notebooks/famousFaces_000_checkingForFamiliarity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking which celebrities are known  \n",
    "\n",
    "And using this information for analysis of other experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import fnmatch\n",
    "import qgrid\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get logfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getLogfile(whichfolder, whichexperiment):\n",
    "\n",
    "    loglist = []\n",
    "    for fileName in os.listdir(whichfolder):\n",
    "        if fnmatch.fnmatch(fileName, whichexperiment):\n",
    "            loglist.append(whichfolder+fileName)\n",
    "    loglist.sort()\n",
    "    return loglist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logList = getLogfile('../famousCheck/experiment/data/','*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../famousCheck/experiment/data/6_textInputTest_2017_Jan_27_1111.csv',\n",
       " '../famousCheck/experiment/data/7_textInputTest_2017_Jan_30_1524.csv',\n",
       " '../famousCheck/experiment/data/8_textInputTest_2017_Feb_01_1735.csv',\n",
       " '../famousCheck/experiment/data/9_textInputTest_2017_Feb_02_1329.csv',\n",
       " '../famousCheck/experiment/data/jb_textInputTest_2016_Nov_23_1453.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logList[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load a logfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../famousCheck/experiment/data/14_textInputTest_2017_Feb_08_1348.csv'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logList[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(logList[4],index_col=[0],header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### extract relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getCols(df):\n",
    "    thisDf = pd.concat([df['name'], df['ratingBekannt.response'] ],axis=1)\n",
    "    thisDf.index = df.index\n",
    "    thisDf.columns = ['name','bekannt']\n",
    "    thisDf = thisDf.dropna()\n",
    "    return thisDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thisDf = getCols(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>bekannt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>img</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>./img/BarackObama2.jpg</th>\n",
       "      <td>Barack Obama</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JudithRakers7.jpg</th>\n",
       "      <td>Judith Rakers</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/OmarEpps(DrEricForeman)1.jpg</th>\n",
       "      <td>Omar Epps</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JimParsons(SheldonCooper)1.jpg</th>\n",
       "      <td>Jim Parsons</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/BritneySpears5.jpg</th>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/HughLaurie(DrHouse)9.jpg</th>\n",
       "      <td>Hugh Laurie</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/EmilyDeschanel3.jpg</th>\n",
       "      <td>Emily Deschanel</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/CarenMiosga4.jpg</th>\n",
       "      <td>Caren Miosga</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JoachimLoew1.jpg</th>\n",
       "      <td>Joachim Löw</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/AlysonHannigan(Lily)1.jpg</th>\n",
       "      <td>Alyson Hannigan</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/KaleyCuoco(Penny)2.jpg</th>\n",
       "      <td>Kaley Cuoco</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/SahraWagenknecht6.jpg</th>\n",
       "      <td>Sahra Wagenknecht</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/GeorgeClooney6.jpg</th>\n",
       "      <td>George Clooney</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JasonSegel(Marshall)2.jpg</th>\n",
       "      <td>Jason Segel</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JoshRadnor(Ted)1.jpg</th>\n",
       "      <td>Josh Radnor</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/Shakira1.jpg</th>\n",
       "      <td>Shakira</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/NeilPatrickHarris(Barney)4.jpg</th>\n",
       "      <td>Neil Patrick Harris</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/BastianSchweinsteiger1.jpg</th>\n",
       "      <td>Bastian Schweinsteiger</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/LisaEdelstein(DrLisaCuddy)2.jpg</th>\n",
       "      <td>Lisa Edelstein</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/GuentherJauch2.jpg</th>\n",
       "      <td>Günther Jauch</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/RobertSeanLeonard(Wilson)2.jpg</th>\n",
       "      <td>Robert Sean Leonard</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/SigmarGabriel1.jpg</th>\n",
       "      <td>Sigmar Gabriel</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/Usher1.jpg</th>\n",
       "      <td>Usher</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/DannFlorek(DonaldDonCragen)1.jpg</th>\n",
       "      <td>Dann Florek</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/NicoleKidman4.jpg</th>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/TaylorSwift1.jpg</th>\n",
       "      <td>Taylor Swift</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/Ice-T(OdafinFinTutuola)1.jpg</th>\n",
       "      <td>Ice-T</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/ElvisPresley1.jpg</th>\n",
       "      <td>Elvis Presley</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/MariskaHargitay(Liv)1.jpg</th>\n",
       "      <td>Mariska Hargitay</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/CobieSmulders(Robin)2.jpg</th>\n",
       "      <td>Cobie Smulders</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/MilaKunis5.jpg</th>\n",
       "      <td>Mila Kunis</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/KatjaBurkard5.jpg</th>\n",
       "      <td>Katja Burkard</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/UrsulavonderLeyen1.jpg</th>\n",
       "      <td>Ursula von der Leyen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/BenedictCumberbatch4.jpg</th>\n",
       "      <td>Benedict Cumberbatch</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/AngelaMerkel1.jpg</th>\n",
       "      <td>Angela Merkel</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/Beyonce2.jpg</th>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/TomCruise2.jpg</th>\n",
       "      <td>Tom Cruise</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/LeonardoDiCaprio1.jpg</th>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/CristinMilioti(Tracy_Mutter)1.jpg</th>\n",
       "      <td>Cristin Milioti</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/WillSmith4.jpg</th>\n",
       "      <td>Will Smith</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/JanHofer1.jpg</th>\n",
       "      <td>Jan Hofer</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./img/EmmaWatson(HermioneGranger)1.jpg</th>\n",
       "      <td>Emma Watson</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           name bekannt\n",
       "img                                                                    \n",
       "./img/BarackObama2.jpg                             Barack Obama      ja\n",
       "./img/JudithRakers7.jpg                           Judith Rakers    nein\n",
       "./img/OmarEpps(DrEricForeman)1.jpg                    Omar Epps      ja\n",
       "./img/JimParsons(SheldonCooper)1.jpg                Jim Parsons      ja\n",
       "./img/BritneySpears5.jpg                         Britney Spears      ja\n",
       "./img/HughLaurie(DrHouse)9.jpg                      Hugh Laurie      ja\n",
       "./img/EmilyDeschanel3.jpg                       Emily Deschanel    nein\n",
       "./img/CarenMiosga4.jpg                             Caren Miosga    nein\n",
       "./img/JoachimLoew1.jpg                              Joachim Löw    nein\n",
       "./img/AlysonHannigan(Lily)1.jpg                 Alyson Hannigan      ja\n",
       "./img/KaleyCuoco(Penny)2.jpg                        Kaley Cuoco      ja\n",
       "./img/SahraWagenknecht6.jpg                   Sahra Wagenknecht    nein\n",
       "./img/GeorgeClooney6.jpg                         George Clooney      ja\n",
       "./img/JasonSegel(Marshall)2.jpg                     Jason Segel      ja\n",
       "./img/JoshRadnor(Ted)1.jpg                          Josh Radnor      ja\n",
       "./img/Shakira1.jpg                                      Shakira      ja\n",
       "./img/NeilPatrickHarris(Barney)4.jpg        Neil Patrick Harris      ja\n",
       "./img/BastianSchweinsteiger1.jpg         Bastian Schweinsteiger    nein\n",
       "./img/LisaEdelstein(DrLisaCuddy)2.jpg            Lisa Edelstein      ja\n",
       "./img/GuentherJauch2.jpg                          Günther Jauch    nein\n",
       "./img/RobertSeanLeonard(Wilson)2.jpg        Robert Sean Leonard      ja\n",
       "./img/SigmarGabriel1.jpg                         Sigmar Gabriel    nein\n",
       "./img/Usher1.jpg                                          Usher    nein\n",
       "./img/DannFlorek(DonaldDonCragen)1.jpg              Dann Florek    nein\n",
       "./img/NicoleKidman4.jpg                           Nicole Kidman      ja\n",
       "./img/TaylorSwift1.jpg                             Taylor Swift    nein\n",
       "./img/Ice-T(OdafinFinTutuola)1.jpg                        Ice-T      ja\n",
       "./img/ElvisPresley1.jpg                           Elvis Presley      ja\n",
       "./img/MariskaHargitay(Liv)1.jpg                Mariska Hargitay    nein\n",
       "./img/CobieSmulders(Robin)2.jpg                  Cobie Smulders      ja\n",
       "./img/MilaKunis5.jpg                                 Mila Kunis    nein\n",
       "./img/KatjaBurkard5.jpg                           Katja Burkard    nein\n",
       "./img/UrsulavonderLeyen1.jpg               Ursula von der Leyen    nein\n",
       "./img/BenedictCumberbatch4.jpg             Benedict Cumberbatch      ja\n",
       "./img/AngelaMerkel1.jpg                           Angela Merkel      ja\n",
       "./img/Beyonce2.jpg                                      Beyoncé      ja\n",
       "./img/TomCruise2.jpg                                 Tom Cruise      ja\n",
       "./img/LeonardoDiCaprio1.jpg                   Leonardo DiCaprio      ja\n",
       "./img/CristinMilioti(Tracy_Mutter)1.jpg         Cristin Milioti      ja\n",
       "./img/WillSmith4.jpg                                 Will Smith      ja\n",
       "./img/JanHofer1.jpg                                   Jan Hofer    nein\n",
       "./img/EmmaWatson(HermioneGranger)1.jpg              Emma Watson      ja"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## do this for all participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {}\n",
    "for logFile in logList:\n",
    "    \n",
    "    pNum = logFile.split('/')[-1].split('_')[0]\n",
    "    \n",
    "    df = pd.read_csv(logFile,index_col=[0],header=0)\n",
    "    thisDf = getCols(df)\n",
    "    thisDf.to_csv('../famousCheck/output/famousFamiliarity_%s.csv' % pNum)\n",
    "    # count number of known people\n",
    "    a = list(thisDf.bekannt)\n",
    "    d[pNum] = float(a.count('ja'))/len(a)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d,index=['known']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('../famousCheck/output/familiarFaces.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "snsDf = df.copy()\n",
    "snsDf['hue'] = [['control','JB'][x=='jb'] for x in snsDf.index]\n",
    "snsDf['x'] = [0 for x in snsDf.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>known</th>\n",
       "      <th>hue</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76.190476</td>\n",
       "      <td>control</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>76.190476</td>\n",
       "      <td>control</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90.476190</td>\n",
       "      <td>control</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>85.714286</td>\n",
       "      <td>control</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jb</th>\n",
       "      <td>88.095238</td>\n",
       "      <td>JB</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        known      hue  x\n",
       "6   76.190476  control  0\n",
       "7   76.190476  control  0\n",
       "8   90.476190  control  0\n",
       "9   85.714286  control  0\n",
       "jb  88.095238       JB  0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snsDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "swarmFig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "sns.swarmplot(data=snsDf,x='x',y='known',hue='hue',\n",
    "              size=16,orient='v',edgecolor='white',linewidth=1,alpha=0.8,ax=ax)\n",
    "\n",
    "plt.title('Celebrity knowledge')\n",
    "plt.ylim(0,100)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gX/zQVeRErczLjfXxpS/9JadPnyaRSPCpT/0JDz/8c+rr6/B9n3e/+73cfPNtPPDAx7js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\nhbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d61f8fd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swarmFig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### who is best known overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "famList = getLogfile('../famousCheck/output/','famousFamiliarity*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "famFile = famList[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigDf = pd.DataFrame()\n",
    "for famFile in famList:\n",
    "    pName = famFile.split('/')[-1].split('_')[-1].split('.')[0]\n",
    "\n",
    "    thisDf = pd.read_csv(famFile,index_col=[1])\n",
    "    thisDf = thisDf.drop('img',axis=1).T\n",
    "    thisDf.index = [pName]\n",
    "    bigDf = pd.concat([bigDf,thisDf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bigDf[bigDf=='ja'] = 1\n",
    "bigDf[bigDf=='nein'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "meanDf = pd.DataFrame( bigDf.mean() )\n",
    "meanDf.columns = ['mean']\n",
    "meanDf = meanDf.sort_values(by='mean',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "matplotlib.rc('font', family='Arial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ykdKYzaokSZJi5dijhlJeVkjNazuofsGjbKR0ZbMqSZKk2Ll4xjgGZGVw1zyP\nspHSlc2qJEmSYmfEEQM5p3wM23bu5YH5takuR1IK2KxKkiQpls4tL2F4Xg4PLXjFo2ykNGSzKkmS\npFjKGZDJRdPH0bzfo2ykdGSzKkmSpNgqn1T4xlE2q1/xKBspndisSpIkKbYyEgkuOXCUzaMeZSOl\nE5tVSZIkxdq4o4YytayQ2td28PTzHmUjpYusZL8YRdE1wAeAAcBNwDzgNqAVeAG4vP3We4Ai4NoQ\nwpwoisYCV4YQruxC3ZIkSUojF80Yx3OrG7h73jpOPa6AgQOS/mOspD4iqZnVKIpmAO8GTgemA0cD\nP6KtIT0TSADnAycCNcA5wBXtX78WuLErRUuSJCm9vHGUTdNeqqo9ykZKB8m+Bvw+4HngXuB+4C/A\nKbTNrgI8CMwEdgKD2n81RVF0OrAmhFDflaIlSZKUfs6d2naUzcMLX2HTVo+ykfq7ZJvVkcCpwMXA\nF4DbgYwQwoEV7zuAoSGE1UAd8EPgeuAq4A9RFP08iqIboyhyzawkSZLekZzsTC6a0XaUzR89ykbq\n9xKtSeyoFkXRd4GGEMJ/tP+8FBgfQsht//l84L0hhCsO+s4ltDXHk4C7gRnA0hDCnLd6VnPz/tas\nrMxO1yhJkqT+p7W1lat/+iShdgvfvfwMJo3NT3VJkrou0dFgsivT/wZcGUXRj2jbPGkw8GgURTNC\nCI8D5wKPHbg5iqKBwIXARcCPgf20bcQ05O0etGXLriRL7B0FBXk0NOwwL8VZ5sUny7z4ZJkXnyzz\n4pNlXnyyupJ30fSxfPu3z/Lzu5bytU+dSkYiEav6eiMvzrWlW16ca+uJvJ5QUJDX4XhSr+GGEP4C\nLAYW0rZm9XLg34BvRlFUTdsOwXcd9JWrgJ+0vyZ8K/AL2jZdeiSZ50uSJCl9jSseSsWkQmrrd/DU\n8xtSXY6kHpL0nt8hhH/vYHj6Ye797kGflwDlyT5XkiRJunD6OJ4NDdwz70VOjUYxKMejbKT+xg2O\nJEmS1OeMOGIg504tYVvTXh6Y71E2Un9ksypJkqQ+6ZzyMW8cZdPgUTZSv+P7EpIkSeqTcrIzuXjG\nOH55/wq+cctC9jS3UJyfS2VFKeVlhakuT1IX2axKkiSpz3t9734A6hqamDV7OYANq9TH+RqwJEmS\n+qzDrVetqnYdq9TX2axKkiSpz1q/addhxpt6uRJJ3c1mVZIkSX1W8cjcDsdbWlu57cFVbN+1t5cr\nktRdbFZkXqMZAAAgAElEQVQlSZLUZ1VWlHY4PjwvhyeWrueaWfN55JlXaN7f0ruFSeoyN1iSJElS\nn3VgE6Wq6lo2NDZRlD+YyooSTj2ugMeee5X7nnyJ3z+6hnlLXuWSmROYdMyIFFcs6Z2yWZUkSVKf\nVl5WSHlZIQUFeTQ07HhjfOapR1NeVsi9T77EvMWv8h9/WMJJ40fykbOPZdTwjl8flhQfNquSJEnq\nt/JyB/DJ90XMOLGYO+asZvGaTTz/YiPvO20MlRUlDBzgH4eluHLNqiRJkvq9MYV5fPmfTuYL50/i\niMEDqKqu5Su/nE/18tdobW1NdXmSOmCzKkmSpLSQSCQ4bWIh3/7sVD5weilNu5v51f0r+M7vnqPm\nte2pLk/S3/G9B0mSJKWVnOxMPnjmWM6YXMQfHlvLs6GBb922iDNOKOLC6eNYWbuFquoa1jfuojg/\nl8qK0jc2cpLUe2xWJUmSlJZGDhvE5R+azMqazdzx6BqeXLaB+SteY1/z/74WXNfQxKzZywFsWKVe\n5mvAkiRJSmsTS0fwjU+/i3967wSa93e8frWquraXq5JksypJkqS0l5mRwXtOGU3iMNc3NDb1aj2S\nbFYlSZKkNxSPHNzheFF+x+OSeo7NqiRJktSusqK0w/Fzyo/u3UIk2axKkiRJB5SXFfL5D0xidMEQ\nMjMSDMpp24902bpGWjyPVepV7gYsSZIkHaS8rJDyskIKCvJYv2ErP7hzCQtXbmTU8FwumDY21eVJ\nacOZVUmSJOkwsrMyueLCyRQMG8hfnq7hqec3pLokKW3YrEqSJElv4YjcAVx18RQGD8zitgdXsbJ2\nS6pLktKCzaokSZL0NoryB3P5hyYD8N/3PO9RNlIvsFmVJEmS3oHjSobzqXOPY9eeZn78p6Vs37U3\n1SVJ/VrSGyxFUfQcsL39x5eAWcB/Ac3AIyGEb0ZRNASYDQwCPh9CWBZF0RnA6SGE73WtdEmSJKl3\nnT65iI1bXuf+p2v46d3L+PePnUR2Vmaqy5L6paSa1SiKBgKJEMKMg8aWABcCLwJVURSdBBxDW7M6\nD7gsiqKrgCuBT3SxbkmSJCklPnjmMTRsfZ35K+q5uWoln/vAJDISiVSXJfU7yc6sTgFyoyh6pD3j\nG0BOCGEdQBRFDwMzgaW0zaoOApqAS4B7Qwi7u1i3JEmSlBKJRIJPn3ccjdt3s3DlRgqGDeLC6eNS\nXZbU7yRakzjcOIqiycBU4NfAeOBBYGsI4ZT265cCY4GvAz8ChgPXAT9s/9cvAetCCN9/u2c1N+9v\nzfLVCkmSJMXMtp17uPonT7KhsYkrP3IiM08rSXVJUl/V4asJyc6srgbWhhBagdVRFG0DRhx0PY+2\n5rUFuAogiqJraFvTei3wReAbURRNCCGsfqsHbdmyK8kSe0dBQR4NDTvMS3GWefHJMi8+WebFJ8u8\n+GSZF5+s/pL3xQsn8+3fLuJnf1rKgARMLB1x2Hv9z6J/5sW5tp7I6wkFBXkdjie7G/ClwH8ARFFU\nDOQCTVEUjYuiKAG8D3jywM1RFI0CohDCk+337gdagcFJPl+SJElKuSNH5HLFBW1H2vzs3hdYv8kj\nbaTukmyzejMwLIqivwF/oK15/QxwO7AQWBxCWHDQ/dcCN7R/vgl4GCiibU2rJEmS1GdFY4Zz6XkT\nef3AkTZNHmkjdYekXgMOIeylbbOkvzf1MPd/6aDPD9PWrEqSJEn9QsXxR1K/ZRezn2o70ubqj53E\ngGz3XZG6ItmZVUmSJEkHOf+MY6iYVMi69dv5ddVKWpLYyFTS/7JZlSRJkrpBIpHgU+dOZMLooSxa\ntZF75r2Y6pKkPs1mVZIkSeom2VkZXHHhCRQOH8QD82t5Yun6VJck9VnJHl0jSZIkqQNDBmVz1cVT\nuOG3i/ifhwMNW19n6dpNrG/cRXF+LpUVpZSXFSaVvWBFPVXVNd2SJcWdM6uSJElSNysckcsXLzyB\n1tZWqqprqWtooqWllbqGJmbNXs6CFfWdzlywop5Zs5d3S5bUFzizKkmSJPWACUcPY9iQHDbv2HPI\ntTvnru70mayPL3m1w/Gq6lpnV9Uv2axKkiRJPWTrzo7PXN2+ax/3P13TLc/Y0Ni5plfqK2xWJUmS\npB5SPDKXuoZDm8mCYQO59LyJncq65YGVNGzdfch4RkaCNXVbGT96WNJ1SnFksypJkiT1kMqKUmbN\nXn7I+AXTxhGNGd6prAumjeswa19zC9/53XNMnVTIxTOOZXheTtL1SnFisypJkiT1kANrSauqa9nQ\n2ERR/mAqK0qSWmN6uKz8oQO5fc5q5i+vZ/HqTVRWlPC+044mOyuzW38vUm+zWZUkSZJ6UHlZIeVl\nhRQU5NHQsKNHsr72f07lqWUbuHveOu554kWeXLaej5w9npPGjySRSHT1tyClhM2qJEmS1MdlJBKc\nOaWYU6JR3P/0S8xdVMfP7nmestLhfGzmBI4aOTjVJUqd5jmrkiRJUj+ROzCLj5w9nusvO43jx45g\nRc0Wrrt5IXfMWU3T7n2pLk/qFJtVSZIkqZ8pyh/Mv148hS9ddAIjhw1k7rN1XDNrPo8veZWWltZU\nlye9I74GLEmSJPVDiUSCE48dyaTSEcxd9Aqzn67htw8FHl/8KpfMnMCEoz3qRvFmsypJkiT1Y9lZ\nGZw7tYSK44/k7sfX8dQLr/Hd25/jtImjGD96GPOWvMr6xl0U5+dSWVGa1E7FByxYUU9VdU1s89S3\n2KxKkiRJaWDYkBwue38ZM04+ijvmrGHhyo0sXLnxjet1DU1vnOOaTEO4YEX9m86BjVue+h6bVUmS\nJCmNjCseylc/eQpX3/Q0W3bsOeT6rNnLublqRadzm/d3vBa2u/OqqmttVtOEzaokSZKUZjISCbbt\n3HvY6yWFeZ3OXLd+e6/kbWhs6nSW+iabVUmSJCkNFY/Mpa7h0MZvdMEQvvrJUzud9/WbF/RKXlG+\nZ8amC4+ukSRJktJQZUXpYcZLYp1XMclXgNOFM6uSJElSGjqw7rOqupYNjU0U5Q+msqIk6fWgPZ03\nbMgAGrfv4a/P1VFx/JEMG5KTVK76DptVSZIkKU2VlxVSXlZIQUEeDQ07Yp/3l6druOeJF/nJXcv4\n8iUnkzMgs8vPUHz5GrAkSZKkPqGyooQzJhdR89oOfnn/clpaOt4xWP1Dl2ZWoygaBTwLvBdoBm4D\nWoEXgMvbb7sHKAKuDSHMiaJoLHBlCOHKrjxbkiRJUnpJJBJ88pyIxu27WbxmE398bC0ffc/4VJel\nHpL0zGoURdnALOD19qEf0daQngkkgPOBE4Ea4Bzgivb7rgVuTPa5kiRJktJXVmYG//Kh4ynKz+WR\nZ17hsefqUl2SekhXXgP+IfALYH37z6cA89o/PwjMBHYCg9p/NUVRdDqwJoRQ34XnSpIkSUpjgwdm\nc+XFU8jLzeZ3c1azbF1jqktSD0i0tnb+Pe8oij4FjA4h3BBF0ePAF4C/hhCK26+fDVwaQvh4FEVf\nAyYC1wPfAr4MXA1soW0mtuWtntXcvL81K8uF05IkSZLebFXtZr5601NkZib43hVnckzx0FSXpOQk\nOhxMsll9gra1qa20veq7Gjg5hJDVfv184L0hhCsO+s4ltM3kTgLuBmYAS0MIc97qWQ0NO2K9arq7\ndjpLx7w415ZueXGuLd3y4lxbuuXFubZ0y4tzbemWF+fa0i0vzrX1dt4zqzby8/teYHheDtd+8lSG\n5739kTb+ZxEvBQV5HTarSb0GHEKYFkKYHkKYASwBPgk8GEXRjPZbzgWePHB/FEUDgQuB24FcYD9t\nje6QZJ4vSZIkSQDvOm4UF04fy5Yde/jJXcvYs3d/qktSN+nOo2v+DfhmFEXVwADgroOuXQX8JITQ\nCtxK21rXc4BHuvH5kiRJktLQeVNLOPOEImrrdzBrtkfa9BddOroGoH129YDph7nnuwd9XgKUd/W5\nkiRJkgRtR9p84n0Rm7btZslaj7TpL7pzZlWSJEmSUiIrM4PLP3Q8xSMH88gzr/Dosx5p09fZrEqS\nJEnqF3IHZnPlRSdwRG42d8xdzbJ1m1JdkrrAZlWSJElSv1EwbBBfvOgEsjIz+Pmfl/Nyfbx3wtXh\n2axKkiRJ6lfGFQ/ls+8vY8/e/fzXXcvYsmNPqktSEmxWJUmSJPU7px43iotnjGPLjj38111L2b23\nOdUlqZNsViVJkiT1S+eUj2HalGJert/JL2ev8EibPqbLR9dIkiRJUhwlEgk+/g8TaNz2OkvWbuLH\ndy1l6449rG/cRXF+LpUVpZSXFaa6TB2GM6uSJEmS+q2szAz++YOTGZ6XwwsvbqauoYmWllbqGpqY\nNXs5C1bUp7pEHYbNqiRJkqR+LXdgFjnZmR1eq6qu7eVq9E7ZrEqSJEnq9zZueb3D8Q2NTb1cid4p\nm1VJkiRJ/V7xyNwOx7MyM3i1YWcvV6N3wmZVkiRJUr9XWVHa4fieffu57pZnuGPOapp27+vdovSW\n3A1YkiRJUr93YNffqupaNjQ2UZQ/mMqKEgYOyOT3j65h7rN1zF9RzwXTxjJtSjEZGYkUVyybVUmS\nJElpobyskPKyQgoK8mho2PHGeFnpCOYueoXZT9fw24cDjy9+lUveO4EJRw9LYbXyNWBJkiRJaS07\nK4Nzp5bwnc9N5fTjj+TljTv57u3P8Ys/v8Dm7btTXV7acmZVkiRJkoBhQ3K47P1lzDj5KO6Ys4aF\nKzeyZM0mzqso4ZzTxjDgMMffqGc4sypJkiRJBxlXPJSvfvIULqucyKCcLO578iW++qsFLFq1kdbW\n1lSXlzacWZUkSZKkv5ORSHD65CJOnlDAX56u4ZFnXuGm+17guDHDuGTmBEaPGpLqEvs9m1VJkiRJ\nOoxBOVlcfNaxnDmlmN8/uoZl6xq57taFlJUMZ8uOPby25XWK83OprCh9Y8dhdQ+bVUmSJEl6G0eO\nyOWqi6ewbF0jtz6wkuU1W964VtfQxKzZywFsWLuRa1YlSZIk6R06YVw+QwZld3itqrq2l6vp32xW\nJUmSJKkTNjTuOsx4Uy9X0r/ZrEqSJElSJxSPzO1wPP+Igb1cSf9msypJkiRJnVBZUdrh+LamvWzc\n0vGsqzovqQ2WoijKBH4FREAr8AVgN3Bb+88vAJe3334PUARcG0KYE0XRWODKEMKVXStdkiRJknrf\ngU2Uqqpr2dDYRFH+YI4pzuPJpRv48Z+W8ZVPnHLYda1655LdDfgfAUIIp0dRNAP4NpCgrSF9PIqi\nXwDnA7VADfBp2hrZOcC1wDVdqlqSJEmSUqi8rJDyskIKCvJoaNgBwOCB2Ty04GVuuvd5/u9HTiQr\n0xdZuyKpf/dCCPcBn2v/sQTYCpwCzGsfexCYCewEBrX/aoqi6HRgTQihvitFS5IkSVLcXDRjHKdM\nKGDVy1v5zYOraG1tTXVJfVqiK/8GRlH0G+BDwEXAbSGE4vbxs4FLQwgfj6Loa8BE4HrgW8CXgauB\nLbTNxLa81TOam/e3ZmVlJl2jJEmSJPWW3Xub+cpNT7Hmla18/Jzj+Mh7o1SX1BckOhzsarcfRdGR\nwALgiBDC8Pax84H3hhCuOOi+S2ibyZ0E3A3MAJaGEOa8VX5Dw45Y/3XEwdP+5qUuy7z4ZJkXnyzz\n4pNlXnyyzItPlnnxyTKv+7O27dzDDb99lsbtu/ncB8qYWnZkSmrribyeUFCQ12GzmtRrwFEUfSKK\nogPrTncBLcCi9vWrAOcCTx50/0DgQuB2IBfYT9tGTEOSeb4kSZIkxdXQITlcdfEJDMrJ5Jaqlax+\nZWuqS+qTkl3xew9wUhRFTwAPA1fRtvvvN6MoqgYGAHcddP9VwE9CCK3ArcAvgHOAR5ItXJIkSZLi\n6qiCIfzLByfT0gI/u+d56j3SptOS2g04hNAEfLiDS9MPc/93D/q8BChP5rmSJEmS1FdMOmYEn3jf\nBH7zUODHf1zKVz95qkfadIJ7KUuSJElSD5l+4lGcO3UM9Vte52f3PM++5rfcX1YHsVmVJEmSpB50\n4fRxnBoVsPqVrdzmkTbvmM2qJEmSJPWgjESCz7y/jLHFR1C9/DXuf6om1SX1CTarkiRJktTDBmRn\n8sULT2Dk0IHc97eXqF7+WqpLij2bVUmSJEnqBUMHD+DKi6cwKCeLWx/wSJu3Y7MqSZIkSb3kqJGD\nufxDx9PaCj+9exn1mz3S5nBsViVJkiSpF5WVjuAT74to2t3Mf/5pKTtf35fqkmLJZlWSJEmSetm0\nKcVUVpSwccvr/PTuZR5p0wGbVUmSJElKgQ9NG8upx41iTd02bn1wpUfa/J2sVBcgSZIkSekoI5Hg\nM5UT2bJ9N/OX17P8pc007W6mOD+XyopSyssKU11iSjmzKkmSJEkpMiA7k9MnFwGwY9c+WlpaqWto\nYtbs5SxYUZ/i6lLLZlWSJEmSUuivz9V1OF5VXdvLlcSLzaokSZIkpdD6TR0fX7OhsamXK4kXm1VJ\nkiRJSqHikbkdjheOGNTLlcSLzaokSZIkpVBlRWmH4/tbWtnXvL93i4kRm1VJkiRJSqHyskI+/4FJ\njC4YQmZGgtEFgxlXfAT1m1/nlgdWpe2RNh5dI0mSJEkpVl5WSHlZIQUFeTQ07GBf835+cOcSFqyo\nZ9SwQXxo2thUl9jrnFmVJEmSpJjJzsrkigsnUzBsIPc/XcNTz29IdUm9zmZVkiRJkmLoiNwBXHXx\nFHJzsrjtwVWsqt2S6pJ6lc2qJEmSJMVUUf5grrhgMgA/u+f5tDrOxmZVkiRJkmLsuJLhfOrc49i1\np5kf/2kp23ftTXVJvcJmVZIkSZJi7vTJRfzju0tp2Lqbn939fFocaWOzKkmSJEl9wAfPPIbyskLW\nvrqNm6tW0tLPj7SxWZUkSZKkPiCRSHDpecdx7OihLFy5kfuefDHVJfUom1VJkiRJ6iOyszL54gWT\nGTVsEH95upYnl61PdUk9JiuZL0VRlA3cApQCOcANwArgNqAVeAG4vP32e4Ai4NoQwpwoisYCV4YQ\nruxS5ZIkSZKUhvJyB3DVh6fw7d8u4rcPBUYeMZCJpSNSXVa3S3Zm9eNAYwjhTOAc4GfAj2hrSM8E\nEsD5wIlATfs9V7R/91rgxi7ULEmSJElp7cgRuW8cafPf977A+k3970ibZJvVPwFfa/+cAJqBU4B5\n7WMPAjOBncCg9l9NURSdDqwJIdQnXbEkSZIkiWjMcD593kFH2jT1ryNtEq1d2EEqiqI8YDbwK+CH\nIYTi9vGzgUtDCB+PouhrwETgeuBbwJeBq4EttM3EtrzVM5qb97dmZWUmXaMkSZIk9Wd3PLyKOx8J\nHFcynBv++XRysvtc/5ToaDCpNasAURQdDdwL3BRCuCOKou8fdDkP2AoQQvhW+/2XAH8GPgvcDMwA\n3gPMeavnbNmyK9kSe0VBQR4NDTvMS3GWefHJMi8+WebFJ8u8+GSZF58s8+KTZV58spLNm3lSMS+9\nupX5y+v53m0L+fz5k8hIJHqkvp5QUJDX4XhSrwFHUVQIPAJ8OYRwS/vw4iiKZrR/Phd48qD7BwIX\nArcDucB+2jZiGpLM8yVJkiRJbRKJBJ8+dyLjRw/lmVUbufeJ/nGkTbJrVr8CDAe+FkXR41EUPU7b\nxknfjKKoGhgA3HXQ/VcBPwkhtAK3Ar+gbdOlR5ItXJIkSZLUJjsrgy9eeAKFwwdRVV3Lk0v7/pE2\nSb0G3H7sTEdHz0w/zP3fPejzEqA8medKkiRJkjo2ZFA2V108hRt+u4jbHlrF/U/XsHnHHorzc6ms\nKKW8rDDVJXZKsjOrkiRJkqSYKRyRy3tOGU1rK2zatpuWllbqGpqYNXs5C1b0rUNZbFYlSZIkqR95\nbnVDh+NV1bW9XEnX2KxKkiRJUj+yflPHJ6psaGzq5Uq6xmZVkiRJkvqR4pG5HY4X5Q/u5Uq6xmZV\nkiRJkvqRyorSw4yX9G4hXZTUbsCSJEmSpHg6sOtvVXUtGxqbKMofTGVFSZ/bDdhmVZIkSZL6mfKy\nQsrLCikoyKOhYUeqy0mKrwFLkiRJkmLHZlWSJEmSFDs2q5IkSZKk2LFZlSRJkiTFjs2qJEmSJCl2\nbFYlSZIkSbFjsypJkiRJih2bVUmSJElS7NisSpIkSZJix2ZVkiRJkhQ7NquSJEmSpNixWZUkSZIk\nxY7NqiRJkiQpdmxWJUmSJEmxY7MqSZIkSYodm1VJkiRJUuzYrEqSJEmSYierK1+Ooqgc+F4IYUYU\nRccCtwGtwAvA5e233QMUAdeGEOZEUTQWuDKEcGVXni1JkiRJ6r+SnlmNoujfgV8DA9uHfkRbQ3om\nkADOB04EaoBzgCva77sWuDHZ50qSJEmS+r+uvAa8DrjgoJ9PAea1f34QmAnsBAa1/2qKouh0YE0I\nob4Lz5UkSZIk9XOJ1tbWpL8cRVEp8PsQwtQoitaHEIrbx88GLg0hfDyKoq8BE4HrgW8BXwauBrbQ\nNhPb8lbPaG7e35qVlZl0jZIkSZKkWEt0NNilNat/5+CmMw/YChBC+BZAFEWXAH8GPgvcDMwA3gPM\neavQLVt2dWOJ3a+gII+Ghh3mpTjLvPhkmRefLPPik2VefLLMi0+WefHJMi8+WX0hrycUFOR1ON6d\nuwEvjqJoRvvnc4EnD1yIomggcCFwO5AL7KdtI6Yh3fh8SZIkSVI/0Z3N6r8B34yiqBoYANx10LWr\ngJ+EEFqBW4Ff0Lbp0iPd+HxJkiRJUj/RpdeAQwg1wNT2z6uB6Ye577sHfV4ClHfluZIkSZKk/q07\nZ1YlSZIkSeoWNquSJEmSpNixWZUkSZIkxY7NqiRJkiQpdmxWJUmSJEmxY7MqSZIkSYodm1VJkiRJ\nUuzYrEqSJEmSYsdmVZIkSZIUOzarkiRJkqTY+f/ZO+8wy6oqfb9NaoIgkpOggHyABAMijEQzpp+B\nKMiQg6IoIIqApJakIyNJkCAgSRoEJOooIJIGQSTKRwaxAQljgxIk9O+PtU/fU5eubu7eu6YL5rzP\n00/de7vuql11T9grfatzVjs6Ojo6Ojo6Ojo6OjpGHZ2z2tHR0dHR0dHR0dHR0THq6JzVjo6Ojo6O\njo6Ojo6OjlFH56x2dHR0dHR0dHR0dHR0jDo6Z7Wjo6Ojo6Ojo6Ojo6Nj1NE5qx0dHR0dHR0dHR0d\nHR2jjs5Z7ejo6Ojo6Ojo6Ojo6Bh1dM5qR0dHR0dHR0dHR0dHx6ijc1Y7Ojo6Ojo6Ojo6Ojo6Rh2d\ns9rR0dHR0dHR0dHR0dEx6uic1Y6Ojo6Ojo6Ojo6Ojo5RR+esdnR0dHR0dHR0dHR0dIw6Ome1o6Oj\no6Ojo6Ojo6OjY9TROasdHR0dHR0dHR0dHR0do46ZahqTNANwNLAy8AKwDbA2sC3wR9tfTt93OrCD\n7adr/vyOjo6Ojo6Ojo6Ojo6ONwZVnVXgs8CstleXtBrwH8DcwL8B50p6S3r8+85R7ejo6Ojo6Ojo\n6Ojo6BiO2mXAawCXAti+DlgFeBaYhXCMXwG2Ao6r/HM7Ojo6Ojo6Ojo6Ojo63kCMmTRpUjVjko4H\nzrF9SXr+EPBF4GvArwmn9UGiTPitwH/adrUFdHR0dHR0dHR0dHR0dLwhqJ1ZfRqYs23f9lW2NwTG\nA2sC9wCLAHsD36388zs6Ojo6Ojo6Ojo6OjreANR2Vq8GPgGQelZvbf3ft4GDgdmBl4FJwJsq//yO\njo6Ojo6Ojo6Ojo6ONwC1ndVzgeclXQMcBnwDQNLbgLlt3wzcDCwOXAwcWfnnd3R0dHR0dHR0dHR0\ndLwBqNqz2tHR0dHR0dHR0dHR0dFRg9qZ1Y6Ojo6Ojo6Ojo6Ojo6OYjpntaOjo6Ojo6Ojo6Ojo2PU\n0TmrHR0dHR0dHR0dHR0dHaOOzlntmCaS1pa0laQVJI2tYG8GSTNKWlPSLDXW+H8BSQtN7zW8XpG0\ncGV7m9a019ExCJLmmd5rGA5J75S0gaR3VbC1Xt/zL5Ta/L+IpFG515M0n6TFm3+VbL61go11a6yl\n4/8ekmYufP/6kmaqtJa5+p7/Ww2704Mqf5D/a0jaB9gJeBEYA0yyvUiBvSOB423/qdL6FgZmTmtb\nxPa1BbYOAJYCliHGDe0BZG/UJf0n8GdgCeA9wGPAvxfY+w6wO/AsdT6LuYgZwMsDdwEH2H5qQBsf\nHe7/bP86d23A2ZIeB04ALrb9SoGtUU/l8+ykFGi5APiF7fsLl7cdcFqhjclImhNYD5i1ec32KQX2\nPkxc32cAjgD2tn16gb23Apv0rW//6bk2SWsN93+2r8xZW7Jb5bOQtN1w/2f7J5lrWxs4CphR0njg\nQdsn5Nhq2dzN9g9KbLRsfQ34InAd8E1JZ+XYlvRJYDVgM0k/Sy/PCHweOKdgfafb/mLu+/tsrQTM\nAbwCHAgcaPu3Bfb2sj2u9fwg23sU2NuUGBM4Fvi+pEMzP4tlhvs/23cVrO8nwIeIPcAYYn+RtZmW\n9E3g78DcwJaSLrW9S+7agP2Aywve36zrWuL3atPcywb+XSU9kuyNJcZA/gVYDPib7bcVrHNzYm83\ntrW+JQe0sY3t4yUdRN/vbPs7metaz/YlOe+dis3a5+0OwC7EPW0M8BLwjoIlrgLsLem/gBNs/7nA\n1rnpWvoScADwMWLf/bqjc1bz+BSwuO3nKtm7EPiOpEWBU4HTbD+dY0jSCcDqxMk4O3AvcdPPZW3b\na0m63PZPJW1fYAvgfba/nuytKyn7IpHYiHDIny2003AicCXhiKwNnAR8ZkAbmwzz+iQg21m1vYak\n5YEtgb3S3+4E2/fl2EvRu/cxNLBxRu76ks0PEcGN64C7bD9fYK7aeWb7YykQsR5wqqTZbb+7wORY\nSTcBJm56FG6CzwcmEJsPePUGZ1C+RzgNRwEfAM4Csp1VYDzwG3rrK6HW2nZMX5cCZgH+ALwb+Aew\nTsH6an0WVbP5iQOAtQiH7UBitnmRswp8QtJhtl8uXRxx7VvD9kspw3ANkOMI30r8/V4AHkyvvQJs\nVjA+zIEAACAASURBVLi+sWmzehe98/ZfmbaOIYJp+wF7AocCA9/PJG0NbAMsJ+kT6eUZietytrMK\n7Exc784E3krce3I+i2OHeX0S8MG8pQGwErC07RojKb5AnBeX2l5e0mWF9iZJOpeh1/cch2vjwnUM\nwfbCAJJOBfaw/RdJixCjIkv4FvBpyq7vzXvv7Hu95PP9JlDVWaXSedviy8RecS/iPvn1ksXZ/nZK\nwqwHjEsVdccRfsGLA5o7DDgPeAvwK+D9JWubnnTOah5/I7I9VbB9KXCppPmBHxFR0LOJrN69A5pb\nGXgncYP5DnB24fJmShmpSZJmJCK1Jcwo6b3AA6kEeM5Ce/cDtYIGAPPaPjw9/pOk9Qc1YHvL5nGK\nSi8N3EJsgEv5K3Af8F5gBeBHkm63/e0MW+cSG6JFic3RBGBgZ1XSm21PlHQgEeVdjthk7sHwjvtr\nodp5JumzwIeJi/VDxIW7hG8VL2ooM9gu3Yi3eZbIWLxk+1FJpRvCZ2zvVWFdUGlttjcBkHQR8P+S\ngzQjcFHh+qp8Frb3ax6nbPKSpCBOgdlXbD8laZLt5yU9U7pOYD5ggqT7iY1lVtYnMcb2SwC2X5SU\ne/4eY/sTkhYuzRz3sQwRjGiYRHwuOTwP3A7MYvs6Sbn3xlOJzfJ3iEAOhIP0t0x7Dc198RnbL+SW\nFtqeXBIr6c3A24B7bf+jcH0TiPt/VmC+j5eBhYjrCkSgvoQTC98PgO0HAVIi4hBgAcKhuYVeECaH\nJW3/Jf2MCRVKqO+zfU+JAdvNPfV9tndqXpd0CpBbJTRDCnqNmcLPyw0y1TpvGybYfkTSnLavSBVh\n2UgaA3wU2JyoQDyNuEZfAHz8NdpoqiHuAn5HBJVOBd5O2f1nutE5qwMg6Qzi5rYgcJOk29LzoqyK\npOWALYjI1hXAmsRncxbhlAzCk7YnSZrD9hOScpfVcDhwA3GRvRY4stDeycDRwFZERGu4qO1rZRbg\nVkm3pueTCjNcs0laKG2iFyScuCwk7QR8DpiHyNC+g4jo5do7i3BQTwU2sz0hvX5Dpsn5bK8u6Xjg\nq8B/Zdr5LrArkVFpsvAnS9pxWm+cEsOcZ1D22R5EONAHE9H3v2faafgj4bAuQlRG3FJo7xZJ7wf+\nRO+aknszhtgAXgr8RNJXKN/43iZpY+Cm1vpyb3rPVF5bO4M5E3GtKqHqZ1E5iHNPKrObV9K3Kdvw\nNny6go2Gq1Kg9ffEfezqTDsLp+vAOpKGlNTZ3jx3cbZXBJC0AHGvLNmoTiI24RdL2pDMwJrtF4jg\n7e5EQK3taGW3AhBBzeuAb6QNdNE1StEvvBdpb5ICJuOm8bYp2WlKYxcA7pbUVAaVBEmuSP82k3QY\n5QGr04g92eLAZcBtU/3uafMT4D+IFqMriX1QScXbHak8/nqiku7GwvU9K+kShl7zBsokp2v5XsBb\nJH2ecDDHEI5hLu8nsttNmTitx7lBpirnbYuJKRg+KVUezldo727i+nm47cnXT0nvHMDGlPbVx1Je\nDTHd6JzVwThmhOwel/7t1y5nlZQT3btR0m5EpPxMCiOMts+U9BvC0brH9uMl9oA5bDelCEXlEolD\nKthoszdwjaSniajvsH1nr4GNidKk39r+kaQ/FK7tONtTcijXyLTXHGtz2H6uIPt2YPo6k6RZKc/C\nVz/PbC8n6W1Ez8YvUhlwyWbhRKI8aW3gUaIUc+0Ce2sz1GkouRkDbAgsZfsOSSsQ15cS3pX+NZTc\n9DaovLbjgdtTUOOdRECihNqfRZUgTmIHomT0KqLcedsCWw0vUSnrY3u31CO1HHCi7Ysz1/Qh4nhb\njtjYV0HSOsS5O5HYVG87zDX1tbARsCpxHViH8pLPXxB/90fT86JqCNtbSnqT7X9I+oPtx6b9rqmy\nC+FgXQqMI4LYAzurDP07NY7HWCKQk4XtPSV9l3AUds8ol+znGCLz+xGiveAU4BNTfcfUmc32ZYq+\nZEsqaY+B0OlYl6gU+Lnt86fx/dMi9zydjO2jgKMkfcf2gdN8w2vjunZmvxK1z9ttiOq5PYigfXZC\nIvGedhugpJltv9iu2JsWU6iGWILInpdWQ0w3Omd1MK4iMm1nEgf8mPT8IgqiFakX8cNEVHByr186\n+Qe19R1JbyJKHdYD/jt3XTBZFW9m4ve8StJ3bf+8wGTN/iiI3qaP0eq7JMoeskgblyUlzWf7icK1\nzUAqq0vPs2/GiUck/Z7oPzgVuM32hQV9ob+QtDdwczrusi5ktp9MDw8jIrzzE8ddVh+N7d8BSPo0\nsIrtfSRdmmsv2XoPsdn4COGkn5VrKzGv7RMlbWb7GhWqbdpeOa2zRsYHwvH4VF8Ze5YgUlrfuqUl\ngEoCHMC+RECj/d9ZAhyJPxNZvKWAu0vP2+azqEhxEEfSKrZvIO4z96V/EJutEtE2qJj16T9nJb3k\nPFG5sWlzvxm9oFoNxhHBgwmpNPMX5FeUnGe7CRQWi/EQJdSveUM6HMkhGtdUqDTnmaTSvvqXUznx\npFS99c8cI63S2G2BZWx/U9KvgZ+lfwMj6XPADwmRpTkl7VgQhIAIpm0jaU3bF6QqhhKel/Qxog1q\nNWJ/VsIvW8deNq3ryiOltlocJelQInCYJVI5wtQ+bz+Xvq4I3AwsImkN21dl2ttE0q5UEGyqVQ0x\nGuic1cHYithULUSvNOFlwonNpmaZmKRViUhRo2S5HtEAnsvBhPrvEUS0+wygxFmdn3r9URB9l38m\nLhTPk7mxkXSk7Z1aJUo0N/mC9Z1ObP6WkHQx0ehewo8IcaXjiEzeJUQJai53AFekjcdFQGnPyvjk\n9C4EPGb7oRJ7hABCEyHciPh9c3tN9yI2pp+xPbFwXQBIWjZ9XYy4oZTYWod6GR+oK4hU66bXrOVu\nynvf2+xney3Ky4kBkPQZ4Cv0AmDz2l6pwGSNIM6HiExW/32hSLQtUTPrM6VzNmd93yYEgn7c9/ok\nololl5eb9gnbfy38XZ+StDNDRXgG/l3VG992n6TViRaDkvLzC9LX2hUqVyUHeDFJxxAZxxJ2JDJc\nAJ8k7pVZzirRivJ+239TtO9cQH4QAiLANB/h7M9J+nwL2I4Qt5oP2I2eOFwuVY49Rua6cgLxWZ5O\nvkhlw1fbTxQ97KWOda2/XcPGhKDpNcTxPCvwsqQbbX8jw95XqCfYVKsaYrrTOasDYPs44DhJW9mu\n0oCfqFkmdjJR0vU/ldb2TyLq9orthyWVXrQ/VWFNbcbY3iGVTG9D1PrncED6uiWVBJtsH6lQ7F2B\nuDCWSJA3Nu9JjsLjKhdXaTb52L51Wt88LRR9UWNTdn+8pBtsl5Rpv9g4lg4BpxIHZ0vi4r+JpBrR\n3p2BnxIBprMpCwhB3YwP1BVEggo3PfcEODa2Pex4pwxqKXc2jAO2J0puLyey8dmkIM5viFKx+1qV\nCIPYaM6jp2zvWrKeKVAz61PlnLW9c3p4OHCR66m9Py3pq8Rmei2g5BrwJEPL43M3+E7vHUNkzpvH\nWeXntm9OD/9I3wimEtJ1/ePJ7p9tlwRKIQIHbTGukrLnJ23/Ldl6LLXxlLAn0W+9MNH3W6rw+jB1\nlYGrHHvNdSWVjNcSgywWqWytr79X+DTKey5rnbcNMwPr2n4lVVhdbPvjkq7JtFdTsKlKNcRooHNW\nB6BVxvaOlA2dTOHmqFavH0QZ3EkF7+/nWaKf4aeKeVKlpbEzEz1r7bLdknE4L6W/3RzERSdX8bDp\n5zm+RnkNgKTdbR8K/FnSikRWpWTG1VOKBv45FGI3pSJBtTf5n7H93mRnA0lXU9ZTfL2k0wlhr1UJ\ncZ+BkLS/7e8S0d7fUTaSaDLJuV899/1ToGbGB+oKIjXrq3XT+5+UvWyPDylZW83AIcAjtq+VtIPt\nkyRtUWJM0uW0+g8VCrl/AcbZfmBAc8tLmtvlAmFtamZ9is/ZPpYDdpb0NyKA80tnjnVLbEYErb5H\nVJZslWuo1gbf9tthsgroYo5xJO+zXZq5rDKCSdKnbF+o3tzgiUSp43bOnBfcrE/R1nI9cV/8ZYGt\nZyT9irjGrwLM3uzRBrmnSfqe7T2Bt0SRgeYHnnDheB1VngffXy4uqWhMluqKQVYTqZwCr1IFHpTK\njjnAvMR+9oX0dZ70+thMezUFm65K1+Na1RDTjc5ZHYzh5kiVUqXXL3GOQljpjuYF29m9aoRj+Q7b\nt0hamcgmlXA6Ubq7BnGReFOhvaOAbxCRsb9QWJIN/FOhJth24HJvyCskB/9NhAx5aenP1kQZ+hPE\nDXnrQnu1N/mvSJrF9r8UcvOlfZxfTRdtAeNt52xmmpL1eW0fkR4XRXsBJP2V6At9nLiZPE+MTfhy\nZvluzYwP1BVEgrolgAsQ52yttZ1G37zgAlsAL0haC5g5ZRxL1R0fILI0vycCHJ8mnLkTiDK8QVge\neFLS4/TaKIp+31QxsxPl4z7a5+wywFm2L5jWe6Zhb39gf0mrEK0oJ5C/CWyyvd+nl22ck8wqpMob\nfIiS53uIwMFmkja1XZLRqzUOa970tfbc4IOJNhYRAkYlytZNi80kYrxbLhtKmgB8NTlawOR+3xLH\nvOo8eEn7E/uJWYjz9i6iRzSXmmKQNUUq+ykdxTgS5+1RhIL87cCywKEpOHFppr2agk2HEPecm4A7\nS6/H05POWR2AVhnbeUR2plZ5TbtM7H6XCYR8hRgYXxR5l7Sl7Z8SztGkvs39dwtM/8P2QZLeYXur\nFFnNxvY5zWNJ4wuj7hB9BxBjU0rZgthIz0/MHssSWFJvZhYMdTDnI8Op0ciIKkD0SN2mGCO0LDGa\nKBtJzYiKR4C5JW1ue9BRDt8GvkT9aO+VwL6px28pYB9CwOhU8sp3q2V8YLIg0ryE6NB9hdeU/hLA\n0pvexba/X7KePqrMC26xI3H8jiPaAw6Y+rdPk8VbmRAnJ+SE1vH9mrG9ROFaXoWknxDBgr/RKz/N\n6tOXNBfwAWLjvLCkq0vK7SX9IK3l74QoWlGATtLRRGnsIxT+rtRXe3+P7R0gyqAlXVlor8oIJtuN\nGrNcJtAURqSFgLkIB/VLxEZ6BiLgvOpU3jo1xhMVWssQo1KOdZ4i8KaEYONY6jrntefBf4bQOTmM\nEJY6utBeNTFI1xWpBEDRN/wtIqP/eeAW58+FrXrepmv5ecT+/W7HHOwZnS+SeJB7c2p3VcypzRUN\nvShVCuY6zqOGzlnN49fEhrJxCCdRoC4q6V1E9GnW9BzbuZvVJ13WJ9jQlMU+UMFWm0npZjWnpDko\nzKymMokdSNH29LdbvsDk/kCTGbg9py9HLZEmYhO9MnB5WlvOxmi4WbS5GakREWtJF+1fEn1W91a4\nUS2Xvo4hsoRPMfjcwR3S1ybaO5HYKJWO/FjMtgFs3ytpcUc/cZbQUsr4jCOy+Z8tXBuSNiCcrT8T\nGf59bZ9aYO8GwhE/ocT5SKwn6YcFN/N+as0LbliKmL03HyFq9qKkxVLfWQ6zpAzttYRjNLOkJcnI\nZGoK48wK7hUNKxHVM0WljokTqVhuD7yZ2DjfTVS7PFC2PFYllF5LtRegvto7kua1/aSkuSnfn9Ue\nwTSLpJUYWr6fIwC1GtHzL0KJmmQvVzwPomLrTmJT/gGiAmzgrLLt64lS9kuBe4lrQWkCAerPg38k\ntWXMme47s0z7LVPlDArFIDUyIpUNNUfFVT1vFZM8ZiICpadL2tv26Rl2mjm186g3pxZaVZIZ1BaT\nmm50zmoeE/t7Bgo5CTiSOsqdT0g6lqGKggOXr7g3H69YFKiP/YgSjJ8R4xdy1f8adiZGktQSlDqO\ncKCvBTaX9EHbuwxooxFSmI0K0VRPYc6YpLfazjpeWsGMvdsbcEWPYzaVgy7Y3qNlewwZyse2/5m+\nDon2Slo6d12JRyQdTGTi/w14VNJHgJzNG6l0/8Jkawbg8/Qk8XPYBXivY8binMRg+2xnFfgw8EXg\nAkl/IXq7f5Npq7YieK15wQ3jCEXrG4F3E5/prJKOy8wIbwF8n8iC3EZkzVcjPqNBacraxxB9fqUl\nzxCZ6DmB0qoUqFxub3tbgJQhPJjoWy2paLonvb9GOWZttff9gRsk/Q/hpBeJtrn+CCYRfbANuQJQ\n5wHnSfqE8+fw9jOv7Wa8TNMLW8LbiYDLHVQI9lF/HvzDkrYi2pYOAuYutPdjQj2+EYPMUfJvKlBq\nCkk11BwVV/u8/R5xbzyKCJSclX7GQHhk5tTWFpOabnTOah6/UvQitvtCS0p2HnUIN9WgKY1YKH0t\n3bh9I9mYgeiXup+Qmc/C9pWS/kTMa1zK5UOKbwH+UjFLs6Lt96fHP1KMYhkI9+bIXeVKYk3J3jeJ\nbP7cwJaSLs1wpNuMl/QpYuzKj4n5rWcW2DuJekGX9kgHiJKst5fabEXITye/5AyiB3k7oqTwVmJ2\n6LvJHDlF9DOdKmnrVMKb6wg2vNKcW7afUaFgk0PQ52iFWNDeRAT5fuBg2+cOaK62IniVecEtngVW\nsv28pLFEW8XniQ3OwM5qyrx/m+iNuhn4q+37pvG24Wy1s0+XKuZTZtHKgCwA3C2pWVNJ8KBqub2k\nrxHn2NxEZuWrU3/HNFkceFBSc5/M/l3dp/Zu+5aShTlEjC4h7t0TSjPd6hP2Sj+jZB78iiloOG+l\n8s6nUmB9cq+57Y9l2rpd0gdsX60QM3xQoZswJjP7+w2iLLtWsK/qPHii5PmtRPnzFoSzNDBTKMm+\nmThnc0qyvz6VQGGJcCNQb1Rc/3lLeULmWaIS8aV03SsSMiM0CYb0+eYknNL7tpS0ArF3v8v2n3Ls\njAY6ZzWPNYmy06YMYRKxkcnlgbSZaSt3Zm1CbO+nUIarIjZie4Pmcdq4lcxYHYkhxZcR8+nupaey\nVyLWco+kt9u+X9IC5EUYG2qKNQF8gei1uNT28mkzUsLORKT8zcBhLh/HVDPoAkNHOjxHhqMwFbJU\nBdXr912LKDtrxNbWKiyvmSWV/tyhmO83Z4EtiHPiP+gJNt1bYkzSlwkH/WngeODfiWvMdUTP6CBU\nVQS3fZSkMe7NCy7dSM9v+/lk+4WUjf9XbjRfFQU9JLVH/ixMWW/9SGRAaourzESIlt1fvLIgN5g0\nGUlT0mxYTtJnXSBmKGltIjszIxFIfND2Cbn26LVAjAHey1DBtZz1rUcEIydKehOwne0rCkz+mNA1\nWJ9w5kpKWdcEPibpXy07d5Ff+lw12EelefAt5gDeT1QJTCQEF3PKRdsl2ccSx0puSXZt4dE2X6PS\nqDjVn9LwNFF+/pNUyps777sRMluo7/XsoJVCtPGLxO+4m6SzbP8g1970pHNW83iT7Q9XtDeWuFgo\nPc9O1Us6gVD/moMoQ72PuCDVYAbKs1u1hxRvD2xI+RiXhtWBOyU9RAi2vCDpEfJUN2uKNUGMNFqI\nXj/xbDlG+ja8vyXmSD4s6aOFDle1oEt6b3EmdSrk3gD6+33bMxFL/naHJJu7EDfmEgVviLmy2xOf\n7R2E0FQJixLzUR9ovfaiomd8UKoqgks6wnaTcVuEyIQuM5W3TIvzJF1FjNR4H/BLxezr/pl/r5Wa\ngh5tZ+t5ykavNBUgKxH3i1eAA9O/LGXW/nL73LVJeguRjfkWsGzKAr8AbGv77gx7UxMFHPRca66/\nnyUqja4mjpPFB11XHwcQx8k5xGdwNdGbl4UdPfWJOyWVqsfvC6zmmPG9EFE+WbK3eML2Gem+s6+k\n7Eyj7RIl3ClRNdhHvXnwDVXGElUuyb7d9g19+4ta3E0ErW5SqI2XzIWvPaVhQ6JK8A5J7ySCuQPj\nnpDZFQz9PEs0E74IrGn7pVRpcA2hNv66o3NW86g6w7Byqn5lQonxWOJmXyT1nfrTmg35rMT4gBJq\nDyl+GPiD6whmYLtEgKLf1n6K5vsliQxUySxJiIvYFcRYg8OAizLt9GcXnF4rdbiqBV0AFLM4v0Iv\nAzef7RUHtHEGr76RjyFTaMQjM0QdorRuw/T4u6n8sYSxxIbmHEJMahHKRkMcAuwuaRGit/YW2/fY\nvjbDVlVFcGLsz8HE5mMFomw0G9sHSDqfiOKfaPs2xbzFYzJN1lTarDpfMXEMkendD9iTyHb9NsdQ\nu/RUPXGVnEqXY4hsAMQ953hig3o4eZ9vNQfT9rEQVUK2mwzPaZJKhb1ecSiJTkol6M+UGOsrJVyY\n8jFxz9h+HCCVO5beu19Jm/vZFQfLPNN6Qz+SfmR75/R4U9unpcfn2i7p+a8d7KsyD75FrbFEDf9S\nqL3PQJxvOSJBIyLcmDiN2O/cRAQiNySz9JlKUxpazA/sJ2l5Yo/3DcqE4A6gnmbCGNsvAdh+UTHj\n+3VJ56zmsXL611A0J7Byqv7J5ATO4RCSyV1WwxaEg0SlvtCa8xohNuU3S7qNXuAgW2VP0mrEjaq4\nj0YxlHwxYtP7AjE3K7sMzTGsfE9J8wDfyuzFedWGt6F04zsC/RHjiA3DDsDlhMjPoAznYOQ6HkC9\n0k5JmxBqqetKaq4hMxDlYocXLPFsosxufWKz9ROiZyqXE6inxlhVEdz2norZmUvbXifXjqRtbB+v\nECxpHMuVFUJhJT1X1QQ9VH++IkSG9nZgFtvXSSq5ztcqPV3E9g8V/YLvAj6c7mtZx8oIOZjzSFrK\n0ZMsop2ihHvSsTdvqlApCS7B0NErzxOl9wOT7mMAM0m6kJhlvirl6se7EMfu4cQ5ktOG0g5ebk04\nIZApOKSYr9xwK70M3uqUtXrVngdfZSxRi2KRoHYgt2Adw7GoY5Qitg9VRguU6k9paDiOuNdeCaxD\n3vzsNjU1E66SdDaRyV+TCNK9Lumc1Qyc1FkV8vIv2y6KgFI3VX+jpN0Itc0zySwVbbGP7awo+5Tw\n0HmNf3bGaJg+DqqwrDY1+2jWsL2WpMttn5xKCbNJN9KjqdTTVHvjOwL9EY/YvlbSDrZPkrTFoAZs\nl4hYTI1apZ2XEjMf56U3ougVysvOZgcuAL5ue/OU4S+hphpjFUXwpjw/PR0DLChpAkBGyT70Suqq\n9l55qKDHnbZLSthqz1eE+BueAlwkaUNibE8WFUtPm6zdWsDv3RMbmiN3bYmaDubXgXMVQlIP03PU\nc9mBKBG9ihAJyxqvJWld25fb3q/12hhCKyJnZrD7vsJQVeAsbN+eriPLAJvazjnvxgzzOLfNo32P\nXpfQxGjaPLKdVdefB197LFGxSJCkV4jxcm2nudERKVUtnyRpGdt3KZT8c4TbRqJHH2BW279Mj8+T\nVCJ6CRU1E2zvJumT9KqEaqlv/6/TOasDIOk9RNRkVULR8ljgfyTtZvuCAtPVUvXJGXwTIUjzCaLv\nqoRXJI1nqEjQ1Pp/popCtGg9olR0QcXQ+IHHzkjaPHcN06BaHw0RiZ6VuNDOSPScljCOij1N1N/4\n1u6PeCE56DMr5lTOV7i+mlQp7UzH/hXAFRoqjLYEZaXFsxDCGTem8qTSTX5NNcYr6W38fjm1752G\nnYXTej5o+7JcOy17jajIKUSJaMmIFNK5vz2ROXoG2Ah4Pt0vHs00W22+YusaegkhCvIskZHK3tRV\nLD19LAXTPg4cnO5pXyUywCVUczBtX0XMqK3Fy0QQtxHKWY08B+kYSV9yzAxFIdh2Gvl9jScnOzMR\n50UV8UZJexGf7x+AXZIT958Dmpk0zOMsbE+ufEpB5pJZqJORtA2RWZ0tPS9qOXIaS5T2U09WqHp7\nhnKRoN2Ivd29wGm2S9s72nwdODNV5EwgQ5DPvR79pako8Efs81a0fatCsKn0OCzWTOi7DkOIQC0m\naTuXiXxONzpndTC+D/x7cii/R5yYdxM3+xJntVqqXjFs/vtEtPI2okykhNI5qP38PP07kSg3+Rl5\noyyWS1/fTzjm19C7kZ5SsL7iPpoWhxF9B/MT2cbDCmxB5Z4m6g8Wr90fsSOwLOGkH0CZEFdtioeo\nt9FQYbTZiRt+iXjJrkR/3veAzQjHtYR+NcaBqwTUm6v6Kko2boTwS7Gz2uIc4pxtBC1ysyqHExmy\nGYjyuj8QztaPyZ+hW3O+4nKtx/MSjuVaRFAyVxm8v/R0w+G+cRrsSJR1/sD2LyStTgTWiqpT+h3M\nFFQbiFZGv8m4QZ0M0i+IgNxfKMvm/T/CId+AGEd2MnCk7R8WrA1CFG1mQmxtRsJpOKPA3ieBD9h+\nJTnCVwGDOqtLpTLlMX2Pa2hPFDu/LXYkkge5QaohSFqHOEcnAm+RtK1D3CyXDeiJBK1AlLYORDq+\nfpiCmptK2pfYl53aV3Hxmukr3X2BXmn8EcRM8hyqCvwR98YTFXoOf6VcAf0sonKhRDOhhpbBqKJz\nVgdjRtu3pINyDts3wuTyh2xaqfplKU/Vn0CUsV5DbDxOJEQCBkbRQ3di6hVaCViw8IIIgO3mpLs5\nlZ3l2NgjrfFS25Pnvqpg7mCiv4+mRI1xvGJe5tLA/S6fTVe7p6n2YPGrW0GXNcgMuiiEixoaga+t\nHbM+s5C0KpExmpwta/Wu5VBjiHqbqsJowE6trMCRhbawfRvhTAMgac0MM8umr2OI0Qi1VCMnSTqX\nodUfJT2mCxX2MDW80/YHUoZ1TWD9FMTZtcBm/3zFkh74PfpfS2u9ggxnVdKyTemppKWA2TxUPXqQ\ntT1LS8zPIeSVI+bVv8btiWt8k1V5ieg3H2RtI7URXLDGcWf7TknrE87vi8AXbN9UvLoQuFtd0vFE\nlrt0L/AYEZj7B1EJ8niGje8O83ifgnWNBE80mb1KjCPajCZIWpT4rAf+PDS0P3+yKFoi6xqayrn3\nThU4/0HMbs2tUhmJ0t2qAn/p3HpfpbUBnGB7DUK4sfkZg54bN1ZosRtVdM7qYDSZoo8TG9UmMls0\nE1HSXERj9juJVP11tp/KNPey7UvS4wskfT1zTdsRke0PEyUiY4DvSVq8pE+S6GPalBDMeS8xAHkZ\nyFZUXkDS3Lb/LmleerOqBkbSPLZvJwaMLwh8LMfBVMjTT+l1bGePmiBmi21FRKD/SWZPU4vt6/1t\n8AAAIABJREFUiWxF8cYXwPaurf6Ik2znqhUfO4XX5k9lYvtN4f9eCycTirYDl5y3Ud0h6m1qC6ON\nTQGmu+g5cCUCHP38BwP+vm6pLkp62eUqjA2l84H7uVPSIrZLFZ6byocPANfbbu4f2ToCDtXzydkF\nST8jjsMqpIqNgY8TxfzsAyWtansiMa7rp5K+5RiPMVr4CnGv3Yu47mXdH2HK1/nC63ut467pB92A\nqIbIcQKnRDMbdA7bzymjrxGGZMsWAO6WdDMhyvfkoLbcG/dRhT7nrcnUNj9rYOet9f5ZJP2KKPNu\nBJFKAmovN8eJ7b8qfw5s1f58hfjjhvQqKn5OQTVEZQe/oYrAn6Szba+vV2snlFZY/FMx7aEdfB20\ndHcXQrUfST+3vVHBekYFnbM6GL+RdDUR2f5Mih4fSZyQJZwI/I7oK1mbUBf9zCAG1Jtt9U9JuxPl\nQ6vSk+wflK2BdWw/B2D7ZkkfIsrtSpzVZdO/bVqvHUu+ovL3gD9JeooQy/jqNL5/iigGsp8i6V2p\nj3Bl4DiFHP6gyn2rEBHjU4kM95ipf/tr5kLbNWeYLU4ISc1OrHEDCuZ7SrqB+J1PLAi2TBYw67M9\nAzH+J9dZvdv2SblralFziHqb2sJoyzBUBKVUgKOfWsd0De6vbG8N4CFJzSY/d/PxjxT0Wx84PR3D\nm1KehW9THNUYYiw2cTn9zbsBqydHFYcI15pET/JoclYn2H4ktT5cIakkA9fc98cA76Gwh5PIvhcf\nd32b57mAOxRjZorLlBXzam+WdB2REc2hyZbNRrTwjCbazlu2NkeLKYlT1eBphaBhMwc2637b14+8\nBbEnuIyMmdKpHWZRIgi0LT3ButHGfkSLTJHAn+3109falRbXpK8Lpq85QaH2/XmBsuWMDjpndQBs\nHyLpl8DEVH6xFPAT2+cWmp7XdlPy9KdUwjMoTVbsKSKz1fQj5Ubcnm0c1Qbbz6iwT9KVlZRtn6OY\niTg/8LcCoYFxwNrJUcX2ryV9hHDMByp5tL1S6vvYjJjPdiXRt3FP5toa/kcxe7SdLSuZ3XoGIapQ\npY+GyMJ/kcjo/wU43vZvSo0qxKnWoMxBOic5gY14CbYHdsxdd4h62+53FGM6niN64YuE0TzgPNoM\nctQi2+Xds0t6B+kzLTyOm8j9GKI65QHKlDuXmfZ3vSZ2AL5JnGMnE8G49SkT86iGXj2DeFZiTEyO\nmuXz/QEq238ryPgAk9s8Pl5io4+Jkj5LZFe2p0C0zT1BLoBLS1tQbA8pR05Zqhw7I1KmbPuo5rGk\ni4Cs+5l7QjdXpXLHYiS9uQmUlFA7U9tyBlcDVrV9uKTTiMqUEjYjqgPGAX8mKq5KOIbo3/wI0Vt/\nCtFjOwjLk1p2Wutpeq9rBkqLcJ/An0IUaWCGq6BLPyP787C9n4aKLeYEmKoKj40GOmd1QGz/ufX4\nXspHTADMJmkhh2T4gmTIcjvNtpK0BKEk+lBuv1DipVQWO3kDkspsBxakSO+tqqQs6UjbO2loA35T\napvT9/NS/9/LIZOe1Y/s6PH7dlrTWsBBkt5qu0Q0ZwFCUbChaL4vEZDIzVS+CkdP6dGKGWh7E9mk\n+4GDCwM6sxIZ86yseeIrhHBOdt9rHzWGqCPpYeLGeSnwqxS8KRFra+x+hvidmxvevLYHVi7tP78S\nY+j1nw5Cu7z7OWL2KxQexx6q4DkLIVCRjaR3ESIZ7f7mgTcfqYXgW62Xfpv+5axpShUVY4jMWS79\ngh3PEePEcgKIkyTN1g5wSpqdzPtFi7+n1oJ2Sdx9Bfa2ITQE9iBEyAaejdzQ95ksTC8TkmvvCNtf\nbdk+kqiQmK4oWp02t32CpNOBhYhzdjvK9j81yh0bLiICmqOVI+hllPcmqufWGva7p4Htiams+G6i\n4qg0Q72U7W0krWn7AoUmxqBrelvhGkYUxUSBHxAJnS2JCpcDic9l8QyTI1JBp6Fii7MR2d9B941T\nEh4DisvPpxudszo62Bu4RtLTRP/rwGpiqfb+TKJn8wFg6VROtInzZnqNA/4rRY/uI07mbclsuqe+\nknKjDNvfgJ9bPjmDpBlSTxgwOaNXMhpiTmKY8ybEhefUAltzAZ90CI8U0cpwPSbpi4RicdNHk53h\nkvRlYHNCJv144N+Jzep1hPpeFrb/SWSkSnjSaWh5JYqHqCeWIm5M6xDS/GMJgZtLUwQ4l3FEBm8H\noj88S2SNigIXUyrvHgFmojyKfxLhKIymMrbh+smzRYdcdwbx4cDFkv6TuF+8lcgql4p7LUoK+iUm\nkbnJTwHTiU5iQ4rRKfsSLTg5tD+T5ynPbj0t6WCih24F4h45GjiU5EwS+4CtiRmkexOlo7n0lzuW\n8JSknRnq+JaKLdbkxZTcwPZ9uUHwhuR8LEZU0L1ABF9KNCdmUow4mpT2LUXrG6UcCnwBeBtwEBH8\n/wvR8jUwI1hBV0NscTjhsdctnbM6CnAo7C6pGP6bqxh7CDDe9uSxLYrZXt8nbybV7yRtRDgf7yZE\nPTYsiGrXVlLei1A8ndyAL0mE05Az++5U4Ix0E2g2W3uT0Y+sUDjemMhwnwPsUJLllrQTkQV4SdJO\nfeVnOTQ9wmOIAMTuxIy/iZRlahcFNu77XV9M5XbTmyckHctQgYuSeWPFQ9TTGl6gN2d1PsJp3Zn4\nXOYvWN8jtq+VtIPtkyRtkbm+kRC4qIqGjhKZicHHX/TzqO3jixdWkaZyZrRi+zxJjxHH7SJEwHQP\n29cV2l0ztYwsSSiqZwmkSTqCuI+9WdI4IlB6NgWKtra3TEHEonm8LXt7Svo+sLTtdWrYrMTKtpv7\nwou2DVjS1iVGU7njh4nP9jqivSWXJ4kS9nel55MI0bssksO2HkOrK0pG4j2Y9hbXEtVlfy2wBaEE\nvJZiFuzJijmcJexFqPcvTHwW2cJjo5i/p2D8XSl7ub/tgUf0tBmhCrpiscXa5eyjgc5ZHQD1yXy3\nyUmtT6nErjkwM0pZV7Y9pKTJ9vElN5QUIaoVlamtpDyfpO/Z3jPZ+iLhmO+eY8z2cSmzfRi9zdZP\nbeeIZ51JCDXcDKxIqGQ2PydnyPgXCSGVuQgxgFJndVdC1KspyT6GKI8tLQk+BNg9BSQuBG6xfY9j\n7MTASFLaGNWgiXYulL6W9nE8TfkQdSS9l+gNavqDfkVkpIp6VoEX0s1z5lT+lN2bN9oZgR69B1IZ\n3E30AhtFWRpJCzB041tTZGlU4ErjZdpI+jxwMOHILC9pT9s5sz0/YPs9kt5M3H/eBGxrO6ssO63t\nZKL0dCK93rz3ZNjpVxNdUFKj9JotiJRaAXYi9nljiNEzOf157bak9rijnIqtydTMDvYHc1LPXwnn\nEz2cTXVF6f1iS6LK5ROEbkLpzPCZFGOmJqUKsFytDmBylYUU8zyfsP2G6HPso/03eqjUUW2oWUGX\n6BdbnL3Q3huCzlkdjKoy39SdIfXiMK+/VPFnlFBbSXlT4KxUyrUYkU1dw3a2MmhyTEuVnSFKpGry\nvGPsyBOpJ6+U7xM9SP9KWYaPE87cJYR6Zy4nJBtrE6JNJ6THJfaK+pAkLWb7YcqG10+JDSkcop64\nnqgG2KSwx7yfHYm+0nHAARRujiSta/vyGgtrfSbN841tn5lhZy6iB/ExotT5ZKLsfBfbfyxY4lgi\nONSEtEuzNEcTm9QJ9JyaGnNc/y/wTeC9DnG/uYie35xzuVEpnpjsfKzC+bas7aUKbYzk3Nb+VoAP\nZ9oZo1BPfqbJlKe/YWmPXrXsoKT9iWveLMTm/i6ilDKXGWxvVvD+fmYkMvkvEdUHC1E2J/2HRPvO\n/MB/E0H2gVHoS7zKMVVof2RVWUnanAg8jKU3ymU0CCzNoRD1mwGYUYUCf7Ur6Br8arHF/y61+Uag\nc1YH45HK9iYQ2az9HfPtPkVszvfKsPWUpFVs39C8IGkVMiXNa+PKSsq2X05lyr8gbk5rtPtNB0UV\nZ2ZV7gXrp0YTf39J9h8BcktZW8xr+0RJmzlGV8xQaK/WvLFdePXs1lJxqgWAT2mocnfO2J81iBvS\naZL+QWRrL3EMVh+Y9Df/BDH4vMkafSHHVh/7ERveGpytEM15Cfgx8BaiGmFQTieqF5YmepD2AR4m\n+iezgxwjkKVZFViy5PrUWsuMRDnr5Gh7YW/zaGeyYrztpyXlCsm0r20TKgWGrq9Z/aFKwl4tqrQC\nAEcTY2t2IwSV3k4EPI+Y6rumTc3s4GeIoPVhhCN3dOHabpH0fuBP9KorSuZUn01c69YnMqs/AT6W\na8z22ZJ+S1z77qc3A3dQdkhf9yFGTF1Nr+Iql28Bn2Z09fxD/I2a/UMNgb+qFXSKsVBT4t1kjhSU\nNAZ4H0OvKa/L+0XnrA7GcCUquZH3w4iMaLOJuQb4KHGx/dqAtnYjZLivoHdD+TBx0chG0uLEhat9\nsP8wx5YrKimrp8R4PLE53VnS7cn2wJ+FR25mVg3eqVBhHNN6DGSXFQ9Xkp01HLuNpGXT18Uoz+oX\nC3DY3iV9rZ3tHk/87YpuyK3Sye+mUtGPE6XFi2VGo39MzBt+k6T32i4dkdAwSdK5DA0c5IqtfY0o\ns3szcJjtYUcATIM3t9oAPmD7p+lxVitAwwhkae4hrp/F4mjEpndueuOmJlEwpqc2ki4krskXOH+M\nWJsHJR1Cb57kA5l22uqYS6qOOuZE4A8pyJQV2OzjJOoKe1VpBbB9ZmqROYjYVzwEHOkMFf8+DqNC\ndjDxiO0XUgb4ngoVSGszdO9UOn5ldkJI8uu2N1f06haR+rf/ACDpesLJHNSG0/sXtN2oqJ+rmOGa\ny30uFxmqzgjsAWrbe6z1+BuUnQ8N59ATkoJRdr8YhM5ZHYxtbb9UqRQTorxp9eaJ7UbRbuC0v+0H\nJK0KfJK4qF4P7OlQUi3hgvQvS9hiBGkHDi4nyoBXoqBkTyFktCFxU38YOKPZAE9nNmw97h83kUPt\nkuyGrwE/JUQuzqY3/zILVxTgSFHLnWg50IUby2ds51RA9K9rDPH3WpPYjL8DuIXY8OewvEOUZmai\nJLuWs5rrUE5GQ0d9/JZQKH5Y0kcze0LbrQ9Pth4PPPqrj9pZmsUJp6vZwE3K0CRomM/2QHOf/5fZ\njVDF3VcxWuN423cX2NsC+DLhONxBvobCSKhjfhCYx3atVpvawl7VWgEcM6WrzZVONsdL+g2hiH6/\n7Sen9Z6p8LCkrYhqnIOIgE7J2rIUYqfCLIRw3o2Slid6G2tSXHGl0De5nmhRKMkiPyvpEoZmpV+X\n41KmRu0KOtuTq79Sa0x/NVgOCxXca0YVnbM6GKcQYjemr1SUvKjbq0qaHCpgWQ6m7eeJSEpN/lpj\nU94gaZbCchqgvjqmpH2JPpKtiQjX24BdJS1qO+smL2lpYANaw51tZykz5/z8qdirWpItaWViI/QY\nsRH8OeF0rUCI1GRRU4CD2Owu4dYcyEJuk7QxQ0V4cpzpCcnGb4D9bN9auK4X01perFCG3eY0wnFY\nHLgMuC3DRv9nZ3p9+znO6qKStiPOrfbjkiAE1M/SlIyU6OdBhdrkaCuxAyCVr+8u6VCi4uU2SVcC\n3/UAQmuS2mV5t6Z/EGOiLstY10ioY95FVH2UKrs2VBH2Um80GfQyKntM6XunJ6lN6Vjib/iQpO0L\nrn/bE8HX8cR1quicU6U51S12BT5LjDzbjHBca1LawrMpsCcRGL89Pc+lalDj/yi1BK7ulLSI7QmV\n7E03Omd1MA4GsP32SvYeH6bPtEa5WC0uUIjw3NG8YDtnnmTDDZIuIyLuORvekeJj7Sw30bOyJZG1\nzY1In07MF12DcEqKy2xrUbMkmyg93QeYh/h93w08TvRf/qzAbk15/r8xvAhZDu0xCZDf97JEjeDN\n/wLHEMfwR4jSs1PoKRi/JpoAk2JEz7tt/1eqZshVTzydGLXQ/7hUTKtqloboxTsMWJ5wcL4xqIFW\nL/2swIaSnkrPS0tPqyJpPcJZWI44979ObPgvZrB5hk0wshlJROv5wM7qCPEBwsF8kjqfRS1hr+Ey\nMqV9+rU5HPiSQ6RuRaKCIbdqYAkiINm0K30a+PPw3z5Nas2pBiBpOMxOOINXklklpClPpBhDjI3L\nsbew7UdsPwp8tfX6+xlarfJabDV72draLtVJ96B23/8bTp09sQYRCHo8PR9V94tB6JzVwfhR6uH8\nHbER/7XtvxfY2xU4X9JDxGzPxYmM3galC22QNLPtkk36hkTP1dj0fBKxMczlXURf3j4KmfRTgTNt\n/6PAZg2e73/B9iuSSkq8/mH7IEnvsL2VpN8X2BrN/MsxKxhJOzdlf6mXq4RiAQ5JZxDH7ILATZKa\nAMmkzH5foF7/ywg4qmsoxl6MAeZpPS69SS1lextJa9q+IGWAcjkD+FF6/BRxDRhY0MN26ail4ejP\n0mQfJ4njiIDOlcQc3ROADw1iYJT20k+JzYCj+6tBUuXKa8b2l9L7vgGcbHtUCAW2sf2Oyva2TFnR\npYlWgKxsyAj05gEg6fSSa+YUeM72HQC2b5VUci08nxBbrNWuVEucCqhaJTSc8F5ume1ppACGpJ81\n5x3RnzxoYONDwA28+vcqUlOvjaSfEGt9jArq7JLeSvzObV2XgQWR1BtlOYYY03UNvXt31vpsLzPt\n73p90DmrA2B7XUljgdWJTce2qdTudzkHp+2HJb2PiH4sQvT5XeeCGVeSdrd9aHq8IjHOYeDZby3+\nZXvbgvcPITmAlxAn5TZENG9LSWfYPjLHpqQPEX0v1wF3pXLoQRnub17SCzJJ0kLAnJLmoDCzKmkl\notflFeBA4EAXzAmsSFvltP23Ly1DrSHAUaPHdzKqqBo9Etiu1U/fz0wpGj1JIatfomw7h+0LIao0\nJFW7vpSgGLnQz0RgFVqVJRnMarsZCXWepF1yDaWqlDYvEqWe41x39FEu/w5ska7JlwG32X4it8WA\n+P0ukvQgcEITFMtF0tsIRdZ2VmWge7ekeYC9iWDzcoQw0gvAVpmtAI3dnYDPERUqJxGtFDtN7T3D\n2Bmpa9TYdA+6i57I2sAOZirXB3hRMdbpSkIcqGRu619s71vw/n5qz6muUiU0AuXs7f3NYsO8/pqw\nfUh6eHW791rSoGKhI81KwNIl++w+qogtUneUJTAiCuPTjc5ZHZDUy3QjcUOZk3AE3zX1d03V3ivU\nVedaQdIOhGO0OYUiN0SZ0zeBP9Lro8kuw0q9TJ8FrgAOsX19cvhvJER+BrVXK2LZZKTajCHGauSy\nH7H5+BmROS8piYVwvHZKdvcEDiWEaqY3U1IrHkOUPWbTEuBYmlAYHFiAo8nwSPo0sIrtfSRdSqbS\nnkdINVrSTITEfLu/ufZs2BL2IsYaLEwEhb5eYOtfkj6S7KxK4UD7iizXerwJvXLi0k3NTJJWTNmj\nFQvtPUh8Dr8ngqafJtSkB87WjhDF5eJtUgDzyNQX/62UFTkBOML2xAyTZxBVUY9O6xunwjHAVenx\nEcR961airPXjBXY3JkTWfmv7R5L+kGNkpK5RwDJEBrMhV6ujWVfTwywiKPSn/KVxgaSDGdqudEqB\nvapzqqk7pud/g4GvUZI2IcTp1m31nM9AjHU5vOLaSplA7N1LgiNtqogt2i6ZuzscJ1FXYXy60Tmr\nAyBpV+LGOzcRSbkQ+HZhmW1ttiBKO+YH3mf7hUJ7s9NT2oXynqG7iX61ySJSKdv6uUx7tSKW1TNS\njnlWTSDil1P73tfI84T4wSy2r5M0Wm54w6kVF2U1FbM4dyRlQVQwqJxw8JvyuI0IpdxfFaztncTv\n9xaijPW2JluYybmEo7oooWY7gfLey2okp1+pdP+Jwqj0NsAPiA3MHUTZbTa1HH3bk0VoJK3Wfl7I\n14ATFXON/0pEunNZ3D1xOUva1PYJw2SFpwdNufgaFcrFSVn8jYiM7bPA7sT5cRF5s3SfrVA+vrDt\nw9PaVgJOcQgjliq8zkDqfU3Ps+7dkoZV7i7JqtheMfe9fXb2g8kzg99JK+tTwMZEj2oTcMq6PunV\n4lSTgK0L273g1VVCWeP/RoBJwzzO4VKiX3Veen3Tr1Cmh1GNVpntAsDdku5L/1Wizg71xBZHgtoK\n49ONzlkdjL2JE/IgovR31DiprRMRYtO2MnB52uBnn4i2vyRpOeImcJfLRZEuAY5VzJQcD9xi+78L\nSthGXcSyr/wKeiU1pWVYk4hMxcWSNqSuYFA2/f1pFTmAEKMpyYI0vNhkYmxPrODoH06IwBxHZHou\nIYJXucxne3VJxxOl8UXljrWQdDlT2MTkBA4kzeQY8/EQEeBo+oVKGQlHv1aJGLZvIpxpVK7kO0sq\nS7yW6LOaWdKStMpapzNNuXjjaJaUi0NsAM8ANrd9f/NiKm97zbSckMdSBqhdKTToxrIJtK4N/L4V\nuCl1Vs8ggptLSLoYOC/TzirE8XAqMau6eKwJjIhC7sXESJfGEZwEfD7T1gu2S6vIYMriVPNLGl8S\n5OirErrf9hPZK2TyubUeQ8s7czLJw2kcDFxR5pj7eoWk3xGZy1eIyrLRIqTZlNnOwtDRPPMU2q0l\ntjgSVFEYHw10zupgzE+o1X0CODA5JZcAF7tATUzSCoQAR0mWpl3v3mwCx5IZnW2t7ctEtvZ64DuS\nTrNdMqz4WGL2497EjflkYLUCezUHi1dhBMqvGjYCVrV9saR1GYEeh1HGUxUd4etTefK1ROlp9kid\nBsdYk0m2H5f0TKG5RgF8DtvPSSpyliQ1SpZj6fWr5ZRl75C+7kNsnq8m/n4DCyJRf/RXw6h09BtS\nG8XfiYqcLSVdaju3b3UL4PvAfxKlp1sR18/sPtjK1CwXB1gmtcoAIGl+24/bHjRj23ZC2pntnI3l\nI6n95KPAuOQ4fJ0QRcrG9hHJoVkBuNOZY1xsr5T2FJsB3ybus6favmfq75wmVRVyiV7utQttNDwo\naQ+GBiEG3pR7CuJUqU3pOqI6Z2BSL/JGRMbxYSIoUTq//XwiKNcEvrLuFyOkcXAGEbj9N6Ja4POE\n0zq9eQGYi7gPfYm498xAXBtWzTXq0LJ5MyGOeq8LxUIlrUYEwtuVQh/LNFdLYXy60zmrA5AyqZel\nf0j6OKHCdhRlg+h/RGGWpql3T2Ily9j+pqRfE32SJb2SXwI+4JjbODMRqS1xCGezfZmkvWxbUo4Y\n0mRqRywbUrnjk+2NUoaNKqWikj6V3vf59LzZbL0D+Enu+kYrrd/vX6lH7UZ6G5Cs39f2VyV9lrho\nj3dP8CaXp5JDOEcqASotE/uFpO8CN0u6DihVUt6ZCKoVqWPaNoCkBW2flV4+V9JXp/K24Ww1SqJ7\n284dVzMlqjj66ilHt3uvgSFrz+ELRC/ipbaX16tFkl4zjjFT/dmn+6b0vdODyuXiAHulzf7MROby\nXob2Fr/WdU3JCcnNcu9I3K8PtH1e2lzOR2Qds+kr311PUiOedVTKWr1mUgXUt5PdtYCD0u9bEhiu\nqpALXJmqBNpj1HKD/jMTPbVNBr3KpjxVa61BZnZavfntWxHqs0sAu6lgfntiBtubFbx/JFnE9qmS\ntk6O3G+m94ISqxH3RdHbN71CQTsQgKQvEEG6mYCzUgC75LP9MaFHsj4RkMwOKLRaRpq1vl5U5V9F\n56wOgGIG6prp37LAzURmsPiiUTFLsyO9KNEniahqibM6pil3Tg5r6aiN59MNasZ0k89yVlsby/7X\nizaWKWN5AtF8P7ekbZ2vQFmrVHTe9PV1e6EZkOb3/O/0daH0tUQlex5gNqKn5i2S9rB9UP4S2ZoI\nVD1BlN1tXWAL20e11noR0dtdwi2EQma1snhJWxMVFv/G0DKqQdmW/NmqU6KWo1+t37qPl4lj+LH0\nPLtkt9ViMIYoX7vP9sDOW2362lDarxe1oRCO+eJENc7hFAq11MhyO9Tmf9x6fh2ReStlNsIZ/z2x\nsX4fMR/6ZEK4ZiBSxvfzhFjYHJSfc7UVchckKgTaZcC5x8p+xHHyUEFL0ZSYlajWGDg4l+if336r\nyue3Q8yBfz8hStUEckfLvO5ZJH0euCO1BMw5vRcEYPs8Qo39E7Yvrmh6F+J8vZT4TG+g7LN9wvYZ\nkj5qe99UVp2FpP0Jn2AW4r5zF9En/rqjc1YH42AiWjcOuKlC1LihZpbm5dQX1jiXpWu8VtKZxA10\nDXoORC7bEeIq8wG7ka9WXHUkSYsDCNGmCZIWJWa3ZZcV1ghCuCdX/xbgJ06z6d6ouCfAsVc7QqkY\niJ7LuUQEf0UiQPLs1L99mhxKHBt71nAIFWrFWzJUbCRbRZWo/rhP0r30yoBL+mg2JRSoNyREvjYt\nsDVW0k1EOXAzAqNk5m0VR38Ee6+vSP82k3QYIQ6URbvFQNISwL6Fa6vFSLUkPGL7eUlzpUqc0rLF\n/iz35RXWWIv5bTdK9r+S9Gvbe0saaFqAQs9gYyKLdw6wQyUHrl8h94BCe8uWBlokvYkoO50XeABY\nWtLjwCa2i9VeHUKQ6xeYGIn57RD90p9uPS9tpajJocTxtwshLld6nFShtZ/4kqQhCabCypmXHVNC\nJjmE1v457bdMlVdSVd7skkRZT+1niGkZhxGiXkcXrm260TmrA2D7wyNkumaW5nxJvycyIO+hUIXW\n9jck/T+i9OpM2+dP6z3TsPcwFTY27o0kmYvof12eiBqVXhhftj0h/Yy/FpYp1y4V/T1waIqY/xT4\nue3nCm2OOlIGbxtgOUmNwzYDER3MVWgdY3uHVGq3DfG3LOEU4P8B+0i6BzinsLT4B0Q/WK2h9tsT\njmXpMQeA7UfJzy70860aRprNx5SqLFIFyIW2z67xs0qwvSfh6CPpD64kzGf7QUnL1rBVSqsNZWlg\nA1r9VpSpPU9I5abPSTqAslFi8Oos92yF9moyl6Rlbd+ZPtc5Jc3L4PO5zwTuJCq/ViT0NYDiTfmj\nhFP4JsIhKQ2E35Kqq9riL4NmBw8m2jomiwtJ2obo6y5SGa/ESMxvx/bKJe/vRyGetRMRlnY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QHXaN1QTznlXpv5F/tV9bqKjhM7oBtSrYFO/5vplLoprKLvJKa/YcQz1K8zrX2T2tuxlLQD8Ebb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\nppRSSimllFLqnP8PnenI93/pmA0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c95b4b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(list(meanDf['mean']),'-o')\n",
    "plt.xticks(range(meanDf.index.size),[x.decode(\"utf8\") for x in meanDf.index],rotation=90);\n",
    "plt.ylim(0,1.01)\n",
    "plt.yticks(np.arange(0,1.01,0.1),['%s%%' % x for x in np.arange(0,101,10)] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}