generate_tables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import warnings\n",
    "import rpy2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import matplotlib\n",
    "# matplotlib.rc('text', usetex = True)\n",
    "from glob import glob\n",
    "from os import path, makedirs\n",
    "from rpy2.robjects.packages import importr\n",
    "from pandas.plotting import table \n",
    "\n",
    "from matplotlib.font_manager import FontProperties\n",
    "    \n",
    "from utils import formats, short_names, condition_name_dict, full_passed_turkers #constants\n",
    "from utils import read_cond_file, violation_analysis, calc_SSRT, filter_ssrt_subs #helper funcs\n",
    "\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TABLE_DIR = 'Tables'\n",
    "makedirs(TABLE_DIR, exist_ok=True)\n",
    "TABLE2_DIR = path.join(TABLE_DIR, 'Table2Jasp')\n",
    "makedirs(TABLE2_DIR, exist_ok=True)\n",
    "T2_INPUT_DIR = path.join(TABLE2_DIR, 'Input')\n",
    "makedirs(T2_INPUT_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_corr_RT(sub_df, trial_type=''):\n",
    "    assert((trial_type=='GoCritical') | (trial_type=='GoNoncritical') | (trial_type=='Go'))\n",
    "\n",
    "    # get relevant columns\n",
    "    rt_col = trial_type+'RT'\n",
    "    response_col = trial_type+'TrialResponse'\n",
    "    corr_resp_col = trial_type+'TrialCorrectResponse'\n",
    "    \n",
    "    #convert columns to string but leave nans alone\n",
    "    sub_df[response_col] = np.where(pd.isnull(sub_df[response_col]),sub_df[response_col],sub_df[response_col].astype(str))\n",
    "    sub_df[corr_resp_col] = np.where(pd.isnull(sub_df[corr_resp_col]),sub_df[corr_resp_col],sub_df[corr_resp_col].astype(str))\n",
    "    \n",
    "    #subset to correct answers\n",
    "    correct_trials = sub_df[sub_df[response_col].str.lower()==sub_df[corr_resp_col].str.lower()] \n",
    "    #take mean of correct RTs\n",
    "    mean_rt = correct_trials[rt_col].mean()\n",
    "\n",
    "    return(mean_rt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Table 1__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "table1_conditions_papers = [\n",
    "    (['FixedSSDs1'], 'Bissett (2014)'),\n",
    "    (['FixedSSDs2'], 'Bissett (2014)'),\n",
    "    (['Deadline1300ms', 'Deadline1500ms', 'Deadline1700ms'], 'Bissett (2014)'),\n",
    "    (['Deadline2300ms', 'Deadline2500ms', 'Deadline2700ms'], 'Bissett (2014)'),\n",
    "    (['StopProbabilityLow', 'StopProbabilityHigh'], 'Bissett & Logan (2011) $E.1$'),\n",
    "    (['TurkSimpleLow', 'TurkSimpleHigh', 'TurkStimSelec', 'TurkMotorSelec'], 'Eisenberg et al. (2019)'),\n",
    "    (['Saccades'], 'Bissett (2014)'),\n",
    "    (['BtwnSubjAuditory1'], 'Bissett & Logan (2012) $E.1$'),\n",
    "    (['BtwnSubjAuditory2'], 'Bissett & Logan (2012) $E.2$'),\n",
    "    (['BtwnSubjVisual1'], 'Bissett & Logan (2012) $E.3$'),\n",
    "    (['BtwnSubjVisual2'], 'Bissett & Logan (2012) $E.4$'),\n",
    "    (['BtwnSubjStimSelec'], 'Bissett & Logan (2014) $E.1$'),\n",
    "    (['WithinSubjCentralGoSimple', 'WithinSubjCentralGoSelec',\n",
    "     'WithinSubjPeriphGoSimple', 'WithinSubjPeriphGoSelec'], 'Bissett (2014)'),\n",
    "    (['Matzke'], 'Matzke et al (2019)')\n",
    "]\n",
    "\n",
    "sciAdv_reference_to_citation_map = {\n",
    "    'Bissett (2014)': '$($36$)$',\n",
    "    'Bissett & Logan (2011)': '$($21$)$',\n",
    "    'Eisenberg et al. (2019)' : '$($40$)$',\n",
    "    'Bissett & Logan (2012)': '$($42$)$',\n",
    "    'Bissett & Logan (2014)': '$($27$)$',\n",
    "    'Matzke et al (2019)': '$($38$)$',\n",
    "}\n",
    "\n",
    "table1_conditions_sciAdvCitations = []\n",
    "for conds, ref in table1_conditions_papers:\n",
    "    for ref_key, citation in sciAdv_reference_to_citation_map.items():\n",
    "        if ref_key in ref:\n",
    "            table1_conditions_sciAdvCitations.append((conds, ref.replace(ref_key, citation)))\n",
    "\n",
    "\n",
    "\n",
    "# default = in-person\n",
    "collection_method_map = {\n",
    "    'TurkSimpleLow': 'Mechanical Turk',\n",
    "    'TurkSimpleHigh': 'Mechanical Turk',\n",
    "    'TurkStimSelec': 'Mechanical Turk',\n",
    "    'TurkMotorSelec': 'Mechanical Turk',\n",
    "}\n",
    "\n",
    "# default = Visual\n",
    "stop_modality_map = {\n",
    "    'TurkSimpleLow': 'Visual',\n",
    "    'TurkSimpleHigh': 'Visual',\n",
    "    'TurkStimSelec': 'Visual',\n",
    "    'TurkMotorSelec': 'Visual',\n",
    "    'BtwnSubjVisual1': 'Visual',\n",
    "    'BtwnSubjVisual2': 'Visual',\n",
    "    'Matzke': 'Visual'\n",
    "}\n",
    "\n",
    "# default = 'Simple'\n",
    "stop_rule_map = {\n",
    "    'TurkStimSelec': 'Stim Selective',\n",
    "    'TurkMotorSelec': 'Motor Selective',\n",
    "    'BtwnSubjStimSelec': 'Stim Selective',\n",
    "    'WithinSubjCentralGoSelec': 'Stim Selective',\n",
    "    'WithinSubjPeriphGoSelec': 'Stim Selective',\n",
    "}\n",
    "\n",
    "table1_data = []\n",
    "counter = 0\n",
    "for curr_conds, paper in table1_conditions_sciAdvCitations:\n",
    "    for check, cond_name in enumerate(curr_conds):\n",
    "        file = 'Data/Data'+cond_name+'.xlsx'\n",
    "        curr_df = read_cond_file(file)\n",
    "        \n",
    "        counter += 1 #increment condition number\n",
    "\n",
    "        # get num_subs\n",
    "        curr_subs = curr_df.Subject.unique()\n",
    "        if len(curr_subs) == 522: curr_subs = full_passed_turkers\n",
    "        num_subs = ''\n",
    "        if check==0:\n",
    "            num_subs = len(curr_subs)\n",
    "        # get number of trials for the condition\n",
    "        max_trials = 0\n",
    "        for subject in curr_subs:\n",
    "            max_trials = np.max([max_trials, len(curr_df.query('Subject == %d' % subject))])\n",
    "            \n",
    "        #append condition info\n",
    "        table1_data.append([counter,\n",
    "                            condition_name_dict[cond_name],\n",
    "                            num_subs,\n",
    "                            max_trials,\n",
    "                            collection_method_map.get(cond_name, 'In-Person'),\n",
    "                            stop_modality_map.get(cond_name, 'Auditory'),\n",
    "                            stop_rule_map.get(cond_name, 'Simple'),                    \n",
    "                            paper])\n",
    "        \n",
    "    #append gap row between subject groups\n",
    "    table1_data.append(['', '', '', '', '', '', '', ''])\n",
    "\n",
    "\n",
    "table1_df = pd.DataFrame(table1_data, columns=['Condition #', 'Condition Description', 'N', \n",
    "                                         'trial N', 'Collection Method', 'Stop Modality', 'Stop Rule', '(Citation No.)'])\n",
    "table1_df = table1_df.drop([38]) #drop last blank row\n",
    "\n",
    "table1_df.to_csv(path.join(TABLE_DIR, 'Table1.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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hgyIjI9WpUyft2rVLS5Ys8cizb98+JScnq3r16qpevfo1tAr8WU5+p09LS1OTJk302WefqVWrVnr44Ye1c+dOtW3bVkuXLlXHjh31zjvvqEGDBnr00Ud16tQp9evXL9OpzdevX68GDRooJCREffr0UYsWLbR06VLVrVtXq1atyladkpOTFRcXpw8//FD33nuvBgwYoMaNG2vOnDmqUaOGvv766+veb/ivyZMny8zUsWNHj3VBQUEeU605lztfJ3D5VP9bt27Vs88+q7Zt2+rNN99USEiIR35vyzp16qTU1FSv51jcAi6P7Dg/8sETKmlpaVa5cmWTZMnJyblWDm49vjg+fc25P4mJiR6fy59AyWz0y8qVKy0gIMAqVKhgp06dsl9++cWKFi1q+fLlsy1btrilXbx4sUmyOnXqeDyhOGHCBJNkzzzzjGvZ8ePHrWDBghYUFGRff/21W/q+ffu66p+dETnp6ekWFxdnkqxq1ar29ttv26ZNm7J86vGnn36y/PnzmyRr1aqVTZ8+3bZv357pk1xm2RuRY2a2detWk2SBgYGu6Vj69+9vkmz+/Pke6Q8fPpxluVdy/vx51xOsS5YsuebtAMCtIKeu6VndV+7Zs8cKFChgXbt2dS1jRM61yal23rx5s0myypUrey3nq6++MklWo0YN17L58+ebJLv//vu95pk1a5ZJsg4dOlzr7uUIX92neivDOSJHkg0bNsxtnXOKpxYtWlx3OWYXp5B1Tq22Z88eM7v4NGpQUJCVKVPG9u3b55Z+6dKllidPHmvdurXbcucxcvfdd9uhQ4fc1h07dswcDodVr17d65Olv/32m+vfe/bsseDgYMufP7/Hfa/zKfDLn5J1lh0XF2eHDx92LT916pSVKVPG8uTJY7/++mtWzYOblC/66T/+8Q+TZK+++qprWVxcnDkcDo9z4tWOyMnIyHBNyXP5VM1jxoxx7duVRuSYXXmaqKZNm5oke+WVV9yWr1mzxgICAqxQoUJ28uRJ13LnKAqHw2ELFy702J7z/N62bVuPdc66/Pvf//ZaF/hObvSRr7/+2oKCgszhcFjXrl0tKSnJdf3wJqsROc77gUtHJKxcudIkWWRkpMXHx9vRo0dd63bu3GlBQUFWtWpVt205+5fkOUOHc9rW2NhYt2lbvfWZI0eOWMGCBa1w4cL2ww8/uG1n8+bNli9fPqtWrdoV2wi+kRvHd/Xq1S0gIMDryJvMrFq1ysLCwqxy5coev4e1b9/e8uTJYzt37rTvv//e3nrrLRs1apTNmzfPzp8/n+k2P//8c5OubjYb+JfLjk//mVpt0KBBJslatmyZa2Xg1nQrB3K8fS6/2c5qGrO///3vJsm6detmjRo18nrjY2Z2//33m6RMh/hWrlzZihcv7vp76tSpJsl69uzpkfbIkSOuIEt2AjlmF39oqFevntt+BgUFWc2aNW3kyJEec+KbXbwg3XnnnW55ChQoYM2bN7cPP/zQY0787AZyTp486dqe80u8M5CzdOnSbO3P1RgwYIBJsgcffDDHtw0AN5ucuqZndl+Znp5u9evXt9tuu82OHDniWk4g59rkVDs7p1mtXbu213J+/PFHk2TlypVzLfvwww9Nkj300ENe8zgfUmnatOm17l6O8IdATkxMjNfAR8mSJa1w4cLXVI7zAaMhQ4ZY27ZtLSAgwCTZU0895Uo7cODATB+EMbs47V5AQIDbfZ7zGPH23sDjx4+bdPE9Sld6iOaVV14xSfbCCy94rHPeq4aGhrr96Ocs29sUbS+//LJJuqbp6OD/crufZmRkuIKBlwY133zzTZNkzz33nFv6qw3krF692iRZvXr1PNJfuHDBypQpkyOBnL1795okK1mypNcfDrt27WqS7L333nMtc/74fnnQ9lLx8fEWGBjoFii9cOGC3XHHHZY/f363wBBujNzqIzNnzrTo6Gi37RcqVMhat25t8+bNc0t7pUDOjh07PLbv/L3A2/f4Bg0aWEBAgNv10dm/Lg/WODmvE8uXL3ct89ZnnAHUcePGed1v5/Xx8iAPboycPr5PnTplAQEBmT6g5PTaa69ZYmKiDRw40O677z4LDQ21zp07ezzI8ttvv1lwcLDFx8db+/btPX4jvOuuuzweWnE6duyYSbJ77703R/YNvpdVIOeGzenz5ptv6vXXX1eFChU0bdq0G1UNwO/Y/5+i4loNGTJEy5cv1/vvvy9J6tq1q3r06OGR7osvvlBISIhmzJjhdTsXLlzQr7/+quPHjysiIsI1DNg59P5SkZGRuueee7RmzZps17NUqVJasWKFfvjhBy1ZskQbN27U+vXrXZ93331Xy5cvV0xMjCvPfffdpx07dmj16tVauXKlvv76a61Zs0bJyclKTk7W+++/r3nz5ik4ODjb9ZDc29w5NUinTp00btw4PfDAA2rXrp3uu+8+1a5dW6VLl76qbV9u9OjRGjt2rCpVqqSpU6de17YAABdldV/5xhtvaMWKFVqwYIEiIyNvUA1vDb5sZ+e1+dIpu3Ijz62qatWqCggI8FheokQJffHFF66/9+zZ4/V+ZNiwYR7Lhg8fLuli+xYsWFB169ZVr1691LVrV1ca57ZXrFihr776ymMbBw8eVHp6un788UePaZNq1Kjhkb5AgQJ64IEH9Omnn6pq1apq27at6tatq5o1ayosLMwtrfNetVGjRh7biYyMVLVq1bRy5Upt3bpVVapUcVsfHx/vkadEiRKSpKNHj3qsA65k2bJl2rlzp5o1a6bbb7/dtbxLly565plnNHXqVP397393TVV5tbL6bhYQEKA6depo586d11b5S2zatEmSVLduXa91bdSokT744ANt2rRJ3bp1c1vnrU87Pfnkk+rZs6cmT56sIUOGSJI+++wz7du3T71791Z4ePh11x3+qUOHDmrTpo1SUlK0evVqbdq0SatXr9bcuXM1d+5cdevWTVOnTr3itbxgwYIqU6aMx/LbbrtNu3fv9jo13+2336709HTt37/frV9KF4/xPHk83z7RoEEDrVixQps2bfLa35yc179vv/3W6zXUOZ3cli1bVLFixSz3DTefn3/+Wenp6SpevHimaY4fP65nn33W7fenli1batiwYSpSpIhb2iVLluj8+fPauHGjTp8+rUWLFqlu3brav3+/BgwYoE8//VTdunXTl19+6VFORESEQkNDM53KFje3GxLIefvttzVgwABVrFhRS5cuVaFChW5ENYBbUp48edSmTRstXbpUkvTUU095TXfkyBGZmetLeWZOnTqliIgIHT9+XJJUrFgxr+mio6Ovqb6VKlVSpUqVXH//97//Vc+ePbV+/XoNGjRIs2fPdkufJ08e1atXT/Xq1ZN08UebRYsWqXv37lq0aJH+/e9/q2/fvldVh19++UXSxflJCxYsKEn605/+pJUrV+rVV1/Vxx9/7AqMVahQQcOGDfM67+mVjB07VoMGDVLlypW1dOlSflAEgByQ1X3l9u3b9eKLL+qRRx5Ry5Ytb2Atb3453c4RERGS5Lq/uNyJEyfc0l1rnj8q5/3M5QIDA5WRkeH6e8+ePV7vBb39CJWdh40OHz4sSXrttdeyTHfq1CmPZZndS86cOVMjR47U9OnTXe/tCA0NVbt27TRq1CjXvanzuMjsRxTncm/vyfHWXs53QKWnp2e5L4A3zvfgXP5AXeHChfXAAw8oKSlJn3zyidq1a3dN28+t72aZlXMt/SqrOnTq1EmDBg3ShAkT9PzzzytPnjz617/+JUm8m+oPICgoSE2bNlXTpk0lXTzPJiUlqWfPnnr//ffVpk0btW7dOsttZHatd567va13rvP2vtsr9aXM7j2cnNe/CRMmZJnO2/UPNz/n/39Wv/FEREQoIyND6enp+uWXXzRt2jS99NJLWr9+vX788Ue3e2tngKZYsWJasWKFoqKiJEl33nmnpk2bpujoaH311Vc6ePCgx7t1JKlQoUI6cOBATu4i/IRnuDmXjRkzRn379lXlypWVkpKSYzcYAC7aunWrnnvuORUqVEgOh0O9evXSuXPnPNIVKFBAUVFRmU6v6Pw4n1Rx3ghldjHYv39/jtS/YsWKeu+99yRl72XFDodDzZs319/+9rds57mc86W/9957r9uTP7Vr19aCBQt07NgxrV69Wi+++KJ+/fVXde7cWcuXL7+qMkaNGqWBAweqSpUqSklJ8XqxBQBcnSvdV/7www86d+6cpkyZIofD4fZZsWKFJKls2bJyOByaO3fujdiFm0JutHNsbKwCAgK0a9cuXbhwwaPM7du3S5LKlSvnWla+fHlJmb8k2VseZK1BgwaZTbN9TS4NtmV1f+ntqebMnr7Omzevhg0bph9//FGpqan64IMPVKdOHX3wwQduP4I7y87snvTXX391SwfklkOHDrnOdZ07d/Y4LyYlJUn6X7BHkmskgLfzobcgia++m11Pv8pqREXevHnVo0cP7dmzR4sXL9a+ffuUnJysmjVreoyYw60vICBAHTp0cD2Eei3f6a/XlfrSla4dzvXffvttlte/7t2752zF4Rfy5s0rSTp79uwV0wYEBKhEiRIaMmSIOnbsqMOHD2v+/Pluafbt2ydJ6tevnyuI4xQREeGauSaz8s6cOeOqE24tPh2RM3LkSD3//POqWrWqPv/8c4+hYwCuz9mzZ9WxY0edOXNG8+bN0+LFizVy5EgNGjRI48aNc0tbq1YtLVq0SNu2bXP9MJKVuLg4SRenyrh82PzRo0f13Xff5dh+5M+fX9LVTTN3LXkk6fTp0xo9erQk6aGHHvKaJiQkRLVr11bt2rVVpkwZ9ezZU5988okaNGiQrTJGjBihoUOHKi4uTp9//jmjEAEgB2TnvrJUqVLq1auX1/wLFizQ/v371b59exUoUEClSpXK5RrfnHKrnUNCQpSQkKBVq1Zp1apVatiwoVu+hQsXSnKfJqtMmTIqWbKkfvzxR+3evVt33nnnFfPAt2rVqqWNGzdq1apV+vOf/5zj2y9RooQeeughde7cWRUqVNDq1at1+PBhFS5cWNWqVdOcOXO0fPlyNW7c2C3fsWPH9M033yg0NFR33XVXjtcLuNR7772n8+fPq3r16qpatarXNPPmzdOSJUtc5zLnU9x79+5VbGysW9oNGzZ45L/0u9nl0tPTtXr16mzX1zkNo7fRZ9WqVZMkrV69WhcuXHCNaHByPhDnrM/V6N27t8aMGaN//etfqlKlitLT0xmN8wd3rd/pc8Lq1auVkZHhMb2a8wFOZ1/ITK1atZSUlKRVq1bpnnvuya1qwk85H9R1jszJLmeQ5vKAjHOUubdzq5npt99+U2hoqNfBERkZGTp27JjHfTJuEZlFiZXDL37629/+ZpKsevXqrpeJA9cqp49Pf3A1+/PQQw+ZJNu7d6/b8ieeeMIk2YsvvmhmZmlpaVarVi2TZElJSW5pFy1aZJKsTp069ssvv3iUcfLkSVu3bp3r7+PHj1tERIQFBQXZ119/7Za2b9++rvpfXidvtm/fbm+++aYdP37cY116ero9+eSTJskefPBB1/IFCxbYf/7zH0tLS/PIc+LECatRo4ZJstGjR7uVI8nKlCnjtR47duywevXqmSSLj4+3c+fOudatWLHCa/3+8Y9/mCQbMmTIFffT7H8vQqxRo4YdPXo0W3kA4I/kWq7pOXFf6Xx57fbt268p/83oats6t9t5+vTprhfZnzlzxrX8yy+/tODgYIuKivK4Fr/66qsmydq1a+f2UuK5c+eaJKtYsaLXlxX7kq/uU72VsXv3bpNk3bt395rH+f9xveVkZsuWLRYUFGRly5a1bdu2eaw/d+6crVy5Mtt1OnjwoNv9qNOJEycsOjraAgMD7cSJE2Z2cd+DgoIsIiLC43hz3qs++uij2S47s5ds49aQm/20fPnyJsnWr1+faZqhQ4e6faf46KOPTJJ17tzZLd13331n4eHhJskSExNdyzMyMlzlzJ071y2P86Xruuxl7GYX97t+/fpuy95++22TZJMnT/Za1yZNmpgke+2119yWr1u3zgICAiwyMtLVD82uru/cd999FhgYaMWKFbOCBQva6dOnr5gHvpEbfWT69Om2ePFir9fpX3/91WJjY02Sffzxx2aW+bEUExNjMTExXsvI6rzevXt3k2S7d+92LUtJSXHt51tvveWW3nlvERsb61Zn53f8S/vXb7/9ZgULFrSoqCivfT89Pd2jP+LGyenjOyMjw6KioqxIkSLZznP+/HnXMf/NN9+4revcubNJso8++sgjX3Jyskmy1q1be93uf//7X5Nkf/nLX65uJ+A3Ljs+3eI1PhmR89577+nll19WQECA6tatqzfffNMjTalSpby+kB1A9syePVvjx49XQkKCa17zwMBAzZgxQ9WqVVOvXr0UFxfnehK2adOmeuWVV/TSSy+pbNmyatGihe68806dOnVKe/bs0YoVK9SwYUPXEM8CBQpo/Pjx6tKli2rXrq2OHTsqOjpaK1eu1JYtW1SnTp1sP/l17Ngx9e/fX4MHD1ZCQoLuvvtuhYeH6+DBg1q6dKl2796t6Ohot/nV//vf/2rw4MEqVKiQ6tatq9jYWAUGBmrv3r1asGCBjh8/roSEBPXu3dujvCNHjrja5MKFCzp69Ki++eYbrVu3ThkZGWrZsqWmTp2q4OBgV55//vOfSklJUYMGDVS6dGnly5dPmzdv1sKFC1WoUCE99thjV9zPSZMmafjw4QoICFDt2rU1ZswYjzSlS5f2GOEEAMgc95W+4Yt27tSpk+bMmaPZs2erWrVqeuCBB3T48GHNnDlT6enpmjBhggoUKOCW5+mnn9b8+fM1e/Zs1axZU40bN1ZqaqpmzZqlsLAwTZ482evLiuEbFSpU0OTJk9WzZ09VqlRJzZs3V7ly5ZSWlqbU1FStWrVKUVFR2rp1a7a29/PPP6tWrVq66667FBcXpxIlSujEiROaP3++9u/fr/79+7ue4C5VqpTGjBmjPn36KC4uTh06dFBUVJRWrFihL774QhUqVNDIkSNzc/cBLV++XNu2bdPdd9+tGjVqZJquV69eGjFihKZMmaLhw4erVatWKlu2rGbMmKF9+/apZs2aSk1N1SeffKJWrVrp448/dsvvcDg0adIkNWnSRG3bttVf/vIXxcbG6ttvv9WSJUvUvHlzJScnZ6vODRs2VJ48efTCCy9o8+bNrtFBQ4cOlSSNHz9etWvX1uDBg7V48WLFx8dr7969mjVrlvLkyaMpU6a4+uHVevLJJ7VkyRIdOHBA/fr1U1hY2DVtBzeH9evXa+zYsYqOjladOnVcIwZ2796tBQsW6MyZM2rVqtU1vzvqejRv3lyDBg3SwoULVaVKFe3YsUNz5sxRaGioJk2adMV7i8KFC2v27Nlq06aNatWqpcaNG6tSpUrKkyePUlNT9cUXX+jw4cPZmnoLNx+Hw6F69eopKSlJO3bscI2sXLNmjSpWrOjx7pwDBw6oT58+2rFjh1q3bu0xpeR9992nGTNmaOrUqWrfvr3r+Nu5c6cee+wxBQUFZfq+63Xr1kmSx2h33CIuj+w4P8rB6KQzWp3V5/KnQoCs5OTx6S+uZn8uH5Gza9cui4iIsMjISPvpp5880iclJZkkq1Wrlp0/f95t3YoVK6xt27ZWvHhxCwoKsiJFiljVqlXt6aefto0bN3psKzk52RISEixv3rwWGRlprVq1sm3btmU6SsibM2fOWFJSkj3xxBNWrVo1K1q0qAUGBlqBAgWsevXqNnToUDt06JBbnoMHD9rEiROtY8eOdtddd1lERIQFBgZaVFSUNWrUyN59912PfXOOyLn0ExISYlFRUVazZk3r27evrVmzxmsdFy5caN27d7cKFSpYgQIFLF++fFa+fHnr37+/1zb25sUXX7ziua9x48bZ2hYA3Kqu9pqeU/eVjMjJmq/aOS0tzUaPHm2VK1e20NBQK1iwoLVo0SLT67OZ2e+//24vv/yyxcbGWnBwsBUpUsTatWtnP/zwwxXr4wu+uk/1VsaNHpHj9N1331n37t2tZMmSFhwcbJGRkVapUiV7/PHHbenSpdmu09GjR2348OHWsGFDu+222yw4ONiio6Otfv36Nn36dMvIyPDIs2jRImvSpIkVLFjQgoODrUyZMjZ48GCvI6MZkfPHlVv9tEuXLibJxo4de8W0zpEuc+bMMTOz1NRU69Chg0VGRlpoaKjFx8dbUlKSa8TApSNynDZs2GDNmjWz8PBwCw8Pt8aNG9vatWu9jhgw8z4ix8xs2rRpVqVKFQsNDfXaLvv27bMnnnjCSpYsaUFBQVa4cGFr1aqVffnllx7bupq+c+HCBStSpIhJss2bN18xPXwnN/pIamqqjRs3zlq3bm3lypWz/PnzW1BQkEVHR1uLFi1s2rRpbiNffDkiJzEx0dauXWuNGze2/PnzW3h4uDVp0sTrMZ5Z/zK7eB3u06ePxcbGWkhIiOXPn9/Kly9vXbt2tf/85z9XbCP4Rm4c386R5uPGjXMtu/feey00NNRatGhhffr0sUGDBlnr1q0tLCzMJFnTpk3t2LFjHts6d+6ca9aZe++915599ll7+OGHLSwszIKCgmzGjBmZ1qNTp04WEBBgqampObZv8K3Ljk+3eI3DMpl70uFwuFZklga4US59cSLHJwAANy+u6b5DW/sG7Qz4P/qpf9i1a5diY2NVu3ZtrVq16kZXB5f4o/SR5cuXq2HDhkpMTHTN4oFbX24c3+fPn1fJkiUVExOj9evXS5LeffddzZ8/X99//70OHjwoSYqOjlZcXJy6dOmitm3butXlUidPntSwYcOUlJSkn3/+WYUKFVLDhg1d76705vjx44qOjlazZs00d+7cHNkv+N5lx6fbAeKTqdUAAAAAAAAAp1GjRsnM1Ldv3xtdFQC4LsHBwRowYICGDBmiTZs2qbOb7LkAACAASURBVFq1aurdu7fX6f+zI3/+/Hr99df1+uuvZzvP+++/r7Nnz2rQoEHXVCb8HxNIAwAAAAAAINelpqbq//7v/9SrVy+NHz9eVapUUfv27W90tQDguj311FMqWbKkXn75ZZ+XfebMGf3jH/9Q27ZtVbduXZ+XD99gRA4AAAAAAABy3a5du/TCCy8oLCxMTZo00bvvvnvFF8kDwM0gNDRU06ZNU0pKik6fPq18+fL5rOw9e/bo8ccfV48ePXxWJnyPd+TgpvRHma8VAIBbHdd036GtfYN2Bvwf/RTIGn0EtzKOb/izrN6Rw2MPAAAAAAAAAAAAfopADgAAAAAAAAAAgJ8ikAMAAAAAAAAAAOCnCOQAAAAAAAAAAAD4KQI5AAAAAAAAAAAAfopADgAAAAAAAAAAgJ9ymJn3FQ6H9xUAAAAAAAAAAADIFWbmuPRvRuQAAAAAAAAAAAD4KQI5AAAAAAAAAAAAfiowO4kym34NuFEcjv+NLOP4BADg5sU13Xdoa9+gnQH/Rz8FskYfwa2M4xv+7NLj83KMyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIEcAAAAAAAAAAAAP0UgBwAAAAAAAAAAwE8RyAEAAAAAAAAAAPBTBHIAAAAAAAAAAAD8FIE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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111, frame_on=False) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "\n",
    "table = ax.table(table1_df.values, colLabels=table1_df.columns,\n",
    "                 loc='center', cellLoc='left',\n",
    "                 colWidths = [0.25, 0.749, 0.135, 0.135, 0.35, 0.3, 0.275, 0.25], fontsize=20)\n",
    "\n",
    "\n",
    "for (row, col), cell in table.get_celld().items():\n",
    "    if (row == 0):\n",
    "#         if col==7:\n",
    "#             cell.set_text_props(fontproperties=FontProperties(weight='bold', style='italic'))\n",
    "#         else:\n",
    "        cell.set_text_props(fontproperties=FontProperties(weight='bold'))\n",
    "    if col==7 and row!=0:\n",
    "        cell.set_text_props(fontproperties=FontProperties(style='italic'))\n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(20)\n",
    "table.scale(2, 3)\n",
    "\n",
    "for key, cell in table.get_celld().items():\n",
    "    cell.set_linewidth(3)\n",
    "\n",
    "plt.savefig(path.join(TABLE_DIR, 'Table1.png'), bbox_inches='tight', transparent=True, dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __TABLE 2__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Table 2 data frame\n",
    "table2_df = pd.DataFrame([], columns=['Comparison', 'Condition', 'Violation', 'p-value', 'BF\\u2080\\u2081', '\\u03B7\\N{SUPERSCRIPT TWO}'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __ROW 1__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond1_df = read_cond_file('Data/DataFixedSSDs1.xlsx')\n",
    "cond2_df = read_cond_file('Data/DataFixedSSDs2.xlsx')\n",
    "\n",
    "#shift cond2 subjects to prevent confusion with cond1 subjs\n",
    "cond2_df.Subject += cond1_df.Subject.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get median no-stop-signal RT and separate subjects\n",
    "# Condition 1\n",
    "Go_RTs1 = []\n",
    "cond1_subs = cond1_df.Subject.unique()\n",
    "for subject in cond1_subs:\n",
    "    subdata = cond1_df.query('Subject == %d' % subject).copy()\n",
    "    # go RT\n",
    "    curr_corrRT = get_mean_corr_RT(subdata, trial_type='Go')\n",
    "    Go_RTs1.append(curr_corrRT)\n",
    "    \n",
    "median_RT1 = np.median(Go_RTs1)\n",
    "\n",
    "slow_idx1 = Go_RTs1 > median_RT1\n",
    "fast_idx1 = Go_RTs1 < median_RT1\n",
    "\n",
    "slow_subjects1 = cond1_subs[slow_idx1]\n",
    "fast_subjects1 = cond1_subs[fast_idx1]\n",
    "\n",
    "# Conditon 2\n",
    "Go_RTs2 = []\n",
    "cond2_subs = cond2_df.Subject.unique()\n",
    "for subject in cond2_subs:\n",
    "    subdata = cond2_df.query('Subject == %d' % subject).copy()\n",
    "    # go RT\n",
    "    curr_corrRT = get_mean_corr_RT(subdata, trial_type='Go')\n",
    "    Go_RTs2.append(curr_corrRT)\n",
    "    \n",
    "median_RT2 = np.median(Go_RTs2)\n",
    "\n",
    "slow_idx2 = Go_RTs2 > median_RT2\n",
    "fast_idx2 = Go_RTs2 < median_RT2\n",
    "\n",
    "slow_subjects2 = cond2_subs[slow_idx2]\n",
    "fast_subjects2 = cond2_subs[fast_idx2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/henrymj/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "/Users/henrymj/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:22: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n"
     ]
    }
   ],
   "source": [
    "# Generate violation analysis for each condition\n",
    "VA1_df = violation_analysis(cond1_df)\n",
    "VA2_df = violation_analysis(cond2_df)\n",
    "\n",
    "# Process the datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA1_df = VA1_df.loc[VA1_df.ssd < 200, :]\n",
    "VA2_df = VA2_df.loc[VA2_df.ssd < 200, :]\n",
    "\n",
    "# #SUBSET TO SHARED SSDs\n",
    "# shared_ssds = list(set(VA1_df.ssd.unique()).intersection(set(VA2_df.ssd.unique())))\n",
    "# VA1_df = VA1_df.loc[VA1_df.ssd.isin(shared_ssds), :]\n",
    "# VA2_df = VA2_df.loc[VA2_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "# get mean violation per subject\n",
    "VA1_means = VA1_df.groupby('subject').mean()\n",
    "VA2_means = VA2_df.groupby('subject').mean()\n",
    "\n",
    "#concatenate and clean up\n",
    "slowVA_mean_df = pd.concat([VA1_means.loc[slow_subjects1].copy(), VA2_means.loc[slow_subjects2].copy()])\n",
    "fastVA_mean_df = pd.concat([VA1_means.loc[fast_subjects1].copy(), VA2_means.loc[fast_subjects2].copy()])\n",
    "\n",
    "slowVA_mean_df.dropna(inplace=True)\n",
    "fastVA_mean_df.dropna(inplace=True)\n",
    "\n",
    "# add condition label\n",
    "slowVA_mean_df['condition'] = 'slow'\n",
    "fastVA_mean_df['condition'] = 'fast'\n",
    "\n",
    "#move subject into columns\n",
    "slowVA_mean_df.reset_index(inplace=True)\n",
    "fastVA_mean_df.reset_index(inplace=True)\n",
    "\n",
    "#assert no subject overlap\n",
    "assert((set(slowVA_mean_df.subject.unique())&set(fastVA_mean_df.subject.unique()))==set())\n",
    "\n",
    "\n",
    "fastSlow_meanVA_df = pd.concat([slowVA_mean_df, fastVA_mean_df])\n",
    "#remove subject column for JASP and save to csv\n",
    "del fastSlow_meanVA_df['subject']\n",
    "fastSlow_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row1SlowFast.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Comparison                                        Condition  \\\n",
       "0  Fast vs. Slow subjects  RT Below Median (1/2) vs. RT Above Median (1/2)   \n",
       "\n",
       "      Violation  p-value   BF₀₁        η²  \n",
       "0  6ms vs. 15ms    0.549  3.034  0.000985  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slow_violations = slowVA_mean_df['mean_violation'].values\n",
    "fast_violations = fastVA_mean_df['mean_violation'].values\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.549\n",
    "BF_01 = 3.034\n",
    "eta_sq = 9.846e-4\n",
    "\n",
    "#build up Table 2\n",
    "row1 = ['Fast vs. Slow subjects', 'RT Below Median (1/2) vs. RT Above Median (1/2)', f'{fast_violations.mean():.0f}ms vs. {slow_violations.mean():.0f}ms', p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row1\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __ROW 2__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond3_df = read_cond_file('Data/DataDeadline1300ms.xlsx')\n",
    "cond4_df = read_cond_file('Data/DataDeadline1500ms.xlsx')\n",
    "cond5_df = read_cond_file('Data/DataDeadline1700ms.xlsx')\n",
    "cond6_df = read_cond_file('Data/DataDeadline2300ms.xlsx')\n",
    "cond7_df = read_cond_file('Data/DataDeadline2500ms.xlsx')\n",
    "cond8_df = read_cond_file('Data/DataDeadline2700ms.xlsx')\n",
    "\n",
    "#shifting subjects in conditions 6-8 to keep IDs unique for combined analyses\n",
    "assert((cond3_df['Subject'].max()==cond4_df['Subject'].max()) & (cond4_df['Subject'].max()==cond5_df['Subject'].max()))\n",
    "\n",
    "cond6_df['Subject'] = cond6_df['Subject'] + cond5_df['Subject'].max()\n",
    "cond7_df['Subject'] = cond7_df['Subject'] + cond5_df['Subject'].max()\n",
    "cond8_df['Subject'] = cond8_df['Subject'] + cond5_df['Subject'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUBSET SSDS, THEN SUBJECTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate violation analysis for each condition\n",
    "VA3_df = violation_analysis(cond3_df)\n",
    "VA4_df = violation_analysis(cond4_df)\n",
    "VA5_df = violation_analysis(cond5_df)\n",
    "VA6_df = violation_analysis(cond6_df)\n",
    "VA7_df = violation_analysis(cond7_df)\n",
    "VA8_df = violation_analysis(cond8_df)\n",
    "\n",
    "# process Deadline 1 datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA3_df = VA3_df.loc[VA3_df.ssd < 200, :]\n",
    "VA4_df = VA4_df.loc[VA4_df.ssd < 200, :]\n",
    "VA5_df = VA5_df.loc[VA5_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA3_df.ssd.unique()).intersection(set(VA4_df.ssd.unique()).intersection(set(VA5_df.ssd.unique()))))\n",
    "VA3_df = VA3_df.loc[VA3_df.ssd.isin(shared_ssds), :]\n",
    "VA4_df = VA4_df.loc[VA4_df.ssd.isin(shared_ssds), :]\n",
    "VA5_df = VA5_df.loc[VA5_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA3_df.subject.unique()).intersection(set(VA4_df.subject.unique()).intersection(set(VA5_df.subject.unique()))))\n",
    "VA3_df = VA3_df.loc[VA3_df.subject.isin(shared_subs), :]\n",
    "VA4_df = VA4_df.loc[VA4_df.subject.isin(shared_subs), :]\n",
    "VA5_df = VA5_df.loc[VA5_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA3_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA4_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA5_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA3_means = VA3_df.groupby('subject').mean()\n",
    "VA4_means = VA4_df.groupby('subject').mean()\n",
    "VA5_means = VA5_df.groupby('subject').mean()\n",
    "\n",
    "# process Deadline 2 datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA6_df = VA6_df.loc[VA6_df.ssd < 200, :]\n",
    "VA7_df = VA7_df.loc[VA7_df.ssd < 200, :]\n",
    "VA8_df = VA8_df.loc[VA8_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA6_df.ssd.unique()).intersection(set(VA7_df.ssd.unique()).intersection(set(VA8_df.ssd.unique()))))\n",
    "VA6_df = VA6_df.loc[VA6_df.ssd.isin(shared_ssds), :]\n",
    "VA7_df = VA7_df.loc[VA7_df.ssd.isin(shared_ssds), :]\n",
    "VA8_df = VA8_df.loc[VA8_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA6_df.subject.unique()).intersection(set(VA7_df.subject.unique()).intersection(set(VA8_df.subject.unique()))))\n",
    "VA6_df = VA6_df.loc[VA6_df.subject.isin(shared_subs), :]\n",
    "VA7_df = VA7_df.loc[VA7_df.subject.isin(shared_subs), :]\n",
    "VA8_df = VA8_df.loc[VA8_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA6_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA7_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA8_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA6_means = VA6_df.groupby('subject').mean()\n",
    "VA7_means = VA7_df.groupby('subject').mean()\n",
    "VA8_means = VA8_df.groupby('subject').mean()\n",
    "\n",
    "#combine and run ANOVA\n",
    "\n",
    "#combine like conditions\n",
    "VA300_mean_df = pd.concat([VA3_means, VA6_means])\n",
    "VA500_mean_df = pd.concat([VA4_means, VA7_means])\n",
    "VA700_mean_df = pd.concat([VA5_means, VA8_means])\n",
    "\n",
    "#assert that the conditions have the same number of subjects\n",
    "assert((len(VA300_mean_df)==len(VA500_mean_df)) & (len(VA500_mean_df)==len(VA700_mean_df)))\n",
    "\n",
    "# add condition label\n",
    "VA300_mean_df['condition'] = '300'\n",
    "VA500_mean_df['condition'] = '500'\n",
    "VA700_mean_df['condition'] = '700'\n",
    "\n",
    "#move subject into columns\n",
    "VA300_mean_df.reset_index(inplace=True)\n",
    "VA500_mean_df.reset_index(inplace=True)\n",
    "VA700_mean_df.reset_index(inplace=True)\n",
    "\n",
    "#prefix columns and save for JASP input\n",
    "VA300_mean_df = VA300_mean_df.add_prefix('short_')\n",
    "VA500_mean_df = VA500_mean_df.add_prefix('intermediate_')\n",
    "VA700_mean_df = VA700_mean_df.add_prefix('long_')\n",
    "\n",
    "deadline_meanVA_df = pd.concat([VA300_mean_df, VA500_mean_df, VA700_mean_df], axis=1)\n",
    "deadline_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row2Deadline.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Comparison  \\\n",
       "0         Fast vs. Slow subjects   \n",
       "1  Varying Go Response Deadlines   \n",
       "\n",
       "                                         Condition               Violation  \\\n",
       "0  RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1       300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_300 = VA300_mean_df['short_mean_violation'].mean()\n",
    "violation_500 = VA500_mean_df['intermediate_mean_violation'].mean()\n",
    "violation_700 = VA700_mean_df['long_mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.011\n",
    "BF_01 = 2.478\n",
    "eta_sq = 0.008\n",
    "\n",
    "row2 = ['Varying Go Response Deadlines', '300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)', f'{violation_300:.0f}ms vs. {violation_500:.0f}ms vs. {violation_700:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row2\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond9_df = read_cond_file('Data/DataStopProbabilityLow.xlsx')\n",
    "cond10_df = read_cond_file('Data/DataStopProbabilityHigh.xlsx')\n",
    "cond11_df = read_cond_file('Data/DataTurkSimpleLow.xlsx')\n",
    "cond12_df = read_cond_file('Data/DataTurkSimpleHigh.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "VA9_df = violation_analysis(cond9_df)\n",
    "VA10_df = violation_analysis(cond10_df)\n",
    "VA11_df = violation_analysis(cond11_df)\n",
    "VA12_df = violation_analysis(cond12_df)\n",
    "\n",
    "#shift cond11/12 subjects to prevent confusion in the next analysis\n",
    "assert(VA9_df.subject.max()==VA10_df.subject.max())\n",
    "VA11_df.subject += VA9_df.subject.max()\n",
    "VA12_df.subject += VA9_df.subject.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process local datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA9_df = VA9_df.loc[VA9_df.ssd < 200, :]\n",
    "VA10_df = VA10_df.loc[VA10_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA9_df.ssd.unique()).intersection(set(VA10_df.ssd.unique())))\n",
    "VA9_df = VA9_df.loc[VA9_df.ssd.isin(shared_ssds), :]\n",
    "VA10_df = VA10_df.loc[VA10_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA9_df.subject.unique()).intersection(set(VA10_df.subject.unique())))\n",
    "VA9_df = VA9_df.loc[VA9_df.subject.isin(shared_subs), :]\n",
    "VA10_df = VA10_df.loc[VA10_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA9_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA10_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA9_means = VA9_df.groupby('subject').mean()\n",
    "VA10_means = VA10_df.groupby('subject').mean()\n",
    "\n",
    "\n",
    "# process turk datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA11_df = VA11_df.loc[VA11_df.ssd < 200, :]\n",
    "VA12_df = VA12_df.loc[VA12_df.ssd < 200, :]\n",
    "\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA11_df.subject.unique()).intersection(set(VA12_df.subject.unique())))\n",
    "VA11_df = VA11_df.loc[VA11_df.subject.isin(shared_subs), :]\n",
    "VA12_df = VA12_df.loc[VA12_df.subject.isin(shared_subs), :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA11_df.ssd.unique()).intersection(set(VA12_df.ssd.unique())))\n",
    "VA11_df = VA11_df.loc[VA11_df.ssd.isin(shared_ssds), :]\n",
    "VA12_df = VA12_df.loc[VA12_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA11_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA12_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA11_means = VA11_df.groupby('subject').mean()\n",
    "VA12_means = VA12_df.groupby('subject').mean()\n",
    "\n",
    "#combine and run ANOVA\n",
    "\n",
    "#combine like conditions\n",
    "VAlow_mean_df = pd.concat([VA9_means, VA11_means])\n",
    "VAhigh_mean_df = pd.concat([VA10_means, VA12_means])\n",
    "\n",
    "#assert that the conditions have the same number of subjects\n",
    "assert(len(VAlow_mean_df)==len(VAhigh_mean_df))\n",
    "       \n",
    "# add condition label\n",
    "VAlow_mean_df['condition'] = 'low'\n",
    "VAhigh_mean_df['condition'] = 'high'\n",
    "\n",
    "#move subject into columns\n",
    "VAlow_mean_df.reset_index(inplace=True)\n",
    "VAhigh_mean_df.reset_index(inplace=True)\n",
    "\n",
    "#save data for JASP input\n",
    "VAlow_mean_df = VAlow_mean_df.add_prefix('low_')\n",
    "VAhigh_mean_df = VAhigh_mean_df.add_prefix('high_')\n",
    "row3_meanVA_df = pd.concat([VAlow_mean_df, VAhigh_mean_df], axis=1)\n",
    "row3_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row3StopProb.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Comparison  \\\n",
       "0         Fast vs. Slow subjects   \n",
       "1  Varying Go Response Deadlines   \n",
       "2  Low vs. High Stop Probability   \n",
       "\n",
       "                                         Condition               Violation  \\\n",
       "0  RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1       300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "2        .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  \n",
       "2    0.127  2.582  0.007000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_low = VAlow_mean_df['low_mean_violation'].mean()\n",
    "violation_high = VAhigh_mean_df['high_mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.127\n",
    "BF_01 = 2.582\n",
    "eta_sq = 0.007\n",
    "\n",
    "row3 = ['Low vs. High Stop Probability', '.2 (9/11) vs. .4 Stop Probability (10/12)', f'{violation_low:.0f}ms vs. {violation_high:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "\n",
    "table2_df.loc[len(table2_df)] = row3\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond15_df = read_cond_file('Data/DataSaccades.xlsx')\n",
    "# shifting subject IDs to prevent confusion in following analyses\n",
    "cond15_df.Subject += cond9_df.Subject.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAmanual_df = violation_analysis(cond9_df)\n",
    "VAsaccade_df = violation_analysis(cond15_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VAmanual_df = VAmanual_df.loc[VAmanual_df.ssd < 200, :]\n",
    "VAsaccade_df = VAsaccade_df.loc[VAsaccade_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VAmanual_df.ssd.unique()).intersection(set(VAsaccade_df.ssd.unique())))\n",
    "VAmanual_df = VAmanual_df.loc[VAmanual_df.ssd.isin(shared_ssds), :]\n",
    "VAsaccade_df = VAsaccade_df.loc[VAsaccade_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VAmanual_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VAsaccade_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VAmanual_mean_df = VAmanual_df.groupby('subject').mean()\n",
    "VAsaccade_mean_df = VAsaccade_df.groupby('subject').mean()\n",
    "\n",
    "       \n",
    "# add condition label\n",
    "VAmanual_mean_df['condition'] = 'manual'\n",
    "VAsaccade_mean_df['condition'] = 'saccade'\n",
    "\n",
    "#move subject into columns\n",
    "VAmanual_mean_df.reset_index(inplace=True)\n",
    "VAsaccade_mean_df.reset_index(inplace=True)\n",
    "\n",
    "#anova\n",
    "row4_meanVA_df = pd.concat([VAmanual_mean_df, VAsaccade_mean_df])\n",
    "\n",
    "#remove subjects as save for JASP input\n",
    "del row4_meanVA_df['subject']\n",
    "row4_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row4ManualvsSaccade.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saccadic vs. Manual Responses</td>\n",
       "      <td>Saccadic (15) vs. Manual Responses (9)</td>\n",
       "      <td>-9ms vs. -3ms</td>\n",
       "      <td>0.754</td>\n",
       "      <td>3.799</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Comparison  \\\n",
       "0         Fast vs. Slow subjects   \n",
       "1  Varying Go Response Deadlines   \n",
       "2  Low vs. High Stop Probability   \n",
       "3  Saccadic vs. Manual Responses   \n",
       "\n",
       "                                         Condition               Violation  \\\n",
       "0  RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1       300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "2        .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "3           Saccadic (15) vs. Manual Responses (9)           -9ms vs. -3ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  \n",
       "2    0.127  2.582  0.007000  \n",
       "3    0.754  3.799  0.000094  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_saccade = VAsaccade_mean_df['mean_violation'].mean()\n",
    "violation_manual = VAmanual_mean_df['mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.754\n",
    "BF_01 = 3.799\n",
    "eta_sq = 9.382e-5\n",
    "\n",
    "row4 = ['Saccadic vs. Manual Responses', 'Saccadic (15) vs. Manual Responses (9)', f'{violation_saccade:.0f}ms vs. {violation_manual:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row4\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond16_df = read_cond_file('Data/DataBtwnSubjAuditory1.xlsx')\n",
    "cond17_df = read_cond_file('Data/DataBtwnSubjAuditory2.xlsx')\n",
    "cond18_df = read_cond_file('Data/DataBtwnSubjVisual1.xlsx')\n",
    "cond19_df = read_cond_file('Data/DataBtwnSubjVisual2.xlsx')\n",
    "\n",
    "#shift subjects for following analyses\n",
    "cond17_df['Subject'] += cond16_df['Subject'].max()\n",
    "cond18_df['Subject'] += cond17_df['Subject'].max()\n",
    "cond19_df['Subject'] += cond18_df['Subject'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "VA16_df = violation_analysis(cond16_df)\n",
    "VA17_df = violation_analysis(cond17_df)\n",
    "VA18_df = violation_analysis(cond18_df)\n",
    "VA19_df = violation_analysis(cond19_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA16_df = VA16_df.loc[VA16_df.ssd < 200, :]\n",
    "VA17_df = VA17_df.loc[VA17_df.ssd < 200, :]\n",
    "VA18_df = VA18_df.loc[VA18_df.ssd < 200, :]\n",
    "VA19_df = VA19_df.loc[VA19_df.ssd < 200, :]\n",
    "\n",
    "# assert there are 5 subjects per ssd\n",
    "assert(VA16_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA17_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA18_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA19_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA16_means = VA16_df.groupby('subject').mean()\n",
    "VA17_means = VA17_df.groupby('subject').mean()\n",
    "VA18_means = VA18_df.groupby('subject').mean()\n",
    "VA19_means = VA19_df.groupby('subject').mean()\n",
    "\n",
    "#concatenate\n",
    "VAaudio_df = pd.concat([VA16_means, VA17_means])\n",
    "VAvis_df = pd.concat([VA18_means, VA19_means])\n",
    "       \n",
    "# add condition label\n",
    "VAaudio_df['condition'] = 'audio'\n",
    "VAvis_df['condition'] = 'visual'\n",
    "\n",
    "#move subject into columns\n",
    "VAaudio_df.reset_index(inplace=True)\n",
    "VAvis_df.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "#remove subjects as save for JASP input\n",
    "AV_meanVA_df = pd.concat([VAaudio_df, VAvis_df])\n",
    "del AV_meanVA_df['subject']\n",
    "AV_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row5Modality.csv'), index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saccadic vs. Manual Responses</td>\n",
       "      <td>Saccadic (15) vs. Manual Responses (9)</td>\n",
       "      <td>-9ms vs. -3ms</td>\n",
       "      <td>0.754</td>\n",
       "      <td>3.799</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auditory vs. Visual Responses</td>\n",
       "      <td>Auditory (16/17) vs. Visual Responses (18/19)</td>\n",
       "      <td>-22ms vs. -13ms</td>\n",
       "      <td>0.637</td>\n",
       "      <td>3.095</td>\n",
       "      <td>0.000766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Comparison  \\\n",
       "0         Fast vs. Slow subjects   \n",
       "1  Varying Go Response Deadlines   \n",
       "2  Low vs. High Stop Probability   \n",
       "3  Saccadic vs. Manual Responses   \n",
       "4  Auditory vs. Visual Responses   \n",
       "\n",
       "                                         Condition               Violation  \\\n",
       "0  RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1       300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "2        .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "3           Saccadic (15) vs. Manual Responses (9)           -9ms vs. -3ms   \n",
       "4    Auditory (16/17) vs. Visual Responses (18/19)         -22ms vs. -13ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  \n",
       "2    0.127  2.582  0.007000  \n",
       "3    0.754  3.799  0.000094  \n",
       "4    0.637  3.095  0.000766  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_audio = VAaudio_df['mean_violation'].mean()\n",
    "violation_visual = VAvis_df['mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row5ModalityOutput.jasp\n",
    "p_val = 0.637\n",
    "BF_01 = 3.095\n",
    "eta_sq = 7.657e-4\n",
    "\n",
    "row5 = ['Auditory vs. Visual Responses', 'Auditory (16/17) vs. Visual Responses (18/19)', f'{violation_audio:.0f}ms vs. {violation_visual:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row5\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond20_df = read_cond_file('Data/DataBtwnSubjStimSelec.xlsx')\n",
    "\n",
    "#shifting subject IDs for analysis\n",
    "cond20_df.Subject += cond9_df.Subject.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAsimple_df = violation_analysis(cond9_df)\n",
    "VAselective_df = violation_analysis(cond20_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VAsimple_df = VAsimple_df.loc[VAsimple_df.ssd < 200, :]\n",
    "VAselective_df = VAselective_df.loc[VAselective_df.ssd < 200, :]\n",
    "\n",
    "# assert there are 5 subjects per ssd\n",
    "assert(VAsimple_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VAselective_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VAsimple_df = VAsimple_df.groupby('subject').mean()\n",
    "VAselective_df = VAselective_df.groupby('subject').mean()\n",
    "\n",
    "       \n",
    "# add condition label\n",
    "VAsimple_df['condition'] = 'simple'\n",
    "VAselective_df['condition'] = 'selective'\n",
    "\n",
    "#move subject into columns\n",
    "VAsimple_df.reset_index(inplace=True)\n",
    "VAselective_df.reset_index(inplace=True)\n",
    "\n",
    "#remove subjects as save for JASP input\n",
    "simpleSelec_meanVA_df = pd.concat([VAsimple_df, VAselective_df])\n",
    "del simpleSelec_meanVA_df['subject']\n",
    "simpleSelec_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row6SimplevsSelective.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saccadic vs. Manual Responses</td>\n",
       "      <td>Saccadic (15) vs. Manual Responses (9)</td>\n",
       "      <td>-9ms vs. -3ms</td>\n",
       "      <td>0.754</td>\n",
       "      <td>3.799</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auditory vs. Visual Responses</td>\n",
       "      <td>Auditory (16/17) vs. Visual Responses (18/19)</td>\n",
       "      <td>-22ms vs. -13ms</td>\n",
       "      <td>0.637</td>\n",
       "      <td>3.095</td>\n",
       "      <td>0.000766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stimulus Selective vs. Simple Stopping</td>\n",
       "      <td>Stimulus Selective (20) vs. Simple Stopping (9)</td>\n",
       "      <td>55ms vs. -1ms</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.018000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Comparison  \\\n",
       "0                  Fast vs. Slow subjects   \n",
       "1           Varying Go Response Deadlines   \n",
       "2           Low vs. High Stop Probability   \n",
       "3           Saccadic vs. Manual Responses   \n",
       "4           Auditory vs. Visual Responses   \n",
       "5  Stimulus Selective vs. Simple Stopping   \n",
       "\n",
       "                                         Condition               Violation  \\\n",
       "0  RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1       300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "2        .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "3           Saccadic (15) vs. Manual Responses (9)           -9ms vs. -3ms   \n",
       "4    Auditory (16/17) vs. Visual Responses (18/19)         -22ms vs. -13ms   \n",
       "5  Stimulus Selective (20) vs. Simple Stopping (9)           55ms vs. -1ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  \n",
       "2    0.127  2.582  0.007000  \n",
       "3    0.754  3.799  0.000094  \n",
       "4    0.637  3.095  0.000766  \n",
       "5    0.064  0.802  0.018000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_simple = VAsimple_df['mean_violation'].mean()\n",
    "violation_selective = VAselective_df['mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.064\n",
    "BF_01 = 0.802\n",
    "eta_sq = 0.018\n",
    "\n",
    "row6 = ['Stimulus Selective vs. Simple Stopping', 'Stimulus Selective (20) vs. Simple Stopping (9)', f'{violation_selective:.0f}ms vs. {violation_simple:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row6\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond11_df = read_cond_file('Data/DataTurkSimpleLow.xlsx')\n",
    "cond12_df = read_cond_file('Data/DataTurkSimpleHigh.xlsx')\n",
    "cond13_df = read_cond_file('Data/DataTurkStimSelec.xlsx')\n",
    "\n",
    "cond21_df = read_cond_file('Data/DataWithinSubjCentralGoSimple.xlsx')\n",
    "cond22_df = read_cond_file('Data/DataWithinSubjCentralGoSelec.xlsx')\n",
    "cond23_df = read_cond_file('Data/DataWithinSubjPeriphGoSimple.xlsx')\n",
    "cond24_df = read_cond_file('Data/DataWithinSubjPeriphGoSelec.xlsx')\n",
    "\n",
    "#shift block nums to prevent block matches\n",
    "cond12_df.Block += cond11_df.Block.max()\n",
    "cond23_df.Block += cond21_df.Block.max()\n",
    "cond24_df.Block += cond22_df.Block.max()\n",
    "\n",
    "#combine datasets\n",
    "cond1112_df = pd.concat([cond11_df, cond12_df], ignore_index=True)\n",
    "cond2123_df = pd.concat([cond21_df, cond23_df], ignore_index=True)\n",
    "cond2224_df = pd.concat([cond22_df, cond24_df], ignore_index=True)\n",
    "\n",
    "# Simple Stopping (11/12/21/23)\n",
    "VA1112_df = violation_analysis(cond1112_df)\n",
    "VA2123_df = violation_analysis(cond2123_df)\n",
    "\n",
    "# Stimulus Selective (13/22/24) \n",
    "VA13_df = violation_analysis(cond13_df)\n",
    "VA2224_df = violation_analysis(cond2224_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process turk datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA13_df = VA13_df.loc[VA13_df.ssd < 200, :]\n",
    "VA1112_df = VA1112_df.loc[VA1112_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA13_df.ssd.unique()).intersection(set(VA1112_df.ssd.unique())))\n",
    "VA13_df = VA13_df.loc[VA13_df.ssd.isin(shared_ssds), :]\n",
    "VA1112_df = VA1112_df.loc[VA1112_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA13_df.subject.unique()).intersection(set(VA1112_df.subject.unique())))\n",
    "VA13_df = VA13_df.loc[VA13_df.subject.isin(shared_subs), :]\n",
    "VA1112_df = VA1112_df.loc[VA1112_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA13_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA1112_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA13_means = VA13_df.groupby('subject').mean()\n",
    "VA1112_means = VA1112_df.groupby('subject').mean()\n",
    "\n",
    "# process local datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VA2224_df = VA2224_df.loc[VA2224_df.ssd < 200, :]\n",
    "VA2123_df = VA2123_df.loc[VA2123_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VA2224_df.ssd.unique()).intersection(set(VA2123_df.ssd.unique())))\n",
    "VA2224_df = VA2224_df.loc[VA2224_df.ssd.isin(shared_ssds), :]\n",
    "VA2123_df = VA2123_df.loc[VA2123_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VA2224_df.subject.unique()).intersection(set(VA2123_df.subject.unique())))\n",
    "VA2224_df = VA2224_df.loc[VA2224_df.subject.isin(shared_subs), :]\n",
    "VA2123_df = VA2123_df.loc[VA2123_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VA2224_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VA2123_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VA2224_means = VA2224_df.groupby('subject').mean()\n",
    "VA2123_means = VA2123_df.groupby('subject').mean()\n",
    "#combine and run ANOVA\n",
    "\n",
    "#combine like conditions\n",
    "VAsimple_df = pd.concat([VA1112_means, VA2123_means])\n",
    "VAselective_df = pd.concat([VA13_means, VA2224_means])\n",
    "\n",
    "#assert that the conditions have the same number of subjects\n",
    "assert(len(VAsimple_df)==len(VAselective_df))\n",
    "\n",
    "# add condition label\n",
    "VAsimple_df['condition'] = 'simple'\n",
    "VAselective_df['condition'] = 'selective'\n",
    "\n",
    "#move subject into columns\n",
    "VAsimple_df.reset_index(inplace=True)\n",
    "VAselective_df.reset_index(inplace=True)\n",
    "\n",
    "#prefix columns and save for JASP input\n",
    "VAsimple_df = VAsimple_df.add_prefix('simple_')\n",
    "VAselective_df = VAselective_df.add_prefix('selective_')\n",
    "\n",
    "simpleSelec_meanVA_df = pd.concat([VAsimple_df, VAselective_df], axis=1)\n",
    "simpleSelec_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row7SimplevsSelectiveWinSubjs.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saccadic vs. Manual Responses</td>\n",
       "      <td>Saccadic (15) vs. Manual Responses (9)</td>\n",
       "      <td>-9ms vs. -3ms</td>\n",
       "      <td>0.754</td>\n",
       "      <td>3.799</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auditory vs. Visual Responses</td>\n",
       "      <td>Auditory (16/17) vs. Visual Responses (18/19)</td>\n",
       "      <td>-22ms vs. -13ms</td>\n",
       "      <td>0.637</td>\n",
       "      <td>3.095</td>\n",
       "      <td>0.000766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stimulus Selective vs. Simple Stopping</td>\n",
       "      <td>Stimulus Selective (20) vs. Simple Stopping (9)</td>\n",
       "      <td>55ms vs. -1ms</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stimulus Selective vs. Simple Stopping</td>\n",
       "      <td>Stimulus Selective (13/22/24) vs. Simple Stopp...</td>\n",
       "      <td>49ms vs. 24ms</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.014000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Comparison  \\\n",
       "0                  Fast vs. Slow subjects   \n",
       "1           Varying Go Response Deadlines   \n",
       "2           Low vs. High Stop Probability   \n",
       "3           Saccadic vs. Manual Responses   \n",
       "4           Auditory vs. Visual Responses   \n",
       "5  Stimulus Selective vs. Simple Stopping   \n",
       "6  Stimulus Selective vs. Simple Stopping   \n",
       "\n",
       "                                           Condition               Violation  \\\n",
       "0    RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "1         300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "2          .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "3             Saccadic (15) vs. Manual Responses (9)           -9ms vs. -3ms   \n",
       "4      Auditory (16/17) vs. Visual Responses (18/19)         -22ms vs. -13ms   \n",
       "5    Stimulus Selective (20) vs. Simple Stopping (9)           55ms vs. -1ms   \n",
       "6  Stimulus Selective (13/22/24) vs. Simple Stopp...           49ms vs. 24ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "0    0.549  3.034  0.000985  \n",
       "1    0.011  2.478  0.008000  \n",
       "2    0.127  2.582  0.007000  \n",
       "3    0.754  3.799  0.000094  \n",
       "4    0.637  3.095  0.000766  \n",
       "5    0.064  0.802  0.018000  \n",
       "6    0.018  0.794  0.014000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_simple = VAsimple_df['simple_mean_violation'].mean()\n",
    "violation_selective = VAselective_df['selective_mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.018\n",
    "BF_01 = 0.794\n",
    "eta_sq = 0.014\n",
    "\n",
    "row7 = ['Stimulus Selective vs. Simple Stopping', 'Stimulus Selective (13/22/24) vs. Simple Stopping (11/12/21/23)', f'{violation_selective:.0f}ms vs. {violation_simple:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row7\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Row 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond14_df = read_cond_file('Data/DataTurkMotorSelec.xlsx')\n",
    "cond11_df = read_cond_file('Data/DataTurkSimpleLow.xlsx')\n",
    "cond12_df = read_cond_file('Data/DataTurkSimpleHigh.xlsx')\n",
    "\n",
    "#shift block nums to prevent block matches\n",
    "cond12_df.Block += cond11_df.Block.max()\n",
    "\n",
    "#combine\n",
    "cond1112_df = pd.concat([cond11_df, cond12_df], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAmotor_df = violation_analysis(cond14_df)\n",
    "VAsimple_df = violation_analysis(cond1112_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process local datasets\n",
    "\n",
    "#subset to SSDs < 200\n",
    "VAmotor_df = VAmotor_df.loc[VAmotor_df.ssd < 200, :]\n",
    "VAsimple_df = VAsimple_df.loc[VAsimple_df.ssd < 200, :]\n",
    "\n",
    "#SUBSET TO SHARED SSDs\n",
    "shared_ssds = list(set(VAmotor_df.ssd.unique()).intersection(set(VAsimple_df.ssd.unique())))\n",
    "VAmotor_df = VAmotor_df.loc[VAmotor_df.ssd.isin(shared_ssds), :]\n",
    "VAsimple_df = VAsimple_df.loc[VAsimple_df.ssd.isin(shared_ssds), :]\n",
    "\n",
    "#SUBSET TO SHARED SUBJECTS\n",
    "shared_subs = list(set(VAmotor_df.subject.unique()).intersection(set(VAsimple_df.subject.unique())))\n",
    "VAmotor_df = VAmotor_df.loc[VAmotor_df.subject.isin(shared_subs), :]\n",
    "VAsimple_df = VAsimple_df.loc[VAsimple_df.subject.isin(shared_subs), :]\n",
    "\n",
    "# assert there are still 5 subjects per ssd\n",
    "assert(VAmotor_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "assert(VAsimple_df.groupby('ssd').count()['subject'].min()>=5)\n",
    "\n",
    "# get mean violation per subject\n",
    "VAmotor_df = VAmotor_df.groupby('subject').mean()\n",
    "VAsimple_df = VAsimple_df.groupby('subject').mean()\n",
    "\n",
    "\n",
    "#assert that the conditions have the same number of subjects\n",
    "assert(len(VAmotor_df)==len(VAsimple_df))\n",
    "\n",
    "# add condition label\n",
    "VAmotor_df['condition'] = 'motor'\n",
    "VAsimple_df['condition'] = 'simple'\n",
    "\n",
    "#move subject into columns\n",
    "VAmotor_df.reset_index(inplace=True)\n",
    "VAsimple_df.reset_index(inplace=True)\n",
    "\n",
    "# #anova\n",
    "# simpleMotor_meanVA_df = pd.concat([VAsimple_df, VAmotor_df])\n",
    "# simpleMotor_melt_df = pd.melt(simpleMotor_meanVA_df, id_vars=['subject', 'condition'], value_vars=['mean_stopFailureRT', 'mean_precedingGoRT'], var_name='TrialType', value_name='RT')\n",
    "# aos_res = pg.rm_anova(dv='RT', within=['TrialType', 'condition'], subject='subject', data=simpleMotor_melt_df)\n",
    "# aos_res = aos_res.set_index('Source')\n",
    "# aos_res\n",
    "\n",
    "#prefix columns and save for JASP input\n",
    "VAsimple_df = VAsimple_df.add_prefix('simple_')\n",
    "VAmotor_df = VAmotor_df.add_prefix('motor_')\n",
    "\n",
    "simpleMotor_meanVA_df = pd.concat([VAsimple_df, VAmotor_df], axis=1)\n",
    "simpleMotor_meanVA_df.to_csv(path.join(T2_INPUT_DIR, 'row8SimplevsMotor.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Violation</th>\n",
       "      <th>p-value</th>\n",
       "      <th>BF₀₁</th>\n",
       "      <th>η²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fast vs. Slow subjects</td>\n",
       "      <td>RT Below Median (1/2) vs. RT Above Median (1/2)</td>\n",
       "      <td>6ms vs. 15ms</td>\n",
       "      <td>0.549</td>\n",
       "      <td>3.034</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Varying Go Response Deadlines</td>\n",
       "      <td>300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)</td>\n",
       "      <td>-16ms vs. -5ms vs. 3ms</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Low vs. High Stop Probability</td>\n",
       "      <td>.2 (9/11) vs. .4 Stop Probability (10/12)</td>\n",
       "      <td>8ms vs. 27ms</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.582</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saccadic vs. Manual Responses</td>\n",
       "      <td>Saccadic (15) vs. Manual Responses (9)</td>\n",
       "      <td>-9ms vs. -3ms</td>\n",
       "      <td>0.754</td>\n",
       "      <td>3.799</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Auditory vs. Visual Responses</td>\n",
       "      <td>Auditory (16/17) vs. Visual Responses (18/19)</td>\n",
       "      <td>-22ms vs. -13ms</td>\n",
       "      <td>0.637</td>\n",
       "      <td>3.095</td>\n",
       "      <td>0.000766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stimulus Selective vs. Simple Stopping</td>\n",
       "      <td>Stimulus Selective (20) vs. Simple Stopping (9)</td>\n",
       "      <td>55ms vs. -1ms</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stimulus Selective vs. Simple Stopping</td>\n",
       "      <td>Stimulus Selective (13/22/24) vs. Simple Stopp...</td>\n",
       "      <td>49ms vs. 24ms</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Motor Selective vs. Simple Stopping</td>\n",
       "      <td>Motor Selective (14) vs. Simple Stopping (11/12)</td>\n",
       "      <td>69ms vs. 36ms</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.467</td>\n",
       "      <td>0.015000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Comparison  \\\n",
       "1                  Fast vs. Slow subjects   \n",
       "2           Varying Go Response Deadlines   \n",
       "3           Low vs. High Stop Probability   \n",
       "4           Saccadic vs. Manual Responses   \n",
       "5           Auditory vs. Visual Responses   \n",
       "6  Stimulus Selective vs. Simple Stopping   \n",
       "7  Stimulus Selective vs. Simple Stopping   \n",
       "8     Motor Selective vs. Simple Stopping   \n",
       "\n",
       "                                           Condition               Violation  \\\n",
       "1    RT Below Median (1/2) vs. RT Above Median (1/2)            6ms vs. 15ms   \n",
       "2         300ms (3/6) vs.500ms (4/7) vs. 700ms (5/8)  -16ms vs. -5ms vs. 3ms   \n",
       "3          .2 (9/11) vs. .4 Stop Probability (10/12)            8ms vs. 27ms   \n",
       "4             Saccadic (15) vs. Manual Responses (9)           -9ms vs. -3ms   \n",
       "5      Auditory (16/17) vs. Visual Responses (18/19)         -22ms vs. -13ms   \n",
       "6    Stimulus Selective (20) vs. Simple Stopping (9)           55ms vs. -1ms   \n",
       "7  Stimulus Selective (13/22/24) vs. Simple Stopp...           49ms vs. 24ms   \n",
       "8   Motor Selective (14) vs. Simple Stopping (11/12)           69ms vs. 36ms   \n",
       "\n",
       "   p-value   BF₀₁        η²  \n",
       "1    0.549  3.034  0.000985  \n",
       "2    0.011  2.478  0.008000  \n",
       "3    0.127  2.582  0.007000  \n",
       "4    0.754  3.799  0.000094  \n",
       "5    0.637  3.095  0.000766  \n",
       "6    0.064  0.802  0.018000  \n",
       "7    0.018  0.794  0.014000  \n",
       "8    0.019  1.467  0.015000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build up Table 2\n",
    "violation_simple = VAsimple_df['simple_mean_violation'].mean()\n",
    "violation_motor = VAmotor_df['motor_mean_violation'].mean()\n",
    "# VALUES RETRIEVED FROM TABLE_DIR/row1SlowFastOutput.jasp\n",
    "p_val = 0.019\n",
    "BF_01 = 1.467\n",
    "eta_sq = 0.015\n",
    "\n",
    "row8 = ['Motor Selective vs. Simple Stopping', 'Motor Selective (14) vs. Simple Stopping (11/12)', f'{violation_motor:.0f}ms vs. {violation_simple:.0f}ms',  p_val, BF_01, eta_sq]\n",
    "table2_df.loc[len(table2_df)] = row8\n",
    "table2_df.index += 1 \n",
    "table2_df.to_csv(path.join(TABLE_DIR, 'Table2.csv'))\n",
    "table2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111, frame_on=False) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "table = ax.table(table2_df.values,colLabels=table2_df.columns,\n",
    "                 loc='center',rowLabels=[' '+str(d)+' ' for d in range(1, len(table2_df.index)+1)],\n",
    "                 cellLoc='left',\n",
    "                 colWidths = [0.75, 1.25, 0.5, 0.25, 0.25, 0.25])\n",
    "\n",
    "for (row, col), cell in table.get_celld().items():\n",
    "    if (row == 0) or (col == -1):\n",
    "        cell.set_text_props(fontproperties=FontProperties(weight='bold'))\n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(23)\n",
    "table.scale(2, 4)\n",
    "\n",
    "for key, cell in table.get_celld().items():\n",
    "    cell.set_linewidth(3)\n",
    "    \n",
    "plt.savefig(path.join(TABLE_DIR, 'Table2.png'), bbox_inches='tight', transparent=True, dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}