IsoSolve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IsoSolve: Integrate, clarify and consolidate isotopic measurements by MS and/or NMR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. General information\n",
    "\n",
    "This notebook is part of Supporting information of the following publication:\n",
    "    \n",
    "> IsoSolve: an integrative framework to improve isotopic coverage and consolidate isotopic measurements by MS and/or NMR. Millard <i>et al.</i>, 2021, [bioRxiv preprint](https://www.biorxiv.org/)\n",
    "\n",
    "This notebook contains examples on IsoSolve usage, and reproduces the analysis detailed in the publication, including all equations and Figures 3-6.\n",
    "\n",
    "More information can be found at the [IsoSolve git repository](https://github.com/MetaSys-LISBP/IsoSolve).\n",
    "\n",
    "### Content\n",
    "\n",
    "- [Prepare environment](#2.-Prepare-environment)\n",
    "- [Examples: Integrate specific datasets for Alanine](#3.-Examples:-Integrate-specific-datasets-for-Alanine)\n",
    "  - [Isotopic data collected by NMR (HSQC, C<sub>α</sub>; TOCSY, H<sub>α</sub>)](#3.1.-Integrate-isotopic-data-collected-by-NMR-(HSQC,-C%CE%B1;-TOCSY,-H%CE%B1)-to-reproduce-Equations-8-12-of-the-publication.)\n",
    "  - [Isotopic data collected by NMR (HSQC, C<sub>α</sub>; TOCSY, H<sub>α</sub>) and MS (LC-MS, ion [M+H]<sup>+</sup>)](#3.2.-Integrate-isotopic-data-collected-by-NMR-(HSQC,-C%CE%B1;-TOCSY,-H%CE%B1)-and-MS-(LC-MS,-[M+H]+)-to-reproduce-Equations-17-23-of-the-publication.)\n",
    "  - [Isotopic data collected by NMR (HSQC, C<sub>α</sub> and C<sub>β</sub>) and MS (LC-MS, ion [M+H]<sup>+</sup>)](#3.3.-Integrate-isotopic-data-collected-by-NMR-(HSQC,-C%CE%B1-and-C%CE%B2)-and-MS-(LC-MS,-ion-[M+H]+)-to-reproduce-Equations-31-38-of-the-publication.)\n",
    "  - [All datasets](#3.4.-Integrate-all-datasets.)\n",
    "- [Analyze all combinations of datasets for Alanine](#4.-Analyze-all-combinations-of-datasets-for-Alanine)\n",
    "- [Determine isotopic coverage for all amino acids](#5.-Determine-isotopic-coverage-for-all-amino-acids)\n",
    "- [Overview of integration results from different combinations of datasets for Alanine](#6.-Overview-of-integration-results-from-different-combinations-of-datasets-for-Alanine)\n",
    "- [Detailed analysis of integration results for Alanine](#7.-Detailed-analysis-of-integration-results-for-Alanine)\n",
    "- [Evaluate self-consistency of measurements](#8.-Evaluate-self-consistency-of-measurements)\n",
    "- [Generate isotopically-resolved InChIs](#9.-Generate-isotopically-resolved-InChIs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Prepare environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dependencies\n",
    "\n",
    "The following Python packages are required:\n",
    "  - numpy\n",
    "  - pandas\n",
    "  - matplotlib\n",
    "  - seaborn\n",
    "\n",
    "These packages can be installed by running the following command in a terminal:\n",
    "\n",
    "```console\n",
    "pip install --user X\n",
    "```\n",
    "\n",
    "where `X` is the package name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sys, time, itertools, pickle\n",
    "from IPython.display import Markdown, display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# change Pandas option to display full (i.e. non-truncated) dataframes\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None, 'display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import IsoSolve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import isosolve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load functions to calculate metrics, create measurements-isotopomers mapping matrix, display results summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from misc import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Examples: Integrate specific datasets for Alanine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Path of the file containing the relationships between measurements and the isotopic space for alanine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Integrate isotopic data collected by NMR (HSQC, C<sub>α</sub>; TOCSY, H<sub>α</sub>) to reproduce Equations 8-12 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate datasets (columns 1 and 4 of the measurements mapping file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test_1 = isosolve.main(mm=dataset_file, colsel=[1,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Equations**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isotopomer</th>\n",
       "      <th>equation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>b*f</td>\n",
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       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "      <td>c*f</td>\n",
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       "  isotopomer             equation\n",
       "0        000  e - 001 - 100 - 101\n",
       "1        010                  a*f\n",
       "2        011                  b*f\n",
       "3        110                  c*f\n",
       "4        111                  d*f"
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    {
     "data": {
      "text/markdown": [
       "**Summary**"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/markdown": [
       "Number of undefined isotopic species: 4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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     "data": {
      "text/markdown": [
       "&nbsp; &nbsp; *Undefined isotopic species: 000, 001, 100, 101*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Number of redundant measurements: 0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_summary(res_test_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Integrate isotopic data collected by NMR (HSQC, C<sub>α</sub>; TOCSY, H<sub>α</sub>) and MS (LC-MS, [M+H]<sup>+</sup>) to reproduce Equations 17-23 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate datasets (columns 1, 4 and 8 of the measurements mapping file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test_2 = isosolve.main(mm=dataset_file, colsel=[1,4,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Equations**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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       "  isotopomer         equation\n",
       "0        000                g\n",
       "1        001   -a*f + h - 100\n",
       "2        101  a*f + e - g - h\n",
       "3        010              a*f\n",
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      "text/markdown": [
       "**Summary**"
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     "data": {
      "text/markdown": [
       "Number of undefined isotopic species: 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/markdown": [
       "&nbsp; &nbsp; *Undefined isotopic species: 001, 100*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "Number of redundant measurements: 1"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "&nbsp; &nbsp; *Redundant measurements: d*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "print_summary(res_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Integrate isotopic data collected by NMR (HSQC, C<sub>α</sub> and C<sub>β</sub>) and MS (LC-MS, ion [M+H]<sup>+</sup>) to reproduce Equations 31-38 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate datasets (columns 1, 2 and 8 of the measurements mapping file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test_3 = isosolve.main(mm=dataset_file, colsel=[1,2,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Equations**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "output_type": "display_data"
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       "  isotopomer                                   equation\n",
       "0        000                                          g\n",
       "1        001           -i - j*(1 - (b + c*l + d)/(d*l))\n",
       "2        100  h + i + j*(1 - (b + d + l*(a + c))/(d*l))\n",
       "3        101                            i - j*(b + c)/d\n",
       "4        010                                      a*j/d\n",
       "5        011                                      b*j/d\n",
       "6        110                                      c*j/d\n",
       "7        111                                          j"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "**Summary**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "Number of undefined isotopic species: 0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Number of redundant measurements: 0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "print_summary(res_test_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Integrate all datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate datasets (all columns of the measurements mapping file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test_4 = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Equations**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>-f*n + i + 2*j - q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010</td>\n",
       "      <td>-f*n + j - o + t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>011</td>\n",
       "      <td>-j + q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>f*n - j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>111</td>\n",
       "      <td>j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  isotopomer             equation\n",
       "0        000                    g\n",
       "1        001  f*n + g + h - j - t\n",
       "2        100               -g + o\n",
       "3        101   -f*n + i + 2*j - q\n",
       "4        010     -f*n + j - o + t\n",
       "5        011               -j + q\n",
       "6        110              f*n - j\n",
       "7        111                    j"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Summary**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Number of undefined isotopic species: 0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Number of redundant measurements: 10"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "&nbsp; &nbsp; *Redundant measurements: a, b, c, d, e, f, k, l, r, s*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_summary(res_test_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analyze all combinations of datasets for Alanine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code computes all combinations of isotopic datasets that can be collected for alanine and reproduces Figure 3 of the publication.\n",
    "\n",
    "Combinations are evaluated based on the following metrics:\n",
    "- number of <b>isotopomers</b>, <b>cumomers</b> and <b>EMUs</b> that can be quantified individually,\n",
    "- number of <b>redundant measurements</b>,\n",
    "- number of <b>additional isotopomers</b> that can be quantified individually via data integration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Path of the file containing the relationships between measurements and the isotopic space for alanine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define all combinations of methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of datasets/methods\n",
    "measurement_methods = [\"D1\", \"D2\", \"D3\", \"D4\", \"D5\", \"D6\", \"D7\", \"D8\"]\n",
    "\n",
    "# define all combinations\n",
    "all_combinations = []\n",
    "for r in range(1, len(measurement_methods) + 1):\n",
    "    combinations_object = itertools.combinations(range(0, len(measurement_methods)), r)\n",
    "    all_combinations += list(combinations_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate datasets for all combinations and calculate evaluation metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "combination: 197/255\n",
      " (datasets included: [1, 5, 6, 7, 8])\n",
      "combination: 198/255\n",
      " (datasets included: [2, 3, 4, 5, 6])\n",
      "combination: 199/255\n",
      " (datasets included: [2, 3, 4, 5, 7])\n",
      "combination: 200/255\n",
      " (datasets included: [2, 3, 4, 5, 8])\n",
      "combination: 201/255\n",
      " (datasets included: [2, 3, 4, 6, 7])\n",
      "combination: 202/255\n",
      " (datasets included: [2, 3, 4, 6, 8])\n",
      "combination: 203/255\n",
      " (datasets included: [2, 3, 4, 7, 8])\n",
      "combination: 204/255\n",
      " (datasets included: [2, 3, 5, 6, 7])\n",
      "combination: 205/255\n",
      " (datasets included: [2, 3, 5, 6, 8])\n",
      "combination: 206/255\n",
      " (datasets included: [2, 3, 5, 7, 8])\n",
      "combination: 207/255\n",
      " (datasets included: [2, 3, 6, 7, 8])\n",
      "combination: 208/255\n",
      " (datasets included: [2, 4, 5, 6, 7])\n",
      "combination: 209/255\n",
      " (datasets included: [2, 4, 5, 6, 8])\n",
      "combination: 210/255\n",
      " (datasets included: [2, 4, 5, 7, 8])\n",
      "combination: 211/255\n",
      " (datasets included: [2, 4, 6, 7, 8])\n",
      "combination: 212/255\n",
      " (datasets included: [2, 5, 6, 7, 8])\n",
      "combination: 213/255\n",
      " (datasets included: [3, 4, 5, 6, 7])\n",
      "combination: 214/255\n",
      " (datasets included: [3, 4, 5, 6, 8])\n",
      "combination: 215/255\n",
      " (datasets included: [3, 4, 5, 7, 8])\n",
      "combination: 216/255\n",
      " (datasets included: [3, 4, 6, 7, 8])\n",
      "combination: 217/255\n",
      " (datasets included: [3, 5, 6, 7, 8])\n",
      "combination: 218/255\n",
      " (datasets included: [4, 5, 6, 7, 8])\n",
      "combination: 219/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 6])\n",
      "combination: 220/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 7])\n",
      "combination: 221/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 8])\n",
      "combination: 222/255\n",
      " (datasets included: [1, 2, 3, 4, 6, 7])\n",
      "combination: 223/255\n",
      " (datasets included: [1, 2, 3, 4, 6, 8])\n",
      "combination: 224/255\n",
      " (datasets included: [1, 2, 3, 4, 7, 8])\n",
      "combination: 225/255\n",
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      "combination: 226/255\n",
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      "combination: 227/255\n",
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      "combination: 228/255\n",
      " (datasets included: [1, 2, 3, 6, 7, 8])\n",
      "combination: 229/255\n",
      " (datasets included: [1, 2, 4, 5, 6, 7])\n",
      "combination: 230/255\n",
      " (datasets included: [1, 2, 4, 5, 6, 8])\n",
      "combination: 231/255\n",
      " (datasets included: [1, 2, 4, 5, 7, 8])\n",
      "combination: 232/255\n",
      " (datasets included: [1, 2, 4, 6, 7, 8])\n",
      "combination: 233/255\n",
      " (datasets included: [1, 2, 5, 6, 7, 8])\n",
      "combination: 234/255\n",
      " (datasets included: [1, 3, 4, 5, 6, 7])\n",
      "combination: 235/255\n",
      " (datasets included: [1, 3, 4, 5, 6, 8])\n",
      "combination: 236/255\n",
      " (datasets included: [1, 3, 4, 5, 7, 8])\n",
      "combination: 237/255\n",
      " (datasets included: [1, 3, 4, 6, 7, 8])\n",
      "combination: 238/255\n",
      " (datasets included: [1, 3, 5, 6, 7, 8])\n",
      "combination: 239/255\n",
      " (datasets included: [1, 4, 5, 6, 7, 8])\n",
      "combination: 240/255\n",
      " (datasets included: [2, 3, 4, 5, 6, 7])\n",
      "combination: 241/255\n",
      " (datasets included: [2, 3, 4, 5, 6, 8])\n",
      "combination: 242/255\n",
      " (datasets included: [2, 3, 4, 5, 7, 8])\n",
      "combination: 243/255\n",
      " (datasets included: [2, 3, 4, 6, 7, 8])\n",
      "combination: 244/255\n",
      " (datasets included: [2, 3, 5, 6, 7, 8])\n",
      "combination: 245/255\n",
      " (datasets included: [2, 4, 5, 6, 7, 8])\n",
      "combination: 246/255\n",
      " (datasets included: [3, 4, 5, 6, 7, 8])\n",
      "combination: 247/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 6, 7])\n",
      "combination: 248/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 6, 8])\n",
      "combination: 249/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 7, 8])\n",
      "combination: 250/255\n",
      " (datasets included: [1, 2, 3, 4, 6, 7, 8])\n",
      "combination: 251/255\n",
      " (datasets included: [1, 2, 3, 5, 6, 7, 8])\n",
      "combination: 252/255\n",
      " (datasets included: [1, 2, 4, 5, 6, 7, 8])\n",
      "combination: 253/255\n",
      " (datasets included: [1, 3, 4, 5, 6, 7, 8])\n",
      "combination: 254/255\n",
      " (datasets included: [2, 3, 4, 5, 6, 7, 8])\n",
      "combination: 255/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 6, 7, 8])\n"
     ]
    }
   ],
   "source": [
    "# create empty panda dataframe\n",
    "metrics = ['nb_id_ipm', 'nb_id_cumo', 'nb_id_emu', 'nb_rdd_meas', 'nb_add_ipm']\n",
    "res_hm = pd.DataFrame(columns = measurement_methods + metrics)\n",
    "\n",
    "# compute all combinations of datasets\n",
    "for i in range(len(all_combinations)):\n",
    "    \n",
    "    # define columns (i.e. datasets) to include\n",
    "    print(\"combination: {}/{}\".format(i+1, len(all_combinations)))\n",
    "    columns = [j+1 for j in all_combinations[i]]\n",
    "    print(\" (datasets included: {})\".format(columns))\n",
    "    \n",
    "    # integrate datasets\n",
    "    res_comb = isosolve.main(mm=dataset_file, colsel=columns, fast=True)\n",
    "    \n",
    "    # calculate evaluation metrics\n",
    "    #   number of individual isotopomers that can be quantified\n",
    "    nb_id_ipm = len(res_comb[\"metrics\"][\"idef\"])\n",
    "    #   number of individual cumomers that can be quantified\n",
    "    nb_id_cumo = len(res_comb[\"metrics\"][\"idef_c\"])\n",
    "    #   number of individual EMUs that can be quantified\n",
    "    nb_id_emu = len([1 for i,j in res_comb[\"metrics\"][\"idef_e\"].items() if len(j) == i.count(\"E\")+1])\n",
    "    #   number of redundant measurements\n",
    "    nb_rdd_meas = len(res_comb[\"rdn_meas\"])\n",
    "    #   information gain (additional isotopomers) brought by data integration\n",
    "    nb_add_ipm = nb_id_ipm - (2 if 7 in all_combinations[i] else 0)\n",
    "    \n",
    "    # fill the results dataframe\n",
    "    #   datasets included\n",
    "    row_tmp = [1 if j in all_combinations[i] else 0 for j in range(len(measurement_methods))]\n",
    "    #   metrics\n",
    "    row_tmp += [nb_id_ipm, nb_id_cumo, nb_id_emu, nb_rdd_meas, nb_add_ipm]\n",
    "    #   add row to dataframe\n",
    "    res_hm.loc[i] = row_tmp\n",
    "\n",
    "# change dataframe type to 'int' (required for plotting)\n",
    "res_hm = res_hm.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>8</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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>13</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>60</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     D1  D2  D3  D4  D5  D6  D7  D8  nb_id_ipm  nb_id_cumo  nb_id_emu  \\\n",
       "0     1   0   0   0   0   0   0   0          0           0          0   \n",
       "1     0   1   0   0   0   0   0   0          0           0          0   \n",
       "2     0   0   1   0   0   0   0   0          0           0          0   \n",
       "3     0   0   0   1   0   0   0   0          0           1          1   \n",
       "4     0   0   0   0   1   0   0   0          0           1          1   \n",
       "5     0   0   0   0   0   1   0   0          0           0          0   \n",
       "6     0   0   0   0   0   0   1   0          0           1          1   \n",
       "7     0   0   0   0   0   0   0   1          2           1          1   \n",
       "8     1   1   0   0   0   0   0   0          0           0          0   \n",
       "9     1   0   1   0   0   0   0   0          0           0          0   \n",
       "10    1   0   0   1   0   0   0   0          4           4          1   \n",
       "11    1   0   0   0   1   0   0   0          0           1          1   \n",
       "12    1   0   0   0   0   1   0   0          0           0          0   \n",
       "13    1   0   0   0   0   0   1   0          4           5          3   \n",
       "14    1   0   0   0   0   0   0   1          6           5          3   \n",
       "15    0   1   1   0   0   0   0   0          0           0          0   \n",
       "16    0   1   0   1   0   0   0   0          0           1          1   \n",
       "17    0   1   0   0   1   0   0   0          0           2          1   \n",
       "18    0   1   0   0   0   1   0   0          0           0          0   \n",
       "19    0   1   0   0   0   0   1   0          0           3          3   \n",
       "20    0   1   0   0   0   0   0   1          2           1          1   \n",
       "21    0   0   1   1   0   0   0   0          0           1          1   \n",
       "22    0   0   1   0   1   0   0   0          0           1          1   \n",
       "23    0   0   1   0   0   1   0   0          0           0          0   \n",
       "24    0   0   1   0   0   0   1   0          0           1          1   \n",
       "25    0   0   1   0   0   0   0   1          2           1          1   \n",
       "26    0   0   0   1   1   0   0   0          0           2          2   \n",
       "27    0   0   0   1   0   1   0   0          0           2          1   \n",
       "28    0   0   0   1   0   0   1   0          0           3          3   \n",
       "29    0   0   0   1   0   0   0   1          2           2          2   \n",
       "30    0   0   0   0   1   1   0   0          0           1          1   \n",
       "31    0   0   0   0   1   0   1   0          0           3          3   \n",
       "32    0   0   0   0   1   0   0   1          2           2          2   \n",
       "33    0   0   0   0   0   1   1   0          0           1          1   \n",
       "34    0   0   0   0   0   1   0   1          2           1          1   \n",
       "35    0   0   0   0   0   0   1   1          4           3          3   \n",
       "36    1   1   1   0   0   0   0   0          0           0          0   \n",
       "37    1   1   0   1   0   0   0   0          4           5          3   \n",
       "38    1   1   0   0   1   0   0   0          4           5          3   \n",
       "39    1   1   0   0   0   1   0   0          6           7          7   \n",
       "40    1   1   0   0   0   0   1   0          4           5          3   \n",
       "41    1   1   0   0   0   0   0   1          8           7          7   \n",
       "42    1   0   1   1   0   0   0   0          4           5          3   \n",
       "43    1   0   1   0   1   0   0   0          0           1          1   \n",
       "44    1   0   1   0   0   1   0   0          0           0          0   \n",
       "45    1   0   1   0   0   0   1   0          4           6          5   \n",
       "46    1   0   1   0   0   0   0   1          8           7          7   \n",
       "47    1   0   0   1   1   0   0   0          4           5          3   \n",
       "48    1   0   0   1   0   1   0   0          4           4          1   \n",
       "49    1   0   0   1   0   0   1   0          4           5          3   \n",
       "50    1   0   0   1   0   0   0   1          6           5          3   \n",
       "51    1   0   0   0   1   1   0   0          0           1          1   \n",
       "52    1   0   0   0   1   0   1   0          4           5          3   \n",
       "53    1   0   0   0   1   0   0   1          8           7          7   \n",
       "54    1   0   0   0   0   1   1   0          4           5          3   \n",
       "55    1   0   0   0   0   1   0   1          6           5          3   \n",
       "56    1   0   0   0   0   0   1   1          8           7          7   \n",
       "57    0   1   1   1   0   0   0   0          0           1          1   \n",
       "58    0   1   1   0   1   0   0   0          0           2          1   \n",
       "59    0   1   1   0   0   1   0   0          0           0          0   \n",
       "60    0   1   1   0   0   0   1   0          0           3          3   \n",
       "61    0   1   1   0   0   0   0   1          2           1          1   \n",
       "62    0   1   0   1   1   0   0   0          0           3          3   \n",
       "63    0   1   0   1   0   1   0   0          0           2          1   \n",
       "64    0   1   0   1   0   0   1   0          0           3          3   \n",
       "65    0   1   0   1   0   0   0   1          2           2          2   \n",
       "66    0   1   0   0   1   1   0   0          0           2          1   \n",
       "67    0   1   0   0   1   0   1   0          0           3          3   \n",
       "68    0   1   0   0   1   0   0   1          3           3          2   \n",
       "69    0   1   0   0   0   1   1   0          0           4          3   \n",
       "70    0   1   0   0   0   1   0   1          2           1          1   \n",
       "71    0   1   0   0   0   0   1   1          4           5          5   \n",
       "72    0   0   1   1   1   0   0   0          0           2          2   \n",
       "73    0   0   1   1   0   1   0   0          0           3          3   \n",
       "74    0   0   1   1   0   0   1   0          0           3          3   \n",
       "75    0   0   1   1   0   0   0   1          2           2          2   \n",
       "76    0   0   1   0   1   1   0   0          0           1          1   \n",
       "77    0   0   1   0   1   0   1   0          0           3          3   \n",
       "78    0   0   1   0   1   0   0   1          2           2          2   \n",
       "79    0   0   1   0   0   1   1   0          0           1          1   \n",
       "80    0   0   1   0   0   1   0   1          2           1          1   \n",
       "81    0   0   1   0   0   0   1   1          6           5          3   \n",
       "82    0   0   0   1   1   1   0   0          0           3          2   \n",
       "83    0   0   0   1   1   0   1   0          0           3          3   \n",
       "84    0   0   0   1   1   0   0   1          2           4          4   \n",
       "85    0   0   0   1   0   1   1   0          0           4          3   \n",
       "86    0   0   0   1   0   1   0   1          3           3          2   \n",
       "87    0   0   0   1   0   0   1   1          4           5          5   \n",
       "88    0   0   0   0   1   1   1   0          0           4          3   \n",
       "89    0   0   0   0   1   1   0   1          2           2          2   \n",
       "90    0   0   0   0   1   0   1   1          4           5          5   \n",
       "91    0   0   0   0   0   1   1   1          4           3          3   \n",
       "92    1   1   1   1   0   0   0   0          4           6          5   \n",
       "93    1   1   1   0   1   0   0   0          4           6          5   \n",
       "94    1   1   1   0   0   1   0   0          0           0          0   \n",
       "95    1   1   1   0   0   0   1   0          4           6          5   \n",
       "96    1   1   1   0   0   0   0   1          8           7          7   \n",
       "97    1   1   0   1   1   0   0   0          4           5          3   \n",
       "98    1   1   0   1   0   1   0   0          4           5          3   \n",
       "99    1   1   0   1   0   0   1   0          4           5          3   \n",
       "100   1   1   0   1   0   0   0   1          8           7          7   \n",
       "101   1   1   0   0   1   1   0   0          4           5          3   \n",
       "102   1   1   0   0   1   0   1   0          4           5          3   \n",
       "103   1   1   0   0   1   0   0   1          8           7          7   \n",
       "104   1   1   0   0   0   1   1   0          4           5          3   \n",
       "105   1   1   0   0   0   1   0   1          8           7          7   \n",
       "106   1   1   0   0   0   0   1   1          8           7          7   \n",
       "107   1   0   1   1   1   0   0   0          4           6          5   \n",
       "108   1   0   1   1   0   1   0   0          4           5          3   \n",
       "109   1   0   1   1   0   0   1   0          4           6          5   \n",
       "110   1   0   1   1   0   0   0   1          8           7          7   \n",
       "111   1   0   1   0   1   1   0   0          0           1          1   \n",
       "112   1   0   1   0   1   0   1   0          4           6          5   \n",
       "113   1   0   1   0   1   0   0   1          8           7          7   \n",
       "114   1   0   1   0   0   1   1   0          4           6          5   \n",
       "115   1   0   1   0   0   1   0   1          8           7          7   \n",
       "116   1   0   1   0   0   0   1   1          8           7          7   \n",
       "117   1   0   0   1   1   1   0   0          4           5          3   \n",
       "118   1   0   0   1   1   0   1   0          4           5          3   \n",
       "119   1   0   0   1   1   0   0   1          8           7          7   \n",
       "120   1   0   0   1   0   1   1   0          4           5          3   \n",
       "121   1   0   0   1   0   1   0   1          6           5          3   \n",
       "122   1   0   0   1   0   0   1   1          8           7          7   \n",
       "123   1   0   0   0   1   1   1   0          4           5          3   \n",
       "124   1   0   0   0   1   1   0   1          8           7          7   \n",
       "125   1   0   0   0   1   0   1   1          8           7          7   \n",
       "126   1   0   0   0   0   1   1   1          8           7          7   \n",
       "127   0   1   1   1   1   0   0   0          0           3          3   \n",
       "128   0   1   1   1   0   1   0   0          0           3          3   \n",
       "129   0   1   1   1   0   0   1   0          0           3          3   \n",
       "130   0   1   1   1   0   0   0   1          2           2          1   \n",
       "131   0   1   1   0   1   1   0   0          0           2          1   \n",
       "132   0   1   1   0   1   0   1   0          0           3          3   \n",
       "133   0   1   1   0   1   0   0   1          3           3          2   \n",
       "134   0   1   1   0   0   1   1   0          0           5          5   \n",
       "135   0   1   1   0   0   1   0   1          2           1          1   \n",
       "136   0   1   1   0   0   0   1   1          8           7          7   \n",
       "137   0   1   0   1   1   1   0   0          0           4          3   \n",
       "138   0   1   0   1   1   0   1   0          0           3          3   \n",
       "139   0   1   0   1   1   0   0   1          4           5          5   \n",
       "140   0   1   0   1   0   1   1   0          0           4          3   \n",
       "141   0   1   0   1   0   1   0   1          3           3          2   \n",
       "142   0   1   0   1   0   0   1   1          4           5          5   \n",
       "143   0   1   0   0   1   1   1   0          0           4          3   \n",
       "144   0   1   0   0   1   1   0   1          3           3          2   \n",
       "145   0   1   0   0   1   0   1   1          4           5          5   \n",
       "146   0   1   0   0   0   1   1   1          8           7          7   \n",
       "147   0   0   1   1   1   1   0   0          0           4          4   \n",
       "148   0   0   1   1   1   0   1   0          0           3          3   \n",
       "149   0   0   1   1   1   0   0   1          4           5          5   \n",
       "150   0   0   1   1   0   1   1   0          0           5          5   \n",
       "151   0   0   1   1   0   1   0   1          4           5          5   \n",
       "152   0   0   1   1   0   0   1   1          8           7          7   \n",
       "153   0   0   1   0   1   1   1   0          0           5          5   \n",
       "154   0   0   1   0   1   1   0   1          4           5          5   \n",
       "155   0   0   1   0   1   0   1   1          8           7          7   \n",
       "156   0   0   1   0   0   1   1   1          8           7          7   \n",
       "157   0   0   0   1   1   1   1   0          0           4          3   \n",
       "158   0   0   0   1   1   1   0   1          4           5          5   \n",
       "159   0   0   0   1   1   0   1   1          4           5          5   \n",
       "160   0   0   0   1   0   1   1   1          8           7          7   \n",
       "161   0   0   0   0   1   1   1   1          8           7          7   \n",
       "162   1   1   1   1   1   0   0   0          4           6          5   \n",
       "163   1   1   1   1   0   1   0   0          4           6          5   \n",
       "164   1   1   1   1   0   0   1   0          4           6          5   \n",
       "165   1   1   1   1   0   0   0   1          8           7          7   \n",
       "166   1   1   1   0   1   1   0   0          4           6          5   \n",
       "167   1   1   1   0   1   0   1   0          4           6          5   \n",
       "168   1   1   1   0   1   0   0   1          8           7          7   \n",
       "169   1   1   1   0   0   1   1   0          4           6          5   \n",
       "170   1   1   1   0   0   1   0   1          8           7          7   \n",
       "171   1   1   1   0   0   0   1   1          8           7          7   \n",
       "172   1   1   0   1   1   1   0   0          4           5          3   \n",
       "173   1   1   0   1   1   0   1   0          4           5          3   \n",
       "174   1   1   0   1   1   0   0   1          8           7          7   \n",
       "175   1   1   0   1   0   1   1   0          4           5          3   \n",
       "176   1   1   0   1   0   1   0   1          8           7          7   \n",
       "177   1   1   0   1   0   0   1   1          8           7          7   \n",
       "178   1   1   0   0   1   1   1   0          4           5          3   \n",
       "179   1   1   0   0   1   1   0   1          8           7          7   \n",
       "180   1   1   0   0   1   0   1   1          8           7          7   \n",
       "181   1   1   0   0   0   1   1   1          8           7          7   \n",
       "182   1   0   1   1   1   1   0   0          4           6          5   \n",
       "183   1   0   1   1   1   0   1   0          4           6          5   \n",
       "184   1   0   1   1   1   0   0   1          8           7          7   \n",
       "185   1   0   1   1   0   1   1   0          4           6          5   \n",
       "186   1   0   1   1   0   1   0   1          8           7          7   \n",
       "187   1   0   1   1   0   0   1   1          8           7          7   \n",
       "188   1   0   1   0   1   1   1   0          4           6          5   \n",
       "189   1   0   1   0   1   1   0   1          8           7          7   \n",
       "190   1   0   1   0   1   0   1   1          8           7          7   \n",
       "191   1   0   1   0   0   1   1   1          8           7          7   \n",
       "192   1   0   0   1   1   1   1   0          4           5          3   \n",
       "193   1   0   0   1   1   1   0   1          8           7          7   \n",
       "194   1   0   0   1   1   0   1   1          8           7          7   \n",
       "195   1   0   0   1   0   1   1   1          8           7          7   \n",
       "196   1   0   0   0   1   1   1   1          8           7          7   \n",
       "197   0   1   1   1   1   1   0   0          0           5          5   \n",
       "198   0   1   1   1   1   0   1   0          0           3          3   \n",
       "199   0   1   1   1   1   0   0   1          8           7          7   \n",
       "200   0   1   1   1   0   1   1   0          0           5          5   \n",
       "201   0   1   1   1   0   1   0   1          8           7          7   \n",
       "202   0   1   1   1   0   0   1   1          8           7          7   \n",
       "203   0   1   1   0   1   1   1   0          0           5          5   \n",
       "204   0   1   1   0   1   1   0   1          8           7          7   \n",
       "205   0   1   1   0   1   0   1   1          8           7          7   \n",
       "206   0   1   1   0   0   1   1   1          8           7          7   \n",
       "207   0   1   0   1   1   1   1   0          0           4          3   \n",
       "208   0   1   0   1   1   1   0   1          8           7          7   \n",
       "209   0   1   0   1   1   0   1   1          4           5          5   \n",
       "210   0   1   0   1   0   1   1   1          8           7          7   \n",
       "211   0   1   0   0   1   1   1   1          8           7          7   \n",
       "212   0   0   1   1   1   1   1   0          0           5          5   \n",
       "213   0   0   1   1   1   1   0   1          4           5          5   \n",
       "214   0   0   1   1   1   0   1   1          8           7          7   \n",
       "215   0   0   1   1   0   1   1   1          8           7          7   \n",
       "216   0   0   1   0   1   1   1   1          8           7          7   \n",
       "217   0   0   0   1   1   1   1   1          8           7          7   \n",
       "218   1   1   1   1   1   1   0   0          4           6          5   \n",
       "219   1   1   1   1   1   0   1   0          4           6          5   \n",
       "220   1   1   1   1   1   0   0   1          8           7          7   \n",
       "221   1   1   1   1   0   1   1   0          4           6          5   \n",
       "222   1   1   1   1   0   1   0   1          8           7          7   \n",
       "223   1   1   1   1   0   0   1   1          8           7          7   \n",
       "224   1   1   1   0   1   1   1   0          4           6          5   \n",
       "225   1   1   1   0   1   1   0   1          8           7          7   \n",
       "226   1   1   1   0   1   0   1   1          8           7          7   \n",
       "227   1   1   1   0   0   1   1   1          8           7          7   \n",
       "228   1   1   0   1   1   1   1   0          4           5          3   \n",
       "229   1   1   0   1   1   1   0   1          8           7          7   \n",
       "230   1   1   0   1   1   0   1   1          8           7          7   \n",
       "231   1   1   0   1   0   1   1   1          8           7          7   \n",
       "232   1   1   0   0   1   1   1   1          8           7          7   \n",
       "233   1   0   1   1   1   1   1   0          4           6          5   \n",
       "234   1   0   1   1   1   1   0   1          8           7          7   \n",
       "235   1   0   1   1   1   0   1   1          8           7          7   \n",
       "236   1   0   1   1   0   1   1   1          8           7          7   \n",
       "237   1   0   1   0   1   1   1   1          8           7          7   \n",
       "238   1   0   0   1   1   1   1   1          8           7          7   \n",
       "239   0   1   1   1   1   1   1   0          0           5          5   \n",
       "240   0   1   1   1   1   1   0   1          8           7          7   \n",
       "241   0   1   1   1   1   0   1   1          8           7          7   \n",
       "242   0   1   1   1   0   1   1   1          8           7          7   \n",
       "243   0   1   1   0   1   1   1   1          8           7          7   \n",
       "244   0   1   0   1   1   1   1   1          8           7          7   \n",
       "245   0   0   1   1   1   1   1   1          8           7          7   \n",
       "246   1   1   1   1   1   1   1   0          4           6          5   \n",
       "247   1   1   1   1   1   1   0   1          8           7          7   \n",
       "248   1   1   1   1   1   0   1   1          8           7          7   \n",
       "249   1   1   1   1   0   1   1   1          8           7          7   \n",
       "250   1   1   1   0   1   1   1   1          8           7          7   \n",
       "251   1   1   0   1   1   1   1   1          8           7          7   \n",
       "252   1   0   1   1   1   1   1   1          8           7          7   \n",
       "253   0   1   1   1   1   1   1   1          8           7          7   \n",
       "254   1   1   1   1   1   1   1   1          8           7          7   \n",
       "\n",
       "     nb_rdd_meas  nb_add_ipm  \n",
       "0              0           0  \n",
       "1              0           0  \n",
       "2              0           0  \n",
       "3              0           0  \n",
       "4              0           0  \n",
       "5              0           0  \n",
       "6              0           0  \n",
       "7              0           0  \n",
       "8              0           0  \n",
       "9              0           0  \n",
       "10             0           4  \n",
       "11             0           0  \n",
       "12             0           0  \n",
       "13             0           4  \n",
       "14             0           4  \n",
       "15             0           0  \n",
       "16             0           0  \n",
       "17             0           0  \n",
       "18             0           0  \n",
       "19             0           0  \n",
       "20             0           0  \n",
       "21             0           0  \n",
       "22             0           0  \n",
       "23             0           0  \n",
       "24             0           0  \n",
       "25             0           0  \n",
       "26             0           0  \n",
       "27             0           0  \n",
       "28             0           0  \n",
       "29             0           0  \n",
       "30             0           0  \n",
       "31             0           0  \n",
       "32             0           0  \n",
       "33             0           0  \n",
       "34             0           0  \n",
       "35             0           2  \n",
       "36             0           0  \n",
       "37             0           4  \n",
       "38             0           4  \n",
       "39             8           6  \n",
       "40             0           4  \n",
       "41             0           6  \n",
       "42             0           4  \n",
       "43             0           0  \n",
       "44             2           0  \n",
       "45             0           4  \n",
       "46             0           6  \n",
       "47             0           4  \n",
       "48             0           4  \n",
       "49             0           4  \n",
       "50             1           4  \n",
       "51             0           0  \n",
       "52             0           4  \n",
       "53             0           6  \n",
       "54             0           4  \n",
       "55             0           4  \n",
       "56             0           6  \n",
       "57             0           0  \n",
       "58             0           0  \n",
       "59             0           0  \n",
       "60             0           0  \n",
       "61             0           0  \n",
       "62             0           0  \n",
       "63             0           0  \n",
       "64             2           0  \n",
       "65             0           0  \n",
       "66             0           0  \n",
       "67             2           0  \n",
       "68             0           1  \n",
       "69             0           0  \n",
       "70             0           0  \n",
       "71             0           2  \n",
       "72             0           0  \n",
       "73             0           0  \n",
       "74             0           0  \n",
       "75             0           0  \n",
       "76             0           0  \n",
       "77             0           0  \n",
       "78             0           0  \n",
       "79             0           0  \n",
       "80             0           0  \n",
       "81             0           4  \n",
       "82             0           0  \n",
       "83             0           0  \n",
       "84             0           0  \n",
       "85             0           0  \n",
       "86             0           1  \n",
       "87             0           2  \n",
       "88             0           0  \n",
       "89             0           0  \n",
       "90             0           2  \n",
       "91             0           2  \n",
       "92             0           4  \n",
       "93             0           4  \n",
       "94             2           0  \n",
       "95             0           4  \n",
       "96             0           6  \n",
       "97             0           4  \n",
       "98             0           4  \n",
       "99             2           4  \n",
       "100            1           6  \n",
       "101            0           4  \n",
       "102            2           4  \n",
       "103            0           6  \n",
       "104            0           4  \n",
       "105            0           6  \n",
       "106            2           6  \n",
       "107            0           4  \n",
       "108            2           4  \n",
       "109            0           4  \n",
       "110            1           6  \n",
       "111            0           0  \n",
       "112            0           4  \n",
       "113            0           6  \n",
       "114            0           4  \n",
       "115            2           6  \n",
       "116            0           6  \n",
       "117            0           4  \n",
       "118            0           4  \n",
       "119            1           6  \n",
       "120            0           4  \n",
       "121            2           4  \n",
       "122            2           6  \n",
       "123            0           4  \n",
       "124            0           6  \n",
       "125            2           6  \n",
       "126            0           6  \n",
       "127            0           0  \n",
       "128            0           0  \n",
       "129            2           0  \n",
       "130            0           0  \n",
       "131            0           0  \n",
       "132            2           0  \n",
       "133            0           1  \n",
       "134            0           0  \n",
       "135            0           0  \n",
       "136            0           6  \n",
       "137            0           0  \n",
       "138            2           0  \n",
       "139            0           2  \n",
       "140            2           0  \n",
       "141            0           1  \n",
       "142            2           2  \n",
       "143            2           0  \n",
       "144            0           1  \n",
       "145            2           2  \n",
       "146            0           6  \n",
       "147            0           0  \n",
       "148            0           0  \n",
       "149            0           2  \n",
       "150            0           0  \n",
       "151            0           2  \n",
       "152            0           6  \n",
       "153            0           0  \n",
       "154            0           2  \n",
       "155            0           6  \n",
       "156            0           6  \n",
       "157            0           0  \n",
       "158            0           2  \n",
       "159            2           2  \n",
       "160            0           6  \n",
       "161            0           6  \n",
       "162            2           4  \n",
       "163            0           4  \n",
       "164            2           4  \n",
       "165            3           6  \n",
       "166            0           4  \n",
       "167            2           4  \n",
       "168            2           6  \n",
       "169            0           4  \n",
       "170            2           6  \n",
       "171            4           6  \n",
       "172            0           4  \n",
       "173            2           4  \n",
       "174            3           6  \n",
       "175            2           4  \n",
       "176            3           6  \n",
       "177            4           6  \n",
       "178            2           4  \n",
       "179            2           6  \n",
       "180            4           6  \n",
       "181            4           6  \n",
       "182            2           4  \n",
       "183            0           4  \n",
       "184            3           6  \n",
       "185            2           4  \n",
       "186            3           6  \n",
       "187            4           6  \n",
       "188            2           4  \n",
       "189            2           6  \n",
       "190            4           6  \n",
       "191            4           6  \n",
       "192            0           4  \n",
       "193            2           6  \n",
       "194            4           6  \n",
       "195            4           6  \n",
       "196            4           6  \n",
       "197            0           0  \n",
       "198            2           0  \n",
       "199            0           6  \n",
       "200            2           0  \n",
       "201            0           6  \n",
       "202            2           6  \n",
       "203            2           0  \n",
       "204            0           6  \n",
       "205            2           6  \n",
       "206            2           6  \n",
       "207            2           0  \n",
       "208            0           6  \n",
       "209            4           2  \n",
       "210            2           6  \n",
       "211            2           6  \n",
       "212            0           0  \n",
       "213            2           2  \n",
       "214            2           6  \n",
       "215            2           6  \n",
       "216            2           6  \n",
       "217            2           6  \n",
       "218            2           4  \n",
       "219            2           4  \n",
       "220            5           6  \n",
       "221            4           4  \n",
       "222            4           6  \n",
       "223            6           6  \n",
       "224            2           4  \n",
       "225            2           6  \n",
       "226            6           6  \n",
       "227            6           6  \n",
       "228            2           4  \n",
       "229            5           6  \n",
       "230            6           6  \n",
       "231            6           6  \n",
       "232            6           6  \n",
       "233            0           4  \n",
       "234            4           6  \n",
       "235            6           6  \n",
       "236            6           6  \n",
       "237            6           6  \n",
       "238            6           6  \n",
       "239            2           0  \n",
       "240            2           6  \n",
       "241            4           6  \n",
       "242            4           6  \n",
       "243            4           6  \n",
       "244            4           6  \n",
       "245            4           6  \n",
       "246            2           4  \n",
       "247            7           6  \n",
       "248            8           6  \n",
       "249            8           6  \n",
       "250            8           6  \n",
       "251            8           6  \n",
       "252            8           6  \n",
       "253            6           6  \n",
       "254           10           6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res_hm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results as an heatmap (Figure 3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 330x800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set font size\n",
    "plt.rcdefaults\n",
    "plt.rcParams.update({'font.size': 7})\n",
    "\n",
    "# create figure and panels\n",
    "fig, ax = plt.subplots(1, 2, figsize=(3.3, 8.0), dpi=100.0, gridspec_kw={'width_ratios': [1.3, 1]})\n",
    "\n",
    "# panel 1 (combinations of datasets)\n",
    "h1 = sns.heatmap(res_hm[measurement_methods],\n",
    "                 cmap=['white', '#74C476'],\n",
    "                 cbar=False,\n",
    "                 ax=ax[0],\n",
    "                 xticklabels=True)\n",
    "\n",
    "#   add border to heatmap\n",
    "for _, spine in h1.spines.items():\n",
    "    spine.set_visible(True)\n",
    "h1.set_ylabel('combinations')\n",
    "\n",
    "#   add lines between columns\n",
    "ax[0].vlines(range(len(measurement_methods)), *ax[0].get_ylim(), lw=0.5, colors=\"black\")\n",
    "\n",
    "# panel 2 (metrics)\n",
    "data = res_hm[metrics]\n",
    "cmap = plt.get_cmap('RdBu', 1 + max(data.max()) - min(data.min()))\n",
    "x_axis_labels = ['nb isotopomers', 'nb cumomers', 'nb EMUs', 'nb redundant meas.', 'nb add. isotopomers']\n",
    "h2 = sns.heatmap(data,\n",
    "                 cmap=cmap,\n",
    "                 ax=ax[1],\n",
    "                 yticklabels=False,\n",
    "                 xticklabels=x_axis_labels)\n",
    "\n",
    "#   add colorbar (with centered ticks)\n",
    "c_bar = ax[1].collections[0].colorbar\n",
    "c_bar.ax.set_ylim(0, 1 + max(data.max()))\n",
    "c_bar.set_ticks([i*max(data.max())/(max(data.max())+1)+0.5 for i in range(0, 1 + max(data.max()))])\n",
    "c_bar.set_ticklabels([i for i in range(0, 1 + max(data.max()))])\n",
    "c_bar.outline.set_linewidth(1)\n",
    "c_bar.set_label('value')\n",
    "\n",
    "#   add lines between columns\n",
    "ax[1].vlines(range(len(metrics)), *ax[1].get_ylim(), lw=0.5, colors=\"black\")\n",
    "\n",
    "#   add border to heatmap\n",
    "for _, spine in h2.spines.items():\n",
    "    spine.set_visible(True)\n",
    "    \n",
    "#   adjust space between panels\n",
    "fig.tight_layout(pad=1.0)\n",
    "\n",
    "# save figure\n",
    "plt.savefig(\"./results/Figure_3.jpeg\", dpi=300.0, bbox_inches='tight', pad_inches=0.05)\n",
    "\n",
    "# display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of combinations which do not bring any novel isotopic information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74\n"
     ]
    }
   ],
   "source": [
    "print(len(res_hm[res_hm['nb_id_ipm'] == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of combinations with full isotopic coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87\n"
     ]
    }
   ],
   "source": [
    "print(len(res_hm[res_hm['nb_id_ipm'] == 8]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save results to an excel file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_hm.to_excel(\"./results/res_combinations_ALA.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Determine isotopic coverage for all amino acids\n",
    "\n",
    "This code integrates isotopic measurements collected by NMR and MS for all amino acids and determines the number of isotopomers, cumomers and EMUs that can be quantified. It reproduces Figure 4 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experimental datasets can be easily defined in a dictionary to generate the isotopomers-measurements mapping dataframe. Dictionary keys are dataset name, and values represent measurements which can be defined as:\n",
    "\n",
    "- `dict()` where keys are isotopomers or cumomers and values are measurements symbol, this is typically used to map NMR datasets\n",
    "- `str()` of elementary metabolite units, this is typically used to map MS datasets\n",
    "- `tuple()` where the first value is the elementary metabolite unit of the parent ion and the second value is the elementary metabolite unit of the daughter ion, this is typically used to map MS/MS datasets with established parent/daughter ions relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NMR_1</th>\n",
       "      <th>NMR_2</th>\n",
       "      <th>GC-MS</th>\n",
       "      <th>LC-MS</th>\n",
       "      <th>MS_MS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aa_m0</td>\n",
       "      <td>ab_m0</td>\n",
       "      <td>ac_m0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aa_m0</td>\n",
       "      <td>ab_m1</td>\n",
       "      <td>ac_m0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>d</td>\n",
       "      <td>aa_m1</td>\n",
       "      <td>ab_m1</td>\n",
       "      <td>ac_m1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001</th>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aa_m1</td>\n",
       "      <td>ab_m1</td>\n",
       "      <td>ac_m1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>aa_m1</td>\n",
       "      <td>ab_m2</td>\n",
       "      <td>ac_m1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aa_m1</td>\n",
       "      <td>ab_m2</td>\n",
       "      <td>ac_m1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>b</td>\n",
       "      <td>e</td>\n",
       "      <td>aa_m2</td>\n",
       "      <td>ab_m2</td>\n",
       "      <td>ac_m2_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>b</td>\n",
       "      <td>f</td>\n",
       "      <td>aa_m2</td>\n",
       "      <td>ab_m3</td>\n",
       "      <td>ac_m2_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NMR_1 NMR_2  GC-MS  LC-MS    MS_MS\n",
       "000   NaN   NaN  aa_m0  ab_m0  ac_m0_0\n",
       "100   NaN   NaN  aa_m0  ab_m1  ac_m0_0\n",
       "010   NaN     d  aa_m1  ab_m1  ac_m1_1\n",
       "001     a   NaN  aa_m1  ab_m1  ac_m1_0\n",
       "110     c     d  aa_m1  ab_m2  ac_m1_1\n",
       "101     a   NaN  aa_m1  ab_m2  ac_m1_0\n",
       "011     b     e  aa_m2  ab_m2  ac_m2_1\n",
       "111     b     f  aa_m2  ab_m3  ac_m2_1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define measurements\n",
    "list_datasets_example = {\"NMR_1\":{\"x01\":\"a\", \"x11\":\"b\", \"110\":\"c\"},\n",
    "                         \"NMR_2\":{\"010\":\"d\", \"110\":\"d\", \"011\":\"e\", \"111\":\"f\"},\n",
    "                         \"GC-MS\":\"xEE\",\n",
    "                         \"LC-MS\":\"EEE\",\n",
    "                         \"MS_MS\":(\"xEE\", \"xEx\")}\n",
    "\n",
    "# Generate mapping dataframe\n",
    "mapping_example = build_mapping_dataframe(list_datasets_example)\n",
    "\n",
    "display(mapping_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of accessible isotopic measurements for all amino acids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "amino_acids_datasets = {\"ALA\":{'HSQC_Ca': {'010': 'a1', '011': 'a2', '110': 'a3', '111': 'a4'},\n",
    "                               'HSQC_Cb': {'x01': 'b1', 'x11': 'b2'},\n",
    "                               'HACO': {'10x': 'c1', '11x': 'c2'},\n",
    "                               'TOCSY_Ha': {'x0x': 'd1','x1x': 'd2'},\n",
    "                               'TOCSY_Hb': {'xx0': 'e1', 'xx1': 'e2'},\n",
    "                               'HNCA': {'01x': 'f1', '11x': 'f2'},\n",
    "                               'GC-MS': 'xEE',\n",
    "                               'LC-MS': 'EEE'},\n",
    "                        \"ASP\":{'HSQC_Ca': {'010x': 'a', '011x': 'b', '110x': 'c', '111x': 'd'},\n",
    "                               'HSQC_Cb': {'x010': 'k', 'x110': 'l1', 'x011': 'l2', 'x111': 'l'},\n",
    "                               'HACO': {'10xx': 'r', '11xx': 's'},\n",
    "                               'TOCSY_Ha': {'x0xx': 'e','x1xx': 'f'},\n",
    "                               'TOCSY_Hb': {'xx0x': 't', 'xx1x': 'u'},\n",
    "                               'HNCA': {'01xx': 'm', '11xx': 'n'},\n",
    "                               'GC-MS': 'xEEE',\n",
    "                               'LC-MS': 'EEEE'},\n",
    "                        \"GLU\":{'HSQC_Ca': {'010xx': 'a1', '011xx': 'a2', '110xx': 'a3', '111xx': 'a4'},\n",
    "                               'HSQC_Cb': {'x010x': 'b1', 'x110x': 'b2', 'x011x': 'b2', 'x111x': 'b3'},\n",
    "                               'HSQC_Cg': {'xx010': 'c1', 'xx110': 'c2', 'xx011': 'c3', 'xx111': 'c4'},\n",
    "                               'HACO': {'10xxx': 'd1', '11xxx': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xxx': 'e1','x1xxx': 'e2'},\n",
    "                               'TOCSY_Hb': {'xx0xx': 'f1', 'xx1xx': 'f2'},\n",
    "                               'HNCA': {'01xxx': 'g1', '11xxx': 'g2'},\n",
    "                               'GC-MS': 'xEEEE',\n",
    "                               'LC-MS': 'EEEEE'},\n",
    "                        \"GLY\":{'HSQC_Ca': {'01': 'a1', '11': 'a2'},\n",
    "                               'HACO': {'10': 'b1', '11': 'b2'},\n",
    "                               'TOCSY_Ha': {'x0': 'c1','x1': 'c2'},\n",
    "                               'GC-MS': 'xE',\n",
    "                               'LC-MS': 'EE'},\n",
    "                        \"HIS\":{'HSQC_Ca': {'010xxx': 'a1', '011xxx': 'a2', '110xxx': 'a3', '111xxx': 'a4'},\n",
    "                               'HSQC_Cb': {'x010xx': 'b1', 'x110xx': 'b2', 'x011xx': 'b3', 'x111xx': 'b4'},\n",
    "                               'HSQC_Cd': {'xxx01x': 'c1', 'xxx11x': 'c2'},\n",
    "                               'TOCSY_Ha': {'x0xxxx': 'e1','x1xxxx': 'e2'},\n",
    "                               'TOCSY_Hd': {'xxxx0x': 'f1', 'xxxx1x': 'f2'},\n",
    "                               'TOCSY_He': {'xxxxx0': 'g1', 'xxxxx1': 'g2'},\n",
    "                               'LC-MS': 'EEEEEE'},\n",
    "                        \"ILE\":{'HSQC_Ca': {'010xxx': 'a1', '011xxx': 'a2', '110xxx': 'a3', '111xxx': 'a4'},\n",
    "                               'HSQC_Cg1': {'xx0x10': 'b1', 'xx1x10': 'b2', 'xx0x11': 'b2', 'xx1x11': 'b3'},\n",
    "                               'HSQC_Cg2': {'xx01xx': 'c1', 'xx11xx': 'c2'},\n",
    "                               'HSQC_Cd': {'xxxx01': 'd1', 'xxxx11': 'd2'},\n",
    "                               'HACO': {'10xxxx': 'e1', '11xxxx': 'e2'},\n",
    "                               'TOCSY_Ha': {'x0xxxx': 'f1', 'x1xxxx': 'f2'},\n",
    "                               'TOCSY_Hb': {'xx0xxx': 'g1', 'xx1xxx': 'g2'},\n",
    "                               'TOCSY_Hg': {'xxxx0x': 'h1', 'xxxx1x': 'h2'},\n",
    "                               'TOCSY_Hd': {'xxxxx0': 'i1', 'xxxxx1': 'i2'},\n",
    "                               'HNCA': {'01xxxx': 'j1', '11xxxx': 'j2'},\n",
    "                               'LC-MS': 'EEEEEE',\n",
    "                               'GC-MS': 'xEEEEE'},\n",
    "                        \"LEU\":{'HSQC_Ca': {'010xxx': 'a1', '011xxx': 'a2', '110xxx': 'a3', '111xxx': 'a4'},\n",
    "                               'HSQC_Cb': {'x010xx': 'b1', 'x110xx': 'b2', 'x011xx': 'b2', 'x111xx': 'b3'},\n",
    "                               'HSQC_Cd': {'xxx010': 'c1', 'xxx011': 'c1', 'xxx001': 'c1', 'xxx110': 'c2', 'xxx111': 'c2', 'xxx101': 'c2'},\n",
    "                               'HACO': {'10xxxx': 'd1', '11xxxx': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xxxx': 'e1', 'x1xxxx': 'e2'},\n",
    "                               'TOCSY_Hg': {'xxx0xx': 'f1', 'xxx1x0': 'f2'},\n",
    "                               'HNCA': {'01xxxx': 'g1', '11xxxx': 'g2'},\n",
    "                               'LC-MS': 'EEEEEE',\n",
    "                               'GC-MS': 'xEEEEE'},\n",
    "                        \"LYS\":{'HSQC_Cb': {'x010xx': 'a1', 'x110xx': 'a2', 'x011xx': 'a2', 'x111xx': 'a3'},\n",
    "                               'HSQC_Cd': {'xxx010': 'b1', 'xxx110': 'b2', 'xxx011': 'b2', 'xxx111': 'b3'},\n",
    "                               'HSQC_Ce': {'xxxx01': 'c1', 'xxxx11': 'c2'},\n",
    "                               'TOCSY_Ha': {'x0xxxx': 'd1', 'x1xxxx': 'd2'},\n",
    "                               'TOCSY_Hd': {'xxxx0x': 'e1', 'xxxx1x': 'e2'},\n",
    "                               'TOCSY_He': {'xxxxx0': 'f1', 'xxxxx1': 'f2'},\n",
    "                               'LC-MS': 'EEEEEE'},\n",
    "                        \"PRO\":{'HSQC_Ca': {'010xx': 'a1', '011xx': 'a2', '110xx': 'a3', '111xx': 'a4'},\n",
    "                               'HSQC_Cb': {'x010x': 'b1', 'x110x': 'b2', 'x011x': 'b2', 'x111x': 'b3'},\n",
    "                               'HSQC_Cg': {'xx010': 'c1', 'xx110': 'c2', 'xx011': 'c2', 'xx111': 'c3'},\n",
    "                               'HSQC_Cd': {'xxx01': 'd1', 'xxx11': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xxx': 'e1','x1xxx': 'e2'},\n",
    "                               'TOCSY_Hb': {'xx0xx': 'f1', 'xx1xx': 'f2'},\n",
    "                               'TOCSY_Hg': {'xxx0x': 'g1', 'xxx1x': 'g2'},\n",
    "                               'TOCSY_Hd': {'xxxx0': 'h1', 'xxxx1': 'h2'},\n",
    "                               'LC-MS': 'EEEEE'},\n",
    "                        \"SER\":{'HSQC_Ca': {'010': 'a1', '011': 'a2', '110': 'a3', '111': 'a4'},\n",
    "                               'HSQC_Cb': {'x01': 'b1', 'x11': 'b2'},\n",
    "                               'HACO': {'10x': 'c1', '11x': 'c2'},\n",
    "                               'TOCSY_Ha': {'x0x': 'd1','x1x': 'd2'},\n",
    "                               'TOCSY_Hb': {'xx0': 'e1', 'xx1': 'e2'},\n",
    "                               'HNCA': {'01x': 'f1', '11x': 'f2'},\n",
    "                               'GC-MS': 'xEE',\n",
    "                               'LC-MS': 'EEE'},\n",
    "                        \"THR\":{'HSQC_Ca': {'010x': 'a1', '011x': 'a2', '110x': 'a3', '111x': 'a4'},\n",
    "                               'HSQC_Cb': {'x010': 'b1', 'x110': 'b2', 'x011': 'b2', 'x111': 'b3'},\n",
    "                               'HSQC_Cg': {'xx01': 'c1', 'xx11': 'c2'},\n",
    "                               'HACO': {'10xx': 'd1', '11xx': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xx': 'e1','x1xx': 'e2'},\n",
    "                               'TOCSY_Hb': {'xx0x': 'f1', 'xx1x': 'f2'},\n",
    "                               'JRES_Hg': {'xxx0': 'g1', 'xxx1': 'g2'},\n",
    "                               'HNCA': {'01xx': 'h1', '11xx': 'h2'},\n",
    "                               'GC-MS': 'xEEE',\n",
    "                               'LC-MS': 'EEEE'},\n",
    "                        \"VAL\":{'HSQC_Ca': {'010xx': 'a1', '011xx': 'a2', '110xx': 'a3', '111xx': 'a4'},\n",
    "                               'HSQC_Cb': {'x0100': 'b1', 'x0110': 'b2', 'x0101': 'b2', 'x1100': 'b2', 'x1110': 'b3', 'x1101': 'b3', 'x0111': 'b3', 'x1111': 'b4'},\n",
    "                               'HSQC_Cg': {'xx010': 'c1', 'xx001': 'c1', 'xx110': 'c2', 'xx101': 'c2'},\n",
    "                               'HACO': {'10xxx': 'd1', '11xxx': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xxx': 'e1','x1xxx': 'e2'},\n",
    "                               'TOCSY_Hb': {'xx0xx': 'f1', 'xx1xx': 'f2'},\n",
    "                               'HNCA': {'01xxx': 'g1', '11xxx': 'g2'},\n",
    "                               'GC-MS': 'xEEEE',\n",
    "                               'LC-MS': 'EEEEE'},\n",
    "                        \"PHE\":{'HSQC_Ca': {'010xxxxxx': 'a1', '011xxxxxx': 'a2', '110xxxxxx': 'a3', '111xxxxxx': 'a4'},\n",
    "                               'HSQC_Cb': {'x010xxxxx': 'b1', 'x011xxxxx': 'b2', 'x110xxxxx': 'b3', 'x111xxxxx': 'b4'},\n",
    "                               'HSQC_Cd': {'xxx010xxx': 'c1', 'xxx011xxx': 'c2', 'xxx110xxx': 'c3', 'xxx111xxx': 'c4'},\n",
    "                               'HSQC_Ce': {'xxxx010xx': 'd1', 'xxxx011xx': 'd2', 'xxxx110xx': 'd3', 'xxxx111xx': 'd4'},\n",
    "                               'HACO': {'10xxxxxxx': 'e1', '11xxxxxxx': 'e2'},\n",
    "                               'JRES_Ha': {'x0xxxxxxx': 'f1', 'x1xxxxxxx': 'f2'},\n",
    "                               'JRES_Hb': {'xx0xxxxxx': 'g1', 'xx1xxxxxx': 'g2'},\n",
    "                               'JRES_Hd': {'xxxx0xxxx': 'h1', 'xxxx1xxxx': 'h2'},\n",
    "                               'JRES_He': {'xxxxx0xxx': 'i1', 'xxxxx1xxx': 'i2'},\n",
    "                               'HNCA': {'01xxxxxxx': 'j1', '11xxxxxxx': 'j2'},\n",
    "                               'LC-MS': 'EEEEEEEEE',\n",
    "                               'GC-MS_1': 'xEEEEEEEE',\n",
    "                               'GC-MS_2': 'EExxxxxxx'},\n",
    "                        \"ARG\":{'HSQC_Cb': {'x010xx': 'a1', 'x110xx': 'a2', 'x011xx': 'a2', 'x111xx': 'a3'},\n",
    "                               'HSQC_Cd': {'xxx01x': 'b1', 'xxx11x': 'b2'},\n",
    "                               'TOCSY_Ha': {'x0xxxx': 'c1', 'x1xxxx': 'c2'},\n",
    "                               'TOCSY_Hd': {'xxxx0x': 'd1', 'xxxx1x': 'd2'},\n",
    "                               'LC-MS': 'EEEEEE'},\n",
    "                        \"TYR\":{'HSQC_Cb': {'x010xxxxx': 'b1', 'x011xxxxx': 'b2', 'x110xxxxx': 'b2', 'x111xxxxx': 'b3'},\n",
    "                               'HSQC_Cd': {'xxx010xxx': 'c1', 'xxx011xxx': 'c2', 'xxx110xxx': 'c2', 'xxx111xxx': 'c3'},\n",
    "                               'HSQC_Ce': {'xxxx010xx': 'd1', 'xxxx011xx': 'd2', 'xxxx110xx': 'd2', 'xxxx111xx': 'd3'},\n",
    "                               'JRES_Ha': {'x0xxxxxxx': 'f1', 'x1xxxxxxx': 'f2'},\n",
    "                               'JRES_Hb': {'xx0xxxxxx': 'g1', 'xx1xxxxxx': 'g2'},\n",
    "                               'JRES_Hd': {'xxxx0xxxx': 'h1', 'xxxx1xxxx': 'h2'},\n",
    "                               'JRES_He': {'xxxxx0xxx': 'i1', 'xxxxx1xxx': 'i2'},\n",
    "                               'LC-MS': 'EEEEEEEEE',\n",
    "                               'GC-MS': 'EExxxxxxx'},\n",
    "                        \"MET\":{'HSQC_Ca': {'010xx': 'a1', '011xx': 'a2', '110xx': 'a3', '111xx': 'a4'},\n",
    "                               'HACO': {'10xxx': 'd1', '11xxx': 'd2'},\n",
    "                               'TOCSY_Ha': {'x0xxx': 'e1','x1xxx': 'e2'},\n",
    "                               'TOCSY_Hg': {'xxx0x': 'f1', 'xxx1x': 'f2'},\n",
    "                               'TOCSY_He': {'xxxx0': 'h1', 'xxxx1': 'h2'},\n",
    "                               'HNCA': {'01xxx': 'g1', '11xxx': 'g2'},\n",
    "                               'GC-MS': 'xEEEE',\n",
    "                               'LC-MS': 'EEEEE'}}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate isotopic coverage for all amino acids.\n",
    "\n",
    "**Notes**:\n",
    "  - Processing is fast for small amino acids but can take a few dozen minutes for larger molecules (e.g. PHE and TYR), hence results are saved in pickle files to facilitate data analysis. Pre-calculated results files are provided with this notebook. Uncomment the following code to re-run the calculation.\n",
    "  - Here we use the flag `fast=True`, which makes the calculation faster by skipping some simplication steps. We strongly recommand using this flag to reduce calculation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#import pickle\n",
    "\n",
    "#for k,v in amino_acids_datasets.items():\n",
    "    \n",
    "#    # generate mapping table\n",
    "#    print(k)\n",
    "#    mm = build_mapping_dataframe(v)\n",
    "#    display(mm)\n",
    "    \n",
    "#    # integrate datasets\n",
    "#    res_comb = isosolve.main(mm=mm, fast=True)\n",
    "    \n",
    "#    # save results in a pickle file\n",
    "#    with open(\"./results/coverage_\" + k + \"_res.pkl\", \"wb\") as f:\n",
    "#        pickle.dump(res_comb, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results file and calculate isotopic coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of identifable isotopomers, cumomers and EMUs:\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>amino_acid</th>\n",
       "      <th>isotopomers</th>\n",
       "      <th>cumomers</th>\n",
       "      <th>emus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLY</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALA</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SER</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASP</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THR</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GLU</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MET</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRO</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VAL</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ARG</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HIS</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LEU</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ILE</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LYS</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PHE</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TYR</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   amino_acid  isotopomers  cumomers  emus\n",
       "0         GLY            4         3     3\n",
       "1         ALA            8         7     7\n",
       "2         SER            8         7     7\n",
       "3         ASP            6        11    10\n",
       "4         THR            6        11    10\n",
       "5         GLU            4        12     8\n",
       "6         MET            4        10     9\n",
       "7         PRO            2        13    10\n",
       "8         VAL            4         9     8\n",
       "9         ARG            2         4     3\n",
       "10        HIS            2         9     5\n",
       "11        LEU            4         7     5\n",
       "12        ILE            4        15    13\n",
       "13        LYS            2         7     5\n",
       "14        PHE            4        17    10\n",
       "15        TYR            2        10     7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative isotopic coverage for isotopomers, cumomers and EMUs:\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>amino_acid</th>\n",
       "      <th>isotopomers</th>\n",
       "      <th>cumomers</th>\n",
       "      <th>emus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLY</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALA</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SER</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASP</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THR</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GLU</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>38.709677</td>\n",
       "      <td>25.806452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MET</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>32.258065</td>\n",
       "      <td>29.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRO</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>41.935484</td>\n",
       "      <td>32.258065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VAL</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>29.032258</td>\n",
       "      <td>25.806452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ARG</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>6.349206</td>\n",
       "      <td>4.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HIS</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>7.936508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LEU</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>7.936508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ILE</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>23.809524</td>\n",
       "      <td>20.634921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LYS</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>7.936508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PHE</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>3.326810</td>\n",
       "      <td>1.956947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TYR</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>1.956947</td>\n",
       "      <td>1.369863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   amino_acid  isotopomers    cumomers        emus\n",
       "0         GLY   100.000000  100.000000  100.000000\n",
       "1         ALA   100.000000  100.000000  100.000000\n",
       "2         SER   100.000000  100.000000  100.000000\n",
       "3         ASP    37.500000   73.333333   66.666667\n",
       "4         THR    37.500000   73.333333   66.666667\n",
       "5         GLU    12.500000   38.709677   25.806452\n",
       "6         MET    12.500000   32.258065   29.032258\n",
       "7         PRO     6.250000   41.935484   32.258065\n",
       "8         VAL    12.500000   29.032258   25.806452\n",
       "9         ARG     3.125000    6.349206    4.761905\n",
       "10        HIS     3.125000   14.285714    7.936508\n",
       "11        LEU     6.250000   11.111111    7.936508\n",
       "12        ILE     6.250000   23.809524   20.634921\n",
       "13        LYS     3.125000   11.111111    7.936508\n",
       "14        PHE     0.781250    3.326810    1.956947\n",
       "15        TYR     0.390625    1.956947    1.369863"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# read calculation results from pickle files and determine the number of identifiable species\n",
    "res = {}\n",
    "for fi in amino_acids_datasets.keys():\n",
    "    with open(\"./results/coverage_\" + fi + \"_res.pkl\", \"rb\") as f:\n",
    "        tmp = pickle.load(f)\n",
    "    p_ip = len(tmp[\"metrics\"][\"idef\"])\n",
    "    p_c = len(tmp[\"metrics\"][\"idef_c\"])\n",
    "    p_e = len([1 for i,j in tmp[\"metrics\"][\"idef_e\"].items() if len(j) == i.count(\"E\")+1])\n",
    "    res[fi] = {\"identifiable\":{\"isotopomers\":p_ip, \"cumomers\":p_c, \"emus\":p_e},\n",
    "               \"total\":{\"isotopomers\":len(tmp[\"xbc\"]), \"cumomers\":len(tmp[\"cusol\"]), \"emus\":len(tmp[\"emusol\"])}}\n",
    "\n",
    "# parse results to extract isotopomers, cumomers and EMUs for all amino acids\n",
    "list_files = [\"GLY\", \"ALA\", \"SER\", \"ASP\", \"THR\", \"GLU\", \"MET\", \"PRO\", \"VAL\", \"ARG\", \"HIS\", \"LEU\", \"ILE\", \"LYS\", \"PHE\", \"TYR\"]\n",
    "\n",
    "print(\"Number of identifable isotopomers, cumomers and EMUs:\")\n",
    "p_ip = get_coverage(res, list_files)\n",
    "display(p_ip)\n",
    "\n",
    "print(\"Relative isotopic coverage for isotopomers, cumomers and EMUs:\")\n",
    "p_ip_rel = get_coverage_relative(res, list_files)\n",
    "display(p_ip_rel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results to generate Figure 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 330x590 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib as mpl\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import math\n",
    "\n",
    "# set font size\n",
    "plt.rcdefaults\n",
    "plt.rcParams.update({'font.size': 6})\n",
    "plt.rcParams.update({\"axes.labelsize\": 6})\n",
    "sns.set_style({'axes.color':'black'})\n",
    "\n",
    "# initialize plot\n",
    "plt.figure(3, figsize=(3.3, 5.9), dpi=100.0)\n",
    "the_grid = GridSpec(3, 1)\n",
    "\n",
    "# plot isotopomers\n",
    "plt.subplot(the_grid[0, 0],  title='')\n",
    "ax = sns.barplot(x='amino_acid', y='isotopomers', data=p_ip, palette='Spectral', edgecolor=\"black\", linewidth=0.5)\n",
    "plt.ylabel(\"number of isotopomers\", fontsize = 7)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks([0,2,4,6,8])\n",
    "plt.ylim(0, 10.5)\n",
    "ax.set(xticklabels=[])\n",
    "ax.tick_params(labelsize=7)\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x()+0.1, p.get_height()+0.4, \"{:0.0f}%\".format(max(1, p_ip_rel.iloc[math.ceil(p.get_x())]['isotopomers'])), fontsize=7, rotation=90)\n",
    "ax.get_yaxis().set_label_coords(-0.1,0.5)\n",
    "\n",
    "# plot cumomers\n",
    "plt.subplot(the_grid[1, 0], title='')\n",
    "ax2 = sns.barplot(x='amino_acid', y='cumomers', data=p_ip, palette='Spectral', edgecolor=\"black\", linewidth=0.5)\n",
    "plt.ylabel(\"number of cumomers\", fontsize = 7)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks([0,2,4,6,8,10,12,14,16,18])\n",
    "plt.ylim(0, 20)\n",
    "ax2.set(xticklabels=[])\n",
    "ax2.tick_params(labelsize=7)\n",
    "for p in ax2.patches:\n",
    "    ax2.text(p.get_x()+0.1, p.get_height()+0.8, \"{:0.0f}%\".format(p_ip_rel.iloc[math.ceil(p.get_x())]['cumomers']), fontsize=7, rotation=90)\n",
    "ax2.get_yaxis().set_label_coords(-0.1,0.5)\n",
    "\n",
    "# plot EMUs\n",
    "plt.subplot(the_grid[2, 0], title='')\n",
    "ax3 = sns.barplot(x='amino_acid', y='emus', data=p_ip, palette='Spectral', edgecolor=\"black\", linewidth=0.5)\n",
    "plt.ylabel(\"number of EMUs\", fontsize = 7)\n",
    "plt.xlabel(\"\")\n",
    "plt.yticks([0,2,4,6,8,10,12,14,16])\n",
    "plt.ylim(0, 16.5)\n",
    "ax3.tick_params(labelsize=7)\n",
    "ax3.set_xticklabels(list_files, rotation=90)\n",
    "for p in ax3.patches:\n",
    "    ax3.text(p.get_x()+0.1, p.get_height()+0.8, \"{:0.0f}%\".format(p_ip_rel.iloc[math.ceil(p.get_x())]['emus']), fontsize=7, rotation=90)\n",
    "ax3.get_yaxis().set_label_coords(-0.1,0.5)\n",
    "\n",
    "# save figure\n",
    "plt.savefig(\"./results/Figure_4.jpeg\", dpi=300.0, bbox_inches='tight', pad_inches=0.05)\n",
    "\n",
    "# display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Overview of integration results from different combinations of datasets for Alanine\n",
    "\n",
    "This code performs data integration for different combinations of isotopic measurements of Alanine collected by NMR and MS and calculates evaluation metrics for each combination. It reproduces Figure 5 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measurements datafile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\"\n",
    "measurements_file = \"./data/ALA_measurements.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define combinations of datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of datasets/methods\n",
    "measurement_methods = ['HSQC_C'+chr(945)+\" (D1)\", 'HSQC_C'+chr(946)+\" (D2)\", 'HACO-DIPSY (D3)', 'TOCSY_H'+chr(945)+\" (D4)\", 'TOCSY_H'+chr(946)+\" (D5)\", 'HNCA (D6)', 'GC-MS (D7)', 'LC-MS (D8)']\n",
    "\n",
    "# define all combinations\n",
    "all_combinations = []\n",
    "for r in range(1, len(measurement_methods) + 1):\n",
    "    combinations_object = itertools.combinations(range(0, len(measurement_methods)), r)\n",
    "    all_combinations += list(combinations_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combination: 42/255\n",
      " (datasets included: [1, 2, 8])\n",
      "combination: 47/255\n",
      " (datasets included: [1, 3, 8])\n",
      "combination: 54/255\n",
      " (datasets included: [1, 5, 8])\n",
      "combination: 57/255\n",
      " (datasets included: [1, 7, 8])\n",
      "combination: 97/255\n",
      " (datasets included: [1, 2, 3, 8])\n",
      "combination: 101/255\n",
      " (datasets included: [1, 2, 4, 8])\n",
      "combination: 104/255\n",
      " (datasets included: [1, 2, 5, 8])\n",
      "combination: 106/255\n",
      " (datasets included: [1, 2, 6, 8])\n",
      "combination: 107/255\n",
      " (datasets included: [1, 2, 7, 8])\n",
      "combination: 111/255\n",
      " (datasets included: [1, 3, 4, 8])\n",
      "combination: 114/255\n",
      " (datasets included: [1, 3, 5, 8])\n",
      "combination: 116/255\n",
      " (datasets included: [1, 3, 6, 8])\n",
      "combination: 117/255\n",
      " (datasets included: [1, 3, 7, 8])\n",
      "combination: 120/255\n",
      " (datasets included: [1, 4, 5, 8])\n",
      "combination: 123/255\n",
      " (datasets included: [1, 4, 7, 8])\n",
      "combination: 125/255\n",
      " (datasets included: [1, 5, 6, 8])\n",
      "combination: 126/255\n",
      " (datasets included: [1, 5, 7, 8])\n",
      "combination: 127/255\n",
      " (datasets included: [1, 6, 7, 8])\n",
      "combination: 137/255\n",
      " (datasets included: [2, 3, 7, 8])\n",
      "combination: 147/255\n",
      " (datasets included: [2, 6, 7, 8])\n",
      "combination: 153/255\n",
      " (datasets included: [3, 4, 7, 8])\n",
      "combination: 156/255\n",
      " (datasets included: [3, 5, 7, 8])\n",
      "combination: 157/255\n",
      " (datasets included: [3, 6, 7, 8])\n",
      "combination: 161/255\n",
      " (datasets included: [4, 6, 7, 8])\n",
      "combination: 162/255\n",
      " (datasets included: [5, 6, 7, 8])\n",
      "combination: 166/255\n",
      " (datasets included: [1, 2, 3, 4, 8])\n",
      "combination: 169/255\n",
      " (datasets included: [1, 2, 3, 5, 8])\n",
      "combination: 171/255\n",
      " (datasets included: [1, 2, 3, 6, 8])\n",
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      "combination: 185/255\n",
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      "combination: 187/255\n",
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      "combination: 188/255\n",
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      "combination: 190/255\n",
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      "combination: 191/255\n",
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      "combination: 194/255\n",
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      "combination: 195/255\n",
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      "combination: 196/255\n",
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      "combination: 197/255\n",
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      "combination: 200/255\n",
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      "combination: 202/255\n",
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      "combination: 207/255\n",
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      "combination: 209/255\n",
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      "combination: 211/255\n",
      " (datasets included: [2, 4, 6, 7, 8])\n",
      "combination: 212/255\n",
      " (datasets included: [2, 5, 6, 7, 8])\n",
      "combination: 215/255\n",
      " (datasets included: [3, 4, 5, 7, 8])\n",
      "combination: 216/255\n",
      " (datasets included: [3, 4, 6, 7, 8])\n",
      "combination: 217/255\n",
      " (datasets included: [3, 5, 6, 7, 8])\n",
      "combination: 218/255\n",
      " (datasets included: [4, 5, 6, 7, 8])\n",
      "combination: 221/255\n",
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      "combination: 228/255\n",
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      "combination: 235/255\n",
      " (datasets included: [1, 3, 4, 5, 6, 8])\n",
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      "combination: 246/255\n",
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      "combination: 249/255\n",
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      "combination: 250/255\n",
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      "combination: 251/255\n",
      " (datasets included: [1, 2, 3, 5, 6, 7, 8])\n",
      "combination: 252/255\n",
      " (datasets included: [1, 2, 4, 5, 6, 7, 8])\n",
      "combination: 253/255\n",
      " (datasets included: [1, 3, 4, 5, 6, 7, 8])\n",
      "combination: 254/255\n",
      " (datasets included: [2, 3, 4, 5, 6, 7, 8])\n",
      "combination: 255/255\n",
      " (datasets included: [1, 2, 3, 4, 5, 6, 7, 8])\n"
     ]
    }
   ],
   "source": [
    "# create empty dataframe\n",
    "metrics_num = ['mean_error_iso', 'mean_sd_iso', 'mean_error_emu', 'mean_sd_emu', 'mean_error_cumo', 'mean_sd_cumo']\n",
    "res_num = pd.DataFrame(columns = measurement_methods + metrics_num)\n",
    "\n",
    "ab_13C, err_13C = 0.508, 0.01\n",
    "\n",
    "# integrate measurements for all combinations of datasets\n",
    "for i in range(len(all_combinations)):\n",
    "    \n",
    "    # define columns (i.e. datasets) to include\n",
    "    columns = [j+1 for j in all_combinations[i]]\n",
    "    \n",
    "    # integrate datasets\n",
    "    res_comb = isosolve.main(mm=dataset_file, colsel=columns, data=measurements_file)\n",
    "    \n",
    "    # compute evaluation metrics if all isotopomers are quantifiable\n",
    "    if len(res_comb[\"metrics\"][\"idef\"]) == len(res_comb[\"xbc\"]):\n",
    "        print(\"combination: {}/{}\".format(i+1, len(all_combinations)))\n",
    "        print(\" (datasets included: {})\".format(columns))\n",
    "        # calculate evaluation metrics\n",
    "        me = calculate_metrics(res_comb, ab_13C, err_13C)\n",
    "        # fill results dataframe\n",
    "        res_num.loc[i] = [1 if j in all_combinations[i] else 0 for j in range(len(measurement_methods))] + [me[i] for i in metrics_num]\n",
    "\n",
    "# change dataframe type to 'float' (required for plotting)\n",
    "res_num = res_num.astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSQC_Cα (D1)</th>\n",
       "      <th>HSQC_Cβ (D2)</th>\n",
       "      <th>HACO-DIPSY (D3)</th>\n",
       "      <th>TOCSY_Hα (D4)</th>\n",
       "      <th>TOCSY_Hβ (D5)</th>\n",
       "      <th>HNCA (D6)</th>\n",
       "      <th>GC-MS (D7)</th>\n",
       "      <th>LC-MS (D8)</th>\n",
       "      <th>mean_error_iso</th>\n",
       "      <th>mean_sd_iso</th>\n",
       "      <th>mean_error_emu</th>\n",
       "      <th>mean_sd_emu</th>\n",
       "      <th>mean_error_cumo</th>\n",
       "      <th>mean_sd_cumo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033271</td>\n",
       "      <td>0.033476</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.047081</td>\n",
       "      <td>0.046152</td>\n",
       "      <td>0.044046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033776</td>\n",
       "      <td>0.034156</td>\n",
       "      <td>0.049718</td>\n",
       "      <td>0.048223</td>\n",
       "      <td>0.045770</td>\n",
       "      <td>0.044770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017320</td>\n",
       "      <td>0.024050</td>\n",
       "      <td>0.022056</td>\n",
       "      <td>0.031886</td>\n",
       "      <td>0.027819</td>\n",
       "      <td>0.033351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009387</td>\n",
       "      <td>0.012765</td>\n",
       "      <td>0.013636</td>\n",
       "      <td>0.017093</td>\n",
       "      <td>0.010966</td>\n",
       "      <td>0.017763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.011615</td>\n",
       "      <td>0.007256</td>\n",
       "      <td>0.015726</td>\n",
       "      <td>0.005132</td>\n",
       "      <td>0.014712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.016825</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.023192</td>\n",
       "      <td>0.005978</td>\n",
       "      <td>0.021037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005586</td>\n",
       "      <td>0.018548</td>\n",
       "      <td>0.009782</td>\n",
       "      <td>0.024912</td>\n",
       "      <td>0.007929</td>\n",
       "      <td>0.022986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030096</td>\n",
       "      <td>0.031560</td>\n",
       "      <td>0.044549</td>\n",
       "      <td>0.044338</td>\n",
       "      <td>0.041617</td>\n",
       "      <td>0.041319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.010676</td>\n",
       "      <td>0.014010</td>\n",
       "      <td>0.013810</td>\n",
       "      <td>0.012462</td>\n",
       "      <td>0.013089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.017933</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.005978</td>\n",
       "      <td>0.022207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006723</td>\n",
       "      <td>0.013671</td>\n",
       "      <td>0.011408</td>\n",
       "      <td>0.018257</td>\n",
       "      <td>0.009651</td>\n",
       "      <td>0.017095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>0.032844</td>\n",
       "      <td>0.046689</td>\n",
       "      <td>0.046669</td>\n",
       "      <td>0.043025</td>\n",
       "      <td>0.043292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008539</td>\n",
       "      <td>0.011106</td>\n",
       "      <td>0.013169</td>\n",
       "      <td>0.014948</td>\n",
       "      <td>0.010449</td>\n",
       "      <td>0.014645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006624</td>\n",
       "      <td>0.015493</td>\n",
       "      <td>0.009235</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.007121</td>\n",
       "      <td>0.018601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009850</td>\n",
       "      <td>0.011180</td>\n",
       "      <td>0.014265</td>\n",
       "      <td>0.014727</td>\n",
       "      <td>0.012889</td>\n",
       "      <td>0.014370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015728</td>\n",
       "      <td>0.023056</td>\n",
       "      <td>0.020043</td>\n",
       "      <td>0.030685</td>\n",
       "      <td>0.025087</td>\n",
       "      <td>0.031778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009297</td>\n",
       "      <td>0.011503</td>\n",
       "      <td>0.013451</td>\n",
       "      <td>0.015197</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>0.015239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009507</td>\n",
       "      <td>0.012476</td>\n",
       "      <td>0.013694</td>\n",
       "      <td>0.016804</td>\n",
       "      <td>0.010959</td>\n",
       "      <td>0.017461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014322</td>\n",
       "      <td>0.015751</td>\n",
       "      <td>0.017621</td>\n",
       "      <td>0.019406</td>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.018218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023294</td>\n",
       "      <td>0.016625</td>\n",
       "      <td>0.026575</td>\n",
       "      <td>0.019968</td>\n",
       "      <td>0.024359</td>\n",
       "      <td>0.018876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014322</td>\n",
       "      <td>0.016645</td>\n",
       "      <td>0.017621</td>\n",
       "      <td>0.021047</td>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.019668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014322</td>\n",
       "      <td>0.017132</td>\n",
       "      <td>0.018002</td>\n",
       "      <td>0.021541</td>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.019668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026526</td>\n",
       "      <td>0.020230</td>\n",
       "      <td>0.039584</td>\n",
       "      <td>0.027848</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>0.026567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023308</td>\n",
       "      <td>0.017209</td>\n",
       "      <td>0.026587</td>\n",
       "      <td>0.021250</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.020789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023321</td>\n",
       "      <td>0.017176</td>\n",
       "      <td>0.026598</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>0.026358</td>\n",
       "      <td>0.021394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.011344</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.015288</td>\n",
       "      <td>0.004980</td>\n",
       "      <td>0.014249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005350</td>\n",
       "      <td>0.010726</td>\n",
       "      <td>0.006801</td>\n",
       "      <td>0.014354</td>\n",
       "      <td>0.004280</td>\n",
       "      <td>0.013181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.011110</td>\n",
       "      <td>0.007166</td>\n",
       "      <td>0.015081</td>\n",
       "      <td>0.005097</td>\n",
       "      <td>0.013898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007042</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>0.010644</td>\n",
       "      <td>0.012573</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>0.011926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006160</td>\n",
       "      <td>0.013875</td>\n",
       "      <td>0.008443</td>\n",
       "      <td>0.018476</td>\n",
       "      <td>0.007107</td>\n",
       "      <td>0.016655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005083</td>\n",
       "      <td>0.016472</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.022834</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.020558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.010456</td>\n",
       "      <td>0.014276</td>\n",
       "      <td>0.013433</td>\n",
       "      <td>0.012969</td>\n",
       "      <td>0.012574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005540</td>\n",
       "      <td>0.018232</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.024579</td>\n",
       "      <td>0.007872</td>\n",
       "      <td>0.022596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009394</td>\n",
       "      <td>0.010409</td>\n",
       "      <td>0.013577</td>\n",
       "      <td>0.013352</td>\n",
       "      <td>0.012231</td>\n",
       "      <td>0.012512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009837</td>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.014084</td>\n",
       "      <td>0.013492</td>\n",
       "      <td>0.012484</td>\n",
       "      <td>0.012684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.012489</td>\n",
       "      <td>0.006024</td>\n",
       "      <td>0.016623</td>\n",
       "      <td>0.004244</td>\n",
       "      <td>0.015072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005083</td>\n",
       "      <td>0.017570</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.024600</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.021674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007167</td>\n",
       "      <td>0.010138</td>\n",
       "      <td>0.010915</td>\n",
       "      <td>0.013499</td>\n",
       "      <td>0.008903</td>\n",
       "      <td>0.012968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006412</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.011222</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>0.009727</td>\n",
       "      <td>0.016429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006830</td>\n",
       "      <td>0.010104</td>\n",
       "      <td>0.010242</td>\n",
       "      <td>0.013444</td>\n",
       "      <td>0.007797</td>\n",
       "      <td>0.012974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008557</td>\n",
       "      <td>0.010774</td>\n",
       "      <td>0.013236</td>\n",
       "      <td>0.014558</td>\n",
       "      <td>0.010254</td>\n",
       "      <td>0.014201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.015181</td>\n",
       "      <td>0.009238</td>\n",
       "      <td>0.020526</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.018285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.010703</td>\n",
       "      <td>0.013918</td>\n",
       "      <td>0.013937</td>\n",
       "      <td>0.012897</td>\n",
       "      <td>0.013390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009994</td>\n",
       "      <td>0.010854</td>\n",
       "      <td>0.014338</td>\n",
       "      <td>0.014423</td>\n",
       "      <td>0.012901</td>\n",
       "      <td>0.014005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>0.011185</td>\n",
       "      <td>0.013509</td>\n",
       "      <td>0.014897</td>\n",
       "      <td>0.011897</td>\n",
       "      <td>0.014908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012602</td>\n",
       "      <td>0.020776</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.025500</td>\n",
       "      <td>0.018292</td>\n",
       "      <td>0.022591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014524</td>\n",
       "      <td>0.023707</td>\n",
       "      <td>0.018342</td>\n",
       "      <td>0.030667</td>\n",
       "      <td>0.022261</td>\n",
       "      <td>0.029373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014323</td>\n",
       "      <td>0.015076</td>\n",
       "      <td>0.017622</td>\n",
       "      <td>0.018312</td>\n",
       "      <td>0.022159</td>\n",
       "      <td>0.017367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014999</td>\n",
       "      <td>0.017295</td>\n",
       "      <td>0.016625</td>\n",
       "      <td>0.021442</td>\n",
       "      <td>0.013263</td>\n",
       "      <td>0.018605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013560</td>\n",
       "      <td>0.014817</td>\n",
       "      <td>0.016659</td>\n",
       "      <td>0.017776</td>\n",
       "      <td>0.020802</td>\n",
       "      <td>0.016516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011963</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.015794</td>\n",
       "      <td>0.016870</td>\n",
       "      <td>0.014816</td>\n",
       "      <td>0.015918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019843</td>\n",
       "      <td>0.020817</td>\n",
       "      <td>0.022955</td>\n",
       "      <td>0.025442</td>\n",
       "      <td>0.021407</td>\n",
       "      <td>0.022785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023218</td>\n",
       "      <td>0.016260</td>\n",
       "      <td>0.026546</td>\n",
       "      <td>0.019167</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>0.017847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022033</td>\n",
       "      <td>0.015779</td>\n",
       "      <td>0.025086</td>\n",
       "      <td>0.018422</td>\n",
       "      <td>0.023483</td>\n",
       "      <td>0.017125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014009</td>\n",
       "      <td>0.015173</td>\n",
       "      <td>0.017226</td>\n",
       "      <td>0.018458</td>\n",
       "      <td>0.021716</td>\n",
       "      <td>0.017252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011646</td>\n",
       "      <td>0.014143</td>\n",
       "      <td>0.015632</td>\n",
       "      <td>0.018197</td>\n",
       "      <td>0.014490</td>\n",
       "      <td>0.017356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011040</td>\n",
       "      <td>0.013401</td>\n",
       "      <td>0.014470</td>\n",
       "      <td>0.017084</td>\n",
       "      <td>0.013131</td>\n",
       "      <td>0.016200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022844</td>\n",
       "      <td>0.016045</td>\n",
       "      <td>0.025999</td>\n",
       "      <td>0.019018</td>\n",
       "      <td>0.025236</td>\n",
       "      <td>0.018113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004836</td>\n",
       "      <td>0.010434</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>0.004613</td>\n",
       "      <td>0.012711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.010875</td>\n",
       "      <td>0.006281</td>\n",
       "      <td>0.014700</td>\n",
       "      <td>0.004973</td>\n",
       "      <td>0.013472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006918</td>\n",
       "      <td>0.009410</td>\n",
       "      <td>0.010626</td>\n",
       "      <td>0.012293</td>\n",
       "      <td>0.009298</td>\n",
       "      <td>0.011596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005315</td>\n",
       "      <td>0.010291</td>\n",
       "      <td>0.006768</td>\n",
       "      <td>0.013808</td>\n",
       "      <td>0.004281</td>\n",
       "      <td>0.012507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006589</td>\n",
       "      <td>0.009272</td>\n",
       "      <td>0.010054</td>\n",
       "      <td>0.012085</td>\n",
       "      <td>0.008563</td>\n",
       "      <td>0.011343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007188</td>\n",
       "      <td>0.009254</td>\n",
       "      <td>0.010600</td>\n",
       "      <td>0.012205</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>0.011427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006176</td>\n",
       "      <td>0.013560</td>\n",
       "      <td>0.008437</td>\n",
       "      <td>0.018194</td>\n",
       "      <td>0.007069</td>\n",
       "      <td>0.016279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009573</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.013854</td>\n",
       "      <td>0.013028</td>\n",
       "      <td>0.012689</td>\n",
       "      <td>0.012073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010010</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.014352</td>\n",
       "      <td>0.013110</td>\n",
       "      <td>0.012993</td>\n",
       "      <td>0.012154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009529</td>\n",
       "      <td>0.010053</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>0.013031</td>\n",
       "      <td>0.012253</td>\n",
       "      <td>0.012108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004599</td>\n",
       "      <td>0.012142</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.016223</td>\n",
       "      <td>0.004230</td>\n",
       "      <td>0.014539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006624</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.010201</td>\n",
       "      <td>0.012678</td>\n",
       "      <td>0.008662</td>\n",
       "      <td>0.012049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>0.009824</td>\n",
       "      <td>0.010897</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.008698</td>\n",
       "      <td>0.012510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006854</td>\n",
       "      <td>0.009761</td>\n",
       "      <td>0.010163</td>\n",
       "      <td>0.013053</td>\n",
       "      <td>0.007430</td>\n",
       "      <td>0.012501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009768</td>\n",
       "      <td>0.010361</td>\n",
       "      <td>0.013990</td>\n",
       "      <td>0.013626</td>\n",
       "      <td>0.012908</td>\n",
       "      <td>0.013017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008642</td>\n",
       "      <td>0.015358</td>\n",
       "      <td>0.009812</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>0.009083</td>\n",
       "      <td>0.016896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.014406</td>\n",
       "      <td>0.016745</td>\n",
       "      <td>0.017119</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>0.016016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010805</td>\n",
       "      <td>0.012919</td>\n",
       "      <td>0.014526</td>\n",
       "      <td>0.016016</td>\n",
       "      <td>0.013896</td>\n",
       "      <td>0.015083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010322</td>\n",
       "      <td>0.012378</td>\n",
       "      <td>0.013657</td>\n",
       "      <td>0.015171</td>\n",
       "      <td>0.012972</td>\n",
       "      <td>0.014112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022108</td>\n",
       "      <td>0.015580</td>\n",
       "      <td>0.025204</td>\n",
       "      <td>0.017950</td>\n",
       "      <td>0.023696</td>\n",
       "      <td>0.016498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009935</td>\n",
       "      <td>0.012582</td>\n",
       "      <td>0.013341</td>\n",
       "      <td>0.015643</td>\n",
       "      <td>0.012735</td>\n",
       "      <td>0.014713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004891</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.013394</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.012071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006673</td>\n",
       "      <td>0.009144</td>\n",
       "      <td>0.010139</td>\n",
       "      <td>0.011861</td>\n",
       "      <td>0.008961</td>\n",
       "      <td>0.011078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007117</td>\n",
       "      <td>0.009086</td>\n",
       "      <td>0.010575</td>\n",
       "      <td>0.011925</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.011087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006773</td>\n",
       "      <td>0.008939</td>\n",
       "      <td>0.010017</td>\n",
       "      <td>0.011711</td>\n",
       "      <td>0.008385</td>\n",
       "      <td>0.010840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009713</td>\n",
       "      <td>0.009858</td>\n",
       "      <td>0.013928</td>\n",
       "      <td>0.012702</td>\n",
       "      <td>0.012714</td>\n",
       "      <td>0.011655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006816</td>\n",
       "      <td>0.009286</td>\n",
       "      <td>0.010176</td>\n",
       "      <td>0.012310</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>0.011571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009831</td>\n",
       "      <td>0.012135</td>\n",
       "      <td>0.013209</td>\n",
       "      <td>0.014731</td>\n",
       "      <td>0.012727</td>\n",
       "      <td>0.013679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006784</td>\n",
       "      <td>0.008811</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.011489</td>\n",
       "      <td>0.008780</td>\n",
       "      <td>0.010568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     HSQC_Cα (D1)  HSQC_Cβ (D2)  HACO-DIPSY (D3)  TOCSY_Hα (D4)  \\\n",
       "41            1.0           1.0              0.0            0.0   \n",
       "46            1.0           0.0              1.0            0.0   \n",
       "53            1.0           0.0              0.0            0.0   \n",
       "56            1.0           0.0              0.0            0.0   \n",
       "96            1.0           1.0              1.0            0.0   \n",
       "100           1.0           1.0              0.0            1.0   \n",
       "103           1.0           1.0              0.0            0.0   \n",
       "105           1.0           1.0              0.0            0.0   \n",
       "106           1.0           1.0              0.0            0.0   \n",
       "110           1.0           0.0              1.0            1.0   \n",
       "113           1.0           0.0              1.0            0.0   \n",
       "115           1.0           0.0              1.0            0.0   \n",
       "116           1.0           0.0              1.0            0.0   \n",
       "119           1.0           0.0              0.0            1.0   \n",
       "122           1.0           0.0              0.0            1.0   \n",
       "124           1.0           0.0              0.0            0.0   \n",
       "125           1.0           0.0              0.0            0.0   \n",
       "126           1.0           0.0              0.0            0.0   \n",
       "136           0.0           1.0              1.0            0.0   \n",
       "146           0.0           1.0              0.0            0.0   \n",
       "152           0.0           0.0              1.0            1.0   \n",
       "155           0.0           0.0              1.0            0.0   \n",
       "156           0.0           0.0              1.0            0.0   \n",
       "160           0.0           0.0              0.0            1.0   \n",
       "161           0.0           0.0              0.0            0.0   \n",
       "165           1.0           1.0              1.0            1.0   \n",
       "168           1.0           1.0              1.0            0.0   \n",
       "170           1.0           1.0              1.0            0.0   \n",
       "171           1.0           1.0              1.0            0.0   \n",
       "174           1.0           1.0              0.0            1.0   \n",
       "176           1.0           1.0              0.0            1.0   \n",
       "177           1.0           1.0              0.0            1.0   \n",
       "179           1.0           1.0              0.0            0.0   \n",
       "180           1.0           1.0              0.0            0.0   \n",
       "181           1.0           1.0              0.0            0.0   \n",
       "184           1.0           0.0              1.0            1.0   \n",
       "186           1.0           0.0              1.0            1.0   \n",
       "187           1.0           0.0              1.0            1.0   \n",
       "189           1.0           0.0              1.0            0.0   \n",
       "190           1.0           0.0              1.0            0.0   \n",
       "191           1.0           0.0              1.0            0.0   \n",
       "193           1.0           0.0              0.0            1.0   \n",
       "194           1.0           0.0              0.0            1.0   \n",
       "195           1.0           0.0              0.0            1.0   \n",
       "196           1.0           0.0              0.0            0.0   \n",
       "199           0.0           1.0              1.0            1.0   \n",
       "201           0.0           1.0              1.0            1.0   \n",
       "202           0.0           1.0              1.0            1.0   \n",
       "204           0.0           1.0              1.0            0.0   \n",
       "205           0.0           1.0              1.0            0.0   \n",
       "206           0.0           1.0              1.0            0.0   \n",
       "208           0.0           1.0              0.0            1.0   \n",
       "210           0.0           1.0              0.0            1.0   \n",
       "211           0.0           1.0              0.0            0.0   \n",
       "214           0.0           0.0              1.0            1.0   \n",
       "215           0.0           0.0              1.0            1.0   \n",
       "216           0.0           0.0              1.0            0.0   \n",
       "217           0.0           0.0              0.0            1.0   \n",
       "220           1.0           1.0              1.0            1.0   \n",
       "222           1.0           1.0              1.0            1.0   \n",
       "223           1.0           1.0              1.0            1.0   \n",
       "225           1.0           1.0              1.0            0.0   \n",
       "226           1.0           1.0              1.0            0.0   \n",
       "227           1.0           1.0              1.0            0.0   \n",
       "229           1.0           1.0              0.0            1.0   \n",
       "230           1.0           1.0              0.0            1.0   \n",
       "231           1.0           1.0              0.0            1.0   \n",
       "232           1.0           1.0              0.0            0.0   \n",
       "234           1.0           0.0              1.0            1.0   \n",
       "235           1.0           0.0              1.0            1.0   \n",
       "236           1.0           0.0              1.0            1.0   \n",
       "237           1.0           0.0              1.0            0.0   \n",
       "238           1.0           0.0              0.0            1.0   \n",
       "240           0.0           1.0              1.0            1.0   \n",
       "241           0.0           1.0              1.0            1.0   \n",
       "242           0.0           1.0              1.0            1.0   \n",
       "243           0.0           1.0              1.0            0.0   \n",
       "244           0.0           1.0              0.0            1.0   \n",
       "245           0.0           0.0              1.0            1.0   \n",
       "247           1.0           1.0              1.0            1.0   \n",
       "248           1.0           1.0              1.0            1.0   \n",
       "249           1.0           1.0              1.0            1.0   \n",
       "250           1.0           1.0              1.0            0.0   \n",
       "251           1.0           1.0              0.0            1.0   \n",
       "252           1.0           0.0              1.0            1.0   \n",
       "253           0.0           1.0              1.0            1.0   \n",
       "254           1.0           1.0              1.0            1.0   \n",
       "\n",
       "     TOCSY_Hβ (D5)  HNCA (D6)  GC-MS (D7)  LC-MS (D8)  mean_error_iso  \\\n",
       "41             0.0        0.0         0.0         1.0        0.033271   \n",
       "46             0.0        0.0         0.0         1.0        0.033776   \n",
       "53             1.0        0.0         0.0         1.0        0.017320   \n",
       "56             0.0        0.0         1.0         1.0        0.009387   \n",
       "96             0.0        0.0         0.0         1.0        0.005449   \n",
       "100            0.0        0.0         0.0         1.0        0.005073   \n",
       "103            1.0        0.0         0.0         1.0        0.005586   \n",
       "105            0.0        1.0         0.0         1.0        0.030096   \n",
       "106            0.0        0.0         1.0         1.0        0.009692   \n",
       "110            0.0        0.0         0.0         1.0        0.005073   \n",
       "113            1.0        0.0         0.0         1.0        0.006723   \n",
       "115            0.0        1.0         0.0         1.0        0.031379   \n",
       "116            0.0        0.0         1.0         1.0        0.008539   \n",
       "119            1.0        0.0         0.0         1.0        0.006624   \n",
       "122            0.0        0.0         1.0         1.0        0.009850   \n",
       "124            1.0        1.0         0.0         1.0        0.015728   \n",
       "125            1.0        0.0         1.0         1.0        0.009297   \n",
       "126            0.0        1.0         1.0         1.0        0.009507   \n",
       "136            0.0        0.0         1.0         1.0        0.014322   \n",
       "146            0.0        1.0         1.0         1.0        0.023294   \n",
       "152            0.0        0.0         1.0         1.0        0.014322   \n",
       "155            1.0        0.0         1.0         1.0        0.014322   \n",
       "156            0.0        1.0         1.0         1.0        0.026526   \n",
       "160            0.0        1.0         1.0         1.0        0.023308   \n",
       "161            1.0        1.0         1.0         1.0        0.023321   \n",
       "165            0.0        0.0         0.0         1.0        0.004780   \n",
       "168            1.0        0.0         0.0         1.0        0.005350   \n",
       "170            0.0        1.0         0.0         1.0        0.005368   \n",
       "171            0.0        0.0         1.0         1.0        0.007042   \n",
       "174            1.0        0.0         0.0         1.0        0.006160   \n",
       "176            0.0        1.0         0.0         1.0        0.005083   \n",
       "177            0.0        0.0         1.0         1.0        0.009862   \n",
       "179            1.0        1.0         0.0         1.0        0.005540   \n",
       "180            1.0        0.0         1.0         1.0        0.009394   \n",
       "181            0.0        1.0         1.0         1.0        0.009837   \n",
       "184            1.0        0.0         0.0         1.0        0.004568   \n",
       "186            0.0        1.0         0.0         1.0        0.005083   \n",
       "187            0.0        0.0         1.0         1.0        0.007167   \n",
       "189            1.0        1.0         0.0         1.0        0.006412   \n",
       "190            1.0        0.0         1.0         1.0        0.006830   \n",
       "191            0.0        1.0         1.0         1.0        0.008557   \n",
       "193            1.0        1.0         0.0         1.0        0.006632   \n",
       "194            1.0        0.0         1.0         1.0        0.009628   \n",
       "195            0.0        1.0         1.0         1.0        0.009994   \n",
       "196            1.0        1.0         1.0         1.0        0.009418   \n",
       "199            1.0        0.0         0.0         1.0        0.012602   \n",
       "201            0.0        1.0         0.0         1.0        0.014524   \n",
       "202            0.0        0.0         1.0         1.0        0.014323   \n",
       "204            1.0        1.0         0.0         1.0        0.014999   \n",
       "205            1.0        0.0         1.0         1.0        0.013560   \n",
       "206            0.0        1.0         1.0         1.0        0.011963   \n",
       "208            1.0        1.0         0.0         1.0        0.019843   \n",
       "210            0.0        1.0         1.0         1.0        0.023218   \n",
       "211            1.0        1.0         1.0         1.0        0.022033   \n",
       "214            1.0        0.0         1.0         1.0        0.014009   \n",
       "215            0.0        1.0         1.0         1.0        0.011646   \n",
       "216            1.0        1.0         1.0         1.0        0.011040   \n",
       "217            1.0        1.0         1.0         1.0        0.022844   \n",
       "220            1.0        0.0         0.0         1.0        0.004836   \n",
       "222            0.0        1.0         0.0         1.0        0.004791   \n",
       "223            0.0        0.0         1.0         1.0        0.006918   \n",
       "225            1.0        1.0         0.0         1.0        0.005315   \n",
       "226            1.0        0.0         1.0         1.0        0.006589   \n",
       "227            0.0        1.0         1.0         1.0        0.007188   \n",
       "229            1.0        1.0         0.0         1.0        0.006176   \n",
       "230            1.0        0.0         1.0         1.0        0.009573   \n",
       "231            0.0        1.0         1.0         1.0        0.010010   \n",
       "232            1.0        1.0         1.0         1.0        0.009529   \n",
       "234            1.0        1.0         0.0         1.0        0.004599   \n",
       "235            1.0        0.0         1.0         1.0        0.006624   \n",
       "236            0.0        1.0         1.0         1.0        0.007310   \n",
       "237            1.0        1.0         1.0         1.0        0.006854   \n",
       "238            1.0        1.0         1.0         1.0        0.009768   \n",
       "240            1.0        1.0         0.0         1.0        0.008642   \n",
       "241            1.0        0.0         1.0         1.0        0.013628   \n",
       "242            0.0        1.0         1.0         1.0        0.010805   \n",
       "243            1.0        1.0         1.0         1.0        0.010322   \n",
       "244            1.0        1.0         1.0         1.0        0.022108   \n",
       "245            1.0        1.0         1.0         1.0        0.009935   \n",
       "247            1.0        1.0         0.0         1.0        0.004891   \n",
       "248            1.0        0.0         1.0         1.0        0.006673   \n",
       "249            0.0        1.0         1.0         1.0        0.007117   \n",
       "250            1.0        1.0         1.0         1.0        0.006773   \n",
       "251            1.0        1.0         1.0         1.0        0.009713   \n",
       "252            1.0        1.0         1.0         1.0        0.006816   \n",
       "253            1.0        1.0         1.0         1.0        0.009831   \n",
       "254            1.0        1.0         1.0         1.0        0.006784   \n",
       "\n",
       "     mean_sd_iso  mean_error_emu  mean_sd_emu  mean_error_cumo  mean_sd_cumo  \n",
       "41      0.033476        0.049222     0.047081         0.046152      0.044046  \n",
       "46      0.034156        0.049718     0.048223         0.045770      0.044770  \n",
       "53      0.024050        0.022056     0.031886         0.027819      0.033351  \n",
       "56      0.012765        0.013636     0.017093         0.010966      0.017763  \n",
       "96      0.011615        0.007256     0.015726         0.005132      0.014712  \n",
       "100     0.016825        0.007021     0.023192         0.005978      0.021037  \n",
       "103     0.018548        0.009782     0.024912         0.007929      0.022986  \n",
       "105     0.031560        0.044549     0.044338         0.041617      0.041319  \n",
       "106     0.010676        0.014010     0.013810         0.012462      0.013089  \n",
       "110     0.017933        0.007021     0.025000         0.005978      0.022207  \n",
       "113     0.013671        0.011408     0.018257         0.009651      0.017095  \n",
       "115     0.032844        0.046689     0.046669         0.043025      0.043292  \n",
       "116     0.011106        0.013169     0.014948         0.010449      0.014645  \n",
       "119     0.015493        0.009235     0.020782         0.007121      0.018601  \n",
       "122     0.011180        0.014265     0.014727         0.012889      0.014370  \n",
       "124     0.023056        0.020043     0.030685         0.025087      0.031778  \n",
       "125     0.011503        0.013451     0.015197         0.011903      0.015239  \n",
       "126     0.012476        0.013694     0.016804         0.010959      0.017461  \n",
       "136     0.015751        0.017621     0.019406         0.022252      0.018218  \n",
       "146     0.016625        0.026575     0.019968         0.024359      0.018876  \n",
       "152     0.016645        0.017621     0.021047         0.022252      0.019668  \n",
       "155     0.017132        0.018002     0.021541         0.022252      0.019668  \n",
       "156     0.020230        0.039584     0.027848         0.036697      0.026567  \n",
       "160     0.017209        0.026587     0.021250         0.025367      0.020789  \n",
       "161     0.017176        0.026598     0.021231         0.026358      0.021394  \n",
       "165     0.011344        0.006278     0.015288         0.004980      0.014249  \n",
       "168     0.010726        0.006801     0.014354         0.004280      0.013181  \n",
       "170     0.011110        0.007166     0.015081         0.005097      0.013898  \n",
       "171     0.009577        0.010644     0.012573         0.008931      0.011926  \n",
       "174     0.013875        0.008443     0.018476         0.007107      0.016655  \n",
       "176     0.016472        0.007021     0.022834         0.005966      0.020558  \n",
       "177     0.010456        0.014276     0.013433         0.012969      0.012574  \n",
       "179     0.018232        0.009699     0.024579         0.007872      0.022596  \n",
       "180     0.010409        0.013577     0.013352         0.012231      0.012512  \n",
       "181     0.010331        0.014084     0.013492         0.012484      0.012684  \n",
       "184     0.012489        0.006024     0.016623         0.004244      0.015072  \n",
       "186     0.017570        0.007021     0.024600         0.005966      0.021674  \n",
       "187     0.010138        0.010915     0.013499         0.008903      0.012968  \n",
       "189     0.013189        0.011222     0.017700         0.009727      0.016429  \n",
       "190     0.010104        0.010242     0.013444         0.007797      0.012974  \n",
       "191     0.010774        0.013236     0.014558         0.010254      0.014201  \n",
       "193     0.015181        0.009238     0.020526         0.007109      0.018285  \n",
       "194     0.010703        0.013918     0.013937         0.012897      0.013390  \n",
       "195     0.010854        0.014338     0.014423         0.012901      0.014005  \n",
       "196     0.011185        0.013509     0.014897         0.011897      0.014908  \n",
       "199     0.020776        0.015852     0.025500         0.018292      0.022591  \n",
       "201     0.023707        0.018342     0.030667         0.022261      0.029373  \n",
       "202     0.015076        0.017622     0.018312         0.022159      0.017367  \n",
       "204     0.017295        0.016625     0.021442         0.013263      0.018605  \n",
       "205     0.014817        0.016659     0.017776         0.020802      0.016516  \n",
       "206     0.013428        0.015794     0.016870         0.014816      0.015918  \n",
       "208     0.020817        0.022955     0.025442         0.021407      0.022785  \n",
       "210     0.016260        0.026546     0.019167         0.024634      0.017847  \n",
       "211     0.015779        0.025086     0.018422         0.023483      0.017125  \n",
       "214     0.015173        0.017226     0.018458         0.021716      0.017252  \n",
       "215     0.014143        0.015632     0.018197         0.014490      0.017356  \n",
       "216     0.013401        0.014470     0.017084         0.013131      0.016200  \n",
       "217     0.016045        0.025999     0.019018         0.025236      0.018113  \n",
       "220     0.010434        0.006088     0.013884         0.004613      0.012711  \n",
       "222     0.010875        0.006281     0.014700         0.004973      0.013472  \n",
       "223     0.009410        0.010626     0.012293         0.009298      0.011596  \n",
       "225     0.010291        0.006768     0.013808         0.004281      0.012507  \n",
       "226     0.009272        0.010054     0.012085         0.008563      0.011343  \n",
       "227     0.009254        0.010600     0.012205         0.008745      0.011427  \n",
       "229     0.013560        0.008437     0.018194         0.007069      0.016279  \n",
       "230     0.010222        0.013854     0.013028         0.012689      0.012073  \n",
       "231     0.010101        0.014352     0.013110         0.012993      0.012154  \n",
       "232     0.010053        0.013649     0.013031         0.012253      0.012108  \n",
       "234     0.012142        0.006033     0.016223         0.004230      0.014539  \n",
       "235     0.009615        0.010201     0.012678         0.008662      0.012049  \n",
       "236     0.009824        0.010897     0.013144         0.008698      0.012510  \n",
       "237     0.009761        0.010163     0.013053         0.007430      0.012501  \n",
       "238     0.010361        0.013990     0.013626         0.012908      0.013017  \n",
       "240     0.015358        0.009812     0.018903         0.009083      0.016896  \n",
       "241     0.014406        0.016745     0.017119         0.020886      0.016016  \n",
       "242     0.012919        0.014526     0.016016         0.013896      0.015083  \n",
       "243     0.012378        0.013657     0.015171         0.012972      0.014112  \n",
       "244     0.015580        0.025204     0.017950         0.023696      0.016498  \n",
       "245     0.012582        0.013341     0.015643         0.012735      0.014713  \n",
       "247     0.010033        0.006080     0.013394         0.004527      0.012071  \n",
       "248     0.009144        0.010139     0.011861         0.008961      0.011078  \n",
       "249     0.009086        0.010575     0.011925         0.009105      0.011087  \n",
       "250     0.008939        0.010017     0.011711         0.008385      0.010840  \n",
       "251     0.009858        0.013928     0.012702         0.012714      0.011655  \n",
       "252     0.009286        0.010176     0.012310         0.008451      0.011571  \n",
       "253     0.012135        0.013209     0.014731         0.012727      0.013679  \n",
       "254     0.008811        0.010101     0.011489         0.008780      0.010568  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g7W57G9IZ4zdL2ja3HwbcZvsl+Vorn7kgCIK+ottrxhdL2qWmbX6+u1q+OT/eF9g891kCzGtkV9I4YFxVUyjwgiDoK0qxZizpSuBh4M+2Z0maQkocdHRW4J0kqVmADQVeEAR9TSmCse0dgfWBaZJeBowFNgHOt70d8ADwxSYmQoEXBEFf03MFXgXbCyRdBLwZOBqYb/sP+fIM4PAmY0unwOvE5levCQVeMd63aAITCmzgtbIhtsGYxew9+ZFCfU8v1i0oOT2dGecsa1Pz/XHAbsAttg1cKGmH3HUX4ObeeBkEQTD0lEGBd4yk1UhfDGfbPjd3PwQ4La8V3w18sJu+BkEQdJMyKPC2b9D3DmCnofUoCIKgHJRiAy8IgmCk09VgLGmGpMclTa9qu0vSDZJmSTqvqv0MSbdKmi3pqG76GQRB0G26fZriOODnrLr+u6PtJ2vaTrW9j6QxwJ8kvd72RV3xknLkAu71SYaQQxfjF+MXFpJDryg4NjD3LR3HL+evW6jv5GVm2qLlhfqeU9yFoMv0tAbeAH0vyP8uBW4ENhhC14IgCHpKGdaMDVwq6e+S3lV7MRcYfStwSSMDUZA0CIJ+pwyij51s3y9pQ+AiSdfbvh2eLcl0MvBD2/c2sXEo8NWhdzUIgmBo6PnMuKrW3VzgL8C0qsvfAh6z/Z0BzIQcOgiCvqanM2NJE4FRWQo9BXgN8IN87eOkwPyWgeyUUQ7dLp3YPGs3J3M/S7mDoN/otQLvXcCPc/AcBRxnu1J49AfAncA1+fqxtk/qpr9BEATdogwKvG0a9C3DenYQBEFX6PmacRAEQVBuBd54SSdnFd7Nkl7dTV+DIAi6SZkVeJUaePtJGgtM7IaDw4kyqAhHAkessYg1Ri8dsN/eDxZXyl25yVgO+vhahfpefOgzbDH134X6rjN1NV738ecU6rvBocVyKi9YtpSvF7IYNKPnNfCaULgGXhAEQb9ThjXjVRR4rdbACwVeEAT9ThmC8U65zt07gaMkbUrrNfCiIGkQBH1Nz4NxAwXeo6xaA29aEzOhwAuCoK8ppQLPtiVdKGkH21cxQA28Mirw2lXQxeZZ/3DbI2sXKkg6f7SYNb7Y/Gfx3U/y4Am3Feq7YPFq3PxwsU25xU/P7LjdhcuXFLIXNKfMCryogRcEwYihzAq8qIEXBMGIoedrxkEQBEEE4yAIglLQ7TXjGaTNuL/Y3jO3PYekynsJsBx4m+1/SToM+BgwwfY63fSzDJQhhWYQBN2j2zPj44AP1LQdC5xpe3Nge+DB3P5H4JVd9C0IgqBn9FQOLWlNYHvb++brC6v6XpP7DGhX0jhgXFVTKPCCIOgrer1m/ELgUUm/kDRT0nclDeYLIhR4QRD0Nb0OxmOBVwDfBl4OTAX2H4SdUOAFQdDX9LqaxlzgDtuzACT9jrTB1xKNFHiHXf0Vxk0c12hYUzqhfmvXRrvj202hCbS9idju+H7YgHzvvCuQBp7XTB2zjFnUZoqtz98WL+VtDz9RqO/yFpR9f3lkAS++9aaBO5L8PXn8wgH7LQkFXkfo6czY9gPAI5JemJt2oYnsOQiCYLjS7UoffwTOAt4iaa6k/wAOAs6WdCMwGfhJ7nt4lkyvlft+ppu+BkEQdJMyyKEh5auo7Xs4cPhQ+hMEQVAWer2BFwRBEFBuBd7rge+QvjAeAt5r+7Fu+ttLyqDAa3fzb6Sw2ZQNGD1q9ID9Hl9w95A8/xMsLbwxGJSXMivwjiUF4G2AmcABXfMyCIKgy3Q1GNu+GFhQeVylwDsjX19o+6lKd1Yo6SaRSi/VJWrgBUHQ7/R6zbiZAu8TwAWS7ge2Bk5rYicUeEEQ9DW9DsbNFHgHAbvaXh+4ihRwGxEKvCAI+ppSKvAkrQtsYXtm7ncWcEQjI2WsgVcG2lXgRR2+/mBNxjCNSYX6XjB2dZ4/pdhcZag2HIP6lFWB9ziwblX7G4Bbu+9hEARBd+h1QdJ3sEKBNxaYBfzE9lJJBwLnSloG3EcUJA2CYBhTZgXedGD60HoUBEFQDnq9gRcEQTCkSDpa0mX51NZqVe1jJJ2crx1b1b5A0iX5tlW3/OypAk/SGsBlVV1eCHzV9vdGeg28MmyalUEFOJxYsGwxcxY9OHBHYI3R49hy/HqF+l6x4O5Q4DVA0rbAerZ3lvRlYE/gjHz5bcBc2/tJ+omkHW1fCdxqe5du+9pTBZ7tBban2Z4GbAvMA36XL4/oGnghRQ5GMpKeL2m7JrdNq4Ve+VYvefkOwIX5/gXAjgWubSLpUkk/kjR+SF5gHXpaA6+GHYAHbd+Z+xaugRcEwfBB0vMnTFj97oULn27WbQlJp1DNEaya6XEKcH++/wSwds21+XWubWr735K+AhwIfLcF9wdNr88ZV/Nu4MzBDIyCpEEwrFhn4cKnOflH32WLzTZZ5eLN//wX+33ic2OB1wDXV11avErndEx2cr4/BXhsoGu2/53bzqK52KyjlCIYK01/3wnsNEgThwJf7ZxHQRD0mi0224Rtt3lZsy5P2Z7frANwNfB54FTgTcAVNdd2Ay7N134uaSKwyPYyUrC/fZDut0xZTlO8GrjH9r2DHB9y6CAoIZL2lzSYIsMdIat4H5R0GbAlSdNwYr58LrBRvva07auAzYBrJF0KvIWUPbIrlGJmTBtLFFBOOXS7JxE6IUVud3wnNhE7IckeLrRyQuKCebcVPnnRkhy6UK/W7C5iybO77tXkIPwz4HjgpBaeuqPYPrim6YDcvpQaMVlOzbCK7qEb9LwGnlJZ3XdQI/CIGnhB0L9UBeIf2v50r/3pB8qiwFtlWSFq4AVBfyJpc1IgFvBJSZ8caIzt3v+c7TFlWaYIgmD4cBfwF+CNwCnA5T31pk8oswJvPHAC6fzxcuCjtuM/NQhKju1Fkt4OnAO8C/iZ7csGGDbi6fbM+DhS8dEPQlLgAdPg2eNtd7FCgXcYcFuWKo4FJnbZ174n8hmXi6HaaJu8zExbtHxwTnXA7sLlq/ax/XRVQP4cK0+6gjqUVoEH7AtsnsctIUmlgyDoE6oCsnvtSz9QlnPGUHW8TdIUYClwtKTrJJ2UlzTqEgVJg6Cc2H7a9qJe+9EPlCIYVynwzspNY4FNgPNtb0eqDP3FJiaiIGkQBH1NKYIxqyrwHgXm2/5DfjyDvLbcgFDgBUHQ15TlaNtKCjzblnShpB2yRHEXUm28upRRgTcciHzGneUJlhbOO9xSPuPRd3Py+IU9s7tk+ZJC9oLmlKEG3j/yv/9R0/0Q4LS8Vnw3UQMvCIJhTJkVeHcw+CxuQRD0OUvvvI6lYx+r035X953pAmVZMw6CIBjRRDAOgiAoAWWWQ+8KfJt0zO1C2wd109deU5bNs3YVeJFCcwUtKfBaKF660bLR7LdoQs/sLlxeP4Vm0BqlLEia02r+FNjD9kuB8ZJ267KvQRAEXaOrwdj2xcCCBper5dDrAAts35WvXUQShdQlFHhBEPQ7ZVozrj5r/AgwSdJWeZa8O7BBk7GhwAuCoK8pRTCulUPbNilR0AnAlcCDpFwVjQgFXhAEfU1ZFHirFCTNuYt3ApC0L00yP4UCb2goyybicCEUeEEzSjEzpk5BUklT87+TgE+TyrgEQRAMS8oshz5U0n/m+9+wfUv3PA2CIOguZZZDHwSMqLPFQRCMXMqyTBEEQTCi6akCL7e9F/gyqaz3bOCDthdLOgz4GDDB9jrd9HO4EDXwggAkHQ28ErgH2N/2M7l9DElctglwne3PVo3ZgXSSaw3bxXZd26SnCrx8pO27wC62X5abK+KOP5LewCAIgkEhaVtgPds7A3OAPasuvw2Ym69NkLRj1bXPkPazukYZFHgivRGjgQmkEkvYvsb2A0XshgIvCEYkE6v/7iWNq9NnB+DCfP8CYMeBrkl6NXADFDyH2CF6es44V/T4FGl5YjHwZ9uXDMLUocBXO+lbEAS9xbffjBc/umr7vY9U7l5ac+kI4PCatinA/fn+E8DaNdfm17n2WeBDQKMDB0NCTzfwJI0lrQtvBTwvNWnfQZgKBV4QjDxew8p/90fV6fM4MDnfnwI81uyapNcC19tulENnyOi1Am8asNT2PQCSfgO8Dji9FSNDocArw+ZXr8d3QoHX7vh+2Dz857z7SClUmjO1hb+2BS2kumwlNef0RfOY8cC1hfpuNqaY3UX0LIXmU7bnD9DnauDzwKmkme4VNdd2I82w3wT8nKR3eENeqtg6t727w37XpddH2+4Dtpa0Vn78BuDWHvoTBMEwwvZM4EFJlwFbAmdLOjFfPhfYKF972vZVto+z/Trb/0laN/5Qt3wtgwLv/4ArJS0lrR2fmPseDnwEWCv3/Zbt47rpbxAE/Y/tg2uaDsjtS2lS6Nj2LkPo1iqUQYF3DXB8nb6Hs+pifBAEwbCk18sUQRAEAeVW4I0n5TPeAVgOfDSn1QwKUoZNyJHAZlM2YPSo0QP2W77gvuI18Fp4/snLzLRFywv1nTR2dZ4/pdhho0nz5hayu3B5secOmlNmBd5hwG22X0La1ZzdTUeDIAi6SbfXjC+WtEtNc0WBN48qBR6p0sfmedwSUrHSumTlTbX6JhR4QRD0FT1dM87llSoKvAeAJ21fImkKqczS0ZKuk3SSpGYBNmrgBUHQ15RVgTeWlEnpfNvbkQL1F5uYCgVeEAR9Ta9PU0wjK/BsLwN+Q0rW8Sgw3/Yfcr8ZuW9dbC+2Pb9yY9VkREEQBKWm13LoZxV4th8nKfBuzgmELpS0g+2rSCcwbu6loyORKEjaWcpQkLQV7h29LAqSdpHSKvCAQ4DT8lrx3TRRygRBEPQ7ZVbg3QHsNOROBUEQlIBBB+N8nOw5tu8fsHMQBEGLLJ1zJ0se+veq7f8eKFFbf9LSBp6kGZLWkLQmaQ33L5KObHH845KmV7W9V9KNkmZL+lUlW7+kXSXNknSTpGNa8TMIgqDfaHVmvLHtBZI+DPySpJK7HvhKwfHHkfKDfhBWUuBtZfvfkn4FvFPSmaRCga+1fZekH0nazfaFDS0PM8qyedauHLoTkuygOa3kM25FZl3Ubg/zGQ8rWj3aNk7SJsA+wDlZtFGYFmrgrQMssH1X7nMRK2TSQRAEw45Wg/GRpDO/t9r+m6QXAbcP9skbKfCAR4BJkrZSKqGwO7BBIztRkDQIgn6npWBs+1e2t7Z9YH58h+1Bz1gbKfBykN6XlLXtSuBBkjy6ESGHDoKgr2lpzVjSc0nVNzYGns0ZaHuwpUmm0aAGXk6XuVNu3xdotiRyFGntucIaREAOgqCPaHUD7xzSHsC5wLIOPH9dBR6ApKm2H5Y0Cfg0zcujdLwgaVCeTcSgOa0o+4bC7hJCgdcJWg3GY21/dbBP1qIC71BJ/5nvf8P2LYN93iAIgrLTajD+naQPkmbIz85EbRcSxreowDsIOKhF/4IgCPqSVoPxfvnfw6vaDLyoE84EQRCMVFoKxrZfOFSOBEEQjGRaPU2xGnAgsDNpRnwpcILtZwqOr1eQ9H2ko2kCTrb97dx+GOnY2wTb67TiZ5CIgqRBLS2l5lxw9xB70x0kHQ28ErgH2L8SrySNISl9NwGus/1ZSesAvyMdpV0OvK9b+XdaFX38GHgxcDTpKNmLc1tRaguSrgN8jRTctwJeK+kl+fIfSW9gEATBoJC0LbCe7Z2BOcCeVZffBszN1yZI2hF4HHiN7dcCJwMf7pavrQbjabYPtH2V7Sttf4omFThqqSOHfhEwx/bjtpcDfyWdsMD2NbYfqGNmFUKBFwQjkonVf/eVJGM17ABUctpcQKok1PCa7WW56hDAZLpYlb7VDbzFkra3fS2ApJdTdapiENwObCVpA1KppbcwuBd/KDDoI3dBEJSPJ+aM4t+rrzpffOLpZ9surbl0BCsfLgCYAlSWGZ4A1q65Nr/2mqStSMsXU4B6J8CGhFaD8SeAk/PasYBFwP6DfXLbj0n6LPBb4BlSBrhmsudGhAIvCEYeryHFjAr1JoaPk2a4kILrYwNds30j8EpJ7yYVQv54xzxuQqunKa4jKeYmA7L9RLsO2P4tKRhXNu0eH4SN0inw2lWvdWLzrAybb5FCcwUtpbpctpg5ix4s1HejZaPZb9GEjtvtgxSaT+UCxM24Gvg8cCpplntFzbXdSDPsNwE/l7Ra1YGEJ4CnOutyYwoFY0l72T5L0oE17QDY/uFgHaiSPa8HvAd47WBtBUEQVGN7pqQHJV1GOk3xbUkn2j6AlNZhj3xtpu2rJL08F7NYBiykixt4RWfGlXWWdetcK5zTuIEc+guSXkp68Qfbfiz3PZyUlGit3Pdbto8r+lxBEAQAtg+uaTogty+lJueN7X+Qlj9aImeg3KAqB3vLFArGtiv5Iq60/acaJ3Yt+mQN5NB7Neh7OKsuxgdBEJQKSW8n5dhZHXihpG2A/6loKYrS6tG2eot//9eijSAIguHE4SRNxDwA29eTNBgtUXTNeFfSQvf6kr5VdWkyLaTSbEWBVzVmOqn23vZFnyfoDJFCs7O0kuqyJaXc6Ls5eXyhXF0tK/AihWYhnsm1QQHI1Ylapuigh0nnfxcDN1XdLqS1c3itKPAqXwKdyJscBEEwVFydj+iOk/Rq4DSSgrgliq4ZXw9cL+m0rJQbFLYvlrRLVdOzCjwASRUF3v/lBfEvAZ8BTmpmNytvqtU3ocALgqBbfJ502GAOKe3vn1mRl70wrYo+NpN0FLAFVcHP9mBTaDZT4H0OOIVVq0nXIxR4QRD0hCyfPpFBBOBqWg3GJ5G+BU4grf1+AJg42CdvpMDLwXk34I3ACwqYCgVeEAQ9QdKd1Dni2+oktdVgvFo+GD3K9kOkA9SX08YRtAYKvGnAlsCd2cd1JZ1n+y0NbJROgVcGIoVmEHSFl1XdHwe8C9igVSOtBuOFeS33BklfIyXgWLPVJ62mngIvCz+el69vDExvFIiDIAh6ie1qyfRTwE8kXUmLk9TBlF0aDXwS+C9gM9K3QCFaUeAFQRD0A5KqJ4qjgG1JcbIlWk0UdEe+u4hBLE20osCrGnMXEGeMgyAoK9UxbClwN7B7q0ZaLbu0M+nUwguoivxtnKYIgiCoy52PrcnosWuv2r6kXHtCtgedRriaVpcpfkrK7fkPBiHGaLEG3njSqY0dSLWoPmr78lafs18J9VsQlBtJ36ZJojTbX2jFXqvB+LFcOmmwHAf8nJwpqUqB93JS7tBzJJ1j+1bgMOA22/vlTcNBH6ELgiAYAjpakqnVYPw3SScB51B1lMz2eUUGt6LAA/YFNs/jlpCTcNQjFHhBEHQb26d00l6rwbhyjO3tVW0GCgXjOtRV4EmaQloIPzpXbL0e+IztRmq8UOAFQdATcj6dtpXJrZ6m6MhCdZW9RjXwxgKbAOfb/pSkb5BqUX25galQ4AVB0Cs6okwumkLzs7aPrUmf+SytLlTXjP0tqyrwHgXm2/5D7jaDJkfpQoEXBEEP6YgyuejMuHK++KZWjBehgQLPki6UtIPtq0jfNje3avvrrzqSyZMnD9yxDp04idDvBUU7caKj3fH9cBpk8/FTGTtq7ID97pk3t3Dh0NktFC99fMwyvjn56UJ93/pIcbv3FCx0unB5zwqSloWOKJOLptA8N/97St4sewlprfi2PCstRIsKvEOA0yStQTpE/cF6NoMgCHrMdNJa8aCUyRVaFX28Hfg+aZYq4MWSPlMJ1gPRYg28O4CdWvEvCIKgB0wj7XddBvwauDAXO22JVk9TfAvYyfZcAEkbkhIpFwrGQRAEww3bH5I0hpTydy/gOEmXtXrgodVaTQ/kW4WHah43RNJGki6RNEfSDZL2yu2vkHSTpNslfaWq/2GS7pH0aIs+BkEQdJU8E74SuAT4J2mfqyWKnqY4MN+9G7gyy5oN7AHMLPhcS4H/sj1L0lTgOknnAccDe5NKllwl6Te2Z5NqSP0MuLHoiwlWJvIZjwzGToHnbF+sGtrk28y0RcX6XjzuGbaY+u8B+y1YtjSdfyopko4mVW++B9jf9jO5fQwpxcMmwHW2Pyvp+cCppGXYhcDetucNYH8/0ox4E5Ig7qu2/96qn0Vnxuvm250kgcc4YDxwAWl2PCC2H7A9K99/GHgMWAcYY/uG/M1yBvC23Oca24Vm3UEQBPWQtC2wnu2dSRO+Pasuvw2Ym69NyAKz+cA7bb+WdKT2owWeZgtSAN7c9hcGE4ih+GmKIwZjvBGStid9EawL3Fd1aS7w2kHYCzl0EIw8JkqqPru6uM7prh1IVewhTR73J036Ktd+X3VtR9tXVo1dQvpF3xTbHfnp2OppileSpMe1KTS3bsHGc0g/Az5C+ilQS8MsSE0IOXQQDDNuGzeKJ1db9cf7/aOebbu05tIRrCq0mEI69wspGdnaNdfm17smaU3gAODNrfo9WFo9TXEa8GngBlJay5bIM9gZwFG2r5S0PivXitqQghuCNYQcOghGHq8hHSmrUE/z8DhQmT1PIS2PNr2WBRxnkHQPj3fO3ea0Gowfsv3HwTyRkkb5ZOAi26cB2L5f0jJJW5PWc/YGPtyq7ZBDDw2RU7mzzB8tZo0vuE3TKCVWHe56dBw/uOi5BXsXF7Let3Qcv5y/7oD9Fi1fUthmh3nK9vwB+lxNyhtxKvAm4Iqaa7uRZthvIqX3Bfgh8Otu509v9WjboZJ+Kemjkj5QuRUcuxNJ7ryHpFn5thXwKeCXwK3AebZvBJB0eFbprSVprqTPtOhrEAQjHNszgQclXUaqOH+2pBPz5XOBjfK1p3N+iR2AfYD981Hcz3bL11Znxp8HJpDyDFeWKQqt8eZvmUbB/6V1+h/OIOrsBUEQVGP74JqmA3L7UmrSLORcOD0pZNFqMN7Y9rZD4kkQBMEIptVlioskvWYwT9SqAq9q3HRJ1w7mOYMgCPqFVmfGHwAOkvQkKzbMbHtqgbGtKvCQtCuDKHw6HCjL5lm7CrxOqACD5kxeVlxVd/zoZZw8fmExwy1sIgbt09LM2Pa6tkfZnpzvr1swELeswMvHS74EfL0VH4MgCPqRVmfGSNoD2Jm0cXep7XMGYaOIAu9zwCkU+H4OBV4QBP1OSzNjSd8hnQO+FrgO+EhOwtGKjYoC72M0UODlAqW7kYJxEQ4lKWgqtxB8BEHQV7Q6M961Wvos6VfArKKDW1DgTSOdCbwz+7iupPNsv6WB6VDgBUHQ17QajJdJ2sj2vfnxBhTcYGtFgZeFH8/L4zYGpjcJxKHAGyLKsok4XHiCpcziyY7bbUXZt9G8YnXtAGoP5wZDS6vB+AvAZZJuIS0xbEZabihCRYF3Q153Bng/KxR444HTKgq8IAiCkUSrwfh1wI6kjTdIKaUPJJVeakqrCryqcXcB27fkZRAEQZ/Rqujjzbbvt319vt1HF1PMBUEQDFdanRmPljTJ9pMAObHz2M67FQTBSOdWFvIwq63S/jgFRSt9RqvB+PvAFZLOzI/fAxxTZKCkjUj5kKeS1Hhfs32WpFcAJ5HOCZ9q+8jcfzxwAikb/3Lgo91OadfvRA28oJZ7Q4FXWloKxrZ/IulqVlQ+3cf2TQWHtyqHPgy4zfZ+WY3Xk0xKQRAE3aBlBV4+7dDyiYdcXPSBfP9hSSvJoQEkVeTQs4F9Sak6sb0EmNfIdijwgiDod1rdwOsIA8ihN5A0hTSTPlrSdZJOktQswIYCLwiCvqbrwbiIHJq0KbgJcL7t7Ugz6i82MXsUsGbVbcNO+hwEQTDUtLxM0Q4tyKEfBebb/kNun0GTqh9lVOC1q17rxOZZGTbfRkIKza2YxPgCh4ruWVZc/XZkC2kxLx6zmL0nP1Ko7zkPdt6Hhctbrk0c1KFrM+NGcmiSxHprSWNIG3nn2jZwYa5HBWnDsHglxSAIgj6jmzPjVuXQhwCn5bXiu6mpVRUEQTCc6FowHkRB0jtIATwIgmDY05PTFEEQBMHKdG1m3ESBdzywF3CP7e2r+m8CnAlMISUi+kReSw4KUobNszJsIg41Wy9azoRRA29ind5Cqss1xj3DFlP/Xajvff8axy/nrztwxxYpmppz0fKY03WCbr6LFQXelsAbgWMkTSTVvauXbOhbwOG2NwWeC7y1a54GQTBskHS0pMsk/ULSalXtYySdnK8dW9X+J0nzJP2/bvrZtWDcoCDp2ravAFaaAuSTFzsAlaNtp5ILldZD0jhJkys3QoEXBAEgaVtgPds7k1Iu7Fl1+W3A3HxtgqQdc/sHgO911VF6rMCrqhhSy3OAx6qWJeay8nnkWkKBFwQjj4nVk7CsY6hlB+DCfP8CUj72ptdy6oau02sFXsNuddqarReHAi8IRh6XsvIk7NA6faYA8/P9J4C1C17rOj1V4DXp+iiwtiTl2XFFmVeXMirw2iXqzwUjnTsW/5vVly9apf3pJU9V7r4GuL7q0uJVOsPjwOR8fwppebTIta7TUwVeI3IAvpoVm3YfAM4dUgeDIOg3nrI9v+pWLxhfDeyW778JuKLgta7TzWWKigJvD0mz8m0rST8FrgK2ljRX0jty/0OAIyT9C3iEFZt5QRAEhbA9E3hQ0mXAlsDZkk7Ml88FNsrXnrZ9FYCkn5MmgF+X1CxBWUcpgwLvIw36/xN4+ZA6FQTBsMf2wTVNB+T2pdRJs2D7Q93wq5Y4rR0EQVACurlmvJGkSyTNkXSDpL1y+/GSHpZ0bYNx0xtdC4IgGC508zRFoxp4ZwA/B06sHSBpV2BZF318ljIU8+y1lLgTJzpmcHYHPSonvxi/kLGjChRJb6HA531Lh0biHJSXUirwAHIR0i8BX++Wj0EQBL2iq+eMKxRQ4AF8DjiFAvOJKEgaBEG/U0oFnqQNSOf/TiloNuTQQRD0NV0Nxi0o8KaRzgTeCVwObJXXlxsRcuggCPqabuYzbkWB9wfgeXncxsB0229p0n/YyaE7QRk2IYMVPMFSZvFkT324d/QyTh6/sFjnFjYcg/YpswIvCIJgxFBaBV7VuLuA7Zv1CYIg6HdCgRcEQVACylwDbzxwAikB9HLgo3l2HQTBCODuBQ9RVSXpWexneuDN0FNmBd5hwG2298sCkIld9LXnRD7jkU0ZNvuC7tLNNeMHyAnibT8s6VkFXj4xUcu+wOa5/xJgXpdcDYIg6DqlrIEnaQppJn20pOsknSSpoaouCpIGQdDvlFKBB4wFNgHOt70daUbdLMlzKPCCIOhryqrAexSYn8Uf5DHTmvQPBV4QBH1NWRV4lnShpB1yKZRdgJub9C+dAq/dDbhQv/UP71s0gQkFUmjOZgzTmFTI5uNjlvHNyU8X6vvWRwp1A2DLIbIbtE83T1NUFHg3SNojt70f+Cyp8OhzJM0FPm17BqkG3ml5rfhu6pRHCYIgGC6UVoFn+w5SAA+CIBj2hAIvCIKgBEQwDoIgKAE9lUMDfwCmAy/KbSfa/n7uvwlwJjAF+DPwCdtu5TkPu/orjJs4buCOdejE5lm/b8B1QgXY7vh+eP/eO+8KpIHnNVPHLCusqvvb4qW87eEnCvVtRa33lycX8OJbbyrUt6i/S1hSyF7QnG7OjCty6C2BNwLHAAK+aXtz4JXAgZI2zf2/BRxue1PguaRNviAIgpaQdLSkyyT9QlXJLiSNkXRyvnZsL32EchQk/Wtuewr4J/C8fAxuB9LMGZJI5G2NbIcCLwiCekjaFljP9s7AHGDPqstvA+bmaxMk7dgLHyuURg6dlzG2Bq4DngM8VrUsMRfYoInJUOAFwTDDXor9TJ3b0kqXidWTsCwqq2UH4MJ8/wJgx4LXuk4p5NA5XeaZwMF5hlxPtdFsvTgUeEEwfHgUWOhl81i+9NFVbl42D2AJcCkrT8IOrWNrCjA/338CWLvgta7TTdFHXTl0XpI4BTjP9vTc9VFgbUnKs+MNyRnf6lFGBV4ZiBp4QT9i+x5JWwDrNOk2H3i4pm1xnX6PA5Pz/Smk5dEi17pOGeTQRwELbX+90pDl0FeTNu1+D3yAlPM4CIIRgO17gHs6YOpq4POkX+NvAq6oubYbaYb9JnocY3pdkHRnkuz5FVVtb8r9DwGOkPQv4BFWbOYFQRAUwvZM4EFJlwFbAmdLqhSyOBfYKF97OufB6RllkEPXXVOw/U/g5UPqVBAEwx7bB9c0HZDbl1KinDehwAuCICgBZVbgnQ5sRfrCuBz4pO3l3fI3iDp8RdlsygaMHjV6wH73zLuNOYseLGRzjdHj2HL8eoX6XrHg7kL9ACaNXZ3nTyl22OjxFuwG7VNmBd6BtrexvRXpyMnuXfQ1CIKgq5ShIOmzCjxJ/wSeB9xuez4kySKwOk3OGecjc9UHvkOBFwRBX1FWBV6lbTrwEPAkcE4Tk6HAC4KgrymrAg8A23uSZsoC3tDEbCjwgiDoa8qqwHsW289I+i2wB/CnenaHowKvLJtn7SrwOqECHC60sil3QQubfa3w5JKnufXxYj8cp3Y1OgRdmxm3osDLqe02zvdHk5R4t3TL1yAIgm5TVgXeaOCXkm4ErietGZ/QRV+DIAi6SmkVeKT0dkEQBCOCUOAFQRCUgG6uGW8k6RJJcyTdIGkvSRMknSfpFkmzJX26qv/4XBLlVkk3S3p1t3wNgiDoNt3cL60o8GZJmko6T3weSYH3V0kTgWslnW/7duAw4Dbb+0kaC0zsoq8B5TnRMVxYsGxxyKGDhpRWgQfsC2yery0B5nXL1yAIgm5TSgWepCmkmfTRkq6TdJKkhhLnKEgaBEG/U1YF3lhgE+B829uRZtRfbGI25NBBEPQ1XQ3GLdbAm2+7Ut1jBjCtiemQQwdB0NeUuQbehZJ2yKVQdgFubmR7OMqhO0EUJA2C/qGbpykqCrwbJO2R2z5NUuDNkTQrtx1i+4+5/bS8Vnw3JSqPEgRB0GlKq8CzfQcpgAdBEAx7QoEXBEFQAsqswNs1Jw66SdIx3fIzCIKgF5RSgQfcAfwUeK3tuyT9SNJuti/sor9t0a56rRObZ2XYfIt8xisYqnzGazKGaUwqZrdQr9bsLmIJv2vBblCfrs2MbT9ge1a+/zCwigIPqCjw1gEW2L4rD78IeGe3fA2CIOg2PcnlX6AG3kJgkqStgJtIlaHXbGIvCpIGQdDXlFKBZ9uk3BQnAFcCD5KWORoRCrwgCPqasirwsH257Z1svwqYRUoe1IhQ4AVB0NeUUoGX+0/N2d0mkcQhDUUfocAbGiKFZu9oNYXmLJ7smd0lLClkL2hOWWvgARwq6WbgGuAHtqMgaRAEw5YyK/AOAg4aUqeCIAhKQijwgiAISkAE4yAIghLQzQ28jYDTgKmkY2pfs32WpIuBtbMvZ9o+Mvc/jHT8bYLtdbrl53AiUmh2h83HT2XsqLED9mulVl0w8ujmzLgih94SeCNwTJZA7257G5Lg482Sts39/wi8sov+BUEQ9IwyFCStqPBWyzfnPtdAsWNqocALgqDfKUVBUklXAg8Df67kr2iRUOAFQdDXlEIObXtHYH1gmqSXDcJsKPCCIOhrupooqJ4cuoLtBZIuAt4MzG7F7nBU4IX6rX+4ZdHDjB41utduBH1ON5PLryKHljQ55zauBOrdgFDaBUEw4uipHBrYBjhP0g3AP4DLbZ8LIOlwSXOBtSTNlfSZLvoaBEHQVcogh96+Qf/DgcOH0KUgCILSEAq8IAiCElBaBV7VuOnAxrbrzqCHijKo14aD+m0k1MD757z7kAae10xt4a9twbLFQ1IDb/qiecx44NpCfVvxN2ifnhYklXQeSYE3X9Jo4HJJ59qeCalCNLCsiz4GQRD0hDIUJJ2fu6ykwJM0FvgS8PVVra2MpHH5ZMZkSZMJBV4QBH1GmRV4nyOVY1pQwGQo8IIg6GtKqcCTtAHpzPEpBc2GAi8Igr6mrAq8OcCWwJ3Zx3UlnWf7LfXsDkcFXhkIFWAxNpuyQSEF3j3zbiu8KTdUNfAmjV2d508pNld5PFJ+dpVSKvBs/8H282xvDLwauLFRIA6CIBgOdHNmXFHg3SBpj9z2aVJe49VIXwxnVxR4QRAEI4nSKvCqxt01UJ8gCIJ+JxR4QRAEJaCba8YbSbpE0hxJN0jaK7dfLOl6STdJ+kpV//GSTpZ0q6SbJb26W74GQRB0mzIr8A4DbrO9XxaATOyir8OCMki6gyAoRmlr4AH7Apvn/kuAed3yNQiCoNuUUoEnaQppJn20pOsknSSpocQ55NBBEPQ7pVTgAWOBTYDzbW9HmlF/sYnZkEMHQdDXdDUYD6TAAyoKvEeB+bb/kC/PAKY1MR1y6CAI+ppu5jOuq8ADxuc15IoC73u2LelCSTvYvgrYBbi5ke3hKIcOKXIQjCzKrMA7BDgtrxXfDXywi74GQRB0ldIq8GzfQQrgQRAEw55Q4AVBEJSAMivwXi9pZr52oaS1u+VrEARBtymzAu9YYE/bt0r6JnAA6dREX9DuBlyo34YfreYoHq4+BPUpbQ28/G9FvDGJrN4LgiAYjpRSgZe7fQK4QNL9wNbAaU3shQIvCIK+pqwKPICDgF1trw9cRVLZNSIUeEEQ9DWlVOBJWhfYIq8dA5wF7NjEdCjwgiDoa0qpwAMeJxUhfaHtO4E3ALc2st1Igff1Vx3J5MmTB+VvJ9RrsQHXmTSew4U1GcM0JhXqe8GyxYWLl/ba7iKW8LtCFoNmlFaBJ+lA4FxJy4D7CAVeEATDmDIr8KYD04fUqSAIgpIQCrwgCIISEME4CIKgBHRzA28j0lnhqSQ13tdsn5WvjQKuBu6xvWduO4x0/G2C7XW65WewgkjjWYzNx09l7KixA/a7YsHdzOLJQjY3HD2RfcZvWqjv6QvuK9QPYEsm8BVeVKjvwRS3G7RPN2fGFTn0lsAbSRt3lSKjHwburOn/R+CVXfQvCIKgZ/RcDp0TAL0X+HFN/2tyEdMBCQVeEAT9Thnk0P8LfA1Y1obJUOAFQdDX9FQOLWlbYC3bl7RpNhR4QRD0Nd0Ufawih5b0CWBnSXcB44E1JP3Y9sea2allONbA6wSdUL+FirA3PMHSwpt9rXDv6GWcPH5hsc4LOv70QRO6mVx+FTm07R/Z3sD2xqR14/NbDcRBEATDgW4uU1Tk0HtImpVvWzXqLOlwSXOBtSTNlfSZrnkaBEHQZcogh65cvwS4pOrx4cDhQ+xWEARBKQgFXhAEQQkoswLvdGAr0hfG5cAnbS/vlr+9JtRvQa8puom4hCVd8Gb4U2YF3oG2t7G9FbA2sHv3XA2CIOguZVbgzQeQNAZYnRWFSlchFHhBEPQ7pVbgSZoOPAQ8CZzTxGQo8IIg6GtKrcDL68fPA0QqvdSIUOAFQdDXlF6BZ/sZSb8F9gD+VM9uGRV47W7Ahfqtf3jfoglMKJBCc3YLteoeH7OMb05+ulDftz5SqBsAGy0bzX6LJhTqW9TfqIHXGUqpwJM0RtLGedxo4K3ALd3yNQiCoNuUVYE3GvilpBuB60lrxid0yc8gCIKuU0oFXl522KErjgVBEJSAUOAFQRCUgDIr8C4B1gMWAdie1i1fIdJPBsX5H+5iNKMH7NdKWsw5S0dzyPzV23VtFVpJofnEglDgdZNunqaoKPBmSZoKXCfpPNtPsUKBV/uJ3tP27C76GARB0BNKq8ALgiAYSXT1nHGFagWepB+RFHj1OEPSUuCntn/YxN44YFxVU8ihgyDoK8qswNvH9takpEL7SXptE7Mhhw6CoK/pajCuVeABr2KFAu9XwJsl/RjA9v3538eAs4H/aGI65NBBEPQ13TxNUVeBB/woX98F+FRFgQdMsf2opPHAm4BvN7LdSA592NVfYdzEcY2GNSVOQnSGTpxKKTv/nHcf6UBQc6a28Ne2YNli5ix6sFDfVk5pPLBoHjMeuLZQ31b8Ddqnm293RYF3g6Q9ctv7bd9Yp+844I+SxpJOWPza9vndcTMIgqD7lFWB9xTw8q44FgRBUAJCgRcEQVACupm1bSNJl0iaI+kGSXtVXRsl6e85mXyl7fWSZkq6XtKF+TxyEATBsKTMCrxjSQq8WyV9EziAdGoi6BJRFLUYm03ZgNGjBpZD3zPvtsKbcmuMHseW49cr1PeKBXcX6gcwaezqPH9KscNGj7dgN2ifMivwzArxxiTggS65GgRB0HXKrMD7BHCBpGeAfwGfaWIvFHhBEPQ1ZVbgHQTsant94CqSyq4RocALgqCvKaUCT9K6wBa2Z+ahZwE7NjEdCrwgCPqaMivw1pX0Qtt3kipD39rIdhkLkpaByMkcBP1DKRV4tpdKOhA4V9Iy4D7gg13zNAiCoMuUUoGXH08HpjfqHwRBMJwIBV4QBEEJiGAcBEFQAnpekDSfpJgPLAfut/2W3H8T4ExgCvBn4BO23S1/e02o34JgZNHNmXFFDr0lqXrHMZIm5ms72p5WCcSZbwGH294UeC7w1i76GgRB0FV6Loeu1zcfg9sB+ENuOhV4WyPbksZJmly5EQq8IAj6jJ6sGVfLoUk5KC7NWdvelbs8B3isalliLrBBE5OhwAuCoK/pem6KKjn0R3LTTrbvl7QhcJGk60kBtZZm68VHAd+terwGEZCDIOgjuhqM68ihqwuPzpX0F2AaqQDp2pKUZ8cb0iRrWxkVeO1uwIX6LRgKhio1Z9A+3Uwuv4ocWtJESWvk+1OA1wA35wB8NSs27T4AnNstX4MgCLpNN9eMK3LoPSTNkjQL2BS4PC9NXAYcZ/um3P8Q4AhJ/wIeYcVmXhAEwbCjDHLobRr0/ydRlDQIghFCKPCCIAhKQJkVeGeQZsZLgHNtN0su33Ei/WQwHFmwbHHhOnxBd+l5QdJ8bUfbT9b0P9X2Pjm38Z8kvd72RV30NwiCoGt0c834AfLxNNsPS2qowMt9Lsj/LpV0I01EH1EDLwiCfqesCrzqvpNJR9wuaWIyFHhBEPQ1PS1Impt2sr0d8E7gKEmbVvWtnE3+YQ7cjYgaeEEQ9DVlVeDdnod8i5Sj4jvN7DZS4H39VUcyefLkQfka6SeDTlMG9VsZfAjqU0oFXn78cVJg/kS3fAyCIOgVvS5I+kHg1DyTHcXKCrwfAHcC1+Trx9o+qYv+BkEQdI0yK/C6nlEuCIKgV4QCLwiCoASUVoFXNW46sLHt7Vt9zsOu/grjJo4buGMQdIEyqN+67YOk/QFiiXFgylwDD0m7Asu66GMQBB0iB+KfAdv12pd+oJQ18AAkjQW+BHx9INtRAy8IykVVIP6h7U/32p9+oMwKvM8BpwALCpgMBV4QlARJm5MCsYBPSvJAtx67XArKWgPvaWA30nLGCwqYjRp4QVAe7gL+Qvr7PQW4vKfe9AllVeA9DWxJOmc8BlhX0nm1a8oVGinwnnlqcb3uhVi+dDmL2xjfCRu9Hj8cfGjnM1CUZcuLbWvYLtx3uZezZPmSjvcdCh9q+9heJOntwDnAu4Cf2b6s0JOOYJTKzXXhiVKEPAO41fbhuW0iabliQVbgXQG8u0r4gaSNgemtnKaQtAExMw5WZkPb93XSYHzOVmGl91jS6qSA/KTtd/TOrf6gzAq8driflCyo0XpzZRmjWZ9mtDs+fOjM+KI21iB9JjpNo89ZK69ruPRd5T22/XSeIceacAFKq8CrGncX0NIZ41xduuEsqLKMASywPb8V250YHz50ZnwLNgZleyAafc5aeV3DqG/dcbafbmYvWEEo8IIgCEpABOMgCIISMFKD8WLgCKpOYHR5fPjQmfGdstFpWvFpOPcNWqBrpymCIAiCxozUmXEQBEGpiGAcBEFQAiIYB0EQlIAIxkEQBCUgShu1iKQtgecBi4CbbT82CBujbfcsT3MnXkOH/Ojp+xAEZWLEnKaQtDbwKlYEoTnALBd4AyRtBnwe2IWUkepRYBywCbAQ+DlwSqPAImk0sBfwXuDluXk08DBwASmRyj+H8nW0+xqyjfXz69ip5vnPBy60vXwAH9p+HyS9Etg7+7BejQ+/tP1Es/FDhaTdgD8V+TwNZ7vB4Bn2wVjS64GDgbWAG4BHSEFoU+CFpEQm37U9r4mNU4ATbF9V59rapOAy3/bpDcZfBFwInAvMqfwBSFoTeC0puJxn+7Sheh0deA0nA+sC5wHX1Tz/a4H/AP7H9iVNXkNb74Ok84B78vh6PrwNON72bxv5MFRIOg14BSkr4am25wzQ/93A/wJrVppICuupZbNb1GbQHiMhGP8vqdpAvRwCo4D/BMba/t0Q+jDgz/GB+vT6dUh6se3bmlwfQ8radVeTPm29D5KmNPvSLNpnqMhZCPciJcCaQKr5eEa9ZSBJtwO72b6j7HZbsRkMnmG/gWf7y41SJ9pebvu8IgFM0i6S3pfTJla3f7CAD8skrZVnoEhaV9K7JL2suk83XkeV3ztL+pykNxXpXxuIJa0p6eWV12R7abNAnHlrTqvY7Hkavg+250l6bqV2oqSNJe0paavqPgP4MGTYfgr4K3AJ8BzgNaQqNl+q0/0+Ur7ufrBb2GYweIb9zLgZkv7P9hcL9PsGaZ32RuCtwA9sfy9fu85204KLkj5CKg01CvgW8D7SUsNrSGlDfzxI/2fZnlaw77N+SvoEsB9wNvAm4GLbTWsNSvol8Gnbj0p6C3A8cBOwOXCY7V8V8GEh8CSpCsSZpCWJZ4r4n8d/EfgYqfjA94BPAZcBOwK/sv2torY6jaSPAu8nVTk/BTjL9pP5F8M/bb+wpv9PgI1Jyz7PSott/7BsdovaDNpj2J+mkDSh0SVSUBwwGAP/D5hme7mk/wFOkrQFcGC2MxCfBl5K+il4D/BC248oFU+9FBgwGEt6hBV5YSvPuValvcD6XbWfHwZ2tf24pOOAaxi48OtLbT+a73+FFeWyppBmVwMGY+BW4NXA20k/jU+U9EdSYP6j7aUDjH8/qQLM6qRNyI3zaxgPXEv6ousVzwf2t/2v6kbbS6vyd1czN98m94HdojaDNhj2wZiUAPtuVg5Gzo+fW9DGapWTAjmH67skfYs0U2gU7KtZZnsRsEjS7bYfqdhS8WKMp2R/D7U9F0DSnbUzoyaMz18go4Axth/PPiySVOR4mSVNdars/Qxp8wxSAdiinyPnn8a/BH4paQ1gd+AA4KekExoDjV+U37OlwFO5/RmKfSkOJdeRTqgAae0a2MX2b21fX9vZ9hG53xrpoZ8sq90WbAbtYHtY34DbgBc0uHZvQRu/IG1g1LZ/HFhSYPzfgfH5/ppV7WsAM1t4LVuQThL8D+kUwR0tjL245rZebn8OcG2B8buSlmm+BBxHWmf8AvB70hdEER8avlZgcoHx3yL9kvh7vn826bjeucBPevw5W+W1DfB6twZmAbfkz+h1wNZltFvUZtza/Az12oEhf4HpJ/lLG1z7UJd8mESq9VfbPhXYbhD23k2quPtAB3wbDUwo2HcKaWnmGOAHpCWeLVt4rhd3wN/tgG3z/U1Ix/3eU+/97eYNuKHmsYDZTfpfDbyy6vErgKvLaLeozbi1dxvRG3itIGld0jnYl+SmW0gig0cbj1rFxtbV423f2IY/E4AX2Z7dwpi2X0OVHZyXW1ql3fdB0lqks8WQNpvmDcaPTpLX3iezYv3/Y8ATtj/boP8Ntreuabve9jY1bT23W9Rm0B4jIhgryX8/wspB6Ke2by44fivgz6Q14pmkWcQ00kmEXT1AEdV8/OsckmLshjx+K9KRod2LBpN2XkcHXsMo4GukpRlIa8/LSDPkI1zgg9Tu+5A36n4GvIV01EqkXf7fAQfY7lnC83wK4QDg9bnpz8CP3fjM9KmkunEVkc0+wFq23182u0VtBu0x7IOxpNcAvwZ+xMpB6GPAXravLGDjt8DJrlF2SdqdtCO9xwDjfwTMA77svBEoSSRV0xTbBw716+jAazgEeB3wcefzxJI2Bn4IXGT76AKvoa33QdL/kTb5Pum8iSRpEvB94EHbhw7kQ1nIXyyfJJ0ugbTsdLzTRm+p7A6Vr0ENvV4nGeob8Cdg5zrtO5HyKRSxcWuTa7cUGH8z9deMRxcZ34nX0YHXcD1pNlTbvjZwfcHX0Nb7QDrXPL5O++rATT3+nL0E+E1+jXdUbiPNbtwGfxsJR9ueb/uy2kbbV0h6QUEbTzW5trDA+EWuk0THSZlXdHbR7uto9zWMcj4OV/P8j+UljCK0+z4sdZ3ZmO2nJQ10RnmoOYl0suMEUjKmDwATaztJOs32+yVdw4pz47Ai38MrymJ3EDaDNhgJwbjZmcii5yU3yOeKaxGwfoHxk7Nqrd74SQV9aPd1tPsalmUpc+15XpHWjovQ7vuwWj4rXc+HsQV9GCpWs32VpFG2HwK+Lely4PCafl/I/+7ZB3ZbtRm0wUgIxi+S9Os67SJlOyvCF5pcO6TA+EtJCVnqscpstwHtvo52X8MU0jJBPfFM0Y2Hdt+Hh0hr1I2u9ZKFksYCN0j6GnA/K7KcPYvtB/LdNYDbnUQs/490lvenZbI7CJtBG4yEDbzXNrtu+6/d8qUdhsvrGK5IehEpoI0H/ot0bOwEN8h0J2kW6cz0lqSN2dOBN9h+Q9nsFrUZtEmvF6374QZ8B9i0wbXVSLO9PZuMf/sA9p8H/EfJX8PUAs+x7gDX23ofgG0GGD8O2KwHn49RpERFrYyZmf/9KvDRfP+6MtotYjNu7d+G/TKFpF+RflJd5JrNI0nPA/YlHYtqmNidlATnm5I2IUmCK1UyNiXli/gF6bxtI14i6Vrgb6zICVCpsvFaUiaspksFHXgd7b6GE5Syrv2uzmvYhXRe+f9IFTsa0e778LG8Znx+Ax82z+MLVU3pFE4JpCZJmuiUe6MIT+XlgX2AVytVQVlp3btEdge0GbTPSFim2BA4iFQF4iFW/AG/EHiA9NOs3lpsPVtrkMoFVUoO3eLiwpExwBtZuWTSLcAFtu/s1uto8zVsTfqDrLyGp/Nr+CPw6yJ/2B14H9YhbSjVG39xkdcxFEg6g1Tt5I9UnU6xXXetPn+B7g1cY/sySc8nJeo5tWx2i9oM2mPYB+Nqskih8gd8WwuzjVIxXF7HcEINigzYPmUk2Q0Gz4gKxkHQayT9xfYbtHJ+alhxdndQdeWGwu5Q+RrUJ4JxEHQASXdS54if7ReNJLvB4Bn2G3hlQNImrqmoEAw7XlZ1fxzwLmCDBn2R9EngF87JkZQy0e3tVUsZ9dxuCzaDduj1cY5u3ci/AmraCiXIJuU+2B3YlqQU+yVJADED2KLA+Nmkskar9+p1tPsaGtjcgVSL7u6C/d9GVaJ/4AhS9rZzgU0KjH8FOSl+frw/KQvc94Hn9PozVsffK5tcm1mnrdBxsW7bbcdm3IrfRtLM+HJJdwF/IFUt2Bz4HCsyUTXjbOBhUqWNscB0Ul27VwE/JwWlZmxHSoJ+raTDbM8YhP8VBvs62n0NAEj6D+C9wDtIR+I+C3yjoO//m58PSe8gnczYm6ToOpF0yqIZJ5JTPkp6Q37eT5NmeT8D9ijoR8epkXmPIn3pjW4yZIykMc51/yStRjrvXUa7hWwG7TFi1ozzH/8xJNXRk6RsZ++3/ZsCY2fa3jYfC7ufVDqpkgLyZttbFPRhK1L6wSWkqrwtb4QM9nW0+xqU0le+k5RH+Czgt6SjTkUl5aiqmrVSjtwbbX87Py5SZfvZhOaSTgTu94r6bM/a7gWSTqp6uJRUd/Gnth9s0P8rwM6khD2QZvl/dU2V7jLYLWozaI+RFIznAK+2/Vh+PBX4ne0BZ4SSrgO+TfpJvTUpnSTAG0hVjj8AYHtOExsfJ50T/h7pTPCg3vjBvo52X4Oku0nnmX8EzHAqpnqHW9jwyT68kZRB7i7gP52LX0qaY3vLAcZfT1qqeIaU8nEf21flazfZfmlRX8qApLdSldzd9vlltTtUvgYrGEnLFAtJxTcfy49NOqdbhH1Jyq6PkH7SVbMAOD7bez11kPQPUkL4ndxiiaM6DPZ1tPUabL9A0g6kenP/k78UJkma4uJlj44gvQ+jgD9WBeKdScF5IH4A/IP0i+BfVYF4G9ISTM+Q9CdSkv95+fFaJMnxm5oMmwnMdxJSjJO0uu2nS2p3QJtBm/R60bpbN9Ka6C35NhuYQ0p20o3nftVweB01fuxM2ji7F/hLC+PGUJOknpRHd1LB8RuQ1jdHVbU9j5TvuZefr1l12mY26f8xkiz8X/nxi0kzztLZLWozbu3dRszM2GkWtbmkKaTlmceLjs1LDAu9qqT0gGTaP64/8lm2kvSo7duVErGfTNoAu4tU8ujaoX4dkj4wgN2WpK1Oie4vk/QZUl6JIj68pup+vS6XDjD++fnuv4ENG9joFYskbeEsLVeqV/hMk/4HkuTIfwewfVteciqj3aI2gzYY9sG4NpB6xc+yooEUVnwYazmFVMZ8IBv/xYrNj31Ju/8vJK3dHkvahGtKo2BaCUgFgulWVfffD1QnFBpw/VrS4cC9tn9Wc+mTwFrAJQPZyH1uACppGmtzIzcNxqQvr9tIxTHrje9l5YnPAb+X9C+SXy8gvc+NeMb2EknpvGKqM1dWu0VtBm0w7DfwJN1ASsu4uKZ9PHC1C+zAS7rR9lYNrs22/bJ616r6zPKKUwRnApfb/n5+POApgtzv21UPVwmmbpA4poGtmba3Ldo/j7kJ2MqrZowbTfrJW/f9qen7HlKSn/VJZ4t/bfuOFnw4hHRW+WFSXt1zXaK8HPnI17OVu20vadL3S8BU4P+Rqmt8GPiD6xR27bXdVmwGbdDrdZKhvpGOTzW6NrugjVnUWZMkzSYGLMZJmj2/iFS8899U5dwFbh7Ea5rZ5nvS8oF9YM5grjXoP5F0Vnk66ajf61oc/yLgi3nsWcB2vfyMZZ++QapwXXm8FvC/DfqKlLr0TcDRpFzTby6j3VZsxq3Nz1CvHRjyF9hmIM1930HaMHs/6ef+y0hHwW4G3lFg/E6kjbaHgcOr2t9Mi8nD87i21E+DDMZXU0fpB2wD/K1FW6uTTmVMB65sNRhnG6/MgeFWYI8SfM5mtvI+F33PymC31f/fuA3uNuzXjEnHqS6U9L+kwGySIu5Q4EtFDNiekVVvnyOt/4okJd7H9swC468glaypbT+flCh9yNGKzFsCpkiqHAUrKjz5b+AcST9j5fdxf9I55SI+7EmqKLIh8HvgELeQs0PS9sC7gdeRksv/GviC7aIFUYeS0ZIm2X4SQNJkmidgv0jSf9puloy/LHaL2gzaYNivGQNI2pYUSLdkRSD9bpFA2uLzfM/2f9VpfyVpk69SZePDbiIQaWB7pWAKVE5RdDSdoaTJtuc3uLY+8AlWvI9zgB/avr+g7eWk11/ZwFvpw2f73QXHX0RSjdWOL7xu3mkkfZQkzf4V6b15D/A92z9v0P8R0rLV06yc3H1qTb+e2y1qM2iPERGMi9AokLZoo+5mnFKpoUNJpwV2Bz5k+z/bea4mPjQMpgXHF9pQHKTtpkfgPEBRVTVIiF41vqeJ0ZUqoexO+qK8CHjS9j0N+o4iZUrbkfSlcgVJ2bi8Tt+e2m3FZjB4IhhnOhGEmgTjma46vTDEAa8t241OWki6mMZH4OxBVgqW9CrbVw9mbB7//EaBqZtI2gM4CtiIJITZjLR5XPfUiqQfA+sCZ5JmpnsBj9r+WNnsFrUZtMdIWDMuA5O1cpasNasf2z6vg8/VrhKiUcD9VJ22N5Cy0d3VxvP9kLT2PFh+2+b4TnEkaVPxUtvTJL2CtKTTiB1cdRxQqeDsDSW1W9Rm0AYRjDtLo0B4KWk2Ue+xgU4G4yH5qWP7psp9SbuTll0eA97npMYbLO1+eZRFhrfYKXkSklaz/XdJJzTpf4OkabZn5cfbkCTHZbRb1GbQBhGMV9Dwj1rSmqR8CnfVtG8MPFa1Rlu3zLzt/Tvk46BRVT7aAagric3rhnuTTlXcCny86o+zHQ5sc3xZ0jg+oCRRPxc4T9K/gUea9H8ZKb/17fnxZqSgdw1p2aeiJiyD3aI2gzYY9mvGRQOppN1sX9jAxmnAKbb/XNP+/0iZr5puLElqGnBcoHxN0WAq6Wrbr6rTfgNwkO2/DGSjgd07SMnHj2HFaYhnKbLUIumdwFjbZ9a0f4CUEey3A4x/DSkP87k17XsCD7U5Q+8YknYBJpMy0y1u0OcFzWzYvrssdgdjM2idkRCM2wqkuW9DybOaSKWr+ny12XXnBOkD2Gg3mG5OyqW8APic7XtbHH8yzTfwPlTAxj9IAo/5Ne1rkrKA1cv/Ud3vSpLI5qGa9vVJ0uoiVVuCoJSMhGDcViDN/W63vWmDa7fZfnG7fhbwoa1gWmVnd1KC+CtJ1UaAgc/4dgJJN9jeutVrBcc/WwUkCPqRkbBm3CzD1LiCNm6U9D7bv6hulLQPSRI9IJLeSFoffTYxC3C87YuKjLd9C/CfOZj+Lc8SWwqmSmkSPwNcBpxQPb7A2E6k4BwjaS3XpP2UtDbNVWUVxksaV/tTWtLqwIQC44OgtIyEYNx2ICUplWZI+hArS4EnU6AIplLduu+QjhNdR9os3Bb4saSDatdAm9hpJ5geS5IRD3apo60UnJljSZtF/00q+2TS+/B/+dpAnAb8WtInbc8FkLQRcFyNP0HQd4yEZYoNSeXo51MnkFb+qAvaegNVUuDadegm4y4nnT6YXdP+UlI9vJ0L2GgrmCqln/xOwRMVA9mqKwwpOHZ30tnkSq6OOcDRtn9XcPxn8/jVWVFy6tu2jxuMP51C0nqdsOOagqBDYXeofA3aY9gH4wqDDaRV4wXsCmyem661fWXBsbfY3rzBtULVpdsNpvlQ/z2VPyBJ+5Oy0d1NyiT37xZsDZmCsAUf1gCwvSA/fpfts3voT0f+kGyvdMRyKOwOla9Be4yEZYpKIB3NirPEC5t0rzd+E5LS6xHS7BrgHZKWAG8HPmj7xCYmmiVAL+rLxcA6wGCD6YnkYqP5i+kbpOWXlwE/o8ByS7soZV17ke1f58cnkvLoAhzjXGC0CJUgXMUxQM+CMemLul/sDpWvQRsM+5lxg0C6LVA0kCLpQuDHtqfXtO8FfAWY12ypQdJTpExxq1wCtrA9qcDrmAm83vbjOZiezopgOs32HgOMf/a0QQ6C91eO1KmqEkmT8W1njcvv48G2b8iPbyJtak4APmX7rQPZaGL7XtsbDXZ8EPSakTAz/hFwRINAeg0wjzRrbMbza8cD2D5LqRzSDgOMXyWX8SAYVXUK4d2ktebpwHRJs4oYkDSOpLDbDdin6tKAJxlsr9uau3VZqxKIM3OcM7VJOqxN28N7VhEMe0ZCMG43kEJa6Zjomnpred3yGecE3Y1ooHp6p+3fFHju6jGDDqbAD4B/AE+SSq5flW1uQ6pA0g3Wqn5guzpfx4CbSlWz81UukWbrQdC3jOq1A11AkibWaSwUSDM/JFULebWkSfn2GuCCfG0wtDoTrATTqxhEMLX9E1Ids0+QgnmFhylYqaMDzFKdnMSS3k866tYU2+vanlrntq7tIl9IQVBaRsKa8WdJP+sPYcWa8XakXK5n2f5eQTvvZEW1EEhHso4Z7A7+YI6HSdqAVKX3eufE3pKeR8r30DSnbxnyOmT/f8vKxwy3ZcUxw/uG2oduI2lX4CrbTyrlKJlGqjJzS5t21yHtVSyVNI30uZxh++l2fa56Dnm4B4gSMeyDMXQ+kHbIpxfUW75o0r+tYFqmvA5ZjbgFgzxm2E9UNk4lvYokbDkW+KTtndq0ex1piW19UpWOPwHr2n5HGzaPJ1XdXkpK8/oC4Cu2m6XsDDrEiAjG7aJUL+xi27fnY3Ins+JY2f62ry1gYzzwPlIQgqT+O6PoTKbdYBp5HXpDVTD+P2C27dPbEc1U2b3O9naSvkDKTXxsu3Yrp2ok7U2qaP554O/x2egOw37NWNJHJW2a70vSKZLmS7oxn3stwn+xoprF+0nS4BcBn6WAjDf/jJxD+oDfk2+vBubka0WYVBuIAZyKga5RYPz4vAFY61vkdRha7lfKHPhe4A/5/2B0B+xa0oeB/Ug5iaHYRm4zxufPw17A2W6QpjMYGoZ9MKbNQJpZUqV8eytwku1HnZL8rF5g/A+AD9j+kO3j8m3/7M/xBX1oN5hW8jpsWDV2I+AMIq/DUPIekhx/13w0cW2SnLtd9iNV3Pia7TskvZB09rwdfgTMJSXXukQpj3GtuCYYIob9MkW1oEHSmcDltr+fHxeS9Uq6mnSUbB7wT+BVtv+Zrw0oZ5Y0x3bds8YtyKH/B9ietN5YmyRnpu0jC9iozusAqfT60cBxsVHTeZSqo1w3kKCmDGRf97F9elWbgNHuQD6TYGBGwsx4kaQXKaVpfCPpOFqFIrNaSGtnvyelvfx+VSB+MwWOZJF+Uj63tjGfhCgUBG1/jbRRc5WkRyU9SspJfAnwtYI2js0qtRcCL7S9UW6LQDwE5BMvf1dKCNURJD0i6eGq20OSbpJ0gqTntOnrR2raHIG4e4yEmfFOwE9IeR1+aPvw3P5mkhT6vV3w4b3AV0n5IGbm5peTinoeYfuXLdpbKUlOC+O2JZ0qqd5EPMb2da3YCYqT1ZFbkkpVLWSFfLxjdeMkrQXsC+xi+11t2PlfkhDsLKpyptie07aTwYAM+2DcCdSBxOp5s/C/WPl43feKnMSosjHoYCrpPcARpPPVlS+E7UhfCF+1/auifgTFUYP6ca0ca2zhudrKpifp4jrNtv36NtwKCjLsg3GHAum3qx6ukljd9hcGGD8aGGd7YU37BNKxpGUFfGgrmCol5Xmj7Qdq2tcH/mS7Yz+lg5WR9EpgE9tn5FnsRLeQR7vgc6xOEpdM66TdoHuMhGDcViCtY6/ls5ySfgBcUbscoVQ5ZJrtzxSw0VYwVfOcyg2vBe0h6XDSl+bmtl+cVYhnDlZko/qVxtcipUD9ue0fteHrc4HDgefZ3kOp7uIrikxYgvYZ9omCbP935b6kN1Y/HqzJQYx5ne1P1Wk/CbixoI3RtYEY0jnjPPMeiH9L2tE1CfHzmvpjBX0IWmcPkuT7OgDb90ma3Ia92ux5Bv4NfNgrZ8QbDKcAPwX+Jz++nbR+HMG4Cwz7YFxDr34G1H2fbVtS0f+DdoPpgaQ6fhez8ibiLsA7C/oQtE5FOGEASVNooXZhLc45qIeI59ieLunL+bmWShpwCS3oDCMtGA8K1SRWl1TJklY0sfq9kl7vmkrQkl4H3F/QjXaD6XGkRPT7sGIT8UrSueWWKp8ELfEj4ExgHaWcze8BvjlYY5J+3uy67Q8N1jbweD67XvnieDOpKEPQBUbCmnHbFSo64MPLSNnK/srKRVFfQ8pWNrvh4BU2/gq8mZWD6c3AL4oE007kQwgGh6QtgDeQPnMX2a5X9aWorXkkRemvSZ+llerQ2f5DG7Y3JaWE3Ql4gBSI97X9r8HaDIoz7INxWci73ZVAKtLRtl+4eKKgtoKppHtJJzHqYnuweZmDAciCo42oykkx2LPdksaS8lK/m1Qc9wJSKtiiew9FnmMSKTaEFLqLxDJFl8hB92eVx/kUxAGS3m17xwIm1mmwk16xP1AwHUMSvkRF3y4i6RukJEG3sWLPwsBbBmPP9hKSGvT3klYjfcFfLOlI28e16evapBNHGwOjkxoaipz2CdongnEXkbQesCdp3XB74DtA0Q96u8H0gSL5K4KO807gJTmIdgRJa2a7e5KSRH0NaEnF2YDzSFXI/04bm4zB4Ihg3AUkfZwUgJ8L/IZU1XmG7VZKL7UbTGNG3Btmkv7fOyLykHQuqV7gb4GDqu1KmtDmZuxY24e252EwWGLNuAtIeop0zvQwr6iGfIftF7Vgo90141E5GUzQRZQqfJxLWqZYzIqN40FJjCXdxcrLHc9eynYLf6bq2D4AGAecz4ojeXiAkl5BZ4hg3AWUCqK+nTQ73pq05ren7fVbsBHBtA+RdCtJxn4jVT/92zlRMVRIOpSUP+VeqgJ+J5MaBY2JYNxlcsa1PUi74dOAC21/uJc+BUOHpIttv66D9vauyOol7WT7iqprn7L9gzZs30aS58e58x4QwbiH5I2Yd9g+ude+BEODpO8Dk4BzWPmn/3mDtPdsZrbaLG0dyNo2HfiY7ZDH94DYwOsCA2SOi6WH4c2k/O/bq9pMOrkwGNTgfr3HrbImcIukq1j5i+PdbdoNChDBuDtsVXV/lcxxXfYl6CJOtQ47arLB/XqPW+XrbY4P2iCWKbpMyJJHFpKeD3wf+A9SsPw78NnBnlCQtJSUGKqevH9N26u16e96Vb5e4zoVyYOhYSTUwCsb8e03sjiZJHtf3/YGpGrcJw/WmO0xtqfaXtf22Hy/8rjdQLwPcDmpAvrbgctyybCgC8TMuMu0u8kS9BeSrre9zUBtZUDS9aQ6eo/nx2sBl5TR1+FIrBl3gQ6k4Az6l7slHQycnh+/DyiriGIU8GTV4yeJX89dI4JxF7BdW50hGDnsTypldH5+fDmwX6+cGYDTgSslnZ0fv5Oo8tE1YpkiCLpAPlO+vOxpKSW9HKjU57vc9j966c9IIoJxEAwhOTfFT4CxuekZ4CO2/947rxrTydzLQWtEMA6CIUTSbOCDlRmmpO2AU22/rLeerUqj3Mu2B5V7OWiNWDMOgqFlXvVPfdvX5dJJZaTjuZeD4kQwDoKh5UZJM0hFSQ3sBVwt6S0w+BwVQ0RHcy8HrRHLFEEwhEg6qcllt1nNuaN0Ovdy0BoRjIMgAPor9/JwJJYpgmAI6XRuiiHmfttn9NqJkUrMjINgCJF0EXCC7V/nx3sBnyjjT/9O514OWiOCcRAMIX2Wm6Le+nap1rWHM7FMEQRDS9/kphiC3MtBC8TMOAiGEEnPIeWmeDVpzfhy4PAyljbKM+NVAkLMjLtDzIyDYGjZzvanqxsk7Qr8qUf+NGN61f1xpMK5j9fvGnSamBkHwRBSL3+1pH/YfnmvfGqFTle3DhoTM+MgGALy7Hc3YH1J36q6NBlY1huvmiNpQtXDUcC2wPo9cmfEEcE4CIaGh4HZpCNi1aKJBcChPfFoYG5iRRGEpcBdwEd76dBIIpYpgmAIkTSKNOl5CSnQ3Wb7md56VR9JRwHfqim7dLDtL/fWs5FBBOMgGEIkvR74GXAr6af/JqR8xhf31LE61KtcHjUbu0csUwTB0HI0qcjn3QCSXgDMAMoY4EZLmmT7SQBJk1mRFD8YYiIYB8HQMrYSiDP3UN4A933gCkln5sfvAY7poT8jilimCIIhRNIxpKWJSgKevYE7bB/UO68aI2krYJf88KLI2NY9IhgHwRAj6V1UFfkEfuP4wwtqiGAcBEOIpHWAJ2wvkbQtsAUww/bTPXYtKBmjeu1AEAxzLgRGSXohcDZpCSByBgerEME4CIYY24uBdwPH2v4YsHFvPQrKSJymCIKhxZI+DOwHvDW3lfU0RdBDYmYcBEPLB4GtgSNs3yFpY+C03roUlJHYwAuCIUDSabbfL+maquZK3gfbfkWPXAtKSgTjIBgCJD3P9gNZcbcKNUKQIIhgHARBUAZizTgIgqAERDAOgiAoARGMgyAISkAE4yAIghIQwTgIgqAERDAOgiAoARGMgyAISsD/B6qywUxYw30/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 330x590 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set font size\n",
    "plt.rcdefaults\n",
    "plt.rcParams.update({'font.size': 7})\n",
    "\n",
    "# create figure and panels\n",
    "fig, ax = plt.subplots(1, 2, figsize=(3.3, 5.9), dpi=100.0, gridspec_kw={'width_ratios': [1.3, 1]})\n",
    "\n",
    "# panel 1 (combinations of datasets)\n",
    "h1 = sns.heatmap(res_num[measurement_methods],\n",
    "                 cmap=['white', '#74C476'],\n",
    "                 cbar=False,\n",
    "                 ax=ax[0],\n",
    "                 xticklabels=True)\n",
    "\n",
    "#   add border to heatmap\n",
    "for _, spine in h1.spines.items():\n",
    "    spine.set_visible(True)\n",
    "h1.set_ylabel('combinations')\n",
    "h1.yaxis.label.set_size(7)\n",
    "\n",
    "#   add lines between columns\n",
    "ax[0].vlines(range(len(measurement_methods)), *ax[0].get_ylim(), lw=0.5, colors=\"black\")\n",
    "\n",
    "# panel 2 (metrics)\n",
    "data = res_num[metrics_num]\n",
    "cmap = plt.get_cmap(\"rocket\", 10)\n",
    "x_axis_labels = ['accuracy', 'precision', 'accuracy', 'precision', 'accuracy', 'precision']\n",
    "h2 = sns.heatmap(data,\n",
    "                 cmap=cmap,\n",
    "                 ax=ax[1],\n",
    "                 yticklabels=False,\n",
    "                 xticklabels=x_axis_labels,\n",
    "                 vmin=0.0,\n",
    "                 vmax=0.05,\n",
    "                 cbar_kws={\"use_gridspec\":False, \"location\":\"right\", \"pad\":0.2, \"shrink\":0.35})\n",
    "\n",
    "#   add colorbar (with centered ticks)\n",
    "c_bar = ax[1].collections[0].colorbar\n",
    "c_bar.ax.set_ylim(0, 0.05)\n",
    "c_bar.set_ticks([0., 0.01, 0.02, 0.03, 0.04, 0.05])\n",
    "c_bar.set_ticklabels([0., 0.01, 0.02, 0.03, 0.04, 0.05])\n",
    "c_bar.outline.set_linewidth(1)\n",
    "c_bar.set_label('value')\n",
    "c_bar.ax.yaxis.label.set_size(7)\n",
    "c_bar.ax.tick_params(labelsize=6)\n",
    "\n",
    "#   add lines between columns\n",
    "ax[1].vlines(range(len(metrics_num)), *ax[1].get_ylim(), lw=0.5, colors=\"black\")\n",
    "\n",
    "#   add border to heatmap\n",
    "for _, spine in h2.spines.items():\n",
    "    spine.set_visible(True)\n",
    "\n",
    "#   highlight combinations #41, #174 and #254\n",
    "plt.text(0.8, 0.865, \"\\u2190\", fontsize=12, transform=plt.gcf().transFigure)\n",
    "plt.text(0.8, 0.615, \"\\u2190\", fontsize=12, transform=plt.gcf().transFigure)\n",
    "plt.text(0.8, 0.12, \"\\u2190\", fontsize=12, transform=plt.gcf().transFigure)\n",
    "\n",
    "#   add legend\n",
    "plt.text(0.612, -0.08, \"isotopomers\", fontsize=7, transform=plt.gcf().transFigure, rotation=90)\n",
    "plt.text(0.6, 0.016, \"\\u2015\", fontsize=13, transform=plt.gcf().transFigure)\n",
    "plt.text(0.679, -0.024, \"EMUs\", fontsize=7, transform=plt.gcf().transFigure, rotation=90)\n",
    "plt.text(0.665, 0.016, \"\\u2015\", fontsize=13, transform=plt.gcf().transFigure)\n",
    "plt.text(0.742, -0.067, \"cumomers\", fontsize=7, transform=plt.gcf().transFigure, rotation=90)\n",
    "plt.text(0.73, 0.016, \"\\u2015\", fontsize=13, transform=plt.gcf().transFigure)\n",
    "\n",
    "# save figure\n",
    "plt.savefig(\"./results/Figure_5.jpeg\", dpi=300.0, bbox_inches='tight', pad_inches=0.05)\n",
    "\n",
    "# display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Detailed analysis of integration results for Alanine\n",
    "\n",
    "This code integrates isotopic measurements collected by NMR and MS for alanine and reproduces Figure 6 of the publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measurements datafile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\"\n",
    "measurements_file = \"./data/ALA_measurements.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrates datasets for combinations 41, 174 and 254."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_128 = isosolve.main(mm=dataset_file, colsel=[1,2,8], data=measurements_file)\n",
    "res_12458 = isosolve.main(mm=dataset_file, colsel=[1,2,4,5,8], data=measurements_file)\n",
    "res_12345678 = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8], data=measurements_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate consistency of all datasets (combination 254)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>Value</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>number of measurements</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>system rank (number of statistically defined parameters)</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>degree of freedom</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>dof=m - p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>χ²(14)</td>\n",
       "      <td>4.199899</td>\n",
       "      <td>Σ((estimated-measured)/sd)²&lt;br/&gt;95% confidence interval is [0; 24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>χ²(14)/14</td>\n",
       "      <td>0.299993</td>\n",
       "      <td>Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p-value of one-tail χ² test, i.e.&lt;br/&gt;P(χ²(14) &gt; 4.2)</td>\n",
       "      <td>0.994139</td>\n",
       "      <td>Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       Item      Value  \\\n",
       "0                                    number of measurements  21.000000   \n",
       "1  system rank (number of statistically defined parameters)   7.000000   \n",
       "2                                         degree of freedom  14.000000   \n",
       "3                                                    χ²(14)   4.199899   \n",
       "4                                                 χ²(14)/14   0.299993   \n",
       "5     p-value of one-tail χ² test, i.e.<br/>P(χ²(14) > 4.2)   0.994139   \n",
       "\n",
       "                                                                                                                                                                            Comment  \n",
       "0                                                                                                                                                                                 m  \n",
       "1                                                                                                                                                                                 p  \n",
       "2                                                                                                                                                                         dof=m - p  \n",
       "3                                                                                                                Σ((estimated-measured)/sd)²<br/>95% confidence interval is [0; 24]  \n",
       "4                                                       Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd  \n",
       "5  Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res_12345678['chi2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate theoretical distribution of isotopomers, cumomers and EMUs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Theoretical distributions:\n",
      "\n",
      "isotopomer '000': 0.119 ± 0.007\n",
      "isotopomer '001': 0.123 ± 0.005\n",
      "isotopomer '100': 0.123 ± 0.005\n",
      "isotopomer '101': 0.127 ± 0.006\n",
      "isotopomer '010': 0.123 ± 0.005\n",
      "isotopomer '011': 0.127 ± 0.006\n",
      "isotopomer '110': 0.127 ± 0.006\n",
      "isotopomer '111': 0.131 ± 0.008\n",
      "\n",
      "cumomer '1xx': 0.508 ± 0.010\n",
      "cumomer 'x1x': 0.508 ± 0.010\n",
      "cumomer 'xx1': 0.508 ± 0.010\n",
      "cumomer '11x': 0.258 ± 0.010\n",
      "cumomer '1x1': 0.258 ± 0.010\n",
      "cumomer 'x11': 0.258 ± 0.010\n",
      "cumomer '111': 0.131 ± 0.008\n",
      "\n",
      "emu 'Exx, M0': 0.492 ± 0.014\n",
      "emu 'Exx, M1': 0.508 ± 0.014\n",
      "emu 'xEx, M0': 0.492 ± 0.014\n",
      "emu 'xEx, M1': 0.508 ± 0.014\n",
      "emu 'xxE, M0': 0.492 ± 0.014\n",
      "emu 'xxE, M1': 0.508 ± 0.014\n",
      "emu 'EEx, M0': 0.242 ± 0.007\n",
      "emu 'EEx, M1': 0.500 ± 0.028\n",
      "emu 'EEx, M2': 0.258 ± 0.007\n",
      "emu 'ExE, M0': 0.242 ± 0.007\n",
      "emu 'ExE, M1': 0.500 ± 0.028\n",
      "emu 'ExE, M2': 0.258 ± 0.007\n",
      "emu 'xEE, M0': 0.242 ± 0.007\n",
      "emu 'xEE, M1': 0.500 ± 0.028\n",
      "emu 'xEE, M2': 0.258 ± 0.007\n",
      "emu 'EEE, M0': 0.119 ± 0.003\n",
      "emu 'EEE, M1': 0.369 ± 0.031\n",
      "emu 'EEE, M2': 0.381 ± 0.032\n",
      "emu 'EEE, M3': 0.131 ± 0.004\n"
     ]
    }
   ],
   "source": [
    "# 13C-enrichment of labeled carbon input with standard deviation\n",
    "ab_13C, err_13C = 0.508, 0.01\n",
    "\n",
    "# calculate abundance and standard deviation\n",
    "ipm_th = get_theoretical_iso(res_128, ab_13C)\n",
    "ipm_th_sd = get_theoretical_iso_sd(res_128, ab_13C, err_13C)\n",
    "\n",
    "# calculate abundance and standard deviation for cumomers\n",
    "cumo_th = get_theoretical_cumo(res_128, ab_13C)\n",
    "cumo_th_sd = get_theoretical_cumo_sd(res_128, ab_13C, err_13C)\n",
    "\n",
    "# calculate abundance and standard deviation for EMUs\n",
    "emu_th = get_theoretical_emu_full(res_128, ab_13C)\n",
    "emu_th_sd = get_theoretical_emu_full_sd(res_128, ab_13C, err_13C)\n",
    "\n",
    "# display results\n",
    "print(\"Theoretical distributions:\")\n",
    "\n",
    "print(\"\")\n",
    "for i in range(len(ipm_th)):\n",
    "    print(\"isotopomer '{}': {:.3f} \\u00B1 {:.3f}\".format(res_128['xbc'].index[i], ipm_th[i],\n",
    "                                                         ipm_th_sd[i]))\n",
    "print(\"\")\n",
    "\n",
    "for i in range(len(cumo_th)):\n",
    "    print(\"cumomer '{}': {:.3f} \\u00B1 {:.3f}\".format(list(res_128['cusol'].keys())[i], cumo_th[i], cumo_th_sd[i]))\n",
    "print(\"\")\n",
    "\n",
    "for i in res_128['ls']['emu'][0].keys():\n",
    "    for j in range(len(res_128['ls']['emu'][0][i])):\n",
    "        print(\"emu '{}, M{}': {:.3f} \\u00B1 {:.3f}\".format(i, j, emu_th[i][j], emu_th_sd[i][j]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display metrics for combination 41 (HSQC + LC-MS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isotopomer</th>\n",
       "      <th>theoretical_fraction</th>\n",
       "      <th>measured_fraction</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000</td>\n",
       "      <td>0.119095</td>\n",
       "      <td>0.116864</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.009354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001</td>\n",
       "      <td>0.122969</td>\n",
       "      <td>0.186771</td>\n",
       "      <td>-0.063803</td>\n",
       "      <td>0.065798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>0.122969</td>\n",
       "      <td>0.021316</td>\n",
       "      <td>0.101652</td>\n",
       "      <td>0.082901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>0.126967</td>\n",
       "      <td>0.097768</td>\n",
       "      <td>0.029200</td>\n",
       "      <td>0.039313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010</td>\n",
       "      <td>0.122969</td>\n",
       "      <td>0.146504</td>\n",
       "      <td>-0.023535</td>\n",
       "      <td>0.020459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>011</td>\n",
       "      <td>0.126967</td>\n",
       "      <td>0.146504</td>\n",
       "      <td>-0.019536</td>\n",
       "      <td>0.020459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.126967</td>\n",
       "      <td>0.143149</td>\n",
       "      <td>-0.016182</td>\n",
       "      <td>0.020168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>111</td>\n",
       "      <td>0.131097</td>\n",
       "      <td>0.141125</td>\n",
       "      <td>-0.010028</td>\n",
       "      <td>0.009354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  isotopomer  theoretical_fraction  measured_fraction  accuracy  precision\n",
       "0        000              0.119095           0.116864  0.002232   0.009354\n",
       "1        001              0.122969           0.186771 -0.063803   0.065798\n",
       "2        100              0.122969           0.021316  0.101652   0.082901\n",
       "3        101              0.126967           0.097768  0.029200   0.039313\n",
       "4        010              0.122969           0.146504 -0.023535   0.020459\n",
       "5        011              0.126967           0.146504 -0.019536   0.020459\n",
       "6        110              0.126967           0.143149 -0.016182   0.020168\n",
       "7        111              0.131097           0.141125 -0.010028   0.009354"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combination_41 = {'isotopomer': list(res_128['xbc'].index),\n",
    "                  'theoretical_fraction': get_theoretical_iso(res_128, ab_13C),\n",
    "                  'measured_fraction': res_128['ls']['iso'][0].flatten().tolist()[0],\n",
    "                  'accuracy': [ipm_th[i] - res_128['ls']['iso'][0].flatten().tolist()[0][i] for i in range(len(ipm_th))],\n",
    "                  'precision': res_128['ls']['iso'][1].flatten().tolist()}  \n",
    "\n",
    "display(pd.DataFrame(combination_41))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x990 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set font size\n",
    "plt.rcdefaults\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.rcParams.update({\"axes.labelsize\": 12})\n",
    "\n",
    "# create figure\n",
    "fig, ax = plt.subplots(3, 1, figsize=(7, 9.9), dpi=100.0)\n",
    "\n",
    "# plot isotopomers\n",
    "#   x locations for the groups\n",
    "ind = np.arange(8)\n",
    "#   width of the bars\n",
    "width = 0.2\n",
    "#   -> theoretical data\n",
    "p1 = ax[0].bar(ind - width, ipm_th, width, bottom=0, yerr=ipm_th_sd, edgecolor='black', color=\"#C8595E\")\n",
    "#   -> HSQC + LC-MS\n",
    "p2 = ax[0].bar(ind, res_128['ls']['iso'][0].flatten().tolist()[0], width, bottom=0, yerr=res_128['ls']['iso'][1].flatten().tolist(), edgecolor='black', color=\"#EDC588\")\n",
    "#   -> HSQC + GC-MS + LC-MS\n",
    "p3 = ax[0].bar(ind + width, res_12458['ls']['iso'][0].flatten().tolist()[0], width, bottom=0, yerr=res_12458['ls']['iso'][1].flatten().tolist(), edgecolor='black', color=\"#88C2A5\")\n",
    "#   -> all datasets\n",
    "p4 = ax[0].bar(ind + 2*width, res_12345678['ls']['iso'][0].flatten().tolist()[0], width, bottom=0, yerr=res_12345678['ls']['iso'][1].flatten().tolist(), edgecolor='black', color=\"#5273A4\")\n",
    "#   set y limits\n",
    "ax[0].set(ylim=(0.,0.3))\n",
    "#   add ticks\n",
    "ax[0].set_xticks(ind + width / 2)\n",
    "#   add labels\n",
    "ax[0].set_xticklabels(res_128['xbc'].index)\n",
    "#   add legend\n",
    "ax[0].legend((p1[0], p2[0], p3[0], p4[0]),\n",
    "             ('Theoretical values',\n",
    "              'HSQC+LC-MS (accuracy=0.033, precision=0.033)',\n",
    "              'HSQC+TOCSY+LC-MS (accuracy=0.006, precision=0.014)',\n",
    "              'All datasets (accuracy=0.006, precision=0.009)'),\n",
    "              loc='upper right', borderaxespad=0., framealpha=1., frameon=False, fontsize=10)\n",
    "#   add axis labels\n",
    "ax[0].set(xlabel='isotopomer', ylabel='abundance')\n",
    "\n",
    "# plot cumomers\n",
    "#   x locations for the groups\n",
    "ind = np.arange(7)\n",
    "#   width of the bars\n",
    "width = 0.2\n",
    "#   -> theoretical data\n",
    "p1 = ax[1].bar(ind - width, cumo_th, width, bottom=0, yerr=cumo_th_sd, edgecolor='black', color=\"#C8595E\")\n",
    "#   -> HSQC + LC-MS\n",
    "p2 = ax[1].bar(ind, res_128['ls']['cumo'][0], width, bottom=0, yerr=res_128['ls']['cumo'][1], edgecolor='black', color=\"#EDC588\")\n",
    "#   -> HSQC + GC-MS + LC-MS\n",
    "p3 = ax[1].bar(ind + width, res_12458['ls']['cumo'][0], width, bottom=0, yerr=res_12458['ls']['cumo'][1], edgecolor='black', color=\"#88C2A5\")\n",
    "#   -> all datasets\n",
    "p4 = ax[1].bar(ind + 2*width, res_12345678['ls']['cumo'][0], width, bottom=0, yerr=res_12345678['ls']['cumo'][1], edgecolor='black', color=\"#5273A4\")\n",
    "#   set y limits\n",
    "ax[1].set(ylim=(0.,0.7))\n",
    "#   add ticks\n",
    "ax[1].set_xticks(ind + width / 2)\n",
    "#   add labels\n",
    "ax[1].set_xticklabels(res_128['cusol'].keys())\n",
    "#   add axis labels\n",
    "ax[1].set(xlabel='cumomer', ylabel='abundance')\n",
    "\n",
    "# plot EMUs\n",
    "def transform_emu(d):\n",
    "    l, n = [], []\n",
    "    for k,v in d.items():\n",
    "        for i,j in enumerate(v):\n",
    "            l.append(j)\n",
    "            n.append(k + \"_M\" + str(i))\n",
    "    return (n, l)\n",
    "#   get theoretical EMUs \n",
    "r_th = transform_emu(emu_th)\n",
    "#   x locations for the groups\n",
    "ind = np.arange(len(r_th[0]))\n",
    "#   width of the bars\n",
    "width = 0.2\n",
    "#   -> theoretical data\n",
    "p1 = ax[2].bar(ind - width, transform_emu(emu_th)[1], width, bottom=0, yerr=transform_emu(emu_th_sd)[1], edgecolor='black', color=\"#C8595E\")\n",
    "#   -> HSQC + LC-MS\n",
    "p2 = ax[2].bar(ind, transform_emu(res_128['ls']['emu'][0])[1], width, bottom=0, yerr=transform_emu(res_128['ls']['emu'][1])[1], edgecolor='black', color=\"#EDC588\")\n",
    "#   -> HSQC + GC-MS + LC-MS\n",
    "p3 = ax[2].bar(ind + width, transform_emu(res_12458['ls']['emu'][0])[1], width, bottom=0, yerr=transform_emu(res_12458['ls']['emu'][1])[1], edgecolor='black', color=\"#88C2A5\")\n",
    "#   -> all datasets\n",
    "p4 = ax[2].bar(ind + 2*width, transform_emu(res_12345678['ls']['emu'][0])[1], width, bottom=0, yerr=transform_emu(res_12345678['ls']['emu'][1])[1], edgecolor='black', color=\"#5273A4\")\n",
    "#   set y limits\n",
    "ax[2].set(ylim=(0.,0.8))\n",
    "#   add ticks\n",
    "ax[2].set_xticks(ind + width / 2)\n",
    "#   add labels\n",
    "xlabels = [i.split(\"_\")[1] for i in transform_emu(res_12458['ls']['emu'][0])[0]]\n",
    "ax[2].set_xticklabels(xlabels, fontsize=10)\n",
    "#   add axis labels\n",
    "ax[2].set(ylabel='abundance')\n",
    "plt.xlabel(xlabel='EMU', labelpad=25)\n",
    "lemus = [i.split(\"_\")[0] for i in transform_emu(res_12458['ls']['emu'][0])[0]]\n",
    "xemus =  [(x, lemus.count(x)) for i, x in enumerate(lemus) if i == lemus.index(x)]\n",
    "i=0\n",
    "for e in xemus:\n",
    "    ax[2].text(0.18+i/len(lemus)*0.82, 0.048, e[0], fontsize=12, transform=plt.gcf().transFigure)\n",
    "    ax[2].text(0.16+i/len(lemus)*0.8, 0.063, \"\\u2015\"*(e[1]+1), fontsize=12, transform=plt.gcf().transFigure)\n",
    "    i += e[1]\n",
    "\n",
    "# adjust space between panels\n",
    "fig.tight_layout(pad=1.0)\n",
    "\n",
    "# save figure\n",
    "plt.savefig(\"./results/Figure_6.jpeg\", dpi=300.0, bbox_inches='tight', pad_inches=0.05)\n",
    "\n",
    "# display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Evaluate self-consistency of measurements\n",
    "\n",
    "This code evaluates the self-consistency of different datasets to consolidate measurements and identify biased datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define mapping and measurements datafiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\"\n",
    "measurements_file = \"./data/ALA_measurements.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate measurements and evaluate the consistency of the different datasets based on chi2 statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>Value</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>number of measurements</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>system rank (number of statistically defined parameters)</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>degree of freedom</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>dof=m - p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>χ²(14)</td>\n",
       "      <td>4.199899</td>\n",
       "      <td>Σ((estimated-measured)/sd)²&lt;br/&gt;95% confidence interval is [0; 24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>χ²(14)/14</td>\n",
       "      <td>0.299993</td>\n",
       "      <td>Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p-value of one-tail χ² test, i.e.&lt;br/&gt;P(χ²(14) &gt; 4.2)</td>\n",
       "      <td>0.994139</td>\n",
       "      <td>Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       Item      Value  \\\n",
       "0                                    number of measurements  21.000000   \n",
       "1  system rank (number of statistically defined parameters)   7.000000   \n",
       "2                                         degree of freedom  14.000000   \n",
       "3                                                    χ²(14)   4.199899   \n",
       "4                                                 χ²(14)/14   0.299993   \n",
       "5     p-value of one-tail χ² test, i.e.<br/>P(χ²(14) > 4.2)   0.994139   \n",
       "\n",
       "                                                                                                                                                                            Comment  \n",
       "0                                                                                                                                                                                 m  \n",
       "1                                                                                                                                                                                 p  \n",
       "2                                                                                                                                                                         dof=m - p  \n",
       "3                                                                                                                Σ((estimated-measured)/sd)²<br/>95% confidence interval is [0; 24]  \n",
       "4                                                       Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd  \n",
       "5  Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_all = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8], data=measurements_file)\n",
    "display(res_all['chi2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we alter two measurements ('b' and 'c') in the measurements file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0.253300</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>0.053300</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>0.447500</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>0.244000</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>0.509600</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>0.490400</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>g</td>\n",
       "      <td>0.116864</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>h</td>\n",
       "      <td>0.354591</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>i</td>\n",
       "      <td>0.387421</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>j</td>\n",
       "      <td>0.141125</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>k</td>\n",
       "      <td>0.497300</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>l</td>\n",
       "      <td>0.502700</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>m</td>\n",
       "      <td>0.496000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>n</td>\n",
       "      <td>0.504000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>o</td>\n",
       "      <td>0.258864</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p</td>\n",
       "      <td>0.494754</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>q</td>\n",
       "      <td>0.246383</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>r</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>s</td>\n",
       "      <td>0.498000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>t</td>\n",
       "      <td>0.496000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>u</td>\n",
       "      <td>0.504000</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name     value    sd\n",
       "0     a  0.253300  0.02\n",
       "1     b  0.053300  0.02\n",
       "2     c  0.447500  0.02\n",
       "3     d  0.244000  0.02\n",
       "4     e  0.509600  0.03\n",
       "5     f  0.490400  0.03\n",
       "6     g  0.116864  0.01\n",
       "7     h  0.354591  0.01\n",
       "8     i  0.387421  0.01\n",
       "9     j  0.141125  0.01\n",
       "10    k  0.497300  0.02\n",
       "11    l  0.502700  0.02\n",
       "12    m  0.496000  0.03\n",
       "13    n  0.504000  0.03\n",
       "14    o  0.258864  0.01\n",
       "15    p  0.494754  0.01\n",
       "16    q  0.246383  0.01\n",
       "17    r  0.502000  0.03\n",
       "18    s  0.498000  0.03\n",
       "19    t  0.496000  0.03\n",
       "20    u  0.504000  0.03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biased_measurements = pd.read_csv(measurements_file, delimiter=\"\\t\", comment=\"#\")\n",
    "biased_measurements.at[1, 'value'] = biased_measurements.at[1, 'value'] - 0.2\n",
    "biased_measurements.at[2, 'value'] = biased_measurements.at[2, 'value'] + 0.2\n",
    "display(biased_measurements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data integration indicates that the altered datasets are not self-consistent (p(chi2) < 0.05)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>Value</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>number of measurements</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>system rank (number of statistically defined parameters)</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>degree of freedom</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>dof=m - p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>χ²(14)</td>\n",
       "      <td>1.066792e+02</td>\n",
       "      <td>Σ((estimated-measured)/sd)²&lt;br/&gt;95% confidence interval is [0; 24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>χ²(14)/14</td>\n",
       "      <td>7.619945e+00</td>\n",
       "      <td>Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p-value of one-tail χ² test, i.e.&lt;br/&gt;P(χ²(14) &gt; 106.679)</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        Item         Value  \\\n",
       "0                                     number of measurements  2.100000e+01   \n",
       "1   system rank (number of statistically defined parameters)  7.000000e+00   \n",
       "2                                          degree of freedom  1.400000e+01   \n",
       "3                                                     χ²(14)  1.066792e+02   \n",
       "4                                                  χ²(14)/14  7.619945e+00   \n",
       "5  p-value of one-tail χ² test, i.e.<br/>P(χ²(14) > 106.679)  2.220446e-16   \n",
       "\n",
       "                                                                                                                                                                            Comment  \n",
       "0                                                                                                                                                                                 m  \n",
       "1                                                                                                                                                                                 p  \n",
       "2                                                                                                                                                                         dof=m - p  \n",
       "3                                                                                                                Σ((estimated-measured)/sd)²<br/>95% confidence interval is [0; 24]  \n",
       "4                                                       Reduced χ², χ²(dof)/dof: if ≫ 1 then poor fitting, ~ 1 means 'good' fitting and ≪ 1 means over-fitting or overestimating sd  \n",
       "5  Value close to 0 (e.g. under 0.05) means poor fitting. Value close to 1 can be an evidence for over-fitting or that sd are overestimated. It can be NaN (not a number) for dof=0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_biased = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8], data=biased_measurements)\n",
    "display(res_biased['chi2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Generate isotopically-resolved InChIs\n",
    "\n",
    "The [IUPAC International Chemical Identifier](https://iupac.org/who-we-are/divisions/division-details/inchi/) (InChI) is a textual identifier for chemical substances, designed to provide a standard way to encode molecular information and to facilitate the search for such information in databases and on the web.\n",
    "\n",
    "The identifiers describe chemical substances in terms of layers of information. IsoSolve may generate an isotopic layer that specifies the isotopic species of the tracer element, following the extended representation proposed by the [InChI Isotopologue and Isotopomer Development Team](https://github.com/MSI-Metabolomics-Standards-Initiative/inchi-isotopologue-extension)\n",
    "\n",
    "To generate isotopic layers of InChIs, use `isosolve.main(..., inchi=True)`, as shown here for Alanine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define mapping and measurements datafiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_file = \"./data/ALA_mapping.tsv\"\n",
    "measurements_file = \"./data/ALA_measurements.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate measurements and display isotopic layers for isotopomers, cumomers, and EMUs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotopic layers of isotopomers:\n"
     ]
    },
    {
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       "      <th>000</th>\n",
       "      <td>/i1+0,2+0,3+0</td>\n",
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       "      <td>/i1+0,2+0,3+1</td>\n",
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       "      <th>110</th>\n",
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       "      <th>111</th>\n",
       "      <td>/i1+1,2+1,3+1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             inchi\n",
       "000  /i1+0,2+0,3+0\n",
       "001  /i1+0,2+0,3+1\n",
       "100  /i1+1,2+0,3+0\n",
       "101  /i1+1,2+0,3+1\n",
       "010  /i1+0,2+1,3+0\n",
       "011  /i1+0,2+1,3+1\n",
       "110  /i1+1,2+1,3+0\n",
       "111  /i1+1,2+1,3+1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotopic layers of cumomers:\n"
     ]
    },
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       "      <th>x11</th>\n",
       "      <td>/i2+1,3+1</td>\n",
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       "      <th>111</th>\n",
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      "text/plain": [
       "             inchi\n",
       "1xx          /i1+1\n",
       "x1x          /i2+1\n",
       "xx1          /i3+1\n",
       "11x      /i1+1,2+1\n",
       "1x1      /i1+1,3+1\n",
       "x11      /i2+1,3+1\n",
       "111  /i1+1,2+1,3+1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotopic layers of EMUs:\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>Exx+0</th>\n",
       "      <td>/a(C1+0,1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exx+1</th>\n",
       "      <td>/a(C1+1,1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xEx+0</th>\n",
       "      <td>/a(C1+0,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xEx+1</th>\n",
       "      <td>/a(C1+1,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xxE+0</th>\n",
       "      <td>/a(C1+0,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xxE+1</th>\n",
       "      <td>/a(C1+1,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEx+0</th>\n",
       "      <td>/a(C2+0,1,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEx+1</th>\n",
       "      <td>/a(C1+1,1,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEx+2</th>\n",
       "      <td>/a(C2+1,1,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExE+0</th>\n",
       "      <td>/a(C2+0,1,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExE+1</th>\n",
       "      <td>/a(C1+1,1,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExE+2</th>\n",
       "      <td>/a(C2+1,1,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xEE+0</th>\n",
       "      <td>/a(C2+0,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xEE+1</th>\n",
       "      <td>/a(C1+1,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xEE+2</th>\n",
       "      <td>/a(C2+1,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEE+0</th>\n",
       "      <td>/a(C3+0,1,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEE+1</th>\n",
       "      <td>/a(C1+1,1,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEE+2</th>\n",
       "      <td>/a(C2+1,1,2,3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEE+3</th>\n",
       "      <td>/a(C3+1,1,2,3)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                inchi\n",
       "Exx+0      /a(C1+0,1)\n",
       "Exx+1      /a(C1+1,1)\n",
       "xEx+0      /a(C1+0,2)\n",
       "xEx+1      /a(C1+1,2)\n",
       "xxE+0      /a(C1+0,3)\n",
       "xxE+1      /a(C1+1,3)\n",
       "EEx+0    /a(C2+0,1,2)\n",
       "EEx+1    /a(C1+1,1,2)\n",
       "EEx+2    /a(C2+1,1,2)\n",
       "ExE+0    /a(C2+0,1,3)\n",
       "ExE+1    /a(C1+1,1,3)\n",
       "ExE+2    /a(C2+1,1,3)\n",
       "xEE+0    /a(C2+0,2,3)\n",
       "xEE+1    /a(C1+1,2,3)\n",
       "xEE+2    /a(C2+1,2,3)\n",
       "EEE+0  /a(C3+0,1,2,3)\n",
       "EEE+1  /a(C1+1,1,2,3)\n",
       "EEE+2  /a(C2+1,1,2,3)\n",
       "EEE+3  /a(C3+1,1,2,3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_all = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8], inchi=True)\n",
    "print(\"Isotopic layers of isotopomers:\")\n",
    "display(res_all['inchi']['iso'])\n",
    "print(\"Isotopic layers of cumomers:\")\n",
    "display(res_all['inchi']['cumo'])\n",
    "print(\"Isotopic layers of EMUs:\")\n",
    "display(res_all['inchi']['emu'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When measurements are provided as input, the integration results are provided for each isotopic InChI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>inchi</th>\n",
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       "      <th>sd</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>/i1+0,2+0,3+0</td>\n",
       "      <td>0.123579</td>\n",
       "      <td>0.008452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001</th>\n",
       "      <td>/i1+0,2+0,3+1</td>\n",
       "      <td>0.104716</td>\n",
       "      <td>0.012772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>/i1+1,2+0,3+0</td>\n",
       "      <td>0.129994</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>/i1+1,2+0,3+1</td>\n",
       "      <td>0.141941</td>\n",
       "      <td>0.010580</td>\n",
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       "    <tr>\n",
       "      <th>010</th>\n",
       "      <td>/i1+0,2+1,3+0</td>\n",
       "      <td>0.123621</td>\n",
       "      <td>0.008616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>/i1+0,2+1,3+1</td>\n",
       "      <td>0.121670</td>\n",
       "      <td>0.006702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>/i1+1,2+1,3+0</td>\n",
       "      <td>0.124574</td>\n",
       "      <td>0.007433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>/i1+1,2+1,3+1</td>\n",
       "      <td>0.129905</td>\n",
       "      <td>0.005822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             inchi     value        sd\n",
       "000  /i1+0,2+0,3+0  0.123579  0.008452\n",
       "001  /i1+0,2+0,3+1  0.104716  0.012772\n",
       "100  /i1+1,2+0,3+0  0.129994  0.010114\n",
       "101  /i1+1,2+0,3+1  0.141941  0.010580\n",
       "010  /i1+0,2+1,3+0  0.123621  0.008616\n",
       "011  /i1+0,2+1,3+1  0.121670  0.006702\n",
       "110  /i1+1,2+1,3+0  0.124574  0.007433\n",
       "111  /i1+1,2+1,3+1  0.129905  0.005822"
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       "      <th>1xx</th>\n",
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       "      <th>xx1</th>\n",
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       "      <th>11x</th>\n",
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       "      <th>x11</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>/i1+1,2+1,3+1</td>\n",
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      "text/plain": [
       "             inchi     value        sd\n",
       "1xx          /i1+1  0.526414  0.017891\n",
       "x1x          /i2+1  0.499770  0.010169\n",
       "xx1          /i3+1  0.498232  0.012152\n",
       "11x      /i1+1,2+1  0.254479  0.008186\n",
       "1x1      /i1+1,3+1  0.271846  0.013694\n",
       "x11      /i2+1,3+1  0.251574  0.006063\n",
       "111  /i1+1,2+1,3+1  0.129905  0.005822"
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       "      <th>EEx+2</th>\n",
       "      <td>/a(C2+1,1,2)</td>\n",
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       "      <th>ExE+0</th>\n",
       "      <td>/a(C2+0,1,3)</td>\n",
       "      <td>0.247200</td>\n",
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       "      <th>ExE+1</th>\n",
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       "      <th>ExE+2</th>\n",
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       "      <th>EEE+0</th>\n",
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       "      <th>EEE+1</th>\n",
       "      <td>/a(C1+1,1,2,3)</td>\n",
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       "      <td>0.009030</td>\n",
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       "    <tr>\n",
       "      <th>EEE+2</th>\n",
       "      <td>/a(C2+1,1,2,3)</td>\n",
       "      <td>0.388185</td>\n",
       "      <td>0.008448</td>\n",
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       "    <tr>\n",
       "      <th>EEE+3</th>\n",
       "      <td>/a(C3+1,1,2,3)</td>\n",
       "      <td>0.129905</td>\n",
       "      <td>0.005822</td>\n",
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      ],
      "text/plain": [
       "                inchi     value        sd\n",
       "Exx+0      /a(C1+0,1)  0.473586  0.017891\n",
       "Exx+1      /a(C1+1,1)  0.526414  0.017891\n",
       "xEx+0      /a(C1+0,2)  0.500230  0.010169\n",
       "xEx+1      /a(C1+1,2)  0.499770  0.010169\n",
       "xxE+0      /a(C1+0,3)  0.501768  0.012152\n",
       "xxE+1      /a(C1+1,3)  0.498232  0.012152\n",
       "EEx+0    /a(C2+0,1,2)  0.228296  0.016769\n",
       "EEx+1    /a(C1+1,1,2)  0.517225  0.016368\n",
       "EEx+2    /a(C2+1,1,2)  0.254479  0.008186\n",
       "ExE+0    /a(C2+0,1,3)  0.247200  0.012105\n",
       "ExE+1    /a(C1+1,1,3)  0.480954  0.018130\n",
       "ExE+2    /a(C2+1,1,3)  0.271846  0.013694\n",
       "xEE+0    /a(C2+0,2,3)  0.253573  0.007485\n",
       "xEE+1    /a(C1+1,2,3)  0.494853  0.007319\n",
       "xEE+2    /a(C2+1,2,3)  0.251574  0.006063\n",
       "EEE+0  /a(C3+0,1,2,3)  0.123579  0.008452\n",
       "EEE+1  /a(C1+1,1,2,3)  0.358331  0.009030\n",
       "EEE+2  /a(C2+1,1,2,3)  0.388185  0.008448\n",
       "EEE+3  /a(C3+1,1,2,3)  0.129905  0.005822"
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       "      <td>101</td>\n",
       "      <td>i4</td>\n",
       "      <td>/i1+1,2+0,3+1</td>\n",
       "      <td>0.141941</td>\n",
       "      <td>0.010580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010</td>\n",
       "      <td>i5</td>\n",
       "      <td>/i1+0,2+1,3+0</td>\n",
       "      <td>0.123621</td>\n",
       "      <td>0.008616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>011</td>\n",
       "      <td>i6</td>\n",
       "      <td>/i1+0,2+1,3+1</td>\n",
       "      <td>0.121670</td>\n",
       "      <td>0.006702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>i7</td>\n",
       "      <td>/i1+1,2+1,3+0</td>\n",
       "      <td>0.124574</td>\n",
       "      <td>0.007433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>111</td>\n",
       "      <td>i8</td>\n",
       "      <td>/i1+1,2+1,3+1</td>\n",
       "      <td>0.129905</td>\n",
       "      <td>0.005822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  accu group          inchi     value        sd\n",
       "0  000    i1  /i1+0,2+0,3+0  0.123579  0.008452\n",
       "1  001    i2  /i1+0,2+0,3+1  0.104716  0.012772\n",
       "2  100    i3  /i1+1,2+0,3+0  0.129994  0.010114\n",
       "3  101    i4  /i1+1,2+0,3+1  0.141941  0.010580\n",
       "4  010    i5  /i1+0,2+1,3+0  0.123621  0.008616\n",
       "5  011    i6  /i1+0,2+1,3+1  0.121670  0.006702\n",
       "6  110    i7  /i1+1,2+1,3+0  0.124574  0.007433\n",
       "7  111    i8  /i1+1,2+1,3+1  0.129905  0.005822"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_all = isosolve.main(mm=dataset_file, colsel=[1,2,3,4,5,6,7,8], data=measurements_file, inchi=True)\n",
    "display(res_all['inchi']['iso'])\n",
    "display(res_all['inchi']['cumo'])\n",
    "display(res_all['inchi']['emu'])\n",
    "display(res_all['inchi']['mcomb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the system is partly undetermined, IsoSolve provides InChIs for all species and numerical values only for the identifiable species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "             inchi     value        sd\n",
       "000  /i1+0,2+0,3+0  0.119806  0.009268\n",
       "001  /i1+0,2+0,3+1       NaN       NaN\n",
       "100  /i1+1,2+0,3+0       NaN       NaN\n",
       "101  /i1+1,2+0,3+1  0.143343  0.018438\n",
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       "             inchi     value        sd\n",
       "1xx          /i1+1       NaN       NaN\n",
       "x1x          /i2+1  0.504293  0.021176\n",
       "xx1          /i3+1       NaN       NaN\n",
       "11x      /i1+1,2+1  0.254342  0.013399\n",
       "1x1      /i1+1,3+1  0.275640  0.018735\n",
       "x11      /i2+1,3+1  0.257273  0.013480\n",
       "111  /i1+1,2+1,3+1  0.132297  0.007282"
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       "      <th>ExE+1</th>\n",
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       "      <th>ExE+2</th>\n",
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       "      <th>EEE+0</th>\n",
       "      <td>/a(C3+0,1,2,3)</td>\n",
       "      <td>0.119806</td>\n",
       "      <td>0.009268</td>\n",
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       "    <tr>\n",
       "      <th>EEE+1</th>\n",
       "      <td>/a(C1+1,1,2,3)</td>\n",
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       "      <td>0.009268</td>\n",
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       "    <tr>\n",
       "      <th>EEE+2</th>\n",
       "      <td>/a(C2+1,1,2,3)</td>\n",
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       "      <td>0.009268</td>\n",
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       "    <tr>\n",
       "      <th>EEE+3</th>\n",
       "      <td>/a(C3+1,1,2,3)</td>\n",
       "      <td>0.132297</td>\n",
       "      <td>0.007282</td>\n",
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      ],
      "text/plain": [
       "                inchi     value        sd\n",
       "Exx+0      /a(C1+0,1)       NaN       NaN\n",
       "Exx+1      /a(C1+1,1)       NaN       NaN\n",
       "xEx+0      /a(C1+0,2)  0.495707  0.021176\n",
       "xEx+1      /a(C1+1,2)  0.504293  0.021176\n",
       "xxE+0      /a(C1+0,3)       NaN       NaN\n",
       "xxE+1      /a(C1+1,3)       NaN       NaN\n",
       "EEx+0    /a(C2+0,1,2)       NaN       NaN\n",
       "EEx+1    /a(C1+1,1,2)       NaN       NaN\n",
       "EEx+2    /a(C2+1,1,2)  0.254342  0.013399\n",
       "ExE+0    /a(C2+0,1,3)  0.244782  0.014480\n",
       "ExE+1    /a(C1+1,1,3)  0.479578  0.021297\n",
       "ExE+2    /a(C2+1,1,3)  0.275640  0.018735\n",
       "xEE+0    /a(C2+0,2,3)       NaN       NaN\n",
       "xEE+1    /a(C1+1,2,3)       NaN       NaN\n",
       "xEE+2    /a(C2+1,2,3)  0.257273  0.013480\n",
       "EEE+0  /a(C3+0,1,2,3)  0.119806  0.009268\n",
       "EEE+1  /a(C1+1,1,2,3)  0.357533  0.009268\n",
       "EEE+2  /a(C2+1,1,2,3)  0.390363  0.009268\n",
       "EEE+3  /a(C3+1,1,2,3)  0.132297  0.007282"
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       "      <th>4</th>\n",
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       "      <td>i5</td>\n",
       "      <td>/i1+1,2+1,3+0</td>\n",
       "      <td>0.122045</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>111</td>\n",
       "      <td>i6</td>\n",
       "      <td>/i1+1,2+1,3+1</td>\n",
       "      <td>0.132297</td>\n",
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      ],
      "text/plain": [
       "  accu group          inchi     value        sd\n",
       "0  000    i1  /i1+0,2+0,3+0  0.119806  0.009268\n",
       "1  101    i2  /i1+1,2+0,3+1  0.143343  0.018438\n",
       "2  010    i3  /i1+0,2+1,3+0  0.124975  0.011181\n",
       "3  011    i4  /i1+0,2+1,3+1  0.124975  0.011181\n",
       "4  110    i5  /i1+1,2+1,3+0  0.122045  0.011113\n",
       "5  111    i6  /i1+1,2+1,3+1  0.132297  0.007282"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_partial = isosolve.main(mm=dataset_file, colsel=[1,4,8], data=measurements_file, inchi=True)\n",
    "display(res_partial['inchi']['iso'])\n",
    "display(res_partial['inchi']['cumo'])\n",
    "display(res_partial['inchi']['emu'])\n",
    "display(res_partial['inchi']['mcomb'])"
   ]
  }
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