{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0327d175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.15'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyAMARES\n",
    "import numpy as np\n",
    "\n",
    "pyAMARES.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50c6216",
   "metadata": {},
   "source": [
    "# Fitting Simulated In Vivo 31P MRS Data\n",
    "**[Try This Tutorial on Google Colab!](https://colab.research.google.com/drive/1H8GdP4XX292JovAF5TNOEBaYQT5vk_DT)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8954278",
   "metadata": {},
   "source": [
    "## Simulating an In Vivo MRS Spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3958be",
   "metadata": {},
   "source": [
    "- **Load Prior Knowledge**: Use the dataset based on the 7T brain data reported by Ren et al. in NMR Biomedicine, 28(11): 1455–1462."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8641feac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning, fid is None!\n",
      "Checking comment lines in the prior knowledge file\n",
      "Comment: in line 0 \"#  Ren et al, NMR Biomed . 2015 Nov;28(11):1455-62. doi: 10.1002/nbm.3384.\",,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 14 # Phase not fixed ,,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 15 \" # 2024/06/24 In Ren et al, the chemical shift range of BATP (0.1 ppm), AATP (0.04 ppm), and GATP (0.02 ppm) are smaller than the J-couplin range (0.125 - 0.25 ppm). \",,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 16 \"# So, increase the chemical shift range\",,,,,,,,,,,,,,,,\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the Prior Knowledge File ./attachment/example_human_brain_31P_7T_flex_phase.csv\n"
     ]
    },
    {
     "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>BATP</th>\n",
       "      <th>BATP2</th>\n",
       "      <th>BATP3</th>\n",
       "      <th>AATP</th>\n",
       "      <th>AATP2</th>\n",
       "      <th>GATP</th>\n",
       "      <th>GATP2</th>\n",
       "      <th>UDPG</th>\n",
       "      <th>NAD</th>\n",
       "      <th>PCr</th>\n",
       "      <th>GPC</th>\n",
       "      <th>GPE</th>\n",
       "      <th>Pin</th>\n",
       "      <th>Pex</th>\n",
       "      <th>PC</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</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>Initial Values</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amplitude</th>\n",
       "      <td>1.41</td>\n",
       "      <td>BATP/2</td>\n",
       "      <td>BATP/2</td>\n",
       "      <td>1.545</td>\n",
       "      <td>AATP</td>\n",
       "      <td>1.5</td>\n",
       "      <td>GATP</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.41</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chemicalshift</th>\n",
       "      <td>-16.15</td>\n",
       "      <td>BATP-15Hz</td>\n",
       "      <td>BATP+15Hz</td>\n",
       "      <td>-7.49</td>\n",
       "      <td>AATP-16Hz</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>GATP-16Hz</td>\n",
       "      <td>-9.72</td>\n",
       "      <td>-8.25</td>\n",
       "      <td>0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.82</td>\n",
       "      <td>5.24</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linewidth</th>\n",
       "      <td>58.12</td>\n",
       "      <td>BATP</td>\n",
       "      <td>BATP</td>\n",
       "      <td>32.28</td>\n",
       "      <td>AATP</td>\n",
       "      <td>39.02</td>\n",
       "      <td>GATP</td>\n",
       "      <td>32.37</td>\n",
       "      <td>40.49</td>\n",
       "      <td>15.41</td>\n",
       "      <td>19.96</td>\n",
       "      <td>19.1</td>\n",
       "      <td>21.04</td>\n",
       "      <td>30.91</td>\n",
       "      <td>19.96</td>\n",
       "      <td>22.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phase</th>\n",
       "      <td>0</td>\n",
       "      <td>BATP</td>\n",
       "      <td>BATP</td>\n",
       "      <td>0</td>\n",
       "      <td>AATP</td>\n",
       "      <td>0</td>\n",
       "      <td>GATP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bounds</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amplitude</th>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "      <td>(0,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chemicalshift</th>\n",
       "      <td>(-16.30,-16.00)</td>\n",
       "      <td>(-16.30,-16.00)</td>\n",
       "      <td>(-16.30,-16.00)</td>\n",
       "      <td>(-7.72,-7.42)</td>\n",
       "      <td>(-7.72,-7.42)</td>\n",
       "      <td>(-2.65,-2.39)</td>\n",
       "      <td>(-2.65,-2.39)</td>\n",
       "      <td>(-9.76,-9.68)</td>\n",
       "      <td>(-8.25,-8.17)</td>\n",
       "      <td>(-0.5, 0.5)</td>\n",
       "      <td>(2.94,2.96)</td>\n",
       "      <td>(3.49,3.51)</td>\n",
       "      <td>(4.81,4.83)</td>\n",
       "      <td>(5.19,5.29)</td>\n",
       "      <td>(6.22,6.26)</td>\n",
       "      <td>(6.71,6.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linewidth</th>\n",
       "      <td>(54.335, 61.911)</td>\n",
       "      <td>(54.335, 61.911)</td>\n",
       "      <td>(54.335, 61.911)</td>\n",
       "      <td>(31.226, 33.327)</td>\n",
       "      <td>(31.226, 33.327)</td>\n",
       "      <td>(36.892, 41.157)</td>\n",
       "      <td>(36.892, 41.157)</td>\n",
       "      <td>(29.125, 35.619)</td>\n",
       "      <td>(34.823, 46.155)</td>\n",
       "      <td>(13.21, 17.603)</td>\n",
       "      <td>(18.557, 21.359)</td>\n",
       "      <td>(17.348, 20.849)</td>\n",
       "      <td>(19.353, 22.727)</td>\n",
       "      <td>(22.091, 39.725)</td>\n",
       "      <td>(15.756, 24.16)</td>\n",
       "      <td>(20.658, 24.605)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phase</th>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "      <td>(-180, 180)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>(0,1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th># 2024/06/24 In Ren et al, the chemical shift range of BATP (0.1 ppm), AATP (0.04 ppm), and GATP (0.02 ppm) are smaller than the J-couplin range (0.125 - 0.25 ppm).</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th># So, increase the chemical shift range</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                BATP  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       1.41   \n",
       "chemicalshift                                                 -16.15   \n",
       "linewidth                                                      58.12   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                        (-16.30,-16.00)   \n",
       "linewidth                                           (54.335, 61.911)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                               BATP2  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                     BATP/2   \n",
       "chemicalshift                                              BATP-15Hz   \n",
       "linewidth                                                       BATP   \n",
       "phase                                                           BATP   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                        (-16.30,-16.00)   \n",
       "linewidth                                           (54.335, 61.911)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                               BATP3  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                     BATP/2   \n",
       "chemicalshift                                              BATP+15Hz   \n",
       "linewidth                                                       BATP   \n",
       "phase                                                           BATP   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                        (-16.30,-16.00)   \n",
       "linewidth                                           (54.335, 61.911)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                AATP  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                      1.545   \n",
       "chemicalshift                                                  -7.49   \n",
       "linewidth                                                      32.28   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-7.72,-7.42)   \n",
       "linewidth                                           (31.226, 33.327)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                               AATP2  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       AATP   \n",
       "chemicalshift                                              AATP-16Hz   \n",
       "linewidth                                                       AATP   \n",
       "phase                                                           AATP   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-7.72,-7.42)   \n",
       "linewidth                                           (31.226, 33.327)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                GATP  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                        1.5   \n",
       "chemicalshift                                                  -2.46   \n",
       "linewidth                                                      39.02   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-2.65,-2.39)   \n",
       "linewidth                                           (36.892, 41.157)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                               GATP2  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       GATP   \n",
       "chemicalshift                                              GATP-16Hz   \n",
       "linewidth                                                       GATP   \n",
       "phase                                                           GATP   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-2.65,-2.39)   \n",
       "linewidth                                           (36.892, 41.157)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                UDPG  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       0.08   \n",
       "chemicalshift                                                  -9.72   \n",
       "linewidth                                                      32.37   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-9.76,-9.68)   \n",
       "linewidth                                           (29.125, 35.619)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                 NAD  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       0.41   \n",
       "chemicalshift                                                  -8.25   \n",
       "linewidth                                                      40.49   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                          (-8.25,-8.17)   \n",
       "linewidth                                           (34.823, 46.155)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                PCr  \\\n",
       "Index                                                                 \n",
       "Initial Values                                                  NaN   \n",
       "amplitude                                                      4.37   \n",
       "chemicalshift                                                     0   \n",
       "linewidth                                                     15.41   \n",
       "phase                                                             0   \n",
       "g                                                                 0   \n",
       "Bounds                                                          NaN   \n",
       "amplitude                                                      (0,    \n",
       "chemicalshift                                           (-0.5, 0.5)   \n",
       "linewidth                                           (13.21, 17.603)   \n",
       "phase                                                   (-180, 180)   \n",
       "g                                                             (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...              NaN   \n",
       "# So, increase the chemical shift range                         NaN   \n",
       "NaN                                                             NaN   \n",
       "\n",
       "                                                                 GPC  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       1.32   \n",
       "chemicalshift                                                   2.95   \n",
       "linewidth                                                      19.96   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                            (2.94,2.96)   \n",
       "linewidth                                           (18.557, 21.359)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                 GPE  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                        0.8   \n",
       "chemicalshift                                                    3.5   \n",
       "linewidth                                                       19.1   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                            (3.49,3.51)   \n",
       "linewidth                                           (17.348, 20.849)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                 Pin  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                       0.85   \n",
       "chemicalshift                                                   4.82   \n",
       "linewidth                                                      21.04   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                            (4.81,4.83)   \n",
       "linewidth                                           (19.353, 22.727)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                 Pex  \\\n",
       "Index                                                                  \n",
       "Initial Values                                                   NaN   \n",
       "amplitude                                                        0.3   \n",
       "chemicalshift                                                   5.24   \n",
       "linewidth                                                      30.91   \n",
       "phase                                                              0   \n",
       "g                                                                  0   \n",
       "Bounds                                                           NaN   \n",
       "amplitude                                                       (0,    \n",
       "chemicalshift                                            (5.19,5.29)   \n",
       "linewidth                                           (22.091, 39.725)   \n",
       "phase                                                    (-180, 180)   \n",
       "g                                                              (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN   \n",
       "# So, increase the chemical shift range                          NaN   \n",
       "NaN                                                              NaN   \n",
       "\n",
       "                                                                 PC  \\\n",
       "Index                                                                 \n",
       "Initial Values                                                  NaN   \n",
       "amplitude                                                       0.3   \n",
       "chemicalshift                                                  6.24   \n",
       "linewidth                                                     19.96   \n",
       "phase                                                             0   \n",
       "g                                                                 0   \n",
       "Bounds                                                          NaN   \n",
       "amplitude                                                      (0,    \n",
       "chemicalshift                                           (6.22,6.26)   \n",
       "linewidth                                           (15.756, 24.16)   \n",
       "phase                                                   (-180, 180)   \n",
       "g                                                             (0,1)   \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...              NaN   \n",
       "# So, increase the chemical shift range                         NaN   \n",
       "NaN                                                             NaN   \n",
       "\n",
       "                                                                  PE  \n",
       "Index                                                                 \n",
       "Initial Values                                                   NaN  \n",
       "amplitude                                                       2.27  \n",
       "chemicalshift                                                   6.76  \n",
       "linewidth                                                      22.63  \n",
       "phase                                                              0  \n",
       "g                                                                  0  \n",
       "Bounds                                                           NaN  \n",
       "amplitude                                                       (0,   \n",
       "chemicalshift                                            (6.71,6.81)  \n",
       "linewidth                                           (20.658, 24.605)  \n",
       "phase                                                    (-180, 180)  \n",
       "g                                                              (0,1)  \n",
       " # 2024/06/24 In Ren et al, the chemical shift ...               NaN  \n",
       "# So, increase the chemical shift range                          NaN  \n",
       "NaN                                                              NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "priorknowledge = pyAMARES.initialize_FID(\n",
    "    fid=None,\n",
    "    priorknowledgefile=\"./attachment/example_human_brain_31P_7T_flex_phase.csv\",\n",
    "    preview=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2247c3",
   "metadata": {},
   "source": [
    "- **Perturb Peak Parameters**: Randomly adjust the 31P spectra peak parameters by 5%, chemical shift by 10 Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a13a8bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23412e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "params0 = deepcopy(\n",
    "    priorknowledge.initialParams\n",
    ")  # Make a copy of initialParams to be perturbed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71dc27a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturb_value(value, percentage=5):\n",
    "    percentage = float(percentage)\n",
    "    # Generate a random perturbation factor between 0.95 and 1.05\n",
    "    factor = np.random.uniform(1 - percentage / 100, 1 + percentage / 100)\n",
    "    # Apply the perturbation factor\n",
    "    result = value * factor\n",
    "    # print(f\"Perturbing input {value=} to {result=}\")\n",
    "    return result\n",
    "\n",
    "\n",
    "def perturb_table(inputparams, percentage=5, freq_shift=5, phase_shift=0):\n",
    "    params = deepcopy(inputparams)\n",
    "    for i in params:\n",
    "        if params[i].name.startswith(\"ak\") or params[i].name.startswith(\"dk\"):\n",
    "            params[i].value = perturb_value(params[i].value)\n",
    "        if params[i].name.startswith(\"freq\"):\n",
    "            params[i].value += np.random.uniform(-freq_shift, freq_shift)\n",
    "        if params[i].name.startswith(\"phi\"):\n",
    "            params[i].value += np.random.uniform(\n",
    "                -np.deg2rad(phase_shift), np.deg2rad(phase_shift)\n",
    "            )\n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdfa4eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = perturb_table(params0, percentage=5, freq_shift=5, phase_shift=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "222e43d1",
   "metadata": {},
   "source": [
    "- **Simulate the 31P MRS Spectra Using Scanner Parameters**:\n",
    "    - **MHz (Field Strength)**: 120 MHz, corresponding to 31P at 7T.\n",
    "    - **sw (Spectral Width)**: 10000.0 Hz.\n",
    "    - **Deadtime**: 200 microseconds (200e-6 seconds).\n",
    "    - **Number of Points (fid_len)**: 1024.\n",
    "    - **SNR (Signal to Noise Ratio, snr_target)**: 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1181362b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZSKNCGmMeAB6oNnkjMNjPsiXAJXUvmlJNS9MyKpLpFapKBaEdeWsKls5V9as2n7cGd6UcaPzyLz4+nn379mmAbySe8dzj4+PDWk9v1qGUCounZ8eePXuauihHDc+dmMKhwV0pB5qO8S8mJiasOwKppqFpGaUcaNZBRTIN7koFoTV4FYk0uCsVhNbgVSTS4K6UUi2QBnelgtC0jIpEGtyVcmD0GlUVwTS4KxWEjueuIpEGd6WC0Bq8ikQa3JVSqgXS4K5UEJqWUZFIg7tSSrVAGtyVcqAXL6lIpsFdKaVaIA3uSinVAmlwVyoIvUJVRSIN7kop1QJpcFfKgbanqkimwV0ppVogDe5KOdBUu4pkGtyVcqBpGRXJNLgrpVQLpMFdKaVaIA3uSinVAmlwV0qpFkiDu1IOjI4cpiKYBnelghAdf0BFIA3uSgWhNXgViTS4K6VUC6TBXakgNC2jIlFIwV1E2ojIeyKyWkRWicgwEUkTkWkiss7+m2ovKyLynIisF5GlInJCw+6CUg1DkzEqkoVac38W+MIY0xPoD6wC7gZmGGO6AzPs1wBnA93tx03AC/VaYqUamdbbVSQKGtxFpDVwKjAJwBhzxBhzEDgfeM1e7DXgAvv5+cDrxjIPaCMiHeq53Eo1Gq3Bq0gUSs29K7AHeEVEFonIRBFpBbQzxuywl9kJtLOfdwS2+qyfZ0+rQkRuEpGFIrJwz549td8DpZRSNYQS3KOBE4AXjDEDgcNUpmAAMFZfsbAqOMaYF40xucaY3MzMzHBWVapRaVpGRaJQgnsekGeMmW+/fg8r2O/ypFvsv7vt+duATj7rZ9vTlFJKNZKgwd0YsxPYKiLH2ZNGASuBT4Bx9rRxwMf280+Aq+1eM0OBQz7pG6WUUo0gOsTlbgHeFJFYYCNwLdaJYbKIXA9sAS61l/0MGAusB4rsZZVSSjWikIK7MWYxkOtn1ig/yxrgt3UrllLNgHaTURFMr1BVyoGxo7teoKoikQZ3pRzoeGEqkmlwV0qpFkiDu1IOtOauIpkGd6UcaGxXkUyDu1IO9CYdKpJpcFfKgSe0a2cZFYk0uCsVhNbfVSTS4K6UA83KqEimwV0pR/ZFTE1cCqVqQ4O7Ug605q4imQZ3pZRqgTS4K+VAK+4qkmlwV8qBpmVUJNPgrpSDylEhtUlVRR4N7ko50Jq7imQa3JVSqgXS4K6UA624q0imwV0pBzpwmIpkGtyVCkKbU1Uk0uCulAOtuKtIpsFdKaVaIA3uSjkw2qSqIpgGd6UcaFpGRTIN7ko50OCuIpkGd6UceG+zp91lVATS4K6UUi2QBnelHOhFTCqSaXBXyoGGdhXJNLgr5USju4pgGtyVUqoF0uCulAO9iElFMg3uSjnQ9lQVyTS4K+WgMrZrR3cVeUIO7iLiEpFFIjLFft1VROaLyHoReUdEYu3pcfbr9fb8nAYqu1INSmvuKpKFU3P/A7DK5/VjwNPGmG7AAeB6e/r1wAF7+tP2ckoppRpRSMFdRLKBc4CJ9msBRgLv2Yu8BlxgPz/ffo09f5To7eNVBNIGVRXJQq25PwP8CXDbr9OBg8aYcvt1HtDRft4R2Apgzz9kL1+FiNwkIgtFZOGePXtqV3qlGpCmZVQkCxrcReRcYLcx5of6fGNjzIvGmFxjTG5mZmZ9blqpeqEDh6lIFh3CMsOBn4nIWCAeSAGeBdqISLRdO88GttnLbwM6AXkiEg20BvbVe8mVamhadVcRLGjN3RhzjzEm2xiTA1wOfGWMuRKYCVxsLzYO+Nh+/on9Gnv+V0ZHYFJKqUZVl37udwG3i8h6rJz6JHv6JCDdnn47cHfdiqhU09AaiYpkoaRlvIwxs4BZ9vONwGA/y5QAl9RD2ZRqUvp7U0UyvUJVKQeaTVSRTIO7Ug68vWWatBRK1Y4Gd6UceCru2hVSRSIN7ko5cNvRXbTuriKQBnellGqBNLgr5cBbc9eKu4pAGtyVcqA5dxXJNLgr5aCyt4xGdxV5NLgr5cDtrbo3bTmUqg0N7ko50GuYVCTT4K6UA+PtCqlU5NHgrpSDygZVDe8q8mhwV8qBW9MyKoJpcFfKgeceqlpvV5FIg7tSDrSfu4pkGtyVcqBD/qpIpsFdKQee0K4xXkUiDe5KOfBcxKSxXUUiDe5KOdAau4pkGtyVclCZltEoryKPBnelHGhaRkUyDe5KOdGoriKYBnelHHhHhdQgryKQBnelHGiqXUUyDe5KOXB7K+4a5VXk0eCulANPUNcavIpEGtyVcuBNuTdgcC+vcPPAx8vZcai44d5EHZU0uCvlwNO/3d2A0f37zQd47bst3Pnu0gZ7D3V00uCulANPSG/I4O6KsoacLC2vaLD3UEcnDe5KOXB7a+4N9x7RLiu4l1VoYl/VLw3uSjnwVNgbsuYe67K+gmUV7gZ7D3V00uCulAO3qfq3IVTW3DW4q/qlwV0pR56ukA0X3T2b1rSMqm9Bg7uIdBKRmSKyUkRWiMgf7OlpIjJNRNbZf1Pt6SIiz4nIehFZKiInNPROKNUQPIG3ogGr7p6Uj9bcVX0LpeZeDtxhjOkNDAV+KyK9gbuBGcaY7sAM+zXA2UB3+3ET8EK9l1qpRuBuhK6QlTV3De6qfgUN7saYHcaYH+3nBcAqoCNwPvCavdhrwAX28/OB141lHtBGRDrUd8GVamimEXLulTV3Tcuo+hVWzl1EcoCBwHygnTFmhz1rJ9DOft4R2OqzWp49rfq2bhKRhSKycM+ePeGWW6kG5x1bpgFr7p73KCvXmruqXyEHdxFJAt4HbjXG5PvOM9bRH9Y3wBjzojEm1xiTm5mZGc6qStXZ3sJStuw7HHAZz9gyjVFzP6JpGVXPQgruIhKDFdjfNMZ8YE/e5Um32H9329O3AZ18Vs+2pynVbJz+xCxOe3xWwGUao5+751dBeUOeQdRRKZTeMgJMAlYZY57ymfUJMM5+Pg742Gf61XavmaHAIZ/0jVLNQkFJOQDuAEHVO7ZMg/aWsf42ZI8cdXQKpeY+HLgKGCkii+3HWOBR4AwRWQeMtl8DfAZsBNYDLwG/qf9iq6PRtJW7yLn7Uw4VldXbNvceLnWcVzm2TL29XQ0NeeJQR7foYAsYY74BxGH2KD/LG+C3dSyXUjW8NGcjACt35DPs2PR62eahojLaJsf7neduhLSMxnbVUPQKVRUxEmJdABSXldfbNgN1QTSNMHBYQ/bEUUc3De4qYiTEWMG96Ej9DY9b7nbupWIasSukUvVNg7uKGJ6au6cxtLZ889yBrgz1dIVsjOEHlKpvGtxVxPDUoI/U8YIf3z7lR8qdg6unUt+wOXcN7qphaHBXEcPTF7yufcJLfU4OAdMyjXCDbI3tqqFocFcRw5MeKa/j1Zy+Nf+AaZlG6S1TuW1tXFX1SYO7ihieni11rbn7pmUC9ZZpjJt1+G5br1JV9UmDu4oYnqBcXscRFEOtuRNmg+pny3ZwuDS8xl7fmntd2xKU8qXBXUUMT+AMlCcPhW8QDXSiCGdUyCVbD/KbN3/kb1NWhlUW322XanBX9UiDu4oY+cXWsAN1Tsv4BNFAozGGcxHTtoPFABwoOhJWWXy3XVpef/33ldLgriJGfokd3OvaoFpRGUQDpWXCGX7goD3eTZuE2LDK4rvt0jKtuav6o8FdRYz8Yk9aph67QgYafsD+G8rbHbJ/VaQkBB2uqQrffL6mZVR90uCuIsKRcjfFZVaNu64Nqr415MBdIU2Vv4F4auBRUcJ9Hy1jWd6hkMriu2ltUFX1SYO7iggFJZXD/Na15u45SUDgrpCeWnVFCMFd7HFT1+4s4I15P3HX+0tDKkuVtIzm3FU90uCuIoIn7QF1z7mXlIWWc/f8QghlzHWxR8XetNe6dV+H1v6HEa7Od9P1OSCaUhrcVUTI9xksrK4DefnW3AOdKMrsLpfhXDhaYqd84mJC+2r51tx9T2BK1ZUGdxUR8n0CX1kdg3tJmW9XSOdteWvuYeTcPX3wQ+2KbzS4qwaiwV1FBN/AV1HHi5g8aZkYlwRMy3jmhXIu8ZwIPDl8Q2gnIN9ta3BX9UmDu4oInj7u6a1iAzaChqKkrAIRSIyNDpiW8TTchpIG8jS6ek4IvpX9Q0VlPPjJCor95NR9fxXsPxzeBVBKBaLBXUUET602rVVsnRtUi49UkBDjIsYVFSQtY71PmdsdtDukp9HV01fd93zw0JSVvPrtZr5avbvmevZyqYkx5B0oCmc3lApIg7tq9vIOFPHSnI3EuqJoFRdd566QJeUVxMe4iHVJ4AZVT4rFBO9+6am5V9byK5ffsKcQgHg/jayek0bXjFZs2afBXdUfDe6q2bty4nwOFJXhNoYYl9T5IqbiI27io6OIdkUF7grpDu1iJ6jZXdK3oh/jsrpJHvaXlnF7gnsSm/cd1jHdVb3R4K6aPU+Ntm92a1xRUueukCXlFcTHuqwG1QDb8n2fYFePVq/Z++bS46LtG3v7GQ7Ys1rXjERKytzsLigNWn6lQqHBXTVr36zbC0BCjIv/XDWIGFeUt/95bZWWVRAfbeXcywIE7bIKQ3SUVesOFtyrn3B8X8VFW1+zQr/B3VqyY2oCALvzNbir+qHBXTVrv5w0H4A/jO5O2+T4eqm5F5dVkBBrBfdAufTyCjeJsVatO9DQwFCzL7zvy1g7uPu7AtXz/plJ1hWt+w5rcFf1Q4O7igi9O6QAEB0VVQ9dId3Ex0QRHayfu9uQGGuN8hhuzb3qvVGtv/7u0uRZLzM5DtDukKr+aHBXzdYtby0CoGf7ZE7pngFYjZN1vYjJtytkoGF2yyvcJMZZNfdgJ5TqNXffk4anYfbwEefg3i7FCu63T17CM9PXhrAXSgWmwV01S6XlFfxvyXYAbjjlGMQedtEVVbfeMgeLjrBudwGZyXHEx7gcg7vbbXAbKtMywRpUq5WppMqwwta8w6XOaZnWCTHeac9MX8fybaENGayUEw3uqtmocBuem7GOPQWlTF2xyzs9I6ny7kbB8uTB/LS/iLIKw4jj2hIXHUVpmf+RGD059laetEyQnHv1YYF9b7fnqbnPXrunxjjvbrfBFSWICBMu7ued/vp3m0PbIaUcaHBXfhljWL7tEG63wRjD+t0F9f4e+wpLWb0z3/t65urdPDVtLY9/uZqleQe909v7DJ9r1dxrn5bZW2g1WLYNUnP3XBGbYefCg9Xcq/dz982de2r1+w8f4bznv6myXLkd3AEuze3Ea9cNBmDywjwe+2I1fR/4krveW+r4/oWl5ZqnV35pcK+F7QeLm+2NFSrcxpvH/WL5Dk6Z8FWNRsO9haUcKqo6SNXTdlD1eGPeFs79xzcc8+fPeGvBVkY/NYezn/2aaSt3UV8GjZ/OmGe+9r7+Zr3V7XHywjxm+Fyq3zkt0fs8xiV1qrnvLbQCYUZSHPHRUVXGdvflCe7ZdhfF/JLAg3pVzxQVlJR7P/dA5a1wu3F57vQBnNYjk4lX5wLwwqwNFJSW887Crfxr1nrKK9w8/OlK7824Ac54ajYn/G2a436oo1dEB/fdBSUs3Ly/1lf1fbdhH8Mf/apG/+OV2/O55wOrtlQ9iFe4DSc9+hW3vr045PeZtWY3t769qFGuPjzjqdmcOmEmAHd/sIyt+4uZstTKXVe4DTNW7eKUx2bS/6Gp/LSviKemrsHtNjw7Yx3/nLkBsGqDf/l4hXeb9360DIBVO/K58fWF9VLO+Rv3eZ9v2FPIHZOX8Oq3m73TNu6xbnqR2yXV22MF7Jp7HYL72p0FxEZH0TYljriY4MG9Z/tkAHbllwTcrr8benhSM4F+aVS48fal9xjdux1nH9++yrSPFm3jzGfm8NLXm7j+1e+903ccssp1/8fLHd/jhy0H2HawmKJqDbrGmFr9CqpwmxrbcnLpf77zNowXHSlns30zk4a2eOtBFv10IOTll2w9SM7dnzq2dXyxfCffbdhXY/pTU9fwid021NyEdzffZuaDH7fx6OerWfnQWd4AcM8HS3lrwVYmXNyPS3M7+V2voKSMkjI3d0xezPZDJazekU+vDiks2LSf03u25ab/W0jegWKmr9rNnoJSNj96jnddT4+Hz5fv5Olpayk6Uk6FG87r34Evlu/kj2cdR4yr6jnzmlesL+NHi7fz0Pl9+Fn/LBJiXd4rFz3cbsMzM9ZxaW42qYmxDHlkBjEu4cvbTiXO5aJ1YgyTv9/KiOMy+XLFTgZ2TiWrTQL7Dx9h9FOzufOs49jo58tz2ztL+GzZzhq17lMft04CvoNnXTVpPl/bFw55NMQ5aU9hZX/uUU/O9rvMpbnZTLi4f5VpVlfI8APSul0FnPH0HMCqHcdFu4iPdk7LHLR/2RyTkUSMS7xB1Im/vvf7Dx+hbXJ8kFv5uYmqFtwBrh6Ww+fLd3pfb/YZd2b1zgKOuedThh2b7p02f9N+SssrWLuzkL7ZrQHreDpUXMZFL3wLWOPXfPK74YgISXHRPDF1Df+cuYHVfxvD/R8v5+phOSzYtJ9rTsrxlqmwtJyHP13JZSd2ZkCnNgD85ePl/Hf+T2x8ZCyHisuIiY7i3YVbeW7GOt7/9Ul0aJ1AQqyLFdsPsWDTfgD+ccVAfv3Gj8xeu4cNj4xl/e5CurdN8rvvvnYcKmbxTwc5vWdb4mNc7C0sJb1VrLeBvbqiI+U8NXUtE7/ZZH1uPt9dj6V5B2mXEs/SvEOkxEezdnch2w5Yv4Zmrt7N8R1be5d9auoavlixk7W7Cr3b23awmMQYF6mtYnnuq/UAnNm7HSLU+E577DxUwtC/zyA2Ooq1488OuM/1pUGCu4iMAZ4FXMBEY8yjDfE+reyeDL3v/5L/u34wL87Z6A1M/561gd4dUnh34VbuP6+PN6/55NQ1/MP+h3g88tkqfvzpIADv//okb3/kPfal4CVlFQx8aBqd0xJ55doTves9O2Od9/nLc62D6T9zNvL0Zf05pXsmbrepceHK+E9Xcf/HK8jtkkq527Arv4Q7zjyOCwZksSTvEM/NWGd/SYZ5f1EMfngGAF/dcRp/en8pmclx3rL5evzLNd7nW/cXVamVBkqn/Hv2Bu/z6oHd4783DOEXE60LigpLy0mKcz50io9U8MWKHTzx5VpevfZEurdL9s677Z3FfLp0h98GyuzUBGb9cQS7C0rZsKeQfh3b1FgmupYXMc1eu8f7fMRxmQDEx7goKavAGFMjWHhq6u1S4mmXEs/OIMHd36+Jtxds5cGf9aHc7easPu3olJrIa99txhiDMfC/pdspLXfXqLkDDD0mjYlX53KDwy8lt4G56ytrklv2FXHcfV8AcF7/LGJdUSzJO8j63YXeZTbtPUzfB6cC8PCFx3t/qfX8i7Xe5IV5AHywKI9fnXosvbNS+O2bP7J6ZwFvLdjK0gfPJCU+hv/O/wmA/UVHyB0/HZHKCsBI+0Q9blgXXvtuS5Uye/4Ho5+azaa9h+mUlsCvTj2W5Phonp62lkcv6se9Hy7j378cxPrdhazake8Nnr89/Vg6tE7gvo+WM+Giflx6olVxm7t+L4Wl5fTukMKfP1zGCZ1TvYEdrGsLWtnHatGRcqau2MWt7yz2+5kCRLui+H7zfiZ8sZqXrs71vr9H0ZFyhj/6lfcz9PB8hqN7tWXiuMoY8e7CraQnxVJg30nsSLmbkx/7iicu6c/lL85jcNc0Jv9qmGN56kLqO1UgIi5gLXAGkAd8D1xhjFnptE5ubq5ZuDD8n/sf/JjH7ZOXhLTs+QOy+Hhx8/z51BiyWsezPUiA8tW3Y2uuGtqF7zfvp1+nNvxySGeunDifbzfs46s7TuOYzCTHdW97ZzEfLtoW8nsf1y6Z343sRq8OKXRr67xdgEc/X83L32xi7cOh134OFZcx6slZ3nz7v648gbF9O/CPGet4ctpa1o4/23sVqe/7TPx6I2vGn83lL35HlAjvBPgSXvfq936H9N386DmMfHIWvTqkcGbvdvzh7cW8ecMQdh4q4Y53rWO3bXIcC+4d7Xe7OXd/6n3+8IXHU1Lm5o15W7z3am1sl5/Yibe/3xr2em9cP4QbX19Y5RaHTq4/uSuTfAI0WOmx1TsrG/Xf//UwLn9xXtDrD3K7pLJwywFuHd2dZ6avC7gsUOVEVRdj+7an6EgFs9bsCbrsqofGkBDrv8YfjIj8YIzJ9TevIWrug4H1xpiN9pu/DZwPOAb32moVoPZYXUME9sE5aSzYvL9O2+jRLsn7k68+vHXjUK54aV6VaScdm86kcSfS6/4vQtrGpHG5nNg1jZT4GG8NCeC3p3fj2w372Jlf4je4bz9YzPRVu6oEdiBgYL9ySGcuye3k/ckfTHSUVBmt0Ynbbcg7UExSfDSnPPZVlREZPcdNcrz191BxmfcKUY+pK3ZybGYSriihU1oiH/y4jX2FpaQnVV3Ow1/N3RUldl7bEBMlnNG7HTEuYc66PcT5pO5cAVITU245meKyCnq2TyY53uoLf9XQLvyw5QBZbeIZ9/IC2iTGsnjrQb/r3zu2Fw9/tspx++EKJ7CPOC7TG9w8w0iEonpgB6oEdoCLXvgupG0t3GLl3UMJ7FB/KcjPlu0MvpBt28HioJWa2miI4N4R8D0C8oAhDfA+JMRUnu1EIDHGxbBjM5i+KrQeHaf1yGTiuFyuf20hc9YGP8NeMbgzby2wfpL+5dzeXH9yV/YWlvLSnI10SW9Fp7QErpq0gOT4aO/PMIDubZOYOC6XA0Vl3Pj6Qm9K5eELj+fKIV2q1M42/X0sB4rKOOFv0wB48pL+vP7dZpbY/aOvHtaFBZv20zktkQsGdmRfYSmPf7mGrDYJXHNSDsOOTWfjI2MxWD1eZq3ZzROX9Cch1sXmR89h+bZDtEmMITs1kY8XbyMzKY68A8VckpvNp8t20KF1PIO6pPnd/+72Abhyez4nHZtRY/64lxewbnfVE9XFg7J574e8Gsu2S4nj6z+NrFFjDibaJbiNFbyd8rU/bNkf8Ms/ILsNAO1bWz1hduWXVAnuhaXlbNx7mDvO6AHAuf068MGP21iad4hTumcQ7apZ5sOl5WSnJpBn526fvKQ/d7y7hKV5hygus8aPT4yNpndWa5ZsPUjvDpV53UDB3Tf/6xEbHeXNt8+683TAavB7ee4mendIoUt6Ii/P3cTW/cVcNCibnfklzF2/l7Yp8UGP8/EXHM99H9VsnHVKBTp54/ohVBhTo+Y64aJ+TPxmY60qNKN7teWRC/vyqzd+YJGdRn37pqHMWrOnSmqxS3oiXTNasWJ7vmOZ/zTmOF6YuYGHLujDOX2z+Gl/EaOfslJKHVrHB21j8VgzfgxPTV3Lf+ZsDGn5CwZk8ZFd0fRUDhuqK2uTNaiKyE3ATQCdO3eu1TY8DWun9cj09g/2mLt+L2t3FfDX/60kKS6awtJyUuKj+fK2U3GJ8P3mA5zTrwNg1VRLy928NGcjORmJ3PbOEjqlJXD98K48+D/rB8fgrmn8/ed9uXV0dx6aspLL7BptRlIc94zt5X3fzY+eg9ttuPrlBXRrm8Q1J+XQvnU88TEuuqTD9/eO5pznvmbF9nyuHNIFgE9+N5yfPT+XU3tkIiKktYrlxasG8dLXGzm3fwdO6JLK6U/M8p5QqrtqWE6V156gN+6kHMadVHWeb7A4f0DHKvPO7ZcV8PNumxJPm8QYNu+rmRIYP2VljcAO8OjP+zJl6XaOyUjirZuGVrkSszY8+elytyHWT1B0uw3vONQubz7tWO486zhvMO1g95/PO1Ds/Vwe/3K1d31PbWpAp1QArrV7qfhrpDtcWk5OeitvcB9qB9/z/zmX+JgoUuz97tsxhY8Xbad728p2CH8593CNOb49Y3x62FxzUg75JeW0TojhL+f2BmDGql3MWbuH6befRtGRchZuPsDHi7exJO8QPx/YkfMGZNG3Y2vu+2g5j1zYl9YJMTwxdQ3/uGIgx7VP5h9frWfexn0s2LSfy3I78c7CrRyT0QoD3H9ub1JbxfLV6t1cNzyHNomxuN2GW0d3JyMpjosHZRNvV8ZG9MzkyxW7KDlSQW5OKtmpieSXlLFmZwE92iWxO7+UX0ycT7/s1rx78zBvW8KLV+USFSX0aJvsDe6Dc9IY0jXNG9xn3zmCzmmJiAgFJWW8Oncz+SVlvPT1Jj753fDKMYpcUfxmRDfv59WtbRJZreO5YnBnbhnVnTfmbfGe5Hp1SOFQ0RG2Hyqha0YrurVN4paR3chMjiMu2sU9Y3txYk4aCbEuslMTmLxwq7c9A6xK3OHScq4b3pVoVxTPXD4QsHqfnf3s1+wrbJjB4hoiuG8DfLupZNvTqjDGvAi8CFbOvTZv1Mnu/zyyZ9sa84Z3y2B4twwuze1EWYWbAQ9N46phXehg19Y8gR2sqx5jXFHcZtfUTuicSvvW8d4WdIDfj+wOWA1s//zFCQHLFRUlvHGD84+V924+qcpFKf2y27Dg3lHeqyEBzuzTnjP7WF/WrhmtmP/nUbRN9p8SaEytYqOrNBK/8/1PjP90VZVfKmClh7YdLCbaFcWyB88iSiRgDTVUnlpzudtNrN2Td9vBYtxuQ6e0RCZ+s9HbMOhdJ0pY/bcxNWrcPdolExttNaCN7NmW579aV+VL6Uk9pbWKrbLeja8v5LbRPeidleKd5mnUA2iTGEOWz4VXJWVuku1UUN+OrXlj3k9VGjqD9RipDRGpcSId1atdlRNTv+w2XHNSDj/8dIATcyp/ra1/+GzvZ+X7Pbn9jB4Ulpbzzbq9jDm+PY/8vC9RQpXGaN/0WlSUcOvoHjXK1jY5nquGdqkyLTM5jmPtz7tzWisuHpTNb0YcS1y0i09/fzIp8THez+mWUd1Iio/mzrOO806bcsvJ/LS/iC7prbzbTI6P4ZZR3Skpq+CyEzvRzeeE6s+394zyPj+rT3tvcO/ZPpmnLu3PtJW7GNWrnd/jeHTvdt7nI45ryz9nbuCZywYw5Jg0b8ypLt0+rvZGUM39e6C7iHTFCuqXA79ogPehR7tk5t0zyjvokj+e/Oryv55FYkxojRaeAySrTQLRUcKTl/bn5O410xC1lRDrqtGA0jY53mFpS7uUwPMbS6s4F0U+Y6Tc9f6yGsukt4qt0k2vetfQuvCtuXt4ei/MuOM0HvnMuhDrwfN60zurNZf+5zsSYlx+UykJsS6GdE1j0jeb+GL5zioXB4H1896jd4cUVu6wrqadtnIXs9bs5rrhXfnTmJ64ooTDpeUkxUfz3xuH0CW9FSLCr049xvtz3ZPfH9zV+ly+8+nnXx8199qKipIqgR3w+1l5JMVFe38h1MfJ2p/Y6CieuKSyC2yfrKqpqezURO+vEY/jO7b2m8ICq1dUsMBeXWZyHP93/WCumrTA7uYo3spWMCfmpPHt3SPJauM/qHukJ8Ux+VfDODazVcDlaqveg7sxplxEfgd8idUV8mVjzIogq9Wa76XpgQTquuckPsbF+kfGhr1eS5YQG+13dEOw2gMuGNiR9g14IvIGdz+9JH5vXywDcM3wrpRXuBmck8Z5A5zTTce1S+brdXtrBPb3bh7mTSMAvHLtibwwa4P3QquyCsN/5mzkP3M2khIfTX6J1eXOty2iTWJljb/YHkgsJz2xRptMlEOfbdW0TumeyYq/nhVWxw2PYIEdrJPj4K7+27fqQ4Pk3I0xnwGfNcS2VdNqFeui2E7LFPukZ1648gTO7tvBabV645uWeWnORp72GR53xXarZt0nqzKvOvnmwH2I/cXVdilx5FarzbZLiXfs0ZBvB+rqabOUhMqv17l2ekNESI6rGtyjXRrcm6vaBPbmInJLrppEYmw0B4qsWq7vRVz9QuzKWFe+NXd/Xfzm/3kUGQ7dFYP5Wf8sTujcxjEFd/mJnUiIsVJqv3nzxxrzT+6WUeP16F5tuXV0D2/7EFjtQe/69CByRUX0KCCqmdLgrsKSGOui2E7LrN9dQGpiDI9e1I+OIfwMrQ+emnv1q1R/1j+LvYWlYbdNnD+gIy99vYnpt58aNC8b7YriokHZAKwdfzY97vscgNG92jF91a4aNfsu6a2qXK3oMeHifizfns8qO4evFXfVEDS4q7C0inN5LwgqKCmne7tkzgqxoak+eGruZRXuKv2R//qzPqRW69USiuM7tvbbtTEYT6PfiTmpdE5LpNxdcwgDJyLC5384hVvfXsRHi7dXGRhNqfqivwdVWBJioik+UkFhaTnzN+2nsSudnvz0kQo3hT5569oE9rq6eFC2t2dMbXoEeRpftx8qDrKkUuHTKoMKi1VzL+cte/Co+ZvqNvxCuDw19xmrdlNQWs7vR3ZjhJ/rHCKBp598kZ/b7ylVV1pzV2FJiHVhTM3byjWWaLvx0TMC5sndMzmhc2qTlKWuPN1442P0a6jqn9bcVVg8V9F6bmbx4Hm9Ay1e71zVWh87pTVOQ25DSG8Vy11jenJmn3bBF1YqTBrcVVg8V9bmHSgmOkpqjF3T0GJ8ug12b5vkeGl3JBARfj3i2KYuhmqh9PegCoun5p53oIjUAHfEaSi+F/xckpvdqO+tVCTR4K7CkmSPkbJ1fxFpiY3fQ8V3HBansUSUUhrcVZg8A2DtLTxCq7ja3T2mLnwHtYrUhlSlGoMGdxWWZJ+xNoLdV7QhZKdaOfaMpNgqA3spparSBlUVFs+t3gCubuTGVLBujvLKNSfS2Wc4XqVUTRrcVVg8aRmwbjzRFE6P0IuWlGpMmpZRYfEdAjWxlndsV0o1PA3uqtYieaxrpVo6De6q1hK0QVOpZkuDuwqbZ+x27a2iVPOlv6tV2P574xA+WrSdjKTGv4hJKRUaDe4qbF3SW/GH0d2buhhKqQA0LaOUUi2QBnellGqBNLgrpVQLpMFdKaVaIA3uSinVAmlwV0qpFkiDu1JKtUAa3JVSqgUSY0xTlwER2QNsaepy1EIGsLepC9HIjrZ9Ptr2F3SfI0kXY0ymvxnNIrhHKhFZaIzJbepyNKajbZ+Ptv0F3eeWQtMySinVAmlwV0qpFkiDe9282NQFaAJH2z4fbfsLus8tgubclVKqBdKau1JKtUAa3JVSqgXS4B6EiNwhIkZEMuzXIiLPich6EVkqIif4LDtORNbZj3E+0weJyDJ7nedERJpiX4IRkcdFZLW9Xx+KSBufeffY5V8jImf5TB9jT1svInf7TO8qIvPt6e+ISMTdtslp3yKRiHQSkZkislJEVojIH+zpaSIyzT5mp4lIqj097OO8ORIRl4gsEpEp9mu/x6WIxNmv19vzc3y24ffYb/aMMfpweACdgC+xLrDKsKeNBT4HBBgKzLenpwEb7b+p9vNUe94Ce1mx1z27qffNYX/PBKLt548Bj9nPewNLgDigK7ABcNmPDcAxQKy9TG97ncnA5fbzfwO/bur9C/OzcNy3SHwAHYAT7OfJwFr7/zoBuNuefrfP/zzs47w5PoDbgf8CU+zXfo9L4DfAv+3nlwPv2M/9HvtNvV+hPLTmHtjTwJ8A31bn84HXjWUe0EZEOgBnAdOMMfuNMQeAacAYe16KMWaesY6W14ELGnUvQmSMmWqMKbdfzgOy7efnA28bY0qNMZuA9cBg+7HeGLPRGHMEeBs43/5lMhJ4z17/NZrpPgfgd9+auEy1ZozZYYz50X5eAKwCOmLt02v2Yr7/p7CO88bbk9CJSDZwDjDRfh3ouPT9HN4DRtnLOx37zZ4Gdwcicj6wzRizpNqsjsBWn9d59rRA0/P8TG/ursOquUH4+5wOHPQ5UUTKPvty2reIZ6ccBgLzgXbGmB32rJ1AO/t5uP/z5ugZrMqZ234d6Lj07pc9/5C9fCTtbxVH9Q2yRWQ60N7PrHuBP2OlKVqUQPtsjPnYXuZeoBx4szHLphqeiCQB7wO3GmPyfZt/jDFGRFpE32gRORfYbYz5QURGNHFxmsRRHdyNMaP9TReRvlj5tSX2wZ8N/Cgig4FtWLl4j2x72jZgRLXps+zp2X6WbxJO++whItcA5wKj7DQSOO8zDtP3Yf2Mj7ZrQU26z7UUaJ8jkojEYAX2N40xH9iTd4lIB2PMDjvtstueHu5x3twMB34mImOBeCAFeBbn49Kzv3kiEg20xjqOI/c4aOqkfyQ8gM1UNqieQ9WGpgX29DRgE1YjU6r9PM2eV71BdWxT75PDfo4BVgKZ1ab3oWqj0kasBsdo+3lXKhsd+9jrvEvVhqvfNPX+hflZOO5bJD7sY+914Jlq0x+naoPqBPt52Md5c31gnYw8Dap+j0vgt1RtUJ1sP/d77Df1PoW0301dgEh4VAvuAvwTq9V8GZDrs9x1WA0u64FrfabnAsvtdZ7HvjK4uT3scm8FFtuPf/vMu9cu/xp8evtg9apYa8+712f6MfZJbb39hYpr6v2rxefhd98i8QGcjNUxYKnP/3csVl55BrAOmE5lhSTs47y5PqoFd7/HJVbt/l17+gLgGJ/1/R77zf2hww8opVQLpL1llFKqBdLgrpRSLZAGd6WUaoE0uCulVAukwV0ppVogDe5K2USksNrra0Tk+aYqj1J1ocFdKaVaoKN6+AGlQiUii31eHgeMMcbMbqLiKBWUBnelKiVUC+JpwCcAxpgBACJyHtZIg982duGUCocGd6UqFXuCOHgHUcv1ed0dayyW040xZY1eOqXCoDl3pUJgD5U7GbjRVI5/rlSzpcFdqdC8DLxijPm6qQuiVCg0uCsVhIh0AS4GrhORxfYjN9h6SjUlHRVSKaVaIK25K6VUC6TBXSmlWiAN7kop1QJpcFdKqRZIg7tSSrVAGtyVUqoF0uCulFIt0P8DKuVzeRZo1JQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fid = pyAMARES.kernel.fid.simulate_fid(\n",
    "    params,\n",
    "    MHz=120.0,\n",
    "    sw=10000.0,\n",
    "    deadtime=200e-6,\n",
    "    fid_len=1024,\n",
    "    snr_target=20,\n",
    "    preview=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674557de",
   "metadata": {},
   "source": [
    "## Simple Tutorial on pyAMARES Fitting\n",
    "- **Initialize the FID Object**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "608be3e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking comment lines in the prior knowledge file\n",
      "Comment: in line 0 \"#  Ren et al, NMR Biomed . 2015 Nov;28(11):1455-62. doi: 10.1002/nbm.3384.\",,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 14 # Phase not fixed ,,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 15 \" # 2024/06/24 In Ren et al, the chemical shift range of BATP (0.1 ppm), AATP (0.04 ppm), and GATP (0.02 ppm) are smaller than the J-couplin range (0.125 - 0.25 ppm). \",,,,,,,,,,,,,,,,\n",
      "\n",
      "Comment: in line 16 \"# So, increase the chemical shift range\",,,,,,,,,,,,,,,,\n",
      "\n"
     ]
    }
   ],
   "source": [
    "FIDobj = pyAMARES.initialize_FID(\n",
    "    fid=fid,\n",
    "    MHz=120.0,\n",
    "    sw=10000.0,\n",
    "    deadtime=200e-6,\n",
    "    normalize_fid=False,\n",
    "    priorknowledgefile=\"./attachment/example_human_brain_31P_7T_flex_phase.csv\",\n",
    "    preview=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8819c729",
   "metadata": {},
   "source": [
    "- **A. HSVD Optimization of Initial Parameters (Optional)**: Utilize HSVD to optimize the initial parameters for fitting, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b23db0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm of residual = 65.858\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.027\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params_hsvd = pyAMARES.HSVDinitializer(\n",
    "    fid_parameters=FIDobj,\n",
    "    num_of_component=12,  # If error happens, decreasae this number\n",
    "    fitting_parameters=FIDobj.initialParams,\n",
    "    preview=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ceb1a12",
   "metadata": {},
   "source": [
    "- **Fitting AMARES Using HSVD-Initialized Parameters**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c594022c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A copy of the input fid_parameters will be returned because inplace=False\n",
      "Autogenerated tol is 3.158e-06\n",
      "Fitting with method=least_squares took 5.937595 seconds\n",
      "Estimated CRLBs are calculated using the default noise variance estimation used by OXSA.\n",
      "Lmfit Fitting Results:\n",
      "----------------\n",
      "Number of function evaluations (nfev): 15\n",
      "Reduced chi-squared (redchi): 0.030735869137296347\n",
      "Fit success status: Success\n",
      "Fit message: `ftol` termination condition is satisfied.\n",
      "Norm of residual = 61.472\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIDresult1 = pyAMARES.fitAMARES(\n",
    "    fid_parameters=FIDobj,\n",
    "    fitting_parameters=params_hsvd,\n",
    "    method=\"least_squares\",\n",
    "    ifplot=True,\n",
    "    inplace=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3964de1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_773b2_level0_col0\" class=\"col_heading level0 col0\" >amplitude</th>\n",
       "      <th id=\"T_773b2_level0_col1\" class=\"col_heading level0 col1\" >sd</th>\n",
       "      <th id=\"T_773b2_level0_col2\" class=\"col_heading level0 col2\" >CRLB(%)</th>\n",
       "      <th id=\"T_773b2_level0_col3\" class=\"col_heading level0 col3\" >chem shift(ppm)</th>\n",
       "      <th id=\"T_773b2_level0_col4\" class=\"col_heading level0 col4\" >sd(ppm)</th>\n",
       "      <th id=\"T_773b2_level0_col5\" class=\"col_heading level0 col5\" >CRLB(cs%) </th>\n",
       "      <th id=\"T_773b2_level0_col6\" class=\"col_heading level0 col6\" >LW(Hz)</th>\n",
       "      <th id=\"T_773b2_level0_col7\" class=\"col_heading level0 col7\" >sd(Hz)</th>\n",
       "      <th id=\"T_773b2_level0_col8\" class=\"col_heading level0 col8\" >CRLB(LW%)</th>\n",
       "      <th id=\"T_773b2_level0_col9\" class=\"col_heading level0 col9\" >phase(deg)</th>\n",
       "      <th id=\"T_773b2_level0_col10\" class=\"col_heading level0 col10\" >sd(deg)</th>\n",
       "      <th id=\"T_773b2_level0_col11\" class=\"col_heading level0 col11\" >CRLB(phase%)</th>\n",
       "      <th id=\"T_773b2_level0_col12\" class=\"col_heading level0 col12\" >g</th>\n",
       "      <th id=\"T_773b2_level0_col13\" class=\"col_heading level0 col13\" >g_sd</th>\n",
       "      <th id=\"T_773b2_level0_col14\" class=\"col_heading level0 col14\" >g (%)</th>\n",
       "      <th id=\"T_773b2_level0_col15\" class=\"col_heading level0 col15\" >SNR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row0\" class=\"row_heading level0 row0\" >BATP</th>\n",
       "      <td id=\"T_773b2_row0_col0\" class=\"data row0 col0\" >2.774</td>\n",
       "      <td id=\"T_773b2_row0_col1\" class=\"data row0 col1\" >0.056</td>\n",
       "      <td id=\"T_773b2_row0_col2\" class=\"data row0 col2\" >2.795</td>\n",
       "      <td id=\"T_773b2_row0_col3\" class=\"data row0 col3\" >-16.123</td>\n",
       "      <td id=\"T_773b2_row0_col4\" class=\"data row0 col4\" >0.010</td>\n",
       "      <td id=\"T_773b2_row0_col5\" class=\"data row0 col5\" >0.068</td>\n",
       "      <td id=\"T_773b2_row0_col6\" class=\"data row0 col6\" >61.099</td>\n",
       "      <td id=\"T_773b2_row0_col7\" class=\"data row0 col7\" >1.958</td>\n",
       "      <td id=\"T_773b2_row0_col8\" class=\"data row0 col8\" >4.287</td>\n",
       "      <td id=\"T_773b2_row0_col9\" class=\"data row0 col9\" >-1.814</td>\n",
       "      <td id=\"T_773b2_row0_col10\" class=\"data row0 col10\" >1.113</td>\n",
       "      <td id=\"T_773b2_row0_col11\" class=\"data row0 col11\" >88.258</td>\n",
       "      <td id=\"T_773b2_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row0_col13\" class=\"data row0 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row0_col15\" class=\"data row0 col15\" >11.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row1\" class=\"row_heading level0 row1\" >AATP</th>\n",
       "      <td id=\"T_773b2_row1_col0\" class=\"data row1 col0\" >3.037</td>\n",
       "      <td id=\"T_773b2_row1_col1\" class=\"data row1 col1\" >0.068</td>\n",
       "      <td id=\"T_773b2_row1_col2\" class=\"data row1 col2\" >3.121</td>\n",
       "      <td id=\"T_773b2_row1_col3\" class=\"data row1 col3\" >-7.512</td>\n",
       "      <td id=\"T_773b2_row1_col4\" class=\"data row1 col4\" >0.004</td>\n",
       "      <td id=\"T_773b2_row1_col5\" class=\"data row1 col5\" >0.077</td>\n",
       "      <td id=\"T_773b2_row1_col6\" class=\"data row1 col6\" >31.613</td>\n",
       "      <td id=\"T_773b2_row1_col7\" class=\"data row1 col7\" >0.996</td>\n",
       "      <td id=\"T_773b2_row1_col8\" class=\"data row1 col8\" >4.376</td>\n",
       "      <td id=\"T_773b2_row1_col9\" class=\"data row1 col9\" >-0.015</td>\n",
       "      <td id=\"T_773b2_row1_col10\" class=\"data row1 col10\" >1.288</td>\n",
       "      <td id=\"T_773b2_row1_col11\" class=\"data row1 col11\" >11728.739</td>\n",
       "      <td id=\"T_773b2_row1_col12\" class=\"data row1 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row1_col15\" class=\"data row1 col15\" >12.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row2\" class=\"row_heading level0 row2\" >GATP</th>\n",
       "      <td id=\"T_773b2_row2_col0\" class=\"data row2 col0\" >2.914</td>\n",
       "      <td id=\"T_773b2_row2_col1\" class=\"data row2 col1\" >0.044</td>\n",
       "      <td id=\"T_773b2_row2_col2\" class=\"data row2 col2\" >2.112</td>\n",
       "      <td id=\"T_773b2_row2_col3\" class=\"data row2 col3\" >-2.484</td>\n",
       "      <td id=\"T_773b2_row2_col4\" class=\"data row2 col4\" >0.004</td>\n",
       "      <td id=\"T_773b2_row2_col5\" class=\"data row2 col5\" >0.211</td>\n",
       "      <td id=\"T_773b2_row2_col6\" class=\"data row2 col6\" >36.104</td>\n",
       "      <td id=\"T_773b2_row2_col7\" class=\"data row2 col7\" >0.906</td>\n",
       "      <td id=\"T_773b2_row2_col8\" class=\"data row2 col8\" >3.483</td>\n",
       "      <td id=\"T_773b2_row2_col9\" class=\"data row2 col9\" >-0.368</td>\n",
       "      <td id=\"T_773b2_row2_col10\" class=\"data row2 col10\" >0.871</td>\n",
       "      <td id=\"T_773b2_row2_col11\" class=\"data row2 col11\" >328.653</td>\n",
       "      <td id=\"T_773b2_row2_col12\" class=\"data row2 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row2_col13\" class=\"data row2 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row2_col15\" class=\"data row2 col15\" >11.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row3\" class=\"row_heading level0 row3\" >UDPG</th>\n",
       "      <td id=\"T_773b2_row3_col0\" class=\"data row3 col0\" >0.071</td>\n",
       "      <td id=\"T_773b2_row3_col1\" class=\"data row3 col1\" >0.038</td>\n",
       "      <td id=\"T_773b2_row3_col2\" class=\"data row3 col2\" >73.621</td>\n",
       "      <td id=\"T_773b2_row3_col3\" class=\"data row3 col3\" >-9.760</td>\n",
       "      <td id=\"T_773b2_row3_col4\" class=\"data row3 col4\" >0.080</td>\n",
       "      <td id=\"T_773b2_row3_col5\" class=\"data row3 col5\" >1.142</td>\n",
       "      <td id=\"T_773b2_row3_col6\" class=\"data row3 col6\" >27.638</td>\n",
       "      <td id=\"T_773b2_row3_col7\" class=\"data row3 col7\" >19.262</td>\n",
       "      <td id=\"T_773b2_row3_col8\" class=\"data row3 col8\" >96.796</td>\n",
       "      <td id=\"T_773b2_row3_col9\" class=\"data row3 col9\" >44.761</td>\n",
       "      <td id=\"T_773b2_row3_col10\" class=\"data row3 col10\" >30.378</td>\n",
       "      <td id=\"T_773b2_row3_col11\" class=\"data row3 col11\" >94.238</td>\n",
       "      <td id=\"T_773b2_row3_col12\" class=\"data row3 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row3_col13\" class=\"data row3 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row3_col15\" class=\"data row3 col15\" >0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row4\" class=\"row_heading level0 row4\" >NAD</th>\n",
       "      <td id=\"T_773b2_row4_col0\" class=\"data row4 col0\" >0.441</td>\n",
       "      <td id=\"T_773b2_row4_col1\" class=\"data row4 col1\" >0.078</td>\n",
       "      <td id=\"T_773b2_row4_col2\" class=\"data row4 col2\" >24.589</td>\n",
       "      <td id=\"T_773b2_row4_col3\" class=\"data row4 col3\" >-8.250</td>\n",
       "      <td id=\"T_773b2_row4_col4\" class=\"data row4 col4\" >0.036</td>\n",
       "      <td id=\"T_773b2_row4_col5\" class=\"data row4 col5\" >0.609</td>\n",
       "      <td id=\"T_773b2_row4_col6\" class=\"data row4 col6\" >46.155</td>\n",
       "      <td id=\"T_773b2_row4_col7\" class=\"data row4 col7\" >8.687</td>\n",
       "      <td id=\"T_773b2_row4_col8\" class=\"data row4 col8\" >26.131</td>\n",
       "      <td id=\"T_773b2_row4_col9\" class=\"data row4 col9\" >-3.662</td>\n",
       "      <td id=\"T_773b2_row4_col10\" class=\"data row4 col10\" >10.139</td>\n",
       "      <td id=\"T_773b2_row4_col11\" class=\"data row4 col11\" >384.739</td>\n",
       "      <td id=\"T_773b2_row4_col12\" class=\"data row4 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row4_col13\" class=\"data row4 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row4_col15\" class=\"data row4 col15\" >1.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row5\" class=\"row_heading level0 row5\" >PCr</th>\n",
       "      <td id=\"T_773b2_row5_col0\" class=\"data row5 col0\" >4.159</td>\n",
       "      <td id=\"T_773b2_row5_col1\" class=\"data row5 col1\" >0.025</td>\n",
       "      <td id=\"T_773b2_row5_col2\" class=\"data row5 col2\" >0.842</td>\n",
       "      <td id=\"T_773b2_row5_col3\" class=\"data row5 col3\" >-0.012</td>\n",
       "      <td id=\"T_773b2_row5_col4\" class=\"data row5 col4\" >0.001</td>\n",
       "      <td id=\"T_773b2_row5_col5\" class=\"data row5 col5\" >6.211</td>\n",
       "      <td id=\"T_773b2_row5_col6\" class=\"data row5 col6\" >15.005</td>\n",
       "      <td id=\"T_773b2_row5_col7\" class=\"data row5 col7\" >0.126</td>\n",
       "      <td id=\"T_773b2_row5_col8\" class=\"data row5 col8\" >1.166</td>\n",
       "      <td id=\"T_773b2_row5_col9\" class=\"data row5 col9\" >-0.142</td>\n",
       "      <td id=\"T_773b2_row5_col10\" class=\"data row5 col10\" >0.347</td>\n",
       "      <td id=\"T_773b2_row5_col11\" class=\"data row5 col11\" >340.441</td>\n",
       "      <td id=\"T_773b2_row5_col12\" class=\"data row5 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row5_col13\" class=\"data row5 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row5_col15\" class=\"data row5 col15\" >16.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row6\" class=\"row_heading level0 row6\" >GPC</th>\n",
       "      <td id=\"T_773b2_row6_col0\" class=\"data row6 col0\" >1.208</td>\n",
       "      <td id=\"T_773b2_row6_col1\" class=\"data row6 col1\" >0.033</td>\n",
       "      <td id=\"T_773b2_row6_col2\" class=\"data row6 col2\" >3.848</td>\n",
       "      <td id=\"T_773b2_row6_col3\" class=\"data row6 col3\" >2.953</td>\n",
       "      <td id=\"T_773b2_row6_col4\" class=\"data row6 col4\" >0.003</td>\n",
       "      <td id=\"T_773b2_row6_col5\" class=\"data row6 col5\" >0.130</td>\n",
       "      <td id=\"T_773b2_row6_col6\" class=\"data row6 col6\" >18.826</td>\n",
       "      <td id=\"T_773b2_row6_col7\" class=\"data row6 col7\" >0.663</td>\n",
       "      <td id=\"T_773b2_row6_col8\" class=\"data row6 col8\" >4.893</td>\n",
       "      <td id=\"T_773b2_row6_col9\" class=\"data row6 col9\" >-1.532</td>\n",
       "      <td id=\"T_773b2_row6_col10\" class=\"data row6 col10\" >1.588</td>\n",
       "      <td id=\"T_773b2_row6_col11\" class=\"data row6 col11\" >143.921</td>\n",
       "      <td id=\"T_773b2_row6_col12\" class=\"data row6 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row6_col13\" class=\"data row6 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row6_col15\" class=\"data row6 col15\" >4.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row7\" class=\"row_heading level0 row7\" >GPE</th>\n",
       "      <td id=\"T_773b2_row7_col0\" class=\"data row7 col0\" >0.873</td>\n",
       "      <td id=\"T_773b2_row7_col1\" class=\"data row7 col1\" >0.035</td>\n",
       "      <td id=\"T_773b2_row7_col2\" class=\"data row7 col2\" >5.500</td>\n",
       "      <td id=\"T_773b2_row7_col3\" class=\"data row7 col3\" >3.510</td>\n",
       "      <td id=\"T_773b2_row7_col4\" class=\"data row7 col4\" >0.004</td>\n",
       "      <td id=\"T_773b2_row7_col5\" class=\"data row7 col5\" >0.156</td>\n",
       "      <td id=\"T_773b2_row7_col6\" class=\"data row7 col6\" >19.040</td>\n",
       "      <td id=\"T_773b2_row7_col7\" class=\"data row7 col7\" >0.947</td>\n",
       "      <td id=\"T_773b2_row7_col8\" class=\"data row7 col8\" >6.904</td>\n",
       "      <td id=\"T_773b2_row7_col9\" class=\"data row7 col9\" >2.568</td>\n",
       "      <td id=\"T_773b2_row7_col10\" class=\"data row7 col10\" >2.269</td>\n",
       "      <td id=\"T_773b2_row7_col11\" class=\"data row7 col11\" >122.719</td>\n",
       "      <td id=\"T_773b2_row7_col12\" class=\"data row7 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row7_col13\" class=\"data row7 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row7_col15\" class=\"data row7 col15\" >3.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row8\" class=\"row_heading level0 row8\" >Pin</th>\n",
       "      <td id=\"T_773b2_row8_col0\" class=\"data row8 col0\" >0.909</td>\n",
       "      <td id=\"T_773b2_row8_col1\" class=\"data row8 col1\" >0.045</td>\n",
       "      <td id=\"T_773b2_row8_col2\" class=\"data row8 col2\" >6.935</td>\n",
       "      <td id=\"T_773b2_row8_col3\" class=\"data row8 col3\" >4.810</td>\n",
       "      <td id=\"T_773b2_row8_col4\" class=\"data row8 col4\" >0.005</td>\n",
       "      <td id=\"T_773b2_row8_col5\" class=\"data row8 col5\" >0.157</td>\n",
       "      <td id=\"T_773b2_row8_col6\" class=\"data row8 col6\" >22.727</td>\n",
       "      <td id=\"T_773b2_row8_col7\" class=\"data row8 col7\" >1.307</td>\n",
       "      <td id=\"T_773b2_row8_col8\" class=\"data row8 col8\" >7.987</td>\n",
       "      <td id=\"T_773b2_row8_col9\" class=\"data row8 col9\" >2.573</td>\n",
       "      <td id=\"T_773b2_row8_col10\" class=\"data row8 col10\" >2.862</td>\n",
       "      <td id=\"T_773b2_row8_col11\" class=\"data row8 col11\" >154.417</td>\n",
       "      <td id=\"T_773b2_row8_col12\" class=\"data row8 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row8_col13\" class=\"data row8 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row8_col15\" class=\"data row8 col15\" >3.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row9\" class=\"row_heading level0 row9\" >Pex</th>\n",
       "      <td id=\"T_773b2_row9_col0\" class=\"data row9 col0\" >0.219</td>\n",
       "      <td id=\"T_773b2_row9_col1\" class=\"data row9 col1\" >0.043</td>\n",
       "      <td id=\"T_773b2_row9_col2\" class=\"data row9 col2\" >27.094</td>\n",
       "      <td id=\"T_773b2_row9_col3\" class=\"data row9 col3\" >5.242</td>\n",
       "      <td id=\"T_773b2_row9_col4\" class=\"data row9 col4\" >0.019</td>\n",
       "      <td id=\"T_773b2_row9_col5\" class=\"data row9 col5\" >0.495</td>\n",
       "      <td id=\"T_773b2_row9_col6\" class=\"data row9 col6\" >20.041</td>\n",
       "      <td id=\"T_773b2_row9_col7\" class=\"data row9 col7\" >4.489</td>\n",
       "      <td id=\"T_773b2_row9_col8\" class=\"data row9 col8\" >31.101</td>\n",
       "      <td id=\"T_773b2_row9_col9\" class=\"data row9 col9\" >-0.272</td>\n",
       "      <td id=\"T_773b2_row9_col10\" class=\"data row9 col10\" >11.179</td>\n",
       "      <td id=\"T_773b2_row9_col11\" class=\"data row9 col11\" >5697.685</td>\n",
       "      <td id=\"T_773b2_row9_col12\" class=\"data row9 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row9_col13\" class=\"data row9 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row9_col15\" class=\"data row9 col15\" >0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row10\" class=\"row_heading level0 row10\" >PC</th>\n",
       "      <td id=\"T_773b2_row10_col0\" class=\"data row10 col0\" >0.332</td>\n",
       "      <td id=\"T_773b2_row10_col1\" class=\"data row10 col1\" >0.035</td>\n",
       "      <td id=\"T_773b2_row10_col2\" class=\"data row10 col2\" >14.789</td>\n",
       "      <td id=\"T_773b2_row10_col3\" class=\"data row10 col3\" >6.220</td>\n",
       "      <td id=\"T_773b2_row10_col4\" class=\"data row10 col4\" >0.010</td>\n",
       "      <td id=\"T_773b2_row10_col5\" class=\"data row10 col5\" >0.224</td>\n",
       "      <td id=\"T_773b2_row10_col6\" class=\"data row10 col6\" >18.355</td>\n",
       "      <td id=\"T_773b2_row10_col7\" class=\"data row10 col7\" >2.404</td>\n",
       "      <td id=\"T_773b2_row10_col8\" class=\"data row10 col8\" >18.188</td>\n",
       "      <td id=\"T_773b2_row10_col9\" class=\"data row10 col9\" >-8.989</td>\n",
       "      <td id=\"T_773b2_row10_col10\" class=\"data row10 col10\" >6.102</td>\n",
       "      <td id=\"T_773b2_row10_col11\" class=\"data row10 col11\" >94.260</td>\n",
       "      <td id=\"T_773b2_row10_col12\" class=\"data row10 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row10_col13\" class=\"data row10 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row10_col15\" class=\"data row10 col15\" >1.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_773b2_level0_row11\" class=\"row_heading level0 row11\" >PE</th>\n",
       "      <td id=\"T_773b2_row11_col0\" class=\"data row11 col0\" >2.281</td>\n",
       "      <td id=\"T_773b2_row11_col1\" class=\"data row11 col1\" >0.039</td>\n",
       "      <td id=\"T_773b2_row11_col2\" class=\"data row11 col2\" >2.348</td>\n",
       "      <td id=\"T_773b2_row11_col3\" class=\"data row11 col3\" >6.790</td>\n",
       "      <td id=\"T_773b2_row11_col4\" class=\"data row11 col4\" >0.002</td>\n",
       "      <td id=\"T_773b2_row11_col5\" class=\"data row11 col5\" >0.042</td>\n",
       "      <td id=\"T_773b2_row11_col6\" class=\"data row11 col6\" >23.277</td>\n",
       "      <td id=\"T_773b2_row11_col7\" class=\"data row11 col7\" >0.492</td>\n",
       "      <td id=\"T_773b2_row11_col8\" class=\"data row11 col8\" >2.935</td>\n",
       "      <td id=\"T_773b2_row11_col9\" class=\"data row11 col9\" >0.706</td>\n",
       "      <td id=\"T_773b2_row11_col10\" class=\"data row11 col10\" >0.969</td>\n",
       "      <td id=\"T_773b2_row11_col11\" class=\"data row11 col11\" >190.518</td>\n",
       "      <td id=\"T_773b2_row11_col12\" class=\"data row11 col12\" >0.000</td>\n",
       "      <td id=\"T_773b2_row11_col13\" class=\"data row11 col13\" >0.000</td>\n",
       "      <td id=\"T_773b2_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_773b2_row11_col15\" class=\"data row11 col15\" >9.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f0351a8f820>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FIDresult1.styled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14656853",
   "metadata": {},
   "source": [
    "- **B. Initialization Using Levenberg-Marquardt Method**: Instead of using the HSVD initializer, initialize the parameters using the Levenberg-Marquardt method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b70a4d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A copy of the input fid_parameters will be returned because inplace=False\n",
      "Autogenerated tol is 3.158e-06\n",
      "Fitting with method=leastsq took 3.309779 seconds\n",
      "Estimated CRLBs are calculated using the default noise variance estimation used by OXSA.\n",
      "a_sd is all None, use crlb instead!\n",
      "freq_sd is all None, use crlb instead!\n",
      "lw_sd is all None, use crlb instead!\n",
      "phase_sd is all None, use crlb instead!\n",
      "g_std is all None, use crlb instead!\n",
      "Lmfit Fitting Results:\n",
      "----------------\n",
      "Number of function evaluations (nfev): 595\n",
      "Reduced chi-squared (redchi): 0.034074975457039455\n",
      "Fit success status: Success\n",
      "Fit message: Fit succeeded. Could not estimate error-bars.\n",
      "Norm of residual = 68.150\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.028\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params_LM = pyAMARES.fitAMARES(\n",
    "    fid_parameters=FIDobj,\n",
    "    fitting_parameters=FIDobj.initialParams,\n",
    "    method=\"leastsq\",\n",
    "    ifplot=True,\n",
    "    inplace=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b673d670",
   "metadata": {},
   "source": [
    "- **Fitting AMARES Using Levenberg-Marquardt-Initialized Parameters**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6d3a0c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A copy of the input fid_parameters will be returned because inplace=False\n",
      "Autogenerated tol is 3.158e-06\n",
      "Fitting with method=least_squares took 2.848357 seconds\n",
      "Estimated CRLBs are calculated using the default noise variance estimation used by OXSA.\n",
      "Lmfit Fitting Results:\n",
      "----------------\n",
      "Number of function evaluations (nfev): 7\n",
      "Reduced chi-squared (redchi): 0.030735869852873087\n",
      "Fit success status: Success\n",
      "Fit message: `ftol` termination condition is satisfied.\n",
      "Norm of residual = 61.472\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIDresult2 = pyAMARES.fitAMARES(\n",
    "    fid_parameters=FIDobj,\n",
    "    fitting_parameters=params_LM.fittedParams,\n",
    "    initialize_with_lm=False,\n",
    "    method=\"least_squares\",\n",
    "    ifplot=True,\n",
    "    inplace=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82efe742",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_fd793_row3_col0, #T_fd793_row3_col1, #T_fd793_row3_col2, #T_fd793_row3_col3, #T_fd793_row3_col4, #T_fd793_row3_col5, #T_fd793_row3_col6, #T_fd793_row3_col7, #T_fd793_row3_col8, #T_fd793_row3_col9, #T_fd793_row3_col10, #T_fd793_row3_col11, #T_fd793_row3_col12, #T_fd793_row3_col13, #T_fd793_row3_col14, #T_fd793_row3_col15, #T_fd793_row4_col0, #T_fd793_row4_col1, #T_fd793_row4_col2, #T_fd793_row4_col3, #T_fd793_row4_col4, #T_fd793_row4_col5, #T_fd793_row4_col6, #T_fd793_row4_col7, #T_fd793_row4_col8, #T_fd793_row4_col9, #T_fd793_row4_col10, #T_fd793_row4_col11, #T_fd793_row4_col12, #T_fd793_row4_col13, #T_fd793_row4_col14, #T_fd793_row4_col15, #T_fd793_row9_col0, #T_fd793_row9_col1, #T_fd793_row9_col2, #T_fd793_row9_col3, #T_fd793_row9_col4, #T_fd793_row9_col5, #T_fd793_row9_col6, #T_fd793_row9_col7, #T_fd793_row9_col8, #T_fd793_row9_col9, #T_fd793_row9_col10, #T_fd793_row9_col11, #T_fd793_row9_col12, #T_fd793_row9_col13, #T_fd793_row9_col14, #T_fd793_row9_col15 {\n",
       "  background-color: rgba(255, 0, 0, 0.2);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fd793\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fd793_level0_col0\" class=\"col_heading level0 col0\" >amplitude</th>\n",
       "      <th id=\"T_fd793_level0_col1\" class=\"col_heading level0 col1\" >sd</th>\n",
       "      <th id=\"T_fd793_level0_col2\" class=\"col_heading level0 col2\" >CRLB(%)</th>\n",
       "      <th id=\"T_fd793_level0_col3\" class=\"col_heading level0 col3\" >chem shift(ppm)</th>\n",
       "      <th id=\"T_fd793_level0_col4\" class=\"col_heading level0 col4\" >sd(ppm)</th>\n",
       "      <th id=\"T_fd793_level0_col5\" class=\"col_heading level0 col5\" >CRLB(cs%) </th>\n",
       "      <th id=\"T_fd793_level0_col6\" class=\"col_heading level0 col6\" >LW(Hz)</th>\n",
       "      <th id=\"T_fd793_level0_col7\" class=\"col_heading level0 col7\" >sd(Hz)</th>\n",
       "      <th id=\"T_fd793_level0_col8\" class=\"col_heading level0 col8\" >CRLB(LW%)</th>\n",
       "      <th id=\"T_fd793_level0_col9\" class=\"col_heading level0 col9\" >phase(deg)</th>\n",
       "      <th id=\"T_fd793_level0_col10\" class=\"col_heading level0 col10\" >sd(deg)</th>\n",
       "      <th id=\"T_fd793_level0_col11\" class=\"col_heading level0 col11\" >CRLB(phase%)</th>\n",
       "      <th id=\"T_fd793_level0_col12\" class=\"col_heading level0 col12\" >g</th>\n",
       "      <th id=\"T_fd793_level0_col13\" class=\"col_heading level0 col13\" >g_sd</th>\n",
       "      <th id=\"T_fd793_level0_col14\" class=\"col_heading level0 col14\" >g (%)</th>\n",
       "      <th id=\"T_fd793_level0_col15\" class=\"col_heading level0 col15\" >SNR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row0\" class=\"row_heading level0 row0\" >BATP</th>\n",
       "      <td id=\"T_fd793_row0_col0\" class=\"data row0 col0\" >2.774</td>\n",
       "      <td id=\"T_fd793_row0_col1\" class=\"data row0 col1\" >0.056</td>\n",
       "      <td id=\"T_fd793_row0_col2\" class=\"data row0 col2\" >2.795</td>\n",
       "      <td id=\"T_fd793_row0_col3\" class=\"data row0 col3\" >-16.123</td>\n",
       "      <td id=\"T_fd793_row0_col4\" class=\"data row0 col4\" >0.010</td>\n",
       "      <td id=\"T_fd793_row0_col5\" class=\"data row0 col5\" >0.068</td>\n",
       "      <td id=\"T_fd793_row0_col6\" class=\"data row0 col6\" >61.099</td>\n",
       "      <td id=\"T_fd793_row0_col7\" class=\"data row0 col7\" >1.958</td>\n",
       "      <td id=\"T_fd793_row0_col8\" class=\"data row0 col8\" >4.287</td>\n",
       "      <td id=\"T_fd793_row0_col9\" class=\"data row0 col9\" >-1.814</td>\n",
       "      <td id=\"T_fd793_row0_col10\" class=\"data row0 col10\" >1.113</td>\n",
       "      <td id=\"T_fd793_row0_col11\" class=\"data row0 col11\" >88.257</td>\n",
       "      <td id=\"T_fd793_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row0_col13\" class=\"data row0 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row0_col15\" class=\"data row0 col15\" >11.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row1\" class=\"row_heading level0 row1\" >AATP</th>\n",
       "      <td id=\"T_fd793_row1_col0\" class=\"data row1 col0\" >3.037</td>\n",
       "      <td id=\"T_fd793_row1_col1\" class=\"data row1 col1\" >0.068</td>\n",
       "      <td id=\"T_fd793_row1_col2\" class=\"data row1 col2\" >3.121</td>\n",
       "      <td id=\"T_fd793_row1_col3\" class=\"data row1 col3\" >-7.512</td>\n",
       "      <td id=\"T_fd793_row1_col4\" class=\"data row1 col4\" >0.004</td>\n",
       "      <td id=\"T_fd793_row1_col5\" class=\"data row1 col5\" >0.077</td>\n",
       "      <td id=\"T_fd793_row1_col6\" class=\"data row1 col6\" >31.614</td>\n",
       "      <td id=\"T_fd793_row1_col7\" class=\"data row1 col7\" >0.996</td>\n",
       "      <td id=\"T_fd793_row1_col8\" class=\"data row1 col8\" >4.376</td>\n",
       "      <td id=\"T_fd793_row1_col9\" class=\"data row1 col9\" >-0.015</td>\n",
       "      <td id=\"T_fd793_row1_col10\" class=\"data row1 col10\" >1.288</td>\n",
       "      <td id=\"T_fd793_row1_col11\" class=\"data row1 col11\" >11709.060</td>\n",
       "      <td id=\"T_fd793_row1_col12\" class=\"data row1 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row1_col15\" class=\"data row1 col15\" >12.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row2\" class=\"row_heading level0 row2\" >GATP</th>\n",
       "      <td id=\"T_fd793_row2_col0\" class=\"data row2 col0\" >2.914</td>\n",
       "      <td id=\"T_fd793_row2_col1\" class=\"data row2 col1\" >0.044</td>\n",
       "      <td id=\"T_fd793_row2_col2\" class=\"data row2 col2\" >2.112</td>\n",
       "      <td id=\"T_fd793_row2_col3\" class=\"data row2 col3\" >-2.484</td>\n",
       "      <td id=\"T_fd793_row2_col4\" class=\"data row2 col4\" >0.004</td>\n",
       "      <td id=\"T_fd793_row2_col5\" class=\"data row2 col5\" >0.211</td>\n",
       "      <td id=\"T_fd793_row2_col6\" class=\"data row2 col6\" >36.104</td>\n",
       "      <td id=\"T_fd793_row2_col7\" class=\"data row2 col7\" >0.906</td>\n",
       "      <td id=\"T_fd793_row2_col8\" class=\"data row2 col8\" >3.483</td>\n",
       "      <td id=\"T_fd793_row2_col9\" class=\"data row2 col9\" >-0.368</td>\n",
       "      <td id=\"T_fd793_row2_col10\" class=\"data row2 col10\" >0.871</td>\n",
       "      <td id=\"T_fd793_row2_col11\" class=\"data row2 col11\" >328.587</td>\n",
       "      <td id=\"T_fd793_row2_col12\" class=\"data row2 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row2_col13\" class=\"data row2 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row2_col15\" class=\"data row2 col15\" >11.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row3\" class=\"row_heading level0 row3\" >UDPG</th>\n",
       "      <td id=\"T_fd793_row3_col0\" class=\"data row3 col0\" >0.071</td>\n",
       "      <td id=\"T_fd793_row3_col1\" class=\"data row3 col1\" >0.038</td>\n",
       "      <td id=\"T_fd793_row3_col2\" class=\"data row3 col2\" >73.624</td>\n",
       "      <td id=\"T_fd793_row3_col3\" class=\"data row3 col3\" >-9.760</td>\n",
       "      <td id=\"T_fd793_row3_col4\" class=\"data row3 col4\" >0.080</td>\n",
       "      <td id=\"T_fd793_row3_col5\" class=\"data row3 col5\" >1.142</td>\n",
       "      <td id=\"T_fd793_row3_col6\" class=\"data row3 col6\" >27.634</td>\n",
       "      <td id=\"T_fd793_row3_col7\" class=\"data row3 col7\" >19.260</td>\n",
       "      <td id=\"T_fd793_row3_col8\" class=\"data row3 col8\" >96.801</td>\n",
       "      <td id=\"T_fd793_row3_col9\" class=\"data row3 col9\" >44.759</td>\n",
       "      <td id=\"T_fd793_row3_col10\" class=\"data row3 col10\" >30.380</td>\n",
       "      <td id=\"T_fd793_row3_col11\" class=\"data row3 col11\" >94.246</td>\n",
       "      <td id=\"T_fd793_row3_col12\" class=\"data row3 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row3_col13\" class=\"data row3 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row3_col15\" class=\"data row3 col15\" >0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row4\" class=\"row_heading level0 row4\" >NAD</th>\n",
       "      <td id=\"T_fd793_row4_col0\" class=\"data row4 col0\" >0.441</td>\n",
       "      <td id=\"T_fd793_row4_col1\" class=\"data row4 col1\" >0.078</td>\n",
       "      <td id=\"T_fd793_row4_col2\" class=\"data row4 col2\" >24.589</td>\n",
       "      <td id=\"T_fd793_row4_col3\" class=\"data row4 col3\" >-8.250</td>\n",
       "      <td id=\"T_fd793_row4_col4\" class=\"data row4 col4\" >0.036</td>\n",
       "      <td id=\"T_fd793_row4_col5\" class=\"data row4 col5\" >0.609</td>\n",
       "      <td id=\"T_fd793_row4_col6\" class=\"data row4 col6\" >46.155</td>\n",
       "      <td id=\"T_fd793_row4_col7\" class=\"data row4 col7\" >8.687</td>\n",
       "      <td id=\"T_fd793_row4_col8\" class=\"data row4 col8\" >26.130</td>\n",
       "      <td id=\"T_fd793_row4_col9\" class=\"data row4 col9\" >-3.662</td>\n",
       "      <td id=\"T_fd793_row4_col10\" class=\"data row4 col10\" >10.139</td>\n",
       "      <td id=\"T_fd793_row4_col11\" class=\"data row4 col11\" >384.695</td>\n",
       "      <td id=\"T_fd793_row4_col12\" class=\"data row4 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row4_col13\" class=\"data row4 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row4_col15\" class=\"data row4 col15\" >1.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row5\" class=\"row_heading level0 row5\" >PCr</th>\n",
       "      <td id=\"T_fd793_row5_col0\" class=\"data row5 col0\" >4.159</td>\n",
       "      <td id=\"T_fd793_row5_col1\" class=\"data row5 col1\" >0.025</td>\n",
       "      <td id=\"T_fd793_row5_col2\" class=\"data row5 col2\" >0.842</td>\n",
       "      <td id=\"T_fd793_row5_col3\" class=\"data row5 col3\" >-0.012</td>\n",
       "      <td id=\"T_fd793_row5_col4\" class=\"data row5 col4\" >0.001</td>\n",
       "      <td id=\"T_fd793_row5_col5\" class=\"data row5 col5\" >6.211</td>\n",
       "      <td id=\"T_fd793_row5_col6\" class=\"data row5 col6\" >15.005</td>\n",
       "      <td id=\"T_fd793_row5_col7\" class=\"data row5 col7\" >0.126</td>\n",
       "      <td id=\"T_fd793_row5_col8\" class=\"data row5 col8\" >1.166</td>\n",
       "      <td id=\"T_fd793_row5_col9\" class=\"data row5 col9\" >-0.142</td>\n",
       "      <td id=\"T_fd793_row5_col10\" class=\"data row5 col10\" >0.347</td>\n",
       "      <td id=\"T_fd793_row5_col11\" class=\"data row5 col11\" >340.405</td>\n",
       "      <td id=\"T_fd793_row5_col12\" class=\"data row5 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row5_col13\" class=\"data row5 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row5_col15\" class=\"data row5 col15\" >16.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row6\" class=\"row_heading level0 row6\" >GPC</th>\n",
       "      <td id=\"T_fd793_row6_col0\" class=\"data row6 col0\" >1.208</td>\n",
       "      <td id=\"T_fd793_row6_col1\" class=\"data row6 col1\" >0.033</td>\n",
       "      <td id=\"T_fd793_row6_col2\" class=\"data row6 col2\" >3.848</td>\n",
       "      <td id=\"T_fd793_row6_col3\" class=\"data row6 col3\" >2.953</td>\n",
       "      <td id=\"T_fd793_row6_col4\" class=\"data row6 col4\" >0.003</td>\n",
       "      <td id=\"T_fd793_row6_col5\" class=\"data row6 col5\" >0.130</td>\n",
       "      <td id=\"T_fd793_row6_col6\" class=\"data row6 col6\" >18.826</td>\n",
       "      <td id=\"T_fd793_row6_col7\" class=\"data row6 col7\" >0.663</td>\n",
       "      <td id=\"T_fd793_row6_col8\" class=\"data row6 col8\" >4.893</td>\n",
       "      <td id=\"T_fd793_row6_col9\" class=\"data row6 col9\" >-1.532</td>\n",
       "      <td id=\"T_fd793_row6_col10\" class=\"data row6 col10\" >1.588</td>\n",
       "      <td id=\"T_fd793_row6_col11\" class=\"data row6 col11\" >143.915</td>\n",
       "      <td id=\"T_fd793_row6_col12\" class=\"data row6 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row6_col13\" class=\"data row6 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row6_col15\" class=\"data row6 col15\" >4.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row7\" class=\"row_heading level0 row7\" >GPE</th>\n",
       "      <td id=\"T_fd793_row7_col0\" class=\"data row7 col0\" >0.873</td>\n",
       "      <td id=\"T_fd793_row7_col1\" class=\"data row7 col1\" >0.035</td>\n",
       "      <td id=\"T_fd793_row7_col2\" class=\"data row7 col2\" >5.500</td>\n",
       "      <td id=\"T_fd793_row7_col3\" class=\"data row7 col3\" >3.510</td>\n",
       "      <td id=\"T_fd793_row7_col4\" class=\"data row7 col4\" >0.004</td>\n",
       "      <td id=\"T_fd793_row7_col5\" class=\"data row7 col5\" >0.156</td>\n",
       "      <td id=\"T_fd793_row7_col6\" class=\"data row7 col6\" >19.040</td>\n",
       "      <td id=\"T_fd793_row7_col7\" class=\"data row7 col7\" >0.947</td>\n",
       "      <td id=\"T_fd793_row7_col8\" class=\"data row7 col8\" >6.904</td>\n",
       "      <td id=\"T_fd793_row7_col9\" class=\"data row7 col9\" >2.568</td>\n",
       "      <td id=\"T_fd793_row7_col10\" class=\"data row7 col10\" >2.269</td>\n",
       "      <td id=\"T_fd793_row7_col11\" class=\"data row7 col11\" >122.723</td>\n",
       "      <td id=\"T_fd793_row7_col12\" class=\"data row7 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row7_col13\" class=\"data row7 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row7_col15\" class=\"data row7 col15\" >3.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row8\" class=\"row_heading level0 row8\" >Pin</th>\n",
       "      <td id=\"T_fd793_row8_col0\" class=\"data row8 col0\" >0.909</td>\n",
       "      <td id=\"T_fd793_row8_col1\" class=\"data row8 col1\" >0.045</td>\n",
       "      <td id=\"T_fd793_row8_col2\" class=\"data row8 col2\" >6.935</td>\n",
       "      <td id=\"T_fd793_row8_col3\" class=\"data row8 col3\" >4.810</td>\n",
       "      <td id=\"T_fd793_row8_col4\" class=\"data row8 col4\" >0.005</td>\n",
       "      <td id=\"T_fd793_row8_col5\" class=\"data row8 col5\" >0.157</td>\n",
       "      <td id=\"T_fd793_row8_col6\" class=\"data row8 col6\" >22.727</td>\n",
       "      <td id=\"T_fd793_row8_col7\" class=\"data row8 col7\" >1.307</td>\n",
       "      <td id=\"T_fd793_row8_col8\" class=\"data row8 col8\" >7.987</td>\n",
       "      <td id=\"T_fd793_row8_col9\" class=\"data row8 col9\" >2.573</td>\n",
       "      <td id=\"T_fd793_row8_col10\" class=\"data row8 col10\" >2.862</td>\n",
       "      <td id=\"T_fd793_row8_col11\" class=\"data row8 col11\" >154.405</td>\n",
       "      <td id=\"T_fd793_row8_col12\" class=\"data row8 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row8_col13\" class=\"data row8 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row8_col15\" class=\"data row8 col15\" >3.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row9\" class=\"row_heading level0 row9\" >Pex</th>\n",
       "      <td id=\"T_fd793_row9_col0\" class=\"data row9 col0\" >0.219</td>\n",
       "      <td id=\"T_fd793_row9_col1\" class=\"data row9 col1\" >0.043</td>\n",
       "      <td id=\"T_fd793_row9_col2\" class=\"data row9 col2\" >27.093</td>\n",
       "      <td id=\"T_fd793_row9_col3\" class=\"data row9 col3\" >5.242</td>\n",
       "      <td id=\"T_fd793_row9_col4\" class=\"data row9 col4\" >0.019</td>\n",
       "      <td id=\"T_fd793_row9_col5\" class=\"data row9 col5\" >0.495</td>\n",
       "      <td id=\"T_fd793_row9_col6\" class=\"data row9 col6\" >20.041</td>\n",
       "      <td id=\"T_fd793_row9_col7\" class=\"data row9 col7\" >4.489</td>\n",
       "      <td id=\"T_fd793_row9_col8\" class=\"data row9 col8\" >31.101</td>\n",
       "      <td id=\"T_fd793_row9_col9\" class=\"data row9 col9\" >-0.281</td>\n",
       "      <td id=\"T_fd793_row9_col10\" class=\"data row9 col10\" >11.179</td>\n",
       "      <td id=\"T_fd793_row9_col11\" class=\"data row9 col11\" >5530.322</td>\n",
       "      <td id=\"T_fd793_row9_col12\" class=\"data row9 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row9_col13\" class=\"data row9 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row9_col15\" class=\"data row9 col15\" >0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row10\" class=\"row_heading level0 row10\" >PC</th>\n",
       "      <td id=\"T_fd793_row10_col0\" class=\"data row10 col0\" >0.332</td>\n",
       "      <td id=\"T_fd793_row10_col1\" class=\"data row10 col1\" >0.035</td>\n",
       "      <td id=\"T_fd793_row10_col2\" class=\"data row10 col2\" >14.789</td>\n",
       "      <td id=\"T_fd793_row10_col3\" class=\"data row10 col3\" >6.220</td>\n",
       "      <td id=\"T_fd793_row10_col4\" class=\"data row10 col4\" >0.010</td>\n",
       "      <td id=\"T_fd793_row10_col5\" class=\"data row10 col5\" >0.224</td>\n",
       "      <td id=\"T_fd793_row10_col6\" class=\"data row10 col6\" >18.356</td>\n",
       "      <td id=\"T_fd793_row10_col7\" class=\"data row10 col7\" >2.404</td>\n",
       "      <td id=\"T_fd793_row10_col8\" class=\"data row10 col8\" >18.187</td>\n",
       "      <td id=\"T_fd793_row10_col9\" class=\"data row10 col9\" >-8.991</td>\n",
       "      <td id=\"T_fd793_row10_col10\" class=\"data row10 col10\" >6.102</td>\n",
       "      <td id=\"T_fd793_row10_col11\" class=\"data row10 col11\" >94.245</td>\n",
       "      <td id=\"T_fd793_row10_col12\" class=\"data row10 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row10_col13\" class=\"data row10 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row10_col15\" class=\"data row10 col15\" >1.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd793_level0_row11\" class=\"row_heading level0 row11\" >PE</th>\n",
       "      <td id=\"T_fd793_row11_col0\" class=\"data row11 col0\" >2.281</td>\n",
       "      <td id=\"T_fd793_row11_col1\" class=\"data row11 col1\" >0.039</td>\n",
       "      <td id=\"T_fd793_row11_col2\" class=\"data row11 col2\" >2.348</td>\n",
       "      <td id=\"T_fd793_row11_col3\" class=\"data row11 col3\" >6.790</td>\n",
       "      <td id=\"T_fd793_row11_col4\" class=\"data row11 col4\" >0.002</td>\n",
       "      <td id=\"T_fd793_row11_col5\" class=\"data row11 col5\" >0.042</td>\n",
       "      <td id=\"T_fd793_row11_col6\" class=\"data row11 col6\" >23.277</td>\n",
       "      <td id=\"T_fd793_row11_col7\" class=\"data row11 col7\" >0.492</td>\n",
       "      <td id=\"T_fd793_row11_col8\" class=\"data row11 col8\" >2.935</td>\n",
       "      <td id=\"T_fd793_row11_col9\" class=\"data row11 col9\" >0.707</td>\n",
       "      <td id=\"T_fd793_row11_col10\" class=\"data row11 col10\" >0.969</td>\n",
       "      <td id=\"T_fd793_row11_col11\" class=\"data row11 col11\" >190.440</td>\n",
       "      <td id=\"T_fd793_row11_col12\" class=\"data row11 col12\" >0.000</td>\n",
       "      <td id=\"T_fd793_row11_col13\" class=\"data row11 col13\" >0.000</td>\n",
       "      <td id=\"T_fd793_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_fd793_row11_col15\" class=\"data row11 col15\" >9.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f03505ddcd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FIDresult2.styled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b917de53",
   "metadata": {},
   "source": [
    "- **New after 0.3.10**: Fitting AMARES using internally initialized Levenberg-Marquardt parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56fad2d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A copy of the input fid_parameters will be returned because inplace=False\n",
      "Autogenerated tol is 3.158e-06\n",
      "Run internal leastsq initializer to optimize fitting parameters for the next least_squares fitting\n",
      "Fitting with method=leastsq took 3.33159 seconds\n",
      "Fitting with method=least_squares took 3.026501 seconds\n",
      "Estimated CRLBs are calculated using the default noise variance estimation used by OXSA.\n",
      "Lmfit Fitting Results:\n",
      "----------------\n",
      "Number of function evaluations (nfev): 7\n",
      "Reduced chi-squared (redchi): 0.030735869852873087\n",
      "Fit success status: Success\n",
      "Fit message: `ftol` termination condition is satisfied.\n",
      "Norm of residual = 61.472\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIDresult2b = pyAMARES.fitAMARES(\n",
    "    fid_parameters=FIDobj,\n",
    "    fitting_parameters=FIDobj.initialParams,\n",
    "    initialize_with_lm=True,  # Turn on the Levenberg-Marquardt initializer\n",
    "    method=\"least_squares\",\n",
    "    ifplot=True,\n",
    "    inplace=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10f04a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_52340_row0_col0, #T_52340_row0_col1, #T_52340_row0_col2, #T_52340_row0_col3, #T_52340_row0_col4, #T_52340_row0_col5, #T_52340_row0_col6, #T_52340_row0_col7, #T_52340_row0_col8, #T_52340_row0_col9, #T_52340_row0_col10, #T_52340_row0_col11, #T_52340_row0_col12, #T_52340_row0_col13, #T_52340_row0_col14, #T_52340_row0_col15, #T_52340_row1_col0, #T_52340_row1_col1, #T_52340_row1_col2, #T_52340_row1_col3, #T_52340_row1_col4, #T_52340_row1_col5, #T_52340_row1_col6, #T_52340_row1_col7, #T_52340_row1_col8, #T_52340_row1_col9, #T_52340_row1_col10, #T_52340_row1_col11, #T_52340_row1_col12, #T_52340_row1_col13, #T_52340_row1_col14, #T_52340_row1_col15, #T_52340_row2_col0, #T_52340_row2_col1, #T_52340_row2_col2, #T_52340_row2_col3, #T_52340_row2_col4, #T_52340_row2_col5, #T_52340_row2_col6, #T_52340_row2_col7, #T_52340_row2_col8, #T_52340_row2_col9, #T_52340_row2_col10, #T_52340_row2_col11, #T_52340_row2_col12, #T_52340_row2_col13, #T_52340_row2_col14, #T_52340_row2_col15, #T_52340_row5_col0, #T_52340_row5_col1, #T_52340_row5_col2, #T_52340_row5_col3, #T_52340_row5_col4, #T_52340_row5_col5, #T_52340_row5_col6, #T_52340_row5_col7, #T_52340_row5_col8, #T_52340_row5_col9, #T_52340_row5_col10, #T_52340_row5_col11, #T_52340_row5_col12, #T_52340_row5_col13, #T_52340_row5_col14, #T_52340_row5_col15, #T_52340_row6_col0, #T_52340_row6_col1, #T_52340_row6_col2, #T_52340_row6_col3, #T_52340_row6_col4, #T_52340_row6_col5, #T_52340_row6_col6, #T_52340_row6_col7, #T_52340_row6_col8, #T_52340_row6_col9, #T_52340_row6_col10, #T_52340_row6_col11, #T_52340_row6_col12, #T_52340_row6_col13, #T_52340_row6_col14, #T_52340_row6_col15, #T_52340_row7_col0, #T_52340_row7_col1, #T_52340_row7_col2, #T_52340_row7_col3, #T_52340_row7_col4, #T_52340_row7_col5, #T_52340_row7_col6, #T_52340_row7_col7, #T_52340_row7_col8, #T_52340_row7_col9, #T_52340_row7_col10, #T_52340_row7_col11, #T_52340_row7_col12, #T_52340_row7_col13, #T_52340_row7_col14, #T_52340_row7_col15, #T_52340_row8_col0, #T_52340_row8_col1, #T_52340_row8_col2, #T_52340_row8_col3, #T_52340_row8_col4, #T_52340_row8_col5, #T_52340_row8_col6, #T_52340_row8_col7, #T_52340_row8_col8, #T_52340_row8_col9, #T_52340_row8_col10, #T_52340_row8_col11, #T_52340_row8_col12, #T_52340_row8_col13, #T_52340_row8_col14, #T_52340_row8_col15, #T_52340_row10_col0, #T_52340_row10_col1, #T_52340_row10_col2, #T_52340_row10_col3, #T_52340_row10_col4, #T_52340_row10_col5, #T_52340_row10_col6, #T_52340_row10_col7, #T_52340_row10_col8, #T_52340_row10_col9, #T_52340_row10_col10, #T_52340_row10_col11, #T_52340_row10_col12, #T_52340_row10_col13, #T_52340_row10_col14, #T_52340_row10_col15, #T_52340_row11_col0, #T_52340_row11_col1, #T_52340_row11_col2, #T_52340_row11_col3, #T_52340_row11_col4, #T_52340_row11_col5, #T_52340_row11_col6, #T_52340_row11_col7, #T_52340_row11_col8, #T_52340_row11_col9, #T_52340_row11_col10, #T_52340_row11_col11, #T_52340_row11_col12, #T_52340_row11_col13, #T_52340_row11_col14, #T_52340_row11_col15 {\n",
       "  background-color: rgba(0, 255, 0, 0.5);\n",
       "}\n",
       "#T_52340_row3_col0, #T_52340_row3_col1, #T_52340_row3_col2, #T_52340_row3_col3, #T_52340_row3_col4, #T_52340_row3_col5, #T_52340_row3_col6, #T_52340_row3_col7, #T_52340_row3_col8, #T_52340_row3_col9, #T_52340_row3_col10, #T_52340_row3_col11, #T_52340_row3_col12, #T_52340_row3_col13, #T_52340_row3_col14, #T_52340_row3_col15, #T_52340_row4_col0, #T_52340_row4_col1, #T_52340_row4_col2, #T_52340_row4_col3, #T_52340_row4_col4, #T_52340_row4_col5, #T_52340_row4_col6, #T_52340_row4_col7, #T_52340_row4_col8, #T_52340_row4_col9, #T_52340_row4_col10, #T_52340_row4_col11, #T_52340_row4_col12, #T_52340_row4_col13, #T_52340_row4_col14, #T_52340_row4_col15, #T_52340_row9_col0, #T_52340_row9_col1, #T_52340_row9_col2, #T_52340_row9_col3, #T_52340_row9_col4, #T_52340_row9_col5, #T_52340_row9_col6, #T_52340_row9_col7, #T_52340_row9_col8, #T_52340_row9_col9, #T_52340_row9_col10, #T_52340_row9_col11, #T_52340_row9_col12, #T_52340_row9_col13, #T_52340_row9_col14, #T_52340_row9_col15 {\n",
       "  background-color: rgba(255, 0, 0, 0.2);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_52340\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_52340_level0_col0\" class=\"col_heading level0 col0\" >amplitude</th>\n",
       "      <th id=\"T_52340_level0_col1\" class=\"col_heading level0 col1\" >sd</th>\n",
       "      <th id=\"T_52340_level0_col2\" class=\"col_heading level0 col2\" >CRLB(%)</th>\n",
       "      <th id=\"T_52340_level0_col3\" class=\"col_heading level0 col3\" >chem shift(ppm)</th>\n",
       "      <th id=\"T_52340_level0_col4\" class=\"col_heading level0 col4\" >sd(ppm)</th>\n",
       "      <th id=\"T_52340_level0_col5\" class=\"col_heading level0 col5\" >CRLB(cs%) </th>\n",
       "      <th id=\"T_52340_level0_col6\" class=\"col_heading level0 col6\" >LW(Hz)</th>\n",
       "      <th id=\"T_52340_level0_col7\" class=\"col_heading level0 col7\" >sd(Hz)</th>\n",
       "      <th id=\"T_52340_level0_col8\" class=\"col_heading level0 col8\" >CRLB(LW%)</th>\n",
       "      <th id=\"T_52340_level0_col9\" class=\"col_heading level0 col9\" >phase(deg)</th>\n",
       "      <th id=\"T_52340_level0_col10\" class=\"col_heading level0 col10\" >sd(deg)</th>\n",
       "      <th id=\"T_52340_level0_col11\" class=\"col_heading level0 col11\" >CRLB(phase%)</th>\n",
       "      <th id=\"T_52340_level0_col12\" class=\"col_heading level0 col12\" >g</th>\n",
       "      <th id=\"T_52340_level0_col13\" class=\"col_heading level0 col13\" >g_sd</th>\n",
       "      <th id=\"T_52340_level0_col14\" class=\"col_heading level0 col14\" >g (%)</th>\n",
       "      <th id=\"T_52340_level0_col15\" class=\"col_heading level0 col15\" >SNR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row0\" class=\"row_heading level0 row0\" >BATP</th>\n",
       "      <td id=\"T_52340_row0_col0\" class=\"data row0 col0\" >2.774</td>\n",
       "      <td id=\"T_52340_row0_col1\" class=\"data row0 col1\" >0.056</td>\n",
       "      <td id=\"T_52340_row0_col2\" class=\"data row0 col2\" >2.795</td>\n",
       "      <td id=\"T_52340_row0_col3\" class=\"data row0 col3\" >-16.123</td>\n",
       "      <td id=\"T_52340_row0_col4\" class=\"data row0 col4\" >0.010</td>\n",
       "      <td id=\"T_52340_row0_col5\" class=\"data row0 col5\" >0.068</td>\n",
       "      <td id=\"T_52340_row0_col6\" class=\"data row0 col6\" >61.099</td>\n",
       "      <td id=\"T_52340_row0_col7\" class=\"data row0 col7\" >1.958</td>\n",
       "      <td id=\"T_52340_row0_col8\" class=\"data row0 col8\" >4.287</td>\n",
       "      <td id=\"T_52340_row0_col9\" class=\"data row0 col9\" >-1.814</td>\n",
       "      <td id=\"T_52340_row0_col10\" class=\"data row0 col10\" >1.113</td>\n",
       "      <td id=\"T_52340_row0_col11\" class=\"data row0 col11\" >88.257</td>\n",
       "      <td id=\"T_52340_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row0_col13\" class=\"data row0 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row0_col15\" class=\"data row0 col15\" >11.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row1\" class=\"row_heading level0 row1\" >AATP</th>\n",
       "      <td id=\"T_52340_row1_col0\" class=\"data row1 col0\" >3.037</td>\n",
       "      <td id=\"T_52340_row1_col1\" class=\"data row1 col1\" >0.068</td>\n",
       "      <td id=\"T_52340_row1_col2\" class=\"data row1 col2\" >3.121</td>\n",
       "      <td id=\"T_52340_row1_col3\" class=\"data row1 col3\" >-7.512</td>\n",
       "      <td id=\"T_52340_row1_col4\" class=\"data row1 col4\" >0.004</td>\n",
       "      <td id=\"T_52340_row1_col5\" class=\"data row1 col5\" >0.077</td>\n",
       "      <td id=\"T_52340_row1_col6\" class=\"data row1 col6\" >31.614</td>\n",
       "      <td id=\"T_52340_row1_col7\" class=\"data row1 col7\" >0.996</td>\n",
       "      <td id=\"T_52340_row1_col8\" class=\"data row1 col8\" >4.376</td>\n",
       "      <td id=\"T_52340_row1_col9\" class=\"data row1 col9\" >-0.015</td>\n",
       "      <td id=\"T_52340_row1_col10\" class=\"data row1 col10\" >1.288</td>\n",
       "      <td id=\"T_52340_row1_col11\" class=\"data row1 col11\" >11709.060</td>\n",
       "      <td id=\"T_52340_row1_col12\" class=\"data row1 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row1_col15\" class=\"data row1 col15\" >12.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row2\" class=\"row_heading level0 row2\" >GATP</th>\n",
       "      <td id=\"T_52340_row2_col0\" class=\"data row2 col0\" >2.914</td>\n",
       "      <td id=\"T_52340_row2_col1\" class=\"data row2 col1\" >0.044</td>\n",
       "      <td id=\"T_52340_row2_col2\" class=\"data row2 col2\" >2.112</td>\n",
       "      <td id=\"T_52340_row2_col3\" class=\"data row2 col3\" >-2.484</td>\n",
       "      <td id=\"T_52340_row2_col4\" class=\"data row2 col4\" >0.004</td>\n",
       "      <td id=\"T_52340_row2_col5\" class=\"data row2 col5\" >0.211</td>\n",
       "      <td id=\"T_52340_row2_col6\" class=\"data row2 col6\" >36.104</td>\n",
       "      <td id=\"T_52340_row2_col7\" class=\"data row2 col7\" >0.906</td>\n",
       "      <td id=\"T_52340_row2_col8\" class=\"data row2 col8\" >3.483</td>\n",
       "      <td id=\"T_52340_row2_col9\" class=\"data row2 col9\" >-0.368</td>\n",
       "      <td id=\"T_52340_row2_col10\" class=\"data row2 col10\" >0.871</td>\n",
       "      <td id=\"T_52340_row2_col11\" class=\"data row2 col11\" >328.587</td>\n",
       "      <td id=\"T_52340_row2_col12\" class=\"data row2 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row2_col13\" class=\"data row2 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row2_col15\" class=\"data row2 col15\" >11.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row3\" class=\"row_heading level0 row3\" >UDPG</th>\n",
       "      <td id=\"T_52340_row3_col0\" class=\"data row3 col0\" >0.071</td>\n",
       "      <td id=\"T_52340_row3_col1\" class=\"data row3 col1\" >0.038</td>\n",
       "      <td id=\"T_52340_row3_col2\" class=\"data row3 col2\" >73.624</td>\n",
       "      <td id=\"T_52340_row3_col3\" class=\"data row3 col3\" >-9.760</td>\n",
       "      <td id=\"T_52340_row3_col4\" class=\"data row3 col4\" >0.080</td>\n",
       "      <td id=\"T_52340_row3_col5\" class=\"data row3 col5\" >1.142</td>\n",
       "      <td id=\"T_52340_row3_col6\" class=\"data row3 col6\" >27.634</td>\n",
       "      <td id=\"T_52340_row3_col7\" class=\"data row3 col7\" >19.260</td>\n",
       "      <td id=\"T_52340_row3_col8\" class=\"data row3 col8\" >96.801</td>\n",
       "      <td id=\"T_52340_row3_col9\" class=\"data row3 col9\" >44.759</td>\n",
       "      <td id=\"T_52340_row3_col10\" class=\"data row3 col10\" >30.380</td>\n",
       "      <td id=\"T_52340_row3_col11\" class=\"data row3 col11\" >94.246</td>\n",
       "      <td id=\"T_52340_row3_col12\" class=\"data row3 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row3_col13\" class=\"data row3 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row3_col15\" class=\"data row3 col15\" >0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row4\" class=\"row_heading level0 row4\" >NAD</th>\n",
       "      <td id=\"T_52340_row4_col0\" class=\"data row4 col0\" >0.441</td>\n",
       "      <td id=\"T_52340_row4_col1\" class=\"data row4 col1\" >0.078</td>\n",
       "      <td id=\"T_52340_row4_col2\" class=\"data row4 col2\" >24.589</td>\n",
       "      <td id=\"T_52340_row4_col3\" class=\"data row4 col3\" >-8.250</td>\n",
       "      <td id=\"T_52340_row4_col4\" class=\"data row4 col4\" >0.036</td>\n",
       "      <td id=\"T_52340_row4_col5\" class=\"data row4 col5\" >0.609</td>\n",
       "      <td id=\"T_52340_row4_col6\" class=\"data row4 col6\" >46.155</td>\n",
       "      <td id=\"T_52340_row4_col7\" class=\"data row4 col7\" >8.687</td>\n",
       "      <td id=\"T_52340_row4_col8\" class=\"data row4 col8\" >26.130</td>\n",
       "      <td id=\"T_52340_row4_col9\" class=\"data row4 col9\" >-3.662</td>\n",
       "      <td id=\"T_52340_row4_col10\" class=\"data row4 col10\" >10.139</td>\n",
       "      <td id=\"T_52340_row4_col11\" class=\"data row4 col11\" >384.695</td>\n",
       "      <td id=\"T_52340_row4_col12\" class=\"data row4 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row4_col13\" class=\"data row4 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row4_col15\" class=\"data row4 col15\" >1.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row5\" class=\"row_heading level0 row5\" >PCr</th>\n",
       "      <td id=\"T_52340_row5_col0\" class=\"data row5 col0\" >4.159</td>\n",
       "      <td id=\"T_52340_row5_col1\" class=\"data row5 col1\" >0.025</td>\n",
       "      <td id=\"T_52340_row5_col2\" class=\"data row5 col2\" >0.842</td>\n",
       "      <td id=\"T_52340_row5_col3\" class=\"data row5 col3\" >-0.012</td>\n",
       "      <td id=\"T_52340_row5_col4\" class=\"data row5 col4\" >0.001</td>\n",
       "      <td id=\"T_52340_row5_col5\" class=\"data row5 col5\" >6.211</td>\n",
       "      <td id=\"T_52340_row5_col6\" class=\"data row5 col6\" >15.005</td>\n",
       "      <td id=\"T_52340_row5_col7\" class=\"data row5 col7\" >0.126</td>\n",
       "      <td id=\"T_52340_row5_col8\" class=\"data row5 col8\" >1.166</td>\n",
       "      <td id=\"T_52340_row5_col9\" class=\"data row5 col9\" >-0.142</td>\n",
       "      <td id=\"T_52340_row5_col10\" class=\"data row5 col10\" >0.347</td>\n",
       "      <td id=\"T_52340_row5_col11\" class=\"data row5 col11\" >340.405</td>\n",
       "      <td id=\"T_52340_row5_col12\" class=\"data row5 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row5_col13\" class=\"data row5 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row5_col15\" class=\"data row5 col15\" >16.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row6\" class=\"row_heading level0 row6\" >GPC</th>\n",
       "      <td id=\"T_52340_row6_col0\" class=\"data row6 col0\" >1.208</td>\n",
       "      <td id=\"T_52340_row6_col1\" class=\"data row6 col1\" >0.033</td>\n",
       "      <td id=\"T_52340_row6_col2\" class=\"data row6 col2\" >3.848</td>\n",
       "      <td id=\"T_52340_row6_col3\" class=\"data row6 col3\" >2.953</td>\n",
       "      <td id=\"T_52340_row6_col4\" class=\"data row6 col4\" >0.003</td>\n",
       "      <td id=\"T_52340_row6_col5\" class=\"data row6 col5\" >0.130</td>\n",
       "      <td id=\"T_52340_row6_col6\" class=\"data row6 col6\" >18.826</td>\n",
       "      <td id=\"T_52340_row6_col7\" class=\"data row6 col7\" >0.663</td>\n",
       "      <td id=\"T_52340_row6_col8\" class=\"data row6 col8\" >4.893</td>\n",
       "      <td id=\"T_52340_row6_col9\" class=\"data row6 col9\" >-1.532</td>\n",
       "      <td id=\"T_52340_row6_col10\" class=\"data row6 col10\" >1.588</td>\n",
       "      <td id=\"T_52340_row6_col11\" class=\"data row6 col11\" >143.915</td>\n",
       "      <td id=\"T_52340_row6_col12\" class=\"data row6 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row6_col13\" class=\"data row6 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row6_col15\" class=\"data row6 col15\" >4.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row7\" class=\"row_heading level0 row7\" >GPE</th>\n",
       "      <td id=\"T_52340_row7_col0\" class=\"data row7 col0\" >0.873</td>\n",
       "      <td id=\"T_52340_row7_col1\" class=\"data row7 col1\" >0.035</td>\n",
       "      <td id=\"T_52340_row7_col2\" class=\"data row7 col2\" >5.500</td>\n",
       "      <td id=\"T_52340_row7_col3\" class=\"data row7 col3\" >3.510</td>\n",
       "      <td id=\"T_52340_row7_col4\" class=\"data row7 col4\" >0.004</td>\n",
       "      <td id=\"T_52340_row7_col5\" class=\"data row7 col5\" >0.156</td>\n",
       "      <td id=\"T_52340_row7_col6\" class=\"data row7 col6\" >19.040</td>\n",
       "      <td id=\"T_52340_row7_col7\" class=\"data row7 col7\" >0.947</td>\n",
       "      <td id=\"T_52340_row7_col8\" class=\"data row7 col8\" >6.904</td>\n",
       "      <td id=\"T_52340_row7_col9\" class=\"data row7 col9\" >2.568</td>\n",
       "      <td id=\"T_52340_row7_col10\" class=\"data row7 col10\" >2.269</td>\n",
       "      <td id=\"T_52340_row7_col11\" class=\"data row7 col11\" >122.723</td>\n",
       "      <td id=\"T_52340_row7_col12\" class=\"data row7 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row7_col13\" class=\"data row7 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row7_col15\" class=\"data row7 col15\" >3.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row8\" class=\"row_heading level0 row8\" >Pin</th>\n",
       "      <td id=\"T_52340_row8_col0\" class=\"data row8 col0\" >0.909</td>\n",
       "      <td id=\"T_52340_row8_col1\" class=\"data row8 col1\" >0.045</td>\n",
       "      <td id=\"T_52340_row8_col2\" class=\"data row8 col2\" >6.935</td>\n",
       "      <td id=\"T_52340_row8_col3\" class=\"data row8 col3\" >4.810</td>\n",
       "      <td id=\"T_52340_row8_col4\" class=\"data row8 col4\" >0.005</td>\n",
       "      <td id=\"T_52340_row8_col5\" class=\"data row8 col5\" >0.157</td>\n",
       "      <td id=\"T_52340_row8_col6\" class=\"data row8 col6\" >22.727</td>\n",
       "      <td id=\"T_52340_row8_col7\" class=\"data row8 col7\" >1.307</td>\n",
       "      <td id=\"T_52340_row8_col8\" class=\"data row8 col8\" >7.987</td>\n",
       "      <td id=\"T_52340_row8_col9\" class=\"data row8 col9\" >2.573</td>\n",
       "      <td id=\"T_52340_row8_col10\" class=\"data row8 col10\" >2.862</td>\n",
       "      <td id=\"T_52340_row8_col11\" class=\"data row8 col11\" >154.405</td>\n",
       "      <td id=\"T_52340_row8_col12\" class=\"data row8 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row8_col13\" class=\"data row8 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row8_col15\" class=\"data row8 col15\" >3.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row9\" class=\"row_heading level0 row9\" >Pex</th>\n",
       "      <td id=\"T_52340_row9_col0\" class=\"data row9 col0\" >0.219</td>\n",
       "      <td id=\"T_52340_row9_col1\" class=\"data row9 col1\" >0.043</td>\n",
       "      <td id=\"T_52340_row9_col2\" class=\"data row9 col2\" >27.093</td>\n",
       "      <td id=\"T_52340_row9_col3\" class=\"data row9 col3\" >5.242</td>\n",
       "      <td id=\"T_52340_row9_col4\" class=\"data row9 col4\" >0.019</td>\n",
       "      <td id=\"T_52340_row9_col5\" class=\"data row9 col5\" >0.495</td>\n",
       "      <td id=\"T_52340_row9_col6\" class=\"data row9 col6\" >20.041</td>\n",
       "      <td id=\"T_52340_row9_col7\" class=\"data row9 col7\" >4.489</td>\n",
       "      <td id=\"T_52340_row9_col8\" class=\"data row9 col8\" >31.101</td>\n",
       "      <td id=\"T_52340_row9_col9\" class=\"data row9 col9\" >-0.281</td>\n",
       "      <td id=\"T_52340_row9_col10\" class=\"data row9 col10\" >11.179</td>\n",
       "      <td id=\"T_52340_row9_col11\" class=\"data row9 col11\" >5530.322</td>\n",
       "      <td id=\"T_52340_row9_col12\" class=\"data row9 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row9_col13\" class=\"data row9 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row9_col15\" class=\"data row9 col15\" >0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row10\" class=\"row_heading level0 row10\" >PC</th>\n",
       "      <td id=\"T_52340_row10_col0\" class=\"data row10 col0\" >0.332</td>\n",
       "      <td id=\"T_52340_row10_col1\" class=\"data row10 col1\" >0.035</td>\n",
       "      <td id=\"T_52340_row10_col2\" class=\"data row10 col2\" >14.789</td>\n",
       "      <td id=\"T_52340_row10_col3\" class=\"data row10 col3\" >6.220</td>\n",
       "      <td id=\"T_52340_row10_col4\" class=\"data row10 col4\" >0.010</td>\n",
       "      <td id=\"T_52340_row10_col5\" class=\"data row10 col5\" >0.224</td>\n",
       "      <td id=\"T_52340_row10_col6\" class=\"data row10 col6\" >18.356</td>\n",
       "      <td id=\"T_52340_row10_col7\" class=\"data row10 col7\" >2.404</td>\n",
       "      <td id=\"T_52340_row10_col8\" class=\"data row10 col8\" >18.187</td>\n",
       "      <td id=\"T_52340_row10_col9\" class=\"data row10 col9\" >-8.991</td>\n",
       "      <td id=\"T_52340_row10_col10\" class=\"data row10 col10\" >6.102</td>\n",
       "      <td id=\"T_52340_row10_col11\" class=\"data row10 col11\" >94.245</td>\n",
       "      <td id=\"T_52340_row10_col12\" class=\"data row10 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row10_col13\" class=\"data row10 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row10_col15\" class=\"data row10 col15\" >1.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52340_level0_row11\" class=\"row_heading level0 row11\" >PE</th>\n",
       "      <td id=\"T_52340_row11_col0\" class=\"data row11 col0\" >2.281</td>\n",
       "      <td id=\"T_52340_row11_col1\" class=\"data row11 col1\" >0.039</td>\n",
       "      <td id=\"T_52340_row11_col2\" class=\"data row11 col2\" >2.348</td>\n",
       "      <td id=\"T_52340_row11_col3\" class=\"data row11 col3\" >6.790</td>\n",
       "      <td id=\"T_52340_row11_col4\" class=\"data row11 col4\" >0.002</td>\n",
       "      <td id=\"T_52340_row11_col5\" class=\"data row11 col5\" >0.042</td>\n",
       "      <td id=\"T_52340_row11_col6\" class=\"data row11 col6\" >23.277</td>\n",
       "      <td id=\"T_52340_row11_col7\" class=\"data row11 col7\" >0.492</td>\n",
       "      <td id=\"T_52340_row11_col8\" class=\"data row11 col8\" >2.935</td>\n",
       "      <td id=\"T_52340_row11_col9\" class=\"data row11 col9\" >0.707</td>\n",
       "      <td id=\"T_52340_row11_col10\" class=\"data row11 col10\" >0.969</td>\n",
       "      <td id=\"T_52340_row11_col11\" class=\"data row11 col11\" >190.440</td>\n",
       "      <td id=\"T_52340_row11_col12\" class=\"data row11 col12\" >0.000</td>\n",
       "      <td id=\"T_52340_row11_col13\" class=\"data row11 col13\" >0.000</td>\n",
       "      <td id=\"T_52340_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_52340_row11_col15\" class=\"data row11 col15\" >9.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f03550fb3d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FIDresult2b.styled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a697e2dc",
   "metadata": {},
   "source": [
    "## Visualize Fitting Results\n",
    "\n",
    "- **Visualization with pyAMARES**: pyAMARES utilizes the `plotParameter` object to display fitting results visually.\n",
    "- **Template for `plotParameter`**: Within the initialized `FIDobj`, there is a pre-configured template for `plotParameter` to facilitate customization and usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31603a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(deadtime=0.0002, ifphase=False, lb=2.0, sw=10000.0, xlim=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotParameter = (\n",
    "    FIDobj.plotParameters\n",
    ")  # plotParameter is a pointer to FIDobj.plotParameters\n",
    "# If you do not want to modify FIDobj.plotParameters,\n",
    "# do plotParameter = deepcopy(FIDobj.plotParameters) instead\n",
    "plotParameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2782df6f",
   "metadata": {},
   "source": [
    "- **Modify the visualization parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "479407bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plotParameter.ifphase = True  # Phasing the spectrum for visualization\n",
    "plotParameter.xlim = (10, -20)  # Show 10 to -20 ppm only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d25bc6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting_parameters is None, just use the fid_parameters.out_obj.params\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyAMARES.plotAMARES(FIDresult2, plotParameters=plotParameter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22764621",
   "metadata": {},
   "source": [
    "- **Uniform Phase for All Peaks**: Previously, each peak's phase was fitted independently. We can now attempt to use the same phase for all peaks.\n",
    "- **Editing Parameters**: Parameters can be edited programmatically using Python. Alternatively, you can manually edit them using Excel or similar software. Use the `pyAMARES.kernel.lmfit.save_parameter_to_csv` function to save parameters to a CSV file, and `pyAMARES.kernel.lmfit.load_parameter_from_csv` to reload them as an lmfit parameter object.\n",
    "``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b012038",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_params_fixedphase = deepcopy(\n",
    "    params_LM.fittedParams\n",
    ")  # Starting from the Levenberg-Marquardt-Initialized Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "016296f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constrain all phase parameters (starting with `phi` ) to the phase of PCr (`phi_Pcr`)\n",
    "for peak_para in initial_params_fixedphase:\n",
    "    if peak_para.startswith(\"phi\"):\n",
    "        initial_params_fixedphase[peak_para].expr = \"phi_PCr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "03551dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# But do not fix phi_PCr itself because it will be fitted\n",
    "initial_params_fixedphase[\"phi_PCr\"].expr = None\n",
    "initial_params_fixedphase[\"phi_PCr\"].vary = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67904509",
   "metadata": {},
   "source": [
    "- If you modified the `FIDobj.plotParameters` above and turned on `ifphase`, the following preview will show phased spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dbfba30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A copy of the input fid_parameters will be returned because inplace=False\n",
      "Autogenerated tol is 3.158e-06\n",
      "Fitting with method=least_squares took 2.330929 seconds\n",
      "Estimated CRLBs are calculated using the default noise variance estimation used by OXSA.\n",
      "Lmfit Fitting Results:\n",
      "----------------\n",
      "Number of function evaluations (nfev): 7\n",
      "Reduced chi-squared (redchi): 0.030836590708162432\n",
      "Fit success status: Success\n",
      "Fit message: `ftol` termination condition is satisfied.\n",
      "Norm of residual = 62.012\n",
      "Norm of the data = 2408.147\n",
      "resNormSq / dataNormSq = 0.026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIDresult3 = pyAMARES.fitAMARES(\n",
    "    fid_parameters=FIDobj,\n",
    "    fitting_parameters=initial_params_fixedphase,\n",
    "    method=\"least_squares\",\n",
    "    ifplot=True,\n",
    "    inplace=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "21d0c3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "#T_99267_row3_col0, #T_99267_row3_col1, #T_99267_row3_col2, #T_99267_row3_col3, #T_99267_row3_col4, #T_99267_row3_col5, #T_99267_row3_col6, #T_99267_row3_col7, #T_99267_row3_col8, #T_99267_row3_col9, #T_99267_row3_col10, #T_99267_row3_col11, #T_99267_row3_col12, #T_99267_row3_col13, #T_99267_row3_col14, #T_99267_row3_col15 {\n",
       "  background-color: rgba(255, 0, 0, 0.2);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_99267\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_99267_level0_col0\" class=\"col_heading level0 col0\" >amplitude</th>\n",
       "      <th id=\"T_99267_level0_col1\" class=\"col_heading level0 col1\" >sd</th>\n",
       "      <th id=\"T_99267_level0_col2\" class=\"col_heading level0 col2\" >CRLB(%)</th>\n",
       "      <th id=\"T_99267_level0_col3\" class=\"col_heading level0 col3\" >chem shift(ppm)</th>\n",
       "      <th id=\"T_99267_level0_col4\" class=\"col_heading level0 col4\" >sd(ppm)</th>\n",
       "      <th id=\"T_99267_level0_col5\" class=\"col_heading level0 col5\" >CRLB(cs%) </th>\n",
       "      <th id=\"T_99267_level0_col6\" class=\"col_heading level0 col6\" >LW(Hz)</th>\n",
       "      <th id=\"T_99267_level0_col7\" class=\"col_heading level0 col7\" >sd(Hz)</th>\n",
       "      <th id=\"T_99267_level0_col8\" class=\"col_heading level0 col8\" >CRLB(LW%)</th>\n",
       "      <th id=\"T_99267_level0_col9\" class=\"col_heading level0 col9\" >phase(deg)</th>\n",
       "      <th id=\"T_99267_level0_col10\" class=\"col_heading level0 col10\" >sd(deg)</th>\n",
       "      <th id=\"T_99267_level0_col11\" class=\"col_heading level0 col11\" >CRLB(phase%)</th>\n",
       "      <th id=\"T_99267_level0_col12\" class=\"col_heading level0 col12\" >g</th>\n",
       "      <th id=\"T_99267_level0_col13\" class=\"col_heading level0 col13\" >g_sd</th>\n",
       "      <th id=\"T_99267_level0_col14\" class=\"col_heading level0 col14\" >g (%)</th>\n",
       "      <th id=\"T_99267_level0_col15\" class=\"col_heading level0 col15\" >SNR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row0\" class=\"row_heading level0 row0\" >BATP</th>\n",
       "      <td id=\"T_99267_row0_col0\" class=\"data row0 col0\" >2.766</td>\n",
       "      <td id=\"T_99267_row0_col1\" class=\"data row0 col1\" >0.055</td>\n",
       "      <td id=\"T_99267_row0_col2\" class=\"data row0 col2\" >2.782</td>\n",
       "      <td id=\"T_99267_row0_col3\" class=\"data row0 col3\" >-16.132</td>\n",
       "      <td id=\"T_99267_row0_col4\" class=\"data row0 col4\" >0.005</td>\n",
       "      <td id=\"T_99267_row0_col5\" class=\"data row0 col5\" >0.047</td>\n",
       "      <td id=\"T_99267_row0_col6\" class=\"data row0 col6\" >60.804</td>\n",
       "      <td id=\"T_99267_row0_col7\" class=\"data row0 col7\" >1.876</td>\n",
       "      <td id=\"T_99267_row0_col8\" class=\"data row0 col8\" >4.306</td>\n",
       "      <td id=\"T_99267_row0_col9\" class=\"data row0 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row0_col10\" class=\"data row0 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row0_col11\" class=\"data row0 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row0_col13\" class=\"data row0 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row0_col15\" class=\"data row0 col15\" >11.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row1\" class=\"row_heading level0 row1\" >AATP</th>\n",
       "      <td id=\"T_99267_row1_col0\" class=\"data row1 col0\" >3.024</td>\n",
       "      <td id=\"T_99267_row1_col1\" class=\"data row1 col1\" >0.051</td>\n",
       "      <td id=\"T_99267_row1_col2\" class=\"data row1 col2\" >2.345</td>\n",
       "      <td id=\"T_99267_row1_col3\" class=\"data row1 col3\" >-7.511</td>\n",
       "      <td id=\"T_99267_row1_col4\" class=\"data row1 col4\" >0.002</td>\n",
       "      <td id=\"T_99267_row1_col5\" class=\"data row1 col5\" >0.041</td>\n",
       "      <td id=\"T_99267_row1_col6\" class=\"data row1 col6\" >31.468</td>\n",
       "      <td id=\"T_99267_row1_col7\" class=\"data row1 col7\" >0.859</td>\n",
       "      <td id=\"T_99267_row1_col8\" class=\"data row1 col8\" >3.808</td>\n",
       "      <td id=\"T_99267_row1_col9\" class=\"data row1 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row1_col10\" class=\"data row1 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row1_col11\" class=\"data row1 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row1_col12\" class=\"data row1 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row1_col15\" class=\"data row1 col15\" >12.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row2\" class=\"row_heading level0 row2\" >GATP</th>\n",
       "      <td id=\"T_99267_row2_col0\" class=\"data row2 col0\" >2.911</td>\n",
       "      <td id=\"T_99267_row2_col1\" class=\"data row2 col1\" >0.043</td>\n",
       "      <td id=\"T_99267_row2_col2\" class=\"data row2 col2\" >2.069</td>\n",
       "      <td id=\"T_99267_row2_col3\" class=\"data row2 col3\" >-2.485</td>\n",
       "      <td id=\"T_99267_row2_col4\" class=\"data row2 col4\" >0.003</td>\n",
       "      <td id=\"T_99267_row2_col5\" class=\"data row2 col5\" >0.147</td>\n",
       "      <td id=\"T_99267_row2_col6\" class=\"data row2 col6\" >36.051</td>\n",
       "      <td id=\"T_99267_row2_col7\" class=\"data row2 col7\" >0.893</td>\n",
       "      <td id=\"T_99267_row2_col8\" class=\"data row2 col8\" >3.457</td>\n",
       "      <td id=\"T_99267_row2_col9\" class=\"data row2 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row2_col10\" class=\"data row2 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row2_col11\" class=\"data row2 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row2_col12\" class=\"data row2 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row2_col13\" class=\"data row2 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row2_col15\" class=\"data row2 col15\" >11.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row3\" class=\"row_heading level0 row3\" >UDPG</th>\n",
       "      <td id=\"T_99267_row3_col0\" class=\"data row3 col0\" >0.043</td>\n",
       "      <td id=\"T_99267_row3_col1\" class=\"data row3 col1\" >0.023</td>\n",
       "      <td id=\"T_99267_row3_col2\" class=\"data row3 col2\" >74.386</td>\n",
       "      <td id=\"T_99267_row3_col3\" class=\"data row3 col3\" >-9.686</td>\n",
       "      <td id=\"T_99267_row3_col4\" class=\"data row3 col4\" >0.028</td>\n",
       "      <td id=\"T_99267_row3_col5\" class=\"data row3 col5\" >0.397</td>\n",
       "      <td id=\"T_99267_row3_col6\" class=\"data row3 col6\" >12.628</td>\n",
       "      <td id=\"T_99267_row3_col7\" class=\"data row3 col7\" >9.479</td>\n",
       "      <td id=\"T_99267_row3_col8\" class=\"data row3 col8\" >104.751</td>\n",
       "      <td id=\"T_99267_row3_col9\" class=\"data row3 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row3_col10\" class=\"data row3 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row3_col11\" class=\"data row3 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row3_col12\" class=\"data row3 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row3_col13\" class=\"data row3 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row3_col15\" class=\"data row3 col15\" >0.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row4\" class=\"row_heading level0 row4\" >NAD</th>\n",
       "      <td id=\"T_99267_row4_col0\" class=\"data row4 col0\" >0.452</td>\n",
       "      <td id=\"T_99267_row4_col1\" class=\"data row4 col1\" >0.058</td>\n",
       "      <td id=\"T_99267_row4_col2\" class=\"data row4 col2\" >17.787</td>\n",
       "      <td id=\"T_99267_row4_col3\" class=\"data row4 col3\" >-8.250</td>\n",
       "      <td id=\"T_99267_row4_col4\" class=\"data row4 col4\" >0.019</td>\n",
       "      <td id=\"T_99267_row4_col5\" class=\"data row4 col5\" >0.320</td>\n",
       "      <td id=\"T_99267_row4_col6\" class=\"data row4 col6\" >46.155</td>\n",
       "      <td id=\"T_99267_row4_col7\" class=\"data row4 col7\" >7.350</td>\n",
       "      <td id=\"T_99267_row4_col8\" class=\"data row4 col8\" >22.225</td>\n",
       "      <td id=\"T_99267_row4_col9\" class=\"data row4 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row4_col10\" class=\"data row4 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row4_col11\" class=\"data row4 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row4_col12\" class=\"data row4 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row4_col13\" class=\"data row4 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row4_col15\" class=\"data row4 col15\" >1.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row5\" class=\"row_heading level0 row5\" >PCr</th>\n",
       "      <td id=\"T_99267_row5_col0\" class=\"data row5 col0\" >4.156</td>\n",
       "      <td id=\"T_99267_row5_col1\" class=\"data row5 col1\" >0.025</td>\n",
       "      <td id=\"T_99267_row5_col2\" class=\"data row5 col2\" >0.826</td>\n",
       "      <td id=\"T_99267_row5_col3\" class=\"data row5 col3\" >-0.012</td>\n",
       "      <td id=\"T_99267_row5_col4\" class=\"data row5 col4\" >0.000</td>\n",
       "      <td id=\"T_99267_row5_col5\" class=\"data row5 col5\" >5.726</td>\n",
       "      <td id=\"T_99267_row5_col6\" class=\"data row5 col6\" >14.994</td>\n",
       "      <td id=\"T_99267_row5_col7\" class=\"data row5 col7\" >0.125</td>\n",
       "      <td id=\"T_99267_row5_col8\" class=\"data row5 col8\" >1.159</td>\n",
       "      <td id=\"T_99267_row5_col9\" class=\"data row5 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row5_col10\" class=\"data row5 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row5_col11\" class=\"data row5 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row5_col12\" class=\"data row5 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row5_col13\" class=\"data row5 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row5_col15\" class=\"data row5 col15\" >16.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row6\" class=\"row_heading level0 row6\" >GPC</th>\n",
       "      <td id=\"T_99267_row6_col0\" class=\"data row6 col0\" >1.186</td>\n",
       "      <td id=\"T_99267_row6_col1\" class=\"data row6 col1\" >0.028</td>\n",
       "      <td id=\"T_99267_row6_col2\" class=\"data row6 col2\" >3.322</td>\n",
       "      <td id=\"T_99267_row6_col3\" class=\"data row6 col3\" >2.951</td>\n",
       "      <td id=\"T_99267_row6_col4\" class=\"data row6 col4\" >0.002</td>\n",
       "      <td id=\"T_99267_row6_col5\" class=\"data row6 col5\" >0.084</td>\n",
       "      <td id=\"T_99267_row6_col6\" class=\"data row6 col6\" >18.540</td>\n",
       "      <td id=\"T_99267_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
       "      <td id=\"T_99267_row6_col8\" class=\"data row6 col8\" >4.608</td>\n",
       "      <td id=\"T_99267_row6_col9\" class=\"data row6 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row6_col10\" class=\"data row6 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row6_col11\" class=\"data row6 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row6_col12\" class=\"data row6 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row6_col13\" class=\"data row6 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row6_col15\" class=\"data row6 col15\" >4.761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row7\" class=\"row_heading level0 row7\" >GPE</th>\n",
       "      <td id=\"T_99267_row7_col0\" class=\"data row7 col0\" >0.857</td>\n",
       "      <td id=\"T_99267_row7_col1\" class=\"data row7 col1\" >0.028</td>\n",
       "      <td id=\"T_99267_row7_col2\" class=\"data row7 col2\" >4.642</td>\n",
       "      <td id=\"T_99267_row7_col3\" class=\"data row7 col3\" >3.510</td>\n",
       "      <td id=\"T_99267_row7_col4\" class=\"data row7 col4\" >0.002</td>\n",
       "      <td id=\"T_99267_row7_col5\" class=\"data row7 col5\" >0.099</td>\n",
       "      <td id=\"T_99267_row7_col6\" class=\"data row7 col6\" >18.780</td>\n",
       "      <td id=\"T_99267_row7_col7\" class=\"data row7 col7\" >0.866</td>\n",
       "      <td id=\"T_99267_row7_col8\" class=\"data row7 col8\" >6.433</td>\n",
       "      <td id=\"T_99267_row7_col9\" class=\"data row7 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row7_col10\" class=\"data row7 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row7_col11\" class=\"data row7 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row7_col12\" class=\"data row7 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row7_col13\" class=\"data row7 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row7_col15\" class=\"data row7 col15\" >3.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row8\" class=\"row_heading level0 row8\" >Pin</th>\n",
       "      <td id=\"T_99267_row8_col0\" class=\"data row8 col0\" >0.905</td>\n",
       "      <td id=\"T_99267_row8_col1\" class=\"data row8 col1\" >0.035</td>\n",
       "      <td id=\"T_99267_row8_col2\" class=\"data row8 col2\" >5.385</td>\n",
       "      <td id=\"T_99267_row8_col3\" class=\"data row8 col3\" >4.812</td>\n",
       "      <td id=\"T_99267_row8_col4\" class=\"data row8 col4\" >0.003</td>\n",
       "      <td id=\"T_99267_row8_col5\" class=\"data row8 col5\" >0.092</td>\n",
       "      <td id=\"T_99267_row8_col6\" class=\"data row8 col6\" >22.628</td>\n",
       "      <td id=\"T_99267_row8_col7\" class=\"data row8 col7\" >1.161</td>\n",
       "      <td id=\"T_99267_row8_col8\" class=\"data row8 col8\" >7.162</td>\n",
       "      <td id=\"T_99267_row8_col9\" class=\"data row8 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row8_col10\" class=\"data row8 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row8_col11\" class=\"data row8 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row8_col12\" class=\"data row8 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row8_col13\" class=\"data row8 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row8_col15\" class=\"data row8 col15\" >3.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row9\" class=\"row_heading level0 row9\" >Pex</th>\n",
       "      <td id=\"T_99267_row9_col0\" class=\"data row9 col0\" >0.257</td>\n",
       "      <td id=\"T_99267_row9_col1\" class=\"data row9 col1\" >0.035</td>\n",
       "      <td id=\"T_99267_row9_col2\" class=\"data row9 col2\" >19.168</td>\n",
       "      <td id=\"T_99267_row9_col3\" class=\"data row9 col3\" >5.241</td>\n",
       "      <td id=\"T_99267_row9_col4\" class=\"data row9 col4\" >0.011</td>\n",
       "      <td id=\"T_99267_row9_col5\" class=\"data row9 col5\" >0.301</td>\n",
       "      <td id=\"T_99267_row9_col6\" class=\"data row9 col6\" >22.970</td>\n",
       "      <td id=\"T_99267_row9_col7\" class=\"data row9 col7\" >4.191</td>\n",
       "      <td id=\"T_99267_row9_col8\" class=\"data row9 col8\" >25.463</td>\n",
       "      <td id=\"T_99267_row9_col9\" class=\"data row9 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row9_col10\" class=\"data row9 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row9_col11\" class=\"data row9 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row9_col12\" class=\"data row9 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row9_col13\" class=\"data row9 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row9_col15\" class=\"data row9 col15\" >1.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row10\" class=\"row_heading level0 row10\" >PC</th>\n",
       "      <td id=\"T_99267_row10_col0\" class=\"data row10 col0\" >0.331</td>\n",
       "      <td id=\"T_99267_row10_col1\" class=\"data row10 col1\" >0.029</td>\n",
       "      <td id=\"T_99267_row10_col2\" class=\"data row10 col2\" >12.180</td>\n",
       "      <td id=\"T_99267_row10_col3\" class=\"data row10 col3\" >6.220</td>\n",
       "      <td id=\"T_99267_row10_col4\" class=\"data row10 col4\" >0.006</td>\n",
       "      <td id=\"T_99267_row10_col5\" class=\"data row10 col5\" >0.143</td>\n",
       "      <td id=\"T_99267_row10_col6\" class=\"data row10 col6\" >18.782</td>\n",
       "      <td id=\"T_99267_row10_col7\" class=\"data row10 col7\" >2.258</td>\n",
       "      <td id=\"T_99267_row10_col8\" class=\"data row10 col8\" >16.778</td>\n",
       "      <td id=\"T_99267_row10_col9\" class=\"data row10 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row10_col10\" class=\"data row10 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row10_col11\" class=\"data row10 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row10_col12\" class=\"data row10 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row10_col13\" class=\"data row10 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row10_col15\" class=\"data row10 col15\" >1.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99267_level0_row11\" class=\"row_heading level0 row11\" >PE</th>\n",
       "      <td id=\"T_99267_row11_col0\" class=\"data row11 col0\" >2.258</td>\n",
       "      <td id=\"T_99267_row11_col1\" class=\"data row11 col1\" >0.032</td>\n",
       "      <td id=\"T_99267_row11_col2\" class=\"data row11 col2\" >1.989</td>\n",
       "      <td id=\"T_99267_row11_col3\" class=\"data row11 col3\" >6.792</td>\n",
       "      <td id=\"T_99267_row11_col4\" class=\"data row11 col4\" >0.001</td>\n",
       "      <td id=\"T_99267_row11_col5\" class=\"data row11 col5\" >0.028</td>\n",
       "      <td id=\"T_99267_row11_col6\" class=\"data row11 col6\" >23.059</td>\n",
       "      <td id=\"T_99267_row11_col7\" class=\"data row11 col7\" >0.453</td>\n",
       "      <td id=\"T_99267_row11_col8\" class=\"data row11 col8\" >2.739</td>\n",
       "      <td id=\"T_99267_row11_col9\" class=\"data row11 col9\" >-0.108</td>\n",
       "      <td id=\"T_99267_row11_col10\" class=\"data row11 col10\" >0.293</td>\n",
       "      <td id=\"T_99267_row11_col11\" class=\"data row11 col11\" >379.135</td>\n",
       "      <td id=\"T_99267_row11_col12\" class=\"data row11 col12\" >0.000</td>\n",
       "      <td id=\"T_99267_row11_col13\" class=\"data row11 col13\" >0.000</td>\n",
       "      <td id=\"T_99267_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_99267_row11_col15\" class=\"data row11 col15\" >9.061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f03506b8160>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FIDresult3.styled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47dc81c",
   "metadata": {},
   "source": [
    "- **Convert lmfit Parameter to Pandas DataFrame**:\n",
    "    - For comparison and easier editing, import functions that enable conversion between an lmfit Parameter object and a Python pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37c25c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyAMARES import parameters_to_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1fac02d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "origin = parameters_to_dataframe(params)  # Original parameters\n",
    "result1 = parameters_to_dataframe(\n",
    "    FIDresult1.fittedParams\n",
    ")  # Fitting Result using HSVD initialized parameters\n",
    "result2 = parameters_to_dataframe(\n",
    "    FIDresult2.fittedParams\n",
    ")  # Fitting Result using Levenberg-Marquardt initialized parameters\n",
    "result3 = parameters_to_dataframe(\n",
    "    FIDresult3.fittedParams\n",
    ")  # Fitting Result using fixed phase of all peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8296442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "75     True\n",
       "76    False\n",
       "77    False\n",
       "78    False\n",
       "79    False\n",
       "Name: name, Length: 80, dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate index for peak amplitudes only.\n",
    "amplitude_index = origin.name.str.startswith(\"ak\")\n",
    "amplitude_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0ffd039c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to do linear regression between two lists\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "\n",
    "\n",
    "def compare_plot(x, y, labellist, title=\"\", xlabel=\"\", ylabel=\"\"):\n",
    "    assert len(x) == len(y) == len(labellist)\n",
    "    x = x / x[0]\n",
    "    y = y / y[0]\n",
    "    plt.scatter(x, y)\n",
    "    for i, j, l in zip(x, y, labellist):\n",
    "        plt.annotate(l, (i * 1.02, j * 1.02))\n",
    "\n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "\n",
    "    x_fit = np.linspace(min(x), max(x), 100)\n",
    "    y_fit = slope * x_fit + intercept\n",
    "    plt.plot(x_fit, y_fit, \"r\", label=f\"{slope=:.3f}\")  # Linear fit line\n",
    "\n",
    "    combined_min = min(min(x), min(y)) * 0.95\n",
    "    combined_max = max(max(x), max(y)) * 1.05\n",
    "    plt.plot(\n",
    "        [combined_min, combined_max], [combined_min, combined_max], \"k--\"\n",
    "    )  # Dashed diagonal line\n",
    "\n",
    "    # Beautify the plot\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    # plt.axis('equal')  # Use the same scale for both x and y axes\n",
    "\n",
    "    # Print the results\n",
    "    print(f\"Slope: {slope:.3f}\")\n",
    "    print(f\"Pearson's R: {r_value:.4f}\")\n",
    "    print(f\"P-value: {p_value:.2e}\")\n",
    "    plt.title(f\"{title} {r_value=:.2f} {p_value=:.2f}\")\n",
    "    plt.xlim(combined_min, combined_max)\n",
    "    plt.ylim(combined_min, combined_max)\n",
    "    plt.legend()\n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "\n",
    "    # Return the slope, Pearson's R, and p-value\n",
    "    return slope, r_value, p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1c7a67",
   "metadata": {},
   "source": [
    "- Now we have three fitting results:\n",
    "    - `result1`: Fitting result using HSVD-initialized parameters.\n",
    "    - `result2`: Fitting result using Levenberg-Marquardt-initialized parameters.\n",
    "    - `result3`: Fitting result with a fixed phase for all peaks.\n",
    "- Compare them to the ground truth by linear regressions\n",
    "    - Compare these fitting results to the ground truth using linear regression analyses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "470781c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.996\n",
      "Pearson's R: 0.9991\n",
      "P-value: 1.47e-20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.9955220179593912, 0.9990817576877268, 1.4722833669761668e-20)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_plot(\n",
    "    y=origin[amplitude_index][\"value\"],\n",
    "    x=result1[amplitude_index][\"value\"],\n",
    "    labellist=FIDresult1.peaklist,\n",
    "    xlabel=\"Fitted Result\",\n",
    "    ylabel=\"Ground Truth\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46df9b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.996\n",
      "Pearson's R: 0.9991\n",
      "P-value: 1.47e-20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.9955204123080279, 0.9990817398883923, 1.472483082362872e-20)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_plot(\n",
    "    x=result2[amplitude_index][\"value\"],\n",
    "    y=origin[amplitude_index][\"value\"],\n",
    "    labellist=FIDresult1.peaklist,\n",
    "    xlabel=\"Fitted Result\",\n",
    "    ylabel=\"Ground Truth\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "277bf41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.995\n",
      "Pearson's R: 0.9992\n",
      "P-value: 4.65e-21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.9954449822381952, 0.9992212974530363, 4.645693969839231e-21)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_plot(\n",
    "    x=result3[amplitude_index][\"value\"],\n",
    "    y=origin[amplitude_index][\"value\"],\n",
    "    labellist=FIDresult1.peaklist,\n",
    "    xlabel=\"Fitted Result\",\n",
    "    ylabel=\"Ground Truth\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4388e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MRS 3.8",
   "language": "python",
   "name": "py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}