09_human_stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import dataframe_image as dfi\n",
    "import numpy as np\n",
    "\n",
    "from src.database import *\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('custom_matplotlibrc')\n",
    "plt.rcParams[\"figure.figsize\"] = (20,5)\n",
    "fig_id = 0\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "pd.set_option('display.precision', 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "database_path = Path('databases/3T_human_T1maps_database.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(database_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare stats table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'genu WM',\n",
    "    'splenium WM',\n",
    "    'cortical GM',\n",
    "    'deep GM'\n",
    "]\n",
    "\n",
    "col_vals = [\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None\n",
    "]\n",
    "\n",
    "df_setup = {\n",
    "    'all submissions mean T1': col_vals,\n",
    "    'inter-submission mean T1': col_vals,\n",
    "    'intra-submission mean T1': col_vals,\n",
    "    'all submissions T1 STD': col_vals,\n",
    "    'inter-submission T1 STD': col_vals,\n",
    "    'intra-submission T1 STD': col_vals,\n",
    "    'all submissions COV [%]': col_vals,\n",
    "    'inter-submission COV [%]': col_vals,\n",
    "    'intra-submission COV [%]': col_vals\n",
    "}\n",
    "\n",
    "stats_table = pd.DataFrame.from_dict(df_setup, orient='index', columns=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/mathieuboudreau/opt/anaconda3/envs/analysis_arm/lib/python3.8/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "genu_estimate = np.array([])\n",
    "genu_std = np.array([])\n",
    "splenium_estimate = np.array([])\n",
    "splenium_std = np.array([])\n",
    "deepgm_estimate = np.array([])\n",
    "deepgm_std = np.array([])\n",
    "cgm_estimate = np.array([])\n",
    "cgm_std = np.array([])\n",
    "\n",
    "ii = 0\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    genu_estimate = np.append(genu_estimate, np.mean(df.loc[index]['T1 - genu (WM)']))\n",
    "    splenium_estimate = np.append(splenium_estimate, np.mean(df.loc[index]['T1 - splenium (WM)']))\n",
    "    deepgm_estimate = np.append(deepgm_estimate, np.mean(df.loc[index]['T1 - deep GM']))\n",
    "    cgm_estimate = np.append(cgm_estimate, np.mean(df.loc[index]['T1 - cortical GM']))\n",
    "\n",
    "    ii = ii +1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['genu WM']['all submissions mean T1'] = np.nanmean(genu_estimate)\n",
    "stats_table['genu WM']['all submissions T1 STD'] = np.nanstd(genu_estimate)\n",
    "stats_table['genu WM']['all submissions COV [%]'] = np.divide(np.nanstd(genu_estimate),np.nanmean(genu_estimate)) * 100\n",
    "\n",
    "stats_table['splenium WM']['all submissions mean T1'] = np.nanmean(splenium_estimate)\n",
    "stats_table['splenium WM']['all submissions T1 STD'] = np.nanstd(splenium_estimate)\n",
    "stats_table['splenium WM']['all submissions COV [%]'] = np.divide(np.nanstd(splenium_estimate),np.nanmean(splenium_estimate)) * 100\n",
    "\n",
    "stats_table['cortical GM']['all submissions mean T1'] = np.nanmean(cgm_estimate)\n",
    "stats_table['cortical GM']['all submissions T1 STD'] = np.nanstd(cgm_estimate)\n",
    "stats_table['cortical GM']['all submissions COV [%]'] = np.divide(np.nanstd(cgm_estimate),np.nanmean(cgm_estimate)) * 100\n",
    "\n",
    "stats_table['deep GM']['all submissions mean T1'] = np.nanmean(deepgm_estimate)\n",
    "stats_table['deep GM']['all submissions T1 STD'] = np.nanstd(deepgm_estimate)\n",
    "stats_table['deep GM']['all submissions COV [%]'] = np.divide(np.nanstd(deepgm_estimate),np.nanmean(deepgm_estimate)) * 100\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inter-submision stats\n",
    "One subject/measurement per submission were selected. The protocol closest to the proposed one was selected, preferring complex data over magnitude-only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting one subject/measurement per submission, protocol closest to proposed, complex if available.\n",
    "ids_intersubmissions = (\n",
    "    1.001,\n",
    "    2.003,\n",
    "    3.001,\n",
    "    5.001,\n",
    "    6.001,\n",
    "    7.001,\n",
    "    8.002,\n",
    "    9.001,\n",
    "    10.001\n",
    ")\n",
    "\n",
    "genu_estimate = np.array([])\n",
    "genu_std = np.array([])\n",
    "splenium_estimate = np.array([])\n",
    "splenium_std = np.array([])\n",
    "deepgm_estimate = np.array([])\n",
    "deepgm_std = np.array([])\n",
    "cgm_estimate = np.array([])\n",
    "cgm_std = np.array([])\n",
    "\n",
    "ii = 0\n",
    "for index in ids_intersubmissions:\n",
    "    \n",
    "    genu_estimate = np.append(genu_estimate, np.mean(df.loc[index]['T1 - genu (WM)']))\n",
    "    splenium_estimate = np.append(splenium_estimate, np.mean(df.loc[index]['T1 - splenium (WM)']))\n",
    "    deepgm_estimate = np.append(deepgm_estimate, np.mean(df.loc[index]['T1 - deep GM']))\n",
    "    cgm_estimate = np.append(cgm_estimate, np.mean(df.loc[index]['T1 - cortical GM']))\n",
    "    \n",
    "    ii = ii +1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['genu WM']['inter-submission mean T1'] = np.nanmean(genu_estimate)\n",
    "stats_table['genu WM']['inter-submission T1 STD'] = np.nanstd(genu_estimate)\n",
    "stats_table['genu WM']['inter-submission COV [%]'] = np.divide(np.nanstd(genu_estimate),np.nanmean(genu_estimate)) * 100\n",
    "\n",
    "stats_table['splenium WM']['inter-submission mean T1'] = np.nanmean(splenium_estimate)\n",
    "stats_table['splenium WM']['inter-submission T1 STD'] = np.nanstd(splenium_estimate)\n",
    "stats_table['splenium WM']['inter-submission COV [%]'] = np.divide(np.nanstd(splenium_estimate),np.nanmean(splenium_estimate)) * 100\n",
    "\n",
    "stats_table['cortical GM']['inter-submission mean T1'] = np.nanmean(cgm_estimate)\n",
    "stats_table['cortical GM']['inter-submission T1 STD'] = np.nanstd(cgm_estimate)\n",
    "stats_table['cortical GM']['inter-submission COV [%]'] = np.divide(np.nanstd(cgm_estimate),np.nanmean(cgm_estimate)) * 100\n",
    "\n",
    "stats_table['deep GM']['inter-submission mean T1'] = np.nanmean(deepgm_estimate)\n",
    "stats_table['deep GM']['inter-submission T1 STD'] = np.nanstd(deepgm_estimate)\n",
    "stats_table['deep GM']['inter-submission COV [%]'] = np.divide(np.nanstd(deepgm_estimate),np.nanmean(deepgm_estimate)) * 100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "genu_estimate_inter = genu_estimate\n",
    "splenium_estimate_inter = splenium_estimate\n",
    "deepgm_estimate_inter = deepgm_estimate\n",
    "cgm_estimate_inter = cgm_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intra-submision stats\n",
    "All unique T1 maps from Site 9 were used for the stats calculations. Site 9 acquired maps on 3 different scanners, 2 different vendors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting one submission\n",
    "ids_intrasubmissions = (\n",
    "9.001,\n",
    "9.002,\n",
    "9.006,\n",
    "9.007,\n",
    "9.009,\n",
    "9.01,\n",
    "9.012,\n",
    "9.013,\n",
    "9.015,\n",
    "9.016,\n",
    "9.018,\n",
    "9.019,\n",
    "9.02,\n",
    "9.021,\n",
    "9.023,\n",
    "9.025,\n",
    "9.027,\n",
    "9.028,\n",
    "9.03,\n",
    "9.031,\n",
    "9.033,\n",
    "9.034,\n",
    "9.036,\n",
    "9.037\n",
    ")\n",
    "\n",
    "genu_estimate = np.array([])\n",
    "genu_std = np.array([])\n",
    "splenium_estimate = np.array([])\n",
    "splenium_std = np.array([])\n",
    "deepgm_estimate = np.array([])\n",
    "deepgm_std = np.array([])\n",
    "cgm_estimate = np.array([])\n",
    "cgm_std = np.array([])\n",
    "\n",
    "ii = 0\n",
    "for index in ids_intrasubmissions:\n",
    "    \n",
    "    genu_estimate = np.append(genu_estimate, np.mean(df.loc[index]['T1 - genu (WM)']))\n",
    "    splenium_estimate = np.append(splenium_estimate, np.mean(df.loc[index]['T1 - splenium (WM)']))\n",
    "    deepgm_estimate = np.append(deepgm_estimate, np.mean(df.loc[index]['T1 - deep GM']))\n",
    "    cgm_estimate = np.append(cgm_estimate, np.mean(df.loc[index]['T1 - cortical GM']))\n",
    "\n",
    "    ii = ii +1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['genu WM']['intra-submission mean T1'] = np.nanmean(genu_estimate)\n",
    "stats_table['genu WM']['intra-submission T1 STD'] = np.nanstd(genu_estimate)\n",
    "stats_table['genu WM']['intra-submission COV [%]'] = np.divide(np.nanstd(genu_estimate),np.nanmean(genu_estimate)) * 100\n",
    "\n",
    "stats_table['splenium WM']['intra-submission mean T1'] = np.nanmean(splenium_estimate)\n",
    "stats_table['splenium WM']['intra-submission T1 STD'] = np.nanstd(splenium_estimate)\n",
    "stats_table['splenium WM']['intra-submission COV [%]'] = np.divide(np.nanstd(splenium_estimate),np.nanmean(splenium_estimate)) * 100\n",
    "\n",
    "stats_table['cortical GM']['intra-submission mean T1'] = np.nanmean(cgm_estimate)\n",
    "stats_table['cortical GM']['intra-submission T1 STD'] = np.nanstd(cgm_estimate)\n",
    "stats_table['cortical GM']['intra-submission COV [%]'] = np.divide(np.nanstd(cgm_estimate),np.nanmean(cgm_estimate)) * 100\n",
    "\n",
    "stats_table['deep GM']['intra-submission mean T1'] = np.nanmean(deepgm_estimate)\n",
    "stats_table['deep GM']['intra-submission T1 STD'] = np.nanstd(deepgm_estimate)\n",
    "stats_table['deep GM']['intra-submission COV [%]'] = np.divide(np.nanstd(deepgm_estimate),np.nanmean(deepgm_estimate)) * 100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "genu_estimate_intra = genu_estimate\n",
    "splenium_estimate_intra = splenium_estimate\n",
    "deepgm_estimate_intra = deepgm_estimate\n",
    "cgm_estimate_intra = cgm_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
       "      <th></th>\n",
       "      <th>genu WM</th>\n",
       "      <th>splenium WM</th>\n",
       "      <th>cortical GM</th>\n",
       "      <th>deep GM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all submissions mean T1</th>\n",
       "      <td>827.9</td>\n",
       "      <td>851.7</td>\n",
       "      <td>1548.4</td>\n",
       "      <td>1188.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission mean T1</th>\n",
       "      <td>836.1</td>\n",
       "      <td>869.3</td>\n",
       "      <td>1550.6</td>\n",
       "      <td>1268.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission mean T1</th>\n",
       "      <td>819.5</td>\n",
       "      <td>839.7</td>\n",
       "      <td>1586.6</td>\n",
       "      <td>1196.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all submissions T1 STD</th>\n",
       "      <td>37.5</td>\n",
       "      <td>48.9</td>\n",
       "      <td>155.5</td>\n",
       "      <td>132.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission T1 STD</th>\n",
       "      <td>49.2</td>\n",
       "      <td>92.5</td>\n",
       "      <td>248.8</td>\n",
       "      <td>278.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission T1 STD</th>\n",
       "      <td>25.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>109.6</td>\n",
       "      <td>84.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all submissions COV [%]</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission COV [%]</th>\n",
       "      <td>5.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission COV [%]</th>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         genu WM splenium WM cortical GM deep GM\n",
       "all submissions mean T1    827.9     851.7      1548.4    1188.4\n",
       "inter-submission mean T1   836.1     869.3      1550.6    1268.2\n",
       "intra-submission mean T1   819.5     839.7      1586.6    1196.3\n",
       "all submissions T1 STD      37.5      48.9       155.5     132.6\n",
       "inter-submission T1 STD     49.2      92.5       248.8     278.5\n",
       "intra-submission T1 STD     25.9      26.0       109.6      84.6\n",
       "all submissions COV [%]      4.5       5.7        10.0      11.2\n",
       "inter-submission COV [%]     5.9      10.6        16.0      22.0\n",
       "intra-submission COV [%]     3.2       3.1         6.9       7.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1204/154924.315752:WARNING:angle_platform_impl.cc(49)] RendererVk.cpp:1962 (initialize): ANGLE VMA version: 2003000\n",
      "63039 bytes written to file /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/tmp907n_ef_/temp.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/mathieuboudreau/neuropoly/github/analysis/plots/09_human_stats/human_stats.png')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd = os.getcwd()\n",
    "folder_path = Path(\"plots/09_human_stats/\")\n",
    "dfi.export(stats_table, 'human_stats.png')\n",
    "\n",
    "shutil.move('human_stats.png', Path(pwd) / folder_path / 'human_stats.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Violin plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_36173/2399684766.py:16: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='count'` for the same effect.\n",
      "  ax = sns.violinplot(x='Region', y='T1 (ms)', hue='Type', data=df, split=True, inner=\"quartile\", scale=\"count\", palette=\"Set2\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Combine data into a single DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'T1 (ms)': np.concatenate([genu_estimate_inter, genu_estimate_intra, splenium_estimate_inter, splenium_estimate_intra, cgm_estimate_inter, cgm_estimate_intra, deepgm_estimate_inter, deepgm_estimate_intra]),\n",
    "    'Group': ['Genu'] * len(genu_estimate_inter) + ['Genu'] * len(genu_estimate_intra) + ['Splenium'] * len(splenium_estimate_inter) + ['Splenium'] * len(splenium_estimate_intra) + ['Cgm'] * len(cgm_estimate_inter) + ['Cgm'] * len(cgm_estimate_intra) + ['DeepGM'] * len(deepgm_estimate_inter) + ['DeepGM'] * len(deepgm_estimate_intra),\n",
    "    'Type': ['Inter'] * len(genu_estimate_inter) + ['Intra'] * len(genu_estimate_intra) + ['Inter'] * len(splenium_estimate_inter) + ['Intra'] * len(splenium_estimate_intra) + ['Inter'] * len(cgm_estimate_inter) + ['Intra'] * len(cgm_estimate_intra) + ['Inter'] * len(deepgm_estimate_inter) + ['Intra'] * len(deepgm_estimate_intra),\n",
    "    'Region': ['Genu'] * len(genu_estimate_inter) + ['Genu'] * len(genu_estimate_intra) + ['Splenium'] * len(splenium_estimate_inter) + ['Splenium'] * len(splenium_estimate_intra) + ['Cgm'] * len(cgm_estimate_inter) + ['Cgm'] * len(cgm_estimate_intra) + ['DeepGM'] * len(deepgm_estimate_inter) + ['DeepGM'] * len(deepgm_estimate_intra),\n",
    "})\n",
    "\n",
    "# Set the color palette\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "# Create a violin plot using seaborn\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(x='Region', y='T1 (ms)', hue='Type', data=df, split=True, inner=\"quartile\", scale=\"count\", palette=\"Set2\")\n",
    "\n",
    "\n",
    "# Make the legend larger\n",
    "ax.legend(loc='upper left', title=\"Legend\", prop={'size':25})\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "vscode": {
   "interpreter": {
    "hash": "021e32979ba0b507138b043ad23cc6ba88d193e810337e0e3a2521ed1faaaccb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}