09-Task-Evaluate_Data/01d2_PRE_VIS_Fixation_Calculation.ipynb

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    "# Calculate Fixations\n",
    "Status: Implemented\n",
    " -- Fully automatized"
   ]
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   "source": [
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from utils.utils import *\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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   "source": [
    "# get all files from participants\n",
    "folders = get_participant_folder_per_participant()\n",
    "sequence_orders = get_sequence_order_per_participant()\n",
    "assert (set(folders.keys()) == set(sequence_orders.keys()))\n",
    "snippets = {p: list(sequence_orders[p][SNIPPET].values)\n",
    "            for p in sequence_orders}"
   ]
  },
  {
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   "source": [
    "### Calculate fixation data\n",
    "* calculate fixations"
   ]
  },
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     "text": [
      "----------------------------------------------\n",
      "Participant 003\n",
      "(01/05) Load gaze data\n"
     ]
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      "(02/05) Transform gaze data to fixation data\n"
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      "(03/05) Basic check of each snippet's fixations\n",
      "(04/05) Check fixations present for snippet\n"
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      "(05/05) Store fixation data\n"
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      "----------------------------------------------\n",
      "Participant 004\n",
      "(01/05) Load gaze data\n"
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      "(02/05) Transform gaze data to fixation data\n"
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      "(03/05) Basic check of each snippet's fixations\n",
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      "----------------------------------------------\n",
      "Participant 005\n",
      "(01/05) Load gaze data\n"
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      "----------------------------------------------\n",
      "Participant 007\n",
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      "----------------------------------------------\n",
      "Participant 008\n",
      "(01/05) Load gaze data\n"
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      "----------------------------------------------\n",
      "Participant 009\n",
      "(01/05) Load gaze data\n"
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      "----------------------------------------------\n",
      "Participant 010\n",
      "(01/05) Load gaze data\n"
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     "text": [
      "----------------------------------------------\n",
      "Participant 101\n",
      "(01/05) Load gaze data\n"
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      "(02/05) Transform gaze data to fixation data\n"
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      "(03/05) Basic check of each snippet's fixations\n",
      "(04/05) Check fixations present for snippet\n"
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      "(05/05) Store fixation data\n"
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     "text": [
      "----------------------------------------------\n",
      "Participant 102\n",
      "(01/05) Load gaze data\n"
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      "(02/05) Transform gaze data to fixation data\n"
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      "(03/05) Basic check of each snippet's fixations\n",
      "(04/05) Check fixations present for snippet\n"
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      "(05/05) Store fixation data\n"
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     "text": [
      "----------------------------------------------\n",
      "Participant 106\n",
      "(01/05) Load gaze data\n"
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      "(02/05) Transform gaze data to fixation data\n"
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     "text": [
      "(03/05) Basic check of each snippet's fixations\n",
      "(04/05) Check fixations present for snippet\n"
     ]
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      "(05/05) Store fixation data\n"
     ]
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     "text": [
      "Excluded trials per participant {'003': {}, '004': {}, '005': {}, '007': {}, '008': {}, '009': {}, '010': {}, '011': {}, '012': {}, '013': {}, '014': {}, '015': {}, '016': {}, '017': {}, '018': {}, '019': {}, '020': {}, '021': {}, '022': {}, '023': {}, '024': {}, '101': {}, '102': {}, '106': {}}\n",
      "All trials 0\n"
     ]
    }
   ],
   "source": [
    "# Iterate over all participants\n",
    "all_excluded_fixations = {}\n",
    "for participant in tqdm(folders):\n",
    "    print('----------------------------------------------')\n",
    "    print(PARTICIPANT, participant)\n",
    "\n",
    "    i2mc_options = I2MC_options.copy()\n",
    "    i2mc_options['freq'] = get_eyetracking_frequency(participant)\n",
    "\n",
    "    participant_snippet_gazes = {}\n",
    "    participant_fixation_cross_gazes = {}\n",
    "    excluded_fixations = {}\n",
    "    exclusions: dict[str, dict[str, dict[str, str]]] = get_exclusions(\n",
    "        participant, [SNIPPET], [BEHAVIORAL, VISUAL])\n",
    "\n",
    "    print('(01/05) Load gaze data')\n",
    "    for snippet in tqdm(snippets[participant], leave=True):\n",
    "        if exclusions[SNIPPET][snippet][BEHAVIORAL] or any(s.startswith('01d1_PRE_VIS_Event_Gaze_Data') for s in exclusions[SNIPPET][snippet][VISUAL]):\n",
    "            continue\n",
    "        snippet_gaze_path = get_snippet_gaze_path(\n",
    "            participant, snippet)\n",
    "        if not snippet_gaze_path.exists():\n",
    "            if not snippet in excluded_fixations:\n",
    "                excluded_fixations[snippet] = '01d2_PRE_VIS_Fixation_Calculation: '\n",
    "            excluded_fixations[snippet] += 'No gaze data for snippet view found.'\n",
    "            continue\n",
    "        fixation_cross_gaze_path = get_snippet_gaze_path(\n",
    "            participant, snippet, fixation_cross=True)\n",
    "        if not fixation_cross_gaze_path.exists():\n",
    "            if not snippet in excluded_fixations:\n",
    "                excluded_fixations[snippet] = '01d2_PRE_VIS_Fixation_Calculation: '\n",
    "            excluded_fixations[snippet] += 'No gaze data for fixation cross view found.'\n",
    "            continue\n",
    "        participant_snippet_gazes[snippet] = pd.read_csv(\n",
    "            snippet_gaze_path, index_col=False, sep=SEPARATOR, dtype={PARTICIPANT: str})\n",
    "        participant_fixation_cross_gazes[snippet] = pd.read_csv(\n",
    "            fixation_cross_gaze_path, index_col=False, sep=SEPARATOR, dtype={PARTICIPANT: str})\n",
    "\n",
    "    print('(02/05) Transform gaze data to fixation data')\n",
    "    # insert frequency to algorithm options before calculating fixations for each gaze data\n",
    "    participant_snippet_fixations = calculate_fixations(\n",
    "        participant_snippet_gazes, participant, i2mc_options)\n",
    "    participant_fixation_cross_fixations = calculate_fixations(\n",
    "        participant_fixation_cross_gazes, participant, i2mc_options)\n",
    "\n",
    "    print('(03/05) Basic check of each snippet\\'s fixations')\n",
    "    # per snippet perform basic check\n",
    "    check_fixation_cross_fixation_outliers(\n",
    "        participant_snippet_fixations, participant_fixation_cross_fixations, participant)\n",
    "\n",
    "    print('(04/05) Check fixations present for snippet')\n",
    "    for snippet, fixations in tqdm(participant_snippet_fixations.items(), leave=True):\n",
    "        if fixations.empty or fixations.shape[0] < 2:\n",
    "            if not snippet in excluded_fixations:\n",
    "                excluded_fixations[snippet] = '01d2_PRE_VIS_Fixation_Calculation: '\n",
    "            excluded_fixations[\n",
    "                snippet] += 'Too few fixations identified in snippet view (<2).'\n",
    "    if excluded_fixations:\n",
    "        update_exclusions(participant, SNIPPET, VISUAL, excluded_fixations)\n",
    "    all_excluded_fixations[participant] = excluded_fixations\n",
    "\n",
    "    print('(05/05) Store fixation data')\n",
    "    # save fixation data\n",
    "    for snippet in tqdm(participant_snippet_fixations):\n",
    "        fixation_data = participant_snippet_fixations[snippet]\n",
    "        fixation_data.to_csv(get_fixations_path(\n",
    "            participant, snippet, iteration=lambda:1), index=False, sep=SEPARATOR)\n",
    "    for snippet in tqdm(participant_fixation_cross_fixations):\n",
    "        fixation_data = participant_fixation_cross_fixations[snippet]\n",
    "        fixation_data.to_csv(get_fixations_path(\n",
    "            participant, snippet, fixation_cross=True), index=False, sep=SEPARATOR)\n",
    "print('Excluded trials per participant', all_excluded_fixations)\n",
    "print('All trials', sum([len(trials)\n",
    "      for trials in all_excluded_fixations.values()]))"
   ]
  }
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