subj-lvl_time_place.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2241ae9a-bf49-421c-a198-294f64339814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# General\n",
    "import sys\n",
    "import os.path as op\n",
    "from time import time\n",
    "from collections import OrderedDict as od\n",
    "from glob import glob\n",
    "import itertools\n",
    "import warnings\n",
    "from importlib import reload\n",
    "from cluster_helper.cluster import cluster_view\n",
    "\n",
    "# Scientific\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows = 100\n",
    "pd.options.display.max_columns = 999\n",
    "\n",
    "# Stats\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "# Plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "plt.style.use('/home1/dscho/code/general/preferred.mplstyle')\n",
    "\n",
    "# Personal\n",
    "sys.path.append('/home1/dscho/code/general')\n",
    "sys.path.append('/home1/dscho/code/projects')\n",
    "import data_io as dio\n",
    "import array_operations as aop\n",
    "from formatting import *\n",
    "from helper_funcs import *\n",
    "from eeg_plotting import plot_trace\n",
    "from time_cells import (spike_sorting, spike_preproc,\n",
    "                        events_preproc, events_proc,\n",
    "                        time_bin_analysis, pop_decoding)\n",
    "\n",
    "font = {'tick': 12,\n",
    "        'label': 14,\n",
    "        'annot': 12,\n",
    "        'fig': 16}\n",
    "\n",
    "# Colors\n",
    "colors, palettes = get_colors()\n",
    "co = colors\n",
    "mpl.rcParams['axes.prop_cycle'] = mpl.cycler('color', palettes['economist'])\n",
    "\n",
    "colws = od([(1, 1.555118),\n",
    "            (2, 3.169291),\n",
    "            (3, 6.456693)])\n",
    "\n",
    "proj_dir = '/home1/dscho/projects/time_cells'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aeaf65d0-1614-40cd-948a-6f42f9fdb283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 subjects, 12 sessions\n"
     ]
    }
   ],
   "source": [
    "# Get sessions.\n",
    "sessions = np.unique([op.basename(f).split('-')[0] \n",
    "                      for f in glob(op.join(proj_dir, 'analysis', 'events', '*-Events.pkl'))])\n",
    "print('{} subjects, {} sessions'.format(len(np.unique([x.split('_')[0] for x in sessions])), len(sessions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a47b89-85e2-48f3-b058-262ddd277110",
   "metadata": {},
   "source": [
    "# Load subject-level place and time decoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c840b623-1618-459a-ad78-fb80028725e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_onto(sort_on, \n",
    "              apply_to, \n",
    "              n_split=None):\n",
    "    \"\"\"Sort the values within each split of sort_on.\n",
    "    \n",
    "    Apply the sorting indices to apply_to.\"\"\"\n",
    "    # Split the input vectors.\n",
    "    if (len(np.array(apply_to).shape) == 1) & (n_split is None):\n",
    "        n_split = 1\n",
    "    if n_split is not None:\n",
    "        sort_on = np.split(np.ravel(sort_on), n_split)\n",
    "        apply_to = np.split(np.array(apply_to), n_split)\n",
    "    \n",
    "    # Sort each row.\n",
    "    apply_to = np.array(apply_to)\n",
    "    assert len(apply_to.shape) == 2\n",
    "    sort_idx = np.argsort(sort_on, axis=-1)\n",
    "    apply_to = np.squeeze([apply_to[ii, sort_idx[ii, :]]\n",
    "                           for ii in range(sort_idx.shape[0])])\n",
    "    return apply_to\n",
    "\n",
    "\n",
    "def clf_error(y_test, y_test_pred, err_mult=1):\n",
    "    \"\"\"Return the difference between predicted and actual values.\"\"\"\n",
    "    return list((np.array(y_test_pred) - np.array(y_test)) * err_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "45324977-57fb-4d03-a16b-43e1657a207f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in path_lens:\n",
    "    path_lens[col] = path_lens[col].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "862c19ca-8177-4ac6-84fe-a6910dcd0fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(np.tril(path_lens.values), np.triu(path_lens.values).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "152941d8-51fc-4ded-997d-a43b412e19c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved /home1/dscho/projects/time_cells/figs/plot_behav/maze_path_length.pdf\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3803.15x2852.36 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig = 0\n",
    "overwrite = 0\n",
    "figsize = (colws[2], colws[2] * 0.75)\n",
    "dpi = 1200\n",
    "font = {'tick': 5, 'label': 6, 'fig': 8, 'annot': 5}\n",
    "spine_lw = 0.5\n",
    "ticklen = 2.5\n",
    "tickpad = 2.5\n",
    "labelpad = 5\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "iax = 0\n",
    "_ax = ax[iax]\n",
    "_ax = sns.heatmap(path_lens.values, vmin=0, vmax=4, cmap='binary_r', square=True)\n",
    "for axis in ['left', 'bottom']:\n",
    "    _ax.spines[axis].set_linewidth(spine_lw)\n",
    "_ax.tick_params(labelsize=font['tick'], pad=tickpad, length=ticklen, width=spine_lw)\n",
    "_ax.set_xticks(np.arange(path_lens.shape[0]) + 0.5)\n",
    "_ax.set_xticklabels([x.replace('_', ' ') for x in path_lens.index.tolist()], rotation=90)\n",
    "_ax.set_yticks(np.arange(path_lens.shape[1]) + 0.5)\n",
    "_ax.set_yticklabels([x.replace('_', ' ') for x in path_lens.columns.tolist()], rotation=0)\n",
    "_ax.set_title('Path length', fontsize=font['fig'])\n",
    "\n",
    "if savefig:\n",
    "    filename = op.join('/home1/dscho/projects/time_cells',\n",
    "                       'figs', 'plot_behav', \n",
    "                       'maze_path_length.pdf')\n",
    "    if overwrite or not op.exists(filename):\n",
    "        fig.savefig(filename, format='pdf', bbox_inches='tight', pad_inches=0)\n",
    "        print('Saved {}'.format(filename))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "059be175-6b20-40ac-8779-75213e152be1",
   "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>Base</th>\n",
       "      <th>C_Hall</th>\n",
       "      <th>NE_Hall</th>\n",
       "      <th>NE_Room</th>\n",
       "      <th>NW_Hall</th>\n",
       "      <th>NW_Room</th>\n",
       "      <th>N_Passage</th>\n",
       "      <th>SE_Hall</th>\n",
       "      <th>SE_Room</th>\n",
       "      <th>SW_Hall</th>\n",
       "      <th>SW_Room</th>\n",
       "      <th>S_Passage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_Hall</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE_Hall</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE_Room</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NW_Hall</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NW_Room</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Passage</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE_Hall</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE_Room</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW_Hall</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW_Room</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_Passage</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Base  C_Hall  NE_Hall  NE_Room  NW_Hall  NW_Room  N_Passage  \\\n",
       "Base          0       2        4        3        4        3          3   \n",
       "C_Hall        2       0        2        1        2        1          1   \n",
       "NE_Hall       4       2        0        1        2        3          1   \n",
       "NE_Room       3       1        1        0        3        2          2   \n",
       "NW_Hall       4       2        2        3        0        1          1   \n",
       "NW_Room       3       1        3        2        1        0          2   \n",
       "N_Passage     3       1        1        2        1        2          0   \n",
       "SE_Hall       1       2        3        2        4        3          3   \n",
       "SE_Room       2       1        2        1        3        2          2   \n",
       "SW_Hall       1       2        4        3        3        2          3   \n",
       "SW_Room       2       1        3        2        2        1          2   \n",
       "S_Passage     1       1        3        2        3        2          2   \n",
       "\n",
       "           SE_Hall  SE_Room  SW_Hall  SW_Room  S_Passage  \n",
       "Base             1        2        1        2          1  \n",
       "C_Hall           2        1        2        1          1  \n",
       "NE_Hall          3        2        4        3          3  \n",
       "NE_Room          2        1        3        2          2  \n",
       "NW_Hall          4        3        3        2          3  \n",
       "NW_Room          3        2        2        1          2  \n",
       "N_Passage        3        2        3        2          2  \n",
       "SE_Hall          0        1        2        3          2  \n",
       "SE_Room          1        0        3        2          2  \n",
       "SW_Hall          2        3        0        1          3  \n",
       "SW_Room          3        2        1        0          2  \n",
       "S_Passage        2        2        3        2          0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "49eacb1d-50af-4daa-bd1b-91bd56a56b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [60, 60]\n",
       "1     [60, 60]\n",
       "2     [60, 60]\n",
       "3     [60, 60]\n",
       "4     [60, 60]\n",
       "        ...   \n",
       "67    [60, 60]\n",
       "68    [60, 60]\n",
       "69    [60, 60]\n",
       "70    [60, 60]\n",
       "71    [60, 60]\n",
       "Length: 708, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df.apply(lambda x: [len(x['y_test']), len(x['y_test_null'])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "20861c8d-ee32-42be-9837-afca18e819c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/dscho/anaconda3/envs/memlab/lib/python3.6/site-packages/pandas/core/series.py:4213: RuntimeWarning: Mean of empty slice\n",
      "  mapped = lib.map_infer(values, f, convert=convert_dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clfs: (354, 21)\n",
      "Ran in 13.9s\n"
     ]
    }
   ],
   "source": [
    "average = 'micro'\n",
    "game_states = ['Encoding']\n",
    "err_mult = 2\n",
    "\n",
    "# ----------------------------------------------\n",
    "# Load subject-level place decoder dataframes.\n",
    "timer = Timer()\n",
    "\n",
    "path_lens = dio.open_pickle('/home1/dscho/projects/time_cells/unity/maps/path_lengths.pkl')\n",
    "\n",
    "globstr = op.join(proj_dir, 'analysis', 'classifiers',\n",
    "                  'SVC_predicting_maze_region-{}-*.pkl')\n",
    "\n",
    "clfs = []\n",
    "clf_results = []\n",
    "for subj_sess in sessions:\n",
    "    fpath = glob(globstr.format(subj_sess))\n",
    "    if len(fpath) == 1:\n",
    "        _clf_results = dio.open_pickle(fpath[0])\n",
    "        _clf_results.insert(0, 'subj_sess', subj_sess)\n",
    "        clf_results.append(_clf_results)\n",
    "clf_results = pd.concat(clf_results)\n",
    "if 'subj' not in clf_results:\n",
    "    clf_results.insert(0, 'subj', clf_results['subj_sess'].apply(lambda x: x.split('_')[0]))\n",
    "if 'testvar' not in clf_results:\n",
    "    clf_results.insert(0, 'testvar', 'place')\n",
    "\n",
    "# Drop columns that we don't need.\n",
    "drop_cols = ['mean_acc', 'acc_by_time', 'accuracy', 'clf',\n",
    "             'mean_acc_null', 'acc_by_time_null', 'accuracy_null', 'clf_null']\n",
    "for _df in (clf_results,):\n",
    "    _drop_cols = [col for col in drop_cols if col in _df.columns]\n",
    "    if len(_drop_cols) > 0:\n",
    "        _df.drop(columns=_drop_cols, inplace=True)\n",
    "\n",
    "# Calculate performance metrics.\n",
    "for _df in (clf_results,):\n",
    "    _df['recall'] = _df.apply(lambda x: metrics.recall_score(x['y_test'], x['y_test_pred'], average=average), axis=1)\n",
    "    _df['precision'] = _df.apply(lambda x: metrics.precision_score(x['y_test'], x['y_test_pred'], average=average), axis=1)\n",
    "    _df['f1'] = _df.apply(lambda x: metrics.f1_score(x['y_test'], x['y_test_pred'], average=average).tolist(), axis=1)\n",
    "    _df['abs_err'] = _df.apply(lambda x: [path_lens.at[x['y_test'][ii], x['y_test_pred'][ii]]\n",
    "                                          for ii in range(len(x['y_test']))\n",
    "                                          if x['y_test'][ii]!='Base'\n",
    "                                         ],\n",
    "                               axis=1)\n",
    "    _df['mean_abs_err'] = _df['abs_err'].apply(np.nanmean)\n",
    "    \n",
    "    _df['recall_null'] = _df.apply(lambda x: metrics.recall_score(x['y_test_null'], x['y_test_pred_null'], average=average), axis=1)\n",
    "    _df['precision_null'] = _df.apply(lambda x: metrics.precision_score(x['y_test_null'], x['y_test_pred_null'], average=average), axis=1)\n",
    "    _df['f1_null'] = _df.apply(lambda x: metrics.f1_score(x['y_test_null'], x['y_test_pred_null'], average=average).tolist(), axis=1)\n",
    "    _df['abs_err_null'] = _df.apply(lambda x: [path_lens.at[x['y_test_null'][ii], x['y_test_pred_null'][ii]]\n",
    "                                               for ii in range(len(x['y_test_null']))\n",
    "                                               if x['y_test'][ii]!='Base'\n",
    "                                              ], axis=1)\n",
    "    _df['mean_abs_err_null'] = _df['abs_err_null'].apply(np.nanmean)\n",
    "    \n",
    "clfs.append(clf_results)\n",
    "\n",
    "# ----------------------------------------------\n",
    "# Load subject-level time decoders.\n",
    "testvar = 'time_step'\n",
    "clf_results = []\n",
    "for subj_sess in sessions:\n",
    "    event_spikes = time_bin_analysis.load_event_spikes(subj_sess, verbose=0)\n",
    "    for game_state in game_states:\n",
    "        globstr = op.join(proj_dir, 'analysis', 'classifiers',\n",
    "                          'SVC_predicting_{}-{}-{}units-{}-random_search-*fold.pkl'\n",
    "                          .format(testvar, subj_sess, len(event_spikes.column_map['neurons']), game_state))\n",
    "        files = glob(globstr)\n",
    "        assert len(files) == 1\n",
    "        clf_results.append(dio.open_pickle(files[0]))\n",
    "clf_results = pd.concat(clf_results)\n",
    "if 'subj' not in clf_results:\n",
    "    clf_results.insert(0, 'subj', clf_results['subj_sess'].apply(lambda x: x.split('_')[0]))\n",
    "if 'testvar' not in clf_results:\n",
    "    clf_results.drop(columns=['testvar'])\n",
    "    clf_results.insert(0, 'testvar', 'time')\n",
    "    \n",
    "# --------------------------\n",
    "# Resort y_test_null and y_test_pred_null on the values within each y_test_null test trial.\n",
    "for _df in (clf_results,):\n",
    "    cols = ['accuracy_null', 'acc_by_time_null', 'y_test_pred_null', 'y_test_null']\n",
    "    cols = [col for col in cols if col in _df.columns]\n",
    "    for col in cols:\n",
    "        _df[col] = _df.apply(lambda x: sort_onto(x['y_test_null'], x[col], len(x['test_trials'])), axis=1)\n",
    "        \n",
    "# Drop columns that we don't need.\n",
    "drop_cols = ['mean_acc', 'acc_by_time', 'accuracy', 'clf',\n",
    "             'mean_acc_null', 'acc_by_time_null', 'accuracy_null', 'clf_null']\n",
    "for _df in (clf_results,):\n",
    "    _drop_cols = [col for col in drop_cols if col in _df.columns]\n",
    "    if len(_drop_cols) > 0:\n",
    "        _df.drop(columns=_drop_cols, inplace=True)\n",
    "\n",
    "# Calculate performance metrics.\n",
    "for _df in (clf_results,):\n",
    "    _df['recall'] = _df.apply(lambda x: metrics.recall_score(x['y_test'], x['y_test_pred'], average=average), axis=1)\n",
    "    _df['precision'] = _df.apply(lambda x: metrics.precision_score(x['y_test'], x['y_test_pred'], average=average), axis=1)\n",
    "    _df['f1'] = _df.apply(lambda x: metrics.f1_score(x['y_test'], x['y_test_pred'], average=average).tolist(), axis=1)\n",
    "    _df['err'] = _df.apply(lambda x: clf_error(x['y_test'], x['y_test_pred'], err_mult=err_mult), axis=1)\n",
    "    _df['abs_err'] = _df.apply(lambda x: np.abs(x['err']).tolist(), axis=1)\n",
    "    _df['mean_abs_err'] = _df['abs_err'].apply(np.nanmean)\n",
    "    \n",
    "    _df['recall_null'] = _df.apply(lambda x: metrics.recall_score(x['y_test_null'], x['y_test_pred_null'], average=average), axis=1)\n",
    "    _df['precision_null'] = _df.apply(lambda x: metrics.precision_score(x['y_test_null'], x['y_test_pred_null'], average=average), axis=1)\n",
    "    _df['f1_null'] = _df.apply(lambda x: metrics.f1_score(x['y_test_null'], x['y_test_pred_null'], average=average).tolist(), axis=1)\n",
    "    _df['error_null'] = _df.apply(lambda x: clf_error(x['y_test_null'], x['y_test_pred_null'], err_mult=err_mult), axis=1)\n",
    "    _df['abs_err_null'] = _df.apply(lambda x: np.abs(x['error_null']).tolist(), axis=1)\n",
    "    _df['mean_abs_err_null'] = _df['abs_err_null'].apply(np.nanmean)\n",
    "    \n",
    "clfs.append(clf_results)\n",
    "clfs = (pd.concat(clfs, axis=0)\n",
    "        .query(\"(gameState_train==gameState_test=={})\".format(game_states))\n",
    "        .reset_index(drop=True))\n",
    "clfs.insert(clfs.columns.tolist().index('test_trials')+1,\n",
    "            'test_trial',\n",
    "            clfs['test_trials'].apply(lambda x: x[0]))\n",
    "clfs.drop(columns=['test_trials'], inplace=True)\n",
    "\n",
    "cols = ['subj', 'subj_sess', 'gameState_train', 'gameState_test', 'test_trial',\n",
    "        'y_test', 'y_test_pred', 'abs_err', 'mean_abs_err',\n",
    "        'y_test_null', 'y_test_pred_null', 'abs_err_null', 'mean_abs_err_null']\n",
    "grp_cols = ['testvar', 'subj', 'subj_sess', 'gameState_train', 'gameState_test', 'test_trial']\n",
    "clfs = pd.merge(clfs.sort_values(grp_cols).query(\"(testvar=='place')\")[cols],\n",
    "                clfs.sort_values(grp_cols).query(\"(testvar=='time_step')\")[cols],\n",
    "                on=grp_cols[1:], suffixes=('_place', '_time')).reset_index(drop=True)\n",
    "\n",
    "keep_idx = np.where(np.isfinite(clfs['mean_abs_err_place']))[0]\n",
    "clfs = clfs.iloc[keep_idx].reset_index(drop=True)\n",
    "\n",
    "print('clfs: {}'.format(clfs.shape))\n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "3fb91a69-f60f-4b4b-b2bc-0a4e2d8786c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gold_views: (594, 11), gold_dug: (277, 2)\n"
     ]
    }
   ],
   "source": [
    "# Load all gold view events.\n",
    "output_dir = '/home1/dscho/projects/unit_activity_and_hpc_theta/data2/goldmine/nav/events'\n",
    "gold_views = []\n",
    "for subj_sess in np.unique(clfs['subj_sess']):\n",
    "    filename = op.join(output_dir, '{}-gold_views.pkl'.format(subj_sess))\n",
    "    gold_views.append(dio.open_pickle(filename))\n",
    "gold_views = pd.concat(gold_views, axis=0).reset_index()\n",
    "\n",
    "# Get the no. of (viewed) golds dug on each trial, for each session.\n",
    "gold_dug = (gold_views\n",
    "            .groupby(['subj_sess', 'trial', 'gold_id'])\n",
    "            .agg({'dur': np.sum,\n",
    "                  'dig_success': lambda x: np.any(x) * 1})\n",
    "            .reset_index()\n",
    "            .groupby(['subj_sess', 'trial'])\n",
    "            .agg({'dur': np.sum,\n",
    "                  'dig_success': np.sum}))\n",
    "\n",
    "print('gold_views: {}, gold_dug: {}'.format(gold_views.shape, gold_dug.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "d41b1a5b-b66e-4ac6-9c74-36991657eb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get prop. time spent moving during each Encoding trial.\n",
    "is_moving = []\n",
    "for subj_sess in np.unique(clfs['subj_sess']):\n",
    "    events = events_proc.load_events(subj_sess, verbose=False)\n",
    "    is_moving.append(pd.DataFrame([[subj_sess,\n",
    "                                    trial, \n",
    "                                    np.mean(np.concatenate(df.apply(lambda x: np.repeat(x['moved_pos'],\n",
    "                                                                                        x['dur']),\n",
    "                                                                    axis=1).tolist()))]\n",
    "                                   for (trial, df) in events.positions.query(\"(gameState=='Encoding')\").groupby('trial')],\n",
    "                                  columns=['subj_sess', 'trial', 'is_moving']))\n",
    "    \n",
    "is_moving = pd.concat(is_moving, axis=0).set_index(['subj_sess', 'trial'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "380b8f5e-4b27-4b77-a9d9-74515793f7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs.insert(clfs.columns.tolist().index('test_trial')+1,\n",
    "            'is_moving',\n",
    "            clfs.apply(lambda x: is_moving['is_moving'].get((x['subj_sess'], x['test_trial']), 0), axis=1))\n",
    "clfs.insert(clfs.columns.tolist().index('test_trial')+2,\n",
    "            'gold_view_dur',\n",
    "            clfs.apply(lambda x: gold_dug['dur'].get((x['subj_sess'], x['test_trial']), 0), axis=1))\n",
    "clfs.insert(clfs.columns.tolist().index('test_trial')+3,\n",
    "            'gold_dug',\n",
    "            clfs.apply(lambda x: gold_dug['dig_success'].get((x['subj_sess'], x['test_trial']), 0), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "ce5f3b94-38b2-404e-921e-67877a78d063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subject-level decoding accuracy:\n",
      "-------------------------------\n",
      " time, U518_ses0: obs err: 6.9 ± 6.9; null err: 10.1 ± 10.1; t(35) = -6.4, p = 0.0000\n",
      " time, U518_ses1: obs err: 7.0 ± 7.0; null err: 10.1 ± 10.1; t(32) = -7.1, p = 0.0000\n",
      " time, U521_ses0: obs err: 9.1 ± 9.1; null err: 10.3 ± 10.3; t(32) = -3.0, p = 0.0054\n",
      " time, U526_ses0: obs err: 6.7 ± 6.7; null err: 10.0 ± 10.0; t(35) = -8.2, p = 0.0000\n",
      " time, U527_ses0: obs err: 8.1 ± 8.1; null err: 9.9 ± 9.9; t(35) = -4.6, p = 0.0001\n",
      " time, U530_ses0: obs err: 7.0 ± 7.0; null err: 10.4 ± 10.4; t(34) = -7.0, p = 0.0000\n",
      " time, U530_ses1: obs err: 8.2 ± 8.2; null err: 9.8 ± 9.8; t(35) = -3.2, p = 0.0028\n",
      " time, U532_ses0: obs err: 8.0 ± 8.0; null err: 10.0 ± 10.0; t(32) = -3.2, p = 0.0029\n",
      " time, U536_ses0: obs err: 7.2 ± 7.2; null err: 10.3 ± 10.3; t(35) = -7.7, p = 0.0000\n",
      " time, U540_ses0: obs err: 7.3 ± 7.3; null err: 9.9 ± 9.9; t(35) = -6.2, p = 0.0000\n",
      "\n",
      "place, U518_ses0: obs err: 1.8 ± 1.8; null err: 1.7 ± 1.7; t(35) = 0.9, p = 0.3608\n",
      "place, U518_ses1: obs err: 1.6 ± 1.6; null err: 1.8 ± 1.8; t(32) = -1.3, p = 0.1962\n",
      "place, U521_ses0: obs err: 1.7 ± 1.7; null err: 1.4 ± 1.4; t(32) = 4.3, p = 0.0002\n",
      "place, U526_ses0: obs err: 2.0 ± 2.0; null err: 1.5 ± 1.5; t(35) = 5.2, p = 0.0000\n",
      "place, U527_ses0: obs err: 1.7 ± 1.7; null err: 1.9 ± 1.9; t(35) = -1.9, p = 0.0724\n",
      "place, U530_ses0: obs err: 1.8 ± 1.8; null err: 1.4 ± 1.4; t(34) = 4.7, p = 0.0000\n",
      "place, U530_ses1: obs err: 1.9 ± 1.9; null err: 1.9 ± 1.9; t(35) = -0.8, p = 0.4359\n",
      "place, U532_ses0: obs err: 1.9 ± 1.9; null err: 1.7 ± 1.7; t(32) = 2.8, p = 0.0097\n",
      "place, U536_ses0: obs err: 1.7 ± 1.7; null err: 1.6 ± 1.6; t(35) = 1.0, p = 0.3051\n",
      "place, U540_ses0: obs err: 1.6 ± 1.6; null err: 1.4 ± 1.4; t(35) = 2.1, p = 0.0395\n",
      "\n",
      "Time ~ place decoding accuracy across trials:\n",
      "---------------------------------------------\n",
      "U518_ses0: r =  0.15, p = 0.368694257676513\n",
      "U518_ses1: r = -0.15, p = 0.4024021886351454\n",
      "U521_ses0: r =  0.03, p = 0.8591995628568917\n",
      "U526_ses0: r =  0.18, p = 0.2844599772459302\n",
      "U527_ses0: r = -0.00, p = 0.9897426717667608\n",
      "U530_ses0: r = -0.26, p = 0.12717381868885178\n",
      "U530_ses1: r = -0.17, p = 0.3193344012929451\n",
      "U532_ses0: r = -0.38, p = 0.02825951036549063\n",
      "U536_ses0: r =  0.18, p = 0.28359694095393784\n",
      "U540_ses0: r =  0.17, p = 0.32326247509890704\n"
     ]
    }
   ],
   "source": [
    "def _test(obs, null):\n",
    "    n = len(obs)\n",
    "    dof = n - 1\n",
    "    mean_obs = np.mean(obs)\n",
    "    mean_null = np.mean(null)\n",
    "    sem_obs = np.mean(obs)\n",
    "    sem_null = np.mean(null)\n",
    "    tval, pval = stats.ttest_rel(obs, null)\n",
    "    output = ('obs err: {:.1f} ± {:.1f}; null err: {:.1f} ± {:.1f}; t({}) = {:.1f}, p = {:.4f}'\n",
    "              .format(mean_obs, sem_obs, mean_null, sem_null, dof, tval, pval))\n",
    "    return output\n",
    "\n",
    "print('Subject-level decoding accuracy:', '-'*31, sep='\\n')\n",
    "for testvar in ['time', 'place']:\n",
    "    for (subj, subj_sess), df in clfs.groupby(['subj', 'subj_sess']):\n",
    "        print('{:>5}, {}: {}'.format(testvar, subj_sess,\n",
    "                                     _test(df['mean_abs_err_{}'.format(testvar)].values,\n",
    "                                           df['mean_abs_err_null_{}'.format(testvar)].values)))\n",
    "    print()\n",
    "    \n",
    "print('Time ~ place decoding accuracy across trials:', '-'*45, sep='\\n')\n",
    "for (subj, subj_sess), df in clfs.groupby(['subj', 'subj_sess']):\n",
    "    rval, pval = stats.pearsonr(df['mean_abs_err_time'],\n",
    "                                df['mean_abs_err_place'])\n",
    "    print('{}: r = {:>5.2f}, p = {}'\n",
    "          .format(subj_sess, rval, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "4b57c133-710a-4061-9627-4373b5414637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'U518_ses0'"
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       "      <th>is_moving</th>\n",
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       "      <th>mean_abs_err_time</th>\n",
       "      <th>mean_abs_err_place</th>\n",
       "      <th>gold_dug</th>\n",
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       "      <td>0.488527</td>\n",
       "      <td>-0.472342</td>\n",
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       "      <td>0.092182</td>\n",
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      "text/plain": [
       "                    is_moving  gold_view_dur  mean_abs_err_time  \\\n",
       "is_moving            1.000000      -0.376225          -0.342020   \n",
       "gold_view_dur       -0.376225       1.000000           0.282937   \n",
       "mean_abs_err_time   -0.342020       0.282937           1.000000   \n",
       "mean_abs_err_place   0.488527       0.092182           0.154367   \n",
       "gold_dug            -0.472342       0.601699           0.284332   \n",
       "\n",
       "                    mean_abs_err_place  gold_dug  \n",
       "is_moving                     0.488527 -0.472342  \n",
       "gold_view_dur                 0.092182  0.601699  \n",
       "mean_abs_err_time             0.154367  0.284332  \n",
       "mean_abs_err_place            1.000000 -0.213214  \n",
       "gold_dug                     -0.213214  1.000000  "
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'U518_ses1'"
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       "                    is_moving  gold_view_dur  mean_abs_err_time  \\\n",
       "is_moving            1.000000      -0.176954          -0.261894   \n",
       "gold_view_dur       -0.176954       1.000000          -0.063174   \n",
       "mean_abs_err_time   -0.261894      -0.063174           1.000000   \n",
       "mean_abs_err_place  -0.106640       0.110984          -0.150739   \n",
       "gold_dug            -0.286141       0.563507           0.246190   \n",
       "\n",
       "                    mean_abs_err_place  gold_dug  \n",
       "is_moving                    -0.106640 -0.286141  \n",
       "gold_view_dur                 0.110984  0.563507  \n",
       "mean_abs_err_time            -0.150739  0.246190  \n",
       "mean_abs_err_place            1.000000  0.224395  \n",
       "gold_dug                      0.224395  1.000000  "
      ]
     },
     "metadata": {},
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    {
     "data": {
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       "'U521_ses0'"
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       "                    is_moving  gold_view_dur  mean_abs_err_time  \\\n",
       "is_moving            1.000000      -0.383295          -0.215929   \n",
       "gold_view_dur       -0.383295       1.000000           0.261764   \n",
       "mean_abs_err_time   -0.215929       0.261764           1.000000   \n",
       "mean_abs_err_place   0.450247      -0.298526           0.032110   \n",
       "gold_dug            -0.347475       0.417314           0.334299   \n",
       "\n",
       "                    mean_abs_err_place  gold_dug  \n",
       "is_moving                     0.450247 -0.347475  \n",
       "gold_view_dur                -0.298526  0.417314  \n",
       "mean_abs_err_time             0.032110  0.334299  \n",
       "mean_abs_err_place            1.000000 -0.503475  \n",
       "gold_dug                     -0.503475  1.000000  "
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       "      <th></th>\n",
       "      <th>is_moving</th>\n",
       "      <th>gold_view_dur</th>\n",
       "      <th>mean_abs_err_time</th>\n",
       "      <th>mean_abs_err_place</th>\n",
       "      <th>gold_dug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_moving</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.399915</td>\n",
       "      <td>-0.101765</td>\n",
       "      <td>0.498619</td>\n",
       "      <td>-0.299311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gold_view_dur</th>\n",
       "      <td>-0.399915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.304623</td>\n",
       "      <td>-0.148613</td>\n",
       "      <td>0.551962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_abs_err_time</th>\n",
       "      <td>-0.101765</td>\n",
       "      <td>0.304623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169414</td>\n",
       "      <td>-0.024532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_abs_err_place</th>\n",
       "      <td>0.498619</td>\n",
       "      <td>-0.148613</td>\n",
       "      <td>0.169414</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.275636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gold_dug</th>\n",
       "      <td>-0.299311</td>\n",
       "      <td>0.551962</td>\n",
       "      <td>-0.024532</td>\n",
       "      <td>-0.275636</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    is_moving  gold_view_dur  mean_abs_err_time  \\\n",
       "is_moving            1.000000      -0.399915          -0.101765   \n",
       "gold_view_dur       -0.399915       1.000000           0.304623   \n",
       "mean_abs_err_time   -0.101765       0.304623           1.000000   \n",
       "mean_abs_err_place   0.498619      -0.148613           0.169414   \n",
       "gold_dug            -0.299311       0.551962          -0.024532   \n",
       "\n",
       "                    mean_abs_err_place  gold_dug  \n",
       "is_moving                     0.498619 -0.299311  \n",
       "gold_view_dur                -0.148613  0.551962  \n",
       "mean_abs_err_time             0.169414 -0.024532  \n",
       "mean_abs_err_place            1.000000 -0.275636  \n",
       "gold_dug                     -0.275636  1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testvars = ['is_moving', 'gold_view_dur', 'mean_abs_err_time', 'mean_abs_err_place']\n",
    "\n",
    "[display(subj_sess, df[testvars+['gold_dug']].corr())\n",
    " for (subj_sess, df) in clfs.groupby(['subj_sess'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "73912024-4e50-4ec0-9c69-5921766a964a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold dug ~ is_moving:\n",
      "---------------------\n",
      "is_moving, U518_ses0: r = -0.47, p = 0.0036289076811207815\n",
      "is_moving, U518_ses1: r = -0.29, p = 0.1064557531413984\n",
      "is_moving, U521_ses0: r = -0.35, p = 0.0475515441814387\n",
      "is_moving, U526_ses0: r = -0.12, p = 0.4769760115822975\n",
      "is_moving, U527_ses0: r = -0.23, p = 0.18457273734815213\n",
      "is_moving, U530_ses0: r = -0.52, p = 0.0013490140398969151\n",
      "is_moving, U530_ses1: r =  0.01, p = 0.9683223332161425\n",
      "is_moving, U532_ses0: r = -0.00, p = 0.9841882430230522\n",
      "is_moving, U536_ses0: r = -0.16, p = 0.33712089240719545\n",
      "is_moving, U540_ses0: r = -0.30, p = 0.07616366808287123\n",
      "\n",
      "Gold dug ~ gold_view_dur:\n",
      "-------------------------\n",
      "gold_view_dur, U518_ses0: r =  0.60, p = 0.0001037220665856488\n",
      "gold_view_dur, U518_ses1: r =  0.56, p = 0.0006385170792184803\n",
      "gold_view_dur, U521_ses0: r =  0.42, p = 0.015681187208608456\n",
      "gold_view_dur, U526_ses0: r =  0.57, p = 0.00025767274871513484\n",
      "gold_view_dur, U527_ses0: r =  0.55, p = 0.0005626661118103455\n",
      "gold_view_dur, U530_ses0: r =  0.54, p = 0.000912371777053511\n",
      "gold_view_dur, U530_ses1: r =  0.41, p = 0.012084301044845236\n",
      "gold_view_dur, U532_ses0: r =  0.78, p = 8.666828563238682e-08\n",
      "gold_view_dur, U536_ses0: r =  0.44, p = 0.0066268554171666294\n",
      "gold_view_dur, U540_ses0: r =  0.55, p = 0.0004832205430246676\n",
      "\n",
      "Gold dug ~ mean_abs_err_time:\n",
      "-----------------------------\n",
      "mean_abs_err_time, U518_ses0: r =  0.28, p = 0.092826142623402\n",
      "mean_abs_err_time, U518_ses1: r =  0.25, p = 0.16725263621778957\n",
      "mean_abs_err_time, U521_ses0: r =  0.33, p = 0.05724081023330541\n",
      "mean_abs_err_time, U526_ses0: r = -0.07, p = 0.6893792760160796\n",
      "mean_abs_err_time, U527_ses0: r =  0.01, p = 0.961075537026645\n",
      "mean_abs_err_time, U530_ses0: r =  0.03, p = 0.8630524591171024\n",
      "mean_abs_err_time, U530_ses1: r = -0.40, p = 0.01615895468957621\n",
      "mean_abs_err_time, U532_ses0: r = -0.15, p = 0.412841828582048\n",
      "mean_abs_err_time, U536_ses0: r = -0.15, p = 0.3972775605329073\n",
      "mean_abs_err_time, U540_ses0: r = -0.02, p = 0.8870662163875181\n",
      "\n",
      "Gold dug ~ mean_abs_err_place:\n",
      "------------------------------\n",
      "mean_abs_err_place, U518_ses0: r = -0.21, p = 0.21182893297901786\n",
      "mean_abs_err_place, U518_ses1: r =  0.22, p = 0.2093227875164421\n",
      "mean_abs_err_place, U521_ses0: r = -0.50, p = 0.002818815800206189\n",
      "mean_abs_err_place, U526_ses0: r =  0.01, p = 0.9754510868222314\n",
      "mean_abs_err_place, U527_ses0: r = -0.24, p = 0.157266924300721\n",
      "mean_abs_err_place, U530_ses0: r = -0.35, p = 0.038797770467491034\n",
      "mean_abs_err_place, U530_ses1: r =  0.12, p = 0.4840351981092386\n",
      "mean_abs_err_place, U532_ses0: r = -0.15, p = 0.4129699895189451\n",
      "mean_abs_err_place, U536_ses0: r = -0.24, p = 0.1569514521107141\n",
      "mean_abs_err_place, U540_ses0: r = -0.28, p = 0.10370595371587589\n",
      "\n",
      "Cross-session stats\n",
      "-------------------\n",
      "         is_moving: r = -0.24 ± 0.06 , t(9) = -4.3, p = 0.0019356737367303986\n",
      "     gold_view_dur: r =  0.54 ± 0.03 , t(9) = 16.0, p = 6.365475002624606e-08\n",
      " mean_abs_err_time: r =  0.01 ± 0.07 , t(9) = 0.2, p = 0.871558375678111\n",
      "mean_abs_err_place: r = -0.16 ± 0.07 , t(9) = -2.3, p = 0.045011320332043685\n"
     ]
    }
   ],
   "source": [
    "testvars = ['is_moving', 'gold_view_dur', 'mean_abs_err_time', 'mean_abs_err_place']\n",
    "\n",
    "rvals = {testvar: [] for testvar in testvars}\n",
    "for testvar in testvars:\n",
    "    title = 'Gold dug ~ {}:'.format(testvar)\n",
    "    print(title, '-'*len(title), sep='\\n')\n",
    "    for (subj, subj_sess), df in clfs.groupby(['subj', 'subj_sess']):\n",
    "        rval, pval = stats.pearsonr(df['gold_dug'],\n",
    "                                    df[testvar])\n",
    "        rvals[testvar].append(rval)\n",
    "        print('{:>5}, {}: r = {:>5.2f}, p = {}'\n",
    "              .format(testvar, subj_sess, rval, pval))\n",
    "        \n",
    "    print()\n",
    "    \n",
    "title = 'Cross-session stats'\n",
    "print(title, '-'*len(title), sep='\\n')\n",
    "for testvar in rvals:\n",
    "    _mean = np.mean(rvals[testvar])\n",
    "    _sem = stats.sem(rvals[testvar])\n",
    "    tval, pval = stats.ttest_1samp(rvals[testvar], popmean=0)\n",
    "    dof = len(rvals[testvar]) - 1\n",
    "    print('{:>18}: r = {:>5.2f} ± {:<5.2f}, t({}) = {:.1f}, p = {}'\n",
    "          .format(testvar, _mean, _sem, dof, tval, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "a51fe792-42c3-4ab2-8f90-0faa67ac65ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session-level OLS regression\n",
      "----------------------------\n",
      "U518_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.459\n",
      "Model:                            OLS   Adj. R-squared:                  0.409\n",
      "Method:                 Least Squares   F-statistic:                     9.064\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):           0.000172\n",
      "Time:                        19:28:50   Log-Likelihood:                -13.613\n",
      "No. Observations:                  36   AIC:                             35.23\n",
      "Df Residuals:                      32   BIC:                             41.56\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  1.0347      0.569      1.819      0.078      -0.124       2.193\n",
      "gold_view_dur        8.11e-05   1.89e-05      4.289      0.000    4.26e-05       0.000\n",
      "mean_abs_err_time      0.0379      0.031      1.205      0.237      -0.026       0.102\n",
      "mean_abs_err_place    -0.6888      0.310     -2.219      0.034      -1.321      -0.057\n",
      "==============================================================================\n",
      "Omnibus:                        2.488   Durbin-Watson:                   2.151\n",
      "Prob(Omnibus):                  0.288   Jarque-Bera (JB):                2.236\n",
      "Skew:                           0.585   Prob(JB):                        0.327\n",
      "Kurtosis:                       2.654   Cond. No.                     5.23e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.23e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U518_ses1                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.440\n",
      "Model:                            OLS   Adj. R-squared:                  0.382\n",
      "Method:                 Least Squares   F-statistic:                     7.585\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):           0.000684\n",
      "Time:                        19:28:50   Log-Likelihood:                -26.056\n",
      "No. Observations:                  33   AIC:                             60.11\n",
      "Df Residuals:                      29   BIC:                             66.10\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                 -1.6227      0.879     -1.846      0.075      -3.420       0.175\n",
      "gold_view_dur          0.0001   2.51e-05      4.000      0.000    4.91e-05       0.000\n",
      "mean_abs_err_time      0.1217      0.055      2.225      0.034       0.010       0.234\n",
      "mean_abs_err_place     0.6741      0.455      1.482      0.149      -0.256       1.605\n",
      "==============================================================================\n",
      "Omnibus:                        0.457   Durbin-Watson:                   1.329\n",
      "Prob(Omnibus):                  0.796   Jarque-Bera (JB):                0.578\n",
      "Skew:                          -0.056   Prob(JB):                        0.749\n",
      "Kurtosis:                       2.361   Cond. No.                     7.00e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large,  7e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U521_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.412\n",
      "Model:                            OLS   Adj. R-squared:                  0.351\n",
      "Method:                 Least Squares   F-statistic:                     6.765\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):            0.00135\n",
      "Time:                        19:28:50   Log-Likelihood:                -8.5527\n",
      "No. Observations:                  33   AIC:                             25.11\n",
      "Df Residuals:                      29   BIC:                             31.09\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  0.4231      0.313      1.353      0.186      -0.216       1.063\n",
      "gold_view_dur       1.587e-05    1.2e-05      1.318      0.198   -8.75e-06    4.05e-05\n",
      "mean_abs_err_time      0.0493      0.025      1.985      0.057      -0.001       0.100\n",
      "mean_abs_err_place    -0.4070      0.135     -3.005      0.005      -0.684      -0.130\n",
      "==============================================================================\n",
      "Omnibus:                        6.027   Durbin-Watson:                   2.295\n",
      "Prob(Omnibus):                  0.049   Jarque-Bera (JB):                4.890\n",
      "Skew:                           0.932   Prob(JB):                       0.0867\n",
      "Kurtosis:                       3.290   Cond. No.                     3.29e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.29e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U526_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.334\n",
      "Model:                            OLS   Adj. R-squared:                  0.271\n",
      "Method:                 Least Squares   F-statistic:                     5.345\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):            0.00424\n",
      "Time:                        19:28:50   Log-Likelihood:                -28.667\n",
      "No. Observations:                  36   AIC:                             65.33\n",
      "Df Residuals:                      32   BIC:                             71.67\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  0.3115      0.517      0.603      0.551      -0.741       1.364\n",
      "gold_view_dur       8.158e-05   2.05e-05      3.974      0.000    3.98e-05       0.000\n",
      "mean_abs_err_time     -0.0105      0.054     -0.196      0.846      -0.120       0.099\n",
      "mean_abs_err_place     0.0945      0.195      0.486      0.631      -0.302       0.491\n",
      "==============================================================================\n",
      "Omnibus:                        0.428   Durbin-Watson:                   1.387\n",
      "Prob(Omnibus):                  0.808   Jarque-Bera (JB):                0.578\n",
      "Skew:                           0.185   Prob(JB):                        0.749\n",
      "Kurtosis:                       2.501   Cond. No.                     4.12e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.12e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U527_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.395\n",
      "Model:                            OLS   Adj. R-squared:                  0.338\n",
      "Method:                 Least Squares   F-statistic:                     6.962\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):           0.000977\n",
      "Time:                        19:28:50   Log-Likelihood:                -25.978\n",
      "No. Observations:                  36   AIC:                             59.96\n",
      "Df Residuals:                      32   BIC:                             66.29\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  2.1115      0.710      2.976      0.006       0.666       3.557\n",
      "gold_view_dur          0.0001   2.47e-05      4.221      0.000    5.39e-05       0.000\n",
      "mean_abs_err_time     -0.0197      0.052     -0.382      0.705      -0.125       0.085\n",
      "mean_abs_err_place    -0.7359      0.330     -2.228      0.033      -1.409      -0.063\n",
      "==============================================================================\n",
      "Omnibus:                        1.690   Durbin-Watson:                   1.881\n",
      "Prob(Omnibus):                  0.429   Jarque-Bera (JB):                1.526\n",
      "Skew:                           0.476   Prob(JB):                        0.466\n",
      "Kurtosis:                       2.666   Cond. No.                     5.68e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.68e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U530_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.354\n",
      "Model:                            OLS   Adj. R-squared:                  0.292\n",
      "Method:                 Least Squares   F-statistic:                     5.670\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):            0.00324\n",
      "Time:                        19:28:50   Log-Likelihood:                -13.039\n",
      "No. Observations:                  35   AIC:                             34.08\n",
      "Df Residuals:                      31   BIC:                             40.30\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  0.8369      0.395      2.117      0.042       0.031       1.643\n",
      "gold_view_dur       5.225e-05   1.58e-05      3.302      0.002       2e-05    8.45e-05\n",
      "mean_abs_err_time     -0.0372      0.033     -1.115      0.274      -0.105       0.031\n",
      "mean_abs_err_place    -0.2420      0.151     -1.606      0.118      -0.549       0.065\n",
      "==============================================================================\n",
      "Omnibus:                        5.662   Durbin-Watson:                   2.685\n",
      "Prob(Omnibus):                  0.059   Jarque-Bera (JB):                5.199\n",
      "Skew:                           0.941   Prob(JB):                       0.0743\n",
      "Kurtosis:                       2.856   Cond. No.                     3.12e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.12e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U530_ses1                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.282\n",
      "Model:                            OLS   Adj. R-squared:                  0.215\n",
      "Method:                 Least Squares   F-statistic:                     4.190\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):             0.0131\n",
      "Time:                        19:28:50   Log-Likelihood:                -26.745\n",
      "No. Observations:                  36   AIC:                             61.49\n",
      "Df Residuals:                      32   BIC:                             67.82\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  1.4706      0.775      1.898      0.067      -0.108       3.049\n",
      "gold_view_dur          0.0001   4.88e-05      2.319      0.027    1.37e-05       0.000\n",
      "mean_abs_err_time     -0.1226      0.056     -2.208      0.035      -0.236      -0.009\n",
      "mean_abs_err_place    -0.1543      0.318     -0.485      0.631      -0.802       0.493\n",
      "==============================================================================\n",
      "Omnibus:                        0.227   Durbin-Watson:                   2.097\n",
      "Prob(Omnibus):                  0.893   Jarque-Bera (JB):                0.056\n",
      "Skew:                          -0.092   Prob(JB):                        0.973\n",
      "Kurtosis:                       2.946   Cond. No.                     3.34e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.34e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U532_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.622\n",
      "Model:                            OLS   Adj. R-squared:                  0.583\n",
      "Method:                 Least Squares   F-statistic:                     15.91\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):           2.64e-06\n",
      "Time:                        19:28:50   Log-Likelihood:                -27.177\n",
      "No. Observations:                  33   AIC:                             62.35\n",
      "Df Residuals:                      29   BIC:                             68.34\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  1.3076      0.824      1.587      0.123      -0.378       2.993\n",
      "gold_view_dur          0.0002   2.55e-05      6.507      0.000       0.000       0.000\n",
      "mean_abs_err_time     -0.0315      0.055     -0.570      0.573      -0.145       0.082\n",
      "mean_abs_err_place    -0.2592      0.259     -1.000      0.325      -0.789       0.271\n",
      "==============================================================================\n",
      "Omnibus:                        2.718   Durbin-Watson:                   1.677\n",
      "Prob(Omnibus):                  0.257   Jarque-Bera (JB):                1.548\n",
      "Skew:                          -0.477   Prob(JB):                        0.461\n",
      "Kurtosis:                       3.465   Cond. No.                     4.99e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.99e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U536_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.235\n",
      "Model:                            OLS   Adj. R-squared:                  0.164\n",
      "Method:                 Least Squares   F-statistic:                     3.281\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):             0.0334\n",
      "Time:                        19:28:50   Log-Likelihood:                -33.328\n",
      "No. Observations:                  36   AIC:                             74.66\n",
      "Df Residuals:                      32   BIC:                             80.99\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  1.2379      0.637      1.944      0.061      -0.059       2.535\n",
      "gold_view_dur       4.967e-05   1.88e-05      2.640      0.013    1.13e-05     8.8e-05\n",
      "mean_abs_err_time     -0.0774      0.068     -1.132      0.266      -0.217       0.062\n",
      "mean_abs_err_place    -0.0737      0.280     -0.263      0.794      -0.644       0.497\n",
      "==============================================================================\n",
      "Omnibus:                        2.576   Durbin-Watson:                   2.647\n",
      "Prob(Omnibus):                  0.276   Jarque-Bera (JB):                1.754\n",
      "Skew:                           0.330   Prob(JB):                        0.416\n",
      "Kurtosis:                       2.143   Cond. No.                     5.63e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.63e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "U540_ses0                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               gold_dug   R-squared:                       0.369\n",
      "Model:                            OLS   Adj. R-squared:                  0.310\n",
      "Method:                 Least Squares   F-statistic:                     6.243\n",
      "Date:                Thu, 14 Apr 2022   Prob (F-statistic):            0.00185\n",
      "Time:                        19:28:50   Log-Likelihood:                -21.401\n",
      "No. Observations:                  36   AIC:                             50.80\n",
      "Df Residuals:                      32   BIC:                             57.14\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  1.0978      0.486      2.256      0.031       0.107       2.089\n",
      "gold_view_dur       6.868e-05   1.78e-05      3.854      0.001    3.24e-05       0.000\n",
      "mean_abs_err_time     -0.0572      0.050     -1.153      0.257      -0.158       0.044\n",
      "mean_abs_err_place    -0.2758      0.252     -1.095      0.282      -0.789       0.237\n",
      "==============================================================================\n",
      "Omnibus:                        1.243   Durbin-Watson:                   1.658\n",
      "Prob(Omnibus):                  0.537   Jarque-Bera (JB):                1.051\n",
      "Skew:                           0.203   Prob(JB):                        0.591\n",
      "Kurtosis:                       2.267   Cond. No.                     3.93e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.93e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "Cross-session stats\n",
      "-------------------\n",
      "     gold_view_dur: r =  3.64 ± 0.44 , t(9) = 8.3, p = 1.7185350614097884e-05\n",
      " mean_abs_err_time: r = -0.13 ± 0.46 , t(9) = -0.3, p = 0.7793232767400455\n",
      "mean_abs_err_place: r = -0.99 ± 0.43 , t(9) = -2.3, p = 0.045801336631843714\n"
     ]
    }
   ],
   "source": [
    "# testvars = ['is_moving', 'gold_view_dur', 'mean_abs_err_time', 'mean_abs_err_place']\n",
    "testvars = ['gold_view_dur', 'mean_abs_err_time', 'mean_abs_err_place']\n",
    "# testvars = ['mean_abs_err_time', 'mean_abs_err_place']\n",
    "\n",
    "tvals = {testvar: [] for testvar in testvars}\n",
    "title = 'Session-level OLS regression'\n",
    "print(title, '-'*len(title), sep='\\n')\n",
    "for (subj, subj_sess), df in clfs.groupby(['subj', 'subj_sess']):\n",
    "    Y = df['gold_dug']\n",
    "    X = sm.add_constant(df[testvars])\n",
    "    mod = sm.OLS(Y, X)\n",
    "    res = mod.fit()\n",
    "    for testvar in testvars:\n",
    "        tvals[testvar].append(res.tvalues[testvar])\n",
    "    print(subj_sess, res.summary(), end='\\n'*3)\n",
    "    \n",
    "title = 'Cross-session stats'\n",
    "print(title, '-'*len(title), sep='\\n')\n",
    "for testvar in tvals:\n",
    "    _mean = np.mean(tvals[testvar])\n",
    "    _sem = stats.sem(tvals[testvar])\n",
    "    tval, pval = stats.ttest_1samp(tvals[testvar], popmean=0)\n",
    "    dof = len(tvals[testvar]) - 1\n",
    "    print('{:>18}: r = {:>5.2f} ± {:<5.2f}, t({}) = {:.1f}, p = {}'\n",
    "          .format(testvar, _mean, _sem, dof, tval, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9266ea-7e47-4228-8f87-b0a656fbabe5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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