run_questionnaires.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import utils.conversion as conv\n",
    "import utils.summary_scores as sums\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = '/nobackup/adenauer2/LSD/Originals/Raw'\n",
    "\n",
    "internal_dir = '/nobackup/adenauer2/LSD/Originals/Converted/Questionnaires'\n",
    "restricted_dir = '/nobackup/adenauer2/LSD/Restricted/Questionnaires'\n",
    "open_dir = '/nobackup/adenauer2/LSD/Open/Questionnaires'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_A = '%s/Questionnaires/surveyA_151013.csv' % data_dir\n",
    "df_A = pd.read_csv(f_A, sep = \",\", parse_dates =[1,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-control scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_SelfCtrl(df_A.copy(), out_dir = '%s/SCS' % internal_dir, public = False)\n",
    "conv.run_SelfCtrl(df_A.copy(), out_dir = '%s/SCS' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/SCS/SCS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_SelfCtrl(raw_A.copy(), out_dir = '%s/SCS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet addiction test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_IAT(df_A.copy(), out_dir = '%s/IAT' % internal_dir, public = False)\n",
    "conv.run_IAT(df_A.copy(), out_dir = '%s/IAT' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/IAT/IAT.csv' % restricted_dir, \n",
    "                    sep = \",\", parse_dates =[1,5], \n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_IAT(raw_A.copy(), out_dir = '%s/IAT' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Varieties of inner speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_VIS(df_A.copy(), out_dir = '%s/VISQ' % internal_dir, public = False)\n",
    "conv.run_VIS(df_A.copy(), out_dir = '%s/VISQ' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/VISQ/VISQ.csv' % restricted_dir, \n",
    "                    sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_VIS(raw_A.copy(), out_dir = '%s/VISQ' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spontaneous and Deliberate Mind Wandering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_MW_SD(df_A.copy(), out_dir = '%s/S-D-MW' % internal_dir, public = False)\n",
    "conv.run_MW_SD(df_A.copy(), out_dir = '%s/S-D-MW' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/S-D-MW/S-D-MW.csv' % restricted_dir, \n",
    "                    sep = \",\", parse_dates =[1,5], \n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_MW_SD(raw_A.copy(), out_dir = '%s/S-D-MW' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short dark triad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_SDT(df_A.copy(), out_dir = '%s/SD3' % internal_dir, public = False)\n",
    "conv.run_SDT(df_A.copy(), out_dir = '%s/SD3' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/SD3/SD3.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_SDT(raw_A.copy(), out_dir = '%s/SD3' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Social desirability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_SDS(df_A.copy(), out_dir = '%s/SDS' % internal_dir, public = False)\n",
    "conv.run_SDS(df_A.copy(), out_dir = '%s/SDS' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/SDS/SDS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_SDS(raw_A.copy(), out_dir = '%s/SDS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impulsivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_UPPSP(df_A.copy(), out_dir = '%s/UPPS-P' % internal_dir, public = False)\n",
    "conv.run_UPPSP(df_A.copy(), out_dir = '%s/UPPS-P' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/UPPS-P/UPPS-P.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_UPPSP(raw_A.copy(), out_dir = '%s/UPPS-P' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuckmann Procrastination Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_TPS(df_A.copy(), out_dir = '%s/TPS' % internal_dir, public = False)\n",
    "conv.run_TPS(df_A.copy(), out_dir = '%s/TPS' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/TPS/TPS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_TPS(raw_A.copy(), out_dir = '%s/TPS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ASR 18 - 59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_ASR(df_A.copy(), out_dir = '%s/ASR' % internal_dir, public = False)\n",
    "conv.run_ASR(df_A.copy(), out_dir = '%s/ASR' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/ASR/ASR.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_ASR(raw_A.copy(), out_dir = '%s/ASR' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-esteem scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_SE(df_A.copy(), out_dir = '%s/SE' % internal_dir, public = False)\n",
    "conv.run_SE(df_A.copy(), out_dir = '%s/SE' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/SE/SE.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_SE(raw_A.copy(), out_dir = '%s/SE' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Involuntary Musical Imagery Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_IMIS(df_A.copy(), out_dir = '%s/IMIS' % internal_dir, public = False)\n",
    "conv.run_IMIS(df_A.copy(), out_dir = '%s/IMIS' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/IMIS/IMIS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_IMIS(raw_A.copy(), out_dir = '%s/IMIS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goldsmiths Musical Sophistication Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_GoldMSI(df_A.copy(), out_dir = '%s/Gold-MSI' % internal_dir, public = False)\n",
    "conv.run_GoldMSI(df_A.copy(), out_dir = '%s/Gold-MSI' % restricted_dir, public = True)\n",
    "\n",
    "raw_A = pd.read_csv('%s/Gold-MSI/Gold-MSI.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_GoldMSI(raw_A.copy(), out_dir = '%s/Gold-MSI' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-gender identity questionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_MGIQ(df_A.copy(), out_dir = '%s/MGIQ' % internal_dir, public = False)\n",
    "conv.run_MGIQ(df_A.copy(), out_dir = '%s/MGIQ' % restricted_dir, public = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_B = '%s/Questionnaires/surveyB_151013.csv' % data_dir\n",
    "f2_B = '%s/Questionnaires/surveyF_151013.csv' % data_dir # due to neo ffi items\n",
    "df_B = pd.read_csv(f_B, sep = \",\", parse_dates =[1,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NEO PI-R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_NEOPIR(f_B, f2_B, out_dir = '%s/NEO-PI-R' % internal_dir, public = False)\n",
    "conv.run_NEOPIR(f_B, f2_B, out_dir = '%s/NEO-PI-R' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/NEO-PI-R/NEO-PI-R.csv' % restricted_dir, \n",
    "                    sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_NEOPIR(raw_B.copy(), out_dir = '%s/NEO-PI-R' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Epsworth sleepiness scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_ESS(df_B.copy(), out_dir = '%s/ESS' % internal_dir, public = False)\n",
    "conv.run_ESS(df_B.copy(), out_dir = '%s/ESS' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/ESS/ESS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_ESS(raw_B.copy(), out_dir = '%s/ESS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_BDI(df_B.copy(), out_dir = '%s/BDI' % internal_dir, public = False)\n",
    "conv.run_BDI(df_B.copy(), out_dir = '%s/BDI' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/BDI/BDI.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_BDI(raw_B.copy(), out_dir = '%s/BDI' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hamilton Anxiety Depression Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_HADS(df_B.copy(), out_dir = '%s/HADS' % internal_dir, public = False)\n",
    "conv.run_HADS(df_B.copy(), out_dir = '%s/HADS' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/HADS/HADS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_HADS(raw_B.copy(), out_dir = '%s/HADS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boredom proness scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_BPS(df_B.copy(), out_dir = '%s/BP' % internal_dir, public = False)\n",
    "conv.run_BPS(df_B.copy(), out_dir = '%s/BP' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/BP/BP.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_BPS(raw_B.copy(), out_dir = '%s/BP' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Derryberry Attention Control Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_ACS(df_B.copy(), out_dir = '%s/ACS' % internal_dir, public = False)\n",
    "conv.run_ACS(df_B.copy(), out_dir = '%s/ACS' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/ACS/ACS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_ACS(raw_B.copy(), out_dir = '%s/ACS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSSI - Persönlichkeitsstil- und Störungsinventar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_PSSI(df_B.copy(), out_dir = '%s/PSSI' % internal_dir, public = False)\n",
    "conv.run_PSSI(df_B.copy(), out_dir = '%s/PSSI' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/PSSI/PSSI.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_PSSI(raw_B.copy(), out_dir = '%s/PSSI' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-media inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_MMI(df_B.copy(), out_dir = '%s/MMI' % internal_dir, public = False)\n",
    "conv.run_MMI(df_B.copy(), out_dir = '%s/MMI' % restricted_dir, public = True)\n",
    "\n",
    "raw_B = pd.read_csv('%s/MMI/MMI.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_MMI(raw_B.copy(), out_dir = '%s/MMI' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mobile phone usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_mobile(df_B.copy(), out_dir = '%s/MPU' % internal_dir, public = False)\n",
    "conv.run_mobile(df_B.copy(), out_dir = '%s/MPU' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey C (scanning day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_C1 = '%s/Questionnaires/surveyCactive_151013.csv' % data_dir\n",
    "f_C2 = '%s/Questionnaires/surveyCinactive_151013.csv' % data_dir\n",
    "f_C3 = '%s/Questionnaires/surveyCcorrected_151013.csv' % data_dir\n",
    "\n",
    "df_C1 = pd.read_csv(f_C1, sep = \",\", parse_dates =[1,5])\n",
    "df_C1['DS14 answer codes'] = pd.Series(np.zeros(len(df_C1)), index=df_C1.index)\n",
    "\n",
    "df_C2 = pd.read_csv(f_C2, sep = \",\", parse_dates =[1,5])\n",
    "df_C2['DS14 answer codes'] = pd.Series(np.zeros(len(df_C2)), index=df_C2.index)\n",
    "\n",
    "df_C3 = pd.read_csv(f_C3, sep = \",\", parse_dates =[1,5])\n",
    "df_C3['DS14 answer codes'] = pd.Series(np.ones(len(df_C3)), index=df_C3.index)\n",
    "\n",
    "df_C = pd.concat([df_C1, df_C2, df_C3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Facebook intensity scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_FIS(df_C.copy(), out_dir = '%s/FBI' % internal_dir, public = False)\n",
    "conv.run_FIS(df_C.copy(), out_dir = '%s/FBI' % restricted_dir, public = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NYC-Q on scanning day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### full NYC-Q (LIMIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_NYCQ_postscan = '%s/Questionnaires/LIMIT -NYC-Q post Scan.ods - LIMIT_20151215.csv' % data_dir\n",
    "df_postscan = pd.read_csv(f_NYCQ_postscan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_postscan(df_postscan, out_dir = '%s/NYC-Q_postscan' % internal_dir, public = False)\n",
    "conv.run_NYCQ_postscan(df_postscan, out_dir = '%s/NYC-Q_postscan' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_posttasks(df_C.copy(), out_dir = '%s/NYC-Q_posttasks' % internal_dir, public = False) \n",
    "conv.run_NYCQ_posttasks(df_C.copy(), out_dir = '%s/NYC-Q_posttasks' % open_dir, public = True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### short NYC-Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_NYCQ_prescan = '%s/Questionnaires/Prescan short NYC-Q_20151215.csv' % data_dir\n",
    "f_NYCQ_inscan = '%s/Questionnaires/NYCQ-short_inscanner.csv' % data_dir\n",
    "f_NYCQ_postETS = '%s/Questionnaires/NYCQ-short-slider post Win_20151215.csv' % data_dir\n",
    "\n",
    "df_prescan = pd.read_csv(f_NYCQ_prescan)\n",
    "df_inscan = pd.read_csv(f_NYCQ_inscan)\n",
    "df_postETS = pd.read_csv(f_NYCQ_postETS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_prescan(df_prescan.copy(), out_dir = '%s/Short-NYC_prescan' % internal_dir, public = False)\n",
    "conv.run_NYCQ_prescan(df_prescan.copy(), out_dir = '%s/Short-NYC_prescan' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=1, out_dir = '%s/Short-NYC_inscan1' % internal_dir, public = False)\n",
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=1, out_dir = '%s/Short-NYC_inscan1' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=2, out_dir = '%s/Short-NYC_inscan2' % internal_dir, public = False)\n",
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=2, out_dir = '%s/Short-NYC_inscan2' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=3, out_dir = '%s/Short-NYC_inscan3' % internal_dir, public = False)\n",
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=3, out_dir = '%s/Short-NYC_inscan3' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=4, out_dir = '%s/Short-NYC_inscan4' % internal_dir, public = False)\n",
    "conv.run_NYCQ_inscan(df_inscan.copy(), scan=4, out_dir = '%s/Short-NYC_inscan4' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# where to put this\n",
    "conv.run_NYCQ_postETS(df_postETS.copy(), out_dir = '%s/Short-NYC_postETS' % internal_dir, public = False)\n",
    "conv.run_NYCQ_postETS(df_postETS.copy(), out_dir = '%s/Short-NYC_postETS' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_F = '%s/Questionnaires/surveyF_151013.csv' % data_dir\n",
    "df_F = pd.read_csv(f_F, sep = \",\", parse_dates =[1,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_STAI(df_F.copy(), out_dir = '%s/STAI-G-X2' % internal_dir, public = False)\n",
    "conv.run_STAI(df_F.copy(), out_dir = '%s/STAI-G-X2' % restricted_dir, public = True)\n",
    "\n",
    "raw_F = pd.read_csv('%s/STAI-G-X2/STAI-G-X2.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_STAI(raw_F.copy(), out_dir = '%s/STAI-G-X2' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STAXI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_STAXI(df_F.copy(),  out_dir = '%s/STAXI' % internal_dir, public = False)\n",
    "conv.run_STAXI(df_F.copy(),  out_dir = '%s/STAXI' % restricted_dir, public = True)\n",
    "\n",
    "raw_F = pd.read_csv('%s/STAXI/STAXI.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_STAXI(raw_F.copy(),  out_dir = '%s/STAXI' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIS BAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_BISBAS(df_F.copy(),  out_dir = '%s/BISBAS' % internal_dir, public = False)\n",
    "conv.run_BISBAS(df_F.copy(),  out_dir = '%s/BISBAS' % restricted_dir, public = True)\n",
    "\n",
    "raw_F = pd.read_csv('%s/BISBAS/BISBAS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_BISBAS(raw_F.copy(),  out_dir = '%s/BISBAS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_G  = '%s/Questionnaires/surveyG_151013.csv' % data_dir\n",
    "\n",
    "# AMAS was part of F and G\n",
    "df_G = pd.read_csv(f_G, sep = \",\", parse_dates =[1,5])\n",
    "df_G = pd.concat([df_F, df_G])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abbreviated Math Anxiety Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_AMAS(df_G.copy(), out_dir = '%s/AMAS' % internal_dir, public = False)\n",
    "conv.run_AMAS(df_G.copy(), out_dir = '%s/AMAS' % restricted_dir, public = True)\n",
    "\n",
    "raw_G = pd.read_csv('%s/AMAS/AMAS.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                   dtype={'ids':str})\n",
    "\n",
    "sums.run_AMAS(raw_G.copy(), out_dir = '%s/AMAS' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey Creativity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# raw data\n",
    "\n",
    "f_Cr = '%s/Questionnaires/survey_creativity_metacog.csv' % data_dir\n",
    "f_syn = '%s/Questionnaires/synesthesia_color_picker.csv' % data_dir\n",
    "\n",
    "df_Cr = pd.read_csv(f_Cr, sep = \",\", parse_dates =[1,5], \n",
    "                 encoding=\"utf-8-sig\").rename(columns = {'IDcode' : 'ID'})\n",
    "df_syn = pd.read_csv(f_syn, sep = \",\", parse_dates =[1,5]).rename(columns = {'DB_ID' : 'ID'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creative achievement questionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_CAQ(df_Cr.copy(), out_dir = '%s/CAQ' % internal_dir, public = False)\n",
    "conv.run_CAQ(df_Cr.copy(), out_dir = '%s/CAQ' % restricted_dir, public = True)\n",
    "\n",
    "raw_Cr = pd.read_csv('%s/CAQ/CAQ.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_CAQ(raw_Cr.copy(), out_dir = '%s/CAQ' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metacognition questionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_MCQ30(df_Cr.copy(), out_dir = '%s/MCQ-30' % internal_dir, public = False)\n",
    "conv.run_MCQ30(df_Cr.copy(), out_dir = '%s/MCQ-30' % restricted_dir, public = True)\n",
    "\n",
    "raw_Cr = pd.read_csv('%s/MCQ-30/MCQ30.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_MCQ30(raw_Cr.copy(), out_dir = '%s/MCQ-30' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Body Consciousness Questionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_BCQ(df_Cr.copy(), out_dir = '%s/BCQ' % internal_dir, public = False)\n",
    "conv.run_BCQ(df_Cr.copy(), out_dir = '%s/BCQ' % restricted_dir, public = True)\n",
    "\n",
    "raw_Cr = pd.read_csv('%s/BCQ/BCQ.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_BCQ(raw_Cr.copy(), out_dir = '%s/BCQ' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Five Facet Mindfulness Questionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv.run_FFMQ(df_Cr.copy(), out_dir = '%s/FFMQ' % internal_dir, public = False)\n",
    "conv.run_FFMQ(df_Cr.copy(), out_dir = '%s/FFMQ' % restricted_dir, public = True)\n",
    "\n",
    "raw_Cr = pd.read_csv('%s/FFMQ/FFMQ.csv' % restricted_dir, \n",
    "                   sep = \",\", parse_dates =[1,5],\n",
    "                    dtype={'ids':str})\n",
    "\n",
    "sums.run_FFMQ(raw_Cr.copy(), out_dir = '%s/FFMQ' % open_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synesthesia Color picker test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv.run_syn(df_syn.copy(), out_dir = '%s/SYN' % internal_dir, public = False)\n",
    "conv.run_syn(df_syn.copy(), out_dir = '%s/SYN' % open_dir, public = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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