bridges_nis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'Number of vertices':[100,1000,10000],'Number of edges':[3*100,3*1000,3*10000], \n",
    "                    'Deterministic':[0.001,0.004,0.062003], \n",
    "                    'Random 1-bridges':[0.0015, 0.01, 0.157], 'Random 2-bridges':[0.0019, 0.001, 0.193]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of vertices</th>\n",
       "      <th>Number of edges</th>\n",
       "      <th>Deterministic</th>\n",
       "      <th>Random 1-bridges</th>\n",
       "      <th>Random 2-bridges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.062003</td>\n",
       "      <td>0.1570</td>\n",
       "      <td>0.1930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of vertices  Number of edges  Deterministic  Random 1-bridges  \\\n",
       "0                 100              300       0.001000            0.0015   \n",
       "1                1000             3000       0.004000            0.0100   \n",
       "2               10000            30000       0.062003            0.1570   \n",
       "\n",
       "   Random 2-bridges  \n",
       "0            0.0019  \n",
       "1            0.0010  \n",
       "2            0.1930  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Number of vertices'].values, data['Deterministic'].values )\n",
    "plt.plot(data['Number of vertices'].values, data['Random 1-bridges'].values)\n",
    "plt.plot(data['Number of vertices'].values, data['Random 2-bridges'].values)\n",
    "plt.legend(['Deterministic','Random 1-bridges', 'Random 2-bridges'])\n",
    "plt.plot(0,1.4)\n",
    "plt.ylabel('time (sec)')\n",
    "#plt.yscale('linear')\n",
    "plt.xlabel('Number of vertices')\n",
    "plt.title(\"Comparison of algorythms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.DataFrame({'Number of vertices':[100,500,750,1000],\n",
    "                     'Number of edges':[int((100**2-100)/5),int((500**2-500)/5),int((750**2-750)/5),int((1000**2-1000)/5)], \n",
    "                    'Deterministic':[0.006,0.156,0.318, 0.575], \n",
    "                    'Random 1-bridges':[0.017, 0.265, 0.561, 0.979], 'Random 2-bridges':[0.018, 0.32, 0.782, 1.437]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of vertices</th>\n",
       "      <th>Number of edges</th>\n",
       "      <th>Deterministic</th>\n",
       "      <th>Random 1-bridges</th>\n",
       "      <th>Random 2-bridges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>49900</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>750</td>\n",
       "      <td>112350</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>199800</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.979</td>\n",
       "      <td>1.437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of vertices  Number of edges  Deterministic  Random 1-bridges  \\\n",
       "0                 100             1980          0.006             0.017   \n",
       "1                 500            49900          0.156             0.265   \n",
       "2                 750           112350          0.318             0.561   \n",
       "3                1000           199800          0.575             0.979   \n",
       "\n",
       "   Random 2-bridges  \n",
       "0             0.018  \n",
       "1             0.320  \n",
       "2             0.782  \n",
       "3             1.437  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data2['Number of vertices'].values, data2['Deterministic'].values )\n",
    "plt.plot(data2['Number of vertices'].values, data2['Random 1-bridges'].values)\n",
    "plt.plot(data2['Number of vertices'].values, data2['Random 2-bridges'].values)\n",
    "plt.legend(['Deterministic','Random 1-bridges', 'Random 2-bridges'])\n",
    "plt.plot(0,1.4)\n",
    "plt.ylabel('time (sec)')\n",
    "#plt.yscale('linear')\n",
    "plt.xlabel('Number of vertices')\n",
    "plt.title(\"Comparison of algorythms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorts = pd.DataFrame({'Number of elements':[10**4,10**5,10**6],'Radix Sort':[0.002,0.024,0.22], \n",
    "                    'Standard sort':[0.003,0.026,0.476]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of elements</th>\n",
       "      <th>Radix Sort</th>\n",
       "      <th>Standard sort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000000</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of elements  Radix Sort  Standard sort\n",
       "0               10000       0.002          0.003\n",
       "1              100000       0.024          0.026\n",
       "2             1000000       0.220          0.476"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zzmDKlCls2bKl5ld/SkoKH/3oRwFYs2YN+fn5DB8+HDOrWVddZ599Nvfeey/33XcfmzZtIiMjg1deeYUrr7ySrKwsunbtylVXXVUzoFBubm5NcmgpKtYnIiemgV/6iZSSksL06dOZPn06Y8eO5ec//znz5s07qs2GDRu4//77Wbp0Kb169WLevHk1pb+BmkNUKSkpVFRU1EyPVxZ8yZIlvPDCC7z22mtkZmYyffr0mnWlp6cfNdZDU8qKX3vttUyePJlnnnmGiy66iIULF9LQEZ1klAtXD0JE2py1a9fW/HqHo0tux5bS3r9/P1lZWfTo0YMdO3bw3HPPNbruadOm8eijjwLw3HPPsXfvXgCKi4vp1asXmZmZrFmzhtdffz3u8iNHjmTDhg188MEHADz22GNx261fv56hQ4dy2223MXv2bN555x2mTZvGk08+SWlpKSUlJfzud7+rt9JramrqMaXITzb1IESkzTl48CC33nor+/bto3PnzgwbNqzmpPH8+fO55JJL6N+/Py+99BITJkzg9NNPZ+jQoZx77rmNrvuuu+5i7ty5TJw4kfPOO48hQ4YAcPHFF/OjH/2IcePGMWLEiHoP96Snp7NgwQIuvfRScnJymDp1KitWrDim3a9+9SseeeQRUlNTOeWUU7jzzjvp3bs38+bNY9KkSQD84z/+IxMmTKg5/BVr/vz5jBs3jokTJ9YktJNNJ6lFpNlU7rt100lqERFJKCUIERGJSwlCRI5LWzs83VGczM9FCUJEmi09PZ2ioiIliVbG3SkqKiI9Pf2krE9XMYlIsw0aNIjCwkJ27dqV7FCkjvT0dAYNGnRS1qUEISLNlpqaSn5+frLDkATTISYREYlLCUJEROJSghARkbiUIEREJC4lCBERiUsJQkRE4lKCEBGRuBKaIMzsYjNba2brzOyOOPOHmNlLZvaWmb1jZh9JZDwiItJ0CUsQZpYCPARcAowG5prZ6DrNvgr82t0nANcAP0xUPCIi0jyJ7EFMAta5+3p3PwI8Dlxep40D3aPnPYBtCYxHRESaIZEJYiCwJeZ1YTQt1t3A9WZWCDwL3BpvRWY238yWmdky1X4REWkZiUwQ8Ubtrlv6cS7wM3cfBHwE+F8zOyYmd1/g7gXuXtCnT58EhCoiInUlMkEUAoNjXg/i2ENINwG/BnD314B0ICeBMYmISBMlMkEsBYabWb6ZdSGchF5cp81mYAaAmY0iJAgdQxIRaQUSliDcvQK4BXgeWE24Wmmlmd1jZrOjZp8HPmVmfwMeA+a5RiAREWkVEjoehLs/Szj5HDvtzpjnq4BzExmDiIgcH91JLSIicSlBiIhIXEoQIiISlxKEiIjEpQQhIiJxKUGIiEhcShAiIhKXEoSIiMSlBCEiInEpQYiISFxKECIiEpcShIiIxKUEISIicSlBiIhIXEoQIiISlxKEiIjEpQQhIiJxKUGIiEhcShAiIhKXEoSIiMSlBCEiInEpQYiISFxKECIiEpcShIiIxKUEISIicSlBiIhIXEoQIiISlxKEiIjEpQQhIiJxKUGIiEhcTUoQZtbXzK40s8+a2Y1mNsnMGl3WzC42s7Vmts7M7qinzcfNbJWZrTSzXzZ3B0REJDE6NzTTzM4H7gB6A28BO4F04ArgVDN7AvgPd98fZ9kU4CHgAqAQWGpmi919VUyb4cCXgXPdfa+Z9T05uyUiIieqwQQBfAT4lLtvrjvDzDoDswgJ4P/FWXYSsM7d10ftHwcuB1bFtPkU8JC77wVw953N3gMREUmIBhOEu3+xgXkVwJMNLD4Q2BLzuhCYXKfNaQBm9hcgBbjb3X9fd0VmNh+YDzBkyJCGQhYRkZOkqecg7jWznjGve5nZNxpbLM40r/O6MzAcmA7MBRbGbqdmIfcF7l7g7gV9+vRpSsgiInKCmnoV0yXuvq/6RXRI6CONLFMIDI55PQjYFqfNIncvd/cNwFpCwhARkSRraoJIMbO06hdmlgGkNdAeYCkw3MzyzawLcA2wuE6bJ4Hzo3XmEA45rW9iTCIikkCNnaSu9gjwopn9lHCY6Ebg5w0t4O4VZnYL8Dzh/MLD7r7SzO4Blrn74mjehWa2CqgEvujuRce5LyIichKZe93TAvU0NLsYmEk4t/AHd38+kYHVp6CgwJctW5aMTYuItFlmttzdC5qzTFN7EACrgQp3f8HMMs2sm7sfaF6IIiLSVjT1KqZPAU8AP44mDaThS1xFRKSNa+pJ6s8C5wL7Adz9fUB3PYuItGNNTRCH3f1I9YvoLuqmnbwQEZE2qakJ4mUz+wqQYWYXAL8BnkpcWCIikmxNTRB3ALuAd4FPA88CX01UUCIiknxNuorJ3auAnwA/MbPewCBv6vWxIiLSJjX1KqYlZtY9Sg5vAz81s+8lNjQREUmmph5i6hGN+XAV8FN3P5Nw05yIiLRTTU0Qnc2sP/Bx4OkExiMiIq1EUxPEPYS6SevcfamZDQXeT1xYIiKSbE09Sf0bwqWt1a/XAx9NVFAiIpJ8DfYgzOyr0Ynp+uZ/2MxmnfywREQk2RrrQbwLPGVmh4A3CfdCpBMG9RkPvADcm9AIRUQkKRobk3oRsMjMhhNqMfUn1GN6BJjv7mWJD1FERJKhqecg3kcnpUVEOpSmXsUkIiIdjBKEiIjEpQQhItKalRTBm7+AR66G9S+36KabdA7CzE4D/hvo5+5jzGwcMNvdv5HQ6EREOqIDO2DNU7BqMWx8BbwSeg6BQ/taNIymjkn9E+CLREOOuvs7ZvZLQAlCRORkKC6E1VFS2Pwa4JA9DKb+M4yaDf3PALMWDampCSLT3f9qRwdXkYB4REQ6jj0bYPXikBS2LgvT+p4O0+8ISaHvqBZPCrGamiB2m9mpRMOMmtnVwPaERSUi0l7teg9WLwpJ4e/vhGn9x8OMO2HU5ZAzLLnxxWhqgvgssAAYaWZbgQ3A9QmLSkSkvXCHHSujnsIi2LUmTB80CS78Boy6DHrlJTXE+jT1Rrn1wEwzywI6ufuBxIYlItKGucO2t2oPH+35AKwTDDkHLvlOSArdByQ7ykY19SqmnsANQB5hbAgA3P22hEUmItKWVFVB4dLapFC8GSwF8qfBObfAyFnQtW+yo2yWph5iehZ4nVC8rypx4YiItCFVlbDp1XDoaM3TcGA7pHSBoeeHE80jLoHMegtit3pNTRDp7n57QiMREWkLKsthw5+ipPAMlO6GzhkwbAaMvgJOuxDSeyQ7ypOiqQnif83sU4ThRg9XT3T3PQmJSkSkNak4DB+8FJLC2mfDDWtdusJpF4XLUYdfAF2ykh3lSdfUBHEE+C7wb0SXukb/Dk1EUCIiSXekFNb9Xzif8N7zcOQApPWAkR8JSeHUD0NqerKjTKimJojbgWHuvjuRwYiIJNWh/fD+H0JPYd0LUF4KGb1hzJXhHoX8adC5S7KjbDFNTRArgdLmrtzMLgb+E0gBFrr7t+tpdzVhzOuz3H1Zc7cjInLcyvbC2udCT+GDP0LlYejaD8ZfG3oKuedCSlO/KtuXpu51JfC2mb3E0ecg6r3M1cxSgIeAC4BCYKmZLXb3VXXadQNuA95oZuwiIsenZHe46mjVYtjwMlRVQPdBcNZNISkMngydVOy6qQniyejRHJOAddFNdpjZ48DlwKo67b4OfAf4QjPXLyLSdPu3R0lhEWz6C3gV9MqHsz8bDh8NnJjUuketUVPvpP75cax7ILAl5nUhMDm2gZlNAAa7+9NmVm+CMLP5wHyAIUOGHEcoItIh7dtcWyF1yxuAQ84I+NDnQ0/hlLFKCg1oMEGY2a/d/eNm9i61Vy/VcPdxDS0eZ1rNOsysE/B9YF5jQbr7AkItKAoKCo6JQ0SkRtEHtXczb3szTOs3Fs7/SlQhdWRy42tDGutBfC76d9ZxrLsQGBzzehCwLeZ1N2AMsCQq3XEKsNjMZutEtYg0y841tUlhx7th2oCJMPPukBSyT01mdG1WgwnC3atLen/G3f81dp6Z3Qf867FL1VgKDDezfGArcA1wbcy6i4GcmPUtAb6g5CAijXKHv79bWyF193uAhZPLF90biuH11OHoE9XUk9QXcGwyuCTOtBruXmFmtwDPEy5zfdjdV5rZPcAyd198PAGLSAflDlvfjMZSWAR7N4YKqbnnwqT5oRhe9/7JjrJdaewcxM3AZ4ChZvZOzKxuwF8aW7m7P0so9Bc77c562k5vbH0i0sFUVYWTy9WHj/YXQqfOkH8eTL0dRl4KWTmNr0eOS2M9iF8CzwHfAu6ImX5AdZhEJCEqK8JlqNUVUg/ugJS0UAzvw1+FERdDRq9kR9khNHYOohgoBua2TDgi0iFVHIkqpD4ZiuGVFkFqZiiCN2p2KIqX1i3ZUXY4HfP+cRFJvvKyUNpi1eJQ6uJwMXTpFnoIo2bDsJnQJTPZUXZoShAi0nKOlETF8BaHf48chPSeMGpWSApDp7f7CqltiRKEiCTWoeJQLnvVIlj3IlSUQWYOjL06JIX8aZCSmuwoJQ4lCBE5+Ur3hHMJqxbD+peg8gh06w8TPxFVSD0HOqUkO0pphBKEiJwcB3fGVEj9E3gl9BgS7lEYfTkMLFCF1DZGCUJEjt/+bbXF8Da/Giqk9j4Vzr0t9BQGTFAxvDZMCUJEmmfvptob1wr/Gqb1GQXTvhh6Cn1HKym0E0oQItK43euiEheLYfvbYdop4+DD/x6SQs7w5MYnCaEEISLHcoedq2uL4e2MxvkaWAAXfD0Uw+udn9wYJeGUIEQkcIftf6tNCkXrAIMhZ8PF94V7FXoMSnaU0oKUIEQ6sqoq2Lq89vDRvk1gKZA3FaZ8JlRI7dYv2VFKkihBiHQ0VZWw+fXQS1j9FBzYBp1S4dTzw4nmER+BrOxkRymtgBKESEdQWQ4b/xx6CWuehpJd0Dk91DsadXcohpfRM9lRSiujBCHSXlUchvUvh57C2megbC+kZsFpF4Z7FIZfCGldkx2ltGJKECLtSXkZrHsh9BTe+z0c3g9p3WHEJVGF1BmQmpHsKKWNUIIQaesOH4T3n48qpP4flJeEAXVGz4ZRl8PQ86BzWrKjlDZICUKkLSrbF3oIqxbDBy9CxSHI6gtnzAk9hbypqpAqJ0wJQqStKCkK5xJWLYb1S6CqHLoPhDPnhbuZB09WhVQ5qZQgRFqzAztgTVQMb+MroUJqz1yY8k8w+goYMFEVUiVhlCBEWpviwpgKqa8BDtnDYeo/h57CKeNUDE9ahBKESGuwZ0NtiYuty8O0vqfD9C+Hk819RiopSItTghBJll3vRSUuFsHf3w3T+o+HGXeFnkL2qcmNTzo8JQiRluIOO1bW9hR2rQnTB02CC78ZKqT2yk1ujCIxlCBEEskdtr0V1T1aDHvWg3WC3HOh4KZQIbX7gGRHKRKXEoTIyVZVBYVLa4vhFW+GTp0hfxqcc1uokNq1T7KjFGmUEoTIyVBVCZteDUlhzdNwYDukdIFTPwzT7wilLjJ7JztKkWZRghA5XpXlsOFPUVJ4Bkp3Q+cMGD4zlLg47SJI757sKKWdKK+sosqdtM4tdzOkEoRIc5QfgvUvhXsU1j4Lh/ZBl64hGYyaDcMvgC5ZyY5S2qgjFVUU7i1lY1EJG3eXsqmohI1F4XXh3jK+ddVYPl4wuMXiUYIQacyRUlj3f1GF1OfhyAFI7xEG1hk1OxxGSk1PdpTSRhyuqGTLnjI27i5hY1EJm6IEsLGohK17y6jy2rZd0zqTl5PJ2IE9uGzcAEad0rI90oQmCDO7GPhPIAVY6O7frjP/duAfgQpgF3Cju29KZEwiTXJoP7z/h3D4aN0LUF4Kmdkw5spwj0LeNOjcJdlRSit1qLySLXtK2bC7NgFsKgqvtxWX4TFJoFt6Z/JzspgwuBdXjh9IbnYWeTlZ5GVn0jurC5bEGyQTliDMLAV4CLgAKASWmtlid18V0+wtoMDdS83sZuA7wJxExSTSoLK9sPa5qELqH6HyMHTtB+OvDUlhyDmQok63BGVHKtlckwTCoaBNRSVs3F3C9v2HjkoCPTNTyc3O4qy8XuRmDyIvJ5O87CzysrPomZma1CTQkET+tU8C1rn7egAzexy4HKhJEO7+Ukz714HrExiPyLFKdoerjlYthg2uAKUUAAAVMUlEQVQvQ1UFdB8EZ90UksKgSSqG14GVHqkIPYDdtQmgulfw9/2HjmrbO6sLedmZTBmaHfUCQhLIzc6kZ2bb7G0mMkEMBLbEvC4EJjfQ/ibguXgzzGw+MB9gyJAhJys+6aj2b4+SwiLY9BfwKuiVD2ffEuoeDZioukcdyMHDFWw86lBQOEG8saiEnQcOH9U2p2sX8rKzOHdYDnnZmeTmZJGfncWQ7Ex6ZLS/8TcSmSDi/Q/zONMws+uBAuC8ePPdfQGwAKCgoCDuOkQatG9zbYXULW8ADjkj4ENfCEmh3xglhXbswKHymi/9miuDol7B7oNHJ4E+3dLIz87ivNP6kJcTegDVPYFu6e0vCTQkkQmiEIi9HmsQsK1uIzObCfwbcJ67H647X+S4FX1QW/do21thWr+xcP6/RRVSRyQ3PjmpisvKj70yKOoZFJUcOaptv+5p5GVnMWNkX3JjzgfkZmeSlabzTNUS+U4sBYabWT6wFbgGuDa2gZlNAH4MXOzuOxMYi3QUO9fUJoUdK8K0ARNh5tdCMTxVSG3T9pUeOerKoNhzA3tLy49q279HOnnZWVx4er9wTiA6LzCkdyaZXZQEmiJh75K7V5jZLcDzhMtcH3b3lWZ2D7DM3RcD3wW6Ar+JzuJvdvfZiYpJ2iH3UCq7Oinsfg8wGDIFLvpWSAo9W+7GIjkx7s7e0vK4VwZtLCqluKw2CZjBgB4Z5OVkcsnY/uRFh4LycrIY0juT9FQNv3qizL1tHdIvKCjwZcuWJTsMSSZ32Ppm7VgKezeGCql5U8ONa6Mug26nJDtKqYe7U1RyJO6VQRuLSjhwqKKmbSeDgb0yag7/5MX0BAb1UhJoDjNb7u4FzVlG/SxpG6qqwsnl6gqp+wtDhdSh02Hq7TDyUsjKSXaUEnF3dh08fPSJ4d21N4wdPFybBFI6GYN6ZZCbncWEIT3Jzc4iPyeT3OwsBvXKaNHaQ3I0JQhpvSorYNMr4cqjNU/DwR2QkgbDZsCHvwojLoaMXsmOssNyd3bsPxz3yqBNRSWUHqmsaZvSyRjcK4O8nCzOyusdegM5oTcwsGcGXTrrXpPWSAlCWpeKI1GF1CdDhdSyPZCaGYrgjZodiuKldUt2lB1GVZXz9/2H4l4ZtLGohEPlVTVtU1OMwb3DYaApQ3vXnA/Iy85kQM8MUlOUBNoaJQhJvvKyUNpi1eJQ6uJwMXTpFnoIo2bDsJnQJTPZUbZbVVXOtuKyY2oGbYqeH66oTQJdUjoxJDuTvOzMmpvFqnsC/Xuk01lJoF1RgpDkOFISFcNbHP49chDSe4YhOEdfHs4tdE5LdpTtRmWVs21fWVQ1tDTqBYTnm/eUciQmCaR17kRudjgHUH2zWPVJ4v49MkjppBsKOwolCGk5h4pDuezqCqkVhyAzB8ZeHVVI/RCkdKw7VU+misoqtu4ri3tl0JY9pZRX1l6xmJ7aibzsLE7tE24Wi71j+JTu6XRSEhCUICTRSveEgXVWLQ4D7VQegW79YeINUYXUs6GTrlJpqvLKKgr3hp7Apt21g8lsKiply55SKmIGE8jskkJudhYj+nXjwtGn1FwZlJedRd9uaUoC0iglCDn5Du6MqZD6J/BK6DEEJs0PSWFggSqkNuBIRRVb9pYedWloda+gcG8ZlTFJIKtLCnk5WYzu351LxpxSczgoLzuTPt3SWm0ZaWkblCDk5Ni/rbYY3uZXQ4XU3qfCuZ8LdY/6j1cxvBiHyivD0JK7a0cTqz4cVHdUsW5pncnLyaoZVaz6yqDc7CxyuiZ3QBlp35Qg5Pjt3VRb4qJwaZjWZxRM+1JICn1Hd+ikcKg8DChTfVnohpgbxuqOKta9zqhi4ZxA6xhVTDouJQhpnt3raktcbP9bmNb/DPjwv4fDRznDkxtfCys7UsmmPSXH3DG8qejYUcV61RlVLD/mxHCvrLY5oIy0b0oQ0jB32Lm6tqewMxoQcNBZcMHXQ92j3vnJjTHBSg6HUcU2FZWEXkBMyYi6o4plZ3UhN86oYnnZWfTI1BVa0rYoQcix3EPvYNWikBiK1gEGuefAxfeFpNBjYLKjPKnqjioW+/zYUcXS4t4o1l5HFZOOSwlCgqoq2Lo8lLhYvTiMwGYpkP8hmPIZGDkLuvVLdpQnZP+h8ppf/7E1gzYWlbD74NEDyvTtFgaUqXujWEccVUw6LiWIjqyqEja/Xlsh9cA26JQKp54fTjSPvBQyeyc7ymYpLi2vuSqo+lxA9WWie+qMKnZK93RyszOZMbLfUVcGaVQxkUD/CzqaynLY+OfaCqklu6Bzeqh3NOruUAwvo2eyo6yXu7MvSgKxNYOqbxjbV2dUsQE90snNzuKi0/tFvQCNKibSVPof0hFUHIb1S6JieM9A2V5IzYLTLgxXHg27ANK6JjvKGu7OnpIjx4wmVl0+Yn/MgDKxo4p9ZGx/8qsHltGoYiInTAmivSovC/WOVi2G934Ph/dDWo9QIXX05XDqhyE1I2nhuTu7Dx45pmZQY6OKzR4/4KhRxQb3ztSAMiIJogTRnhw+CO8/X1shtbwUMnqHm9ZGXwH550Hnlrve3t3ZdeBwzEAyRyeChkYVq04AudlZDO6VqQFlRJJACaKtK9sXegirFsMHL4YKqVl94Yy5ITHkToWUxH3MVVXOzgOH41wZdOyoYp07hQFlcrMzOSuvdzgpHF0hNKiXBpQRaW2UINqikqJwLmHV4nBuoaocug+EM/8hJIXBk09qhdTYUcWOujJodymb9jQ8qlh+TMmIgT0zNKCMSBuiBNFWHNgBa54Kl6Ru/EuokNozF6bcHM4pDJh4QhVSK6uc7cVlR5eMqB5Yps6AMrGjin1oeE7UCwhJYUBPDSgj0l4oQbRmxYVRhdRF4X4FHLKHw9R/CT2FU8Y1qxhe9ahisZeGVp8k3rKnjCOVx44qlpeTxfkj+9bUDNKoYiIdhxJEa7NnQ23do63Lw7R+Y2D6l0NS6DOywaRQParYMVcG7S5hy974o4oN69uVmaP71SQAjSomIqAE0Trseq+2Qurf3w3TBkyAGXeFw0fZpx7VvGZUsTpXBm3cHQaUiTuq2CnduGjMKTV3C+dlZ9GvuwaUEZH6KUEkgzvsWFlbDG/XmjB98GS48Jsw6jKOdBvMlr2lbNxZwsbVG446J7B1X/xRxU4f0INLx/WvSQB5OZn06aokICLHRwmipbjDtrdqk8Ke9bh1oqz/ZDZP+HfezJzKqpIsNq4qZeOfP2DbvhVxRxUbN6gHl48fUHNlUF5OFtkaUEZEEkAJIlHK9sGuNVT8fTUHN79F2voXyCjdSiUprEwbz3Odb+Y3JePYvb4HrAcoont6Mfk5WUwc0ourJg6qORyUn5NFr8xUJQERaVFKECeqbC/sWkvZthUc2LwC37mazOJ1dCvfDYQ3uIun8Zeq0fy+ahZLu0ymV/dwLuD6mDLS+TlZ9MzUqGIi0nooQTRV2V5852oObFnBwS0rsF1ryNq/ju4VRQBkAFWexjofyAeMZnfGUMp7n0aX/qPpM2gY+X268e8aVUxE2pCEJggzuxj4TyAFWOju364zPw34BXAmUATMcfeNiYypUaV7qNixmn2b3qW0cAWddq+l+8F1dK/YgwHdgRRP430fyAYbS3HXoZT3HkH6gNH0GzyMYf26M7t3pu4YFpE2L2EJwsxSgIeAC4BCYKmZLXb3VTHNbgL2uvswM7sGuA+Yk6iYjlK6h8PbVrF7w9sc3r6KlKK19Dy4nh6Ve+gM5AAlUSJ4K+UM9nc/lcqcEWQOHEP/ISERnNFNVwiJSPuVyB7EJGCdu68HMLPHgcuB2ARxOXB39PwJ4AdmZu7uJNA7T3ybcSu+RRowEDjo6azzgazqMoEDvU/F+4yk68AxDMgdxtC+3RmvcYZFpANKZIIYCGyJeV0ITK6vjbtXmFkxkA3sjm1kZvOB+QBDhgw54cAqB5/Dkzv+Ces7im5DxjJwyHBG9clivMYVEBGpkcgEEe/YS92eQVPa4O4LgAUABQUFJ9y7mDB5GhMmTzvR1YiItGuJPJNaCAyOeT0I2FZfGzPrDPQA9iQwJhERaaJEJoilwHAzyzezLsA1wOI6bRYDn4yeXw38MdHnH0REpGkSdogpOqdwC/A84TLXh919pZndAyxz98XA/wD/a2brCD2HaxIVj4iINE9C74Nw92eBZ+tMuzPm+SHgY4mMQUREjo/u5hIRkbiUIEREJC4lCBERiUsJQkRE4rK2dlWpme0CNh3n4jnUuUu7A9A+dwza547hRPY51937NGeBNpcgToSZLXP3gmTH0ZK0zx2D9rljaOl91iEmERGJSwlCRETi6mgJYkGyA0gC7XPHoH3uGFp0nzvUOQgREWm6jtaDEBGRJlKCEBGRuDpMgjCzi81srZmtM7M7kh1PY8xssJm9ZGarzWylmX0umt7bzP7PzN6P/u0VTTczezDav3fMbGLMuj4ZtX/fzD4ZM/1MM3s3WuZBiwbYrm8bLbjvKWb2lpk9Hb3ON7M3onh+FZWPx8zSotfrovl5Mev4cjR9rZldFDM97t9Bfdtoof3taWZPmNma6PM+u71/zmb2L9Hf9Qoze8zM0tvb52xmD5vZTjNbETMtaZ9rQ9uol7u3+weh3PgHwFCgC/A3YHSy42ok5v7AxOh5N+A9YDTwHeCOaPodwH3R848AzxFG6ZsCvBFN7w2sj/7tFT3vFc37K3B2tMxzwCXR9LjbaMF9vx34JfB09PrXwDXR8x8BN0fPPwP8KHp+DfCr6Pno6DNOA/Kjzz6lob+D+rbRQvv7c+Afo+ddgJ7t+XMmDDW8AciIee/ntbfPGZgGTARWxExL2uda3zYa3IeW+k+QzEf0Jj4f8/rLwJeTHVcz92ERcAGwFugfTesPrI2e/xiYG9N+bTR/LvDjmOk/jqb1B9bETK9pV982Wmg/BwEvAh8Gno7+mHcDnet+loSxRs6OnneO2lndz7e6XX1/Bw1towX2tzvhy9LqTG+3nzO1Y9H3jj63p4GL2uPnDORxdIJI2uda3zYair+jHGKq/oOsVhhNaxOiLvUE4A2gn7tvB4j+7Rs1q28fG5peGGc6DWyjJTwAfAmoil5nA/vcvSJOnDX7Fs0vjto3971oaBuJNhTYBfzUwmG1hWaWRTv+nN19K3A/sBnYTvjcltO+P+dqyfxcm/092FEShMWZ1iau7zWzrsD/A/7Z3fc31DTOND+O6UljZrOAne6+PHZynKbeyLy29F50JhyG+G93nwCUEA4L1Kct7Vtc0THxywmHhQYAWcAlcZq2p8+5MS2xL81epqMkiEJgcMzrQcC2JMXSZGaWSkgOj7r7b6PJO8ysfzS/P7Azml7fPjY0fVCc6Q1tI9HOBWab2UbgccJhpgeAnmZWPfphbJw1+xbN70EYura578XuBraRaIVAobu/Eb1+gpAw2vPnPBPY4O673L0c+C1wDu37c66WzM+12d+DHSVBLAWGR1cwdCGc6Fqc5JgaFF2R8D/Aanf/XsysxUD1lQyfJJybqJ5+Q3SlwhSgOOpePg9caGa9ol9uFxKOu24HDpjZlGhbN9RZV7xtJJS7f9ndB7l7HuEz+qO7Xwe8BFwdJ57YOK+O2ns0/Zro6pd8YDjhhF7cv4Nomfq2kVDu/ndgi5mNiCbNAFbRjj9nwqGlKWaWGcVUvc/t9nOOkczPtb5t1K8lTkq1hgfhDP57hKsb/i3Z8TQh3qmE7t87wNvR4yOE46gvAu9H//aO2hvwULR/7wIFMeu6EVgXPf4hZnoBsCJa5gfU3lkfdxstvP/Tqb2KaSjhP/464DdAWjQ9PXq9Lpo/NGb5f4v2ay3R1R0N/R3Ut40W2tfxwLLos36ScLVKu/6cga8Ba6K4/pdwJVK7+pyBxwjnWMoJv95vSubn2tA26nuo1IaIiMTVUQ4xiYhIMylBiIhIXEoQIiISlxKEiIjEpQQhIiJxKUFI0pmZm9l/xLz+gpndfZLW/TMzu7rxlie8nY9ZqMT6UjOWOZjImBrZ9leStW1pO5QgpDU4DFxlZjnJDiSWmaU0o/lNwGfc/fxExXOSKUFIo5QgpDWoIIy1+y91Z9TtAVT/6jaz6Wb2spn92szeM7Nvm9l1ZvbXqEb+qTGrmWlmf47azYqWTzGz75rZ0qg2/qdj1vuSmf2ScDNR3XjmRutfYWb3RdPuJNzY+CMz+26cZb4Ys52vxXsD4rUxszwLY0QsjLb3qJnNNLO/WKj1Pylql2Vh7IGlFgr+XR5Nn2dmvzWz30ftvxNN/zaQYWZvR+vMMrNnzOxv0XbmNPqJScfQUndO6qFHfQ/gIKHs9UZCnZ0vAHdH834GXB3bNvp3OrCPUM44DdgKfC2a9znggZjlf0/4MTSccEdrOjAf+GrUJo1wJ3N+tN4SID9OnAMIZSL6EIrs/RG4Ipq3hDh3phJKIywg3MXaiVDaelqdfYnbhlAqugIYG01fDjwctbsceDJa/l7g+uh5T8IdxFmEMRbWR+9pOrAJGBy77ej5R4GfxLzukey/CT1ax0M9CGkVPFSq/QVwWzMWW+ru2939MKF8wB+i6e8Svlyr/drdq9z9fcIX5kjCl/INZvY2oYx6NiGBAPzV3TfE2d5ZwBIPReYqgEcJX+QNuTB6vAW8GW17eDPabHD3d929ClgJvOjuXmcfLwTuiPZlCSEZDInmvejuxe5+iFDvKDdOjO8Seln3mdmH3L24kX2SDqJz401EWswDhC/In8ZMqyA6FBoVJYsdIvJwzPOqmNdVHP23XbeeTHW55Fvd/fnYGWY2ndCDiCdeueTGGPAtd/9xc9tYGAekKftowEfdfW2d5SfXWb6SOP/n3f09MzuTUL/oW2b2B3e/p5H9kg5APQhpNdx9D2FIyJtiJm8EzoyeXw6kHseqP2ZmnaLzEkMJhd2eB262UFIdMzvNwkA9DXkDOM/McqIT2HOBlxtZ5nngRgvjemBmA82s7sA8TWnT2DZujRIoZjahCcuUx+z7AKDU3R8hDOTT+FjF0iGoByGtzX8At8S8/gmwyMz+SqhMWd+v+4asJXyR9wP+yd0PmdlCwiGaN6Mv1l3AFQ2txN23m9mXCSWjDXjW3RssF+3ufzCzUcBr0ff3QeB6YsZeaKBNZRP37+uE3tc70b5sBGY1ssyCqP2bhEN73zWzKkLl0ZubuF1p51TNVURE4tIhJhERiUsJQkRE4lKCEBGRuJQgREQkLiUIERGJSwlCRETiUoIQEZG4/j8N7Vi/SOA2EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorts['Number of elements'].values, sorts['Radix Sort'].values )\n",
    "plt.plot(sorts['Number of elements'].values, sorts['Standard sort'].values)\n",
    "\n",
    "plt.legend(['Radix Sort','Standard sort'])\n",
    "plt.plot(0,1)\n",
    "plt.ylabel('time (sec)')\n",
    "#plt.yscale('linear')\n",
    "plt.xlabel('Number of elements')\n",
    "plt.title(\"Comparison of sorts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}