{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "316b12f5",
   "metadata": {},
   "source": [
    "# Test-retest reliability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df68aae-5365-4fe9-a69e-e9589a2338d0",
   "metadata": {},
   "source": [
    "In this notebook, the goal is to assess the test-retest reliability of each framework. For this, we used 289 subjects from the OASIS-3 dataset. The protocol followed in the OASIS-3 dataset, implied that at least two MRI images per subject were acquired in the same day within a 60-minute period. The two images per subject were pre-processed by both frameworks and the test-retest reliability within framework was assessed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10c64c57",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fmachado/anaconda3/envs/fs-cat12/lib/python3.9/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.1, the latest is 0.5.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "import os\n",
    "import math\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from settings import RESOURCE_DIR\n",
    "from utils import get_regression_metrics, bland_altman_plot\n",
    "\n",
    "# Tables format\n",
    "pd.set_option('display.float_format', lambda x: f\"{x: 0.2e}\")\n",
    "\n",
    "# Visualization format\n",
    "font = {'size'   : 18}\n",
    "matplotlib.rc('font', **font)\n",
    "sns.set_theme(style=\"whitegrid\", font_scale=2)\n",
    "\n",
    "# Global variables\n",
    "var_compare = \"software\"\n",
    "template_name = \"a2009s\"\n",
    "metric_analysis = \"corticalThicknessAverage\"\n",
    "path_csv_cortical_data = \"cortical_thicknesss_test_restest.csv\"\n",
    "\n",
    "pipeline1 = \"ACPC_CAT12\"\n",
    "pipeline2 = \"FREESURFER\"\n",
    "\n",
    "color_pallete = {pipeline1: \"#365162\", pipeline2: \"#9C5315\"}\n",
    "\n",
    "var_analyse = [\"r_square\", \"slope\", \"intercept\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7623873-4eae-4e98-bdda-ed4b936338ec",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "#@title\n",
    "df_names_rois = pd.read_csv(os.path.join(RESOURCE_DIR, \"template\", f\"{template_name}_atlas_labels.csv\"))\n",
    "df_areas = pd.read_csv(os.path.join(RESOURCE_DIR, \"template\", f\"{template_name}_rois_areas.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1eba92b-bfaf-4430-ac8f-ed480426d913",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd463e4c-6889-40a1-ad40-ef20492e394e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>repositoryName</th>\n",
       "      <th>software</th>\n",
       "      <th>subjectID</th>\n",
       "      <th>sessionID</th>\n",
       "      <th>run</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>software.1</th>\n",
       "      <th>roiName</th>\n",
       "      <th>corticalThicknessAverage</th>\n",
       "      <th>template</th>\n",
       "      <th>cjv_run1</th>\n",
       "      <th>cnr_run1</th>\n",
       "      <th>snr_total_run1</th>\n",
       "      <th>cjv_run2</th>\n",
       "      <th>cnr_run2</th>\n",
       "      <th>snr_total_run2</th>\n",
       "      <th>cjv_mean</th>\n",
       "      <th>cnr_mean</th>\n",
       "      <th>snr_total_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</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>OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001_ses-d0129_run-01_T1w.nii.gz</th>\n",
       "      <td>OASIS3</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>sub-OAS30001</td>\n",
       "      <td>ses-d0129</td>\n",
       "      <td>1</td>\n",
       "      <td>6.55e+01</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>lG_Ins_lg_and_S_cent_ins</td>\n",
       "      <td>2.97e+00</td>\n",
       "      <td>a2009s</td>\n",
       "      <td>4.01e-01</td>\n",
       "      <td>3.41e+00</td>\n",
       "      <td>1.04e+01</td>\n",
       "      <td>3.93e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "      <td>3.97e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001_ses-d0129_run-01_T1w.nii.gz</th>\n",
       "      <td>OASIS3</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>sub-OAS30001</td>\n",
       "      <td>ses-d0129</td>\n",
       "      <td>1</td>\n",
       "      <td>6.55e+01</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>lG_and_S_cingul-Ant</td>\n",
       "      <td>2.49e+00</td>\n",
       "      <td>a2009s</td>\n",
       "      <td>4.01e-01</td>\n",
       "      <td>3.41e+00</td>\n",
       "      <td>1.04e+01</td>\n",
       "      <td>3.93e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "      <td>3.97e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001_ses-d0129_run-01_T1w.nii.gz</th>\n",
       "      <td>OASIS3</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>sub-OAS30001</td>\n",
       "      <td>ses-d0129</td>\n",
       "      <td>1</td>\n",
       "      <td>6.55e+01</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>lG_and_S_cingul-Mid-Ant</td>\n",
       "      <td>2.57e+00</td>\n",
       "      <td>a2009s</td>\n",
       "      <td>4.01e-01</td>\n",
       "      <td>3.41e+00</td>\n",
       "      <td>1.04e+01</td>\n",
       "      <td>3.93e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "      <td>3.97e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001_ses-d0129_run-01_T1w.nii.gz</th>\n",
       "      <td>OASIS3</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>sub-OAS30001</td>\n",
       "      <td>ses-d0129</td>\n",
       "      <td>1</td>\n",
       "      <td>6.55e+01</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "      <td>lG_and_S_cingul-Mid-Post</td>\n",
       "      <td>2.39e+00</td>\n",
       "      <td>a2009s</td>\n",
       "      <td>4.01e-01</td>\n",
       "      <td>3.41e+00</td>\n",
       "      <td>1.04e+01</td>\n",
       "      <td>3.93e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "      <td>3.97e-01</td>\n",
       "      <td>3.42e+00</td>\n",
       "      <td>1.03e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   repositoryName    software  \\\n",
       "path                                                                            \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...         OASIS3  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...         OASIS3  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...         OASIS3  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...         OASIS3  ACPC_CAT12   \n",
       "\n",
       "                                                       subjectID  sessionID  \\\n",
       "path                                                                          \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  sub-OAS30001  ses-d0129   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  sub-OAS30001  ses-d0129   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  sub-OAS30001  ses-d0129   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  sub-OAS30001  ses-d0129   \n",
       "\n",
       "                                                    run       age  gender  \\\n",
       "path                                                                        \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...    1  6.55e+01  FEMALE   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...    1  6.55e+01  FEMALE   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...    1  6.55e+01  FEMALE   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...    1  6.55e+01  FEMALE   \n",
       "\n",
       "                                                    software.1  \\\n",
       "path                                                             \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  ACPC_CAT12   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  ACPC_CAT12   \n",
       "\n",
       "                                                                     roiName  \\\n",
       "path                                                                           \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  lG_Ins_lg_and_S_cent_ins   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...       lG_and_S_cingul-Ant   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...   lG_and_S_cingul-Mid-Ant   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  lG_and_S_cingul-Mid-Post   \n",
       "\n",
       "                                                    corticalThicknessAverage  \\\n",
       "path                                                                           \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...                  2.97e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...                  2.49e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...                  2.57e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...                  2.39e+00   \n",
       "\n",
       "                                                   template  cjv_run1  \\\n",
       "path                                                                    \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...   a2009s  4.01e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...   a2009s  4.01e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...   a2009s  4.01e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...   a2009s  4.01e-01   \n",
       "\n",
       "                                                    cnr_run1  snr_total_run1  \\\n",
       "path                                                                           \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.41e+00        1.04e+01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.41e+00        1.04e+01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.41e+00        1.04e+01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.41e+00        1.04e+01   \n",
       "\n",
       "                                                    cjv_run2  cnr_run2  \\\n",
       "path                                                                     \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.93e-01  3.42e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.93e-01  3.42e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.93e-01  3.42e+00   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.93e-01  3.42e+00   \n",
       "\n",
       "                                                    snr_total_run2  cjv_mean  \\\n",
       "path                                                                           \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...        1.03e+01  3.97e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...        1.03e+01  3.97e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...        1.03e+01  3.97e-01   \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...        1.03e+01  3.97e-01   \n",
       "\n",
       "                                                    cnr_mean  snr_total_mean  \n",
       "path                                                                          \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.42e+00        1.03e+01  \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.42e+00        1.03e+01  \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.42e+00        1.03e+01  \n",
       "OASIS3/sub-OAS30001/ses-d0129/anat/sub-OAS30001...  3.42e+00        1.03e+01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_software_raw = pd.read_csv(os.path.join(RESOURCE_DIR, \"data\", path_csv_cortical_data), low_memory=False)\n",
    "df_software_raw.set_index(\"path\", inplace=True)\n",
    "# Get only the template ROIs\n",
    "df_software_raw = df_software_raw.loc[df_software_raw.roiName.str[1:].isin(df_names_rois.label.to_list())]\n",
    "\n",
    "df_software_raw.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77956868",
   "metadata": {},
   "source": [
    "### Check preprocessing problems\n",
    "\n",
    "Some images were only ran by one of the softwares, in the code below images with preprocessing problems are discoved, and those subjects/session/run are removed from the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eb7fa96",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 images were only processed by: FREESURFER\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "roi_name = df_software_raw.loc[(~df_software_raw[metric_analysis].isna()) & (df_software_raw.template == template_name)].roiName.to_list()[1]\n",
    "\n",
    "df_group = df_software_raw[df_software_raw[\"roiName\"] == roi_name].copy()\n",
    "df_group = df_group[df_group.template == template_name].groupby(by=['path']).apply(lambda x: list(x[\"software\"]))\n",
    "\n",
    "# Get the images run by only one\n",
    "df_problems_running = df_group[df_group.apply(len) == 1].to_frame()\n",
    "\n",
    "\n",
    "software_problems = [item for sublist in df_problems_running[0].to_list() for item in sublist]\n",
    "\n",
    "\n",
    "print(\"\\n\".join([f\"{number_problems} images were only processed by: {soft_name}\" for soft_name, number_problems in Counter(software_problems).items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c99b59-79e3-4057-b732-368713d5e9d8",
   "metadata": {},
   "source": [
    "#### Exclude the subjects only run by only one software"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52857b3b-a47d-484f-977a-04f0c03e3339",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">sub-OAS30001</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">ses-d0129</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">a2009s</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">ACPC_CAT12</th>\n",
       "      <th>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>2.97e+00</td>\n",
       "      <td>2.85e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>2.49e+00</td>\n",
       "      <td>2.41e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <td>2.57e+00</td>\n",
       "      <td>2.62e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Post</th>\n",
       "      <td>2.39e+00</td>\n",
       "      <td>2.43e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    corticalThicknessAverage  \\\n",
       "run                                                                                        1   \n",
       "subjectID    sessionID template software   roiName                                             \n",
       "sub-OAS30001 ses-d0129 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins                 2.97e+00   \n",
       "                                           lG_and_S_cingul-Ant                      2.49e+00   \n",
       "                                           lG_and_S_cingul-Mid-Ant                  2.57e+00   \n",
       "                                           lG_and_S_cingul-Mid-Post                 2.39e+00   \n",
       "\n",
       "                                                                               \n",
       "run                                                                         2  \n",
       "subjectID    sessionID template software   roiName                             \n",
       "sub-OAS30001 ses-d0129 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins  2.85e+00  \n",
       "                                           lG_and_S_cingul-Ant       2.41e+00  \n",
       "                                           lG_and_S_cingul-Mid-Ant   2.62e+00  \n",
       "                                           lG_and_S_cingul-Mid-Post  2.43e+00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_software_raw = df_software_raw[~df_software_raw.index.isin(df_problems_running.index)]\n",
    "df_software = df_software_raw.reset_index().pivot(['subjectID', 'sessionID', 'template', var_compare,'roiName'], ['run'], [metric_analysis])\n",
    "df_software.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599caee7-9943-4ca8-a2c2-e43b2b8f2f00",
   "metadata": {},
   "source": [
    "### Search subjects with at least one ROI NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1e3f779-044e-4922-b7a5-1d01feb93d29",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">corticalThicknessAverage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subjectID</th>\n",
       "      <th>sessionID</th>\n",
       "      <th>template</th>\n",
       "      <th>software</th>\n",
       "      <th>roiName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sub-OAS30896</th>\n",
       "      <th>ses-d0439</th>\n",
       "      <th>a2009s</th>\n",
       "      <th>FREESURFER</th>\n",
       "      <th>lS_interm_prim-Jensen</th>\n",
       "      <td>2.13e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30986</th>\n",
       "      <th>ses-d1188</th>\n",
       "      <th>a2009s</th>\n",
       "      <th>FREESURFER</th>\n",
       "      <th>rLat_Fis-ant-Vertical</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.27e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">sub-OAS31073</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">ses-d0196</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">a2009s</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">ACPC_CAT12</th>\n",
       "      <th>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>3.09e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>2.35e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <td>2.51e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">FREESURFER</th>\n",
       "      <th>rS_suborbital</th>\n",
       "      <td>1.88e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_subparietal</th>\n",
       "      <td>2.33e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_inf</th>\n",
       "      <td>2.40e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_sup</th>\n",
       "      <td>2.45e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_transverse</th>\n",
       "      <td>2.26e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    corticalThicknessAverage  \\\n",
       "run                                                                                        1   \n",
       "subjectID    sessionID template software   roiName                                             \n",
       "sub-OAS30896 ses-d0439 a2009s   FREESURFER lS_interm_prim-Jensen                    2.13e+00   \n",
       "sub-OAS30986 ses-d1188 a2009s   FREESURFER rLat_Fis-ant-Vertical                         NaN   \n",
       "sub-OAS31073 ses-d0196 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins                 3.09e+00   \n",
       "                                           lG_and_S_cingul-Ant                      2.35e+00   \n",
       "                                           lG_and_S_cingul-Mid-Ant                  2.51e+00   \n",
       "...                                                                                      ...   \n",
       "                                FREESURFER rS_suborbital                            1.88e+00   \n",
       "                                           rS_subparietal                           2.33e+00   \n",
       "                                           rS_temporal_inf                          2.40e+00   \n",
       "                                           rS_temporal_sup                          2.45e+00   \n",
       "                                           rS_temporal_transverse                   2.26e+00   \n",
       "\n",
       "                                                                               \n",
       "run                                                                         2  \n",
       "subjectID    sessionID template software   roiName                             \n",
       "sub-OAS30896 ses-d0439 a2009s   FREESURFER lS_interm_prim-Jensen          NaN  \n",
       "sub-OAS30986 ses-d1188 a2009s   FREESURFER rLat_Fis-ant-Vertical     2.27e+00  \n",
       "sub-OAS31073 ses-d0196 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins       NaN  \n",
       "                                           lG_and_S_cingul-Ant            NaN  \n",
       "                                           lG_and_S_cingul-Mid-Ant        NaN  \n",
       "...                                                                       ...  \n",
       "                                FREESURFER rS_suborbital                  NaN  \n",
       "                                           rS_subparietal                 NaN  \n",
       "                                           rS_temporal_inf                NaN  \n",
       "                                           rS_temporal_sup                NaN  \n",
       "                                           rS_temporal_transverse         NaN  \n",
       "\n",
       "[298 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_rois_nan = df_software.loc[df_software[[('corticalThicknessAverage', 1), ('corticalThicknessAverage', 2)]].isna().sum(axis=1)>0]\n",
    "df_rois_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b391b2f3-6f8c-403e-94a7-8f5082ef502b",
   "metadata": {},
   "source": [
    "#### Exclude subjects with at least one ROI NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73a06b3b-186d-4681-bcc1-71afc6cda777",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">corticalThicknessAverage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subjectID</th>\n",
       "      <th>sessionID</th>\n",
       "      <th>template</th>\n",
       "      <th>software</th>\n",
       "      <th>roiName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">sub-OAS30001</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">ses-d0129</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">a2009s</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">ACPC_CAT12</th>\n",
       "      <th>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>2.97e+00</td>\n",
       "      <td>2.85e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>2.49e+00</td>\n",
       "      <td>2.41e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <td>2.57e+00</td>\n",
       "      <td>2.62e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Post</th>\n",
       "      <td>2.39e+00</td>\n",
       "      <td>2.43e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_frontomargin</th>\n",
       "      <td>2.11e+00</td>\n",
       "      <td>2.09e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">sub-OAS31172</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">ses-d0407</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">a2009s</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">FREESURFER</th>\n",
       "      <th>rS_suborbital</th>\n",
       "      <td>2.16e+00</td>\n",
       "      <td>1.90e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_subparietal</th>\n",
       "      <td>2.09e+00</td>\n",
       "      <td>2.03e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_inf</th>\n",
       "      <td>2.31e+00</td>\n",
       "      <td>2.29e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_sup</th>\n",
       "      <td>2.32e+00</td>\n",
       "      <td>2.32e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_transverse</th>\n",
       "      <td>1.80e+00</td>\n",
       "      <td>2.28e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87616 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    corticalThicknessAverage  \\\n",
       "run                                                                                        1   \n",
       "subjectID    sessionID template software   roiName                                             \n",
       "sub-OAS30001 ses-d0129 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins                 2.97e+00   \n",
       "                                           lG_and_S_cingul-Ant                      2.49e+00   \n",
       "                                           lG_and_S_cingul-Mid-Ant                  2.57e+00   \n",
       "                                           lG_and_S_cingul-Mid-Post                 2.39e+00   \n",
       "                                           lG_and_S_frontomargin                    2.11e+00   \n",
       "...                                                                                      ...   \n",
       "sub-OAS31172 ses-d0407 a2009s   FREESURFER rS_suborbital                            2.16e+00   \n",
       "                                           rS_subparietal                           2.09e+00   \n",
       "                                           rS_temporal_inf                          2.31e+00   \n",
       "                                           rS_temporal_sup                          2.32e+00   \n",
       "                                           rS_temporal_transverse                   1.80e+00   \n",
       "\n",
       "                                                                               \n",
       "run                                                                         2  \n",
       "subjectID    sessionID template software   roiName                             \n",
       "sub-OAS30001 ses-d0129 a2009s   ACPC_CAT12 lG_Ins_lg_and_S_cent_ins  2.85e+00  \n",
       "                                           lG_and_S_cingul-Ant       2.41e+00  \n",
       "                                           lG_and_S_cingul-Mid-Ant   2.62e+00  \n",
       "                                           lG_and_S_cingul-Mid-Post  2.43e+00  \n",
       "                                           lG_and_S_frontomargin     2.09e+00  \n",
       "...                                                                       ...  \n",
       "sub-OAS31172 ses-d0407 a2009s   FREESURFER rS_suborbital             1.90e+00  \n",
       "                                           rS_subparietal            2.03e+00  \n",
       "                                           rS_temporal_inf           2.29e+00  \n",
       "                                           rS_temporal_sup           2.32e+00  \n",
       "                                           rS_temporal_transverse    2.28e+00  \n",
       "\n",
       "[87616 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_software_raw = df_software_raw.loc[~df_software_raw.subjectID.isin(df_rois_nan.index.get_level_values(0).to_list())]\n",
    "df_software = df_software.loc[~df_software.index.get_level_values(0).isin(df_rois_nan.index.get_level_values(0).to_list())]\n",
    "df_software"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ea1c49-fe35-42cc-9673-3cb868951d12",
   "metadata": {},
   "source": [
    "### Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28f90570-71a5-4555-83dd-1f497442ad90",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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",
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       "    }\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>Total of participants</th>\n",
       "      <th>Number males</th>\n",
       "      <th>Mean and standard deviation [years]</th>\n",
       "      <th>Min Age [years]</th>\n",
       "      <th>Max Age [years]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repositoryName</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>OASIS3</th>\n",
       "      <td>592</td>\n",
       "      <td>222</td>\n",
       "      <td>68.94+/- 8.85</td>\n",
       "      <td>45.78</td>\n",
       "      <td>88.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Total of participants  Number males  \\\n",
       "repositoryName                                        \n",
       "OASIS3                            592           222   \n",
       "\n",
       "               Mean and standard deviation [years] Min Age [years]  \\\n",
       "repositoryName                                                       \n",
       "OASIS3                               68.94+/- 8.85           45.78   \n",
       "\n",
       "               Max Age [years]  \n",
       "repositoryName                  \n",
       "OASIS3                   88.86  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_subjects = df_software_raw[[\"repositoryName\", \"subjectID\", \"sessionID\", \"run\", \"age\", \"snr_total_mean\", \"gender\"]].drop_duplicates().copy()\n",
    "\n",
    "\n",
    "groupby_col = [\"repositoryName\"]\n",
    "    \n",
    "df_summary = df_subjects.groupby(groupby_col).apply(lambda x: pd.Series({\"Total of participants\": len(x), \n",
    "                                                                         \"Number males\": (x[\"gender\"]==\"MALE\").sum(),\n",
    "                                                                         \"Mean and standard deviation [years]\": f\"{x['age'].mean(): 0.2f}+/-{x['age'].std(): 0.2f}\",\n",
    "                                                                         \"Min Age [years]\": f\"{x['age'].min(): 0.2f}\",\n",
    "                                                                         \"Max Age [years]\": f\"{x['age'].max(): 0.2f}\"}))\n",
    "\n",
    "\n",
    "df_ct = df_software.reset_index()[[\"subjectID\", var_compare, \"roiName\", \"corticalThicknessAverage\"]].melt(id_vars=[\"subjectID\", var_compare, \"roiName\"])\n",
    "\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35479195-cd9a-482e-900b-264fb2807ca5",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897a50e2-3d5d-4141-8059-0b6e69098c64",
   "metadata": {},
   "source": [
    "### Overall Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26d21e7-75c6-46ab-b2b9-fd943bec8d1c",
   "metadata": {},
   "source": [
    "#### Bland-Altman plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "831ab6d8-9dc0-43d1-83ff-e35b7cbf23ce",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title\n",
    "y_label_txt = \"Difference Run$_1$ - Run$_2$ [mm]\"\n",
    "\n",
    "\n",
    "for var in df_software.index.get_level_values(3).unique():\n",
    "    df_filt = df_software.loc[df_software.index.get_level_values(3) == var]\n",
    "\n",
    "\n",
    "    bland_altman_plot(df_filt.copy().reset_index(),\n",
    "                      ('corticalThicknessAverage', 1), \n",
    "                      ('corticalThicknessAverage', 2),\n",
    "                      var_compare, color_pallete, x_label_reg='Run$_1$ [mm]', y_label_reg='Run$_2$ [mm]',\n",
    "                      y_label_diff=y_label_txt)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dc0622a-c575-4f71-91d0-563451406ea1",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br><h5>ACPC_CAT12</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cortical Thickness Average Mean</th>\n",
       "      <th>Difference Run1 - Run 2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.36e+00</td>\n",
       "      <td>2.36e+00</td>\n",
       "      <td>-1.76e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br><p>One sample t-test to verify whether the means difference is equal to zero</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>-4.23e-01</td>\n",
       "      <td>43807</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>6.72e-01</td>\n",
       "      <td>[-0.0, 0.0]</td>\n",
       "      <td>2.02e-03</td>\n",
       "      <td>0.006</td>\n",
       "      <td>7.07e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence interval (95%) of the means difference: [-0.171;0.171]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<br><h5>FREESURFER</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cortical Thickness Average Mean</th>\n",
       "      <th>Difference Run1 - Run 2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.38e+00</td>\n",
       "      <td>2.38e+00</td>\n",
       "      <td>4.66e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br><p>One sample t-test to verify whether the means difference is equal to zero</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>9.39e+00</td>\n",
       "      <td>43807</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>6.23e-21</td>\n",
       "      <td>[0.0, 0.01]</td>\n",
       "      <td>4.49e-02</td>\n",
       "      <td>7.257e+16</td>\n",
       "      <td>1.00e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence interval (95%) of the means difference: [-0.199;0.208]\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "df_means = []\n",
    "for soft in ['ACPC_CAT12', 'FREESURFER']:\n",
    "    df_soft_filter = df_software.loc[df_software.index.get_level_values(3) == soft]\n",
    "    diff = df_soft_filter[(\"corticalThicknessAverage\", 1)] - df_soft_filter[(\"corticalThicknessAverage\", 2)]\n",
    "    df_means_diff = df_soft_filter[[(\"corticalThicknessAverage\", 1), (\"corticalThicknessAverage\", 2)]].mean().to_frame().T.rename(columns={\"corticalThicknessAverage\": \"Cortical Thickness Average Mean\"})\n",
    "    df_means_diff[\"Difference Run1 - Run 2\"] = diff.mean()\n",
    "    \n",
    "    \n",
    "    test = pg.ttest(diff, 0)\n",
    "    display(HTML(f\"<br><h5>{soft}</h5>\"))\n",
    "    display(HTML(df_means_diff.to_html()))\n",
    "    \n",
    "    display(HTML(f\"<br><p>One sample t-test to verify whether the means difference is equal to zero</p>\"))\n",
    "    display(HTML(test.to_html()))\n",
    "    print(f\"Confidence interval (95%) of the means difference: [{round(diff.mean() - 1.96*diff.std(), 3)};{round(diff.mean() + 1.96*diff.std(), 3)}]\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb2b0a5",
   "metadata": {},
   "source": [
    "### Participant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c42ed70",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subjects in this analysis is: 296\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "print(f'Number of subjects in this analysis is: {len(df_software.reset_index()[\"subjectID\"].unique())}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25ea536-c89f-4042-84a2-7b8030668164",
   "metadata": {},
   "source": [
    "#### Compute metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ece11d7-3d7f-4e14-9191-27a2382182c3",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>slope</th>\n",
       "      <th>intercept</th>\n",
       "      <th>r_value</th>\n",
       "      <th>p_value</th>\n",
       "      <th>std_err</th>\n",
       "      <th>r_square</th>\n",
       "      <th>ICC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subjectID</th>\n",
       "      <th>sessionID</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>sub-OAS30001</th>\n",
       "      <th>ses-d0129</th>\n",
       "      <td>9.34e-01</td>\n",
       "      <td>2.16e-01</td>\n",
       "      <td>8.12e-01</td>\n",
       "      <td>5.31e-36</td>\n",
       "      <td>5.55e-02</td>\n",
       "      <td>6.60e-01</td>\n",
       "      <td>8.04e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30003</th>\n",
       "      <th>ses-d0558</th>\n",
       "      <td>9.17e-01</td>\n",
       "      <td>2.64e-01</td>\n",
       "      <td>8.12e-01</td>\n",
       "      <td>5.66e-36</td>\n",
       "      <td>5.45e-02</td>\n",
       "      <td>6.59e-01</td>\n",
       "      <td>8.06e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30004</th>\n",
       "      <th>ses-d1101</th>\n",
       "      <td>8.73e-01</td>\n",
       "      <td>3.13e-01</td>\n",
       "      <td>7.88e-01</td>\n",
       "      <td>1.46e-32</td>\n",
       "      <td>5.64e-02</td>\n",
       "      <td>6.21e-01</td>\n",
       "      <td>7.84e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30007</th>\n",
       "      <th>ses-d0061</th>\n",
       "      <td>9.56e-01</td>\n",
       "      <td>7.94e-02</td>\n",
       "      <td>8.44e-01</td>\n",
       "      <td>2.78e-41</td>\n",
       "      <td>5.03e-02</td>\n",
       "      <td>7.12e-01</td>\n",
       "      <td>8.37e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30008</th>\n",
       "      <th>ses-d0061</th>\n",
       "      <td>9.82e-01</td>\n",
       "      <td>6.48e-02</td>\n",
       "      <td>8.45e-01</td>\n",
       "      <td>1.74e-41</td>\n",
       "      <td>5.15e-02</td>\n",
       "      <td>7.14e-01</td>\n",
       "      <td>8.35e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30009</th>\n",
       "      <th>ses-d0148</th>\n",
       "      <td>9.90e-01</td>\n",
       "      <td>8.28e-03</td>\n",
       "      <td>8.01e-01</td>\n",
       "      <td>2.24e-34</td>\n",
       "      <td>6.12e-02</td>\n",
       "      <td>6.42e-01</td>\n",
       "      <td>7.84e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30014</th>\n",
       "      <th>ses-d0196</th>\n",
       "      <td>8.89e-01</td>\n",
       "      <td>3.07e-01</td>\n",
       "      <td>8.40e-01</td>\n",
       "      <td>1.39e-40</td>\n",
       "      <td>4.76e-02</td>\n",
       "      <td>7.05e-01</td>\n",
       "      <td>8.39e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30015</th>\n",
       "      <th>ses-d0116</th>\n",
       "      <td>8.80e-01</td>\n",
       "      <td>2.86e-01</td>\n",
       "      <td>7.88e-01</td>\n",
       "      <td>1.63e-32</td>\n",
       "      <td>5.69e-02</td>\n",
       "      <td>6.20e-01</td>\n",
       "      <td>7.83e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30017</th>\n",
       "      <th>ses-d0054</th>\n",
       "      <td>9.85e-01</td>\n",
       "      <td>5.89e-02</td>\n",
       "      <td>8.58e-01</td>\n",
       "      <td>4.69e-44</td>\n",
       "      <td>4.88e-02</td>\n",
       "      <td>7.36e-01</td>\n",
       "      <td>8.50e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-OAS30018</th>\n",
       "      <th>ses-d0070</th>\n",
       "      <td>1.02e+00</td>\n",
       "      <td>4.25e-02</td>\n",
       "      <td>7.82e-01</td>\n",
       "      <td>8.69e-32</td>\n",
       "      <td>6.69e-02</td>\n",
       "      <td>6.12e-01</td>\n",
       "      <td>7.56e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           slope  intercept   r_value   p_value   std_err  \\\n",
       "subjectID    sessionID                                                      \n",
       "sub-OAS30001 ses-d0129  9.34e-01   2.16e-01  8.12e-01  5.31e-36  5.55e-02   \n",
       "sub-OAS30003 ses-d0558  9.17e-01   2.64e-01  8.12e-01  5.66e-36  5.45e-02   \n",
       "sub-OAS30004 ses-d1101  8.73e-01   3.13e-01  7.88e-01  1.46e-32  5.64e-02   \n",
       "sub-OAS30007 ses-d0061  9.56e-01   7.94e-02  8.44e-01  2.78e-41  5.03e-02   \n",
       "sub-OAS30008 ses-d0061  9.82e-01   6.48e-02  8.45e-01  1.74e-41  5.15e-02   \n",
       "sub-OAS30009 ses-d0148  9.90e-01   8.28e-03  8.01e-01  2.24e-34  6.12e-02   \n",
       "sub-OAS30014 ses-d0196  8.89e-01   3.07e-01  8.40e-01  1.39e-40  4.76e-02   \n",
       "sub-OAS30015 ses-d0116  8.80e-01   2.86e-01  7.88e-01  1.63e-32  5.69e-02   \n",
       "sub-OAS30017 ses-d0054  9.85e-01   5.89e-02  8.58e-01  4.69e-44  4.88e-02   \n",
       "sub-OAS30018 ses-d0070  1.02e+00   4.25e-02  7.82e-01  8.69e-32  6.69e-02   \n",
       "\n",
       "                        r_square       ICC  \n",
       "subjectID    sessionID                      \n",
       "sub-OAS30001 ses-d0129  6.60e-01  8.04e-01  \n",
       "sub-OAS30003 ses-d0558  6.59e-01  8.06e-01  \n",
       "sub-OAS30004 ses-d1101  6.21e-01  7.84e-01  \n",
       "sub-OAS30007 ses-d0061  7.12e-01  8.37e-01  \n",
       "sub-OAS30008 ses-d0061  7.14e-01  8.35e-01  \n",
       "sub-OAS30009 ses-d0148  6.42e-01  7.84e-01  \n",
       "sub-OAS30014 ses-d0196  7.05e-01  8.39e-01  \n",
       "sub-OAS30015 ses-d0116  6.20e-01  7.83e-01  \n",
       "sub-OAS30017 ses-d0054  7.36e-01  8.50e-01  \n",
       "sub-OAS30018 ses-d0070  6.12e-01  7.56e-01  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "all_subjects_icc = []\n",
    "for var in df_ct[var_compare].unique():\n",
    "    for subject in df_ct[\"subjectID\"].unique():\n",
    "        df_icc_roi = pg.intraclass_corr(data=df_ct.loc[(df_ct[\"subjectID\"] == subject) & (df_ct[var_compare] == var)], \n",
    "                                        targets='roiName', raters='run',\n",
    "                                        ratings='value')\n",
    "        df_icc_roi[\"subjectID\"] = subject\n",
    "        df_icc_roi[var_compare] = var\n",
    "        \n",
    "        all_subjects_icc.append(df_icc_roi.loc[df_icc_roi[\"Type\"]==\"ICC3\"])\n",
    "        \n",
    "df_icc_sub = pd.concat(all_subjects_icc)\n",
    "\n",
    "\n",
    "sub_grouped_by, sub_grouped_by_stats = get_regression_metrics(df_software, ['subjectID', 'sessionID', var_compare], (metric_analysis, 1), \n",
    "                                                              (metric_analysis, 2))\n",
    "\n",
    "\n",
    "sub_grouped_by_stats = sub_grouped_by_stats.reset_index().set_index([\"subjectID\", \"software\"]).join(df_icc_sub.set_index([\"subjectID\", \"software\"])[[\"ICC\"]])\n",
    "sub_grouped_by_stats = sub_grouped_by_stats.reset_index().set_index([\"subjectID\", \"sessionID\", var_compare])\n",
    "\n",
    "\n",
    "df_subject_info = df_subjects.loc[df_subjects[\"run\"] ==1].reset_index().set_index(['subjectID', 'sessionID']).join(sub_grouped_by_stats)\n",
    "df_subject_info = df_subject_info[~df_subject_info[\"slope\"].isna()]\n",
    "\n",
    "\n",
    "df_mean_runs = df_software.mean(axis=1).reset_index().rename(columns={0: \"corticalThicknessAverage\"})\n",
    "\n",
    "df_ct_mean_runs = df_mean_runs.reset_index()[[\"subjectID\", var_compare, \"roiName\", \"corticalThicknessAverage\"]].melt(id_vars=[\"subjectID\", var_compare, \"roiName\"])\n",
    "\n",
    "all_subjects_icc_mean_runs = []\n",
    "\n",
    "for subject in df_ct[\"subjectID\"].unique():\n",
    "    df_icc_roi_mean_runs = pg.intraclass_corr(data=df_ct_mean_runs.loc[df_ct_mean_runs[\"subjectID\"] == subject], \n",
    "                                    targets='roiName', raters='software',\n",
    "                                    ratings='value')\n",
    "    df_icc_roi_mean_runs[\"subjectID\"] = subject\n",
    "    df_icc_roi_mean_runs[var_compare] = var\n",
    "\n",
    "    all_subjects_icc_mean_runs.append(df_icc_roi_mean_runs.loc[df_icc_roi_mean_runs[\"Type\"]==\"ICC3\"])\n",
    "        \n",
    "df_icc_sub_mean_runs = pd.concat(all_subjects_icc_mean_runs)\n",
    "\n",
    "df_ct_means = df_mean_runs.reset_index().pivot_table(values=\"corticalThicknessAverage\", columns=\"software\", \n",
    "                                                     index=[\"subjectID\", \"sessionID\", \"template\", \"roiName\"])\n",
    "sub_grouped_by_mean_runs, sub_grouped_by_stats_mean_runs = get_regression_metrics(df_ct_means, ['subjectID', 'sessionID'], \n",
    "                                                                                  \"ACPC_CAT12\", \"FREESURFER\")\n",
    "\n",
    "\n",
    "sub_grouped_by_stats_mean_runs = sub_grouped_by_stats_mean_runs.reset_index().set_index([\"subjectID\"]).join(df_icc_sub_mean_runs.set_index([\"subjectID\"])[[\"ICC\"]])\n",
    "sub_grouped_by_stats_mean_runs = sub_grouped_by_stats_mean_runs.reset_index().set_index([\"subjectID\", \"sessionID\"])\n",
    "\n",
    "sub_grouped_by_stats_mean_runs.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399617eb-8b05-4d36-8c85-b57f0ba9ed91",
   "metadata": {},
   "source": [
    "#### Test-retest metrics analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "77dcfc25-bc4a-44df-afb9-d60694ea2a5a",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>ICC</th>\n",
       "      <th>$R^2$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>0.97+/- 0.04</td>\n",
       "      <td>0.94+/- 0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>0.96+/- 0.04</td>\n",
       "      <td>0.93+/- 0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ICC          $R^2$\n",
       "software                                \n",
       "ACPC_CAT12   0.97+/- 0.04   0.94+/- 0.06\n",
       "FREESURFER   0.96+/- 0.04   0.93+/- 0.07"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_subject_info.groupby(by=var_compare).apply(lambda x: pd.Series([f'{x[\"ICC\"].mean(): 0.2f}+/-{x[\"ICC\"].std(): 0.2f}',\n",
    "                                                                   f'{x[\"r_square\"].mean(): 0.2f}+/-{x[\"r_square\"].std(): 0.2f}'], \n",
    "                                                                   index=['ICC', '$R^2$']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68601497-f9d3-4fdd-941a-0e3f28c4c24f",
   "metadata": {},
   "source": [
    "##### Statistical analysis -- Paired t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "185f4aa9-c8ee-42b2-9b9a-21afce62641d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br><p>ICC</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>3.55</td>\n",
       "      <td>295</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0.0, 0.01]</td>\n",
       "      <td>0.13</td>\n",
       "      <td>29.406</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br><p>r_square</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>4.14</td>\n",
       "      <td>295</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0.01, 0.01]</td>\n",
       "      <td>0.15</td>\n",
       "      <td>253.979</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sub_t = df_subject_info.reset_index()\n",
    "\n",
    "for var in [\"ICC\", \"r_square\"]:\n",
    "\n",
    "    df_result = pg.ttest(df_sub_t.loc[df_sub_t[\"software\"] == \"ACPC_CAT12\"][var], \n",
    "                         df_sub_t.loc[df_sub_t[\"software\"] == \"FREESURFER\"][var], paired = True)\n",
    "    \n",
    "    display(HTML(f\"<br><p>{var}</p>\"))\n",
    "    display(HTML(df_result.to_html()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dee79657-3959-4015-bfd0-639b215292f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def latex_float(float_str):\n",
    "    \n",
    "    if \"e\" in float_str:\n",
    "        base, exponent = float_str.split(\"e\")\n",
    "        return r\"{0} \\times 10^{{{1}}}\".format(base, int(exponent))\n",
    "    else:\n",
    "        return float_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158df7bb-d5df-4860-bc51-6cf7f721cae1",
   "metadata": {},
   "source": [
    "#### Analysis of age effect on test-retest metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71f7527e-51f3-4b31-b455-5164b164aba1",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle ICC_{CAT12}= 3.9 \\times 10^{-4}age +  2.6 \\times 10^{-2}SNR +  7.0 \\times 10^{-1}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle ICC_{FREESURFER}= 2.1 \\times 10^{-4}age +  2.6 \\times 10^{-2}SNR +  7.0 \\times 10^{-1}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle R^2_{CAT12}= 6.4 \\times 10^{-4}age +  4.6 \\times 10^{-2}SNR +  4.6 \\times 10^{-1}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle R^2_{FREESURFER}= 3.3 \\times 10^{-4}age +  4.7 \\times 10^{-2}SNR +  4.6 \\times 10^{-1}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math\n",
    "\n",
    "import re, seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "reliability_measures = [(\"ICC\", \"ICC\"), (\"r_square\", \"$R^2$\")]\n",
    "\n",
    "for rel_measure, label_measure in reliability_measures:\n",
    "    for el in df_subject_info.index.get_level_values(2).unique():\n",
    "        legend = el\n",
    "        if legend == \"ACPC_CAT12\":\n",
    "            legend = \"CAT12\"\n",
    "            \n",
    "        data_filtered = df_subject_info.loc[df_subject_info.index.get_level_values(2) == el]\n",
    "                \n",
    "        x_values = data_filtered[[\"age\", \"snr_total_mean\"]]\n",
    "        y_values = data_filtered[rel_measure]\n",
    "        \n",
    "        model = LinearRegression().fit(x_values, y_values)\n",
    "\n",
    "        \n",
    "        age_weight = latex_float(f'{model.coef_[0]: 0.1e}')\n",
    "        SNR_weight = latex_float(f'{model.coef_[1]: 0.1e}')\n",
    "        intercept = latex_float(f'{model.intercept_: 0.1e}')\n",
    "        \n",
    "        eq = f'${label_measure.replace(\"$\", \"\")}_' + \"{\" + legend + \"}\" + f'={age_weight}age + {SNR_weight}SNR + {intercept}$'\n",
    "        display(Math(r'{}'.format(eq)))\n",
    "        \n",
    "        \n",
    "    x= df_subject_info[\"age\"].values\n",
    "    y= df_subject_info[\"snr_total_mean\"].values\n",
    "    z= df_subject_info[rel_measure].values\n",
    "\n",
    "    # axes instance\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    ax = Axes3D(fig, auto_add_to_figure=False)\n",
    "    fig.add_axes(ax)\n",
    "\n",
    "    # get colormap from seaborn\n",
    "    cmap = ListedColormap(sns.color_palette(\"husl\", 256).as_hex())\n",
    "\n",
    "    # plot\n",
    "    sc = ax.scatter(x, y, z, s=40, c=[color_pallete[el] for el in df_subject_info.index.get_level_values(2).values], marker='o', cmap=cmap, alpha=1)\n",
    "    sc = ax.scatter(x, y, z, s=40, c=[color_pallete[el] for el in df_subject_info.index.get_level_values(2).values], marker='o', cmap=cmap, alpha=1)\n",
    "\n",
    "    ax.set_xlabel('Age', labelpad=15)\n",
    "    ax.set_ylabel('SNR', labelpad=20)\n",
    "    ax.set_zlabel(label_measure, labelpad=15)\n",
    "\n",
    "    ax.view_init(elev=10., azim=45)\n",
    "\n",
    "    plt.subplots_adjust(bottom=0.2, top=2)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6a74d6-8bcf-4c08-941f-f2cf792d0821",
   "metadata": {},
   "source": [
    "#### Reproducibility considering the mean of two runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "59cd7a4b-2455-47cf-a868-9521f9414e04",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r_square</th>\n",
       "      <td>0.67 +/- 0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC</th>\n",
       "      <td>0.81 +/- 0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "r_square  0.67 +/- 0.07\n",
       "ICC       0.81 +/- 0.06"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_grouped_by_stats_mean_runs.apply(lambda x: f\"{round(x.mean(), 2)} +/- {round(x.std(), 2)}\")[[\"r_square\", \"ICC\"]].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86943ed3",
   "metadata": {},
   "source": [
    "### ROI Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970169c3-c485-4136-ab77-a8f1c3eadfd9",
   "metadata": {},
   "source": [
    "#### Compute metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79ac3205",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>slope</th>\n",
       "      <th>intercept</th>\n",
       "      <th>r_value</th>\n",
       "      <th>p_value</th>\n",
       "      <th>std_err</th>\n",
       "      <th>r_square</th>\n",
       "      <th>ICC</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>name</th>\n",
       "      <th>hem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roiName</th>\n",
       "      <th>software</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 rowspan=\"2\" valign=\"top\">lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>7.42e-01</td>\n",
       "      <td>7.82e-01</td>\n",
       "      <td>7.70e-01</td>\n",
       "      <td>2.36e-59</td>\n",
       "      <td>3.58e-02</td>\n",
       "      <td>5.93e-01</td>\n",
       "      <td>7.70e-01</td>\n",
       "      <td>[0.72, 0.81]</td>\n",
       "      <td>Long insular gyrus and central sulcus of the i...</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>7.57e-01</td>\n",
       "      <td>7.19e-01</td>\n",
       "      <td>8.03e-01</td>\n",
       "      <td>3.85e-68</td>\n",
       "      <td>3.27e-02</td>\n",
       "      <td>6.45e-01</td>\n",
       "      <td>8.02e-01</td>\n",
       "      <td>[0.76, 0.84]</td>\n",
       "      <td>Long insular gyrus and central sulcus of the i...</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">lG_and_S_cingul-Ant</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>8.40e-01</td>\n",
       "      <td>4.09e-01</td>\n",
       "      <td>9.15e-01</td>\n",
       "      <td>7.91e-118</td>\n",
       "      <td>2.16e-02</td>\n",
       "      <td>8.37e-01</td>\n",
       "      <td>9.12e-01</td>\n",
       "      <td>[0.89, 0.93]</td>\n",
       "      <td>Anterior part of the cingulate gyrus and sulcus</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>8.27e-01</td>\n",
       "      <td>4.42e-01</td>\n",
       "      <td>8.29e-01</td>\n",
       "      <td>3.69e-76</td>\n",
       "      <td>3.26e-02</td>\n",
       "      <td>6.87e-01</td>\n",
       "      <td>8.29e-01</td>\n",
       "      <td>[0.79, 0.86]</td>\n",
       "      <td>Anterior part of the cingulate gyrus and sulcus</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>9.43e-01</td>\n",
       "      <td>1.52e-01</td>\n",
       "      <td>9.59e-01</td>\n",
       "      <td>1.39e-163</td>\n",
       "      <td>1.62e-02</td>\n",
       "      <td>9.20e-01</td>\n",
       "      <td>9.59e-01</td>\n",
       "      <td>[0.95, 0.97]</td>\n",
       "      <td>Middle-anterior part of the cingulate gyrus an...</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_inf</th>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>8.18e-01</td>\n",
       "      <td>4.29e-01</td>\n",
       "      <td>8.66e-01</td>\n",
       "      <td>2.29e-90</td>\n",
       "      <td>2.76e-02</td>\n",
       "      <td>7.49e-01</td>\n",
       "      <td>8.64e-01</td>\n",
       "      <td>[0.83, 0.89]</td>\n",
       "      <td>Inferior temporal sulcus</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">rS_temporal_sup</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>9.44e-01</td>\n",
       "      <td>1.38e-01</td>\n",
       "      <td>9.38e-01</td>\n",
       "      <td>4.18e-137</td>\n",
       "      <td>2.04e-02</td>\n",
       "      <td>8.79e-01</td>\n",
       "      <td>9.38e-01</td>\n",
       "      <td>[0.92, 0.95]</td>\n",
       "      <td>Superior temporal sulcus</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>8.84e-01</td>\n",
       "      <td>2.78e-01</td>\n",
       "      <td>9.00e-01</td>\n",
       "      <td>5.36e-108</td>\n",
       "      <td>2.50e-02</td>\n",
       "      <td>8.10e-01</td>\n",
       "      <td>9.00e-01</td>\n",
       "      <td>[0.88, 0.92]</td>\n",
       "      <td>Superior temporal sulcus</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">rS_temporal_transverse</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>8.88e-01</td>\n",
       "      <td>2.37e-01</td>\n",
       "      <td>8.77e-01</td>\n",
       "      <td>9.65e-96</td>\n",
       "      <td>2.83e-02</td>\n",
       "      <td>7.70e-01</td>\n",
       "      <td>8.77e-01</td>\n",
       "      <td>[0.85, 0.9]</td>\n",
       "      <td>Transverse temporal sulcus</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>8.27e-01</td>\n",
       "      <td>3.83e-01</td>\n",
       "      <td>8.41e-01</td>\n",
       "      <td>1.42e-80</td>\n",
       "      <td>3.10e-02</td>\n",
       "      <td>7.08e-01</td>\n",
       "      <td>8.41e-01</td>\n",
       "      <td>[0.8, 0.87]</td>\n",
       "      <td>Transverse temporal sulcus</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>296 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        slope  intercept   r_value    p_value  \\\n",
       "roiName                  software                                               \n",
       "lG_Ins_lg_and_S_cent_ins ACPC_CAT12  7.42e-01   7.82e-01  7.70e-01   2.36e-59   \n",
       "                         FREESURFER  7.57e-01   7.19e-01  8.03e-01   3.85e-68   \n",
       "lG_and_S_cingul-Ant      ACPC_CAT12  8.40e-01   4.09e-01  9.15e-01  7.91e-118   \n",
       "                         FREESURFER  8.27e-01   4.42e-01  8.29e-01   3.69e-76   \n",
       "lG_and_S_cingul-Mid-Ant  ACPC_CAT12  9.43e-01   1.52e-01  9.59e-01  1.39e-163   \n",
       "...                                       ...        ...       ...        ...   \n",
       "rS_temporal_inf          FREESURFER  8.18e-01   4.29e-01  8.66e-01   2.29e-90   \n",
       "rS_temporal_sup          ACPC_CAT12  9.44e-01   1.38e-01  9.38e-01  4.18e-137   \n",
       "                         FREESURFER  8.84e-01   2.78e-01  9.00e-01  5.36e-108   \n",
       "rS_temporal_transverse   ACPC_CAT12  8.88e-01   2.37e-01  8.77e-01   9.65e-96   \n",
       "                         FREESURFER  8.27e-01   3.83e-01  8.41e-01   1.42e-80   \n",
       "\n",
       "                                      std_err  r_square       ICC  \\\n",
       "roiName                  software                                   \n",
       "lG_Ins_lg_and_S_cent_ins ACPC_CAT12  3.58e-02  5.93e-01  7.70e-01   \n",
       "                         FREESURFER  3.27e-02  6.45e-01  8.02e-01   \n",
       "lG_and_S_cingul-Ant      ACPC_CAT12  2.16e-02  8.37e-01  9.12e-01   \n",
       "                         FREESURFER  3.26e-02  6.87e-01  8.29e-01   \n",
       "lG_and_S_cingul-Mid-Ant  ACPC_CAT12  1.62e-02  9.20e-01  9.59e-01   \n",
       "...                                       ...       ...       ...   \n",
       "rS_temporal_inf          FREESURFER  2.76e-02  7.49e-01  8.64e-01   \n",
       "rS_temporal_sup          ACPC_CAT12  2.04e-02  8.79e-01  9.38e-01   \n",
       "                         FREESURFER  2.50e-02  8.10e-01  9.00e-01   \n",
       "rS_temporal_transverse   ACPC_CAT12  2.83e-02  7.70e-01  8.77e-01   \n",
       "                         FREESURFER  3.10e-02  7.08e-01  8.41e-01   \n",
       "\n",
       "                                            CI95%  \\\n",
       "roiName                  software                   \n",
       "lG_Ins_lg_and_S_cent_ins ACPC_CAT12  [0.72, 0.81]   \n",
       "                         FREESURFER  [0.76, 0.84]   \n",
       "lG_and_S_cingul-Ant      ACPC_CAT12  [0.89, 0.93]   \n",
       "                         FREESURFER  [0.79, 0.86]   \n",
       "lG_and_S_cingul-Mid-Ant  ACPC_CAT12  [0.95, 0.97]   \n",
       "...                                           ...   \n",
       "rS_temporal_inf          FREESURFER  [0.83, 0.89]   \n",
       "rS_temporal_sup          ACPC_CAT12  [0.92, 0.95]   \n",
       "                         FREESURFER  [0.88, 0.92]   \n",
       "rS_temporal_transverse   ACPC_CAT12   [0.85, 0.9]   \n",
       "                         FREESURFER   [0.8, 0.87]   \n",
       "\n",
       "                                                                                  name  \\\n",
       "roiName                  software                                                        \n",
       "lG_Ins_lg_and_S_cent_ins ACPC_CAT12  Long insular gyrus and central sulcus of the i...   \n",
       "                         FREESURFER  Long insular gyrus and central sulcus of the i...   \n",
       "lG_and_S_cingul-Ant      ACPC_CAT12   Anterior part of the cingulate gyrus and sulcus    \n",
       "                         FREESURFER   Anterior part of the cingulate gyrus and sulcus    \n",
       "lG_and_S_cingul-Mid-Ant  ACPC_CAT12  Middle-anterior part of the cingulate gyrus an...   \n",
       "...                                                                                ...   \n",
       "rS_temporal_inf          FREESURFER                           Inferior temporal sulcus   \n",
       "rS_temporal_sup          ACPC_CAT12                          Superior temporal sulcus    \n",
       "                         FREESURFER                          Superior temporal sulcus    \n",
       "rS_temporal_transverse   ACPC_CAT12                         Transverse temporal sulcus   \n",
       "                         FREESURFER                         Transverse temporal sulcus   \n",
       "\n",
       "                                       hem  \n",
       "roiName                  software           \n",
       "lG_Ins_lg_and_S_cent_ins ACPC_CAT12   Left  \n",
       "                         FREESURFER   Left  \n",
       "lG_and_S_cingul-Ant      ACPC_CAT12   Left  \n",
       "                         FREESURFER   Left  \n",
       "lG_and_S_cingul-Mid-Ant  ACPC_CAT12   Left  \n",
       "...                                    ...  \n",
       "rS_temporal_inf          FREESURFER  Right  \n",
       "rS_temporal_sup          ACPC_CAT12  Right  \n",
       "                         FREESURFER  Right  \n",
       "rS_temporal_transverse   ACPC_CAT12  Right  \n",
       "                         FREESURFER  Right  \n",
       "\n",
       "[296 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "roi_grouped_by, roi_grouped_by_stats = get_regression_metrics(df_software, [\"roiName\", var_compare], \n",
    "                                                              (metric_analysis, 1), \n",
    "                                                              (metric_analysis, 2))\n",
    "\n",
    "all_rois = []\n",
    "df_ct = df_software.reset_index()[[\"subjectID\", var_compare, \"roiName\", \"corticalThicknessAverage\"]].melt(id_vars=[\"subjectID\", var_compare, \"roiName\"])\n",
    "\n",
    "for var in df_ct[var_compare].unique():\n",
    "    for roi_name in df_ct[\"roiName\"].unique():\n",
    "        df_icc_roi = pg.intraclass_corr(data=df_ct.loc[(df_ct[\"roiName\"] == roi_name) & (df_ct[var_compare] == var)], \n",
    "                                        targets='subjectID', raters='run',\n",
    "                                        ratings='value')\n",
    "        df_icc_roi[\"roiName\"] = roi_name\n",
    "        df_icc_roi[var_compare] = var\n",
    "        \n",
    "        all_rois.append(df_icc_roi.loc[df_icc_roi[\"Type\"]==\"ICC3\"])\n",
    "        \n",
    "df_icc_rois = pd.concat(all_rois)\n",
    "\n",
    "\n",
    "roi_grouped_by_stats = roi_grouped_by_stats.join(pd.concat(all_rois).set_index([\"roiName\", var_compare])[[\"ICC\", \"CI95%\"]])\n",
    "\n",
    "map_abbrev_to_name = df_names_rois.set_index(\"label\")[[\"name\"]].to_dict(\"series\")[\"name\"]\n",
    "hem_label = {\"r\": \"Right\", \"l\": \"Left\"}\n",
    "\n",
    "roi_grouped_by_stats[\"name\"] = roi_grouped_by_stats.index.to_frame().roiName.apply(lambda x: map_abbrev_to_name[x[1:]])\n",
    "roi_grouped_by_stats[\"hem\"] = roi_grouped_by_stats.index.to_frame().roiName.apply(lambda x: hem_label[x[0].lower()])\n",
    "\n",
    "roi_melted = roi_grouped_by_stats.reset_index()[[var_compare, 'r_square', 'ICC', 'CI95%', 'name', 'hem']].melt(id_vars=[var_compare, 'name', 'hem'])\n",
    "\n",
    "df_sorted_r2_icc = pd.pivot_table(roi_melted, index=\"name\", values=[\"value\"], columns=[var_compare, \"hem\", \"variable\"])\n",
    "\n",
    "\n",
    "dfs = []\n",
    "\n",
    "rois_info = roi_grouped_by_stats.copy()\n",
    "rois_info[\"label\"] = roi_grouped_by_stats.index.get_level_values(0).str[1:].str.strip()\n",
    "rois_info[\"hem\"] = roi_grouped_by_stats.index.get_level_values(0).str[0]\n",
    "rois_info = rois_info.reset_index().set_index([\"label\", \"hem\", var_compare])\n",
    "\n",
    "for var in rois_info.index.get_level_values(2).unique():\n",
    "    for hem in rois_info.index.get_level_values(1).unique():\n",
    "        df_areas_metrics_var = pd.concat([df_areas, df_areas[\"Label\"].apply(lambda x: rois_info.loc[(x, hem, var)])], axis=1)\n",
    "        df_areas_metrics_var[var_compare] = var\n",
    "        dfs.append(df_areas_metrics_var)\n",
    "df_areas_metrics = pd.concat(dfs)\n",
    "df_areas_metrics[\"hem\"] = df_areas_metrics[\"roiName\"].str[0]\n",
    "\n",
    "\n",
    "df_r2 = df_areas_metrics.pivot_table(index=\"name\", columns=[\"software\", \"hem\"], values=[\"r_square\"])\n",
    "\n",
    "df_icc = df_areas_metrics.pivot_table(index=\"name\", columns=[\"software\", \"hem\"], values=[\"ICC\"])\n",
    "df_ci = df_areas_metrics.pivot_table(index=\"name\", columns=[\"software\", \"hem\"], values=[\"CI95%\"])\n",
    "\n",
    "df_ci_icc = df_icc.join(df_ci) \n",
    "df_ci_icc.columns = df_ci_icc.columns.swaplevel(0, 2)\n",
    "df_ci_icc.columns = df_ci_icc.columns.swaplevel(0, 1)\n",
    "df_ci_icc = df_ci_icc[[df_ci_icc.columns[0], df_ci_icc.columns[-4], \n",
    "                     df_ci_icc.columns[1], df_ci_icc.columns[-3], \n",
    "                     df_ci_icc.columns[2], df_ci_icc.columns[-2], \n",
    "                     df_ci_icc.columns[3], df_ci_icc.columns[-1]]] \n",
    "\n",
    "roi_grouped_by_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbbace1-d188-4a15-9560-7810628dc4cc",
   "metadata": {},
   "source": [
    "#### Test-retest metrics analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7dca8477-8d12-4a3d-9cb8-e5c1d36e919b",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>ICC</th>\n",
       "      <th>$R^2$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <td>0.88+/- 0.05</td>\n",
       "      <td>0.78+/- 0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREESURFER</th>\n",
       "      <td>0.84+/- 0.06</td>\n",
       "      <td>0.70+/- 0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ICC          $R^2$\n",
       "software                                \n",
       "ACPC_CAT12   0.88+/- 0.05   0.78+/- 0.09\n",
       "FREESURFER   0.84+/- 0.06   0.70+/- 0.10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "roi_grouped_by_stats.groupby(by=var_compare).apply(lambda x: pd.Series([f'{x[\"ICC\"].mean(): 0.2f}+/-{x[\"ICC\"].std(): 0.2f}',\n",
    "                                                                        f'{x[\"r_square\"].mean(): 0.2f}+/-{x[\"r_square\"].std(): 0.2f}'], \n",
    "                                                                        index=['ICC', '$R^2$']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ddac6663-5026-44a3-a226-e48dc2090e61",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br><h5>r_square</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>9.83e+00</td>\n",
       "      <td>147</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>7.86e-18</td>\n",
       "      <td>[0.06, 0.1]</td>\n",
       "      <td>8.32e-01</td>\n",
       "      <td>6.861e+14</td>\n",
       "      <td>1.00e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br><h5>ICC</h5>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>9.28e+00</td>\n",
       "      <td>147</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>2.10e-16</td>\n",
       "      <td>[0.04, 0.06]</td>\n",
       "      <td>8.08e-01</td>\n",
       "      <td>2.775e+13</td>\n",
       "      <td>1.00e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_roi_t = roi_grouped_by_stats.reset_index()\n",
    "\n",
    "for var in [\"r_square\", \"ICC\"]:\n",
    "\n",
    "    df_result = pg.ttest(df_roi_t.loc[df_roi_t[\"software\"] == \"ACPC_CAT12\"][var], \n",
    "                         df_roi_t.loc[df_roi_t[\"software\"] == \"FREESURFER\"][var], paired = True)\n",
    "    \n",
    "    display(HTML(f\"<br><h5>{var}</h5>\"))\n",
    "    display(HTML(df_result.to_html()))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647c71ab-92f7-4fc1-8185-d0ad3008c29d",
   "metadata": {},
   "source": [
    "##### Mean per lobe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f563ece-854b-42d9-9b8f-b471a29d3dc3",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">r_square</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <th>FREESURFER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frontal Lobe</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insula</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Limbic lobe</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal lobe</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal and occipital lobes</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                r_square           \n",
       "software                      ACPC_CAT12 FREESURFER\n",
       "Area                                               \n",
       " Frontal Lobe                       0.76       0.63\n",
       " Insula                             0.77       0.72\n",
       " Limbic lobe                        0.77       0.72\n",
       " Parietal lobe                      0.83       0.73\n",
       " Temporal and occipital lobes       0.80       0.77"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: f\"{x: 0.2f}\")\n",
    "to_group_by = [\"Area\"]\n",
    "df_areas_metrics_grouped = df_areas_metrics.groupby(to_group_by + [var_compare, \"hem\"]).mean()\n",
    "df_lobes = pd.pivot_table(df_areas_metrics_grouped.reset_index(), index=to_group_by, values=[\"r_square\"], columns=[var_compare])\n",
    "\n",
    "df_lobes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9f905d-3514-4ccb-8316-3b6964a7cac4",
   "metadata": {},
   "source": [
    "#### Paired t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7262e51-1871-4b30-b5de-295b34adf982",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>Contrast</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Paired</th>\n",
       "      <th>Parametric</th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>cohen</th>\n",
       "      <th>roiName</th>\n",
       "      <th>software</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>295.00</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>lG_Ins_lg_and_S_cent_ins</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>295.00</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>lG_and_S_cingul-Ant</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>295.00</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.135</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>lG_and_S_cingul-Mid-Ant</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.13</td>\n",
       "      <td>295.00</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.00</td>\n",
       "      <td>lG_and_S_cingul-Mid-Post</td>\n",
       "      <td>ACPC_CAT12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Contrast  A  B  Paired  Parametric     T     dof alternative  p-unc   BF10  \\\n",
       "0      run  1  2    True        True -0.41  295.00   two-sided   0.68  0.071   \n",
       "0      run  1  2    True        True -0.64  295.00   two-sided   0.52   0.08   \n",
       "0      run  1  2    True        True -1.21  295.00   two-sided   0.23  0.135   \n",
       "0      run  1  2    True        True  0.13  295.00   two-sided   0.89  0.066   \n",
       "\n",
       "   cohen                   roiName    software  \n",
       "0  -0.02  lG_Ins_lg_and_S_cent_ins  ACPC_CAT12  \n",
       "0  -0.02       lG_and_S_cingul-Ant  ACPC_CAT12  \n",
       "0  -0.02   lG_and_S_cingul-Mid-Ant  ACPC_CAT12  \n",
       "0   0.00  lG_and_S_cingul-Mid-Post  ACPC_CAT12  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "all_df = []\n",
    "for var in df_ct[var_compare].unique():\n",
    "    for roiname in df_ct['roiName'].unique():\n",
    "        df_posthoc = pg.pairwise_ttests(data=df_ct.loc[(df_ct['roiName'] == roiname) & (df_ct[var_compare] == var)], \n",
    "                                        dv='value', within='run', subject='subjectID',\n",
    "                                     parametric=True, padjust='fdr_bh', effsize='cohen')\n",
    "\n",
    "        # Pretty printing of table\n",
    "        df_posthoc['roiName'] = roiname\n",
    "        df_posthoc[var_compare] = var\n",
    "        all_df.append(df_posthoc)\n",
    "df_f_p_value = pd.concat(all_df)\n",
    "df_f_p_value.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e29157fd-db35-4cbe-9dc6-129fb840779e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">cohen-p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ACPC_CAT12</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FREESURFER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hem</th>\n",
       "      <th>Left</th>\n",
       "      <th>Right</th>\n",
       "      <th>Left</th>\n",
       "      <th>Right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angular gyrus</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anterior occipital sulcus and preoccipital notch</th>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anterior part of the cingulate gyrus and sulcus</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anterior segment of the circular sulcus of the insula</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anterior transverse collateral sulcus</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal pole</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transverse frontopolar gyri and sulci</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transverse temporal sulcus</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Triangular part of the inferior frontal gyrus</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vertical ramus of the anterior segment of the lateral sulcus</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   cohen-p-value           \\\n",
       "software                                              ACPC_CAT12            \n",
       "hem                                                         Left    Right   \n",
       "name                                                                        \n",
       "Angular gyrus                                              -0.01   0.0008   \n",
       "Anterior occipital sulcus and preoccipital notch          -0.007    -0.02   \n",
       "Anterior part of the cingulate gyrus and sulcus            -0.02    -0.05   \n",
       "Anterior segment of the circular sulcus of the ...         -0.02    -0.05   \n",
       "Anterior transverse collateral sulcus                       0.06     0.05   \n",
       "...                                                          ...      ...   \n",
       "Temporal pole                                               0.06     0.04   \n",
       "Transverse frontopolar gyri and sulci                      -0.02    0.004   \n",
       "Transverse temporal sulcus                                 -0.01    -0.01   \n",
       "Triangular part of the inferior frontal gyrus              -0.04    -0.03   \n",
       "Vertical ramus of the anterior segment of the l...         0.003     0.02   \n",
       "\n",
       "                                                                      \n",
       "software                                           FREESURFER         \n",
       "hem                                                      Left  Right  \n",
       "name                                                                  \n",
       "Angular gyrus                                          -0.008  -0.02  \n",
       "Anterior occipital sulcus and preoccipital notch         0.02  -0.02  \n",
       "Anterior part of the cingulate gyrus and sulcus         0.007  -0.01  \n",
       "Anterior segment of the circular sulcus of the ...       0.04   0.03  \n",
       "Anterior transverse collateral sulcus                    0.07   0.08  \n",
       "...                                                       ...    ...  \n",
       "Temporal pole                                             0.1    0.1  \n",
       "Transverse frontopolar gyri and sulci                  -0.004   0.02  \n",
       "Transverse temporal sulcus                               0.01   0.05  \n",
       "Triangular part of the inferior frontal gyrus            0.01   0.02  \n",
       "Vertical ramus of the anterior segment of the l...        0.1   0.01  \n",
       "\n",
       "[74 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "from scipy.stats import ttest_rel\n",
    "\n",
    "df_f_p_value[\"hem\"] = df_f_p_value.apply(lambda x: hem_label[x.roiName[0].lower()], axis=1)\n",
    "df_f_p_value[\"name\"] = df_f_p_value.apply(lambda x: map_abbrev_to_name[x.roiName[1:]], axis=1)\n",
    "\n",
    "# Bonferroni correction\n",
    "significance_level = .05\n",
    "\n",
    "df_f_p_value[\"p-value\"] = df_f_p_value[\"p-unc\"].apply(lambda x: min(x*df_f_p_value.shape[0], 1))\n",
    "df_f_p_value_rejected = df_f_p_value[df_f_p_value[\"p-value\"] < significance_level]\n",
    "\n",
    "df_f_p_value[\"cohen-p-value\"] = df_f_p_value[[\"p-value\", \"cohen\"]].apply(lambda x: f'{x[\"cohen\"]: 0.1}*' if x['p-value'] < significance_level else f'{x[\"cohen\"]: 0.1}', axis=1).astype(str)\n",
    "\n",
    "df_p_value = pd.pivot_table(df_f_p_value.reset_index(), index=\"name\", values=[\"cohen-p-value\"], columns=[var_compare, \"hem\"], aggfunc=lambda x: ' '.join(x))\n",
    "df_p_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c7650fc-448b-4f5c-87e6-0cc5fe71190d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ROIs with at least on group that rejected the paired t-test hypothesis: 1\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "print(f\"Number of ROIs with at least on group that rejected the paired t-test hypothesis: {df_f_p_value_rejected.reset_index().roiName.unique().shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d26ec48-b710-4ed8-b09e-811cc5cd3bc7",
   "metadata": {},
   "source": [
    "##### Number of regions per site in which the hypothesis was rejected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "695865ae-a0c8-4405-861b-288a5b1f8eaf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Contrast</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Paired</th>\n",
       "      <th>Parametric</th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>cohen</th>\n",
       "      <th>roiName</th>\n",
       "      <th>software</th>\n",
       "      <th>hem</th>\n",
       "      <th>name</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.08</td>\n",
       "      <td>295.00</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.00</td>\n",
       "      <td>196.679</td>\n",
       "      <td>0.14</td>\n",
       "      <td>lG_insular_short</td>\n",
       "      <td>FREESURFER</td>\n",
       "      <td>Left</td>\n",
       "      <td>Short insular gyri</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Contrast  A  B  Paired  Parametric     T     dof alternative  p-unc  \\\n",
       "0      run  1  2    True        True  4.08  295.00   two-sided   0.00   \n",
       "\n",
       "      BF10  cohen           roiName    software   hem                name  \\\n",
       "0  196.679   0.14  lG_insular_short  FREESURFER  Left  Short insular gyri   \n",
       "\n",
       "   p-value  \n",
       "0     0.02  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "df_f_p_value_rejected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca96ceb-7b12-4053-82cd-1e80bd4e9415",
   "metadata": {},
   "source": [
    "#### Reproducibility of Cortical Thickness estimation (mean of both runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2f44430-4bf1-4ae0-969c-875d2405a76f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>software</th>\n",
       "      <th>ACPC_CAT12</th>\n",
       "      <th>FREESURFER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subjectID</th>\n",
       "      <th>sessionID</th>\n",
       "      <th>template</th>\n",
       "      <th>roiName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">sub-OAS30001</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">ses-d0129</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">a2009s</th>\n",
       "      <th>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>2.91</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>2.45</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <td>2.59</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "software                                                  ACPC_CAT12  \\\n",
       "subjectID    sessionID template roiName                                \n",
       "sub-OAS30001 ses-d0129 a2009s   lG_Ins_lg_and_S_cent_ins        2.91   \n",
       "                                lG_and_S_cingul-Ant             2.45   \n",
       "                                lG_and_S_cingul-Mid-Ant         2.59   \n",
       "\n",
       "software                                                  FREESURFER  \n",
       "subjectID    sessionID template roiName                               \n",
       "sub-OAS30001 ses-d0129 a2009s   lG_Ins_lg_and_S_cent_ins        3.13  \n",
       "                                lG_and_S_cingul-Ant             2.59  \n",
       "                                lG_and_S_cingul-Mid-Ant         2.50  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ct_means.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4413458-d8e3-4207-9955-ca6bf7921388",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slope</th>\n",
       "      <th>intercept</th>\n",
       "      <th>r_value</th>\n",
       "      <th>p_value</th>\n",
       "      <th>std_err</th>\n",
       "      <th>r_square</th>\n",
       "      <th>ICC</th>\n",
       "      <th>CI95%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roiName</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>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>0.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.57</td>\n",
       "      <td>[0.49, 0.64]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_Ins_lg_and_S_cent_ins</th>\n",
       "      <td>0.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.57</td>\n",
       "      <td>[0.49, 0.64]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.46</td>\n",
       "      <td>[0.36, 0.54]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Ant</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.46</td>\n",
       "      <td>[0.36, 0.54]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lG_and_S_cingul-Mid-Ant</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.69</td>\n",
       "      <td>[0.63, 0.75]</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_inf</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.42, 0.59]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_sup</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.83</td>\n",
       "      <td>[0.8, 0.87]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_sup</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.83</td>\n",
       "      <td>[0.8, 0.87]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_transverse</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.49, 0.65]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rS_temporal_transverse</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.49, 0.65]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>296 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          slope  intercept  r_value  p_value  std_err  \\\n",
       "roiName                                                                 \n",
       "lG_Ins_lg_and_S_cent_ins   0.51       1.51     0.58     0.00     0.04   \n",
       "lG_Ins_lg_and_S_cent_ins   0.51       1.51     0.58     0.00     0.04   \n",
       "lG_and_S_cingul-Ant        0.44       1.46     0.46     0.00     0.05   \n",
       "lG_and_S_cingul-Ant        0.44       1.46     0.46     0.00     0.05   \n",
       "lG_and_S_cingul-Mid-Ant    0.67       0.71     0.70     0.00     0.04   \n",
       "...                         ...        ...      ...      ...      ...   \n",
       "rS_temporal_inf            0.48       1.25     0.51     0.00     0.05   \n",
       "rS_temporal_sup            0.82       0.37     0.83     0.00     0.03   \n",
       "rS_temporal_sup            0.82       0.37     0.83     0.00     0.03   \n",
       "rS_temporal_transverse     0.77       0.66     0.60     0.00     0.06   \n",
       "rS_temporal_transverse     0.77       0.66     0.60     0.00     0.06   \n",
       "\n",
       "                          r_square   ICC         CI95%  \n",
       "roiName                                                 \n",
       "lG_Ins_lg_and_S_cent_ins      0.33  0.57  [0.49, 0.64]  \n",
       "lG_Ins_lg_and_S_cent_ins      0.33  0.57  [0.49, 0.64]  \n",
       "lG_and_S_cingul-Ant           0.21  0.46  [0.36, 0.54]  \n",
       "lG_and_S_cingul-Ant           0.21  0.46  [0.36, 0.54]  \n",
       "lG_and_S_cingul-Mid-Ant       0.48  0.69  [0.63, 0.75]  \n",
       "...                            ...   ...           ...  \n",
       "rS_temporal_inf               0.26  0.51  [0.42, 0.59]  \n",
       "rS_temporal_sup               0.70  0.83   [0.8, 0.87]  \n",
       "rS_temporal_sup               0.70  0.83   [0.8, 0.87]  \n",
       "rS_temporal_transverse        0.35  0.58  [0.49, 0.65]  \n",
       "rS_temporal_transverse        0.35  0.58  [0.49, 0.65]  \n",
       "\n",
       "[296 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "roi_grouped_by_mean_runs, roi_grouped_by_stats_mean_runs = get_regression_metrics(df_ct_means, [\"roiName\"], \n",
    "                                                                                  (\"ACPC_CAT12\"), \n",
    "                                                                                  (\"FREESURFER\"))\n",
    "\n",
    "all_rois_mean_runs = []\n",
    "df_ct_roi_means = df_mean_runs.reset_index()[[\"subjectID\", \"roiName\", \"corticalThicknessAverage\", \"software\"]].melt(id_vars=[\"subjectID\", \"roiName\", \"software\"])\n",
    "\n",
    "for var in df_ct[var_compare].unique():\n",
    "    for roi_name in df_ct[\"roiName\"].unique():\n",
    "        df_icc_roi_mean_runs = pg.intraclass_corr(data=df_ct_roi_means.loc[(df_ct_roi_means[\"roiName\"] == roi_name)], \n",
    "                                        targets='subjectID', raters='software',\n",
    "                                        ratings='value')\n",
    "        df_icc_roi_mean_runs[\"roiName\"] = roi_name\n",
    "        df_icc_roi_mean_runs[var_compare] = var\n",
    "        \n",
    "        all_rois_mean_runs.append(df_icc_roi_mean_runs.loc[df_icc_roi_mean_runs[\"Type\"]==\"ICC3\"])\n",
    "        \n",
    "df_icc_rois_mean_runs = pd.concat(all_rois_mean_runs)\n",
    "\n",
    "\n",
    "roi_grouped_by_stats_mean_runs = roi_grouped_by_stats_mean_runs.join(pd.concat(all_rois_mean_runs).set_index([\"roiName\"])[[\"ICC\", \"CI95%\"]])\n",
    "\n",
    "roi_grouped_by_stats_mean_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f51ae5a7-7a89-415b-aceb-97aa49efb488",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean +/- std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICC</th>\n",
       "      <td>0.59+/- 0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$R^2$</th>\n",
       "      <td>0.40+/- 0.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean +/- std\n",
       "ICC     0.59+/- 0.21\n",
       "$R^2$   0.40+/- 0.21"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([f'{roi_grouped_by_stats_mean_runs[\"ICC\"].mean(): 0.2f}+/-{roi_grouped_by_stats_mean_runs[\"ICC\"].std(): 0.2f}',\n",
    "           f'{roi_grouped_by_stats_mean_runs[\"r_square\"].mean(): 0.2f}+/-{roi_grouped_by_stats_mean_runs[\"r_square\"].std(): 0.2f}'], \n",
    "          index=['ICC', '$R^2$']).to_frame().rename(columns={0: \"mean +/- std\"})"
   ]
  }
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