notebooks/001_questionnaires.ipynb

{
 "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": {
    "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": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last updated: 2017-04-22 \n",
      "\n",
      "CPython 2.7.9\n",
      "IPython 5.3.0\n",
      "\n",
      "numpy 1.12.1\n",
      "scipy 0.19.0\n",
      "sklearn 0.18.1\n",
      "pandas 0.19.2\n",
      "matplotlib 2.0.0\n",
      "seaborn 0.7.1\n",
      "\n",
      "compiler   : GCC 4.9.2\n",
      "system     : Linux\n",
      "release    : 3.16.0-4-amd64\n",
      "machine    : x86_64\n",
      "processor  : \n",
      "CPU cores  : 4\n",
      "interpreter: 64bit\n",
      "Git hash   : b8121bec24b05956ce8732a407e4cd9a156ee323\n"
     ]
    }
   ],
   "source": [
    "# Sebastian Raschka; https://github.com/rasbt/watermark/blob/master/docs/watermark.ipynb\n",
    "%load_ext watermark\n",
    "%watermark -v -m -d -u -p numpy,scipy,sklearn,pandas,matplotlib,seaborn -g"
   ]
  },
  {
   "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": 3,
   "metadata": {
    "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": 4,
   "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": 5,
   "metadata": {
    "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": 6,
   "metadata": {
    "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": 6,
     "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": 7,
   "metadata": {
    "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": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K009</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "<p>1 rows × 30 columns</p>\n",
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       "      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": 8,
     "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": 9,
   "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": 10,
   "metadata": {
    "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": 11,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
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       "      <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": 11,
     "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": 12,
   "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": 13,
   "metadata": {},
   "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": 13,
     "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": 14,
   "metadata": {},
   "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": 15,
   "metadata": {},
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigDf[bigDf.columns[fAgr]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "meanDf = makeMean(bigDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save for re-use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABECAYAAAAiJuZQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAPx5PqQBBIkuQJDoAgSJLkDQDzFxXvkWdD/yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c92e04a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(myPal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sortDf = meanDf.sort_values('group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sortDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aS5Ys0fbt29WoUSPb2Vk3F33pn+iZzebbJtz0DRsAZJR+Np90Y9fJzP52ExMT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\n2HqytPfX7Ev+hXPpZ3G38eDR+n3RqrQP9FJWNaLS9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3J2dq60fP36668kJiYSGRmJvb09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\nCyYrfZIkSZIkSZIkSRbs/wHy6scNZuzOqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c92d38790>"
      ]
     },
     "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 Sex\\nOffenders','General\\nPopulation'],fontsize=15)\n",
    "    #ax.set_xticks(range(3),['Violence\\nOffenders','Child Sex\\nOffenders','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": 24,
   "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,alternative='two-sided')\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": 25,
   "metadata": {},
   "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": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rQElRzZs3x/nz5xu8VmMJOQB/++2349FHH8XDDz9c5zXLly/Hhx9+iN27dzfI\nCJvNhieffBIHDhzAiBEj8Ne//hXqCA8gYlw/RHp4lR8KBeSLlyJS0S6dOQPr/AWQfv7Z7zjfogVM\ns2bC3acLLLIzIt5IfZXr8a4VaSyUUhQ7zmJvaQGKKg6DoLYI8uDRJb0X+mcNRbahfcR2WK4+wC0W\nC0zVvN/yGtMsa5KWlgYAMJvNtc6Vlpb6zl+1k9TwDh2O2i8WFQF6vlksFjRr1iyoLZGkTjEpLi6G\n3X6tl9CFCxdw5swZFBUVBbxeFEXs3r27wZ6JIAiYMmUKDh48iHHjxmHevHksg4uRkERqRjuVJDjX\nrYNjzbuA5L+WdswYaKY9iSqNDEl2hnUfoP7K9WStFZGIiGPlB1BYWoDLruKA1xiUJvTNvAX9Mm+B\nSZUacRv69esHpVKJr7/+2i+++3XNqZY16NChAzIzM/H5559j4sSJvuPl5eUoKCjAbbfdBgC+Gr5L\nly75rvF4PDh8+HCtNQ8fPoySkhKfwF2+fBmHDh3C448/3ujv11DqFJPDhw/jpZde8qkjx3H4+OOP\n8fHHH9e5GKXUF2QKlfnz5+PgwYP4wx/+gAULFkQ9LsNgNIZIzWiXfvsN1nnzIdV4KeMzM2Ga+TLc\nud1RFilvJEjlerLWilR5KrDPvAsHzXvgqmPrL9vQDnlZQ9E1rRcUfPRaz+fk5ODBBx/E8uXLQQhB\nr169sHv3bqxbtw4A6pw8y/M8Xn75ZcyePRvTp0/HhAkT4HA4sHLlSlBKfbGUzp07Izs7G2+99RbS\n0tKg1+uxdu1aeDyeWi/cHo8HkydPxjPPPANCCJYtW4bU1FRMmjQpat+/JnX+pMeMGYMTJ06gvLwc\nlFJs2bIFnTt3rnN2iUqlQlZWFh566KGQb341G0Kj0SA3Nxdba85igHd7LdwqewYjHCLROp7KMpzr\n18PxzhrA49+AUXvHaGimPY0qHYmMNxIkuC4TCkeS1YpQSvG7/RcUlhbgVOVR0ABKq+AU6JreB/2z\nhqK1oXa5QbSYM2cO9Ho9PvroI7z55pvo06cPZs2ahVdffRX6IB2Vx40bB6PRiFWrVuHZZ5+FXq9H\n//79sXTpUl+dCc/zWLZsGebPn49Zs2YhNTUVEyZMQG5uLj744AO/9Xr27Inhw4fj//7v/+DxeDB4\n8GDMmjUrpttcIbdTGTZsGB599NGw0n5r8sknn2Du3LlBr9m2bZvvhxsI1k6FES0iNaNdOnfOGxs5\nftzvON/mAmoiAAAgAElEQVSsGUyzZkIc0BtWKfz29MGC61drReyCFMli+ajikd04Vr4fheYClLou\nBbzGpEpFv8zB6JsxCAZV+MPLgNBb0FdWVmLnzp0YMmSI30P7ww8/xKuvvoqffvrJL5YSLR5++GG4\n3W6sX78+6vcKRsg+YH09shrDvffeG3KWGIMRSyIxo53KMlybNsH+99W1vBHNyBHQPzsdVj3gDlNI\nggXXk7FWpMJdhn3mXThk2QOhjgr/NsYbkJeZj87pPaHg4hNj1el0ePXVV9GxY0c89thjSElJQVFR\nEf72t7/h3nvvjYmQJBJxrYBnMBIR6nJ5A+1hpMdKxcWwLVgI8cgRv+NcejpSXn4J5JY8WCRbWDGY\n+oLryVQrQinFWdvP2Fu6E6erTiBQ0EjJqdC9WV/kZeWjpT7+uxAajQbvv/8+3njjDfzpT3+Cw+FA\n69atMXnyZEyePDne5sWcOre5OnfujGeeeQbPPPOM7++hVsCfPHky8pbWAdvmYkQSUlUFUlbe6EJE\nSghcmzfD/tYqwO3fDl4zbBj0zz0Lm1ERdqt4Tq/zdvQNEFxPploRtyzgSFkhCs0FKBNqD7gCgFR1\nOvplDkafjEHQKw0Br4kkrAV940ioCngGI15QQryjdcOIj8gXL8K6YCHEQ4f8jnOpqTC9+ALkoQNh\nkeygpPHBb06j9opIgNHByVQrUiaYsc9cgMOWvXCTwDU77U03IS8zHzendQMfp60sRugkTAU8gxEv\nqMfj3dZqZKNGSimErVthf3MlaI3ff83QodC9+BxsJgXcdVRlhwKn4MGlpQbs6JsstSKUEpyxFqGw\ntAC/WAPvXqh4NXo0y0VeVj6ydGz0RDKREBXwDEasoW43OI0GxOn0TkRsZOxCvnwZ1kWLIO7b73ec\nS0mB6YXnId46EGWyE5Q0TqiCxUWSpVZEkF04bNmLfeZdKHfXrvoGgHR1c+RmDUGv5gOgU9adUstI\nXOJeAc9gxApis8G5fgOE7d+CVFWC0xug7p8H3R13gDM0bC+eUgrhiy9gX74C1OlfG6IePBi6F5+H\nNVUZVoffuuIiySIiZtdl7DPvwpGyQnjqiBHdkNIJeZlD0TG1C3gucJEfIzmIewU8gxELiM2Gypdn\nQTr3uze4LkmgFeUQvv4a4pGjSJk9K2RBkUtLYVv0Gjx79/od54xGGGfMgHj7YJQRJ0Ab97CvKy4i\nywR2QYIjgUWEUILTVSdQWLoTZ20/B7xGzWvQq3l/5GYNQYY2MlNbGfEnrhXwDEascK7fcE1IRAmo\nNi9dvnQRrq++gr6ekQeUUghffQ37smWgdv/aEPXAgdC+/DysaRpIpHFV7BzPgUtPqxUXSYaqdZfk\nwCHLT9hn/gGVnrKA1zTTZCIvKx+9mveHRhF4JDAjeQkaM5k5c6bvz3v37sW9997LakgYSYnwzfYr\nQiICAdJ+3bt+CComsqUMtsWvwfOjfzyQMxhgfOYZuEcOQTkRGuWN1BUXIYTC4U7sqvUS50UUmnfi\naNn+Ouaoc7gptQvyMvNxQ0oncGwrq8kS1wp4BiMWULcbcmWF1yOp47FM7Tav0Kj8Z1xQSuHe/i1s\nS5eC1pgzoc7Lg2bm87A200KuI721Pjid1rulVe2+lFI43RJsgpSQY3IJlVFUeRSFpQU4Zz8T8BqN\nQovezQcgNysfzTS153swmh4Nbqm5c+dOfPLJJzh58iSsVit2796NrVu34ty5c3jiiSegq2PoDoMR\nNwgBr9GCiHWn5nJGUy0hIeXlsC5ZAk/BLv9rdToYpj0N9x23oYK6/bbMQoVTq7x9tLT+2z3eqnUR\nUpjdiaOBU7LjgHk39pt/gFWsDHhNhrYl+mflo0ezXKgVtWthGPHl4sWLeO2117B7926IooiBAwdi\nzpw5aNOmTdhrN0hM/ud//gcbNmwApRQKhcI3tOXYsWNYu3YtCgoKsGbNGr+pYgxGrLma9gsAxG4H\nKTVDPfgWCEHmTGiG+CeOCDv+Bdsbb4BWVfkdV/XtC+3MF1CVqQehDa9i53jOKyI1OmG7PTKsgggx\nAavWLznPo7C0AMfKD0AOsI3HgcPNad2Rl5mP9qabWHFzglJZWYlHHnkEdrsdkyZNgkqlwpo1a/DQ\nQw9hy5YtYXcYDllM1q1bh/Xr12PUqFF46aWX8Omnn+LNN98EAEyfPh0OhwObNm3Cu+++62vBwmDE\nipppv3xqGtSDb4H21lvBGQzQ3XEHxCNHIV+6WOuzilatoRs92rtOZSVsr78O97+/879Iq4Xxqafg\nuWtYo7wRjgNgNHrni1SLiyRq1bpMZZysOIzC0gIUO84GvEan0KN3xkDkZg5GmqZ5jC1kNJT33nsP\nxcXF2LhxI7p37w4AGDJkCO655x6sXr0as2fPDmv9kFvQjxs3DpRSfPrppwC8BYorVqzw68N1//33\nw+Fw4IsvvgjLqIbAenMxqqf9AvCl/oIQKFq19qX9UocDrq++gnvXD6B2GzijCZohg6EbPRqcwQDh\n+52wLVkCWum/haPq1Qu6WS/B2sIIiTb8oc9pNd64SLX5IrJMYBUSr2rdLlpxwPwj9lt+hF20Brym\nha418rKGonuzvlDxTW+8diR7c1X3kuPN8OHDkZGR4RvedZVJkybh7Nmz2LlzZ1jrh+yZnD17Nmj1\nOwDk5eXhww8/DMsgBqOhXE37BVArY6t62i9nMEB/333erK1qwXZSVQXrX/4C97c7/BfWaGB88knI\n40ajnDiBBgpJoPkiMqGwu8SEqxW54PgNe0sLcKLiEEiA78mBR+f0nuifmY82xhvYVlYQAnnJ2hHD\nob9/Avg4taWvqqrC+fPnfSOBq9OjRw/s2bMHpaWlyMrKavQ9QhYTrVaLsrLA+eNXKS0thVbL8scZ\nsUX4ZjsAb7NGiLXTUwOm/V4REveuH2BbvBikvNz/dPfuMMydBVvLFLgbWDcSaL5IIqb5SkTCiYqD\nKCwtwEXnuYDX6JVG9M0YhH6Zg5GiTouxhclHTS8ZAEhVJZwbN8KztxBpixfFRVBKSkoAwDcjvjpX\nBeTSpUuxEZN+/fph+/btmDFjBlq1qt2A7bfffsO3336LQYMGNdoYBqOhULcbxFoFKsmAHPhtP1Da\nL7HZYP/bstpBebUaxsmTgXvvQhltWE8tX1wkJQXclRndiZjma/VUYr/5Rxy0/AhHHYO5WunbIC8r\nH93S+0DJqwJe05SQCcXZEgHHzzvD2uaq7iXXRDr3O5wbNsL4+GONXr+xOBzetj6Bsm2vHnM6wxsZ\nHbKYTJ8+Hbt27cKECRPwxBNP4OxZb1Bu7969OHr0KFavXg1RFDF16tSwDGIwGoRaDU6rA60KnKoK\n1E77de/eA9trr4FYLH7XKbt0gXHuHDiym8FFGtZTK1BcJJHSfCmlOO84i8LSnSiqOAKC2gkEPKdA\n1/TeyMscgmxD+ya/lWVzyThx3onj550ouuCCy+P9mfy/4I0QgnLVSw52Ph5icjU0HuzflA8wYK0h\nhCwm3bp1w7JlyzBnzhwsXLjQd3zSpEmglMJoNGLx4sXo1atXWAYxEptECihSUYRcUgL1wAEhpf0S\nux325SsgbNvmf4FKBcPjj0M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OOjWPLjk6X/GgSde0Cgd58D7RUPFqqHh1UjfGDIeNGzdi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c92e0aa10>"
      ]
     },
     "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": 27,
   "metadata": {},
   "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": 27,
     "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": 28,
   "metadata": {},
   "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": 29,
   "metadata": {},
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppiDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppiDf.to_csv('../outputs/ppiDf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 Sex\\nOffenders','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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3H3n06BFWrVoFf39/UYci5OzZs5CSkqqTfW3fvh2vXr3C5MmTARRfbAQCQZ3su7koXTaA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RsY0+X3qKioCIjvVIwqhhhV1+MOEtjEx8fT67PVmI8edQ2UlLiP2TIy3H+vffwJyh55mP7XzfZ1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VGU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VH+rdRqNR06dBDNhr2Q+vaSjSELDSFwvn2DLeDAAfLz870ijTsvL++c2mLK5XIiIiIoKCjw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c90997210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(ppiDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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": 33,
     "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": 34,
   "metadata": {},
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeUTests(ppiDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SCID-II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scidDf.to_csv('../outputs/scidDf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scidDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "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 Sex\\nOffenders','General\\nPopulation'],fontsize=9)\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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Qf369WnXrh3m5ub07t2btLQ0du/erdnP0NCQfv36oSgKBw4c0Knn2rVrhIeH06pVK03W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iFzrnsRPmTIEDZs2IC/v79JJJ4qKio4fvw4Y8eO5f4uXXhmTCASCVhbVF2SFpZVoqrQ4GZv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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c8e7c3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeScidPlot(scidDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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": 40,
     "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": 41,
   "metadata": {},
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeUTests(scidDf)"
   ]
  }
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