brainlife-structural-mri.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e2fb91f-ed75-4055-8daa-3ff539e35a38",
   "metadata": {},
   "source": [
    "# Structural MRI Analysis using Jupyter Notebooks and Python3 on brainlife.io\n",
    "\n",
    "This example notebook will guide the user through grabbing data for their project from the secondary warehouse, compiling data across the entire project, and analyzing and visualizing the compiled results on brainlife.io via the 'Analysis' tab. This example is written for python3, and uses one of the Python3 notebook types.\n",
    "\n",
    "Within this notebook, I will guide the user through analyses comprising derivatives generated from one of the three main datatypes available on brainlife.io, specifically neuro/dwi. Specifically, I will guide the user through:\n",
    "        \n",
    "    1. Load sample data for parcellation statistics.\n",
    "    2. doing some simple data manipulations\n",
    "    3. generating simple visualizations\n",
    "    \n",
    "All of these functions used here are provided in a self-contained python package called \"pybrainlife\" [https://pypi.org/project/pybrainlife/].\n",
    "\n",
    "First thing we'll do is load our python modules, specifically pandas, and pybrainlife!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73393344-a4e3-48ed-9396-3673f0e927a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae257775-c182-43dd-8023-61134b96a37a",
   "metadata": {},
   "source": [
    "### Import pandas and pybrainlife modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "047b8de5-a369-4434-b833-fa58d8201f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys\n",
    "import pandas as pd\n",
    "import pybrainlife as pbl\n",
    "from pybrainlife.data.collect import collect_data\n",
    "from pybrainlife.data.collect import collect_subject_data\n",
    "import pybrainlife.data.manipulate as pybldm\n",
    "import pybrainlife.vis.plots as pyblvp\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import random\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31bc8e7-a0ba-4346-8228-b170d7be37e2",
   "metadata": {},
   "source": [
    "Next thing we'll do is we'll define some useful functions. Specifically, we will define a few fuctions to peform bootstrapping analyses on the data and plotting the histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04161fc-3abc-4333-ae2e-2f6eab420555",
   "metadata": {},
   "source": [
    "### Define some useful functions for this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed40ac31-3148-4ecc-8af5-05ccb82df668",
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs a bootstrapping analysis comparing data from two different groups\n",
    "def bootstrap_analysis_groups(df,group_1,group_2,measures,iterations=1000,sample_size=10,compare_measure='corr'):\n",
    "\n",
    "    compare = 'corr'\n",
    "    if compare_measure == 'ttest':\n",
    "        compare = 'p-value'\n",
    "        \n",
    "    correlation = {}\n",
    "    for meas in measures:\n",
    "        correlation[meas] = []\n",
    "        for i in range(0,iterations):\n",
    "            group_1_df = df.loc[df['classID'] == group_1].sample(sample_size).reset_index(drop=True)\n",
    "            group_2_df = df.loc[df['classID'] == group_2].sample(sample_size).reset_index(drop=True)\n",
    "\n",
    "            if compare_measure == 'ttest':\n",
    "                corr = ttest_ind(group_1_df[meas], group_2_df[meas],equal_var=False)[1]\n",
    "            else:\n",
    "                corr = np.corrcoef(group_1_df[meas].values.tolist(),group_2_df[meas].values.tolist())[0][1]\n",
    "            correlation[meas].append(corr)\n",
    "\n",
    "    corrs = pd.DataFrame()\n",
    "\n",
    "    for meas in measures:\n",
    "        corrs[meas+'_'+compare] = correlation[meas]\n",
    "    corrs['iterations'] = [ f+1 for f in range(iterations) ]\n",
    "    \n",
    "    return corrs[['iterations']+[ f for f in corrs.keys().tolist() if f != 'iterations'] ]\n",
    "\n",
    "# performs a bootstrapping analysis within two individual groups comparing between different measures\n",
    "def bootstrap_analysis_within_groups(df,group_1,group_2,measures,iterations=1000,sample_size=10,compare_measure='corr'):\n",
    "\n",
    "    compare = 'corr'\n",
    "    if compare_measure == 'ttest':\n",
    "        compare = 'p-value'\n",
    "\n",
    "    group_1_corrs = {}\n",
    "    group_2_corrs = {}\n",
    "    for meas in range(len(measures)):\n",
    "        for meas_2 in range(len(measures)):\n",
    "            if measures[meas] != measures[meas_2]:\n",
    "                measures_name = measures[meas]+'_'+measures[meas_2]\n",
    "                inv_measures_name = measures[meas_2]+'_'+measures[meas]\n",
    "                if measures_name not in list(group_1_corrs.keys()):\n",
    "                    if inv_measures_name not in list(group_1_corrs.keys()):\n",
    "                        group_1_corrs[measures_name] = []\n",
    "                        group_2_corrs[measures_name] = []\n",
    "                        for i in range(0,iterations):\n",
    "                            group_1_df = df.loc[df['classID'] == group_1].sample(sample_size).reset_index(drop=True)\n",
    "                            group_2_df = df.loc[df['classID'] == group_2].sample(sample_size).reset_index(drop=True)\n",
    "\n",
    "                            if compare_measure == 'ttest':\n",
    "                                corr_group_1 = ttest_ind(group_1_df[measures[meas]],group_1_df[measures[meas_2]],equal_var=False)[1]\n",
    "                                corr_group_2 = ttest_ind(group_2_df[measures[meas]],group_2_df[measures[meas_2]],equal_var=False)[1]\n",
    "                            else:\n",
    "                                corr_group_1 = np.corrcoef(group_1_df[measures[meas]].values.tolist(),group_1_df[measures[meas_2]].values.tolist())[0][1]\n",
    "                                corr_group_2 = np.corrcoef(group_2_df[measures[meas]].values.tolist(),group_2_df[measures[meas_2]].values.tolist())[0][1]\n",
    "                            group_1_corrs[measures_name].append(corr_group_1)\n",
    "                            group_2_corrs[measures_name].append(corr_group_2)\n",
    "\n",
    "    corrs = pd.DataFrame()\n",
    "    for meas in list(group_1_corrs.keys()):\n",
    "        corrs[meas+'_'+compare] = group_1_corrs[meas] + group_2_corrs[meas]\n",
    "        corrs['classID'] = [ group_1 for f in range(len(group_1_corrs[meas])) ] + [ group_2 for f in range(len(group_2_corrs[meas])) ]\n",
    "\n",
    "    corrs['iterations'] = [ f+1 for f in range(0,iterations) ] + [ f+1 for f in range(0,iterations) ]\n",
    "    return corrs[['classID','iterations']+[f for f in corrs.keys().tolist() if f != 'classID'] ]\n",
    "\n",
    "# plots overall data\n",
    "def plot_histogram(df,plot_measure,compare_measure,ax=''):\n",
    "    \n",
    "    if ax == '':\n",
    "        sns.histplot(x=plot_measure,data=df,alpha=0.5)\n",
    "        ax = plt.gca()\n",
    "    else:\n",
    "        sns.histplot(x=plot_measure,data=df,alpha=0.5,ax=ax)\n",
    "\n",
    "    ax.vlines(x=df[plot_measure].mean(),ymin=0,ymax=ax.containers[1].datavalues.max(),linewidth=2,color='r')\n",
    "    ax.text(x=df[plot_measure].max() * .4,y=ax.containers[1].datavalues.max() *.75,s='average '+compare_measure+': %s' %(str('{:0.3e}'.format(df[plot_measure].mean()))))\n",
    "    \n",
    "# plots individual group data\n",
    "def plot_histogram_groups(df,plot_measure,palette='',ax=''):\n",
    "\n",
    "    if ax == '':\n",
    "        if palette != '':\n",
    "            sns.histplot(x=plot_measure,hue='classID',data=df,palette=palette,alpha=0.25)\n",
    "        else:\n",
    "            sns.histplot(x=plot_measure,hue='classID',data=df,alpha=0.25)\n",
    "        ax = plt.gca()\n",
    "    else:\n",
    "        if palette != '':\n",
    "            sns.histplot(x=plot_measure,hue='classID',data=df,palette=palette,alpha=0.25,ax=ax)\n",
    "        else:\n",
    "            sns.histplot(x=plot_measure,hue='classID',data=df,alpha=0.25,ax=ax)\n",
    "\n",
    "    if palette:\n",
    "        ax.vlines(x=df.loc[df['classID'] == group_1].mean()[plot_measure],ymin=0,ymax=ax.containers[1].datavalues.max(),color=palette[group_1])\n",
    "        ax.vlines(x=df.loc[df['classID'] == group_2].mean()[plot_measure],ymin=0,ymax=ax.containers[0].datavalues.max(),color=palette[group_2])\n",
    "    else:\n",
    "        ax.vlines(x=df.loc[df['classID'] == group_1].mean()[plot_measure],ymin=0,ymax=ax.containers[1].datavalues.max(),color='r')\n",
    "        ax.vlines(x=df.loc[df['classID'] == group_2].mean()[plot_measure],ymin=0,ymax=ax.containers[0].datavalues.max(),color='g')\n",
    "\n",
    "# simple function to compute the number of columns and rows needed based on number of measures and having an n x 3 style figure layout\n",
    "def set_up_figure(measures):\n",
    "    number_measures = int(len(measures))\n",
    "    if number_measures < 3:\n",
    "        num_rows = 1\n",
    "    else:\n",
    "        num_rows = int(np.floor(number_measures / 3))\n",
    "        if number_measures % 3 > 0:\n",
    "            num_rows = num_rows +1\n",
    "    \n",
    "    if num_rows > 1:\n",
    "        total_columns = 3\n",
    "    else:\n",
    "        total_columns = (number_measures %3)\n",
    "        if number_measures % 3 == 0:\n",
    "            total_columns = 3\n",
    "\n",
    "    return num_rows,total_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd0fd18-8f94-4c32-bc6c-672208895063",
   "metadata": {},
   "source": [
    "### Load sample subjects data\n",
    "\n",
    "Now that we have our modules, imported, we can load our sample datasets!\n",
    "\n",
    "First, we will load our subjects dataframe using pandas! We will also build a column containing a color for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a98b542b-5330-4c0d-923f-6a2a37f29a87",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>70061</td>\n",
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      "text/plain": [
       "    subjectID  classID  age gender bart  bht  dwi pamenc pamret rest  ...  \\\n",
       "19      10304  CONTROL   23      F  1.0  1.0  1.0                1.0  ...   \n",
       "158     50051     SCHZ   37      M  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "114     11090  CONTROL   24      M  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "256     70061     ADHD   31      F  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "89      10940  CONTROL   25      M  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "146     50029     SCHZ   49      F  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "42      10478  CONTROL   21      F  1.0  1.0  1.0                1.0  ...   \n",
       "116     11098  CONTROL   39      M  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "226     60084  BIPOLAR   32      F  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "250     70051     ADHD   21      F  1.0  1.0  1.0    1.0    1.0  1.0  ...   \n",
       "\n",
       "    crt_time1 crt_err1 crt_pr1 crt_pr2  crt_err2 crt_nm1  crt_time2  crt_nm2  \\\n",
       "19         28        1       0       0         0       0         37        0   \n",
       "158        51        0       0       0         0       0         77        0   \n",
       "114        23        0       0       0         0       1         48        0   \n",
       "256        32        0       0       0         1       0         65        0   \n",
       "89         17        0       0       0         0       0         44        0   \n",
       "146        41        0       0       2         0       0        147        1   \n",
       "42         32        0       0       0         0       0         64        0   \n",
       "116        22        0       0       0         0       0         47        0   \n",
       "226        28        0       0       0         1       0         69        0   \n",
       "250        28        1       0       0         0       1         55        0   \n",
       "\n",
       "     crt_index      colors  \n",
       "19        0.32    tab:blue  \n",
       "158       0.51  tab:orange  \n",
       "114       1.09    tab:blue  \n",
       "256       1.03    tab:cyan  \n",
       "89        1.59    tab:blue  \n",
       "146       2.59  tab:orange  \n",
       "42        1.00    tab:blue  \n",
       "116       1.14    tab:blue  \n",
       "226       1.46  tab:purple  \n",
       "250       0.96    tab:cyan  \n",
       "\n",
       "[10 rows x 154 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Load the subjects dataframe\n",
    "## First, let's load the subjects dataframe using the collect_subject_data function in pybrainlife\n",
    "## collect_subject_data():\n",
    "## inputs = path where we would like to save the dataframe. if we don't want to save, just leave blank\n",
    "\n",
    "# load the subjects data\n",
    "if not os.path.isfile('subjects_data.csv'):\n",
    "    subjects_data = collect_subject_data()\n",
    "    subjects_data.to_csv('subjects_data.csv',index=False)\n",
    "else:\n",
    "    subjects_data = pd.read_csv('subjects_data.csv')\n",
    "\n",
    "# remove the index column just to keep things clean\n",
    "subjects_data = subjects_data.drop(columns=\"index\")\n",
    "\n",
    "# rename the subject column to 'subjectID'\n",
    "subjects_data = subjects_data.rename(columns={'subject': 'subjectID'})\n",
    "\n",
    "# rename the diagnosis column to 'classID'\n",
    "subjects_data = subjects_data.rename(columns={'diagnosis': 'classID'})\n",
    "\n",
    "# make sure subjectID column is string\n",
    "subjects_data['subjectID'] = subjects_data['subjectID'].astype(str)\n",
    "\n",
    "### Create a color for each group: DON'T DO THIS\n",
    "## first, let's define a color dictionary for each group\n",
    "# make a list of all the unique groups in the dataframe\n",
    "groups = subjects_data.classID.unique().tolist()\n",
    "\n",
    "# generate a random color for each group\n",
    "colors = [ ]\n",
    "for num_group in range(len(groups)):\n",
    "    colors.append(\n",
    "#         list(np.random.choice(range(256), size=3))\n",
    "        random.choice(list(mcolors.TABLEAU_COLORS.keys()))\n",
    "    )\n",
    "\n",
    "# create a dictionary mapping a color with each group\n",
    "groups_colors = dict(zip(groups,colors))\n",
    "\n",
    "# map the groups_colors dictionary to the classID column to create a new column called colors \n",
    "subjects_data['colors'] = subjects_data.classID.map(groups_colors)\n",
    "\n",
    "### Visualize the dataframe \n",
    "## now let's visualize the dataframe to inspect\n",
    "# print out a random sample of 10 rows\n",
    "subjects_data.sample(10).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4458d4-c704-46b3-b712-55be40db8cb6",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Cortical analysis\n",
    "Now that we have our subjects information loaded, let's load some actual data!\n",
    "\n",
    "For this notebook, we will load the parc-stats datatype containing all of the morphometric information for the cortical parcellations generated using the \"collect_data\" function from pybrainlife"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa49bade-4596-4a2d-a65e-81485e70b12b",
   "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>subjectID</th>\n",
       "      <th>structureID</th>\n",
       "      <th>parcellationID</th>\n",
       "      <th>number_of_vertices</th>\n",
       "      <th>surface_area_mm^2</th>\n",
       "      <th>gray_matter_volume_mm^3</th>\n",
       "      <th>thickness</th>\n",
       "      <th>thickness_std</th>\n",
       "      <th>mean_curv</th>\n",
       "      <th>gaus_curv</th>\n",
       "      <th>...</th>\n",
       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20525</th>\n",
       "      <td>10882</td>\n",
       "      <td>L_STV_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>701</td>\n",
       "      <td>439</td>\n",
       "      <td>1663</td>\n",
       "      <td>2.798</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.024</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12373</th>\n",
       "      <td>10557</td>\n",
       "      <td>L_47s_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>489</td>\n",
       "      <td>394</td>\n",
       "      <td>1631</td>\n",
       "      <td>3.392</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.047</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18653</th>\n",
       "      <td>10708</td>\n",
       "      <td>L_2_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>1826</td>\n",
       "      <td>1203</td>\n",
       "      <td>2626</td>\n",
       "      <td>2.274</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.020</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.56</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6801</th>\n",
       "      <td>10377</td>\n",
       "      <td>R_7m_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>490</td>\n",
       "      <td>348</td>\n",
       "      <td>1089</td>\n",
       "      <td>2.452</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.038</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54920</th>\n",
       "      <td>50014</td>\n",
       "      <td>R_AAIC_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>182</td>\n",
       "      <td>127</td>\n",
       "      <td>578</td>\n",
       "      <td>3.512</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.019</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>2.73</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4325</th>\n",
       "      <td>10506</td>\n",
       "      <td>R_IFSa_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>1015</td>\n",
       "      <td>747</td>\n",
       "      <td>1930</td>\n",
       "      <td>2.171</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.031</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39865</th>\n",
       "      <td>50025</td>\n",
       "      <td>rh_middletemporal</td>\n",
       "      <td>aparc</td>\n",
       "      <td>4367</td>\n",
       "      <td>2985</td>\n",
       "      <td>9343</td>\n",
       "      <td>2.626</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38221</th>\n",
       "      <td>50060</td>\n",
       "      <td>L_PCV_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>527</td>\n",
       "      <td>383</td>\n",
       "      <td>897</td>\n",
       "      <td>2.136</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.037</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15895</th>\n",
       "      <td>10697</td>\n",
       "      <td>lh_G_precuneus</td>\n",
       "      <td>aparc.a2009s</td>\n",
       "      <td>3095</td>\n",
       "      <td>2166</td>\n",
       "      <td>6762</td>\n",
       "      <td>2.512</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.030</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20687</th>\n",
       "      <td>10882</td>\n",
       "      <td>R_FEF_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>671</td>\n",
       "      <td>471</td>\n",
       "      <td>940</td>\n",
       "      <td>1.910</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.035</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subjectID        structureID parcellationID  number_of_vertices  \\\n",
       "20525     10882          L_STV_ROI      hcp-mmp-b                 701   \n",
       "12373     10557          L_47s_ROI      hcp-mmp-b                 489   \n",
       "18653     10708            L_2_ROI      hcp-mmp-b                1826   \n",
       "6801      10377           R_7m_ROI      hcp-mmp-b                 490   \n",
       "54920     50014         R_AAIC_ROI      hcp-mmp-b                 182   \n",
       "4325      10506         R_IFSa_ROI      hcp-mmp-b                1015   \n",
       "39865     50025  rh_middletemporal          aparc                4367   \n",
       "38221     50060          L_PCV_ROI      hcp-mmp-b                 527   \n",
       "15895     10697     lh_G_precuneus   aparc.a2009s                3095   \n",
       "20687     10882          R_FEF_ROI      hcp-mmp-b                 671   \n",
       "\n",
       "       surface_area_mm^2  gray_matter_volume_mm^3  thickness  thickness_std  \\\n",
       "20525                439                     1663      2.798          0.635   \n",
       "12373                394                     1631      3.392          0.472   \n",
       "18653               1203                     2626      2.274          0.452   \n",
       "6801                 348                     1089      2.452          0.429   \n",
       "54920                127                      578      3.512          0.763   \n",
       "4325                 747                     1930      2.171          0.592   \n",
       "39865               2985                     9343      2.626          0.546   \n",
       "38221                383                      897      2.136          0.480   \n",
       "15895               2166                     6762      2.512          0.397   \n",
       "20687                471                      940      1.910          0.483   \n",
       "\n",
       "       mean_curv  gaus_curv  ...  crt_time1  crt_err1 crt_pr1 crt_pr2  \\\n",
       "20525      0.097      0.024  ...         42         0       0       0   \n",
       "12373      0.158      0.047  ...         72         1       0       0   \n",
       "18653      0.101      0.020  ...         18         0       0       0   \n",
       "6801       0.137      0.038  ...         37         0       0       0   \n",
       "54920      0.091      0.019  ...         26         0       0       0   \n",
       "4325       0.141      0.031  ...         24         0       0       0   \n",
       "39865      0.115      0.026  ...         58         0       1       0   \n",
       "38221      0.132      0.037  ...         49         0       0       0   \n",
       "15895      0.137      0.030  ...         20         0       0       0   \n",
       "20687      0.121      0.035  ...         42         0       0       0   \n",
       "\n",
       "      crt_err2 crt_nm1 crt_time2 crt_nm2  crt_index      colors  \n",
       "20525        0       0        66       0       0.57    tab:blue  \n",
       "12373        0       0        62       1      -0.14    tab:blue  \n",
       "18653        0       0        46       0       1.56    tab:blue  \n",
       "6801         0       0        75       0       1.03    tab:blue  \n",
       "54920        0       0        97       2       2.73  tab:orange  \n",
       "4325         0       0        67       0       1.79    tab:blue  \n",
       "39865        0       1        93       0       0.60  tab:orange  \n",
       "38221        0       1        91       0       0.86  tab:orange  \n",
       "15895        0       0        39       0       0.95    tab:blue  \n",
       "20687        0       0        66       0       0.57    tab:blue  \n",
       "\n",
       "[10 rows x 170 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## load parc-stats data\n",
    "# function: collect_data()\n",
    "# inputs: datatype = name of the datatype (example: 'parc-stats')\n",
    "#         datatype_tags = list of datatype tags to search for (can leave blank for this analysis)\n",
    "#         tags = list of object tags to search for (can leave blank for this analysis)\n",
    "#         filename = name of the file within the datatype (you can search for this by using the File Viewer on the parc-stats datatype)\n",
    "#         outPath = filepath where to save the concatenated data (can be left blank if you don't want to save the file)\n",
    "#         duplicates = True or False; True = keep duplicates, False = remove duplicates\n",
    "\n",
    "# set the output directory and output filename\n",
    "data_directory = 'data'\n",
    "output_filename = 'cortical-statistics-test.csv'\n",
    "output_filepath = data_directory+'/'+output_filename\n",
    "\n",
    "# make the output directory if not already made\n",
    "if not os.path.isdir(data_directory):\n",
    "    os.mkdir(data_directory)\n",
    "\n",
    "# if the output file doesn't exist, comb through the warehouse to find the data for the project and concatenate to single dataframe\n",
    "# if it does exist, just load the dataframe\n",
    "if not os.path.isfile(output_filename):\n",
    "    cortex_df,_,_ = collect_data(datatype='neuro/parc-stats', \\\n",
    "                                 filename='parc-stats/merged.csv', \\\n",
    "                                 outPath=output_filename, \\\n",
    "                                 duplicates=True)\n",
    "    \n",
    "    # make sure to set subjectID as string\n",
    "    cortex_df['subjectID'] = cortex_df['subjectID'].astype(str)\n",
    "else:\n",
    "    # read the dataframe \n",
    "    cortex_df = pd.read_csv(output_filename)\n",
    "    \n",
    "    # make sure to set subjectID as string\n",
    "    cortex_df['subjectID'] = cortex_df['subjectID'].astype(str)\n",
    "    \n",
    "# merge subjects data frame with tractmeasures\n",
    "cortex_df = cortex_df.merge(subjects_data,on='subjectID')\n",
    "\n",
    "# visualize the dataframe\n",
    "cortex_df.sample(10).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e007feb8-3060-412d-8e97-8a86cb303b7d",
   "metadata": {},
   "source": [
    "For this example, let's just look at cortical data from the hcp-mmp-b atlas (i.e. Glasser atlas).\n",
    "\n",
    "To do this, we can just use some simple pandas .loc functionality to identify only those data that correspond to the hcp-mmp-b atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4b896fb-cd70-4bfc-9645-106e1b40a700",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        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>subjectID</th>\n",
       "      <th>structureID</th>\n",
       "      <th>parcellationID</th>\n",
       "      <th>number_of_vertices</th>\n",
       "      <th>surface_area_mm^2</th>\n",
       "      <th>gray_matter_volume_mm^3</th>\n",
       "      <th>thickness</th>\n",
       "      <th>thickness_std</th>\n",
       "      <th>mean_curv</th>\n",
       "      <th>gaus_curv</th>\n",
       "      <th>...</th>\n",
       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27188</th>\n",
       "      <td>50077</td>\n",
       "      <td>R_3b_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>1509</td>\n",
       "      <td>1041</td>\n",
       "      <td>1543</td>\n",
       "      <td>1.502</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.034</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23539</th>\n",
       "      <td>50027</td>\n",
       "      <td>L_TPOJ2_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>612</td>\n",
       "      <td>404</td>\n",
       "      <td>1224</td>\n",
       "      <td>2.719</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.025</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>10707</td>\n",
       "      <td>R_V8_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>461</td>\n",
       "      <td>306</td>\n",
       "      <td>790</td>\n",
       "      <td>2.345</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.025</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1.27</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22762</th>\n",
       "      <td>50025</td>\n",
       "      <td>L_p9-46v_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>675</td>\n",
       "      <td>479</td>\n",
       "      <td>1258</td>\n",
       "      <td>2.238</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.039</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>11122</td>\n",
       "      <td>R_RI_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>664</td>\n",
       "      <td>419</td>\n",
       "      <td>770</td>\n",
       "      <td>2.227</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.017</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24809</th>\n",
       "      <td>50059</td>\n",
       "      <td>R_PGi_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>1673</td>\n",
       "      <td>1080</td>\n",
       "      <td>2963</td>\n",
       "      <td>2.624</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.027</td>\n",
       "      <td>...</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21558</th>\n",
       "      <td>50005</td>\n",
       "      <td>R_TPOJ1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>983</td>\n",
       "      <td>647</td>\n",
       "      <td>1534</td>\n",
       "      <td>2.548</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.022</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0.67</td>\n",
       "      <td>tab:orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13078</th>\n",
       "      <td>11067</td>\n",
       "      <td>L_PreS_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>491</td>\n",
       "      <td>301</td>\n",
       "      <td>1069</td>\n",
       "      <td>2.588</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.030</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13516</th>\n",
       "      <td>11090</td>\n",
       "      <td>R_IPS1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>479</td>\n",
       "      <td>312</td>\n",
       "      <td>648</td>\n",
       "      <td>2.175</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.021</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9329</th>\n",
       "      <td>10697</td>\n",
       "      <td>R_PGi_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>1999</td>\n",
       "      <td>1332</td>\n",
       "      <td>3736</td>\n",
       "      <td>2.529</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.023</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subjectID   structureID parcellationID  number_of_vertices  \\\n",
       "27188     50077      R_3b_ROI      hcp-mmp-b                1509   \n",
       "23539     50027   L_TPOJ2_ROI      hcp-mmp-b                 612   \n",
       "9546      10707      R_V8_ROI      hcp-mmp-b                 461   \n",
       "22762     50025  L_p9-46v_ROI      hcp-mmp-b                 675   \n",
       "1003      11122      R_RI_ROI      hcp-mmp-b                 664   \n",
       "24809     50059     R_PGi_ROI      hcp-mmp-b                1673   \n",
       "21558     50005   R_TPOJ1_ROI      hcp-mmp-b                 983   \n",
       "13078     11067    L_PreS_ROI      hcp-mmp-b                 491   \n",
       "13516     11090    R_IPS1_ROI      hcp-mmp-b                 479   \n",
       "9329      10697     R_PGi_ROI      hcp-mmp-b                1999   \n",
       "\n",
       "       surface_area_mm^2  gray_matter_volume_mm^3  thickness  thickness_std  \\\n",
       "27188               1041                     1543      1.502          0.358   \n",
       "23539                404                     1224      2.719          0.453   \n",
       "9546                 306                      790      2.345          0.353   \n",
       "22762                479                     1258      2.238          0.456   \n",
       "1003                 419                      770      2.227          0.399   \n",
       "24809               1080                     2963      2.624          0.475   \n",
       "21558                647                     1534      2.548          0.373   \n",
       "13078                301                     1069      2.588          0.820   \n",
       "13516                312                      648      2.175          0.327   \n",
       "9329                1332                     3736      2.529          0.454   \n",
       "\n",
       "       mean_curv  gaus_curv  ...  crt_time1  crt_err1 crt_pr1 crt_pr2  \\\n",
       "27188      0.115      0.034  ...         42         0       0       0   \n",
       "23539      0.121      0.025  ...         27         0       0       0   \n",
       "9546       0.118      0.025  ...         26         0       0       0   \n",
       "22762      0.139      0.039  ...         58         0       1       0   \n",
       "1003       0.089      0.017  ...         43         0       0       0   \n",
       "24809      0.111      0.027  ...        126         0       1       1   \n",
       "21558      0.105      0.022  ...         42         0       0       0   \n",
       "13078      0.105      0.030  ...         38         0       0       0   \n",
       "13516      0.109      0.021  ...         23         0       0       0   \n",
       "9329       0.117      0.023  ...         20         0       0       0   \n",
       "\n",
       "      crt_err2 crt_nm1 crt_time2 crt_nm2  crt_index      colors  \n",
       "27188        0       0        87       0       1.07  tab:orange  \n",
       "23539        0       0        57       0       1.11  tab:orange  \n",
       "9546         0       1        59       0       1.27    tab:blue  \n",
       "22762        0       1        93       0       0.60  tab:orange  \n",
       "1003         0       0        58       0       0.35    tab:blue  \n",
       "24809        0       0       125       0      -0.01  tab:orange  \n",
       "21558        1       1        70       1       0.67  tab:orange  \n",
       "13078        0       0        61       0       0.61    tab:blue  \n",
       "13516        0       1        48       0       1.09    tab:blue  \n",
       "9329         0       0        39       0       0.95    tab:blue  \n",
       "\n",
       "[10 rows x 170 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## subsample data for only parcels in hcp-mmp-b\n",
    "cortex_df_glasser = cortex_df.loc[cortex_df['parcellationID'] == 'hcp-mmp-b'].reset_index(drop=True)\n",
    "\n",
    "# visualize the dataframe\n",
    "cortex_df_glasser.sample(10).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca66ce70-cfbe-4c5a-a581-e926574304cb",
   "metadata": {},
   "source": [
    "# Describe the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143b3700-eb4a-403c-b8eb-4766c4f45433",
   "metadata": {},
   "source": [
    "#### compute mean, min-max, and quantiles of each column using pandas function describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b269f29-030e-4094-b19e-e9c49dd61062",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_vertices</th>\n",
       "      <th>surface_area_mm^2</th>\n",
       "      <th>gray_matter_volume_mm^3</th>\n",
       "      <th>thickness</th>\n",
       "      <th>thickness_std</th>\n",
       "      <th>mean_curv</th>\n",
       "      <th>gaus_curv</th>\n",
       "      <th>foldind</th>\n",
       "      <th>curvind</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>crt_ne2</th>\n",
       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.00000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "      <td>31680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>735.616824</td>\n",
       "      <td>504.33286</td>\n",
       "      <td>1365.883049</td>\n",
       "      <td>2.484791</td>\n",
       "      <td>0.473022</td>\n",
       "      <td>0.122729</td>\n",
       "      <td>0.030681</td>\n",
       "      <td>8.342929</td>\n",
       "      <td>0.905979</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>38.238636</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.295455</td>\n",
       "      <td>0.295455</td>\n",
       "      <td>74.556818</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>1.099659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>645.116533</td>\n",
       "      <td>439.71017</td>\n",
       "      <td>1103.201393</td>\n",
       "      <td>0.355910</td>\n",
       "      <td>0.131896</td>\n",
       "      <td>0.021738</td>\n",
       "      <td>0.011218</td>\n",
       "      <td>8.734938</td>\n",
       "      <td>1.005325</td>\n",
       "      <td>9.842088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105995</td>\n",
       "      <td>18.684874</td>\n",
       "      <td>0.330724</td>\n",
       "      <td>0.324616</td>\n",
       "      <td>0.558564</td>\n",
       "      <td>0.676887</td>\n",
       "      <td>0.624493</td>\n",
       "      <td>27.180325</td>\n",
       "      <td>0.382338</td>\n",
       "      <td>0.624108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.318000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>369.000000</td>\n",
       "      <td>250.00000</td>\n",
       "      <td>661.000000</td>\n",
       "      <td>2.253000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.667500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>562.000000</td>\n",
       "      <td>387.00000</td>\n",
       "      <td>1055.000000</td>\n",
       "      <td>2.458000</td>\n",
       "      <td>0.452000</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.029000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>847.000000</td>\n",
       "      <td>587.00000</td>\n",
       "      <td>1681.000000</td>\n",
       "      <td>2.685000</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.357500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7304.000000</td>\n",
       "      <td>4918.00000</td>\n",
       "      <td>9262.000000</td>\n",
       "      <td>4.614000</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>0.483000</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_of_vertices  surface_area_mm^2  gray_matter_volume_mm^3  \\\n",
       "count        31680.000000        31680.00000             31680.000000   \n",
       "mean           735.616824          504.33286              1365.883049   \n",
       "std            645.116533          439.71017              1103.201393   \n",
       "min              1.000000            1.00000                 1.000000   \n",
       "25%            369.000000          250.00000               661.000000   \n",
       "50%            562.000000          387.00000              1055.000000   \n",
       "75%            847.000000          587.00000              1681.000000   \n",
       "max           7304.000000         4918.00000              9262.000000   \n",
       "\n",
       "          thickness  thickness_std     mean_curv     gaus_curv       foldind  \\\n",
       "count  31680.000000   31680.000000  31680.000000  31680.000000  31680.000000   \n",
       "mean       2.484791       0.473022      0.122729      0.030681      8.342929   \n",
       "std        0.355910       0.131896      0.021738      0.011218      8.734938   \n",
       "min        1.318000       0.000000      0.045000      0.004000      0.000000   \n",
       "25%        2.253000       0.380000      0.109000      0.024000      3.000000   \n",
       "50%        2.458000       0.452000      0.122000      0.029000      6.000000   \n",
       "75%        2.685000       0.541000      0.136000      0.036000     10.000000   \n",
       "max        4.614000       1.310000      0.483000      0.248000    159.000000   \n",
       "\n",
       "            curvind           age  ...       crt_ne2     crt_time1  \\\n",
       "count  31680.000000  31680.000000  ...  31680.000000  31680.000000   \n",
       "mean       0.905979     33.500000  ...      0.011364     38.238636   \n",
       "std        1.005325      9.842088  ...      0.105995     18.684874   \n",
       "min        0.000000     21.000000  ...      0.000000     16.000000   \n",
       "25%        0.400000     24.000000  ...      0.000000     26.000000   \n",
       "50%        0.600000     30.500000  ...      0.000000     32.500000   \n",
       "75%        1.100000     43.000000  ...      0.000000     46.250000   \n",
       "max       14.800000     50.000000  ...      1.000000    126.000000   \n",
       "\n",
       "           crt_err1       crt_pr1       crt_pr2      crt_err2       crt_nm1  \\\n",
       "count  31680.000000  31680.000000  31680.000000  31680.000000  31680.000000   \n",
       "mean       0.125000      0.090909      0.227273      0.295455      0.295455   \n",
       "std        0.330724      0.324616      0.558564      0.676887      0.624493   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      2.000000      3.000000      3.000000      4.000000   \n",
       "\n",
       "          crt_time2       crt_nm2     crt_index  \n",
       "count  31680.000000  31680.000000  31680.000000  \n",
       "mean      74.556818      0.113636      1.099659  \n",
       "std       27.180325      0.382338      0.624108  \n",
       "min       33.000000      0.000000     -0.140000  \n",
       "25%       57.750000      0.000000      0.667500  \n",
       "50%       71.500000      0.000000      1.045000  \n",
       "75%       88.250000      0.000000      1.357500  \n",
       "max      201.000000      2.000000      3.180000  \n",
       "\n",
       "[8 rows x 85 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cortex_df_glasser.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcc800a-02fd-4a58-99b5-82332f2e846f",
   "metadata": {},
   "source": [
    "#### compute meta data on the dataframe using pandas function info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "da61a387-9d00-4888-b933-854d3b4caecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31680 entries, 0 to 31679\n",
      "Columns: 170 entries, subjectID to colors\n",
      "dtypes: float64(15), int64(70), object(85)\n",
      "memory usage: 41.1+ MB\n"
     ]
    }
   ],
   "source": [
    "cortex_df_glasser.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58797419-ec0d-4120-a418-e851dbeb136f",
   "metadata": {},
   "source": [
    "#### count the number of parcels for each subject. ideal value == 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "2cde356d-a5cf-4039-98a1-d6d20e3c0219",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureID</th>\n",
       "      <th>parcellationID</th>\n",
       "      <th>number_of_vertices</th>\n",
       "      <th>surface_area_mm^2</th>\n",
       "      <th>gray_matter_volume_mm^3</th>\n",
       "      <th>thickness</th>\n",
       "      <th>thickness_std</th>\n",
       "      <th>mean_curv</th>\n",
       "      <th>gaus_curv</th>\n",
       "      <th>foldind</th>\n",
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       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subjectID</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>10159</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10171</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10189</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10228</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10290</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50077</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50080</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50081</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50083</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50085</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           structureID  parcellationID  number_of_vertices  surface_area_mm^2  \\\n",
       "subjectID                                                                       \n",
       "10159              360             360                 360                360   \n",
       "10171              360             360                 360                360   \n",
       "10189              360             360                 360                360   \n",
       "10228              360             360                 360                360   \n",
       "10290              360             360                 360                360   \n",
       "...                ...             ...                 ...                ...   \n",
       "50077              360             360                 360                360   \n",
       "50080              360             360                 360                360   \n",
       "50081              360             360                 360                360   \n",
       "50083              360             360                 360                360   \n",
       "50085              360             360                 360                360   \n",
       "\n",
       "           gray_matter_volume_mm^3  thickness  thickness_std  mean_curv  \\\n",
       "subjectID                                                                 \n",
       "10159                          360        360            360        360   \n",
       "10171                          360        360            360        360   \n",
       "10189                          360        360            360        360   \n",
       "10228                          360        360            360        360   \n",
       "10290                          360        360            360        360   \n",
       "...                            ...        ...            ...        ...   \n",
       "50077                          360        360            360        360   \n",
       "50080                          360        360            360        360   \n",
       "50081                          360        360            360        360   \n",
       "50083                          360        360            360        360   \n",
       "50085                          360        360            360        360   \n",
       "\n",
       "           gaus_curv  foldind  ...  crt_time1  crt_err1  crt_pr1  crt_pr2  \\\n",
       "subjectID                      ...                                          \n",
       "10159            360      360  ...        360       360      360      360   \n",
       "10171            360      360  ...        360       360      360      360   \n",
       "10189            360      360  ...        360       360      360      360   \n",
       "10228            360      360  ...        360       360      360      360   \n",
       "10290            360      360  ...        360       360      360      360   \n",
       "...              ...      ...  ...        ...       ...      ...      ...   \n",
       "50077            360      360  ...        360       360      360      360   \n",
       "50080            360      360  ...        360       360      360      360   \n",
       "50081            360      360  ...        360       360      360      360   \n",
       "50083            360      360  ...        360       360      360      360   \n",
       "50085            360      360  ...        360       360      360      360   \n",
       "\n",
       "           crt_err2  crt_nm1  crt_time2  crt_nm2  crt_index  colors  \n",
       "subjectID                                                            \n",
       "10159           360      360        360      360        360     360  \n",
       "10171           360      360        360      360        360     360  \n",
       "10189           360      360        360      360        360     360  \n",
       "10228           360      360        360      360        360     360  \n",
       "10290           360      360        360      360        360     360  \n",
       "...             ...      ...        ...      ...        ...     ...  \n",
       "50077           360      360        360      360        360     360  \n",
       "50080           360      360        360      360        360     360  \n",
       "50081           360      360        360      360        360     360  \n",
       "50083           360      360        360      360        360     360  \n",
       "50085           360      360        360      360        360     360  \n",
       "\n",
       "[88 rows x 169 columns]"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cortex_df_glasser.groupby('subjectID').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072a6dc8-6987-4d53-9ac9-3d3ddeea7b93",
   "metadata": {},
   "source": [
    "#### count the number of unique subjects per group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "38cb638f-55ec-4744-98cf-e827be338025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classID\n",
       "CONTROL    42\n",
       "SCHZ       46\n",
       "Name: subjectID, dtype: int64"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cortex_df_glasser.groupby(['subjectID','classID']).mean().reset_index().groupby(['classID']).count()['subjectID']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5537e402-991e-46a4-a791-5f8e1525162c",
   "metadata": {},
   "source": [
    "#### subsample to only the primary visual cortex (V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "317bdfe0-1afe-477f-bbba-b44733890cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subjectID</th>\n",
       "      <th>structureID</th>\n",
       "      <th>parcellationID</th>\n",
       "      <th>number_of_vertices</th>\n",
       "      <th>surface_area_mm^2</th>\n",
       "      <th>gray_matter_volume_mm^3</th>\n",
       "      <th>thickness</th>\n",
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       "      <th>mean_curv</th>\n",
       "      <th>gaus_curv</th>\n",
       "      <th>...</th>\n",
       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
       "      <th>colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17100</th>\n",
       "      <td>50061</td>\n",
       "      <td>R_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5316</td>\n",
       "      <td>3702</td>\n",
       "      <td>6683</td>\n",
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       "      <td>0.589</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.047</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5580</th>\n",
       "      <td>10565</td>\n",
       "      <td>R_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>4920</td>\n",
       "      <td>3298</td>\n",
       "      <td>6604</td>\n",
       "      <td>1.921</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.040</td>\n",
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       "      <td>tab:blue</td>\n",
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       "    <tr>\n",
       "      <th>22860</th>\n",
       "      <td>50025</td>\n",
       "      <td>R_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5268</td>\n",
       "      <td>3450</td>\n",
       "      <td>5867</td>\n",
       "      <td>1.647</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.043</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>10228</td>\n",
       "      <td>R_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5013</td>\n",
       "      <td>3541</td>\n",
       "      <td>6456</td>\n",
       "      <td>1.747</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.045</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>tab:blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18720</th>\n",
       "      <td>50006</td>\n",
       "      <td>L_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>6015</td>\n",
       "      <td>4422</td>\n",
       "      <td>7356</td>\n",
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       "      <td>0.518</td>\n",
       "      <td>0.161</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.43</td>\n",
       "      <td>tab:green</td>\n",
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       "    <tr>\n",
       "      <th>15120</th>\n",
       "      <td>50076</td>\n",
       "      <td>L_V1_ROI</td>\n",
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       "      <td>5824</td>\n",
       "      <td>3944</td>\n",
       "      <td>7546</td>\n",
       "      <td>1.794</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.036</td>\n",
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       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>tab:green</td>\n",
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       "    <tr>\n",
       "      <th>25020</th>\n",
       "      <td>50052</td>\n",
       "      <td>R_V1_ROI</td>\n",
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       "      <td>3259</td>\n",
       "      <td>5588</td>\n",
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       "      <td>0.554</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.042</td>\n",
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       "      <td>0</td>\n",
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       "      <td>tab:green</td>\n",
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       "    <tr>\n",
       "      <th>31140</th>\n",
       "      <td>50014</td>\n",
       "      <td>R_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5842</td>\n",
       "      <td>4014</td>\n",
       "      <td>7214</td>\n",
       "      <td>1.734</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.044</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>2.73</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29160</th>\n",
       "      <td>50054</td>\n",
       "      <td>L_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5609</td>\n",
       "      <td>3848</td>\n",
       "      <td>7259</td>\n",
       "      <td>1.758</td>\n",
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       "      <td>0.038</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20880</th>\n",
       "      <td>50004</td>\n",
       "      <td>L_V1_ROI</td>\n",
       "      <td>hcp-mmp-b</td>\n",
       "      <td>5162</td>\n",
       "      <td>3396</td>\n",
       "      <td>6687</td>\n",
       "      <td>1.879</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.039</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>tab:green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subjectID structureID parcellationID  number_of_vertices  \\\n",
       "17100     50061    R_V1_ROI      hcp-mmp-b                5316   \n",
       "5580      10565    R_V1_ROI      hcp-mmp-b                4920   \n",
       "22860     50025    R_V1_ROI      hcp-mmp-b                5268   \n",
       "1980      10228    R_V1_ROI      hcp-mmp-b                5013   \n",
       "18720     50006    L_V1_ROI      hcp-mmp-b                6015   \n",
       "15120     50076    L_V1_ROI      hcp-mmp-b                5824   \n",
       "25020     50052    R_V1_ROI      hcp-mmp-b                4852   \n",
       "31140     50014    R_V1_ROI      hcp-mmp-b                5842   \n",
       "29160     50054    L_V1_ROI      hcp-mmp-b                5609   \n",
       "20880     50004    L_V1_ROI      hcp-mmp-b                5162   \n",
       "\n",
       "       surface_area_mm^2  gray_matter_volume_mm^3  thickness  thickness_std  \\\n",
       "17100               3702                     6683      1.685          0.589   \n",
       "5580                3298                     6604      1.921          0.471   \n",
       "22860               3450                     5867      1.647          0.478   \n",
       "1980                3541                     6456      1.747          0.513   \n",
       "18720               4422                     7356      1.688          0.518   \n",
       "15120               3944                     7546      1.794          0.496   \n",
       "25020               3259                     5588      1.602          0.554   \n",
       "31140               4014                     7214      1.734          0.480   \n",
       "29160               3848                     7259      1.758          0.493   \n",
       "20880               3396                     6687      1.879          0.563   \n",
       "\n",
       "       mean_curv  gaus_curv  ...  crt_time1  crt_err1 crt_pr1  crt_pr2  \\\n",
       "17100      0.153      0.047  ...         49         0       0        0   \n",
       "5580       0.139      0.040  ...         28         0       0        0   \n",
       "22860      0.137      0.043  ...         58         0       1        0   \n",
       "1980       0.150      0.045  ...         21         0       0        0   \n",
       "18720      0.161      0.054  ...         42         0       1        1   \n",
       "15120      0.133      0.036  ...         51         0       0        0   \n",
       "25020      0.144      0.042  ...         43         0       0        0   \n",
       "31140      0.151      0.044  ...         26         0       0        0   \n",
       "29160      0.139      0.038  ...         24         0       0        0   \n",
       "20880      0.137      0.039  ...         51         1       0        0   \n",
       "\n",
       "      crt_err2 crt_nm1 crt_time2 crt_nm2  crt_index     colors  \n",
       "17100        0       0        86       0       0.76  tab:green  \n",
       "5580         0       0        65       0       1.32   tab:blue  \n",
       "22860        0       1        93       0       0.60  tab:green  \n",
       "1980         0       0        50       1       1.38   tab:blue  \n",
       "18720        0       0       102       0       1.43  tab:green  \n",
       "15120        3       0        97       0       0.90  tab:green  \n",
       "25020        0       0        91       0       1.12  tab:green  \n",
       "31140        0       0        97       2       2.73  tab:green  \n",
       "29160        0       1        64       0       1.67  tab:green  \n",
       "20880        0       0        85       0       0.67  tab:green  \n",
       "\n",
       "[10 rows x 170 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the v1's from the dataframe\n",
    "regions = ['L_V1_ROI','R_V1_ROI']\n",
    "v1 = cortex_df_glasser.loc[cortex_df_glasser['structureID'].isin(regions)]\n",
    "\n",
    "v1.sample(10).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4d7943-2ff7-40de-bf52-d8a3681c3311",
   "metadata": {},
   "source": [
    "#### compute mean across hemispheres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4004ee31-cf89-48d5-97f4-f9a4827b92fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gaus_curv</th>\n",
       "      <th>foldind</th>\n",
       "      <th>...</th>\n",
       "      <th>crt_ne2</th>\n",
       "      <th>crt_time1</th>\n",
       "      <th>crt_err1</th>\n",
       "      <th>crt_pr1</th>\n",
       "      <th>crt_pr2</th>\n",
       "      <th>crt_err2</th>\n",
       "      <th>crt_nm1</th>\n",
       "      <th>crt_time2</th>\n",
       "      <th>crt_nm2</th>\n",
       "      <th>crt_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10159</td>\n",
       "      <td>CONTROL</td>\n",
       "      <td>3006.0</td>\n",
       "      <td>2486.5</td>\n",
       "      <td>4936.5</td>\n",
       "      <td>1.8325</td>\n",
       "      <td>0.6450</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.1565</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10171</td>\n",
       "      <td>CONTROL</td>\n",
       "      <td>4672.5</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>6541.0</td>\n",
       "      <td>1.8625</td>\n",
       "      <td>0.6105</td>\n",
       "      <td>0.1570</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>69.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10189</td>\n",
       "      <td>CONTROL</td>\n",
       "      <td>4706.0</td>\n",
       "      <td>3034.0</td>\n",
       "      <td>6101.0</td>\n",
       "      <td>1.8215</td>\n",
       "      <td>0.5555</td>\n",
       "      <td>0.1345</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>59.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10228</td>\n",
       "      <td>CONTROL</td>\n",
       "      <td>5189.5</td>\n",
       "      <td>3630.0</td>\n",
       "      <td>6624.0</td>\n",
       "      <td>1.7640</td>\n",
       "      <td>0.4895</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10290</td>\n",
       "      <td>CONTROL</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>3446.5</td>\n",
       "      <td>6255.5</td>\n",
       "      <td>1.7045</td>\n",
       "      <td>0.5555</td>\n",
       "      <td>0.1385</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>61.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>50077</td>\n",
       "      <td>SCHZ</td>\n",
       "      <td>5295.5</td>\n",
       "      <td>3494.0</td>\n",
       "      <td>6012.5</td>\n",
       "      <td>1.6755</td>\n",
       "      <td>0.4620</td>\n",
       "      <td>0.1345</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>50080</td>\n",
       "      <td>SCHZ</td>\n",
       "      <td>5729.5</td>\n",
       "      <td>3963.0</td>\n",
       "      <td>7022.5</td>\n",
       "      <td>1.7420</td>\n",
       "      <td>0.4635</td>\n",
       "      <td>0.1435</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>76.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>50081</td>\n",
       "      <td>SCHZ</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>3358.5</td>\n",
       "      <td>5907.0</td>\n",
       "      <td>1.7055</td>\n",
       "      <td>0.5635</td>\n",
       "      <td>0.1385</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>50083</td>\n",
       "      <td>SCHZ</td>\n",
       "      <td>6611.0</td>\n",
       "      <td>4325.5</td>\n",
       "      <td>8342.0</td>\n",
       "      <td>1.8440</td>\n",
       "      <td>0.5400</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>80.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>50085</td>\n",
       "      <td>SCHZ</td>\n",
       "      <td>5715.5</td>\n",
       "      <td>4182.5</td>\n",
       "      <td>7741.5</td>\n",
       "      <td>1.7785</td>\n",
       "      <td>0.5735</td>\n",
       "      <td>0.1505</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subjectID  classID  number_of_vertices  surface_area_mm^2  \\\n",
       "0      10159  CONTROL              3006.0             2486.5   \n",
       "1      10171  CONTROL              4672.5             3383.0   \n",
       "2      10189  CONTROL              4706.0             3034.0   \n",
       "3      10228  CONTROL              5189.5             3630.0   \n",
       "4      10290  CONTROL              5055.0             3446.5   \n",
       "..       ...      ...                 ...                ...   \n",
       "83     50077     SCHZ              5295.5             3494.0   \n",
       "84     50080     SCHZ              5729.5             3963.0   \n",
       "85     50081     SCHZ              4940.0             3358.5   \n",
       "86     50083     SCHZ              6611.0             4325.5   \n",
       "87     50085     SCHZ              5715.5             4182.5   \n",
       "\n",
       "    gray_matter_volume_mm^3  thickness  thickness_std  mean_curv  gaus_curv  \\\n",
       "0                    4936.5     1.8325         0.6450     0.1925     0.1565   \n",
       "1                    6541.0     1.8625         0.6105     0.1570     0.0455   \n",
       "2                    6101.0     1.8215         0.5555     0.1345     0.0415   \n",
       "3                    6624.0     1.7640         0.4895     0.1460     0.0420   \n",
       "4                    6255.5     1.7045         0.5555     0.1385     0.0370   \n",
       "..                      ...        ...            ...        ...        ...   \n",
       "83                   6012.5     1.6755         0.4620     0.1345     0.0370   \n",
       "84                   7022.5     1.7420         0.4635     0.1435     0.0395   \n",
       "85                   5907.0     1.7055         0.5635     0.1385     0.0385   \n",
       "86                   8342.0     1.8440         0.5400     0.1330     0.0385   \n",
       "87                   7741.5     1.7785         0.5735     0.1505     0.0420   \n",
       "\n",
       "    foldind  ...  crt_ne2  crt_time1  crt_err1  crt_pr1  crt_pr2  crt_err2  \\\n",
       "0      81.0  ...      0.0       32.0       0.0      0.0      0.0       0.0   \n",
       "1      69.5  ...      0.0       51.0       0.0      0.0      0.0       0.0   \n",
       "2      59.5  ...      0.0       22.0       0.0      0.0      0.0       0.0   \n",
       "3      77.0  ...      0.0       21.0       0.0      0.0      0.0       0.0   \n",
       "4      61.5  ...      0.0       26.0       0.0      0.0      0.0       0.0   \n",
       "..      ...  ...      ...        ...       ...      ...      ...       ...   \n",
       "83     63.0  ...      0.0       42.0       0.0      0.0      0.0       0.0   \n",
       "84     76.5  ...      0.0       44.0       0.0      0.0      0.0       0.0   \n",
       "85     57.0  ...      0.0       28.0       0.0      0.0      0.0       1.0   \n",
       "86     80.5  ...      0.0       42.0       1.0      0.0      0.0       2.0   \n",
       "87     76.0  ...      0.0       39.0       0.0      0.0      0.0       0.0   \n",
       "\n",
       "    crt_nm1  crt_time2  crt_nm2  crt_index  \n",
       "0       1.0       59.0      0.0       0.84  \n",
       "1       0.0       56.0      0.0       0.10  \n",
       "2       1.0       51.0      0.0       1.32  \n",
       "3       0.0       50.0      1.0       1.38  \n",
       "4       1.0       58.0      0.0       1.23  \n",
       "..      ...        ...      ...        ...  \n",
       "83      0.0       87.0      0.0       1.07  \n",
       "84      0.0       78.0      0.0       0.77  \n",
       "85      0.0       88.0      0.0       2.14  \n",
       "86      1.0       86.0      0.0       1.05  \n",
       "87      0.0       76.0      0.0       0.95  \n",
       "\n",
       "[88 rows x 88 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1_mean = v1.groupby(['subjectID','classID']).mean().reset_index()\n",
    "v1_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb9068a-c754-4b7c-87f1-310289a0575a",
   "metadata": {},
   "source": [
    "### Data visualizations\n",
    "\n",
    "Now, let's generate some visualizations to examine differences between groups across multiple brain and behavior measures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c0be31-e9c5-4249-9101-f0b511bde59a",
   "metadata": {},
   "source": [
    "#### v1 morphometrical measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "266a6c88-2760-4569-9766-82c8d2404bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QnRzodOzubSYk2ruHohk4ZARZCCGEEKKXGnlOBHU1Vjbu2MXBwI3EDQujxDqMIIIAyKzIxGazEekT2cOR9i+SIAshOkVpfjX71mZSY60maeogwiNCejokIYTo8zTgM9HCO0VPUW2pZnUaLM/7is8u/ownNz7J0rSlAJwXeR5/m/03zAZ54tsZJEEWbM3ZyobsDQwNGMrcqLkoddodaIVopqqsjk+e3sqqwE/ZHbYKvtNcEn0Zj8/+Q7PtzYUQQrTPwY3ZrPskhdUBn1IdXu1oz67M5uVdLzuSY4CV6StZnracRfGLeiLUfkcS5AHu08Of8viGxx3HPxn6E343+Xet9i/KqmT/+ixMJgMjZoXjHeDWDVGK3u7o9jxSjHvZGf69o+1/Jz5lxvFpzI+Z34ORCdF3lORW4eHjgou7/GoWUFlayw+LD3LUZzfZ3qnNXi+rLWvWllGe0R2hDQhdPrSjlLpfKbVPKbVXKfW+UspNKRWrlNqklEpRSn2olHJp6OvacJzS8HpMk+v8tqH9kFLq/K6Oe6B4Z/87TsefHP6EqvqqFvuW5Fbx8V+2suu7dLYtP87HT2+lprK+O8IUPay0tpRntjzDT1f8lDf3vonF5rxDmIu7iULPrGbnHSo+1F0hCtFnVZbU8uFTm3n3sY28+fCP7F2T2dMhiV6gKLOSzWFL+Wbof8jxSXN6bVTwKG5JvsVpOoVRGTk36tzuDrPf6tIEWSkVDtwHTNBaJwNG4FrgL8BzWusEoBi4veGU24HihvbnGvqhlBrecN4IYAHwklJKtpLpBEaD839GgzK0+kj80KYcLLVWx3F1WR1pu/K7ND7ROzy0+iHe2f8Om7I38ey2Z3lp50tOr8ePDWak+ziUdp6eM33w9O4MU4g+afPXaRSkVwBgqbPx40dHqKmQwYeBLiTGmz1hq53aXJUb/zj3H7y14C3i/eJ5Ze4rzAyfyfTB03lxzosMDRjaQ9H2P90xOdAEuCulTIAHkA2cB3zS8PrbwKUN/76k4ZiG1+co+4TYS4APtNa1Wus0IAWY1A2x93s/Tf4pisak5oZhN+BmannahItb88d+LbWJ/qWkpoSN2Rud2palLXM6NrkY+fmDl/B/cb8nzj2BBN8Enpj2BOMGjevOUIXok0pynJ/aWS02ygqrW+ktBgpXDzPu7s6/j71cPZkTNccxcjwpbBIvzX2JV+a9wozwGT0RZr/VpdmN1jpTKfU34ARQDawAtgElWuuTz2gzgJN7JoYD6Q3nWpRSpUBgQ3vT39BNz3FQSt0J3AkQFRXV6e+nP7og7gLi/eLZmL2RpIAkpoRNabXvsOlh7FubSWm+/cY9KNaHmFFB3RWq6CGeZk98XX0prS11tA32Gtysn8Fo4MZZV3EjV3VneEL0ebGjgxw7pQF4B7gRFOHVcwGJXuNn4+7hT5v+5Di+a/RdPRjNwNKuBFkp+zN3rbWtYb5wMnBMa13Uxnn+2Ed/Y4ES4GPsUyS6hNb6NeA1gAkTJuiu+jr9TVJAEkkBSW32qzKWM/peb1wzY3Axm4kaHoDBKBUK+juz0czDEx/m8fWPU2erI8AtgAfGP3BW16yprMdoNmB2kZlSQow+LxKbVXN0ex7ege5MuSRO7q0CsC+cHxU8ip15OxkTMoYRgSN6OqQBo80EWSl1KfAqYFNK3Q38DqgAkpRS92itvzzN6XOBNK11fsO1PgOmA35KKVPDKHIEcHJFQiYQCWQ0TMnwBQqbtJ/U9BzRDRbvX8yz257FYrMQ6R3Ja/Nekxv4ALIofhEzw2dyrOwYwwKH4Wo8sy1OrfU2vnt7Pynb8jCZDEy4MIbxC2I6N1ghepHtudt5ceeLlNSWcFnCZdw4/MZmfZRBMe78aMadH90DEYrebkTgCEmMe0B7MpzHgNHANGAxcJPWeg72RPexNs49AUxRSnk0zCWeA+wHfgCubOhzM/BFw7+XNBzT8PpKrbVuaL+2ocpFLJAIbG5H7KITFNcU89y25xyVC9LL03l518s9HJXobn5ufowJGdNiclxcU8zG7I2U1TUvO9TUvh+zSNmaBxos9TY2fp5KQUZFV4UsRI8qqSnh7u/uZkvOFo4UH+GvW/7K0tSlbZ8oRBfRNs3uH9L56sVdbPhfCrVVshi0Ne0aAtRa5zQsjjuhtT7U0Ha8rfO11puwL7bbDuxp6P8a8DDwgFIqBfsc49cbTnkdCGxofwD4TcN19gEfYU+ulwM/11pbEd0iryqPepvzD5HUWhQnfXv8W+Z+PJc7VtzB3I/nsj5zfat9C7OaJ8OFmZIgi/5pW+42qi3Oi+3WZq7toWhEf/bG3jc496NzmffJPD4+/HGr/bYuO8baD49wfG8h2785wfLX9nZjlH1Lu+cga61twG1N2oyAS1vnaq0fo/lIcyotVKHQWtdAyyt8tNZPAU+1J17RuRL9E4n1iSWtrLEOo2z+MHDtyt/F4+sfJ7U0lRmDZ3Cg6AB1tjoAqi3VPLP1Gf4X/r8Wz40aHsD+tY31kg0mRfgQ/26JW4juFu8X3642Ic7Gj5k/8ty25xzHT2x4grI1rlx5yTx8g92d+h7enOt0nHGwmOryOty920znBpz2jCDfSUMirLVuOq0hEni6K4ISvYtBGbil8v8YmjuFwaWJzEi9klF55/R0WKIHWG1WHlr1ECklKdi0jTWZayioLnDqk1eV1+r58WNDmHF1Iv5hngyK9eHCe0bh5X9m85mF6O1ifGO4f/z9uBntpbpmRcziuqHX9XBUoi+pqaxn4+dHWf7qHg5tzG6xz7bcbc3aNmds5asXd2GfpdrI08/5fuviZsTsJoulW9LmCLLWeksr7ceAY50cj+iF6mos5K63MNv2E0fbnh8yGHlORA9GJXpCXlUeOVU5Tm0miyv1phrHcYxPDOuz1jNt8LQWrzH6vEhGnxfZ4mtC9De3Jd/GtUnXUmWpIshdymKKjln60m6yj9pLbB7dkU9ttZVR5zr/7h0ZNLLZeSEV0ZRUVFGSW4V/qKejfepl8Xz1wi5qKusxGBXTr0zEZJYEuSXtLkOglJrXlYGI3stgUBiMzjukGc1SwWIgGuQ5CH9bsFNbbNEorgu7hQS/BAB2F+zmrm/varbbnhADlYfZQ5Jj0WFlBdWO5PiklkaRz4s6jztG3oGrdsPF4s6UY5cQWhGL2dXYbMR4UIwPN/15Gpc+MJab/jSN4TOa17QXdu3KcpRSN2FfNCcGIJOLkTHzGjdeUQopzTVAGZSBBwc/SnBFJCarmbjC0czKvIL7p91HVb3zbmBv73sbq03W0gohxJlwcTdhNDmnaR6+LU9Ju2/cfXx/0WoezPwnY7LPw8XNyDnXJbW8263RRpZPCoUqt/lrwqE9dZAfwV7P+MKuD0f0VpMvjiNyeAAF6RVEDPUnIMyz7ZNEv7Ro3rkEVURxaGM27t4uTPtpAm5e5mb9NLJXjxhYDhUdYuWJlQz2GszC2IW4GM9u4VNdjYW6agte/m5tdxb9jpunmUmLYtnw+VHQ4OppYtJFsa329w3y4Lo/TKE0vwoPH1fMrs5TJ3LTytiyay9/r3qUgvp8FIrbkm/jV+N/1cXvpG9qTxWLPwDDtNZVbfYUfc6eVRlsWXoMm8XG6DmRTLyw9R++wQl+DE7w677gRK9kMCimX5HA9CsSnNpvSb7FaUvUm4bfhNEgc9vEwLAxeyP3fHsPFm2vF7/82HJennvm9eJ3rUxn4+dHsdTZCB/ix4K7RuLm2fyDqOjfxp0fTfy4EErzqghL8GuW9LbEN9ijWduRrbmseH0fq2Lfp2BQPmAfxHhz35tck3QNYV5hnR57X9eeBPlW4BOl1HytdUGbvUWfkXusjDUfHHYcb/4yjaBIb2JHyVw50XE/GfoThvgPYWvOVpKDkpkePr2nQxKi27x74F1Hcgz20lupJanE+cV1+FrlRTWs+/gIJwsQZB4uYce3J5h6qZSIG4h8g92blWtrjyNbc0k/UERQhBeHNuWAhioX582cbNpGfnW+JMgtaE8Vi/eUUjnA58CMLo9IdJucUyb/n2yTBFmcqfGDxjN+0PieDkOIbmdoYUnPmT5BKcmt4pTqXBRnV57RtUTfZamzYqmztTiFrS07Vpxg/WcpjuOTTx8S8ydwwn+/oz3GJ4bkoOSzD7YfatdGIVrrlUqp1oubij4pNN63XW1CdKeUbXlsXWaf9jPqvEiSZ4X3dEj9jlLqDeAiIE9r3ey3o1LKH3gDiAdqgNu01rLlVhP11no2ZG/A1ejKxNCJ3DziZn7M/NGxac686HlE+0Sf0bVD43xx9TRRW9k4Ih0zUgYuBpLdP2Sw8fOj1NdaiR4ZyPzbR7S84K4V+9dlOR3XVNWDgsTC8ajDBvKT9jNy6BBuTb4Vg5KqVC1p939tuTn2TQXVBXx19CsMysBF8RcR4BbgeG1QjA8zrxnC1qVp2Kya0XMiZfRYdLv0snSqLFUkBSRRlF3Jiv/sdYyerX7vEL4h7kQODTj9RURHvQW8CLzTyuu/A3ZqrS9TSg0F/gXM6abYer2SmhJuXHYjx8qOATApdBKvznuVzy/5nB/SfyDcK5zZkbPP+PpmVyMX3zeGTUvSqCytJWlSKMOmyyPwgaKssJofPzrsuA+m7cljyTf5zFswEV/X9g1iubg7p3dmFyOXPTiOjEPFXBQ2mqgRASilWjlbQAcSZKWUH3ATENP0PK31fZ0elegUBdUFXLnkSgprCgFYfGAxnyz6xOkHbNS5Ec2KjndEbVU9JlcjRqN8AhUd9+iPj/LF0S8AGBsylp+7PNrs0XL6/iJJkDuZ1nqNUirmNF2G07BTqtb6oFIqRik1SGstdaGAT4986kiOATbnbObHzB+ZHTmbm0bc1ClfIyTah0W/GN0p1xJ9S1FWpeM+WOSew9Jhr1BRUszTH7vyyORHuCzxsjavMWlRLMte3oPVYgNg4oWxBEd5Exzl3ZWh9yvtH6+HpcBGYA9g65pwRGf66uhXjuQYIKcyhxXHV3DVkKvO+to1lfWseH0f6fuLcPM0M/OaRIZMCj3r64qBY0vOFkdyDLAjbwdbYn5AkejULyjCq7tDE7ALuBxYq5SaBEQDEUCzBFkpdSdwJ0BUVNSpL/dLZXVlzdpKa5uv6RDiTITF+2J2M1JfY2Vz1JdUuBYDUGut5S9b/sKC2AW4m06/aC96RCA3/nEqmUeKCQr3JmBwy6VZj5Ue41jZMcYPGo+3iyTPTXVk2M9Na/2A1vpNrfXbJ/90WWTirLU0r8ioOqfs1tZlx0jfXwTYk+WViw9SU1nfKdcWfcd3x7/jjxv/yGdHPsNis7R9QhNZFVnN2irdS5hwQQxGswGDQTF85mASJgzqrHBF+z0N+CmldgK/AHYALe76orV+TWs9QWs9ITg4uKUu/c6iuEW4Ghs3bAhwCzirKRVCNOXqYeaie0czONGPSs9ip9cq6yspqSlp13U8/VwZMjG01eT437v/zaLPF/GLlb/g/E/OZ2+BzKRtqiMjyIuVUncAXwG1Jxu11kWdHpXoFIviF7H4wGJyKnMAiPKOYn70/E65dmFGhdOxtd5GSW4VoXGyyG+geHPvmzy77VnH8e783Tw+7fF2nz8jfAaeZk8q6+2r8xWKedHzGDcpjnELotE23aFFKaLzaK3LsJf4RNknKqYBqT0aVC+S4J/Afy/4L58c/gR3kzvXDr223XNDhWiPwQl+XPbgOHJ2XcJLO19ytCcHJndKSbayujJe3tVYp7u8vpzntj3H6+e/ftbX7i868tunDngGeAQcW2RpoONFHkW38Hfz55NFn7Di+AqMysj86Pl4uXTO4+qweF8yDjZ+snVxNxIUKY/CB5IPD33odPxFyhf8ZtJvcDO1b9evQPdA3jj/Dd7c+ybVlmquTrqacYPGAfYFJaLnNKw5qdJa1wE/BdY0JM2iwdCAoTw65dGeDkP0c3eOvBM3oxurM1YT5xvHz8b8rFOuW1lXSb3N+anvlpwt7MjbwdiQsZ3yNfq6jiTIDwIJsllI3+Lr6tspc45Pdep0CqtVY7NqkI2eBgwPs/NuTa4m1w5P4RkeOJxnznmmM8MS7aCUeh+YDQQppTKAx2j46dVavwIMA95WSmlgH3B7D4UqgP2F+3lmyzNkVGQwN2ouD4x/ALNRbrYDgdFg5NbkW7k1+VYAdubt5IFVD5BTmcPC2IXcN/a+M6q3HeYVxujg0ezK3+Vo02je2PsGL5z3QqfF35d1JEFOAWS7aQFAQfopUyzqbJTmVcsK2QHk56N/zkOrH3LsHnbP6HvO6pd2TWU9e1ZlUFZYQ8K4EKKTAzsrVHEKrfVP2nh9AzCkm8Lp17TWZ1VOq95az73f30t+tX174P8e+C8pJSm8MvcV2cp9gKmqr+Ln3//csUj0jb1vEOAWwM0jbj6j6z0y+RGu/upqp7ZaS20rvQeejiTIlcBOpdQPOM9BljJv/UhVfRXHyo4R7xfvtAjlVOFJ/mQ32YnP3dtMQFjLCwFE/7I2Yy2r0lcR4xvDxxd/zN6CvQwLGEZSQNJZXffLf+4k73g5AAfXZ3P+HckkjA/phIiF6H6Weiur3zvE4c25ePq6MuPqROLGdHwRY0pJiiM5Pmlj9kZe3vUy9469t7PCFX3A/sL9zSqobMjacMYJ8rDAYUwfPJ11WesA+zqQa4dee9Zx9hcdSZA/b/gj+qk1GWt4eM3DVNRXEOAWwB+n/5EqSxWxvrEM8XceTBq/MJqainqO7sjDJ8idGVclYjRLLeT+7vOUz/n9ut87jmeGz+SluS+d5oz2KcgodyTHJx1YnyUJsuizdn57goMb7Auky4tqWPH6Pm758/QObxsc5ROFu9Gdamu1U/uK4yskQR5g4v3icTG4OHZrBNocmNi+4jgH12fj7u3C5ItjGZzo7/T68+c9zxcpX5Bens6cqDmMCRnTFaH3SR3ZSU9KuvVjWmue3PgkFfX2qRNFNUXc+/292BpKXt816i6nm7HJbOSc65I457qzGzUUfcvHhz92Ol6buZacyhxCPc+uBraLuwkUjct/sZc6EqKvyklzHumz1tsoyCgnooOb3niaPfl/0/4fD699GN3kByTcS7ZgH2j83fx5fNrjPLPlGUpqS5gZMZM7Rt7Rav9DG7PZ8NlRAIpzqvjyxd3c/KdpuHk23ltdja5cnXR1a5cY0No95KeUukgptUMpVaSUKlNKlSulZFVzP2GxWcitdN4DwNZkP5jX974uhfAFXmbnSiUmZcLN2L6qFafjE+jOyNmNOzq6epoYvyD6rK8rRE8JiHMlz/M49Qb7aJ/JxXDGazQWxi3kielPOKa9hbiH8Ktxv+qsUEUfYbPaiM8ez+PWV3h3+Bf8a86/TluZ6vg+5yq8llorWUdKujjK/qMjUyz+gX1npT1an7oZrOjrzEYzwwOHs69wX4uvW2wWquqrpNbnAKa15trBN7E9dzs11hoAbhpxE35ufp1y/VnXDGHY1DDKCquJHBpgH1UWog9an7meB4sfomJUOa4Wd67I/xnXXbTorJ6KXJpwKedGnktGeQZDAoZgNsgTloFm9XuH2L8uG4ADa6G+2MC4+a0PJASGe3JkS5MGZW8T7dOR30DpwF5JjvuvpICkVhPkqWFTO6U4ueibKopr+fKFnRRl1XKt26PoSXmMjhvOuaOmtOv81B35rP9fCjWV9QybGsbUyxMwGJqv7A+O8pZKKKLPe2rTU1TU2+fU15qq2TzsC36bfMtZX9fX1VcGKQYoS53VMaf9pL2rM50S5NpqC1mHi/Eb5IF/qCejzo0kJ7WMY7sLMLkambwoFt9gj1MvLVrRkQT518BSpdRqnKtYPNv6KaIvGRU0is+OfObUdm7kuYwOHs1Php62KpTo57YuTaMoy77jnVuNN6zx5vCaWrKWbOSK/xuPl3/r0ywqS2r55j977XWygZ3fpeMb4kHyLJlDKfqnU7dRz6zI7KFIRH+hjAqTi4G6msYd301mA+VFNXgHuJF3vIwv/rGTump72c1Ji2KZeGEsF/5sFNUVdZhcjLIBUwd1JEF+CqgA3ACXrglH9KRLEi5hT8Eevkj5AleTK3ePuptbOmHUQ/R9pfnVLbZXFNWye2UG065IaPXc4/sKHcnxSWs/PExJbhXTrmh5JFmIvqKu2kLqrnyMRgOxY4IwmY3Mi5nHsrRljj7zo+f3YISir6upqCd1Vz6xY4I5tLFxFLk4p4p3freeIZMHUVtlcSTHAFuXHmPkORG4eZlx92o5ZSvMrGDzV2lUltSSNDnUaR2I6FiCPFhrndxlkYgeZzKYeHza4zw86WFMyiQ7NQmH+LHBTluLN1VTWU9OZQ75VfkMDxzutHnBsT0FrH7/ULNzbFbNru/T8QlyZ9S5clMWfVN1eR0f/3kr5UX2OfmB4V5c+fB4Hp/6OGGeYezK38W4kHHcMar1SgMtqSytpTCjgpAYH6eKA2LgKSuo5pO/bKW63L57bXCkN8HRXuz/MdvR5/CmXPwGuTudZ7NqaqstrZYVtNRZ+eL5nVSX2ReR5qaVYXIxMmyaTKU8qSMJ8lKl1Hyt9Youi0b0Cu4m97Y7iQFlxKxwbDbNkS25FGRUYKmzVzhRCjaHL+X+T9/Bpm3E+MTw7/n/dpR92/h5KjZL68sWvj/6A3+q/JrK+kquGnIVN424qVvejxCd4cCGbEdyDPYRudSd+QyZFMr94+8/o2se2pjNysUHsVk1JlcjF9w9kshhHSsNJ/qPPaszHckxQH56OQEtLLQLGOxFSW7jk77BiX74Brf+uzwnrcyRHJ+UujNfEuQmOpIg3wM8pJSqBeppqFqqtfbpkshEj6mx1LA+az2+rr6MCRxLTUU9nn6t76on+j+lFKPOjWTUuZFUltSy+4d0qivq8R+juHn7rxz1WY+VHeM/e/7Do1MeBeyjy62pcCnmffOzWAvtjwWf2foMYV5hzIue1/VvSIhOYK23NWuztNDWXjabZt2nKY4pSZZaK+s/S+GaRyad8TVF39bS91hItDeHN+eibfbvE4NRMeWSOBInDCJtVz5+gzwYdV7kaa/rE+SGUtC07ILfIFnA11RHNgo57dJypdQIrXXLJRBEj8qrymNfwT6Sg5IJ9jj9Vqe5lbncuOxGsivtj2/iSkcxf//tBEd5s/DukXgHnH3NW9G3efq5MvUy+5zjHXk7nDYvAMipbJwjN2xaGFuXHnMchyX4UppfTU1FPXp8AVYsTuduyNogCbLoM5KmhLLr+3Rqq+zfx17+rsSP7fh20gBfHv2SDZkbKPIyMbxiBkZt//VcVVaHzabZ/2MWuamlhMb7Mmz6YJm7P0AMnzGYA+uyHB+8/EM9GDEzHN9gD3Z+dwJlUIydG4V/qCf+oZ7t3n3UJ9CdKZfGs+lL+1O+kGhvxp0f1ZVvpc/pzEKji4FxnXg90Qm+OfYNv1n7Gyw2CyaDib/M/AvzY1pfMPLewfccyTFAqu9usrxT2KwP8fclv8TXy4t7x97LxfEXd0f4opdLDkom3CvcaZX++THnO/49aVEs3oFuZBwsJiTam5GzIzCaDPbpGiWDeffLF52ul+QvOzOKvsMn0J2rfzeRgxtzMJoUw6YNPqNax6/tfo0XdrxgP4iBPM905qTcCEDS5FDWfXKE3SszADi4MYfinCpmXJXYWW9D9GJBEV5c/chEDm3Kwc3TzNCpYRhNBqKTA4lODjyra487P5rhMwZTU1Evo8ctaPdOeu0gH2d7oWe3PovFZh/dsNgsPLvNuSrf98e/57ltz7E2Yy1Ai7vlHfffx/aIFVSpcrIrs/n9ut+TVprW9cGLXs9sMPOf+f/hisQrmB4+nadmPMWi+EWO15VS6KHF7Bj5FZtDllNmsX9/GQyKpIAk7h9/P+4md4zKyEVxF3H5kMt76q0IcUZ8gtyZdFEs4xfE4OFzZgWePj38qdPx0eDtRIzxYeY1iUy5NN6xOcRJ+9c5l5ET/Zt/qCdTLolnzNyoTl+06eZpluS4FZ05giwbiPRCxbXOlQeKahq3nnx++/P8Z89/AHhj7xvcP/5+Lk24lC9SvsCiGx4Z1gRQZ6xxuoZN29iZt5NY39gujl70BRHeETw+7fEWX9uVv4tblt/i+JD25dEv+eySzxy7gN2WfBvXD7ueemv9abdMFaKvqKmspzSvmqAIL4zm9o1BuVicExRPsycX3Dja8XPi5mGiorax/q2rh+wy2d9ZLTYK0ivwDXHv1KQ4K6WENe8foiSvmrjRQcy+YSgubvL91BL5r9LPXRx/MR8e+tBxfGnCpYA9yX33wLtOff+7/7/clnwbby18y14L2eJB9L6J7DJs5wDrHf0UilHBo7olftG3fXbkM0dyDPZFfJuzNzM9fLqjzdXoiqtRFoGKvu/wlhx+eOcglnob7t5mLrp3NCHRp1/HXphZwbBd53FiSBo2gxW04tqgW5y2kp5yaTzfv30AbdMog2LqZfFd/VZEDyrIqODLF3ZSVVqH0WzgnJ8kdUp1CavFxvLX9jqqVxzZmoe7jwszrx5y1tfujzozQa5ru4vobr+Z9BtifWPZmbeTMSFjuCbpGsCe5JoMzv/7T96QRwePZnTwaHvjTLhazyRwq5WPDn+Eu8mde8feS7yf3KBF2zxMzR/deZqdSxRpmybjYDHVFXVEjwzC1V0+t4u+x2q1sfaDI47FVNXl9az/NIVLHzj90pyc1FKii0dw/fbHyPZJJagynLEezuckTQ4lLMGX3LQyBsX64BMopTj7sw3/O0pVqT2lstbb+PHjIyROCMF0ljvhleRVNSvtlp3SfFqlsGv3byKllAKuB+K01k8opaKAUK31ZgCt9ZQuilGchdUZq/kx80e8zd5MGDTBkRQrpbhz5J38fdvfHX3vHHVni9cwKAMPTXyIByc8iP3bQIj2uX7Y9SxNW+qY2jMzfCZjQsY49Vn6yh6O7S4AwN3bzBW/Ho9vsMyJE71fbVU9+3/MprqijpiRQc3KGhZlV/Legfd4c9+bKBS3J9/ONUOvceoTGucLgGe9LwmFY53amvIJdJfEeIAoL3TeubSu2kJtleWsE2S/YA/cvc1OdZVb+l4Tdh0ZqnkJsAHnAU8A5cCnwMQuiEt0gk3Zm/jVD79yHP+Y+SPLrliGr6v9B+KW5FsYO2gse/L3MH7QeIYFDjvt9SQ5Fh0V4R3B15d9zZqMNfi5+TElzPlzdE5qqSM5Bvuo266VGcy6Rh75id7NZrXx2d+2U5RVCcCulel4+rtSWVzr6JNqOMgXm//pOP7jpj+SFJDk9CExMNyLc65LYvNXaVjqrIycHdHuUl2if0oYH8KWr485jsPifTtlLwKj2cD5dySz+r1DlOZVEzsmiMmXxJ31dfurjiTIk7XW45RSOwC01sVKqTNbsiu6xYpjzpselteXsy5zHRfEXeBoc5pOIUQrKusrya3MJcY3BoPqWPEbLxcvp+85sM+Bz6vKo7a6+S2ovsbSrE2I3ibzcIkjOQawWXSzUk5ZXinNztuau7XZU5TkWeEkzwrvgihFXzThwljMriaO7ysgIMyLiRfGnNF1rDYr2/O242n2ZHjgcADCh/hz3ePywL89OpIg1yuljDRUq1BKBWMfURa91GCvwe1qa8v7B9/n/YPv42Z04+7Rd3Ne1HmdEZ7oI748+iVPbnySaks1MT4xvDTnJSJ9Tr9L0+nsyd/DQ6sfIqsyi3CvcOZH3YrrCfvmCgaDYvgMSRRE79fS4+66Uz7cDapuvvFCclByl8Uk+geDQTF2fhRj57d/4w6tNenl6QR7BONucqe0tpRblt9CSon9Q9q86Hn8/Zy/y5PgDuhIgvxP4H9AiFLqKeBK4NEuiUp0imuSruGH9B/Ylb8LheLKIVc2G7loy5qMNfxp058cxw+uepAlly0h0vvMEyTRd1TVV/HHjX+k2mKfE3es7BjP73iev53ztzO+5mMbHiOr0l7HNbMik43Jn/HrsX+hpryeIZMHtbnqX4jeICzel6jhAZzY31g6s67aitnNiLXOhpu3mVsvu4Jwk2bx/sUopbh5xM3NphmdKn1/ETu/T0cpGDM3koihAV39VkQfl16ezk1Lb6KgpgCFItE/kfEh4x3JMcC3x79lc85mJodN7sFI+5aObDX9rlJqGzAH+6Ygl2qtD3RZZOKsebl48d8L/suhokN4mj2J8I7o8DU2ZG1wOrZoC5uyN0mCPEAUVBdQZalyajtRduKsrnm05KjTcWrZUSbcEHNW1xSiJ1x472gW/249FSWN847ra6xc/8RkfIM9UEoxhHu5Z/Q9KKXanJ5UmFnBVy/uwmazbyuQfqCIa38/Cf9Qz9OeJwa2/1v9fxTU2NdyaDSHiw+3uJlXQXVBszbRuo7upJcLrAXWA+5KKdlaug9ICkg6o+QYYIh/88VShqN+FGVXttBb9DdRPlEk+CU4tZ0bde5ZXXNG+Ayn45nhM8/qekL0FINBEZbo59Tm7uOCd6C706Nso8HYrrn7absKHMkxgM2qSdstSY04vZaS4XpbPYYmKZ6/qz8zI2ZSklfF1//axTuPrGf1+4eor7M2O1fYdaTM25PALcBRGnfN09irWoh+alH8IrblbuOr1K8waCOj0+eQvcHAB19uZuHdI4kdFdTTIYou9tKcl3hhxwuklaYxO3I2Px35U6fX62osbP36GLnHyhic6Mf4hdGYzK2XI3py+pP8bcvf2F2wm7EhY3lowkNd/RaE6DLTr0igoriG7JRSvAPcOPemoRiNHR17sv8c5R6316Qt9Mii0COL8NJEfIOktJs4vRjfGPYX7m/W/oepf2BL7ha8zF7cOPxGfFx8eP+VTY7FpXtXZ6IMSqoGtUJp3b4dopVSh4CRWus+sSHIhAkT9NatW3s6jH6joKSY9x7djNHSuLtTaJwvV/x6fA9GJXqD5a/u4eiOfMfx8BmDOfeGoT0YUd+llNqmtZ7Q03GcqYF8362rsWB2MaIMZ7YIavlrezm6PY8dg79lU/RXAJi0mX/O/SczI2a0cbbozzIOFnF0ez7egW4knxPebGvoQ8ePctPKG6gyVAD2jcBuGn4TD01sHHzYX7ifT/d9xtE1JYzInYFHvX2th98gD67/f1OoKqujvLCGoCivM/qA15e1dt/tyCK9vYAfkNdZQYm+w9PkiclqpunHKZtVipgMdMXZFU7JMcCRrblUldVRXV7H0KlhUr6qCymlfgm8ib0u/X+AscBvtNYrTnui6HSnJi2nY7XYqK+14uZpH3CwWW2k7sij3lDHtojG/3UWVc8ru1+WBHkAO7ojj+Wv7nUcH9tdwOX/5zwwte/jEm5M+SNFHtnUGWoYEhHLTRPnNL5euI8bl95Iva0eIuFw8Bau2fVbTDYXAsM92f1DBus+OYLNqvHyd+XiX46Ree90LEH+M7BDKbUXcKxI0Fpf3OlRiV7H3cuFuHEhHN3W+Plo9BxZqDeQ7f8xix/ePdis3VJrdWz+kZtWhquHicQJg5r1K68rp7yu/IxKDwqH27TWzyulzgf8gRuBxYAkyD2ortpCxqFivAPdCI70dnrtwPos1n2SQm2VhagRAcz/aTKu7iY8/VypKavCanDeja+irqI7Qxe9zP61WU7H2UdLKcysIDDcy9GWc7QUhSKwyn4vrUxxforx+ZHP7clxg3K3Ig4Eb2SO24VMuCCGT/6yDZvVPvxVUVzL5i/TOP8OKUfYkXH0t4G/AE8Df2/yRwwAuWllHN/TuFgkbkwQQyaF9mBEojtYbVa25GxpNr9Na83GL47CKTO0zK5GTp21lbar+SKjt/e9zbkfncv5n57PzctuprS2tLNDHyhO/ia8AFistd7XpK31k5R6QymV1zDg0dLrvkqpL5VSu5RS+5RSt3ZizP1aYVYFix/dwLJX9vDRU1tY+9Fhx2tVZXWsevcQtVX2eskn9hWx/ZvjAMy6dgieJm/iG7abPunKIVd2X/Ci1zGf+mRCgYu7c9up20WHnXLs5eLFqXYOWc6CB4diMhux1js/DS4rrDmLiPuPjiTIVVrrf2qtf9Barz75p8siE11Ca03KtjzWf5bC8b2F7T5v2/JjWOoaf4jSdhVQ0WRLVdH/lNaWctVXV3HbN7dxzVfXcP8P93NyzYLWUFvtvCmCq4eJyx5qXtjGb5CH03FmRSbPbnuWWqv9+2d73nbe3vd2F72Lfm+bUmoF9gT5G6WUN+3bwOktYMFpXv85sF9rPRqYDfxddk5tn+3Lj1NT2That/uHDMoK7XXEi7MrHSN1JxVm2EeIY0cHc8vT0/nHxX/j1+N+wxWJV/Ds7Ge5cfiN3Re86HXGL4jGxa1x0XPyzHC8A9yc+sy5ZRhhCb4og2Jwoh/n3TSMgoxydq1MJye1lGuTrsXd5LzYs9JSybrMdfgN8iAo0jmBTpwgW51Dx6ZYrFVK/RlYgvMUi+2dHpXoMus+TWHXd+kA7FhxgulXJjBmbsu79RRmVVBTXk9Ygi91Nc6lYLSG+loLcPb7w4ve6ePDH3Ok+Ijj+LsT3zkKzRsMivixwRzZ0jjlJvmccIIjvZm0KJatS49hs2oGJ/o1m4pzvOw4Nu2cw6WWpnbtm+m/bgfGAKla6yqlVADQ5miv1nqNUirmdF0Ab2WvVeYFFAGyB3g7VFecso5dYx8xDoSQGB9cPUyOEWQAS3whm7I3MX7QeFzdTUQnBnMj13dz1KK3Co7y5sY/TuPE/kJ8gtybjRYD+AZ7cPlDjfOSD6zPZuXiA44nfFMvi+eW4bfw8u6Xna/tYd/B9KJ7R7P162OU5FUROzqYkbNl3Qh0LEE++dyn6TZApy3zppRKAj5s0hQH/AF4p6E9BjgGXK21Lm64GT+PfTSkCrjlZAKulLqZxp37/qi1liGnJtJK03hq41McKDrAlLAp/H7K7/Fz83PqY7Xa2Ls606lt53cnqKmsx2bRDJse5piYv3LxAQ6sywbAN8SdsfOiyDxU7DgvPMlPJvH3c4XVzZ8w5FfbF+Sl7sznyNbG5DhubDCTL44DYMIFMRiMitSdBfgGu1NXbcG1ySPBMcFj8LB5OVZcA8SXj+qqt9HfTQV2aq0rlVI3AOOw30PP1ovYB0OyAG/gGq21rMptIr8qny+OfgHAwogL8Kzzw83bREmO88Y6QZFejnnIZlcjF907mg3/O0p5cTUrh7/DK3nrYQXE+cbx1oK38Hfz7/b3Ino3Ny9zh6Y0bl12zGn627Zlx7juz9ezMn0lh4oPAXBR3EWMH2RPqj19XTnnuqTODLlf6MhOeh3eHUBrfQj76AZKKSOQiX276t8A32utn1ZK/abh+GFgIZDY8Gcy8DIwuWFU5DFgAvb/7duUUku01sUIAB5a/RCHi+1z3VYcX4HJYOIvs/7i1EcBBqPC2mQNSFVZHduW2efA7V2TyTWPTqSu2sq2zYdwwxMDRkrzqinNr+aSX43h6I58fIPdGT5DFlb1dxfEXsD7B9/Hqu1PD/xc/ZgVMQuALV+nOd2ADx88QViOlREhI9i/KoeNn9tHhPOOlZGTWspPHpvs2DjBWm5g4d672RK5lGpzOYn5EwjJSAZZ7nsmXgZGK6VGAw9ir2TxDnDOWV73fGAn9gGQeOBbpdRarXXZqR2VUncCdwJERbX8NKq/Kawu5Kovr6Kwxv4h8rVNb3DVrl/jafFpNgc/+Rzn0bjQOF8ue3AcW3K28Mw36x3tqaWp3P7N7fxt9t+I843r8vcg+i+bxfmzrM2q8XHx5qNFH7E7fzdeZi8S/BNaOVuc1JGNQv7QUrvW+ol2XmIOcFRrfVwpdQn2eW1gX/y3CnuCfAnwjrZPdNyolPJTSoU19P1Wa13UEMu32OfPvd/e+Puz0tpSR3J80tac5rVIDUYDExbGsOF/jVv9Nh0Tqq+1suzrjbzr/jzHJqTiUefLeSnXE1GaRFVZHRFDA4gYGtBl70P0LiODR/Lv+f/mk8Of4Gn25KbhN+HjYq+d2XQ++uGgLayO/5D/rKgn2D2YKzN/ATQ+BizOqaIoq9Kx6trF3URoXTQXHLzL0cdjhEzVOUMWrbVuuKe+qLV+XSl1eydc91bg6YZ7cYpSKg0YCmw+taPW+jXgNbDXQe6Er93rLT+23JEcA1Sby0kJ3M6onNmA/Wci3e8ggZXhTKppZQpbTfMnNEdKjnDHN3ew7IpluBhlyvdAV22p5r0D75FSksKM8BlcGHdhu84bdV4k6z9NcRwnz47A0FDbeEzImK4ItV/qyBSLpnsLuwEXAQc6cP61NCa0g7TW2Q3/zgFO1oAKB9KbnJPR0NZauwB8XHyI8o7iRPkJR9vwoOGOf+dV5bHyxEqC3IOYPW82gxP9yDtehqXexobPjjpd678lr3Fc2Uf/qlxK+SH+Xa7f8ThJUrFiQJoYOpGJoRMdxzWV9RzenIt/qAcluVVYVD0/xn7qKE2VX53PD/6fMpvbHOcYTQY8fRsTYFd3E5MXxbHhfylobV/cN/ni2O57U/1LuVLqt9jLu81UShkAcxvntMcJ7IMaa5VSg4AkQCaKNzAbmv8nNmr7r9OmG30cCd6Kh62SiTzXrP+MwTMIcAugqKbIqT2vOo9d+bucfu7EwPTrNb9mVfoqAL5K/YrC6kJuGnFTm+eNnRdFwGBPsg4XExLtQ9zY4K4NtJ/qyBQLp5JuSqm/Ad+059yG1c8XA79t4bpaKdUpow4D8VEfgFKKv8z6C4/8+AippamMCxnHI5MfAeBQ0SFuXn4zlfX2zzfTw6fzytxXCI3zxVpvY9d3J6gqa5xzUeie7XTtStdSzrkrlsjhMnI80NXVWPj46a2U5dtX5Lu4Gwkf404d1U79qrxK8A12pzS/GoNJMfXyeNy8nBOKsfOjiB8XTEluFaHxvo5NFgqqC/jr5r+yp2APE0In8NCEh/B1bb4oRThcA1yHvR5yjlIqCnimrZOUUu9jfzIXpJTKwD6FzQygtX4FeBJ4Sym1B/vsrIe11s3r9Q1QC2MX8va+tx2DEj7VQSQU2Cu4HAjZ6NR3Vc5KyuvK8XZxrofs5eLFfxf+l/tX3e+YFwpgUAZ86wL5/p0DVBbXkDhxEMOmyZS2gaaopsiRHJ/0v5T/tStBBogeEUj0iMDOD2wA6cgI8qk8gIh29l0IbNda5zYc5yqlwrTW2Q1TKE6u9skEmi55j2hoy6RxSsbJ9lWnfpGB+KjvpOSgZL649AtqrbW4GhtH69498K4jOQZYl7mOfQX7GBE0AqPZwMRFcax+t/HmHFUylP2hjfPihgcOZ9SYeMdxfZ2V1B35WC024seFOC2+Ev1b6s58R3IMUFdtJSIkjPFqPNtytznaF8Yv4LrLp1CYUYFXgCvuXi0/KvYJcscnyLn00G/W/oZN2ZsAyEjJoMZSwzPntJnvDVgNSfGn2NdtABRgX+fR1nk/aeP1LGD+2UfYP3m7ePPxoo/57sR3VJXV4bkthrLAeupqrXhoL8po/CzhbnTnwKo8KvMziR0VRMzIIMdrkT6RvDLvFe5cfhdHyg5jxMStcbez8dUcyhtq0aYfKEYpxdCpYd3+PkXPcTO64W5yp9rSeM+VwYLu1ZE5yHtoXJZjBIKB9s4//gnO84WXADdj33TkZuCLJu33KqU+wL5Ir7Qhif4G+JNS6uTy3vm0MBotcEqOAafdc1pqGzY1jPT9RaTuyEcZFLdH3sPmuEjWZa4jKSCJ/5v4f46+pQXVfPTUZuqq7Yu2Ni9J5epHJuHhI3PlBoKWdp9QCp6b/Rwv73rZMU/u5uE3YzAogqO8WzijdfW2ekdyfNK6zHVnEXH/p5S6A/tTswDsi+nCgVewT48QXcjD7MHF8faVpSWDq/jwqc1Y6mxMOHYBy4f+G4uhHoVidtllbFlrXwi9f20Wc28ZRtKUxmTX0+LDhet+RZb1BO713rht9aG83nmjhiNb8yRBHmA8zB78bPTP+Ps2+8N7d5M7Px/z8x6OamDpyPDfRU3+bQFytdZt1sVUSnkC84C7mjQ/DXzUsJjkOHB1Q/tS7CXeUrCXebsVQGtdpJR6EtjS0O+Jkwv2xOldO/Ravjn2jSMpjjMn4ZURhi1IYzAojCYDC+8aSUVxDQajAQ8fF+bbC480s/Sl3Y7kGKCytI6DG7IZd350d7wV0cPixobgt+w4Jbn2MlYePi4MnRqGp5srv5v8u7O+vtlgJtY3lrTSNEdbon/iac4Q2Df0mARsAtBaH1FKSZX/bnZoU45j4WpEaRLXb3ucwEU1TBo6mlV/di6tuW9tllOCfHRHPjUV9QRgb7O0sM+LT6BbszbR/92SfAvnRJ7D0ZKjjB80Hk+zJ6vTV+Np9mT8oPGOykCia7SZIDeUWAMoP+UlH6UUbSWqWutKIPCUtkJaGOFoWDHd4kckrfUbwBttxTuQ7V2Tyc7vTqCUYvzCaIZOCWN08Gg+uugjPtz8PzI3VDMkbyLfrNnH0CmFzLmlcSGfl//pb8A2q42irMoW28XAYLXY8PR1oSS3CpOLgYkXxTgtvusMT0x7gofXPExWZRYxPjE8MuWRTr1+P1Srta47+YtSKWWi2QbgoquduvWvu8WLmUFTCA/wY7UhC23Tzfoe2ZrLpi9SqSo/ZWMRIGpEAOkHitE2jd8gD8YvlEGIgSrWN5ZY31gKqgu49ItLSS+31yuYET6Dl+a8JElyF2rPCPI27Dfclv4vaOybf4gelnWkmNXvNc4l/v7tAwQO9iI4ypsE/wSG752DX06J4/WDm3KYcll8uxMcg9GAb4g7pXmN86GUAaeRENG/bV16jMzDJYC9zNuPH6eQMH4Qbp6dUTTBbkzIGJZdsYzC6kKC3IPk5t+21Uqp3wHuSql5wM+AL3s4pgFn2LQw9v+Y5Xi6EhrnQ+zoIIwmA2PmRrJjhX0xn8nVyIQLYigrqObbN/Y7Jc4n+Yd6MP/2EdTX2qgqqyU40htlkJ+DgWZX/i525+9m3KBxjAgcwceHPnYkxwA/Zv7IppxNTAmbcpqrtI/WmvVZ6zlWdoyZ4TOJ8hk4RQ5Op80EWWst9Zf6gPSDp+yZoiHjYHGH54G2pL7OitnFyNxbhrP8tb1UltTi6mli/m0jmu0JL/qvgowKp2Nrvc1ehaKFrU/PhkEZHFugijb9Bvt203uwT2Nbin2zENGN3DzNXPvoJI7vK8RoNhA5LABDQ1I77fIEEsaHUJJXReTQANy9XTi0KceRHFtUPbvDVlE5OIfpMVO4ctZtuLiYcfUAL3+pDz4Qvb3vbf629W+O4z9M/QPZBfnN+pXUlnTK13ti4xN8cvgTAP5m+Bv/mvMvpg2e1inX7ss6VIJAKXUxMKvhcJXW+qvOD0mciZNbmTYVFOXl+PeYeVFkHy113JSTJoe2OXpcVljNt6/vIye1DL9BHsy9ZTg3/WkaVaV1ePq5yOjeABM1PMBpu3F3b3OL33ei+zRs//zvhj+iBxnNBuLGtPzBLiTah5BoH8fxoBgf+zNZDavi3ycl2F4FZl/mVup3V/LAhAe6I2TRC2mteXX3q05tr+1+jYUpP8UwyIjNYF8H5G3zY1b4rJYu4WC12CjNr8Y32B2jydBin4LqAj478pnj2GKz8ObeNyVBpmNVLJ4GJgLvNjT9Uik1TWt99qtzxFmLHR3E6LmR7F2ViVL2hDiyya53saOCuPp3Ezm+twC/QR7Ejm57hG7tB4fJSbXvLFuSW8W3b+zj+iemyKjGADVmbiS1VfUc2ZqHT6Ab065IwGhu+aYruodSajrwOBCN/X6usC/nkKlvvZjfIA/OvWEo6744xNGgHU6vLTm6RBLkAc5qszodW6wWXI8Fc0nBLzkUvBGz1Y1xJefhYfZo9RpZKSUsf20v1WV1uPu4sOCOZAYn+lFTWc/mJankHi8jfIg/0ed5YNPOa4laqn41EHVkBPkCYEzDiAVKqbeBHYAkyL2AUooZVyYy5ZI4FKrFxCUowougCC8yDhXzwRObKCuoIX5cMLOuTSLrSDE1lRZiRwc55pTmHXdel1maX01djVVqHw9QBqOBqZclMPWyhJ4ORTR6Hbgf+1oRaxt9RS8yfPpghk4L5b8f+TntphfkHnSas0R/p5Ti+mHX8+89jQ+Fbhh+A/7pvpASzaAK+4LNtnbHW/3eIarL7AtAq8vqWP3+IX7yh8l89+Z+ju+1b3Oed6yc9P1ezJsxj2+Pf2v/+ihuGHZDV7y1PqejmY4fcPInWSpW9xLapknZlkfeiXIikvyJTm599xxLnZXlr+6htspeoe/w5lyyUkqoKKoF7I/Nr/j1BHyD3QlP8ufIllzHucFR3pIcC9G7lGqtl/V0EAPZlpwt/GP7PyiqLuLihIu5e9Td7Z5+ZlAGHprwEH9Y9wcs2oKb0Y0HxjcfPd67JpPdK9MxmAxMWBhDwnip5Nef3TfuPkYEjWBP/h7GDxrPzIiZlA2uZvlreynOqWRQjC+zr0867TVK8qqaHVvqrY7k+KSCjAru9vk/Zs+YzbHSY8yOnM2o4FGd/p76oo5kO38GdiilfsD+GG8W9gUioof9+PERdv+QAcDOb08w46pERs+JbLFvcU6VIzk+6WRyDFBdXs+e1RnMuDKRWdcOAa1JP1hMcKQXs37i/ANZWVrL8b2FeAe6EZHkL3OSheh+PyilngE+Axw/yFrr7T0X0sBRWlvKz7//uWO3s5d2vkSwezBXDrmy3ddYFL+IlJIU3tn3DjXWGhYfWMyYkDF4mD0oL6ph1bsHObGvcYR5xX/2EjB4MgFhnp3+fkTvMSdqDnOi5mC12Nj8VRr712ZSWWofEc48VMyx3QWn3YI8dlQwR7fnOR0bTQZcPIzUVTk/bCo8UcXFl17cNW+kD2t3gqy1fl8ptQr7PGSAh7XWOV0SlWg3a72NfWuznNp2rUxvNUH2D/XA1cPULEl2umZDwXs3TzPzf5rcYp/cY2V8/twOLLX2H7ShU0OZc/PwFvsKIbrM5Ia/JzRp08B5PRDLgPPJ4U+ctgIG2JC1oUMJ8tGSo7yxt7HE/4+ZP/LY+sf468y/suT5nY7ScSfphgpFkiAPDOs+TWFPwwBYUzu/SyduciAHCg8Q5RNFgFuA0+vn3TgUDx8XclJLCY3ztU+/VIpJF8Xy40cpTn3Dh/h15VvoszqySO9L4D1gScPmH6I3MIDBqLA2yXdNp1k4ZXIxsuDOZNZ8cJjSgmriRgdTkF5OSUN9Y6PJwPAZrX8qPWnHN8cdyTHAwQ05TLggFt9g9zN/L0KIjlqotXbal1gp1focK9Fp6q31vLnvzWbtSQGnf/R9qtTS1GZty48tJ0xF4prb8gBFUKRXi+2i/0nZltdie67rCeZ/8muKaoowG8w8OuVRLk+83PG6i7vJ/hT4FKPPi8JgNLBtmX378zFzI4kaLreMlnRkisXfgGuAp5VSW4APgK9OvTmL7mU0Ghi/MJqNn9tvskrBhAtiTntOxNAArnu8sbh4TWU9+9dlUVtpYcjkQQQObvvmW1/XfAc9S72sERKim32qlLpEa20BUEqFAl8D43s2rP4vvTyd0tpSpzZvszc3Dr+xQ9eZOGgibkY3aqzOv0qX5izhCuNIbNbGzUSUgvELYxic4HfGcYu+xSfQzbHYrqlt8V9TVGmfelNvq+eZLc9wQewFuJla3pvAUm8lfX8Rrh4mkmeFM/KciC6Nuz/oyBSL1dh3bTJif3x3B/atn31Oe6LocuMXxBA+xJ/39r3PV5Uf8VGajYtKryBx3wwMRsXYeVFEDA1o9Xw3TzPj5ndsK9ORs8M5sb/Qsalt+BC/diXWon86sD6LzV+lYamzMercCCZeaN9f6KNDH7HyxEoivCO4a9RdsgFI5/sc+FgpdSUQCSwBHurRiAaISO9IgtyDKKgucLRdFH8R7qbTP0XLr8qnqKaIIf5DUErh5+bHy3Nf5qcrfopVNw4y+Lj5MOXSeDZ+fhSbVeMT5MaFPx9FQJjcZweS6Vcm8tULO6mrafzecHE3UaKKnPpV1FewO383k8ImObVrrXlu4/N8cOADTPWuTEy/gHmhC7jw56Nk3VAbOrpRiDuwCPtI8jjg7a4ISnRcvvcJ3iz6l+P47azXuDDbQGTpMDIOFfOTP0zGL8SDWmstRdVFhHmd+RbRX6V+xaspr1J5ThUzbAu4Muza0y4WEP1bYVYFKxcfdHxY2vxlGgFhnmx0/5anNz/t6LcjbwefLPpEbsqdSGv9b6WUC/ZEOQa4S2u9vkeDGiDMRjPPzX6OP278I8fLjnNu5LncN/a+057z4o4X+c+e/2DVVoYGDOXVea8S4BbAhNAJPDzpYf686c9oNEZlZHzIeI4HbeeqJ2djKzcQFOnt2J1PDBxh8b7EjQ3m4IbGJV911RaCDg7lWESaU9/bV9zO8MDhvDL3Ffzd/AFYmraUNw+/DkbAWMkPCe8SsjOaMYejiEjy78630ud0ZA7yR8AkYDnwIrD6ZE1k0fO25zVftJ7tk0pk6TBsFs3xPYVsil/LExueoKyujCT/JF4474UOJ8pHS47yyI+POAqL/493OC9hImbXlhcFiv4vO6XUkRyflJVSwjfe3zi1HS4+TGppKvF+8d0YXf+klGpaC0wBUcBOYIpSaorW+tkeCWyAGRMyhk8u/qRdfU+UneC13a+hG35YDhYd5K29bzk2BfnJ0J8wJWwKW3O28sruV/jg0AdwCGJ8Yvjwog8lOR7AfIKaP5UYnz4fs8WV1ICd5Pocc7TvL9zP4v2LuW+c/cPa9lzn3EArTY53GlklWRw5sZORwSOl9nYrOrIN1utAvNb6bq31D6cmx0qpeZ0bmuiI0cGjm7WFVDROm3ANUvy/9f+Psjr7zniHig/x3PbnOvx1tuVua7brzuaczR2+jug/QuOaz7IKjfUl1CPUqc1sMBPoJotBOol3kz9e2Mu8pTRpE71IVX0Vi/cvdiTHJ2VUOFcncDG6kF+dT15V48KsY2XHWHF8RbfEKXqnkbMjCIxonFpTZS6j3LWI0dnnMj7z/Gb908vSWfvRYd74v7XUbDrldqAVNu8a7jp0A/f9cB/zP5nPyhMru/ot9EkdmYP8TRtd/gJ8e3bhiDM1JmQMD45/kH/v+TdWm5Wp1ecTU5wMCoZOCcU92kr5Tued8VJLmq+ebsuIwBHtahMDR1CEN7OuHWKfg1xvY9jUUGLHBPGzqp+xM38n2ZXZmJSJX477JX5ufs3Ot9RZKcquxG+QBy5ushFNe2it/19PxyDaR2vNnd/eya78Xc1eG2WzV+mrtdby4KoHWZ2xGkXzkWKLrfWynKJ/25C1gbf2vYV1opWLFlzOvsrdfJj2PlrZCC8ZwpwjN+Ft8KXc1rhgNKFkLLtX2j98hR1MZkz8eRwIXYeLzY2L3a7jm6iPsdTav6fqbfU8t+05zouSypCn6szfRvL8p4fdknwLtyTfgtYapRRll1ZjMCq8/N3QWhPrG0taaeOcpVkRszr8NUYEjeDB8Q/y6u5XqbPWcXXS1SyMXdiZb0P0QSNnR5AwPoRlr+5hz6pMjmzNY9a1Q/j68q/ZV7CPcK/wFhfoZR0pYdkre6iprMfFzci820cQM1Ie97WXUupb4CqtdUnDsT/wgda6+bCS6BH7Cvc1S47d67yYfOJiYs1jAPgi5QtWZ6wGaDbK7O/mz7xoeUA7EKWVpvGz73/m+IC0OWez/fujIdvK9DtM1YwU3j7/Tf69598U1RRxSfwl6K8jSW/Y9FhhYMrRS7gh4Kcc21kIRk3J2BKnr9N0q3PRqDMTZN12F9EdTi6CajpvSSnFi+e9yHPbniOtNI1zo87lntH3nNH1b0m+hRuH34gNG2aDuVNiFn3f5q/S7PORgZqKelYuPsgtw6cRWBBFzv4qXEZUN6uTvfajw9RU1gNQV2NlzQeHJUHumOCTyTGA1rpYKSX7EPciLkaXZm2RJcMYmj8ZhttHmE+UnWj1/ETfRHxdfbsyRNFLrclY4/T04NQPTwD7Thwm4ysLf73xr462jVFHSd/fmPQaTIqULfn2g3pIyJ3IwUEbHK9fmnBp5wffD8jzzAEkyieK587t+LzjlhgNRowYO+Vaon8ozKhwOrbUWln5zkFSd9pvzAaTYtG9o51KDpYXOtd+rSiqwWa1YTB2ZHnEgGZVSkVprU8AKKWikcGKXmWI/xDmRM3h+xPfA2C2ujEq+1wA9q7OxGQ2cO7Mc3ln/zstJkBWpL78QBXlHdWszcXgQp2tsS5ybNEo9qdmk3xOBMFR9vnG4xZEU5JbRerOfNx9XAiN9SF1Z2M5wplpVzFuyAiK/DMYN2gcVw+5uuvfTB/UrgRZKWUAprRRPuhYp0QkekxZXRk1lhpCPGQASrTu/YPv8+beN1Eobh95O1cn2W+ukcMDyD7aOA/O3dtM2q58x7HNotm+4oRTghw/Npj967Idx7FjgiU57phHgB+VUquxP3idCdzZsyGJUz07+1l+zPyR3KpcCt71wlbl6nhtz+pM7rhkFn8752+8f/B9UktTHY+8FYqfDP1JT4UtetjkgGkkFUziUOAWUJq4wjEsCr6Cb/X/KC4vZVjuVKJKhgFQWVpLcMP6XBc3EwvuGoml3orRaCDjYLFTgmzURn467jZHQi1a1q4EWWttU0r9Cxh7mj6Xt/aa6F75J8rZ+PlRKkpqSZw4iPELotusPfuvnf/i9T2vU2+r55yIc3jmnGfaLHgvBp6tOVv506Y/OY6f3PgkQ/yHMCZkDOMWRFNfY+Xojjx8gtwZMy+Sr17Y7XS+td7GttxtvL7ndWqsNVw19WrG+ySSdaSEQTE+jg1GRPtorZcrpcYBJ7fG/JXWuuB054juZ1AGx5qPd9lICVWO15RBgQHmx8xnfsx86qx1LDm6hONlx5kdOZvxg2RTxIGqorCWc49cz8TjF6Kx4V0XQKD245mFz/LlC7scz4o8fV1arGlsMtuf8kYOD2DGVYns/P4EBqOBCQujJTluh45MsfheKXUF8JnWWh7h9VKWOitL/rmTmgr7vM5NX6Ti6m5i5OzWt5U8WHSQV3a94jhenbGajw99zE0jburyeEXfsjV3a4ttY0LGYDQamHZFAtOuSHC8Fjs6iLRdDfmagsEzXLnr25uptdbaz83Zyhvnv8Hll0zolvj7C6XUUK31wYbkGCCr4e+ohikXzQuji15h/IJovn/ngCO5GTsvCmOTpyYuRheuHHJlD0UnepOgCC+8/F2h2M/RFjMqiKjhgVz4s1Ec3JCDu7eZsfOiMLmcfsrj6DmRjJ4j+xV0REcS5LuAB7DPeavG/jhPa61lq+leJPdYmSM5Pun43sLTJsgtlXtLLe14CTjR/yUHJTdrO12Zv/PvSObw5hxKcquJHR3E8qr/OZJjsC86+SH9ByaESoLcQQ9gn0rx9xZe04DUbOqlhk4NIzDci4xDxYREeRMuu5mJVhiMBhb9YgwbvzhKdnEeviNg1HnhAMSMDDqjBc3fHv+W57Y9R0lNCZcmXsqD4x/EaJD1RC3pSB1kGY/vA/wGeWAwKGy2xkH+gDDP054zKWwSbkY3aqyNC6bOiTiny2IUfdeM8BncOepOFu9fjEJxy4hbmDp4aqv9jSYDljobKdtyObQjk/8kvtGsT0sLUcTpaa3vbPj73J6ORXRccJS3POIW7RIw2JP8WTt4cfs/sJRYeGtJNK/Ne43BXoM7fK28qjx+vebXjsoYi/cvJto7mmuGXtPZYfcLHdlqWgHXA7Fa6yeVUpFAmNZatlHrRTx9XZl57RDWf5pCfa2V8CF+jFsQfdpzgtyDeHnuy7yy+xXK68q5ashVnBslv3dFy34x9hfcM/oeFMpp5CHzcDF7VmVgMBoYMzeSkGgfMg8Vs+aDwwAc899Lqa3Y6Vq+1kAuTby0O8Pvd5RS04AYmtzPtdbv9FhAQohOU1BdwD+2/8OR1B4vO84ru17hielPdPhae/L3NNt05t2D7zIvZh4BbgGtnDVwdWSKxUuADfujuyeBCuBfwMQuiEucheRZ4SRNCaWu2oKnr2vbJwATQifwn9D/dHFkor8wGZxvHQUZFSz5x07Hk4u03QVc99hkMg43JsQuFrdm14nPH4ersX3fo6I5pdRiIB7YCY56YBqQBLkbHS05yh83/pHDxYeZPng6j0x5RGoXi06RV5XXLKnNrMh0/NumbaSVphHiEYK3y+mfSowIGoFJmbDoxuullaZxz3f38OFFH3Zu4P1ARxLkyVrrcUqpHeAoSN+8ArroFcwuRsxtTNoXoj3qa63sW5tJSV41caODiBoR2KzP0e15TtN6LLVWju0uIKTJY+Sw8niiiodzwn8/AJ61fsz1uKjr30D/NgEYLgune9YDqx5wrNtYdmwZZqOZp2Y81cNRif4gyT+JaJ9ojpcdd7TNj54PQEZ5Bj/7/meklabhZnTjN5N+wxVDrmj1WqGeofxxxh/5/brfU29rXKu0v3A/qSWpxPnFdd0b6YM6kiDXK6WMNKy9VUoFYx9RFkL0Yx+8uoqslFK86vzYtyaTebcNZ8ikUKc+Xv7NR4E9/VyJHR3M2PlR7PkhAwyKXw96gq25W8jOL2BS0BQW3jymm95Fv7UXCAWy2+rYlFLqDeAiIE9r3WzlpVLq/7BPqQP774lh2Hftkz1pT7E+c32zRc1bc5pXexHiTBgNRl6d9yqv7nqVzIpM5kfPZ1H8ItZnrufDQx+SVpoGQI21hqc3P838mPmnHUm+MO5Cfkj/gW+OfeNoMxlM+Ln5dfVb6XM6kiD/E/gfEKKUegq4Evh9l0QlhOhxNm3j4e9/y3KfZTBek1AwjvOO3MDeNZnNEuSkyaEc3pxL1pESwF7eLWaUfYX1tMsTmHxxHHXV9ax69zDuh8MZNyiRGRcl4R3QfNqFaJtS6kvsgxXewH6l1GbAUR5Ea31xG5d4C3iRVqZiaK2fAZ5p+FqLgPslOXZmtVm574f7WJOxptlrI4Jar+zS4rUsNsqLavAJcsdgOH3NejHwhHuFc0nCJazLXEdZXRnzPplHWV1Zs3411hryq/LbnGpxz+h72JqzlcKaQhSKu0fdLXOQW9CRKhbvKqW2AXOwl3i7VGt9oMsiE0L0qFXpq1ieudT+0w6kBG0npmgkse7zmvU1uRi57MFx5J8ox2BUBIZ7Ob1uNBnY/OUxx7bTxdmVLP/3Xm7+0zSMJtk57wz8Dfv/mb8AlzZpP9l2WlrrNUqpmHZ+rZ8A73cwvn5vbebaFpPjCYMm8PDEh9t9nfSDRXz7+j6qy+vxDnBj4T0jCY6UChei0ZKjS3jkx0fa7BfrG0usb9ubLcX7xbP8iuXszN9JhFcEEd6tl4EdyDpSxWKx1vpG4GALbUKIfuZE2YlmbWVeBUxYGNPqOacrXZWdWup0XF1WR1lBNf6hpy9DKJrTWq8GUEqZT/77JKVUp22BqZTyABYA93bWNfuLgurmGxbeOuJWHpjwQLuvobVm1X8PUl1unw9aXlTD2g8Pc/lDsnueaLR4/+LTvj40YCgJfgncO/Zep11zU4pTqLXVtlir3s3kxpSwKc3aRaOOTLFw+i/cMB9ZfoqF6KdmRczi+e3PO1Y8GzBw7/XXERp1ZqvzB8f7UphR4Tj28HXBJ1i2Mz8TSql7gJ8BcUqppvt5ewPrOvFLLQLWnW56hVLqTuyblhAVNXBqWs+OnM2zW5+lvL4csM/jvDDuwg5dw1pvo6ygxqmtOKeqld5ioDKp1lO1iaETeeP8xvryu/J3sSFrA+sy17EzfycAY4LH8Oq8V/Ewe3R1qP1KmwmyUuq3wO8Ad6XUyUkvCqgD/t2FsQkhelCcXxwvznmRN/e9idaam4bfxNjIUWd8vSmXxlNdWU/aznzMbibC4n0py5cR5DP0HrAM+DPwmybt5Z08V/ha2pheobV+DXgNYMKECQOmmkaQexDvLHyHt/e+Q3FhOZdEXk5SQJLj9aqyOjIPFeMf5kFQhPOTFa01W3K2UFZXRshQL/IONibFJ+fuC3HS7SNv58HVD2LT9roI0wZPw8PkQaxvLLck3+Lo19pUjJ35O/k85XOuG3Zdd4XcL7SZIGut/wz8WSn1Z631b7shJiFELzE9fDrTw6efts+m7E28te8tbNrGDcNuYGbEzBb7ubibmHJJHMf2FFJTUc/R7fmkHyjmuscm4+kntZA7QmtdCpRinx/cJZRSvsA5wA1d9TX6umBLOLFfzie0rI7D1GPbvpfz70gm60gJX76wE0udPaGZcEEMky+2l9DSWnPvynsd85cHDR7EPf6PU3/CTPgQfyZfIqW2hLO50XP5dNGnbMjeQKJ/YotTI46XHee1Xa+1eo3cqlyn45zUUg5vycXDx4XkWeG4eZo7Pe6+riNTLCad2qCU+l5rPacT4xFC9CHHSo9x93d3OwrZb8rexAcXfcDQgKEt9j+yNQ9LrdVxXFdt4eiOPEadG9kt8Qo7pdT7wGwgSCmVATwGmAG01q80dLsMWKG1ruyRIPuAXd+nU11W5zhO2ZbHuPPL2fJ1miM5Bti+4jij50Ti5mlmW+42p8V9udW5pCdv5Vc3/4qyujKU8fSD8HU1FpRBSZ37ASbBP4EE/4Rm7TZt4+E1D7P82PJWzzUqI/Nj5juOMw4WseSfu9ANtetTtuZyzSOTUFJBxUl7pli4AZ7Yb6T+ONa04wOEd2Fs4iwVVBfwzv53yK/K58K4C5kRPqNTrmuz2jixrwir1UZ0ciAms9yoB6rVGauddnmyaisrT6xsNUFuaZTi+N5CEicMwt1b9h3qLlrrNkeetdZvYS8HJ1pRW13fvK2qnvT6YxzzP0F4WSIuVjdsFm1PmD2hvK682TlF1UX87LufsTZzLb6uvvx64q+5OP5iDhUdIqsii0lhk/AwerDmg8Ps/zELZVSMmRvJlEviu+Ntil5sTcaa0ybHsyNnc8OwG5wW6u3/McuRHAMUZlaSfbSUwYl+XRlqn9OeEeS7gF8Bg4FtNCbIZdjraIpeyGKzcOvyWzlWdgyAr1K/4sXzXuScyHMcfQoyKvjxo8MU51QRMyqIGVclYnY9fbJrrbfxwR83U5JrnzPnG+LOlQ9PkMczA1Skd/OR35baThoyaRD71mZSkN64WO/EviK+fmk3Vz48oUtiFKKrDJ82mCObczm5j6F/qAcflr/JWyFvQQi41ntw8f57GRc3yrGZzrTwaYR7hTu2CzYZTFixsjZzLQCltaU8vv5xduXt4qPDHwHg5+rH3SEP8HbBOxSOzySiNInKb68mclgA4UP8u/19i96j6bbTp3I3ufPXWX/F3eS8GNrs3jz1c3GXga5TtVmAVGv9vNY6FnhIax2ntY5t+DNaay0Jci+1K3+XIzk+6YujXzj+rW2aZa/sJvNwCVVldez/MYtNX6TSlrUfH3YkxwCledUc2pjTaXGLvmV25GynlftzouawIHZBq/1d3Exc9duJ+IU437Bz08oozZfV+6JvCU/y57KHxpM8K5yJF8Uy4+eRvHOgce+VWnMVJ6Zs4Pw7GzcrdDW6snjhYu4cdSfXJl3L4oWLKapxXldZb6vn48MfO45Lakt4Pv1psn1TqDNVkxq4k3Uxnzp90BQD0+zI2bgaW17DccfIO5olxwBjGqb7nBQ/LqTZQlLRsY1CXlBKJQPDAbcm7S3uxCR6lr9b81GFpjvllBZUNysvlHGo7cXvJ/YWNmurKKlpoacYCAzKwNMzn+a+sfdh1dbTjh47zjEo/MM8KcmrdrQZzQbcvGSKheh7wuJ9CYu3lz5MKU5xVBpw8K1rNl842COYX4z9heN4athUfsz80amPxnkucjXOHyCzfFKIGCajxwNduFc4/5n/H97e9zb1tnoWxC7Apm0M8R/S6lQ3/1BPbnhyCsf3FeLh40r4EL/uDbqP6MhGIY9hX9QxHFgKLAR+pJWtSkXPivON46ohVzlGIULcQ7h5xM2O17393XD3NjsK1APt2r3Jw8+V8qJap7b6Witaa6cC5WJgGew1GICVJ1ayPms9Q/yHcFniZZgNLU+9mXxxHLlpZVSV1aEMiqmXxuPawmM/IfqSBP8ERgWNYndBY2nqSxMvbfO864ddT35VPm/vf7tZYnxSoFsghTWNAxRDAocQONirxb6i/8o4VEz6gSKCIrxIGBeCMijGhIxhTMiYDl3H1cPMkImhXRNkP9GR30hXAqOBHVrrW5VSg4D/dk1Y4kztK9zHmow1xPrG8sjkR7gm6RryqvKYGDoRN5Nj4B+j2cDcW4fzw38PUlFUS3iSH1Mvb75C9lSTL4pjyYs7ockgyb41WQRFeJM8S9ZsDmTvHniXpzc/7TjekbeDP8/8c4t9A8O9uPGpqeQdK8MnyMMxP1OIvsambRhU42zFl+a+xLsH3iWjPIN50fM4N+rcNq9hNBi5IO4C3tr/llP7II9BTAqdxPyY+ZgNZn7+/c+xansVmGOWFIpqipyeDIr+bd/aTFa9e8hxnDWrhHOuSzrNGeJsdCRBrtZa25RSFqWUD5AHSG2mXuS749/xwKoHHCMQF8dfzFMznnIqXt9U1PBAbvrjNOrrrLi4te9bIXJ4AHNuGsb3bx1wak/fXyQJ8gD34aEPnY6Xpi3ld5N/h7dLy08mTGYjgxPlEbHom75I+YLntj1HWV0ZlyZcyu8m/w6TwYSvqy8/G/OzDl8vwS+h2SjxJQmXOKZiLN6/2JEcAxTXFLMsbRnXD7v+7N+M6BN2fZ/udLx/XRZTL49v9+9v0TFtLtJrYqtSyg/77nnbgO3Ahq4ISpyZd/a/4/R47qvUryioLjjtOcqgOvzDFZHk36xeYmC47IY20HmanL8HXI2urU6xEKIvy6zI5A/r/0BhTaFjQV3TRXVnwmw088J5LzA6eDQBbgFck3QNd466kz2rMnjv/21iz7dZzc+Rn68BxWB0TtmUQWGQ2sVdpt0Jstb6Z1rrkoYi8vOAm7XWt3ZdaKKjFM1/UJo++jvpcPFhntr4FH/Z/BeOlx3v8Nfx8ndj1rVDcHGzLzyJHhnImHlRHQ9Y9Cv3jLkHk6Hxw9YdI+9wmtYjRH+xr2Bfs8V4e/L3nPV1RwaP5L8X/JfV16zm0SmPkrWvnDUfHKY4u5LQwyPwqWnchjrKO4qFsQvP+muKvmP8wmiaLvUZMzcSk2wY02U6NHSolBoFxJw8TymVoLX+rAviEmfg1uRb2fXDLsdjuMsSLms2P+1E2QluWHoD1RZ7BYElR5fwxaVfEOQe1Ox6p5M8K5yhU0Ox1NmkBrIAYFbELJZetpRNOZtI8k9iWOCwVvvWVtVzZEsuVosmceIgPHykgoXoO0YHj8ZkMDltkjN+0PhO/zrp+xsrC7laPbhy1/9RMS6N4dPDWJBwPp5meXI3kCROGETAYE8yDhQTFOklNbC7WEeqWLwBjAL20bhESwOSIPcSsyNn8/Gij1mbuZY43zhmRcxyvHao6BDuJneWpi11JMcAZXVlfHf8O64dem2Hv57JbJRd9ISTMK8wLk249LR96mosfPTnrZTl278Pt31znGsemYinryzUE33DIM9BPDPrGZ7f/jwltSVclngZlyVedkbXslltbP4qjdSdBfgGuzPt8nj8Q+2J76lT11xsbgRsHYax3A/P+yU5HogCB3udVfWSiroKTAaTPN1rh46MIE/RWg/vskhEp0j0TyTRP9FxXFVfxd3f3c2OvB0AjAwa2ewcX1ffbotPiNQd+Y7kGKC6rI6DG7IZvyCm54ISooPmRs9lbvTcs77OtuXH2bbMPtWtOLuSoqwKrn9iKgaDYui0MLJTSjm0yXkzpsxDJZTkVuE3yOOsv74YGOpt9Ty+/nG+Tv0aF6MLPx35U+4cdWdPh9WrdWSR3gallCTIfcynRz51JMcAewr24Ofq5zgeEzyGOVFzsNk0lnprC1cQomtUmypI9z1ItalCamiLPqm8rpxDRYew2s783nn8lM2XygpqKM6uBMBotJfjjE52niqnFJjd5OmdaFthdSH3rbyPKe9NYcnRJVi1lWpLNS/seIF9hft6OrxerSMjyO9gT5JzgFpAAVprPapLIhOdIqui+crnktoSfjnulyQHJTMpdBIH1+Ww/n8p1FVbSZwYwnk3DMNo7shnJyHaL25sMO+v/pwvBv0bq6Eeo83MsKg/UVLji5+bX0+HJ0S7LDm6hCc3PEmNtQY/Vz/+NedfjAru+K9D/zBPctPKHMdmVyPegc6PvydeGEfWkVLqa+2J+MjZETIlaQDIqsji+e3Pk1aaxjmR53DnqDs7XLnkqU1P8UP6Dy2+drjoMCMCR3RGqP1SRxLk14EbgT04bRMheqs6ax3FNcUtvlZZX8mUsCmUFVSz6t2D6IbqcIc35RIU4c1YqUohuoiLm4lNCUuwVtp3cbQa6nl0y2+xbLaQ4JfA38/5O3F+cT0cpRCtq7ZU89TGp6ix1gD2QYfbvrmN5Vcs7/CC5ykXx1GUWUHe8XJcPUwNFYLsv5pLa0t5e9/bnCg/wfS7ZjK8fDK+Ie6Exsq0uIHg3pX3cqT4CAAHig6gtebesfd26Bpbc7a22G5SJiaFTTrrGPuzjgwT5mutl2it07TWx0/+aeskpZSfUuoTpdRBpdQBpdRUpVSAUupbpdSRhr/9G/oqpdQ/lVIpSqndSqlxTa5zc0P/I0qpm1v/iuKkf+38F1+nfd3iayf3aC/IqHAkxyflnyjv6tDEAFdU6/xY2aLt1QBSSlK4fun1vL3v7WZltIToLYpriqmyVDm11Vpr+SLlixb7pxSn8OKOF3nvwHuU1Zax5OgS/rHtH2zP3Y6nnytX/XYiN/1pGrc8PZ0hkxq3/733+3v5955/882xb/jDlkfZ5bdGkuMBIqM8w5Ecn9TaSPDpJAclOx2bDWZGBI7gH+f+g3Av2dzrdDoygrxDKfUe8CX2KRYAtKPM2/PAcq31lUopF8AD+B3wvdb6aaXUb4DfAA8DC4HEhj+TgZeByUqpAOAxYAL2yhnblFJLtNYtD48KANZkrGnWZlZmrh56NfOj5wMQGueL0WzAWt+YjEQkSekY0ejIllwyDhYRFOnN8JmDMRrPfvrNRXEXtbqxQkV9BX/b+jdqrbWyiET0SoO9BhPmGUZ2ZbZTe52trlnf3fm7uWX5LdTb7E9MXtr1EqW1pQC8vvd1np75NBfGXYh3QOO0CovNQlZFFjvzdzpda8nRJVyddHUnvxvRGwW6B+Jt9qa8vnHAKtonusPX+f2U3/Obtb9he952hvgP4akZTzkGyMTpdSRBdseeGM9v0nbaMm9KKV9gFnALgNa6DqhTSl0CzG7o9jawCnuCfAnwjtZaAxsbRp/DGvp+q7Uuarjut8AC4P0OxD/gJPglkFKS4jj2cfFh6eVLnapWePi4sPCukWz84ijV5fUMmxbGsOlhVNVXcaj4EHG+cVLlYgD75JX1LMv7klpzFYk7JpB3fDxzbj77tbq/nfRbIr0j2ZG3g5TiFNIr0pv1WXFshSTIotd6Zd4r/OSrnzhGkn1cfLgk/pJm/T489KEjOQYcyfFJ7x54lwvjLgRgU/Ym/t+G/0dGeQZDjaMxYsRK4wLAQLfArngrohdyN7nz6JRHeXLjk1TUVxDjE8P94+7v8HXCvML4fcgzHM3JJcjFmziPiC6Itn9qd4Lc1q55Sqnfaq3/fEpzLJAPvKmUGo19i+pfAoO01ic/eucAgxr+HQ40/U2Z0dDWWvupMdwJ3AkQFSVzaO8ffz9ppWkcKj6Er6svj099vMVkNzo5kOjkxhvvttxt/GLlLyivK8fN6MafZv6JedHzujN00Qvs/vE4z/N7SqLzANgTuhr23s+suiTMZ7l7k9lo5tbkW7mVW8mtzOXJjU+yJmON01bpg70Gn9XXEKIrxfnGseyKZXyR8gV11joujr+YMK+wZv1cjKffBOfkoqs6ax2/XvNrimrsm4McsO4kuDySfK8MUBpfV1/uGXNP578R0WtdEHcBsyNnk1eVR7RP9BlV+9m7OoPV7x8GIIV8sg6XsOi+MZ0caf/UmaUKrmqhzQSMA17WWo8FKrFPp3BoGC3WLZzbYVrr17TWE7TWE4KDgzvjkn3aYK/BfHLxJ3x75besvGplu2t2PrPlGcrr7I91aqw1PL3pafSpE5VFv/fN7lWUuOc5jm0GKwcGbcBg7NySbIM8B/HinBd5bOpjuBjsyUSoZyi/HPfLTv06QnS2ALcAbk2+lbtG39Vicgxw/dDr8TZ7O47DPBv7mZSJ20feDkBGRYYjOT5JK81Pdj7CTy0Ps+KKFfJofADyMHsQ4xtzxqUw969zngZ0Yn8RFcW1rfQWTXVoq+k2tPR/LwPI0Fpvajj+BHuCnKuUCtNaZzdMoTj5WzgTiGxyfkRDWyaNUzJOtq/qvND7t1DP0LY7NXHqvLqCmgLqbfUcKDrA0tSlBLkHcXXS1TL1op/z8/GGU8q7hkcGYTQasNqslNeVd2pZtiuGXMGcqDlkVmQyJGBIh8sZCdEbJfgnsOSyJaw8sZJAt0DOiTyHrblbSS1JZXr4dMe80ijvKELcQ8irbvxQGlYWj29NMCM9wvEwy6YgouPcvJzvo0rBntUZjJ0b1ew14awzR5CbDTFqrXOAdKVUUkPTHGA/sAQ4WYniZuDk0t8lwE0N1SymAKUNUzG+AeYrpfwbKl7Mb2gTXWBBzAKn4/Miz2NX/i5uXnYz7x18j3/u+Ce3LL/lrIrji97vmoULia1qnG/sjS+XhF3O3196h3MWn8vMD2dy/dfXk1OZc5qrdIyfmx8jgkZIciz6lZODCnOi52AymJgSNoXrhl3ntOjKZDDx/HnPMzJoJG7agyF5E5mUfiHKoCjMqqAgQ6oLiY6beGEMBlPj+KXWsH35cf737HZsNnkyfDpdPYIM8Avg3YYKFqnArdgT84+UUrcDx4GTy3KXAhcAKUBVQ1+01kVKqSeBLQ39nji5YE90vocmPESQexCbczYzInAEd4y6gyc3PolVNybEKSUpbM/bzsTQiT0YqehKPoHufHbHuyzb/T21LlXEl49i7TupfDjuZaqpAGB3wW6e3fosfz3nrz0crRB9X3JQMu9d+B4Aaz86zO6jGWg0WUdK+fKfu7jpqWmyiZPokKKsSmyW5olwUVYlOUdLGZzo1/1B9RHtTpCVUoFa68LTdGmxZpPWeif28mynmtNCXw38vJXrvAG80Xak4mwd2ZiPx4qRnKtGMm5BNJ5mTzxNns36eZm9eiA60V1Ka0t5YsMTrM1cS6JfIuekG6ky11HtUuHU72DxwTP+GhuzN/LNsW8I8wzj2qHX4uPic7ZhC9EvFGVVOh1XldWRn15OaJxMbRPtd+pW5k25uHfmGGn/05GPohuVUh8rpS5QLcwW11r/qRPjEj0kO6WEHxYfpCS3iuKcKr5/6wBZR0q4acRN+Ls21keeFz2PYYHDejBS0dWe3fYsK46voNpSze6C3bzv/k8863zxqxrk1G/a4GlndP2VJ1Zyx4o7+OTwJ7yw4wXuWnFXZ4QtRL8QEOY8KGEwKXxD3HsoGtFXnfp9dFLihBCCImSQ63Q68vFhCDAXuA34p1LqI+AtrfXhLolM9IgTB5rPXNn4+VEu/7/xfH3516zNWEuwRzATBrX0UED0J9tytzkdF5CLaXA9Cw7dzvqY/1ERWMC8xPP45bhfUpxTyYl9RQSEexI5NICcyhw2ZW8i3i++2U5OJ3165FOn472FezlQeEA+eIk+aUfeDj469BFuJjduHH4jcb5nt136hAtjyE8vJzulFBc3I9OvSsTd6/Ql40T/UWetY2vOVgLcA86qesnY86PJPVZGxsFizK5G4scFM2zaYJla0Q4dqYOsgW+Bb5VS5wL/BX6mlNoF/EZrvaGLYhQdtLdgLy/vepnS2lIuT7ycyxMvb/e5nr6uzdry08v58oVdJM8azAWjL+jMUEUvNipoFMfLGneTD/UM5Y7fzacwvYr7fC9z7PyVtruAZa/sQTcs+HCdXczL9X9y7Cp2x8g7uG/cfc2u7+3i3aztcPFh/rXzX5gNZm5JvoXRwaO74q0J0akOFB7gtuW3ObZMX3FsBV9d9hX+bme+K6m7lwuXPzSeypJaXD1MmM6y9rjoO3Iqc7h52c1kVWYBcGnCpTw5/ckzuparu4lLfjWWytJaXNxMmF3l+6i9OjQHGbgBuBHIxb74bgkwBvv849guiE90UGltKXesuIOKevs80V35u/B18cXV5Mrft/6d/Op8FsYsZETQCADmRs3Fy6XxMcvQKaH8+PERp62n84yZrKhZTP3KGm6vuZFLJjtXuRD900MTH6K4tph1meuI843j8WmPYzKZGBTrPE9427JjjuQY4OP8/1Ln07jl7lv73uKW5FuazS++Pfl21maspayuDLB/Lz62/jHHYtAfM39kyaVLWq0vK0RvsSxtmSM5BiirK2NV+iouS7zsrK/t6dd80EL0b2/ve9uRHAN8nvI5Nwy7gaSApNOcdXotDX6J0+vIFIsNwGLgUq11RpP2rUqpVzo3LHGmtuRscSTHJy0/tpxV6auosdYA8MGhD+CQ/bVXdr3CBxd+4Khna3IxcuHPRrFy8QEqimqpMVXyRfI/qTXZt1P9/cFfExkTwrhB47rrLYkeEuAWwMtzX8ambRhU68sVbFbnFdJ1hhqnY4vNQp21jlMl+iey7IplrM9az2DPwaxKX8V3J75zvF5jrWF1xmquHXrt2b0RIbpYoHvzLaCD3IN6IBLRH5y6YQxAYc3paiSIrtCRRXpJWusnT0mOAdBa/6UTYxJnoWldzZMUypEcnyqzIpMvU790aoscFsBNT03j4l+NId3vgCM5BtBolqUt69ygRa92uuQYYPScSKfjEbkznI7nRM1pNVnwcfFhQcwCRgWPanFradluWvQFlyde7jRPdHbEbKaHT+/BiERfdkn8JagmlXPDvcKZOMheUjWnMoeq+qrWThWdqCMjyEFKqV8DIwC3k41a6/M6PSpxxmJ8Y/AweVBlcU5qzQYz9bb6Fs9paXRPKUXk0ABGDInn+1NKKA7yHNSsvxi4kiaH4uZt5usXdqE1DMubimedL7mRh1kwZ1q758Avil/EimMr2JBtX86wMHYhM8JntHGWOBNKqTeAi4A8rXWLqyiVUrOBfwBmoEBrfU53xdcXZFZk8uquV8mqzGJBzAI+vOhDtudux93k7pjCJsSZmBY+jVfmvsKS1CUEuAVw0/CbqLJUcd+K+9ieZ/8eu3/8/fxk6E/avJZN21i8fzFrMtYQ6xvLPaPvafGJh2iuIwnyu8CH2G+qd2PfAS+/K4ISZy6rIsspOQY4WnqUP834E89seYbCmkLMBrNjRNnP1Y+L4i5q9Xp33HQFh7/bxPJM+6jx8MDhXJ10dav9xcAUFuuLUgr7Wl6IKhnOWM9JXDt0Uruv4Wp05bX5r5FSnILZaG7xaYjoNG8BLwLvtPSiUsoPeAlYoLU+oZQK6b7Qej+rzcodK+4gvTwdgE3ZmwC4csiVPRmW6EemhU9jWnhjCc2/b/072/O2A1Btqeavm//KnKg5hHic/kfztd2v8a+d/wJgc85mDhQd4N0L3u26wPuRjiTIgVrr15VSv9RarwZWK6W2tHmW6FbhXuGEeISQV5XnaBsXMo4FsQtYELsAq81KcW0xS44uwWKzcHH8xW2OCD8z96/cU3o3lXWVJAcl00IZbDHAubibGD5jMHvXZDraxsyNPM0ZrUvwT+issEQrtNZrlFIxp+lyHfCZ1vpEQ/+80/QdcA4UHXAkxyd9c+ybLk+Qi7IqWf+/FErzqokbG8ykRbEYjbKzXn9XWF3IkeIjTm0WbeF42fE2E+Rvjn3jdLw7fzc5lTmEeoZ2epz9TUcS5JPP57OVUhcCWUBA54ckzobJYOK52c/xx41/JK00jdmRs/nluF86XjcajAS5B3Fb8m0duu7Z1vQUfVPqznwObcrBw8eFsfOj8AlsfaOCWdcOIWKoPwWZFUQNDyQsXnb86sOGAGal1CrAG3hea93aaPOdwJ0AUVFR3RZgTxrkMQiTMjlVrgj3Cu/Sr2mzab56cRflRfanf9uXH8dkNjDxQikg1V8V1RRx/w/3O6ZVNBXgFsDIoJFtXiPcK5yUkhTHsafZE19XuTe3R0cS5D8qpXyBB4EXAB/g/i6JSpyVUcGj+GjRRz0dhujjTtY3PunYngJueGIqRlPLI1bKoIgfF0L8OHka3w+YgPHAHMAd2KCU2tjSxlBa69eA1wAmTJigT329Pwr2CObesffy4o4XsWgL0T7R3Dnqzi79msXZlY7k+KTjewslQe7HXtr5ktO0CrPBTJRPFIM9B3PfuPtwM7m1cQX45bhfcqDoAHlVebgYXPj1xF83S7ZFy9qVICuljECi1voroBQ4t0ujEmekttqCi5tRpkCITnF4U47TcUVRLVlHSogcduYPjr5K/Yrntz9PeV05VyRewYMTHmyzSoboERlAoda6EqhUSq0BRgOyc2qD20feziUJl5BblcuwgGEtfh9X1VdhNpoxG8xn/fW8A90wuxqpr7U62gIHt7yNsOgfTp1WUW+r59YRt3JJwiXtvkaifyLLr1jOoaJDRHhFOEq6ira16zeT1toKtL1cUvSI8qIaPvnLVv5z/xoWP7qBrCPFPR2S6AdaKix/umLzB4sOcvs3tzP/k/n8ZfNfqLc6V03JrMjk0R8fJacyh8r6St7Z/w6fp3ze2WGLzvEFMEMpZVJKeQCTgQM9HFOvE+QexIjAEc2S42pLNQ+seoCp709l9oez+fTwp61cof1c3Eyce8NQXD3s41qDYn2YdLFMfevPpg2e1qztw4Mfdvg6ZoOZ5KBkSY47qCNTLNYppV7EXsmi8mSj1np7p0clOuTHj46Qm2bfjay8sIZv39jPjU9Nw2CQkWRx5sbMiyJtTwFl+dUAjJwdQUArI1b1tnp+/v3PHYtD/3vgv3iaPbl37L2OPrvzdzt2yTtpR96ODm2FLjqHUup9YDb28p0ZwGPYy7mhtX5Fa31AKbUc2A3YgP9orff2VLx9zX/3/5dvj38L2HfVe3Ljk0wPn37WC6MSJw4ibkwwNVX1sjPaAHDD8Bt4ceeLTm2ZlZmt9BadrSMJ8piGv/9fw98K0IDUQe5h+enlTscVxbXUVNTj4ePSQxGJ/sDL35XrH59MTmop7t4u+Ie2/jg3tSTVqXIKwIbsDU4J8sigkRiUAZtu3MZ8TPCYTo9btE1r3eYTQa31M8Az3RBOv3OgyHmw3aqtHC4+3CmVA4xmgyTHA4Sn2ZNzIs5hdcZqR9u86Hk9GNHA0pHJf18BXwJfN/z5EnhPKTWmC+ISHRA51B8Ai6EOG1YCwz2bJcd78vfw6eFPyShvthGiEK0yGA0MTvQ/bXIM9h3xjMro1Jbkn+R0HOEdwR+n/5FBHoNwN7lzw7AbuCzxsk6PWYieNiVsitOxu8md0cGjeyga0Zf9aeafuGHYDYwLGcddo+7i1xN/3dMhDRgdGUEeD0wAlmAfPb4I++O3u5RSH2ut/9oF8Yl2mHBZFG/bnme73oCb9uC+Ub/EPmXQ7qWdL/HyrpcBexm45899nlkRs3ooWtEfvbL7FafpEy5GF3425mfN+i2KX8Si+EXdGZoQ3e7KIVeSV5XHF0eWEOQRyK/G/0pKa4kz4uPiw8OTHu7pMAakjiTIEcA4rXUFgFLqMewjybOAbYAkyD3kk2MfsY11oKBaVfC3fU8zJ+kcwrzCqKqv4o29bzj6WmwWXt31qlOCnJNWypr3D1OcW0XsqCBmX5+Ei1tHvjXEQHCk+Aj/2P4PMsszmRczj7tH3Y3RYB813p7rvBShzlrXbJGeEANFWV4N/p9P4NLc4bh6mgiJioewno5KCNERHcmCQoDaJsf1wCCtdbVSqraVc0Q32F+43+nYqq0cLDpImFcYFm2h3uacqDTditpqtbH8lT1UltYBcHhLNkfd9xA8wcTsyNkEuQd1/RsQvV69rZ57vruH3KpcAI7uOoq7yd2x4czo4NEcKzvm6B/qGdrmDk9C9AfLjy1nXeY6hvgP4eqkq3E1urLu0xRKcu332dpKCz8sPkDU09Nl1zsh+pCOJMjvApuUUl80HC/CPgfZE9jf+mmiq00Km8TyY8sdx25GN0aH2Oe7+bj4cEHsBXyV+pXj9XEh49hfuJ/hgcMpy692JMcA3w55i9T6XbAB/rb1b/+/vTuPrrI6+z7+vTITwpxIIBECIQwyBQhEQJHBOiCPgDjgDG+tFRVpq7U4vfogUlnVVl1qK2hBfHidWycetIpEbVEUMEQi8xxMIMwkIWTa7x85xIQEMpDkJCe/z1pZnHuffe/7OsnJzsU++96bVy97lR5ty84llaZn06FNJcnxSV+mfVmSIN+bcC+HTxzmqz1f0bVVVx4b9ljJ6PKpDmVks+bjnWzJ3Uyr8xxXDr2E0MDQOn8NIrVtUeoi/rTq5/sYk/cl8/TIpzmYnl2m3vFj+eRmaeUJqbo1e9ewau8q+oT3qXC5N6l7VU6QnXOPm9lSYLin6A7n3CrP4xtrPTKpsklxk8jIzuC9Le/RLqQdMwbOoG3Iz5s5zBo+i4T2Cazeu5rlu5fz1qa3eGvTW1zX4zoeSHiQZi0COX4sn4PNfmJbu7Ul52XnZ/Nq6qvMuXCON16WNCDRYdEE+wdzovDnD4u6te5W8rhNSBueH/N8RaeWkZdbwD+eWsMn57zG+vZfw3Z4cfdfeO2/FtG5Zec6iV2krryz+Z0yx5/u/JQjJ47QpW84az/fXVJ+TucWSo6lyt7Y8AZPrHyi5PjO/ncyLX6aFyNqmqr1eY9zbpVz7lnP16rKz5D64Gd+TB8wnWXXLOOt/3qL4VHDyzwf6BfIpO6T8PfzJys/q6T8zY1vsicnjUt/1YfW7UMp9C8o13ZuYW65Mml6WgW34pHzH6FFYAugeDvzO/rfUel56VnpHM07WnKctv4Q6QVpxcmxx6GCgyxYt6D2gxapYy2DWpY5DgkIIdg/mPMndmXALzrRtmNz4ga357Jf9/VShNIYlb5vCGBh6kIKiwpPU1vqiu7EakIOnzhcruzIiSP07d6JG//7fJxLZMvSz1ibWTyK7G/+TO4xuZ6jlIZqfLfxXBpzKUdOHKF98/ZnrHss7xgzls/gu4zvCPILYlr8NG7rexvN2wRzIiCnXP0jJ47UVdgideau+LuY/vn0kk9WpvWfRkhACADDJnVj2KRuZzpdRBowJchNyMRuE0nanVRyHNcmjt7hvUuOzYx5v5jHP7f8k705e7k05lJ6t+tdviFpskICQkoSgDN57cfX+C7jOwDyivJ4bs1z/KLzL+gc05lR8efzxbFIDoVmlNSf0G1CXYUsUmeGdhzKx5M+ZlXGKuLaxBHbOtbbIYkPmNpnKnNW/jy18dbet572ng6pO0qQm5DRnUbz14v/ytLtS4lsHslNvW7Cz8rOsgkNDOXGXppSLmdn+5HtZY4djh1HdtC5ZWdG39SbLrsX8MaWN8gOPMzYrmN1E4o0WuHNwrmsy2XeDkN8yPU9r6dHmx7FN+m168OwKPWP3qAEuYm5IOoCLoi6wNthiI+76NyLyqys0iKoBYPaDyo57nJuNA+ce583QhMRafAGth/IwPYD66z9/KJ8vtj9BcfyjjG602htZFMBJcgiUuvGdR3HsbxjvL/lfdqGtOWu+LsICworVy8rL4snv32SFT+tIK5NHA8MeYCYVjH1H7CISBNRWFTILz/5Jd/v+x6AZ9Y8w+tXvE7HsI5ejqxh0arlPiS/KJ/v933P3uy9lVcWqSVHThyhyBWVK7++5/W8Me4NXrz4xTJz3Ut7atVTvL/1fTKPZ7LipxX8Num3dR2uiEiT9k36NyXJMcDB3IO8tfEtL0bUMGkE2UfsPLqT2/51GxnZGfibP9MHTOeXfX9Z5fO3rtnHD1/sITDIj4GXdqZDt9Z1F6z4hPSsdO794l5+2P8DHZt3ZPYFsxkcObhabXyT/k2Z4y2Ht7D/+H7t4CgiUkdKr2d/prKmTiPIPuLF5BfJyC5eFaDQFfJ88vMcOH6gSuf+tPkwH89fx56Nh9jxwwE+eDaZrEP6ZZEzm/vdXH7Y/wMAP2X/xANfPVDttTrPa3demeP2oe1pE9ym1mIUEZGyLoi6gJiWMSXHzQKacVXcVd4LqIHSCLKPOHUb4IKiAg7kHqBds3aVnrstORNcqXPzi9j14wHOG675SHJ6Gw5uKHO8N2cvh04cqtbo7/2D72dvzl5SMlOICovi8eGPazkjEZE6FOQfxP+M/R/e2/IeR/OOMq7rOLq06uLtsBocJcg+YmyXsazeu7rkuFvrbsS1jqvSua0imlWpTKS08zucz7ub3y05jmsTV+2pEZHNI1k8djFZeVmEBoaWW3ZQRERqX6vgVtza+1Zvh9GgKUH2Edf2uJYAvwA+3fkp57Y4l9v63oaZVencXsM6sCNlP7t+PAgGvS+MIqq7PuaWM/v94N/jcPx7z7/p3qY7M4fMrHFbFa1wISIi4i3mnKu8ViOUkJDgVq1a5e0wGpQ1e9eQkpnCwPYD6RfRr9zzh/flEBDoT1ibYC9EJyJmtto5l+DtOGpK/a6INDan63c1gtxELFy3kKdXP11y/FDiQ0zuOblMndbnhNZ3WCLlnCg8wbfp3xIRGkHPtj29HY6IiDRBSpCbiPk/zC93fGqCLOJte7L2cOvSW0tuOp0UN4nHhj3m3aBERKTJ0R0xTUShK7v8VnWX4xKpDwvXLSyzIsu7m99ly6EtXoxIRESaIiXITcQt591S5lh3r0pDdOjEoSqViYiI1CVNsWgi7oy/kz7hfVibuZZB5wxiWNQwb4ckjcj+4/tZsG4Be7L28IvOv+CKrlfUyXWujL2ST3Z8UnLcqUUnBpwzoE6uJSIicjpKkJuQEdEjGBE9wtthSCPjnOPXn/6aTYc2AbBs1zLyCvOYGDex1q81InoEL138Eh9u+5CIZhHcfN7NBPipmxLfl5WXRVJaEi0CWzA8arje9yJept9AETmjTYc2lSTHJ3207aM6SZABhkUN0ycc0qRkZGdw45Ib2Xd8HwAJ7RN45dJXtHGOiBfpt89HuSLHjh/280NSGscO5no7HGnE2oa0xd/Kbv8cERrhpWhEfM9bG98qSY4BVu1dxTc/fePFiEREI8g+6tMFP7L5u+LVAFa8u4Xxvx1AZNdWVTp31f9uZ+3nafgH+DH4ihh6XxhVl6FKAxcRGsGv+/2av679Kw5HRLPiYxGpHccLjlepTETqj0aQfdDhfTklyTFAQX4RyZ/uqtK529dmsvKD7eRm5ZN9+ARJizeSuetYXYUqjcS0+GksuWoJL1/yMksnLaVLqy7eDknEZ0zoNoFg/593MI0Oi2Z41HAvRiQiGkH2QYUFRVUqq8hPW46UK0vfeoSITi3OOi5p3M5tcS7ntjjX22FILTCzvwPjgH3OuT4VPD8SeB/Y7in6h3NuVr0F2MT0aNuDN654gw+2fkBYUBiT4iYREhDi7bBEmjQlyD6oXccwonu2IW1D8fqx5mf0HRldpXMju7asUpmINGoLgeeBRWeo85Vzblz9hCPd2nTjdwm/83YYIuKhBNlHXXFnPzauzODogVxiB0RwTueqJbmxA85h0OWdSVn+8xzkqp4rIo2Dc+5LM4vxdhwiIg2VEmQfFRDkX+Ob684fH8v542NrOSIRaWSGmtla4CfgPudcakWVzOx24HaATp061WN4vuO7jO+YnzKfE4UnmNxzMpd3udzbIYk0eXWeIJvZDuAYUAgUOOcSzKwt8CYQA+wArnXOHTIzA54FxgI5wBTn3BpPO7cCD3uane2ce7WuY/cFGdkZrNu/jn4R/Tgn9BxvhyMijcMaoLNzLsvMxgLvAXEVVXTOzQPmASQkJLh6i9BHpGelc8end5BXlAfAmn1rCG8WzuDIwV6OTKRpq68R5FHOuf2ljmcCy5xzT5rZTM/xH4DLKe6E44BE4K9AoiehfhRIAByw2sw+cM4dqqf4G6UPt37II/95hEJXSIBfAH8a8Scu7nyxt8MSkQbOOXe01OP/NbMXzSz8lH5casF/fvpPSXJ8UtLupHpLkPPz80lLSyM3V+vln62QkBCio6MJDAz0dihSC7w1xWI8MNLz+FUgieIEeTywyDnngG/MrLWZdfDU/dQ5dxDAzD4FLgNer9+wG5e/rP4Lha4QgIKiAp5Z84wSZBGplJlFAnudc87MhlC8JOgBL4flkzq37FylsrqSlpZGixYtiImJofhDXKkJ5xwHDhwgLS2NLl20DKYvqI91kB3wLzNb7ZmrBtDeOZfueZwBtPc8jgJ2lzo3zVN2uvIyzOx2M1tlZqsyMzNr8zU0Os45jpwou2TbodzqDbgfOH6A3cd2V15RRBoVM3sd+BroYWZpZvZLM7vDzO7wVLkaWOeZg/wcMNkzcCG1bHDkYG7oeUPJbpWjzh3FhG4T6u36ubm5tGvXTsnxWTIz2rVrp5F4H1IfI8gXOOf2mNk5wKdmtqH0k54RilrpeDUX7mdmxvhu43l709slZRO7Taywbm52PgfSsgjv1ILgZsVviT+v+jOLflxEoSsksUMiz416jtDA0HqJXUTqlnPu+kqef57iZeCkHjyQ+AC397ud/KJ8IptH1vv1lRzXDn0ffUudJ8jOuT2ef/eZ2T+BIcBeM+vgnEv3TKE4uQn9HqD0TgTRnrI9/Dwl42R5Uh2H3ug9mPggsa1jSclMYVD7QVzd/epydbYlZ/LpK6kU5BcRGOzPZb/uw7H2GSxIXVBSZ2X6St7c+CZT+0ytz/BFRJqMds3aeTsEESmlTqdYmFlzM2tx8jFwCbAO+AC41VPtVop3bMJTfosVOx844pmK8QlwiZm1MbM2nnY+qcvYfUGAXwA39rqRuSPmcm2Pa/Gz8j/ur97aREF+8S57+ScK+c87W9h1tPy21DuP7qzzeEVExPc99thjPPXUU7XWXlhYGAA7duygWbNmDBgwgF69ejFkyBAWLlxYa9eRpqWuR5DbA//0fOwQAPw/59zHZvYd8JaZ/RLYCVzrqf+/FC/xtoXiZd6mAjjnDprZ48B3nnqzTt6wJzXnihw5h8vePZ116ASXdTif0IBQcgpySspHdxpd3+GJiIhUS2xsLN9//z0A27Zt46qrrsI5x9Sp+gRUqqdOR5Cdc9ucc/09X72dc094yg8458Y45+KccxefTHZdsbucc7HOub7OuVWl2vq7c66b52vB6a4pVWd+RreEn9dGzvPPJTIhmDYhbZh/yXwuir6IgecMZM4FcxgRPcKLkYqISGO1aNEi+vXrR//+/bn55pvLPDd//nwGDx5M//79mTRpEjk5xQMzb7/9Nn369KF///6MGFH89yc1NZUhQ4YQHx9Pv3792Lx58xmv27VrV/785z/z3HPP1c0LE5+mnfSauFE39aRVRDMWpy/gy+CPKCgoYNnyMcwdMZfnx+geHRERqbnU1FRmz57NihUrCA8P5+DBg2US1quuuopf/epXADz88MO88sorTJ8+nVmzZvHJJ58QFRXF4cOHAfjb3/7GjBkzuPHGG8nLy6OwsLDS6w8cOJANGzZUWk/kVPWxzJs0UCv2rOC1TYs40Hsjnwe/RwEFACzbtYx3Nr3j5eikMUjJTGH13tUUuSJvhyIiDdDnn3/ONddcQ3h4OABt27Yt8/y6deu48MIL6du3L4sXLyY1tXhH8+HDhzNlyhTmz59fkggPHTqUOXPmMHfuXHbu3EmzZs0qvb5WJ5Sa0giyD0jel8y7m9+lWUAzbu51M+e2PLfSc55e9TQLUxcCYJRfmmbr4a21Hab4kPyifO5edjcrfloBQJ92fXjl0ldOuxSgc46j+4/TvHUwAYH+9RmqiDRgU6ZM4b333qN///4sXLiQpKQkoHi0eOXKlSxZsoRBgwaxevVqbrjhBhITE1myZAljx47lpZdeYvToM98f8/3339OrV696eCXia5QgN3KpB1KZ+vFUClzx6O8nOz7hw4kf0jKo5WnPycnPYfH6xSXHDodhOH7+n7bmHMuZfLH7i5LkGGDdgXV8sPUDJvecXK7u4b05LHkxhcN7cwgODWD0Lb3oGh9Rn+GKiJeMHj2aiRMn8rvf/Y527dpx8GDZ++uPHTtGhw4dyM/PZ/HixURFFe8BtnXrVhITE0lMTGTp0qXs3r2bI0eO0LVrV+655x527dpFSkrKGRPkHTt2cN999zF9+vQ6fY3im5QgN3JLti0pSY4BDuYe5Mu0LxnXddwZzyudDAN0DOtIh+YdyM7P5poe1zDy3JF1Ea74iAPHy+86fCC34p2I//POZg7vLb7x5kROAUmLN9C5bzv8/TXDS8TX9e7dm4ceeoiLLroIf39/BgwYQExMTMnzjz/+OImJiURERJCYmMixY8cA+P3vf8/mzZtxzjFmzBj69+/P3Llzee211wgMDCQyMpIHH3yw3PW2bt3KgAEDyM3NpUWLFtxzzz1MmTKlnl5t3crNymflh9vI3HWM6B5tSLgiRp/I1SElyI1c25C2VSorLTQwlGu6X8PrG14vKbuj/x31ur2pNG6jO43mmTXPkJWfBUCQXxCXxVxWYd1DGTlljo8fyyc3K5/mrYLrPE4R8b5bb72VW2+9tcLnpk2bxrRp08qV/+Mf/yhXNnPmTGbOnFmuPCuruB+KiYnh+PHjZxltw/Wvv6ey+8fiEfi924+Sd7yAEdf38HJUvksJciN3ddzVfLj1Q7Yd2QbARdEXcX6H8ys974EhD5AYmcjGQxsZ1nEY8efE13Gk4ksiQiNYdPkiFq9fTH5RPpN7TCa2dWyFdWP6hbN22e6S43M6t1ByLCJSDXm5BSXJ8UlbkzOVINchJciNXOuQ1rx75bt8l/EdoYGh9I/oX6XzzIwxnccwpvOYOo5QfFVcmzgeG/ZYpfWGTojFz9/YlXqQdlHNGTqx4kRaREQqFhjkT/NWQWQf+Xlzr1YRla/iITWnBNkHBPgFMLTjUG+HIVIh/0A/hl3VjWFXeTsSEZHGyfyMi27syWcLfiTveAHNWwVxwTVx3g7LpylBlhpxzrH72G7Cm4WfdmkvERERqR1d+oUz5cnhHMk8TpsOobrRuY4pQW5Csg7l8t2SHRzJPE7sgAj6XBSFWfk1kCuzJ2sPdy+7my2Ht9A8sDkPn/9wpatmiIiIyNkJDPYnPDrM22E0CUqQmwjnHB88t5ZD6dkA7Nl4COcc/UZVvqnIqZ5d8yxbDm8BIDs/m9nfzGb0uaM1kiwiIiI+QePzTcTBn7JLkuOTtqzeV6O2dhzZUeY4Oz+bfTk1a0tERJq2jIwMJk+eTGxsLIMGDWLs2LFs2rSJ1NRURo8eTY8ePYiLi+Pxxx8v2Tp64cKF+Pn5kZKSUtJOnz592LFjB4mJicTHx9OpUyciIiKIj48nPj6eHTt2EBMTQ9++fenXrx8XXXQRO3fuLDk/LS2N8ePHExcXR2xsLDNmzCAvr/imuKSkJMaN0yelTYkS5Caieatg/ALKTqdo0S6kRm2duolI11Zd6dyyc01DExGRRmLTygxeffA/vHDH57z64H/YtDLjrNpzzjFx4kRGjhzJ1q1bWb16NX/84x/Zu3cvV155JTNnzmTjxo2sXbuWFStW8OKLL5acGx0dzRNPPFGuzZUrV5KcnMysWbO47rrrSE5OJjk5uWSDkuXLl5OSksLIkSOZPXt2SRxXXXUVEyZMYPPmzWzatImsrCweeuihs3p90ngpQW4iQsICGTaxG37+xUlyy/AQhozrUqO2bu93O3f2v5NebXtxeczlvDDmhRrNZRY5KSc/h8O5h70dhoicwaaVGSxfvIGsgycAyDp4guWLN5xVkrx8+XICAwO54447Ssr69+/Ppk2bGD58OJdccgkAoaGhPP/88zz55JMl9caNG0dqaiobN26s0bWHDh3Knj17APj8888JCQlh6tSpAPj7+/OXv/yFv//97+Tk5JypGfFRmoPchPQfcy5xg9tz7GAuEZ1a4OdXs6Q2wC+AafHTmBZffvcjkep6+YeXeWntS5woPMGYTmN4csSTBPtrIxGRhubr97dSkFdUpqwgr4iv399K98TIGrW5bt06Bg0aVK48NTW1XHlsbCxZWVkcPXoUAD8/P+6//37mzJnDq6++Wu1rf/zxx0yYMOG012vZsiWdOnViy5Yt1W5bGj+NIDcxoS2DaB/TssbJsUht2nxoM8+ueZbcwlwcjs92fcabG970dlgiUoGTI8dVLa8PN9xwA9988w3bt2+v8jmjRo0iKiqKpUuXcv3119dhdNKYKUEWEa85uRpKZWUi4n1hbSv+ZOd05VXRu3dvVq9eXa78vPPOK1e+bds2wsLCaNmyZUlZQEAA9957L3Pnzq3yNZcvX87OnTuJj4/n0UcfPe31jh49yq5du+jWrVt1XpL4CCXIIuI1gyMHl5tOcUHUBV6KRkTOZOj4WAKCyqYNAUF+DB1f8+3jR48ezYkTJ5g3b15JWUpKCj169ODf//43n332GQDHjx/nnnvu4f777y/XxpQpU/jss8/IzMys8nUDAgJ45plnWLRoEQcPHmTMmDHk5OSwaNEiAAoLC7n33nuZMmUKoaFawrQpUoIsIl4T3iycF8a8wMBzBtKtdTdmDpnJJTGXeDssEalA98RIRt3Ys2TEOKxtMKNu7Fnj+ccAZsY///lPPvvsM2JjY+nduzcPPPAAkZGRvP/++8yePZsePXrQt29fBg8ezN13312ujaCgIO655x727avecqMdOnTg+uuv54UXXiiJ4+233yYuLo7u3bsTEhLCnDlzSuovW7aM6Ojokq+vv/66xq9bGj47uaagr0lISHCrVq3ydhgiIlVmZqudcwnejqOm1O82PuvXr6dXr17eDsNn6PvZ+Jyu39UIsoiIiIhIKUqQRURERERKUYIsIiIiIlKKEmQRERERkVKUIIuINDFm9ncz22dm6yqpN9jMCszs6vqKTUSkIVCCLCLS9CwELjtTBTPzB+YC/6qPgEREGhIlyCIiTYxz7kvgYCXVpgPvAtVbXFakmp544gl69+5Nv379iI+PZ+XKleTn5zNz5kzi4uIYOHAgQ4cOZenSpQDExMSwf//+kvOTkpIYN24cAFOnTiU+Pr7kKyYmhvbt23vldUnjFuDtAKTuHMrIZsvqfTQLC6R7YiRBIfpxi0jlzCwKmAiMAgZXUvd24HaATp061X1wPu7YwVw2rswgMMifHudHEtI80NshlbFk2xKeXfMsGdkZRDaPZMbAGVzR9Yoat/f111/z0UcfsWbNGoKDg9m/fz95eXk88sgjpKens27dOoKDg9m7dy9ffPFFpe0tWLCg5HFRUREjR47klltuqXF80nQpY/JR+3Ye5R9PraEwvwiA9SvSufoPCZifeTkyEWkEngH+4JwrMjtzn+GcmwfMg+KNQuo+NN91dP9x3przHSdyCgBISUpj8sNDCAz293JkxZZsW8JjKx4jtzAXgPTsdB5b8RhAjZPk9PR0wsPDCQ4u3p0vPDycnJwc5s+fz/bt20vK27dvz7XXXluttufMmUNERAS33XZbjWKTpk0Jso9a9+WekuQYYN/OY/y0+TBRPdp4MSoRaSQSgDc8yXE4MNbMCpxz73k1Kh+3fkV6SXIMcDTzONtTMuk+uOZbOdemZ9c8W5Icn5RbmMuza56tcYJ8ySWXMGvWLLp3787FF1/MddddR5s2bejUqRMtW7Y87XmjRo3C37/4Pw5ZWVn07NmzzPPffvstL7/8MmvWrKlRXCKag+yj/PzL/2j9/DV6LCKVc851cc7FOOdigHeAO5Uc172K+mg/v4bzZzojO6Na5VURFhbG6tWrmTdvHhEREVx33XUkJSVVet7y5ctJTk4mOTmZl19+ucxzWVlZ3HTTTbzyyiu0bdu2xrFJ06YRZB/Vb2Q0m7/NIC+3EICo7q2JjG3l5ahEpCEws9eBkUC4maUBjwKBAM65v3kxtCat17COrPtiDzlH8wBoFxVGTL92Xo7qZ5HNI0nPTq+w/Gz4+/szcuRIRo4cSd++fXnppZfYtWsXR48ePeMo8ulMnz6d8ePHM2bMmLOKS5o2Jcg+qm3H5tzw2PlsS86kWYsgusSHU9lcQhFpGpxz11ej7pQ6DEVKCWsTzPX/N5Gt3+8jIMifrgMiCAhsGPOPAWYMnFFmDjJAiH8IMwbOqHGbGzduxM/Pj7i4OACSk5Pp0aMHAwYMYMaMGbz00ksEBQWRmZlJUlIS11xzzRnbe+edd1i7di3ffPNNjWMSASXIPq1562D6joz2dhgiIlJFIWGB9L4wytthVOjkPOPaXMUiKyuL6dOnc/jwYQICAujWrRvz5s2jZcuWPPzww5x33nmEhITQvHlzZs2aVWl7Dz30EDk5OQwZMqRM+ddff02zZs1qHKc0Peacb950nJCQ4FatWuXtMEREqszMVjvnErwdR02p32181q9fT69evbwdhs/Q97PxOV2/23Bm/4uIiIiINABKkEVERERESlGCLCIi0oT56lTL+qbvo29RgiwiItJEhYSEcODAASV3Z8k5x4EDBwgJCfF2KFJLtIqFiIhIExUdHU1aWhqZmZneDqXRCwkJITpaK0f5CiXIIiIiTVRgYCBdunTxdhgiDY6mWIiIiIiIlKIEWURERESkFCXIIiIiIiKl+OxOemaWCez0dhyNSDiw39tBiE/Se6vqOjvnIrwdRE2p360W/V5IXdF7q3oq7Hd9NkGW6jGzVY15i1tpuPTeEilPvxdSV/Teqh2aYiEiIiIiUooSZBERERGRUpQgy0nzvB2A+Cy9t0TK0++F1BW9t2qB5iCLiIiIiJSiEWQRERERkVKUIIuIiIiIlKIEWURERESkFCXIjYyZZVWhzkVm9vUpZQFmttfMOprZNWaWamZFZnbGtRLNbKSZHTGzZDPbYGZPnfL8BDNLMbP1ZvaDmU0o9dxCM7u6mi9RvMDMCj0/43Vm9qGZtT5D3W1m1uOUsmfM7A9m1s7MlptZlpk9X4Xr7vC8b1LM7Asz61zquWgze9/MNpvZVjN71syCPM+NNLOPzuIli1SJ+lypC+pzGz4lyL7pKyC69BsfuBhIdc79BKwDrgK+rGp7zrl4YAAwzsyGA5hZf+ApYLxzrhdwJfCUmfWrnZch9ei4cy7eOdcHOAjcdYa6bwCTTx6YmR9wtac8F3gEuK8a1x7lnOsHJAEPe9o04B/Ae865OKA7EAY8UY12ReqL+lypLvW5DZwSZB/knCsC3qLUL5Tn8eue59c75zbWoN3jQDIQ5Sm6D5jjnNvueX478Efg9zUOXhqCr/n5Z1yR14HrSh2PAHY653Y657Kdc/+muNM+m+uOBnKdcwsAnHOFwG+B/2NmoTVoW6TOqM+Vs6Q+twFSguy7XsfTWZtZMDAWePdsGjSzNkAcP4+C9AZWn1JtladcGiEz8wfGAB+cro5z7gegyDOaBaUSgbN0GfCe53G595Zz7iiwC+hWC9cSqW3qc6Xa1Oc2XEqQfZRzbhUQ5pm3dDmw0jl3sIbNXWhma4E9wCfOuYzailMajGZmlgxkAO2BTyup/zow2cwCgAnA22dx7eVmtofi92ltdPoi9U59rlST+twGTgmybzs5onG2/9v8yjnXn+L/Yf7SzOI95T8Cg06pOwhIPYtriXcc98x57AwYZ54PB8Vz366leJ5linNu71lce5TnusnAf3vKyr23zKwl0AnYchbXEqlL6nOlqtTnNnBKkH3b68BNFM8tev9sG/PMd3sS+IOn6CngATOLAfD8+yDw9NleS7zDOZcD3APc6xmpOF29rcB+it8PZz0C4ZwrAH4D3GJmbYFlQKiZ3QIlH0M+DSz0xCjSEKnPlWpRn9twKUFufELNLK3U1+9OV9E5tx7IBj53zmWfLDeziWaWBgwFlpjZJ9W4/t+AEWYW45xLprjj/tDMNgAfAvd7yqWRcs59D6QA11dS9XWgJ8V3Ppcwsx3An4EpnvfoeVW8brqnzbuccw6YCFxjZpuBTRTfhPJgNV6KSG1Qnyt1Sn1uw2TF3xMREREREQGNIIuIiIiIlHHa+S7SOJjZQ8A1pxS/7Zyr1uLeZnYpMPeU4u3OuYlnE580XmbWF3jtlOITzrnEGrS1Egg+pfhmz/JFIo2G+lypK+pzGxZNsRARERERKUVTLERERERESlGCLCIiIiJSihJk8Vlm9pva3EPezGp1uRszW2hmV3seJ5nZRjNLMbMNZva8mbWuzeuJiNQl9bniS5Qgiy/7DVBhZ+1ZBL26qt1Zn2nh9wrc6JzrB/QDTlALGw2IiNSj36A+V3yEEmTxCWbW3MyWmNlaM1tnZo8CHSnec365p06WmT1tZmuBoWa2w8zCPc8lmFmS53GYmS0wsx88owuTzOxJoJmZJZvZYjOLMbN1pa5/n5k95nmcZGbPmNkqYIaZDTKzL8xstZl9YmYdzvRanHN5wP1AJzPrX+vfLBGRs6Q+V3ydlnkTX3EZ8JNz7goAM2sFTAVGOef2e+o0B1Y65+711DldW48AR5xzfT312jjn3jWzu51z8Z6ymEriCXLOJZhZIPAFMN45l2lm1wFPAP/nTCc75wo9f1R6AmsruZaISH1Tnys+TQmy+IofgKfNbC7wkXPuqwo640Lg3Sq0dTEw+eSBc+5QDeJ50/NvD6AP8KknHn8gvYptnPaviYiIl6nPFZ+mBFl8gnNuk5kNBMYCs81sWQXVcp1zhaWOC/h5mlFINS9Z+tyKzs/2/GtAqnNuaHUa98zX6wusr2ZcIiJ1Tn2u+DrNQRafYGYdgRzn3P8AfwIGAseAFmc4bQcwyPN4UqnyT4G7SrXdxvMw3/PxHcBe4Bwza2dmwcC401xjIxBhZkM9bQWaWe9KXksg8Edgt3Mu5Ux1RUS8QX2u+DolyOIr+gLfmlky8CgwG5gHfHzyhpEK/DfwrOfGjtKjHLOBNp4bT9YCozzl84AUM1vsnMsHZgHfUty5b6joAp6bP64G5nraSgaGnSaexWaWAqyjeO7e+EpftYiId6jPFZ+mraZFRERERErRCLKIiIiISClKkEVERERESlGCLCIiIiJSihJkEREREZFSlCCLiIiIiJSiBFlEREREpBQlyCIiIiIipfx/DIWtc8zkhhIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## compute categorical scatter plot for the left and right V1s for gray matter volume and cortical thickness\n",
    "# create a subplot figure with 1 row and 2 columns\n",
    "fig, axes = plt.subplots(1,2,figsize=(10,5),sharex=True)\n",
    "\n",
    "# create a strip plot for each measure\n",
    "sns.stripplot(x='structureID',y='gray_matter_volume_mm^3',data=v1,hue='classID',palette=groups_colors,ax=axes[0],size=5,legend=False)\n",
    "sns.stripplot(x='structureID',y='thickness',data=v1,hue='classID',palette=groups_colors,ax=axes[1],size=5)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b049ab78-814d-47bf-bf08-cdc42ef3bbaf",
   "metadata": {},
   "source": [
    "#### behavioral measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "48aaa3e6-0e4c-4322-949d-2e91ba8568cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## compute categorical scatter plot for the left and right V1s for gray matter volume and cortical thickness\n",
    "# create a subplot figure with 1 row and 2 columns\n",
    "fig, axes = plt.subplots(1,2,figsize=(10,5),sharex=True)\n",
    "\n",
    "# create a strip plot for each measure\n",
    "sns.stripplot(x='classID',y='vr2dr_totalraw',data=v1,hue='classID',palette=groups_colors,ax=axes[0],size=5,legend=False)\n",
    "sns.stripplot(x='classID',y='voc_totalraw',data=v1,hue='classID',palette=groups_colors,ax=axes[1],size=5)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa56c93c-d0f2-47ee-9832-2c03a590ffe6",
   "metadata": {},
   "source": [
    "#### group scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "e9aa093f-1643-4e5a-9a96-486edfb3b0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## compute categorical scatter plot for group across behavioral and cortical measures\n",
    "# create a subplot figure with 1 row and 3 columns\n",
    "fig, axes = plt.subplots(1,3,figsize=(15,5),sharex=True)\n",
    "\n",
    "# create a strip plot for each measure\n",
    "sns.stripplot(x='classID',y='gray_matter_volume_mm^3',data=v1,hue='classID',palette=groups_colors,ax=axes[0],size=5,legend=False)\n",
    "sns.stripplot(x='classID',y='thickness',data=v1,hue='classID',palette=groups_colors,ax=axes[1],size=5,legend=False)\n",
    "sns.stripplot(x='classID',y='vr2dr_totalraw',data=v1,hue='classID',palette=groups_colors,ax=axes[2],size=5)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4055ce73-f104-4376-a678-24435639877e",
   "metadata": {},
   "source": [
    "It looks like there's a difference in the behavioral measures between our groups! Let's investigate a bit more!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c7e4277c-16fe-46e8-98be-041d523bc02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='vr2dr_totalraw', ylabel='Count'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate a histogram for each group for the v42dr_totalraw measure\n",
    "sns.histplot(x='vr2dr_totalraw',hue='classID',data=v1_mean,palette=groups_colors,bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15abf809-ff5c-451e-8226-caff4ac68fc0",
   "metadata": {},
   "source": [
    "Let's quantify the differences in our measures of interest. Because we have unequal sample sizes, we can use a bootstrapping procedure and compute ttests from random samples of data from each group. We will be using our pre-defined functions from above, specifically 'bootstrap_analysis_groups' to perform the bootstrapping analysis and 'plot_histogram' to plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "44220c9a-d0e7-4eb7-b188-f57c13c2279c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define our input variables\n",
    "df = v1_mean\n",
    "group_1 = 'CONTROL'\n",
    "group_2 = 'SCHZ'\n",
    "measures = ['gray_matter_volume_mm^3','surface_area_mm^2','vr2dr_totalraw']\n",
    "compare_measure = 'ttest'\n",
    "iterations = 1000\n",
    "\n",
    "# perform our bootstrapping analysis\n",
    "hc_schz_correlation = bootstrap_analysis_groups(df,group_1,group_2,measures,sample_size=40,compare_measure=compare_measure,iterations=iterations)\n",
    "\n",
    "# visualize the results\n",
    "rows, columns = set_up_figure(measures)\n",
    "fig, axes = plt.subplots(rows,columns,figsize=(15,5*(rows)),sharex=False)\n",
    "\n",
    "for i in range(0,len(measures)):        \n",
    "    row_index = int(np.floor(i / 3))\n",
    "    col_index = int(i % 3)\n",
    "    if len(measures) > 3:\n",
    "        plot_histogram(hc_schz_correlation,measures[i]+'_p-value','p-value',ax=axes[row_index][col_index])\n",
    "        axes[row_index][col_index].title.set_text(measures[i])\n",
    "        axes[row_index][col_index].set(xlabel='p-value')\n",
    "    else:\n",
    "        plot_histogram(hc_schz_correlation,measures[i]+'_p-value','p-value',ax=axes[col_index])\n",
    "        axes[col_index].title.set_text(measures[i])\n",
    "        axes[col_index].set(xlabel='p-value')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8f6acc-7e04-4f79-a47c-947bbec236c4",
   "metadata": {},
   "source": [
    "As we can see from the data, it appears there's a statistically significant difference between our groups in the v42dr_totalraw measure!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca786c01-d587-4491-8153-42045cf36965",
   "metadata": {},
   "source": [
    "Next, let's see if we can identify any relationships between measures within each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "5e38addb-6008-4b90-9e0c-1cd38327e187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define our input variables\n",
    "df = v1_mean\n",
    "group_1 = 'CONTROL'\n",
    "group_2 = 'SCHZ'\n",
    "measures = measures\n",
    "compare_measure = 'corr'\n",
    "iterations = 1000\n",
    "\n",
    "# perform our bootstrapping analysis\n",
    "within_group_comparison = bootstrap_analysis_within_groups(df,group_1,group_2,measures,sample_size=40,compare_measure=compare_measure,iterations=iterations)\n",
    "new_measures = [ f for f in within_group_comparison.keys().tolist() if f not in ['classID','iterations'] ]\n",
    "\n",
    "# visualize the results\n",
    "rows, columns = set_up_figure(new_measures)\n",
    "fig, axes = plt.subplots(rows,columns,figsize=(18,5*(rows)),sharex=False)\n",
    "\n",
    "for i in range(0,len(new_measures)):\n",
    "    row_index = int(np.floor(i / 3))\n",
    "    col_index = int(i % 3)\n",
    "    if len(new_measures) > 3:\n",
    "        plot_histogram_groups(within_group_comparison,new_measures[i],palette=groups_colors,ax=axes[row_index][col_index])\n",
    "        axes[row_index][col_index].title.set_text(new_measures[i].replace('_corr',''))\n",
    "        axes[row_index][col_index].set(xlabel='pearsons r')\n",
    "    else:\n",
    "        plot_histogram_groups(within_group_comparison,new_measures[i],palette=groups_colors,ax=axes[col_index])\n",
    "        axes[col_index].title.set_text(new_measures[i].replace('_corr',''))\n",
    "        axes[col_index].set(xlabel='pearsons r')        \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "29f09ccb-4040-451b-8323-f77ea37c3f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gray_matter_volume_mm^3', ylabel='vr2dr_totalraw'>"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a linear regression plot for the overall data between variables of interest\n",
    "sns.regplot(x='gray_matter_volume_mm^3',y='vr2dr_totalraw',data=v1_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "2e9d73d0-eb78-4a76-af08-6c427590784a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6aa06e3160>"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a linear regression plot for the each group data between variables of interest\n",
    "sns.regplot(x='gray_matter_volume_mm^3',y='vr2dr_totalraw',data=v1_mean.loc[v1_mean['classID'] == 'CONTROL'],color=groups_colors['CONTROL'],label='control')\n",
    "sns.regplot(x='gray_matter_volume_mm^3',y='vr2dr_totalraw',data=v1_mean.loc[v1_mean['classID'] == 'SCHZ'],color=groups_colors['SCHZ'],label='schizophrenia')\n",
    "plt.legend(bbox_to_anchor=(1.01,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadb2f4c-1ade-498f-a23e-95f1f6ac840a",
   "metadata": {},
   "source": [
    "## You've now completed your first set of analyses on brainlife.io using the jupyter notebooks!"
   ]
  }
 ],
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