04_vsd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f33d03dc-3029-4a09-851a-a5fa17755d0c",
   "metadata": {},
   "source": [
    "# Calculating Density-controlled Vowel Space Area\n",
    "\n",
    "This notebook applies the methodology demonstrated in [Story & Bunton (2017)](https://doi.org/10.1121/1.4983342) for calculating density-controlled vowel space area, ideally for measuring vowel space area across task types or mediums. Formant measures are obtained at 5ms intervals of the vowel duration and then normalized. A (200 x 200) grid is generated and for each point on the grid a density value is obtained, equal to the number of formant data points that fall within a 0.05 radius field-of-view around the grid point. A heatmap is created with scaled density values, showing the relative concentrations of vowel productions. A convex hull measure is taken at several density cut-offs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e24e333-ad96-409f-a586-c36807e3086c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65e204eb-9f9d-4ea6-aebc-26a4c5f11d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "spanish = pd.read_csv(\"data/spanish_vowels.csv\")\n",
    "english = pd.read_csv(\"data/english_vowels.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d67d16-3f50-4f85-83e1-bdab3628d3bf",
   "metadata": {},
   "source": [
    "## Define function to calculate density-controlled vowel space area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85cfa1c6-4617-4396-966c-5ffaa0be1ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vsd(df, vowel_column):\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from scipy.spatial import ConvexHull\n",
    "    from scipy.spatial import distance\n",
    "    \n",
    "    # get lists of participants in dataset\n",
    "    parts = list(df['Participant'].unique())\n",
    "    vowels_list = list(df[vowel_column].unique())\n",
    "        \n",
    "    # define empty list to fill with participant values in form of dataframes\n",
    "    areas_all = []\n",
    "    \n",
    "    # define necessary functions and global variables\n",
    "    ################################################################################\n",
    "    def rem_outliers(df, vowel_column):\n",
    "        print(\"Removal of outliers:\")\n",
    "        print(\"Initial length: \", len(df))\n",
    "    \n",
    "        # establish 25% and 75% for each formant\n",
    "        f1_qrts = df.groupby(['Participant', vowel_column])[\"F1\"].describe()[['25%', '75%']]\n",
    "        f2_qrts = df.groupby(['Participant', vowel_column])[\"F2\"].describe()[['25%', '75%']]\n",
    "    \n",
    "        # find interquartile range for each formant\n",
    "        f1_qrts['IQR'] = f1_qrts['75%'] - f1_qrts['25%']\n",
    "        f2_qrts['IQR'] = f2_qrts['75%'] - f2_qrts['25%']\n",
    "    \n",
    "        # determine upper limit for each formant\n",
    "        f1_qrts['upper'] = f1_qrts['75%'] + (1.5 * f1_qrts['IQR'])\n",
    "        f2_qrts['upper'] = f2_qrts['75%'] + (1.5 * f2_qrts['IQR'])\n",
    "    \n",
    "        # determine lower limit for each formant\n",
    "        f1_qrts['lower'] = f1_qrts['25%'] - (1.5 * f1_qrts['IQR'])\n",
    "        f2_qrts['lower'] = f2_qrts['25%'] - (1.5 * f2_qrts['IQR'])\n",
    "    \n",
    "        # create smaller df with only limits for each formant\n",
    "        f1_limits = f1_qrts[['upper','lower']]\n",
    "        f2_limits = f2_qrts[['upper','lower']]\n",
    "    \n",
    "        # merge limits into original df\n",
    "        df = df.merge(f1_limits, left_on = [\"Participant\", vowel_column], right_index = True)\n",
    "        df = df.merge(f2_limits, left_on = [\"Participant\", vowel_column], right_index = True, suffixes = (\"_f1\", \"_f2\"))\n",
    "    \n",
    "        # drop rows with outlier formants\n",
    "        df = df[(df[\"F1\"] > df[\"lower_f1\"]) & (df[\"F1\"] < df[\"upper_f1\"])]\n",
    "        df = df[(df[\"F2\"] > df[\"lower_f2\"]) & (df[\"F2\"] < df[\"upper_f2\"])]\n",
    "\n",
    "        print(\"Final length: \", len(df))\n",
    "        return df\n",
    "    \n",
    "    ####################################################################################\n",
    "    # define function to scale all formant measures\n",
    "    def scale_formants(df):\n",
    "        medians = df.groupby([\"Participant\", \"is_stress\"])[[\"F1\", \"F2\"]].median()\n",
    "\n",
    "        df = df.merge(medians, left_on = [\"Participant\", \"is_stress\"], right_index = True, suffixes = (\"\",\"_med\"))\n",
    "        df[\"F1_vsd\"] = (df[\"F1\"]-df[\"F1_med\"])/df[\"F1_med\"]\n",
    "        df[\"F2_vsd\"] = (df[\"F2\"]-df[\"F2_med\"])/df[\"F2_med\"]\n",
    "        \n",
    "        print(\"Formant data scaled\")\n",
    "        return df \n",
    "    \n",
    "    ####################################################################################\n",
    "    \n",
    "    # define variable `grid`\n",
    "    xvalues = np.flip(np.arange(-1., 1.01, 0.01))\n",
    "    yvalues = np.arange(-1., 1.01, 0.01)\n",
    "\n",
    "    grid = [(round(x, 2), round(y,2)) for x in xvalues for y in yvalues]\n",
    "      \n",
    "    #####################################################################################\n",
    "    \n",
    "    # define function get_density\n",
    "    def density(grid, df):\n",
    "        density_dict = {}\n",
    "        for c in grid:\n",
    "            # define x and y coordinate\n",
    "            x = c[0]\n",
    "            y = c[1]\n",
    "    \n",
    "            # define max and min coordinates that form a square of length 0.1 around the coordinate\n",
    "            x_max = x + 0.05\n",
    "            x_min = x - 0.05\n",
    "            y_max = y + 0.05\n",
    "            y_min = y - 0.05\n",
    "    \n",
    "            # pull a subset of data that fall within the box\n",
    "            opts = df[(df[\"F2_vsd\"] <= x_max) &\n",
    "               (df[\"F2_vsd\"] >= x_min) &\n",
    "               (df[\"F1_vsd\"] <= y_max) &\n",
    "               (df[\"F1_vsd\"] >= y_min)].copy()\n",
    "            opts_list = list(zip(opts[\"F2_vsd\"], opts[\"F1_vsd\"]))\n",
    "    \n",
    "             # define density for this point\n",
    "            dens = 0\n",
    "    \n",
    "            for o in opts_list:\n",
    "                # define x and y coordinate of each point in my data\n",
    "                o_x = o[0]\n",
    "                o_y = o[1]\n",
    "        \n",
    "                # calculate distance to grid point\n",
    "                dist = distance.euclidean([x, y], [o_x, o_y])\n",
    "        \n",
    "                if dist <= 0.05:\n",
    "                    dens += 1\n",
    "        \n",
    "            density_dict[c] = dens\n",
    "    \n",
    "        # convert to df\n",
    "        density_df = pd.DataFrame(density_dict.items())\n",
    "        density_df = density_df.rename(columns = {0: \"coord\", 1:\"density\"})\n",
    "    \n",
    "        # make grid into df\n",
    "        grid_df = pd.DataFrame(grid, columns=[\"x\", \"y\"])\n",
    "        grid_df[\"coord\"] = list(zip(grid_df[\"x\"], grid_df[\"y\"]))\n",
    "    \n",
    "        # join df and grid_df\n",
    "        grid_df = grid_df.join(density_df.set_index('coord'), on=\"coord\")\n",
    "              \n",
    "        return grid_df\n",
    "    \n",
    "    ##################################################################################\n",
    "    # remove outliers by participant, by task, by vowel\n",
    "    df = rem_outliers(df, vowel_column)\n",
    "    \n",
    "    # Lobanov normalization of formants, by participant by task\n",
    "    df = scale_formants(df)\n",
    "    \n",
    "    for i in parts:\n",
    "        print(\"\\n\",i)\n",
    "        \n",
    "        # get subset of data for speaker across modality\n",
    "        df_stress = df[(df[\"Participant\"]==i) & (df[\"is_stress\"]== 1)].copy()\n",
    "        df_unstress = df[(df[\"Participant\"]==i) & (df[\"is_stress\"]== 0)].copy()  \n",
    "           \n",
    "        # get density for each point across the modalities\n",
    "        grid_stress_df = density(grid, df_stress)\n",
    "        print(\"Densities calculated for stressed vowels\")\n",
    "        grid_unstress_df = density(grid, df_unstress)\n",
    "        print(\"Densities calculated for unstressed vowels\")\n",
    "              \n",
    "        # scale density measures\n",
    "        grid_stress_df[\"density_norm\"] = grid_stress_df[\"density\"].apply(lambda x: x/grid_stress_df[\"density\"].max())\n",
    "        grid_unstress_df[\"density_norm\"] = grid_unstress_df[\"density\"].apply(lambda x: x/grid_unstress_df[\"density\"].max())\n",
    "        \n",
    "        # pull out coordinates at various density cutoffs and get areas\n",
    "        cutoffs = [0.10, 0.15, 0.20, 0.25, 0.3]\n",
    "\n",
    "        areas_list = []\n",
    "\n",
    "        for c in cutoffs:\n",
    "            grid_stress_density = grid_stress_df[grid_stress_df[\"density_norm\"] >= c].copy()\n",
    "            grid_unstress_density = grid_unstress_df[grid_unstress_df[\"density_norm\"] >= c].copy()\n",
    "    \n",
    "            points_tuples_stress = list(grid_stress_density[\"coord\"])\n",
    "            points_tuples_unstress = list(grid_unstress_density[\"coord\"])\n",
    "    \n",
    "            points_stress = [list(k) for k in points_tuples_stress]\n",
    "            points_unstress = [list(k) for k in points_tuples_unstress]\n",
    "    \n",
    "            hull_stress = ConvexHull(points_stress)\n",
    "            hull_unstress = ConvexHull(points_unstress)\n",
    "    \n",
    "            area_stress = hull_stress.area\n",
    "            area_unstress = hull_unstress.area\n",
    "\n",
    "            areas = {\"is_stress\": [1, 0], \"Area\": [area_stress, area_unstress], \"Cutoff\": c}\n",
    "            areas_list.append(areas)\n",
    "            \n",
    "        # to show progress while running, print areas at density cutoff of 0.25\n",
    "        print(\"Stress, unstress areas at 0.25 cutoff are: \", areas_list[3][\"Area\"])\n",
    "        \n",
    "        # generate dataframe for this participant and reformat\n",
    "        areas_df = pd.DataFrame(areas_list)\n",
    "        areas_df_stress = areas_df.explode('is_stress')\n",
    "        areas_df_stress = areas_df_stress.drop([\"Area\", \"Cutoff\"], axis = 1)\n",
    "        areas_df_area = areas_df.explode('Area')\n",
    "        areas_df_area = areas_df_area.drop(\"is_stress\", axis = 1)\n",
    "        areas_df = pd.concat([areas_df_stress, areas_df_area], axis = 1)\n",
    "        areas_df[\"Participant\"] = i\n",
    "\n",
    "        # append dataframe to list of dataframes\n",
    "        areas_all.append(areas_df)\n",
    " \n",
    "    # return dataframe of areas\n",
    "    areas_to_return = pd.concat(areas_all)\n",
    "    \n",
    "    return areas_to_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64a2882d-17fd-48f0-bf06-15ee973d6ef9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removal of outliers:\n",
      "Initial length:  355115\n",
      "Final length:  324111\n",
      "Formant data scaled\n",
      "\n",
      " p111\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.5524433175361674, 2.7128532528829874]\n",
      "\n",
      " p113\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.6301294587911466, 2.1054029973381465]\n",
      "\n",
      " p114\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.666707564899484, 2.3660280216394174]\n",
      "\n",
      " p117\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.915201082725338, 3.0106180586550755]\n",
      "\n",
      " p118\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.858556038286394, 3.0197287305961265]\n",
      "\n",
      " p119\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.5694565489996277, 3.0209626585575706]\n",
      "\n",
      " p120\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.989894996068776, 2.8008782502763254]\n",
      "\n",
      " p121\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.2666274504880777, 3.1603670381604347]\n",
      "\n",
      " p122\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.8380424693231587, 2.9912022379580145]\n",
      "\n",
      " p123\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.240118188862711, 1.980087195951907]\n",
      "\n",
      " p124\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [1.6159283205576116, 1.9557107695050866]\n",
      "\n",
      " p126\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [1.953156038368953, 1.5222843704779068]\n",
      "\n",
      " s001\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.3973038964770947, 2.7704639865510123]\n",
      "\n",
      " s002\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.7397443488647966, 2.7844693679518264]\n",
      "\n",
      " s051\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.245300045869665, 3.0105430889154667]\n",
      "\n",
      " s053\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.2184843501064284, 3.4730444011048043]\n",
      "\n",
      " s055\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.1784900439398642, 2.2425208956889042]\n",
      "\n",
      " s056\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.844987788363107, 3.2245064627011266]\n"
     ]
    }
   ],
   "source": [
    "spa_vsd = vsd(spanish, \"Vowel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e29ab801-6e57-452a-a60d-117dda8d5a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removal of outliers:\n",
      "Initial length:  16524\n",
      "Final length:  15453\n",
      "Formant data scaled\n",
      "\n",
      " p111\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.0763469208390757, 2.1789235534106774]\n",
      "\n",
      " p113\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.7572730610212806, 2.7397367513194633]\n",
      "\n",
      " p114\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.3373550060606427, 2.1201743716229857]\n",
      "\n",
      " p117\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.0600573159516746, 3.4594645761921505]\n",
      "\n",
      " p118\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.570705075097101, 2.240494517139016]\n",
      "\n",
      " p120\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [4.26499401171259, 1.9999281150934574]\n",
      "\n",
      " p121\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.43765177119438, 3.0323565907368493]\n",
      "\n",
      " p122\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.646371534721039, 2.6845836541430095]\n",
      "\n",
      " p123\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.304366072960754, 2.7324251771604295]\n",
      "\n",
      " p124\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [4.205075973845043, 3.1015874075859036]\n",
      "\n",
      " p126\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [2.9045029552721693, 2.127020773659901]\n",
      "\n",
      " p119\n",
      "Densities calculated for stressed vowels\n",
      "Densities calculated for unstressed vowels\n",
      "Stress, unstress areas at 0.25 cutoff are:  [3.0668335221741065, 2.3786187315325367]\n"
     ]
    }
   ],
   "source": [
    "eng_vsd = vsd(english, \"Vowel_bare\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717abb9f-7a9a-465b-b9d4-fe31f2b70e34",
   "metadata": {},
   "source": [
    "Add in demographic information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b59ae12-d50c-475e-bc01-4c550020afb9",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Participant</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.990106</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.429103</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  is_stress      Area  Cutoff Participant Dom\n",
       "0         1  4.017514    0.10        p111  L2\n",
       "1         0  3.348619    0.10        p111  L2\n",
       "2         1  3.508462    0.15        p111  L2\n",
       "3         0  2.990106    0.15        p111  L2\n",
       "4         1  3.429103    0.20        p111  L2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_dom = pd.DataFrame({\"Participant\": ['p111', 'p113', 'p114', 'p117','p118', 'p119', 'p120', 'p121', 'p122', 'p123', 'p124', 'p126'],\n",
    "                       \"Dom\": [\"L2\", \"biling\", \"biling\", \"L2\", \"biling\", \"biling\", \"biling\", \"L2\", \"biling\", \"L2\", \"biling\", \"L2\"]})\n",
    "eng_vsd = eng_vsd.merge(eng_dom, on = [\"Participant\"])\n",
    "eng_vsd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "05fad6ab-0789-4fa4-9a1a-7a5a34f918ce",
   "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>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Dom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.302681</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3.539401</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.032504</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.023459</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.816748</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  is_stress      Area  Cutoff Participant Dom\n",
       "0         1  3.302681    0.10        p111  L2\n",
       "1         0  3.539401    0.10        p111  L2\n",
       "2         1  3.032504    0.15        p111  L2\n",
       "3         0  3.023459    0.15        p111  L2\n",
       "4         1  2.816748    0.20        p111  L2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_dom = pd.DataFrame({\"Participant\": ['p111', 'p113', 'p114', 'p117','p118', 'p119', 'p120', 'p121', 'p122', 'p123', 'p124', 'p126', 's051', 's053', 's055', 's056', 's001', 's002'],\n",
    "                       \"Dom\": [\"L2\", \"biling\", \"biling\", \"L2\", \"biling\", \"biling\", \"biling\", \"L2\", \"biling\", \"L2\", \"biling\", \"L2\", \"mono\", \"mono\", \"mono\", \"mono\", \"mono\", \"mono\"]})\n",
    "spa_vsd = spa_vsd.merge(spa_dom, on=[\"Participant\"])\n",
    "spa_vsd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b36544-c09a-42d8-9b5c-130660f89435",
   "metadata": {},
   "source": [
    "Add in average duration of phones by speaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44ea5f0e-8322-4bc0-8119-3052720d0395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Participant</th>\n",
       "      <th>avg_dur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p111</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p113</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p114</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p117</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p118</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Participant   avg_dur\n",
       "0        p111  0.120470\n",
       "1        p113  0.136585\n",
       "2        p114  0.158571\n",
       "3        p117  0.110605\n",
       "4        p118  0.113432"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_vowels = pd.read_csv(\"data/spanish_vowels.csv\")\n",
    "spadur = pd.DataFrame(spa_vowels.groupby([\"Participant\"])[\"avg_dur\"].mean())\n",
    "spadur = spadur.reset_index(drop = False)\n",
    "spadur.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eafe2555-c802-4186-b142-4b850cd59ad7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.302681</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3.539401</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.032504</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.023459</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.816748</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  is_stress      Area  Cutoff Participant Dom  avg_dur\n",
       "0         1  3.302681    0.10        p111  L2  0.12047\n",
       "1         0  3.539401    0.10        p111  L2  0.12047\n",
       "2         1  3.032504    0.15        p111  L2  0.12047\n",
       "3         0  3.023459    0.15        p111  L2  0.12047\n",
       "4         1  2.816748    0.20        p111  L2  0.12047"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_vsd = spa_vsd.merge(spadur, on=[\"Participant\"])\n",
    "spa_vsd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "965e53b3-c68f-4fa4-bbf0-26181993205c",
   "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>Participant</th>\n",
       "      <th>avg_dur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p111</td>\n",
       "      <td>0.121433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p113</td>\n",
       "      <td>0.148272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p114</td>\n",
       "      <td>0.176165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p117</td>\n",
       "      <td>0.130144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p118</td>\n",
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      ],
      "text/plain": [
       "  Participant   avg_dur\n",
       "0        p111  0.121433\n",
       "1        p113  0.148272\n",
       "2        p114  0.176165\n",
       "3        p117  0.130144\n",
       "4        p118  0.133981"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_vowels = pd.read_csv(\"data/english_vowels.csv\")\n",
    "engdur = pd.DataFrame(eng_vowels.groupby([\"Participant\"])[\"avg_dur\"].mean())\n",
    "engdur = engdur.reset_index(drop = False)\n",
    "engdur.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2be5de4a-ca3b-460a-9463-ebd811063062",
   "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>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
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       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3.348619</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.508462</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.990106</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.429103</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  is_stress      Area  Cutoff Participant Dom   avg_dur\n",
       "0         1  4.017514    0.10        p111  L2  0.121433\n",
       "1         0  3.348619    0.10        p111  L2  0.121433\n",
       "2         1  3.508462    0.15        p111  L2  0.121433\n",
       "3         0  2.990106    0.15        p111  L2  0.121433\n",
       "4         1  3.429103    0.20        p111  L2  0.121433"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_vsd = eng_vsd.merge(engdur, on=[\"Participant\"])\n",
    "eng_vsd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5715ab83-1cb8-47e0-bdcb-1c11de00ef5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "spa_vsd.to_csv(\"data/spa_areas.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a147c0d-1b78-43ab-91db-bb71897a0e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_vsd.to_csv(\"data/eng_areas.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92f3ccb2-971a-46c9-bf18-8aa65869795d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Spanish</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.121433</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "      <td>3.646372</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p122</td>\n",
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       "      <td>0.139943</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>4.267161</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.148272</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3.68368</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.148272</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>2.000076</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>2.630129</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>2.127021</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p126</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.143693</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2.242783</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p122</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>4.586473</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p120</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.140621</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>3.900084</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p122</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1</td>\n",
       "      <td>3.13751</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p119</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.126013</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>3.463852</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p123</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>2.858556</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>3.780527</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p121</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.153949</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>3.101587</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p124</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.115026</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>4.063463</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>3.106921</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p123</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>3.768125</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p120</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.140621</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    is_stress      Area  Cutoff Participant     Dom   avg_dur Language\n",
       "53          0  3.301232    0.15        p119  biling  0.141896  Spanish\n",
       "49          0  2.848389    0.30        p118  biling  0.113432  Spanish\n",
       "8           1  2.832535    0.30        p111      L2  0.121433  English\n",
       "76          1  3.646372    0.25        p122  biling  0.139943  English\n",
       "12          1  4.267161    0.15        p113  biling  0.148272  English\n",
       "18          1   3.68368    0.30        p113  biling  0.148272  English\n",
       "19          0  2.000076    0.30        p113  biling  0.136585  Spanish\n",
       "16          1  2.630129    0.25        p113  biling  0.136585  Spanish\n",
       "107         0  2.127021    0.25        p126      L2  0.143693  English\n",
       "78          1  2.242783    0.30        p122  biling  0.139943  English\n",
       "60          1  4.586473    0.10        p120  biling  0.140621  Spanish\n",
       "74          1  3.900084    0.20        p122  biling  0.139943  English\n",
       "114         1   3.13751    0.20        p119  biling  0.126013  English\n",
       "82          1  3.463852    0.15        p123      L2  0.131732  English\n",
       "46          1  2.858556    0.25        p118  biling  0.113432  Spanish\n",
       "71          0  3.780527    0.10        p121      L2  0.153949  Spanish\n",
       "97          0  3.101587    0.25        p124  biling  0.115026  English\n",
       "31          0  4.063463    0.10        p117      L2  0.110605  Spanish\n",
       "88          1  3.106921    0.30        p123      L2  0.131732  English\n",
       "68          1  3.768125    0.30        p120  biling  0.140621  Spanish"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas_eng = eng_vsd.copy()\n",
    "cbas_eng[\"Language\"] = \"English\"\n",
    "cbas_spa = spa_vsd[spa_vsd[\"Dom\"]!=\"mono\"].copy()\n",
    "cbas_spa[\"Language\"] = \"Spanish\"\n",
    "cbas_vsd = pd.concat([cbas_eng, cbas_spa])\n",
    "cbas_vsd.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1c843f7-3824-4b52-9173-e65f864d4a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "cbas_vsd.to_csv(\"data/cbas_areas.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0888660-e33e-4317-ba03-6be630356d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Dom</th>\n",
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       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.023459</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>p111</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_stress      Area  Cutoff Participant Dom  avg_dur\n",
       "0          1  3.302681    0.10        p111  L2  0.12047\n",
       "1          0  3.539401    0.10        p111  L2  0.12047\n",
       "2          1  3.032504    0.15        p111  L2  0.12047\n",
       "3          0  3.023459    0.15        p111  L2  0.12047\n",
       "4          1  2.816748    0.20        p111  L2  0.12047"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "spa = pd.read_csv(\"data/spa_areas.csv\")\n",
    "spa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b43ed39-162d-4fc7-8d01-8de600de28cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>44</th>\n",
       "      <td>1</td>\n",
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       "      <th>104</th>\n",
       "      <td>1</td>\n",
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       "      <th>58</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>166</th>\n",
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       "      <td>2.178490</td>\n",
       "      <td>0.25</td>\n",
       "      <td>s055</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>4.255101</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p122</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_stress      Area  Cutoff Participant     Dom   avg_dur  Corpus\n",
       "44           1  2.929383    0.20        p118  biling  0.113432    True\n",
       "104          1  1.923681    0.20        p124  biling  0.112746    True\n",
       "58           1  2.343957    0.30        p119  biling  0.141896    True\n",
       "166          1  2.178490    0.25        s055    mono  0.081750   False\n",
       "80           1  4.255101    0.10        p122  biling  0.122210    True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa[\"Corpus\"] = spa[\"Dom\"]!=\"mono\"\n",
    "spa.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffe21923-bd1c-4806-9e15-257822c178cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "spa.to_csv(\"data/spa_cbas.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f482d489-2a7b-4c78-adc7-2184f223f054",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.990106</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.429103</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_stress      Area  Cutoff Participant Dom   avg_dur Language\n",
       "0          1  4.017514    0.10        p111  L2  0.121433  English\n",
       "1          0  3.348619    0.10        p111  L2  0.121433  English\n",
       "2          1  3.508462    0.15        p111  L2  0.121433  English\n",
       "3          0  2.990106    0.15        p111  L2  0.121433  English\n",
       "4          1  3.429103    0.20        p111  L2  0.121433  English"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas = pd.read_csv(\"data/cbas_areas.csv\")\n",
    "cbas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4bf6fa5e-6bef-409d-9e38-8e55c38afca2",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Area_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Participant</th>\n",
       "      <th>Language</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">p111</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">English</th>\n",
       "      <th>0.10</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.121433</th>\n",
       "      <td>[4.017514030303099, 3.3486187218481938]</td>\n",
       "      <td>0.833505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.121433</th>\n",
       "      <td>[3.5084623046006245, 2.990105514519839]</td>\n",
       "      <td>0.852255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.121433</th>\n",
       "      <td>[3.4291032144182463, 2.464699785629263]</td>\n",
       "      <td>0.718759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.121433</th>\n",
       "      <td>[3.076346920839076, 2.178923553410677]</td>\n",
       "      <td>0.708283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.121433</th>\n",
       "      <td>[2.8325346761905905, 2.108241617979124]</td>\n",
       "      <td>0.744295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                             Area  \\\n",
       "Participant Language Cutoff Dom avg_dur                                             \n",
       "p111        English  0.10   L2  0.121433  [4.017514030303099, 3.3486187218481938]   \n",
       "                     0.15   L2  0.121433  [3.5084623046006245, 2.990105514519839]   \n",
       "                     0.20   L2  0.121433  [3.4291032144182463, 2.464699785629263]   \n",
       "                     0.25   L2  0.121433   [3.076346920839076, 2.178923553410677]   \n",
       "                     0.30   L2  0.121433  [2.8325346761905905, 2.108241617979124]   \n",
       "\n",
       "                                          Area_ratio  \n",
       "Participant Language Cutoff Dom avg_dur               \n",
       "p111        English  0.10   L2  0.121433    0.833505  \n",
       "                     0.15   L2  0.121433    0.852255  \n",
       "                     0.20   L2  0.121433    0.718759  \n",
       "                     0.25   L2  0.121433    0.708283  \n",
       "                     0.30   L2  0.121433    0.744295  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas_list = pd.DataFrame(cbas.groupby([\"Participant\", \"Language\",\"Cutoff\", \"Dom\", \"avg_dur\"])[\"Area\"].agg(list))\n",
    "cbas_list[\"Area_ratio\"] = cbas_list[\"Area\"].apply(lambda x: x[1]/x[0])\n",
    "cbas_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dc6d7e2f-8151-4c36-9b6f-c5db07f8f4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>Language</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "      <th>Area</th>\n",
       "      <th>Area_ratio</th>\n",
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       "      <th>0</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>[4.017514030303099, 3.3486187218481938]</td>\n",
       "      <td>0.833505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>[3.5084623046006245, 2.990105514519839]</td>\n",
       "      <td>0.852255</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>[3.4291032144182463, 2.464699785629263]</td>\n",
       "      <td>0.718759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>[3.076346920839076, 2.178923553410677]</td>\n",
       "      <td>0.708283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>[2.8325346761905905, 2.108241617979124]</td>\n",
       "      <td>0.744295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Participant Language  Cutoff Dom   avg_dur  \\\n",
       "0        p111  English    0.10  L2  0.121433   \n",
       "1        p111  English    0.15  L2  0.121433   \n",
       "2        p111  English    0.20  L2  0.121433   \n",
       "3        p111  English    0.25  L2  0.121433   \n",
       "4        p111  English    0.30  L2  0.121433   \n",
       "\n",
       "                                      Area  Area_ratio  \n",
       "0  [4.017514030303099, 3.3486187218481938]    0.833505  \n",
       "1  [3.5084623046006245, 2.990105514519839]    0.852255  \n",
       "2  [3.4291032144182463, 2.464699785629263]    0.718759  \n",
       "3   [3.076346920839076, 2.178923553410677]    0.708283  \n",
       "4  [2.8325346761905905, 2.108241617979124]    0.744295  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas_list = cbas_list.reset_index(drop = False)\n",
    "cbas_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "918dff4c-14af-45b0-a7e1-536c2d39e89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>Language</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "      <th>Area_ratio</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p111</td>\n",
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       "      <td>0.121433</td>\n",
       "      <td>0.833505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.852255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.718759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.708283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.744295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Participant Language  Cutoff Dom   avg_dur  Area_ratio\n",
       "0        p111  English    0.10  L2  0.121433    0.833505\n",
       "1        p111  English    0.15  L2  0.121433    0.852255\n",
       "2        p111  English    0.20  L2  0.121433    0.718759\n",
       "3        p111  English    0.25  L2  0.121433    0.708283\n",
       "4        p111  English    0.30  L2  0.121433    0.744295"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas_list = cbas_list.drop([\"Area\"], axis = 1)\n",
    "cbas_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d23c6a57-4b2a-4a5c-a0bd-a7b33f538f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Area_ratio</th>\n",
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       "  <tbody>\n",
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       "      <th>43</th>\n",
       "      <td>p118</td>\n",
       "      <td>English</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>0.133981</td>\n",
       "      <td>0.627466</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>p122</td>\n",
       "      <td>Spanish</td>\n",
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       "      <td>0.122210</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>p118</td>\n",
       "      <td>English</td>\n",
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       "      <th>36</th>\n",
       "      <td>p117</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "      <td>1.100988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>p118</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>1.056383</td>\n",
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       "      <th>2</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.718759</td>\n",
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       "      <th>0</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
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       "      <td>0.121433</td>\n",
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       "      <th>84</th>\n",
       "      <td>p122</td>\n",
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       "      <td>0.30</td>\n",
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       "      <td>0.139943</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>p117</td>\n",
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       "      <td>L2</td>\n",
       "      <td>0.130144</td>\n",
       "      <td>1.165189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>p117</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "      <td>1.201820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>p119</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.141896</td>\n",
       "      <td>1.184854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>p118</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>1.124269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>p124</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.186615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>p123</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.843680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>p121</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.153949</td>\n",
       "      <td>0.944019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>p121</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.153949</td>\n",
       "      <td>0.967471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>p114</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "      <td>1.022433</td>\n",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>p126</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.779397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>p117</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "      <td>1.032731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>p126</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.143693</td>\n",
       "      <td>0.703935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>p123</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>0.843172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p113</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "      <td>0.831193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p111</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "      <td>1.071675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.852255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>p114</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.176165</td>\n",
       "      <td>0.720642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>p123</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.937153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>p122</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>0.806836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>p114</td>\n",
       "      <td>English</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.176165</td>\n",
       "      <td>0.893590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>p124</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.210271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>p126</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.821101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>p111</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "      <td>1.012583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>p123</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>0.859911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>p111</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "      <td>0.997017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>p119</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.126013</td>\n",
       "      <td>0.776669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>p113</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "      <td>0.800494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>p118</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.133981</td>\n",
       "      <td>0.523779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>p119</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.141896</td>\n",
       "      <td>1.143071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>p120</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.140621</td>\n",
       "      <td>0.826995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>p114</td>\n",
       "      <td>English</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.176165</td>\n",
       "      <td>0.635286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>p114</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "      <td>1.015392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>p118</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.133981</td>\n",
       "      <td>0.802431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p113</td>\n",
       "      <td>English</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.148272</td>\n",
       "      <td>0.703276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>p124</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.115026</td>\n",
       "      <td>0.776495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>p118</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>1.046323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>p114</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.176165</td>\n",
       "      <td>0.625523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>p123</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>0.852863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>p120</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.140621</td>\n",
       "      <td>0.736314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>p123</td>\n",
       "      <td>English</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>0.800847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>p114</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "      <td>0.857641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>p123</td>\n",
       "      <td>English</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131732</td>\n",
       "      <td>0.826914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>p117</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.130144</td>\n",
       "      <td>0.869209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>p122</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.779356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>p119</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.141896</td>\n",
       "      <td>1.235892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>p121</td>\n",
       "      <td>English</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.157690</td>\n",
       "      <td>0.772784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>p123</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.935336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>p118</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>1.074872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p111</td>\n",
       "      <td>English</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.121433</td>\n",
       "      <td>0.744295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>p122</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>0.816109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>p121</td>\n",
       "      <td>English</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.157690</td>\n",
       "      <td>0.889340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>p124</td>\n",
       "      <td>English</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.115026</td>\n",
       "      <td>0.906842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Participant Language  Cutoff     Dom   avg_dur  Area_ratio\n",
       "43         p118  English    0.25  biling  0.133981    0.627466\n",
       "86         p122  Spanish    0.15  biling  0.122210    0.900878\n",
       "40         p118  English    0.10  biling  0.133981    0.942999\n",
       "36         p117  Spanish    0.15      L2  0.110605    1.100988\n",
       "48         p118  Spanish    0.25  biling  0.113432    1.056383\n",
       "2          p111  English    0.20      L2  0.121433    0.718759\n",
       "0          p111  English    0.10      L2  0.121433    0.833505\n",
       "84         p122  English    0.30  biling  0.139943    0.988295\n",
       "34         p117  English    0.30      L2  0.130144    1.165189\n",
       "35         p117  Spanish    0.10      L2  0.110605    1.201820\n",
       "57         p119  Spanish    0.20  biling  0.141896    1.184854\n",
       "46         p118  Spanish    0.15  biling  0.113432    1.124269\n",
       "105        p124  Spanish    0.10  biling  0.112746    1.186615\n",
       "99         p123  Spanish    0.30      L2  0.131626    0.843680\n",
       "75         p121  Spanish    0.10      L2  0.153949    0.944019\n",
       "78         p121  Spanish    0.25      L2  0.153949    0.967471\n",
       "26         p114  Spanish    0.15  biling  0.158571    1.022433\n",
       "118        p126  Spanish    0.25      L2  0.165465    0.779397\n",
       "38         p117  Spanish    0.25      L2  0.110605    1.032731\n",
       "114        p126  English    0.30      L2  0.143693    0.703935\n",
       "92         p123  English    0.20      L2  0.131732    0.843172\n",
       "16         p113  Spanish    0.15  biling  0.136585    0.831193\n",
       "5          p111  Spanish    0.10      L2  0.120470    1.071675\n",
       "1          p111  English    0.15      L2  0.121433    0.852255\n",
       "21         p114  English    0.15  biling  0.176165    0.720642\n",
       "97         p123  Spanish    0.20      L2  0.131626    0.937153\n",
       "82         p122  English    0.20  biling  0.139943    0.806836\n",
       "20         p114  English    0.10  biling  0.176165    0.893590\n",
       "108        p124  Spanish    0.25  biling  0.112746    1.210271\n",
       "115        p126  Spanish    0.10      L2  0.165465    0.821101\n",
       "7          p111  Spanish    0.20      L2  0.120470    1.012583\n",
       "94         p123  English    0.30      L2  0.131732    0.859911\n",
       "6          p111  Spanish    0.15      L2  0.120470    0.997017\n",
       "54         p119  English    0.30  biling  0.126013    0.776669\n",
       "18         p113  Spanish    0.25  biling  0.136585    0.800494\n",
       "44         p118  English    0.30  biling  0.133981    0.523779\n",
       "55         p119  Spanish    0.10  biling  0.141896    1.143071\n",
       "65         p120  Spanish    0.10  biling  0.140621    0.826995\n",
       "23         p114  English    0.25  biling  0.176165    0.635286\n",
       "27         p114  Spanish    0.20  biling  0.158571    1.015392\n",
       "41         p118  English    0.15  biling  0.133981    0.802431\n",
       "10         p113  English    0.10  biling  0.148272    0.703276\n",
       "104        p124  English    0.30  biling  0.115026    0.776495\n",
       "49         p118  Spanish    0.30  biling  0.113432    1.046323\n",
       "22         p114  English    0.20  biling  0.176165    0.625523\n",
       "91         p123  English    0.15      L2  0.131732    0.852863\n",
       "67         p120  Spanish    0.20  biling  0.140621    0.736314\n",
       "90         p123  English    0.10      L2  0.131732    0.800847\n",
       "29         p114  Spanish    0.30  biling  0.158571    0.857641\n",
       "93         p123  English    0.25      L2  0.131732    0.826914\n",
       "31         p117  English    0.15      L2  0.130144    0.869209\n",
       "88         p122  Spanish    0.25  biling  0.122210    0.779356\n",
       "59         p119  Spanish    0.30  biling  0.141896    1.235892\n",
       "70         p121  English    0.10      L2  0.157690    0.772784\n",
       "96         p123  Spanish    0.15      L2  0.131626    0.935336\n",
       "45         p118  Spanish    0.10  biling  0.113432    1.074872\n",
       "4          p111  English    0.30      L2  0.121433    0.744295\n",
       "81         p122  English    0.15  biling  0.139943    0.816109\n",
       "72         p121  English    0.20      L2  0.157690    0.889340\n",
       "101        p124  English    0.15  biling  0.115026    0.906842"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbas_list.sample(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "07073aaa-0a96-4824-a576-aa8c61e4239c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cbas_list.to_csv(\"data/cbas_ratios.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f57c237-52dc-474f-a045-8751467e5b4e",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_stress</th>\n",
       "      <th>Area</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.302681</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3.539401</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.032504</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.023459</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.816748</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2.852190</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2.552443</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2.712853</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2.394428</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2.589947</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p111</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.120470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3.322740</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>2.802345</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2.875613</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>2.390191</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2.706308</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>2.226065</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>2.630129</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>2.105403</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>2.535291</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>2.000076</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p113</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.136585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3.127569</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>3.278911</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>2.976109</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>3.042872</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2.843557</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>2.887325</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>2.666708</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>2.366028</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>2.557575</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>2.193481</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p114</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>3.381090</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>4.063463</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>3.238070</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>3.565078</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>3.028032</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>3.259071</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>2.915201</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>3.010618</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>2.797935</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>2.828541</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p117</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.110605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>3.369229</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>3.621489</td>\n",
       "      <td>0.10</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>3.029101</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>3.405525</td>\n",
       "      <td>0.15</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>2.929383</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>3.253495</td>\n",
       "      <td>0.20</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>2.858556</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>3.019729</td>\n",
       "      <td>0.25</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>2.722284</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>2.848389</td>\n",
       "      <td>0.30</td>\n",
       "      <td>p118</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.113432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    is_stress      Area  Cutoff Participant     Dom   avg_dur\n",
       "0           1  3.302681    0.10        p111      L2  0.120470\n",
       "1           0  3.539401    0.10        p111      L2  0.120470\n",
       "2           1  3.032504    0.15        p111      L2  0.120470\n",
       "3           0  3.023459    0.15        p111      L2  0.120470\n",
       "4           1  2.816748    0.20        p111      L2  0.120470\n",
       "5           0  2.852190    0.20        p111      L2  0.120470\n",
       "6           1  2.552443    0.25        p111      L2  0.120470\n",
       "7           0  2.712853    0.25        p111      L2  0.120470\n",
       "8           1  2.394428    0.30        p111      L2  0.120470\n",
       "9           0  2.589947    0.30        p111      L2  0.120470\n",
       "10          1  3.322740    0.10        p113  biling  0.136585\n",
       "11          0  2.802345    0.10        p113  biling  0.136585\n",
       "12          1  2.875613    0.15        p113  biling  0.136585\n",
       "13          0  2.390191    0.15        p113  biling  0.136585\n",
       "14          1  2.706308    0.20        p113  biling  0.136585\n",
       "15          0  2.226065    0.20        p113  biling  0.136585\n",
       "16          1  2.630129    0.25        p113  biling  0.136585\n",
       "17          0  2.105403    0.25        p113  biling  0.136585\n",
       "18          1  2.535291    0.30        p113  biling  0.136585\n",
       "19          0  2.000076    0.30        p113  biling  0.136585\n",
       "20          1  3.127569    0.10        p114  biling  0.158571\n",
       "21          0  3.278911    0.10        p114  biling  0.158571\n",
       "22          1  2.976109    0.15        p114  biling  0.158571\n",
       "23          0  3.042872    0.15        p114  biling  0.158571\n",
       "24          1  2.843557    0.20        p114  biling  0.158571\n",
       "25          0  2.887325    0.20        p114  biling  0.158571\n",
       "26          1  2.666708    0.25        p114  biling  0.158571\n",
       "27          0  2.366028    0.25        p114  biling  0.158571\n",
       "28          1  2.557575    0.30        p114  biling  0.158571\n",
       "29          0  2.193481    0.30        p114  biling  0.158571\n",
       "30          1  3.381090    0.10        p117      L2  0.110605\n",
       "31          0  4.063463    0.10        p117      L2  0.110605\n",
       "32          1  3.238070    0.15        p117      L2  0.110605\n",
       "33          0  3.565078    0.15        p117      L2  0.110605\n",
       "34          1  3.028032    0.20        p117      L2  0.110605\n",
       "35          0  3.259071    0.20        p117      L2  0.110605\n",
       "36          1  2.915201    0.25        p117      L2  0.110605\n",
       "37          0  3.010618    0.25        p117      L2  0.110605\n",
       "38          1  2.797935    0.30        p117      L2  0.110605\n",
       "39          0  2.828541    0.30        p117      L2  0.110605\n",
       "40          1  3.369229    0.10        p118  biling  0.113432\n",
       "41          0  3.621489    0.10        p118  biling  0.113432\n",
       "42          1  3.029101    0.15        p118  biling  0.113432\n",
       "43          0  3.405525    0.15        p118  biling  0.113432\n",
       "44          1  2.929383    0.20        p118  biling  0.113432\n",
       "45          0  3.253495    0.20        p118  biling  0.113432\n",
       "46          1  2.858556    0.25        p118  biling  0.113432\n",
       "47          0  3.019729    0.25        p118  biling  0.113432\n",
       "48          1  2.722284    0.30        p118  biling  0.113432\n",
       "49          0  2.848389    0.30        p118  biling  0.113432"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa = pd.read_csv(\"data/spa_areas.csv\")\n",
    "spa.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "468fd711-7c0c-4646-a0b9-13a3cab06ddb",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Area_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Participant</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">p111</th>\n",
       "      <th>0.10</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.12047</th>\n",
       "      <td>[3.3026809703024367, 3.5394013984844914]</td>\n",
       "      <td>1.071675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.12047</th>\n",
       "      <td>[3.0325041482489112, 3.0234585075031206]</td>\n",
       "      <td>0.997017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.12047</th>\n",
       "      <td>[2.8167481848160705, 2.8521900566246963]</td>\n",
       "      <td>1.012583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.12047</th>\n",
       "      <td>[2.552443317536168, 2.7128532528829874]</td>\n",
       "      <td>1.062846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <th>L2</th>\n",
       "      <th>0.12047</th>\n",
       "      <td>[2.3944277317889227, 2.5899467535805867]</td>\n",
       "      <td>1.081656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    Area  \\\n",
       "Participant Cutoff Dom avg_dur                                             \n",
       "p111        0.10   L2  0.12047  [3.3026809703024367, 3.5394013984844914]   \n",
       "            0.15   L2  0.12047  [3.0325041482489112, 3.0234585075031206]   \n",
       "            0.20   L2  0.12047  [2.8167481848160705, 2.8521900566246963]   \n",
       "            0.25   L2  0.12047   [2.552443317536168, 2.7128532528829874]   \n",
       "            0.30   L2  0.12047  [2.3944277317889227, 2.5899467535805867]   \n",
       "\n",
       "                                Area_ratio  \n",
       "Participant Cutoff Dom avg_dur              \n",
       "p111        0.10   L2  0.12047    1.071675  \n",
       "            0.15   L2  0.12047    0.997017  \n",
       "            0.20   L2  0.12047    1.012583  \n",
       "            0.25   L2  0.12047    1.062846  \n",
       "            0.30   L2  0.12047    1.081656  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_list = pd.DataFrame(spa.groupby([\"Participant\", \"Cutoff\", \"Dom\", \"avg_dur\"])[\"Area\"].agg(list))\n",
    "spa_list[\"Area_ratio\"] = spa_list[\"Area\"].apply(lambda x: x[1]/x[0])\n",
    "spa_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "78b39650-a46c-4aa0-97a6-4df7c9b8859b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Area_ratio</th>\n",
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       "    <tr>\n",
       "      <th>Participant</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">p111</th>\n",
       "      <th>0.10</th>\n",
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       "      <td>1.071675</td>\n",
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       "    <tr>\n",
       "      <th>0.20</th>\n",
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       "      <th>0.25</th>\n",
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       "      <th>0.12047</th>\n",
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       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <th>L2</th>\n",
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      ],
      "text/plain": [
       "                                Area_ratio\n",
       "Participant Cutoff Dom avg_dur            \n",
       "p111        0.10   L2  0.12047    1.071675\n",
       "            0.15   L2  0.12047    0.997017\n",
       "            0.20   L2  0.12047    1.012583\n",
       "            0.25   L2  0.12047    1.062846\n",
       "            0.30   L2  0.12047    1.081656"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_list = spa_list.drop([\"Area\"], axis = 1)\n",
    "spa_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f60bf53a-a47a-45ba-8818-8670ba5b4dcf",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cutoff</th>\n",
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       "      <th>Area_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>p111</td>\n",
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       "      <td>1.071675</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p111</td>\n",
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       "      <td>L2</td>\n",
       "      <td>0.12047</td>\n",
       "      <td>0.997017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p111</td>\n",
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       "      <td>0.12047</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p111</td>\n",
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       "      <td>0.12047</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p111</td>\n",
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       "      <td>0.12047</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Participant  Cutoff Dom  avg_dur  Area_ratio\n",
       "0        p111    0.10  L2  0.12047    1.071675\n",
       "1        p111    0.15  L2  0.12047    0.997017\n",
       "2        p111    0.20  L2  0.12047    1.012583\n",
       "3        p111    0.25  L2  0.12047    1.062846\n",
       "4        p111    0.30  L2  0.12047    1.081656"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_list = spa_list.reset_index(drop = False)\n",
    "spa_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d00945a0-6fe7-45a3-ad13-c8e7665a8833",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>Cutoff</th>\n",
       "      <th>Dom</th>\n",
       "      <th>avg_dur</th>\n",
       "      <th>Area_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.122210</td>\n",
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       "      <th>41</th>\n",
       "      <td>p122</td>\n",
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       "      <th>44</th>\n",
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       "      <td>p123</td>\n",
       "      <td>0.10</td>\n",
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       "      <td>0.958471</td>\n",
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       "      <th>46</th>\n",
       "      <td>p123</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.935336</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>p123</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>p123</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.883921</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>p123</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.131626</td>\n",
       "      <td>0.843680</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>p124</td>\n",
       "      <td>0.10</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.186615</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>p124</td>\n",
       "      <td>0.15</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.172925</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>p124</td>\n",
       "      <td>0.20</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.153337</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>p124</td>\n",
       "      <td>0.25</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.210271</td>\n",
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       "      <th>54</th>\n",
       "      <td>p124</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biling</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>1.408389</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>p126</td>\n",
       "      <td>0.10</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.821101</td>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>p126</td>\n",
       "      <td>0.15</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.765266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>p126</td>\n",
       "      <td>0.20</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.717106</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>p126</td>\n",
       "      <td>0.25</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.779397</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>p126</td>\n",
       "      <td>0.30</td>\n",
       "      <td>L2</td>\n",
       "      <td>0.165465</td>\n",
       "      <td>0.768526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>s001</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.090275</td>\n",
       "      <td>1.001871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>s001</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.090275</td>\n",
       "      <td>1.029035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>s001</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.090275</td>\n",
       "      <td>1.100331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>s001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.090275</td>\n",
       "      <td>1.155658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>s001</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.090275</td>\n",
       "      <td>1.140897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>s002</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>1.040735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>s002</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>1.016657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>s002</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>1.035043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>s002</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>1.016325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>s002</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>1.030258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>s051</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.953922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>s051</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.928907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>s051</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.926980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>s051</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.927662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>s051</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.872789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>s053</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.074608</td>\n",
       "      <td>1.076994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>s053</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.074608</td>\n",
       "      <td>1.118780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>s053</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.074608</td>\n",
       "      <td>1.108429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>s053</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.074608</td>\n",
       "      <td>1.079093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>s053</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.074608</td>\n",
       "      <td>1.153472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>s055</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>1.000576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>s055</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>1.025657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>s055</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>1.019472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>s055</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>1.029392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>s055</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>1.057223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>s056</td>\n",
       "      <td>0.10</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>1.008766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>s056</td>\n",
       "      <td>0.15</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>0.993258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>s056</td>\n",
       "      <td>0.20</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>0.996424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>s056</td>\n",
       "      <td>0.25</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>1.133399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>s056</td>\n",
       "      <td>0.30</td>\n",
       "      <td>mono</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>1.191042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Participant  Cutoff     Dom   avg_dur  Area_ratio\n",
       "40        p122    0.10  biling  0.122210    0.944772\n",
       "41        p122    0.15  biling  0.122210    0.900878\n",
       "42        p122    0.20  biling  0.122210    0.827414\n",
       "43        p122    0.25  biling  0.122210    0.779356\n",
       "44        p122    0.30  biling  0.122210    0.785435\n",
       "45        p123    0.10      L2  0.131626    0.958471\n",
       "46        p123    0.15      L2  0.131626    0.935336\n",
       "47        p123    0.20      L2  0.131626    0.937153\n",
       "48        p123    0.25      L2  0.131626    0.883921\n",
       "49        p123    0.30      L2  0.131626    0.843680\n",
       "50        p124    0.10  biling  0.112746    1.186615\n",
       "51        p124    0.15  biling  0.112746    1.172925\n",
       "52        p124    0.20  biling  0.112746    1.153337\n",
       "53        p124    0.25  biling  0.112746    1.210271\n",
       "54        p124    0.30  biling  0.112746    1.408389\n",
       "55        p126    0.10      L2  0.165465    0.821101\n",
       "56        p126    0.15      L2  0.165465    0.765266\n",
       "57        p126    0.20      L2  0.165465    0.717106\n",
       "58        p126    0.25      L2  0.165465    0.779397\n",
       "59        p126    0.30      L2  0.165465    0.768526\n",
       "60        s001    0.10    mono  0.090275    1.001871\n",
       "61        s001    0.15    mono  0.090275    1.029035\n",
       "62        s001    0.20    mono  0.090275    1.100331\n",
       "63        s001    0.25    mono  0.090275    1.155658\n",
       "64        s001    0.30    mono  0.090275    1.140897\n",
       "65        s002    0.10    mono  0.073278    1.040735\n",
       "66        s002    0.15    mono  0.073278    1.016657\n",
       "67        s002    0.20    mono  0.073278    1.035043\n",
       "68        s002    0.25    mono  0.073278    1.016325\n",
       "69        s002    0.30    mono  0.073278    1.030258\n",
       "70        s051    0.10    mono  0.078200    0.953922\n",
       "71        s051    0.15    mono  0.078200    0.928907\n",
       "72        s051    0.20    mono  0.078200    0.926980\n",
       "73        s051    0.25    mono  0.078200    0.927662\n",
       "74        s051    0.30    mono  0.078200    0.872789\n",
       "75        s053    0.10    mono  0.074608    1.076994\n",
       "76        s053    0.15    mono  0.074608    1.118780\n",
       "77        s053    0.20    mono  0.074608    1.108429\n",
       "78        s053    0.25    mono  0.074608    1.079093\n",
       "79        s053    0.30    mono  0.074608    1.153472\n",
       "80        s055    0.10    mono  0.081750    1.000576\n",
       "81        s055    0.15    mono  0.081750    1.025657\n",
       "82        s055    0.20    mono  0.081750    1.019472\n",
       "83        s055    0.25    mono  0.081750    1.029392\n",
       "84        s055    0.30    mono  0.081750    1.057223\n",
       "85        s056    0.10    mono  0.076845    1.008766\n",
       "86        s056    0.15    mono  0.076845    0.993258\n",
       "87        s056    0.20    mono  0.076845    0.996424\n",
       "88        s056    0.25    mono  0.076845    1.133399\n",
       "89        s056    0.30    mono  0.076845    1.191042"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spa_list.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54945bd8-0313-4a34-8f73-b53b77a07d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "spa_list.to_csv(\"data/spa_ratios.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b94a88-497d-4019-b2dc-f5eae6410926",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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