workbook_su_ol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workbook for Eye Tracking Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Basics\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "\n",
    "# Data processing\n",
    "import pandas as pd\n",
    "import awkward as ak\n",
    "\n",
    "# ML\n",
    "import sklearn\n",
    "\n",
    "# Misc\n",
    "from pathlib import Path\n",
    "import pyarrow as pa\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import users and their score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imports the user score information\n",
    "user_data = pd.read_csv(r\"data\\scores_WtG_PrePost.csv\", delimiter=\",\", usecols=[\"User\", \"Pre score\", \"Post score\", \"Difference\", \"Group cat\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defines my directory with the user data\n",
    "user_dir = r'data\\with ET'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filters and drops non-relevant users\n",
    "to_drop = []\n",
    "for i, cat in enumerate(user_data[\"Group cat\"]):\n",
    "    if math.isnan(cat):\n",
    "        to_drop.append(i)\n",
    "user_data = user_data.drop(to_drop)\n",
    "user_data = user_data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filters and drops users with no directory\n",
    "not_existing_names = []\n",
    "for i, user in enumerate(user_data[\"User\"]):\n",
    "    if not os.path.isdir(user_dir + '/' + user):\n",
    "        not_existing_names.append(i)\n",
    "user_data = user_data.drop(not_existing_names)\n",
    "user_data = user_data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert to awkward array\n",
    "array_user = ak.zip(dict(user_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Eye Tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creates dictionary with all the files for one user\n",
    "file_names = {}\n",
    "for user in user_data[\"User\"]:\n",
    "    #print(user)\n",
    "    available_files = []\n",
    "    available_files_temp = os.listdir(user_dir + '/' + user)\n",
    "    for file in available_files_temp:\n",
    "        if \"graph01-ET_planning\" in file:\n",
    "            available_files.append(file)\n",
    "    # print(available_files)\n",
    "    file_names[user] = available_files\n",
    "#file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read each CSV file for one user, stored for each attempt\n",
    "df_attempt1 = []\n",
    "df_attempt2 = []\n",
    "attempt2_mask = []\n",
    "for user in user_data['User']:\n",
    "    files = file_names[user]\n",
    "    if len(files) == 2:\n",
    "        attempt2_mask.append(True)\n",
    "        df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "        df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[1], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "    elif len(files) == 1:\n",
    "        attempt2_mask.append(False)\n",
    "        df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "        df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add delta t list\n",
    "for attempt in [df_attempt1, df_attempt2]:\n",
    "    for i in range(len(attempt)):\n",
    "        temp_delta_t_list = []\n",
    "        for j in range(len(attempt[i][\"eyeDataTimestamp\"]) - 1):\n",
    "            temp_delta_t_list.append(attempt[i][\"eyeDataTimestamp\"][j+1] - attempt[i][\"eyeDataTimestamp\"][j])\n",
    "        temp_delta_t_list.append(np.mean(temp_delta_t_list))\n",
    "        attempt[i][\"deltaTimestamp\"] = temp_delta_t_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert df_attempts to ak.Array\n",
    "array_attempt1 = []\n",
    "array_attempt2 = []\n",
    "for df in df_attempt1:\n",
    "    array_attempt1.append(ak.Array(dict(df)))\n",
    "for df in df_attempt2:\n",
    "    array_attempt2.append(ak.Array(dict(df)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Add Eye Tracking Data to user data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def add_column_old_broken(ak_array1, ak_array2, col_name):\n",
    "    entries = []\n",
    "    for entry, dataframe in zip(ak_array1, ak_array2):\n",
    "        entry_with_column = {**entry, col_name: dataframe}\n",
    "        print(entry_with_column)\n",
    "        entries.append(entry_with_column)\n",
    "    print(entries)\n",
    "    return ak.Array(entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def add_column_old(ak_array1, arrays, col_name):\n",
    "    return ak.zip({**{k: ak_array1[k] for k in ak_array1.fields}, col_name: arrays})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adds a list of arrays in a new column to an array\n",
    "def add_column(ak_array, arrays, col_name):\n",
    "    combined_entries = [\n",
    "        {**{k: ak_array[k][i] for k in ak_array.fields}, col_name: array} for i, (entry, array) in enumerate(zip(ak_array, arrays))\n",
    "    ]\n",
    "    return ak.Array(combined_entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_str = []\n",
    "labels_int_expert = []\n",
    "labels_int_good = []\n",
    "labels_int_bad = []\n",
    "\n",
    "for subject_name, pre_score, diff in zip(array_user[\"User\"], array_user[\"Pre score\"], array_user[\"Difference\"]):\n",
    "    if pre_score == 2 and diff == 0:\n",
    "        label_str = \"expert\"\n",
    "        label_int_expert = 1\n",
    "        label_int_good = 0  \n",
    "        label_int_bad = 0\n",
    "    elif diff <= 0:\n",
    "        label_str = \"bad\"\n",
    "        label_int_expert = 0\n",
    "        label_int_good = 0  \n",
    "        label_int_bad = 1\n",
    "    else:\n",
    "        label_str = \"good\"\n",
    "        label_int_expert = 0\n",
    "        label_int_good = 1\n",
    "        label_int_bad = 0\n",
    "    labels_str.append(label_str)\n",
    "    labels_int_expert.append(label_int_expert)   \n",
    "    labels_int_good.append(label_int_good)    \n",
    "    labels_int_bad.append(label_int_bad)    \n",
    " \n",
    "labels_str = ak.Array(labels_str)\n",
    "labels_int_expert = ak.Array(labels_int_expert)\n",
    "labels_int_good = ak.Array(labels_int_good)\n",
    "labels_int_bad = ak.Array(labels_int_bad)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creates array with first and second attempts added as well as the labels arrays\n",
    "array_data = add_column(array_user, array_attempt1, 'Attempt1')\n",
    "array_data = add_column(array_data, array_attempt2, 'Attempt2')\n",
    "array_data['Labels Str'] = labels_str\n",
    "array_data['Labels Expert'] = labels_int_expert\n",
    "array_data['Labels Good'] = labels_int_good\n",
    "array_data['Labels Bad'] = labels_int_bad\n",
    "array_data[\"Attempt 2 Mask\"] = ak.Array(attempt2_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def minmax(data):\n",
    "    \"\"\"Get the min and max of an iterable in O(n) time and constant space.\"\"\"\n",
    "    minValue = data[0]\n",
    "    maxValue = data[0]\n",
    "    for d in data[1:]:\n",
    "        minValue = d if d < minValue else minValue\n",
    "        maxValue = d if d > maxValue else maxValue\n",
    "    return (minValue,maxValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.4961899999999999 0.4198 -0.49335 0.24327\n"
     ]
    }
   ],
   "source": [
    "# Get Range of field of view\n",
    "min_max_x = []\n",
    "min_max_y = []\n",
    "for i, user in enumerate(array_data[\"User\"]):\n",
    "    min_x, max_x = minmax(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"])\n",
    "    min_y, max_y = minmax(array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"])\n",
    "    min_max_x.extend([min_x, max_x])\n",
    "    min_max_y.extend([min_y, max_y])\n",
    "\n",
    "    if array_data[\"Attempt 2 Mask\"][i]:\n",
    "        min_x, max_x = minmax(array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"])\n",
    "        min_y, max_y = minmax(array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"])\n",
    "        min_max_x.extend([min_x, max_x])\n",
    "        min_max_y.extend([min_y, max_y])\n",
    "min_x, max_x = minmax(min_max_x)\n",
    "min_y, max_y = minmax(min_max_y)\n",
    "print(min_x, max_x, min_y, max_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for i, user in enumerate(array_data[\"User\"]):\n",
    "    plt.plot(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"], array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"], label=user + \" A1\")\n",
    "    if array_data[\"Attempt 2 Mask\"][i]:\n",
    "        plt.plot(array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"], array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"], label=user + \" A2\")\n",
    "\n",
    "# plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Window Sliding Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "windows = []\n",
    "# windows_labels_int_expert = []\n",
    "# windows_labels_int_good = []\n",
    "# windows_labels_int_bad = []\n",
    "windows_labels_int = {\"Expert\": [], \"Good\": [], \"Bad\":[]}\n",
    "windows_delta_t = []\n",
    "\n",
    "for i, user in enumerate(array_data[\"User\"]):\n",
    "    for j in range(int((len(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"])-21)/14)):\n",
    "        try:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"][j*14:(j*14 + 21)], \"gazePointAOI_target_y\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"][j*14:(j*14 + 21)]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt1\"][i][\"deltaTimestamp\"][j*14:(j*14 + 21)])\n",
    "\n",
    "        except IndexError:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"][-21:], \"gazePointAOI_target_y\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"][-21:]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt1\"][i][\"deltaTimestamp\"][-21:])\n",
    "\n",
    "        try:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"][j*14:(j*14 + 21)], \"gazePointAOI_target_y\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"][j*14:(j*14 + 21)]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt2\"][i][\"deltaTimestamp\"][j*14:(j*14 + 21)])\n",
    "\n",
    "        except IndexError:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"][-21:], \"gazePointAOI_target_y\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"][-21:]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt2\"][i][\"deltaTimestamp\"][-21:])\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "windows_dict = {\"GazePoints\": windows, \"Labels\": windows_labels_int, \"DeltaTimestamps\": windows_delta_t}\n",
    "array_windows = ak.Array(windows_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 33, 33, 34, 33, 33, 34, 33, 33, ..., 33, 33, 34, 33, 33, 34, 33, 33, 33.3]\n",
      "2429\n",
      "[33, 33, 34, 33, 33, 34, 33, 33, 34, ..., 33, 33, 34, 33, 33, 34, 33, 33, 33.3]\n",
      "445\n",
      "[34, 33, 33, 34, 33, 33, 34, 33, 33, ..., 33, 33, 34, 33, 33, 34, 33, 33, 33.3]\n",
      "992\n",
      "[33, 33, 34, 33, 33, 34, 33, 33, 34, ..., 33, 34, 33, 33, 34, 33, 33, 34, 33.3]\n",
      "153\n"
     ]
    }
   ],
   "source": [
    "for array in array_data[\"Attempt1\"][:4][\"deltaTimestamp\"]:\n",
    "    print(array)\n",
    "    print(len(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#AOIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 33, 33, 34, 33, 33, 34, 33, 33, 34, 33, 33, 34, 33, 33, 34, 33, 33.3]\n",
      "18\n",
      "[33, 34, 33, 33.3]\n",
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      "[33, 34, 33, 33, 34, 33, 33, 34, 33, 33.3]\n",
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      "[33, 33, 34, 33, 34, 33, 33, 34, 33, ..., 33, 33, 34, 33, 33, 34, 33, 33, 33.3]\n",
      "20\n",
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      "[33, 34, 33, 33, 34, 33, 33, 34, 33, 33, 34, 33, 33, 33.3]\n",
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