dataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a615ecdc",
   "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>query_id</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>n_token</th>\n",
       "      <th>time_index</th>\n",
       "      <th>algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1030303</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>DAAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1037496</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DAAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1043135</td>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>DAAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1045109</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>DAAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1049519</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>DAAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>985594</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>CONJUNCTIVE RETRIEVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>99005</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>197</td>\n",
       "      <td>CONJUNCTIVE RETRIEVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>997622</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>198</td>\n",
       "      <td>CONJUNCTIVE RETRIEVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>999466</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>199</td>\n",
       "      <td>CONJUNCTIVE RETRIEVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>132622</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>CONJUNCTIVE RETRIEVAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     query_id  elapsed_time  n_token  time_index              algorithm\n",
       "0     1030303            28        2           0                   DAAT\n",
       "1     1037496            38        2           1                   DAAT\n",
       "2     1043135            72        4           2                   DAAT\n",
       "3     1045109            61        3           3                   DAAT\n",
       "4     1049519            52        2           4                   DAAT\n",
       "..        ...           ...      ...         ...                    ...\n",
       "595    985594             1        1         196  CONJUNCTIVE RETRIEVAL\n",
       "596     99005            18        5         197  CONJUNCTIVE RETRIEVAL\n",
       "597    997622             5        2         198  CONJUNCTIVE RETRIEVAL\n",
       "598    999466             1        1         199  CONJUNCTIVE RETRIEVAL\n",
       "599    132622             8        3         200  CONJUNCTIVE RETRIEVAL\n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data from csv to pandas dataframe\n",
    "pathData = 'Search-Java/data/dataset/resultsDataset.csv'\n",
    "df = pd.read_csv(pathData, sep=',', header=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a215b1b",
   "metadata": {},
   "source": [
    "# **MEAN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fea048fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of DAAT:  68.25\n",
      "Mean of MAXSCORE:  29.27\n",
      "Mean of CONJUNCTIVE:  16.87\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean of the elapsed_time column with algorithm DAAT\n",
    "dfDAAT = df[df['algorithm'] == 'DAAT']\n",
    "meanDAAT = dfDAAT['elapsed_time'].mean()\n",
    "print(\"Mean of DAAT: \", meanDAAT.round(2))\n",
    "\n",
    "# Calculate the mean of the elapsed_time column with algorithm MAXSCORE\n",
    "dfMAXSCORE = df[df['algorithm'] == 'MAXSCORE']\n",
    "meanMAXSCORE = dfMAXSCORE['elapsed_time'].mean()\n",
    "print(\"Mean of MAXSCORE: \", meanMAXSCORE.round(2))\n",
    "\n",
    "# Calculate the mean of the elapsed_time column with algorithm CONJUNCTIVE\n",
    "dfCONJUNCTIVE = df[df['algorithm'] == 'CONJUNCTIVE RETRIEVAL']\n",
    "# Delete the rows with 0 in the elapsed_time column\n",
    "dfCONJUNCTIVE = dfCONJUNCTIVE[dfCONJUNCTIVE.elapsed_time != 0]\n",
    "meanCONJUNCTIVE = dfCONJUNCTIVE['elapsed_time'].mean()\n",
    "print(\"Mean of CONJUNCTIVE: \", meanCONJUNCTIVE.round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d96f093",
   "metadata": {},
   "source": [
    "# **VARIANCE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "24064df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance of DAAT:  2747.69\n",
      "Variance of MAXSCORE:  374.51\n",
      "Variance of CONJUNCTIVE:  193.41\n"
     ]
    }
   ],
   "source": [
    "# Calculate the variance of the elapsed_time column with algorithm DAAT\n",
    "varDAAT = dfDAAT['elapsed_time'].var()\n",
    "print(\"Variance of DAAT: \", varDAAT.round(2))\n",
    "\n",
    "# Calculate the variance of the elapsed_time column with algorithm MAXSCORE\n",
    "varMAXSCORE = dfMAXSCORE['elapsed_time'].var()\n",
    "print(\"Variance of MAXSCORE: \", varMAXSCORE.round(2))\n",
    "\n",
    "# Calculate the variance of the elapsed_time column with algorithm CONJUNCTIVE\n",
    "varCONJUNCTIVE = dfCONJUNCTIVE['elapsed_time'].var()\n",
    "print(\"Variance of CONJUNCTIVE: \", varCONJUNCTIVE.round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9b61f1",
   "metadata": {},
   "source": [
    "# **MAX**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6637e2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max of DAAT:  496 ms, with query  1051399\n",
      "Max of MAXSCORE:  101 ms,  with query  1121879\n",
      "Max of CONJUNCTIVE:  64 ms,  with query  1114166\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the max of the elapsed_time column with algorithm DAAT\n",
    "maxDAAT = dfDAAT['elapsed_time'].max()\n",
    "maxDAATQuery = dfDAAT[dfDAAT['elapsed_time'] == maxDAAT]\n",
    "print(\"Max of DAAT: \", maxDAAT, \"ms, with query \", maxDAATQuery.values[0][0])\n",
    "\n",
    "# Evaluate the max of the elapsed_time column with algorithm MAXSCORE\n",
    "maxMAXSCORE = dfMAXSCORE['elapsed_time'].max()\n",
    "maxMAXSCOREQuery = dfMAXSCORE[dfMAXSCORE['elapsed_time'] == maxMAXSCORE]\n",
    "print(\"Max of MAXSCORE: \", maxMAXSCORE, \"ms,  with query \", maxMAXSCOREQuery.values[0][0])\n",
    "\n",
    "# Evaluate the max of the elapsed_time column with algorithm CONJUNCTIVE\n",
    "maxCONJUNCTIVE = dfCONJUNCTIVE['elapsed_time'].max()\n",
    "maxCONJUNCTIVEQuery = dfCONJUNCTIVE[dfCONJUNCTIVE['elapsed_time'] == maxCONJUNCTIVE]\n",
    "print(\"Max of CONJUNCTIVE: \", maxCONJUNCTIVE, \"ms,  with query \", maxCONJUNCTIVEQuery.values[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be67da26",
   "metadata": {},
   "source": [
    "# **MIN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "808991e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min of DAAT:  3 ms, with query  1110678\n",
      "Min of MAXSCORE:  0 ms, with query  583468\n",
      "Min of CONJUNCTIVE:  1 ms, with query  1109850\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the min of the elapsed_time column with algorithm DAAT\n",
    "minDAAT = dfDAAT['elapsed_time'].min()\n",
    "minDAATQuery = dfDAAT[dfDAAT['elapsed_time'] == minDAAT]\n",
    "print(\"Min of DAAT: \", minDAAT, \"ms, with query \", minDAATQuery.values[0][0])\n",
    "\n",
    "# Evaluate the min of the elapsed_time column with algorithm MAXSCORE\n",
    "minMAXSCORE = dfMAXSCORE['elapsed_time'].min()\n",
    "minMAXSCOREQuery = dfMAXSCORE[dfMAXSCORE['elapsed_time'] == minMAXSCORE]\n",
    "print(\"Min of MAXSCORE: \", minMAXSCORE, \"ms, with query \", minMAXSCOREQuery.values[0][0])\n",
    "\n",
    "# Evaluate the min of the elapsed_time column with algorithm CONJUNCTIVE\n",
    "minCONJUNCTIVE = dfCONJUNCTIVE['elapsed_time'].min()\n",
    "minCONJUNCTIVEQuery = dfCONJUNCTIVE[dfCONJUNCTIVE['elapsed_time'] == minCONJUNCTIVE]\n",
    "print(\"Min of CONJUNCTIVE: \", minCONJUNCTIVE, \"ms, with query \", minCONJUNCTIVEQuery.values[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "abdebde4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot of the elapsed_time column against the n_token column with algorithm DAAT\n",
    "dfDAAT.boxplot(column='elapsed_time', by='n_token')\n",
    "plt.title('DAAT')\n",
    "plt.xlabel('n_token')\n",
    "plt.ylabel('elapsed_time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6be3b3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5EmvWrMHLL7+MhIQEeHt7Y8uWLXB0dCzTeyySlpaGPn364LnnnsPx48fx+eefY9CgQcXWVmrevDmCgoJ0J0C3aNHisV7HHEJCQnDgwAEsW7YMPj4+8Pf3R5s2bcr8Wfq3xYsXIzMzE6NHj4azszOGDBmCoUOH4quvvsIbb7yBgwcPIiwsDBqNBsnJyfjqq6/w448/6i3+SlTuLHchHlHFVdLSAfb29qJZs2Zi7dq1QqvV6vW/f/++mDhxovDx8RG2traiXr16YvHixbp+CQkJwsbGRm85ACGEKCwsFK1atRI+Pj7i7t27QoiHl86rVCqRmpqqW5vG09NTzJo1S2g0Gr3n419LBwghxOnTp0W3bt2Ek5OTcHR0FJ06ddKtnfOoDRs2iDp16ghra+syXRa/fft2ERgYKJRKpQgKChLffvut6NevnwgMDNT1KVo6YPHixSWOceDAAREWFiYcHByEi4uL6N27tzh37lyxfvv37xdBQUHCzs5ONGjQQHz++eelLh0wevRo8fnnn4t69eoJpVIpmjdvXuJ7uXHjhhg9erTw9fUVtra2wsvLS3Tu3FmsX79er9/ly5dFnz59hKOjo6hWrZoYP3682Ldv32MtHXDu3DnRv39/4ezsLNzc3MSYMWNEXl5eic9ZtGiRACDmzZsnOfaj/Pz8RM+ePYu1h4eHi/Dw8DKPI4QQycnJokOHDsLBwUEA0FtGoCyfpX+vsyTEw3WVXnrpJWFjYyNiYmKEEEI8ePBALFy4UDRu3FgolUrh5uYmQkJCxOzZs0VmZqbuuUXf05Lec0lLHBCZg0IInhFHVJEMGzYMO3fuLPMeG0tq1qwZqlevjtjYWIu8vkKhwOjRo4sdAq1IVq5ciYkTJ+LSpUslXgVGRE8ez1kiIpOp1Wrdie1FDh06hLNnz6Jjx46WKaoSEELgk08+QXh4OIMSkQXxnCUiMtm1a9cQERGBIUOGwMfHB8nJyVi3bh28vLzwxhtvWLq8CicnJwfffvstDh48iMTERHzzzTdmf42MjAzJ7Q4ODsWuwiR6WnHPEhGZzM3NDSEhIVi1ahVef/11LF++HG3atMHRo0dRtWpVXT8hBHx9faFQKNCrV69i49y7dw/29vZQKBTFbqALPFx7KiAgAIGBgbpFPx/VvXt3uLq64vr167q2rKwsDB8+HHXr1oW9vT28vLzQoUMHzJo1q8T3snv3bnTv3h3VqlWDnZ0dfHx8MGDAAPz888/F+l65cgVvvPEGateuDaVSCQ8PD0RFReHYsWPF+h46dEh3M1qFQgFra2t4eHigf//+xd7rrVu3MGjQIN3VYZGRkXrPtbe3L7H2x+Ht7S35NX78eJNfg6iy4J4logpm06ZN2LRpk6XL0OPq6oovv/wSmzZtwvDhw2Fvb4+GDRvq3ZICeLicwN9//w2lUlniODt27IBCoYCXlxe2bt2KuXPn6m23t7fH2rVr0bVrV8yfP18v8Gzfvh379u3D6tWr4ePjA+DhekatWrWCg4MDRowYgdq1ayM9PR2nT5/GwoUL9a6IE0JgxIgR2LRpE5o3b45JkybBy8tLtyxB586dcezYMTzzzDMAgGPHjumuUHz11VfRqFEjZGRkYNOmTWjfvj1Wrlypu43Jo8aNG4dWrVpBrVbj999/x7p163Do0CEkJSXBy8sLwMO1sqKjo7F9+3Z8/PHHxcYwx1pLhs4jK5pDIgKvhiMi8ym68qlv376iWrVqQq1W620fOXKkCAkJKfVqrQ4dOoi+ffuKiRMnCn9//1JfZ9CgQUKpVIoLFy4IIYS4e/eu8PLyEq1atdK7InDUqFHCxsZG76atRW7cuKH3ePHixQKAmDBhQrGrGYUQYvPmzeLEiRNCCCHu3LkjvLy8hKenp94NXYUQIjc3V7Rv315YWVnp3QD24MGDAoDYsWOHXv+1a9cKAGLhwoV67UVXPRKR5fEwHBGZ3UsvvYR//vlHb+/FgwcPsHPnTgwaNKjE5xTdB23gwIEYOHAg0tLS8Msvv5TYd/ny5XB0dNSdDzVt2jTcunULH330Eays/v/HWmpqKmrWrKm3SnqRR9eMysvLw/z58xEYGIglS5aUuIr00KFD0bp1awDARx99hIyMDCxevLjY3jMHBwd89tlnUCgUmDNnTmlTpFN0s9vU1FSDfYnIMhiWiMjsateujdDQUGzbtk3XtnfvXmRmZmLgwIElPmfbtm1QqVTo1asXWrdujbp162Lr1q0l9vXw8NAtJDl27FisX78e48aNQ/PmzfX6+fn54erVqyWeb/Soo0eP4s6dOxg0aFCZDnF99913sLe3x4ABA0rc7u/vj3bt2uHnn39GXl6e5FhFK2C7ubmVuP327dvFvrKysgzWSETmw7BERE/EoEGDEBMTowsLW7duRXh4eKnnwmzduhWRkZG6m+y++OKL+Oqrr4otSVBk5MiRCAsLw5o1a1CzZs0S9+KMGzcOdnZ26Ny5M5o3b44JEybgm2++QW5url6/ohOsg4ODy/Tezp07hwYNGpR67hUANG3aFGq1utj98e7fv4/bt28jPT0dP/74IyZMmACFQoF+/foVGyMnJwfVq1cv9lVaSCOiJ4NhiYieiAEDBiAvLw979uzB/fv3sWfPnlIPwf3+++9ITEzESy+9pGt76aWXcPv2bfz4448lPkehUMDd3R0AEBoaCicnp2J9GjdujDNnzmDIkCG4dOkSVq5ciaioKHh6eurdh6xoT82jNzqW8u+bIpekaPu/9wKNGDEC1atXh4+PD5577jlkZmZiy5YtaNWqVbEx7O3tERsbW+xrwYIFZaqTiMyDV8MR0RNRvXp1RERE4IsvvkBubi40Gg369+9fYt/PP/8cKpUKderU0e2Jsbe3R+3atbF161b07Nmz2HN27dqF7777TnfvtDFjxujO/3lU/fr1sWXLFmg0Gpw7dw579uzBokWL8Nprr8Hf3x8RERG6+/jdv3+/TO/N2dnZYN+i7f8OVTNnzkT79u2RnZ2N3bt3Y/v27XrnWT3K2toaERERZaqJiJ4chiUiemIGDRqEkSNHIiMjA927d0eVKlWK9RFCYNu2bcjJyUGjRo2Kbb958yays7P19hzdv38f48aNQ0hICA4ePIgmTZrgzTffxG+//QZbW9sSa7G2tkZwcDCCg4MRGhqKTp06YevWrYiIiEBgYCAAIDExEVFRUQbfV8OGDfHbb7+hoKCg1ENxv//+O2xtbVGvXj299uDgYF0AioqKQm5uLkaOHIl27drB19fX4GsTUfnjYTgiemKef/55WFlZIT4+vtRDcEVrL82ZMwc7duzQ+1q/fj1yc3MRExOj95x3330X6enp+Oijj+Ds7IzVq1fjjz/+wNKlS8tUV9Ed7NPT0wEA7dq1g5ubG7Zt2waNRmPw+b169UJ+fj527NhR4vZLly4hLi4Ozz77rO4crNIsWLAA+fn5+O9//1um2onIAiy9dgERVR4l3WF+06ZN4v333xe5ubm6tkfXWXrllVeESqUSeXl5JY5Zr1498dxzz+kenzp1SlhbW4tx48bp9Xv++eeFo6Oj3ppKR44cEQ8ePCg25pdffikA6I2xYMECAUBMnjy5xHWWtmzZoltn6fbt28LDw0N4eXmJ1NRUvX55eXmiY8eOZV5nSQghBgwYIJRKpUhPT9e1cZ0lIvngYTgieqKio6NL3VZQUICvv/4aXbp0KfUWHn369MHKlStx8+ZNVK1aFa+99hq8vLyKre69cuVKNGrUCGPHjsW3334LAFi4cCESEhLQt29fNGnSBABw+vRpbN68Ge7u7pgwYYLu+W+99ZZu79TBgwfRv39/eHl5ISMjAzExMfj111916z5VrVoVO3fuRM+ePdGiRYtiK3inpKRg5cqVutW+DXnrrbfw1VdfYcWKFXonbxcWFuLzzz8v8TnPP/88VCpVmcYnIhNZOq0RUeVR0p6lkhTtWfr6668FAPHJJ5+U2vfQoUMCgFi5cqVYvny5ACB27txZYt8lS5YIAGLXrl1CCCGOHTsmRo8eLYKCgoSrq6uwtbUVtWrVEsOGDSu2R6jIzp07RdeuXYW7u7uwsbER3t7e4sUXXxSHDh0q1jctLU2MHDlS1KpVS9ja2opq1aqJPn36iLi4uGJ9pfYsCSFEx44dhYuLi7h3754Q4uGeJQClfqWlpZU6Z0RkXgohhLBISiMiIiKqAHiCNxEREZEEhiUiIiIiCQxLRERERBIYloiIiIgkMCwRERERSWBYIiIiIpLARSkBaLVaXL9+Hc7OzlAoFJYuh4iIiMqBEAL379+Hj49PqTe0BhiWAADXr1/nDSyJiIieUlevXkXNmjVL3c6wBMDZ2RnAw8lycXGxcDWAWq3G/v370bVr11LvoE6cp7LgHBnGOTKMc2QY58gwOc5RVlYWfH19dTmgNAxLgO7Qm4uLi2zCkqOjI1xcXGTzgZIjzpNhnCPDOEeGcY4M4xwZJuc5MnQKDk/wJiIiIpLAsEREREQkgWGJiIiISALDEhEREZEEhiUiIiIiCQxLRERERBIYloiIiIgkMCwRERERSWBYIiIiIpLAsEREREQkgWGJiIiISALDEhEREZEE3kiXiIiITJKbm4vk5GTJPtl5BfglMRVu1U7ByUEp2TcwMBCOjo7mLNEkFg1LR44cweLFi5GQkID09HTs3r0bUVFRuu1CCMyaNQsbNmzAvXv3EBYWhrVr16JevXq6Pnfu3MHYsWPx3XffwcrKCv369cPKlSvh5ORkgXdERET09ElOTkZISEiZ+i4qQ5+EhAS0aNHCtKLMyKJhKScnB02bNsWIESPQt2/fYtsXLVqEVatW4bPPPoO/vz/ee+89dOvWDefOnYO9vT0AYPDgwUhPT0dsbCzUajWGDx+O1157DV988UV5vx0iIqKnUmBgIBISEiT7XEi/h0k7ErHshWA08K5icDw5sWhY6t69O7p3717iNiEEVqxYgXfffReRkZEAgM2bN8PT0xMxMTEYOHAgzp8/j3379uHkyZNo2bIlAGD16tXo0aMHlixZAh8fnxLHLigoQEFBge5xVlYWAECtVkOtVpvzLRqlqAY51CJnnCfDOEeGcY4M4xwZ9rTPka2tLYKDgyX7aF3vQOmVh3qBjRFcy93gmOUxl2V9Ddmes5SWloaMjAxERETo2lxdXdGmTRscP34cAwcOxPHjx1GlShVdUAKAiIgIWFlZ4cSJE3j++edLHHv+/PmYPXt2sfb9+/fL6hhpbGyspUuoEDhPhnGODOMcGcY5MoxzVLqr2QBgg/j4eFxLsnQ1D+Xm5papn2zDUkZGBgDA09NTr93T01O3LSMjAx4eHnrbbWxs4O7urutTkunTp2PSpEm6x1lZWfD19UXXrl3h4uJirrdgNLVajdjYWHTp0gW2traWLke2OE+GcY4M4xwZxjkyjHNk2Nkrd4DEU2jbti2almHPUnkoOrJkiGzD0pOkVCqhVBY/E9/W1lZWH3K51SNXnCfDOEeGcY4M4xyVTKPR4JdffsGRI0egUqnQqVMnWFtbW7os2bGxsdH9KZfPUVnrkO06S15eXgCAGzdu6LXfuHFDt83Lyws3b97U215YWIg7d+7o+hARET0pu3btQkBAALp06YJly5ahS5cuCAgIwK5duyxdGpmRbMOSv78/vLy88NNPP+nasrKycOLECYSGhgIAQkNDce/ePb0z8H/++WdotVq0adOm3GsmIqKnx65du9C/f38EBwcjLi4O27ZtQ1xcHIKDg9G/f38GpkrEoofhsrOzkZKSonuclpaGM2fOwN3dHbVq1cKECRMwd+5c1KtXT7d0gI+Pj24tpoYNG+K5557DyJEjsW7dOqjVaowZMwYDBw4s9Uo4IiIiU2k0GkyePBm9evVCTEwMNBoN/vnnH7Rp0wYxMTGIiorClClTEBkZyUNylYBFw9KpU6fQqVMn3eOik66jo6OxadMmTJ06FTk5OXjttddw7949tGvXDvv27dOtsQQAW7duxZgxY9C5c2fdopSrVq0q9/dCRFQRVfaVl5+UuLg4XLp0Cdu2bYOVlRU0Go1um5WVFaZPn45nnnkGcXFx6Nixo+UKJbOwaFjq2LEjhBClblcoFJgzZw7mzJlTah93d3cuQElEZKTKvvLyk5Keng4ACAoKKnF7UXtRP6rYnsqr4YiI6KHKvvLyk+Lt7Q0ASEpKQtu2bYttT0pK0utHFRvDEhHRU8zR0dHgniCry/9AGZeHhkFN0cyvajlVJm/t27dH7dq1MW/ePMTExOht02q1mD9/Pvz9/dG+fXvLFEhmJdur4YiIiOTK2toaS5cuxZ49exAVFYX4+Hjk5eUhPj4eUVFR2LNnD5YsWcKTuysJ7lkiIiIyQt++fbFz505MnjwZHTp00LX7+/tj586dJd4gniomhiUiIiIj9e3bF5GRkTh48CD27t2L7t27cwXvSohhiYiIyATW1tYIDw9HTk4OwsPDGZQqIZ6zRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJkHVY0mg0eO+99+Dv7w8HBwfUrVsX//nPfyCE0PURQmDmzJnw9vaGg4MDIiIicPHiRQtWTURERJWJrMPSwoULsXbtWqxZswbnz5/HwoULsWjRIqxevVrXZ9GiRVi1ahXWrVuHEydOQKVSoVu3bsjPz7dg5URERFRZ2Fi6ACm//PILIiMj0bNnTwBA7dq1sW3bNvz6668AHu5VWrFiBd59911ERkYCADZv3gxPT0/ExMRg4MCBFqudiIiIKgdZh6VnnnkG69evx59//on69evj7NmzOHr0KJYtWwYASEtLQ0ZGBiIiInTPcXV1RZs2bXD8+PFSw1JBQQEKCgp0j7OysgAAarUaarX6Cb6jsimqQQ61yBnnyTDOkWGcI8MKCwt1f3KeSsbPkWFy/ByVtQ5Zh6Vp06YhKysLgYGBsLa2hkajwX//+18MHjwYAJCRkQEA8PT01Huep6enbltJ5s+fj9mzZxdr379/PxwdHc34DkwTGxtr6RIqBM6TYZwjwzhHpbuaDQA2iI+Px7UkS1cjb/wclU6On6Pc3Nwy9ZN1WPrqq6+wdetWfPHFF2jcuDHOnDmDCRMmwMfHB9HR0UaPO336dEyaNEn3OCsrC76+vujatStcXFzMUbpJ1Go1YmNj0aVLF9ja2lq6HNniPBnGOTKMc2TY2St3gMRTaNu2LZrWcrd0ObJU2T9Hl/7JQU6BxqQxCjIygcTz8AgIhp+Xq8k1qZTWqF1VZdIYRUeWDJF1WHrrrbcwbdo03eG04OBgXL58GfPnz0d0dDS8vLwAADdu3IC3t7fueTdu3ECzZs1KHVepVEKpVBZrt7W1ldWHXG71yBXnyTDOkWGco9LZ2Njo/uQcSauMn6O02znosuKY2cabuvu82cY6OKUj/KsZH5jK+r2SdVjKzc2FlZX+BXvW1tbQarUAAH9/f3h5eeGnn37ShaOsrCycOHECb775ZnmXS0REVOnkFDw812jFi80Q4OFk/Dh5Bdhz6Dh6dQyFyqH4DovHkXIzGxO+PKOr7UmTdVjq3bs3/vvf/6JWrVpo3LgxfvvtNyxbtgwjRowAACgUCkyYMAFz585FvXr14O/vj/feew8+Pj6IioqybPFERESVSICHE4JqGH/4TK1WI6M60MLPrcLtfZN1WFq9ejXee+89jBo1Cjdv3oSPjw9ef/11zJw5U9dn6tSpyMnJwWuvvYZ79+6hXbt22LdvH+zt7S1YOREREVUWsg5Lzs7OWLFiBVasWFFqH4VCgTlz5mDOnDnlVxgRERE9NWS9gjcRERGRpTEsEREREUlgWCIiIiKSwLBEREREJIFhiYieWhqNBocPH8aRI0dw+PBhaDSmrVBMRJUTwxIRPZV27dqFgIAAdOnSBcuWLUOXLl0QEBCAXbt2Wbo0IpIZhiUieurs2rUL/fv3R3BwMOLi4rBt2zbExcUhODgY/fv3Z2AiIj0MS0SVFA8xlUyj0WDy5Mno1asXYmJi0KZNGzg4OKBNmzaIiYlBr169MGXKFM4XEekwLBFVQjzEVLq4uDhcunQJM2bMKHbvSSsrK0yfPh1paWmIi4uzUIVEJDcMS0SVDA8xSUtPTwcABAUFlbi9qL2oHxERwxJRJcJDTIZ5e3sDAJKSkkrcXtRe1I+IiGGJqBLhISbD2rdvj9q1a2PevHnQarV627RaLebPnw9/f3+0b9/eQhUSkdwwLBFVIjzEZJi1tTWWLl2KPXv2ICoqCvHx8cjLy0N8fDyioqKwZ88eLFmyBNbW1pYulYhkwsbSBRCR+Tx6iKlt27bFtvMQ00N9+/bFzp07MXnyZHTo0EHX7u/vj507d6Jv374WrI6I5IZ7logqER5iKru+ffsiJSUFsbGxmDRpEmJjY3Hx4kUGJSIqhmGJqBLhIabHY21tjfDwcHTo0AHh4eGcFyIqEQ/DEVUyPMRERGReDEtElVDfvn0RGRmJgwcPYu/evejevTs6derEPSf/8uDBA6xevRo///wzUlJSMHbsWNjZ2Vm6LCKSGR6GI6qkeIhJ2tSpU6FSqTBlyhT88MMPmDJlClQqFaZOnWrp0ohIZrhniYieOlOnTsXixYvh6emJ2bNnQ6lUoqCgALNmzcLixYsBAIsWLbJwlUQkF9yzRERPlQcPHmD58uXw9PTE33//jREjRsDNzQ0jRozA33//DU9PTyxfvhwPHjywdKlEJBMMS0T0VPnwww9RWFiIuXPnwsZGf+e6jY0N5syZg8LCQnz44YcWqpCI5IZhiYieKqmpqQCAXr16lbi9qL2oHxERwxIRPVXq1q0LANizZ0+J24vai/oRETEsEdFTZdSoUbCxscG7776LwsJCvW2FhYWYOXMmbGxsMGrUKAtVSERyw7BERE8VOzs7TJw4ETdu3EDNmjXx8ccf486dO/j4449Rs2ZN3LhxAxMnTuR6S0Skw6UDiOipU7QswPLly/X2INnY2OCtt97isgFEpId7lojoqbRo0SLk5ORgyZIl6NGjB5YsWYKcnBwGJSIqhnuWiOipZWdnh3HjxiEgIAA9evSAra2tpUsiIhniniUiIiIiCQxLRERERBJ4GI6IKq3c3FwkJydL9snOK8Avialwq3YKTg5Kyb6BgYFwdHQ0Z4lEVAEwLBFRpZWcnIyQkJAy9S3Lad0JCQlo0aKFaUURUYXDsERElVZgYCASEhIk+1xIv4dJOxKx7IVgNPCuYnA8Inr6MCwRUaXl6OhocE+Q1eV/oIzLQ8OgpmjmV7WcKiOiioQneBMRERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkQSGJSIiIiIJDEtEREREEhiWiIiIiCQwLBERERFJYFgiIiIikmByWMrPzzdHHURERESyZFRY0mq1+M9//oMaNWrAyckJf/31FwDgvffewyeffGLWAomIiIgsyaiwNHfuXGzatAmLFi2CnZ2drj0oKAgff/yx2YojIiIisjQbY560efNmrF+/Hp07d8Ybb7yha2/atCmSk5PNVhwREdGTlnY7BzkFhaVuz8vLRVrKn5JjaDQanElMRa5NHKytrSX7+gfUh4ODY6nbVUob+FdTSRdN5cqosHTt2jUEBAQUa9dqtVCr1SYXRUREVB7Sbueg05JDkn0KMlKQ8dkEs72mV/QKKL2K/w591MEpHRmYZMSosNSoUSPExcXBz89Pr33nzp1o3ry5WQojIiJ60or2KK14sRkCPJxK7JOX1wJpA5tJjqPRaHDmtzNo1ryZSXuWUm5mY8KXZyT3dFH5MyoszZw5E9HR0bh27Rq0Wi127dqFCxcuYPPmzdizZ4+5ayQiInqiAjycEFTDtZStrmgV4C35fLVaDcfC++jRtT1sbW3NXyBZlFEneEdGRuK7777DgQMHoFKpMHPmTJw/fx7fffcdunTpYu4aiYiIiCzGqD1LANC+fXvExsaasxYiIiIi2TF5Ucrs7GxkZWXpfZnTtWvXMGTIEFStWhUODg4IDg7GqVOndNuFEJg5cya8vb3h4OCAiIgIXLx40aw1EBER0dPLqLCUlpaGnj17QqVSwdXVFW5ubnBzc0OVKlXg5uZmtuLu3r2LsLAw2NraYu/evTh37hyWLl2q9xqLFi3CqlWrsG7dOpw4cQIqlQrdunXjyuJERERkFkYdhhsyZAiEEPj000/h6ekJhUJh7roAAAsXLoSvry82btyoa/P399f9XQiBFStW4N1330VkZCSAh2tAeXp6IiYmBgMHDnwidREREdHTw6iwdPbsWSQkJKBBgwbmrkfPt99+i27duuGFF17A4cOHUaNGDYwaNQojR44E8HAPV0ZGBiIiInTPcXV1RZs2bXD8+PFSw1JBQQEKCgp0j4sOHarValmsE1VUgxxqkbOnfZ5yc3Nx4cIFyT7ZeQX4JTEVzlXi4eSglOzboEEDODqWvlBeZVVYWKj782n9LBlSmefIXO/NXD+P5DjXcpujJ1GTIUaFpVatWuHq1atPPCz99ddfWLt2LSZNmoQZM2bg5MmTGDduHOzs7BAdHY2MjAwAgKenp97zPD09ddtKMn/+fMyePbtY+/79+2X1y4In0JfN0zpPqampmDx5cpn6LipDn6VLl6Ju3bqmFVUBXc0GABvEx8fjWpKlq5GnyjxHRe/t6NGjuFzyMkuPxdSfR+auxxzkNkeA+WrKzc0tUz+jwtLHH3+MN954A9euXUNQUFCxNSWaNGlizLDFaLVatGzZEvPmzQMANG/eHElJSVi3bh2io6ONHnf69OmYNGmS7nFWVhZ8fX3RtWtXuLi4mFy3qdRqNWJjY9GlSxeu1yHhaZ+n3NxctGvXTrLPn+mZeGv3OSx+vhHqe5e2hsxDT+uepbNX7gCJp9C2bVs0reVu6XJkqTLP0R/Xs7AkMR7t2rVDYx/jf/6b6+eRueoxJ7nNkTlrKutFaUaFpVu3biE1NRXDhw/XtSkUCgghoFAooNFojBm2GG9vbzRq1EivrWHDhvj6668BAF5eXgCAGzduwNv7/xcMu3HjBpo1a1bquEqlEkpl8UMStra2svqlK7d65OppnSdXV1e0bt1aso/d5X+gPP4AQc1aoJlf1XKqrGKxsbHR/VkZP0eG7ntWFpfvFuj+tLcv2//ESyO3+56Z+/tv6s8jOX4e5TZH5qyprM81KiyNGDECzZs3x7Zt257oCd5hYWHFzsn4888/dbdZ8ff3h5eXF3766SddOMrKysKJEyfw5ptvPpGaiIgqirLc9+xxTN6ZaJZxeN8zqmiMCkuXL1/Gt99+W+LNdM1p4sSJeOaZZzBv3jwMGDAAv/76K9avX4/169cDeLg3a8KECZg7dy7q1asHf39/vPfee/Dx8UFUVNQTrY2ISO7Kct+zMo2TV4A9h46jV8dQqAxcKCCF9z2jisqosPTss8/i7NmzTzwstWrVCrt378b06dMxZ84c+Pv7Y8WKFRg8eLCuz9SpU5GTk4PXXnsN9+7dQ7t27bBv3z7Y29s/0dqIiCoK6fueGaZWq5FRHWjh5yabQ0NUfgo0+bCyv4a0rAuwsjc+dBcWFuJ64XWcv3NedxjNWGlZ2bCyv4YCTT4A4z/bZWVUtb1798bEiRORmJiI4ODgYv94+vTpY5biAKBXr17o1atXqdsVCgXmzJmDOXPmmO01iYiI6KHrOZeh8l+NGb+aZ7wP931olnFU/sD1nGYIgafhziYyKiy98cYbAFBiQDHnCd5ERERkWT4qP+SkjcXKF5uhrgmHcwsLC3Hs6DGEtQszec9S6s1sjP/yDHw6+Zk0TlkZVa1WqzV3HURERCRDSmt7aPNrwN+lARpVNe1wbppNGhq6NzT5cK42PxPa/FtQWpfPKTcm30iXiIiIqDIr856lVatW4bXXXoO9vT1WrVol2XfcuHEmF0ZEREQkB2UOS8uXL8fgwYNhb2+P5cuXl9pPoVAwLBEREVGlUeawlJaWVuLfiYiIiCozo85ZmjNnTok3n8vLy+Ml/ERERFSpGBWWZs+ejezs7GLtubm5mD17tslFEREREcmFUUsHFN0w99/Onj0Ld/fKdUdqKn+5ublITk422C87rwC/JKbCrdopOBm4BUNgYCAcHR3NVSIRET1FHissubm5QaFQQKFQoH79+nqBSaPRIDs7W7dgJZGxkpOTERISUub+i8rQJyEhAS1atDC+KCIiemo9VlhasWIFhBAYMWIEZs+eDVfX/1+cys7ODrVr10ZoaKjZi6SnS2BgIBISEgz2u5B+D5N2JGLZC8Fo4F3F4JhERETGeKywFB0dDQDw9/dHWJjh5coXLFiAN954A1WqVDG6QHr6ODo6lmkvkNXlf6CMy0PDoKZo5le1HCojIqKnkVEneIeHh5fpvi7z5s3DnTt3jHkJIiIiIll4orc7EUI8yeGJiIiInjjeG46IiIhIAsMSERERkQSGJSIiIiIJDEtEREREEp5oWGrfvj0cHBye5EsQERERPVFlXmcpKyurzIO6uLgAAH744YfHr4iIiIhIRsoclqpUqVLi/eBKotFojC6IiIiISE7KHJYOHjyo+/ulS5cwbdo0DBs2THd7k+PHj+Ozzz7D/PnzzV8lERERWUSe+uEOkKRrmSaNk5NXgFO3AK/Ld6EycPNzQ1JuZpv0/MdV5rAUHh6u+/ucOXOwbNkyvPTSS7q2Pn36IDg4GOvXr9fdFoWIiIgqttT/BZNpuxLNMJoNtqScNMM4D6mUj3XXNqMZ9SrHjx/HunXrirW3bNkSr776qslFERERkTx0bewFAKjr4QQHW2ujx7mQnonJOxOxtH8wGni7mlyXSmkD/2oqk8cpC6PCkq+vLzZs2IBFixbptX/88cfw9fU1S2FERERkee4qOwxsXcvkcQoLCwEAdaurEFTD9LBUnowKS8uXL0e/fv2wd+9etGnTBgDw66+/4uLFi/j666/NWiARERGRJRm1zlKPHj3w559/onfv3rhz5w7u3LmD3r17488//0SPHj3MXSMRERGRxRh9ZpSvry/mzZtnzlqIiIiIZMfoFbzj4uIwZMgQPPPMM7h27RoAYMuWLTh69KjZiiMiIiKyNKPC0tdff41u3brBwcEBp0+fRkFBAQAgMzOTe5uIiIioUjEqLM2dOxfr1q3Dhg0bYGtrq2sPCwvD6dOnzVYcERERkaUZFZYuXLiADh06FGt3dXXFvXv3TK2JiIiISDaMCkteXl5ISUkp1n706FHUqVPH5KKIiIiI5MKosDRy5EiMHz8eJ06cgEKhwPXr17F161ZMmTIFb775prlrJCIiIrIYo5YOmDZtGrRaLTp37ozc3Fx06NABSqUSU6ZMwdixY81dIxER0RNRoMmHlf01pGVdgJW9k9HjFBYW4nrhdZy/cx42NsbfrywtKxtW9tdQoMkHULFWua7MjPqOKhQKvPPOO3jrrbeQkpKC7OxsNGrUCE5Oxn/QiIiIytv1nMtQ+a/GjF/NM96H+z40eQyVP3A9pxlC4GmGisgcTLpdr52dHRo1aoSsrCwcOHAADRo0QMOGDc1VGxER0RPlo/JDTtpYrHyxGep6mLZn6djRYwhrF2bSnqXUm9kY/+UZ+HTyM3oMMj+jvqMDBgxAhw4dMGbMGOTl5aFVq1ZIS0uDEALbt29Hv379zF0nERGR2Smt7aHNrwF/lwZoVNX4w15qtRppNmlo6N5Qb0mdx6XNz4Q2/xaU1vZGj0HmZ9QJ3keOHEH79u0BALt374ZWq8W9e/ewatUqzJ0716wFEhEREVmSUWEpMzMT7u7uAIB9+/ahX79+cHR0RM+ePXHx4kWzFkhERERkSUYdhvP19cXx48fh7u6Offv2Yfv27QCAu3fvwt6euw6JqHyk3c5BTkGhSWOk3srR/WnKuSYAoFLawL+ayqQxiEh+jPrJMGHCBAwePBhOTk7w8/NDx44dATw8PBccHGzO+oiISpR2Owedlhwy23iTdyaaZZyDUzoyMBFVMkaFpVGjRqF169a4evUqunTpAiurh0fz6tSpw3OWiKhcFO1RWvFiMwSYcBVTTl4B9hw6jl4dQ6FyUBo9TsrNbEz48ozJe7qISH6M3ufcsmVLtGzZEkIICCGgUCjQs2dPc9ZGRGRQgIcTgmqYdhVTRnWghZ+bSVcxEVHlZXRY+uSTT7B8+XLdCd316tXDhAkT8Oqrr5qtOCIiMh5Xp6bykpubi+TkZMk+F9LvoSAjBeeTHKD9p4pk38DAQDg6OpqxQtMY9amfOXMmli1bhrFjxyI0NBQAcPz4cUycOBFXrlzBnDlzzFokERE9Pq5OTeUlOTkZISEhZeo76DPDfRISEtCiRQsTqzIfo8LS2rVrsWHDBrz00ku6tj59+qBJkyYYO3YswxIRkQxwdWoqL4GBgUhISJDsk51XgO8PHkfPTqFwMnB+YGBgoDnLM5lRn3q1Wo2WLVsWaw8JCUFhIU9uJCKSA65OTeXF0dHR4J4gtVqNu7dvIrR1ywp3fqBRYWno0KFYu3Ytli1bpte+fv16DB482CyFERERPWl5ag0AIOlapknj5OQV4NQtwOvyXZOvqiT5MekE7/3796Nt27YAgBMnTuDKlSt4+eWXMWnSJF2/fwcqIiIiuUj9XziZtssc62zZYEvKSTOM83CBU5IPo74bSUlJut1tqampAIBq1aqhWrVqSEpK0vVTKBRmKJGIiOjJ6NrYCwBQ18MJDrbWJfbJy8tFWsqfkuNcvp2NZQdSMCkiAH7VpM8P8w+oDweH0q/04krw8mNUWDp48KC56yAiIip37io7DGxdS7LP6dOpeLF7xzKNN7WMV3oFBcjnSi8yjPv5iIiIJFT2K73IMKPD0qlTp/DVV1/hypUrePDggd62Xbt2mVwYERGRHFT2K73IMCtjnrR9+3Y888wzOH/+PHbv3g21Wo0//vgDP//8M1xdn9yqrAsWLIBCocCECRN0bfn5+Rg9ejSqVq0KJycn9OvXDzdu3HhiNRAREdHTxaiwNG/ePCxfvhzfffcd7OzssHLlSiQnJ2PAgAGoVUv62K+xTp48iY8++ghNmjTRa584cSK+++477NixA4cPH8b169fRt2/fJ1IDERERPX2MCkupqam6m+ba2dkhJycHCoUCEydOxPr1681aIABkZ2dj8ODB2LBhA9zc3HTtmZmZ+OSTT7Bs2TI8++yzCAkJwcaNG/HLL78gPj7e7HUQERHR08eoc5bc3Nxw//59AECNGjWQlJSE4OBg3Lt3D7m5uWYtEABGjx6Nnj17IiIiAnPnztW1JyQkQK1WIyIiQtcWGBiIWrVq4fjx47o1oP6toKAABQUFusdZWVkAHh5zVqvVZq//cRXVIIda5KxotfjCwkLOVSkq8xyZ672Z69+bHOeac1R++HPbMDnOUVlrMSosdejQAbGxsQgODsYLL7yA8ePH4+eff0ZsbCw6d+5szJCl2r59O06fPo2TJ4sv9JWRkQE7OztUqVJFr93T0xMZGRmljjl//nzMnj27WPv+/ftldZfj2NhYS5cga1ezAcAG8fHxuJZkqPfTqTLPUdF7O3r0KC4bf9szHVP/vZm7HnPgHJU//tw2TE5zVNYdPEaFpTVr1iA/Px8A8M4778DW1ha//PIL+vXrh3fffdeYIUt09epVjB8/HrGxsbC3N9+9hKZPn663ynhWVhZ8fX3RtWtXuLi4mO11jKVWqxEbG4suXbrwqgoJZ6/cARJPoW3btmhay93S5chSZZ6jP65nYUliPNq1a4fGPsb/uzXXvzdz1WNOnKPyw5/bhslxjoqOLBliVFhyd///H7pWVlaYNm2aMcMYlJCQgJs3b+pdsqnRaHDkyBGsWbMGP/74Ix48eIB79+7p7V26ceMGvLy8Sh1XqVRCqSy+Doatra1svoGA/OqRm6K7n9vY2HCeSlGZ58jc783Uf29ynGvOUfnjz23D5DRHZa2jzGGprOkLgNn2znTu3BmJifr36xk+fDgCAwPx9ttvw9fXF7a2tvjpp5/Qr18/AMCFCxdw5coVhIaGmqUGIiIierqVOSxVqVLF4L3ehBBQKBTQaDQmFwYAzs7OCAoK0mtTqVSoWrWqrv2VV17BpEmT4O7uDhcXF4wdOxahoaGlntxNRERE9DjKHJbkej+45cuXw8rKCv369UNBQQG6deuGDz/80NJlEZkk7XYOcgoKTR4n9VaO7s+iQyDG4s09iehpVeafnuHh4XqP4+Li8NFHHyE1NRU7d+5EjRo1sGXLFvj7+5u9yEcdOnRI77G9vT0++OADfPDBB0/0dYnKS9rtHHRacsisY07emWi4UxkcnNKRgYmInjpG/Vfz66+/xtChQzF48GD89ttvujWLMjMzMW/ePPzwww9mLZLoaVK0R2nFi80Q4GHa9dU5eQXYc+g4enUMhcrAzT2lpNzMxoQvz5hlbxcRUUVjVFiaO3cu1q1bh5dffhnbt2/XtYeFhektGklExgvwcEJQDdPutahWq5FRHWjh5yabq0+IiCoao253cuHCBXTo0KFYu6urK+7du2dqTURERESyYVRY8vLyQkpKSrH2o0ePok6dOiYXRURERCQXRoWlkSNHYvz48Thx4gQUCgWuX7+OrVu3YsqUKXjzzTfNXSMRERGRxRh1ztK0adOg1WrRuXNn5ObmokOHDlAqlZgyZQrGjh1r7hqJiIop0OTDyv4a0rIuwMre+BPhCwsLcb3wOs7fOW/S8gppWdmwsr+GAk0+ANPONSMieTHqJ4NCocA777yDt956CykpKcjOzkajRo3g5FRJ74xIRLJzPecyVP6rMeNX84z34T7T12dT+QPXc5ohBJ5mqIiI5MKkVers7OzQqFEjc9VCRFRmPio/5KSNxcoXm6GuCUssFBYW4tjRYwhrF2bSnqXUm9kY/+UZ+HTyM3oMIpIn05b0JSKyEKW1PbT5NeDv0gCNqhp/2EutViPNJg0N3RuatLyCNj8T2vxbUFrbGz0GEcmTUSd4ExERET0tGJaIiIiIJDAsEREREUlgWCIiIiKSwLBEREREJIFhiYiIiEgCwxIRERGRBK6zRERUSeWpNQCApGuZJo2Tk1eAU7cAr8t3oXJQGj1Oys1sk+ogshSGJSKiSir1f+Fk2q5EM4xmgy0pJ80wDqBS8lcPVSz8xBIRVVJdG3sBAOp6OMHB1trocS6kZ2LyzkQs7R+MBt6m3SRYpbSBfzWVSWMQlTeGJSKiSspdZYeBrWuZPE5hYSEAoG51FYJqmBaWiCoinuBNREREJIFhiYiIiEgCwxIRERGRBIYlIiIiIgkMS0REREQSGJaIiIiIJDAsEREREUngOktEMlOgyYeV/TWkZV2Alb2TSWMVFhbieuF1nL9zHjY2xv9zT8vKhpX9NRRo8gFwnR0ierowLBHJzPWcy1D5r8aMX8035of7PjR5DJU/cD2nGULgaYaKiIgqDoYlIpnxUfkhJ20sVr7YDHU9TN+zdOzoMYS1CzNpz1LqzWyM//IMfDr5mVSPOfEmsURUXhiWqNyl3c5BTkGhyeOk3srR/WlKEADkdb8qpbU9tPk14O/SAI2qmnbIS61WI80mDQ3dG8LW1tbocbT5mdDm34LS2t6kesyJN4klovLCf9VUrtJu56DTkkNmHXPyTnP8sgQOTukom8BEhvEmsURUXhiWqFwV7VFa8WIzBJh4iCknrwB7Dh1Hr46hJh8+mfDlGbPs7aLyw5vEElF5YVgiiwjwcDL5F5NarUZGdaCFn5tJh5iIiIikcJ0lIiIiIgkMS0REREQSGJaIiIiIJDAsEREREUlgWCIiIiKSwLBEREREJIFhiYiIiEgCwxIRERGRBIYlIiIiIgkMS0REREQSGJaIiIiIJDAsEREREUlgWCIiIiKSwLBEREREJIFhiYiIiEgCwxIRERGRBIYlIiIiIgkMS0REREQSGJaIiIiIJNhYugAi0pen1gAAkq5lmjxWTl4BTt0CvC7fhcpBafQ4KTezTa6FiKiikn1Ymj9/Pnbt2oXk5GQ4ODjgmWeewcKFC9GgQQNdn/z8fEyePBnbt29HQUEBunXrhg8//BCenp4WrJzIOKn/CybTdiWaaUQbbEk5aZaRVErZ/8ggIjI72f/kO3z4MEaPHo1WrVqhsLAQM2bMQNeuXXHu3DmoVCoAwMSJE/H9999jx44dcHV1xZgxY9C3b18cO3bMwtUTPb6ujb0AAHU9nOBga23SWBfSMzF5ZyKW9g9GA29Xk8ZSKW3gX01l0hhERBWR7MPSvn379B5v2rQJHh4eSEhIQIcOHZCZmYlPPvkEX3zxBZ599lkAwMaNG9GwYUPEx8ejbdu2xcYsKChAQUGB7nFWVhYAQK1WQ61WP8F3UzZFNcihFnMrLCzU/Wnq+zPXPJmzJnNwtlOgX3Nvs4yVn58PAPBzU6KBh6PJ48lhfsxNbt9/OeIcGVaZf26bixznqKy1yD4s/Vtm5sPzONzd3QEACQkJUKvViIiI0PUJDAxErVq1cPz48RLD0vz58zF79uxi7fv374ejo+m/UMwlNjbW0iWY3dVsALDB0aNHcdnJPGOaOk9Poia5KHpv8fHxuJZk6WrkiXNkGOeo7Crjz21zk9Mc5ebmlqlfhQpLWq0WEyZMQFhYGIKCggAAGRkZsLOzQ5UqVfT6enp6IiMjo8Rxpk+fjkmTJukeZ2VlwdfXF127doWLi8sTq7+s1Go1YmNj0aVLF9ja2lq6HLP643oWliTGo127dmjsY9pcm2uezFmT3Jy9cgdIPIW2bduiaS13S5cjS5wjwzhHhlXmn9vmIsc5KjqyZEiFCkujR49GUlISjh49atI4SqUSSmXxK4NsbW1l8w0E5FePOdjY2Oj+NNd7M3WenkRNclGZ35u5cI4M4xyVXWX8uW1ucpqjstZRYcLSmDFjsGfPHhw5cgQ1a9bUtXt5eeHBgwe4d++e3t6lGzduwMvLywKVEhFVHLm5uUhOTpbscyH9HgoyUnA+yQHaf6pI9g0MDJTV6QxE5iD7sCSEwNixY7F7924cOnQI/v7+ettDQkJga2uLn376Cf369QMAXLhwAVeuXEFoaKglSiYiqjCSk5MREhJSpr6DPjPcJyEhAS1atDCxKiJ5kX1YGj16NL744gt88803cHZ21p2H5OrqCgcHB7i6uuKVV17BpEmT4O7uDhcXF4wdOxahoaElntxNRET/LzAwEAkJCZJ9svMK8P3B4+jZKRROBhY3DQwMNGd5RLIg+7C0du1aAEDHjh312jdu3Ihhw4YBAJYvXw4rKyv069dPb1FKIiKS5ujoaHBPkFqtxt3bNxHauqVszjUhKk+yD0tCCIN97O3t8cEHH+CDDz4oh4qIiIjoacIb6RIRERFJYFgiIiIiksCwRERERCSBYYmIiIhIguxP8KbKpUCTDyv7a0jLugAre9NuxFZYWIjrhddx/s553QrDxkjLyoaV/TUUaPIBuJpUExERVT4MS1Surudchsp/NWb8ar4xP9xn+jIRKn/gek4zhMDTDBUREVFlwrBE5cpH5YectLFY+WIz1PUwfc/SsaPHENYuzKQ9S6k3szH+yzPw6eRnUj1ERFQ5MSxRuVJa20ObXwP+Lg3QqKpph7zUajXSbNLQ0L2hSQvlafMzoc2/BaW1vUn1EBFR5cQTvImIiIgkMCwRERERSWBYIiIiIpLAsEREREQkgWGJiIiISALDEhEREZEELh1ARJVWbm4ukpOTJftcSL+HgowUnE9ygPafKpJ9AwMD4ejoaMYKiagiYFgiokorOTkZISEhZeo76DPDfRISEtCiRQsTqyKiioZhiYgqrcDAQCQkJEj2yc4rwPcHj6Nnp1A4OSgNjkdETx+GJSKqtBwdHQ3uCVKr1bh7+yZCW7c0aSV4Iqq8eII3ERERkQSGJSIiIiIJDEsyo9FocPjwYRw5cgSHDx+GRqOxdElERERPNYYlGdm1axcCAgLQpUsXLFu2DF26dEFAQAB27dpl6dKIiIieWgxLMrFr1y70798fwcHBiIuLw7Zt2xAXF4fg4GD079+fgYmIiMhCGJZkQKPRYPLkyejVqxdiYmLQpk0bODg4oE2bNoiJiUGvXr0wZcoUHpIjIiKyAIYlGYiLi8OlS5cwY8YMWFnpf0usrKwwffp0pKWlIS4uzkIVEhERPb0YlmQgPT0dABAUFFTi9qL2on5ERERUfhiWZMDb2xsAkJSUVOLVcElJSXr9iIiIqPxwBW8ZaN++PWrXro2xY8fi1q1buHz5MgBg2bJl8PPzQ/Xq1eHv74/27dtbuFIiIqKnD/csyYC1tTVeeOEFnDp1Cvn5+Vi7di0+/fRTrF27Fvn5+Th16hT69+8Pa2trS5dKRET01OGeJRnQaDTYsWMHWrZsidu3b+PNN9/UbfP390fLli2xc+dOzJ8/n4GJiIionHHPkgwUXQ23evVqpKSkIDY2FpMmTUJsbCwuXryIVatW8Wo4IiIiC+GeJRl49Go4a2trhIeHIycnB+Hh4bC2tq5UV8PlqR+uFZV0LdPksXLyCnDqFuB1+S5UDkqjx0m5mW1yLUREVHkxLMnAo1fDtW3bttj2ynQ1XOr/gsm0XYlmGtEGW1JOmmUklZL/HIiIqDj+dpCBoqvh5s2bh5iYGL1tWq0W8+fPrzRXw3Vt7AUAqOvhBAdb086/upCeick7E7G0fzAaeLuaNJZKaQP/aiqTxiAiosqJYUkGrK2tsXTpUvTv3x9RUVF46623kJeXh/j4eCxevBh79uzBzp07K8XJ3e4qOwxsXcssYxUWFgIA6lZXIaiGaWGJiIioNAxLMtG3b1/s3LkTkydPRocOHXTt/v7+2LlzJ/r27WvB6oiIiJ5eDEsy0rdvX0RGRuLgwYPYu3cvunfvjk6dOlWKPUpEREQVFcOSzJR0NRwRERFZDtdZIiIiIpLAsEREREQkgYfhiCqo3NxcJCcnS/a5kH4PBRkpOJ/kAO0/VST7BgYGwtHR0YwVEhFVDgxLRBVUcnIyQkJCytR30GeG+yQkJKBFixYmVkVEVPkwLBFVUIGBgUhISJDsk51XgO8PHkfPTqFwMnBLmMDAQHOWR0RUaTAsEVVQjo6OBvcEqdVq3L19E6GtW8LW1racKiMiqlx4gjcRERGRBIYlIiIiIgkMS0REREQSGJaIiIiIJDAsEREREUlgWCIiIiKSwLBEREREJIFhiYiIiEgCw5LM5OXlYdy4cXj//fcxbtw45OXlWbokIiKipxpX8DajvMI8HLt8DnkPNKX2KSjIx7WrV0rctnnDBzj3+xnd4+S9ydi0dxMaNWmGl0eOLnXMGr61oFTal7rdwc4aYX6N4GDjYPhNyEBZbhAL8CaxRERUPipNWPrggw+wePFiZGRkoGnTpli9ejVat25drjX8lJKE6SdGGD9APyCgX0Cx5gfIxsd3Fpb+vDuGh16OTYioW7abrlra49wgFuBNYomI6MmqFGHpyy+/xKRJk7Bu3Tq0adMGK1asQLdu3XDhwgV4eHiUWx1377kiJ22sZJ8HN/7CP3tXmvV1q3YfDzvPOpJ9aj5X26yv+SSV5QaxAG8SS0RE5aNShKVly5Zh5MiRGD58OABg3bp1+P777/Hpp59i2rRpxfoXFBSgoKBA9zgrKwvAw5uOqtVqo+voGlgDVqIL6lRXwcHWusQ+eXm5uNSrq17bpx+uxIEfvkWfFwZiYPRIaAo1SExMRHBwMKxtrPHFxvXY8/WXiOjRByNGjS82Zu269eDgUPohJpXSGrXdVSa9t/Jka2uL4OBgg/2KbhLbsnnTMt0ktqK8f3Mqes9P43svK86RYZwjwzhHhslxjspaS4UPSw8ePEBCQgKmT5+ua7OyskJERASOHz9e4nPmz5+P2bNnF2vfv3+/yee1OAG4eVO6z79f4fb1h+cwdX82HI6F9wEAzwTXBZALFALdn+2APV9/idvXr+i2P+rmhdMG6zpXhtorqtjYWEuXIHucI8M4R4ZxjgzjHBkmpznKzc0tU78KH5Zu374NjUYDT09PvXZPT89STxKePn06Jk2apHuclZUFX19fdO3aFS4uLk+03pLs27cPZ86cQVpaGl555RWo1WrExsaiS5cusLW1xYwZMwAAbdu2RY8ePcq9Prn69zxRcZwjwzhHhnGODOMcGSbHOSo6smRIhQ9LxlAqlVAqi5/jYmtra5Fv4LJly7Bu3TqsWLEC//nPf3Q12NraQgiBVatW6frJ5QMmJ5b6vlUknCPDOEeGcY4M4xwZJqc5KmsdFX6dpWrVqsHa2ho3btzQa79x4wa8vLwsVNXjcXBwQGRkJB48eABnZ2fMmDED165dw4wZM+Ds7IwHDx4gMjISDg4V49J/IiKiyqTC71mys7NDSEgIfvrpJ0RFRQEAtFotfvrpJ4wZM8ayxT2GmJgYREVF4ZtvvsGSJUv0tkVGRiImJsYyhRERET3lKnxYAoBJkyYhOjoaLVu2ROvWrbFixQrk5OToro6rKGJiYpCXl4dJkyYhPj4ebdu2xbJly7hHiYiIyIIqRVh68cUXcevWLcycORMZGRlo1qwZ9u3bV+yk74rAwcEBq1atwg8//IAePXrI5rguERHR06pShCUAGDNmTIU67EZEREQVQ4U/wZuIiIjoSWJYIiIiIpLAsEREREQkgWGJiIiISALDEhEREZEEhiUiIiIiCQxLRERERBIYloiIiIgkVJpFKU0hhAAAZGVlWbiSh9RqNXJzc5GVlcUVvCVwngzjHBnGOTKMc2QY58gwOc5R0e/9ohxQGoYlAPfv3wcA+Pr6WrgSIiIiKm/379+Hq6trqdsVwlCcegpotVpcv34dzs7OUCgUli4HWVlZ8PX1xdWrV+Hi4mLpcmSL82QY58gwzpFhnCPDOEeGyXGOhBC4f/8+fHx8YGVV+plJ3LMEwMrKCjVr1rR0GcW4uLjI5gMlZ5wnwzhHhnGODOMcGcY5MkxucyS1R6kIT/AmIiIiksCwRERERCSBYUmGlEolZs2aBaVSaelSZI3zZBjnyDDOkWGcI8M4R4ZV5DniCd5EREREErhniYiIiEgCwxIRERGRBIYlIiIiIgkMS0REREQSGJZk5MiRI+jduzd8fHygUCgQExNj6ZJkZ/78+WjVqhWcnZ3h4eGBqKgoXLhwwdJlycratWvRpEkT3cJvoaGh2Lt3r6XLkrUFCxZAoVBgwoQJli5FVt5//30oFAq9r8DAQEuXJTvXrl3DkCFDULVqVTg4OCA4OBinTp2ydFmyUbt27WKfI4VCgdGjR1u6tDJjWJKRnJwcNG3aFB988IGlS5Gtw4cPY/To0YiPj0dsbCzUajW6du2KnJwcS5cmGzVr1sSCBQuQkJCAU6dO4dlnn0VkZCT++OMPS5cmSydPnsRHH32EJk2aWLoUWWrcuDHS09N1X0ePHrV0SbJy9+5dhIWFwdbWFnv37sW5c+ewdOlSuLm5Wbo02Th58qTeZyg2NhYA8MILL1i4srLj7U5kpHv37ujevbuly5C1ffv26T3etGkTPDw8kJCQgA4dOlioKnnp3bu33uP//ve/WLt2LeLj49G4cWMLVSVP2dnZGDx4MDZs2IC5c+dauhxZsrGxgZeXl6XLkK2FCxfC19cXGzdu1LX5+/tbsCL5qV69ut7jBQsWoG7duggPD7dQRY+Pe5aoQsvMzAQAuLu7W7gSedJoNNi+fTtycnIQGhpq6XJkZ/To0ejZsyciIiIsXYpsXbx4ET4+PqhTpw4GDx6MK1euWLokWfn222/RsmVLvPDCC/Dw8EDz5s2xYcMGS5clWw8ePMDnn3+OESNGyOLG9WXFPUtUYWm1WkyYMAFhYWEICgqydDmykpiYiNDQUOTn58PJyQm7d+9Go0aNLF2WrGzfvh2nT5/GyZMnLV2KbLVp0wabNm1CgwYNkJ6ejtmzZ6N9+/ZISkqCs7OzpcuThb/++gtr167FpEmTMGPGDJw8eRLjxo2DnZ0doqOjLV2e7MTExODevXsYNmyYpUt5LAxLVGGNHj0aSUlJPIeiBA0aNMCZM2eQmZmJnTt3Ijo6GocPH2Zg+p+rV69i/PjxiI2Nhb29vaXLka1HTwto0qQJ2rRpAz8/P3z11Vd45ZVXLFiZfGi1WrRs2RLz5s0DADRv3hxJSUlYt24dw1IJPvnkE3Tv3h0+Pj6WLuWx8DAcVUhjxozBnj17cPDgQdSsWdPS5ciOnZ0dAgICEBISgvnz56Np06ZYuXKlpcuSjYSEBNy8eRMtWrSAjY0NbGxscPjwYaxatQo2NjbQaDSWLlGWqlSpgvr16yMlJcXSpciGt7d3sf+ENGzYkIcrS3D58mUcOHAAr776qqVLeWzcs0QVihACY8eOxe7du3Ho0CGeSFlGWq0WBQUFli5DNjp37ozExES9tuHDhyMwMBBvv/02rK2tLVSZvGVnZyM1NRVDhw61dCmyERYWVmz5kj///BN+fn4Wqki+Nm7cCA8PD/Ts2dPSpTw2hiUZyc7O1vsfW1paGs6cOQN3d3fUqlXLgpXJx+jRo/HFF1/gm2++gbOzMzIyMgAArq6ucHBwsHB18jB9+nR0794dtWrVwv379/HFF1/g0KFD+PHHHy1dmmw4OzsXO89NpVKhatWqPP/tEVOmTEHv3r3h5+eH69evY9asWbC2tsZLL71k6dJkY+LEiXjmmWcwb948DBgwAL/++ivWr1+P9evXW7o0WdFqtdi4cSOio6NhY1MBo4cg2Th48KAAUOwrOjra0qXJRknzA0Bs3LjR0qXJxogRI4Sfn5+ws7MT1atXF507dxb79++3dFmyFx4eLsaPH2/pMmTlxRdfFN7e3sLOzk7UqFFDvPjiiyIlJcXSZcnOd999J4KCgoRSqRSBgYFi/fr1li5Jdn788UcBQFy4cMHSpRhFIYQQlolpRERERPLHE7yJiIiIJDAsEREREUlgWCIiIiKSwLBEREREJIFhiYiIiEgCwxIRERGRBIYlIiIiIgkMS0REREQSGJaIiB6hUCgQExNj6TKISEYYloioUmLoISJzYVgiIiIiksCwRESy1bFjR4wbNw5Tp06Fu7s7vLy88P777xt8Xu3atQEAzz//PBQKhe4xAKxduxZ169aFnZ0dGjRogC1btkiONWvWLHh7e+P3338HABw9ehTt27eHg4MDfH19MW7cOOTk5Oi99rx58zBixAg4OzujVq1avAM9UQXHsEREsvbZZ59BpVLhxIkTWLRoEebMmYPY2FjJ55w8eRIAsHHjRqSnp+se7969G+PHj8fkyZORlJSE119/HcOHD8fBgweLjSGEwNixY7F582bExcWhSZMmSE1NxXPPPYd+/frh999/x5dffomjR49izJgxes9dunQpWrZsid9++w2jRo3Cm2++iQsXLphpRoiovCmEEMLSRRARlaRjx47QaDSIi4vTtbVu3RrPPvssFixYIPlchUKB3bt3IyoqStcWFhaGxo0b6+3pGTBgAHJycvD999/rnrdjxw7s3r0bv/32G2JjY1GjRg0AwKuvvgpra2t89NFHuucfPXoU4eHhyMnJgb29PWrXro327dvr9lgJIeDl5YXZs2fjjTfeMHlOiKj8cc8SEclakyZN9B57e3vj5s2bRo11/vx5hIWF6bWFhYXh/Pnzem0TJ07EiRMncOTIEV1QAoCzZ89i06ZNcHJy0n1169YNWq0WaWlpJdasUCjg5eVldM1EZHkMS0Qka7a2tnqPFQoFtFrtE33NLl264Nq1a/jxxx/12rOzs/H666/jzJkzuq+zZ8/i4sWLqFu3rkVrJqInx8bSBRARPQm2trbQaDR6bQ0bNsSxY8cQHR2tazt27BgaNWqk169Pnz7o3bs3Bg0aBGtrawwcOBAA0KJFC5w7dw4BAQFP/g0QkWwwLBFRpVS7dm389NNPCAsLg1KphJubG9566y0MGDAAzZs3R0REBL777jvs2rULBw4cKPb8559/Hlu2bMHQoUNhY2OD/v374+2330bbtm0xZswYvPrqq1CpVDh37hxiY2OxZs0aC7xLIioPDEtEVCktXboUkyZNwoYNG1CjRg1cunQJUVFRWLlyJZYsWYLx48fD398fGzduRMeOHUsco3///tBqtRg6dCisrKzQt29fHD58GO+88w7at28PIQTq1q2LF198sXzfHBGVK14NR0RERCSBJ3gTERERSWBYIqIKZ+vWrXqX7z/61bhxY0uXR0SVDA/DEVGFc//+fdy4caPEbba2tvDz8yvnioioMmNYIiIiIpLAw3BEREREEhiWiIiIiCQwLBERERFJYFgiIiIiksCwRERERCSBYYmIiIhIAsMSERERkYT/A1/z6PuibGhUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot of the elapsed_time column against the n_token column with algorithm MAXSCORE\n",
    "dfMAXSCORE.boxplot(column='elapsed_time', by='n_token')\n",
    "plt.title('MAXSCORE')\n",
    "plt.xlabel('n_token')\n",
    "plt.ylabel('elapsed_time')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5ddc8287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot of the elapsed_time column against the n_token column with algorithm CONJUNCTIVE\n",
    "dfCONJUNCTIVE.boxplot(column='elapsed_time', by='n_token')\n",
    "plt.title('CONJUNCTIVE')\n",
    "plt.xlabel('n_token')\n",
    "plt.ylabel('elapsed_time')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}