periph_rec_analyzer_913.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3794cc2a-5c11-416c-bdc9-b0bf4502f089",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ibmm import EyeClassifier\n",
    "# from ibmm_online import EyeClassifierOnline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "from parser_utils import read_periph_recording, parse_new_dreyevr_rec, GetGazeDeviationFromHead\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "022acadb-3082-415a-890e-118c6eca0cdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib ipympl\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "762d5aac-efed-40cd-811b-08d6eb152977",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "path_to_recording = \"C://carla//carla.periph//RecordingTxts//tab/tab55.txt\"\n",
    "path_to_recording = \"C://carla//carla.periph//RecordingTxts//esther//esther55.txt\"\n",
    "# path_to_recording = \"C://carla//carla.periph//RecordingTxts//jacob/jacob55.txt\"\n",
    "# path_to_recording = \"C://carla//carla.periph//RecordingTxts//george//george-55.txt\"\n",
    "\n",
    "recording_name = Path(path_to_recording).stem\n",
    "recdf_pklname = Path(\"temp_data\").joinpath(recording_name+\".pkl\")\n",
    "\n",
    "# try:\n",
    "#     df1 = pd.read_pickle(recdf_pklname)\n",
    "# except FileNotFoundError:\n",
    "#     df1 = read_periph_recording(path_to_recording)\n",
    "#     df1.to_pickle(recdf_pklname)\n",
    "# df1 = df1.convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7bc68a89-3f85-4130-bb1b-64c0b4d509be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 7840/7840 [00:34<00:00, 226.13it/s]\n"
     ]
    }
   ],
   "source": [
    "df = parse_new_dreyevr_rec(path_to_recording, False)\n",
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05e31b2-4f75-40f3-8445-867551a05b66",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Playground and Sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3f70fe8-c60e-4456-9e47-005eb06d1cfe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 targets spawned\n",
      "9 responses recorded\n"
     ]
    }
   ],
   "source": [
    "# find the indices where lights came on and went off\n",
    "lighton_rows = df1[\"LightOn\"].diff().fillna(0)==1\n",
    "lightoff_rows = df1[\"LightOn\"].diff().fillna(0)==-1\n",
    "df1[lighton_rows].head()\n",
    "lighton_idcs = df1[lighton_rows].index\n",
    "num_targets_spawned = sum(lighton_rows)\n",
    "# find the indices where the button was pressed\n",
    "buttonPress_rows = df1[\"ButtonPressed\"].diff().fillna(0)==1\n",
    "buttonRelease_rows = df1[\"ButtonPressed\"].diff().fillna(0)==-1\n",
    "num_button_presses = sum(buttonPress_rows)\n",
    "print(\"{} targets spawned\".format(num_targets_spawned))\n",
    "print(\"{} responses recorded\".format(num_button_presses)) # may or may not be all accurate responses that correspond to targets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e137fd2-da10-4dbe-b169-97747f140725",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-16.798371781305296 11.607415675794943\n",
      "-29.645261313723815 38.33752289340374\n"
     ]
    }
   ],
   "source": [
    "df2 = df1.copy()\n",
    "df2['gaze_x'] = df1.GazeDir_COMBINED.apply(lambda x: x[0])\n",
    "df2['gaze_y'] = df1.GazeDir_COMBINED.apply(lambda x: x[1])\n",
    "df2['gaze_z'] = df1.GazeDir_COMBINED.apply(lambda x: x[2])\n",
    "\n",
    "gaze_pitches = np.arctan2(df2.gaze_z, df2.gaze_x)*180/np.pi\n",
    "gaze_yaws = np.arctan2(df2.gaze_y, df2.gaze_x)*180/np.pi\n",
    "\n",
    "low_conf_gazeidcs = (gaze_pitches*gaze_yaws == 0)\n",
    "gaze_pitches = gaze_pitches[~low_conf_gazeidcs]\n",
    "gaze_yaws = gaze_yaws[~low_conf_gazeidcs]\n",
    "\n",
    "\n",
    "print(np.min(gaze_pitches),np.max(gaze_pitches))\n",
    "print(np.min(gaze_yaws),np.max(gaze_yaws))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1435bb0-ed68-43f7-ac7c-005d4a2088a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8afa5532f0ed4414a91280253cfa5f82",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gaze_pitches = gaze_pitches[1000:7000]\n",
    "#gaze_yaws = gaze_yaws[6000:7000]\n",
    "\n",
    "# Generate heat map of eye gaze wrt head pos\n",
    "plt.figure()\n",
    "plt.hist2d(gaze_yaws, gaze_pitches,\n",
    "           bins=50, cmap='hot')\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Number of points')\n",
    "plt.title('Heatmap of eye gaze wrt head direction')\n",
    "# plt.xlim(-20,20)\n",
    "# plt.ylim(-20,20)\n",
    "plt.xlabel('yaw')\n",
    "plt.ylabel('pitch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5115e652-b8de-4a4d-a2fe-3b1c1e5cdc0b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Hits and Misses calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2da4c798-bcf9-49e5-9f4e-65c3e42915e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12it [00:00, 35.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/12 hits with a 0.6859911111111104s average reaction time\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# for every light appearance\n",
    "# find the nearest button press, before the next target appearance\n",
    "max_reaction_time_allowed = 5 #seconds\n",
    "time_offsets = []\n",
    "hits_and_misses = []\n",
    "for idx_num, lighton_idx in tqdm(enumerate(lighton_idcs)):\n",
    "    # while not found_buttonpress or lighton_idcs[idx_num+1]:\n",
    "    offset = 0\n",
    "    target_tuple = (df1.loc[lighton_idx], False)\n",
    "    while lighton_idx+offset < max(df1.index):\n",
    "        time_offset = df1.loc[lighton_idx+offset, \"TimeElapsed\"] - df1.loc[lighton_idx, \"TimeElapsed\"]   \n",
    "        if (df1.loc[lighton_idx+offset, \"ButtonPressed\"] == 1):\n",
    "            # print(\"{0:1.2f}s\".format(time_offset))\n",
    "            time_offsets += [time_offset]\n",
    "            target_tuple = (df1.loc[lighton_idx], df1.loc[lighton_idx+offset])\n",
    "            break\n",
    "        else:\n",
    "            if time_offset > max_reaction_time_allowed:\n",
    "                break\n",
    "            offset += 1\n",
    "    hits_and_misses += [target_tuple]\n",
    "    \n",
    "print(\"{}/{} hits with a {}s average reaction time\".format(len(time_offsets), len(lighton_idcs), sum(time_offsets)/len(time_offsets)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77994187-a699-4863-8b04-33c47b4316d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Prelim analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d4bba594-3a02-49bd-995d-1d04e9ee0597",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeElapsed                                           35.2724\n",
      "TimestampCarla                                          42626\n",
      "TimestampDevice                                       1237338\n",
      "FrameSequence                                       101597981\n",
      "GazeDir_COMBINED                       [0.981, 0.148, -0.128]\n",
      "GazeOrigin_COMBINED                   [-2.978, -0.092, 0.069]\n",
      "GazeValid_COMBINED                                          1\n",
      "GazeVergence_COMBINED                                 38.6292\n",
      "GazeDir_LEFT                           [0.982, 0.146, -0.118]\n",
      "GazeOrigin_LEFT                        [-2.915, 3.115, 0.316]\n",
      "GazeValid_LEFT                                              1\n",
      "EyeOpenness_LEFT                                          1.0\n",
      "EyeOpennessValid_LEFT                                       1\n",
      "PupilDiameter_LEFT                                     2.6894\n",
      "PupilPosition_LEFT                              [0.123, 0.01]\n",
      "PupilPositionValid_LEFT                                     1\n",
      "GazeDir_RIGHT                           [0.979, 0.15, -0.139]\n",
      "GazeOrigin_RIGHT                     [-3.041, -3.298, -0.177]\n",
      "GazeValid_RIGHT                                             1\n",
      "EyeOpenness_RIGHT                                         1.0\n",
      "EyeOpennessValid_RIGHT                                      1\n",
      "PupilDiameter_RIGHT                                    2.5843\n",
      "PupilPosition_RIGHT                           [0.217, -0.313]\n",
      "PupilPositionValid_RIGHT                                    1\n",
      "VehicleLoc                        [1447.763, 18779.99, 0.181]\n",
      "VehicleRot                   [-0.01004, 164.559113, 0.000344]\n",
      "VehicleVel                                               <NA>\n",
      "CameraLoc                               [1.499, 1.827, 1.467]\n",
      "CameraRot                     [5.370612, 7.724596, -4.561883]\n",
      "CameraLocAbs                   [1437.183, 18822.516, 121.644]\n",
      "CameraRotAbs                 [5.360612, 172.28363, -4.562905]\n",
      "Throttle                                                  0.0\n",
      "Steering                                               0.1868\n",
      "Brake                                                     0.0\n",
      "ToggledReverse                                              0\n",
      "TurnSignalLeft                                              0\n",
      "TurnSignalRight                                             0\n",
      "HoldHandbrake                                               0\n",
      "WorldPos                       [1440.724, 18821.857, 121.623]\n",
      "WorldRot                    [5.309877, 172.405502, -4.479755]\n",
      "CombinedOrigin                 [1443.675, 18821.559, 121.409]\n",
      "gaze2target_pitch                                       0.462\n",
      "gaze2target_yaw                                       -0.2362\n",
      "head2target_pitch                                      0.3353\n",
      "head2target_yaw                                       -0.0753\n",
      "LightOn                                                     1\n",
      "ButtonPressed                                               0\n",
      "Name: 1464, dtype: object\n",
      "TimeElapsed                                           61.6373\n",
      "TimestampCarla                                          68991\n",
      "TimestampDevice                                       1263708\n",
      "FrameSequence                                       101601146\n",
      "GazeDir_COMBINED                         [0.982, 0.18, -0.06]\n",
      "GazeOrigin_COMBINED                    [-2.868, -0.101, 0.06]\n",
      "GazeValid_COMBINED                                          1\n",
      "GazeVergence_COMBINED                                202.9666\n",
      "GazeDir_LEFT                           [0.979, 0.194, -0.055]\n",
      "GazeOrigin_LEFT                        [-2.829, 3.101, 0.316]\n",
      "GazeValid_LEFT                                              1\n",
      "EyeOpenness_LEFT                                       0.9726\n",
      "EyeOpennessValid_LEFT                                       1\n",
      "PupilDiameter_LEFT                                      2.866\n",
      "PupilPosition_LEFT                             [0.136, 0.049]\n",
      "PupilPositionValid_LEFT                                     1\n",
      "GazeDir_RIGHT                          [0.984, 0.166, -0.065]\n",
      "GazeOrigin_RIGHT                     [-2.907, -3.304, -0.196]\n",
      "GazeValid_RIGHT                                             1\n",
      "EyeOpenness_RIGHT                                      0.9828\n",
      "EyeOpennessValid_RIGHT                                      1\n",
      "PupilDiameter_RIGHT                                    2.9574\n",
      "PupilPosition_RIGHT                           [0.234, -0.278]\n",
      "PupilPositionValid_RIGHT                                    1\n",
      "VehicleLoc                    [-23058.654, 9638.592, 992.023]\n",
      "VehicleRot                  [0.178596, -90.218323, -0.037994]\n",
      "VehicleVel                                               <NA>\n",
      "CameraLoc                              [0.715, -0.754, 1.058]\n",
      "CameraRot                    [2.277123, -0.280982, -5.788293]\n",
      "CameraLocAbs                  [-23099.566, 9618.41, 1113.118]\n",
      "CameraRotAbs                [2.455521, -90.500847, -5.827209]\n",
      "Throttle                                                  0.0\n",
      "Steering                                               0.0058\n",
      "Brake                                                     0.0\n",
      "ToggledReverse                                              0\n",
      "TurnSignalLeft                                              0\n",
      "TurnSignalRight                                             0\n",
      "HoldHandbrake                                               0\n",
      "WorldPos                     [-23099.398, 9652.744, 1113.009]\n",
      "WorldRot                     [2.49767, -90.566719, -5.734797]\n",
      "CombinedOrigin               [-23099.457, 9655.597, 1112.926]\n",
      "gaze2target_pitch                                       0.467\n",
      "gaze2target_yaw                                       -0.6915\n",
      "head2target_pitch                                      0.3772\n",
      "head2target_yaw                                       -0.5376\n",
      "LightOn                                                     1\n",
      "ButtonPressed                                               0\n",
      "Name: 2637, dtype: object\n",
      "TimeElapsed                                          106.227\n",
      "TimestampCarla                                        113580\n",
      "TimestampDevice                                      1308290\n",
      "FrameSequence                                      101606497\n",
      "GazeDir_COMBINED                      [0.993, 0.116, -0.027]\n",
      "GazeOrigin_COMBINED                  [-2.835, -0.064, 0.052]\n",
      "GazeValid_COMBINED                                         1\n",
      "GazeVergence_COMBINED                               119.9629\n",
      "GazeDir_LEFT                          [0.992, 0.129, -0.013]\n",
      "GazeOrigin_LEFT                        [-2.83, 3.136, 0.315]\n",
      "GazeValid_LEFT                                             1\n",
      "EyeOpenness_LEFT                                         1.0\n",
      "EyeOpennessValid_LEFT                                      1\n",
      "PupilDiameter_LEFT                                    3.0217\n",
      "PupilPosition_LEFT                             [0.09, 0.057]\n",
      "PupilPositionValid_LEFT                                    1\n",
      "GazeDir_RIGHT                         [0.994, 0.103, -0.041]\n",
      "GazeOrigin_RIGHT                     [-2.84, -3.264, -0.212]\n",
      "GazeValid_RIGHT                                            1\n",
      "EyeOpenness_RIGHT                                        1.0\n",
      "EyeOpennessValid_RIGHT                                     1\n",
      "PupilDiameter_RIGHT                                    3.026\n",
      "PupilPosition_RIGHT                          [0.183, -0.259]\n",
      "PupilPositionValid_RIGHT                                   1\n",
      "VehicleLoc                       [2971.29, -16725.15, 0.198]\n",
      "VehicleRot                  [-0.019958, 95.065536, -0.00473]\n",
      "VehicleVel                                              <NA>\n",
      "CameraLoc                             [1.122, -0.812, 0.807]\n",
      "CameraRot                   [0.983452, -3.935579, -4.450174]\n",
      "CameraLocAbs                 [3010.084, -16700.465, 120.994]\n",
      "CameraRotAbs                [0.963214, 91.129906, -4.453516]\n",
      "Throttle                                                 0.0\n",
      "Steering                                             -0.0044\n",
      "Brake                                                    0.0\n",
      "ToggledReverse                                             0\n",
      "TurnSignalLeft                                             0\n",
      "TurnSignalRight                                            0\n",
      "HoldHandbrake                                              0\n",
      "WorldPos                     [3011.698, -16719.627, 120.998]\n",
      "WorldRot                    [0.952593, 91.258209, -4.388309]\n",
      "CombinedOrigin               [3011.828, -16722.461, 120.998]\n",
      "gaze2target_pitch                                     0.3599\n",
      "gaze2target_yaw                                      -0.5406\n",
      "head2target_pitch                                     0.3204\n",
      "head2target_yaw                                      -0.4182\n",
      "LightOn                                                    1\n",
      "ButtonPressed                                              0\n",
      "Name: 4606, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for target_tuple in hits_and_misses:\n",
    "    if target_tuple[1] is False:\n",
    "        # miss\n",
    "        print(target_tuple[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce0f1776-7cd4-4591-8c17-0b506964278c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d4f10c1207d454b927476b23f35cb51",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preliminary analysis\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# plots of hits misses vs ecc\n",
    "for target_tuple in hits_and_misses:\n",
    "    target_response = True\n",
    "    if target_tuple[1] is False:\n",
    "        # miss\n",
    "        target_response = False\n",
    "        pass\n",
    "    else:\n",
    "        # hits\n",
    "        pass\n",
    "        # print(target_tuple[1].ButtonPressed)\n",
    "    ax.scatter(target_tuple[0].gaze2target_yaw*180/np.pi,\n",
    "               target_tuple[0].gaze2target_pitch*180/np.pi,\n",
    "               c='g' if target_response else 'r')\n",
    "    ax.set_title(\"Hits/Misses vs. Eccentricity\\n(Pitch and Yaw of Target from EYE Gaze during onset)\")\n",
    "    \n",
    "    # ax.scatter(target_tuple[0].head2target_yaw*180/np.pi, target_tuple[0].head2target_pitch*180/np.pi, c='g' if target_response else 'r')\n",
    "    # ax.set_title(\"Hits/Misses vs. Eccentricity\\n(Pitch and Yaw of Target from HEAD Gaze during onset)\")\n",
    "    \n",
    "    ax.set_xlabel(\"yaw (degrees)\")\n",
    "    ax.set_ylabel(\"pitch (degrees)\")\n",
    "    ax.set_xlim(-60, 60)\n",
    "    ax.set_ylim(-40, 60)\n",
    "    ax.set_aspect('equal')\n",
    "    # ax.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780cef8f-dbb5-4624-abba-77c9f0e26083",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Gaze event detection (gaze vector/gaze+head only)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cfa789f-6a22-4d55-8896-a875242880e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from ibmm import EyeClassifier\n",
    "from ibmm_online import EyeClassifierOnline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98ba8802-c2b5-4207-a0d2-f67f34863373",
   "metadata": {},
   "outputs": [],
   "source": [
    "gazeORgazeHead = \"gazeHead\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aca14dcd-7e84-4824-9160-76a413ff633c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = df1.copy()\n",
    "if gazeORgazeHead == \"gaze\":\n",
    "    df2['Lgaze_x'] = df1.GazeDir_LEFT.apply(lambda x: x[0])\n",
    "    df2['Lgaze_y'] = df1.GazeDir_LEFT.apply(lambda x: x[1])\n",
    "    df2['Lgaze_z'] = df1.GazeDir_LEFT.apply(lambda x: x[2])\n",
    "\n",
    "    df2['Rgaze_x'] = df1.GazeDir_RIGHT.apply(lambda x: x[0])\n",
    "    df2['Rgaze_y'] = df1.GazeDir_RIGHT.apply(lambda x: x[1])\n",
    "    df2['Rgaze_z'] = df1.GazeDir_RIGHT.apply(lambda x: x[2])\n",
    "\n",
    "\n",
    "    LgazeRaySplitDF = pd.DataFrame(df2[['Lgaze_x', 'Lgaze_y', 'Lgaze_z']])\n",
    "    LgazeRaySplitDF.rename(columns={'Lgaze_x': 'x', 'Lgaze_y': 'y', 'Lgaze_z': 'z'}, inplace=True)\n",
    "    LgazeRaySplitDF['timestamp'] = df2[\"TimeElapsed\"]\n",
    "    LgazeRaySplitDF['confidence'] = df2[\"EyeOpennessValid_LEFT\"].astype(bool) # remove all gazes where an eye was closed\n",
    "\n",
    "    RgazeRaySplitDF = pd.DataFrame(df2[['Rgaze_x', 'Rgaze_y', 'Rgaze_z']])\n",
    "    RgazeRaySplitDF.rename(columns={'Rgaze_x': 'x', 'Rgaze_y': 'y', 'Rgaze_z': 'z'}, inplace=True)\n",
    "    RgazeRaySplitDF['timestamp'] = df2[\"TimeElapsed\"]\n",
    "    RgazeRaySplitDF['confidence'] = df2[\"EyeOpennessValid_RIGHT\"].astype(bool) # remove all gazes where an eye was closed\n",
    "    \n",
    "    velL = EyeClassifier.preprocess(LgazeRaySplitDF, dist_method='vector')\n",
    "    velL.velocity = velL.velocity.astype(float)\n",
    "    velR = EyeClassifier.preprocess(RgazeRaySplitDF, dist_method='vector')\n",
    "    velR.velocity = velR.velocity.astype(float)\n",
    "    model = EyeClassifier()\n",
    "    model.fit(eyes=(velL, velR))\n",
    "    labels, indiv_labels = model.predict(eyes=(velL, velR))\n",
    "    labels_unique = labels[1::2]\n",
    "    \n",
    "elif gazeORgazeHead == \"gazeHead\":\n",
    "    # add approx head compensation\n",
    "    df2['Cgaze_x'] = df1.GazeDir_COMBINED.apply(lambda x: x[0])\n",
    "    df2['Cgaze_y'] = df1.GazeDir_COMBINED.apply(lambda x: x[1])\n",
    "    df2['Cgaze_z'] = df1.GazeDir_COMBINED.apply(lambda x: x[2])\n",
    "\n",
    "    # gaze+head values\n",
    "    gaze_pitches, gaze_yaws = GetGazeDeviationFromHead(df2.Cgaze_x, df2.Cgaze_y, df2.Cgaze_z)\n",
    "    head_rots = df2.CameraRot.values\n",
    "    head_pitches =   df2.CameraRot.apply(lambda x: x[0])\n",
    "    head_yaws = df2.CameraRot.apply(lambda x: x[2])\n",
    "    gaze_head_pitches = gaze_pitches + head_pitches\n",
    "    gaze_head_yaws = gaze_yaws + head_yaws       \n",
    "\n",
    "    # Create the new pd\n",
    "    gazeHeadDF = pd.DataFrame(df2[['TimeElapsed']])\n",
    "    gazeHeadDF = gazeHeadDF.rename(columns={'TimeElapsed':'timestamp'})\n",
    "    gazeHeadDF['confidence'] = (df2.EyeOpennessValid_LEFT*df2.EyeOpennessValid_RIGHT).astype(bool)\n",
    "    gazeHeadDF['x'] = gaze_head_pitches\n",
    "    gazeHeadDF['y'] = gaze_head_yaws\n",
    "    gazeHeadDF['z'] = np.zeros(len(gaze_head_pitches))\n",
    "    \n",
    "    vel_w = EyeClassifier.preprocess(gazeHeadDF, dist_method='euclidean')\n",
    "    model = EyeClassifier()\n",
    "    model.fit(world=vel_w)\n",
    "    # raw_vel = vel_w[np.logical_not(vel_w.velocity.isna())].velocity.values\n",
    "    # raw_vel[raw_vel > raw_vel.mean() + 3 * raw_vel.std()]\n",
    "    # print(\"Velocity Means: \",model.world_model.means_)\n",
    "    \n",
    "    labels, indiv_labels = model.predict(world=vel_w)\n",
    "    labels_unique = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ae73a96-7093-46c7-aada-85e37db92c8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels_np.shape:  (5127, 5)\n"
     ]
    }
   ],
   "source": [
    "# 0- fix, 1- sac, -1 -> noise\n",
    "labels_unique.index = np.arange(1, len(labels_unique) + 1) # start index from 1 instead of 0\n",
    "labels_np = labels_unique.to_numpy()\n",
    "print(\"labels_np.shape: \", labels_np.shape)\n",
    "# add the labels to the original df:\n",
    "recdf_with_labels = df2.join(labels_unique[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8dba3631-38df-4c1b-b315-33f6c959ac43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num fixation total pts:  4898\n",
      "Num saccades total pts:  204\n",
      "Num noise total pts:  25\n",
      "\n",
      "Num fixations:  155\n",
      "Num saccades:  151\n",
      "Num noise:  8\n",
      "Num LightOns:  12\n"
     ]
    }
   ],
   "source": [
    "print(\"Num fixation total pts: \", np.sum(labels_np[:,2]))\n",
    "print(\"Num saccades total pts: \", np.sum(labels_np[:,3]))\n",
    "print(\"Num noise total pts: \", np.sum(labels_np[:,4]))\n",
    "print()\n",
    "# filter the consecutives\n",
    "print(\"Num fixations: \", np.sum(np.diff(labels_np[:, 2]) == 1))\n",
    "print(\"Num saccades: \", np.sum(np.diff(labels_np[:, 3]) == 1))\n",
    "print(\"Num noise: \", np.sum(np.diff(labels_np[:, 4]) == 1))\n",
    "print(\"Num LightOns: \", num_targets_spawned)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f32de9-2469-46ef-a40f-1f24fe7c7fc0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## OFD analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "82adff87-4755-43cc-89df-b591c3992632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# need a data format that stores:\n",
    "# all the data from hits and misses\n",
    "# what trial/recording file it corresponds to: recording_name -- this is also stored in the pkl name\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2fa2cdd-dcd8-494f-bbff-82186d5679c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 valid targets\n",
      "11 fixation onsets, 1 sacc, 0 noise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hits/Misses on Gaze Eccentricity vs. OFD')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d29edf09f04408e9ff0c9dc8811246c",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target tuples, go back and find labels\n",
    "saccade_onsets = 0\n",
    "fixation_onsets = 0\n",
    "noise_onsets = 0\n",
    "fig, ax = plt.subplots()\n",
    "graph_tuples = []\n",
    "\n",
    "for target_tuple in hits_and_misses:\n",
    "    # for either hit or miss, go back and find the ofd\n",
    "    # check if you're in a fixation rn\n",
    "    if target_tuple[0].GazeValid_COMBINED==0: # check gaze validity \n",
    "        continue\n",
    "    target_locindf = target_tuple[0].name\n",
    "    onset_gaze_event = recdf_with_labels.loc[target_locindf].label  \n",
    "    OFD = 0\n",
    "    pitch = recdf_with_labels.loc[target_locindf].gaze2target_pitch\n",
    "           # + recdf_with_labels.loc[target_locindf].head2target_pitch\n",
    "    yaw = recdf_with_labels.loc[target_locindf].gaze2target_yaw \n",
    "           # + recdf_with_labels.loc[target_locindf].head2target_yaw\n",
    "    \n",
    "    if onset_gaze_event == 0:\n",
    "        fixation_onsets += 1\n",
    "        # go back and look at when this current fixation ends\n",
    "        # labels going back from current gaze\n",
    "        labels_upto_curr = recdf_with_labels.loc[target_locindf::-1].label \n",
    "        np.diff(labels_upto_curr)\n",
    "        labels_upto_curr = recdf_with_labels.loc[target_locindf::-1].label \n",
    "        label_diff = np.diff(labels_upto_curr)!=0\n",
    "        first_fixation_idx = target_locindf - np.argmax(label_diff)\n",
    "        OFD = recdf_with_labels.loc[target_locindf].TimeElapsed -\\\n",
    "                recdf_with_labels.loc[first_fixation_idx].TimeElapsed\n",
    "        \n",
    "        eccentricity = np.linalg.norm([recdf_with_labels.loc[target_locindf].gaze2target_pitch,\n",
    "                                      recdf_with_labels.loc[target_locindf].gaze2target_yaw])*180/np.pi\n",
    "        ax.scatter(OFD, eccentricity, c='r' if target_tuple[1] is False else 'g')\n",
    "        graph_tuples += (OFD, eccentricity, target_tuple[1] is False, pitch, yaw)\n",
    "    elif onset_gaze_event == 1:\n",
    "        saccade_onsets += 1\n",
    "        OFD = 0        \n",
    "        eccentricity = np.linalg.norm([recdf_with_labels.loc[target_locindf].gaze2target_pitch,\n",
    "                                      recdf_with_labels.loc[target_locindf].gaze2target_yaw])*180/np.pi\n",
    "        ax.scatter(OFD, eccentricity, c='r' if target_tuple[1] is False else 'g')\n",
    "        graph_tuples += (OFD, eccentricity, target_tuple[1] is False, pitch, yaw)\n",
    "    else:\n",
    "        noise_onsets += 1\n",
    "    # print(target_locindf, OFD)\n",
    "print(\"{}/{} valid targets\".format(\n",
    "        fixation_onsets+saccade_onsets+noise_onsets,\n",
    "        len(hits_and_misses)))\n",
    "print(\"{} fixation onsets, {} sacc, {} noise\".format(fixation_onsets, saccade_onsets, noise_onsets))\n",
    "\n",
    "#plt.xlim(0, )\n",
    "ax.set_ylim(-1, 60)\n",
    "ax.set_xlabel(\"Onset Fixation Duration (seconds)\")\n",
    "ax.set_ylabel(\"Eccentricity (degrees)\")\n",
    "ax.set_title(\"Hits/Misses on Gaze Eccentricity vs. OFD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26187812-5481-4646-8d4c-e849265d0f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp hack for henny meeting: save graph tuples\n",
    "graph_data_filname = Path(\"temp_data\").joinpath(recording_name+\"_OFDvEcc.pkl\")\n",
    "with open(graph_data_filname, 'wb') as f:\n",
    "    pkl.dump(graph_tuples, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0205c3b-2a30-40ef-b4fb-828d48cab868",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Saccade amplitude analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fafb11cd-e3ad-4e57-92e7-d41b68d3d489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 valid targets\n",
      "11 fixation onsets, 1 sacc, 0 noise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hits/Misses on Gaze Eccentricity vs. OFD')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29f6935501b646f6af20fba290448e7a",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target tuples, go back and find labels\n",
    "saccade_onsets = 0\n",
    "fixation_onsets = 0\n",
    "noise_onsets = 0\n",
    "fig, ax = plt.subplots()\n",
    "graph_tuples = []\n",
    "\n",
    "for target_tuple in hits_and_misses:\n",
    "    # for either hit or miss, go back and find the ofd\n",
    "    # check if you're in a fixation rn\n",
    "    if target_tuple[0].GazeValid_COMBINED==0: # check gaze validity \n",
    "        continue\n",
    "    target_locindf = target_tuple[0].name\n",
    "    onset_gaze_event = recdf_with_labels.loc[target_locindf].label  \n",
    "    OSA = 0\n",
    "    pitch = recdf_with_labels.loc[target_locindf].gaze2target_pitch\n",
    "           # + recdf_with_labels.loc[target_locindf].head2target_pitch\n",
    "    yaw = recdf_with_labels.loc[target_locindf].gaze2target_yaw \n",
    "           # + recdf_with_labels.loc[target_locindf].head2target_yaw\n",
    "    \n",
    "    if onset_gaze_event == 0:\n",
    "        fixation_onsets += 1\n",
    "        # go back and look at when this current fixation ends\n",
    "        # labels going back from current gaze\n",
    "        labels_upto_curr = recdf_with_labels.loc[target_locindf::-1].label \n",
    "        label_diff = np.diff(labels_upto_curr)!=0\n",
    "        first_fixation_idx = target_locindf - np.argmax(label_diff) # first fixation of this idx\n",
    "        \n",
    "        labels_upto_curr = recdf_with_labels.loc[target_locindf::-1].label \n",
    "        rev_fix_idcs = np.where(labels_upto_curr==0)\n",
    "        last_nonfix_offset = np.where(np.diff(rev_fix_idcs)>1)[1][0]# where is the break in the fixation\n",
    "        last_fixend_idx = rev_fix_idcs[0][last_nonfix_offset+1]\n",
    "        # onset saccade idx\n",
    "        OSA = np.linalg.norm(recdf_with_labels.loc[target_locindf].GazeDir_COMBINED - recdf_with_labels.loc[last_fixend_idx].GazeDir_COMBINED)*180/np.pi\n",
    "        \n",
    "                \n",
    "        eccentricity = np.linalg.norm([recdf_with_labels.loc[target_locindf].gaze2target_pitch,\n",
    "                                      recdf_with_labels.loc[target_locindf].gaze2target_yaw])*180/np.pi\n",
    "        ax.scatter(OSA, eccentricity, c='r' if target_tuple[1] is False else 'g')\n",
    "        graph_tuples += (OSA, eccentricity, target_tuple[1] is False, pitch, yaw)\n",
    "        \n",
    "    elif onset_gaze_event == 1:\n",
    "        saccade_onsets += 1\n",
    "        # OFD = 0        \n",
    "        # eccentricity = np.linalg.norm([recdf_with_labels.loc[target_locindf].gaze2target_pitch,\n",
    "        #                               recdf_with_labels.loc[target_locindf].gaze2target_yaw])*180/np.pi\n",
    "        # ax.scatter(OFD, eccentricity, c='r' if target_tuple[1] is False else 'g')\n",
    "        # graph_tuples += (OFD, eccentricity, target_tuple[1] is False, pitch, yaw)\n",
    "    else:\n",
    "        noise_onsets += 1\n",
    "    # print(target_locindf, OFD)\n",
    "print(\"{}/{} valid targets\".format(\n",
    "        fixation_onsets+saccade_onsets+noise_onsets,\n",
    "        len(hits_and_misses)))\n",
    "print(\"{} fixation onsets, {} sacc, {} noise\".format(fixation_onsets, saccade_onsets, noise_onsets))\n",
    "\n",
    "#plt.xlim(0, )\n",
    "ax.set_ylim(-1, 60)\n",
    "ax.set_xlabel(\"Onset Saccade Amplitude (degrees)\")\n",
    "ax.set_ylabel(\"Eccentricity (degrees)\")\n",
    "ax.set_title(\"Hits/Misses on Gaze Eccentricity vs. OFD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef243a60-4cd3-43eb-8c8c-8a6a25c957a3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# OFD analysis with Eye gaze + Head compensation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "427bb9a0-4f58-45c5-8ecd-7f4bf8c7997d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()\n",
    "df2['Lgaze_x'] = df1.GazeDir_LEFT.apply(lambda x: x[0])\n",
    "df2['Lgaze_y'] = df1.GazeDir_LEFT.apply(lambda x: x[1])\n",
    "df2['Lgaze_z'] = df1.GazeDir_LEFT.apply(lambda x: x[2])\n",
    "\n",
    "df2['Rgaze_x'] = df1.GazeDir_RIGHT.apply(lambda x: x[0])\n",
    "df2['Rgaze_y'] = df1.GazeDir_RIGHT.apply(lambda x: x[1])\n",
    "df2['Rgaze_z'] = df1.GazeDir_RIGHT.apply(lambda x: x[2])\n",
    "\n",
    "df2['Cgaze_x'] = df1.GazeDir_COMBINED.apply(lambda x: x[0])\n",
    "df2['Cgaze_y'] = df1.GazeDir_COMBINED.apply(lambda x: x[1])\n",
    "df2['Cgaze_z'] = df1.GazeDir_COMBINED.apply(lambda x: x[2])\n",
    "\n",
    "\n",
    "LgazeRaySplitDF = pd.DataFrame(df2[['Lgaze_x', 'Lgaze_y', 'Lgaze_z']])\n",
    "LgazeRaySplitDF.rename(columns={'Lgaze_x': 'x', 'Lgaze_y': 'y', 'Lgaze_z': 'z'}, inplace=True)\n",
    "LgazeRaySplitDF['timestamp'] = df2[\"TimeElapsed\"]\n",
    "LgazeRaySplitDF['confidence'] = df2[\"EyeOpennessValid_LEFT\"].astype(bool) # remove all gazes where an eye was closed\n",
    "\n",
    "RgazeRaySplitDF = pd.DataFrame(df2[['Rgaze_x', 'Rgaze_y', 'Rgaze_z']])\n",
    "RgazeRaySplitDF.rename(columns={'Rgaze_x': 'x', 'Rgaze_y': 'y', 'Rgaze_z': 'z'}, inplace=True)\n",
    "RgazeRaySplitDF['timestamp'] = df2[\"TimeElapsed\"]\n",
    "RgazeRaySplitDF['confidence'] = df2[\"EyeOpennessValid_RIGHT\"].astype(bool) # remove all gazes where an eye was closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "af4ac86a-ac2f-4f97-b3ad-ed03e7c2733a",
   "metadata": {},
   "outputs": [],
   "source": [
    "velL = EyeClassifier.preprocess(LgazeRaySplitDF, dist_method='vector')\n",
    "velL.velocity = velL.velocity.astype(float)\n",
    "velR = EyeClassifier.preprocess(RgazeRaySplitDF, dist_method='vector')\n",
    "velR.velocity = velR.velocity.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d3fd3cd3-f142-4c13-b3df-b2025ee7fff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add approx head compensation\n",
    "# gaze+head values\n",
    "gaze_pitches, gaze_yaws = GetGazeDeviationFromHead(df2.Cgaze_x, df2.Cgaze_y, df2.Cgaze_z)\n",
    "\n",
    "head_rots = df2.CameraRot.values\n",
    "head_pitches =   df2.CameraRot.apply(lambda x: x[0])\n",
    "head_yaws = df2.CameraRot.apply(lambda x: x[2])\n",
    "    \n",
    "gaze_head_pitches = gaze_pitches + head_pitches\n",
    "gaze_head_yaws = gaze_yaws + head_yaws       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "ac25fa1e-8caf-4a15-a064-723d04b35a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new pd\n",
    "\n",
    "gazeHeadDF = pd.DataFrame(df2[['TimeElapsed']])\n",
    "gazeHeadDF = gazeHeadDF.rename(columns={'TimeElapsed':'timestamp'})\n",
    "gazeHeadDF['confidence'] = (df2.EyeOpennessValid_LEFT*df2.EyeOpennessValid_RIGHT).astype(bool)\n",
    "gazeHeadDF['x'] = gaze_head_pitches\n",
    "gazeHeadDF['y'] = gaze_head_yaws\n",
    "gazeHeadDF['z'] = np.zeros(len(gaze_head_pitches))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "cf7bc620-ea39-49c2-b63d-0d77e2ca9b6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Velocity Means:  [[  6.05573297]\n",
      " [106.9746424 ]]\n"
     ]
    }
   ],
   "source": [
    "vel_w = EyeClassifier.preprocess(gazeHeadDF, dist_method='euclidean')\n",
    "model = EyeClassifier()\n",
    "model.fit(world=vel_w)\n",
    "\n",
    "# raw_vel = vel_w[np.logical_not(vel_w.velocity.isna())].velocity.values\n",
    "# raw_vel[raw_vel > raw_vel.mean() + 3 * raw_vel.std()]\n",
    "# print(\"Velocity Means: \",model.world_model.means_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "a453999b-a33d-4b1a-b98c-5a9d0c290bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels_npw.shape:  (2563, 5)\n"
     ]
    }
   ],
   "source": [
    "labelsw, indiv_labelsw = model.predict(world=vel_w)\n",
    "# print(labels)\n",
    "# 0- fix, 1- sac, -1 -> noise\n",
    "labels_uniquew = labelsw[1::2]\n",
    "labels_uniquew.index = np.arange(1, len(labels_uniquew) + 1) # start index from 1 instead of 0\n",
    "labels_npw = labels_uniquew.to_numpy()\n",
    "print(\"labels_npw.shape: \", labels_npw.shape)\n",
    "# add the labels to the original df:\n",
    "recdf_with_labelsw = df2.join(labels_uniquew[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "64cf177a-225c-4390-af8f-dc0bf90ebcad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num fixation total pts:  2041\n",
      "Num saccades total pts:  503\n",
      "Num noise total pts:  19\n",
      "\n",
      "Num fixations:  268\n",
      "Num saccades:  270\n",
      "Num noise:  10\n",
      "Num LightOns:  12\n"
     ]
    }
   ],
   "source": [
    "print(\"Num fixation total pts: \", np.sum(labels_npw[:,2]))\n",
    "print(\"Num saccades total pts: \", np.sum(labels_npw[:,3]))\n",
    "print(\"Num noise total pts: \", np.sum(labels_npw[:,4]))\n",
    "print()\n",
    "# filter the consecutives\n",
    "print(\"Num fixations: \", np.sum(np.diff(labels_npw[:, 2]) == 1))\n",
    "print(\"Num saccades: \", np.sum(np.diff(labels_npw[:, 3]) == 1))\n",
    "print(\"Num noise: \", np.sum(np.diff(labels_npw[:, 4]) == 1))\n",
    "print(\"Num LightOns: \", num_targets_spawned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42edf54a-7d46-4b6d-a9ab-ea16967bccc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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