Ant colony optimization for Travelling Salesman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importação das bibliotecas\n",
    "\n",
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# intância do grafo Caixeiro Viajante\n",
    "\n",
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adição dos vértices e grafos \n",
    "\n",
    "G.add_node('A')\n",
    "G.add_node('B')\n",
    "G.add_node('C')\n",
    "G.add_node('D')\n",
    "G.add_node('E')\n",
    "G.add_weighted_edges_from([('A','B', 22), ('A','C', 50), ('A','D', 48), ('A', 'E', 29)], tx_y=0, nx_y=0, txn=0, pxy=0, color='black')\n",
    "G.add_weighted_edges_from([('B','A', 22), ('B','C', 30), ('B','D', 34), ('B', 'E', 32)], tx_y=0, nx_y=0, txn=0, pxy=0, color='black')\n",
    "G.add_weighted_edges_from([('C','A', 50), ('C','B', 30), ('C','D', 22), ('C', 'E', 23)], tx_y=0, nx_y=0, txn=0, pxy=0, color='black')\n",
    "G.add_weighted_edges_from([('D','A', 48), ('D','B', 34), ('D','C', 22), ('D', 'E', 35)], tx_y=0, nx_y=0, txn=0, pxy=0, color='black')\n",
    "G.add_weighted_edges_from([('E','A', 29), ('E','B', 32), ('E','C', 23), ('E', 'D', 35)], tx_y=0, nx_y=0, txn=0, pxy=0, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "  b = plt.ishold()\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "  plt.hold(b)\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
      "  warnings.warn(self.msg_depr_set % key)\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n",
      "  warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n"
     ]
    },
    {
     "data": {
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ddv36dSYWi1l+fj6bNWsWc3JyYm/evGFZWVnM0dGRzZ8/nzHG2Pz589m0adNYUVERKyoq\nYlevXmUSiaTcz6Vfv37szZs3LDY2lrVp04bt2bOHMcaYt7c3q1OnDtu2bRsrLi5meXl57I8//mBm\nZmbs+fPnLDs7m7m4uLDx48czxhh7+fIlA8BGjx7NcnJyWEREBGvUqBG7cOGCzOedkJDA9PX1WWBg\nIBOLxeyvv/5i+vr6LCUlhTHGWN++fZmZmRmLjo5mGRkZrF27dqxNmzbswoUL0vd+4sSJ0u04cOAA\nS0tLY8XFxWzjxo3MyMiI5efnM8YY69atG/P19WWMMZadnc1u3bpV5nsh799gWFgYu3XrFisuLmYv\nX75kFhYW7JdffuG8t+/+zd+5c4cZGBiw27dvM5FIxHx8fJipqSkrKChgjDFmamrKunbtyl6/fs0S\nEhKYgYEBs7a2Znfu3GH5+fmsf//+bPny5dLl7dmzh2VlZbGCggLm6enJLC0tpdMmTJjAFi1axNk+\nU1NT6Wfh5eXFOnbsyCIjI5lEImH37t1jaWlplbavEMVAIUykKhLCGhoaTCQSMcYYy8rKYgDY7du3\npfN36dKFnTp1SmbZIpGIqampsSdPnkifW7BggTSEfX19mZ2dHec13bp1Y97e3iwnJ4fp6emxEydO\nsLy8vA9uw7Jly9igQYOkj8+ePcvq168vU3N6enqZrx82bBjbsmWLdHtLQ/jq1avMyMiIicVi6byj\nR49my5YtY4yVHFDd3d2l0yQSCdPS0mLR0dHS527evCn9ArBkyRLm7Ows836XBQALDg6WPt6xYwcb\nMGAAY6wkhE1MTDjzDxgwgO3YsUP6ODIykqmpqUmDCADnc5g7dy6bPHkyY4z7ea9bt04a3qUGDx7M\nfHx8GGMlIbxq1SrptNmzZ7OhQ4dKH589e5YTOu8TCATs3r17jDHGevfuzZYuXcpSU1M/+F58zt8g\nY4z98ssvbPjw4dLH7//NT58+nS1evJjzGnNzc3b58mXGWElIHjx4UDptxIgRbPr06dLH27ZtY8OG\nDStz3enp6QwAy8jIYIx9PITNzc3Z6dOnZZZTWfsKUQzUHE2k6tSpg+LiYs5zxcXFqFu3rvRxw4YN\nUadOHQCApqYmAMDIyEg6XVNTEzk5OTLLTk1NhUgkgomJifQ5U1NT6b+TkpI4j0unJyYmon79+jh2\n7Bh27dqFJk2awMHBAZGRkeVux/v1NGrUSKbm0hqDg4PRrVs36OvrQyAQICgoCGlpaTLLTEpKgomJ\nCVRV/9tlSusr9e62paamIi8vDzY2NhAIBBAIBBg6dChSU1MBAHPnzkXr1q0xePBgtGrVCuvWrSt3\ne95ftqmpKZKSksqcVlrru++lqakpRCIRkpOTK7S8UrGxsTh+/Li0foFAgOvXr+PVq1fSed5/rz/0\nt7Bx40a0a9cOenp6EAgEyMzMlL7Xf/zxB549ewYLCwvY2dkhICCg3PdCnr/BZ8+ewdHREY0bN4au\nri4WLlxY5uf77jZv2rSJs83x8fGc96ei2yyRSLB8+XJ06NABJiYmsLKyAoAPrv9d8fHxMDMzk3m+\nMvcVwj8KYSLVvHlzzjk8AHj58qXMDv8pDAwMoKamJj2nBQBxcXHSfxsbGyM2Npbzmri4ODRt2hQA\nMGTIEFy4cAGvXr2ChYUFpk6d+tk1FRYW4ssvv8ScOXOQnJyMjIwMCIVCsDJusW1sbIz4+HjOedt3\n6wNKzt2WatSoETQ1NfHo0SNkZGQgIyMDmZmZ0gO0jo4ONm3ahBcvXuDs2bPYvHkz/v7773Jrff99\nMzY2LnO9pbW++17GxcVBTU2NExYfWl4pExMTuLu7S+vPyMhAbm4u5s+fX26d5bl27Rq8vLzg5+eH\n9PR0ZGRkQE9PT/pet2nTBkeOHEFKSgrmzZsHV1dX5Obmyr2e982YMQMWFhaIiopCVlYW1qxZU+bn\nW8rExASLFi3ibHNeXh7GjBkj97qPHDmC48eP4+LFi4iPj8fdu3cBQLr+9z+3smp5/7oJgJ99hVQd\nCmEiNWrUKKxatQoJCQmQSCS4ePEi/P394erq+tnLrlOnDkaMGIHly5cjLy8Pjx8/xv79+6XThUIh\nnj17hsOHD0MkEuHYsWN4/PgxHB0dkZycjDNnziA3Nxfq6urQ1tbm/CL9VEVFRSgsLJR+QQgODuZc\ntPWurl27QktLC15eXiguLsbly5fh7++P0aNHlzm/qqoqpk6dih9++AEpKSkAgMTERJw/fx5AycU/\n0dHRYIxBT08PderU+eA2bdiwAenp6YiPj8fWrVsxatSocucdM2YMfvnlF7x8+RI5OTlYuHAhRo0a\nBTU1Nek8P//8M/Ly8vDo0SN4e3uXubzx48fD398f58+fh1gsRkFBAS5fvoyEhIRy112e7OxsqKmp\nwcDAACKRCCtXruT0mT148CBSU1OhqqoKgUAAAJXyGWdnZ0NXVxfa2tqIjIzEb7/9xpluZGSEFy9e\nSB9PnToVu3btQkhICBhjyM3NRWBgILKzs+Ved0ZGBurUqQNNTU3k5uZi0aJFH1z3+77++mssWbIE\nUVFRYIwhIiICb9684WVfIVWHPh0itXTpUvTo0QO9evVCgwYN8NNPP+HQoUPo2LFjpSx/+/btyMnJ\nQePGjTFx4kRMmjRJOq1hw4YICAjApk2b0LBhQ3h5eSEgIACNGjWCRCLB5s2bYWxsDH19fVy5ckXm\nYPopdHR0sG3bNri5uaFBgwY4fPgwnJ2dy5y3Xr168Pf3R3BwMBo1agQPDw/4+vrCwsKi3OWvX78e\nrVu3Rrdu3aCrq4tBgwZJ+x1HRUVh0KBB0NbWRvfu3eHh4YH+/fuXu6xhw4bBxsYGVlZWcHBwwJQp\nU8qdd/LkyXB3d0efPn3QsmVLaGho4Ndff+XM07dvX7Ru3RoDBw7EnDlzMHjwYJnlmJiY4MyZM1iz\nZg0MDAxgYmKCDRs2lHsV94cMGTIEQ4cOhbm5OUxNTaGhocFpEj937hw6dOgAbW1teHp64ujRo9Km\n5s+xceNGHD58GDo6Opg6darMl43ly5djwoQJEAgE8PPzg62tLfbs2YOZM2eiQYMGaN26NXx8fD5p\n3RMmTEDLli3RrFkztG/fHt26deNMnzJlCh4/fgyBQIDhw4fLvH727Nlwc3PD4MGDoauriylTpiA/\nP5+XfYVUHRX2obYZQgjvVFRUEBUVhdatW/NdSvVJSQF8fICICCAzE9DTAzp3BiZNAgwM+K6OkEpD\nIUyIgqtVIRwaCqxdCwQHlzwuKPhvmqYmwBhgbw8sWADY2fFTIyGViJqjCSGK4bffgH79gNOnS8L3\n3QAGgPz8kudOny6Zj5pZiRKgX8KEEP799hswZw6Ql1fx12hpARs3AjNmVF1dhFQxCmFCCL9CQ0t+\n2b4TwC0AJAOoA6AugB4AdgEwef+1WlrAlSuArW21lEpIZaPmaEIIv9auLWlqfo8/gBwArwAYAfiu\nrNfm55e8npAaikKYEMKflJSSi7A+0CCnAcAVwOOyJjIGBAUB/xuJjJCahkKYEMKfCvTBzQNwDEC3\n8mZQUanQcghRRGofn4UQQqpIRITsVdD/MxwlB6hcAAYAzpe3jPx84MGDKimPkKpGv4QJIbwp/sDN\nDE4DyABQAGA7gL4AXpc38zv38CWkJqFfwoSQapGbm4u7d+8iLCwMoaGhCA0NxYoXL/CxWyPUATAC\nwDQA11FyflhGgwaVXC0h1YNCmBBS6YqKihARESEN27CwMERHR6NDhw6ws7PDoEGDMH/+fLQPDARW\nrCi3SRoAGICzANIBtCtrBk1NoFOnqtkQQqoY9RMmhHwWsViMJ0+ecAL30aNHMDMzg62tLezs7GBn\nZ4dOnTpBXV2d++KUFMDUVCaEW+C/fsIqAEwBLAAwrqwCNDSAuDgaU5rUSBTChJAKY4whOjqa06R8\n7949NGnSBHZ2dtLQtba2Rv369Su0TMnw4cCZM592gYqKCuDiAvz556e8mhDeUXM0IaRMjDEkJCRI\nf92W/l9HR0f663bZsmWwsbFBg088J5uXl4flmZlYqaoKjU+4RaJEQwOqCxZ80roJUQQUwoQQAEBq\naiqnSTk0NBQSiUQauLNmzYKtrS2MjIwqZX0pKSlwdnZGmzZtUGfLFmD+fLnGji6qWxcrNDUxTksL\n7SulIkKqH4UwIbVQZmYmwsPDpaEbGhqKzMxM2NrawtbWFhMnTsSOHTtgYmICFRWVSl//s2fPIBQK\nMWbMGKxcubJkHWpqJTdxyM//4AhaUFEBNDVRb+NGWGhro3///jh27Bj69etX6XUSUtXonDAhSi4v\nL0+ma1BiYiKsrKw453Fbt24NVdWqHzrgxo0b+PLLL7F69WpMmTKFOzEsrGQs6KCgkrB9d0zp0vsJ\nC4Ul9xP+300b/vnnH4wePRqbN2/G+PHjq7x+QioThTAhSqSoqAgPHjzgNClHRUWhffv20mZlW1tb\ntG/fHmpq1d8Qdvz4cXz77bc4cOAAhgwZUv6MqaklQ1E+eICzBw7A2d29pBvSxIllXgX96NEjODg4\nYOrUqVi4cGGV/HonpCpQCBNSQ4nFYkRGRnLO4z58+BAtW7aUBq6dnR06d+4s2zWomjHGsGnTJmzd\nuhX+/v6wsrKq8GtVVFRQkcPUq1ev4ODgABsbG+zcuRN169b9nJIJqRYUwoTUAIwxvHjxgnMO9+7d\nu2jcuLFM1yBtbW2+y+UQiUTw9PTEtWvXEBgYCBMTmbsCf1BFQxgAcnJy4ObmBolEguPHj0NHR+dT\nSiak2lAIE6JgGGNITEyU6RpUv359TpOyra3tJ3cNqi65ubkYPXo0CgoKcOLECejp6cm9DHlCGCgJ\n/ZkzZ+L27dsIDAxE06ZN5V4nIdWFQpgQnqWlpcl0DRKJRJwmZVtbWzRu3JjvUuXy+vVrODk5oWPH\njvj9998/uXlY3hAGSr7IeHl5YefOnQgICEAnGtaSKCgKYUKqUVZWFqdrUFhYGN6+fQsbGxtO6DZv\n3rxGX1z05MkTCIVCTJo0CUuWLPmsbfmUEC515MgReHp64vDhwxg0aNAn10BIVaEQJqSK5Ofn4969\ne5zzuAkJCbC0tOSMqdymTZtq6RpUXa5cuQI3Nzd4eXlhwoQJn728zwlhALh69SpGjhyJ9evXY+LE\niZ9dDyGViUKYkEpQXFyMBw8ecPriPnv2DO3ateNcONWhQwdeugZVl8OHD2PWrFk4cuQIBg4cWCnL\n/NwQBoDIyEgIhUJMmDABS5curdGtDES5UAiTWuHvv//GuXPnoK6ujqVLl6JevXqfvCyxWIynT59y\nmpQfPHiAFi1ayHQN0tDQqMStUFyMMaxbtw67du2q9HOwlRHCAJCcnAxHR0d07NgRu3fv/qy/AUIq\nC4UwUSoZGRkIDQ3FjRs30LRpU7i7uyM7OxuTJ0+GtbU1EhIS0KJFC0ybNq1CYyAzxvDy5UuZrkGG\nhoacJuUuXbooXNeg6iISieDh4YHQ0FAEBgbC2Ni4UpdfWSEMlFytPWbMGOTl5eHPP//8pKu1CalM\nFMJEqUybNg0vXrxAz5498fDhQzg4OODFixcAgJ9//hkJCQlYtWoVbG1t8fXXX0MikXDOxyYmJnKa\nlMPCwqCpqclpUra1tYW+vj5fm6hQsrOzMWrUKDDG4OfnVyX9ciszhIGSlgxPT09cuXIFQUFBcvdb\nJqQyUQgTpSIWi1GnTh0AwP79+xEWFoaGDRsiKioKhw4dAgDs2rULV65cwZEjRzghHBMTw/l1W/rv\nJk2a8LY9iiwpKQmOjo6wtbXFjh07qmyEqsoOYaCkhWPz5s3YsmWL3CN4EVKZlPcKEVIr1alTB+np\n6di5cycuXryIKVOmwMzMDBcuXJDOY21tDW9vbwDg/Ao2NTVFamoqXbRTAaWtDNOnT8f8+fNr3Hum\noqKCH3/8Eaamphg8eDB8fX0xdOhQvssitZDy9Isg5H9UVVURHx+PwYMH49ChQxCJREhMTARQ8gtI\nX18f+vr6yM7O5rxORUWlxoUJH/755x8MGDAAa9euxYIFC2r0e+bq6opTp05h4sSJ2Lt3L9/lkFqI\nfgmTGk0sFkNVVZUTBHp6eti1axcAQE1NDffv34dIJML58+cxZMgQhISEwNLSkq+SazRfX1/MnTsX\nfn5+SnOcj7BBAAAgAElEQVT/3p49e+LatWuwt7fHy5cvsWrVqhr9xYLULHROmNQYEolE2jWo9OKp\nxo0bw9fXt9wLglasWIF69erB0NAQISEhyMzMRHR0NBYvXgwXFxcwxuiAWwGMMfz888/w9vZGUFAQ\n2rVrV23rropzwmVJTU2Fs7MzzMzM8Mcff/B+5ylSO1AIE4XEGENMTAyna9CdO3dgYGAg0zXo3QAu\nLi7G8+fPcevWLURFRSEkJAQLFizAoEGDEBISgn///Re9evWCtbU1BXAFFRcXY9q0aYiIiEBAQEC1\nj2FdXSEMlIxyNm7cOKSnp+PkyZMKf4MMUvNRCBOFkJSUJNM1SF1dXeZq5YYNG350WX5+fjh27Bh6\n9OiBvn37wsbGhsL2E2VlZcHV1RXq6uo4cuQIL32hqzOEgZJTHHPmzMH58+cRFBSEFi1aVNu6Se1D\nIUyq3Zs3bxAWFsYJ3YKCApmuQZU96AORT0JCAhwcHNCzZ09s27aNt+E2qzuES23btg3r16/H2bNn\nYWNjU+3rJ7UDhTCpUtnZ2bhz5w7nPG5qaiq6dOnCCd2WLVvSr1UFcv/+fTg6OuL777/HnDlzeP1s\n+AphADh16hS++eYbeHt7w9HRkZcaiHKjECaVpqCgAPfv3+c0KcfExKBTp06cMZXNzc2lA2oQxfPX\nX39h/Pjx2L59O9zc3Pguh9cQBoCQkBAMHz4cS5cuxYwZM3irgygnCmHySYqLi/H48WPOhVORkZFo\n27Yt5zxux44dq2wkJVL59u3bh4ULF+LEiRPo1asX3+UA4D+EAeD58+cQCoUYNmwY1q1bp1S3niT8\nohAmHyWRSPDs2TNOk/L9+/fRvHlzTpOylZUVNDU1+S6XfALGGJYuXYrDhw8jKCgIbdu25bskKUUI\nYaDkWoZhw4ahadOm2L9/f625QxapWhTChIMxhtjYWE6Tcnh4OPT19TlNyl26dIGuri7f5ZJKUFRU\nhK+//hpPnz6Fv78/DA0N+S6JQ1FCGCg55fLVV1/h1atXOH36dIWu1ifkQyiEa7nXr19zmpTDwsKg\npqbGCVxbW1s0atSI71JJFcjIyMCIESOgp6eHQ4cOQUtLi++SZChSCAMlLUPz58/HmTNnEBwcjFat\nWvFdEqnBKIRrkbdv3yI8PJwTunl5eTJdg5o2bcp3qaQaxMbGwsHBAQMHDsTmzZsV9mI5RQvhUjt3\n7sTPP/+M06dPo2vXrnyXQ2ooCmEllZOTI9M1KDk5WaZrUKtWrahrUC10584dODs7Y86cOZg1axbf\n5XyQooYwAPj7+2Py5MnYu3cvhg0bxnc5pAaiEK6IlBTAxweIiAAyMwE9PaBzZ2DSJMDAgO/qUFhY\nKNM16OXLl+jYsSOnWblt27YK+2uHVJ+goCBMmDABu3fvxogRI/gu56MUOYQBICwsDM7Ozpg/fz6+\n//57vsshNQyF8IeEhgJr1wLBwSWPCwr+m6apCTAG2NsDCxYAdnbVUpJIJJLpGvTkyROYm5vLdA2q\nV69etdREao7du3dj+fLlOHnyJLp37853ORWi6CEMADExMRAKhRg6dCg2btxIXZhIhVEIl+e334A5\nc4D8/JKwLY+KSkkgb9wIVHJHfolEgqioKM7wjvfv30ezZs0453CtrKwU8oIaojgkEgkWLVqEP//8\nE0FBQWjdujXfJVVYTQhhAEhPT4eLiwsaNmyIgwcPUnc9UiEUwmUpDeC8vIq/Rkvrs4KYMYa4uDiZ\nrkECgYBzDtfGxgZ6enqftA5SOxUWFmLixImIi4vDmTNnatyV7jUlhIGS93ry5Ml4+fIlzpw5AwMF\nOF1FFBuF8PtCQ4F+/WQC+DCAzQAiAegAsAKwCABnTCEtLeDKFcDW9qOrSU5O5gRuaGgoVFVVZboG\n0U5MPsfbt2/h4uICQ0ND+Pr61shfZzUphIGSVoclS5bAz88PQUFBaNOmDd8lEQVGIfy+ESOA06c5\nTdCbAawDsAvAEAD1AJwDcBXAhndfq6ICuLgAf/7JWWR6erpM16Dc3FzY2tpyzuM2bdqUrlQmlebl\ny5ewt7eHk5MT1q9fX2PPU9a0EC61Z88eLFmyBCdPnkSPHj34LocoKArhd6WkAKamnAuwMgE0BeAN\nYGQFFsHU1RFy/DhuRUdLA/f169ewtrbmnMc1MzOjwCVVJjQ0FMOGDcPChQsxc+ZMvsv5LDU1hAEg\nODgYEyZMwM6dO+Hq6sp3OUQBUQi/y8sLWLaME8LnADgCKABQkbup5gHY26wZnjo7S0O3Xbt21DWI\nVJuzZ8/i66+/xt69e+Hs7Mx3OZ+tJocwANy9exdOTk6YPXs2fvjhB/ryTTj4uUu3ooqI4HZDAvAG\nQCNU/I3SAvB9//7Ajh2VXBwhH7djxw6sXr0agYGBsKumbnPkw6ytrXHz5k0IhUK8fPkSW7ZsoS/l\nRKpmniSqKpmZMk81BJAGQCTPctLTK6kgQipGIpFgzpw5+PXXX3Hjxg0KYAXTvHlzXL9+HY8fP8aI\nESOQm5vLd0lEQVAIv6uMrj/dAagDOC3HYuJzc5GcnFxZVRHyQfn5+Rg1ahRCQ0Nx8+ZNtGzZku+S\nSBkEAgGCg4MhEAjQv39/OkYQABTCXJ07A+/dI1QPwEoA36IkiPMAFAMIBvBTGYsoUlNDcHw8LCws\n0Lx5c4wYMQJr167FxYsXkZGRUcUbQGqbtLQ0DBo0CGpqavjrr7+gr6/Pd0nkA+rVqwcfHx8IhUJ0\n794dkZGRfJdEeEYXZr2rjKujSx0C8AuAJyjpJ2yDkn7CMh0PNDSAuDiwRo3w/PlzTl/gu3fvonHj\nxpy+wNbW1qhfv34VbxhRRtHR0RAKhfjyyy+xevXqGtsF6WNq+oVZ5fH29sb8+fNx/Phx9OnTh+9y\nCE8ohN9XRj/hCiunn3ApsViMJ0+ecAboePjwIczMzDgDdHTu3Bnq6uqfuSFEmd26dQsjRozA8uXL\nMW3aNL7LqVLKGsIAcOHCBYwbNw7btm3D6NGj+S6H8IBC+H3ljJhVEUxLCyoVHDGrVGFhIR48eMAZ\nHzo6OhodOnTgDOTRrl07qKnRxewEOHnyJKZNm4b9+/dDKBTyXU6VU+YQBoCIiAg4Ojri22+/xU8/\n/URdmGoZCuGyfMLY0YV16mCLiQkmhYTA0NDws1afl5eHu3fvcpqyk5KSYGVlxQlmMzMzpW2CJGXb\nsmULNmzYAH9/f3Tp0oXvcqqFsocwACQkJMDBwQHdu3fH9u3b6Qt3LUIhXB4576LENm7E0qQkHDly\nBEFBQTA3N6/UcjIyMqRDX5b+as7KyoKNjQ2nKdvExIS+SSshsViM2bNn4+LFiwgKCoKpqSnfJVWb\n2hDCAJCVlYWRI0fC2dkZHh4e5e7H2dnZ0NHRqebqSFWhEP6QsLCS+wkHBZWEbX7+f9NK7ycsFJbc\nT/h/TdB//PGH9JZxPXv2rNLykpOTERYWxmnKBsAJZTs7u8/+ZU74lZeXh3HjxiEzMxMnT56EQCDg\nu6RqVVtCGCi5X7hEIin3XuBxcXFYsmQJunfvjunTp1dzdaQqUAhXRGoq4OMDPHiAswcOwNndHejU\nCZg4ESjjLkfnz5+Hu7s7duzYgZEjKzLidOVgjCE+Pp4TymFhYdDT0+OEso2NTa07kNdUKSkpcHZ2\nRps2bfDHH3+Ue3BWZrUphD8mNzcXoaGhWLhwIfr374/Vq1fzXRL5TBTCcqroAeHevXtwcnKCp6cn\nfvzxR96aiCUSSZldpYyNjTldpaysrKirlIJ5+vQphEIhxo4di5UrV9ba0wwUwv+RSCRQVVVFcXEx\nhg8fjv3799e4+0MTLgphOclzQIiPj4eDgwN69+6NrVu3KszFFiKRSKar1KNHj9C6dWuZrlK18ZeX\nIrh+/TpcXV2xZs0aTJ48me9yeFXbQ7g0eN+1d+9e+Pr64ty5c/jnn38waNAgaLw30BCpGSiE5STv\nASEzMxOurq7Q0NDA0aNHFfbXZmFhISIiIjhN2c+fP0fHjh05Tdl0R6iq5+fnh5kzZ+LgwYMYPHgw\n3+XwrraHcGxsLEQiEZ49e4Z79+7h7du3uHHjBn766Se0atUKe/bswe3bt3HixIladcGesqAQltOn\nHBCKi4vxzTff4OHDh/D390fjxo2rqLrKlZuby+kq9e69kd/vKlVbm0orE2MMGzduxLZt2xAQEABL\nS0u+S1IItTWEGWPYunUr5s6di7Fjx0JbWxsA0K1bN3Tt2pXTA+OPP/7A/v37ceLECboQs4ahEJbT\npx4QGGP4+eef4e3tjaCgILRr164Kqqt66enpMl2lsrOzOaFsa2uLZs2aUTDLQSQSwdPTE9euXUNQ\nUBCaNWvGd0kKo7aGMAC8efMGbm5uaNmyJfbu3cuZJhaLwRiDmpoaYmJisGjRIvz222/Q1dXlqVry\nKSiE5fS5B4T9+/fjp59+gp+fH/r27VuJlfEnOTmZE8qhoaFQVVWV6SplUMaV5KSkxWH06NEoLCzE\n8ePHoVfG3bxqs9ocwqVcXV0hkUhw8uRJmWnh4eEICwvDP//8g82bN6Np06Y8VEg+FYWwnCrjgPD3\n339jzJgx2LJlC8aOHVtJlSkOxhji4uI4oRweHg6BQCDTVaq2B87r16/h6OiIzp07Y/fu3ahbty7f\nJSkcCuESp06dQkFBAXJzcxETE4OnT59KuyUOHjwYvXv3pmsIaiAKYTlV1gHhwYMHcHR0xPTp0zF/\n/nylb7qVSCSIjo7mnF++f/8+mjVrxmnKtrKygpaWFt/lVosnT55AKBRi8uTJWLx4sdL/DXwqCuH/\nbNmyBXPnzsX48ePh6emJhg0bQlVVlX791mAUwnKqzANCUlISHBwcYGdnh507dypMF6bqIhKJ8Pjx\nY05T9uPHj9GmTRtOH+aOHTsqXVepK1euwM3NDRs2bMBXX33FdzkKjUKY6/Tp01i1ahW8vLwwYMAA\nMMboC1wNRiEsp8o+IGRnZ8PNzQ0qKio4duxYrR8TtqCgQKar1MuXL6VdpUqbsy0sLGpsV6nDhw9j\n1qxZOHLkCAYOHMh3OQqPQljWpUuXsHLlSmzevBnW1tbS5ymQax4KYTlVxQGhuLgYHh4eCA8PR0BA\nAIyNjSt1+TVdTk6OTFeplJQUWFtbc84xt2rVSqEPQIwxrFu3Drt27UJgYCA6duzId0k1AoVw2VJS\nUqCvry9tQcvPz0dQUBCcnJyUruVImVEIy6mqDgiMMaxduxa7d++mA3QFvH37VtpVqrQ5Ozc3l3N+\n2c7ODsbGxgoRzCKRCB4eHggNDUVgYCB90ZIDhXDFFBcXw9XVFTk5OTh58mStv+ixpqAQllNVHxBK\nmyqPHj2KAQMGVNl6lNHr1685oRwaGgo1NTVOKNva2lb7WLulpxyAktGwavspB3lRCFecWCzGrFmz\ncOnSJQQFBaF58+Z8l0Q+gkJYTtVxQLh8+TJGjRpFF+18JsYYYmNjOaEcHh4OfX19Tijb2NhU2QAH\ntf3iu8pAISwfxhh++eUXbN68Gf7+/pxzxkTxUAjLqboOCKXdVyZNmoQlS5YoRJOqMpBIJIiKipLp\nKtW8eXOZrlKampqfta6HDx/CwcGh1nRDqyoUwp/mxIkTmDFjBnx9fWFvb893OaQcFMJyqs4DAg3k\nUD1EIhEePXrEacp+8uQJzM3NZbpKVfQzUPYBWaoThfCnu3nzJkaMGIGVK1fim2++4bscUgYKYTlV\n9wEhJycHY8aMQWFhIU6cOEHjwlaTgoIC3L9/n9OUHRMTg06dOnGastu2bSvTVUoZhyblE4Xw54mK\nioK9vT1GjRqFVatWUYuMgqEQlhMfBwSRSITvv/8eN27cQGBgIA3uz5Ps7GyZrlKpqano0qWLdBjO\nkJAQnD59ukbfpEPRUAh/vtTUVDg7O6NVq1bYt28f1NXV+S6J/A+FsJz4OiDQbe4U09u3bxEWFobb\nt29j7969eP36NerXr48vvvhCpqsU+TQUwpUjPz8f48ePx5s3b3Dq1Ck0aNCA75IIKITlxvcBgW74\nrngyMzPh6uoKDQ0NHD16FJmZmdIm7NL/q6ury9zusWHDhnyXXiPwvc8pE7FYjLlz5+LcuXMICgpC\nixYt+C6p1qMQlpMiHBCuX78OV1dXrFmzBpMnT+a1ltouISEBQqEQvXv3xtatW8vsgsQYQ0xMjExX\nqUaNGsl0laI+xLIUYZ9TNtu2bcP69etx5swZ2Nra8l1OrUYhLCdFOSA8ffoUQqEQY8eOxcqVK+li\nCx7cv38fjo6O8PT0xI8//ijXZyCRSPDs2TPO+eWIiAiYmppyhuK0srKChobGZ9caFxeH48ePo1u3\nbrC0tIS2tnaNGWdYUfY5ZXP69GlMnToV+/btg5OTE9/l1FoUwnJSpANCSkoKnJyc0LZtW+zdu5fG\ni61G58+fh7u7O7Zv3y4dDetzFRcXy3SVioyMhIWFBacpu0OHDnJ3V/vll1+wdu1a9OrVC/fv38f4\n8eOxYsUKpKamIjExEe3bt0e9evUUMpgVaZ9TNiEhIXBxccHixYvh4eHBdzm1EoWwnBTtgJCXl4dx\n48YhMzMTJ0+ehEAg4LskpffHH39g0aJF+PPPP9GzZ88qXVd+fr5MV6nY2Fh07txZpquUqqpqucsZ\nM2YMRo0aheHDhwMoOTeYlJSEbdu24eLFi8jJycHq1asr7QtFZVK0fU7ZvHjxAvb29nB2dsb69es/\n+HdEKh+FsJwU8YAgFosxe/Zs/P333wgMDISpqSnfJSklxhiWLl2Kw4cPIzg4GObm5rzUkZWVhTt3\n7nBu9/jmzRt06dIFvXv3xvLly2UOpObm5nB2dsbkyZNhbm4ONTU1uLu7o3379liwYAHOnTuHCxcu\nYOHChQp3wZgi7nPK5s2bNxg+fDiaNGkCX1/fSjkFQiqGQlhOinxA2LJlCzZu3IizZ8+iS5cufJej\nVIqKijBlyhRERUXh7NmzMDQ05LskjrS0NISHhyM1NRUuLi6oX7++dJpEIsFvv/2GyMhIREREYNy4\ncfjmm2/QpEkT3L9/H4aGhsjPz0e/fv1w8uRJNG3alMctkaXI+5wyKSgowIQJE5CYmIgzZ84o3Jcx\nZUUhLCdFPyCcPHkS06ZNw/79+yEUCvkuRylkZGRgxIgREAgEOHjwILS0tPgu6ZM9fPgQ3333HVas\nWIExY8YgMTERQEl/ZwsLC6SkpPBcoSxF3+eUiUQiwYIFC6QDzpiZmfFdktKjxn8lM2LECJw9exZT\npkzB7t27+S6nxouNjUXPnj3RuXNnHD9+vMYF8PvhVVxcjAEDBsDb2xvdunWTPh8SEiIdiU0ikVRr\njURxqKqqYv369Zg1axZ69eqFkJAQvktSenRfNSXUvXt3XLt2DUKhEDExMVi9ejVdbPEJ7ty5A2dn\nZ8ydOxeenp58l/NJJBIJ5s2bB8YYhEIhjh07BiMjIzRv3hz5+fnS+f755x/06dOH89qCggL8+eef\naN26NSwtLek8YS0yY8YMmJiYwNHREb///jtcXFz4LklpUXO0nGpS01haWhqGDRuG5s2bw8fHh8aL\nlUNQUBAmTJigFAegGzdu4PLlywgJCUHnzp0xc+ZMqKmp4bvvvsOIESNQWFiIv/76C99++y26du0q\n7aYkEong4eGB0NBQPH36FO3atZPpKlUd90euSfucsgkLC8OwYcPw008/1dgvooqOQlhONe2AkJ+f\nD3d3d6SmpuLUqVPQ19fnuySFt3v3bixfvhynTp3iNNkqm3379uHo0aMQi8X49ddf0b59+3Lnzc/P\nx7179zh9mOPi4mBpackZI7tNmzaV3upS0/Y5ZRMTEwOhUIghQ4Zg48aNMncNI5+HQlhONfGAIJFI\n8NNPPyEgIADBwcFo2bIl3yUpJIlEgoULF+LkyZMICgpC69at+S5JoWVlZSE8PJzTVert27ewsbHh\n9GE2NTX9rAFAauI+p2zS09Ph4uICfX39Gn9xoqKhEJZTTT4gbN++HWvWrMGZM2dgZ2fHdzkKpbCw\nEBMnTkRcXBzOnDmDRo0a8V1SjZSamorw8HDOcJwikYgzFKednR0aN25c4WXW5H1OmRQWFmLy5Ml4\n/vw5/P39YWBgwHdJSoFCWE41/YBw9uxZfP3119i7dy+cnZ35LkchvH37FsOHD4eRkRF8fX2hqanJ\nd0lKgzGGpKQkTiiHhYWhfv36nGC2tbUt99Z6NX2fUyaMMSxZsgRHjx5FUFAQbwPWKBMKYTkpwwEh\nNDQUw4YNw6JFi/Dtt9/yXU6VK73QSCKRyJyvlEgk2LZtGxITE2nIvmrCGMOLFy84oXznzh0YGRlx\nzi9bW1tDW1tbKfY5ZbNnzx4sWbKkWoZuVXYUwnJSlgPCy5cvYW9vD0dHR3h5eSl1+Dx//lw66EBZ\nNygoLi6W+4YIpHKJxWJERkZyzi8/fPgQLVu2xKNHj7Bjxw7Y2trC0tKSrvJXEOfOnYO7uzt27tyJ\nkSNH8l1OjUUhLCdlCWGgpBnWxcUFhoaGStsMe+3aNfTt2xd79uzBlClTFPIuQaRsRUVFePjwIWxs\nbDBlyhSEhoYiKioK7du35zRlt2/fvlq6ShFZ9+7dg6OjI3744QfMnj2b9q1PQCEsJ2UKYUD5L0i6\ndesWpk+fjri4OOzbtw8uLi70y7eGeXefy8vLk+kqlZCQINNVqnXr1krduqNI4uLi4ODggL59+2Lr\n1q3UhUlOFMJyUrYQBkrOi5bemi84OFipxotNSUnByZMn0b59ezg6OuLp06do0qQJ32UROXxsn8vM\nzJTpKpWRkSHTVap58+b0S62KZGZm4ssvv4SWlhaOHDnCuYEI+TAKYTkpYwiXUoZBKoqKirB//340\nb94cPXr0QN26dSEUCnHu3DkcPXoU33//PTQ0NPDkyRNoaWnR+cUa4FP2uZSUFJmuUhKJhBPKdnZ2\nMDIyqqKqa5+ioiJ88803ePToEQICAui9rSAKYTkpcwgD/w3XuHv3bowYMYLvcuSSmJiIUaNGwcLC\nAhEREXBycsKSJUuwYMECrF27FgEBAZg0aRK0tLQQGxvLd7mkgipjn2OMITExUaarlI6OjkxXKYFA\nUEmV1z6MMaxcuRL79+9HYGAg2rVrx3dJCo9CWE7KHsJAyY0LnJycMHfuXMyaNYvvciokOzsb//d/\n/wcXFxfMmzcPt27dgre3N3799VesXbsWPj4+0NbWxsWLFzFt2jSoqqri1KlTfJdNKqCq9jnGGJ4/\nf84J5bt376Jx48YyXaWoeVU+Pj4+mDdvHo4fPy5zYxDCRSEsp9oQwkDJLfyEQiEGDRqEzZs314iL\nLd7tijRw4EDk5ubC0NAQgwcPRlhYGLZu3Qo9PT0AJcPwlTc4BFEs1bnPicViPHnyRBrKpV2lzMzM\nOE3ZnTt3plMZH3Hx4kWMHTsWW7duxZgxY/guR2FRCMuptoQw8N/N7PX09HDo0KEaM15sdHQ01q9f\njz179uDChQs4deoUnJycYG9vj/z8fKXsiqXM+N7nCgsL8eDBA86FX9HR0ejQoQOnKbtdu3bUVeo9\nDx48gIODAzw8PDBv3jy6MK4MFMJy4vuAUN2KioowZcoUREVF4ezZszA0NOS7pA8qqx/w7NmzYWZm\nVitGB1NGirjP5eXl4e7du5ym7KSkJFhZWXHGyG7dunWtD57ExEQ4ODiga9eu2LFjB31ReQ+FsJwU\n8YBQ1RhjWLp0KQ4fPoygoCC0bduW75Iq7MWLFxg1ahSWLFlCY2XXUDVln8vIyJBekV36qzkrK4vT\nVcrOzg7NmjWrdcGclZUFNzc3qKqq4tixY9DR0eG7JIVBISynmnJAqAr79u3DwoULceLECfTq1Yvv\ncjgkEgkASAdoyM/Px5UrV/Ddd99h0aJFmDhxIo/Vkc9Rk/e55ORkhIWFcZqyAch0lVL0FqbKUFxc\nDA8PD4SHhyMgIADGxsZ8l6QQKITlVJMPCJXh/PnzcHd3x/bt2+Hm5sZ3OQBK+oR6eHjA19dXet5a\nIpHg5s2bAKBwXxiIfJRpn2OMIT4+nhPKYWFh0NPTk+kqVXoRoTJhjGHNmjX4/fffERQUhA4dOvBd\nEu8ohOWkTAeET3X//n04Ojri+++/x5w5c3htWnv69CmEQiHGjRuHFStW1LpmvtpA2fc5iURSZlep\npk2bcs4vW1tb15iLIz/m4MGDmD17No4ePYoBAwbwXQ6vKITlpOwHhIpKSEiAUChE7969sXXrVl4u\ntrh+/TpcXV2xZs0aTJ48udrXT6pHbdznRCKRTFepR48eoXXr1jJdperVq8d3uZ/k0qVLGDVqFDZt\n2gR3d3e+y+ENhbCcauMBoTyZmZlwdXWFhoYGjh49Wq0DGvj5+WHmzJk4ePAgBg8eXG3rJdWP9rkS\nhYWFiIiI4DRlP3/+HB07dpTpKlUT+vUDwKNHj+Dg4IApU6Zg8eLFtbIli0JYTnRA4CouLsY333yD\nBw8eICAgAI0bN67S9THGsGHDBmzfvh3+/v6wtLSs0vUR/tE+V77c3FxOV6nQ0FC8fv0a1tbWnKZs\nMzMzhQ24V69ewdHREVZWVti1a1etu8MZhbCc6IAgizGGVatWYd++fQgKCqqy8WJFIhG+//573Lhx\nA4GBgWjWrFmVrIcoFtrn5JOeni7TVSonJ0emq1TTpk0VJphzcnIwevRoFBUV4cSJE9DV1eW7pGpD\nISynhg0b4s2bN3yXoZB8fX0xd+5c+Pn5oW/fvpW67NzcXIwePRqFhYW1biet7erUqQOxWMx3GTVa\ncnIyJ5RDQ0OhqqrKCWVbW1sYGBjwVqNIJMJ3332Hmzdv1qov2RTCcqJhDz/s77//xpgxY7BlyxaM\nHTu2Upb5+vVrODo6onPnzti9e3eta66q7SQSibT/N6kcjDHExcVxQjk8PBwCgYATyjY2NtXaVar0\ndNOvv/6KgICAWnG6iUKYVLqHDx/CwcEB06dPx/z58z+ryevx48dwcHDA5MmTa+2FG4RUB4lEgujo\naIkzaQwAACAASURBVM755fv376NZs2ac88tWVlZV3lXq2LFj+O6778q/8DIlBfDxASIigMxMQE8P\n6NwZmDQJ4PHX/KegECZVIikpCQ4ODrCzs8POnTs/qQvT5cuXMWrUKGzYsAFfffVVFVRJCPkQkUiE\nx48fc5qyHz9+DHNzc04wd+zYsdK7Sl27dg2urq5Yu3btf10QQ0OBtWuB4OCSxwUF/71AUxNgDLC3\nBxYsAOzsKrWeqkIhTKpMdna2dFQtPz8/ucaLPXToEH744QfqzE+IgikoKJDpKvXy5UtpV6nSpmwL\nC4vP7ioVGRkJoVCI8ePHY0WTJlCZMwfIzy8J2/KoqJQE8saNwIwZn7X+6kAhTKqUSCSCh4cHQkND\nERgY+NHxYhljWLt2LX7//XcEBgbSsHaE1AA5OTkyXaVSUlJgbW3N6cPcqlUruU8pJScnw7trV/yQ\nkAB1eS7Q09KqEUFMIUyqHGMM69atw65duxAYGIiOHTuWOR8N8E6I8nj79q20q1Rpc3ZeXh5sbW05\nTdlNmzb98IJCQ8H69oVKfr7MpH4A7gN4DUC9rNdqaQFXrgC2tp+9PVWFQphUm8OHD2PWrFk4cuQI\nBg4cyJmWnZ2NkSNH0q3OCFFir1+/5oRyaGgo6tatywllW1tbNGrU6L8XjRgBnD4t0wQdA8AMgB6A\n3QBGlrVCFRXAxQX488+q2qTPRiH8AVlZWVi7di0SEhJgb2/P6XLj4eGBnTt38lhdzXTlyhW4ublx\nLrYqvYjriy++oJt+12JisRh79+5FQkIChg4dip49e0qnrVq1CosXL+axOlIVGGOIjY3lhHJ4eDj0\n9fVhZ2eHvu3aYfq6dahTVCTz2pUAzgPoCuAZgIDyVqKhAcTFKexV0xTCH/Dll1+iTZs26NatG/bt\n24e6devi8OHDUFdXR5cuXXDnzh2+S6yRnjx5AqFQiEmTJmH48OFwcnLCjBkzMG/ePOqCVIt9/fXX\nyMvLwxdffIEDBw6gb9++2Lx5MwDQ/laLSCQSREVFITQ0FDq//YYht25Bo4yYag1gNkpCuBuABABG\nZS1QUxNYsQKYO7cqy/50jJTL0tKS83jVqlWsR48eLC0tjVlbW/NUlXJ49eoVMzc3Z+rq6mz//v18\nl0MUQKdOnaT/Li4uZlOnTmUuLi6soKCAWVlZ8VgZ4c24cYyVNERz/rsGMDWApf7vcVuAbS5jPul/\n7u58b0m5aBiaDygsLIREIpE+XrRoEaZOnYo+ffrQ0JWf6fz580hPT0eXLl1w4MABZGZm8l0S4VnR\nO02Oampq+P3332FpaYkBAwYgJyeHx8oIXyQZGWU+vx/AYAClZ47H/u+5cqWnV2pdlYlC+AOcnJzw\nzz//cJ6bOHEiNm3aVGPv4ck3xhhWrFiBFStW4MqVK7h69SrMzc3Ru3dvJCQk8F0e4ZGtrS3OnTvH\neW7ZsmWYNGkSYmJi+CmKVBuJRIJnz55Jxwjo1asXjr339wAA+QD8AFwB0Ph///2Ckquk75e38AYN\nqqboSkDnhD8iMjISiYmJ6Nq1K7S1taXPBwcHw97ensfKap6ioiJMmzYNDx8+REBAAIyMSs7gMMaw\nadMmbN26tdaMF0vK9u+//0JFRQV2dnZ4/Pgxzp07BwsLCwiFQr5LI5WIMYb4+HjOldLh4eHQ09Pj\nXCnd/fp1aK5dyxkZ6wiAbwHcA/DuTyE3AHYANr2/MgU/J0wh/AG//vortm/fjnbt2uHevXvYunUr\nhg0bBoAuFJFXZmYmXF1doampiSNHjqB+/foy8/j5+WHmzJk4cOAAhgwZwkOVhE8rVqxAcHAwRCIR\n/u///g8hISHo378/Lly4gCFDhmDRokV8l0g+UUpKCmcgj7CwMOmXrdLQtbW1haGh4fsvBExNOSE8\nFEAHyIatH4DvUXKBFqd/BV0dXXN16tQJt27dgra2NmJiYuDq6gp3d3d4enrC2toad+/e5bvEGiE+\nPh4ODg7o06cPtm7d+sGh7K5fvw5XV1esXr0aU6ZMqcYqCd86deqEe/fuobCwEI0bN0ZCQgJ0dXXx\n/+3deVhT17oG8BeHImhV6ogyKaCggoy1ttS2HntaAsigOKEUcba1SqtVFKkDzh6HarFacVa0qCBD\nqPVcrzgrQRAElYoDoCiICChDSLLvH1xSt0ElELIzfL/nOQ9HQ5IP6t5v9trrW6uyshKDBg1Ceno6\n1yWSBnj+/DkEAgFrWcvy8nKZXmAjI6OGdUP4+EASE4MWjYkqNegTpobMt5BIJNIhaDMzM5w5cwYj\nR47EgwcPaJPxBkpLS4OHhwfmzJmDH3744Z0HnYuLC86ePQtXV1fcv38fy5Yto7YlLdGqVSu0bNkS\n+vr6MDc3l+4ZraenR1sZqqiXL19Kl6usC91Hjx5Jl6scOXIk1qxZA3Nz80Yfx4l2dvgsOhqN2rdJ\nT692MwcVRiH8Ft26dUNaWhrs7OwAAO3atUN8fDwCAwORkZHBcXWq7+TJk5gwYQJ+/fVX+PrWu55N\nvfr06YNLly5h+PDhuH//PiIiImginBZ47733UFFRAX19faSkpEj/vrS0lEJYBQiFQqSnp7OGlHNy\nctC/f384OTlh2LBhCA4OhrW1dZM3bgBq7xuHhYUhYtcuXAwJgf6GDUBFRcNfoG7taBVeshKg4ei3\nys/PR6tWrdC9e3eZxy5cuMBa0YewRUREYNGiRTh27Fijf08VFRXw8/NDaWkpjh8/jo4dOyq4SqJK\nqquroasruwLw06dPUVBQABsbGw6q0k5isRg3b95k3cfNzMyEhYUFa0jZxsam3v9mTVVTU4Pp06cj\nLS0N8fHxMDQ0BLZtA2gXJULejmEYLF68GIcPHwafz0efPn2a9HpisRg//vgjTp06BT6fD1NTUwVV\nSggBao/ZO3fusO7hpqWlwdDQUBq4zs7OsLOzq3dCpaKVlZXB19cXrVq1wpEjR1hdKRAIavcT5vNr\nw/bVTR3q9hPm8WqHoFX8CrgOhbCcTp06hS+//JLrMlSSUCjEpEmT8PfffyMuLg5dFDgbcfPmzVi3\nbh1iY2Ph4OCgsNclqu/w4cMYM2YM12VoBIZhkJ+fzxpSFggEaN++PWvilKOjIycjT/n5+XBzc8Pg\nwYOxdevWN68jX1QE7NkDZGTULsRhYADY2AABASo7C/pNKITlpKOjQ5Oy6lFSUgIfHx8YGBjgwIED\n0Ndv1DSKt4qOjsbUqVOxd+9e6hvVInTMNV5RUZFMaxDDMDKtQXU9+1xKT0+Hu7s7vvvuO8ybN09r\nJmRSCMuJTgiyHjx4AFdXV3z11VdYv369QiZlvMnly5fh7e2NJUuWYNq0ac32PkR10DHXMKWlpaz9\ne5OTk1FaWsoKW2dnZxgbG6tcwJ06dQp+fn745ZdftG7Ug0JYTnRCYEtJScHw4cPx008/Yfbs2Up5\nzzt37oDH48HHxwcrV66kmbMajo45WRUVFUhNTWXdx3348CHs7OxYE6csLCxU/vjYvXs3FixYgKio\nKAwZMoTrcpSOQlhOdEL4R0JCAgICArBjxw54e3sr9b2fPn0KT09PmJiYYM+ePc0yQ5OoBm0/5oRC\nITIyMli9uH///Tf69evHmjhlbW2tVntxMwyDJUuWYP/+/eDz+bCysuK6JE5QCMtJ208IdX777Tcs\nXboU0dHR+OijjzipobKyEv7+/njy5AliYmLwwQcfcFIHaV7adMyJxWLcunWLdQ/3xo0b6N27N2tI\n2dbWVq0/eAqFQkyZMgU3b95EXFycStyT5gqFsJy06YRQH4lEguDgYERHRyMxMRHm5uac1zN//nzE\nxcVBIBCw2xleUV5ejvfff1/J1RFF0NRjjmEY5OTksIaUU1NT0b17d9aQsr29/Rv/Xauj58+fY8SI\nEWjXrh0OHTqklLYnVUYhLCdNPSE0RFVVFQICApCfn4+YmBh07tz53U9SEoFAAGtr63oP6NzcXCxe\nvBiDBw/G9OnTOaiONIUmHHMMw+Dhw4esIWWBQIC2bduyhpQdHR1hoMLb7jVVbm4ueDwevvjiC2za\ntKlZJ3GqCwphOWnCCaExnj17Bk9PTxgaGmLfvn1o06YN1yU12MuXL5GcnIyFCxfiiy++wIoVK7gu\nichBHY+5p0+fsoaUk5OTIRKJWIHr5ORU72p8mio1NRUeHh744YcfEBQUpHIztLlCISwndTwhNNXd\nu3fB4/Hg4eGBNWvWqPxsy9dJJBK0aNECNTU18PLywr59+9CpUyeuyyINpOrHXFlZGas1SCAQoKSk\nBI6Ojqz7uCYmJlobPImJifD398e2bdswcuRIrstRKeozlY5w4urVq/Dy8kJISAhmzpzJdTkNVhe8\nAKRf9+3bh5KSEujp6SEuLg5ffvmlWl3RE+5VVlYiLS2N1Yubn5+PgQMHwtnZGV5eXggLC4OlpaXa\nfVhtLjt27EBoaChOnDiBjz/+mOtyVA6FMHmjEydOYPLkydi1axc8PDy4LkcueXl5EIlEyM7ORlpa\nGp49e4bz589j/vz50NXVRWZmJlavXo1Dhw7RetSkXjU1NcjIyGBNnMrOzoa1tTWcnZ3x+eefY968\neejXr59atQYpC8MwCAkJwZEjR3Du3DlYWlpyXZJKouFoOan60JiibNmyBatXr8aJEyfgpCYLoQO1\nB/7mzZsxb948jBs3Tjqr9KOPPoKzszOrFzE8PBx79+7F8ePH0bNnT65KJu+gjGNOLBbj9u3brCHl\njIwM9OrVS6Y1iEZP3q26uhqBgYG4e/cuYmNjFbqOvKahj2+ERSKRYN68eeDz+bhw4QLMzMy4Lkku\nOjo6mDBhAuLi4tC6dWv8+uuvrMeLioqka1D7+/sjIyODZmhqGYZhcO/ePdaQcmpqKrp27SqdNDVq\n1Cg4ODhoVGuQspSUlMDb2xudOnXC6dOnoaenx3VJKo1CmEhVVlZiwoQJKCoqwsWLF9W2VaJTp074\nn//5H/j6+sLHxwfHjx+XPtalSxdkZWXBysoK7u7uyM3NxcuXLzmsljS3hw8fsoaUBQIB9PT0pIEb\nEhICR0dHWuxFAe7duwcejwdXV1esW7eOPuA2AA1Hy0lTh6OLiorg6emJXr16YdeuXWq9Gs+roqKi\n0KZNG3Tr1g0ffvghgNoPGyNGjMDixYvRpk0b2Nvbc1wleRt5jrni4mJWW1BycjKEQqFMa5ChoWEz\nV619BAIBhg8fjgULFuD777/nuhy1QSEsJ00M4Tt37sDV1RWjRo3C8uXLNW5WZ2RkJDZu3IgjR46g\nV69eKCgowE8//YSNGzeq1IIjpH5vOubKy8ulrUF1oVtcXAxHR0fW3rimpqZa2xqkLHFxcQgMDMTv\nv/8OLy8vrstRKzQcreUuXboEb29vLFu2DFOnTuW6nGYxduxYiEQieHt7Y9iwYcjKyoKRkREFsBqp\nrKzE9evXWUPKubm5sLW1hbOzM9zd3bF06VL06dNH4z5Eqrrw8HCEhYUhPj4egwYN4roctUNXwnLS\npCvhY8eOYcaMGdi7dy9cXV25LqfZXblyBXfu3EH79u2lLVd1/y3pSkl11NTUIDMzUxq4v//+O/T0\n9GBlZcUaVu7Xrx9at27Ndblaq27d9tjYWCQmJqJ3795cl6SW6EpYCzEMg02bNuE///kPTp48qTX3\nRAcNGsT6pM4wDHR0dPDy5UtUVVXRKlockEgk0taguiHl9PR0mJmZSYeUgdplU6k1SHVUVVXB398f\nBQUFuHjxIh07TUBXwnJS9ythsViMoKAgnD59Gnw+HyYmJlyXxCmGYRAREYFVq1aBz+ejb9++XJek\nsRiGwf3791lDyteuXUPnzp1ZvbgODg6sHa/U/ZjTNMXFxfD09ETPnj2xd+9e+nDURHQlrEUqKiow\nbtw4lJeX4/z58+jYsSPXJXFOR0cHkydPRosWLfDZZ5/h6NGjcHFx4bosjVBQUMDqxRUIBNDV1ZUO\nJy9YsABOTk50FaVGcnJy4OrqCm9vb6xatYruvysAXQnLSV0/lRcWFsLDwwNWVlb4/fff8d5773Fd\nksr566+/MH78eGzduhWjRo3iupxmI5FI8Oeff+LSpUvIzc3Fzz//3OT7ecXFxaxNDJKTk1FVVSXT\nGtSjRw+5X1tdjzlNc+XKFXh5eSE0NBQzZszguhyNQSEsJ3U8Idy+fRs8Hg/jx4/HkiVLaBLSW1y/\nfh0eHh747rvvMG/ePI35Xe3btw8CgQDLly9HdnY2wsLC8NFHH0mXawwODka/fv0a9Frl5eW4du0a\nqxe3qKhIpjXIzMxMIb8/dTzmNE3dKnO7d++Gu7s71+VoFAphOanbCeHcuXMYOXIkVq1ahcDAQK7L\nUQv5+flwc3PDJ598gl9++UWtF+ev201q1KhRuHz5MpKSkrBmzRr06NEDoaGhePz4MRYuXIiZM2fW\nu0Z4VVWVtDWoLnTv378vbQ2qC92+ffs229Ckuh1zmmbz5s1Yu3at2q0jry4ohOWkTieEI0eOYNas\nWTh48CC+/PJLrstRK2VlZRg5ciR0dXVx+PBhtG3bluuSGi0hIQF8Ph/t2rWDvb092rdvj4iICBgb\nG0MgEMDOzg5bt26VeZ5QKESnTp1gYWHBGlIeMGCAUluD1OmY0yRisRhz587FyZMnwefz1W4deXVB\nISwndTghMAyDtWvX4tdff0V8fDxsbW25Lkkt1dTUYNq0aUhPT0d8fDy6d+/OdUmNEh4ejqqqKnTu\n3BkXL17Eb7/9hu3btyMhIQFeXl5ITU2FRCKR2exCJBJBJBJxPvtVHY45TVNRUYHx48fj2bNniI6O\nVtt15NWB+o6zkXqJRCLMmjULFy9exMWLF2FkZMR1SWqrdevWiIiIQFhYGAYPHoyEhIQG3zdVFcXF\nxUhKSsKRI0dw9epV8Pl8iMViREZGIiEhAW3btsWTJ0/wr3/9C8+ePWNtYtCqVSu1HoonjVNUVAQP\nDw+Ym5vj5MmTGrOOvKqi+eUa5MWLF/Dy8sLdu3dx7tw5CmAF0NHRweLFi7F06VJ88cUXSEpK4rqk\nd5JIJNL/n5WVBYZhsGXLFqxcuRIJCQlwd3fHjRs3kJOTAwAoLS2FhYUFiouLuSqZqIjs7GwMHjwY\nw4YNw4EDByiAlYA+5mqIgoICuLu7w87ODr/99hst56dg/v7+MDIygq+vLzZu3Ag/Pz+uSwJQu5LU\nq61BDx8+RFJSknQP15qaGpSWluLy5ctwdHREdXW1dGGSJUuWwNDQEMnJyfDz84OlpSXHPw3h0oUL\nFzBixAiEhYVh8uTJXJejNeiesJxU8f5UVlYWeDweJk+ejEWLFmlMW40qyszMhJubG6ZOnYrg4GCl\n/q5fvHgh0xr05MkTODg4sPpxe/XqJa2rbmnOOkFBQTAxMUFQUBCOHz+OvLw8DBkyBHZ2dir770YV\njzlNExUVhZkzZ2L//v34+uuvuS5Hq1AIy0nVTghnzpzB6NGjsX79ekyYMIHrcrTCo0eP4O7uDkdH\nR4SHhzfLqEN1dbVMa9C9e/dgY2PD6sXt27fvOzdOrxueFovF0vu+6jRSomrHnCZhGAYbNmzAxo0b\nER8fDzs7O65L0joUwnJSpRPCgQMH8OOPPyIyMhJDhw7luhytUl5ejtGjR0MikSAqKoq11rG8RCIR\nsrKyWMs7ZmVloU+fPjKtQdq40pkqHXOaRCwWY/bs2UhKSgKfz4exsTHXJWklCmE5qcIJgWEYrFy5\nEr///jsSEhLQv39/TuvRViKRCN9++y2uXr2KhISEBi3JKJFIcOfOHdbyjtevX4eRkRFrSHngwIHQ\n19dXwk+h+lThmNM0L1++xNixY1FRUYFjx46hQ4cOXJektSiE5cT1CaGmpgYzZ85ESkoKEhISYGho\nyFktpPYD0Zo1a7Bt2zbEx8fDxsaG9Vhubi7rHm5KSgoMDAxYQ8oODg50EnwLro85TfP48WN4eHig\nf//+2LFjh1aOrqgSCmE5cXlCKC8vh6+vL1q2bIkjR46gXbt2nNRBZB06dAjff/89goKCIBQKpcHb\nsmVL1pCyk5MTunTpwnW5aoVCWHFu3rwJHo+HgIAAhIaGquxkPG1CISwnrk4IDx8+hJubGz766CNs\n3bqVFlHg2PPnz1lXuAKBACUlJRAKhfj3v/+NSZMmwcnJCT179qQTXRNRCCvG2bNn4evrizVr1iAg\nIIDrcsj/o8U61EBGRgY+/vhjjB07Ftu2baMAVrKXL1/i/Pnz2LhxI8aNGwdLS0sYGxtj2bJlePr0\nKUaNGoX//d//RVlZGdLS0nDjxg1cv36dApiojMjISIwcORIHDx6kAFYxdCUsJ2V/Kv/vf/+LcePG\nYfPmzRg7dqzS3ldbVVdXIz09nXWVe/fuXQwYMIB1H9fKyuqNrUF199wGDBiA7du30z23JqIr4car\nm7MQHh6OhIQE1pwFohoohOWkzBPCnj17MH/+fERFRWHIkCFKeU9tIhKJcPPmTVYvblZWlnTXoLrQ\ntbGxkTtI62afVlZW4ujRozTxqgkohBunbvb+lStXkJCQgJ49e3JdEqkHhbCclHFCYBgGy5Ytw969\ne8Hn82FlZdWs76cNJBIJcnJyWPdw09LS0KNHD9bEKXt7e4W1BtX1YZ49exYJCQnUh9lIFMLyU2Qf\nO2leFMJyau4TglAoxLRp03Djxg3Ex8ejW7duzfZemophGOTl5cm0BnXo0IE1pOzo6NjsV6h1KxJt\n2rQJcXFxtCJRI1AIy0cZK7oRxaEQllNznhBKS0sxYsQI6OvrIzIyUq03klemwsJC1pBycnIydHR0\nWEPKTk5O6Nq1K2c1RkVF4dtvv8X+/fvx1VdfcVaHOqIQbrjMzEzweDxMnToVCxcupImBaoBCWE7N\ndULIy8sDj8fDZ599hs2bN79zPWBtVVpaKg3buq9lZWWssHV2doaRkZHKnYDqdqlZsWIFJk2axHU5\naoNC+B+vb8jxqurqatjb22PRokUqs8sXeTcKYTk1xwkhLS0NHh4emDNnDn744QeVCw+uVFRUIDU1\nlXUf99GjR7Czs2Pdx7WwsFCb31l2djZ4PB7GjBmD5cuXq03dXKIQ/kdhYSFrROfVUBYKhRAKhbSI\nj5qhEJaTok8If/75J/z9/REeHo6RI0cq7HXVjVAolGkNunPnDvr3789aU9nKykrt+6SLiorg4eEB\nS0tLREREUAvTO1AI19q5cyf27t2LYcOGoVWrVli0aBGAt18dE9VHISwnRZ4Qdu7ciZCQEBw7dgyf\nfPKJQl5THYjFYpnWoMzMTJibm8u0Bunq6nJdbrOoqKjA+PHjUVJSguPHj8PAwIDrklQWhTBw7Ngx\nLF++HPv27YO+vj4mTpwIR0dHbNq0ievSSBOp9yWFmmIYBiEhIThy5AjOnTsHS0tLrktqNgzDyLQG\npaamwtDQUHp1O3bsWNjb22vVRDR9fX1ERUVh7ty5+OSTT5CYmAhTU1OuyyIqSiQSISAgALa2tgAA\nHx8fhISEwNTUFEFBQRxXR5qCroTl1NRP5dXV1Zg0aRJycnIQGxurUYv5MwyDhw8fsrbpS0lJQbt2\n7VhDyg4ODnTl94rNmzdj7dq1iI2NhaOjI9flqBxtvRIWCoWIjY2FsbExHjx4gKCgIJw5cwa9e/dG\nSEgIevTogf379yMiIoJWwlJjdCWsRCUlJfDx8YGBgQFOnz4NPT09rktqkqKiIplNDCQSiXRIec6c\nOXBycqJe53eYPXs2TExM4Orqit27d8PNzY3rkgjHHj58CG9vb/Tr1w9XrlzB0qVLMWHCBEyZMgX6\n+vro0qULVq1ahezsbFRVVXFdLmkCuhKWU2M/ld+/fx88Hg9fffUV1q9fr3YtSKWlpUhJSWGF7vPn\nz6Xb89Vd5RobG9MkkUa6fPkyvL298fPPP2P69Olcl6MytO1KuLS0FL6+vhg6dCgWLFiAq1evYvr0\n6YiNjUWHDh1QXFwMMzMzAMCYMWMQGBiIf//739wWTRqNQlhOjTkhpKSkYPjw4Zg/fz6+//77ZqpM\ncSoqKpCWlsa6ws3Pz6+3NahFC9qIS5FycnLg6uoKb29vrFq1in6/0L4QZhgGp0+fxqBBg6TtRlOm\nTMHo0aMxbNgwMAwDkUiEWbNmIS0tDSdPnqS1ydUYDUc3s/j4eAQGBmLHjh3w8vLiuhwZQqEQN27c\nYAVudnY2+vXrB2dnZwwdOhTz58+HtbW12rcGqQNzc3NcvHgRXl5eGDduHPbs2YM2bdpwXRZRIh0d\nHbi4uEBXV1fafvT06VM8fvxY+vhff/2Fjh074sKFC2o3qkbY6EpYTvJ8Kt+2bRuWLVuGmJgYDBo0\nqJkrezexWIxbt26xhpRv3LiB3r17s4aUbW1tNbY1SF1UVVXB398fBQUFOHHiBD744AOuS+KMtl0J\nv6qmpgatW7fG2LFjERQUhIEDB2L+/PkIDAyUzpQm6o1CWE4NOSFIJBIEBwcjJiYGfD4f5ubmSqru\nHwzD4O7du6xe3NTUVHTr1o3Vi2tvb08r7KgoiUSC+fPnIy4uDnw+H7179+a6JE5ocwjXWbhwIVq1\naoWkpCQMGTIEy5cv57okoiA0vtgQhYXAnj1AejpiAWD8eMDWFpg4EXitxaiqqgoBAQHIz8/HxYsX\n0alTJ6WU+GprkEAggEAggL6+vvTqdvHixXB0dKTWIDXSokULrFu3Dr169YKLiwtiYmLw4Ycfcl0W\naWb1rYB1/fp1/Pnnn/jjjz8wYsQIjiojzYGuhN8mORlYtQpITKz986utAHp6AMMArq5AcDDg7Izi\n4mJ4eXmhR48e2Lt3b7Pdy3v69KlMa1BNTQ2rF9fJyQndu3dvlvcnyhcXF4fAwEDs3LkTnp6eXJej\nVNp0JZyeno4uXbqgW7durEl5J06cwPvvv4+hQ4dyWB1pDhTCb7JtGzB3LlBZWRu2b6KjA+jpoWjB\nArgcOABPT0+sXr1aYbNay8rKZFqDSkpK4OjoyLqPa2JiQq1BGk4gEGD48OEIDg7GrFmzuC5Ho630\nMQAAEh9JREFUabQlhPl8Pr755hvs378fX3/9NdflECWhEK5PXQBXVDT4KRUAro4ejc8PH27021ZW\nVkpbg+pCNy8vDwMHDmTdx7W0tKTWFS1179498Hg88Hg8rFu3Tiv+HWhDCO/YsQOhoaGIjo7G4MGD\nuS6HKBGF8OuSk4HPP5cGsBmAJ6i9ed4SQD8A/gCmApA5/enrA0lJgJPTO9+mpqZGpjXo9u3bsLa2\nZg0r9+vXj1qDCEtJSQm8vLzQpUsX7N+/X+1XXnsXTQ5hiUSCkJAQREVFgc/na/Q68qR+FMKv8/EB\nYmKkQ9BmAHYCGAagFEASgNkAPgew+/Xn6ugA3t7AsWOsvxaLxcjOzmatqZyRkYFevXrJtAZRTyhp\niOrqagQGBuLevXs4ceKERq1B/jpNDeHq6mpMnDgR9+/fR2xsLDp37sx1SYQDFMKvKiwETE1ZE7DM\n8E8I17kK4CMA6QAGvPYSTJs2eHD2LK7euycN3GvXrqFr166sIWUHBwdqDSJNIpFIsHjxYvzxxx9I\nTEyEhYUF1yU1C00M4WfPnsHb21trRjPIm9E456v27GnQt30IwAjAOciGcFVVFfYPG4ZrQ4fC2dkZ\nCxcuhJOTk1YvtkCaR4sWLbBixQqYmprCxcWF7ieqCW28r0/ejEL4Venp7Dakt+gB4Fk9f68HYLGn\nJ7BvnyIrI+SNpk6dCmNjY3h6emLbtm3UR6rCkpOT4enpqXUz3MmbUQi/qrS0wd/6EMAbr21LShRR\nDSEN5urqipMnT8LDwwO5ubmYM2cOtaypmNjYWEyaNEkre73Jm9E4yKsauBNJMmpD2OUNjxczDGpq\nahRVFSENYm9vj4sXLyIiIgKzZ8+GWCzmuiTy/3799VdMnz4dCQkJFMCEhUL4Vba2wFtmJ5cBiAcw\nBsB4ADb1fE91y5bYlZyMjh07YvDgwZg1axb27duHmzdvQiKRNE/dhPw/ExMTnD9/HpmZmRgxYgQq\n5Oh1J4onkUgwb948bNmyBefPn6dlR4kMmh39qjfMjq7rE26B2j7h8QCmo7ZvWEabNkBuLl7o6eHa\ntWusPuDCwkI4ODiw+oDNzMxo2JAonFAoxJQpU3Dr1i3ExcWha9euXJfUaOo6O7puJ6zHjx8jJiaG\nJmeSelEIv+61PmG5vKFPuM6zZ8+kK2HVfa2qqpK2LdV97dGjRxN/CEJqNwJYsmQJDhw4AD6fj759\n+3JdUqOoYwgXFxfD09MTxsbG2L17N/X/kzeiEH7daytmyUWOFbPqFBQUsEI5OTkZurq6rFB2cnJS\n2m5MRPPs2rULCxcuRFRUFD799FOuy5GbuoVwTk4OXF1d4ePjg5UrV1ILEnkrCuH6NGLtaOjrA+vX\nAzNmNOmtGYbBgwcPWKtrXbt2DZ07d2atruXg4ID333+/Se9FtMepU6fg5+eHLVu2YPTo0VyXIxd1\nCuHLly/D29sbP//8M6ZPn851OUQNUAi/iZy7KCkigN9EIpGwlr0UCARIT0+HiYkJ6/7ywIEDadiL\nvFF6ejrc3d3x7bff4qefflKbuQjqEsLR0dGYOnUq9uzZAzc3N67LIWqCQvhtBILa/YT5/Nqwraz8\n57G6/YR5vNr9hOUYglaEmpoaZGZmsoayb926BSsrK9aewv3790fr1q2VWhtRXfn5+XBzc8PHH3+M\nLVu2qMXmIOoQwps2bcK6desQGxsLR0dHrsshaoRCuCGKimqXtMzIqF2Iw8AAsLEBAgIAFVo4v7Ky\nEtevX2fdX87NzYWtrS3rHnOfPn3oPpUWKysrg6+vL1q3bo3Dhw+r/BrmqhzCYrEYP/74I06dOgU+\nnw9TU1OuSyJqhkJYw5WVlSE1NZU1lP306VOZVilTU1O1GZ4kTVdTU4Pp06cjLS0N8fHxMDQ05Lqk\nN1LVEK6oqMD48eNRUlKC6OhodOzYkeuSiBqiENZCxcXFMq1SQqGQNfHLyclJpU/MpOkYhsGKFSuw\nc+dO8Pl89OvXj+uS6qWKIVxYWIjhw4fDwsICERER0NXV5bokoqYohAkA4NGjRzKtUnp6eqxQpt2g\nNNP+/fsxd+5cHDlyBJ9//jnX5chQtRDOzs4Gj8fD2LFjsWzZMhpBIk1CIUzqxTAM7t27xwpl2hdZ\nc50+fRpjxozBhg0bMH78eK7LYVGlEL5w4QJGjBiBFStWYNKkSVyXQzQAhTBpMLFYXG+rVK9evVhD\n2QMHDqThOTWUmZkJNzc3TJkyBQsXLlSZKzxVCeGoqCh8++232L9/P7766iuuyyEagkKYNElNTQ1u\n3LjBGsq+ffs2rK2tZVql1KEdRtsVFBTAzc0Njo6OCA8PV4n2Nq5DmGEY/Oc//8HmzZsRFxcHOzs7\nzmohmodCmChcZWUl0tLSWEPZeXl5GDhwIGso29LSklqlVNCLFy8wevRoiMViREVFcb4yG5chLBKJ\nMHv2bJw7dw4JCQkwNjbmpA6iuSiEiVKUlZXJ7Cr17NkzODo6soayTUxMFDIM+vfff+Po0aNwcXGB\ng4MD2rZtq4CfQnuIRCJ89913uHz5MhISEtCzZ0/OauEqhF++fIkxY8agqqoKR48eRYcG7jdOiDwo\nhAlnnj59yrpaTk5OhlgslmmV6t69u9yvfevWLWzYsAG5ubkoLy/H1q1b0atXL/z555+wsLCAra0t\n3nvvvWb4qTQHwzBYu3YtwsPDER8fDxub+nbQbn5chPDjx4/h4eGBAQMGYMeOHSoxLE80E4UwUSkP\nHz5k3V8WCATQ19dnLSzi6OgIAwODBr/mggUL0LNnT3To0AGbN29Gq1atUFJSgjFjxmDRokU0iewd\nIiMjMXv2bBw6dAjDhg1T+vsrO4Rv3rwJHo+HiRMnYvHixSozQY1oJgphotIYhsHdu3dlWqW6d+8O\nZ2dneHh4wNfXlzXpi2EY6Ojo4PDhw+jbty+Cg4Ph5+eH7OxsWFlZwc/PD0DtggvqvNm9Mp09exa+\nvr5Ys2YNAgIClPreygzhpKQkjBo1CmvXrsU333yjlPck2o1CmKgdsViM27dvIzk5GW3btoWHh4fM\n1axYLMaMGTOQmJiIMWPGYOXKlZg4cSJ0dXURFBSEAQMGcFS9+rp16xZ4PB6++eYbhIaGKu0KUVkh\nfOjQIcyZMweRkZH417/+1ezvRwhAIUw03IMHDzBz5kxERkYiJiYGZ86cwa1bt/D1118jNDSU6/LU\nzpMnT+Du7o4BAwZg+/btSrmv3twhzDAMVq9ejd9++43Te99EO1F/CNEoIpEIIpFI+ufS0lJYWlri\n+vXr8Pf3x65du3Dw4EGkpaXhwoULHFaqnrp164YzZ86guLgYPB4PpaWlXJfUJCKRCNOmTcMff/yB\nS5cuUQATpaMQJhqluroac+fOxY8//ojTp09j+/btEIvFrC3mOnXqBFNTU2RlZQGA9CpLJBLh+PHj\nyM3NVYkVmlRV27ZtER0dDSsrK7i4uCAvL4/rkhqlvLwcw4cPR15eHs6ePYsePXpwXRLRQhTCRKPo\n6+tj1KhR+OCDD7Bp0ya0bdsW69atw65du7Bo0SJcunQJ27dvx5MnT6Sbr9fd23z+/Dl2796NDz/8\nEN27d4ebmxuWLFmChIQEFBYWcvljqZyWLVtiy5YtCAgIwMcff4y0tDSuS5LLo0eP8Nlnn6Fnz56I\njY3lfEESor3onjDRCklJSfjvf/+La9euwcDAAEuXLoW5uXm938swjLRVqq5NSiAQoF27djKtUrSH\nLHD06FHMnDkT+/btw9dff63w11f0PeEbN27Azc0N06ZNQ3BwMLUgEU5RCBPSAAzDICcnh9XDnJqa\nCkNDQ9bCIvb29lq5Olfd7kJhYWGYPHmyQl9bkSFct1vUpk2bMG7cOIW8JiFNQSFMSCOJxWLcvHmT\n1cOcmZkJc3Nz1hrZ2rI6199//w1XV1eMHj0aYWFhCrvCVFQI79u3D/PmzVPZfZOJdqIQJkSBhEIh\nMjIyWEtx5uTkoH///qzlOK2trdGyZUuuy1W4oqIiDB8+HObm5oiIiFDIamRNDWGGYbB8+XLs3r0b\nfD4f1tbWTa6JEEWhECakmVVUVCA1NZU1lP3o0SPY2dmxhrItLCw04v5kZWUl/Pz8UFJSguPHj8u1\nxGh9mhLCNTU1mDZtGtLT0xEfH9+odcgJaU4UwoRw4Pnz50hJSWENZZeVlUmvluu+GhkZqWUwi8Vi\nzJ07FydPngSfz4eZmVmjX6uxIVxWVoaRI0dCV1cXkZGRaNeuXaNrIKS5UAgToiIKCwtldpXS0dFh\nhbKzszO6dOnCdakN9ssvv2DNmjWIjY2VtoTJqzEhnJ+fDzc3N3zyySf45ZdfWGuLE6JKKIQJUVEM\nwyA/P1+mVap9+/YyrVKqvNdtdHQ0pk6dit27d8Pd3V3u58sbwtevX4e7uzu+//57zJ07Vy1HEoj2\noBAmRI1IJBJpq1RdMKelpaFHjx4yrVL6+vpclyt15coVeHl5ITQ0FDNmzJDrufKE8F9//YXx48dj\n69atGDVqVGNKJUSpKIQJUXMikUimVSorKwsWFhasoWwbGxtOW6VycnLA4/Hg5eWFVatWoUWLhi3Y\n19AQ3rVrFxYuXIijR4/CxcWlqeUSohQUwoRooOrqaplWqbt372LAgAGs+8tWVlZKbZUqLi6Gp6cn\njIyMsGfPHrRp0+adz3lXCDMMg9DQUBw6dAh8Ph99+/ZVZMmENCsKYUK0xMuXL6WtUnVD2QUFBbC3\nt2fdY+7du3ez3ketqqqCv78/CgoKEBMTg06dOsl+U2EhsGcPkJ6OuIMH4eHnB9jaAhMnAq9MTBMK\nhZg8eTJu376NuLg4dO3atdnqJqQ5UAgTosVKSkqQkpLC6mF+8eKFTKtUz549FRrMEokECxYswIkT\nJ5CYmIjevXvXPpCcDKxaBSQm1v65quqfJ+npAQwDuLoCwcF4bmkJHx8fdOjQAQcPHlSpe+CENBSF\nMCGE5cmTJzKtUi1btpRplercuXOT3ys8PBxhYWGIiYnBhykpwNy5QGVlbdi+iY4OJG3aIKxjRxT7\n+mLDhg0aufoY0Q4UwoSQt2IYBnl5eaxQTklJgYGBASuUHR0d0b59e7lfPz4+HmfGjMFqkQitqqsb\n/Lya1q3RevNmQM7Z1oSoEgphQojcJBIJ7ty5I9MqZWRkxLq/bGdnBz09vbe/WHIyxEOGoOUrQ89m\nAJ4AePX6NgDA1tefq68PJCUBTk4K+KkIUT4KYUKIQohEImRlZbHuL2dlZaFPnz6sHmYbGxu0bt36\nnyf6+AAxMawhaDMAOwEMe9eb6ugA3t7AsWOK/4EIUQIKYUJIs6murkZ6ejprKPvevXuwsbGBk5MT\nPu3bF75z56KFUMh6nhkaGMIA0KYNkJvLmjVNiLqgBVUJIc1GV1dXehVc58WLF9JWKdHOnaiuqcE7\nBqzfTkentp1p3rymlkuI0lEIE0KUql27dvj000/x6aefAteuAenp9X6fF9gnqHUAptT3jZWVQEaG\n4gslRAkohAkh3CktfeNDMWjgcDQAlJQoohpClK5hi7cSQkhzUNTuTwYGinkdQpSMQpgQwh1b29qJ\nVU2hpwfY2CimHkKUjGZHE0K4U1gImJqyl6dE/X3CXwKIru81aHY0UWN0JUwI4U7XrrVrQb+2LvV9\nAJUAXrzyv3oDWEcH4PEogInaoithQgi3kpOBzz8HKirkfy6tmEXUHF0JE0K45ewMrF9fG6jy0Nev\nfR4FMFFj1KJECOFe3SYMDdxFCXp6tQFMmzcQNUfD0YQQ1SEQ1O4nzOfXhm1l5T+P1e0nzOMBwcF0\nBUw0AoUwIUT1FBXVLkWZkVG7EIeBQW0bUkAATcIiGoVCmBBCCOEITcwihBBCOEIhTAghhHCEQpgQ\nQgjhCIUwIYQQwhEKYUIIIYQjFMKEEEIIRyiECSGEEI5QCBNCCCEcoRAmhBBCOEIhTAghhHCEQpgQ\nQgjhCIUwIYQQwhEKYUIIIYQjFMKEEEIIRyiECSGEEI5QCBNCCCEcoRAmhBBCOEIhTAghhHCEQpgQ\nQgjhCIUwIYQQwhEKYUIIIYQjFMKEEEIIRyiECSGEEI5QCBNCCCEcoRAmhBBCOEIhTAghhHCEQpgQ\nQgjhCIUwIYQQwhEKYUIIIYQjFMKEEEIIRyiECSGEEI5QCBNCCCEcoRAmhBBCOEIhTAghhHCEQpgQ\nQgjhCIUwIYQQwhEKYUIIIYQjFMKEEEIIR/4PuqVN5d8vc1YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f961b285c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotando grafo\n",
    "\n",
    "labels = nx.get_edge_attributes(G,'weight')\n",
    "pos=nx.circular_layout(G)\n",
    "nx.draw(G,pos, with_labels=True)\n",
    "colors = [G[u][v]['color'] for u,v in G.edges()]\n",
    "#nx.draw_circular(G,node_color='r',with_labels=True, edge_labels=labels)\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n",
    "plt.title(\"Grafo caixeiro viajante\")\n",
    "plt.suptitle(\"Um dos maiores problemas matemáticos\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# inicialização dos dados!\n",
    "\n",
    "def sumAllNeighborsAndTxyNxy(vertice):\n",
    "    s = 0\n",
    "    for vizinho in G.neighbors(vertice):\n",
    "        Atx_y = float(1/G.get_edge_data(vertice, vizinho)['weight'])\n",
    "        Anx_y = 0.1\n",
    "        Atxn = float(Atx_y*Anx_y)\n",
    "        s += Atxn\n",
    "    return s\n",
    "\n",
    "def preparingPlot():\n",
    "    all_atributes, routs, distances, tx_y, nx_y, txn, pxy, ppxy = [], [], [], [], [], [], [], []\n",
    "\n",
    "    for vertice in G.nodes():\n",
    "        for vizinho in G.neighbors(vertice):\n",
    "            routs.append([vertice, vizinho])\n",
    "            distances.append(G.get_edge_data(vertice, vizinho)['weight'])\n",
    "            tx_y.append(G.get_edge_data(vertice, vizinho)['tx_y'])\n",
    "            nx_y.append(G.get_edge_data(vertice, vizinho)['nx_y'])\n",
    "            txn.append(G.get_edge_data(vertice, vizinho)['txn'])\n",
    "            pxy.append(G.get_edge_data(vertice, vizinho)['pxy'])\n",
    "            ppxy.append(G.get_edge_data(vertice, vizinho)['pxy'] * 100)\n",
    "    all_atributes.append(routs)\n",
    "    all_atributes.append(distances)\n",
    "    all_atributes.append(tx_y)\n",
    "    all_atributes.append(nx_y)\n",
    "    all_atributes.append(txn)\n",
    "    all_atributes.append(pxy)\n",
    "    all_atributes.append(ppxy)\n",
    "    \n",
    "    return all_atributes\n",
    "        \n",
    "    \n",
    "    \n",
    "for vertice in G.nodes():\n",
    "    sumTxVertice = sumAllNeighborsAndTxyNxy(vertice)\n",
    "    for vizinho in G.neighbors(vertice):\n",
    "        Atx_y = 1/G.get_edge_data(vertice, vizinho)['weight']\n",
    "        Anx_y = 0.1\n",
    "        Atxn = Atx_y*Anx_y\n",
    "        Apxy = Atxn/sumTxVertice\n",
    "        G[vertice][vizinho]['tx_y'] = Atx_y\n",
    "        G[vertice][vizinho]['nx_y'] = Anx_y\n",
    "        G[vertice][vizinho]['txn'] = Atxn\n",
    "        G[vertice][vizinho]['pxy'] = Apxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. Rotas</th>\n",
       "      <th>2. Distância</th>\n",
       "      <th>3. t(xy)</th>\n",
       "      <th>4. n(xy)</th>\n",
       "      <th>5. t(xy)*n(xy)</th>\n",
       "      <th>6. P(xy)</th>\n",
       "      <th>7. P(xy) %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[A, B]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.325957</td>\n",
       "      <td>32.595670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[A, C]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.140582</td>\n",
       "      <td>14.058159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[A, D]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>16.764427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A, E]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.250270</td>\n",
       "      <td>25.026960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[B, A]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.325957</td>\n",
       "      <td>32.595670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[B, C]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>23.430265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[B, D]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002941</td>\n",
       "      <td>0.236674</td>\n",
       "      <td>23.667427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[B, E]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.226807</td>\n",
       "      <td>22.680683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[C, A]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.140582</td>\n",
       "      <td>14.058159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[C, B]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>23.430265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[C, D]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.365769</td>\n",
       "      <td>36.576932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[C, E]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004348</td>\n",
       "      <td>0.315557</td>\n",
       "      <td>31.555733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[D, A]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>16.764427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[D, B]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002941</td>\n",
       "      <td>0.236674</td>\n",
       "      <td>23.667427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[D, C]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.365769</td>\n",
       "      <td>36.576932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[D, E]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.207366</td>\n",
       "      <td>20.736624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[E, A]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.250270</td>\n",
       "      <td>25.026960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[E, B]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.226807</td>\n",
       "      <td>22.680683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[E, C]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.004348</td>\n",
       "      <td>0.315557</td>\n",
       "      <td>31.555733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[E, D]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.207366</td>\n",
       "      <td>20.736624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1. Rotas  2. Distância  3. t(xy)  4. n(xy)  5. t(xy)*n(xy)  6. P(xy)  \\\n",
       "0    [A, B]            22  0.045455       0.1        0.004545  0.325957   \n",
       "1    [A, C]            50  0.020000       0.1        0.002000  0.140582   \n",
       "2    [A, D]            48  0.020833       0.1        0.002083  0.167644   \n",
       "3    [A, E]            29  0.034483       0.1        0.003448  0.250270   \n",
       "4    [B, A]            22  0.045455       0.1        0.004545  0.325957   \n",
       "5    [B, C]            30  0.033333       0.1        0.003333  0.234303   \n",
       "6    [B, D]            34  0.029412       0.1        0.002941  0.236674   \n",
       "7    [B, E]            32  0.031250       0.1        0.003125  0.226807   \n",
       "8    [C, A]            50  0.020000       0.1        0.002000  0.140582   \n",
       "9    [C, B]            30  0.033333       0.1        0.003333  0.234303   \n",
       "10   [C, D]            22  0.045455       0.1        0.004545  0.365769   \n",
       "11   [C, E]            23  0.043478       0.1        0.004348  0.315557   \n",
       "12   [D, A]            48  0.020833       0.1        0.002083  0.167644   \n",
       "13   [D, B]            34  0.029412       0.1        0.002941  0.236674   \n",
       "14   [D, C]            22  0.045455       0.1        0.004545  0.365769   \n",
       "15   [D, E]            35  0.028571       0.1        0.002857  0.207366   \n",
       "16   [E, A]            29  0.034483       0.1        0.003448  0.250270   \n",
       "17   [E, B]            32  0.031250       0.1        0.003125  0.226807   \n",
       "18   [E, C]            23  0.043478       0.1        0.004348  0.315557   \n",
       "19   [E, D]            35  0.028571       0.1        0.002857  0.207366   \n",
       "\n",
       "    7. P(xy) %  \n",
       "0    32.595670  \n",
       "1    14.058159  \n",
       "2    16.764427  \n",
       "3    25.026960  \n",
       "4    32.595670  \n",
       "5    23.430265  \n",
       "6    23.667427  \n",
       "7    22.680683  \n",
       "8    14.058159  \n",
       "9    23.430265  \n",
       "10   36.576932  \n",
       "11   31.555733  \n",
       "12   16.764427  \n",
       "13   23.667427  \n",
       "14   36.576932  \n",
       "15   20.736624  \n",
       "16   25.026960  \n",
       "17   22.680683  \n",
       "18   31.555733  \n",
       "19   20.736624  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tabela dos dados iniciais\n",
    "\n",
    "pre_dados = preparingPlot()\n",
    "\n",
    "dados = {\n",
    "    '1. Rotas':pre_dados[0],\n",
    "    '2. Distância':pre_dados[1],\n",
    "    '3. t(xy)':pre_dados[2],\n",
    "    '4. n(xy)':pre_dados[3],\n",
    "    '5. t(xy)*n(xy)':pre_dados[4],\n",
    "    '6. P(xy)':pre_dados[5],\n",
    "    '7. P(xy) %':pre_dados[6]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dados inicializados (As formigas formam rotas aleatórias):\n",
      "\n",
      "A formiga 1 fez o caminho ['A', 'B', 'D', 'C', 'E', 'A'] com o custo 130\n",
      "A formiga 2 fez o caminho ['B', 'E', 'C', 'D', 'A', 'B'] com o custo 147\n",
      "A formiga 3 fez o caminho ['C', 'D', 'B', 'E', 'A', 'C'] com o custo 167\n",
      "A formiga 4 fez o caminho ['D', 'C', 'B', 'E', 'A', 'D'] com o custo 161\n",
      "A formiga 5 fez o caminho ['E', 'A', 'B', 'D', 'C', 'E'] com o custo 130\n"
     ]
    }
   ],
   "source": [
    "# declarando as formigas\n",
    "\n",
    "import random\n",
    "\n",
    "class Ant(object):\n",
    "    '''\n",
    "    Classe que movimenta as formigas\n",
    "    '''\n",
    "    def __init__(self, saida):\n",
    "        self.saida = saida\n",
    "        self.pontos_visitados = [saida]\n",
    "        self.arestas_visitadas = []\n",
    "        self.arestas_visitadas_literal = []\n",
    "    \n",
    "    def caminhar(self):\n",
    "        self.roulette_wheel(self.saida)        \n",
    "        self.pontos_visitados.append(self.saida)\n",
    "        for i in range(len(self.pontos_visitados)-1):\n",
    "            self.arestas_visitadas.append(G[self.pontos_visitados[i]][self.pontos_visitados[i+1]])\n",
    "            self.arestas_visitadas_literal.append([self.pontos_visitados[i], self.pontos_visitados[i+1]])\n",
    "    \n",
    "    # método da roleta\n",
    "    def roulette_wheel(self, inicio):\n",
    "        if len(self.pontos_visitados) >= 5:\n",
    "            return\n",
    "        \n",
    "        lista_com_possiveis_destinos = []\n",
    "        \n",
    "        for l in G.neighbors(inicio):\n",
    "            if l not in self.pontos_visitados:\n",
    "                lista_com_possiveis_destinos.append(l)\n",
    "            \n",
    "        \n",
    "        sum_l = 0\n",
    "        \n",
    "        lista_com_pesos = []\n",
    "            \n",
    "        for i in lista_com_possiveis_destinos:\n",
    "            lista_com_pesos.append(G.get_edge_data(inicio, i)['pxy']*100)\n",
    "        \n",
    "        new_key_drawn = random.choices(lista_com_possiveis_destinos, lista_com_pesos,k=1)[0]\n",
    "        self.pontos_visitados.append(new_key_drawn)\n",
    "        self.roulette_wheel(new_key_drawn)\n",
    "\n",
    "    def sum_route(self):\n",
    "        distancia_caminho = 0\n",
    "        for i in range(len(self.pontos_visitados)-1):\n",
    "            distancia_caminho += G.get_edge_data(self.pontos_visitados[i], self.pontos_visitados[i+1])['weight']\n",
    "        return distancia_caminho\n",
    "\n",
    "\n",
    "# criação das formigas\n",
    "f = list()\n",
    "\n",
    "for i in range(5):\n",
    "    f.append(Ant(G.nodes()[i]))\n",
    "    \n",
    "print(\"Dados inicializados (As formigas formam rotas aleatórias):\\n\")\n",
    "for i in range(5):\n",
    "    f[i].caminhar()    \n",
    "    print(\"A formiga {} fez o caminho {} com o custo {}\".format(i+1, f[i].pontos_visitados, f[i].sum_route()))\n",
    "\n",
    "    #print(f[i].arestas_visitadas_literal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# atualizando as rotas\n",
    "\n",
    "def sum_pre(vertice):\n",
    "    sum_all = 0\n",
    "    for edge in G.neighbors(vertice):\n",
    "        sum_all += G.get_edge_data(vertice, edge)['txn']\n",
    "    return sum_all\n",
    "    \n",
    "\n",
    "def updateRoutes():\n",
    "    for vertice in G.nodes():\n",
    "        sum_pre(vertice)\n",
    "        sumTxVertice = sum_pre(vertice)\n",
    "        for vizinho in G.neighbors(vertice):\n",
    "            Anx_y = G.get_edge_data(vertice, vizinho)['nx_y']\n",
    "            Atx_y = G.get_edge_data(vertice, vizinho)['tx_y']\n",
    "            Atxn = Atx_y*Anx_y\n",
    "            Apxy = Atxn/sumTxVertice\n",
    "            G[vertice][vizinho]['tx_y'] = Atx_y\n",
    "            G[vertice][vizinho]['txn'] = Atxn\n",
    "            G[vertice][vizinho]['pxy'] = Apxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def iterar():\n",
    "    f = list()\n",
    "\n",
    "    for i in range(5):\n",
    "        f.append(Ant(G.nodes()[i]))\n",
    "\n",
    "    for i in range(5):\n",
    "        f[i].caminhar()\n",
    "        print(\"A formiga {} fez o caminho {} com o custo {}\".format(i+1, f[i].pontos_visitados, f[i].sum_route()))\n",
    "\n",
    "    \n",
    "    # atualização de feromônio\n",
    "\n",
    "    coeficiente_feromonio = 0.001 # coeficiente eficaz para o caso\n",
    "\n",
    "    for edge in G.edges():\n",
    "        total_feromonio = coeficiente_feromonio\n",
    "        for formiga in f:\n",
    "            if G[edge[0]][edge[1]] in formiga.arestas_visitadas:\n",
    "                total_feromonio += float(10/formiga.sum_route())\n",
    "        G[edge[0]][edge[1]]['nx_y'] = total_feromonio\n",
    "   \n",
    "    updateRoutes()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A formiga 1 fez o caminho ['A', 'B', 'D', 'E', 'C', 'A'] com o custo 164\n",
      "A formiga 2 fez o caminho ['B', 'C', 'D', 'A', 'E', 'B'] com o custo 161\n",
      "A formiga 3 fez o caminho ['C', 'E', 'A', 'B', 'D', 'C'] com o custo 130\n",
      "A formiga 4 fez o caminho ['D', 'B', 'A', 'E', 'C', 'D'] com o custo 130\n",
      "A formiga 5 fez o caminho ['E', 'C', 'B', 'A', 'D', 'E'] com o custo 158\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. Rotas</th>\n",
       "      <th>2. Distância</th>\n",
       "      <th>3. t(xy)</th>\n",
       "      <th>4. n(xy)</th>\n",
       "      <th>5. t(xy)*n(xy)</th>\n",
       "      <th>6. P(xy)</th>\n",
       "      <th>7. P(xy) %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[A, B]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.279113</td>\n",
       "      <td>0.012687</td>\n",
       "      <td>0.574422</td>\n",
       "      <td>57.442207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[A, C]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.061976</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.086400</td>\n",
       "      <td>8.639989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[A, D]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.126403</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.121355</td>\n",
       "      <td>12.135480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A, E]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.216958</td>\n",
       "      <td>0.007481</td>\n",
       "      <td>0.297255</td>\n",
       "      <td>29.725544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[B, A]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.279113</td>\n",
       "      <td>0.012687</td>\n",
       "      <td>0.574422</td>\n",
       "      <td>57.442207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[B, C]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.126403</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>0.293696</td>\n",
       "      <td>29.369619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[B, D]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.215822</td>\n",
       "      <td>0.006348</td>\n",
       "      <td>0.292521</td>\n",
       "      <td>29.252121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[B, E]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.063112</td>\n",
       "      <td>0.001972</td>\n",
       "      <td>0.078363</td>\n",
       "      <td>7.836332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[C, A]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.061976</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.086400</td>\n",
       "      <td>8.639989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[C, B]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.126403</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>0.293696</td>\n",
       "      <td>29.369619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[C, D]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.216958</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.454458</td>\n",
       "      <td>45.445819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[C, E]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.279113</td>\n",
       "      <td>0.012135</td>\n",
       "      <td>0.482175</td>\n",
       "      <td>48.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[D, A]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.126403</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.121355</td>\n",
       "      <td>12.135480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[D, B]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.215822</td>\n",
       "      <td>0.006348</td>\n",
       "      <td>0.292521</td>\n",
       "      <td>29.252121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[D, C]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.216958</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.454458</td>\n",
       "      <td>45.445819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[D, E]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.125267</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>0.142207</td>\n",
       "      <td>14.220668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[E, A]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.216958</td>\n",
       "      <td>0.007481</td>\n",
       "      <td>0.297255</td>\n",
       "      <td>29.725544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[E, B]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.063112</td>\n",
       "      <td>0.001972</td>\n",
       "      <td>0.078363</td>\n",
       "      <td>7.836332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[E, C]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.279113</td>\n",
       "      <td>0.012135</td>\n",
       "      <td>0.482175</td>\n",
       "      <td>48.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[E, D]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.125267</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>0.142207</td>\n",
       "      <td>14.220668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1. Rotas  2. Distância  3. t(xy)  4. n(xy)  5. t(xy)*n(xy)  6. P(xy)  \\\n",
       "0    [A, B]            22  0.045455  0.279113        0.012687  0.574422   \n",
       "1    [A, C]            50  0.020000  0.061976        0.001240  0.086400   \n",
       "2    [A, D]            48  0.020833  0.126403        0.002633  0.121355   \n",
       "3    [A, E]            29  0.034483  0.216958        0.007481  0.297255   \n",
       "4    [B, A]            22  0.045455  0.279113        0.012687  0.574422   \n",
       "5    [B, C]            30  0.033333  0.126403        0.004213  0.293696   \n",
       "6    [B, D]            34  0.029412  0.215822        0.006348  0.292521   \n",
       "7    [B, E]            32  0.031250  0.063112        0.001972  0.078363   \n",
       "8    [C, A]            50  0.020000  0.061976        0.001240  0.086400   \n",
       "9    [C, B]            30  0.033333  0.126403        0.004213  0.293696   \n",
       "10   [C, D]            22  0.045455  0.216958        0.009862  0.454458   \n",
       "11   [C, E]            23  0.043478  0.279113        0.012135  0.482175   \n",
       "12   [D, A]            48  0.020833  0.126403        0.002633  0.121355   \n",
       "13   [D, B]            34  0.029412  0.215822        0.006348  0.292521   \n",
       "14   [D, C]            22  0.045455  0.216958        0.009862  0.454458   \n",
       "15   [D, E]            35  0.028571  0.125267        0.003579  0.142207   \n",
       "16   [E, A]            29  0.034483  0.216958        0.007481  0.297255   \n",
       "17   [E, B]            32  0.031250  0.063112        0.001972  0.078363   \n",
       "18   [E, C]            23  0.043478  0.279113        0.012135  0.482175   \n",
       "19   [E, D]            35  0.028571  0.125267        0.003579  0.142207   \n",
       "\n",
       "    7. P(xy) %  \n",
       "0    57.442207  \n",
       "1     8.639989  \n",
       "2    12.135480  \n",
       "3    29.725544  \n",
       "4    57.442207  \n",
       "5    29.369619  \n",
       "6    29.252121  \n",
       "7     7.836332  \n",
       "8     8.639989  \n",
       "9    29.369619  \n",
       "10   45.445819  \n",
       "11   48.217457  \n",
       "12   12.135480  \n",
       "13   29.252121  \n",
       "14   45.445819  \n",
       "15   14.220668  \n",
       "16   29.725544  \n",
       "17    7.836332  \n",
       "18   48.217457  \n",
       "19   14.220668  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1ª iteração\n",
    "iterar()\n",
    "pre_dados = preparingPlot()\n",
    "\n",
    "dados = {\n",
    "    '1. Rotas':pre_dados[0],\n",
    "    '2. Distância':pre_dados[1],\n",
    "    '3. t(xy)':pre_dados[2],\n",
    "    '4. n(xy)':pre_dados[3],\n",
    "    '5. t(xy)*n(xy)':pre_dados[4],\n",
    "    '6. P(xy)':pre_dados[5],\n",
    "    '7. P(xy) %':pre_dados[6]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A formiga 1 fez o caminho ['A', 'E', 'D', 'B', 'C', 'A'] com o custo 178\n",
      "A formiga 2 fez o caminho ['B', 'C', 'E', 'D', 'A', 'B'] com o custo 158\n",
      "A formiga 3 fez o caminho ['C', 'E', 'D', 'B', 'A', 'C'] com o custo 164\n",
      "A formiga 4 fez o caminho ['D', 'B', 'A', 'E', 'C', 'D'] com o custo 130\n",
      "A formiga 5 fez o caminho ['E', 'D', 'C', 'B', 'A', 'E'] com o custo 138\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. Rotas</th>\n",
       "      <th>2. Distância</th>\n",
       "      <th>3. t(xy)</th>\n",
       "      <th>4. n(xy)</th>\n",
       "      <th>5. t(xy)*n(xy)</th>\n",
       "      <th>6. P(xy)</th>\n",
       "      <th>7. P(xy) %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[A, B]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.274654</td>\n",
       "      <td>0.012484</td>\n",
       "      <td>0.499018</td>\n",
       "      <td>49.901830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[A, C]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.118155</td>\n",
       "      <td>0.002363</td>\n",
       "      <td>0.076746</td>\n",
       "      <td>7.674587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[A, D]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.064291</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.076573</td>\n",
       "      <td>7.657288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A, E]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.206567</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.307030</td>\n",
       "      <td>30.702955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[B, A]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.274654</td>\n",
       "      <td>0.012484</td>\n",
       "      <td>0.499018</td>\n",
       "      <td>49.901830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[B, C]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.192935</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>0.208863</td>\n",
       "      <td>20.886254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[B, D]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.195078</td>\n",
       "      <td>0.005738</td>\n",
       "      <td>0.328016</td>\n",
       "      <td>32.801636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[B, E]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[C, A]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.118155</td>\n",
       "      <td>0.002363</td>\n",
       "      <td>0.076746</td>\n",
       "      <td>7.674587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[C, B]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.192935</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>0.208863</td>\n",
       "      <td>20.886254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[C, D]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.150387</td>\n",
       "      <td>0.006836</td>\n",
       "      <td>0.390798</td>\n",
       "      <td>39.079794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[C, E]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.202190</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.378922</td>\n",
       "      <td>37.892171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[D, A]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.064291</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.076573</td>\n",
       "      <td>7.657288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[D, B]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.195078</td>\n",
       "      <td>0.005738</td>\n",
       "      <td>0.328016</td>\n",
       "      <td>32.801636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[D, C]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.150387</td>\n",
       "      <td>0.006836</td>\n",
       "      <td>0.390798</td>\n",
       "      <td>39.079794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[D, E]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.253910</td>\n",
       "      <td>0.007255</td>\n",
       "      <td>0.312702</td>\n",
       "      <td>31.270174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[E, A]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.206567</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.307030</td>\n",
       "      <td>30.702955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[E, B]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[E, C]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.202190</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.378922</td>\n",
       "      <td>37.892171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[E, D]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.253910</td>\n",
       "      <td>0.007255</td>\n",
       "      <td>0.312702</td>\n",
       "      <td>31.270174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1. Rotas  2. Distância  3. t(xy)  4. n(xy)  5. t(xy)*n(xy)  6. P(xy)  \\\n",
       "0    [A, B]            22  0.045455  0.274654        0.012484  0.499018   \n",
       "1    [A, C]            50  0.020000  0.118155        0.002363  0.076746   \n",
       "2    [A, D]            48  0.020833  0.064291        0.001339  0.076573   \n",
       "3    [A, E]            29  0.034483  0.206567        0.007123  0.307030   \n",
       "4    [B, A]            22  0.045455  0.274654        0.012484  0.499018   \n",
       "5    [B, C]            30  0.033333  0.192935        0.006431  0.208863   \n",
       "6    [B, D]            34  0.029412  0.195078        0.005738  0.328016   \n",
       "7    [B, E]            32  0.031250  0.001000        0.000031  0.001347   \n",
       "8    [C, A]            50  0.020000  0.118155        0.002363  0.076746   \n",
       "9    [C, B]            30  0.033333  0.192935        0.006431  0.208863   \n",
       "10   [C, D]            22  0.045455  0.150387        0.006836  0.390798   \n",
       "11   [C, E]            23  0.043478  0.202190        0.008791  0.378922   \n",
       "12   [D, A]            48  0.020833  0.064291        0.001339  0.076573   \n",
       "13   [D, B]            34  0.029412  0.195078        0.005738  0.328016   \n",
       "14   [D, C]            22  0.045455  0.150387        0.006836  0.390798   \n",
       "15   [D, E]            35  0.028571  0.253910        0.007255  0.312702   \n",
       "16   [E, A]            29  0.034483  0.206567        0.007123  0.307030   \n",
       "17   [E, B]            32  0.031250  0.001000        0.000031  0.001347   \n",
       "18   [E, C]            23  0.043478  0.202190        0.008791  0.378922   \n",
       "19   [E, D]            35  0.028571  0.253910        0.007255  0.312702   \n",
       "\n",
       "    7. P(xy) %  \n",
       "0    49.901830  \n",
       "1     7.674587  \n",
       "2     7.657288  \n",
       "3    30.702955  \n",
       "4    49.901830  \n",
       "5    20.886254  \n",
       "6    32.801636  \n",
       "7     0.134700  \n",
       "8     7.674587  \n",
       "9    20.886254  \n",
       "10   39.079794  \n",
       "11   37.892171  \n",
       "12    7.657288  \n",
       "13   32.801636  \n",
       "14   39.079794  \n",
       "15   31.270174  \n",
       "16   30.702955  \n",
       "17    0.134700  \n",
       "18   37.892171  \n",
       "19   31.270174  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2ª iteração\n",
    "iterar()\n",
    "pre_dados = preparingPlot()\n",
    "\n",
    "dados = {\n",
    "    '1. Rotas':pre_dados[0],\n",
    "    '2. Distância':pre_dados[1],\n",
    "    '3. t(xy)':pre_dados[2],\n",
    "    '4. n(xy)':pre_dados[3],\n",
    "    '5. t(xy)*n(xy)':pre_dados[4],\n",
    "    '6. P(xy)':pre_dados[5],\n",
    "    '7. P(xy) %':pre_dados[6]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A formiga 1 fez o caminho ['A', 'E', 'D', 'B', 'C', 'A'] com o custo 178\n",
      "A formiga 2 fez o caminho ['B', 'A', 'E', 'C', 'D', 'B'] com o custo 130\n",
      "A formiga 3 fez o caminho ['C', 'E', 'A', 'D', 'B', 'C'] com o custo 164\n",
      "A formiga 4 fez o caminho ['D', 'C', 'E', 'A', 'B', 'D'] com o custo 130\n",
      "A formiga 5 fez o caminho ['E', 'C', 'B', 'D', 'A', 'E'] com o custo 164\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. Rotas</th>\n",
       "      <th>2. Distância</th>\n",
       "      <th>3. t(xy)</th>\n",
       "      <th>4. n(xy)</th>\n",
       "      <th>5. t(xy)*n(xy)</th>\n",
       "      <th>6. P(xy)</th>\n",
       "      <th>7. P(xy) %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[A, B]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>0.007038</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>36.585351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[A, C]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.057180</td>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.050287</td>\n",
       "      <td>5.028726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[A, D]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.122951</td>\n",
       "      <td>0.002561</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>9.612303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A, E]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.332977</td>\n",
       "      <td>0.011482</td>\n",
       "      <td>0.455967</td>\n",
       "      <td>45.596674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[B, A]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>0.007038</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>36.585351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[B, C]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.179131</td>\n",
       "      <td>0.005971</td>\n",
       "      <td>0.262564</td>\n",
       "      <td>26.256391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[B, D]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.332977</td>\n",
       "      <td>0.009793</td>\n",
       "      <td>0.367512</td>\n",
       "      <td>36.751187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[B, E]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.124099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[C, A]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.057180</td>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.050287</td>\n",
       "      <td>5.028726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[C, B]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.179131</td>\n",
       "      <td>0.005971</td>\n",
       "      <td>0.262564</td>\n",
       "      <td>26.256391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[C, D]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>0.007038</td>\n",
       "      <td>0.264127</td>\n",
       "      <td>26.412749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[C, E]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.276797</td>\n",
       "      <td>0.012035</td>\n",
       "      <td>0.477915</td>\n",
       "      <td>47.791522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[D, A]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.122951</td>\n",
       "      <td>0.002561</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>9.612303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[D, B]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.332977</td>\n",
       "      <td>0.009793</td>\n",
       "      <td>0.367512</td>\n",
       "      <td>36.751187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[D, C]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>0.007038</td>\n",
       "      <td>0.264127</td>\n",
       "      <td>26.412749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[D, E]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.057180</td>\n",
       "      <td>0.001634</td>\n",
       "      <td>0.064877</td>\n",
       "      <td>6.487706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[E, A]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.332977</td>\n",
       "      <td>0.011482</td>\n",
       "      <td>0.455967</td>\n",
       "      <td>45.596674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[E, B]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.124099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[E, C]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.276797</td>\n",
       "      <td>0.012035</td>\n",
       "      <td>0.477915</td>\n",
       "      <td>47.791522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[E, D]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.057180</td>\n",
       "      <td>0.001634</td>\n",
       "      <td>0.064877</td>\n",
       "      <td>6.487706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1. Rotas  2. Distância  3. t(xy)  4. n(xy)  5. t(xy)*n(xy)  6. P(xy)  \\\n",
       "0    [A, B]            22  0.045455  0.154846        0.007038  0.365854   \n",
       "1    [A, C]            50  0.020000  0.057180        0.001144  0.050287   \n",
       "2    [A, D]            48  0.020833  0.122951        0.002561  0.096123   \n",
       "3    [A, E]            29  0.034483  0.332977        0.011482  0.455967   \n",
       "4    [B, A]            22  0.045455  0.154846        0.007038  0.365854   \n",
       "5    [B, C]            30  0.033333  0.179131        0.005971  0.262564   \n",
       "6    [B, D]            34  0.029412  0.332977        0.009793  0.367512   \n",
       "7    [B, E]            32  0.031250  0.001000        0.000031  0.001241   \n",
       "8    [C, A]            50  0.020000  0.057180        0.001144  0.050287   \n",
       "9    [C, B]            30  0.033333  0.179131        0.005971  0.262564   \n",
       "10   [C, D]            22  0.045455  0.154846        0.007038  0.264127   \n",
       "11   [C, E]            23  0.043478  0.276797        0.012035  0.477915   \n",
       "12   [D, A]            48  0.020833  0.122951        0.002561  0.096123   \n",
       "13   [D, B]            34  0.029412  0.332977        0.009793  0.367512   \n",
       "14   [D, C]            22  0.045455  0.154846        0.007038  0.264127   \n",
       "15   [D, E]            35  0.028571  0.057180        0.001634  0.064877   \n",
       "16   [E, A]            29  0.034483  0.332977        0.011482  0.455967   \n",
       "17   [E, B]            32  0.031250  0.001000        0.000031  0.001241   \n",
       "18   [E, C]            23  0.043478  0.276797        0.012035  0.477915   \n",
       "19   [E, D]            35  0.028571  0.057180        0.001634  0.064877   \n",
       "\n",
       "    7. P(xy) %  \n",
       "0    36.585351  \n",
       "1     5.028726  \n",
       "2     9.612303  \n",
       "3    45.596674  \n",
       "4    36.585351  \n",
       "5    26.256391  \n",
       "6    36.751187  \n",
       "7     0.124099  \n",
       "8     5.028726  \n",
       "9    26.256391  \n",
       "10   26.412749  \n",
       "11   47.791522  \n",
       "12    9.612303  \n",
       "13   36.751187  \n",
       "14   26.412749  \n",
       "15    6.487706  \n",
       "16   45.596674  \n",
       "17    0.124099  \n",
       "18   47.791522  \n",
       "19    6.487706  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3ª iteração\n",
    "iterar()\n",
    "pre_dados = preparingPlot()\n",
    "\n",
    "dados = {\n",
    "    '1. Rotas':pre_dados[0],\n",
    "    '2. Distância':pre_dados[1],\n",
    "    '3. t(xy)':pre_dados[2],\n",
    "    '4. n(xy)':pre_dados[3],\n",
    "    '5. t(xy)*n(xy)':pre_dados[4],\n",
    "    '6. P(xy)':pre_dados[5],\n",
    "    '7. P(xy) %':pre_dados[6]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A formiga 1 fez o caminho ['A', 'B', 'D', 'C', 'E', 'A'] com o custo 130\n",
      "A formiga 2 fez o caminho ['B', 'A', 'E', 'C', 'D', 'B'] com o custo 130\n",
      "A formiga 3 fez o caminho ['C', 'E', 'A', 'B', 'D', 'C'] com o custo 130\n",
      "A formiga 4 fez o caminho ['D', 'B', 'A', 'E', 'C', 'D'] com o custo 130\n",
      "A formiga 5 fez o caminho ['E', 'C', 'D', 'B', 'A', 'E'] com o custo 130\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. Rotas</th>\n",
       "      <th>2. Distância</th>\n",
       "      <th>3. t(xy)</th>\n",
       "      <th>4. n(xy)</th>\n",
       "      <th>5. t(xy)*n(xy)</th>\n",
       "      <th>6. P(xy)</th>\n",
       "      <th>7. P(xy) %</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[A, B]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.605788</td>\n",
       "      <td>60.578767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[A, C]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.058229</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[A, D]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.072040</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A, E]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.441429</td>\n",
       "      <td>44.142930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[B, A]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.605788</td>\n",
       "      <td>60.578767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[B, C]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.097048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[B, D]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.392186</td>\n",
       "      <td>39.218599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[B, E]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.103742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[C, A]</td>\n",
       "      <td>50</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.058229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[C, B]</td>\n",
       "      <td>30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.097048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[C, D]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.606106</td>\n",
       "      <td>60.610562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[C, E]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.016766</td>\n",
       "      <td>0.556585</td>\n",
       "      <td>55.658478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[D, A]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.072040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[D, B]</td>\n",
       "      <td>34</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.392186</td>\n",
       "      <td>39.218599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[D, C]</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.606106</td>\n",
       "      <td>60.610562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[D, E]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.094850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[E, A]</td>\n",
       "      <td>29</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.441429</td>\n",
       "      <td>44.142930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[E, B]</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.103742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[E, C]</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.385615</td>\n",
       "      <td>0.016766</td>\n",
       "      <td>0.556585</td>\n",
       "      <td>55.658478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[E, D]</td>\n",
       "      <td>35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.094850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1. Rotas  2. Distância  3. t(xy)  4. n(xy)  5. t(xy)*n(xy)  6. P(xy)  \\\n",
       "0    [A, B]            22  0.045455  0.385615        0.017528  0.605788   \n",
       "1    [A, C]            50  0.020000  0.001000        0.000020  0.000582   \n",
       "2    [A, D]            48  0.020833  0.001000        0.000021  0.000720   \n",
       "3    [A, E]            29  0.034483  0.385615        0.013297  0.441429   \n",
       "4    [B, A]            22  0.045455  0.385615        0.017528  0.605788   \n",
       "5    [B, C]            30  0.033333  0.001000        0.000033  0.000970   \n",
       "6    [B, D]            34  0.029412  0.385615        0.011342  0.392186   \n",
       "7    [B, E]            32  0.031250  0.001000        0.000031  0.001037   \n",
       "8    [C, A]            50  0.020000  0.001000        0.000020  0.000582   \n",
       "9    [C, B]            30  0.033333  0.001000        0.000033  0.000970   \n",
       "10   [C, D]            22  0.045455  0.385615        0.017528  0.606106   \n",
       "11   [C, E]            23  0.043478  0.385615        0.016766  0.556585   \n",
       "12   [D, A]            48  0.020833  0.001000        0.000021  0.000720   \n",
       "13   [D, B]            34  0.029412  0.385615        0.011342  0.392186   \n",
       "14   [D, C]            22  0.045455  0.385615        0.017528  0.606106   \n",
       "15   [D, E]            35  0.028571  0.001000        0.000029  0.000948   \n",
       "16   [E, A]            29  0.034483  0.385615        0.013297  0.441429   \n",
       "17   [E, B]            32  0.031250  0.001000        0.000031  0.001037   \n",
       "18   [E, C]            23  0.043478  0.385615        0.016766  0.556585   \n",
       "19   [E, D]            35  0.028571  0.001000        0.000029  0.000948   \n",
       "\n",
       "    7. P(xy) %  \n",
       "0    60.578767  \n",
       "1     0.058229  \n",
       "2     0.072040  \n",
       "3    44.142930  \n",
       "4    60.578767  \n",
       "5     0.097048  \n",
       "6    39.218599  \n",
       "7     0.103742  \n",
       "8     0.058229  \n",
       "9     0.097048  \n",
       "10   60.610562  \n",
       "11   55.658478  \n",
       "12    0.072040  \n",
       "13   39.218599  \n",
       "14   60.610562  \n",
       "15    0.094850  \n",
       "16   44.142930  \n",
       "17    0.103742  \n",
       "18   55.658478  \n",
       "19    0.094850  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# n iterações = 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15\n",
    "#               16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26\n",
    "#               27 (Convegiu), \n",
    "iterar()\n",
    "pre_dados = preparingPlot()\n",
    "\n",
    "dados = {\n",
    "    '1. Rotas':pre_dados[0],\n",
    "    '2. Distância':pre_dados[1],\n",
    "    '3. t(xy)':pre_dados[2],\n",
    "    '4. n(xy)':pre_dados[3],\n",
    "    '5. t(xy)*n(xy)':pre_dados[4],\n",
    "    '6. P(xy)':pre_dados[5],\n",
    "    '7. P(xy) %':pre_dados[6]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "  b = plt.ishold()\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "  plt.hold(b)\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
      "  warnings.warn(self.msg_depr_set % key)\n",
      "/home/diego/virtual/ACO3.6/lib/python3.6/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n",
      "  warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGjWibdu2Vhe8p59+mkOHDqFWq61sQgCvvPIKw4YNY8CAAfj4+PD0009TWFhY\nYedKcHAw/fr1Y82aNTaXh4eHExERYXPZzY7t8tC/f3/ee+89HnvsMRo0aMDJkyf58ccfy93OjbRq\n1Yply5bxwgsvEBgYyLp161i3bh2urq4AvPHGG8yaNQu1Ws3cuXMZO3YsYWFhNGzY0Ob3VJoVK1aw\ne/du/P39effddy266yvynLkZ7dq148svv2TUqFE0aNAAPz8/CyfHrY77FStWMG7cONzc3Co0r7vF\nQdysn0oisRNVMaCEpBqTng5TpsDhw6DVgqcntGkD8+aZ/78LDh06xLhx49izZ4/dvauSqkGn09Gp\nUye2b99OvXr17J2OBVKEJdUSKcJ1lMWL4Y034GZFPQ0awAcfwLhxVZeXRFJJ1PnuaIlEUk0YORLG\nj7+5AIN5+fjx5niJpIZT58eOllRPZAdNHWPkSCjvc9GS+BuGm5RIahryTlgikdiXxYutBNgZcPj/\n/xyBesBuW5/98Ue4YbhJiaSmIZ8JSyQS+xISYtUF7QzMBl4DcoHuQD5gs6M6JATKGJRCIqnuyDth\niURiP9LTb/kMWA08AWSXFZCZaa6ilkhqIFKEJRKJ/Zgy5ZYhWcAS4KYDOU6eXEEJSSRVi+yOlkgk\n9qNpUyg1bCyYu6NvnJ3aEVgFPHazdm4yEpdEUl2Rd8ISiaRKMZlMHD16lGXLlnH90qUy4z4CBKAD\nXgUeBw6UFVwBY25LJPZAWpQkEkmlIYTg7NmzpKamsnPnThITEzl06JAydvNJwPcWbbgCc4C5wGJg\nnq2gUmM9SyQ1BSnCEomkwrh06RIpKSkkJyeTkJDAgQMHbM405OfnR5cuXTBkZCCOH+dmg0eagGmY\n74qjywq6y6EsJRJ7IZ8JSySSOyInJ4fU1FR2795NQkIC+/btIycnxypOpVLRsWNHYmNj6dWrFxER\nETRo0MC8MD0dbEyWUvqZsBvwFPBVWckcOiSFWFIjkSIskUhuSUFBAfv372fPnj3Ex8eTmprK5cuX\nreLc3Nxo164d0dHRREVFERkZSVhY2E0nStD6heCRe/Gmd8NlIQAH6ROW1GBkd7REIrFAp9Px119/\nkZKSQmJiIsnJyZw/f95qKFFnZ2datmxJnz596NOnD5GRkbRs2fK250QWAmbOLOZU7gcsYvwd52t4\n7wN5IZPUWOSdsERShzEYDBw6dIiUlBSSkpL4888/OXXqlNVcsI6OjjRt2pSoqCiio6OJjIykbdu2\nuLi43NGcsS18AAAgAElEQVR69Xp48slCli/3AEwc6jyKNmk/lasNAaxgBF/0+I5Nm1So1XeUikRi\nV6QISyR1BJPJxIkTJ0hJSWHHjh0kJSVx7Ngx9Hq9VWyjRo3o0aMHMTExdO/enU6dOuHu7l4heeTn\nw6BBGnbsUOHsrOenn5x59FGHck/ikD1gBOG7v+f6dU+aNNGQmKiiceMKSVEiqTKkCEsktRAhBOfO\nnbOwBqWnpyvWoBupV68eERERxMbG0r17d7p27Yq3t3el5HXhAsTFFXD8uBdeXoVs3uxBz543BCxZ\nYp5PODOz7EZCQszzCY8dy9mzEB2t4dw5Fb6+hWzb5kHXrpWSukRSKUgRlkhqAZcvXyYlJYXdu3cT\nHx/PX3/9ZdMapFar6dKlC3FxcXTv3p2IiAgCAgKqJMe//4a4OC3Z2Z40bKghIUFF8+ZlBB8+DJMn\nc/K331AB9Zs2NVc/z51rVQWdkwP33achJUWFq2sx//2vCw88cCdlXhJJ1SNFWCKpYeTm5lpZg65d\nu2YVp1Kp6NChg4U1KCQkxA4Zw5YtggcfLKaoyI1OnQrYssWLwMBbf66kqvpWlymdDkaNKuS///XA\nwcHIl18KJk6U5VqS6o8UYYmkGqPRaBRrUEJCAikpKVyyMdSjm5sbbdu2pW/fvvTu3ZvIyEiaNGly\nU2tQVfHDD0YmTACTyYlBg7T88osnHh6399nbFWFzDLz+ejEff+wKwCuv6Pj4Yzdus1hbIrELUoQl\nkmqCTqfjwIEDFtagjIwMm9agFi1aWAhuq1atbtsaVFUIAdOmFTN7tlkUX3hBx2eflU8UyyPCJXzz\njZGJE0EIJ4YM0fLTT564uZUrdYmkypAiLJHYAYPBwOHDh5VK5R07dnDq1CmMRqNFnIODA02bNqVX\nr17ExMQQERFB+/bt79gaVFUUF8PYsWYBdHAw8dlngn/+06nc7dyJCANs2iQYMqSY4mI3unUr4I8/\nvPD3L/fqJZJKR4qwRFLJCCEUa9Cff/5JUlISR44csWkNatiwoZU1yON2+26rCdevw/33F7Brlxcu\nLnp+/tmZIUPurFv8TkUYIC0N+vXTkpPjSWio2cLUtOkdpSGRVBpShCWSCkQIwfnz50lJSWHXrl0k\nJCSQnp5OYWGhVWxQUJCVNcjHx8cOWVccGRkQE1PA6dNe+PgUsmWLB5GRd97e3Yjw//LRcPq0qkLy\nkUgqGinCEsldcOXKFStrUH5+vlWcr6+vlTUo8HbKg2sQN955Nm5stiDd7Z3n3YowlNyZa9i1S4WL\nSzE//+xyx3fmEklFI0VYIrlNrl+/bmUNys7Otorz9PS0sgY1bNjQDhlXHZX1DLYiRBjMz6jHjClk\n1SoPHBxMfPqpiRdflBYmif2RIiyR2ECr1VpZgy5evGgV5+bmRps2bSwqlZs2bVotrEFVxY3VyA8/\nrOXHHyuuGrmiRNjchmW19vPP6/j8c2lhktgXKcKSOk9xcTEHDhwgNTWVhIQEkpOTOXfunE1rUPPm\nzS1mDWrVqhVOTuWv+q0NCAGvvaZj7lyz4r76qo45cypW1CpShEtYuNDEM88ITCYnHnjA7FuuoGGx\nJZJyI0VYUqcwGo1W1qCTJ0/atAY1adLEyhrk6upqp8yrF+YRqrT897+elTpCVWWIMMDmzYKHHir/\nCF4SSUUjRVhSaxFCcPLkSStrUHFxsVVsSEgI3bt3VyqVO3XqhKenpx2yrv6Yx2ouICXFC1fXYlav\ndmHQoMrpfq8sEQbrsawTE1WEh1f4aiSSmyJFWFIrEEJw4cIFUlJSlFmDDh48aNMaFBgYSLdu3ZRK\n5a5du+Lr62uHrGseN85apFYXsnVr5c5aVJkiDOZZnWJjCzhxooxZnSSSSkaKsKRGcvXqVVJTU0lO\nTlasQXl5eVZxPj4+dO7cmbi4OHr06EFERARBQUF2yLjms3cv3HOPtkrn761sEQbz/MYPPKAhKanU\n/MYSSRUgRVhS7bl+/Tp79+4lJSWFbdu2sW/fPrKysqziPD09ad++PTExMURFRSnWoLpUqVxZrF8v\nePRRPXq9Kz16aNi0SYVaXfnrrQoRBtDr4amnClm2zAMw8fHHJiZPlhYmSeUjRVhSrdBqtaSlpVlY\ngzJtTPDu6upK69atiY6OVqxBzZo1k4JbCXz5pZEXXnBACEeGDi1k+XIPqqo+rapE2LwOmDlTz4wZ\n5nG5//GPIr76yp06WvwuqSKkCEvsRnFxMQcPHlRmDdq1axdnz561uuA6OTnRvHlzevfuTd++fYmM\njKR169Z11hpUVZhM5ukAP//cbEF6/fViPvjAlar8nVOVIlzC0qUmxo83YTI5M2CAhtWrVcgaPUll\nIUVYUiUYjUaOHDlCamoqSUlJijXIYDBYxDk4OBAWFkavXr2Ijo4mMjKSDh06SGtQFVNUBMOGaVm3\nzhNHRyNffw0TJlT9jx57iDBAQgIMGlREYaE77dsXsHWrF/XqVWkKkjqCFGFJhSOE4NSpU0ql8vbt\n2zly5Ag6nc4qtkGDBoo1KDIyki5dukhrkJ3JzoZ77y1g/34v3Nx0rFnjyn332aeb314iDHDoEMTG\narh6VUX9+uZCtJYtqzwNSS1HirDkrimxBpXMGnTw4EG0Wq1VXGBgIF27diU2NpYePXrQrVs3aQ2q\nZpw6ZZ516Px5Ff7+WrZt86RTJ/vlY08RBrh0CeLiCjhyxAuVqohNm9zp08cuqUhqKVKEJeUiKytL\nsQYlJCSQlpbG9evXreJ8fHzo1KkTsbGx9OzZk4iICOrJ/rxqzZ490L9/Ifn5HoSHm2dBatTIvjnZ\nW4QBCgrgoYc0xMercHLSs3y5E8OHywGnJRWDFGFJmeTl5SnWoPj4eFJTU21agzw8PBRrUK9evYiM\njKRRo0ayUrkG8euvgscf12MwuBIVpWHjRhXVYWrj6iDCAAaDuVp64ULzINPvv6/njTdcqrRITVI7\nkSIsAaCwsFCxBiUmJrJnzx4uXLhgFefi4mJhDYqIiKB58+ZScGswn35q4JVXHAFHRowoZMkSD1xc\n7J2VmeoiwuYcYPZsPdOmmXfOU08V8c037jhLO7HkLpAiXAfR6/U2rUEmk8kizsnJifDwcAtrUJs2\nbaQ1qJZgMsE//1nEl1+a7+7efruYd9+tWgvSrahOIlzCypUmxowxYTQ606+fhrVrVahU9s5KUlOR\nIlzLMRqNHD161MIadOLECZvWoMaNG9OzZ09iYmIUa5BbRU0MK6lWFBbCY4+Zu50dHY18/70D48dX\nv+ec1VGEAZKSYODAIrRad9q0KWDbNi/q17d3VpKaiBThWoQQgtOnT1tYgw4fPmzTGlS/fn0ra5BK\n/pyvE1y9Cv37F3DggBfu7jo2bHCjXz97Z2Wb6irCAEePmivJL19WERSkJTHRkzZt7J2VpKYhRfh2\nSE+HKVPg8GHQasHTE9q0gXnzsOdZl5mZqViDEhMT+fvvv9FoNFZx/v7+dO3aVZnEoFu3bqirYuBf\nSbXjxAmzcGRmqggM1JKQ4Em7dvbOqmyqswgDXLkC/foVkJ7uhYdHERs3uhMTY++sJDUJKcI3Y/Fi\neOMNuHix7JgGDeCDD2DcuEpNJTs7m9TUVHbv3k18fDxpaWnk5uZaxXl7e1tZg4KDgys1N0nNYOdO\nGDCgEI3Gg5YtC4iP9yIkxN5Z3ZzqLsJg/l3+8MNaNm/2xMnJwKJFjoweXf269iXVEynCZTFyJPz4\n4+3HjxgBK1dWyKrz8/OtrEFXr161inN3d6d9+/ZER0cTFRVFZGQkoaGhslJZYsXPP5sYOdKI0ehC\nTIyG9etVeHnZO6tbUxNEGMBohEmTivj2W3OR27vv6nn7bWlhktwaKcK2KK8Al3AHQlxUVGRhDdq9\ne3eZ1qBWrVrRt29f+vTpo1iDHB3lL25J2QgBc+caeO01swVp7NhCvv/eo8bYamqKCJcwd66BKVPM\n+3rMmEJ++KHm7GuJfZAiXJrFi2H8eKu3/w9YBGgBR8AfeBeYZOvzY8fabFqv15Oenk5KSgrbt29n\n586dnDlzxqY1qGnTpvTu3VuZxKBNmzY4y7NZUg6MRpg4sYgFC8x3ZzNnmj2uNenurKaJMJh7HUaN\nMmIwuNC3r4YNG1R4e9s7K0l1RYpwaUJCrJ4BDwHWAZP//z8v4ANgE5Bi6/MXLmAymTh27BgpKSns\n2LGDpKQkjh8/btMaFBoaSo8ePYiJiaF79+507NhRWoMkd4X5OaWGzZtVODkZWLzYkSeeqHm9JjVR\nhAF27TI/fy8o8KBFiwISEqr/83eJfZAifCPp6dC+vcVb54Aw4GXgk9toQgCPtm7NxtOnbVqDgoOD\niYyMJDY2lu7du9OlSxe8asLDOUmNoXTF7qZN7kRH2zurO6OmijBYVqIHBJgr0UtdXiQSKcIWDBoE\nGzdavDULeBsoBNxvowkBrAceAvz8/KysQX5+fhWdtUSicKN3tV498yQMNdm7WpNFGCArC+6553+e\n7HXrXOnfvwY9D5BUOlKEb6RpUzhzxuKtScA3gLEczWiCg8lPS6O+HEJHUoVYjuJknvWnprvTaroI\nQ8noZFo2bvSs1qOTSeyDPBJuxMYcuCGACSgqRzMqIaQAS6qUlStNxMUZ0GrdueceDSkpNV+Aawse\nHrB+vSfPP1+EyeTEk086Mm1aMTX4d4WkApEifCOenlZvldQ5v1WOZs5eu8bYsWNZsGABaWlp6PX6\nCklPIimNEDBrlp5RoxwxGp15+ukifv9dTihQ3XB0hC++cOfTTw04OJh4/31XRo4sRF4aJLI7+kZs\nPBMGeBDYALwOvAp4AnMxP/vdUyr2xmfCJTg7O9OiRQvF4xsZGUnLli2lx1dyVxgMMGFCIYsWeQDw\n4YcGXnvNuUZZkG5FbeiOLs2aNYKhQw0YDC7Vau5miX2QInwjNqqjS5gELOZ/PuEA4D3g2VJxAti7\ndCmJly+TkJBASkoKly9ftmrPzc2Ntm3bEhMTQ1RUFBERETRp0kSOdiW5LQoK4MEHzYVXTk56li93\nYvjw2vejrjaKMMCePXDvvYXk5XnQrFkBiYleNGpk76wk9kCKcGls+ITL/flSI17l5OSQmprKnj17\niI+PZ9++feTk5Fh91MvLiw4dOijjPkdGRtKgQYM7z0VSK7l4EeLiCjh61AuVqpDff/egd297Z1U5\n1FYRBjh1ylzJfv68Cj8/LfHxnnTqZO+sJFWNFOHSlDFi1u0gABYtxmGc7RGzbuTSpUukpqaya9cu\nEhISOHDgAAUFBVZxfn5+dOnShdjYWHr06EFERAT+/v53lJ+k5nPoEMTEaMnK8qR+fQ2JiSpatrR3\nVpVHbRZhgGvX4N57C9i3zws3Nx1r1rhy332yN6wuIUXYFncwdrQAVjCCjU/8wMKFHri4lG+VQgjO\nnTunzAWcmJjIoUOHKCqyrssODg6mW7duxMXF0b17d7p27SoH/KgDxMfDoEE6iorc6NChgK1bvQgK\nsndWlUttF2GAoiIYPlzL2rWeODgY+eYbmDDByd5pSaoIKcJlUU4hPtdnBOHJSzAYXOjTR8Nvv939\neLEmk4njx48rQ1/u2LGDY8eO2ay2btSoET179rQY+tLd/XaGF5HUBJYsMfHkkyZMJmfuu0/L6tWe\neHjYO6vKpy6IMIDJBK++quOzz8zD1b72WjEffuhaq4rsJLaRInwzliwxzyecmVl2TEiIeT7hsWNJ\nTjYXW5SMFxsf70XDhhWbksFgUCaBSEpK4s8//+T06dNWk0A4Ojoqk0D07duXyMhI2rVrJyeBqGEI\nAe++W8y777oC5gkZvvjCHac6cqNUV0S4hK++MvL88w4I4cjQoVqWL/fE1dXeWUkqEynCt8PhwzB5\nMid/O4wKDfWbqqBNG5g7l9JjAp48aS62uHDBPF5sfLwnHTpUbnpFRUX89ddfpKSkkJiYSHJyMufP\nn7eKc3FxoWXLlvTp00exSrVo0UJapaopej08+WQhy5d7ACbmzjXx6qt160dUXRNhgPXrBY89pqe4\n2JXu3Qv4/Xcv1Gp7ZyWpLKQIl4OSrqFb7bHsbOjfv4C0NPuNF5ufn8/+/fuViuy9e/fatEq5u7tb\nWKUiIyNp3LixtErZmbw8eOABDTt2qHB21vPTT848+mjd+07qoggD7N0L99xTyPXrHoSFmQvwwsLs\nnZWkMpAiXA5uV4TBXGwxdKiWDRvM48UuWABPPWXfPsRr166RmprK7t27SUhIYP/+/WVapTp27Ehc\nXBw9e/YkIiJCDsNZhVy4ALGxBZw44YWXVyGbN3vQs6e9s7IPdVWEAc6eNfeqnT2rwte3kG3bPOja\n1d5ZSSoaKcLloDwiDOZii5df1jF/vrnY4s03i5k1q3oVW1y8eJGUlBSSk5MVq5RGo7GKK5kR6kar\nlJwRquL5+2+Ii9OSne1Jw4bmO6DwcHtnZT/qsggD5ObCgAHmscBdXYv5739deOCBanQBkdw1UoTL\nQXlFuIQvvjDy4ovmYovhw7UsWVJ9iy2EEJw5c4bU1FQLq1RZcyNHRERYWKVUctDiO2bzZsFDDxVT\nVORG584atmxRERBg76zsS10XYQCdDp54Qst//uOJg4OJf/1LMGlSHanMqwNIES4HdyrCAGvXCoYO\n1aPXu9KrVwEbN3rh61ux+VUWJpOJY8eOKVappKQkjh8/jsFgsIhzcHCwaZVyc3OzU+Y1h++/N/KP\nf4DJ5MTgwVp+/tkT6TCTIlyCEDB1ajFz5ph/vb/8so65c92QNZU1HynC5eBuRBggNdVcbJGX50HT\npubxYkNDKy6/qkSv1ytWqe3bt7Nz507OnDljZZVycnJSrFLR0dFERETQtm1baZX6/wgBb71VzAcf\nmC+u//ynjk8/lRfXEqQIW/Ltt0aeew6EcGLIEC0//eSJ/I1bs5EiXA7uVoQBzpwxF1ucO2ceL3bb\nNk86d66Q9OxOYWGhYpVKSEhgz549ZVqlWrVqZWGVat68eZ2zShUXw5gxhaxa5YGDg4nPPxe88ILs\nZrwRKcLWbNokGDLEbGHq2rWAzZu9kCPZ1lykCJeDihBhgJwc83ixe/d64epqHi924MDaWWyRl5dn\nYZVKTU3l6tWrVnHu7u60b9+e6OhoxSoVGhpaa61S16/DwIEakpNVuLgU88svLjz0UO3c1rtBirBt\n0tKgXz8tOTmeNGqkYft2FU2b2jsryZ0gRbgcVJQIg7nYYuRI8/CDDg5G/v1vePbZunEXlJ2drVil\n4uPjSUtLIzc31yrO29vbyioVHBxsh4wrlowMiIkp4PRpL3x9C9myxYOICHtnVT2RIlw2GRlmK9up\nU174+JiPo8hIe2clKS9ShMtBRYowmC1Mr79uLrAAmDxZx5w5btXKwlRVZGZmkpKSwq5du0hMTOTv\nv/+2aZXy9/ena9euSkV2REQE6ho0nNCNdzCNG5stSE2a2Dur6osU4Ztz/Trcf7+GXbtUODube1SG\nDKmDF5AajBThclDRIlzCv/9t4P/+zwEhnHj0US0rVshiCyEEp0+fVmaV2r59O4cPH7Zplapfvz7d\nu3dXKrK7dOlSLa1SGzcKHn64mOJiN7p107B5swpptb45UoRvjV5vri346SdzbcEnn5h46SVZ+FhT\nkCJcDipLhAF++03wyCPmYovISPN4sfICbYnRaFSsUklJSezYsYMTJ07YtEqFhobSq1cvoqOj6d69\nOx06dLCrVeqbb4xMnGiuan3kES0rV8ofWreDFOHbQwh4++1i3n/fXGX//PM6Pv9cVtnXBKQIl4PK\nFGGA/fvNXZW5ueauyu3b5Xixt0Kv13Pw4EFl8opdu3Zx5swZq4u2k5MTzZo1o0+fPvTt21exSjlV\n8nREQsBrr1k+cvjoI3lxvF2kCJePhQtNPPOMwGRyYtAgLb/8UjemvKzJSBEuB5UtwgDnzpktTGfO\nqPD11bJ1qyfdulXe+mojhYWFpKWlsWfPHhITE9mzZw8XLlywinNxcaF169YWFdnNmzevsIrs0sV3\nX34JEyfWjeK7ikKKcPnZskXw4IPmkdc6dSpgyxYvAgPtnZWkLKQIl4OqEGEwjxc7cKCG3bvN9pX/\n/teFwYNlscXdkJeXx969e0lJSVGsUllZWVZxHh4etG/fnpiYGHr16kVkZCSNGjUqtzDn5JjH/E1N\nVeHqqmP1alcGDZLfYXmRInxn3DgGeUiIuQCweXN7ZyWxhRThclBVIgzmgRxGjy7k55/NxRbz5wue\nf17eRVUkV69eZe/evRZWqevXr1vF+fj4WFilIiMjCQoKKrPdGwdkUavNA7J06VKJG1KLkSJ852Rm\nmi1Mx4+bZ+P64w8PevWyd1aS0kgRLgdVKcIl63nzzWI+/NBcbPHiizo++UQ+T6wshBA2rVJardYq\nNiAgwMoq5evrazE0aZMm5juQxo3tsDG1BCnCd0d+vnle6qQk87zUP/7ozGOPyR6Z6oQU4XJQ1SJc\nwoIF5vFiTSYnHnxQy6pVcnD/qkIIwalTp6ysUsXFxVaxfn5jyM39GiE8adv2Eps3+xAS4mmHrGsP\nUoTvHr0enn66kKVLPQATH39sYvJkaWGqLkgRLgf2EmGAP/4wT3On07nRpYt5vNi6Ps2dvTAajRw5\ncoTU1FTFKnXs2D0IMR9wAhYDE3BwMBAWFqbMKhUZGUmHDh1wra7zWFZDpAhXDELAe+/pmT7dBYB/\n/KOIr75yp5LNAZLbQIpwObCnCAMcOGAutrh2zTxebGKiimbN7JOLxIzJZJ5Wbv58swXpvvt24+f3\nOcnJuzh79qxNq1R4eLhilYqMjKR169aVbpWqqUgRrliWLTMxbpwJk8mZAQM0rF6twlN21tgVKcLl\nwN4iDHD+PMTGajh5UoW3t3m82O7d7ZdPXaaoCB5/XMv69Z44Ohr55ht45pn/ialWq1WsUgkJCaSk\npJCZmWnVjqurq2KV6t27NxEREYSHh9faySvKgxThiichAQYNKqKw0J127QrYts2LevXsnVXdRYpw\nOagOIgyQlweDBmn480/zeLE//+zCww/LC3ZVkp1tnglr/34v3Nx0rF3ryoABt/4Orl+/bmGV2rt3\nr02rlKenJ+3btyc2NlapyG7YsGGdE2YpwpXD4cMQE6Pl6lVP6tfXkJCgolUre2dVN5EiXA6qiwiD\nudhi3LhCVq40F1t88omJl1+WxRZVwalTEB2t4cIFFf7+WuLjPenY8c7bu3LlCnv37iU5OZmEhATS\n0tLIy8uzivPx8aFz586KMEdERNzUKlUbkCJceVy6BHFxBRw54oVKVcSmTe706WPvrOoeUoTLQXUS\nYTDnMWNGMTNnmgt9/u//ipg/311amCqR3bvh3nsLyc/3IDzcfAfRqFHFrkMIwYULF5SK7MTERA4e\nPEhhYaFVbGBgIF27dqVfv350796drl274uvrW7EJ2REpwpVLQQEMGaJh2zYVTk56li1zYsQIeQGp\nSqQIl4PqJsIlLF5s4qmnzMUW99+v4T//UcnxYiuBX38VPP64HoPBlT59NGzYoMLHp2rWLYTg5MmT\npKSk8Oeff5KUlMThw4fR6/VWsSEhIXTv3p3Y2FgiIyPp0qULHjX0gJAiXPkYDOZq6YULzb7H99/X\n88YbLnVySlV7IEW4HFRXEQbYtg0eeEBHUZEbHTuax4ut5T2VVconnxh49VVHwJGRIwtZvNgDFxf7\n5mQ0Gjl8+LAyq9Sff/7JyZMnMRqNFnEODg40adLEyirlYu8NuA2kCFcNQsAHH+h56y3zMfHkk0V8\n+607zvIJV6UjRbgcVGcRBkhPh9hYLVlZcrzYisJkghdeMHsqAd55p5gZM1yr7V2CTqfj77//JjU1\nVZlV6ty5c1Yi5uzsbGWVatWqVbWzSkkRrlp+/NHE6NFGjEYX4uI0rFunohpOzV2rkCJcDqq7CANc\nvGgeL/bYMS9UKvN4sVFR9s6qZlJYCI8+qmHTJhWOjgYWLnRk7Nia97xMq9Wyf/9+xSq1Z88eLl26\nZBXn5uZmZZVq1qyZXSuypQhXPTt2wMCBRWg07rRuXUB8vBf169s7q9qLFOFyUBNEGMzFFoMHm++E\nnZz0rFzpxOOP1zzxsCdXr0L//gUcOOCFu7uO335zIy7O3llVHLm5ucrkFQkJCezbt4/s7GyrOE9P\nTzp06GBllaoqpAjbh6NHzeMRXLqkIijIfC1p08beWdVOpAiXg5oiwmAutnjmGfOzSzDx0Ucmpkxx\nrrbdqNWJ48fNsyBdvKgiMFBLQoIn7drZO6vK5/Lly6SmpiqzSv3111/k5+dbxfn6+tK5c2fi4uLo\n0aMHERERBFbShLVShO3HlStwzz0FHDzohYdHERs3uhMTY++sah9ShMtBTRJhMOf5/vt63n7bXGzx\nzDNFfP21HC/2ZuzcCQMGFKLReNCqlbkrrkEDe2dlH4QQZGRkkJqayq5du0hISCA9Pd2mVSooKIhu\n3bops0p17doVnwooHZcibF+0WnjkEQ1//GF+JLNokSNjxshetYpEinA5qGkiXMLy5ebxYo1GZ+69\nV8Ovv8rxYm2xapWJUaPMRSkxMRrWr1fh5WXvrKoXJpPJwiq1fft2jh49atMq1bBhQwurVOfOnctt\nlZIibH+MRpg0yVwtDeaxCd55p/oWJ9Y0pAiXg5oqwgDbt8P99xeh1brTtq15vNjgYHtnVT0QAj7+\n2MDrr5stSOPGFfLddx7SnnGbGAwGxSq1fft2du7cyalTp6ysUo6OjoSFhREVFUV0dDSRkZG0b9/+\nplYpKcLVh7lzDUyZYj5Hxowp5Icf5DlSEUgRLgc1WYQBjhwxP+u8ckVFcLC52KKujxdrNMJzzxXx\n3XfmX/nvvWf2Sspf+XeHTqfjwIED7Nmzh6SkJHbt2kVGRoZNq1SLFi3o06cPffr0UaxSjv9/2Dcp\nwtWL//xHMGKEAYPBhb59zQPWeHvbO6uajRThclDTRRjg8mWIi9Nw+LAKT0/zeLF9+9o7K/ug0cDD\nD0nNv8UAACAASURBVGvYskWFk5OBJUscGTVKPu+qLAoKCqxmlSrLKtWmTRuio6OZP38+YO4Gr2uT\nV1RXdu0y100UFHjQokUBCQlehITYO6uaixThclAbRBjM4jNkiIatW83is3SpIyNH1i3xuXwZ+vUr\n4NAhLzw9zZWf0dH2zqrukZOTo1il4uPj2b9/P9euXbOKU6lUVlapEHnltxsnTpgtTBcuqAgIMDsI\n2re3d1Y1EynC5aC2iDCYu2GffbaI77+ve92wR46YLyCXL6uoV8/cLd+6tb2zkpRw6dIlUlNTSU5O\n5v333y8zTq1W06VLF2JjYxWrVEBAQBVmWrfJyjJ76f/6y+ylX7fOlf7968AFpIKRIlwOapMIg3k7\n5swxMHWqubpi/PhCFiyo3cUWskCtZlHSBX3mzBlSUlLYtWsXiYmJpKenU1RUZBVfr149K6uUt3xo\nWWkUFsLQoVp++80TR0cj333nwJNP1q1etbtFinA5qG0iXMKN1py4OA1r19ZOa86KFSbGjjVbte65\nR8OaNXJc3OpOWYVZJpOJ48ePk5qayo4dO0hKSuLYsWM2rVKNGjVSrFLdu3enU6dOuLu7V0n+dQGT\nCV56SccXX7gB8NZbxbz3nrQw3S5ShMtBbRVhgD//hPvuq52DVMhBS2ou5amONhgMHDp0yMoqZTKZ\nLOIcHR1p0qQJvXv3ViavaNeuXY2YVao689lnBl55xREhHBk+vJAlSzxwdbV3VtUfKcLloDaLMFgP\n15iY6EnbtvbO6u6wHL4TPvrIIIfvrEHcrUWpqKhIsUpt376d5ORkMjIyrOKcnZ1p2bKlhVWqZcuW\nilVKcnusWSMYOtRsYerVq4CNG73w9bV3VtUbKcLloLaLMJgnLrjnngL+/rvmT1xQeiKLFSucGDZM\nXlRrEpXhEy4oKGDfvn2kpKQQHx9Pamoqly9ftopzc3Ojbdu2xMTEEBUVRWRkJGFhYdIqdQtSUqB/\n/0Ly8jxo1qyAxEQvGjWyd1bVFynC5aAuiDDUjin8Ll6EuLgCjh41T+n4++8e9O5t76wk5aWqBuu4\ndu2alVUqJyfHKs7Ly0uxSvXq1YuIiAga1JbnNhXI6dPmXrWMDBV+flq2bfOkc2d7Z1U9kSJcDuqK\nCIPZwvTPf/5vMvvp04uZPr1mFFukp5stSFlZKho0MN8Jt2hh76wkd4I9R8y6ePGixeQVBw4cQKPR\nWMX5+fnRpUsXpSI7IiICf3//Ks+3unHtGgwYUMDevV64uupYs8aVgQNrwAWkipEiXA7qkgiX8Mkn\nBl591Txe7BNPFLJwoQfVuX4lPh4GDdJRVORGhw4FbN3qRVCQvbOS3CnVadhKIQRnz54lJSWFnTt3\nsn37dtLT09HpdFaxwcHBREREKBXZXbt2xas2Wg5ugU4Hw4drWbPGEwcHI19/Df/4h6yIvBEpwuWg\nLoowwOrVgmHD9BgMrvTpYx4vtgJmqatwliwx8eSTJkwmZwYO1PLf/3pSzkl7JNWM6iTCtjCZTBw7\ndkyZVarEKmUwGKxiGzVqRM+ePYmJiaF79+507NixTlilTCaYPFnHp5+aLUyTJ+uYM8etRvSqVQVS\nhMtBXRVhgN274d57C8nP96B5c/N4sQ0b2jsrM0KYp1ebOdPsh5g0qYj586UFqTZQ3UXYFnq93soq\ndfr0aZtWqaZNm9K7d2+io6OJiIigXbt2ONfS0XK++srI8887IIQjjz6qZeVKT2lhQopwuajLIgxw\n8qS52KJkvNj4eE86dLBvTnq9eaSvFSs8ABPz5pl45ZXaeRGri9REEbZFUVERf/31F3v27CExMZHd\nu3dz/vx5qzgXFxdatmxJ37596d27N5GRkbRo0aLWWKU2bBA8+qie4mJXuncv4PffvVCr7Z2VfZEi\nXA7quggDZGdD//4a0tJUuLvrWLvWlXvvtU+/Ul4ePPCAhh07VDg7F7NqlQuPPCL7uGoTtUWEbZGf\nn8++ffuUWaVSU1O5cuWKVZy7u7uVVapx48Y11iq1bx/061fI9esehIWZLUxhYfbO6v+1d9/xUVV5\nH8c/6WVClRqKhCahhJJMQklFEMWOitjL6tp27bqPPrqr+6xlV3Rdy666rixYsC2KSlGRFEogM3SS\n0GvoBAhMn8zc548rs4RJMJNkcqf83q/XvngtnMwcQPK9c+/5nqMdCWEfSAirbDa47joL332n7hf7\n3nvwq1+17r3fffvUT+Xbt+to08bKjz8mkJXVqlMQrSCUQ7g+1dXVGI1GysrKPFWpEydOeI1LSkoi\nLS2NgoICRo8eTUZGBt26ddNgxk2ze7f673f3bh3t2ln46adE0tO1npU2JIR9ICH8X243PPKInTfe\nUBdbPPWUgxdeaJ0K04YNUFBgobo6kZ49zRQV6ejXz//vK1pfuIVwffbv31+nKrVhw4YGq1KjRo2q\nU5Xq0KGDBjNunBMnYNIkE2VlScTGOvjPf2K47LLg/HTfHBLCPpAQ9vbmmy4eekhdbDF1qpUPP/Tv\nfrE//KBwxRUO7PY4Ro408eOPScjpdaFLQtiboiieU6VOV6UqKioarErp9fo6VSldAJ1a4nDATTdZ\n+PLLRCIi3Lz5psIDD4TXikoJYR9ICNfv228VrrnGidMZy+jR6k5b/tgv9v33XdxzD7jdUVx2mYUv\nvkgkDBoeYU1CuHHcbjebN2+uU5XaunWrV1UqIiKi3qpUXFycRjNXv58+9ZSDP/9ZvXp/+GE7r74a\nR4isRftFEsI+kBBumNGo7hdbU5NASoq62KJXr5Z5bUWBp5928PLL6j/SBx9UO4fh8o80nEkIN53T\n6WTjxo0YjcY6Vamz/yyjoqK8qlKDBw9u9arUP/+pXmQrShRXXGHhs8/C4yJbQtgHEsLntmuXuthi\nz56W2y/W4YCbb7byxRcJRES4eeMNhd/8JrxuV4UzCeGWZbVavapS+/bt8xoXExPDBRdcUKcq1b9/\nf79Xpb7/XuHKK//7uGnRoiRCfQdQCWEfSAj/suPH4aKLzBiNOmJj7Xz9dSyXXNK0xRY1NTBpkpmV\nK3XExKgLNy6/PPwWboQzCWH/O3nypKcqVVhYyKpVqzhy5IjXuPj4eIYOHUpubq6nKtWrV68Wr0qt\nWwfjx1s4dkxdeFlcrKNv37MGlZfDE09AZSVYLJCYCKmp8Oqr6o9BRELYBxLCjWO3ww03WPjqK3W/\n2L//He6917dPr3v2qJ+qd+3S0a6dlUWLEsjI8NOERcCSENbG0aNHvapSNTU1XuPatGlTpyql1+vp\n0qVLs9+/qkr9979jh462bdUKYmYmMHMmPPWUekxaQ7p3h5degttua/Y8WoOEsA8khBvP7Ybf/c7O\n9Om+7xe7Zo16JXziRCK9e6tXwn36+He+IjBJCAeOffv2eVWlLBaL17iOHTt6VaXaN2FbrJoamDzZ\nzPLl6mY8O0bfRq+lnzb+BaZNg9mzfX7f1iYh7AMJYd+9846L++9XF1tcfbW6X+y5FmLOn69w9dUO\nHI44MjLM/PCDjgCuOgo/kxAOXIqisHPnTs+K7JKSEiorK3E4HF5ju3XrRmZmpmdF9siRIxtVlXI6\n4dZbrVz26Z3cyKf4fOM7CIJYQtgHEsJN09hgfeedWu6/P6LRgS1Cn4RwcHG5XJ6q1JIlS1i2bBnb\ntm2rtyrVu3dvT1VKr9eTlpZGbD2bDCj/ngl33F5vALcHTgIngAYPdps5E269tXm/MT+SEPaBhHDT\nnesWs6LAk0827da1CG0SwsHP4XCwceNGz6lSpaWl7Nq1q96qVN++fcnOziYnJwe9Xk9qaipRvXrV\n+wx4KZADRAAPA681NIHkZHWf2wAlIewDCeHmqW+x1bBhzV/EJUKXhHBoslgsrF27FoPBQFFREWVl\nZezfv99r3LCoKNa5XPV+Ch4PGIEBQBVw6FxvWFERsKumJYR9ICHcfDU1cPHFZlasUGtH55/vYtu2\nhGbXmURokhAOHzU1NV5VqRlHj3Ip1BvCMcA1wGXALcAGYGhDLz55Msyb55d5N5eEsA8khFuGwwFT\nptiYN0/dDichwcHy5bHN3thDhB4J4fBW27s30Xv3ev3834EHgE3ABUAccAnwdUMvlJICO3b4aZbN\nIxv/iVa3fj2UlPz3m6rVGsuMGXbcbg0nJYQIGNXV1fzwww/Yjh2r99ffADqjBjDAOOD7c71gPadO\nBQr5JOwD+STcfGcf9nDjjfE8/LAcyiDqJ5+EQ9+pU6c8t6GLioowGo0cPnwYgO3A2ZtlHQNOH5x2\n+lOk8vP/PgOm1vcmAfxJuHV36BZh7Y03ann44UgUJfbnYw91xMbCoEEKl19u57vvEhk7Vo4nFCJU\n2Ww2r72rq6qqvMbFxMQwcOBATCdPouzdW+eZ8B9QnxEvA85sGucAf6GBEA7QRVkgn4R9Ip+Em8bt\nhkcesfPGG2oF6amnHLzwQmydCtL69VBQoO4X26OHWmHq10+jCYuAIZ+Eg5fT6aSiosJTTTp9ipP7\nrOdOkZGRnlOcTleThgwZop7iVF4OQ+sut+oE9EFdGX2mR1BvU5sBr5tpsjo6NEgI+85mg+uus/Dd\nd4lERrr45z8juPPO+pci7NsH+fkmtm1Lok0bdb/YrKxWnrAIKBLCwcHtdrNly5Y65xlv2bLFa5MO\ngF69epGVleXZpGP48OHEn+MZVG3XZKIOH/B9t6zTpCccOiSEfVNdDRMmmFi7Nom4ODvffhvLxInn\n/qd08iRceqmZpUvV/WI//zyGq6+W2lK4khAOPIqisHv3bgwGA8uXL6ekpISKigpsNpvX2K5du5KR\nkUF+fr5nu8o2bdo0+r1Wr4b3xr3HP2z3ND2EZces0CEh3Hjbt6sbc+zbp+O88ywUFiYybFjjvtbp\nhNtvt/LJJwmAm1dfdfPoo7J8IRxJCGvv4MGDGAwGVqxYQWFhIRs2bMBkMnmN69ChAyNHjiQ/P5+s\nrCwyMjLo2IzDgOfNU5gyxYnDEcuCjtcx6diXsnd0uJMQbpyVK2HiRCunTiXQv7+JoqIkevTw7TUU\nBZ57zsEf/6juJXv//TbefDMeP58pLgKMhHDrOnbsGKtWrWLlypWeIwyPHz/uNU6n05GWlkZ+fr7n\nCMPu3bu32Dz+/ncXv/lNBIoSyTXXWPj440Tibr8BPpVTlMKahPAv++orhalTndTWxpKdbWbePB1t\nG9xZ/ZfNmuXmjjvcuN3RXHyxmTlzdCQktNx8RWCTEPYfk8nE6tWrMRgMFBYWYjQaOXTIe/PHuLg4\nBg8eTF5eHmPHjiUjI4M+ffp4/m5aktut7h3/17+qiziffNLByy+fsYhz1iz1POF6trj0SE5WzxMO\n4FvQZ5IQ9oGE8Lm99lotjz0WCURy001WZsxIICam+a9bWAiTJ9ux2eIYNszETz8l0blz819XBD4J\n4ZZht9s91aCSkhJWrFhBVVWV159rdHQ0AwYMICcnh+zsbPR6PQMHDiSyFW5B2e0wdaqFb75R95F/\n5x349a8b2Ee+shIef1z90WwGnU5d/Tx9esCugm6IhLAPJITr53LBb35j45131BWOf/iDgz/8IbZF\nT0EqL4f8fAtHjybSvbtaYRowoOVeXwQmCWHf1dbW1qkGLVu27JzVoDFjxpCbm+upBsW0xJWzj44d\ng4kTTaxenURsrIO5c2O4+OLwWJApIewDCWFvVitMmWJm4UIdkZG1zJgRya23+ueq+cABKCgwsXlz\nEjqdlR9+SGDsWL+8lQgQEsLn5na72bp1K0ajkaVLl3qqQU6n02tsz549ycrKIjc3l8zMTIYPH05C\nADzb2bkTcnPNVFXp6NDBwuLFiWG1j7yEsA8khOs6cgQuvNDEhg1JxMfbmT8/joIC/76nyQSXXaZ+\nEo6KcvLJJ1FMnSqrtUKVhPB/KYrCnj17MBgMlJaWUlxcTHl5eb3VoM6dO5ORkUFBQQF6vZ5Ro0bR\ntjmLM/zEYIAJE6ycPJlA377qIs5evbSeVeuSEPaBhPB/bdkC+flmDhzQ0amThaKiRIYMaZ33rq2F\nu+6yMnOmWmF6+WU3Tz4Z3aK3v0VgCOcQPnTokKcaVFRUxLp16+qtBrVv354RI0ZQUFDgqQadFwT7\nvs6dq3DtteoizjFjzCxYoKNdO61n1fokhH0gIaxavhwuusiK2ZzABReYKCxMogXbCY2iKPDii06e\neUZ9fnXXXeoz6agG1nGI4BQuIXz8+PE61aDVq1c3WA0aNmwYeXl5jBkzBr1eT3JysgYzbp6//a2W\nRx6JRFEiuf56Kx9+2DKLOIORhLAPJIThs8/c3HSTC5crhoICM998oyMpSbv5fPKJm1tvdeNyRTNh\ngpmvv9ah0/3y14ngEIohbDabvapBBw8e9BoXFxdHamoqubm5jB07Fr1eT0pKil+qQa3F7YaHHrLx\n1lvqIs7//V8H//d/LbuIM9hICPsgnENYUeDPf3by1FPq5ertt1v55z8TiA6AjaxKSuCSS2xYLPEM\nHmxi8eIkunbVelaiJQR7CNvtdtavX1+nGrR37956q0H9+/f3qgZFhdCtHasVrr3Wwvz56j7y778f\nwR13yHoOCWEfhGsIu1xw77023n9fvXr905+cPP10TEBdvW7apD6jPnRIR5cu6sKtQYO0npVormAK\n4draWiorKzEYDCxZsoRly5axY8cOXC5XnXERERGkpKQwevRozyEGQ4cO1aQa1FqOHlX3kV+3Tl3E\n+e23sUyYEEDfQDQkIeyDcAxhsxmuusrMokU6oqJq+fDDSG64ITCvXg8dgvHjTVRUJJGYaGPBgnhy\nc7WelWiOQA1ht9vNtm3bMBqNLFu2jJKSEjZv3lxvNahHjx51qkEjRowIiGpQa9m2Td1Hfv9+3/eR\nDwcSwj4ItxA+dEjt5VZWqqG2cGE8OTlaz+rczr5omDUrkhtvDMyLBvHLAiGEFUVh7969dVYql5eX\nY7VavcZ27tyZ9PR0TzUoPT09IKtBraW0VF3EaTIlMGCAWkEKwnVkfiUh7INwCuFNm9Sr18OHdXTt\naqaoKHhu7559+/z//s/J//5vYN0+F42jRQgfPnzYqxp06tQpr3Ht2rXzqgZ16tSp1eYZ6P7zH4Vp\n02qprY0hJ0fdR96HUwzDhoSwD8IlhOsudDKzeLEu6BY6KQq88kotv/udupf1bbdZef/9wFhIJhrP\n3yF84sQJr1ODqqurvcYlJibWWw0K5pXK/jR9ei1PPKH+27v5ZisffBC+FaRfIiHsg3AI4Y8/dnPb\nbacrPxa+/joxqCs/n3/u5sYb1UpVXp6Z777TtlIlfNOSIWw2m1mzZg0Gg4GioiLKysrqrQbFxsZ6\nVYP69u0rgdsILpd67Oh776l3oZ57zsnvfy93oc5FQtgHoRzCigIvvODk2WfVy9Vf/9rG3/8eGptf\nBMLmIqJpmhrCDoeD9evXYzAYKC4uZsWKFezZs8frdaKioujfvz/Z2dnk5OSQkZHBoEGDQqoa1Fos\nFrj6ajM//KDuIz9zZiQ33yzrMX6JhLAPQjWEa2vhV7+yMmuWug3kX/7i5vHHQ2sbyK1b1Wfc6jab\n6jPu1tpmUzRdY0LY5XJ5VYO2b99ebzWoT58+XtWg2NhYv/4ewsHhw2ozobw8iYQEG/Pnx5Ofr/Ws\ngoOEsA9CMYTPPBAhOlo9EOG660Lz6lWLAydE85wdwoqisH37dgwGg6catGnTpnqrQcnJyWRmZpKX\nl+epBiUmJrbq/MPB5s1qR//gQR2dO1soLk4MtiN9NSUh7INQC+G6RwPa+PHHeMaM0XpW/tWaRy+K\n5lEUxXOY/OOPP05xcTEbN26stxrUqVMnr2pQu3A8DaCVLV0KF19sw2yOZ9Ag9VFPt25azyq4SAj7\nIJRCuLxcvXo9elRHcrJ6e3bAAK1n1TpcLnjwQfWZN8Czzzp4/vnw3r82EBw5cgSDwcDKlSspKipi\n7dq1nDx50mtc27ZtGTFiBPn5+YwePZr09HS6dOmiwYzD26efurn5ZnXR4/jxZubOlUWPTSEh7INQ\nCeGfflK47DI7Nls8aWkmFi1KonNnrWfV+l57rZbHHlNrFDfcoB6NKDWK1lFTU8OqVasoKyvznBp0\n9OjRBsc/8cQTnmpQjx49ZKWyhhQFXnpJ7d4D3HGHuhpa6n9NIyHsg1AI4Vmz3Nxxhxu3O5pLLrHw\nn/8kEkY76Hn56iuFqVPVM03HjTMzf76OMN7gyC8sFgtr166lrKzMUw06cOCA17jY2FgGDRpUpxo0\n4OfbM/JtKjDU1qrNiRkz1LtIL7ygHuoi10RNJyHsg2AOYUWBP/xBPTYM4IEHbLzxRjyR8jiUlSth\n4kQrp04l0K+femu+Z0+tZxWcHA4HGzZswGg0UlxcTGlpKbt37663GtSvX7861aDU1FSvalAgbFsp\nVGYzXH65mcJCHVFRTj76KIpp0+QbSHNJCPsgWEPY6YTbbrMye7ZaQXrtNTePPCL3js60Y4daYaqq\n0tGxo7rJfFqa1rMKbC6Xi02bNnlVg2pra+uMi4iIoHfv3owZM8ZTDRo2bFijqkESwoHh4EF1Eeem\nTeoizoUL48nO1npWoUFC2AfBGMInT8LkyWaWLdMRHe3giy9iuOoquXdUn+pqmDjRxJo1ScTF2fnm\nm1guukj+rEANwR07dnhVgxwOh9fY7t27o9fryc/PJzMzk5EjRza5GiQhrL3KSvUC9cgRdR/54mId\nF1yg9axCh4SwD4IthKuqID/fxPbtSbRpY2XRogQyM7WeVWCz2eC66yx895168Pi778Jdd4XX7kmK\norBv3z6MRiOlpaUUFRWxceNGLBaL19jzzjuP9PR0T+Cmp6fTvn37FpuLhLC2iovVfeSt1niGDDGx\neHESshC9ZUkI+yCYQnj9eigosHDsWCI9e6pXr337aj2r4OB2w6OP2vnb3+IA+J//cfDii6FbYTp6\n9KhXNaimpsZrXJs2bRg+fDgFBQWMHj2ajIwMv1eDJIS189FH6j7ybnc0F11k5quvdMheJy1PQtgH\nwRLC33+vcOWVDuz2OEaOVM/W7dhR61kFn7fecvHggxEoSiTXXWflo48SCPYdDk+ePFmnGrRq1ap6\nq0EJCQkMGTKE/Px8xowZQ0ZGBr169Wr1apCEcOtTFPjjHx0895z6H3so7SMfiCSEfRAMIfz++y7u\nuQfc7iguv9zC558nEh+v9ayC17ffKlxzjROnM5asLHWnrRa82+pXVqu1TjXIYDCwb98+r3ExMTEM\nGjSInJwcxo0bh16vp1+/fp7dqrQkIdy6amvhzjutfPihuojzlVfUfeSF/0gI+yCQQ1hR4OmnHbz8\nsnr1+tBDdl57LU4qSC3AaIQJE6zU1CTQp496a793b61nVZfT6fSqBu3atavealDfvn3Jzs4mOzsb\nvV5Pamoq0QG604KEcOs5dQouvdTMkiXqPvKffhrNNdeE6DOYACIh7INADWGHA26+2cIXXyQSEeHm\nzTcVHnhA7h21pF271BWie/boaN/ewuLFiYwcqc1cXC4XmzdvxmAwsHTpUpYuXcq2bdvqrQb16tXL\nUw3KyMggLS2NuLg4bSbeBBLCrWP/fnUR59atSSQlWfnhh4SQ30c+UEgI+yAQQ/jECbj4YjMrV+qI\niXHwn//EcPnlcvXqD8ePw6RJZgwGHbGxdr76KpbJk/37Z60oCjt37vRUg5YsWUJlZSV2u91rbLdu\n3byqQTqdzq/z8zcJYf/buBHy8y1UVyeSnKze6enfX+tZhQ8JYR8EWgjv2aN+Otu1S0e7dhZ++imR\n9HStZxXa7Ha44QYLX32VSESEi7ffVrjvvpa7lXt2NWjDhg31VoM6duzIqFGjyM/PJysri/T0dDp0\n6NBi8wgUEsL+tWiRwuWXO7DZ4hg+XN1HvlMnrWcVXiSEfRBIIbxmDYwfb+HEiUR69zZTUqLj/PO1\nnlV4UBR48kk706ert3Uff9zOX/4S53OFqbq6GoPB4FmpvGbNmgarQWlpaRQUFJCVlYVer6dr164t\n8VsJeBLC/jNjhpu77lJwu6OYPNnCl1+G9z7yWpEQ9kGghPD8+QpXX+3E4YhFrzfz/fc6QvBDUMB7\n910X990HihLF1VdbmD07kYYet546dcpTDSoqKsJoNHLkyBGvcfHx8QwZMoS8vDzGjh1LRkYGvXv3\nDttTgySEW56iqMd3vvCCuojzt7+18/rrsohTKxLCPgiEEP7HP2p54IEIFCWKKVOsfPJJQoPf+IX/\nLVigcNVVDhyOODIyTPzwQxLx8VbWrVtHWVkZxcXFlJWVUVVV5fW1MTExDBw4kJycHM9K5f79+wdE\nNShQSAi3LIcDbr3VwmefqYs4X3vNzcMPB+bK+HAhIewDLUPY7VZvgb76qpq4Tzzh4M9/Dt1dnIKF\n0+lkzpwd3HlnFyyWDkRFbcXtvhhF2VFnXFRUFCkpKYwbN46cnBz0ej2DBw8O2GpQoJAQbjk1NXDJ\nJSZKS5OIjnby5ZfRXHmlfAPRmoSwD7QKYbsdrr/ewty56mKgf/wD7rlHKkitze12e6pBp1cqb926\n9edqUE9gPjAMOETXrr8iNzfRc2pQWloa8bJris8khFtGVRXk5ZnYsSOJtm3VfeT1eq1nJUBC2Cda\nhPDx43DRRSaMxiRiY+3MnRvLxRfL1au/KYrCrl27MBgMLF++nJKSEioqKuqtBnXt2hW9Xk9W1kXM\nnn0tFRXdiYlx8OWXMVxxhfxdNYeEcPOtXasu4jx+PJFevdQKUkqK1rMSp0kI+6C1Q3jnTrWCtHev\njg4d1A0iRoxonfcONwcOHMBgMNSpBpnNZq9xHTp0YOTIkZ6Vyunp6XQ8Y2NuhwNuucXK558nEBHh\n5vXX3Tz4oNxybioJ4eZZuFDdR97hiCM9XV2zIPvIBxYJYR+0ZggbjXDhhVZOnkwgJUW9eu3Vy//v\nGw6qq6tZtWoVK1eu9FSDTpw44TUuKSnJqxrUrVu3X3x9RYFnnlFPXgJZfdocEsJN9957Lu69MfcH\nNAAAGkRJREFUV129f+WV6mIsWcQZeCSEfdBaIfzNNwrXXqseGjBmjJkFC3S0a+ff9wxVp06dYvXq\n1XWqQYcPH/YaFx8fz+DBg+tUg84///xmVYM++MDN3XerPcxLL1V7mPJY2DcSwr47u8f+6KN2XnlF\nLgIDlYSwD1ojhN94o5aHH45EUSKZOlU9zSTYj89rLTabrU41aOXKlfVWg6Kjo72qQQMGDPBLNejH\nHxWuuELdkWjECHVHovPOa/G3CVkSwr5xONQd3ebMUStIb72lcP/9sogzkEkI+8CfIex2w8MP23nz\nTfXq9emnHfzpT1JBaojT6aSiogKDwUBJSQnLly9n586duN3uOuMiIyO9qkFDhgxp1WrQhg1QUKDu\nzdujh/pooV+/Vnv7oCYh3HgnTsCkSSbKypKIjXUwZ04Ml14q30ACnYSwD/wVwjYbXHuthXnzEomM\ndPH++xHccYfcOzrN7XazZcuWOtWgLVu2eJ0aBNCzZ09Gjx7tqQYNHz48IKpB+/app9Rs25ZEmzZW\nfvwxgawsrWcV+CSEG2f3brWCtHt3Eu3aWVm8OIFRo7SelWgMCWEf+COEq6thwgQTa9cmER9v55tv\nYpk4MXyvXhVFYffu3V7VIJvN5jW2S5cuZGRkUFBQ4Dk1qE2bNhrMunFOnlTPa126VEd0tIPPPoth\nypTw/btuDAnhX7ZqFVx4oYWamkTOP1+90yL7yAcPCWEftHQIb9+uVpD27dNx3nkWCgsTGTasZV47\nWBw8eBCDwcCKFSsoLCxkw4YNmEwmr3EdOnRgxIgRnpXKGRkZdapBwcLphDvvtPLRRwmAm+nT3Tz2\nmFSYGiIhfG7z5ilMmaLuI5+Zqe4j37691rMSvpAQ9kFLhvCKFXDRRVZOnUqgf38TRUVJ9OjR/NcN\nZMeOHfOqBh0/ftxrnE6nIy0tjby8PMaMGYNer6d79+4azNg/FAWef97B88+rK+7uvdfGW2/FEyXr\nZ7xICDfs7bdd/Pa3EShKJNdco+4jL4s4g4+EsA9aKoTnzFG4/vpaamtjyMkxM2+ejgC+i9okJpOJ\n1atXYzAYKCwsxGg0cujQIa9xcXFxnmrQ6cDt06dPWJwaNGuWmzvucON2RzNpkrqiNTFR61kFFglh\nb243PPaY2j0HePJJBy+/LIs4g5WEsA9aIoRffbWWxx+PBCK5+WYrH3yQQExMi0xPM3a73VMNKikp\nYcWKFVRVVXl944yOjmbAgAF1qkEDBw4M61ODCgth8mQ7NlscQ4eaWLw4ic6dtZ5V4JAQrstuh6lT\nLXzzjbqP/Lvvwt13yy2UYCYh7IPmhLDLBQ88YOPdd9WVus895+T3v48JuqvX2traeqtBLperzrjT\n1aAxY8aQm5vrqQbFBPsVhx9UVEB+vpkjR3R0726mqEjHwIFazyowSAj/17FjMHGiidWr1QrS3Lkx\nso98CJAQ9kFTQ9higSlT1EUTkZG1/PvfkdxyS+B/+nO73WzduhWj0cjSpUs91SCn0+k1tmfPnmRl\nZZGbm0tmZibDhw8nISFBg1kHp4MH1QrT5s1J6HQ2vv8+nnHjtJ6V9iSEVTt2qIs4q6p0dOyo7iM/\nfLjWsxItQULYB00J4SNHYPx4Exs3JpGQYGP+/Hjy8/0yvWZRFIU9e/Z4DjEoLi6mvLy83mpQ586d\nPdUgvV7PqFGjaNu2rQazDi0mE1xxhZnCQh1RUU4+/jiK668P/Is1f5IQBoMBJkxQ95Hv21etIPXs\nqfWsREuREPaBryG8ZYt6m/HAAR2dO1soKkpk8GD/zc8Xhw4d8lSDioqKWL9+PadOnfIa165dO69T\ngzp16qTBjMNDbS3cfbeVf/9bvYvw4otO/ud/gu+xRUsJ9xD++muF665zUlsby9ixZubPl33kQ42E\nsA98CeFly2DSJBtmczyDBpkoLEyiEQfw+MXx48e9qkHHjh3zGqfT6Rg2bFidlcrJyckazDi8KYoa\nvs88oz4//9Wv1LUE4VhhCucQfv31Wh55RF3EOW2alVmzgn8Rp/AmIeyDxobwZ5+5uekmFy5XDAUF\nZr75RkdSkv/nB2A2mz3VoKKiIgwGAwcPHvQaFxcXR2pqKrm5uYwdOxa9Xk9KSkpYVIOCxezZbm65\nxY3LFc2FF5qZO1eHTqf1rFpXOIaw2w0PPmjj7bfVRZzPPqt2yuWfZmiSEG6M8nJ44gl2LKgkEQvd\n+iRCaiq8+qr6488UBV5+2cnTT6uXq3fcYeO99+Lx11kBdrud9evXe1Yql5aWsnfv3nqrQf379/dU\ngzIyMrjggguICsePVkFmyRK4+GIbFks8qalqhUmrOypaCLcQtlrhmmssLFig7iP/r39FcPvt4b0u\nINRJCJ/LzJnw1FNw4EDDY7p3h5dewnXzbdxzj41//Uu9ev3Tn9Qwbqmr19raWiorKzEYDCxZsoRl\ny5axY8cOr2pQREQEKSkpdQ4xGDJkCLGylU7Q2rxZXRl76JCOLl3UCtMZ134hLZxC+OhRuPBCE+vX\nq/vIf/ttLBMmyMffUCch3JAbboBPP23UUAVY3OVaJhz+gqioWj78MJIbbmj61avb7Wbbtm0YjUaW\nLVtGSUkJmzdvrrca1KNHDzIzM8nLyyMzM5MRI0ZINSgEHToE48ebqajQkZBgY+HCeHJztZ6V/4VL\nCG/bpl5o7d+vo1MndR/5oUO1npVoDRLC9fEhgE9TgM8jryO56HNycnz4OkVh7969dVYql5eXY7Va\nvcZ26tSpTjUoPT1dqkFhxGyGq6828+OPOqKiapk5M5KbbgrtW5XhEMKlpTBxorqIc8AA9U6HrIcM\nHxLCZ5s5E26/3fN/o4Ezb/gmAZcDs37+tTMpQMTMmXDrrQ2+/OHDh+sE7rp16xqsBp15alB6ejqd\nZT/DsOdywf33q2sNAP74R3UVdagu2gn1EP7iCzc33ugK6X3kxblJCJ8tObnOM+Bo4EXgSWAP8C7w\nF6APsLWhr9+3D4ATJ054qkFFRUWsXr2a6upqry9JTExk6NCh5Ofn16kGyUplUR9FgenTa3nySbW+\ncsst6h7k/loAqKVQDWHvv0MbH3zgv0WcInBJCJ+pvJyzH8ScGcKnzQDuBOYAV5/1EgrwQEEBX2/a\nxIF6FnTFxsaSmppKTk4O48aNQ6/X07dvXwlc4bMzP0Xl5Zn59tvQ+xQViiHscsF999n45z/VuxnP\nP+/k2WdD926GODcJ4TNNngwLFtT5qfpC+PTPXwfMPuvnFeA74AogKiqK/v37k52dTU5ODhkZGQwa\nNEiqQaLFnPk8ceBAdVOYUHqeGGohbLHAVVf997n+v/8dyc03h/ZzfXFucvPjTJWVjR4aDxyt5+cj\ngNxOnVj1/fcMHTpUqkHCr8aMgTVr4snPN7NlSxLDh6vbow4ZovXMxNkOH1b3kS8vV/eRX7Agnrw8\nrWcltCaXYGeyWBo91AY0tINyu8hIRo0aJQEsWsWAAbB2rY60NBNHjyaSkWHjp59C45NjqNi8GdLS\nzJSXJ9Gli5lVqySAhUpC+EyJiY0aNhN1xfS1Dfz6vpoafv/73zNv3jwOHz7cUrMTokGdO8OKFUlc\ncokFmy2eiy5yMXOmW+tpCWDpUhg1ysahQzoGDTKzbl34bLYifpk8Ez7TLzwTrgLeA14CegPb63mJ\nM58Jn3beeeeRnp5Ofn4+mZmZpKen0759e3/8DkSYC7V9h4P9mbC6/7e6j/z48eo+8uG2/7c4Nwnh\nMzWwOvrsnvAlwEdAfTebFWD+9Oks3LWLJUuWUFlZicPh8BrXvXt39Hq9J5hHjBiBTv51ihby17/W\n8uijwX8CT7CG8NknYd15p3oSllSQxNkkhM92Vk+4SV//c08YwOVysWnTJgwGA0uXLmXp0qVs27at\n3j2fe/fuzZgxY8jLyyMjI4O0tDR5riya7OyzaBcs0BFsG6wFYwjX1sKvf21jxgz1bkS4nwktzk1C\n+Gxn7ZjVpK8/x45ZAA6Hgw0bNmA0GikuLqa0tJTdu3d7faOJioqiX79+ZGdnk52djV6vJzU1VSpO\notHKymDiRCsnTybQr5+JoqIkevbUelaNF2whbDLBFVeYKSzUERXl5KOPopg2TZbeiIZJCNenCXtH\nAzBtGsw+uzncOBaLhbVr11JWVkZRURFlZWUNbvYxaNCgOpt99OvXTzb7EA3asUM9HKCqSkfHjhYW\nL05k+HCtZ9U4wRTCBw9Cfr6JzZuT0OnUQzays7WelQh0EsIN8TWImxHADampqWHVqlWeYF61ahVH\nj3q3kxMSEupse5mRkUHPnj0lmIXHsWMwYYKZNWt0xMXZmTs3lkmTAv+/j2AJ4YoKyM83c+SIjm7d\nzBQX6xg4UOtZiWAgIXwus2ap5wnv39/wmORkeOmlX7wF3VIOHz6M0Wj07Ee9du1aTp486TWubdu2\njBgxgvz8fLKystDr9XIARJiz2WDqVAvffqseGP/OO3D33YH9aCMYQrioCCZPtmG1xjN0qImffkqi\nSxetZyWChYRwY1RWwuOPqz+azaDTQWoqTJ+O1oU/RVGoqqrCaDSyfPlyiouL2bhxY4NHIZ5dlWrX\nrp0GsxZacbvhscfsvP56HAC/+52Dl14K3ApToIfwhx+6uf12N253NJMmWZgzJ7Gx2w0IAUgIhyRF\nUdi+fTsGg4Fly5Z5qlJOp9NrbHJysldVKlG+i4S8t9928dvfRqAokVx7rYWPP04kEBfiB2oIKwo8\n/7zawQa49147b70Vh6yZFL6SEA4TLpeLyspKDAYDS5YsYdmyZWzfvr3eqtT555/vqUrp9XrZAztE\nffedwjXXOHE4YsnKMrNwoY5A20MmEEPY6YQ77rDy8ccJgJvp09089pgUgEXTSAiHMbvd7lWV2rNn\nj9c3vOjoaE9VSk6DCi2rVsGFF1qoqUmkTx91QVHv3lrP6r8CLYRPnYLJk80sXaojOtrJZ59FM2VK\ngN7LF0FBQljUYTab61SlDAZDvVWpuLg4Bg0aRG5uLmPHjpVzkYPY7t2Qm2tmzx4d7durFaaRI7We\nlSqQQnjfPigoMLF1axJJSVZ+/DGB0aO1npUIdhLC4hedOHGCVatWeVZkr169murqaq9xiYmJdapS\ner2e5ORkCeYgcPw4TJpkxmDQERvrYM6cGC69VPu/t0AJ4Q0boKDAQnV1Ij16mCkq0tG/v6ZTEiFC\nQlg0yaFDhzAajaxYsYKioiLWrVvHqVOnvMa1a9eOESNGUFBQQFZWFhkZGXTq1NAhkEJLdjvceKOV\nOXMSiIhw8fbbCvfdp+2zzkAI4UWLFC6/3IHNFsfw4SYWLUpC/hMWLUVCWLQIRVHYu3cvRqOR0tJS\nioqKKC8vb7AqlZGRQUFBAXq9nvT0dNoG26bGIUpR1NrSK6+oC/EefdTOK6/EEanRzotah/AHH7i4\n+25wu6OYPNnCl18mkpCgyVREiJIQFn7jdrvrVKVKSkrYvHlzvVWpHj16kJmZSV5enqcqlSDf7TTz\n7rsu7rsPFCWKK6+08NlnicTFtf48tAphRYFnnnHw4ovqxchvf6t2q7W6GBGhS0JYtKra2lqvqtSO\nHTvqrUr16dPHqyoVE4zn8QWphQsVrrzSgcMRR3q6iR9+SKJjx9adgxYh7HDArbeqFx4REW5ef13h\nwQelCSD8Q0JYaM5ut7N+/XoMBgMlJSWUlpayd+/eeqtS/fv3Jycnh+zsbDIyMrjgggukKuVHa9fC\n+PEWjh9PpFcvtcKUktJ679/aIVxTA5dcYqK0NImYGCdffBHNlVdqv0BNhC4JYRGQTCaTV1Xq4MGD\nXuPi4uJITU0lLy/PsyI7JSVFVmS3oL171VOYdu7U0batlUWLEtDrW+e9WzOE1d+niZ07k1r99ynC\nl4SwCBrHjx/3qkodO3bMa5xOp6u3KiWaTv2EaKa0VEdMjIMvvohplU+IrRXCZ37i791brSC15id+\nEb4khEVQO3jwYJ2q1Pr16+utSrVv396rKnXeeedpMOPg5XDALbdY+fzzBCIi3Pz1r24eesi/FabW\nCOFAePYtwpeEsAgpiqKwZ88eDAYDpaWlFBcXU15ejs1m8xrbuXNnr6pUmzZtNJh18Dh71fBvfmPn\nb3/z36phf4fwmavAr7rKwqefarMKXIQvCWER8txuN1u3bsVoNLJ06VKWLFnCli1b6q1K9ezZk6ys\nLHJzcz1Vqfj4eA1mHdhmzHBz110KbncUl16q9mf98cfkrxBWFHjySTvTp6uJ+9hjdv7yF6kgidYn\nISzCUm1tLRUVFZ4V2cuXL2fnzp1eVanIyEhSUlIYM2YMubm56PV6hgwZIlUp4McfFa64wr87Sfkj\nhNWdwdSzfyMi3Lz1lsL998sKe6ENCWEhfmaz2Vi/fj1lZWWUlJSwYsUK9u7d6zUuOjqaAQMGeKpS\ner2egQMHEhmGH6PO3lO5uFhHv34t9/otHcLqHtkmDIakgNojW4QvCWEhzsFkMrF69WoMBgOFhYUY\njUYOHTrkNS4uLo7BgwfXqUr16dMnLKpS+/ZBfr6Jbdta/nShlgzhuqdFWfnppwRGjWr2ywrRLBLC\nQvjo2LFjnqpUYWEha9as4fjx417jkpKSGDZsGPn5+YwePRq9Xk/37t01mLH/nToFl15qZsmSlj1n\nt6VCONDPTRbhS0JYiBZw4MABr1OlzGaz17gOHTp4VaU6hkgfxumEO++08tFHCYCbV15x8/jjzasw\ntUQIf/edwpQpTpzOWLKyzCxcqKN9+2ZNS4gWIyEshB8oisLu3bu9qlJ2u91rbJcuXTxVqczMTEaO\nHBm0VSlFgT/+0clzz6kL1+65x8bbb8fT1J1FmxvCb73l4sEHI1CUSK691srHHycQG9u0uQjhDxLC\nQrQSt9vNli1bvKpStbW1XmN79uzJ6NGjPVWp4cOHB1VV6sMP3dx+uxu3O5pJk8zMmaMjMdH312lq\nCLvd6jGMf/ubWkH63e8cvPRSLGHwiF4EGQlhITTkdDrrrUq53e46405XpcaOHVunKhUd7d8dq5qj\nqAgmT7ZhtcYzdKiJn35KoksX316jKSFss8HUqRa+/TaRyEgX77wDd98tFSQRmCSEhQgwNpuNdevW\nUVZWRnFxMStXrqSqqsprXExMjFdVasCAAQFVlaqogPx8M0eO6OjWTV0QNXBg47/e1xCuroaJE02s\nWZNEXJyduXNjmTRJPv6KwCUhLEQQOHXqlFdV6vDhw17j4uPjPVWpsWPHkpGRwfnnn69pVergQSgo\nMLFpUxI6nY2FC+PJzm7c1/oSwjt2qKc9VVXp6NjRwuLFiQwf3pyZC+F/EsJCBKnq6mqvqtSJEye8\nxiUlJZGWllanKtWtW7dWnavJBFdcYaawUEdUlJOPP47i+ut/+RN7Y0O4rAwmTLBy6lQC/fqppyD1\n7NkiUxfCrySEhQgh+/fvx2g0UlpaSlFRERs2bGiwKjVq1Cjy8/M9VakOHTr4dW61tfDrX9uYMUNd\nYPbCC06eeirmnIulGhPCX3+tcN11TmprYxk71syCBTratm3RqQvhNxLCQoQwRVHYtWtXnapURUVF\nvVWprl27otfryc/P91SlkpKSWng+8OKLTp55Rq0w3XmnjXffjcdrfVl5OTzxBDsWLCAR6NanD6Sm\nwquvqj/+7K9/reXRRyOBSKZNszJrVgKyrbcIJhLCQoQZt9vN5s2b61Sltm7d6lWVioiI8FSl8vLy\n0Ov1DB8+nLgWOOtv9mw3t9zixuWKZvx4M998o0OnA2bOhKeeggMHGv7i7t1xv/ASD666nrffVj9V\nP/usg+eflwqSCD4SwkIInE4n5eXlXlWps789REVFkZKSQnZ2tmdF9uDBg5tUlVqyBC6+2IbFEk9q\nqgnjwLtJnPvpL37d6Rl9wjRujfyIf/0rgttvD5wV4UL4QkJYCFEvq9VapypVVlbWYFXqggsuICcn\nh3HjxqHX6+nfv3+jqlKbN6srml89dBc38im+fJBVgIMF0+i+eLYPXyVEYJEQFkI02smTJ1m9ejVl\nZWUUFRVhNBo5cuSI17iEhAQGDx5Mfn4+Y8aMISMjg969e9dblap5YyZtH7q9TgBHA66zxg0D1tc3\nqZkz4dZbm/6bEkJDEsJCiGY5evQoRqORsrIyT1WqpqbGa1ybNm1IS0vzHF6h1+vp2rUrJCejHDjg\nFcIvAk82ZgLJyep5ikIEIQlhIUSL279/v2dFdlFRERs3bqy3KjW6bVuWnzzpdRvapxAGdWuuM1ZN\nCxEsJISFEH6nKAo7d+7EYDCwbNkylixZQmVlJV/a7VwKzQ/hyZNh3ryWnLIQrUJCWAihCZfLhev8\n84mt51Zyfc+EbwFmNfRiKSnqvpVCBBlZ1y+E0ERUVBSxTmeDv/5n1BXQp//XYAAD1HOrW4hgICEs\nhNBOUw4Zro9O1zKvI0QrkxAWQminpRZTyaIsEaTkmbAQQjvl5TB0qNdP1/dMuDuwv6HXkdXRIkhJ\nCAshtJWcfO69ohvz9dITFkFKbkcLIbT10kvafr0QGpJPwkII7d1wA3z6y4c3eJk2DWbL3tEieMkn\nYSGE9mbPVgPVFxLAIgRICAshAsPs2ephDMnJ5x6XnKyOkwAWIUBuRwshAk9lJTz+uPqj2az2gFNT\nYfp0WQUtQoqEsBBCCKERuR0thBBCaERCWAghhNCIhLAQQgihEQlhIYQQQiMSwkIIIYRGJISFEEII\njUgICyGEEBqREBZCCCE0IiEshBBCaERCWAghhNCIhLAQQgihEQlhIYQQQiMSwkIIIYRGJISFEEII\njUgICyGEEBqREBZCCCE0IiEshBBCaERCWAghhNCIhLAQQgihEQlhIYQQQiMSwkIIIYRGJISFEEII\njUgICyGEEBqREBZCCCE0IiEshBBCaERCWAghhNCIhLAQQgihEQlhIYQQQiMSwkIIIYRGJISFEEII\njUgICyGEEBqREBZCCCE0IiEshBBCaERCWAghhNCIhLAQQgihEQlhIYQQQiMSwkIIIYRGJISFEEII\njfw/nus6Oj7VmAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f961eeb5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Foi descoberta a melhor rota com 27 iterações\n",
    "\n",
    "# plotando grafo\n",
    "\n",
    "pos=nx.circular_layout(G)\n",
    "nx.draw(G,pos, with_labels=True)\n",
    "for g in f[0].arestas_visitadas:\n",
    "    g['color'] = 'b'\n",
    "colors = [G[u][v]['color'] for u,v in G.edges()]\n",
    "#nx.draw_circular(G,node_color='r',with_labels=True, edge_labels=labels)\n",
    "nx.draw_networkx(G, pos, edge_labels=labels, edge_color=colors, width=2)\n",
    "plt.title(\"Grafo caixeiro viajante\")\n",
    "plt.suptitle(\"Um dos maiores problemas matemáticos (Melhor rota definida)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}