What's new in 1.0.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# An overview of what's new in IPython 1.0"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`%matplotlib`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After many years of `%pylab`, the community has repeatedly found that the `%pylab` magic does too many things.\n",
      "In particular, it imported a bunch of things into the user's namespace, hiding where functionality comes from.\n",
      "\n",
      "For the future, we now recommend using `%matplotlib` to hook up matplotlib machinery,\n",
      "and always using explicit imports:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "plt.plot(np.sin(np.linspace(0,10)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[<matplotlib.lines.Line2D at 0x110c87bd0>]"
       ]
      },
      {
       "metadata": {
        "png": {
         "height": 380,
         "width": 609
        }
       },
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAABMMAAAL4CAYAAAB2hDBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3XeY3WWd///X59TpfSbTUyaTHkgjEFoIpFBDQNzFgqio\n/NR13XXd9eviT7Hgcv2+ruuuu+sCiqtYQOkCIaQRSiCQnjCpk8xMpvdeTvv8/jgzEZCQNjP3Kc/H\ndeUSM2fmvHJdOSef8/rc9/u2bNu2BQAAAAAAAMQBh+kAAAAAAAAAwHihDAMAAAAAAEDcoAwDAAAA\nAABA3KAMAwAAAAAAQNygDAMAAAAAAEDcoAwDAAAAAABA3KAMAwAAAAAAQNygDAMAAAAAAEDcoAwD\nAAAAAABA3KAMAwAAAAAAQNygDAMAAAAAAEDcoAwDAAAAAABA3KAMAwAAAAAAQNxwmQ7wfhs2bNBD\nDz2kL37xi7rqqqvO62fZtq2XX35ZmzZt0okTJxQIBJSXl6dLLrlEa9askcfjGZ3QAAAAAAAAiAoR\nVYbV19frqaeekiRZlnXeP+9nP/uZtmzZIpfLpalTp8rj8aiyslJPPPGEduzYoe9+97tKSEg47+cB\nAAAAAABAdIiIMuy+++5TR0eHTpw4MWo/880339SWLVuUm5urb3/728rLy5MkDQ0N6cc//rF2796t\nP/zhD/rUpz41as8JAAAAAACAyBYRM8P27t07qkWYJK1du1aS9MlPfvJkESZJXq9Xd999tyzL0saN\nG+X3+0f1eQEAAAAAABC5IqIMe+yxx07+mj9//nn/vMHBQR06dEgOh0MLFy78i69nZWWprKxMg4OD\nOnDgwHk/HwAAAAAAAKJDRJRho62urk62bSsnJ0dut/sDH1NYWChJqq2tHc9oAAAAAAAAMCgmy7D2\n9nZJUmJi4ikfk5aWJklqbW0dl0wAAAAAAAAwLybLsKGhIUnh+WCnMrJibHBwcFwyAQAAAAAAwLyY\nLMNs25YkuVynPyzTsqyxjgMAAAAAAIAIcfq2KAolJCRI0oeeFDnytQ9bPXYqGzduPLdgAAAAAAAA\n+EDXXHPNuDxPTK4My87OliT19fWd8jHd3d2SwidLng2KMAAAAAAAgNE3Xp1LTK4MKywslGVZam5u\nViAQ+MDtkvX19ZKk4uLic3qOBQsWnFdG4GwFQ7baB/xq6vGpscenxl6fmnqG1NjjU1OvTy29PgXt\nU3+/JSk72a38FI/yUz2q6hjU0bYBSVJuslufmJ+vldOy5XJE7tbhu+++Ww888IDpGEDc4bUHmMFr\nD9Gssq1fP3nthA619I/Zczgs6ba5ebpjQYG8rtFb58FrDzBj586d4/ZcMVmGJSQkaPr06Tp48KC2\nb9+uSy655D1fb29vV2VlpTwej2bNmmUoJfDhfIGQHt3TpHea+tTU61Nzr0+B0Ie0XZKyklzKT/Fq\nQqrnZOk1IdWjCSle5aW45Xb++SLBtm1tre7Sr3Y0qKpjUD957YQe29OkT8zP1zVTs+SM4FIMAAAA\nkWnAH9Rvdjbqif3NCtlSTpJbl03KkGQraIevQUO2FBr+X9sO/37ItmW/6/dH/n/wL34//D2BkK3D\nrf36w95mvV7Vpb+/okQXFKSa/uMDiBIRW4aNDME/lYqKCn3ve9+TZVn64he/qCuvvPI9X1+1apUO\nHjyo3/72t5o6dapycnIkST6fTw888IBs29ayZcvk8XjG7M8AnKv67iH9YOPxkyu3RmQkuMJF13DZ\nNSHVGy68UsK/PGdxR8yyLF02KUNLJqZry7FOPbKzQbVdQ/rRKzV6dE+T7lhQoKVTMuTgkAkAAACc\ngbdPdOs/Xj+hpl6fLEk3z8rRpxcVKtnjHJPnO9Dcpx+/WqPqjkF9/fmjunFGju5aPHbPByB2REQZ\n9uCDD57875qaGknSli1bdPjwYUnS9OnTtXTp0vd8j23b7/n1fpdeeql27Nih1157TV/96lc1depU\nud1uHTt2TH19fSotLdXtt98+hn8q4Ny8VtWpf32lRn2+oApSPfr84iKVZHg1IdWrhFFc/j3CYVla\nVpapKydnaFNlux7Z2ajariH9y+YqPbo7QXcsLNBlE9M5eRUAAAAfqL3fr5+9WastxzolSVOyEvV3\nl5doRl7ymD7vzLxk/fea6Xp0T5N+v7tJzx1s1ZsnuvS3l5XoktL0MX1uANEtIsqwDxqQVlFRoYqK\nCklSIBD4izLsTD6Yf+UrX9Hs2bO1adMmVVdXKxgMKi8vT9dee63WrFnDqjBElEDI1i/eqtMT+1sk\nSZdNTNc/XFmqFO/4vEydDksryrO1rCxLLx1u0293Nep4x6C+t+G4ynMSdefCAl1UnEYpBgAAAEnh\nrYtrD7XpF2/Vq9cXlNdp6Y6FBbp1Tt64zaF1Ox26Y0GBLp+UoR+/WqNDLf369kvHtKwsU1+8pEgZ\nie5xyQEgukREGfbYY4+d9ffMmjXrjL7v6quv1tVXX30usYBx09zr0w83VamiuU9OS7prcZE+MifX\nSPHkcli6fkaOlpdnae3BNv1+d6OOtA7oW+uOaVZesu5cVKD5hcxjAAAAiGfVHQP6yWsn9E5TnyRp\nUXGqvnJZiQpSvUbyTM5K1E9umqan32nR/+5o0ObKDu2o7daXlhRrWVkmN3QBvEdElGFAPNte2637\nN1epeyionCS37rlmkmZPSDEdSx6nQzfPztWq6dl6rqJFj+1tVkVzn77xwlFdWJCiOxcWaE6++ZwA\nAAAYP75ASL/b3ag/7G1WIGQrM9GlL15SrKVTMowXTk6HpY/MzdOlE9P1k9dqtKu+V/e/XK3NlR36\nymUlykthZxCAMMowwJBgyNZvdjXqd7saZSt8N+2flk6MuKXcCS6Hbrtggq6fkaNnKlr0x73N2tPQ\nq689d0SLilN158ICTc8d23kQAAAAMG9XfY/+/bUTqu8ekiRdPyNbd11UqNRxGutxpgrSvLr/uqla\nd7hdD2yr07YT3dr3xAHddVGhbpiZwwFRACjDABM6+v26/+Uq7arvlSXpUwsL9PF5EyL6H+Ykj1Mf\nm5evm2bm6Mn9LXpyf7O21/Zoe22PlpSm61ML81WWnWQ6JgAAAEZZ12BAD2yr04Yj7ZKkiRkJ+url\nJRG9S8CyLF07PVsXFafpp1tPaGt1l366tVabj3Xoa1eUqjg9wXREAAZRhgHjbF9jr+7bdFzt/QFl\nJLj0zWWTNL8oemZwpXhd+tTCAq2Znas/7m3S0xWteqOmS2/UdOnKyRm6Y0G+JmYmmo4JAACA82Tb\nttYfadeD2+rUPRSU22npE/Py9dEL8uR2jv4p52MhO9mt7yyfrFerOvVfW2u1v7FPdz95UJ9aUKDb\n5ubJOU6D/gFEFsowYJyEbFuP723Ww9vrFbKlOfnJumfZZGUnR9a2yDOVluDSXYuLdOucPD26t0nP\nHWjVK8c79erxTn1xSbHWzM41HREAAADnqK5rUP/++gntru+VJM0rTNFXLytRURSuqLIsS1dOztS8\nglQ9uK1OLx1p1y/erteWYx36hytL2d0AxCHnvffee6/pENHk+PHjkqSCggLDSRBNugcD+uHmKj17\noFW2pL++IE//tHSSkr1O09HOW6LbqYuK07RqWpaGArYOt/br7dpulWYkaNIYrBCbPn36qP9MAKfH\naw8wg9cexps/GNKje5r0w81Vqu/2Kc3r1N9eXqK7Ly5SWkJ03sQd4XU5dOmkDM3MS9b+xj6d6BrS\n2kNt8gVtzZ6Q/J5VYrz2gPHX0NAgSZoyZcqYP5dl27Y95s8SQzZu3ChJWrBggeEkiBaHWvr0g41V\naur1KdXr1NevnKglE9NNxxozf9jTpJ+/XS+3w9K/XDdVFxRE7iwJAAAA/Nn+xl795LUTqukclCSt\nKM/SFy4uUnpC7G0oGvAH9cvtDXrmnRbZkorTvfraFaURPQcNiHU7d+6UJF1zzTVj/lyx964GRAjb\ntvWnA6164M06+UO2puUk6VvXTFJ+qtd0tDH10Qvy1Nzn07MVrbp3/TH9203lzBADAACIYP2+oB7Y\nVqe1h9okSUVpXn318hLNK4yeubZnK9Ht1JeWFOuqKZn68as1qukc1NeeO6LVs3L02UWFSvJE/w4O\nAKcWHVMPgSjT7wvqXzZX6T+31sofsrV6Vo5+fFN5zBdhUngmwxcvKdaSienq9QV1z7pKtfX5TccC\nAADABwiGbN23qUprD7XJ5bD0ifn5euDWGTFdhL3brAnJ+u9bpusT8/PltKRnK1r1+ScOqLKt33Q0\nAGOIMgwYZcfbB/Q3zxzSy8c6leh26J+XTdLfXFoiT5ScuDManA5L31w2STPzktTc69e3XqpUvy9o\nOhYAAADe51c7GvR2bbfSvE7915rpunNhgTyu+LlulSSP06E7Fxbov9bM0LScJLX0+fWd9cfUNRgw\nHQ3AGImvdzlgjL10uE1/+8wh1XYNaVJmgv7z5um6qizTdCwjElwOfW9lmYrSvKpsG9D3Nx5XIMSI\nQgAAgEix5ViHHt3TJIcl3XP1ZE3Oiu/RFlOyE/Xjm8o1PTd8Q/e+TccV5PoViEmUYcAoGAqE9ONX\navSjV2o0FLS1sjxL/3HzdJVkRN/R06MpPcGl+64tU3qCSzvqevSTV2vEmR0AAADmVbb160ev1EiS\nvnBxkeYXxce2yNPxOB369vLJykhwaXd9r37xdr3pSADGAGUYcJ7a+vz66rOH9eLhNnmclr52Ram+\nvnSiEuJsefmpFKZ59YNVU+R1OfTSkXY9srPRdCQAAIC41j0Y0L3rj2soENLy8izdMjvXdKSIkpvs\n0beumSynJT2+r1kvV3aYjgRglPFpHTgPwZCt+zYf17H2ARWlefXvq6fp2unZpmNFnOm5ybrn6kly\nWNJvdjVq7cFW05EAAADiUjBk6webjqup16dpOUn66mUlsizLdKyIc0FBiu6+pFiS9K+v1uhY24Dh\nRABGE2UYcB5+u6tR+xv7lJXk0r/dVK6y7CTTkSLWJaXp+ptLSyRJ//76Cb11ostwIgAAgPjz0Ft1\n2l3fq4wEl769fLK87GY4pZtn5Wj51EwNBUL67oZj6hlioD4QK3jnA87Rnvoe/W53oyxJ/+eqScpI\ndJuOFPFunJmjj82boJAtfX9jlQ63cGQ1AADAeNlwpF1P7m+R05K+vXyy8lI8piNFNMuy9NXLSzU1\nO1ENPT7dv7magfpAjKAMA85B12BA979crZAtfWzeBM0rZODomfr0wgItL8/SUCCkb62rVEP3kOlI\nAAAAMe9wS7/+7bXwwPwvX1qiOfkphhNFB68rPFA/zevU27Xd+vXOBtORAIwCyjDgLNm2rR9tqVZb\nv1+zJyTrjgUFpiNFFcuy9PeXl2h+Yao6BwO6Z12lugdZcg4AADBWOvr9unfDMfmDtq6fka0bZ+aY\njhRV8lO9+ufh+be/392k16o6TUcCcJ4ow4Cz9PQ7Ldp2olupXqe+uWySnA4Gjp4t9/CR1VOyElTb\nNaRvv3RMQ4GQ6VgAAAAxxx8M6fsbj6u1z69Zecn60pJi05Gi0oKiNN11UaEk6f9uqVZNx6DhRADO\nB2UYcBaOtPbrobfqJUl/f0UpcxbOQ7LHqftWTVVuslsVzX26f3MVMxgAAABG2c/erNP+pj5lJ7n1\n/y6fLI+Tj4Dn6ra5eVo6JUMD/pDu3XBMfb6g6UgAzhHvhMAZ6vcF9cNNVQqEbN00M0eXT8owHSnq\nZSe7dd+1ZUrxOPV6dZf+58062TaFGAAAwGh44WCrnjvQKrfD0neWT1Z2Egc+nQ/LsvS1K0o1KTO8\nu+H/21KtENeuQFSiDAPO0H9uPaG67iFNyUrQ3RcXmY4TMyZlJureFZPldlh6pqJFT+xrNh0JAAAg\n6r3T1Kv/3ForSfrq5SWakZdsOFFsSHQ79Z3lU5TiceqN6i79bneT6UgAzgFlGHAG1h9p04ajHfK6\nHPrnZZPlcfHSGU0XFKTqH5dOlCQ9+Fa9Nld2GE4EAAAQvVr7fPr+huMKhGytmZ2rldOyTUeKKUXp\nXv2fZRNlSXpkR4O21XSZjgTgLPGJHjiN2q5B/fT18F21Ly8pVmlmguFEsemqskx9YXF4KOmPtlRr\nT32P4UQAAADRxxcI6bsbjqt9IKALC1L0BXY0jInFJem6c2GBbEn3v1ytui4G6gPRhDIM+BC+YEg/\n3FSlwUBIy8oytWpalulIMe0jc/O0Znau/CFb9244rqqOAdORAAAAooZt2/qP10/oUEu/JqR4dM/V\nk+Ti5PMxc/u8Cbp0Yrr6fEHdu+G4BvwM1AeiBWUY8CF+8Va9jrYNqCDVo7+9rESWxcXEWLIsS3df\nXKTLJ4UvKu55sVKtfT7TsQAAAKLCsxWteulIu7zO8MD8jEQG5o8lh2XpH5dOVEm6V9Udg/rXV2o4\nDAqIEpRhwCm8Ud2lp95pkdOS/vnqSUr2OE1HigtOh6VvXDVJs/KS1dLn17fWVXJsNQAAwGnsqe/R\nz94Mj/b42pWlmpqTZDhRfEj2OPWdFVOU5HboleOd+uNeDoMCogFlGPABWvp8+tdXqiVJd11UqOm5\nnL4znrwuh763coqK07061j6o7204Ln8wZDoWAABARGrq8ekHm6oUsqW/uiBPy8oY7TGeSjMS9E9X\nhQ+Denh7vXbUdhtOBOB0KMOA9wmGbN2/uVrdQ0EtKk7VrXPzTEeKS2kJLt13bZkyElzaVd+jf3uV\nZecAAADvNxgI6bsbjqlrMKBFxan6zKJC05Hi0qUTM/SJ+fkK2dIPN1epoWfIdCQAH4IyDHif3+1u\n1L7GXmUluvSPSyfKwZwwYwpSvfrBqjIluBzacLRDj+xsNB0JAAAgYti2rX97tUZH2wZUmObRN5dN\nkpOB+cbcsSBfi0vS1DMU1Pc2HNdggJ0NQKSiDAPeZW9Dr367q1GWpG9cNUmZDB01blpukr51zSQ5\nrHBRebydEyYBAAAk6fF9zdpc2aEEl0P3rpiiVK/LdKS45rAsfeOqiSpM86iybUA/YWcDELEow4Bh\n3YMB3b85PGvh9gsnaH5RqulIGLa4JF03zcxRyJb++41aLioAAEDc217brV+8XS9J+qerJmpSZqLh\nRJCkVK9L31k+RQkuhzZVdujpd1pMRwLwASjDAIWXmP/rKzVq7fdrVl6y7lhYYDoS3ueOBQVK8zq1\np6FXr1Z1mo4DAABgTF3XkH44PDD/k/PzdfmkDNOR8C6TsxL1D1eWSpIe2FanvQ09hhMBeD/KMEDS\nMxWteqOmSykep765bJJczFqIOGkJLn16eCDsg9vqmMEAAADiUr8vqHs3HFOvL6glpen65IJ805Hw\nAZZOydRfXZCnkC19f2OVmnt9piMBeBfKMMS9o639emhbnSTp768o1YRUj+FEOJXrpmdranaimnv9\n+sOeJtNxAAAAxlXItvV/t1SrumNQJele/dNVHPYUyT6zqFDzC1PVNRjQ9zcel4+buUDEoAxDXBvw\nB/XDzVXyh2zdOCNHV0xmiXkkczosfXlJsSTpsb1NHFkNAADiymN7mvR6dZeS3A59d+UUJXucpiPh\nQzgdlu65epImpHh0qKVfP916gtm3QISgDENc+8+ttartGtKkzATdfUmR6Tg4A7PzU3R1Wab8Qfvk\nij4AAIBY19gzpN/sapQkfXPZJBWnJ5gNhDOSluDSd5ZPlsdpad3hdr1wqM10JACiDEMc23CkXeuP\ntMvrDN+x8bp4OUSLzy0uVILLodequrSzrtt0HAAAgDH3y+0N8gdtLSvL1MWl6abj4CxMzUnS310e\nHqj/0LY6dQ74DScCwKd/xKW6rkH9dOsJSdKXlhRrIkdRR5WcZI8+Pn+CJOm/36hTIMRycwAAELsO\nNvdpc2WH3E5Lnx0+UAjRZXl5lhYVp6rfH9JvdzH7FjCNMgxxxxcM6b5NVRrwh7R0SoaunZ5tOhLO\nwa1z8lSY5lVN56CerWgxHQcAAGBM2LatB98Kj4a4dXYuhz1Fsc8vLpLDkp470KK6rkHTcYC4RhmG\nuPPw2/U62jag/FSP/u7yUlmcwBOVPE6Hvjg85+3XOxrUwXJzAAAQg7ZWd2l/Y5/SvE7dPi/fdByc\nh8lZiVpRnqWgLT28vcF0HCCuUYYhrmyr6dKT+1vktMKDRzmBJ7pdXJquxSVp6veH9Mu3uaAAAACx\nJRCy9fO36iVJdywo4No1Bty5sEBep6VXj3eqoqnPdBwgblGGIW609vn0o1dqJEmfuahQM/OSDSfC\naPh/LimSy2Fp3eE2HWrhggIAAMSO5w+0qq57SMXpXt0wM8d0HIyCnGSPPjI3T5L04LY62TazbwET\nKMMQN/79tRPqGgxoUXGqbhv+BwjRrzg9QbfOyZUt6b+21irEBQUAAIgBfb6gHtkZXvn+ucWFcjkY\n7RErPnrBBKUnuFTR3KfXq7pMxwHiEmUY4sL+xl5tO9GtRLdDX79yohzMCYspH5+Xr6wklw629GvD\nkXbTcQAAAM7b73c3qnsoqLn5KVpSmm46DkZRssepOxaE57/94u16TkYHDKAMQ8yzbVsPbw/PWvjI\nnDxlJbkNJ8JoS/I49bmLwsP0f/F2vfp8QcOJAAAAzl1jz5Ceeid8WvYXLi7kwKcYdP2MHBWne1XX\nPaTnD7SajgPEHcowxLwddT3a39inVK/z5P58xJ5rpmZqVl6yOgYC+u2uRtNxAAAAztkvtzfIH7S1\nrCxT03OZcxuLXA5Ld11UKEn6za5GbuYC44wyDDHNtm09/HZ4VdhfXziBE3himGVZ+vKlxbIkPbW/\nWTWdg6YjAQAAnLVDLX3aXNkht9PSZxYVmI6DMXTpxHTNmZCsrsGAHtvTZDoOEFcowxDTXqvq0tG2\nAWUlubR6Vq7pOBhj5TlJum5GtoK29LM3ajmdBwAARBXbtvXgtvCN3Ftm5yo/1Ws4EcaSZVn6/MXh\nUR9P7m9Wc6/PcCIgflCGIWYFQ7b+d3hW2Cfm5SvBxV/3ePDphQVK8Ti1o65Hb9Z0m44DAABwxt6o\n6dK+xl6leZ362Lx803EwDmbmJWvp5Az5grZ+vaPBdBwgbtAOIGZtPNquE11Dyk/16Nrp2abjYJxk\nJLr1qYXhLQX/82atfIGQ4UQAAACnFwjZ+vlb4Ru5dywoYLxHHPnMRYVyOSytP9KuyrZ+03GAuEAZ\nhpjkC4b0yM7wEPVPLSiQ28lf9Xhy08wcTcpMUEOPT4/vazYdBwAA4LSeP9Cq2q4hFaV5dcPMHNNx\nMI4K07y6aWaObOlkIQpgbNEQICa9eKhNTb0+TcxI0LKyTNNxMM6cDktfWlIsSfr9nibmLwAAgIjW\n5wvqkZ3hLXKfWxxeJYT48on5+UoeHvWxvZZRH8BYowxDzBnwB/XbXeFVYXcuKpCTi4m4NK8wVVdO\nztBQIKSfv1VnOg4AAMApPbq7Ud1DQc3JT9alE9NNx4EBaQkufezCCZKkn79Vp2CIg6CAsUQZhpjz\nbEWrOgYCmpaTpMu4mIhrn19cJK/T0svHOrW3odd0HAAAgL/Q1OPTk++0SJLuvrhIlsWN3Hi1Znau\n8lLcOtY+qI1H203HAWIaZRhiSu9QQH/Y2yRJ+syiAi4m4tyEVI/+evgO23+/cYI7bAAAIOL8cnu9\n/EFby8oyNT032XQcGORxOfTphYWSpP/d3qBBDoICxgxlGGLKE/tb1DMU1AX5KVpQlGo6DiLARy+Y\noAkpHh1rH9TzB1tNxwEAADjpcEu/NlV2yO209JlFBabjIAJcPTVTU7MT1drv11P7OQgKGCuUYYgZ\nHQN+PTF8ciCrwjDC63Lo7kuKJEm/2tGg7sGA4UQAAACSbdt6YFt4rukts3OVn+o1nAiRwGFZ+vzF\n4WvXx/Y0qXPAbzgREJsowxAzHtvTpMFASBeXpGl2forpOIggl01M1/zCVPUMBfW/OxpMxwEAANAb\nNV3a19irNK9Ttw+PdQAkaX5hqhaXpKnfHzp5MBiA0UUZhpjQ3OvTnw6Et8B9miXmeB/LsvSlJUVy\nWtILB1tV2dZvOhIAAIhjgZCtn79VL0n65IICpXhdhhMh0tx1UaEclvTcgVbVdg2ajgPEHMowxITf\n7mqUP2hr6ZQMlWUnmY6DCDQxM1E3z85VyJb+a2utbJth+gAAwIwXDraqtmtIRWle3TAj23QcRKDJ\nWYlaWZ6toC09/Ha96ThAzKEMQ9Sr6xrUusNtcljSnQtZFYZTu2NBgTISXNrf1KeXj3WYjgMAAOJQ\nny+oR3aGt759bnGh3E4+kuGD3bmwQF6XQ69Vdemdxl7TcYCYwjsvot6vdzYqZEsry7NVnJ5gOg4i\nWLLHqc9eFD6u+qFt9RrwBw0nAgAA8ebRPU3qGgxoTn6yLp2YbjoOIlh2slu3zc2TJD30Vj07G4BR\nRBmGqFbZ1q/NlR1yOyx9ckG+6TiIAiunZWl6bpJa+/36/e4m03EAAEAcaerx6cn94dPPv7C4iNPP\ncVofnZunjASXKpr79GpVp+k4QMygDENU+9XwyYA3zsxRXorHcBpEA4dl6UtLiiVJT+xrVl3XkOFE\nAAAgXvxye738QVvLyjI1Iy/ZdBxEgSSPU58aHgXz8Nv18gdDhhMBsYEyDFGroqlPb9Z0K8Hl4Dhq\nnJWZeclaWZ4lf8jWA9tqTccBAABx4HBLvzYN72j4DKef4yxcNz1bJele1Xf79NyBVtNxgJhAGYao\nZNu2frk9fKrKLXNylZnkNpwI0eazFxUqye3QmzXdeutEl+k4AAAghtm2rQe31UmS1szOVX6q13Ai\nRBOnw9LnFhdJkn67q1F9PubeAueLMgxRaVd9j/Y09CrF49RHh4dKAmcjK8mtT84Pz5n7nzfrWHIO\nAADGzJs13drb2Ks0r1Mfm8eOBpy9S0rTNDc/Rd1DQT26h7m3wPmiDEPUCa8KC88K+6sL85TidRlO\nhGh18+xcFad7Vds1pM2VHabjAACAGBQI2XrorfCqsE8uKODaFefEsix9fnH4VPSn9jeruddnOBEQ\n3SjDEHW2VnfpUEu/MhNdunlWruk4iGJu55/nzT2+r5njqgEAwKh74WCraruGVJTm1Q0zsk3HQRSb\nkZespVNPRVMdAAAgAElEQVQy5Ava+t/hg8QAnBvKMESVYOjPb/wfn5evRLfTcCJEu2VlmcpOcquq\nY1A76npMxwEAADGkzxfUIzsbJUl3LS6U28nHL5yfzy4qlMthaeORdlW29ZuOA0Qt3o0RVTZXdqi6\nY1ATUjy6jjtrGAVup0NrZodXGP5xb7PhNAAAIJY8uqdJXYMBzclP1mUT003HQQwoSPNq9awc2ZIe\n3FbPzgbgHFGGIWr4gyE9sjO8KuyOBfnycGcNo+SGGdlKdDu0q76HO2wAAGBUNPf69OT+8I22Lywu\nkmVZhhMhVnx8Xr5SPE7tqu/R9lp2NgDngjYBUWPd4XY19PhUku7VNVOzTMdBDEnxunTt9PBKw8f3\nsToMAACcv19ur5c/aGtZWaZm5CWbjoMYkpbgOnkq6UNv1SkYYnUYcLYowxAVhgIh/WZXeFXYnYsK\n5HRwZw2j65bZuXJY0suVHZzOAwAAzsvh1n5tPNoht8PSZxYVmI6DGHTzrFxNSPGoqmNQ64+0m44D\nRB3KMESFZyta1N4f0NTsRF0+KcN0HMSg/FSvrpycoaAtPf1Oi+k4AAAgStm2rYe21UmS1szOVX6q\n13AixCKPy3GyaP3VjgYNBkKGEwHRhTIMEa/PF9Sje5okSZ9ZVCgH8xYwRm6bG15u/sLBVvX5gobT\nAACAaLSvsVd7GnqV6nWe3MoGjIWryjJVnpOotn6/nmTUB3BWKMMQ8Z7Y16yeoaDm5CdrUXGq6TiI\nYdNyk3RhQYr6/SG9cLDVdBwAABCFntwfXmF+86xcpXhdhtMgljksS59fXCRJemxvk7oHA4YTAdGD\nMgwRrWswoCeGT+H5zKJCTuHBmLttbp4k6an9LfIHWW4OAADOXEP3kN6o7pLbYenGmTmm4yAOzCtM\n1YKiVA34Q3rxUJvpOEDUoAxDRHtsT5MG/CEtKk7V3PwU03EQBy4qSdPEjAS19vu15Vin6TgAACCK\nPF3RIlvh7WtZSW7TcRAnbp2TK0l6pqKFkyWBM0QZhojV0ufTMxXhZeafXlRoOA3ihcOy9JHh1WGP\n72uWbXNBAQAATq/PF9S64ZU5t8zONZwG8WRRcZqK071q6fPr9Spu5gJngjIMEet3uxrlD9q6YnKG\npuUkmY6DOHL11ExlJrp0rH1Au+p7TMcBAABR4KXDber3h3RBfoqmcu2KceSwLK0ZLmCf4lR04IxQ\nhiEi1XcP6cVDbXJY0p0LCkzHQZzxOB0nLyge52QeAABwGsGQraeHS4hb5rAqDONvRXmWkj1OvdPU\np8Mt/abjABGPMgwR6ZGdDQra0vKpWSrNTDAdB3Hohhk58roc2l7bo2NtA6bjAACACLbtRJcaenzK\nT/XoktJ003EQhxLdTl03PVuS9NQ73MwFTocyDBHnePuANh3tkMth6ZML8k3HQZxKS3Dp2mnhC4qR\nE00BAAA+yFP7w6vC1szOldPB6ecwY/WsHDksacuxTrX1+03HASIaZRgizq92NMiWdMOMbOWnek3H\nQRy7dU6uHJa0ubJDrX0+03EAAEAEqmzr156GXiW5HVo1fCMNMCE/1aslpekKhGw9f6DVdBwgolGG\nIaJUdwxoa3WXvE5LH5vHqjCYVZDm1eWTMhQI2XqGYaQAAOADjMwKWzUtW8kep+E0iHcjM+v+dKBV\nvkDIcBogclGGIaI8WxG+g7GiPFtZSW7DaQDpI3PzJEnPHWxTvy9oOA0AAIgkHQN+bTraIUvSzbMZ\nnA/z5uanqCw7UV2DAb18rMN0HCBiUYYhYvT5gtpwtF2SdNOsHMNpgLCZecmak5+sPl9Qaw+1mY4D\nAAAiyPMHWuUP2bpkYroK0xjvAfMsy9Itw8XsU++0yLZtw4mAyEQZhoix4Ui7BvwhXZCfoslZiabj\nACd9dO4ESeGTeQIhLigAAIDkC4b0p+G5TLeyKgwR5KopmUpPcKmybUD7GvtMxwEiEmUYIoJt23q2\nIjxvYfVsVoUhslxcmqbidK+ae/169TjLzQEAgLTlWIc6BgKakpWoCwpSTMcBTvK4HLppZvgz1VOc\nig58IMowRITd9b060TWk7CS3Lp2YYToO8B4Oy9Jtw7PD/ri3meXmAADEOdu29dT+8I3cW+fkyrIs\nw4mA97pxZo5cDktv1HSpsWfIdBwg4lCGISI8M7wq7IbhN20g0iyfmqWMBJeOtg1oT0Ov6TgAAMCg\nfY19Oto2oIwEl66akmk6DvAXspLcWjolQyH7z4eUAfgzyjAY19zr05s1XXI5LF0/Pdt0HOADeVwO\nrR6eB/L4PpabAwAQz0a2nt04M0ceFx+pEJlumR3e2bD2UJsG/JyKDrwb79ww7rkDrQrZ0hWTM5SV\n5DYdBzilm2bmyOu09NaJblV1DJiOAwAADGjoHtLW6vCN3BtnMusWkWtabpJmTwifir7+SLvpOEBE\noQyDUb5ASGsPtUmSVs/iYgKRLT3BpZXTwqsXn2B1GAAAcemZihbZkq4qy+RGLiLeLcM7G55+p0Uh\n5t4CJ1GGwahXjneqazCgsuxEzcpLNh0HOK1b5+TJkrTpaIfa+v2m4wAAgHHU5wvqxeEbuSMlAxDJ\nLpuUodxkt2q7hrS9ttt0HCBiUIbBqJHB+atncQoPokNRuleXTUqXP2Tr2XdaTMcBAADj6KXDber3\nhzQ3P0XlOUmm4wCn5XRYunlWuLgdOQEVAGUYDDrU0qdDLf1K9Tq1rIxTeBA9bps7QZL03MFWhpEC\nABAngiH75I3cW+awKgzR49rp2fI6Le2o61FNx6DpOEBEoAyDMSNH/K6alq0ETuFBFJk1IVmz8pLV\nM/TnrRIAACC2vXWiW/XdPk1I8WhJabrpOMAZS0twaXl5lqTw7DAAlGEwpHPAr5ePdciSOIUHUem2\nC8JHVT+5v0XBEMNIAQCIdU/uDx+es2Z2rpwOxnsguqwZnnG3/mi7ugcDhtMA5lGGwYgXD7fJH7S1\nuCRNhWle03GAs7akNF2FaV419fr0WlWn6TgAAGAMVbb1a09DrxLdDl07Pdt0HOCsTcxM1MKiVA0F\nQuxsAEQZBgOCIVvPHQhvkbxpFqvCEJ2cDku3zQ2vDnt8X7NsjqoGACBmjWwtWzUtW8kep+E0wLkZ\nmXX3TAU7GwDKMIy7bSe61NzrV2GaR4uK00zHAc7Z8vIspSe4dKilX/sa+0zHAQAAY6BjwK9NR8Pj\nPUZO5QOi0aLiNBWne9XS59fr1exsQHyjDMO4Gxmcf+PMXDks5i0geiW4HLppeObd4/uaDKcBAABj\n4fkDrfKHbF1Smq6idMZ7IHo5LOtkofv0fgbpI75RhmFc1XQOamddj7xOS6umZZmOA5y3m2blyOO0\n9GZNt2o6OaoaAIBY4guG9Kfh8R4jW8yAaLZyWpaSPU7tb+rT4dZ+03EAYyjDMK7+NLwq7OqpWUr1\nugynAc5fZqJbK4aPqn5iX7PhNAAAYDRtOdahjoGApmQl6MKCFNNxgPOW6Hbq2uFFCU/v59oV8Ysy\nDOOm3xfU+iPhk0tWMzgfMeTWOXmyJG042q6Ofr/pOAAAYBTYtq2nhreS3TInTxbjPRAjVs/OlcOS\nXj7WqXauXRGnKMMwbjYebVe/P6Q5E5JVlp1kOg4wakoyEnTJxHT5g7aeHd5KAQAAotv+pj4dbRtQ\neoJLy6Zkmo4DjJqCVK+WlKYrELL1HNeuiFOUYRgXtm2fHJy/mlN4EIM+OjdPkvRsRYsG/EHDaQAA\nwPl6angL2U0zc+Rx8bEJsWVkBt5zB1rlC4YMpwHGH+/qGBd7G3pV3TmorESXLpuUbjoOMOpmT0jW\njNwk9QwFtf5Iu+k4AADgPDT0DGlrdZdcDks3zmS8B2LP3PwUTclKVOdgQC9XdpiOA4w7yjCMi2eG\nV4VdPyNHbid/7RB7LMvSbReEV4c9sa9ZwZBtOBEAADhXz77TopAtXTUlQ1lJbtNxgFFnWdbJ1WFP\nv9Mi2+baFfGFVgJjrqXPp63VnXJa0g0zuLOG2HXZxAzlp3rU0OPT1uou03EAAMA56PcFtfZQ+NCn\nNXPyDKcBxs6yKZlKT3DpaNuA9jX2mY4DjCvKMIy55w+0KmRLl0/KUHYyd9YQu5wOSx8Zvmj+494m\n7rABABCFXjoyfOhTfrKm5XDoE2KXx+U4uQ346XeaDacBxhdlGMaULxjSCwfDd9ZWz2ZwPmLfymlZ\nSvU6dbClXxVN3GEDACCahGz7ZClw62xWhSH23TgzRy6Hpa3VXWrsGTIdBxg3lGEYU68d71TnYECT\nMxM0Z0Ky6TjAmEt0O3XT8B22P+7jDhsAANFkW0236rt9mpDi0ZKJHPqE2Jed5NbSKRkK2dKzw3Oe\ngXhAGYYxNfKGunp2rizLMpwGGB83z8qV22Hpjeou1XYNmo4DAADO0FPDq8Junp0rp4NrV8SHW4ZX\nQa491KYBf9BwGmB8UIZhzBxp7VdFc5+SPU5dXZZpOg4wbjKT3FpeniVb3GEDACBaHGsb0O76XiW6\nHbpuerbpOMC4mZabpFl5yerzBbX+SLvpOMC4oAzDmHm2okVSeIZSottpOA0wvkaGkW482i5fMGQ4\nDQAAOJ2RVWEry7OV7OHaFfHlljnh+c5Pv9OiEIdAIQ5QhmFMdA8GtLmyQ5K0ergUAOLJ1OxETclK\nVM9QUG9Wd5mOAwAAPkTHgF+bKjtkSVozm2tXxJ/LJ2UoN9mt2q4h7ajtMR0HGHOUYRgTLx5uky9o\na1FxqorSE0zHAcadZVlaNS1LkrTuMMvNAQCIZM8fbJM/aOvi0jSuXRGXnA5Lq2eFV4eNrJIEYhll\nGEZdMGTruQPDg/OH31CBeHTN1Cy5HZZ21HWrpc9nOg4AAPgAvmBIzw2P97hlTp7hNIA5103Pltdp\naXttj2o6OQQKsY0yDKPu7dpuNfb4lJ/q0UXFaabjAMakJbi0ZGK6Qra0gWGkAABEpFeOdap9IKDJ\nmQmaV5BiOg5gTFqCS9eUh3c2PP1Oi+E0wNiiDMOoGxmcf+PMHI6kRtxbNS18GtW6w22yGUYKAEBE\nsW1bT+4Pbwm7ZU6eLItrV8S3W2aHd/asP9KunqGA4TTA2KEMw6iq6xrU9toeeZyWrp3GkdTAgqJU\n5SS5Vd/t077GPtNxAADAu+xv6tPRtgGlJ7h0dVmm6TiAcRMzE7WgKFVDgZDWHmozHQcYM5RhGFXP\nDs8KW1aWqbQEl+E0gHlOh6UVJwfpc0EBAEAkeWp4VdiNM3PkcfHRCJD+vDrs2YoWBUPsbEBs4h0f\no2bAH9RLw6fmMTgf+LOV5eFVkq8c71SfL2g4DQAAkKSGniFtre6Sy2Hpxpk5puMAEeOikjQVpXnV\n3OvX1uou03GAMUEZhlGzqbJDfb6gZuUlqzwnyXQcIGIUpXt1QX6KhgIhvXKsw3QcAAAgae3BNoVs\naemUDGUnuU3HASKGw7K0Znh12MjqSSDWUIZhVNi2rWeHTxxZPYs7a8D7rTy5VZJTJQEAMC0Ysk+e\n9Hz9DK5dgfdbUZ6lJLdD+5v6dKS133QcYNRRhmFU7G/q0/GOQWUkuHT55AzTcYCIc8XkDCW6Hapo\n7lNNx6DpOAAAxLVd9T1q7ferINWjOROSTccBIk6Sx6lV08OjPtYeZO4tYg9lGEbFyKqw62dky+Pk\nrxXwfolup66aEj6likH6AACYtX54VdiK8ixZlmU4DRCZrp0WLsNePtYhXyBkOA0wumgtcN7a+vx6\nrapTDku6geGjwCmtGr6g2HC0XQFO5gEAwIg+X1CvV3VKkpaXZxlOA0SuyVmJmpqdqF5fUG/WMEgf\nsYUyDOft+YOtCtrSpRMzlJvsMR0HiFgz85JUku5Vx0BAb5/oNh0HAIC4tOVYh3xBWxcWpCg/1Ws6\nDhDRVgwXxi8dYe4tYgtlGM6LPxjSCwdbJUk3Mzgf+FCWZZ2cvcBWSQAAzHj3FkkAH25ZWaaclrS9\ntlvt/X7TcYBRQxmG8/J6VZfaBwKamJmgCwpSTMcBIt7yqVlyWNK2mi51cEEBAMC4qusa1DtNfUpw\nOXQFhz4Bp5WR6Nbi0nSFbGnTUVaHIXZQhuG8PFsRHpy/emYOw0eBM5CV5NbFJekK2tJGLigAABhX\nI6vCwqc8Ow2nAaLDyndtlbRt5t4iNlCG4ZxVtvVrf1OfktwOho8CZ2HV9PDrZd1hLigAABgvIdvW\nhuEbUSu5dgXO2OKSNKV5narqGNTRtgHTcYBRQRmGc/ZsRXhW2IrybO6sAWdhcUm6MhJcqu4c1MGW\nftNxAACIC3saetXc69eEFI/mMt4DOGNup0PLysIF8noG6SNGUIbhnPQMBU7uGb+JwfnAWXE5rJOr\nKRmkDwDA+Fg//G/uivIsORjvAZyVFdPC166bKzvkD4YMpwHOH2UYzsnGox0aCtqaX5iq0owE03GA\nqLNq+ILi5coODQa4oAAAYCz1+4J6tapLkhjvAZyD8uxETcpMUNdgQG/XdpuOA5w3yjCck5HB39dO\nzzacBIhOEzMTNSM3Sf3+kF473mk6DgAAMe3Vqk4NBUKak5+swjSv6ThA1LEsSytGBukfZqskoh9l\nGM5aTeegDrX0K8nt0KUT003HAaLWquEyma2SAACMrZEP7yvLuZELnKurp2bJYUlvnehW12DAdBzg\nvFCG4axtfNeR1F4Xf4WAc3XVlEx5nZb2NPSqoXvIdBwAAGJSQ/eQ9jX2yuu0dMXkDNNxgKiVneTW\nwqI0BUK2Nld2mI4DnBeaDJyVkG1rY2W4DFs+lXkLwPlI9jhPXpS/xMk8AACMiZHT7y6fnKFkDyeg\nA+dj5bSRUyXZ2YDoRhmGs7K/MXwkdV6KmyOpgVGwalp4u8ZLh9sUDNmG0wAAEFtCtn2yDGOLJHD+\nlpSmK8Xj1JHWAR1vHzAdBzhnlGE4KxuOhJfDXl3GkdTAaJhbkKKCVI9a+vzaVd9jOg4AADFlf2Ov\nmnp9yk1268JCbuQC58vjcmjplPDOhvXsbEAUowzDGRsKhPTK8XAZxhZJYHQ4LEsrpzFIHwCAsTAy\nOH95OTdygdGyYniV5aaj7exsQNSiDMMZe6O6S/3+kKblJKk0M8F0HCBmrCjPkiVpa1WXujmZBwCA\nUTHgD+qV452Swv/WAhgdM/OSVJzuVftAQDvquk3HAc4JZRjO2Maj4Ttr10zNNJwEiC15KR4tLE6V\nn5N5AAAYNa9VdWowENKsvGQVp3MjFxgtlmWdLJjXH2arJKITZRjOSMeAX2/XdsthSVeVUYYBo20V\nWyUBABhVI1skR06/AzB6rpk6vLOhpks9Q+xsQPShDMMZebmyQyFbuqg4TZmJbtNxgJizZGK6Ur1O\nHW0b0NHWftNxAACIak09Pu1p6JXHaWnpFG7kAqMtL8WjeYUp8gdtbTnWaToOcNYow3BGNhz98/BR\nAKPP43To6rLw62sdy80BADgv64evXS+blKFkj9NwGiA2jQzS38CpkohClGE4rZqOQR1pHVCS26FL\nStNNxwFi1rXTw2XYpsp2+YIhw2kAAIhOtm1rw5Hw2AEG5wNj57JJ6Up0O1TR3KcTnYOm4wBnhTIM\npzWyKuzKyZnyuvgrA4yVsuwklWUnqmcoqDeru0zHAQAgKr3T1Kf6bp9yktyaX5hqOg4QsxLdTl05\nOUMSq8MQfWg28KFCtq1NlSNbJJm3AIy1kUH6LzJIHwCAczIyOP+a8iw5HZbhNEBsG1l9ueFou0K2\nbTgNcOYow/Ch9jX0qrnXr7wUt+bkp5iOA8S8q8sy5XZY2lHbo+Zen+k4AABElcFASK8c75DEFklg\nPMzJT1F+qkctfX7tqe81HQc4Yy7TAUYEg0GtXbtWr776qhoaGiRJBQUFuvLKK3XdddfJ4Ti73q65\nuVlf+cpXTvu4W265Rbfffvs5ZY4HI1skr5maJYfFnTVgrKUluHTpxHRtOd6pDUfa9fH5+aYjAQAQ\nNV6v6lS/P6QZuUkqzUgwHQeIeQ7L0vKpWfrNrkatP9Km+UVsTUZ0iIgyLBAI6P7779e+ffvk9XpV\nXl4uSTp8+LB+/etfa+/evfrGN75x1oWYJDkcDi1cuPCUXy8pKTnn3LFuMBDSq8fDx+Qun8qdNWC8\nrJqerS3HO7XucJtunzeBIhoAgDO0fnhu0crhsQMAxt6K8nAZ9mpVl/7GF1QSJ7giCkREGfbCCy9o\n3759mjJliu655x6lpIS34/X09Oi+++7T7t27tW7dOl133XVn/bM9Ho++/vWvj3bkuPBGdZf6/SFN\nz01SCXfWgHEzvzBVOcluNfT4tL+xVxcUcIcNAIDTae71aVddj9xOS0unZJiOA8SNgjSv5uQna39j\nn16t6jw5AxeIZBExM2zdunWSpM9//vMnizBJSk1N1V133SVJWrt2rZFs8Wzju7ZIAhg/ToellcNz\nTl48zMk8AACciY1H22VLurQ0XaneiLjnD8SNleXhAmw9166IEsbLsLq6OrW2tiorK0tTpkz5i6+X\nl5crPT1dTU1NJ2eJYex19Pu1vbZbTku6ijtrwLgb2d7x6rEO9fmChtMAABDZbNs+uUVyxTRu5ALj\n7YrJGfI6Le1t7FVDz5DpOMBpGS/DTpw4ISk8LP9UCgsL3/NYjL3NxzoUsqWLStKUkeg2HQeIO4Vp\nXl1YkKKhoK0txzpMxwEAIKIdaO5XbdeQshJdWliUZjoOEHeSPU5dNim8iGLDEVaHIfIZXz/c3h5+\noSQmJp7yMWlpae957NkYHBzUJz7xCQUCAblcLmVlZWnmzJm6/vrrNWnSpHPKHA9G3sAYnA+Ys2pa\ntvY09Grd4TZdPyPHdBwAACLW+iNtksLjPZwODp4BTFhRnqVNlR3acKRdn5yfL4tDoBDBjK8MGxwc\nlCR5vd5TPsbtdr/nsWfK7XartLRU06dP17x581RcXKyuri5t2bJF3/zmN7Vhw4ZzDx7DqjsGdLRt\nQMkepy4pTTcdB4hbl0/OUJLboQPN/arpOLv3PwAA4sVQIKSXj4VPQGeLJGDOvHcfAtXUZzoO8KGM\nrwwb4XKdOopt22f983Jzc/Wb3/zmL37f5/Ppd7/7ndauXauHH35Yc+bMUX5+/ln//Fi24Wh4S9aV\nkzPkcRnvS4G4leBy6KqyTL1wsE3rDrfp8xcXmY4EAEDE2VrdpT5fUNNykjQp89S7TQCMLafD0vKp\nWXp0T5PWH27X3PyU038TYIjxpiMhIUGS5Pf7T/mYQCDwnseeiVMtyfR4PPr0pz+t6dOnKxgM6o03\n3jiLtLEvZNucIglEkJGjqTccbVcgdPY3BgAAiHUjWyRXlHPtCpg28jp85XiHBgMhw2mAUzNehmVl\nhV8sfX2nXkbZ3d39nseOhhkzZkiS2traRu1nxoI9Db1q7fNrQopHc/KTTccB4t6M3CSVZiSoYyCg\nt090m44DAEBEaevza2ddj1wOS8vKMk3HAeJeSUaCZuQmqd8f0utVnabjAKdkvAwrKSmRJNXV1Z3y\nMSNfKy4uHrXnHRgYkHR2q83iwcYjI6vCMuVg4CFgnGVZWjU8/+TFw5T3AAC828aj7QrZ0iWl6UpL\niJgJMEBcWzm8s2E9p0oighkvw4qKipSdna3W1lZVVlb+xdePHDmi7u5u5ebmqrCwcFSeMxAIaOfO\nnZKkmTNnjsrPjAWDgZBeHW7vl7PMHIgYy6dmyWFJ22q61N5/6i3lAADEE9u29dLwh222SAKRY+mU\nDLmdlnbV9ailz2c6DvCBjJdhkrRq1SpJ0s9//nP19vae/P3e3l49/PDDkqRrr732Pd9TUVGh22+/\nXR/72Mf0yiuv/MXP/OEPf6jnn39ePT097/n9vr4+/fSnP1Vra6tKS0u1YMGC0f7jRK03qjs14A9p\nem6SitNZMQdEiswkty4uTVfI1smZfgAAxLtDLf2q6RxURoJLF5WkmY4DYFiq16UlpemyJW1gdRgi\nVESsJb7hhhu0e/duVVRU6Mtf/rLKysokhVeF+Xw+XXDBBbruuuve8z22bb/n1/t1dnbq17/+tR55\n5BEVFxcrKytLPT09OnHihPx+v/Ly8vQP//APpxy0H49GlrEuZ3A+EHGunZatN6q7tO5wu26bm8d7\nFwAg7o1cu149NVMuB/8uApFkRXmWXjneqfVH2nX7hRO4dkXEiYgyzOVy6Z577tELL7yg11577eR2\nycLCQl1xxRW6/vrr5XC8dxHb6V5Mn/3sZ/XGG2/o6NGjamxsVF1dnbxer0pKSnTRRRfpuuuuU2Ii\nRy+PaO8PDx91WtJVDB8FIs5FJWnKTHSppnNQB1v6NTOPAy4AAPHLFwzp5WMdkqSV5dmG0wB4v0XF\n4WvX2q4hrl0RkSKiDJPChdjq1au1evXqM3r8rFmz9Nhjj53y6zNmzDh5YiROb3Nlh0K2tKQ0XekM\nHwUijsthafnULP1xX7NePNTGBQUAIK69WdOlnqGgpmYnako2N7iBSON0WLpmapYe39es9UfauXZF\nxImImWEwb2QO0TXlrAoDItWq4ZN5thzr0IA/aDgNAADmrD/M4Hwg0o28Pl+u7JAvEDKcBngvyjDo\nePuAjrYNKNnj1CUl6abjADiF0swEzcpLVr8/pNerukzHAQDAiPZ+v/5/9u7kN64zvfv+79TMIquK\ng0RJnEXJk9y2bEvWQFKS7c7QnQCNXj3oAEHeRRBkEQT5AxrJLgiyyeZZJAGCLBJkkeB9lm83Ok/a\ntiRSg23Z8iS32hbFQaQkilMNrCrWdN5F1SlJtiVxKPI+VfX9AL2xJOJy2666z3Xu33V9eCchryW9\nzXgPwLUOdrbocFeLUrmirsxwdoW70AxD9VbY2YPtCvj4VwJws99/vvyG7Ve/XTJcCQAAZrz7zbJK\ntnRiIKb2Fr/pcgA8hXM77P+yVRIuQ+ejyRVLtt79pjx8lGvmgPudHe6Q32vps7spLa3lTZcDAMCu\nsm1b/115qP49zq6A6719qENeS/rwTkLLac6ucA+aYU3us7spLabz2h8J6OV9DDUE3K414NXJ/qhs\nSWaONb4AACAASURBVBdur5guBwCAXfXNUkZTK1nFQj6d6I+aLgfAM7S3+HViIKaSXb7VCbgFzbAm\n9z/O4PzDnbIsy3A1ADbi3HB5PoqzUh4AgGbx35XB+W8f6pDfy6MMUA8ejUratm24GqCMb5AmlskX\nNT61Kkn6ncMMHwXqxYn+qEI+j75aSOtuct10OQAA7Ip8saT3bhGRBOrNyf6ookGvbq9kdWspY7oc\nQBLNsKZ2aTquTL6kF/eG1RsLmS4HwAa1+L06PVje/HphctVwNQAA7I6rswkl1os62BHSoa4W0+UA\n2CC/16O3DzFIH+5CM6yJOVskf4c3a0DdeYuoJACgyfzfSkTyd5/vYrwHUGd+t7IR/d1bK8oXS4ar\nAWiGNa2ldF4fzyXl81jVh2oA9eNYX0StAa9uLWU0u5o1XQ4AADtqJZPXB7NxeSzph4c4uwL15rmu\nFg12hBTPFvThnYTpcgCaYc3qvVsrKtnSm/1RRUM+0+UA2KSA16PRSlTyPLfDAAAN7r1bKyra0pt9\nUXWE/abLAbBJlmVVZ/05tzwBk2iGNalqRPIwEUmgXr1VeTP+3q0VNvMAABqaM2fo957vMlwJgK16\n53CnPFZ5/l88WzBdDpoczbAmdHs5o1tLGbUFvDo5EDVdDoAteq0noljIp9n4um4vE5UEADSmW0tp\n3VrKKBLk7ArUs66wX8d6oyqUbL13i2QDzKIZ1oT+p/Jm7dxwuwJe/hUA6pXPY+nMULskBukDABrX\nf1fOrm8f6uDsCtS533Wikl8vGa4EzY5vkyZTLNl6t9KFJyIJ1L9zww+bYUQlAQCNpliydZ6zK9Aw\nRgZjag149fViRlMrGdPloInRDGsyn95Naimd14FIQEf2tZouB8A2/WB/mzrDPt1L5vTbxbTpcgAA\nqKkv7qW0nCnoQCSgF/aGTZcDYJsCPo/eqrzM/W8G6cMgmmFN5n++Kb9Z++HhTlmWZbgaANvl9Vg6\nd7A8SP99Zi8AABrM+durkqRzwx2cXYEG8TuVqOT7kysqkWyAITTDmkgmX9R45UDxw8MdhqsBUCvO\nVsnzk6scKAAADaNYsnWx2gxrN1wNgFo50t2q7ja/Ftfy+mphzXQ5aFI0w5rIxFRc2UJJR7pb1RsL\nmS4HQI28uDesfW0BLabz+vI+BwoAQGP47G5K8WxBfbGghjtbTJcDoEYsy9LZgw9f5gIm0AxrIr/+\nppzJ5lYY0Fgsy3o4SJ+oJACgQTibks8ebCciCTQY5+x64TZRSZhBM6xJLKXz+mQ+KZ/H0rlhmmFA\no3mr8t/1xdurKpY4UAAA6luhZGt86uG8MACN5fk9Ye2PBLScLpBsgBE0w5rEe98sq2RLJ/qjioZ8\npssBUGOHulrUFwtqNVvQp3eTpssBAGBbrs8nlVwvaqA9pKEOxnsAjcayLJ07WL4ddn6SZAN2H82w\nJvE/lYjk7xzuNFwJgJ1gWVb1dtj7t5i9AACob87D8blhIpJAozpLsgEG0QxrApNLGU0uZxUJenVi\nIGq6HAA7xJm9MD61qnyxZLgaAAC2Jl8saWIqLkk6d5CIJNCoDne1qCca0EqmoM/vpUyXgyZDM6wJ\nOLfCzh3sUMDLP3KgUQ12tOhgR0ipXFHX5ohKAgDq08dzSaVyRR3sCGmAiCTQsMpRyXLD+wJbJbHL\n6Iw0uGLJ1ru3Klskn+PNGtDo3jrkrKlm9gIAoD6dv83gfKBZnK0kGy5OEZXE7qIZ1uA+mU9qOV1Q\nTzSgI92tpssBsMOcB4dL03GtF4hKAgDqS65Q0qXqFsl2w9UA2GnDneUlUHGWQGGX0QxrcO9WIpLv\nHOpk+CjQBHqiQb2wN6xMvqQPZhOmywEAYFOuzSWVzpd0uKtFvTEikkCjsyyr+jL3PFFJ7CKaYQ0s\nVyzp0nR5+Og7h7lmDjQL50DxPlFJAECdcb67znIrDGgaZw+W/3ufmFpVgagkdgnNsAZ27U75zVr5\n6ilv1oBm4Rwors7Elc4VDVcDAMDGrBdKujLDFkmg2RzsbNFge0iJ9aKuzxOVxO6gGdbALtwuv1lj\n3gLQXLrbAvrBvlblinb1oQIAALf7cDahTL6k5/eEdSAaNF0OgF3k3AZlCRR2C82wBpUrlnS5EpF0\nbokAaB7OVkmikgCAeuE8BPMiF2g+zm3QS9Nx5YssgcLOoxnWoB6NSDJ8FGg+Z4ba5bGkj+4klVwv\nmC4HAICnyuSLulJZ/HKWiCTQdAY6QjrYEVJyvahPiEpiF9AMa1BEJIHm1hH26+iBiAolWxNTRCUB\nAO72wWxC64WSXuoOa18kYLocAAacZaskdhHNsAZERBKAJL1VaYYTlQQAuJ3z8OtsRAbQfJyLHJem\n48oRlcQOoxnWgIhIApCk0aF2eS3p+nxSK5m86XIAAPhe6VxRH8yWX+Se4UUu0LT6YiEd6mrRWq6o\na3eISmJn0QxrQBcrEUluhQHNLRry6XhfVCVbGr/NdXMAgDtdnY0rV7T1g32t2ttKRBJoZs4zrDP2\nB9gpNMMaTK5Y0iUnIsm8MKDpOXGT95m9AABwKec76iwRSaDpOWfXy9Nx5QpEJbFzaIY1mEcjkn1E\nJIGmd3owJr/X0hf3UnqwljNdDgAAj1nLFfXRbEKWiEgCkHqiQT23p0XpfEkf3kmYLgcNjGZYgyEi\nCeBRrQGvTvZHZUu6wO0wAIDLXJ6OK1+y9cr+NnWF/abLAeAC5w6Wb4ddYMwHdhDNsAZCRBLA93mr\nuqaa2QsAAHdxvpvOcXYFUOE8y16ejitLVBI7hGZYAyEiCeD7nBiIKeTz6DcP0rqbXDddDgAAkqTk\nekHX5pLyWNIYqQYAFfsjQb2wN6xsoaQPZ4lKYmfQDGsgRCQBfJ+Qz6PTgzFJ3A4DALjHpem4CiVb\nRw+0qaOFiCSAh845WyU5u2KH0AxrEEQkATyNE5V8/xazFwAA7vAwIskWSQCPc7bLXplNKJMvGq4G\njYhmWIMgIgngaY71RdQa8GpyOaOZlazpcgAATS6RLeiTSkRydIgXuQAe190W0JHuVq0XSvqAqCR2\nAM2wBkFEEsDTBLwejQ2Vo5Lvc90cAGDY+NSqirb0Rm9EsZDPdDkAXMhJPDHmAzuBZlgDICIJYCPO\nPbJV0rZtw9UAAJrZ+clybP/sQSKSAL6fc9Hjg9mE0jmikqgtmmENgIgkgI14vaf89n02vq7J5Yzp\ncgAATWolk9end5PyeSyNVm4tA8C37WkN6Af7WpUr2ro6GzddDhoMzbAGQEQSwEZ4PZbOVOayvD/J\nIH0AgBkTU3GVKhHJSJCIJIAncwbpc3ZFrdEMq3NEJAFsxluHKs2wW0QlAQBmPNwiydkVwNOdOdgu\nS9JHswmtEZVEDdEMq3MfzzkRyRARSQDP9PK+NnWF/bqfyuk3D9KmywEANJnldF6f3U3J77E0Mkgz\nDMDTdYX9emV/m/IlW5eniUqidmiG1bkLk05EkuGjAJ7N67HYzAMAMObi7VXZko73RdUa8JouB0Ad\nOMfZFTuAZlgdIyIJYCveqm6VXFWJqCQAYBedv01EEsDmjA21y2NJ1+aSSq0XTJeDBkEzrI4RkQSw\nFS/uDWtfW0BL6by+uLdmuhwAQJNYXMvpy3trCngtnRpgiySAjekI+/XqgTYVSnb1MgiwXTTD6hgR\nSQBbYVmW3hp2tkpy3RwAsDsuVCKSJ/qjChORBLAJ5x5JNgC1QDOsThGRBLAdbx0qHygu3l5VsURU\nEgCw8y5UHmJ5kQtgs0YHY/JY0sdzCSWyRCWxfTTD6hQRSQDbMdzZor5YUPFsQdfnk6bLAQA0uIVU\nTjcW1hT0eXRyIGq6HAB1pr3Fr9d6Iira0gRRSdQAzbA6deF2+c3aGd6sAdiCclSy/PlBVBIAsNOc\n8R6n+qNq8RORBLB5TlTyAmdX1ADNsDqUK5Z0acq5Zk5EEsDWOM2wiam4csWS4WoAAI3sfOVF7tlh\nXuQC2JrRwZi8lvTJfFJxopLYJpphdejRiGR/OxFJAFsz0BHScGdIqVxR1+4QlQQA7Iy7yXXdfJBW\nyOfRiX4ikgC2Jhry6fXeiEq2ND7FIH1sD82wOkREEkCtnCMqCQDYYc7g/NODMQV9PH4A2Lq3iEqi\nRvg2qjNEJAHUknOguDITV7ZAVBIAUHvnKw+t59iADmCbTg/G5PNY+vRuSiuZvOlyUMdohtUZIpIA\naulANKgX9oaVyZf0wSybeQAAtTUXX9c3SxmF/R4d7yUiCWB7IkGfjjlRydtEJbF1NMPqDBFJALVW\njUre4kABAKgt51bYyGBMASKSAGqgulWSZhi2gW+kOpIrlnR5unxzg4gkgFpxYisfzMa1lisargYA\n0Egu3C43w9giCaBWTg/G5PdY+uxuSktpopLYGpphdeTjuaTWckUikgBqam9rQD/Y36pc0a423AEA\n2K6Z1awml7NqC3h1rDdiuhwADaI14NXx/qhsEZXE1tEMqyNEJAHsFGeQ/nk28wAAasTZ9jY6FJPf\ny2MHgNo5V0lKnb/N2RVbw7dSnSAiCWAnnRlql8eSrs0llcgWTJcDAGgA5yedDei8yAVQW6cGYgp4\nLX15b02LaznT5aAO0QyrE58QkQSwgzrCfh090KZCydaVGaKSAIDtmVrJaHo1q0jQq9eJSAKosXDA\nqxOVqORFopLYApphdeI8EUkAO2xsqHzrdHyKAwUAYHucW2FjQ+3yeSzD1QBoRM6tU+fzBtgMmmF1\ngIgkgN0wMtQuS+WoZJqtkgCALbJtuzqD0tlYDAC1dnIgqqDX0o2FNS2kiEpic2iG1QEikgB2Q1fY\nryP7WpUv2vpgNmG6HABAnZpczuhOfF2xkE9HDxCRBLAzWvxenRyISXq4bA7YKJphdYCIJIDdQlQS\nALBdTmTpzFC7vEQkAeygs5XbpxfYiI5NohnmckQkAeym0aHy27UPZhNaL5QMVwMAqDe2bevCbSKS\nAHbHif6YQj6PfvMgrXvJddPloI7QDHM5JyJ5sIOIJICdtz8S1HN7WpQtlHRtjqgkAGBzvl7KaD6R\nU2eLTz/Y32a6HAANLuTz6NRAVBJRSWwOzTCXc/6DPjtMRBLA7qhGJTlQAAA2yYkqnTlIRBLA7nCe\nlc8TlcQm0AxzsVyxpEtEJAHsMqcZdnkmoXyRqCQAYGPKWyR5kQtgd53oi6rF79HXixnNJ4hKYmNo\nhrkYEUkAJvS3hzTYEdJarqhP76ZMlwMAqBM3H6R1P5VTV9ivl/e1mi4HQJMI+Dw6Xd0qye0wbAzN\nMBcjIgnAlDOV22EXiUoCADbIiSidPdguj0VEEsDuOVeNSnJ2xcbQDHMpIpIATHKikpem4yqWbMPV\nAADcrmTb1Re553iRC2CXHeuLKOz36NZSRnfiWdPloA7QDHMpIpIATDrYGVJPNKB4tqAv7xOVBAA8\n3c0HaT1Yy2tvq18vdodNlwOgyQS8Ho1UXuZe4HYYNoBmmEs5b9bO8GYNgAGWZVVvh128HTdcDQDA\n7ZwNxGNDRCQBmHGukqhiqyQ2gmaYC+WJSAJwAacZNjG1qpJNVBIA8P1s29bEdLkZNjrE2RWAGW/0\nRtQW8Or2Slazq0Ql8XQ0w1zo40cikgNEJAEY8vzesPa0+rWYzuvmg7TpcgAALnV7Oav5RE7tIR9b\nJAEY4/d6dGqwvFXSadADT0IzzIWISAJwA88jUclxtkoCAJ5gfKr8HTEyFJPXQ0QSgDljQ5Vm2BRj\nPvB0NMNchogkADepNsOmVmUTlQQAfA+nGTZGRBKAYcd6owr6PLr5IK2FVM50OXAxmmEuQ0QSgJu8\nvK9V7SGf7iZzmlzOmC4HAOAyd+JZTa1k1Rrw6uiBNtPlAGhyQZ9HJ/qjkspzb4EnoRnmMkQkAbiJ\n12NppHLdfJzr5gCAb3GiSKcGovJ7ebQAYN4YZ1dsAN9YLpIvlnSZiCQAl2FuGADgSYhIAnCbE/0x\n+T2WvriX0ko6b7ocuBTNMBf5ZD6pFBFJAC7zWk95TfX0alYzrKkGAFQspHK6+SCtoM+jY31R0+UA\ngCSpNeDVG70R2ZIuz3A7DN+PZpiLnJ8kIgnAfXwe6+GaamYvAAAqnO+EN/uiCvl4rADgHqOPLIEC\nvg/fWi5BRBKAm52pHCguEpUEAFQ488Kc+TwA4BanB2PyWNL1+ZRS6wXT5cCFaIa5BBFJAG52rDei\nkM+jb5YyuptcN10OAMCwlUxeX9xPyeexdHKAZhgAd4mFfHplf5sKJVtXZxOmy4EL0QxzCSKSANws\n4PPoZHVNNbMXAKDZXZmOq2RLb/RG1Brwmi4HAL6DJVB4GpphLkBEEkA9GDvIgQIAUDZeeTEyyhZJ\nAC41Uolwf3QnoUy+aLgauA3NMBf49G5KqVxRg0QkAbjYif6o/F5LNxbWtLTGmmoAaFap9YI+mU/K\nY0mnB9giCcCd9rYG9OLesNaLtq7dSZouBy5DM8wFnE08Y7xZA+BiLX6vjvdWopLT3A4DgGZ1dTah\nQsnWK/vb1N7iN10OADzRGFsl8QQ0wwwrlmxdmmYTD4D6MHaw/DnFgQIAmpfzIpeIJAC3cz6nrszE\nlS+WDFcDN6EZZthXC2tayRS0PxLQcGeL6XIA4KlODcTktaTP7qYUz7KmGgCaTbZQ0oeVzWyjvMgF\n4HK9saCGO0NK50u6Pp8yXQ5chGaYYeOPRCQtyzJcDQA8XSTo0+u9EZVsVW+1AgCax0d3Elov2npx\nb1h7WwOmywGAZxolKonvQTPMINu2NeFs4hnkzRqA+uAcKCY4UABA03E2CjPrFkC9cD6vLk3HVSzZ\nhquBW9AMM+jWUkb3Uzl1tvj00r5W0+UAwIaMDMbksaSP55Jay7GmGgCaRb5Y0pWZyotcIpIA6sRQ\nR0g90aDi2YK+vE9UEmU0wwxyrmmODLbLQ0QSQJ3oaPHrB/vaVCjZ1YciAEDjuz6fUjpf0sGOkHpj\nIdPlAMCGWJZVXVY3PsXZFWU0wwyqRiR5swagzjifW0QlAaB5jLNFEkCdenRumG0TlQTNMGNmV7Oa\nXs2qLeDV0Z6I6XIAYFPGDpYPFB/OJpTJE5UEgEZXLNnVxSlnDtIMA1BfXtgb1p6wX4tref12MW26\nHLgAzTBDJqbLb9ZODUTl8xCRBFBf9rYG9OLesNaLtj66kzRdDgBgh315P6V4tqCeaFBDHUQkAdQX\nj2VVkw1EJSHRDDPmYUSSN2sA6tMYa6oBoGk4D49jQzFZzLoFUIeqUcnbRCVBM8yIhVRONx+kFfR5\ndKwvarocANgSJyp5dSauXLFkuBoAwE6xbZt5YQDq3iv72xQL+TSXWNf0atZ0OTCMZpgBzryFN/si\nCvn4RwCgPvVEgxrubFE6X9Inc0QlAaBR/XYxrcW1vPaE/Xphb9h0OQCwJV6PpdMDRCVRRifGgAne\nrAFoEM7tMKKSANC4xh8Z7+EhIgmgjo0drDTDbnN2bXY0w3bZaiavz++l5PNYOtlPRBJAfRurDCK9\nNB1XscTsBQBoNLZtVx8anc98AKhXr/VEFPZ7NLmc0Xxi3XQ5MIhm2C67PJNQyZZe62lTW9BnuhwA\n2JbB9pD6YkEl14v67G7KdDkAgBqbWslqLrGuWMinH+xvM10OAGxLwOvRyUpUcoJkQ1OjGbbLLk05\nb9aISAKof5Zl6Uzl8+wiBwoAaDjOw+LpgZi8HiKSAOrf6JDTDGNuWDOjGbaL1nJFfTyXlCVVB/cB\nQL0brcwNuzS1qhJrqgGgoTjzwpw5OwBQ797siyrgtXRjYU1La3nT5cAQmmG76IPZhPIlWy/vb1VH\n2G+6HACoiee6WrSvLaDlTEFf3V8zXQ4AoEbmE+uaXM4o7PfotZ6I6XIAoCZa/F4d6yvP756YJtnQ\nrGiG7aIJIpIAGpBlWdWhykQlAaBxOJuCTw7EFPDy2ACgcThjPpgb1rz4VtsluUJJH8wmJEmjgzTD\nADSWseqBIi6bqCQANARe5AJoVCcHovJa0qd3U0pkC6bLgQE0w3bJtbmksoWSDne1aF8kYLocAKip\nl/a1qjPs0/1UTl8vZUyXAwDYpsW1nL5aSCvgtXS8j4gkgMYSCfr0Wk9EJVu6PMMg/WZEM2yX8GYN\nQCPzWFb11uv4ba6bA0C9uzRdfjg83hdVi99ruBoAqL3RIc6uzYxm2C4oluxqt5lmGIBGNVbZKjk+\ntUpUEgDq3DgvcgE0uJHBmCxJH88llc4VTZeDXUYzbBd8di+l5HpR/bGgBjpCpssBgB3x6v42RYNe\n3Ymva3o1a7ocAMAWxbMFfXY3Ja8lnRqImi4HAHZEZ9ivl/e1Kl+yq/O90Txohu0CJyI5yps1AA3M\n67F0erC8VZLr5gBQvy5Px1Wypdd7I2oL+kyXAwA7ZpStkk2LZtgOK9m2JqaISAJoDmceiUoCAOoT\nL3IBNIvRofKL3KuzCeUKJcPVYDfRDNthNx+ktZTOa2+rX8/taTFdDgDsqNd6Igr7PZpczmouvm66\nHADAJq3livp4LilL0shAzHQ5ALCj9keCOtzVomyhpGtzSdPlYBfRDNthj26RtCzLcDUAsLMCXo9O\nVh6euG4OAPXng9m48iVbL+9vVUfYb7ocANhxTrKBs2tzoRm2g2zb1nglIulcvwSARnemEqu5yIEC\nAOqOc3Y9Q0QSQJNwIuGXZ+IqlNiI3ixohu2gqZWs5hPrioV8enlfm+lyAGBXHOuLKOi1dPNBWgup\nnOlyAAAbtF4oVTeqMS8MQLMYaA9poD2k5HpRn90lKtksaIbtIGeA9MhgTF4PEUkAzaHF79Wb/VFJ\nXDcHgHpybS6h9UJJz+8Jq7stYLocANg1TpLLuR2LxkczbAdNEJEE0KSc7bkcKACgfjDeA0Czcs6u\nl6ZWVbKJSjYDmmE7ZD6xrsnljMJ+j17riZguBwB21cmBmPweS1/cS2klnTddDgDgGfLFkq5Ml5th\nY0QkATSZw10t2tcW0HKmoK8W1kyXg11AM2yHONGgkwMxBbz83wygubQGvHq9NyJb0qUZbocBgNt9\nejelVK6owY6Q+ttDpssBgF1lWVb1VuwEyYamQJdmhxCRBNDsqlHJ28wNAwC3c17kcisMQLN6OOZj\nVTZRyYZHM2wHLKXzurGwpoDX0pt9UdPlAIARpwdj8ljS9fmkkusF0+UAAJ6gWLJ1qRqR5EUugOb0\nUnerOlp8upfMaXI5Y7oc7DCaYTvgUuXN2rG+qFr8XsPVAIAZsZBPrx5oU9GWrhCVBADX+mphTSuZ\ngvZHAhrubDFdDgAY4fVYGh0s3w67SLKh4dEM2wHVTTyDvFkD0NweRiVphgGAW118JCJpWZbhagDA\nHOaGNQ+aYTWWyBb06d2kPJZ0aoBmGIDmNjrULkvSR3MJpXNF0+UAAL7Ftm3mhQFAxdGeiNoCXk2v\nZjWzmjVdDnYQzbAauzobV8mWjh6IKBrymS4HAIzqCvv1Uner8kVbH8wmTJcDAPiWr5cyWkjl1Rn2\n6cXusOlyAMAon8fSqUHndhhRyUZGM6zGnIgkw0cBoGzsYPmmwcQ0BwoAcJuJylyc0cF2eYhIAkD1\nWZ6oZGOjGVZDmXxR1+6Ubz6MDHLNHACkh7MXPphNKFcoGa4GAPCocSKSAPCYY71RBX0e/XYxrYVU\nznQ52CE0w2rowzsJ5Yq2jnS3qqvVb7ocAHCFA5GgDnW1KJMv6ZP5pOlyAAAV0ysZzcbXFQl69cqB\nNtPlAIArBH0eneiPSiIq2chohtWQc41ylIgkADxmdJDr5gDgNs54j5HBmHweIpIA4HCikuOcXRsW\nzbAayRVLujrjNMO4Zg4Aj3I+Fy/PxFUs2YarAQBID288cHYFgMed6I/J77H0xb2UVtJ50+VgB9AM\nq5Hr80ml8yUNd7aoJxo0XQ4AuMpQR0g90aDi2YK+vJ8yXQ4ANL27yXV9s5RRi9+jN3oipssBAFdp\nDXj1Rm9EtqRLM9wOa0Q0w2qEiCQAPJllWUQlAcBFnM/iE/1RBXw8EgDAtzm3Zpkb1pj45quBYsnW\npenygYJNPADw/aoHiulV2TZRSQAwafw2WyQB4GlOD8bksaRP5pJKrRdMl4MaoxlWA1/eX1M8W1BP\nNKihjpDpcgDAlV7sDqsz7NNCKq+vlzKmywGAprWUzuvGwpr8Xqu6MQ0A8LhYyKdX9repaEtXZhKm\ny0GN0QyrAefa5NhQTJbFJh4A+D4ey9LIINfNAcC0S5XP4OO9UbX4vYarAQD3GiMq2bBohm2Tbdsa\nZxMPAGwIc8MAwLxxZt0CwIaMVD4nP7qTUCZfNFwNaolm2DZ9vZjRg7W8usJ+vbA3bLocAHC1oz0R\ntQW8mlnNanY1a7ocAGg6iWxBn95NymNJpwZohgHA0+xtDejFvWGtF21du5M0XQ5qiGbYNk1Ub4XF\n5CEiCQBP5fNYOjVQnk8zMc11cwDYbVdm4irZ0tEDEUVDPtPlAIDrOVHJi0QlGwrNsG0iIgkAmzNS\nnb1AVBIAdtsEEUkA2BTnWf/qTFy5YslwNagVmmHbMLOS1Wx8XZGgV6/ubzNdDgDUheN9UQW9lm4+\nSOvBWs50OQDQNDL5oj6aK29EGx3kRS4AbERvLKjhzpDS+ZKuzxOVbBQ0w7bBuRV2eiAmr4eIJABs\nRMjn0fG+clTyErfDAGDXfHgnoXzR1pHuVnW1+k2XAwB14+FGdM6ujYJm2DY4826ISALA5jifm+PM\nXgCAXeM8xI0QkQSATXHmhl2ajqtYsg1Xg1qgGbZF95M5fb2YUcjn0bHeiOlyAKCunByIymtJn99L\nKZEtmC4HABperljS1ZnKvDAikgCwKQc7QzoQCSieLejL+2umy0ENuGaFTLFY1C9/+UtdvHhRd+/e\nlSQdOHBAZ8+e1Y9//GN5PJvv29m2rffff1/vvvuuZmdnVSgU1N3drVOnTumnP/2pAoHAlut1boWd\n6I8q4KOnCACbEQn6dLQnoo/nkroyE9fvPd9luiQAaGjX55NK50s62BFSbyxouhwAqCuWZWl0V6Mg\n1AAAIABJREFUqF3/7+cLmphe1asHmBle71zRxSkUCvq7v/s7/fu//7vu3r2r5557Ts8995zm5+f1\nb//2b/r7v/97lUqb39rwj//4j/qnf/onTU5OanBwUC+99JJWV1f1f/7P/9Ff//VfK5vNbrlmtkgC\nwPaMEZUEgF3zcIskZ1cA2ApnC+/E1Kpsm6hkvXPFzbBf/OIX+vzzzzU8PKyf//znamsrd1mTyaT+\n9m//VtevX9evfvUr/fjHP97wz7xy5YrOnz+vvXv36m/+5m/U3d0tSVpfX9c//MM/6Pr16/qv//ov\n/cmf/MmWav7y3pr8Hksn+qNb+vMA0OxOD8b0vydmdW0uqUy+qBa/13RJANCQiiVbl6adZhjzwgBg\nK17qblVni08Lqby+WcrouT1h0yVhG1xxM+xXv/qVJOnP/uzPqo0wSYpEIvrTP/1TSdIvf/nLTf1M\n5/f/8R//cbURJknBYFB//ud/Lsuy9Otf/1r5fH5LNduS3uiNqDXAwxsAbEVX2K+XuluVL9r6cDZh\nuhwAaFg3FtYUzxZ0IBLQcGeL6XIAoC55LKu6VZJkQ/0z3gybm5vT4uKiOjs7NTw8/J1ff+655xSL\nxXT//v3qLLFnyWazunnzpjwej44dO/adX+/s7NShQ4eUzWb11Vdfbbl2rpkDwPZUr5tPs6YaAHbK\nxCPjPSzLMlwNANQv5+x6aYqza70z3gybnZ2VVB6W/yQ9PT2P/d5nmZubk23b2rNnj/x+/1N/5p07\ndzZTbpXHkk4NEJEEgO1wXipcnYkrV9z8bEgAwNPZtv3IvDAikgCwHUd7ImoLeDW9mtXs6tZnkMM8\n482w5eVlSVJLy5OvbEej0cd+by1/5uLi4oZ+5re9sr9N7S3f32gDAGxMTzSogx0hpfMlXZ9Pmi4H\nABrOraWM7qdy6mzx6aXuVtPlAEBd83ms6qWYiWmikvXMeDPM2egYDD55xbNzu2uj2x/X19dr/jO/\njYgkANSG83k6wXVzAKg5Z67NyGC7PEQkAWDbRji7NgTjzTCHz/fkxZabXVvq/P6n/UzHVucmjAxy\nzRwAasGJ7VyejqtYYk01ANSSM5NxhIgkANTE8b6ogl5LNx+k9WAtZ7ocbJHxZlgoFJKkp251LBQK\nj/3eWvxM59eednvsabrbAlv6cwCAxw13tmh/JKDVbEE3FtZMlwMADeNOPKvplazaAl4dPdD27D8A\nAHimkM+j433lqCSD9OuX8WZYZ2enJGlt7ckPQIlE4rHf+yxdXV01/5kAgJ1hWZbGqtfNmb0AALXi\nRHhODkTl9xo/9gNAw3DGfIxzdq1bxr8V+/v7JZU3QD6J82t9fX0b+pk9PT2yLEsLCwvVW2XfNj8/\nv6mfCQDYOaOV6PnEVHzT0XgAwPdzHtJGB5l1CwC1dHIgKq8lfX4vpXj2+3sOcDfjzbDe3l51dXVp\ncXFRt27d+s6vf/3110okEtq7d696eno29DNDoZBeeOEFFQoFffTRR9/59eXlZd26dUuBQEBHjhzZ\n9t8DAGB7XtrXqo4Wn+6ncrq1lDFdDgDUvQdrOd18kFbQa+lYX8R0OQDQUCJBn17riahkS1dmiErW\nI+PNMEn6/d//fUnSv/zLvyiVSlX/eiqV0r/+679Kkn70ox899mdu3Lihn/3sZ/qjP/ojXbhw4Yk/\n8z/+4z+0uLhY/eu5XE7//M//LNu29fbbbysQYPYXAJjmsazqYhJn2DMAYOucOTbH+qJq8XsNVwMA\njWeUMR917dnrFnfBH/7hH+r69eu6ceOG/uIv/kKHDh2SVL4Vlsvl9Oqrr+rHP/7xY3/Gtu3H/vdt\nIyMjunbtmsbHx/VXf/VXOnz4sPx+vyYnJ7W2tqaBgQH97Gc/25W/PwDAs40Otev/+82SJqZW9f8c\nO2C6HACoaxPT5YczZyYjAKC2Tg/G9L8nZnVtLql0rqhwgBcP9cQVzTCfz6ef//zn+sUvfqHx8fFq\nXLKnp0dnzpzRH/zBH8jjefwSm2VZz/y5f/mXf6mXX35Z7777rqanp1UsFtXd3a0f/ehH+ulPf8qt\nMABwkaMH2tQa8GpqJau5eFa9sY1tEAYAPC6RLeizuyl5rfJcGwBA7XWF/Xqpu1U3Ftb00Z2Ezg53\nmC4Jm+CKZphUboj95Cc/0U9+8pMN/f4jR47oP//zP5/5+9555x2988472y0PALDD/F6PTvZH9e6t\nFU1MxfW/jtIMA4CtuDITV8mW3uiNKBJ0zXEfABrO2FBMNxbWND61SjOszrhiZhgAANIjsxemmb0A\nAFv1cItkzHAlANDYnLPrB7MJ5Yolw9VgM2iGAQBc43hfRAGvpa8W0lpcy5kuBwDqTiZf1LW5pCxJ\nI8wLA4AddSAa1HBni9L5kq7PJ02Xg02gGQYAcI0Wv1fH+srzbS6xVRIANu3D2YTyRVsvdbeqK+w3\nXQ4ANLzRocpG9CnOrvWEZhgAwFWcWA9rqgFg8yYqLxKchzMAwM5ytvZemo6rWLINV4ONohkGAHCV\nUwMxeSzp07spJbIF0+UAQN3IFUu6OuM0w4hIAsBuGOoIqScaUDxb0Jf310yXgw2iGQYAcJVoyKej\nB9pUsqWrs1w3B4CNuj6fVDpf0nBnSD3RoOlyAKApWJal0cHKEiiSDXWDZhgAwHWcGw3jzF4AgA1z\n5tWMDHIrDAB206Mb0W2bqGQ9oBkGAHCdkcrcsGt3Esrki4arAQD3K5bs6uIR5oUBwO56sTuszrBP\nC6m8vl7KmC4HG0AzDADgOntaA3pxb1i5oq1rd1hTDQDPcmNhTfFsQQciAQ13tpguBwCaiseyqrdy\niUrWB5phAABXehiV5EABAM/ifFaODrXLsizD1QBA8xkbcjaiM+ajHtAMAwC4khPzuTqbUL5YMlwN\nALiXbdu6NEVEEgBMevVARJGgVzOrWc2sZk2Xg2egGQYAcKW+WEiDHSGt5Yr69G7KdDkA4FrfLGV0\nP5VTZ4tPL3W3mi4HAJqSz2Pp5IBzO4xkg9vRDAMAuNboIAcKAHgW5zNyZLBdHiKSAGCMc3Z1FprA\nvWiGAQBca6wyN+zydFwl1lQDwPdy5tOMEJEEAKOO9UUV9Hl080FaC6mc6XLwFDTDAACudairRfva\nAlrOFPTVwprpcgDAdWZXs5pezaot4NXRA22mywGAphbyefRmX0QSt8PcjmYYAMC1LMuq3nRgMw8A\nfNfEdDkieXIgKr+Xoz0AmOZsRGfMh7vxjQkAcLWxRw4UNlFJAHjMRHWLZLvhSgAAknSyPyqvJX1+\nL6V4tmC6HDwBzTAAgKsd6W5VLOTT3WROt5dZUw0AjgdrOd18kFbQa+l4X9R0OQAASW1Bn17vjahk\nl+fewp1ohgEAXM3rsTRS2cwzznVzAKi6VLkVdrwvqpCPYz0AuMXIIFFJt+NbEwDgeqNDzppqDhQA\n4HBeEBCRBAB3GRmMyZL08VxS6VzRdDn4HjTDAACu91pPRGG/R5PLWc0n1k2XAwDGxbMFfX4vJa9V\nHp4PAHCPzrBfR/a1Kl+y9eGdhOly8D1ohgEAXC/g9ehEf/lhj+vmACBdmYmrZEtHeyKKBH2mywEA\nfItza5cxH+5EMwwAUBcebpVkECkAOC8GRiszFQEA7uKM+fhgNqFcoWS4GnwbzTAAQF14sz8qv9fS\njYU1LaXzpssBAGPSuaKuzSVlSRphXhgAuNKBSFCHulqUyZf0yXzSdDn4FpphAIC60OL36o2eiCTW\nVANobh/dSShftPVSd6u6wn7T5QAAnsC5vUuywX1ohgEA6sbYQdZUA8BE5YWAE8EBALiTMzfs8kxc\nxZJtuBo8imYYAKBunBqIyWNJ1+eTSq4XTJcDALsuVyzp6ozTDCMiCQBuNtQRUk80qHi2oC/vp0yX\ng0fQDAMA1I1YyKdX9repaEtXZ1hTDaD5XJ9PKp0vabiz/IAFAHAvy7I0VrnFO05U0lVohgEA6opz\nE+LSNFFJAM3HmTszMsitMACoB6NDD8d82DZRSbegGQYAqCsjlUGkH84mlGVNNYAmUizZulSZFzZG\nRBIA6sILe8PqCvv1YC2vrxczpstBBc0wAEBd6W4L6Pk9Ya0XbV27Q1QSQPP48v6a4tmCDkQCOtgZ\nMl0OAGADPJZVfZnLEij3oBkGAKg7zgY1Z6MaADSDiUo8fHSoXZZlGa4GALBRzm3ecZphrkEzDABQ\nd5zZC1em4yqwphpAE7Btu3qjwHkhAACoD68caFMk6NVsfF0zK1nT5UA0wwAAdWigPaSB9pBSuaI+\nu5s0XQ4A7LhvljJaSOXV2eLTS92tpssBAGyCz2Pp1ICTbOB2mBvQDAMA1KXRQdZUA2geTrRmZLBd\nHiKSAFB3qmM+OLu6As0wAEBdcqKSl6ZXVWJNNYAGd6ny8EREEgDq07HeqII+j367mNZCKme6nKZH\nMwwAUJee29OifW0BLacL+ur+mulyAGDHzK5mNb2aVVvAq6M9EdPlAAC2IOjz6M2+qCS2SroBzTAA\nQF2yLEtjlRsSFzhQAGhgznyZkwNR+TxEJAGgXo0RlXQNmmEAgLo1drCypvr2qmyikgAa1EQ1Itlu\nuBIAwHacHIjJ57H0xf2UVjN50+U0NZphAIC69VJ3q7rCfj1Yy+vmg7TpcgCg5hZSOd18kFbQa+l4\nJV4DAKhPrQGvXutpU8mWLs8kTJfT1GiGAQDqlueRqOQ4UUkADejSdPlW2PG+qEI+ju4AUO+qS6A4\nuxrFNyoAoK6dcaKSU0QlATQeZ8gyEUkAaAwjAzFZkj6eS2otVzRdTtOiGQYAqGsv72tTe8in+URO\nk8sZ0+UAQM3EswV9fi8lr1Ueng8AqH8dYb9e3teqfMnWh7NEJU2hGQYAqGtej6XRSlTy4m2umwNo\nHFdm4irZ0tGeiCJBn+lyAAA14tz2nSAqaQzNMABA3RurHChohgFoJM5D0hgRSQBoKM6L3A/uJJQr\nlAxX05xohgEA6l751oRXs/F1Ta8QlQRQ/9K5oq7NJWVJOj0YM10OAKCG9keCOtzVoky+pI/nk6bL\naUo0wwAAdc/nsTQySFQSQOP46E5C+aKtl7pb1RX2my4HAFBjI0QljaIZBgBoCE6MaJwDBYAGMF7d\nIsmtMABoRGOVz/crMwkVS2xE3200wwAADeH13ojCfo8ml7Oai2dNlwMAW7ZeKOlqZcMY88IAoDEN\ntofUGw0qni3oi3sp0+U0HZphAICGEPB6dGqgEpXkdhiAOnZtLqFMvqTDXS06EA2aLgcAsAMsy6re\nDhufihuupvnQDAMANIwzBytRydscKADUL2f2ofOZBgBoTKOPzA0r2UQldxPNMABAwzjeF1XI59Fv\nF9O6l1w3XQ4AbFquWNLl6XJDn2YYADS2F/aGtbfVr8V0Xr9ZSJsup6nQDAMANIygz6OT/VFJXDcH\nUJ8+mUsqnS9puDOkvljIdDkAgB1kWVb1xceF2yuGq2kuNMMAAA1lrBqVZG4YgPpzoRqR7DBcCQBg\nN5ytfN5fvL0qm6jkrqEZBgBoKCf6owp4Ld1YWNPiWs50OQCwYXkikgDQdF7sDmtP2K8Ha3ndfEBU\ncrfQDAMANJQWv1fH+4hKAqg/1+dTSuWKGuwIaaCdiCQANAOPZVWTDRdINuwammEAgIYzNkRUEkD9\nqW6RHOJWGAA0k7OVZhhRyd1DMwwA0HBOD8bk81j6/F5KK+m86XIA4JkKJVsT0868MJphANBMjuxr\nVWfYp/upnL5ezJgupynQDAMANJzWgFdv9EZkS5qYJioJwP0+nU8quV5UfyyooQ4ikgDQTDyWVU02\nXGSr5K6gGQYAaEjOzYrxKaKSANzv4tTDW2GWZRmuBgCw26pRySmikruBZhgAoCGdHojJY0nX55NK\nZAumywGAJyqWbE1MsUUSAJrZy/va1B7yaT6R060lopI7jWYYAKAhRUM+vdYTUcmWLs8QlQTgXp/f\nSymeLagnGtRwZ4vpcgAABng9j0YlSTbsNJphAICGxYECQD24UPmMOktEEgCa2pnh8tn1AlsldxzN\nMABAwxodLEclP55Lai1XNF0OAHxHOSLJFkkAgPTq/jbFQj7NJdZ1ezlrupyGRjMMANCwOsJ+/WBf\nmwolW5fZKgnAhb68v6aVTEH7IwEd7iIiCQDNzOuxNDoUk/RwsQp2Bs0wAEBDG2OrJAAXc2LcZ4aI\nSAIAyt8HknRhcoWo5A6iGQYAaGhjlbdrH91JKJMnKgnAPUq2XW3Unx0mIgkAkI72RBQJejUbX9f0\nKlHJnUIzDADQ0Pa0BnSku1W5oq0PZhOmywGAqq/ur2kpnVd3m1/P7wmbLgcA4AI+j6XRQZZA7TSa\nYQCAhleNSnKgAOAiF6aISAIAvstZqHKBs+uOoRkGAGh4TlTy6mxC64WS4WoAoByRrM4LO9hhuBoA\ngJu81tOmtoBX0ytZzawQldwJNMMAAA1vfySo5/eElS2U9NEdopIAzLv5IK3Ftbz2hP16sZuIJADg\nIb/Xo5FBtkruJJphAICmMHawcqDgujkAF3h4K6xdHiKSAIBvcaKSF2+vGK6kMdEMAwA0BWdN9ZWZ\nuHJFopIAzLEfi0iyRRIA8F2v90YU9ns0uZzVnThRyVqjGQYAaAq9sZCGO0NK50v6ZC5puhwATezr\nxYzup3LqDPt0ZF+r6XIAAC4UeDQqSbKh5miGAQCaxljldtg4sxcAGOREXsaGiEgCAJ7MWbBCM6z2\naIYBAJqGE0e6NB1XoWQbrgZAM7JtuzoM+SwRSQDAUxyrRCW/WcpoPrFuupyGQjMMANA0Bjta1B8L\nKrle1KfzRCUB7L7J5YzmEzm1h3x6eV+b6XIAAC4W8Hl0coCo5E6gGQYAaCrO7TCikgBMuDBZ/uwZ\nG2qX10NEEgDwdA+3SnJ2rSWaYQCApuIcKCam4ioSlQSwi2zb1gW2SAIANuHNvqhCPo9+u5jWvSRR\nyVqhGQYAaCrDnS3qiQa0mi3oy/sp0+UAaCJTK1nNJdYVC/n06gEikgCAZwv6PDo5EJXE7bBaohkG\nAGgqlmVVt0pyoACwm5zPnJHBGBFJAMCGEZWsPZphAICm83BuWFwlm6gkgN1xkYgkAGALTvTHFPR5\n9JsHaS2kcqbLaQg0wwAATef5PWF1t/m1lM7rq4U10+UAaALTKxlNr2YVCXr1Wk/EdDkAgDoS8nl0\nop+oZC3RDAMANB3LsjRaiUqOc6AAsAsejUj6iEgCADbpDGM+aopmGACgKZ0dehiVtIlKAthhRCQB\nANtxciCqgNfSjYU1PVgjKrldNMMAAE3ppX2t6gz7dD+V09eLGdPlAGhgs6tZ3V7JqjXg1etEJAEA\nW9Di9+rNvnJUkmTD9tEMAwA0Jc+jWyWnOFAA2DnOrbDTgzH5vRy/AQBbU90qydl12/g2BgA0rbFH\nZi8QlQSwU5yHFmfeCwAAW3FyICa/19KX99a0lM6bLqeu0QwDADStV/a3KRbyaT6xrtvLWdPlAGhA\nc/F13VrKKOz36FgfEUkAwNa1Brw63huVLWmC22HbQjMMANC0vB5LI4MxSVw3B7AzxiufLacGYgoQ\nkQQAbFM1KsncsG3hGxkA0NScAwWDSAHsBLZIAgBq6fRgTD6Ppc/vpbRCVHLLaIYBAJraaz0RRYJe\nTa9mNbNCVBJA7dxNruu3i2mFfB4dr2wAAwBgO1oDXh3rjahkSxPTcdPl1C2aYQCApubzWDo1QFQS\nQO05N05PDkQV9HHsBgDUxsOo5IrhSuoX38oAgKbH7AUAO8H5TDl7sMNwJQCARnJ6MCavJX16N6XV\nDFHJraAZBgBoem/0RhT2ezS5nNF8Yt10OQAawEIqp988SCvo8+jNfiKSAIDaiQR9er0SlbxEVHJL\naIYBAJpewOvRyUpUkkH6AGrB2SJ5oj+qEBFJAECNObeOL3B23RK+mQEAkHRmqBKVZG4YgBq4MFnZ\nIjnEFkkAQO2NDMbksaTr80klsgXT5dQdmmEAAEg63l8ecH3zQVr3kznT5QCoY4trOd1YWFPAa+nk\nABFJAEDtRUM+vdZDVHKraIYBACAp5PPoRGWuzzi3wwBsw/hU+aHkzb6oWvxew9UAABrV2coSqAts\nldw0mmEAAFQ4cSaaYQC2w9ki6WyqBQBgJzhRyU/mkkquE5XcDJphAABUnOiPyu+19OX9NS2tsaYa\nwOYtp/P64l5Kfq9VXcwBAMBOaG/x6+iBNhVt6TJRyU2hGQYAQEU44NXx3nJUcmKa22EANm98alW2\npOO9UbUGiEgCAHbWmcpWyYtsldwUmmEAADzCiTVxoACwFUQkAQC7abQSlbw2l9Rarmi6nLpBMwwA\ngEecGojK57H0+b2UVtJEJQFs3Eomr8/vpeTzWDrFFkkAwC7oCPv1yv42FUo2UclNoBkGAMAj2oI+\nHestr6m+wO0wAJtwaTquki290RtRW9BnuhwAQJMg2bB5NMMAAPiWtw+VZy+8d4s11QA2jogkAMCE\n0aF2WZI+mksQldwgmmEAAHzL6cGYgl5LNxbWdC+5brocAHUgni3o+nxSXks6zRZJAMAu6gr79fL+\nVuWLtq7OEJXcCJphAAB8S4vfq9OD5YfZ9ye5HQbg2ZyI5Ou9EUVDRCQBALvrzBBRyc2gGQYAwPd4\n+1CnJOm9b2iGAXi2i7fLnxXOwwgAALtprBLR//BOQpk8UclnoRkGAMD3ON4XUSTo1e2VrG4vZ0yX\nA8DFkusFfTKXlMeSRmiGAQAM2Nsa0JHuVuWKtq7OJEyX43o0wwAA+B5+r0djlYfa9xmkD+ApLk/H\nVbSlowciihGRBAAYUt0qOUVU8llohgEA8ATOVsn3J1dk27bhagC4FVskAQBu4HwPfTBLVPJZaIYB\nAPAEr+xvU2fYp7vJnH7zIG26HAAutJYr6lolIjk6yBZJAIA53W0Bvbg3rPVCSR/eISr5NDTDAAB4\nAq/H0lvD5dth7xGVBPA9Lk/HVSjZemV/mzrCftPlAACaXDUqyVbJp6IZBgDAUzhRyfOTKyqWiEoC\neJwzl4WIJADADZzvo6szCa0XSoarcS+aYQAAPMXze8LqiQa0kino07tJ0+UAcJG1XFEf3UnIkjTK\nFkkAgAvsjwT1/J6wskQln4pmGAAAT2FZlt4+1CmJqCSAx01MrSpfLEcku4hIAgBcgqjks9EMAwDg\nGd6uzA0bn4orV+S6OYCyX39TbpC/c7jDcCUAADzkNMOuzMSJSj4BzTAAAJ5hoCOkQ10tWssV9eEs\n180BSEtreX16Nym/x2JeGADAVXqiQT23p0WZfElXZ+Kmy3ElmmEAAGzA22yVBPCI9yZXVLKlE/1R\nRYI+0+UAAPCYHx4uj/lwbjHjcTTDAADYgLcqWyWvzMSVzhUNVwPAtHe/WZYkvVN52AAAwE3eGu6Q\nx5I+vJNQIlswXY7r0AwDAGADutsC+sG+VuWKti5Nc90caGYzq1l9s5RRa8Crk/1R0+UAAPAdnWG/\nXuuJqFCydYFB+t9BMwwAgA16+xBRSQAPb4WNDcUU8HGcBgC40w8rC16c7y08xLc3AAAbdOZguzyW\ndG0uodVM3nQ5AAywbbvaECciCQBws9HBdgW9lr64v6b7yZzpclyFZhgAABvU3uLXsd6oSrZ0kevm\nQFP6aiGtu8mcusJ+vbq/zXQ5AAA8UTjg1enBmCTp3VvcDnsUzTAAADaBqCTQ3JyHibcPdcjrsQxX\nAwDA0zm3mN/9ZkW2bRuuxj1ohgEAsAkjgzEFKtfNF1JcNweaSaFk6/xk+VboO5XGOAAAbna8L6po\n0Kvp1awmlzOmy3ENmmEAAGxCOODVqYHydfP3J7kdBjSTj+cSimcLGmgP6VBXi+lyAAB4Jp/H0rnh\n8gucX3/D2dVBMwwAgE0iKgk0J+ch4p1DHbIsIpIAgPrwzuGHZ9diiaikRDMMAIBNe7M/qtaAV7eW\nMppZyZouB8AuyOSLujQdlyS9fZiIJACgfhzpbtX+SEBL6bw+u5cyXY4r0AwDAGCTAl6PxobKUcn3\niEoCTeHSdFzrhZKOdLfqQCRouhwAADbMsqzqrMt3v2GrpEQzDACALXk0KslmHqDxvVuJSP6QW2EA\ngDrkbJW8eHtVuULJcDXm0QwDAGALjh6IqKPFp/nEun67mDZdDoAdtJLJ69pcQl5LOjtMMwwAUH8G\n2kM63NWidL6kK7Nx0+UYRzMMAIAt8D6ymYdB+kBjOz+5qpJdXk8fC/lMlwMAwJb8sHI7jK2SNMMA\nANgyJyr5/iSbeYBG5sxXcSImAADUo7cOdchjSR/OJpTIFkyXYxTNMAAAtujFvWHtjwS0nC7oczbz\nAA1pLr6u3zxIq8Xv0enBmOlyAADYsq6wX6/1RFQo2bpwe9V0OUbRDAMAYIssy9LbRCWBhvberfKt\nsNHBmEI+js4AgPpW3Sp5q7m3SvKNDgDANrxd2Sw3PrWqXJHNPEAjsW1b71Ya3UQkAQCNYHSoXQGv\npS/urel+Mme6HGNohgEAsA1DHS062BFScr2oa3eSpssBUENfL2Z0J76u9pBPr/dETJcDAMC2tQa8\n1dh/M98OoxkGAMA2ObfD3mviAwXQiH5d+W/6rUMd8nosw9UAwP/f3p3H113X+R5//845Odn3tWmS\ntkmXtJQuULbS0pVuKIpepSDj9eL1ImCvG44yKDNyHyAzjiNz0VEWcUQUQRyuSlvKktIFSqF7a9qk\nTZdszX6ynGwnZ7l/nCQQSrokJ/md5fV8PPp41JzfSd7Vfs2v73y/nx8QGMuL/LudSyoc8vki8yFQ\nlGEAAIzS0v65Ybsq29Xd5zE5DYBA8Hh92jZwRLJ/vgoAAOFgQV6iEqOtOuPo0cmWbrPjmIIyDACA\nUcpJjNasrHj1ur3adabN7DgAAuBAbYdaut3KTYrWjMw4s+MAABAwUVaLlvT/MPfNE5H5ECjKMAAA\nAmBZEU+VBMJJyYd2hRkGRyQBAOFlRf+961sVDnm8kXdUkjIMAIAAuGFKiiyGtKe6Xe3y65uwAAAg\nAElEQVQ9brPjABiFXrdXb59ulSStmMoRSQBA+JmVHa/sBLuauvp0uM5pdpxxRxkGAEAApMZFaX5u\nojw+aUf/P6IBhKZ3K9vU1efVjMw4TUyOMTsOAAABZxiGlk8dOCoZeQ+BogwDACBABo9KRujsBSBc\nlJxgcD4AIPyt6H+q5I5TrXK5vSanGV+UYQAABMj1k1MUZTV0uM6pxk6X2XEAjEB7j1vvVbXJYnzw\npFgAAMJRQWqMpqbHqqvPq3erIushUJRhAAAESLzdqmvyk+STtI1B+kBI2n6qVR6fdMXERKXGRZkd\nBwCAMbV8qn93WEmEnWygDAMAIICW9W8333oysm4ogHBR0j83ZXn/WgYAIJwtK0yVIen9qsh6CBRl\nGAAAAXR1fpLioiw63tSt6rYes+MAuAT1HS4dqe9UtNXQwknJZscBAGDMpcdHaV5uovq8voh6CBRl\nGAAAARRts+j6ySmSpK0clQRCSkmFf1fYdZOSFWe3mpwGAIDxsaL/qZKRdFSSMgwAgAAbfKpkhUM+\nn8/kNAAuhs/nU0l/gT0wPwUAgEhw/eQU2fsfAtXgjIyHQFGGAQAQYPNzE5UcY1N1W69ONHebHQfA\nRTjZ0q0zjh4lRVu1IC/J7DgAAIybeLtV1xX4xwMM7JIOd5RhAAAEmNViaEkhRyWBUDJwNOSGwlTZ\nLIbJaQAAGF+R9lRJyjAAAMbAwFHJt0465OWoJBDUvD7f4BNgV/SvXQAAIsmCvEQlRlt12tGjkxFw\nsoEyDACAMTArK17ZCXY1dfbpSF2n2XEAnMfhs041dfYpO8GuWdnxZscBAGDcRVktWjLF/wOhN0+E\n/1FJm9kBPB6PNm/erB07dujs2bOSpAkTJuiGG27Q2rVrZbFcel/X0NCgDRs2XPC6W265RevXr7/k\nzw8AwIUYhqGlRal64WC9tla0aM6EBLMjARjG4OD8olQZBkckAQCRafnUVL1yrElbKxz68tW5soTx\n90RTyzC3261HH31Uhw8fVnR0tKZNmyZJKi8v17PPPqtDhw7pu9/97ogKMUmyWCy68sorh309Pz9/\nRJ8XAICLsazQX4ZtP9Wqe67LU5SVDdlAsHF5vNpxqlWS/x8BAABEqlnZ/pMN9U6XDp11al5uotmR\nxoypZdimTZt0+PBhFRYW6oEHHlBCgv+n5h0dHXr44Yd14MABbdmyRWvXrh3R57fb7brvvvsCGRkA\ngIs2JS1Gk1JjdMbRo301Hbqm/yk9AILHe1Xtcro8mpoeq0mpsWbHAQDANBbD0PKiVD1/sF4lJxxh\nXYaZ+iPqLVu2SJK+8pWvDBZhkpSYmKgvf/nLkqTNmzebkg0AgNEyDEPLCv07TXiqJBCcSvrnoixn\ncD4AAIO7pHecbpXL7TU5zdgxrQyrqalRU1OT0tLSVFhYeM7r06ZNU3Jysurr6wdniQEAEGoGnir5\nzpk29YTxDQUQipy9bu2ubJchaSllGAAAmpQaq6npsep0ebS7qt3sOGPGtDKsqqpKkn9Y/nByc3OH\nXAsAQKiZkBSt4sw49bi9evdMm9lxAHzIjtNt6vP6NDc3QRnxdrPjAAAQFAZ2S5eE8VMlTZsZ1tLi\n/y81Nnb42QxJSUlDrr1UPT09+sIXviC32y2bzaa0tDTNnDlT69at0+TJk0f0OQEAuFTLilJ1rLFL\nWysc7D4BgsgHRyTTTE4CAEDwWFqUqqfeq9V7Ve3q6HUrMdrUcfNjwrSdYT09PZKk6OjoYa+Jiooa\ncu2liIqKUkFBgWbMmKF58+YpLy9PbW1t2rZtm+6//3698cYbIwsOAMAlWlKYKoshvV/tv6EAYL6m\nTv+TsqKshhZPSTE7DgAAQSMj3q55uQnq8/oGn7gcbkZc7z333HPas2fPJb1nwYIFuuOOO4YGsA0f\nwefzjShbZmamnnvuuXM+7nK59Pvf/16bN2/WM888o9mzZysnJ2dEXwMAgIuVFheluRMStL/WqZ2n\nWrW2OMPsSEDE21rhkE/SNfnJirdbzY4DAEBQWT41TftrnSo54dC6MLx3HXEZ5nA4LnmwfWvrB41i\nTEyMJKmvr2/Y691u95BrL5ZhGB/7cbvdri996Us6efKkysrKtGvXLt1yyy2X9LkBABiJZUX+G4qt\nJx2UYUAQKOl/wuvAU7MAAMAHFk1O0eNvV+lQnVMNTpeyEsJrtuaIy7ANGzZow4YNI/7CaWn+2Qyd\nnZ3DXtPe3j7k2kApLi5WWVmZmpubA/p5AQAYzqLJyXr8bUMHa51q7uxTenyU2ZGAiHXG0a2K5m4l\n2K26Oj/J7DgAAASdeLtV1xYka/upVm2tcOjWudlmRwoo02aG5efnS5JqamqGvWbgtby8vIB+7e7u\nbkmXvuMMAICRSoi26ar8JPkklVSE75N5gFBQcsK/K2zxlBTZrabdDgMAENQGdk+H41MlTfvuP3Hi\nRKWnp6upqUkVFRXnvH78+HG1t7crMzNTubm5Afu6brdb+/btkyTNnDkzYJ8XAIALuXGaf6fz5rLm\nEc/FBDA6Pp/vgyOSPN0VAIBhXZWXpMRoq045enSyudvsOAFl6o/CVq9eLUl6+umn5XQ6Bz/udDr1\nzDPPSJLWrFlzzvtKS0u1fv163Xbbbdq+ffs5rz/yyCPauHGjOjo6hny8s7NTjz/+uJqamlRQUKAr\nrrgikH8cAADO65qCZKXF2lTd1qsj9cOPCQAwdkrrO1XvdCkjPkqXT0gwOw4AAEErymrRDf1PXA63\nkw0jnhkWCDfddJMOHDig0tJS3XvvvSoqKpLk3xXmcrk0Z84crV279pz3+Xy+Ib8+qrW1Vc8++6x+\n+9vfKi8vT2lpaero6FBVVZX6+vqUlZWlb3/728MO2gcAYCzYLIZWTU/XHw7Wa3NZsy7P4R/iwHh7\n80O7wizcCwIAcF7Lp6Zp47FmlVQ4dOdVuWHzvdPUMsxms+mBBx7Qpk2btHPnzsHjkrm5uVq8eLHW\nrVsni+XczWsXKrHuvPNO7dq1SydOnFBdXZ1qamoUHR2t/Px8XXXVVVq7dq1iY2PH5M8EAMD5rJnh\nL8N2nHTonmsnKiHa1G/FQERxe33afnKgDAvsA5oAAAhHl2XHKzvBrnqnS4fPOjU3N9HsSAFh+h24\nzWbTzTffrJtvvvmi3zNr1iy98MILw75eXFys4uLiQMQDACCgcpOiNT83QftrnSqpcOjmWZlmRwIi\nxp7qdrX3ejQ5NUaF6fxgFACAC7EYhpYVpeoPB+tVUuEImzKMx+cAADDO1s7IkCRtOsYgfWA8vdn/\nNKyBp2MBAIALG/i+uf1Uq1xur8lpAoMyDACAcbZwcrKSoq062dKt8qYus+MAEaHL5dG7Z9okScsK\nOSIJAMDFmpwaq6L0WHW6PHqvqt3sOAFBGQYAwDizWy26cZr/H+ObjjWbnAaIDG+faVWvx6fZOfHK\nTrSbHQcAgJCyvMi/OyxcnipJGQYAgAkGjkq+ddKh7j6PyWmA8FdygsH5AACM1LKiVBmSdle2q6PX\nbXacUaMMAwDABAWpMZqdHa/uPq/eqnCYHQcIay1dfdpf2yGbxdANU1LMjgMAQMjJiLdrbm6C+rw+\n7TzVanacUaMMAwDAJGtmpEuSNpVxVBIYSyUnWuT1SVflJSkpxvSHqQMAEJIGdleXhMEPcinDAAAw\nyQ2FqYq3W1XW2KWTzd1mxwHCktfn08b+2XwDBTQAALh0i6ekKMpq6OBZpxqcLrPjjAplGAAAJomx\nWQaHkW4uazI5DRCeDtY6VdPeq8z4KF2dn2R2HAAAQla83aprC5IlKeTHfFCGAQBgonXF/p0qb55w\nqNftNTkNEH42HvMXzWtnpMtqMUxOAwBAaPvM7Ex9d+kkfXJWhtlRRoUyDAAAExWlx2l6RpycLo92\nhMEwUiCYtHT16e3TrbIYHJEEACAQLstO0IqpaYqNspodZVQowwAAMNna4oFB+hyVBAJpS3mzPD7p\nuoJkZcTbzY4DAACCBGUYAAAmW1aYqhibRUfqOlXZ2mN2HCAseLw+beofnH/TzNA+ygEAAAKLMgwA\nAJPF2a1aWugfpP9qWbPJaYDwsLemXfVOlyYk2nXFxESz4wAAgCBCGQYAQBAYOCr5+vEWuTwM0gdG\na+NRf7G8rjhDFoPB+QAA4AOUYQAABIHizDhNSY1RW49b755pMzsOENIanC7trmqTzWJo1fQ0s+MA\nAIAgQxkGAEAQMAxDa4v9c402cVQSGJVXy5rl9UmLJicrNTbK7DgAACDIUIYBABAkVkxNld1qaF9N\nh8529JodBwhJbq9v8Mmsn2BwPgAA+BiUYQAABInEaJsWT0mRxCB9YKTerWxTS5db+cnRujwnwew4\nAAAgCFGGAQAQRNbO8O9kea28RR6vz+Q0QOjZePSDXWEGg/MBAMDHoAwDACCIXJ4Tr7zkaDV39em9\nqnaz4wAh5Wx7r/bWdMhuNbRyGoPzAQDAx6MMAwAgiBiGobUz0iVJm441mZwGCC0Da2ZJYaoSo20m\npwEAAMGKMgwAgCBz47Q02SyG3q9uV2Ony+w4QEhwebx6tbxFEoPzAQDA+VGGAQAQZFJio7RwUrK8\nPmlL/z/uAZzf26fb1NbjVmFarIoz48yOAwAAghhlGAAAQWhN/1HJLWXN8voYpA9cCIPzAQDAxaIM\nAwAgCF0xMVHZCXbVO13aV9NhdhwgqFU6enSozqkYm0XLilLNjgMAAIIcZRgAAEHIYhiDu8M2HWs2\nOQ0Q3DaW+XeFLZ+aqni71eQ0AAAg2FGGAQAQpFZPT5PFkHadaZWjq8/sOEBQ6nV79frA4PxiBucD\nAIALowwDACBIZcTbdXV+kjw+6fXjDNIHPs62kw45XR7NyIzT1AwG5wMAgAujDAMAIIitneHf6bK5\nrFk+BukD59h47IPB+QAAABeDMgwAgCB2dX6S0uOiVNPeq0NnnWbHAYJKRXOXjjZ0Kd5u1ZJCBucD\nAICLQxkGAEAQs1oMrZ6eJsm/OwzABzYe9a+JlVPTFGPjthYAAFwc7hoAAAhya2aky5C043Sr2nvc\nZscBgkKXy6M3K/yz9G6amW5yGgAAEEoowwAACHI5idG6YmKi+jw+vXmCQfqAJG096VB3n1ezc+I1\nOTXW7DgAACCEUIYBABAC1hb7d74wSB+QfD6fNh7tH5xfzOB8AABwaSjDAAAIAdcVJCs5xqbTjh4d\na+wyOw5gqrLGLp1o7lZyjE2LpqSYHQcAAIQYyjAAAEJAlNWiVdP8g/Q3HWsyOQ1gro39a+DGaWmy\nW7mdBQAAl4a7BwAAQsTAUcm3Traq0+UxOQ1gDmevW29VOCRJNxUzOB8AAFw6yjAAAEJEXnKM5uQk\nqNft1db+MgCING+ccKjX49P83ERNTI4xOw4AAAhBlGEAAISQDwbpc1QSkWfI4PyZDM4HAAAjQxkG\nAEAIWTw5RYnRVh1v6taJJgbpI7Icqe/UmdYepcXadN2kZLPjAACAEEUZBgBACLHbLFpe1D9Iv6zZ\n5DTA+Hqlf1fYmhnpslkMk9MAAIBQRRkGAECIWdd/VLLkRIu6+xikj8jQ1uPWzlOtMiStncERSQAA\nMHKUYQAAhJgpabEqzoxTV59XO061mh0HGBevlTerz+vT1flJyk60mx0HAACEMMowAABC0Npi/86Y\nTcc4Konw5/X5tLH/7/pNDM4HAACjRBkGAEAIWlqYotgoi0obOnXa0W12HGBMHajtUG17rzLjo3RV\nXpLZcQAAQIijDAMAIATFRlm1rChVkrSZQfoIc68c9f8dX1ecISuD8wEAwChRhgEAEKLW9Q8Rf+N4\ni1xur8lpgLHR3Nmnd860ymJIa6anmx0HAACEAcowAABC1LSMWBWlx6qj16O3z7SZHQcYE6+WN8vr\nkxZOSlZ6fJTZcQAAQBigDAMAIEQZhqG1M/w7ZTaXNZmcBgg8j9c3+Hf7pmIG5wMAgMCgDAMAIISt\nmJqmaKuhA7VO1bb3mh0HCKg91e1qcPYpN8mu+RMTzY4DAADCBGUYAAAhLN5u1Q2F/YP0j7E7DOHl\nlaP+v9PrijNkMRicDwAAAoMyDACAELeu2H9UclNZs7pcHpPTAIFR3+HSe1XtirIYWjUtzew4AAAg\njFCGAQAQ4mZlxeuy7Hh19Hq0id1hCBOby5rkk7RoSopSYhmcDwAAAocyDACAEGcYhtbPzZYkvXSk\nQS6P1+REwOi4vT69WtYsicH5AAAg8CjDAAAIA1fnJ6kwLUYtXW69frzF7DjAqLx7pk0t3W4VpMTo\n8px4s+MAAIAwQxkGAEAY8O8Oy5EkvXiwXh6vz+REwMi90n/c96bidBkMzgcAAAFGGQYAQJhYPCVF\nuUnROtvh0vZTDrPjACNS09arfTUdirYaWsngfAAAMAYowwAACBNWi6Fb52RJkp4/UC+vj91hCD0D\nD4FYWpSqxGibyWkAAEA4ogwDACCMrJiWpvS4KJ129Gh3ZbvZcYBL4vJ49Vr/zLt1DM4HAABjhDIM\nAIAwYrda9N8u9+8O+8PBOvnYHYYQ8vbpVrX1uFWUHqvizDiz4wAAgDBFGQYAQJhZV5yuxGirjjZ0\n6dBZp9lxgIvi8/n0X0caJUk3FWcwOB8AAIwZyjAAAMJMbJRVt1yWKUn6w8F6k9MAF+f96naVNXYp\nJcbG4HwAADCmKMMAAAhDN8/KVGyURXtrOlTe2GV2HOC8fD6fnttXJ0n63Jwsxdi4RQUAAGOHOw0A\nAMJQUoxNN/UPIP/DwTqT0wDnt7emQ8cau5QcY9MnZjI4HwAAjC3KMAAAwtRnL89SlMXQ26fbVOno\nMTsO8LGG7Aq7PEuxUVaTEwEAgHBHGQYAQJhKj4vSqulp8kl64RCzwxCc9tV0qLShU0nRVn1yFrvC\nAADA2KMMAwAgjH1+TrYshlRyokX1HS6z4wBD+Hw+Pbffvyvss+wKAwAA44QyDACAMDYhKVpLClPl\n8UkvHWZ3GILLgbNO/a2+U4nRVn1qVqbZcQAAQISgDAMAIMytn5stSdpc1ixHd5/JaYAPDMwK++zs\nLMXZ2RUGAADGB2UYAABhbkparK4rSJbL49PLRxrNjgNIkg7WduhwndO/K+wydoUBAIDxQxkGAEAE\nWD/PvzvsL6WN6nR5TE4DaHBW2C2zsxTPrjAAADCOKMMAAIgAM7PiNXdCgrr6vPpLKbvDYK5DZ506\neNapBLtVt7ArDAAAjDPKMAAAIsTA7LD/OtKoHrfX5DSIZM/tPytJumV2JrvCAADAuKMMAwAgQlwx\nMVHTM+LU1uPWlrJms+MgQh2pc+pArVNxURZ2hQEAAFNQhgEAECEMwxicHfbioXr1edgdhvH3230f\nzApLiLaZnAYAAEQiyjAAACLIwknJKkiJUWNnn7ZWOMyOgwjzt3qn9td2sCsMAACYijIMAIAIYjEM\n3To3S5L0h4P18nh9JidCJHmuf1fYpy7LVFIMu8IAAIA5KMMAAIgwy4rSlJ1gV3Vbr94502Z2HESI\now2d2lvTodgoiz47O8vsOAAAIIJRhgEAEGFsFkOfmzOwO6xOPh+7wzD2BneFzWJXGAAAMBdlGAAA\nEWj19HSlxNh0vKlbe2s6zI6DMHesoVPvV7crxmbRZy9nVxgAADAXZRgAABEo2mbRZy73DzB/4WC9\nyWkQ7n63f2BXWIaS2RUGAABMRhkGAECE+uTMTMXbrTp41qm/1TvNjoMwVd7Ypd1V7YpmVxgAAAgS\nlGEAAESoeLtVN8/MkCT94QC7wzA2ntt/VpL0yZkZSomNMjkNAAAAZRgAABHt07MzFW01tLuqXSeb\nu82OgzBzvKlL71a2K9pq6HPsCgMAAEGCMgwAgAiWGhulNTP8u8NeOMTuMATWc/2zwj4xM0OpcewK\nAwAAwYEyDACACPe5OVmyGtK2kw7VtveaHQdhoqK5S7vOtMluNfS5OdlmxwEAABhEGQYAQITLSrBr\nxdQ0eX3Si+wOQ4A8t8+/K+ymmRlKY1cYAAAIIpRhAABAn5+bLUPS6+Utau7sMzsOQtzJ5m693b8r\n7PPsCgMAAEGGMgwAAKggJUaLpqSoz+vTn440mB0HIW5gVti64gylsysMAAAEGcowAAAgSVo/17+D\n55WjTWrvcZucBqHqVEu3dp5uVZTV0K3sCgMAAEGIMgwAAEiSpmXEaUFeonrcXv25tNHsOAhRv+/f\nFbZ2RrrS49kVBgAAgg9lGAAAGDSwO+z//a1R3X0ek9Mg1JxxdGv7qVZFWQzdOpddYQAAIDhRhgEA\ngEGX5yRoVla8Ono92nis2ew4CDG/218nn6TVM9KVGW83Ow4AAMDHogwDAACDDMPQ+nn+HT0vHa6X\ny+M1ORFCRaWjR9tOtspmMQZ3GAIAAAQjyjAAADDENflJKkyLUUuXW68fbzE7DkLE7w707wqbnqas\nBHaFAQCA4EUZBgAAhjAMQ7fOzZEk/fFQvTxen8mJEOyqWnu07aSjf1dYjtlxAAAAzosyDAAAnOOG\nKSnKTbKrtt2l7accZsdBkPv9gTp5fdKN09KUnciuMAAAENwowwAAwDmsFkOfn+Of+/SHA/Xy+tgd\nho9X3dajrRUOWQ3ptnnMCgMAAMGPMgwAAHysldPSlBEfpVOOHr1ytMnsOAhSvz9QL6/P//clJzHa\n7DgAAAAXRBkGAAA+lt1q0VevnShJeub9WjV39pmcCMGmpq1XJSdaZDGk2+cxKwwAAIQGyjAAADCs\nxZNTdE1+krr6vPr5rmqz4yDI/OGgf1bYyqlpmpDErjAAABAaKMMAAMCwDMPQhuvzFWOzaOfpVu06\n02Z2JASJs+29ev24f1fYbewKAwAAIYQyDAAAnFdWgl1fWjBBkvSzd6rU5fKYnAjB4Pn+WWHLp6Zp\nYjK7wgAAQOigDAMAABf0qVmZmpYRq8bOPv1m71mz48BkdR29ev14syyG9AWeIAkAAEIMZRgAALgg\nq8XQNxYVyGJIfy5tVHljl9mRYKLnD9TL45OWFaVqYnKM2XEAAAAuCWUYAAC4KNMy4vSZ2Vny+qSf\n7qyUx+szOxJMcLypS6+VN/MESQAAELIowwAAwEX7uytylJ1gV0Vzt14+0mB2HIwzl8erf912Rh6f\n9MmZGcpPYVcYAAAIPZRhAADgosVGWbXh+jxJ0m/21amuo9fkRBhPv9tfp1OOHuUm2XXnVblmxwEA\nABgRyjAAAHBJrs5P1pIpKep1e/X429Xy+TguGQnKGjv1wsF6GZLuu2GSYqOsZkcCAAAYEcowAABw\nye6+Lk/xdqver27XtpOtZsfBGHO5vfrxtkp5fdJnZmdqdk6C2ZEAAABGjDIMAABcsrS4KP3Pq/3H\n5H7xbrU6et0mJ8JYenbfWVW29igvOVpfWsDxSAAAENoowwAAwIisnZGuy7Lj5eh261fv15odB2Ok\ntL5TLx1ukMWQvrNkkqJt3D4CAIDQxt0MAAAYEYth6BuL8mWzGNp0rFlH6pxmR0KA9bi9+tftZ+T1\nSf/t8izNzIo3OxIAAMCoUYYBAIARm5Qaq8/PyZIkPbazSi6P1+RECKT/3FOr6rZeTUqJ0RevmGB2\nHAAAgICgDAMAAKNy+7wcTUyKVmVrj1481GB2HATI4TqnXj7SKIsh3bekQHaORwIAgDDBXQ0AABgV\nu82iry/KlyQ9f6BO1W09JifCaHX3efST7Wfkk3Tr3GzNyOR4JAAACB+UYQAAYNTm5SZq1bQ09Xl8\n+vedVfL5fGZHwig8836tattdmpIaoy/MzzE7DgAAQEBRhgEAgID4X9dMVHKMTQfPOvX68Raz42CE\nDtR26M+lTbL2Pz3SbuV2EQAAhBfubgAAQEAkxdh01zUTJUlP7K5Ra3efyYlwqbpcHv1ke6Uk6fb5\nOZqaEWdyIgAAgMCjDAMAAAGzYmqqrpiYqI5ej57YXWN2HFyip9+rVb3TpanpsbptHscjAQBAeKIM\nAwAAAWMYhv739fmyWw29ecKhvdXtZkfCRdpb3a5XjjXJZjH0nSWTZLMYZkcCAAAYE5RhAAAgoHKT\nogeHrj/+TpV63V6TE+FCOl0e/dsO//HIv7siR1PSYk1OBAAAMHYowwAAQMB9bk62JqfGqLbdpd/t\nrzM7Di7giXdr1NjZp+kZcfr8nGyz4wAAAIwpyjAAABBwNouhbywqkCHpj4fqdaql2+xIGMZ7VW16\ntbxZUVZD31lSICvHIwEAQJijDAMAAGNiVna8PjEzQx6f9NjOSnl9PrMj4SM6et366Y4qSdJ/v3KC\nJqVyPBIAAIQ/yjAAADBm7rwqV2lxNh1t6NIrR5vMjoOP+MW7NWru6tPMrDh9dnaW2XEAAADGBWUY\nAAAYM/F2q+69Ll+S9Mz7tWru7DM5EQbsOtOmN463yG41dN8NkzgeCQAAIgZlGAAAGFOLJifruoJk\ndfV59fNd1WbHgaT2Hrf+faf/6ZH/Y0Gu8lNiTE4EAAAwfijDAADAmDIMQ/cuzFNslEU7T7dq15k2\nsyNFvJ/vqlZLt1uzc+J1y+xMs+MAAACMK8owAAAw5rIS7PrSlRMkSY+/U6Uul8fkRJFrx6lWba1w\nKNpm0bcXT5LF4HgkAACILJRhAABgXNw8K1PTM+LU1Nmn3+w9a3aciNTa3af/+7b/6ZH/86pcTUyO\nNjkRAADA+KMMAwAA48JqMfSNRfmyGNKfSxtV3thldqSI87N3qtXW49bcCQn65KwMs+MAAACYgjIM\nAACMm6kZcfrM7Cx5fdJPd1bK4/WZHSlibDvp0PZTrYqNsujbNxRwPBIAAEQsyjAAADCu/u6KHGUn\n2FXR3K3HdlbK66MQG2uOrj493n888itXT1ROIscjAQBA5KIMAwAA4yo2yqrvLp2kaKuhLeUt+ved\nVRRiY8jn8+mxt6vU3uvR/NxE3VScbnYkAAAAU1GGAQCAcTc7J0EPrS5StNXQ5rJmCrExVFLh0K4z\nbYrrPx5pcDwSAABEOMowAABgivm5iXpoVZHs/YXY/32bQizQmjv79B+7qiVJX5gS5KsAABP9SURB\nVL02T1kJdpMTAQAAmI8yDAAAmGb+xET9n/5CbNOxZj1OIRYwPp9Pj+2sVEevR1flJWn19DSzIwEA\nAAQFyjAAAGCq+RMT9dCqQtmthjYea9bP3q6mEBslr8+nX+6u0e6qdsXbrfrm4nyORwIAAPSjDAMA\nAKa7YmKSfnijvxB75ViTfvZOtXwUYiPi8nj1o62n9fKRRtkshu67oUAZ8RyPBAAAGEAZBgAAgsKV\neUn6pxsLFWU19MrRJj1OIXbJOl0ePfBqhbadbFVclEUPry7S9ZNTzI4FAAAQVCjDAABA0FiQ598h\nNlCIsUPs4jV1uvStv5br4Fmn0mJt+sknpmn+xESzYwEAAAQdyjAAABBUPlyI/fVok36+i0LsQiod\nPfr6X8p1ytGjvORoPXbzdBWlx5kdCwAAIChRhgEAgKCzIC9J/7TSX4j9pbRJ/0EhNqwjdU5985Vy\nNXb2aVZWvB775HTlJEabHQsAACBoUYYBAICgdFV+fyFmMfTn0ib9x64aCrGP2Hm6Vd/bfEIdvR5d\nV5CsR9dNVVKMzexYAAAAQY0yDAAABK2r8pP0jzdO6S/EGvWLdynEBvyltFH/541Tcnl8uqk4XQ+u\nnKIYG7d2AAAAF8IdEwAACGpX5ycPFmL/72+N+mWEF2I+n0+/fr/W/3ABSV+8coL+9/X5sloMs6MB\nAACEBMowAAAQ9K7OT9aDK/2F2Mt/a9Qvd0dmIeb2+vSv2yv1/MF6WQzpW4sLdMf8HBkGRRgAAMDF\nogwDAAAh4ZqCZP1g5RTZLIZePtKoJyKsEOvu8+jB1yr0+vEWRdss+uGNhVozI93sWAAAACGHMgwA\nAISMawuS9YMV/kLsv4406skIKcQc3X36zsYT2lPdoeQYm368bqquKUg2OxYAAEBIogwDAAAh5bpJ\nHxRifzrSqKfeqw3rQqymrVff/Gu5ypu6NCHRrsc+OU3FWfFmxwIAAAhZlGEAACDkXDcpWd9fMVk2\ni6GXDjeEbSFW1tipb/y1XLXtLk1Nj9Vjn5yuickxZscCAAAIaZRhAAAgJC2clKLvr5gsqyG9dLhB\nT4dZIfZeVZvu23hCbT1uLchL1L/eNE2pcVFmxwIAAAh5lGEAQsZf//pXsyMAESmY156/EJsiqyH9\n8XCDfvV+eBRiW8qb9eBrJ9Xr9mrltDQ9tKpIcXar2bEwzoJ57QHhjLUHhD/KMAAh45VXXjE7AhCR\ngn3tXT85RQ/0F2IvHmrQMyFciPl8Pv1uf51+sr1SXp9029xsfeeGAtkshtnRYIJgX3tAuGLtAeHP\nZnaAD3vjjTf01FNP6e6779bSpUtH9bl8Pp/eeustlZSUqKqqSm63W1lZWbr22mv16U9/Wna7PTCh\nAQCA6RZNTtEDy6fo4ZJTeuFQg047erSuOENX5yfJGiJFksfr0893VeuVo00yJN27ME83z8o0OxYA\nAEDYCZoyrLa2Vi+//LIkyTBGf9P6i1/8Qtu2bZPNZtPUqVNlt9tVUVGhP/3pT9q7d69++MMfKiaG\nAbQAAISLRVNS9A/Lp+jRt05rd1W7dle1KzXWphVT07R6epompcaaHXFYvW6vfrT1tN4506Yoq6H7\nl07WoikpZscCAAAIS6aXYQ8//LAcDoeqqqoC9jnfffddbdu2TZmZmXrwwQeVlZUlSert7dW//du/\n6cCBA3rxxRf1xS9+MWBfEwAAmG/xlBTNyr5Mbx5v0ZbyZlW19eqlww166XCDZmTGafX0dC0tTFFC\ntOm3QJL8O9lr21368bYzKm3oVILdqodWFWp2ToLZ0QAAAMKW6XeChw4dCvjn3Lx5syTpjjvuGCzC\nJCk6Olp33XWX7rnnHr355pu67bbbFBXFU5kAAAgn6XFR+vzcbH1uTpaONXZpS3mz3qpwqKyxS2WN\nXfrFu9W6flKyVk1P1/zcxHE/Runo6tOBsx3aX+PU/toO1TtdkqTM+Cg9sqYoqHewAQAAhAPTy7AX\nXnhh8PePPvqo9u/fP6rP19PTo7KyMlksFl155ZXnvJ6WlqaioiKdOHFCR48e1Zw5c0b19QAAQHAy\nDEMzs+I1MyteX702T2+fbtVr5c06UOvUWydb9dbJVmXER+nGaWlaNS1dE5OjxyRHp8ujw3X+4mt/\nTYdOO3qGvJ4YbdWVExP1lWsmKjOemaYAAABjzfQyLNBqamrk8/mUmZk57K6v3NxcnThxQtXV1ZRh\nAABEgBibRSumpmnF1DTVd7j0+vFmvXa8RXUdLj1/oF7PH6jX7Jx4rZ6ersWTUxRnt474a7k8Xh1r\n6NS+mg4dqHXqWGOnvB96uGW01dDsnATNn5io+bmJKkqPlSUA81IBAABwccKuDGtpaZEkxcYOf8Qg\nKSlJktTU1DQumQAAQPDITrTrjism6Pb5OTpS59SW8hZtP9WqI3WdOlLXqZ+/U63FU1K0enqaLs9J\nuOCDfTxenypaunWgpkP7azt0pM6pXs8H7ZfFkGZlxfeXXwkqzoqX3WoZ6z8mAAAAhhF2ZVhvb68k\n/3yw4QzsGOvp6Rn2GgAAEN4shqE5ExI1Z0Ki7r0uT9tP+Y9RHqnv1OvHW/T68RZNSLTrxunpWjUt\nTVkJ/iOMPp9PNe292l/Tof21Th0826GOXs+Qzz05NWZw59flOQmKH8VOMwAAAATWiMuw5557Tnv2\n7Lmk9yxYsEB33HHHSL/kRfH5/D+Jtdku/Ee70E96AQBAZIizW7VmRrrWzEhXTVuPXiv3l2FnO1x6\ndu9Z/XbvWc3LTVRmfJT213aosbNvyPuzE+yan5uo+RMTNG9ColLjeEAPAABAsBpxGeZwOHT27NlL\nek9ra+tIv9xFi4mJkST19fUNe83Aa+fbPXYh+/btG/F7AYzMXXfdxdoDTBCJa2+uVZpb/NGPtkuS\nls/4uHf0+n+1NelUm3RqbOMhQkTi2gOCAWsPCH8jLsM2bNigDRs2BDJLQKSnp0uSOjs7h72mvd1/\nM5uWlnbJn3/FihV68803RxYOAAAAAAAAH6u5uXlcvk7YzQzLzc2VYRhqaGiQ2+3+2OOStbW1kqS8\nvLwRfY0VK1aMKiMAAAAAAADMEXaPMoqJidGMGTPkdrs/dqZZS0uLKioqZLfbNWvWLBMSAgAAAAAA\nwCxBWYYNDMEfTmlpqdavX6/bbrtN27dvP+f11atXS5J+97vfqampafDjLpdLTzzxhHw+n5YtWya7\n3R7Y4AAAAAAAAAhqph+TfPLJJwd/X1lZKUnatm2bysvLJUkzZszQkiVLhrzH5/MN+fVRCxcu1N69\ne7Vz5059/etf19SpUxUVFaWTJ0+qs7NTBQUFWr9+/Rj+qQAAAAAAABCMTC/DPm4YfWlpqUpLSyVJ\nbrf7nDLMMIwLft4NGzbosssuU0lJic6cOSOPx6OsrCytWbNGn/70p9kVBgAAAAAAEIEM34XOJAIA\nAAAAAABhIihnhgEAAAAAAABjgTIMAAAAAAAAEYMyDAAAAAAAABGDMgwAAAAAAAARgzIMAAAAAAAA\nEcNmdoBQ4PF4tHnzZu3YsUNnz56VJE2YMEE33HCD1q5dK4uFThEYrTfeeENPPfWU7r77bi1duvS8\n17ImgdE7fvy4du/erbKyMtXW1qqrq0t2u10FBQVatGiRbrzxxo9dS6w/YHQqKyv1zjvv6NixY6qu\nrlZnZ6dsNpuys7O1cOFCrVy5UklJSee8j7UHjA2Xy6V//Md/1MmTJ7V69Wrdeeed51zD+gNG5m9/\n+5seeuihC1531113afny5YP/eTzWnOHz+Xyj/ixhzO1269FHH9Xhw4cVHR2tadOmSZLKy8vlcrk0\nb948ffe73+X/AIFRqK2t1cMPP6ympibdc889WrJkybDXsiaBwNiwYYMaGhpks9lUUFCg5ORkNTc3\nq7KyUpI0a9Ys/cM//IOioqIG38P6A0bve9/7nk6dOiWr1aqCggKlpKSoq6tLFRUVcrvdSk5O1kMP\nPaScnJzB97D2gLHh8/n005/+VLt375akjy3DWH/AyA2UYbGxsZo9e/aw161Zs2bw9fFac+wMu4BN\nmzbp8OHDKiws1AMPPKCEhARJUkdHhx5++GEdOHBAW7Zs0dq1a01OCoSehx9+WA6HQ1VVVRf9HtYk\nEBgZGRn61Kc+pUWLFikmJmbw48eOHdMjjzyi0tJSbdq0SZ/61KcGX2P9AaOXlZWlVatWaeHChUPW\nntPp1I9+9COdOHFCv//97/Wtb31r8DXWHjA2XnjhBe3evVtxcXHq6ur62GtYf8DoZWRk6L777ruo\na8drzVFfX8CWLVskSV/5ylcG/0eQpMTERH35y1+WJG3evNmUbECoO3To0CUVYRJrEgiUH/zgB1q5\ncuWQf4xLUnFxsVatWiVJ2rNnz5DXWH/A6H3rW9/S8uXLz1l7CQkJ+sxnPiNJqqmpGfIaaw8IvB07\ndujll1/WnDlztHr16mGvY/0B42u81hxl2HnU1NSoqalJaWlpKiwsPOf1adOmKTk5WfX19YPnWAFc\nvBdeeGHw1/z58y94PWsSCJzzbS3PzMyUpCE/JWf9AWOvqalJkn8uygDWHhB4ZWVl+uUvf6nc3Fx9\n85vfHPZ7IusPGF/jueYow85jYMfKh29IPio3N3fItQDGDmsSGB8DNxcDpZjE+gPGWnl5uf74xz8q\nJiZG69evH/w4aw8IrIaGBv34xz9WbGysvve97ykuLm7Ya1l/wPgazzXHzLDzaGlpkSTFxsYOe83A\n034GrgUwdliTwNjr6enRjh07JEnz5s0b/DjrDwi8kpIS7dq1Sw0NDWpoaND8+fN1++23Ky8vb/Aa\n1h4QOF1dXfrnf/5ndXd36/vf/76ys7PPez3rDwiM6upq3XbbbfJ6vbLb7crIyNDs2bP1iU98Ysg6\nHM81Rxl2Hj09PZKk6OjoYa8ZeMrWwLUAxg5rEhh7zz77rJxOp9LS0oY84pr1BwReZWWlDh06JMm/\nfiwWizo7O4dcw9oDAsPr9eqxxx5TdXW17r77bs2cOfOC72H9AaNjGIZiYmKUk5OjxMREWSwWORwO\n1dbW6rXXXtNbb72lb3zjG7ryyislje+aowy7CDbb8P81+Xy+cUwCQGJNAmNl48aNevPNN2Wz2fTN\nb35Tdrv9nGtYf0DgfOlLX9IXv/hFtbe3a//+/Xr++ef14IMP6mtf+5oWL1485FrWHjA6//mf/6mD\nBw/q5ptv1tKlSy/pvaw/YGSKi4v1m9/85pyPd3V16cknn9SuXbv0s5/9TD//+c+HHFkejzXHzLDz\nGHjKT19f37DXuN3uIdcCGDusSWDslJSU6Nlnn5XVatWGDRs0ffr0Ia+z/oCxYbFYlJKSomXLlumr\nX/2qJOlXv/rV4Fpj7QGjV11drS1btmjBggW6/fbbL/p9rD9gdIZ7OEVcXJy+9rWvKSsrS11dXdq3\nb5+k8V1z7Aw7j7S0NEk6Z7v6h7W3tw+5FsDYYU0CY+PVV1/Vr3/9a9ntdn3961/XggULzrmG9QeM\nvdmzZ0uSuru7VVVVpcLCQtYeEAAD/3jes2fPkAdUfNSWLVu0ZcsWxcXF6de//jXrDxhDNptNRUVF\namhoUHNzs6Txvd+kDDuP/Px8Sf7Hew5n4LUPDzoFMDZYk0Dgvfjii/rTn/6kuLg4/f3f//2wM1RY\nf8DY83g8g783DEMSaw8IhLi4OC1YsGBwXX1UTU2NamtrlZWVpUmTJg3OKxpYU6w/YGwMzP0aGJg/\nnt/zKMPOY+LEiUpPT1dTU5MqKipUVFQ05PXjx4+rvb1dmZmZg4/3BDB2WJNA4Hg8Hj311FPaunWr\n0tLSdP/996ugoGDY61l/wOh5PB5ZrdZhXz948KAkyW63a+LEiZJYe0AgZGVl6Tvf+c6wr//xj3/U\nSy+9pPnz5+vOO+8c/HheXh7rDxgjTqdTR48eleSfLSaN7/c8ZoZdwOrVqyVJTz/9tJxO5+DHnU6n\nnnnmGUnSmjVrTMkGhKMLDURkTQKj19PTo0cffVRbt27VlClT9Mgjj5y3CBvA+gNG59ixY3rwwQe1\nd+/eIbvAJOno0aN6+umnJUk33njjkAdYsPaAsXW++0/WHzBy999/v0pKSs558qPD4dBPfvIT9fT0\naN68eUPuQ8drzbEz7AJuuukmHThwQKWlpbr33nsHm8njx4/L5XJpzpw5Wrt2rckpgdD05JNPDv6+\nsrJSkrRt2zaVl5dLkmbMmKElS5YMeQ9rEhi9iooKHTp0SJKUkJCgp556athrL7/88sE1xfoDRscw\nDJWVlelf/uVfFBMTo4KCAsXExKiurk4NDQ2SpPnz558z4Ju1B5iH9QeMXF1dnZ544gk99dRTKigo\nUEpKilpaWlRdXS2v16vJkyfra1/72pD3jNeaM3w8C/aC3G63Nm3apJ07d6q+vl6SlJOTo8WLF2vd\nunXDPiEBwPndeuut5319yZIluueee875OGsSGJ3S0lL98Ic/vKhrP7oOWX/AyLndbr3//vvav3+/\nTp06pcbGRvX29io+Pl5TpkzRkiVLtGjRomHfy9oDxsbAMcnVq1cPOSY5gPUHjMy+ffu0Z88eVVRU\nqKGhQT09PYqJiVF+fr4WLlyolStXymY7d4/WeKw5yjAAAAAAAABEDCpsAAAAAAAARAzKMAAAAAAA\nAEQMyjAAAAAAAABEDMowAAAAAAAARAzKMAAAAAAAAEQMyjAAAAAAAABEDMowAAAAAAAARAzKMAAA\nAAAAAEQMyjAAAAAAAABEDMowAAAAAAAARAzKMAAAAAAAAEQMyjAAAAAAAABEDMowAAAAAAAARAzK\nMAAAAAAAAEQMyjAAAAAAAABEDMowAAAAAAAARAzKMAAAAAAAAESM/w8s/KbaIaR1HAAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110c7e7d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For backward-compatiblity, `%pylab` is not going away,\n",
      "but we don't recommend using it for instruction.\n",
      "It now includes a message about what it does:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`raw_input`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One of the major missing features in the notebook prior to 1.0 is the lack of `raw_input` support.\n",
      "This has been remedied"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "answer = raw_input(\"What is your favourite colour? \")\n",
      "answer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "What is your favourite colour? blue\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'blue'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Having `raw_input` support means that all kinds of things based on raw_input now work, such as `%debug`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def foo(x):\n",
      "    return 1/x\n",
      "\n",
      "def bar(y):\n",
      "    return foo(1-y)\n",
      "\n",
      "bar(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ZeroDivisionError",
       "evalue": "integer division or modulo by zero",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m in \u001b[0;36mbar\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m in \u001b[0;36mfoo\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%debug"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "> \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(2)\u001b[0;36mfoo\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> up\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "> \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(5)\u001b[0;36mbar\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m      4 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m----> 5 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      6 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> down\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "> \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(2)\u001b[0;36mfoo\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> bt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(7)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n",
        "\u001b[0;32m      3 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m      4 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m      5 \u001b[0m    \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m      6 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m----> 7 \u001b[1;33m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n",
        "  \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(5)\u001b[0;36mbar\u001b[1;34m()\u001b[0m\n",
        "\u001b[0;32m      3 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m      4 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m----> 5 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[0;32m      6 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m      7 \u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\n",
        "> \u001b[1;32m<ipython-input-5-e30cf484dce8>\u001b[0m(2)\u001b[0;36mfoo\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      4 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\u001b[1;32m      5 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> exit\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Autosave"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As of 1.0, IPython notebooks are autosaved at a frequency of approximately two minutes.\n",
      "\n",
      "You can change this frequency with the `%autosave` magic:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%autosave?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%autosave 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.set_autosave_interval(0)"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Autosave disabled\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%autosave 300"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.set_autosave_interval(300000)"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Autosaving every 300 seconds\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you save manually, IPython creates a **checkpoint** to which you can revert at a later time,\n",
      "so that autosave won't result in overwriting unwanted changes.\n",
      "In this way, autosave is *strictly safer* than the previous behavior in terms of data loss - manually saved state will never be lost due to autosave."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "New cell magics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have added a few cell magics for raw display data.\n",
      "There is one for each basic display type:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%html\n",
      "\n",
      "<audio controls height=\"100\" width=\"100\">\n",
      "  <source src=\"files/data/wilhelm.mp3\" type=\"audio/mpeg\">\n",
      "  <source src=\"files/data/wilhelm.ogg\" type=\"audio/ogg\">\n",
      "  <embed height=\"50\" width=\"100\" src=\"files/data/wilhelm.mp3\">\n",
      "</audio>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<audio controls height=\"100\" width=\"100\">\n",
        "  <source src=\"files/data/wilhelm.mp3\" type=\"audio/mpeg\">\n",
        "  <source src=\"files/data/wilhelm.ogg\" type=\"audio/ogg\">\n",
        "  <embed height=\"50\" width=\"100\" src=\"files/data/wilhelm.mp3\">\n",
        "</audio>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x110af1bd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%latex\n",
      "\n",
      "\n",
      "\\begin{align}\n",
      "    \\LaTeX \\\\\n",
      "\n",
      "    \\nabla\\cdot{\\bf D} =& \\rho \\\\\n",
      "    \\nabla\\cdot{\\bf B} =& 0 \\\\\n",
      "    \\nabla\\times{\\bf E} =& - {{\\partial{\\bf B}}\\over{\\partial t}} \\\\\n",
      "    \\nabla\\times{\\bf H} =& {\\bf J} + {{\\partial{\\bf D}}\\over{\\partial t}}\n",
      "\n",
      "\\end{align}\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\n",
        "\n",
        "\\begin{align}\n",
        "    \\LaTeX \\\\\n",
        "\n",
        "    \\nabla\\cdot{\\bf D} =& \\rho \\\\\n",
        "    \\nabla\\cdot{\\bf B} =& 0 \\\\\n",
        "    \\nabla\\times{\\bf E} =& - {{\\partial{\\bf B}}\\over{\\partial t}} \\\\\n",
        "    \\nabla\\times{\\bf H} =& {\\bf J} + {{\\partial{\\bf D}}\\over{\\partial t}}\n",
        "\n",
        "\\end{align}"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Latex at 0x110af18d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%javascript\n",
      "\n",
      "IPython.notebook.save_notebook();\n",
      "IPython.notebook.insert_cell_above(\"code\");\n",
      "var cell = IPython.notebook.get_selected_cell();\n",
      "cell.set_text(\"print 'hello'\");\n",
      "alert(\"I created a new cell!\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "\n",
        "IPython.notebook.save_notebook();\n",
        "IPython.notebook.insert_cell_above(\"code\");\n",
        "var cell = IPython.notebook.get_selected_cell();\n",
        "cell.set_text(\"print 'hello'\");\n",
        "alert(\"I created a new cell!\");"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x110af1a50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Linking notebooks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Heading cells automatically get anchors with their text,\n",
      "so you can link to them:\n",
      "\n",
      "    [like this](#Autosave)\n",
      "\n",
      "[like this](#Autosave)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also link to headings in other notebooks:\n",
      "\n",
      "    [Coming in 2.0](Coming%20in%202.0.ipynb#Using-Two-Widgets-together)\n",
      "\n",
      "[Coming in 2.0](Coming%20in%202.0.ipynb#Using-Two-Widgets-together)\n",
      "\n",
      "\n",
      "\n",
      "Note that for HTML / HTTP reasons, spaces in the notebook filename should be replaced with `%20`,\n",
      "and spaces in heading anchors should be replaced with `-`."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "nbconvert"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipython nbconvert --to html \"What's new in 1.0\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[NbConvertApp] Using existing profile dir: u'/Users/minrk/.ipython/profile_default'\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[NbConvertApp] Converting notebook What's new in 1.0.ipynb to html\r\n",
        "[NbConvertApp] Support files will be in What's new in 1.0_files/\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[NbConvertApp] Loaded template html_full.tpl\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[NbConvertApp] Writing 282948 bytes to What's new in 1.0.html\r\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[static html output](files/What's%20new%20in%201.0.html)\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}