travelling_slesman_problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h1skl3JZjpvI"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zRhfyq5rkZrE"
   },
   "outputs": [],
   "source": [
    "def cities_distance(cities):\n",
    "    data = dict()\n",
    "    for index, value in enumerate(cities):\n",
    "        x1 = cities[index][0]\n",
    "        y1 = cities[index][1]\n",
    "        if index + 1 <= len(cities)-1:\n",
    "            x2 = cities[index+1][0]\n",
    "            y2 = cities[index+1][1]\n",
    "            xd = x2 - x1\n",
    "            yd = y2 - y1\n",
    "            dst = (xd*xd + yd*yd)** 0.5\n",
    "            data['Distance from city '+ str(index+1) +' to city ' + str(index+2)] = dst \n",
    "        elif index + 1 > len(cities)-1:\n",
    "            x2 = cities[0][0]\n",
    "            y2 = cities[0][1]\n",
    "            xd = x2 - x1\n",
    "            yd = y2 - y1\n",
    "            dst = (xd*xd + yd*yd)** 0.5\n",
    "            data['Distance from city '+ str(index+1) + ' to city ' + str(index +2 -len(cities))] = dst\n",
    "              \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q9TCaX4ckclz"
   },
   "outputs": [],
   "source": [
    "N=9\n",
    "List_of_cities  = [[1,2],[3,4],[8,7],[10,1]\n",
    "             ,[3,1],[5,10],[6,7],[12,4],[9,5]]\n",
    "val = cities_distance(List_of_cities).values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vtb6Vnx2kevj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.78024179537479"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def totaldist(cities):\n",
    "    total = sum(cities_distance(cities).values())\n",
    "    return total\n",
    "totaldist(List_of_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8, 7], [12, 4], [6, 7], [1, 2], [9, 5], [5, 10], [3, 4], [3, 1], [10, 1]]\n"
     ]
    }
   ],
   "source": [
    "def path_gen(cities):\n",
    "    path = random.sample(cities, len(cities))\n",
    "    return path\n",
    "list= path_gen(List_of_cities)\n",
    "print(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K0GW2U8Oks6C"
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "# city_names = ['Kalyani', 'Ranaghat', 'Sealdah', 'Bidhannagar','Naihati','Halisahar','DumDum']\n",
    "def plot_p(cities):\n",
    "    plt.figure(figsize=(20,10))\n",
    "    x = [i[0] for i in cities]\n",
    "    y = [i[1] for i in cities]\n",
    "    x1=[x[0],x[-1]]\n",
    "    y1=[y[0],y[-1]]\n",
    "    plt.plot(x, y, 'b', x1, y1, 'b')\n",
    "    plt.scatter (x, y)\n",
    "    j =[item[0] for item in list]\n",
    "    k = [item[1] for item in list]\n",
    "   \n",
    "    \n",
    "#     for i in range(N):\n",
    "#         plt.annotate((j[i], k[i]),horizontalalignment='center', verticalalignment='bottom',\n",
    "#                     )\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9JsXEuK4GoVK/vrRypVS6tPTQQ9KTT0rHj7uuCgAAIO0i+AEABEXHjlKmTFKvXq4rQagVLizNmyd17mwLoMuXl374wXVVAAAAaRPBDwAg4BYskCZNsvCnYEHX1cCFjBnt1PvMmdLevVJsrPTee5Lvu64MAAAgbSH4AQAEVHKy1Lq1VKiQ1Lat62rgWs2aNvp1661Ss2ZSkybSkSOuqwIAAEg7CH4AAAE1dqy0fLnUp4+UNavrahAOChSQZs2ysb/x423x84oVrqsCAABIGwh+AAABc/y47XWJjZUaN3ZdDcJJ+vRS1662++fECesAGjqU0S8AAIBgI/gBAATMwIHSjh12vj0df8PgPKpUsdGvmjWlF16QGjSQDhxwXRUAAED04p/lAICA2LlT6ttXatjQntwDfyVPHmnyZAsIp06VypaVFi1yXRUAAEB0IvgBAARE165SUpLUr5/rShAJPM+WgH/9tY2BVa1qwWFysuvKAAAAogvBDwAg1RITpQ8+kJ5/Xipe3HU1iCQ332wfPw0b2n6oWrWkPXtcVwUAABA9CH4AAKni+3a2PXduqUsX19UgEuXMade+RoyQ5s+XYmKkOXNcVwUAABAdCH4AAKkyZYo0d67Us6eUK5frahCpPE966ilp2TL7OKpeXerWzcYHAQAAkHIEPwCAFDt9WmrXTipZUmre3HU1iAalS0sJCdIjj0i9ekl33CFt3+66KgAAgMhF8AMASLE335Q2bJAGDJAyZnRdDaJFtmzS++9LY8ZIK1bY6Fd8vOuqAAAAIhPBDwAgRQ4csPGuGjWk2rVdV4No1KSJBT9XXSXVq2e7pE6fdl0VAABAZCH4AQCkSK9e0qFD0sCBtp8FCIZrrpEWL5aefVaKi5MqV5Z+/NF1VQAAAJGD4AcAcNHWr5eGD5eaNbOdLEAwXXKJfbxNmmQfe2XLSh995LoqAACAyEDwAwC4aB062JPxl192XQnSkgYNpJUrpeuukxo1kp5+WjpxwnVVAAAA4Y3gBwBwUebOlT7/XHrxRemKK1xXg7SmaFFpwQILH996S6pQQVq71nVVAAAA4YvgBwBwwc6eldq0kQoXllq1cl0N0qqMGaV+/aRp06Rdu6Ry5aRRo1xXBQAAEJ4IfgAAF2zMGBu16dtXypLFdTVI62rVkr79VipfXnr0UalpU+nIEddVAQAAhBeCHwDABTl61Ma7KlSQHnzQdTWAKVhQmj1b6tlTGjtWio21cBIAAACG4AcAcEH697exmrg4zrcjvKRPL3XrJs2ZYwHlLbdIr78u+b7rygAAANwj+AEA/KPt2y34adRIqljRdTXA+VWrZt0+d9whtWwp3X+/dPCg66oAAADcIvgBAPyjLl2k5GTb7QOEs7x5pfh4CyonT5ZiYqQlS1xXBQAA4A7BDwDgbyUkSKNH2xWvokVdVwP8s3TppHbtpIULbSyxShULgpKTXVcGAAAQegQ/AIC/5Pt2vj1vXlvsDESSChWkxETp3nulDh2kunWlvXtdVwUAABBaBD8AgL/06afSggVSr15SjhyuqwEuXq5c0sSJ0htv2PLnmBhp3jzXVQEAAIQOwQ8A4LxOnbIuiRtukJo1c10NkHKeJ7VoIS1dKmXPLt15p9Sjh3T2rOvKAAAAgo/gBwBwXq+/Lm3aJA0cKGXI4LoaIPXKlJGWL5eaNJF69rQAaMcO11UBAAAEF8EPAOBP9u2TXn5Zuvtu6a67XFcDBE727NKoUdIHH0jffGOjX9Onu64KAAAgeAh+AAB/0rOndPSoNGCA60qA4HjkEev+KVhQql3bxhrPnHFdFQAAQOAR/AAA/sfatdKbb0rNm9t+HyBalSwpLVkiPf20nXuvUkX66SfXVQEAAAQWwQ8A4H+0by9ly2bLb4FolyWLBZ0ffSStWWOjX5Mmua4KAAAgcAh+AAD/b/ZsKT5e6tJFypfPdTVA6DzwgJSYKF1zjXT//dKzz0onT7quCgAAIPUIfgAAkuy0dZs2UtGi0vPPu64GCL1//UtauFBq21Z64w3pllukdetcVwUAAJA6BD8AAEnS++9L330nvfaadMklrqsB3MiUyZaax8dL27dL5cpJY8a4rgoAACDlCH4AADpyROraVapY0cZcgLSuTh1p5UoLfpo2lR591C7dAQAARJpUBT+e57X2PO97z/NWe573oed5vEYMABGoXz9pzx4pLk7yPNfVAOGhUCHpyy+lbt2k0aOlm2+WVq1yXRUAAMDFSXHw43nelZKelxTr+34pSeklPRiowgAAobF1qzRwoNS4sVShgutqgPCSIYPUs6ctPj94UCpfXhoxQvJ915UBAABcmNSOemWQlMXzvAySskramfqSAACh1Lmzfd2nj9s6gHB2xx3St99K1apJLVpIjRpJhw65rgoAAOCfpTj48X1/h6QBkrZK2iXpkO/7swJVGAAg+JYulcaNsytGhQu7rgYIb/nySdOnS337Sp98IpUtK33zjeuqAAAA/l5qRr0uk3SvpGKSCkrK5nlek/N8v+ae5yV4npewd+/elFcKAAgo37fz7fnzSx07uq4GiAzp0tmflwULpLNnpUqVbDcWo18AACBcpWbUq7qkzb7v7/V9/4ykTyRV/ON38n3/bd/3Y33fj82bN28qHg4AEEgTJ0qLFkmvvCJdeqnraoDIcuutdvWrbl3rmKtXT9q3z3VVAAAAf5aa4GerpFs8z8vqeZ4n6U5JawJTFgAgmE6etK6FG2+UHnvMdTVAZLrsMmnSJGnYMOmLL6SYGGn+fNdVAQAA/K/U7PhZKuljSSskfffr+3o7QHUBAIJo6FDpp5/smlf69K6rASKX50ktW0pLlkhZski33y716mVjYAAAAOHA80M4lB4bG+snJCSE7PEAAH/2889SiRJS1arSlCmuqwGix5EjdvFr7Fi7Avbf/0oFCriuCgAApAWe5y33fT/2fP8vtefcAQARpkcP6dgxqX9/15UA0eXSS6UxY6T33pMWL5bKlJFmznRdFQAASOsIfgAgDfn+e+mtt6wroWRJ19UA0cfzbG9WQoJdzLv7bqlzZ+nMGdeVAQCAtIrgBwDSkHbtrCuhe3fXlQDR7frrpaVLpSeflPr2lapVk7ZscV0VAABIiwh+ACCNmDHDvnTrJuXJ47oaIPplzSq9/bY0fry0erVd/frsM9dVAQCAtIbgBwDSgKQk6/YpXlx69lnX1QBpS6NGUmKi/fm77z7p+eelU6dcVwUAANIKgh8ASAPeecf2+7z2mpQ5s+tqgLSneHHp66+lVq2kYcOkW2+VNmxwXRUAAEgLCH4AIModOmTjXVWrWrcBADcyZ5YGDZImT7Z9PzfdJI0b57oqAAAQ7Qh+ACDK9ekj7d0rxcXZxSEAbtWrJ61caefeH35YatZMOnbMdVUAACBaEfwAQBTbvNk6DJo2lcqVc10NgN9cdZU0b57UpYv0/vtS+fK2ABoAACDQCH4AIIp17iylTy/17u26EgB/lCGD9Mor0qxZ0v790s03SyNHSr7vujIAABBNCH4AIEotWiRNmCC1by8VKuS6GgB/pXp1G/2qUkVq3lxq3Fg6fNh1VQAAIFoQ/ABAFEpOllq3lgoUsOAHQHi74gppxgzp1VeliRNt8XNCguuqAABANCD4AYAoNGGCtGyZPYnMnt11NQAuRLp0Np751VfS6dNSxYrS4MGMfgEAgNQh+AGAKHPihNSpk1S2rC11BhBZKlWy0a9ataxzr3592wEEAACQEgQ/ABBlBg2Stm6VBg60DgIAkSd3bumzz6zjZ/p0KSZGWrjQdVUAACAS8ZQAAKLI7t1Snz7SvfdKt9/uuhoAqeF50gsv2KL2zJml226z8c3kZNeVAQCASELwAwBRpFs36eRJqX9/15UACJTYWGnFCumBB6QuXaS77rKQFwAA4EIQ/ABAlFi1Snr3XallS6lECdfVAAikHDmkceOkkSNt5CsmRpo923VVAAAgEhD8AEAU8H2pbVspZ07ppZdcVwMgGDxPeuIJ6ZtvpMsvl2rWlLp2lZKSXFcGAADCGcEPAESBadPs1f/u3W0pLIDoVaqUhT+PPy717m27f7Ztc10VAAAIVwQ/ABDhzpyR2rWz8a4WLVxXAyAUsmaV3nlHGjtW+vZbG/2aPNl1VQAAIBwR/ABAhHv7bWntWmnAAClTJtfVAAilxo1t8XORInbNr1Ur6dQp11UBAIBwQvADABHs4EEb77r9dqlePdfVAHChRAlp8WLpueekIUOkSpWkTZtcVwUAAMIFwQ8ARLBXXpEOHJAGDrTFrwDSpsyZpaFDpU8/tdCnbFlp/HjXVQEAgHBA8AMAEWrTJnui9+ij9iQPAOrXl1autAXQDz0kNW8uHT/uuioAAOASwQ8ARKiOHW2nzyuvuK4EQDgpUkT66iupUydp5EipfHnphx9cVwUAAFwh+AGACLRggTRpkoU/BQu6rgZAuMmYUerTR5o5U9q7V4qNld57T/J915UBAIBQI/gBgAiTnCy1aSNdeaXUtq3ragCEs5o1bfTr1lulZs2kJk2kI0dcVwUAAEKJ4AcAIszYsVJCgr2anzWr62oAhLsCBaRZs6RevWzh80032Ql4AACQNhD8AEAEOX5c6tzZxjYefth1NQAiRfr0Uteu0ty50okT1gE0bBijXwAApAUEPwAQQQYOlHbskOLipHR8BgdwkapWtdGvGjWk55+XGjSQfvnFdVUAACCYeNoAABFi506pb1+pYUOpShXX1QCIVHnySFOmWJA8daoUEyMtXuy6KgAAECwEPwAQIbp2lc6ckfr1c10JgEjnebYkfuFCGwOrUsU+tyQnu64MAAAEGsEPAESAxETpgw9sNKN4cdfVAIgW5cvb55cGDaROnaTataWff3ZdFQAACCSCHwAIc75vZ9tz57auHwAIpJw5pQkTpBEjpK++ksqUkebMcV0VAAAIFIIfAAhzU6bYJZ6ePaVcuVxXAyAaeZ701FPSsmX2eaZ6dalbNykpyXVlAAAgtQh+ACCMnT4ttWsnlSwpNW/uuhoA0a50aSkhQXrkEalXL+mOO6Tt211XBQAAUoPgBwDC2JtvShs2SAMGSBkzuq4GQFqQLZv0/vvS6NHSihV29WvqVNdVAQCAlCL4AYAwdeCAjXdVr24LVwEglP7zH2n5cqlQIaluXds1dvq066oAAMDFIvgBgDDVq5d06JAUF2f7NwAg1K69VlqyRHrmGftcVLmy9OOPrqsCAAAXg+AHAMLQ+vXS8OFSs2a2cwMAXLnkEun116WPP7bPTWXLShMnuq4KAABcKIIfAAhDHTrYk62XX3ZdCQCYhg2llSul666T/v1vqUUL6cQJ11UBAIB/QvADAGFm7lzp88+lzp2lK65wXQ0AnFO0qLRggYXTI0ZIt9wirV3ruioAAPB3CH4AIIycPSu1aSMVLiy1bu26GgD4s4wZpX79pGnTpJ07pXLlpFGjXFcFAAD+CsEPAISRMWNslKJvXylLFtfVAMBfq1VL+vZbqXx56dFHpaZNpaNHXVcFAAD+iOAHAMLEsWPSiy9KFSpIDz7ouhoA+GcFC0qzZ0s9ekhjx1r3z8qVrqsCAAC/R/ADAGHitdekXbs43w4gsqRPL3XvLn35pXTkiO39eeMNyfddVwYAACQpg+sCAADS9u1S//52KadiRdfVAIHxWeIO9Z+5TjsPnlDBXFnU/q5rVb/sla7LQpDcdpuNfj3yiPTss9KcOdI770i5crmuLPD42AYARBI6fgAgDHTpIiUn28JUIBp8lrhDnT/5Ttv3n1RyUjrtOHhCnT/5Tp8l7nBdGoIob14pPt6C7M8/l8qWlZYudV1VYP32sb3151M6tiEfH9sAgLBH8AMAjiUkSKNHS61a2alkIBq8Nn2d9q4ooO1v3KltQ2rq54k36+elhdRr/BbXpSHI0qWT2rWTFi60b1euLA0YYOF2NOg/c51OnDmrA9PKaO8nsdo9roJ+2ZRTr81Y57o0AADOy/NDOIAdGxvrJyQkhOzxACDc+b5UrZq0dq20YYOUM6frioDUW75cqnTPLzq18zJlKviLMhc4qBOb8inpYDZJUunSUt26Up06tg8mfXrHBSNoDh6UnnhCmjTJroCNGmVdQZGsWKep8ioHot8AACAASURBVCWd3ptdu0ZVlnxJyemVKf8hvdM/pxo1kjJlcl0lACCt8Txvue/7sef7f3T8AIBDn34qLVggvfwyoQ8i37590lNPSTffLCUfzqrLa6/UFU0WKXf1H1Sw+TwVfGKeitTeoDx5bBSocmUpf37pP/+Rxo+XfvnF9c8AgZYrlzRxovT667bzJyZGmjfPdVWpUzBXFklSprxHdVnVdVJyemUv96PS+xnUtKl1bvbpIx044LZOAAB+Q/ADAI6cOiV16CDdcIO9Ig5EqqQke2J/zTXSu+/a2OL7U/cr7027//9CnedJOa44qcGvZNWcOdLevdKECVLt2tKMGdJDD1knyG232VjQmjVchYoWnic984y0ZImUPbt0551Sz57S2bOuK0uZ9nddqywZrU3t0pu2KEOuYzq9Na9GTT6o6dOlUqWkF1+UChWyn/f69Y4LBgCkeQQ/AODI669LmzZJAwdKGbixiAi1cKEUGyu1bGmLfL/9VoqLkx6uWlB9GpTWlbmyyJN0Za4s6tOg9P9fPsqVy67YjR4t7d4tLVokdexoXT/t20vXXy9dfbX0wgvSF19YUIrIFhNjY4APPyz16CFVry7t3Om6qotXv+yV//+xnS5DsorX+VGn916qAyuv1N13S7NmSatWWZj57rvStddK9epJc+cSZgIA3GDHDwA4sG+fPam99VZp+nTX1QAXb+dO61gbO1a66ioLexo21P93+KTG1q3StGl2HerLL6WTJ61TpEYN2w1Uu7Z0xRWpfxy4M2qUdcNkzSqNGSPdfbfrilLu97vaNm6UcuQ49//27JHefFN64w3rcitTRmrTRnrwQfYAAQACix0/ABBmevaUjhyxkRYgkpw+bft5rr3Wdrd07WpjWfffH5jQR5IKF5aeftqCn/377esmTaRvvpGaNZMKFLA9Qj17WgdJtFyLSkseecQuGhYoYEufO3SQzpxxXVXKeJ4Fn3v3Sq+++r//L39+627aulV65x37OT7yiFSkiNS7t318AwAQbHT8AECIrV1rOyCefNJeCQYixaxZ0vPPS+vW2ejKoEFS8eKhe3zfl777zoKg+HjbGeP7Fh7Urm3dQNWrW3cQIsOJE9YBM2KEXXj78ENbjhyJmja1vVVr10rFip3/+/i+jS7GxUkzZ0pZstiPa9VKKlkytPUCAKLL33X8EPwAQIjVqyfNn2/n2/Plc10N8M82b7Yn5599ZiOKQ4ZY0OLa3r22GDo+3r4+fNjGZ2677dy5+H/9y3WVuBATJ9qS+3TpbC9OgwauK7p427fbgvN77rErdf/k+++lwYNt1O3UKft4bd1auuOOwHXPAQDSDka9ACBMzJ5tT1K7dCH0Qfg7ccLGVK6/3rp9Xn1VWr06PEIfya6A/ec/1mWxb58tz33uOWnLFutMKl7cruZ16GBha1KS64rxVx54QEpMlEqUsF1RLVvabqdIUqiQLSafMEFavPifv/8NN0gjR9oYWI8eNspYvbotwf7gAxaaAwACh44fAAiRs2elm26yroQ1a6RLLnFdEXB+vm/dPW3aSD/9JDVqZHt9rrrKdWUXbuNGaepUC1q/+sp2q+TKZftk6tSxZcKXX+66SvzR6dN2Cn3gQFuE/NFH1kUTKY4etXoLF7bw52I6d06elMaNsxHK1attP1DLlrbvKk+e4NUMAIgOdPwAQBh4/3078duvH6EPwtfatRaKNGhgu3LmzrWxlUgKfaT/PQW/b5/08cfSfffZlbAmTazjrkoVqW9fe5LNme3wkCmTLb2Pj7fRqZtuslGoSJE9uy1tXrr0wsa9fu+SS6THH7e/J2bNksqWlV56yf7sPfWUvWAAAEBK0PEDACFw5IiNMBQvLi1cyP4GhJ8jR6SXX7adI9my2X8/84yUIYPrygIrOdmuScXHW0fQihX29iJFzu0Fuv12wtlwsH271LixtGCB9Oij0vDh9rEZ7s6elWJjpQMHLEjNkiXl7+uHH87tATp50jrWWre2kTD+HgEA/B4dPwDgWL9+0p49dsmFf6wjnPi+NHasnWcfMMAuDK1fbztyoi30kWx5cPnyFmwtX27hwttv21jR++/b/qLLL5fuvdf2r+zY4britKtQIWnOHOt6GTXKwpRVq1xX9c/Sp7fP9Vu3WmiTGtdfbx+fW7fax+yKFVLNmuc+XiNtDxIAwA06fgAgyLZutSfVDRrYE2wgXKxcacuQFy6Ubr5ZGjZMqlDBdVXunDwpzZt37lz8li329rJlrRuobl0LH9LxslnIzZkjPfywdPCghSnNm4d/iF6/vo0Wbtxo+3oC4dQpO3kfFyd9952NLD77rNSihS07BwCkXZxzBwCHHn5Y+uQTad06W/gJuHbggHVRjBgh5c5te24ee4xA4/d838ZsfguBFi2yMbF8+awrqG5dqUYNKUcO15WmHT//bFfcZs2S/v1v64TJmdN1VX9t/Xq73PX449JbbwX2ffu+hWFxcdK0aVLmzPZr07q1dQkBANIegh8AcGTpUumWW+xKTe/erqtBWnf2rPTuu/bx+Msv1inQs6d02WWuKwt/+/dLM2daCDR9unWeZMwoVa16rhvo6qtdVxn9kpPtwlyXLraXafx461YLV61aWSfdypVS6dLBeYy1a60LatQo61q76y67yFejRvh3RQEAAofgBwAc8H2pcmVp0yZpwwbp0ktdV4S0bPFiG+tavtzCimHDpBtvdF1VZEpKsl/P37qBfvjB3n7NNedCoMqVLRhCcCxaJD30kLRrl3WstW4dniHHgQMWCJYrZ51Kwaxx3z7rLBo+XNq927qNWre2rlOWlQNA9GO5MwA48PHH9uTklVcIfeDO7t12EaliRXuSPG6c7bEh9Em5DBnsFHy/ftL330s//mhBWrFi9qT7jjukPHlsHGn0aGnvXtcVR5+KFaXERLvC1ratVK+eBR/hJnduqXt3afZs6xQLpjx5rBPqp5+kDz6wj9MnnrAR4x49bFQOAJA20fEDAEFw8qR03XUW+CQm2pUXIJTOnLEQokcP6cQJG/3o2lXKnt11ZdHt6FF7kj91qnUD7d5tXR633GIhRd26FrqFY3dKJPJ9+zhv186WG3/4oYVy4eT0aalUKft7YNWq0HWC+b40d640aJB9LGbOLDVpYuNnpUqFpgYAQOjQ8QMAITZ0qL3qGhdH6IPQmzNHiomxsKdiRWn1ahuHIfQJvuzZ7ZrTb6fgExIsfEtKsuAtJsY6MJ5+2p6MHz/uuuLI5nk2wrh4sZQli3TbbdZlefas68rOyZTJ9hKtXWsLqUPF86z7bMoUe+zHHrOOv9KlbQ/QjBkWDgEAoh8dPwAQYD//LJUoYa86x8e7rgZpydatNvby8cc2djR4sI3A0F0SHnbvtgtMU6favpejR233yp13WidQnTrSVVe5rjJyHTligdq4cRZ4/Pe/UoECrqsyvm81ffednXfPlctNHfv3n9sDtGuXXQBr1co6gbJkcVMTACAwWO4MACH0zDP2qu7q1VLJkq6rQVpw8qQ0cKBdjvN9u9rVrh1P5MLZqVPS/PnnFkT/+KO9/cYbz4VAFSrQMXixfN/22zz7rI3ajhkj1azpuiqTmGhLntu2tQ4gl06fliZMsK7UlSttP9Azz9iX/Pnd1gYASBmCHwAIke+/tyduzzxjy16BYIuPl154wYKDhg0tACpSxHVVuBi+L61bZ7+XU6dKCxbYqNLll0u1a1sQVLOmuy6RSPTDD1KjRhbAd+okvfxyeFxZe/xxaexYq694cdfV2MfeV19ZABQfb79GDz9s18CCdX4eABAcBD8AECK1atmuiY0b7RVUIFg2bLARjWnTrLNs2DCpenXXVSEQDh6UZs60J+LTp9t4Tvr0Nj7627n4a65hhO+fHD9uf0ZGjrRdV+PGuQ9Fd+60UeDataWJE93W8kfr10tDhljH1PHj9vmkTRvbB5SOraAAEPZY7gwAITBjhn156SVCHwTPsWM2ylWqlHWGDBggffstoU80yZXLulXGjJH27JG+/lrq0MECoHbtLOgrUcJCjdmzbWwHf5Y1q43dfvih7daJiZE++8xtTQULSh072h6uBQvc1vJH11wjvf66tG2b1KePdSXVrm2fa95+264DAgAiEx0/ABAASUn2pOLkSRv3ypzZdUWINr4vffSRPfHfvl1q2tQudYXL8lqExtat507Fz5ljn3OyZ7dRsLp17Yk6O1r+bONG6cEHpeXL7QpY//7uPk8fOyZde6392V26NHy7aU6ftq6kuDhpxQp7QePpp21/0hVXuK4OAPBHdPwAQJC9+64FPq+9RuiDwPvuO7sI9OCDUt681gEyahShT1pUuLDUooWFP/v326nuhx+2AOHxx+0JefnyttNmxQrOdf/m6qvtz02rVjYWWbGijUu6kC2bddQkJNj4WbjKlMk+thISpHnzpEqVbIF8kSLSo49apyEAIDLQ8QMAqXTokI1dlCxpSzLZu4FAOXhQ6t7dxi9y5rQnXU8+yaUn/Jnv2xPx37qBli61txUoYBfC6ta1ccBs2VxX6t7kyRZcnDljp80bNw59DcnJFtDt2WOLvbNmDX0NKbFhgzR0qPTee7YH6M47bRF0rVrh27kEAGkFHT8AEER9+kh791o7PKEPAiE52Z5YXXONdSc8+aQtXn36aUIfnJ/n2bhply62YH7PHusKq1zZRgTr17crYXffLQ0fLv30k+uK3bnnHgvJypSxjpYnnrAQI5TSpbO/M7Zvt0t8kaJECfuctH27jZquXWuh4vXXSyNGhP7XEQBwYej4AYBU2LzZOn0aNZJGj3ZdDaLBN99ILVtKy5bZOMqwYdJNN7muCpHs9Glp4cJz3UDr19vbb7jhXDfQrbdKGTK4rTPUkpKkHj2kV1+VrrvOArIbbghtDQ0b2lGADRts8XOkOXPm3B6g5cul3LltFPHZZxlFBYBQ45w7AATJgw/a2MD69VKhQq6rQSTbu9eudb37rpQvny2fbdKELjIE3vr1FgJNnWrjqUlJ0mWX2bhOnTrWFZQ7t+sqQ2f2bPuzdviwjTE1axa6P3cbN1q3zH/+Y3/2I5XvW7gYFyd9/rmFiA89ZGNgMTGuqwOAtIHgBwCCYPFi68jo1k3q2dN1NYhUSUk2IvHSS9LRo9ILL9jHVI4critDWnDokPTFF+eCoL17bQypUiXrBKpTx4KJaA8gd++28GX2bAv033ordH8G27U7dzkrGkKSTZukIUNsXPXYMen226U2beziHHuAACB4CH4AIMCSky302brVXj3Pnt11RYhE8+fbWNd339ni3aFDbeQEcCE52UYN4+MtBEpMtLcXLXouBLrtNumSS1xWGTzJyba3pls3+zlPmCCVKxf8xz140K6OlSljwVO0hGwHD0ojR9rnte3bbWdZq1ZS06YsGQeAYGC5MwAE2IQJdjWnd29CH1y8HTvsklC1atZx8fHH0qxZhD5wK106qUIFqVcv6z7Zts06X0qXtjGkWrVsQXT9+tI770g7d7quOLDSpbNxy3nzpFOnbO/RkCE2xhRMuXLZrqE5c6QpU4L7WKGUK5fUvr3044/Shx/aZcJnnpEKF7Zf52j7+AGAcEbHDwBcpBMnbKFz7txSQgJXlnDhTp2SBg+2J9ZJSVLHjvYlUk45I+06ccICkfh4+7J1q739ppusG6huXeuOiZZRnv37pccesyDmnnuk998P7t6jM2ekG2+Uzp6VVq+WMmUK3mO54vvSokU21vbZZ/Z3Z6NGtgeIBfYAkHp0/ABAAA0ebE964uIIfXDhZsywzolOnWys64cfbDcUoQ8iQZYs1vHz+ut2Cv6776Q+feztr7wilS9vV6kef1z65BPpyBHXFafO5ZfbkuJBg6Tp0233ztdfB+/xMmaUBgyw614jRgTvcVzyPNsdNWmS/TyfecYCoHLlbA/Q5Mk2bgcACDw6fgDgIuzeLZUoId15p/2DFfgnP/5or2hPnmwfO0OH2tUkIFrs32/BZny8fX3woAUZ1aqd6wYqXtx1lSmXkGALn3/6ybr1OnYMTmeT70s1a9pZ9I0b08ZltYMHbWxw6FAbLSxRwhbcP/ooe4AA4GKx3BkAAqR5c2v5//57W1QJ/JXjx21R7Guv2Wnjl16yxaaZM7uuDAiepCQb5/ltJGzNGnt7yZK2HLpuXev6yJjRbZ0X6/Bh6amnpPHjpRo1pDFjpPz5A/84q1ZZd9ELL1i3UVqRlGSdQHFx0rJl0mWX2d+3LVtKhQq5rg4AIgPBDwAEwKpVUtmy0nPP2bgXcD6+b6MubdrYSOBDD0n9+0tXXum6MiD0fvzRLoTFx9uOoNOnbcnvXXdZCFSrlpQnj+sqL4zv25Lr556zn8N//2tjm4H25JPSBx+kzRcYfF9avNhCr08+sc6q3/YAheLCGgBEMoIfAEiltNiCj4u3Zo30/PN2krl0aWnYMBt3AWB7f2bPtiBo6lQbnfU8u571WzdQ6dLhf8589WoLI9assetUPXpYV1+g/DZSXL269OmngXu/kWbzZhsBe/dd+9ipWtUC9bp12a8HAOfDcmcASKXp0+0JS/fuhD74s8OHpXbt7CpPQoIFPitWEPoAv3fppdJ999lOlx07pG++sc+pp09LXbpIZcpIRYrY0t+pU+2SWDgqVcrGkR57TOrd2xYTb9sWuPd/xRVS5862R27evMC930hTrJh1/mzbJg0cKG3ZItWvL117rTR8uHT0qOsKASBy0PEDAP8gLZzZRcokJ9u4R8eO0p49UrNm0quvSnnzuq4MiCy7dknTptlI2BdfSMeO2cWwO++0bqA6daSrrnJd5Z+NG2e7fzJlsvGsevUC835PnLCAI08eC5ODsUw60iQlWQfUoEE2DpYrl+0Beu459gABgETHDwCkyttvS2vX2p4WQh/8JjFRqlJFeuQRqXBhaelSaeRIQh8gJQoUsOD000/tStjMmdITT9iemxYt7M9YTIzUtas96T971nXFpnFj6+4rUkS65x7bRXP6dOrfb5Ysthw+MVEaPTr17y8aZMggPfCALQ9ftMiWbA8YYJ1BjRtbQAYAOD86fgDgbxw8KF19te2dmDMn/HdPIPj277cnn2+9Za/G9+tn4Q+vyAOB5/sWvP92Jezrry30yZNHql3bOoHuusuWLbt06pTUvr2NecbG2vWv1J6wT062/UfbtkkbNnDe/Hx++sl+zd95x0ZuK1e2PUD33MMeIABpDx0/AJBCvXtLBw7YiVlCn7Tt7FlpxAi7sjNypC1xXr/e9nwQ+gDB4XnSdddZqPLVV9LevdKHH1rYEx9vS5bz5JHuuMM+T69f76bOzJltEfGnn9oBgLJlpQkTUvc+06WzsaZdu6zjFH9WtKjt/9m2zX6ttm+XGjSwz9NDh9pSaAAAHT8A8Jc2bbInHE2aSO+957oauLRokdSypY1d3HabPaEoXdp1VUDalpRkI5a/dQOtXm1vv/pqu/xUt66NY4Z6RHfLFumhh2wkrXlzafBgG91KqUaNpClTrOvnyisDV2c0SkqypdiDBtnn7Zw5pSeftD1AhQu7rg4Agotz7gCQAvffb9e8NmyQChZ0XQ1c2LXLFjePGWPLQwcMkP79b7q/gHC0ZYtdA4uPt9HcU6fskljNmhYC1aol5c8fmlrOnJG6dbM9PaVKWffP9den7H1t3iyVLCk9+KA0alRg64xmS5daAPTxx/btBx6wHUzly7utCwCChVEvALhICxZIkybZk35Cn7TnzBkbH7j2WnvC1rmztGaNvfJO6AOEp99OwU+bZru4Jk8+13nz2GO2QLpCBalXL+veC+ZrnxkzSn36SDNm2MW/2FjrHE3JYxYrJrVqZUueef30wlWoYLuWfvzRAp9p0+xtlStLn3wSPgvCASAU6PgBgD9ITrZ/HO7aZfsismZ1XRFCafZs29+zZo0tjx08WCpRwnVVAFLK96Vvvz03ErZsmb2tYEFbDl23rp2ND9by5F27bGR4zhzp4YelN9+0TqSLceiQfR667jpp3jwC6JQ4csTCtyFDrIuqWDHphRekxx+/+N8PAAhHdPwAwEUYN85eVe3Th9AnLdmyxcb7atSwc8xTptjYCKEPENk879wp+CVLLIh5/32pYkXrCLn3Xunyyy3off11uxQVSAUKSLNmSS+/bIupy5WzjqOLkTOn/fj5822HDS7epZda0LNhg3X0XnmldVIVKiS1a2d/BwBAtEpVx4/nebkkvSOplCRf0uO+7y/+q+9Pxw+AcHf8uI335M9vrwpzrSn6nThhF3P69rVvd+kitW0rXXKJ27oABN/p0zbaO3Wqhb0bN9rbS5U61w10yy1ShgyBebz58238bN8+Gyd99tkL795JSpLKlLHdRT/8EPql1dFo2TLbAzRxon27YUM7B1+hgtu6ACAlgtnxM0TSDN/3S0oqI2lNKt8fADg1cKCdg42LI/SJdr4vff65dMMNUvfu9gRv7VoLfgh9gLQhUyYb84qLs06Qdevs74F8+ezrKlXshYAmTaxb55dfUvd4Vava2FmNGnZpqmHDC3+fGTJYTZs2ScOHp64OmPLl7fd182YLfGbOtKCvYkVbCp2U5LpCAAiMFHf8eJ6XQ9K3kv7lX+A7oeMHQDjbudPGeu6+29rAEb3Wr7eW/xkz7NLOsGHSHXe4rgpAODl0yEa04uNtMfC+fVL69FKlShYU16ljO3dSsm/H963TpFMn2zX04YfSrbde2I+9+24bWdu4UcqT5+IfG3/t6FEbAxwyxAK2IkXs74pmzaQcOVxXBwB/L1gdP/+StFfS+57nJXqe947neUFaiwcAwffSS3bN6bXXXFeCYDl61J5olSolLVpkT7xWriT0AfBnOXPaCfBRo6Tdu+06WKdO0uHDUocO1i1YvLgtg581y0awLpTnWYfJwoXWXVqliv3dk5z8zz924EBbVPzyyyn/ueH8sme3Tqx166RPP5UKF7bfp0KF7OtA738CgFBJTcdPrKQlkir5vr/U87whkg77vv/SH75fc0nNJalw4cLltrA5DUAYSky0hZtt2kgDBriuBoHm+/aKevv21tn16KO2vPuKK1xXBiASbdtmXUDx8dKXX9qusGzZbISrbl1bFF2gwIW9r0OHpCeftD0zd91lZ9vz5fv7H9OihTRypLR6tVSyZOp/PvhrCQn2IsFHH1kw16CB/VvhQju0ACBU/q7jJzXBzxWSlvi+X/TXb1eR1Mn3/Tp/9WMY9QIQjnzfdjysWmWt87lyua4IgbRqlb2CO3++hXvDhvEPdgCBc+KENHfuuXPx27bZ28uVsxCobl3pppv+fm+c70tvv21jRZddJo0d+/ediD//LF19tVStmi2lRvBt3267ld56Szp40HYBtWkj3Xdf4JZ/A0BqBGXUy/f93ZK2eZ537a9vulPSDyl9fwDgypQp9o/2Hj0IfaLJL79Y4FO2rPT99/aP9aVLCX0ABFaWLNbh88YbdhJ81Srp1VelzJltHOvmm22PT7NmNj505Mif34fnSU89ZVemcuWSqleXunX76+XC+fLZIvr4eGn27OD+/GAKFbLrj9u22QsI+/ZJ//63BXBxcda5BQDhKrXn3GNk59wzSfpR0mO+7//lbQI6fgCEm9Onbd9L+vT2j/WMGV1XhNRKTpbee0/q3Fk6cEB6+mmpVy8pd27XlQFIa/btsyXy8fH29aFDdkmsWrVz3UD/+tf//pijR6WWLW23UNWq0rhx0pVX/vl9nzxpy6Vz5JBWrLC/xxA6Z8/a7+ugQdJXX0mXXmrh3vPPS8WKua4OQFoUlFGvlCD4ARBuhg611vr4eLvQgsi2bJk9YfrmG6lyZXtVNibGdVUAYMcDvv5amjrV/s5Zu9beft119vdP3bp2Rvy3FyDGjLFdPpdcYiHQ+f6O+ugjqVEj6Z13LHSAGytWWAA0fry9+HDffef2AKXk6hsApATBDwCcx4ED1qJdrpxdZOEfZ5Hr55+tw+e992yhav/+UuPG/J4CCF8bN1oINHWqNG+eBUO5ctmC57p1pVq1rGOoUSPp22+ltm1thCxTpnPvw/ftvPzmzdL69dZ1And27Di3B+iXX6Ty5S0AatiQPUAAgo/gBwDOo3VracgQO+d9442uq0FKJCVJr78ude8uHTtmv6cvvcSTHwCR5cgR6YsvzgVBe/bYMuhbb7XjA3PnSgsWWJAwfvz/jhItWWLfr0sX6ZVX3P0ccM6xY9alNXiwtGGDdNVVNgL2xBPsEgQQPAQ/APAH69dLN9xgZ71HjnRdDVJi3jxb3rx6tVSzpoV4nDUGEOmSk6Xly20cbOpU++8/GjNGatLk3LcbN7bF0evWSYULh65W/L3kZPs9HDTIwrvs2aXHH7cR8z/udgKA1CL4AYA/uO8+u4SyYYN0xRWuq8HF2L5datdOmjBBKlLEXlG9917GugBEp507pWnTLAj6/PP//X+DB0v332+Lhq+91kaK/vtfN3Xi7yUmntsDlJQk1a9vY2CVKvH3F4DAIPgBgN+ZO1e64w6pd2/pxRddV4MLdeqUncx95RV7FbVjR/uSJYvrygAgNE6etBct6tX737fHxEjZstny6EWLbPQL4WnnThtRHjHCdg3efLONKd9/P5dFAaQOwQ8A/OrsWSk2Vtq/31riCQ0iw7Rp1hq/caN1a8XFSUWLuq4KANyZNu1/L315ni17zpDBRr/q1bMx2Bw53NWIv3b8uDR6tHUBrV8vFSpke4CefJI9QABS5u+Cn3ShLgYAXBozxpY59+1L6BMJNm2yJy916kjp00szZ0qffELoAwC1a9voa7Vq9u06daT//MfGiCZNkh54QLr8clsOPWiQjTYjfGTNKj39tLRmjTRlinTNNVKHDucCoE2bXFcIIJrQ8QMgzTh2TCpRwhZfLl7MTH04O3ZM6tPHzrJnyiR162YdP78/YwwAsE7WXr3sy9VXW9dPUpIdLvjiC9sN9P339n1Le8J2ZQAAIABJREFUlLBT8XXrSpUr8zk13KxcaXubxo2z38N777U9QJUr828WAP+Mjh8AkIUIu3bZmBD/gApPvi9NnChdd53tYHrgARvJa9+eJygAcD7p00s9ekhffmln4X/6Sdq82a6B9e1rlw83b5aGD5eKF7f9MnfeKeXJY59jR42Sfv7Z9c8Cku1q+uADacsWqUsXacECqWpV2wM0bpx05ozrCgFEKjp+AKQJ27dbG3W9enYNCuHn+++tvX3OHKlMGWnYMKlKFddVAUDk2LtXeuQRafp02/WzZo11Af3e0aMWEk2dat1Au3bZiyEVKti4WN269jmYF0jcO37cRtQHD5bWrpWuvFJ67jmpeXPpsstcVwcg3LDcGUCa98gjdkJ17VqpWDHX1eD3Dv0fe/cdHlW5dQF8JaFFeu+d0HtNUBQFpQVEREVREBQBKQLSi1TphCYKYhcV7EpARERsJNRQpUXA0HsLJIQk5/tjfXNPRhQpM3mnrN/z8OSaA2SD3syZfda79wU+rZ47l0NIJ0zgTW2GDKYrExHxPqmpnBUzYwaQPTuPezVo8M8/17K4ZtzRBFq/np8vWtRuAjVpwnk0Yk5qKrBiBWc1rVrFfx9duvAIdEiI6epExFOo8SMifm3jRsakBw8GpkwxXY04pKbySebgwXxK3a0bj3fly2e6MhER79e+PYc8BwXxyNeAAUDgfwx5OH6caaFlyzhMPz4eyJIFeOABNoJatQJKlkyf+uWfbdvGBpDj6Ffr1vx3e++9SmmJ+Ds1fkTEb1kW0Lgx4+779gE5c5quSADOnujdG4iOBkJDOXuiTh3TVYmI+I7TpznTJzgYOHECaNGC83zy57+5X5+UBPzyC5tAS5faW6aqVWMSqFUrfv8OCnLfn0H+3fHjwOuvA2+8wX/XtWsD/fsDjz+umXgi/krDnUXEb331FW9cx41T08cTnD4NdO/OBNb+/cC77wK//66mj4iIq+XLx42IJ04AvXpxrk/NmsDPP9/cr8+UCWja1F4Fv3s3MH06V8RPm8ZNUwULcoX84sXAuXPu/fOIs0KFeG8TFwe8+SaQkMB/F6VLcyvm2bOmKxQRT6LEj4j4rKtXgSpVgMyZga1bNTPGpJQUYMECYORI4OJFDnEePVrNOBERd7p6Fahcmamf994DOnYEYmPZEBo58vbTOufPAytXci7Qd9+xqR8UxGaQIw1UsaKOHqWn1FT+O4mI4Fynu+4Cnn2Wc4DKlzddnYikByV+RMQvzZvHaPqMGWr6mPTbb0z09OoF1KrFJlxEhJo+IiLuljkzMHUqtyZu2sQfHTtyoP6DDwJHj97e75srF48UffABjxytXQsMGcLUz6BBbDaVK8emw8qVbECJewUGAs2b8+972zagQwfgrbfYgGvTBvjpJx5/FxH/pMSPiPik06d50xkWxqeRkv6OHuXg5o8+AooXZwOufXs9ARYRSU+WBdx3H49qxcZye+L77wMvvghkzcrmTfPmrvt6cXHA8uVMA/34I5CYyK/z0ENMA7VsyWNK4n4nTnAG0Ouvc4lCzZocBP3EE5oDJOKLlPgREb8zbhxw6RLnEUj6Skri/IcKFYDPPgNGjOBw7cceU9NHRCS9BQQwZXnqFGe/AEDnztx4WagQhz4PGcINUa5QogTQowcbP2fO8OMzzwAbNgDPPQcULsw5b2PHMoGUmuqaryvXK1iQ6a6//gIWLuTrc6dOQKlSwMSJ/PcjIv5BiR8R8Tm7dwNVq3I9+BtvmK7Gv6xcyfk9e/bwye7MmUxeiYiIWZ06AZ9+ytfIUqX4uYQEJkDmz+eGrk8+sa+5mmUB27ezERQZya2OlsXmU6tWfM1o2hTIls09X1/4971yJV+bv/+es586dwb69ePDGhHxblrnLiJ+pXVrbi2JjQUKFDBdjX84cIBvHr7+mo2eWbN4Iy8iIp7h0CG+uW/Thlu40vrsM+D55zkn5u23gXbt3F/PqVPAihVsAq1YwcH/mTIBjRvbA6LLlHF/Hf5q506+Vn/4IWcwtWrF1/H771c6V8Rb6aiXiPiNVat4EzlihJo+6SEhgXH9ypX5FHHiRGDHDjV9REQ8TfHiwMCBwJIlQFSU87XHHgNiYti4f/RRoHdvzuZxp/z5eQRsyRLO5Vu9GujTh8eS+vYFypblZs7Bg4FffgGSk91bj7+pUoXHv+LieBxswwagSRPOAXr/fQ3kFvE1SvyIiM9ISQFq1+ZTw127gCxZTFfkuyyL6Z4BA4CDBzkocto0vrEQERHPFB/P1d4lSrD58/dkR1ISMGwYZwLVrMmmjIlV4LGxwLJlfJDz88+cP5QrF+cRtWrFYdR586Z/Xb4sMRH4+GMeA9uxg0fwevXivKZ8+UxXJyI3Q4kfEfEL777LFaZTpqjp4067d/Omu107zmJYvZrHBtT0ERHxbNmyAa++Cqxbx6bO32XKxA2MS5fyaFjt2sCiRelfp2MV/A8/MA30+edA27bcEvb000z0NmoETJ7MJoXWlN+5LFmArl15H7VyJRt/o0bxtb1HD772i4j3UuJHRHzCpUtASAjnAfz+u86nu8OlS8D48XwamDUrN6e9+CKQIYPpykRE5GalpAB16wJnz/LNfHDwP/+8w4eBp54Cfv0V6NIFmDuX3/tNSk3lNrLISCaCNm/m50uWtOcC3X+/Hv64yh9/cA7QBx/w6FfLlkD//jwSpvssEc+jxI+I+LwpU4ATJ9iU0M2Ia1kW8NFHHAo6bRo3w+zdyxkMavqIiHiXoCAe5YqL45v6f1OsGBOdo0YB773HFezbt6dbmf8oMBCoX58PHjZtYnPqzTeBGjWY+m3ZkkfAHn6Y82uOHDFbr7erXJl/v4cO8e9840bgwQftv2/NARLxHkr8iIjXi4tjU6JdOzYoxHW2bOGwzd9+4xPi114DGjQwXZWIiNwpx9Gp2FigYMEb/1zHEavz54HZs4Fu3TzvIUtiIrBmjb0u/q+/+PlatZgGCg/n61igHnvftqtXgU8+YeNw+3b+d+OYA5Q/v+nqRETr3EXEpz39NPDFF8CePRxYKXfu7Fk+5Z0/H8iTB5g0iWf/dcMsIuIb9u7lZqeuXYEFC/775584wcTnypXA448zCZIzp/vrvB2WxWNKjibQ2rU8JlagAFNB4eFMruTIYbpS72RZbAbOnAksX86jdc88A/Trx5SQiJihxo+I+Kz165lAGT6cAyvlzqSkAG+/zb/Pc+c4w2fcOCB3btOViYiIq/Xrx9k9W7YA1ar9989PTQWmTgVGjuRcncWLeQTM0505A3z/PZtA333H5FLGjMC999ppoHLlTFfpnXbtYgrs/feZumrenBs/mzb1vFSYiK9T40dEfJJlAffcw5h6bCyQPbvpirxbVBSPdW3axG0pc+fyHL+IiPims2fZ8Khbl42Rm32jvnYt8OSTwLFjnLHXr5/3vMlPTubrnSMN9Mcf/Hz58nYT6J572BiSm3f6NJNjr70GHD8OVK3KQdBPPaVh2yLpRcOdRcQnff45bz4nTFDT506cOMGNLQ0b8ib+44+Bn39W00dExNflyQOMHs216d99d/O/rmFDICaGW7QGDADatGGqxhtkyMCHG1OmADt3Avv380FH6dJsWjzwAJAvH4+zffABcOqU6Yq9Q758wIgRwMGDHAYeFAQ89xyTYWPHAidPmq5QxL8p8SMiXikxkefIs2XjzWdQkOmKvM+1a7zJHTMGSEjgzfvIkfw7FRER/5CUxHRGUBCwbdutJV0si68jAwdyfs7HH7Op4q3i44FVq7gqPjKSyZWAACA0lE2u8HCgenXvSTeZZFnATz9xDlBkJJA5M2cy9u/P2VIi4npK/IiIz5kzBzhwgJsl1PS5datXAzVrstnTsCG3c0yerKaPiIi/yZQJmDYN2L2bK9BvRUAAjwhHRfE4T+PGTOGmpLilVLfLlo3bzhyr4DduZCIqOZkPRmrW5BKJHj3YzLhyxXTFnisggOmppUv531aXLmwMVq3KOUDff8/mkIikDyV+RMTrnDwJhITwqWJkpOlqvEtcHJ/MfvYZY+2zZgGtW+vppYiIP7MsvknfsQPYtw/IlevWf49Ll9gQ+fhjoEkTYNEioFAh19dqyvHj3GC1bBk3m8XHs9nVpAnTQK1aabPofzlzxp4DdOwYkz/9+jEJpDlAIndOw51FxKe8+CLXyG7fDlSqZLoa75CYCMyYwc1nlsWtXQMHAsHBpisTERFPEBMD1KkDvPwyE0C3w7KAd98Fevfm7L0PPwQeesi1dXqCq1eBX36xB0Tv38/PV69uHwlr0ECJ5H+TlAQsWcLU9pYtQP78vLfr2RMoWNB0dSLeS40fEfEZO3fyxqpnTz4xkv8WGQm89BJvTB99lA2gkiVNVyUiIp6ma1fgo4+46aps2dv/fXbuBJ54gh+HDgXGjfPdLVmWBezZYzeBfvuNR93y5gVatmQT6KGHbi9F5essi8skIiJ4JCxTJnsOUNWqpqsT8T5q/IiIz2jZkpu8YmO5QUL+3b59jFAvXw5UrMitJU2bmq5KREQ81dGjPErdsiWPBN+JK1f4GrRwIWfJffKJfxyFOneOR8EiI/n6e/Yskz+NGrEJ1KoVUKGCjlj/3d69wOzZTIwlJAAPPsgGULNmQKCm0orcFA13FhGf8P33XDc7apSaPjdy+TKPclWtCvz6KzB9OrB1q5o+IiJyY0WKAEOGAJ9/zuTKnbjrLh7L/uQTHs2uWRP45hvX1OnJcudm2unDDzmT8PffgcGDOd9m4EAeUQ8JYVNs1SoeexKgfHlg3jzg8GFg0iSmxVq25L3MwoVsBonI7VPiR0S8QnIybxoTEhhBz5zZdEWex7KATz/ljeXhw0CnTtzUVbiw6cpERMRbXL7MRErhwsC6da5JW8TGAh06AJs2AX37AlOn+ufreFycvSp+9WrO38uWjUfBwsPZ6NCMG0pK4j3NzJnA5s184NezJ2cB+dLQcBFXUuJHRLze22/z6Y+/3iz+lx07uJGlQwfeHP32G/D++2r6iIjIrcmalYmLjRu5ocsVypVj8qVfP2DOHB79io11ze/tTUqUYPNi2TImgJYuBTp2ZIOta1c2NOrX50ykzZv9e925Y97Pxo3AmjXA3XcDEyZwRmGXLsC2baYrFPEuSvyIiMe7eJE3jRUrcgigzsXbzp8HxozhoOucObm1q1s3bRIREZHbl5rKBsSJExxcfNddrvu9v/0WePZZJnkXLACefNJ1v7e3siweyXakgdat4+cKF7a3hDVtyqacP9u3j43Dd97hDKmmTTkHqHlzzQESAZT4EREvN3EicOoUtz6o6UOpqbzxKV+eN0HdunEwYo8eavqIiMidCQzka+7hw/zoSm3acIV39erAU0/x9evKFdd+DW8TEMDj7CNGAFFRwPHjwHvvAffcw+NObdtyS1jz5nzQc/Cg6YrNCAnhoorDh3mUfdcuNsaqVGET0d//OxK5ESV+RMSjHTjApM8TTwAffGC6Gs+wYQPQuzewfj3j8nPnArVrm65KRER8zaOPcrHCvn2uPzqcnAyMHs1jZZUrA0uW8A28OEtK4vFtRxpo715+vkoVOw0UFgZkyGC2ThOuXeP2uYgIzo/Km5cPwHr10lF38U9a5y4iXqtDB8bC9+wBihc3XY1Zp05xW9fbbwMFCgDTpvH8u1JQIiLiDrGxbMo88wxfe9zhhx/4WnbpEhOszz2n17Ub2buXTaBly3j8PTmZm8RatGAjqHlzIE8e01WmL8ticywigpvjMmRgmqx/f6BGDdPViaQfNX5ExCtFRTHRMmoUBx36q+RkYP58/j3ExwMvvQS88gqQI4fpykRExNcNHMg31Js38ziSOxw/zubPjz/ygc+CBXqNuxkXLrBxFhkJLF/OB0SBgRyEHB7ORlDlyv7VSIuNtecAXb7MxRf9+3NjmuYAia9T40dEvI5lMbocF8enW9myma7IjF9+4bGu7ds5xHDOHKBSJdNViYiIvzh/ngsWatQAVq1yXxMhJQWYMoUPNkqV4tGvOnXc87V8UWoqj4JHRjINFBPDz5cqZTeBGjcGsmQxWWX6OXcOeOst3jcdPsyZiP36AZ07u3ZYuYgn0XBnEfE6ixdzq8Wrr/pn0+fIEcaU77uPT/Q+/xxYuVJNHxERSV+5cnF75OrVbCq4S1AQjzOvWQNcvcqHP3Pm+PdK81sRGAg0aACMH8901qFDTE5Vq8Zjei1acAZO27ZsiBw9arpi98qdGxg0CNi/H/jkEybIXnyRYwNGjPD9P7/I3ynxIyIeJyGBA53z5AE2bvSvLVVXrwKzZvHGLTkZGDKEP/R0SkRETLl2jVu4UlOZQM2Uyb1f78wZoEsXYOlS4OGHgUf7HcX86N04ej4BRXIFY1CzCmhbq6h7i/AhCQlsqEVG8kdcHD9fuzbTQOHhTFf58lEoywJ+/x2YORP46ivOAerQgcfAatUyXZ2Ia+iol4h4lUmT+NRv9Wrg/vtNV5N+Vqzg/J69e7nuduZMoEwZ01WJiIjw+FB4ODB7NtC3r/u/nmXxaw0aZAF3JSJv6xhkKXYOABCcMQiT2lVT8+c2WBawc6fdBIqKYkOvYEHOwQkPBx58EMie3XSl7rN/P9Nkb7/N2YmNGwMDBvA4nC83v8T3qfEjIl7jxAnOEmjSBPj6a9PVpI/9+3nD8c03QEgIb0aaNzddlYiIiM2ygIce4trs2Nj02xxVucsW7F5UDVZKAPI8tAPZax4CABTNFYzfhz6QPkX4sDNn+OApMpIfz58HMmbkUXNHGqhsWdNVusf58/YcoEOHeA/mmAOUNavp6kRunWb8iIjXGDUKSEwEpk41XYn7XbnCIZaVK3Ng5uTJjNCr6SMiIp4mIACYMYNvlidMcM/XSE7mfJrXX+cK+XLlgF3v1YSVHAQEAMnn7HfjR88nuKcIP5M3L9CxI+fgnDzJI2H9+nHWYL9+/HdQsSK3u61Zw2N/viJXLv659u/nbMncuYFevTgHaNgw/h2I+AolfkTEY2zbxnPWffpwzo2vsiyeL+/fn+fsn3wSmDYNKKrEuoiIeLhu3YD33+dxoZCQO/u9Tp7kUaPoaH7csIEPRQCgUCEOeN6cGIvEPCeRqeAFBGZM/d+vVeLH/fbv5xG/yEg2fZKSgJw5gWbNmARq0QLIl890la5jWfzvcOZM4MsveezriSeYyq5d23R1Iv9NR71ExOOZipCnt127OBth1Spu2pg7l3FqERERb3D8OBs+TZvyIcbNunaNqdaoKPvH/v28liEDH/yEhdk/SpRgyujrmCMY9uV2JFxL+d/vpRk/6e/SJd67LFvGH8eP899PWBhn44SH874mIMB0pa5x4ACPgL31FucA3XcfH9iFh/vX0hHxLmr8iIjHW76cNw4zZzJa7GsuXgTGjeOgymzZuLWrRw/e7IqIiHiTiRO5EvunnzgY95840jyOHxs32mmewoWdmzy1awPBwf/+9b6OOYJp3+/RVi8PkZrKI3mRkWwCOd7eFS9uN4EeeODG/069xYULHAI9ezZT2uXKcRHHs8/yfk7Ek6jxIyIezbEmNiUF2LHD/Wti05NlAYsWAYMHc3D1c8/xhjl/ftOViYiI3J6EBKBCBR7z2biRr9/btjk3eg4c4M/NmPH6NE/x4r6TDBHg2DE+wIuMBH74Abh8mU2fJk3YCGrViv/OvVlyMhNuERE8mpgrF9C9O9C7N1CsmOnqREiNHxHxaPPm8YXz66+Bhx82XY3rxMTwz7V2LVC/PvDaa0C9eqarEhERuTMnTgBTpjClW748NyIl/P+s5SJFrk/zZMlitl5JP1evAj//bK+LdzQAa9RgEqhVK94TefNxKcccoC++4Bygxx/nMbC6//h2WyT9qPEjIh7r/HnGZqtVA1av9o0ngGfOACNHAgsW8GnolClcDRqoPYoiIuJlrl0Dtm51TvMcPMhrAQE8svzCC8C99wKhoUrziM2ygN277SbQ778zHZYvH9CyJZtAzZpxYLQ3OniQsxoXLuQMpEaNOAi6dWvvbmyJ91LjR0Q81qBBXA+7aROj4N4sJYUv/iNG8Ex4r17A2LGMA4uIiHiD48ftBk90NI9yOdI8RYvaSZ7QUKY7HngAGD0aGDPGaNniBc6dA77/nk2g774Dzp5l47BRI6aBwsOZIPM2Fy8C77zDOUAHDwJly3IOUJcumgMk6UuNHxHxSH/+CVSuDHTsyBdMb7Z2LY91xcRw0OWcOUwxiYiIeKpr14AtW5xXqjvSPJky8ZiWo8njmM3zd088ASxdCuzbx8aQyM1ITgbWrbPTQDt28PPlytlNoEaNvGvuY3IyxxbMnMn7wpw5mYbr08f7ZxyJd1DjR0Q8Uvv2fOKzbx9nAnijY8eAIUOADz/kDe+MGTzrrZi7iIh4mmPHnJs8GzcCiYm8VqyYc5OnVq2bm81z4ABQsSLw5JPAe++5tXzxYX/9xQ1hkZE8+n/1KpA9O/DQQ2wCtWgBFCxousqbt24dG0Cff85/fuwxHgPTrEdxJzV+RMTj/Por5wGMHQu88orpam7dtWs81z1mDG9OXn4ZGD5ckV4REfEMSUlM8ziaPFFRfHMNMEVRp47d5AkLu7PNREOGAFOnspFUp45r6hf/dfkymz+ONNDRo3ygVq+enQaqWdM7HrLFxfF+8c03eSTsnns4CPrhhzUHSFxPjR8R8SipqUCDBnzyuGcPkDWr6YpuzapVQN++wK5dHE44axYQEmK6KhER8WdHjzo3eTZtstM8xYs7N3lq1QIyZ3bd175wga+DlSoBa9Z4xxty8Q6WxQZmZCQTQevX83NFinA4dHg418Z7+r3kpUv2HKADB4DSpTkHqGtXJptEXEGNHxHxKIsWAc88A7z/PtCpk+lqbt5ffzHZ88UXQJkyfPEODzddlYiI+BtHmiftpq24OF5zpHnSrlRPj9k78+cDPXsCX34JPPKI+7+e+KcTJzgmYNkyDoq+dIlNzAceYCOoVSugVCnTVf67lBTgm2+AiAhuOcuRA+jWjQ8US5QwXZ14OzV+RMRjXLkCVKjAc9rr13vHivPERGDaNGDSJP7ziBFsAN3M7AMREZE7dfSoc5Nn0yYeMwaY5knb5KlZ07VpnpuVnAzUqMG6/vjDu4byindKSuLoAMeRsNhYfr5qVTsNFBrKzWGeaP16zgH67DP+c/v2nANUv77ZusR7qfEjIh5j/HjO9Pn5Z8748WSWxU0l/foxlvvYY8D06XoiIyIi7pOUxA2RaRs9hw7xWubMzmme0FDP2qS1YgWH8EZEcI6JSHrau9c+EvbLL2xG5snD/yZbtQKaNwdy5zZd5fXi4oDXXuMcoAsXgIYN2QBq21ZzgOTWqPEjIh7h6FHOAGjenMelPNnevTx7vWIFV87PncsYsYiIiCsdOeLc5Nm82U7zlChxfZrH05M0zZtzo1FsLJA3r+lqxF9duACsXMlG0PLlwOnTbKLcfTeTQK1acSaVJ82junSJm/FmzQL27+eRNcccoBw5TFcn3kCNHxHxCM89x7Xnf/wBlCtnupp/Fh8PTJjAp5XBwdza1bs3kDGj6cpERMTbXb1qp3kcg5jTpnnq1nVO8xQpYrbe27FzJ1C9OtCrFzBnjulqRDhXZ8MG+0jY1q38fOnS9paw++4zc0Tyn6SkMHEeEcGjbDlyAM8/zzlAJUuark48mRo/ImLcli1A7dqMrk6fbrqa61kWsHgxMGgQn74++yxn+hQqZLoyERHxVocP20me6GjO5klK4rWSJZ2bPN6Q5rlZPXsCCxcCO3YAFSuarkbE2aFDTAFFRgI//ggkJHAr2IMPsgnUsiVQuLDpKmnjRs4B+vRTbsV99FHeS4eGmq5MPJEaPyJilGVx1ea2bYx+58pluiJn27YBffrwPHjt2jxnHRZmuioREfEmV6/ymFbaNM/hw7yWJQvTPGlXqnvKG0t3OHmSyd7GjYFvvzVdjci/S0gAfvrJTgM5Enh16thpoNq1zS8jOXyY96cLFgDnz/N7yYAB3KDnqcOrJf2p8SMiRn37LfDww5yT07u36Wps585x0PTrr3PY38SJPI6mQXoiIvJfDh1ybvJs3myneUqVspM8YWHcduUraZ6bNWUKMHQosGoVH/6IeDrLYkrN0QSKiuLnCha0t4Q1bQpkz26uxvh4zgGaPZsPU0uW5BGw554DcuY0V5d4BjV+RMSYpCSu1QwMBLZv94xZOampwDvvAMOGAWfPAj16cNtYnjymKxMREU+UmMjGjqPJExXFY8EA0zz16tlNntBQ307z3KzERA7PzZGDf3d6qCLe5vRpLvmIjOTHCxfYwL3vPntAdNmyZmpLSWFdERFMrGfPzuZP376cXST+SY0fETFmzhxuJFi6lC+Spq1fz9TRhg3c7PDaa5yrICIiAvAJ/6FDzk2emJjr0zyOHzVqeMZDDU/06afAE08Ab73FN6Ui3uraNeD337kqPjIS2L2bn69Y0T4S1rChme8FmzZxDtCSJXy42a4dj4FpbIH/UeNHRIw4e5Zn/OvU4UpNkyszT55kwuedd/gkdto04KmnPGuNp4iIpD9HmiftSvWjR3ktOPj6TVsa+n/zLIsPWQ4cAPbuNXtERsSVYmPZBFq2DFizho2hXLmAZs3YBGrRAsibN31rOnLEngN07hzQoAEbQO3aaQ6Qv1DjR0SM6N+fZ5C3bOFqVxOSkznD55VXgMuXWdOoUbr5FBHxR440T9omT0wM37QBPCKRNs1TvbrSPHcqOpp/lyNH8li1iK+5dAn44QcmgZYvB06c4IiDsDB7NlDVqun3sPHyZeD995kCio0FSpTgEbDnn9ccIF+nxo+IpLu9e4EqVbgWfeFCMzWsWcNtXTt2cEXnnDlaKysi4k8SE3kMIm2j59gxXgsO5myetGmeggXN1uurnnoK+Oor3hsUL266GhH3SU3l95zISKaBNm3i50uUsOcC3X9wyzPzAAAgAElEQVQ/v/+kRy3LlnEO0Jo1QLZs9hygMmXc//Ul/anxIyLp7pFHuMlj3770j8UfPgwMHMizziVL8olH27Y61iUi4sssC4iLsxs80dHOaZ4yZZybPErzpJ+4OKBCBeDRR4FFi0xXI5J+jh5lCigykqmgK1fY9Gna1G4EFS3q/jpiYng//MknbAi1bctjYA0b6v7Yl6jxIyLpas0aPs149VVg+PD0+7pXr/KpxoQJfFEbMoQ/0uOpioiIpK+EBDvN4xjE7Ejz3HWXneYJDVWaxxOMGAFMnAisWwfUr2+6GpH0l5gI/PyzvS7+4EF+vmZNuwlUr557N+AdPQrMmwfMn89ZnPXqsQH06KNqhPsCNX5EJN2kpPBF5PRpYM+e9Gu6LF/O7WGxsUwbRURw84qIiHg/ywL++uv6NE9yMq+XLWs3eRyzeTTM1LNcusSFDyEhwK+/KmUg/s2ygF277CbQ77/zoWX+/EDLlmwEPfQQkCOHe77+lSvABx8wBbR3L1CsGI+AdevGIdXindT4EZF08957QJcuwEcf8Uy/u/35J9CvH180y5fnHJ9mzdz/dUVExH0SEoCNG53TPMeP89pddzEx4mjyhIYCBQqYrVduzsKFwAsvAJ99BrRvb7oaEc9x9izw/fe8n/3uO27lypABuPdee118SIjrv25qKh+ezpwJrF4NZM0KdO3Kh6lly7r+64l7qfEjIuni8mW+KBUvzpv0wED3fq1Jk7iWPVMmbu166SX+bxER8R6WxSMPaZs8W7Zcn+Zx/KhWTWkeb5WSAtSqBcTHA3/8AWTJYroiEc+TnMzvg8uWsRG0cyc/HxJiN4Huucf197xbtgCzZgEff8wa2rblNtx77lFCz1uo8SMi6WLMGGDsWOC334C773bP17As4PPPgZdf5krejh2BqVOBIkXc8/VERMS1rly5ftPWiRO85kjzpB3CnD+/2XrFtVat4qbNqVOBQYNMVyPi+Q4etJtAq1cDSUlA9uxMuIeHAy1auDb1eOyYPQfozBmgbl3OAWrfXnOAPJ0aPyLidkeO8ElE69bcpuUOO3fy/PHq1UCNGsDcuUCjRu75WiIicucsCzhwwE7yREUBW7faaZ5y5ZzTPFWrKs3jD8LDOecnNlaNPZFbER8P/Pij3Qg6doxpnAYNOBw6PJz3yK5I6Fy5Anz4IY+B7dnDOUB9+nAOUO7cd/77i+up8SMibte5M7B4MbB7N1C6tGt/7wsXmCSaM4dD7iZM4IwAvTkQEfEsV67Ys3kcR7ccaZ6sWa9P8+TLZ7ZeMWPXLh7Z696dyQIRuXWWxSH3kZFsBK1fz88XLWo3gZo0YZLyTqSmAitWcHHKjz/ye3mXLhyxUK7cnf85xHXU+BERt9q4kZu8Bg8Gpkxx3e+bmsonDUOGACdP8gnDq6/qjYKIiCdwpHnSHtnaupVzXACmQNOmeapUUcNebL178yjJtm1A5cqmqxHxfsePczD0smUcFB0fzzlaDzzARlCrVkDJknf2NbZutecAXbsGtGnDY2CNGmkOkCdQ40dE3MaygMaN+fRu3z4gZ07X/L6bNvGmMDqaT4XnzuUZYxERMePy5evTPCdP8lq2bM5pngYN1KSXGzt9mmmBhg25VUhEXOfqVR6ndKyL//NPfr5aNTsNFBoKBAXd3u9//Djw+uv8ceYMULs2G0CPPaZFKyap8SMibvPVV0C7dsAbbwA9etz573f6NDBiBFe+5s/PBFGnTu7dECYiIs4sC9i/37nJkzbNU76885GtqlVv/w2E+K8ZM4CBA3mMpFkz09WI+CbLAvbutZtAv/3GOWt583IwdKtW/P/f7cztSUgAFi3iHKBdu7hspU8fjmTIk8f1fxa5MTV+RMQtrl5ldD9zZr4huJMIf0oKsGABMHIkcPEihziPHu26BJGIiPy7y5eBDRucV6qfOsVr2bIxweNo8oSG8g2DyJ26epXHvIKDuUpaRwFF3O/8eWDlSjaBli9nYicoiGvbw8PZCKpY8daObqWm8veMiAB++IFzhZ59lnOAypd32x9F/kaNHxFxi4gIrlX/7jugefPb/31++43HurZuBe6/n8e6qlRxXZ0iImKzLMb+06Z5tm2z0zwVKthNHsdsHqV5xF2++IJrohcsYEpARNJPSgqHQjvSQNu28fNlythNoPvu40Pem7V9O+cALVrEOUCtWwP9+/P30Rwg91LjR0RcznE2PzSUEe3bcfQoB0J/9BFQvDgj3+3b60VBRMSV4uOZ5nEkeaKj7TRP9uzXz+ZRmkfSk2XxDeGePZwVmCOH6YpE/FdcHFNAkZHc4JWYyC1eDz3ERlDLlkChQjf3e504wVEQr7/O15xatdgAeuIJzQFyFzV+RMTl+vblCtatWznb4VYkJQGzZwPjxvF/DxoEDBvGFxYREbl9lgXExtpNnqgoPsFNTeV1R5rH8aNyZaV5xDzHdtChQ4FJk0xXIyIAcOUK8NNPdhro8GF+vm5dNoHCw9nM+a85nAkJfMg7cybwxx9A4cKcA9S9u+YAuZoaPyLiUrt3s9nTrRs7+bdi5Uo2jfbs4QvGzJlMDomIyK1zpHnSHts6fZrXsme3Z/M40jy6yRZP1akT8OmnvMcoVcp0NSKSlmXxIcKyZWwCRUfzc4UK2VvCmjblTLgb/R6OOUArV3K217PPAv36aQ6Qq6jxIyIu1bo18PPPfKpcoMDN/ZqDB7nm8auv2OiZNYsvFCIicnMcaR5HkycqirMUHGmeihWd0zyVKinNI97j0CEm0h5+GPjkE9PViMiNnDrFUQ+Rkfx48SKPbzVubM8GKlPm33/9jh32HKCrV/lrBgzgr9fIh9unxo+IuMyqVcCDDwKTJwNDhvz3z09IAKZO5c8PDOTWrgEDbm1InIiIP4qP59DNtGmeM2d4LUeO69M8t7OKV8STvPIKMH48sHYt/7sWEc937RoXtTjSQHv28POVK9tpoIYN/3lr38mTPD0wbx6bSTVq8H1Chw6aA3Q71PgREZdISQFq1wYuXGAUO0uWf/+5lgV8/TW/eR88yEFu06ZxiLOIiDizLA62TZvm2bHDTvNUqmQ3eUJDleYR3xQfzyMfJUuy+aMn/yLeZ98+NoGWLeMJgWvXgFy5gBYt2Ahq3vz6JQKJicDHH/MY2M6dPELWuzfnAOXLZ+bP4Y3U+BERl3j7beD554HFi9nI+Td79nCOz8qVnAU0Zw7XtIuICF26dH2a5+xZXsuZ007zhIYqzSP+5d13ga5dedyrQwfT1YjInbh4EfjhByaBli9nwicwkAkgRxqoShW7yWtZPF0QEcEjZFmyAJ07cw5QxYpm/yzeQI0fEbljly4BISE8r/v77//8FO7SJUa0Z83iwLbx44EXX/znaKeIiL+wLGDvXucmT9o0T+XKdpPHMZvnv7akiPiqlBRuDTp7luni4GDTFYmIK6SmcoOfY0tYTAw/X7KkPRfo/vvtEwV//MH3FB98wDlALVvyJMEDDygN+G/U+BGROzZyJPDqq3zTEhrqfM2yGM8cNAg4dgzo0oXrWAsWNFOriIhJFy/aaZ7o6OvTPI4GjyPNkyuX2XpFPM1PP/HN3aRJXPEuIr7nyBGmgCIjmfK5cgW46y5uBwsPZ6OnaFHO/pk/H3jtNSaGqlcH+vcHnnxSM0P/To0fEbkjcXHctPHII2zwpLVlC9CnD4e61a3Lb8oNGpipU0QkvaWm2mme6Gh7No9l8Ylk5cp2oycsjFF1pXlE/lvbtsDq1ZwXogdJIr4tMRFYs8ZOA/31Fz9fqxabQOHhQLVqwJIlPAa2fTu/L/TuDfTooTlADmr8iMgdefpp4IsvGLkuWZKfO3sWGDWKHfg8efhUrmtXvaEREd928SKwbp3d5ImOBs6d47VcuZw3bdWvrzSPyO3au5ezP7p2BRYsMF2NiKQXy+IxL0cTaO1aPmQpUIApoFatuNzgrbeYGMqSBejUiXOAKlUyXb1ZavyIyG1bv55vZIYP51GvlBQOeR4+nG92XnwRGDdOg0dFxPekpnJYvaPJExXFbSNp0zyOJk9YGJORan6LuE6/fsDcuUwXV6tmuhoRMeHMGeD779kE+u474Px5IGNG4N57gbJlgdhYNocSE7k5rH9/HhfzxzlAbm38BAQEBAHYCOCIZVnhN/q5avyIeBfLAho1Ysw6NpZveHr3BjZt4ufnzgVq1DBdpYiIa1y44Lxpa9065zRP2iNb9etzXo+IuM/Zs0C5cjxK/v33/vlGTkRsycl8fXakgf74g5/Pk8eepQewUeyYA+QYFu0P3N34GQCgLoAcavyI+IavY45g2vd7sC86J059UwdPvXgRma7kwHvvAUWKANOnc8WqbsBExFs50jyOJk9UFG8gHWmeKlWc0zzlyyvNI2LC7NlM/ixfzqf5IiIO+/cDy5bxx08/AUlJztcLFAB69QJ69gTy57ff4xw9n4AiuYIxqFkFtK1V1EzxbuC2xk9AQEAxAO8DeBXAADV+RLzf1zFHMOzL7biSYOHIm42RcikYARmTEWQF4eWXAzBiBJA9u+kqRURuzYULTPCkTfOcP89ruXM7p3nq1VOaR8RTJCUBVasCGTIAW7fyiIeIyN/Fx3M72LJlTAMdP25fy5wZaNTyMv4sEIPUXBf+9/ngjEGY1K6azzR/3Nn4+RzAJADZAQxU40fE+909eTWOnE/AiU/rIfFAAQBAltInUemR/dg8I/Q/frWIiHmpqRxGnzbNs2uXneapWtVu8oSGKs0j4um++YZbvubN42xBEZEbSU0FYmLYAFq2DNiwwb6WpfRJ5Gq0F5kLswFUNFcwfh/6gKFKXetGjZ8Md/CbhgM4aVnWpoCAgMY3+HkvAHgBAEqUKHG7X05E0klcnIWzq2v9r+mTv90GBJc7iXM61iUiHur8+evTPBf+/4Fenjxs7jz5JD/Wrw/kyGG2XhG5NW3aAI0bA6NHA089pW15InJjgYFAnTr8MXo00z9Vu27FlT8LIvFgPpz/pQIKPrEeAHD0fILhatPHbTd+ANwNoE1AQEBLAFkA5AgICFhkWdbTaX+SZVlvAngTYOLnDr6eiLhRYiIwYwZw9K3GcAQBg8uewF0hJwEARXIFG6xORIRSU5necTR5oqPt4Y6BgUzzdOhgH90qX17zyES8XUAAEBHBN3ETJwJTp5quSES8SaFCQIV7z+BI9cOwUgKANPcF/vIe57YbP5ZlDQMwDAD+P/Ez8O9NHxHxDpGRwEsvcUBaWJMknKy0DnGf1EFAhhQAPP86qFkFw1WKiD86d85O80RHX5/mCQtjmsexaUszyER8U61awLPPcthzjx5AmTKmKxIRbzKoWQUM+3I7EpDyv8/503ucO0n8iIiX27fP3pRRsSKwciXw4IPB+DomBE99yoEXRX1w4r2IeKbUVKZ3oqOdZ/MATPNUq8Y0j2M+T0iI0jwi/mTCBGDJEmDIEOCzz0xXIyLexPFexpe3et3IHa9zvxUa7iziGS5fBl59lUe7Mmfm2dc+fYBMmeyfU7kyj0x8+qm5OkXEt507xyaPo9Gzbh1w8SKv5c17/aYtpXlEZNw43rf8+itwzz2mqxER8RxuGe4sIt7HstjIGTgQOHwYeOYZYMoUoHBh05WJiK9LSbk+zbN7N6850jxPPWU3esqVU5pHRK738svAm28CAwbw+4k28omI/Dc1fkT8xI4dTPWsWQPUrAksXgzcfbfpqkTEV509e/2mrUuXeC1vXjZ3nnmGH+vWVZpHRG5O1qzApElAp07AJ58AHTuarkhExPOp8SPi486fB8aMAV57DciZE3jjDaBbNyAoyHRlIuIrHGkeR5MnKgrYs4fXAgOB6tWBp5+20zxlyyrNIyK3r2NHDnkeOhR45BHgrrtMVyQi4tnU+BHxUampwPvvcwDi6dNA9+4cipg3r+nKRMTbnT3rfGRr/Xo7zZMvH5s7nTvbaZ5s2czWKyK+JTCQ693vu48fR440XZGIiGdT40fEB23YwGNd69bxjdeKFUDt2qarEhFvlJIC7NzpnObZu5fXgoKY5nEc2QoNVZpHRNLHvfcC7doBkycDzz2neYUiIjeixo+IDzl1Chg+HHj7baBAASZ+nn5agw9F5OadOXN9mic+ntfy52eDp0sXNnnq1eO8DRERE6ZMAZYuBUaNAt56y3Q1IiKeS40fER+QnAzMn88bn/h4oH9/rjrNkcN0ZSLiyVJSOPjd0eSJjnZO89SowQGqjtk8ZcoozSMinqNcOaBvXx736t2byytEROR6avyIeLlffuGxrm3bgCZNgDlzgMqVTVclIp7o9Gk2dxyJnrRpngIF7DSPYzaP0jwi4ulGjgTee49r3letUnNaROSfqPEj4qWOHAEGDeIq0xIlgM8/51l33fCICMAk4I4dzse29u3jtaAgPhl3DGAOCwNKl9b3DxHxPrlycXtpnz5AZCTQurXpikREPI8aPyJeJikJmDkTGD+eb+xGjeI6U60yFfFvp0/bx7UcaZ7Ll3nNkeZ57jk7zaPvGSLiK7p3B+bNAwYOBJo3BzJmNF2RiIhnUeNHxIusWAG89BJncLRpwwZQmTKmqxKR9OZI86TdtBUby2sZMnA2j+PIVlgYUKqU0jwi4rsyZgSmTwfCwznzsE8f0xWJiHgWNX5EvMD+/cCAAcA33wAhIcDy5UCLFqarEpH0cuqU85GtDRvsNE/BgmzudOvGj3XqKM0jIv6nZUugaVMe+3r6aSB3btMViYh4DjV+RDzYlStcVTplCp/iT54M9OsHZM5sujIRcZfkZGD7duc0z59/8lqGDJzN07UrmzyhoUrziIgA/D44Ywa/R44fz01fIiJCavyIeCDLAr76imvZ4+KAJ58Epk0DihY1XZmIuNrJk9enea5c4bVChdjg6d6dTR6leURE/l316pxl9tprQM+eTEmLiIgaPyIeZ9cuoG9friStVg1Yswa47z7TVYmIKyQnA9u2Oad59u/ntQwZgFq1gOefZ5MnLAwoWVJpHhGRWzF+PLB4MTBkCPDll6arERHxDGr8iHiIixeBceOA2bOBbNmAOXP4tCqD/l8q4rVOnnRu8mzcaKd5Chdmc6dHD3s2T3Cw2XpFRLxdoULAsGHAiBHAzz/r4ZmICKDGj4hxlgUsWgQMHgycOMHZHRMncv2yiHiPa9fsNI/j6JYjzZMxo53mcWzaKlFCaR4REXfo35/bvQYM4PHZwEDTFYmImKXGj4hBMTFA797A2rVAvXrc2lW/vumqRORmnDjh3OTZsAFISOC1IkXY3OnZkx9r11aaR0QkvQQHcyFGx47Ahx8CnTubrkhExCw1fkQMOHMGGDkSWLAAyJcPePtt4Nln9URKxFNduwZs3eo8hPnAAV5zpHleeMFO8xQvrjSPiIhJHTrw+Pzw4UD79kDWrKYrEhExR40fkXSUkgIsXMhz5xcuAH36AGPHArlyma5MRNI6fty5ybNx4/Vpnl697DRPlixm6xUREWeBgcDMmcDddwPTpwOjR5uuSETEHDV+RNLJ2rU81hUTw0GDc+dya5eImOVI86QdwnzwIK9lzMjGTvfudpqnWDGleUREvEHDhsDjjwNTp3LGWtGipisSETFDjR8RNzt+nCtFP/iANxyLF/MmRG8cRcw4fvz6TVuJibxWtCibO3368GOtWkrziIh4s8mTga+/Ztr6vfdMVyMiYoYaPyJucu0aUz1jxvBN5bBhPGeeLZvpykT8R1LS9Wmev/7itUyZmOZxDGAODeVsHhER8R2lSwP9+jH106cPUKeO6YpERNKfGj8ibrBqFdC3L7BrF9CiBYcLhoSYrkrE9x075tzk2bTJTvMUK8YGz0svsclTuzaQObPZekVExP2GDwfefRd4+WXgp5+UuhYR/6PGj4gL/fUXbyq++AIoUwb49lsgPFw3GCLukJQEbNni3OiJi+O1TJn4VPfFF9nkcczmERER/5MzJzBuHBOe33wDtG1ruiIRkfSlxo+ICyQmAtOmAZMm8Z8nTGADSLNBRFzn6FG7wRMd7ZzmKV6czZ1+/ezZPErziIiIw/PP8wj+oEFAy5Z8QCAi4i/U+BG5A5YFLF3KN5sHDgCPPcaVoSVKmK5MxLslJXEDnqPJ829pHsemLW1qERGRG8mQAZgxg0fw580D+vc3XZGISPpR40fkNu3dy1khK1YAlStzrk+TJqarEvFOR444N3k2bQKuXuU1R5qnf39+rFlTaR4REbl1zZsDzZrx2FenTkDevKYrEhFJH2r8iNyi+Hge5YqIAIKD+bF3byBjRtOViXiHq1eZ5nE0eaKigEOHeC1zZqZ5eve2N20pzSMiIq4yYwZQvTqbP7Nnm65GRCR9qPEjcpMsC1i8mGfDjxwBOncGJk8GChUyXZmIZzt82LnJs3mzneYpUQJo2NA+slWzpuYuiIiI+1SpArzwAvD66zwyXKGC6YpERNxPjR+Rm7BtG9CnD/DLL1wB/dlnfJMqIs4caZ60m7YOH+a1zJmBunX5/yVHmqdIEbP1ioiI/xk7FvjoIz7M+/Zb09WIiLifGj8iN3DuHDB6NIcA5s4NLFgAPPccEBRkujIRz3D4sHOTZ/NmDmYGgJIlgXvusZs8SvOIiIgnKFAAGDECGDoU+PFHzWgUEd+nxo/IP0hNBd55Bxg2DDh7FujRAxg/HsiTx3RlIuZcvcrGTtpGz5EjvJYlC9M8L73EJk9YGFC4sNl6RURE/s1LLwHz5wMvv8yFAnqoJyK+TI0fkb9Zv56DZTdsAO6+G3jtNSYVRPzNoUPOTZ6YGDvNU6oUcO+9dpqnRg2leURExHtkyQJMmQI88QTw/vtA166mKxIRcR81fkT+38mTTPi88w6TCosWAU89BQQEmK5MxP0SE+00j2MQc9o0T716QL9+dppHQ81FRMTbPfYYMGsWj3099hiQPbvpikRE3EONH/F7ycnc7PDKK8Dlyxz0N2qUXvzFd1mWc5onOppNn2vXeL10aTvNExbGNE/GjGZrFhERcbWAACAigq91U6fyWL+IiC9S40f82po13DC0Ywfw4IPAnDlAxYqmqxJxrcREzi9Im+Y5epTXgoM5m6d/f/vYltI8IiLiL0JDgSefBKZP55r34sVNVyQi4npq/IhfOnwYGDgQWLKEm4e+/BJo21bHusT7WRYQF+fc5ImJcU7zNG5sp3mqV1eaR0RE/NvkycBXXwHDhwMffmi6GhER11PjR/zK1auM9E6YwM1do0cDQ4Yw9SDijRISrt+0dewYrwUHczbPgAF2mqdgQbP1ioiIeJoSJfhaOXEik+D165uuSETEtdT4Eb+xfDlXd8bGMt0TEcH0g4i3sCzgr7/sJE9UFLBli53mKVMGeOABO81TrZrSPCIiIjdj6FDgrbfYAPr1V6XARcS3qPEjPu/PPzm/ZOlSoHx5YMUKoFkz01WJ/LeEBHs2j+PH8eO8dtddTPO8/DKbPA0aKM0jIiJyu7JnZyL8hReAL74A2rc3XZGIiOuo8SM+68oVYNIkYNo0ph6mTmXiJ1Mm05WJXM+R5knb5NmyhVvnAKBsWaBpU/vIVvXqQAZ9BxcREXGZrl2BuXOBwYOB1q2BzJlNVyQi4hp62yA+x7KAzz9nEuLQIaBjRzZ9ihQxXZmILSEB2LjReaV62jRP/frAoEFs8oSGAgUKmK1XRETE1wUFcRTAgw+yATRwoOmKRERcQ40f8Sl//MGhfKtXAzVqAB99BDRqZLoq8XeWBRw86NzkSZvmKVeON5mhofZsHqV5RERE0l/TpkCrVsD48UDnzkD+/KYrEhG5c3prIT7hwgVg7Fg+ncmWDZg3j2e09eZZTLhyxU7zOAYxnzjBa1mz2mkex7Et3VSKiIh4jmnT+BBmzBjeU4qIeDu9LRavlpoKfPghV7KfPAk8/zzw6qt6Iy3px7KAAwec0zxbt9ppnpAQ4KGH7E1bVauqISkiIuLJKlUCevQA5s8HevUCKlc2XZGIyJ3R2w/xWps2Ab178412aCgQGQnUrWu6KvF1V64AGzY4r1Q/eZLXHGmewYPtNE++fGbrFRERkVs3ZgywaBETusuWma5GROTOqPEjXuf0aWDECGDhQiZ73n0X6NQJCAw0XZn4GssC9u93bvJs3QqkpPB6SAjQvLlzmicoyGzNIiIicufy5QNGjeKA55Urmd4VEfFWavyI10hJARYsAEaOBC5e5Gr2MWOAnDlNVya+4vLl6zdtOdI82bIxzTN0qJ3myZvXbL0iIiLiPr17A6+/DgwYwKUMOqotIt5K377EK/z2G7d1bdkC3H8/hzhXqWK6KvFmlgX8+adzmmfbNjvNU7480KKFneapUkVpHhEREX+SOTMwdSrQvj3wzjtcHCIi4o3U+BGPduwY56UsWgQUKwZ8+ilffAMCTFcm3iY+/vo0z6lTvJYtG9CgATBsGJs8DRoozSMiIiJAu3ZAo0Y89tWhA5Ajh+mKRERunRo/4pGSkoDZs4Fx4/i/R4zgm/KsWU1XJt7AkeZxNHmiooDt2+00T4UKQKtW9pEtpXlERETknwQEABERQL16wOTJwMSJpisSEbl1avyIx1m5EujbF9izBwgPB2bOBMqVM12VeLL4eG7aSpvmOX2a17JnZ4Jn+HA2eUJDgTx5zNYrIiIi3qNuXeCZZ9gAeuEFoFQp0xWJiNwaNX7EYxw8yOF5X30FlC3L9eytWpmuSjyNZQGxsc5Nnm3bgNRUXq9YkQ1Dx2yeypWV5hEREZE78+qrwOefM4H+ySemqxERuTVq/IhxCQkcnDd5Mleyv/oqG0BZspiuTDxBfDywfr3d5Emb5smRg2meESPs2TxK84iIiIirFS/O1e7jxzOZHhZmuiIRkZunxo8YY1nAN98A/fsz7fPEE8C0aXxhFf9kWcC+fXaTxzGbx5HmqVQJaN3aTvNUqqQ0j4iIiKSPwYOBt97iA8q1a7VsRES8hxo/YsSePXxasmxOWVgAABmTSURBVHIlB+uuXs017eJfLl1imsfR5ImOBs6c4TVHmmfkSDvNkzu32XpFRETEf2XLxmR6167AkiXc8iUi4g3U+JF0dekSI7KzZgHBwfz44otAxoymKxN3syxg7167yRMVBezY4Zzmefhh5zRPYKDZmkVERETS6tQJmDMHGDKE9y3BwaYrEhH5b2r8SLqwLODjj4FBg4Bjx4AuXYBJk4CCBU1XJu5y8eL1m7bOnuW1nDmZ4HnkETZ56tdXmkdEREQ8X1AQt3s98AAwezYwdKjpikRE/psaP+J2W7cCffoAv/7KdZhffcU3/eI7HGkeR5PHkeaxLF6vXNlu8oSFcfOW0jwiIiLije6/n2mfiRP5MFMPMkXE06nxI25z9iwwahQwfz43LS1cyDPResPv/S5etDdtOdI8587xWs6cQGgo8Oij/NigAZArl9l6RURERFxp6lTOqRw9mve6IiKeTI0fcbmUFOCdd4Bhw9gMePFFYNw4HeXxVqmp16d5du5kmicggGkeR5NHaR4RERHxB+XLA716AXPn8mO1aqYrEhH5dwGW4yxGOqhbt661cePGdPt6kv6io4HevYFNm4BGjfhiWKOG6arkVly8CKxbBzz0EP85d247zZMrFxs8jiZPgwZM+IiIiIj4m7NngXLlOMrg+++13l1EzAoICNhkWVbdf7qmxI+4xIkTHG733ntAkSLARx8BTz6pF0BPl5oK7NnjfGTLkeZxePRRezZPhQpK84iIiIgAHGXwyitA//7AihVAixamKxIR+WdK/MgduXYNmDeP55sTEoABA4ARI4Ds2U1XJv/kwgWmeRxNnuho4Px5Xsud2znN8+yzwN13A59+arRkEREREY+VlARUrQpkyABs28aPIiImKPEjbrF6NdC3LxMizZpxpWWFCqarEofUVGD3brvJExUF/PGHPZunShXgscfsNE/58s5pHh3hEhEREbmxTJmAadOAtm2BN9/kbEsREU+jxo/csrg4YOBA4LPPgNKlga+/Btq00bEu086fZ5rH0eRZt+76NM8TT7DJU78+kCOH2XpFREREfEGbNkDjxkzAP/WUtpmKiOdR40duWmIiMGMGMHEi0yRjxwKDBgHBwaYr8z+pqcCuXXaTJyqK/+xI81StCjz+uJ3mCQnRbB4RERERdwgIACIigDp1eJ88darpikREnKnxIzclMhLo1w/480+gXTs2gEqVMl2V/3CkeRxNnnXrOK8H4GDB0FAO0w4LA+rVU5pHREREJD3VqsX5iLNnAz16AGXKmK5IRMSmxo/c0L59bPgsXw5UrAisXAk8+KDpqnybI83jaPI40jwAUztVqwIdOrDJExrK2Tw6ZiciIiJi1oQJwJIl3HSr5Rgi4knU+JF/dPkyo6rTpwOZM/Njnz4cYCeude7c9Wmeixd5LU8eNng6dmSTp359bUwTERER8URFigBDhnDWz2+/AffcY7oiERFS40ecWBafUAwcCBw+DDzzDDBlClC4sOnKfENqKjdrpU3z7N7Na4GBQLVqHAroWKkeEqI0j4iIiIi3ePllbvcaMICzGDVjUUQ8gRo/8j87djDVs2YNULMmsHgxcPfdpqvybufOOQ9gXr/eTvPkzcvmztNP27N5lOYRERER8V5ZswKTJgGdOgGffMLUtoiIaWr8CM6fB8aMAV57DciZE3jjDaBbNyAoyHRl3iUlxTnNEx39z2kex6atcuWU5hERERHxNR07csjz0KHAI48Ad91luiIR8Xdq/Pix1FTg/ff5onTqFNC9O4fS5c1rujLvcPYsmzuORM+6dcClS7yWLx+Paz3zjJ3myZbNbL0iIiIi4n6BgVzvft99wMyZwIgRpisSEX+nxo+f2rCBx7rWrWNj4rvvgNq1TVfluVJSgJ07nY9t7dnDa4GBQPXq9pGtsDCgbFmleURERET81b33Au3a8dhX166alykiZqnx42dOnQKGDwfefhsoUICJn6ef1uC5vztzxjnNs369c5onLAzo3Jkf69ZVmkdEREREnE2ZAixdCowaBbz1lulqRMSfqfHjJ5KTgfnz+cITHw/0789VkzlymK7MvJQUDrZOm+bZu5fXgoKY5nEc2QoLA8qUUZpHRERERG6sXDmgb18e++rTB6hRw3RFIuKv1PjxA7/8whebbduAJk2AOXOAypVNV2WOI82TdtNWfDyv5c/P5k6XLpzRU68etzOIiIiIiNyqkSOB997jevdVq/TwUETMUOPHhx05AgweDHz8MVCiBPD55zxr7E8vOI40j6PJExUF7NvHa0FBfPLSuTObPErziIiIiIgr5crF7bl9+gCRkUDr1qYrEhF/pMaPD0pKAmbNAsaN4xGvUaO4ucsfVkmePn19mufyZV4rUIDNneeeY6Onbl2leURERETEvbp3B+bNAwYOBJo3BzJmNF2RiPgbNX58zIoVwEsvcUZNmzZcIVmmjOmq3CM5+fo0T2wsrwUFATVr2ke2wsKA0qWV5hERERGR9JUxIzB9OhAezpmbffqYrkhE/I0aPz7iwAEObP7mGyAkBFi+HGjRwnRVrnXqlHOaZ8OG69M8zz9vb9ryh4STiIiIiHi+li2Bpk157Ovpp4HcuU1XJCL+RI0fL3flCldFTpkCZMgATJ4M9OsHZM5surI7k5wMbN/OBo+j2eNI82TIwNk8XbrYm7ZKlVKaR0REREQ8U0AAMGMGE+kTJvB/i4ikFzV+vJRlAV99xZRPXBzQoQMwbRpQrJjpym7PqVPOTZ60aZ6CBdnc6daNH+vUUZpHRERERLxL9eqcNTl3LtCjB1P6IiLpQY0fL7RrF9C3L1dCVqsGrFkD3Hef6apuXnIyV8unPbb155+8liEDn4R07WqneUqWVJpHRERERLzf+PHA4sXAkCHAl1+arkZE/IUaP17k4kVu6po9m9uo5swBevZks8STnTx5fZrnyhVeK1SIzZ3u3e00T3Cw2XpFRERERNyhUCFg2DBgxAjg55+96+GtiHgvD28ZCMBjXYsWAYMHAydOMA0zcSIHGnuaa9fs2TyOH/v381qGDECtWvYA5tBQpXlERERExL/078/tXgMG8IFoYKDpikTE16nx4+FiYrjy8fffgXr1uLWrfn3TVdkcaR7Hj40b7TRP4cJs8PTsySaP0jwiIiIi4u+Cg7mQpWNH4MMPgc6dTVckIr5OjR8PdeYMMGoUsGABkDcv8PbbwLPPmn0icO0aZ/OkbfQcOMBrGTMyzdOtG5s8YWFAiRJK84iIiIiI/F2HDhzfMHw40L49xziIiLiLGj8eJiUFWLiQ534vXAB69wbGjgVy5Ur/Wk6cuD7Nk5DAa0WKsLnz4ov8WLu20jwiIiIiIjcjMBCYORO4+25g+nRg9GjTFYmIL1Pjx4OsXctGT0wMB73NncutXenh2jVg61bnRs/Bg7yWMSMbOy+8YG/aKl5caR4RERERkdvVsCHw+OPA1KmcgVm0qOmKRMRXqfHjAY4f50rHDz7gN/zFi/ki4M7GyvHjdoMnOvqf0zy9e9tpnixZ3FeLiIiIiIg/mjwZ+PprYORI4N13TVcjIr5KjR+Drl1jqmfMGCAxERg6lEe8smVz/dfZssV5pfrf0zyOdeqONI+IiIiIiLhX6dJAv37AtGlc6FK7tumKRMQXqfFjyKpVQN++wK5dQIsWHO4WEuKa3/vYMecmz8aNbCwBTBSFhfGFJSyMA5mV5hERERERMWP4cKZ9BgwAfvpJ4xRExPXU+Elnf/0FvPwy8MUXQJkywLffAuHht/8NPimJaR5Hkycqil8DADJl4lODnj3tNE+xYq77s4iIiIiIyJ3JmRMYN4737N98A7Rta7oiEfE1avykk8RERjgnTeI/jx8PDBx462mbo0edmzybNtlpnmLF2Nx56SU7zZM5s2v/HCIiIiIi4lrPP88REIMGAS1b8gGuiIirqPHjZpYFLF0K9O8P7N8PtG8PzJgBlCjx37/WkeZJu2krLo7XMmUC6tSx16mHhirNIyIiIiLijTJk4HuEFi2AefP43kFExFXU+HGjvXs5rO2774BKlTjXp0mTf//5R486N3k2bQKuXuW14sXZ4Onfn00epXlERERERHxH8+ZAs2Y89tWpE5A3r+mKRMRXqPHjBvHxwIQJQEQEj3JFRHA1esaM9s9JSgJiYpwbPYcO8VrmzEzz9O7NJk9YGIcyi4iIiIiI75oxA6henc2f2bNNVyMivuK2Gz8BAQHFAXwAoBCAVABvWpbl19+eLAtYsoSze44cATp3BiZPBgoV4j+nbfJs3myneUqUABo2tJs8NWsqzSMiIiIi4m+qVAFeeAF4/XWOdKhQwXRFIuIL7iTxkwzgZcuyNgcEBGQHsCkgIOAHy7L+cFFtHu/rmCOY9v0eHD2fgJwJ+ZD8e03s3JQZVapwcxfAQct/T/PUrcs0j2PTVpEi5v4MIiIiIiLiOcaOBT76CBg8mFu+RETu1G03fizLOgbg2P//70sBAQG7ABQF4BeNn69jjmDYl9sRfykAF36rjIOb/6+9e4+xuyrwAP49nRYYKTJ2oS4tL1kR2ABL1/7BQiSINRgwQEhY1yyIgQSMuOBSCkVQSeRRKFbUTRZRWIkQiPLaB7BAxM0GFlQQBRaoZOWxlPejVKG82rN/3AGti9mlc2fO3N98Pslk7v3dmd7vHyfNme89v3O2TWrvTPbly5Pjj+/93Dbb9FbzvFny7LabXfoBAIC3N3t2csopyeLFyc03J/vs0zoRMOj6ssdPKWXbJPOS/Lgf/94gWHrD8vxm1bQ8ceFeWfPS6JnsQ2uyyZa/ztGHjLx10pbVPAAAwDtx3HHJ+ef3Pky+885kaKh1ImCQjbn4KaXMTHJlks/XWle9zetHJTkqSbb+/5xhPiAeX7k60zYo2XjnFRma+Uo2nPtCNpi9KtOGapYu2b91PAAAYEBttFFy9tnJJz6RXHxxcsQRrRMBg2zaWH65lDIjvdLn0lrrVW/3M7XWC2qt82ut8zfffPOxvN2kMmdkOGWo5j17P5B3z384G27xYspQzZyR4dbRAACAAXfIIb2tIk45pXdqMMD6Wu/ip5RSklyY5P5a67L+RRoMi/bdIcMz1l1zOTxjKIv2tfU+AAAwNqUky5YlTz7ZW/0DsL7GsuJnzySHJdmnlPLz0a/9+pRr0jto3tycdfAumTsynJJk7shwzjp4lxw0b27raAAAQAfsvnvyyU8m557721OCAd6psZzqdUuS0scsA+egeXMVPQAAwLhZsiS5+urkC19Ivve91mmAQTSmPX4AAAAYP1tv3Tvd65JLkp/+tHUaYBApfgAAACaxxYuT2bN7BVCtrdMAg0bxAwAAMIltskly+unJLbckV17ZOg0waBQ/AAAAk9wRRyS77JKceGLy6qut0wCDRPEDAAAwyQ0N9Y53f+ih5JvfbJ0GGCSKHwAAgAGwYEGy//7JV76SPPNM6zTAoFD8AAAADIilS5OXXkpOO611EmBQKH4AAAAGxE47JZ/5TPKtbyX33dc6DTAIFD8AAAAD5LTTkpkzk0WLWicBBoHiBwAAYIBstlnyxS8m112X3Hhj6zTAZKf4AQAAGDCf+1yy3XbJwoXJmjWt0wCTmeIHAABgwGy4YXLOOcm99yYXXtg6DTCZKX4AAAAG0MEHJx/6UO+2r1WrWqcBJivFDwAAwAAqJVm2LHn66WTJktZpgMlK8QMAADCg5s9PDjusVwA98kjrNMBkpPgBAAAYYGeckUyblixe3DoJMBkpfgAAAAbYVlslJ5yQXH55ctttrdMAk43iBwAAYMCdeGKyxRbJ8ccntbZOA0wmih8AAIABN3Nm75av229Pvv/91mmAyUTxAwAA0AGf+lSy227JSSclr7zSOg0wWSh+AAAAOmBo6Lene513Xus0wGSh+AEAAOiID384OfDA5Mwzk6eeap0GmAwUPwAAAB1yzjnJ6tXJl7/cOgkwGSh+AAAAOuQDH0iOOSb59reTe+9tnQZoTfEDAADQMV/6UrLppskJJ7ROArSm+AEAAOiYWbN65c8NNyTXX986DdCS4gcAAKCDPvvZZPvtk4ULkzfeaJ0GaEXxAwAA0EEbbJAsXZrcf39vvx9galL8AAAAdNQBByR779277evFF1unAVpQ/AAAAHRUKcmyZclzzyVnnNE6DdCC4gcAAKDD5s1LPv3p5OtfT371q9ZpgImm+AEAAOi4009Ppk9PFi9unQSYaIofAACAjpszJznppOQHP0huvbV1GmAiKX4AAACmgIULk7lzk+OPT9aubZ0GmCiKHwAAgClg442TM89MfvKT5LLLWqcBJoriBwAAYIo49NDkgx/s7fXz8sut0wATQfEDAAAwRUyb1jve/bHHkq99rXUaYCIofgAAAKaQvfZKDj44Oeus5MknW6cBxpviBwAAYIo5++zktdeSU09tnQQYb4ofAACAKeb970+OPTa56KLkF79onQYYT4ofAACAKejUU5NZs3rHu9faOg0wXhQ/AAAAU9DISHLaacnNNyfXXts6DTBeFD8AAABT1NFHJzvumJxwQvL6663TAONB8QMAADBFzZiRnHtusnx5cv75rdMA40HxAwAAMIXtt1+yYEHvtq8XXmidBug3xQ8AAMAUVkry1a/2Sp/TT2+dBui36a0DAJPPNXetyMPPjeSRu1/MnkseyKJ9d8hB8+a2jgUAwDjZddfkyCOTb3yj5kfl9rww/fnMGRk2D4QOsOIHWMc1d63IyVfdkzfWrE2SrFi5OidfdU+uuWtF42QAAIynPf7yiaydtiYP/PP7UmMeCF1hxQ+wjqU3LM/q19ckSV59YiTPXrtrkuSo66blml1aJgMAYDxdd08yNPKbrP7lH2flf/xJRvb4r6x+fU2W3rDcqh8YYIofYB2Pr1ydJNlo22fz8oPvzSuP/lGS5JUk//Zcw2AAAIyr51/YNLUmKWvz2lObvnX9zfkhMJgUP8A65owMZ8XK1Zm14L7MWnDfW9fnjgzn1sX7NEwGAMB42nPJ7VmxcnXqG9NSpq996/qckeGGqYCxsscPsI5F++6Q4RlD61wbnjGURfvu0CgRAAAT4c154O+WPuaBMPis+AHW8eb920tvWJ7HV652mgMAwBRhHgjdVGqtE/Zm8+fPr3fccceEvR8AAABA15VS7qy1zn+719zqBQAAANBRih8AAACAjlL8AAAAAHSU4gcAAACgoxQ/AAAAAB2l+AEAAADoKMUPAAAAQEcpfgAAAAA6SvEDAAAA0FGKHwAAAICOUvwAAAAAdJTiBwAAAKCjFD8AAAAAHaX4AQAAAOgoxQ8AAABARyl+AAAAADpK8QMAAADQUYofAAAAgI5S/AAAAAB0lOIHAAAAoKMUPwAAAAAdpfgBAAAA6CjFDwAAAEBHlVrrxL1ZKc8keWTC3pAu2izJs61DwDgxvukqY5uuMrbpMuObrurq2N6m1rr5270wocUPjFUp5Y5a6/zWOWA8GN90lbFNVxnbdJnxTVdNxbHtVi8AAACAjlL8AAAAAHSU4odBc0HrADCOjG+6ytimq4xtusz4pqum3Ni2xw8AAABAR1nxAwAAANBRih8GQillq1LKj0op95dS/rOUclzrTNBPpZShUspdpZR/aZ0F+qmUMlJKuaKU8sDo/+F/0ToT9EMp5W9H5yT3llIuK6Vs1DoTrK9SykWllKdLKff+zrVZpZSbSikPjn5/T8uMsD7+wNheOjovubuUcnUpZaRlxomg+GFQvJFkYa11pyS7JzmmlPKnjTNBPx2X5P7WIWAcfD3Jv9Zad0zyZzHO6YBSytwkxyaZX2vdOclQkr9qmwrG5LtJPvZ71xYn+WGtdfskPxx9DoPmu/nfY/umJDvXWndN8sskJ090qImm+GEg1FqfqLX+bPTxr9P7w2Fu21TQH6WULZPsn+Q7rbNAP5VS3p1kryQXJkmt9bVa68q2qaBvpicZLqVMT/KuJI83zgPrrdb670me/73LBya5ePTxxUkOmtBQ0AdvN7ZrrTfWWt8YfXp7ki0nPNgEU/wwcEop2yaZl+THbZNA35yX5MQka1sHgT7bLskzSf5h9FbG75RSNm4dCsaq1roiyblJHk3yRJIXa603tk0FfffeWusTSe9D2CSzG+eB8XBEkutbhxhvih8GSillZpIrk3y+1rqqdR4Yq1LKx5M8XWu9s3UWGAfTk/x5kr+vtc5L8lLcKkAHjO51cmCS9yWZk2TjUsqhbVMB8E6UUk5Jb0uRS1tnGW+KHwZGKWVGeqXPpbXWq1rngT7ZM8kBpZSHk1yeZJ9SyiVtI0HfPJbksVrrmys0r0ivCIJBtyDJQ7XWZ2qtrye5KskejTNBvz1VStkiSUa/P904D/RNKeXwJB9P8te11to6z3hT/DAQSiklvT0i7q+1LmudB/ql1npyrXXLWuu26W0MenOt1afGdEKt9ckk/11K2WH00keS3NcwEvTLo0l2L6W8a3SO8pHYuJzu+ackh48+PjzJPzbMAn1TSvlYkpOSHFBrfbl1nomg+GFQ7JnksPRWQ/x89Gu/1qEA+D/9TZJLSyl3J9ktyZmN88CYja5iuyLJz5Lck96c+oKmoWAMSimXJbktyQ6llMdKKUcmWZLko6WUB5N8dPQ5DJQ/MLb/LskmSW4a/bvy/KYhJ0CZAquaAAAAAKYkK34AAAAAOkrxAwAAANBRih8AAACAjlL8AAAAAHSU4gcAAACgoxQ/AAAAAB2l+AEAAADoKMUPAAAAQEf9DyaUyyTtZfFVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_p(list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fjsa54Udkx2B"
   },
   "outputs": [],
   "source": [
    "def initialPopulation(cities, populationSize):\n",
    "    population = [path_gen(cities) for i in range(0, populationSize)]\n",
    "    return population\n",
    "population = initialPopulation(List_of_cities,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Population 0 [[1, 2], [3, 1], [6, 7], [12, 4], [5, 10], [3, 4], [10, 1], [9, 5], [8, 7]]\n",
      "Initial Population 1 [[12, 4], [6, 7], [5, 10], [1, 2], [10, 1], [3, 1], [9, 5], [8, 7], [3, 4]]\n",
      "Initial Population 2 [[1, 2], [8, 7], [3, 4], [12, 4], [6, 7], [10, 1], [3, 1], [5, 10], [9, 5]]\n",
      "Initial Population 3 [[5, 10], [10, 1], [6, 7], [8, 7], [3, 1], [12, 4], [3, 4], [1, 2], [9, 5]]\n",
      "Initial Population 4 [[6, 7], [5, 10], [3, 4], [9, 5], [3, 1], [12, 4], [1, 2], [8, 7], [10, 1]]\n",
      "Initial Population 5 [[8, 7], [3, 1], [1, 2], [10, 1], [6, 7], [5, 10], [9, 5], [12, 4], [3, 4]]\n",
      "Initial Population 6 [[12, 4], [1, 2], [9, 5], [8, 7], [6, 7], [3, 1], [5, 10], [3, 4], [10, 1]]\n"
     ]
    }
   ],
   "source": [
    "for index, p_plot in enumerate (population):\n",
    "    print('Initial Population '+ str(index),p_plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "FeNJEVlWk0yy",
    "outputId": "6d881ae8-6c93-4e57-fc4c-57f849328d75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018946483873206344"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def path_fitness(cities):\n",
    "    total_dis = totaldist(cities)\n",
    "    fitness= 0.0\n",
    "    if fitness == 0:\n",
    "        fitness = 1 / float(total_dis)\n",
    "    return fitness\n",
    "path_fitness(List_of_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "Z6ndn4ofk3Ly",
    "outputId": "ccf595dc-378f-44f6-b77d-142d54eccf58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.018596400382396218),\n",
       " (5, 0.01856271262642847),\n",
       " (6, 0.017411277444395516),\n",
       " (1, 0.016906667787184224),\n",
       " (3, 0.015728372156418674),\n",
       " (4, 0.015247190331051248),\n",
       " (2, 0.0145944374929325)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def path_ranking(population):\n",
    "    fitnessResults = {}\n",
    "    for i in range(len(population)):\n",
    "        fitnessResults[i] = path_fitness(population[i])\n",
    "        \n",
    "    return sorted(fitnessResults.items(), key = operator.itemgetter(1), reverse = True)\n",
    "path_ranking(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jP0Jx4JJk43S"
   },
   "outputs": [],
   "source": [
    "def selection(pop, eliteSize):\n",
    "    #output = path_ranking(population)\n",
    "    df = pd.DataFrame(np.array(pop), columns=[\"Index\",\"Fitness\"])\n",
    "#A cumulative sum is a sequence of partial sums of a given sequence\n",
    "    df['cumulative_sum'] = df.Fitness.cumsum()\n",
    "#Cumulative percentage is another way of expressing frequency distribution. \n",
    "#It calculates the percentage of the cumulative frequency within each interval, much as relative frequency distribution calculates the percentage of frequency.\n",
    "    df['cum_percentage'] = 100*df.cumulative_sum/df.Fitness.sum()\n",
    "    selected_values = [pop[i][0] for i in range(eliteSize)]\n",
    "    \n",
    "    for i in range(len(pop) - eliteSize):\n",
    "        pick = 100*random.random()\n",
    "        for i in range(0, len(pop)):\n",
    "            if pick <= df.iat[i,3]:\n",
    "                selected_values.append(pop[i][0])\n",
    "                break\n",
    "                \n",
    "    return selected_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "htqusM3ElO7q",
    "outputId": "cac635f6-89c1-4f0d-ac55-fd551b4ae084"
   },
   "outputs": [],
   "source": [
    "out11 = path_ranking(population)\n",
    "selected_values = selection(out11,N-2)\n",
    "# print(selected_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OZ0deE16k8Qh"
   },
   "outputs": [],
   "source": [
    "def mating(population, selected_values):\n",
    "    matingpool = [population[selected_values[i]] for i in range(len(selected_values))]\n",
    "    return matingpool\n",
    "mp = mating(population, selected_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ioEfFpK0lMtx"
   },
   "outputs": [],
   "source": [
    "def breed(first_parent, second_parent):\n",
    "    gen1= int(random.random() * len(first_parent))\n",
    "    gen2 = int(random.random() * len(second_parent))\n",
    "    \n",
    "    first_gen = min(gen1, gen2)\n",
    "    last_gen = max(gen1, gen2)\n",
    "\n",
    "    total_parent1 = [first_parent[i] for i in range(first_gen, last_gen)]\n",
    "    total_parent2 = [i for i in second_parent if i not in total_parent1]\n",
    "\n",
    "    total = total_parent1 + total_parent2\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "90xMExfKmFJB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1, 2], [3, 1], [6, 7], [12, 4], [5, 10], [3, 4], [10, 1], [9, 5], [8, 7]],\n",
       " [[8, 7], [3, 1], [1, 2], [10, 1], [6, 7], [5, 10], [9, 5], [12, 4], [3, 4]],\n",
       " [[12, 4], [6, 7], [10, 1], [8, 7], [3, 1], [1, 2], [5, 10], [9, 5], [3, 4]],\n",
       " [[6, 7], [5, 10], [1, 2], [10, 1], [3, 1], [12, 4], [3, 4], [9, 5], [8, 7]],\n",
       " [[8, 7], [6, 7], [5, 10], [10, 1], [3, 1], [12, 4], [3, 4], [1, 2], [9, 5]],\n",
       " [[6, 7], [5, 10], [3, 4], [9, 5], [3, 1], [12, 4], [1, 2], [8, 7], [10, 1]],\n",
       " [[5, 10], [12, 4], [1, 2], [9, 5], [8, 7], [6, 7], [3, 1], [3, 4], [10, 1]]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def breed_population(my_mating_pool, eliteSize):\n",
    "    ln = len(my_mating_pool) - eliteSize\n",
    "    pl = random.sample(my_mating_pool, len(my_mating_pool))\n",
    "    tot1 = [my_mating_pool[i] for i in range(eliteSize)]\n",
    "    tot2 = [breed(pl[i], pl[len(my_mating_pool)-i-1]) for i in range(ln)]\n",
    "    tot = tot1+tot2\n",
    "    return tot\n",
    "breed_population(mp,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wvo7Nyy7mIow"
   },
   "outputs": [],
   "source": [
    "def mutatation(ind, mutat_rate):\n",
    "    for exc in range(len(ind)):\n",
    "        if(random.random() < mutat_rate):\n",
    "            exc_with = int(random.random() * len(ind))\n",
    "            \n",
    "            city1 = ind[exc]\n",
    "            city2 = ind[exc_with]\n",
    "            \n",
    "            ind[exc] = city2\n",
    "            ind[exc_with] = city1\n",
    "    return ind\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "puBz5ia7mRVg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1, 2], [3, 1], [6, 7], [12, 4], [5, 10], [3, 4], [10, 1], [9, 5], [8, 7]],\n",
       " [[12, 4], [6, 7], [5, 10], [1, 2], [10, 1], [3, 1], [9, 5], [8, 7], [3, 4]],\n",
       " [[1, 2], [8, 7], [3, 4], [12, 4], [6, 7], [10, 1], [3, 1], [5, 10], [9, 5]],\n",
       " [[5, 10], [10, 1], [6, 7], [8, 7], [3, 1], [12, 4], [3, 4], [1, 2], [9, 5]],\n",
       " [[6, 7], [5, 10], [3, 4], [9, 5], [3, 1], [12, 4], [1, 2], [8, 7], [10, 1]],\n",
       " [[8, 7], [3, 1], [1, 2], [10, 1], [6, 7], [5, 10], [9, 5], [12, 4], [3, 4]],\n",
       " [[12, 4], [1, 2], [9, 5], [8, 7], [6, 7], [3, 1], [5, 10], [3, 4], [10, 1]]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mutatation_population(population, mutat_rate):\n",
    "    mutated_population = [mutatation(population[i], mutat_rate) for i in range(len(population))]\n",
    "    return mutated_population\n",
    "mutatation_population(population, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pj9CynBDmWzf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1, 2], [3, 1], [6, 7], [12, 4], [5, 10], [3, 4], [10, 1], [9, 5], [8, 7]],\n",
       " [[8, 7], [3, 1], [1, 2], [10, 1], [6, 7], [5, 10], [9, 5], [12, 4], [3, 4]],\n",
       " [[12, 4], [1, 2], [9, 5], [8, 7], [6, 7], [3, 1], [5, 10], [3, 4], [10, 1]],\n",
       " [[12, 4], [6, 7], [5, 10], [1, 2], [10, 1], [3, 1], [9, 5], [8, 7], [3, 4]],\n",
       " [[5, 10], [10, 1], [6, 7], [8, 7], [3, 1], [12, 4], [3, 4], [1, 2], [9, 5]],\n",
       " [[12, 4], [6, 7], [5, 10], [1, 2], [10, 1], [3, 1], [9, 5], [8, 7], [3, 4]],\n",
       " [[1, 2], [10, 1], [6, 7], [5, 10], [3, 1], [12, 4], [3, 4], [9, 5], [8, 7]]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def next_gen(existing_gen, eliteSize, mutat_rate):\n",
    "    pop = path_ranking(existing_gen)\n",
    "    \n",
    "    selected_values = selection(pop, eliteSize)\n",
    "   \n",
    "    my_mating_pool = mating(existing_gen, selected_values)\n",
    "    tot = breed_population(my_mating_pool, eliteSize)\n",
    "    following_gen = mutatation(tot, mutat_rate)\n",
    "    #print(following_gen)\n",
    "    return following_gen\n",
    "next_gen(population, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NwvesumYmaZf"
   },
   "outputs": [],
   "source": [
    "# def get_names(result_lst, cities, name_lst):\n",
    "#     names = []\n",
    "#     for index,value in enumerate(result_lst):\n",
    "#         for i,v in enumerate(cities):\n",
    "#             if value == v:\n",
    "#                 names.append(name_lst[i])\n",
    "#     return names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "isFHFF1Fmctv",
    "outputId": "237ee523-56e2-40ed-f539-5e0d67feb098"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incipient distance: 42.20537375935296\n",
      "Eventual distance: 32.55522499588327\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def GA(cities, population_size, eliteSize, mutat_rate, generations):\n",
    "    population = initialPopulation(cities,population_size)\n",
    "    #print(population_)\n",
    "    print(\"Incipient distance: \" + str(1 / path_ranking(population)[0][1]))\n",
    "    for i in range(generations):\n",
    "        population = next_gen(population, eliteSize, mutat_rate)\n",
    "        #print(population)\n",
    "    \n",
    "    print(\"Eventual distance: \" + str(1 / path_ranking(population)[0][1]))\n",
    "    optimal_route_id = path_ranking(population)[0][0]\n",
    "    optimal_route = population[optimal_route_id]\n",
    "#     ordered_cities = get_names(optimal_route,cities)\n",
    "#     print([(indx,val) for indx,val in enumerate(ordered_cities)])\n",
    "    plot_p(optimal_route)\n",
    "    return optimal_route\n",
    "\n",
    "result_lst = GA(List_of_cities, population_size=100, \n",
    "                 eliteSize=5, mutat_rate=0.01, \n",
    "                 generations=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dRQbtgFQmfv_"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "travelling slesman problem.ipynb",
   "provenance": []
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}