aminoacids_properties.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install pandas seaborn requests matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Name</th>\n",
       "      <th>Sym</th>\n",
       "      <th>ResidueWeight</th>\n",
       "      <th>pKa1</th>\n",
       "      <th>pKb2</th>\n",
       "      <th>pKx3</th>\n",
       "      <th>pl4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>Alanine</td>\n",
       "      <td>A</td>\n",
       "      <td>71.08</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.69</td>\n",
       "      <td>–</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>Arginine</td>\n",
       "      <td>R</td>\n",
       "      <td>156.19</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.04</td>\n",
       "      <td>12.48</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>Asparagine</td>\n",
       "      <td>N</td>\n",
       "      <td>114.11</td>\n",
       "      <td>2.02</td>\n",
       "      <td>8.80</td>\n",
       "      <td>–</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>Aspartic acid</td>\n",
       "      <td>D</td>\n",
       "      <td>115.09</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.60</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>Cysteine</td>\n",
       "      <td>C</td>\n",
       "      <td>103.15</td>\n",
       "      <td>1.96</td>\n",
       "      <td>10.28</td>\n",
       "      <td>8.18</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>E</td>\n",
       "      <td>129.12</td>\n",
       "      <td>2.19</td>\n",
       "      <td>9.67</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>Glutamine</td>\n",
       "      <td>Q</td>\n",
       "      <td>128.13</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.13</td>\n",
       "      <td>–</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>Glycine</td>\n",
       "      <td>G</td>\n",
       "      <td>57.05</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.60</td>\n",
       "      <td>–</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>Histidine</td>\n",
       "      <td>H</td>\n",
       "      <td>137.14</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyp</th>\n",
       "      <td>Hydroxyproline</td>\n",
       "      <td>O</td>\n",
       "      <td>113.11</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.65</td>\n",
       "      <td>–</td>\n",
       "      <td>–</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>Isoleucine</td>\n",
       "      <td>I</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>–</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>Leucine</td>\n",
       "      <td>L</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>–</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>Lysine</td>\n",
       "      <td>K</td>\n",
       "      <td>128.18</td>\n",
       "      <td>2.18</td>\n",
       "      <td>8.95</td>\n",
       "      <td>10.53</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>Methionine</td>\n",
       "      <td>M</td>\n",
       "      <td>131.20</td>\n",
       "      <td>2.28</td>\n",
       "      <td>9.21</td>\n",
       "      <td>–</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>Phenylalanine</td>\n",
       "      <td>F</td>\n",
       "      <td>147.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>9.13</td>\n",
       "      <td>–</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>Proline</td>\n",
       "      <td>P</td>\n",
       "      <td>97.12</td>\n",
       "      <td>1.99</td>\n",
       "      <td>10.60</td>\n",
       "      <td>–</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glp</th>\n",
       "      <td>Pyroglutamatic</td>\n",
       "      <td>U</td>\n",
       "      <td>121.09</td>\n",
       "      <td>–</td>\n",
       "      <td>–</td>\n",
       "      <td>–</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>Serine</td>\n",
       "      <td>S</td>\n",
       "      <td>87.08</td>\n",
       "      <td>2.21</td>\n",
       "      <td>9.15</td>\n",
       "      <td>–</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>Threonine</td>\n",
       "      <td>T</td>\n",
       "      <td>101.11</td>\n",
       "      <td>2.09</td>\n",
       "      <td>9.10</td>\n",
       "      <td>–</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>Tryptophan</td>\n",
       "      <td>W</td>\n",
       "      <td>186.22</td>\n",
       "      <td>2.83</td>\n",
       "      <td>9.39</td>\n",
       "      <td>–</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>Tyrosine</td>\n",
       "      <td>Y</td>\n",
       "      <td>163.18</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.11</td>\n",
       "      <td>10.07</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>Valine</td>\n",
       "      <td>V</td>\n",
       "      <td>99.13</td>\n",
       "      <td>2.32</td>\n",
       "      <td>9.62</td>\n",
       "      <td>–</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name Sym ResidueWeight  pKa1   pKb2   pKx3    pl4\n",
       "Abbr.                                                             \n",
       "Ala           Alanine   A         71.08  2.34   9.69      –   6.00\n",
       "Arg          Arginine   R        156.19  2.17   9.04  12.48  10.76\n",
       "Asn        Asparagine   N        114.11  2.02   8.80      –   5.41\n",
       "Asp     Aspartic acid   D        115.09  1.88   9.60   3.65   2.77\n",
       "Cys          Cysteine   C        103.15  1.96  10.28   8.18   5.07\n",
       "Glu     Glutamic acid   E        129.12  2.19   9.67   4.25   3.22\n",
       "Gln         Glutamine   Q        128.13  2.17   9.13      –   5.65\n",
       "Gly           Glycine   G         57.05  2.34   9.60      –   5.97\n",
       "His         Histidine   H        137.14  1.82   9.17   6.00   7.59\n",
       "Hyp    Hydroxyproline   O        113.11  1.82   9.65      –      –\n",
       "Ile        Isoleucine   I        113.16  2.36   9.60      –   6.02\n",
       "Leu           Leucine   L        113.16  2.36   9.60      –   5.98\n",
       "Lys            Lysine   K        128.18  2.18   8.95  10.53   9.74\n",
       "Met        Methionine   M        131.20  2.28   9.21      –   5.74\n",
       "Phe     Phenylalanine   F        147.18  1.83   9.13      –   5.48\n",
       "Pro           Proline   P         97.12  1.99  10.60      –   6.30\n",
       "Glp    Pyroglutamatic   U        121.09     –      –      –   5.68\n",
       "Ser            Serine   S         87.08  2.21   9.15      –   5.68\n",
       "Thr         Threonine   T        101.11  2.09   9.10      –   5.60\n",
       "Trp        Tryptophan   W        186.22  2.83   9.39      –   5.89\n",
       "Tyr          Tyrosine   Y        163.18  2.20   9.11  10.07   5.66\n",
       "Val            Valine   V         99.13  2.32   9.62      –   5.96"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get tables from url\n",
    "path = \"./data/aminoacids_properties_sigmaaldrich.html\"\n",
    "url = \"https://www.sigmaaldrich.com/life-science/metabolomics/learning-center/amino-acid-reference-chart.html\"\n",
    "if not os.path.isfile(path):\n",
    "    with open(path, \"w\") as file:\n",
    "        file.write(requests.get(url).text)\n",
    "tables = pd.read_html(path, header=0)\n",
    "# Get first table and remove last comment line\n",
    "aminoacids = tables[0].copy()[:-1]\n",
    "# Fix table header\n",
    "aminoacids.columns = [c.replace(' ', '') for c in aminoacids.columns]\n",
    "aminoacids = aminoacids.rename(mapper={\"ResidueWeight(-H2O)\": \"ResidueWeight\", \"Abbr..1\": \"Sym\"}, axis=1)\n",
    "aminoacids.set_index(\"Abbr.\", inplace=True)\n",
    "aminoacids.drop([\"MolecularFormula\", \"ResidueFormula\", \"MolecularWeight\"], axis=1, inplace=True)\n",
    "aminoacids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Class</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Hydropathy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>−4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>−3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>−3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>Basic aromatic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive, 10%Neutral, 90%</td>\n",
       "      <td>−3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>−3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>Cyclic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>−1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Class      Polarity                     Charge Hydropathy\n",
       "Abbr.                                                                    \n",
       "Ala         Aliphatic      Nonpolar                    Neutral        1.8\n",
       "Arg             Basic   Basic polar                   Positive       −4.5\n",
       "Asn             Amide         Polar                    Neutral       −3.5\n",
       "Asp              Acid  Acidic polar                   Negative       −3.5\n",
       "Cys          Sulfuric      Nonpolar                    Neutral        2.5\n",
       "Gln             Amide         Polar                    Neutral       −3.5\n",
       "Glu              Acid  Acidic polar                   Negative       −3.5\n",
       "Gly         Aliphatic      Nonpolar                    Neutral       −0.4\n",
       "His    Basic aromatic   Basic polar  Positive, 10%Neutral, 90%       −3.2\n",
       "Ile         Aliphatic      Nonpolar                    Neutral        4.5\n",
       "Leu         Aliphatic      Nonpolar                    Neutral        3.8\n",
       "Lys             Basic   Basic polar                   Positive       −3.9\n",
       "Met          Sulfuric      Nonpolar                    Neutral        1.9\n",
       "Phe          Aromatic      Nonpolar                    Neutral        2.8\n",
       "Pro            Cyclic      Nonpolar                    Neutral       −1.6\n",
       "Ser        Hydroxylic         Polar                    Neutral       −0.8\n",
       "Thr        Hydroxylic         Polar                    Neutral       −0.7\n",
       "Trp          Aromatic      Nonpolar                    Neutral       −0.9\n",
       "Tyr          Aromatic         Polar                    Neutral       −1.3\n",
       "Val         Aliphatic      Nonpolar                    Neutral        4.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get tables from url\n",
    "path = \"./data/aminoacids_properties_wikipedia.html\"\n",
    "url = \"https://en.wikipedia.org/wiki/Amino_acid\"\n",
    "if not os.path.isfile(path):\n",
    "    with open(path, \"w\") as file:\n",
    "        file.write(requests.get(url).text)\n",
    "tables = pd.read_html(path, header=0)\n",
    "# Get table and remove comment line\n",
    "class_polarity = tables[1].copy()\n",
    "# Fix table header\n",
    "class_polarity.columns = class_polarity.iloc[0]\n",
    "class_polarity = class_polarity.iloc[1:, 1:7]\n",
    "class_polarity.columns = [\"Abbr.\", \"Sym\", \"Class\", \"Polarity\", \"Charge\", \"Hydropathy\"]\n",
    "class_polarity.drop(\"Sym\", axis=1, inplace=True)\n",
    "class_polarity.set_index(\"Abbr.\", inplace=True)\n",
    "class_polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Class</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Hydropathy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>Basic aromatic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>Cyclic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Class      Polarity    Charge Hydropathy\n",
       "Abbr.                                                   \n",
       "Ala         Aliphatic      Nonpolar   Neutral        1.8\n",
       "Arg             Basic   Basic polar  Positive       -4.5\n",
       "Asn             Amide         Polar   Neutral       -3.5\n",
       "Asp              Acid  Acidic polar  Negative       -3.5\n",
       "Cys          Sulfuric      Nonpolar   Neutral        2.5\n",
       "Gln             Amide         Polar   Neutral       -3.5\n",
       "Glu              Acid  Acidic polar  Negative       -3.5\n",
       "Gly         Aliphatic      Nonpolar   Neutral       -0.4\n",
       "His    Basic aromatic   Basic polar  Positive       -3.2\n",
       "Ile         Aliphatic      Nonpolar   Neutral        4.5\n",
       "Leu         Aliphatic      Nonpolar   Neutral        3.8\n",
       "Lys             Basic   Basic polar  Positive       -3.9\n",
       "Met          Sulfuric      Nonpolar   Neutral        1.9\n",
       "Phe          Aromatic      Nonpolar   Neutral        2.8\n",
       "Pro            Cyclic      Nonpolar   Neutral       -1.6\n",
       "Ser        Hydroxylic         Polar   Neutral       -0.8\n",
       "Thr        Hydroxylic         Polar   Neutral       -0.7\n",
       "Trp          Aromatic      Nonpolar   Neutral       -0.9\n",
       "Tyr          Aromatic         Polar   Neutral       -1.3\n",
       "Val         Aliphatic      Nonpolar   Neutral        4.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fix value from Lehninger\n",
    "class_polarity.loc[\"His\", \"Charge\"] = \"Positive\"\n",
    "# Fix minus sign\n",
    "class_polarity[\"Hydropathy\"] = class_polarity[\"Hydropathy\"].apply(\n",
    "    lambda x: x if x[0].isnumeric() else \"-\" + x[1:]\n",
    ")\n",
    "class_polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Steric</th>\n",
       "      <th>Polar</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Hydoph</th>\n",
       "      <th>IsoPoint</th>\n",
       "      <th>HelixProb</th>\n",
       "      <th>SheetProb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>1.28</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>3.67</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.22</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>2.59</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>6.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>4.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>6.04</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>2.94</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.89</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.67</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>2.94</td>\n",
       "      <td>0.30</td>\n",
       "      <td>6.47</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.66</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>3.21</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.08</td>\n",
       "      <td>2.25</td>\n",
       "      <td>5.94</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>3.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.26</td>\n",
       "      <td>5.60</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>1.31</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.60</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>2.34</td>\n",
       "      <td>0.29</td>\n",
       "      <td>6.13</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>10.74</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>1.89</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.77</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>2.99</td>\n",
       "      <td>0.23</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.13</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>1.60</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.78</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>1.56</td>\n",
       "      <td>0.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>1.60</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.95</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>6.52</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>1.56</td>\n",
       "      <td>0.18</td>\n",
       "      <td>3.95</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>5.65</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>2.35</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.43</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.71</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>2.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>6.80</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>1.77</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.54</td>\n",
       "      <td>6.35</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Steric  Polar  Volume  Hydoph  IsoPoint  HelixProb  SheetProb\n",
       "Abbr.                                                               \n",
       "Ala      1.28   0.05    1.00    0.31      6.11       0.42       0.23\n",
       "Gly      0.00   0.00    0.00    0.00      6.07       0.13       0.15\n",
       "Val      3.67   0.14    3.00    1.22      6.02       0.27       0.49\n",
       "Leu      2.59   0.19    4.00    1.70      6.04       0.39       0.31\n",
       "Ile      4.19   0.19    4.00    1.80      6.04       0.30       0.45\n",
       "Phe      2.94   0.29    5.89    1.79      5.67       0.30       0.38\n",
       "Tyr      2.94   0.30    6.47    0.96      5.66       0.25       0.41\n",
       "Trp      3.21   0.41    8.08    2.25      5.94       0.32       0.42\n",
       "Thr      3.03   0.11    2.60    0.26      5.60       0.21       0.36\n",
       "Ser      1.31   0.06    1.60   -0.04      5.70       0.20       0.28\n",
       "Arg      2.34   0.29    6.13   -1.01     10.74       0.36       0.25\n",
       "Lys      1.89   0.22    4.77   -0.99      9.99       0.32       0.27\n",
       "His      2.99   0.23    4.66    0.13      7.69       0.27       0.30\n",
       "Asp      1.60   0.11    2.78   -0.77      2.95       0.25       0.20\n",
       "Glu      1.56   0.15    3.78   -0.64      3.09       0.42       0.21\n",
       "Asn      1.60   0.13    2.95   -0.60      6.52       0.21       0.22\n",
       "Gln      1.56   0.18    3.95   -0.22      5.65       0.36       0.25\n",
       "Met      2.35   0.22    4.43    1.23      5.71       0.38       0.32\n",
       "Pro      2.67   0.00    2.72    0.72      6.80       0.13       0.34\n",
       "Cys      1.77   0.13    2.43    1.54      6.35       0.17       0.41"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From \"Generation and evaluation of dimension-reduced...\" (Meiler, 2001)\n",
    "meiler = pd.read_csv(\"./data/meiler2001.txt\", sep=\" \")\n",
    "meiler = meiler.iloc[:, :-1]\n",
    "meiler[\"Abbr.\"] = meiler[\"Abbr.\"].apply(lambda x: x[0] + x[1:].lower())\n",
    "meiler.set_index(\"Abbr.\", inplace=True)\n",
    "meiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>DeltaG</th>\n",
       "      <th>DeltaDeltaG</th>\n",
       "      <th>ChouParam</th>\n",
       "      <th>ZiggBraggParam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>12.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>11.9</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>13.4</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>12.2</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>11.7</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>12.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>11.7</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>12.1</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>12.1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>12.8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>11.3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>12.8</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DeltaG  DeltaDeltaG  ChouParam  ZiggBraggParam\n",
       "Abbr.                                                \n",
       "Trp      12.4         0.45        1.5            1.11\n",
       "Val      11.9         0.14        1.2            0.95\n",
       "Ala      13.4         0.77        1.8            1.07\n",
       "Ser      12.2         0.35        0.6            0.76\n",
       "Asp      11.7         0.15        1.0            0.68\n",
       "Arg      13.3         0.68        1.3            1.03\n",
       "Glu      12.2         0.27        0.8            0.97\n",
       "Thr      11.7         0.11        1.0            0.82\n",
       "Lys      13.0         0.65        1.1            0.94\n",
       "Tyr      12.1         0.17        0.8            1.02\n",
       "His      11.6         0.06        1.0            0.69\n",
       "Leu      13.0         0.62        1.2            1.14\n",
       "Phe      12.1         0.41        1.3            1.09\n",
       "Cys      11.6         0.23        0.7            0.99\n",
       "Met      12.8         0.50        1.5            1.20\n",
       "Ile      12.0         0.23        1.2            1.14\n",
       "Gly      11.3         0.00        0.5            0.59\n",
       "Gln      12.8         0.33        1.3            0.98\n",
       "Asn      12.0         0.07        0.9            0.78\n",
       "Pro       6.5         3.00        0.3            0.19"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltaG = pd.read_csv(\"./data/aminoacids_deltaG.txt\", sep=\" \", index_col=0)\n",
    "deltaG.drop(\"ConfParamAlphaHelix\", axis=1, inplace=True)\n",
    "deltaG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "aminoacids = pd.concat([\n",
    "    aminoacids.iloc[:, :2],\n",
    "    class_polarity,\n",
    "    meiler[[\"HelixProb\", \"SheetProb\"]],\n",
    "    aminoacids.iloc[:, 2:],\n",
    "    deltaG\n",
    "], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>Sym</th>\n",
       "      <th>Class</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Hydropathy</th>\n",
       "      <th>HelixProb</th>\n",
       "      <th>SheetProb</th>\n",
       "      <th>ResidueWeight</th>\n",
       "      <th>pKa1</th>\n",
       "      <th>pKb2</th>\n",
       "      <th>pKx3</th>\n",
       "      <th>pl4</th>\n",
       "      <th>DeltaG</th>\n",
       "      <th>DeltaDeltaG</th>\n",
       "      <th>ChouParam</th>\n",
       "      <th>ZiggBraggParam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbr.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>Alanine</td>\n",
       "      <td>A</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.23</td>\n",
       "      <td>71.08</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>Arginine</td>\n",
       "      <td>R</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>156.19</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.04</td>\n",
       "      <td>12.48</td>\n",
       "      <td>10.76</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>Asparagine</td>\n",
       "      <td>N</td>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>114.11</td>\n",
       "      <td>2.02</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.41</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>Aspartic acid</td>\n",
       "      <td>D</td>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.20</td>\n",
       "      <td>115.09</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.60</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.77</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>Cysteine</td>\n",
       "      <td>C</td>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "      <td>103.15</td>\n",
       "      <td>1.96</td>\n",
       "      <td>10.28</td>\n",
       "      <td>8.18</td>\n",
       "      <td>5.07</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>E</td>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>129.12</td>\n",
       "      <td>2.19</td>\n",
       "      <td>9.67</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.22</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>Glutamine</td>\n",
       "      <td>Q</td>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>128.13</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.65</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>Glycine</td>\n",
       "      <td>G</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>57.05</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.97</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>Histidine</td>\n",
       "      <td>H</td>\n",
       "      <td>Basic aromatic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>137.14</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.59</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyp</th>\n",
       "      <td>Hydroxyproline</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.11</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>Isoleucine</td>\n",
       "      <td>I</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.45</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>Leucine</td>\n",
       "      <td>L</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.31</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.98</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>Lysine</td>\n",
       "      <td>K</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>128.18</td>\n",
       "      <td>2.18</td>\n",
       "      <td>8.95</td>\n",
       "      <td>10.53</td>\n",
       "      <td>9.74</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>Methionine</td>\n",
       "      <td>M</td>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.32</td>\n",
       "      <td>131.20</td>\n",
       "      <td>2.28</td>\n",
       "      <td>9.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.74</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>Phenylalanine</td>\n",
       "      <td>F</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>147.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.48</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>Proline</td>\n",
       "      <td>P</td>\n",
       "      <td>Cyclic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.34</td>\n",
       "      <td>97.12</td>\n",
       "      <td>1.99</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glp</th>\n",
       "      <td>Pyroglutamatic</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>Serine</td>\n",
       "      <td>S</td>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>87.08</td>\n",
       "      <td>2.21</td>\n",
       "      <td>9.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.68</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>Threonine</td>\n",
       "      <td>T</td>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.36</td>\n",
       "      <td>101.11</td>\n",
       "      <td>2.09</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.60</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>Tryptophan</td>\n",
       "      <td>W</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "      <td>186.22</td>\n",
       "      <td>2.83</td>\n",
       "      <td>9.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.89</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>Tyrosine</td>\n",
       "      <td>Y</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>163.18</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.11</td>\n",
       "      <td>10.07</td>\n",
       "      <td>5.66</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>Valine</td>\n",
       "      <td>V</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>99.13</td>\n",
       "      <td>2.32</td>\n",
       "      <td>9.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.96</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name Sym           Class      Polarity    Charge  Hydropathy  \\\n",
       "Abbr.                                                                           \n",
       "Ala           Alanine   A       Aliphatic      Nonpolar   Neutral         1.8   \n",
       "Arg          Arginine   R           Basic   Basic polar  Positive        -4.5   \n",
       "Asn        Asparagine   N           Amide         Polar   Neutral        -3.5   \n",
       "Asp     Aspartic acid   D            Acid  Acidic polar  Negative        -3.5   \n",
       "Cys          Cysteine   C        Sulfuric      Nonpolar   Neutral         2.5   \n",
       "Glu     Glutamic acid   E            Acid  Acidic polar  Negative        -3.5   \n",
       "Gln         Glutamine   Q           Amide         Polar   Neutral        -3.5   \n",
       "Gly           Glycine   G       Aliphatic      Nonpolar   Neutral        -0.4   \n",
       "His         Histidine   H  Basic aromatic   Basic polar  Positive        -3.2   \n",
       "Hyp    Hydroxyproline   O             NaN           NaN       NaN         0.0   \n",
       "Ile        Isoleucine   I       Aliphatic      Nonpolar   Neutral         4.5   \n",
       "Leu           Leucine   L       Aliphatic      Nonpolar   Neutral         3.8   \n",
       "Lys            Lysine   K           Basic   Basic polar  Positive        -3.9   \n",
       "Met        Methionine   M        Sulfuric      Nonpolar   Neutral         1.9   \n",
       "Phe     Phenylalanine   F        Aromatic      Nonpolar   Neutral         2.8   \n",
       "Pro           Proline   P          Cyclic      Nonpolar   Neutral        -1.6   \n",
       "Glp    Pyroglutamatic   U             NaN           NaN       NaN         0.0   \n",
       "Ser            Serine   S      Hydroxylic         Polar   Neutral        -0.8   \n",
       "Thr         Threonine   T      Hydroxylic         Polar   Neutral        -0.7   \n",
       "Trp        Tryptophan   W        Aromatic      Nonpolar   Neutral        -0.9   \n",
       "Tyr          Tyrosine   Y        Aromatic         Polar   Neutral        -1.3   \n",
       "Val            Valine   V       Aliphatic      Nonpolar   Neutral         4.2   \n",
       "\n",
       "       HelixProb  SheetProb  ResidueWeight  pKa1   pKb2   pKx3    pl4  DeltaG  \\\n",
       "Abbr.                                                                           \n",
       "Ala         0.42       0.23          71.08  2.34   9.69   0.00   6.00    13.4   \n",
       "Arg         0.36       0.25         156.19  2.17   9.04  12.48  10.76    13.3   \n",
       "Asn         0.21       0.22         114.11  2.02   8.80   0.00   5.41    12.0   \n",
       "Asp         0.25       0.20         115.09  1.88   9.60   3.65   2.77    11.7   \n",
       "Cys         0.17       0.41         103.15  1.96  10.28   8.18   5.07    11.6   \n",
       "Glu         0.42       0.21         129.12  2.19   9.67   4.25   3.22    12.2   \n",
       "Gln         0.36       0.25         128.13  2.17   9.13   0.00   5.65    12.8   \n",
       "Gly         0.13       0.15          57.05  2.34   9.60   0.00   5.97    11.3   \n",
       "His         0.27       0.30         137.14  1.82   9.17   6.00   7.59    11.6   \n",
       "Hyp          NaN        NaN         113.11  1.82   9.65   0.00   0.00     NaN   \n",
       "Ile         0.30       0.45         113.16  2.36   9.60   0.00   6.02    12.0   \n",
       "Leu         0.39       0.31         113.16  2.36   9.60   0.00   5.98    13.0   \n",
       "Lys         0.32       0.27         128.18  2.18   8.95  10.53   9.74    13.0   \n",
       "Met         0.38       0.32         131.20  2.28   9.21   0.00   5.74    12.8   \n",
       "Phe         0.30       0.38         147.18  1.83   9.13   0.00   5.48    12.1   \n",
       "Pro         0.13       0.34          97.12  1.99  10.60   0.00   6.30     6.5   \n",
       "Glp          NaN        NaN         121.09  0.00   0.00   0.00   5.68     NaN   \n",
       "Ser         0.20       0.28          87.08  2.21   9.15   0.00   5.68    12.2   \n",
       "Thr         0.21       0.36         101.11  2.09   9.10   0.00   5.60    11.7   \n",
       "Trp         0.32       0.42         186.22  2.83   9.39   0.00   5.89    12.4   \n",
       "Tyr         0.25       0.41         163.18  2.20   9.11  10.07   5.66    12.1   \n",
       "Val         0.27       0.49          99.13  2.32   9.62   0.00   5.96    11.9   \n",
       "\n",
       "       DeltaDeltaG  ChouParam  ZiggBraggParam  \n",
       "Abbr.                                          \n",
       "Ala           0.77        1.8            1.07  \n",
       "Arg           0.68        1.3            1.03  \n",
       "Asn           0.07        0.9            0.78  \n",
       "Asp           0.15        1.0            0.68  \n",
       "Cys           0.23        0.7            0.99  \n",
       "Glu           0.27        0.8            0.97  \n",
       "Gln           0.33        1.3            0.98  \n",
       "Gly           0.00        0.5            0.59  \n",
       "His           0.06        1.0            0.69  \n",
       "Hyp            NaN        NaN             NaN  \n",
       "Ile           0.23        1.2            1.14  \n",
       "Leu           0.62        1.2            1.14  \n",
       "Lys           0.65        1.1            0.94  \n",
       "Met           0.50        1.5            1.20  \n",
       "Phe           0.41        1.3            1.09  \n",
       "Pro           3.00        0.3            0.19  \n",
       "Glp            NaN        NaN             NaN  \n",
       "Ser           0.35        0.6            0.76  \n",
       "Thr           0.11        1.0            0.82  \n",
       "Trp           0.45        1.5            1.11  \n",
       "Tyr           0.17        0.8            1.02  \n",
       "Val           0.14        1.2            0.95  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fix columns types\n",
    "numerics = [\"ResidueWeight\", \"pKa1\", \"pKb2\", \"pKx3\", \"pl4\", \"Hydropathy\"]\n",
    "# Apply column-wise casting and map missing to zero\n",
    "aminoacids[numerics] = aminoacids[numerics].applymap(lambda x: pd.to_numeric(x, errors=\"coerce\")).fillna(0)\n",
    "aminoacids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 22 entries, Ala to Val\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Name            22 non-null     object \n",
      " 1   Sym             22 non-null     object \n",
      " 2   Class           20 non-null     object \n",
      " 3   Polarity        20 non-null     object \n",
      " 4   Charge          20 non-null     object \n",
      " 5   Hydropathy      22 non-null     float64\n",
      " 6   HelixProb       20 non-null     float64\n",
      " 7   SheetProb       20 non-null     float64\n",
      " 8   ResidueWeight   22 non-null     float64\n",
      " 9   pKa1            22 non-null     float64\n",
      " 10  pKb2            22 non-null     float64\n",
      " 11  pKx3            22 non-null     float64\n",
      " 12  pl4             22 non-null     float64\n",
      " 13  DeltaG          20 non-null     float64\n",
      " 14  DeltaDeltaG     20 non-null     float64\n",
      " 15  ChouParam       20 non-null     float64\n",
      " 16  ZiggBraggParam  20 non-null     float64\n",
      "dtypes: float64(12), object(5)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "aminoacids.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Hydropathy</th>\n",
       "      <th>HelixProb</th>\n",
       "      <th>SheetProb</th>\n",
       "      <th>ResidueWeight</th>\n",
       "      <th>pKa1</th>\n",
       "      <th>pKb2</th>\n",
       "      <th>pKx3</th>\n",
       "      <th>pl4</th>\n",
       "      <th>DeltaG</th>\n",
       "      <th>DeltaDeltaG</th>\n",
       "      <th>ChouParam</th>\n",
       "      <th>ZiggBraggParam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.445455</td>\n",
       "      <td>0.283000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>118.726364</td>\n",
       "      <td>2.061818</td>\n",
       "      <td>9.004091</td>\n",
       "      <td>2.507273</td>\n",
       "      <td>5.735000</td>\n",
       "      <td>11.98000</td>\n",
       "      <td>0.459500</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.907000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.844833</td>\n",
       "      <td>0.089801</td>\n",
       "      <td>0.093012</td>\n",
       "      <td>29.389193</td>\n",
       "      <td>0.516616</td>\n",
       "      <td>2.055394</td>\n",
       "      <td>4.163517</td>\n",
       "      <td>2.116252</td>\n",
       "      <td>1.42223</td>\n",
       "      <td>0.639148</td>\n",
       "      <td>0.367781</td>\n",
       "      <td>0.240747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.500000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>57.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.425000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>101.620000</td>\n",
       "      <td>1.967500</td>\n",
       "      <td>9.115000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.510000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>0.147500</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.750000</td>\n",
       "      <td>0.285000</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>114.600000</td>\n",
       "      <td>2.175000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>12.10000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.387500</td>\n",
       "      <td>130.680000</td>\n",
       "      <td>2.310000</td>\n",
       "      <td>9.615000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>5.995000</td>\n",
       "      <td>12.80000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>186.220000</td>\n",
       "      <td>2.830000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>12.480000</td>\n",
       "      <td>10.760000</td>\n",
       "      <td>13.40000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Hydropathy  HelixProb  SheetProb  ResidueWeight       pKa1       pKb2  \\\n",
       "count   22.000000  20.000000  20.000000      22.000000  22.000000  22.000000   \n",
       "mean    -0.445455   0.283000   0.312500     118.726364   2.061818   9.004091   \n",
       "std      2.844833   0.089801   0.093012      29.389193   0.516616   2.055394   \n",
       "min     -4.500000   0.130000   0.150000      57.050000   0.000000   0.000000   \n",
       "25%     -3.425000   0.210000   0.245000     101.620000   1.967500   9.115000   \n",
       "50%     -0.750000   0.285000   0.305000     114.600000   2.175000   9.300000   \n",
       "75%      1.875000   0.360000   0.387500     130.680000   2.310000   9.615000   \n",
       "max      4.500000   0.420000   0.490000     186.220000   2.830000  10.600000   \n",
       "\n",
       "            pKx3        pl4    DeltaG  DeltaDeltaG  ChouParam  ZiggBraggParam  \n",
       "count  22.000000  22.000000  20.00000    20.000000  20.000000       20.000000  \n",
       "mean    2.507273   5.735000  11.98000     0.459500   1.050000        0.907000  \n",
       "std     4.163517   2.116252   1.42223     0.639148   0.367781        0.240747  \n",
       "min     0.000000   0.000000   6.50000     0.000000   0.300000        0.190000  \n",
       "25%     0.000000   5.510000  11.70000     0.147500   0.800000        0.775000  \n",
       "50%     0.000000   5.710000  12.10000     0.300000   1.050000        0.975000  \n",
       "75%     4.100000   5.995000  12.80000     0.530000   1.300000        1.075000  \n",
       "max    12.480000  10.760000  13.40000     3.000000   1.800000        1.200000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aminoacids.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5oV4CVEr5PS3585w/N93Jz88nPj6+1LL4+Hjy83WyX6VUzdTl1OPKx2o6eU1RUREtWrQgNjY2KMa/DTYHDhwgISGB6OhoYmJieOGFF8qts3HjRlq3bu16DHWGtnrgKPkrmnsrzDzhfFw091ZnRlv5v6ioKDZv3lxq2ebNm4mKivJRREqpQKcZ6iBRm8lrALp06RI0498Gm5CQEJ577jny8vLYunUrCxYscHtsb7zxRtdjqKNgKFWB1NRUUlJS2LBhA+fOnWPDhg2kpKQ4ayKVUqq6NEMdJGozeY3ybx06dKBPnz4AXHbZZURFRZUbR10p5bkxY8aQlpbGww8/TPPmzXn44YdJS0sLivbTSinf0Ax1kKjN5DUAhYWF9O7dm5///OcAl7p7j2CYbCHQFRUVsXPnznI1DAAffvghvXr14pZbbgFrNJpy9BgqZRkzZgy7d++mpKSE3bt3a2a6gUhOTqZ9+/Z07969wnU2btxIbGwsMTExAN28FpwKaA2mU2JDV9nkNR06dODLL7+kbdu2fPzxx8TFxUWISCtjTKkOmIE62UKw+PHHHxk5ciR/+tOfaNWqVanX+vTpw/79+7n00ktZuXIl2dnZke72ocewdla9O5X8d6e6fw1gbvmbVaWU/xg3bhyTJk1i7Nixbl///vvv+e1vf0t2djadOnVCRD73cogqQGmGOkjUZvKauLg4mjVrBkDfvn0BzgLXAjleS4Cq1Llz5xg5ciT33HMPd9xxR7nXXTPY9mg2IiJXGmOOlltZ1dgtt//RZTiy0sKnryg1PJlSyv8MGjSIoqKiCl9funQpd9xxB506Ofvm+2zyoWDmOmISBMeoSZqhDhK1mbzmyJEjtGnThsaNG/PFF18ANAO+8HoilFvGGFJSUoiKimLKlClu1/n666+56qqrEBG2b3fOO/Sd14JUSqkgsG/fPs6dO8fgwYM5efIkQFtfxxSMSo+VDsEwXrpmqINEbSavef/993niiSdo0qQJjRo1AmvymmNeC15VasuWLbz++uv06NHDMSQeTz/9NF9+aQ3GMmHCBN566y1efvllQkJCaNGiBcAXxhht0qGUUtVw/vx5Pv74Y9atW8fp06dp165dBxG51hizr+y6IvIA8ADgWqKtGijNUAeRmk5eM3LkSEaOHOl8LiIn6iVAVSPx8fFUlTeeNGkSkyZNcj4XkVP1HVdD5Sg5KWp+N+FnXGqBWjTxVUh1LiMjg7S0NPLz84mKiiI1NVU77akGISwsjLZt23LJJZdwySWXAJwEegHlMtTaJ0W50gy1Ukp5qFQV5UwqbE8dyDIyMkhNTSU9PZ34+Hg2b95MSkoKgGaqVdC77bbbmDRpEufPn6e4uBisUa90Ck1VJR02TymllFNaWhrp6ekkJCTQpEkTEhISSE9PJy0tzdehqXrWEGZlHTNmDAMHDmTv3r2EhYWRnp7OwoULWbhwIWDNonnzzTfTs2dPrrvuOoAjxpjdPg1aBQQtoVZKKeWUn59PfHx8qWXx8fHk52shXbBzzMrap08fTp48Sd++fRk6dCjR0dGl1rvxxhtZvnw5ACISULOyZmRkVLnOtGnTmDZtGgAi8m19x6SCg5ZQK6WUcoqKimLz5s2llm3evJmoqCgfRaS8RWdlVarmtIRaKVWnFkxYX27ZxIU3+SASVROpqamkpKSUa0OtTT4aFk9mZb3mmmugkllZ0REwVAOiGWqlVJ1yZp5ntoaZwTlgjHXT8A7YNw/BdMPg6Hj48MMPO0f5SEtL0w6JDYjOyqpU9WmGWimlqimYMtDujBkzRjPQDZTOyqpUzWiGWikvK9skItgzZ6q85ORkli9fTvv27dm92xpA4NixY9x1110UFRURHh4O0NiXMaqGR2dlDQ7uzi+ujDFMnjyZlStX0rJlS4CWXg8yCGmGWgWkik4YL730EgsWLKBx48bceuutPPvsswDMmTOH9PR0GjduDNDK/V69Y+LCm4KyOUSvWas5cfqc83lR84uToLRu0YRPnhzmq9D8zrhx45g0aRJjx451Lps7dy5Dhgxh+vTpzJ07l7Vr117twxBVFdydg6ZNm8a//vUvmjZtSpcuXcBHN0Wr3p1K/rtT3b8GUMH46Tora3Bwd35xtWrVKgoKCigoKGDbtm0MHDhQG7nXAc1Qq4Dk7oSxYcMGsrKy+OSTT2jWrBnffmuNdpSXl0dmZiZ79uzhq6++IiIiopOINDbGlPgq/mB04vS5MhOdnKDIfuTIWCvLoEGDKCoqKrUsKyvLOYPpvffeyx/+8IcrvB+ZRTuWVs3dOWjo0KHMmTOHkJAQHn/8cQCf3BTdcvsfK5x0KHz6CufvsiydlTU4uDu/uMrKymLs2LGICAMGDAAIEZEOxpiAGgLR32iGWgUkdyeMl19+menTp9OsWTMA2rdvD1gnj6SkJJo1a0bnzp0BzgLXAR96M2alKvPNN9/QoYM1R8bVV18NPjw/N4SOpbXl7hw0bNjFWhg7o9LUq0Ep5YFDhw7RsWNH10XFQChQKkOtI7VUj45DrYLGvn37+OCDD+jfvz8///nP+eijj4BKTx7liMgDIpIjIjlHjhyp/6CVckNEKntNv6MB4LXXXgOo8G5Ej6Pyd8aYRcaYOGNMXLt27Xwdjt/TEuoA4do+1bVtKmj7VIfz589z7Ngxtm7dykcffcTo0aP54osvqrUPHepJ+cpVV13F4cOH6dChA4cPHwY47249/Y76v7S0NEJCQgCOVbSOHkflK6GhoRw4cMB1UVNAZ/CpJc1QB4jS7VNPlGoDp+1TLWFhYdxxxx2ICNdddx2NGjXi6NGjevJQASExMZElS5Ywffp0lixZAvC9j0NSNbB48WKWL1/OunXruOSSS3wdjlLlJCYmMn/+fJKSkti2bRtAibafrj1t8qGCxu23386GDRsAq/lHcXExV155JYmJiWRmZnL27FkKCwvBmtlre2X7Uqo+jRkzhoEDB7J3717CwsJIT09n+vTprFmzhq5du7J27Voo055R+b/s7GyeffZZli1b5hiOTCmvc3d+WbhwIQsXLgSs8cMjIiKIjIzk/vvvB9jv04CDhJZQq4A0ZswYNm7cyNGjRwkLC2PWrFkkJyeTnJxM9+7dadq0KUuWLEFEiImJYfTo0URHRzuqYffrCB/KlzIyMtwuX7dunfOxiOh31I+5OwfNmTOHs2fPMnToUMdq2pNLeV1F5xcHEWHBggWuz3+q75jKqmxoR6h8eEd/1WAy1NnZ2UyePJmSkhLGjx/P9OnTS72+cOFC5/jFl156KYsWLSI6OpqioiKioqLo1q2bY1U9QfqBik4Yb7zxhtvlqamppKamAiAiP9RbYEqpBsHdOSglJaXUcxH50lvxKBVIKhvaESof3tFfNYgMdUlJCRMnTmTNmjWEhYXRr18/EhMTiY6Odq5z9913M2HCBACWLVvGlClTyM7OBqBLly7k5uYCeoJUqiI1nUyiOp5//nleffVVgBgRyQDuM8acqfWOlVJKqVpoEBnq7du3ExkZSUREBABJSUlkZWWVylC3anVx8rxTp05VOmyVUqq8mk4m4alDhw7x4osvkpeXR8uWLfdgzUKXBCyu5a6VUkqpWmkQGeqy4xCHhYU5eraWsmDBAubNm0dxcTHr11+cKaywsJDevXs7Mt2XVvQ+Ogi6UvXr/PnznD592vG0JfCVD8NRNeCoZbALLTqLSHOtZVBKBTod5cPFxIkT+fzzz3nmmWd46qmnAOjQoQNffvklO3fuZN68eQARItLK3fa+HgQ9Ozubbt26ERkZydy5cytc7+2330ZEyMnJcS6bM2cOkZGRjrbibtOnlC+FhoYydepUx81qL+CEMWZ12fV0wgz/5ahlyMnJYffu3QCCVcug6lj49BXWkKozWzsfh09fQesWTXwdmlJBqUGUUJcdh/jgwYOEhrqdKA+wmoQ89NBDADRr1sw5lXXfvn3Bmrb6WiCnou19wZN24gAnT57khRdeoH///s5leXl5ZGZmsmfPHr766isiIiI6iUhjHQlD+ZPjx4+TlZVFYWEh7du33wVcIiK/NsaU6omqE2b4N0ctQ5MmTcAq1NFahjpWqunVTCrt/NXQJCcns3z5ctq3b++4qXPro48+YuDAgQBXeC04FdAaRAl1v379KCgooLCwkOLiYjIzM0lMTCy1TkFBgfPxihUr6Nq1KwBHjhyhpMTKV9qz7jUDqjf9nhe4thNv2rSps514WTNmzODxxx+nefPmzmVZWVkkJSXRrFkzOnfuDNZNw3VeC14pD6xdu5bOnTtj1/4Y4J/A9b6NSlWHay1Dhw4dwJpQQmsZlNeMGzfOOeBARUpKSnj88ccZNkxnIFaeq3WGWkQ6isgGEckTkT0iMrkuAqtLISEhzJ8/n+HDhxMVFcXo0aOJiYnhiSeeYNmyZQDMnz+fmJgYYmNjmTdvnmOmMt5//3169uxJbGwso0aNAmsM4wqnk/UVd+3EDx0qPRngjh07OHDgALfeemul2wLFQMVF+Er5QKdOndi6dSs//eQcMnUIkO/DkFQ1udYyfPXVVwCNROTXZdfzdfM5FbwGDRpEmzZtKl3npZdeYuTIkbRv395LUalgUBdNPs4DvzfG7BCRy4CPRWSNMSavDvZdZ0aMGMGIESNKLZs9e7bz8QsvvOB2u5EjRzJy5EjncxE5UT8R1q8LFy4wZcoUFi9eXON9aKdL5Uv9+/dn1KhR9OnTByAGKzO9yLdRqeooU8sA1vTq1wPuB5BXyssOHTrEO++8w4YNG/joo48qXVevicpVrUuojTGHjTE77McnsS5yWrrpZVW1Ez958iS7d+9m8ODBhIeHs3XrVhITE8nJySm3LdAUKF28jZYaKd+bNWsWn332GcAeY8xvjDFnfR2T8pxrLYMxBuAytJZB+ZFHH32UZ555hkaNqs4e6TVRuarTNtQiEg70BsqNSadt4upXVe3EW7duzdGjRykqKqKoqIgBAwawbNky4uLiSExMJDMzk7Nnz1JYWAjQHNjuq7So0g4cOEBCQgLR0dHExMS4rU0xxvDII48QGRlJz549wRpSTim/4lrL0KNHD7BG+fBJLUNGRgbdu3encePGdO/evcrpmlXDkJOTQ1JSEuHh4bz11lsAnUTkdh+HpQJAnWWoReRS4G3gUWNMuamd9U6ufnnSTrwiMTExjB49mujoaG6++Waw2onrCB9+IiQkhOeee468vDy2bt3KggULyMsr3aJq1apVFBQUUFBQwKJFiwC0/lH5JUctgz3CQqEvahkyMjJITU3lpZde4syZM7z00kukpqZqplpRWFjoLHiy+019aYx518dhqQBQJ8PmiUgTrMz0m8aYf9bFPlX1VdVO3NXGjRtLPU9NTSU1NRUAESl3Q6R8p0OHDo4REbjsssuIiori0KFDpYZEzMrKYuzYsYgIAwYMAAgRkQ7GmMO+iVop/5WWlkZ6ejoJCQkAJCQkkJ6ezsMPP8yYMWN8HJ2qT2PGjGHjxo0cPXqUsLAwZs2axblz5wCYMGGCj6NTgazWGWqxprtKB/KNMfNqH1L19Zq1mhOnzzmfFzW3BrUHaN2iCZ88qUPfqOBQVFTEzp07S40jDpWO1FIqQ62daJSC/Px84uPjSy2Lj48nP1+bcwe76tRCLF68mCVLlhyvx3BUEKmLEuobgN8An4pIrr3sf4wxK+tg3x45cfpcmYHrT1BkP3JkrJUKdD/++CMjR47kT3/6E61a1WwyS530RCmIiopi8+bNzhJqgM2bNxMVFeXDqJRSgazWGWpjzGasjiVK1ZjWMlTu3LlzjBw5knvuuYc77rij3OuejtRS31xvYIua3034maUAOt2x8iupqamkpKSQnp5OfHw8mzdvJiUlhbS0NF+HVmcWTFhvP3oH7McTF97ku4CUCnINYupx5f+0lqFixhhSUlKIiopiypQpbtdJTExk/vz5JCUlsW3bNrBmoPNq++ly0xvP1CmPlX9ytJN++OGHyc/PJyoqirS0tKBqP62ZZ6W8SzPUSvm5LVu28Prrr9OjRw9iY2MBePrpp/nyyy8BqyPNiBEjWLlyJZGRkbRs2RJgv88CVioAjBkzJqgy0Eop39IMtVJ+Lj4+3jEJRoVEhAULFrg+/6mS1ZVSqpwDBw4wduxYvvnmG0SEBx54gMmTJ5daxxjD5MmTWblypePmXce8VwrNUCullFKKi2Pe9+nTh5MnT9K3b1+GDh1aaohO1zHvt23bxsCBA3W4IKWo45kSlVJKKRWYOnToQJ8+fYDSY967qmjMe+9Hq5R/0RJqpZRSqpqSk5NZvnw57du3d8z6yD/+8Q9mzpxJfn4+27dv93GEtaNj3vuW68hXrqNegY585a80Q62UUkpV07hx45g0aRJjx451LuvevTv//Oc/efDBB30YWe3pmPe+V3rkq4ujXoGOfOWvtMmHCkjJycm0b9+e7t27l3vtueeeQ0Q4evQoYHWieeSRR4iMjKRnz56gnWiUH3v++eeJiYkBiBGRDBFp7uuYVHmDBg2iTZs2pZZFRUXRrVs3H0VUNwJlzHul/I1mqFVAGjduHNnZ2eWWHzhwgNWrV5eqYnTtRLNo0SIArX9UfunQoUO8+OKL5OTkAOwBGgNJvo1K1QcReUBEckQk58iRI74OB/B8zPu//vWvGGPYunUr+GDMe6X8kTb5UAFp0KBBFBUVlVv+u9/9jmeffZbbbrvNuayiTjR6EVD+6Pz585w+fdrxtCXwlQ/DUfXEH5tE6Jj3StWcZqhV0MjKyiI0NJRevXqVWu5pJxrQjjR1Qac8rrnQ0FCmTp3q+O71AvYaY1a7rqPfUVVfdMx7pWpOM9QqKPz00088/fTTrF69uuqVK+GPpUaBRjPPNXf8+HGysrIoLCykffv2u4BLROTXxpg3HOvod1QppfyPtqFWQeHzzz+nsLCQXr16ER4ezsGDB+nTpw9ff/21dqJRAWPt2rV07tyZdu3aARjgn8D1vo1KuTNmzBgGDhzI3r17CQsLIz09nXfeeYewsDA+/PBDbr31VoCuvo5TKeUdWkKtgkKPHj349ttvnc/Dw8PJycnhyiuvJDExkfnz55OUlMS2bdtAO9EoP9WpUye2bt3KTz85a9GHADk+DElVICMjw+3yX/3qV87HIlLgrXiUUr6lJdQqILkrHarIiBEjiIiIIDIykvvvvx+0E43yU/3792fUqFGO2episM7Ri3wblVIqkGRnZ9OtWzciIyOZO3duude//PJLEhIS6N27t2Mo2dZeDzIIaQm1CkgVlQ45uI4Aop1oVCCZNWsWs2bNQkT2GGN+4+t4lAom7ma4dPXmm2/yzDPPYIzhsssuA2jh9SBroaSkhIkTJ7JmzRrCwsLo168fiYmJREdHO9d56qmnGD16NA899BB5eXnExMRo7+Y6oCXUSimllGoQKprDwKFz585s2rSJTz/9lBkzZgD8zGvB1YHt27cTGRlJREQETZs2JSkpiaysrFLriAg//PADACdOnAA45/1Ig49mqINIVdU8CxcudI4vGh8fT15eHmCV5rZo0YLY2FjH2KN6t6qUUirouJvh0tX111/PFVdcAeCYt6CpdyKrG2WHiQ0LC+PQodJ98GfOnMkbb7xBWFgYI0aMAPjSu1EGpwaToa5pZhNgzpw5REZGOqaUbeW9qD3nqOZZtWoVeXl5ZGRklEoDwN13382nn35Kbm4ujz32WKmZsLp06UJubi65ubmgPy6llFINnN0350RFr/vjbJeeyMjIYNy4cRw8eJCVK1cCdBaRcvnBQE2frzSIDHVtMpt5eXlkZmayZ88eRzVRJxFp7P1UVM6Tap5WrS7eC5w6dQoR8XaYSimllN/bsGGDI0N9sKJ1jDGLjDFxxpg4e6hLnys7TOzBgwcJDQ0ttU56ejqjR48GYODAgWDlBa8suy9/TJ8/axAZ6tpkNrOyskhKSqJZs2Z07twZ4Cxwnfei94wn1TwACxYsoEuXLjz22GO8+OKLzuWFhYX07t2bn//85wCXeiFkpZRSyu/s2rWL8ePHO/IJJb6Opzr69etHQUEBhYWFFBcXk5mZSWJiYql1OnXqxLp16wDIz88HEECLoGupQYzy4S6zaY9HXMqCBQuYN28excXFrF+/3rmt3Y7KwTFtdTmBMCXwxIkTmThxIkuXLuWpp55iyZIldOjQgS+//JK2bdvy8ccfExcXFyEirYwxP7huGwjpU0opTyyYsL7Uc53hU4E1pNwdd9zB66+/zrXXXuvrcKotJCSE+fPnM3z4cEpKSkhOTiYmJoYnnniCuLg4EhMTee6557j//vt5/vnnHYWHRaaqOedVlRpEhtpT7jKb1eHLKYE9qeZxlZSUxEMPPQRAs2bNaNasGQB9+/YFqxT+WspMKKFTHitf+/777xk/fjxAjIjkA8nGmA99HJYKQM4M9MzWMLPCZrIqyIwZM4aNGzdy9OhRwsLCmDVrFufOWYNcTJgwgdmzZ/Pdd9/x29/+1rFJlM+CraERI0Y4Ohs6zZ492/k4OjqaLVu2OJ+LSKnCM28Jn77C+bio+d2En1nqfN66RRNfhFQrDSJDXZvMZqBMW+1azRMaGkpmZiZLly4ttU5BQQFdu1oz4a5YscL5+MiRI7Rp04bGjRvzxRdfADQDvvBuCpSq2uTJk7n55pt5++2392BNyd3S1zEppQJHVXMYvPrqq7z66qvO5/aNu6pjRXNvLb1gpptlAaZBZKhrk9lMTEzk7rvvZsqUKXz11VcAzYHt3k1B1Typ5pk/fz5r166lSZMmXHHFFc4S+Pfff58nnniCJk2a0KhRI4D9xphjPk1QEOo1azUnTlslIUXNS9+dt27RhE+eHOar0ALCiRMneP/991m8eDH3338/xphirCZYKoA4ahnsSTViRGSg1jIopQJdg8hQ1yazGRMTw+jRo4mOjiYkJASszKZfdlKoqprnhRdecLvdyJEjGTlypPO5iGj9Zz04cfqcyx34CYpcXnPNXCv3CgsLadeuHffddx9AtIi8Ckw2xpxyXU/b+vs3Ry3DW2+9hYjkAVoCqJQKeA0iQw01z2wCpKamkpqaCviurZFSDd358+fZsWMHL730EkuWLMkDTgHTgRmu62lbf//lWstgM8aY730XkVJK1Y0Gk6FWSgW2sLAwwsLC6N+/v2PRW1gZahUgXGsZPvnkE4Cficgl3qxlcG16BaWbX2nTK6VUTWmGWikVEK6++mo6duzI3r17HYuGAHmVbKL8jGstQ//+/RGRC3i5lqF00ytwbX6lTa+UUjXVICZ2USqQJScn0759e7p37+729Y0bN9K6dWtiY2OJjY0t1ZQp2Lz00kvcc889ANFALPC0TwNS1eKmluE40MeHISmlVJ3QDLVSfm7cuHGOae8rdOONN5Kbm0tubi5PPPGElyLzvtjYWHJycgDyjDG3G2OO+zom5Tk3tQyt0FoGpVQQ0CYfAWLVu1PJf3eq+9cAAnz8RlWxQYMGUVRU5OswlKoTjlqG4uJigBZoLYNSKghohjpA3HL7Hysc9Dx8+opSQ7CphufDDz+kV69eXHPNNfzxj3+scD0dUk75mkstAyLyubdrGbRwQilVHzRDrVSA69OnD/v37+fSSy9l5cqV3H777RWuq0PKqYaurgonkpOTWb58Oe3bt3dMUsOxY8e46667KCoqIjw8HKBxHYSslAoA2oZaqQDXqlUrLr30UsAab/3cuXOgN8tK1St3fRvmzp3LkCFDKCgoYMiQIQBX+yQ4pZTX1UmGWkRuFpG9IvIfEdFxYZXyoq+//hpjrMLm7du3c+HCBYDzPg1KqSA3aNAg2rRpU2pZVlYW9957L4Dj/xXej6zmdEQhpWqu1qVYItIYWAAMBQ4CH4nIMmOM9txWqg6MGTOGjRs3cvToUcLCwpg1a5ajFJoJEybw1ltv8fLLLxMSEkKLFi3IzMzkhhtu8HHUSvkv1/Gmi5rfTfiZpYA1sUttfPPNN3To0AGwRjQhwGqKxo0bx6RJkxg7dmyF69x4440sX77c+fzJJ5/0RmhK+b26+LFfB/zHGPMFgIhkArehQyGpeuSu/eK0adP417/+RdOmTenSpQt/+ctfuPzyywGYM2cO6enpNG7cGKyhugJGRkZGpa9PmjSJSZMmeSkapQJbufbTM90sqwMiUtXrftdBWEcUUqrm6qLJRyhwwOX5QXtZKSLygIjkiEjOkSNH6uBtVUPmrv3i0KFD2b17N7t27eLaa69lzpw5AOTl5ZGZmcmePXsc23Sya1aUUqrOXHXVVRw+fBjA8b/CplfGmEXGmDhjTFy7du28FGHtOUYUuuWWW9izZ0+F6+k1v3ZWvTuV/P+Kcvu3qoJRapRvea1TYqCePJR/ctd+cdiwYYSEWJUuAwYM4ODBg4DVrjEpKYlmzZrRuXNngLNYNStK+Z3vv/+eUaNGAcSISL6IDPR1TMoziYmJLFmyBMDx/3tfxlPXHCMKffLJJzz88MNVjiik1/yau+X2PxL1Wb7bv1tur3hoVOU7dZGhPgR0dHkeZi9Tymdee+01brnlFgAOHTpEx46uX1GKcVOLopQ/mDx5MjfffDPAHqAXkO/biJQ7Y8aMYeDAgezdu5ewsDDS09OZPn06a9asoWvXrqxduxbgsK/jrEs6opBSFauLH8JHQFcR6YyVkU4C7q6D/SpVI2lpaYSEhHDPPfdUe1t/bNeoGo4TJ07w/vvvs3jxYu6//36MMcVYN4DKz1TUt2HdunXOxyJS4q14vOHrr7/mqquuQkR0RCGlyqh1CbUx5jwwCXgPqyTl78aYihtWKVWPFi9ezPLly3nzzTednYJCQ0M5cMC1mT9NqaAWRasplS8VFhbSrl077rvvPoBoEXlVRC5xXUfbpqr64q7UfeHChSxcuBCAt956i+7du9OrVy8eeeQRMjMzfRxx9VU1NKAxhkceeYTIyEh69uwJ0NKrAaqAVSdVNcaYlcDKutiXUjWVnZ3Ns88+y6ZNm2jZ8uI5MDExkbvvvpspU6bw1VdfATQHtvsqTqUqcv78eXbs2MFLL73EkiVL8oBTwHRghmMdne1S1ZeGMKJQVUMDrlq1ioKCAgoKCti2bRsDBw7UqkrlEW37pAKSu7GZ58yZw9mzZxk6dChgdUxcuHAhMTExjB49mujoaEenxf3GmKCqilXBISwsjLCwMPr37+9Y9BZWhlrVsQUT1tuP3oEJ65m48CafxqO8o6qhAbOyshg7diwiwoABAwBCRKSDMSao2sOruqcZahWQ3JWkpKSkVLh+amoqqampAIjID/UWmFK1cPXVV9OxY0f27t3rWDQEHdO/XmgGWrlTSSf2chlq7XOjXHlt2DyllFJVe+mllxwdaqOBWOBpnwaklHJL+9woV5qhDiLZ2dl069aNyMhI5s6dW+71hQsX0qNHD2JjY4mPjycv72LB15w5c4iMjKRbt27gg5kEdRB7pSyxsbHk5OQA5BljbjfGHPd1TEo1FNXpxK6UK23yESRKSkqYOHEia9asISwsjH79+pGYmEh0dLRznbvvvpsJEyYAsGzZMqZMmUJ2dnapmQS/+uorIiIiOolIY2+2M77l9j9WOP1v+PQVFHkrEKWUUg1WYmIi8+fPJykpiW3btgGUaPtp5QnNUAeJ7du3ExkZSUREBABJSUlkZWWVylC3anWx4PnUqVPOYeUqmUnwQ++lIPitencq+RWUtq8CqOCGQimlVN1w16HdnqCGCRMmMGLECFauXElkZKRjtKj9Pg1YBQzNUAeJsh0pwsLCHHfXpSxYsIB58+ZRXFzM+vXrndvavZkd3M4kqB0wakdL4ZVSyreqGhpQRFiwYIHr85/qOyYVHLQNdQMzceJEPv/8c5555hmeeuqpam2rHTCUUkoppcprMBnqqjrszZs3j+joaHr27MmQIUPYv/9iLU/jxo2JjY0lNjYWINJrQVdD2Y4UBw8eJDS0XCGzU1JSEu+++67bbdFOGEoppZRSHmsQGWpHh71Vq1aRl5dHRkZGqREuAHr37k1OTg67du1i1KhRPPbYY87XWrRoQW5uLrm5uQD/8WrwHurXrx8FBQUUFhZSXFxMZmYmiYmJpdYpKChwPl6xYgVdu3YFrE4YmZmZnD17lsLCQtCZBJVSSimlPNYgMtSuHfaaNm3q7LDnKiEhwTld9YABAzh48KAvQq2xkJAQ5s+fz/Dhw4mKimL06NHExMTwxBNPsGzZMgDmz59PTEwMsbGxzJs3jyVLlgCUmknw5ptvBp1JUCmllApIVdXIA/z9738nOjqamJgYgM5eDTBIBUWnxKpGT9gT978eddhzSE9P55ZbbnE+P3PmDHFxcY5pqy+vaDtfd9obMWIEI0aMKLVs9uzZzscvvPBChdvqTIJKKaVUYPNkCN2CggLmzJnDli1buOKKKxCRA5XsUnkoKDLUVY2e8EdOe7yvN954g5ycHDZt2uRctn//fkJDQ/niiy/o0qVLRxHpYoz5vOy2xphFwCKAuLg4U910KKWUUkrVlCdD6L7yyitMnDiRK664wrHovPcjDT5BkaGuiqcd9tauXUtaWhqbNm2iWbNmpbYHHF/Qk0BvoFyGWimllFLKVzwZQnffvn0A3HDDDZSUlEAFsyP7utY90DSINtSedNjbuXMnDz74IMuWLaN9+/bO5cePH+fs2bMAHD16FOBSoHSPRqWUUkqpAHD+/HkKCgrYuHGjY1zucBG5vOx6OlRu9TSIEmrXDnslJSUkJyc7O+zFxcWRmJjItGnT+PHHH7nzzjsB625s2bJl5Ofn8+CDD9KoUSMuXLgA8LUxRjPUSvmIXaISLSLLjTG/8HU8SinlLzypkQ8LC6N///40adLEMTvyGaAr8JE3YwVYMGG9/egdsB9PXHiTt8OoEw0iQw1Vd9hbu3at2+2uv/56Pv30U+dzETlaPxEqpTxhd671vGOE8jslJSXExcWBn47rr1Sgcq2RDw0NJTMzk6VLl5Za5/bbbycjI4P77rvPUfPeHPjCF/EGaubZnQbR5EMpFRwOHjzIihUrAPTGNoC98MILREVF+ToMpYKOJ0PoDh8+nLZt2xIdHU1CQgLAAWPMdz4NPAhohlopFTAeffRRnn322UrXEZEHRCRHRHKOHDnipciUpxw3RePHj/d1KPXmhRdeoHv37gAxIvKoj8NRDcyIESPYt28fn3/+uXM43NmzZzv7jokI8+bNIy8vz1EDf9x30QYPzVArpQLC8uXLad++PX379q10Pe1I498cN0WNGlV8+Qnkm6Ldu3fzyiuvsH37doA9wC9ERJu2KBXkNEOtVABITk6mffv2jlKvcowxPPLII0RGRtKzZ0+All4N0Au2bNnCsmXLCA8PB4gAbhKRN3wblaqOhnBTlJ+fT//+/Z0z7wKbgDt8GJJSygs0Q61UABg3bhzZ2dkVvr5q1SoKCgooKChg0aJFAEE3aOicOXM4ePAgRUVFYHWgWW+M+bVvo1LV4XpTlJSUBHBZsN0Ude/enQ8++IDvvvsOrGvsCKBj2fUCuRReKVWeZqiVCgCDBg2iTZs2Fb6elZXF2LFjEREGDBgAECIiHbwWoFIecL0pyszMBDgZbDdFUVFRPP744wwbNgysochygZKy6wVyKbxSqjzNUCsVBMrOjgUUA+WmAw2iUrGTOga18lcpKSl8/PHHAHuxOnzt821ESqn6phlqFZDctSk+duwYQ4cOpWvXrgwdOpTjx62Oyw2hfbGntFRM+YvBgwcD/MfHYdSLb7/91vGwKVb76aUVr+0/qttXY8eOHV6OsG5kZ2fTrVs3IiMjmTt3brnXv/zySxISEujduzdYk0iNKLeSUmVohloFJHdtiufOncuQIUMoKChgyJAhzhOlP7UvDp++gvDpK2Bma+fj8OkraN2iSa32W3Z2LKwL+aFa7VQpVSMjR44kOjoarIlrJhpjvvdtRJ6pbl+Nhx56yIvR1Y2SkhImTpzIqlWryMvLIyMjg7y80pMfP/XUU4wePZqdO3eC1V/jz76IVQWWBjNTogougwYNcnROc8rKymLjxo0A3HvvvQwePJhnnnmmwvbFxpjD3oy5aO6tF5/MLPO8lhITE5k/fz5JSUls27YNoMTb6VN1R6dXD2wffPABACKSZ4xZ5+NwPObuvOqq7Ln0+++/B6hdaYCXbd++ncjISCIiIgBISkoiKyvLcQMEWOM0//DDD46njYGvvB6oCjiaoVZB45tvvqFDB6sf3tVXX80333wDVNq+uFyGU0QeAB4A6NTJfwbKGDNmDBs3buTo0aOEhYUxa9Yszp07B8CECRMYMWIEK1euJDIy0jFc136fBqxqRadXV/6o7Lk0LCyMffv2uc1Q++u51F0a7EIIp5kzZzJs2DBeeuklsDqW9ne3r/pOY/j0FQAUNb+b8DMXWw3VtkZT1Q/NUKugJCKISLW3M8YsAhYBxMXFmbqOq6YyMjIqfV1EWLBggevzn+o7JlU/fD29eq9Zqzlx2rpZK2p+8aIO1oX8kyeH+SIsFWD89VzqiYyMDMaNG8fvf/97RKQAeF1EuhtjLriuV59prM8aTVU/NEOtgsZVV13F4cOH6dChA4cPH6Z9+/aAti9WgcUxk2BcXJxP3v/E6XMuF+8TFLm85pq5Vg1P2XPpwYMHAc75LKAacJeG0NDSAyKlp6e7tiU/BbQBrgS+RakKaKdEFTQSExNZsmQJAEuWLOG2225zLv/rX/+KMYatW7eCti9WfsqTmQSDaOhDFWDKnktbt24NAZah7tevHwUFBRQWFlJcXExmZiaJiYml1unUqRPr1jmbvje3//THpiqlGWoVkMaMGcPAgQPZu3cvYWFhpKenM336dNasWUPXrl1Zu3Yt06dPB2DEiBFEREQQGRnJ/fffD9q+WPkpT6ZX16EP60ZGRgbdu3encePGdO/evcpmVQ2Bu/PqwoULWbhwIVD+XPrnPwfe4BchISHMnz+f4cOHExUVxejRo4mJieGJJ55g2bJlADz33HO88sor9OrVC6zf4ThjTEA1W1Hep00+gkR2djaTJ0+mpKSE8ePHOzOTDvPmzePVV18lJCSEdu3a8dprr/Gzn/0MgMaNG9OjRw/AvzqPVKaii59LqYKTti9WgWLOnDnMmTMHABH5AigItpkE/UFGRgapqamkp6cTHx/P5s2bSUlJAaxMZUNV3b4agWrEiBGMGFF6aOnZs2c7H0dHR7NlyxbAOVLLaq8GqAKSllAHAXOh6nE1e/fuTU5ODrt27WLUqFE89thjztdatGhBbm4uubm5zjt0pZQKVmlpaaSnp5OQkECTJk1ISEggPT2dtLQ0X4emlApQmqEOAsWH9znH1WzatKlzXE1XCQkJjuHUGDBggKMziVLKf+n06vUkPz+f+Pj4Usvi4+PJz8/3UURKqUBXqwy1iPyfiHwmIrtE5B0RubyO4lLVcP7kd+XG1Tx0qOJBLNLT07nlllucz8+cOUNcXBwDBgzg3XffrXA77QyllAoGUVFRbN68udSyzZs3ExUV5aOIlFKBrrYl1GuA7saYnsA+4A+1D0nVpzfeeIOcnBymTZvmXLZ//35ycnJYunQpjz76KEAzd9tqZyilVDBITU0lJSWFDRs2cO7cOTZs2EBKSgqpqam+Dk0pFaBq1SmxTEP9rcCo2oWjaiLksrYcOLDD+dzduJoAa9euJS0tjU2bNtGs2cU8s2PdiIgIBg8ezJIlS1rWf9RKKeUbjo6HDz/8MPn5+URFRZGWltagOyQqpWqnLkf5SAb+VtGL/joNaSCpaBrSdp1jKNjyZwoLCwkNDSUzM5OlS5eW2nbnzp08+OCDZGdnOyc8ATh+/DgtW7akWbNmHD161NGz2SdTHrtOGuGaRp1mVSlV18aMGaMZaKVUnakyQy0ia4Gr3byUaozJstdJBc4Db1a0n0CehtQfVDUN6coB1riaJSUlJCcnO8fVjIuLIzExkWnTpvHjjz9y5513AtZNzbJly8jPz+fBBx+kUaNGXLhwgenTpzN+/PgzXkwa4GZa1Zk61apSqv5kZGSQlpbmLKFOTU3VDLZSqsaqzFAbY/67stdFZBzwC2CIDnzuO1WNq7l27Vq3211//fV8+umnpZaNHz++7gNUSik/oeNQK6XqWm1H+bgZeAxINMboZBlKKaX8no5DrZSqa7VtQz0fa0SINSICsNUYM6HWUdWD2swkuGTJEp566inHqm29G7lSSqm6pONQB4cFE9aXWzZx4U0+iESp2o/yEVlXgdSnkhJrJsE1a9YQFhZGv379SExMJDo62rmOYybBli1b8vLLL/PYY4/xt7/9jWPHjjFr1ixycnIQEdq0aXONiFxhjDnuwyQppZSqIcc41AkJCc5lOg514HFmnme2hpknfBuMavAaxEyJ27dvr/FMgu+99x5Dhw6lTZs2XHHFFQA/ADd7NwVKKaXqio5DrZSqa3U5bJ7fOnToULmZBLdt21bh+q4zCZbdFigGyg/yjA4NqJRSgUDHoVZK1bUGkaGuDsdMgps2bar2tjo0oFJKBQYdh1opVZcaRJOP0NBQDhw44Hxe1UyCy5Ytc84kWHZboClwqH4jVkopFaief/55YmJiAGJEJENEmvs6JqVU/WoQGep+/fpRUFBAYWEhxcXFZGZmkpiYWGodx0yCy5YtKzWT4PDhw1m9ejXHjx/n+PHjAK2A97yaAKWUUgHh0KFDvPjii+Tk5ADsARoDSb6NSilV3xpEhjokJIT5862ZBKOiohg9erRzJsFly5YBlJpJMDY21pnhbtOmDTNmzKBfv37069cP4CtjzDHfpUYppZQ/O3/+PKdPn3Y8bQl85cNwVAOTnZ1Nt27diIyMZO7cuRWu9/bbb2MPedzSa8EFsQbThrqmMwkCJCcnk5ycDICIfFc/ESqllAp0oaGhTJ061dExvRew1xizuux62ondd6qalwLg73//OzNnzgSr2c5SY8zd3o6zJjwZJhjg5MmTvPDCC/Tv37/SQRqU5xpECbVSSinlDcePHycrK4vCwkKAXcAlIvLrsusZYxYZY+KMMXHt2rXzepwNlSPDuWrVKvLy8sjIyCAvL6/UOgUFBcyZM4ctW7aA1WznUR+EWiOeDBMMMGPGDB5//HGaN9fm/XVFM9RKqYBw4MABEhISHCUtMSIy2dcxKVXW2rVr6dy5M3Ym2QD/BK73bVTKwZMM5yuvvMLEiRMdc09gjPnWF7HWhLthgg8dKj2Owo4dOzhw4AC33nprpfsSkQdEJEdEco4cOVIv8QYTzVArpQJCSEgIzz33nKM0KR+YKCLRVWym/IjrTZE9Ckb7qrYJNJ06dWLr1q389NNPjkVDsL6vyg94kuHct28f+/bt44YbbgD4LxFxO5lbIGY4L1y4wJQpU3juueeqXFdrUapHM9RKqYDQoUMH+vTp43h6ASuT4naSJeWfXG+Ktm7dCtA+2G6K+vfvz6hRoxzf1Ris6+wi30alquP8+fMUFBSwceNGgC+AV0Tk8rLr+WOGs6phgk+ePMnu3bsZPHgw4eHhjt9hpIjEeT/a4KIZahV0HGPAdu/enTFjxnDmzBkKCwvp378/kZGRABEi0tTXcVZHVb22Fy9eTLt27YiNjSU2NhbgSm/H6GVNgd6A9qYJIK43RZdddhnAaYLwpmjWrFl89tlnAHuMMb8xxpz1dUzK4sm8FGFhYSQmJtKkSROwZkfeB3T1Zpw1VdUwwa1bt+bo0aMUFRVRVFTEgAEDAP5jjMnxWdBBQjPUKqi4jgG7e/duSkpKyMzM5PHHH+d3v/sd//nPfwDOAyk+DtVjnnSiAbjrrrvIzc0lNzcX4Ki34/SWH3/8EaAL8Kgx5oeyrwdiNWxDVFRUBNZwXeVuivQYqvriybwUt99+u6N0GqzR0K7FKqn2e54ME6zqh2aoVdBxjAF7/vx5fvrpJzp06MD69esZNWqUY5XvgNt9F2H1eNpruyE4d+4cI0eOBDhmjPmnu3X8sRpWlfbjjz86juMBdzdFegx9J9hrwzzJcA4fPpy2bds6OkBfC0wzxgTMkLkjRoxg3759fP7556SmpgLWMMFlbxwAx43DT+VeUNXWYMahVg2D6xiwLVq0YNiwYfTt25fLL7+ckBDn172YCqqZ/XFsWHedaNyNG/r222/z/vvvc+211wI0cbcvf0yfp4wxpKSkEBUVxerVq7/xdTz14cCBA4wdO5ZvvvkG7JFMjDEv+DquuuS4KbrnnnvYsWPH976OR13k6RjGd911F/PnzwdARLxeG9Zr1mpOnD7nfF7UHMKnrwCgdYsmfPLksEq3r2peChFh3rx5zJs3DxHJM8Zk1mH4KkhpCbUKKq5jwH711VecOnWK7Oxsj7cP1JKxX/7ylxQVFbFr1y6GDh0K0NndeoGaPoAtW7bw+uuvs379eoBoEckVkRFVbRdIgn0kE9eboilTpvg6HFVGoNSGnTh9jqK5tzr/mHnC+dg1o62UN2mGWgUV1zFgmzRpwh133MGWLVv4/vvvOX/+vGO1psChSnbjVzzpRNO2bVuaNWsGwPjx4yEIp5KNj4/HGMOuXbsA8owxscaYlb6Oqy4F+0gmrjdFdnOB6GC7KQpkngwpB1ZtWM+ePR3N6CqsDdN28Koh0Qx1EKmq7du8efOIjo6mZ8+eDBkyhP379ztfW7JkCV27dqVr164Abb0Xdd1yHQPWGMO6deuIjo4mISGBt956y7FaW8D/il0q4EknmsOHDzsf2+0Az3g3SlUP3I5kEsgZFdebIrvzbF6w3RQFu4ZQG6ZUTWiGOkh4MhJE7969ycnJYdeuXYwaNYrHHnsMgGPHjjFr1iy2bdvG9u3bAa4RkSu8n4racx0DtkePHly4cIEHHniAZ555hnnz5jmGzQsB0n0cqsc86UTz4osvEhMTQ69evXjxxRcBinwZs6qdykYy0YyKqi9aG6ZUzWmnxCDh2vYNcLZ9c+1MkpCQ4Hw8YMAA3njjDQDee+89hg4dSps2bRwv/wDcDGR4J/q6NWvWLGbNmlVqWUREhONmARH5ItDGha2qE82cOXOYM2eO87mIaAl1gPJkJBOl6oNrbVhoaCiZmZksXbq01DqHDx+mQ4cOgNaGKeVKS6iDhKdt3xzS09O55ZZb3G5LBaNgBHJVs1KBwLXTHhCUI5ko/6W1YUrVnJZQN0BvvPEGOTk5bNq0qVrbGWMWYU+hGxcXZ+ojNqUaMkenvR49eoA9kgnwP9rOWHmL1oYpVTOaoQ4SnrR9A2sUjLS0NDZt2uRsBxcaGuo6KxQE2CgYSgULR6c9wDH+bZyPQ1JKKeUBbfIRJDwZCWLnzp08+OCDLFu2jPbt2zuXDx8+nNWrV3P8+HGOHz8O0Ap4z6sJUEoppZQKUFpCHSRc276VlJSQnJzsbPsWFxdHYmIi06ZN48cff+TOO+8ErCHmli1bRps2bZgxYwb9+vVz7O4rY8wxnyVGKaWUUiqABE2G2jHtKEBR87sJP2P1TG7dwu2Y80GpqrZva9eurXDb5ORkkpOTARCR7+onQqWUUkqp4BMUGeqiubeWXjDTzTKllFJKKaXqQVBkqJUKJAsmrAfegQnrAZi48CbfBqSUUkqpWtEMtVJephlopZRSKrhohloppZRSAWPVu1PJf3eq+9cAtMmn8gHNUCullFIqYNxy+x8r7CcVPn2FTt2ofELHoVZKKaXqyN69e4mNjSU2Nhas2S5/EJFHfRuVUqq+NZgMdXZ2Nt26dSMyMpK5c+eWe/3999+nT58+hISE8NZbb5V6rXHjxq4nyEivBKyUUirgdOvWjdzcXHJzcwHygJ+Ad3walFKq3jWIDHVJSQkTJ05k1apV5OXlkZGRQV5eXql1OnXqxOLFi7n77rvLbd+iRQvXE+R/vBK0UkqpQNcK+NwYs9/XgaiLqipgc3j77bcB+opInNeCUwGrQWSot2/fTmRkJBERETRt2pSkpCSysrJKrRMeHk7Pnj1p1KhBfCRKKaXqXxsgw9dBqIs8KWADOHnyJC+88ALAKa8HqQJSneQeReT3ImJE5Mq62F9dO3ToEB07dnQ+DwsL49ChQx5vf+bMGeLi4hgwYADA5RWtJyIPiEiOiOQcOXKkFhErpZQKZMXFxQCtgX+4e12vF77hSQEbwIwZM3j88ccBjNeDVAGp1hlqEekIDAO+rH04/mn//v3k5OSwdOlSgI4i0sXdesaYRcaYOGNMXLt27bwbpFJKKb+xatUqgJ+MMd+4e12vF77hSQHbjh07OHDgALfeWvnwe3pTpFzVRQn188Bj+PFdXGhoKAcOHHA+P3jwIKGhodXaHiAiIgLgJNC7biNUSikVTDIyMgCO+ToOVT0XLlxgypQpPPfcc1WuqzdFylWtMtQichtwyBjziQfr+uxOrl+/fhQUFFBYWEhxcTGZmZkkJiZ6tO3x48c5e/YsAEePHgW4FKvntlJKKVXOqVOnWLNmDcD3Pg5FlVFVAdvJkyfZvXs3gwcPJjw8HOASYJl2TFRVqXJiFxFZC1zt5qVU4H+wmntUyRizCFgEEBcX59XS7JCQEObPn8/w4cMpKSkhOTmZmJgYnnjiCeLi4khMTOSjjz7iV7/6FcePH+df//oXTz75JHv27CE/P58HH3yQRo0aceHCBYCvjTGaoVZKKeXWJZdcwnfffYeIlPg6lmAVPn2F83FR87sJP7MUgNYtmlS6nWsBW2hoKJmZmY7mnNb2rVs7Cs8AEJFTQKIxJqduU6CCTZUZamPMf7tbLiI9gM7AJyICEAbsEJHrjDFf12mUdWDEiBGMGDGi1LLZs2c7H/fr14+DBw+W2+7666/n008/dT4XkaPlVlJKKaWUV5SbJXGmm2UV8KSATamaqPHU48aYT4H2juciUgTEGWM0w6l86vvvv2f8+PHs3r0bEeG1116jW7du3HXXXRQVFQF0FZErjDHHfRyqUkopL6uqgK2MvVo6rTyhgy6roDN58mRuvvlmPvvsMz755BOioqKYO3cuQ4YMoaCgAKyOpdN9HKZSSimlgkSNS6jLMsaE19W+lKqpEydO8P7777N48WIAmjZtStOmTcnKymLjxo2O1b4Dbgce90WMSimllAouWkKtgkphYSHt2rXjvvvuo3fv3owfP55Tp07xzTff0KFDB8dq54Cr3G2v44oqpZQKZFVNrT5v3jyio6Pp2bMnQ4YMAWjq9SCDkGaoVVA5f/48O3bs4KGHHmLnzp1ccsklbk8oVDBuuo4rqpRSKlB5MrV67969ycnJYdeuXYwaNQqsQSVULWmGOkhUdUf6/vvv06dPH0JCQnjrrbdKvda4cWNiY2OJjY0N+B7OYWFhhIWF0b9/fwBGjRrFjh07uOqqqzh8+LBjtSbAt76ILyMjg+7du9O4cWO6d+/umPyhSlUd37Nnz3LXXXcRGRnpSLvPShxqmkZPOD4HoLuI+KQdfH2mD/wjjfXJ9buM+yFZvaK+j6OvNYRzjSrPk6nVExISaNmyJQADBgyAIL1eeJ0xxut/ffv2NfXqyVb1tmsgx/g6jWXSd/78eRMREWE+//xzc/bsWdOzZ0+zZ8+eUusUFhaaTz75xPzmN78x//jHP0q9dskll5R67kka/fkYxsfHm88++8zazZNPmqlTp5qpU6eaOXPmGGOMAQ4Czxovp3Hp0qWmc+fOZv369aa4uNisX7/edO7c2SxdurTS7Tw5vgsWLDAPPvigMcaYjIwMAxzzdvpqk0ZPuH4OwMfAJ0C0CYBj6KnqprE+juHPHl9eo9c8Ufa7DPzk7WNoTP0fRwdfXS8awrmmlAZ2za/MP/7xD5OSkuJ8/te//tVMnDixwvUnTpxosCboK5cm4AEgB8jp1KlTTaOvkLd+h8Z4fhxr86cZ6mryxx/Xv//9bzNs2DDn86effto8/fTTbje99957gz5DvXPnTtO3b1/To0cPc9ttt5ljx46Zo0ePmptuuslERkYa4AegjfFyGmNiYsz69etLLVu/fr2JiYmpdDtPju+wYcPMv//9b2OMMefOnTNY7cTFePkY1jSNnnD9HOyT/B+AP5gAOIaeqm4aAy1DXfa7bN/cevUYGlP/x9HBV9eLhnCuKaWBXfMrU50M9euvv2769+9vgI+ND9Lnrd+hMZ4fx9r81dkoH/5gwYT19qN3YMJ6Ji68yafx1LWy6QOYuPAmDh06RMeOHZ3rhYWFsW3bNo/3e+bMGeLi4ggJCWH69IprmEXkAaw7Vjp16lT9BHigLo5hbGwsOTnlhw1dt24dACKyzxhzrDZx1kR+fj7x8fGllsXHx5Ofn1/pdp4cX9d1QkJCAEqAtkCpceHr+xjWNI2eKPs5YGXG+pddrz7TWJ/pA8/SWN/HcNW7U8l/d6r71wA8nEDDHTfpKwZCy64XyN9Tf9AQzjVQ8TUxmFhp9Dx9VU2t7rB27VrS0tLYtGkTV111lVdnr3YItt9hUGWog+2HVFZ9pW///v2EhobyxRdfcNNNNwE0c7ee8cL08cF8DKOioti8eTMJCQnOZZs3byYqKsprMdT3MQz2NAZ7+gCiPvP9xawhfE/rkz+kT68XdaO6aaxqanWAnTt38uCDD5KdnU379u0r2FP984fvaV3STolBwNM70sq2B4iIiGDw4MEALes2QgWQmppKSkoKGzZs4Ny5c2zYsIGUlBRSU1Mr3c6T4+u6zvnz5wEaY4237VU1TaMnyn4OWD3TD9V6x9VQn+kD/0hjfXKTvqb4IH31fRx9rSGca5R7rlOrR0VFMXr0aOfU6suWLQNg2rRp/Pjjj9x5553ExsYCRPoi1qD7HdZ3mxJ3f/Xenqoe4Q/tqco4d+6c6dy5s/niiy+cHUl2797tdt2ybaiPHTtmzpw5Y4wx5siRI442xruNH6WvrvnyGC5dutTExMSYRo0amZiYGI86X3hyfOfPn+83HYVqkkZPuH4OXOywF2MC4Bh6qrppDLTfYdnvMlanRK8fQ2Pq9zg66LkmML+nrvzxml/XPEljIP8OjfH8ONbmz+uZadMAvnjGB2lcsWKF6dq1q4mIiDBPPfWUMcaYGTNmmKysLGOMMdu3bzehoaGmZcuWpk2bNiY6OtoYY8yWLVtM9+7dTc+ePU337t3Nq6++6h+dEuuRvx7DylR1fE+fPm1GjRplunTpYvr162eAXSaA0ucpx+cAnAFSTQNPYyCnLyIiwgAHTRAeQwc91/hf+qorEI9hdQX7Nd8Yz49jbf7Eeh/viouLM+46jQUCEfnYGBNX1XrBnsZgTx8EfxqDPX0Q/GkM5PRB8KdRv6eWYE8fBH8aAzl94PlxrA1tQ62UUkoppVQtaIZaKaWUUkqpWtAMtVJKKaWUUrWgGWqllFJKKaVqwSedEkXkCLC/Ht/iSsrM2lSHfmaMaVfVSvWcxvpMH3iQRj2GtabHsHb0GBLwxxCCP436PUWPYR3QY1h7Hh3H2vBJhrq+iUhOfffm9KVgTx8EfxqDPX0Q/GkM9vSBpjEYBHv6IPjTGOzpg+BIozb5UEoppZRSqhY0Q62UUkoppVQtBGuGepGvA6hnwZ4+CP40Bnv6IPjTGOzpA01jMAj29EHwpzHY0wdBkMagbEOtlFJKKaWUtwRrCbVSSimllFJeoRlqpZRSSimlaqHeM9Qi8mOZ5+NEZH41tg8Xkd11H1mp97hcRH7r8nywiCyv5T6rnW7XdURkgoiMrWL9jSKyV0Q+EZEtItKtGvHV6nMVkVQR2SMiu0QkV0T6i0iRiFxZ032Wie1ul+eDReSE/T75IvJkNfe3WERK7O13i8i/ROTyGsQVJyIvVvBajdJuf/e+ExGxnw8UESMiYfbz1iJyTETc/lZF5BoRecuD9/mxguW3i0h0deMus4+NIhJnP+4sIgUiMryS9duKyAYR+bE65wJfqiiNFf2uRaSjncY8+3cy2ftRe64Gx/A2l99+jojEey/auuOabpdly+r7mlMdLueuPfa5/vcVnQ9ctnGe30UkVkRGePhe19mfSYGI7BCRFSLSw99idbkm7LSvge+LyC882M71GuvxuU9Efm1/3x3pelVErhWRTBH5XEQ+FpGVIvKA1DLvUCbWI/bnmSci99diX7+y9+P6d0FE7vHk+lHJfmeKyCF7f5+JyMtVHe+6INY1vdB+3x0iMrC+39MTAVtCLSIhdbi7y4HfVrWSNxljFhpj/urBqvcYY3oBS4D/K/uiiDSu69jsL+8vgD7GmJ7AfwMH6vAtwoG7yyz7wBgTC8QBvxaRPmViqur7UGyMiTXGdAeOAROrG5QxJscY80h1t6tin98Dh4Eoe9H1wE77P8AAYLsx5kIF239ljBlVixBuB2qVoXYQ6yYgG/i9Mea9SlY9A8wAptbF+3pTNdJ43l4nGusYTqztjYs3VCN964Be9m8yGXjVC+HVOxG5A3B78+lDp+1zVwwwFLgFqE6hQizgSSb1KuDvwP8YY7oaY/oAc4Au/har7QNjTG9jTDfgEWC+iAypxnvdjgfnPhG5GfgdcIudrj7Av4G/ARuNMV2MMX2BPwBXVeP9PfE3+zc2GHjaPkZVKnvdN8a8Yx+XWHt/fwY+ADJqef0AeN7eZzTQA/i5m3jqMr/mMM1+3+nA//NkA7HUW77XZxlqEbnMvsNoYj9v5XguIn3tu8BPcMn42Hdsy0RkPbBORNqIyLv2neNWEelprzdTRF4XkQ/tO+377eWXisg6+47mUxG5zd71XKCLfbfjyJReKiJv2Xddb9oH4iYRedclnqEi8k4N0t5ORN4WkY/svxvcrDNTRKaKSIi9zmB7+RwRSXOz2/eBSHudH0XkOfvzGygiU8Qqmd0tIo+6bBNipy3fTmtLD5PQAThqjDkLYIw5aoz5yn7tYZfP97/seC4RkddEZLtYJQq32csbi8j/2enbJSIP2vuYC9xoH4/fub6xMeYU8DEQ6XKctwCvi1XSsd7e1zoR6VQmrTkisg/rghlqx9BFRLLFKmH4wCXmO+3P6xMRed9e5qy5EKuUdbVYJRavAo4S5lIl//YxnFnZe2GdnB0Z6OuB58s831LRZyWlS3daisjfxSrNeEdEtolL6ZuIpNnp2SoiV4nI9UAi8H/2Z13phdN+L8fvoex3pgOwGkg1xiyz17/O/g3uFJF/i12DYow5ZYzZjJWx9ivVTaOto1ws1XsSwBhz2Bizw358EsjH/s75Ug2O4e9E5DX7cQ/7N9HSGPOjudij/RLAr3u3V5FuxzqXAlOAp3wTZdWMMd8CDwCTxFLRORQAEWkKzAbusn/jd1X0uwQmAUuMMf92eb/Nxph3/TDWsu+Va287yd5XpddYd+c+EbnfXvcTe1vH9yMVmGqMOWS/VwlQBJw0xix0ieETrExqubyD/Z5D7HR8Ktb1sJm9vEjs2k2xakE3VvBZfg78TKxS4Byxrj2zXNJUJCLPiMgO4M6K0iMi/wTmAZcBRWLlu16z4y0UN9cPEUkRkX1iXcNfEfc1i02B5sBxe5uNIvInEckBJovIL+197hSRtWLfHNjHao2dnldFZL/L5zFDrBqIzSKSISLuCmHex8oPuM3f2b/9vSLyV2A31vm6ss9wjlyseesjIu+JVQsxwc17lztQ9foHlAC5Ln9fAvPt1/4C3G4/fgB4zn68CxhkP/4/YLf9eBxwEGhjP38JeNJ+fBOQaz+eCXwCtMCazvIAcA0QArSy17kS+A9WRijc8R72a4OBE0AY1k3Hh0C8ve5nQDt7vaXAL2uQ7qVAvP24E5Dvkr75LmmYaj+Owbog/zdW6WVTe/lGIM5+PA3rbhasi9to+3Ff4FOsi96lwB6gt51mA9xgr/ea4/08OKaX2mnah3Wn+3N7eRHwsP34t8Cr9uOngV/bjy+3t7vEPub/n728GZADdLY//+Vljsdy+3Fb+31i7M/oY6CF/dq/gHvtx8nAu/bjxVglho2AbsBPjuOGVdLW1X7cH1hvP/4UCHXE7CaOF4En7Me32p/llZT/Lk0FZlbxXvcCr9mPd2KdlDbbz9cAQyr5rJzvZ7/X/7Mfd7fT7Ph+GJc0P+uyr8XAKA+PezhuvjNY38NjwG/LrN8KCLEf/zfwdpnXx2F/3/3lrwZpHIdVw9AW63yz2/GZl9nnl9jnngBLXyOsC9av7O/cDS6v/QrrfHgMGOjrtNUi3Y7fyPN2mpy/KX/4A350s+x7rNJQT84LpX5nFf0ugX8CtwVIrINxuUbYy2K5eC315Bq7GJdzH9DW5fFTXLyWHQNal3mvR7BKZsumdTDu8w7NsfIh19rr/RV41H5cBFxpP47DKvUuG2sE8C3Qhov5n8b297eny34eqyw9QBOs6b23YOVn7sfKq/TAykMcsT9H5/UDK+9UZL93E6ybBtd8yiGs/MBxYKnLe24E/uzy/Aoujiw3nov5vfnAH+zHN3PxWtrP3m9zrMx/ARfzRM5jB9wJbKPy/N0FYIBLLJV9hg+5nA922e/dDvimqu+/N0qoHVVAscYqnn/C5bVXgfvsx/cBfxGrbevlxpj37eWvl9nfGmPMMftxvON1Y8x6oK2ItLJfyzLGnDbGHAU2ANdhfbhPi8guYC1WiVFFVSjbjTEHjVXVnguEG+tTfh2rycHlwEBgVQ3S/d9Y1VO5wDKglVilI24ZY/bY77scSDbGFLu8/Ka9nxu4WIVeArxtP44H3jFWqeCPWCfNG+3XDhhjttiP37DXrZK9n75YJ8gjwN9EZJz98j/t/x9jfZEBhgHT7Tg3Yv1AOtnLx9rLt2FlSrpW8LY3ishOrBK0ufZnArDMGHPafjwQ60QK1uflmp5GwA5gM1AMHLI/8+uBf9gx/D+sUjqwTjiLxardcNdsZhDWZ4YxZgX2XXlFqnivfwPXi0hnoMgYc8baRC7F+py34dlnFQ9k2jHtxjoZOBRjfX+g9LGproq+M2uxfheupX6t7fTuxjo5xdTwPb2tOmkE65z0nf09/KfL+o7j/jbWxfOHeo7bUx6nzz7/jcP6PW1y2Q5jVSP/F1bV+f96Ie7aqvB8JyKxQBdjzDu+CKwWqnMOdfDod2mXJuaLyAv+HqtNXB5X6xpr6y5WzeGnwD3u3kusWppcrOv5tRXsp1zeAasgp9AYs89eZwnWNaQqd9nvlwE8aOd9Rtul0DvtGF2brfytivT8L9YNzp/t/MxnwHljzKdYeYgdWHkd1+vHdVi//WPGmHPAP8rE+Lydx2kPXCIiSRXEEwa8Z8czjYufr+t1K5uL19IbsPJxZ4xVy/evMu/7f/Zn8wCQQuX5u/3GmK0u21b2GTpqHz8FthljThpjjgBnpYq+V/XRrsVjxpgtdnH8YKCxMWZ3VQEDpzzdvZvn92DdafQ1xpwTkSKszJ07Z10el3Dxs/oL1oE9A/zDGHPew3hcNcK6WypV5S0iFawOWHeQ32N9aV3dY4zJKbPsjLGqpari7jPyiL3/jcBG+wdyr/2S43Nz/cwEGGmM2eu6D7ES/LAp01bT/j6U9YExxl2nE0+/D8XGmFg7s3AYGI1Vcv69fTIoxRgzQUT6Y5U+fywifT18H0dJuIPj+9WokvcqsL/3v8Qq0QAr03sfVgb7x0o+q3AP4zpnn0Ch9LGproq+M88Cv8G6+N1m/y7+F9hgjPmVHefGGr6nt1UnjRWuL1ZztreBN40x/8R/VDd9XbGaSV3jdmfGvC8iESJypV2A4a8qO98NBOLsa0II0F5ENhpjBnspNo+JSATWb/hbrHNrdc8LFf0u92C1D84CMMb0F5FRWP1l/C1Wd3pj1eRCza6xi7FqzD+xC4gG28sdn8sGO+MZK1bTzyg3+4CK8w4Vcb1mlM2P/M0YM8kl/s5YBWf9jDHHRWRxmW1cr4dl05OE1R5+q0uMhtK/A+NBvG7ZeapsrBuFTDfxvATMM8Yss6/xM2vyPi6mGWOcHSrtNFaUvzvlsl5Vn6Hjs7lA6WN5gSo+G3/olPhXrFLFv4Czk9b3crHX+D2VbPuB43X7AB11KQW6TUSai0hbrB/GR1h3u9/aH3YC8DN73ZNYxfpVMlZb4a+A/88Rcw2sxqp+wY49trKVxeoo0wbri/qSBzcdrj4Abherfe0lWFWaH9ivdZKLvWPvxiq9rZKIdBMR15KFWGB/JZu8h9W22tGWrLfL8ofkYjv6a+0YPT4eZfwb66QB1vfiA5fXQsTqjNAB62ZoDFbTj0IRudN+fxGRXvbjLsaYbcaYJ7BK4TuWea/3sTtOisgtWNVZAN9gXYzbitVG7hcA9vfS7XvZtgKTuZih/hB4FKukHCr+rFxtwbpRQKwOcJ70zq/uZ13Zd+ZR4Acg3T7WrbGqA8Eq5QwU1UkjwFCx+nO0wCqt3WK/lo5V1TzPO2F7zOP0iUhrrOZNg7BqAEcBiEiky++5D1YV/ndeir+mKky3MeZlY8w1xphwrBKzfX6amW4HLMSqcjd4dl4o+xuv6He5ABgnVvtiB0/71Xg71rLv1ROro/MCe5En19iy73UZcNiOzzXfMQf4o9gjL9m+w7qmPFAmhhtxby8QLiKR9vPfAJvsx0VYNZEAIyvY3qEVVubwhFhtkG+pZF3X9NyLVeI7FisD784W7JrLMtePj4Cfi8gVYnUudBujfT64Aauttzuux/Jel+Wu161hXLyWbgF+aefjLqXqG7uK8ndlVeczrBZ/yFC/ifUBZrgsuw9YIFZxfmW3lDOBvmIV8c+l9EHahdXUYyvwv3ZG+E2sUohPsb5YnwEYY77DugjuloudEquK+YAxJr/KNd17xI5jl4jkARU2dhercf5cYLxdXTQf8LgKzlgdoxYD27Gq2V41xuy0X96LNfpAPtYxeNnD3V4KLBGr88IurOqSmZWs/79Yba92icgeLlYPvwrkATvEqtL7f1h3gLuAErE6U/zO3Q4r8DBwnx3Tb7AyqA4G6zNYhVU9tAsrU30PkCJWB849wG32+v8nVseG3VgZ9U/KvNcsYJCdnjuw2shiV4nNtt9rDfZ3zFbRe4F18uiI1a4QrAx1hP3eUPFn5erPQDv7O/WU/R4nKvqwbJnANLE6injSm7/C74x90bwX66blWftvjlhNdUrFapcezMO6gB8U/xoBozppBOtYv431nXrbrjG6Aes7eJNcHKbK09EL6lt10vc8sMA+96QAc0WkPdZFdbd9jl4A3OVSA+Kvanq+87UW9vdnD1ZV9mqs8w94dl7YAETb+7iLCn6Xxpivgbvs1/4jIv8GRmFdc/wqVtuN9nlrL9Z38BFjzDr7NU+usWXPfTOwrpFbcDlvG2NWYt1UrrKvef/GKnkeCfy3WB3W9mBlvL9296HYJeX3YdX+fIpV2uno0DgLeEGsznuV1iwbq+PjTju+pVwscHHHNT3NsEphX8alMyalM6l/ttd5Hpfrh7E6Yz6NdZ7bgnUD4Hpd+Z29r91YzSP/XEE8M7HS/zFWO26HWcAw+ztxJ9ZneNIY8xFW84tdWNftT6n8euY2f1dWNT/DavH51ON2icdtxpjf1OE+Z2J1jvhjXe2zzP7nAzuNMen1sX+lakKsoZKaGGPO2BeItUA3U7rNfW32H47VEah7XezPHwV7GoM9fRVpqOlWylOVXT9E5FK76WEI8A5WJ/o66W8gVk1uiTHmvF2D9LKjaaTL+7bEqhV+wC4k9Es+bUMtIi9hFbf7S8lNley7q1PA730di1JltAQ22FV8gjViQ51kppVSSgW1yq4fM0Xkv7FKsFcD79bh+3YC/i5Wk8xirJFHHBbZtZfNsYZ09NvMNPhBCbVSSimllFKBzB/aUCullFJKKRWwNEOtlFJKKaVULWiGWimllFJKqVrQDLVSSimllFK1oBlqpZRSSimlauH/ByQibmTaEp7xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = {\n",
    "    \"boxes\": sns.color_palette(\"tab10\")[0],\n",
    "    \"whiskers\": sns.color_palette(\"tab10\")[1],\n",
    "    \"medians\": sns.color_palette(\"tab10\")[3],\n",
    "    \"caps\": sns.color_palette(\"tab10\")[4],\n",
    "}\n",
    "column = list(aminoacids)[5:]\n",
    "colors = sns.color_palette(\"tab10\")[1:(len(column)+1)]\n",
    "palette = dict(zip(column, colors)) \n",
    "\n",
    "_ = aminoacids.plot(kind = 'box', subplots=True, figsize=(12, 4), color=color)\n",
    "#_, ax = plt.subplots(figsize=(12, 4))\n",
    "#plot = sns.boxplot(data = aminoacids, ax=ax, palette=palette)\n",
    "#plot.set_yscale(\"log\")\n",
    "#plt.ylim(min(log(aminoacids)), max(log(aminoacids)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = aminoacids.corr()\n",
    "_, ax = plt.subplots(figsize=(12, 8))\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "_ = sns.heatmap(\n",
    "    corr,\n",
    "    mask=mask,\n",
    "    cmap=cmap,\n",
    "    vmin=-1,\n",
    "    vmax=+1,\n",
    "    center=0,\n",
    "    square=True, \n",
    "    linewidths=.5,\n",
    "    annot=True,\n",
    "    fmt=\".2f\",\n",
    "    ax=ax,\n",
    "    cbar_kws={\"shrink\": .5}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "aminoacids.to_csv(\"./data/aminoacids_properties.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}