Investigate_SD_of_SSRTs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "from stopsignalmetrics import StopData, SSRTmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_cond_file(filey):\n",
    "    cond_df = pd.read_excel(filey)\n",
    "    cond_df = cond_df.replace(r'^\\s*$', np.nan, regex=True).replace('?', np.nan)  \n",
    "    return cond_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#these are the 339 subjects who passed the criteria discussed in the manuscriptinfo\n",
    "full_passed_turkers = [4, 5, 9, 11, 12, 13, 15, 16, 17, 19, 20, 23, 25, 28, 30, 31, 32,\n",
    "                       33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 51,\n",
    "                       52, 53, 54, 55, 56, 58, 59, 60, 62, 63, 64, 66, 71, 74, 76, 80,\n",
    "                       81, 82, 85, 87, 88, 89, 90, 91, 92, 94, 96, 97, 99, 100, 101,\n",
    "                       102, 103, 105, 106, 107, 108, 109, 112, 113, 114, 117, 122, 123,\n",
    "                       124, 125, 126, 127, 128, 132, 133, 134, 136, 139, 140, 141, 142,\n",
    "                       144, 146, 150, 151, 154, 156, 158, 160, 161, 162, 165, 167, 168,\n",
    "                       171, 172, 173, 175, 177, 179, 180, 183, 184, 185, 186, 188, 189,\n",
    "                       190, 193, 195, 198, 199, 201, 202, 203, 205, 207, 208, 210, 212,\n",
    "                       214, 215, 216, 217, 219, 220, 221, 222, 223, 226, 227, 228, 230,\n",
    "                       232, 233, 234, 235, 236, 237, 239, 240, 241, 243, 244, 245, 246,\n",
    "                       247, 251, 252, 254, 256, 257, 259, 260, 266, 268, 270, 271, 273,\n",
    "                       274, 276, 278, 280, 282, 283, 284, 285, 288, 292, 293, 294, 295,\n",
    "                       296, 297, 298, 299, 301, 302, 304, 305, 307, 309, 311, 313, 314,\n",
    "                       317, 323, 324, 325, 327, 328, 330, 333, 334, 335, 336, 338, 340,\n",
    "                       342, 343, 345, 346, 349, 350, 354, 355, 357, 358, 360, 362, 363,\n",
    "                       364, 365, 368, 369, 375, 376, 377, 383, 384, 386, 387, 388, 389,\n",
    "                       390, 394, 396, 397, 398, 399, 400, 403, 405, 406, 407, 409, 410,\n",
    "                       411, 412, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426,\n",
    "                       427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 438, 439, 441,\n",
    "                       443, 444, 445, 446, 448, 449, 450, 451, 454, 456, 457, 458, 459,\n",
    "                       460, 461, 463, 465, 466, 467, 469, 470, 473, 474, 476, 477, 479,\n",
    "                       480, 481, 482, 483, 486, 489, 492, 493, 494, 495, 496, 497, 499,\n",
    "                       501, 504, 505, 507, 508, 509, 512, 514, 515, 516, 520, 521, 522,\n",
    "                       523, 526, 527, 529, 533, 535, 539, 543, 545, 546, 548, 549, 550,\n",
    "                       559, 560]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_map = {\n",
    "        'columns': {'ID': 'Subject',\n",
    "                    'block': 'Block',\n",
    "                    'condition': 'TrialType',\n",
    "                    'SSD': 'StopSignalDelay',\n",
    "                    'goRT': 'GoRT',\n",
    "                    'stopRT': 'StopFailureRT',\n",
    "                    'response': 'GoTrialResponse',\n",
    "                    'correct_response': 'GoTrialCorrectResponse',\n",
    "                   },\n",
    "        'key_codes': {'go': 'go',\n",
    "                      'stop': 'stop',\n",
    "                      'correct': 1,\n",
    "                      'incorrect': 0,\n",
    "                      'noResponse': 0,\n",
    "                     }\n",
    "    }\n",
    "mapping_dicts = {\n",
    "    'Matzke': {\n",
    "        'columns': {'ID': 'Subject',\n",
    "                    'condition': 'SS',\n",
    "                    'SSD': 'StopSignalDelay',\n",
    "                    'goRT': 'Target.RT',\n",
    "                    'stopRT': 'TargetDelay.RT',\n",
    "                    'response': 'R',\n",
    "                    'correct_response': 'S',\n",
    "                   },\n",
    "        'key_codes': {'go': 'go',\n",
    "                      'stop': 'stop',\n",
    "                      'correct': 1,\n",
    "                      'incorrect': 0,\n",
    "                      'noResponse': 0,\n",
    "                     }\n",
    "    },\n",
    "    'Saccades': {\n",
    "        'columns': {'ID': 'Subject',\n",
    "                    'block': 'Block',\n",
    "                    'condition': 'TrialType',\n",
    "                    'SSD': 'StopSignalDelay',\n",
    "                    'goRT': 'GoRT',\n",
    "                    'stopRT': 'StopFailureRT',\n",
    "                    'response': 'GoTrialResponse',\n",
    "                    'correct_response': 'GoTrialCorrectResponse',\n",
    "                   },\n",
    "        'key_codes': {'go': 'go',\n",
    "                      'stop': 'Stop',\n",
    "                      'correct': 1,\n",
    "                      'incorrect': 0,\n",
    "                      'noResponse': 0,\n",
    "                     }\n",
    "    }\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Deadline1500ms': 34.720568393562615,\n",
       " 'Saccades': 34.211348598704376,\n",
       " 'BtwnSubjVisual2': 29.775339862087897,\n",
       " 'FixedSSDs2': 55.90494002179574,\n",
       " 'BtwnSubjAuditory2': 35.76913227708826,\n",
       " 'Deadline2700ms': 56.488884882220276,\n",
       " 'TurkSimpleHigh': 44.977463388777046,\n",
       " 'Deadline1300ms': 27.6090681978959,\n",
       " 'WithinSubjCentralGoSimple': 36.33724505817492,\n",
       " 'WithinSubjPeriphGoSimple': 30.4702282005005,\n",
       " 'Matzke': 84.85650409521057,\n",
       " 'TurkSimpleLow': 53.42240767811976,\n",
       " 'Deadline1700ms': 39.60555763489168,\n",
       " 'Deadline2300ms': 53.48093612753764,\n",
       " 'BtwnSubjAuditory1': 35.26281790159863,\n",
       " 'BtwnSubjVisual1': 31.693386222585243,\n",
       " 'FixedSSDs1': 34.55797481318032,\n",
       " 'Deadline2500ms': 56.45605367392923,\n",
       " 'StopProbabilityLow': 37.0234708800525,\n",
       " 'StopProbabilityHigh': 41.95826722296842}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_dict = {}\n",
    "ssrt_df_dict = {}\n",
    "for filey in [f for f in glob('normative_stopping_data/*') if '~$' not in f]:\n",
    "    condition_name = filey.split('/Data')[-1].replace('.xlsx', '')\n",
    "    curr_df = read_cond_file(filey)\n",
    "    if 'Turk' in condition_name:\n",
    "        curr_df = curr_df[curr_df.Subject.isin(full_passed_turkers)].copy()\n",
    "    if 'Matzke' in condition_name:\n",
    "        del curr_df['SSD']\n",
    "    clean_data = StopData(mapping_dicts.get(condition_name, default_map)).fit_transform(curr_df)\n",
    "    clean_data = clean_data[(clean_data.SSD >= 0) | (clean_data.SSD.isnull())].copy()  # ignore negative SSDs\n",
    "    ssrt_metrics = SSRTmodel(model='replacement').fit_transform(clean_data, level='group')\n",
    "    ssrt_df_dict[condition_name] = ssrt_metrics\n",
    "    sd_dict[condition_name] = ssrt_metrics['SSRT'].std()\n",
    "sd_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>SD of SSRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Deadline1500ms</th>\n",
       "      <td>34.720568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saccades</th>\n",
       "      <td>34.211349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BtwnSubjVisual2</th>\n",
       "      <td>29.775340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FixedSSDs2</th>\n",
       "      <td>55.904940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BtwnSubjAuditory2</th>\n",
       "      <td>35.769132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadline2700ms</th>\n",
       "      <td>56.488885</td>\n",
       "    </tr>\n",
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       "      <th>TurkSimpleHigh</th>\n",
       "      <td>44.977463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadline1300ms</th>\n",
       "      <td>27.609068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WithinSubjCentralGoSimple</th>\n",
       "      <td>36.337245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WithinSubjPeriphGoSimple</th>\n",
       "      <td>30.470228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matzke</th>\n",
       "      <td>84.856504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TurkSimpleLow</th>\n",
       "      <td>53.422408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadline1700ms</th>\n",
       "      <td>39.605558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadline2300ms</th>\n",
       "      <td>53.480936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BtwnSubjAuditory1</th>\n",
       "      <td>35.262818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BtwnSubjVisual1</th>\n",
       "      <td>31.693386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FixedSSDs1</th>\n",
       "      <td>34.557975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadline2500ms</th>\n",
       "      <td>56.456054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StopProbabilityLow</th>\n",
       "      <td>37.023471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StopProbabilityHigh</th>\n",
       "      <td>41.958267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           SD of SSRT\n",
       "Deadline1500ms              34.720568\n",
       "Saccades                    34.211349\n",
       "BtwnSubjVisual2             29.775340\n",
       "FixedSSDs2                  55.904940\n",
       "BtwnSubjAuditory2           35.769132\n",
       "Deadline2700ms              56.488885\n",
       "TurkSimpleHigh              44.977463\n",
       "Deadline1300ms              27.609068\n",
       "WithinSubjCentralGoSimple   36.337245\n",
       "WithinSubjPeriphGoSimple    30.470228\n",
       "Matzke                      84.856504\n",
       "TurkSimpleLow               53.422408\n",
       "Deadline1700ms              39.605558\n",
       "Deadline2300ms              53.480936\n",
       "BtwnSubjAuditory1           35.262818\n",
       "BtwnSubjVisual1             31.693386\n",
       "FixedSSDs1                  34.557975\n",
       "Deadline2500ms              56.456054\n",
       "StopProbabilityLow          37.023471\n",
       "StopProbabilityHigh         41.958267"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_df = pd.DataFrame(sd_dict, index=['SD of SSRT']).T\n",
    "sd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SD of SSRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.729080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.804978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>27.609068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.471318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.680358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.437040</td>\n",
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       "      <th>max</th>\n",
       "      <td>84.856504</td>\n",
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      "text/plain": [
       "       SD of SSRT\n",
       "count   20.000000\n",
       "mean    42.729080\n",
       "std     13.804978\n",
       "min     27.609068\n",
       "25%     34.471318\n",
       "50%     36.680358\n",
       "75%     53.437040\n",
       "max     84.856504"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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