F2F_Growth_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71e4089e",
   "metadata": {},
   "source": [
    "# Statistical analysis of all subjects\n",
    "Growth modeling, correlations, and other group-level analyses. Much of the code is adapted from Explore_Growth_Statistics.  Only single-ROI models are made in this notebook, and all three Pars regions are used (instead of 2, which was tested earlier).\n",
    "** Includes creation of figures for the first F2F paper. **\n",
    "\n",
    "In 2021 Dec, import of re-linearized ROI values was added.\n",
    "In 2022 Jan, merging of Superior and Middle Temporal regions was added, then Transverse and Bank of Superior Temporal Sulcus. In 2022 Feb, the temporal merging was pared back to Superior and Middle along with the Temporal Pole.\n",
    "\n",
    "In 2022 Jun, added scatter plots of attend-ignore contrast versus vocab score difference.\n",
    "\n",
    "In 2023 Jun, adjusted font, font size, color ordering, and temporal ROI for final (?) manuscript push."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ebc4007",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Preliminaries\" data-toc-modified-id=\"Preliminaries-1\">Preliminaries</a></span></li><li><span><a href=\"#Load-and-transform-ROI-data\" data-toc-modified-id=\"Load-and-transform-ROI-data-2\">Load and transform ROI data</a></span></li><li><span><a href=\"#Load-and-transform-linearized-ROI-data\" data-toc-modified-id=\"Load-and-transform-linearized-ROI-data-3\">Load and transform linearized ROI data</a></span></li><li><span><a href=\"#Series-of-linear-models\" data-toc-modified-id=\"Series-of-linear-models-4\">Series of linear models</a></span><ul class=\"toc-item\"><li><span><a href=\"#Baseline-linear-models\" data-toc-modified-id=\"Baseline-linear-models-4.1\">Baseline linear models</a></span></li><li><span><a href=\"#Models-(mdf2)-with-Inferior-Frontal-Gyrus-slope-covariants\" data-toc-modified-id=\"Models-(mdf2)-with-Inferior-Frontal-Gyrus-slope-covariants-4.2\">Models (mdf2) with Inferior Frontal Gyrus slope covariants</a></span></li><li><span><a href=\"#Models-(mdf3)-with-Inferior-Parietal-slope-covariants\" data-toc-modified-id=\"Models-(mdf3)-with-Inferior-Parietal-slope-covariants-4.3\">Models (mdf3) with Inferior Parietal slope covariants</a></span></li><li><span><a href=\"#Models-(mdf4)-with-Superior-Temporal-slope-covariants\" data-toc-modified-id=\"Models-(mdf4)-with-Superior-Temporal-slope-covariants-4.4\">Models (mdf4) with Superior Temporal slope covariants</a></span></li><li><span><a href=\"#Models-(mdf4b)-with-Combined-Temporal-slope-covariants\" data-toc-modified-id=\"Models-(mdf4b)-with-Combined-Temporal-slope-covariants-4.5\">Models (mdf4b) with Combined Temporal slope covariants</a></span></li><li><span><a href=\"#Models-(mdf5)-with-TWO-ROI-slope-covariants\" data-toc-modified-id=\"Models-(mdf5)-with-TWO-ROI-slope-covariants-4.6\">Models (mdf5) with TWO ROI slope covariants</a></span></li></ul></li><li><span><a href=\"#**FIG**-Graphically-represent-the-model-results\" data-toc-modified-id=\"**FIG**-Graphically-represent-the-model-results-5\">**FIG** Graphically represent the model results</a></span></li><li><span><a href=\"#**FIG**-Plot-RTP-versus-scores,-with-regression-lines\" data-toc-modified-id=\"**FIG**-Plot-RTP-versus-scores,-with-regression-lines-6\">**FIG** Plot RTP versus scores, with regression lines</a></span></li><li><span><a href=\"#**TEST**-Graphically-represent-the-model-results-(w/-linearized-ROI-instead-of-zscore)\" data-toc-modified-id=\"**TEST**-Graphically-represent-the-model-results-(w/-linearized-ROI-instead-of-zscore)-7\">**TEST** Graphically represent the model results (w/ linearized ROI instead of zscore)</a></span></li><li><span><a href=\"#**TEST**-Plot-scores-versus-RTP\" data-toc-modified-id=\"**TEST**-Plot-scores-versus-RTP-8\">**TEST** Plot scores versus RTP</a></span></li><li><span><a href=\"#**TEST**-Plot-RTP-contrasts-versus-scores\" data-toc-modified-id=\"**TEST**-Plot-RTP-contrasts-versus-scores-9\">**TEST** Plot RTP contrasts versus scores</a></span></li><li><span><a href=\"#**FIG**-Plot-labels-on-an-inflated-brain-map\" data-toc-modified-id=\"**FIG**-Plot-labels-on-an-inflated-brain-map-10\">**FIG** Plot labels on an inflated brain map</a></span></li><li><span><a href=\"#Plot-residuals-from-various-models\" data-toc-modified-id=\"Plot-residuals-from-various-models-11\">Plot residuals from various models</a></span></li><li><span><a href=\"#**FIG**-Plot-average-attend-and-ignore-levels-as-a-bar-graph---linear-validation\" data-toc-modified-id=\"**FIG**-Plot-average-attend-and-ignore-levels-as-a-bar-graph---linear-validation-12\">**FIG** Plot average attend and ignore levels as a bar graph - linear validation</a></span></li><li><span><a href=\"#End\" data-toc-modified-id=\"End-13\">End</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ecf005",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7762de0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from functools import partial\n",
    "from scipy.optimize import minimize_scalar\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "import mne\n",
    "\n",
    "%config InlineBackend.figure_format = 'png'\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.rcParams.update({'xtick.labelsize': 14, 'ytick.labelsize': 14})\n",
    "plt.rcParams.update({'font.family':'sans-serif', 'font.sans-serif':['Arial']})\n",
    "# adapted from stackoverflow.com/questions/21321670/how-to-change-fonts-in-matplotlib-python\n",
    "\n",
    "mne.viz.set_3d_backend(\"pyvista\")\n",
    "pd.options.display.float_format = '{:,.5f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "af0b20a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/share/fonts/truetype/msttcorefonts/Arial.ttf\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.font_manager import findfont, FontProperties\n",
    "# font_used = findfont(FontProperties(family=['sans-serif']))\n",
    "font_used = findfont(FontProperties(family=plt.rcParams['font.family']))\n",
    "print(font_used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1d707edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define data space for loading and analysis #\n",
    "# Originally this was done by loading a pre-derived .csv file (F2F_ROIBehav_2021Apr.xlsx',\n",
    "# which was taken from \"roi_ORIGINAL_THETA.csv\" and \"roi_extra_ORIGINAL_THETA.csv\"\n",
    "# in the \"srcpsd_groups_2021Jan\" results directory.).\n",
    "score_file = './supporting_files/F2F_BehavOnly_2021Apr.xlsx'\n",
    "score_columns = {'subject':'Subject', 'VOCAB_18m':'18', 'VOCAB_21m':'21', \n",
    "               'VOCAB_24m':'24', 'VOCAB_27m':'27', 'VOCAB_30m':'30'}\n",
    "\n",
    "datadb_file = './results/srcpsd_groups_2022Jan/roi_REDUCED_THETA.csv'\n",
    "datalin_file = './results/srcpsd_groups_2022Jan/roilinear_REDUCED_THETA.csv'\n",
    "roi_abbrv = {'parstriangularis':'ParsTri', 'parsopercularis':'ParsOper',\n",
    "             'parsorbitalis':'ParsOrb', 'inferiorparietal':'InfPar',\n",
    "             'superiortemporal':'SupTemp', 'middletemporal':'MidTemp',\n",
    "             'transversetemporal':'TransTemp', 'bankssts':'BanksTemp',\n",
    "             'temporalpole': 'PoleTemp'\n",
    "              }   # later add 'transversetemporal', 'bankssts'\n",
    "\n",
    "months = np.arange(18,33, 3)\n",
    "month_cols = [str(x) for x in months]\n",
    "\n",
    "anatomy_dir = os.path.expanduser('~/Data/MEG/anatomy')\n",
    "graphics_dir = './manuscript/graphics/'   # make sure the current directory is for this notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2de11a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some ROI groupings #\n",
    "runParams = {'pars_grouping':('ParsTri', 'ParsOper', 'ParsOrb'),\n",
    "             'temporal_grouping':('SupTemp', 'MidTemp', 'PoleTemp')  # 'TransTemp', 'BanksTemp'\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d02c2a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Define some ROI groupings - TEST ONLY #\n",
    "# runParams = {'pars_grouping':('ParsTri', 'ParsOper', 'ParsOrb'),\n",
    "#              'temporal_grouping':('SupTemp',)\n",
    "#             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "74423658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function for showing MSE, R-sq. (1 - MSE/var(scores)), and Bayesian Info. Crit. metrics #\n",
    "# Note that the mean of the scores should be 0 (so below, scores_model.mean() < 2e-16),\n",
    "# and because of the z-score tranformation, the sum of the scores _squared_ is 105 and the variance\n",
    "# of the scores is 1.0.\n",
    "def show_metrics(model_list):\n",
    "    robj = re.compile('^month_t:Q\\((.*)\\)')\n",
    "    r2_list, mse_list, bic_list, pval_list = [], [], [], []\n",
    "    \n",
    "    for model in model_list:  # m.resid := scores_model - m.fittedvalues\n",
    "        r2 = 1 - ( sum(model.resid**2) / sum((scores_model - scores_model.mean())**2) )\n",
    "        mse = sum(model.resid**2) / len(model.resid)\n",
    "        r2_list.append(r2)\n",
    "        mse_list.append(mse)\n",
    "        bic_list.append(model.bic)  # bic := k * np.log(105) - 2 * model.llf (k=5 for 1 ROI, 6 for 2 ROI)\n",
    "    \n",
    "        fe_list = set(model.fe_params.index) - {'Intercept', 'month_t'}\n",
    "        pval = None\n",
    "        for param in fe_list:\n",
    "            phit = robj.match(param)\n",
    "            if phit:\n",
    "                phit = phit.group(1).strip(r\"'\")\n",
    "                pval = model.pvalues[param]\n",
    "            pval_list.append(pval)\n",
    "\n",
    "    print('R2: ', end='')\n",
    "    for r2 in r2_list:\n",
    "        print(r2, ' ', end='')\n",
    "    print('\\nMSE: ', end='')\n",
    "    for mse in mse_list:\n",
    "        print(mse, ' ', end='')\n",
    "    print('\\nBIC: ', end='')\n",
    "    for bic in bic_list:\n",
    "        print(bic, ' ', end='')\n",
    "    print('\\nPval: ', end='')\n",
    "    for pval in pval_list:\n",
    "        print(pval, ' ', end='')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2784ce6",
   "metadata": {},
   "source": [
    "## Load and transform ROI data\n",
    "Note that the z-score transformation for the ROI scores will depend on the pool of labels pulled into the dataframe (matching USE_COLUMNS). So the values will be different for this version of the notebook (with more temporal lobe ROIs) than in previous versions (see _archived\\_files_ folder).  \n",
    "\n",
    "Note that the ROI data here is from Jan 2022 REDUCED set rather than Jan 2021 ORIGNAL set as in the previous notebook versions, though these two sets of values track very closely (within ~0.3 log units).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "624dd6ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expand the ROI abbreviations to ROI column names #\n",
    "use_columns = {f'{roibase}-{hemi}-{cond1}':f'{cond2}_{colbase}-{hemi}' for\n",
    "    (roibase, colbase) in roi_abbrv.items() for\n",
    "    cond1, cond2 in zip(('talk', 'ignore'), ('attend', 'ignore')) for\n",
    "    hemi in ('lh', 'rh')\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1dad37e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the vocab scores and ROI data #\n",
    "df_scores = pd.read_excel(score_file, index_col=0, usecols=score_columns.keys())\n",
    "\n",
    "df_roidb = pd.read_csv(datadb_file, index_col=0, header=0)\n",
    "df_roidb = df_roidb[[key for key in use_columns]]\n",
    "\n",
    "# Join the scores and data, then rename columns #\n",
    "df_roidb = df_roidb.join(df_scores, how='right')\n",
    "combine_columns = score_columns; combine_columns.update(use_columns)\n",
    "\n",
    "order_list = list(combine_columns.keys())\n",
    "order_list.pop(0)\n",
    "df_roidb = df_roidb[order_list]\n",
    "df_roidb.rename(columns=combine_columns, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5fb393de",
   "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>18</th>\n",
       "      <th>21</th>\n",
       "      <th>24</th>\n",
       "      <th>27</th>\n",
       "      <th>30</th>\n",
       "      <th>attend_ParsTri-lh</th>\n",
       "      <th>attend_ParsTri-rh</th>\n",
       "      <th>ignore_ParsTri-lh</th>\n",
       "      <th>ignore_ParsTri-rh</th>\n",
       "      <th>attend_ParsOper-lh</th>\n",
       "      <th>attend_ParsOper-rh</th>\n",
       "      <th>ignore_ParsOper-lh</th>\n",
       "      <th>ignore_ParsOper-rh</th>\n",
       "      <th>attend_ParsOrb-lh</th>\n",
       "      <th>attend_ParsOrb-rh</th>\n",
       "      <th>ignore_ParsOrb-lh</th>\n",
       "      <th>ignore_ParsOrb-rh</th>\n",
       "      <th>attend_InfPar-lh</th>\n",
       "      <th>attend_InfPar-rh</th>\n",
       "      <th>ignore_InfPar-lh</th>\n",
       "      <th>ignore_InfPar-rh</th>\n",
       "      <th>attend_SupTemp-lh</th>\n",
       "      <th>attend_SupTemp-rh</th>\n",
       "      <th>ignore_SupTemp-lh</th>\n",
       "      <th>ignore_SupTemp-rh</th>\n",
       "      <th>attend_MidTemp-lh</th>\n",
       "      <th>attend_MidTemp-rh</th>\n",
       "      <th>ignore_MidTemp-lh</th>\n",
       "      <th>ignore_MidTemp-rh</th>\n",
       "      <th>attend_TransTemp-lh</th>\n",
       "      <th>attend_TransTemp-rh</th>\n",
       "      <th>ignore_TransTemp-lh</th>\n",
       "      <th>ignore_TransTemp-rh</th>\n",
       "      <th>attend_BanksTemp-lh</th>\n",
       "      <th>attend_BanksTemp-rh</th>\n",
       "      <th>ignore_BanksTemp-lh</th>\n",
       "      <th>ignore_BanksTemp-rh</th>\n",
       "      <th>attend_PoleTemp-lh</th>\n",
       "      <th>attend_PoleTemp-rh</th>\n",
       "      <th>ignore_PoleTemp-lh</th>\n",
       "      <th>ignore_PoleTemp-rh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>f2f_009</th>\n",
       "      <td>-1.20258</td>\n",
       "      <td>-0.84281</td>\n",
       "      <td>-0.30567</td>\n",
       "      <td>0.59538</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_010</th>\n",
       "      <td>-1.33074</td>\n",
       "      <td>-1.29473</td>\n",
       "      <td>-0.92429</td>\n",
       "      <td>-0.41760</td>\n",
       "      <td>0.18365</td>\n",
       "      <td>-2.56157</td>\n",
       "      <td>-1.02438</td>\n",
       "      <td>-1.51046</td>\n",
       "      <td>0.32705</td>\n",
       "      <td>-2.94051</td>\n",
       "      <td>-1.32984</td>\n",
       "      <td>-1.44769</td>\n",
       "      <td>0.19501</td>\n",
       "      <td>-1.95453</td>\n",
       "      <td>-0.81947</td>\n",
       "      <td>-1.42426</td>\n",
       "      <td>0.34961</td>\n",
       "      <td>-3.68550</td>\n",
       "      <td>-0.88759</td>\n",
       "      <td>-3.10786</td>\n",
       "      <td>-0.09687</td>\n",
       "      <td>-2.02400</td>\n",
       "      <td>-0.46551</td>\n",
       "      <td>-1.67712</td>\n",
       "      <td>0.33557</td>\n",
       "      <td>-2.08646</td>\n",
       "      <td>-0.83105</td>\n",
       "      <td>-1.52576</td>\n",
       "      <td>0.06262</td>\n",
       "      <td>-2.86213</td>\n",
       "      <td>-0.35547</td>\n",
       "      <td>-1.93928</td>\n",
       "      <td>0.43321</td>\n",
       "      <td>-2.81135</td>\n",
       "      <td>-0.87470</td>\n",
       "      <td>-2.59115</td>\n",
       "      <td>-0.03681</td>\n",
       "      <td>-2.02328</td>\n",
       "      <td>-1.46402</td>\n",
       "      <td>-1.25918</td>\n",
       "      <td>-0.15755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_012</th>\n",
       "      <td>-1.08202</td>\n",
       "      <td>-0.98107</td>\n",
       "      <td>-0.58665</td>\n",
       "      <td>-0.00758</td>\n",
       "      <td>0.44438</td>\n",
       "      <td>0.14815</td>\n",
       "      <td>0.74592</td>\n",
       "      <td>0.30814</td>\n",
       "      <td>0.90209</td>\n",
       "      <td>-0.20589</td>\n",
       "      <td>1.47618</td>\n",
       "      <td>0.58182</td>\n",
       "      <td>0.60562</td>\n",
       "      <td>0.30319</td>\n",
       "      <td>0.59145</td>\n",
       "      <td>0.15312</td>\n",
       "      <td>0.89745</td>\n",
       "      <td>0.06141</td>\n",
       "      <td>0.34839</td>\n",
       "      <td>0.25024</td>\n",
       "      <td>1.08829</td>\n",
       "      <td>0.06661</td>\n",
       "      <td>0.83729</td>\n",
       "      <td>0.24237</td>\n",
       "      <td>0.57032</td>\n",
       "      <td>0.21034</td>\n",
       "      <td>0.42859</td>\n",
       "      <td>-0.14911</td>\n",
       "      <td>0.55638</td>\n",
       "      <td>0.24505</td>\n",
       "      <td>1.21552</td>\n",
       "      <td>0.14389</td>\n",
       "      <td>0.82279</td>\n",
       "      <td>0.11164</td>\n",
       "      <td>0.80292</td>\n",
       "      <td>0.01083</td>\n",
       "      <td>1.28234</td>\n",
       "      <td>0.80210</td>\n",
       "      <td>0.86838</td>\n",
       "      <td>0.08114</td>\n",
       "      <td>-0.45452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_013</th>\n",
       "      <td>-1.09410</td>\n",
       "      <td>-0.63672</td>\n",
       "      <td>-0.03494</td>\n",
       "      <td>0.89220</td>\n",
       "      <td>1.72450</td>\n",
       "      <td>-1.40686</td>\n",
       "      <td>0.95020</td>\n",
       "      <td>1.22323</td>\n",
       "      <td>-0.82806</td>\n",
       "      <td>-2.11945</td>\n",
       "      <td>0.95536</td>\n",
       "      <td>0.97080</td>\n",
       "      <td>-0.87814</td>\n",
       "      <td>-0.35711</td>\n",
       "      <td>0.98936</td>\n",
       "      <td>1.23876</td>\n",
       "      <td>-0.68803</td>\n",
       "      <td>-0.44363</td>\n",
       "      <td>0.86847</td>\n",
       "      <td>0.42152</td>\n",
       "      <td>-2.55315</td>\n",
       "      <td>-1.35179</td>\n",
       "      <td>0.63137</td>\n",
       "      <td>1.01847</td>\n",
       "      <td>-2.00393</td>\n",
       "      <td>-0.69714</td>\n",
       "      <td>0.53829</td>\n",
       "      <td>0.91142</td>\n",
       "      <td>-2.69577</td>\n",
       "      <td>-1.19030</td>\n",
       "      <td>0.76581</td>\n",
       "      <td>0.99461</td>\n",
       "      <td>-1.72626</td>\n",
       "      <td>-1.09237</td>\n",
       "      <td>0.38994</td>\n",
       "      <td>0.65911</td>\n",
       "      <td>-2.79683</td>\n",
       "      <td>0.25707</td>\n",
       "      <td>0.96066</td>\n",
       "      <td>1.14195</td>\n",
       "      <td>-1.43861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_018</th>\n",
       "      <td>-1.18252</td>\n",
       "      <td>0.15043</td>\n",
       "      <td>1.29829</td>\n",
       "      <td>2.28091</td>\n",
       "      <td>2.28091</td>\n",
       "      <td>1.92089</td>\n",
       "      <td>0.83451</td>\n",
       "      <td>-0.88199</td>\n",
       "      <td>-1.46315</td>\n",
       "      <td>1.82046</td>\n",
       "      <td>1.82392</td>\n",
       "      <td>-1.09481</td>\n",
       "      <td>-1.44422</td>\n",
       "      <td>1.72644</td>\n",
       "      <td>1.55221</td>\n",
       "      <td>-1.41957</td>\n",
       "      <td>-1.79294</td>\n",
       "      <td>-0.77097</td>\n",
       "      <td>1.63713</td>\n",
       "      <td>-1.64253</td>\n",
       "      <td>-1.88914</td>\n",
       "      <td>0.21539</td>\n",
       "      <td>1.96388</td>\n",
       "      <td>-1.17673</td>\n",
       "      <td>-2.21965</td>\n",
       "      <td>-0.14063</td>\n",
       "      <td>1.78734</td>\n",
       "      <td>-0.41173</td>\n",
       "      <td>-2.03407</td>\n",
       "      <td>0.57110</td>\n",
       "      <td>1.97763</td>\n",
       "      <td>-0.81267</td>\n",
       "      <td>-1.90401</td>\n",
       "      <td>-0.36665</td>\n",
       "      <td>1.62424</td>\n",
       "      <td>-1.05729</td>\n",
       "      <td>-2.36685</td>\n",
       "      <td>1.12908</td>\n",
       "      <td>2.04094</td>\n",
       "      <td>-0.48719</td>\n",
       "      <td>-1.20050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              18       21  ...  ignore_PoleTemp-lh  ignore_PoleTemp-rh\n",
       "subject                    ...                                        \n",
       "f2f_009 -1.20258 -0.84281  ...            -0.29513             0.27690\n",
       "f2f_010 -1.33074 -1.29473  ...            -1.25918            -0.15755\n",
       "f2f_012 -1.08202 -0.98107  ...             0.08114            -0.45452\n",
       "f2f_013 -1.09410 -0.63672  ...             1.14195            -1.43861\n",
       "f2f_018 -1.18252  0.15043  ...            -0.48719            -1.20050\n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard arcsine transformation to deal with 0|100% saturating language scores #\n",
    "# The ROI z-scores are slightly different than those obtained in the Explore_ notebook.\n",
    "# Note that \"np.array()\" is necessary since June 2023 switch to newer conda and MNE stack. \n",
    "orig_scores = df_roidb.copy()[month_cols]\n",
    "\n",
    "max_score = df_roidb[month_cols].max().max()\n",
    "ya = np.arcsin(df_roidb[month_cols]/max_score * 0.97)\n",
    "df_roidb[month_cols] = stats.zscore(np.array(ya), axis=None)   # update DF w/ transformed scores\n",
    "\n",
    "df = df_roidb.copy()  # keep non-transformed ROI values unchanged\n",
    "temp = df.iloc[:, 5:]\n",
    "df.iloc[:, 5:] = stats.zscore(np.array(temp), axis=None)  # simpler transform for the ROI levels\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "64b9eff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average the Pars and Temporal ROIs, by condition and hemisphere #\n",
    "# Pars Triangularis region was left out in one version, but currently is kept for analysis.\n",
    "avg_pars, avg_temp = {}, {}\n",
    "for pre, post in zip(('attend', 'attend', 'ignore', 'ignore'),\n",
    "                     ('lh', 'rh', 'lh', 'rh')):\n",
    "    col_names = []\n",
    "    for base in runParams['pars_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'ParsAvg' + '-' + post\n",
    "    avg_pars[avg_name] = avg\n",
    "    \n",
    "    col_names = []\n",
    "    for base in runParams['temporal_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'TempAvg' + '-' + post\n",
    "    avg_temp[avg_name] = avg\n",
    "\n",
    "# Add new columns to the existing data frame #\n",
    "df_pars = pd.DataFrame(avg_pars)\n",
    "df_temp = pd.DataFrame(avg_temp)\n",
    "df = pd.concat([df, df_pars, df_temp], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e2a54629",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average the Pars ROIs for the non-zscore version, too #\n",
    "avg_pars, avg_temp = {}, {}\n",
    "for pre, post in zip(('attend', 'attend', 'ignore', 'ignore'),\n",
    "                     ('lh', 'rh', 'lh', 'rh')):\n",
    "    col_names = []\n",
    "    for base in runParams['pars_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df_roidb[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'ParsAvg' + '-' + post\n",
    "    avg_pars[avg_name] = avg\n",
    "    \n",
    "    col_names = []\n",
    "    for base in runParams['temporal_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df_roidb[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'TempAvg' + '-' + post\n",
    "    avg_temp[avg_name] = avg\n",
    "\n",
    "# Add new columns to the existing data frame #\n",
    "df_pars2 = pd.DataFrame(avg_pars)\n",
    "df_temp2 = pd.DataFrame(avg_temp)\n",
    "df_roidb = pd.concat([df_roidb, df_pars2, df_temp2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c4c7e1c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reconfigure dataframe for linear modeling so month becomes a variable #\n",
    "df_time = df.copy().reset_index()\n",
    "df_time = df_time.melt(id_vars = 'subject', value_vars=month_cols,\n",
    "                       var_name='month', value_name='score')\n",
    "df_time = df_time.astype({'month': 'int32'})\n",
    "\n",
    "# Merge with static variables #\n",
    "df_model = df_time.merge(df.iloc[:,5:], how='outer', left_on='subject', right_index=True)\n",
    "df_model.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "77f4f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a minimization function for transforming time #\n",
    "def corr_expx_trim(df, p):\n",
    "    '''Returns the corr. coef between vector y and e**(df['months']/p),\n",
    "    averaged across all df['subject'].'''\n",
    "    subject_list = df['subject'].unique()\n",
    "    r_list = []\n",
    "    for subject in subject_list:\n",
    "        idx = df.index[df['subject'] == subject]\n",
    "        x = df.loc[idx[:-1], 'month']  # don't include the last month\n",
    "        y = df.loc[idx[:-1], 'score']\n",
    "        r = np.corrcoef(np.exp(x/p), y)\n",
    "        r_list.append(abs(r[0][1]))\n",
    "    r_avg = stats.trim_mean(r_list, .1)\n",
    "    return -r_avg\n",
    "\n",
    "cost_function = partial(corr_expx_trim, df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b957a0ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  message: Solution found.\n",
      " success: True\n",
      "  status: 0\n",
      "     fun: -0.992064110722993\n",
      "       x: 13.081749960655813\n",
      "     nit: 18\n",
      "    nfev: 18\n"
     ]
    }
   ],
   "source": [
    "# Find the optimal exponent for transforming months #\n",
    "# 11.88, skipping the last month. Or later 13.08.\n",
    "min_result = minimize_scalar(cost_function, bounds=[.5, 400], method='bounded')\n",
    "best_coef = min_result.x\n",
    "print('\\n', min_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1c03deac",
   "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>subject</th>\n",
       "      <th>month</th>\n",
       "      <th>month_t</th>\n",
       "      <th>score</th>\n",
       "      <th>attend_ParsTri-lh</th>\n",
       "      <th>attend_ParsTri-rh</th>\n",
       "      <th>ignore_ParsTri-lh</th>\n",
       "      <th>ignore_ParsTri-rh</th>\n",
       "      <th>attend_ParsOper-lh</th>\n",
       "      <th>attend_ParsOper-rh</th>\n",
       "      <th>ignore_ParsOper-lh</th>\n",
       "      <th>ignore_ParsOper-rh</th>\n",
       "      <th>attend_ParsOrb-lh</th>\n",
       "      <th>attend_ParsOrb-rh</th>\n",
       "      <th>ignore_ParsOrb-lh</th>\n",
       "      <th>ignore_ParsOrb-rh</th>\n",
       "      <th>attend_InfPar-lh</th>\n",
       "      <th>attend_InfPar-rh</th>\n",
       "      <th>ignore_InfPar-lh</th>\n",
       "      <th>ignore_InfPar-rh</th>\n",
       "      <th>attend_SupTemp-lh</th>\n",
       "      <th>attend_SupTemp-rh</th>\n",
       "      <th>ignore_SupTemp-lh</th>\n",
       "      <th>ignore_SupTemp-rh</th>\n",
       "      <th>attend_MidTemp-lh</th>\n",
       "      <th>attend_MidTemp-rh</th>\n",
       "      <th>ignore_MidTemp-lh</th>\n",
       "      <th>ignore_MidTemp-rh</th>\n",
       "      <th>attend_TransTemp-lh</th>\n",
       "      <th>attend_TransTemp-rh</th>\n",
       "      <th>ignore_TransTemp-lh</th>\n",
       "      <th>ignore_TransTemp-rh</th>\n",
       "      <th>attend_BanksTemp-lh</th>\n",
       "      <th>attend_BanksTemp-rh</th>\n",
       "      <th>ignore_BanksTemp-lh</th>\n",
       "      <th>ignore_BanksTemp-rh</th>\n",
       "      <th>attend_PoleTemp-lh</th>\n",
       "      <th>attend_PoleTemp-rh</th>\n",
       "      <th>ignore_PoleTemp-lh</th>\n",
       "      <th>ignore_PoleTemp-rh</th>\n",
       "      <th>attend_ParsAvg-lh</th>\n",
       "      <th>attend_ParsAvg-rh</th>\n",
       "      <th>ignore_ParsAvg-lh</th>\n",
       "      <th>ignore_ParsAvg-rh</th>\n",
       "      <th>attend_TempAvg-lh</th>\n",
       "      <th>attend_TempAvg-rh</th>\n",
       "      <th>ignore_TempAvg-lh</th>\n",
       "      <th>ignore_TempAvg-rh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f2f_009</td>\n",
       "      <td>18</td>\n",
       "      <td>3.95889</td>\n",
       "      <td>-1.20258</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "      <td>0.13611</td>\n",
       "      <td>0.94969</td>\n",
       "      <td>-0.39476</td>\n",
       "      <td>0.09914</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>0.93765</td>\n",
       "      <td>-0.41164</td>\n",
       "      <td>0.27819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f2f_009</td>\n",
       "      <td>21</td>\n",
       "      <td>4.97930</td>\n",
       "      <td>-0.84281</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "      <td>0.13611</td>\n",
       "      <td>0.94969</td>\n",
       "      <td>-0.39476</td>\n",
       "      <td>0.09914</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>0.93765</td>\n",
       "      <td>-0.41164</td>\n",
       "      <td>0.27819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f2f_009</td>\n",
       "      <td>24</td>\n",
       "      <td>6.26273</td>\n",
       "      <td>-0.30567</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "      <td>0.13611</td>\n",
       "      <td>0.94969</td>\n",
       "      <td>-0.39476</td>\n",
       "      <td>0.09914</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>0.93765</td>\n",
       "      <td>-0.41164</td>\n",
       "      <td>0.27819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f2f_009</td>\n",
       "      <td>27</td>\n",
       "      <td>7.87698</td>\n",
       "      <td>0.59538</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "      <td>0.13611</td>\n",
       "      <td>0.94969</td>\n",
       "      <td>-0.39476</td>\n",
       "      <td>0.09914</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>0.93765</td>\n",
       "      <td>-0.41164</td>\n",
       "      <td>0.27819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f2f_009</td>\n",
       "      <td>30</td>\n",
       "      <td>9.90729</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>0.04274</td>\n",
       "      <td>1.02224</td>\n",
       "      <td>-0.46355</td>\n",
       "      <td>0.08903</td>\n",
       "      <td>0.70741</td>\n",
       "      <td>1.14799</td>\n",
       "      <td>0.20788</td>\n",
       "      <td>0.24348</td>\n",
       "      <td>-0.34182</td>\n",
       "      <td>0.67883</td>\n",
       "      <td>-0.92861</td>\n",
       "      <td>-0.03508</td>\n",
       "      <td>0.58229</td>\n",
       "      <td>0.90861</td>\n",
       "      <td>-0.40781</td>\n",
       "      <td>-0.06931</td>\n",
       "      <td>0.35568</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>-0.38491</td>\n",
       "      <td>0.34036</td>\n",
       "      <td>0.08309</td>\n",
       "      <td>0.89636</td>\n",
       "      <td>-0.55487</td>\n",
       "      <td>0.21732</td>\n",
       "      <td>0.57708</td>\n",
       "      <td>1.34408</td>\n",
       "      <td>0.05115</td>\n",
       "      <td>0.69358</td>\n",
       "      <td>0.62667</td>\n",
       "      <td>1.26729</td>\n",
       "      <td>-0.38849</td>\n",
       "      <td>0.40643</td>\n",
       "      <td>0.06108</td>\n",
       "      <td>0.85968</td>\n",
       "      <td>-0.29513</td>\n",
       "      <td>0.27690</td>\n",
       "      <td>0.13611</td>\n",
       "      <td>0.94969</td>\n",
       "      <td>-0.39476</td>\n",
       "      <td>0.09914</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>0.93765</td>\n",
       "      <td>-0.41164</td>\n",
       "      <td>0.27819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  month  ...  ignore_TempAvg-lh  ignore_TempAvg-rh\n",
       "0  f2f_009     18  ...           -0.41164            0.27819\n",
       "1  f2f_009     21  ...           -0.41164            0.27819\n",
       "2  f2f_009     24  ...           -0.41164            0.27819\n",
       "3  f2f_009     27  ...           -0.41164            0.27819\n",
       "4  f2f_009     30  ...           -0.41164            0.27819\n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add transformed months to the modeling data frame #\n",
    "df_model.insert(2, 'month_t', np.exp(df_model['month']/best_coef))\n",
    "df_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b327b6a",
   "metadata": {},
   "source": [
    "## Load and transform linearized ROI data\n",
    "Added 2021-12-14, to compare linear to original log values of the epoch-averaged ROI data. The linearized version is created to report statistics that match the bar chart in the section \"\\*FIG\\* Plot attend and ignore levels\", below.  \n",
    "\n",
    "The dataframe _df\\_roilin_ here is essentially analogous to _df\\_roidb_ above. No modeling is performed with the linear version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f577fd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the ROI data #\n",
    "df_roilin = pd.read_csv(datalin_file, index_col=0, header=0)\n",
    "df_roilin = df_roilin[[key for key in use_columns]]\n",
    "\n",
    "# Join the scores and data, then rename columns #\n",
    "df_roilin = df_roilin.join(df_scores, how='right')\n",
    "combine_columns = score_columns; combine_columns.update(use_columns)\n",
    "\n",
    "order_list = list(combine_columns.keys())\n",
    "order_list.pop(0)\n",
    "df_roilin = df_roilin[order_list]\n",
    "df_roilin.rename(columns=combine_columns, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5cbc1fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard arcsine transformation to deal with 0|100% saturating language scores #\n",
    "max_score = df_roilin[month_cols].max().max()\n",
    "ya = np.arcsin(df_roilin[month_cols]/max_score * 0.97)\n",
    "df_roilin[month_cols] = stats.zscore(ya.values, axis=None)   # update dframe w/ transformed scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "aa7f0b4c",
   "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>18</th>\n",
       "      <th>21</th>\n",
       "      <th>24</th>\n",
       "      <th>27</th>\n",
       "      <th>30</th>\n",
       "      <th>attend_ParsTri-lh</th>\n",
       "      <th>attend_ParsTri-rh</th>\n",
       "      <th>ignore_ParsTri-lh</th>\n",
       "      <th>ignore_ParsTri-rh</th>\n",
       "      <th>attend_ParsOper-lh</th>\n",
       "      <th>attend_ParsOper-rh</th>\n",
       "      <th>ignore_ParsOper-lh</th>\n",
       "      <th>ignore_ParsOper-rh</th>\n",
       "      <th>attend_ParsOrb-lh</th>\n",
       "      <th>attend_ParsOrb-rh</th>\n",
       "      <th>ignore_ParsOrb-lh</th>\n",
       "      <th>ignore_ParsOrb-rh</th>\n",
       "      <th>attend_InfPar-lh</th>\n",
       "      <th>attend_InfPar-rh</th>\n",
       "      <th>ignore_InfPar-lh</th>\n",
       "      <th>ignore_InfPar-rh</th>\n",
       "      <th>attend_SupTemp-lh</th>\n",
       "      <th>attend_SupTemp-rh</th>\n",
       "      <th>ignore_SupTemp-lh</th>\n",
       "      <th>ignore_SupTemp-rh</th>\n",
       "      <th>attend_MidTemp-lh</th>\n",
       "      <th>attend_MidTemp-rh</th>\n",
       "      <th>ignore_MidTemp-lh</th>\n",
       "      <th>ignore_MidTemp-rh</th>\n",
       "      <th>attend_TransTemp-lh</th>\n",
       "      <th>attend_TransTemp-rh</th>\n",
       "      <th>ignore_TransTemp-lh</th>\n",
       "      <th>ignore_TransTemp-rh</th>\n",
       "      <th>attend_BanksTemp-lh</th>\n",
       "      <th>attend_BanksTemp-rh</th>\n",
       "      <th>ignore_BanksTemp-lh</th>\n",
       "      <th>ignore_BanksTemp-rh</th>\n",
       "      <th>attend_PoleTemp-lh</th>\n",
       "      <th>attend_PoleTemp-rh</th>\n",
       "      <th>ignore_PoleTemp-lh</th>\n",
       "      <th>ignore_PoleTemp-rh</th>\n",
       "      <th>attend_ParsAvg-lh</th>\n",
       "      <th>attend_ParsAvg-rh</th>\n",
       "      <th>ignore_ParsAvg-lh</th>\n",
       "      <th>ignore_ParsAvg-rh</th>\n",
       "      <th>attend_TempAvg-lh</th>\n",
       "      <th>attend_TempAvg-rh</th>\n",
       "      <th>ignore_TempAvg-lh</th>\n",
       "      <th>ignore_TempAvg-rh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>f2f_009</th>\n",
       "      <td>-1.20258</td>\n",
       "      <td>-0.84281</td>\n",
       "      <td>-0.30567</td>\n",
       "      <td>0.59538</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>43.37030</td>\n",
       "      <td>54.15661</td>\n",
       "      <td>38.66636</td>\n",
       "      <td>43.82789</td>\n",
       "      <td>50.42518</td>\n",
       "      <td>55.72306</td>\n",
       "      <td>45.02516</td>\n",
       "      <td>45.39004</td>\n",
       "      <td>39.74855</td>\n",
       "      <td>50.09948</td>\n",
       "      <td>34.79650</td>\n",
       "      <td>42.61166</td>\n",
       "      <td>49.01462</td>\n",
       "      <td>52.77906</td>\n",
       "      <td>39.15818</td>\n",
       "      <td>42.28220</td>\n",
       "      <td>46.55966</td>\n",
       "      <td>54.58409</td>\n",
       "      <td>39.36209</td>\n",
       "      <td>46.39820</td>\n",
       "      <td>43.76894</td>\n",
       "      <td>52.63263</td>\n",
       "      <td>37.87391</td>\n",
       "      <td>45.12160</td>\n",
       "      <td>48.95679</td>\n",
       "      <td>58.25663</td>\n",
       "      <td>43.45308</td>\n",
       "      <td>50.26728</td>\n",
       "      <td>49.51043</td>\n",
       "      <td>57.25103</td>\n",
       "      <td>39.33015</td>\n",
       "      <td>47.09860</td>\n",
       "      <td>43.55096</td>\n",
       "      <td>52.19667</td>\n",
       "      <td>40.17161</td>\n",
       "      <td>45.73534</td>\n",
       "      <td>44.51468</td>\n",
       "      <td>53.32638</td>\n",
       "      <td>39.49601</td>\n",
       "      <td>43.94320</td>\n",
       "      <td>44.62652</td>\n",
       "      <td>53.13780</td>\n",
       "      <td>39.13587</td>\n",
       "      <td>45.75171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_010</th>\n",
       "      <td>-1.33074</td>\n",
       "      <td>-1.29473</td>\n",
       "      <td>-0.92429</td>\n",
       "      <td>-0.41760</td>\n",
       "      <td>0.18365</td>\n",
       "      <td>24.02841</td>\n",
       "      <td>34.04899</td>\n",
       "      <td>30.49554</td>\n",
       "      <td>46.25840</td>\n",
       "      <td>22.04996</td>\n",
       "      <td>31.77046</td>\n",
       "      <td>30.93266</td>\n",
       "      <td>44.89385</td>\n",
       "      <td>27.57434</td>\n",
       "      <td>35.66840</td>\n",
       "      <td>31.09742</td>\n",
       "      <td>46.49559</td>\n",
       "      <td>18.62267</td>\n",
       "      <td>35.12168</td>\n",
       "      <td>21.22889</td>\n",
       "      <td>42.01879</td>\n",
       "      <td>27.14334</td>\n",
       "      <td>38.64925</td>\n",
       "      <td>29.36454</td>\n",
       "      <td>46.34781</td>\n",
       "      <td>26.76163</td>\n",
       "      <td>35.57484</td>\n",
       "      <td>30.38990</td>\n",
       "      <td>43.56625</td>\n",
       "      <td>22.44533</td>\n",
       "      <td>39.62574</td>\n",
       "      <td>27.66984</td>\n",
       "      <td>47.38545</td>\n",
       "      <td>22.70526</td>\n",
       "      <td>35.22449</td>\n",
       "      <td>23.86779</td>\n",
       "      <td>42.59497</td>\n",
       "      <td>27.14778</td>\n",
       "      <td>30.81836</td>\n",
       "      <td>32.28354</td>\n",
       "      <td>41.44458</td>\n",
       "      <td>24.55090</td>\n",
       "      <td>33.82928</td>\n",
       "      <td>30.84188</td>\n",
       "      <td>45.88262</td>\n",
       "      <td>27.01759</td>\n",
       "      <td>35.01415</td>\n",
       "      <td>30.67933</td>\n",
       "      <td>43.78622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_012</th>\n",
       "      <td>-1.08202</td>\n",
       "      <td>-0.98107</td>\n",
       "      <td>-0.58665</td>\n",
       "      <td>-0.00758</td>\n",
       "      <td>0.44438</td>\n",
       "      <td>44.41944</td>\n",
       "      <td>50.86747</td>\n",
       "      <td>46.06049</td>\n",
       "      <td>52.70099</td>\n",
       "      <td>40.99281</td>\n",
       "      <td>60.02810</td>\n",
       "      <td>49.00944</td>\n",
       "      <td>49.27465</td>\n",
       "      <td>46.00877</td>\n",
       "      <td>49.11661</td>\n",
       "      <td>44.46946</td>\n",
       "      <td>52.64565</td>\n",
       "      <td>43.55427</td>\n",
       "      <td>46.48281</td>\n",
       "      <td>45.45963</td>\n",
       "      <td>54.97386</td>\n",
       "      <td>43.60565</td>\n",
       "      <td>51.93230</td>\n",
       "      <td>45.37859</td>\n",
       "      <td>48.88175</td>\n",
       "      <td>45.05026</td>\n",
       "      <td>47.33585</td>\n",
       "      <td>41.52395</td>\n",
       "      <td>48.72750</td>\n",
       "      <td>45.40617</td>\n",
       "      <td>56.58291</td>\n",
       "      <td>44.37653</td>\n",
       "      <td>51.76183</td>\n",
       "      <td>44.05319</td>\n",
       "      <td>51.52916</td>\n",
       "      <td>43.05755</td>\n",
       "      <td>57.44676</td>\n",
       "      <td>51.51964</td>\n",
       "      <td>52.29969</td>\n",
       "      <td>43.74953</td>\n",
       "      <td>38.74564</td>\n",
       "      <td>43.80700</td>\n",
       "      <td>53.33739</td>\n",
       "      <td>46.51313</td>\n",
       "      <td>51.54043</td>\n",
       "      <td>46.72518</td>\n",
       "      <td>50.52261</td>\n",
       "      <td>43.55069</td>\n",
       "      <td>45.45163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_013</th>\n",
       "      <td>-1.09410</td>\n",
       "      <td>-0.63672</td>\n",
       "      <td>-0.03494</td>\n",
       "      <td>0.89220</td>\n",
       "      <td>1.72450</td>\n",
       "      <td>31.22038</td>\n",
       "      <td>53.27914</td>\n",
       "      <td>56.68191</td>\n",
       "      <td>35.59895</td>\n",
       "      <td>26.56218</td>\n",
       "      <td>53.34155</td>\n",
       "      <td>53.52853</td>\n",
       "      <td>35.19701</td>\n",
       "      <td>39.61100</td>\n",
       "      <td>53.75435</td>\n",
       "      <td>56.88189</td>\n",
       "      <td>36.74750</td>\n",
       "      <td>38.84148</td>\n",
       "      <td>52.30082</td>\n",
       "      <td>47.26001</td>\n",
       "      <td>24.07432</td>\n",
       "      <td>31.61270</td>\n",
       "      <td>49.56321</td>\n",
       "      <td>54.11035</td>\n",
       "      <td>27.26717</td>\n",
       "      <td>36.67166</td>\n",
       "      <td>48.52809</td>\n",
       "      <td>52.81260</td>\n",
       "      <td>23.30824</td>\n",
       "      <td>32.79178</td>\n",
       "      <td>51.09743</td>\n",
       "      <td>53.81833</td>\n",
       "      <td>29.03919</td>\n",
       "      <td>33.52804</td>\n",
       "      <td>46.92273</td>\n",
       "      <td>49.87592</td>\n",
       "      <td>22.78015</td>\n",
       "      <td>45.53009</td>\n",
       "      <td>53.40561</td>\n",
       "      <td>55.64676</td>\n",
       "      <td>30.99644</td>\n",
       "      <td>32.46452</td>\n",
       "      <td>53.45834</td>\n",
       "      <td>55.69744</td>\n",
       "      <td>35.84782</td>\n",
       "      <td>37.93815</td>\n",
       "      <td>50.49897</td>\n",
       "      <td>54.18990</td>\n",
       "      <td>27.19061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_018</th>\n",
       "      <td>-1.18252</td>\n",
       "      <td>0.15043</td>\n",
       "      <td>1.29829</td>\n",
       "      <td>2.28091</td>\n",
       "      <td>2.28091</td>\n",
       "      <td>66.39702</td>\n",
       "      <td>51.89962</td>\n",
       "      <td>35.16627</td>\n",
       "      <td>30.82444</td>\n",
       "      <td>64.90206</td>\n",
       "      <td>64.95302</td>\n",
       "      <td>33.50955</td>\n",
       "      <td>30.95703</td>\n",
       "      <td>63.53308</td>\n",
       "      <td>61.07193</td>\n",
       "      <td>31.13054</td>\n",
       "      <td>28.60342</td>\n",
       "      <td>36.06277</td>\n",
       "      <td>62.25940</td>\n",
       "      <td>29.59578</td>\n",
       "      <td>27.98619</td>\n",
       "      <td>45.10187</td>\n",
       "      <td>67.04746</td>\n",
       "      <td>32.89285</td>\n",
       "      <td>25.96551</td>\n",
       "      <td>41.60387</td>\n",
       "      <td>64.41654</td>\n",
       "      <td>39.12342</td>\n",
       "      <td>27.08147</td>\n",
       "      <td>48.89049</td>\n",
       "      <td>67.25683</td>\n",
       "      <td>35.72339</td>\n",
       "      <td>27.89204</td>\n",
       "      <td>39.52542</td>\n",
       "      <td>62.07761</td>\n",
       "      <td>33.79585</td>\n",
       "      <td>25.11311</td>\n",
       "      <td>55.48468</td>\n",
       "      <td>68.22932</td>\n",
       "      <td>38.45969</td>\n",
       "      <td>32.71602</td>\n",
       "      <td>64.94405</td>\n",
       "      <td>59.30819</td>\n",
       "      <td>33.26879</td>\n",
       "      <td>30.12830</td>\n",
       "      <td>47.39681</td>\n",
       "      <td>66.56444</td>\n",
       "      <td>36.82532</td>\n",
       "      <td>28.58766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              18       21  ...  ignore_TempAvg-lh  ignore_TempAvg-rh\n",
       "subject                    ...                                      \n",
       "f2f_009 -1.20258 -0.84281  ...           39.13587           45.75171\n",
       "f2f_010 -1.33074 -1.29473  ...           30.67933           43.78622\n",
       "f2f_012 -1.08202 -0.98107  ...           43.55069           45.45163\n",
       "f2f_013 -1.09410 -0.63672  ...           54.18990           27.19061\n",
       "f2f_018 -1.18252  0.15043  ...           36.82532           28.58766\n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average the Pars ROIs #\n",
    "avg_pars, avg_temp = {}, {}\n",
    "for pre, post in zip(('attend', 'attend', 'ignore', 'ignore'),\n",
    "                     ('lh', 'rh', 'lh', 'rh')):\n",
    "    col_names = []\n",
    "    for base in runParams['pars_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df_roilin[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'ParsAvg' + '-' + post\n",
    "    avg_pars[avg_name] = avg\n",
    "    \n",
    "    col_names = []\n",
    "    for base in runParams['temporal_grouping']:\n",
    "        col_names.append(pre + '_' + base + '-' + post)\n",
    "    avg = df_roilin[col_names].mean(axis=1)\n",
    "    avg_name = pre + '_' + 'TempAvg' + '-' + post\n",
    "    avg_temp[avg_name] = avg\n",
    "\n",
    "# Add new columns to the existing data frame #\n",
    "df_pars2 = pd.DataFrame(avg_pars)\n",
    "df_temp2 = pd.DataFrame(avg_temp)\n",
    "df_roilin = pd.concat([df_roilin, df_pars2, df_temp2], axis=1)\n",
    "\n",
    "df_roilin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55b24b3",
   "metadata": {},
   "source": [
    "## Series of linear models\n",
    "Note that for the mixed models (Model 2+), R2 values can be calculated in different ways (e.g. the marginal and conditional r-squared values of Nakagawa + Shielzeth 2012). Regardless, here I've calculated it by hand using the traditional formula (1 - resid var./total var.).  \n",
    "Also, models below are calculated _without_ the restricted ML method (which is otherwise on by default); mainly this is so a value for BIC can be obtained when .fit() is called."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ead92dd",
   "metadata": {},
   "source": [
    "### Baseline linear models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec98cdc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0742</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-42.0208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>-2.599</td>   <td>0.137</td>  <td>-18.983</td> <td>0.000</td> <td>-2.867</td> <td>-2.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>    <td>0.394</td>   <td>0.013</td>  <td>31.296</td>  <td>0.000</td>  <td>0.369</td>  <td>0.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>  <td>0.234</td>   <td>0.315</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0742    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -42.0208  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "          &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept & -2.599 &    0.137 & -18.983 &       0.000 & -2.867 & -2.331  \\\\\n",
       "month\\_t  &  0.394 &    0.013 &  31.296 &       0.000 &  0.369 &  0.419  \\\\\n",
       "Group Var &  0.234 &    0.315 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "        Mixed Linear Model Regression Results\n",
       "======================================================\n",
       "Model:            MixedLM Dependent Variable: score   \n",
       "No. Observations: 105     Method:             ML      \n",
       "No. Groups:       21      Scale:              0.0742  \n",
       "Min. group size:  5       Log-Likelihood:     -42.0208\n",
       "Max. group size:  5       Converged:          Yes     \n",
       "Mean group size:  5.0                                 \n",
       "------------------------------------------------------\n",
       "           Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "------------------------------------------------------\n",
       "Intercept  -2.599    0.137 -18.983 0.000 -2.867 -2.331\n",
       "month_t     0.394    0.013  31.296 0.000  0.369  0.419\n",
       "Group Var   0.234    0.315                            \n",
       "======================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 1 - Random intercept for each subject, no covariants; transformed time #\n",
    "# As expected, log-likelihood and R2 value are better for transformed time.\n",
    "md = smf.mixedlm(\"score ~ month_t\", df_model, groups=df_model['subject'])\n",
    "scores_model = md.data.endog    # actual scores, stored for later\n",
    "mdf1 = md.fit(method='lbfgs', reml=False)\n",
    "mdf1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "155005b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0781</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-44.1806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>-4.694</td>   <td>0.189</td>  <td>-24.865</td> <td>0.000</td> <td>-5.064</td> <td>-4.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month</th>      <td>0.196</td>   <td>0.006</td>  <td>30.433</td>  <td>0.000</td>  <td>0.183</td>  <td>0.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>  <td>0.233</td>   <td>0.307</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0781    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -44.1806  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "          &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept & -4.694 &    0.189 & -24.865 &       0.000 & -5.064 & -4.324  \\\\\n",
       "month     &  0.196 &    0.006 &  30.433 &       0.000 &  0.183 &  0.208  \\\\\n",
       "Group Var &  0.233 &    0.307 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "        Mixed Linear Model Regression Results\n",
       "======================================================\n",
       "Model:            MixedLM Dependent Variable: score   \n",
       "No. Observations: 105     Method:             ML      \n",
       "No. Groups:       21      Scale:              0.0781  \n",
       "Min. group size:  5       Log-Likelihood:     -44.1806\n",
       "Max. group size:  5       Converged:          Yes     \n",
       "Mean group size:  5.0                                 \n",
       "------------------------------------------------------\n",
       "           Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "------------------------------------------------------\n",
       "Intercept  -4.694    0.189 -24.865 0.000 -5.064 -4.324\n",
       "month       0.196    0.006  30.433 0.000  0.183  0.208\n",
       "Group Var   0.233    0.307                            \n",
       "======================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 1n - Random intercept for each subject, no covariants; linear with time #\n",
    "#\n",
    "md = smf.mixedlm(\"score ~ month\", df_model, groups=df_model['subject'])\n",
    "mdf1_n = md.fit(method='lbfgs', reml=False)\n",
    "mdf1_n.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d9ce290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9397855786773035  0.9365589499172551  \n",
      "MSE: 0.060214421322696554  0.0634410500827449  \n",
      "BIC: 102.65745604024308  106.97711884614604  \n",
      "Pval: None  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf1, mdf1_n))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb094508",
   "metadata": {},
   "source": [
    "### Models (mdf2) with Inferior Frontal Gyrus slope covariants\n",
    "The average Broca's with only pars opercularis and orbitalis (column \"ParsAvg2\") gives slightly better results than with all three regions. That was used in the archived notebook, F2F_Growth_Statistics_Pars2Version. But here, all three are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d65397dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0688</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-33.2442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.115</td>  <td>-22.504</td> <td>0.000</td> <td>-2.825</td> <td>-2.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.388</td>   <td>0.012</td>  <td>31.832</td>  <td>0.000</td>  <td>0.364</td>  <td>0.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_ParsAvg-lh')</th>  <td>0.038</td>   <td>0.008</td>   <td>4.560</td>  <td>0.000</td>  <td>0.022</td>  <td>0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.132</td>   <td>0.195</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0688    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -33.2442  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.115 & -22.504 &       0.000 & -2.825 & -2.373  \\\\\n",
       "month\\_t                         &  0.388 &    0.012 &  31.832 &       0.000 &  0.364 &  0.412  \\\\\n",
       "month\\_t:Q('attend\\_ParsAvg-lh') &  0.038 &    0.008 &   4.560 &       0.000 &  0.022 &  0.054  \\\\\n",
       "Group Var                        &  0.132 &    0.195 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0688  \n",
       "Min. group size:          5             Log-Likelihood:           -33.2442\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.115 -22.504 0.000 -2.825 -2.373\n",
       "month_t                         0.388    0.012  31.832 0.000  0.364  0.412\n",
       "month_t:Q('attend_ParsAvg-lh')  0.038    0.008   4.560 0.000  0.022  0.054\n",
       "Group Var                       0.132    0.195                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 2_lha - Left ParsAvg|attend predicting slope #\n",
    "# Left is better than right and attend is better than ignore.\n",
    "formula = \"score ~ month_t + month_t:Q('attend_ParsAvg-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf2_lha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf2_lha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cb43d3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0713</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-36.2835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.121</td>  <td>-21.452</td> <td>0.000</td> <td>-2.837</td> <td>-2.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.377</td>   <td>0.013</td>  <td>28.573</td>  <td>0.000</td>  <td>0.351</td>  <td>0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_ParsAvg-rh')</th>  <td>0.045</td>   <td>0.013</td>   <td>3.589</td>  <td>0.000</td>  <td>0.021</td>  <td>0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.155</td>   <td>0.222</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0713    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -36.2835  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.121 & -21.452 &       0.000 & -2.837 & -2.362  \\\\\n",
       "month\\_t                         &  0.377 &    0.013 &  28.573 &       0.000 &  0.351 &  0.403  \\\\\n",
       "month\\_t:Q('attend\\_ParsAvg-rh') &  0.045 &    0.013 &   3.589 &       0.000 &  0.021 &  0.070  \\\\\n",
       "Group Var                        &  0.155 &    0.222 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0713  \n",
       "Min. group size:          5             Log-Likelihood:           -36.2835\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.121 -21.452 0.000 -2.837 -2.362\n",
       "month_t                         0.377    0.013  28.573 0.000  0.351  0.403\n",
       "month_t:Q('attend_ParsAvg-rh')  0.045    0.013   3.589 0.000  0.021  0.070\n",
       "Group Var                       0.155    0.222                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 2_rha - Right ParsAvg|attend predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_ParsAvg-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf2_rha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf2_rha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e28101e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0746</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-41.6950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.135</td>  <td>-19.276</td> <td>0.000</td> <td>-2.863</td> <td>-2.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.396</td>   <td>0.013</td>  <td>30.552</td>  <td>0.000</td>  <td>0.371</td>  <td>0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_ParsAvg-lh')</th>  <td>0.009</td>   <td>0.011</td>   <td>0.812</td>  <td>0.417</td> <td>-0.013</td>  <td>0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.221</td>   <td>0.303</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0746    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -41.6950  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.135 & -19.276 &       0.000 & -2.863 & -2.335  \\\\\n",
       "month\\_t                         &  0.396 &    0.013 &  30.552 &       0.000 &  0.371 &  0.422  \\\\\n",
       "month\\_t:Q('ignore\\_ParsAvg-lh') &  0.009 &    0.011 &   0.812 &       0.417 & -0.013 &  0.031  \\\\\n",
       "Group Var                        &  0.221 &    0.303 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0746  \n",
       "Min. group size:          5             Log-Likelihood:           -41.6950\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.135 -19.276 0.000 -2.863 -2.335\n",
       "month_t                         0.396    0.013  30.552 0.000  0.371  0.422\n",
       "month_t:Q('ignore_ParsAvg-lh')  0.009    0.011   0.812 0.417 -0.013  0.031\n",
       "Group Var                       0.221    0.303                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 2_lhi - Left ParsAvg|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_ParsAvg-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf2_lhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf2_lhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "016a8d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0674</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-38.2261</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.135</td>  <td>-19.182</td> <td>0.000</td> <td>-2.865</td> <td>-2.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.385</td>   <td>0.012</td>  <td>31.029</td>  <td>0.000</td>  <td>0.361</td>  <td>0.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_ParsAvg-rh')</th> <td>-0.031</td>   <td>0.011</td>  <td>-2.774</td>  <td>0.006</td> <td>-0.053</td> <td>-0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.240</td>   <td>0.344</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0674    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -38.2261  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.135 & -19.182 &       0.000 & -2.865 & -2.334  \\\\\n",
       "month\\_t                         &  0.385 &    0.012 &  31.029 &       0.000 &  0.361 &  0.410  \\\\\n",
       "month\\_t:Q('ignore\\_ParsAvg-rh') & -0.031 &    0.011 &  -2.774 &       0.006 & -0.053 & -0.009  \\\\\n",
       "Group Var                        &  0.240 &    0.344 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0674  \n",
       "Min. group size:          5             Log-Likelihood:           -38.2261\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.135 -19.182 0.000 -2.865 -2.334\n",
       "month_t                         0.385    0.012  31.029 0.000  0.361  0.410\n",
       "month_t:Q('ignore_ParsAvg-rh') -0.031    0.011  -2.774 0.006 -0.053 -0.009\n",
       "Group Var                       0.240    0.344                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 2_rhi - Right ParsAvg|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_ParsAvg-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf2_rhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf2_rhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a110ac6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9436736790794188  0.9417982137730988  0.9394128193830761  0.9453487775984666  \n",
      "MSE: 0.056326320920581256  0.05820178622690124  0.060587180616923884  0.05465122240153345  \n",
      "BIC: 89.75822272326735  95.8367471726437  106.65971560736591  99.72197305265416  \n",
      "Pval: 5.1212725883291135e-06  0.00033151216898320265  0.4166573529806247  0.005541010123795197  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf2_lha, mdf2_rha, mdf2_lhi, mdf2_rhi))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c4f5de",
   "metadata": {},
   "source": [
    "### Models (mdf3) with Inferior Parietal slope covariants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bd451a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0728</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-39.9395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.131</td>  <td>-19.789</td> <td>0.000</td> <td>-2.857</td> <td>-2.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.398</td>   <td>0.013</td>  <td>31.512</td>  <td>0.000</td>  <td>0.373</td>  <td>0.423</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_InfPar-lh')</th>  <td>0.018</td>   <td>0.009</td>   <td>2.076</td>  <td>0.038</td>  <td>0.001</td>  <td>0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.206</td>   <td>0.283</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0728    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -39.9395  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.131 & -19.789 &       0.000 & -2.857 & -2.342  \\\\\n",
       "month\\_t                        &  0.398 &    0.013 &  31.512 &       0.000 &  0.373 &  0.423  \\\\\n",
       "month\\_t:Q('attend\\_InfPar-lh') &  0.018 &    0.009 &   2.076 &       0.038 &  0.001 &  0.036  \\\\\n",
       "Group Var                       &  0.206 &    0.283 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0728  \n",
       "Min. group size:         5             Log-Likelihood:           -39.9395\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.131 -19.789 0.000 -2.857 -2.342\n",
       "month_t                        0.398    0.013  31.512 0.000  0.373  0.423\n",
       "month_t:Q('attend_InfPar-lh')  0.018    0.009   2.076 0.038  0.001  0.036\n",
       "Group Var                      0.206    0.283                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 3_lha - Left InfPar|attend predicting slope #\n",
    "# Right is better than left and ignore is better than attend.\n",
    "formula = \"score ~ month_t + month_t:Q('attend_InfPar-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf3_lha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf3_lha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a1609e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0649</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-30.9799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.115</td>  <td>-22.699</td> <td>0.000</td> <td>-2.824</td> <td>-2.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.365</td>   <td>0.013</td>  <td>27.994</td>  <td>0.000</td>  <td>0.340</td>  <td>0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_InfPar-rh')</th>  <td>0.079</td>   <td>0.016</td>   <td>5.106</td>  <td>0.000</td>  <td>0.049</td>  <td>0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.136</td>   <td>0.202</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0649    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -30.9799  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.115 & -22.699 &       0.000 & -2.824 & -2.375  \\\\\n",
       "month\\_t                        &  0.365 &    0.013 &  27.994 &       0.000 &  0.340 &  0.391  \\\\\n",
       "month\\_t:Q('attend\\_InfPar-rh') &  0.079 &    0.016 &   5.106 &       0.000 &  0.049 &  0.110  \\\\\n",
       "Group Var                       &  0.136 &    0.202 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0649  \n",
       "Min. group size:         5             Log-Likelihood:           -30.9799\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.115 -22.699 0.000 -2.824 -2.375\n",
       "month_t                        0.365    0.013  27.994 0.000  0.340  0.391\n",
       "month_t:Q('attend_InfPar-rh')  0.079    0.016   5.106 0.000  0.049  0.110\n",
       "Group Var                      0.136    0.202                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 3_rha - Right InfPar|attend predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf3_rha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf3_rha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "22c4c3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-41.2366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.134</td>  <td>-19.420</td> <td>0.000</td> <td>-2.861</td> <td>-2.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.400</td>   <td>0.014</td>  <td>29.482</td>  <td>0.000</td>  <td>0.374</td>  <td>0.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_InfPar-lh')</th>  <td>0.010</td>   <td>0.008</td>   <td>1.264</td>  <td>0.206</td> <td>-0.006</td>  <td>0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.217</td>   <td>0.296</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0741    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -41.2366  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.134 & -19.420 &       0.000 & -2.861 & -2.337  \\\\\n",
       "month\\_t                        &  0.400 &    0.014 &  29.482 &       0.000 &  0.374 &  0.427  \\\\\n",
       "month\\_t:Q('ignore\\_InfPar-lh') &  0.010 &    0.008 &   1.264 &       0.206 & -0.006 &  0.026  \\\\\n",
       "Group Var                       &  0.217 &    0.296 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0741  \n",
       "Min. group size:         5             Log-Likelihood:           -41.2366\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.134 -19.420 0.000 -2.861 -2.337\n",
       "month_t                        0.400    0.014  29.482 0.000  0.374  0.427\n",
       "month_t:Q('ignore_InfPar-lh')  0.010    0.008   1.264 0.206 -0.006  0.026\n",
       "Group Var                      0.217    0.296                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 3_lhi - Left InfPar|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_InfPar-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf3_lhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf3_lhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e688312f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0689</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-39.3356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.137</td>  <td>-19.002</td> <td>0.000</td> <td>-2.867</td> <td>-2.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.389</td>   <td>0.012</td>  <td>31.677</td>  <td>0.000</td>  <td>0.365</td>  <td>0.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_InfPar-rh')</th> <td>-0.023</td>   <td>0.010</td>  <td>-2.324</td>  <td>0.020</td> <td>-0.042</td> <td>-0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.244</td>   <td>0.346</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0689    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -39.3356  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.137 & -19.002 &       0.000 & -2.867 & -2.331  \\\\\n",
       "month\\_t                        &  0.389 &    0.012 &  31.677 &       0.000 &  0.365 &  0.414  \\\\\n",
       "month\\_t:Q('ignore\\_InfPar-rh') & -0.023 &    0.010 &  -2.324 &       0.020 & -0.042 & -0.004  \\\\\n",
       "Group Var                       &  0.244 &    0.346 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0689  \n",
       "Min. group size:         5             Log-Likelihood:           -39.3356\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.137 -19.002 0.000 -2.867 -2.331\n",
       "month_t                        0.389    0.012  31.677 0.000  0.365  0.414\n",
       "month_t:Q('ignore_InfPar-rh') -0.023    0.010  -2.324 0.020 -0.042 -0.004\n",
       "Group Var                      0.244    0.346                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 3_rhi - Right InfPar|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf3_rhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf3_rhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "df222278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9408179363876492  0.9469847587034098  0.9397482548570304  0.9441230102544105  \n",
      "MSE: 0.05918206361235079  0.05301524129659015  0.0602517451429696  0.055876989745589536  \n",
      "BIC: 103.14879163157946  85.22966734569354  105.7429861181821  101.94090268247692  \n",
      "Pval: 0.037880297313512994  3.2863425289838487e-07  0.20618287313665407  0.020133534047874668  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf3_lha, mdf3_rha, mdf3_lhi, mdf3_rhi))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7b0d4b",
   "metadata": {},
   "source": [
    "### Models (mdf4) with Superior Temporal slope covariants\n",
    "Added for completeness, though likley not going to be in the paper. Not as predictive as for Inferior Parietal or Inferior Frontal (Avg. Pars)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "352b6981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0740</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-39.7362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.129</td>  <td>-20.225</td> <td>0.000</td> <td>-2.851</td> <td>-2.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.396</td>   <td>0.013</td>  <td>31.414</td>  <td>0.000</td>  <td>0.371</td>  <td>0.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_SupTemp-lh')</th>  <td>0.023</td>   <td>0.010</td>   <td>2.198</td>  <td>0.028</td>  <td>0.002</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.188</td>   <td>0.263</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0740    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -39.7362  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.129 & -20.225 &       0.000 & -2.851 & -2.347  \\\\\n",
       "month\\_t                         &  0.396 &    0.013 &  31.414 &       0.000 &  0.371 &  0.420  \\\\\n",
       "month\\_t:Q('attend\\_SupTemp-lh') &  0.023 &    0.010 &   2.198 &       0.028 &  0.002 &  0.043  \\\\\n",
       "Group Var                        &  0.188 &    0.263 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0740  \n",
       "Min. group size:          5             Log-Likelihood:           -39.7362\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.129 -20.225 0.000 -2.851 -2.347\n",
       "month_t                         0.396    0.013  31.414 0.000  0.371  0.420\n",
       "month_t:Q('attend_SupTemp-lh')  0.023    0.010   2.198 0.028  0.002  0.043\n",
       "Group Var                       0.188    0.263                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_lha - Left SupTemp|attend predicting slope #\n",
    "# Right is better than left and _ignore_ is better than attend.\n",
    "formula = \"score ~ month_t + month_t:Q('attend_SupTemp-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4_lha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4_lha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1d7b0de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0707</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-36.6100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.123</td>  <td>-21.129</td> <td>0.000</td> <td>-2.840</td> <td>-2.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.374</td>   <td>0.014</td>  <td>27.570</td>  <td>0.000</td>  <td>0.348</td>  <td>0.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_SupTemp-rh')</th>  <td>0.049</td>   <td>0.014</td>   <td>3.454</td>  <td>0.001</td>  <td>0.021</td>  <td>0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.165</td>   <td>0.235</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0707    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -36.6100  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.123 & -21.129 &       0.000 & -2.840 & -2.358  \\\\\n",
       "month\\_t                         &  0.374 &    0.014 &  27.570 &       0.000 &  0.348 &  0.401  \\\\\n",
       "month\\_t:Q('attend\\_SupTemp-rh') &  0.049 &    0.014 &   3.454 &       0.001 &  0.021 &  0.077  \\\\\n",
       "Group Var                        &  0.165 &    0.235 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0707  \n",
       "Min. group size:          5             Log-Likelihood:           -36.6100\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.123 -21.129 0.000 -2.840 -2.358\n",
       "month_t                         0.374    0.014  27.570 0.000  0.348  0.401\n",
       "month_t:Q('attend_SupTemp-rh')  0.049    0.014   3.454 0.001  0.021  0.077\n",
       "Group Var                       0.165    0.235                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_rha - Right SupTemp|attend predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_SupTemp-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4_rha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4_rha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "22989d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0743</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-41.1879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.133</td>  <td>-19.484</td> <td>0.000</td> <td>-2.861</td> <td>-2.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.399</td>   <td>0.013</td>  <td>30.488</td>  <td>0.000</td>  <td>0.373</td>  <td>0.424</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_SupTemp-lh')</th>  <td>0.012</td>   <td>0.009</td>   <td>1.305</td>  <td>0.192</td> <td>-0.006</td>  <td>0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.214</td>   <td>0.293</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0743    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -41.1879  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.133 & -19.484 &       0.000 & -2.861 & -2.338  \\\\\n",
       "month\\_t                         &  0.399 &    0.013 &  30.488 &       0.000 &  0.373 &  0.424  \\\\\n",
       "month\\_t:Q('ignore\\_SupTemp-lh') &  0.012 &    0.009 &   1.305 &       0.192 & -0.006 &  0.031  \\\\\n",
       "Group Var                        &  0.214 &    0.293 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0743  \n",
       "Min. group size:          5             Log-Likelihood:           -41.1879\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.133 -19.484 0.000 -2.861 -2.338\n",
       "month_t                         0.399    0.013  30.488 0.000  0.373  0.424\n",
       "month_t:Q('ignore_SupTemp-lh')  0.012    0.009   1.305 0.192 -0.006  0.031\n",
       "Group Var                       0.214    0.293                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_lhi - Left SupTemp|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_SupTemp-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4_lhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4_lhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "22dac405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0648</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-36.1031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.133</td>  <td>-19.603</td> <td>0.000</td> <td>-2.859</td> <td>-2.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.389</td>   <td>0.012</td>  <td>32.894</td>  <td>0.000</td>  <td>0.366</td>  <td>0.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_SupTemp-rh')</th> <td>-0.038</td>   <td>0.011</td>  <td>-3.494</td>  <td>0.000</td> <td>-0.060</td> <td>-0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.230</td>   <td>0.334</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0648    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -36.1031  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.133 & -19.603 &       0.000 & -2.859 & -2.339  \\\\\n",
       "month\\_t                         &  0.389 &    0.012 &  32.894 &       0.000 &  0.366 &  0.413  \\\\\n",
       "month\\_t:Q('ignore\\_SupTemp-rh') & -0.038 &    0.011 &  -3.494 &       0.000 & -0.060 & -0.017  \\\\\n",
       "Group Var                        &  0.230 &    0.334 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0648  \n",
       "Min. group size:          5             Log-Likelihood:           -36.1031\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.133 -19.603 0.000 -2.859 -2.339\n",
       "month_t                         0.389    0.012  32.894 0.000  0.366  0.413\n",
       "month_t:Q('ignore_SupTemp-rh') -0.038    0.011  -3.494 0.000 -0.060 -0.017\n",
       "Group Var                       0.230    0.334                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_rhi - Right SupTemp|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_SupTemp-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4_rhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4_rhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d79af852",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9397473043666289  0.9422983434363081  0.939625039692499  0.9474426434054843  \n",
      "MSE: 0.06025269563337113  0.05770165656369188  0.060374960307501016  0.05255735659451566  \n",
      "BIC: 102.74213260829634  96.48976094509851  105.64555392578382  95.4759403386089  \n",
      "Pval: 0.027952518967171696  0.0005522372382559007  0.19201826505749608  0.00047531273481204523  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf4_lha, mdf4_rha, mdf4_lhi, mdf4_rhi))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e827703",
   "metadata": {},
   "source": [
    "### Models (mdf4b) with Combined Temporal slope covariants\n",
    "Added 2022 Jan 5, then updated 2022 Jan 25 with more temporal regions.  Then updated again with a final (?) set of temporal regions. Results are similar to the Superior Temporal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89b3e863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0733</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-38.8234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.126</td>  <td>-20.562</td> <td>0.000</td> <td>-2.847</td> <td>-2.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.391</td>   <td>0.013</td>  <td>31.042</td>  <td>0.000</td>  <td>0.366</td>  <td>0.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_TempAvg-lh')</th>  <td>0.029</td>   <td>0.011</td>   <td>2.621</td>  <td>0.009</td>  <td>0.007</td>  <td>0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.178</td>   <td>0.250</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0733    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -38.8234  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.126 & -20.562 &       0.000 & -2.847 & -2.351  \\\\\n",
       "month\\_t                         &  0.391 &    0.013 &  31.042 &       0.000 &  0.366 &  0.415  \\\\\n",
       "month\\_t:Q('attend\\_TempAvg-lh') &  0.029 &    0.011 &   2.621 &       0.009 &  0.007 &  0.050  \\\\\n",
       "Group Var                        &  0.178 &    0.250 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0733  \n",
       "Min. group size:          5             Log-Likelihood:           -38.8234\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.126 -20.562 0.000 -2.847 -2.351\n",
       "month_t                         0.391    0.013  31.042 0.000  0.366  0.415\n",
       "month_t:Q('attend_TempAvg-lh')  0.029    0.011   2.621 0.009  0.007  0.050\n",
       "Group Var                       0.178    0.250                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4b_lha - Left TempAvg|attend predicting slope #\n",
    "# Right is better than left and _ignore_ is better than attend.\n",
    "formula = \"score ~ month_t + month_t:Q('attend_TempAvg-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4b_lha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4b_lha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d5ed84d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0682</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-33.7987</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.118</td>  <td>-22.042</td> <td>0.000</td> <td>-2.830</td> <td>-2.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.373</td>   <td>0.013</td>  <td>28.623</td>  <td>0.000</td>  <td>0.347</td>  <td>0.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_TempAvg-rh')</th>  <td>0.055</td>   <td>0.013</td>   <td>4.351</td>  <td>0.000</td>  <td>0.030</td>  <td>0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.145</td>   <td>0.211</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0682    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -33.7987  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.118 & -22.042 &       0.000 & -2.830 & -2.368  \\\\\n",
       "month\\_t                         &  0.373 &    0.013 &  28.623 &       0.000 &  0.347 &  0.398  \\\\\n",
       "month\\_t:Q('attend\\_TempAvg-rh') &  0.055 &    0.013 &   4.351 &       0.000 &  0.030 &  0.080  \\\\\n",
       "Group Var                        &  0.145 &    0.211 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0682  \n",
       "Min. group size:          5             Log-Likelihood:           -33.7987\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.118 -22.042 0.000 -2.830 -2.368\n",
       "month_t                         0.373    0.013  28.623 0.000  0.347  0.398\n",
       "month_t:Q('attend_TempAvg-rh')  0.055    0.013   4.351 0.000  0.030  0.080\n",
       "Group Var                       0.145    0.211                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_rha - Right SupTemp|attend predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_TempAvg-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4b_rha = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4b_rha.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "445fd68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-40.9470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.133</td>  <td>-19.586</td> <td>0.000</td> <td>-2.859</td> <td>-2.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.397</td>   <td>0.013</td>  <td>31.105</td>  <td>0.000</td>  <td>0.372</td>  <td>0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_TempAvg-lh')</th>  <td>0.015</td>   <td>0.010</td>   <td>1.485</td>  <td>0.138</td> <td>-0.005</td>  <td>0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.210</td>   <td>0.289</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0741    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -40.9470  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.133 & -19.586 &       0.000 & -2.859 & -2.339  \\\\\n",
       "month\\_t                         &  0.397 &    0.013 &  31.105 &       0.000 &  0.372 &  0.422  \\\\\n",
       "month\\_t:Q('ignore\\_TempAvg-lh') &  0.015 &    0.010 &   1.485 &       0.138 & -0.005 &  0.035  \\\\\n",
       "Group Var                        &  0.210 &    0.289 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0741  \n",
       "Min. group size:          5             Log-Likelihood:           -40.9470\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.133 -19.586 0.000 -2.859 -2.339\n",
       "month_t                         0.397    0.013  31.105 0.000  0.372  0.422\n",
       "month_t:Q('ignore_TempAvg-lh')  0.015    0.010   1.485 0.138 -0.005  0.035\n",
       "Group Var                       0.210    0.289                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_lhi - Left SupTemp|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_TempAvg-lh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4b_lhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4b_lhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "644e4a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0678</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-38.5010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.136</td>  <td>-19.146</td> <td>0.000</td> <td>-2.865</td> <td>-2.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.389</td>   <td>0.012</td>  <td>31.884</td>  <td>0.000</td>  <td>0.365</td>  <td>0.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_TempAvg-rh')</th> <td>-0.032</td>   <td>0.012</td>  <td>-2.669</td>  <td>0.008</td> <td>-0.056</td> <td>-0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.241</td>   <td>0.344</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0678    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -38.5010  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.136 & -19.146 &       0.000 & -2.865 & -2.333  \\\\\n",
       "month\\_t                         &  0.389 &    0.012 &  31.884 &       0.000 &  0.365 &  0.413  \\\\\n",
       "month\\_t:Q('ignore\\_TempAvg-rh') & -0.032 &    0.012 &  -2.669 &       0.008 & -0.056 & -0.009  \\\\\n",
       "Group Var                        &  0.241 &    0.344 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0678  \n",
       "Min. group size:          5             Log-Likelihood:           -38.5010\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.136 -19.146 0.000 -2.865 -2.333\n",
       "month_t                         0.389    0.012  31.884 0.000  0.365  0.413\n",
       "month_t:Q('ignore_TempAvg-rh') -0.032    0.012  -2.669 0.008 -0.056 -0.009\n",
       "Group Var                       0.241    0.344                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 4_rhi - Right SupTemp|ignore predicting slope #\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_TempAvg-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf4b_rhi = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf4b_rhi.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dd0d5385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9402472944739343  0.9442406068763782  0.9397163072266883  0.9450238661981057  \n",
      "MSE: 0.05975270552606574  0.05575939312362173  0.06028369277331173  0.05497613380189428  \n",
      "BIC: 100.91655574442694  90.86727842405651  105.16374137458018  100.27183600169985  \n",
      "Pval: 0.008754874456410967  1.3563295285733806e-05  0.1376212688325003  0.007607591265111703  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf4b_lha, mdf4b_rha, mdf4b_lhi, mdf4b_rhi))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2316afbc",
   "metadata": {},
   "source": [
    "### Models (mdf5) with TWO ROI slope covariants\n",
    "Added 2022 Feb 6. All variations of left-attend and right-ignore have positive and negative coefficients, respectively. Currently, the two being considered for the paper are mdf5_lharhiIFC and mdf5_lharhiMIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ffb8bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0623</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-29.2158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.113</td>  <td>-22.945</td> <td>0.000</td> <td>-2.821</td> <td>-2.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.380</td>   <td>0.012</td>  <td>31.876</td>  <td>0.000</td>  <td>0.357</td>  <td>0.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_ParsAvg-lh')</th>  <td>0.038</td>   <td>0.008</td>   <td>4.605</td>  <td>0.000</td>  <td>0.022</td>  <td>0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_ParsAvg-rh')</th> <td>-0.028</td>   <td>0.010</td>  <td>-2.824</td>  <td>0.005</td> <td>-0.048</td> <td>-0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.135</td>   <td>0.214</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0623    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -29.2158  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.113 & -22.945 &       0.000 & -2.821 & -2.377  \\\\\n",
       "month\\_t                         &  0.380 &    0.012 &  31.876 &       0.000 &  0.357 &  0.404  \\\\\n",
       "month\\_t:Q('attend\\_ParsAvg-lh') &  0.038 &    0.008 &   4.605 &       0.000 &  0.022 &  0.054  \\\\\n",
       "month\\_t:Q('ignore\\_ParsAvg-rh') & -0.028 &    0.010 &  -2.824 &       0.005 & -0.048 & -0.009  \\\\\n",
       "Group Var                        &  0.135 &    0.214 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0623  \n",
       "Min. group size:          5             Log-Likelihood:           -29.2158\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.113 -22.945 0.000 -2.821 -2.377\n",
       "month_t                         0.380    0.012  31.876 0.000  0.357  0.404\n",
       "month_t:Q('attend_ParsAvg-lh')  0.038    0.008   4.605 0.000  0.022  0.054\n",
       "month_t:Q('ignore_ParsAvg-rh') -0.028    0.010  -2.824 0.005 -0.048 -0.009\n",
       "Group Var                       0.135    0.214                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 5_lharhiIFC - IFC left attend and IFC right ignore! #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_ParsAvg-lh') + month_t:Q('ignore_ParsAvg-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf5_lharhiIFC = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf5_lharhiIFC.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "198b77e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0652</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-35.3436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.129</td>  <td>-20.209</td> <td>0.000</td> <td>-2.851</td> <td>-2.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.394</td>   <td>0.012</td>  <td>32.680</td>  <td>0.000</td>  <td>0.370</td>  <td>0.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_InfPar-lh')</th>  <td>0.026</td>   <td>0.009</td>   <td>2.896</td>  <td>0.004</td>  <td>0.008</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_InfPar-rh')</th> <td>-0.030</td>   <td>0.010</td>  <td>-3.056</td>  <td>0.002</td> <td>-0.049</td> <td>-0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.207</td>   <td>0.304</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0652    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -35.3436  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.129 & -20.209 &       0.000 & -2.851 & -2.347  \\\\\n",
       "month\\_t                        &  0.394 &    0.012 &  32.680 &       0.000 &  0.370 &  0.418  \\\\\n",
       "month\\_t:Q('attend\\_InfPar-lh') &  0.026 &    0.009 &   2.896 &       0.004 &  0.008 &  0.043  \\\\\n",
       "month\\_t:Q('ignore\\_InfPar-rh') & -0.030 &    0.010 &  -3.056 &       0.002 & -0.049 & -0.011  \\\\\n",
       "Group Var                       &  0.207 &    0.304 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0652  \n",
       "Min. group size:         5             Log-Likelihood:           -35.3436\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.129 -20.209 0.000 -2.851 -2.347\n",
       "month_t                        0.394    0.012  32.680 0.000  0.370  0.418\n",
       "month_t:Q('attend_InfPar-lh')  0.026    0.009   2.896 0.004  0.008  0.043\n",
       "month_t:Q('ignore_InfPar-rh') -0.030    0.010  -3.056 0.002 -0.049 -0.011\n",
       "Group Var                      0.207    0.304                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 5_lharhiIPC - IPC left attend and IPC right ignore! #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_InfPar-lh') + month_t:Q('ignore_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf5_lharhiIPC = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf5_lharhiIPC.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "10f4290b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0596</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-27.8189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td>-2.599</td>   <td>0.114</td>  <td>-22.880</td> <td>0.000</td> <td>-2.822</td> <td>-2.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                        <td>0.361</td>   <td>0.013</td>  <td>28.440</td>  <td>0.000</td>  <td>0.336</td>  <td>0.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_InfPar-rh')</th>  <td>0.080</td>   <td>0.015</td>   <td>5.204</td>  <td>0.000</td>  <td>0.050</td>  <td>0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_InfPar-rh')</th> <td>-0.021</td>   <td>0.009</td>  <td>-2.500</td>  <td>0.012</td> <td>-0.038</td> <td>-0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                      <td>0.143</td>   <td>0.226</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0596    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -27.8189  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                       & -2.599 &    0.114 & -22.880 &       0.000 & -2.822 & -2.376  \\\\\n",
       "month\\_t                        &  0.361 &    0.013 &  28.440 &       0.000 &  0.336 &  0.386  \\\\\n",
       "month\\_t:Q('attend\\_InfPar-rh') &  0.080 &    0.015 &   5.204 &       0.000 &  0.050 &  0.110  \\\\\n",
       "month\\_t:Q('ignore\\_InfPar-rh') & -0.021 &    0.009 &  -2.500 &       0.012 & -0.038 & -0.005  \\\\\n",
       "Group Var                       &  0.143 &    0.226 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "=========================================================================\n",
       "Model:                   MixedLM       Dependent Variable:       score   \n",
       "No. Observations:        105           Method:                   ML      \n",
       "No. Groups:              21            Scale:                    0.0596  \n",
       "Min. group size:         5             Log-Likelihood:           -27.8189\n",
       "Max. group size:         5             Converged:                Yes     \n",
       "Mean group size:         5.0                                             \n",
       "-------------------------------------------------------------------------\n",
       "                              Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-------------------------------------------------------------------------\n",
       "Intercept                     -2.599    0.114 -22.880 0.000 -2.822 -2.376\n",
       "month_t                        0.361    0.013  28.440 0.000  0.336  0.386\n",
       "month_t:Q('attend_InfPar-rh')  0.080    0.015   5.204 0.000  0.050  0.110\n",
       "month_t:Q('ignore_InfPar-rh') -0.021    0.009  -2.500 0.012 -0.038 -0.005\n",
       "Group Var                      0.143    0.226                            \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 5_lharhiIPC - IPC right attend and IPC right ignore! #\\\n",
    "# Going off script here, but IPC right attend was the \"best\" of the models above.\n",
    "formula = \"score ~ month_t + month_t:Q('attend_InfPar-rh') + month_t:Q('ignore_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf5_rharhiIPC = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf5_rharhiIPC.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "935600a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0617</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-28.5240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.112</td>  <td>-23.181</td> <td>0.000</td> <td>-2.819</td> <td>-2.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.382</td>   <td>0.012</td>  <td>32.674</td>  <td>0.000</td>  <td>0.359</td>  <td>0.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_ParsAvg-lh')</th>  <td>0.041</td>   <td>0.008</td>   <td>5.094</td>  <td>0.000</td>  <td>0.026</td>  <td>0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_InfPar-rh')</th>  <td>-0.026</td>   <td>0.009</td>  <td>-3.070</td>  <td>0.002</td> <td>-0.043</td> <td>-0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.131</td>   <td>0.208</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0617    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -28.5240  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.112 & -23.181 &       0.000 & -2.819 & -2.379  \\\\\n",
       "month\\_t                         &  0.382 &    0.012 &  32.674 &       0.000 &  0.359 &  0.405  \\\\\n",
       "month\\_t:Q('attend\\_ParsAvg-lh') &  0.041 &    0.008 &   5.094 &       0.000 &  0.026 &  0.057  \\\\\n",
       "month\\_t:Q('ignore\\_InfPar-rh')  & -0.026 &    0.009 &  -3.070 &       0.002 & -0.043 & -0.009  \\\\\n",
       "Group Var                        &  0.131 &    0.208 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0617  \n",
       "Min. group size:          5             Log-Likelihood:           -28.5240\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.112 -23.181 0.000 -2.819 -2.379\n",
       "month_t                         0.382    0.012  32.674 0.000  0.359  0.405\n",
       "month_t:Q('attend_ParsAvg-lh')  0.041    0.008   5.094 0.000  0.026  0.057\n",
       "month_t:Q('ignore_InfPar-rh')  -0.026    0.009  -3.070 0.002 -0.043 -0.009\n",
       "Group Var                       0.131    0.208                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 5_lharhiMIX - IFC left attend and IPC right ignore! #\n",
    "formula = \"score ~ month_t + month_t:Q('attend_ParsAvg-lh') + month_t:Q('ignore_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf5_lharhiMIX = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf5_lharhiMIX.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87fbe691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>score</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>105</td>         <td>Method:</td>          <td>ML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>21</td>          <td>Scale:</td>         <td>0.0568</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>5</td>      <td>Log-Likelihood:</td>   <td>-25.2096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>5</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>5.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                 <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>-2.599</td>   <td>0.111</td>  <td>-23.508</td> <td>0.000</td> <td>-2.816</td> <td>-2.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t</th>                         <td>0.354</td>   <td>0.013</td>  <td>28.001</td>  <td>0.000</td>  <td>0.330</td>  <td>0.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('ignore_ParsAvg-rh')</th> <td>-0.033</td>   <td>0.010</td>  <td>-3.406</td>  <td>0.001</td> <td>-0.052</td> <td>-0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_t:Q('attend_InfPar-rh')</th>   <td>0.083</td>   <td>0.015</td>   <td>5.577</td>  <td>0.000</td>  <td>0.054</td>  <td>0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                       <td>0.134</td>   <td>0.218</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/latex": [
       "\\begin{table}\n",
       "\\caption{Mixed Linear Model Regression Results}\n",
       "\\label{}\n",
       "\\begin{center}\n",
       "\\begin{tabular}{llll}\n",
       "\\hline\n",
       "Model:            & MixedLM & Dependent Variable: & score     \\\\\n",
       "No. Observations: & 105     & Method:             & ML        \\\\\n",
       "No. Groups:       & 21      & Scale:              & 0.0568    \\\\\n",
       "Min. group size:  & 5       & Log-Likelihood:     & -25.2096  \\\\\n",
       "Max. group size:  & 5       & Converged:          & Yes       \\\\\n",
       "Mean group size:  & 5.0     &                     &           \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\n",
       "\\begin{center}\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\hline\n",
       "                                 &  Coef. & Std.Err. &       z & P$> |$z$|$ & [0.025 & 0.975]  \\\\\n",
       "\\hline\n",
       "Intercept                        & -2.599 &    0.111 & -23.508 &       0.000 & -2.816 & -2.382  \\\\\n",
       "month\\_t                         &  0.354 &    0.013 &  28.001 &       0.000 &  0.330 &  0.379  \\\\\n",
       "month\\_t:Q('ignore\\_ParsAvg-rh') & -0.033 &    0.010 &  -3.406 &       0.001 & -0.052 & -0.014  \\\\\n",
       "month\\_t:Q('attend\\_InfPar-rh')  &  0.083 &    0.015 &   5.577 &       0.000 &  0.054 &  0.113  \\\\\n",
       "Group Var                        &  0.134 &    0.218 &         &             &        &         \\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "\\end{center}\n",
       "\\end{table}\n",
       "\\bigskip\n"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Mixed Linear Model Regression Results\n",
       "==========================================================================\n",
       "Model:                    MixedLM       Dependent Variable:       score   \n",
       "No. Observations:         105           Method:                   ML      \n",
       "No. Groups:               21            Scale:                    0.0568  \n",
       "Min. group size:          5             Log-Likelihood:           -25.2096\n",
       "Max. group size:          5             Converged:                Yes     \n",
       "Mean group size:          5.0                                             \n",
       "--------------------------------------------------------------------------\n",
       "                               Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "--------------------------------------------------------------------------\n",
       "Intercept                      -2.599    0.111 -23.508 0.000 -2.816 -2.382\n",
       "month_t                         0.354    0.013  28.001 0.000  0.330  0.379\n",
       "month_t:Q('ignore_ParsAvg-rh') -0.033    0.010  -3.406 0.001 -0.052 -0.014\n",
       "month_t:Q('attend_InfPar-rh')   0.083    0.015   5.577 0.000  0.054  0.113\n",
       "Group Var                       0.134    0.218                            \n",
       "==========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 5_lharhiMIX - IFC right ignore and IPC right attend! #\n",
    "# A bit of a switch here, but going with the optimal single-ROI models.\n",
    "formula = \"score ~ month_t + month_t:Q('ignore_ParsAvg-rh') + month_t:Q('attend_InfPar-rh')\"\n",
    "md = smf.mixedlm(formula, df_model, groups=df_model['subject'])\n",
    "mdf5_lharhiMIX = md.fit(method=['lbfgs'], reml=False)\n",
    "mdf5_lharhiMIX.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "69feccbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9491298509906176  0.9470277811847709  0.9514331655348984  0.9536645731572623  0.9536645731572623  \n",
      "MSE: 0.05087014900938235  0.05297221881522908  0.04856683446510162  0.04633542684273774  0.04633542684273774  \n",
      "BIC: 86.35526891931161  98.61094034308977  83.5614643952421  78.34293787708626  78.34293787708626  \n",
      "Pval: 0.00473556619476042  4.125986157733898e-06  0.0037787180782876  0.002245503126266037  1.9489870901840987e-07  0.012413787934505887  0.0006598869072071736  2.4482535615552768e-08  0.0006598869072071736  2.4482535615552768e-08  \n"
     ]
    }
   ],
   "source": [
    "# R-square and BIC summary #\n",
    "show_metrics((mdf5_lharhiIFC, mdf5_lharhiIPC, mdf5_rharhiIPC, mdf5_lharhiMIX, mdf5_lharhiMIX))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b61148",
   "metadata": {},
   "source": [
    "## \\*\\*FIG\\*\\* Graphically represent the model results\n",
    "2021 Dec (and prior):  Using IPC left-attend model (mdf2_lha).\n",
    "2022 Feb:  Using the 2-ROI model (mdf5_lharhiMIX)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d0f6455f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variant 3 from earlier notebook - June 2023: Only show overall mean now #\n",
    "mdf_try = mdf5_lharhiMIX   # mdf2_lha\n",
    "\n",
    "fig, axset = plt.subplots(1, 2, figsize=[14, 5])\n",
    "\n",
    "for subject in df_model['subject'].unique():       # all subjects\n",
    "    ridx = df_model['subject'] == subject\n",
    "    x = df_model.loc[ridx, 'month']\n",
    "    y_actual = df_model.loc[ridx, 'score']\n",
    "    axset[0].plot(x, y_actual, color='#b0b0b0', linewidth=1);\n",
    "\n",
    "cutoff = df['24'].median()\n",
    "subjects_low = df.loc[df['24']<=cutoff].index\n",
    "subjects_high = df.loc[df['24']>cutoff].index\n",
    "\n",
    "# y_mean = df.loc[subjects_low, month_cols].mean()   # low subgroup\n",
    "# axset[0].plot(x, y_mean, color='#00008B', linewidth=2);\n",
    "# y_mean = df.loc[subjects_high, month_cols].mean()  # high subgroup\n",
    "# axset[0].plot(x, y_mean, color='#A52A2A', linewidth=2);\n",
    "y_mean = df.loc[:, month_cols].mean()    # all subjects combined subjects\n",
    "axset[0].plot(x, y_mean, color='k', linewidth=2)\n",
    "\n",
    "for subject in ('f2f_116', 'f2f_030'):             # specific subjects\n",
    "    ridx = df_model['subject'] == subject\n",
    "    x = df_model.loc[ridx, 'month']\n",
    "    y_actual = df_model.loc[ridx, 'score']\n",
    "    y_fitted = mdf_try.fittedvalues[ridx]\n",
    "    \n",
    "    axset[1].plot(x, y_actual, color='#202020', linewidth=2);\n",
    "    axset[1].plot(x, y_fitted, color='#202020', linewidth=2, linestyle='--');\n",
    "\n",
    "axset[1].text(0.77, 0.20, 'F2F_116', transform = axset[1].transAxes,\n",
    "              horizontalalignment='left', verticalalignment='center');\n",
    "axset[1].text(0.65, 0.87, 'F2F_030', transform = axset[1].transAxes,\n",
    "              horizontalalignment='left', verticalalignment='center');\n",
    "axset[0].set_ylabel('Vocabulary Score');\n",
    "plt.setp(axset, xlabel='Age (months)', ylim=(-1.60, 2.80), yticks=[-1, 0, 1, 2]);\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'fitv_highlow.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "071ae7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEzCAYAAAAhPviHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaWUlEQVR4nO3df4xU5b3H8c/Z3dndWX64SyH8UqCaZi8gimUTMSxKqZE2F4qhVrDa1ha7hiaW292uwUKrrabYgl6T4h+uf6gJP+ql4U5riqHS1tTlV9lmIOuSSwpCkSmbrilbEJb9+dw/tjPODDM7MztnZs45834lxDtnhpnHY/O5T77neb6PZYwxAgC4XkmhBwAAsAeBDgAeQaADgEcQ6ADgEQQ6AHgEgQ4AHlFWqB+eOHGiZs2aVaifBwBXOnv2rD766KOE7xUs0GfNmqW2trZC/TwAuFJdXV3S9yi5AIBHEOgA4BEEOgB4BIEOAB5BoAOARxDoAOARBDoAeASBDgAeQaADgEcQ6ADgEQQ6AHgEgQ4AHkGgA4BHEOgA4BEEOgB4BIEOAB5BoAOARxDoAOARBDoAeASBDgAeQaADgEcQ6ADgEQQ6AHgEgQ4AHkGgA4BHEOgA4BEEOgB4RFmhBwAAxWJToF27jnyoQWNUall66M6b9Nz982z7fgIdAPJgU6Bd2w+fi7weNCby2q5Qp+QCAHmw68iHGV0fDQIdAPJg0JiMro8GgQ4AeVBqWRldHw0CHQDy4KE7b8ro+mjwUBQA8iD84JNVLgDgAc/dP8/WAI9HyQUAPIIZOgDkQCAY0pZ9J/X37h5Nq/areVmt7r9jek5/k0AHAJsFgiE9taddPf2DkqRQd4+e2tMuSTkNdUouAGCzLftORsI8rKd/UFv2nczp7xLoAGCzv3f3ZHTdLgQ6ANhoU6BdyfZ+Tqv25/S3CXQAsEl8A65ofl+pmpfV5vT3bQn006dPa8WKFaqpqdGNN96opqYmXbt2zY6vBgDXGKnR1uZV85y/yqWvr08rVqzQnDlzdPDgQf3jH//Qt771LUnSCy+8kPUAAcAtRmq0leswl2yYof/5z3/WqVOn9Prrr2v27Nm655579Oyzz2rHjh12jA8AXCMfDbhGknWg19bWau/evRo7dmzkmmVZ6u3tzfarAcBV8tGAayRZl1wmTZqke++9N/J6aGhI27Zt0+LFi7P9agBwlXw04BqJ7TtFGxsbFQwGdfTo0evea2lpUUtLiySpq6vL7p8GgILLdQOukdi2bNEYo/Xr1+vll1/Wrl27NHfu3Os+09DQoLa2NrW1tWnSpEl2/TQAQDbN0IeGhrR27Vrt2LFDb775plauXGnH1wIAMmBLoDc1NWnnzp3as2ePli9fbsdXAgAylHWgHz58WC+99JI2b96suro6dXZ2Rt6bMmVKtl8PAI5TiNa46ci6hv6rX/1KkvTUU09p6tSpMX8GBgayHiAAOEm4NW6ou0dGn7TGDQRDhR5a9oG+detWGWMS/ikro906AG8pVGvcdNCcCwAyUKjWuOkg0AEgA8la4Oa6NW46CHQAyEDzslr5faUx1/LRGjcdFLkBIAPh1SxOXOVCoANAhu6/Y7ojAjweJRcA8AgCHQA8gpILAMRx6k7QVAh0AIgS3gka3jwU3gkq5ecYuWxQcgGAKE7eCZoKgQ4A/xYIhhRy8E7QVAh0ANAnpZZknLATNBUCHQCUuNQS5pSdoKkQ6ACgkUsqm1fNc/wDUYlABwBJyUsq06v9rghziUAHAEnObrqVLgIdQFEKBENa9Pwf9OkNv9Wi5/8gabi0Mr3aL0vDM3O3lFrC2FgEoOgk2zy0edU8HdiwtMCjGz1m6ACKjps3D42EQAdQdJx8jFw2CHQARcfJx8hlg0AHUHS8sKIlER6KAig6Tj5GLhsEOoCi5NRj5LJByQUAPIJABwCPINABwCMIdADwCB6KAvAMtx7ubBcCHYAnuPlwZ7tQcgHgCV7tz5IJAh2AJ3i1P0smKLkAcKVAMKQfv9Whi1f7JUlWks+5vT9LJgh0AK4TCIbUtPu4BodM5JpJ8Dkv9GfJBIEOwHU2/m97TJhHK7UsDRnDKhcAcLpAMKQrfYNJ3x8yRmee/888jsg5eCgKwFVSrVopppp5PAIdgKukWrVSTDXzeJRcADhe9A7QEsvSoElcP39k4YyiqpnHI9ABOFr8DtBEYW5JenjhDD13/7w8j85ZCHQAjpZoB6hU3KtZkiHQAThaspp5Ma9mSYZAB+AomwLt2nXkQw0ao1LLUqWvRD39Q9d9rphXsyRDoANwjE2Bdm0/fC7yetAY9fQblUiKjvRi2wGaLpYtAnCEQDAUE+YxLGl6tV+Whv+5edU8auYJMEMHUHDhlSzJDBnpwIaleRyROzFDB1BwyVayhJVayXopIhqBDqDgUu3+fOjOm/I0Enej5AKgIDLZ/VnsG4bSRaADyKv4gymkxLs//b5SHn5myNaSS29vr2699Vbt37/fzq8F4BHhh5/RYR6t1LJYyZIF22bo165d01e/+lV1dHTY9ZUAPCbVw092f2bHlhn6iRMntHDhQp0+fdqOrwPgUakefrL7Mzu2BPp7772n++67T4cOHbLj6wB4SCAY0qLn/6BPb/itSkZYfsjuz+zZUnJ5/PHH7fgaAB6TTutbSar2+/TMl+ZSM89SXle5tLS0qKWlRZLU1dWVz58GUAC0vs2vvAZ6Q0ODGhoaJEl1dXX5/GkABUDr2/xipyiAnEn2kJOHn7nBxiIAtoje+RkupTQvq42poUs8/MwlZugAsrIp0K5Pb/it/uvNYwp198hICnX3RLonbl41j9a3ecIMHcCoPfzqIR04/c+E7/X0D2rLvpM6sGEpAZ4ntge6SbIsCYC3BIKhpGEelmojEexFyQVAxgLBkJr+53jKz/HwM78IdAAZCW8WSrZJKIyHn/lHoAPISKoGW5I0ppzWt4XAQ1EAGUlVF190ywTt+PZdeRoNohHoADIyrdqvUIJQL7UsvfDg7czKC4iSC4CMNC+rld9XGnPN7yslzB2AGTqAGIl2fEYHdfj/HukzKAwCHUDEpkC7th8+F3kd6u5R8+7h5YnxoU6AOw8lFwCShmfm0WEe1j9k9MxvOFrSDQh0AJKGSyjJdPckPtQZzkKgA5DENn0voIYOFKn4h5/VVT5dvJp4Jl5T5cvz6DAaBDpQhOLP+gx198hXYqnEkobidvSXllh6esXcAowSmSLQgSISnpUn2hjUP2RU7R+eiYdr5jVVPj29gsOb3YJAB4pE/Kw8kX/19HPWp4sR6IDHBYIhPfObjrRWqtDu1t0IdMDDAsGQmncfV398YTwB2t26H4EOeNiWfSfTCvPpbN/3BAId8LBUa8v9PvqWewmBDnhEoqZayVrdSsPtbglzb2GnKOAB4RUsoe4eGQ2vK39qT7s+9x+T5Cuxrvu8r5Te5V5EoAMekOhYuJ7+Qf3x/7q05Su3R9aXS8Nry7c8QJh7ESUXwMVG2igkDdfQaXVbPAh0wKXS2SjEuvLiQskFcKlEZZZorCsvPszQAReJXsky0upy1pUXJwIdcIl0SizScJgf2LA0T6OCk1ByAVwiVYlFosxS7JihAw4Vv1Eo2UoWSbKkyGYiyizFi0AHHCjRARSWlLBuTokFYZRcAAdKVF4xGp6JR6PEgmgEOuBAyZpqGQ3PyK1//5NeLIhGyQVwgHQPbKa8gpEQ6ECBJTuw2VdqqX/wk6o55RWkQskFKLBE9fL+IaMx5WWUV5ARZuhAgSWrl/+rp1/Hnr4vz6OBmzFDBwosWQMtGmshUwQ6UGDNy2rl95XGXKNejtGg5ALkUKJj4eLr4OHXqT4HpEKgAzmyKdCuHYfPRXZ3ho+Fk5Qw1AlwZIuSC5ADgWAoJszDevoHtWXfyYKMCd7HDB2w0aZAu3Yd+VCDJnm38mSrWoBsEeiATTYF2rX98LmUn2P1CnKFQAeylOqg5miWxOoV5AyBDmQh3VOEpOEwf3jhDB5+ImcIdCAL6ZwiJHHGJ/KDQAfSlGhNeToPOB9ZOEPP3T8vDyNEsSPQgTQk6oj41J523eD3qbvn+ja3klRqWXrozpsIc+QNgQ6MYKRliD39g6r0lcjvK40pu/h9pXRGREGwsQhIIrwMcaQ15d1X+7V51Tza3MIRbJmh9/b26oknntDu3btVUVGhxsZGPfnkk3Z8NZB3mSxDnFbtZ9s+HMOWQG9ubtbBgwe1f/9+nT9/Xl/72tc0Y8YMrVmzxo6vB/Imk2WIdESE02Qd6FeuXNGrr76qt956SwsWLNCCBQv05JNPatu2bQQ6XCGd7frxWIYIJ8q6hn78+HH19vaqvr4+cq2+vl5Hjx7VwMBAtl8P5FQ6dfJ4jyycoQMblhLmcJysZ+gXLlzQhAkTVFlZGbk2efJk9fX1qaurS1OnTs32JwDbZVInD2MZIpwu60C/evWqKioqYq6FX/f29sZcb2lpUUtLiySpq6sr258GRiWTOrnEMkS4R9Yll8rKyuuCO/y6qqoq5npDQ4Pa2trU1tamSZMmZfvTwKiku12fZYhwm6xn6NOnT9fFixfV19en8vJySVJnZ6cqKio0YcKErAcIZCt+y346ZZYqX4lOPPvFPIwOsE/WM/T58+ervLxcBw8ejFxrbW3VggULVFbGRlQUVri8EurukdHwln0rxd8psaSfrrotH8MDbJV14lZVVekb3/iGvvOd7+j1119XZ2entm7dGqmVA4WUqLxiNFxOiV7XEn7NckS4mS1T6BdffFHr1q3T0qVLNX78eP3oRz/S6tWr7fhqICvJuiGGwzu6cyIhDrezJdCrqqr0xhtv6I033rDj6wDbJKuZT6/268CGpQUYEZA7NOeCpzUvq5XfVxpzjS378CqeWsLTwmWU+IMpKK/Aiwh0uFaiE4QSBTXdEFEsCHS4UrIThCQR3ihaBDpcI3pGXmJZ1zXU6ukf1JZ9Jwl0FC0CHY4XCIb047c6dPHqJ2d3JuuOmM6hzYBXEehwtEwbaU2r9ud4RIBzsWwRjpZuIy2J5YgAM3Q4TnStPNWxE6WWpSFjWI4IiECHgwSCIT3zmw519/Sn/rDoUw7EI9DhCJnWyqv9Pj3zpbmEORCFQEfBZHo4syVRWgFGQKCjIMKHM6eLZlpAagQ68iaTh53RWL0CpIdAR15kWiMPq6ny6ekV1MqBdBDoyItM1pNTKwdGh0BHzoymxPLIwhl67v55OR0X4FUEOmyXqPdKKqWWpYfuvIkwB7JAoMM2mQY5G4MAexHoyFqmOzypkQO5QaAjK5muXmE9OZA7dFtEVuiGCDgHgY6spHugRLXfR70cyDFKLsjKtGq/QiOEOk20gPxhho6sNC+rld9Xet31miqfXlo9X8eevo8wB/KEGTqSit4YlGxVSvh1qs8ByD0CHdfZFGjXjsPnYnZ3hrp79NSedklKGOoEOFB4lFwQEQiGNOeHb2t7XJiH9fQPasu+k3kfF4D0MEOHpMSz8kTSXdUCIP8I9CIWrpGPtEol3rRqfw5HBCAbBHqRGk1/cjYGAc5GDb1IZbLDU5LGlNNIC3A6ZuhFKt1auGVJD99Jj3LADQh0j0u2ljzVDk9L0sMcNgG4CoHuYfF18ui15M3Laq+roVuSjIY7IrI5CHAfAt1jomfkJZalQRO7EDG8ljzcwpYdnoB3EOgeEj8jjw/zsHD9nB2egLcQ6B6Q6Xpy1pID3kSgu1i6uzujsZYc8C4C3aU2Bdq1/fC5tD5balkaMoY6OeBxBLrLZFpe8fvYEAQUCwLdJQLBkJ75TYe6e/rT/jssPwSKC4HucKMJckvSf6+eT5ADRYZAd7DRNNCShnd4EuZA8SHQHSzTBlpVvhL9dNVthDlQpAh0B0ungRYPPQGE0T7XwVJtAKqp8hHmACKYoRdQsk6IYYkaaEnDQf70irkEOYAYBHoBbAq0a+eRcxqK2uIZ3QkxHNThf9JAC0A6bAt0Y4yWLVumBx98UI899phdX+s5I+3wDHdCjA5sGmgBSJctNfShoSF997vf1TvvvGPH13lSIBjSouf/kHK7fronCQFAvKxn6KFQSI888og++OADVVdX2zAk78mkiRadEAGMVtYz9GAwqFtuuUV/+ctfdMMNN9gxJk8JBENphzmdEAFkI+sZ+vLly7V8+XI7xuIJ8Vv1LUtphnkJSxABZCVloF+7dk3nz59P+N7kyZM1btw42wflVoFgSM27j6s/avlKkkODIkotSw/deROHMQPIWspAb2tr0+LFixO+99prr+nRRx9N+8daWlrU0tIiSerq6kr77zldpi1tJRpoAbBfykCvr6+XSTXNTFNDQ4MaGhokSXV1dbZ8Z6GNpoGWJRpoAbAfG4tGIXqHZ4llJT2MORqnBgHINQI9A4FgSBv/t11X+j6ZjacT5r5SS1seuJ0QB5BTBHqaAsGQmn91XP2DmZWf6LsCIF9sDfSzZ8/a+XWOsmXfybTDnJa2AAqBGXoCibogptqST40cQKER6HHit+mHuyDe4PclPdfTkvTCg9TIARQWgf5vIx3G3NM/qEpfiXylVsKyC0sQAThB0Qf6SO1so3Vf7dd/r56vH7/VoYtXh0O/2u/TM1/igScAZyjqQH/41UM6cPqfaX12WrWf3uQAHK0oAz0QDMXMtFOxJLogAnC8ogv0TLfqs00fgFsURaBvCrRr15EP09rVGY1NQQDcxPOBnu5Dz3iPLJxBS1sAruK5QM+0Ph6voqxEP/vybczKAbiOpwJ9tP1WJJYgAnA/TwT6aA6YkIYfeLJVH4BXuD7QR3PAhESNHID3uD7Qt+w7mVGYc4YnAK9yVaCPpgtiGIdMAPA61wR6fGklnS6IYawnB1AMXBPoiUor4S6Ifl9pzHscMAGgGJUUegDpSlZa6b7ar82r5ml6tV+WpOnVfsIcQFFyzQx9WrU/4bJEuiACwDDXzNCbl9XK7yuNueb3ldIFEQD+zTUz9PAMPH6VCzNzABjmmkCXRGkFAEbgmpILAGBkBDoAeASBDgAeQaADgEcQ6ADgEQQ6AHgEgQ4AHkGgA4BHEOgA4BGWMSbzE5VtMHHiRM2aNSutz3Z1dWnSpEm5HRAkca/ziXudP16612fPntVHH32U8L2CBXom6urq1NbWVuhhFAXudf5wr/OnWO41JRcA8AgCHQA8whWB3tDQUOghFA3udf5wr/OnWO61K2roAIDUXDFDBwCk5thA7+3tVUNDg2pqajRlyhT9/Oc/L/SQXO306dNasWKFampqdOONN6qpqUnXrl2TJP3tb3/TfffdpzFjxmj27Nl6++23Y/7uH//4R912222qqqrSkiVLdOrUqUL8K7jOY489piVLlkRec5/t19/fr8bGRk2cOFGf+tSntG7dOvX29koq0vttHOqJJ54wc+fONW1tbSYQCJhx48aZXbt2FXpYrtTb22tmz55tvvzlL5sTJ06Yd99919x8882msbHRDA0Nmdtvv92sWbPGdHR0mM2bNxu/328++OADY4wx586dM2PHjjU/+9nPTEdHh1m9erWZM2eOGRwcLPC/lbPt37/fSDL33HOPMcZwn3Nk/fr1ZubMmaa1tdUcOHDAzJw502zcuLFo77cjA/3jjz82lZWV5p133olce/bZZ82iRYsKOCr3eu+994zP5zOXL1+OXNuxY4eZPHmy+f3vf28qKyvNpUuXIu99/vOfNxs3bjTGGPPDH/4w5r5fuXLFjBs3Lua/DWJ9/PHH5uabbzaLFi2KBDr32X4XL1405eXl5ne/+13k2muvvWa+8IUvFO39dmTJ5fjx4+rt7VV9fX3kWn19vY4ePaqBgYECjsydamtrtXfvXo0dOzZyzbIs9fb26vDhw7rjjjs0bty4yHv19fU6dOiQJOnw4cO6++67I+9VVVXps5/9bOR9XG/jxo1asmRJTLmF+2y/1tZW+f1+3XvvvZFrjz76qN5+++2ivd+ODPQLFy5owoQJqqysjFybPHmy+vr61NXVVcCRudOkSZNi/kc/NDSkbdu2afHixbpw4YKmTZsW8/nJkyfr/PnzkpTyfcQ6dOiQdu/era1bt8Zc5z7b7/Tp05o1a5Z27dqluXPnaubMmfr+97+vvr6+or3fZYUeQCJXr15VRUVFzLXw6/ADD4xeY2OjgsGgjh49qhdffDHhvQ7f52T/LfjvcL3e3l6tXbtWL730kmpqamLeS3Ufuc+Zu3z5ss6cOaNf/OIXeuWVV3T58mWtW7dOAwMDRXu/HTlDr6ysvO7Ghl9XVVUVYkieYIzR+vXr9fLLL0dmNcnudfg+p3ofn/jJT36iz3zmM/rKV75y3XvcZ/uVlZXp0qVL2r59u+rr6/XFL35RW7du1SuvvKLy8vKivN+OnKFPnz5dFy9eVF9fn8rLyyVJnZ2dqqio0IQJEwo8OncaGhrS2rVrtWPHDr355ptauXKlpOF7ffz48ZjPdnZ2aurUqZH3Ozs7r3v/1ltvzc/AXWTnzp26cOFC5FlFX1+fBgcHNXbsWP3gBz/gPtts2rRpKisr0y233BK5Vltbq2vXrmnKlClqb2+P+Xwx3G9HztDnz5+v8vJyHTx4MHKttbVVCxYsUFmZI/9/kOM1NTVp586d2rNnj1atWhW5vnDhQh07dkxXrlyJXGttbdXChQsj77e2tkbeu3r1qoLBYOR9fOLdd9/V+++/r2PHjunYsWP69re/rbq6Oh07doz7nAN33XWXBgYGYoL7xIkTGjdunO66667ivN+FXmaTzOOPP25mz55tjhw5Yn7961+b8ePHm1/+8peFHpYrHTp0yEgymzdvNhcuXIj5MzAwYObMmWMeeOAB8/7775vnn3/eVFVVmTNnzhhjjDlz5oyprKw0zz33nOno6DBr1qwxc+fOdf163XzYuHFjZNki9zk3Vq5caRYsWGDa2trMn/70JzNr1izT2NhYtPfbsYF+5coV8/Wvf92MGTPGTJ061WzdurXQQ3KtpqYmIynhn/7+fvPXv/7V3H333aaiosLMmTPH7Nu3L+bv792719TW1hq/328+97nPmVOnThXo38RdogPdGMN9zoFLly6Zb37zm2b8+PFmwoQJ5nvf+57p6+szxhTn/aY5FwB4hCNr6ACAzBHoAOARBDoAeASBDgAeQaADgEcQ6ADgEQQ6AHgEgQ4AHkGgA4BH/D8CTr1xGEl+3QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For generating original vocab score numbers on the y-axis, plot transformed score versus original #\n",
    "fig, ax = plt.subplots(1, 1, figsize=[6, 5])\n",
    "\n",
    "yy = np.array(df_roidb[month_cols]).flatten()\n",
    "xx = np.array(orig_scores)[:].flatten()\n",
    "\n",
    "ax.plot(xx, yy, 'o');\n",
    "plt.setp(ax, ylim=(-1.60, 2.80), yticks=[-1, 0, 1, 2])   # to match above\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'vocabscore_transformation.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a8ffd29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up a function to determine per-subject/month estimated slopes #\n",
    "# Note that the random effects was only used in a previous notebook.\n",
    "def get_slope(subject, mdf):\n",
    "    fe_list = set(mdf.fe_params.index) - {'Intercept', 'month_t'}\n",
    "    for param in fe_list:\n",
    "        assert(param[:].startswith('month_t'))  # make sure it's a slope parameter!\n",
    "    \n",
    "    b0, bx = mdf.fe_params[\"month_t\"], 0\n",
    "    if \"month_t\" in mdf.random_effects[subject].keys():\n",
    "        bx = mdf.random_effects[subject]['month_t']\n",
    "    \n",
    "    ridx = df_model['subject'] == subject\n",
    "    idx = np.where(ridx)[0][0]  # take first time pt (all same anyway)\n",
    "    robj = re.compile('^month_t:Q\\((.*)\\)')\n",
    "    \n",
    "    slope = b0 + bx\n",
    "    var = mdf.bse['month_t']**2\n",
    "    for param in fe_list:\n",
    "        col = robj.match(param).group(1).strip(r\"'\")\n",
    "        slope += mdf.fe_params[param] * df_model.iloc[idx][col]\n",
    "        var += \\\n",
    "            (mdf.bse[param] * df_model.iloc[idx][col])**2\n",
    "    std = np.sqrt(var)\n",
    "        \n",
    "    return slope, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d8f269b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Like above, but only returns the ROI value #\n",
    "def get_roival(subject, mdf):\n",
    "    fe_list = set(mdf.fe_params.index) - {'Intercept', 'month_t'}\n",
    "    for param in fe_list:\n",
    "        assert(param[:].startswith('month_t'))  # make sure it's a slope parameter!\n",
    "    fe_list = list(fe_list)\n",
    "    \n",
    "    if len(fe_list) > 1:\n",
    "        raise ValueError('Only one ROI allowed')\n",
    "    \n",
    "    b0, bx = mdf.fe_params[\"month_t\"], 0\n",
    "    if \"month_t\" in mdf.random_effects[subject].keys():\n",
    "        bx = mdf.random_effects[subject]['month_t']\n",
    "    \n",
    "    ridx = df_model['subject'] == subject\n",
    "    idx = np.where(ridx)[0][0]  # take first time pt (all same anyway)\n",
    "    robj = re.compile('^month_t:Q\\((.*)\\)')\n",
    "    \n",
    "    param = fe_list[0]\n",
    "    col = robj.match(param).group(1).strip(r\"'\")\n",
    "    val = df_model.iloc[idx][col]\n",
    "        \n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d3bd5f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Like above, but less complicated by using the non-expanded (dB or linear) dataframe #\n",
    "def get_roival2(subject, mdf, df_use=df_roidb):\n",
    "    fe_list = set(mdf.fe_params.index) - {'Intercept', 'month_t'}\n",
    "    for param in fe_list:\n",
    "        assert(param[:].startswith('month_t'))  # make sure it's a slope parameter!\n",
    "    fe_list = list(fe_list)\n",
    "    \n",
    "    if len(fe_list) > 1:\n",
    "        raise ValueError('Only one ROI allowed')\n",
    "    \n",
    "    b0, bx = mdf.fe_params[\"month_t\"], 0\n",
    "    if \"month_t\" in mdf.random_effects[subject].keys():\n",
    "        bx = mdf.random_effects[subject]['month_t']\n",
    "    \n",
    "    robj = re.compile('^month_t:Q\\((.*)\\)')    \n",
    "    param = fe_list[0]\n",
    "    col = robj.match(param).group(1).strip(r\"'\")\n",
    "    \n",
    "    val = df_use.loc[subject][col]\n",
    "        \n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "35ee4d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All subjects' fitted slopes versus vocab differences, with 95% confidence intervals #\n",
    "# Using the ParsAvg model (mdf2_lha) yields a similar result as the 2-ROI model (no longer made).\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "mdf_try = mdf3_rha  # mdf2_lha, mdf3_rha\n",
    "\n",
    "y1 = df_model.loc[df_model['month']==18, 'score'].values\n",
    "y2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "yfits, yconfs = [], []  # store slope est. AND confidence intvl (as abs. offset) for each subject\n",
    "for subject in df_model['subject'].unique():\n",
    "    slope, std = get_slope(subject, mdf_try)\n",
    "    yfits.append(slope)\n",
    "    yconfs.append(std * stats.norm.ppf(.025))\n",
    "\n",
    "yhi = np.array(yfits) + np.array(yconfs)\n",
    "ylo = np.array(yfits) - np.array(yconfs)\n",
    "slope_control = mdf1.fe_params['month_t']\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot(np.array([y2-y1, y2-y1]), np.array([ylo, yhi]), color='grey')\n",
    "ax.plot([1.35, 3.65], [slope_control, slope_control], color='grey', linestyle=':')\n",
    "ax.scatter(y2 - y1, yfits, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(1.30, 3.70), ylim=(0.235, 0.555), \n",
    "         xlabel='Vocab Score Difference',\n",
    "         ylabel='Estimated Slope');\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'slope_vocabdiff_IPrh-attend.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "0cc68941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All subjects' fitted slopes versus vocab differences, with 95% confidence intervals #\n",
    "# Using the ParsAvg model (mdf2_lha) yields a similar result as the 2-ROI model (no longer made).\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "mdf_try = mdf5_lharhiMIX  # mdf2_lha, mdf3_rha, mdf4_rhi, mdf3_rhi\n",
    "\n",
    "y1 = df_model.loc[df_model['month']==18, 'score'].values\n",
    "y2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "yfits, yconfs = [], []  # store slope est. AND confidence intvl (as abs. offset) for each subject\n",
    "for subject in df_model['subject'].unique():\n",
    "    slope, std = get_slope(subject, mdf_try)\n",
    "    yfits.append(slope)\n",
    "    yconfs.append(std * stats.norm.ppf(.025))\n",
    "\n",
    "yhi = np.array(yfits) + np.array(yconfs)\n",
    "ylo = np.array(yfits) - np.array(yconfs)\n",
    "slope_control = mdf1.fe_params['month_t']\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot(np.array([y2-y1, y2-y1]), np.array([ylo, yhi]), color='grey')\n",
    "ax.plot([1.35, 3.65], [slope_control, slope_control], color='grey', linestyle=':')\n",
    "ax.scatter(y2 - y1, yfits, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(1.30, 3.7), ylim=(0.22, 0.62), \n",
    "         xlabel='Vocab Score Difference',\n",
    "         ylabel='Estimated Slope');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, 'slope_vocabdiff_IPrh-attend.jpg')\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89ab7d7",
   "metadata": {},
   "source": [
    "## \\*\\*FIG\\*\\* Plot RTP versus scores, with regression lines\n",
    "2022 Jun: Added IFC lh-ignore by request of PK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "d0262ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r, p (pearsons): PearsonRResult(statistic=0.3050141623884981, pvalue=0.17879894169103303)\n",
      "r, p (regression): (0.3050141623884982, 0.17879894169103264)\n",
      "slope (regression): 0.46188259779599194\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All subjects' ROI value (theta level) versus vocab differences, with regression line #\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "mdf_try = mdf2_rha  # 2023.06.05 for paper: mdf2_lha, lhi, rha, rhi, mdf3_rha, rhi\n",
    "mdf_figname = 'roi_vocabdiff_IFCrh-attend.jpg'  # not allowed here: 2-ROI model\n",
    "\n",
    "v1 = df_model.loc[df_model['month']==18, 'score'].values\n",
    "v2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "roivals = []  # unlike previous section, no confidence interval!\n",
    "for subject in df_model['subject'].unique():\n",
    "    roival = get_roival(subject, mdf_try)\n",
    "    roivals.append(roival)\n",
    "\n",
    "xmin, xmax = 1.35, 3.65\n",
    "x = np.arange(xmin, xmax, 0.02)\n",
    "r_pearsonr = stats.pearsonr(v2 - v1, roivals)       # also make regression line\n",
    "r_linregress = stats.linregress(v2 - v1, roivals)   # identical\n",
    "rline = r_linregress.intercept + r_linregress.slope * x\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot(x, rline, color='grey', linestyle=':', linewidth=2)\n",
    "ax.scatter(v2 - v1, roivals, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(xmin-0.05, xmax+0.05), ylim=(-3, 3))  # now leaving out labels for simplicity\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, mdf_figname)\n",
    "fig.savefig(fig_name, dpi=500)\n",
    "\n",
    "# Also report the correlation coefficient #\n",
    "print(f'r, p (pearsons): {r_pearsonr}')\n",
    "print(f'r, p (regression): {r_linregress.rvalue, r_linregress.pvalue}')\n",
    "print(f'slope (regression): {r_linregress.slope}\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c8a655b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAF/CAYAAAAirDIZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAxIUlEQVR4nO3deZxcVZ3//9ebsDUgKApoR1miMiCLIA2CtMDgzwUdNYMzjgoobuCCoE4PStyigxGwAXXYFMECWTQiNoP4BWRYJCxKBzWyE8I23SzJiNGQBkL8/P44t5tKUdVdt/tWV3X1+/l41CN1z71165Pbt+pT59xzz1FEYGZmZs+3VrMDMDMza1VOkmZmZjU4SZqZmdXgJGlmZlaDk6SZmVkNTpJmZmY1OEmamZnV0JJJUtJ5kh6R9FdJ90j6WLNjMjOz6UetOJiApB2AxRHxtKTtgGuBd0TEwuZGZmZm00lL1iQj4vaIeHp4MXu8sokhmZnZNNSSSRJA0mmSVgJ3AY8Av2pySGZmNs20ZHPrMEkzgL2A/YDjI2JVxfrDgMMANtxww9222267SY/RzMxa08KFC5dFxGYT2UdLJ8lhks4A7oiI79XapqurK/r7+ycxKjMza2WSFkZE10T20bLNrRXWxtckzcxskrVckpS0uaT3SdpI0gxJbwXeD1zd7NjMzGx6WbvZAVQRwCeBM0hJ/EHgsxFxSVOjMjOzaaflkmRELAX2bXYcZmZmLdfcamZm1iqcJM3MzGpwkjQzM6vBSdLMzKwGJ0kzM7ManCTNzMxqcJI0MzOrwUnSzMysBidJMzOzGpwkzczManCSNDMzq8FJ0szMrAYnSTMzsxqcJM3MzGpwkjQzM6vBSdLMzKwGJ0kzM7ManCTNzMxqcJI0MzOrwUnSzMysBidJMzOzGpwkzczManCSNDMzq8FJ0szMrAYnSTMzsxqcJM3MzGpwkjQzM6vBSdLMzKwGJ0kzM7ManCTNzMxqWDvPxpLWAzqBDmBpRCxtSFRmZmYtYMyapKQXSPqkpN8Ay4HFwG3Ao5IelnSmpN0bHaiZmdlkGzVJSvoc8ADwEeDXwLuBXYBtgb2Ar5Fqo7+WdLmkVzcyWDMzs8k0VnPrG4B9I+K2Gut/B5wt6RPAR4F9gXsLjM/MzKxpRk2SEfGv9ewkIp4GTiskIjMzsxbh3q1mZmY1jFqTlHQNEPXsKCL2LyQiMzOzFjHWNcnya5EzgIOAR4HfZmV7AC8Dzis+NDMzs+Ya65rkZ4afSzoZOAc4KiKirPw7gBoVoJmZWbPkuSb5QeCU8gSZOQ04pLiQzMzMWkOeJClgpyrl1crMzMymvDzD0p0N/DAbMODmrGxP4GjgR0UHZmZm1mx5kuTRwOPAUcC8rOwR4DjgxILjMjMza7q6k2RE/B04AThB0sZZ2V8bFZiZmVmz5R5MQFIXcACwOlveUFKu2UTMzMymgrqTpKQtJP2WNF7rBcAW2aqTKLC5VdJ6ks6S9KCkv0n6vaQDitq/mZlZvfLUJE8mDSTwYmBlWfnPgLcUGNPawMOkwdI3Ab4CzJe0dYHvYWbWlvr6+uju7mbWrFl0d3fT19fX7JCmtDzNpG8C3hQRT0hrjB1wH7BlUQFFxJPA3LKiX0q6H9iNNG2XmZlV0dfXx5w5cxgaGgJgYGCAOXPmADB79uwmRjZ15alJdgDPVCnfDHiqmHCeT9IWpPkrb6+y7jBJ/ZL6ly5d2qgQzMymhN7e3pEEOWxoaIje3t4mRTT15UmSvwEOLVsOSTOALwD/U2RQwyStA5wPnBMRd1Wuj4gfRERXRHRtttlmjQjBzGzKGBwczFVuY8t7n+R1knYH1iN11tmBdN1w76IDk7QW8GNS7fWIovdvZtZuOjs7GRgYqFpu41N3TTIi7gB2Bm4ErgTWJ3Xa2TUi7isyKKWLnmeRetC+JyJWFbl/M7N21NPTQ0dHxxplHR0d9PT0NCmiqS/X/Y0R8QjwtQbFUu50YHvg/4uIobE2NjOz5zrn9Pb2Mjg4SGdnJz09Pe60MwF6/qQeNTaUlgDXAJ+MiGfKyl8C/C4iZhUSkLQVqRfr08CzZasOj4jza72uq6sr+vv7iwjBzMzagKSFEdE1kX3kqUluDewPXCvp3REx3J10BrDVRIIoFxEP4vkpzcysBeTp3RrAm4E/A7dI2rExIZmZ2VTUjgMZ5J1P8q/AO0kddm6Q9E8NicrMzKaU4YEMBgYGiIiRgQymeqLMW5Mkkv8AjgR+Avx7IwIzM7Opo10HMshzTXKN64QRcY6ke4GLiw3JzMymmnYdyCBPTXIbYFl5QUTcCLyW1KHHzMymqVoDFkz1gQzyDCbwYDbxcmX5YxFxXbFhmZnZVNKuAxmM2twqaRGwbzbzx5/IrktWExE7Fx2cmZlNDe06kMFY1yR/TrqpH+CiBsdiZmZT2OzZs6d8Uqw0apKMiK9Xe25mZjYd5Om4Y2ZmNq2MdU3yfka5DlmuqLFbzczMWsVY1yRPKXu+EfB54HfATVnZXsAepLklzczM2spY1yRHkp+kEnB8RMwr30bSMaTJl83MzNpKnmuSBwLzq5T/DHhXMeGYmZm1jjxJ8klgvyrl+wEriwjGzMysleQZu/Vk4FRJXcDNWdmewIeAuQXHZWZm1nR1J8mIOEHSA8BRwHuz4juBD0VEtWZYMzOzKa2uJClpHeCbwKkRsXdjQzIzM2sNdV2TjIhVwKeomC7LzMysneXpuHMFnhLLzMymkTwdd/4HmCdpZ2AhqbfriIjw5MtmZtZW8iTJ4dF3jqyyLoAZEw/HzMysdeTp3erB0M3MbFpx4jMzM6shT3MrkjYF3gZsCaxbvi4ivlFgXGZmZk1Xd5KUtCdwGfA0sBkwALwsW34AcJI0M7O2kqe59dvA+cBM4CnS7SBbAv3A8cWHZmZm1lx5kuTOwCkREcBqYL2IeAz4Ah671czM2lCeJPlM2fPHgK2y5yuAzsIiMjMzaxF5Ou7cCuwO3ANcCxwraQvgYGBR8aGZmZk1V56a5JeAwez5l4GlwH8BLwIOKzguMzOzpsszmEB/2fOlwAENicjMzKxFeDABMzOzGkatSUq6nzQu65giYlYhEZmZmbWIsZpbTyl7vhHweeB3wE1Z2V7AHsCJxYdmZmbWXKMmyYgYSX6SSsDxETGvfBtJxwA7NCQ6MzOzJspzTfJAYH6V8p8B7yomHDMzs9aRJ0k+CexXpXw/YGURwZiZmbWSPIMJnAycKqkLuDkr2xP4EB6WzszM2lDdNcmIOAE4BNgJOCl77AR8KCI8wLlZm+rr66O7u5tZs2bR3d1NX19fs0MymzS55pOMiPlUvy5pZm2or6+POXPmMDQ0BMDAwABz5swBYPbs2U2MzGxyjGswAUkvlLRp+aPowMys+Xp7e0cS5LChoSF6e3ubFJHZ5Moz6fJWwBnAPwLrlK8iDTgwo9jQzKzZBgcHc5WbtZs8NckfAVsAHwHeRJp0eX9S0ty/+NDMbCyNvl7Y2Vl9Frxa5WbtJk+S3AP4YERcEBHXRsR15Y8ig5J0hKR+SU9ngxiYWYXh64UDAwNExMj1wiITZU9PDx0dHWuUdXR00NPTU9h7mLWyPEnyfmC9RgVSYRA4Fjh7kt7PbMqZjOuFs2fPZt68ecycORNJzJw5k3nz5rnTjk0beXq3HgV8S9KnImJxowICiIiLAbJ7Ml/eyPcym6om63rh7NmznRRt2sqTJC8h1STvlvQ08Gz5yojYuMjA6iHpMLIJn7fccsvJfnuzpurs7GRgYKBquZkVI09z6xHAx0kddz4JfKbiMeki4gcR0RURXZtttlkzQjBrGl8vbH0eiGHqq7smGRHnNDIQM8tnuAm0t7eXwcFBOjs76enpcdNoi/BADO1BEXXNqbzmi6SXAuuWl0XEQ0UFVfY+xwIvj4hDx9q2q6sr+vv7iw7BzGxcuru7qzaHz5w5kwULFjQhoulH0sKI6JrIPupubpW0iaRzJA0BA6TeruWPwkhaW9L6pAEKZkhaX1KuIfTMJpub1qycB2JoD3muSfYCrwVmA08BHwD+A/hf4N8KjuvLwBDwReDg7PmXC34Ps8JMxj2LNrV4IIb2kCdJHgB8JiKuAFYDCyPiJFIiO7zIoCJibkSo4jG3yPcwK5LHOLVK7ljVHvIkyRcCD2bPlwMvzp7fBLyhwJjMphw3rVklD8TQHvJc57sPmAU8BNwJvE/S74ADgT83IDazKcP3LFo1Hohh6stTkywBO2fPjyM1sT4DfBvwpMs2rblpzaw91Z0kI+LkiPhe9vxqYDtSh51dIuKUBsVnNiW4ac2azb2rG6Pu+yQlfRD4aUQ8XVG+LvC+iDi3AfHVzfdJmtl0VTlwAaSWjOn+Q21S75MkzSe5SZXyF2TrzMysCdy7unHyJEkB1aqdW5J6u5qZWRO4d3XjjJkkJf1J0iJSgrxO0qKyx+3A9cBVjQ7UrB35OpIVwQMXNE49t4BclP27I3AZsKJs3TPAA8DPiw3LrP15AGwrSk9PT9Vrku5dPXF5Ou58CPhJZcedVuGOOzbVeABsK1JfX59nhKlQRMed8c4Csj7wXmAj4MqIWDyRIIrgJGlTzaxZs6j2+ZPEkiVLmhCRWXspIkmO2dwq6RvABhHRky2vDdwA7Jpt8qSkN0fEzRMJxGy68Sg9Zq2vnt6t7yaNzzrs/cD2QDfwEuA6YE7xoZm1N4/SY9b66um4sxVwe9nyW4CfR8SNMDIxsjvumOU0fL3I15HMWlc9SXIGqRfrsNcDJ5ctDwKbFhmU2XThAbDNWls9za33AvsDSNoGeCWpiXXYy4FlxYdmZmbWXPXUJE8DvitpH2AP4OaIuKNs/f7A7xsRnJmZWTONWZOMiB8CnyGN0XoN8J6KTTqBs4sPzczMrLnqmnQ5Is6mRiKMiE8VGpGZmVmLGLUmKekFeXaWd3szM7NWNlZz672Svizp5bU2kLSWpAMk/Rr4dLHhmZmZNc9Yza1vBL4JLMlmAukHHgGeAl4EvAbYExgC5gFnNi5UMzOzyTVqkoyIe4H3SnoFaazWN5Luk+wg3fbxe+AHwK8i4u8NjtXMzGxS1TXpckQ8HBEnRsTsiNg1IraLiO6I+ExE/NIJ0sxsavAcpvnUlSTNxssfSLPWMTyH6cDAABExMoepP5e1OUlaw/gDadZaent715iYGWBoaIje3t4mRdT6nCStYfyBNGstg4ODucrNSdIayB9Is9ZSa65Sz2FaW11JUtLakj4lyUfS6uYPpFlr8Rym+dXbu/VZ4NvAOo0Nx9qJP5BmrWX27NnMmzePmTNnIomZM2cyb948T9c2irrGbs3cDLwOeLBBsVib8aTCZq3Hc5jmkydJngmcKGkrYCHwZPnKiLi1yMCsPfgDaWZTWZ4keUH270lV1gUwY+LhmJmZtY48vVu3GeUxq/jQzKYvD8Jg1hrqrklGhK9Fmk2C4UEYhu8xHR6EAXDTtdkky3WfZDYl1i8l3ZENeo6kj0l6U2PCM5t+PAiDWeuoO0lKOgiYD9xLamIdvh1kBnB08aGZTU8ehMGsdeSpSR4NfDwiPgc8W1Z+M7BLkUGZtaLJuk7oQRjMWkeeJPlq4KYq5SuAjYsJx6w1TeZg7R6Ewax15EmSg8C2Vcr3Ae4rJhyz1jSZ1wk9KopZ68hzn+QPgO9J+li2/ApJbwROAOYWHZiNX19fn0e5KdhkXyf0IAxmrSHPLSAnSNoE+DWwPnAN8DTQGxGnNig+y8m3DzRGZ2cnAwMDVcvNrH3lugUkIr4EvATYA9gT2CwivtKIwGx8fPtAY/g6odn0VHdNUtJewO8iYiXQ37iQbCJ8+0BjeLB2s+kpzzXJa4FnJN2YPb+WlDRXFx2UpE2Bs4C3AMuAYyLigtFfZeBmwUbydUKz6SdPc+sLgQOBW4B3kJLkXyRdIemLBcd1KvAMsAVwEHC6pB0Kfo+25GZBM7Pi1J0kI2IoIn4dEV+OiG5gB+AiYH/gm0UFJGlD4D3AVyJiRUQsAP4bOKSo92hnvn2gdXiQcrOpL8+wdJtLeq+k0yXdCSwiDU/3TVKiLMq2wOqIuKes7I+kpDxupVKJP/zhDwCsXr2aUqnEokWLAFi1ahWlUonbbrsNgKeeeopSqcSdd94JwMqVKymVStx9990ArFixglKpxOLFiwFYvnw5pVKJJUuWAPDEE09QKpV44IEHAFi2bBmlUomHH34YgMcff5xSqTTSLProo49SKpV49NFHgdQjtVQq8fjjjwPw8MMPUyqVWLZsGQAPPPAApVKJJ554AoAlS5ZQKpVYvnw5ADvuuCMf+9jHWLRoEQsWLGD77benVCqxcuVKAO68805KpRJPPfUUALfddhulUolVq1YBsGjRIkqlEqtXp5b0P/zhD5RKpZFjuXDhQs4999yR5VtuuYXzzz9/ZPnmm2/mwgsvHFm+8cYbmT9//sjyggULuOiii0aWr7vuOi6++OKR5WuuuYZLLrlkZPmqq67i0ksvHVm+8sorueyyy0aWL7/8ci6//PKR5csuu4wrr7xyZPnSSy/lqquuGlm+5JJLuOaaa0aWL774Yq677rqR5YsuuogFCxaMLM+fP58bb7xxZPnCCy/k5ptvHlk+//zzueWWW0aWzz33XE4++eSqgw/84he/aOtzb/HixZRKJVasWAHA3Xff7XNvks+9hQsXjixPh++9RsvT3Poo8F+ka4SfAF4UEftFxNyIuG70l+ayEbC8omw58ILKDSUdJqlfUv/SpUsLDMFsYi644AL3MjZrA4qI+jaUzieNrrMJ8BvSfZLXArdGvTup7312BW6IiA3Kyv4d2C8i3lnrdV1dXdHf70631hpmzZpFtY+FpJFf3mbWWJIWRkTXRPaR55rkQRHxCmA3oA/YFfgF8GdJl4z22pzuAdaW9OqystcCtxf4HmYN5UHKzdpDrsEEMvcBfyIlrbtIzaNvKyqgiHgSuBj4hqQNJe0NvBv4cVHvYdZo7mVs1h7ydNz5D0m/Av5Cam59F3Ar8E5g04Lj+hTQATwOXAh8MiJck7Qpw72MzdpDnmuSN/PcIALXZzW+luFrkhPjQdGnH//Nrd0VcU0yzwDne07kjax1eVD06cd/c7P61F2TBJC0BfBp4DVAAHcAp0XEY40Jr36uSY5fd3d31aHsZs6cucY9W9Y+/De36WBSe7dmHWgWAx8AhoCnSEPG3ZsNfm5TlAdFn378NzerT57erb2kTjTbRsQhEXEIaXScnwAnNiI4mxy+XWH68d/crD55kuQuwIkR8ffhguz5SaR7Jm2K8u0K04//5mb1yTNV1nLSWK13V5RvQ7otxKYoz5U4/fhvblafPLeAfAf4V+Bo4EZSx51u4DhgfkR8vkEx1sUdd8zMrNyk3gJCSo4Czi573SrgdKDo+STNzMyaLs99ks8AR0k6BnglKWEujoiVjQrOzMysmcbsuCNpA0mnShqQ9DjwQ+CRiFjkBGlmZu2snt6tXwcOBS4j3e7xZlITq5mZWVurp7n1QOCjEfETAEnnATdImhERqxsanZmZWRPVU5N8BXD98EJE/A54FvBdx2Zm1tbqSZIzgGcqyp4lX89YMzOzKaeeJCngPEn/PfwA1gfOrCgzy62vr4/u7m5mzZpFd3c3fX19zQ7JzGxEPbXBc6qUnVd0IDb9eLomM2t1uabKamUecWfq8XRNZtZIkzpVllnRPF2TmbU6J0lrGk/XZGatzknSmsbTNZlZq/NtHNY0nq7JzFqdO+6YmVlbmuypsobftBPYEli3vDwifjORQMzMzFpN3UkyS44XAPuQJlxW9u+wGcWGZmZm1lx5Ou58B1gNvAZYCbwR+FfgTuBthUdmZmbWZHmaW/cF3hERd0kKYGlE3CDpaeA/gV83JEIzM7MmyVOT7ACWZc//DGyePb8D2LnIoMzMzFpBniR5F7Bd9vwPwCckbQV8Gnj+2GJmZmZTXJ7m1u8CL82efwO4HHg/8DTwoYLjMjMza7q6k2REnF/2/FZJW5Nqlg9FxLKaLzQzM5ui6m5ulfRVSRsML0fEyoi4FXhS0lcbEp2ZmVkT5bkm+TVgoyrlG2TrzMzM2kqeJFk5eMCwXUm9Xc3MzNrKmNckJf2NlBwDWJLdIzlsBrA+cEZjwjMzM2ueejruHEGqRZ4NfAlYXrbuGeCBiLipAbGZmZk11ZhJMiLOAZB0P3BjRKxqeFRmZmYtoO5rkhFxHbCppB5Jp0t6CYCkvSVt07AIzczMmiTPLSC7AXcDBwEfBTbOVr0Z+GbxoZmZmTVXnt6tvcB3I2JX0ig7w64A9i40KjMzsxaQJ0nuBpxTpfwRYItiwjEzM2sdeZLkEPCiKuXbAY8XE46ZmVnryJMkLwG+Jmm9bDmy8VuPB35edGBmZmbNlidJ9gCbAktJQ9EtABYDfwG+XHhkZmZmTZZnFpC/At2S9gdeR0qwt0bEVY0KzszMrJnyzCcJQERcDVzdgFjMzMxaSq4kKen1wJuAzaloqo2IIycajKQjgEOBnYALI+LQie7TzMxsvOpOkpJ6gBNI1yEHWXNGkGqzg4zHIHAs8Fago6B9mpmZjUuemuRRwJERcUqjgomIiwEkdQEvb9T7mJmZ1SNP79aNgV81KpDxkHSYpH5J/UuXLm12OGZm1mbyJMkLgbc1KpDxiIgfRERXRHRtttlmzQ7HzMzazKjNrZI+X7b4MPB1SXsDi4A1psyKiJPG2Ne1wL41Vt8QEd1jRmtmZjaJxrom+ZmK5RXAG7JHuQBGTZIRsV+uyMzMzJps1CQZEZM6T6SktUkxzQBmSFofeDYinp3MOMzMzKCOa5KSlkh68WQEQxrebgj4InBw9txD3pmZWVPU03Fna1LNruEiYm5EqOIxdzLe26yV9PX10d3dzaxZs+ju7qavr6/ZIZlNS7mHpTOzxurr62POnDkMDQ0BMDAwwJw5cwCYPXt2EyMzm34UMfpgOZL+Dvwz8MRo20XEbwqMK7eurq7o7+9vZghmheju7mZgYOB55TNnzmTBggVNiMhsapK0MCK6JrKPemuSFwMaZX0wSU2yZu1ucHAwV7mZNU69SXIH0jySZtZgnZ2dVWuSnZ2dTYjGbHqrd8SdP0fE/432aGiUZtNIT08PHR1rju/f0dFBT09PkyIym77cccesxQx3zunt7WVwcJDOzk56enrcacesCepJktcBzzQ6EDN7zuzZs50UG6Svr88/QKxuYybJiPjHyQjEzKzRfHuN5ZVnFhAzsymtt7d3JEEOGxoaore3t0kRWatzkjSzacO311heTpJmNm3Uuo3Gt9dYLU6SZjZt+PYayytXkpT0KUm3S1opaVZW9kVJ721MeGZmxZk9ezbz5s1j5syZSGLmzJnMmzfPnXasprrvk5T0WeBo4HjguLJVA8ARwPxCIzMzawDfXmN55KlJfgL4eER8FyifBPlW0rB1ZmZmbSVPktwKuK1K+Sqgo0q52aTzPIxmVqQ8w9ItAV4HPFhR/nbgjsIiMhsn3yhuZkXLU5PsBU6RdBBp2qy9JH0N+Cbw7UYEZ5aHbxQ3s6LVXZOMiB9JWhuYB2wA/JjUaefIiPhpg+Izq5tvFDezouW6BSQizoyIrYDNgZdGxCsi4qzGhGaWj28UN7Oi1Z0kJZ0s6XUAEbEsIh5vXFhm+flGcTMrWp6a5OuBfkl3SpojaesGxWQ2Lr5R3MyKpoiof2NpG+Ag4APAPwA3AucB8yPiiYZEWKeurq7o7+9vZghmZtZCJC2MiK6J7CPvNcn7I+LYiHgNsDvwW+ArgHtGmJlZ25nIAOfrAOsB6wKriwnHzMysdeQd4HxbSV+XdC+wgNTk2gNs0YjgzMzMminPAOf9wK7AH4HTgQsi4tFGBWZmZtZseYaluxI4JCLubFQwZmZmrSTPiDtzGhmImZlZqxk1SUr6HnBMRDyZPa8pIo4sNDIzM7MmG6smuROpF+vwczMzs2lj1CQZEf9Y7bmZmdl0kGfs1q9K2qBKeYekrxYblpmZWfPluU/ya8BGVco3yNaZmZm1lTxJUkC1gV53Bf5cTDhmZmatY8xbQCT9jZQcA1giqTxRzgDWB85oTHhmZmbNU899kkeQapFnA18ClpetewZ4ICJuakBsZmZmTTVmkoyIcwAk3Q/cGBGrGh6VmZlZC8gz4s51w88lvZQ0+0f5+ocKjMvMzKzp8gxwvjHwX8B7qUiQmRlFBWVmZtYK8vRuPRF4LTAbeAr4APAfwP8C/1Z4ZGZmZk2WZxaQA4D3R8T1klYDCyPip5IeAQ4HLmpIhGZmZk2Spyb5QuDB7Ply4MXZ85uANxQYk5mZWUvIkyTvA2Zlz+8E3idJwIF4MAEzM2tDeZJkCdg5e34cqYn1GeDbwPETDUTSepLOkvSgpL9J+r2kAya6XzMzs/HKcwvIyWXPr5a0HdAF3BsRfyooloeBfYGHgLcD8yXtFBEPFLB/MzOzXPJ03FlDdl9kYfdGRsSTwNyyol9mAxjsBjxQ1PuYmZnVa9QkKenz9e4oIk6aeDhrvPcWwLbA7aNscxhwGMCWW25Z5NubmZmhiGoTe2QrU02uHhERs8berD6S1gH+H3BfRBxez2u6urqiv7+/qBDMzGyKk7QwIromso9Ra5IRsc1Edl5O0rWk643V3BAR3dl2awE/JnUKOqKo9zczM8tr3Nck84qI/cbaJrul5CxgC+DtHkzdzMyaqe5bQJR8StLtklZKmpWVf1HSewuK53Rge+CdETFU0D7NzMzGJc99kkcBXwZ+QJpfctgABTSLStqKdO/lLsCjklZkj4Mmum8zM7PxyNPc+gng4xFxmaRjy8pvBXaYaCAR8SBrJl8zM7OmylOT3Aq4rUr5KqCjmHDMzMxaR54kuQR4XZXytwN3FBOOmZlZ68jT3NoLnCJpA1Kz6F6SDgGOBj7SiODMzMyaKc/YrT+StDYwD9iAdC/jAHBkRPy0QfGZmZk1Ta77JCPiTOBMSS8B1oqIxwEkvSIiHm5EgGZmZs2S55rkiIhYFhGPS3qppFOBewqOy8zMrOnGTJKSXijpfElLJQ1KOjIbWOBrpM48e+BrkmZm1obqaW6dB+wDnAO8DTgZeDOwIXBARFzXuPDMzMyap54k+Q7gwxFxlaTTgMWk2Tk+29DIzMzMmqyea5KdZPdBRsQS4CngzEYGZWZm1grqSZJrkUbVGbYaWNmYcMzMzFpHPc2tAs6T9HS2vD7pNpA1EmVEvKvo4MzMzJqpniR5TsXyeY0IxMzMrNWMmSQj4sOTEYiZmVmrGddgAmZmZtOBk6SZmVkNTpJmZmY1OEmOQ19fH93d3cyaNYvu7m76+vqaHZKZmTVArllALCXIOXPmMDQ0BMDAwABz5swBYPbs2U2MzMzMiuaaZE69vb0jCXLY0NAQvb29TYrIzMwaxUkyp8HBwVzlZmY2dTlJ5tTZ2Zmr3MzMpi4nyZx6enro6OhYo6yjo4Oenp4mRWRmZo3ijjs5DXfO6e3tZXBwkM7OTnp6etxpx8ysDSkimh1DIbq6uqK/v7/ZYZiZWYuQtDAiuiayDze3mpmZ1eAkaWZmVoOTpJmZWQ1OkmZmZjU4SZqZmdXgJGlmZlaDk6SZmVkNTpJmZmY1OEmamZnV4CRpZmZWg5OkmZlZDU6SZmZmNThJmpmZ1eAkaWZmVoOTpJmZWQ1OkmZmZjU4SZqZmdXgJGlmZlaDk6SZmVkNLZUkJZ0n6RFJf5V0j6SPNTsmMzObvloqSQLfAraOiI2BdwHHStqtyTGZmdk01VJJMiJuj4inhxezxyubGJKZmU1jLZUkASSdJmklcBfwCPCrJodkZmbTlCKi2TE8j6QZwF7AfsDxEbGqxnaHAYdlizsCt01KgO3lJcCyZgcxBfm4jY+P2/j4uI3PP0TECyayg0lLkpKuBfatsfqGiOiu8pozgDsi4nt17L8/IromFuX04+M2Pj5u4+PjNj4+buNTxHFbu6hgxhIR+43jZWvja5JmZtYkLXNNUtLmkt4naSNJMyS9FXg/cHWzYzMzs+lp0mqSdQjgk8AZpOT9IPDZiLikztf/oFGBtTkft/HxcRsfH7fx8XEbnwkft5bsuGNmZtYKWqa51czMrNU4SZqZmdUwZZKkpE0l/ULSk5IelPSBUbb9nKRHJS2XdLak9SYz1lZS73GTdKik1ZJWlD32m9xoW4ekIyT1S3paUmmMbX2+Zeo9bj7f1iRpPUlnZZ/Rv0n6vaQDRtne5xz5jtt4z7kpkySBU4FngC2Ag4DTJe1QuVHWK/aLwJuArYFZwNcnL8yWU9dxy9wUERuVPa6drCBb0CBwLHD2aBv5fHueuo5bxufbc9YGHibdS74J8BVgvqStKzf0ObeGuo9bJvc5NyWSpKQNgfcAX4mIFRGxAPhv4JAqm38IOCsbB/YJ4D+BQyct2BaS87hZmYi4OCL6gP8bY1Ofb2VyHDcrExFPRsTciHggIv4eEb8E7geqTfDgcy6T87iNy5RIksC2wOqIuKes7I9AtRrRDtm68u22kPTiBsbXqvIcN4BdJS3Lpin7iqRWukWoVfl8Gz+fbzVI2oL0+b29ymqfczWMcdxgHOfcVDkpNwKWV5QtB6qNyVe57fDzFzD9ft3mOW6/IY1/+yDpQ/hT4FnS9GVWm8+38fH5VoOkdYDzgXMi4q4qm/icq6KO4zauc26q1CRXABtXlG0M/K2ObYefV9u23dV93CJiSUTcnzVZ/An4BvAvkxDjVOfzbRx8vlUnaS3gx6R+BEfU2MznXIV6jtt4z7mpkiTvAdaW9OqystdSvUp9e7aufLvHImI6/sLKc9wqBaCGRNVefL4VY9qfb5IEnEXqZPeeWrMf4XNuDTmOW6W6zrkpkSQj4kngYuAbkjaUtDfwbtIvh0rnAh+V9BpJLwK+DJQmLdgWkue4SToga89H0nakXmL1DgnYdiStLWl9YAYwQ9L6Na5f+HwrU+9x8/lW1enA9sA7I2JolO18zq2pruM27nMuIqbEA9gU6AOeBB4CPpCVb0lqftiybNvPA48BfwV+BKzX7Phb/bgBvdkxexJYQmqKWKfZ8TfxuM0l/dIsf8z1+VbMcfP59rzjtlV2rJ7KjtPw4yCfc8Uct/Gecx671czMrIYp0dxqZmbWDE6SZmZmNThJmpmZ1eAkaWZmVoOTpJmZWQ1OkmZmZjU4SZqZmdXgJGlTlqSSpF82O47xkBSS2n6sUkn7ZjMuzGh2LKORtLekRZKekXRts+OpVzZx8KHZ880lLZX08iaH1VacJNuMpE9nH/a/Zo+bJL2jYhtJmitpUNKQpGtHmYi56SRtnSWVrgnuZ7dsP9011s+XdMNE3qORJG0gaZ6kxZKeyqb8uUHS+5sd2yi+DXwzIlZn51mM8nigiXF+lzTl1CuBA5sYx7hFxOOkIeum6wTMDeEk2X7+F/gC8DqgC7ga6JO0c9k2RwP/DnwG2B14HPi1pGpTaLWNiFgI/B74aOW6bC6+d5EGSm5VZwD/BnwW2A54C3AeaejBhpC07gRe+wZSnD/Lig4EXpY9hn+UvaesbPei3nscXgVcHREPR8Sfx7ODSY63lh8BB0lq2Dkx7TR77D0/JmV8wz8Dh2fPBTwCfKlsfQdpmp3DR9nH1qQxEt8HXAcMkRLOzqQ52m4kjYm4ANim4rWHA4tJ09gsBj5esT6Aw0hfpsPjKh5csb78cW1WXgJ+CRwFDABPkL4kNhjl//Fp0niOG1WUH5Udgw2B9YDvkMZ5fAq4Geiu2H474L9Jc/mtAG4CdsrW7Q5cCSwjja25ANiryv/5COAyYCVpjruDa8WdveYvwMfG2EakH0D3Ak+TfjR9q2z9TsBV2d/vz9kx3KRs/fAx/UL22sez8pnAT7Jj/EQW96vHiOUU4OIa616SHYP9ysoeII3zenb2f/1ZVn4ccHcW8wPACcD6Za+bC9xGOjfvy/6OfcBLKv7f/5P9Pf5GqjX+I8+d1+WPQ7PX7AP8NjsHHgNOBtYt2+e1pMG1e4GlwC3Aftk+DgAWZjFfD7wc2Dd73xXZMX5xxTH5MHBH9n73AJ8D1ipb/6rsPZ/Kjsc/Zfs6tGI/S8Y6T/yo/9H0APxo4B83zcTwPlJyGv4Cn5V9iHev2PYy0mSltfY1/GVyN/B2UpK4Jvtyuib7wtkB6AcuLXvdPwOrSAlhW1LtdRVpxP7hbYL0hXxw9kXwrSzmrbL1u2fbvBV4KbBpVl4iJakzSbMAvIX05XrMKP+PF2ZfXB+tKP8jcGb2/LukHxLvyPZ7ZvZl9LJsfScpAV4C7JH9vw4GdsnW7w8ckr12O1KyeII1v7SDNEHu4dnrvwT8HegaJfa7gIsoS2pVtvlWdgw+kh3LvYBPZes2IP2Y6CMljX1JX8Y/L3t9iZREzif9+Nkpe9092bqds//TD0mJfbQfJH8E5tRYVytJ/pXU0vEqsiRMmq1hb9I5+HbSQP3/Wfa6udnf5xdZfHtlsX2/bJs/kWrd22X7/udsuxnZOfUk6YfSS0k/GmdmZWdkf8d/Ah4FTizb57XZsTox2+/2PJckfwe8MYvnNuAGUpJ+PamF537gv8r29XHSOfcvwDbAO7P3OyJbv1b2f/gNsGt2PPpJn6VDK47tT4AfN/v7p10eTQ/Ajwb8UdMX2wrSrNt/Ad5Rtu4N2Yd4y4rXnA1cMco+t85ed3hZ2T9lZQeWlR0KrChbvgE4u2JfJWBB2XKwZm1nbVLt6uCK9+6qsp+HgbXLys4Erhrj+JwH3Fi2PJyEX0+qST4DfLBs/QxSDeXYbPmbpC/hdUd7n7LXD9feK2vHZ1ZsdxVw3ij72Sf7/64CbiUl3zeXrd+IVMv4RI3Xf5z0o+IFZWX7ZbG8quyYLqVsVglSwr0X0oQIZcfk/4D3jhLvX4AP11hXK0leWmt/Zdt9Alhctjw3+39vUlb2pYpt/gp8aJR9rlEjy/7Gi1mzJncoqXa+QbZ8LbCoYj/Dx/OtZWVHZGWvq4j5trLlh4BDKvb1WeCO7PlbgNWsORNIN2U137Lyk4Dr6zk3/Rj74WuS7eluYBdgT1Jz0DmSdqzYJiqWNVwm6Yys19wKSSsqtltU9vyx7N8/VZRtKGmDbHl7UqIstwB4Ta39RsSzpC/qzZ//X3ueO7Lthw3W8bqzgL2yOeUgJYHbIuK3pI4b65THHBGrSc2pwzHvSkryz1TbedbL8PtZr87lpNrG5qSpe8rdVGW58riMiIjfkFoC9gfmk2qgV0r6frbJa0hNxf9TYxfbk77Uy2ewv5FUgy1/39si4umy5d1ItZu/lZ0Ty4EXkY5XLR2k5JVHf2WBpH+RtEDSo9l7n8zzj+WDEbG8bLnyPDgJ+KGkqyV9qexvX8v2wE0R8feysgXAuqSa6LCFNV5fz+dkcwBJmwGvAL5f8bk7jueO7/bAQEQ8VLaP35L+dpWGSMfeCuAk2YYi4pmIWBwR/RFxDPAH0vUNSE04kJqVym3Ocx/mr5KS7PCjXPms3zFK2VpVyhilrHI28aC+83M8r7uWVEv4iKQO4P0812FneKby0WIeazbzc0i108+Rau67kJqTJ9yxIyJWRcT1EXFcRLyF1BR5mKSt64hr5IdQtV2XPX+yYt1apHNol4rHtsD3qW0ZKZHmscZ7S9qT1Hx4BakJclfSJMPrVLxu1PMgIuaSfgj0kf4miyR9ZJQ4xnusqsUTWQyVZcPxDf/7CdY8vjvyXAensf625TYl/ci0AjhJTg9rkWoYkK6FPAq8eXhlNpP8G0m1CiLi8SzJLo6IxRN87ztJzULlukkdFOo1XGMr5F67SG1SZwMfJCXIDuDH2erhDkYjMWf3+O3FczHfCnSP0puxm3S96bKIuJ1Uk3xZle32rLJ8Z87/znBMG2XPnwbeNMq2r63oxfwG0vkx2vveSqo9LSs/L7LHaD1Bf88oNeM67U2qQf1nRNwSEfeSJtrNLSLujYjvRcQ7SD+KPjbK5neQWhvKvyO7SefGfeN5/1Hieox0rfiVVY7v8OfvDmCmpFeUvXQPqn+H70j6m1kBnCTbjKTjJL0xu7dwJ0nfIl0nOR9GEsR3gC9KOjBrhi2Rrslc0ICQvg0ckt2/+WpJnyHNGn5Cjn08TmpCequkLSRtUkBcJdJ1sV6gLyL+DyAiniQ1UR8n6e2Sts+WtwBOy157GikpzZe0u6RXSXq/pF2y9fcAB0t6jaTdSTWhak2zB0r6eHZcjiElt+/UCji7z/Dw7H7PrSW9HZhHal6/M2tG/S7wLUkflvRKSXtI+mS2i/NJNZ9zs3NjH1JN8OIxfgydT2pluERpcIBtJO0j6URJrx7ldVfw/B9Ied1DSg4HSZqV/V9y3RcqqUPSqZL2y47b6xn7h9pppA5ap0naXule4+OAUyJi5Tj/L6OZCxwt6XOS/kHSjpI+mJ0XkK5X30X62+0iaS9Ss3P5pQayyxy7AZc3IMZpyUmy/byU1DHlbtK1qd2BAyLi/5VtcwLpGs2ppGtALwPeUnGtqhAR0Ufq0fo50pfSUaTelpfm2MezwJGkX/6DpF6lE43rEeBXpObAH1as/gLpmt+PSM2MOwNvy15DRAyQOtGsS+rZ+3vS/3H4C+sjpCS6kJQgzyZ1Sqk0l3Sf4CLgk6ROLreMEvYVpF6zV5C+ME8j3V7w5uy6KcAxwPGkZtg7gZ+Tbj8g+3J/K7AxqfflJaTroKM1Ow6/bh/SrQU/y977HNKxe2KUl54HbKsJDFSRnSffJv14WERqAflqzt2sJsV6Dulz8QvS//vzo7zvAOk2jl1J58DZwIXAnJzvXZeI+CHp73AIqVfw9aTbou7P1v+d1CN3LdK1yHOBY0ktB+XeDTwUEdc3Is7pSKliYWZWPEnHAZtFxPMGcLDiSfod8J2IaESr0LTkmqSZNdI8YIlafOzWdiBpc9J9tBc2O5Z24pqkmZlZDa5JmpmZ1eAkaWZmVoOTpJmZWQ1OkmZmZjU4SZqZmdXgJGlmZlbD/w/eNDsragfzfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Like above, but use only the 30-month vocabulary score instead of the difference #\n",
    "# *TEST ONLY*\n",
    "mdf_try = mdf2_lha  # 2022.01.27 for paper: mdf2_lha, mdf3_rhi; not in paper: mdf4b_rhi\n",
    "mdf_figname = 'roi_vocab30_IFClh-attend.jpg'\n",
    "\n",
    "y2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "yfits = []  # unlike above, no confidence interval!\n",
    "for subject in df_model['subject'].unique():\n",
    "    roival = get_roival(subject, mdf_try)\n",
    "    yfits.append(roival)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot([0.05, 2.45], [0, 0], color='grey', linestyle=':')  # 0 because ROI is z-score normed\n",
    "ax.scatter(y2, yfits, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(0.00, 2.50), ylim=(-3, 3), \n",
    "         xlabel='30-month Vocab Score (Transformed)',\n",
    "         ylabel='Relative Theta Power (Standardized)');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, mdf_figname)\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92458392",
   "metadata": {},
   "source": [
    "## \\*\\*TEST\\*\\* Graphically represent the model results (w/ linearized ROI instead of zscore)\n",
    "Weird, so not using. Could be linearizing from df_roidb would have been more similar to the above section, but moving on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "48cc649d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb0AAAF/CAYAAADdDhf7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAzeUlEQVR4nO3deZxcVZn/8c+XsDWEyBbAbg0Q1AmLCtIKaCsIo6gzag/4QxAXcAFk2JSG0RYwggTFVkZFcEA2hUFEsR11dFwDBkFtYATCTli7A4aRLUknbM/vj3MLKkV1Vd1OVVd11/f9et1Xqs7dnrp9U0+de889RxGBmZlZO1ij2QGYmZlNFCc9MzNrG056ZmbWNpz0zMysbTjpmZlZ23DSMzOztuGkZ2ZmbWPCk56k/SXdKmmZpLslvTkr30vSbZKWS/q9pC0nOjYzM5vaJjTpSXob8GXgYGAD4C3AIkmbAlcAJwIbA0PAZRMZm5mZTX2ayB5ZJP0ROC8izispPwQ4KCLemL1fH3gE2CkibpuwAM3MbEqbsJqepGlANzBT0l2SHpR0pqQOYHvgr4VlI2IZcHdWbmZmVhdrTuC+NgfWAt4HvBl4GvgJcAIwHVhSsvzjpEugq8hqhYcArL/++jvPmTOngSGbmdlkc9111z0SETPLzZvIpDea/fvNiFgMIOlrpKR3FTCjZPkZwJOlG4mIc4BzALq7u2NoaKhhAZuZ2eQj6b6x5k3Y5c2IeBR4ECh3E3Eh8NrCm+ye3jZZuZmZWV1M9CMLFwBHStpM0kbAMcDPgB8DO0jaV9K6wEnAjW7EYmZm9TTRSe8U4C/AHcCtwA3AqRGxBNgXOBV4FNgF2H+CYzMzsyluIu/pERFPA4dnU+m83wBulWJmZg3jbsjMzKxtOOmZmVnbcNIzM7O24aRnZmZtw0nPzMzahpOemZm1DSc9MzNrG056ZmbWNpz0zMysbTjpmZlZ23DSMzOztuGkZ2ZmbcNJz8zM2oaTnpmZtQ0nPTMzaxtOemZm1jac9MzMrG046ZmZWdtw0jMzs7bhpGdmZm3DSc/MzNqGk56ZmbUNJz0zM2sbTnpmZtY2nPTMzKxtOOmZmVnbcNIzM7O24aRnZmZtw0nPzMzahpOemZm1jVxJT9I6kraWtJ2kmY0KymwqGhwcpKenh9mzZ9PT08Pg4GCzQzJrO1WTnqQNJH1S0lXA48BdwM3AQ5IekHSupNfXsjNJ8yWtkLQ0m27PyreSFEXlSyWduDofzKyVDA4O0t/fz/DwMBHB8PAw/f39TnxmE6xi0pP0KeBe4KPAr4H3AjsCrwJ2Az4PrAn8WtIvJb2yhn0eERHTs+kfSuZtWDTvlHwfxax1DQwMMDo6ukrZ6OgoAwMDTYrIrD2tWWX+G4HdI+LmMeb/GThf0mHAx4DdgTvrGJ/ZlDAyMpKr3Mwao2JNLyL+X4WEV7zcyog4KyK+U8M+T5P0iKSrJe1RMu8+SQ9KukDSpuVWlnSIpCFJQ0uWLKlhd2bN19nZmavczBpjoltv/hswG+gCzgF+Kmkb4BHg9cCWwM7ABsAl5TYQEedERHdEdM+c6bY0Njn09fXR0dGxSllHRwd9fX1NisisPVW8vCnp90DUsqGI2LOGZf5U9PYiSQcA74qIbwJDWfnDko4AFkuaERFP1LJ/s1bW29sLpHt7IyMjdHZ20tfX93y5mU2Mavf0ii9tTgMOBB4CCsnrDcBLgYvHuf8ANEY5Y8wzm5R6e3tzJbnBwUEnSbM6q5j0IuLIwmtJZwAXAUdHRBSV/zs1JCdJGwK7AFcCzwDvB94CHCNpF+AxUiOYjYBvAPMj4vFcn8Zsiig84lBo8Vl4xAFw4jNbDXnu6X0YOLM44WXOAj5Uw/prAV8ElpDu4R0J9EbE7aT7fL8EniTVLlcCB+SIzWxK8SMOZo1R7fJmMQGvBu4oKX91LStHxBJSY5Vy8y4FLs0Ri9mU5kcczBojT9I7H/hO9gD6tVnZrsDxwAX1DsysnXV2djI8PFy23MzGL8/lzeOB00iXJX+XTUcCX8rmmVmd+BEHs8aouaYXEc8BpwOnS5qRlflxArMG8CMOZo2hF7dLqbKC1A1sA/wsIpZJWh9YGRHPNCLASrq7u2NoaKj6gmZm1jYkXRcR3eXm1VzTk7Q58F+kxigBvBJYBHwNWAEcvfqhmpmZNU6ee3pnkB5M3wRYXlR+OfD2egZlZmbWCHmS3l7A5yLi0ZLyu4FZ9QvJbGryILJmzZfnkYUO4Kky5TNJlzfNbAzuYcWsNeSp6V0FHFT0PiRNI42c8Nt6BmU21biHFbPWkKemdzxwpaTXA+sAXwW2B14CvKkBsZlNGe5hxaw11FzTi4hbgNcAfwR+BaxLasSyU0Tc3ZjwzKYGDyJr1hpyDSIbEYsj4vMR8c8R8a6IOCEiFjcqOKs/N6ZoDvewYtYaak56khZJOk/S2iXlm0paVP/QrN4KjSmGh4eJiOcbUzjxNV5vby/z5s2jq6sLSXR1dTFv3jw3YjGbYDX3yCLpOeA+YDHw3mzUhMJD6yMRMa1hUY7BPbLk09PTU7YT466uLhYsWNCEiMzM6q9Sjyx5Lm8G8Dbg78BfJO1Qj+Bs4rgxhZm1uzxJT8ATwLtJDViulvTPDYnKGsKNKcys3eWt6RHJccBRwPeBYxsRmNWfG1OYWbvLO3L68yLiIkl3AlfUNyRrFA9XY2btLk9Dli2BB7Jx9YrLNwfmRMSVDYivIjdkMTOzUnUZWigi7huj/GHg4XHGZmZmNmEqJj1JNwK7R8Sjkm4iu69XTkS8pt7BmZmZ1VO1mt6PgJXZ6x82OBYzM7OGqpj0IuIL5V6bmdnYBgcH3WCsReVpvWlmZlV47MTWVrH1pqR7qHAfr1hEzK5XULVy600zazXu7q/5Vqf15plFr6cDnwb+DFyTle0GvIE0tp6ZWdtzd3+trdo9veeTmaQLgS9HxLziZSR9ljSYrJlZ2+vs7Cxb03N3f60hTzdk+wA/KFN+OfCe+oRjZja5ubu/1pYn6S0D9ihTvgewvB7BmLU7D/I7+XnsxNaWp/XmGcC3JHUD12ZluwIfAebWOS6ztuNWf1NHb2+v/2Ytqua+NwEk7QccDWybFd0KfD0iyl32bDi33rSpxK3+zOpjtQeRlbSWpNOBP0XEmyJi42x6U56EJ2m+pBWSlmbT7UXz9pJ0m6Tlkn6fdXBt1jbc6s+s8WpKehHxNHA4JcMLjdMRETE9m/4BQNKmpCGKTgQ2BoaAy+qwL7NJw4P8mjVenoYs/wPs2aA49gEWRsTlEbGCdI/wtZLmNGh/Zi3Hrf7MGi9PQ5bfAvMkvQa4jtSa83kRUetgsqdJ+hJwO/C5iJhPes7vr0XbWibp7qz8tuKVJR0CHAIwa9asHOGbtTYP8mvWeHkGkX2uwuyIiGk1bGMX4BbgKWB/Uo8vOwL9wJKI+EzRslcD50bEhWNtzw1ZzMysVL0Gkc1zKXSsbfyp6O1Fkg4A3gUsBWaULD4DeHJ192lmZlaw2olsNQWpccxC4LWFQknrA9tk5WZmZnWRa2ghSRsD7wBmAWsXz4uIk6usuyGwC3Al8AzwfuAtwDHA34GvSNoX+DlwEnBjRNxWdmNmZmbjUHPSk7QrKSGtBGYCw8BLs/f3AhWTHrAW8EVgDvAsqYFKb0Tcnm1/X9I9vouBP5Hu+ZmZmdVNnpreV4BLSD2yPEF6fGEZcClwXrWVI2IJ8PoK839DSohmZmYNkeee3muAMyM193wWWCciHgb+Dfe9aWZmk0CepPdU0euHgUI3YUsBdxlhZmYtL8/lzetJlyfvAOYDX5S0OfBB4Mb6h2ZmZlZfeWp6nwMKPd+eACwBvglsRNZDipmZWSvL83D6UNHrJcA7GxKRmZlZgzT74XQzM7MJU7GmJ+keUq8pVUXE7LpEZGZm1iDVLm+eWfR6OvBp4M/ANVnZbsAbgK/WPzQzM7P6qpj0IuL5ZCbpQuDLETGveBlJnyUNAWRmZtbS8tzT2wf4QZnyy4H31CccMzOzxsmT9JYBe5Qp3wNYXo9gzMzMGinPw+lnAN+S1A1cm5XtCnwEd0NmZmaTQJ7n9E6XdC+pw+n9suJbgY9ERLnLnmZmZi0l13h6WXJzgjMzs0kpV9IryAaEXeV+YET8vR4BmZmZNUrNDVkkbSnpF5JWAP9H6ntzCfBI9q+1kcHBQXp6epg9ezY9PT0MDg42OySzsnyuWrE8Nb0LgA2Bj5I6nq6ppxabegYHB+nv72d0dBSA4eFh+vv7Aejt7W1iZGar8rlqpZTGhK1hQWkpsGtE3NzYkGrX3d0dQ0ND1Re0uurp6WF4ePhF5V1dXSxYsKAJEZmV53O1PUm6LiK6y83L85zePcA69QnJJrORkZFc5WbN4nPVSuVJekcDp0l6RaOCscmhs7MzV7lZs/hctVJ5kt5PSL2v3C5puaQniqfGhGetqK+vj46OjlXKOjo66Ovra1JEZuX5XLVSeRqyHNGwKGxSKTQAGBgYYGRkhM7OTvr6+twwwFqOz1UrVXNDllbkhixmZlaqUkOW8T6cvgWwdnFZRNw/nm2ZmZlNlJqTnqSXAN8g9bu5dplFptUrKDMzs0bI05BlAHgt0AusAD4AHAc8CLy/7pGZmZnVWZ7Lm+8EDoiIP0h6FrguIi6TtBg4FPhhQyI0MzOrkzw1vQ2B+7LXjwObZK+vAd5Yx5jMzMwaIk/SuxuYnb2+FdhfkoB9AI+wYGZmLS9P0rsQeE32+kukS5pPAV8BvlzfsMzal0cFMGucPCOnn1H0+neS5gDdwJ0RcVMjgjNrNx4VwKyx8oyn92FJz3c4HRH3R8QVpG7JPpxnp5JeKWmFpIuz91tJCklLi6YT82zTbCoYGBh4PuEVjI6OMjAw0KSIzKaWPJc3LwBeUqZ8g2xeHt8C/lKmfMOImJ5Np+Tcptmk51EBzBorT9IT5QeOnUVqzVnbRqT9gceA3+bYt1lb8KgAZo1VNelJuknSjaSEd6WkG4umhcAfgN/UsjNJM4CTgWPHWOQ+SQ9KukDSpmNs4xBJQ5KGlixZUstuzSYNjwpg1li11PR+CPyIVNP7efa6MF0MfAL4YI37OwU4LyIeKCl/BHg9sCWwM+mS6SXlNhAR50REd0R0z5w5s8bdmpXXai0le3t7mTdvHl1dXUiiq6uLefPmuRGLWZ3UPMqCpI8A34+IlePakbQjKZHtFBFPSZoLvCIiXpQwsw6tFwMviYgxx+rzKAu2OkpbSkKqVTnJmE1ulUZZqPmeXkRcVEh4ktbNWnMenmMk9T2ArYD7JT0E9AH7Srq+3O4Ksdcan1lebilp1n6qPqcn6WRgvYjoy96vCVwN7JQtskzS2yLi2iqbOgf4ftH7PlIS/KSkXUiNW+4ENiKN5jA/ImpuIGOWl1tKmrWfWmp67yX1r1lwALAt0ANsClwJ9FfbSEQsj4iHChOwFFgREUtI3Zv9EngSuBlYme3HrGHcUtKs/dSS9LYEFha9fzvwo4j4Y0T8HfgiqfFJLhExt3A/LyIujYitI2L9iHhpRHw4S4xmDeOWkmbtp5akN43Ux2bBLsAfi96PABvXM6jJqNVaAVp1bilp1n5q6XvzTmBPYJGkrYFtSJc0C15GeuSgbbm/xMmrt7fXfyOzNlJLTe8s4OuSvgv8Arg2Im4pmr8ncEMjgpss3ArQzGxyqJr0IuI7wJGkB8Z/D+xbskgncH79Q5s83ArQzGxyqGlooYg4nzESW0QcXteIJqHOzk6Gh4fLlpuZWeuoWNOTtEGejeVdfqpwK0Azs8mh2uXNOyWdIOllYy0gaQ1J75T0a+Bf6xve5OBWgGZmk0PFvjclvRI4FegFbgSGSH1iriD1nLIdsCswCswDzo2I5xob8gvc96aZmZUad9+bEXFnROxHekzhUmALUgI8mNQjy33AR4GtI+I/JjLhmTWDn8c0m9xqbcjyAPDVbDJrS34e02zyyzNyullb8/OYZpOfk55Zjfw8ptnk56RnViOPymA2+TnpmdXIz2OaTX41JT1Ja2ajpPsnrbUtP49pNvlVfE5vlQWlZcB2EXFfY0OqnZ/TMzOzUuN+Tq/EtcDr6hOSmZnZxMuT9M4FvirpGElvlvS64qlRAZq1Ej+cbja55Ul6/wlsBXyNNIjsUNH0l7pHZk3hL/WxFR5OHx4eJiKefzjdx8hs8shzT2/LSvObca/P9/Tqq7THEUitE91YI+np6Sk7hFRXVxcLFixoQkRmVk6le3o1J71W5KRXX/5Sr2z27NmU+/8iiUWLFjUhIjMrp14NWciGEPqZpFskvTwr+7ikveoRqDWXexypzA+nm01+NSc9SQcCPwDuBLYG1spmTQOOr39oNtH8pV6ZH043m/zy1PSOBz4REZ8CnikqvxbYsZ5BWXP4S70yP5xuNvnVNLRQ5pXANWXKlwIz6hOONVPhy3tgYICRkRE6Ozvp6+vzl3qR3t5eHw+zSSxP0hsBXkUaOLbYW4C76xaRNZW/1M1sKstzefMc4BuS3pS9f7mkjwCnA2fXPTIzM7M6q7mmFxGnS3oJ8GtgXeD3wEpgICK+1aD4zMzM6ibP5U0i4nOSTgW2I9USb4mIpQ2JzMzMrM5qTnqSdgP+HBHLSV2PmZmZTSp57unNBx6T9D+SPitpN0nTxrNTSa+UtELSxUVle0m6TdJySb+v1u2ZmZlZXnmS3obAPqTOpf+JVZPgZ3Lu91sUdVItaVPgCuBEYGNSTfKynNs0MzOrqOakFxGjEfHriDghInqA7YEfAnsCp9a6HUn7A48Bvy0q3gdYGBGXR8QKYC7wWklzat2umZlZNXm6IdtM0n6SzpZ0K3AjqTuyU0mJr5ZtzABOBo4tmbU98NfCm4hYRnr2b/ta4zMzM6smT+vNh4AlpOf1DgOujYiVOfd3CnBeRDwgqbh8erbtYo8DG5RuQNIhwCEAs2bNyrl7MzNrZ3nu6V0KPAUcDRwHHCFpZ5Vkr7FI2hH4R+CMMrPLdWU2A3iydMGIOCciuiOie+bMmTnCNzOzdpfn4fQDIbW8BHYH9iAlwA0kXRUR762yiT1II6/fn+XJ6cA0SdsB3wY+UlhQ0vrANsDCWuMzMzOrJtd4epm7gZtICek2UvJ6Rw3rnUNKZDtm07eBnwN7Az8GdpC0r6R1gZOAGyPitnHEZw02ODhIT08Ps2fPpqenh8HBwWaHZGZWkzwPpx8HvBXoAdYBrgeuBL4G/KHa+tlD7cuLtrcUWBERS7L3+wJnAhcDfwL2r/lT2IQZHBykv7+f0dFRAIaHh+nv7wdwR9Vm1vIUEbUtKF1LejZvPvCHrIVlU3V3d8fQkDuHmUg9PT0MDw+/qLyrq4sFCxY0ISIzs1VJui4iusvNy3NPb9f6hWST1cjISK5yM7NWkqvDaUmbA/9K6nA6gFuAsyLi4QbEZi2os7OzbE2vs7OzCdGYmeWT5+H0NwF3AR8ARoEVwIHAnVln1NYG+vr66OjoWKWso6ODvr6+JkVkZla7PDW9AdKzeodFxHMAktYgtcL8KvDG+odnrabQWGVgYICRkRE6Ozvp6+tzIxYzmxTyNGQZBXaMiNtLyucAN0RER/k1G8cNWczMrFSlhix5ntN7nNTXZqmtSR1Im5mZtbQ8lze/D5wn6Xjgj6SGLD3Al0iXPc3MzFpanqR3PCDg/KL1ngbOBvKOp2dmZjbh8jyn9xRwtKTPkroTE3BX1tOKmZlZy6t6T0/SepK+JWlY0t+A7wCLI+JGJzwzM5tMamnI8gXgIFLn0N8H3ka6pGlmZjap1HJ5cx/gYxHxfQBJFwNXS5oWEc82NDozM7M6qqWm93KKRlGIiD8DzwDud8rMzCaVWpLeNNKI6cWeIWe/nWZmZs1WS+IScLGklUVl6wLnSnq+IUtEvKfewZmZmdVTLUnvojJlF9c7EDMzs0armvQi4uCJCMTMzKzR8vS9aWZmNqk56ZmZWdtw0jMzs7bhpGdmZm3DSc/MzNpG7gfMJXUCs4C1i8sj4qp6BWVmZtYINSe9LNn9J/AW0gCyyv4tmFbf0MzMzOorz+XNfweeBbYDlgNvBv4fcCvwjrpHZmZmVmd5Lm/uDvxTRNwmKYAlEXF11j3ZKcCvGxKhmZlZneSp6XUAj2Sv/w5slr2+BXhNPYMyMzNrhDxJ7zZgTvb6f4HDJG0J/CswXOe4zMzM6i7P5c2vA1tkr08GfgkcAKwEPlLnuMzMzOqu5qQXEZcUvb5e0lakmt/9EfHImCuamZm1iJovb0o6SdJ6hfcRsTwirgeWSTqpIdGZmZnVUZ57ep8HppcpXy+bV5WkiyUtlvSEpDskfTwr30pSSFpaNJ2YIzYzM7Oq8tzTK30YvWAnUmvOWpwGfCwiVkqaA8yXdAPwf9n8DSPimRwxmZmZ1axq0pP0JCnZBbAoe0avYBqwLvDtWnYWEQuL32bTNryQ9MzMzBqmlpreEaRa3vnA54DHi+Y9BdwbEdfUukNJZwEHkZ77uwH4b2DTbPZ9WVL9NXBcuQYykg4BDgGYNWtWrbs1MzNDEeWuWJZZUNod+GNEPL3aO5WmAbsBewBfBtYhtQT9X2AT4FvABhGxd6XtdHd3x9DQ0OqGY2ZmU4ik6yKiu9y8mhuyRMSVwMaS+iSdLWnTbONvkrR1noAi4tmIWAC8DPhkRCyNiKGIeCYiHibVLt8uaUae7ZqZmVWS55GFnYHbgQOBjwGFhPQ24NRx7n9N0j29UoXqp8a5XTMzsxfJ88jCAPD1iNiJ1AtLwf8Ab6q2sqTNJO0vabqkaZL2JvXo8jtJu0j6B0lrSNoE+AYwPyIer7xVMzOz2uVJejsDF5UpXwxsXsP6AXwSeBB4lJREj4mInwCzSd2aPQncTEqqB+SIzczMrKo8z+mNAhuVKZ8D/K3ayhGxhDQ8Ubl5lwKX5ojFzMwstzw1vZ8An5e0TvY+sv43vwz8qN6BmZmZ1VuepNcHbAwsIXU9tgC4C3gMOKHukZmZmdVZnlEWngB6JO0JvI6UMK+PiN80KjgzM7N6ynNPD4CI+B3wuwbEYmZm1lC5kp6kXYC9gM0ouTQaEUfVMS4zM7O6qznpSeoDTifdxxth1REXauvLzMzMrIny1PSOBo6KiDMbFYyZmVkj5Wm9OYM0IoKZ5TQ4OEhPTw+zZ8+mp6eHwcHBZodk1pbyJL1LgXc0KhCzqWpwcJD+/n6Gh4eJCIaHh+nv73fiM2uCikMLSfp00dsO4BjgV8CNwCpDDEXE1xoQX0UeWsgmg56eHoaHh19U3tXVxYIFC5oQkdnUVmlooWr39I4seb8UeGM2FQtgwpOe2WQwMjKSq9zMGqfi5c2I2LrGafZEBWw22XR2duYqb3e+/2mNVPWenqRF2XA/ZjYOfX19dHR0rFLW0dFBX19fkyJqXb7/aY1WS0OWrYBpDY7DbMrq7e1l3rx5dHV1IYmuri7mzZtHb29vs0NrOQMDA4yOjq5SNjo6ysDAQJMisqkmdzdkZpZfb2+vk1wNfP/TGq3WpLebpEcrLRARV9UhHjNrY52dnWVbuvr+p9VLrUnvCkAV5ge+BGpmq6mvr4/+/v5VLnH6/qfVU61Jb3vSOHpmZg1TuAQ8MDDAyMgInZ2d9PX1+dKw1U3Fh9MBJD0HbBERf5uYkGrnh9PNzKxUpYfT83RDZmZmNqnVkvSuBJ5qdCBmZmaNVvWeXkS8dSICMTMzazRf3jQzs7bhpGdmZm3DSc+siDs7Npva3A2ZWabQ2XHhwehCZ8eAnxMzmyJy1fQkHS5poaTlkmZnZZ+RtF9jwjObOO7s2GzqqznpSToGOAE4h1W7JBsGjqhvWGYTz50dm019eWp6hwGfiIivA88UlV9P6qbMbFLzYK9mU1+epLclcHOZ8qeBjjLlZpOKB3s1m/ryNGRZBLwOuK+k/F3ALXWLyKxJ3Nmx2dSXJ+kNAGdKWo90T283SR8Cjgc+WssGJF0M7AWsDzwEnB4R38nm7QV8C5gF/Ak4KCJKE6xZQ3mwV7OpreakFxEXSFoTmAesB3yP1IjlqIi4rMbNnAZ8LCJWSpoDzJd0A6n2eAXwceCnwCnAZcCuNX8SMzOzKnI9pxcR5wLnStoUWCPvcEMRsbD4bTZtA+wMLIyIywEkzQUekTQnIm7Lsw8zM7Ox5Hlk4QxJrwOIiEfGO76epLMkLQduAxYD/01q/fnXwjIRsQy4mzKtQiUdImlI0tCSJR7X1szMapen9eYuwJCkWyX1S9pqPDuMiMOBDYA3ky5prgSmA4+XLPp4tlzp+udERHdEdM+cOXM8IZiZWZuqOelFxBtJlyIvAT4I3C3pD5IOlbRRnp1GxLMRsQB4GfBJYCkwo2SxGcCTebZrZmZWSa5uyCLinoj4YkRsB7ye1MryRGC8XVasSUqkC4HXFgolrV9UbmZmVherM8rCWsA6wNrAs9UWlrSZpP0lTZc0TdLewAHA74AfAztI2lfSusBJwI1uxGJmZvWUt8PpV0n6gqQ7gQXAPwB9wOY1rB6kS5kPAo+Snvs7JiJ+EhFLgH2BU7N5uwD754nNzMysmpofWZA0BOxEamV5NvCfEfFQretniW33CvN/A8ypdXtmZmZ55XlO71fAhyLi1kYFY2Zm1kh5emTpb2QgZmZmjVYx6Un6BvDZiFiWvR5TRBxV18jMzMzqrFpN79WkVpqF12ZmZpNWxaQXEW8t99rMzGwyytP35knZsEKl5R2STqpvWGZmZvWX5zm9z5P6yCy1XjbPzMyspeVJeiI9YF5qJ+Dv9QnHzMyscao+siDpSV4Y+26RpOLENw1YF/h2Y8IzMzOrn1qe0zuCVMs7H/gcqw4B9BRwb0Rc04DYzMzM6qpq0ouIiwAk3QP8MSKebnhUZmZmDZCnR5YrC68lbUEaXaF4/v11jMvMzKzu8nQ4PQP4JrAfJQkvM61eQZmZmTVCntabXyUN9NoLrAA+ABxHGiro/XWPzMzMrM7yjLLwTuCAiPiDpGeB6yLiMkmLgUOBHzYkQjMzszrJU9PbELgve/04sEn2+hrgjXWMyczMrCHyJL27gdnZ61uB/SUJ2Ac/nG5mZpNAnqR3IfCa7PWXSJc0nwK+Any5vmGZmZnVX55HFs4oev07SXOAbuDOiLipEcGZmZnVU56GLKvInsvzs3lmZjZpVBs5/dO1bigivrb64ZiZmTVOtZrekTVuJwAnPTMza2nVRk7feqICMTMza7Q8rTfNzMwmtZqTnpLDJS2UtFzS7Kz8M5L2a1yIZmZm9ZGnpnc0cAJwDml8vYJh0ph7ZmZmLS1P0jsM+EREfB14pqj8emD7ukZlZmbWAHmS3pbAzWXKnwY66hOOmZlZ4+RJeouA15UpfxdwS33CMTMza5w8PbIMAGdKWo90T283SR8Cjgc+2ojgzMzM6ilP35sXSFoTmAesB3yP1IjlqIi4rEHxmZmZ1U2u5/Qi4tyI2BLYDNgiIl4eEedJenm1dSWtI+k8SfdJelLSDZLemc3bSlJIWlo0nTi+j2RmZlbeuDqcjohHACRtAZxIurxZrTHLmsADwO6kjqrfBfxA0quLltkwIp4pt7KZmdnqqlrTk7ShpEskLZE0Iumo7EH1z5Mat7yBGu7pRcSyiJgbEfdGxHMR8TPgHmDn1f4UZmZmNailpjcPeAtwEfAO4AzgbcD6wDsj4srx7FjS5sCrgIVFxfdJCuDXwHGFGmXJeocAhwDMmjVrPLs2M7M2Vcs9vX8CDo6IPuA9pJabd0fEnquR8NYCLgEuiojbgEeA15OeBdwZ2CCb/yIRcU5EdEdE98yZM8ez+7oaHBykp6eH2bNn09PTw+DgYLNDMjOzMdRS0+skew4vIhZJWgGcO94dSlqD1PLzKbLuyyJiKTCULfKwpCOAxZJmRMQT491Xow0ODtLf38/o6CgAw8PD9Pf3A9Db29vEyMzMrJxaanprkHpdKXgWWD6enUkScB6wObBvRDw9xqJRWGU8+5koAwMDzye8gtHRUQYGBpoUkZmZVVJLTU/AxZJWZu/XBc6VtErii4j31LCts4FtgX+MiOezhaRdgMeAO4GNgG8A8yPi8Rq22TQjIyO5ys3MrLlqSXoXlby/eDw7krQlcCiwEngoVfogK3uO1GBmM+AJUkOWA8azn4nU2dnJ8PBw2XIzM2s9VZNeRBxcjx1FxH1Uvlx5aT32M5H6+vpWuacH0NHRQV9fXxOjMjOzsYzr4XRLCo1VBgYGGBkZobOzk76+PjdiMTNrUYqI6ku1qO7u7hgaGqq+oJmZtQ1J10VEd7l5ufreNDMzm8yc9MzMrG046ZmZWdtw0jMzs7bhpGdmZm3DSc/MzNqGk56ZmbUNJz0zM2sbTnpmZtY2nPTMzKxtOOmZmVnbcNIzM7O24aRnZmZtw0nPzMzahpOemZm1DSc9MzNrG056ZmbWNpz0zMysbTjpmZlZ23DSMzOztuGkZ2ZmbcNJz8zM2oaTnpmZtQ0nPTMzaxtOemZm1jac9MzMrG046ZmZWdtw0jMzs7YxYUlP0jqSzpN0n6QnJd0g6Z1F8/eSdJuk5ZJ+L2nLiYrNzMzaw0TW9NYEHgB2B14CnAj8QNJWkjYFrsjKNgaGgMsmMDYzM2sDa07UjiJiGTC3qOhnku4BdgY2ARZGxOUAkuYCj0iaExG3TVSMZmY2tTXtnp6kzYFXAQuB7YG/FuZlCfLurNzMzKwuJqymV0zSWsAlwEURcZuk6cCSksUeBzYos+4hwCHZ26WSbm9osNVtCjzS5BgmOx/D+vBxXH0+hvXR7OM4ZpuQCU96ktYAvgc8BRyRFS8FZpQsOgN4snT9iDgHOKeRMeYhaSgiupsdx2TmY1gfPo6rz8ewPlr5OE7o5U1JAs4DNgf2jYins1kLgdcWLbc+sE1WbmZmVhcTfU/vbGBb4N0RMVpU/mNgB0n7SloXOAm40Y1YzMysnibyOb0tgUOBHYGHJC3NpgMjYgmwL3Aq8CiwC7D/RMW2mlrmUusk5mNYHz6Oq8/HsD5a9jgqIpodg5mZ2YRwN2RmZtY2nPTMzKxtOOlVIekISUOSVkq6sMJyB0l6tuhe5VJJe0xYoC2sWr+rZZb/lKSHJD0u6XxJ60xkvK0ozzH0uTg2SRdLWizpCUl3SPp4hWV9Ho6h1uPYiueik151I8AXgfNrWPaaiJheNM1vbGiTxpj9rpYuKGlv4DPAXsBWwGzgCxMVaAur+RhmfC6WdxqwVUTMAN4DfFHSzqUL+TysqqbjmGmpc9FJr4qIuCIiBoH/a3Ysk1VELIuIuRFxb0Q8FxE/Awr9rpb6CHBeRCyMiEeBU4CDJjDclpTzGNoYsvNqZeFtNm1TZlGfhxXkOI4tx0mvvnaS9EhW3T9RUlO6eWt1Jf2ullqlH9bs9eaSNpmI2CaLKscQfC6OSdJZkpYDtwGLgf8us5jPwypqPI7QYueik179XAXsAGxGeubwAOC4pkbUgkr7XS2zyHRSv6sFhdcv6oe1XdVwDH0uVhARh5POpzeThjRbWWYxn4dV1HgcW+5cdNKrk4hYFBH3ZJeebgJOBt7X7LhayRj9rpYq7Ye18PpF/bC2o1qOoc/F6iLi2YhYALwM+GSZRXwe1qDacWzFc9FJr3ECULODaBUV+l0ttUo/rNnrhyOi7e+p5jiGpXwujm1Nyt+L8nmYz1jHsVTTz0UnvSokrZn1BzoNmCZp3XLXpCW9M7vPgqQ5pNZ1P5nYaFvaWP2ulvou8DFJ20naCDgBuHAC4psMajqGPhfLk7SZpP0lTZc0LWuheQDwuzKL+zwcQ57j2JLnYkR4qjCRRnuPkmkuMIt0CWRWttwA8DCwDFhEqsav1ez4W2EijW0VwIrsmBWmA0uPY7b8p7Nj+QRwAbBOsz9Ds6c8x9Dn4pjHcCZwJfBYdm7dBHwim+fzsAHHsRXPRfe9aWZmbcOXN83MrG046ZmZWdtw0jMzs7bhpGdmZm3DSc/MzNqGk56ZmbUNJz0zM2sbTnq2WiTNlXRzs+NoFeWOR1b2sKSQdNBYZVNR9jlrGYuyqSQdIul+Sc9JmtvseGohqTs7f7bK3v+zpP/N+me1MfjgTFKSfirpN2PM2zb7z/C2iY6rVpJ2l/TbbMiR5ZLulnSJpBnV155Y2ejPkU3PSnpM0pCkUyVtVrL4AGmg18K6OwCfBw4DXgpcVq5sYj7JxMqOzadJgzBTdAzHmi5sUpwbAd8CvgJ0kf6Gk06kMRafJfXSY2Nw0pu8vgPsOcbI2R8D7gN+O6ER1UjSdsAvgRuBt5KGHvkkafiWdRq437VWY/XlpAT1MmAX4N9JI0bfLGnbwkIRsTRW7ZT4Fdm/gxHxUKQ+M8uV5SZp7fGsN4E+Dvw5IhZl719aNH2iTNnRxSuv5t8rjy1JHSb/LCIWR8TS8WykRf4eFwBHNTuIltbsftw8jW8i/SddDHyhpHwtUl93J2Xv3wL8idRn48PAGcDaRcsLOBa4kzQe1oPAaUXzvwTcDowC9wKnA+sWzZ8L3Ez6grs/W24Q2LRC7McAD9bwGecA/0VKhkuBa4BXZ/PWIHVe+0AW903Ae4vW3YrUV2WhI9xR4Ihs3sHALdkxuQP4FLBGhTgOApaWKZ+erX9l6fEoel2u39ZVyorWrRhXtvy/ksYuWwYMZOXvBq7L1rsHOLXkb3wvqcPk/yD1lfggcFzJZ5lB6tB6cbadW4H3F81/I6m/xeXAcLbsjCp/v5uBo8aY976Sz1727wVsAlyaxTxKGv3g4JJtzQfOAuYBjwB/I9XWio/dPqQfWaPA37PPsnn2ty39G22VrXMocBdpGKe7yPqXrPT34IX/Dx/JjvtSUiJaGzicdL7+H/C1kvjWBr6cfc5lwF+AvUv29w7SgK0rgD8AHyiON1tmVlb2imZ/R7Xq1PQAPK3GHy8lpPvK/Od+Fng56VLNMuDbpN75/xl4CPhq0fKnkTqO/SipBrIbcHjR/BOBN2VfSu8iJbZTiubPzf5jzwd2ypZdCPxXhbj3JyWqt1ZYpjP7AvsJ8AbSKOEfBHbM5n+K9AX+gWzeydnnLszfKvvPfy/pC3ZrUi3tE6Qv9kLZu7NjckSFWA6iTNLL5n0628/MouNRSHrTST8GAtgim15Uli1bNa5snb9l68/Olts7Ow4Hk4Z2eSvpR8pA0Xr3kr5oj8j+xkdm29otmy/galLCfUe27XcC/5LNf3X2Nz4WeCWppnsN8MMKx2xj4DngjWPMHyvplf69ukiDju6YxXUIKQntVbTufNIPo5Ozc2E/4BnggGz+Ftk6x2b72SE7hpsDHdkxDOD12bLTgH8Bns6O2auyY/Y0aYSLSn+PudmxuiLbz97Z+1+Qkt+2Rdvet2hblwDXkn6kzs72+xTw2mz+y0nJ7pukH4P7kRLkKkkvW/Yh4GPN/n5q1anpAXhajT9e+gIK4O1FZT8HfpG9PpX0C7U4KR5ESjjrkb6AVwCH5djnYcBdRe/nkpJNce/0PVlcrxxjG9OyL4Ag1T5/SkoeM4uWOZWU0NceYxvDZLXZorL5wMXZ662y7R9bssz9wIdKyo4BbqnwmQ9i7KT3jmw/byg6HjcXzV/ly71CWdW4sv18s2SZq4ATS8p6sy/aQofy9wKXlixzJ3BC9vptpAS17Rif8bvAeSVlO2bxbDbGOoX5W48xf6ykd2y55UvW/T7wnZK/+zUly/y6sAzwumzbW46xvW5eXGO6Gji/ZLkLgQVV/h5zSbXJlxSV/RBYwqq17/nAmdnrbbLjP6tkW4PAWdnreaTav4rmn1Aad1Z+PUU/TD2tOr1oXDibPCLiTklXkWppv5LUSfpl+f5skW1JXwbPFa22gHQp5RXAuqR7aGPe+5P0PtKX7ytISXJaNhUbjoj7i97/iexLlPTlWhr3s8DBkk4A9gR2Jf2a/5ykt0TEQlKtcUFEPFUmphmkmuDVJbMWkGqjxYaK1ptJ+sX8H5LOLlpmTcY/sGVhvRjn+nnjGip5vzPwBkn/VlS2BqkGswWp9gjp0l6xEaDQCGcnYHFE3DpGiDsDr5D0/qKyQlzbkGo7pTqyf1eMsc2xrPL5JE0DPkM6p7tI5+vapKRRrNLn+yvwG9L9119lr38YEUsqxLEtUNrqdAHpPu6Y8Wbuj4jHi94/DNxRci4/XBTf60jH85Y0TvDz1uGFMeq2Ba6NLKtlrhkj9lFeOP5Wwklv8vsOcK6kjUk1kr+T7oNB+o801pdxUOWLXtKupF/VXyBdTnyM9J++Lq3bImIY+B7wvSwB3kFKfgdVi62wiRrKlhW9LjTcOgz4Y65gx7YdL1yWG688cS0reb8G6e9zeZlli7/US0dZj6L9VjvWa5DOszPKzBseY51Hsn834oXEW4vSz9dHuix5NOm+7VJSrae01eyYny8inpX0dtKPq7eTGnqdJmn3iPhrhVjynl+VYilXVvjxuAYvXF4tXa7QyCnPj7KNWfVvb0Wc9Ca/H5Ku83+QVOP7bkQU/uPcAuwnaY2i2l4P6V7B3aT/dCuBvShTIyPdnxuOiFMKBZK2LLNcl6SXR8QD2fs3kP4jj1VzeJGIeFTSYlJtEtIlmg9KWru0thcRT0gayT5L8WjNPdlnHmsfD0saBraJiO/WGttYJE0nJaorq9QaKlrNuK4H5kTEXePdf7aNl0radoza3vXA9jn3cTfpXuN2VPib1KAH+GlEfA9AqSr0KtIPsJplNaRrgGsknUy67/x+Ui2wnFuzfRfX9iqeX6vhBlJS2yIifj/GMrcA+0pSUW1v19KFJK1Lqn1f34A4pwQnvUkuIkYl/SfpXsJGwHlFs88iXZo8S9LXSTfIv0S6l7AcICs/TdJK0v2hTYCdI+JsUs2rS9KBpC+MvUmt60qNAhdJ+jTpssq3gZ9HRLlEiqRDSfd8fkz6clwX+DCpwcTpRbEfBvxA0qnAo6RfwrdGxP+Snqk6WdKdpJaLHwTeTLoUV8lc4JuSHgP+m9Ta9XVAV0ScVmE9Sdoie/2SLJZ/y16XXvIaj/HGdTLwM0n3AT8gNeDYgXSP8fga9/1b0iXpH0n6FOnv/gpg/YgYJLUqvFbSt0ktQJ8kNaZ4d0QcWm6DEfFc9hxpD+mH2XjdAbxfUg+p9ngkqcHIDbVuILti8Y/A/5AuK+5EupxcKYF9Bbhc0nXAr0j3bg8kNRSrq4i4Q9IlwIWSjiUlrI2BPYBFEXEF6f/UscC/SzqL9H/lsDKb25X0Q7b00r8VNPumoqfVn3jhRv3VZeYVHllYyQuPLKxTNH8N0j2TRaQa4APAqUXzTyNdKim0SPskqzZAmEtqon1Itu4oqcXlzArx7gRcREp4o6SWhdfy4oYc25MSwFLSF+0fgR2K4i48svAU6dJXb9G6W2XHpLvM/g8gfbGsICXTBcD+FeI9iBeasz9Hail4PdlltpJl5zKOhiy1xJXt/31l1ns7qQn7clLtaohVW33eC/SVrDOfrCFF9n5D4Nzsb72C7CpB0fxu0rOVT5Au6d0EnFzlvNybdGlzWpl5YzVk6S5ZbqPsvHuSdO/wdNIPovljfZas7ELSc3eQ7of9gnT+ryQ17jq+5LOVaxByWLbs04z9yML7SspW+ftnZWcWx5uVfZ+i1q+kHzlzeeH/4UOk2xQ7Fy3zT6SWuStISe3A0rhJP0q+Xe/vmKk0FVp3mZnVnaRrSC0Qv9fsWKa6rEHUbaQfDvc0O55W5R5ZzKyRDsXfMxNla9Iztk54FbimZ2ZmbcO/wMzMrG046ZmZWdtw0jMzs7bhpGdmZm3DSc/MzNqGk56ZmbWN/w8WuRYoHdpGUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All subjects' ROI value (theta level) versus vocab differences, with 95% confidence intervals #\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "mdf_try = mdf2_lha  # 2022.01.27 for paper: mdf2_lha, mdf3_rhi; not in paper: mdf4b_rhi\n",
    "mdf_figname = 'roi_vocabdiff_test-attend.jpg'\n",
    "\n",
    "y1 = df_model.loc[df_model['month']==18, 'score'].values\n",
    "y2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "yfits = []  # unlike above, no confidence interval!\n",
    "for subject in df_model['subject'].unique():\n",
    "    roival = get_roival2(subject, mdf_try, df_roilin)\n",
    "    yfits.append(roival)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot([1.35, 3.65], [0, 0], color='grey', linestyle=':')  # 0 because ROI is z-score normed\n",
    "ax.scatter(y2 - y1, yfits, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(1.30, 3.70), ylim=(20, 60), \n",
    "         xlabel='Vocab Score Difference (Transformed)',\n",
    "         ylabel='Relative Theta Power (Standardized)');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, mdf_figname)\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1dadc18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Like above, but use only the 30-month vocabulary score instead of the difference #\n",
    "mdf_try = mdf3_rhi  # 2022.01.27 for paper: mdf2_lha, mdf3_rhi; not in paper: mdf4b_rhi\n",
    "mdf_figname = 'roi_vocab30_test-attend.jpg'\n",
    "\n",
    "y2 = df_model.loc[df_model['month']==30, 'score'].values\n",
    "\n",
    "yfits = []  # unlike above, no confidence interval!\n",
    "for subject in df_model['subject'].unique():\n",
    "    roival = get_roival2(subject, mdf_try, df_roilin)\n",
    "    yfits.append(roival)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=[7, 6]);\n",
    "ax.plot([0.05, 2.45], [0, 0], color='grey', linestyle=':')  # 0 because ROI is z-score normed\n",
    "ax.scatter(y2, yfits, marker='o', color='#202020', zorder=3);\n",
    "\n",
    "plt.setp(ax, xlim=(0.00, 2.50), ylim=(20, 60), \n",
    "         xlabel='30-month Vocab Score (Transformed)',\n",
    "         ylabel='Relative Theta Power (Standardized)');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, mdf_figname)\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403f6a64",
   "metadata": {},
   "source": [
    "##  \\*\\*TEST\\*\\* Plot scores versus RTP\n",
    "Note all the regions show relatively high activity for ignore vs attend. These plots aren't being used (they show similar info as the \"roi\" ones above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "34964b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a plotting function #\n",
    "def plot_growth(col, ax=None, color = '#353535', subject=None):\n",
    "    '''Uses global dataframe 'df'.'''\n",
    "    if subject:\n",
    "        subject_list = [subject]\n",
    "    else:\n",
    "        subject_list = df.index\n",
    "    for subject in subject_list:\n",
    "        x = df.loc[subject, col]\n",
    "        y1, y2 = df.loc[subject, low], df.loc[subject, high]\n",
    "        ax.plot([x, x], [y1, y2], color=color)\n",
    "        ax.plot(x, y1, marker='o', color=color, fillstyle='full', markerfacecolor='white')\n",
    "        ax.plot(x, y2, marker='o', color=color, fillstyle='full')\n",
    "    \n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Also define a labeling function #\n",
    "def add_text(ax_array):\n",
    "    ax_array[0, 0].text(0.02, 0.98, 'Attend, Left', transform = ax_array[0, 0].transAxes,\n",
    "                 horizontalalignment='left', verticalalignment='center')\n",
    "    ax_array[0, 1].text(0.02, 0.98, 'Attend, Right', transform = ax_array[0, 1].transAxes,\n",
    "                 horizontalalignment='left', verticalalignment='center')\n",
    "    ax_array[1, 0].text(0.02, 0.98, 'Ignore, Left', transform = ax_array[1, 0].transAxes,\n",
    "             horizontalalignment='left', verticalalignment='center')\n",
    "    ax_array[1, 1].text(0.02, 0.98, 'Ignore, Right', transform = ax_array[1, 1].transAxes,\n",
    "         horizontalalignment='left', verticalalignment='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "d6fa4694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Brocas: Plot 18- and 30-month scores (connected vertically) in order of ROI value #\n",
    "# Pars Orbitalis by itself shows stronger attend-lh correlation.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(2, 2, figsize = (14, 8))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_growth('attend_ParsAvg-lh', axset[0, 0])\n",
    "plot_growth('attend_ParsAvg-rh', axset[0, 1])\n",
    "plot_growth('ignore_ParsAvg-lh', axset[1, 0])\n",
    "plot_growth('ignore_ParsAvg-rh', axset[1, 1])\n",
    "\n",
    "plt.setp(axset, xlim=(-2.5, 2.2), ylim=(-1.8, 2.85));\n",
    "plt.setp(axset[:, 0], ylabel='Vocab Score (transformed)');\n",
    "plt.setp(axset[1, :], xlabel='ROI Amplitude (normalized)');\n",
    "add_text(axset);\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'growth_ParsAvg.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "f0421094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inferior Parietal: Similar to above, this time highlighting exemplary subjects #\n",
    "# Quite the correlation for attend-rh, but activity relatively high here for all subjects!\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(2, 2, figsize = (14, 8))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_growth('attend_InfPar-lh', axset[0, 0])\n",
    "plot_growth('attend_InfPar-rh', axset[0, 1])\n",
    "plot_growth('ignore_InfPar-lh', axset[1, 0])\n",
    "plot_growth('ignore_InfPar-rh', axset[1, 1])\n",
    "\n",
    "# plot_growth('ignore_InfPar-rh', axset[0, 0], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('ignore_InfPar-rh', axset[0, 1], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 0], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 1], subject='f2f_116', color='#00008B')\n",
    "\n",
    "# plot_growth('ignore_InfPar-rh', axset[0, 0], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('ignore_InfPar-rh', axset[0, 1], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 0], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 1], subject='f2f_030', color='#A52A2A')\n",
    "\n",
    "plt.setp(axset, xlim=(-3.6, 1.7), ylim=(-1.8, 2.85));\n",
    "plt.setp(axset[:, 0], ylabel='Vocab Score (transformed)');\n",
    "plt.setp(axset[1, :], xlabel='ROI Amplitude (normalized)');\n",
    "add_text(axset);\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'growth_InfPar.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "8fef3e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAHqCAYAAAAgZV6kAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABxnklEQVR4nO3dfZyUdb3/8dcHdmURlABxNzBuyntFERE0NTRTttT1WMe8y6NkCmyeUyaZrYgmuvVTNO3kgrdoN2blDZF2hlNH0/RkQopH84ZIQQMWFQhFWF3g8/tjZrbZ2dnduXavmWtu3s/H43qw871mrusz1wzXZ77X9b0xd0dERERERKSc9Yk6ABERERERkaipYiQiIiIiImVPFSMRERERESl7qhiJiIiIiEjZU8VIRERERETKnipGIiIiIiJS9lQxKkBm9qKZXZXnfe5sZveb2SYzczMbnc/9i4hINHKVc8xsdCKfTAjwmvPMbHPYsYiIZKPsKkZmdoiZbTezpzKsy3hCNrOVZjYzPxH2TAgxfhn4FHAU8FGgb9CEJiIi7ZV4zvHEstXMXjGzb5qZpTztTeL5ZFnI+77KzF4Mc5siIlCGFSPgAqAJONDM9os6mAKyJ/Cyu7/g7s3A9qgDEhEpAaWcc64mXvHZD5gLNAIXJle6+3Z3b3b3bRHFJyISjLuXxDJlyhTvzpYtW3zQoEH+/PPP+5e//GW/5JJL2tY99thjDrRbrrzySp88eXKH8qSnnnrKP/WpT3n//v19+PDhPn36dN+0aVPb+smTJ/uMGTP829/+tg8dOtSHDRvml1xyiW/fvr3tOevWrfO6ujqvqqrykSNH+p133ukHHHCAX3nlld2+n1SjRo3y66+/vtP1ixYt8vHjx3u/fv189OjR3tDQ4B988EFbnKnvL9N7njx5cqB4REQCiDyHBF2UczrmnPHjx/vnP//5tsevv/66A75kyZK2socfftj33ntv79evnx999NH+s5/9zAF//fXX3d19wYIFPmDAAP/d737nBxxwgO+8885+zDHH+Guvvda2Pv34LFiwIFDsIlL2Oj23R55cwloOPfTQbo/Cj370Iz/ooIPcPZ6Uhg0b5h9++KG7u3/wwQd+0003+c477+xr1671tWvX+nvvvefr16/3PfbYw2fPnt1W7u7+f//3fz5gwACfO3euL1++3J9++mk//PDD/Qtf+ELb/iZPnuy77rqrX3HFFf7qq6/6z3/+c+/bt6/fe++9bc/57Gc/6/vvv78/+eST/uyzz/rkyZN9wIABoVaMYrGY77LLLn7XXXf5ihUr/NFHH/W99967LUmvX7/ep06d6kcccYSvXbvW169f788884wDHovF2spERHIk8hwSdFHO+WfO2bFjhz/22GPev39/P/3009uek14xWrVqle+0005+8cUX+yuvvOK//OUv/WMf+1iHilFFRYUfd9xx/qc//cmff/55HzdunJ9wwgnuHq9sXnLJJb7PPvu0HZ8tW7YEil1Eyp4qRu7un/rUp9qdyEeNGuX3339/2/rklap0mSod55xzjn/5y19uV/bcc8854OvWrXP3eJI6/PDD2z3nM5/5jJ9//vnu7v7qq6864E8++WTb+pUrV3qfPn1CrRgdffTRfvXVV7cre+ihh3zAgAG+Y8cOd3f/6le/2u6uUKYrfSIiORJ5Dgm6KOeM8p122skHDBjglZWVDnhVVZU/9dRTbc9JzyOXXXaZ77vvvm15x9392muv7VAxAvyVV15pe85PfvITr6ysbLvzdeWVV/oBBxwQKF4RkRSdntvLpo/RihUreOqppzjrrLMAMDPOPvts7rjjjh5t789//jM/+clPGDhwYNty5JFHAvC3v/2t7XkHHXRQu9cNHz6ct956C4CXX36ZPn36MHHixLb1o0aNYvjw4T2KqatYr7322naxnnXWWbz//vs0NzeHui8RESmPnPONb3yDZcuW8fjjj3Psscdy5ZVX8slPfrLT57/yyiscdthhpI7PMGnSpA7P69evH/vss0+799Da2so//vGPHsUpIpKtiqgDyJc77riD7du3M3LkyLYydwfgzTff5GMf+1ig7e3YsYOvfOUrXHzxxR3WjRgxou3vysrKduvMjB07drTbf67t2LGDK6+8ktNOO63DumHDhuUlBhGRclIOOWfo0KHsueee7LnnnjzwwAPstddeTJo0iWOPPTbj892d9oPWZVZR0f6nSfI1yfchIpIrZVEx2rZtG/fccw/f/e53Oemkk9qtO+ecc1iwYAGzZ89mp512Yvv2joOxZSofP348f/nLX9hzzz17HNd+++3Hjh07WLJkSdtVtjfeeIM1a9b0eJuZjB8/nldeeSVQrDvttBNAxuMhIiKdK8ecM3jwYC666CIuvvhinnvuuYwVoP32249f/epX7cqeeeaZwPvq7LiJiPRWWTSle+SRR3jnnXe44IILOPDAA9stZ5xxBnfddRc7duxg9OjRtLS08Nvf/pZ33nmHLVu2ADB69Gj+8Ic/sHr1at555x0AvvWtb/HMM88wffp0nnvuOVasWMHDDz/MtGnTso5rn332oba2lmnTpvHHP/6RZcuWcd5559G/f/8evc81a9awbNmydss777zD7Nmzuffee5k9ezYvvvgir7zyCvfffz+XXnppp9vafffd6d+/P4sXL2bdunVs2rSpRzGJiJSbcsk56b761a/y6quv8stf/jLj+unTp/O3v/2NmTNn8uqrr/Lggw9y6623AmR1Jylp9OjRrFq1imeffZZ33nmHDz74IJT4RUTKomJ05513cuyxxzJ06NAO60477TRWrVrF7373Oz75yU8yffp0zjzzTIYNG8Z1110HwNVXX82bb77JJz7xibamZwcddBBPPPEEK1euZPLkyRx88MF8+9vfprq6OlBsd999N2PGjOHTn/40J598MmeddRajR49u95zzzjuvQ1km3//+9znkkEPaLffddx9TpkzhkUce4bHHHmPixIlMnDiR733ve+2aeKSrqKjgBz/4AXfccQfDhw/nlFNOCfS+RETKVbnknHTDhg3jnHPO4aqrrsrY7G3UqFE88MADLFq0iIMPPpjvf//7XHnllQBUVVVlvZ8vfOELfO5zn+O4445j2LBh/OxnPwscq4hIJpavfi65NmHCBF+6dGnUYeTE5MmT2XfffduurImIlJjsbxcUCOWccNx8883Mnj2bjRs30qdPWVyrFZHodZpzAvUxMrN+wHCgP/C2u7/dy8CkG5s2bWprciAiIpJLuc45t9xyC4cddhjDhg3j6aefZs6cOZx33nmqFIlIQei2YmRmuwBfAs4EJgKVxGtabmZrgBhwm7svyWWg5WrQoEEaUltERPIi1zlnxYoVNDY2sn79evbYYw+mT5/O7Nmzc7Y/EZEgumxKZ2YXA7OA14BFwDPAGmArMAQ4EDgaOBV4Gvh3d/9rjmPOqJSbNYiIlDg1pRMRkXzpcVO6TwKT3f3FTtY/A9xlZtOB84HJQCQVIxERERERkZ7qsmLk7h1nBM38vA+AplAiEhERERERyTP1dhQRERERkbLX5R0jM3sMyGo8b3f/dCgRiYiIiIiI5Fl3fYxS+xb1Bc4GmoE/JcomAh8FfhJ+aCIiIiIiIvnRXR+jf0/+bWbfB+4BvuYpQ9mZ2U0U4YhCIiIiIiIiSUH6GP0b8EPvOL53E3BOeCGJiIiIiIjkV5CKkQFjM5RnKhMRERERESka3fUxSnUXcIeZ7UV8MleAw4FLgQVhByYiIiIiIpIvQSpGlwJvAV8DGhNla4HvATeEHJeIiIiIiEjeZN2Uzt13uPt17j4C+AjwEXcfkSjbnrMIRaRHYrEYdXV1TJo0ibq6OmKxWNQhiYiIiEQmFosxceLElZ2tD3LHCAAzmwB8Ang48XgA8IG7b+tpkCISrlgsRmNjIy0tLQA0NzfT2Bi/0VtbWxtlaCIiIiJ5l/xtBIzq7DlZ3zEys2oz+xPwDHAvUJ1YdSNqSidSUJqamtoqRUktLS00NTVFFJGIiIhIdDL9NkoXZFS67xOf3HUosCWl/JfACYGjE5GcWbduXaByERERkVKWzW+gIBWj44DL3X1jWvnfgJEBtiMiOVZdXR2oXERERKSUZfMbKEjFqD/wYYbyYUDX96VEJK/q6+upqqpqV1ZVVUV9fX1EEYmIiIhEJ9Nvo3RBKkZPAOelPHYz6wt8C/ifwNGJSM7U1tbS0NBAZWUlADU1NTQ0NGjgBRERESlLyd9GwKrOnmPuntXGzGx/4HFgGTCZ+Kh0BwCDgCPd/W+9jLdXJkyY4EuXLo0yBJGCM336dADmz58fcSQiXbKoAwhKOUdEpGh1mnOCzGP0EjAW+F/gv4Eq4gMvHBJ1pUhERERERKQ3As1j5O7NwJU5ikVERERERCQSgSpGZrYTcCCwO2l3m9z9NyHGJSIiIiIikjdZV4zM7Hjgx8QrRekc6BtGQGbWD2gCPgMMAVYADe7+X2FsX0REJEk5R0REkoKMSncL8QEXxgA7Ex++O7nsHGJMFcCbxAd4GARcAfzCzEaHuA8RERFQzhERkYQgTek+CjS6e6dD3IXB3d8HrkopetjMXgcOBVbmct8iIlJelHNERCQpyB2jh4FP5iqQzphZNbA38Jd871tERMqLco6ISPkKcsdoOvBTMzsUeBFoTV3p7j8KMzAAM6sEfgrc4+6vZFh/IXAhwMiRI8PevYiIlBHlHBGR8hakYjQFOA74HLCF+IALSQ6EWjEysz7EB3v4ELgo03Pc/TbgNohPthfm/kVEpHwo54iISJCmdHOBHwK7uPtAd98lZdk1zKDMzIA7gWrgC+7e2s1LREREekQ5R0REINgdo48A8xMdVXNtHrAf8Bl335qH/YmISPlSzhERkUB3jB4gPs9DTpnZKGAaMA5oNrPNieXsXO9bRETKi3KOiIgkBblj9BpwrZl9Cvg/Og6+cGMYASWGA7cwtiUiItIV5RwREUkKUjH6MvAe8SG704ftdiCUipGIiIiIiEi+ZVUxSozWcxKwyt035zYkERERERGR/Mq2j5EDzwE1OYxFREREREQkEllVjNzdgVeBYbkNR0REREREJP+CjEp3KXC9mY1LzPkgIiIiIiJSEoJUjH4BTAL+DLSY2bupS27CExEpbrFYjLq6OiZNmkRdXR2xWCzqkERERDpVznkryKh0F+UsChGREhSLxWhsbKSlpQWA5uZmGhsbAaitrY0yNBERkQ7KPW9lXTFy93tyGYiISKlpampqSy5JLS0tNDU1lUWCERGR4lLueSvIHSPMrB9wNrA/8ZHq/gL8zN0/yEFsIiJFbd26dYHKRUREolTueSvrPkZmtj/wV+ITuU4CDgduApab2X45iU5EJI/CblddXV0dqFxERASi6+dT7nkryOALNxOfy2ikux/t7kcDI4HniVeQRESKVrJddXNzM+7e1q66N8movr6eqqqqdmVVVVXU19f3NlwRESlRuchH2Sr3vBWkYnQk0ODubSPQJf6+HDgq7MBERPKpq3bVPVVbW0tDQwOVlZUA1NTU0NDQUBbttEVEpGdykY+yVe55K0gfoxbgIxnKByXWiYgUrVy1q66trWXhwoUAzJ8/v1fbEhGR0hd1P59yzltB7hj9GrjdzI40s76J5SjgVmBRbsITEcmPcm9XLSIihUH5KDpBKkZfIz74wh+I3yFqAR4HlgNfDz0yEZE8Kvd21SIiUhiUj6LTZVM6MxsJvOlx/wBOMbM9gf0AA15y9xW5D1NEJLeS7afnzJlDa2srNTU11NfXl027ahERKQzKR9Hpro/R68BHgbfM7FHg84mKkCpDIlJyyrldtYiIFA7lo2h015TuPWC3xN/HAJU5jUZERERERCQC3d0x+h3wqJm9nHj8kJl9mOmJ7v7pUCMTERERERHJk+4qRucAXwb2BCYDrwJbch2UiIiIiIhIPnVZMXL3rcAtAGY2DrgkMQiDiEikYrEYTU1NrFu3jurqanVMFRGRoqEcVpiyHq7b3Y9NrxSZ2Z5mVtXJS0REciIWi9HY2EhzczPuTnNzM42NjcRisVC2v2HDBurq6pg0aRJ1dXWhbVdERKQ3OSwWiyk/5VDWFSMzazSzcxN/m5n9lvgcRmvN7PBcBSgikq6pqYmWlpZ2ZS0tLTQ1NfV62xs2bGDVqlU5q3SJiEh562kOy/VFQQk2wevZxPsYAXwWGAccDvwI+G64YYmIdG7dunWByoNYvXo17t6uLKxKl4iISE9zWC4vCkpckIpRNfD3xN+fA37h7s8A/wkcEnZgIiKdqa6uDlQeRGtra8byMCpdIiIiPc1hubwoKHFBKkbrgVGJv08AHk38XQFYmEGJiHSlvr6eqqr23Rurqqqor6/v9bYrKzNP1xZGpUtERKSnOSyXFwUlLkjF6AHg3kTfoiFAskHjOGBFyHGJiHSqtraWhoaGtkpMTU0NDQ0NoYzoM2LECMzaX+sJq9IlIiLS0xyWy4uCEtfdPEapvgGsAkYCl7r7+4nyjwLzwg5MRKQrtbW1LFy4EID58+eHtt0hQ4YA8b5Gra2t1NTUaBhVEREJVU9yWDIPzZkzR/kpR7KuGLn7NuCGDOXfDzUiEZGIDRkypK2CFGalS0REpDdydVFQ4oLcMcLMdibedG532jfDc3d/KMS4RERERERE8ibripGZfQb4GTA0w2oH+oYVlIiIiIiISD4FGXzhZuARYA9375O2qFIkIiIiIiJFK0hTutFAnbuvyVEsIiIiIiIikQhyx+gpYJ9cBSIiIiIiIhKVIBWj+cBcM/uKmU0ys/GpS5hBmdlFZrbUzD4ws7vD3LaIiEiS8o2IiCQFaUp3f+Lf2zKsC3vwhTXANcAUoH+I2xUREUmlfCMiIkCwitGYnEWRxt0fBDCzCcAe+dqviIiUF+UbERFJCjLB66pcBtITZnYhcCHAyJEjI45GRERKmXKOiEhpCzrBawUwERgJ7JS6zt1/FGJcWXH320g07ZswYYLne/8iIlI+lHNEREpb1oMvmNm+wMvAE8BPgTuAu4HbgR/mIjgRKUyxWIy6ujomTZpEXV0dsVgs6pAkQvo+iIgUj+7O2bFYjBdeeIFnn3227M7pQe4Y3QT8GRgHNCf+HQTMA2aFHJeIdCIWi9HU1MS6deuorq6mvr6e2travO6/sbGRlpYWAJqbm2lsbATIaxxSGPR9EBHJvdTcX1FRwfDhw3u8na7O2cn1ra2tGdeXuiDDdR8GXOPu7wM7gAp3fxa4FLghzKDMrMLMqoiPdNfXzKoSzfhEylryhNXc3Iy7t52w8nk1p6mpqe2EmtTS0kJTU1PeYpDCUezfB+UbESl06bm/tbWVN954o0e5v7tzdrGf03srSMXIgC2Jv98GRiT+/juwZ5hBEb8DtRW4DPhS4m/dlZKyVwgnrHXr1gUql9JWAt8H5RsRKWiZcr+79yj3d3fOLoFzeq8EqRi9CByc+PsZ4FtmNhn4DrAizKDc/Sp3t7TlqjD3IVKMCuGEVV1dHahcSluxfx+Ub0Sk0IWZ+7s7Zxf7Ob23glSMriV+1wjiV9M+BjwGnAD8R8hxiUgGhXDCqq+vp6qqql1ZVVUV9fX1eYtBCoe+DyIiuRVm7u/unF3u5/QgFaM/AL8CcPfX3H1/YDeg2t1/n4PYRCRNIZywamtraWhooLKyEoCamhoaGhrKolOmdKTvg4hIbmXK/WbWo9zf3Tk7ud7MMq4vdVl1MDWzvsAm4k3pXkqWu/uGHMUlIhkkT0xz5syhtbWVmpqavI9Kl4xj4cKFAMyfPz+v+5bCo++DiEjupOf+yspKhg8f3uPc3905u7a2luuuuw6ARYsW9SzoIpVVxcjdt5vZKtImdRWR/NOPUBERkfKSmvsld4I0pZsDfM/MdstVMCIiIiIiIlEIMlfDTGAMsNrM/g68n7rS3Q8KMzAREREREZF8CVIxegDwXAUiIiIiIiISlawrRprXQUSk+MViMZqamli3bh3V1dWRDN4hIlJodG4UCNDHyMxeM7OhGco/YmavhRuWiEjvxGIx6urqmDRpEnV1dcRiscj2vWFDYQzgGYvFaGxspLm5GXenubmZxsbGvB4bEZFCU4znxg0bNuQlx0WZS6MQZPCF0UDfDOX9gD1CiUZEJARRJrlM+161alVBVI6amppoaWlpV9bS0kJTU1NEEYmIRK/Yzo0bNmxg1apVOc9xra2tRVdh7K1uK0Zm9nkz+3zi4YnJx4nlNOA7wOs5jVJEJIAok1ymfbs7q1evzvm+u7Nu3bpA5SIi5aDYzo2rV6/GvX23/1zkuA8//LCoKoxhyKaP0f2Jfx24M21dK7ASuCTEmEREeiXKJNfZPlpbW3O+7+5UV1fT3NycsVxEpFwV27mxs3wSdo5Lr3zlaj+FpNs7Ru7ex937AG8AuycfJ5Z+7r6Puz+c+1BFRLLTWTLLR5LrbB+VlZU533d36uvrqaqqaldWVVVFfX19RBGJiESv2M6NneWTsHOcmeVlP4Uk6z5G7j7G3d/JZTAipaDcOioWoiiTXKZ9mxkjRozI+b67U1tbS0NDQ1tSrampoaGhQSMviUhZy/bcWCj5fcSIER0qLbnIcTvttFNRVRjD0GVTOjP7EvBT7+xeWvvnjgY+5u5/CCk2kaKT7HifbJOb7KgI6MdnHiWP9Zw5c2htbaWmpiZvQ69m2ndVVRVDhgzJ+b6zUVtby8KFCwGYP39+tMGIiBSI7s6NhZTfk/lk9erVOc1xlZWVXHrppZHk0qh0d8foy8CrZtZgZgdaWvXUzIaYWZ2Z/QJ4BhiUq0BFikGxjWxTympraxk7dizjx49n0aJFeT2Rp++7UCpFIiLSM4WW34cMGZKXHBdlLo1ClxUjd/80cDEwGXge2Gxmr5vZy2bWDLwN3AqsAA5QXyMpd8U2so2IiIh0T/m9PHQ7Kp27PwI8Yma7AUcBo4D+wDvAc8Bz7r4jp1GKFIliG9lGREREuqf8Xh6CDL7wjrsvdPeb3f177n6Hu/9ZlSKRfyq2kW1ERESke8rv5SGbeYxEJEtRdvoXERGR3FB+Lw+qGImETKN+iYiIlB7l99KXdVM6ERERERGRUqWKkYiIiIiIlD1VjEREREREpOwFqhiZWb2Z/cXMtpjZxxNll5nZF3MTnkjpisVi1NXVMWnSJOrq6ojFYlGHJCIiUnSUTyUsWVeMzOzrwCzgNsBSVq0GLgo3LJHSFovFaGxspLm5GXenubmZxsbGkjyZK2GJiEiulFM+7Y7ybe8FuWM0HbjA3W8GtqWUPwscEGpUIiWuqamJlpaWdmUtLS00NTVFFFFuKGGJiEgulUs+7Y7ybTiCVIxGAS9mKG8F+ocTjkh5WLduXaDyYqWEJSIiuVQu+bQ7yrfhCFIxeg0Yn6H8c8BL4YQjUh6qq6sDlRcrJSwREcmlcsmn3VG+DUeQitFc4IdmdjbxPkZHmNmVwLXA9bkITqRU1dfXU1VV1a6sqqqK+vr6iCLKDSUsERHJpXLJp91Rvg1H1hUjd18AXAU0AjsDPwa+AvyHu/88J9GJlKja2loaGhqorKwEoKamhoaGBmprayOOLFxKWCIikkvlkk+7o3wbjopsnmRmFcCFwEJ3v93MdgP6uPtbOY1OpITV1taycOFCAObPnx9tMDmSTExz5syhtbWVmpoa6uvryy5hiYhI7pRDPu2O8m04srpj5O7biDeXq0w8fkeVIhHJRm1tLWPHjmX8+PEsWrSoZE/S6cOkrl+/PuqQRESkjKTnW0DDdwcUpI/R08ChuQoklZkNMbOHzOx9M1tlZmflY78iIj2RaZjUN954Q5WjIqGcIyKlRsN390yQitHtwFwz+7qZHW1m41OXkOO6BfgQqAbOBuaZmeZKkoKjydQKQ9SfQ6ZhUt2dNWvW5DUO6THlHJECFvU5Ptdy8f40fHfPZNXHKOHexL83ZljnQN/ehwNmNgD4AnCgu28GnjSzRcA5wGVh7EMkDMmrMckTT/JqjOTXhg0bIv8cOhsOtbW1Na9xSHDKOSKFratcWwpNs3OVwzR8d88EuWM0povl4yHGtDew3d2Xp5Q9D+jqnRQUXY0pDKtXr478c+hsONTkKElS0JRzRApYqefaXOUwDd/dM0GG617V1RJiTAOBTWllm4Bd0p9oZhea2VIzW/r222+HGIJI93Q1pjB0dlcmn59DpmFSzYzhw4fnLQbpMeUckQJW6rk2VzlMw3f3TJA7RpjZQWb2o0RiWGJm95jZ2JBj2gzsmla2K/Be+hPd/TZ3n+DuE4YNGxZyGCJd09WYwtDZXZl8fg6Z5tEYOXIkQ4cOzVsM0mPKOSIFrNRzba5ymOZ36pmsK0ZmVgc8C3wM+C8gBowEnjWzk0OMaTlQYWZ7pZQdDPwlxH2I9JquxhSGESNGFMTnkD5MqipFRUM5R6SAlXquzWUOK5fpMsIU5I7RNcC17n6su1+RWI4FvptYFwp3fx94ELjazAaY2ZHAKcCPw9qHSBh0NaYwDBkyRJ+D9JhyjkhhK/VcqxxWWIKMSrc3mRPFj4FLwwmnTT1wF/AWsB6Y4e66eicFR7NtFwZ9DtJLyjkiBazUz/Gl/v6KSZCK0VvEJ3hdkVZ+KBBqDzh33wD8S5jbFMm1WCxGU1MT69ato6KiQh3vRYqEco5I8UnNudXV1fTr109NmKXXglSMbgduNbM9gf8lPnfRUcBM4PocxCZSNNavX99uHoLW1lbeeOMNYrGYboeLiIiEKNPcRmYWcVRSCoL2MfoOMAP4H+BRYDpwJaBZLaWsrVmzpsM8BO5eMvMsSGEo9dnfRUSykWluI3dnzZo1EUVUHFJzyAsvvMCGDRuiDqngBJnHyN39++6+BzAIGOTue7j7ze7uuQtRpPAVwlw6UtqSV0ibm5tx97bZ0VU5EpFy01lu7SwXS8cc0trayqpVq5RD0gQZrvsAMzsIwN3fc/f3EuUHmdn+uQpQpBgUwlw6UtpKffZ3EZFsdZZbO8vF0vldNuWQ9oI0pbsNODBD+f6JdSJla/jw4R3mITCzkplnQaJX6rO/i4hkK9PcRmamQY+6oBySnSAVo4OAZzKULwHGhhOOSHEaOnRou3kIKisrGTlypAZekNCU+uzvIiLZyjS30ciRIzUqXReUQ7ITpGK0nXjfonSDAQ0FImUvdYbpsWPH6gQtoSr12d9FRIJIzbmLFi1Szu1GZ3fZlEPaC1Ixehy43Mz6JgvMrAK4HHgi7MBEROSfSn32dxERyZ30HFJZWcmoUaOUQ9IEmcfoUuBJYIWZPZkoOwoYCHwq7MBERKQ9zY4uIiI9lZpDJLMgw3W/Sryf0b3AEGAo8FPgYHd/OTfhiYiIiEiUNIealIsgd4xw97XEm86JiIiISIlLzn+THOo5OYcaoGZYUnK6vWNkZgPNbGha2X5mdpeZ/cLMzsxdeCIiwaTP7L1+/frA29iwYQOvvfYazz33HGeccQaLFy/OQaQiIoVPc6jFJXPLs88+ywsvvBDZXbPU/PTaa6+xcePGSOIoVdk0pZsHfCf5wMx2A/4AnATsA/zEzM7KTXgiItlbv359h5m933jjjUAJbMOGDaxZs4bGxkaeeuopZs6cybx581Q5EpGypPlv/nnXrLm5GYDW1lYaGxvzXjlKz0+NjY289957yk8hyqZidASwMOXxOcCHwF7ufjAwF7go/NBERIJZs2ZNr2f2bm5uZtSoUUyYMIGKigomTJjArFmzWLBgQdjhiogUPM1/Uzh3zTLlp6uvvlr5KUTZVIw+CqxIeXws8IC7b0o8vgfYK+zARESCam1tzVge5MpmS0sLAwcObFc2btw4Vq5c2ZvQRESKkuZQK5y7ZspPuZdNxWgLMCDl8UTg6ZTHLcDOYQYlItITyfkZ0gW5sllVVcXmzZvblS1btozRo0f3KKbFixerv5KIFC3NoVY4d83Czk+dWbx4Me7O+++/zxlnnFFW/ZiyqRg9D0wFMLNjgGHAoynrPwGsCTswEZGghg8f3uuZvWtqali1ahVLly5l27ZtLF26lGuuuYapU6cGjmfjxo3MmzdP/ZVEpKjV1tYyduxYxo8fz6JFi8qqUgSFc9csU36aPXt2j/JTZxYvXsy8efO4/vrr2/LWe++9VzaVo2yG654D/JeZfZF4pejuxLDdSacSn/hVRCRSQ4cO5fzzz2fOnDm0trZSWVnJ8OHDAyXxIUOGADB37lxWrlzJ6NGjmTFjBlOmTAkcz8aNG2lsbGTChAkAbf2V5s6d26PtiYhI/iVzSGpuieKuWXp+2nXXXRk8eHCo+WTBggXMmjWrXd66+uqraWhoCG0fhazbipG7P25mhwInAM3AL9Oesgx4JvzQRESCC2Nm7yFDhjB//vxex7Jp0ybGjRvXrkztwUVEik8ytyxfvpy99947srtmqflp+vTpoW9/5cqVGfPWpk2bMr+gxGTTlA53f9ndb3b3n7v7jrR1t7n7spxEJyJSxAYNGsSyZcvaleWiPbiIiEgYRo8enTFvDRo0KJqA8iyripGIiAQ3ePBgrrnmmlD6K4mIiOTa1KlTO+St2bNnM3jw4KhDy4ts+hiJiEgPDB48mFNPPTWU/koiIiK5lsxPDQ0NbNq0iTFjxrDLLruoYiQiIr03ZcoUVYRERKRoTJkyhYceegiA+fPn56QvU6FSUzoRKUkbNmzQ/EEiIlKSNEdebgS+Y2RmnwD2Szx82d3/Fm5IIiK9s2HDBtasWcNNN93EuHHjWLZsGddccw2A7t6IiEhRS8411NjY2CHHSe9kfcfIzIaa2ULgr8DCxLLczH5lZkNzEp2ISA80NzczatQoJkyYQEVFRdv8QQsWLIg6NBERkV5JnWtIOS5cQZrS3QHsCRwNVCWWTwFjgNvDD01EpGdaWloYOHBgu7Js5g9S0wQRESkEXeWjzuYa0hx5vRekKd0U4Dh3/2NK2VNmNg34XbhhiYj0XFVVFZs3b25X1t38QV01TVDzOxERyZfO8lG/fv0YPHhw21xDEyZMaHuN5sgLR5A7Rm8D72co3wKsDyccEZHeq6mpYdWqVYHmD1LTBBERKQSd5aONGzcCmeca0hx54Qhyx+hq4CYzO8fdVwOY2QjghsQ6EZGCMGTIEIBA8wepaYKIiBSCzvLRpk2bgH+2YsiU45LDbEvPdFkxMrMXAE8pGgOsNLPViccjgBZgd+J9kERECsKQIUOYP39+1s9X0wQRESkEneWjQYMGtT3WHHm50d0do/vzEoWISMSSTRNmzZrVrk33jBkzog5NRETKSGf5aPDgwVGHVvK6rBi5+3fyFYiIZCc5Us2mTZs444wzmDp1qq4ahaCrpgkiIiL5kikfHXnkkfz2t79l5cqVyv05FHiCVxGJjkZOyy01TegZVdZFRMKVmo/Czv0bN25k48aNOmdnEGSC153M7DtmttzMWsxse+oSRjBmdpGZLTWzD8zs7jC2KVJKNHKaFJrUhP3UU08xc+ZM5s2bVxRzQCnniEgxCDP3b9y4kffee68oz9n5EGS47jnAucRHodsBfBO4hfhQ3fUhxbMGuAa4K6TtiZQUjZwmhabIK+vKOSJS8MLM/Rs3buTqq68u1nN2zgWpGH0RmO7utwLbgV+5+38AVwLHhxGMuz/o7gvRvEgiGSVHqkmlkdMkSsVcWVfOEZFiEGbu37RpU9Ges/MhSMWoGngp8fdm4COJv2PACSHGlDUzuzDRDGLp22+/HUUIInmlSd3+Kdmv5bnnnuOMM85om/hO8qucKuvKOSISliA5LMzcP2jQoLI5Z/dEkIrRG8DwxN8rgGQvrSOArWEGlS13v83dJ7j7hGHDhkURgkheTZkyhRkzZjB37lyOOuoo5s6dG8nIaekn9Hy3Tc7Ur+Xdd99ti2fDhg15jaeclVNlXTlHRMLQXQ5Lz6lh5v7Bgwcze/bsLs/Z5XzhMciodA8BxwFPAzcDPzOzC4hP8np9dy82s98DkztZ/ZS7HxUgFpGyFfXIaYUwMl5qvxaACRMmMGfOHObOncvMmTP5+te/npc4pHCHOVfOEZFC1V0Oy5RTw8r9ybmQOjtnZ8rxs2fP7vV+i0XWFSN3/3bK3/eb2ZvAkcByd384i9cf06MIRaSgZDqhz5o1i7lz5+btx3BX/VomTJjAqFGjePPNN/MSi0RfWc9EOUdEClV3OSzXOXXw4MHMnz8/47pMOf7qq6+moaEhJ7EUmiBN6dpx9z+5+43ZVIqyZWYVZlYF9AX6mlmVmWmuJZECUgid7bvr1zJw4EBaWlryFo8UJ+UcEYlCdzksysEQOsvxmzZtiiSefAsyj9G1ZjY9Q/l0M5sTUjyziPdXugz4UuLvWSFtW6Sg5LqfTq62Xwid7bvr17J582aqqqryFo8ULeUckRIQdb/XoLrLYVEOhtBZjh80aFAk8YRt8eLFHHvssSs7Wx/kjtE5wHMZyv8M/FvAuDJy96vc3dKWq8LYtki+bNiwodsTdK4nxczl9oN2ts9FwkrtiHrkkUfyrW99iylTpnDcccexdOlSVq1aRU1NTa/301PFlqTLlXKOSPHbsGFD3iaZ3rBhA1u3bu31ub27HNbTAWzCyD2ZcvwVV1zBpk2bij6fJX8bXX/99aM6e06QJgO7A5nGJ11PfChvkbK3YcMG1qxZw0033dTlwAS57qeTy+0H6Wyfy4EaUvu1LF68mAULFnDPPfcwevRohg8fzpAhQ3q1/aCSCWnTpk389a9/5V//9V+54IILIhmcoiupcZ5xxhlMnTq1IOISEemJ5uZmbrzxxl7nu+7OjYsXL2bNmjV8/OMf55577un1ub2rHNaTAWwy5dvLL7+cbdu2sXnz5rb3lE1cEM/xr7/+OgMHDizYfBZU+m+jjNw9qwVYDpybofw8YEW228nVcuihh7pIFKZNm+bTpk1zd/ejjz7azzrrrHbrlyxZ4qeffnq7skmTJnlra2u7stbWVp80aVIoMeV6+9k6/fTTfcmSJe3KMh2PsKV+JvkQi8X8lFNO8SVLlnhra6svWbLETznlFI/FYu4e7nvuzXvrLs4IRZo/erIo50i5yfd5NZv9T5s2zQ877LBe57tszo2nn366n3XWWe1iyEc+y1Z6vo3FYn7yySd3eE9f/OIXs/4co8rhuZL22yjjuT1IU7pbge+b2QVm9onEciFwA3BbDytvIiVj8eLFmBkrVqxod7s5UyfKXPfTKYR+QFAYAzXkQ+pVqIqKirYrlgsWLAAK5z13F6eISLFI3uExM774xS+2a+IVNN9lc25cuXIlAwcObPe6Qjm3Q8d8u2DBAmbPnt3hPQWZk6jUcnim30bpsq4YufsNxCtHPyB+92g58fmMbnf363oepkjx27hxI/PmzeOGG27o0MY50wk615NiFsqkm4VSQcu17pJHWO+5t+3HSy3JiUh5Su9H29DQwC233MJvfvObHuW7bM6No0ePZvPmze2eU0j5LD3fdjW6XLY5pNRyeOpvo84EGpbU3b9tZtcA+wMGvOTum7t5mUjJ27hxI42NjR3aODc2NrJjxw5mzJjR7vm5nhSzUCbdTJ6EZs2a1a6PUfrxCFMUfWiSySO13XIyeSSTdG/fcxj9tbqKU0SkWGTqRzt79mxmzpxJTU1N4HyXzbnx0EMP5ZFHHmHr1q2cccYZTJ48mcWLF+c0nwWRnm+HDx+e8T2NGDGCX/ziF1nlkChyeC4l3+c3v/nNVY899ljmJ3XWxq6zBagCDgQOAKqCvj5Xi9p7S1SmTZvmEydOzNjGeeLEiYXQfyNSsVjMTz/9dJ80aZKffvrpOT0eUfWhybTf2tpanzhxYmjvOYy23upjpJwj0hOxWMyPP/74UM9pQaX2MQq7H21358bOzvHXXXdd795UyFLz7YknnugnnXRSu5hPPvnkdp9dNjkknzk8jzqv53S1st0T43eXric+z8N2YEfi7+uAymy3k6tFSUqiMm3aND/++ONLqoNisYqyo2iuk0dYPwQKNMlFXtEJuijnSLkolAsqqRWjXJzruzo3FusgBKnvafLkyf7II4+0Wx/FoEwFIpSK0Y3AWuBc4BOJ5bxE2dxst5OrRUlKopB6Fa22tjbyxFHuCmU0vlwo1sScpcgrOkEX5RwpF4Vw7km/Y3XdddfltbJWCrmlED7HAtLpuT3IqHRnAee7+z3u/rfEcjfwFeDsANsRKQnpnT/r6ur41re+xZFHHsncuXMj6dNT7kqto2iqQhlQQ0TKS9SDtmSasPypp55qy7VHHXVUznNuKeQW5ZAsdVVrSl2IN5vbJ0P5vsDWbLeTq0VX7yTfdPWl8BRKk49cKdBmcGGI/A5Q0EU5R8pF1Lku6v27l05uKeEcElSn5/Ygo9I9D/wH8NW08q8By3pfRRMpLlFfRZOOCmU0vlxJnSldRCQfoh6ZrBBybankFuWQ7gWpGF0K/MbMjgf+CDhwBDAc+GwOYhMpaBr6uDDpxC8iEp6oKwWFkmuVW8pDt32MzOw4AHd/AtgH+CUwENg18fc+7v5kLoMUKURqrysiIuVgypQp3HfffTz99NPcd999ea0gKNdKPmVzx+i3ZrYSuBNY4O6X5zYkkeIQ9VU0ERGRUqdcK/lkHh9AofMnmO0HnA98CRgK/DdwO/Brd9+e8wizNGHCBF+6dGnUYYiISHAWdQBBKeeIiBStTnNOt03p3P1ld58J7AGcTrxv0S+B1Wb2/8xsn9DCFBERERERiUDW8xi5+zZ3f9DdTwJGAT8APg+8ZGZP5CpAERERERGRXAsywWsbd18DNBGvHP0DODLEmERERERERPIqyHDdAJjZZ4AvA/8CtAA/A+4INywREREREZH8yapiZGYjganAecSb0T0BXAjc7+4tOYtOREREREQkD7qtGJnZb4FjgbeAe4A73X1FrgMTERERERHJl2zuGG0lPsjCI4U0PLeIiIiIiEhYuq0YuXtdPgIRERERERGJSo9GpRMRERERESklqhiJiIiIiEjZU8VIRERERETKnipGIiIiIiJS9lQxEhERERGRsmfuHnUMoTCzt4FVUceRhd2Ad6IOosDomGSm49KRjklmxX5c3nH32qiDCKJIck6xfy9yRcelIx2TzHRcOiqFY9JpzimZilGxMLOl7j4h6jgKiY5JZjouHemYZKbjIpnoe5GZjktHOiaZ6bh0VOrHRE3pRERERESk7KliJCIiIiIiZU8Vo/y7LeoACpCOSWY6Lh3pmGSm4yKZ6HuRmY5LRzommem4dFTSx0R9jEREREREpOzpjpGIiIiIiJQ9VYxERERERKTsqWIUATP7iZmtNbN3zWy5mX0l6piiZmb9zOxOM1tlZu+Z2XNm9tmo44qamV1kZkvN7AMzuzvqeKJiZkPM7CEzez/xHTkr6piipu+GZEs5pz3lm87pvBKnnNNRuXw3KqIOoEx9Fzjf3T8ws32B35vZc+7+56gDi1AF8CYwGXgD+BzwCzMb6+4rowwsYmuAa4ApQP+IY4nSLcCHQDUwDnjEzJ53979EGlW09N2QbCnntKd80zmdV+KUczoqi++G7hhFwN3/4u4fJB8mlk9EGFLk3P19d7/K3Ve6+w53fxh4HTg06tii5O4PuvtCYH3UsUTFzAYAXwCucPfN7v4ksAg4J9rIoqXvhmRLOac95ZvO6byinNOZcvluqGIUETNrMrMtwCvAWuA3EYdUUMysGtgbKOerMxK3N7Dd3ZenlD0PHBBRPCJFRzmnc8o3kkY5p4ypYhQRd68HdgGOBh4EPuj6FeXDzCqBnwL3uPsrUccjkRsIbEor20T8/4+IZEE5JzPlG8lAOaeMqWIUMjP7vZl5J8uTqc919+2JW7R7ADOiiTg/sj0uZtYH+DHxtr0XRRZwHgT5rpS5zcCuaWW7Au9FEItIQVHO6Uj5JjPlnKwp55QxDb4QMnc/pgcvq6DE23tnc1zMzIA7iXd2/Jy7t+Y6rij18LtSjpYDFWa2l7v/NVF2MGr2IqKck4HyTWbKOVlTziljumOUZ2a2u5mdYWYDzayvmU0BzgQejTq2AjAP2A842d23Rh1MITCzCjOrAvoCfc2syszK6oKGu79PvOnP1WY2wMyOBE4hfqW3bOm7IdlQzumU8k0GOq8o53SmXL4bqhjlnxNvwvB3YCMwF/i6u/8q0qgiZmajgGnEh8VsNrPNieXsaCOL3CxgK3AZ8KXE37MijSga9cSHB30L+Bkwo8yHTQV9NyQ7yjlplG+6pPNKnHJOR2Xx3TB3jzoGERERERGRSOmOkYiIiIiIlD1VjEREREREpOypYiQiIiIiImVPFSMRERERESl7qhiJiIiIiEjZU8VIRERERETKnipGIiIiIiJS9lQxEhERERGRsqeKkYiIiIiIlD1VjEREREREpOypYlTmzGxfM/ujmbWY2cqo4xERkeJmZueZ2eaAr7nbzB7OVUwiItkom4pRKZ50zWy0mbmZTejFZq4BtgD7Aof1JKGJiEh7JZ5zkssmM3vazE5Oe+rPgY/nYP+/N7Mfhr1dEZGksqkYFQoz62NmfaOOI8WewJPuvtLd3446GBERCU+Ock4t8FFgEvAM8ICZHZhc6e5b3f2tkPcpIpJzJVMxqq2tdaDT5dxzzz33xBNPPDH5eNu2bX7xxRf74MGDffDgwX7xxRf7jBkz/Jhjjml7zTHHHOP19fXe0NDgu+22m+++++4+c+ZM37FjR9tzNm7c6Oeee64PHjzY+/fv75/5zGf8L3/5S9v6u+++2wcOHOi/+c1v/MADD/S+fftuf+GFF7Z9+OGH/q1vfcv32GMPHzBggB922GG+ePHiLt9D+vL666+/DrBkyZIlmda7u1933XX+iU98wvv37+9jx471n/zkJ23rzcyBg4HZZpZ87wuAAckrgldddVWgmLRo0aKlB0vRKfOcE3P3te7+8rvvvvvvQOUPfvCDF9JjSH3td7/7Xa+urvaBAwf6v/3bv/l3vvMdHz16dIfjdfPNN/uIESN88ODBPnXqVN+yZYsDft555zkwGfhqMj+tXLky6u+tFi1ainPpVMlUjN55551Az587dy533303d9xxB08//TQ7duzg3nvv7fC8n/70p1RUVPC///u//PCHP+Smm27i5z//edv68847jz/96U/86le/4plnnmHnnXemtraWrVu3tj2npaWFa665hltvvZWXXnqJUaNGMXXqVB5//HHuvfdeXnjhBc4991xOPvlknn/++Z4fhDSzZs3izjvv5JZbbuGll17i29/+NtOmTeORRx4BYO3ateyzzz5ccsklrF27lkWLFnHTTTex8847s3btWtauXcvMmTNDi0dEpFQo50Brayu33347AJWVlZ0+77777uM73/kO1157Lc8++yz77bcfN954Y4fn/eEPf+DFF1/kd7/7HT//+c956KGHuPnmmwG4+eabOeKII5g6dWpbfvrYxz7W49hFRDJy95JYDj30UO/Kueee6yeeeGLb45qaGv/ud7/b9njHjh2+zz77+OTJk9vKJk+e7Icffni77XzmM5/x888/393dly9f7oA//vjjbev/8Y9/+K677uq33367u7svWLDAAV+6dGnbc1asWOFm5qtWrWq37VNOOcVnzJjR5ftI9frrrzvgS5Ys6bBu8+bNXlVV5U888US78q997Wv+2c9+tu3xAQcc4FdeeWXb4wULFviAAQOyjkFEJASR55CgSznnnP79+/uAAQO8T58+DviYMWN8/fr1bc9LzyOHH364T5s2rd22jj/+eB81alTb43PPPdf32GMPb21tbSv7yle+4scdd1y74/PVr34163hFRDrR6bm9IsI6WWQ2bdpEc3MzEydObCszMw477DDefPPNds896KCD2j0ePnw4b70Vbzr98ssv06dPH4444oi29YMGDWLs2LG89NJLbWUVFRWMGzeu7fGzzz6Lu7P//vu32/YHH3zApz/96V6/P4CXXnqJlpYWamtrMbO28tbWVkaPHh3KPkREpHullnPuvfdeDjjgAJYvX87FF1/MbbfdxpAhQzp9/iuvvMIFF1zQrmzSpEksX768Xdn+++9PRcU/f5YMHz6cP/3pT4HjExHpqbKsGCWlVhg6k948wMzYsWMHEL/bls22+/XrR9++/+z7umPHDsyMJUuWdNh+//79s4q9O8kYf/3rXzNy5Mh267pq8iAiIrlRKjlnjz32YK+99mKvvfZi4MCBnHbaabz00kvstttuWcXXma7eu4hIPpRMH6MgBg0aRE1NDc8880xbmbsTH8Mge/vvvz87duzgj3/8Y1vZu+++ywsvvNDhylyqQw45BHenubmZPffcs90yYsSI4G+ok9j69evHqlWrOuxj1KhRnb5up512Yvv27aHEICIipZ1zJk+ezP7778/VV1/d6XP23Xffdu8d6PA4G8pPIpJrZXvH6Gtf+xrXXXcde++9N/vvvz+33nora9eu5aMf/WjW29hrr7045ZRTmDZtGrfddhsf+chHuPzyy9l1110566yzOn3d3nvvzdlnn815553HDTfcwPjx49mwYQO///3v+fjHP87nP//5QO9l+fLl7ZofQDwRzZw5k5kzZ+LufOpTn2Lz5s08/fTT9OnThwsvvDDjtkaPHk1LSwu//e1vOeSQQ9h5553ZeeedA8UjIiLtlVLOSXfJJZdw2mmn8c1vfjPjgAhf+9rXmDp1KocddhhHH300Dz30EH/6058YPHhwoP2MHj2aZ555hpUrVzJw4ECGDBlCnz5leX1XRHKkbM8oM2fO5JxzzmHq1KkcfvjhAJx66qlUVVUF2s6CBQuYOHEidXV1TJw4kS1bthCLxbptnrBgwQKmTp3KpZdeyr777stJJ53EE0880e5uzujRoznvvPO6jeHss8/mkEMOabesWLGCOXPmcNVVVzF37lwOOOAAjj/+eB544AHGjBnT6bY++clPMn36dM4880yGDRvGddddl/WxEBGRzEop56Q76aSTGD16NHPmzMm4/owzzuCKK67gsssu45BDDuHFF19k+vTpgd/7zJkz2Wmnndh///0ZNmwYb7zxRuBYRUS6Yl21WS4mEyZM8KVLl/ZqG+PHj+fII4/kP//zP0OKque2bNnC0KFDueuuuzjzzDOjDkdEJJe674BSYJRzeufUU09l27Zt/PrXv875vkRE0nSacwI1pTOzfsBwoD/wtru/3cvAIrNq1SoWL17M5MmT2bZtG7fddhvPP/88t912W9ShAfDYY48xadIkVYpEREpAOeecLVu2MG/ePGpra6moqOCBBx7gV7/6FQ888EDo+xIR6Y1u7xiZ2S7Al4AzgYlAJfGalgNrgBhwm7sH60UasqBX7958803OPPNMXnjhBXbs2MH+++/PnDlzOOGEE3IYpYiIZFDyd4zKOeds3bqVk08+meeee46tW7ey1157cemll3L22WdHHZqIlKdOc06XFSMzuxiYBbwGLAKeIV4Z2goMAQ4EjgZOBZ4G/t3d/xpa2AGE0axBREQiUfIVIxERKRg9bkr3SWCyu7/YyfpngLvMbDpwPjAZiKRiJCIiIiIi0lNdVozc/bRsNuLuHwBNoUQkIiIiIiKSZ2U7XLeIiIiIiEhSl3eMzOwx4oMsdMvdPx1KRCIiIiIiInnWXR+j1L5FfYGzgWbgT4myicBHgZ+EH5qIiIiIiEh+dNfH6N+Tf5vZ94F7gK95ylB2ZnYTRTiikIiIiIiISFKQPkb/BvzQO47v3QScE15IIiIiIiIi+RWkYmTA2AzlmcpERERERESKRnd9jFLdBdxhZnsRn8wV4HDgUmBB2IGJiIiIiIjkS5A7RpcC3wX+HXg0sfw78L3EOpGCFIvFqKurY9KkSdTV1RGLxaIOSURECoxyhYhkfcfI3XcA1wHXmdmuibJ3cxWYSBhisRiNjY20tLQA0NzcTGNjIwC1tbVRhiYiIgVCuUJEoAcTvJrZBOCzwPbE4wFmFqRJnkjeNDU1tSW6pJaWFpqamiKKSERECo1yhYhAgDtGZlYNLAIOIz7p617Aa8CNQAvwtVwEKNIb69atC1QuIiLlR7lCRCDYHaPvE5/cdSiwJaX8l8AJYQYlEpbq6upA5SIiUn6UK0QEglWMjgMud/eNaeV/A0aGF5JIeOrr66mqqmpXVlVVRX19fUQRiYhIoVGuEBEINlx3f+DDDOXDiDelEyk4yU6zc+bMobW1lZqaGurr69WZVkRE2ihXiAgEqxg9AZwHNCQeu5n1Bb4F/E/IcYmEpra2loULFwIwf/78aIMREZGCpFwhIkEqRpcCj5vZYUA/4AbgAGAQcGQOYhMREREREcmLrPsYuftLwFjgf4H/BqqID7xwiLv/LTfhiYiIiIiI5F6g+YfcvRm4MkexiIiIiIiIRCJQxcjMdgIOBHYn7W6Tu/8mxLhERERERETyJsgEr8cDPyZeKUrnQN8wAjKzfkAT8BlgCLACaHD3/wpj+yIiIknKOSIikhRkHqNbgIeBMcDOxIfvTi47hxhTBfAmMJn4wA5XAL8ws9Eh7kNERASUc0REJCFIU7qPAo3uvipXwQC4+/vAVSlFD5vZ68ChwMpc7ltERMqLco6IiCQFuWP0MPDJXAXSGTOrBvYG/pLvfYuISHlRzhERKV9B7hhNB35qZocCLwKtqSvd/UdhBgZgZpXAT4F73P2VDOsvBC4EGDlyZNi7FxGRMqKcIyJS3oJUjKYAxwGfA7YQH3AhyYFQK0Zm1of4YA8fAhdleo673wbcBjBhwgTP9BwREZHuKOeIiEiQpnRzgR8Cu7j7QHffJWXZNcygzMyAO4Fq4Avu3trNS0RERHpEOUdERCDYHaOPAPMTHVVzbR6wH/AZd9+ah/2JiEj5Us4REZFAd4weID7PQ06Z2ShgGjAOaDazzYnl7FzvW0REyotyjoiIJAW5Y/QacK2ZfQr4PzoOvnBjGAElhgO3MLYlIiLSFeUcERFJClIx+jLwHvEhu9OH7XYglIqRiIiIiIhIvmVVMUqM1nMSsMrdN+c2JBERERERkfzKto+RA88BNTmMRUREREREJBJZVYzc3YFXgWG5DUdERERERCT/goxKdylwvZmNS8z5ICIiWYjFYtTV1TFp0iTq6uqIxWJRhyQiIr2g83ppCjL4wi+AKuDPwDYz+yB1ZdiTvIqIlIJYLEZjYyMtLS0ANDc309jYCEBtbW2UoYmISA/ovF66glSMLspZFCIiJaqpqakteSa1tLTQ1NSkBCoiUoR0Xi9dWVeM3P2eXAYiIlKK1q1bF6hcREQKm87rpStIHyPMrJ+ZfdnM5prZ9WZ2npn1y1VwIiJhiLIteHV1daByEREpTMlcEh+TrCOd14tf1hUjM9sf+CvxiVwnAYcDNwHLzWy/nEQnItJLybbgzc3NuHtbW/B8VY7q6+upqqpqV1ZVVUV9fX1e9i8iIr2Xmksy0Xm9NAS5Y3Qz8bmMRrr70e5+NDASeJ54BUlEpOB01RY8H2pra2loaKCyshKAmpoaGhoa1A5dRKSIZMolSTqvl44ggy8cCRzm7u8mC9z9XTO7HHg69MhEREJQCG3Ba2trWbhwIQDz58/P235FRCQcXeWMRYsW5TESyaUgd4xagI9kKB+UWCciUnDUx0dERHqrs5yRbA0gpSFIxejXwO1mdqSZ9U0sRwG3Aqoqi0hBUh8fERHprUy5xMwYPnx4RBFJLgRpSvc14B7gD8D2RFkf4pWir4cblohIOJJtvufMmUNrays1NTXU19erLbiIiGQtUy7p168fQ4cOjTgyCVOXFSMzGwm86XH/AE4xsz2B/QADXnL3FbkPU0Sk59THR0REeis9l0yfPj3agCR03d0xeh34KPCWmT0KfD5REVJlSERERERESkZ3fYzeA3ZL/H0MoB5mIiIiIiJScrq7Y/Q74FEzeznx+CEz+zDTE93906FGJiIiIiIikifdVYzOAb4M7AlMBl4FtuQ6KBERERERkXzqsmLk7luBWwDMbBxwSWIQBhGRrMRiMZqamli3bh3V1dUaEU5ERKSHlFNzK+t5jNz92PRKkZntaWZVnbxERMpcLBajsbGR5uZm3J3m5mYaGxuJxWJRhwbE46urq2PSpEnU1dUVTFwiIlKeuspLhZ5TS0HWFSMzazSzcxN/m5n9FlgOrDWzw3MVoIgUr6amJlpaWtqVtbS00NTUFFFE/6QEIyIihaS7vFTIObVUZF0xAs4m3scI4LPAOOBw4EfAd8MNS0RKwbp16wKV55MSjIiIFJLu8lIh59RSEaRiVA38PfH354BfuPszwH8Ch4QdmIgUv+rq6kDl+aQEIyIihaS7vFTIObVUBKkYrQdGJf4+AXg08XcFYGEGJSKlob6+nqqq9t0Qq6qqqK+vjyiif1KCERGRQtJdXirknFoqglSMHgDuTfQtGgIkG+KPA1aEHJeIlIDa2loaGhqorIzPDV1TU0NDQ0NBjKCjBCMiIoWku7xUyDm1VHQ3j1GqbwCrgJHApe7+fqL8o8C8sAMTkdJQW1vLwoULAZg/f360waRIJpI5c+bQ2tpKTU2Nhj0VEZHIZJOXCjWnloqsK0buvg24IUP590ONSEQkT5RgRESkkCgvRSvIHSPMbGfiTed2p30zPHf3h0KMS0REREREJG+yrhiZ2WeAnwFDM6x2oG9YQYmIiIiIiORTkMEXbgYeAfZw9z5piypFIiIiIiJStII0pRsN1Ln7mhzFIiIiIiIiEokgd4yeAvbJVSCpzOwiM1tqZh+Y2d352KeIiJQf5RsREUkKcsdoPjDXzIYDLwCtqSvd/dkQ41oDXANMAfqHuF0REZFUyjciIgIEqxjdn/j3tgzrQh18wd0fBDCzCcAeYW1XREQklfKNiIgkBakYjclZFCIiIiIiIhEKMsHrqlwG0hNmdiFwIcDIkSMjjkZEREqZco6ISGkLOsFrBTARGAnslLrO3X8UYlxZcffbSDTtmzBhgud7/yIiUj6Uc0RESlvWo9KZ2b7Ay8ATwE+BO4C7gduBH+YiOJFiFYvFqKurY9KkSdTV1RGLxaIOSXpJn6mOgXRP3xGJir57pS8fn3GQO0Y3AX8GxgHNiX8HAfOAWWEGlbgzVUF8QIe+ZlYFbHP3bWHuRyQXYrEYjY2NtLS0ANDc3ExjYyMAtbW1UYYmPaTPtHSPgfJNeEr1OyKFT9+90pevzzjIPEaHAde4+/vADqAiMUT3pcANoUUUNwvYClwGfCnxd6iVL5FcaWpqavuPm9TS0kJTU1NEEUlv6TMt6WOgfBOSEv6OSIHTd6/05eszDlIxMmBL4u+3gRGJv/8O7BlmUO5+lbtb2nJVmPsQyZV169YFKpfCp8+0dI+B8k14SvU7IoVP373Sl6/POEjF6EXg4MTfzwDfMrPJwHeAFaFGJVLEqqurA5VL4dNnqmMg3dN3RKKi717py9dnHKRidC3xu0YQb2bwMeAx4ATgP0KNSqSI1dfXU1VV1a6sqqqK+vr6iCKS3tJnqmMg3dN3RKKi717py9dnHGTwhT8AHwC4+2vA/mY2BNjo7hq2VCQh2Qlwzpw5tLa2UlNTQ319vTqAFjF9pjoG0j19RyQq+u6Vvnx9xllVjMysL7CJeFO6l5Ll7r4h1GhESkRtbS0LFy4EYP78+dEGI6HQZ6pjIN3Td0Siou9e6cvHZ5xVUzp33w6sIm1SVxERERERkVIQpI/RHOB7ZrZbroIRERERERGJQpA+RjOBMcBqM/s78H7qSnc/KMzARERERERE8iVIxegBQIMslJFYLEZTUxPr1q2jurpaHRlFRKRoKaeJSHeyrhhpwrvyEovFaGxsbJtluLm5mcbGRgAlEilKGzZsYPXq1UyaNEk/ikTKjHKaSO+Uy4WFrPsYmdlrZjY0Q/lHzOy1cMOSqDU1NbUlkKSWlhaampoiikgKUSwWo66ujkmTJlFXV0csFos6pIxisRirVq2itbUVd2/7UVSo8YpIuJTTJBvFktPyLXlhobm5ueRzaJDBF0YDfTOU9wP2CCUaKRjr1q0LVC7lp5hOlE1NTaRPt6YfRSLlQzlNulNMOS3fyunCQrcVIzP7vJl9PvHwxOTjxHIa8B3g9ZxGKXlXXV0dqFzKTzGdKPWjSKS8KadJd4opp+VbOeXQbO4Y3Z9YHLgz5fH9wE+AY4FLchWgRKO+vp6qqqp2ZVVVVdTX10cUkRSaYjpR6keRSHlTTpPuFFNOy7dyyqHdVozcvY+79wHeAHZPPk4s/dx9H3d/OPehSj7V1tbS0NBAZWUlADU1NTQ0NJRERzu1IQ5HMZ0o6+vrMbN2ZfpRJFI+SjmnSXa6y/3FlNPyrZwuLGTdx8jdx7j7O7kMRgpLbW0tY8eOZfz48SxatKgkEojaEIenmE6UtbW1jBo1qq1ypB9FIuWnFHOaZCeb3F9MOS3fyunCQpcVIzP7kqVfZu38uaPN7OhwwhLJDbUhDk+xnSiHDBnCgAED9KNIRKTMZJP7iy2n5Vu5XFjobh6jLwOzzexuYBHwF08Z2snMhgBHAV8Cjkk8X6RgqQ1xuGpra1m4cCEA8+fPjzYYERGRDLLN/cpp0uUdI3f/NHAxMBl4HthsZq+b2ctm1gy8DdwKrAAOUF8jKXRqQywiIlJelPslW9kMvvCIu08BqoGzgZuAe4BZwERghLs3uPvbuQxUJAxqQywiIlJelPslW901pWuTGHhhYe5CEcm9ZJvYOXPm0NraSk1NDfX19SXbVlZERKTcKfdLtrKuGImUCrUhFhERKS/K/ZKNrIfrFhERERERKVWqGImIiIiISNlTxUhERERERMqeKkYiIiIiIlL2Ag2+YGb1wFeBMcCB7v6amV0GvObuv8hFgJI/sViMpqYm1q1bR3V1tYax7IHUY1hRUcGIESOiDklERKQoZPod0puR49K3V1VVxZAhQ0KMWEpN1neMzOzrxOcuug2wlFWrgYvCDUvyLRaL0djYSHNzM+5Oc3MzjY2NrF+/PurQikb6MWxtbWXVqlXEYrGoQxMRESlonf0O6WkOzbS9VatWsWHDhpAjl1ISpCnddOACd78Z2JZS/ixwQKhRSd41NTXR0tLSrqylpYU1a9ZEFFHxyXQM3Z2mpqaIIhIRESkOnf0O6WkO7Swnr169uscxSukLUjEaBbyYobwV6B9OOBKVdevWZSxvbW3NcyTFq7Nj2Fm5iIiIxIWdQ/W7RnoiSMXoNWB8hvLPAS+FE45Epbq6OmN5ZWVlniMpXp0dw87KRUREJC7sHKrfNdITQSpGc4EfmtnZxPsYHWFmVwLXAtfnIjjJn/r6eqqqqtqVVVVVMXz48IgiKj6ZjqGZaRALERGRbnT2O6SnObSznKxBkaQrWY9K5+4LzKwCaAR2Bn5MfOCF/3D3n+coPsmT5Kgvc+bMobW1lZqaGurr61m4cGG0gRWR9GNYWVnJiBEjejWijoiISDno7HdIT3Nopu1pVDrpTlYVo0SF6EJgobvfbma7AX3c/a2cRid5VVtb21YRmj9/PoAqRgGlHkMRERHJXqbfIWFub/r06b3eppS2rJrSufs24s3lKhOP31GlSESKQSwWo66ujmeffZb3339fQ7WKiEhOJPPNpEmTqKur03QdRShIH6OngUNzFUgqMxtiZg+Z2ftmtsrMzsrHfkWktKTOYwHxoVo1t5SkU84Rkd4Kex4miUaQitHtwFwz+7qZHW1m41OXkOO6BfgQqAbOBuaZmeZKEolYsV0N62puqdQ7SS+88ELBvxfJKeUckTITdj4Lex6mYlZsvxVSZT34AnBv4t8bM6xzoG/vwwEzGwB8ATjQ3TcDT5rZIuAc4LIw9iEiwSWvhiVP/MmrYdXV1QwdOjTi6DLrbB6LZOzJ99La2kpjYyOABssoM8o5IuWns3zWG5rLMK6rY1sM+TXIHaMxXSwfDzGmvYHt7r48pex5QFfvRCLU2dWwNWvWRBRR9zqbx6JPnz66sidJyjkiZSYXd3c0l2Fcsd85y7pi5O6rulpCjGkgsCmtbBOwS/oTzexCM1tqZkvffvvtEEMQkXTFOIt4Z/NY7NixI+Pzy+3KngDKOSJlJxd3d8Keh6lYFfudsyB3jDCzg8zsR4nEsMTM7jGzsSHHtBnYNa1sV+C99Ce6+23uPsHdJwwbNizkMEQkVTHOIl5bW0tDQ0NbjGbGqFGjqKmpyfj8cruyJ4ByjkjZycXdnfR8U1NTQ0NDQ1E0HwtTsd85y7piZGZ1wLPAx4D/AmLASOBZMzs5xJiWAxVmtldK2cHAX0Lch4gE1NnVsOHDh0cUUXZqa2sZO3YsAwcOZMCAAQwZMkRX9iSVco5ImclVDkjmm/Hjx7No0aKyqxRB8d85C3LH6BrgWnc/1t2vSCzHAt9NrAuFu78PPAhcbWYDzOxI4BTgx2HtQ0SC6+xqWKEOvNCV9PdSWVlZllf2RDlHpBzp7k7uFPuxDTIq3d5kThQ/Bi4NJ5w29cBdwFvAemCGu+vqnUjEMs1KnnxcbJLvZfny5ey9995Fc9KWnFDOESkzmfKZhKOYj22QO0ZvkXmC10OBUHtUufsGd/8Xdx/g7iPd/d7uXyVJxTx+vIhIvinnSK4pL4sUhyB3jG4HbjWzPYH/JT530VHATOD6HMQmPVDs48eLSM/FYjGamppYt24d1dXV1NfX6/+9SMSUl0XiiiFHBe1j9B1gBvA/wKPAdOBKoHezYkloin38eBHpmeSPr+bmZty97ceXrkyLREt5WaR4clSQeYzc3b/v7nsAg4BB7r6Hu9/s7p67ECWIYh8/XkR6Rj++RAqT8rJI8eSoIMN1H2BmBwG4+3vu/l6i/CAz2z9XAUowxT5+vIj0jH58iRQm5WWR4slRQZrS3QYcmKF8/8Q6KQDFPn68iPSMfnyJFCblZZHiyVFBKkYHAc9kKF8CjA0nHOmtYh8/XkR6Rj++RAqT8rJI8eSoIKPSbSfetyjdYMDCCUfCUMzjx4tIzyR/ZM2ZM4fW1lZqamoKcsQfkXKkvCzlrlhyVJCK0ePA5WZ2mrtvBzCzCuBy4IlcBCciItnTjy8RESlUxZCjglSMLgWeBFaY2ZOJsqOAgcCnwg5MREREREQkX4IM1/0q8X5G9wJDgKHAT4GD3f3l3IRXHDSjtYiIiEhxSv0d98ILL7B+/fqoQ5KIBBl8AXdf6+6Xu/uJ7v45d5/l7mtyFVwxKJYJq0RyZf369bzwwgu6MCAiIjmRywvQGzZsaPc7rrW1lTfeeEO5rEx1WzEys4FmNjStbD8zu8vMfmFmZ+YuvMJXLBNWieRCLBbjjTfeoLW1tSgvDGzcuBF357nnnuOMM85g8eLFUYckIiIpcn0BevXq1R1+x7l7ZL/jFi9ezGuvvaa8FJFs+hjNAzYBFwGY2W7AH4AdwFrgJ2Zm7n5vzqIsYMUyYZVILjQ1NeHu7cqSFwYKbaSZdIsXL+a9997j+uuvZ9y4cSxbtoxrrrkGgClTpkQcnYiIQNcXoMPIM62trRnLo/gdt3jxYubNm0djY6PyUkSyaUp3BLAw5fE5wIfAXu5+MDCXRKWpHBXLhFUiuVDMFwYWLFjA1VdfzYQJE6ioqGDChAnMmjWLBQsWRB2aiIgk5DrPJOeXShfF77gFCxYwa9Ys5aUIZVMx+iiwIuXxscAD7r4p8fgeYK+wAysWxTJhlUguFPOFgZUrVzJu3Lh2ZePGjWPlypWRxCMiIh3lOs+MGDGiw+84M4vkd5zyUvSyqRhtAQakPJ4IPJ3yuAXYOcygiolmtJZyVl9fj1n7+Z2L5cLA6NGjWbZsWbuyZcuWMXr06ND3pTbjIiI9k+sL0EOGDGn3O66yspKRI0d2+zsuF+f1fOYlySybitHzwFQAMzsGGAY8mrL+E0BZj0xXW1vL2LFjGT9+PIsWLVKlSMpGbW0tI0eObKscFdOFgalTpzJ79myWLl3Ktm3bWLp0Kddccw1Tp04NdT+pbcafeuopZs6cybx581Q5EhHJQj4uQKf+jhs7dixDhw7t8vkbNmzIyXl96tSpXHPNNTnPS9K5bAZfmAP8l5l9kXil6G53X5uy/lTiE7+KSBkaOnQo69evZ++99y7YmawzmTJlCnfddRcNDQ28++67jB49mhkzZoTewTW1zTjQ1mZ87ty56kwrIpKF2tpaFi5cCFAQeaa5uZkbb7wx43n9Ix/5SI+3m8wJc+fOZeXKlTnLS9K5bitG7v64mR0KnAA0A79Me8oy4JnwQxMRya3BgwczePDgnCZatRkXESktLS0tnZ7X08uDmjJliipCEcpqgld3f9ndb3b3n7v7jrR1t7n7spxEJyJS5NRmXESktFRVVem8XqKyqhiJiEjPqM24iEhpqamp0Xm9RGXTx0hERHpIbcZFRErLkCFDOPXUUzOe1x966KGow5NeUMVIRCTH1GZcRKS06LxemtSUTkTKwrZt29i6davmEhIREQmoXObjC3zHyMw+AeyXePiyu/8t3JBERMK1ceNGtm3bxsc//nHuueceli1bxjXXXEO/fv0YPHhw1OGJiIgUrNT5+MaNG9eWQ4GSu2uW9R0jMxtqZguBvwILE8tyM/uVmXU9E5aISIQ2btzImDFj2GWXXaioqGibc2Ljxo1RhyYiIlLQUufjS82hCxYsiDq00AVpSncHsCdwNFCVWD4FjAFuDz80EZFwbNq0ibfeeqtdE4Bx48axadOmqEMTEZEiUS7NydKV03x8QZrSTQGOc/c/ppQ9ZWbTgN+FG5aISDgWL17MkCFDmDNnTrsmAK+99hqDBg2KOjwRESkC5dScLF1yPr4JEya0lZXqvE1B7hi9DbyfoXwLsD6ccEREwrVgwQLmzJnToQnA/fffr/5FIiKSlXJqTpaunObjC3LH6GrgJjM7x91XA5jZCOCGxDoRkYLTWROAzZs3s9dee0UTlIiIFJVyak6Wrpzm4+uyYmRmLwCeUjQGWGlmqxOPRwAtwO7E+yCJiBSUzpoAjBkzJsKoRESkmJRTc7JMymXepu7uGN2flyhERHJk6tSpzJ49m6uvvrpdu/AZM2ZohnIREclKsjnZrFmzOuQSKR1dVozc/Tv5CkSklGzcuJGNGzeyadMmzjjjDKZOnVoWV1oK0ZQpU7jrrrtoaGjg3XffbdcEQBUjEZHSkBwxLld5t5yak5WzwBO85pKZXQScB4wFfubu50UakEgPbNy4kffee68sR64pVIMHD2bw4MHMnz8/6lCKWq5/eOSbco5IaehqxLgwlUtzskKVjxyUdcXIzHYCLgfOBEYClanr3b1vCPGsAa4hPjR4/xC2J5J3GzdupLGxsa0dcnLkmrlz5+qEKkWrRIeqVc4RKQGpI8ZB+7z7kY98JNrgJBT5ykFBhuueA5xLfBS6HcA3gVuID9VdH0Yw7v6guy9Ew39LEdu0aVPZjlwjpasUh6pVzhEpDeU8Yly5yFcOClIx+iIw3d1vBbYDv3L3/wCuBI4PNSqRIjZo0CCWLVvWrqycRq5JV64zhZca/fAQkUKVHDEuVanm3XLNqfnKQUEqRtXAS4m/NwMfSfwdA04IMaasmdmFZrbUzJa+/fbbUYQg0sHgwYOZPXt2WUyE1p3UW99PPfUUM2fOZN68eWVzIi8l5fTDozPKOSKFqVwmIC3nnJqvHBSkYvQGMDzx9wribbIBjgC2dvdiM/u9mXkny5PBwo5z99vcfYK7Txg2bFhPNiESusGDB7PLLrswd+5cjjrqKObOnVu2I9cUcvOroFfdyvUqXVKx/fBQzolGuf8/kWhMmTKFGTNmlHzeLeScmioX54F85aAgo9I9BBwHPA3cDPzMzC4gPsnr9d292N2P6UmAIsVII6DFFWrzq6CdOEt04IFAim2oWuWc/NP/E4lSOYwYV6g5NVWuzgP5ykFZV4zc/dspf99vZm8CRwLL3f3hMIIxs4pETH2BvmZWBWxz921hbF9E8qtQZwrvagSjTCfZoM8vVaX2w0M5J1z6fyKSW4WaU1Pl8jyQjxwUpCldO+7+J3e/MaxKUcIs4s3yLgO+lPh7VojbF8kpNSNpr1CbXwW96lYMV+mkR5RzQqT/J9JbyqFdK9ScmqrYzwNB5jG6FnjT3eenlU8HRrj7Fb0Nxt2vAq7q7XZEUqVPCHbooYfmZIIwNSPpqFCbXwW96lYMV+kkOOWccBX7/5NSm8C42OQ6h2b6fItNoebUVMV+Hghyx+gc4LkM5X8G/i2ccCRK6Vdqrr/++qK/cpM+gsvkyZN59NFHczKiS7F0igxT8juzefNmXnrppYzHccqUKdx33308/fTT3HfffQVxAg961a0YrtKJRK3Q/590dTeinEf7KhS5yKGpn/lNN93U4fPdsGFDu+dv2LCh4H/3FGJOTVXo54HuBBl8YXcg0/ik64kP5S1FLNOVmiuuuIJTTz2VCy64oGjvfqS3dX388ceZM2dOTtq+Fvvt46BSvzO33347mzdvZt68eUBhfUe6ugqc7VW3YrhKJxK1Qv5/0t3dCPWPil7YOTT1M587dy4zZ87s8Pl+4xvfYMiQIUC8UrRmzRpuuummHt+x0l3Hwj4PZMXds1qA5cC5GcrPA1Zku51cLYceeqhHadq0aT5t2rRIY0iKxWJ+/PHH+8SJE/3000/3WCzW7WtOP/10X7JkSbuyJUuW+Omnn97p42IwadIkb21t7fSxu3tra6tPmjSp1/vK5hiWguR3PfX9JssK7f3GYjE/5ZRTfMmSJd7a2upLlizxU045Jav/E8WqkM5FAUSaP3qyRJ1zJJjuzs+5zA3uPcvL5aa7zyjouS11e519vocddljbNo8++mg/66yzOt1/d8ox3/REgeSoTs/tQZrS3Qp838wuMLNPJJYLgRuA20Kur0kP9bQ5QDZXaorx7kf6hGC5nCCs2G8fB1UMd8jKsXmjiHTU3fkql7lBzfSyE3YOTf3MO/t8q6qq2h63tLQwcODAds8JktOUb0pD1hUjd7+BeOXoB8TvHi0nPp/R7e5+XW7Ck6B6+h8zm6RQTJ3nktJPtJMnT+aKK67ISeWlXCaYS0p+Z1LbcH/xi19kt912izq0NsVQeQuTRnQSyay7HJfLC1v6wZydrnJoT85tqZ95Z59vTU1N2/OrqqrYvHlzu20E+d1TbvmmJ4oiR3V1OynTAgwADgMmAgODvj5XS5TNGgrpFnlPmwNkugVcW1vrTU1NRX9LOBaL+emnn+6TJk3y008/3a+77rp2j4vxPUUpeRs8Fov5CSec4CeffHK7781JJ51UMMe0XJo3uhd9M47Ic0jQRU3piks2/z/Sc0VY/3dy3Uyv1PX03Jb+uqamJv/0pz/d7rdaaj5Lrjv11FP9kUceCXwOLad80xMFlqM6r+d0tTLjC6AKOBA4AKgK+vpcLVElqQL7oHv1H1MVCMlGavvgurq6gk4Ehfb/M5eKPClHnkOCLqoYFZ9cVXy6U+T/NyMX5u+a9M982rRpftppp3XIE1OmTPETTzwx0HeknPJNTxTY/4PeV4yIj2B3PfEJ8LYDOxJ/XwdUZrudXC1RJakC+6D1H1NyLrViVAxXQqP6MZRvxfBZdCHyik7QRRUjyZbycu/k8tw2bdo0P/roo0P7HVcu+aYnCixHdXpuDzJc93XAmcB04MlE2dHAd4n3VZrZixZ9RavQ2pQW/TCJUtDShyKtrq4u+IncpkyZUhbf/2KfVE+kVCkv906uzm3JfPbBBx8wd+7cdkNr9/R3XLnkm54omhzVVa0pdQGagc9lKD8RWJvtdnK16I6RSG5luup5wgkn+EknnaQroQWgyK9KR34HKOiiO0Yi+ZGLc1t329TvuPAVWI7q9NxuHq/cdMvMtgLj3P3VtPJ9gefcvX+oNbaAJkyY4EuXLs37fpPDcM6aNavdhGC6GiSl5owzzmg3QR7A0qVLueqqqxg4cGDbldBynNCuUCxevJgFCxYU42dhUQcQVFQ5R6QchX1u6yyfJSeC1e+43CigHNVpzglSMXoa+LO7fzWtfB7xCtMRvQqxl6JMUgX0QYvkzOGHH86TTz5JRcU/W+Bu27aNo446iqeffjrCyKQEqGIkInnTWT478sgjGTNmjH7Hlb5Oc06QPkaXAr8xs+OBPwIOHAEMBz7bq/CKnNqUSjkomvbBIiIiXegsn40ZM4b77rsvwsgkat1O8GpmxwG4+xPAPsAvgYHArom/93H3JzvfgoiUglxOgCgiIpIvymfSmWzuGP3WzFYCdwIL3P3y3IYkIoVIIyuJiEgpUD6TznTbx8jM9gPOB74EDAX+G7gd+LW7b895hFlSe28RkaKlPkYiIpIvneacbpvSufvL7j4T2AM4nXjfol8Cq83s/5nZPqGFKSIiIiIiEoFuK0ZJ7r7N3R9095OAUcAPgM8DL5nZE7kKUEREREREJNeyrhilcvc1QBPxytE/gCNDjElERERERCSvggzXDYCZfQb4MvAvQAvwM+COcMMSERERERHJn6wqRmY2EpgKnEe8Gd0TwIXA/e7ekrPoRERERERE8qDbipGZ/RY4FngLuAe4091X5DowERERERGRfMnmjtFW4oMsPFJIw3OLiIiIiIiEpduKkbvX5SMQERERERGRqPRoVDoREREREZFSooqRiIiIiIiUPVWMRERERESk7KliJCIiIiIiZU8VIxERERERKXvm7lHHEAozextYFXUcWdgNeCfqIAqMjklmOi4d6ZhkVuzH5R13r406iCCKJOcU+/ciV3RcOtIxyUzHpaNSOCad5pySqRgVCzNb6u4Too6jkOiYZKbj0pGOSWY6LpKJvheZ6bh0pGOSmY5LR6V+TNSUTkREREREyp4qRiIiIiIiUvZUMcq/26IOoADpmGSm49KRjklmOi6Sib4Xmem4dKRjkpmOS0clfUzUx0hERERERMqe7hiJiIiIiEjZU8VIRERERETKnipGETCzn5jZWjN718yWm9lXoo4pambWz8zuNLNVZvaemT1nZp+NOq6omdlFZrbUzD4ws7ujjicqZjbEzB4ys/cT35Gzoo4pavpuSLaUc9pTvumczitxyjkdlct3oyLqAMrUd4Hz3f0DM9sX+L2ZPefuf446sAhVAG8Ck4E3gM8BvzCzse6+MsrAIrYGuAaYAvSPOJYo3QJ8CFQD44BHzOx5d/9LpFFFS98NyZZyTnvKN53TeSVOOaejsvhu6I5RBNz9L+7+QfJhYvlEhCFFzt3fd/er3H2lu+9w94eB14FDo44tSu7+oLsvBNZHHUtUzGwA8AXgCnff7O5PAouAc6KNLFr6bki2lHPaU77pnM4ryjmdKZfvhipGETGzJjPbArwCrAV+E3FIBcXMqoG9gXK+OiNxewPb3X15StnzwAERxSNSdJRzOqd8I2mUc8qYKkYRcfd6YBfgaOBB4IOuX1E+zKwS+Clwj7u/EnU8ErmBwKa0sk3E//+ISBaUczJTvpEMlHPKmCpGITOz35uZd7I8mfpcd9+euEW7BzAjmojzI9vjYmZ9gB8Tb9t7UWQB50GQ70qZ2wzsmla2K/BeBLGIFBTlnI6UbzJTzsmack4Z0+ALIXP3Y3rwsgpKvL13NsfFzAy4k3hnx8+5e2uu44pSD78r5Wg5UGFme7n7XxNlB6NmLyLKORko32SmnJM15ZwypjtGeWZmu5vZGWY20Mz6mtkU4Ezg0ahjKwDzgP2Ak919a9TBFAIzqzCzKqAv0NfMqsysrC5ouPv7xJv+XG1mA8zsSOAU4ld6y5a+G5IN5ZxOKd9koPOKck5nyuW7oYpR/jnxJgx/BzYCc4Gvu/uvIo0qYmY2CphGfFjMZjPbnFjOjjayyM0CtgKXAV9K/D0r0oiiUU98eNC3gJ8BM8p82FTQd0Oyo5yTRvmmSzqvxCnndFQW3w1z96hjEBERERERiZTuGImIiIiISNlTxUhERERERMqeKkYiIiIiIlL2VDESEREREZGyp4qRiIiIiIiUPVWMRERERESk7KliJNIJMxttZm5mEzI9zsH+Hjazu0PYzmAzW2dmRT2zfeJY/2tnj3Owv90S+zgm8Xisma02swG52qeICCjfRE35RpJUMZKsmNndif/EbmbbzOwNM5tnZoMzPHeSmS0ysw1m9oGZvWJmVyZmTE593kozm5nl/v8vsd+9w3pPPfAm8FFgWSKmYxLHY7cIY8qkAfiNu/8t6kBC9lHg1/nambu/ADwNfCNf+xQR5ZsE5ZtoKd+UKVWMJIjfET9ZjAa+ApwMNKU+wczqgD8A64HPAHsD3wEuBP7bzHYKulMzmwgMA34EnN/z8HvH3be7e7O7b4sqhu6Y2c7EP5s787CvwJ9lbySO/Qf53CewAJhhZhV53q9IuVO+Ub5J3ZfyjeSFKkYSxAeJk8Xf3f2/gZ8DJyRXJk6SdxK/ejTV3Z9191Xu/jPiSe0o4Gs92O/5wL3ETxrnpp80zOz3iauJNySuGr5tZl8zs35mdouZ/SNxxfGclNckmymcZWZPmllL4krjCXQitWmDmY0GHkusejtRfndKPD9Me+3dZvZw6rFKlG1ONENoyLC/nczs/5nZ383sfTNbYmZTujlWnwN2AE+lbCd5pfE4M/uTmW0xs6VmNj5tf583sxcSV13fNLPLzcxS1q80s6vM7C4z+wfwUzM7L/EePps4flsSV28Hmdm/mtlfzWyTmf3YzPqnbKvWzP5gZhsTn9liM9uvqzdmKU0b0q4opy7nJdabmV1qZn8zs62J9/WltO0dZmZ/Tnz2zwGTMuz2v4EhwDHdHHcRCZfyjfKN8o3knSpG0iNm9nGgFmhNKZ4C7AZcl/58d38W+B/grID72Rk4A/gJ8CSwBTgpw1PPBt4jfrL5HnATsBBYDkwA7gHuMLPhaa+7DvgBMA74LfArMxuRRWhvAl9I/H0A8SubQZLwXOD4xDaOAw4BPpX2nAXAZOLHbGziPfzazA7uYrtHA392d8+w7rvAZcB44ldYf5pMRGZ2KPBL4MHEvi4Dvg1clLaNbwCvED+myeTaD7iE+GdwXGLd/cC5iff3L8Q/s/qU7Qwg/hlNJJ4ENiXeW7ZXBb9G/Jgnl8uJfzeWJtZfQ/wHzleB/RPv/VYzOzHxfgcAjwCvJeK9jPhn0o67f0i8KcvkLOMSkZAp3yjfoHwj+eLuWrR0uwB3A9uAzcBWwBPLxSnP+VaibHAn27gZ2JLyeCUws5v9TgVeTHl8NfDrtOf8HvhjymMD3gYWpZRVAh8C/5p4PDoR6+Upz+lDPLFdk/acCZ08PibxeLcM8fwww/F7OPH3QOAD4OyU9QOBfwB3Jx5/gviVuJFp21kINHVxvBYC96SVJeOcklJ2ZKJsj8TjnwKPpr3uKuDvaZ9X+rE/L7GdfVLK5gLbU49L6vvvJO4BidcclVLmyc8r0+OU8qOAFuDzKdvaChyd9rybiF9dhnhTm38AA1PWfymxj2PSXvcg8OOo/w9q0VIuC8o3mR4nz+PKN8o3WnK46I6RBPEE8StdE4H/BH5D/OpXtoz4iSCI84Efpzz+MVCb4Urc/yX/8PjZ5S3ghZSyVmAjsHva6/6Y8pwdwJ+IX/HJpU8AO6XtezMp8RK/ymbAS4mmA5vNbDNwYuL1nelP/KSdyf+l/L0m8W/yeOxHSnOIhCeBEWa2a0rZUjr6wN1fTXm8Dmh293fSytqOvZl9wszuTTQ9eDexvg8wspPYM0o0MXkQuNrdH0wU7w9UAbG0YzeDfx67/YD/Sxz3pD+S2Vbix1VE8kf5JhzKN8o3EoA6eEkQW9x9ReLv/zCzx4AriF/pgfjVL4ifKNJPehA/Ofw1252Z2b7ErzQdYWbXpqzqS/zKXmpZahMLiCfETGX5uBiwg3iSSVWZ8nf6ukz6EI/3MDq+j61dvO4doMPITQmp20n+YEgej65+RKSWv59hfXrn4GyO/a+B1cC0xL/bgJeIJ/CsmNlAYBGw2N0bU1Yl93My8Ebay5JxZfMZJA0hfvVSRPJH+SY7yjfKNxIi3TGS3vgO8K2Uq2n/Tbwt8TfTn5joeHkc8Vvo2Tqf+BW1g4lfOUwuVwFfTu2o2QuHp8RoxK9Ovpzlaz9M/Ns3rfxt4u2QUx2c8vcK4ifM1H0PAA5Mec5zxE+mNe6+Im1Z3UVMz9GzK5AvEW8ikOoo4k0b3uvB9jplZkOJ/2hpdPffufvLwC4EuFBjZn2If5feIz4qUqqXiDcdGZXh2K1Kec5Yaz9nxOFkdiDwbLaxiUhOKN/EKd8EoHwjQaliJD3m7r8H/gLMSjx+H7gAODExkswhZjbSzM4gfqXlSeLtvrtlZpXAvwH3uvuLqQtwG/H218eG8DZmJEaz2Yd4m+BRwLwsX7uK+JWpE81sWOKKEsCjwGfNrM7M9jGzG4GPJV+UuJ1+J/D/zOx4MzsAuIuUhOfuy4mfiO9OxPdxi49ONNPMPt9FTIuB/RLJIIgbgMkWHwVobzM7m3gH1w4dm0OwkfiVxgvMbE8zmwzMp+OVwK5cCRxBvLnCYDOrSSz9E4l1LjDXzL6c2Mc4M5tuZhcmXn9vYn93mdkBZnY88Q617SSaTowg/iNMRCKifKN800PKNxKIKkbSWzcC55vZKAB3f4j4aDe7Ez9h/5X4lb47gBM8PupKNk4mPpfEA+kr3H0t8aYT6VdueuIy4iPfPE981KNT3f3v2bwwcSXtSuJNLNYBySFT70pZniLegfihtJfPJD786kOJf18k3qY+1VTiIwVdR3xknoeJH9tVdMLjk8Q9Q3xkpax5fBSn04iP6vMi8ZGWvpfynkKTaFt/OnBQYl+3EG8iE2TOiMnEvx/PA2tTltMT65NNbmYS/zH1W+Lv7fVEDJuJj1y0F/Grc3OJd+ZOdybw3ylX/kQkOso3yjeBKN9IUBbvNyhSXhJXZl4HDnP3TB08i5aZ1RK/Urq/u2+POp5iZWb9iP/QOtPdM/VhEBHplvKNdEf5pnDojpFIiXH3GPGrYntEHUuRGwVcqyQlIpKZ8k1olG8KhO4YSVkq5St4IiJSOJRvRIqHKkYiIiIiIlL21JRORERERETKnipGIiIiIiJS9lQxEhERERGRsqeKkYiIiIiIlD1VjEREREREpOypYiQiIiIiImXv/wPST3cZI1PK5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inferior Parietal: Similar to above, this with narrower month range #\n",
    "# Quite the correlation for attend-rh, but activity relatively high here for all subjects!\n",
    "low, high = '18', '27'\n",
    "fig, axset = plt.subplots(2, 2, figsize = (14, 8))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_growth('attend_InfPar-lh', axset[0, 0])\n",
    "plot_growth('attend_InfPar-rh', axset[0, 1])\n",
    "plot_growth('ignore_InfPar-lh', axset[1, 0])\n",
    "plot_growth('ignore_InfPar-rh', axset[1, 1])\n",
    "\n",
    "# plot_growth('attend_InfPar-lh', axset[0, 0], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('attend_InfPar-rh', axset[0, 1], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('ignore_InfPar-lh', axset[1, 0], subject='f2f_116', color='#00008B')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 1], subject='f2f_116', color='#00008B')\n",
    "\n",
    "# plot_growth('attend_InfPar-lh', axset[0, 0], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('attend_InfPar-rh', axset[0, 1], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('ignore_InfPar-lh', axset[1, 0], subject='f2f_030', color='#A52A2A')\n",
    "# plot_growth('ignore_InfPar-rh', axset[1, 1], subject='f2f_030', color='#A52A2A')\n",
    "\n",
    "plt.setp(axset, xlim=(-3.6, 1.7), ylim=(-1.8, 2.85));\n",
    "plt.setp(axset[:, 0], ylabel='Vocab Score (transformed)');\n",
    "plt.setp(axset[1, :], xlabel='ROI Amplitude (normalized)');\n",
    "add_text(axset);\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'growth_InfPar_18to27.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "ff3401c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Superior Temporal: Similar to above #\n",
    "# No discernable patterns.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(2, 2, figsize = (14, 8))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_growth('attend_SupTemp-lh', axset[0, 0])\n",
    "plot_growth('attend_SupTemp-rh', axset[0, 1])\n",
    "plot_growth('ignore_SupTemp-lh', axset[1, 0])\n",
    "plot_growth('ignore_SupTemp-rh', axset[1, 1])\n",
    "\n",
    "plt.setp(axset, xlim=(-2.5, 2.2), ylim=(-1.8, 2.85));\n",
    "plt.setp(axset[:, 0], ylabel='Vocab Score (transformed)');\n",
    "plt.setp(axset[1, :], xlabel='ROI Amplitude (normalized)');\n",
    "add_text(axset);\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'growth_SupTemp.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca293e76",
   "metadata": {},
   "source": [
    "##  \\*\\*TEST\\*\\* Plot RTP contrasts versus scores\n",
    "At request from PK, these plots are sort of the inverse of the above sets, and without the lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "777acc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a plotting function #\n",
    "def plot_contrast(col_partial, ax=None, color = '#353535', subject=None):\n",
    "    '''Uses global dataframe 'df'.'''\n",
    "    if subject:\n",
    "        subject_list = [subject]\n",
    "    else:\n",
    "        subject_list = df.index\n",
    "    for subject in subject_list:\n",
    "        x = df.loc[subject, high] - df.loc[subject, low]\n",
    "        y1 = df.loc[subject, 'ignore_' + col_partial]\n",
    "        y2 = df.loc[subject, 'attend_' + col_partial]\n",
    "        ax.plot(x, y2 - y1, marker='o', color=color, fillstyle='full')\n",
    "    \n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Also define a labeling function #\n",
    "def add_text2(ax_array, roi_str):\n",
    "    ax_array[0].text(0.02, 0.98, roi_str + ', left',\n",
    "                     transform = ax_array[0].transAxes,\n",
    "                     horizontalalignment='left', verticalalignment='center')\n",
    "    ax_array[1].text(0.02, 0.98, roi_str + ', right',\n",
    "                     transform = ax_array[1].transAxes,\n",
    "                     horizontalalignment='left', verticalalignment='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e65ab29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inferior Parietal: Right hemisphere shows high correlation.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(1, 2, figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_contrast('InfPar-lh', axset[0])\n",
    "plot_contrast('InfPar-rh', axset[1])\n",
    "\n",
    "plt.setp(axset, xlim=(1.2, 3.8), ylim=(-3.0, 5.0));\n",
    "plt.setp(axset[0], ylabel='RTP Contrast (Standardized)');\n",
    "plt.setp(axset[:], xlabel='Vocab Score Difference (Transformed)');\n",
    "add_text2(axset, 'IPC');\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'contrast_vocabdiff_IPC.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8db7d24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inferior Frontal: Right hemisphere shows correlation, but not as much as IPC right.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(1, 2, figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_contrast('ParsAvg-lh', axset[0])\n",
    "plot_contrast('ParsAvg-rh', axset[1])\n",
    "\n",
    "plt.setp(axset, xlim=(1.2, 3.8), ylim=(-3.0, 5.0));\n",
    "plt.setp(axset[0], ylabel='RTP Contrast (Standardized)');\n",
    "plt.setp(axset[:], xlabel='Vocab Score Differnce (Transformed)');\n",
    "add_text2(axset, 'IFC');\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'contrast_vocabdiff_IFC.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f0d95148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Temporal: Right hemisphere shows correlation, but not as much as IPC right.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(1, 2, figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_contrast('TempAvg-lh', axset[0])\n",
    "plot_contrast('TempAvg-rh', axset[1])\n",
    "\n",
    "plt.setp(axset, xlim=(1.2, 3.8), ylim=(-3.0, 5.0));\n",
    "plt.setp(axset[0], ylabel='RTP Contrast (Standardized)');\n",
    "plt.setp(axset[:], xlabel='Vocab Score Differnce (Transformed)');\n",
    "add_text2(axset, 'TC');\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'contrast_vocabdiff_TC.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a6c9445e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a plotting function #\n",
    "def plot_contrastVattend(col_partial, ax=None, color = '#353535', subject=None):\n",
    "    '''Uses global dataframe 'df'.'''\n",
    "    if subject:\n",
    "        subject_list = [subject]\n",
    "    else:\n",
    "        subject_list = df.index\n",
    "    for subject in subject_list:\n",
    "#         x = df.loc[subject, high] - df.loc[subject, low]\n",
    "        y1 = df.loc[subject, 'ignore_' + col_partial]\n",
    "        y2 = df.loc[subject, 'attend_' + col_partial]\n",
    "        ax.plot(y2, y2 - y1, marker='o', color=color, fillstyle='full')\n",
    "    \n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "19661f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contrast growth for various regions; testing a hunch Part 1 #\n",
    "# IPC: correlation for rh; IFC: correlation for both, esp lh\n",
    "# TC: correlation for rh\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(1, 2, figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_contrastVattend('TempAvg-lh', axset[0])\n",
    "plot_contrastVattend('TempAvg-rh', axset[1])\n",
    "\n",
    "plt.setp(axset, xlim=(-3.0, 3.0), ylim=(-3.0, 5.0));\n",
    "plt.setp(axset[0], ylabel='RTP Contrast (Standardized)');\n",
    "plt.setp(axset[:], xlabel='RTP Attend (Standardized)');\n",
    "add_text2(axset, 'TC');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, 'contrast_attend_IPC.jpg')\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "97252d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a plotting function #\n",
    "def plot_attendignore(col_partial, ax=None, color = '#353535', subject=None):\n",
    "    '''Uses global dataframe 'df'.'''\n",
    "    if subject:\n",
    "        subject_list = [subject]\n",
    "    else:\n",
    "        subject_list = df.index\n",
    "    for subject in subject_list:\n",
    "#         x = df.loc[subject, high] - df.loc[subject, low]\n",
    "        y1 = df.loc[subject, 'ignore_' + col_partial]\n",
    "        y2 = df.loc[subject, 'attend_' + col_partial]\n",
    "        ax.plot(y2, y2, marker='o', color=color, fillstyle='full')\n",
    "        ax.plot(y2, y1, marker='o', color=color, fillstyle='full',\n",
    "                markerfacecolor='white')\n",
    "    \n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7665f039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contrast growth for various regions; testing a hunch Part 2 #\n",
    "# No apparent contrast versus attend RTP magnitude trends. Pat's idea\n",
    "# that ignore and attend anti-correlate isn't supported here.\n",
    "low, high = '18', '30'\n",
    "fig, axset = plt.subplots(1, 2, figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace=.12, hspace=.15)\n",
    "\n",
    "plot_attendignore('ParsAvg-lh', axset[0])\n",
    "plot_attendignore('ParsAvg-rh', axset[1])\n",
    "\n",
    "plt.setp(axset, xlim=(-5.0, 5.0), ylim=(-5.0, 5.0));\n",
    "plt.setp(axset[0], ylabel='RTP Contrast (Standardized)');\n",
    "plt.setp(axset[:], xlabel='RTP Attend (Standardized)');\n",
    "add_text2(axset, 'Chosen Region');\n",
    "\n",
    "# fig_name = os.path.join(graphics_dir, 'contrast_attend_IPC.jpg')\n",
    "# fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d1ea14",
   "metadata": {},
   "source": [
    "## \\*\\*FIG\\*\\* Plot labels on an inflated brain map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "47ba78a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Reading labels from parcellation...\n",
      "   read 34 labels from /home/sbierer/Data/MEG/anatomy/14mo_surr/label/lh.aparc.annot\n",
      "   read 34 labels from /home/sbierer/Data/MEG/anatomy/14mo_surr/label/rh.aparc.annot\n"
     ]
    }
   ],
   "source": [
    "# Read in anatomical labels #\n",
    "# On Thicke, make sure the data drive has been activated.\n",
    "use_parc = 'aparc'\n",
    "source_subject = '14mo_surr'\n",
    "\n",
    "parc_exists = os.path.isfile(os.path.join(anatomy_dir, source_subject, 'label', f'lh.{use_parc}.annot'))\n",
    "print(parc_exists)  # check for the existence of the chosen Freesurfer parcellation set\n",
    "\n",
    "anat_labels = mne.read_labels_from_annot(source_subject, use_parc, subjects_dir=anatomy_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "4619dad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Reading a source space...\n",
      "    [done]\n",
      "    Reading a source space...\n",
      "    [done]\n",
      "    2 source spaces read\n"
     ]
    }
   ],
   "source": [
    "# Import morphed source space #\n",
    "morph_subject = '14mo_surr'\n",
    "src_name = os.path.join(anatomy_dir, morph_subject,\n",
    "            'bem', morph_subject + '-oct-6-src.fif')\n",
    "src_morph = mne.read_source_spaces(src_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ffc4ab0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some plotting parameters #\n",
    "# No longer including entorhinal (orange).\n",
    "labels_to_plot = ['parsopercularis', 'parsorbitalis', 'parstriangularis',\n",
    "                  'inferiorparietal',\n",
    "                  'superiortemporal', 'middletemporal', 'temporalpole']  # 'transversetemporal', 'bankssts'\n",
    "\n",
    "label_colors = ['#008000', '#008020', '#208000',             # greens\n",
    "                '#A52A2A',                                   # red\n",
    "                '#0000FF', '#0000CC', '#4444FF']             # blues   '#000099'   3d3dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c5f62e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some plotting parameters #\n",
    "# No longer including entorhinal (orange).\n",
    "labels_to_plot = ['parsopercularis', 'parsorbitalis', 'parstriangularis',\n",
    "                  'inferiorparietal',\n",
    "                  'superiortemporal']\n",
    "\n",
    "label_colors = ['#008000', '#008020', '#208000',             # greens\n",
    "                '#A52A2A',                                   # red\n",
    "                '#0000FF']    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7ef78438",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show the labels of interest and optionally save #\n",
    "# Getting a VTK / numpy conflict as of 2023-June. Skipping this for now.\n",
    "kwargs = dict(surf='inflated', hemi='split', views='lateral',\n",
    "              subjects_dir=anatomy_dir, size=(1600, 800))\n",
    "brain = mne.viz.Brain(morph_subject, figure=99, **kwargs)\n",
    "\n",
    "for lbl_base, lbl_color in zip(labels_to_plot, label_colors):\n",
    "    for hemi in ('-lh', '-rh'):\n",
    "        label = [lbl for lbl in anat_labels if lbl.name==lbl_base + hemi][0]\n",
    "        brain.add_label(label, color=lbl_color)\n",
    "\n",
    "brain.add_annotation(use_parc, color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c9ceab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the figure #\n",
    "# PNG format looks much better than JPG! Note that the entorhinal is hidden on the medial side.\n",
    "fig_name = os.path.join(graphics_dir, 'roi_labelmap_lateral.png')\n",
    "brain.save_image(fig_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72c4300",
   "metadata": {},
   "source": [
    "## Plot residuals from various models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b877859f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for homoskedasticity #\n",
    "# Not bad. Model 5 is obviously lower scatter.\n",
    "mdf = mdf5_lharhiMIX    # mdf2_lha, mdf3_rha, mdf5_lharhiMIX\n",
    "\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "ax = sns.scatterplot(y = mdf.resid, x = mdf.fittedvalues)\n",
    "\n",
    "ax.set_title(\"Model XX: Residuals versus Fits\")\n",
    "ax.set_xlabel(\"Fitted Values\")\n",
    "ax.set_ylabel(\"Residuals\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "fd6b7e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at residuals of one of the better models #\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "ax = sns.distplot(mdf.resid, hist=False, kde_kws={\"shade\" : True, \"lw\": 1}, fit=stats.norm)\n",
    "\n",
    "ax.set_title(\"Model L IFG attend KDE: Residuals (blue), Gaussian Distribution (black)\")\n",
    "ax.set_xlabel(\"Residuals\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "55741af9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShapiroResult(statistic=0.9802371263504028, pvalue=0.11903943121433258)\n",
      "ShapiroResult(statistic=0.9845940470695496, pvalue=0.2657117247581482)\n",
      "ShapiroResult(statistic=0.9847633242607117, pvalue=0.27383309602737427)\n"
     ]
    }
   ],
   "source": [
    "# Statistics for normality #\n",
    "# The assumption of normality is violated for each model at .05 level.\n",
    "# Orig time-transform: mdf2_lha=.00087, mdf3_rha=.0035.\n",
    "for mdf in (mdf2_lha, mdf3_rha, mdf5_lharhiMIX):\n",
    "    norm_sw = stats.shapiro(mdf.resid)\n",
    "    print(norm_sw)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "539167fd",
   "metadata": {},
   "source": [
    "## \\*\\*FIG\\*\\* Plot average attend and ignore levels as a bar graph\n",
    "Averaging is across subjects.  This procedure uses the original ROI data, _without_ standardizing to a z-score, linearized using 10**(x+2) to express each frequency band's value as a percentage.  \n",
    "\n",
    "From 2022-January, bar chart color order matches the left-right order of the anatomical map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "3401c581",
   "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>18</th>\n",
       "      <th>21</th>\n",
       "      <th>24</th>\n",
       "      <th>27</th>\n",
       "      <th>30</th>\n",
       "      <th>attend_ParsTri-lh</th>\n",
       "      <th>attend_ParsTri-rh</th>\n",
       "      <th>ignore_ParsTri-lh</th>\n",
       "      <th>ignore_ParsTri-rh</th>\n",
       "      <th>attend_ParsOper-lh</th>\n",
       "      <th>...</th>\n",
       "      <th>ignore_PoleTemp-lh</th>\n",
       "      <th>ignore_PoleTemp-rh</th>\n",
       "      <th>attend_ParsAvg-lh</th>\n",
       "      <th>attend_ParsAvg-rh</th>\n",
       "      <th>ignore_ParsAvg-lh</th>\n",
       "      <th>ignore_ParsAvg-rh</th>\n",
       "      <th>attend_TempAvg-lh</th>\n",
       "      <th>attend_TempAvg-rh</th>\n",
       "      <th>ignore_TempAvg-lh</th>\n",
       "      <th>ignore_TempAvg-rh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>f2f_009</th>\n",
       "      <td>-1.20258</td>\n",
       "      <td>-0.84281</td>\n",
       "      <td>-0.30567</td>\n",
       "      <td>0.59538</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>-3.62808</td>\n",
       "      <td>-2.66349</td>\n",
       "      <td>-4.12667</td>\n",
       "      <td>-3.58249</td>\n",
       "      <td>-2.97353</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.96081</td>\n",
       "      <td>-3.39748</td>\n",
       "      <td>-3.53613</td>\n",
       "      <td>-2.73493</td>\n",
       "      <td>-4.05892</td>\n",
       "      <td>-3.57253</td>\n",
       "      <td>-3.50609</td>\n",
       "      <td>-2.74679</td>\n",
       "      <td>-4.07554</td>\n",
       "      <td>-3.39621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_010</th>\n",
       "      <td>-1.33074</td>\n",
       "      <td>-1.29473</td>\n",
       "      <td>-0.92429</td>\n",
       "      <td>-0.41760</td>\n",
       "      <td>0.18365</td>\n",
       "      <td>-6.19275</td>\n",
       "      <td>-4.67896</td>\n",
       "      <td>-5.15764</td>\n",
       "      <td>-3.34809</td>\n",
       "      <td>-6.56592</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.91019</td>\n",
       "      <td>-3.82532</td>\n",
       "      <td>-6.11787</td>\n",
       "      <td>-4.71196</td>\n",
       "      <td>-5.10874</td>\n",
       "      <td>-3.38404</td>\n",
       "      <td>-5.68363</td>\n",
       "      <td>-4.57635</td>\n",
       "      <td>-5.13489</td>\n",
       "      <td>-3.59118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              18       21       24       27      30  attend_ParsTri-lh  \\\n",
       "subject                                                                  \n",
       "f2f_009 -1.20258 -0.84281 -0.30567  0.59538 0.99481           -3.62808   \n",
       "f2f_010 -1.33074 -1.29473 -0.92429 -0.41760 0.18365           -6.19275   \n",
       "\n",
       "         attend_ParsTri-rh  ignore_ParsTri-lh  ignore_ParsTri-rh  \\\n",
       "subject                                                            \n",
       "f2f_009           -2.66349           -4.12667           -3.58249   \n",
       "f2f_010           -4.67896           -5.15764           -3.34809   \n",
       "\n",
       "         attend_ParsOper-lh  ...  ignore_PoleTemp-lh  ignore_PoleTemp-rh  \\\n",
       "subject                      ...                                           \n",
       "f2f_009            -2.97353  ...            -3.96081            -3.39748   \n",
       "f2f_010            -6.56592  ...            -4.91019            -3.82532   \n",
       "\n",
       "         attend_ParsAvg-lh  attend_ParsAvg-rh  ignore_ParsAvg-lh  \\\n",
       "subject                                                            \n",
       "f2f_009           -3.53613           -2.73493           -4.05892   \n",
       "f2f_010           -6.11787           -4.71196           -5.10874   \n",
       "\n",
       "         ignore_ParsAvg-rh  attend_TempAvg-lh  attend_TempAvg-rh  \\\n",
       "subject                                                            \n",
       "f2f_009           -3.57253           -3.50609           -2.74679   \n",
       "f2f_010           -3.38404           -5.68363           -4.57635   \n",
       "\n",
       "         ignore_TempAvg-lh  ignore_TempAvg-rh  \n",
       "subject                                        \n",
       "f2f_009           -4.07554           -3.39621  \n",
       "f2f_010           -5.13489           -3.59118  \n",
       "\n",
       "[2 rows x 49 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roidb.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "a91724fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart w/ averaged ROIs, highlighting attend vs ignore in left and right hemispheres #\n",
    "# Note that the standard error bars, since they are expressed in linear units, are\n",
    "# calculated according to the following link:\n",
    "#   https://stats.stackexchange.com/questions/123514/calculating-standard-error-after-a-log-transform\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "roi_base = {'ParsAvg':'IF', 'TempAvg':'ST', 'InfPar':'IP'}\n",
    "lh_colors = ['#33B333', '#6666FF', '#F27777']  # rh match ROI map or darker, lh are lighter\n",
    "rh_colors = ['#008000', '#0000B3', '#A52A2A']  # shading via https://www.hexcolortool.com/\n",
    "\n",
    "width, offset = 0.16, 0.20\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (10, 4))\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey');  # fake data for legend\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey', hatch = '/');\n",
    "\n",
    "div = np.sqrt(len(df_roidb) - 1)\n",
    "for xx, roi in enumerate(roi_base):\n",
    "    avg_a = df_roidb['attend_' + roi + '-lh'].mean()/10 + 2\n",
    "    ste_a = df_roidb['attend_' + roi + '-lh'].std()/(10*div)  # see note in header\n",
    "    ste_a = (10**avg_a) * ste_a\n",
    "    avg_i = df_roidb['ignore_' + roi + '-lh'].mean()/10 + 2\n",
    "    ste_i = df_roidb['ignore_' + roi + '-lh'].std()/(10*div)\n",
    "    ste_i = (10**avg_i) * ste_i\n",
    "    ax.bar(xx - 1.5*offset, 10**avg_a, width, edgecolor='k', yerr=np.array([[0],[ste_a]]),\n",
    "           color=lh_colors[xx]);\n",
    "    ax.bar(xx - .5*offset, 10**avg_i, width, edgecolor='k', yerr=np.array([[0],[ste_i]]),\n",
    "           color=lh_colors[xx], hatch = '/');\n",
    "    \n",
    "    avg_a = df_roidb['attend_' + roi + '-rh'].mean()/10 + 2\n",
    "    ste_a = df_roidb['attend_' + roi + '-rh'].std()/(10*div)\n",
    "    ste_a = (10**avg_a) * ste_a\n",
    "    avg_i = df_roidb['ignore_' + roi + '-rh'].mean()/10 + 2\n",
    "    ste_i = df_roidb['ignore_' + roi + '-rh'].std()/(10*div)\n",
    "    ste_i = (10**avg_i) * ste_i\n",
    "    ax.bar(xx + .5*offset, 10**avg_a, width, edgecolor='k', yerr=np.array([[0],[ste_a]]),\n",
    "           color=rh_colors[xx]);\n",
    "    ax.bar(xx + 1.5*offset, 10**avg_i, width, edgecolor='k', yerr=np.array([[0],[ste_i]]),\n",
    "           color=rh_colors[xx], hatch = '/');\n",
    "\n",
    "ax.set_xlim((-.6, 2.5))\n",
    "\n",
    "ax.set_xticks(np.arange(len(roi_base)))\n",
    "ax.tick_params('x', length=0, pad=-20)\n",
    "ax.set_xticklabels([(roi_base[roi] + '\\nleft     right') for roi in roi_base], fontsize=20)\n",
    "ax.set_ylim((32, 50))\n",
    "ax.set_ylabel('Relative Theta Power (%)', fontsize=16)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "leg = ax.legend(['attend', 'ignore'], loc=(0.98, 0.68), prop={'size': 16})\n",
    "\n",
    "fig.tight_layout();\n",
    "plt.show();\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'barchart_ROIs_pct.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "2056d06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TEST ONLY #\n",
    "roi_base = {'SupTemp':'SUP', 'MidTemp':'MID', 'TransTemp':'TRN', 'BanksTemp':'BNK'}\n",
    "lh_colors = ['#33B333', '#6666FF', '#F27777', '#999999']\n",
    "rh_colors = ['#008000', '#0000B3', '#A52A2A', '#666666']\n",
    "width, offset = 0.16, 0.20\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 4))\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey');  # fake data for legend\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey', hatch = '/');\n",
    "\n",
    "div = np.sqrt(len(df_roidb) - 1)\n",
    "for xx, roi in enumerate(roi_base):\n",
    "    avg_a = df_roidb['attend_' + roi + '-lh'].mean()/10 + 2\n",
    "    ste_a = df_roidb['attend_' + roi + '-lh'].std()/(10*div)  # see note in header\n",
    "    ste_a = (10**avg_a) * ste_a\n",
    "    avg_i = df_roidb['ignore_' + roi + '-lh'].mean()/10 + 2\n",
    "    ste_i = df_roidb['ignore_' + roi + '-lh'].std()/(10*div)\n",
    "    ste_i = (10**avg_i) * ste_i\n",
    "    ax.bar(xx - 1.5*offset, 10**avg_a, width, edgecolor='k', yerr=np.array([[0],[ste_a]]),\n",
    "           color=lh_colors[xx]);\n",
    "    ax.bar(xx - .5*offset, 10**avg_i, width, edgecolor='k', yerr=np.array([[0],[ste_i]]),\n",
    "           color=lh_colors[xx], hatch = '/');\n",
    "    \n",
    "    avg_a = df_roidb['attend_' + roi + '-rh'].mean()/10 + 2\n",
    "    ste_a = df_roidb['attend_' + roi + '-rh'].std()/(10*div)\n",
    "    ste_a = (10**avg_a) * ste_a\n",
    "    avg_i = df_roidb['ignore_' + roi + '-rh'].mean()/10 + 2\n",
    "    ste_i = df_roidb['ignore_' + roi + '-rh'].std()/(10*div)\n",
    "    ste_i = (10**avg_i) * ste_i\n",
    "    ax.bar(xx + .5*offset, 10**avg_a, width, edgecolor='k', yerr=np.array([[0],[ste_a]]),\n",
    "           color=rh_colors[xx]);\n",
    "    ax.bar(xx + 1.5*offset, 10**avg_i, width, edgecolor='k', yerr=np.array([[0],[ste_i]]),\n",
    "           color=rh_colors[xx], hatch = '/');\n",
    "\n",
    "ax.set_xlim((-.6, 3.5))\n",
    "\n",
    "ax.set_xticks(np.arange(len(roi_base)))\n",
    "ax.tick_params('x', length=0, pad=-20)\n",
    "ax.set_xticklabels([(roi_base[roi] + '\\nleft     right') for roi in roi_base], fontsize=20)\n",
    "ax.set_ylim((32, 52))\n",
    "ax.set_ylabel('Relative Theta Power (%)', fontsize=16)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "leg = ax.legend(['attend', 'ignore'], loc=(0.98, 0.68), prop={'size': 16})\n",
    "\n",
    "fig.tight_layout();\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1cd143e",
   "metadata": {},
   "source": [
    "## \\*\\*FIG\\*\\* Plot average attend and ignore levels as a bar graph - linear validation\n",
    "Like the figure in the previous section, except here the linearization was done BEFORE averaging (because statistics from that per-subject / per-band ROI data are being calculated for the F2F paper).  \n",
    "\n",
    "The two figures, here and above, are very comparable in both mean and standard error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3562fce0",
   "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>18</th>\n",
       "      <th>21</th>\n",
       "      <th>24</th>\n",
       "      <th>27</th>\n",
       "      <th>30</th>\n",
       "      <th>attend_ParsTri-lh</th>\n",
       "      <th>attend_ParsTri-rh</th>\n",
       "      <th>ignore_ParsTri-lh</th>\n",
       "      <th>ignore_ParsTri-rh</th>\n",
       "      <th>attend_ParsOper-lh</th>\n",
       "      <th>attend_ParsOper-rh</th>\n",
       "      <th>ignore_ParsOper-lh</th>\n",
       "      <th>ignore_ParsOper-rh</th>\n",
       "      <th>attend_ParsOrb-lh</th>\n",
       "      <th>attend_ParsOrb-rh</th>\n",
       "      <th>ignore_ParsOrb-lh</th>\n",
       "      <th>ignore_ParsOrb-rh</th>\n",
       "      <th>attend_InfPar-lh</th>\n",
       "      <th>attend_InfPar-rh</th>\n",
       "      <th>ignore_InfPar-lh</th>\n",
       "      <th>ignore_InfPar-rh</th>\n",
       "      <th>attend_SupTemp-lh</th>\n",
       "      <th>attend_SupTemp-rh</th>\n",
       "      <th>ignore_SupTemp-lh</th>\n",
       "      <th>ignore_SupTemp-rh</th>\n",
       "      <th>attend_MidTemp-lh</th>\n",
       "      <th>attend_MidTemp-rh</th>\n",
       "      <th>ignore_MidTemp-lh</th>\n",
       "      <th>ignore_MidTemp-rh</th>\n",
       "      <th>attend_TransTemp-lh</th>\n",
       "      <th>attend_TransTemp-rh</th>\n",
       "      <th>ignore_TransTemp-lh</th>\n",
       "      <th>ignore_TransTemp-rh</th>\n",
       "      <th>attend_BanksTemp-lh</th>\n",
       "      <th>attend_BanksTemp-rh</th>\n",
       "      <th>ignore_BanksTemp-lh</th>\n",
       "      <th>ignore_BanksTemp-rh</th>\n",
       "      <th>attend_PoleTemp-lh</th>\n",
       "      <th>attend_PoleTemp-rh</th>\n",
       "      <th>ignore_PoleTemp-lh</th>\n",
       "      <th>ignore_PoleTemp-rh</th>\n",
       "      <th>attend_ParsAvg-lh</th>\n",
       "      <th>attend_ParsAvg-rh</th>\n",
       "      <th>ignore_ParsAvg-lh</th>\n",
       "      <th>ignore_ParsAvg-rh</th>\n",
       "      <th>attend_TempAvg-lh</th>\n",
       "      <th>attend_TempAvg-rh</th>\n",
       "      <th>ignore_TempAvg-lh</th>\n",
       "      <th>ignore_TempAvg-rh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>f2f_009</th>\n",
       "      <td>-1.20258</td>\n",
       "      <td>-0.84281</td>\n",
       "      <td>-0.30567</td>\n",
       "      <td>0.59538</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>43.37030</td>\n",
       "      <td>54.15661</td>\n",
       "      <td>38.66636</td>\n",
       "      <td>43.82789</td>\n",
       "      <td>50.42518</td>\n",
       "      <td>55.72306</td>\n",
       "      <td>45.02516</td>\n",
       "      <td>45.39004</td>\n",
       "      <td>39.74855</td>\n",
       "      <td>50.09948</td>\n",
       "      <td>34.79650</td>\n",
       "      <td>42.61166</td>\n",
       "      <td>49.01462</td>\n",
       "      <td>52.77906</td>\n",
       "      <td>39.15818</td>\n",
       "      <td>42.28220</td>\n",
       "      <td>46.55966</td>\n",
       "      <td>54.58409</td>\n",
       "      <td>39.36209</td>\n",
       "      <td>46.39820</td>\n",
       "      <td>43.76894</td>\n",
       "      <td>52.63263</td>\n",
       "      <td>37.87391</td>\n",
       "      <td>45.12160</td>\n",
       "      <td>48.95679</td>\n",
       "      <td>58.25663</td>\n",
       "      <td>43.45308</td>\n",
       "      <td>50.26728</td>\n",
       "      <td>49.51043</td>\n",
       "      <td>57.25103</td>\n",
       "      <td>39.33015</td>\n",
       "      <td>47.09860</td>\n",
       "      <td>43.55096</td>\n",
       "      <td>52.19667</td>\n",
       "      <td>40.17161</td>\n",
       "      <td>45.73534</td>\n",
       "      <td>44.51468</td>\n",
       "      <td>53.32638</td>\n",
       "      <td>39.49601</td>\n",
       "      <td>43.94320</td>\n",
       "      <td>44.62652</td>\n",
       "      <td>53.13780</td>\n",
       "      <td>39.13587</td>\n",
       "      <td>45.75171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2f_010</th>\n",
       "      <td>-1.33074</td>\n",
       "      <td>-1.29473</td>\n",
       "      <td>-0.92429</td>\n",
       "      <td>-0.41760</td>\n",
       "      <td>0.18365</td>\n",
       "      <td>24.02841</td>\n",
       "      <td>34.04899</td>\n",
       "      <td>30.49554</td>\n",
       "      <td>46.25840</td>\n",
       "      <td>22.04996</td>\n",
       "      <td>31.77046</td>\n",
       "      <td>30.93266</td>\n",
       "      <td>44.89385</td>\n",
       "      <td>27.57434</td>\n",
       "      <td>35.66840</td>\n",
       "      <td>31.09742</td>\n",
       "      <td>46.49559</td>\n",
       "      <td>18.62267</td>\n",
       "      <td>35.12168</td>\n",
       "      <td>21.22889</td>\n",
       "      <td>42.01879</td>\n",
       "      <td>27.14334</td>\n",
       "      <td>38.64925</td>\n",
       "      <td>29.36454</td>\n",
       "      <td>46.34781</td>\n",
       "      <td>26.76163</td>\n",
       "      <td>35.57484</td>\n",
       "      <td>30.38990</td>\n",
       "      <td>43.56625</td>\n",
       "      <td>22.44533</td>\n",
       "      <td>39.62574</td>\n",
       "      <td>27.66984</td>\n",
       "      <td>47.38545</td>\n",
       "      <td>22.70526</td>\n",
       "      <td>35.22449</td>\n",
       "      <td>23.86779</td>\n",
       "      <td>42.59497</td>\n",
       "      <td>27.14778</td>\n",
       "      <td>30.81836</td>\n",
       "      <td>32.28354</td>\n",
       "      <td>41.44458</td>\n",
       "      <td>24.55090</td>\n",
       "      <td>33.82928</td>\n",
       "      <td>30.84188</td>\n",
       "      <td>45.88262</td>\n",
       "      <td>27.01759</td>\n",
       "      <td>35.01415</td>\n",
       "      <td>30.67933</td>\n",
       "      <td>43.78622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              18       21  ...  ignore_TempAvg-lh  ignore_TempAvg-rh\n",
       "subject                    ...                                      \n",
       "f2f_009 -1.20258 -0.84281  ...           39.13587           45.75171\n",
       "f2f_010 -1.33074 -1.29473  ...           30.67933           43.78622\n",
       "\n",
       "[2 rows x 49 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roilin.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1bb7686a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAELCAYAAADUXz4tAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAABFrElEQVR4nO3deVxU9f7H8feoiMgmgSiCgIoJaKKBIlcMNDWXck1MsyQ11C7uaxph4pa7lJqUay631CsaLlcRMTdMUVQETVzYBDdc2GRzfn/4c4qA4QzOnDMzvJ+PB4+bs5zzGe+53pen75wjk8vlchARERER6bEaUg9ARERERKRpjF4iIiIi0nuMXiIiIiLSe4xeIiIiItJ7jF4iIiIi0nuMXiIiIiLSe4xeem2bNm2CTCbDpk2bAABz5syBTCZT+hMXFyfpzFQ9/fNY9ff3L3Ns1qxZE2ZmZvD09MTmzZulHZiqnYKCAixatAhubm4wNjaGmZkZ3N3d8e233+L58+cAhP0ZK5PJ4OvrK+2HIdIytaQegPTXrFmz4OLiUu5zDg4OIk9DVLEVK1bAysoKACCXy/H06VNs3boV/v7+ePjwIaZMmSLxhFQdFBcXo0ePHjhz5gw+/fRTjB49GsXFxThx4gS+/PJL7Nu3D1FRURgwYACcnJwU70tMTMSCBQvQv39/DBgwQPF4gwYNpPgYRFqL0Usa061bN55pIJ3Qr18/ODo6lnps5MiRcHV1xdy5cxEYGAhDQ0NphqNq49dff0V0dDR2795dKl7Hjx+PJUuWYPr06diwYQPGjh2L1q1bK56Pjo7GggUL0Lp1awwbNkyK0Yl0Apc3EBGVw8jICB988AGePXuGq1evSj0OVQOnT58GAHTv3r3Mc//+979hYGCAM2fOiD0Wkd5g9BIRVaBGjZd/RBYXF0s8CVUHpqamAIB169aVea5u3brIycnBli1bxB6LSG8wekljnj59iocPH5b5KSoqkno0okq9ePEC0dHRMDQ0hKurq9TjUDUwbNgw1K5dG1OnTkWrVq0QFBSE6OhoFBQUAABq164t8YREuo3RSxrTr18/1K9fv8zPqVOnpB6NqJTHjx8r/lKWmZmJmJgYDBkyBJcuXcIXX3wBExMTqUekaqBly5b473//C2tra1y9ehXz5s1D586dYWlpiaFDh+LPP/+UekQincYvspHGLF26FG5ubmUeL+8xIim9/fbbZR4zNDTEuHHjsGjRIgkmouqqd+/eSE5Oxr59+xAREYHIyEhkZGRgx44d2LNnDw4dOgQfHx+pxyTSSYxe0hh3d3devYF0wtatWxWXd6pZsybq1asHFxcX1KlTR+LJqDqqU6cO/Pz84OfnBwC4cOECli5dih07dmDMmDFITEyUeEIi3cToJaJqr2PHjmUuWUYkptzcXCxYsADu7u6lLlcGvPw3Edu3b8eTJ09w8OBBPHr0CJaWlhJNSqS7uKaXiIhIYnXq1MHSpUvx3XffVfiali1bQiaTwcjISMTJiPQHo5eIiEhiNWvWxODBgxEdHY2tW7eWeT4rKwu7du1C165dUbduXQkmJNJ9XN5ARESkBZYvX44//vgDn3zyCbZu3Yr33nsP5ubmSEpKwqZNm1BYWIjVq1dLPSaRzmL0EhERaQErKyvExsZixYoV2Lt3L+bOnYu8vDw0atQIAwYMwOzZs2FjYyP1mEQ6SyaXy+VSD0FEREREpElc00tEREREeo/RS0RERER6j9FLRERERHpPL6K3R48eUo9ARERERFpML6L34cOHUo9ARERERFpML6KXiIiIiEgZRq+OcnR0hK+vr8rvk8vlmDFjBiwtLWFsbIw1a9YAAO7fv4/c3Fw1T1k1/v7+kMlkKr9vzpw5kMlkuHPnTqWvffHihaDX0evjsVoWj1XtxeO1LB6vpC8YvdXM/v37sXjxYnh5eWHVqlV49913cfDgQbRo0QIPHjyQejwAwOjRo/Hzzz9rbPvPnj1Dhw4dsGnTJo3tg14fj1Ueq7qExyuPV9J+vCNbNXP58mUAwMKFC/HWW28BAHbs2IEnT55IOFVpXl5e8PLy0tj2s7KycO7cOfTq1Utj+6DXx2OVx6ou4fHK45W0H8/0VjOFhYUAAFNTU4knIVKOxyrpEh6vRNqP0atHzpw5g27dusHU1BSmpqbo3r07/vjjD8Xzjo6O+OabbwAATZo0gaOjI/z9/Us9VpW1bFXl6+uLHj164KuvvoKJiQmsra1x5cqVctedXb9+HX379kW9evVgZWWF8ePH48cffyx3nVlSUhI++OADmJiY4I033oC/vz+ysrIAANHR0WjSpAkA4JtvvhG8To3Ui8fqSzxWdQOP15d4vJKu4/IGPXHkyBH07t0bbdq0QUhICAoKCrBx40a88847OHLkCDp16oSVK1diy5Yt2LNnD1asWAFHR0c0aNAAz549UzzWsmVLUec+efIkbty4gSVLluD27dtwdXUt85qUlBR4e3sDAKZOnYpatWph9erV2LZtW7nb7Nu3L/r27Yvly5fj1KlT2Lx5M548eYLw8HC4uLhgxYoVmDRpEvr3748BAwagfv36Gv2MVBqP1b/wWNV+PF7/wuOVdJ5cD7i7u0s9gugcHBzkPj4+crlcLi8pKZE3bdpU3rFjR3lxcbHiNTk5OXInJyd5mzZtFI8FBwfLAchv376t9DEx+Pj4yAHIjx07Vurx4cOHy/9+aI4YMUJeq1YteUJCguKxtLQ0uYmJSam5X32O8ePHl9pe586d5QYGBvLnz5/L5XK5/Pbt23IA8uDgYI18LiqNxyqPVV3C45XHK+kvLm/QAxcvXsStW7fQr18/PH78GA8fPsTDhw+Rn5+PDz74AHFxcUhLS5N6zHIZGRnhnXfeqfB5uVyO8PBw9OzZEy4uLorHbW1tMWzYsHLfM2TIkFK/bteuHYqKivDo0SP1DE1VxmO1NB6r2o3Ha2k8XknXcXmDHrh58yYAYNq0aZg2bVq5r0lNTYWdnZ2YYwliaWmJGjUq/rtXVlYWsrKy0Lx58zLPOTs7l/sea2vrUr82MjIC8NcXTUg6PFZL47Gq3Xi8lsbjlXQdo1cPlJSUAABCQkLQoUOHcl9T0R9iUqtZs6bS54uKigAAhoaGZZ6rU6dOue9R9gc9SYvHamk8VrUbj9fSeLySrmP06gFHR0cAgImJCbp27VrquXPnziErK0vxN3JdY21tDRMTE/z5559lnrtx44YEE9Hr4LFKuoTHK5F+4V/b9ICHhwdsbGwQGhqKnJwcxePPnj2Dn58fPvvsM9SqVfHfb16dEXjx4oXGZ1VVjRo10KdPHxw8eBC3b99WPP748WPs2LGjStvU5s+r73isqkabP291wONVNdr8eYkARq9eMDAwwHfffYfk5GS8/fbbWLx4MUJDQ9GxY0ckJydj2bJlSv9gfnVZmSVLlmDfvn1ijS3Y3LlzYWBggA4dOmD+/PlYunQp2rVrh8ePHwOAyveSf7XWbd++fVi/fr1iO6R5PFZ5rOoSHq88Xkm/MHr1xMCBA3H48GHY2dkhJCQEQUFBMDMzw759+8p84/afPvroI3Tt2hUbN27EjBkzRJpYuGbNmuH48eNo3bo1FixYgEWLFqFPnz4IDAwEUP6aNGXq1q2L+fPnIzU1FePGjcOlS5c0MTZVgMeqcDxWpcfjVTger6TtZHK5XC71EK/Lw8MD58+fl3oM0pD79++jfv36Zc46jBs3DmvXrkV+fj4MDAwkmo7oLzxWSZfweKXqhmd6SesNGjQILVu2LLVOLC8vD7/99hvatGnDP5RJa/BYJV3C45WqG169gbTeJ598gs8//xy9e/dG37598fz5c/z8889IS0vDunXrpB6PSIHHKukSHq9U3XB5A+mEbdu2YdWqVbh27Rpq1KgBDw8PBAUFwcfHR+rRiErhsUq6hMcrVSeMXiIiIiLSe1zTS0RERER6j9FLRERERHpP0BfZkpKS8Msvv+DYsWO4c+cOnj59CktLS9jb26N79+7o168fnJycND0rEREREVGVKD3Tm5CQgH79+qFFixZYv3496tWrh759+2LMmDHo2rUrjI2NsWzZMjg7O2PAgAGIj48Xa24iIiIiIsEqPNMbEhKC1atXw9/fH8HBwWjbtm2FG7l06RLWr1+Pzp07IzAwEMHBwRoZloiIiIioKio80/v06VNcv34dixYtUhq8AODm5obQ0FBcu3YNT58+VfuQRERERESvg5csIyIiIiK9V6U7sqWmpiIzMxPW1tZwcHBQ90xEREREAICCggJkZWUhOzsbJSUlUo9DWqhmzZowNTXFG2+8AUNDwwpfp1L03rlzB4MHD8a5c+cAADKZDG5ubti2bRtcXFxeb2IiIiKivykoKEBKSgosLCzg6OgIAwMDyGQyqcciLSKXy1FUVIRnz54hJSUF9vb2FYavStfpDQwMhI+PD+7du4fCwkLcvn0bLVu2xPDhw9UyOBEREdErWVlZsLCwgJWVFWrXrs3gpTJkMhlq164NKysrWFhYICsrq8LXVhi9CxYsQF5eXqnHbt++jaFDh6J+/fqoVasW7O3t0b9/f9y6dUt90xMREREByM7OhpmZmdRjkI4wMzNDdnZ2hc9XGL0XLlyAk5MTvv/+exQVFQEAPvnkE7z//vsIDAzE119/jYCAAIwaNQqfffaZ4IG2b98OmUxW6qdfv34AgOTkZHTv3h3GxsZwcXHBwYMHBW+XiIiI9EtJSQkMDAykHoN0hIGBgdJ13xVG765duxAREYH9+/ejefPm2LRpE6ZPn461a9ciOzsb586dQ0FBAX744QcsXrxY8EAJCQno378/MjIyFD+bNm2CXC5H3759YWlpiXPnzmH48OEYOHAgbt++rdonJiIiIr3BJQ0kVGXHitIvsr399ts4ePAgTpw4gdmzZ2Px4sUICQnB5s2bqzxQQkICWrdujYYNG5Z6PCoqCtevX8eJEydgamoKV1dXREZGYv369Zg3b16V90dEREREJOiLbJ06dcLvv/+OpUuXYsGCBXB3d8fhw4ertMOEhAS0aNGizOMxMTFo27YtTE1NFY95e3vjzJkzVdoPEREREdErFUZvXl4evv76a3zwwQcYN24cMjIy0KtXL8TGxmLGjBmYOHEifHx8cPr0acE7KywsxM2bNxEREYHmzZujWbNmmDlzJgoKCpCRkYFGjRqVen2DBg2QlpZW9U9HRERERKX4+/tXadlIVd+nLSpc3jBy5EhcvHgR/fv3x+nTp9GtWzdcuXIFMpkMfn5++PDDD7Fp0yZ8/PHHcHV1xf79+yvd2Y0bN1BcXAxjY2Ps2rULN2/exIQJE5CdnY3nz5+Xua6aoaEhCgoKyt1WWFgYwsLCAAAPHjxQ5TMTERGRjrOzs0N6errUYyjY2trqzIm60aNHo2vXrlKPIboKo/fAgQPYtWsXunXrhqysLFhZWeHmzZtwcnICANSoUQMjRozAsGHDFPFZmZYtW+Lhw4ewtLQEALi5uUEul2PIkCH4/PPP8fTp01KvLygoQN26dcvdVkBAAAICAgC8vA0xERERVR/p6emYM2eO1GMoaNMslfHy8oKXl5fUY4iuwui1tbVFeHg4HBwccOTIEdSuXRvW1tZlXle7dm0EBgYK3uGr4H3FxcUFRUVFaNSoES5dulTquczMTNjY2AjeNhERERFReSpc0/vDDz9g3759cHZ2xqxZs7B69erXvkD0f//7XzRo0ACFhYWKxy5evIh69eqhQ4cOiIuLQ25uruK5kydPokOHDq+1TyIiIiJt9/jxY/j7+ytuo9usWTN8+eWXeP78ueI1V65cQb9+/VCvXj0YGRmhQ4cOCA8PL7Ota9euwc/PD1ZWVjA3N4evry9OnDiheL68tblRUVHo2bMnLC0tYWBgAFtbW4wePRpPnjzR1EcWXYVnet955x2kpqbiwYMHsLKyUsvCZR8fH8jlcgQEBGDWrFm4ceMGpk2bhmnTpsHX1xcODg7w9/fHnDlzEBERgZiYGKxfv/6190tERESkzfz8/HDx4kVMmDABNjY2OHPmDBYtWoRHjx4hLCwM586dg6+vL8zMzDBlyhSYmJjg559/Rv/+/fH999/j3//+N4CX35/y9PSEgYEBAgMDUb9+faxbtw7dunXDiRMn0K5duzL7Pnz4MHr27ImOHTti7ty5qFGjBg4fPoywsDAUFhZi48aNYv92aESF0Xvz5k00a9YM9evXV2mDSUlJinW//2RpaYn//e9/mDx5Mt5++22Ym5tjzJgx+PLLLyGTybB3716MHDkS7u7uaNasGfbs2QNHR0eV9k9ERESkS+7fv4/IyEgsWbIEU6dOBQCMGjUKcrkct27dAgCMGzcONWrUwLlz52BnZwcAGDt2LDp27Ihp06Zh8ODBsLKywldffYWioiLExsYqeuyjjz5Cs2bNsGTJEvz6669l9r9ixQo0btwYkZGRqF27tmLbXl5e2L17t/5Hb69evdCjRw/MmDGjzKXEynPz5k0sW7YMkZGR+PPPPyt8Xdu2bXHs2LFyn3NycsLx48cFjE1ERESkH8zNzWFiYoI1a9agSZMm6NGjB4yNjbFhwwYAwL1793D27FmMHTtWEbwAUKdOHUybNg1DhgzBkSNHMHjwYBw4cAC9evUqdQLS0tISJ0+ehJWVVbn7j4iIwJMnTxTBCwCPHj2CmZkZcnJyNPSpxVfhmt6LFy9CLpejSZMm8PHxwapVq3DixAncuXMH9+/fx40bN3Ds2DEsWLAAvr6+cHV1hVwuR2xsrJjzExERkQp8fX3h6+sr9Rj0N4aGhli3bh3u3buHDz/8EJaWlnjvvfcQFhaG58+f486dOwBQ7s29XFxcAADJycl49OgRcnJy0Lx58zKva9WqVZm74b5Ss2ZN3Lp1C1988QV8fHxgZ2cHKysrHD58GHK5XH0fVGIVRm/dunURGhqKa9euoU2bNli4cCF8fHzQrFkz2NjYwNnZGe+++y7WrFmDVq1aITExEWvXri11RzWi18U/nImIqDoYOnQoUlNTsX79evTu3RsxMTEYPXo0OnToUOrLbP/04sULAC+vplVSUgLg5WVlVbFu3Tq0b98eUVFRePPNNzF9+nTExMTg448/rvoH0kIVLm94pUmTJli1ahVWrVqFhIQE3LlzB0+fPoWlpSUaN26s+BsGEREREakuJycHcXFxaNmyJUaMGIERI0agsLAQ06dPx6pVq5CSkgLg5VUZ/un69esAgMaNG8PKygpGRkZISkoq87qlS5ciMzMTS5cuLfX48+fPMXnyZHTu3BmHDx9GrVp/pWFQUJA6P6bkVPqrgKurK3r16oUhQ4age/fuDF4iIiKi1xQfH49OnTqVumJV7dq10bZtWwAv1+R6eHhg69atpe76VlhYiOXLl8PQ0BDdunVDrVq10L17dxw4cACpqamK1z1+/BhLlizBzZs3y+w7Pz8feXl5ePPNN0sFb1xcnOJ7VsXFxWr/zFKo9EwvEREREWmOp6cnOnXqhNmzZyMlJQWtW7dGamoqvvvuOzg7O6Nr166wsLBAly5d0K5dO3zxxRcwNTXF1q1bERsbi9DQUNSrVw8AsHDhQnh6eqJ9+/YIDAyEmZkZfvzxR+Tk5GDevHll9m1hYQFPT09s2LABZmZmaNGiBeLj4/HTTz8plklkZ2fDwsJCzN8SjWD0EhERkc6xtbXVqlv/2traVvm9MpkM4eHh+Oabb/Dbb78hLCwMFhYWGDhwIEJCQlC7dm14eXnh1KlT+Prrr7F06VKUlJSgTZs2CA8PR9++fRXbcnFxwZkzZzBr1iwsXrwYNWrUQPv27bF582a0bNmy3P3v3LkTkydPxoYNG1BQUAAHBwfMnDkTLi4uGDhwIKKiojBw4MAqfz5tIZPrwdfyPDw8cP78eanHIA149SW26OhoSecgItIXuvTnamJiIpdSkkqUHTOC1vS+WkBNRERERKSLBEVvp06dsG3bNk3PQkRERESkEYKit6ioSLFAmoiIyuI1pYmItJugL7LNmTMHY8eOxaRJk+Di4gJra+syr3n77bfVPlx1o0vrrIiIiIh0iaDoHTNmDABgypQpAF5+y/AVuVwOmUymuAsIEREREZG2ERS9x44d0/QcREREREQaIyh6fXx8FP9cXFyMhw8fwsrKqtSdO4iIiIiItJXg2xDHxMSga9euMDExgZ2dHS5fvoxhw4bhq6++0uR8RERERESvTVD0RkVFKc72zp8/H6/uZ9GqVSssWrQIy5cv19yERERERESvSdD6hBkzZmDw4MHYsmULiouLMW3aNADAzJkzkZ+fj7CwMEyePFmjgxIREVFpjRo5IiMjuUrv/fuX0oWysXHA3bt3qrQ/IqkJit74+HjMnz8fQNn/kXTu3BmLFy9W/2SkdxraNcS99HtVeq+qfzg3sG2AzLTMKu2LiEhXZGQko1MnuUrvuXzZFwDQunW0yvs7cUL1UCbSFoKi19raGgkJCejevXuZ5xITE8u9bi/RP91LvwfMUfFNG///Pz9TcV9zqhbXREREpJ8ErekdPnw4goKCsHHjRjx48ADAy6s4REZGYs6cORg6dKhGhyQiIiIi3ebv71+lZTVVfd8/CTrTGxwcjNTUVIwcOVKx03/961+Qy+UYMGAA5s6d+9qDEBEREQllZ2eH9PR0qcdQsLW1RVpamtRjaLXRo0eja9euku1fUPTWrFkTGzduxIwZMxAdHY2srCyYm5vD29sbbm5ump6RiEhUjRs1QlpGRpXeW5WzEXY2Nki9e7dK+6PK8Rbv+ik9PR1z5sxRy7Zu376NnTt3YtCgQWjSpEmVtqGuWfSZl5cXvLy8JNu/oOj96KOP0KtXL/To0UNxS2IiIn2VlpGBba6uKr1n3p07AICvHB1V3t/HCQkqv4eI1EMdwUu6QdCa3pycHAQGBqJRo0Zo164dgoODcfbsWcX1eomIiIh0jTYFr6OjI8aMGYOtW7eiZcuWqFOnDpo3b47Vq1eXet2JEycUNwszMTFBly5d8Pvvv1dpW48fP4a/vz/s7e1haGiIZs2a4csvv8Tz589Lve7KlSvo168f6tWrByMjI3To0AHh4eFlPsO1a9fg5+cHKysrmJubw9fXFydOnFA8X97a3KioKPTs2ROWlpYwMDCAra0tRo8ejSdPnlThd1E5QdEbERGBrKwsREZGolu3bjhw4AA6duwIa2trDBs2DDt27FD7YERERESaok3B+8rBgwcxfvx4fPjhh1ixYgWMjY0RGBiIAwcOAAD27dsHX19fpKSkICgoCEFBQUhJScG7776Lffv2qbQtAPDz80NERAQ+//xzrF69Gr6+vli0aBHGjx+veM25c+fQoUMHnD17FlOmTMGCBQtQWFiI/v37l4roGzduwNPTE1FRUQgMDMSCBQuQlZWFbt264dy5c+V+3sOHD6Nbt27Izc3F3LlzERoaivbt2yMsLAyTJk1S528tAIHLGwCgVq1a8PX1ha+vLxYsWIDIyEgEBwdj+/bt2LFjB4YMGaL24YiIiIjUTRuDFwBSU1MRFxeH1q1bAwD69++PRo0aYdu2bejevTv+/e9/w9bWFufPn4eZmRmAl18Oa9WqFb744gv07NkTBgYGlW6rV69euH//PiIjI7FkyRJMnToVADBq1CjI5XLcunVLMdO4ceNQo0YNnDt3DnZ2dgCAsWPHomPHjpg2bRoGDx4MKysrfPXVVygqKkJsbCycnJwAvFwe26xZMyxZsgS//vprmc+7YsUKNG7cGJGRkahdu7Zi215eXti9ezc2btxY5j2vQ9CZ3sLCQpw8eRILFy5E7969YWFhgffeew/379/HZ599pvahiIiIiDRBW4MXAFq0aKGIVABo2LAhGjRogMzMTFy4cAFpaWkIDAxUBC8A1KtXD4GBgUhPT8f58+cFbQsAzM3NYWJigjVr1mD37t3Izc0FAGzYsAGRkZEAgHv37uHs2bP45JNPFMELAHXq1MG0adOQn5+PI0eO4MWLFzhw4AB69eqlCF4AsLS0xMmTJxEaGlru542IiEBsbKwieAHg0aNHMDMzQ05OTpV+D5URdKbXzMwMRUVFcHV1ha+vLz755BP4+PjAxsZG7QMRERERaYI2By8A1K9fv8xjhoaGKCkpwe3btwG8jNl/cnFxAQAkJycrro6gbFuv/nndunX4/PPP8eGHH8LQ0BA+Pj4YOHAgPv30U9SpUwd3/v8LupXt89GjR8jJyUHz5s3LvK5Vq1YVft6aNWvi1q1bCAoKwtWrV3Hz5k2NXoZO0JneHj16wMLCAklJSbh8+TLi4+MRHx+v+FsBERERkTbT9uAFgBo1Ks4yZRcPePHiBQCUOmOqbFuvDB06FKmpqVi/fj169+6NmJgYjB49Gh06dEBBQYHgfb4KaSH7/Lt169ahffv2iIqKwptvvonp06cjJiYGH3/8sUrbEUrQdOHh4Xj48CFiYmIwYMAAXL16FUOHDoWFhQXat2+PadOmaWQ4IiIiotel6eB9dRZWkxz//3KI165dK/Pc9evXAQCNGzcWvL2cnBycPHkSMpkMI0aMwO7du/HgwQNMmDABly5dwuHDhwXv08rKCkZGRkhKSirzuqVLlyrWDP/d8+fPMXnyZHTu3Bnx8fH48ccfMX78eHh6euL+/fuCP4cqVEpyNzc3TJgwAdu3b8fWrVvRq1cvnD9/HsuXL9fIcERERESvQ4zg3blzp9q3+0/u7u6wsbHBmjVr8OzZM8Xjz549w5o1a2BjYwN3d3fB24uPj0enTp2wfv16xWO1a9dG27ZtAbxcetCwYUN4eHhg69atpe42V1hYiOXLl8PQ0BDdunVDrVq10L17dxw4cACpqamK1z1+/BhLlizBzZs3y+w/Pz8feXl5ePPNN1Gr1l+rbePi4nD8+HEAQHFxseDPI4SgNb2FhYU4ffo0jh07hqioKJw7dw4ymQwdO3bE4sWL0bNnT7UORURERPS6xAreQYMGYfPmzWrf/t8ZGBjgu+++g5+fHzw8PDBq1CgAwE8//YS7d+9i165dKi0v8PT0RKdOnTB79mykpKSgdevWSE1NxXfffQdnZ2fF7YJDQ0PRpUsXtGvXDl988QVMTU2xdetWxMbGIjQ0FPXq1QMALFy4EJ6enmjfvr3iy3Y//vgjcnJyMG/evDL7t7CwgKenJzZs2AAzMzO0aNEC8fHx+OmnnxSfIzs7GxYWFq/5O/cXQdFbr149FBQUwNHREe+99x6mT5+OLl26wNjYWG2D6JtG9o2QkSrebUxtGtvgbgpvY0pERMq1bh0t9QhqYWtrK/jWv5oO0s2bN8PW1laj+wCAgQMH4vDhwwgJCcE333wDAwMDeHp6Yv369ejUqZNK25LJZAgPD8c333yD3377DWFhYbCwsMDAgQMREhKiWB/s5eWFU6dO4euvv8bSpUtRUlKCNm3aIDw8HH379lVsz8XFBWfOnMGsWbOwePFi1KhRA+3bt8fmzZvRsmXLcmfYuXMnJk+ejA0bNqCgoAAODg6YOXMmXFxcMHDgQERFRWHgwIFV/w3752eWC7it2qpVq9CjR49yv72nDTw8PEpdpkMbyGQyuP8m/F8zAMD1L1+uj2mxUPXf59gPYrX+DnkymQyYI9LO5ihf9E+kjEwmE/02xDxeNcfX1xcAEB0dLekcmiCTydCpk3jHzokTMlGP1cTERMVVAoiEUHbMCDrTO2HCBOTn52P16tU4fvw4nj17BktLS3h7e+PTTz/lGV8iqvaqErtERCQeQYs/7t+/D3d3d0yYMAFJSUmQyWRISEjAuHHj0LZt2yp9y27UqFGKv30DQEBAAGQyWamflStXqrxdIiIiIqJ/EhS9U6dORV5eHq5cuYILFy7g4MGDuHjxIq5cuYLi4mLMmDFDpZ0ePXq01LcFASAhIQFLlixBRkaG4icgIECl7RIRERERlUdQ9O7fvx/z588vs0bCxcUFISEh2L9/v+Ad5ubmIiAgAB07diz1eGJiIjw8PNCwYUPFT926dQVvl4iIiIioIoKvbfHqkhTlPa7Kndlmz54NX1/fUksbMjMzkZWVpbVflCMiIiIi3SYoetu3b49Vq1YpbjP3SnFxMVasWIF27doJ2tmZM2ewc+dOLF26tNTjCQkJqFWrFoKCgmBraws3Nzds2rRJ2CcgIqJqo3FjxzLf/6js5/jx4zh+/LjK72vc2FHqj0tEaiTo6g3ffvstvLy84OTkhL59+6JBgwa4d+8e9u7di8zMTERFRVW6jYKCAowcORIrV64sc6HhxMREAC/v+DZ+/HhER0dj9OjRMDY2xqBBg8rdXlhYGMLCwgAADx48EPIxiIhIx6WlJWPFCtUumfX9974AgMDAaJXeN2mS6tdMJ/WTy+VVun49VT+VXU5PUPS2bt0ap06dQkhICLZv347Hjx/jjTfegLe3N2bPno2333670m3MnTsXzZs3Lzdiv/jiCwwZMgRvvPGGYn83btzA2rVrK4zegIAAxRfdPDw8hHwMIiIi0iG1a9dGfn4+v+NDguTn58PQ0LDC5wVFLwC0adMGu3fvrvIg27dvR0ZGBkxMTAC8vLVxSUkJTExMkJOTowjeV1xcXHD48OEq74+IiIh0m5WVFdLS0mBlZQVTU1PUqlWLZ32pFLlcjuLiYmRnZ+Phw4do0KBBha9VGr2XLl3CunXrkJycjKZNm2LUqFFwc3Or0lDR0dEoKipS/HrFihU4f/48tm3bhilTpuD69euIiIhQPH/x4kU4OztXaV9ERESk+8zNzWFoaIgHDx7g0aNHKC4ulnok0kK1atVCnTp1YG9vjzp16lT8uoqeiIqKQo8ePfDixQtYWVnhf//7H3744QesX78en376qcoDOTg4lPq1hYUFjIyM4OTkhD59+qBLly4IDQ1F7969cejQIWzZsgVHjx5VeT9ERESkP+rUqYPGjRtLPQbpgQqv3hASEoI2bdrgzp07yMzMxIMHD9C9e3d8+eWXah/Cx8cHO3bsQFhYGFq2bInVq1djx44d8Pb2Vvu+iIiIiKj6qfBMb1xcHH7++WfY2dkBeHlm9ttvv4WbmxtSUlJgb2//WjueN29eqV/7+fnBz8/vtbZJRERERFSeCs/0Zmdno379+qUec3Jyglwux+PHjzU+GBERERGRulQYvS9evCjzDUkDAwMAKHOTCiIi0g7/vOMlaQ/+d0MkLcG3ISYieoX/501ERLpG6SXLduzYgZMnTyp+/ers77Zt2xAdHa14XCaTYdKkSRobkoiIiIjodSiN3lWrVpX7+IoVK0r9mtFLREREqnj1b4v+fhKNSJMqjN4XL16IOQcRERERkcZwTS8RERER6T2lyxtIXC0WtpB6BCIiIiK9xDO9RERERKT3GL1EREREpPe4vIHK4DdqiUifBAZGSz0CEWkBlaI3Ly8PBQUFkMvlAAC5XI68vDycOXMGfn5+GhmQSBvwLwJERES6TVD0xsfH47PPPsOFCxcqfA2jl4iIiIi0laDonTx5MjIyMrBs2TL89ttvqF27Nvr06YODBw9i//79OHr0qKbnJCIiIiKqMkFfZIuJicGCBQswceJEDBkyBNnZ2Rg7diz27duHwYMHIzQ0VNNzEhERERFVmaDoLSoqQpMmTQAAzs7OuHTpkuK54cOH4+zZs5qZjoiIiIhIDQRFb/PmzRWh6+zsjNzcXCQmJgIASkpK8OzZM81NSERERET0mgSt6R05ciSmTZuGnJwczJw5Ex07dsTIkSMREBCAlStXws3NTdNzEhERERFVmaDonTBhAoqKipCWlgYA+PHHH/HBBx9gxIgRsLe3x4YNGzQ6JBERERHR6xB8nd6pU6cq/tnZ2Rl//vknHjx4AGtra7x48UIjwxEREZFuaNTIERkZySq/TyaTqfweS8uGePgwQ+X3UfUmKHqbNm2K8PBwtG7dWvGYTCaDtbU1/vjjD/Tq1QsPHz7U2JBERNVR40aNkJZRtf9jr0pI2NnYIPXu3Srtr7qxtbXH3bupVXqvqv/d2Nk5IDX1TpX2JaaMjGS89dYxJCYOgovLTtSr56v09Zcvv3y+detowft48iQaiYmD8OhRZtUHpWqrwuhdu3Yt8vPzAQB37tzBhg0bYG9vX+Z1J0+eRElJieYmJCKqptIyMrDN1VXx64TcXISmpWG8nR1cjY3Lfc+8O3cAAF85Oqq8v48TEqoyZrV0924qVqyQq/Se77/3BVDxbZGTkqKxadMg+PvvhJOTr+LxSZNU/wuMVIQGb1W8Cl4Xl524cqWz2rdP+q/C6H348CGCg4MBvPxbaXnX4q1Rowbq1auHefPmaW5CItI4Rzs7JKenq/y+qpxNdLC1xZ3//34ACSckeF93+ySdioJX14gRvJrYPlUPFUZvUFAQgoKCALyM25iYGLRv3160wYhIPMnp6Xg2Y4bg1/favh0AcGDoUJX3Zfbttyq/p7oTI3hD+RcRyehL8AJg8JJWE3Sd3hcvXiiCt7i4GJmZmSguLtboYEREJF7wjrezU/u2qXL6FLyawOAldRIUvcDLWxF37doVJiYmsLOzw+XLlzFs2DB89dVXmpyPiKhaEyt4NbF9Uo7BqxyDl9RNUPRGRUXBx8cHADB//nzI5S8X77dq1QqLFi3C8uXLNTchEVE1xuDVTwxe5Ri8pAmConfGjBkYPHgwIiMjMWHCBEX0zpw5E7Nnz0ZYWJhGhyQiqq4YvPqHwascg5c0RVD0xsfHY9iwYQDKflu7c+fOSE5W/WLUJJ5G9o0gk8kE/xw/fhzHjx9X6T2vfhrZN5L64xKREgxeaeXnP2HwKsHgJU0SdHMKa2trJCQkoHv37mWeS0xMhLW1tdoHI/XJSM2A+2/ugl9//cvrAIAWC1uUeS77SjZuLbqFpjObwvQt0zLPx34QW/VBRWJpbYmsB1lVem9VLtHVwLYBMtN4IXWSHoNXWvn5T5CZeRVjxx5h8JaDwUuaJih6hw8fjqCgIJibm6Nnz54AXl7FITIyEnPmzMGIESM0OiRph8qCV1dkPcgC5qj4po3//5+fKXnNbQA7AQwC0OSvh+/NuafizojUj8ErraSkaGRmXkXDhi0ZvOVg8JIYBEVvcHAwUlNTMXLkSMWZrn/961+Qy+UYMGAA5s6dq9EhSXr6ErwaU0HwEmkDBq+0Xq3hbdiwJYyM6kk9jtZh8JJYBEVvzZo1sXHjRsyYMQPR0dHIysqCubk5vL294ebmpukZSWIM3koweCXl6+sLAIiOjpZ0Dm3F4JXW37+0dujQHKnH0TrFxU8YvCQaQdH7irOzM5ydnTU1C2khBm8lGLykxRi80uJVGpQrLn6C3NyreOutIwxeEoWg6C0pKcEPP/yAY8eO4cmTJ3jx4kWp52UyGY4ePaqRAUk6DN5KMHhJizF4pcXgVe7Jk2jk5l6FsXFLBi+JRtAly6ZNm4Zx48bhxo0bMDY2hrm5eakfMzMzlXc8atQoxb+WBIDk5GR0794dxsbGcHFxwcGDB1XeJqlPSW4Jg1cZBi9psdySEgavhBi8yr1aw/vWW0fw9ttxUo9D1YigM70///wzvvrqK7V9Ye3o0aNYv3694i5vcrkcffv2hYuLC86dO4d9+/Zh4MCBuHr1Kpo0YVGIrSS3BPkp+Xgz5E0Gb3kYvKTFcktKkF5QgJkODgxeCTB4leOX1khKgqK3oKAAXbp0UcsOc3NzERAQgI4dOyoeO3bsGK5fv44TJ07A1NQUrq6uiIyMxPr16zFv3jy17JeEyb6SjfyUfBjZGzF4y8PgJS2WkJuL9IIC2BoaMnglwOBVjsFLUhO0vOH999/Hnj171LLD2bNnw9fXt9TShpiYGLRt2xampn9Flre3N86cOaOWfZIwr9bwGtkboaZxTanH0T7PweAlrfVqDa+toSGMa/J/v2Jj8CrH4CVtUOGZ3uXLlyv+uUmTJli8eDGSkpLg5eWFunXrlnqtTCbDpEmTKt3ZmTNnsHPnTsTHx2PZsmWKxzMyMtCoUenb1zZo0ABpaWmCPwi9nr9/ae3u9rtSj6N9ngN4AOATMHhJ6/BLa9Ji8CrH4CVtUWH0Tp06tcxjBw8eLPcLZkKit6CgACNHjsTKlSthYWFR6rm8vDwYGhqWeszQ0BAFBQUVbi8sLAxhYWEAgAcPHijdNynHqzRU4jZeBm99MHhJ6zB4paVK8AYGRldp+7qMwUvapMLo/edlyV7X3Llz0bx5cwwaNKjMc3Xq1MHTp09LPVZQUFDmjPLfBQQEICAgAADg4eGh1lmrEwZvJV6t4a0PoI7Es2iRA0OHSj0CgcErNU2f4X21fV3F4CVtU+Ga3hEjRuD27dtq29H27dvxv//9DyYmJjAxMcHixYtx4sQJmJiYwNbWFpmZmaVen5mZCRsbG7Xtn8pi8Fbi719aY/CSlmHwSk+M4PX336n2bYuBwUvaqMLo3bRpk1qXDURHRyM+Ph5xcXGIi4vD559/Dg8PD8TFxaFDhw6Ii4tDbm6u4vUnT55Ehw4d1LZ/Ko3BWwlepYG0GINXO4gRvLq4RljTwfvkSbTat0nVg0q3IX4dDg4OpX5tYWEBIyMjODk5oUmTJnBwcIC/vz/mzJmDiIgIxMTEYP369WKNV60weCvB4CUtxuDVHgzessQI3sRE3V3yQdJSeskymUwmyhA1a9bE3r17cf/+fbi7u2PLli3Ys2cPHB0dRdl/dcLgrQSDl7QYg1e/6XrwAhAleF1cdHPJB0lP6ZnesWPHCrrFsEwmw9GjR1Xa8T9vOuHk5ITjx4+rtA1SjdDgbbGwRZW3r9OUBe9n4o9D9HeaDt6Evy0vI/HpQ/ACECV4uUaYqkpp9JqYmMDc3FysWUiDNH2G99X2dRbP8JIWEyN4Q3lddMnoS/ACYPCSVlMavYsXL0b79u3FmoU0SIzgbTqzKf6c9afat69xDF7SYmIF73g7O8xPTlb79kk5fQpeTWDwkjoJug0x6T4xglcn1whrOnjVd9U/qqbECl6uERYfg1c5Bi+pG6O3mmDwlkOM4OX3Leg1MXj1E4NXOQYvaUKF0RscHAw7OzsxZyEdwuBVYftEr4HBq38YvMoxeElTKlzTGxwcLOYcpEN0PngB8YKXa4RJyzB4pcXgVY7BS5ok2s0pSD/oRfACDF4t5Whnh+T09Cq9V9XrijvY2uJONbtiAYNXWgxe5Ri8pGmMXhJMb4IXYPBqqeT0dDybMUOl9/Tavh0A8KW3Nz4ND8eWfv3Qyd6+0veZffttlWbUVQxeaTF4lWPwkhj4RTYSRK+CVxMYvJJ6WlCgUvBWNwxeaTF4lWPwklhUjt7k5GTExMQgNzcXOTk5mpiJtAyDtxIMXkk9LShA4sOHDN4KMHilxeBVjsFLYhIcvbt370bz5s3RpEkTeHt74/r16/j4448xbNgwFBUVaXJGkhCDtxIMXkmdSElB4sOHcLGyYvCWg8ErLQavcgxeEpug6P3111/h5+cHHx8f/Prrr3jx4gUAoH///tizZw/mzp2r0SFJGgzeSjB4JXUiJQWfhofDxcoK5oaGUo+jdRi80mLwKsfgJSkIit65c+diwoQJ+Omnn9C/f3/F4/7+/pg3bx62bdumsQFJGgzeSjB4JfUqeLf068fgLQeDV1oMXuUYvCQVQdGblJSEXr16lftc27ZtkZGRodahSFoM3koweCX19+DlkoayGLzSYvAqx+AlKQmKXnt7e5w8ebLc5/744w80btxYrUORdBi8lWDwSorBqxyDV1oMXuUYvCQ1QdfpDQwMxNSpUyGXy9GrVy/IZDKkp6fjwoULmD9/PoKCgjQ9J4mAwVsJBq+kGLzKMXilxeBVjsFL2kBQ9I4fPx6PHz/Gt99+i3nz5kEul6Nv374wMDDA+PHjMXXqVE3PSRrG4K0Eg1dSDF7lGLzSYvAqx+AlbSH4jmzBwcGYOHEizpw5g6ysLJibm8PT0xNWVlaanI9EwOCtBINXUgxe5Ri80tJ08CYlRat9m2Ji8JI2ERS9/v7+GDp0KLp27YoePXpoeiYSEYO3EgxeSTF4lWPwSkuM4N20aZDatysWBi9pG0FfZIuPj0ePHj1gY2ODcePG4fTp05qei0TA4K0Eg1dSDF7lGLzSEyN4/f13qn3bYmDwkjYSFL3nz5/HrVu3MGnSJJw+fRre3t5wdHTEzJkzcenSJU3PSBrA4K0Eg1dSmg7eEykpat+mmBi82kGM4NXFNcKaDt4nT6LVvk2qHgTfhvhV5MbGxuLGjRsYM2YMoqKi4O7ujpYtW2pyRlIzBm8lGLySEiN4Pw0PV/t2xcLg1R4M3rLECN7ERN1d8kHSEhy9f/f8+XMUFhYCAORyOQwMDNQ6FGkOg7cSDF7JiRG8W/r1U/u2xcDg1W+6HrwARAleFxfdXPJB0hN89Yb4+Hjs3LkTO3fuxPXr19GkSRN89NFH2Lx5M1xcXDQ5I6mJpoM3+0q22rcpKgavVhAjeHVxjbCmgzchN1ft2yTh9CF4AYgSvFwjTFUlKHqdnZ1x48YNWFtbw8/PDxs3boSnp6emZyM1EiN4by26pfbtiobBqzUYvGWJEbyhaWlq3y4Joy/BC4DBS1pN0PIGLy8vHDp0COnp6Vi1ahWDVweJEbxNZzZV+7ZFweDVawxe4dsn8elT8GoCg5fUSdCZ3o0bN2p6DtIwMYJXJ9cIazp4b2tgmySYrgcvANGCl2uExcfgVY7BS+pWYfSamZnh2LFjcHd3h6mpKWQymdINPXv2TO3DkfoweMshRvDy+xYad2Do0HIf14fgBcDg1VMMXuUYvKQJFUbvlClTYGNjo/jnyqKXqhcGrwrb36yB7ZNS+hK8ABi8eojBqxyDlzSlwugNDg5W/POIESPQsGFD1K5du8zrnj9/jri4OI0MR9pJ54MXEC94uUZYdPoUvJrA4JUWg1c5Bi9pkqAvsjVp0qTCO6+dPXsWXbp0UetQpL30IngBBq+eYvAqx+CVFoNXOQYvaVqFZ3rHjBmDu3fvAnh5A4opU6agXr16ZV6XmJgIKysrjQ1I2kNvghdg8OohBq9yDF5pMXiVY/CSGCo809urVy9kZ2cjO/vlDQdyc3MVv371k5eXBzc3N/znP/8RbWCShl4FryYweCXF4FWOwSstBq9yDF4SS4Vnevv06YM+ffoAADp37ow1a9bwzmvVFIO3EgxeSTF4lWPwSovBqxyDl8Qk6Dq9x44dU/p8ZmYmGjZsqJaBSLsweCvB4JUUg1c5Bq+0GLzKMXhJbIKi99mzZwgJCcHx48dRUFAAuVwO4OVa37y8PKSkpKCoqEijg5L4GLyVYPBKisGrHINXWgxe5Ri8JAVBV28YP348Vq1ahUaNGiE/Px81atSAi4sLsrKykJycjNWrVwve4bVr19C1a1eYmJjAwcEBS5YsUTwXEBAAmUxW6mflypUqfyh6fQzeSjB4JcXgVY7BKy0Gr3IMXpKKoOg9cOAA5s+fj/DwcIwdOxa2trb45Zdf8Oeff8Ld3R2XL18WtLOioiL07NkT9vb2iIuLw+rVqxESEoJt27YBABISErBkyRJkZGQofgICAqr+6ahKGLyVYPBKisGrHINXWgxe5Ri8JCVB0fv06VN4enoCAFq1aoXz588DAIyNjTFlyhQcOHBA0M7S09PRvn17rF69Gk5OTnj//ffRtWtXHD9+HMDLy595eHigYcOGip+6detW5XNRFTF4K8HglRSDVzkGr7QYvMoxeElqgqLXxsYGmZmZAIA333wTDx8+REZGBgCgfv36iucq4+joiF9++QVGRkaQy+U4deoUfv/9d7z77rvIzMxEVlYWWrRoUcWPQq+LwVsJBq+kGLzKMXilxeBVjsFL2kBQ9Pbt2xczZ87EkSNH4ODggCZNmmDu3Lm4c+cO1q5dCwcHB5V3bGdnB29vb3h5eeHDDz9EQkICatWqhaCgINja2sLNzQ2bNm2q8P1hYWHw8PCAh4cHHjx4oPL+qTQGbyUYvJJi8CrH4JUWg1c5Bi9pC0HRO2/ePLi6umLZsmUAgOXLl2Pjxo1o1qwZ9uzZg+DgYJV3vHfvXuzduxcXLlzApEmTkJiYCABwc3PDwYMHMXLkSIwePRo7d+4s9/0BAQE4f/48zp8/j/r166u8f/oLg7cSDF5JMXiVY/BKS9PBm5QUrfZtionBS9pE0CXLTE1NERERgYKCAgAvb1xx5coVXLx4EW3btkXz5s1V3rGHhwcAIC8vD8OHD8ezZ88wZMgQvPHGGwCA1q1b48aNG1i7di0GDRqk8vZJGAZvJRi8kmLwKsfglZYYwbtpk+7+/x+Dl7SNoDO9rxgaGir+uXnz5vDz81MpeNPT07Fv375Sj7m6uqKwsBDZ2dmK4H3FxcUF6enpqoxIKmDwVoLBKykGr3IMXumJEbz+/uX/205tx+AlbVThmd633noLMplM0EZkMhkuXbpU6esSExMxYMAA3L17F9bW1gCA2NhY1K9fHwsXLsT169cRERGheP3Fixfh7OwsaAZSDYO3EgxeSWk6eE+kpKh9m2Ji8GoHMYJXF9cIazp4nzyJVvs2qXqoMHrd3d0FR69QPj4+cHV1hb+/P5YtW4abN29i5syZmD17Ntq0aYMuXbogNDQUvXv3xqFDh7BlyxYcPXpUrTMQg7dSDF5JiRG8n4aHq327YmHwag8Gb1liBG9iou4u+SBpVRi9yq6cUFUGBgaIiIhAYGAgPD09YWpqiokTJ2L8+PGQyWTYsWMH5s6di+nTp6Np06bYsWMHvL291T5HdcbgrQSDV3JiBO+Wfv3Qe8cOtW9f0xi8+k3XgxeAKMHr4rITV650Vvv2Sf8J+iIbABQXF+M///kPjh49iszMTISGhuLkyZNwd3dH69atBe/Q3t6+zLreV/z8/ODn5yd4W6QaTQdv9pVstW9TVAxerSBG8OriGmFNB29Cbq7at0nC6UPwAhAleLlGmKpK0BfZHj16BE9PT3z22We4cOECDh8+jOzsbPz3v//Fv/71L5w9e1bTc9JrEiN4by26pfbtiobBqzUYvGWJEbyhaWlq3y4Joy/BC4DBS1pNUPROmjQJz549Q1JSEmJjYyGXywEAu3btgqenJ2bNmqXRIen1iRG8TWc2Vfu2RcHg1WsMXuHbJ/HpU/BqAoOX1ElQ9P7222+YP38+HBwcSn25zdDQEFOmTEFsbKzGBiT1ECN4dXKNsKaD97YGtkmC6XrwAhAteLlGWHwMXuUYvKRugqK3pKQEderUKfe54uJixZlf0l4M3nKIEby6eYlNvaAPwQuAwaunGLzKMXhJEwRFb5cuXfDNN9/g8ePHisdkMhmKioqwatUq+Pj4aGxA0k4MXhW2T6LTl+AFwODVQwxe5Ri8pCmCrt6wbNkydOzYEc2aNYOXlxdkMhmCgoJw7do1PHnyBCdPntT0nKRFdD54AfGCl2uERadPwasJDF5pMXiVY/CSJgk609usWTNcvnwZo0ePRlZWFpo1a4Z79+7hgw8+4F3Tqhm9CF6AwaunGLzKMXilxeBVjsFLmib4Or3W1tZYuHBhuc89evQIlpaWahuKtJPeBC/A4NVDDF7lGLzSYvAqx+AlMSg901tYWIhDhw7h0KFDyMvLK/N8cXExli1bhubNm2tsQNIOehW8msDglRSDVzkGr7QYvMoxeEksFZ7pvX79Ot577z2kpqYCABwcHBAZGYmmTV9ei3X//v2YPHkybty4AUdHR1GGJWkweCvB4JUUg1c5Bq+0GLzKMXhJTBWe6Z0xYwZycnKwbt06bN++HTVr1sSUKVNQVFQEf39/9OnTB/fv38e3336La9euiTkziYjBWwkGr6QYvMoxeKXF4FWOwUtiq/BM7+nTpzFnzhyMGjUKANCgQQP07t0bn376KX799VeMHDkSCxcu5FpePcbgrQSDV1IMXuUYvNJi8CrH4CUpVBi9jx8/hpubm+LX7dq1Q35+PiIjIxEZGYnOnTuLMiBJg8FbCQavpBi8yjF4pcXgVY7BS1KpcHlDSUkJDA0NFb9+dUe2ZcuWMXj1HIO3EgxeSTF4lWPwSovBqxyDl6Qk6Dq9f9eqVStNzEFagsFbCQavpBi8yjF4pcXgVY7BS1JTGr0ymUzQY6QfGLyVYPBKisGrHINXWgxe5Ri8pA2U3pxiypQpqFevHgBALpcDACZOnAhzc/NSr5PJZNi7d69mJiRRMHgrweCVFINXOQavtBi8yjF4SVtUGL3vvPMOZDIZsrOzFY/5+PgAQKnHSPcxeCvB4JUUg1c5Bq+0NB28SUnRat+mmBi8pE0qjN7o6GgRxyCpMHgrweCVFINXOQavtMQI3k2bBql9u2Jh8JK2UfmLbKQ/GLyVYPBKisGrHINXemIEr7//TrVvWwwMXtJGjN5qisFbCQavpDQdvCdSUtS+TTExeLWDGMGri2uENR28T55Eq32bVD0weqshBm8lGLySEiN4Pw0PV/t2xcLg1R4M3rLECN7ERN1d8kHSYvRWMwzeSjB4JSdG8G7p10/t2xYDg1e/6XrwAhAleF1cdHPJB0mP0VuNaDp4s6/o+FU9GLxaQYzg1cU1wpoO3oTcXLVvk4TTh+AFIErwco0wVRWjt5oQI3hvLbql9u2KhsGrNRi8ZYkRvKFpaWrfLgmjL8ELgMFLWo3RW02IEbxNZzZV+7ZFweDVawxe4dsn8elT8GoCg5fUidFbTYgRvDq5RljTwXtbA9skwXQ9eAGIFrxcIyw+Bq9yDF5SN0ZvNcHgLYcYwcvvW0hGH4IXAINXTzF4lWPwkiYweqlKGLwqbJ9Epy/BC4DBq4cYvMoxeElTGL2kMp0PXkC84OUaYdHpU/BqAoNXWgxe5Ri8pEmMXlKJXgQvwODVUwxe5Ri80mLwKsfgJU1j9JJgehO8AINXDzF4lWPwSovBqxyDl8TA6CVB9Cp4NYHBKykGr3IMXmkxeJVj8JJYGL1UKQZvJRi8kmLwKsfglRaDVzkGL4mJ0UtKMXgrweCVFINXOQavtBi8yjF4SWyiR++1a9fQtWtXmJiYwMHBAUuWLFE8l5ycjO7du8PY2BguLi44ePCg2OPR3zB4K8HglRSDVzkGr7QYvMoxeEkKokZvUVERevbsCXt7e8TFxWH16tUICQnBtm3bIJfL0bdvX1haWuLcuXMYPnw4Bg4ciNu3eUsrKTB4K8HglRSDVzkGr7QYvMoxeEkqtcTcWXp6Otq3b4/Vq1fDyMgITk5O6Nq1K44fPw4bGxtcv34dJ06cgKmpKVxdXREZGYn169dj3rx5Yo5Z7TF4K8HglRSDVzkGr7QYvMoxeElKop7pdXR0xC+//AIjIyPI5XKcOnUKv//+O959913ExMSgbdu2MDX9K7K8vb1x5swZMUes9hi8lWDwSorBqxyDV1oMXuUYvCQ1mVwul0uxY1tbW9y9exfvv/8+wsPDMXHiRGRkZGDXrl2K16xduxYrV67E9evXy7w/LCwMYWFhAF6uE3Z2dhZtdiIiItIfVlZWOHTokNRjkIaJurzh7/bu3Yu7d+9i7NixmDRpEvLy8mBoaFjqNYaGhigoKCj3/QEBAQgICBBjVCIiIiLScZJFr4eHBwAgLy8Pw4cPx4gRI/D06dNSrykoKEDdunWlGI+IiIiI9Iioa3rT09Oxb9++Uo+5urqisLAQNjY2yMzMLPVcZmYmbGxsxByRiIiIiPSQqNGbmJiIAQMG4P79+4rHYmNjUb9+fXh7eyMuLg65ubmK506ePIkOHTqIOSIRERER6SFRv8hWVFQEd3d32NnZYdmyZbh58yZGjhyJWbNmITAwEK1bt4arqyvmzJmDiIgIzJ07F1evXoWjo6NYIxIRERGRHhL1TK+BgQEiIiJQq1YteHp6YvTo0Zg4cSLGjx+PmjVrYu/evbh//z7c3d2xZcsW7Nmzh8FLRERERK9NskuWERERERGJRdQzvUREREREUmD0EhEREZHeY/QSERERkd5j9BIRERGR3mP0EhEREZHeY/QSERERkd5j9BIRERGR3mP0EhEREZHe+z+onMJXlb+rnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a grouped bar chart, to highlight attend vs ignore in left and right hemispheres #\n",
    "# The stds are calculated without transformation, unlike above. This may not be strictly valid,\n",
    "# but it's being done this way for comparison.\n",
    "# MAKE SURE TO UNCOMMENT 'SAVEFIG()' LINE TO SAVE THE FIGURE!\n",
    "roi_base = {'ParsAvg':'IF', 'InfPar':'IP', 'TempAvg':'ST'}\n",
    "# lh_colors = ['#33B333', '#6666FF', '#F27777']  # rh match ROI map or darker, lh are lighter\n",
    "# rh_colors = ['#008000', '#0000B3', '#A52A2A']  # shading via https://www.hexcolortool.com/\n",
    "lh_colors = ['#33B333', '#F27777', '#8f8fff']  # rh match ROI map or darker, lh are lighter\n",
    "rh_colors = ['#008000', '#A52A2A', '#3d3dff']  # shading via https://www.hexcolortool.com/\n",
    "width, offset = 0.16, 0.20\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (10, 4))\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey');  # fake data for legend\n",
    "ax.bar(-5, 40, width, edgecolor='k', color='grey', hatch = '/');\n",
    "\n",
    "div = np.sqrt(len(df_roilin) - 1)\n",
    "for xx, roi in enumerate(roi_base):\n",
    "    avg_a = df_roilin['attend_' + roi + '-lh'].mean()\n",
    "    ste_a = df_roilin['attend_' + roi + '-lh'].std()  # see note in header\n",
    "    ste_a /= np.sqrt(len(df_roilin))\n",
    "    avg_i = df_roilin['ignore_' + roi + '-lh'].mean()\n",
    "    ste_i = df_roilin['ignore_' + roi + '-lh'].std()\n",
    "    ste_i /= np.sqrt(len(df_roilin))\n",
    "    ax.bar(xx - 1.5*offset, avg_a, width, edgecolor='k', yerr=np.array([ste_a]),\n",
    "           color=lh_colors[xx]);\n",
    "    ax.bar(xx - .5*offset, avg_i, width, edgecolor='k', yerr=np.array([ste_i]),\n",
    "           color=lh_colors[xx], hatch = '/');\n",
    "    \n",
    "    avg_a = df_roilin['attend_' + roi + '-rh'].mean()\n",
    "    ste_a = df_roilin['attend_' + roi + '-rh'].std()\n",
    "    ste_a /= np.sqrt(len(df_roilin))\n",
    "    avg_i = df_roilin['ignore_' + roi + '-rh'].mean()\n",
    "    ste_i = df_roilin['ignore_' + roi + '-rh'].std()\n",
    "    ste_i /= np.sqrt(len(df_roilin))\n",
    "    ax.bar(xx + .5*offset, avg_a, width, edgecolor='k', yerr=np.array([ste_a]),\n",
    "           color=rh_colors[xx]);\n",
    "    ax.bar(xx + 1.5*offset, avg_i, width, edgecolor='k', yerr=np.array([ste_i]),\n",
    "           color=rh_colors[xx], hatch = '/');\n",
    "\n",
    "ax.set_xlim((-.6, 2.5))\n",
    "\n",
    "ax.set_xticks(np.arange(len(roi_base)))\n",
    "ax.tick_params('x', length=0, pad=-20)\n",
    "ax.set_xticklabels([(roi_base[roi] + '\\nleft     right') for roi in roi_base], fontsize=18)\n",
    "ax.set_ylim((30, 54))\n",
    "ax.set_ylabel('Relative Theta Power (%)', fontsize=16)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "leg = ax.legend(['social', 'nonsocial'], loc=(0.95, 0.61), prop={'size': 18})\n",
    "\n",
    "fig.tight_layout();\n",
    "plt.show();\n",
    "\n",
    "fig_name = os.path.join(graphics_dir, 'barchart_linearROIs_pct.jpg')\n",
    "fig.savefig(fig_name, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50cb8605",
   "metadata": {},
   "source": [
    "## End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ca5d696",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "262.783px",
    "left": "0px",
    "top": "110.883px",
    "width": "343.667px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "378.183px",
    "left": "876.917px",
    "right": "20px",
    "top": "120px",
    "width": "378.167px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}