psifr_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pybeh_pd' from '/Users/davidhalpern/Documents/GitHub/pybeh_pd/pybeh_pd.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from psifr import fr\n",
    "import pybeh_pd as pb\n",
    "import cmlreaders as cml\n",
    "import pkg_resources\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "data_file = pkg_resources.resource_filename('psifr', 'data/Morton2013.csv')\n",
    "import importlib\n",
    "importlib.reload(pb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using psifr sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_file, dtype={'category': 'category'})\n",
    "df.category.cat.as_ordered(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>prob</th>\n",
       "      <th>actual</th>\n",
       "      <th>possible</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th>lag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>-23.0</th>\n",
       "      <td>0.020833</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-22.0</th>\n",
       "      <td>0.035714</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-21.0</th>\n",
       "      <td>0.026316</td>\n",
       "      <td>3</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-20.0</th>\n",
       "      <td>0.024000</td>\n",
       "      <td>3</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-19.0</th>\n",
       "      <td>0.014388</td>\n",
       "      <td>2</td>\n",
       "      <td>139</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\">47</th>\n",
       "      <th>19.0</th>\n",
       "      <td>0.061224</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.045455</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.071429</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1880 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   prob  actual  possible\n",
       "subject lag                              \n",
       "1       -23.0  0.020833       1        48\n",
       "        -22.0  0.035714       3        84\n",
       "        -21.0  0.026316       3       114\n",
       "        -20.0  0.024000       3       125\n",
       "        -19.0  0.014388       2       139\n",
       "...                 ...     ...       ...\n",
       "47       19.0  0.061224       3        49\n",
       "         20.0  0.055556       2        36\n",
       "         21.0  0.045455       1        22\n",
       "         22.0  0.071429       1        14\n",
       "         23.0  0.000000       0         6\n",
       "\n",
       "[1880 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Organizing data for psifr\n",
    "data = fr.merge_free_recall(df, study_keys=['category'])\n",
    "crp = fr.lag_crp(data)\n",
    "crp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'psifr CRP')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# psiFR plotting\n",
    "g = fr.plot_lag_crp(crp)\n",
    "plt.title('psifr CRP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing lag crp curves with pd_to_pb\n",
    "crp_pb = df.groupby('subject').apply(pb.pd_crp, itemno_column=\"item_number\", pres_type=\"study\", \n",
    "                            rec_type=\"recall\", type_column='trial_type', lag_num=23).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pd_pybeh CRP')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the pd_to_pb results using psifr\n",
    "g = fr.plot_lag_crp(crp_pb)\n",
    "plt.title('pd_pybeh CRP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lag', ylabel='prob'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zf0mv+ec5Djd3cMjyHEkp1CuO1TjDl38KuBGoA9Z4VSljQtHZ00vFQSe/MXviKPLdns3GW/4dLC3PkZxCDRyFqvqvqnrAffxfoMDDehlzWtuqmuno7gOs/0Y0LbH5OZJeqIHjFRG5VURS3MfNwG+8rJgxp+P/oWX5jeiZNjaH8aOcPMf6/ZbnSEbDBg4RaRWRFuBLwBNAl/tYDXzN++oZM7T1+52Jm3x+80UY74nIwFXH0ZZOKo9ZniPZDBs4VDVPVUe5P1NUNdV9pKjqqGhV0phAHd29bD7UBMAFxfnkZVp+I5psOtnkFvLtuCLyCRG53318zMtKGXM6mw820tVj+Y1YGTTgoSXIk06ot+N+B/gqsMt9fNVdZkxM2PhUsTW5MJtJ7iyL660/R9IJ9YrjOuBqVV2lqquAa9xlxsREf/NIaopQNmV0jGuTfPz7c9Qf72Rf3YkY18hEUzg9xwv8nueHsoOIXCMiu0Vkr4jcG2T9LBFZLyKdInKP3/JSEXlFRN4RkZ0i8lW/df8sIjUistV9WABLMm1dPWyrbgJgTmkBORmpsa1QklpszVVJK9R33L8DW0TkFUCAy4G/H24HEfEBDwBXA9VAuYisU9Vdfps1AH8NXB+wew/wN6q6WUTygE0i8pLfvt9T1ftDrLtJMBWVjXT3Ok0jlt+IHf/f/YZ9x1i2eEoMa2Oi6bRXHCKSAvQBi4Gn3ccSVV19ml0XAntVdb+q9t/Cu9R/A1WtVdVyoDtg+RFV3ew+bwXeAYpDOyWT6Cy/ER9KC7MpLsgCnB7kludIHqcNHKraB6xwP8zXqepaVX0/hGMXA1V+r6s5gw9/EZkKXAxs9Fu8QkS2i8gqEQnawC0iy0WkQkQq6urqwi3WxLH+/Ea6L4X5lt+Iqf7AfexEF3tqj8e4NiZaQs1xvCQi97i5h8L+x2n2kSDLwvpKIiK5wK+Bu1W1xV38IDAdmAscAb4bbF9VXamqZapaVlRUFE6xJo4d7+xhR00zAHMnF5CZ5otxjZKbDT+SnEINHF8A/gr4I1Dh9xhONVDq97oEOBxqxUQkDSdo/EJVn+5frqpHVbXXvRL6KU6TmEkS5Qca6O2z/Ea8GJQgt8CRNEINHLNxEt3bgK3Aj4DzT7NPOTBTRKaJSDpwK7AulMJERICHgXdU9f8FrJvo9/IG4O1QjmkSg+U34ktxQRaTC7MB2HjgGH19ludIBqHeVfUo0AL80H19m7vs5qF2UNUeEVkBvAj4gFWqulNE7nLXPyQiE3CuXEYBfSJyN06QughYBuwQka3uIb+pqs8D94nIXJxmr0qccbRMkuj/VpuRmsLFkwtiWxkDOFd+hxraaGzrZvfRVj4w0UYjSnShBo7zVHWO3+tXRGTb6XZyP+ifD1j2kN/z93GasAL9ieA5ElR1WUg1Ngmnub2bnYed/Mb8KaPJSLX8RjxYMn0Mayqc+2DW7ztmgSMJhNpUtUVEFve/EJFFwBveVMmY4N460EB/S4jlN+KH/4CHNrFTcgg1cCwC3hSRShGpBNYDHxKRHSKy3bPaGePH5t+ITxPyM5k2NgeAjQcaLM+RBEJtqrrG01oYE4L+xHhWmo+LSgpiWxkzyOJzxnCg/gTN7d3sOtLCBcUhjUpkRqiQrjhU9eBwD68raUzjiS7eOeJ05VkwrZD01HCGWTNe878CtOaqxGfvPjMibDzg10xl+Y24s9hvBkYLHInPAocZESy/Ed/GjcpketHJPEev5TkSmgUOMyK86QaO3IxULphkt3vGo/6A3trRM3DbtElMFjhM3Ktr7RwYQG/htEJSffZvG4/sttzkYe9AE/f8P4QsvxG/FtuAh0nDAoeJezY+1cgwNjeDc8fnAlBe2UhPb1+Ma2S8YoHDxL0N7rfXUZmpNpxFnOu/6jje2cPbh1tOs7UZqSxwmLh2tKWD/fUnAFh0zhh8KUGHMDNxwubnSA4WOExcG3QbruU34t4i/8BhCfKEZYHDxDXrvzGyFOakM2tCHgAVlQ10W54jIVngMHGt/1vr6Ow0zhufF+PamFD05znaunrZXm39ORKRBQ4Tt2qa2jnU0AY4H0Yplt8YEWzcqsRngcPELWumGpkWTStE3BhvCfLEZIHDxC1LjI9MBdnpfGCCc9t0xcEGunosz5FoLHCYuKSqA80cY3MzmDEuN8Y1MuHov0Ls6O5jW3VTbCtjIs7TwCEi14jIbhHZKyL3Blk/S0TWi0iniNwTyr4iUigiL4nIHvfnaC/PwcRGVUM7NU3tACw+pxARy2+MJNafI7F5FjhExAc8AFwLzAZuE5HZAZs1AH8N3B/GvvcCL6vqTOBl97VJMOv31w88v2T62BjWxJyJBdMK6b+XwRLkicfLK46FwF5V3a+qXcBqYKn/Bqpaq6rlQHcY+y4FHnWfPwpc71H9TQxZYnxky89K4/xJzvSxmw420tnTG+MamUjyMnAUA1V+r6vdZWe773hVPQLg/hx3lvU0cUZVB/pvTBiVydQx2TGukTkT/QG/s6ePLYeaYlsZE1FeBo5gjdKhTgt2Nvs6BxBZLiIVIlJRV1cXzq4mxvbXn+BoSyfgfPhYfmNksjxH4vIycFQDpX6vS4DDEdj3qIhMBHB/1gY7gKquVNUyVS0rKioKq+Imtuw23MRQNnX0wKCUludILF4GjnJgpohME5F04FZgXQT2XQfc7j6/HVgbwTqbOGDzbySGvMw0Lih28hxbDjXR0W15jkThWeBQ1R5gBfAi8A7wpKruFJG7ROQuABGZICLVwNeB/yMi1SIyaqh93UN/B7haRPYAV7uvTYJQVTa6gaO4IIvSQstvjGT9V4xdvX1sPtgY49qYSEn18uCq+jzwfMCyh/yev4/TDBXSvu7yY8BVka2piRd7ao9Tf7wLsKuNRLD4nEIe+uM+wGmuumSG3VqdCKznuIkrlt9ILAumFpLq5jlsfo7EYYHDxBXrv5FYcjJSuajEyXNsrWqivcvyHInAAoeJG319yoYDTuCYMiabSQVZMa6RiYT++Tm6e5WKgw0xro2JBAscJm68+34rTW3OIALWTJU4/K8cv/n0Dq68/1WWPbwxhjUyZ8vT5LgxoVr28EZ2Hm4ZeG3NVImjbEohaT6hu1epbe2k04ZZH/HsisPEhaqGNhpOdA28tiuOxJGV7mNuaQGABY0EYYHDxFRzWzf/8uxODrpTxIIzQF52hl0MJ5LF9kUgoVjgMDHT2tHNLSvX88gblajfSGTN7d185qcb7A6cBBJ4Bdna0U1bV0+MamPOlgUOEzOPvlnJu++3Bl23rbqZX751KMo1Ml7o7u3jiYC/Zf3xLi6/7xV2VDfHqFbmbFjgMDGzduvwY16u3VoTpZoYL/3o5T08t/3IKcvrj3fx+Z+/xYlOu/IYaSxwmJhpbAucv2uwpvbh15v419nTy2PrDw65vv54F+u2hTpotokXFjhMzJw7PnfY9TPH5UWpJsYrVQ3tp/0CsL26KTqVMRFjgcPEzOJzCodd/7klU6JUE+OVnAzfabfJTrc76EYaCxwmJvYcbeVnrx8Ycv3XPnwul59rE3CNdBPzs5jj9uEYStmU4deb+GOBw0RdbUsHdzxSTkuHkxT9s1njyHP7beRlpvLsisv46odnxrKKJoLuvWbWwAi5wXz7uXfYV3c8ijUyZ8sCh4mqE509fOHRcmqa2gH44Myx/GTZfMbmZQAwNjeDC93RVE1iWDJ9DI/fuYg5fn9XERg/yvmbH2nu4JafrOedIy1DHcLEGQscJmp6evtY8cRm3q5xPiBmTcjjx5+ZR5rP/g0T3ZLpY1i74jJKRjsjHk8uzOYPf3MFl7hjktUf7+KWn6xnyyGbJXAksHesiQpV5R/X7uSV3XUATMzP5OefX0heZhoAJaOzmDY2Z+CDxSSm/i8JKSLkZKSy6o4FXDVrHAAtHT189mcbB83JYuKTBQ4TFQ/+cd9AT/C8jFQe+fwCJuRnDqx//M5FvHLPFTx+56JYVdHEQGaaj4eWzedjF00E4ERXL3c88hav7K6Ncc3McCxwGM+t3VrDfS/sBiA1RXjws/OZNWFUjGtl4kWaL4Uf3HoxN5eVAM4Iussfq+C3O07tbW7ig6eBQ0SuEZHdIrJXRO4Nsl5E5Ifu+u0iMs9dfp6IbPV7tIjI3e66fxaRGr9113l5DubsbNh/jL99avvA6//45IVcNnNsDGtkYmmoJklfivCdT17EHZdMBZzZAr/8xGZ+tak6BrU0p+NZzxsR8QEPAFcD1UC5iKxT1V1+m10LzHQfi4AHgUWquhuY63ecGuAZv/2+p6r3e1V3Exl7a1tZ/lgFXb3OHAx3f3gmN5WVxrhWJpaGa4pMSRH+6eOzyc1I5b9f2Uufwj1PbaO9q4dlS6ZGr5LmtLy84lgI7FXV/araBawGlgZssxR4TB0bgAIRmRiwzVXAPlUdesAbE3dqWzu4fdXJvho3zi/hq1dZ3wwzPBHhnj8/j29cM2tg2T+u3cmDr+6LYa1MIC8DRzFQ5fe62l0W7ja3Ar8MWLbCbdpaJSKjgxUuIstFpEJEKurq6sKvvTljJzp7+MLPB/fV+I9PXojI0J3AjPH3l1dM59tLzx94/Z8vvMv9L+5G/SduMTHjZeAI9ikR+FcfdhsRSQc+ATzlt/5BYDpOU9YR4LvBClfVlapapqplRUU2dEW09PT28ZVfbrG+GuasfW7JVO6/aQ79nc7/+5W9fPu5XRY84oCX7+ZqwL9BuwQIHD/5dNtcC2xW1aP9C1T1qKr2qmof8FOcJjETB1SVf1q3kz+869xKOWFUJo98fsFAXw1jwnXj/BL++9PzSPM50eORNyq599c76O2z4BFLXgaOcmCmiExzrxxuBdYFbLMO+Jx7d9VioFlV/e/Bu42AZqqAHMgNwNuRr7o5Ew/9cT+/2Oj01ch1+2pMzLcOfebsXHfhRFYuKyMj1fm4WlNRxVdXb6HbvenCRJ9ngUNVe4AVwIvAO8CTqrpTRO4SkbvczZ4H9gN7ca4e/qp/fxHJxrkj6+mAQ98nIjtEZDtwJfA1r87BhG7t1hr+84V3gf6+GvP4wETrq2Ei48pZ4/j55xeSk+4M0/7c9iP85f9soqPb5qWPBUmG9sKysjKtqKiIdTUS1ob9x/jcw28N3Hb7XzdeZLfdGk9sOdTI7aveGrhb79IZY1i5rIycDJvTwwsisklVywKXW8bSnJXAvhpfvcr6ahjvXDx5NKuXL2FMTjoAb+w9xrKHN9Js0wxHlQUOc8YC+2p8al4Jd9s8GsZjsyeN4sm7ljDRHets86EmPv3TDRw73hnjmiUPCxzmjAT21bhshvXVMNEzvSiXJ7+0hMmF2QDsPNzCLSs38H5zR4xrlhwscJiwBe2r8dl5pKfav5OJntLCbJ66awkzx+UCsLf2ODf/ZD1VDW0xrlnis3e6CctQfTVGWV8NEwPjR2Wy5ktLOH+ScwffoYY2bnpoPXtrbSpaL1ngMGEJ7Kux6g7rq2FiqzAnnSf+YjHzpzijD73f4kxFu+uwTUXrFQscJmSBfTV+/Jl5zJ5kfTVM7OVnpfH4nQu5dIYzFe2xE13cunI9m/2mol328EauvP9Vlj28MVbVTBgWOExINgbMq/Hvn7yQy8+1McBM/MhOT+Xh2xfw4Q+MB05ORfu/W2q474V32XSwkQP1J9hz9LiNd3WWLHCY09pb28pfBPTVuNn6apg4lJnm48HPzuPjcyYB0NbVy91rtvLjV/fR1uX0Mn+/pYOvP7nNxrs6CxY4zLBqWzu44xHrq2FGjjRfCt+/ZS4fvTBwap+TntlSwyNvHIhirRKLBQ4zpLauHu78eQXVjU5fjUtnjLG+GmZE8KUIE0ZlDLvNY+sPWpPVGbIBXsyAju5ebv7JeupaO5kyJpuc9FR21DQDcN74PB787Hzrq2FGjPdOc0vuoYY22jp7ycm0j8Fw2W/MoKr87PUD/PjVvTS2OWP+HPHrgTt+VIb11TAjTn7W6f9fP/L917iprISbykopLrDbykNlXx8ND/5xH//2/DsDQcNfmk945I6FTLI3lRlhPuEmyIdT09TO93+/h8v+8w8se3gjz20/TGePDdV+OnbFkeRaO7p54A97h1zf26eMyU2PYo2MiYwPf2A8V80ax8vuKAf+8rNSuWT6WF7ZXUtHdx+q8Pqeel7fU8/o7DSuv7iYWxaUMmuC9VMKxgJHktuwv4ETXUN/w+pTeHV3LbcsmBzFWhlz9lJShAc/O58HXtnLLzYeot4dPTcnw8dzX/kgpYXZtHZ08+y2I6ypqGJbVRMAjW3dPPJGJY+8UcmcknxuXlDKx+dMsqZaP9ZUlcR2Hm7mfzYcPO12XT02RacZmdJTU/ja1eey8ZtXDYykOy4vk1L3eV5mGp9eNJm1X76UF+7+IF+4dBqjs08GiG3VzfzDM2+z8N9+z9ef3MrG/cfsTizsiiPpNLd1s3ZbDWvKq9gZ4lg+ZVMLPa6VMd7ypQhTxmTjSxFKRgfP182aMIpvfXw237j2PH6/q5Y1FVW8vqcOVejo7uPpzTU8vbmGaWNzuKmshBvnlTBuVGaUzyQ+2NSxSaCvT9mw/xhrKqp44e336Qy4gkhPTRnyquLyc4t47AsLo1FNY+JOTVM7v6qo5smKqoG5Z/r5UoQrzyvi5rJSrpw1jjRf8AacZQ9vpLqxnZLRWTx+56JoVDtihpo61tPAISLXAD8AfMDPVPU7AevFXX8d0Abcoaqb3XWVQCvQC/T0V15ECoE1wFSgErhZVRsZRrIGjiPN7j/9piqqGgb/06cIfOhc559+yfQx3PPUNn7/zuAk4qJphfxk2XwKsi05bpJbX5/y5j7ny9eLb78/MPxOv7G5GXxqfjE3l5UyvciZH+TgsROsfG0/q8ur6O1TstN9/OKLi7h48uhYnMIZiXrgEBEf8B5wNVANlAO3qeouv22uA76CEzgWAT9Q1UXuukqgTFXrA457H9Cgqt8RkXuB0ar6jeHqkkyBo6unj9+/c5QnK6p47b06AofjmVyYzc1lJXxqfskpw6HvqG7mc6s20tjWzcT8TN6898+sl7gxAZrauvjfLTWsqajmnSOnNvcumDqaS2eM5Wev7+d45+AbT3wi/OC2uXzsotPfKhwPhgocXuY4FgJ7VXW/W4HVwFJgl982S4HH1IleG0SkQEQmquqRYY67FLjCff4o8CowbOBIBu8dbWVNeRXPbKmh4UTXoHUZqSlce8EEbl5QyuJpY0hJCR4MLizJ54Li/IHLagsaxpyqIDudOy6dxu2XTOXtmhbWVBxi7dbDtLrjuZVXNlJeGbwRpFeVe3+9gyvOG0duxshNMXtZ82Kgyu91Nc5Vxem2KQaOAAr8TkQU+ImqrnS3Gd8fWFT1iIiMC1a4iCwHlgNMnpyYt5K2dnTz3PYjrCmvYqt7K6G/C4pHcUtZKZ+YWxxSL1pgxLXBGhMrIsKFJflcWHIh/3DdbF7Y6bwXN+xvGHa/4509/MuzO/nEnElMKshiUn4WWem+iNatt095c189//LsLlo7upkxLpdffHFxxI7vZeAI9nU1sF1suG0uVdXDbmB4SUTeVdXXQi3cDTQrwWmqCnW/frvfb2X5YxU0tXdTXJDFs1+5DN8Q39Qjqb2rl9++fYTvvfQebV29zBiXy5ovLRlYr6qUVzaypryK53ccob178KVwflYaN1xczE1lJZw/Kd/z+hpjICvdxw0Xl3DDxSWsfusQ9z69Y9jtn6qo5qmK6oHXo7PTmJifxaSCLIoLMplY4Pc8P4txeRmkDpF8D7TpYCN3r9kyKK9Zf7yLP+2p57KZY8/sBAN4GTiqAf9JG0qAw6Fuo6r9P2tF5Bmcpq/XgKP9zVkiMhE4tVvoWeju7ePvn97Brzad/KM2t3fzke/9kUfuWMjkMdmRLG6QN/fVs+KJLYOamo4daODx9ZX8+QUT+PWmGp6qqGJ//YlT9r1sxlhuXlDKR2aPJzMtst9ejDGh65/CNhyNbd00tnWzK0jOBPpH+81kYn6mc5VSkMWkgkwm5Z98np+VRlVDO59btZETAbmV3j7lzkfLWbvi0oj0hvcyOZ6Kkxy/CqjBSY5/WlV3+m3zUWAFJ5PjP1TVhSKSA6Soaqv7/CXg26r6goj8F3DMLzleqKp/N1xdwkmO3/fCu/z41X1B151TlMPv7r485MgfjurGNj7yvdcGJpsJlCKckuielJ/JjWWl3DS/ZKBDkzEm9j754zfYfKgp6Lq8DB//ev2F1LV2cri5ncNN7Rxp7uBwUzv1x7uC7hOK7HQfab4UmttPHXOu36fmlfDdm+eEfMyoJ8dVtUdEVgAv4tyOu0pVd4rIXe76h4DncYLGXpzbcT/v7j4eeMZNzqYCT6jqC+667wBPisidwCHgpkjVua2rh8fWD92Ten/dCb62ZiszxuUxVN54qMas4fLMIsKru2uHDBpwMmik+1K4+vzx3FJWyqUzxkal+cwYE577bpzDrSvXnxII0lNT+NGn53HFeUFTs3R09/K+G0Rq/AKK//OhPiec5cMP0PinvXVndD6BrAOgn21VTSx94I0o1OjMfOtjs7nh4mJG51i/CmPiXW1rB4+9eZCfvLaP7l4lLzOVZ/7qEmaMyzvjY6oqze3dHG7qcK9U2qnxe77lUBM9w0yJO2FUJhu+eVXI5cXidtwRJyMtfofuyk5L4QuXTYt1NYwxIRqXl8k9f34ev9lxhAP1Jxibm3FWQQOc1omC7HQKstOZPenUXMW3n93FqmGmxP3gCEiOjzjnjstjyphsDh5rG3Kb7940h+njcoOuG+rqbbhruv5dntt+mEfeqBxyu6vPnzDMUYwx8ap/bKyhxsiKpM9fOpWnNlUN9Cnxl5mWwvLLz4lIORY4/KSkCH/75+ex4oktQddfd+EEPjW/xJOyzx2fyyvv1lIZJGhlp/v48pUzPCnXGOOtaPaNKi3M5vE7F/G1NVs54Hf3pS/FmZBt5vizu+LpF79tMzHysYsm8aPbLh707UBwIvn3bpnrWbl5mWmsXr6EK88rGrQ83ZfCL764iHMj9Ac3xiS2uaUFvPz1D/HLv1jMWHcSttLRWSyZPiZiZVjgCOLjcybxx7+9kkkFzpDJkwuz+aePn09Gqrf9IybkZ/LI5xfyp29cyQR3uOZJBZkjalA0Y0zspaQIS6aP4QMTRzFtbE7Eb9e3pqoh+FKE6UW5ZKT6otI26a9kdDYzx+eSlR79so0xicOrZjK7HdcYY0xQQ92Oa01VxhhjwmKBwxhjTFgscBhjjAmLBQ5jjDFhscBhjDEmLBY4jDHGhMUChzHGmLAkRT8OEakDhp5oY3hjgfoIVmcklJ1s5caybDvn5Ch7pJ7zFFUtClyYFIHjbIhIRbAOMIlcdrKVG8uy7ZyTo+xEO2drqjLGGBMWCxzGGGPCYoHj9FYmYdnJVm4sy7ZzTo6yE+qcLcdhjDEmLHbFYYwxJiwWOIwxxoTFAodLRK4QkWYR2eo+vjXEdtNEZKOI7BGRNSKSHqHyF4hIr4jcGK1yRWSpiGx3z7dCRC6LRtki8hm33O0i8qaIzIlGue4xZ4nIehHpFJF7htku0ud8jYjsFpG9InJvkPUiIj90128XkXlnU57fcVeJSK2IvD3Eeq/KLRWRV0TkHRHZKSJfjUbZIpIpIm+JyDa33H+JRrkBx/eJyBYReS5aZYtIpYjs6H8ve16uqtrDyfNcATwXwnZPAre6zx8C/jICZfuAPwDPAzdGsdxcTua5LgLejUbZwCXAaPf5tcDGKJ7zOGAB8G/APdH4O7t/333AOUA6sA2YHbDNdcBvcaa4XzzU7+QMyr4cmAe8PcR6r8qdCMxzn+cB70XjnN1j5brP04CNwOJonLPf8b8OPBHs88TD33clMHaY9REt1644wiAiAvwZ8Ct30aPA9RE49FeAXwO10SxXVY+r+18F5ACn3CnhRdmq+qaqNrovNwAl0SjXLbtWVcuB7qG28aDshcBeVd2vql3AamBpwDZLgcfUsQEoEJGJZ1EmAKr6GtAwzCZelXtEVTe7z1uBd4Bir8t2j3XcfZnmPgL/rz05ZwARKQE+CvxsiE08K/s0IlquBY7BlriXuL8VkfODrB8DNKlqj/u6mlPfDGERkWLgBpxvtUOJeLl+5d8gIu8CvwG+EM2yXXfifBOKdrnDiXTZxUCV3+tgxwtlGy94Xq6ITAUuxvn273nZblPRVpwvYi+palTKdX0f+Dugb4j1XpWtwO9EZJOILPe6XAscJ23GGZdlDvAj4H+DbCNBlp3t/czfB76hqr3DbONFuc5BVJ9R1Vk436j/NZpli8iVOIHjG9EsNwSRLjuU48XqfD0tV0Ryca6m71bVlmiUraq9qjoX50p2oYhcEI1yReRjQK2qbhpuMy/KBi5V1Xk4Tb9fFpHLvSw3qQOHiHzZTSZtxWkXPQ6gqs8DaSIyNmCXepxLvFT3dQlw+CzLLQNWi0glcCPwYxG53otyA8sWkUn9y90mjenROGcRmSQiF+Fczi9V1WNBdvH8nIcRsbJd1UCp3+tgxwtlGy94Vq6IpOEEjV+o6tPRLBtAVZuAV4FrolTupcAn3PfyauDPROR/olG2qh52f9YCz+A0j3pXbiQSM4nwACZwMlG8EDjU/zpgu6cYnDT9qwjW4ecMnRyPeLnADL9zngfUROOcgcnAXuCS02zn5e/6nxk+OR6xsoFUYD8wjZPJ8fMDtvkog5OXb0XwXKcydHLck3Ld4z0GfH+YbSJeNlAEFLjPs4DXgY9F63ftV8YVBE+Oe3HOOUCe3/M3gWu8LDeiv6yR/ABWADvdN/UG/w81nLudJrnPzwHecj/4ngIyIliHQYHD63Jxmoh2AluB9cBl0Sgb50qj0S13K1ARxXOegPPtqwVocp+PisI5X4dzZ9E+4B/cZXcBd7nPBXjAXb8DKIvQ/9QvgSM4NwNU4zQNRqPcy3CaQrb7/Z2v87psnLsDt7jlvg18K1q/64B6XIEbOKJwzufgfG5tc9/Pnv9/2ZAjxhhjwpLUOQ5jjDHhs8BhjDEmLBY4jDHGhMUChzHGmLBY4DDGGBMWCxzGRIGIHD/9VsaMDBY4jDHGhMUChzFRJCK5IvKyiGx2509Y6rfuH0XkXRF5SUR+KcPMF2JMLKWefhNjTAR1ADeoaos7LtgGEVkHzAc+hTOKbCrOoJvDDZZnTMxY4DAmugT4d3f00j6coa3H4wzRsVZV2wFE5NnYVdGY4VngMCa6PoMzEN98Ve12R1LNJPiw18bEJctxGBNd+ThzNnS785FMcZf/Cfi4O2d2Ls5opsbEJbviMCa6fgE8KyIVOCPGvgugquVurmMbcBCoAJpjVUljhmOj4xoTJ0QkV1WPi0g28BqwXN15u42JJ3bFYUz8WCkis3FyHo9a0DDxyq44jDHGhMWS48YYY8JigcMYY0xYLHAYY4wJiwUOY4wxYbHAYYwxJiz/H/+OpjoTdPkoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting using pure seaborn\n",
    "sns.pointplot(x='lag', y='prob', data=crp.reset_index().query(\n",
    "    'lag <= 5 and lag >= -5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing on CML data (single session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ix = cml.CMLReader.get_data_index(rootdir='~/rhino_mount/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sess = ix.query('experiment == \"FR1\"').iloc[0]\n",
    "events = cml.CMLReader(subject=df_sess['subject'], experiment=df_sess['experiment'], \n",
    "                       session=df_sess['session'], localization=df_sess['localization'], \n",
    "                       montage=df_sess['montage'], rootdir='~/rhino_mount/'\n",
    "                      ).load('task_events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidhalpern/opt/anaconda3/envs/cml/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/Users/davidhalpern/opt/anaconda3/envs/cml/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Reorganizing for psifr\n",
    "events.rename(columns={'item_name': 'item'}, inplace=True)\n",
    "word_evs = events.query('type == \"WORD\"')\n",
    "rec_evs = events.query('type == \"REC_WORD\"')\n",
    "rec_evs['position'] = rec_evs.groupby(['subject', 'session', 'list']).cumcount() \n",
    "word_evs['position'] = word_evs['serialpos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = fr.merge_lists(word_evs, rec_evs, \n",
    "                        merge_keys=['subject', 'session', 'list', 'item'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>prob</th>\n",
       "      <th>actual</th>\n",
       "      <th>possible</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th>lag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">R1001P</th>\n",
       "      <th>-5.0</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-4.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3.0</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.266667</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prob  actual  possible\n",
       "subject lag                             \n",
       "R1001P  -5.0  0.111111       1         9\n",
       "        -4.0  0.000000       0         9\n",
       "        -3.0  0.062500       1        16\n",
       "        -2.0  0.000000       0        16\n",
       "        -1.0  0.117647       2        17\n",
       "         0.0       NaN       0         0\n",
       "         1.0  0.285714       6        21\n",
       "         2.0  0.111111       2        18\n",
       "         3.0  0.117647       2        17\n",
       "         4.0  0.266667       4        15\n",
       "         5.0  0.000000       0        14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crp = fr.lag_crp(merged)\n",
    "crp.query('lag <= 5 and lag >= -5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>subject</th>\n",
       "      <th>level_1</th>\n",
       "      <th>prob</th>\n",
       "      <th>lag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>-5</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>-4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>-3</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>1</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>3</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>4</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  level_1      prob  lag\n",
       "0   R1001P       -5  0.111111   -5\n",
       "1   R1001P       -4  0.000000   -4\n",
       "2   R1001P       -3  0.062500   -3\n",
       "3   R1001P       -2  0.000000   -2\n",
       "4   R1001P       -1  0.117647   -1\n",
       "5   R1001P        0       NaN    0\n",
       "6   R1001P        1  0.285714    1\n",
       "7   R1001P        2  0.111111    2\n",
       "8   R1001P        3  0.117647    3\n",
       "9   R1001P        4  0.266667    4\n",
       "10  R1001P        5  0.000000    5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is all you need to do with pd_to_pb to get th esame thing:\n",
    "events.groupby('subject').apply(pb.pd_crp, itemno_column=\"item_num\").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing on CML data (multiple sessions from one subject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_events = []\n",
    "for _, df_sess in ix.query('experiment == \"FR1\" and subject == \"R1001P\"').iterrows():\n",
    "    try:\n",
    "        sess_events = cml.CMLReader(subject=df_sess['subject'], \n",
    "                                    experiment=df_sess['experiment'], \n",
    "                                    session=df_sess['session'], \n",
    "                                    localization=df_sess['localization'], \n",
    "                                    montage=df_sess['montage'],\n",
    "                                    rootdir='~/rhino_mount/'\n",
    "                                   ).load('task_events')\n",
    "        all_events.append(sess_events)\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "events = pd.concat(all_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidhalpern/opt/anaconda3/envs/cml/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/Users/davidhalpern/opt/anaconda3/envs/cml/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Organizing for psifr\n",
    "events.rename(columns={'item_name': 'item'}, inplace=True)\n",
    "word_evs = events.query('type == \"WORD\"')\n",
    "rec_evs = events.query('type == \"REC_WORD\"')\n",
    "rec_evs['position'] = rec_evs.groupby(['subject', 'session', 'list']).cumcount() \n",
    "word_evs['position'] = word_evs['serialpos']\n",
    "\n",
    "merged = fr.merge_lists(word_evs, rec_evs, \n",
    "                        merge_keys=['subject', 'session', 'list', 'item'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "psifr_crp = fr.lag_crp(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_crp = events.groupby('subject').apply(pb.pd_crp, \n",
    "                                itemno_column='item_num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the psifr result\n",
    "g = fr.plot_lag_crp(psifr_crp)\n",
    "g.ax.set_ylim(0, .3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the pd_to_pb result\n",
    "g = fr.plot_lag_crp(pb_crp)\n",
    "g.ax.set_ylim(0, .3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reason for the difference is becaue psifr only uses one list index when it computes CRP -- to take into account session, you need [session, list] as index so the pd_to_pb version is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
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