notebook/demo_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import pynwb\n",
    "except ImportError:\n",
    "    %pip install pynwb\n",
    "    import pynwb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from pynwb import NWBFile, TimeSeries, NWBHDF5IO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aind_dynamic_foraging_models.logistic_regression import (\n",
    "    fit_logistic_regression, MODEL_MAPPER, \n",
    "    plot_logistic_regression, exp_func, COLOR_MAPPER\n",
    ")\n",
    "\n",
    "import warnings\n",
    "# Ignore all UserWarnings from hdmf.utils\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='hdmf.utils')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get example data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming you're in Code Ocean and the data asset `foraging_nwb_bonsai` is attached.\n",
    "\n",
    "Or, if you're debugging locally, download some sample nwb files to your local computer with the same path.\n",
    "\n",
    "In Windows, it should be `{the hard drive on which your code resides}:\\root\\capsule\\data\\foraging_nwb_bonsai\\`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "session_name = '/root/capsule/data/foraging_nwb_bonsai/703548_2024-03-20_10-47-42.nwb'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "io = NWBHDF5IO(session_name, mode='r')\n",
    "nwb = io.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>animal_response</th>\n",
       "      <th>rewarded_historyL</th>\n",
       "      <th>rewarded_historyR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    animal_response  rewarded_historyL  rewarded_historyR\n",
       "id                                                       \n",
       "0               1.0              False              False\n",
       "1               1.0              False               True\n",
       "2               1.0              False               True\n",
       "3               1.0              False              False\n",
       "4               1.0              False              False\n",
       "5               1.0              False              False\n",
       "6               1.0              False              False\n",
       "7               1.0              False               True\n",
       "8               0.0               True              False\n",
       "9               0.0              False              False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trial = nwb.trials.to_dataframe()\n",
    "df_trial[['animal_response', 'rewarded_historyL', 'rewarded_historyR']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn to 0 and 1 coding (assuming all trials are non-autowater trials)\n",
    "choice_history = df_trial['animal_response'].values\n",
    "choice_history[choice_history == 2] = np.nan\n",
    "reward_history = ((df_trial['rewarded_historyL'] == True) + (df_trial['rewarded_historyR'] == True)).astype(int).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do one fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_logistic_result = fit_logistic_regression(choice_history, reward_history,\n",
    "                            logistic_model='Su2022',\n",
    "                            n_trial_back=15,\n",
    "                            selected_trial_idx=None,\n",
    "                            solver='liblinear', \n",
    "                            penalty='l2',\n",
    "                            Cs=10,\n",
    "                            cv=10,\n",
    "                            n_jobs_cross_validation=-1,\n",
    "                            n_bootstrap_iters=1000, \n",
    "                            n_bootstrap_samplesize=None,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_design = dict_logistic_result['df_design']\n",
    "\n",
    "X = df_design.X.to_numpy()\n",
    "Y = df_design.Y.to_numpy()\n",
    "trials, var_names = df_design.index, df_design.X.columns\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "im = ax.imshow(np.concatenate([X[:30,:], Y[:30, :]], axis=1), cmap='gray')\n",
    "ax.set_xticks(range(len(var_names) + 1))\n",
    "ax.set_xticklabels(var_names.to_list() + ['Choice'], rotation=90)\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['model', 'model_terms', 'n_trial_back', 'df_design', 'X', 'Y', 'df_beta', 'df_beta_exp_fit', 'logistic_reg_cv', 'beta_bootstrap'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_logistic_result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>cross_validation</th>\n",
       "      <th>bootstrap_mean</th>\n",
       "      <th>bootstrap_std</th>\n",
       "      <th>bootstrap_CI_lower</th>\n",
       "      <th>bootstrap_CI_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <th>trial_back</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">RewC</th>\n",
       "      <th>15</th>\n",
       "      <td>-0.146186</td>\n",
       "      <td>-0.148285</td>\n",
       "      <td>0.170583</td>\n",
       "      <td>-0.481912</td>\n",
       "      <td>0.191639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.065598</td>\n",
       "      <td>0.070367</td>\n",
       "      <td>0.162751</td>\n",
       "      <td>-0.250171</td>\n",
       "      <td>0.390693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.136498</td>\n",
       "      <td>0.137155</td>\n",
       "      <td>0.164113</td>\n",
       "      <td>-0.199445</td>\n",
       "      <td>0.457772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.108426</td>\n",
       "      <td>-0.115379</td>\n",
       "      <td>0.169206</td>\n",
       "      <td>-0.439150</td>\n",
       "      <td>0.208339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.280671</td>\n",
       "      <td>0.291238</td>\n",
       "      <td>0.165501</td>\n",
       "      <td>-0.038180</td>\n",
       "      <td>0.626229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.297412</td>\n",
       "      <td>0.318254</td>\n",
       "      <td>0.167488</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.644545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.048291</td>\n",
       "      <td>-0.052463</td>\n",
       "      <td>0.166269</td>\n",
       "      <td>-0.378479</td>\n",
       "      <td>0.264709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000896</td>\n",
       "      <td>-0.006470</td>\n",
       "      <td>0.174186</td>\n",
       "      <td>-0.377825</td>\n",
       "      <td>0.335459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.211107</td>\n",
       "      <td>0.231546</td>\n",
       "      <td>0.177568</td>\n",
       "      <td>-0.118295</td>\n",
       "      <td>0.598944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.197905</td>\n",
       "      <td>0.222949</td>\n",
       "      <td>0.163353</td>\n",
       "      <td>-0.116126</td>\n",
       "      <td>0.523999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.396052</td>\n",
       "      <td>0.419938</td>\n",
       "      <td>0.171117</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>0.765757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.246317</td>\n",
       "      <td>0.246930</td>\n",
       "      <td>0.170917</td>\n",
       "      <td>-0.109703</td>\n",
       "      <td>0.566344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.193063</td>\n",
       "      <td>0.220010</td>\n",
       "      <td>0.166610</td>\n",
       "      <td>-0.099748</td>\n",
       "      <td>0.549808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.430427</td>\n",
       "      <td>0.446575</td>\n",
       "      <td>0.174449</td>\n",
       "      <td>0.102007</td>\n",
       "      <td>0.764398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.049212</td>\n",
       "      <td>1.100681</td>\n",
       "      <td>0.153731</td>\n",
       "      <td>0.809082</td>\n",
       "      <td>1.407691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">UnrC</th>\n",
       "      <th>15</th>\n",
       "      <td>0.166719</td>\n",
       "      <td>0.179992</td>\n",
       "      <td>0.167028</td>\n",
       "      <td>-0.151660</td>\n",
       "      <td>0.485894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.213752</td>\n",
       "      <td>0.221590</td>\n",
       "      <td>0.177469</td>\n",
       "      <td>-0.117213</td>\n",
       "      <td>0.585757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.150454</td>\n",
       "      <td>0.152673</td>\n",
       "      <td>0.176042</td>\n",
       "      <td>-0.207317</td>\n",
       "      <td>0.501019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.069872</td>\n",
       "      <td>0.067689</td>\n",
       "      <td>0.172210</td>\n",
       "      <td>-0.281508</td>\n",
       "      <td>0.383473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.204016</td>\n",
       "      <td>-0.213244</td>\n",
       "      <td>0.174624</td>\n",
       "      <td>-0.563773</td>\n",
       "      <td>0.119444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.094110</td>\n",
       "      <td>-0.101732</td>\n",
       "      <td>0.166843</td>\n",
       "      <td>-0.437123</td>\n",
       "      <td>0.235814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.164363</td>\n",
       "      <td>0.176670</td>\n",
       "      <td>0.179103</td>\n",
       "      <td>-0.193022</td>\n",
       "      <td>0.536174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.087382</td>\n",
       "      <td>-0.092642</td>\n",
       "      <td>0.173942</td>\n",
       "      <td>-0.429348</td>\n",
       "      <td>0.259625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.333966</td>\n",
       "      <td>0.364881</td>\n",
       "      <td>0.179535</td>\n",
       "      <td>0.025090</td>\n",
       "      <td>0.718761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.104845</td>\n",
       "      <td>-0.118944</td>\n",
       "      <td>0.166026</td>\n",
       "      <td>-0.442735</td>\n",
       "      <td>0.194659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.022298</td>\n",
       "      <td>-0.021830</td>\n",
       "      <td>0.174067</td>\n",
       "      <td>-0.384349</td>\n",
       "      <td>0.327153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.012063</td>\n",
       "      <td>-0.020308</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>-0.375438</td>\n",
       "      <td>0.353492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.117698</td>\n",
       "      <td>-0.123528</td>\n",
       "      <td>0.179226</td>\n",
       "      <td>-0.446246</td>\n",
       "      <td>0.262828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015973</td>\n",
       "      <td>0.010865</td>\n",
       "      <td>0.183519</td>\n",
       "      <td>-0.348152</td>\n",
       "      <td>0.383664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.244197</td>\n",
       "      <td>-0.251708</td>\n",
       "      <td>0.175285</td>\n",
       "      <td>-0.596992</td>\n",
       "      <td>0.108142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.143977</td>\n",
       "      <td>0.150851</td>\n",
       "      <td>0.122880</td>\n",
       "      <td>-0.081231</td>\n",
       "      <td>0.398597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cross_validation  bootstrap_mean  bootstrap_std  \\\n",
       "var  trial_back                                                    \n",
       "RewC 15                 -0.146186       -0.148285       0.170583   \n",
       "     14                  0.065598        0.070367       0.162751   \n",
       "     13                  0.136498        0.137155       0.164113   \n",
       "     12                 -0.108426       -0.115379       0.169206   \n",
       "     11                  0.280671        0.291238       0.165501   \n",
       "     10                  0.297412        0.318254       0.167488   \n",
       "     9                  -0.048291       -0.052463       0.166269   \n",
       "     8                   0.000896       -0.006470       0.174186   \n",
       "     7                   0.211107        0.231546       0.177568   \n",
       "     6                   0.197905        0.222949       0.163353   \n",
       "     5                   0.396052        0.419938       0.171117   \n",
       "     4                   0.246317        0.246930       0.170917   \n",
       "     3                   0.193063        0.220010       0.166610   \n",
       "     2                   0.430427        0.446575       0.174449   \n",
       "     1                   1.049212        1.100681       0.153731   \n",
       "UnrC 15                  0.166719        0.179992       0.167028   \n",
       "     14                  0.213752        0.221590       0.177469   \n",
       "     13                  0.150454        0.152673       0.176042   \n",
       "     12                  0.069872        0.067689       0.172210   \n",
       "     11                 -0.204016       -0.213244       0.174624   \n",
       "     10                 -0.094110       -0.101732       0.166843   \n",
       "     9                   0.164363        0.176670       0.179103   \n",
       "     8                  -0.087382       -0.092642       0.173942   \n",
       "     7                   0.333966        0.364881       0.179535   \n",
       "     6                  -0.104845       -0.118944       0.166026   \n",
       "     5                  -0.022298       -0.021830       0.174067   \n",
       "     4                  -0.012063       -0.020308       0.181205   \n",
       "     3                  -0.117698       -0.123528       0.179226   \n",
       "     2                   0.015973        0.010865       0.183519   \n",
       "     1                  -0.244197       -0.251708       0.175285   \n",
       "bias NaN                 0.143977        0.150851       0.122880   \n",
       "\n",
       "                 bootstrap_CI_lower  bootstrap_CI_upper  \n",
       "var  trial_back                                          \n",
       "RewC 15                   -0.481912            0.191639  \n",
       "     14                   -0.250171            0.390693  \n",
       "     13                   -0.199445            0.457772  \n",
       "     12                   -0.439150            0.208339  \n",
       "     11                   -0.038180            0.626229  \n",
       "     10                    0.002419            0.644545  \n",
       "     9                    -0.378479            0.264709  \n",
       "     8                    -0.377825            0.335459  \n",
       "     7                    -0.118295            0.598944  \n",
       "     6                    -0.116126            0.523999  \n",
       "     5                     0.058309            0.765757  \n",
       "     4                    -0.109703            0.566344  \n",
       "     3                    -0.099748            0.549808  \n",
       "     2                     0.102007            0.764398  \n",
       "     1                     0.809082            1.407691  \n",
       "UnrC 15                   -0.151660            0.485894  \n",
       "     14                   -0.117213            0.585757  \n",
       "     13                   -0.207317            0.501019  \n",
       "     12                   -0.281508            0.383473  \n",
       "     11                   -0.563773            0.119444  \n",
       "     10                   -0.437123            0.235814  \n",
       "     9                    -0.193022            0.536174  \n",
       "     8                    -0.429348            0.259625  \n",
       "     7                     0.025090            0.718761  \n",
       "     6                    -0.442735            0.194659  \n",
       "     5                    -0.384349            0.327153  \n",
       "     4                    -0.375438            0.353492  \n",
       "     3                    -0.446246            0.262828  \n",
       "     2                    -0.348152            0.383664  \n",
       "     1                    -0.596992            0.108142  \n",
       "bias NaN                  -0.081231            0.398597  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_logistic_result['df_beta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <th colspan=\"2\" halign=\"left\">amp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stat</th>\n",
       "      <th>fitted</th>\n",
       "      <th>standard_error</th>\n",
       "      <th>fitted</th>\n",
       "      <th>standard_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RewC</th>\n",
       "      <td>1.292674</td>\n",
       "      <td>0.32109</td>\n",
       "      <td>2.638506</td>\n",
       "      <td>0.758506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UnrC</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "var        amp                      tau               \n",
       "stat    fitted standard_error    fitted standard_error\n",
       "RewC  1.292674        0.32109  2.638506       0.758506\n",
       "UnrC       NaN            NaN       NaN            NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_logistic_result['df_beta_exp_fit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Su2022, L2 penalty, best C = 0.359 from 10-fold CV\\nscore $\\\\pm$ std = 0.694 $\\\\pm$ 0.11; CI from 1000 bootstraps'}, xlabel='Past trials', ylabel='Logistic regression coeffs'>"
      ]
     },
     "execution_count": 62,
     "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": [
    "plot_logistic_regression(dict_logistic_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try a tiny panel project here :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import panel\n",
    "except ImportError:\n",
    "    %pip install panel jupyter_bokeh holoviews\n",
    "\n",
    "import glob\n",
    "import panel as pn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all nwb files from /root/capsule/data/foraging_nwb_bonsai/ using glob\n",
    "def get_all_nwbs():\n",
    "    return glob.glob('/root/capsule/data/foraging_nwb_bonsai/*.nwb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a plot function with holoviz (thank you ChatGPT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import holoviews as hv\n",
    "pn.extension()\n",
    "hv.extension('bokeh')\n",
    "\n",
    "def plot_logistic_regression_holoviz(dict_logistic_result,\n",
    "                                     ax=None, \n",
    "                                    ls='-o',\n",
    "                                    alpha=0.3,\n",
    "                                    ):\n",
    "    # Define empty list to hold plots\n",
    "    plots = []\n",
    "    \n",
    "    # Unpack necessary items from dict_logistic_result\n",
    "    logistic_reg = dict_logistic_result['logistic_reg_cv']\n",
    "    df_beta = dict_logistic_result['df_beta']\n",
    "    df_beta_exp_fit = dict_logistic_result['df_beta_exp_fit']\n",
    "    \n",
    "    for var in dict_logistic_result['model_terms']:\n",
    "        col = COLOR_MAPPER[var]\n",
    "        \n",
    "        var_mean = df_beta.loc[var, 'cross_validation']\n",
    "        trials_back = var_mean.index\n",
    "        \n",
    "        if np.all(np.isnan(var_mean)):\n",
    "            continue\n",
    "        \n",
    "        # Create curve for the beta values\n",
    "        beta_curve = hv.Curve((trials_back, var_mean), label=var + ' ± CI').opts(color=col)\n",
    "        \n",
    "        # Add confidence intervals as shaded area if available\n",
    "        if 'bootstrap_CI_upper' in df_beta.columns:\n",
    "            var_CI_upper = df_beta.loc[var, 'bootstrap_CI_upper']\n",
    "            var_CI_lower = df_beta.loc[var, 'bootstrap_CI_lower']\n",
    "            \n",
    "            confidence_area = hv.Area((trials_back, var_CI_lower, var_CI_upper), vdims=['y', 'y2']).opts(\n",
    "                fill_color=col, fill_alpha=alpha)\n",
    "            beta_curve *= confidence_area\n",
    "        \n",
    "        # Add exponential fit if applicable\n",
    "        if var != 'bias':\n",
    "            xx = np.linspace(1, trials_back.max(), 100)\n",
    "            yy = exp_func(xx, *df_beta_exp_fit.loc[var, (slice(None), 'fitted')])\n",
    "            exp_fit_curve = hv.Curve((xx, yy), label=f\"τ = {df_beta_exp_fit.loc[var, ('tau', 'fitted')]:.2f}\").opts(\n",
    "                color=col, line_dash='dashed', line_width=4)\n",
    "            beta_curve *= exp_fit_curve\n",
    "        \n",
    "        plots.append(beta_curve)\n",
    "        \n",
    "    # Define the horizontal line at y=0\n",
    "    hline = hv.HLine(0).opts(color='black', line_dash='dotted', line_width=0.5)\n",
    "    plots.append(hline)\n",
    "    \n",
    "    # Combine all the plots\n",
    "    final_plot = hv.Overlay(plots).opts(\n",
    "        title=f'{logistic_reg.cv}-fold CV, score ± std = {np.mean(logistic_reg.scores_[1.0]):.3g} ± {np.std(logistic_reg.scores_[1.0]):.2g}\\nbest C = {logistic_reg.C_[0]:.3g}',\n",
    "        xlabel='Past trials',\n",
    "        ylabel='Logistic regression coeffs',\n",
    "        show_legend=True\n",
    "    ).redim.range(y=(-3, 3))  # Example y-axis range, adjust as necessary\n",
    "    \n",
    "    return final_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_plot_logistic_regression_from_nwb(\n",
    "    session_name= '/root/capsule/data/foraging_nwb_bonsai/703548_2024-03-20_10-47-42.nwb',\n",
    "    logistic_model='Su2022',\n",
    "    n_trial_back=15,\n",
    "    penalty='l2',\n",
    "    n_bootstrap_iters=1000,\n",
    "):\n",
    "    io = NWBHDF5IO(session_name, mode='r')\n",
    "    nwb = io.read()\n",
    "    \n",
    "    df_trial = nwb.trials.to_dataframe()\n",
    "    df_trial[['animal_response', 'rewarded_historyL', 'rewarded_historyR']].head(10)\n",
    "    \n",
    "    # Turn to 0 and 1 coding (assuming all trials are non-autowater trials)\n",
    "    choice_history = df_trial['animal_response'].values\n",
    "    choice_history[choice_history == 2] = np.nan\n",
    "    reward_history = ((df_trial['rewarded_historyL'] == True) + (df_trial['rewarded_historyR'] == True)).astype(int).values\n",
    "\n",
    "    dict_logistic_result = fit_logistic_regression(choice_history, reward_history,\n",
    "                                logistic_model=logistic_model,\n",
    "                                n_trial_back=n_trial_back,\n",
    "                                selected_trial_idx=None,\n",
    "                                solver='liblinear', \n",
    "                                penalty=penalty,\n",
    "                                Cs=10,\n",
    "                                cv=10,\n",
    "                                n_jobs_cross_validation=-1,\n",
    "                                n_bootstrap_iters=n_bootstrap_iters, \n",
    "                                n_bootstrap_samplesize=None,)\n",
    "    ax = plot_logistic_regression_holoviz(dict_logistic_result)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b0aab17f0994f818978f12c2650a9df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BokehModel(combine_events=True, render_bundle={'docs_json': {'1c5b8ce5-df23-4283-8055-70c978ff0aa2': {'version…"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_widget = pn.widgets.Select(options=get_all_nwbs(), name='Select NWB file', width=500, \n",
    "                                value='/root/capsule/data/foraging_nwb_bonsai/703548_2024-03-20_10-47-42.nwb')\n",
    "model_widget = pn.widgets.Select(options=list(MODEL_MAPPER.keys()), name='Select model')\n",
    "trial_back_widget = pn.widgets.IntSlider(name='Number of trials back', start=1, end=30, value=15)\n",
    "penalty_widget = pn.widgets.Select(options=['l1', 'l2'], name='Penalty')\n",
    "n_bootstrap_iters_widget = pn.widgets.IntSlider(name='Number of bootstrap iterations', start=0, end=2000, value=1000, step=100)\n",
    "\n",
    "logistic_plot = pn.bind(fit_and_plot_logistic_regression_from_nwb,\n",
    "                        session_name=file_widget,\n",
    "                        logistic_model=model_widget,\n",
    "                        n_trial_back=trial_back_widget,\n",
    "                        penalty=penalty_widget,\n",
    "                        n_bootstrap_iters=n_bootstrap_iters_widget)\n",
    "\n",
    "first_app = pn.Column(file_widget, \n",
    "                      pn.Row(model_widget, trial_back_widget), \n",
    "                      pn.Row(penalty_widget, n_bootstrap_iters_widget),\n",
    "                      pn.pane.HoloViews(logistic_plot, width=500, height=500, backend='bokeh')\n",
    "                      )\n",
    "first_app.servable()"
   ]
  }
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