code/04_experimental_validation_of_learned_emotions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimental validation of the learned emotions\n",
    "\n",
    "**I. Emotional attribution test with humans**\n",
    "\n",
    "Verify the distinguishability and correspondence of the learned emotions with independent human observations.\n",
    "\n",
    "1. Select a representative list of sequences.\n",
    "2. Gather and validate human feedback for each of the sequences.\n",
    "3. Verify distinguishability of emotions across participants.\n",
    "4. Analyze the values obtained for Pleasure / Arousal / Dominance on each emotion.\n",
    "\n",
    "**II. Mapping PAD values to referenced PAD values**\n",
    "\n",
    "Map the distribution of PAD values obtained for each learned emotion vs select pivotal experimental findings in psychology literature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following code was run on Python 3.9.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Attempting uninstall: numpy\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Installation of libraries (in the specific versions used).\n",
    "%pip install numpy==1.22.2\n",
    "%pip install pandas==1.4.1\n",
    "%pip install matplotlib==3.1.1\n",
    "%pip install seaborn==0.12.2\n",
    "\n",
    "%pip install statsmodels==0.14.0\n",
    "%pip install scipy==1.8.0\n",
    "%pip install pingouin==0.5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable automatic reloading of external modules before executing code here.\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages.\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None  # default='warn'; disable warning when copying a slice of a dataframe.\n",
    "\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "import statsmodels.api as sm\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation\n",
    "import pingouin as pg\n",
    "from scipy.stats import f\n",
    "from scipy.stats import kstwobign, pearsonr\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.patches import Circle\n",
    "import mpl_toolkits.mplot3d.art3d as art3d\n",
    "\n",
    "import seaborn as sns\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Alberto/Code/love-emotional-framework/.venv/lib/python3.9/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.3, the latest is 0.5.4.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "# Import modules.\n",
    "module_path = os.path.abspath(os.path.join('modules'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import time_series_tools as ts_tools\n",
    "import clustering as cl\n",
    "from conv_autoencoder import ConvolutionalAutoencoder\n",
    "import statistical_tests as st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auxiliary code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_clusterized_trajectories(cluster_file_path):\n",
    "    # Load clustering info of the full latent space:\n",
    "    clustering_df = pd.read_csv(\n",
    "    cluster_file_path,\n",
    "    sep='\\t', header=0, float_precision='round_trip',\n",
    "    )\n",
    "    num_clusters = max(clustering_df['Cluster']) + 1  # From 0 to max. value.\n",
    "\n",
    "    # Check if there's an emotion names dict.\n",
    "    dict_file_path = f\"{cluster_file_path.split('.txt')[0]}.dic\"\n",
    "    try:\n",
    "        emotion_dict = pickle.load(open(dict_file_path, 'rb'))\n",
    "    except:\n",
    "        emotion_dict = None\n",
    "\n",
    "    print(f\"Number of clusters: {num_clusters}\")\n",
    "    print(f\"Emotion names dict: {emotion_dict}\")\n",
    "    print(clustering_df.head())\n",
    "\n",
    "    return clustering_df, num_clusters, emotion_dict\n",
    "\n",
    "def rearrange_samples_from_a_b_tests(df_a_rates_single_col, df_b_rates_single_col, vid_to_emotion):\n",
    "    # Test A:\n",
    "    # Create columns 'test', 'video' and 'emotion' for each data point (int 1-9), rearranging columns order.\n",
    "    df_a_rates_single_col['video'] = df_a_rates_single_col.apply(lambda x: x['test'] [x['test'].find('(')+1:x['test'].find(')')], axis=1)\n",
    "    df_a_rates_single_col['test_ab'] = df_a_rates_single_col['video'].str[0]\n",
    "\n",
    "    emotion_col = [vid_to_emotion.loc[v]['emotion'] for v in df_a_rates_single_col['video']]\n",
    "    df_a_rates_single_col['emotion']  = emotion_col\n",
    "\n",
    "    df_a_rates_single_col = df_a_rates_single_col[['test_ab', 'video', 'emotion', 'rater', 'test', 'dimension', 'rating']]\n",
    "    df_a_rates_single_col  # <n raters> x <24 videos/rater> x <3 rates/video> = m single-rate rows    \n",
    "\n",
    "    # Test B:\n",
    "    # Create columns 'test', 'video' and 'emotion' for each data point (int 1-9), rearranging columns order.\n",
    "    df_b_rates_single_col['video'] = df_b_rates_single_col.apply(lambda x: x['test'] [x['test'].find('(')+1:x['test'].find(')')], axis=1)\n",
    "    df_b_rates_single_col['test_ab'] = df_b_rates_single_col['video'].str[0]\n",
    "\n",
    "    emotion_col = [vid_to_emotion.loc[v]['emotion'] for v in df_b_rates_single_col['video']]\n",
    "    df_b_rates_single_col['emotion']  = emotion_col\n",
    "\n",
    "    df_b_rates_single_col = df_b_rates_single_col[['test_ab', 'video', 'emotion', 'rater', 'test', 'dimension', 'rating']]\n",
    "    df_b_rates_single_col  # <n raters> x <24 videos/rater> x <3 rates/video> = m single-rate rows    \n",
    "\n",
    "    # Test A & B:\n",
    "    df_ab_rates_single_col = pd.concat([df_a_rates_single_col, df_b_rates_single_col])\n",
    "    df_ab_rates_single_col.reset_index(inplace=True)\n",
    "    \n",
    "    return df_ab_rates_single_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Emotional attribution test with humans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Select a representative list of sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load all emotional trajectories and their step-wise clustering info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters: 8\n",
      "Emotion names dict: None\n",
      "   Unnamed: 0             0             1         2         3         4  \\\n",
      "0           0  4.903072e-26  6.492333e-10  0.726463  0.000088  0.015700   \n",
      "1           1  5.480444e-25  3.246398e-10  0.744999  0.000080  0.014492   \n",
      "2           2  2.368872e-24  2.164266e-10  0.766861  0.000070  0.012918   \n",
      "3           3  1.342064e-23  1.623199e-10  0.786888  0.000066  0.012053   \n",
      "4           4  1.373973e-22  1.298559e-10  0.800981  0.000063  0.011557   \n",
      "\n",
      "          5         6         7  Episode  Cluster  Probability  Seed  Step  \n",
      "0  0.000023  0.253094  0.004633        4        2     0.726463    30    19  \n",
      "1  0.000042  0.235605  0.004782        4        2     0.744999    30    20  \n",
      "2  0.002945  0.212597  0.004608        4        2     0.766861    30    21  \n",
      "3  0.002633  0.193686  0.004674        4        2     0.786888    30    22  \n",
      "4  0.002185  0.180269  0.004945        4        2     0.800981    30    23  \n"
     ]
    }
   ],
   "source": [
    "traj_clustering_file = 'gaussian_mixture_02_clustering.txt'\n",
    "cluster_file_path = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'interpretation', traj_clustering_file)\n",
    "\n",
    "clustering_df, num_clusters, emotion_dict = read_clusterized_trajectories(cluster_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Automated selection of a top-matching list of sequences\n",
    "For each of the 8 possible clusters in the emotional space, we select samples that:\n",
    "i. Show a high probability for that cluster during enough steps (for representativity).\n",
    "ii. Happen at different states of the episode (for variety)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stable_sequences_list = [\n",
    "    cl.obtain_stable_sequences(clustering_df, min_probability=0.8, min_length=5, cluster_value=c,)\n",
    "    for c in range(8)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>0.864725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>107</td>\n",
       "      <td>122</td>\n",
       "      <td>16</td>\n",
       "      <td>0.839470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>239</td>\n",
       "      <td>254</td>\n",
       "      <td>16</td>\n",
       "      <td>0.835697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>260</td>\n",
       "      <td>15</td>\n",
       "      <td>0.819133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>0.880757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Seed  Episode  Cluster  Start  End  Length  Avg_Probability\n",
       "0     1        5        6     23   42      20         0.841484\n",
       "1     1        5        6     52   71      20         0.846854\n",
       "2     0        5        6     34   52      19         0.846327\n",
       "3    30        0        6    124  142      19         0.860484\n",
       "4     0        2        6     99  115      17         0.836103\n",
       "5     2        0        6     19   35      17         0.864725\n",
       "6     0        8        6    107  122      16         0.839470\n",
       "7     2        2        6    239  254      16         0.835697\n",
       "8    20        9        6    246  260      15         0.819133\n",
       "9    30        9        6     19   33      15         0.880757"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See what one looks like:\n",
    "cluster_number = 6\n",
    "stable_sequences_list[cluster_number][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final manual selection (off this notebook)\n",
    "For each of the 8 lists of sequences, we visualize and pick 6 sequences of around 3-5 seconds each, at different states of the episode (for variety).\n",
    "\n",
    "The final set of videos can be found in path [love-emotional-framework/data/original/survey/videos/](./../data/original/survey/videos/)\n",
    "\n",
    "File name convention: sequence_cluster_\\<cluster\\>\\_s\\<seed\\>\\_ep\\<episode\\>\\_\\<from_step\\>\\_\\<to_step\\>.mp4 (e.g. 'sequence_cluster_0_s10_ep1_196_296.mp4')."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Gather and validate human feedback for each of the sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read, merge and format the surveys (types A1, A2, B1, B2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tests = 24  # Number of videos in each of the test versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read Test A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the tests 1 & 2 (all responses): \t\t25 + 27 = 52 data points\n",
      "Shape: (52, 73): 52 raters x 24 tests\n"
     ]
    },
    {
     "data": {
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       "      <td>Rater A01</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rater A02</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>Rater A04</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "       Rater  (A01) Placer  (A01) Activación  (A01) Dominancia  (A02) Placer  \\\n",
       "0  Rater A01             1                 7                 2             1   \n",
       "1  Rater A02             3                 3                 3             3   \n",
       "2  Rater A03             1                 9                 1             1   \n",
       "3  Rater A04             1                 9                 1             1   \n",
       "4  Rater A05             2                 5                 5             1   \n",
       "\n",
       "   (A02) Activación  (A02) Dominancia  (A03) Placer  (A03) Activación  \\\n",
       "0                 9                 1             5                 5   \n",
       "1                 4                 2             2                 3   \n",
       "2                 9                 1             5                 5   \n",
       "3                 9                 1             4                 7   \n",
       "4                 9                 1             3                 7   \n",
       "\n",
       "   (A03) Dominancia  ...  (A21) Dominancia  (A22) Placer  (A22) Activación  \\\n",
       "0                 5  ...                 7             2                 5   \n",
       "1                 2  ...                 4             2                 2   \n",
       "2                 3  ...                 6             2                 7   \n",
       "3                 1  ...                 9             6                 8   \n",
       "4                 2  ...                 7             2                 7   \n",
       "\n",
       "   (A22) Dominancia  (A23) Placer  (A23) Activación  (A23) Dominancia  \\\n",
       "0                 4             6                 6                 6   \n",
       "1                 2             2                 3                 3   \n",
       "2                 2             3                 7                 3   \n",
       "3                 6             5                 5                 4   \n",
       "4                 2             1                 8                 2   \n",
       "\n",
       "   (A24) Placer  (A24) Activación  (A24) Dominancia  \n",
       "0             1                 6                 1  \n",
       "1             3                 2                 1  \n",
       "2             3                 6                 1  \n",
       "3             5                 7                 5  \n",
       "4             3                 7                 3  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read and merge test from two files (different order of tests).\n",
    "path_a1 = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'responses', 'Test A1 responses.csv')\n",
    "path_a2 = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'responses', 'Test A2 responses.csv')\n",
    "df_a = st.read_and_merge_surveys_1_and_2(path_a1, path_a2)\n",
    "\n",
    "# Generate a version with ONLY rater info and data points.\n",
    "df_a_rates = st.generate_rates_only_df(df_a, 'A', n_tests)\n",
    "df_a_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read Test B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the tests 1 & 2 (all responses): \t\t22 + 22 = 44 data points\n",
      "Shape: (44, 73): 44 raters x 24 tests\n"
     ]
    },
    {
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
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       "       Rater  (B01) Placer  (B01) Activación  (B01) Dominancia  (B02) Placer  \\\n",
       "0  Rater B01             1                 9                 1             1   \n",
       "1  Rater B02             1                 1                 1             1   \n",
       "2  Rater B03             1                 5                 1             1   \n",
       "3  Rater B04             2                 8                 1             3   \n",
       "4  Rater B05             4                 9                 3             4   \n",
       "\n",
       "   (B02) Activación  (B02) Dominancia  (B03) Placer  (B03) Activación  \\\n",
       "0                 8                 1             3                 6   \n",
       "1                 4                 3             1                 5   \n",
       "2                 5                 1             4                 5   \n",
       "3                 7                 3             3                 9   \n",
       "4                 6                 2             2                 2   \n",
       "\n",
       "   (B03) Dominancia  ...  (B21) Dominancia  (B22) Placer  (B22) Activación  \\\n",
       "0                 4  ...                 8             1                 6   \n",
       "1                 2  ...                 3             2                 8   \n",
       "2                 4  ...                 2             1                 7   \n",
       "3                 2  ...                 3             1                 9   \n",
       "4                 2  ...                 2             3                 3   \n",
       "\n",
       "   (B22) Dominancia  (B23) Placer  (B23) Activación  (B23) Dominancia  \\\n",
       "0                 2             3                 6                 5   \n",
       "1                 3             1                 6                 1   \n",
       "2                 1             4                 5                 4   \n",
       "3                 1             3                 8                 3   \n",
       "4                 2             4                 8                 2   \n",
       "\n",
       "   (B24) Placer  (B24) Activación  (B24) Dominancia  \n",
       "0             3                 6                 2  \n",
       "1             1                 6                 2  \n",
       "2             2                 6                 1  \n",
       "3             2                 8                 3  \n",
       "4             4                 6                 2  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read and merge test from two files (different order of tests).\n",
    "path_b1 = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'responses', 'Test B1 responses.csv')\n",
    "path_b2 = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'responses', 'Test B2 responses.csv')\n",
    "df_b = st.read_and_merge_surveys_1_and_2(path_b1, path_b2)\n",
    "\n",
    "# Generate a version with ONLY rater info and data points.\n",
    "df_b_rates = st.generate_rates_only_df(df_b, 'B', n_tests)\n",
    "df_b_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test reliability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ICC2k on Pleasure / Arousal / Dominance (all responses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d906d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_d906d_level0_col0\" class=\"col_heading level0 col0\" >Dimension</th>\n",
       "      <th id=\"T_d906d_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
       "      <th id=\"T_d906d_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_d906d_level0_col3\" class=\"col_heading level0 col3\" >ICC</th>\n",
       "      <th id=\"T_d906d_level0_col4\" class=\"col_heading level0 col4\" >F</th>\n",
       "      <th id=\"T_d906d_level0_col5\" class=\"col_heading level0 col5\" >df1</th>\n",
       "      <th id=\"T_d906d_level0_col6\" class=\"col_heading level0 col6\" >df2</th>\n",
       "      <th id=\"T_d906d_level0_col7\" class=\"col_heading level0 col7\" >pval</th>\n",
       "      <th id=\"T_d906d_level0_col8\" class=\"col_heading level0 col8\" >CI95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_d906d_row0_col0\" class=\"data row0 col0\" >Pleasure</td>\n",
       "      <td id=\"T_d906d_row0_col1\" class=\"data row0 col1\" >ICC2k</td>\n",
       "      <td id=\"T_d906d_row0_col2\" class=\"data row0 col2\" >Average random raters</td>\n",
       "      <td id=\"T_d906d_row0_col3\" class=\"data row0 col3\" >0.991303</td>\n",
       "      <td id=\"T_d906d_row0_col4\" class=\"data row0 col4\" >143.006427</td>\n",
       "      <td id=\"T_d906d_row0_col5\" class=\"data row0 col5\" >23</td>\n",
       "      <td id=\"T_d906d_row0_col6\" class=\"data row0 col6\" >1173</td>\n",
       "      <td id=\"T_d906d_row0_col7\" class=\"data row0 col7\" >0.000000</td>\n",
       "      <td id=\"T_d906d_row0_col8\" class=\"data row0 col8\" >[0.99 1.  ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d906d_row1_col0\" class=\"data row1 col0\" >Arousal</td>\n",
       "      <td id=\"T_d906d_row1_col1\" class=\"data row1 col1\" >ICC2k</td>\n",
       "      <td id=\"T_d906d_row1_col2\" class=\"data row1 col2\" >Average random raters</td>\n",
       "      <td id=\"T_d906d_row1_col3\" class=\"data row1 col3\" >0.869813</td>\n",
       "      <td id=\"T_d906d_row1_col4\" class=\"data row1 col4\" >10.420163</td>\n",
       "      <td id=\"T_d906d_row1_col5\" class=\"data row1 col5\" >23</td>\n",
       "      <td id=\"T_d906d_row1_col6\" class=\"data row1 col6\" >1173</td>\n",
       "      <td id=\"T_d906d_row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
       "      <td id=\"T_d906d_row1_col8\" class=\"data row1 col8\" >[0.79 0.93]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d906d_row2_col0\" class=\"data row2 col0\" >Dominance</td>\n",
       "      <td id=\"T_d906d_row2_col1\" class=\"data row2 col1\" >ICC2k</td>\n",
       "      <td id=\"T_d906d_row2_col2\" class=\"data row2 col2\" >Average random raters</td>\n",
       "      <td id=\"T_d906d_row2_col3\" class=\"data row2 col3\" >0.987786</td>\n",
       "      <td id=\"T_d906d_row2_col4\" class=\"data row2 col4\" >108.758808</td>\n",
       "      <td id=\"T_d906d_row2_col5\" class=\"data row2 col5\" >23</td>\n",
       "      <td id=\"T_d906d_row2_col6\" class=\"data row2 col6\" >1173</td>\n",
       "      <td id=\"T_d906d_row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
       "      <td id=\"T_d906d_row2_col8\" class=\"data row2 col8\" >[0.98 0.99]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f020910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test A (generating sample-wise dataframe for further use)\n",
    "icc_pleasure_a, icc_arousal_a, icc_dominance_a, df_a_rates_single_col = st.run_icc_test(df_a_rates, type='ICC2k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_269ab\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_269ab_level0_col0\" class=\"col_heading level0 col0\" >Dimension</th>\n",
       "      <th id=\"T_269ab_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
       "      <th id=\"T_269ab_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_269ab_level0_col3\" class=\"col_heading level0 col3\" >ICC</th>\n",
       "      <th id=\"T_269ab_level0_col4\" class=\"col_heading level0 col4\" >F</th>\n",
       "      <th id=\"T_269ab_level0_col5\" class=\"col_heading level0 col5\" >df1</th>\n",
       "      <th id=\"T_269ab_level0_col6\" class=\"col_heading level0 col6\" >df2</th>\n",
       "      <th id=\"T_269ab_level0_col7\" class=\"col_heading level0 col7\" >pval</th>\n",
       "      <th id=\"T_269ab_level0_col8\" class=\"col_heading level0 col8\" >CI95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_269ab_row0_col0\" class=\"data row0 col0\" >Pleasure</td>\n",
       "      <td id=\"T_269ab_row0_col1\" class=\"data row0 col1\" >ICC2k</td>\n",
       "      <td id=\"T_269ab_row0_col2\" class=\"data row0 col2\" >Average random raters</td>\n",
       "      <td id=\"T_269ab_row0_col3\" class=\"data row0 col3\" >0.987127</td>\n",
       "      <td id=\"T_269ab_row0_col4\" class=\"data row0 col4\" >95.329519</td>\n",
       "      <td id=\"T_269ab_row0_col5\" class=\"data row0 col5\" >23</td>\n",
       "      <td id=\"T_269ab_row0_col6\" class=\"data row0 col6\" >989</td>\n",
       "      <td id=\"T_269ab_row0_col7\" class=\"data row0 col7\" >0.000000</td>\n",
       "      <td id=\"T_269ab_row0_col8\" class=\"data row0 col8\" >[0.98 0.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_269ab_row1_col0\" class=\"data row1 col0\" >Arousal</td>\n",
       "      <td id=\"T_269ab_row1_col1\" class=\"data row1 col1\" >ICC2k</td>\n",
       "      <td id=\"T_269ab_row1_col2\" class=\"data row1 col2\" >Average random raters</td>\n",
       "      <td id=\"T_269ab_row1_col3\" class=\"data row1 col3\" >0.809354</td>\n",
       "      <td id=\"T_269ab_row1_col4\" class=\"data row1 col4\" >6.115296</td>\n",
       "      <td id=\"T_269ab_row1_col5\" class=\"data row1 col5\" >23</td>\n",
       "      <td id=\"T_269ab_row1_col6\" class=\"data row1 col6\" >989</td>\n",
       "      <td id=\"T_269ab_row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
       "      <td id=\"T_269ab_row1_col8\" class=\"data row1 col8\" >[0.69 0.9 ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_269ab_row2_col0\" class=\"data row2 col0\" >Dominance</td>\n",
       "      <td id=\"T_269ab_row2_col1\" class=\"data row2 col1\" >ICC2k</td>\n",
       "      <td id=\"T_269ab_row2_col2\" class=\"data row2 col2\" >Average random raters</td>\n",
       "      <td id=\"T_269ab_row2_col3\" class=\"data row2 col3\" >0.984978</td>\n",
       "      <td id=\"T_269ab_row2_col4\" class=\"data row2 col4\" >83.461934</td>\n",
       "      <td id=\"T_269ab_row2_col5\" class=\"data row2 col5\" >23</td>\n",
       "      <td id=\"T_269ab_row2_col6\" class=\"data row2 col6\" >989</td>\n",
       "      <td id=\"T_269ab_row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
       "      <td id=\"T_269ab_row2_col8\" class=\"data row2 col8\" >[0.97 0.99]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f146070>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test B (generating sample-wise dataframe for further use)\n",
    "icc_pleasure_b, icc_arousal_b, icc_dominance_b, df_b_rates_single_col = st.run_icc_test(df_b_rates, type='ICC2k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Verify distinguishability of emotions across participants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read association of the 48 videos to the 8 emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 2)\n"
     ]
    },
    {
     "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>emotion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       emotion\n",
       "video         \n",
       "A01          0\n",
       "A02          0\n",
       "B01          0\n",
       "A03          0\n",
       "B02          0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data associating tests with videos, keeping just video - emotion columns:\n",
    "file_with_vid_emotion_correspondence = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'responses', 'emotional_attribution_videos_and_clusters.csv')\n",
    "vids_vs_emotions_full_data_df = pd.read_csv(file_with_vid_emotion_correspondence)\n",
    "vids_vs_emotions_df = vids_vs_emotions_full_data_df[['New file name', 'Cluster']].copy()\n",
    "vids_vs_emotions_df.rename(columns={'New file name': 'video', 'Cluster': 'emotion'}, inplace=True)\n",
    "vid_to_emotion = vids_vs_emotions_df.set_index('video')\n",
    "\n",
    "print(vids_vs_emotions_df.shape)\n",
    "vid_to_emotion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label attributed emotion numbers:\n",
    "emotional_attribution = {\n",
    "    0: 'Distress',\n",
    "    1: 'Optimism',\n",
    "    2: 'slight Concern',\n",
    "    3: 'Satisfaction',\n",
    "    4: 'high Optimism',\n",
    "    5: 'Concern',\n",
    "    6: 'Excitement',\n",
    "    7: 'Fear',\n",
    "}\n",
    "n_emotions = len(emotional_attribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15c11a430>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show colors x emotions table:\n",
    "x = np.zeros(n_emotions)\n",
    "y = np.arange(n_emotions)\n",
    "\n",
    "fig = plt.figure(figsize=(1, 2.5))\n",
    "ax1 = plt.axes()\n",
    "\n",
    "palette = 'tab10'\n",
    "cmap = matplotlib.colors.ListedColormap(matplotlib.cm.get_cmap(palette).colors[:n_emotions])\n",
    "ax1.axes.get_xaxis().set_visible(False)\n",
    "\n",
    "plt.yticks(ticks=y, labels=[f\"{emotional_attribution[i]} ({i})\" for i in range(n_emotions)])\n",
    "plt.scatter(x, y, c=y, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>index</th>\n",
       "      <th>test_ab</th>\n",
       "      <th>video</th>\n",
       "      <th>emotion</th>\n",
       "      <th>rater</th>\n",
       "      <th>test</th>\n",
       "      <th>dimension</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>A01</td>\n",
       "      <td>0</td>\n",
       "      <td>Rater A01</td>\n",
       "      <td>(A01) Placer</td>\n",
       "      <td>Placer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>A01</td>\n",
       "      <td>0</td>\n",
       "      <td>Rater A01</td>\n",
       "      <td>(A01) Activación</td>\n",
       "      <td>Activación</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>A01</td>\n",
       "      <td>0</td>\n",
       "      <td>Rater A01</td>\n",
       "      <td>(A01) Dominancia</td>\n",
       "      <td>Dominancia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>A02</td>\n",
       "      <td>0</td>\n",
       "      <td>Rater A01</td>\n",
       "      <td>(A02) Placer</td>\n",
       "      <td>Placer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>A02</td>\n",
       "      <td>0</td>\n",
       "      <td>Rater A01</td>\n",
       "      <td>(A02) Activación</td>\n",
       "      <td>Activación</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>3163</td>\n",
       "      <td>B</td>\n",
       "      <td>B23</td>\n",
       "      <td>7</td>\n",
       "      <td>Rater B44</td>\n",
       "      <td>(B23) Activación</td>\n",
       "      <td>Activación</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6908</th>\n",
       "      <td>3164</td>\n",
       "      <td>B</td>\n",
       "      <td>B23</td>\n",
       "      <td>7</td>\n",
       "      <td>Rater B44</td>\n",
       "      <td>(B23) Dominancia</td>\n",
       "      <td>Dominancia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6909</th>\n",
       "      <td>3165</td>\n",
       "      <td>B</td>\n",
       "      <td>B24</td>\n",
       "      <td>7</td>\n",
       "      <td>Rater B44</td>\n",
       "      <td>(B24) Placer</td>\n",
       "      <td>Placer</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6910</th>\n",
       "      <td>3166</td>\n",
       "      <td>B</td>\n",
       "      <td>B24</td>\n",
       "      <td>7</td>\n",
       "      <td>Rater B44</td>\n",
       "      <td>(B24) Activación</td>\n",
       "      <td>Activación</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>3167</td>\n",
       "      <td>B</td>\n",
       "      <td>B24</td>\n",
       "      <td>7</td>\n",
       "      <td>Rater B44</td>\n",
       "      <td>(B24) Dominancia</td>\n",
       "      <td>Dominancia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6912 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index test_ab video  emotion      rater              test   dimension  \\\n",
       "0         0       A   A01        0  Rater A01      (A01) Placer      Placer   \n",
       "1         1       A   A01        0  Rater A01  (A01) Activación  Activación   \n",
       "2         2       A   A01        0  Rater A01  (A01) Dominancia  Dominancia   \n",
       "3         3       A   A02        0  Rater A01      (A02) Placer      Placer   \n",
       "4         4       A   A02        0  Rater A01  (A02) Activación  Activación   \n",
       "...     ...     ...   ...      ...        ...               ...         ...   \n",
       "6907   3163       B   B23        7  Rater B44  (B23) Activación  Activación   \n",
       "6908   3164       B   B23        7  Rater B44  (B23) Dominancia  Dominancia   \n",
       "6909   3165       B   B24        7  Rater B44      (B24) Placer      Placer   \n",
       "6910   3166       B   B24        7  Rater B44  (B24) Activación  Activación   \n",
       "6911   3167       B   B24        7  Rater B44  (B24) Dominancia  Dominancia   \n",
       "\n",
       "      rating  \n",
       "0          1  \n",
       "1          7  \n",
       "2          2  \n",
       "3          1  \n",
       "4          9  \n",
       "...      ...  \n",
       "6907       7  \n",
       "6908       6  \n",
       "6909       5  \n",
       "6910       5  \n",
       "6911       4  \n",
       "\n",
       "[6912 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arrange all samples in a single dataframe.\n",
    "df_ab_rates_single_col = rearrange_samples_from_a_b_tests(df_a_rates_single_col, df_b_rates_single_col, vid_to_emotion)\n",
    "df_ab_rates_single_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3D representation of each data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "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>pleasure</th>\n",
       "      <th>arousal</th>\n",
       "      <th>dominance</th>\n",
       "      <th>emotion</th>\n",
       "      <th>video</th>\n",
       "      <th>test_ab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A01</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A02</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>A03</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>A04</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>A05</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>B20</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>B21</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>B22</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>B23</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>B24</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2304 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pleasure  arousal  dominance  emotion video test_ab\n",
       "0            1        7          2        0   A01       A\n",
       "1            1        9          1        0   A02       A\n",
       "2            5        5          5        0   A03       A\n",
       "3            8        6          7        1   A04       A\n",
       "4            8        6          9        1   A05       A\n",
       "...        ...      ...        ...      ...   ...     ...\n",
       "2299         7        5          5        6   B20       B\n",
       "2300         4        7          2        6   B21       B\n",
       "2301         2        8          2        7   B22       B\n",
       "2302         4        7          6        7   B23       B\n",
       "2303         5        5          4        7   B24       B\n",
       "\n",
       "[2304 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3d = pd.DataFrame({\n",
    "    'pleasure': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Placer']['rating']),\n",
    "    'arousal': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Activación']['rating']),\n",
    "    'dominance': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Dominancia']['rating']),\n",
    "    'emotion': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Placer']['emotion']),\n",
    "    'video': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Placer']['video']),\n",
    "    'test_ab': list(df_ab_rates_single_col[df_ab_rates_single_col['dimension'] == 'Placer']['test_ab']),\n",
    "})\n",
    "df_3d  # <n raters> x <24 videos> = ... triple-rate rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Emotion of each video (1 point = 1 video)\n",
    "Representations in space of the average PAD by video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "vid_names = [f\"A{i+1:02}\" for i in range(n_tests)] + [f\"B{i+1:02}\" for i in range(n_tests)]  # 'A01', 'A02', ...,'A24', 'B01'...'B24'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean and stdev P, A and D across raters for each video in the test.\n",
    "vid_pleasure_means = [np.mean(df_3d[df_3d['video'] == v]['pleasure']) for v in vid_names]\n",
    "vid_pleasure_stds = [np.std(df_3d[df_3d['video'] == v]['pleasure'], ddof=1) for v in vid_names]\n",
    "vid_arousal_means = [np.mean(df_3d[df_3d['video'] == v]['arousal']) for v in vid_names]\n",
    "vid_arousal_stds = [np.std(df_3d[df_3d['video'] == v]['arousal'], ddof=1) for v in vid_names]\n",
    "vid_dominance_means = [np.mean(df_3d[df_3d['video'] == v]['dominance']) for v in vid_names]\n",
    "vid_dominance_stds = [np.std(df_3d[df_3d['video'] == v]['dominance'], ddof=1) for v in vid_names]\n",
    "\n",
    "# List the hypothetical emotions attributed to each video.\n",
    "vid_emotions = [vid_to_emotion.loc[v]['emotion'] for v in vid_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame with PAD values for each video.\n",
    "df_video_pad = pd.DataFrame({\n",
    "    'vid_name': vid_names,\n",
    "    'vid_emotion': vid_emotions,\n",
    "    'vid_pleasure_mean': vid_pleasure_means,\n",
    "    'vid_pleasure_std': vid_pleasure_stds,\n",
    "    'vid_arousal_mean': vid_arousal_means,\n",
    "    'vid_arousal_std': vid_arousal_stds,\n",
    "    'vid_dominance_mean': vid_dominance_means,\n",
    "    'vid_dominance_std': vid_dominance_stds\n",
    "})\n",
    "print (len(df_video_pad))\n",
    "df_video_pad.head()\n",
    "\n",
    "# Save the DataFrame to a CSV file.\n",
    "output_path = os.path.join(os.path.dirname(os.getcwd()), 'data', 'new_runs', 'pad_values_of_videos.csv')\n",
    "df_video_pad.to_csv(output_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2D & 3D animated representation of each data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are matplotlib's Qualitative color palettes:\n",
    "# https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
    "palette = 'tab10'\n",
    "cmap = matplotlib.colors.ListedColormap(matplotlib.cm.get_cmap(palette).colors[:n_emotions])\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(vid_pleasure_means, vid_arousal_means, vid_dominance_means, s=30, alpha=1.0, c=vid_emotions, cmap=cmap)\n",
    "\n",
    "ax.set_xlabel('Pleasure')\n",
    "ax.set_ylabel('Arousal')\n",
    "ax.set_zlabel('Dominance')\n",
    "ax.set_title('Mean P/A/D for each video')\n",
    "\n",
    "for i in range(len(vid_pleasure_means)):\n",
    "    p = Circle(\n",
    "        (vid_pleasure_means[i], vid_arousal_means[i]), 0.11,  # Center and radius.\n",
    "        color='grey', alpha=0.3\n",
    "    )\n",
    "    ax.add_patch(p)\n",
    "    art3d.pathpatch_2d_to_3d(p, z=1, zdir='z')\n",
    "\n",
    "ax.set_xlim(left=1, right=9)\n",
    "ax.set_ylim(bottom=1, top=9)\n",
    "ax.set_zlim(bottom=1, top=9)\n",
    "\n",
    "# Set the desired viewing angle\n",
    "ax.view_init(elev=20, azim=220)  # Adjust the elevation (elev) and azimuth (azim) angles\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotate(angle):\n",
    "     ax.view_init(azim=angle)\n",
    "\n",
    "anim_name = os.path.join(os.path.dirname(os.getcwd()), 'data', 'new_runs', 'test_3d_anim_all_videos_vs_emotion_test_AB.gif')\n",
    "angle = 2\n",
    "ani = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 360, angle), interval=50)\n",
    "ani.save(anim_name, writer=animation.PillowWriter(fps=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result\n",
    "\n",
    "<img src=\"../data/new_runs/test_3d_anim_all_videos_vs_emotion_test_AB.gif\" alt=\"drawing\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The animation above should look like this:\n",
    "\n",
    "<img src=\"../data/original/survey/test_3d_anim_all_videos_vs_emotion_test_AB.gif\" alt=\"drawing\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PAD values for each emotion (1 point = 1 emotion)\n",
    "Representations in space of the average of data points by emotion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean P, A and D across raters for each hypothetical emotion in the test.\n",
    "emo_pleasure_means = [np.mean(df_3d[df_3d['emotion'] == e]['pleasure']) for e in range(n_emotions)]\n",
    "emo_pleasure_stds = [np.std(df_3d[df_3d['emotion'] == e]['pleasure'], ddof=1) for e in range(n_emotions)]\n",
    "emo_arousal_means = [np.mean(df_3d[df_3d['emotion'] == e]['arousal']) for e in range(n_emotions)]\n",
    "emo_arousal_stds = [np.std(df_3d[df_3d['emotion'] == e]['arousal'], ddof=1) for e in range(n_emotions)]\n",
    "emo_dominance_means = [np.mean(df_3d[df_3d['emotion'] == e]['dominance']) for e in range(n_emotions)]\n",
    "emo_dominance_stds = [np.std(df_3d[df_3d['emotion'] == e]['dominance'], ddof=1) for e in range(n_emotions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>name</th>\n",
       "      <th>pleasure (mean)</th>\n",
       "      <th>pleasure (stdev)</th>\n",
       "      <th>arousal (mean)</th>\n",
       "      <th>arousal (stdev)</th>\n",
       "      <th>dominance (mean)</th>\n",
       "      <th>dominance (stdev)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distress</td>\n",
       "      <td>2.614583</td>\n",
       "      <td>1.827222</td>\n",
       "      <td>6.954861</td>\n",
       "      <td>2.227804</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.627293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Optimism</td>\n",
       "      <td>7.378472</td>\n",
       "      <td>1.669303</td>\n",
       "      <td>5.468750</td>\n",
       "      <td>2.186209</td>\n",
       "      <td>7.100694</td>\n",
       "      <td>1.859227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>slight Concern</td>\n",
       "      <td>4.722222</td>\n",
       "      <td>1.822983</td>\n",
       "      <td>6.649306</td>\n",
       "      <td>1.838023</td>\n",
       "      <td>4.642361</td>\n",
       "      <td>1.864918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Satisfaction</td>\n",
       "      <td>8.427083</td>\n",
       "      <td>1.066547</td>\n",
       "      <td>5.486111</td>\n",
       "      <td>2.571585</td>\n",
       "      <td>8.218750</td>\n",
       "      <td>1.360491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high Optimism</td>\n",
       "      <td>7.336806</td>\n",
       "      <td>1.749395</td>\n",
       "      <td>5.850694</td>\n",
       "      <td>2.245072</td>\n",
       "      <td>7.184028</td>\n",
       "      <td>1.969494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Concern</td>\n",
       "      <td>3.843750</td>\n",
       "      <td>1.851611</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>2.107623</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>1.991270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Excitement</td>\n",
       "      <td>5.288194</td>\n",
       "      <td>2.329241</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>2.059122</td>\n",
       "      <td>5.291667</td>\n",
       "      <td>2.404627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fear</td>\n",
       "      <td>3.156250</td>\n",
       "      <td>1.704645</td>\n",
       "      <td>6.447917</td>\n",
       "      <td>2.091312</td>\n",
       "      <td>3.149306</td>\n",
       "      <td>1.721538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  pleasure (mean)  pleasure (stdev)  arousal (mean)  \\\n",
       "emotion                                                                      \n",
       "0              Distress         2.614583          1.827222        6.954861   \n",
       "1              Optimism         7.378472          1.669303        5.468750   \n",
       "2        slight Concern         4.722222          1.822983        6.649306   \n",
       "3          Satisfaction         8.427083          1.066547        5.486111   \n",
       "4         high Optimism         7.336806          1.749395        5.850694   \n",
       "5               Concern         3.843750          1.851611        6.437500   \n",
       "6            Excitement         5.288194          2.329241        6.437500   \n",
       "7                  Fear         3.156250          1.704645        6.447917   \n",
       "\n",
       "         arousal (stdev)  dominance (mean)  dominance (stdev)  \n",
       "emotion                                                        \n",
       "0               2.227804          2.500000           1.627293  \n",
       "1               2.186209          7.100694           1.859227  \n",
       "2               1.838023          4.642361           1.864918  \n",
       "3               2.571585          8.218750           1.360491  \n",
       "4               2.245072          7.184028           1.969494  \n",
       "5               2.107623          3.833333           1.991270  \n",
       "6               2.059122          5.291667           2.404627  \n",
       "7               2.091312          3.149306           1.721538  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emo_stats_df = pd.DataFrame({\n",
    "    'emotion': np.arange(n_emotions),\n",
    "    'name': [emotional_attribution[i] for i in range(n_emotions)],\n",
    "    'pleasure (mean)': emo_pleasure_means,\n",
    "    'pleasure (stdev)': emo_pleasure_stds,\n",
    "    'arousal (mean)': emo_arousal_means,\n",
    "    'arousal (stdev)': emo_arousal_stds,\n",
    "    'dominance (mean)': emo_dominance_means,\n",
    "    'dominance (stdev)': emo_dominance_stds,\n",
    "})\n",
    "emo_stats_df.set_index('emotion', inplace=True)\n",
    "emo_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are matplotlib's Qualitative color palettes:\n",
    "# https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
    "palette = 'tab10'\n",
    "cmap = matplotlib.colors.ListedColormap(matplotlib.cm.get_cmap(palette).colors[:n_emotions])\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(emo_pleasure_means, emo_arousal_means, emo_dominance_means, s=50, alpha=1.0, c=np.arange(n_emotions), cmap=cmap)\n",
    "\n",
    "for i in range(len(emo_pleasure_means)):\n",
    "    ax.plot(\n",
    "        [emo_pleasure_means[i], emo_pleasure_means[i]],\n",
    "        [emo_arousal_means[i], emo_arousal_means[i]],\n",
    "        [1, emo_dominance_means[i]],\n",
    "        color=cmap.colors[i], alpha=0.5\n",
    "    )\n",
    "\n",
    "ax.set_xlabel('Pleasure')\n",
    "ax.set_ylabel('Arousal')\n",
    "ax.set_zlabel('Dominance')\n",
    "ax.set_title('Mean P/A/D for each emotion')\n",
    "\n",
    "# ax.set_xlim(left=1, right=9)\n",
    "# ax.set_ylim(bottom=1, top=9)\n",
    "ax.set_zlim(bottom=1, top=9)\n",
    "\n",
    "# Set the desired viewing angle\n",
    "ax.view_init(elev=20, azim=210)  # Adjust the elevation (elev) and azimuth (azim) angles\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotate(angle):\n",
    "     ax.view_init(azim=angle)\n",
    "\n",
    "anim_name = os.path.join(os.path.dirname(os.getcwd()), 'data', 'new_runs', 'test_3d_anim_all_emotions_dimensions_test_AB.gif')\n",
    "angle = 2\n",
    "ani = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 360, angle), interval=50)\n",
    "ani.save(anim_name, writer=animation.PillowWriter(fps=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result\n",
    "\n",
    "<img src=\"../data/new_runs/test_3d_anim_all_emotions_dimensions_test_AB.gif\" alt=\"drawing\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The animation above should look like this:\n",
    "\n",
    "<img src=\"../data/original/survey/test_3d_anim_all_emotions_dimensions_test_AB.gif\" alt=\"drawing\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Analyze the values obtained for Pleasure / Arousal / Dominance on each emotion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations among PAD for all data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>method</th>\n",
       "      <th>alternative</th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>arousal</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>2304</td>\n",
       "      <td>-0.187508</td>\n",
       "      <td>[-0.23, -0.15]</td>\n",
       "      <td>1.125364e-19</td>\n",
       "      <td>1.962e+16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>2304</td>\n",
       "      <td>0.865263</td>\n",
       "      <td>[0.85, 0.88]</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arousal</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>2304</td>\n",
       "      <td>-0.182182</td>\n",
       "      <td>[-0.22, -0.14]</td>\n",
       "      <td>1.209051e-18</td>\n",
       "      <td>1.882e+15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X          Y   method alternative     n         r           CI95%  \\\n",
       "0  pleasure    arousal  pearson   two-sided  2304 -0.187508  [-0.23, -0.15]   \n",
       "1  pleasure  dominance  pearson   two-sided  2304  0.865263    [0.85, 0.88]   \n",
       "2   arousal  dominance  pearson   two-sided  2304 -0.182182  [-0.22, -0.14]   \n",
       "\n",
       "          p-unc       BF10  power  \n",
       "0  1.125364e-19  1.962e+16    1.0  \n",
       "1  0.000000e+00        inf    1.0  \n",
       "2  1.209051e-18  1.882e+15    1.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.pairwise_corr(df_3d[['pleasure', 'arousal', 'dominance']])  # Pearson pairwise correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations among PAD for all videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>method</th>\n",
       "      <th>alternative</th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>arousal</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>48</td>\n",
       "      <td>-0.693290</td>\n",
       "      <td>[-0.82, -0.51]</td>\n",
       "      <td>4.715668e-08</td>\n",
       "      <td>3.424e+05</td>\n",
       "      <td>0.999933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>48</td>\n",
       "      <td>0.994360</td>\n",
       "      <td>[0.99, 1.0]</td>\n",
       "      <td>1.758086e-46</td>\n",
       "      <td>1.492e+42</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arousal</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>48</td>\n",
       "      <td>-0.688291</td>\n",
       "      <td>[-0.81, -0.5]</td>\n",
       "      <td>6.432830e-08</td>\n",
       "      <td>2.559e+05</td>\n",
       "      <td>0.999913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X          Y   method alternative   n         r           CI95%  \\\n",
       "0  pleasure    arousal  pearson   two-sided  48 -0.693290  [-0.82, -0.51]   \n",
       "1  pleasure  dominance  pearson   two-sided  48  0.994360     [0.99, 1.0]   \n",
       "2   arousal  dominance  pearson   two-sided  48 -0.688291   [-0.81, -0.5]   \n",
       "\n",
       "          p-unc       BF10     power  \n",
       "0  4.715668e-08  3.424e+05  0.999933  \n",
       "1  1.758086e-46  1.492e+42  1.000000  \n",
       "2  6.432830e-08  2.559e+05  0.999913  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.pairwise_corr(\n",
    "    pd.DataFrame({\n",
    "        'pleasure': vid_pleasure_means,\n",
    "        'arousal': vid_arousal_means,\n",
    "        'dominance': vid_dominance_means,\n",
    "    })\n",
    ")  # Pearson pairwise correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arousal')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pleasure vs Arousal:\n",
    "plt.scatter(vid_pleasure_means, vid_arousal_means, s=10, alpha=1)\n",
    "plt.xlabel('Pleasure')\n",
    "plt.ylabel('Arousal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dominance')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pleasure vs Dominance:\n",
    "plt.scatter(vid_pleasure_means, vid_dominance_means, s=10, alpha=1)\n",
    "plt.xlabel('Pleasure')\n",
    "plt.ylabel('Dominance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dominance')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Arousal vs Dominance:\n",
    "plt.scatter(vid_arousal_means, vid_dominance_means, s=10, alpha=1)\n",
    "plt.xlabel('Arousal')\n",
    "plt.ylabel('Dominance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations among PAD for all emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>method</th>\n",
       "      <th>alternative</th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>arousal</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.922272</td>\n",
       "      <td>[-0.99, -0.62]</td>\n",
       "      <td>1.106621e-03</td>\n",
       "      <td>33.71</td>\n",
       "      <td>0.961207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pleasure</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999416</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>4.987303e-10</td>\n",
       "      <td>6.819e+05</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arousal</td>\n",
       "      <td>dominance</td>\n",
       "      <td>pearson</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.917867</td>\n",
       "      <td>[-0.99, -0.6]</td>\n",
       "      <td>1.301206e-03</td>\n",
       "      <td>30.062</td>\n",
       "      <td>0.955431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X          Y   method alternative  n         r           CI95%  \\\n",
       "0  pleasure    arousal  pearson   two-sided  8 -0.922272  [-0.99, -0.62]   \n",
       "1  pleasure  dominance  pearson   two-sided  8  0.999416      [1.0, 1.0]   \n",
       "2   arousal  dominance  pearson   two-sided  8 -0.917867   [-0.99, -0.6]   \n",
       "\n",
       "          p-unc       BF10     power  \n",
       "0  1.106621e-03      33.71  0.961207  \n",
       "1  4.987303e-10  6.819e+05  1.000000  \n",
       "2  1.301206e-03     30.062  0.955431  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.pairwise_corr(\n",
    "    pd.DataFrame({\n",
    "        'pleasure': emo_pleasure_means,\n",
    "        'arousal': emo_arousal_means,\n",
    "        'dominance': emo_dominance_means,\n",
    "    })\n",
    ")  # Pearson pairwise correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arousal')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pleasure vs Arousal:\n",
    "plt.scatter(emo_pleasure_means, emo_arousal_means, s=20, alpha=1)\n",
    "plt.xlabel('Pleasure')\n",
    "plt.ylabel('Arousal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dominance')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pleasure vs Dominance:\n",
    "plt.scatter(emo_pleasure_means, emo_dominance_means, s=20, alpha=1)\n",
    "plt.xlabel('Pleasure')\n",
    "plt.ylabel('Dominance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dominance')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zAAAuMDYzW1m5BT5jWbkFGpP5mUuJgMBydEZlzJgxeuyxx/TNN9+oW7duCgvzbfXdu3f3SzgAaMj25p/2OZNyTrkxWpeTr30FJVwGQr3nqKjce++9kqSRI0dWjnk8HhljWEwLAH6Sd6LqO33vP05RQf3nqKjs27fP3zkAABeIbxlR5faEaEoK6j9HRSU+Pt7fOQAAF2gf20y9kmOVlVug8vNuBRHi8SitQwxnU9AgVLuovPfee+rfv7/CwsL03nvvVbnvoEGDah0MACDNSk/RmMzPfNaqpHWI0az0FBdTAYFT7VvoN2rUSN98841atWqlRo0u/2GhQK5R4Rb6ABqKfQUl2n+8hPuooF6ok1von3+b/AtvmQ8AqFuJMRQUNEyO7qMCAAAQCI6/lHDz5s1au3btJb+U8KWXXqp1MAAAAEdFZfr06XrqqafUsWNHtW7d2ue7fvjeHwAA4C+Oisqrr76quXPnavjw4X6OAwAA8P85WqPSqFEjpaWl+TsLAACAD0dFZfz48frTn/7k7ywAAAA+HF36efzxxzVgwAAlJSWpS5cuF30p4dKlS/0SDgAANGyOzqiMGTNGf//733XttdcqOjpaXq/X51FdU6dOlcfj8XlcddVVTiIBAIB6yNEZlfnz5+udd97RgAEDah2ga9euWr16deXPISEhtX5NAABQPzgqKi1btlRSUpJ/AoSGchYFAABckqNLP1OnTtXTTz+t0tLSWgfIyclR27ZtlZiYqKFDh2rv3r2X3besrExFRUU+DwAAUH9V+0sJz5eSkqI9e/bIGKOEhISLFtNu27atWq+zYsUKlZaW6tprr9XRo0f17LPP6p///Kd27Nih6Ojoi/afOnWqpk2bdtE4X0oIAEDwqMmXEjoqKpcqC+d7+umna/qSkqSSkhIlJSVp4sSJmjBhwkXby8rKVFZWVvlzUVGR4uLiKCoAAASROvn25PM5LSJXEhkZqW7duiknJ+eS28PDwxUeHl4nvxsAANjH8ZcSStKnn36qXbt2yePxqEuXLkpJSalVmLKyMu3atUs/+MEPavU6AACgfnBUVI4dO6ahQ4dq7dq1atGihYwxKiwsVO/evbVo0SLFxsZW63Uef/xx3XXXXbrmmmt07NgxPfvssyoqKtKwYcOcxAIAAPWM4xu+FRUVaceOHTpx4oROnjypL7/8UkVFRRo7dmy1X+frr79Wenq6OnbsqHvuuUeNGzfWpk2bFB8f7yQWAACoZxwtpvV6vVq9erVuuOEGn/HNmzfrtttu06lTp/wWsCo1WYwDAADsUJP3b0dnVCoqKi76SLIkhYWFqaKiwslLAgAAXMRRUfnRj36kcePG6fDhw5Vjhw4d0vjx49WnTx+/hQMAAA2bo6Lyxz/+UcXFxUpISFBSUpI6dOigxMREFRcXa9asWf7OCAAAGihHn/qJi4vTtm3btHr1au3atUvGGHXp0kV9+/b1dz4AANCA1bioVFRUaN68eVq6dKn2798vj8ejxMTEyo8pezyeusgJAAAaoBpd+jHGaNCgQRo9erQOHTqkbt26qWvXrsrLy9Pw4cP1k5/8pK5yAgCABqhGZ1TmzZundevWac2aNerdu7fPto8//lg//vGPNX/+fD300EN+DQkAABqmGp1RyczM1K9//euLSor0708CPfHEE3r77bf9Fg4AADRsNSoqn3/+ue64447Lbu/fv7+2b99e61AAAABSDYvKiRMn1Lp168tub926tU6ePFnrUAAAAFIN16iUl5crNPTyTwkJCdHZs2drHQoAgPPtzT+tvBOlSoiOVGJMpNtxEEA1KirGGA0fPlzh4eGX3F5WVuaXUAAASNKp0jMam5mtdTn5lWO9kmM1Kz1F3oiLv8oF9U+NisqwYcOuuA+f+AEA+MvYzGxl5Rb4jGXlFmhM5meaP+pGl1IhkGpUVDIyMuoqBwAAPvbmn/Y5k3JOuTFal5OvfQUlXAZqABx91w8AAHUt70Rpldv3Hy8JUBK4iaICALBSfMuIKrcnRHM2pSGgqAAArNQ+tpl6Jccq5ILvkAvxeNQrOZbLPg0ERQUAYK1Z6SlK6xDjM5bWIUaz0lNcSoRAq/G3JwMAECjeiDDNH3Wj9hWUaP/xEu6j0gBRVAAA1kuMoaA0VFz6AQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWtYUlRkzZsjj8ejRRx91OwoAALCEFUVly5Ytmj17trp37+52FAAAYBHXi8rp06d1//33680339R//Md/uB0HAABYxPWi8sgjj2jAgAHq27fvFfctKytTUVGRzwMAANRfoW7+8kWLFmnbtm3asmVLtfafMWOGpk2bVsepAACALVw7o3Lw4EGNGzdOCxYsUJMmTar1nMmTJ6uwsLDycfDgwTpOCQAA3OQxxhg3fvG7776rn/zkJwoJCakcKy8vl8fjUaNGjVRWVuaz7VKKiork9XpVWFioqKiouo4MAAD8oCbv365d+unTp4+++OILn7ERI0aoU6dOmjRp0hVLCgAAqP9cKyrNmzfXdddd5zMWGRmp6Ojoi8YBAEDD5OpiWgBA9ezNP628E6VKiI5UYkyk23GAgLGqqKxdu9btCABglVOlZzQ2M1vrcvIrx3olx2pWeoq8EWEuJgMCw/X7qAAALm9sZraycgt8xrJyCzQm8zOXEgGBRVEBAEvtzT+tdTn5Kr/gw5nlxmhdTr72FZS4lAwIHIoKAFgq70Rpldv3H6eooP6jqACApeJbRlS5PSGaRbWo/ygqAGCp9rHN1Cs5ViEej894iMejXsmxfPoHDQJFBQAsNis9RWkdYnzG0jrEaFZ6ikuJgMCy6uPJAABf3ogwzR91o/YVlGj/8RLuo4IGh6ICAEEgMYaCgoaJSz8AAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANYKdTsAAKBm9uafVt6JUiVERyoxJtLtOECdoqgAQJA4VXpGYzOztS4nv3KsV3KsZqWnyBsR5mIyoO5w6QcAgsTYzGxl5Rb4jGXlFmhM5mcuJQLqHkUFAILA3vzTWpeTr3JjfMbLjdG6nHztKyhxKRlQtygqABAE8k6UVrl9/3GKCuonigoABIH4lhFVbk+IZlEt6ieKCgAEgfaxzdQrOVYhHo/PeIjHo17JsXz6B/UWRQUAgsSs9BSldYjxGUvrEKNZ6SkuJQLqHh9PBoAg4Y0I0/xRN2pfQYn2Hy/hPipoECgqABBkEmMoKGg4XL3089prr6l79+6KiopSVFSUevbsqRUrVrgZCQAAWMTVotKuXTs9//zz2rp1q7Zu3aof/ehHuvvuu7Vjxw43YwEAAEt4jLng7kEua9mypV588UWNGjXqom1lZWUqKyur/LmoqEhxcXEqLCxUVFRUIGMCAACHioqK5PV6q/X+bc2nfsrLy7Vo0SKVlJSoZ8+el9xnxowZ8nq9lY+4uLgApwQAAIHk+hmVL774Qj179tS3336rZs2aaeHChbrzzjsvuS9nVAAACH41OaPi+qd+OnbsqOzsbJ06dUrvvPOOhg0bpk8++URdunS5aN/w8HCFh4e7kBIAALjB9TMqF+rbt6+SkpL0xhtvXHHfmjQyAABgh6Bco3KOMcbn8g4AAGi4XL308+tf/1r9+/dXXFyciouLtWjRIq1du1YfffSRm7EAAIAlXC0qR48e1YMPPqgjR47I6/Wqe/fu+uijj9SvXz83YwEAAEu4WlTmzJnj5q8HAACWs26NCgAAwDkUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGCtULcDAAAAu+zNP628E6VKiI5UYkykq1koKgAAQJJ0qvSMxmZma11OfuVYr+RYzUpPkTcizJVMXPoBAACSpLGZ2crKLfAZy8ot0JjMz1xKRFEBAAD69+WedTn5KjfGZ7zcGK3Lyde+ghJXclFUAACA8k6UVrl9/3GKCgAAcEl8y4gqtydEu7OolqICAADUPraZeiXHKsTj8RkP8XjUKznWtU//UFQAAIAkaVZ6itI6xPiMpXWI0az0FJcS8fFkAADwf7wRYZo/6kbtKyjR/uMl3EcFAADYJzHG/YJyDpd+AACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaQX0LfWOMJKmoqMjlJAAAoLrOvW+fex+vSlAXleLiYklSXFycy0kAAEBNFRcXy+v1VrmPx1SnzliqoqJChw8fVvPmzVVcXKy4uDgdPHhQUVFRbkcLiKKiogY1Z+Zb/zW0OTPf+q+hzbm68zXGqLi4WG3btlWjRlWvQgnqMyqNGjVSu3btJEkej0eSFBUV1SD+ZThfQ5sz863/GtqcmW/919DmXJ35XulMyjkspgUAANaiqAAAAGuFTJ06darbIfwlJCREP/zhDxUaGtRXtGqkoc2Z+dZ/DW3OzLf+a2hz9vd8g3oxLQAAqN+49AMAAKxFUQEAANaiqAAAAGtRVAAAgLWCoqhMnTpVHo/H53HVVVdV67lZWVkKDQ1Vjx496jilfzmZc1lZmZ588knFx8crPDxcSUlJmjt3boAS146T+b799tv63ve+p4iICLVp00YjRozQ8ePHA5S49g4dOqQHHnhA0dHRioiIUI8ePfTpp59W+ZxPPvlE119/vZo0aaL27dvr9ddfD1Ba/6jpnJcuXap+/fopNjZWUVFR6tmzp1auXBnAxLXj5G98TjAeu5zMN5iPW07mG8zHrYSEhIuO0x6PR4888shln+OPY1bQfFaqa9euWr16deXPISEhV3xOYWGhHnroIfXp00dHjx6ty3h1oqZzHjJkiI4ePao5c+aoQ4cOOnbsmM6ePVvXMf2mJvNdv369HnroIb388su66667dOjQIT388MMaPXq0li1bFoi4tXLy5EmlpaWpd+/eWrFihVq1aqU9e/aoRYsWl33Ovn37dOedd+pnP/uZFixYoKysLP3iF79QbGys7r333gCmd8bJnNetW6d+/fpp+vTpatGihTIyMnTXXXfpH//4h1JSUgKYvuaczPecYDx2OZ1vsB63nMw32I9bW7ZsUXl5eeXPX375pfr166fBgwdfcn+/HbNMEHj66afN9773vRo/77777jNPPfWU4+e7qaaZV6xYYbxerzl+/Hgdpqo7NZ3viy++aNq3b+8z9oc//MG0a9fO39HqxKRJk8wtt9xSo+dMnDjRdOrUyWfsv//7v81NN93kz2h1xsmcL6VLly5m2rRpfkhUt2oz32A8djmZbzAft5zMN9iPWxcaN26cSUpKMhUVFZfc7q9jVlBc+pGknJwctW3bVomJiRo6dKj27t1b5f4ZGRnas2ePnn766QAl9L+azPm9995TamqqXnjhBV199dW69tpr9fjjj+tf//pXABPXTk3me/PNN+vrr7/Whx9+KGOMjh49qiVLlmjAgAEBTOzcub/X4MGD1apVK6WkpOjNN9+s8jkbN27Ubbfd5jN2++23a+vWrfruu+/qMq5fOJnzhSoqKlRcXKyWLVvWUUr/cTrfYD12OZlvMB+3nMw32I9b5ztz5owWLFigkSNHVn7X3oX8dsxyWKQC6sMPPzRLliwxn3/+uVm1apW59dZbTevWrU1BQcEl99+9e7dp1aqV+eqrr4wxzs/IuKmmc056Pr4AAAmBSURBVL799ttNeHi4GTBggPnHP/5hPvjgAxMfH29GjBgR4OTO1HS+xhjz17/+1TRr1syEhoYaSWbQoEHmzJkzAUztXHh4uAkPDzeTJ08227ZtM6+//rpp0qSJeeutty77nOTkZPPcc8/5jGVlZRlJ5vDhw3UdudaczPlCL7zwgmnZsqU5evRoHSb1DyfzDeZjl5P5BvNxy+m/z8F83Drf4sWLTUhIiDl06NBl9/HXMSsoisqFTp8+bVq3bm1+//vfX7Tt7NmzJjU11bz22muVY8H0H/vlVDVnY4zp16+fadKkiTl16lTl2DvvvGM8Ho8pLS0NVEy/udJ8d+zYYdq0aWNeeOEFs337dvPRRx+Zbt26mZEjRwY4qTNhYWGmZ8+ePmNjxoyp8pRocnKymT59us/Y+vXrjSRz5MiROsnpT07mfL6FCxeaiIgIs2rVqrqI53c1nW+wH7uc/H2D+bjlZL7Bftw632233WYGDhxY5T7+OmYFzaWf80VGRqpbt27Kycm5aFtxcbG2bt2qX/7ylwoNDVVoaKieeeYZbd++XaGhofr4449dSFx7Vc1Zktq0aaOrr77a52uzO3fuLGOMvv7660DF9JsrzXfGjBlKS0vTr371K3Xv3l233367/vznP2vu3Lk6cuRIgNPWXJs2bdSlSxefsc6dO+vAgQOXfc5VV12lb775xmfs2LFjCg0NVXR0dJ3k9Ccncz5n8eLFGjVqlP7yl7+ob9++dRXRr2o632A/djn5+wbzccvJfIP9uHVOXl6eVq9erdGjR1e5n7+OWUFZVMrKyrRr1y61adPmom1RUVH64osvlJ2dXfl4+OGH1bFjR2VnZ+v73/++C4lrr6o5S1JaWpoOHz6s06dPV47t3r1bjRo1Urt27QIV02+uNN/S0lI1auT7r++5TwmZIPj6qrS0NH311Vc+Y7t371Z8fPxln9OzZ0+tWrXKZ+xvf/ubUlNTFRYWVic5/cnJnCUpMzNTw4cP18KFC4PqWn5N5xvsxy4nf99gPm45mW+wH7fOycjIUKtWra7436Pfjlk1Pt/jgscee8ysXbvW7N2712zatMkMHDjQNG/e3Ozfv98YY8wTTzxhHnzwwcs+P5hOn55T0zkXFxebdu3amZ/+9Kdmx44d5pNPPjHJyclm9OjRbk2hRmo634yMDBMaGmr+/Oc/mz179pj169eb1NRUc+ONN7o1hRrZvHmzCQ0NNc8995zJyckxb7/9tomIiDALFiyo3OfCOe/du9dERESY8ePHm507d5o5c+aYsLAws2TJEjemUGNO5rxw4UITGhpq/vSnP5kjR45UPs6/VGArJ/O9UDAdu5zMN5iPW07mG+zHLWOMKS8vN9dcc42ZNGnSRdvq6pgVFEXlvvvuM23atDFhYWGmbdu25p577jE7duyo3D5s2DBz6623Xvb5wfQf+zlO5rxr1y7Tt29f07RpU9OuXTszYcIE66/znuNkvn/4wx9Mly5dTNOmTU2bNm3M/fffb77++usAJ3fu/fffN9ddd50JDw83nTp1MrNnz/bZfqk5r1271qSkpJjGjRubhIQEn/UMwaCmc7711luNpIsew4YNC2xwh5z8jc8XbMcuJ/MN5uOWk/kG+3Fr5cqVRlLlgu/z1dUxy2NMEJ1vAgAADUpQrlEBAAANA0UFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigqABmfq1Knq0aOH2zEAVANFBUCtbNiwQSEhIbrjjjvcjgKgHqKoAKiVuXPnasyYMVq/fn2VX3FvjNHZs2cDmAxAfUBRAeBYSUmJ/vKXv+jnP/+5Bg4cqHnz5lVuW7t2rTwej1auXKnU1FSFh4frf//3fyVJr732mpKSktS4cWN17NhR//M//1P5vP3798vj8Sg7O7ty7NSpU/J4PFq7dq0k6eTJk7r//vsVGxurpk2bKjk5WRkZGZX7T5o0Sddee60iIiLUvn17TZkyRd99913d/sMAUCdC3Q4AIHgtXrxYHTt2VMeOHfXAAw9ozJgxmjJlijweT+U+EydO1MyZM9W+fXu1aNFCy5Yt07hx4/TKK6+ob9++Wr58uUaMGKF27dqpd+/e1fq9U6ZM0c6dO7VixQrFxMQoNzdX//rXvyq3N2/eXPPmzVPbtm31xRdf6Gc/+5maN2+uiRMn+v2fAYC6RVEB4NicOXP0wAMPSJLuuOMOnT59WmvWrFHfvn0r93nmmWfUr1+/yp9nzpyp4cOH6xe/+IUkacKECdq0aZNmzpxZ7aJy4MABpaSkKDU1VZKUkJDgs/2pp56q/N8JCQl67LHHtHjxYooKEIS49APAka+++kqbN2/W0KFDJUmhoaG67777NHfuXJ/9zpWJc3bt2qW0tDSfsbS0NO3atavav/vnP/+5Fi1apB49emjixInasGGDz/YlS5bolltu0VVXXaVmzZppypQpVa6fAWAvigoAR+bMmaOzZ8/q6quvVmhoqEJDQ/Xaa69p6dKlOnnyZOV+kZGRFz33/EtD0r8X2p4ba9SoUeXYOReuL+nfv7/y8vL06KOP6vDhw+rTp48ef/xxSdKmTZs0dOhQ9e/fX8uXL9dnn32mJ598UmfOnPHPxAEEFEUFQI2dPXtW8+fP1+9//3tlZ2dXPrZv3674+Hi9/fbbl31u586dtX79ep+xDRs2qHPnzpKk2NhYSdKRI0cqt5+/sPac2NhYDR8+XAsWLNArr7yi2bNnS5KysrIUHx+vJ598UqmpqUpOTlZeXl6t5wzAHaxRAVBjy5cv18mTJzVq1Ch5vV6fbT/96U81Z84cvfzyy5d87q9+9SsNGTJE//mf/6k+ffro/fff19KlS7V69WpJUtOmTXXTTTfp+eefV0JCggoKCnzWnEjSb37zG11//fXq2rWrysrKtHz58sqi06FDBx04cECLFi3SDTfcoA8++EDLli2rg38KAAKBMyoAamzOnDnq27fvRSVFku69915lZ2dr27Ztl3zuj3/8Y7366qt68cUX1bVrV73xxhvKyMjQD3/4w8p95s6dq++++06pqakaN26cnn32WZ/XaNy4sSZPnqzu3burV69eCgkJ0aJFiyRJd999t8aPH69f/vKX6tGjhzZs2KApU6b4b/IAAspjzr8QDAAAYBHOqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWv8Pq3+JYHfZ4SMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Arousal vs Dominance:\n",
    "plt.scatter(emo_arousal_means, emo_dominance_means, s=20, alpha=1)\n",
    "plt.xlabel('Arousal')\n",
    "plt.ylabel('Dominance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distinguishability of the emotion PAD values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.663554e-143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.033336e-44</td>\n",
       "      <td>4.320217e-65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.064668e-211</td>\n",
       "      <td>1.037083e-17</td>\n",
       "      <td>1.906671e-123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.341631e-135</td>\n",
       "      <td>1.161902e-01</td>\n",
       "      <td>6.788755e-58</td>\n",
       "      <td>8.627224e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.705928e-18</td>\n",
       "      <td>1.831517e-92</td>\n",
       "      <td>4.693267e-08</td>\n",
       "      <td>2.451758e-155</td>\n",
       "      <td>8.906829e-86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.141574e-49</td>\n",
       "      <td>1.364904e-33</td>\n",
       "      <td>2.125553e-03</td>\n",
       "      <td>1.617304e-71</td>\n",
       "      <td>1.227084e-28</td>\n",
       "      <td>3.482231e-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.459782e-06</td>\n",
       "      <td>2.373908e-127</td>\n",
       "      <td>1.300688e-25</td>\n",
       "      <td>1.571769e-197</td>\n",
       "      <td>1.292886e-118</td>\n",
       "      <td>1.387915e-05</td>\n",
       "      <td>4.343177e-33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0              1              2              3              4  \\\n",
       "0            NaN            NaN            NaN            NaN            NaN   \n",
       "1  3.663554e-143            NaN            NaN            NaN            NaN   \n",
       "2   8.033336e-44   4.320217e-65            NaN            NaN            NaN   \n",
       "3  9.064668e-211   1.037083e-17  1.906671e-123            NaN            NaN   \n",
       "4  3.341631e-135   1.161902e-01   6.788755e-58   8.627224e-17            NaN   \n",
       "5   4.705928e-18   1.831517e-92   4.693267e-08  2.451758e-155   8.906829e-86   \n",
       "6   1.141574e-49   1.364904e-33   2.125553e-03   1.617304e-71   1.227084e-28   \n",
       "7   4.459782e-06  2.373908e-127   1.300688e-25  1.571769e-197  1.292886e-118   \n",
       "\n",
       "              5             6   7  \n",
       "0           NaN           NaN NaN  \n",
       "1           NaN           NaN NaN  \n",
       "2           NaN           NaN NaN  \n",
       "3           NaN           NaN NaN  \n",
       "4           NaN           NaN NaN  \n",
       "5           NaN           NaN NaN  \n",
       "6  3.482231e-15           NaN NaN  \n",
       "7  1.387915e-05  4.343177e-33 NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Hotelling T-squared test (= multivariate T-test) for each pair of emotions across all data points.\n",
    "multivar_ttest = np.zeros([n_emotions, n_emotions])\n",
    "multivar_ttest[:] = np.nan\n",
    "\n",
    "for emo1 in range(n_emotions):\n",
    "    for emo2 in range(0, emo1, 1):\n",
    "        mv_ttest = pg.multivariate_ttest(\n",
    "            np.array(df_3d[df_3d['emotion'] == emo1][['pleasure', 'arousal', 'dominance']]),\n",
    "            np.array(df_3d[df_3d['emotion'] == emo2][['pleasure', 'arousal', 'dominance']]),\n",
    "        )\n",
    "        multivar_ttest[emo1, emo2] = float(mv_ttest['pval'].iloc[0])\n",
    "\n",
    "multivar_ttest_df = pd.DataFrame(multivar_ttest)\n",
    "multivar_ttest_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(multivar_ttest, cmap='hot')\n",
    "plt.xticks(np.arange(n_emotions))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results (high values mean overlapping distributions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11619024822609848"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The distribution of emotions 4 and 1 are less distinguishable ('highOptimism' and 'Optimism').\n",
    "multivar_ttest[4, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Mapping PAD values to referenced PAD values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References and methodology overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of the PAD values attributed to each learned emotion with referential experimental accounts from psychology:\n",
    "\n",
    "* Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. J. Res. Personal. 11,\n",
    "273–294 (1977)\n",
    "* Bradley, M.M., Lang, P.J.: Affective norms for english words (ANEW): Instruction manual and affective ratings. Technical Report C-1, Center for Research in Psychophysiology, University of Florida (1999)\n",
    "* Redondo, J., Fraga, I., Padrón, I., Comesaña, M.: The spanish adaptation of anew (affective norms for english words). Behavior Research Methods 39(3), 600–605 (2007)\n",
    "* Landowska, A.: Towards new mappings between emotion representation models. Applied Sciences 8(2), 274 (2018)\n",
    "* Scott, G.G., Keitel, A., Becirspahic, M., et al.: The glasgow norms: Ratings of 5,500 words on nine scales. Behavior Research Methods 51, 1258–1270 (2019)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methodology:\n",
    "1. For each of the learned emotions (0, 1, ... 7), obtain its separate PAD samples from the survey data. That will make 'Sample 1'.\n",
    "2. Compare the distribution of 'Sample 1' with each of the distributions reported (N, mean of each PAD dim., std.dev of each PAD dim.) as 'Sample 2'. Statistical test = Hotelling T-squared (a multivariate extension of the t-test to compare the distribution of two multivariate samples) making assumptions about Sample 2's unknown covariance matrix.\n",
    "3. For this agent’s task (short life-or-death landing maneuvers), ignore:\n",
    "\n",
    "    - off-scope emotions: emotions associated with social relationships (*unfaithful, loyal, insolent, admired*), moral judgements (*dignity, paranoid, emotional*) or self-conscious reflections (*guilty, kind, repentant, lonely*);\n",
    "    - redundant or vague terms (*achievement* and *achieved*, *frightened* and *fright*);\n",
    "    - bodily needs (*hungry*);\n",
    "    - non-emotions (*butter, cemetery, chair*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data from the test\n",
    "2,304 datapoint & 8 emotions\n",
    "* df_3d, emo_stats_df (original [1, 9] scale).\n",
    "* df_3d_norm, emo_stats_df_norm (normalized to [-1, 1] scale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_pad_references = os.path.join(os.path.dirname(os.getcwd()), 'data', 'original', 'survey', 'pad_reference_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>name</th>\n",
       "      <th>pleasure (mean)</th>\n",
       "      <th>pleasure (stdev)</th>\n",
       "      <th>arousal (mean)</th>\n",
       "      <th>arousal (stdev)</th>\n",
       "      <th>dominance (mean)</th>\n",
       "      <th>dominance (stdev)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distress</td>\n",
       "      <td>2.614583</td>\n",
       "      <td>1.827222</td>\n",
       "      <td>6.954861</td>\n",
       "      <td>2.227804</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.627293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Optimism</td>\n",
       "      <td>7.378472</td>\n",
       "      <td>1.669303</td>\n",
       "      <td>5.468750</td>\n",
       "      <td>2.186209</td>\n",
       "      <td>7.100694</td>\n",
       "      <td>1.859227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>slight Concern</td>\n",
       "      <td>4.722222</td>\n",
       "      <td>1.822983</td>\n",
       "      <td>6.649306</td>\n",
       "      <td>1.838023</td>\n",
       "      <td>4.642361</td>\n",
       "      <td>1.864918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Satisfaction</td>\n",
       "      <td>8.427083</td>\n",
       "      <td>1.066547</td>\n",
       "      <td>5.486111</td>\n",
       "      <td>2.571585</td>\n",
       "      <td>8.218750</td>\n",
       "      <td>1.360491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high Optimism</td>\n",
       "      <td>7.336806</td>\n",
       "      <td>1.749395</td>\n",
       "      <td>5.850694</td>\n",
       "      <td>2.245072</td>\n",
       "      <td>7.184028</td>\n",
       "      <td>1.969494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Concern</td>\n",
       "      <td>3.843750</td>\n",
       "      <td>1.851611</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>2.107623</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>1.991270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Excitement</td>\n",
       "      <td>5.288194</td>\n",
       "      <td>2.329241</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>2.059122</td>\n",
       "      <td>5.291667</td>\n",
       "      <td>2.404627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fear</td>\n",
       "      <td>3.156250</td>\n",
       "      <td>1.704645</td>\n",
       "      <td>6.447917</td>\n",
       "      <td>2.091312</td>\n",
       "      <td>3.149306</td>\n",
       "      <td>1.721538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  pleasure (mean)  pleasure (stdev)  arousal (mean)  \\\n",
       "emotion                                                                      \n",
       "0              Distress         2.614583          1.827222        6.954861   \n",
       "1              Optimism         7.378472          1.669303        5.468750   \n",
       "2        slight Concern         4.722222          1.822983        6.649306   \n",
       "3          Satisfaction         8.427083          1.066547        5.486111   \n",
       "4         high Optimism         7.336806          1.749395        5.850694   \n",
       "5               Concern         3.843750          1.851611        6.437500   \n",
       "6            Excitement         5.288194          2.329241        6.437500   \n",
       "7                  Fear         3.156250          1.704645        6.447917   \n",
       "\n",
       "         arousal (stdev)  dominance (mean)  dominance (stdev)  \n",
       "emotion                                                        \n",
       "0               2.227804          2.500000           1.627293  \n",
       "1               2.186209          7.100694           1.859227  \n",
       "2               1.838023          4.642361           1.864918  \n",
       "3               2.571585          8.218750           1.360491  \n",
       "4               2.245072          7.184028           1.969494  \n",
       "5               2.107623          3.833333           1.991270  \n",
       "6               2.059122          5.291667           2.404627  \n",
       "7               2.091312          3.149306           1.721538  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emo_stats_df  # Statistics of PAD values in 1-9 scale (8 emotions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization of data points and statistics to [-1, 1] scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pleasure</th>\n",
       "      <th>arousal</th>\n",
       "      <th>dominance</th>\n",
       "      <th>emotion</th>\n",
       "      <th>video</th>\n",
       "      <th>test_ab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>0</td>\n",
       "      <td>A01</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>A02</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>A03</td>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>A04</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>A05</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>B20</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>6</td>\n",
       "      <td>B21</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>7</td>\n",
       "      <td>B22</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>7</td>\n",
       "      <td>B23</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>7</td>\n",
       "      <td>B24</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2304 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pleasure  arousal  dominance  emotion video test_ab\n",
       "0        -1.00     0.50      -0.75        0   A01       A\n",
       "1        -1.00     1.00      -1.00        0   A02       A\n",
       "2         0.00     0.00       0.00        0   A03       A\n",
       "3         0.75     0.25       0.50        1   A04       A\n",
       "4         0.75     0.25       1.00        1   A05       A\n",
       "...        ...      ...        ...      ...   ...     ...\n",
       "2299      0.50     0.00       0.00        6   B20       B\n",
       "2300     -0.25     0.50      -0.75        6   B21       B\n",
       "2301     -0.75     0.75      -0.75        7   B22       B\n",
       "2302     -0.25     0.50       0.25        7   B23       B\n",
       "2303      0.00     0.00      -0.25        7   B24       B\n",
       "\n",
       "[2304 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3d_norm = df_3d.copy()\n",
    "for e in ('pleasure', 'arousal', 'dominance'):\n",
    "    df_3d_norm[e] = (df_3d_norm[e] - 5) / 4\n",
    "df_3d_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean P, A and D across raters for each hypothetical emotion in the test.\n",
    "emo_pleasure_means_norm = [np.mean(df_3d_norm[df_3d_norm['emotion'] == e]['pleasure']) for e in range(n_emotions)]\n",
    "emo_pleasure_stds_norm = [np.std(df_3d_norm[df_3d_norm['emotion'] == e]['pleasure'], ddof=1) for e in range(n_emotions)]\n",
    "emo_arousal_means_norm = [np.mean(df_3d_norm[df_3d_norm['emotion'] == e]['arousal']) for e in range(n_emotions)]\n",
    "emo_arousal_stds_norm = [np.std(df_3d_norm[df_3d_norm['emotion'] == e]['arousal'], ddof=1) for e in range(n_emotions)]\n",
    "emo_dominance_means_norm = [np.mean(df_3d_norm[df_3d_norm['emotion'] == e]['dominance']) for e in range(n_emotions)]\n",
    "emo_dominance_stds_norm = [np.std(df_3d_norm[df_3d_norm['emotion'] == e]['dominance'], ddof=1) for e in range(n_emotions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>pleasure (mean)</th>\n",
       "      <th>pleasure (stdev)</th>\n",
       "      <th>arousal (mean)</th>\n",
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       "      <th>dominance (mean)</th>\n",
       "      <th>dominance (stdev)</th>\n",
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       "      <th>emotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distress</td>\n",
       "      <td>-0.596354</td>\n",
       "      <td>0.456806</td>\n",
       "      <td>0.488715</td>\n",
       "      <td>0.556951</td>\n",
       "      <td>-0.625000</td>\n",
       "      <td>0.406823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Optimism</td>\n",
       "      <td>0.594618</td>\n",
       "      <td>0.417326</td>\n",
       "      <td>0.117188</td>\n",
       "      <td>0.546552</td>\n",
       "      <td>0.525174</td>\n",
       "      <td>0.464807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>slight Concern</td>\n",
       "      <td>-0.069444</td>\n",
       "      <td>0.455746</td>\n",
       "      <td>0.412326</td>\n",
       "      <td>0.459506</td>\n",
       "      <td>-0.089410</td>\n",
       "      <td>0.466230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Satisfaction</td>\n",
       "      <td>0.856771</td>\n",
       "      <td>0.266637</td>\n",
       "      <td>0.121528</td>\n",
       "      <td>0.642896</td>\n",
       "      <td>0.804688</td>\n",
       "      <td>0.340123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high Optimism</td>\n",
       "      <td>0.584201</td>\n",
       "      <td>0.437349</td>\n",
       "      <td>0.212674</td>\n",
       "      <td>0.561268</td>\n",
       "      <td>0.546007</td>\n",
       "      <td>0.492374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Concern</td>\n",
       "      <td>-0.289062</td>\n",
       "      <td>0.462903</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.526906</td>\n",
       "      <td>-0.291667</td>\n",
       "      <td>0.497818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Excitement</td>\n",
       "      <td>0.072049</td>\n",
       "      <td>0.582310</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.514780</td>\n",
       "      <td>0.072917</td>\n",
       "      <td>0.601157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fear</td>\n",
       "      <td>-0.460938</td>\n",
       "      <td>0.426161</td>\n",
       "      <td>0.361979</td>\n",
       "      <td>0.522828</td>\n",
       "      <td>-0.462674</td>\n",
       "      <td>0.430384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  pleasure (mean)  pleasure (stdev)  arousal (mean)  \\\n",
       "emotion                                                                      \n",
       "0              Distress        -0.596354          0.456806        0.488715   \n",
       "1              Optimism         0.594618          0.417326        0.117188   \n",
       "2        slight Concern        -0.069444          0.455746        0.412326   \n",
       "3          Satisfaction         0.856771          0.266637        0.121528   \n",
       "4         high Optimism         0.584201          0.437349        0.212674   \n",
       "5               Concern        -0.289062          0.462903        0.359375   \n",
       "6            Excitement         0.072049          0.582310        0.359375   \n",
       "7                  Fear        -0.460938          0.426161        0.361979   \n",
       "\n",
       "         arousal (stdev)  dominance (mean)  dominance (stdev)  \n",
       "emotion                                                        \n",
       "0               0.556951         -0.625000           0.406823  \n",
       "1               0.546552          0.525174           0.464807  \n",
       "2               0.459506         -0.089410           0.466230  \n",
       "3               0.642896          0.804688           0.340123  \n",
       "4               0.561268          0.546007           0.492374  \n",
       "5               0.526906         -0.291667           0.497818  \n",
       "6               0.514780          0.072917           0.601157  \n",
       "7               0.522828         -0.462674           0.430384  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emo_stats_df_norm = pd.DataFrame({\n",
    "    'emotion': np.arange(n_emotions),\n",
    "    'name': [emotional_attribution[i] for i in range(n_emotions)],\n",
    "    'pleasure (mean)': emo_pleasure_means_norm,\n",
    "    'pleasure (stdev)': emo_pleasure_stds_norm,\n",
    "    'arousal (mean)': emo_arousal_means_norm,\n",
    "    'arousal (stdev)': emo_arousal_stds_norm,\n",
    "    'dominance (mean)': emo_dominance_means_norm,\n",
    "    'dominance (stdev)': emo_dominance_stds_norm,\n",
    "})\n",
    "emo_stats_df_norm.set_index('emotion', inplace=True)\n",
    "emo_stats_df_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference 1: Russell and Mehrabian (1977)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. J. Res. Personal. 11,273–294 (1977)\n",
    "\n",
    "In this pivotal study, 151 different terms denoting emotional states were rated according to the three PAD values. The study reports the mean and standard deviation values for each, along with the number of samples.\n",
    "\n",
    "The dataset, obtained from the original paper, is included in this repository for reproducibility purposes, for which author's permission was granted. Further use of these data for other purposes may require additional persmission from the authors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read Russell-Mehrabian PAD values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Number</th>\n",
       "      <th>Term</th>\n",
       "      <th>N</th>\n",
       "      <th>Pleasure-mean</th>\n",
       "      <th>Pleasure-sd</th>\n",
       "      <th>Arousal-mean</th>\n",
       "      <th>Arousal-sd</th>\n",
       "      <th>Dominance-mean</th>\n",
       "      <th>Dominance-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bold</td>\n",
       "      <td>27</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Useful</td>\n",
       "      <td>27</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mighty</td>\n",
       "      <td>27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kind</td>\n",
       "      <td>27</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Self-satisfied</td>\n",
       "      <td>27</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>Uninterested</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>Detached</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>Discontented</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>Discouraged</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>Sad</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Number            Term   N  Pleasure-mean  Pleasure-sd  Arousal-mean  \\\n",
       "0         1            Bold  27           0.44         0.32          0.61   \n",
       "1         2          Useful  27           0.70         0.20          0.44   \n",
       "2         3          Mighty  27           0.48         0.37          0.51   \n",
       "3         4            Kind  27           0.73         0.22          0.19   \n",
       "4         5  Self-satisfied  27           0.86         0.10          0.20   \n",
       "..      ...             ...  ..            ...          ...           ...   \n",
       "146     147    Uninterested  30          -0.47         0.26         -0.50   \n",
       "147     148        Detached  30          -0.37         0.34         -0.26   \n",
       "148     149    Discontented  30          -0.53         0.19         -0.16   \n",
       "149     150     Discouraged  30          -0.61         0.25         -0.15   \n",
       "150     151             Sad  30          -0.63         0.23         -0.27   \n",
       "\n",
       "     Arousal-sd  Dominance-mean  Dominance-sd  \n",
       "0          0.24            0.66          0.30  \n",
       "1          0.28            0.47          0.40  \n",
       "2          0.28            0.69          0.31  \n",
       "3          0.32            0.57          0.27  \n",
       "4          0.40            0.62          0.31  \n",
       "..          ...             ...           ...  \n",
       "146        0.22           -0.08          0.24  \n",
       "147        0.28           -0.14          0.29  \n",
       "148        0.41           -0.26          0.30  \n",
       "149        0.32           -0.29          0.32  \n",
       "150        0.34           -0.33          0.22  \n",
       "\n",
       "[151 rows x 9 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm_pad_df = pd.read_csv(os.path.join(path_to_pad_references, 'PAD_values_Russell_Mehrabian_1977.csv'))\n",
    "rm_pad_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Hotelling T-squared - Sample 2's covariance assumption: independent vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Russell-Mehrabian PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103</td>\n",
       "      <td>Helpless</td>\n",
       "      <td>3.698365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>Fearful</td>\n",
       "      <td>5.234994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>Insecure</td>\n",
       "      <td>5.793690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>Humiliated</td>\n",
       "      <td>6.157902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112</td>\n",
       "      <td>Pain</td>\n",
       "      <td>7.144085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>96</td>\n",
       "      <td>Distressed</td>\n",
       "      <td>7.485591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>7.572980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120</td>\n",
       "      <td>Confused</td>\n",
       "      <td>7.852914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104</td>\n",
       "      <td>Troubled</td>\n",
       "      <td>8.074359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95</td>\n",
       "      <td>Frustrated</td>\n",
       "      <td>8.129714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx     rm_term  statistic\n",
       "0     103    Helpless   3.698365\n",
       "1     100     Fearful   5.234994\n",
       "2      97    Insecure   5.793690\n",
       "3      98  Humiliated   6.157902\n",
       "4     112        Pain   7.144085\n",
       "5      96  Distressed   7.485591\n",
       "6     117      Guilty   7.572980\n",
       "7     120    Confused   7.852914\n",
       "8     104    Troubled   8.074359\n",
       "9      95  Frustrated   8.129714"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Russell-Mehrabian PAD data...\")\n",
    "\n",
    "rm_statistics_df = st.match_sample_vs_rm_pad_values(emotion_to_match, df_3d_norm, rm_pad_df)\n",
    "rm_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Helpless, Fearful, Insecure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Hotelling T-squared - Sample 2's covariance assumption: = Sample 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Russell-Mehrabian PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103</td>\n",
       "      <td>Helpless</td>\n",
       "      <td>3.733884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>Fearful</td>\n",
       "      <td>5.285386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>Insecure</td>\n",
       "      <td>5.728299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>Humiliated</td>\n",
       "      <td>6.207736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112</td>\n",
       "      <td>Pain</td>\n",
       "      <td>7.129982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>96</td>\n",
       "      <td>Distressed</td>\n",
       "      <td>7.220849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>7.478089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120</td>\n",
       "      <td>Confused</td>\n",
       "      <td>7.707590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104</td>\n",
       "      <td>Troubled</td>\n",
       "      <td>7.808646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>Terrified</td>\n",
       "      <td>8.123117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx     rm_term  statistic\n",
       "0     103    Helpless   3.733884\n",
       "1     100     Fearful   5.285386\n",
       "2      97    Insecure   5.728299\n",
       "3      98  Humiliated   6.207736\n",
       "4     112        Pain   7.129982\n",
       "5      96  Distressed   7.220849\n",
       "6     117      Guilty   7.478089\n",
       "7     120    Confused   7.707590\n",
       "8     104    Troubled   7.808646\n",
       "9     101   Terrified   8.123117"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Russell-Mehrabian PAD data...\")\n",
    "\n",
    "rm_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, rm_pad_df,\n",
    "    method='same'\n",
    ")\n",
    "rm_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Helpless, Fearful, Insecure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Hotelling T-squared - Euclidean distance (compare the means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Russell-Mehrabian PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103</td>\n",
       "      <td>Helpless</td>\n",
       "      <td>0.175676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>Fearful</td>\n",
       "      <td>0.228723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98</td>\n",
       "      <td>Humiliated</td>\n",
       "      <td>0.254174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>Frustrated</td>\n",
       "      <td>0.280194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112</td>\n",
       "      <td>Pain</td>\n",
       "      <td>0.296123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>96</td>\n",
       "      <td>Distressed</td>\n",
       "      <td>0.337599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>0.354234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120</td>\n",
       "      <td>Confused</td>\n",
       "      <td>0.381135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101</td>\n",
       "      <td>Terrified</td>\n",
       "      <td>0.385141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>104</td>\n",
       "      <td>Troubled</td>\n",
       "      <td>0.399763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx     rm_term  statistic\n",
       "0     103    Helpless   0.175676\n",
       "1     100     Fearful   0.228723\n",
       "2      98  Humiliated   0.254174\n",
       "3      95  Frustrated   0.280194\n",
       "4     112        Pain   0.296123\n",
       "5      96  Distressed   0.337599\n",
       "6     117      Guilty   0.354234\n",
       "7     120    Confused   0.381135\n",
       "8     101   Terrified   0.385141\n",
       "9     104    Troubled   0.399763"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Russell-Mehrabian PAD data...\")\n",
    "\n",
    "rm_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, rm_pad_df,\n",
    "    method='means'\n",
    ")\n",
    "rm_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Helpless, Fearful, Frustrated\n",
    "\n",
    "Off scope: Humiliated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference 2: ANEW, by Bradley & Lang (1977)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bradley, M.M., Lang, P.J.: Affective norms for english words (ANEW): Instruction manual and affective ratings. Technical Report C-1, Center for Research in Psychophysiology, University of Florida (1999)\n",
    "\n",
    "A lexicon of 1,034 frequent words \"developed to provide a set of normative emotional ratings for a large number of words in the English language\".\n",
    "\n",
    "Unfortunately, the dataset permissions exclude their placement on the internet, so only the first two rows have been left in this repository for orientative purposes, while preserving here the analytical results obtained from the full data.\n",
    "The authors provide a link to request permission at: https://csea.phhp.ufl.edu/media/anewmessage.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read ANEW PAD values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>Description</th>\n",
       "      <th>Word No.</th>\n",
       "      <th>Valence Mean</th>\n",
       "      <th>Valence SD</th>\n",
       "      <th>Arousal Mean</th>\n",
       "      <th>Arousal SD</th>\n",
       "      <th>Dominance Mean</th>\n",
       "      <th>Dominance SD</th>\n",
       "      <th>Word Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abduction</td>\n",
       "      <td>621</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.06</td>\n",
       "      <td>5.53</td>\n",
       "      <td>2.43</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abortion</td>\n",
       "      <td>622</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.59</td>\n",
       "      <td>2.54</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>absurd</td>\n",
       "      <td>623</td>\n",
       "      <td>4.26</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.73</td>\n",
       "      <td>1.72</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abundance</td>\n",
       "      <td>624</td>\n",
       "      <td>6.59</td>\n",
       "      <td>2.01</td>\n",
       "      <td>5.51</td>\n",
       "      <td>2.63</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2.16</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abuse</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.23</td>\n",
       "      <td>6.83</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.69</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>yacht</td>\n",
       "      <td>1037</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.61</td>\n",
       "      <td>2.72</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>yellow</td>\n",
       "      <td>545</td>\n",
       "      <td>5.61</td>\n",
       "      <td>1.94</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.05</td>\n",
       "      <td>5.47</td>\n",
       "      <td>1.58</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>young</td>\n",
       "      <td>1038</td>\n",
       "      <td>6.89</td>\n",
       "      <td>2.12</td>\n",
       "      <td>5.64</td>\n",
       "      <td>2.51</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2.49</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>youth</td>\n",
       "      <td>1039</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.67</td>\n",
       "      <td>2.52</td>\n",
       "      <td>5.11</td>\n",
       "      <td>2.55</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>zest</td>\n",
       "      <td>1040</td>\n",
       "      <td>6.79</td>\n",
       "      <td>2.04</td>\n",
       "      <td>5.59</td>\n",
       "      <td>2.66</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.99</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Description  Word No.  Valence Mean  Valence SD  Arousal Mean  \\\n",
       "0      abduction       621          2.76        2.06          5.53   \n",
       "1       abortion       622          3.50        2.30          5.39   \n",
       "2         absurd       623          4.26        1.82          4.36   \n",
       "3      abundance       624          6.59        2.01          5.51   \n",
       "4          abuse         1          1.80        1.23          6.83   \n",
       "...          ...       ...           ...         ...           ...   \n",
       "1029       yacht      1037          6.95        1.79          5.61   \n",
       "1030      yellow       545          5.61        1.94          4.43   \n",
       "1031       young      1038          6.89        2.12          5.64   \n",
       "1032       youth      1039          6.75        2.29          5.67   \n",
       "1033        zest      1040          6.79        2.04          5.59   \n",
       "\n",
       "      Arousal SD  Dominance Mean  Dominance SD Word Frequency  \n",
       "0           2.43            3.49          2.38              1  \n",
       "1           2.80            4.59          2.54              6  \n",
       "2           2.20            4.73          1.72             17  \n",
       "3           2.63            5.80          2.16             13  \n",
       "4           2.70            3.69          2.94             18  \n",
       "...          ...             ...           ...            ...  \n",
       "1029        2.72            6.10          2.13              4  \n",
       "1030        2.05            5.47          1.58             55  \n",
       "1031        2.51            5.30          2.49            385  \n",
       "1032        2.52            5.11          2.55             82  \n",
       "1033        2.66            6.00          1.99              5  \n",
       "\n",
       "[1034 rows x 9 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anew_pad_df = pd.read_csv(os.path.join(path_to_pad_references, 'PAD_values_ANEW_sample.csv'))\n",
    "anew_pad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Term</th>\n",
       "      <th>Pleasure-mean</th>\n",
       "      <th>Pleasure-sd</th>\n",
       "      <th>Arousal-mean</th>\n",
       "      <th>Arousal-sd</th>\n",
       "      <th>Dominance-mean</th>\n",
       "      <th>Dominance-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abduction</td>\n",
       "      <td>-0.5600</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.6075</td>\n",
       "      <td>-0.3775</td>\n",
       "      <td>0.5950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abortion</td>\n",
       "      <td>-0.3750</td>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>-0.1025</td>\n",
       "      <td>0.6350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>absurd</td>\n",
       "      <td>-0.1850</td>\n",
       "      <td>0.4550</td>\n",
       "      <td>-0.1600</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>-0.0675</td>\n",
       "      <td>0.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abundance</td>\n",
       "      <td>0.3975</td>\n",
       "      <td>0.5025</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.6575</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abuse</td>\n",
       "      <td>-0.8000</td>\n",
       "      <td>0.3075</td>\n",
       "      <td>0.4575</td>\n",
       "      <td>0.6750</td>\n",
       "      <td>-0.3275</td>\n",
       "      <td>0.7350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>yacht</td>\n",
       "      <td>0.4875</td>\n",
       "      <td>0.4475</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.5325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>yellow</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.4850</td>\n",
       "      <td>-0.1425</td>\n",
       "      <td>0.5125</td>\n",
       "      <td>0.1175</td>\n",
       "      <td>0.3950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>young</td>\n",
       "      <td>0.4725</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.6225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>youth</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.5725</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>0.6375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>zest</td>\n",
       "      <td>0.4475</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>0.6650</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.4975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Term  Pleasure-mean  Pleasure-sd  Arousal-mean  Arousal-sd  \\\n",
       "0     abduction        -0.5600       0.5150        0.1325      0.6075   \n",
       "1      abortion        -0.3750       0.5750        0.0975      0.7000   \n",
       "2        absurd        -0.1850       0.4550       -0.1600      0.5500   \n",
       "3     abundance         0.3975       0.5025        0.1275      0.6575   \n",
       "4         abuse        -0.8000       0.3075        0.4575      0.6750   \n",
       "...         ...            ...          ...           ...         ...   \n",
       "1029      yacht         0.4875       0.4475        0.1525      0.6800   \n",
       "1030     yellow         0.1525       0.4850       -0.1425      0.5125   \n",
       "1031      young         0.4725       0.5300        0.1600      0.6275   \n",
       "1032      youth         0.4375       0.5725        0.1675      0.6300   \n",
       "1033       zest         0.4475       0.5100        0.1475      0.6650   \n",
       "\n",
       "      Dominance-mean  Dominance-sd  \n",
       "0            -0.3775        0.5950  \n",
       "1            -0.1025        0.6350  \n",
       "2            -0.0675        0.4300  \n",
       "3             0.2000        0.5400  \n",
       "4            -0.3275        0.7350  \n",
       "...              ...           ...  \n",
       "1029          0.2750        0.5325  \n",
       "1030          0.1175        0.3950  \n",
       "1031          0.0750        0.6225  \n",
       "1032          0.0275        0.6375  \n",
       "1033          0.2500        0.4975  \n",
       "\n",
       "[1034 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adapt data to process (column names and scale):\n",
    "anew_pad_df_norm = pd.DataFrame()\n",
    "anew_pad_df_norm['Term'] = anew_pad_df['Description']\n",
    "# N is an undocumented value in dataset.\n",
    "\n",
    "# Adapt to [-1, 1] scale.\n",
    "anew_pad_df_norm['Pleasure-mean'] = anew_pad_df['Valence Mean']/4 - 1.25\n",
    "anew_pad_df_norm['Pleasure-sd'] = anew_pad_df['Valence SD']/4\n",
    "\n",
    "anew_pad_df_norm['Arousal-mean'] = anew_pad_df['Arousal Mean']/4 - 1.25\n",
    "anew_pad_df_norm['Arousal-sd'] = anew_pad_df['Arousal SD']/4\n",
    "\n",
    "anew_pad_df_norm['Dominance-mean'] = anew_pad_df['Dominance Mean']/4 - 1.25\n",
    "anew_pad_df_norm['Dominance-sd'] = anew_pad_df['Dominance SD']/4\n",
    "\n",
    "anew_pad_df_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Hotelling T-squared - Sample 2's covariance assumption: independent vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW PAD data...\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>808</td>\n",
       "      <td>scared</td>\n",
       "      <td>2.605335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>901</td>\n",
       "      <td>surgery</td>\n",
       "      <td>4.309269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>438</td>\n",
       "      <td>hostage</td>\n",
       "      <td>5.500752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>325</td>\n",
       "      <td>fear</td>\n",
       "      <td>6.699121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>448</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>8.519073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>676</td>\n",
       "      <td>panic</td>\n",
       "      <td>9.969456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>445</td>\n",
       "      <td>humiliate</td>\n",
       "      <td>10.039632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>ambulance</td>\n",
       "      <td>10.083964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>393</td>\n",
       "      <td>guilty</td>\n",
       "      <td>10.459087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>829</td>\n",
       "      <td>shark</td>\n",
       "      <td>11.801609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx    rm_term  statistic\n",
       "0     808     scared   2.605335\n",
       "1     901    surgery   4.309269\n",
       "2     438    hostage   5.500752\n",
       "3     325       fear   6.699121\n",
       "4     448  hurricane   8.519073\n",
       "5     676      panic   9.969456\n",
       "6     445  humiliate  10.039632\n",
       "7      35  ambulance  10.083964\n",
       "8     393     guilty  10.459087\n",
       "9     829      shark  11.801609"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW PAD data...\")\n",
    "\n",
    "anew_tls_statistics_df = st.match_sample_vs_rm_pad_values(emotion_to_match, df_3d_norm, anew_pad_df_norm)\n",
    "anew_tls_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: scared, fear, panic\n",
    "\n",
    "Non-emotions: surgery, hostage, hurricane"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Hotelling T-squared - Sample 2's covariance assumption: = Sample 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW PAD data...\n"
     ]
    },
    {
     "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",
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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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>901</td>\n",
       "      <td>surgery</td>\n",
       "      <td>4.061015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>808</td>\n",
       "      <td>scared</td>\n",
       "      <td>4.169270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>448</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>8.226641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>676</td>\n",
       "      <td>panic</td>\n",
       "      <td>9.510779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>445</td>\n",
       "      <td>humiliate</td>\n",
       "      <td>12.220827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>292</td>\n",
       "      <td>embarrassed</td>\n",
       "      <td>12.472010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>438</td>\n",
       "      <td>hostage</td>\n",
       "      <td>13.325588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>551</td>\n",
       "      <td>lost</td>\n",
       "      <td>13.375994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>325</td>\n",
       "      <td>fear</td>\n",
       "      <td>13.504699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>340</td>\n",
       "      <td>flood</td>\n",
       "      <td>15.579705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx      rm_term  statistic\n",
       "0     901      surgery   4.061015\n",
       "1     808       scared   4.169270\n",
       "2     448    hurricane   8.226641\n",
       "3     676        panic   9.510779\n",
       "4     445    humiliate  12.220827\n",
       "5     292  embarrassed  12.472010\n",
       "6     438      hostage  13.325588\n",
       "7     551         lost  13.375994\n",
       "8     325         fear  13.504699\n",
       "9     340        flood  15.579705"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW PAD data...\")\n",
    "\n",
    "anew_tls_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, anew_pad_df_norm,\n",
    "    method='same')\n",
    "anew_tls_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: scared, panic, embarrassed\n",
    "\n",
    "Non-emotions: surgery, hurricane\n",
    "\n",
    "Off-scope: humiliate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Hotelling T-squared - Euclidean distance (compare the means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW PAD data...\n"
     ]
    },
    {
     "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",
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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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>808</td>\n",
       "      <td>scared</td>\n",
       "      <td>0.122257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>438</td>\n",
       "      <td>hostage</td>\n",
       "      <td>0.141145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901</td>\n",
       "      <td>surgery</td>\n",
       "      <td>0.174747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>325</td>\n",
       "      <td>fear</td>\n",
       "      <td>0.183639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>ambulance</td>\n",
       "      <td>0.206160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>676</td>\n",
       "      <td>panic</td>\n",
       "      <td>0.216461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>277</td>\n",
       "      <td>drown</td>\n",
       "      <td>0.217968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>948</td>\n",
       "      <td>trauma</td>\n",
       "      <td>0.219495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>445</td>\n",
       "      <td>humiliate</td>\n",
       "      <td>0.225598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>448</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>0.232744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx    rm_term  statistic\n",
       "0     808     scared   0.122257\n",
       "1     438    hostage   0.141145\n",
       "2     901    surgery   0.174747\n",
       "3     325       fear   0.183639\n",
       "4      35  ambulance   0.206160\n",
       "5     676      panic   0.216461\n",
       "6     277      drown   0.217968\n",
       "7     948     trauma   0.219495\n",
       "8     445  humiliate   0.225598\n",
       "9     448  hurricane   0.232744"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW PAD data...\")\n",
    "\n",
    "anew_tls_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, anew_pad_df_norm,\n",
    "    method='means')\n",
    "anew_tls_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: scared, fear, panic\n",
    "\n",
    "Non-emotion: hostage, surgery, ambulance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference 3: Spanish ANEW, by Redondo et al (2007)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Redondo, J., Fraga, I., Padrón, I., Comesaña, M.: The spanish adaptation of anew (affective norms for english words). Behavior Research Methods 39(3), 600–605 (2007)\n",
    "\n",
    "Mapping between the 1,034 Spanish ANEW words and PAD values (from Spanish speakers).\n",
    "\n",
    "The original dataset can be accessed in the public link of the paper at Springer.com, section [\"Electronic supplementary material\"](https://link.springer.com/article/10.3758/BF03193031#rightslink:~:text=Supplementary%20material%2C%20approximately%20340%20KB). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read Spanish ANEW PAD values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Number</th>\n",
       "      <th>E-Word</th>\n",
       "      <th>S-Word</th>\n",
       "      <th>Val-Mn-All</th>\n",
       "      <th>Val-Sd-All</th>\n",
       "      <th>Aro-Mn-All</th>\n",
       "      <th>Aro-Sd-All</th>\n",
       "      <th>Dom-Mn-All</th>\n",
       "      <th>Dom-Sd-All</th>\n",
       "      <th>Nlett</th>\n",
       "      <th>Nsyll</th>\n",
       "      <th>GClass</th>\n",
       "      <th>Freq</th>\n",
       "      <th>Neigh</th>\n",
       "      <th>Fam</th>\n",
       "      <th>Con</th>\n",
       "      <th>Imag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>722</td>\n",
       "      <td>derelict</td>\n",
       "      <td>abandonado</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.88</td>\n",
       "      <td>2.21</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2.03</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>V/A</td>\n",
       "      <td>27.86</td>\n",
       "      <td>1</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>583</td>\n",
       "      <td>bees</td>\n",
       "      <td>abejas</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.85</td>\n",
       "      <td>6.59</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2.32</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622</td>\n",
       "      <td>abortion</td>\n",
       "      <td>aborto</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.13</td>\n",
       "      <td>6.07</td>\n",
       "      <td>1.87</td>\n",
       "      <td>4.17</td>\n",
       "      <td>2.37</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.47</td>\n",
       "      <td>4.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>975</td>\n",
       "      <td>scorching</td>\n",
       "      <td>abrasador</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.58</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.19</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>218</td>\n",
       "      <td>hug</td>\n",
       "      <td>abrazo</td>\n",
       "      <td>8.13</td>\n",
       "      <td>1.34</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.57</td>\n",
       "      <td>6.12</td>\n",
       "      <td>2.16</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>619</td>\n",
       "      <td>volcano</td>\n",
       "      <td>volcán</td>\n",
       "      <td>3.52</td>\n",
       "      <td>2.02</td>\n",
       "      <td>6.63</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.26</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>5.54</td>\n",
       "      <td>1</td>\n",
       "      <td>3.87</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>481</td>\n",
       "      <td>vomit</td>\n",
       "      <td>vómito</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.16</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2.54</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.15</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>1037</td>\n",
       "      <td>yacht</td>\n",
       "      <td>yate</td>\n",
       "      <td>6.57</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.23</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.92</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>9.64</td>\n",
       "      <td>7</td>\n",
       "      <td>3.57</td>\n",
       "      <td>5.95</td>\n",
       "      <td>6.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>371</td>\n",
       "      <td>sapphire</td>\n",
       "      <td>zafiro</td>\n",
       "      <td>6.71</td>\n",
       "      <td>1.83</td>\n",
       "      <td>5.21</td>\n",
       "      <td>2.12</td>\n",
       "      <td>5.12</td>\n",
       "      <td>1.87</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>990</td>\n",
       "      <td>spanking</td>\n",
       "      <td>zurra</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.54</td>\n",
       "      <td>6.63</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2.40</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>0.36</td>\n",
       "      <td>5</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "      <td>UnAv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Number     E-Word      S-Word  Val-Mn-All  Val-Sd-All  Aro-Mn-All  \\\n",
       "0        722   derelict  abandonado        1.68        1.00        5.88   \n",
       "1        583       bees      abejas        3.18        1.85        6.59   \n",
       "2        622   abortion      aborto        2.80        2.13        6.07   \n",
       "3        975  scorching   abrasador        2.46        1.58        6.00   \n",
       "4        218        hug      abrazo        8.13        1.34        5.70   \n",
       "...      ...        ...         ...         ...         ...         ...   \n",
       "1029     619    volcano      volcán        3.52        2.02        6.63   \n",
       "1030     481      vomit      vómito        1.76        1.16        5.80   \n",
       "1031    1037      yacht        yate        6.57        1.81        5.19   \n",
       "1032     371   sapphire      zafiro        6.71        1.83        5.21   \n",
       "1033     990   spanking       zurra        1.96        1.54        6.63   \n",
       "\n",
       "      Aro-Sd-All  Dom-Mn-All  Dom-Sd-All  Nlett  Nsyll  GClass  Freq   Neigh  \\\n",
       "0           2.21        3.11        2.03     10       5    V/A  27.86      1   \n",
       "1           2.05        3.42        2.32      6       3      N   0.00      0   \n",
       "2           1.87        4.17        2.37      6       3      N  10.00      1   \n",
       "3           2.16        4.00        2.19      9       4      A   0.71      0   \n",
       "4           2.57        6.12        2.16      6       3      N  15.71      0   \n",
       "...          ...         ...         ...    ...     ...    ...    ...    ...   \n",
       "1029        2.07        2.58        2.26      6       2      N   5.54      1   \n",
       "1030        2.54        3.12        2.15      6       3      N   3.04      1   \n",
       "1031        2.23        5.49        1.92      4       2      N   9.64      7   \n",
       "1032        2.12        5.12        1.87      6       3      N   1.43      0   \n",
       "1033        2.04        3.97        2.40      5       2      N   0.36      5   \n",
       "\n",
       "      Fam   Con  Imag   \n",
       "0     UnAv  UnAv  UnAv  \n",
       "1     UnAv  UnAv  UnAv  \n",
       "2     5.26  5.47  4.83  \n",
       "3     UnAv  UnAv  UnAv  \n",
       "4     5.41   5.9   6.2  \n",
       "...    ...   ...   ...  \n",
       "1029  3.87   6.2  6.15  \n",
       "1030  UnAv  UnAv  UnAv  \n",
       "1031  3.57  5.95  6.32  \n",
       "1032  UnAv  UnAv  UnAv  \n",
       "1033  UnAv  UnAv  UnAv  \n",
       "\n",
       "[1034 rows x 17 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanew_pad_df = pd.read_csv(os.path.join(path_to_pad_references, 'PAD_values_Spanish_ANEW.csv'))\n",
    "sanew_pad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Term</th>\n",
       "      <th>Pleasure-mean</th>\n",
       "      <th>Pleasure-sd</th>\n",
       "      <th>Arousal-mean</th>\n",
       "      <th>Arousal-sd</th>\n",
       "      <th>Dominance-mean</th>\n",
       "      <th>Dominance-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>derelict</td>\n",
       "      <td>-0.8300</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.5525</td>\n",
       "      <td>-0.4725</td>\n",
       "      <td>0.5075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bees</td>\n",
       "      <td>-0.4550</td>\n",
       "      <td>0.4625</td>\n",
       "      <td>0.3975</td>\n",
       "      <td>0.5125</td>\n",
       "      <td>-0.3950</td>\n",
       "      <td>0.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abortion</td>\n",
       "      <td>-0.5500</td>\n",
       "      <td>0.5325</td>\n",
       "      <td>0.2675</td>\n",
       "      <td>0.4675</td>\n",
       "      <td>-0.2075</td>\n",
       "      <td>0.5925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scorching</td>\n",
       "      <td>-0.6350</td>\n",
       "      <td>0.3950</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.5400</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>0.5475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hug</td>\n",
       "      <td>0.7825</td>\n",
       "      <td>0.3350</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.6425</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>volcano</td>\n",
       "      <td>-0.3700</td>\n",
       "      <td>0.5050</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>0.5175</td>\n",
       "      <td>-0.6050</td>\n",
       "      <td>0.5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>vomit</td>\n",
       "      <td>-0.8100</td>\n",
       "      <td>0.2900</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.6350</td>\n",
       "      <td>-0.4700</td>\n",
       "      <td>0.5375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>yacht</td>\n",
       "      <td>0.3925</td>\n",
       "      <td>0.4525</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.5575</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>sapphire</td>\n",
       "      <td>0.4275</td>\n",
       "      <td>0.4575</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.4675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>spanking</td>\n",
       "      <td>-0.7600</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>-0.2575</td>\n",
       "      <td>0.6000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Term  Pleasure-mean  Pleasure-sd  Arousal-mean  Arousal-sd  \\\n",
       "0      derelict        -0.8300       0.2500        0.2200      0.5525   \n",
       "1          bees        -0.4550       0.4625        0.3975      0.5125   \n",
       "2      abortion        -0.5500       0.5325        0.2675      0.4675   \n",
       "3     scorching        -0.6350       0.3950        0.2500      0.5400   \n",
       "4           hug         0.7825       0.3350        0.1750      0.6425   \n",
       "...         ...            ...          ...           ...         ...   \n",
       "1029    volcano        -0.3700       0.5050        0.4075      0.5175   \n",
       "1030      vomit        -0.8100       0.2900        0.2000      0.6350   \n",
       "1031      yacht         0.3925       0.4525        0.0475      0.5575   \n",
       "1032   sapphire         0.4275       0.4575        0.0525      0.5300   \n",
       "1033   spanking        -0.7600       0.3850        0.4075      0.5100   \n",
       "\n",
       "      Dominance-mean  Dominance-sd  \n",
       "0            -0.4725        0.5075  \n",
       "1            -0.3950        0.5800  \n",
       "2            -0.2075        0.5925  \n",
       "3            -0.2500        0.5475  \n",
       "4             0.2800        0.5400  \n",
       "...              ...           ...  \n",
       "1029         -0.6050        0.5650  \n",
       "1030         -0.4700        0.5375  \n",
       "1031          0.1225        0.4800  \n",
       "1032          0.0300        0.4675  \n",
       "1033         -0.2575        0.6000  \n",
       "\n",
       "[1034 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename and select columns.\n",
    "sanew_pad_df = sanew_pad_df[['E-Word', 'Val-Mn-All', 'Val-Sd-All', 'Aro-Mn-All', 'Aro-Sd-All', 'Dom-Mn-All', 'Dom-Sd-All']].copy()\n",
    "sanew_pad_df.rename(columns={\n",
    "    'E-Word': 'Term',\n",
    "    'Val-Mn-All': 'Pleasure-mean',\n",
    "    'Val-Sd-All': 'Pleasure-sd',\n",
    "    'Aro-Mn-All': 'Arousal-mean',\n",
    "    'Aro-Sd-All': 'Arousal-sd',\n",
    "    'Dom-Mn-All': 'Dominance-mean',\n",
    "    'Dom-Sd-All': 'Dominance-sd',\n",
    "}, inplace=True\n",
    ")\n",
    "\n",
    "# Adapt to [-1, 1] scale.\n",
    "sanew_pad_df['Pleasure-mean'] = sanew_pad_df['Pleasure-mean']/4 - 1.25\n",
    "sanew_pad_df['Pleasure-sd'] = sanew_pad_df['Pleasure-sd']/4\n",
    "\n",
    "sanew_pad_df['Arousal-mean'] = sanew_pad_df['Arousal-mean']/4 - 1.25\n",
    "sanew_pad_df['Arousal-sd'] = sanew_pad_df['Arousal-sd']/4\n",
    "\n",
    "sanew_pad_df['Dominance-mean'] = sanew_pad_df['Dominance-mean']/4 - 1.25\n",
    "sanew_pad_df['Dominance-sd'] = sanew_pad_df['Dominance-sd']/4\n",
    "\n",
    "sanew_pad_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Hotelling T-squared - Sample 2's covariance assumption: independent vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Spanish ANEW PAD data...\n"
     ]
    },
    {
     "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",
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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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>avalanche</td>\n",
       "      <td>1.112740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>124</td>\n",
       "      <td>beast</td>\n",
       "      <td>2.048543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>964</td>\n",
       "      <td>tornado</td>\n",
       "      <td>3.157791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>3.170539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>787</td>\n",
       "      <td>python</td>\n",
       "      <td>5.990484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102</td>\n",
       "      <td>wasp</td>\n",
       "      <td>6.251874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>91</td>\n",
       "      <td>robber</td>\n",
       "      <td>6.742385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>366</td>\n",
       "      <td>scorpion</td>\n",
       "      <td>6.940294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>163</td>\n",
       "      <td>chaos</td>\n",
       "      <td>7.215493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>87</td>\n",
       "      <td>scared</td>\n",
       "      <td>8.409766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>802</td>\n",
       "      <td>cliff</td>\n",
       "      <td>8.557850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>180</td>\n",
       "      <td>jealousy</td>\n",
       "      <td>9.675426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>864</td>\n",
       "      <td>revolt</td>\n",
       "      <td>9.774499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>532</td>\n",
       "      <td>insecure</td>\n",
       "      <td>9.835362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>763</td>\n",
       "      <td>lost</td>\n",
       "      <td>9.835518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rm_idx    rm_term  statistic\n",
       "0       96  avalanche   1.112740\n",
       "1      124      beast   2.048543\n",
       "2      964    tornado   3.157791\n",
       "3      504  hurricane   3.170539\n",
       "4      787     python   5.990484\n",
       "5      102       wasp   6.251874\n",
       "6       91     robber   6.742385\n",
       "7      366   scorpion   6.940294\n",
       "8      163      chaos   7.215493\n",
       "9       87     scared   8.409766\n",
       "10     802      cliff   8.557850\n",
       "11     180   jealousy   9.675426\n",
       "12     864     revolt   9.774499\n",
       "13     532   insecure   9.835362\n",
       "14     763       lost   9.835518"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Spanish ANEW PAD data...\")\n",
    "\n",
    "sanew_statistics_df = st.match_sample_vs_rm_pad_values(emotion_to_match, df_3d_norm, sanew_pad_df)\n",
    "sanew_statistics_df[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top three: scared, insecure, lost\n",
    "\n",
    "Non-emotions: avalanche, beast, tornado, hurricane, python, wasp, robber, scorpion, chaos, cliff, revolt\n",
    "\n",
    "Off-scope: jealousy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Hotelling T-squared - Sample 2's covariance assumption: = Sample 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Spanish ANEW PAD data...\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>avalanche</td>\n",
       "      <td>1.023440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>964</td>\n",
       "      <td>tornado</td>\n",
       "      <td>2.531220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>beast</td>\n",
       "      <td>2.679740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>5.449177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>robber</td>\n",
       "      <td>7.007544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>864</td>\n",
       "      <td>revolt</td>\n",
       "      <td>8.420657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>787</td>\n",
       "      <td>python</td>\n",
       "      <td>8.655445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>689</td>\n",
       "      <td>nervous</td>\n",
       "      <td>9.874125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>198</td>\n",
       "      <td>cyclone</td>\n",
       "      <td>10.058237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>366</td>\n",
       "      <td>scorpion</td>\n",
       "      <td>10.512370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>163</td>\n",
       "      <td>chaos</td>\n",
       "      <td>12.277188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>771</td>\n",
       "      <td>nightmare</td>\n",
       "      <td>12.602726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>763</td>\n",
       "      <td>lost</td>\n",
       "      <td>13.246740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1008</td>\n",
       "      <td>shy</td>\n",
       "      <td>13.351761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>706</td>\n",
       "      <td>obsession</td>\n",
       "      <td>13.527992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>532</td>\n",
       "      <td>insecure</td>\n",
       "      <td>14.356667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>102</td>\n",
       "      <td>wasp</td>\n",
       "      <td>14.548517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rm_idx    rm_term  statistic\n",
       "0       96  avalanche   1.023440\n",
       "1      964    tornado   2.531220\n",
       "2      124      beast   2.679740\n",
       "3      504  hurricane   5.449177\n",
       "4       91     robber   7.007544\n",
       "5      864     revolt   8.420657\n",
       "6      787     python   8.655445\n",
       "7      689    nervous   9.874125\n",
       "8      198    cyclone  10.058237\n",
       "9      366   scorpion  10.512370\n",
       "10     163      chaos  12.277188\n",
       "11     771  nightmare  12.602726\n",
       "12     763       lost  13.246740\n",
       "13    1008        shy  13.351761\n",
       "14     706  obsession  13.527992\n",
       "15     532   insecure  14.356667\n",
       "16     102       wasp  14.548517"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Spanish ANEW PAD data...\")\n",
    "\n",
    "sanew_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, sanew_pad_df,\n",
    "    method='same')\n",
    "sanew_statistics_df[:17]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: nervous, lost, insecure\n",
    "\n",
    "Non-emotions: avalanche, tornado, beast, hurricane, robber, revolt, python, cyclone, scorpion, chaos, nightmare\n",
    "\n",
    "Off-scope: shy, obsession"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Hotelling T-squared - Euclidean distance (compare the means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Spanish ANEW PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>avalanche</td>\n",
       "      <td>0.072624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504</td>\n",
       "      <td>hurricane</td>\n",
       "      <td>0.103359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>beast</td>\n",
       "      <td>0.105144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>964</td>\n",
       "      <td>tornado</td>\n",
       "      <td>0.124858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>163</td>\n",
       "      <td>chaos</td>\n",
       "      <td>0.156516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102</td>\n",
       "      <td>wasp</td>\n",
       "      <td>0.156725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89</td>\n",
       "      <td>terrified</td>\n",
       "      <td>0.172347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>180</td>\n",
       "      <td>jealousy</td>\n",
       "      <td>0.174273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>937</td>\n",
       "      <td>fear</td>\n",
       "      <td>0.177551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>295</td>\n",
       "      <td>despairing</td>\n",
       "      <td>0.178435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx     rm_term  statistic\n",
       "0      96   avalanche   0.072624\n",
       "1     504   hurricane   0.103359\n",
       "2     124       beast   0.105144\n",
       "3     964     tornado   0.124858\n",
       "4     163       chaos   0.156516\n",
       "5     102        wasp   0.156725\n",
       "6      89   terrified   0.172347\n",
       "7     180    jealousy   0.174273\n",
       "8     937        fear   0.177551\n",
       "9     295  despairing   0.178435"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Spanish ANEW PAD data...\")\n",
    "\n",
    "sanew_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, sanew_pad_df,\n",
    "    method='means')\n",
    "sanew_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: terrified, fear, despairing\n",
    "\n",
    "Non-emotions: avalanche, hurricane, beast, tornado, chaos, wasp\n",
    "\n",
    "Off-scope: jealousy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference 4: ANEW_MEHR, by Landowska (2018)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Landowska, A.: Towards new mappings between emotion representation models. Applied Sciences 8(2), 274 (2018)\n",
    "\n",
    "Assignment of the preexisting ANEW PAD values to 112 Russel-Mehrabian’s emotions (out of 151).\n",
    "\n",
    "The original dataset is downloadable as a semicolon-separated .csv file [in the public link provided by the author](https://www.mdpi.com/2076-3417/8/2/274/s1).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read ANEW_MEHR PAD values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Term</th>\n",
       "      <th>P_anew</th>\n",
       "      <th>P_SD</th>\n",
       "      <th>A_anew</th>\n",
       "      <th>A_SD</th>\n",
       "      <th>D_anew</th>\n",
       "      <th>D_SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bold</td>\n",
       "      <td>0.4500</td>\n",
       "      <td>0.4025</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.5525</td>\n",
       "      <td>0.4175</td>\n",
       "      <td>0.4525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Useful</td>\n",
       "      <td>0.5350</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>-0.1850</td>\n",
       "      <td>0.6175</td>\n",
       "      <td>0.2325</td>\n",
       "      <td>0.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mighty</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>0.5475</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>0.5575</td>\n",
       "      <td>0.5275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kind</td>\n",
       "      <td>0.6475</td>\n",
       "      <td>0.4175</td>\n",
       "      <td>-0.1350</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.4825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Admired</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>0.2775</td>\n",
       "      <td>0.5900</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Listless</td>\n",
       "      <td>-0.2200</td>\n",
       "      <td>0.4325</td>\n",
       "      <td>-0.2250</td>\n",
       "      <td>0.5775</td>\n",
       "      <td>-0.2150</td>\n",
       "      <td>0.4325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Weary</td>\n",
       "      <td>-0.3025</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>-0.2975</td>\n",
       "      <td>0.5725</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>0.4775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Detached</td>\n",
       "      <td>-0.2850</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>-0.1850</td>\n",
       "      <td>0.6425</td>\n",
       "      <td>-0.3425</td>\n",
       "      <td>0.5375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Discouraged</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>0.5400</td>\n",
       "      <td>-0.1175</td>\n",
       "      <td>0.5275</td>\n",
       "      <td>-0.3475</td>\n",
       "      <td>0.5025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Sad</td>\n",
       "      <td>-0.8475</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>-0.2175</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.5450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Term  P_anew    P_SD  A_anew    A_SD  D_anew    D_SD\n",
       "0           Bold  0.4500  0.4025  0.1500  0.5525  0.4175  0.4525\n",
       "1         Useful  0.5350  0.4000 -0.1850  0.6175  0.2325  0.5250\n",
       "2         Mighty  0.3850  0.5475  0.1525  0.5950  0.5575  0.5275\n",
       "3           Kind  0.6475  0.4175 -0.1350  0.6375  0.2375  0.4825\n",
       "4        Admired  0.6850  0.4600  0.2775  0.5900  0.6325  0.4850\n",
       "..           ...     ...     ...     ...     ...     ...     ...\n",
       "107     Listless -0.2200  0.4325 -0.2250  0.5775 -0.2150  0.4325\n",
       "108        Weary -0.3025  0.5300 -0.2975  0.5725 -0.2500  0.4775\n",
       "109     Detached -0.2850  0.4700 -0.1850  0.6425 -0.3425  0.5375\n",
       "110  Discouraged -0.5000  0.5400 -0.1175  0.5275 -0.3475  0.5025\n",
       "111          Sad -0.8475  0.2375 -0.2175  0.5950 -0.3875  0.5450\n",
       "\n",
       "[112 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anew_pad_df = pd.read_csv(os.path.join(path_to_pad_references, 'PAD_values_ANEW_MEHR.csv'))\n",
    "anew_pad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Term</th>\n",
       "      <th>Pleasure-mean</th>\n",
       "      <th>Pleasure-sd</th>\n",
       "      <th>Arousal-mean</th>\n",
       "      <th>Arousal-sd</th>\n",
       "      <th>Dominance-mean</th>\n",
       "      <th>Dominance-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bold</td>\n",
       "      <td>0.4500</td>\n",
       "      <td>0.4025</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.5525</td>\n",
       "      <td>0.4175</td>\n",
       "      <td>0.4525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Useful</td>\n",
       "      <td>0.5350</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>-0.1850</td>\n",
       "      <td>0.6175</td>\n",
       "      <td>0.2325</td>\n",
       "      <td>0.5250</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mighty</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>0.5475</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>0.5575</td>\n",
       "      <td>0.5275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kind</td>\n",
       "      <td>0.6475</td>\n",
       "      <td>0.4175</td>\n",
       "      <td>-0.1350</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.4825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Admired</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>0.2775</td>\n",
       "      <td>0.5900</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Listless</td>\n",
       "      <td>-0.2200</td>\n",
       "      <td>0.4325</td>\n",
       "      <td>-0.2250</td>\n",
       "      <td>0.5775</td>\n",
       "      <td>-0.2150</td>\n",
       "      <td>0.4325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Weary</td>\n",
       "      <td>-0.3025</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>-0.2975</td>\n",
       "      <td>0.5725</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>0.4775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Detached</td>\n",
       "      <td>-0.2850</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>-0.1850</td>\n",
       "      <td>0.6425</td>\n",
       "      <td>-0.3425</td>\n",
       "      <td>0.5375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Discouraged</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>0.5400</td>\n",
       "      <td>-0.1175</td>\n",
       "      <td>0.5275</td>\n",
       "      <td>-0.3475</td>\n",
       "      <td>0.5025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Sad</td>\n",
       "      <td>-0.8475</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>-0.2175</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.5450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Term  Pleasure-mean  Pleasure-sd  Arousal-mean  Arousal-sd  \\\n",
       "0           Bold         0.4500       0.4025        0.1500      0.5525   \n",
       "1         Useful         0.5350       0.4000       -0.1850      0.6175   \n",
       "2         Mighty         0.3850       0.5475        0.1525      0.5950   \n",
       "3           Kind         0.6475       0.4175       -0.1350      0.6375   \n",
       "4        Admired         0.6850       0.4600        0.2775      0.5900   \n",
       "..           ...            ...          ...           ...         ...   \n",
       "107     Listless        -0.2200       0.4325       -0.2250      0.5775   \n",
       "108        Weary        -0.3025       0.5300       -0.2975      0.5725   \n",
       "109     Detached        -0.2850       0.4700       -0.1850      0.6425   \n",
       "110  Discouraged        -0.5000       0.5400       -0.1175      0.5275   \n",
       "111          Sad        -0.8475       0.2375       -0.2175      0.5950   \n",
       "\n",
       "     Dominance-mean  Dominance-sd  \n",
       "0            0.4175        0.4525  \n",
       "1            0.2325        0.5250  \n",
       "2            0.5575        0.5275  \n",
       "3            0.2375        0.4825  \n",
       "4            0.6325        0.4850  \n",
       "..              ...           ...  \n",
       "107         -0.2150        0.4325  \n",
       "108         -0.2500        0.4775  \n",
       "109         -0.3425        0.5375  \n",
       "110         -0.3475        0.5025  \n",
       "111         -0.3875        0.5450  \n",
       "\n",
       "[112 rows x 7 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anew_pad_df.rename(columns={\n",
    "    'P_anew': 'Pleasure-mean',\n",
    "    'P_SD': 'Pleasure-sd',\n",
    "    'A_anew': 'Arousal-mean',\n",
    "    'A_SD': 'Arousal-sd',\n",
    "    'D_anew': 'Dominance-mean',\n",
    "    'D_SD': 'Dominance-sd',\n",
    "}, inplace=True\n",
    ")\n",
    "anew_pad_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Hotelling T-squared - Sample 2's covariance assumption: independent vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW_MEHR PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>10.459087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>Embarrassed</td>\n",
       "      <td>12.197758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71</td>\n",
       "      <td>Insecure</td>\n",
       "      <td>20.439105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>Pain</td>\n",
       "      <td>25.680109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>Helpless</td>\n",
       "      <td>27.230186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68</td>\n",
       "      <td>Crushed</td>\n",
       "      <td>27.327148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>90</td>\n",
       "      <td>Regretful</td>\n",
       "      <td>28.288259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69</td>\n",
       "      <td>Frustrated</td>\n",
       "      <td>28.934919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>94</td>\n",
       "      <td>Despairing</td>\n",
       "      <td>29.170100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73</td>\n",
       "      <td>Fearful</td>\n",
       "      <td>30.310303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx      rm_term  statistic\n",
       "0      88       Guilty  10.459087\n",
       "1      81  Embarrassed  12.197758\n",
       "2      71     Insecure  20.439105\n",
       "3      84         Pain  25.680109\n",
       "4      75     Helpless  27.230186\n",
       "5      68      Crushed  27.327148\n",
       "6      90    Regretful  28.288259\n",
       "7      69   Frustrated  28.934919\n",
       "8      94   Despairing  29.170100\n",
       "9      73      Fearful  30.310303"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW_MEHR PAD data...\")\n",
    "\n",
    "br_statistics_df = st.match_sample_vs_rm_pad_values(emotion_to_match, df_3d_norm, anew_pad_df)\n",
    "br_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Insecure, Pain, Helpless\n",
    "\n",
    "Off-scope: Guilty, Embarrassed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Hotelling T-squared - Sample 2's covariance assumption: = Sample 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW_MEHR PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>Embarrassed</td>\n",
       "      <td>12.472010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>17.159882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71</td>\n",
       "      <td>Insecure</td>\n",
       "      <td>22.197742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>Helpless</td>\n",
       "      <td>31.561847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>46.611713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>94</td>\n",
       "      <td>Despairing</td>\n",
       "      <td>47.333009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>Shamed</td>\n",
       "      <td>49.553086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69</td>\n",
       "      <td>Frustrated</td>\n",
       "      <td>51.020288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83</td>\n",
       "      <td>Defeated</td>\n",
       "      <td>51.553852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "      <td>Regretful</td>\n",
       "      <td>53.589039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx      rm_term  statistic\n",
       "0      81  Embarrassed  12.472010\n",
       "1      88       Guilty  17.159882\n",
       "2      71     Insecure  22.197742\n",
       "3      75     Helpless  31.561847\n",
       "4     102     Rejected  46.611713\n",
       "5      94   Despairing  47.333009\n",
       "6      79       Shamed  49.553086\n",
       "7      69   Frustrated  51.020288\n",
       "8      83     Defeated  51.553852\n",
       "9      90    Regretful  53.589039"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW_MEHR PAD data...\")\n",
    "\n",
    "br_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, anew_pad_df,\n",
    "    method='same')\n",
    "br_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Insecure, Helpless, Despairing\n",
    "\n",
    "Off-scope: Embarrassed, Guilty, Rejected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Hotelling T-squared - Euclidean distance (compare the means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs ANEW_MEHR PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>Guilty</td>\n",
       "      <td>0.272180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>Embarrassed</td>\n",
       "      <td>0.304794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>0.319449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>Fearful</td>\n",
       "      <td>0.337544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>Pain</td>\n",
       "      <td>0.345129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74</td>\n",
       "      <td>Terrified</td>\n",
       "      <td>0.349639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>71</td>\n",
       "      <td>Insecure</td>\n",
       "      <td>0.357015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>Distressed</td>\n",
       "      <td>0.383286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>Regretful</td>\n",
       "      <td>0.391530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>94</td>\n",
       "      <td>Despairing</td>\n",
       "      <td>0.397197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx      rm_term  statistic\n",
       "0      88       Guilty   0.272180\n",
       "1      81  Embarrassed   0.304794\n",
       "2     102     Rejected   0.319449\n",
       "3      73      Fearful   0.337544\n",
       "4      84         Pain   0.345129\n",
       "5      74    Terrified   0.349639\n",
       "6      71     Insecure   0.357015\n",
       "7      70   Distressed   0.383286\n",
       "8      90    Regretful   0.391530\n",
       "9      94   Despairing   0.397197"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs ANEW_MEHR PAD data...\")\n",
    "\n",
    "br_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, anew_pad_df,\n",
    "    method='means')\n",
    "br_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: Fearful, Pain, Terrified\n",
    "\n",
    "Off-scope: Guilty, Embarrassed, Rejected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference 5: Glasgow Norms, by Scott et al (2019)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scott, G.G., Keitel, A., Becirspahic, M., et al.: The glasgow norms: Ratings of 5,500 words on nine scales. Behavior Research Methods 51, 1258–1270 (2019)\n",
    "\n",
    "Ratings of 5,553 English words on nine scales (including PAD).\n",
    "\n",
    "The original dataset can be accessed in the public link of the paper at Springer.com, section [\"Electronic supplementary material\"](https://link.springer.com/article/10.3758/s13428-018-1099-3#Sec13). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read Glasgow Norms PAD values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Words</th>\n",
       "      <th>Length</th>\n",
       "      <th>AROU_M</th>\n",
       "      <th>AROU_SD</th>\n",
       "      <th>AROU_N</th>\n",
       "      <th>VAL_M</th>\n",
       "      <th>VAL_SD</th>\n",
       "      <th>VAL_N</th>\n",
       "      <th>DOM_M</th>\n",
       "      <th>DOM_SD</th>\n",
       "      <th>...</th>\n",
       "      <th>AOA</th>\n",
       "      <th>SD.3</th>\n",
       "      <th>N.3</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>SD.4</th>\n",
       "      <th>N.4</th>\n",
       "      <th>GEND</th>\n",
       "      <th>SD.5</th>\n",
       "      <th>N.5</th>\n",
       "      <th>N_AR_VA_DO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abattoir</td>\n",
       "      <td>8</td>\n",
       "      <td>4.200</td>\n",
       "      <td>2.400</td>\n",
       "      <td>25</td>\n",
       "      <td>2.864</td>\n",
       "      <td>1.740</td>\n",
       "      <td>22</td>\n",
       "      <td>4.333</td>\n",
       "      <td>1.285</td>\n",
       "      <td>...</td>\n",
       "      <td>6.760</td>\n",
       "      <td>0.427</td>\n",
       "      <td>25</td>\n",
       "      <td>4.652</td>\n",
       "      <td>1.549</td>\n",
       "      <td>23</td>\n",
       "      <td>5.391</td>\n",
       "      <td>1.170</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abbey</td>\n",
       "      <td>5</td>\n",
       "      <td>3.125</td>\n",
       "      <td>2.342</td>\n",
       "      <td>32</td>\n",
       "      <td>5.781</td>\n",
       "      <td>1.268</td>\n",
       "      <td>32</td>\n",
       "      <td>4.667</td>\n",
       "      <td>1.551</td>\n",
       "      <td>...</td>\n",
       "      <td>5.177</td>\n",
       "      <td>1.339</td>\n",
       "      <td>34</td>\n",
       "      <td>5.121</td>\n",
       "      <td>1.629</td>\n",
       "      <td>33</td>\n",
       "      <td>3.303</td>\n",
       "      <td>1.193</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abbreviate</td>\n",
       "      <td>10</td>\n",
       "      <td>3.273</td>\n",
       "      <td>1.582</td>\n",
       "      <td>33</td>\n",
       "      <td>5.250</td>\n",
       "      <td>1.031</td>\n",
       "      <td>32</td>\n",
       "      <td>5.235</td>\n",
       "      <td>1.238</td>\n",
       "      <td>...</td>\n",
       "      <td>5.543</td>\n",
       "      <td>1.130</td>\n",
       "      <td>35</td>\n",
       "      <td>2.667</td>\n",
       "      <td>1.700</td>\n",
       "      <td>33</td>\n",
       "      <td>3.971</td>\n",
       "      <td>0.954</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abdicate</td>\n",
       "      <td>8</td>\n",
       "      <td>4.194</td>\n",
       "      <td>1.941</td>\n",
       "      <td>31</td>\n",
       "      <td>3.767</td>\n",
       "      <td>1.383</td>\n",
       "      <td>30</td>\n",
       "      <td>4.419</td>\n",
       "      <td>2.182</td>\n",
       "      <td>...</td>\n",
       "      <td>6.233</td>\n",
       "      <td>0.844</td>\n",
       "      <td>30</td>\n",
       "      <td>4.679</td>\n",
       "      <td>1.441</td>\n",
       "      <td>28</td>\n",
       "      <td>5.167</td>\n",
       "      <td>1.186</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abdication</td>\n",
       "      <td>10</td>\n",
       "      <td>3.846</td>\n",
       "      <td>2.282</td>\n",
       "      <td>26</td>\n",
       "      <td>3.880</td>\n",
       "      <td>1.608</td>\n",
       "      <td>25</td>\n",
       "      <td>4.800</td>\n",
       "      <td>1.960</td>\n",
       "      <td>...</td>\n",
       "      <td>6.407</td>\n",
       "      <td>0.913</td>\n",
       "      <td>27</td>\n",
       "      <td>5.083</td>\n",
       "      <td>1.351</td>\n",
       "      <td>24</td>\n",
       "      <td>4.571</td>\n",
       "      <td>1.015</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5548</th>\n",
       "      <td>zeppelin</td>\n",
       "      <td>8</td>\n",
       "      <td>6.185</td>\n",
       "      <td>2.037</td>\n",
       "      <td>27</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.414</td>\n",
       "      <td>27</td>\n",
       "      <td>5.333</td>\n",
       "      <td>1.743</td>\n",
       "      <td>...</td>\n",
       "      <td>6.036</td>\n",
       "      <td>0.944</td>\n",
       "      <td>28</td>\n",
       "      <td>5.464</td>\n",
       "      <td>1.721</td>\n",
       "      <td>28</td>\n",
       "      <td>5.269</td>\n",
       "      <td>1.021</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5549</th>\n",
       "      <td>zero</td>\n",
       "      <td>4</td>\n",
       "      <td>4.031</td>\n",
       "      <td>2.023</td>\n",
       "      <td>32</td>\n",
       "      <td>4.182</td>\n",
       "      <td>1.585</td>\n",
       "      <td>33</td>\n",
       "      <td>4.567</td>\n",
       "      <td>2.124</td>\n",
       "      <td>...</td>\n",
       "      <td>2.636</td>\n",
       "      <td>1.123</td>\n",
       "      <td>33</td>\n",
       "      <td>1.758</td>\n",
       "      <td>1.393</td>\n",
       "      <td>33</td>\n",
       "      <td>4.172</td>\n",
       "      <td>0.647</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5550</th>\n",
       "      <td>zest</td>\n",
       "      <td>4</td>\n",
       "      <td>5.969</td>\n",
       "      <td>2.158</td>\n",
       "      <td>32</td>\n",
       "      <td>6.818</td>\n",
       "      <td>1.313</td>\n",
       "      <td>33</td>\n",
       "      <td>6.121</td>\n",
       "      <td>1.320</td>\n",
       "      <td>...</td>\n",
       "      <td>5.364</td>\n",
       "      <td>1.123</td>\n",
       "      <td>33</td>\n",
       "      <td>2.636</td>\n",
       "      <td>1.738</td>\n",
       "      <td>33</td>\n",
       "      <td>3.452</td>\n",
       "      <td>1.011</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>zoo</td>\n",
       "      <td>3</td>\n",
       "      <td>5.909</td>\n",
       "      <td>2.137</td>\n",
       "      <td>33</td>\n",
       "      <td>6.235</td>\n",
       "      <td>1.800</td>\n",
       "      <td>34</td>\n",
       "      <td>5.485</td>\n",
       "      <td>1.598</td>\n",
       "      <td>...</td>\n",
       "      <td>2.324</td>\n",
       "      <td>0.674</td>\n",
       "      <td>34</td>\n",
       "      <td>4.844</td>\n",
       "      <td>1.873</td>\n",
       "      <td>32</td>\n",
       "      <td>4.059</td>\n",
       "      <td>0.802</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5552</th>\n",
       "      <td>zoology</td>\n",
       "      <td>7</td>\n",
       "      <td>4.571</td>\n",
       "      <td>2.060</td>\n",
       "      <td>35</td>\n",
       "      <td>5.765</td>\n",
       "      <td>0.972</td>\n",
       "      <td>34</td>\n",
       "      <td>5.114</td>\n",
       "      <td>1.545</td>\n",
       "      <td>...</td>\n",
       "      <td>5.824</td>\n",
       "      <td>1.150</td>\n",
       "      <td>34</td>\n",
       "      <td>4.571</td>\n",
       "      <td>1.609</td>\n",
       "      <td>35</td>\n",
       "      <td>3.657</td>\n",
       "      <td>1.068</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5553 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Words  Length  AROU_M  AROU_SD  AROU_N  VAL_M  VAL_SD  VAL_N  \\\n",
       "0       abattoir       8   4.200    2.400      25  2.864   1.740     22   \n",
       "1          abbey       5   3.125    2.342      32  5.781   1.268     32   \n",
       "2     abbreviate      10   3.273    1.582      33  5.250   1.031     32   \n",
       "3       abdicate       8   4.194    1.941      31  3.767   1.383     30   \n",
       "4     abdication      10   3.846    2.282      26  3.880   1.608     25   \n",
       "...          ...     ...     ...      ...     ...    ...     ...    ...   \n",
       "5548    zeppelin       8   6.185    2.037      27  5.000   1.414     27   \n",
       "5549        zero       4   4.031    2.023      32  4.182   1.585     33   \n",
       "5550        zest       4   5.969    2.158      32  6.818   1.313     33   \n",
       "5551         zoo       3   5.909    2.137      33  6.235   1.800     34   \n",
       "5552     zoology       7   4.571    2.060      35  5.765   0.972     34   \n",
       "\n",
       "      DOM_M  DOM_SD  ...    AOA   SD.3  N.3   SIZE   SD.4  N.4   GEND   SD.5  \\\n",
       "0     4.333   1.285  ...  6.760  0.427   25  4.652  1.549   23  5.391  1.170   \n",
       "1     4.667   1.551  ...  5.177  1.339   34  5.121  1.629   33  3.303  1.193   \n",
       "2     5.235   1.238  ...  5.543  1.130   35  2.667  1.700   33  3.971  0.954   \n",
       "3     4.419   2.182  ...  6.233  0.844   30  4.679  1.441   28  5.167  1.186   \n",
       "4     4.800   1.960  ...  6.407  0.913   27  5.083  1.351   24  4.571  1.015   \n",
       "...     ...     ...  ...    ...    ...  ...    ...    ...  ...    ...    ...   \n",
       "5548  5.333   1.743  ...  6.036  0.944   28  5.464  1.721   28  5.269  1.021   \n",
       "5549  4.567   2.124  ...  2.636  1.123   33  1.758  1.393   33  4.172  0.647   \n",
       "5550  6.121   1.320  ...  5.364  1.123   33  2.636  1.738   33  3.452  1.011   \n",
       "5551  5.485   1.598  ...  2.324  0.674   34  4.844  1.873   32  4.059  0.802   \n",
       "5552  5.114   1.545  ...  5.824  1.150   34  4.571  1.609   35  3.657  1.068   \n",
       "\n",
       "      N.5  N_AR_VA_DO  \n",
       "0      23          21  \n",
       "1      33          32  \n",
       "2      34          32  \n",
       "3      30          30  \n",
       "4      28          25  \n",
       "...   ...         ...  \n",
       "5548   26          27  \n",
       "5549   29          30  \n",
       "5550   31          32  \n",
       "5551   34          33  \n",
       "5552   35          34  \n",
       "\n",
       "[5553 rows x 30 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gn_pad_df = pd.read_csv(os.path.join(path_to_pad_references, 'PAD_values_Glasgow_norms.csv'))\n",
    "gn_pad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Term</th>\n",
       "      <th>N</th>\n",
       "      <th>Pleasure-mean</th>\n",
       "      <th>Pleasure-sd</th>\n",
       "      <th>Arousal-mean</th>\n",
       "      <th>Arousal-sd</th>\n",
       "      <th>Dominance-mean</th>\n",
       "      <th>Dominance-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abattoir</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.53400</td>\n",
       "      <td>0.43500</td>\n",
       "      <td>-0.20000</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>-0.16675</td>\n",
       "      <td>0.32125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abbey</td>\n",
       "      <td>32</td>\n",
       "      <td>0.19525</td>\n",
       "      <td>0.31700</td>\n",
       "      <td>-0.46875</td>\n",
       "      <td>0.58550</td>\n",
       "      <td>-0.08325</td>\n",
       "      <td>0.38775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abbreviate</td>\n",
       "      <td>32</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>0.25775</td>\n",
       "      <td>-0.43175</td>\n",
       "      <td>0.39550</td>\n",
       "      <td>0.05875</td>\n",
       "      <td>0.30950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abdicate</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.30825</td>\n",
       "      <td>0.34575</td>\n",
       "      <td>-0.20150</td>\n",
       "      <td>0.48525</td>\n",
       "      <td>-0.14525</td>\n",
       "      <td>0.54550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abdication</td>\n",
       "      <td>25</td>\n",
       "      <td>-0.28000</td>\n",
       "      <td>0.40200</td>\n",
       "      <td>-0.28850</td>\n",
       "      <td>0.57050</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>0.49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5548</th>\n",
       "      <td>zeppelin</td>\n",
       "      <td>27</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.35350</td>\n",
       "      <td>0.29625</td>\n",
       "      <td>0.50925</td>\n",
       "      <td>0.08325</td>\n",
       "      <td>0.43575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5549</th>\n",
       "      <td>zero</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.20450</td>\n",
       "      <td>0.39625</td>\n",
       "      <td>-0.24225</td>\n",
       "      <td>0.50575</td>\n",
       "      <td>-0.10825</td>\n",
       "      <td>0.53100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5550</th>\n",
       "      <td>zest</td>\n",
       "      <td>32</td>\n",
       "      <td>0.45450</td>\n",
       "      <td>0.32825</td>\n",
       "      <td>0.24225</td>\n",
       "      <td>0.53950</td>\n",
       "      <td>0.28025</td>\n",
       "      <td>0.33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>zoo</td>\n",
       "      <td>33</td>\n",
       "      <td>0.30875</td>\n",
       "      <td>0.45000</td>\n",
       "      <td>0.22725</td>\n",
       "      <td>0.53425</td>\n",
       "      <td>0.12125</td>\n",
       "      <td>0.39950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5552</th>\n",
       "      <td>zoology</td>\n",
       "      <td>34</td>\n",
       "      <td>0.19125</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>-0.10725</td>\n",
       "      <td>0.51500</td>\n",
       "      <td>0.02850</td>\n",
       "      <td>0.38625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5553 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Term   N  Pleasure-mean  Pleasure-sd  Arousal-mean  Arousal-sd  \\\n",
       "0       abattoir  21       -0.53400      0.43500      -0.20000     0.60000   \n",
       "1          abbey  32        0.19525      0.31700      -0.46875     0.58550   \n",
       "2     abbreviate  32        0.06250      0.25775      -0.43175     0.39550   \n",
       "3       abdicate  30       -0.30825      0.34575      -0.20150     0.48525   \n",
       "4     abdication  25       -0.28000      0.40200      -0.28850     0.57050   \n",
       "...          ...  ..            ...          ...           ...         ...   \n",
       "5548    zeppelin  27        0.00000      0.35350       0.29625     0.50925   \n",
       "5549        zero  30       -0.20450      0.39625      -0.24225     0.50575   \n",
       "5550        zest  32        0.45450      0.32825       0.24225     0.53950   \n",
       "5551         zoo  33        0.30875      0.45000       0.22725     0.53425   \n",
       "5552     zoology  34        0.19125      0.24300      -0.10725     0.51500   \n",
       "\n",
       "      Dominance-mean  Dominance-sd  \n",
       "0           -0.16675       0.32125  \n",
       "1           -0.08325       0.38775  \n",
       "2            0.05875       0.30950  \n",
       "3           -0.14525       0.54550  \n",
       "4           -0.05000       0.49000  \n",
       "...              ...           ...  \n",
       "5548         0.08325       0.43575  \n",
       "5549        -0.10825       0.53100  \n",
       "5550         0.28025       0.33000  \n",
       "5551         0.12125       0.39950  \n",
       "5552         0.02850       0.38625  \n",
       "\n",
       "[5553 rows x 8 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adapt Glasgow Norms data to process (column names and scale):\n",
    "gn_pad_df_norm = pd.DataFrame()\n",
    "gn_pad_df_norm['Term'] = gn_pad_df['Words']\n",
    "gn_pad_df_norm['N'] = gn_pad_df['N_AR_VA_DO']\n",
    "\n",
    "# Adapt to [-1, 1] scale.\n",
    "gn_pad_df_norm['Pleasure-mean'] = gn_pad_df['VAL_M']/4 - 1.25\n",
    "gn_pad_df_norm['Pleasure-sd'] = gn_pad_df['VAL_SD']/4\n",
    "\n",
    "gn_pad_df_norm['Arousal-mean'] = gn_pad_df['AROU_M']/4 - 1.25\n",
    "gn_pad_df_norm['Arousal-sd'] = gn_pad_df['AROU_SD']/4\n",
    "\n",
    "gn_pad_df_norm['Dominance-mean'] = gn_pad_df['DOM_M']/4 - 1.25\n",
    "gn_pad_df_norm['Dominance-sd'] = gn_pad_df['DOM_SD']/4\n",
    "\n",
    "gn_pad_df_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Hotelling T-squared - Sample 2's covariance assumption: independent vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Glasgow Norms PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>fearful</td>\n",
       "      <td>1.373253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2030</td>\n",
       "      <td>frightened</td>\n",
       "      <td>1.568709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>addicted</td>\n",
       "      <td>1.755871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2029</td>\n",
       "      <td>fright</td>\n",
       "      <td>2.156662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2631</td>\n",
       "      <td>jeopardy</td>\n",
       "      <td>2.743655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2529</td>\n",
       "      <td>infectious</td>\n",
       "      <td>2.913056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2940</td>\n",
       "      <td>madness</td>\n",
       "      <td>2.913573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>abyss</td>\n",
       "      <td>3.279528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2547</td>\n",
       "      <td>insanity</td>\n",
       "      <td>3.449645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>524</td>\n",
       "      <td>bonds (restraints)</td>\n",
       "      <td>3.477017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3803</td>\n",
       "      <td>prisoner</td>\n",
       "      <td>3.919348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3455</td>\n",
       "      <td>panic</td>\n",
       "      <td>4.015559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4245</td>\n",
       "      <td>scary</td>\n",
       "      <td>4.278406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3467</td>\n",
       "      <td>paranoid</td>\n",
       "      <td>4.340883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5212</td>\n",
       "      <td>underworld</td>\n",
       "      <td>4.911314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rm_idx             rm_term  statistic\n",
       "0     1856             fearful   1.373253\n",
       "1     2030          frightened   1.568709\n",
       "2       60            addicted   1.755871\n",
       "3     2029              fright   2.156662\n",
       "4     2631            jeopardy   2.743655\n",
       "5     2529          infectious   2.913056\n",
       "6     2940             madness   2.913573\n",
       "7       26               abyss   3.279528\n",
       "8     2547            insanity   3.449645\n",
       "9      524  bonds (restraints)   3.477017\n",
       "10    3803            prisoner   3.919348\n",
       "11    3455               panic   4.015559\n",
       "12    4245               scary   4.278406\n",
       "13    3467            paranoid   4.340883\n",
       "14    5212          underworld   4.911314"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Glasgow Norms PAD data...\")\n",
    "\n",
    "gn_statistics_df = st.match_sample_vs_rm_pad_values(emotion_to_match, df_3d_norm, gn_pad_df_norm)\n",
    "gn_statistics_df[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: fearful, frightened, panic\n",
    "\n",
    "Redundant: fright\n",
    "\n",
    "Non-emotion: addicted, jeopardy, infectious, madness, abyss, insanity, bonds (restraints), prisoner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Hotelling T-squared - Sample 2's covariance assumption: = Sample 1's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Glasgow Norms PAD data...\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>fearful</td>\n",
       "      <td>1.335195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2030</td>\n",
       "      <td>frightened</td>\n",
       "      <td>1.542863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>addicted</td>\n",
       "      <td>1.756242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2029</td>\n",
       "      <td>fright</td>\n",
       "      <td>2.390863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2631</td>\n",
       "      <td>jeopardy</td>\n",
       "      <td>2.918126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2940</td>\n",
       "      <td>madness</td>\n",
       "      <td>3.130790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2529</td>\n",
       "      <td>infectious</td>\n",
       "      <td>3.133750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>abyss</td>\n",
       "      <td>3.415521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>524</td>\n",
       "      <td>bonds (restraints)</td>\n",
       "      <td>3.481637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3803</td>\n",
       "      <td>prisoner</td>\n",
       "      <td>3.771720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2547</td>\n",
       "      <td>insanity</td>\n",
       "      <td>3.907999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3455</td>\n",
       "      <td>panic</td>\n",
       "      <td>4.115062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4245</td>\n",
       "      <td>scary</td>\n",
       "      <td>4.326358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3467</td>\n",
       "      <td>paranoid</td>\n",
       "      <td>4.642242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5212</td>\n",
       "      <td>underworld</td>\n",
       "      <td>4.983263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rm_idx             rm_term  statistic\n",
       "0     1856             fearful   1.335195\n",
       "1     2030          frightened   1.542863\n",
       "2       60            addicted   1.756242\n",
       "3     2029              fright   2.390863\n",
       "4     2631            jeopardy   2.918126\n",
       "5     2940             madness   3.130790\n",
       "6     2529          infectious   3.133750\n",
       "7       26               abyss   3.415521\n",
       "8      524  bonds (restraints)   3.481637\n",
       "9     3803            prisoner   3.771720\n",
       "10    2547            insanity   3.907999\n",
       "11    3455               panic   4.115062\n",
       "12    4245               scary   4.326358\n",
       "13    3467            paranoid   4.642242\n",
       "14    5212          underworld   4.983263"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Glasgow Norms PAD data...\")\n",
    "\n",
    "gn_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, gn_pad_df_norm,\n",
    "    method='same')\n",
    "gn_statistics_df[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: fearful, frightened, panic\n",
    "\n",
    "Redundant: fright\n",
    "\n",
    "Non-emotion: addicted, jeopardy, infectious, madness, abyss, insanity, bonds (restraints), prisoner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Hotelling T-squared - Euclidean distance (compare the means)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match hypothetical emotion 0 (Distress) vs Glasgow Norms PAD data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>rm_idx</th>\n",
       "      <th>rm_term</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>fearful</td>\n",
       "      <td>0.186359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2030</td>\n",
       "      <td>frightened</td>\n",
       "      <td>0.202815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>addicted</td>\n",
       "      <td>0.223033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2029</td>\n",
       "      <td>fright</td>\n",
       "      <td>0.226475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2547</td>\n",
       "      <td>insanity</td>\n",
       "      <td>0.231770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1291</td>\n",
       "      <td>danger</td>\n",
       "      <td>0.235421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1706</td>\n",
       "      <td>epidemic</td>\n",
       "      <td>0.260645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2631</td>\n",
       "      <td>jeopardy</td>\n",
       "      <td>0.263747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2940</td>\n",
       "      <td>madness</td>\n",
       "      <td>0.264450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5094</td>\n",
       "      <td>tragedy</td>\n",
       "      <td>0.283671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rm_idx     rm_term  statistic\n",
       "0    1856     fearful   0.186359\n",
       "1    2030  frightened   0.202815\n",
       "2      60    addicted   0.223033\n",
       "3    2029      fright   0.226475\n",
       "4    2547    insanity   0.231770\n",
       "5    1291      danger   0.235421\n",
       "6    1706    epidemic   0.260645\n",
       "7    2631    jeopardy   0.263747\n",
       "8    2940     madness   0.264450\n",
       "9    5094     tragedy   0.283671"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_to_match = 0  # Run for all the emotion numbers (0 to 7 in this use case).\n",
    "print(f\"Match hypothetical emotion {emotion_to_match} ({emotional_attribution[emotion_to_match]}) vs Glasgow Norms PAD data...\")\n",
    "\n",
    "gn_statistics_df = st.match_sample_vs_rm_pad_values(\n",
    "    emotion_to_match, df_3d_norm, gn_pad_df_norm,\n",
    "    method='means')\n",
    "gn_statistics_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3: fearful, frightened, danger\n",
    "\n",
    "Redundant: fright\n",
    "\n",
    "Non-emotions: addicted, insanity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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