EmoVar Analysis (Study 1) - Authenticity vs. Power.ipynb

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       "<p>20 rows × 25 columns</p>\n",
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      "text/plain": [
       "      date      time      group    subject     build  blocknum  trialnum  \\\n",
       "0   103117  12:52:10  806904944  340280316  5.0.10.0         1         1   \n",
       "1   103117  12:52:10  806904944  340280316  5.0.10.0         2         1   \n",
       "2   103117  12:52:10  806904944  340280316  5.0.10.0         2         2   \n",
       "3   103117  12:52:10  806904944  340280316  5.0.10.0         2         3   \n",
       "4   103117  12:52:10  806904944  340280316  5.0.10.0         2         4   \n",
       "5   103117  12:52:10  806904944  340280316  5.0.10.0         2         5   \n",
       "6   103117  12:52:10  806904944  340280316  5.0.10.0         2         6   \n",
       "7   103117  12:52:10  806904944  340280316  5.0.10.0         2         7   \n",
       "8   103117  12:52:10  806904944  340280316  5.0.10.0         2         8   \n",
       "9   103117  12:52:10  806904944  340280316  5.0.10.0         2         9   \n",
       "10  103117  12:52:10  806904944  340280316  5.0.10.0         2        10   \n",
       "11  103117  12:52:10  806904944  340280316  5.0.10.0         2        11   \n",
       "12  103117  12:52:10  806904944  340280316  5.0.10.0         2        12   \n",
       "13  103117  12:52:10  806904944  340280316  5.0.10.0         2        13   \n",
       "14  103117  12:52:10  806904944  340280316  5.0.10.0         2        14   \n",
       "15  103117  12:52:10  806904944  340280316  5.0.10.0         2        15   \n",
       "16  103117  12:52:10  806904944  340280316  5.0.10.0         2        16   \n",
       "17  103117  12:52:10  806904944  340280316  5.0.10.0         2        17   \n",
       "18  103117  12:52:10  806904944  340280316  5.0.10.0         2        18   \n",
       "19  103117  12:52:10  806904944  340280316  5.0.10.0         2        19   \n",
       "\n",
       "           blockcode         trialcode  pretrialpause       ...        \\\n",
       "0         expt_intro        expt_intro              0       ...         \n",
       "1   hv3_female_block  hv3_female_trial              0       ...         \n",
       "2   hv3_female_block  hv3_female_trial              0       ...         \n",
       "3   hv3_female_block  hv3_female_trial              0       ...         \n",
       "4   hv3_female_block  hv3_female_trial              0       ...         \n",
       "5   hv3_female_block  hv3_female_trial              0       ...         \n",
       "6   hv3_female_block  hv3_female_trial              0       ...         \n",
       "7   hv3_female_block  hv3_female_trial              0       ...         \n",
       "8   hv3_female_block  hv3_female_trial              0       ...         \n",
       "9   hv3_female_block  hv3_female_trial              0       ...         \n",
       "10  hv3_female_block  hv3_female_trial              0       ...         \n",
       "11  hv3_female_block  hv3_female_trial              0       ...         \n",
       "12  hv3_female_block  hv3_female_trial              0       ...         \n",
       "13  hv3_female_block  hv3_female_trial              0       ...         \n",
       "14  hv3_female_block  hv3_female_trial              0       ...         \n",
       "15  hv3_female_block  hv3_female_trial              0       ...         \n",
       "16  hv3_female_block  hv3_female_trial              0       ...         \n",
       "17  hv3_female_block  hv3_female_trial              0       ...         \n",
       "18  hv3_female_block  hv3_female_trial              0       ...         \n",
       "19  hv3_female_block  hv3_female_trial              0       ...         \n",
       "\n",
       "    latency  stimulusnumber1  \\\n",
       "0    249349                1   \n",
       "1       500               85   \n",
       "2       500               73   \n",
       "3       500               62   \n",
       "4       500               79   \n",
       "5       500               65   \n",
       "6       500               76   \n",
       "7       500               87   \n",
       "8       500               64   \n",
       "9       500               83   \n",
       "10      500               72   \n",
       "11      500               74   \n",
       "12      500               80   \n",
       "13      500               86   \n",
       "14      500               66   \n",
       "15      500               84   \n",
       "16      500               71   \n",
       "17      500               67   \n",
       "18      500               88   \n",
       "19      500               69   \n",
       "\n",
       "                                        stimulusitem1  stimulusonset1  \\\n",
       "0   Welcome to the study!    During this session, ...              30   \n",
       "1                                       WF-011_11.jpg               3   \n",
       "2                                      WF-011_-04.jpg               2   \n",
       "3                                      WF-011_-15.jpg               1   \n",
       "4                                       WF-011_05.jpg               1   \n",
       "5                                      WF-011_-12.jpg               1   \n",
       "6                                       WF-011_02.jpg               1   \n",
       "7                                       WF-011_13.jpg               1   \n",
       "8                                      WF-011_-13.jpg               1   \n",
       "9                                       WF-011_09.jpg               1   \n",
       "10                                     WF-011_-05.jpg               2   \n",
       "11                                     WF-011_-03.jpg               1   \n",
       "12                                      WF-011_06.jpg               2   \n",
       "13                                      WF-011_12.jpg               2   \n",
       "14                                     WF-011_-11.jpg               2   \n",
       "15                                      WF-011_10.jpg               3   \n",
       "16                                     WF-011_-06.jpg               2   \n",
       "17                                     WF-011_-10.jpg               2   \n",
       "18                                      WF-011_14.jpg               2   \n",
       "19                                     WF-011_-08.jpg               2   \n",
       "\n",
       "    stimulusnumber2  stimulusitem2  stimulusonset2 stimulusnumber3  \\\n",
       "0                 1   cbs_logo.jpg              30               1   \n",
       "1                 0              0               0               0   \n",
       "2                 0              0               0               0   \n",
       "3                 0              0               0               0   \n",
       "4                 0              0               0               0   \n",
       "5                 0              0               0               0   \n",
       "6                 0              0               0               0   \n",
       "7                 0              0               0               0   \n",
       "8                 0              0               0               0   \n",
       "9                 0              0               0               0   \n",
       "10                0              0               0               0   \n",
       "11                0              0               0               0   \n",
       "12                0              0               0               0   \n",
       "13                0              0               0               0   \n",
       "14                0              0               0               0   \n",
       "15                0              0               0               0   \n",
       "16                0              0               0               0   \n",
       "17                0              0               0               0   \n",
       "18                0              0               0               0   \n",
       "19                0              0               0               0   \n",
       "\n",
       "                                        stimulusitem3  stimulusonset3  \n",
       "0   If you need to exit the study early, you can d...              30  \n",
       "1                                                   0               0  \n",
       "2                                                   0               0  \n",
       "3                                                   0               0  \n",
       "4                                                   0               0  \n",
       "5                                                   0               0  \n",
       "6                                                   0               0  \n",
       "7                                                   0               0  \n",
       "8                                                   0               0  \n",
       "9                                                   0               0  \n",
       "10                                                  0               0  \n",
       "11                                                  0               0  \n",
       "12                                                  0               0  \n",
       "13                                                  0               0  \n",
       "14                                                  0               0  \n",
       "15                                                  0               0  \n",
       "16                                                  0               0  \n",
       "17                                                  0               0  \n",
       "18                                                  0               0  \n",
       "19                                                  0               0  \n",
       "\n",
       "[20 rows x 25 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Why, when, and how variability in emotion expression makes people look authentic and leader-like\"\n",
    "# Analysis script for Study 1 \n",
    "# Evan W. Carr, 05-01-2018 (Columbia Business School)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from plotnine import *\n",
    "from rpy2.robjects import r, pandas2ri\n",
    "\n",
    "%matplotlib inline  \n",
    "\n",
    "# Set working directory to location of data files, and read in .csv's:\n",
    "rawData = pd.read_csv('amanic1_exptrun_final_clean_17_11_01.csv')\n",
    "rawDemog = pd.read_csv('demographics_survey_survey_17_11_01.csv')\n",
    "rawJobs = pd.read_csv('job_survey_survey_17_11_01.csv')\n",
    "\n",
    "rawData.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>159159933</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>940965457</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>292839563</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>773929842</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>886406741</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>460234065</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>955204288</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>776094049</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>548694195</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58519494</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52981696</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>614959507</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>529377873</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>141467720</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>900356126</td>\n",
       "      <td>125</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>221796339</td>\n",
       "      <td>165</td>\n",
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       "      <th>90</th>\n",
       "      <td>340280316</td>\n",
       "      <td>193</td>\n",
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       "      <th>98</th>\n",
       "      <td>363540134</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>167059365</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>65292312</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>427039206</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>594715388</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>649291712</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>596158045</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>603400630</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>687082924</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>613487610</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>683074910</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>682362134</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>622212991</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  counts\n",
       "36   159159933       1\n",
       "213  940965457       1\n",
       "78   292839563      24\n",
       "178  773929842      29\n",
       "201  886406741      32\n",
       "111  460234065      32\n",
       "215  955204288      32\n",
       "180  776094049      33\n",
       "138  548694195      33\n",
       "11    58519494      34\n",
       "9     52981696      64\n",
       "148  614959507      64\n",
       "130  529377873      96\n",
       "32   141467720     104\n",
       "203  900356126     125\n",
       "61   221796339     165\n",
       "90   340280316     193\n",
       "98   363540134     321\n",
       "39   167059365     353\n",
       "15    65292312     353\n",
       "107  427039206     353\n",
       "144  594715388     372\n",
       "154  649291712     386\n",
       "145  596158045     386\n",
       "146  603400630     386\n",
       "161  687082924     386\n",
       "147  613487610     386\n",
       "160  683074910     386\n",
       "159  682362134     386\n",
       "149  622212991     386"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All subjects should have 386 observations (rows) in the dataset ...\n",
    "# Any that have more/less than 386 need to be removed before analysis:\n",
    "rawObsCount = (rawData\n",
    "               .groupby('subject', as_index=False)\n",
    "               .size()\n",
    "               .reset_index(name='counts')\n",
    "               .sort_values(by='counts'))\n",
    "\n",
    "rawObsCount.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([159159933, 940965457, 292839563, 773929842, 886406741, 460234065,\n",
       "       955204288, 776094049, 548694195,  58519494,  52981696, 614959507,\n",
       "       529377873, 141467720, 900356126, 221796339, 340280316, 363540134,\n",
       "       167059365,  65292312, 427039206, 594715388], dtype=int64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of subject ID's to exclude:\n",
    "exclSbj = rawObsCount.query('counts != 386')\n",
    "exclSbj = np.array(exclSbj['subject'])\n",
    "exclSbj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>stimulusitem1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>465293704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4171</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>hv1_female_block</td>\n",
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       "      <td>465293704</td>\n",
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       "      <td>2</td>\n",
       "      <td>hv1_female_block</td>\n",
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       "      <td>500</td>\n",
       "      <td>WF-009_04.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_09.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  blocknum  trialnum         blockcode         trialcode  \\\n",
       "193  465293704         1         1        expt_intro        expt_intro   \n",
       "194  465293704         2         1  hv1_female_block  hv1_female_trial   \n",
       "195  465293704         2         2  hv1_female_block  hv1_female_trial   \n",
       "196  465293704         2         3  hv1_female_block  hv1_female_trial   \n",
       "197  465293704         2         4  hv1_female_block  hv1_female_trial   \n",
       "\n",
       "     response  latency                                      stimulusitem1  \n",
       "193      57.0     4171  Welcome to the study!    During this session, ...  \n",
       "194       0.0      500                                      WF-009_11.jpg  \n",
       "195       0.0      500                                      WF-009_04.jpg  \n",
       "196       0.0      500                                      WF-009_09.jpg  \n",
       "197       0.0      500                                     WF-009_-06.jpg  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to important columns, and exclude subjects that didn't finish the study:\n",
    "cleanCols = ['subject', 'blocknum', 'trialnum', 'blockcode', 'trialcode', 'response', 'latency', 'stimulusitem1']\n",
    "cleanData = rawData.loc[~rawData['subject'].isin(exclSbj), cleanCols]\n",
    "cleanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>latency</th>\n",
       "      <th>latency_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>891765</td>\n",
       "      <td>442378</td>\n",
       "      <td>7.372967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6822400</td>\n",
       "      <td>670745</td>\n",
       "      <td>11.179083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7504972</td>\n",
       "      <td>692462</td>\n",
       "      <td>11.541033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12191860</td>\n",
       "      <td>310893</td>\n",
       "      <td>5.181550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13417493</td>\n",
       "      <td>354598</td>\n",
       "      <td>5.909967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22024596</td>\n",
       "      <td>499076</td>\n",
       "      <td>8.317933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33510929</td>\n",
       "      <td>320256</td>\n",
       "      <td>5.337600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40037666</td>\n",
       "      <td>346593</td>\n",
       "      <td>5.776550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49563899</td>\n",
       "      <td>326455</td>\n",
       "      <td>5.440917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54532176</td>\n",
       "      <td>286538</td>\n",
       "      <td>4.775633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  latency  latency_mins\n",
       "0    891765   442378      7.372967\n",
       "1   6822400   670745     11.179083\n",
       "2   7504972   692462     11.541033\n",
       "3  12191860   310893      5.181550\n",
       "4  13417493   354598      5.909967\n",
       "5  22024596   499076      8.317933\n",
       "6  33510929   320256      5.337600\n",
       "7  40037666   346593      5.776550\n",
       "8  49563899   326455      5.440917\n",
       "9  54532176   286538      4.775633"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quickly check subject times to complete the study (should usually be around 5-8 minutes):\n",
    "completionTimes = (cleanData\n",
    "                   .groupby('subject')\n",
    "                   .agg({'latency': 'sum'})\n",
    "                   .reset_index())\n",
    "\n",
    "completionTimes['latency_mins'] = (completionTimes['latency'] / 1000) / 60\n",
    "\n",
    "completionTimes.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.87"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get quick demographics summary, without excluded subjects:\n",
    "demogSummary = (rawDemog\n",
    "                .groupby('subject')\n",
    "                .agg({'age_response': np.mean, \"gender_response\": pd.Series.unique})\n",
    "                .reset_index())\n",
    "\n",
    "demogSummary = demogSummary[~demogSummary['subject'].isin(exclSbj)]\n",
    "\n",
    "demogSummary['age_response'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.995632211950523"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['age_response'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      108\n",
       "Female     92\n",
       "Name: gender_response, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['gender_response'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>currently_employed_response</th>\n",
       "      <th>years_at_job_response</th>\n",
       "      <th>mgr_status_response</th>\n",
       "      <th>industry_type_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>465293704</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>911299474</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62276385</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Corporate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>649291712</td>\n",
       "      <td>Yes</td>\n",
       "      <td>12</td>\n",
       "      <td>non-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>870963081</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>208383413</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>non-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>926899964</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Consumer Goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70465461</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Non-profit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>188487282</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>69429657</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Non-profit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>689157329</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200003390</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>327545437</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>846311920</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Non-profit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>131935159</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>40037666</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>194056049</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>891765</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>225363170</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>490363871</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Arts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject currently_employed_response  years_at_job_response  \\\n",
       "0   465293704                         Yes                      9   \n",
       "1   911299474                         Yes                      4   \n",
       "2    62276385                         Yes                      9   \n",
       "3   649291712                         Yes                     12   \n",
       "4   870963081                         Yes                      1   \n",
       "5   208383413                         Yes                      3   \n",
       "6   926899964                         Yes                     10   \n",
       "7    70465461                         Yes                      5   \n",
       "8   188487282                         Yes                      8   \n",
       "9    69429657                         Yes                      5   \n",
       "10  689157329                         Yes                     20   \n",
       "11  200003390                         Yes                      8   \n",
       "12  327545437                         Yes                     15   \n",
       "13  846311920                         Yes                      3   \n",
       "14  131935159                         Yes                      6   \n",
       "15   40037666                         Yes                     17   \n",
       "16  194056049                         Yes                     10   \n",
       "17     891765                         Yes                      4   \n",
       "18  225363170                         Yes                      2   \n",
       "19  490363871                         Yes                      2   \n",
       "\n",
       "   mgr_status_response industry_type_response  \n",
       "0    middle-management          Manufacturing  \n",
       "1       non-management                Finance  \n",
       "2       non-management              Corporate  \n",
       "3       non-management              High Tech  \n",
       "4       non-management         Transportation  \n",
       "5       non-management              High Tech  \n",
       "6     lower-management         Consumer Goods  \n",
       "7       top-management             Non-profit  \n",
       "8       non-management                Service  \n",
       "9    middle-management             Non-profit  \n",
       "10   middle-management                Service  \n",
       "11    lower-management              High Tech  \n",
       "12   middle-management                Service  \n",
       "13      non-management             Non-profit  \n",
       "14      top-management                Service  \n",
       "15   middle-management          Manufacturing  \n",
       "16      non-management             Government  \n",
       "17      top-management            Educational  \n",
       "18      non-management              High Tech  \n",
       "19      non-management                   Arts  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the data from the job survey.  Questions asked included ...\n",
    "#    - Currently employed? (y/n) -- if so, how many years?\n",
    "#    - Managerial status? (top, middle, lower, non)\n",
    "#    - Job industry\n",
    "\n",
    "cleanJobs = rawJobs[['subject', \n",
    "                     'currently_employed_response', \n",
    "                     'years_at_job_response',\n",
    "                     'mgr_status_response',\n",
    "                     'industry_type_response']]\n",
    "\n",
    "cleanJobs.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.53694581280789"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are most of the subjects currently employed?  (Yes, ~97.5% have a job)\n",
    "100 * (len(cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']) / cleanJobs.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mgr_status_response</th>\n",
       "      <th colspan=\"2\" halign=\"left\">years_at_job_response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lower-management</td>\n",
       "      <td>7.265306</td>\n",
       "      <td>6.092261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>middle-management</td>\n",
       "      <td>9.875000</td>\n",
       "      <td>7.069939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>non-management</td>\n",
       "      <td>5.527473</td>\n",
       "      <td>4.924633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>top-management</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.477226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mgr_status_response years_at_job_response          \n",
       "                                       mean       std\n",
       "0    lower-management              7.265306  6.092261\n",
       "1   middle-management              9.875000  7.069939\n",
       "2      non-management              5.527473  4.924633\n",
       "3      top-management              6.000000  5.477226"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Of the subjects that are currently employed, how long have they been at their jobs, by management type?\n",
    "# (average range between 5-10 years across levels)\n",
    "(cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']\n",
    " .groupby('mgr_status_response')\n",
    " .agg({'years_at_job_response': [np.mean, np.std]})\n",
    " .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>percent_of_sbj</th>\n",
       "      <th>mean_yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High Tech</td>\n",
       "      <td>14.646465</td>\n",
       "      <td>5.310345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Service</td>\n",
       "      <td>12.626263</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finance</td>\n",
       "      <td>11.616162</td>\n",
       "      <td>6.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Educational</td>\n",
       "      <td>11.616162</td>\n",
       "      <td>8.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>10.606061</td>\n",
       "      <td>9.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Consumer Goods</td>\n",
       "      <td>10.101010</td>\n",
       "      <td>6.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Medical</td>\n",
       "      <td>8.080808</td>\n",
       "      <td>4.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Non-profit</td>\n",
       "      <td>5.050505</td>\n",
       "      <td>8.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Construction</td>\n",
       "      <td>4.040404</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arts</td>\n",
       "      <td>2.525253</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Government</td>\n",
       "      <td>2.525253</td>\n",
       "      <td>9.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2.020202</td>\n",
       "      <td>7.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Corporate</td>\n",
       "      <td>1.515152</td>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Recreational</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Legal</td>\n",
       "      <td>0.505051</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Media</td>\n",
       "      <td>0.505051</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  percent_of_sbj   mean_yrs\n",
       "0        High Tech       14.646465   5.310345\n",
       "1          Service       12.626263   5.600000\n",
       "2          Finance       11.616162   6.478261\n",
       "3      Educational       11.616162   8.043478\n",
       "4    Manufacturing       10.606061   9.571429\n",
       "5   Consumer Goods       10.101010   6.600000\n",
       "6          Medical        8.080808   4.812500\n",
       "7       Non-profit        5.050505   8.700000\n",
       "8     Construction        4.040404   8.000000\n",
       "9             Arts        2.525253   6.800000\n",
       "10      Government        2.525253   9.400000\n",
       "11  Transportation        2.020202   7.250000\n",
       "12       Corporate        1.515152  12.333333\n",
       "13     Agriculture        1.010101  10.000000\n",
       "14    Recreational        1.010101  13.000000\n",
       "15           Legal        0.505051   4.000000\n",
       "16           Media        0.505051   7.000000"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What percentages of our subjects fall into which industries, and how many years have they been there?\n",
    "# (Many subjects work in high-tech, service, finance, and/or education -- all > 10% of our subject pool, \n",
    "# and all industry types have average number of years > 4)\n",
    "industryPerc = (100 * (cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes', 'industry_type_response']\n",
    "                      .value_counts() \\\n",
    "                      / cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes', 'industry_type_response'].shape[0])\n",
    "                .to_frame()).reset_index().rename(index=str, columns={'industry_type_response': 'percent_of_sbj'})\n",
    "\n",
    "industryYrs = (cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']\n",
    "               .groupby('industry_type_response')\n",
    "               .agg({'years_at_job_response': np.mean})\n",
    "               .reset_index()\n",
    "               .rename(index=str, columns={'industry_type_response': 'index', 'years_at_job_response': 'mean_yrs'}))\n",
    "\n",
    "industryPerc.merge(industryYrs, on='index', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>stimulusitem1</th>\n",
       "      <th>model_block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>465293704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4171</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "      <td>expt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
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       "      <td>500</td>\n",
       "      <td>WF-009_11.jpg</td>\n",
       "      <td>hv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_04.jpg</td>\n",
       "      <td>hv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_09.jpg</td>\n",
       "      <td>hv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>hv1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  blocknum  trialnum         blockcode         trialcode  \\\n",
       "193  465293704         1         1        expt_intro        expt_intro   \n",
       "194  465293704         2         1  hv1_female_block  hv1_female_trial   \n",
       "195  465293704         2         2  hv1_female_block  hv1_female_trial   \n",
       "196  465293704         2         3  hv1_female_block  hv1_female_trial   \n",
       "197  465293704         2         4  hv1_female_block  hv1_female_trial   \n",
       "\n",
       "     response  latency                                      stimulusitem1  \\\n",
       "193      57.0     4171  Welcome to the study!    During this session, ...   \n",
       "194       0.0      500                                      WF-009_11.jpg   \n",
       "195       0.0      500                                      WF-009_04.jpg   \n",
       "196       0.0      500                                      WF-009_09.jpg   \n",
       "197       0.0      500                                     WF-009_-06.jpg   \n",
       "\n",
       "    model_block  \n",
       "193        expt  \n",
       "194         hv1  \n",
       "195         hv1  \n",
       "196         hv1  \n",
       "197         hv1  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create column for emotion variability condition ('model_block'):\n",
    "cleanData = (cleanData\n",
    "             .join(cleanData['blockcode'].str.split('_', 1, expand=True)\n",
    "                   .rename(columns={0:'model_block', 1:'junk'})))\n",
    "\n",
    "cleanData = cleanData.drop(['junk'], axis=1)\n",
    "\n",
    "cleanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
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       "      <th>stimulusitem1</th>\n",
       "      <th>model_block</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>465293704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4171</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "      <td>expt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>hv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>hv1_female_block</td>\n",
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       "      <td>WF-009_04.jpg</td>\n",
       "      <td>hv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_09.jpg</td>\n",
       "      <td>hv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>465293704</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>hv1_female_block</td>\n",
       "      <td>hv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>hv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  blocknum  trialnum         blockcode         trialcode  \\\n",
       "193  465293704         1         1        expt_intro        expt_intro   \n",
       "194  465293704         2         1  hv1_female_block  hv1_female_trial   \n",
       "195  465293704         2         2  hv1_female_block  hv1_female_trial   \n",
       "196  465293704         2         3  hv1_female_block  hv1_female_trial   \n",
       "197  465293704         2         4  hv1_female_block  hv1_female_trial   \n",
       "\n",
       "     response  latency                                      stimulusitem1  \\\n",
       "193      57.0     4171  Welcome to the study!    During this session, ...   \n",
       "194       0.0      500                                      WF-009_11.jpg   \n",
       "195       0.0      500                                      WF-009_04.jpg   \n",
       "196       0.0      500                                      WF-009_09.jpg   \n",
       "197       0.0      500                                     WF-009_-06.jpg   \n",
       "\n",
       "    model_block  \n",
       "193        expt  \n",
       "194          hv  \n",
       "195          hv  \n",
       "196          hv  \n",
       "197          hv  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map emotion variability condition ('model_block') to umbrella factor: \n",
    "varDict = {'expt': 'expt',\n",
    "           'lv1': 'lv',\n",
    "           'lv2': 'lv',\n",
    "           'lv3': 'lv',\n",
    "           'hv1': 'hv',\n",
    "           'hv2': 'hv',\n",
    "           'hv3': 'hv'}\n",
    "\n",
    "cleanData['model_block'] = cleanData['model_block'].map(varDict)\n",
    "cleanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>891765</td>\n",
       "      <td>3</td>\n",
       "      <td>[WF-009_03.jpg, WF-009_05.jpg, WF-009_06.jpg, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891765</td>\n",
       "      <td>4</td>\n",
       "      <td>[WM-006_02_crop.jpg, WM-006_-10_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891765</td>\n",
       "      <td>5</td>\n",
       "      <td>[WF-011_15.jpg, WF-011_04.jpg, WF-011_11.jpg, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891765</td>\n",
       "      <td>6</td>\n",
       "      <td>[WM-029_04_crop.jpg, WM-029_-15_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>891765</td>\n",
       "      <td>7</td>\n",
       "      <td>[WM-009_-04_crop.jpg, WM-009_04_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>891765</td>\n",
       "      <td>8</td>\n",
       "      <td>[WF-027_-02.jpg, WF-027_-03.jpg, WF-027_03.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>891765</td>\n",
       "      <td>9</td>\n",
       "      <td>[WF-015_-07.jpg, WF-015_-11.jpg, WF-015_06.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>891765</td>\n",
       "      <td>10</td>\n",
       "      <td>[WM-033_-04_crop.jpg, WM-033_05_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  blocknum                                      stimulusitem1\n",
       "0   891765         1  Welcome to the study!    During this session, ...\n",
       "1   891765         2  [WM-003_04_crop.jpg, WM-003_-03_crop.jpg, WM-0...\n",
       "2   891765         3  [WF-009_03.jpg, WF-009_05.jpg, WF-009_06.jpg, ...\n",
       "3   891765         4  [WM-006_02_crop.jpg, WM-006_-10_crop.jpg, WM-0...\n",
       "4   891765         5  [WF-011_15.jpg, WF-011_04.jpg, WF-011_11.jpg, ...\n",
       "5   891765         6  [WM-029_04_crop.jpg, WM-029_-15_crop.jpg, WM-0...\n",
       "6   891765         7  [WM-009_-04_crop.jpg, WM-009_04_crop.jpg, WM-0...\n",
       "7   891765         8  [WF-027_-02.jpg, WF-027_-03.jpg, WF-027_03.jpg...\n",
       "8   891765         9  [WF-015_-07.jpg, WF-015_-11.jpg, WF-015_06.jpg...\n",
       "9   891765        10  [WM-033_-04_crop.jpg, WM-033_05_crop.jpg, WM-0..."
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access and log stimulus model ID codes for inclusion in mixed-effects models (shown later):\n",
    "ratingsList = ['authenticity_slider', 'power_slider']\n",
    "modelSummary = (cleanData\n",
    "                .groupby(['subject', 'blocknum'])\n",
    "                .agg({\"stimulusitem1\": pd.Series.unique})\n",
    "                .reset_index())\n",
    "\n",
    "modelSummary.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>891765</td>\n",
       "      <td>3</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891765</td>\n",
       "      <td>4</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891765</td>\n",
       "      <td>5</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891765</td>\n",
       "      <td>6</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>891765</td>\n",
       "      <td>7</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>891765</td>\n",
       "      <td>8</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>891765</td>\n",
       "      <td>9</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>891765</td>\n",
       "      <td>10</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>891765</td>\n",
       "      <td>11</td>\n",
       "      <td>WF-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>891765</td>\n",
       "      <td>12</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>891765</td>\n",
       "      <td>13</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>891765</td>\n",
       "      <td>16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6822400</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  blocknum model_id\n",
       "0    891765         1        W\n",
       "1    891765         2   WM-003\n",
       "2    891765         3   WF-009\n",
       "3    891765         4   WM-006\n",
       "4    891765         5   WF-011\n",
       "5    891765         6   WM-029\n",
       "6    891765         7   WM-009\n",
       "7    891765         8   WF-027\n",
       "8    891765         9   WF-015\n",
       "9    891765        10   WM-033\n",
       "10   891765        11   WF-003\n",
       "11   891765        12   WM-016\n",
       "12   891765        13   WF-016\n",
       "13   891765        16        A\n",
       "14  6822400         1        W"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'modelSummary' dataframe shows all unique stimuli shown to each subject, by blocknum ...\n",
    "# Not useful yet, but will be if we can split stimulus names apart:\n",
    "modelList = []\n",
    "for i in range(0, modelSummary.shape[0]):\n",
    "    modelID = modelSummary.iloc[i]['stimulusitem1'][0].split('_', 1)[0]\n",
    "    modelList.append(modelID)\n",
    "    \n",
    "modelSummary['model_id'] = modelList\n",
    "modelSummary = modelSummary.drop('stimulusitem1', axis=1)\n",
    "\n",
    "modelSummary.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>model_block</th>\n",
       "      <th>model_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19574</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>10997</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4728</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4360</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6166</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4536</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5081</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4158</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43472</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3150</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6489</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3538</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5174</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3382</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30731</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4550</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4413</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3120</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>9777</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3658</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>12386</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3746</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>465293704</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24146</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>465293704</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>86.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4490</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15448</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3056</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4230</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3398</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5864</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3912</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>82.0</td>\n",
       "      <td>31370</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3605</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4921</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3612</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6881</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8747</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>911299474</td>\n",
       "      <td>power_slider</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6088</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>911299474</td>\n",
       "      <td>authenticity_slider</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6509</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject            trialcode  response  latency model_block model_id\n",
       "31   465293704  authenticity_slider      51.0    19574          hv   WF-009\n",
       "32   465293704         power_slider      51.0    10997          hv   WF-009\n",
       "63   465293704  authenticity_slider      52.0     4728          hv   WF-016\n",
       "64   465293704         power_slider      51.0     4360          hv   WF-016\n",
       "95   465293704         power_slider      51.0     6166          lv   WF-011\n",
       "96   465293704  authenticity_slider      51.0     4536          lv   WF-011\n",
       "127  465293704         power_slider      51.0     5081          hv   WM-009\n",
       "128  465293704  authenticity_slider      51.0     4158          hv   WM-009\n",
       "159  465293704  authenticity_slider      51.0    43472          hv   WF-015\n",
       "160  465293704         power_slider      51.0     3150          hv   WF-015\n",
       "191  465293704  authenticity_slider      51.0     6489          lv   WM-016\n",
       "192  465293704         power_slider      51.0     3538          lv   WM-016\n",
       "223  465293704         power_slider      51.0     5174          lv   WM-006\n",
       "224  465293704  authenticity_slider      51.0     3382          lv   WM-006\n",
       "255  465293704         power_slider      51.0    30731          lv   WM-003\n",
       "256  465293704  authenticity_slider      51.0     4550          lv   WM-003\n",
       "287  465293704  authenticity_slider      51.0     4413          hv   WM-029\n",
       "288  465293704         power_slider      51.0     3120          hv   WM-029\n",
       "319  465293704         power_slider      51.0     9777          lv   WF-027\n",
       "320  465293704  authenticity_slider      51.0     3658          lv   WF-027\n",
       "351  465293704         power_slider      51.0    12386          hv   WM-033\n",
       "352  465293704  authenticity_slider      51.0     3746          hv   WM-033\n",
       "383  465293704  authenticity_slider      51.0    24146          lv   WF-003\n",
       "384  465293704         power_slider      51.0     3673          lv   WF-003\n",
       "417  911299474         power_slider      86.0     8737          hv   WM-016\n",
       "418  911299474  authenticity_slider      70.0     4490          hv   WM-016\n",
       "449  911299474  authenticity_slider      82.0    15448          lv   WM-009\n",
       "450  911299474         power_slider      52.0     3056          lv   WM-009\n",
       "481  911299474  authenticity_slider      64.0     4230          hv   WF-016\n",
       "482  911299474         power_slider      49.0     3398          hv   WF-016\n",
       "513  911299474  authenticity_slider      80.0     5864          lv   WF-027\n",
       "514  911299474         power_slider      57.0     3912          lv   WF-027\n",
       "545  911299474  authenticity_slider      82.0    31370          lv   WM-033\n",
       "546  911299474         power_slider      82.0     3605          lv   WM-033\n",
       "577  911299474         power_slider      91.0     4921          lv   WM-003\n",
       "578  911299474  authenticity_slider      80.0     3612          lv   WM-003\n",
       "609  911299474         power_slider      43.0     6881          hv   WM-006\n",
       "610  911299474  authenticity_slider      51.0     8747          hv   WM-006\n",
       "641  911299474         power_slider      77.0     6088          hv   WF-011\n",
       "642  911299474  authenticity_slider      53.0     6509          hv   WF-011"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subjects only saw real stimuli in blocknum's 2-13 ...\n",
    "# Do an inner join with the main data to get only those blocks, then filter to only when subjects gave ratings:\n",
    "cleanRatings = pd.merge(cleanData, modelSummary, on=('subject', 'blocknum'), how='inner')\n",
    "cleanRatings = cleanRatings.loc[(cleanRatings['model_block'] != 'expt') & (cleanRatings['trialcode'].isin(ratingsList)), :]\n",
    "cleanRatings = cleanRatings.drop(['blocknum', 'trialnum', 'blockcode', 'stimulusitem1'], axis=1)\n",
    "\n",
    "cleanRatings.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create individual datasets for each major DV:\n",
    "authenticityData = cleanRatings.query(\"trialcode == 'authenticity_slider'\")\n",
    "powerData = cleanRatings.query(\"trialcode == 'power_slider'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Mixed Linear Model Regression Results\n",
      "============================================================\n",
      "Model:             MixedLM  Dependent Variable:  response   \n",
      "No. Observations:  2400     Method:              REML       \n",
      "No. Groups:        200      Scale:               493.0465   \n",
      "Min. group size:   12       Likelihood:          -10955.8423\n",
      "Max. group size:   12       Converged:           Yes        \n",
      "Mean group size:   12.0                                     \n",
      "------------------------------------------------------------\n",
      "                  Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "------------------------------------------------------------\n",
      "Intercept         62.312    0.914 68.143 0.000 60.519 64.104\n",
      "model_block[T.lv] -5.282    0.907 -5.826 0.000 -7.058 -3.505\n",
      "groups RE         85.060    0.595                           \n",
      "============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Run mixed-effects models on each DV:\n",
    "#    - fixed = variability (high vs. low)\n",
    "#    - random = subject & model_id (will add model_id random-effects in R models later)\n",
    "\n",
    "a_m1 = smf.mixedlm(\"response ~ model_block\", data=authenticityData, groups=authenticityData['subject']).fit()\n",
    "print(a_m1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Mixed Linear Model Regression Results\n",
      "=============================================================\n",
      "Model:              MixedLM  Dependent Variable:  response   \n",
      "No. Observations:   2400     Method:              REML       \n",
      "No. Groups:         200      Scale:               435.2489   \n",
      "Min. group size:    12       Likelihood:          -10843.7344\n",
      "Max. group size:    12       Converged:           Yes        \n",
      "Mean group size:    12.0                                     \n",
      "-------------------------------------------------------------\n",
      "                   Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-------------------------------------------------------------\n",
      "Intercept          55.793    0.996 56.014 0.000 53.840 57.745\n",
      "model_block[T.lv]  -2.464    0.852 -2.893 0.004 -4.133 -0.795\n",
      "groups RE         125.882    0.814                           \n",
      "=============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "p_m1 = smf.mixedlm(\"response ~ model_block\", data=powerData, groups=powerData['subject']).fit()\n",
    "print(p_m1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "# Models suggest that high-variability leads to greater ratings of both authenticity and power ...\n",
    "\n",
    "# Push objects to R to confirm and extend analyses\n",
    "# (statsmodels package in python has trouble fitting certain random-effect structures):\n",
    "\n",
    "pandas2ri.activate()\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Send datasets with ratings to R: \n",
    "%R -i cleanRatings,authenticityData,powerData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "# Use R to create mixed-effects models with both subject- and stimulus-level random effects:\n",
    "\n",
    "library(lmerTest)\n",
    "\n",
    "a_m2 <- lmer(response ~ model_block + (1|subject) + (1|model_id), data=authenticityData)\n",
    "a_m2_anova <- anova(a_m2)\n",
    "a_m2_lsmeans <- lsmeans(a_m2)\n",
    "             \n",
    "p_m2 <- lmer(response ~ model_block + (1|subject) + (1|model_id), data=powerData)\n",
    "p_m2_anova <- anova(p_m2)\n",
    "p_m2_lsmeans <- lsmeans(p_m2)\n",
    "\n",
    "# Include rating type ('trialcode' = authenticity vs. power) as another factor\n",
    "both_m1 <- lmer(response ~ model_block * trialcode + (1|subject) + (1|model_id), data=cleanRatings)\n",
    "both_m1_anova <- anova(both_m1)\n",
    "both_m1_lsmeans <- lsmeans(both_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pull model results back to python:\n",
    "%R -o a_m2_anova,a_m2_lsmeans,p_m2_anova,p_m2_lsmeans,both_m1_anova,both_m1_lsmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                   Sum Sq       Mean Sq  NumDF        DenDF    F.value  \\\n",
       "model_block  17391.599876  17391.599876      1  2190.221094  36.646797   \n",
       "\n",
       "                   Pr(>F)  \n",
       "model_block  1.661272e-09  "
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     "execution_count": 126,
     "metadata": {},
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   "source": [
    "pandas2ri.ri2py(a_m2_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
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       "      <th>model_block</th>\n",
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       "                  Sum Sq      Mean Sq  NumDF        DenDF   F.value    Pr(>F)\n",
       "model_block  3059.822476  3059.822476      1  2189.113849  7.627141  0.005798"
      ]
     },
     "execution_count": 127,
     "metadata": {},
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   "source": [
    "pandas2ri.ri2py(p_m2_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "      <td>17565.474005</td>\n",
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       "      <td>1</td>\n",
       "      <td>4589.409386</td>\n",
       "      <td>37.823520</td>\n",
       "      <td>8.402368e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trialcode</th>\n",
       "      <td>31339.630208</td>\n",
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       "      <td>4585.983471</td>\n",
       "      <td>67.483242</td>\n",
       "      <td>2.220446e-16</td>\n",
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       "      <td>5.128037</td>\n",
       "      <td>2.358882e-02</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                             Sum Sq       Mean Sq  NumDF        DenDF  \\\n",
       "model_block            17565.474005  17565.474005      1  4589.409386   \n",
       "trialcode              31339.630208  31339.630208      1  4585.983471   \n",
       "model_block:trialcode   2381.491875   2381.491875      1  4585.983471   \n",
       "\n",
       "                         F.value        Pr(>F)  \n",
       "model_block            37.823520  8.402368e-10  \n",
       "trialcode              67.483242  2.220446e-16  \n",
       "model_block:trialcode   5.128037  2.358882e-02  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas2ri.ri2py(both_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_type</th>\n",
       "      <th>slider_mean</th>\n",
       "      <th>slider_sem</th>\n",
       "      <th>variability</th>\n",
       "      <th>slider_mean_45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Authenticity</td>\n",
       "      <td>62.2924</td>\n",
       "      <td>1.3764</td>\n",
       "      <td>High</td>\n",
       "      <td>17.2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Authenticity</td>\n",
       "      <td>57.0493</td>\n",
       "      <td>1.3764</td>\n",
       "      <td>Low</td>\n",
       "      <td>12.0493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Power</td>\n",
       "      <td>55.7732</td>\n",
       "      <td>1.3764</td>\n",
       "      <td>High</td>\n",
       "      <td>10.7732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Power</td>\n",
       "      <td>53.3476</td>\n",
       "      <td>1.3764</td>\n",
       "      <td>Low</td>\n",
       "      <td>8.3476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_type  slider_mean  slider_sem variability  slider_mean_45\n",
       "0  Authenticity      62.2924      1.3764        High         17.2924\n",
       "1  Authenticity      57.0493      1.3764         Low         12.0493\n",
       "2         Power      55.7732      1.3764        High         10.7732\n",
       "3         Power      53.3476      1.3764         Low          8.3476"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variability main effect significant for both authenticity and power, but\n",
    "# combined mixed-effects model ('both_m1') shows that the strength of the \n",
    "# variability effect depends on the rating (authenticity vs. power) ...\n",
    "\n",
    "# Create dataset to plot the variability x rating type interaction:\n",
    "plotData = pd.DataFrame({\n",
    "    'rating_type': ['Authenticity', 'Authenticity', 'Power', 'Power'],\n",
    "    'variability': ['High', 'Low', 'High', 'Low'],\n",
    "    'slider_mean': [pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[4],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[5],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[6],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[7]],\n",
    "    'slider_sem': [pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][4],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][5],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][6],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][7]]\n",
    "})\n",
    "\n",
    "# Adjust numbers to start at 45 (plotnine isn't great at scaling charts to non-zero y-axes):\n",
    "plotData['slider_mean_45'] = plotData['slider_mean']-45\n",
    "plotData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WFmbOnIlLly5BCKGd/u233yI5ORm9e/dGYmIixowZY5JCiYioZXoNraxfvx5BQUFQqVRI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\nawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbc6a042b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (127731480156)>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use plotnine package to plot using ggplot2 (R) style ...\n",
    "# Manually recoding y-axis labels using 'slider_mean_45' variable (starting y-axis at zero doesn't look good):\n",
    "(ggplot(plotData, aes(x='factor(variability)', y='slider_mean_45', fill='factor(variability)')) \n",
    " + geom_errorbar(aes(ymax='slider_mean_45 + slider_sem', ymin='slider_mean_45'), position=position_dodge(width=0.9), width=0.2, color='black')\n",
    " + geom_bar(stat='identity', position=position_dodge(width=0.9), alpha=0.5, color='black')\n",
    " + scale_fill_manual(values=['red', 'deepskyblue'], guide=False)\n",
    " + facet_wrap('~ rating_type', nrow=1)\n",
    " + scale_y_continuous(labels=['45', '50', '55', '60'])\n",
    " + labs(x='Variability', y='Slider Rating (0-100)')\n",
    " + theme_classic()\n",
    " + theme(figure_size = (4, 3.5),\n",
    "         text = element_text(family='Gill Sans MT'),\n",
    "         axis_text_x = element_text(size = 8),\n",
    "         axis_title_x = element_text(size = 9, weight = 'bold'),\n",
    "         axis_text_y = element_text(size = 9),\n",
    "         axis_title_y = element_text(size = 9, weight = 'bold'),\n",
    "         axis_line_x = element_line(color = 'black'),\n",
    "         axis_line_y = element_line(color = 'black'),\n",
    "         strip_text_x = element_text(weight = 'bold', color = 'white', size = 9), \n",
    "         strip_background = element_rect(fill = 'black')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>emotion</th>\n",
       "      <th>feature</th>\n",
       "      <th>variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Angry</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>Happy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>Happy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>15.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>Happy</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>Happy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>25.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>Happy</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>30.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>35.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>40.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>45.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>50.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>55.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>60.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>65.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>70.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>Happy</td>\n",
       "      <td>75.0</td>\n",
       "      <td>High Variability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "      <td>Happy</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Low Variability</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    count emotion  feature       variability\n",
       "0       1   Angry    -75.0  High Variability\n",
       "1       0   Angry    -75.0   Low Variability\n",
       "2       1   Angry    -70.0  High Variability\n",
       "3       0   Angry    -70.0   Low Variability\n",
       "4       1   Angry    -65.0  High Variability\n",
       "5       0   Angry    -65.0   Low Variability\n",
       "6       1   Angry    -60.0  High Variability\n",
       "7       0   Angry    -60.0   Low Variability\n",
       "8       1   Angry    -55.0  High Variability\n",
       "9       0   Angry    -55.0   Low Variability\n",
       "10      1   Angry    -50.0  High Variability\n",
       "11      0   Angry    -50.0   Low Variability\n",
       "12      1   Angry    -45.0  High Variability\n",
       "13      0   Angry    -45.0   Low Variability\n",
       "14      1   Angry    -40.0  High Variability\n",
       "15      0   Angry    -40.0   Low Variability\n",
       "16      1   Angry    -35.0  High Variability\n",
       "17      0   Angry    -35.0   Low Variability\n",
       "18      1   Angry    -30.0  High Variability\n",
       "19      0   Angry    -30.0   Low Variability\n",
       "20      1   Angry    -25.0  High Variability\n",
       "21      3   Angry    -25.0   Low Variability\n",
       "22      1   Angry    -20.0  High Variability\n",
       "23      3   Angry    -20.0   Low Variability\n",
       "24      1   Angry    -15.0  High Variability\n",
       "25      3   Angry    -15.0   Low Variability\n",
       "26      1   Angry    -10.0  High Variability\n",
       "27      3   Angry    -10.0   Low Variability\n",
       "28      1   Angry     -5.0  High Variability\n",
       "29      3   Angry     -5.0   Low Variability\n",
       "..    ...     ...      ...               ...\n",
       "32      1   Happy      5.0  High Variability\n",
       "33      3   Happy      5.0   Low Variability\n",
       "34      1   Happy     10.0  High Variability\n",
       "35      3   Happy     10.0   Low Variability\n",
       "36      1   Happy     15.0  High Variability\n",
       "37      3   Happy     15.0   Low Variability\n",
       "38      1   Happy     20.0  High Variability\n",
       "39      3   Happy     20.0   Low Variability\n",
       "40      1   Happy     25.0  High Variability\n",
       "41      3   Happy     25.0   Low Variability\n",
       "42      1   Happy     30.0  High Variability\n",
       "43      0   Happy     30.0   Low Variability\n",
       "44      1   Happy     35.0  High Variability\n",
       "45      0   Happy     35.0   Low Variability\n",
       "46      1   Happy     40.0  High Variability\n",
       "47      0   Happy     40.0   Low Variability\n",
       "48      1   Happy     45.0  High Variability\n",
       "49      0   Happy     45.0   Low Variability\n",
       "50      1   Happy     50.0  High Variability\n",
       "51      0   Happy     50.0   Low Variability\n",
       "52      1   Happy     55.0  High Variability\n",
       "53      0   Happy     55.0   Low Variability\n",
       "54      1   Happy     60.0  High Variability\n",
       "55      0   Happy     60.0   Low Variability\n",
       "56      1   Happy     65.0  High Variability\n",
       "57      0   Happy     65.0   Low Variability\n",
       "58      1   Happy     70.0  High Variability\n",
       "59      0   Happy     70.0   Low Variability\n",
       "60      1   Happy     75.0  High Variability\n",
       "61      0   Happy     75.0   Low Variability\n",
       "\n",
       "[62 rows x 4 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Main interaction plot shows that high-variability leads to especially high ratings of authenticity ...\n",
    "\n",
    "# Create new dataframe for figure showing high vs. low variability stimulus distributions:\n",
    "expDesignData = pd.DataFrame({\n",
    "    'feature': np.repeat(np.linspace(-75, 75, num=31), [2]*31),\n",
    "    'emotion': ['Angry']*30 + ['Neutral']*2 + ['Happy']*30,\n",
    "    'variability': ['High Variability', 'Low Variability']*31,\n",
    "    'count': np.array([1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,3,1,3,1,3,1,3,1,3,0,0,1,3,1,3,1,3,1,3,1,3,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0])\n",
    "})\n",
    "\n",
    "expDesignData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6VC1bttTSpUvtp/C22WzasmWL5cMBAAAAgKs4XI4GDRqk9PR07dixw76NzzkC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\nNh0TzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbd64c4160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371909143160583)>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate figure for high vs. low variability stimulus presentation distributions:\n",
    "(ggplot(expDesignData, aes(x='feature', y='count', fill='variability'))\n",
    " + geom_bar(stat='identity', position=position_dodge(width=2.5), alpha=0.5, color='black')\n",
    " + scale_fill_manual(values = ['red', 'deepskyblue'])\n",
    " + geom_vline(xintercept=0, color='black')\n",
    " + scale_x_continuous(breaks=np.linspace(-75, 75, num=31), labels=np.linspace(-75, 75, num=31).astype(int))\n",
    " + scale_y_continuous(limits=[0, 3])\n",
    " + theme_classic()\n",
    " + labs(x='Type of Face Image for Model', y='Number of Exposures\\n')\n",
    " + theme(figure_size = (10,3),\n",
    "         text = element_text(family='Gill Sans MT'),\n",
    "         axis_text_x = element_text(size=8),\n",
    "         axis_title_x = element_text(size=9, weight='bold'),\n",
    "         axis_text_y = element_text(size=8),\n",
    "         axis_title_y = element_text(size=9, weight='bold'),\n",
    "         axis_line_x = element_line(color='black'),\n",
    "         axis_line_y = element_line(color='white'),\n",
    "         legend_title = element_blank(),\n",
    "         legend_text = element_text(size=8),\n",
    "         legend_position = 'top'))"
   ]
  }
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