RR 1998 dataset - JF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "15ec71b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run SAT_help_functions.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "88e18e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/Users/wangjiashun/Desktop/Reserach on SAT/datasets/Ratcliff 1998/jf.dat\"\n",
    "df = pd.read_csv(path, delimiter='\\t')\n",
    "\n",
    "df.columns=[\"Nothing\", \"Condition\", \"Choice\", \"RT\"]\n",
    "df[\"RT\"] = df[\"RT\"]/1000\n",
    "df[\"Choice\"] = df[\"Choice\"]-1\n",
    "\n",
    "df.loc[df[\"Condition\"] <= 33, \"SAT\"] = \"Accuracy\"\n",
    "df.loc[df[\"Condition\"] > 33, \"SAT\"] = \"Speed\"\n",
    "\n",
    "df.loc[df[\"Condition\"] <= 33, \"Brightness\"] = df[\"Condition\"]\n",
    "df.loc[df[\"Condition\"] > 33, \"Brightness\"] = df[\"Condition\"]-33\n",
    "\n",
    "df.loc[df[\"Brightness\"] <= 17, \"Correct\"] = df.loc[df[\"Brightness\"] <= 17, \"Choice\"]==0\n",
    "df.loc[df[\"Brightness\"] >17, \"Correct\"] = df.loc[df[\"Brightness\"] >17, \"Choice\"]==1\n",
    "\n",
    "\n",
    "\n",
    "# to plot latency function\n",
    "df.loc[df[\"Brightness\"] < 17, \"Difficulty\"] = df[\"Brightness\"]\n",
    "df.loc[df[\"Brightness\"] > 17, \"Difficulty\"] = 33-df[\"Brightness\"]+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "fc073263",
   "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>Nothing</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Choice</th>\n",
       "      <th>RT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Brightness</th>\n",
       "      <th>Correct</th>\n",
       "      <th>Difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.265</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.712</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.623</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.456</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7884</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.193</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7885</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7886</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.361</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7887</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.338</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7888</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.348</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7889 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nothing  Condition  Choice     RT       SAT  Brightness  Correct  \\\n",
       "0           1          1       0  0.995  Accuracy         1.0        1   \n",
       "1           1          1       0  1.265  Accuracy         1.0        1   \n",
       "2           1          1       0  0.712  Accuracy         1.0        1   \n",
       "3           1          1       0  0.623  Accuracy         1.0        1   \n",
       "4           1          1       0  0.456  Accuracy         1.0        1   \n",
       "...       ...        ...     ...    ...       ...         ...      ...   \n",
       "7884        1         66       0  0.193     Speed        33.0        0   \n",
       "7885        1         66       1  0.249     Speed        33.0        1   \n",
       "7886        1         66       1  0.361     Speed        33.0        1   \n",
       "7887        1         66       1  0.338     Speed        33.0        1   \n",
       "7888        1         66       1  0.348     Speed        33.0        1   \n",
       "\n",
       "      Difficulty  \n",
       "0            1.0  \n",
       "1            1.0  \n",
       "2            1.0  \n",
       "3            1.0  \n",
       "4            1.0  \n",
       "...          ...  \n",
       "7884         1.0  \n",
       "7885         1.0  \n",
       "7886         1.0  \n",
       "7887         1.0  \n",
       "7888         1.0  \n",
       "\n",
       "[7889 rows x 8 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a6eb826",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc3a008",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ff27bfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32053844",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05bd4c30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "0111d582",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb16b6e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fdef9bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e53d38b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "ef10c73a",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Correct</th>\n",
       "      <th>RT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT</th>\n",
       "      <th>Difficulty</th>\n",
       "      <th>Correct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">Accuracy</th>\n",
       "      <th>1.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.628532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.589057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.025424</td>\n",
       "      <td>0.414333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.645287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.674712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <th>True</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.683502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.007547</td>\n",
       "      <td>0.802500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.992453</td>\n",
       "      <td>0.692605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.018519</td>\n",
       "      <td>1.190800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.981481</td>\n",
       "      <td>0.700521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.024316</td>\n",
       "      <td>1.146375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.975684</td>\n",
       "      <td>0.700246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.032164</td>\n",
       "      <td>1.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.967836</td>\n",
       "      <td>0.791215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>1.185778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.862835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>1.419583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.923764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.243373</td>\n",
       "      <td>1.075347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.756627</td>\n",
       "      <td>0.891729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.380510</td>\n",
       "      <td>1.058116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.619490</td>\n",
       "      <td>0.952184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"32\" valign=\"top\">Speed</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.331325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.333600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.903846</td>\n",
       "      <td>0.321489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.288667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.304133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.271875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.306743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.272375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.929825</td>\n",
       "      <td>0.321189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.112500</td>\n",
       "      <td>0.270611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.323394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.097938</td>\n",
       "      <td>0.292789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.902062</td>\n",
       "      <td>0.318771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.062176</td>\n",
       "      <td>0.280333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.937824</td>\n",
       "      <td>0.322635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.087121</td>\n",
       "      <td>0.273261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.912879</td>\n",
       "      <td>0.317610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.282360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.321724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.276361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.325982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.107362</td>\n",
       "      <td>0.303543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.892638</td>\n",
       "      <td>0.325845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.116223</td>\n",
       "      <td>0.318521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.883777</td>\n",
       "      <td>0.325811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.201635</td>\n",
       "      <td>0.313838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.798365</td>\n",
       "      <td>0.330498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.290993</td>\n",
       "      <td>0.331913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.709007</td>\n",
       "      <td>0.333655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16.0</th>\n",
       "      <th>False</th>\n",
       "      <td>0.381166</td>\n",
       "      <td>0.338494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.618834</td>\n",
       "      <td>0.332286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Correct        RT\n",
       "SAT      Difficulty Correct                    \n",
       "Accuracy 1.0        True     1.000000  0.668628\n",
       "         2.0        True     1.000000  0.628532\n",
       "         3.0        True     1.000000  0.675952\n",
       "         4.0        True     1.000000  0.589057\n",
       "         5.0        False    0.025424  0.414333\n",
       "                    True     0.974576  0.645287\n",
       "         6.0        True     1.000000  0.674712\n",
       "         7.0        True     1.000000  0.660040\n",
       "         8.0        True     1.000000  0.683502\n",
       "         9.0        False    0.007547  0.802500\n",
       "                    True     0.992453  0.692605\n",
       "         10.0       False    0.018519  1.190800\n",
       "                    True     0.981481  0.700521\n",
       "         11.0       False    0.024316  1.146375\n",
       "                    True     0.975684  0.700246\n",
       "         12.0       False    0.032164  1.170455\n",
       "                    True     0.967836  0.791215\n",
       "         13.0       False    0.075000  1.185778\n",
       "                    True     0.925000  0.862835\n",
       "         14.0       False    0.120000  1.419583\n",
       "                    True     0.880000  0.923764\n",
       "         15.0       False    0.243373  1.075347\n",
       "                    True     0.756627  0.891729\n",
       "         16.0       False    0.380510  1.058116\n",
       "                    True     0.619490  0.952184\n",
       "Speed    1.0        False    0.111111  0.245000\n",
       "                    True     0.888889  0.331325\n",
       "         2.0        False    0.096154  0.333600\n",
       "                    True     0.903846  0.321489\n",
       "         3.0        False    0.090909  0.288667\n",
       "                    True     0.909091  0.304133\n",
       "         4.0        False    0.097561  0.271875\n",
       "                    True     0.902439  0.306743\n",
       "         5.0        False    0.070175  0.272375\n",
       "                    True     0.929825  0.321189\n",
       "         6.0        False    0.112500  0.270611\n",
       "                    True     0.887500  0.323394\n",
       "         7.0        False    0.097938  0.292789\n",
       "                    True     0.902062  0.318771\n",
       "         8.0        False    0.062176  0.280333\n",
       "                    True     0.937824  0.322635\n",
       "         9.0        False    0.087121  0.273261\n",
       "                    True     0.912879  0.317610\n",
       "         10.0       False    0.090909  0.282360\n",
       "                    True     0.909091  0.321724\n",
       "         11.0       False    0.115385  0.276361\n",
       "                    True     0.884615  0.325982\n",
       "         12.0       False    0.107362  0.303543\n",
       "                    True     0.892638  0.325845\n",
       "         13.0       False    0.116223  0.318521\n",
       "                    True     0.883777  0.325811\n",
       "         14.0       False    0.201635  0.313838\n",
       "                    True     0.798365  0.330498\n",
       "         15.0       False    0.290993  0.331913\n",
       "                    True     0.709007  0.333655\n",
       "         16.0       False    0.381166  0.338494\n",
       "                    True     0.618834  0.332286"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_plot = df.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({\"Correct\":'count'})\n",
    "\n",
    "data_plot2 = df.groupby([\"SAT\", \"Difficulty\"]).agg({\"Correct\":'count'})\n",
    "\n",
    "data_plot3 = df.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({'RT':'mean'})\n",
    "\n",
    " \n",
    "data_plot = pd.concat((data_plot/data_plot2,data_plot3), axis = 1)\n",
    "data_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27731180",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "0fbcea31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=data_plot, x=\"Correct\", y=\"RT\", hue=\"SAT\", markers=\"True\")\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df96c20f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ca2d011d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DriftConstantBrightness(Drift):\n",
    "    name = \"DriftConstantSpeedAcc\"\n",
    "    required_parameters = ['V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16',\n",
    " 'V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','V29','V30','V31','V32','V33']\n",
    "    required_conditions = ['Brightness']\n",
    "        \n",
    "    def get_drift(self, x, t, conditions, **kwargs):\n",
    "        #print(conditions['Brightness'])\n",
    "        if conditions['Brightness'] == 1:\n",
    "            return self.V1\n",
    "        elif conditions['Brightness'] == 2:\n",
    "            return self.V2\n",
    "        elif conditions['Brightness'] == 3:\n",
    "            return self.V3\n",
    "        elif conditions['Brightness'] == 4:\n",
    "            return self.V4\n",
    "        elif conditions['Brightness'] == 5:\n",
    "            return self.V5\n",
    "        elif conditions['Brightness'] == 6:\n",
    "            return self.V6\n",
    "        elif conditions['Brightness'] == 7:\n",
    "            return self.V7\n",
    "        elif conditions['Brightness'] == 8:\n",
    "            return self.V8\n",
    "        elif conditions['Brightness'] == 9:\n",
    "            return self.V9\n",
    "        elif conditions['Brightness'] == 10:\n",
    "            return self.V10\n",
    "        elif conditions['Brightness'] == 11:\n",
    "            return self.V11\n",
    "        elif conditions['Brightness'] == 12:\n",
    "            return self.V12\n",
    "        elif conditions['Brightness'] == 13:\n",
    "            return self.V13\n",
    "        elif conditions['Brightness'] == 14:\n",
    "            return self.V14\n",
    "        elif conditions['Brightness'] == 15:\n",
    "            return self.V15\n",
    "        elif conditions['Brightness'] == 16:\n",
    "            return self.V16\n",
    "        elif conditions['Brightness'] == 17:\n",
    "            return self.V17\n",
    "        elif conditions['Brightness'] == 18:\n",
    "            return self.V18\n",
    "        elif conditions['Brightness'] == 19:\n",
    "            return self.V19\n",
    "        elif conditions['Brightness'] == 20:\n",
    "            return self.V20\n",
    "        elif conditions['Brightness'] == 21:\n",
    "            return self.V21\n",
    "        elif conditions['Brightness'] == 22:\n",
    "            return self.V22\n",
    "        elif conditions['Brightness'] == 23:\n",
    "            return self.V23\n",
    "        elif conditions['Brightness'] == 24:\n",
    "            return self.V24\n",
    "        elif conditions['Brightness'] == 25:\n",
    "            return self.V25\n",
    "        elif conditions['Brightness'] == 26:\n",
    "            return self.V26\n",
    "        elif conditions['Brightness'] == 27:\n",
    "            return self.V27\n",
    "        elif conditions['Brightness'] == 28:\n",
    "            return self.V28\n",
    "        elif conditions['Brightness'] == 29:\n",
    "            return self.V29\n",
    "        elif conditions['Brightness'] == 30:\n",
    "            return self.V30\n",
    "        elif conditions['Brightness'] == 31:\n",
    "            return self.V31\n",
    "        elif conditions['Brightness'] == 32:\n",
    "            return self.V32\n",
    "        elif conditions['Brightness'] == 33:\n",
    "            return self.V33\n",
    "        else:\n",
    "            print(\"Non-implemented SAT conditions\")\n",
    "\n",
    "class BoundSpeedAcc(Bound):\n",
    "    name = \"BoundSpeedAcc\"\n",
    "    required_parameters = [\"Bspeed\", \"Bacc\"]\n",
    "    required_conditions = ['SAT']\n",
    "    def get_bound(self, conditions, *args, **kwargs):\n",
    "        assert self.Bacc > 0\n",
    "        assert self.Bspeed > 0\n",
    "        if conditions['SAT'] == 'Speed':\n",
    "            return self.Bspeed\n",
    "        elif conditions['SAT'] == 'Accuracy':\n",
    "            return self.Bacc\n",
    "        else:\n",
    "            print(\"Non-implemented SAT conditions\")\n",
    "            \n",
    "class DriftExcitationSpeedAcc(Drift):\n",
    "    name = \"DriftSpeedAcc\"\n",
    "    required_parameters = ['V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16',\n",
    " 'V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','V29','V30','V31','V32','V33', \"Kspeed\", \"Kacc\"]\n",
    "    required_conditions = ['Brightness', \"SAT\"]\n",
    "        \n",
    "    def get_drift(self, x, t, conditions, **kwargs):\n",
    "        #print(conditions['Brightness'])\n",
    "        if conditions['SAT'] == \"Speed\":\n",
    "            if conditions['Brightness'] == 1:\n",
    "                return self.V1 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 2:\n",
    "                return self.V2 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 3:\n",
    "                return self.V3 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 4:\n",
    "                return self.V4 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 5:\n",
    "                return self.V5 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 6:\n",
    "                return self.V6 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 7:\n",
    "                return self.V7 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 8:\n",
    "                return self.V8 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 9:\n",
    "                return self.V9 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 10:\n",
    "                return self.V10 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 11:\n",
    "                return self.V11 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 12:\n",
    "                return self.V12 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 13:\n",
    "                return self.V13 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 14:\n",
    "                return self.V14 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 15:\n",
    "                return self.V15 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 16:\n",
    "                return self.V16 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 17:\n",
    "                return self.V17 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 18:\n",
    "                return self.V18 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 19:\n",
    "                return self.V19 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 20:\n",
    "                return self.V20 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 21:\n",
    "                return self.V21 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 22:\n",
    "                return self.V22 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 23:\n",
    "                return self.V23 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 24:\n",
    "                return self.V24 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 25:\n",
    "                return self.V25 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 26:\n",
    "                return self.V26 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 27:\n",
    "                return self.V27 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 28:\n",
    "                return self.V28 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 29:\n",
    "                return self.V29 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 30:\n",
    "                return self.V30 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 31:\n",
    "                return self.V31 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 32:\n",
    "                return self.V32 + x*self.Kspeed \n",
    "            elif conditions['Brightness'] == 33:\n",
    "                return self.V33 + x*self.Kspeed \n",
    "            else:\n",
    "                print(\"Non-implemented SAT conditions\")  \n",
    "\n",
    "        if conditions['SAT'] == \"Accuracy\":\n",
    "            if conditions['Brightness'] == 1:\n",
    "                return self.V1 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 2:\n",
    "                return self.V2 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 3:\n",
    "                return self.V3 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 4:\n",
    "                return self.V4 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 5:\n",
    "                return self.V5 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 6:\n",
    "                return self.V6 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 7:\n",
    "                return self.V7 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 8:\n",
    "                return self.V8 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 9:\n",
    "                return self.V9 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 10:\n",
    "                return self.V10 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 11:\n",
    "                return self.V11 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 12:\n",
    "                return self.V12 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 13:\n",
    "                return self.V13 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 14:\n",
    "                return self.V14 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 15:\n",
    "                return self.V15 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 16:\n",
    "                return self.V16 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 17:\n",
    "                return self.V17 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 18:\n",
    "                return self.V18 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 19:\n",
    "                return self.V19 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 20:\n",
    "                return self.V20 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 21:\n",
    "                return self.V21 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 22:\n",
    "                return self.V22 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 23:\n",
    "                return self.V23 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 24:\n",
    "                return self.V24 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 25:\n",
    "                return self.V25 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 26:\n",
    "                return self.V26 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 27:\n",
    "                return self.V27 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 28:\n",
    "                return self.V28 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 29:\n",
    "                return self.V29 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 30:\n",
    "                return self.V30 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 31:\n",
    "                return self.V31 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 32:\n",
    "                return self.V32 + x*self.Kacc\n",
    "            elif conditions['Brightness'] == 33:\n",
    "                return self.V33 + x*self.Kacc\n",
    "            else:\n",
    "                print(\"Non-implemented SAT conditions\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "805e80e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "                      drift=DriftExcitationSpeedAcc(drift=Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    Kspeed=Fittable(minval=k[0], maxval=k[1]),\n",
    "                                                    Kacc=Fittable(minval=k[0], maxval=k[1])),     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "31398e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_OUM_result_sub_33v1a2k2ter(samp, v = [0,6], a = [0.5,4], k=[-20,20], ter=[0.1,1], T_dur=5):\n",
    "    \n",
    "    model_fit = Model(name='Simple model (fitted)',\n",
    "                      drift=DriftExcitationSpeedAcc(V1 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V2 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V3 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V4 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V5 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V6 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V7 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V8 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V9 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V10 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V11 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V12 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V13 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V14 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V15 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V16 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V17 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V18 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V19 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V20 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V21 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V22 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V23 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V24 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V25 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V26 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V27 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V28 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V29 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V30 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V31 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V32 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V33 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    Kspeed=Fittable(minval=k[0], maxval=k[1]),\n",
    "                                                    Kacc=Fittable(minval=k[0], maxval=k[1])), \n",
    "                      noise=NoiseConstant(noise=1),\n",
    "                      bound=BoundConstant(B=Fittable(minval=a[0], maxval=a[1])),\n",
    "                      overlay=OverlayNonDecision(nondectime=Fittable(minval=ter[0], maxval=ter[1])),\n",
    "                      dx=.01, dt=.01, T_dur=T_dur)\n",
    "    #set_N_cpus(4)\n",
    "    fit_adjust_model(samp, model_fit,\n",
    "                     fitting_method=\"differential_evolution\",\n",
    "                     lossfunction=LossRobustBIC, verbose=False)\n",
    "    #print(model_fit.get_model_parameters())\n",
    "    df = pd.DataFrame(data = np.array(model_fit.get_model_parameters()).reshape(1,37),\n",
    "                      columns = model_fit.get_model_parameter_names())\n",
    "    \n",
    "    #df['Participant']=i+1\n",
    "    #df['SAT'] = SAT\n",
    "    df['BIC'] = model_fit.get_fit_result().value()\n",
    "    df = df.rename(columns={'V1': 'Drift Rate 1',\n",
    "                            'V2': 'Drift Rate 2',\n",
    "                            'V3': 'Drift Rate 3',\n",
    "                            'V4': 'Drift Rate 4',\n",
    "                            'V5': 'Drift Rate 5',\n",
    "                            'V6': 'Drift Rate 6',\n",
    "                            'V7': 'Drift Rate 7',\n",
    "                            'V8': 'Drift Rate 8',\n",
    "                            'V9': 'Drift Rate 9',\n",
    "                            'V10': 'Drift Rate 10',\n",
    "                            'V11': 'Drift Rate 11',\n",
    "                            'V12': 'Drift Rate 12',\n",
    "                            'V13': 'Drift Rate 13',\n",
    "                            'V14': 'Drift Rate 14',\n",
    "                            'V15': 'Drift Rate 15',\n",
    "                            'V16': 'Drift Rate 16',\n",
    "                            'V17': 'Drift Rate 17',\n",
    "                            'V18': 'Drift Rate 18',\n",
    "                            'V19': 'Drift Rate 19',\n",
    "                            'V20': 'Drift Rate 20',\n",
    "                            'V21': 'Drift Rate 21',\n",
    "                            'V22': 'Drift Rate 22',\n",
    "                            'V23': 'Drift Rate 23',\n",
    "                            'V24': 'Drift Rate 24',\n",
    "                            'V25': 'Drift Rate 25',\n",
    "                            'V26': 'Drift Rate 26',\n",
    "                            'V27': 'Drift Rate 27',\n",
    "                            'V28': 'Drift Rate 28',\n",
    "                            'V29': 'Drift Rate 29',\n",
    "                            'V30': 'Drift Rate 30',\n",
    "                            'V31': 'Drift Rate 31',\n",
    "                            'V32': 'Drift Rate 32',\n",
    "                            'V33': 'Drift Rate 33',\n",
    "                            'B': 'Boundary Separation',\n",
    "                            'Kspeed': 'Self-excitation Speed',\n",
    "                            'Kacc': 'Self-excitation Accuracy',\n",
    "                            'nondectime': 'Non-decision Time'})\n",
    "    return df,model_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b93111a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_DDM_result_sub_33v2a2ter(samp, v = [0,6], a = [0.5,8], ter=[0.1,1], T_dur=5):\n",
    "    \n",
    "    model_fit = Model(name='Simple model (fitted)',\n",
    "                      drift=DriftConstantBrightness(V1 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V2 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V3 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V4 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V5 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V6 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V7 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V8 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V9 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V10 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V11 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V12 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V13 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V14 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V15 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V16 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V17 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V18 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V19 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V20 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V21 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V22 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V23 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V24 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V25 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V26 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V27 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V28 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V29 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V30 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V31 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V32 = Fittable(minval=v[0], maxval=v[1]),\n",
    "                                                    V33 = Fittable(minval=v[0], maxval=v[1])), \n",
    "                      noise=NoiseConstant(noise=1),\n",
    "                      bound=BoundSpeedAcc(\n",
    "                          Bspeed=Fittable(minval=a[0], maxval=a[1]),\n",
    "                          Bacc=Fittable(minval=a[0], maxval=a[1])),\n",
    "                      overlay=OverlayNonDecision(nondectime=Fittable(minval=ter[0], maxval=ter[1])),\n",
    "                      dx=.01, dt=.01, T_dur=T_dur)\n",
    "    #set_N_cpus(4)\n",
    "    fit_adjust_model(samp, model_fit,\n",
    "                     fitting_method=\"differential_evolution\",\n",
    "                     lossfunction=LossRobustBIC, verbose=False)\n",
    "    #print(model_fit.get_model_parameters())\n",
    "    df = pd.DataFrame(data = np.array(model_fit.get_model_parameters()).reshape(1,36),\n",
    "                      columns = model_fit.get_model_parameter_names())\n",
    "    \n",
    "    #df['Participant']=i+1\n",
    "    #df['SAT'] = SAT\n",
    "    df['BIC'] = model_fit.get_fit_result().value()\n",
    "    df = df.rename(columns={'V1': 'Drift Rate 1',\n",
    "                            'V2': 'Drift Rate 2',\n",
    "                            'V3': 'Drift Rate 3',\n",
    "                            'V4': 'Drift Rate 4',\n",
    "                            'V5': 'Drift Rate 5',\n",
    "                            'V6': 'Drift Rate 6',\n",
    "                            'V7': 'Drift Rate 7',\n",
    "                            'V8': 'Drift Rate 8',\n",
    "                            'V9': 'Drift Rate 9',\n",
    "                            'V10': 'Drift Rate 10',\n",
    "                            'V11': 'Drift Rate 11',\n",
    "                            'V12': 'Drift Rate 12',\n",
    "                            'V13': 'Drift Rate 13',\n",
    "                            'V14': 'Drift Rate 14',\n",
    "                            'V15': 'Drift Rate 15',\n",
    "                            'V16': 'Drift Rate 16',\n",
    "                            'V17': 'Drift Rate 17',\n",
    "                            'V18': 'Drift Rate 18',\n",
    "                            'V19': 'Drift Rate 19',\n",
    "                            'V20': 'Drift Rate 20',\n",
    "                            'V21': 'Drift Rate 21',\n",
    "                            'V22': 'Drift Rate 22',\n",
    "                            'V23': 'Drift Rate 23',\n",
    "                            'V24': 'Drift Rate 24',\n",
    "                            'V25': 'Drift Rate 25',\n",
    "                            'V26': 'Drift Rate 26',\n",
    "                            'V27': 'Drift Rate 27',\n",
    "                            'V28': 'Drift Rate 28',\n",
    "                            'V29': 'Drift Rate 29',\n",
    "                            'V30': 'Drift Rate 30',\n",
    "                            'V31': 'Drift Rate 31',\n",
    "                            'V32': 'Drift Rate 32',\n",
    "                            'V33': 'Drift Rate 33',\n",
    "                            'Bspeed': 'Boundary Separation Speed',\n",
    "                            'Bacc': 'Boundary Separation Accuracy',\n",
    "                            'nondectime': 'Non-decision Time'})\n",
    "    return df,model_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3a80445d",
   "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>Nothing</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Choice</th>\n",
       "      <th>RT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Brightness</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.265</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.712</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.623</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.456</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7884</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.193</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7885</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7886</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.361</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7887</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.338</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7888</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.348</td>\n",
       "      <td>Speed</td>\n",
       "      <td>33.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7876 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nothing  Condition  Choice     RT       SAT  Brightness Correct\n",
       "0           1          1       0  0.995  Accuracy         1.0    True\n",
       "1           1          1       0  1.265  Accuracy         1.0    True\n",
       "2           1          1       0  0.712  Accuracy         1.0    True\n",
       "3           1          1       0  0.623  Accuracy         1.0    True\n",
       "4           1          1       0  0.456  Accuracy         1.0    True\n",
       "...       ...        ...     ...    ...       ...         ...     ...\n",
       "7884        1         66       0  0.193     Speed        33.0   False\n",
       "7885        1         66       1  0.249     Speed        33.0    True\n",
       "7886        1         66       1  0.361     Speed        33.0    True\n",
       "7887        1         66       1  0.338     Speed        33.0    True\n",
       "7888        1         66       1  0.348     Speed        33.0    True\n",
       "\n",
       "[7876 rows x 7 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9a16e3c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info: Params [2.20749183 2.49472994 2.43081738 2.89836023 2.59401321 2.42667875\n",
      " 2.31005464 2.33995404 2.37145689 2.27939029 2.19248743 1.82806759\n",
      " 1.54740484 1.07995293 0.56196071 0.         0.40671358 0.77893815\n",
      " 1.13258323 1.32065008 1.60023286 1.81934855 2.00625141 2.0534562\n",
      " 2.06306145 2.22493292 2.37169646 2.14525237 2.1164168  2.43675825\n",
      " 2.24606961 2.38041015 2.43948445 0.5        1.12902559 0.1651424 ] gave -99.07452007768694\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Drift Rate 1  Drift Rate 2  Drift Rate 3  Drift Rate 4  Drift Rate 5  \\\n",
      "0      2.207492       2.49473      2.430817       2.89836      2.594013   \n",
      "\n",
      "   Drift Rate 6  Drift Rate 7  Drift Rate 8  Drift Rate 9  Drift Rate 10  ...  \\\n",
      "0      2.426679      2.310055      2.339954      2.371457        2.27939  ...   \n",
      "\n",
      "   Drift Rate 28  Drift Rate 29  Drift Rate 30  Drift Rate 31  Drift Rate 32  \\\n",
      "0       2.145252       2.116417       2.436758        2.24607        2.38041   \n",
      "\n",
      "   Drift Rate 33  Boundary Separation Speed  Boundary Separation Accuracy  \\\n",
      "0       2.439484                        0.5                      1.129026   \n",
      "\n",
      "   Non-decision Time       BIC  \n",
      "0           0.165142 -99.07452  \n",
      "\n",
      "[1 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "df = df[df[\"RT\"]<5]\n",
    "\n",
    "samp = Sample.from_pandas_dataframe(df, rt_column_name=\"RT\", choice_column_name=\"Correct\")\n",
    "df_result, model_fit = get_DDM_result_sub_33v2a2ter(samp)\n",
    "print(df_result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "861f6063",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "db85a137",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "58428672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'JF')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(df)[0]\n",
    "x[0:15] = -x[0:15].copy()\n",
    "\n",
    "plt.plot(x[0:33], \"-o\")\n",
    "plt.xlabel(\"Brightness\")\n",
    "plt.ylabel(\"Drift Rate\")\n",
    "plt.title(\"JF\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "22e37f4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: Setting undecided probability from -0.000000 to 0\n",
      "Warning: Setting undecided probability from -0.000000 to 0\n",
      "Warning: Setting undecided probability from -0.000000 to 0\n",
      "Warning: Setting undecided probability from -0.000000 to 0\n"
     ]
    }
   ],
   "source": [
    "model_prediction =pd.DataFrame()\n",
    "for i in range(1,34):\n",
    "    for j in [\"Speed\", \"Accuracy\"]:\n",
    "        samp = model_fit.solve({'SAT':j, 'Brightness':i}).resample(1000).to_pandas_dataframe(drop_undecided = True)\n",
    "        model_prediction = pd.concat((model_prediction,samp))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "342bc2b1",
   "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>Correct</th>\n",
       "      <th>RT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Brightness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.246487</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.476394</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.475299</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.563900</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.525220</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1</td>\n",
       "      <td>0.541566</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>0.759837</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1</td>\n",
       "      <td>0.498343</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>0.598305</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.381491</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65968 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Correct        RT       SAT  Brightness\n",
       "0          1  0.246487     Speed           1\n",
       "1          1  0.476394     Speed           1\n",
       "2          1  0.475299     Speed           1\n",
       "3          1  0.563900     Speed           1\n",
       "4          1  0.525220     Speed           1\n",
       "..       ...       ...       ...         ...\n",
       "995        1  0.541566  Accuracy          33\n",
       "996        1  0.759837  Accuracy          33\n",
       "997        1  0.498343  Accuracy          33\n",
       "998        1  0.598305  Accuracy          33\n",
       "999        0  0.381491  Accuracy          33\n",
       "\n",
       "[65968 rows x 4 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model_prediction = model_prediction.rename(columns = {\"choice\":\"Correct\"})\n",
    "model_prediction.loc[model_prediction[\"Brightness\"] <= 15, \"Choice\"] = model_prediction.loc[model_prediction[\"Brightness\"] <= 15, \"Correct\"]==0\n",
    "model_prediction.loc[model_prediction[\"Brightness\"] > 15, \"Choice\"] = model_prediction.loc[model_prediction[\"Brightness\"] > 15, \"Correct\"]==1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "96fb692f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to plot latency function\n",
    "model_prediction.loc[model_prediction[\"Brightness\"] < 17, \"Difficulty\"] = model_prediction.loc[model_prediction[\"Brightness\"] < 17, \"Brightness\"]\n",
    "model_prediction.loc[model_prediction[\"Brightness\"] > 17, \"Difficulty\"] = 33-model_prediction.loc[model_prediction[\"Brightness\"] > 17, \"Brightness\"]+1\n",
    "\n",
    "prediction_plot = model_prediction.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({\"Correct\":'count'})\n",
    "prediction_plot2 = model_prediction.groupby([\"SAT\", \"Difficulty\"]).agg({\"Correct\":'count'})\n",
    "\n",
    "prediction_plot3 = model_prediction.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({'RT':'mean'})\n",
    "\n",
    "prediction_plot = pd.concat((prediction_plot/prediction_plot2,prediction_plot3), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "a41afa6a",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Correct</th>\n",
       "      <th>RT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT</th>\n",
       "      <th>Difficulty</th>\n",
       "      <th>Correct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Accuracy</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.774922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9915</td>\n",
       "      <td>0.606353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.598072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.615999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.681287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Speed</th>\n",
       "      <th>14.0</th>\n",
       "      <th>1</th>\n",
       "      <td>0.6985</td>\n",
       "      <td>0.365537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.3605</td>\n",
       "      <td>0.410500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.6395</td>\n",
       "      <td>0.373046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.4480</td>\n",
       "      <td>0.396697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5520</td>\n",
       "      <td>0.383762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Correct        RT\n",
       "SAT      Difficulty Correct                   \n",
       "Accuracy 1.0        0         0.0085  0.774922\n",
       "                    1         0.9915  0.606353\n",
       "         2.0        0         0.0045  0.598072\n",
       "                    1         0.9955  0.615999\n",
       "         3.0        0         0.0110  0.681287\n",
       "...                              ...       ...\n",
       "Speed    14.0       1         0.6985  0.365537\n",
       "         15.0       0         0.3605  0.410500\n",
       "                    1         0.6395  0.373046\n",
       "         16.0       0         0.4480  0.396697\n",
       "                    1         0.5520  0.383762\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=data_plot, x=\"Correct\", y=\"RT\", hue=\"SAT\", markers=\"True\")\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))prediction_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "5cd08872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## DDM prediction\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=prediction_plot, x=\"Correct\", y=\"RT\", hue=\"SAT\", markers = \"$\\cros$\")\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "5efa418d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info: Params [ 2.09933874  2.90575732  2.43932718  3.16698879  2.76370027  2.70659694\n",
      "  2.03761399  2.17738417  1.78390947  2.15083576  2.25314577  1.1810762\n",
      "  1.33593993  1.21695429  0.72465069  0.13111905  0.44706962  0.75935757\n",
      "  1.18766576  1.35122078  1.70425634  1.61097759  2.0180442   2.17097814\n",
      "  1.85556966  2.16686024  2.05535147  1.98835225  2.05979231  2.84355958\n",
      "  2.65346891  2.33935105  3.28173971 15.79562902  1.81093507  1.6445186\n",
      "  0.15402018] gave -2607.1637996280256\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Drift Rate 1  Drift Rate 2  Drift Rate 3  Drift Rate 4  Drift Rate 5  \\\n",
      "0      2.099339      2.905757      2.439327      3.166989        2.7637   \n",
      "\n",
      "   Drift Rate 6  Drift Rate 7  Drift Rate 8  Drift Rate 9  Drift Rate 10  ...  \\\n",
      "0      2.706597      2.037614      2.177384      1.783909       2.150836  ...   \n",
      "\n",
      "   Drift Rate 29  Drift Rate 30  Drift Rate 31  Drift Rate 32  Drift Rate 33  \\\n",
      "0       2.059792        2.84356       2.653469       2.339351        3.28174   \n",
      "\n",
      "   Self-excitation Speed  Self-excitation Accuracy  Boundary Separation  \\\n",
      "0              15.795629                  1.810935             1.644519   \n",
      "\n",
      "   Non-decision Time        BIC  \n",
      "0            0.15402 -2607.1638  \n",
      "\n",
      "[1 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "df = df[df[\"RT\"]<5]\n",
    "\n",
    "samp = Sample.from_pandas_dataframe(df, rt_column_name=\"RT\", choice_column_name=\"Correct\")\n",
    "OUM_result, model_fit_OUM = get_OUM_result_sub_33v1a2k2ter(samp)\n",
    "print(OUM_result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "40152196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'JF')"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(OUM_result)[0]\n",
    "x[0:15] = -x[0:15].copy()\n",
    "\n",
    "plt.plot(x[0:33], \"-o\")\n",
    "plt.xlabel(\"Brightness\")\n",
    "plt.ylabel(\"Drift Rate\")\n",
    "plt.title(\"JF\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "7a3e660c",
   "metadata": {},
   "outputs": [],
   "source": [
    "OUM_prediction =pd.DataFrame()\n",
    "for i in range(1,34):\n",
    "    for j in [\"Speed\", \"Accuracy\"]:\n",
    "        samp = model_fit_OUM.solve({'SAT':j, 'Brightness':i}).resample(1000).to_pandas_dataframe(drop_undecided = True)\n",
    "        OUM_prediction = pd.concat((OUM_prediction,samp))\n",
    "\n",
    "        \n",
    "OUM_prediction = OUM_prediction.rename(columns = {\"choice\":\"Correct\"})\n",
    "OUM_prediction.loc[OUM_prediction[\"Brightness\"] <= 15, \"Choice\"] = OUM_prediction.loc[OUM_prediction[\"Brightness\"] <= 15, \"Correct\"]==0\n",
    "OUM_prediction.loc[OUM_prediction[\"Brightness\"] > 15, \"Choice\"] = OUM_prediction.loc[OUM_prediction[\"Brightness\"] > 15, \"Correct\"]==1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "90989b0c",
   "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>Correct</th>\n",
       "      <th>RT</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Brightness</th>\n",
       "      <th>Choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.594497</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.363347</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.374526</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.378892</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.373111</td>\n",
       "      <td>Speed</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1</td>\n",
       "      <td>0.502211</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>0.831343</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1</td>\n",
       "      <td>0.624643</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>0.582089</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1</td>\n",
       "      <td>0.433527</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65995 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Correct        RT       SAT  Brightness Choice\n",
       "0          1  0.594497     Speed           1  False\n",
       "1          1  0.363347     Speed           1  False\n",
       "2          1  0.374526     Speed           1  False\n",
       "3          1  0.378892     Speed           1  False\n",
       "4          1  0.373111     Speed           1  False\n",
       "..       ...       ...       ...         ...    ...\n",
       "995        1  0.502211  Accuracy          33   True\n",
       "996        1  0.831343  Accuracy          33   True\n",
       "997        1  0.624643  Accuracy          33   True\n",
       "998        1  0.582089  Accuracy          33   True\n",
       "999        1  0.433527  Accuracy          33   True\n",
       "\n",
       "[65995 rows x 5 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OUM_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "5b094d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to plot latency function\n",
    "OUM_prediction.loc[OUM_prediction[\"Brightness\"] < 17, \"Difficulty\"] = OUM_prediction.loc[OUM_prediction[\"Brightness\"] < 17, \"Brightness\"]\n",
    "OUM_prediction.loc[OUM_prediction[\"Brightness\"] > 17, \"Difficulty\"] = 33-OUM_prediction.loc[OUM_prediction[\"Brightness\"] > 17, \"Brightness\"]+1\n",
    "\n",
    "OUM_prediction_plot = OUM_prediction.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({\"Correct\":'count'})\n",
    "OUM_prediction_plot2 = OUM_prediction.groupby([\"SAT\", \"Difficulty\"]).agg({\"Correct\":'count'})\n",
    "\n",
    "OUM_prediction_plot3 = OUM_prediction.groupby([\"SAT\", \"Difficulty\", \"Correct\"]).agg({'RT':'mean'})\n",
    "\n",
    "OUM_prediction_plot = pd.concat((OUM_prediction_plot/OUM_prediction_plot2,OUM_prediction_plot3), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "37fbb2ff",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Correct</th>\n",
       "      <th>RT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT</th>\n",
       "      <th>Difficulty</th>\n",
       "      <th>Correct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Accuracy</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.009005</td>\n",
       "      <td>1.015358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990995</td>\n",
       "      <td>0.614370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.873736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.996500</td>\n",
       "      <td>0.627522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.687218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Speed</th>\n",
       "      <th>14.0</th>\n",
       "      <th>1</th>\n",
       "      <td>0.668000</td>\n",
       "      <td>0.331811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.355500</td>\n",
       "      <td>0.351225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.644500</td>\n",
       "      <td>0.334720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.428000</td>\n",
       "      <td>0.352401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.572000</td>\n",
       "      <td>0.341506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Correct        RT\n",
       "SAT      Difficulty Correct                    \n",
       "Accuracy 1.0        0        0.009005  1.015358\n",
       "                    1        0.990995  0.614370\n",
       "         2.0        0        0.003500  0.873736\n",
       "                    1        0.996500  0.627522\n",
       "         3.0        0        0.003500  0.687218\n",
       "...                               ...       ...\n",
       "Speed    14.0       1        0.668000  0.331811\n",
       "         15.0       0        0.355500  0.351225\n",
       "                    1        0.644500  0.334720\n",
       "         16.0       0        0.428000  0.352401\n",
       "                    1        0.572000  0.341506\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OUM_prediction_plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0901ec98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "49963f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5g/vqmn12yd48z6l6jf2s2yy62r0000gn/T/ipykernel_72047/2682232630.py:1: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  data_all = pd.concat((data_plot,prediction_plot, OUM_prediction_plot))\n"
     ]
    }
   ],
   "source": [
    "data_all = pd.concat((data_plot,prediction_plot, OUM_prediction_plot))\n",
    "data_all[\"Type\"] = [\"Data\"]*57 + [\"DDM\"]*64 + [\"OUM\"]*64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "3796d894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a09acb10>"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## OUM prediction\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=data_all, x=\"Correct\", y=\"RT\", hue=\"Type\")\n",
    "#sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
    "plt.xlabel(\"Choice Probability\")\n",
    "plt.ylabel(\"Mean RT\")\n",
    "plt.legend(title='', loc='upper left', bbox_to_anchor=(1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "6304163b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29d621290>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## OUM prediction\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=data_all, x=\"Choice\", y=\"RT\", hue=\"Type\")\n",
    "#sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
    "plt.xlabel(\"Choice Probability\")\n",
    "plt.ylabel(\"Mean RT\")\n",
    "plt.legend(title='', loc='upper left', bbox_to_anchor=(1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "dac53b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_plot_cor = df.groupby([\"SAT\", \"Brightness\"]).agg({'RT':'mean', \"Correct\":'mean'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "cf4c4358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DDM\n",
    "prediction_plot_cor = model_prediction.groupby([\"SAT\", \"Brightness\"]).agg({'RT':'mean', \"Correct\":'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6539bfe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "fe5e220f",
   "metadata": {},
   "outputs": [],
   "source": [
    "OUM_prediction_plot_cor = model_prediction.groupby([\"SAT\", \"Brightness\"]).agg({'RT':'mean', \"Correct\":'mean'})\n",
    "OUM_prediction_plot_cor\n",
    "\n",
    "data_all_cor = pd.concat((data_plot_cor,prediction_plot_cor, OUM_prediction_plot_cor))\n",
    "data_all_cor[\"Type\"] = [\"Data\"]*66 + [\"DDM\"]*66 + [\"OUM\"]*66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "260616fa",
   "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></th>\n",
       "      <th>RT</th>\n",
       "      <th>Correct</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT</th>\n",
       "      <th>Brightness</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Accuracy</th>\n",
       "      <th>1.0</th>\n",
       "      <td>0.684182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.606654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.646571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.580894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.595815</td>\n",
       "      <td>0.984615</td>\n",
       "      <td>Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Speed</th>\n",
       "      <th>29.0</th>\n",
       "      <td>0.371006</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.361677</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31.0</th>\n",
       "      <td>0.365226</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0.357845</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>0.357112</td>\n",
       "      <td>0.878000</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           RT   Correct  Type\n",
       "SAT      Brightness                          \n",
       "Accuracy 1.0         0.684182  1.000000  Data\n",
       "         2.0         0.606654  1.000000  Data\n",
       "         3.0         0.646571  1.000000  Data\n",
       "         4.0         0.580894  1.000000  Data\n",
       "         5.0         0.595815  0.984615  Data\n",
       "...                       ...       ...   ...\n",
       "Speed    29.0        0.371006  0.829000   OUM\n",
       "         30.0        0.361677  0.825000   OUM\n",
       "         31.0        0.365226  0.820000   OUM\n",
       "         32.0        0.357845  0.818000   OUM\n",
       "         33.0        0.357112  0.878000   OUM\n",
       "\n",
       "[198 rows x 3 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "bcfac46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29cedb990>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## OUM prediction\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (4,4))\n",
    "ax = sns.scatterplot(data=data_all_cor, x=\"Correct\", y=\"RT\", hue=\"Type\")\n",
    "#sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
    "plt.xlabel(\"Choice Probability\")\n",
    "plt.ylabel(\"Mean RT\")\n",
    "plt.legend(title='', loc='upper left', bbox_to_anchor=(1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2be1de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}