03_singledataset_scatter_NIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import json\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "\n",
    "from src.database import *\n",
    "from src.nist import get_reference_NIST_values, get_NIST_ids\n",
    "from src.tools import calc_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('custom_matplotlibrc')\n",
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "fig_id = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "database_path = Path('databases/3T_NIST_T1maps_database.pkl')\n",
    "output_folder = Path(\"plots/03_singledataset_scatter_NIST/\")\n",
    "\n",
    "estimate_type = 'mean' # median or mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:5: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:16: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:5: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:16: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:5: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if y_type is 'linear':\n",
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if y_type is 'log':\n",
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/368739589.py:16: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if y_type is 'error_t1':\n"
     ]
    }
   ],
   "source": [
    "def plot_single_scatter(x, y, y_std,\n",
    "                        title, x_label, y_label,\n",
    "                        file_prefix, folder_path, fig_id,\n",
    "                        y_type='linear'):\n",
    "    if y_type is 'linear':\n",
    "        plt.errorbar(x,y, y_std, fmt='o', solid_capstyle='projecting')\n",
    "        ax = plt.gca()\n",
    "        ax.axline((1, 1), slope=1, linestyle='dashed')\n",
    "        ax.set_ylim(ymin=0, ymax=2500)\n",
    "        ax.set_xlim(xmin=0, xmax=2500)\n",
    "    if y_type is 'log':\n",
    "        plt.loglog(x,y,'o')\n",
    "        ax = plt.gca()\n",
    "        ax.set_ylim(ymin=20, ymax=2500)\n",
    "        ax.set_xlim(xmin=20, xmax=2500)\n",
    "    if y_type is 'error_t1':\n",
    "        plt.errorbar(x,calc_error(x,y), fmt='o')\n",
    "        ax = plt.gca()\n",
    "        ax.axline((1, 0), slope=0, color='k')\n",
    "        ax.axline((1, -10), slope=0, linestyle='dashed', color='k')\n",
    "        ax.axline((1, 10), slope=0, linestyle='dashed', color='k')\n",
    "        ax.set_ylim(ymin=-100, ymax=100)\n",
    "        ax.set_xlim(xmin=0, xmax=2500)\n",
    "\n",
    "\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "\n",
    "    fig = plt.gcf()\n",
    "    \n",
    "\n",
    "    folder_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    if fig_id<10:\n",
    "        filename = \"0\" + str(fig_id) + \"_\" + file_prefix\n",
    "    else:\n",
    "        filename = str(fig_id) + \"_\" + file_prefix\n",
    "\n",
    "    fig.savefig(folder_path / (str(filename) + '.svg'), facecolor='white')\n",
    "    fig.savefig(folder_path / (str(filename) + '.png'), facecolor='white')\n",
    "    fig_id = fig_id + 1\n",
    "    plt.show()\n",
    "    return fig_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(database_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize variables, store mean and standard deviations of T1 in the ROIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:9: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:9: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/3451975214.py:9: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if estimate_type is 'mean':\n",
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_1546/3451975214.py:11: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  elif estimate_type is 'median':\n"
     ]
    }
   ],
   "source": [
    "dataset_estimate = np.array([])\n",
    "dataset_std = np.array([])\n",
    "\n",
    "index = 6.001\n",
    "\n",
    "serial_number = df.loc[index]['phantom serial number']\n",
    "\n",
    "for key in get_NIST_ids():\n",
    "    if estimate_type is 'mean':\n",
    "        dataset_estimate = np.append(dataset_estimate, np.mean(df.loc[index][key]))\n",
    "    elif estimate_type is 'median':\n",
    "        dataset_estimate = np.append(dataset_estimate, np.median(df.loc[index][key]))\n",
    "    else:\n",
    "        Exception('Unsupported dataset estimate type.')\n",
    "\n",
    "    dataset_std = np.append(dataset_std, np.std(df.loc[index][key]))\n",
    "\n",
    "ref_values = get_reference_NIST_values(serial_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_id = plot_single_scatter(x=ref_values,\n",
    "                             y=dataset_estimate,\n",
    "                             y_std=dataset_std,\n",
    "                             title='Linear axes',\n",
    "                             x_label='Reference T$_1$ (ms)',\n",
    "                             y_label=\"T$_1$ estimate (ms)\",\n",
    "                             file_prefix=str(index),\n",
    "                             folder_path=output_folder,\n",
    "                             fig_id=fig_id,\n",
    "                             y_type='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_id = plot_single_scatter(x=ref_values,\n",
    "                             y=dataset_estimate,\n",
    "                             y_std=dataset_std,\n",
    "                             title='Log-log axes',\n",
    "                             x_label='Reference T$_1$ (ms)',\n",
    "                             y_label=\"T$_1$ estimate (ms)\",\n",
    "                             file_prefix=str(index),\n",
    "                             folder_path=output_folder,\n",
    "                             fig_id=fig_id,\n",
    "                             y_type='log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_id = plot_single_scatter(x=ref_values,\n",
    "                             y=dataset_estimate,\n",
    "                             y_std=dataset_std,\n",
    "                             title='Error relative to reference',\n",
    "                             x_label='Reference T$_1$ (ms)',\n",
    "                             y_label=\"Error (%)\",\n",
    "                             file_prefix=str(index),\n",
    "                             folder_path=output_folder,\n",
    "                             fig_id=fig_id,\n",
    "                             y_type='error_t1')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "analysis_arm",
   "language": "python",
   "name": "analysis_arm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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