdDBohBxR4XH78vFJYLFUoIA7sPGZExBsL2dqVgT7lJYZ7J\n/q3VKqdBg+z/blMIYkRUTxqNpmOR5rU4tVtcs88bnuplV8COecoLWfRJcJPgNskPmhEB4DGIxGgk\nuQ38loHhpP8oDhuNxms0hXYRFEqpBUqpKv+tL4GezuuLgZeVUhVKqe3AFmBco+1FvQ6q6iyqAIsW\ngVgQCkL/yW4WLTq2sW+vsI45CGZ7nsGhnRbZ2cKD9xns3g/L1glH9grlpnDzD2wBFwDCCF8th7Vb\nDHLW1zROV12L27DtE1WqJ41GExukeEzSvCZbK1wcCJoMSHIT5zIoUwYpHvuhsSqYrzhkRIRHqsfg\niFMEqbEI8KbQEWwU3wP+5bzuAUQ/8+92tjVK2CkQEqamMXvTJnDHCYdzDRI6Gy0qzBPN1oCbnEDL\nncVycuDlN4XPPjIpK4OD+wRvorByo5C/w6CiBDyJwqJF1QJvyZeCN0kiKcFr4zENx5gN2utVo4kt\npnSPZ1WZh4GJLrxmtWtriuOsYgfzQXG4utLk6E5+1pR7ORI0SG6FWjFtNq2IyMcisr6Ov4ujjrkH\nCAJ/a0H7N4vIchFZjlKEsEuBgu35VMW+feD2C4dyDbwJ9vtjoTRskBdqudvpokXgTTE5uNv+8sIV\ngssLw8cYFOw3ObTbwJ9u8Ne/wr48+5wJk21hUnKo5tf1k5/Y/92mUB4WTAFDG7A1mpiid4KLs3vG\nMbGrrbqKd54GqwSA1zLsFYUySXWER0aqh4w0P93j3XTyHbugaLM4CqXUmQ3tF5EbgAuA6UpFrAy5\nQHSYXE9nW13tPws8C9A7Y5QKquoglmhBcckl8N4aKDxgYFpw8cwWXIxDWEF5CPLCBmFFnZHNIWWr\nwqwG5uuETgZH9tpf3vnnC14TEnsYjBpm8nWOIj5NOLQFduYKDIQhGbCh3GDNwmpBccklcI5j6vcY\nQlGISLZYjUYTW2R3qvaWqlpRVKmZfKZQGRKKQkKao44SESZ3b75NpD7ay+vpXOCnwEVKqdKoXW8D\nV4qIR0T6AYOApY22R5Vtwn4fjBIUM2bAWecJKQkGloJp57b8ibtc2ZN6isfgrcI4SutIAbypwsXa\nMk+9bWzaBAmdhcN77I9+xw5wIRwoNynOMzmyzyS+k23A7tarqqSp0GeQQWWJgWna1ej+7/+q2/Q4\nOsqecSdO/KRpmowaNYrMzEwuvPBCjhw5clz779u3L/n5+Udtj4+v21vsmWeeYd68eQ22+eKLL3Lb\nbbc1qf9Zs2axbds2SktLmTFjBkOHDiUjI4Of/exnkWMqKiq44oorGDhwIOPHj2fHjh2Rfb/73e8Y\nOHAgQ4YM4cMqP2nnukaMGMGoUaM45ZRT6uw7JyeHqVOnMnz4cDIyMnj88ccj+w4dOsRZZ53FoEGD\nOOusszh8+DAA33zzDRMnTsTj8fDII48c1WYoFCI7O5sLLrggsu3KK69k8+bNTfo8NE3HYwqDk9wk\nOm6vXsugPCyUhoWkNnpabK9n0CeBBOAjEVktIs8AKKU2AK8AG4EPgP9SSoXqb6aaEHZksts4OuBu\nwFDh+msMUuKlRnBKcykL23mUrh+SQne/i/w68rnnhVyU1fpYN5W7CDjCa+kyR1A4K4rVq+HbdUKJ\nYfCnR1wcyjXxpdgV6qouvFIJAYEpE4Q+fWD0aNi+vbp9t5NOvF9S/QKqo+Hz+Vi9ejXr168nNTWV\np556qs36CgaDjR/UCLfccgvXXXddK4wGNmzYQCgUon///gD85Cc/4ZtvvmHVqlV8/vnn/Otftsnu\n+eefJyUlhS1btnDXXXdx9913A7Bx40ZefvllNmzYwAcffMAPf/hDQqHqn8mnn37K6tWrWb58eZ39\nW5bFo48+ysaNG/nyyy956qmn2LhxIwAPPfQQ06dPZ/PmzUyfPp2HHnoIgNTUVObOnctPqvSdtXj8\n8ccZNmxYjW233norv//974/hk9LUhYhwaf9ETEfN7HeZHAqZJFgS2dbatJfX00ClVC+l1Cjn75ao\nff+rlBqglBqilPpXQ+1UIeJEZCu7BkMwXHN/EMFjCj7LdjdtKaVhIcFlYBpCj3hXxL+5irCCvIBB\nea2guQ0lLt7/zGDZXoukzkJ5EQQroqIpDxsU5UP+LoOCAwZxqSZKwb58+5gDh4WDRcLfXjDYtg2+\n+ALOPZdIwF1VqvDmJvrqKEycOJHc3GoN48MPP8zYsWPJysrivvvui2yfN28eWVlZjBw5kmuvvRaA\nHTt2MG3aNLKyspg+fTq7du0C4IYbbuCWW25h/Pjx/PSnP+XgwYOcffbZZGRkcNNNN6EaiMC85557\nGDlyJBMmTGD//v0A/PrXv448SS9btoysrCxGjRrF//zP/5CZmRk5d8+ePZx77rkMGjSIn/70p3W2\n/7e//Y2LL7ZNdX6/n6lTpwLgdrsZPXo0u3fvBuCtt97i+uuvB+wVyCeffIJSirfeeosrr7wSj8dD\nv379GDhwIEuXNrrwjtCtWzdGjx4NQEJCAsOGDYt8/tF9Xn/99bz55psAdO7cmbFjx+JyHW2f2717\nN++99x433XRTje2TJ0/m448/bhVBrakfrynkB6vtE21BTGi1DardQ72m1LBRAASVYQsK06DyGCK0\nS8MGiY4BqZvfxZFwTVVPgRNBWR4lqHJyoDggLFxqsNXrpddok/ydtaKvDxtsX+EChIJ9BnHpgmFA\nWmfBbwl78gW3H8oKawq5qoA70xBm9U8kuRUiMI83oVCITz75hIsuugiABQsWsHnzZpYuXcrq1atZ\nsWIFixcvZsOGDTz44IMsXLiQNWvWRNQlP/rRj7j++utZu3Yt11xzDbfffnuk7d27d/Of//yHP/zh\nD9x///2cdtppbNiwgZkzZ0YESm1KSkqYMGECa9as4fTTT+e555476pgbb7yRP/7xj6xevRrT6o4L\nQQAAIABJREFUrPnjXL16Nf/4xz9Yt24d//jHP8ipI3Dn888/Z8yYMUdtP3LkCO+88w7Tp08HIDc3\nl15OZkvLskhKSuLgwYM1tgP07NkzMtGLCGeffTZjxozh2Wefrf+Dd9ixYwerVq1i/PjxAOzfv59u\n3boB0LVr14igbIg777yT3//+9xhGzfvPMAwGDhzImjVrGm1D03K8plCuhMxUb5v1ceIotRtAxFbT\nCPaKIgQ1ipIHIisKg4qKY1A9KaGrY0Dq6rfIqzB4dxGMzIIePe2qc5mpHtYdqoics2Y9uCYI6QMd\nb4RzXKz4qOaT/7dfuKkotn9kBQdMkjoLf/oT7I03UKZJoIvBoUJQtVZDV11V/XpgUstXEwX339/i\nc+sjKWolUBdlZWWMGjWK3Nxchg0bxllnnQXYgmLBggVkZ2cDUFxczObNm1mzZg2XX3456enpgK0K\nAfjiiy94/fXXAbj22mtrPMVffvnlkYl88eLFkeNmzJhBSkpKneNyu90RPfuYMWP46KOPauw/cuQI\nRUVFTJw4EYCrr76ad999N7J/+vTpJCUlATB8+HB27txZY1IH2Lt3L506daqxLRgMctVVV3H77bdH\nVFIt4bPPPqNHjx4cOHCAs846i6FDh3L66afXeWxxcTGXXXYZc+bMITEx8aj9Io3XK3n33Xfp3Lkz\nY8aMYVEdQUqdO3dmz549dQpGTevQze/i1C4+hmtB0ThBZS+PXIZgOYKj6uICyjYA+V0GpWXNExTR\n3k3lyox4HORuNyg4DH//m1D2pOKh500qk12c2SuedYcqCChwCQwdISwHug6zJyx/V5PcjTWX7+sW\nVH/BJYcETxx0T4NdZZDoMtjvMXDVsq1Eez0dK41N6m1BlY2itLSUc845h6eeeorbb78dpRQ///nP\n+cEPflDj+CeeeKLZfcTFNd/rw+VyRSZH0zSbrTbxeKrtRPWd7/P5KC8vr7Ht5ptvZtCgQdx5552R\nbT169CAnJ4eePXsSDAYpKCggLS0tsr2K3bt306NHj8g5YE/QM2fOZOnSpfTr148LL7wQsG0tt9xy\nC4FAgMsuu4xrrrmGSy+9NNJWly5d2Lt3L926dWPv3r107ty5wev9/PPPefvtt3n//fcpLy+nsLCQ\n7373u/z1r38FoLy8HJ/vBEnlfIKS6jVb1cOpLk48XUUdCLZ9oiqFhSk1XWQrleA17dD2ynoueWmJ\n56hC5uvK3PzzSDzfVtgTe7mSiA/zkiVwaLeBL802PG/bb9A7wYUpgt+JawAIOJNOQieDgzn2ubs3\nVsvnWqt1TFNwK+H9hUJxmZDktmvspiXUPPCWW4gJ/H4/c+fO5dFHHyUYDHLOOefw5z//meLiYsBW\nvxw4cIBp06bx6quvcvDgQcD2zgGYNGkSL7/8MmDr/idPnlxnP6effjp///vfAfjXv/4V8eZpLsnJ\nySQkJPDVV18BRPpuDsOGDWPLli2R9/feey8FBQXMmTOnxnEXXXQRf/nLXwD45z//ybRp0xARLrro\nIl5++WUqKirYvn07mzdvZty4cZSUlFBUVATYKrQFCxaQmZlJr169WL16NatXr+aWW25BKcXs2bMZ\nNmwY//3f/11vn3/5y18itpT6+N3vfsfu3bvZsWMHL7/8MtOmTYsICYBNmzbVsOFoTkxiYkUhTlIs\nQ+wVhctwDNhOco9A2E5z4bUMAqpuQbEvaNE5ECLOqQx1KGiwM+jmzJ5xfJ0XYggBysNCnLOimDwZ\nPnrFIr2PHfOQ0tMkzYmK9Fu2zjABxabtQmV/cPtg7UceMqdXRASGZUHtB87MTNiy1mDjewbZfqG3\n18QQwdoej9cL5eXg9YJj74wJsrOzycrKYv78+Vx77bV8/fXXEdVOfHw8f/3rX8nIyOCee+5hypQp\nmKZJdnY2L774Ik888QQ33ngjDz/8MJ06deKFF16os4/77ruPq666ioyMDCZNmkTv3r1bPN7nn3+e\n73//+xiGwZQpUyKqpqYyY8YMFi1axJlnnsnu3bv53//9X4YOHRoxMN92223cdNNNzJ49m2uvvZaB\nAweSmpoaEUoZGRl85zvfYfjw4ViWxVNPPYVpmuzfv5+ZM+1AoWAwyNVXX8255557VP+ff/45L730\nUsSNFuC3v/0t559/Pj/72c/4zne+w/PPP0+fPn145ZVXANi3bx+nnHIKhYWFGIbBnDlz2LhxY50q\nqyr279+Pz+eja9euzfp8NB0Pacj740RhcNZoNfeVdxBA/IlsK6xkkq+YRKfC05sF8Xx/eCq5JQGW\n5hZwWlzpUW3880g8A9wBRvkq2BMwKVVCpSeREWk+Ptl1hKlxJbxREM8PMlLxO6uKd9aXsnlnAWMT\nK9iSGsfU3kn0TXDz6pYjdA0X0csdYuVei4Vb4uiZGeKvP0lgw8LGXVhn3VdIyZ5Chp/nISUnhTtu\nNdiz3WDUKAiFwDRtt9rBg1v1Y9Q0keLi4ki8xUMPPcTevXtrxCI0RllZGVOnTuXzzz8/yhgeSzz2\n2GMkJiYye/bs9h6Kph5EZIVSqu6AmyhiQ/XkrChCgMsULKO6JoVyKkR5TCHVa3I4JEfVpgg5wXpH\nlEVxWFhS4iMvaNHZ77JdasP2MYGwHQVZxeCeJp37mvTqBYVBIdUxdMe7DPYFLMrDQnyKcOpIk3AY\nDu9p2qRwcLdJfCcDl1c4lCfNTk2saVvee++9SLDgkiVLuPfee5t1vs/n4/7776/hEhyLJCcnR1xt\nNSc2MaF6MrCD7JQCl2FgiUQFq4EpYBlCmsdEIRSHhQSzWlpUKtsAfiggFDiTck6lySSfaXtKhW1V\nks+khheIXfxcqFS2wTzBObdnvJtFhQHiK8MEleAVk7Ufusnf1bQJ//Bek76ZJm4/HMhzEgMusQVi\nZSX4/fZ7vaJoH6644gquuOKKY2rjnNbyROjA3Hjjje09BE0rEROCwnaPFY6EDHq5DCwn7TbAkZBJ\nJ6/hHCf0TXCxv9Ikwaw2DlQqSHDZgiYnYGGJnbm1s9fC46woysNCXK3UrCkek+KQcCRokOKudiUc\nkealMqzYnleJIbaH1Ku/jCNcKxAwmmHDoEcPKCmBPbkmCV0M3D447ATdTZ5sX6ffb/+vx2ar0Wg0\nrU5M6DQEYUeFRch0MSLVg9uozvd0OGTQ1V/tjjogycPOgJvwUV5RQu8EFzmVFsNTPMRZtjutKbYq\nqzBkEOeq6RVlGUKyS9he6aJbXE2X1yS3SakyqVAGwwcKdQS01mDbNli82M5NdSjXILWXLbhmnGf3\nOXgwrFwJc+fa//VqQqPRHC9iQlB4LeHygclcPSgZ0xDi3SblVSuKsEXXqEl8WIoHt8vFlorqbZVh\nW1D0SXATRshM83L9kOTq9k2hIGQQ7zraxtDVb7Gr0jpKUCS7DUqcCnX9ehr88pcNX0NFha1WeuAB\nKDtiEKwU3v6/OH70IyI1NAYPhtmztZDQaDTHl5gQFAL0infhdVRDCW6T0nB1QfIuvqi4BRHGd/Gz\nL6qmRKWyo7Z7x9vb0jxmJFUH2AbsgpBBfB0FQLrHuwkhdPXX1OIlOWqpQwG7//ffb9q1hMMQDAq/\nPSeVZW/4CAZte4RGo9G0FzEhKGqT6DIpVwYVYSgJ2cXGo+nqtzgUNCLeT1WCIsFtcuXAxEisRBU+\nSzgYMiNeTbXbMoBO3pqCwmUIbsO2fcS5DL73vYbHbBjg81UH4IWD9oooVu0RHTXNeH3bjwWdUlxz\nohOTgiLeZVCuDA4ETXrEWZi1KgxVeSdVqacqHEEBdWdg9TsVpPokHG1o6Oq3uKhvAlYdVYySXAZ9\nE+32Zs+GO++ETp2gZ8+jDuX22+GJJ+Dpp2tuf/rp2FQ1nWhpxluKTimuiQViUlAkuA1Kw8L+oEW/\nxKMnfhGhs88kP2iwqNjHzkorIijqwm/ZXk2JdaieTBGGptQdRDc0xRtJ1PXhhzBnDuTl2VHV0bnW\nPB649VZbmIC9soj+H+t0tDTjVfzhD38gMzOTzMzMSHqNhx9+mLlz5wJw1113MW3aNAAWLlzINddc\nc1QbOqW4JhaITUHhMigN2YKivhoN3eNcfFnio9JwURI28JpHrwiqSPRY9K9D4DTGhK5+ejhG7toJ\nWocMsRP73XknrF1bvWqYPNlWP/n99v9YVDtF09HSjFexYsUKXnjhBb766iu+/PJLnnvuOVatWsXk\nyZNZ4hiNli9fTnFxMYFAgCVLltSZpVWnFNfEAjERR1GbqlTjpmHQuZ7C4pO7xzGmkw+fZfDU+kN4\nzfpl5imdvBxropPaKvhvv4WtW+3X555bLSiq3GCXLLGFxPFQOz20qnV18gA/y05vcH9HTTNexWef\nfcbMmTMjGWgvvfRSlixZwq233sqKFSsoLCzE4/EwevRoli9fzpIlSyIrjWh0SnFNLBCTgkJEiLOE\njFRvvTe/KUKCo0qaNSDxKGN0NEYrlBf88Y8herWuFAQC9usLL4T162sKi+Npl2hsUm8LOmqa8cZw\nuVz069ePF198kUmTJpGVlcWnn37Kli1bjtLbg04prokNYlL1BJCd7iMrvWmFPHrEuSJ1p9uK2bPh\nT3+CSZNslVM0Sp28LrAdNc345MmTefPNNyktLaWkpIQ33ngj0vbkyZN55JFHOP3005k8eTLPPPMM\n2dnZdT6U6JTimphAKXXC/40ZM0adSHz7rVIej1K2iFDK57O3nUzExcXVeH/BBReoefPmKaWUmjNn\njsrMzFSZmZlqwoQJasuWLUoppV588UWVkZGhsrKy1PXXX6+UUmrHjh1q6tSpasSIEWratGlq586d\nSimlrr/+evXqq69G2s/Pz1dnnXWWGj58uLrppptU7969VV5e3lHj6tOnT2T7o48+qjIyMlRGRoZ6\n7LHHIsd8/PHHyrIsVVxcrJRSatCgQerRRx+t8zrnzZun7rnnHqWUUjk5OQpQQ4cOVSNHjlQjR45U\nzz33nFJKqbKyMjVr1iw1YMAANXbsWLV169ZIGw8++KDq37+/Gjx4sHr//feVUkpt3bpVZWVlqays\nLDV8+HD14IMP1tn/kiVLFKBGjBgR6fO9996LfCbTpk1TAwcOVNOnT1cHDx5USim1d+9e1aNHD5WQ\nkKCSkpJUjx49VEFBQY12P/30UzVjxozI+3379qmxY8fWOQZNxwVYrpowx8ZEmvFTTjlF1ece2BHZ\ntAmysuxIbIDf/Abuuad9x6RpG3RKcU1H5qRKM36i8eqrdsqOqjXFvffa7rOa2EOnFNfEAlpQdBDm\nz2/vEWjainPOOeeYKuqdCNx4441YVkz6xmjQgqJduPxycNcKy7jqqvYZi0aj0TSGFhTtwODB8Pbb\ncOaZ9t8HH8BJUMdGo9GcoOi1YjuwaRNcdpltnxCBfv3ae0QajUZTP3pF0Q4sWWILidLSkzuGQqPR\nnBhoQdEO6LKmHTfNeHx8fJv09+KLL3Lbbbcdtf2ZZ55h3rx5rdJHfdf09ttvRzLD1seiRYtqpA2v\nj1/96ld8/PHHTR7TRx99xJgxYxgxYgRjxoxh4cKFkX0rVqxgxIgRDBw4MBKVD/Dqq6+SkZGBYRg1\nsuLu2LEDn8/HqFGjGDVqFLfcckuTx6E5NrTqqR1oj3xOHY2qFB5gZy596qmnuKeNgkmCwWCreuS0\nZnvHY7K76KKLIkkXj5UHHnigWcenp6fzzjvv0L17d9avX88555wTcRW+9dZbee655xg/fjznn38+\nH3zwAeeddx6ZmZm8/vrrR6VxARgwYEDkvtEcP/SKop3QZU2r6Whpxu+66y4yMjKYPn06eXl5AJxx\nxhnceeednHLKKTz++OPk5eVx2WWXMXbsWMaOHcvnn38OwNKlS5k4cSLZ2dlMmjSJb7/99qj233vv\nPSZOnEh+fj6//vWvI8WBzjjjDO6++27GjRvH4MGDI1lqS0tL+c53vsPw4cOZOXMm48ePr7f+xBNP\nPMHo0aMZMWIE33zzDVBzNbN161YmTJjAiBEjuPfee2usoIqLi5k1axZDhw7lmmuuqfMzuuGGG/jn\nP/8J2CuY++6776j+osnOzqZ79+4AZGRkUFZWRkVFBXv37qWwsJAJEyYgIlx33XWRNOfDhg1jyJAh\n9X4/muOPFhSaprFpEzz/fHUB71aio6UZLykp4ZRTTmHDhg1MmTKF+6Pyw1dWVrJ8+XJ+/OMfc8cd\nd3DXXXexbNkyXnvttUh9hqFDh7JkyRJWrVrFAw88wC9+8Ysa7b/xxhs89NBDvP/++5FMuNEEg0GW\nLl3KnDlzIn0//fTTpKSksHHjRn7zm9+wYsWKej/P9PR0Vq5cya233lpndbo77riDO+64g3Xr1tGz\nVgWtVatWMWfOHDZu3Mi2bdsiwq8hGusvmtdee43Ro0fj8XjIzc2t0X90+vSG2L59O9nZ2UyZMiUi\nSDVtj1Y9aRpn0yYYPbraTWvlymNeCnXUNOOGYXDFFVcA8N3vfrdGttWq7QAff/xxpFIcQGFhIcXF\nxRQUFHD99dezefNmRIRAVYpg7OJGy5cvZ8GCBXWm+gYi/Y0ZMyZSDvWzzz7jjjvuACAzM5OsrKx6\nPtWa51ddbzRffPFF5Mn96quvrlHFbty4cZHJe9SoUezYsYPTTjut3r6a0l8VGzZs4O6772bBggUN\nttcQ3bp1Y9euXaSlpbFixQouueQSNmzYUO9nqWk99IpC0zht4KZVZaPYuXMnSqlIKVTlpBmvyoC6\nZcuWFucPao0049EZYaPbC4fDfPnll5Fx5ubmEh8fzy9/+UumTp3K+vXreeedd2qkGB8wYABFRUVs\namBV5vHY1RJN02xRtbhjOb/q3Oac35T+du/ezcyZM5k3bx4DBgwA7BTpVdX9qo6pSpveUF9paWmA\nLZgGDBjQ4GepaT20oNA0Thu6aXW0NOPhcDiig//73/9e7xP12WefXaNGRpWBtaCgIDLhvfjiizXO\n6dOnD6+99hrXXXcdGzZsaPiDieLUU0/llVdeAewa2uvWrWvyubWZMGECr732GkDkc2tLjhw5wowZ\nM3jooYc49dRTI9u7detGYmIiX375JUop5s2b12ia87y8vEi98G3btrF58+ZjKvykaTpaUGgap8pN\na+7cVlE71SY7O5usrCzmz5/P2WefzdVXX83EiRMZMWIEs2bNoqioiIyMDO655x6mTJnCyJEjI7UV\nnnjiCV544QWysrJ46aWXIraL2tx3330sXryYjIwMXn/99XpzL8XFxbF06VIyMzNZuHAhv/rVr+o8\nbu7cuSxfvpysrCyGDx/OM888A8BPf/pTfv7zn5OdnV3nE/bQoUP529/+xuWXX87WqhKHjfDDH/6Q\nvLw8hg8fzr333ktGRgZJSUlNOrc2c+bM4Q9/+ANZWVls2bKlxe00lSeffJItW7bwwAMPRNxaDxw4\nANi2l5tuuomBAwcyYMAAzjvvPMC24/Ts2ZMvvviCGTNmcI6TtmDx4sVkZWUxatQoZs2axTPPPBNR\nQWralnZNMy4iPwYeAToppfLFXuc/DpwPlAI3KKVWNtbOiZZmXKNpDqFQiEAggNfrZevWrZx55pl8\n++23uGsnDGsCpaWl+Hw+RISXX36Z+fPn89Zbb7XBqDUnAk1NM95uxmwR6QWcDUS7n5wHDHL+xgP/\nz/mv0Zy0lJaWMnXqVAKBAEopnn766RYJCbCD3G677TaUUiQnJ/PnP/+5lUeriUXa0+vpMeCnQPTj\nzMXAPKfy0pcikiwi3ZRSe9tlhBpNByAhIaHeuInmMnnyZNasWdMqbWlOHtrFRiEiFwO5Sqnad2wP\nICfq/W5nm0aj0WjaiTZbUYjIx0DXOnbdA/wCW+10LO3fDNwMxHxRGI1Go2lP2kxQKKXOrGu7iIwA\n+gFrHB/1nsBKERkH5AK9og7v6Wyrq/1ngWfBNma33sg1Go1GE81xVz0ppdYppTorpfoqpfpiq5dG\nK6X2AW8D14nNBKBA2yc0Go2mfelocRTvA9uALcBzwA/bdziatkKnGbdpzTTjZ5xxRqsZvUGnFNdU\n0+65npxVRdVrBfxX+41Gc7zQacZtOvIEqFOKa6roaCsKzUmITjPeemnGq5g/fz4jRowgMzOTu+++\nG7Cf3qsi2h9//PFI+ott27bVSK9RhU4prqlCCwpNk2ijLOM6zXgtjjXNOMCePXu4++67WbhwIatX\nr2bZsmW8+eabTJ48OSJ8lixZQlpaGrm5uSxZsoTTTz+9wTZBpxQ/mWl31ZOm49MGWcZ1mvE2SjMO\nsGzZMs444ww6deoEwDXXXMPixYu55JJLKC4upqioiJycHK6++moWL17MkiVLalxnfeiU4icvekWh\naZQ2yDKu04zXw7GmGW+MSZMm8cILLzBkyJDICuOLL76oU/XUkrHplOKxiRYUmkZpwyzjOs14E2hu\nmvFx48bx73//m/z8fEKhEPPnz2fKlCmAncLjkUce4fTTTyc7O5tPP/0Uj8fTKllkdUrx2EULCk2j\ntHGWcZ1mvBGam2a8W7duPPTQQ0ydOpWRI0cyZsyYyMQ8efJkcnJyOP300zFNk169ejVaxa6p6JTi\nsUu7phlvLXSacU0s05ppxjWaaDp8mnGNRtM0WjPNuEbTErSg0Gg6OK2ZZlyjaQnaRqHRaDSaBtGC\nQqPRaDQNogWFRqPRaBpECwqNRqPRNIgWFBqNRqNpEC0oNBqNRtMgWlBoNBqNpkFiIjJbRIqAoxP/\nty3pwNEl0k68Ptq7T93fid3f8e5TX1/r0kcp1amxg2Il4O7bpoShtyYisryt+zwefbR3n7q/E7u/\n492nvr72QaueNBqNRtMgWlBoNBqNpkFiRVA8G6N9xup16f5ip7/j3ae+vnYgJozZGo1Go2k7YmVF\nUS8iUnwc+wqJyOqov74NHHuGiLzbgj6UiPw16r0lInktaauZ/V7i9D20Dftol2uL6u+43StN7VNE\nFonIMRk3j8d3V0ef94jIBhFZ6/wWxrdxfz1F5C0R2SwiW0XkcRGpNxe7iNwpIv4W9qVE5NGo9z8R\nkV+3pK0m9lc1r2wQkTUi8mMROa5zd8wLiuNMmVJqVNTfjjboowTIFBGf8/4sILc5DYhIS7zdrgI+\nc/43py+zGYcf87Vp6qRF311LEZGJwAXAaKVUFnAmkNOG/QnwOvCmUmoQMBiIB/63gdPuBFokKIAK\n4FIRSW/h+c2lal7JwP5NnAfcd5z6Bk4SQSEi8SLyiYisFJF1InKxs72viHwtIs850npB1CTVWn2b\nIvKwiCxznq5+ELU7UUTeE5FvReSZZjwlvA/McF5fBcyP6m+ciHwhIqtE5D8iMsTZfoOIvC0iC4FP\nmnkN8cBpwGzgSmfbGSKyuK7xi0ixiDwqImuAic3pq4XXtlhERkUd95mIjGxmv1Xn1ljpiciTInKD\n83qHiNwfdR+1yhN6Q322Qtv1fXf1XeP5IvKNiKwQkbktXM11A/KVUhUASql8pdQeERkjIv922v5Q\nRLo5fS5yVgCrRWS9iIxrZn/TgHKl1AtOfyHgLuB7IhInIo847a4VkR+JyO1Ad+BTEfm0BdcXxLYl\n3FV7hzOnLHT6+kREeotIkojsjPp9xIlIjoi4mtuxUuoAcDNwm9jUO7+IyN3OfbpGRB5qwXVGOCkE\nBVAOzFRKjQamAo86TyEAg4CnHGl9BLjsGPrxSbXa6Q1n22ygQCk1FhgLfF9E+jn7xgE/AoYDA4BL\nm9jPy8CVIuIFsoCvovZ9A0xWSmUDvwJ+G7VvNDBLKTWlmdd1MfCBUmoTcFBExjQy/jjgK6XUSKXU\nZ83sqyXX9jxwA4CIDAa8Sqk1zey3qeQ799H/A37SRn20JvV9d0fhfOZ/BM5TSo0BGg3EqocFQC8R\n2SQiT4vIFGdSfAL7/hsD/JmaT/x+pdQo4IfOvuaQAayI3qCUKgR2ATcBfYFRzurmb0qpucAeYKpS\namrzLw+Ap4BrRKR28fIngL9U9QXMVUoVAKuBqt/dBcCHSqlASzpWSm0DTKAz9cwvInIe9nc/Xik1\nEvh9S/qq4mQRFAL8VkTWAh8DPYAuzr7tSqnVzusV2DdVS4lWPc10tp0NXCciq7EnvTRs4QSwVCm1\nzXkCmo/95NcoSqm1zjivwn4CjyYJeFVE1gOPYf+IqvhIKXWoBdd1FfYEjvO/SoVR3/hDwGst6Kel\n1/YqcIEzGX0PeLElfTeR153/x3qvHC/q++7qYiiwTSm13Xk/v4Fj60UpVQyMwX7yzQP+AfwAyAQ+\ncn4L9wI9o06b75y7GHulndySvuvgDOCPSqmg035L7v+jcATRPOD2WrsmAn93Xr9E9W/iH8AVzusr\nnfetQX3zy5nAC0qpUme8x3TdsRKZ3RjXYD8djVFKBURkB+B19lVEHRcCWlX1hC2kfqSU+rDGRpEz\ngNouZ81xQXsbeAT7h5AWtf03wKdKqZliG9MXRe0raUb7AIhIKvbSfoSIKOwnGQW8V8d4q96XO8Kj\npTTr2pRSpSLyEfYT1HewJ6mWEqTmA5S31v6q+yVE6/1+GuuzRTTw3b3VFv1F43z/i4BFIrIO+C9g\ng1KqPlXksfwWNgKzojeISCLQG9jRjHaayxxgJfBCE459G/thNRX7/lzY0k5FpD/2/XeA+ueXc1ra\nfl2cLCuKJOCAIySmAn2OY98fArdW6SNFZLCIxDn7xjnLRAP7aaM5apo/A/crpdbV2p5EtQH4hpYP\nO8Is4CWlVB+lVF+lVC9gOzCZYxt/Q7Tk2v4EzAWWKaUOH0PfO4HhIuJxnmqnH0Nb7d1nfd+dUU9/\n3wL9pdpb74raDTYFERkiIoOiNo0CvgY6iW3oRkRcIhK92r3C2X4atiqloBldfgL4ReQ6pw0TeBR7\nZfkh8ANxHDiciRqgCEho7rVF4zylv4Kt/qniPzi2IOwH1CXOscXAMuBx4N2WPkiJSCfgGeBJZcc2\n1De/fATcKI5nV9R1t4iYFhTOzVGBrSs8xXmyuQ5b1328+BP2E89KR2XyR6qfRJcBT2Jg78SOAAAC\n5ElEQVT/iLYDb9TZQh0opXY7utba/B74nYisonWeeK+qY1yvOdtbPP6GaMm1KaVWAIU07enuKKru\nFaVUDvaPf73zf1VL2usgfdb33V1ZV39KqTJsG8EHIrICezJtzoRdRTzwFxHZ6Kh7h2PblGYB/ye2\nk8NqYFLUOeXO9/oMNSfeRnEmzJnA5SKyGdiEbZf8Bfbvbxew1un3aue0Z53rbIkxO5pHsRP5VfEj\n7Al6LXAtcEfUvn8A36X5aqcq2+cGbNX5AuB+Z1+d84tS6gPsVcxyRy11TPa0mA64E9vz5TmlVHO9\nKDSN4KjOfqKUuqC9xwIgIt2xVR1DlVLhFpx/3O+Vjnh/iki8UqrYcfZ4CtislHqsjftchH0vLW/L\nfjQtJ2ZXFCJyC7aB7N72HoumbXFUDl8B97RQSBz3e6UD35/fd55AN2Cr+v7YzuPRdABiekWh0Wg0\nmmMnZlcUGo1Go2kdtKDQaDQaTYNoQaHRaDSaBtGCQqNpAdKCjJ5i5wG6uqFjNJqOiBYUGk3LaElG\nz75U+/FrNCcM2utJo2kBIlKslIqPet8fOwAxHTvy/yXs5IgAtyml/iMiXwLDsIMT/4IdSf4QdqoS\nD3ZySu2OqulwaEGh0bSA2oLC2XYEGIId0RxWSpU7qSzmK6VOqR2kKCI3A52VUg+KiAf4HLg8Kimf\nRtMhOFmSAmo0xxMX8KTYNTJC2IV06uJsIEtEqhLaJWFn/tSCQtOh0IJCo2kFamX0vA/YD4zEtgOW\n13cadWT+1Gg6GtqYrdEcI3Vk9EwC9jrpRK7FTu8NR2csbSizsEbTYdArCo2mZficnEgu7HoSLwF/\ncPY9Dbzm5KD6gOo6IGuBkJPF9EXslNN9sTN/CnaRn0uO1wVoNE1FG7M1Go1G0yBa9aTRaDSaBtGC\nQqPRaDQNogWFRqPRaBpECwqNRqPRNIgWFBqNRqNpEC0oNBqNRtMgWlBoNBqNpkG0oNBoNBpNg/x/\nnAdr9cjqWAUAAAAASUVORK5CYII=\n",
  32        "text/plain": [
  33         "<matplotlib.figure.Figure at 0x7fab75c9cc88>"
  34        ]
  35       },
  36       "metadata": {},
  37       "output_type": "display_data"
  38      }
  39     ],
  40     "source": [
  41      "data = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')\n",
  42      "\n",
  43      "# Convert all data to degrees Celsius:\n",
  44      "data['Data_Value'] = data['Data_Value']*0.1\n",
  45      "\n",
  46      "data['Date'] = pd.to_datetime(data['Date'])\n",
  47      "\n",
  48      "data['Year'] = data['Date'].dt.year\n",
  49      "\n",
  50      "data['Month_Day'] = data['Date'].dt.strftime('%m-%d')\n",
  51      "\n",
  52      "# Remove February 29:\n",
  53      "data = data[data['Month_Day']!='02-29']\n",
  54      "\n",
  55      "# Max and min data after grouping by each day:\n",
  56      "max_temp = data[(data.Year >= 2005) & (data.Year < 2015) & (data['Element'] == 'TMAX')].groupby(['Month_Day'])['Data_Value'].max()\n",
  57      "min_temp = data[(data.Year >= 2005) & (data.Year < 2015) & (data['Element'] == 'TMIN')].groupby(['Month_Day'])['Data_Value'].min()\n",
  58      "\n",
  59      "# Record breaking data:\n",
  60      "data = data.merge(max_temp.reset_index(drop=False).rename(columns={'Data_Value':'Max_temp'}), on='Month_Day', how='left')\n",
  61      "data = data.merge(min_temp.reset_index(drop=False).rename(columns={'Data_Value':'Min_temp'}), on='Month_Day', how='left')\n",
  62      "\n",
  63      "record_high = data[(data.Year==2015)&(data.Data_Value > data.Max_temp)]\n",
  64      "record_low = data[(data.Year==2015)&(data.Data_Value < data.Min_temp)]\n",
  65      "\n",
  66      "# X Axis spanning 2015 only:\n",
  67      "date_index = np.arange('2015-01-01','2016-01-01', dtype='datetime64[D]')\n",
  68      "\n",
  69      "plt.figure()\n",
  70      "\n",
  71      "plt.plot(date_index,max_temp,color='lightcoral', linewidth=1) # Max in red.\n",
  72      "plt.plot(date_index,min_temp,color='skyblue', linewidth=1) # Min in blue.\n",
  73      "\n",
  74      "plt.scatter(record_high.Date.values, record_high.Data_Value.values, color='red', s=8)\n",
  75      "plt.scatter(record_low.Date.values, record_low.Data_Value.values, color='blue', s=8)\n",
  76      "\n",
  77      "# Set x and y limits.\n",
  78      "ax = plt.gca()\n",
  79      "ax.axis(['2015/01/01','2015/12/31',-50,50])\n",
  80      "\n",
  81      "# Set axis names and title:\n",
  82      "plt.xlabel('Date', fontsize=10)\n",
  83      "plt.ylabel('° Celsius', fontsize=10)\n",
  84      "plt.title('Temperature in Ann Arbour, Michigan (2005-2015)', fontsize=12)\n",
  85      "\n",
  86      "# Create legend and title\n",
  87      "# loc=0 provides the best position for the legend\n",
  88      "plt.legend(['Record high (2005-2014)','Record low (2005-2014)','Record breaking high in 2015','Record breaking low in 2015'],loc=0,frameon=False)\n",
  89      "\n",
  90      "# Fill colour between highs and lows:\n",
  91      "# alpha adjusts darkness of the shade.\n",
  92      "ax.fill_between(date_index, max_temp, min_temp, facecolor='grey', alpha=0.25)\n",
  93      "\n",
  94      "# Where you locate the major and minor ticks:\n",
  95      "ax.xaxis.set_major_locator(dates.MonthLocator())\n",
  96      "ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15)) # Put the label at the minor tick so it's in the center.\n",
  97      "#ax.yaxis.set_minor_locator()\n",
  98      "\n",
  99      "# What you put at the ticks:\n",
 100      "ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
 101      "ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))\n",
 102      "\n",
 103      "# 1 refers to the bottom of the plot for xticks and the left for yticks\n",
 104      "# 2 refers to the top of the plot for xticks and the right for yticks\n",
 105      "for tick in ax.xaxis.get_minor_ticks():\n",
 106      "    tick.tick1line.set_markersize(0) # Make small ticker disappear\n",
 107      "    tick.label1.set_horizontalalignment('center')\n"
 108     ]
 109    },
 110    {
 111     "cell_type": "code",
 112     "execution_count": null,
 113     "metadata": {
 114      "collapsed": false
 115     },
 116     "outputs": [
 117      {
 118       "name": "stdout",
 119       "output_type": "stream",
 120       "text": [
 121        "Assignment2.ipynb\n",
 122        "Assignment3.ipynb\n",
 123        "Assignment4.ipynb\n",
 124        "Dejunking.ipynb\n",
 125        "Matplotlib_3D.ipynb\n",
 126        "Test.ipynb\n",
 127        "UnderstandingDistributionsThroughSampling.ipynb\n",
 128        "Week2.ipynb\n",
 129        "Week3.ipynb\n",
 130        "Week4.ipynb\n",
 131        "avg_hourly.csv\n",
 132        "avg_weekly.csv\n",
 133        "data/\n",
 134        "data/C2A2_data/\n",
 135        "data/C2A2_data/GHCND_10Year.h5\n"
 136       ]
 137      }
 138     ],
 139     "source": [
 140      "!tar chvfz notebook.tar.gz *"
 141     ]
 142    },
 143    {
 144     "cell_type": "code",
 145     "execution_count": null,
 146     "metadata": {
 147      "collapsed": true
 148     },
 149     "outputs": [],
 150     "source": []
 151    }
 152   ],
 153   "metadata": {
 154    "kernelspec": {
 155     "display_name": "Python 3",
 156     "language": "python",
 157     "name": "python3"
 158    },
 159    "language_info": {
 160     "codemirror_mode": {
 161      "name": "ipython",
 162      "version": 3
 163     },
 164     "file_extension": ".py",
 165     "mimetype": "text/x-python",
 166     "name": "python",
 167     "nbconvert_exporter": "python",
 168     "pygments_lexer": "ipython3",
 169     "version": "3.6.2"
 170    }
 171   },
 172   "nbformat": 4,
 173   "nbformat_minor": 1
 174  }
 175