flanker_test_20220715.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b01c811b",
   "metadata": {},
   "source": [
    "# Flanker task timing test plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7413c4",
   "metadata": {},
   "source": [
    "## Load Test 1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b4e7260",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import urllib.request as urllibrequest #import urllib\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = [7.00, 3.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "def download_google_sheet(filepath, docid):\n",
    "    \"\"\"\n",
    "    Download latest version of a Google Sheet\n",
    "    Parameters\n",
    "    ----------\n",
    "    filepath : string\n",
    "    docid : string\n",
    "    Returns\n",
    "    -------\n",
    "    filepath : string\n",
    "    \"\"\"\n",
    "    if not os.path.exists(os.path.abspath(os.path.dirname(filepath))):\n",
    "        os.makedirs(os.path.abspath(os.path.dirname(filepath)))\n",
    "    urllibrequest.urlretrieve(\"{1}{0}{2}\".format(\n",
    "        docid,\n",
    "        'https://docs.google.com/spreadsheets/d/',\n",
    "        '/export?format=csv'\n",
    "        ), filepath)\n",
    "    return filepath\n",
    "\n",
    "# MindLogger exported Flanker csv table with test data:\n",
    "# Google sheet format: Format -> Number -> Custom number format: 0.0000\n",
    "## block_number\ttrial_number\ttrial_type\tevent_type\tframe\texperiment_start_timestamp\tblock_start_timestamp\ttrial_start_timestamp\tevent_start_timestamp\tvideo_display_request_timestamp\tresponse_touch_timestamp\ttrial_offset\tevent_offset\tresponse_time\tresponse\tresponse_accuracy\n",
    "## 1.0000\t8.0000\t>>>>>\tFixation\t339.0000\t1657918589.9240\t1657918888.0330\t1657918895.0680\t1657918895.0680\t1657918895.0400\t.\t7.0360\t0.0000\t.\t.\t.\t\t\t\t\t\t\t\t\t\t\t\n",
    "base_dir = '/Users/arno/Software/mindlogger-time-tests/'\n",
    "google_sheet = '1nhoswgFJnv4I3YOskfErXW4XQFGiWMSL87UAjl3zQ4M' # 62d1d6cc154fa87efa129b85\n",
    "csv_file = base_dir + 'input/flanker_test_20220715/flanker_test1_excerpt.csv'\n",
    "columns = ['block_number','trial_number','trial_type','event_type','frame_touch','frame_remove','experiment_start_timestamp',\n",
    "           'block_start_timestamp','trial_start_timestamp','event_start_timestamp',\n",
    "           'video_display_request_timestamp','response_touch_timestamp','trial_offset','event_offset',\n",
    "           'response_time','response','response_accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4e2e1b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_table = download_google_sheet(csv_file, google_sheet)\n",
    "\n",
    "df = pd.read_csv(csv_file, usecols=columns)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "# Movie of the Flanker task test 1 block of trials:\n",
    "# Convert movie and add frame numbers:\n",
    "## ffmpeg -i /Users/arno.klein/Downloads/flanker-test-20220715.mp4 -vf \"drawtext=fontfile=Arial.ttf: text='%{frame_num}': fontsize=200: start_number=1: x=(w-tw)/2: y=h-(2*lh): fontcolor=black: fontsize=20: box=1: boxcolor=white: boxborderw=5\" -c:a copy /Users/arno/Downloads/flanker_test_20220715_frames.mp4\n",
    "fps = 240\n",
    "ms_per_frame = 1000/fps\n",
    "\n",
    "# Excel table with test data frame numbers:\n",
    "#frames = df.frame_touch\n",
    "frames = (df.frame_touch + df.frame_remove)/2\n",
    "frame_times = np.array([x * 1000 / fps for x in frames])\n",
    "imax = len(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3425c025",
   "metadata": {},
   "source": [
    "## Touch to fixation\n",
    "\n",
    "  - Define touch to fixation in the movie as the time between contact with the screen and first appearance of the fixation.\n",
    "  - Define touch to fixation in the data export as the time between recorded response_touch_timestamp and recorded event_start_timestamp representing the next fixation event. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cc1fe764",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Touch to fixation time:\n",
      "Movie mean (SD): 33.01 (5.41) +- 8.33 ms (2 frames)\n",
      "Data mean (SD): 36.00 (4.13) ms\n",
      "Error (movie - data) mean (SD): -2.99 (5.93)\n"
     ]
    }
   ],
   "source": [
    "touch_to_fixation_movie = pd.Series(np.zeros(len(frames)))\n",
    "fixation_to_stimulus_movie = pd.Series(np.zeros(len(frames)))\n",
    "i = 0\n",
    "for frame in frames:\n",
    "    i = i + 1\n",
    "    if i > 1:\n",
    "        if i % 3 == 1:\n",
    "            touch_to_fixation_movie[i-1] = (frame - frames[i-2]) * 1000 / fps\n",
    "        else:\n",
    "            touch_to_fixation_movie[i-1] = np.nan\n",
    "        if i % 3 == 2:\n",
    "            fixation_to_stimulus_movie[i-1] = (frame - frames[i-2]) * 1000 / fps\n",
    "        else:\n",
    "            fixation_to_stimulus_movie[i-1] = np.nan\n",
    "            \n",
    "touch_to_fixation = pd.Series(np.zeros(len(frames)))\n",
    "i = 0\n",
    "for frame in frames:\n",
    "    i = i + 1\n",
    "    if i > 1:\n",
    "        if i % 3 == 1:\n",
    "            touch_to_fixation[i-1] = 1000 * (float(df.event_start_timestamp[i-1]) - float(df.response_touch_timestamp[i-2]))\n",
    "        else:\n",
    "            touch_to_fixation[i-1] = np.nan\n",
    "\n",
    "touch_to_fixation_error = touch_to_fixation_movie - touch_to_fixation\n",
    "\n",
    "imin = 1\n",
    "\n",
    "plt.plot(frame_times[imin:imax], touch_to_fixation_movie[imin:imax], marker='s', linestyle='', color='k', label='Movie')\n",
    "plt.plot(frame_times[imin:imax], touch_to_fixation_error[imin:imax], marker='*', linestyle='', color='r', label='Error')\n",
    "plt.plot(frame_times[imin:imax], touch_to_fixation[imin:imax], marker='o', linestyle='', color='b', label='Data')\n",
    "plt.title('Touch to fixation timing')\n",
    "plt.xlabel ('Movie time (ms)')\n",
    "plt.ylabel ('Time difference (ms)')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(\"Touch to fixation time:\")\n",
    "print(\"Movie mean (SD): {0:.2f} ({1:.2f}) +- {2:.2f} ms (2 frames)\".format(np.mean(touch_to_fixation_movie[imin:imax]),\n",
    "                                                                           np.std(touch_to_fixation_movie[imin:imax]),\n",
    "                                                                           2 * ms_per_frame))\n",
    "print(\"Data mean (SD): {0:.2f} ({1:.2f}) ms\".format(np.mean(touch_to_fixation[imin:imax]), \n",
    "                                                           np.std(touch_to_fixation[imin:imax])))\n",
    "print(\"Error (movie - data) mean (SD): {0:.2f} ({1:.2f})\".format(np.mean(touch_to_fixation_error[imin:imax]), \n",
    "                                                                   np.std(touch_to_fixation_error[imin:imax])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daaf1026",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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