{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><h1>In your face:<br>Biased judgement of fear-anger expressions in violence offenders.</h1></center>\n",
    "\n",
    "# 1. Questionnaire Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "This is an anlaysis of the [AFAS](http://www.researchgate.net/publication/270567168_Combat_high_or_traumatic_stress_Violent_offending_is_associated_with_appetitive_aggression_but_not_with_symptoms_of_traumatic_stress), PPI-R and SCID-II scores of the participants.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "import fnmatch\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from myBasics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AFAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Overview of the questions the AFAS contains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Traten Sie aus Frust einen Gegenstand oder warfen ihn umher?\n",
      "2. Fanden Sie Sportarten aufregender, in denen Blut floss?\n",
      "3. Waren Sie so gereizt, dass Sie sich an anderen Menschen abreagierten?\n",
      "4. Wollten Sie jemanden noch mehr einschüchtern, wenn Sie merkten, dass diese Person Angst vor Ihnen hatte?\n",
      "5. Schlugen Sie aus Ärger so fest auf den Tisch, dass Ihnen die Faust danach weh tat?\n",
      "6. Wollten Sie möglichst vor Ort sein, wenn Sie von einem grausamen Ereignis hörten?\n",
      "7. Provozierten Sie andere, einfach weil es Ihnen Spaß machte?\n",
      "8. Wenn Sie wütend auf jemanden waren, malten Sie sich schlimme Dinge aus, die der betreffenden Person zustoßen könnten?\n",
      "9. Schauten Sie einer Schlägerei zu, auch wenn Sie sich selbst dabei in Gefahr gebracht haben?\n",
      "10. Rutschte Ihnen die Hand aus, weil jemand Sie provozierte?\n",
      "11. Machte es Ihnen Spaß, sich zu prügeln?\n",
      "12. Wenn Sie sich bedroht fühlten, ging es Ihnen besser, wenn Sie sich vorstellten der betreffenden Person zu schaden?\n",
      "13. Gewöhnten Sie sich an die Bilder von Gewalt, sodass Sie immer grausamere Bilder anschauen mussten, um genauso fasziniert zu sein?\n",
      "14. Wenn Sie frustriert waren, suchten Sie körperliche Auseinandersetzungen, um den Frust abzubauen?\n",
      "15. Fanden Sie Gewaltfilme aufregend, in denen die Opfer richtig leiden mussten?\n",
      "16. Machte es Ihnen Spaß, mit anderen Ihre Kräfte zu messen, auch wenn Sie den anderen weh taten?\n",
      "17. Fühlten Sie sich für einen Moment besser, wenn Sie Ihren Frust an anderen ausließen?\n",
      "18. Stachelten Sie andere dazu an, jemanden zu beleidigen oder zu mobben?\n",
      "19. Standen Sie so unter Druck, dass Sie andere Menschen beschimpften, um diesen Druck abzubauen?\n",
      "20. Fühlten Sie sich stark, wenn Sie jemanden körperlich angriffen?\n",
      "21. Schlugen Sie zu, wenn Sie sich in die Enge getrieben fühlten?\n",
      "22. Waren Sie gemein und mussten Sie das nächste Mal gemeiner sein, um wieder die positive Aufregung zu spüren?\n",
      "23. Fühlten Sie sich erleichtert, wenn Sie jemanden anschrien?\n",
      "24. Faszinierte Sie eine Prügelei so sehr, dass Sie nicht aufhören konnten, sich zu prügeln?\n",
      "25. Wenn Sie sich ärgerten, schlugen Sie zu, um Ihrem Ärger Luft zu machen?\n",
      "26. Beschimpften oder beleidigten Sie andere, um sich gut zu fühlen?\n",
      "27. Ließen Sie andere Ihren Ärger spüren, wenn Sie wegen denen nicht das bekamen, was Sie wollten?\n",
      "28. Gab Ihnen eine Prügelei ein so gutes Gefühl, dass Sie auch Verletzungen in Kauf nahmen?\n",
      "29. Fühlten Sie sich besser, wenn Sie jemanden schlugen, durch den Sie sich bedroht fühlten?\n",
      "30. Zerstörten Sie Dinge, wenn Ihnen etwas weh tat\n"
     ]
    }
   ],
   "source": [
    "for entry in open('../experiment/quest/app/forms.py','r'):\n",
    "    if 'u\"' in entry:\n",
    "        print entry[entry.find('u\"')+2:-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### find all files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def get_logfile(whichfolder, whichexperiment):\n",
    "\n",
    "    loglist = []\n",
    "    for fileName in os.listdir(whichfolder):\n",
    "        if fnmatch.fnmatch(fileName, whichexperiment):\n",
    "            loglist.append(whichfolder+fileName)\n",
    "    return loglist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "loglist = get_logfile('../experiment/quest/app/static/logfiles/','log*.txt')\n",
    "loglist.sort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../experiment/quest/app/static/logfiles/logfile_A1.txt',\n",
       " '../experiment/quest/app/static/logfiles/logfile_A10.txt',\n",
       " '../experiment/quest/app/static/logfiles/logfile_A11.txt',\n",
       " '../experiment/quest/app/static/logfiles/logfile_A12.txt',\n",
       " '../experiment/quest/app/static/logfiles/logfile_A13.txt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loglist[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### get data of one participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def makeThis(logfile):\n",
    "\n",
    "    pName = logfile[logfile.find('_')+1:logfile.rfind('.')]\n",
    "    pName = pName[0] + ('000'+ pName[1:])[-3:]\n",
    "    \n",
    "    fullDf = pd.read_csv(logfile,\n",
    "                         skiprows=2,\n",
    "                         index_col=[0]\n",
    "                        )\n",
    "    \n",
    "    fullDf.index = [[pName]*len(fullDf.index),fullDf.index]\n",
    "    fullDf.index.names = ['id', 'f']\n",
    "    fullDf.columns = ['ans']\n",
    "    \n",
    "    withinDf = fullDf.unstack(1)\n",
    "    \n",
    "    # sort columns\n",
    "    newCols = []\n",
    "    for entry in withinDf.columns.levels[1]:\n",
    "        newName = '00'+str(entry[1:])\n",
    "        newCols.append('f'+newName[-3:])\n",
    "    withinDf.columns = newCols\n",
    "    withinDf = withinDf.reindex_axis(sorted(withinDf.columns), axis=1)\n",
    "    \n",
    "    return withinDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f001</th>\n",
       "      <th>f002</th>\n",
       "      <th>f003</th>\n",
       "      <th>f004</th>\n",
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       "      <th>f006</th>\n",
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       "      <th>f008</th>\n",
       "      <th>f009</th>\n",
       "      <th>f010</th>\n",
       "      <th>...</th>\n",
       "      <th>f021</th>\n",
       "      <th>f022</th>\n",
       "      <th>f023</th>\n",
       "      <th>f024</th>\n",
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       "      <th>f028</th>\n",
       "      <th>f029</th>\n",
       "      <th>f030</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K009</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      f001  f002  f003  f004  f005  f006  f007  f008  f009  f010  ...   f021  \\\n",
       "id                                                                ...          \n",
       "K009     1     0     0     2     0     0     0     1     1     1  ...      1   \n",
       "\n",
       "      f022  f023  f024  f025  f026  f027  f028  f029  f030  \n",
       "id                                                          \n",
       "K009     0     1     0     1     0     1     0     1     1  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeThis(loglist[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### get data of all participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def makeAll(loglist):\n",
    "    \n",
    "    for p in loglist:\n",
    "        thisDf = makeThis(p)\n",
    "        try:\n",
    "            bigDf = pd.concat([bigDf,thisDf])\n",
    "            \n",
    "        except:\n",
    "            bigDf = thisDf\n",
    "    \n",
    "    group = [a[0] for a in bigDf.index]\n",
    "    \n",
    "    bigDf.index = [group,bigDf.index]\n",
    "    bigDf = bigDf.sort_index(axis=0,level=1)\n",
    "    \n",
    "    return bigDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "bigDf = makeAll(loglist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The final DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A005</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
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       "        f001  f002  f003  f004  f005  f006  f007  f008  f009  f010  ...   \\\n",
       "  id                                                                ...    \n",
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       "  A002     1     0     0     0     1     0     1     0     0     0  ...    \n",
       "  A003     2     0     0     0     1     0     1     0     1     1  ...    \n",
       "  A004     0     0     0     1     0     0     2     1     0     0  ...    \n",
       "  A005     3     0     1     3     2     3     1     4     4     0  ...    \n",
       "\n",
       "        f021  f022  f023  f024  f025  f026  f027  f028  f029  f030  \n",
       "  id                                                                \n",
       "A A001     1     0     2     0     0     1     1     0     2     0  \n",
       "  A002     0     0     1     0     0     0     0     0     0     0  \n",
       "  A003     1     0     1     0     0     0     0     0     0     2  \n",
       "  A004     0     0     1     0     0     0     1     0     0     0  \n",
       "  A005     0     0     1     0     0     3     1     1     2     3  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Export as CSV for re-use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "bigDf.to_csv('../outputs/fullAFAS.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of re-importing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f001</th>\n",
       "      <th>f002</th>\n",
       "      <th>f003</th>\n",
       "      <th>f004</th>\n",
       "      <th>f005</th>\n",
       "      <th>f006</th>\n",
       "      <th>f007</th>\n",
       "      <th>f008</th>\n",
       "      <th>f009</th>\n",
       "      <th>f010</th>\n",
       "      <th>...</th>\n",
       "      <th>f021</th>\n",
       "      <th>f022</th>\n",
       "      <th>f023</th>\n",
       "      <th>f024</th>\n",
       "      <th>f025</th>\n",
       "      <th>f026</th>\n",
       "      <th>f027</th>\n",
       "      <th>f028</th>\n",
       "      <th>f029</th>\n",
       "      <th>f030</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>A001</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A003</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A005</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        f001  f002  f003  f004  f005  f006  f007  f008  f009  f010  ...   \\\n",
       "  id                                                                ...    \n",
       "A A001     1     0     1     0     1     0     0     0     0     1  ...    \n",
       "  A002     1     0     0     0     1     0     1     0     0     0  ...    \n",
       "  A003     2     0     0     0     1     0     1     0     1     1  ...    \n",
       "  A004     0     0     0     1     0     0     2     1     0     0  ...    \n",
       "  A005     3     0     1     3     2     3     1     4     4     0  ...    \n",
       "\n",
       "        f021  f022  f023  f024  f025  f026  f027  f028  f029  f030  \n",
       "  id                                                                \n",
       "A A001     1     0     2     0     0     1     1     0     2     0  \n",
       "  A002     0     0     1     0     0     0     0     0     0     0  \n",
       "  A003     1     0     1     0     0     0     0     0     0     2  \n",
       "  A004     0     0     1     0     0     0     1     0     0     0  \n",
       "  A005     0     0     1     0     0     3     1     1     2     3  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('../outputs/fullAFAS.csv',\n",
    "            index_col =[0,1]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Get avarage scores for each participant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Facilitative Aggression:\n",
    "Items 1,3,5,8,10,12,14,17,19,21,23,25,27,29,30\n",
    "\n",
    "Appetitive Aggression:\n",
    "Items 2,4,6,7,9,11,13,15,16,18,20,22,24,26,28\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  2  4  7  9 11 13 16 18 20 22 24 26 28 29]\n",
      "[ 1  3  5  6  8 10 12 14 15 17 19 21 23 25 27]\n"
     ]
    }
   ],
   "source": [
    "fAgr=np.array([1,3,5,8,10,12,14,17,19,21,23,25,27,29,30])-1 # because we start counting from zero, we subtract 1\n",
    "aAgr=np.array([2,4,6,7,9,11,13,15,16,18,20,22,24,26,28])-1\n",
    "\n",
    "print fAgr\n",
    "print aAgr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: facilitative aggression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f001</th>\n",
       "      <th>f003</th>\n",
       "      <th>f005</th>\n",
       "      <th>f008</th>\n",
       "      <th>f010</th>\n",
       "      <th>f012</th>\n",
       "      <th>f014</th>\n",
       "      <th>f017</th>\n",
       "      <th>f019</th>\n",
       "      <th>f021</th>\n",
       "      <th>f023</th>\n",
       "      <th>f025</th>\n",
       "      <th>f027</th>\n",
       "      <th>f029</th>\n",
       "      <th>f030</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>A001</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A003</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A005</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        f001  f003  f005  f008  f010  f012  f014  f017  f019  f021  f023  \\\n",
       "  id                                                                       \n",
       "A A001     1     1     1     0     1     2     1     1     1     1     2   \n",
       "  A002     1     0     1     0     0     0     0     0     1     0     1   \n",
       "  A003     2     0     1     0     1     1     0     0     0     1     1   \n",
       "  A004     0     0     0     1     0     0     0     1     0     0     1   \n",
       "  A005     3     1     2     4     0     3     0     1     0     0     1   \n",
       "\n",
       "        f025  f027  f029  f030  \n",
       "  id                            \n",
       "A A001     0     1     2     0  \n",
       "  A002     0     0     0     0  \n",
       "  A003     0     0     0     2  \n",
       "  A004     0     1     0     0  \n",
       "  A005     0     1     2     3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigDf[bigDf.columns[fAgr]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeMean(df,fAgr=fAgr,aAgr=aAgr,labelCoding=labelCoding):\n",
    "    # get the mean on both scales\n",
    "    meanF = df[df.columns[fAgr]].T.mean()\n",
    "    meanA = df[df.columns[aAgr]].T.mean()\n",
    "    # get the overall mean\n",
    "    meanBig = df.T.mean()\n",
    "    \n",
    "    assert meanBig.all() == ((meanA+meanF)/2.).all() , 'mean values corrupted; please check'\n",
    "    \n",
    "    # put them in one df\n",
    "    meanDf = pd.DataFrame([meanF,meanA,meanBig]).T\n",
    "    # structure the df\n",
    "    meanDf['group'] = [ labelCoding[x[0]] for x in meanDf.index.levels[1] ]\n",
    "    meanDf.columns = ['Facilitative','Appetitive','Overall','group']\n",
    "    meanDf = meanDf.sort_index()\n",
    "   \n",
    "    return meanDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "meanDf = makeMean(bigDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Facilitative</th>\n",
       "      <th>Appetitive</th>\n",
       "      <th>Overall</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>A001</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A002</th>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A003</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A004</th>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A005</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Facilitative  Appetitive   Overall  group\n",
       "  id                                             \n",
       "A A001      1.000000    0.266667  0.633333      2\n",
       "  A002      0.266667    0.066667  0.166667      2\n",
       "  A003      0.600000    0.200000  0.400000      2\n",
       "  A004      0.266667    0.200000  0.233333      2\n",
       "  A005      1.400000    1.200000  1.300000      2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save for re-use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "meanDf.to_csv('../outputs/meanAFAS.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Plot of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61df39c490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(myPal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sortDf = meanDf.sort_values('group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Facilitative</th>\n",
       "      <th>Appetitive</th>\n",
       "      <th>Overall</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>A014</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A015</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A016</th>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A010</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A001</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Facilitative  Appetitive   Overall  group\n",
       "  id                                             \n",
       "A A014      1.000000    0.533333  0.766667      2\n",
       "  A015      0.200000    0.066667  0.133333      2\n",
       "  A016      0.133333    0.200000  0.166667      2\n",
       "  A010      0.600000    0.200000  0.400000      2\n",
       "  A001      1.000000    0.266667  0.633333      2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sortDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RERER6cSikYiIiIiIiHRi0UhEREREREQ6sWgkIiIiIiIinVg0EhERERERkU4sGomIiIiI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kAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61df26fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "\n",
    "for i,metric in enumerate(meanDf.columns[:-1]):\n",
    "    \n",
    "    ax = fig.add_subplot(int('13'+str(i+1)))\n",
    "\n",
    "    sns.boxplot(x='group',y=metric,data=sortDf,\n",
    "                width=0.4,linewidth=1,color='white',whis=True,notch=True,fliersize=0,ax=ax)\n",
    "\n",
    "    sns.stripplot(x='group', y=metric, data=sortDf,\n",
    "                  jitter=True, edgecolor='white',palette=myPal,size=9,linewidth=1,ax=ax)\n",
    "\n",
    "    ax.axhline(0,color='k',linewidth=1)\n",
    "    ax.set_title(metric)\n",
    "    \n",
    "    ax.set_xticklabels(['Violence\\nOffenders','Child\\nMolesters','General\\nPopulation'],fontsize=15)\n",
    "    #ax.set_xticks(range(3),['Violence\\nOffenders','Child\\nMolesters','General\\nPopulation'])\n",
    "    ax.set_xlabel('')\n",
    "    if i ==  0: \n",
    "        ax.set_ylabel('Mean Score (0-4 scale)')\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "    ax.set_ylim(-0.2,4)\n",
    "    sns.despine()\n",
    "    \n",
    "plt.suptitle('AFAS Scores',fontsize=20, position=(0.51,1.1))\n",
    "# save to file\n",
    "plt.savefig('../figures/afasFig.png',dpi=300) \n",
    "# show in notebook\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferential statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeUTests(df):\n",
    "    bigDf = pd.DataFrame()\n",
    "    d = {}\n",
    "    for comp in [('G','K'),('G','A'),('K','A')]:\n",
    "        for variable in df.columns[:-1]:\n",
    "            group1 = df.ix[comp[0]][variable]\n",
    "            group2 = df.ix[comp[1]][variable]\n",
    "            U,p = stats.mannwhitneyu(group1,group2)\n",
    "            if p<0.05:\n",
    "                thisSig = '*'\n",
    "            else:\n",
    "                thisSig = 'n.s.'\n",
    "            d[variable] = {'U':round(U,2),'p':round(p,3),'sig':thisSig}\n",
    "        thisDf = pd.DataFrame(d)\n",
    "        thisDf = thisDf.reindex_axis(['U','p','sig'], axis=0)\n",
    "        thisDf.index = [ [comp[0]+' > '+comp[1]]*len(thisDf.index),thisDf.index ]\n",
    "\n",
    "        bigDf = pd.concat([bigDf,thisDf])\n",
    "    return bigDf.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; K</th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; A</th>\n",
       "      <th colspan=\"3\" halign=\"left\">K &gt; A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Appetitive</th>\n",
       "      <td>322.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>*</td>\n",
       "      <td>360</td>\n",
       "      <td>0.02</td>\n",
       "      <td>*</td>\n",
       "      <td>90</td>\n",
       "      <td>0.253</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facilitative</th>\n",
       "      <td>329</td>\n",
       "      <td>0.003</td>\n",
       "      <td>*</td>\n",
       "      <td>357.5</td>\n",
       "      <td>0.024</td>\n",
       "      <td>*</td>\n",
       "      <td>80</td>\n",
       "      <td>0.125</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>331</td>\n",
       "      <td>0.002</td>\n",
       "      <td>*</td>\n",
       "      <td>362.5</td>\n",
       "      <td>0.018</td>\n",
       "      <td>*</td>\n",
       "      <td>89</td>\n",
       "      <td>0.241</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              G > K             G > A            K > A             \n",
       "                  U      p sig      U      p sig     U      p   sig\n",
       "Appetitive    322.5  0.005   *    360   0.02   *    90  0.253  n.s.\n",
       "Facilitative    329  0.003   *  357.5  0.024   *    80  0.125  n.s.\n",
       "Overall         331  0.002   *  362.5  0.018   *    89  0.241  n.s."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeUTests(meanDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating how the subscales correlate with other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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tvWPCkZ0yoNNc7IsXL+axxx7j0UcfxWazcc011/Doo49y1113YbFYCAaDjdo2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61df2b2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a categorical scatterplot to show each observation\n",
    "sns.lmplot(x=\"Facilitative\", y=\"Appetitive\", hue=\"group\", data=sortDf,palette=myPal);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Appetitive</th>\n",
       "      <td>0.964444</td>\n",
       "      <td>0.874103</td>\n",
       "      <td>0.252381</td>\n",
       "      <td>0.265611</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>0.288165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facilitative</th>\n",
       "      <td>1.264444</td>\n",
       "      <td>0.924504</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.361384</td>\n",
       "      <td>0.650980</td>\n",
       "      <td>0.411319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>1.114444</td>\n",
       "      <td>0.876893</td>\n",
       "      <td>0.359524</td>\n",
       "      <td>0.272744</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.329723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group                0                   1                   2          \n",
       "                  mean       std      mean       std      mean       std\n",
       "Appetitive    0.964444  0.874103  0.252381  0.265611  0.329412  0.288165\n",
       "Facilitative  1.264444  0.924504  0.466667  0.361384  0.650980  0.411319\n",
       "Overall       1.114444  0.876893  0.359524  0.272744  0.490196  0.329723"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanDf.groupby(\"group\").describe().T[[1,2,9,10,17,18]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# PPI-R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ppiDf = pd.read_csv('../experiment/ppi_r.csv',\n",
    "                   index_col=[0,1])\n",
    "\n",
    "ppiDf['group'] = [ labelCoding[x[0]] for x in ppiDf.index.levels[1]]\n",
    "ppiDf = ppiDf.replace(-99,np.nan)\n",
    "ppiDf = ppiDf.dropna()\n",
    "ppiDf = ppiDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Blame externalization</th>\n",
       "      <th>Rebellious nonconformity</th>\n",
       "      <th>Stress immunity</th>\n",
       "      <th>Social influence</th>\n",
       "      <th>Coldheartedness</th>\n",
       "      <th>Machiavellian egocentricity</th>\n",
       "      <th>Carefree nonplanfulness</th>\n",
       "      <th>Fearlessness</th>\n",
       "      <th>Sum PPI-R</th>\n",
       "      <th>Insincere Answers</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>A001</th>\n",
       "      <td>18.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A002</th>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A003</th>\n",
       "      <td>34.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A004</th>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A005</th>\n",
       "      <td>45.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Blame externalization  Rebellious nonconformity  Stress immunity  \\\n",
       "g id                                                                       \n",
       "A A001                   18.0                      57.0             46.0   \n",
       "  A002                   22.0                      41.0             46.0   \n",
       "  A003                   34.0                      56.0             37.0   \n",
       "  A004                   19.0                      46.0             51.0   \n",
       "  A005                   45.0                      82.0             37.0   \n",
       "\n",
       "        Social influence  Coldheartedness  Machiavellian egocentricity  \\\n",
       "g id                                                                     \n",
       "A A001              53.0             27.0                         35.0   \n",
       "  A002              46.0             30.0                         32.0   \n",
       "  A003              46.0             32.0                         31.0   \n",
       "  A004              34.0             38.0                         32.0   \n",
       "  A005              37.0             37.0                         46.0   \n",
       "\n",
       "        Carefree nonplanfulness  Fearlessness  Sum PPI-R  Insincere Answers  \\\n",
       "g id                                                                          \n",
       "A A001                     24.0          25.0      285.0                 53   \n",
       "  A002                     30.0           9.0      256.0                 43   \n",
       "  A003                     30.0          24.0      290.0                 41   \n",
       "  A004                     22.0          18.0      260.0                 47   \n",
       "  A005                     38.0          27.0      349.0                 31   \n",
       "\n",
       "        group  \n",
       "g id           \n",
       "A A001      2  \n",
       "  A002      2  \n",
       "  A003      2  \n",
       "  A004      2  \n",
       "  A005      2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppiDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppiDf.to_csv('../outputs/ppiDf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeBoxPlot(df):\n",
    "    fig = plt.figure(figsize=(15,8))\n",
    "\n",
    "    sortDf = df.sort_values('group')\n",
    "    \n",
    "    myColumns = df.columns[:-1]\n",
    "    numColumns = len(myColumns)\n",
    "    \n",
    "    for i,metric in enumerate(myColumns):\n",
    "\n",
    "        ax = fig.add_subplot(2,numColumns/2.,i+1)\n",
    "\n",
    "        sns.boxplot(x='group',y=metric,data=sortDf,\n",
    "                    width=0.4,linewidth=1,color='white',whis=True,notch=True,fliersize=0,ax=ax)\n",
    "\n",
    "        sns.stripplot(x='group', y=metric, data=sortDf,\n",
    "                      jitter=True, edgecolor='white',palette=myPal,ax=ax)\n",
    "\n",
    "        ax.set_title(metric,fontsize=15)\n",
    "\n",
    "        ax.set_xticklabels(['Violence\\nOffenders','Child\\nMolesters','General\\nPopulation'],fontsize=9)\n",
    "        ax.set_xlabel('')\n",
    "        if i%5 ==  0: \n",
    "            ax.set_ylabel('Sum Score',fontsize=15)\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        #ax.set_ylim(-0.2,4)\n",
    "        sns.despine()\n",
    "\n",
    "    plt.suptitle('PPI-R Scores',fontsize=20, position=(0.51,1.02))\n",
    "    # save to file\n",
    "    #plt.savefig('../figures/afasFig.png',dpi=300) \n",
    "    # show in notebook\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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27dqJXdNV6yDy8ZO1PNVF1euoNjweT6xerOpt9gW8uTc0NzfH+vXr32o7QmqTkZGBrVu3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4zEx4eQFZ9irK1F8PFB4uP67bLO1ilidKKS9X+cjKpFS3iAJ4IgoG/0p6yszIFRujlfAgMDkUql\nDBo0yGYFaMKECezcuZPCwkIUCgUNp3sKd6cz7dRp4qBCbr0tkcAlVBbx/bEqPtpWSIfeSKx48R/M\nbtycDb1ej0QiYfXq1QTsP8Dgs10gl5/tjH7D29sbtVqNUqlEfkZKrEwi6fNLjlqtxscJ4++oUaPY\nunUrycnJSKVS4gPiiQ+IZ2/Vnh7FBZlgfp6VlZWh0+ksL4K/ZDZv3szOnTtZvnw5Hadf1kVRxGQy\nIQgCbW1t+Pj4OH1cKi4uJjw8HF9fXwCEM8cjQDh9v5haWnpslxcVFYXJZKKurs5iCukIQkNDqaio\n6PFvymA0YTD1fE/V19fj4+NDa2trf4Xo5ldEREQERqOR2tpau5nPYksLbd9uxFhSgsTHx0Zc6Ojo\noLy8vFcPMTfWxMXFcejQIUaMGHHWc02iiR3lP1HUWMiw0OGMiuhKEigtLb1ggcIevc7m77vvPurr\n69m5cycZGRm88MILlmPvvvsutbW1vP322xc9KFflUlpVbWpqOqsaJZNKiAjsqpEKCAjg2LFj/R2a\nm4uATCZj4MCBVq7RnXSa4sjlclJSUsjPz7c5Jy8vD0EQ7HpyOAK/Rx6mfv8BMBgAsxmd9BJxsS9V\naXhh1RFMpxMvagjip9waJqe7y4ncOI7s7Gwuu+wy0tLSME2fTu3xHIw9iMMmqRTtbc7LWkhNTSUn\nJ4eBAwdy76RE/rYhx3Ls3kmJff6c3Nxcp6xuhYeHo1Qqyc3NtXpm3pJyK6/ufcWmfZ6AwK2pt/3q\nsha2b9+O0Wjkrrvusnt85MiRzJkzx+njklarxa+bYOCzYAFt//sfYqO5Y5Zi7Bjw9aVm8lQMBQXI\nBw0i6L13kQ+0fpkXBAE/Pz+HZ6lGRUVRVFRk99jqfaUs++EkrR2x1Kw+wrM3DEEhs/7bq6ioICoq\nihMnTjgiXDe/cARBIDU1lfz8fLviQvXESYinvUwaDx/GWFWF/5+esBwvKCggLi7OktXk5uxERUXx\nww8/9MmE/4PDy/ju1EYA1hWs5b6h91vaVZaWljJq1KjeLj8vehUXJBIJTz/9NE8//bTNsfvvv5+n\nn37aaS6zbnrnbJkL9ggIMBs6iqLoEulybnpn9uzZLF26lMLCQqsVjO3bt6NQKBg8eDCzZ8/mjTfe\n4MiRI1b1cOvWrUOpVDJmzJh+jVEikdh1APacOpXwbT/SsW0bsuSBeJxFsTYYDC4zOc8qbrAIC50c\nPNXA5PRwTla3UNXYzsiEYHw8Lp1MDDeXFjU1NTQ1NZGSYu5nLwkKIuz7zbRv3IhoMCKIIkilCP7+\nmGpqqEpMIL+1lb4nTl9c4uPjyczMpLGxkRtHx5Ie7c/hkkYGxwQwJDawT5/R1tZGRUWF07IlR40a\nxaZNmyx1+gCjIy9n6bR/kV2bRZxfHHKJnMLGAoaGDiM+IIFTp04hiiIJCQlOidnRPPTQQ9x6q62I\n9dJLLyEIAs8//zxKpZKtW7c6dVw6E3lSIuHbt9Hx3SYkwcF4XjWT2qtnYygwtxbV5+TQ+NRThK5a\n6dC4esLPz4/m5mZUKpUl+wKgvKGdJd/mnu6/ILD5SBUDQ7259XJr74tTp06Rnp7eb/XWbn59pKSk\nsHLlSsaNG2fxTgFo/+EHi7DQSevHn1jEBVEUycvLY9y4cQ6N91JHJpMRGRlJeXm5xWzYHlqj1qa9\n64aiDVw38Aba29tRq9VERkZe/PjO98KoqKjz/tJ9+/axbNkycnJyaG9vJzExkdtvv53bbrsNgKef\nfpo1a9bYvXbKlCksW7bsvL/710JzczPx8fHndI2npycSiYSOjg77rq9uXIq77rqLNWvWcN999/Hk\nk08SGBjI+vXr2b9/P4888gi+vr7ceeedrFy5kscff5xFixYRFhbG+vXr2bNnD0uWLOl3Q9agoCDq\n6+vttkSVJSbgm9i3SXdDQwPBLpLZkB5tK9qlRwewdHM+n2cWAxDgLefdBaNJCrefTuvGzYWQlZVl\nMRjsROLnh8/pMfRMkkwmDnz1FdXV1URERDgqTAtSqZSUlBTy8vIYO3YsaVEBpEWdm/idn59PYmKi\n0xY0QkJCCA8P5/jx4wwfPpxDtYfYV7WXaL9oroqfhUJqjmtQiLlAxWQyceDAAcaMGfOrEevj4uLs\nptj6+voiCAKXnTZGdPa4ZA9paCg+99wNgGgwYMjNszquP+oaWZ3BwcHs27cPgP/9739Wq72FrR6I\nWHfh+H7vMUyndlu2TSYTer2euro6goKCfpGm7G4cj4+PDxERERQVFVlll0m6iV+dCN3EB5VKhV6v\nv6B3yl8rcXFxlJaW9iouSAQJMokMg9Fg2edxeqwqKysjOjq6X54BDl9a27FjBw899BATJ07k9ddf\nRyqV8tVXX/HCCy/Q2NjIgw8+CEBYWBjLli2zUVb7w3jibJhaWtCs+Azt7kwETw88Z0zHY/x42r74\nEtFgwOf225H18SXJUZxP5gJ0ZS+4xQXXx8fHh88//5wlS5bw8ssv09LSQnx8PC+++KJl9cjT05MV\nK1ZYztFoNCQnJ7N06VJmzJjR7zGGhIRQWlp6wZ9TV1fn8DZ6NTU1dmtSZcC8UUrWHFajN4oM9Goi\nmGYro7qmNj3/3HiE300yv8iFhoY65dnl5pdHbW0tDQ0NlhZ6fUEikXDZZZeRlZXF7Nmz+zG6nklL\nS2PdunWMGjXKsvLfVzpXt6ZPn95P0fWNUaNGsWHDBlT+tSw91FUSml2TxXPjFludW1RUhFwutyus\n/hrpLrA4e1zqDd2xY2hWrkIICkLsVnboMXGCE6PqIiMjg4yMDHJycqipqWHq1KmWY3sLVGxdcdAq\ns25AfAIqHwVTB4WTER9MdXU1mZmZ3HTTTU6I3s0vmdTUVI4ePWolLniMGYM0Ph5jcbFln/8Tiyz/\nn5+fT2pq6q9GgL2YxMXFsX//fkwmU4+ZvXKJnFtS57Ii5xMAJEiYm3o70H9+C+AEceG1114jLi6O\nd99916KWjB07ltmzZ/PFF19YxAW5XO7w9j72EEUR1R13ou/WirLju03g6QmnDYs0H39C2PebkfXT\nP9K50lsbyrPh7+9PU1OTU1a33Jw7wcHB/PWvf+31HKVSyauvvuqgiKwJDQ0lKyvrgj9HpVIxwI7D\ncH+yYcMG4uLi7A566V6QMgZEUaCqopWTxWWcmWFa29jKqVOnaG9vRyqVOu2lzh46o47/nVxNfkMe\n6cHp3Jh8M3Kp8wz/3PSd7Oxshg8ffs6rDcnJyWRlZVFbW+tQ87lOAgICUCqVFBcX97rSYo/y8nI8\nPDyc3p0qKCiI6Oho/pe7ymr//up91LXVEerdFV9WVhbjx493T5qBTz/91GafM8elntAdOkTdnJvg\njE4WsuRkAl//m5Oisk90dDTZ2dmWMtZj5Y0s+jzLIixIJQKhfh58f6wagFX7Svnb7Zfh21bpXiV2\n0y8MGDCAXbt20djYSGBgV7lb2M6faH1vGYbcPHx/sxDFZWYTQoPBQGFhoaVri5tzw9fXFx8fH2pr\na3t9Z7s1dS7DQodT1FjI0NChxPjFYjKZKC8v77f2yA4XF+bPn09ERITVxKjT9X7Tpk0YDIZernY8\n+qNHrYQFC93aFomtrbSt/h/+j//BgZH1TFtbG3K5/LzSRzszF9y4uRgEBATQ3t6OVqu9ILMelUrF\nyJEjL2JkZ8dkMjFt2rQ+ez38WPMzORVdrVwXTB/G9KHmmrjDhw/3V5jnxXuH3uXHUnPf6ayag9S0\n1fJoxmNOjsrN2VCpVNTV1TFt2jS7x0WjkZZl79O+dh3S8DD8Fz+P4rQvg1QqZcSIEWRlZTFr1ixH\nhm0hPT2d48ePW8SF6sZ21hwoQ2swcX1GDIlhtim0YDZyTEtLc2SodhFFkSOth1Hp66DbY0EQBb78\n7Es8xK5nnEwmo7293QlRuukLlZWV7Nixw2pfyEf/JsBOi0xtVRW7jx+3+zn2TJUdQWcmXHNzMwEB\nAaw/WI7B2KVwG00i1U3d5qki/G9/KdMCqxg+fLjD43Xzy0cikZCcnEz+8eMMKSxCe+QIgiABATzG\nj8P/kYcB0O7fT/uatahlMiKHDrXyDXEmRbWtrM8qx0Mm4cZRsVaG965KZ2mET7AP3xV9S0OHmsmx\nk0lXWi/OpwankhrclVFSXV1NQEAA3t7e/RKXw8WF23qoCS0pKSEuLu6c0yX7G8G7b22v7NUVOYu+\ndIroiYCAAE6dOnWRI3Lza0UikRAcHIxKpSI6+vzs5LRaLe3t7eeVieNI/nHXSL7eW0qVup0rB4cz\nMdV1u8vsKN9utb2z/Ce3uNCN/fv3c/fdd3P55ZfzySefWPZXVVWxZMkSMjMz0Wg0JCQkMH/+fIel\nGGdnZzNs2LAex8nGPz9F2xdfAmDIyaFu127Ct/+I7LT/TkpKCtnZ2dTV1TklC2DAgAHs3r2bxsZG\nJB4+LPxwDw2tOgDWHijn49+OY0CI9Zir0WiorKxkypQpDo/3TJbte49txh+shAWA65NvYOFN91nt\n63x5TUpKchkzWjdmUlJS7Gb+ePTg6yP4+hDq7Y2g1yMGWhuQhoeHn/fYdiEIgkBUVBQVFRXmlwSF\n7TNBALon1HnKpdTV1blkZmp9ez1+Cj+Ld4mbLs7mUwfOH5s6SU1NpfCeBTSekbHa9tnnGEtKkV92\nGfXz7gKTCQWQnpiIePNNCE5+RpaqNCz8YA8derMB+fqsCr783QQCvF377zEuLo6du3ayouUTSpqL\nAdh86jv+b8KLDA/ruU1lf5ZEQB/FhcLCQt5++20KCwstvYu788MPP5x3AJWVlbz//vsUFBRYtbXs\n6Ojg5ZdfZseOHVRXVxMWFsZ1113Hww8/bOVE2t/IBybhdfPNtK9ebbVfGhWJsdLc31qakID3rbc4\nLKaz0alknw8BAQE0Nzef/UQ3bvpIaGjoBYkLKpWK4OBgl5+gB3gruH/quaV7O5KVK1dS2VTJAf99\nGOTWGWJ6g55/fvgO4YERdt3ef03odDqef/55m3T21tZW7rjjDmQyGYsXLyY8PJwtW7bwzDPP0N7e\nzrx58/o1roaGBqqrq61qrLsjmky0rbRO10evp23NWktWnUwmY/jw4WRnZ5+TZ8PFQiqVkpqaSm5u\nLvWecRZhAaBDb+S7w5X8dlqy1TXONnLsRBRFtlf+aLVPKkh5aeJfGXzaxLE7UVFR+Pn5ceLECZfI\nunDThUKhID093Wa/YdEfqdv+E6a6uq6dEgneI0YgmXs76HR4XXMNQUvfQnCBtnnR0dGUlpYyaNAg\n5o6NY8uxKss9NT45hFB/T9YdLAfAWyFldpof6hKl0++l7qg71Px1z8ucUOfjI/fhweEPOTskl6Iv\nPnXOHpu6E+DhQcQhO9negGbFZyjy8sBksuyTFBWhO3AAj8svd1SIdtl0pNIiLACoNTp25NVyXUZM\nL1c5n7CwMMq0pRZhAcCEiS3Fm88qLvQ0l7gY9ElcePLJJwkKCmLevHkXzehv37593HPPPYBZeVm2\nbJlV7UdTUxMSiYRXXnkFg8HAxo0bee+99ygoKOCdd965KDH0hIeHBxqNxvKzBi99C+0dt6E7fBj9\niQJ0u3ZiatXgMX0apsYm9Dk5qO6cR+BfX0ExousfU6PROKVv6/n6LYBrtqPUGXW8f3gZuyt2EuYd\nzn3DHmBY6LCzX+jGJbhQU0eVSuX0WutLDY1GY9MC9KqrruK94/+iudq27MkkmJBcJnBVylU24qKv\nr6/LCzsXk3fffZfW1laGDBlitX/58uXU1NSwZs0ay8tiRkYGpaWlvPXWW9x66639OmnPz88nPT3d\nJmtBNJlo+/IrOvbsBTv/Tmdm1aWlpXHgwAHa29udYtyblpbG2rVrib7M1kPlZFULL645yrjkEGYM\nicRkMpGXl+d0kz8wO2vLRJl5Ofg0vnJfu8JCJ6NGjWLr1q0kJye7XfkvAWSxsYTv/ImOH37ApGlj\nx66dTLnuetT3P2A5p33DBhTjxuK7YL4TIzUTFRXFnj17EEWRqCBvvn50IrtP1F+pTYcAACAASURB\nVBHorWB0ohKJRODq4VFUqtsYOzCEgpzDLue38HnuCk6o8wHQ6DW8m/1PJgn999JzqdEXnzpnj01W\nSKUY5XJkWq3NoUa9HnV5OWeul0t8nd9Vy17r8Myd26k60Gb3/KCgIJdYiJFIJESHxcAZ3uM+8p6z\n7pubm9Fqtf1qkt4ncaGoqIi9e/de1D/OoUOHsnbtWtRqNVu2bOH+++/nscce48EHH+TZZ5/lmWee\nwc+v6w9u3Lhx6HQ61q9fz7Fjx2wmfheTmJgYysrKrH7xHuPGIYuPp3rcBIvZj3ZrV8aG/vARGu57\ngPA9mQinJ39lZWXExDhe9Wpqajpnw6xOFAoFcrmctrY2fHz6VhLS36zM/5qtJeY+rSXNxby29xX+\nO+tjPGSeTo7MTV8ICQm5IFPHurq6fk3f+qXR2NjI6tWr7dbS5ShybFK6OzlUfIigk0qrfVqtlpEj\nRzJ06ND+CNXlOHHiBB999BEvv/wyq8/IVvv+++9JTEy0WYW+9tpr2bZtG5mZmf2aum8wGOyKxs0v\nv0Lr+x/YvUbw9cV7rvUESCaTIZfLbcQnR+Hv749SqSRK0crQ2ECOlpl7oHvJpew+aV4x3niokoYW\nHeOiBTw9PZ0uLoqiyIEDB7hxwM18VvYpJtGEgMBdg+7u9brw8HCUSiW5ubn9Omdxc/GQ+PnhPWcO\nAOWaVkQ7HYMMJ044Oiy7+Pr6olAoUKvVBAcH4+sp56ph1uLBiAFBjBhgbk9ZWVnJ5U5eIT6TrFNZ\nVoJdh7GDdkkbH3xg/UxzlZc5R9MXn7otW7Y4dWzqTnV9PWWzriJh3XrrAzIZA159haTkFFQ33oSp\noQEA3ZQpyAfZZhE5mmsvi2btwXLK6s1iwtDYQF5cMAO5TMIHH3zAAw88cJZPcB4jE0ayL2cPBcaT\nAPgr/Ll+4Jwez+8siejPBeQ+iQupqamoVKqLqnh6eXlZboRx48bh6enJ22+/zfTp00lKSrJ7zYwZ\nM1i3bh3Hjx/v14E6Li6O7OxsS0/mTnRZ2TYuwt0xVlVhLCtDlpCAKIqUlZUxbJjjV9gvJHMBujpG\nuIq4kFNvbaSk0Wsobi6xMidx47oEBgbS1taGTqc7L4FSpVKRkZHRD5H9MtHpdAQHB3PjjTfaHDMc\n17HqxEq7110z/BpuSLa+5uDBg2jtrED8EhFFkcWLFzNmzBjmzJljJS4YjUYKCwvtGikmJycjiiK5\nublO8QXQnPZYsIckKAiJC3qVpKenk5uby7KFs9lXqKKhVcdLa49ZnbMuqxylpt0lSgqKi4sRRZGb\nM25hYtoV5NXnkhyUQrTf2Uu9Ro0axaZNm0hLS3M5Tyk3XWh37aZt9WokYWH4/mYh0tMdVTwnTgQP\nD+j2HPS88kpnhWlDp+9CsB2/CFEU+fZQJfuL6hkY5o22Qe2UTjG9cdWgq/gsd4VlO8w7jMfn/BGp\n4M70gbP71AmCQFFRkcuMTXl5eYQ+9FvCHnsU/fEcJGFhmGprUYy5HNnpxdbw3Tvp2LYdtVRKlraD\nBIdE1jsB3gpWPDSePQUqFDIJlyeFIJW4Rvb22YiNjWXIzmHcM3sBzfomMsJH9pq5UFJS0u/dGPs0\n0r3yyiu88MILXHPNNURGRtqkyI4ePbpPX9bU1MTWrVtJTU21EQcGDx6MKIqcOHGCpKQkDAaDzUDc\n6ffQ36UGkZGRbN261cbhXj58GEil0MOKjyQsDOnpm0etViORSBxuQieKIs3Nzedt6AhdpRGukD63\ncuVK2nXt0G0RVmaS8cPqH/hJ/An49SralwoSiQSlUnleAqVOp6Otrc2qrZGb8+f29DsxiSb2Vu0l\nyDMIk2iiWdfMFTGTuG7gDc4Oz6l8/vnn5Ofn880339gca2pqwmAwEBQUZHOsc199fX2/x2gPSUAA\nxh58ckzNzWi++grvuXNdpswNzP4VVTV1/OEfX1GvVxCu6EAmBGEQu+YWlaomNunUDK2odGpbapPJ\nxIEDBxgzZgyCIBDhE0GET98N8UJCQggPD+f48eNul34XpeOnn6ifdzed/YTbN3xL+Hazv4Y0KhLl\n8v/S8tZbiC2t+My/B8/p9ru1OIPo6GgKCgrsZpd9tL2Qf28vBGAzMCQ4gvtcTOC6OeVWdCY9P1fs\nJtI3knsG3+sWFnrhTJ86VxqbtFotDXv2Mqy+nlaZDEmAP8Yff8Rj7FikUVGIHR20/nc5+mPH8Lhi\nIpFz56L76itUKlW/puj3hMFgQKvVWhZSRRHyKpvYnluLwZRHiK8Ho5OU6E1nHzubm5vx8/Nzyjjr\n4eFBSEgIQdogggOD+fT4J5hEI7MSZpMYmAjAcdUxfijZiqfUC32dgRnR/Vtq2KenzJYtW9i1axc7\nd+60OSYIArm5uX36MolEwuLFixk/fjwffvih1bHs7GzAnEY4evRoxo8fb2XwCLB582akUmmfxYzz\nRSaTERlpbiHXPYtCFhND0Jt/p+mvf8XU2IT3DddjrK9H++M2ZElJBL7+GsJps0lHpJ3Yo9Pn4UJM\nL12pHeWM62cQWhPCjrLtZNdl42Hw5Pfj/oBEkBDkGUSKO3vhkiAkJOSs4oK+oJCm559Hn5eP5+RJ\nBLz0IqqWFpRKpcvV/De36/n7xlz2F9WTGunPE7PTiQ7un5Y+FxO5RM78Ifcyf8i9ABhNRj45vpwt\npzaRVXOQe4f8hnSl81MUHU11dTVvvvkmjzzyiN1SNp3ObJJmL/Om81nrrAwP/6efQv3oY3ZFb7Gp\nicY/PoGxqhr/P/zeCdHZ0rmSli9PZ3+zuSSiqN2HUYnBHChqsJzXYZKyqzEEg1TD3SaT054BRUVF\nyOVyYmNjz/szRo4cyYYNG0hPT3cpMz03Ztq+XmkRFgCMxcXo9u6zbHtOugLPSVc4I7SzEhkZyY4d\nO+z6ZG3IrrDazlFL6NAZ8VS4zsu7VCLlrkF3n7XE6NdOTz511dXVgGuMTad++okxH36EVqez2t++\ndh2GsjKMZWW0r//Gss9YVU3KpCvIy8tj4sSJDomxO9u2bePUqVMsXLgQmUzGC6sP81Nel5lrWX0b\n2SVqojxCeaSXz6mvr2f16tVcd911REZG9n/gdoiLi+N40TE+aVlOm8Fc2rGtbBtLr3yHRm0Tz+16\nBpNoNtL0CfDhPslv+jWePokLy5cv57XXXuOqq67C0/P869z9/Py45557WL58OU888QQ33HADcrmc\nHTt28OWXXzJu3DgyMjK4/fbb+eijj3j++eeZNWsWJpOJDRs2sHXrVhYsWOCQtj+xsbGUlZXZlGh4\n33Iz3rfcbPUgF00mmzYqZWVlTlmluNCSCDCLCwUFBRcpovPncO0hXt7zEjqj+cF475DfcOqnYt4/\n/B5qrbm39JVx0/n9yD84M0w3fSAkJITy8vJez2l44AEM+eZa1raVq0AuR3X3XU5RtM/Gmxtz2XzE\n3C3m55Mqnl15mOUPjnNyVOfO2oI1rC1YA0B9Rz0v//wX/jNr+a/Oz+Qvf/kLcXFxLFy40O7xzgw2\nvZ2yuE7h4ULGxr7QKeRXVFi/MODthez11/DLyibys8/tXlu//GP2Du5a/e/o6HBaJkN1dTUGUWB3\nYaPV/vL6Nv59/xh+8+Feq/15LV6UlJSQkOD45FlRFDl48CATJ068oN9XcHAwMTExHDt2zF3i5YJI\n7Kz6Ss4oM9CXlWE4eRLPyZMRXMicU6vV4unpyapVq1Cr1VbHDG0RQLfsW8GETOo6GUxu+k5PPnVz\n584FnDs2ddK0ahV+ZwgLnbR9+RWmM/4+2776mtT772P16tWMHTvW4WVjSqWSU6dO8Z///IeI2Hh+\nyhPtnlep9WL1hi34e9i/d06dOgXgVNPe2NhY1uavoU3eZUCpM2rZWb4DVbvKIiwAaNCQVZvFuKjx\n9j7qotCnf0mJRMI111xzUf7h//znP5OQkMDXX3/NY489hkQiISYmhgceeIDf/va3ACxatIgBAwaw\nYsUK1q9fjyAIJCUl8eKLLzos/T02NpasrKweuyZ033emsKDVai+6R0VfuRjiQqfngrP5LHeFRVgA\n+CLvc0K8Qy3CAsCPpVuZkzyHAf7xTojQTV8JDQ3lUA/tiQCMtbUWYaET7a7d1M266oJWDPuLA6ca\nrLbzKptp7dDj6+m4NrkXgyN1h622W/WtFDUVka50Xhq6o9m8eTM7d+5k+fLlltI7URQxmUwIgmAx\nt1UoFDQ0NNhc35ly2t+mgxkZGZZVqjORnDyJpLgYURAQRNsJkiw01EooHzRokNM8dXJzcxmUnoZv\ncR3N7V0tUdUaHS/+7ygyiYDB1PUzKP28yMvLc4q4YDAYaG1ttVrQOFx7iK0l3+Or8OWGgTf2uURi\nwIABFBUV9Veobi4A3wcfoH3deovJHB4emDQay/H6+x+gY+N35g2FgpCVX+ExapQTIrWloqKCqKgo\nJk+ebHNs6Mk6/vzlIXQGExIBJkXokEldKwvQTd/oyadu2rRpTh+bOr+rTdFzybpEqUQ0GBC7vVtI\nlMH4+voSGhpKcXHxeRvRny9DhgxBKpWyd+9eIsNC8ZDVoTXYjp9SQSR1YCLedjJ+Tpw2d505c6ZT\njYfb29sRtAKcMQU9vO8IHZIOOGO4D1D0b8l+n9SCe++9lxUrVrBgwYKL8qVz5861qG09ccstt3DL\nLbdclO87H/z9/fHw8DivNngVFRVEREQ4xbypqanpgvwWwJy50Nzc7PR2lK06a5dmraEDvWCrip55\nnhvXIzAwEI1G06Opo0SpRBoVhbGy0rJPPmQIKpXKxljVFUiN9EfV0pU+FxPsZbeVkTMQBAGDwXD2\nEwG9yXq1QyaREePXJebY8775pbF9+3aMRiN33XWX3eMjR45kzpw5pKSkkJ+fb3M8Ly8PQRD6vRuA\nt7c3iYmJNvtb/7ucplde7fE6wdOT0MXP42nnWkfT0dFBaWkp48eP52F9EK9vyKFTR9AaTJTUW7f9\n8pBLePTqQeRnfkdLS4tVBylH0X0MPK46xv/tXowJ8yrQzxWZLJv5IZ59yPRxJc8LN9bIYmPB1xc6\nX9C0Wpqeex5uvhHdkSNdwgKATof6938gYvcu5wR7BpWVlXafCwDjkkNZ+/gkDpc2MjDchx++WeW0\nFrRuzp2++NSdPHnS6WMTmFsl+829FXlBIfqjR60Penjg/8zTmKqqaHz2OTCZEDw98f/zk4C5aUBe\nXp7DxQWFQsHw4cMxmUzU1tbyyIw03vwuz+a8B6elMCTN9h7TarXs3r2bOXPmON0otaSkhFmDrqZd\n02YxwU8KHMhz1z1Ph6GDp3Y8SbXGnG0bZxrAoF5aKF8M+jRrzMnJYf/+/fz3v/8lKirKpvbxs88+\n65fgnE1cXBylpaXnLC50+i04g6amJiIi+m42ZQ+5XI6HhwcajQbfM3qkO5LpA2bw8fH/WrbHRY3H\nkGOk1rMGEfOMNMY3hrRfYY34pYZEIiE4OLjHjB5BKiXonbdRP74IY2kpipEj8X72aTQ//uiSZo6L\nZqeh1mjJqWgmJtibF24a6jIvD8HBwXR0dNDY2Njr706j15Bfbz2QxvjG4Kcwv8CJokhxcbFTOiA4\nkoceeshuRtxLL72EIAg8//zzKJVKtm7dyhtvvMGRI0esugCtW7cOpVLJmDFjHBm2hbaV9rt/AHjd\nfBMB//cCUjtO8o7kyJEjVFZWUlpaCpgFHU/g0RHw3+PQrDvz3hG5NRniA4w0Fmah1Wr54osviIuL\nIyQkhFFOWjXeXrbNIiwAqLVqDtVmMzbq0iuJctOFaDJhOqNsz3A61VmfZ/vSZqqts9nnDEwmE1VV\nVb3Wqwf7ejB1UDgAERERVFZW9tiRzY1r0RefuujoaGbPnu3UscloNFJQUMCcOXPw+/YbdD/vAZkU\nSUwMhmPHUIwcifT0e5THlMnoc3NRjB5tGZfi4+PZvXv3BZvRny/Dhg1j1apVjE2D1b+/gp35tXgr\npHh7yEmJ8CMuxH6W34EDBxgwYIDThQUwl+JPmzaNvypfI6f+OEbRyOCQIUgFKV4yL/45/V2O1h3B\nR+7L3g17+12s75O44OXlxaRJk/otCFclNjaW/fv3M3LkSMs+Q0Ulre+9h7G6Gu85c/C69hqrazpb\nUDqrrrK5ufmidKjoNHV0prhwU8rNKL2UZNUcJCEggasTr+Hjwx/z4sSX+alsO0GewVybdN2v1ln4\n7rvvZv/+/Tb7BUFg3rx5PPfccwBUVVWxZMkSMjMz0Wg0JCQkMH/+fG666SaHxns2U0ePsWMJz9yF\nqNEg8fWlsrKS4OBglzNzBIgK8uY/D4xD02HA20PqMsICmOv+kpOTycvLY+zYsT2e125oxyBaZzhI\nJV33UlVVFRKJxCUGzv4kLi7Orhjs6+uLIAiWzJk777yTlStX8vjjj7No0SLCwsJYv349e/bsYcmS\nJU6rt5SE9Cx+e0wY73RhAcypo6mpqZSWljJs2DArAXxrdRnHq6wzFvw8pNw4fiCS063AIiIi2Ldv\nHwMGDODgwYNOExcCPWzFul3lO/mhZCvjosYzOXYKG4q+4WjdYZICB3Jj8s19ympw41xMtbVIY2Mw\nlpRa9nnOmgWA18wZNJ7RJUwxYYLDY7RHQ0MDXl5eeHt3mQlrOgx8susUhbUtjE0K4abRsZb7KDo6\n2mXFhdLmEtaeXEOHUcushKsZFur4Nu6uRl986oYNG0ZKSopTx6bi4mKCg4MtwoDHxK77Qx4Tg6ml\nhea/vY7umHlF3dTWBu/8E2lUFD7z7sRz8mQGDhzIiRMnnPJsl0qljB8/ng0/ZtIYOJjmDgOjEoPZ\nW6hiW041N4yM5fIkpdU1KpWKoqIil+hU19jYiF6vR6lUIggCg0OGUNpcwrvZ/6Td0MHUuKmcVJ+k\nqLGAYaEjiI6JprS0lMGD+y97oU/iwquv9pxy+UsmIiKCxsZGSxqZaDCgmjsXY3EJAB3fbSJ42Xt4\nXXet5RqVSoWHh4dT1DeTyURLS8tF+e5OccER5pm9MTl2CpNjp1jtGxY6nGGh7pZeYE6Ne+mllxDP\nqLNWKs0PwtbWVu644w5kMhmLFy8mPDycLVu28Mwzz9De3s68efMcFmtoaKitGd0ZCIKAcFrQclZ7\nonPBx9M1SwbS0tL45ptvGD16dI8TixCvEIaFDrfyXbgybrrl//Py8khLS3Mp4cTRdP/ZPT09WbFi\nBUuWLOHll19Go9GQnJzM0qVLmTGjf9s69Yb/nxahOngQsdHaIFExejTe11/vpKhsEQSBoKAgS0vH\nTv5wTSCPfXyAdr3x9Hnw+OxBJCZajz2lpaUYe2gD7SiuSbqO3RW7qWg1r3L7yn3ZWbEDgH3Ve9le\nto3DdWZvmf3V+yltLuXPY552Wrxuzo5oMqG6/U4rYcFjyhQCX30FPvsMSWAgyk8+pvHJP2NqbMRj\n3DiCP1jmxIi76PRb6M6zKw+xp8Bca78rv46mdh2/mWJON4+KiiInJ8fhcZ6NZm0TT+14Eo3e7HPx\nc+VuXp+8hOSgFCdH5nz64lPn7LEpPz+f1NSeO7c1PPw7tD/+aLNfn32Ijo3fEbLqa1JTU9m0aRMZ\nGRlOWVAKDY9idVkgrUXmOeq2nBrLsW05Nbz/mzEMjTWLy6Iosnv3bkaNGuUws8zeOLM7YbO22ep+\n2l3R1elxf/V+poVNR1eqc764sHbt2l6Pz5kz56IE42pIpVKioqIoLy8nOTkZXVaWRVjopG3NGitx\noayszGkGdBqNBk9Pz4tSI+3v709zD73T3bgOPj4+vfaAX758OTU1NaxZs8ZiBpSRkUFpaSlvvfUW\nt956q8Nao4WEhPRo6iiaTOgOHUIaE4sszLwSq1KpnC5unY2Wdj1SiYC3i/gtdBIYGEhgYCAlJSU9\n1uMCPD3mWdYXrqO8pYxREaOZEjsVMNcSdtbG/1r59NNPbfYplUqXE9sVQ4cSsW8Pun37kCUm8r+P\n/s0N11+HYtQolxKGcnNz7YpVQ2MD2fDEFH4uqGPT9z/w59/cQqi/7YQtPT29V1PY/kAQBEwmExs3\nbrTsmy1eS5W8Cq2o5Xv9Jqvzj9Ydsdr+uTKT9d+uRybIaG9vt9uL3o1z0R89iuHkyTP2iki6GZ56\nTplMxL49jg2sD1RWVlrGdYBGjc4iLHSy+UiVRVwIDg5Gq9XS2trq1KzUMzlQfcDyIgRgEk3sLN/h\nFhdO0xefOmeNTQ0NDZSXl2M0Gjlpcx+BVKNhiB1hwYIokrf0HSrumodGoyEnJ8chHhFnsv9UPa22\nDTcAMImw5WiVRVwoKSlBr9db3XvOpKyszEooOFCz3+p+OpNDzYfwqw5Ar9db2pVebPo0I37qqafs\n7pfL5fj5+f1ixQUwl0aUlpaSnJyMNCzMvKzSbZVYGh5udX5paanTUjZNJtNFS3+SSqWYTKazn+jG\npfn+++9JTEy0eQhee+21bNu2jczMTIfV1AcFBdk1dezYu5f6W2+zpJ3Khg8jfOO3qFQqp7Rz7UQQ\nBIxGo10V3WA08er643x3uBKZVMId4+J5aHqy3c8xGo1OeclLS0sjLy+vV3HBW+7N7Wl32Ow/efIk\nsbGxLqHKuzk7Eh8fPKeahaHG2Bg8Ro9Gl5VN6wcfIJpEfBcuwKOXEhlH0NzcTEqK/ZcFH08ZIwYE\n85neg1fWHWdiSijqNh1HyxoZHhfIXRMSSEhIIDMz06HlJzKZjDlz5tDe3m61fzjDadW38uOR7zGK\nXdkUgiBAtyQyX5kvw4cORyKYnyGdGWVuXAdpaBhIJNBtvnPmvM4VMZlMVFdXM/X0fQ/gpZDi4yFD\no+0qdwvx63LwFwSBqKgoKisre7wXnUGwl23pVrCn88u53JydwMBArr766p5P0OkQ/fwQWlp6PCU4\nNZXgoUNJT0932uJsqF/PnS4AinIP80F5VwaAIAhO96UDc7vR2tpaZs6cadkX7Nn7OKP0UhIaGkpl\nZSUDBgzol7j6JC4cP37cattoNFJSUsJ7773Hbbfd1i+BuQqxsbHs27fP/OIeHY3PvQvQ/MdsMiiN\njsb3kYct53Z0dKBWq4mMjHRWuG7cWDAajRQWFjJt2jSbY8nJyYiiSG5ursPEBYlEQlBQEPX19Vb3\nSMOChVb1rIbDR2j+4kta29ucutIXERFBRUUF8fHxNsc2H63i20PmzhY6g4mPdxYxLjmEEQNs4+3s\nHuNoOl/GztW4RxRF8vLyGDfObVJ3qWIoLaXu1luhw9zKt2PLFsI2f4e8l9TV/iYxMREPD/sTOFEU\n+cOnByjQ+EOBij0FKsuxfYX11DR18MwNQ0hJSaGwsNBRIQP0Wpo1uHIwR7plK3QXGmQSGfePeJAB\nsf0zeXNzcZBGReL36O9oWfoOiCLSqCj8HnvU2WGdldraWvz9/a0EYA+5lEdnpvDGt7kYTSL+XjIe\nnm4tIriiuDA8dATjoyaQWbkbMLvcz4yf5eSo3PQFiURyVkFA88JiGp96Gux0sZKlpxH62KNInJzV\nlRTqzSD/DnKazfeTj4cUjdb8PE+J8OO1BVfi59W1yr9//3727NnD9OnT7X6eoygvLyc8PNwqA2F4\n6HAmRE9kd4W5o02Ydxj17fUYRSPeMm8WDLkXY6WJkpIS54oLZ64UdBqGPffcc8yfP59vvvmmX4Jz\nBXx9ffHx8aF6+XKEN9/CpFYjTUrCWF2FsaKCpucXE/TPd5D4+FBWVkZUVJTTjL3c/PpQq9U8/fTT\n7N27F5VKRUxMDHPnzmXBggU0NTVhMBjsvqB37uvsg+woQkNDUalUhEmltK1Zi+ChQLRTftOyYwdB\nM2c41cwxPj6e4uJiu+JCYY1t+9PCmhYbcaGz48KsWY6fKMlkMgYOHEh+fv45ZVPV1dVhMBh6NN50\n4/p0bNpsERYA0Otp3+g8cSExMbHXCeipOg0Fdu6pTr4/Vs0zNwwhPT3dpUo9VO32n593D5rP9AHT\nCfR0l0FcCvg/+Se8b5uLsaISxaiRCA4qFbwQKisr7ZYNzhkVy4SUUEpUGgZFB9iU7EVFRZGdne30\nVuPdEQSBP495muKmU3QYOkgN/nV7/fzS8LnjdjyvnMraf7zF9Y88jLG4BMHbG0xGFCNHIriAaXdW\nVhZ3Z/gTP2Q0TW16hsYGUlzXikZrYEhMoMUUtZPLLruMlStXUlFR4dTyXXvdCQVB4MnLn6K4qZgO\nQzupwWk0aRspaS4hOSgFb7k3jbJGjh492m/PgQsqFPby8qKsrOxixeKyxAcGYnriSQS9uSDH2G3l\npGPL97S++x7+f3qCsrIyp7WgdPPrpLy8nJkzZ/L3v/+d5uZmVq5cyWuvvUZ1dTULFiwAsOup0Kly\narVam2P9SUhICLXHjlF71z2YVKdXJ89w4gZomjTpnFvAXmzi4+M5ePAgJpPJRuQYOzCEzzOLLdtS\nicDoJNtUNJVKZcnYcAZpaWnnbJKUl5dHamqqe3J3CSO1IwzZ2+co7HVPWrlyJWq1GoAOowQJMZiw\n/zcXdtqDITAwsNcOKI4mxCuEylZbk9rLI8e4hYVLDNmAAcj6aRWvP6isrLRqO9idUH9Pu74lgKWb\n2MXqLHYxiQ9IcHYIbi4yhuJiWt5eirGqGq+gQOSxseh+2kH7NxuQxsYgjYxEFhPj1BjVajX5+fnc\ncsstls4rHToj3x+r5kipmqGxgdw7KQlPRdfCsUwmY9y4cezevZubb77ZKYvKZ+tOGB8Qb/n/QM8g\nqzEpICAAqVRKfX19vxin90lcWLVqlc2+jo4OfvjhB6fVxziS4OYWi7Bgj4KNGzkQYO7QUFNTQ3p6\nuqNCc/Mr5l//+hdSqRSfbsZTkydP5u6772bFihXce++9AOjt/O3qdDoAh9fUh4SE0LB5S5ewAGA0\nIgQFIarVIJXi++ADnIgIJ9LJnSJ8fX3x9/enqqrKRpm+PEnJ09cP5uu9JXjIpCyYlEic0rYXcmfm\ng7Ne1JVKJd7e3pSXl/dJ+NTr9RQVFZ3VPMqNa+M56yo8Z86gY8v3AHhc3Y4OwwAAIABJREFUcQXe\nN7hO5wjApoWXcvcp3t16EqNJJMBbTofeiFZvwlsh5fGrXcM460zmD76X/9v9PC36rnri65JuIM7f\nvchwKaEvLKLlrbcx1dTgdfON+Lh4ua/BYKC2tva8yu26+y64mrjg5peFqNejmns7xtNdwoYDai8v\n2r762nKOds9ewndsd1r2Qmfnh4yMDKuWrq9vyGHjYXPpa1axmpqmDv7vZmsxb8CAAeTm5nL06FFG\njBjh0LjBnGV6vt0JBUEgLi6O0tJS54kLzz33nM0+Dw8PEhMTeeGFF875S/ft28eyZcvIycmhvb2d\nxMREbr/9div/hqqqKpYsWUJmZiYajYaEhATmz5/PTTfddM7fd6FUBfgT5+eHpAdDkpR77iHj3gXs\n3LnT/bB24zB6eqBMmzaNAwcOkJeXh0KhoKGhweacznIIZ2QHdJwWNrqTPXkSFcO7PbhPnqSiosLp\nbrzx8fGcOnXKbtrbDSNjuGFk74p7cXExkyZN6q/w+kRaWhq5ubm0+7ax/Nh/ULXXMylmEncOugup\nYK22FxYWEhkZaTXIurn0EGQylP/9D/r8fDAYkQ/uuaOMqzBvQgIzh0ZS1djOoOgAOvRGCmpaSQ73\nc9mWrwODBvKfqz/mhPoEa79Zw/1z7yfcx/H+Kq7GpTTHE7VaVHPnYqo2t57T7t6NoFDgfeONDo3j\nXKipqUGpVJ53p6eoqCjKysrcC2Fu+hVdVpZFWOikU/DuxHjqFPpjx1D0kIXT3xQWFtLR0WHTde2H\n49U222eKC4IgMGHCBNasWcPAgQMdbu5oryTiXIiLi+PAgQM9Zj5cCH0asfPy8i7aF+7YsYOHHnqI\niRMn8vrrryOVSvnqq6944YUXaGxs5MEHH6S1tZU77rgDmUzG4sWLCQ8PZ8uWLTzzzDO0t7czb968\nixbP2RBFkdLaWlLe/Re88w7GyioUl4/GUFKKoaQEaUAARrUaUa8nNjaWY8eO9Ziq5mxOVDVz8FQD\nqVH+ZMS7vhNvi66Fnyt2I5PKGR81AQ+pB3XyWtYXrOOysAxi/X/5WTO9IYqi3Q4hnaUOPj4+pKSk\nkJ+fb3NtXl4egiA4vOWPSqVCdsMNSI4cxVRbC4AsMZGrX/+bVeuvo0eP0tjY6NDY7JGQkMCGDRuY\nMGHCOWcfNDY2otVqCQsL66fo+kZSUhK79+7m493/QWMwtydadWIl/gp/bki2nkDn5eX1y0Djxjk4\n08DxfOiezu0rldg1SHUFjtQdZu3JNYiIXJd0PRnhI8nUZ7qFBS69OZ7uwEGLsNBJ+4ZvXVpcqKys\nvCBPnKioKPbu3etSvgtufnlIo6NtOrFIQkIwnS6HA0AmQ+okE3ydTsfevXuZNm2aTdloRKAXJaqu\ndo6RgV52P8Pf35/Bgwfz888/M2PGjH6N90xKS0svqEwwMjIStVpNe3s7Xl72f77z5byWA8rKylCr\n1QwZMuScDddee+014uLiePfddy0vRWPHjmX27Nl88cUXPPjggyxfvpyamhrWrFljWbnMyMigtLSU\nt956i1tvvfW8FdtzpampCZPJRMjUKQhXdrX8af7HW7Qs+TsGlYrWv79J+6rVRP+0jW3bttm02nMF\nvjtcyYtrjlq6aP5mShL3Tx3o3KB6Qd2h5o/bfk9Dh3nVfc2J1QwOGcK+wD3sO7oHiSDh6THPcnnk\nGCdH6hzKysqYPXs299xzD3/6058s+0VRZMuWLXh7ezNkyBBmz57NG2+8wZEjR6xEr3Xr1qFUKhkz\nxrG/v7q6OoISEwh89a/oDh5EFh+P1/XXIfHxQTQa0e3dh+DvR2hoKAUFBQ6NzR6BgYEoFArq6urO\nWSRwdklEJwqFAnmEDE27dd/j7NpsK3Ghra2NhoYGYpxc/+jGjStT3lLOX3a/gEE0O58fqs3mH1Pf\ndnJUrsOlNseTxkTbtBjPaWwk94MPAPjg9H87CQoKsinpcTSVlZUX1PLcz88PhUKBWq0mONj1F5rc\nXJrIYmLw++PjtPzjLTAaaQkJIeEff0f9u8cwFheDTIb/n59E6iR/rfz8fMLDw+2WFy2anc4zXx+i\ntcOAj4eMRbN7zvIZMWIEX375JWq12mH+Wm1tbTQ3N19QJzKpVEp0dDRlZWUXvXtMr+KCVqvlmWee\n4frrr2fy5MkAvP322yxbtgyAgQMH8umnnxIYGNjnL5w/fz4RERFWq61SqZRBgwaxadMmDAYDW7Zs\nITEx0SYl+tprr2Xbtm1kZmY6rH1eaWkpsbGxNi8Ircvet9o2lpRgOnyYsLAwKioqSEhwLWOa5TuK\nuo+dfLa7mAVXJCKXOd+lFcwKYm5uLuLpIHc0/mQRFgBKW0opa+kyDzWJJj7J/hhFjW1rM4lEQnp6\nulVrll8asbGxzJw5k+XLlyMIAhMnTqStrY0vv/ySnJwcnn32WTw9PbnzzjtZuXIljz/+OIsWLSIs\nLIz169ezZ88elixZ4nATGnVJCYkffETDaVNUz9mz8b7zDowNalQ334LhxAkAPK6ZjXrsGLtmio6m\nszTiXMWFU6dOMXr06H6Kqu+YTCb0dXokPhJMQtcKQnNxMx8ct544SyQS6urqCL8E+ry7ceMM9lfv\nswgLYB6L9lbtcWJErsWlNseTDRhgfgF6620wGpGlpzH+vXe5wskZZz2h0+loaGi44Gd0p++CW1xw\n05/4P/4HfO64HWNdHd9mZpJ22WWE79iO/vhxpBERSJ14n+l0uh7fXy9PUvLNoskU1raSGOpr03Wl\nOzKZDF9fX4uXmSMoLS0lJibmgufHnb4LDhUXli5dyqFDh7jvvvsA8w+zbNkyHnnkEaZOncqbb77J\nv/71L5599tk+f+FtPRjllJSUEBcXhyAIFBUVMW3aNJtzkpOTEUWR3Nxchw08ZWVlDB482PZAtzSf\nTkS9nri4OMrKylxOXDCaRKttkyhiEsUeznY8xcXFnDhxwmIQqtPbdjEQsY7XYDLQ0dFhc15JSQne\n3t4MHOi6mRkXg9dee43Bgwfz9ddf8/HHHyOXy0lPT+edd96x9N719PRkxYoVLFmyhJdffhmNRkNy\ncjJLly51eAqXyWTC7/ut0L3bysaN6DJ/Rrt/v0VYANB+u5Go+HjUajVKpW0XBkeSkJDAjz/+yOWX\nX97nLITW1laam5tdop1jSUkJYQHhPDbiD/z76Ee06JoZGT6KRaP/hI/c2oTy0KFD5OXlucUFN256\nIMJO6UOETyRN2Pdk+rVxqc3xAPz/+Dg+8+7EpKpHNsi12p2eSXV1NaGhochkF+ZDEhUVRWFhocNL\nI938CpHL0Xy6ggk/bqNJVY//Hx93msfC2WjXGXj/xwIOlahJjwrgt9MGWgkLW49Vs2pfKZ5yCfOv\nSOQyJ5WY92bSbRSNrD6xir2Ve4j2i2Ze+t2E+9if08XFxbFnz8UXx3t9Om3ZsoU333zTYvqyadMm\nYmNj+d3vfgfAU089xSOPPHJO4sKZVFZW8v7771NQUMDbb79NU1MTBoPBbmpJ575OM7r+RqfTUVtb\ny8yZM22Oed81D80HH1q2JeFheIwdS2xTE4cPH3a5Wrbbxw1gybe5lu2bRsXiIXd865TeCAkJsdQP\nJbUlcujHbIsLd5h3OGnBaewo/wkAAYE7h89jbKxtvVFra8/90n9JyOVyFi5cyMKFC3s9T6lU8uqr\nrzooqp5paGjA107rS2NdLaa6Opv9SsxlFM4WF0JCQjAYDOeUQlpcXExcXJzTsy7A7KMwMGUguR05\npAenkxKcyk3JNyOV2N7/KSkpfP3114wbN87lSrvcuHEFLo8cwxUxk9hZvgOAcVHjmRA9kXxOnOXK\nXyeuPMfrjjQ8HOklIKpeqN9CJ1FRUezatcslsgPd/LJRP/w7tLt2EQi0vvseYmsrga/+1dlh2eXv\nG/PYkG02ocyrbKa2uYO/zzP7UGUVN/DcysOWc7OL1ax87ArCAhzbdQ3Mnb166va2+sQqPsv5FICC\nxpMUNRbxzrR/2X0n9fLy6peMi17FhZqaGoYOHWrZPnjwIBMnTrRsJyUlUVNTY+/Ss7Jv3z7uuece\nwKycLFu2jPHjx1NdbXbotDex7Uxz19p5QekPKisrCQsLs5te7zPvTrQ/78FYWIgsJYXgLz9HEAQC\nAgKQyWQ0NDQ4/aWoO7dcHkd8iA/7ixpIjfJjarprD6Kh3mH848ql/FS2DblUwdTYK/FR+DA64nJK\nW0oZGT6KdKXb6fhSQqVSob/ySsj8GYxGACTBwXheeSXSmFg0n67o2q9U4jl1KqruLSudhCAIxMfH\nU1xcfE7igt2MJwfT2tpKbW0t+f/P3p2Hx3iuDxz/zkz2PbKSiCz2tZQS1YNG6HGoHsqxVjnHUtQp\n2jqoVJUfrRwVbdFoS0lLq62lq1RLUY3YVe27bGSXPZnl90dOhpgJIcnMJO7PdZ3rOvO875vco27v\nzP0+z/14nWLntV8AiE/ZT1ZRJuPaTjA438HBQf9ESzqJC2FIpVDxcqdXGdVyNDp0RmcyiNrxGa82\nys3NJTAwsMo/x8HBAZ1Oh1qtlkKyqDHavDyK9u4tN1awfbtFFBesra05duwYly9f1o/tOOkE3PoS\n/tvZG3y+6UtUCtiZZAvcypUitZb3Pt9O23ol3Lx506RLsa2srPjtt9+Ij483OPaj8vvb3wLXcq7y\n8Vcf44rx3QxrIu67FhdsbGzQaDQolUq0Wi1Hjhyhf//++uMajeaB12y3adOGLVu2kJmZSWxsLOPG\njWPq1Kn6/dVLSkoMrimrrlRUraluZf0W7qRTq0kfMQpNQgIAJUePUvDZBpwnjEehUNCwYUOuXr1q\nUcUFgI7BHnQMtqyY7sbLwYtnmw0pN/aXht3NFI2oqrS0NFxDu+D5+QbyPtuA0skJp3HjULq4YN2k\nMR7r15P/5abS8fHjwM6OC/v3mztsoHRpRFxc3D13UtBodaRl5ZKammr03w5TO336NMHBwSxL2Fxu\n/JcrPzO61RhKtCXYKG2wVt26ubRo0YKDBw9KcUGIu6homqkoVRs+4wkhapbC3h6lry/alFtbO1oF\nB5sxoltat25tsM34thunOHv9VvNrXxc7dJoseoaHk3/8Bod2XS13fliXdrT2c0alUt1X/8Gq6t69\nOzk5xpfinT11mvTUW7O/bJW29OnaB3sr4ztC2Noa9q6rqrsWF/z9/Tl+/DiPPvooe/fuJScnp1yD\nsjNnzuD1gF0+7e3t9c18QkNDsbOzIyoqirCwMGxsbMjIyDC4pmyq3IP+zvuh0+m4du2a0W0lS06e\n1BcWyhTGxuI8YTxQWqU/cuQI7du3r/E4hagt0tLSCAkJwbZNG2xDQwHQ5uSQNmo0Rb/8gsLNFdeI\nCBz/Ufrh0+N/jassYdqmr68vubm55OTk4OzsbPSc/efTWLDlBKk5Rfg5NaBvXgk+rlVbF1sVWq2W\nM2fO8NRTT+Ea70p64a2bTb46n3988yxanRZHa0dGtRrNX4P6AuDn58eePXtIS0vD09PTXOELIWox\nS/+MJ4SoeQqlEve3FpPx73+jy8pG1aABrvNeN3dYQGkD6zsfAs98ujUzNx4lLacIVwdrZj3TmmM/\nn8XDw4N/dHPjWFIBv51NRamAgZ0C6N420Cyx29jYVPgA+5/t/0XivgQSchNQ6VRMeOQF/H1MuwvY\nXT+x9+3blxkzZrBgwQLmzJlD9+7d9Y2+UlNTeeutt/S7SFRGdnY2X331FSdOnDA41qpVK3Q6HefO\nnaNp06acOXPG4JzTp0+jUChM0oAmIyMDpVKJq6vhNBKVf0O4o9JjFXKreWD9+vVJT0+XqX1C/I9W\nqzW6VCjnvfcp+qV0ur4uK5usmf9B87/+CzY2Njg6OpJ5+57IZqJUKgkICCg3fe52ao2WNzb/QWpO\nac4n5sLy7eZdg52QkICDgwMeHh6MbfMvrJTlCx1aXWlT2rySPKKPriI1/wZQ+l6bNWvG6dOnTR6z\nEKL2qk2f8YQQpmHXK4z6Bw/w66SJ+Pz+GzYWnN+t/N3YMu0vbJj8ONumd6dzyK0HLLbWKv47ogNf\nv/QXvpnRg5f/ZpmzO30cfXmv10re77WSXul9CGvUy+Qx3PWx2r/+9S/S09PZs2cPHTp04PXXb1Wb\nVqxYwY0bN4iKqvz+zkqlkoiICLp27crq1avLHTty5AhQ+tSsb9++LFmyhOPHj5ebObB161Y8PDzo\n3LlzpX/ng7p27Zq+s/GdtOlpuLz6Cjn/XYouPx/r1q1xmf6S/riVlRX169cnISGBkJCQGo/1flxK\nzcXF3hqdDnILSwj0cjJ3SFVy7eY1nG2ccLMzzd6y4sFkZmbi6OhosLaz5OSp8ieWlHDzrbex79sX\nuyd74uXlRVpamtmXGOl0Oo5m2rH394s0iD1BK6ccyv5pyFFbcSbPkYzc8lPizl83b/f406dP658c\ndvN/gtaerVl+OIpD1w8anKtFy5WbV/ByKN0WqlmzZnz11Vd06dKlyl3Ja4v4+HhWrVrFyZMnKSgo\nIDg4mKFDh5brfp+cnExkZCT79u0jLy+PoKAgRo8ezcCBA80Ss/rqVdJHPof66lVU9X2p9/FHZolD\nCKhdn/GEqC3i4uJ4//33OXHiBFZWVoSEhDB27Fh9s/lZs2axefNmo9f26NGDVatWmSzWvA0byVv7\nCQp7e5xfmord/3Z9Udjbk+vlhaIWfJ5Qa3VsOXSNuPPphHg74aEuv/y/gbvx5QWWRKFQ4O/cECud\nef687/pblUols2bNYtasWQbHxo0bx6xZs+6rEYyzszPPPfcca9eu5eWXX2bAgAFYW1uze/duNm7c\nSGhoKG3btqVp06Zs2rSJadOmMWPGDLy9vdm2bRtxcXFERkY+cJ+H+3Ht2jXatWtXbkyTkUn6qFGU\nHD0GVlY4TRiP44jhWDVqZHB9w4YNuXbtmsUUF7Lzi5kWc5iTidn6Ph86oF2AG0tHPIqjneUn/O1u\nFt1k/u/zOJd5FpVCxaCmgxnRcqS5wxIVSEtLMzrV1a77X/QzF8rkb9hI/oaNOE2cgOeAp0lNTaVZ\ns2amCtWo6Z8e5vdz2YAdCYV24NWY5c914nRSNhM+jqeoxHBr2sdCzFcQyc/PJykpqdx2bm527vRq\nFG60uGBnZU/zereq8E5OTnh7e3Px4sVq3//YEu3evZsXXniBbt268fbbb6NSqfj88895/fXXycrK\nYsKECeTm5jJs2DCsrKyIiIjAx8eH2NhYZs+eTUFBASNGjDB53KkDB6FNLl3Lqrl6jbSBz8JLU00e\nhxBQuz7jCVEb7Ny5k0mTJtG1a1eWL1+OQqEgJiaGqVOnMm/ePIYOHQqAt7c3q1atQnfHFvMuLi4m\ni7Vw1y6yXn5F/zr9+bH47PkVKwvoP3U/Vvx0li/2l/ZWuJKWh4e1FzPMHFNt88DfKB90K5yZM2cS\nFBTEF198wdSpU1Eqlfj7+zN+/HgmTpwIlDbziYmJITIykgULFpCXl0eTJk1Yvnw54eHhDxpypRUV\nFZGWlmbwHnM/+KC0sACgVpP7/gocKtjTuWHDhhw+fNjkW1JqNBpSjWzrtz4uiZOJ2UBpUaHMsatZ\nrN15iiEdy3e8tvTtHLee38K5zNJp5xqdhi/ObKRHw574Ofvd40phDqmpqUbX7zuOeR5tRgb5m7eg\nTU1FV1CgP5a7Zi0eI4Zz6dIlU4aqd+7cOW7evEmhWsvv58qvD46/kMGeuAN8eSLPoLDgaKWloW0e\noR55HDp0CCj997J+/fomi/3s2bMEBQUZFH+7+j3O2Db/4sdLP1CoLkCn0+Ht6MOols/hZFN+FlPz\n5s35448/HoriwuLFiwkICGDFihX6LzZdunShb9++bNiwgQkTJrB27VquX7/O5s2b9TNCOnTowNWr\nV1m2bBmDBw82add1bW6uvrBQRnfzJvbZ2SaLQYg71YbPeELUFkuXLtXvtlLW1b9z58707t2bNWvW\n6IsL1tbWtGzZ0pyhUvjLzvIDJSUU7dmL1fBh5gnoAe07V/47VHqJLWk5RXg6V3/jw7rKLI+rhwwZ\nou8YXBEPDw8WLVpkoojKS0xMxNfX12A6sObKFYNzNVeuYB1i2PnUxcUFW1vbCp/Y1gR7e3vc3NzY\ns2ePwbHjV2yp6D/30bNXqF9wzmD8kUceqe4Qq01KXrLRMSkuWKa0tDSCjXQIVqhUuLz6Ci6vvsL1\nnmGoz5bvU+Dp4UF6erpZmjru2rWLdu3aodMpKC3JlS8S6nQ67nhIAMC4Ts7cvHoNbYkvOpUdN2/e\nJCUlhb/97W8miVun03H69Gl69uxp9Hgrj9YcTIknNT+Vbv5/YViL4agUhk8KGzVqxN69e8nKyjJp\nF2RzGD16NL6+vuWemKpUKlq2bMmPP/6IWq0mNjaW4OBgfWGhTL9+/di5cyf79u0rN1OkpikcHVHY\n25cryGmVSoqcnIiOji53rru7O4MHDzZZbA+ztII0PjwezYWsC7T1asvYNv/C0drR3GGZlKV/xhOi\ntpgyZQqurq7ltgu0trYmJCSEAwcOmDEyQ9ZGHkRY1cKHE0FeTiRk3Lqv2is1uDmYbpvJuqB2zYU3\nkatXrxIQEGAwbte3LwXffKt/raxXD5suFa8NDAgI4OrVqyYrLtjY2FT4Bcb1zxTmfHHM6LHn/9qZ\nx5uatzuzra0t+fn5lT6/q9/j7E28VURxsXGhlWdpk5j8/HzZysqClDVzvNfOA04TJ5A1/dbkM6fR\nz2Hr6oqjoyNZWVnUq1evpkM10LFjR5RKJU3iYjmXeauS0DG4Hn8J7YRXQDaHVu9DoystfLQPdGfo\nU48RG5uPl5cXzZo1IyEhgWPHjOdeTShrgOnt7W1wrFhTzPx9r5NdXPp0e9OZz3G0duTvTQx7BiiV\nSkJCQrh06VKd3/nmHxXMQLty5Yq+987FixcJCwszOKdJkybodDpOnTpl2uKCQoFb5BIy//0SqNWg\nVOL+xuv8a+xYk8UgDP33wNucTD8JwI4rP6HWqpnWUSbVCiHuX58+fYyOX7x40egDG3NyGDKYon37\nKNj2DVhb4zR+HLYdHzV3WPft3081JymzgAs3cnF3tKGj3TWsVObdsay2keKCEUlJSUaf2js83R9d\nfh75m75E5emF80tTUTo4VPhz/P39OXLkCI8+av7kCmvlS8EzGr47moiVUkFCYiL16zdgwKP+Zi8s\nQOkOG7/88gtqtbpSDeQe9+vGix3+zS9XfsbNzo0hzYZiZ2VHSUkJ6enp+Pr63vNnCNPIysoy2szx\nTo7/GIJ148YU7tmDdatW2PUq/SLn6elJWlqaWYoLUFocCXNL5sl2nTl0JYcujT0Y3jUIgBZ+rvzd\nOxnvNj3wdLYlrFXp37ugoCAuXLhgll4RSqWSvLw8PvvsM4Njaco0su3KT5v/4dh3JB9IoVhRhIfW\nE+VtmwgVFxeX2374YZGUlMQHH3zA+fPniYqKIjs7G7Vajbu7YePYsrGybfRMyeGZAdj3+xvFx//A\numULlFJUNatCdaG+sFDmyPXDZopGCFEXRUdHk5yczOTJk/VjhYWFLFiwgN27d5OSkoK3tzf9+/dn\n0qRJ5WY91CSFjQ31VryPZsGbKKytUVawbbel86/nwKeTH+dGdiHujjas+fhDc4dU60hxwQi1Wo2t\nrfG1NY5Dh+L4vzVO92JjY4Nara7O0KqkX3s/+rUvXTYQHX2E8WOeMXNEt9jY2FCvXj2uX7+On1/l\nljb0ahROr0bl12cmJyfj5eX10HS4rw0q6rdgjM2jHbB5tEO5MU9PT1JTU8229j8pKQlXVxf+3rM5\nY4wcd7FS89wT5Z8gBAQEsHfvXkpKSkwT5G3c3NwYPnw4Go3G4FhOcQ579uyiRHsrLmsXG7bnfQ9A\nfYcGzO0YgavtrWUQjo4Pz5Tu+Ph4nnvuOQD9OteuXbuSklLa28BYgazsg5u5th5WWFlh26Fuzyyx\nNJs2bTK6Re4nH3+CQz0H8lW3ZuE1cg00YWRCwKFLGVxNy0WlUmKlVPBEM2+c7WVad12wZcsWoqKi\nCA8PZ9CgQfrx7OxslEolCxcuRK1W8/3337Ny5UrOnz/Pu+++a9IYVXd5EKRJTSV7bgRF8fHYPPoo\nbgveROXjY8LoKs/bVYr1D0q+gQmL4efnR2JiYqWLC8ZU9XpR/arad8TLy4vLly9XX0D36fLlywQF\nBd3XNba2tvj4+HDt2jWTNvkrY29vfKskJ5yY3P5FPjweTW5JLq4lbiTkXdMfT85P4ueUHTzX6nkT\nRWpZ2rRpw5YtW8jMzCQ2NpZx48YxdepU/fpxY8Wi4uJiAFmK9RC5W/+KJ9K6sfRgJKkFqQQ4BzCh\n3UQTRiYedm9/e5KvD1wrN+bhZMPH47vg42r5W+iJikVHR/POO+8QFhZGZGSkfnzOnDnMnj0b59tm\nCoSGhlJcXMy2bds4ceIErVu3NkfIBrJefpXCHTsAKPz+BzLz8vD87FMzRyWqmxQXhMXw8/MjLi6u\nSj8jMTGRv/zlL9UUUe1y4MABRo0axWOPPca6dev048nJyURGRrJv3z7y8vIICgpi9OjRDBxouM6+\nJqSlpVX45bxwz17yP/8CzfUUFPYO2HYNxWnsGBS3fSH39PQ0W1NHnU7H5cuX6devHwAHL6bz7ZFE\nXB2sGRoaSH23ij+sBQYGcunSJbNvo3mnngFP0s3vCQrU+URtWM5B1/3ljmcUZFRwZd1nb2+vb9gY\nGhqKnZ0dUVFRhIWFYWNjQ0aG4Z9N2XIIU/XWEZatpWcrPujzITlFN3GzM1xGI0RVJSQkGJ0plVmg\nYfNBw2bX6bnFRP9whIFtXMuNa7WGWygLy7Rw4ULWr1/PyJEjmTNnTrld6JycnIxeEx4eztatW/nz\nzz8tprhQeEfD+aI9e80UiahJUlwQFsPb25usrCwKCwsf6Clgfn4+eXl5lZ6CX5cUFxczd+5cg21P\nc3NzGTZsGFZWVkRERODj40NsbCyzZ8+moKCAESNG1GhcWq2W9PSBtJycAAAgAElEQVR0o/9Nin7b\nR/qw4dy+5ULRzz+jPncO9//eqsrb2Njg4OBAdna20TXvNenGjRvY2Njg5ubGoUsZTF13EO3/wt15\n8gZfTO1W4bWBgYHEx8fTpEkTE0VbedYqa6xVrngWe1LPrh4ZhaVfmhUo6N6wh3mDM7Hs7Gx27NhB\ns2bNDD6AtWrVCp1Ox7lz52jatClnzpwxuP706dMoFAqL+fAmzE+lUElhQdSIFi1acOnSJaPLctLz\ntUZ3MAK4mVdAZmb5YkL79u1Nth5fPLioqChiYmJ49dVXGVtBw15j/coKCwsBKlzmbQ7WrVpRcvhw\nudei7pHiwn3SZmaSNXsOhbv3YN2yJW6LFmLduLG5w6q0lKwC3vr2JIeS/Dn72WFm9muJl4tlTOdV\nqVT4+vqSlJT0QF1wk5KSqF+/vsmfbluCFStWkJuba/AFZ+3atVy/fp3Nmzfrn8h26NCBq1evsmzZ\nMgYPHlyj0/azsrJwcHAw+jvyv/wSY5+E8r/6Gre330Jx27aAZX0XTF1cuH1JxA/HkvSFBYAbNws5\neLHiJn4ODg64u7uTmJhY02E+MBUq/u+Jt/jy7Bfkl+QT1qgX7X063PvCOkSpVBIREUHXrl1ZvXp1\nuWNHjhwBSmdV9e3blyVLlnD8+HHatm2rP2fr1q14eHjQuXPFOwcJIUR18PPzu+vSz+0J8Ry+Ur7w\nYGutZGK/xwjxqZ0N9h5mO3bsYOXKlUyfPt1oYSE/P5/u3bvz+OOPs2zZsnLHtm/fjkqlsqimzO5L\n3iJj0mTUZ85i1bhxuQdJou6Q4sJ9ynr9jdJtVoDiffvImPgCPjt+MnNUlTd/8x8cvpwJqNh7JhWt\n9k+WjjT/bhZlyvouPEhxISEh4aHst3D27Fk+/PBDFixYwFdffVXu2E8//URwcLC+sFCmX79+7Ny5\nk3379tXo9nlpaWkVziRRuLgYH7e1LVdYgFs7Rpi6qePly5fp1asXAO6OhgUSY2O3CwwM5OTJk7hU\n8F7NLd06ndd/e43r+ddp6t6MAJdG5g7J5JydnXnuuedYu3YtL7/8MgMGDMDa2prdu3ezceNGQkND\nadu2LU2bNmXTpk1MmzaNGTNm4O3tzbZt24iLiyMyMhLVHX9nhRDC1PJLyjfybeTpyOKhjxDkZXzq\nvLBcGo2GxYsX06BBA0JDQzlx4oTBOUFBQQwbNozVq1czd+5cnnrqKbRaLd9++y07duzg+eeft6jP\nxdbNm+Pzy89os7JQurnd+wJRK0lx4T4V79tX7rX61Gk0GZmo6ln+FEidTseROyrahy5b1vpqPz8/\nTp48ee8T76DT6UhMTDS6hWhdptPpiIiIoHPnzjzzzDPligsajYYLFy4QFhZmcF2TJk3Q6XScOnWq\nRosL169fx9nZWT8973ZqI2MAutxccg8cxKrNrVkYzs7OXLx4scbirIhOp9MXR/7RpRE7T6aQkFEA\nQJ+29Wnl78Zvd7k+KCiI/fv3W2RxQavTctT5MIX5pe/nbOYZPjwezewur5k5MtObOXMmQUFBfPHF\nF0ydOhWlUom/vz/jx49n4sTShnx2dnbExMQQGRnJggULyMvLo0mTJixfvpzw8PB7/AYhhKhZBcVq\nTifdLDeWnV8shYVaKiUlRT/zsayp8J3WrVvH9OnTCQgIICYmhm3btqFQKAgJCWH+/Pl3bT5rLvlb\nt3Lz7Uh0ubk4jhqJ84zpBkt6ze3PhCz++/0prqbn46vwYGSRGgfb2vGVeX/yfj46Hk1aQRpaTy03\n47N4of1kHK1Nt/NX7fiTsiDW7dqiSb7VMEfVKACle+2ovikUCprVdyl382nRwPUuV5hevXr1KCkp\nIScnp1zn23vJzs4GwNXVst5PTfvss884c+YM33zzjcGx7Oxs1Gq10aUEZWNlzehqSn5+PqdOneL0\n6dMGx9rHxeFRwXW/f/IJSZ066l+XdeQ3tcDAQP1Nz9PZlg2Tu3H4Sgau9jY0b3DvgoGLiwv17rIt\nkzndLL5Joaqg3NjFrAtmisb8hgwZUuEHuDIeHh4sWrTIRBEJIUTl2dtYEejlyOXUPP2Yj6sdOp3O\n4r68iXvz8/Pj1KlTlTr32Wef5dlnn63hiKpOfeUKmVOmwv+aiea8swyrxiE4PPOMmSO7Ra3R8p+N\nR0nNKW2aeh4non85z0t/bX6PK80vszCTt+MXodaqSwcUsCdxNw7WDkxqP8VkcZiluBAXF8f777/P\niRMnsLKyIiQkhLFjx9K7d28AZs2axebNm41e26NHD1atWmXKcMtxnT8fbWYmxfvjsQoJwX3ZO+gK\nCij6PQ5VQEOsLbB52+3mPtOaeV//wbmUHJo3cGH205bVTEWhUNCgQQMSExMNpvLfTdkWlA/TDTQl\nJYWlS5cyefJk/P39DY6XfSE31u+grImTsY7T1alPnz4VHsvJy+fmwv8zPKBS0fPlGVgFBtZcYJUU\neEcM1lZKOofcX8PQwMBArl+/Xo1RVQ83Wzfs1Q4UWOXrxx6m/BFCiLpm3sC2zNx4hOvZpTMDzyTn\nsPibk8yysM964uFUHH9AX1goU/T7fosqLiRk5OsLC2UOX7GsWd4VOZNx+lZh4TYn0gyX1NQkkxcX\ndu7cyaRJk+jatSvLly9HoVAQExPD1KlTmTdvHkOHDgVKdw5YtWoVujsavpl7erGVXwO8vv4KXWEh\nCjs7Ss6e5XqXrmj/9wTYacpkXGf9x6wx3k2IjzPrX+jKig+imTRhvLnDMaqs78L9Fhcq2u6wrnrj\njTcICAiosHtwWYfgkpISg2NlhYcH2ZWjujiNH0fR3t8o+vXXcuMOgwZaRGFhwIAB1bLzSKtWrSxy\nxwgAR60DBdwqLtzIv0FiTiJ+zpazRlMIIUTlNG/gQgM3e31xAeCbwwlMeLIx9ZwsZ9cAUbdt2rTJ\n6I4mm8+dpTtw+2MMm/btTBZXZfz+yw/YKR0p1N7qo6TITiI6Olr/2t3d3SKXnIS4NUapUKLVlS/g\nNHE37WdQkxcXli5dSkBAAKtWrdI/Pe3cuTO9e/dmzZo1+uKCtbU1LVu2NHV4elevXq1UF33VwkWo\nbptanrNiJRm9wsDTUz9V3xJZWfADSn9/f+Lj4ys9lU+r1ZKUlES3bhVvC1jXbN++nT179rB27Vp9\nPwOdTodWq0WhUJCfn4+joyM2NjZkZBhWXMuWQ3h5eZk0bgBdSQk3I/9L4Q8/GhQPAayambZpY0W8\nvb2r5efY29tjb29fLT+ruql0hreAO29KQgghag/tHfdVHUY3ZhKixtzti3eef0Nuvr0EbX4+jsOH\n4WBhX9KH/WMwTS6ms/ibP0nMLKBrEy8i/t4TV4ea21mtung5ePFih3/z0fHV5Jbkgg4e8W7PmDb/\nNGkcJi8uTJkyBVdX13J761pbWxMSEsKBAwdMHY5RLVu25MqVK/c8LzExka5JidzeKkeh1XL1+B8U\nNSydpm7q7vZ1gZOTE7a2tmRkZODhUdGq/FvS0tJwdHTEwcHBBNFZhl27dqHRaBg5cqTR448++ijP\nPPMMTZs25cyZMwbHT58+jUKhMNi60hRylr9L7nvvGz2msLfHYeBAE0f08AosCCLNLhWNrrTD+KM+\nHWno0tDMUQkhhHhQQ0MDOXb1CGXPh/u0qY+Hs8xaEJbB8blROIwaCVqtwc5glqJjsAdf/vsvqDVa\nrFS1a3v7JwPC6NnwSbRoWb16NRMHTjR5DCYvLlS0BvvixYsPtP1gTejYseO9TwKio6NpOHEimVP/\nrR+zbtuG7v80Pk1dVF7Z0ojKFBfK+i08TF544QWjleE333wThULB3Llz8fDwYMeOHSxZsoTjx4/T\ntm1b/Xlbt27Fw8ODzp07mzJsAAp37qzwmLJBfVTVNGPAlM5fz6FEraWFX+1qKOpZ4sXSnlHEJf+O\nt4M3T/j/xdwhCSGEqIKeLX142isFr1bdCPR05MlWvuYOSYhyFAoFWGhh4Xa1rbBQRqFQoNPq+NPp\nDwZvHYizrQtjWo812Wc8i9gtIjo6muTkZCZPnqwfKywsZMGCBezevZuUlBS8vb3p378/kyZNKjfr\nwdwcBg1EYW9PwXffoWrYEKfxltnH4HaXU3PZl1WPlK//4JmO/rQNsLxtNP38/Dh9+jRt27Zl87mv\n+eHi99hZ2RHaoCuJuQl42HkwoMnfqWdXj8TExHJfnB8GAQEBBAQEGIw7OTmhUCho3749AMOHD2fT\npk1MmzaNGTNm4O3tzbZt24iLiyMyMhKVGf5xt27WjJIjR40es2lVu5pOabQ6fk734uMVpVvUtg1w\nI2rUo9jbWMQ/rZUS6BpIoGugucMQQghRTTxtihnXs7G5wxBCmMn3F7/jqv0V0EJ6QRrvHPwvLT1a\n4WF/74e2VWX2T8BbtmwhKiqK8PBwBg0apB/Pzs5GqVSycOFC1Go133//PStXruT8+fO8++67ZozY\nkH3fv2Lf96/mDqNSsvKKGf/Rfm4WOHP6WBKxJ5JZM74LTXzN2yjzTg0aNGDXrl1sPvs1a//8WD9+\n5eZl/f8/kHKAd3pEcePGDXx9pTJf5vY+FXZ2dsTExBAZGcmCBQvIy8ujSZMmLF++nPDwcLPE5/Lq\nK5ScOUvJkSMoHEv33dXl5WHdujUur80xS0wP6vdzqVwpvLUc5/jVLL47msSzjxkWfoQwNZ1WCzqd\nxU49FUIIIUTV3dlE87DzQbitZ7tGp2HF5+/T3LFFjTejNGtxITo6mnfeeYewsDAiIyP143PmzGH2\n7Nk4Ozvrx0JDQykuLmbbtm2cOHHCLGvF64K9Z1O5WXBrmxK1Rsf248kWV1ywtbXF1dWV7y98W+E5\nibkJ7L+wHw8Pj0o133wYrF+/3mDMw8ODRYsWmSEa41Q+Pnh/uw3N9euUnDpN/ubNnLp8mS4frEJV\ni4pEBcVqth1KMBhPvVlo5GwhTCs3ejU3l0VBURGOo5/DZe5rstWoEEIIUQfdWTD47kJ9oo9/oH9t\npbRiytAXcber+dnqZisuLFy4kPXr1zNy5EjmzJlT7kOPk5OT0WvCw8PZunUrf/75pxQX7sPt1axr\nhfZA+TXtZ/84zKbs4xa1rYpOpyM/Px+NVgt3eeh2aO9BnLGswoionJKz50gfOQp0OgKB1L8PwufX\nnShqSaHotU3H+e1sarkxlVJBmKxvFWZWfOwY2W/M17/O/SAa63btcBjwtBmjEkIIIYQpPBXUl6S8\nJH658jMutq4832qMSQoLYKbiQlRUFDExMbz66quMHWu8+aFarcbKqnx4ZVvu2dqap+utrqQEbX4+\nSmdnFMra0+Tj9qKBRqsjf8MR/Zeipr7O/N+YJ3Gys5w+FnBrWcycsLm8+usM1LrS2RYKlOgo3Sqv\nX0h//vXMeGJiYrh58yYuLlJkqE3yN31Zbn8szdWrFP3+O3bdu5sxqsrJzCs2KCw42lrx9rD2NK0v\nfw+FeRUfPWYwVnL0KEhxQQghhKjzVEoV49pOYFzbCSb/3SYvLuzYsYOVK1cyffp0o4WF/Px8unfv\nzuOPP86yZcvKHdu+fTsqlYpOnTqZKly97AULyf0gunTrFEdH3N9bbvIYqoNKqeC/IzrwZ0IWRWot\njwS4o1Ra3lTZhIQE/Pz8CHEP4bN+G9l++Udcbdzo3KALp9JP4mHvSYBL6br2sp0lpLhQuyjrGVZQ\nlfXqmSGSyiubBVSiVWCl8Eetu1VkdNblcuinLzkEuLu7W9RMIPFwse38GCgU5Yp3NqFdzBiREEII\nIR4GJi0uaDQaFi9eTIMGDQgNDeXEiRMG5wQFBTFs2DBWr17N3Llzeeqpp9BqtXz77bfs2LGD559/\n3uTbDhbt/Y3clav0r3V5eWS8MBnVS1NNGkd1auXvZu4Q7ioxMZGQkBAAbK3seLrxM/pj7X06lDvX\nz8+Pa9eu0aJFC5PGKKrGadw4Cr/7Hk1SEgD2A/+OTZs2Zo7q7m4vGLj9dpn3fjqDTgf2NirmjfwL\njzSyvJ1XxMPHunlz3Jb+l5xly9AVFuI0diz2vXubOyzxEImLi+P999/nxIkTWFlZERISwtixY+l9\n29/D5ORkIiMj2bdvH3l5eQQFBTF69GgGDhxoxsiFEEJUhUmLCykpKSQmJgIwZMgQo+esW7eO6dOn\nExAQQExMDNu2bUOhUBASEsL8+fPN8jSw+Phxw8HCQhzTM0wey8NAq9WSnJzME088Uanz/fz8iIuL\nQ6fTScOyWsTKrwE+e36l6Ld9bN2zm3/Mm2fukO7LiMcDeaK5F5du5PJII3dcHWpHrwhjEnMSOXz9\nIH7O/rT37iB5VAc4DhmM4xCZPSNMb+fOnUyaNImuXbuyfPlyFAoFMTExTJ06lXnz5jF06FByc3MZ\nNmwYVlZWRERE4OPjQ2xsLLNnz6agoIARI0aY+20IIYR4ACYtLvj5+XHq1KlKnfvss8/y7LPP1nBE\nlWPbNdRgTOHqSq63lxmiqftSU1NxcnLCwcHh3idT2gDUzs6O9PR0PD09azg6UZ0UdnbYhT1J9oXz\n5g7lgQR4OBLg4WjuMKrk8PVDLPz9TX1fkz6BTzGp/RQzRyWEqK2WLl1KQEAAq1atwtq6tJ9T586d\n6d27N2vWrGHo0KGsXbuW69evs3nzZpo3bw5Ahw4duHr1KsuWLWPw4MGyC1Q1+TMhi59OpODlbMcz\nj/rjaGf2XeiFEHVY7elKaEY2jzyC61uLUDg7g1KJqmFDPGPW43Qjldy1ayk5WbmCiaicxMTEcktf\nNFoN+5P3E3t5O1lFWUavKeu7IIS4P1+f/UpfWAD46XIsWYWZd7lCCCEqNmXKFN544w19YQHA2tqa\nkJAQUlJSAIiNjSU4OFhfWCjTr18/cnNz2bdvn0ljrqsOXkxn/EfxbPz9Cu/GnmHKJwfQ3daLRQgh\nqpuULyvJaeRInEaO1L/O/fAjnlj9IdkACgVukW/jOHSo2eKrSxITE2nXrp3+9Zu/v8GRG4cB+OTE\nGt7qvgR/54blrvH39+fkyZPlrhO1T+4n68j/bAMKFxdcZkzDtos0oasut29Je7uE5AS4bbMYHTo0\nOq0JIxNC1CV9+vQxOn7x4kWCg4PRaDRcvHiRsLAwg3OaNGmCTqfj1KlT9OjRo4Yjrfs2H7yGRnur\nmHAq6SZ/JmTTuqFl990SQtReUlx4ADqdjpvvLLt9gJx3oqS4UA1KSkpITU2lfv36AJzJOK0vLADk\nluTy7YVvmfjIC+Wuq1+/Pjt37jS6hamoHQp++IHs2XP0r9OPHMEnbh8qWepSLSrqV9M6sRVvxy9G\nR+kH0Mf9uuFh72HK0CyCNKATouZER0eTnJzM5MmTyc7ORq1W4+5u2AC3bCw9Pd3UIdZqxorH0dHR\nXM30AJzKjdvZqEwYmagquTeJ2ka+hT2ooqJyL3WFhWYKpG5JSUnBy8tLP52ySFNscE6xpshgzNbW\nFnd3d27cuEGDBg1qPE5R/Qp3/Fzuta6ggKLf9uEw4GkzRfRw6Or3OG91j+RASjz+Tv50869cI9W6\nRBrQCVFztmzZQlRUFOHh4QwaNEi/NMJYTwX9vb/I8D4vKlZR8Tjseg4TP44np7B06VtYKx8a+zib\nMjRRBXJvErWRFBcegEKhwHHM8+SuWKkfc/rnWDNGVHfc2W+hlWcrglyDuZR9EQArpRVPBf3V6LV+\nfn4kJCRIcaGWsmrSxGDMuqnhmKh+zeo1o1m9ZuYOw2zqcgM6TVIyynruKOzszB2KeAhFR0fzzjvv\nEBYWRmRkJFD6MABKZyreqbi49IGCnfx9rRYhPs5smvoEv51LxcvZjk7B9cwdkrgPdfneJOouKS48\nIJfZs/j1xg26e3hg06Wz7CFeTRITE+nWrRsAx24cZfO5r3GwciC8UW9cbV15wr87ga6BRq/18/Mj\nPj7ehNGK6uQ4+jmK4+Io/GkH2Nri/OIUrFu0MHdY4iEwZcoUXF1djTagO3DgAHD3BnQ7d+5k3759\nFrVGXJOSQvrYf1Jy7DgKV1fcFi3EYcAAc4clHiILFy5k/fr1jBw5kjlz5ui3uHV1dcXGxoaMDMPt\nvMuWQ3h5yW5c1cXN0Ya/PeJ37xOFxamL9yZR90lx4QEpFApSWrXEdfx4c4dSZxQUFJCTk4OXlxeJ\nOYnM3zdP38X+VPpJInsurbCwAODj40NmZiZFRUX6JyN1XXx8PKtWreLkyZMUFBQQHBzM0KFD+cc/\n/qE/p7asxVPa2+Oxdg2a69dR2NujdHExd0jiIVEXG9DdfOttSo4dB0CXnU3WKzOxCwtD6eR0jyuF\nqLqoqChiYmJ49dVXGTu2/MxOpVJJ06ZNOXPmjMF1p0+fRqFQ0Lp1a1OFKoTFqov3JlH3yVaUwmIk\nJiZSv359lEolB68fKLc9nhYt+5P33/V6lUqFj48PycnJNR2qRdi9ezdjxozB2tqat99+mxUrVtCw\nYUNef/11PvjgAwD9Wrxjx44RERHB2rVrCQ0NZfbs2Xz66admfgfGqXx8pLAgzK6sAd3IkSNrZQO6\nknPnyr3W5eWhke16hQns2LGDlStXMm3aNIPCQpm+ffty5coVjh8/Xm5869ateHh40LlzZ1OEKkSt\nU9vvTaLuk5kLwmLc3m+hvmN9g+MNHO/dS6Gs70JgYGB1h2dxFi9eTEBAACtWrEClKu3+3KVLF/r2\n7cuGDRuYMGGCrMUT4gHUhQZ0dk8+ScmRo/rXqkYBRvuaCFGdNBoNixcvpkGDBoSGhnLixAmDc4KD\ngxk+fDibNm1i2rRpzJgxA29vb7Zt20ZcXByRkZH6e5oQ4pa6cG8SdZ9Zigt1aVsVTUYG6itXUHn7\nYOUnjQQflE6nIzExkbZt2wLQ0bcTPRs+ya5rO9Gho713h0p1sffz8+Pnn3++53l1wejRo/H19S33\nIUylUtGyZUt+/PFH1Gq1rMUT4j7VlQZ0zi9OQVdcTOGP27EKCsRlzmwUSpmsKGpWSkoKif+bITNk\nyBCj56xbt45OnToRExNDZGQkCxYsIC8vjyZNmrB8+XLCw8NNGbIQtUJduTeJus/kxYW6tK1K0192\nkjJ/AehK94e37R2OxwerUMiT4Pt28+ZNtFotbm5uACgVSl7qOB1PB082n/uaIzcOM3vPf3itSwQu\nthVPmffw8KCoqIjc3Fyc6vja4tv7KtzuypUrBAQEoFAoZC2eEPehLjWgU1hb4/qfmbj+Z6a5QxEP\nET8/P06dOlWpcz08PFi0aFENRyRE7VeX7k2i7jN5caGubKtScuoUTfbsLTdWFPsT+Zs341jBlz5R\nsdTUVNzd3cnMzNSPpRWm8uWZTegoLd6cyTjNhj8+ZXDw3f983d3duXHjRp0vLtwpKSmJDz74gPPn\nzxMVFWXxa/F0ajWFP/2ENj0Dm7w8s8UhBEgDOiGEEJZH7k2itjF5caGubKuivnjJ+PiFiyaOpG5w\nc3OjoKCg3JKGZEUSOpWu3HmHLxym3iXPu/4spVKpnwHxMIiPj+e5554D0BfuunbtatFr8XQ6Hekj\nRlG0t7RA193OjpL+T2PdOMQs8YiHW1kDuunTp9+1Ad2SJUs4fvy4fvkWSAM6IYQQNUPuTaI2Mnlx\noa5sq2LbNRS1tTVWd6xzspO1gg/E09OTZ599ttxYkbqQg9vjyS7O1o/5FPowePRgU4dn0dq0acOW\nLVvIzMwkNjaWcePGMXXqVP16V0tci1d88KC+sABgU1hI3po1uC1cYJZ4xMNLGtAJIYSwNHJvEtUh\npziHtIJUGrkEolSYpu+SRewWUbatyuTJky1+KncZpbs7caNH0fPQEdQXzqP09MR15kxsO3U0d2h1\nhq2VHfO7LeCzU5+SWZhJj4Y9SPz54dhm8n7Y29vrZ/mEhoZiZ2dHVFQUYWFhFrkWb9OmTSgPH6bL\nHeOnjh3nj+ho/Wt3d3cGD5ZCkqhZ0oBOCCGEpZF7k6iqHy5+z0d/rKZEW0IDxwa8/vh8fB19a/z3\nmr24UJu3Vcn288P7jTfMHUadFugaxOwur+lfR/8cfZezHx7Z2dns2LGDZs2aGayna9WqFTqdjnPn\nzlnkWrzBgwejGziQ1IOHKTl5snTQ1pZHF75JaLt2Jo9HPNykAZ0QQghLI/cmURW5xbl8/MeHlGhL\nZy8n5SWx4dSnTOs4o8Z/t1n3pYqOjmbWrFn07NlTtlUR4j4olUoiIiKIiooyOHbkyBGg9MbUt29f\nrly5wvHjx8udY+61eAqVCs+vv8R1/hs4T30R7x+/x0YKC0IIIYQQQlRJRmE6xdricmPJeaaZ/W22\nmQuyrYoQD87Z2ZnnnnuOtWvX8vLLLzNgwACsra3ZvXs3GzduJDQ0lLZt29K0aVOLXYundHbG6Z/G\nGxQJIYQQQggh7l9D5wD8nRuSkHNNP9a1QVeT/G6zFBdkWxUhqm7mzJkEBQXxxRdfMHXqVJRKJf7+\n/owfP56JEycCpbN8ZC2eEEIIIYQQDweFQsG8rm/w2anPSMlLokuDrjwdMsAkv9vkxYW6tq1Kzvsr\nyIv5FKWzM84vz8C+t3xhqy65xbmsPv4Bx24cJdgthPHtJpo7JIszZMiQChv9lJG1eEIIIYQQQjw8\nvBy8+fejL5n895q0uFDXtlWp/+ef3PzyawA0QMb4Cfj8thcrvwbmDayOWH38A3Zd2wnAoesHWRL/\nFs1oYeaohBBCCCGEEELcyaTFhbq2rYrnxUvlB0pKKI6Lw2rQQPMEVMccu3G03OvzWecIVjQ2UzRC\nCCGEEEIIISpi0uJCXdtWJbt+feBIuTHr1q3ME0wdFOwWwqHrB/WvfR3rY5Vq9t1ThRBCCCGEEELc\nwaxbUdZ2iW1aY9u9O6hUKJydcX09AutmzcwdVp0xvt1EGrs1AcDF1pW/BvVFiwaAvJI8jlw/TEah\n4a4iQgghhBBCCCFMSx4DPyD1xUt0f38lRTk5ADiMGonT+HFmjqpu8XX0pbVna85nneNmUTZrTnyE\ntYc1jS835qM/VlOgLkClUDGp/RR6NbKc5TJCCCGEEEII8W3NPUYAACAASURBVLCRmQsPKGf5cuz/\nV1gAyFv1AZqkZDNGVPck5yaz5fzmcmMlyhLWnPiIAnUBABqdhrV/fIxaqzZHiEIIIYQQQgghkJkL\nlbZp0yYyMzP1rx87fBiv20/QatkUHY2qVUsGDx5s8vjqoqyiTKPj+cX5oLj1Oqc4h2JNMVZK+ess\nhBBCCCGEEOYg38Yq6c6CQX49DzL/fWvvUKtmTflHxFwUSpkMUl2aujejvmN9kvPKzwhp5/MIR2/c\naqTZuUEXHKwdTB2eEEIIIYQQQoj/keLCA3J4dhDY2FDwzTdY+fvj9MJEKSxUM5VSxcInFvH56Y38\nkfYHjtaO/DWoLz0DnuSHS99z7MZRgl2DeaaJbP0phBBCCCGEEOYkxYUqcHi6Pw5P9zd3GHWah70n\nk9pPMRj/W3A//hbczwwRCSGEEEIIIYS4kzxqF0IIIYQQQgghRJVIcUEIIYQQQgghhBBVYrbiwvnz\n53n66adp3rw5Bw4cKHds1qxZNG/e3Oj/Jk6caKaIhbA8cXFxjBo1ivbt29OpUyeGDh1KbGxsuXOS\nk5OZMWMGoaGhtG3blgEDBvD111+bKWIhLNfd7ksguSREZUkuCVE95PuSqG3M0nPh888/Z9GiRbi4\nuKBQKIye4+3tzapVq9DpdOXGXVxcTBGiEBZv586dTJo0ia5du7J8+XIUCgUxMTFMnTqVefPmMXTo\nUHJzcxk2bBhWVlZERETg4+NDbGwss2fPpqCggBEjRpj7bQhhEe51X5JcEqJyJJeEqB7yfUnURiYv\nLhw4cID/+7//Y9asWRQVFbF48WKj51lbW9OyZUsTRydE7bF06VICAgJYtWoV1tbWAHTu3JnevXuz\nZs0ahg4dytq1a7l+/TqbN2+mefPmAHTo0IGrV6+ybNkyBg8ejI2NjTnfhhBmV5n7kuSSEPcmuSRE\n9ZDvS6K2MvmyCHd3dzZu3MjQoUNN/auFqFOmTJnCG2+8oS8sQOlNJiQkhJSUFABiY2MJDg7Wf4Ar\n069fP3Jzc9m3b59JYxbCElXmvvTTTz9JLglxD5JLQlQP+b4kaiuTFxcaN25MixYtTP1rhahz+vTp\nQ5cuXQzGL168SHBwMBqNhosXL9K4cWODc5o0aYJOp+PUqVOmCFUIi3av+5JGo+HChQuSS0Lcg+SS\nENVDvi+J2spid4soLCxkwYIF9O7dm7Zt29KrVy+ioqIoKSkxd2hCWKzo6GiSk5MZOXIk2dnZqNVq\n3N3dDc4rG0tPTzd1iELUOpJLQlQPySUhqpd8XxKWxiwNHSsjOzsbpVLJwoULUavVfP/996xcuZLz\n58/z7rvvmjs8ISzOli1biIqKIjw8nEGDBumXRhhbu1q2lKKoqMikMQpRGxUXFwOSS0JUleSSENVL\nvi8JS2ORxYU5c+Ywe/ZsnJ2d9WOhoaEUFxezbds2Tpw4QevWre/5czQaDYD+S5YQleHr64uVlUWm\nRoWio6N55513CAsLIzIyEgBbW1sAo9Xrsg94dnZ2lfr5kkviQdTGXDJGckmYm+SSIckl8SDqSi6B\nfF8S5mcsnywyu5ycnIyOh4eHs3XrVv78889KJUtqaiqAbGsk7svPP/+Mv7+/ucOotIULF7J+/XpG\njhzJnDlz9NsVubq6YmNjQ0ZGhsE1ZdNOvby8KvU7JJfEg6htuVQRySVhbpJLhiSXxIOoK7kE8n1J\nmJ+xfLLI4gKAWq02qIQUFhYCtyrf99K6dWs+/fRTvLy8UKlU1R6jqJt8fX3NHUKlRUVFERMTw6uv\nvsrYsWPLHVMqlTRt2pQzZ84YXHf69GkUCkWlbjoguSQeTG3KpbuRXBLmJrlkSHJJPIi6kktl5PuS\nMCdj+WRxxYX8/Hy6d+/O448/zrJly8od2759OyqVik6dOlXqZ9nZ2dGxY8eaCFMIs9uxYwcrV65k\n+vTpBoWFMn379mXJkiUcP36ctm3b6se3bt2Kh4cHnTt3rtTvklwSDzvJJSGqh+SSEFUn35eEpTJ5\ncSExMZHMzEwAkpOT0el0XLx4EXt7ewCCgoIYNmwYq1evZu7cuTz11FNotVq+/fZbduzYwfPPP4+f\nn5+pwxbComg0GhYvXkyDBg0IDQ3lxIkTBucEBwczfPhwNm3axLRp05gxYwbe3t5s27aNuLg4IiMj\npUItBPe+L0kuCVE5kktCVA/5viRqK4VOp9OZ8hfOmjWLLVu2VHh83bp1dOrUiS+//JKYmBguXbqE\nQqEgJCSEoUOHMnjwYBNGK4RlSkxMpFevXnc9pyyX0tPTiYyM5NdffyUvL48mTZowYcIEwsPDTRSt\nEJatsvclySUh7k5ySYjqId+XRG1l8uKCEEIIIYQQQggh6haluQMQQgghhBBCCCFE7SbFBSGEEEII\nIYQQQlSJFBeEEEIIIYQQQghRJVJcuM2vv/7Kd999Z/RYfHw8b775pokjqj79+/c3GHvllVcAGDVq\nFFlZWeWOGRurzTZv3syaNWvMHcZDQ3LpFsklURWSS7dILomqkFy6RXJJVEVdziWQfKpqPpl8K0pL\nMWbMGBYsWKDfpiUhIYGPPvqIdevWVXiNQqEwVXhVEhsby4YNG7CxsSE/P59Ro0YZPW/JkiWA8fdV\n0+91x44dbNiwAWtra/Ly8vjrX//K8OHDa/R3ipohuSS5JKqH5JLkkqgekkuSS6J61OVcAsmnmvDQ\nFheGDx/Oxo0bmTFjBgBffPEF3bt3Z82aNYwcOZLZs2dja2tLVlYWr776qv46jUbDa6+9hlKppKCg\ngNmzZ3Px4kWio6N55JFH+OOPP5g0aRItWrRg1qxZlJSUoFKpeOuttzh06BDr16/HxcWF1q1bM3Lk\nyGp/X5cvX2bdunV89NFH2NraUlhYSFRUFFqtlvfee4+TJ0/SqFEjZs6cSf/+/fnmm28o2zBky5Yt\n/Pjjj/j7++v31q0JFy5cYN26dXz44YfY2NigVqt59913yc3NZfbs2bi5uVFcXExERATbt29n9+7d\nBAcHc/ToUebNm4e7u7vR837++We8vb158cUXmTVrFu7u7mRmZrJs2bIaey9CcklySVQXySXJJVE9\nJJckl0T1qKu5BJJPNZVPD+2yiCeffJJ9+/ahVqtRq9Xs3bsXV1dXAH766SeCgoJYsGABY8aM4cMP\nP0ShUKDT6di+fTt+fn4sXLiQIUOG8MknnwDg4ODAlClTGDlyJNu3b+fHH3+kefPmLF++nEGDBpGa\nmkpkZCT//e9/Wbx4MTt37iQ3N7fa39dvv/3GgAEDsLW1BcDOzo6ZM2dSVFTEiBEjWLFiBbt27Sp3\nTdl7++STT1ixYgWzZ88mOzu72mMr8/vvv/P0009jY2MDgJWVFdOmTWPDhg307NmT+fPn06lTJ7Zt\n2waAr68vL774IuHh4ezevbvC83x8fIiIiCArK4vx48ezaNEigoKCOHToUI29FyG5dDvJJVEVkku3\nSC6JqpBcukVySVRFXc0lkHyqqXx6aGcuqFQqwsPDiY2NRaFQ0KtXL6ysrNDpdCQmJtKoUSMA/P39\nSUpK0l+XkJDA/v37SUxMRK1W4+npCYC3tzcAtra2FBUVkZKSgr+/PwDdunUDICUlhfnz5wOg1WpJ\nT0/Hycmp2t+bWq02GLO3t8fd3R0o/YtpjEajQaksrTc1aNCg2uMqo1Ao0Gg0ABw/fpzly5eTk5ND\ndnY2gYGBxMfHU1BQQLt27bC1tS33Z5uZmUlCQgKHDh0yOK9sypatrS2bN2/mhx9+4MSJE3Ts2LHG\n3ouQXDJGckk8CMklQ5JL4kFILhmSXBIPoi7nEkg+1UQ+PbTFBYDBgwcze/ZsVCoVb775Jrt370ah\nUNCwYUMuXrwIwLVr12jYsKF+GkxAQAA9evTgn//8J7m5uRQWFurPvZ2/v79+fMeOHQQGBhIQEMD8\n+fOxtrbm0qVL+oSsTo8//jivvPIK/fv3x8nJifz8fCIiItBqtRVeU/bebv8LnJCQUO2xlenWrRsz\nZ87kb3/7G23btuXDDz/kvffew97eHj8/P5566ikyMjKwsrLi559/Nrg+ICCA0NBQg/PK1jytXbuW\nsLAwevTowWuvvXbX9y6qh+RSKcklUVWSS6Ukl0RVSS6VklwSVVUXcwkkn6Bm8umhLi54eHjg5uaG\nUqnEw8NDPx4eHs6cOXOYO3cu2dnZ/Oc//yEhIQGFQqE/Nnv2bDIyMnjxxRcNfm5ZZW/WrFm89NJL\n6HQ6lixZwuTJk5k2bRq2tra4u7vz2muvVft7CgwM5IUXXmDSpEnY2tqi0WgYO3YsZ86cqfAahUKB\nQqFg5MiRTJgwAT8/P3x8fKo9tjKNGjVi8uTJTJ06FVtbW9RqNQEBAQwaNIi3336bX3/9lczMTObO\nnWs01sGDBxMREVHheY899hgff/wxe/bswdfXl/Xr1/PMM8/U2PsRkku3xyu5JKpCculWvJJLoiok\nl27FK7kkqqIu5hJIPkHN5JNCV1aCEUIIIYQQQgghhHgAD21DRyGEEEIIIYQQQlQPKS4IIYQQQggh\nhBCiSqS4IIQQQgghhBBCiCqR4sL/XLhwgWeffZaDBw8SERFhtDFGZbz33nvExsZWc3QPpn///uYO\nodrFx8fz5ptv3vO8+fPn19i+uOLuJJdqB8klyye5VDtILlk+yaXaQXLJ8kku1R7myqeHsriwadMm\nnn/+eSZOnMjYsWPZtWsXf/zxB3379qVjx44cOXKkUv8xTC0+Pp527dqRnZ2tH3vvvfcYNWpUlX92\nWloaCxYsqPLPuVN8fDy9evViypQpTJo0iSlTppCXl1eln1m2fcqdbn8PERERNbYnrrhFcsmQ5JJ4\nEJJLhiSXxIOQXDIkuSQehOSSoZrKJag7+fTQbUW5f/9+fv31Vz7++GOUSiUFBQWMGTMGW1tbioqK\nKC4u5saNG7zzzjsMHTqUBQsW4OHhgVKpJCIighUrVnDjxg08PT05evQoy5cvJyEhgcWLF9OkSRMu\nXLhA06ZNSU5ONnrtlStXaNSoER06dGDDhg04OTnh4uLCrFmzKhV/27Zt+fbbbxkxYgQAiYmJKBQK\nNm/ezM6dO1GpVHTt2pXBgwfrr9m2bRs//fQTNjY29OnTh969e7No0SJycnLIyclh6NCh7Ny5k/37\n9/P7778DsH79elxcXGjTpg0jRoxg1KhRtGrVikceeYSEhATOnDlDSUkJPXr0uOeWJWV7pwJ88skn\nrF27ljNnzuDi4sLNmzd544032LVrFzt27CAkJITz588zd+5c4uLiyMrKYsyYMWzevJnMzExat24N\nQHFxMa+88goODg7cuHGDRYsWER0drX8PK1as4N133yUhIYEVK1bg7u6OjY0Nr7/+OrNmzcLDwwOV\nSsWZM2dYsWIFSuVDWWerEsklySXJpeohuSS5JLlUPSSXJJckl6qH5JLpcwnqRj49dMWFX3/9leHD\nh+v/YOzt7Rk0aBAlJSUUFRUxZswYvvvuO6ZNm8a8efMYPXo0jz32GMuXL+e3334DoGnTpowcOZKl\nS5dy6NAhtm/fziuvvEKrVq2YPn06AB988IHRa1u0aMHYsWNZtGgR/fr1o0+fPpw7d67S8T/22GMc\nOnSIESNGcOjQITp06MCVK1f45JNP2LJlCwDPPPMMf//73wHQarV8+OGHbN26FYBhw4bRu3dv9u/f\nzyeffIK9vT3Jycn07t0bgNDQUAYNGkRMTAz29vb885//ZMCAAUDptKFWrVoxbNgw3nnnHXx9fSsV\n++27nWZkZGBnZ0dYWBgDBgzgm2++4YsvvsDb2xtvb2+mT5/Ojh07+Oqrr/D39y/3c26vvt28eZOB\nAwfSvXt3Pv30U3bt2kWfPn3072HlypXA/7N332FRXF8Dx79LWVBQo6hYkCg27GIFe9dgr7FhVyyJ\nNdGAFTWRWFEsqNEQewd7iRrBxIYllthiRcSKiEiRtu8fvMyPlR6QRT2f5/FJmJndObM7Z8rZe++A\nm5sbP/74I+bm5kydOpVLly4BULNmTZo2bcr48eO5f/8+pUuXTvd3IOJJLkkugeRSVpBcklwCyaWs\nILkkuQSSS1lBcin7cwk+jXz67IoLGo1G64sDiI2NTbYKExAQwJYtW/Dy8uLNmzfKh1m4cGEA1Go1\nkZGRPH/+nCJFigBgYWGBRqNJ8bXFixcHwNHRkRUrVrBu3TqaN29O2bJl0xW/SqWibNmy3L9/nyNH\njjB69GjWrl1LsWLFlGXMzMx4/fo1EL9jvn79GmdnZwAMDAyIiIjAycmJKVOmEB4ezpgxY7TW8fTp\nU2bOnAnEJ9vLly8BlHVMnz6defPm8eLFC4YOHZpm7D4+Pjx79gyNRkPJkiUJDAzEzs5O+TzOnz+P\nubk5RYsWBaBgwYI8f/48SaIkZmhoiI+PDydPnuTBgwc0atQoyfeq0Wh4/vw55ubmyrqePHkC/O87\nNDIyIjIyMtX4RfIklySXQHIpK0guSS6B5FJWkFySXALJpawguZT9uQSfRj59dsWFpk2bsnbtWurU\nqYOhoSFv375l9+7d9OjRg4iICK1lLS0t6dy5M1WqVOHp06fky5ePNWvWJHnPggULEhgYiJmZGQ8e\nPKBy5copvjahknT//n2cnJzQ09OjT58+dOvWjbx586ZrG7p06cLmzZsBMDExoUCBAgQHBwPxO0dw\ncDD58+cH4hOnaNGizJkzB4B79+6hp6dHrly5cHd35+HDh7i6ujJ48GBiY2OV7Z45cyaGhoY8ePCA\nkiVLAv+rgoWGhrJgwQLCwsIYMGAADRs2TDXexE18ANasWYO/vz/Vq1fn0aNHlChRQjnAAAQGBlKk\nSBHUajXv3r0D4hPY2NhY2UZvb29Kly5Nnz59WLFiBXFxcahUKmUbEpKmaNGiPHv2DHNzcx49ekS9\nevXS9RmLtEkuSS6JrCG5JLkksobkkuSSyBqSS9mfS/Bp5NNnV1yoU6cOAQEBDB06FGNjY6Kjoxk5\ncqRSbUps8ODBuLq6YmJiQnh4OD/++GOSZVQqFQ4ODri6umJlZUV0dDQAQ4YMYc6cOSm+9smTJ6xa\ntYr8+fNTtmzZdCcKoHzpPXv2BEBfX5/+/fszbtw4AAYMGIC+vr4SX69evRgzZgwqlQpra2scHR3Z\ntGkTnp6exMTE0K1bNywsLDhz5gwHDx7km2++Ydy4cRgZGZE/f36mTJmi1bzm+PHjbNiwAQMDA7p2\n7ZruuBN0796d6dOnc/78eUJDQ5k5cyZHjx7l+fPnzJ49m7t37+Lq6opKpWL79u1ERUXx9OlTpeKn\nUqmwsbFhzpw53Lt3D3Nzc/bt20fr1q2VbVCpVKhUKsaMGcPs2bPJmzcvJiYmVK1aVTnQiMyRXJJc\nklzKGpJLkkuSS1lDcklySXIpa0gu6T6X4OPMJ5Xm/XYRQuiAl5eXMhCJEOK/k1wSImtILgmRNSSX\nhMg6OT2fZPhUIYQQQgghhBBCZIq0XBBCCCGEEEIIIUSmSMsFIYQQQgghhBBCZIoUF4QQQgghhBBC\nCJEpUlwQQgghhBBCCCFEpkhxQQghhBBCCCGEEJkixQUhhBBCCCGEEEJkihQXhBBCCCGEEEIIkSlS\nXBBCCCGEEEIIIUSmSHFBCCGEEEIIIYQQmSLFBSGEEEIIIYQQQmSKFBeEEEIIIYQQQgiRKVJcEEII\nIYQQQgghRKZIcUEIIYQQQgghhBCZIsUFIYQQQgghhBBCZIoUF4QQQgghhBBCCJEpUlwQQgghhBBC\nCCFEpkhxQQghhBBCCCGEEJkixQUhhBBCCCGEEEJkihQXhBBCCCGEEEIIkSlSXBBCCCGEEEIIIUSm\nSHFBCCGEEEIIIYQQmSLFBSGEEEIIIYQQQmSKFBeEEEIIIYQQQgiRKVJcABwcHJg6dWqK83fv3k21\natXQaDTZEk+zZs1YvHgxAO7u7jRu3BiAwMBAqlatyrlz57Iljk9ZRr7TFStW0Lp162yI6vM0ceJE\natSowcyZM3UdSraztrZmx44dWfZ+9+/fp3379lSvXp1Lly6lufyuXbuwtrYmLi4uy2IQ4ty5c1hb\nW/Po0SNdhyKEECIHq1q1Kt7e3roOQ2QhA10HkBwHBwf8/Pzw8PCgSZMmKc5fv349tWvX/uDxdOzY\nkY4dO37w9SRHpVKhUqkAKFasGFeuXNFJHB+T8PBwtm3bxoABA1JcJiPf6YgRIxgxYoTy94kTJzA3\nN6dChQqZDTVHePHiBatXr+bEiRO8ePECY2NjypQpQ69evbC3t/+g67516xZ79uxh5cqVShFN/Hdb\nt24lLCyMs2fPYmRklObyiY8v4tPk4ODAhQsXMDQ0VKZpNBpUKhV9+/bl+++//yDrlf1KfE7i4uL4\n5ZdfOHDgAAEBAURFRWFubk6rVq0YPXp0uo7HWSW5nC9YsCB169ZlzJgxmJubZ2g5Jycn/P392bhx\nY4bWqVarKVmyJP3796ddu3ZZvZlCR9KzP2TEx35f06ZNGx4+fMj+/fuxsrLSdTg5Qo5tuVCoUCF2\n7tyZZPqjR4+4f/++XLiIFJ05c4Zff/31g73/kiVL+Oeffz7Y+2cnf39/OnfuzJMnT/Dw8ODSpUsc\nPnwYe3t7nJ2dWbRo0Qddf0hICCqVSg7IWSQkJISiRYtm64WsyPnatWvH5cuXlX9Xrlzh8uXLH6yw\nIMTn5ueff2bTpk1MnTqV06dP8/fffzN//nwOHTqEs7Nztsfzfs57enoSEBDA8OHD07Vc4h9U/us6\n//rrL/r06cMPP/zA77//nlWbJkSO8eeff/Lq1SuaNGnCpk2bdB1OjpFjiwuNGzfGx8eHV69eaU33\n9vamWbNmSZZfs2YNrVu3xsbGhqZNmya5Kbp69SoODg7Y2NjQrFkz3NzckjSJX7ZsGQ0aNKBatWqM\nGTOGiIgIIGnT4Xv37uHo6IidnR21a9emb9++XL9+HYBFixbRokWLJPEl7upw8+ZNBg8ejK2tLTVr\n1mTYsGE8ePAgzc/k8ePHWFtbc/r0aSD+F/pp06bRqFEjbGxsaN++PQcOHFCWX7p0KT169ODAgQPK\nZ9O7d2/8/f1TXMfr16+ZNGkSTZo0oXr16nTp0gUfHx9l/rt375Rm7PXq1WPx4sW4uLjg4OCgLHPr\n1i1l+2rUqMGgQYO4efOmMj88PJwZM2ZQr1496taty/Dhw7Waz545c4Y+ffpQu3Zt6taty/jx43n5\n8qUy39ramn379jFmzBhq1apFgwYNWLlyJQBbtmzh22+/5fnz51SrVo3Dhw/j5eWFra0tGzdupHbt\n2nh7e+Pl5aX1nQYFBTFhwgTq1KlDvXr1mDBhgrLvubu7Ky1oGjduzPXr15kxYwadOnXihx9+oFev\nXlqfYXR0NHXr1mXz5s1pfqe65uLiQqFChXB3d1du8PPmzUuvXr2YO3cucXFxSp74+PjQvXt3atWq\nRb169Rg/frxWflpbW+Pp6UmbNm0YNGgQkPr+tH//fmW5tm3bMm3atGS/K0h7n0hrv31fenIjMDCQ\nb7/9lgYNGmBjY0PPnj21uiQ5ODgwd+5cFi1aRP369alduzYTJkwgKioKAC8vL6pXr46vry+tW7em\natWqdOzYUSsXEouNjWX+/Pk0a9YMGxsbWrVqxbp165T5Xl5e1K9fnzNnztChQwdsbGzo3LkzV69e\nBWDYsGHs3r2bixcvUq1aNc6fP4+DgwMTJ07UWk/v3r1xcnJKNobUcivBxo0bad++PTY2NjRp0oT5\n8+drdatwc3NTtqFx48a4uroSExMDwPXr1+nXrx916tShZs2a9OrViwsXLqT4PYkP7927d8yePZvm\nzZtTrVo12rZtm6SJamrn14QuEN7e3tSrV4/ly5dneB2vX79mwoQJ1K9fHxsbG+zt7dm+fbsyf//+\n/XTo0IEaNWpQt25d5RgP6cvlx48fa+Wyg4OD1q9lae2Xqe3TQgCcPHmSFi1aULNmTQwNDdHT06Na\ntWosXbqUDh06KMu93xUuNjZWyR+I/1V47NixrFq1Cjs7O+rWrcuKFSu4e/cuPXv2xMbGhq5du3L3\n7t0MxVeiRAnGjx/PjRs3uHfvXprLXb9+PdXl0kOtVtOpUydsbW3x8vLK1HuJnKtZs2Z4enoybdo0\n6tati62tLbNmzVLmp3V8T5wTTk5OjBs3jvXr19O0aVNq1KjBsGHDtK41k7ufS7gGiYuLY+nSpbRp\n04bq1avTsmVL1qxZo7z2v15jpmTTpk20adOGrl274u3trdw3ZsVn06xZM7Zt26Ysu3HjRqytrfHz\n81Omubq6MnjwYAACAgJSPc85ODgwa9YsBg8ejI2NDRqNhn/++eeDXJPl2OJCkSJFqFWrFnv27NGa\n7u3tTbt27bQKA0eOHGHx4sW4ublx6dIlli1bxm+//aYczIKCghgyZAhNmjTh7NmzrFmzhl27dmld\nNJ84cYLChQvzxx9/sGXLFo4fP86uXbuApE2Hx4wZQ/78+fHx8eGvv/7CwsKCb7/9FoAOHTrw+PFj\nrl27pix/6dIlnjx5QufOnXn16hX9+/fHxsYGX19ffHx8MDMzw9HRMV39/xPHsXDhQi5duoS3tzcX\nL15UbiQSX1Q9ePCAM2fOsGvXLk6cOEFoaCgLFixI8f1HjRpFaGgou3btws/Pj+7duzNq1CgCAgIA\nWLVqFSdPnmTTpk2cOHGC6OhoDh48qMT15s0b+vXrR7ly5fjjjz84efIkhQoVYvDgwYSHhwMwdepU\npTm8r68v+fLlY9iwYQDcuXOH4cOH06lTJ86ePcuBAwcIDQ1lwoQJWnEuXbqU/v374+fnx5gxY1i0\naBF37tyhZ8+ejBgxAnNzcy5fvqyMlfDu3Ttu3LjByZMn6dSpU5LPctSoUURHR3Ps2DEOHz5McHAw\n3333XZLlEm5YXVxc8Pb2pnv37vz9999axSFfX1/evXtH+/bt0/w+dSk4OJhTp04xcODAZOe3atWK\nCRMmoFKpePHiBd988w1dunTh/Pnz7N27lzt37jBvHWHdZwAAIABJREFU3jyt1+zcuZPly5ezdu1a\nIPX9qW3btspy+/fvV8ZceP+7Ss8+kdZ+m5zUciM2NpaBAweiVqs5cOAAZ8+epW7dugwbNownT54o\n7+Ht7U3x4sXx8fFhw4YN/P7771onzcjISHbu3MmWLVs4deoUpUqVSvEXoXXr1uHt7c26deu4dOkS\nU6ZM4aefftIqaLx584bt27fz22+/cfr0afLnz4+LiwsQn5sdO3akRo0aXL58mVq1aqW47alJKbcA\nduzYgbu7OzNnzuTSpUusXLmSAwcO4OHhAcR/jzt27FC2Yd26dfj4+CjH0gkTJlCzZk1Onz7N6dOn\nadq0Kd9//322jWcjkpo6dSpXr15VvrNx48YxZcoUzp8/D6R9fk1w/PhxDh8+zMiRIzO8joULF/L6\n9WsOHz6s7Ptz5szh7t27PHv2jIkTJzJx4kQuXrzIkSNHUKlUWsee1HI5OjqagQMHkj9/fg4fPsyZ\nM2eoUaMGQ4YMISwsDEh9v0xrnxYCoGzZshw+fBhfX1+tYmuFChUy3OXPz88PtVrNyZMncXZ2ZsmS\nJbi6urJw4ULOnj1L/vz5lR+rMiImJgaVSqXVdSEzy6VXVFQUxsbGWfJeImdau3YtTZs25fTp0yxY\nsICNGzcq18upHd+Tc+bMGYKDgzl06BAHDhzg+vXr/PLLL0DK93OrVq0C4n8M3LNnD0uXLuXSpUu4\nurri4eHB7t27lfdP7zVmwj1ASh4/fsyJEyfo1q0bTZo0wcjIKMk9a2Y+mwYNGmhd/509e5ayZcty\n9uxZrWmNGjUiOjqaQYMGpXqeAzh06BC9evXi0qVLqFQqvvvuuw9yTZZjiwsA3bp106rwnjlzBn19\nferUqaO1XMuWLTl58qTSB75ixYqULVuWy5cvA7Bv3z709fUZPHgwarWaUqVK4e7urjVeQ/Hixene\nvTuGhoZUqFCBsmXL8u+//yYb19atW3FxcUGtVqNWq/nqq68IDAwkKCiI0qVLU6FCBQ4ePKgsv3//\nfqpXr46lpSV79+7FyMiIb775BrVajampKU5OTjx69Ehrh0lJ4i980qRJbN68mQIFCqBSqWjfvj0x\nMTFahY2wsDCcnJwwMTEhX758NGrUiFu3biX73jdv3uTChQtMmjSJAgUKYGhoSK9evShXrpxyIXXo\n0CHat2+PtbU1arWacePGkStXLuU99uzZg0qlYsKECeTKlQsTExN++OEHXr16ha+vL69fv+bQoUMM\nHz6cggULYmRkxKRJkxgzZgyRkZFs376dChUq0L17d/T09DAzM+O7777j7NmzWq0bWrZsSY0aNZTt\nBlLcLoi/yRswYECyJ7ibN2/y999/M3r0aPLkyUOePHlwcXFJ0iIhue+hZs2alC5dWms/PXDgAC1b\ntsTU1DTF1+cECZ9n6dKl01y2UKFC/Pnnn3z99dcAmJmZ0bBhQyXHEjRs2FBpAZGe/Sk5739Xae0T\n/3U9qeWGr68vjx49YsqUKeTNmxe1Ws23336LkZGRVusgCwsLevTogYGBAeXLl6d8+fJa+6FKpWL4\n8OHkz58fU1NThg8fzpMnT5LtY9ivXz8OHjyIhYUFAI0aNaJAgQJan3FMTAyjRo0if/78GBsb06JF\nC27fvp3iNv4XqeXWxo0b+frrr7GxsQGgfPnyDBo0SKmuh4aGoq+vj1qtBuDLL7/k4MGD9OjRQ5lv\naGioLDNs2DCOHz8u3dx05PXr10pLleLFi6Onp0eLFi1o1qyZUiRL6/yaoGvXruTJkyfJOkJCQtJc\nx9u3b7X2m3r16nHx4kVKly5NWFgYcXFxyvEgX758LFmyRKu4kFou+/j4EBgYiLOzMyYmJhgZGTF2\n7Fj09fU5dOgQkPp+mdY+LQTA9OnTqVSpEo6Ojtja2uLo6MiqVatSbSmaEgMDAwYMGICBgQGtW7dG\no9HQtm1bihUrhlqtplmzZhlquaDRaLh//z6LFi2idu3alChRIlPLpdfbt2/ZtGkTFy5coGvXrpl6\nL5Gz1axZk6ZNm6Knp0f9+vUpUKCAcgxO7fieHAMDA2WckoQfmhPeK7X7OY1Gw6ZNmxg6dChlypRB\npVJRs2ZNunXrluRHn/RcY545cybVQYk3b95MmTJlqFq1KgYGBnTq1CnZrhH/9bNp2LCh0kpBo9Fw\n9uxZBgwYoNwrvn79mps3b9KwYcN0necg/of7xK3rP9Q1WY4c0DFBixYtcHFx4cqVK8pool26dEmy\n3Lt371i8eDHHjx8nODgYjUZDTEwMZcqUAeL7lRcrVkzrNdWqVdP6O+GCPoGxsTHv3r1LNq4LFy6w\nfPly7ty5Q1RUFLGxsUocED9Y4Lp165Tqz6FDhxgzZgwQP5r7ixcvtNav0WjQ09Pj8ePHGfl4CAwM\nZO7cuVy8eFGpTKlUKqVZNsTfBCa++c+VKxeRkZHJvl9CE7jETfg0Gg0ajYby5csD8OTJEywtLZX5\n+vr6VKlShdevXwPxn/WXX36JgcH/dq38+fNToEAB/P39sbCwIC4ujuLFiyvzCxQoQJs2bZQYLl++\nnOTzMTAwICAgQDnZJY4h4QDxfnOk96V0onz48CEqlUorphIlSqT7xNq9e3dWr17N+PHjeffuHceP\nH0/SlDwnS++vE15eXmzfvp3AwEDi4uKIiYmhSJEiWssk/szSsz+l5P33SW2fCA4OTnE95cqVS3Ed\nqeWGv78/BQoUIH/+/Mp8AwMDvvzyS62LxcT7IcTvi+/nV+LxJBK26+nTp1StWlVruZCQEObMmcPp\n06cJDQ1Fo9EQHR2d5DiUeJ25cuUiOjqauLg49PSyplacWm7du3ePf//9l19//VU5+SQU2mJiYmjX\nrh2HDh2iefPmStep9u3bK8ffiRMnMnPmTHbs2IGdnR1NmzalWbNmWRa7SN6+ffs4fPiw8nfCgI7f\nfPMNcXFxDB8+XOv71Gg0VK9eHUj7/Arx5533z6EJHj58mOY6HB0dGTVqFA0bNqROnTo0aNCAtm3b\nYmpqipWVFQ4ODvTv359y5cpha2vLV199pZU/qeXy/fv3iYmJoW7dulrbHxcXp5xzU9sv09qnhYD4\n6xgPDw+ePXuGn58fFy9eZMeOHSxatIjhw4cr14DpkXjfSjgGFy1aVJmW2nVcgsQ5r1KpKFy4MI0a\nNVJa2WZ0ufRI/F4ajYaoqCiqVKnC0qVLqV+/fobfT3w8krsWSrhuSO34npz3zyW5cuXixYsXQOr3\nc0FBQYSEhDBz5kxmz56tzNdoNBQqVEjrNRm5xkzuXiAqKoqdO3dqtUTt1q0ba9as4cKFC9SsWTPT\nn42dnR1BQUH4+/sTGhpK3rx5adOmDbNnzyYqKopz585RrFgxrKysOHbsWIrnucDAwBRj+VDXZDm6\nuKBWq2nfvj07duygTJkyHD16lP379ydZzsXFhVOnTrFs2TIqVaqESqWiZ8+eynw9Pb00H7WW3g/y\n/v37jBw5kn79+rFy5Ury5s3Ln3/+ydChQ5Vl2rVrx7x587hy5QphYWGEhoby1VdfAfE7Vfny5TP9\n2BWNRsPgwYMpUaIEu3btomjRokRFRSW5YcnIDmJsbIxKpeKvv/5KMek1Gk2qN6Pv3r1LtjlNXFwc\nKpUKfX19ZYdPKYamTZuybNmyVGP9L1W1lOLW19dXYvwvOnXqxMKFC/Hx8SEiIoKCBQsmaV2TE5Uq\nVQqVSsXVq1dTvQmH+MLCvHnzmDdvHi1atECtVrNw4cIk+Zj4M07P/pSS998ntX0iocqa0fWklhtp\n7cfpeY8ECcVH+N+NeHKvGzNmDCEhIaxfv55SpUoB0KBBgwzFnR5p7eep5ZaxsTGjRo2iX79+yc43\nNTXF09OTu3fvcvLkSY4dO8ayZcuUR+p26NCBli1bcvr0af7880+mTJlC2bJlWbdunbRe+IDatWvH\n3Llzk0y/desWCxYsYNu2bVhbWyf72rTOrwkSfnl5X8Lgoqmto3z58vz+++9cuHCBv/76i7Vr17Js\n2TK2bt1K0aJFcXZ2ZujQofz555/4+vrSp08fBg8ezNixY4HUc8LY2Jg8efJo9VN9X2r7ZVr7tBCJ\nmZub065dO+XpCEuXLmXZsmV06dIl2ZuUxOeHBMntzxk97qeU8/91uQTnz59n0KBByvF61qxZSmH/\n/ffq3bs3+fLlo2nTphmKXXx8Uts/0zq+Z+S9UrufSzjXuLm50bx581Tjzcg1ZnL27dtHcHAwixcv\nxt3dXSu+TZs2aRUXMvPZVKtWjbNnzxISEkLdunUxMTGhdOnSXLhwQekSkbANaZ3n3t9uSP3cl5lr\nsmz/ucjBwQFra+sk/ypUqKBUmu7du4e7uzvW1tasX7+erVu3YmNjQ2hoKNOnT0/ynn///TetWrWi\ncuXKqFQqwsLClD7CACVLliQgIEBrAKZz586xd+/eDMd/48YNYmJicHR0JG/evMr6EzMzM6NevXoc\nOXKEgwcP0rx5c+Wmp2TJkjx8+FAZfyBBan3DkxMUFERAQAB9+vRRkvP9ZqoZVbJkSYAkT0JIHFuh\nQoW0mgnFxsZqdcMoVaoUDx8+1Go98eLFC4KDg7GysqJEiRIYGBhoDRQUHBzM2rVrefPmDSVLlkwy\n4F1UVJQyeNeHkLDdiWN69OgRv/76a7oKDvny5aNVq1bs27ePPXv2JNu6Jjv4+flhbW2tdePn5OSU\nbL5ZW1vz/fff06hRI1atWpXs4GR//PEHHTp0IDIykr///psyZcpgb2+v3ES8v9+/Lz37U3qktU9k\n1XoSK1WqFMHBwVoD+kRFReHv75/hJ1s8fPhQ+f+EVg/J/ep5+fJlunTpohQWHj9+nK4BhVLzfksK\njUaTqc+lZMmSyuC1CV69eqUcz6KioggLC6N06dIMGDCA9evX06ZNG7Zu3aosmytXLpo1a8a0adPY\nvn07fn5+KQ5ymZ3OnTvHoEGDsLW1pVq1anTu3FmJO8GTJ0+YMGECdnZ2ygCdH3Pf+xIlSqCvr5/k\nO33y5Ily05PW+TUr1hEaGkpsbCy1atVizJgx7Nu3DyMjIw4fPoxGoyEkJIRChQrRuXNnFi1axPTp\n09mwYUO61l+yZEnevn2bpHlr4r9T2y/T2qc/J+nJkWPHjuHg4EC9evWoUqUKbdq0YenSpVrXBBDf\nUmv69Ok0bNiQypUr06ZNG9asWfNRjr8SGBiIi4uL8utqYgk31gkt7IyMjLSOyYnPDx+DWrVqKU+b\nuXz5slaLwff99NNPnD59WmtQOpFxERER/PzzzzRp0oSqVavStm1brc80teu8958OogupHd8zKrX7\nOVNTUwoWLJjkXPPs2bMkx5/33zOj9x2bNm3C3t6evXv3snv3buXftGnT+P3335M8jCAlaX029evX\nx8/Pj9OnT2NrawtA7dq1OXv2rFZxIT3nueR8qGsynbRFrVSpErt27WLnzp3Kvx07djBkyBBlGRMT\nE3bt2sWuXbsoW7Ys5ubmfPfdd0yePDnJ+1laWnLjxg0iIiJ4/PgxU6dOpXjx4jx9+hRA6Tfs7u5O\nREQE/v7+ODs7Z7gbAvyvKc358+eJiori0KFDyqBUiZuetG/fHh8fH44fP64MIAjxld3cuXMza9Ys\nXr9+TUREBG5ubnTr1k1r0I20JPThvnjxIrGxsVy5cgVPT09MTU214sgIKysr6tevz88//4y/vz9x\ncXH8/vvvtG3bVhk9tHnz5uzevVvpEuLm5qaVtO3atSMuLo558+YRGRnJ69ev+emnnyhevDgNGzbE\n1NSUdu3a4eHhQUBAAJGRkbi5ubFt2zby5MlDz549efnyJYsWLSIsLIyQkBBmzJjBgAED0r0duXLl\nIiQkhOfPn6fZVQKgTJky1KpVCzc3N4KCgggNDWXOnDn4+vomW3HMlSsXDx484M2bN8q0Hj16cPz4\ncU6dOqWT4kJUVBRTp05NttJYuHDhJPm2c+dOJk+ezNSpU3n37h29e/fm6tWraDQa3rx5w8aNGxk/\nfjxdunTB2NgYS0tLnj59SmBgICEhISxdupTIyEhCQkJSbJ6Znv0JSPNiMq19Ir3ryYjGjRtTpEgR\nZs2aRWhoKGFhYcyfP1/p+5peGo2GlStX8urVK0JDQ/Hw8MDS0pKKFSsmWdbS0pIrV64QHR3N3bt3\n+emnn7CwsNAaQDKjrKysuHDhAoGBgURFReHu7p6pUe779+/PgQMHOHToEDExMTx69Ihhw4bh6uoK\nxP+KNWLECCXmZ8+e8eDBA0qXLs2TJ09o1KgR+/fvJzo6mtjYWM6fP4+xsbHOm5j7+voycOBADA0N\nmTt3LsuXL6dEiRJMnz5d6eL09u1bevXqxeXLl5k2bRqenp7Y2dnh7OycZc/7zm65c+emW7duLF26\nlBs3bhAXF4efnx+dOnVSxg1K6/wKyedwwrT0rKNbt24sWLCAt2/fAnD79m3evHmDlZUV+/bto127\ndso4JWFhYVy7di1dY8VA/MVZ6dKlmTFjBs+fPyc6OprNmzfTrl07AgIC0twvU9unPyfpyRFvb29G\njRqFmZkZc+fOZc2aNXz11VcsW7ZM66k1CQPmHjlyRBkZPqEFYEZ+Rc8pChYsyKlTpxg/fjzXrl1T\nuqr9+++/LFy4kDJlylC5cmUApQlzREQEr169YsWKFVk2cGJOU7JkScaOHcucOXP+09gTIv446ujo\nyI4dOxg5ciRr166levXqTJs2jZ07dyrLpXadp2spHd//yzE0pfu5hB9N+vXrx4YNGzhz5gxxcXHc\nvHmTPn36pPp4+ozed1y+fJlr167Rv39/ihUrpvWva9eumJqaprugltZn06BBAy5cuMClS5eULg+1\na9fm2LFjPHr0SCk4pHWeS86HvCbTSbcIExOTZC+uEyQ0n09Ypl+/fixcuJABAwYoB+HEN1ETJ07k\nhx9+oF69elhYWDBp0iTCw8NxdnZm2LBhrFq1ig0bNjB16lTWrVvHF198QceOHZUnFKQWx/uqVKnC\n8OHDmTx5MnFxcbRo0YKlS5fi6OjI0KFDWb16NbVq1aJly5ZMnz4dU1NTGjZsqLze1NSUX375hZ9/\n/pmmTZuiVqupXLkynp6emJiYKOtNqTlKwnR9fX1cXV2ZM2cOmzdvpmrVqsyePZtNmzaxcuVKrTEP\nMmLevHnMmTOH7t27Ex0dTcmSJZk3b57SxGfEiBH4+/vTrVs3vvjiC/r374+tra3yC2vBggVZs2YN\nCxYsoHHjxhgaGlKrVi02btyo/OI9c+ZMfvzxR+Um3MbGhlWrVinjHnh4eODm5sZvv/1G7ty5qVmz\npjISbErfS+JprVq1Ytu2bbRo0YJJkyaRO3fuNLd72bJlTJ8+nVatWqFWq6lXr55Wn63E+vXrx2+/\n/aY87QLik93c3Jwvv/wSc3Pz9HzUWWr58uW8fftWuYBJzNDQMNV8S3hyyvjx43nx4gWmpqZUqFCB\n5cuXY2dnB0CvXr34+++/adeuHXny5KF///4sWLCAfv360aRJE3x8fJL9XtLanyDtLi7p2SfSs56M\nUKvVrF27FldXV2VArcqVK7N582bMzMxSfW3i7VGpVHTo0IHevXvz5MkTrKysWLFihdb8hOVnzJjB\njBkzqFOnDmXKlGHGjBlcuHCBRYsWYWhomGScmPQYPHgwt2/fpm3btuTNm5chQ4YoJ6O0Yk9umr29\nPa9evWLRokXKAJotW7ZURlWeNGkSP/30E127diU8PJwCBQrQrFkzvv32W9RqNW5ubixbtowpU6Zg\naGhImTJlWLFiBfny5cvwtmUlV1dXLC0tWb58udJNytbWFnt7ezZv3oyjoyOenp48e/ZMeYwtQI0a\nNfD398fNzY3u3bun2DUgJ3N2dmbevHkMGTKE8PBwihUrxtixY5Vm3WmdX4cMGZLmfuPk5IShoWGK\n61i+fDk//vgjTZs2JTY2lqJFizJ69GjlV5nAwEDGjRtHUFCQkv/z589P1/bp6enh4eHBnDlzsLe3\nR6VSUaZMGVavXq307U1tv0xtn/6cpCdHfv31VwoWLMiiRYuU779OnTrcuXOHI0eOEBoaSp48edi7\ndy83btxg6dKlSvNlGxsbgoKCWLduHf37908ynk9Oplar2bx5Mx4eHkyYMIEXL14QGxuLubk5jRs3\nZsGCBcoPFZMnT8bFxQU7OztKlCjBlClTOHPmTKrv/zF3Gevfvz9Hjx7l+++/Z/PmzTK+Tgbt37+f\nc+fOsWTJElq1agXEtx4JDAzk8uXLykCZaV3nfUhpHf9TOr4n3Bulds/zvrx58yZ7P+fo6AjAkCFD\nePfuHU5OTrx69YrChQvTpUsXZX5yUrrGXL16dbLLb9q0iYoVKyZ7TWZoaEjXrl3Ztm0bw4YNy/Rn\nU6VKFd6+fUvRokWVcSNq1arFvXv3sLW1VcYaSs957n1Fixb9YNdkKk02t0FzcHBApVJpPb/9fU5O\nTpw7d45jx45lY2QivaKiorQuoh0cHLCwsGDOnDk6jEq3IiIiaN68Oa6ursoFcXa5ffs2Xbp0Yfbs\n2ezcuVMrvySXdMfLywtnZ2f++ecfuaDK4bZu3UqRIkWS9KEfN24chw4d4urVq3Tp0oXY2Ngk44wc\nOHCACRMmsGLFCpo0aZKNUQuRfdKTI7179+bly5ccP35caxknJyf27t3LxYsXUavVjBgxgnPnznH+\n/HmtC+0rV67Qo0cPpk6dSp8+fbJlu4TIyYYMGcLdu3f5448/UlxGrvNETiNXvCJDfv31V5o0acK/\n//5LXFwcx48f5+LFi1qPNvncRERE4OLiQsmSJbO9sKDRaJg2bRp169bV6n4jhEi/r7/+OtnB+R4+\nfIilpSUqlYp79+5pPSEhQdmyZdFoNNy4cSM7QhVCJ9LKkYTHJz59+pSlS5cSGhpKdHQ0J06c4MiR\nI/Tq1Uv5UeL27dvKgMKJJeSX5JIQ8a5cuUKNGjV0HYYQGaKTbhHBwcE4OTlx9uxZXr58qTwrPnH/\nlsjISGbPno2vry9Pnz6lcOHCtG/fnpEjR36y/dM+Bg4ODjx79oyBAwcSFhZG0aJFmTx5cpojs36q\n9u3bx+TJk7GxsWHRokXZvv5NmzZx69atVAcnlVwSImMCAwNZuXIld+7cYfHixYSEhBATE6P1aNIE\nCdOCgoKyO0whdOb9HIH4blNGRkY4OTmxdOlSIL4L59ChQ5Une0D8IGLJDYybO3du1Gq15JIQxA/2\n9+bNG4oUKcLGjRtZv349jx8/plChQvTp00frqR1ynSdyEp0UFwICAmjVqhULFizgzZs3bN++HVdX\nV54+fcoPP/wAxI8krKenx48//khMTAwHDhxgxYoV3LlzR+uxHyJ7GRgY8MMPPyjf0+cu8SOnstvT\np09ZuHAho0aNSrFPFUgu6Urnzp3p3LmzrsMQGXDu3DnlaSuWlpZ4eHhQr149ZfDC5MZUSLhwe/fu\nXfYFKoSOpJQjAFevXmXy5MlUrlyZvn37YmZmxvHjx/nll19Qq9WMHDkSiM+VlMYnMTQ0lFwSApSn\nMB06dAgLCwucnZ1Rq9Xs37+fefPm8fLlSyZNmgTIdZ7IWbK9uLBs2TL09fWVwQshfmR2BwcHNmzY\nwNChQ5k8eTLOzs7kyZNHWcbOzo6oqCj27NnDtWvXkh247n0xMTE8ffqUIkWK/OcBDoXIqVxcXLC0\ntGTQoEEpLiO5JET6ValSBW9vb4KDgzly5AhDhw5l9OjR9OjRA4Do6Ogkr0l4Wo6xsXG61iG5JD5m\nKeWIo6Mj06dPx8TEhNWrVyuDPlarVo2oqCiWLVtG+/btKVGiBEZGRsnmEsTnk+SSECg5FBMTw+rV\nq5WCnK2tLc+ePWP9+vUMGjQoS67zJJdEVsr2MRfy5s2rVVhI0Lx5c2JjY7lx4wampqZaSZKgZcuW\naDSaJM+zT8nTp09p3ry51iOzhPgUHD58mJMnTzJ58mQiIyMJDw8nLCyMuLg44uLiCA8PJyoqSnJJ\niAzIlSsX1tbW2NnZMX36dPr168fixYsJCgpCrVYn++zqhCbcCSM5p0VySXzMUsqRmzdvcv36dezs\n7JSbogS2trbExsby999/A/G5klwuJYzTILkkBHzxxRfKk/Peb+lTv359YmNjuX37dpZc50kuiayU\n7eUpjUZDXFxckpNPQjO4hIp1TExMkupZZGQkAEZGRtkQqRA514kTJ4iNjaVv377Jzq9ZsyadOnVi\nzpw5kktCpCIkJISjR49Svnz5JL/uVKpUCY1Gw7///ku5cuW4detWktffvHkTlUqVrhZAQnyM0pMj\n9+7dA+Kv3d6X0Lon4TqvQoUK+Pr6Ehsbq3UtmJBfkktCxHdDLlOmDMHBwUnmxcbGKsuA3DOJnCVb\nWy48evSIqlWrsnDhQq3pGo2GI0eOYGJigrW1NbVr11aem57Y4cOH0dfXp3bt2tkVshA50ogRI9i4\ncSObNm3S+lehQgUqVqzIpk2bGDhwILVr12bChAlJXi+5JEQ8PT09pk2bpgxKl9ilS5eA+Odg29vb\n8/DhQ65cuaK1zO7duzEzM6Nu3brZEq8Q2S09OWJhYUHRokU5d+6ccuOT4PTp06hUKuW58Pb29kRG\nRnLkyBGt5by8vDAyMvqsnz4lRGL29vZcvXqVu3fvak0/ceIEarWakiVLyj2TyHGyteVCiRIlaNWq\nFZ6enqhUKho0aEB4eDhbtmzh+vXrTJ48GVNTU3r27Mkvv/zC1KlTadOmDXFxcezbt4+jR48yYMAA\nihcvnp1hC5HjWFpaYmlpmWS6qakpKpUKGxsbAHr16sXq1asll4RIQZ48eejXrx+enp589913dOzY\nEUNDQ3x9fdmyZQt2dnZUrVqVcuXKsX37dsaNG8eECRMoXLgwe/bs4cyZM8yfPz9JazwhPhXpzZFx\n48YxadIkhg4dSu/evTE1NeXPP/9k69atfPXVV5QtWxaA1q1bU6tWLVxcXAgLC8PKygofHx+8vLwY\nO3Zssk9lEeJz1LdvX7y8vBgyZAgTJ07kiy8erys5AAAgAElEQVS+YM+ePfj5+TFq1CjMzc3lnknk\nOCqNRqPJzhVGR0ezfv16tm3bxuPHjzE0NKRChQoMHDhQq1q9Y8cONmzYwP3791GpVJQuXZqePXvS\nvXv3dK8rICCA5s2bc+zYsVRH0xfiU+Hg4ICenh6//fabMk1ySYi0bdu2jW3btnH37l309PSwsLCg\nWbNmDB8+XGlWGhQUxPz58/Hx8SEsLIyyZcvi6OhIy5Yt070eySXxsUpPjvj4+PDLL79w/fp1oqOj\nsbCwoGPHjgwZMkSrABcWFoabmxuHDx/m9evXfPnll/Tt25evv/463fFILonPwatXr5TzTmhoKCVL\nlsTBwUHrGi6z13mSSyIrZXtxITtJsgiRNSSXhMgakktCZA3JJSGyhuSSyErZ/rQIIYQQQgghhBBC\nfFqkuCCEEEIIIYQQQohMkeKCEEIIIYQQQgghMkWKC0IIIYQQQgghhMgUKS4IIYQQQgghhBAiU6S4\nIIQQQgghhBBCiEyR4oIQQgghhBBCCCEyRYoLQgghhBBCCCGEyBQpLgghhBBCCCGEECJTpLgghBBC\nCCGEEEKITJHighBCCCGEEEIIITJFigtCCCGEEEIIIYTIFCkuCCGEEEKIVJ07d45BgwZha2tLtWrV\n6Ny5M1u3btVaJjY2lhUrVtCyZUuqVq1Ky5Yt8fDwSPJeISEhTJ8+nYYNG1K5cmXatGnDmjVr0Gg0\n2bU5QnwUIiIi+Pnnn2nSpAlVq1albdu2bNu2TWuZJ0+eMGHCBOzs7KhatSodO3Zk165dOopYfO4M\ndB2AEEIIIYTIuXx9fRkxYgQNGjRg7ty56Ovrs3XrVqZPn87r169xdHQEYPLkyezfv5+xY8dSvXp1\nfHx8cHNzIyoqitGjRwPxBYiBAwfy5MkTvv/+e0qVKsXZs2dZuHAhL1++ZNKkSdm+fZrYWN79+Seo\n9DCqXw+Vvn62xyDE+zQaDY6Ojty4cYPvv/8eKysrvLy8mDZtGvr6+nTt2pW3b9/Sq1cvDAwMmDZt\nGubm5hw5cgRnZ2ciIiLo06ePrjdDfGakuCCEEEIIIVLk6uqKpaUly5cvR///b7xtbW2xt7dn8+bN\nODo6cunSJby9vZk0aRIDBw4EoGbNmjx79ozr168r77V3715u3LjB0qVLad68OQA2NjYEBQWxbt06\n+vfvT5EiRbJt2+LCw3nZpRvRV68CYGhjQ6HtW1HlypVtMQiRnP3793Pu3DmWLFlCq1atAKhVqxaB\ngYFcvnyZrl274unpybNnz/Dy8sLa2hqAGjVq4O/vj5ubG927d0etVutyM8RnRrpFZJImLo6w9Rt4\nNWIkoR4eaN6903VIQgghhBBZpn///vzwww9KYQFAX1+fihUr8uzZM2JiYvD29sbIyIiePXtqvfbn\nn3/W6hpx+PBhcufOTbNmzbSWa9++PbGxsRw7duzDbsx7Iry8lcICQPSlS0TsP5CtMQiRHG9vb4oW\nLaoUFhL8+uuvzJw5E4Dff/8dKysrpbCQoF27drx9+5ZTp05lW7xCgA5aLjg4OODn55dkukqlok+f\nPkyZMgWI7z80f/58Tp06RVhYGKVKlaJ///506dIlu0NO1Zu583jrvhSAiD17if7nBgXcF+s4KiGE\nEJ+yqGv/8M7HB8Py5TFq3gyVSqXrkD4790PucenZRUrksaRWkdqf9Hfw9ddfJzv94cOHWFpaYmBg\nwOXLl6lQoQK50vjF//bt25QqVSrJ51WmTBkAbty4kTVBp1Pc69dJpwUHZ2sMQiTnypUrNGzYMMX5\nsbGx3L17V2kBlFjZsmXRaDTcuHGDJk2afMAohdCmk24RlSpVYtasWUkG7jEzMwP4qPoPhb83qErE\n7t1o5s9FZWSko4iEEEJ8yiL27uPVyFEQFweAyaCBfDFrpo6j+rycfnyKuedciSP+O/iqVFuGVx+h\n46iyT2BgICtXruTOnTssXhz/g8rjx4+pX78+Bw8eZOXKldy7d4+8efPSuXNnvvnmG4z+/7ro1atX\nWFlZJXnP3Llzo1arCQoKytZtydWhPaGLl6AJCwNAlTcvudq1zdYYhHhfaGgob968oUiRImzcuJH1\n69fz+PFjChUqRJ8+fRg0aBAhISHExMSQP3/+JK9PmJbd+SSETooLJiYmVKxYMcX5H1P/Ib0CZsQ9\ne/6/v/PlA0NDHUYkhBDiUxa6YoVSWAAIW7eevN9/h17evDqM6vOy69+dSmEB4PCDg/St6ICp2lSH\nUX14586do1+/fgBYWlri4eFBvXr1AAgPD+fKlSv4+/szatQo8ufPz4kTJ1i7di0PHz5kyZIlALx7\n9y7FazhDQ0PeZXP3UoMSJSi0dzdh6zeAnh4m/RzQL1o0W2MQ4n3h4eEAHDp0CAsLC5ydnVGr1ezf\nv5958+bx8uVL+vfvD5BsPhn+/71IdueTEDlyQMfU+g/98ccfnDp1Ksc08cnn7ETQ0KEQ+Q709ck7\nxRmVngxlIURqYl+94u0KD2Lu3ydX69bk7t5N1yF9NoIjg/H6dyfPw59Tv3gDGlo00nVIIqPitFv9\nxcXF4enpSYyxcbKL58+fn+7du2dHZJ+F7du380zzDBL9jqCJ0+D5mydqTfxF/qf6mVepUgVvb2+C\ng4M5cuQIQ4cOZfTo0Tg6OqKvr09QUBA7d+5UfjWtVasWYWFhbNmyhatXr1KlShWMjIyIjo5O9v2j\noqIwTmE//pAMy5fni9mzsn29QqQkYXyTmJgYVq9erRQQbG1tefbsGevXr1cKfcnlU1RUFIBO8kl8\n3nJcceFj6z9k3KwpRc6dJerCBQwrVcKgeHFdhyREjhfUrz/Rl/4GIPLgIeLCwzHt30/HUX364jRx\nTPtzMv6h/gCcDjxFTFwMTS2bpfFKkZOYDh9G8Dej4f+7Fubp05tB//+Yv1WrVjFs2DBdhvfJ6969\nO0UCzFngNw8N8d+BRaQl3wz9RseRfXi5cuVSfvixs7PD2NiYxYsX06JFCwoWLIipqWmSJtr169dn\n8+bN3LhxgypVqlCoUCFevXqV5L1DQ0OJjo6mUKFC2bItQuRkX3zxhTJo6vstE+rXr8/Jkye5e/cu\narU62XxK6A4h+SSym06KC8HBwTg5OXH27FlevnyJhYUFPXr0YMCAAR9l/yF9MzNyvTeSqxDZzc/P\nDwcHB+rUqcO6deuU6TltcNTou/eUwkKCiJ27pLiQDe6H3FMKCwlOPPpDigsfmdydOmHw5ZdEnogf\n0NG4TWtdh/TZaWjRiCImRbjw7AKWeSy5uv+arkP6YEJCQjh69Cjly5encuXKWvMqVaqERqPh9u3b\nWFtbc+XKlSSvj42NBcDAIP6Ss0KFCvj6+hIbG6v19Ilbt24BJFmHEJ8jAwMDypQpQ3Ayg4sm5JSh\noSHlypVTciexmzdvolKpJJ9EttNJ+/2AgACKFSvGggULcHd3x8rKCldXV1xdXZVmPNJ/SIj0i4qK\nYurUqUlG304YHPXy5ctMmzYNT09P7OzscHZ2ZuPGjTqJVS9/fngvv/XMC+skls/J9u3b2bdjP2i3\nqOel/0tWrVrFqlWr2L59u26CExmmtrEh77ix5LL/Srri6UjZ/OXoad2LesXro+LTfVKEnp4e06ZN\nUwZuTOzSpUsAFC9eHHt7e4KCgjh58qTWMidOnEClUlG9enUA7O3tiYyM5MiRI1rLeXl5YWRkRIsW\nLT7QlqQt+s4dou/c0dn6hUjM3t6eq1evcvfuXa3pJ06cQK1WU6lSJezt7Xn48GGSwt7u3bsxMzOj\nbt262RlymjQaDVFXrxLzOFDXoYgPJNtbLixbtgx9fX1MTEyUaY0bN8bBwYENGzYwcOBAQPoPCZER\ny5cv5+3bt0kq1DlxcFT9AvnJO34cb+bOg7g49AoWJO/4cdkaw+coof+32fUC7Li1DQ0ajGKNmdx+\nCsVMpTuXECJ5efLkoV+/fnh6evLdd9/RsWNHDA0N8fX1ZcuWLdjZ2VG1alUqV67M5s2b+e677/j+\n+++xtLTkjz/+YPfu3XTq1El5QkTr1q2pVasWLi4uhIWFYWVlhY+PD15eXowdOzbZlqsfmiY6mldD\nhxH5+1EAjFu2oMDqVahkgG6hQ3379sXLy4shQ4YwceJEvvjiC/bs2YOfnx+jRo3C1NSU3r17s337\ndsaNG8eECRMoXLgwe/bs4cyZM8yfP1+rdZCuxb58ycuevYm5cQNUKkyHO5JvymRdhyWyWLYXF/Km\nMJp18+bNOX/+PDdv3pT+Q0JkwO3bt/nll1+YPXs2O3fu1JqXUwdHzfPtN+Tq1JGY+w8wql0LVRrP\nRRdZp29FB5pbNudFxAv+8jolhYVPUIy/P3qFCqF58wYM1egXyP6bNfFpmTRpEqVKlWLbtm2MHj0a\nPT09LCwsGDZsGMOHDwfiWzisXr2axYsX4+7uzqtXryhWrBhjx45lyJAhWu/n4eGBm5sbS5Ys4fXr\n13z55ZdMnz6dr7/+WhebR8T+/UphASDy96NEHDhI7o4ddBKPEBD/dL1NmzYxf/58Zs+eTWhoKCVL\nlmTmzJnKDwbGxsZs2LBBWSYsLIyyZcuyZMkSWrZsqeMt0PZ21er4wgKARsPbFR7k/roHhmXL6jYw\nkaWyvbig0WiIi4tLUklL6OpgYmIi/YeESCeNRsO0adOoW7cunTp10iou5PTBUQ1KlMCgRAmdrPtz\nV9S0GEVNi3GGs7oORWShmMePaeixkmcus8DAAGJiwMAAk4ED+GLGdF2HJz5yPXr0oEePHqkukzt3\nbpycnHByckp1ORMTEyZPnszkyTnjV8vYgMdJpp3auZN7z55qTftUnwIicq4CBQrw008/pbqMmZkZ\nc+bMyaaI/rvYR4+STgsIkOLCJyZbO2o+evSIqlWrsnDhQq3pGo2GI0eOkDt3bipXrvzR9R8SQlc2\nbdrErVu3cHFxSTLvYxwcVQjx371xnUveZ8/j/4iJUf4btvoX3p05o7vAhMjhjNu01h4LSK3mmXV5\nhg0bpvVPCgtC/He5OrTX+luvcGGMbG11FI34ULK15UKJEiVo1aoVnp6eqFQqGjRoQHh4OFu2bOH6\n9etMnjwZY2Pjj6r/kBC68vTpUxYuXMioUaOwsLBIMl8GRxUi/c6cOcOyZcu4du0aBgYGlC5dmkGD\nBtHq/58E5OTkhJeXV7KvbdKkCR4eHtkZbrJi7vyb8rx/78hFnBApMCxThoKbN/J29S8AmA4dQlgy\nT74QQvx3ub76ivzL3AnfvgM9s4LkGf2tdIv9BGV7twhXV1cqVarEtm3b+O233zA0NKRChQq4u7sr\nIwR/TP2HhNAVFxcXLC0tGTRoULLzjYyMABkcVYi0/PHHH4wcOZJ69eqxZMkSVCoVGzZsYPTo0cyY\nMYOePXsCULhwYTw8PNBotB+5kdJYQtnNuGVLoq9cTTpDrcaoSePsD0iIj4iRra12AU6KC0Jkudyd\nOpG7UyddhyE+oGwvLhgaGjJo0KAUb4gSfCz9h4TQhcOHD3Py5Ek8PT2JjIwE/jeeiUqlIjw8HBMT\nExkcVYh0WLhwIZaWlnh4eCiteurWrUurVq349ddfleKCoaEhFStW1GWoqcoz+lsu+vlR4cVLVEZG\nEBWFKl8+8oz+RsY3EUIIIcQHl+3FBSFE5p04cYLY2Fj69u2b7PyaNWvSqVMnGRxViHT45ptvyJcv\nn1JYgPhCQunSpfHz89NhZBmjMjDgdtMmNBk2TNehCPFJibp2jagLFzGqWwfD956+JIQQ4n+kuCDE\nR2jEiBHJDiw1a9YsVCoVU6dOxczMjKNHjzJv3jyuXLlC1apVleVkcFQh/qd169bJTr937x5WVlbZ\nHI0QIid5u/ZXQqZOi/9DpeKLBfMw0dEjMz9l72IiiYiJ4AtjeXSuEB8zKS4I8RGytLTE0tIyyXRT\nU1NUKhU2NjYAMjiqEP/RqlWrePLkCaNGjVKmRUZGMnv2bHx9fXn69CmFCxemffv2jBw5UqvVgxDi\n0/FmQaInnGk0hC50k+JCFjt47wCe//xKZEwE1QpVZ1JdJ0wMTXQdlhDiP5DighCfGJVKpfy/DI4q\nRMZ5e3uzePFiWrZsSdeuXZXpISEh6Onp8eOPPxITE8OBAwdYsWIFd+7cwd3dPVtjjP7nOm8WLCD6\n+g0ADMqVI++4Mdkag4gXq4nl39y3Gf/HWIqZFqNvxX4UMSmi67BEFtBoNPDeU5U0/z/OkcgaQREv\nWXXFgzhNHACXX/zNrts7cajUT8eRicyIvnuP0LnziHnkT6527TAd7ohKT0/XYWXI9cchrDlxl5Dw\nKNrZFKdTLRm7KD2kuCDEJ2T9+vVJpsngqEKk36pVq1i0aBHNmzdn/vz5yvTJkyfj7OxMnjx5lGl2\ndnZERUWxZ88erl27lm1jmMRFRPCyZy/iEg3WGvvoES/PncNghGO2xCD+Z9ftndw2uQmv4e7rO9x7\nfY9lLVZoFXrFx0mlUmEycABvl69QppkMHKC7gD5BAaEBSmEhgf+bhzqKRmQFTUwMQX36EvvoEQDR\nl6+gMjTEdOgQHUeWfqER0Yxed563kTEAXAsIIW9uQ5pVlMJxWj6uEpIQQgjxgfz4448sXLiQPn36\n4O7ujlqtVuaZmppqFRYStGzZEo1Gwz///JNtcUb5+WkVFhJoQkMp+OBBtsUh4p19ckbr78dvAwgI\nDdBRNCKr5XV2Iv/yZZgMHUKBNavJO1ZaCGWlcgXKY2poqjWtRpGaOopGZIXo69eVwkKCiMOHdRTN\nf3PxwSulsJDA98ZzHUXzcZGWC0IIIT57ixcvZsOGDUycODHFRyXHxMRgYKB92kx4FKyRkdEHjzGB\nQalSoKcHcXFJ5r01M8u2OES84qbF+Tf4tvK3sb4xZrnke/hUqFQqcnfsQO6OHXQdyicpl0Euptdz\nYf31dbyKfEXjEk1oU/IrXYclMkHfogQYGWl1KTKwKq3DiDLO0izpmB+WBWUckPSQlgtCCCE+a0eP\nHmXFihWM+z/27jwuqnp94PhnZhgYdhgQQQEFNBfUck+09OZSebPsXtfM9f7cy8wlNVNzKyvzppYS\ntnjVcuumaXmV1IzKXFLTUNQAFRcWgRmWAQZm+f1BjI4MiAtzBvm+X69er873nBke1JlzznO+3+d5\n9VWbiYWCggLat2/PtGnTyu3bs2cPCoWC9u3b2yNUAJxCQvCaNRNumlmBkxOek18hPyDAbnEIpYY0\nH4qHoXRWi0qhYszD43BTukkclSDUHA+pm7Cwy2I+6rGaAU0GiiVFNZxC7YvP/DeRuboCoGzeHK8p\nkyWO6s6EBXjwr24ROClK/y22aejLwI4NJI6qZhAzFwRBEIRay2g0smTJEurVq0enTp2Ij48vd0xY\nWBiDBw9mzZo1zJkzh6eeegqTycS3337L3r17GTFiBPXr17dr3J4TxuP+wmBMWi3I5cg8PFGofSEm\nxq5xCBDgFsDjmm48M/gZfFW+D2xi4ciRI0RHR3PmzBkKCwsJDw9n0KBBDKygc8LFixd59tlnqVOn\nDvv27bPal5OTw7Jly9i/fz8ajYbg4GD69+/PqFGjxI2lIPxl6NChHD16tNy4TCZjyJAhvPHGG8yc\nOZPt27fbfH23bt2Ijo6u7jBtch/6Iq59n8N0PROn8DBJYrhXo//WiAEdQ9HpDfh5uLD92BUuXs+n\n80N16NJEJPIrIpILgiAIQq2VlpbG1atXARgwYIDNY9atW8eUKVMIDQ1lw4YN7NixA5lMRkREBAsW\nLKB///72DNlC7uOD3MdHkp8tWJMho76nfRNM9hQXF8f48ePp0qUL7777LgqFgs2bNzNv3jy0Wi1j\nx5YvJDpnzhxKSkrKjRuNRkaOHElqairTp08nLCyMw4cPs2zZMjIzM5kxY4Y9fiVBqBEiIyNZuHBh\naeeSm/j9tQROJpMREBBAdHR0uWO8vLzsFqctck9P5DZqFdUk3m7OeLs589rGE8SdLa25sO23K8x6\nNpLn2gZLHJ1jEskFQRAkYczWkLtgAfojR3Bu2w7vN+eiqGHrxQ+ev070vj+5qilAqZDTo0UgL/Vs\ngspZIXVolbqUe5G18Z9zzvcsilMyCgwFxGfG08inEaNa/l+tWi9ev359EhISqnRsv3796NevXzVH\nJAiOZ8mSJYSGhrJq1SoUitLvt0cffZTevXuzcePGcsmFrVu38scffxAVFcXFW4qM7ty5k4SEBD78\n8EO6d+8OQOvWrcnKymLdunUMHz6cwEBRkV0QANzd3WnevHmlxyiVytseI9y9zDy9JbFQZvtvl0Vy\noQIiuSAIgiS006dTtLu0enDhpRRMWi3+6/8jcVRVd01TwGubTmAwlj0pMPLVkdLqyNP+7rgneaPJ\nyIKD88ksvA5OsCPpG8u+NF0qWr2GxY8tkTBC4W6UnD1L0b79BCb+idloRKZw7ATXzQ4lZnLuWi5t\nw9W0CHbcmRhGs5Ej1w5zTXeNUM8GXM67hMFkBMxkOKdLHV61KrvhV9z070qhUNC8eXN2795tVew0\nMzOTpUuX8n//939cvXq1XHJhz549uLm58cQTT1iN9+nTh/Xr17Nv3z6GDBlS7b+TIAjCrbZu3YpG\no7Ea05tkyAnBxI0lW+4u4ha6IuJPRhAESRQdOGC1rb9l29EdTc6+KbFww6HETAmiqbqUvEuliYUK\nxGfGozfqcVHYr/uBcG+K9v9A1oiRYDTSFtAYzahXfCB1WFWy6vvzrPv5QunGPnijbwueae2YywtW\nHPuAA5d/sL3TGz7/41NGtvyXfYOyk4rqKly6dImQkBCrLiqLFi0iICCAcePGMWfOnHKvOX/+PGFh\nYeVqKzRq1AigyjOJBEEQ7rdblznGxMTw8rjROO/7k8/jkgFwcZIzsmvN6n5hTyK5IAiCJJRNm1Ly\n+0mrbUd3c0Y7o9gZCCp3jLwgi61bt0q2Dv926roFolKoKDIW2dxfz72eSCzUMPnRH4PRaNku/Ppr\njLNmoAgq/+/TkRQbTGw+dMlqbP3PFxwmuWA2mzl37hw6nY5cQw4Hrh2o9PidiTtpVtQcpdzZ5v6Q\nkBACHpBuHteuXePjjz8mMTGR5cuXW8YPHDhAbGwsX3zxRbm2rWWys7MJDw8vN+7m5oazszNZWVnV\nFrcg1DQajYZZs2Zx+PBhMjMzCQ4OZsCAAYwYMcJyTFFREYsWLSIuLo60tDQCAgLo06cPEyZMQKlU\nShf8A2Rs98Z0bRbAhes62of74e9Zc66TdCU6jqYewcPZg9Z126CQVe/MRpFcEARBEj7vLCF77DiM\nFy+hCA3F5713pA7ptm5NGATs/5O1PyZZpso1quvBO4Meo77acavFuyndmNjmZWJ+jyavJI9w7wgK\nDQWk6lLxd63Dy21ekTpE4X4oP6nG4dxafKyiMakYDAZ++uknWrdujcls5vZ/qGZMZrPN30Gj0ZCd\nnU3Pnj2rJVZ7OXLkCMOGDQMgNDSU6OhooqKigNKWrQsWLGDgwIG0bt26wvfQ6/U4O9tOwCiVSvR6\n/f0PXBBqqCtXrtCrVy/ef/99cnNz2bp1K0uWLCEtLY2ZM2cCpd1X5HI5ixcvxmAwsGvXLlavXk1i\nYiIrV66U+Dd4cDSt503Tet5Sh3FHMgoymH5gClq9FoBWdR5mQedF1dqVR/LkwtGjRxk6dCgdOnRg\n3bp1AMyaNYtt27bZPN7ebVVMOh05c+dRtP8HlE2a4L1wPsrGje3282urjIIMYk6uJlGTSMs6rRjd\naixeLtJWvRXuL+cWLaj780+Y0tORBwQgk8ulDumOjXmiMSXnDzD4xWGYzNSYTPbjwV3pFBRF9Ger\nefn5SZjNZrKLsvFR+VR7Rlu4/zzGjEZ/6JBl9oJr3+dQ1HPsWQsALkoF/TqG8sUvFy1jQzo7Vssy\nhUJBu3btAPj96HHirvxY4bG9I54hqlWUzX3JyckkJSVVS4z21LJlS7Zv345GoyE2NpbRo0czadIk\nxo4dy7JlyzAYDEybNq3S93BxcbHZRQKguLgYlUpVHaELQo3z0UcfoVAocHd3t4x17dqVoUOHsmHD\nBkaPHs3s2bN5/fXX8bypK0OnTp0oLi5mx44dxMfH06JFCynCFxzAruRvLYkFgFPXT/JH5h+0qtOq\n2n6mpMmF4uJi5syZYzN74ihtVXIXv0XBps0A6DMyyP6/MQQc2C/6MFez94++x9ns0nWXcVd+xGQ2\nMb2DaE/1oJHJZChqeFVwpdyM2qNmJBVuplQocTGXXsTLZLJa1SHiQaPq0Z2A/+2iaO9efkhOps+y\n96UOqcpe7tWENg3VnLuWS7twNa1CfaUOqUKT202hY9CjpQUdPUK5nH8Zg8kAmInwaUT7wA5Sh1jt\nXF1dafrXErZOnTqhUqlYvnw5bdu2ZePGjbz33nvIZDIKCgowm80YDAbMZjMFBQUoFApcXFyoU6cO\n2dnZ5d47Ly+PkpIS6tSpY+9fSxAcUkX3PN27d+e3334jISGBLl262DymZ8+efPPNN5w+fVokF2oB\nW4UoAeI9ToGr9ZjeYHtZ7P0iaXJh1apV5Ofn2/xH7yhtVfQ//Wy1bUhMxJSaViOeCtVUeqPeklgo\nc/L6yQqOFgRBEJSRzVFGNictJqZGdYoA6PxQHTo/5Pg3lAqZgi7Bj1m2H6WThNHYT05ODnv37qVJ\nkyblrtciIyMxmUzMmzcPk8nE1KlTbS4LadOmjWWGarNmzYiLi8NoNFp1nzh37hyAuBEShL+YzWZM\nJpPV5wSwLB0qm+Vzc7eWMkVFpTeQLi417+GHcOds1fmKiYlhcu9Xee3HaZSYSmeL1XOvxyN1K162\ndj9Illw4f/48n3zyCYsWLeK///2vVGHclrJFJIbkZMt2kYcHn+34BnMFU7h9fX0dtpBbTeGicCHY\nI5gr+VcsY+He5Ys/CYIgCIJQveRyOXPnziUqKoo1a9ZY7Ttx4gQymYwFCxbYnNEZHR1NQkICK1as\nwMPDA4DevXsTGxtLbGwsTz/9tOXYbXpretYAACAASURBVNu24eLiQo8ePar3F7LBbDRiOH8eRf36\nyO08Q1YQbLl8+TK9e/dm2LBhTJ8+3TJuNpuJjY3F3d2dpk2b0r59ezp37swHH1h3CNqzZw8KhYL2\n7dvbO3TBgYT7RLC02785cHk/HkoPejV8EqW8eot8SpJcMJvNzJ07l44dO9K3b1+HTi54z52DMTWN\n4qNHUQQHE/z+UkZ36QyUZoTGjBkjcYQPpsntprDs6FKu6a4R7h3BuEfGSx2SIAiCw1MWFGBMS6vx\ny40Ex+Hp6cmwYcNYu3Yt06ZN47nnnkOpVBIXF8emTZvo1KkTbdu2tflaPz8/nJ2drQo8Pvnkk7Rr\n14758+ej0+kIDw/nxx9/ZNu2bUyePBlfX/sujSlJSiZr6FCMl1KQubri/dZi3AeIh0SCtEJCQujV\nqxdr165FJpPRpUsXCgoK2LRpE2fOnGH27Nl4eHgwaNAgPvnkE+bMmcNTTz2FyWTi22+/Ze/evYwY\nMYL69R2j+44gnYbeDRnhPcpuP0+S5MKXX37JuXPn2LlzZ4XHOEpbFUVQEHW2f40pLw+Zu3uNLDpX\nEzX2fYhVPT+mwFCAu9L99i8QhPts/fr1/POf/8TN7d46PyQnJ5OcnCzJ0zihdtHOfZMen31G2nvv\no+rdG/VHK5FVUJVfEO7EjBkzCAsLY8uWLUyaNAm5XE5wcDBjxoxh3Lhxlb62ohkNH3zwAStWrECr\n1dKgQQPmzZvHwIEDq+tXqFDukiUYL6UAYC4sJGfOXFyf+bvd4xCEWy1ZsoTIyEi2bNnCf/7zH5RK\nJc2aNWPlypWWa4qpU6fSoEEDNmzYwI4dO5DJZERERLBgwQIxk1qQhN2TC2lpaSxbtoyJEycSHBxc\n4XGO1lZFflMVVsE+ZDKZSCwIkikqKkKj0dxzciErK0u0VhOqnf7XX9F9+ill6e+iXbso+Kob7i8M\nljQu4cExYMAABgwYcEevefvtt22Ou7u7M3v2bGbPnn0/QrsnxouXrLbN+fmYMjMlikYQblAqlYwa\nNYpRoyp/6tyvXz/69etnp6gEoXJ2Ty7Mnz+f0NDQSj8ooq2KIAiOIDs7+56nFNqqii4I95shsXyb\nQ0NiogSRCELNour9NCVnzli2lS1a4BQaintmJkU//YxLh/bIRFE8QRCEKrHrHP89e/bw008/MXv2\nbIqKiigoKECn02EymTCZTBQUFFBcXIyHh4dVYqFMz549MZvNnD592p5hC4JQS9lq6yPFewjC7bh0\n6wq3LIFQ9RRLcQThdjwnvYzXzBk4t22L26CB+K39jJz5C+j20WqyBg0mvcvjGFJSpA5TEAShRrDr\nzIUDBw5gNBp58cUXbe5v27Ytffv25e233xZtVQRBkFzZrIPkjHze+iaehGs5eLk6k1dUQkSAJzP7\nVN4ut6SkhNzcXJvJUkdhMBn45FQMP6Tsx0fly8gWo3i0Xu1osfcgcQoJwW/dfzg3axb11H64jxyO\nSyfx9ygItyNTKPB8+SU8X34JAMPFi+TH3OiKYbx2jbyPVuP7ju0lHoIgCI5iZ+I3bD2/FZPZRD1X\naYp52jW5MH78eJvFRRYuXIhMJmPOnDm4u7vTvn17oqKiWL58udVxoq2KIFg7cuQI0dHRnDlzhsLC\nQsLDwxk0aJClKNasWbPYtm2bzdd269aN6Ohoe4Zb42g0GsxmM3O+OklSen7pmK4YgHOpuby+5SRP\nVVKSQavV2iPMe/Jt0g7+d2EXAGm6VJYefZdPn1qLt4u3xJEJd0r1WBcODxsquhgJwj0wXi9fb8GU\nkS5BJHfGZDKz/dgVjiRl8lCQFy90aojKWSF1WIIg2ElCVgKf/HEjMXrOI5fj6cdoU9d2N5/qYtfk\nQmhoKKGhoeXGPTw8kMlkllZFgwcPZs2aNaKtiiBUIi4ujvHjx9OlSxfeffddFAoFmzdvZt68eWi1\nWsaOHQtAQEAA0dHRmM1mq9d7iV7elfL09CQ3N5dVH39CUnr57y2AVG0hBSoFMTExlb5XWX93R5SQ\nlWC1XWIqIUmbaPeTkSAIgiNwbtMap/BwDMnJljG3GlAs79Mfk/j0QGntlQMJGZxLzeWdQa1v8ypB\nEB4UZ7MTyo0lZCU82MmFytzcqmjKlCmEhoaKtiqCUIklS5YQGhrKqlWrUChKn048+uij9O7dm40b\nN1qSC0qlkubNK5++L5Q3cOBAdu3aRYsWLfjl2yskZ+SXOybIR0V9Py/697d94Xno0CFUKhWPPPJI\ndYd715r6NeNQ6q+WbaVcSbhPhIQRCYIgSEemUOC/dTMHJ75EpH8dXPs+i0vnzphLSpDZsRX6ndr1\n+zWr7bizGeiKDLirHOZSXxCEatRU3bTcWDO/ZnaPwyG+cdavX19uTLRVEYTKDR8+nMDAQEtiAUCh\nUNC8eXN2796NwWCQMLqaTyaToVar0Wg0LOzXisXfxHP2Wi5erkryigyEB3gw69lImtevePmARqMh\nMjLSZp93R/FMRB9S869Zai6MavEvfFx8pA5LuA3d5i3oNnyB3MsTz8mv4NK+PWaTifCffyFj23YU\nwSF4zZiOslEjqUMVhBpHERhIwpO96PzCC2gmvkz26LHIvL3xnjcX9wGO+ZDLz8OZVG2hZdtT5YSL\n0q512wVBkFAzv+aMavl/fHVuS2nNBW2wJLNQHSK5IAjCnSurq3CrS5cuERoaWq4gqnDn1Go1V69e\n5ZFHHuGzMXdeHE+j0aBWq6shsvtHKVcyofVLTGj9ktShCFVUtHcf2ilTLdvFh49Q99BBCrdtp9m+\n/ZQAJaf+oOTkSeoe/BmZ+C4QhLuS/9EqivbuBcCs1aJ9bQaqv3VDUaeOxJGVN77HQ0z/8jgFxUYU\nchkTejyEk0IkFwT7Kj5xgrwPP8KsK8B92FBcez8tdUi1ynON+vJco74At12yW13EFYcgPCCuXbvG\nxx9/TGJiolUx1KKiIhYtWkRcXBxpaWkEBATQp08fJkyYgNLBpniaCwuRubpKHYaFr68v8fHxVT6+\nxGCixGjCzcUJvV5vaa3rCIqNxTgrnG9/oOCQzp8/z8mTJwEI37yFwJv2mQsLiVu6lLq/HuLmeTTG\nq1f53/IV6EJDrN7LxcWFp59+2uE+/4LgaIpPn7EeKCnBcP5Ph0wutA1T882UrvxxRUujAE8CvFVS\nhyTUMsbr18kcMAhzQQEA+p9/xn/rZtG5qJYRyQVBqOGOHDnCsGHDgNKiqdHR0URFRVn25+TkIJfL\nWbx4MQaDgV27drF69WoSExNZuXKlVGFbKTl3Ds1Lkyg5cwZly5b4frjCIaZz+/r6otVqMZlMyOUV\nPwEyGE3M//oPvo9PA8DPw4XZTzfA19dX8iURmiIN7x99jz8yTxHoHsSkNq8Q6d9C0piEO5eenk7D\nhg2JiIig5MpVDL8estrf8pk+GIr0GBOTbgw6O9Ox73PIfH2tjv3uu+/Q6/UiuSAIt6F6/DH0+/ZZ\ntmXe3igfeVjCiCrn6aokqrHjJT6E2qHohwOWxAIAZjOF/9stkgu1jEguCBiMJgwmMyqlaFlUE7Vs\n2ZLt27ej0WiIjY1l9OjRTJo0ibFjxzJ79mxef/11PD09Lcd36tSJ4uJiduzYQXx8PC1aSH+jqZk6\njZIzpU+ISv74A+3U6dT5xnYLTXtSKpW4ubmRm5uLj0/FdQh2Hr9qSSwAZOXreed/yUzp4Fvha6pT\nSUmJpTvIpyfX8EfmKaC01eR7R95l9RMfo5CXft6Li4srfS+lUil5gkQo5e7ujlqtxjRuLNnHjqH/\n4QA4OeExbizeXTpjbNyIrISzlMTHI3N1LV0fHlG+OGdliTJBEG5wHzkCU1YWBdu2owgMxGv2LOTu\n7lKHJQgOyalB+c5aTja6BAoPtionF3Q6Hbt37+bq1atMmjQJgCtXrhAcHFxtwQnVb/OhS6z5IZHC\nYiNPP1yPmX2aizV6NYyrqytNm5ZWiO3UqRMqlYrly5fTo0cPImzcWAD07NmTb775htOnT9s9ubB1\n61Y0Go3VWO/fT3Lz7WvhiRNs3brVIbrDqNVqsrOzK00uJFzLKTeWnm/A09v+T5Byc3PZunWr5Qby\nhOdxuClvqNFn89mmz3AzuwHwxRdfVPheRqORqKgo0W3EwchdXfHfsB7D1avIXN1QqEuTWIq6dQnY\n8z8MFy4g9/dHflNSURCEOydTKPCa8RpeM16TOhRBcHguHTviPvRFdBu+ALMZl86dcXthsNRhCXZW\npeTCqVOnGD16NHK5nLy8PCZNmsTly5d55plnWLNmDR06dKjuOIVqcCEjn3//76xl+9sTV2lWz4t/\ndhBZRkeXk5PD3r17adKkSbnkQGRkJGazmfPnzxMREYHBYChX3LGoqAgoXXttb7YSBifXrcf/wkXL\ntttjXRwisQClSyNuTYbcqm2Ymh3Hr1qNBbqZCPC3fzHHoqIi1Go1zz//PACFJ3Tsubjbsj/IPYgJ\nfSdUaTbCsWPHKLh5iqPgUJzq17c9HhZm50iE2uDIkSNER0dz5swZCgsLCQ8PZ9CgQVbFhc+cOcMH\nH3zA8ePHMZlMNGzYkEGDBtG/f3+r75ycnByWLVvG/v370Wg0BAcH079/f0aNGiVmSgnCX4YOHcrR\no0fLjctkMoYMGcIbb7wBQGpqKkuXLuXgwYPodDrCwsIYPnw4//jHP+wdMj5L3sbj5ZcxFxaibFTz\n2loXlRj59vhVrmoK6NqsrtTh1EhVSi689957DBo0iMmTJ/Pww6VrzUJCQpg5cyb//ve/2bhxY7UG\nKVSPc2m55cdSy48JjkculzN37lyioqJYs2aN1b4TJ04AULduXdq3b09UVJRVgUeAPXv2oFAoaN++\nvd1irszJvs/R+1Q8xceO4dK+HT5L3pY6JAu1Ws2FCxcqPebJVvX4My2XLYcvYzCaiAjwoIPzBYfo\nFDGixSiKjcX8lv4boZ6hjHl4nLh4FwThjsTFxTF+/Hi6dOnCu+++i0KhYPPmzcybNw+tVsvYsWM5\nffo0gwcP5qGHHmLJkiV4eHiwY8cO5s6dy+XLl5k6tbTDidFoZOTIkaSmpjJ9+nTCwsI4fPgwy5Yt\nIzMzkxkzZkj82wqC44iMjGThwoWWpY5l/Pz8AMjPz2fw4ME4OTkxd+5c6tatS2xsLK+//jqFhYUM\nGTLE7jE71a9n9595v7y28QRHkrIA2HToEk/4Ok6R8ZqiSsmF+Ph4PvnkE2QymdVFab9+/Vi2bFm1\nBSdUr0dCfXFSyDAYb3xhtQ/3kzAioao8PT0ZNmwYa9euZdq0aTz33HMolUri4uLYtGkTnTp1ok2b\nNgwaNIhPPvmEOXPm8NRTT2Eymfj222/Zu3cvI0aMoH4FTz7trcjLC/91a6UOwya1Ws3x48dve9xL\nvZryUq/S5SkFBQV89VUSrg7Q+cJN6cbkdlOkDkOwM7PRSNHuPRiSk1H16I6yWTOpQyonK1/P3j/S\nMFN6DjKboWfLIPw97T+jSqjckiVLCA0NZdWqVSgUpeusHn30UXr37s3GjRsZO3YsK1euxMXFhc8/\n/9xS5+fRRx8lOTmZDRs28Morr+Dk5MTOnTtJSEjgww8/pHv37gC0bt2arKws1q1bx/DhwwkMDKww\nFkGoTdzd3Stdmrh27VrS09PZtm2bZYlsmzZtSElJ4YMPPqB///44O4tOUVWRkqmzJBag9JyUoBPL\nC+9UlZIL7u7uGAyGclOoNRpNuUyaUHME+rjy1oBHiNn/J/l6A33bhtCzZZDUYQlVNGPGDMLCwtiy\nZQuTJk1CLpcTHBzMmDFjGDduHABTp06lQYMGbNiwgR07diCTyYiIiGDBggUOsezAbDZjysws/QZ3\nUN7e3uTl5dlcXlKR7Oxsh+gUIdRemlenUvjf/wKQ+95S/D77FFWP7hJHdUNGbhHDo39Fo7MuKLr2\np2Q+H9OJeuJpkUMpu+EvSywAKBQKmjdvzu7duzEYDLzwwgsMGDDAqoAwQJMmTTh58iQ5OTn4+fmx\nZ88e3NzceOKJJ6yO69OnD+vXr2ffvn2SPG0VhJro+++/Jzw83JJYKPPMM8/www8/cPDgQbp16yZN\ncDWMi1KOTGZ9Seokc9zrU0dVpSvl1q1b88477/D6669bxi5fvsybb75Jx44dqy04R2U2mzFlZIDJ\ndGNMr8eUm+uQvY8r83jTAB5vGiB1GMJdGjBgAAMGDKj0mH79+tGvXz87RVR1xfGnyR43HuOFC3Tz\n9aW4UyecW7WSOqxyFAoFXl5elgvjqtBoNA6xJEKonYxpaRR+/fVNA0byPo5xqOTCdyeulkssAOQU\nlLDz+BXGdm8sQVRCRW6uq3CzS5cuERISgpOTE48//rjNY5KSkvD09LR8f54/f56wsLByyddGf7Uf\nTkhIuI+RC8KDy2g0kpSUZJkBdLPGjRtjNptJSEgQyYUqquvtSp/W9S01tFRKBa08yhfsFipXpeTC\nzJkzGTlyJO3atcNgMNC+fXvy8/OJiIggOjq6umN0KCXnzpE9eiyGpCSe8PJC37YtxqtX0c6eg1mr\nxblDe9RrYlD4+0sdqiA4NO1rr2H8q5aBu0aDdtprBMTuvs2rpOHr60t2dnaVkwvZ2dnUkSjR6OLi\nYinYea+KiorKPYV8UB06dIiPPvqI+Ph4nJyciIiIYNSoUfTq1ctyjCMVzarp4uPjAZXNfcdPHCcm\n6Qeg9LPnCLOsBGvXrl3j448/JjExsVxNn5t99913/Pbbb0yYMMEylp2dTXh4eLlj3dzccHZ2Jisr\nq9w+QaitNBoNs2bN4vDhw2RmZhIcHMyAAQMYMWIEOTk5GAwGfH3Lt70uGxOfpzvz+nMteLJVEFey\nC+nU2J/tm9ZJHdId0eq1nMo4SX1P6ZY9Vym5UL9+fb777jt+/PFHLly4gEqlomHDhnTu3LnW9cvW\nzn4DQ1ISAK65uWS/8irmjAzMhYUAFB85St7S9x2qIJ0gOKKShLPW22fPVnCk9NRq9W07RtxMo9HQ\npEmTaoyoYp6enhQUFNzRMo6KaLVaQkJC7lNkjuuHH35gwoQJREVFsWLFCmQyGRs2bGDSpEm8+eab\nDBo0yCGLZlVEERiI6/PP35i9oFDgOXaMtEHdYsbQJ20ui/B2UzLvX8+KZREO6siRIwwbNgyA0NBQ\noqOjiYqKsnnszz//zBtvvEHr1q0ZP368ZVyv11e4BlypVKLX6+9/4IJQQ125coVevXrx/vvvW1pN\nL1myhLS0NEaMGAFg8/OkVCoBxOfpLrQN86NtDWy6lJCVwJu/zKHIWPqAKcK9kSRxVOnK84033mDR\nokX06NGjuuNxeLfeEJkuXSp/zNlz9gpHEGosVbeuFMV+b9l26dpVwmgqp1arOVvF5IfZbEaj0dh8\nkmAPcrkcLy8vtFot/vc4g0rK38Oeli1bZrlRKrsg69ixI7169eLzzz9n0KBBNa5olu8Hy3B96kmH\nLegY4KViw4Qoq4KOB3/9lbmj/yEKOjqwli1bsn37djQaDbGxsYwePZpJkyYxduxYq+O++eYbZs+e\nTcuWLYmJibF8rqB0dlVJSYnN9y8uLkalsj2jRRBqm48++giFQoG7u7tlrGvXrgwdOpQNGzYwcuRI\nAJufp+Li0sSt+DzVHlvPbbIkFgCSXZPI1efg5eJt1ziqlFz45ZdfyMjIICBArM1XPfE3Cr/eZtlW\nduyIKSUFY2rqjWP+1k2CyAShZvF5fyk58+ZTfPQoKT7etP33+1KHVKGyZRFVkZeXh7Ozc7kCuPbk\n6+t7z8mF4uJiiouL8fDwuI+ROaaXXnoJb29vqxsgpVJJRESEpcd4bGxsjSqaJVMocP17b6nDqJSf\nhwsDOzWwbOf+ESsSCw7O1dXV8hno1KkTKpWK5cuX06NHDyIiSnvar127lnfeeYeePXvy3nvvlfsu\nrFOnjs3v07y8PEpKSiRbUgZQ8uef5K1YiSkzk3pVXAYnpWuaQj77MYk0bSE9WgTSt92DP9OsNvHy\n8rI53r17d3777TfOnj2Ls7Ozzc9T2XIIKT9Pgn3ll+ists0yM0VGPbb/FVWfKiUXxo8fz5QpU3j6\n6acJCQmxugCD0hNMbeGzeBEyFxf0Bw9y2cODNqs+xKTRkLNoMYZLKbj+vTceEyfc/o0EoZZTqNWo\nV5au1d0RE0MHB65T4unpSVFREcXFxbd9Ou0IxRx9fHzuaBmHLRqNBh8fn1rR8eLJJ5+0OZ6cnEx4\neDhGo5Hk5GRRNEuolXJycti7dy9NmjShRYsWVvsiIyMxm82cP3+eiIgIvvrqK5YsWcKLL77IG2+8\nYfP9mjVrRlxcHEaj0ar7xLlzpbM+b/0Z9mIqLCSz34DSDkZAa6Cwd29cez8tSTy3YzSZmbTuKFey\nS5fl/nah9AZTJBgeHGazGZPJZPU5gRtLHdzd3XnooYcsn52bnT17FplMJtnnSbC/pxo+zbnsG7Ns\n6xTXIcDN/hMDqpRcmDt3LgC//fZbuX0ymaxWVfaVe3nhu/Q9oPSGqH1gIIrAQPy/2CBxZIJQ8xmu\nXEHu4YHcx0fqUKzI5XLLDXvdunUBSNUW4u7ihJerdbK1rA2llHx8fLjwV7HMu6XVavFxsL8He4qJ\niSE1NZWJEyc6RNEsmUyG0Wis9BhjVhYUF6MIKm0pbMrNxaTV4hQaajnGbDZjNBprRdLIHjRFpUk8\nX9WNfxsFJQXkFucS6B4oVVj3lVwuZ+7cuURFRbFmzRqrfSdOnABKa3PFx8fz5ptvMmDAgAoTCwC9\ne/cmNjaW2NhYnn76xo37tm3bcHFxkWwJbvGhQ5bEQpnCnTsdNrlwLjXXklgos+90+gOTXHjQPkd3\n6vLly/Tu3Zthw4Yxffp0y7jZbCY2NhY3NzdatGhB7969ee+99zh16hStbuq49c033+Dn5+dwXf3M\nJhOGCxdxql8PmYMu2TCazFzNLiDQp2bV/nmiQXe8XLw4knqYeh71uPZDmiRxVCm5sG/fvmoL4OjR\nowwdOpQOHTqwbt2NipyiKrcg1B5Oej3X+w+k+OBBcHbGc9LLeL06WeqwrJQVdXT3VvPaxhMcv6hB\nqZDxr26NGPH4jcrnGo2G4OBgCSMtveEtu+i/W2UzF2qj7du3s3z5cnr27Mk///lP0tJKT9BSFs3y\n9/fn2rVrFe7Xzn0T3dq1YDSieupJnNu0IXfZMijS49yuHerPP0Oh9iU/Px+ZTIabm1u1xvugM5lN\nrDy+gh9SSq+PuoX8jZfbvkLshT18Hv8peqOeRj6NmdNpLj6qml23xNPTk2HDhrF27VqmTZvGc889\nh1KpJC4ujk2bNtGpUydatWrFkCFDUKlUPPvss391A7EWHByMj48PTz75JO3atWP+/PnodDrCw8P5\n8ccf2bZtG5MnT5YsOauwUbxWcVNiztEEeKlQyGUYTWbLWE7aRWJijts8viZ1Xvku+VvWxn9OsVFP\nY9/GvNFpHj4utet8FBISQq9evVi7di0ymYwuXbpQUFDApk2bOHPmDLNnz0alUvHCCy+wdetWXn31\nVaZOnUpAQAA7duzg0KFDLF26tNysBymVJCSQNer/MKakIPPxQb1yBaon/iZ1WFb+TMtl+sYTpGmL\n8HZT0tHVMRMgFWkX2J52ge0BiPkhRpIYqtwtAkqfyF2+fBmZTEaDBg3w9r63AhHFxcXMmTOn3BOU\nmlSVWxCEe9fw0OHSxAJAcTF5S9/Htc8zKBtJU+nWlrK6Cz+nOnH8YunTyhKjmY/3/8kTkXUJ9Sst\nuJSdnU3Lli2lDBVvb29yc3MxmUx33dFHq9VK1vFCSjExMfz73/+me/fuLF26FMCyZlzKollBQUEc\nO3YMs9lc7pypP3wY3aefWraLdu+haE8smEtvOop/+4381avxnv06qampBAUFiZkLVWQ2m22OH0k9\nzP6UvZbtHy7vJ9K/BZ+cisFgNgCQqP2TLec2M+bhcRW+T00xY8YMwsLC2LJlC5MmTUIulxMcHMyY\nMWMYN24cAMeOHUMmkzF06FCb7/H222/Tt29fAKKjo/nggw9YsWIFWq2WBg0aMG/ePAYOHGi33+lW\nykaN8HhpIvmrVoPJRE5gXQLHjJYsntvx93RhXPfGRO/7E6PJTKifG+8Oe5xAH1diYmIYM8axOsRU\nlaZIw2enPrF8jv7U/MmWs5sZ8/DY27zywbNkyRIiIyPZsmUL//nPf1AqlTRr1oyVK1daZvioVCo2\nbNjA0qVLWbRoETqdjsaNG7NixQp69uwp8W9gLefNBRhTUgAwa7Vopr9G4JFDyCRIgJhMJrRabbnx\nd3ecJU1bWhQxp6CEX/R+ZGZlIa/knOnk5FRhfYzaqErJhZycHF577TXi4uKA0pOtQqHgySef5O23\n377rwmWrVq0iPz+/3HqgmlaVWxCEe+NhY0q5ISnJoZILbm5uHD9+nH3pacCNys1mM8Rs+JpQ1xvT\nU/fv3y/pRbKTkxNubm7k5ube9ewDrVYr+fIOe1u8eDHr16/nxRdfZPbs2ZYbcG9vb8mLZnl5eWEy\nmcjPz8fT09NqnyExqfwLbrmZNSQmApCWlkZgYO2cZnynnJyckMvlFBUVlUseXcm7Uu74PzXnLTdE\ntx6n0+lq/GyRAQMGMGDAgAr3V7WjDpSuFZ89ezazZ8++H6HdN96zZuIxfDjG7Gy+++Vnmjt4Uceh\nXcLo/XA9rufpeSjQE7m8ZiUNt27dWq4+ULZTNgZf68/R8cTfoBYmF5RKJaNGjWLUqFGVHufn58fb\nb79tp6juXslf56EyprQ0zDodMgluzE+fPs2JEydwdbVe+pCc7gHceCijMzqx5/v9OFeS/8jLy6Nv\n376S19tyFFVKLrz11ltkZGSwfPlyIiIiLMV7Vq9ezYoVK6zWAlXV+fPn+eSTT1i0aBH//e9/rfZ9\n//33NaoqtyAI9ya9yUPU/+PGNFqZlxcuDlYoVqvV0rJlS+q3q8/cr05Zxj1VTswaOxh3VenX6Y4d\nO2jbtq1UYVr4+vre9dIGg8GAfVs6kgAAIABJREFUTqerVZn45cuXs2HDBl577bVyF3JyuVzyolky\nmYygoCBSU1PLJRdcunUFFxe4aWmGzMsLc26uZVvVqxdQuuQwMjKyWmN9UMhkMtRqNdnZ2dSrV89q\nX7vA9nyRsB6T2QSAHDm9Gj7Jb2lHySq6kSztEFS63jkrK4ugv2phCI5NUS8IRb0gOPiL1KFUiZ+n\nC341tMuKrWUa0THRqFVqsotuJHP7tH7OnmEJ1cS1V09069Zbtp07PYpcouuM4uJimjdvTrt27azG\nk3acZvuxG8njdmFqhgyyXfS5zPbt2ytsr1sbVSm5EBcXx1dffWVZHgHQqFEjmjVrxpgxY+44uWA2\nm5k7dy4dO3akb9++VskFo9FIUlKSqMotCLVIamQkPm3boduyBbmvGq+pr0p2wqmIRqOhUaNGtAsP\nIq+whO9+v4qvuwv/1y3CkliAG7UZbv6+lIKPj4/NKX9VkZOTg6en510vqahp9u7dy+rVq5kyZUqF\nT4gcoWhWYGAgqampPPTQQ1bjTvXr479+HXkrP8RcWIj7iGEoH2pC7tKlGNPTcXv+edwHD6KgoIDC\nwsJaNyPlXvj5+ZGVlVUuudDQuyGvd3yD7YlfYzbDc42fp5FvY97svJAvzqznemEGXeo/zt/DnwFK\nl0uJpI4g3J4cOfM7L+SLMxu4Xnidx4Ifp3fY36UOS7gPvOfOQebqiv6nn1G2iMTr9VlSh1TO5Kea\n4uqs4LcL2TQJ8mJCj8ZSh1TjVCm5oNfrLRXSbxYaGlrl3u83+/LLLzl37hw7d+4st88RqnJXxJid\njfFaKsrmzZDVwIvu5Ix8PFROBHiVTu80GE0kZeRTz8cVz1sq3juygpICUnXXCPVsgFJRc+IWKuc+\nbCjuw2yv1XUEN3eB+GeHUP7ZwXahL19fX8m+o26N4+rVq3f12tpUzNFoNLJkyRLq1atHp06dbBai\nCw8Pd4iiWUFBQZw+fdrmPpfOUbh0jrIa8/v8M6vtsiURtSVpdD+o1WoyMjJs7msf1IH2QR2sxkK9\nQpn1qPVUf6PRiFarFVNmBaGKQr0alPscCTWfzNUV77lzpA6jUipnBa881fT2BwoVqlJyoWHDhnz/\n/fdWLYMAdu/eTYiN6rqVSUtLY9myZUycONFmRfWy4lhSVuW2Jf/zteQsWFja5issDP8va07rybzC\nEl7dcIz4KznIZTCoU0N6P1KPKRuOk5FbhEqpYOazzXmqVb3bv5nEDl87xL+PvU+hoRBvFx9mPzqH\nJuraV3TuQWE2myn83x58Ll+WOpRKlS0TqEoRW7VaTeIt6wql4OPjY3UjqjcUkZKXQohnKCqn8sUH\nc/Q5ZBZep6F3WK2qt5CWlmZJwlS0nnzdunW0b99e8qJZarWawsJCCgoK7mr9fmpqqqi3cIf8/f3v\nud22VqvF09MTJ6cqXXIJDqhw924Kd+3GKawhHv8a5XAz6wRBEBxFlc5048aNY/LkyXzzzTeWpQnn\nzp3jl19+4a233rqjHzh//nxCQ0MrnHrqCFW5b2XSaMhZuAj++vnGCxfIXboMWjS3axx3a+vhFOKv\n5ABgMsOXBy8Sf0VLRm5pNdSiEiPvf5fA35rVxUXpOC1rbmUym4g+uYpCQ2nhvBy9lk//WMO7XZdK\nHJlwNwzXrpHR7QnMOh2dgfSfDxKw73uHnBWk0Wjw9vau0hPfsq4Stqr625Ovry9arRaz2cwfmadY\ncvgtdCU63JXuvNZhJo8EtLYcuzNpB2v/+AyD2UCgeyC9eJqHwx+WLHZ7ql+/fpVvHqUumiWTyQgM\nDCQtLY3w8PDbv+AWaWlpdOnSpRoie3CVfY7upfNKVlYWfg5eGFComG7zFrRTplq29XFx1Nn2tYQR\nCYIgOK4qJRfK+qxu2LCBn376Cb1eT8OGDYmOjubxxx+v8g/bs2cPP/30E2vXrqWoqPTG1mw2YzKZ\nkMlkFBQU4O7uLnlV7lsZ09KsCmUBGC+n1Jjkwi8nzgDWM0ESr2Ry819/XpGB/CKDQycXSozFaIqs\nqwqn69Ikika4V9ppr2HW6SzbhvPnKfhyI+4vOl6rWY1GU+UpzSqVCqVSiU6nw8PDo5ojq5izszPO\nzs7odDo+PhmNrqT0z1pXUrq9uufHAOTqc/lP/OeWKvdpujR+Mv1IV5+uksUuVKysqKOt5IIxNZWc\nt5ZQkpCA6m/d8Jo6BdlfyXi9Xk9ubi7+/v72DrlGUyqVeHh43NOyBpFccCwajYZjx45V6di9e/fS\nIPpjbp4nVHzkKD9u2EDJX0uMOnXqVK7ivCAIQm1V5Tl6HTp0oEOHG2sLjUbjHa8xPXDgAEajkRdf\nfNHm/rZt29K3b1/Jq3LfyqlJE5waNbK08gJw/fvfwWS0axx3a2Tvjkz78oRl28vViVBFDvH5N6Z4\ntwr1cfhqwy5OKtrUbcux9N8sY1H1O0sYkXAvDDbqARSfSbipyaPjuLneQlWUzV6QMrlQFodGoyGj\nwHrNeIYu3fL/Gn02JSbrmWI5Rm2tqblQ0wQGBvLzzz/b3Jc1eiwlJ0q/6/MTEjAXF+Mz/00A0tPT\nqVOnjl1qQzxoyoo63m1yITs7m5YtW97nqIS7dfXqVYqLi2nSpPIllenp6YSFhaGsGwDnz1vGzQoF\nwU2bgq8vJ06cIDMz846XCAuCIDyoqpRcyM7OZtq0aQwePNiytnTdunXExcXx/vvvV/mEO378eJtt\nZxYuXIhMJmPOnDn4+fmxd+9eyaty30wml+P3xXrylv0bw6VLuD79NO7/GgVr1tg1jrvVpUkAC/q1\nYsfxK3i7KhnxeDj7vtlEl47t+PXP64QHeDL6bxFSh1klU9pNY+PZL0jSJNEq4GH6P1Rxz23Bsbn1\n+yd5S96xGvMYNUKaYG5Do9HQvHnVZyqVta8LDbVd9NFefHx80Gg0RNWL4sDlHyzjNyflQj0bEOwZ\nwpW8G3UvGhAm1oc7KH9/f3Jzc9Hr9ZZlhADGzExLYqFM0fffw1/JBVFv4e6p1WqysrJo3PjOq4ab\nzWYxc8EBeXt7ExFR+XVP2f7i2a+TOXAw5pzS5aWeE8YT/Ff7OlsPwgRBKK/gq/9SsH07MhcVZpMR\nmcmM24tDcO3ZQ+rQysnK1/P5j8mkZOl4rEkd+nUIlXSZ6534/mIs31/cQ25xLiXeJbRJa027wPZ2\njaFKV4+LFy8GoGnTG9Uze/TowdGjR1m8eDHvv/9+lX5YaGiozYttDw8PZDIZrVuXrgF2hKrct3IK\nDsZz8isY09NRNGhA8aFDKP6qwVAT9GoZRK+WN3ps/yCDEY+HM+LxO1+3KyUPZw9GtxoLQKGhkHOa\ns4R6huLlcvtCe4Jj8Xr5JcwaLQVbtqAzGgl6azHKRo2kDsumu5m5kJqaWo0RVY2Pjw9ZWVlMiJqI\nv6s/CVkJNFE3ZWDTQZZjZDIZb0YtYPPZjaTqUmmiaor/dfsuPROqTqFQEBAQQHp6utX5VO7tjdzf\nH1NmpmXMKeLG5yktLa1cP2+havz8/Gx2EamKgoICgLsqwClIx3DxIkX7f8AprCEu3boReOgg+oMH\ncWrYEGVTUUleEO5Ewdfb0Lwyudx40b59+H+1BZdHH5UgqopN++I4CddyATiSlEWxwcSQzmESR3V7\nsRf38NGJlTcGnGHRrwtY/NjbRPrbb9Z/lZILv/76K3v27MHT09MyFhISwjvvvEOvXr3uSyA3Z4RU\nKpXkVblvlfP2EvI/WgVms2Wsu4sL+vbtcbHzTAoBTmfGs/jQQnQlOpRyJZPaTObxELFGvKbxnvsG\n3nPfICYmhjHP95U6HJv0ej16vd7q++921Gp1hS0D7cnX15ekpCRcnFQMjRxe4XF13OrwUptJAPz+\n++8UqYvsFaJwFwIDA0lNTbVKLsiUSnzeewfNq1Mwa3NQNGyI97zSll8Gg4GsrCybLaWF2/Pz87ur\ntttwo95CTXnqJUDRTz+TNWy4pYi325AX8H33HVyfekriyAShZircscP2DrOZwp3fSpJcUCgUluTv\nza5kF1gSC2W+j0+rNLlQUlJi1wffp0+fJjk5udz4LuO35cbMmNnw83qiFLaLOXt6etKtW7f7Gl+V\nkgsGg8HmeHFxsaWDw71Yv359uTGpq3LfzHDpUrnEAoBSrydn4SICvt0pUWS11+fxn1kK1JWYSvjk\njzV0Du6CQibWEwv3l0ajscxaiL+sxUUpp3Fg5W3I7keF+fuhbFnEndBoNAQFBd3+QEEyQUFBHD16\ntNy4a69eqI79hjE1FUXDhpYb2oyMDNRqtWRLXXJzc3F1dbW0k75bRqOR/Pz8KrWEvZ/c3d0xmUx3\n1QJULImoefJXr7YkFgAKNm7Ca8qrKMSyIkGokq1bt1pde0RqtTSs4FhF/fp2ielWQUFBxMXFlRv3\ndXPGRSlHX2KyjNX1rrhLoU6no6Cg4K5r8tyNlJQUQkJCCAgIsBo/kxzPtevl65k1DWlG26C2Nt/r\n22+/lSa58NhjjzF79mwmTZpE/fr1MZlMJCcn88EHH9xRt4iaypieXi6xYNnnAFOfH2S3fkGVSVGn\nwE15hFx9DiXGEhROIrkg3F/Z2dm4e/nyrzWHOHO1NJvdrVkAbw14BLnc9tNIpVKJm5sbubm5khZG\nLKtgXlhYWOVq5lqtlmbNmlVnWMI9CggIICsrC4PBUC5hIFOpcAqzfsKSmpoqacIoNjaWNm3a3FX7\nzJulp6ezf//+CotCVxeZTGYp6ng3yQWpa68Id8ZcfEsrdJMJs7FmFPAWBEdwa309Y58+JD75FB5Z\n2SCTWe6plK1b4z7Uvt/nZerUqUNhYSF5eXlWM1PdVU683LMJH+w5i8Fopo6XC+OeqLjeTkpKCsHB\nwXZ/kKRWq6lXr57V2AjvUST/kkTaTZ30mvg2oX+rAbgpy5+7zBXc296rKiUXXn/9dSZOnMgzzzxj\neRJiNptp37498+fPr5bAHIlzmzYoGoRivJRSbp9bX8ecyv2guPULKiYmhjFjxqD8w4ntiTf6THcI\n6ojKqeLM4oPqyJEjREdHc+bMGQoLCwkPD2fQoEEMHDjQckxqaipLly7l4MGD6HQ6wsLCGD58OP/4\nxz8kjLzm0Gg0xGtdLIkFgAMJGRxJzuLRRhW39Svr1CBlckEmk1lmL1QluWA2m9FqtXdUX0KwPycn\nJ/z8/MjIyCh3cWFLWlqa5N0KMjIy7jm5kJGRcfuDqklZUcc77QqQnZ1tqSdV093P801OTg7Lli1j\n//79aDQagoOD6d+/P6NGjZJ8CYnHv0aSfeiQ5QZI1ftpnCR6uioIZY4ePcrQoUPp0KED69atA2DW\nrFls27bN5vHdunUjOjraniFWSBEUxI8TxjOiY0fkAXWhWI9Jp8PZzt3/biaXywkJCSElJYXIyEir\nfQHeKp5vF0KQt4r+HRugdKo4cZCSknLP57b7pa57XVb1+JhEbSJGkwGVk4pwH/sX7K9ScsHPz49N\nmzZx9uxZLl26hEKhoGHDhjRy0OJr95vMyQn/rVvIX/khhitXwNkZiov53cmJ7rNmSh1erTQscjg+\nKh9OXT9JuHcE/ZqU70LyoIuLi2P8+PF06dKFd999F4VCwebNm5k3bx5arZaxY8eSn5/P4MGDcXJy\nYu7cudStW5fY2Fhef/11CgsLGTJkiNS/hsPLzs6miPJPfa/nVl6XoKxjRFiYtEWAfHx80Gq1VboJ\n1el0ODk5WXUhEBxTWd2FevXqYbh8mcKd3yLz8UamcMKUkYEiJISCbduQeXujaRRB3R7SVuS+fv36\nPb+HlMkFPz8/rly5UukxqfmpHLz2C74uvnQJfgy5WU5eXt4D0db1fp5vjEYjI0eOJDU1lenTpxMW\nFsbhw4dZtmwZmZmZzJgxQ9Lf1fXpp6mz4xsKY2NxCgvDzUHrAT2ILuZcIF+RL3UYDqe4uJg5c+bY\nTLwFBAQQHR1d7im0l1flyzftTi7H2cESraGhoZw/f94qubDmh0Q+PZBk2dYbTIzsavsG3WAwcO3a\nNbp2dZyabwq5gibqytvsVrc7WoDZtGlTmjZtyuXLl9FoNJKvJ7Ynp/r18V68iOLDR5C5uODctg3J\nMTH0EO3aJKGQK3i+8T94vnHtffq+ZMkSQkNDWbVqlaWQzKOPPkrv3r3ZuHEjY8eOZe3ataSnp7Nt\n2zZLt5c2bdqQkpLCBx98QP/+/XF2dpby13BoZrOZ7Oxsej/Wge/iszGaSk/eHionOj9UeUcFtVrN\nxYsX7RBl5crqP1SFmLVQcwQFBXHq1ClKziRw/bm+mG0UpirTVemEcuRIkDBpdP369Xu6ZjCbzZIn\nF06dOlXh/iRtIjPjZlBs1APw/aU9TG4yFR8fH0k6XN1v9/N8s3PnThISEvjwww/p3r07AK1btyYr\nK4t169YxfPhwydumOrdpjXMbx7oRepDpjXoWHpzPH5mnQA2GIyVMaz8duax23GPczqpVq8jPz6eF\njSf9SqXyjlplCzcEBwcTFxdntcRw06+XrI7ZfOhShcmF1NRU1Go1KlXtmzldmUo/tXq9nqlTp/Lj\njz9axlasWEGvXr0YOHAgzz33XJUvWms6U14e15/+O5n9B3D92efIHPIiMpPp9i8UhGoyfPhwZs6c\naXXhqlAoaN68Oenp6RgMBmJjYwkPD7dqIwvwzDPPkJ+fz8GDB+0ddo1SWFgIwMNhAawc3o4eLQJ5\npnV9Ph7VAbVH5TdqZTMXpHYnRR2lXsYhVF3dunW5fv06+f/5T6WJBQB5iYG81dJOjzUajXdcXPRm\nOp3O8nmUgq+vL7m5uRUWuP4u6VtLYgHgTNYZTlw5/sAUc7yf55s9e/bg5ubGE088YXVcnz59MBqN\n7Nu3r/p/IUFyv2f8zqv7X2HErqEsPrSwNLHwl1+u/sTx9GMSRuc4zp8/zyeffMK0adPETex95uLi\ngr+/P1ev3iiCqFRYzw5xUlS+JKJBgwbVFl9NVWlyYcWKFfz++++WapQpKSmsXr2aiRMn8tVXXxEQ\nEMBHH31kl0DtKSsriwsXLlj9d3nVakpuai2nP/Ajdf78s9xxt/6Xk5Mj4W8iPMgGDhxocyrWpUuX\nCA0NRSaTkZycbHP5UuPGjTGbzSQkJNgj1BpLo9GgVquRyWS0aahmUf+HeaNvCyLq3r4tpbe3N3l5\neRglLgRWtiyiKrRarUgu1DC22lHZJPE6drVafU8zD8o6XkhFoVDg7e19RwmSnNxcSWO+n+7n+eb8\n+fOEhYWVm+Jd9lpxXnrw5Rfn8/ahRSTnJKHRaziZ8Xu5Y64X3PtSqprObDYzd+5cOnbsSF9R461a\nhIaGkpJyo6beyMetZymMfNx2PQWz2UxKSooo2GtDpXP6Y2NjWbZsmaVy+O7duwkJCeGll14CYObM\nmUycOJHZs2dXf6R2tGvXLvz9/a0y9AHJydw6CdqtsIg///yzwvfR6/UYjUbxhSDYxbVr1/j4449J\nTExk+fLl5OTkYDAYbE5zLxvLysqya4yGK1coOfUHzm1aYzYYLP9/M/3RoxTt+R7XPn/H+eGH7Rrf\nrbKzs+96mYBCocDLywutVivp00tPT0/0ej3FxcU4OztzLvscufocGng1JEmbSAPvhmQUZCCXycjO\nznaYwkRC5dLT0wkICKDVwgVcf7YvZp2uwmONSiWe48baMbryAgICyMjIuOtOJBkZGQQEBHDp0qXb\nH1xNyoo61qlTfknUMxF9+PnqT+j/mr0Q6dcClUaF30MPxsyFW93L+aai7xk3NzecnZ3tfl56EOUb\nFOw/k0aLYB8CvBzvafefmvMUGSuuW+SicKF9UAc7RuSYvvzyS86dO8fOnRW3vC8qKmLRokXExcWR\nlpZGQEAAffr0YcKECffc/rc2aNCgAd999x1msxmZTMbATg1oEeJN/OUcWoX60Ky+7dbHWq0Ws9ks\nlpLaUGlyIT093arC9LFjx+jSpYtlOyIigvT09OqLTiJms5lu3bpZVVcvCQ0lI/Z7S+9jmbc33RfM\nR+FfcbX4jIwMfvnll2qPV6jdjhw5wrBhw4DSDGx0dDRRUVGkpZW2orFVU6HshKPX68vtqy66LVvR\nTpsORiMoFKWVuE0mUCqp+4/nAdBMmUbB5s1Aaa9x9zGj8Zk3124x3kqj0eBfyWf8dso6RkiZXJDJ\nZHh7e6PVall3aS0/XSnf17mM2uhHVy/HKUwkVCw1NZXAwECUTZsS8MM+Cnd+i9zHB5mTE8aMDBSh\noRR+/TUyb292hYXR39kZKct0BgQEVFqz4HYyMjJo0qSJpMkFPz+/Cpc6hftEsLL7Rxy8dhBfF1+i\n6nXmi/VfPDDLIsrcj/ONXq+vsNaPUqm063npQbT71DW2ptdny+aTOClkLPhnK56IlLaGxa0aeofh\nJHPCYL6xzOiJ0O7oSnSkXEhhypNT8Xe9+3PvgyAtLY1ly5YxceJEgoODKzwuJycHuVzO4sWLMRgM\n7Nq1i9WrV5OYmMjKlSvtGHHN5O3tjVwuJzs72/J9HRnsQ2Rw5bM4y2YtSN3dxhFVmlxwdnbGaDQi\nl8sxmUycOHGCPn36WPYbjcYHolDRrRQKBcXFxVbJBWXTptT571fo1q9HplLh/q9RlSYWoLS66609\nyAXhfmvZsiXbt29Ho9EQGxvL6NGjmTRpEgMGDACgpKSk3GuK/0qSVff6vZycHDIzM8FsxmXhQmRl\nSwRuXipQUkLTfftIOnUKly1buPlrOv/TT8kaOACUSjz/n73zDmyqXP/45yRputI23emmLWXIUsoq\ngiwZ4hZEHFfE69bLz3VVQEWvePVeuShcUS56Lyi4B8MJMmTvIcvSFkp3OpM0HWkzzu+PkEBIWgq0\nScB8/iLnvCd9SHLG+32f5/uEhNhLtNxFTU0NXbp0ueDjvcV3ITw8nENlB1sVFgBqpNXsq9nLyJBR\nborMx4VSVlbGgAHWlT1ZQoLLzISgG64HIPyHH1Cr1R6tDY2IiECv19szaM4Hi8VCVVWVw+KGJ4iM\njHRInz2b2GCV3WS4trYWuVx+2dVIt8f9xt/f3+U429jL7TNzNwvW5CCeupOazCLvrc3xOnEhPCCc\nv2T+H/879F/0zbUMjr+aR658DH+pP4sOLqKLh93uvYFXX32V5ORk7r///hbHzJw5kxkzZhAScrpU\nMysri+bmZlatWsXhw4ddmkD6OI0gCPbSiPMRgwsLC+nj4exab6XVmW9iYiIHDx4kMzOTLVu2oNfr\n6d+/v33/sWPHXKYHXuokJCRQVFREWJhjKozY3ETAmNH4DxuGJCjonO9TVFREgq83so8OJjAw0G6g\nlZWVRUBAAPPmzWPUqFHI5XKXk1tb2mlHn7+bN29GFEUC/f3pWqunJX1X3mig4NgxMs5qpYTZQkFu\nLiZ/f0pKSpg6dWqHxnsmoiii0WguKuUtPDycY8eOtWNUF4ZMJmPX0Z3Qhmd2vVHf8QH5uChMJhM1\nNTVtFttUKpXHxQWJREJkZCSVlZXnfV+sqalBoVB4vEVqZGQk1dXV9vTZ1qiurr7sshbg4u43tt9r\ndHS0y3F6vR6j0XhZPle6k9pGR+FG2+BayPE0w5NGcE3iMEwWE3Kpr2vVmaxevZrNmzezZMkSDAZr\n+YgoilgsFgRBoKGhAZlMhkKhcHn86NGjWblyJUeOHPGJC20gJSWFvXv3clUbW2U2NTVRVVXVphbf\nf0RaNXQcP348zzzzDLNnz2bmzJkMGzaM2NhYwNpW6h//+IdX9fZsL5KTk51SL6sfeJCqCbdT88BD\nlA8dhukMZ1FXiKJoNzry4aO90el0fPPNNxw+fNhpX48ePRBFkdzcXLp06eJycpudnY0gCB1+0xFF\nkczMTK4dMwbFpNtbHBdx3xSG33470tRODtvlPXsw8sYbGTFihFMP546mrq4OuVx+UROaiIiIi3LI\nby90Oh0Th9xOTFBsq+NkoowB0b46V2+nvLycyMhIZDIZ5spKTCWliKKI8fgJLC68F+Li4igrK/NA\npI7YfBcAynWNHFfrKa5u4GRlnb3NqytsfgueJjAwEJlMxvaCbVQ1VDnsM1qMFOuLMFmsad7V1dWX\njZlje99vunfvzvHjx53Mbm3HemoyZDxylLolS2g+dMgjf7+9uP4qR/Huhqu8d5FLIkhcCgu5mhx+\nPPEDBbUn3R+UF/Drr79iNpu555576Nu3L3379iUzM5O9e/eyZ88eMjMzmTVrFoDLDjY2QcLTguyl\nQlxcHDU1NfbP7VwUFRURFxfnsex0uVxOwzm6RLWFhoaGDvmNtPqpPPDAA1RXV7N582b69u1r/yGD\ntedqRUUF8+bNa/egPE1iYiIbN260p2827dmL4aef7fstajX1//0fYS+/1OJ76HQ6zGbzZbly4cPz\nSCQSXn75ZQYPHswHH3zgsG///v2ANQNn/PjxvPXWWxw8eJDevXvbx6xcuZLIyEgGDhzotpiVf38d\nv27daD5wAHm/fohGI8bffsN/0CCC7pwMQPTPP6F9+lmMBw8i79+P8Lf+6bb4zuZisxbAaqbY2NiI\n0Wj0qLGSRqMhOiKaN6/5J98f/w6tQYMyQEm1oZoI/3AaTI1IJVJkeXKkTb5SLm9HrVajUqnQznyR\n+o+XgsWCEByMWF+PEByM8u+vEzRxgn18TEwMNTU1Hv8dxsTEkJ2Tx6ef7mPzMUcn+JjQALL8Xcfm\nLeJCTk0O3wZ/hXm/dVI8ImkkT/Z7mqNVR/jHrjfQNmkJ9w/n+YHTqampIT3ddW/0S432ut/YynjG\njx/PmjVrWLNmDdddd5193PLly/H39+faa691w//KkfpPPkX73PP212GzX0Mx9T4AxKYmLNU1SOPj\n3B7XhfD0dd0ozfmNsJQe9EkO5+bMluv1vZETgXn88OsqAAQEpmU+ycjkP1ap3qOPPsrttzsvyLz2\n2msIgsBLL71EcHAw/fv3Z/DgwU5zsdWrVyOVSh2yzb0Fi1YLoojEi4wQpVKpPWs9IyPDab/BaEbf\naCQ6NIBqfRN5+Z5dPFaPlXvEAAAgAElEQVSpVJSXlzu1/T1fbM8S7U2rT5ESiYTp06czffp0p30P\nPvgg06dPP+/ayUsBuVxObGwsxcXFpKWlIdbWOo2xnKPF5JntmXz4aG9CQkK49957WbJkCc8++yw3\n33wzfn5+bNq0ic8//5ysrCx69+5Nly5d+Oqrr3jqqad45plniImJYdWqVezYsYM5c+a41TNF8PND\n8eeWawcBpAoFkYsWuimi1qmpqbnolUeJRIJSqUSj0XhscmQwGDCbzQQFBREsBDOl530tjl1XtQ6t\nVusVEzkfLVNWVkbvZiP1Sz6yb7N1ixDr69HOmEnAdeOQBAcD1rKYyMhIKioqPFaqp9PpkMlkrMuu\nZpuLzqgVtQZ2yl17lOTk5BAfH+/xLKC398zBzOnV9g1F65nc7S7eP7AAbZP1P6Vp0rDwwHsMqr7a\nPpm+1Gnv+83YsWPp168fr776KvX19aSlpbFx40aWL1/Ok08+6RH39dq5bzu81s99G8XU+2j8+We0\nzz6HRaPBr0cPIhb/F5mXl7vKpBKuUOh5aELvcw92IyUlJef0ILKIFnKDcuyvRUSWHvyIaJ3jPSkq\nKoq4uEtD7LkQkpOTXU5eFQoFgiDY0/fvvPNOPvjgA1566SXGjRuHxWLh+++/Z+3atdx3333eVZot\nilZBfOkyAIIm34HyzTcQJK0m0bsNW9b62eLCT7+V8q8ff6fOYELhL6OuyYRMsPDISD+u8FCsKpXK\nZSbZ+WIzhm5vLniJ6nKvM7GZe6SlpeF/9WCknVIwnzxVKiGVEjT5jlaP9xl9+Ohonn/+eVJTU/ny\nyy+ZNm0aEomExMREHnroIR555BHAaqC1bNky5syZw+zZs6mvrycjI4P58+czevRoD/8PQDSZMGz4\nFbGxEb+ePWjevQe/jAyn9pSeoKamxuHGLIoie/JrqNI3MTgjirCgtgmr4eHh51Uf397YMjDaInTa\nulv48F7MZjOVlZWEitBS80mxvh6zuhxJ+ul2fyqVirKyMo88bCYkJLB3714AdMaWMyd0Jj/WrVvn\nsM1m/Ld//34kEglJSUkdF+g50DQ5nxsLv36fotAizjSUKdQV0kPfm6A2eDNdKrT3/WbhwoW88847\nzJ8/H61WS0pKCrNmzeKOO1p/tuowzupQITY1IRoMaJ75K6LWKhwZjxyh5v4HiFn9kycivOTZuHEj\n8fHxrS5KWkQLFsHisK3ZbESvP+0FZDAYyM7Odrmy/0fgzHv5008/TXJyMsuWLWPVqlUIgkB6ejp/\n+9vfvO7zicnNo/6zz+2vGz75lIDhwwkcf10rR7mPpKQkdu7cicViQXJK8KhtNPLmd0doMlp/k3VN\n1hIUkyjh/Q35jOmTREyY+w1ow8PDaWxspKGh4aLuM+Xl5S4zNS4WX/5rC6SkpLBv3z7rj8zfn+jl\n31K/5CMsNTUE3X478sy+LR5rMBh8Rh8+3MKkSZPsLt0tERkZyRtvvOGmiNqOaDRSNeF2mk9NOhCA\nU2XXiscfI2yGc8aUO9FoNA6teGd++Rvrj1pb74YG+rHozwPoFO3aTOlMPO27oNVqUSpbb6lkQ6lU\nkpub28ER+bgYqqqqCA0NRTFgAPWvzba3Rz4TWUYGsrRUh21xcXEX1QryYsjKyrL/u+bL1Rw54nrc\n2KtSuP2mHg7bSkpK2LNnDzfffHNHhtgmrojswd7yPfbXUkHKzHtf5F+732J76Tb79qzEq4kUIy+7\nLKD2vN8EBwczc+ZMZs6c2V7hXRTBf74f/Zx/nX59/1TM5eV2YcGG8fBh6j/9jOC77nR3iJcFmZmZ\nDp0NXJFzKJuVeSvsryd0n8DgLoPtr2tqapxEyD8KS5cuddo2ceJEJk6c6IFozg/FKb+dMzEeO+Y1\n4kJQUBCCIPDVV1+hO5WdXtUsp8noOkPGbBHJr6rziLggkUiIjY2lvLyc1NTUcx/ggubmZnQ63UW1\nW28Jn7jQAiEhIQQGBlJZWUlsbCzSmBhCn/urdaV1/Xpq58xFEhFOwOhrkZ21klJcXEx8fPwl0Yay\npq6JxZtO4CeVMPWaNEICPVePey5EUaTKr5K1Bb/QT9UfpX/bJkw+PEtLRoyGdetOCwtgFxYA6hZ9\nQMijj9hr8txt5gjWSbmtY0yuutYuLIBVzf5sewHTz5oIuUKpVFJaWtphcZ6L8/GOCA8PR6t1kbPu\nw2soKyuzGkklJRG19GP0CxYgNjYiiVVhLixAlpZG6PPPOWWqqFQq1q1b5/EW0v07R2P2q2NLqZSK\nWgMSART+fgy/IpaHRnR2Gu8tfgsAMwa9yLSVj1MpqSTUP5S/XPV/+En8+Evf/yM8IJzs6my6R3bn\nnivuZVfDLqqqqrwmdh+tE/rUk/hd0Z3mnbuQZ2YSeP14RFFEmpCA+SwDb8OaNT5xoQOZ2vPPdAnv\nSq4mh55Rvegfd3mUF/3RqeycTvcNv4LlVGaKIBAwYrgnQ3JAo9Hg5+fHpEmT7PdPk9nC9nc2UVnb\n5DQ+JEBGr0TPzUNs2YgXKi6o1Wqio6M75HnAI7PfXbt2sXDhQo4ePUpjYyNpaWlMnjzZng43ffp0\nli9f7vLY4cOHs3Che2qyU1JSKCgosHfIEM1mqibdQfPOXfYxulf/RuSS/xEwYoR9mye7RJxPD/Gi\nqnomL9hqd+n+elchK58eRniwHKPRiFQqtacGeQNz98xhp3I7O/dtJ0gWxN+veZPUsLRzH+jDY0RG\nRqJWq12mYot1LSV1A0Yj4hlpqmVlZR2irraGxWKxC4T1TWan/Q1Nzg7NrpBKpVgslnMP7CC0Wm2b\nU+FDQ0Opq6vz+ATUR8uo1Wq6dOkCgP+Qq/EfcnWbjpPL5YSGhlJVVWW/p3mCmJgYEouL+fTxm9o0\nvqKigs6dnUUHTyBBQj/NQO6++24Hh+1gv2Ae7vOow9jIyMhz1pf78C4Cx44lcOxY+2tBEFDOn0f1\nBMdVYZkHf4+bN28mNDT0ostuDQYDX375JXfeeadHTV5dIQgCQxKHMiRxqKdD8dGO6FUqIha+j/79\n98FiQfHww8ivvNLTYdkpLCwkKSnJQZiXSSW8fU8m7/2SQ4mmgeiQAKrrmjA3aPm/8d0I8vfcIrJK\npWLHjh0XfHxHmTmCB8SFTZs28eijjzJkyBD++c9/IpVK+eKLL5g1axZarZaHH34YsD6ALFy40GnF\nMjQ01G2xJicns2XLFrspU9OmTQ7CAgAmE/p35tvFBYvFQnFxsUMaqLvQaDR89dVXZGZmEhBw7jSd\n17/Z49D+q9lk4c2vd3Fvv3C2bt1KYGAgf/rTnzoy5DZTVFvEpuKN9tcNpgZW5C7nqX7PeDAqH+ci\nLS2NzZs3k5mZ6bQvYMxoJCoVFrXaed/YMUjPuOidOHGCtDTPCUm9k5RkqELIVVvrPqUSgVv6XRoO\n3Fqtts2ZC1KpFIVCgU6nu2za6F1OWCwW1Go111xzzQUdb2tJ6WlxoaqqyqGutSVEUaSiooLBgwe3\nOs5d1NbWEhgY2KbWXZGRkZw4ccINUfm4ENqaDRcwaCBhs15G949/gKEJeWYmisceO+/3aS+ampra\nxdOruLgYg8HgkaxAH5cvFr0e3UsvY/h1I37duhH66is0fvstjd98yxCLhdqUZEzHcsBiQf/uu0gj\nI9sskHc0hYWFdqPMM+kcG8Lw7rEs25pPRa2Be65OJaqpkIDmaiDF/YGeIjo6Go1Gg9FoRGfS8Z8D\n73NMc4wekT14+MpHz5ndXV5e7vL/2x60SVw4fvw48+bN4/jx4y57gJ5P7dObb75JcnIy7733nn1l\nbNCgQYwfP57PPvvMLi74+flxxRWe8uG0EhMTQ0NDA3q9npCQEMSGRpfjxDN6jarVakJDQz1i5GQT\nXvbu3XvOz87f3586rXOtbm29wd6HfNiwYe0f5AXSZHZOSTKY2taP1ofniI2NpampyWVqviQ0lJgf\nvqP+k08RGxuRpnbCdPR3ZBmdCZ482T7ObDZTWFjoUed1iUTgvfv6s3xPEdV1TYzuFUdPD6bDtRWj\n0UhjYyMKxbm9IWzYTB194oL3odFoCAwMvOD7i0qlIicn59wDOxC5XI5CoaCmpuac2Uh1dXUA5/X7\n7UjOp4OMLXNBFEVf1ygvIyIi4ry8ZRQPPUjQ5DuwaLXIzshKtVgsVFVVub3luFqtPq8sVVcUFha2\nY0RtQyKRYDK1LeOvNUwmk1dl1fo4je612TR89TUATZWVVN8xGUulte1wGGAqP11eajr6O9VT70e1\nZxeSUyWonsJgMFBdXe2yA8nhIi1/X3XaKOjvq47w95tSqSg8St++LfvvdTS2LlDl5eUszH+PQ1VW\nT6VtpVsxWUzMzHqpxWNtxtAdVbbXJnHhueeeIzw8nLvvvpvAwMCL+oNTpkxBpVI5pNxKpVKuuOIK\nfv7553a58LQXEonE3pqkZ8+eBIwcgTQ5GfNZF+XgU72QwbMlEVKplHHjxrFv3z6GDBnS6tghQ4bQ\np6CGR/63275NEOD52wdh1JZhMBg82sP1bDqHd6ZreFeOaY4B1vTU69LGezgqH+dCEARSU1PJz893\nuXouVakIfebpVt+jpKQEpVJJ8Km2ep4iJNCPe4deWmU4NjPH83kQUyqVPt8FL8Xmt3ChqFQqNm3a\n1KasgY6iyWhmT20EH/33ABakxIYFcO/QVEb2cE7PtPkteMvkvLq62mkiWdlQwcdHPiJfdwKJIEUU\nLfSI6sW9PaYgl8vR6/Vuzbj0cW5iY2PRarUYDIY2ZXmaiosxHv0deT/HDLyqqiqCgoLcvpgkiiIl\nJSUXXGstiiLFxcXtHNW5iY6Opry8/JyZdOp6NYW1BVwR2QOF3FlYLC8v93mZeAlarZbvv/8es9la\nOjr4p58482ywCQstITY08MM/36Kmi7VjwYgRIzwy92jNK2/3iWqnbSf1EiRaLY2NjRc9L74YrL4L\npXZhwcbByt9aPa6yshKlUnlRAmVrtElcOHHiBDt37myXIFpqM2SblHubCWJycjLZ2dn07NkTITCQ\n6O9XUf/xUpr37kUSEUHQhNsIOGOFv7CwkFGjRnks3sTERDZu3IhOp7Ob0bXElSkRLJjSj/9syEMm\nEXhiTFc6RSv4cXeuvabXm3jl6td47fNXybgyg6sThtI1oqunQ/LRBtLS0ti6desFK7yeLom4lNFo\nNG3uFGEjPDycgoKCDorIx8WgVqsv6sErKCiIwMDANmUNdBTvr81lU5HNw8SMpqGZmV/9xodhAfQ4\nKxvIm8wcwSoudO3qeN95fcds8nWO5Q+F+kLqjXX0iOxFdXW1T1zwMqRSKSqVitLS0nPeW+o/+RTt\nC9PBYkEICCBi8X8JOFWWVFxc7JHWrtHR0RQVFbVZXDg7e6aqqoqAgAD7hNBdxMXFoVar6datW4tj\nVuWt5H+HPkREJEAWyKysV7giytE4uayszPdM4CXU1dURGhrKmDFjANBv3kLzTz/b9wsKBeKpDDSX\nyGSMeuhBJLGx7N69G61W6xFxobCwsMW/2zXe+frdLSGMuuZ4ioqKPDpfUqlUHDp0iNSwNIf7UJqy\n9fOjI/0WoI3iQteuXTustWJpaSn/+c9/yMvLY968efbtBoOB2bNns2nTJtRqNTExMdx444089thj\nbjWfsU3W67Zsxfjdd0iCggicdDt+3bpiLlMjNjdT88RfkERGIt43BaPR6PYUuTORSCSkp6eTm5tL\nv3797NstFpGtuZWU1jQypGs0CRFWbTEzLZJFaafjbWhooKKiwn6h8DT6Zj3bSrYil8oJ9lMgAqNS\nriUltJOnQ/PRRmJjY2lsbDyvlog2LBYLBQUFDr9lH23nQj5zpVLJb7+1rnr7cD+iKFJWVsbAgQMv\n6n1sD/ieEhe25Di3IxNF2JZT5SQuVFdX07t3b3eF5oTFYuHHH3+kvt5qPqvT6SgqKrKLBQ3Uky93\n7auwrWgrIVVhFBYW2oX+7t27e/T/4+M0CQkJlJSUtDpJFY1GdH//u93dXjQYqH3zH3ZxoaSk5KK9\nDy6EpKQkjh07ds6Sm8PFWv6+8gj5lXUMTI/i5Vt7EqHwp6ioiKSkJLKzs90YtfXa09q9pclk4JOj\nSxFPtY4ymBr55PdlvD70dGtTURRRq9Ve48PiwyrW2TKAZK++iqaikua9e5EmJ6N843XqP/kMw88/\nIygUEBSIqLaWRgihoYS9MougFKtvgacWly0WC0VFRS3eWwdnRPOnIal8ucO66DJpUAqDM6I5Zkmh\nsLDQ4+LC+vXr+cst05i7918U64voFNqJx678S6vHnWkM3RG06Zt8/fXXmTVrFtdffz1xcXFO6ZT9\n+/c/7z+8a9cu7r33XsCaHbBw4UKHi4VOp0MikfD6669jMpn48ccfef/998nLy+Pf//73ef+9C0Uu\nl9Nr9x50L75s31a36AOXY8Wly0ib80+Pp3BmZGSwbt06MjMz7bG88u0h1hwqA2DB2hzm/SmTqzo5\n147m5eXRqVMnr8ggqW6s5plfn0JjOMNxWwFPrpvGXwc8z+AE7zCB8dE6EonEXhpxvuYxJSUlhIWF\neU3N9aWGVqs9b6d9pVKJTqfzaOq8p8jLy+Ppp58mJyeHpUuXOtzbPN3FqLa2FolEctHngkqlspf6\neYKUKAXFNc7+RXkHd7Aob73Tdq1Wyz333OOO0Jwwm82Ul5czYcIEDAYDq1atYuLEifb7qtFiZO22\nNdQZnVfmkkKT6depH7m5uYwdO5aioiLULsxrfXiGxMREjhw50uoY0WhErNU7bLNUWVOkjUYjVVVV\nF1WmdKGEhYUhiiLV1dVOHiDahmaWbTnJ5qpovl62l9pGa6nxjrwq3vk5m1du60VBQQGZmZluFxeU\nSiVGo5G6ujqX1zGDuQmD2dFLS9ekc3it1Wrx8/PzPRN4Cf7+/uh0OkwmEzKZDFlCPNGrVmCpqwO5\nHN3MF2neswdZ924o33oL/z69sej1IJUiyOUIp+Yaoiiel6dNe1JRUYFCoXAqvTWZLXy2vYADBRp6\nJIbx/V+HI5dK8PezlvUnJSWxfft2jz4r+fv7o1AoMNaZ6BPdB1WQihHJI0kKTbKPaTQ18m3ON+Tr\njtMn+kquS7ue8vLyCzaGbgttmkGuWbOGLVu2sHnzZqd9giDw+++/n/cf7tWrFytWrECj0bBmzRoe\nfPBBpk2bxsMPP8zMmTOZMWMGISEh9vFZWVk0NzezatUqDh8+7LYHI9FkQrV6TZvGCk1NJO3YCbfd\n1sFRtU5UVBSCIFBRUUFsbCylmga7sADWrhCfbDvZorjgCeO85uZmfv/9dwfn4o2aDY7CwiksWFh6\n4GOCKl3X4EskErp37+517ZX+yKSlpbF9+/bzFhd8JREXx4WURfj5+REYGIherz9nadXlxBdffMEb\nb7xBaGhoiwKxJ7sY2fwWLla8jouLY+fOnR4zGpw2tis5xVVUNpz+DMf1juPFW0Yjkzo+oB09etRu\nMOwpBEFAqVRSWlpKbGysU734E32n8e6++dQZ65AIEiyihajAaB7P/Asxkhiys7NRKpW+tpReRnh4\nOCaTidra2hbPX0lQEIE3XE/jqu/s24Im3Q6cbo/s7ueM0NBQfv31V0RR5Ntvv3U6h1dVxFLZ7A8E\nAY4eZtuPFvFhzU4Afv75Z4KDg906KRIEAZVKhVqtdil6h/mH0S+2P3vKT3uBjUx2LDO+WN8ZH+1L\ndHQ0UVFRHDhwwCHDVKJQUP3Qwxh++BEAS0UFVbfcStxv+5G4ON/y8/NpaGjwSNvhlrzy3v4pm292\nFwGwNaeS4uoGXr6tl31/UFAQYWFhqNXqDsnsbyuxqlj+8dsb1Bitwuee8t2IiAxNtIoHb+/5FzvL\nrC0rd6t3U6ZVE+of1qFeMW0SF5YsWcKbb77J2LFj22R+0xYCAwPtdVdZWVkEBAQwb948rr32WtLT\n010eM3r0aFauXMmRI0fct+oiimBpe11aSWEhSece1qEIgkBGRga5ubnExsZisji3GjKZnbdpNBoa\nGho8cpKcPHmSnJwckpJOf3rNJuduFjZMFpPLziVgvVAEBQV5TW90H9bV0oaGhjZ5gdiwWCycPHnS\no268lzJms5m6uroLEgjCw8PRarV/GHFh9+7d/P3vf2f69Ok0NTXx5ptvuhznyS5GVVVVREdHX/T7\nhISEIIoiDQ0NHjFJTYkK5s9dG0nq2pvEeBUyqYRIhevWjjExMRw+fNjNEbrGlZkjQFb8YDJj+/He\n4gU8PvUJqhqriAmOQSpIEUWRpqYmmpqcux358CyCINhLI1oTB8Pfnotfz54YDx/Gf8gQgu66E7Bm\n1XnCb2HAgAEMGDCAkydPcvjwYW644Qb7vqLqev47f0uLx17TK4WRvbqRl5fHuHHj3BGuE7Z2uC09\nnz074Dm+y1tJQW0BmbH9GJniKC54eiLnw5nBgwfzzTffkJGR4fDMYFi71nFgczONa9cRfNutZ21u\nZvv27YwcOdLB7L+jUavV7Nu3j+LiYuRyOZVnmU9+v08CnBbvfj5YQqZ/EWfqeZWVlXz//fd06tSJ\na665pt3myOdDU2gTNVWOppMbi35laOI1NBgb2FW202Hf1rLNTFU90KExtUlckEgkXH/99RedKq/T\n6Vi7di1du3Z1Egd69OiBKIrk5OSQnp5uT7E5E9tksi09ptsLwc+PqhEjiG5D9oIokyHcPtENUZ2b\njIwMli9fTlZWFsmRwVzdJZqtOdYTRyoRuGOQs0qXm5tL586dPZbeExUVxaBBg+yvU+tTObBhv8uU\n0zt738WglEFO2+F0+zIf3oNEIqFTp07k5+dz5ZVXtumY0tJSQkNDHTKYfLQdnU6HQqG4oJu1UqlE\no9GQkuK5Hs7uJDw8nM8//5zu3bvz0UcfeTocl4ii2G4PXlKp1GP97S0WCzU11YxPTzinSXRERAT1\n9fU0NTW59b7viurqamJjY13uk0vlBFmC8ZP6Eac4vaoqCALh4eG+rAUvJTExkYKCArp3797iGCEg\ngJDHH3PaXlJSwtChQzsyvFZJSEhgw4YNDi0pw4LkyGUSmk0W+7iQABlGs0hWRhTTxnZl386tDos4\n7iYuLq7VbOdAWSCTuk12uc/mO5OZmelyvw/PoFAouOqqq9iyZQvjx4+3Z9NIQkOxVFY5jJUlOAtD\ne/fuJSEhwe0ZKSUlJRiNRgBGjhzplAUUdSyPEu3pRc7wID9693b0KVCpVOzZsweNRoNOp/OIuJAe\nnw7HOVMHISLAmpkul8pRyEPQN9fa9wWIgR1q5ghtFBemTp3KsmXLuO+++y7qj0kkEl5++WUGDx7M\nBx84+hbs378fsJq/9e/fn8GDBzsYPAKsXr0aqVR6QR4PF4rJZGL/kKu57aabMK1ahRAYSNCdd2Iu\nLMRcVoYQFUXTz6sRlGFYHnmE40cOM8AL+lqHhISgVCopKiqiU6dOvHnHlaw+VEappoFrusXS7Sz3\nU1EUycvLY+zYsR6K2JnY4FjeGflvNhZtwE/iR6BfEBUNFVwVcxU9ojxTL+zjwklLS2Pnzp1tFhd8\nJREXx4WYOdpQKpWUn9GP+nLHl+XkPmpqaggJCWlT9ymJREJkZCSVlZUkJia6IbqWqa6uvqCslcjI\nSKqrq93ertDHuUlISLigmumGhgbq6uraJZPoQvHz8yMmJobS0lI6deoEQGigHw+P7MyCX3KwiBAe\nLOffU/rROdYq0NtaUHpycm4TDFtr4We2mPk650t2q3eRoEjkniv+RHRQDHq9HlEUfd1XvJCePXuS\nm5vL8ePH7fdT5euvU/PwI9YMcEA+YAD+Z5kmVlVVkZeXx+233+72mMG6CJORkeGyLOLZGwKY/sVv\nGIxm/GUSnr2hJ0lJjgKzzbvFk9lpadFpdDN155hfNiIiMUGxTOhi/TxlEhn39/ozC/b9G5NoIkgW\nRPf6Ht4hLhw9epTdu3ezePFi4uPjnS7Cn3zySZv+WEhICPfeey9Llizh2Wef5eabb8bPz49Nmzbx\n+eefk5WVRd++fZk8eTIffvghL730EuPGjcNisfD999+zdu1a7rvvPremopWUlBCjVCJraEDSty+B\nN92ILDkZOG0mGDLFakwpiiLC0SNoNBqPmJKcTUZGht2g0U8m4YarrJ+byWxh9cFSSjWNXNMthvTY\nEMrKypDL5R7tdOGKkrpiTKKZjLAu9IlxnJRqDRo2Fm9EKkgZljScELlvhdubiYuLo66uzqnG1VxR\nQd2yTzAetPbplV95JYF33cnJkye55ZZbPBVuizQ2m1hzSI2uoZlre6qID/fOSYNWq7XXh1tECztL\nd1CgLyAzNpOM8NZdgsPDw91u9nUp4C1djFqiaes2GpYvR2w2Enj9dQSMGeNxoftszre9ZExMDBUV\nFR4VFywWi8P5ZOOk7iS7ynagCo7DgsXlsTZx5HIQF3bs2MGCBQs4fPgwMpmM9PR07r//fofuUuvW\nrWPJkiUcP34cvV5PQkICN9xwAw899JCDoKTT6Zg7dy7r169Ho9GQmJjI7bffzv333++232xwcDCB\ngYFUV1cTHR2NuUZD4/LlYLEQeNutSFt4HiopKXH5LOxukpKS7AtINu6+OpVRPVSUaBrokajEbBH5\ndncRDU0m+iX6I5fLPTo5l0gkdt+FllppfnXsCz7L/hSAXE0uJ3X5zBv1rr19nrdd09zF7t27+dOf\n/sSAAQP4+OOP7dvLysqYM2cO27Zto76+ntTUVKZMmcJtbvR/k0gkDBkyhF9++YWkpCT8/f2RpaXh\nP/pazMUlBP/5fhST76BxzS8YjxzBf8jV+GVmsnnzZvr37++RFX+w3tNbakGZlRHNqmeGMfeDz3jy\nz5NRBjsL4oIgkJyczLFjxzo61FYZG3UdN0bdjCRSQF1fxt7yPQxLGk6wXzAjk0fRJbwrP574nmCJ\ngvoDDR1e8tomcSEwMLDdXCWff/55UlNT+fLLL5k2bRoSiYTExEQeeughHnnkEQCeeeYZUlJSWLZs\nGatWrUIQBNLT0xjU8bYAACAASURBVPnb3/7mdnWrID+fnv9egDYnBwD9vPlEr1qBn4s0OtuPrKCg\nwCvEBdtK8dkppTO+/I1N2VaTrA9/Pc7b92TSWJxLRkaGp0J1yVfHvmTZ0dMX0Ck9pnJblwkAaAwa\nnlz/F7RNWgBW5q3gnZHzCfZzfw2xj7ZxZmmErX2XubSM8jFjETUa+7imX9ai//C/KJ992utWKMwW\nkUcX7ya71JpitmTTCT54YCDpsd4nbGk0Gnv668ID77H6pLX39Oe/f8oz/f9qN/txhVKpRKvVesz0\nz1vxli5Grqhb9AG6V/9mf934zTcEP/BnlK++4rmgXGAzGm4rMTEx5Jy6/3oKrVaLQqFwEJD2l+/j\nte2vYhatnkyqUNcpvREREWRnZ3s0Fb092LBhA4899hiDBw9m/vz5CILAsmXLmDZtGq+88gqTJ09m\nxYoVvPDCC4wbN46HH34YuVzO9u3bWbBgAXl5ebzzzjuA1Q9m6tSplJWV8de//pXU1FR27tzJ3Llz\nqaqq4vnnn3fb/ysxMZGSkhIi/fyoHHcd5pISAPTvv0/ML2tcCgye8ls4m6SkJA4fPux0nVYpA1Ep\nA2k2Wbj/wx2cqLCWigb6CTw1yPNx23wXWhIXdpwyn7NxsvYkZXVlf2gzx+bmZl566SWn+3FdXR13\n3nknMpmMl19+mdjYWNasWcOMGTNobGzk7rvvdluMsbGxpKSksHv3bgYEBVN50832rAXds3+lefNm\nGlesBEA/5180PPUkkozOdO3a1W0xno0gCK0K16GBfsQHGFwKCza8QVxQqVTkleXyZeHn6I3WDjff\nHV/FOyPmYRYt/G3bLMobrNmogSFBTGyaaC+d6AjaJC688cYb5x50HkyaNIlJkya1OmbixIlMnOhZ\n/wJRFKn9dSOyMx5sxIYG6j/6GOWbrj+TlJQU9u7de96u+B2Bv78/8fHx5Ofn280zC6vr7cICWCdL\nn28/SR/zSSZMmOChSF2zPPcbh9crcr+1iwsbCtfZhQWAioZytpVsZXSnMfjwXtLS0ti9e7ddXKj/\n8ksHYcGORkPX4677x7uTPXv2ONzMsyuNZJeebk3W0Gxm4Q97mdSrZVFLr9e3uK8j0Wq19OrVi7rm\nOn4pOO0ZIyKyMm95q+JCQEAAUqnUY6Z/3og3dTFyhd5FK8z6jz4m9PnnkHjRqnlFRcV5fU4xMTFs\n2bLFo0KXKzPHVcdX2oUFALV/Gep6Napgx3TTiIgINBoNFovrzIZLhblz59rbhttEloEDBzJmzBgW\nL17M5MmTWbx4MVFRUbz99tv272rAgAHk5eWxZs0a9Ho9ISEhfPfdd/z++++8++67jBplNey76qqr\nqK6u5uOPP2bKlCkdnrZrIyEhgUOHDtH5t4N2YQHAUl5B47fLUTzoaHwmiiIlJSVtLu/rSGxlb66y\nagC25VbahQWARqPIEV0AN7ktQteoVCq2bt3a4v54RTz5Osf7/xs7X6dvZT969erVwlGXN++99x51\ndXVO184lS5ZQXl7O8uXL7c/5ffv2pbCwkHfeeYfbb7+9TSVo7cWAAQP48ssvSfvuB7uwAIAo0rhy\nlcNYcekyhm5Y59EFDJVKddF+PgkJCR7PYlKpVHxyZCl62ennzdK6Enaqd9JsarILCwCNYgMbCtcz\noUvHzbHbJC6sWLGi1f3emLrcHlRVVSHzc/ERtfIjiouLo6amptV6MnezZ88eNm3aBECtSQY4KtdF\nRYV0j2q5M4OnkHDW53zGBUgQnL8D3wqr92PrZbxo0SIA0vfupVsLY8vKy/Gcng3Dhw93EgakUufO\nMVqNBqm05QwLpVLp9g4DtjRupVJJM80InH1unPtcCQ8PR6PR+MSFU7TUV90jXYxc4eKaiCA4XDc9\nTVNTE3V1deeV2Wdrl6fX6z2WyeRKXHA+p1yfVXK5nODgYHQ6XQdF5x6eeOIJwsLCHLI3/Pz8SE9P\nZ/dua+tAf39r2v3Z92KFQoFEIrE/xK9evZqgoCBGjhzpMO7GG29k6dKlrFu3zm0rrnFxcaxfvx6L\nxIVZqotzR6vVIgiCV3TSsa26frbpGJuLTEgEgXuHpjK2t9U0T+Ii/tAQ19cxdxIdHY1Op3MwozyT\nP11xL7maXCrOmBAV1J7EDzlTwqe4M1SvICcnhw8//JDZs2fzzTeOi26//PILaWlpdmHBxg033MCG\nDRvYtm0bw4cPd1usNqPgqppqzs75sYiiw1O9eMZ4T5CRkdEuptVyuZxx48a5FPjchSiKNDcbnWb1\n69etx4wZzrp1urp/tSdtEhdeeOEFl9v9/PwICQm5bMWFgoIClCNGIN+5m+a9ewEQFAqCT3ksuEIq\nlZKQkEBRURFdurRe19zRGAwGSktLueuuuxwu4DVfHGD9UetFWyYVeGHyMOqLDpOXl+cVaryN27pM\n5KMji+2vz1TZRiSPZFXeCmoMVhduVXAcg+OvdnoPH95Ffn4+/fv3p3fv3gCYb7qJ8jHjEKsd2+gQ\nFUnt1YM9EOFpXJUJXWkRWVe4kyPF1olCsL+MjAANffve6jTWk9TV1REQEIBcLkeOnDGdxvFT/g+A\nVbS7LePcWUq20ghPG+l5E97SxcgVIU88ju7Flxy2Ke6fisRLRG6wtu2Kioo6r1UeQRDsvgueFBfO\nXjG9JeNWfqs4gEk0ARBniCc22PVqu83U8VIWwFsyez7TePe+++7j2Wef5d1332XKlCkEBASwdetW\n1qxZw5133ml/DsnJySE1NdXp87AZwbXWTaC9sXlN1WZkIEtOxlxYCIA0Lo7A25yv67aSCG/5Luvl\nUXy8r8j++pVvD5EaraBLXChZnaPoHKsgr/xUWYRM5Nb+ruvL3YlUKiUmJga1Wu2y3j1OEc/jV/2F\nWVtfdNhuDjJ6zefuLkRR5OWXX2bgwIHccsstDuKC2Wzm+PHj9uyfM8nIyEAURX7//Xe3igs7d+4k\nPT2dbv+aQ+UNN4EtY0siEDxhAo1ffW0fK516H5s3b+bmm2/2yPfanvcTTz8nqdVqrokbRnljGbpm\n6/NpUkgy02+agdli5ukN/0dZfRkAQUIwI5NHtvZ2F02bxIUjR444vDabzRQUFPD+++9zxx13dEhg\n3kBhYSGDBg0i6svPafz+Byw1NQRcf73LVipnkpKSQkFBgcfFhRMnTpCYmOikDP9tYm+uza6gVNPI\n0G4xpEQFUxaQwZYtW+jTp4/HLt5ms+Oq8G1dJtAlogvZ1dl0j+zu0CFC6a9k3sh/s7lkMzJBxpDE\noQT5WVN/LRbLH+4GdClgsVg4efIkt956+oFNqlIRu2Ed9Z9/gfG330AQkF/Zh8BJk9D9+KOT+aOn\nkUgEFtzXn/VH1OgajIzqoWLFF8c9HZYTZ6fJPtznETJjMynUF9I3ti+pYefuwmHLXPBhdYcfNmwY\nV199tb1u3IYnuhi5QjH1Pvx69qBx5UrEpmYCx19HwIgRHo3pbM7XzNGGTVzwVFePmpoap2yL3tF9\nmDfq3+wq24kqOI5DPxxu8fjIyEiOHTtGVFRUR4fqVhYtWkRZWRmPP/44AOPHj8ff35/p06fz7rvv\nAtaJ5IMPPsiTTz5pP66mpsZlJ6CgoCDkcjnVZ4vNHUxCQgLq7GyueHEG5jI1gkxG0M03IXGxEllc\nXOxV3WVO1joKdaIIu09U0yUuFD+ZhA8eGMi6I+Uczs5lUCeF1xgQq1QqysrKWjTTuyLyCsIDItAY\nTrdx7RvZz13heQ2ffvopx44d47vvvnPap9PpMJlMLlfMbdvceS6p1WqKi4vtpRgxGzegf/sdRIuF\ngGtHIUtKJvDmmzAdOYokKorITikcKSsjOzu71Xaw3oTBaOa3Ag0qZSApUd6T1alWq8lIyGB+6gK2\nFG9GKkgJDwinsqGCxJAk5o6Yx+biTdQb6qneU0OY/4V1EmsrbRIXzu6tLZVKycjI4MUXX2TKlCku\nf/SXOvX19ej1eqszrURC0MS2+xEkJyezfft2zGZzu/UlvxAOHM2lyj+JkxvyGH9lvP2mIpNKGNnD\ncYVFpVJhNBqprq72yANQfHw827dvp6GhwcFVu2dUL3pGua6xC/UP4/q0Gxy21dfXU1ZWxpAhQzo0\nXh/nT2lpKSEhIQ416wDSyEhCXfQR79SpEydOnPCqbBqAAD8p46/0vClWa2g0Goc2lIIg0D9uAP3j\nBrT5PZRKJfn5+R0RntdRUlJiF1LKysoQRZETJ07YS9tSU1O58847+eCDD7yii1FL+Pfvj7+HRY7W\nqKiouCDRPSYmxp56725MJhNSqdRleVBiSBKJIVajxsMccdpvIyIiAr1ef1mJCytWrGDevHmMHj3a\n7td06NAhZs6cSc+ePbnnnnuIjIxk/fr1fPjhh8jlch57zHqdb2pqarEO3M/Pz+1t3eJ+/AnLfxah\nEUUkERFEffm5S2HBYrFQVlbGsGHD3Bpfa8QEOXt55OzdzKIjvzhsiwDys/0YnuUdE/S4uDj27NnT\n4n65VM7sIa/zRfbn1BiqCS4PYVzGdW6M0POo1Wrmzp3L448/7nJlvLnZWs7s6lyylS+561yyWCxs\n3ryZrKwsezx+aWmE//MfVN15N9onplm3DRqIRCajaYvVc6N/zx5smDiBTp06eU0peUsUVNXz2OJd\nVNdZP/cpQ1N59FrPLiKDNbtFrVbTr18/wvzDyIzNZMbmF+yZ3bd0vpWpvf7M2NRxAHx24DN0Ot0F\ntypvC20SF1oiMDCQoqKicw+8BCkoKCAxMfGCTDoCAwNRKpWUlZV5LFWmrLKG//3uR52pEqjk8x0F\nfPRwFgkRrlVrQRDIyMggNzfXIw9ACoWCzp07c+DAAQYPvvB0+P3799O1a9fLou3X5caJEyfIMJvR\nvjwLc3Ex0tRUgiffgV8LXUrS0tLYtWuX14kLlwJarbbNPdibTAbWFq6lqqGSwQlX29tU2soi/gi8\n++67Dt5CgiDwyiuv2F9//PHHPP300yQnJ3tFF6NLEVEUqaiouCDhNyoqiurqao8J9pGRkReVDedt\nLZ4vlkWLFvH2228zatQo5syZY98+a9YsgoOD+eCDD+zfU58+fWhubmbBggXceOON9jZ1RqPR5Xs3\nNze7tS2duboa8b//QzhV+22pqaF27jtEfvAfp7G20hxvmgTFS3UMTw1ga2ETEkHgjkEpPDbauYSl\nvr6er7/+GovF4nHzObB2FqiurnZZamYjMSSJZ/r/FYPBwGeffXZZiXNt4dVXXyU5OZn777/f5X5b\nKZ6rc8kmPLjrXDp06BAKhcKpA0jDqu9oPkMYNu7Y6bBfPHyE3llZ7NixgxFelml3Nks2nbALCwBL\nt+QzoX8yMWGeaaNpo66uDovFYs/y/Trna7uwANZueuPTbiA22NqlKTY2FrVa7Xlx4euvv3baZjAY\nWLdu3SXfXqklCgsLLyr1zVYa4SlxYcW2bOpMpx/C6gwmfjxQyoMjW/4/ZWRk8P333zNw4ECP3Hyu\nuuoqvvrqK3r37t2ieVpr1NbWcvz48cu6VOdSxWKxoFu3nvTFS6g/o/ylYclHRK9aiV8PZ8PDuLg4\n9Hq93WHcR9vRarVtXiH+2/ZXOFxlTelekbuclwe/wlWxfQkODsZkMmEwGDzWg9pdvPHGG23qiuQN\nXYxsiKKI6fgJpKpYJBdwvXQ3er0eiUTSJoPQOoORmrpmkk+lncrlckJCQqipqWmzaNaeXKw4oFAo\n3OrY3pG8/vrrLF26lHvuuYeZM2faRZf6+nqOHj3KxIkTnQSgQYMGsXjxYg4cOEBSUhLR0dHU1NQ4\nvbder8doNLr1O7ZotHDW5Kz08CG+OWU6fDYtTYQ9RXFxMU+PH8YsZQSCIBDg51p8Cw4OJjg4mIqK\nCrd14mgNmUyGKcLEP7a9QZgijJs630xyqGtzPbVaTWxsrFeIIu5i9erVbN68mSVLlth9fURRtJf9\n2jo5yeVyl+eSrRzCHeeSXq/nwIED3HrrrU4irKWy8pzHJwYGcqisjNLSUuLjWy879yTVdY5ZIBYR\nNA3NHhcXbOeH7bPXGhzLWUVEdE1au7hgK0k62wS0PWnTVfLFF1902ubv709aWhqzZs1q96A8jclk\noqys7KJUtJSUFFavXs3gwYPdXv8viiKV5WrA8SFOLmv9wqxUKgkODqa0tNQjokhQUBDdunVj//79\nDB069LyP37dvHz169LjsJ0JnsmPHDhYsWMDhw4eRyWSkp6dz//33M2aMtSXn9OnTWb58uctjhw8f\nzkIX7es6grKyMlL27oWzfDVEg4H6zz5DOfs1p2MkEom9NMLWutLHuRFFEY1G0ybn4pO6fLuwAGDB\nwo8nfuCq2L4IgmDPXvCGh1EfpzEVFlJ9732YcnMRgoIIe302wZO8O3vC5rdwrvvh17sK+feaYzQZ\nLXRRhfCvu/sSHRpg9124FMUFQRAui+yFefPmsWzZMp577jmn1VRb+rXJZHI6zraKahvTvXt3Nm3a\n5JSJYusV786uK36d05H07oXl4CH7trQnnqDX3Xc5jbWtoHvL6n9dXR2NjY1tNklNSkqiuLjYK67n\nJ3X5/CR+j6XaAtWwrXQr7127EGWA831LrVZ7Rczu5Ndff8VsNnPPPfe43J+Zmcktt9xCly5d7OfN\nmWRnZyMIglvOpW3bttGrVy+X/liB14+3+i6cEkgIDESQSBDr662v/f1R3HoLg+VyNm/e7FKc9BbG\nXxnPruOnPSw6xyroovL8wpdarSYuLs7+ekTySPaUn84WSQxJonP46QzhuLg4fvvttw6NqU3iQnZ2\ndocG4W2UlJQQFRV1Ue7f4eHh9of882m71R5UVlaSHmIkQxVCrtraSi8mNIAb+p67Jrhz587k5uZ6\nLOOiT58+fPHFF/Tp0+e8jPy0Wi2FhYV/qKyFDRs28NhjjzF48GDmz5+PIAgsW7aMadOm8corrzB5\n8mTAWq+8cOFCp5Y/7jRKzM/PJ66Fya7QihiUlpbGnj17fOLCedDY2IhEImmTyOYncV5NlUtPb7OZ\nOv7RHuy8lfr6eqqrq2l+bTaW3FwAxIYGtDNm0jhoIEIb24ZaLM512h1NW8wcNfXNzPs5G6PZeq3K\nUev538YTPH/jFXZ3+R49ergjXAfa4x4eGRlJve2B+hJk7dq1vP/++zz99NMu07QjIiKIi4tj165d\nTqLB9u3bEQTBfh0fP348a9asYc2aNVx33ek6+uXLl+Pv78+1117b8f+hM9C98gpNixeTaBEJHD+O\noBY6oAUEBBAWFkZ5ebnDw7ynKCoqOq/y3aSkJHbu3Em/fp73XdhcvAkLp69D9cZ6dpXtZMypuvAz\nUavVDBjQdr+gy4FHH33UZbnda6+9hiAIvPTSS0RGRrJ27VreeustDh48aO/CBbBy5UoiIyMZOHBg\nh8ZZWVlJTU1Ni+esLDWVqOXfUL/kIxAEgqdOBQHq/rMIQSIheMq9+HXpQiesXWJyc3M7dEX9YhjX\nOx65VMK6I2pUykDuHtzJK8zj1Wq1w2c2JHEoUomUzcWbiQ6M4paM25Cc0apaqVTS3NxMfX19h7Ua\nv6D8rqKiIjQaDT179vQK9ba9KSgouOjep4IgkJycTGFhodvFhdzcXLp16czE3ley6VgFTUYLw7vH\noAjwO+ex6enp7N27F6PR6NDP2l0EBATQo0cP9u3bd17tc/bu3UuvXr083g7OncydO5fk5GQWLlxo\n/64GDhzImDFjWLx4sV1c8PPz44ornMsO3IXFYiE/P58rHn+cxp274IwHbElkJMH3/qnFY+Pj49Hp\ndNTV1V1QqcwfkfP5rBJCErgmcRibijcCECAN4JaM2+z7FQrFJT0hupyIjY3l4MGDFBQU0OvQYRy+\n4cZGdnz3PYbYGHQ6HWFhYa2+l1KpdPu1srKy8pwdNcq0jXZhwUZRtfX3FxMTw8GDBzssPlcIgkBc\nXFy79C+Pj493exeE9sJsNvPmm28SHx9PVlYWhw87d8ZITU3lqaee4vnnn+fBBx/krrvuQqFQsGXL\nFr744guuu+46e3vfsWPH0q9fP1599VXq6+tJS0tj48aNLF++nCeffNLt/eKL9bUkPfUkkV27nnNs\nQkICJSUlXiMunF3j3hoqlQqdTkdjY6PHfSNcZSjs3LiLk78Uuhy/YcMG7r777o4Oy2tITk522UlD\noVAgCAJXXXUVAHfddRdfffUVTz31FM888wwxMTGsWrWKHTt2MGfOnA7PAmhqaiI0NLTVvyPv3Rv5\n3H8B0PjTT2hfmIGlqgr5oIFIzyitt016vZmRPVROhviexGAwUFdX55QZlxU/mKx41/51giDYfRfS\n09M7JK5WxYWmpiZmzJjBTTfdZHfHnTdvnj2VunPnzixdurRDTSE8QUlJib2ntaWhgfqlyzAXFBBw\n3XUEDLWaUZkrKqhfugxTaSkCApJwJcF33YUs7fSFPjk5mYMHD7rVkM5isXD8+HFuueUW/P2kjO7Z\n+g3wQIGGtYfVxIT6c1v/JBRBQcTGxnLy5En7g4C76dWrF59/8Tk/Zv/AycZ8ukV0Z3jyCCSChP3l\n+1ie+y0Gs4FRyaMY3WksmhoNpaWlXHPNNR6J11M88cQThIWFOYhAfn5+pKene8xZ3RVqtZqgoCAi\nBg3EvGUTjT+vxqIuR5qUSOC4sS5duW2cWRpxpirvo3XOR01/ut+zjEgeSWVDJf1U/YkMPH2TEgTB\nKePFh2fo0qWL3UejtrgE/VunjfRkGRlc/9ijCILAokWLvMYXwobZbG5TJ6IuqhBUYQGodQb7tqHd\nrNkO4eHhNDQ00NTU5DZhRCaTceONN7bLe3Xq1IlOnTq1y3u5G7VaTUlJCQCTJk1yOebjjz/mpptu\nIiwsjA8//JDnn38eo9FIYmIi06ZN44EHHnAYv3DhQt555x3mz5+PVqslJSWFWbNmuT37UBRFSkpK\nGDRokNO+hpUrqV/2KZLQEEKm/QV5nz4kJiayZ88ej6/+m81mSktLz6uEVCKREB8fT1FRkcdbpY9K\nvpbvjqyk3FwOQL/Y/sy4ZSZSwXmS+ttvv6HX690dotdy5v09ICCAZcuWMWfOHGbPnk19fT0ZGRnM\nnz+f0aNHezBKZywNDWieegbx1HfZvGMntf+cQ/g/3/RwZJcu5eXlxMTEnPdCf1xcnOfEhfnz53Pg\nwAH7TaGwsJCFCxfy+OOPM2LECObOncuCBQuYOXNmhwTnKYxGoz2luHrKVJq3bQOg/qOPiVj4Pv7X\njqLyxpsxFxc7HFe/7BNi1q5BdqqkICAgoEVH5I6iqKiIsLCwNqW8b8ut5JlP9mGbO/z6ezn/fXAQ\nGRkZ5OTkeExc8Pf3pzKxgm9//wqA1Sd/Jl93gozwLvxrz1v2ccdqsinSF5Gs7kSfPn08kmnhScaO\ndXaEBmtXBlc9xD3FiRMn7Ksr0pgYFK1kKrgiLS2Nffv2+cSFDkIQBPrGZno6DB/nQchfnkAQBBpX\nr0aWnk7oc3/1ivTMlqipqSEkJOScpoYyqYR59/Zj0fo8yrSNjOqhYtJA6+qdRCIhKiqKioqKy9ZI\n2ltJSEjg999/b9PYYcOGtalVY3BwMDNnzvT482N1dTUBAQFO2V6GX39F89gTp1+v30DE//5L7NAh\n1NTU0Nzc7FGTzvLycsLCws47AyEpKckrxIUgvyCG148ifVAakcooOoe3bDYeFxdH7qkysD86S5cu\nddoWGRnZJkNiT2MuLLQLCzaMR1tu4evj3FyoH4lKpWLLli0dEJGVVqWONWvWMHfuXLp37w7Azz//\nTFJSEk888QQ9evTghRdeYOPGjR0WnKcx5h23Cws26pcuo2ndeidhAUDU62lcvsJpuzvJzc1tsyiw\nYk8xZy5KHi2pJbu0lk6dOlFRUUFDQ0MHRXlu9jfsdXj988mf+enEj07jfjrxI+UV5R5N+/cmFi1a\nRFlZmYMJkMFgYPbs2YwZM4bevXtz7bXXMm/ePLcIX6IocvLkyYsSOxISEtBqtdTV1bVjZO1Hk0XC\n/zYe5x/fHeVAgebcB/jwcZEIUikh/zeNmB9/IOLf85F5+WS7LX4LNlKignl9Uh/+99Ag7r7asabV\nZurow0d7UVxc7NJjqvHHnx03NDdTc8+faPrqa2JiYigtLXVThK4pKiqyi2xl2kYqzsj2aQ2bqaMn\nfFfOpK6uDpPRxIBOA1sVFsDailav19u7Jvi4NJF17oz0rG4QAW0QIn20zIWKC1FRUeh0ug4rQ2lV\nXCgvL7eXB4C1rv3MHtXp6emUl5d3SGDegCQ4CM5aDRJCFAghLdczCx6sC29ubqa4uLjNE7kguXP6\nWbC/DJlMRkpKCsePH2/vENtMoPQsNd4oUlVW5TROMAsYGg1uzxDxRlasWMG8efMYPXo0EyZMsG/X\n6XRIJBJef/11/vOf/5CVlWU35upo1Go1AQEBF1U6JZFISElJIT8/vx0jax8sFpEfK2NZtD6P5XuK\neGzxLnbmOf9Offj4I3M+4kJr+MQFH+1NSUkJCQnOZteyTq59t/QL3iMhIYFiFwtM7qSoqIi4+ESe\n/3w/t769iZvf3shryw9hsbRexqZQKAgKCqKqyrP3qbKyMlQqVZsyriQSid3Q1celiyCTEfnREvyH\nDkWamEjwA38mZNpfPB3WJYvJZKK6uvqC7q1SqZTo6OgOm8O3Ki7I5XLMp1rHWSwW9u/fT9++fe37\nz3YEvtyQxsURfN8U+2shOJiQJx7Hf+hQ/K++2mm8LCODoAm3OW13F42NjQQEBLS5FeM9Q1IJCThd\nGTP+ynh7X3GbUuwp+vsNRMB60xEQeLDvQzwz+ln8BMfShwf6PsQVV1zBgQMHPBGm17Bo0SKmT5/O\niBEjmDPndC32zJkz2b59OzNmzKB///5kZWXx2muvcfPNN7N27VqXxlztSX5+fruUaKSlpXHixIl2\niKh9OVysRWM6nRprEWHlXs8+dPrw4W3U1NS0SyvGyMhIlz3dffi4EEwmExUVFcTHx2NpbEQ780XU\nWVdTde99bkRHlwAAIABJREFU+F8zFL+BLjoUiCKJif/P3p0GVFWtDRz/nwEOg0wyI6KAaI7lLGhq\nznm1wSlN07RssF4rtbxq2aSpaSZailhpSleNuk5NcjUNDccsp3AgckJA5pkzvx+Qo0cOCoZnH2D9\nPrzv3Wvvow/Ggr2fvdbzBJpqUEihsLCQoqIi/kjX8UvitfKw+P6PqxyoQnK7cePGXLpkuXCitaSm\nplarKGb5HnGhdijdtZtr/xpC+kN9KVy3DoD8pR+T8fgw1IcOoQwNwXXaa8hssAh7kVrHgu2nefzj\neGZu/J0CnW0+52ZkZODh4XHHLeFns88yZ98sXoibzH8Sv8JgLFu15Ofnd8/m1G1rLgQGBnLixAk6\nduzI/v37KSgoMKv2fPbsWUl6TluT+7z3cXzsMfQXL6Lq1RPF9YJUnhu/Qr1vH/qcXGRKJTKlAoc+\nfWxyolSmma8L37zyIAeSMvFzc+SBJtat0FyZ/Px8HFIcWTZkOcmFyTRv2JxAl7Llf58N+oI9l36m\nWFdCz8BeNHZtTLFvMbGxsbRt27ZedhSYP38+GzZsYNy4ccyZM8fsTUBl/x79+/dn27ZtnD59+p72\nQc7KyjJLSN4tPz8/m6y0bqkDSwOHu2rCIwg1QqHRkPvWXNS//opdm7a4vTkbRQ2sGvgnjEZjjbyI\nUCgUosCoUGPS0tJo2LAh9vb25L79Tlm7PMr2hudcvozvnt3kvDGT4q/+Y/qMy5QXcfL0RK1WS9bF\nqHwrx185JRXPZd15O2vjxo05fPiwpEUp09LSqrWd1d/fn4MHD97DiISaort8maxnJ8P1FcV5c95C\nn5ZO4YpPTNeof4knZ/oMPD9bI1WYlYr86Qzbj5UlD1NzS/Cy82a6xDFZUr7653bUulLeT3iHAm3Z\ny+LNZzbiYteAoc0exc/P7569mL3tXfDgwYOZPn06/fr1Y+fOnfTq1QtfX1+gLGOyaNGiKhXuqe1U\nnTpCJ/OCZzKFAodqtEq0RWqtnu9+v8q5tHy6hHpyf5C7TRQFO3bsGK1bt6apZzBNPcsKAWr0Glb/\nsZIDqQdQKVRMbPMMjV3LEg5OTk60aNGC33//vVqVk+uCyMhIYmJieOONNyz2HoeytzNKpflUL9+7\naI2q6zXxPWUL35eWhPg0INSxkL9Kym4w3Z3sGNu96q3BBKGmtfppJ0W/l90w6M6eQ3/1Kt7ffC1x\nVIJge27eEqHea14/THfuHLqUq3h8uAjHgQPRnDyJqnt3VJ3LHsgDAgK4cuWKWX95a7l06RJNmzYl\nwNmHdfHJlO+EsFPICG9++44sUJasz83NlawlZUlJCcXFxdVq0+7t7U1OTo7khTQFcyqVirS0NL76\n6ivTmN+RI9x3y1bltM1fc2sarnD3z/x00+fUarXZ1nupHPrL/EVWplZFXrEGNyfb+r5LT0+/48+f\n87nnTYmFcr9fO8bQZo/i6+tLRkbGPdmFcNvkwrPPPktWVhb79u2jQ4cOvP3226ZzK1eu5Nq1a0RG\nRlb7Lz18+DBRUVH8+eeflJSUEBISwujRo81aEKWmprJkyRISEhIoKioiODiYCRMmMGyYdNsO6pr3\nt55i16myJTE7T6RyLa+Uib3uTVuSqsrNzeXSpUsV2lHNP/g+f1z7HYAibREfHV2Mk9KRTv5lyxYf\neOABNm/ezP3331+lThl1wa5du0y1EywlFoqLi+nVqxfdu3dn2bJlZud27tyJQqG4Y9954c56Nczi\njUF9yMhXE97MC2exckGQkM8586rqmgMHMBQVIXd2ligiQbBNKSkpRESU9YK3a3kfuqQk0zm5lxcK\n77IHdYe+fXDo28fss+VbI6ydXDAYDFy9epUePXrg5OTEojHt2XzwIkq5jHHdgwnyvPM8VygUpuSI\nFF3BUlNT8fX1rVb7PKVSaeoWY6kApyANb29vxowZY1YgVBcSSlHst2bXuXfphO4786LsqhYtePTR\nR83GnG3g95SzoQC4sSrVWaFj04Z1ZiX4PDw8GDlypPWDu85gMJCWlkbvO7zkDmwQiFKmRGfUmcaa\nuJa9ALO3t8fNzY3MzEzTwoGactu7YLlczqxZs5g1a1aFc5MnT2bWrFnVziDGx8fz4osv0qNHDz78\n8EMUCgWbN2/m7bffJjc3l+eff57CwkLGjBmDUqlk7ty5+Pr6EhcXx+zZsykpKWHs2LHV+yrrCZlM\nRnFxMbt27brjtWqdkd2nzKsFf52QRGNtWdG8vLw8Am6p6nov5efnc+TIEVMRyX379pnOaYwa/ij8\nvcJnPj/yOblO+aZjtVrNpk2bCA0NpWvXrnV6i4Rer2fhwoUEBAQQHh5usXZCcHAwY8aMYc2aNbz1\n1lsMGjQIg8HAd999x65du3j66actFrISqu/+INvYUiQIRd5eOBQVmY4VgYHInJwkjEgQbE9paSl5\neXmmYmiub72J7tIltMdPIPfxweOjJchuc3/bqFEjDh8+jNFotOrKuvT0dFxdXXG6PqcfbOHDgy2q\nv+2pvCWlFMmFtLS0atVbKOfv709qaqpILtgYp1t/v4R3Qz5jOoWffIpRo8HxscdwX/gBJ/5MxDu5\n7BlD7uWJ54pI7GzwPn3RxIeYtfkPktIL8XF14O1hbegY/M9rBtWk7OxsnJyc7rjyyN3BgxcfmMIX\npz6nSFvEAz7tGd58hOm8n5+fKdlXk+76FdvdPnguXLiQoKAgVq5caVqG0a1bNwYPHszGjRt5/vnn\nWbduHenp6WzZssWUFe7QoQOXLl1i2bJljBw5UiyLssDV1ZX+/ftXqbXIrwcO4mDnQ4n2RoLBw1lF\ncPCNlmbWTC6kpaWZqi/37NnTrECJzqBDcVqB3qg3+4ynsyfBTW8sQQ8ICGD//v1cuXKFJk2a0KzZ\n7dsb1WZpaWmmglKjRo2yeM369euZNm0aQUFBxMTEsH37dmQyGaGhobz33nuSZF21iYkUrl0HBgPO\nE8ZjFxZG7ptvoY7fh6JpEzyWfoRS3DjUuNzSHLYlbSWzJJMejR6ks38X4i7s5ETGcULcQhna7BFU\nitpTL0ao3OlBA3nox5/QX7yE3MsL98Uf2uy2IkGQSkpKCv7+/qa358pGjfD54Xv0mZnI3d2R3bKV\nUHvmDIVfrAO9DucJ43Fp1w6VSkVWVhZeXnfeilBTbm5B+U80btyYo0ePWj05AmUrF+5m+bu/vz+/\n/17xRZNge1xfe5UGLzwPGg3qhARyX3+DPH9/ms99i5It25A7OmAsqVgzxBY09nQmZkp3sgrVuDvZ\no5Db3u/PqrSg1Bl0/JD8PWeyE/lXyFAKNQUUaAs4lXmSbgHhQFly4fz587f9c+6G1dfvTpgwAT8/\nP7P9HQqFglatWvHTTz+h0+mIi4sjJCSkwnKzIUOGsGfPHhISEu64FKS+quovnd27d/NS/xZ89GMi\nRiOolHJeGdyG0FDr/ZK8lVqtplu3bhaXGY4zjufL02tNx0qZklfCX8XbyTxjr1arOXLkyD2PVWqN\nGjUiMTGxSteOGDGCESNG3PnCe0x/NZWMx4ZhLCwEoGTLVpStWqE9dqzsfEoK1/r1x//4H7WqMKqt\nMxgNzP31TS7mXwQg/sovhAdEcOBqAgC/puznQv7fzOj8hpRhCjWkwNcX3/370F++jMLf/7ZvXwWh\nvqqsBaU+NRX1L/GoenRH4euLLvlvSn/+mfxFH2IsLiuWWLx1Kz5xcTRq1IiUlBSrJhfy8/Np2rTp\nP/5zXFxc0Ov1aLVaq76sU6vV5Ofn39W/mY+PDxkZGRbrSAm2R+7oSPHOneS8VNZushmQc+AgXN9C\nUT6P7EL/eUexe8Gzge3eh6anp99xBc9nJ9bw49/fVxjfdyWe1zvPpEfgg/j5+bF///4aj8/qs/PW\nvfTlLl68SFBQEDKZjOTkZPr27VvhmrCwMIxGI4mJiSK5UANGdA2iW5gX59MKuD/InYYSTyQnJ6dK\nqwcPaz6cLn5dWPDdfCIe6MGI5iNRKSvG26ZNG06ePCne1Nmgkh9/NCUWAIylpabEgmmsoBB1QgIO\nDz1k7fDqDLlcTl5eHnFxcQCk69O5WHrR7JrDVw+ZHe+/so+W2a0rtHrNycmRZNms8M/I5HKUTZpI\nHYYg2CSj0ciVK1do27at2Xj+h4spiFxeduCgosGkSRSuiirr83izUjUlO3bQ6NFHSExM5P7777dS\n5LVfeno63t7ed1VAzt7eHg8PDzIyMu5qW4VgfcVfx5oP3FSbgVI1Jdu3Y/faq9YNqg7QarUkJiZy\n8eLFSq/5X9HOSs9tPraJ4j9LMBqNaG8pvlkTJE/9Xb16ldWrV5OUlERkZCR5eXnodDo8PCruYS4f\ns4WWdEWbN1Ma9z+UoaG4vPgCcgvx1gaBDZ0IbCj9ftywsDCCgoJum40OdG3M/YUdGNtqHADnss/y\nffJ3yGVyhoQOJdS9GXZ2dowaNcoqXRCE6pF7WdizplSCTnfLddKtnqkLPD096du3L7rr/64N1Q2R\nJcowcuMGWaVwoFh/Y1++k8KJFmEtUMgq3vCJuhyCINQl+fn5GAwG3N3dTWOGvDwKVkXduKhUTeGa\nzyomFq5TeHkREBDA3r17xZv0akhNTf1HiYHyugsiuVA7yD1vfz+nEPd7d6V79+5kZmbe9hrXP13J\n1mRbPOfr6ktYk7IXR+3bt6/x+CT7aXj48GHGjx8PQFBQEFFRUURERJCWVta9wNIyrfJ9+Gq12nqB\nWlD4+Rfkzb3ROUNz+AjeW/8rYUS1n0wmw8HBocrXpxSkMGffLDSGsvoSv6bsZ0Xflfg6+1brzxGs\nx/HhhykK74bmQFmvarv2D+AwaBAFCxaarlE92AP7W94mCdUjk8nMtkcFE8xQzSNs/2sbAB4qD8a1\nGk/0iSjUejVymZyJ7Z6hWdO6W6NEEO41rUzLj8k/UKIr5sHAXng7eUsdklCJ1NRUAgICzFY4GtVq\nuLVeVSVv9OzaP4DjsMeRq1S4u7tL/iZ9x7Er/HQiFW8XFZN6hRLkJX3F/cqkpqbSpUuXO15Xoivh\n6zObOJOdSCvP1oxq8QQqpQN+fn78+eefVohUqAkur/wf6n37MGRkACAPCMBw9SoAdg/cj+Nw0QHw\nbri4uODi4nLbaybbP8/iQ4swyAzIuPGCyUPlwcSOz9DI5d69OJIsudC2bVu2bt1KTk4OcXFxTJ48\nmalTp5qK01laplFeqFDqh8fib81brGiOHEF36RLKoCCJIqp/Eq7+akosAKj1ag6mHuDRZo9JGJVw\nOzJ7e7xiv0Zz9Cjo9dh36YJMLsdxyBBKtmzBvmsXHK63BRNq1jPtJtO/6QAySzJp7dUGlUJFF/+u\nRG5cxgujXhQPQoLwD2j1WhLc9xN3/EcAvjkXy0e9P8a/gfWKIlvDwYMH+fTTTzl16hRKpZLQ0FAm\nTZrEgAEDTNfo9Xqio6P573//S3p6Or6+vgwfPpwXXnjB7M/Ky8tj6dKl/Pzzz+Tk5BAYGMjIkSOZ\nNGnSPd/WqNPpKqxuVPj44DBwAKU74yr9nOOwx3F+cgz2Xbsiu14IUqVSmVaJSeHH41eZv+206fi3\nC9n895We2Cmr3ubRWrRaLdnZ2aYOHbez/NgyElJ+BeDPrD/JLs3mlY6v4efnx549ezAYDNVqZVmb\nHT58mKioKP78809KSkoICQlh9OjRpm3ms2bNYsuWLRY/27t3b6Kioiyeswb91VScJ01E1sCZHamp\njJk9G83R30CnNZtHQs2LaNSdh7L6ET6kG808wijQFJBRfI023m3veQFvyZILjo6OpsJ94eHhODg4\nEBkZSd++fbG3tyc7u+JSjvLtEN7e0t4IK3x8MEt9OKiQu7lJFU695OFgYduMhTHBtshkMlSdO5uN\n2TVtIvbcWUGQaxOCXG/sw3dVueKr8ROJBUH4h46mH6FQWWA6LtIWEXchjgltnpYuqBq2Z88epkyZ\nQkREBMuXL0cmkxETE8PUqVN55513GD16NABz5szh+++/59VXX+WBBx7gl19+YdmyZWg0GqZOnQqU\nJSAmTpxIamoqr7/+OsHBwRw6dIilS5eSmZnJzJkz7+nXolAoSE5OJuP629RysocH4e3igscfx3FN\nSjI7l9uyJUf6PATp6bB9+43x3Ny7qh9QU34+nWZ2nJGv5uSVXDo0bShRRJW7du0anp6ed9xCojfq\nTYmFcvuv7OOVjq/h4OCAi4sLmZmZVUpS1Hbx8fG8+OKL9OjRgw8//BCFQsHmzZt5++23yc3N5fnn\nnwfKil1GRUVhvGUbj6urqxRhA5C3YCGFn3xadiCT4fbYI9fvATtJFlN942B0oLN/2UohDwcPglyt\n8xLcqsmFvLw8du3aRYsWLWjTpo3ZudatW2M0Gjl//jzNmzfn7NmzFT5/5swZZDJZhc9am8uMGWh+\nO4YhOxvkclxfnyGSC1bWM7AXuy/u4s+ssox9O+/7CQ8Qb71tmVGno3DNZ6h/+QWjWgN2SlRduuAw\naCBFn32OIS8PpzGjcbzpLZggCIKtk1Px7Zu8jhUVXrp0qWkLa/kW1a5duzJgwADWrl3L6NGj+f33\n39m6dSszZ85k4sSJAHTs2JH09HSzpew7duwgMTGRTz75xFS8u3379mRlZbF+/XpTV7F7pUWLFjRs\nWMnDd8+eGA4fwfDCi2bDHj0fJNzCyjq5XG7VbhG3CvAw73Mvl4Gfm2MlV0urqrUSjl/7o8LYzQW8\n/fz8SEtLqxfJhYULFxIUFMTKlStNSaxu3boxePBgNm7caEou2NnZVVoQXQrG0lIKP/vspgEjoft/\nrfwDQp1i1eSCXC5n7ty5REREsGbNGrNz5b1rGzVqxODBg1m8eDEnTpygXbt2pmu2bduGp6cnXbt2\ntWbYFdi3aY3v4YNojhxFGRKM8g7tQISaZ6+wZ0HPRZzNPotcJiPMo7nUIQl3kL/oQwpXrjIb0/ya\nQMGnK017XUvj/ofnxq9w6NlTihAFQRCqrYNfR1x1ruQr8wFwsXdlQNNBEkdVs15++WXc3NxMiQUo\ne6AJDQ01tX/eunUrKpXKtIqh3KJFi8yOd+7ciZOTE3369DEbHzp0KBs2bGD37t2MHTv2Hn0lZfei\nvr6+lZ7X9+5F2i3FhlWpqXje5jNSGd8jhKN/Z/NXeiEKuYxneoVWSDjYitTU1Cp11jAYDRXG2njd\nqMXk7+9PUlKS2fNBXVWeaLt5dYxCoaBVq1b89NNPkm7JuS2jEQzmqyhkBsvFUYW6x6rJBRcXF8aP\nH8+6deuYMWMGjz76KHZ2dsTHx7Np0ybCw8Np164dzZs3JzY2ltdee43p06fj4+PD9u3bOXjwIEuW\nLLHqEjRDcTEFy1egPfY79l274PLSFFCpKFy5iqL//AfkChpMfgaX556zWkw15VJWEV/GJ5NVqOHh\n+/0Z2K727Q9t0bCF1CEItyGXyzlw4AAqlYrWGzdhsZv2LUW0zn3yKZfy883GjEajpEtPBUEQKmMn\ntyMipwctBragWFdMj0Y9cK9j2/QGDhxocTw5OZmQkLI+9cePH6dly5Y4Ot7+4fbcuXMEBwdXqK3Q\nrFlZUdnExMQaiLj6Sr7/Ac3Ro8gDAyt0MdLfpuWblDxdVMS8GEFSegENnVV4uthup6yCggKzDh2V\necCnPU1dm3Ih/wIAjkpHxrZ8ynTew8OD/FvuEeqq8roKt7p48eIdO6xJSeboiPP4pyj67HPTWHJE\nOOKOvX6w+nflzJkzCQ4O5uuvv2bq1KnI5XICAwN57rnnTAV/HBwciImJYcmSJcybN4+ioiLCwsJY\nvnw5/fv3t2q8ua+/QcnWsirr6l9/RZ+ejjIoiIKlH5uuyX/3feRu7jg/Mcqqsf0TOiO8tPYIGQVl\nnTcOJmVip5DTp/W9W4oo1D+9e/cmNze37KBxIOTk3PEzni1b4mmhNY6zs+1WwBYEoX5ToKRPk75S\nh2FV0dHRpKam8tJLLwGQkpJC9+7d+fHHH1m9ejXJycm4urry+OOP8/LLL5uKKGZnZ5sSEjdzcnLC\n3t5eknbj+R8upiByuelY1rAhxptqfznY8Ha9K9nFlGj0uPtYTN/blKoU6yzWFvFUqwmkFKWg1Wt4\nMLAXvs62t2pEClevXmX16tUkJSURGRlpGi8tLWXevHnEx8ebtowMHTqUKVOmmK02shZ9WhoOAwdi\n37EDunPnUT3Yg8vHj1s9DkEakqS8Ro0aZeoKURlPT08WLFhgpYjMKRQKdu7ciRzosH0HN/8oLPj2\nv2jc3Lg1L3955UrOOZp3sdBqtZJM6qpIVzuYEgvldp1KE8kFoUY5OzubkgLq994j6+mnMebmmc7L\nHB2x79oV9d69ANi1aoXnK6+gaFi33voJgiDUJVu3biUyMpL+/fszfPhwAIqLizlx4gSXLl3ipZde\nwsPDg7179/LFF19w8eJFli8ve3hXq9UW241D2VYLKdqNF65dZz6g1ZLSpjVN5XIcBgzA5f9etnpM\nVbH0h0S+PnQJgCZeznz6dGe8bHj1wp3sv7KPZb8tRWvQ4qR0Yla3N0VigbKOEePHjwcw1T6JuKkG\nSF5eHnK5nPnz56PT6fjhhx9YtWoVSUlJrFixwqqxFq5dR94774JOh9zLC6//fIVd61Ygkgv1hm2u\np5HY0KFDKSoqAsDg5wtXU03nFI0a4ejrU1Yx+CYu97Wk8y1V8EHaSq2300BRcZ+Wn7tt7tMT6gZV\n5074HT2C9sQJFP7+6FNTsbvvPuRubsQsXMioQYOwu//+e96GTBDqMqesLPTZObUuQVdQoiWrUE1T\n7wZShyLcQXR0NB9//DF9+/ZlyZIlpnGFQkFWVhbffvstHh5l33+dOnWiqKiITZs2cfLkSdq2bYtK\npbLYbhzKWo5L0W5c5uCA8eal9kYjfwwfRhcb3PKanZ3NlStXuJxdakosAFzMLCI67iTju1btJZHB\nULG2gZQMRgOfnYhGayj73ijWFbPu1OcsfSjyDp+s+9q2bcvWrVvJyckhLi6OyZMnM3XqVJ5//nnm\nzJnD7NmzcXFxMV0fHh6ORqNh+/btnDp1ymqF8A2FheTP/8C0rciQmUn+h4vx/HKtVf5+wTaI5IIF\nrq6upqRA6YIFZE95CWNRETJXVzw/mI/c14eMoY9iLChrPSXz9MRn7psoq1AF11a42el4qkcwX/36\nNwYjhPg0YGz3plKHJdRxckdHVNcLsiqDbrTEKW7YEPsHHpAqLEGo9Qw5OWSOf5qHjh0jbfUaXF97\nFZdXpkodVpV8c+gSK+LOotYZaObbgKXjOuLjav0HTOHO5s+fz4YNGxg3bhxz5swxSwZ7eXnRoEED\nU2KhXPfu3dm4cSOJiYm0bdsWb29vi+3GCwoK0Gq1krQbdxo+jMJVUaZjY2Ehnn9fsHocd9KkSRPO\nnTtHRkYGf+XJAPNaRH9dyeC4Qzrp6em3LVoJEBISYlN79vUGPbnqXLOxS/mX+PbcNwwJGYJKWX9/\nJjg6OnLfffcBZYkDBwcHIiMj6devH6GhoRY/079/f7Zt28bp06etl1zIy8NYUmI2pk9NreRqoa6y\nnZ8qNsqhX1/8fjuC9uw57Freh/z6Em//k8fZ+u9ZPDx0CKoHH0RWC4vNvdS/OcM7NyanSEMLf1fk\ncvHGWJCG++UrZD09EWNxCc4TxuP4r8FSh1QnXcj7m81nNpGnzqOZRzMOux3g6q9XeLTZY7T37SB1\neMI/UBC1Gu2xY2UHWi35i5fg+OgjKJs2lTSucrm5uRV6sAPklWhZ9tMZdNcriSelF7Lyp9O83KdJ\nhWtLS0vveZxC5SIjI4mJieGNN95g0qRJFc7fd999nDhxosK4Xq8HMD3ItmzZkvj4ePR6vVmh3vIW\n5FK0G5d7VFzp09AGiziGhYURFhYGgFqr53/L95GRf2MbydODOvJgCx+io6P517/+JVWYd+VC/t8V\nxrQGLetPr+Nc9llmdZsjQVTSycvLY9euXbRo0aLCnGjdujVGo5Fz584RGhqKTqerkCgq/3lZXuvE\nGpSNGmHfuTOa6x1kAByHPWa1v1+wDSK5UAVyFxdUnTqiT00la9p01AkJyFQOuLVoXqsSC7lFGlb/\nfJ6zqfko89yZoNXj5+4otkMIktKnpdFt/QZKry+jUyck4BW7GVV4uMSR1S0luhLe3D+HAk3Z0t/T\nWafAHjKuZXAi4zgf94mkiWtTaYOUWFJSEtOmTePcuXNs2LChwla31NRUlixZQkJCAkVFRQQHBzNh\nwgSGDRsmUcQ36G59y2o0ortwwSaSC4GBgfzxR8Xe9QBpxXJ0BiezsT8vpPHLLxcsXh9004onwXp2\n7drFqlWrmDZtmsXEAsDgwYPZs2cP+/bt48EHHzSN7927F5lMxgPXV6cNHjyYuLg44uLiePjhh03X\nbdmyBZVKRb9+/e7tF2OBwsL3VW6jRlaPozpUdgqiJnZhw/6/yS7SMPiBAB5s4SN1WJWSyWTs37/f\n4moJnVHHr+p9GLHcrvBg6gF2xO3AQeaARqOpF9sn5XI5c+fOJSIigjVr1pid+/333wHw9fWlc+fO\nREREmBV4hLKWrwqFwuKW7Xup4RefU/jJJ2jPJ+HYvx+OY5+ssJpBqNtEcqEasp6ehPbUKQCMQIvU\nVAqWr8D1tVelDayK3ow9ztG/y5ciurEi7hwz/tVS0pgEoXTvXhQ3t/0yGin5aadILtSw05mnTImF\nW+mNeo6kHq7XyYXNmzezYMECXF1dLd64FhYWMmbMGJRKJXPnzsXX15e4uDhmz55NSUkJY8eOlSDq\nGxwfHkjp99+bjuUNG2LfpYuEEd3QrVu3Ss/p9AZ2Ld9Pau6Nm88RPdswvFvFlQuCNPR6PQsXLiQg\nIIDw8HBOXb8PullISAiDBw9m48aNzJgxg9dff52goCD27NnDtm3beOyxx0wdIgYOHEinTp149913\nKSoqIiQkhF9++YUtW7bw6quvVthWYQ1aCysudCrb777QqKET/36ktdRhVMmAAQPIy8szG9MZtGy+\nsommgR6CAAAgAElEQVTfco7e9rMquYrmIc2xk5cVSa9KS8vazsXFhfHjx7Nu3TpmzJjBo48+ip2d\nHfHx8WzatInw8HA6dOjA6NGj+eyzz3jrrbcYNGgQBoOB7777jl27dvH000/TyMpJMkVDD9zmvgVA\n8fYdpHfsjCEzk44tmmMYO9a0Alyou0RyoYp0V66YEgs3K/3xp1qRXCgq1d2UWCjzy5l0kVwQJKds\nUvEhwhbettY1fs63L/Ll51x7asbUtCNHjvDBBx8wa9Ys1Go1CxcurHDNunXrSE9PZ8uWLaa9rx06\ndODSpUssW7aMkSNHVloB3xqcHn8cY2ERyZ9+SkCbNrhOm4bcyenOH5SYUiEn8qmORO9J4vjZi4zp\n046RXcXqBFuSlpZGSkoKQKWdvtavX0/nzp1Zs2YNkZGRrFixguzsbAICAnj11Vd59tlnza6Piopi\n2bJlLF++nNzcXJo0acLbb7/NE088cc+/HksMeRUTr3YlYhtOTfL09MTT09NsbFvSVo7mHLF4vRw5\nBgzIkPFU6/G0aNbCGmHalJkzZxIcHMzXX3/N1KlTkcvlBAYG8txzz/HCCy8AMH36dJo0aUJMTAzb\nt29HJpMRGhrKe++9x8iRIyWLXZ+dQ85rr0Fp2bYdvzNnKVi+ArdZ/5YsJsE6RHKhihSenuDiAteL\nOJrGa8FDUGxsLFnZOTjKG1FiuPGfXFGaR3R0NAAeHh6S/hAS6i9VeDgXO3akybFjYDSi6tUT59HS\n3GDWZYEujRnVYjTfnotFb9TjYudCgbbs51n3Rg8SHhBxhz+h7vLw8GDTpk20bNmSL7/80uI1//vf\n/wgJCTElFsoNGTKEPXv2kJCQQO/eva0QbeWcnxrHgZJinrPBCve3E+TlzLyR9xMdfYgnIx6ROhzh\nFo0aNSIxMbFK1zo5OTFr1ixmzZp12+ucnZ2ZM2cOc+bYxj565ydGUbx5s6nKvaJxYzJDQySOqu77\nKzep0nOrB3xGct5fNHFtin+D+pv8HjVqVKVJvXIjRoxgxIgRVoqoanTnz5kSC+W0J09KFI1gTSK5\nUEUyR0c8PphPzvQZoNEAUOzmhu+/Z0oc2Z2VJw1an73Gu/89SWGpDh9XBxY9GU4Lf9tslSnUL6eG\nDKbr6lUYS0tRBgdLHU6dNbbVOP4V8i8KtYUEujRm2WfLGDNmTL3vI96sWbPbntfr9fz111/07du3\nwrmwsDCMRiOJiYmSJxcEQbg79h074L3lvxTHxiJ3d8f56QkYtm2TOqw6r51XO365vLfCeJhHGD7O\nPvg4224NCeH27Nq0QebmhvGmrTCqHj0kjEiwFpFcqAanYY/jMKA/2nPnkDk6sS7+F8JCas+D0IMt\nfNgxvRdXc0po4uWMUiGXOiRBMFHUolautZm7gwfuDmV7mp0MTvU+sVAVeXl56HQ6i3vBy8eysrKs\nHZYgCDXIvkN77Du0lzqMeqVvk/5cK8kg7sJONHo1RqMR52JnZnS2/Rd3wu3JnZ3x/OIz8t6fh/5q\nKkkhwfR4brLUYQlWIJIL1VAav4+CyEiMxcU4Pz2B4AMHSY/9FoWPN65vvI799UrItszRXkmor4vU\nYVTZyYwTfH5yDamFqbjYu+DnECB1SEINKNnxHQWro5HJ5TSY8gIYDOR/vIyS775DGdgY11kzsbtl\n+bmUzlzNI2r3eTIL1HQK8eRSZhHpeaX0be2Hhe56Qh2jub5azVJNBTu7sgJjarW6wjlrMBQUkD//\nA9QHD2F3//00Lcjn2patyJyccHllKg49e0oSV3X8J+ECPx6/SnGOD92v5NI6sO4XaxMEa/g7o5CV\n/ztHSk4JD7X0ZWKvEJt6sSSTyXiy5ViebHmjIG50dPQdawQJtstoNFL46UqKt25F4e+P+6KF2Ldp\nw47oaB600ClEqHvEf+Uq0qWkkDXhadOWiNxpM2gF6ABdYiKZx4/jd/iQqIJag7JLs3k34W20Bi0A\npSWlZLhkcCT1MJ39baMKutQOHjzIp59+yqlTp1AqlYSGhjJp0iQGDBhgusbW2udp/viD7BenUP5U\nnj35ee7r0oWCgwcB0J05i+bUKfwOJiC7/uAmpRKNjqnrfyO/pOz7MCm90HTur2tJdHGtPck64e6U\n9wnXarUVzpUnHhwcHKwaU7nc2W9S8t//AqA7f57WQHmUWccm4rs/HqUNt9T77vcUlu88e/3IkVc3\n/MbW13rh7CBuTwThn9AbjLwW8xtpuWWFKZOvFWKnlPN0T1FLQrh3ir78kvwFZUWRdYlnyDpxEr+D\nCRJHJViT7aQvbZx63z5TYsESY24emiOWK94Kd+dExnFTYuFmR9PFvzPAnj17mDhxIvb29ixfvpyP\nP/4Yd3d3pk6dyqZNm4Ab7fOOHz/O3LlzWbduHeHh4cyePZuvvvpKkrhL9+zF7HW/wYDf2bNm1xjS\n0ix2Z5HCyct5psSCJZfVjlaMRpCCm5sb9vb2ZGdnVzhXvh3C29vb2mEBUPrz7spPajSo4/dZL5i7\nkHAuw+y4oFTH8cs5EkUjCHVH8rUCU2Kh3K+3zDdBqGmlu382OzZkZqI5flyiaAQpiFcDVWQX1vz2\nF8jlKO9QFEyoniAXy+3ILp24TPThaNNxfe10sXTpUoKCgoiKijItze7atSsDBgxg7dq1jB492ibb\n59mFhVUYK/TyxDnnpgcKlQpFk6bWC+o2mng5oZDL0Bss739wV2pNXVfK1dfvybpKLpfTvHlzzt6S\nBAM4c+YMMpmMNm3aSBBZ2e+m2yW2lRbmmy0J9m4ApJuO5TJo6iVWAArCP+Xv7oiDnYJSrd40Fuwt\n5pZwb9mFhaH+eY/p2CCXs+nXX8HFxexeSdwn1V0iuVBF9h070ODllyhcHQ06Har+/Uk5dw6vCxeQ\nOTriOvMNlIGBUodZp4S4h/LEfWOIPbsZg9EAQBe/rrz+yEzsFdL1k7cVL7/8Mm5ubqbEApTt/w4N\nDeXI9YeNuLg4m2uf5zD4YZxGP0Hx17Egk+E89klOeHvxr73xaH77DVmDBri9+zaKhhWL50nB182R\nVwa2YOWu85Rq9QR5OpFRUEqJxkDbxu4sGt2bhg1UUocp3GODBw9m8eLFnDhxgnbt2pnGt23bhqen\nJ127dpUkLvf588iaPBn9xUvIfXy45uiI16VLoFTS4LnJqDp1lCSuqnoyoiknLudyJDkLhczASwNa\nEuDhJHVYglDrNXCwY+bQVnz0QyKFpTpaNXLluT62nWwUaj+X/3sZzR9/oDl0GJmzMx5zZjNhwnip\nwxKsSCQXqsFt1r9xmfIiRo0Ghbc3/42O5plHHkHm4iJqLdwjT7Ycy6PNHiO3NBdnOydTlXsBBg4c\naHE8OTmZkJAQ9Ho9ycnJNtc+TyaX4/HRElznzEYmkyH38EATHY339q3or6Yi83BH7mhbWw1GdWvC\nkPaNKFSXtXEt1ejJL9Hi4ybNPnuhZqWkpJBzfeVMamoqRqOR5ORkHK9/H4aEhPDkk08SGxvLa6+9\nxvTp0/Hx8WH79u0cPHiQJUuWoFAoJIndrnUrfPfvQ5+SgsLfnx1ffMEzjz+OzN4euZubJDFVh7OD\nkhUTOpGRX8rX/9nAkxEPSx2SINQZD98fwEOtfMkr1uDrZlu/V4W6SebujtOokci9vLHv2AHnJ8dI\nHVK9YzQa+fnSbv5wOcZ3f+1gUPDDKOXWe+SXJLlwpyJ0s2bNYsuWLRY/27t3b6KioqwZrplbb9YU\nfn4Ubf6awtWrQSbDZcoUnIZLUyivrnK2c8bZTiRvqiI6OprU1FReeuklm2+fp2jYsOJYgO22o3RS\nKXFSlf3IdLBX4GAvzcOkUPM++eQTtm7dajqWyWS88847puP169fTuXNnYmJiWLJkCfPmzaOoqIiw\nsDCWL19O//79JYj6BplcjrJxY9OxQqL6D/+Et6sDdnLRekWQnlGtRr3/VzwuXsJoNCKTyaQO6R9x\nsFPgIBILgpXkL/qQwhWfAFD6/ffo/voLjw8XSRxV/fKfxK/4+uwmcIA1J1aTnPsXUzu+arW/3+rJ\nhT179jBlyhQiIiJYvnw5MpmMmJgYpk6dyjvvvMPo0aMB8PHxISoqCuMtfd5cXV2tHfJtqY/+Ru60\n6abjnFdeRRnWDPubls0KgjVs3bqVyMhI+vfvz/Dhw0lLSwNss32eINiSBQsWsGDBgjte5+npWaXr\nBEGonfRZWWQ88hj6CxeIALIuXsRz/Zeg1SJTie1vgnAnxRs3mR/HfoP7B/ORiTaUVrPrYpzZ8d7L\ne3ix/UvYya3Tgc3q/6WrUoQOyh5+WrVqZe3wqk2975ZK3EYj6n37RXJBsKro6Gg+/vhj+vbty5Il\nSwDbbp8nCELN0qemUrxlK01/+w3DqFHI3d2lDqna8nVK1sUn4+Zox8B2/qaVQrbobPZZjqUfpbFL\nEOGNIqQOR6ghRes3oL9wwXSs3rOXawMGoUtMRBkaivvSj2y+jokgSEnu7o4hM/PGsYsLSLRtsL5y\nsXchu/RGdysnO2fkMus1iLT6b+6qFKGzRbGxsaY9uTfbc/kynW4Zs2vV0jpBCQIwf/58NmzYwLhx\n45gzZ45pCactt88TBKHm6K5c4drAhzHm5tIauHbmLD7/24ncqfYURkxKL2DrNX90u88DsOW3y6x9\nLhyF3PaWpO+7Es9HRxZjpGxlZd/0fjhQe/6thcoZcnMrjOkSE8v+/19/kfPSy/ge+BWZXHRyFwRL\nXGfNJPuFKaDVglyO6+x/1/qtRbXNuFbjWXR4ATqDDhkynmo1HoXMegkeqycX7lSEzlZV1i7FaDSS\n18CFog0bQCajwaSJODz0kJWjE+qryMhIYmJieOONN5g0aZLZOVtunycIQs0p3vw1xpseivQXLlC6\ncydOjz8uYVTVs+XIZXTGGw9s51ILOPp3Fl1DvSSMyrJtSVtNiQWAPZd+pq9M2rob1nCnelm3unDh\nAo888gje3t7s3r3b7FxeXh5Lly7l559/Jicnh8DAQEaOHMmkSZMkfRBxGjGcoi/Xlz0YAcjlYDCY\nzuuvXMGQmYnCx0eiCOuWC3l/czrzFM08wmjR8L47f6AeOnz4MFFRUfz555+UlJQQEhLC6NGjeeKJ\nJ0zXpKamsmTJEhISEigqKiI4OJgJEyYwbJj1a8A5DhqE34Ff0Rz9Dbv726EMs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Cjn7KAU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\niIiIiIiIXf4H89jeAbDuu7QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d4b29650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(ppiDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blame externalization</th>\n",
       "      <td>33.172414</td>\n",
       "      <td>9.606802</td>\n",
       "      <td>37.923077</td>\n",
       "      <td>8.836115</td>\n",
       "      <td>26.647059</td>\n",
       "      <td>7.565887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carefree nonplanfulness</th>\n",
       "      <td>27.551724</td>\n",
       "      <td>5.520004</td>\n",
       "      <td>31.153846</td>\n",
       "      <td>6.053183</td>\n",
       "      <td>29.647059</td>\n",
       "      <td>6.660904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coldheartedness</th>\n",
       "      <td>32.758621</td>\n",
       "      <td>5.841081</td>\n",
       "      <td>29.615385</td>\n",
       "      <td>3.819652</td>\n",
       "      <td>30.529412</td>\n",
       "      <td>5.917323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fearlessness</th>\n",
       "      <td>17.827586</td>\n",
       "      <td>5.910457</td>\n",
       "      <td>15.384615</td>\n",
       "      <td>5.058631</td>\n",
       "      <td>18.176471</td>\n",
       "      <td>5.491303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insincere Answers</th>\n",
       "      <td>41.758621</td>\n",
       "      <td>6.456753</td>\n",
       "      <td>45.153846</td>\n",
       "      <td>7.278349</td>\n",
       "      <td>41.588235</td>\n",
       "      <td>7.141943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machiavellian egocentricity</th>\n",
       "      <td>34.620690</td>\n",
       "      <td>6.008410</td>\n",
       "      <td>33.846154</td>\n",
       "      <td>5.459642</td>\n",
       "      <td>33.647059</td>\n",
       "      <td>4.755801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rebellious nonconformity</th>\n",
       "      <td>54.310345</td>\n",
       "      <td>16.444937</td>\n",
       "      <td>50.538462</td>\n",
       "      <td>10.689679</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>13.486104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social influence</th>\n",
       "      <td>44.827586</td>\n",
       "      <td>8.888472</td>\n",
       "      <td>34.538462</td>\n",
       "      <td>8.903327</td>\n",
       "      <td>45.588235</td>\n",
       "      <td>5.916701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress immunity</th>\n",
       "      <td>44.517241</td>\n",
       "      <td>9.649651</td>\n",
       "      <td>44.538462</td>\n",
       "      <td>8.809232</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>6.855655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sum PPI-R</th>\n",
       "      <td>289.586207</td>\n",
       "      <td>34.983177</td>\n",
       "      <td>277.538462</td>\n",
       "      <td>29.452265</td>\n",
       "      <td>280.235294</td>\n",
       "      <td>27.148963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group                                 0                      1             \\\n",
       "                                   mean        std        mean        std   \n",
       "Blame externalization         33.172414   9.606802   37.923077   8.836115   \n",
       "Carefree nonplanfulness       27.551724   5.520004   31.153846   6.053183   \n",
       "Coldheartedness               32.758621   5.841081   29.615385   3.819652   \n",
       "Fearlessness                  17.827586   5.910457   15.384615   5.058631   \n",
       "Insincere Answers             41.758621   6.456753   45.153846   7.278349   \n",
       "Machiavellian egocentricity   34.620690   6.008410   33.846154   5.459642   \n",
       "Rebellious nonconformity      54.310345  16.444937   50.538462  10.689679   \n",
       "Social influence              44.827586   8.888472   34.538462   8.903327   \n",
       "Stress immunity               44.517241   9.649651   44.538462   8.809232   \n",
       "Sum PPI-R                    289.586207  34.983177  277.538462  29.452265   \n",
       "\n",
       "group                                 2             \n",
       "                                   mean        std  \n",
       "Blame externalization         26.647059   7.565887  \n",
       "Carefree nonplanfulness       29.647059   6.660904  \n",
       "Coldheartedness               30.529412   5.917323  \n",
       "Fearlessness                  18.176471   5.491303  \n",
       "Insincere Answers             41.588235   7.141943  \n",
       "Machiavellian egocentricity   33.647059   4.755801  \n",
       "Rebellious nonconformity      53.000000  13.486104  \n",
       "Social influence              45.588235   5.916701  \n",
       "Stress immunity               43.000000   6.855655  \n",
       "Sum PPI-R                    280.235294  27.148963  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppiDf.groupby(\"group\").describe().T[[1,2,9,10,17,18]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferential statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; K</th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; A</th>\n",
       "      <th colspan=\"3\" halign=\"left\">K &gt; A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blame externalization</th>\n",
       "      <td>132</td>\n",
       "      <td>0.127</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>349</td>\n",
       "      <td>0.02</td>\n",
       "      <td>*</td>\n",
       "      <td>182.5</td>\n",
       "      <td>0.003</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carefree nonplanfulness</th>\n",
       "      <td>120</td>\n",
       "      <td>0.064</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>195.5</td>\n",
       "      <td>0.249</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>124.5</td>\n",
       "      <td>0.571</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coldheartedness</th>\n",
       "      <td>254.5</td>\n",
       "      <td>0.074</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>284.5</td>\n",
       "      <td>0.392</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>94</td>\n",
       "      <td>0.501</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fearlessness</th>\n",
       "      <td>241</td>\n",
       "      <td>0.156</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>246.5</td>\n",
       "      <td>0.991</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>78.5</td>\n",
       "      <td>0.185</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insincere Answers</th>\n",
       "      <td>121.5</td>\n",
       "      <td>0.07</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>249</td>\n",
       "      <td>0.964</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>145.5</td>\n",
       "      <td>0.148</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machiavellian egocentricity</th>\n",
       "      <td>193</td>\n",
       "      <td>0.913</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>272</td>\n",
       "      <td>0.568</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>121</td>\n",
       "      <td>0.674</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rebellious nonconformity</th>\n",
       "      <td>203</td>\n",
       "      <td>0.703</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>237.5</td>\n",
       "      <td>0.847</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>102</td>\n",
       "      <td>0.737</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social influence</th>\n",
       "      <td>294</td>\n",
       "      <td>0.004</td>\n",
       "      <td>*</td>\n",
       "      <td>220</td>\n",
       "      <td>0.553</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>34</td>\n",
       "      <td>0.001</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress immunity</th>\n",
       "      <td>212</td>\n",
       "      <td>0.531</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>285.5</td>\n",
       "      <td>0.38</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>117</td>\n",
       "      <td>0.801</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sum PPI-R</th>\n",
       "      <td>219</td>\n",
       "      <td>0.414</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>289.5</td>\n",
       "      <td>0.333</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>108</td>\n",
       "      <td>0.933</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             G > K               G > A               K > A  \\\n",
       "                                 U      p   sig      U      p   sig      U   \n",
       "Blame externalization          132  0.127  n.s.    349   0.02     *  182.5   \n",
       "Carefree nonplanfulness        120  0.064  n.s.  195.5  0.249  n.s.  124.5   \n",
       "Coldheartedness              254.5  0.074  n.s.  284.5  0.392  n.s.     94   \n",
       "Fearlessness                   241  0.156  n.s.  246.5  0.991  n.s.   78.5   \n",
       "Insincere Answers            121.5   0.07  n.s.    249  0.964  n.s.  145.5   \n",
       "Machiavellian egocentricity    193  0.913  n.s.    272  0.568  n.s.    121   \n",
       "Rebellious nonconformity       203  0.703  n.s.  237.5  0.847  n.s.    102   \n",
       "Social influence               294  0.004     *    220  0.553  n.s.     34   \n",
       "Stress immunity                212  0.531  n.s.  285.5   0.38  n.s.    117   \n",
       "Sum PPI-R                      219  0.414  n.s.  289.5  0.333  n.s.    108   \n",
       "\n",
       "                                          \n",
       "                                 p   sig  \n",
       "Blame externalization        0.003     *  \n",
       "Carefree nonplanfulness      0.571  n.s.  \n",
       "Coldheartedness              0.501  n.s.  \n",
       "Fearlessness                 0.185  n.s.  \n",
       "Insincere Answers            0.148  n.s.  \n",
       "Machiavellian egocentricity  0.674  n.s.  \n",
       "Rebellious nonconformity     0.737  n.s.  \n",
       "Social influence             0.001     *  \n",
       "Stress immunity              0.801  n.s.  \n",
       "Sum PPI-R                    0.933  n.s.  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeUTests(ppiDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SCID-II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scidDf = pd.read_csv('../experiment/scid_ii.csv',\n",
    "                   index_col=[0,1])\n",
    "\n",
    "scidDf = scidDf.replace(-99,np.nan)\n",
    "scidDf = scidDf.dropna()\n",
    "scidDf = scidDf[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scidDf.to_csv('../outputs/scidDf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Avoidant</th>\n",
       "      <th>Dependent</th>\n",
       "      <th>OCD</th>\n",
       "      <th>Negativistic</th>\n",
       "      <th>Depressive</th>\n",
       "      <th>Paranoid</th>\n",
       "      <th>Schizotypal</th>\n",
       "      <th>Schizoid</th>\n",
       "      <th>Histrionic</th>\n",
       "      <th>Narcissitic</th>\n",
       "      <th>Borderline</th>\n",
       "      <th>Antisocial</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">K</th>\n",
       "      <th>K010</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K011</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K012</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K013</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K014</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Avoidant  Dependent  OCD  Negativistic  Depressive  Paranoid  \\\n",
       "g id                                                                   \n",
       "K K010         5          5    7             7           7         8   \n",
       "  K011         1          0    3             0           0         1   \n",
       "  K012         2          0    2             2           2         5   \n",
       "  K013         4          6    5             0           3         4   \n",
       "  K014         0          2    5             2           2         0   \n",
       "\n",
       "        Schizotypal  Schizoid  Histrionic  Narcissitic  Borderline  \\\n",
       "g id                                                                 \n",
       "K K010            7         4           0           11          10   \n",
       "  K011            0         1           0            1           0   \n",
       "  K012            7         1           0            2           3   \n",
       "  K013            2         2           1            4           0   \n",
       "  K014            0         0           0            0           0   \n",
       "\n",
       "        Antisocial  group  \n",
       "g id                       \n",
       "K K010           7      1  \n",
       "  K011           0      1  \n",
       "  K012           0      1  \n",
       "  K013           2      1  \n",
       "  K014           0      1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scidDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeScidPlot(df):\n",
    "    fig = plt.figure(figsize=(15,9))\n",
    "    sns.set_style('ticks')\n",
    "\n",
    "    sortDf = df.sort_values('group')\n",
    "    \n",
    "    myColumns = df.columns[:-1]\n",
    "    numColumns = len(myColumns)\n",
    "    \n",
    "    for i,metric in enumerate(myColumns):\n",
    "\n",
    "        ax = fig.add_subplot(3,4,i+1)\n",
    "\n",
    "        sns.boxplot(x='group',y=metric,data=sortDf,\n",
    "                    width=0.4,linewidth=1,color='white',whis=True,notch=True,fliersize=0,ax=ax)\n",
    "\n",
    "        sns.stripplot(x='group', y=metric, data=sortDf,\n",
    "                      jitter=True, edgecolor='white',palette=myPal,ax=ax)\n",
    "\n",
    "        ax.set_title(metric)\n",
    "\n",
    "        ax.set_xticklabels(['Violence\\nOffenders','Child\\nMolesters','General\\nPopulation'],fontsize=9)\n",
    "        #ax.set_xticks(range(3),['Violence\\nOffenders','Child\\nMolesters','General\\nPopulation'])\n",
    "        ax.set_xlabel('')\n",
    "        if i%4 ==  0: \n",
    "            ax.set_ylabel('Sum Score',fontsize=15)\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        #ax.set_ylim(-0.2,4)\n",
    "        sns.despine()\n",
    "    plt.suptitle('SCID-II',fontsize=20, position=(0.51,1.02))\n",
    "    # save to file\n",
    "    #plt.savefig('../figures/afasFig.png',dpi=300) \n",
    "    # show in notebook\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3btjb27NlyxaD39UvO3v2LAcOHKBIkSK0bt1a196uXTsKFCjAmTNn0lxOLtUv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uDgUKjPU65cOUJDQylfvnyhPk9+HDt2jJs3b9KzZ89C+eFvZWVFhw4dOH78OOvWrWPAgAGldoiE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\nhBBCCCFElfw/7lVV2aWMjTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c35c6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeScidPlot(scidDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Antisocial</th>\n",
       "      <td>4.233333</td>\n",
       "      <td>4.190986</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>2.208873</td>\n",
       "      <td>1.823529</td>\n",
       "      <td>2.811479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avoidant</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.696853</td>\n",
       "      <td>2.357143</td>\n",
       "      <td>1.984833</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>1.091410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borderline</th>\n",
       "      <td>3.533333</td>\n",
       "      <td>3.234868</td>\n",
       "      <td>2.214286</td>\n",
       "      <td>2.887069</td>\n",
       "      <td>2.470588</td>\n",
       "      <td>2.648529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependent</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.522249</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>1.833750</td>\n",
       "      <td>1.117647</td>\n",
       "      <td>1.053705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depressive</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>2.069205</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>2.335818</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>1.536325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Histrionic</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.811838</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>1.411765</td>\n",
       "      <td>1.325652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Narcissitic</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.936101</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>3.149219</td>\n",
       "      <td>2.176471</td>\n",
       "      <td>1.776066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Negativistic</th>\n",
       "      <td>1.566667</td>\n",
       "      <td>1.675036</td>\n",
       "      <td>2.071429</td>\n",
       "      <td>1.899971</td>\n",
       "      <td>1.235294</td>\n",
       "      <td>1.437420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>1.631585</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1.785165</td>\n",
       "      <td>4.294118</td>\n",
       "      <td>2.114377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paranoid</th>\n",
       "      <td>2.566667</td>\n",
       "      <td>2.095699</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>2.567763</td>\n",
       "      <td>1.352941</td>\n",
       "      <td>1.656094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schizoid</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.423933</td>\n",
       "      <td>2.642857</td>\n",
       "      <td>1.984833</td>\n",
       "      <td>1.588235</td>\n",
       "      <td>1.003670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schizotypal</th>\n",
       "      <td>1.233333</td>\n",
       "      <td>0.935261</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>2.381245</td>\n",
       "      <td>1.411765</td>\n",
       "      <td>1.371989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group                0                   1                   2          \n",
       "                  mean       std      mean       std      mean       std\n",
       "Antisocial    4.233333  4.190986  2.428571  2.208873  1.823529  2.811479\n",
       "Avoidant      1.500000  1.696853  2.357143  1.984833  0.764706  1.091410\n",
       "Borderline    3.533333  3.234868  2.214286  2.887069  2.470588  2.648529\n",
       "Dependent     1.400000  1.522249  2.142857  1.833750  1.117647  1.053705\n",
       "Depressive    1.833333  2.069205  3.071429  2.335818  0.882353  1.536325\n",
       "Histrionic    1.600000  1.811838  0.571429  0.755929  1.411765  1.325652\n",
       "Narcissitic   4.000000  2.936101  3.071429  3.149219  2.176471  1.776066\n",
       "Negativistic  1.566667  1.675036  2.071429  1.899971  1.235294  1.437420\n",
       "OCD           3.600000  1.631585  4.428571  1.785165  4.294118  2.114377\n",
       "Paranoid      2.566667  2.095699  2.857143  2.567763  1.352941  1.656094\n",
       "Schizoid      1.800000  1.423933  2.642857  1.984833  1.588235  1.003670\n",
       "Schizotypal   1.233333  0.935261  2.142857  2.381245  1.411765  1.371989"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scidDf.groupby(\"group\").describe().T[[1,2,9,10,17,18]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inferential statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; K</th>\n",
       "      <th colspan=\"3\" halign=\"left\">G &gt; A</th>\n",
       "      <th colspan=\"3\" halign=\"left\">K &gt; A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Antisocial</th>\n",
       "      <td>250.5</td>\n",
       "      <td>0.308</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>355</td>\n",
       "      <td>0.025</td>\n",
       "      <td>*</td>\n",
       "      <td>147.5</td>\n",
       "      <td>0.252</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avoidant</th>\n",
       "      <td>154</td>\n",
       "      <td>0.151</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>314</td>\n",
       "      <td>0.171</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>177.5</td>\n",
       "      <td>0.016</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borderline</th>\n",
       "      <td>273.5</td>\n",
       "      <td>0.108</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>309.5</td>\n",
       "      <td>0.227</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>107.5</td>\n",
       "      <td>0.653</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependent</th>\n",
       "      <td>155.5</td>\n",
       "      <td>0.15</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>275</td>\n",
       "      <td>0.642</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>159</td>\n",
       "      <td>0.1</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depressive</th>\n",
       "      <td>137</td>\n",
       "      <td>0.063</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>331.5</td>\n",
       "      <td>0.077</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>194</td>\n",
       "      <td>0.002</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Histrionic</th>\n",
       "      <td>279</td>\n",
       "      <td>0.069</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>256</td>\n",
       "      <td>0.991</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>76</td>\n",
       "      <td>0.074</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Narcissitic</th>\n",
       "      <td>255.5</td>\n",
       "      <td>0.253</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>349.5</td>\n",
       "      <td>0.036</td>\n",
       "      <td>*</td>\n",
       "      <td>132</td>\n",
       "      <td>0.615</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Negativistic</th>\n",
       "      <td>169.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>285.5</td>\n",
       "      <td>0.49</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>153.5</td>\n",
       "      <td>0.164</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>152.5</td>\n",
       "      <td>0.145</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>195</td>\n",
       "      <td>0.182</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>120</td>\n",
       "      <td>0.984</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paranoid</th>\n",
       "      <td>198.5</td>\n",
       "      <td>0.778</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>348.5</td>\n",
       "      <td>0.035</td>\n",
       "      <td>*</td>\n",
       "      <td>169</td>\n",
       "      <td>0.044</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schizoid</th>\n",
       "      <td>158.5</td>\n",
       "      <td>0.188</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>274</td>\n",
       "      <td>0.669</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>155</td>\n",
       "      <td>0.144</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schizotypal</th>\n",
       "      <td>178.5</td>\n",
       "      <td>0.408</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>246</td>\n",
       "      <td>0.841</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>133.5</td>\n",
       "      <td>0.568</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              G > K               G > A               K > A             \n",
       "                  U      p   sig      U      p   sig      U      p   sig\n",
       "Antisocial    250.5  0.308  n.s.    355  0.025     *  147.5  0.252  n.s.\n",
       "Avoidant        154  0.151  n.s.    314  0.171  n.s.  177.5  0.016     *\n",
       "Borderline    273.5  0.108  n.s.  309.5  0.227  n.s.  107.5  0.653  n.s.\n",
       "Dependent     155.5   0.15  n.s.    275  0.642  n.s.    159    0.1  n.s.\n",
       "Depressive      137  0.063  n.s.  331.5  0.077  n.s.    194  0.002     *\n",
       "Histrionic      279  0.069  n.s.    256  0.991  n.s.     76  0.074  n.s.\n",
       "Narcissitic   255.5  0.253  n.s.  349.5  0.036     *    132  0.615  n.s.\n",
       "Negativistic  169.5    0.3  n.s.  285.5   0.49  n.s.  153.5  0.164  n.s.\n",
       "OCD           152.5  0.145  n.s.    195  0.182  n.s.    120  0.984  n.s.\n",
       "Paranoid      198.5  0.778  n.s.  348.5  0.035     *    169  0.044     *\n",
       "Schizoid      158.5  0.188  n.s.    274  0.669  n.s.    155  0.144  n.s.\n",
       "Schizotypal   178.5  0.408  n.s.    246  0.841  n.s.  133.5  0.568  n.s."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeUTests(scidDf)"
   ]
  }
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