Plotting from Simulations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dfe4ea3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, statistics, glob\n",
    "from statistics import median\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a38fac9",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  10,\n",
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       "  80,\n",
       "  120,\n",
       "  20,\n",
       "  15,\n",
       "  15,\n",
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       "  125,\n",
       "  25,\n",
       "  10,\n",
       "  120,\n",
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       "  85,\n",
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       "  20,\n",
       "  90,\n",
       "  130,\n",
       "  25]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "# set the directory path\n",
    "path = \"single baseline\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    # extract the filename from the file path\n",
    "    filename = os.path.basename(file_path)\n",
    "    # open the file and read its contents into a list\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "        #create a dict from the file contents\n",
    "        ttime = [int(line.split(' ')[3]) for line in file_contents]\n",
    "        user_count = int(file_contents[0].split(' ')[2])\n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict[user_count] = ttime\n",
    "    \n",
    "file_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd951bb0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(100, 54.00974025974026),\n",
       " (10, 54.54545454545455),\n",
       " (200, 69.42769857433808),\n",
       " (30, 53.601036269430054),\n",
       " (50, 52.72435897435897)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_times = [(k,median(v)) for k,v in file_dict.items()]\n",
    "avg_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1f85176b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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vp1evXtqzZ4927drlXDp27KhBgwY5/1yzZk2tWbPG+TMZGRnKyspSdHS02dgAAKAKMn1EJjw8XMuXLy8xPmvWLFPbqVu3rlq3bu0yVqdOHdWvX985PnToUCUmJiogIEB+fn566aWXFB0drc6dO5uNDQAAqqBy3UemuLhYBw8e1MmTJ1VcXOwy161bN7cEk34qR9WqVVNcXJyKiooUGxurt99+223bBwAA1ma6yGzZskVPPvmkjh49KsMwXOZsNpuuXr1a7jDr1q1zeezj46OUlBSlpKSUe5sAAKDqMl1kXnjhBXXs2FErVqxQSEiIbDZbReQCAAC4IdNFJjMzU0uWLFGzZs0qIg8AAECZmf7UUlRUlA4ePFgRWQAAAEwxfUTmpZde0siRI+VwONSmTRvVrFnTZf6/v/gRAACgopguMnFxcZKkIUOGOMdsNpsMw7jpi30BAADMKNd3LQEAAFQGpotMo0aNKiIHAACAaeW6Id6hQ4c0e/Zs7d+/X5IUGRmpV155RXfccYdbwwEAAFyP6U8trVq1SpGRkdq2bZvatm2rtm3bauvWrWrVqpVWr15dERkBAABKZfqIzJgxY5SQkKDk5OQS43/4wx/0q1/9ym3hAAAArsf0EZn9+/dr6NChJcaHDBmib7/91i2hAAAAysJ0kWnQoIF27dpVYnzXrl0KDAx0RyYAAIAyMX1q6bnnntOwYcP0/fff695775Ukbdq0SdOmTVNiYqLbAwIAAFyL6SIzduxY1a1bVzNmzFBSUpIkKTQ0VBMmTNDLL7/s9oAAAADXYrrI2Gw2JSQkKCEhQQUFBZKkunXruj0YAADAjZTrPjKSdPLkSWVkZEiSIiIi1KBBA7eFAgAAKAvTF/sWFBTot7/9rUJDQ9W9e3d1795doaGheuqpp5SXl1cRGQEAAEplusj87ne/09atW7VixQrl5uYqNzdXy5cv1/bt2/X8889XREYAAIBSmT61tHz5cq1atUpdu3Z1jsXGxmrevHl64IEH3BoOAADgekwfkalfv77sdnuJcbvdrnr16rklFAAAQFmYLjKvvfaaEhMT5XA4nGMOh0OjR4/W2LFj3RoOAADgekyfWpozZ44OHjyo8PBwhYeHS5KysrLk7e2tH3/8Ue+8845z3R07drgvKQAAwH8xXWQGDhxYATEAAADMM11kxo8fXxE5AAAATDN9jcyxY8d0/Phx5+Nt27ZpxIgRevfdd90aDAAA4EZMF5knn3xSa9eulfTTRb4xMTHatm2b/vjHP2rSpEluDwgAAHAtpovM3r17dc8990iSPvnkE7Vp00abN2/WBx98oIULF7o7HwAAwDWZLjKXL1+Wt7e3JOnf//63+vfvL+mn71vKzs52bzoAAIDrMF1kWrVqpblz5+qrr77S6tWrnXfzPXHihOrXr+/2gAAAANdiushMmzZN77zzjnr06KEnnnhC7dq1kyQtW7bMecoJAADgVjD98esePXro1KlTys/Pd/lKgmHDhql27dpuDQcAAHA9po/ISJJhGEpPT9c777yjgoICSZKXlxdFBgAA3FKmj8gcPXpUDzzwgLKyslRUVKRf/epXqlu3rqZNm6aioiLNnTu3InICAACUYPqIzCuvvKKOHTvq7NmzqlWrlnP84Ycf1po1a9waDgAA4HpMH5H56quvtHnzZnl5ebmMN27cWD/88IPbggEAANyI6SMyxcXFunr1aonx48ePq27dum4JBQAAUBami0zv3r01e/Zs52ObzabCwkKNHz9effv2dWc2AACA6zJ9amnGjBmKjY1VZGSkLl68qCeffFKZmZm67bbb9NFHH1VERgAAgFKZLjINGzbUN998o48//ljffPONCgsLNXToUA0aNMjl4l8AAICKZrrISFKNGjU0aNAgDRo0yDmWnZ2t0aNH66233nJbOAAAgOsxVWT27duntWvXysvLS48++qj8/f116tQpTZ48WXPnzlXTpk0rKicAAEAJZb7Yd9myZWrfvr1efvllvfDCC+rYsaPWrl2rli1bav/+/Vq6dKn27dtXkVkBAABclLnIvP7664qPj1d+fr5mzpyp77//Xi+//LL++c9/KjU11fkt2AAAALdKmYtMRkaG4uPj5evrq5deeknVqlXTrFmz1KlTp4rMBwAAcE1lLjIFBQXy8/OTJFWvXl21atW66Wti5syZo7Zt28rPz09+fn6Kjo7WypUrnfMXL15UfHy86tevL19fX8XFxSknJ+emnhMAAFQdpi72XbVqlex2u6Sf7vC7Zs0a7d2712Wd/v37l3l7DRs2VHJyspo3by7DMPTee+9pwIAB2rlzp1q1aqWEhAStWLFCixcvlt1u1/Dhw/XII49o06ZNZmIDAIAqylSRGTx4sMvj559/3uWxzWYr9esLrqVfv34ujydPnqw5c+Zoy5YtatiwoebPn68PP/xQPXv2lCQtWLBALVu21JYtW9S5c+dSt1lUVKSioiLn4/z8/DLnAQAA1lLmU0vFxcU3XMyUmP929epVLVq0SOfOnVN0dLTS09N1+fJlxcTEONeJiIhQeHi40tLSrrmdqVOnym63O5ewsLByZwIAAJWb6e9acrc9e/bI19dX3t7eeuGFF7R06VJFRkbK4XDIy8tL/v7+LusHBQXJ4XBcc3tJSUnKy8tzLseOHavgVwAAADylXHf2dacWLVpo165dysvL05IlSzR48GCtX7++3Nvz9vaWt7e3GxMCAIDKyuNFxsvLS82aNZMkdejQQV9//bXeeOMNPfbYY7p06ZJyc3Ndjsrk5OQoODjYQ2kBAEBl4vFTS/+tuLhYRUVF6tChg2rWrKk1a9Y45zIyMpSVlaXo6GgPJgQAAJWFR4/IJCUlqU+fPgoPD1dBQYE+/PBDrVu3zvkx76FDhyoxMVEBAQHy8/PTSy+9pOjo6Gt+YgkAAPyylKvI5ObmasmSJTp06JBGjx6tgIAA7dixQ0FBQbr99tvLvJ2TJ0/q6aefVnZ2tux2u9q2batVq1bpV7/6lSRp1qxZqlatmuLi4lRUVKTY2Fi9/fbb5YkMAACqINNFZvfu3YqJiZHdbteRI0f03HPPKSAgQJ999pmysrL0/vvvl3lb8+fPv+68j4+PUlJSlJKSYjYmAAD4BTB9jUxiYqKeeeYZZWZmysfHxznet29fbdiwwa3hAAAArsd0kfn6669L3NFXkm6//fbr3t8FAADA3UwXGW9v71Jv+//dd9+pQYMGbgkFAABQFqaLTP/+/TVp0iRdvnxZ0k/fr5SVlaU//OEPiouLc3tAAACAazFdZGbMmKHCwkIFBgbqwoUL6t69u5o1a6a6detq8uTJFZERAACgVKY/tWS327V69Wpt3LhRu3fvVmFhoe6++26XL3cEAAC4Fcp9Q7yuXbuqa9eu7swCAABgiuki8+abb5Y6brPZ5OPjo2bNmqlbt26qXr36TYcDAAC4HtNFZtasWfrxxx91/vx51atXT5J09uxZ1a5dW76+vjp58qSaNm2qtWvXKiwszO2BAQAAfmb6Yt8pU6aoU6dOyszM1OnTp3X69Gl99913ioqK0htvvKGsrCwFBwcrISGhIvICAAA4mT4i89prr+nTTz/VHXfc4Rxr1qyZ/vznPysuLk7ff/+9pk+fzkexAQBAhTN9RCY7O1tXrlwpMX7lyhXnnX1DQ0NVUFBw8+kAAACuw3SRuf/++/X8889r586dzrGdO3fqxRdfVM+ePSVJe/bsUZMmTdyXEgAAoBSmi8z8+fMVEBCgDh06yNvbW97e3urYsaMCAgKc32bt6+urGTNmuD0sAADAfzJ9jUxwcLBWr16tAwcO6LvvvpMktWjRQi1atHCuc//997svIQAAwDWU+4Z4ERERioiIcGcWAAAAU8pVZI4fP65ly5YpKytLly5dcpmbOXOmW4IBAADciOkis2bNGvXv319NmzbVgQMH1Lp1ax05ckSGYejuu++uiIwAAAClMn2xb1JSkkaNGqU9e/bIx8dHn376qY4dO6bu3bvrN7/5TUVkBAAAKJXpIrN//349/fTTkqQaNWrowoUL8vX11aRJkzRt2jS3BwQAALgW00WmTp06zutiQkJCdOjQIefcqVOn3JcMAADgBkxfI9O5c2dt3LhRLVu2VN++fTVy5Ejt2bNHn332mTp37lwRGQEAAEplusjMnDlThYWFkqSJEyeqsLBQH3/8sZo3b84nlgAAwC1lqshcvXpVx48fV9u2bSX9dJpp7ty5FRIMAADgRkxdI1O9enX17t1bZ8+erag8AAAAZWb6Yt/WrVvr+++/r4gsAAAAppguMq+//rpGjRql5cuXKzs7W/n5+S4LAADArWL6Yt++fftKkvr37y+bzeYcNwxDNptNV69edV86AACA6zBdZNauXVsROQAAAEwzXWS6d+9eETkAAABMM32NjCR99dVXeuqpp3Tvvffqhx9+kCT97W9/08aNG90aDgAA4HpMF5lPP/1UsbGxqlWrlnbs2KGioiJJUl5enqZMmeL2gAAAANdSrk8tzZ07V/PmzVPNmjWd4126dNGOHTvcGg4AAOB6TBeZjIwMdevWrcS43W5Xbm6uOzIBAACUiekiExwcrIMHD5YY37hxo5o2beqWUAAAAGVhusg899xzeuWVV7R161bZbDadOHFCH3zwgUaNGqUXX3yxIjICAACUyvTHr8eMGaPi4mL16tVL58+fV7du3eTt7a1Ro0bppZdeqoiMAAAApTJdZGw2m/74xz9q9OjROnjwoAoLCxUZGSlfX9+KyAcAAHBNpk8t/f3vf9f58+fl5eWlyMhI3XPPPZQYAADgEaaLTEJCggIDA/Xkk0/qn//8J9+tBAAAPMZ0kcnOztaiRYtks9n06KOPKiQkRPHx8dq8eXNF5AMAALgm00WmRo0aeuihh/TBBx/o5MmTmjVrlo4cOaL7779fd9xxR0VkBAAAKJXpi33/U+3atRUbG6uzZ8/q6NGj2r9/v7tyAQAA3FC5vjTy/Pnz+uCDD9S3b1/dfvvtmj17th5++GHt27fP1HamTp2qTp06qW7dugoMDNTAgQOVkZHhss7FixcVHx+v+vXry9fXV3FxccrJySlPbAAAUMWYLjKPP/64AgMDlZCQoKZNm2rdunU6ePCg/vSnPykiIsLUttavX6/4+Hht2bJFq1ev1uXLl9W7d2+dO3fOuU5CQoK++OILLV68WOvXr9eJEyf0yCOPmI0NAACqINOnlqpXr65PPvlEsbGxql69usvc3r171bp16zJvKzU11eXxwoULFRgYqPT0dHXr1k15eXmaP3++PvzwQ/Xs2VOStGDBArVs2VJbtmxR586dS2yzqKjI+Y3ckpSfn2/m5QEAAAsxfUTm51NKP5eYgoICvfvuu7rnnnvUrl27mwqTl5cnSQoICJAkpaen6/Lly4qJiXGuExERofDwcKWlpZW6jalTp8putzuXsLCwm8oEAAAqr3JdIyNJGzZs0ODBgxUSEqI///nP6tmzp7Zs2VLuIMXFxRoxYoS6dOniPKrjcDjk5eUlf39/l3WDgoLkcDhK3U5SUpLy8vKcy7Fjx8qdCQAAVG6mTi05HA4tXLhQ8+fPV35+vh599FEVFRXp888/V2Rk5E0FiY+P1969e7Vx48ab2o63t7e8vb1vahsAAMAaynxEpl+/fmrRooV2796t2bNn68SJE/rLX/7ilhDDhw/X8uXLtXbtWjVs2NA5HhwcrEuXLik3N9dl/ZycHAUHB7vluQEAgHWVucisXLlSQ4cO1cSJE/Xggw+WuNC3PAzD0PDhw7V06VJ9+eWXatKkict8hw4dVLNmTa1Zs8Y5lpGRoaysLEVHR9/08wMAAGsrc5HZuHGjCgoK1KFDB0VFRemtt97SqVOnburJ4+Pj9fe//10ffvih6tatK4fDIYfDoQsXLkiS7Ha7hg4dqsTERK1du1bp6el69tlnFR0dXeonlgAAwC9LmYtM586dNW/ePGVnZ+v555/XokWLFBoaquLiYq1evVoFBQWmn3zOnDnKy8tTjx49FBIS4lw+/vhj5zqzZs3SQw89pLi4OHXr1k3BwcH67LPPTD8XAACoekx/aqlOnToaMmSINm7cqD179mjkyJFKTk5WYGCg+vfvb2pbhmGUujzzzDPOdXx8fJSSkqIzZ87o3Llz+uyzz7g+BgAASLqJj19LUosWLTR9+nQdP35cH330kbsyAQAAlMlNFZmfVa9eXQMHDtSyZcvcsTkAAIAycUuRAQAA8ASKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyKDAAAsCyPFpkNGzaoX79+Cg0Nlc1m0+eff+4ybxiGxo0bp5CQENWqVUsxMTHKzMz0TFgAAFDpeLTInDt3Tu3atVNKSkqp89OnT9ebb76puXPnauvWrapTp45iY2N18eLFW5wUAABURjU8+eR9+vRRnz59Sp0zDEOzZ8/Wa6+9pgEDBkiS3n//fQUFBenzzz/X448/fiujAgCASqjSXiNz+PBhORwOxcTEOMfsdruioqKUlpZ2zZ8rKipSfn6+ywIAAKqmSltkHA6HJCkoKMhlPCgoyDlXmqlTp8putzuXsLCwCs0JAAA8p9IWmfJKSkpSXl6eczl27JinIwEAgApSaYtMcHCwJCknJ8dlPCcnxzlXGm9vb/n5+bksAACgaqq0RaZJkyYKDg7WmjVrnGP5+fnaunWroqOjPZgMAABUFh791FJhYaEOHjzofHz48GHt2rVLAQEBCg8P14gRI/T666+refPmatKkicaOHavQ0FANHDjQc6EBAECl4dEis337dt1///3Ox4mJiZKkwYMHa+HChXr11Vd17tw5DRs2TLm5ueratatSU1Pl4+PjqcgAAKAS8WiR6dGjhwzDuOa8zWbTpEmTNGnSpFuYCgAAWEWlvUYGAADgRigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsixRZFJSUtS4cWP5+PgoKipK27Zt83QkAABQCVT6IvPxxx8rMTFR48eP144dO9SuXTvFxsbq5MmTno4GAAA8rNIXmZkzZ+q5557Ts88+q8jISM2dO1e1a9fW//7v/3o6GgAA8LAang5wPZcuXVJ6erqSkpKcY9WqVVNMTIzS0tJK/ZmioiIVFRU5H+fl5UmS8vPz3Z6vuOi827dZlbnr74D9bg77/dZz5/sN+73s2O+eURH/v/7ndg3DuO56lbrInDp1SlevXlVQUJDLeFBQkA4cOFDqz0ydOlUTJ04sMR4WFlYhGVF29tmeTvDLxH6/9djnnsF+94yK3u8FBQWy2+3XnK/URaY8kpKSlJiY6HxcXFysM2fOqH79+rLZbB5Mdmvk5+crLCxMx44dk5+fn6fj/GKw3z2D/e4Z7HfP+KXtd8MwVFBQoNDQ0OuuV6mLzG233abq1asrJyfHZTwnJ0fBwcGl/oy3t7e8vb1dxvz9/SsqYqXl5+f3i/hFr2zY757BfvcM9rtn/JL2+/WOxPysUl/s6+XlpQ4dOmjNmjXOseLiYq1Zs0bR0dEeTAYAACqDSn1ERpISExM1ePBgdezYUffcc49mz56tc+fO6dlnn/V0NAAA4GGVvsg89thj+vHHHzVu3Dg5HA7dddddSk1NLXEBMH7i7e2t8ePHlzi9horFfvcM9rtnsN89g/1eOptxo881AQAAVFKV+hoZAACA66HIAAAAy6LIAAAAy6LIAAAAy6LIWNSGDRvUr18/hYaGymaz6fPPP3eZNwxD48aNU0hIiGrVqqWYmBhlZmZ6JmwVMWfOHLVt29Z5M6ro6GitXLnSOX/x4kXFx8erfv368vX1VVxcXImbOcK8CRMmyGazuSwRERHOefa7e7jjPeXMmTMaNGiQ/Pz85O/vr6FDh6qwsPAWvgrrmTp1qjp16qS6desqMDBQAwcOVEZGhss6Zfkdz8rK0oMPPqjatWsrMDBQo0eP1pUrV27lS/EYioxFnTt3Tu3atVNKSkqp89OnT9ebb76puXPnauvWrapTp45iY2N18eLFW5y06mjYsKGSk5OVnp6u7du3q2fPnhowYID27dsnSUpISNAXX3yhxYsXa/369Tpx4oQeeeQRD6euGlq1aqXs7GznsnHjRucc+9093PGeMmjQIO3bt0+rV6/W8uXLtWHDBg0bNuxWvQRLWr9+veLj47VlyxatXr1aly9fVu/evXXu3DnnOjf6Hb969aoefPBBXbp0SZs3b9Z7772nhQsXaty4cZ54SbeeAcuTZCxdutT5uLi42AgODjb+53/+xzmWm5treHt7Gx999JEHElZd9erVM/76178aubm5Rs2aNY3Fixc75/bv329IMtLS0jyY0PrGjx9vtGvXrtQ59nvFKM97yrfffmtIMr7++mvnOitXrjRsNpvxww8/3LLsVnfy5ElDkrF+/XrDMMr2O/7Pf/7TqFatmuFwOJzrzJkzx/Dz8zOKiopu7QvwAI7IVEGHDx+Ww+FQTEyMc8xutysqKkppaWkeTFZ1XL16VYsWLdK5c+cUHR2t9PR0Xb582WWfR0REKDw8nH3uBpmZmQoNDVXTpk01aNAgZWVlSRL7/RYpy3tKWlqa/P391bFjR+c6MTExqlatmrZu3XrLM1tVXl6eJCkgIEBS2X7H09LS1KZNG5cbxcbGxio/P995xLgqq/R39oV5DodDkkrc/TgoKMg5h/LZs2ePoqOjdfHiRfn6+mrp0qWKjIzUrl275OXlVeILStnnNy8qKkoLFy5UixYtlJ2drYkTJ+q+++7T3r175XA42O+3QFneUxwOhwIDA13ma9SooYCAAP4uyqi4uFgjRoxQly5d1Lp1a0kq0++4w+Eo9e/m57mqjiIDmNCiRQvt2rVLeXl5WrJkiQYPHqz169d7OlaV1qdPH+ef27Ztq6ioKDVq1EiffPKJatWq5cFkgHvFx8dr7969LteA4cY4tVQFBQcHS1KJq9pzcnKccygfLy8vNWvWTB06dNDUqVPVrl07vfHGGwoODtalS5eUm5vrsj773P38/f1155136uDBg+z3W6Qs7ynBwcE6efKky/yVK1d05swZ/i7KYPjw4Vq+fLnWrl2rhg0bOsfL8jseHBxc6t/Nz3NVHUWmCmrSpImCg4O1Zs0a51h+fr62bt2q6OhoDyareoqLi1VUVKQOHTqoZs2aLvs8IyNDWVlZ7HM3Kyws1KFDhxQSEsJ+v0XK8p4SHR2t3NxcpaenO9f58ssvVVxcrKioqFue2SoMw9Dw4cO1dOlSffnll2rSpInLfFl+x6Ojo7Vnzx6XIrl69Wr5+fkpMjLy1rwQT/L01cYon4KCAmPnzp3Gzp07DUnGzJkzjZ07dxpHjx41DMMwkpOTDX9/f+Mf//iHsXv3bmPAgAFGkyZNjAsXLng4uXWNGTPGWL9+vXH48GFj9+7dxpgxYwybzWb861//MgzDMF544QUjPDzc+PLLL43t27cb0dHRRnR0tIdTW9/IkSONdevWGYcPHzY2bdpkxMTEGLfddptx8uRJwzDY7+7ijveUBx54wGjfvr2xdetWY+PGjUbz5s2NJ554wlMvyRJefPFFw263G+vWrTOys7Ody/nz553r3Oh3/MqVK0br1q2N3r17G7t27TJSU1ONBg0aGElJSZ54SbccRcai1q5da0gqsQwePNgwjJ8+Ljl27FgjKCjI8Pb2Nnr16mVkZGR4NrTFDRkyxGjUqJHh5eVlNGjQwOjVq5ezxBiGYVy4cMH4/e9/b9SrV8+oXbu28fDDDxvZ2dkeTFw1PPbYY0ZISIjh5eVl3H777cZjjz1mHDx40DnPfncPd7ynnD592njiiScMX19fw8/Pz3j22WeNgoICD7wa6yhtn0syFixY4FynLL/jR44cMfr06WPUqlXLuO2224yRI0caly9fvsWvxjNshmEYt/ooEAAAgDtwjQwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigyACnPkyBHZbDbt2rXL01GcDhw4oM6dO8vHx0d33XWXp+MAuEkUGaAKe+aZZ2Sz2ZScnOwy/vnnn8tms3kolWeNHz9ederUUUZGhssX8f2nHj16aMSIESXGFy5cKH9//4oNCMAUigxQxfn4+GjatGk6e/asp6O4zaVLl8r9s4cOHVLXrl3VqFEj1a9f342pbt7ly5c9HQGwHIoMUMXFxMQoODhYU6dOveY6EyZMKHGaZfbs2WrcuLHz8TPPPKOBAwdqypQpCgoKkr+/vyZNmqQrV65o9OjRCggIUMOGDbVgwYIS2z9w4IDuvfde+fj4qHXr1lq/fr3L/N69e9WnTx/5+voqKChIv/3tb3Xq1CnnfI8ePTR8+HCNGDFCt912m2JjY0t9HcXFxZo0aZIaNmwob29v3XXXXUpNTXXO22w2paena9KkSbLZbJowYcJ19tyNrVu3Tvfcc4/q1Kkjf39/denSRUePHnXO/+Mf/9Ddd98tHx8fNW3aVBMnTtSVK1dc8syZM0f9+/dXnTp1NHnyZJ09e1aDBg1SgwYNVKtWLTVv3rzUfQrgJxQZoIqrXr26pkyZor/85S86fvz4TW3ryy+/1IkTJ7RhwwbNnDlT48eP10MPPaR69epp69ateuGFF/T888+XeJ7Ro0dr5MiR2rlzp6Kjo9WvXz+dPn1akpSbm6uePXuqffv22r59u1JTU5WTk6NHH33UZRvvvfeevLy8tGnTJs2dO7fUfG+88YZmzJihP//5z9q9e7diY2PVv39/ZWZmSpKys7PVqlUrjRw5UtnZ2Ro1alS598WVK1c0cOBAde/eXbt371ZaWpqGDRvmPGX31Vdf6emnn9Yrr7yib7/9Vu+8844WLlyoyZMnu2xnwoQJevjhh7Vnzx4NGTJEY8eO1bfffquVK1dq//79mjNnjm677bZy5wSqPE9//TaAijN48GBjwIABhmEYRufOnY0hQ4YYhmEYS5cuNf7zn//48eONdu3aufzsrFmzjEaNGrlsq1GjRsbVq1edYy1atDDuu+8+5+MrV64YderUMT766CPDMAzj8OHDhiQjOTnZuc7ly5eNhg0bGtOmTTMMwzD+9Kc/Gb1793Z57mPHjhmSjIyMDMMwDKN79+5G+/btb/h6Q0NDjcmTJ7uMderUyfj973/vfNyuXTtj/Pjx191O9+7djVdeeaXE+IIFCwy73W4YhmGcPn3akGSsW7eu1G306tXLmDJlisvY3/72NyMkJMT5WJIxYsQIl3X69etnPPvss9fNB+D/cUQG+IWYNm2a3nvvPe3fv7/c22jVqpWqVfv/t42goCC1adPG+bh69eqqX7++Tp486fJz0dHRzj/XqFFDHTt2dOb45ptvtHbtWvn6+jqXiIgIST9dz/KzDh06XDdbfn6+Tpw4oS5duriMd+nS5aZe87UEBATomWeeUWxsrPr166c33nhD2dnZzvlvvvlGkyZNcnldzz33nLKzs3X+/Hnneh07dnTZ7osvvqhFixbprrvu0quvvqrNmze7PTtQlVBkgF+Ibt26KTY2VklJSSXmqlWrJsMwXMZKu/C0Zs2aLo9tNlupY8XFxWXOVVhYqH79+mnXrl0uS2Zmprp16+Zcr06dOmXe5s3y8/NTXl5eifHc3FzZ7Xbn4wULFigtLU333nuvPv74Y915553asmWLpJ9e18SJE11e0549e5SZmSkfHx/nNv77dfXp00dHjx5VQkKCTpw4oV69et3UKTCgqqPIAL8gycnJ+uKLL5SWluYy3qBBAzkcDpcy4857v/z8n7v007Ul6enpatmypSTp7rvv1r59+9S4cWM1a9bMZTFTXvz8/BQaGqpNmza5jG/atEmRkZGm8rZo0UI7duwoMb5jxw7deeedLmPt27dXUlKSNm/erNatW+vDDz90vq6MjIwSr6lZs2YuR7VK06BBAw0ePFh///vfNXv2bL377rum8gO/JDU8HQDArdOmTRsNGjRIb775pst4jx499OOPP2r69On69a9/rdTUVK1cuVJ+fn5ued6UlBQ1b95cLVu21KxZs3T27FkNGTJEkhQfH6958+bpiSee0KuvvqqAgAAdPHhQixYt0l//+ldVr169zM8zevRojR8/XnfccYfuuusuLViwQLt27dIHH3xgKu+LL76ot956Sy+//LJ+97vfydvbWytWrNBHH32kL774QpJ0+PBhvfvuu+rfv79CQ0OVkZGhzMxMPf3005KkcePG6aGHHlJ4eLh+/etfq1q1avrmm2+0d+9evf7669d87nHjxqlDhw5q1aqVioqKtHz5cmfpA1ASR2SAX5hJkyaVOPXTsmVLvf3220pJSVG7du20bds2t57OSE5OVnJystq1a6eNGzdq2bJlzk/i/HwU5erVq+rdu7fatGmjESNGyN/f/4ZHLv7byy+/rMTERI0cOVJt2rRRamqqli1bpubNm5vaTtOmTbVhwwYdOHBAMTExioqK0ieffKLFixfrgQcekCTVrl1bBw4cUFxcnO68804NGzZM8fHxev755yVJsbGxWr58uf71r3+pU6dO6ty5s2bNmqVGjRpd97m9vLyUlJSktm3bqlu3bqpevboWLVpkKj/wS2Iz/vvEOAAAgEVwRAYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFgWRQYAAFjW/wHxhTL6RbtBpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "path = \"single baseline\"\n",
    "file_dict = {}\n",
    "\n",
    "\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "\n",
    "    filename = os.path.basename(file_path)\n",
    "\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "\n",
    "        ttime = [int(line.split(' ')[3]) for line in file_contents]\n",
    "        user_count = int(file_contents[0].split(' ')[2])\n",
    "  \n",
    "    file_dict[user_count] = ttime\n",
    "    \n",
    "avg_times = [(k,median(v)) for k,v in file_dict.items()]\n",
    "\n",
    "avg_times = sorted(avg_times, key=lambda x: x[0])\n",
    "labels = [str(i[0]) for i in avg_times]\n",
    "values = [i[1] for i in avg_times]\n",
    "\n",
    "# create the bar plot\n",
    "plt.bar(labels, values)\n",
    "\n",
    "# add labels to the plot\n",
    "plt.xlabel('Number of Users')\n",
    "plt.ylabel('Average Response Time')\n",
    "# plt.title('Bar Plot with Labels')\n",
    "\n",
    "# add value labels to the bars\n",
    "# for i, v in enumerate(values):\n",
    "#     plt.text(i, v + 1, str(v))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "201845f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(100, 20), (10, 30.0), (200, 20.0), (30, 20.0), (50, 25.0)]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"single trust\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    # extract the filename from the file path\n",
    "    filename = os.path.basename(file_path)\n",
    "    # open the file and read its contents into a list\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "        #create a dict from the file contents\n",
    "        user_count = int(file_contents[0].split(' ')[2]) \n",
    "        ttime = []\n",
    "        \n",
    "#         ttime = [int(line.split(' ')[3]) for line in file_contents]\n",
    "        for line in file_contents:\n",
    "            larr = line.split(' ')\n",
    "            trust_score = int(larr[6])\n",
    "            _time = int(larr[3])\n",
    "            if trust_score > 0:\n",
    "                ttime.append(_time)\n",
    "        \n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict[user_count] = ttime\n",
    "    \n",
    "avg_times2 = [(k, median(v)) for k,v in file_dict.items()]\n",
    "avg_times2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "75af459a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_times2 = sorted(avg_times2, key=lambda x: x[0])\n",
    "labels = [str(i[0]) for i in avg_times2]\n",
    "values = [i[1] for i in avg_times2]\n",
    "\n",
    "# create the bar plot\n",
    "plt.bar(labels, values)\n",
    "\n",
    "# add labels to the plot\n",
    "plt.xlabel('Number of Users')\n",
    "plt.ylabel('Average Response Time')\n",
    "plt.title('Trust Based Avg Time Excluding Request Rejection')\n",
    "\n",
    "# add value labels to the bars\n",
    "# for i, v in enumerate(values):\n",
    "#     plt.text(i, v + 1, str(v))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d57f224",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(100, 39.345692475463466),\n",
       " (10, 14.902439024390244),\n",
       " (200, 42.3455549974887),\n",
       " (30, 45.673352435530084),\n",
       " (50, 46.832)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"single trust\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    # extract the filename from the file path\n",
    "    filename = os.path.basename(file_path)\n",
    "    # open the file and read its contents into a list\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "        #create a dict from the file contents\n",
    "        user_count = int(file_contents[0].split(' ')[2]) \n",
    "        ttime = []\n",
    "        \n",
    "        ttime = [int(line.split(' ')[3]) for line in file_contents]\n",
    "#         for line in file_contents:\n",
    "#             larr = line.split(' ')\n",
    "#             trust_score = int(larr[6])\n",
    "#             _time = int(larr[3])\n",
    "#             if trust_score > 0:\n",
    "#                 ttime.append(_time)\n",
    "        \n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict[user_count] = ttime\n",
    "    \n",
    "avg_times3 = [(k, sum(v)/len(v)) for k,v in file_dict.items()]\n",
    "avg_times3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "387fa612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#including request rejections\n",
    "\n",
    "avg_times = sorted(avg_times3, key=lambda x: x[0])\n",
    "labels = [str(i[0]) for i in avg_times]\n",
    "values = [i[1] for i in avg_times]\n",
    "\n",
    "# create the bar plot\n",
    "plt.bar(labels, values)\n",
    "\n",
    "# add labels to the plot\n",
    "plt.xlabel('Number of Users')\n",
    "plt.ylabel('Average Response Time')\n",
    "plt.title('Trust Based Avg Time Including Request Rejection')\n",
    "\n",
    "# add value labels to the bars\n",
    "# for i, v in enumerate(values):\n",
    "#     plt.text(i, v + 1, str(v))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "49852398",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100: [(10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
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       "  (10, 11),\n",
       "  (35, 3),\n",
       "  (10, 7),\n",
       "  (15, 4),\n",
       "  (10, 14),\n",
       "  (25, 11),\n",
       "  (10, 14),\n",
       "  (65, 4),\n",
       "  (100, 7),\n",
       "  (85, 10),\n",
       "  (10, 4),\n",
       "  (15, 12),\n",
       "  (50, 2),\n",
       "  (130, 13),\n",
       "  (35, 12),\n",
       "  (80, 5),\n",
       "  (10, 9),\n",
       "  (15, 9),\n",
       "  (70, 15),\n",
       "  (10, 1),\n",
       "  (10, 4),\n",
       "  (85, 8),\n",
       "  (10, 2),\n",
       "  (10, 6),\n",
       "  (70, 3),\n",
       "  (100, 14),\n",
       "  (65, 10),\n",
       "  (15, 3),\n",
       "  (10, 6),\n",
       "  (90, 9),\n",
       "  (85, 4),\n",
       "  (80, 11),\n",
       "  (70, 3),\n",
       "  (10, 15),\n",
       "  (15, 10),\n",
       "  (535, 0),\n",
       "  (45, 5),\n",
       "  (10, 8),\n",
       "  (15, 10),\n",
       "  (10, 12),\n",
       "  (15, 13),\n",
       "  (25, 14),\n",
       "  (10, 3),\n",
       "  (15, 5),\n",
       "  (65, 4),\n",
       "  (10, 6),\n",
       "  (55, 9),\n",
       "  (470, 1),\n",
       "  (115, 13),\n",
       "  (270, 3),\n",
       "  (15, 8),\n",
       "  (70, 3),\n",
       "  (30, 4),\n",
       "  (10, 15),\n",
       "  (15, 14),\n",
       "  (45, 15),\n",
       "  (15, 9),\n",
       "  (415, 1),\n",
       "  (55, 4),\n",
       "  (30, 16),\n",
       "  (15, 5),\n",
       "  (15, 7),\n",
       "  (65, 11),\n",
       "  (15, 11),\n",
       "  (10, 1),\n",
       "  (85, 3),\n",
       "  (60, 10),\n",
       "  (35, 12),\n",
       "  (10, 4),\n",
       "  (80, 16),\n",
       "  (10, 9),\n",
       "  (35, 11),\n",
       "  (55, 15),\n",
       "  (50, 11),\n",
       "  (15, 12),\n",
       "  (135, 10),\n",
       "  (20, 10),\n",
       "  (10, 2),\n",
       "  (100, 8),\n",
       "  (10, 10),\n",
       "  (15, 14),\n",
       "  (20, 16),\n",
       "  (420, 1),\n",
       "  (10, 4),\n",
       "  (45, 4),\n",
       "  (30, 4),\n",
       "  (10, 9),\n",
       "  (115, 15),\n",
       "  (75, 10),\n",
       "  (40, 13),\n",
       "  (80, 10),\n",
       "  (10, 12),\n",
       "  (10, 11),\n",
       "  (100, 9),\n",
       "  (20, 14)],\n",
       " 10: [(10, 0),\n",
       "  (15, 0),\n",
       "  (25, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (45, 0),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (15, 2),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (30, 1),\n",
       "  (15, 3),\n",
       "  (15, 4),\n",
       "  (20, 4),\n",
       "  (10, 4),\n",
       "  (25, 2),\n",
       "  (10, 5),\n",
       "  (15, 5),\n",
       "  (15, 5),\n",
       "  (10, 6),\n",
       "  (20, 3),\n",
       "  (15, 6),\n",
       "  (10, 7),\n",
       "  (25, 6),\n",
       "  (15, 4),\n",
       "  (10, 7),\n",
       "  (15, 8),\n",
       "  (15, 7),\n",
       "  (10, 8),\n",
       "  (225, 0),\n",
       "  (10, 9),\n",
       "  (10, 9),\n",
       "  (20, 8),\n",
       "  (10, 10),\n",
       "  (55, 5),\n",
       "  (15, 10),\n",
       "  (10, 11),\n",
       "  (20, 9),\n",
       "  (50, 1),\n",
       "  (10, 11),\n",
       "  (15, 12),\n",
       "  (20, 10),\n",
       "  (10, 12),\n",
       "  (10, 13),\n",
       "  (330, 0),\n",
       "  (65, 6),\n",
       "  (15, 13),\n",
       "  (20, 14),\n",
       "  (35, 11),\n",
       "  (10, 14),\n",
       "  (70, 2),\n",
       "  (15, 15),\n",
       "  (45, 1),\n",
       "  (45, 7),\n",
       "  (25, 15),\n",
       "  (35, 12),\n",
       "  (30, 3),\n",
       "  (30, 16),\n",
       "  (30, 2),\n",
       "  (30, 8),\n",
       "  (25, 16),\n",
       "  (30, 13),\n",
       "  (25, 4),\n",
       "  (30, 17),\n",
       "  (470, 0),\n",
       "  (30, 3),\n",
       "  (35, 9),\n",
       "  (35, 17),\n",
       "  (35, 14),\n",
       "  (35, 5),\n",
       "  (40, 18),\n",
       "  (40, 1),\n",
       "  (35, 4),\n",
       "  (40, 10),\n",
       "  (35, 18),\n",
       "  (35, 15),\n",
       "  (35, 6),\n",
       "  (35, 19),\n",
       "  (35, 2),\n",
       "  (575, 0),\n",
       "  (45, 5),\n",
       "  (45, 11),\n",
       "  (45, 19),\n",
       "  (45, 16),\n",
       "  (610, 0),\n",
       "  (50, 7),\n",
       "  (50, 20),\n",
       "  (50, 3),\n",
       "  (50, 1),\n",
       "  (50, 6),\n",
       "  (45, 12),\n",
       "  (50, 20),\n",
       "  (50, 17),\n",
       "  (55, 1),\n",
       "  (50, 8),\n",
       "  (50, 21),\n",
       "  (690, 0),\n",
       "  (60, 4)],\n",
       " 200: [(10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (15, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (15, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (25, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (25, 0),\n",
       "  (30, 0),\n",
       "  (25, 0),\n",
       "  (10, 1),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (10, 1),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (35, 0),\n",
       "  (10, 1),\n",
       "  (35, 0),\n",
       "  (35, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (35, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (15, 1),\n",
       "  (15, 1),\n",
       "  (40, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (40, 0),\n",
       "  (40, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (20, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (20, 1),\n",
       "  (45, 0),\n",
       "  (10, 1),\n",
       "  (40, 0),\n",
       "  (40, 0),\n",
       "  (15, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (10, 1),\n",
       "  (50, 0),\n",
       "  (45, 0),\n",
       "  (20, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (50, 0),\n",
       "  (15, 1),\n",
       "  (20, 1),\n",
       "  (50, 0),\n",
       "  (50, 0),\n",
       "  (10, 1),\n",
       "  (10, 2),\n",
       "  (15, 1),\n",
       "  (20, 1),\n",
       "  (50, 0),\n",
       "  (50, 0),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (10, 1),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (15, 2),\n",
       "  (20, 1),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (20, 1),\n",
       "  (15, 1),\n",
       "  (55, 0),\n",
       "  (55, 0),\n",
       "  (10, 2),\n",
       "  (55, 0),\n",
       "  (25, 1),\n",
       "  (35, 1),\n",
       "  (15, 2),\n",
       "  (35, 1),\n",
       "  (15, 2),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (10, 1),\n",
       "  (10, 2),\n",
       "  (60, 0),\n",
       "  (10, 2),\n",
       "  (30, 1),\n",
       "  (30, 1),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (20, 1),\n",
       "  (15, 1),\n",
       "  (10, 2),\n",
       "  (15, 1),\n",
       "  (65, 0),\n",
       "  (10, 2),\n",
       "  (20, 1),\n",
       "  (15, 2),\n",
       "  (15, 2),\n",
       "  (40, 1),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (70, 0),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (70, 0),\n",
       "  (20, 2),\n",
       "  (15, 2),\n",
       "  (10, 1),\n",
       "  (70, 0),\n",
       "  (70, 0),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (10, 3),\n",
       "  (70, 0),\n",
       "  (70, 0),\n",
       "  (25, 2),\n",
       "  (10, 3),\n",
       "  (75, 0),\n",
       "  (75, 0),\n",
       "  (10, 1),\n",
       "  (15, 2),\n",
       "  (10, 3),\n",
       "  (10, 2),\n",
       "  (10, 3),\n",
       "  (35, 1),\n",
       "  (80, 0),\n",
       "  (35, 1),\n",
       "  (10, 3),\n",
       "  (10, 1),\n",
       "  (10, 2),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (80, 0),\n",
       "  (80, 0),\n",
       "  (10, 3),\n",
       "  (10, 2),\n",
       "  (20, 2),\n",
       "  (15, 1),\n",
       "  (80, 0),\n",
       "  (35, 2),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (85, 0),\n",
       "  (15, 1),\n",
       "  (10, 3),\n",
       "  (10, 2),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (15, 3),\n",
       "  (10, 2),\n",
       "  (85, 0),\n",
       "  (85, 0),\n",
       "  (15, 3),\n",
       "  (10, 2),\n",
       "  (20, 2),\n",
       "  (10, 3),\n",
       "  (10, 1),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (20, 3),\n",
       "  (10, 3),\n",
       "  (50, 1),\n",
       "  (10, 3),\n",
       "  (10, 1),\n",
       "  (10, 3),\n",
       "  (15, 3),\n",
       "  (10, 4),\n",
       "  (10, 1),\n",
       "  (40, 2),\n",
       "  (20, 3),\n",
       "  (40, 2),\n",
       "  (25, 3),\n",
       "  (15, 3),\n",
       "  (10, 3),\n",
       "  (95, 0),\n",
       "  (30, 1),\n",
       "  (20, 2),\n",
       "  (10, 3),\n",
       "  (95, 0),\n",
       "  (10, 3),\n",
       "  (10, 4),\n",
       "  (15, 1),\n",
       "  (50, 2),\n",
       "  (10, 3),\n",
       "  (10, 3),\n",
       "  (25, 3),\n",
       "  (40, 2),\n",
       "  (25, 2),\n",
       "  (15, 3),\n",
       "  (10, 4),\n",
       "  (10, 1),\n",
       "  (15, 4),\n",
       "  (15, 4),\n",
       "  (20, 3),\n",
       "  (20, 1),\n",
       "  (50, 2),\n",
       "  (10, 3),\n",
       "  (105, 0),\n",
       "  (10, 2),\n",
       "  (70, 1),\n",
       "  (30, 3),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (85, 1),\n",
       "  (10, 1),\n",
       "  (10, 4),\n",
       "  (30, 3),\n",
       "  (15, 2),\n",
       "  (110, 0),\n",
       "  (110, 0),\n",
       "  (10, 4),\n",
       "  (15, 4),\n",
       "  (10, 4),\n",
       "  (20, 4),\n",
       "  (10, 3),\n",
       "  (15, 4),\n",
       "  (10, 3),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (50, 1),\n",
       "  (110, 0),\n",
       "  (10, 5),\n",
       "  (15, 3),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (95, 1),\n",
       "  (115, 0),\n",
       "  (115, 0),\n",
       "  (30, 1),\n",
       "  (10, 3),\n",
       "  (15, 4),\n",
       "  (115, 0),\n",
       "  (120, 0),\n",
       "  (120, 0),\n",
       "  (10, 5),\n",
       "  (10, 3),\n",
       "  (10, 4),\n",
       "  (85, 1),\n",
       "  (50, 2),\n",
       "  (10, 5),\n",
       "  (10, 4),\n",
       "  (10, 1),\n",
       "  (35, 3),\n",
       "  (10, 4),\n",
       "  (15, 4),\n",
       "  (15, 4),\n",
       "  (15, 4),\n",
       "  (10, 5),\n",
       "  (15, 4),\n",
       "  (75, 1),\n",
       "  (15, 3),\n",
       "  (10, 4),\n",
       "  (35, 3),\n",
       "  (20, 4),\n",
       "  (30, 3),\n",
       "  (125, 0),\n",
       "  (125, 0),\n",
       "  (70, 1),\n",
       "  (100, 1),\n",
       "  (10, 4),\n",
       "  (15, 3),\n",
       "  (35, 4),\n",
       "  (20, 1),\n",
       "  (130, 0),\n",
       "  (10, 2),\n",
       "  (30, 4),\n",
       "  (15, 4),\n",
       "  (60, 2),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (10, 4),\n",
       "  (130, 0),\n",
       "  (15, 4),\n",
       "  (135, 0),\n",
       "  (65, 2),\n",
       "  (10, 4),\n",
       "  (45, 1),\n",
       "  (135, 0),\n",
       "  (10, 5),\n",
       "  (55, 3),\n",
       "  (15, 3),\n",
       "  (15, 5),\n",
       "  (10, 5),\n",
       "  (10, 4),\n",
       "  (30, 4),\n",
       "  (15, 5),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (30, 3),\n",
       "  (140, 0),\n",
       "  (10, 5),\n",
       "  (75, 2),\n",
       "  (85, 1),\n",
       "  (10, 2),\n",
       "  (140, 0),\n",
       "  (10, 6),\n",
       "  (140, 0),\n",
       "  (10, 6),\n",
       "  (10, 4),\n",
       "  (15, 6),\n",
       "  (25, 3),\n",
       "  (10, 1),\n",
       "  (10, 2),\n",
       "  (15, 5),\n",
       "  (25, 5),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (25, 2),\n",
       "  (145, 0),\n",
       "  (20, 5),\n",
       "  (70, 2),\n",
       "  (15, 5),\n",
       "  (125, 1),\n",
       "  (15, 5),\n",
       "  (15, 2),\n",
       "  (10, 5),\n",
       "  (10, 2),\n",
       "  (50, 2),\n",
       "  (10, 5),\n",
       "  (15, 6),\n",
       "  (105, 1),\n",
       "  (15, 1),\n",
       "  (15, 1),\n",
       "  (10, 2),\n",
       "  (10, 5),\n",
       "  (35, 3),\n",
       "  (20, 2),\n",
       "  (10, 4),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (25, 4),\n",
       "  (20, 5),\n",
       "  (95, 1),\n",
       "  (155, 0),\n",
       "  (10, 5),\n",
       "  (10, 6),\n",
       "  (15, 3),\n",
       "  (15, 6),\n",
       "  (10, 1),\n",
       "  (15, 5),\n",
       "  (155, 0),\n",
       "  (35, 3),\n",
       "  (55, 2),\n",
       "  (45, 5),\n",
       "  (15, 6),\n",
       "  (160, 0),\n",
       "  (15, 3),\n",
       "  (40, 2),\n",
       "  (10, 7),\n",
       "  (10, 4),\n",
       "  (10, 6),\n",
       "  (10, 6),\n",
       "  (10, 6),\n",
       "  (90, 2),\n",
       "  (25, 5),\n",
       "  (160, 0),\n",
       "  (10, 7),\n",
       "  (10, 2),\n",
       "  (10, 6),\n",
       "  (25, 4),\n",
       "  (25, 5),\n",
       "  (20, 5),\n",
       "  (10, 2),\n",
       "  (15, 6),\n",
       "  (10, 7),\n",
       "  (10, 6),\n",
       "  (10, 4),\n",
       "  (10, 6),\n",
       "  (55, 2),\n",
       "  (170, 0),\n",
       "  (20, 5),\n",
       "  (30, 1),\n",
       "  (10, 3),\n",
       "  (55, 4),\n",
       "  (20, 6),\n",
       "  (10, 6),\n",
       "  (10, 4),\n",
       "  (25, 6),\n",
       "  (15, 6),\n",
       "  (50, 1),\n",
       "  (10, 3),\n",
       "  (15, 3),\n",
       "  (30, 3),\n",
       "  (20, 5),\n",
       "  (10, 6),\n",
       "  (15, 1),\n",
       "  (60, 4),\n",
       "  (15, 3),\n",
       "  (130, 1),\n",
       "  (30, 4),\n",
       "  (10, 5),\n",
       "  (10, 6),\n",
       "  (40, 3),\n",
       "  (110, 1),\n",
       "  (10, 4),\n",
       "  (20, 6),\n",
       "  (10, 6),\n",
       "  (35, 1),\n",
       "  (80, 1),\n",
       "  (10, 6),\n",
       "  (50, 1),\n",
       "  (60, 2),\n",
       "  (25, 6),\n",
       "  (10, 7),\n",
       "  (10, 7),\n",
       "  (15, 6),\n",
       "  (10, 4),\n",
       "  (10, 8),\n",
       "  (10, 6),\n",
       "  (10, 7),\n",
       "  (180, 0),\n",
       "  (75, 2),\n",
       "  (10, 7),\n",
       "  (10, 3),\n",
       "  (10, 7),\n",
       "  (25, 7),\n",
       "  (65, 4),\n",
       "  (10, 8),\n",
       "  (10, 6),\n",
       "  (10, 8),\n",
       "  (15, 4),\n",
       "  (10, 5),\n",
       "  (15, 3),\n",
       "  (95, 1),\n",
       "  (190, 0),\n",
       "  (10, 4),\n",
       "  (95, 2),\n",
       "  (10, 3),\n",
       "  (15, 6),\n",
       "  (10, 6),\n",
       "  (10, 6),\n",
       "  (35, 6),\n",
       "  (15, 7),\n",
       "  (10, 7),\n",
       "  (80, 2),\n",
       "  (10, 7),\n",
       "  (20, 5),\n",
       "  (60, 4),\n",
       "  (10, 7),\n",
       "  (25, 7),\n",
       "  (65, 1),\n",
       "  (115, 2),\n",
       "  (10, 6),\n",
       "  (10, 5),\n",
       "  (195, 0),\n",
       "  (10, 6),\n",
       "  (35, 5),\n",
       "  (50, 6),\n",
       "  (20, 3),\n",
       "  (45, 3),\n",
       "  (25, 3),\n",
       "  (35, 3),\n",
       "  (30, 6),\n",
       "  (10, 2),\n",
       "  (10, 7),\n",
       "  (10, 7),\n",
       "  (10, 4),\n",
       "  (20, 7),\n",
       "  (10, 8),\n",
       "  (15, 7),\n",
       "  (20, 7),\n",
       "  (15, 5),\n",
       "  (10, 3),\n",
       "  (10, 9),\n",
       "  (200, 0),\n",
       "  (65, 4),\n",
       "  (10, 3),\n",
       "  (155, 1),\n",
       "  (10, 8),\n",
       "  (65, 4),\n",
       "  (150, 1),\n",
       "  (10, 5),\n",
       "  (15, 8),\n",
       "  (15, 2),\n",
       "  (20, 5),\n",
       "  (25, 5),\n",
       "  (25, 7),\n",
       "  (30, 7),\n",
       "  (10, 9),\n",
       "  (20, 2),\n",
       "  (25, 4),\n",
       "  (20, 7),\n",
       "  (25, 5),\n",
       "  (40, 6),\n",
       "  (50, 2),\n",
       "  (20, 7),\n",
       "  (10, 7),\n",
       "  (25, 7),\n",
       "  (10, 7),\n",
       "  (55, 6),\n",
       "  (10, 8),\n",
       "  (15, 8),\n",
       "  (10, 7),\n",
       "  (15, 8),\n",
       "  (100, 2),\n",
       "  (55, 2),\n",
       "  (10, 3),\n",
       "  (10, 8),\n",
       "  (20, 7),\n",
       "  (20, 9),\n",
       "  (15, 5),\n",
       "  (15, 4),\n",
       "  (20, 8),\n",
       "  (175, 1),\n",
       "  (50, 2),\n",
       "  (15, 8),\n",
       "  (30, 7),\n",
       "  (10, 7),\n",
       "  (35, 4),\n",
       "  (10, 6),\n",
       "  (10, 8),\n",
       "  (70, 3),\n",
       "  (20, 7),\n",
       "  (220, 0),\n",
       "  (15, 8),\n",
       "  (25, 4),\n",
       "  (15, 8),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (10, 8),\n",
       "  (10, 6),\n",
       "  (15, 4),\n",
       "  (15, 7),\n",
       "  (10, 9),\n",
       "  (10, 9),\n",
       "  (65, 3),\n",
       "  (15, 8),\n",
       "  (20, 7),\n",
       "  (10, 10),\n",
       "  (10, 8),\n",
       "  (75, 4),\n",
       "  (10, 3),\n",
       "  (60, 6),\n",
       "  (15, 7),\n",
       "  (15, 8),\n",
       "  (15, 3),\n",
       "  (10, 6),\n",
       "  (20, 4),\n",
       "  (10, 7),\n",
       "  (10, 5),\n",
       "  (230, 0),\n",
       "  (45, 4),\n",
       "  (25, 2),\n",
       "  (15, 4),\n",
       "  (10, 8),\n",
       "  (50, 2),\n",
       "  (10, 8),\n",
       "  (10, 9),\n",
       "  (25, 6),\n",
       "  (150, 2),\n",
       "  (45, 2),\n",
       "  (10, 6),\n",
       "  (10, 4),\n",
       "  (15, 9),\n",
       "  (15, 8),\n",
       "  (15, 10),\n",
       "  (15, 6),\n",
       "  (10, 9),\n",
       "  (10, 8),\n",
       "  (40, 5),\n",
       "  (20, 6),\n",
       "  (10, 9),\n",
       "  (10, 2),\n",
       "  (235, 0),\n",
       "  (10, 8),\n",
       "  (60, 5),\n",
       "  (90, 5),\n",
       "  (10, 8),\n",
       "  (10, 8),\n",
       "  (240, 0),\n",
       "  (10, 10),\n",
       "  (15, 8),\n",
       "  (40, 7),\n",
       "  (15, 8),\n",
       "  (15, 8),\n",
       "  (10, 8),\n",
       "  (10, 5),\n",
       "  (15, 5),\n",
       "  (10, 9),\n",
       "  (10, 8),\n",
       "  (10, 10),\n",
       "  (10, 9),\n",
       "  (180, 2),\n",
       "  (95, 2),\n",
       "  (245, 0),\n",
       "  (20, 7),\n",
       "  (10, 9),\n",
       "  (10, 5),\n",
       "  (10, 9),\n",
       "  (10, 10),\n",
       "  (155, 1),\n",
       "  (30, 5),\n",
       "  (10, 7),\n",
       "  (10, 11),\n",
       "  (20, 9),\n",
       "  (15, 7),\n",
       "  (15, 1),\n",
       "  (35, 2),\n",
       "  (210, 1),\n",
       "  (80, 1),\n",
       "  (10, 5),\n",
       "  (15, 4),\n",
       "  (250, 0),\n",
       "  (15, 8),\n",
       "  (10, 9),\n",
       "  (15, 4),\n",
       "  (115, 2),\n",
       "  (25, 4),\n",
       "  (15, 9),\n",
       "  (10, 3),\n",
       "  (40, 1),\n",
       "  (15, 6),\n",
       "  (15, 9),\n",
       "  (10, 10),\n",
       "  (10, 11),\n",
       "  (35, 3),\n",
       "  (10, 5),\n",
       "  (10, 9),\n",
       "  (25, 7),\n",
       "  (10, 10),\n",
       "  (10, 9),\n",
       "  (50, 8),\n",
       "  (165, 1),\n",
       "  (10, 6),\n",
       "  (10, 8),\n",
       "  (20, 5),\n",
       "  (15, 3),\n",
       "  (15, 7),\n",
       "  (60, 6),\n",
       "  (10, 10),\n",
       "  (15, 10),\n",
       "  (15, 9),\n",
       "  (140, 1),\n",
       "  (10, 9),\n",
       "  (40, 9),\n",
       "  (15, 6),\n",
       "  (35, 9),\n",
       "  (15, 11),\n",
       "  (265, 0),\n",
       "  (10, 9),\n",
       "  (40, 3),\n",
       "  (25, 4),\n",
       "  (30, 8),\n",
       "  (10, 11),\n",
       "  (15, 9),\n",
       "  (10, 9),\n",
       "  (55, 5),\n",
       "  (270, 0),\n",
       "  (15, 9),\n",
       "  (10, 11),\n",
       "  (85, 2),\n",
       "  (100, 1),\n",
       "  (15, 10),\n",
       "  (270, 0),\n",
       "  (65, 3),\n",
       "  (25, 6),\n",
       "  (270, 0),\n",
       "  (10, 8),\n",
       "  (30, 7),\n",
       "  (20, 9),\n",
       "  (30, 7),\n",
       "  (10, 5),\n",
       "  (25, 9),\n",
       "  (190, 1),\n",
       "  (10, 3),\n",
       "  (10, 10),\n",
       "  (15, 10),\n",
       "  (20, 10),\n",
       "  (10, 11),\n",
       "  (15, 10),\n",
       "  (15, 5),\n",
       "  (10, 10),\n",
       "  (280, 0),\n",
       "  (20, 8),\n",
       "  (75, 5),\n",
       "  (20, 10),\n",
       "  (10, 10),\n",
       "  (80, 2),\n",
       "  (280, 0),\n",
       "  (95, 4),\n",
       "  (25, 6),\n",
       "  (10, 9),\n",
       "  (20, 2),\n",
       "  (85, 1),\n",
       "  (15, 10),\n",
       "  (10, 11),\n",
       "  (210, 2),\n",
       "  (10, 4),\n",
       "  (15, 6),\n",
       "  (10, 11),\n",
       "  (25, 8),\n",
       "  (30, 6),\n",
       "  (10, 7),\n",
       "  (10, 10),\n",
       "  (30, 9),\n",
       "  (40, 3),\n",
       "  (25, 6),\n",
       "  (20, 12),\n",
       "  (10, 10),\n",
       "  (10, 10),\n",
       "  (10, 6),\n",
       "  (50, 7),\n",
       "  (30, 3),\n",
       "  (70, 5),\n",
       "  (10, 2),\n",
       "  (15, 10),\n",
       "  (10, 10),\n",
       "  (10, 9),\n",
       "  (10, 3),\n",
       "  (10, 10),\n",
       "  (20, 10),\n",
       "  (15, 10),\n",
       "  (15, 12),\n",
       "  (10, 12),\n",
       "  (10, 10),\n",
       "  (40, 3),\n",
       "  (70, 3),\n",
       "  (270, 1),\n",
       "  (275, 1),\n",
       "  (10, 1),\n",
       "  (20, 12),\n",
       "  (35, 12),\n",
       "  (30, 2),\n",
       "  (10, 7),\n",
       "  (10, 11),\n",
       "  (300, 0),\n",
       "  (60, 5),\n",
       "  (35, 9),\n",
       "  (10, 11),\n",
       "  (10, 11),\n",
       "  (40, 2),\n",
       "  (15, 4),\n",
       "  (135, 2),\n",
       "  (10, 7),\n",
       "  (40, 6),\n",
       "  (30, 11),\n",
       "  (15, 10),\n",
       "  (10, 6),\n",
       "  (15, 11),\n",
       "  (15, 11),\n",
       "  (55, 3),\n",
       "  (155, 1),\n",
       "  (70, 9),\n",
       "  (15, 11),\n",
       "  (10, 7),\n",
       "  (10, 12),\n",
       "  (15, 11),\n",
       "  (20, 11),\n",
       "  (35, 2),\n",
       "  (145, 2),\n",
       "  (305, 0),\n",
       "  (10, 12),\n",
       "  (35, 9),\n",
       "  (25, 8),\n",
       "  (30, 12),\n",
       "  (45, 2),\n",
       "  (310, 0),\n",
       "  (10, 8),\n",
       "  (15, 3),\n",
       "  (75, 5),\n",
       "  (10, 7),\n",
       "  (10, 9),\n",
       "  (115, 4),\n",
       "  (15, 8),\n",
       "  (10, 13),\n",
       "  (40, 10),\n",
       "  (15, 11),\n",
       "  (10, 6),\n",
       "  (105, 1),\n",
       "  (35, 7),\n",
       "  (15, 7),\n",
       "  (315, 0),\n",
       "  (75, 8),\n",
       "  (65, 3),\n",
       "  (55, 3),\n",
       "  (10, 13),\n",
       "  (10, 11),\n",
       "  (25, 1),\n",
       "  (15, 4),\n",
       "  (10, 2),\n",
       "  (15, 11),\n",
       "  (10, 12),\n",
       "  (15, 10),\n",
       "  (10, 2),\n",
       "  (95, 3),\n",
       "  (35, 4),\n",
       "  (30, 9),\n",
       "  (10, 8),\n",
       "  (15, 13),\n",
       "  (60, 6),\n",
       "  (50, 8),\n",
       "  (10, 7),\n",
       "  (10, 10),\n",
       "  (10, 12),\n",
       "  (45, 4),\n",
       "  (10, 11),\n",
       "  (20, 7),\n",
       "  (15, 13),\n",
       "  (10, 12),\n",
       "  (20, 11),\n",
       "  (25, 11),\n",
       "  (15, 11),\n",
       "  (10, 4),\n",
       "  (45, 11),\n",
       "  (155, 3),\n",
       "  (10, 10),\n",
       "  (10, 7),\n",
       "  (175, 1),\n",
       "  (10, 1),\n",
       "  (160, 1),\n",
       "  (10, 12),\n",
       "  (60, 4),\n",
       "  (10, 13),\n",
       "  (20, 13),\n",
       "  (10, 12),\n",
       "  (10, 13),\n",
       "  (10, 12),\n",
       "  (330, 0),\n",
       "  (190, 1),\n",
       "  (40, 10),\n",
       "  (175, 3),\n",
       "  (20, 6),\n",
       "  (10, 6),\n",
       "  (10, 5),\n",
       "  (10, 9),\n",
       "  (10, 11),\n",
       "  (10, 4),\n",
       "  (50, 6),\n",
       "  (335, 0),\n",
       "  (15, 10),\n",
       "  (210, 2),\n",
       "  (55, 5),\n",
       "  (20, 12),\n",
       "  (340, 0),\n",
       "  (40, 5),\n",
       "  (10, 8),\n",
       "  (15, 9),\n",
       "  (20, 12),\n",
       "  (10, 12),\n",
       "  (20, 2),\n",
       "  (30, 5),\n",
       "  (25, 3),\n",
       "  (10, 3),\n",
       "  (145, 6),\n",
       "  (20, 9),\n",
       "  (340, 0),\n",
       "  (20, 8),\n",
       "  (10, 8),\n",
       "  (20, 14),\n",
       "  (255, 1),\n",
       "  (15, 7),\n",
       "  (30, 12),\n",
       "  (20, 3),\n",
       "  (40, 11),\n",
       "  (10, 14),\n",
       "  (10, 8),\n",
       "  (15, 5),\n",
       "  (145, 3),\n",
       "  (60, 6),\n",
       "  (20, 10),\n",
       "  (10, 12),\n",
       "  (10, 5),\n",
       "  (50, 10),\n",
       "  (10, 12),\n",
       "  (15, 11),\n",
       "  (10, 12),\n",
       "  (35, 8),\n",
       "  (10, 12),\n",
       "  (40, 4),\n",
       "  (10, 5),\n",
       "  (20, 13),\n",
       "  (65, 2),\n",
       "  (125, 6),\n",
       "  (15, 9),\n",
       "  (10, 11),\n",
       "  (40, 8),\n",
       "  (45, 11),\n",
       "  (20, 14),\n",
       "  (25, 2),\n",
       "  (10, 5),\n",
       "  (35, 13),\n",
       "  (10, 4),\n",
       "  (90, 7),\n",
       "  (30, 12),\n",
       "  (10, 13),\n",
       "  (285, 1),\n",
       "  (15, 1),\n",
       "  (10, 1),\n",
       "  (85, 6),\n",
       "  (25, 3),\n",
       "  (10, 10),\n",
       "  (15, 14),\n",
       "  (360, 0),\n",
       "  (95, 5),\n",
       "  (15, 6),\n",
       "  (15, 12),\n",
       "  (15, 7),\n",
       "  (30, 7),\n",
       "  (10, 10),\n",
       "  (10, 10),\n",
       "  (110, 1),\n",
       "  (15, 6),\n",
       "  (10, 9),\n",
       "  (35, 12),\n",
       "  (55, 2),\n",
       "  (10, 6),\n",
       "  (35, 14),\n",
       "  (20, 14),\n",
       "  (30, 13),\n",
       "  (365, 0),\n",
       "  (365, 0),\n",
       "  (10, 12),\n",
       "  (10, 11),\n",
       "  (15, 13),\n",
       "  (30, 13),\n",
       "  (155, 4),\n",
       "  ...],\n",
       " 30: [(10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (25, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (35, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (40, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (20, 1),\n",
       "  (55, 0),\n",
       "  (55, 0),\n",
       "  (15, 2),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (10, 1),\n",
       "  (20, 2),\n",
       "  (70, 0),\n",
       "  (75, 0),\n",
       "  (80, 0),\n",
       "  (15, 2),\n",
       "  (10, 3),\n",
       "  (20, 2),\n",
       "  (25, 2),\n",
       "  (10, 3),\n",
       "  (20, 1),\n",
       "  (45, 2),\n",
       "  (15, 3),\n",
       "  (10, 1),\n",
       "  (95, 0),\n",
       "  (15, 3),\n",
       "  (10, 4),\n",
       "  (15, 3),\n",
       "  (10, 3),\n",
       "  (15, 3),\n",
       "  (10, 3),\n",
       "  (15, 4),\n",
       "  (95, 1),\n",
       "  (15, 4),\n",
       "  (45, 2),\n",
       "  (10, 5),\n",
       "  (10, 4),\n",
       "  (15, 4),\n",
       "  (10, 4),\n",
       "  (15, 4),\n",
       "  (10, 5),\n",
       "  (70, 1),\n",
       "  (15, 4),\n",
       "  (10, 5),\n",
       "  (15, 2),\n",
       "  (10, 5),\n",
       "  (10, 6),\n",
       "  (15, 5),\n",
       "  (20, 3),\n",
       "  (10, 6),\n",
       "  (160, 0),\n",
       "  (15, 5),\n",
       "  (10, 5),\n",
       "  (20, 5),\n",
       "  (10, 3),\n",
       "  (15, 6),\n",
       "  (75, 2),\n",
       "  (10, 4),\n",
       "  (15, 6),\n",
       "  (100, 1),\n",
       "  (20, 7),\n",
       "  (15, 7),\n",
       "  (25, 6),\n",
       "  (20, 6),\n",
       "  (15, 6),\n",
       "  (15, 6),\n",
       "  (10, 7),\n",
       "  (10, 7),\n",
       "  (120, 1),\n",
       "  (15, 3),\n",
       "  (205, 0),\n",
       "  (10, 8),\n",
       "  (10, 7),\n",
       "  (20, 8),\n",
       "  (10, 7),\n",
       "  (170, 1),\n",
       "  (155, 2),\n",
       "  (20, 8),\n",
       "  (20, 7),\n",
       "  (15, 8),\n",
       "  (10, 9),\n",
       "  (120, 1),\n",
       "  (15, 1),\n",
       "  (20, 2),\n",
       "  (30, 7),\n",
       "  (10, 2),\n",
       "  (15, 3),\n",
       "  (20, 9),\n",
       "  (10, 9),\n",
       "  (140, 2),\n",
       "  (10, 10),\n",
       "  (30, 8),\n",
       "  (15, 2),\n",
       "  (10, 3),\n",
       "  (25, 9),\n",
       "  (15, 8),\n",
       "  (15, 3),\n",
       "  (40, 8),\n",
       "  (10, 10),\n",
       "  (10, 11),\n",
       "  (10, 3),\n",
       "  (20, 10),\n",
       "  (15, 9),\n",
       "  (45, 8),\n",
       "  (45, 2),\n",
       "  (15, 10),\n",
       "  (110, 4),\n",
       "  (75, 4),\n",
       "  (20, 4),\n",
       "  (10, 12),\n",
       "  (40, 4),\n",
       "  (20, 11),\n",
       "  (305, 0),\n",
       "  (305, 0),\n",
       "  (310, 0),\n",
       "  (25, 9),\n",
       "  (35, 4),\n",
       "  (20, 9),\n",
       "  (15, 5),\n",
       "  (55, 3),\n",
       "  (20, 5),\n",
       "  (15, 13),\n",
       "  (35, 11),\n",
       "  (25, 11),\n",
       "  (45, 4),\n",
       "  (25, 5),\n",
       "  (40, 10),\n",
       "  (35, 3),\n",
       "  (25, 1),\n",
       "  (65, 9),\n",
       "  (10, 14),\n",
       "  (40, 5),\n",
       "  (35, 12),\n",
       "  (30, 10),\n",
       "  (25, 10),\n",
       "  (15, 5),\n",
       "  (40, 1),\n",
       "  (25, 4),\n",
       "  (20, 4),\n",
       "  (10, 15),\n",
       "  (10, 6),\n",
       "  (50, 1),\n",
       "  (45, 5),\n",
       "  (35, 12),\n",
       "  (45, 6),\n",
       "  (25, 2),\n",
       "  (50, 6),\n",
       "  (20, 11),\n",
       "  (45, 12),\n",
       "  (30, 13),\n",
       "  (10, 16),\n",
       "  (45, 11),\n",
       "  (25, 6),\n",
       "  (400, 0),\n",
       "  (50, 6),\n",
       "  (15, 7),\n",
       "  (50, 10),\n",
       "  (25, 2),\n",
       "  (45, 11),\n",
       "  (10, 17),\n",
       "  (25, 13),\n",
       "  (45, 5),\n",
       "  (20, 13),\n",
       "  (25, 7),\n",
       "  (35, 3),\n",
       "  (55, 2),\n",
       "  (430, 0),\n",
       "  (50, 6),\n",
       "  (10, 12),\n",
       "  (55, 5),\n",
       "  (15, 14),\n",
       "  (35, 12),\n",
       "  (35, 7),\n",
       "  (10, 3),\n",
       "  (40, 1),\n",
       "  (70, 7),\n",
       "  (35, 11),\n",
       "  (265, 5),\n",
       "  (60, 12),\n",
       "  (60, 14),\n",
       "  (35, 18),\n",
       "  (15, 6),\n",
       "  (45, 3),\n",
       "  (10, 8),\n",
       "  (40, 6),\n",
       "  (65, 7),\n",
       "  (30, 7),\n",
       "  (10, 12),\n",
       "  (10, 13),\n",
       "  (70, 8),\n",
       "  (50, 8),\n",
       "  (50, 1),\n",
       "  (60, 14),\n",
       "  (20, 15),\n",
       "  (30, 8),\n",
       "  (10, 8),\n",
       "  (45, 13),\n",
       "  (45, 2),\n",
       "  (335, 1),\n",
       "  (25, 7),\n",
       "  (75, 4),\n",
       "  (15, 9),\n",
       "  (10, 15),\n",
       "  (40, 19),\n",
       "  (45, 7),\n",
       "  (10, 9),\n",
       "  (45, 9),\n",
       "  (80, 13),\n",
       "  (40, 14),\n",
       "  (70, 4),\n",
       "  (60, 4),\n",
       "  (35, 16),\n",
       "  (545, 0),\n",
       "  (40, 9),\n",
       "  (65, 8),\n",
       "  (10, 14),\n",
       "  (15, 10),\n",
       "  (10, 5),\n",
       "  (25, 10),\n",
       "  (110, 15),\n",
       "  (10, 1),\n",
       "  (20, 15),\n",
       "  (70, 9),\n",
       "  (70, 2),\n",
       "  (10, 9),\n",
       "  (20, 17),\n",
       "  (65, 14),\n",
       "  (10, 11),\n",
       "  (10, 16),\n",
       "  (65, 3),\n",
       "  (440, 2),\n",
       "  (15, 2),\n",
       "  (105, 13),\n",
       "  (10, 18),\n",
       "  (600, 0),\n",
       "  (75, 5),\n",
       "  (80, 20),\n",
       "  (15, 4),\n",
       "  (100, 8),\n",
       "  (25, 12),\n",
       "  (10, 14),\n",
       "  (70, 5),\n",
       "  (10, 6),\n",
       "  (160, 6),\n",
       "  (105, 10),\n",
       "  (20, 3),\n",
       "  (20, 19),\n",
       "  (440, 2),\n",
       "  (15, 21),\n",
       "  (60, 10),\n",
       "  (10, 9),\n",
       "  (10, 15),\n",
       "  (115, 16),\n",
       "  (65, 3),\n",
       "  (10, 4),\n",
       "  (55, 3),\n",
       "  (130, 8),\n",
       "  (10, 22),\n",
       "  (10, 11),\n",
       "  (10, 17),\n",
       "  (75, 15),\n",
       "  (20, 10),\n",
       "  (675, 0),\n",
       "  (20, 16),\n",
       "  (115, 15),\n",
       "  (10, 9),\n",
       "  (10, 23),\n",
       "  (125, 10),\n",
       "  (10, 16),\n",
       "  (120, 6),\n",
       "  (10, 1),\n",
       "  (65, 6),\n",
       "  (10, 16),\n",
       "  (10, 10),\n",
       "  (30, 12),\n",
       "  (120, 10),\n",
       "  (10, 11),\n",
       "  (10, 7),\n",
       "  (75, 11),\n",
       "  (10, 7),\n",
       "  (130, 17),\n",
       "  (65, 4),\n",
       "  (10, 11),\n",
       "  (10, 8),\n",
       "  (170, 11),\n",
       "  (25, 17),\n",
       "  (10, 12),\n",
       "  (120, 5),\n",
       "  (170, 16),\n",
       "  (15, 8),\n",
       "  (10, 5),\n",
       "  (15, 12),\n",
       "  (35, 13),\n",
       "  (125, 7)],\n",
       " 50: [(10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (10, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (15, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (20, 0),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (30, 0),\n",
       "  (10, 1),\n",
       "  (30, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (40, 0),\n",
       "  (40, 0),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (10, 1),\n",
       "  (15, 1),\n",
       "  (30, 1),\n",
       "  (15, 1),\n",
       "  (10, 1),\n",
       "  (20, 1),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (60, 0),\n",
       "  (15, 1),\n",
       "  (60, 0),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (35, 1),\n",
       "  (10, 2),\n",
       "  (10, 2),\n",
       "  (70, 0),\n",
       "  (15, 2),\n",
       "  (25, 2),\n",
       "  (10, 2),\n",
       "  (75, 0),\n",
       "  (10, 2),\n",
       "  (15, 2),\n",
       "  (35, 1),\n",
       "  (15, 3),\n",
       "  (15, 2),\n",
       "  (10, 2),\n",
       "  (50, 1),\n",
       "  (10, 3),\n",
       "  (85, 0),\n",
       "  (15, 3),\n",
       "  (10, 2),\n",
       "  (10, 3),\n",
       "  (60, 1),\n",
       "  (90, 0),\n",
       "  (15, 3),\n",
       "  (25, 1),\n",
       "  (10, 3),\n",
       "  (15, 3),\n",
       "  (30, 3),\n",
       "  (50, 1),\n",
       "  (105, 0),\n",
       "  (15, 4),\n",
       "  (10, 3),\n",
       "  (105, 0),\n",
       "  (110, 0),\n",
       "  (10, 4),\n",
       "  (10, 2),\n",
       "  (10, 4),\n",
       "  (10, 4),\n",
       "  (30, 1),\n",
       "  (30, 3),\n",
       "  (15, 3),\n",
       "  (20, 4),\n",
       "  (10, 4),\n",
       "  (15, 1),\n",
       "  (10, 4),\n",
       "  (40, 3),\n",
       "  (20, 2),\n",
       "  (10, 1),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (20, 4),\n",
       "  (135, 0),\n",
       "  (10, 5),\n",
       "  (45, 3),\n",
       "  (10, 4),\n",
       "  (140, 0),\n",
       "  (15, 5),\n",
       "  (20, 4),\n",
       "  (140, 0),\n",
       "  (145, 0),\n",
       "  (15, 5),\n",
       "  (15, 5),\n",
       "  (10, 6),\n",
       "  (20, 4),\n",
       "  (10, 6),\n",
       "  (20, 2),\n",
       "  (65, 2),\n",
       "  (15, 4),\n",
       "  (10, 6),\n",
       "  (25, 5),\n",
       "  (20, 2),\n",
       "  (10, 5),\n",
       "  (10, 5),\n",
       "  (25, 3),\n",
       "  (10, 1),\n",
       "  (45, 3),\n",
       "  (15, 6),\n",
       "  (75, 3),\n",
       "  (25, 5),\n",
       "  (105, 1),\n",
       "  (10, 7),\n",
       "  (175, 0),\n",
       "  (15, 5),\n",
       "  (10, 3),\n",
       "  (10, 7),\n",
       "  (20, 6),\n",
       "  (55, 2),\n",
       "  (10, 6),\n",
       "  (185, 0),\n",
       "  (25, 7),\n",
       "  (190, 0),\n",
       "  (10, 4),\n",
       "  (20, 5),\n",
       "  (15, 4),\n",
       "  (20, 6),\n",
       "  (10, 8),\n",
       "  (25, 6),\n",
       "  (15, 4),\n",
       "  (15, 7),\n",
       "  (105, 2),\n",
       "  (10, 4),\n",
       "  (10, 8),\n",
       "  (10, 3),\n",
       "  (15, 6),\n",
       "  (45, 6),\n",
       "  (10, 7),\n",
       "  (15, 8),\n",
       "  (95, 1),\n",
       "  (90, 1),\n",
       "  (15, 5),\n",
       "  (20, 1),\n",
       "  (15, 5),\n",
       "  (10, 8),\n",
       "  (10, 7),\n",
       "  (170, 1),\n",
       "  (30, 7),\n",
       "  (10, 9),\n",
       "  (10, 5),\n",
       "  (20, 7),\n",
       "  (25, 6),\n",
       "  (15, 3),\n",
       "  (10, 8),\n",
       "  (10, 9),\n",
       "  (25, 9),\n",
       "  (90, 1),\n",
       "  (20, 7),\n",
       "  (20, 7),\n",
       "  (55, 6),\n",
       "  (10, 8),\n",
       "  (10, 8),\n",
       "  (15, 9),\n",
       "  (90, 1),\n",
       "  (30, 4),\n",
       "  (15, 6),\n",
       "  (15, 6),\n",
       "  (30, 2),\n",
       "  (25, 6),\n",
       "  (10, 7),\n",
       "  (20, 10),\n",
       "  (220, 1),\n",
       "  (10, 10),\n",
       "  (95, 3),\n",
       "  (10, 8),\n",
       "  (15, 10),\n",
       "  (75, 1),\n",
       "  (10, 5),\n",
       "  (15, 8),\n",
       "  (10, 9),\n",
       "  (25, 9),\n",
       "  (20, 7),\n",
       "  (20, 9),\n",
       "  (15, 10),\n",
       "  (10, 7),\n",
       "  (10, 11),\n",
       "  (55, 2),\n",
       "  (20, 7),\n",
       "  (15, 7),\n",
       "  (15, 8),\n",
       "  (10, 9),\n",
       "  (10, 11),\n",
       "  (15, 4),\n",
       "  (10, 6),\n",
       "  (10, 9),\n",
       "  (205, 1),\n",
       "  (10, 10),\n",
       "  (20, 11),\n",
       "  (35, 2),\n",
       "  (10, 10),\n",
       "  (10, 12),\n",
       "  (155, 4),\n",
       "  (60, 4),\n",
       "  (20, 10),\n",
       "  (20, 8),\n",
       "  (15, 11),\n",
       "  (70, 8),\n",
       "  (15, 9),\n",
       "  (15, 8),\n",
       "  (10, 10),\n",
       "  (15, 12),\n",
       "  (10, 11),\n",
       "  (15, 7),\n",
       "  (240, 3),\n",
       "  (20, 10),\n",
       "  (25, 8),\n",
       "  (30, 8),\n",
       "  (15, 12),\n",
       "  (20, 5),\n",
       "  (35, 3),\n",
       "  (150, 3),\n",
       "  (10, 12),\n",
       "  (20, 13),\n",
       "  (10, 10),\n",
       "  (25, 11),\n",
       "  (330, 0),\n",
       "  (20, 9),\n",
       "  (20, 9),\n",
       "  (20, 11),\n",
       "  (10, 4),\n",
       "  (10, 11),\n",
       "  (345, 0),\n",
       "  (15, 13),\n",
       "  (15, 8),\n",
       "  (15, 9),\n",
       "  (20, 13),\n",
       "  (170, 2),\n",
       "  (25, 11),\n",
       "  (15, 9),\n",
       "  (240, 2),\n",
       "  (10, 13),\n",
       "  (15, 14),\n",
       "  (10, 12),\n",
       "  (30, 12),\n",
       "  (15, 11),\n",
       "  (40, 9),\n",
       "  (45, 5),\n",
       "  (15, 10),\n",
       "  (20, 12),\n",
       "  (30, 4),\n",
       "  (10, 9),\n",
       "  (20, 10),\n",
       "  (20, 12),\n",
       "  (10, 14),\n",
       "  (15, 14),\n",
       "  (10, 14),\n",
       "  (10, 15),\n",
       "  (20, 10),\n",
       "  (15, 10),\n",
       "  (170, 5),\n",
       "  (380, 0),\n",
       "  (10, 10),\n",
       "  (25, 3),\n",
       "  (50, 6),\n",
       "  (15, 13),\n",
       "  (15, 12),\n",
       "  (10, 13),\n",
       "  (10, 11),\n",
       "  (90, 2),\n",
       "  (25, 13),\n",
       "  (20, 13),\n",
       "  (10, 15),\n",
       "  (40, 12),\n",
       "  (25, 6),\n",
       "  (15, 15),\n",
       "  (25, 11),\n",
       "  (15, 11),\n",
       "  (25, 15),\n",
       "  (205, 1),\n",
       "  (30, 10),\n",
       "  (20, 11),\n",
       "  (20, 6),\n",
       "  (260, 1),\n",
       "  (15, 14),\n",
       "  (10, 3),\n",
       "  (45, 5),\n",
       "  (150, 2),\n",
       "  (10, 14),\n",
       "  (25, 11),\n",
       "  (15, 13),\n",
       "  (425, 0),\n",
       "  (30, 4),\n",
       "  (20, 14),\n",
       "  (25, 12),\n",
       "  (10, 2),\n",
       "  (20, 7),\n",
       "  (185, 2),\n",
       "  (35, 13),\n",
       "  (20, 12),\n",
       "  (100, 4),\n",
       "  (15, 7),\n",
       "  (20, 16),\n",
       "  (50, 16),\n",
       "  (15, 2),\n",
       "  (30, 16),\n",
       "  (15, 6),\n",
       "  (445, 0),\n",
       "  (80, 3),\n",
       "  (50, 7),\n",
       "  (30, 11),\n",
       "  (50, 14),\n",
       "  (10, 3),\n",
       "  (270, 2),\n",
       "  (50, 16),\n",
       "  (45, 12),\n",
       "  (30, 12),\n",
       "  (115, 5),\n",
       "  (15, 14),\n",
       "  (45, 12),\n",
       "  (10, 7),\n",
       "  (15, 17),\n",
       "  (110, 1),\n",
       "  (30, 14),\n",
       "  (45, 15),\n",
       "  (10, 12),\n",
       "  (35, 5),\n",
       "  (15, 8),\n",
       "  (35, 13),\n",
       "  (35, 15),\n",
       "  (15, 15),\n",
       "  (10, 6),\n",
       "  (50, 15),\n",
       "  (30, 17),\n",
       "  (40, 8),\n",
       "  (180, 3),\n",
       "  (30, 17),\n",
       "  (15, 15),\n",
       "  (40, 13),\n",
       "  (40, 5),\n",
       "  (45, 8),\n",
       "  (40, 4),\n",
       "  (30, 3),\n",
       "  (25, 13),\n",
       "  (20, 2),\n",
       "  (25, 13),\n",
       "  (15, 16),\n",
       "  (10, 16),\n",
       "  (20, 6),\n",
       "  (45, 4),\n",
       "  (195, 5),\n",
       "  (10, 18),\n",
       "  (40, 17),\n",
       "  (15, 18),\n",
       "  (285, 2),\n",
       "  (30, 16),\n",
       "  (20, 9),\n",
       "  (30, 9),\n",
       "  (20, 14),\n",
       "  (10, 3),\n",
       "  (45, 18),\n",
       "  (50, 13),\n",
       "  (525, 0),\n",
       "  (15, 9),\n",
       "  (265, 3),\n",
       "  (30, 4),\n",
       "  (55, 15),\n",
       "  (30, 6),\n",
       "  (20, 14),\n",
       "  (25, 4),\n",
       "  (50, 14),\n",
       "  (45, 16),\n",
       "  (25, 17),\n",
       "  (65, 8),\n",
       "  (60, 13),\n",
       "  (10, 14),\n",
       "  (20, 17),\n",
       "  (35, 14),\n",
       "  (10, 1),\n",
       "  (25, 10),\n",
       "  (30, 18),\n",
       "  (45, 5),\n",
       "  (35, 6),\n",
       "  (15, 10),\n",
       "  (35, 7),\n",
       "  (560, 0),\n",
       "  (130, 4),\n",
       "  (25, 19),\n",
       "  (110, 3),\n",
       "  (65, 16),\n",
       "  (15, 17),\n",
       "  (50, 17),\n",
       "  (45, 19),\n",
       "  (40, 10),\n",
       "  (50, 5),\n",
       "  (55, 19),\n",
       "  (10, 6),\n",
       "  (35, 7),\n",
       "  (10, 7),\n",
       "  (15, 19),\n",
       "  (35, 16),\n",
       "  (55, 3),\n",
       "  (10, 5),\n",
       "  (25, 15),\n",
       "  (45, 4),\n",
       "  (35, 15),\n",
       "  (60, 15),\n",
       "  (10, 20),\n",
       "  (55, 4),\n",
       "  (595, 0),\n",
       "  (100, 7),\n",
       "  (25, 1),\n",
       "  (30, 11),\n",
       "  (30, 8),\n",
       "  (50, 5),\n",
       "  (60, 5),\n",
       "  (45, 18),\n",
       "  (35, 20),\n",
       "  (60, 15),\n",
       "  (165, 3),\n",
       "  (50, 15),\n",
       "  (55, 9),\n",
       "  (25, 8),\n",
       "  (20, 17),\n",
       "  (35, 18),\n",
       "  (30, 6),\n",
       "  (60, 14),\n",
       "  (25, 4),\n",
       "  (345, 2),\n",
       "  (70, 18),\n",
       "  (35, 20),\n",
       "  (45, 17),\n",
       "  (25, 5),\n",
       "  (30, 16),\n",
       "  (10, 4),\n",
       "  (15, 6),\n",
       "  (50, 11),\n",
       "  (55, 18),\n",
       "  (45, 20),\n",
       "  (45, 7),\n",
       "  (75, 2),\n",
       "  (30, 8),\n",
       "  (75, 11),\n",
       "  (45, 16),\n",
       "  (30, 6),\n",
       "  (45, 5),\n",
       "  (25, 10),\n",
       "  (215, 3),\n",
       "  (60, 8),\n",
       "  (50, 16),\n",
       "  (420, 2),\n",
       "  (55, 6),\n",
       "  (25, 19),\n",
       "  (25, 15),\n",
       "  (40, 19),\n",
       "  (25, 3),\n",
       "  (10, 3),\n",
       "  (60, 21),\n",
       "  (40, 9),\n",
       "  (55, 9),\n",
       "  (25, 17),\n",
       "  (60, 1),\n",
       "  (60, 12),\n",
       "  (45, 16),\n",
       "  (60, 2),\n",
       "  (25, 12),\n",
       "  (55, 21),\n",
       "  (10, 16),\n",
       "  (40, 19),\n",
       "  (60, 16),\n",
       "  (15, 17),\n",
       "  (30, 17),\n",
       "  (45, 6),\n",
       "  (35, 19),\n",
       "  (60, 18),\n",
       "  (50, 21),\n",
       "  (10, 13),\n",
       "  (40, 21),\n",
       "  (60, 7),\n",
       "  (45, 8),\n",
       "  (40, 4),\n",
       "  (15, 13),\n",
       "  (45, 6),\n",
       "  (50, 9),\n",
       "  (10, 17),\n",
       "  (50, 7),\n",
       "  (70, 18),\n",
       "  (15, 22),\n",
       "  (25, 3),\n",
       "  (30, 10),\n",
       "  (15, 7),\n",
       "  (70, 7),\n",
       "  (40, 20),\n",
       "  (15, 19),\n",
       "  (15, 8),\n",
       "  (40, 10),\n",
       "  (55, 3),\n",
       "  (90, 5),\n",
       "  (65, 11),\n",
       "  (40, 17),\n",
       "  (25, 22),\n",
       "  (10, 19),\n",
       "  (150, 4),\n",
       "  (85, 5),\n",
       "  (15, 4),\n",
       "  (10, 21),\n",
       "  (45, 20),\n",
       "  (65, 7),\n",
       "  (80, 12),\n",
       "  (60, 18),\n",
       "  (15, 11),\n",
       "  (65, 22),\n",
       "  (60, 2),\n",
       "  (25, 18),\n",
       "  (20, 20),\n",
       "  (50, 20),\n",
       "  (35, 10),\n",
       "  (90, 9),\n",
       "  (15, 12),\n",
       "  (90, 20),\n",
       "  (15, 20),\n",
       "  (65, 18),\n",
       "  (760, 0),\n",
       "  (25, 9),\n",
       "  (90, 4),\n",
       "  (10, 23),\n",
       "  (65, 18),\n",
       "  (10, 3),\n",
       "  (765, 0),\n",
       "  (45, 8),\n",
       "  (80, 17),\n",
       "  (770, 0),\n",
       "  (100, 4),\n",
       "  (30, 23),\n",
       "  (35, 5),\n",
       "  (50, 8),\n",
       "  (35, 6),\n",
       "  (15, 21),\n",
       "  (15, 11),\n",
       "  (35, 8),\n",
       "  (35, 13),\n",
       "  (10, 19),\n",
       "  (80, 22),\n",
       "  (10, 4),\n",
       "  (40, 22),\n",
       "  (55, 6),\n",
       "  (85, 14),\n",
       "  (45, 5),\n",
       "  (85, 9),\n",
       "  (80, 8),\n",
       "  (10, 5),\n",
       "  (10, 7),\n",
       "  (40, 19),\n",
       "  (85, 7),\n",
       "  (90, 5),\n",
       "  (15, 9),\n",
       "  (705, 1)]}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make another plot based on task count\n",
    "\n",
    "path = \"single trust\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    filename = os.path.basename(file_path)\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "     \n",
    "        ttime = [int(line.split(' ')[3]) for line in file_contents]\n",
    "        tcount = [int(line.split(' ')[7]) for line in file_contents]\n",
    "        \n",
    "        ttime, tcount = [], []\n",
    "        \n",
    "        for line in file_contents:\n",
    "            larr = line.split(' ')\n",
    "            trust_score = int(larr[6])\n",
    "            _count = int(larr[7])\n",
    "            _time = int(larr[3])\n",
    "            if trust_score > 0:\n",
    "                ttime.append(_time)\n",
    "                tcount.append(_count)\n",
    "        \n",
    "        user_count = int(file_contents[0].split(' ')[2])\n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict[user_count] = list(zip(ttime, tcount))\n",
    "    \n",
    "file_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e22d0e7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trust score 200, count:10 with median 42.5 and std_dev: 167.93041713757518\n",
      "trust score 400, count:10 with median 22.5 and std_dev: 71.34747044192623\n",
      "trust score 600, count:10 with median 15.0 and std_dev: 9.362873420740582\n",
      "trust score 800, count:10 with median 10 and std_dev: 4.803844614152614\n",
      "trust score 1000, count:10 with median 10 and std_dev: 2.886751345948129\n",
      "trust score 200, count:30 with median 35 and std_dev: 115.16649639407173\n",
      "trust score 400, count:30 with median 35 and std_dev: 73.6578432891357\n",
      "trust score 600, count:30 with median 15 and std_dev: 44.32156271679718\n",
      "trust score 800, count:30 with median 15 and std_dev: 7.127864449672372\n",
      "trust score 1000, count:30 with median 10.0 and std_dev: 2.5\n",
      "trust score 200, count:50 with median 45 and std_dev: 127.83897555100842\n",
      "trust score 400, count:50 with median 30.0 and std_dev: 84.20916133065671\n",
      "trust score 600, count:50 with median 15 and std_dev: 24.92803961869397\n",
      "trust score 800, count:50 with median 15.0 and std_dev: 14.066732398569341\n",
      "trust score 1000, count:50 with median 10.0 and std_dev: 5.5781165831653805\n",
      "trust score 200, count:100 with median 122.5 and std_dev: 125.51301652026899\n",
      "trust score 400, count:100 with median 25.0 and std_dev: 74.18106570740098\n",
      "trust score 600, count:100 with median 20.0 and std_dev: 73.46072315334423\n",
      "trust score 800, count:100 with median 15.0 and std_dev: 27.064138093697295\n",
      "trust score 1000, count:100 with median 15 and std_dev: 6.5565085890318775\n",
      "trust score 200, count:200 with median 35.0 and std_dev: 108.78320306364061\n",
      "trust score 400, count:200 with median 25.0 and std_dev: 89.17104616242503\n",
      "trust score 600, count:200 with median 15 and std_dev: 47.612316804852604\n",
      "trust score 800, count:200 with median 12.5 and std_dev: 18.09805552073013\n",
      "trust score 1000, count:200 with median 10 and std_dev: 5.861132087990285\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"single trust\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict2 = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    filename = os.path.basename(file_path)\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "     \n",
    "        \n",
    "        ttime, ttrust = [], []\n",
    "        \n",
    "        for line in file_contents:\n",
    "            larr = line.split(' ')\n",
    "            trust_score = int(larr[6])\n",
    "            _time = int(larr[3])\n",
    "            if trust_score > 0:\n",
    "                ttime.append(_time)\n",
    "                ttrust.append(trust_score)\n",
    "        \n",
    "        user_count = int(file_contents[0].split(' ')[2])\n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict2[user_count] = list(zip(ttime, ttrust))\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "#binning by trust scores is actually the way\n",
    "def get_grouped2(index, file_dict=file_dict2):\n",
    "    grouped = [[],[],[],[],[]]\n",
    "    for i in file_dict[index]:\n",
    "        j,k = i[0], i[1]\n",
    "        \n",
    "        if k <= 200:\n",
    "#             if j < 100:\n",
    "            grouped[0].append(j)\n",
    "        elif k <= 400:\n",
    "            grouped[1].append(j)\n",
    "        elif k <= 600:\n",
    "            grouped[2].append(j)\n",
    "        elif k <= 800:\n",
    "            grouped[3].append(j)\n",
    "        else:\n",
    "            grouped[4].append(j)\n",
    "            \n",
    "            \n",
    "        #get avg_times\n",
    "    avg_grouped = [median(i) for i in grouped]\n",
    "    [print(f\"trust score {200 + 200*j}, count:{index} with median {median(i)} and std_dev: {statistics.stdev(i)}\") for j,i  in enumerate(grouped)]\n",
    "    return avg_grouped\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "species = ['0-200', '201-400', '401-600', '601-800', '801-1000'] #make this task count\n",
    "\n",
    "\n",
    "# create the first bar plot\n",
    "\n",
    "weight_counts = {\n",
    "    '10': get_grouped2(10),\n",
    "    '30': get_grouped2(30),\n",
    "    '50': get_grouped2(50),\n",
    "    '100': get_grouped2(100),\n",
    "    '200':get_grouped2(200)\n",
    "}\n",
    "\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bottom = np.zeros(5)\n",
    "\n",
    "for boolean, weight_count in weight_counts.items():\n",
    "    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom)\n",
    "    bottom += weight_count\n",
    "\n",
    "ax.set_xlabel('Trust Score')\n",
    "ax.set_ylabel('Response Time(s)')\n",
    "ax.set_title('Single Server')    \n",
    "\n",
    "ax.set_ylim(0, 350)\n",
    "\n",
    "ax.legend(loc=\"upper left\", ncols=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801de9f3",
   "metadata": {},
   "source": [
    "## MULTIPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "faef9e63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"multiple_baseline\"\n",
    "file_dict = {}\n",
    "\n",
    "\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "\n",
    "    filename = os.path.basename(file_path)\n",
    "\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "\n",
    "        ttime = [int(line.split(' ')[4]) for line in file_contents]\n",
    "        user_count = int(file_contents[0].split(' ')[3])\n",
    "  \n",
    "    file_dict[user_count] = ttime\n",
    "    \n",
    "avg_times = [(k,median(v)) for k,v in file_dict.items()]\n",
    "\n",
    "avg_times = sorted(avg_times, key=lambda x: x[0])\n",
    "labels = [str(i[0]) for i in avg_times]\n",
    "values = [i[1] for i in avg_times]\n",
    "\n",
    "# create the bar plot\n",
    "plt.bar(labels, values)\n",
    "\n",
    "# add labels to the plot\n",
    "plt.xlabel('Number of Users')\n",
    "plt.ylabel('Average Response Time')\n",
    "# plt.title('Bar Plot with Labels')\n",
    "\n",
    "# add value labels to the bars\n",
    "# for i, v in enumerate(values):\n",
    "#     plt.text(i, v + 1, str(v))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "07f6576c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trust score 200, count:40 with median 295 and std_dev: 78.70995437043555\n",
      "trust score 400, count:40 with median 202.5 and std_dev: 135.30496840769172\n",
      "trust score 600, count:40 with median 60 and std_dev: 147.32518192360828\n",
      "trust score 800, count:40 with median 55.0 and std_dev: 149.32729622680426\n",
      "trust score 1000, count:40 with median 15.0 and std_dev: 162.51766782559554\n",
      "trust score 200, count:120 with median 255.0 and std_dev: 88.86921624548431\n",
      "trust score 400, count:120 with median 170 and std_dev: 131.6908242320832\n",
      "trust score 600, count:120 with median 65 and std_dev: 107.58314354544403\n",
      "trust score 800, count:120 with median 30 and std_dev: 125.08180398191575\n",
      "trust score 1000, count:120 with median 20.0 and std_dev: 143.30773190012624\n",
      "trust score 200, count:200 with median 245.0 and std_dev: 97.58174737436771\n",
      "trust score 400, count:200 with median 185 and std_dev: 129.28024918315907\n",
      "trust score 600, count:200 with median 105.0 and std_dev: 111.62004535051248\n",
      "trust score 800, count:200 with median 35.0 and std_dev: 112.36939960859704\n",
      "trust score 1000, count:200 with median 20.0 and std_dev: 129.64623663378535\n",
      "trust score 200, count:400 with median 175.0 and std_dev: 69.85496958412571\n",
      "trust score 400, count:400 with median 120 and std_dev: 91.4243468331135\n",
      "trust score 600, count:400 with median 45 and std_dev: 82.64234329977187\n",
      "trust score 800, count:400 with median 25.0 and std_dev: 90.99958619807026\n",
      "trust score 1000, count:400 with median 15 and std_dev: 104.91026991731776\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"multiple_baseline\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict2 = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    filename = os.path.basename(file_path)\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "     \n",
    "        \n",
    "        ttime, ttrust = [], []\n",
    "        \n",
    "        for line in file_contents:\n",
    "            larr = line.split(' ')\n",
    "            trust_score = int(larr[7])\n",
    "            _time = int(larr[4])\n",
    "            if trust_score > 0:\n",
    "                ttime.append(_time)\n",
    "                ttrust.append(trust_score)\n",
    "        \n",
    "        user_count = int(file_contents[0].split(' ')[3])\n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict2[user_count] = list(zip(ttime, ttrust))\n",
    "    \n",
    "\n",
    "# print(file_dict2)\n",
    "\n",
    "\n",
    "#binning by trust scores is actually the way\n",
    "def get_grouped2(index, file_dict=file_dict2):\n",
    "    grouped = [[],[],[],[],[]]\n",
    "    for i in file_dict[index]:\n",
    "        j,k = i[0], i[1]\n",
    "        \n",
    "        if k <= 200:\n",
    "            grouped[0].append(j)\n",
    "        elif k <= 400:\n",
    "            grouped[1].append(j)\n",
    "        elif k <= 600:\n",
    "            grouped[2].append(j)\n",
    "        elif k <= 800:\n",
    "            grouped[3].append(j)\n",
    "        else:\n",
    "            grouped[4].append(j)\n",
    "            \n",
    "            \n",
    "        #get avg_times\n",
    "    avg_grouped = [median(i) for i in grouped]\n",
    "    [print(f\"trust score {200 + 200*j}, count:{index} with median {median(i)} and std_dev: {statistics.stdev(i)}\") for j,i  in enumerate(grouped)]\n",
    "    return avg_grouped\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "species = ['0-200', '201-400', '401-600', '601-800', '801-1000'] #make this task count\n",
    "\n",
    "\n",
    "# create the first bar plot\n",
    "\n",
    "weight_counts = {\n",
    "    '40': get_grouped2(40),\n",
    "    '120': get_grouped2(120),\n",
    "    '200': get_grouped2(200),\n",
    "    '400': get_grouped2(400),\n",
    "#     '200':get_grouped2(200)\n",
    "}\n",
    "\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bottom = np.zeros(5)\n",
    "\n",
    "for boolean, weight_count in weight_counts.items():\n",
    "    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom)\n",
    "    bottom += weight_count\n",
    "\n",
    "ax.set_xlabel('Trust Score')\n",
    "ax.set_ylabel('Avg Response Time')\n",
    "ax.set_title('Multiple Component Bar Plot')    \n",
    "\n",
    "ax.set_ylim(0, 1300)\n",
    "\n",
    "ax.legend(loc=\"upper left\", ncols=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5082e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trust score 200, count:40 with median 265 and std_dev: 113.3832846180125\n",
      "trust score 400, count:40 with median 235 and std_dev: 130.3902214450334\n",
      "trust score 600, count:40 with median 15 and std_dev: 116.3456622797478\n",
      "trust score 800, count:40 with median 50.0 and std_dev: 116.61168487027811\n",
      "trust score 1000, count:40 with median 20 and std_dev: 135.46771720172356\n",
      "trust score 200, count:120 with median 252.5 and std_dev: 75.57090404687202\n",
      "trust score 400, count:120 with median 215 and std_dev: 126.57949824480706\n",
      "trust score 600, count:120 with median 55 and std_dev: 105.67368875004398\n",
      "trust score 800, count:120 with median 37.5 and std_dev: 114.51771913518729\n",
      "trust score 1000, count:120 with median 20 and std_dev: 128.62107704014366\n",
      "trust score 200, count:200 with median 240 and std_dev: 81.31343780131073\n",
      "trust score 400, count:200 with median 200 and std_dev: 112.4853865116105\n",
      "trust score 600, count:200 with median 70 and std_dev: 107.22692229855815\n",
      "trust score 800, count:200 with median 40 and std_dev: 117.32995397712307\n",
      "trust score 1000, count:200 with median 25 and std_dev: 138.7216433932548\n",
      "trust score 200, count:400 with median 160.0 and std_dev: 77.15021038916525\n",
      "trust score 400, count:400 with median 125 and std_dev: 93.78550126004552\n",
      "trust score 600, count:400 with median 50 and std_dev: 83.19625170303398\n",
      "trust score 800, count:400 with median 25.0 and std_dev: 85.67928033772854\n",
      "trust score 1000, count:400 with median 20 and std_dev: 91.76626992542448\n"
     ]
    },
    {
     "data": {
      "image/png": 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8889hYWGBkJAQnRvW0fNbsWIFAKBTp04606OjozFo0CAAz97ulpaWiImJwciRIxEQEAAHBweEhYVh1qxZ5bUalQq3NxEBAIQJHThwQLz++uvCw8NDABDbtm2T5hUUFIiJEyeKRo0aCXt7e+Hh4SEGDhworl27prOMW7duiQEDBogqVaoIlUol3nvvPXHnzh2dNqdPnxbt27cXSqVS1KxZU3z22Wd61anRaAQAodFoSs27d++eOHfunLh3755eyySiyo3fDUSm97S/348y6Riiu3fvomnTpli2bFmpeXl5eThx4gSmTZuGEydOYOvWrUhNTUWPHj102oWGhuLs2bOIjY1FTEwM4uPjMWzYMGm+VqtF165d4e3tjaSkJCxYsAAzZszAqlWrjL5+REREVDEohBDC1EUAgEKhwLZt29CzZ88ntjl27BhatWqFy5cvo1atWkhJSYGfnx+OHTuGli1bAnhwJ+Du3bvj6tWr8PT0xIoVK/DJJ59I9xsBgMmTJ+Pnn3/G+fPny1SbVquFSqWCRqOBk5OTzrz79+8jPT0dPj4+vKyXiCT8biAyvaf9/X5UhbrKTKPRQKFQSDdRS0hIgLOzsxSGACAwMBAWFhZITEyU2nTo0EEKQ8CDu8ympqYiOzu7XOsnIiIi81RhBlXfv38fkyZNQv/+/aWUp1ar4erqqtPOysoK1apV07nL7KOXNj98l9mHn2tUIj8/H/n5+dJ7rVZr0HUhIiIi81IhjhAVFhaiT58+EEJIV+kY07x586BSqaSXl5eX0fskIiIi0zH7QFQShi5fvizdDK2Eu7u7zs3SAOCff/7B7du3/9VdZqdMmQKNRiO9rly5YshVIiIiIjNj1oGoJAylpaVhz549cHFx0ZkfEBCAnJwcJCUlSdP27t2L4uJitG7dWmoTHx+PwsJCqU1sbCwaNGjw2NNlAKBUKuHk5KTzIiIiosrLpIEoNzdXup0+AKSnp+PUqVPIyMhAYWEh3n77bRw/fhwbNmxAUVER1Go11Go1CgoKAAANGzbEa6+9hqFDh+Lo0aM4dOgQIiIi0K9fP3h6egIABgwYABsbGwwZMgRnz57F5s2b8eWXX2LcuHGmWm0iIiIyMya97H7//v145ZVXSk0PCwvDjBkzSg2GLrFv3z7pTr+3b99GREQEduzYId1tdvHixXB0dJTa//HHHwgPD8exY8fwwgsvYNSoUZg0aVKZ6+Rl90SkL343EJmePpfdm/Qqs06dOuFpeawsWa1atWrYuHHjU9s0adIEv//+u971ERERkTxUmMvuK6QZqnLuT6NX8/j4eCxYsABJSUn4+++/dW6MWVhYiKlTp+LXX3/FpUuXoFKpEBgYiPnz50unI4EHR+hGjRqlc4Tuyy+/1DlCR0REZO7MelA1GVd5PDqFiIioIuARIhnr1q0bunXr9th5KpUKsbGxOtOWLl2KVq1aISMjQ3p0yq5du3QenbJkyRJ0794dCxcu1DmSREREZM54hIjK7HkenUJERFQRMBBRmTzvo1OIiIgqAgYieqbyfnQKERFReeMYInqqhx+dsnfvXr0fnUJERFQR8AgRPZEhHp1CRERUEfAIkYzl5ubi4sWL0vuSR6dUq1YNHh4eePvtt3HixAnExMRIj04BHtwM08bGRufRKStXrkRhYWGpR6cQERFVBAxEMnb8+HGdR6eUPN+t5NEp27dvBwA0a9ZM53MPPzplw4YNiIiIQOfOnXUenUJERFSRMBAZk553ji5v5fXoFCIiInPHMUREREQkewxEREREJHsMRERERCR7DEREREQkewxEBlKWAchEJB/8TiCqWBiI/iVra2sAQF5enokrISJzUvKdUPIdQUTmjZfd/0uWlpZwdnaWHmFhb28PhUJh4qqIyFSEEMjLy0NWVhacnZ1haWlp6pKIqAwYiAyg5Lldjz7Xi4jky9nZmc/0I6pAGIgMQKFQwMPDA66urigsLDR1OURkYtbW1jwyRFTBMBAZkKWlJb8EiYiIKiAOqiYiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZM2kgio+PxxtvvAFPT08oFAr8/PPPOvOFEIiMjISHhwfs7OwQGBiItLQ0nTa3b99GaGgonJyc4OzsjCFDhiA3N1enzR9//IGXX34Ztra28PLyQlRUlLFXjYiIiCoQkwaiu3fvomnTpli2bNlj50dFRWHx4sVYuXIlEhMT4eDggKCgINy/f19qExoairNnzyI2NhYxMTGIj4/HsGHDpPlarRZdu3aFt7c3kpKSsGDBAsyYMQOrVq0y+voRERFRBSHMBACxbds26X1xcbFwd3cXCxYskKbl5OQIpVIpvv/+eyGEEOfOnRMAxLFjx6Q2O3fuFAqFQly7dk0IIcTy5ctF1apVRX5+vtRm0qRJokGDBmWuTaPRCABCo9E87+oRERFROdPn77fZjiFKT0+HWq1GYGCgNE2lUqF169ZISEgAACQkJMDZ2RktW7aU2gQGBsLCwgKJiYlSmw4dOsDGxkZqExQUhNTUVGRnZz+27/z8fGi1Wp0XERERVV5mG4jUajUAwM3NTWe6m5ubNE+tVsPV1VVnvpWVFapVq6bT5nHLeLiPR82bNw8qlUp6eXl5/fsVIiIiIrNltoHIlKZMmQKNRiO9rly5YuqSiIiIyIjMNhC5u7sDADIzM3WmZ2ZmSvPc3d2RlZWlM/+ff/7B7du3ddo8bhkP9/EopVIJJycnnRcRERFVXmYbiHx8fODu7o64uDhpmlarRWJiIgICAgAAAQEByMnJQVJSktRm7969KC4uRuvWraU28fHxKCwslNrExsaiQYMGqFq1ajmtDREREZkzkwai3NxcnDp1CqdOnQLwYCD1qVOnkJGRAYVCgTFjxuDTTz/F9u3bkZycjHfffReenp7o2bMnAKBhw4Z47bXXMHToUBw9ehSHDh1CREQE+vXrB09PTwDAgAEDYGNjgyFDhuDs2bPYvHkzvvzyS4wbN85Ea01ERERmpxyuenuiffv2CQClXmFhYUKIB5feT5s2Tbi5uQmlUik6d+4sUlNTdZZx69Yt0b9/f+Ho6CicnJzE4MGDxZ07d3TanD59WrRv314olUpRo0YNMX/+fL3q5GX3REREFY8+f78VQghhwjxWIWi1WqhUKmg0Go4nIiIiqiD0+ftttmOIiIiIiMoLAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJnpWpCyAgxbehqUswiIbnU0xdAhER0XPhESIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPSt9P5Ceno7ff/8dly9fRl5eHqpXr47mzZsjICAAtra2xqiRiIiIyKjKHIg2bNiAL7/8EsePH4ebmxs8PT1hZ2eH27dv488//4StrS1CQ0MxadIkeHt7G7NmIiIiIoMqUyBq3rw5bGxsMGjQIPz000/w8vLSmZ+fn4+EhARs2rQJLVu2xPLly9G7d2+jFExERERkaAohhHhWo927dyMoKKhMC7x16xb++usv+Pv7/+vizIVWq4VKpYJGo4GTk5PBl5/i29DgyzSFhudTTF0CERGRRJ+/32U6QlTWMAQALi4ucHFxKXN7IiIiIlPT+yqzEydOIDk5WXr/yy+/oGfPnvj4449RUFBg0OKIiIiIyoPegWj48OG4cOECAODSpUvo168f7O3tsWXLFkycONHgBRIREREZm96B6MKFC2jWrBkAYMuWLejQoQM2btyINWvW4KeffjJ0fURERERGp3cgEkKguLgYALBnzx50794dAODl5YWbN28atjoiIiKicqB3IGrZsiU+/fRTrFu3DgcOHEBwcDCABzdsdHNzM3iBRERERMamdyD64osvcOLECUREROCTTz7Biy++CAD48ccf0bZtW4MXSERERGRsej+6o0mTJjpXmZVYsGABLC0tDVIUERERUXkq0xGiMty7Eba2trC2tv7XBT2sqKgI06ZNg4+PD+zs7FC3bl3Mnj1bpx4hBCIjI+Hh4QE7OzsEBgYiLS1NZzm3b99GaGgonJyc4OzsjCFDhiA3N9egtRIREVHFVaZA9NJLL2HTpk3PvM9QWloaRo4cifnz5xukuM8++wwrVqzA0qVLkZKSgs8++wxRUVFYsmSJ1CYqKgqLFy/GypUrkZiYCAcHBwQFBeH+/ftSm9DQUJw9exaxsbGIiYlBfHw8hg0bZpAaiYiIqOIr06M74uLiMGnSJFy6dAldunRBy5Yt4enpCVtbW2RnZ+PcuXM4ePAgzp49i4iICHz88cdQqVT/urjXX38dbm5u+Pbbb6VpISEhsLOzw/r16yGEgKenJ8aPH48JEyYAADQaDdzc3LBmzRr069cPKSkp8PPzw7Fjx9CyZUsAwK5du9C9e3dcvXoVnp6ez6yDj+4oGz66g4iIzInBH93RuXNnHD9+HAcPHsTmzZuxYcMGXL58Gffu3cMLL7yA5s2b491330VoaCiqVq1qkJUAgLZt22LVqlW4cOEC6tevj9OnT+PgwYNYtGgRgAdXtqnVagQGBkqfUalUaN26NRISEtCvXz8kJCTA2dlZCkMAEBgYCAsLCyQmJqJXr16l+s3Pz0d+fr70XqvVGmydiIiIyPzoNai6ffv2aN++vbFqKWXy5MnQarXw9fWFpaUlioqKMGfOHISGhgIA1Go1AJS63N/NzU2ap1ar4erqqjPfysoK1apVk9o8at68eZg5c6ahV4eIiIjMlN6X3T/s4XE6xvDDDz9gw4YN2LhxI06cOIG1a9di4cKFWLt2rVH7nTJlCjQajfS6cuWKUfsjIiIi09I7EBUVFWH27NmoUaMGHB0dcenSJQDAtGnTdMb6GMJHH32EyZMno1+/fmjcuDEGDhyIsWPHYt68eQAAd3d3AEBmZqbO5zIzM6V57u7uyMrK0pn/zz//4Pbt21KbRymVSjg5Oem8iIiIqPLSOxDNmTMHa9asQVRUFGxsbKTpjRo1wjfffGPQ4vLy8mBhoVuipaWl9OgQHx8fuLu7Iy4uTpqv1WqRmJiIgIAAAEBAQABycnKQlJQktdm7dy+Ki4vRunVrg9ZLREREFZPegei7777DqlWrEBoaqnMjxqZNm+L8+fMGLe6NN97AnDlz8L///Q9//fUXtm3bhkWLFkkDoRUKBcaMGYNPP/0U27dvR3JyMt599114enqiZ8+eAICGDRvitddew9ChQ3H06FEcOnQIERER6NevX5muMCMiIqLKT+87VV+7dk16XMfDiouLUVhYaJCiSixZsgTTpk3DBx98gKysLHh6emL48OGIjIyU2kycOBF3797FsGHDkJOTg/bt22PXrl2wtbWV2mzYsAERERHo3LkzLCwsEBISgsWLFxu0ViIiIqq4ynQfoof5+/tj7NixeOedd1ClShWcPn0aderUwaxZsxAbG4vff//dWLWaDO9DVDa8DxEREZkTg9+H6GGRkZEICwvDtWvXUFxcjK1btyI1NRXfffcdYmJinrtoInPBgEpEJD96jyF68803sWPHDuzZswcODg6IjIxESkoKduzYgS5duhijRiIiIiKj0vsIEQC8/PLLiI2NNXQtRERERCbxXIGoRG5urnQJfAnes4eIiIgqGr1PmaWnpyM4OBgODg5QqVSoWrUqqlatCmdnZ4M+x4yIiIiovOh9hOidd96BEAKrV6+Gm5sbFAqFMeqSlT5T/tWBOrORbOoCiIiInpPef4lPnz6NpKQkNGjQwBj1EBEREZU7vU+Z/ec//+HDTomIiKhS0fsI0TfffIMRI0bg2rVraNSoEaytrXXmN2nSxGDFEREREZUHvQPRjRs38Oeff2Lw4MHSNIVCASEEFAoFioqKDFogERERkbHpHYjee+89NG/eHN9//z0HVRMREVGloHcgunz5MrZv3/7YB7wSERERVUR6D6p+9dVXcfr0aWPUQkRERGQSeh8heuONNzB27FgkJyejcePGpQZV9+jRw2DFEREREZUHvQPRiBEjAACzZs0qNY+DqomIiKgi0jsQPfrsMiIiIqKKTu8xRERERESVTZmOEC1evBjDhg2Dra0tFi9e/NS2H374oUEKIyIiIiovZQpEn3/+OUJDQ2Fra4vPP//8ie0UCgUDEREREVU4ZQpE6enpiI+PR9u2bZGenm7smoiIiIjKVZnHEL3yyiu4ffu2MWshIiIiMokyByIhhDHrICIiIjIZva4y43PLiIiIqDLS6z5EgwYNglKpfGqbrVu3/quCiIiIiMqbXoGoSpUqsLOzM1YtRERERCahVyBavHgxXF1djVULERERkUmUeQwRxw8RERFRZcWrzIiIiEj2yhyI9u3bh2rVqhmzFiIiIiKTKPMYoo4dOxqzDiIiIiKT4dPuiYiISPYYiIiIiEj2GIiIiIhI9p4rEP3555+YOnUq+vfvj6ysLADAzp07cfbsWYMWR0RERFQe9A5EBw4cQOPGjZGYmIitW7ciNzcXAHD69GlMnz7d4AUSERERGZvegWjy5Mn49NNPERsbCxsbG2n6q6++iiNHjhi0OCIiIqLyoNejOwAgOTkZGzduLDXd1dUVN2/eNEhRRKbUZ4revxZmKdnUBRARVSB6HyFydnbG33//XWr6yZMnUaNGDYMURURERFSe9A5E/fr1w6RJk6BWq6FQKFBcXIxDhw5hwoQJePfdd41RIxEREZFR6R2I5s6dC19fX3h5eSE3Nxd+fn7o0KED2rZti6lTpxqjRiIiIiKj0nuwhI2NDb7++mtERkYiOTkZubm5aN68OerVq2eM+oiIiIiM7rlHj3p5ecHLywtFRUVITk5GdnY2qlatasjaiIiIiMqF3qfMxowZg2+//RYAUFRUhI4dO6JFixbw8vLC/v37DV0fERERkdHpHYh+/PFHNG3aFACwY8cOXLp0CefPn8fYsWPxySefGLxAIiIiImPTOxDdvHkT7u7uAIBff/0Vffr0Qf369fHee+8hOZl3PiEiIqKKR+9A5ObmhnPnzqGoqAi7du1Cly5dAAB5eXmwtLQ0eIFERERExqZ3IBo8eDD69OmDRo0aQaFQIDAwEACQmJgIX19fgxd47do1vPPOO3BxcYGdnR0aN26M48ePS/OFEIiMjISHhwfs7OwQGBiItLQ0nWXcvn0boaGhcHJygrOzM4YMGSI9g42IiIhI70A0Y8YMfPPNNxg2bBgOHToEpVIJALC0tMTkyZMNWlx2djbatWsHa2tr7Ny5E+fOncN///tfnavZoqKisHjxYqxcuRKJiYlwcHBAUFAQ7t+/L7UJDQ3F2bNnERsbi5iYGMTHx2PYsGEGrZWIiIgqLoUQQpi6iCeZPHkyDh06hN9///2x84UQ8PT0xPjx4zFhwgQAgEajgZubG9asWYN+/fohJSUFfn5+OHbsGFq2bAkA2LVrF7p3746rV6/C09PzmXVotVqoVCpoNBo4OTkZbgX/v8ZrGxt8maaQHFY5xpBxfxARVQ76/P1+rvsQxcXFIS4uDllZWSguLtaZt3r16udZ5GNt374dQUFB6N27Nw4cOIAaNWrggw8+wNChQwEA6enpUKvV0mk7AFCpVGjdujUSEhLQr18/JCQkwNnZWQpDABAYGAgLCwskJiaiV69epfrNz89Hfn6+9F6r1RpsnYiIiMj86H3KbObMmejatSvi4uJw8+ZNZGdn67wM6dKlS1ixYgXq1auH3bt3Y+TIkfjwww+xdu1aAIBarQbwYKD3w9zc3KR5arUarq6uOvOtrKxQrVo1qc2j5s2bB5VKJb28vLwMul5ERERkXvQ+QrRy5UqsWbMGAwcONEY9OoqLi9GyZUvMnTsXANC8eXOcOXMGK1euRFhYmNH6nTJlCsaNGye912q1DEVERESVmN5HiAoKCtC2bVtj1FKKh4cH/Pz8dKY1bNgQGRkZACDdDykzM1OnTWZmpjTP3d0dWVlZOvP/+ecf3L59W2rzKKVSCScnJ50XERERVV56B6L3338fGzduNEYtpbRr1w6pqak60y5cuABvb28AgI+PD9zd3REXFyfN12q1SExMREBAAAAgICAAOTk5SEpKktrs3bsXxcXFaN26dTmsBREREZk7vU+Z3b9/H6tWrcKePXvQpEkTWFtb68xftGiRwYobO3Ys2rZti7lz56JPnz44evQoVq1ahVWrVgEAFAoFxowZg08//RT16tWDj48Ppk2bBk9PT/Ts2RPAgyNKr732GoYOHYqVK1eisLAQERER6NevX5muMCMiIqLKT+9A9Mcff6BZs2YAgDNnzujMUygUBimqxH/+8x9s27YNU6ZMwaxZs+Dj44MvvvgCoaGhUpuJEyfi7t27GDZsGHJyctC+fXvs2rULtra2UpsNGzYgIiICnTt3hoWFBUJCQrB48WKD1kpEREQVl1nfh8hc8D5EZVNZ7nvD/UFEVDno8/db7zFED7t69SquXr36bxZBREREZHJ6B6Li4mLMmjULKpUK3t7e8Pb2hrOzM2bPnl3qJo1EREREFYHeY4g++eQTfPvtt5g/fz7atWsHADh48CBmzJiB+/fvY86cOQYvkoiIiMiY9A5Ea9euxTfffIMePXpI05o0aSI9VoOBiIiIiCoavU+Z3b59G76+vqWm+/r64vbt2wYpioiIiKg86R2ImjZtiqVLl5aavnTpUjRt2tQgRRERERGVJ71PmUVFRSE4OBh79uyR7gadkJCAK1eu4NdffzV4gURERETGpvcRoo4dO+LChQvo1asXcnJykJOTg7feegupqal4+eWXjVEjERERkVHpfYQIADw9PTl4moiIiCqN5wpE2dnZ+Pbbb5GSkgIA8PPzw+DBg1GtWjWDFkdERERUHvQ+ZRYfH4/atWtj8eLFyM7ORnZ2NhYvXgwfHx/Ex8cbo0YiIiIio9L7CFF4eDj69u2LFStWwNLSEgBQVFSEDz74AOHh4UhO5vOTiIiIqGLR+wjRxYsXMX78eCkMAYClpSXGjRuHixcvGrQ4IiIiovKgdyBq0aKFNHboYSkpKbwPEREREVVIep8y+/DDDzF69GhcvHgRbdq0AQAcOXIEy5Ytw/z58/HHH39IbZs0aWK4SomIiIiMRO9A1L9/fwDAxIkTHztPoVBACAGFQoGioqJ/XyERERGRkekdiNLT041RBxEREZHJ6B2IvL29jVGHrCWnZ5i6BCIiIlnTe1D12rVr8b///U96P3HiRDg7O6Nt27a4fPmyQYsjIiIiKg96B6K5c+fCzs4OwIOHui5duhRRUVF44YUXMHbsWIMXSERERGRsep8yu3LlCl588UUAwM8//4y3334bw4YNQ7t27dCpUydD10dERERkdHofIXJ0dMStW7cAAL/99hu6dOkCALC1tcW9e/cMWx0RERFROdD7CFGXLl3w/vvvo3nz5rhw4QK6d+8OADh79ixq165t6PqIiIiIjE7vI0TLli1DQEAAbty4gZ9++gkuLi4AgKSkJOkeRUREREQVid5HiJydnbF06dJS02fOnGmQgoiIiIjKm95HiADg999/xzvvvIO2bdvi2rVrAIB169bh4MGDBi2OiIiIqDzoHYh++uknBAUFwc7ODidOnEB+fj4AQKPRYO7cuQYvkIiIiMjY9A5En376KVauXImvv/4a1tbW0vR27drhxIkTBi2OiIiIqDzoHYhSU1PRoUOHUtNVKhVycnIMURMRERFRudI7ELm7u+PixYulph88eBB16tQxSFFERERE5UnvQDR06FCMHj0aiYmJUCgUuH79OjZs2IAJEyZg5MiRxqiRiIiIyKj0vux+8uTJKC4uRufOnZGXl4cOHTpAqVRiwoQJGDVqlDFqJCIiIjIqvQORQqHAJ598go8++ggXL15Ebm4u/Pz84OjoiHv37kkPfiUiIiKqKJ7rPkQAYGNjAz8/P7Rq1QrW1tZYtGgRfHx8DFkbERERUbkocyDKz8/HlClT0LJlS7Rt2xY///wzACA6Oho+Pj74/PPPMXbsWGPVSURERGQ0ZT5lFhkZia+++gqBgYE4fPgwevfujcGDB+PIkSNYtGgRevfuDUtLS2PWSkRERGQUZQ5EW7ZswXfffYcePXrgzJkzaNKkCf755x+cPn0aCoXCmDUSlavk9AxTl0BEROWszKfMrl69Cn9/fwBAo0aNoFQqMXbsWIYhIiIiqvDKHIiKiopgY2MjvbeysoKjo6NRiiIiIiIqT2U+ZSaEwKBBg6BUKgEA9+/fx4gRI+Dg4KDTbuvWrYatkIiIiMjIyhyIwsLCdN6/8847Bi+GiIiIyBTKHIiio6ONWQcRERGRyTz3jRmJiIiIKgsGIiIiIpI9BiIiIiKSPQYiIiIikr0KFYjmz58PhUKBMWPGSNPu37+P8PBwuLi4wNHRESEhIcjMzNT5XEZGBoKDg2Fvbw9XV1d89NFH+Oeff8q5eiIiIjJXFSYQHTt2DF999RWaNGmiM33s2LHYsWMHtmzZggMHDuD69et46623pPlFRUUIDg5GQUEBDh8+jLVr12LNmjWIjIws71UgIiIiM1UhAlFubi5CQ0Px9ddfo2rVqtJ0jUaDb7/9FosWLcKrr74Kf39/REdH4/Dhwzhy5AgA4LfffsO5c+ewfv16NGvWDN26dcPs2bOxbNkyFBQUmGqViIiIyIxUiEAUHh6O4OBgBAYG6kxPSkpCYWGhznRfX1/UqlULCQkJAICEhAQ0btwYbm5uUpugoCBotVqcPXv2sf3l5+dDq9XqvIiIiKjyKvONGU1l06ZNOHHiBI4dO1Zqnlqtho2NDZydnXWmu7m5Qa1WS20eDkMl80vmPc68efMwc+ZMA1RPREREFYFZHyG6cuUKRo8ejQ0bNsDW1rbc+p0yZQo0Go30unLlSrn1TUREROXPrANRUlISsrKy0KJFC1hZWcHKygoHDhzA4sWLYWVlBTc3NxQUFCAnJ0fnc5mZmXB3dwcAuLu7l7rqrOR9SZtHKZVKODk56byIiIio8jLrQNS5c2ckJyfj1KlT0qtly5YIDQ2V/m1tbY24uDjpM6mpqcjIyEBAQAAAICAgAMnJycjKypLaxMbGwsnJCX5+fuW+TkRERGR+zHoMUZUqVdCoUSOdaQ4ODnBxcZGmDxkyBOPGjUO1atXg5OSEUaNGISAgAG3atAEAdO3aFX5+fhg4cCCioqKgVqsxdepUhIeHQ6lUlvs6ERERkfkx60BUFp9//jksLCwQEhKC/Px8BAUFYfny5dJ8S0tLxMTEYOTIkQgICICDgwPCwsIwa9YsE1ZNRERE5kQhhBCmLsLcabVaqFQqaDQa44wnmqEy/DJNYYbG1BUYBvcHEVGloM/fb7MeQ0RERERUHhiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9qxMXQABte9vNHUJBvGXqQsgIiJ6TjxCRERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyxxszEj2CN8okIpIfHiEiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItkz60A0b948/Oc//0GVKlXg6uqKnj17IjU1VafN/fv3ER4eDhcXFzg6OiIkJASZmZk6bTIyMhAcHAx7e3u4urrio48+wj///FOeq0JERERmzKwD0YEDBxAeHo4jR44gNjYWhYWF6Nq1K+7evSu1GTt2LHbs2IEtW7bgwIEDuH79Ot566y1pflFREYKDg1FQUIDDhw9j7dq1WLNmDSIjI02xSkRERGSGFEIIYeoiyurGjRtwdXXFgQMH0KFDB2g0GlSvXh0bN27E22+/DQA4f/48GjZsiISEBLRp0wY7d+7E66+/juvXr8PNzQ0AsHLlSkyaNAk3btyAjY3NM/vVarVQqVTQaDRwcnIy+HrVnvw/gy/TFP6aH2zqEgyC+4OIqHLQ5++3WR8hepRGowEAVKtWDQCQlJSEwsJCBAYGSm18fX1Rq1YtJCQkAAASEhLQuHFjKQwBQFBQELRaLc6ePfvYfvLz86HVanVeREREVHlVmEBUXFyMMWPGoF27dmjUqBEAQK1Ww8bGBs7Ozjpt3dzcoFarpTYPh6GS+SXzHmfevHlQqVTSy8vLy8BrQ0REROakwgSi8PBwnDlzBps2bTJ6X1OmTIFGo5FeV65cMXqfREREZDoV4llmERERiImJQXx8PGrWrClNd3d3R0FBAXJycnSOEmVmZsLd3V1qc/ToUZ3llVyFVtLmUUqlEkql0sBrQURERObKrAOREAKjRo3Ctm3bsH//fvj4+OjM9/f3h7W1NeLi4hASEgIASE1NRUZGBgICAgAAAQEBmDNnDrKysuDq6goAiI2NhZOTE/z8/Mp3hYhIbym+DU1dgkE0PJ9i6hKI6CnMOhCFh4dj48aN+OWXX1ClShVpzI9KpYKdnR1UKhWGDBmCcePGoVq1anBycsKoUaMQEBCANm3aAAC6du0KPz8/DBw4EFFRUVCr1Zg6dSrCw8N5FIiIiIgAmHkgWrFiBQCgU6dOOtOjo6MxaNAgAMDnn38OCwsLhISEID8/H0FBQVi+fLnU1tLSEjExMRg5ciQCAgLg4OCAsLAwzJo1q7xWg4iIiMycWQeistwiydbWFsuWLcOyZcue2Mbb2xu//vqrIUsjIiKiSqTCXGVGREREZCwMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQke2Z92T0REZkf3j2cKiMeISIiIiLZYyAiIiIi2WMgIiIiItnjGCIiMmt9plSOr6lkUxdARE/FI0REREQkewxEREREJHuV41g0ERGVG57GpMqIR4iIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYqxwNpiIio3CSnZ5i6BCKDYyAiIiKqwFJ8G5q6BINoeD7FpP3zlBkRERHJHgMRERERyR4DEREREckexxARkVnjAF4iKg8MRERERBVYnymV4095son75ykzIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikr3KMRKLiIjKTe37G01dgkH8ZeoCyKzwCBERERHJHo8QERERVWC8V5dh8AgRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckerzIjIiKqwHhfKMOQVSBatmwZFixYALVajaZNm2LJkiVo1aqVqcsioqfglz0RlQfZnDLbvHkzxo0bh+nTp+PEiRNo2rQpgoKCkJWVZerSiIiIyMRkE4gWLVqEoUOHYvDgwfDz88PKlSthb2+P1atXm7o0IiIiMjFZBKKCggIkJSUhMDBQmmZhYYHAwEAkJCSYsDIiIiIyB7IYQ3Tz5k0UFRXBzc1NZ7qbmxvOnz9fqn1+fj7y8/Ol9xqNBgCg1WqNUl9xfp5RllvejLV9yhv3h3nh/jA/3Cfmhfvj2csUQjyzrSwCkb7mzZuHmTNnlpru5eVlgmoqDtUXpq6AHsb9YV64P8wP94l5Meb+uHPnDlQq1VPbyCIQvfDCC7C0tERmZqbO9MzMTLi7u5dqP2XKFIwbN056X1xcjNu3b8PFxQUKhcLo9RqaVquFl5cXrly5AicnJ1OXI3vcH+aF+8P8cJ+Yl4q8P4QQuHPnDjw9PZ/ZVhaByMbGBv7+/oiLi0PPnj0BPAg5cXFxiIiIKNVeqVRCqVTqTHN2di6HSo3Lycmpwv0wV2bcH+aF+8P8cJ+Yl4q6P551ZKiELAIRAIwbNw5hYWFo2bIlWrVqhS+++AJ3797F4MGDTV0aERERmZhsAlHfvn1x48YNREZGQq1Wo1mzZti1a1epgdZEREQkP7IJRAAQERHx2FNklZ1SqcT06dNLnQYk0+D+MC/cH+aH+8S8yGV/KERZrkUjIiIiqsRkcWNGIiIioqdhICIiIiLZYyAiIiIi2WMgIiIiItljIKoAli1bhtq1a8PW1hatW7fG0aNHn9j2r7/+wpAhQ+Dj4wM7OzvUrVsX06dPR0FBgU67P/74Ay+//DJsbW3h5eWFqKioUsvasmULfH19YWtri8aNG+PXX381+LqZi3nz5uE///kPqlSpAldXV/Ts2ROpqak6be7fv4/w8HC4uLjA0dERISEhpe5+/uGHH8Lf3x9KpRLNmjXTu45Dhw7BysrqsZ991s9BWeqrqObPnw+FQoExY8ZI04y5P/Lz8/HJJ5/A29sbSqUStWvXxurVq3XaPOv3QwiByMhIeHh4wM7ODoGBgUhLS9N73c3JtWvX8M4778DFxQV2dnZo3Lgxjh8/Ls0vyzrPmTMHbdu2hb29vV43vN29ezfatGmDKlWqoHr16ggJCcFff/2l02b//v1o0aIFlEolXnzxRaxZs6bUcvT5PjUnRUVFmDZtms53++zZs3We0WWs7X///n0MGjQIjRs3hpWVlXSD40cZYvub9HtMkFnbtGmTsLGxEatXrxZnz54VQ4cOFc7OziIzM/Ox7Xfu3CkGDRokdu/eLf7880/xyy+/CFdXVzF+/HipjUajEW5ubiI0NFScOXNGfP/998LOzk589dVXUptDhw4JS0tLERUVJc6dOyemTp0qrK2tRXJystHX2RSCgoJEdHS0OHPmjDh16pTo3r27qFWrlsjNzZXajBgxQnh5eYm4uDhx/Phx0aZNG9G2bVud5YwaNUosXbpUDBw4UDRt2lSvGrKzs0WdOnVE165dS322LD8HZamvIjp69KioXbu2aNKkiRg9erQ03Zj7o0ePHqJ169YiNjZWpKeni8OHD4uDBw9K88vy+zF//nyhUqnEzz//LE6fPi169OghfHx8xL179557W5jS7du3hbe3txg0aJBITEwUly5dErt37xYXL16U2pRlnSMjI8WiRYvEuHHjhEqlKlPfly5dEkqlUkyZMkVcvHhRJCUliQ4dOojmzZvrtLG3txfjxo0T586dE0uWLBGWlpZi165dUht9v0/NyZw5c4SLi4uIiYkR6enpYsuWLcLR0VF8+eWXUhtjbf/c3FwxYsQIsWrVKhEUFCTefPPNUm0Mtf1N+T3GQGTmWrVqJcLDw6X3RUVFwtPTU8ybN6/My4iKihI+Pj7S++XLl4uqVauK/Px8adqkSZNEgwYNpPd9+vQRwcHBOstp3bq1GD58+POsRoWTlZUlAIgDBw4IIYTIyckR1tbWYsuWLVKblJQUAUAkJCSU+vz06dP1DkR9+/YVU6dOfexnn/VzoG99FcWdO3dEvXr1RGxsrOjYsaMUiIy5P3bu3ClUKpW4devWE9s86/ejuLhYuLu7iwULFkjzc3JyhFKpFN9//32Z6jA3kyZNEu3bt3/ifH3XOTo6usx/kLds2SKsrKxEUVGRNG379u1CoVCIgoICIYQQEydOFC+99JLO5/r27SuCgoKk94b4PjWV4OBg8d577+lMe+utt0RoaKgQwrjb/2FhYWGPDUSG2P6m/h7jKTMzVlBQgKSkJAQGBkrTLCwsEBgYiISEhDIvR6PRoFq1atL7hIQEdOjQATY2NtK0oKAgpKamIjs7W2rzcL8lbfTptyLTaDQAIG23pKQkFBYW6mwTX19f1KpVyyDbJDo6GpcuXcL06dNLzSvLz4Gx6zOV8PBwBAcHl/pZNOb6bt++HS1btkRUVBRq1KiB+vXrY8KECbh3757U5lm/H+np6VCr1TptVCoVWrduXWH3R8l26d27N1xdXdG8eXN8/fXX0nxjrrO/vz8sLCwQHR2NoqIiaDQarFu3DoGBgbC2tgbw7H1iqO9TU2nbti3i4uJw4cIFAMDp06dx8OBBdOvWDYDpf+YMsf1N/T0mqztVVzQ3b95EUVFRqceLuLm54fz582VaxsWLF7FkyRIsXLhQmqZWq+Hj41NqmSXzqlatCrVa/dh+1Wr186xKhVJcXIwxY8agXbt2aNSoEYAH28XGxqbUOXdDbJO0tDRMnjwZv//+O6ysSv9KluXnwJj1mcqmTZtw4sQJHDt2rNQ8Y67vpUuXcPDgQdja2mLbtm24efMmPvjgA9y6dQvR0dFS/0/7/Sj5b2X6Hbp06RJWrFiBcePG4eOPP8axY8fw4YcfwsbGBmFhYUZdZx8fH/z222/o06cPhg8fjqKiIgQEBOiM23rSPtFqtbh37x6ys7P/9fepKU2ePBlarRa+vr6wtLREUVER5syZg9DQUACm/5kzxPY39fcYjxBVYCNGjICjo6P0etS1a9fw2muvoXfv3hg6dKgJKqyYwsPDcebMGWzatMngy354f40YMQJFRUUYMGAAZs6cifr16xu8v4rqypUrGD16NDZs2ABbW1uj9fPo/gAeBGKFQoENGzagVatW6N69OxYtWoS1a9fqHCWSm+LiYrRo0QJz585F8+bNMWzYMAwdOhQrV640aD8vvfSStE9Kjn6o1WoMHToUYWFhOHbsGA4cOAAbGxu8/fbbOoOKK7MffvgBGzZswMaNG3HixAmsXbsWCxcuxNq1aw3az+O2v1zwCJEZe+GFF2BpaVlqhH1mZibc3d0xa9YsTJgw4bGfvX79Ol555RW0bdsWq1at0pnn7u7+2GWWzHtam5L5lVVERARiYmIQHx+PmjVrStPd3d1RUFCAnJwcnf970XebnDp1Svq3k5MT7ty5g+PHj+PkyZPSc/aKi4shhICVlRV+++03tG/f/qk/B4asz1wkJSUhKysLLVq0kKYVFRUhPj4eS5cuxe7du42yPwDAw8MDNWrUgEqlkuY1bNgQQghcvXoV9erVe+bvR8l/MzMz4eHhodPmea4+NAceHh7w8/PTmdawYUP89NNPAAy3zr/++isKCwsBAHZ2dgAeXJmkUql0roZdv349vLy8kJiYiDZt2jxxnzg5OcHOzg6WlpbP/D0yZx999BEmT56Mfv36AQAaN26My5cvY968eQgLCzPq9i8LQ2x/U3+P8QiRGbOxsYG/vz/i4uKkacXFxYiLi0NAQABcXV3x4osvSq8S165dQ6dOneDv74/o6GhYWOju5oCAAMTHx0s/9AAQGxuLBg0aoGrVqlKbh/staRMQEGCMVTU5IQQiIiKwbds27N27t9QpRX9/f1hbW+tsk9TUVGRkZOi1TR7eX66urnByckJycjJOnTolvUaMGIEGDRrg1KlTaN269TN/DgxZn7no3Llzqe3SsmVLhIaGSv82xv4AgHbt2uH69evIzc2V2l24cAEWFhZSSH7W74ePjw/c3d112mi1WiQmJlbI/QE82C6P3oriwoUL8Pb2BmC4dfb29pb2SY0aNQAAeXl5pb7HLC0tATz4XQCevU/K8ntkzp60DUrW35jbvywMsf1N/j1m9GHb9K9s2rRJKJVKsWbNGnHu3DkxbNgw4ezsLNRq9WPbX716Vbz44ouic+fO4urVq+Lvv/+WXiVycnKEm5ubGDhwoDhz5ozYtGmTsLe3L3XZvZWVlVi4cKFISUkR06dPr9SX3Y8cOVKoVCqxf/9+nW2Wl5cntRkxYoSoVauW2Lt3rzh+/LgICAgQAQEBOstJS0sTJ0+eFMOHDxf169cXJ0+eFCdPntS5ou9ZHndFVFl+DspSX0X28FVmQhhvf9y5c0fUrFlTvP322+Ls2bPiwIEDol69euL999+X2pTl92P+/PnC2dlZ/PLLL+KPP/4Qb775ZoW+7P7o0aPCyspKzJkzR6SlpYkNGzYIe3t7sX79eqlNWdb58uXL4uTJk2LmzJnC0dFR2id37tx5Yt9xcXFCoVCImTNnigsXLoikpCQRFBQkvL29pd/Rksu+P/roI5GSkiKWLVv22Mu+9fk+NSdhYWGiRo0a0mX3W7duFS+88IKYOHGi1MZY218IIc6ePStOnjwp3njjDdGpUyfpcyUMtf1N+T3GQFQBLFmyRNSqVUvY2NiIVq1aiSNHjjyxbXR0tADw2NfDTp8+Ldq3by+USqWoUaOGmD9/fqll/fDDD6J+/frCxsZGvPTSS+J///ufwdfNXDxpm0VHR0tt7t27Jz744ANRtWpVYW9vL3r16qUTNIV48Ef7cctJT08vcy1PukT8WT8HZamvIns0EBlzf6SkpIjAwEBhZ2cnatasKcaNG6cTjoV49u9HcXGxmDZtmnBzcxNKpVJ07txZpKam/qttYGo7duwQjRo1EkqlUvj6+opVq1bpzC/LOoeFhT12n+zbt++pfX///feiefPmwsHBQVSvXl306NFDpKSk6LTZt2+faNasmbCxsRF16tTR+f0toc/3qTnRarVi9OjRolatWsLW1lbUqVNHfPLJJzrh3pjb39vb+5l/Vwyx/U35PaYQQiYj0oiIiIiegGOIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIqNwoFIqnvmbMmGHwPgcNGoSePXs+s92NGzcwcuRI1KpVC0qlEu7u7ggKCsKhQ4cMXhMRmR8+7Z6Iys3ff/8t/Xvz5s2IjIzUeWCoo6Oj9G8hBIqKimBlVT5fUyEhISgoKMDatWtRp04dZGZmIi4uDrdu3TJanwUFBbCxsTHa8omo7HiEiIjKjbu7u/RSqVRQKBTS+/Pnz6NKlSrYuXMn/P39oVQqcfDgwcce4RkzZgw6deokvf/xxx/RuHFj2NnZwcXFBYGBgbh79y5mzJiBtWvX4pdffpGOQu3fv79UXTk5Ofj999/x2Wef4ZVXXoG3tzdatWqFKVOmoEePHjrthg8fDjc3N9ja2qJRo0aIiYmR5v/000946aWXoFQqUbt2bfz3v//V6ad27dqYPXs23n33XTg5OWHYsGEAgIMHD+Lll1+GnZ0dvLy88OGHH+Lu3bv/foMTUZnxCBERmZXJkydj4cKFqFOnDqpWrfrM9n///Tf69++PqKgo9OrVC3fu3MHvv/8OIQQmTJiAlJQUaLVaREdHAwCqVatWahmOjo5wdHTEzz//jDZt2kCpVJZqU1xcjG7duuHOnTtYv3496tati3PnzsHS0hIAkJSUhD59+mDGjBno27cvDh8+jA8++AAuLi4YNGiQtJyFCxciMjIS06dPBwD8+eefeO211/Dpp59i9erVuHHjBiIiIhARESHVTETGx0BERGZl1qxZ6NKlS5nb//333/jnn3/w1ltvwdvbGwDQuHFjab6dnR3y8/Ph7u7+xGVYWVlhzZo1GDp0KFauXIkWLVqgY8eO6NevH5o0aQIA2LNnD44ePYqUlBTUr18fAFCnTh1pGYsWLULnzp0xbdo0AED9+vVx7tw5LFiwQCcQvfrqqxg/frz0/v3330doaCjGjBkDAKhXrx4WL16Mjh07YsWKFbC1tS3ztiCi58dTZkRkVlq2bKlX+6ZNm6Jz585o3Lgxevfuja+//hrZ2dl69xsSEoLr169j+/bteO2117B//360aNECa9asAQCcOnUKNWvWlMLQo1JSUtCuXTudae3atUNaWhqKioqkaY+u3+nTp7FmzRrpKJWjoyOCgoJQXFyM9PR0vdeDiJ4PAxERmRUHBwed9xYWFhBC6EwrLCyU/m1paYnY2Fjs3LkTfn5+WLJkCRo0aPBcYcLW1hZdunTBtGnTcPjwYQwaNEg6tWVnZ/cca1Pao+uXm5uL4cOH49SpU9Lr9OnTSEtLQ926dQ3SJxE9GwMREZm16tWr61ydBjw4WvMwhUKBdu3aYebMmTh58iRsbGywbds2AICNjY3OERp9+Pn5SYObmzRpgqtXr+LChQuPbduwYcNSl+gfOnQI9evXl8YZPU6LFi1w7tw5vPjii6VevAKNqPwwEBGRWXv11Vdx/PhxfPfdd0hLS8P06dNx5swZaX5iYiLmzp2L48ePIyMjA1u3bsWNGzfQsGFDAA+u7Prjjz+QmpqKmzdv6hxdKnHr1i28+uqrWL9+Pf744w+kp6djy5YtiIqKwptvvgkA6NixIzp06ICQkBDExsYiPT0dO3fuxK5duwAA48ePR1xcHGbPno0LFy5g7dq1WLp0KSZMmPDU9Zs0aRIOHz6MiIgInDp1Cmlpafjll18QERFhqE1IRGUhiIhMIDo6WqhUKun9vn37BACRnZ1dqm1kZKRwc3MTKpVKjB07VkRERIiOHTsKIYQ4d+6cCAoKEtWrVxdKpVLUr19fLFmyRPpsVlaW6NKli3B0dBQAxL59+0ot//79+2Ly5MmiRYsWQqVSCXt7e9GgQQMxdepUkZeXJ7W7deuWGDx4sHBxcRG2traiUaNGIiYmRpr/448/Cj8/P2FtbS1q1aolFixYoNOPt7e3+Pzzz0v1f/ToUalGBwcH0aRJEzFnzpyybUgiMgiFEI+cnCciIiKSGZ4yIyIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2ft/Xrsr3NcdW8cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"multiple_trust\"\n",
    "\n",
    "# initialize an empty dictionary to store file contents\n",
    "file_dict2 = {}\n",
    "\n",
    "# iterate over all files in the directory\n",
    "for file_path in glob.glob(os.path.join(path, \"*\")):\n",
    "    filename = os.path.basename(file_path)\n",
    "    with open(file_path, \"r\") as f:\n",
    "        file_contents = f.readlines()\n",
    "     \n",
    "        \n",
    "        ttime, ttrust = [], []\n",
    "        \n",
    "        for line in file_contents:\n",
    "            larr = line.split(' ')\n",
    "            trust_score = int(larr[7])\n",
    "            _time = int(larr[4])\n",
    "            if trust_score > 0:\n",
    "                ttime.append(_time)\n",
    "                ttrust.append(trust_score)\n",
    "        \n",
    "        user_count = int(file_contents[0].split(' ')[3])\n",
    "    # add the file contents to the dictionary with filename as key\n",
    "    file_dict2[user_count] = list(zip(ttime, ttrust))\n",
    "    \n",
    "\n",
    "# print(file_dict2)\n",
    "\n",
    "\n",
    "#binning by trust scores is actually the way\n",
    "def get_grouped2(index, file_dict=file_dict2):\n",
    "    grouped = [[],[],[],[],[]]\n",
    "    for i in file_dict[index]:\n",
    "        j,k = i[0], i[1]\n",
    "        \n",
    "        if k <= 200:\n",
    "            grouped[0].append(j)\n",
    "        elif k <= 400:\n",
    "            grouped[1].append(j)\n",
    "        elif k <= 600:\n",
    "            grouped[2].append(j)\n",
    "        elif k <= 800:\n",
    "            grouped[3].append(j)\n",
    "        else:\n",
    "            grouped[4].append(j)\n",
    "            \n",
    "            \n",
    "        #get avg_times\n",
    "    avg_grouped = [median(i) for i in grouped]\n",
    "    [print(f\"trust score {200 + 200*j}, count:{index} with median {median(i)} and std_dev: {statistics.stdev(i)}\") for j,i  in enumerate(grouped)]\n",
    "    return avg_grouped\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "species = ['0-200', '201-400', '401-600', '601-800', '801-1000'] #make this task count\n",
    "\n",
    "\n",
    "# create the first bar plot\n",
    "\n",
    "weight_counts = {\n",
    "    '40': get_grouped2(40),\n",
    "    '120': get_grouped2(120),\n",
    "    '200': get_grouped2(200),\n",
    "    '400': get_grouped2(400),\n",
    "#     '200':get_grouped2(200)\n",
    "}\n",
    "\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bottom = np.zeros(5)\n",
    "\n",
    "for boolean, weight_count in weight_counts.items():\n",
    "    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom)\n",
    "    bottom += weight_count\n",
    "\n",
    "ax.set_xlabel('Trust Score')\n",
    "ax.set_ylabel('Response Time(s)')\n",
    "ax.set_title('Multiple Servers')    \n",
    "\n",
    "ax.set_ylim(0, 1300)\n",
    "\n",
    "ax.legend(loc=\"upper left\", ncols=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cae725a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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