Final Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "\n",
    "This notebook is used to tabulate final results and compare novel generated molecules vs HIV inhibitors vs Remdesivir which entered clinical trials just a few days ago."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now the best results and save to sdf for last double check tabulation in PyRx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from rdkit import Chem, DataStructs\n",
    "import random\n",
    "import numpy as np\n",
    "import rdkit.Chem.PropertyMol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16548, 7)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gen</th>\n",
       "      <th>smile</th>\n",
       "      <th>source</th>\n",
       "      <th>weight</th>\n",
       "      <th>logp</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABJY</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>821.205</td>\n",
       "      <td>17.61030</td>\n",
       "      <td>-13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARK</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>859.254</td>\n",
       "      <td>18.33260</td>\n",
       "      <td>-13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAXK</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>765.846</td>\n",
       "      <td>6.37382</td>\n",
       "      <td>-12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABXN</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>888.296</td>\n",
       "      <td>18.23730</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACBT</td>\n",
       "      <td>10</td>\n",
       "      <td>OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>1005.443</td>\n",
       "      <td>21.02130</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  gen                                              smile source  \\\n",
       "0  ABJY   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1  AARK   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "2  AAXK    9  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...   LSTM   \n",
       "3  ABXN   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4  ACBT   10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "\n",
       "     weight      logp  score  \n",
       "0   821.205  17.61030  -13.7  \n",
       "1   859.254  18.33260  -13.1  \n",
       "2   765.846   6.37382  -12.8  \n",
       "3   888.296  18.23730  -12.7  \n",
       "4  1005.443  21.02130  -12.7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# './generations/master_results_table_gen10.csv'\n",
    "\n",
    "final = pd.read_csv('./generations/master_results_table_gen10.csv',sep=',')\n",
    "final = final.sort_values('score', ascending=True)\n",
    "print(final.shape)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gen</th>\n",
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       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>821.205</td>\n",
       "      <td>17.61030</td>\n",
       "      <td>-13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARK</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>859.254</td>\n",
       "      <td>18.33260</td>\n",
       "      <td>-13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABXN</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>888.296</td>\n",
       "      <td>18.23730</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACBT</td>\n",
       "      <td>10</td>\n",
       "      <td>OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>1005.443</td>\n",
       "      <td>21.02130</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AAJO</td>\n",
       "      <td>10</td>\n",
       "      <td>COC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>901.291</td>\n",
       "      <td>18.46180</td>\n",
       "      <td>-12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14741</th>\n",
       "      <td>AALR</td>\n",
       "      <td>10</td>\n",
       "      <td>C1CCNCC1</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>85.150</td>\n",
       "      <td>0.75990</td>\n",
       "      <td>-3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14804</th>\n",
       "      <td>AAKS</td>\n",
       "      <td>10</td>\n",
       "      <td>C1CNCNC1</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>86.138</td>\n",
       "      <td>-0.47320</td>\n",
       "      <td>-3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14828</th>\n",
       "      <td>AAJG</td>\n",
       "      <td>10</td>\n",
       "      <td>CCCC</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>58.124</td>\n",
       "      <td>1.80640</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14850</th>\n",
       "      <td>ABYF</td>\n",
       "      <td>10</td>\n",
       "      <td>CO</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>32.042</td>\n",
       "      <td>-0.39150</td>\n",
       "      <td>-2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15096</th>\n",
       "      <td>AAZU</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2=CC=C(c3nnnn3-c3ccc4ccc-4ccc3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>670.657</td>\n",
       "      <td>3.83362</td>\n",
       "      <td>99.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1535 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gen                                              smile source  \\\n",
       "0      ABJY   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1      AARK   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "3      ABXN   10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4      ACBT   10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "6      AAJO   10  COC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC...   LSTM   \n",
       "...     ...  ...                                                ...    ...   \n",
       "14741  AALR   10                                           C1CCNCC1   LSTM   \n",
       "14804  AAKS   10                                           C1CNCNC1   LSTM   \n",
       "14828  AAJG   10                                               CCCC   LSTM   \n",
       "14850  ABYF   10                                                 CO   LSTM   \n",
       "15096  AAZU   10  Cc1ccc(-c2nnnn2C2=CC=C(c3nnnn3-c3ccc4ccc-4ccc3...   LSTM   \n",
       "\n",
       "         weight      logp  score  \n",
       "0       821.205  17.61030  -13.7  \n",
       "1       859.254  18.33260  -13.1  \n",
       "3       888.296  18.23730  -12.7  \n",
       "4      1005.443  21.02130  -12.7  \n",
       "6       901.291  18.46180  -12.5  \n",
       "...         ...       ...    ...  \n",
       "14741    85.150   0.75990   -3.4  \n",
       "14804    86.138  -0.47320   -3.1  \n",
       "14828    58.124   1.80640   -2.6  \n",
       "14850    32.042  -0.39150   -2.1  \n",
       "15096   670.657   3.83362   99.9  \n",
       "\n",
       "[1535 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final[final.gen == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16548, 3)\n"
     ]
    },
    {
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       "      <th>smile</th>\n",
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       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
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       "      <td>Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...</td>\n",
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       "      <td>10</td>\n",
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      "text/plain": [
       "                                                   smile  score  gen\n",
       "6437   CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...  -13.7   10\n",
       "6447   CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...  -13.1   10\n",
       "12610  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...  -12.8    9\n",
       "6438   CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...  -12.7   10\n",
       "16317  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...  -12.7   10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_max = final.groupby(\"smile\").max()[[\"score\",\"gen\"]].reset_index()\n",
    "final_max = final_max.sort_values('score', ascending=True)\n",
    "print(final_max.shape)\n",
    "final_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5742, 7)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>smile</th>\n",
       "      <th>gen</th>\n",
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       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>10</td>\n",
       "      <td>ABJY</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>821.205</td>\n",
       "      <td>17.61030</td>\n",
       "      <td>-13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>10</td>\n",
       "      <td>AARK</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>859.254</td>\n",
       "      <td>18.33260</td>\n",
       "      <td>-13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...</td>\n",
       "      <td>9</td>\n",
       "      <td>AAXK</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>765.846</td>\n",
       "      <td>6.37382</td>\n",
       "      <td>-12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>10</td>\n",
       "      <td>ABXN</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>888.296</td>\n",
       "      <td>18.23730</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>10</td>\n",
       "      <td>ACBT</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>1005.443</td>\n",
       "      <td>21.02130</td>\n",
       "      <td>-12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               smile  gen    id source  \\\n",
       "0  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   10  ABJY   LSTM   \n",
       "1  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   10  AARK   LSTM   \n",
       "2  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...    9  AAXK   LSTM   \n",
       "3  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   10  ABXN   LSTM   \n",
       "4  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   10  ACBT   LSTM   \n",
       "\n",
       "     weight      logp  score  \n",
       "0   821.205  17.61030  -13.7  \n",
       "1   859.254  18.33260  -13.1  \n",
       "2   765.846   6.37382  -12.8  \n",
       "3   888.296  18.23730  -12.7  \n",
       "4  1005.443  21.02130  -12.7  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_joined = pd.merge(final_max, final, on=['smile','gen'], suffixes=('_old','_new'), how='left')\n",
    "\n",
    "# final_joined = final_joined[(final_joined['score_new'] <= -15.0) & (final_joined['weight'] < 900)]\n",
    "# final_joined = final_joined[(final_joined['score_new'] <= -7.8) & (final_joined['logp']<5)]\n",
    "final_joined = final_joined[(final_joined['score_new'] <= -7.8)]\n",
    "\n",
    "final_joined['score'] = final_joined['score_new']\n",
    "final_joined = final_joined.drop('score_old', axis=1)\n",
    "final_joined = final_joined.drop('score_new', axis=1)\n",
    "print(final_joined.shape)\n",
    "final_joined.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### Note I also manually added Remdesivir because as working on this it went into clinical trials\n",
    "###https://en.wikipedia.org/wiki/Remdesivir\n",
    "\n",
    "final_table.to_csv(r'./generations/master_results_table_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "final_table = pd.read_csv('./generations/master_results_table_final.csv')\n",
    "final_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def set_molecule(row):\n",
    "    mol = Chem.MolFromSmiles(row['smile'])\n",
    "    pm = Chem.PropertyMol.PropertyMol(mol)\n",
    "    title = 'id' + str(row['id']) + 'gen'+ str(row['gen'])\n",
    "    # print(title)\n",
    "    # Enables for tracking which molecule is which in PyRx GUI and PyRx results export\n",
    "    pm.SetProp('Title', title)\n",
    "    return pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1144, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_joined[final_joined['gen']==10].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "mols_for_export = final_table.apply(set_molecule, axis=1)\n",
    "### list(mols_for_export)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "len(mols_for_export)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "w = Chem.SDWriter('./generations/genfinal.sdf')\n",
    "for m in mols_for_export:\n",
    "    w.write(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rerun everything through PyRx once more to double check, then publish final metrics (No need run this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "\n",
    "# new_scores = pd.read_csv('./generations/results/results_gen12.csv',sep=',')\n",
    "\n",
    "current_gen = 10\n",
    "for i in range(0, current_gen+1):\n",
    "    dfs.append(pd.read_csv('./generations/results/results_gen' + str(i) +'.csv',sep=','))\n",
    "\n",
    "# Concatenate all data into one DataFrame\n",
    "new_scores = pd.concat(dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ligand</th>\n",
       "      <th>Binding Affinity</th>\n",
       "      <th>rmsd/ub</th>\n",
       "      <th>rmsd/lb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6lu7_idAAAAgen0</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6lu7_idAAAAgen0</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6lu7_idAAAAgen0</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>9.023</td>\n",
       "      <td>3.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6lu7_idAAAAgen0</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>6.626</td>\n",
       "      <td>3.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6lu7_idAAAAgen0</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>6.182</td>\n",
       "      <td>4.837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ligand  Binding Affinity  rmsd/ub   rmsd/lb\n",
       "0  6lu7_idAAAAgen0              -6.7    0.000     0.000\n",
       "1  6lu7_idAAAAgen0              -6.6    2.125     1.628\n",
       "2  6lu7_idAAAAgen0              -6.4    9.023     3.692\n",
       "3  6lu7_idAAAAgen0              -6.4    6.626     3.987\n",
       "4  6lu7_idAAAAgen0              -6.3    6.182     4.837"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new_scores = pd.read_csv('./generations/results/results_genfinal.csv',sep=',')\n",
    "final_table = pd.read_csv('./generations/master_results_table_gen' + str(current_gen) +'.csv')\n",
    "new_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Ligand</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Binding Affinity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>amin</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6LU7_idAAAAgen10_uff_E=407.75</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-6.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6LU7_idAAAAgen2_uff_E=137.22</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-4.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6LU7_idAAAAgen3_uff_E=128.83</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-6.044444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6LU7_idAAAAgen4_uff_E=182.10</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-3.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6LU7_idAAAAgen5_uff_E=14085.47</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-4.088889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Ligand Binding Affinity          \n",
       "                                              amin   average\n",
       "0   6LU7_idAAAAgen10_uff_E=407.75             -6.2 -6.033333\n",
       "1    6LU7_idAAAAgen2_uff_E=137.22             -5.4 -4.944444\n",
       "2    6LU7_idAAAAgen3_uff_E=128.83             -6.2 -6.044444\n",
       "3    6LU7_idAAAAgen4_uff_E=182.10             -3.6 -3.511111\n",
       "4  6LU7_idAAAAgen5_uff_E=14085.47             -4.3 -4.088889"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_scores = new_scores.groupby(\"Ligand\").agg({'Binding Affinity': [np.min,np.average]}).reset_index()\n",
    "new_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gen</th>\n",
       "      <th>score_best</th>\n",
       "      <th>score_avg</th>\n",
       "    </tr>\n",
       "    <tr>\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>0</th>\n",
       "      <td>AAAA</td>\n",
       "      <td>10</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-6.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAA</td>\n",
       "      <td>2</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-4.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAA</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-6.044444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAAA</td>\n",
       "      <td>4</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-3.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAAA</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-4.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>ABTC</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-7.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>ABTD</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-6.144444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17772</th>\n",
       "      <td>ABTE</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.9</td>\n",
       "      <td>-7.488889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17773</th>\n",
       "      <td>ABTF</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-7.477778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17774</th>\n",
       "      <td>ABTG</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.9</td>\n",
       "      <td>-7.688889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17775 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id gen score_best score_avg\n",
       "                                    \n",
       "0      AAAA  10       -6.2 -6.033333\n",
       "1      AAAA   2       -5.4 -4.944444\n",
       "2      AAAA   3       -6.2 -6.044444\n",
       "3      AAAA   4       -3.6 -3.511111\n",
       "4      AAAA   5       -4.3 -4.088889\n",
       "...     ...  ..        ...       ...\n",
       "17770  ABTC   0       -7.8 -7.088889\n",
       "17771  ABTD   0       -6.5 -6.144444\n",
       "17772  ABTE   0       -7.9 -7.488889\n",
       "17773  ABTF   0       -7.8 -7.477778\n",
       "17774  ABTG   0       -7.9 -7.688889\n",
       "\n",
       "[17775 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_scores['id'] = new_scores['Ligand'].str.split(\"_\").str[1].str.split(\"gen\").str[0].str.split(\"id\").str[1]\n",
    "new_scores['gen'] = new_scores['Ligand'].str.split(\"_\").str[1].str.split(\"gen\").str[1]\n",
    "new_scores['score_best'] = new_scores[\"Binding Affinity\"][\"amin\"]\n",
    "new_scores['score_avg'] = new_scores[\"Binding Affinity\"][\"average\"]\n",
    "new_scores = new_scores[['id','gen','score_best','score_avg']]\n",
    "\n",
    "new_scores.head()\n",
    "new_scores.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen</th>\n",
       "      <th>weight</th>\n",
       "      <th>logp</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.947577</td>\n",
       "      <td>349.764273</td>\n",
       "      <td>3.487965</td>\n",
       "      <td>-7.177295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.164130</td>\n",
       "      <td>171.131767</td>\n",
       "      <td>2.463154</td>\n",
       "      <td>1.847241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.070000</td>\n",
       "      <td>-9.962380</td>\n",
       "      <td>-13.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>217.214000</td>\n",
       "      <td>1.877175</td>\n",
       "      <td>-8.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>315.088000</td>\n",
       "      <td>3.148800</td>\n",
       "      <td>-7.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>453.580500</td>\n",
       "      <td>4.658655</td>\n",
       "      <td>-5.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1083.557000</td>\n",
       "      <td>23.358620</td>\n",
       "      <td>-1.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gen        weight          logp         score\n",
       "count  14860.000000  14860.000000  14860.000000  14860.000000\n",
       "mean       4.947577    349.764273      3.487965     -7.177295\n",
       "std        3.164130    171.131767      2.463154      1.847241\n",
       "min        0.000000     30.070000     -9.962380    -13.700000\n",
       "25%        2.000000    217.214000      1.877175     -8.600000\n",
       "50%        4.000000    315.088000      3.148800     -7.100000\n",
       "75%        8.000000    453.580500      4.658655     -5.700000\n",
       "max       10.000000   1083.557000     23.358620     -1.500000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_table = final_table[final_table.score != 99.9]\n",
    "final_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_scores.id = new_scores.id.astype(str)\n",
    "new_scores.gen = new_scores.gen.astype(str)\n",
    "\n",
    "final_table.id = final_table.id.astype(str)\n",
    "final_table.gen = final_table.gen.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:618: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
      "  warnings.warn(msg, UserWarning)\n"
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       "      <td>LSTM</td>\n",
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       "      <td>LSTM</td>\n",
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       "      <td>-11.911111</td>\n",
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       "      <th>4</th>\n",
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       "     id gen                                              smile source  \\\n",
       "0  ABJY  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1  AARK  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "2  AAXK   9  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...   LSTM   \n",
       "3  ABXN  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4  ACBT  10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "\n",
       "     weight      logp  score  score_best  score_avg  \n",
       "0   821.205  17.61030  -13.7       -13.7 -12.577778  \n",
       "1   859.254  18.33260  -13.1       -13.1 -11.488889  \n",
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       "3   888.296  18.23730  -12.7       -12.7 -11.911111  \n",
       "4  1005.443  21.02130  -12.7       -12.7 -11.677778  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    }
   ],
   "source": [
    "new_table = pd.merge(final_table, new_scores, on=['id','gen'], suffixes=('_old','_new'), how='left')\n",
    "new_table['score_best'] = new_table[('score_best', '')]\n",
    "new_table['score_avg'] = new_table[('score_avg', '')]\n",
    "\n",
    "new_table = new_table.drop([('score_best', ''),('score_avg', '')], axis=1)\n",
    "new_table = new_table.sort_values('score_best', ascending=True)\n",
    "\n",
    "new_table.dropna()\n",
    "new_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:618: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
      "  warnings.warn(msg, UserWarning)\n"
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       "      <th>3</th>\n",
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       "      <th>90</th>\n",
       "      <td>ABXN</td>\n",
       "      <td>9</td>\n",
       "      <td>CC1=CC=C(c2nnnn2C2=CC=CC=CC=C2C2=CC=C(c3nnnn3-...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>760.786</td>\n",
       "      <td>5.15360</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.077778</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>ABDD</td>\n",
       "      <td>9</td>\n",
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       "      <td>LSTM</td>\n",
       "      <td>852.915</td>\n",
       "      <td>8.22830</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.111111</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>AAQP</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccc(-c3nnnn3-c3cccnc3-c3cccn...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>818.782</td>\n",
       "      <td>3.38962</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-10.344444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>ACHH</td>\n",
       "      <td>4</td>\n",
       "      <td>CC1=CC=C(c2nnnn2C2=CC=C(c3nnnn3-c3ccccc3)C2=O)...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>500.566</td>\n",
       "      <td>4.64840</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-10.855556</td>\n",
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       "      <th>93</th>\n",
       "      <td>ABJZ</td>\n",
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       "      <td>LSTM</td>\n",
       "      <td>667.937</td>\n",
       "      <td>13.15560</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-10.666667</td>\n",
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       "</table>\n",
       "<p>100 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "      id gen                                              smile source  \\\n",
       "0   ABJY  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1   AARK  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "2   AAXK   9  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...   LSTM   \n",
       "3   ABXN  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4   ACBT  10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "..   ...  ..                                                ...    ...   \n",
       "90  ABXN   9  CC1=CC=C(c2nnnn2C2=CC=CC=CC=C2C2=CC=C(c3nnnn3-...   LSTM   \n",
       "89  ABDD   9  CC(=O)OC1=CC=C(C(=O)Nc2nnnn2-c2ccc(-c3ccc(-c4c...   LSTM   \n",
       "88  AAQP   9  Cc1ccc(-c2nnnn2-c2ccc(-c3nnnn3-c3cccnc3-c3cccn...   LSTM   \n",
       "87  ACHH   4  CC1=CC=C(c2nnnn2C2=CC=C(c3nnnn3-c3ccccc3)C2=O)...   LSTM   \n",
       "93  ABJZ  10  Oc1ccccccccccccccccccccccccccccccccccccccccccc...   LSTM   \n",
       "\n",
       "      weight      logp  score  score_best  score_avg  \n",
       "0    821.205  17.61030  -13.7       -13.7 -12.577778  \n",
       "1    859.254  18.33260  -13.1       -13.1 -11.488889  \n",
       "2    765.846   6.37382  -12.8       -12.8 -11.377778  \n",
       "3    888.296  18.23730  -12.7       -12.7 -11.911111  \n",
       "4   1005.443  21.02130  -12.7       -12.7 -11.677778  \n",
       "..       ...       ...    ...         ...        ...  \n",
       "90   760.786   5.15360  -11.5       -11.5 -11.077778  \n",
       "89   852.915   8.22830  -11.5       -11.5 -11.111111  \n",
       "88   818.782   3.38962  -11.5       -11.5 -10.344444  \n",
       "87   500.566   4.64840  -11.5       -11.5 -10.855556  \n",
       "93   667.937  13.15560  -11.5       -11.5 -10.666667  \n",
       "\n",
       "[100 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table = pd.merge(final_table, new_scores, on=['id','gen'], suffixes=('_old','_new'), how='left')\n",
    "\n",
    "new_table['score_best'] = new_table[('score_best', '')]\n",
    "new_table['score_avg'] = new_table[('score_avg', '')]\n",
    "\n",
    "new_table = new_table.drop([('score_best', ''),('score_avg', '')], axis=1)\n",
    "new_table = new_table.sort_values('score_best', ascending=True)\n",
    "\n",
    "new_table.dropna()\n",
    "new_table.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>AARK</td>\n",
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       "      <td>0.093633</td>\n",
       "      <td>0.094731</td>\n",
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       "      <th>2</th>\n",
       "      <td>AAXK</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>765.846</td>\n",
       "      <td>6.37382</td>\n",
       "      <td>-12.8</td>\n",
       "      <td>-12.8</td>\n",
       "      <td>-11.377778</td>\n",
       "      <td>0.483449</td>\n",
       "      <td>0.678427</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABXN</td>\n",
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       "      <td>0.113226</td>\n",
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       "      <td>LSTM</td>\n",
       "      <td>1005.443</td>\n",
       "      <td>21.02130</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-11.677778</td>\n",
       "      <td>0.039815</td>\n",
       "      <td>0.035607</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ABPG</td>\n",
       "      <td>6</td>\n",
       "      <td>CC1=CC=C(C(=O)Nc2ncnn2C2=CC=C(c3nnc(NC(=O)CC4=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>845.876</td>\n",
       "      <td>6.83532</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>-11.566667</td>\n",
       "      <td>0.513157</td>\n",
       "      <td>0.749751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AAJO</td>\n",
       "      <td>10</td>\n",
       "      <td>COC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>901.291</td>\n",
       "      <td>18.46180</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>-11.900000</td>\n",
       "      <td>0.132315</td>\n",
       "      <td>0.139263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ACEU</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccccc2-c2ccc(-c3ccnnc3-c3ccc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>789.899</td>\n",
       "      <td>10.58302</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>-11.977778</td>\n",
       "      <td>0.414809</td>\n",
       "      <td>0.556896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ACMN</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2=CC=C(C(=O)NC(=O)c3nnnn3-c3cc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>700.675</td>\n",
       "      <td>3.33582</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-11.300000</td>\n",
       "      <td>0.501429</td>\n",
       "      <td>0.714787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ABSY</td>\n",
       "      <td>8</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2CCC=C(c3nnnn3-c3ccccc3-c3cccn...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>788.836</td>\n",
       "      <td>5.95482</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-11.555556</td>\n",
       "      <td>0.499531</td>\n",
       "      <td>0.719219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gen                                              smile source  \\\n",
       "0   ABJY  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1   AARK  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "2   AAXK   9  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...   LSTM   \n",
       "3   ABXN  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4   ACBT  10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "5   ABPG   6  CC1=CC=C(C(=O)Nc2ncnn2C2=CC=C(c3nnc(NC(=O)CC4=...   LSTM   \n",
       "6   AAJO  10  COC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC...   LSTM   \n",
       "7   ACEU  10  Cc1ccc(-c2nnnn2-c2ccccc2-c2ccc(-c3ccnnc3-c3ccc...   LSTM   \n",
       "12  ACMN  10  Cc1ccc(-c2nnnn2C2=CC=C(C(=O)NC(=O)c3nnnn3-c3cc...   LSTM   \n",
       "11  ABSY   8  Cc1ccc(-c2nnnn2C2CCC=C(c3nnnn3-c3ccccc3-c3cccn...   LSTM   \n",
       "\n",
       "      weight      logp  score  score_best  score_avg  \\\n",
       "0    821.205  17.61030  -13.7       -13.7 -12.577778   \n",
       "1    859.254  18.33260  -13.1       -13.1 -11.488889   \n",
       "2    765.846   6.37382  -12.8       -12.8 -11.377778   \n",
       "3    888.296  18.23730  -12.7       -12.7 -11.911111   \n",
       "4   1005.443  21.02130  -12.7       -12.7 -11.677778   \n",
       "5    845.876   6.83532  -12.5       -12.5 -11.566667   \n",
       "6    901.291  18.46180  -12.5       -12.5 -11.900000   \n",
       "7    789.899  10.58302  -12.4       -12.4 -11.977778   \n",
       "12   700.675   3.33582  -12.3       -12.3 -11.300000   \n",
       "11   788.836   5.95482  -12.3       -12.3 -11.555556   \n",
       "\n",
       "    similarity_to_hiv_inhibitors  similarity_to_remdesivir  \n",
       "0                       0.088662                  0.089561  \n",
       "1                       0.093633                  0.094731  \n",
       "2                       0.483449                  0.678427  \n",
       "3                       0.113226                  0.114454  \n",
       "4                       0.039815                  0.035607  \n",
       "5                       0.513157                  0.749751  \n",
       "6                       0.132315                  0.139263  \n",
       "7                       0.414809                  0.556896  \n",
       "12                      0.501429                  0.714787  \n",
       "11                      0.499531                  0.719219  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add HIV inhibitors manually into the table\n",
    "hiv_smiles = pd.read_csv('./datasets/hiv_inhibitors_cleaned.smi',sep=',', header=None)\n",
    "hiv_smiles_list = list(hiv_smiles[0])\n",
    "hiv_fingerprints = []\n",
    "\n",
    "for smile in hiv_smiles_list:\n",
    "    hiv_fingerprints.append(Chem.RDKFingerprint(Chem.MolFromSmiles(smile)))\n",
    "\n",
    "def calc_hiv_similarity_score(row):\n",
    "    fingerprint = Chem.RDKFingerprint(Chem.MolFromSmiles(row['smile']))\n",
    "    similarity = np.mean(DataStructs.BulkTanimotoSimilarity(fingerprint,hiv_fingerprints))\n",
    "    return similarity\n",
    "\n",
    "remdesivir_fingerprint = Chem.RDKFingerprint(Chem.MolFromSmiles('CCC(CC)COC(=O)[C@H](C)NP(=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1'))\n",
    "\n",
    "new_table['similarity_to_hiv_inhibitors'] = new_table.apply(calc_hiv_similarity_score, axis=1)\n",
    "new_table['similarity_to_remdesivir'] = new_table['smile'].apply(lambda x: DataStructs.TanimotoSimilarity(Chem.RDKFingerprint(Chem.MolFromSmiles(x)),remdesivir_fingerprint))\n",
    "new_table.sort_values(by=['similarity_to_hiv_inhibitors', 'similarity_to_remdesivir'])\n",
    "new_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze_table = new_table.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gen</th>\n",
       "      <th>smile</th>\n",
       "      <th>source</th>\n",
       "      <th>weight</th>\n",
       "      <th>logp</th>\n",
       "      <th>score</th>\n",
       "      <th>score_best</th>\n",
       "      <th>score_avg</th>\n",
       "      <th>similarity_to_hiv_inhibitors</th>\n",
       "      <th>similarity_to_remdesivir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABJY</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>821.205</td>\n",
       "      <td>17.61030</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>-12.577778</td>\n",
       "      <td>0.088662</td>\n",
       "      <td>0.089561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARK</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>859.254</td>\n",
       "      <td>18.33260</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>-11.488889</td>\n",
       "      <td>0.093633</td>\n",
       "      <td>0.094731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAXK</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>765.846</td>\n",
       "      <td>6.37382</td>\n",
       "      <td>-12.8</td>\n",
       "      <td>-12.8</td>\n",
       "      <td>-11.377778</td>\n",
       "      <td>0.483449</td>\n",
       "      <td>0.678427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABXN</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>888.296</td>\n",
       "      <td>18.23730</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-11.911111</td>\n",
       "      <td>0.113226</td>\n",
       "      <td>0.114454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACBT</td>\n",
       "      <td>10</td>\n",
       "      <td>OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>1005.443</td>\n",
       "      <td>21.02130</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>-11.677778</td>\n",
       "      <td>0.039815</td>\n",
       "      <td>0.035607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>ACKF</td>\n",
       "      <td>2</td>\n",
       "      <td>CNC</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>45.085</td>\n",
       "      <td>-0.16440</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.855556</td>\n",
       "      <td>0.003513</td>\n",
       "      <td>0.001825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14856</th>\n",
       "      <td>AACE</td>\n",
       "      <td>5</td>\n",
       "      <td>CC(=O)NC(=O)C1CCCCC2CCCCC2CCCC1</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>293.451</td>\n",
       "      <td>4.20610</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-1.744444</td>\n",
       "      <td>0.122913</td>\n",
       "      <td>0.094643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14857</th>\n",
       "      <td>ABOC</td>\n",
       "      <td>7</td>\n",
       "      <td>CC</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>30.070</td>\n",
       "      <td>1.02620</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-1.477778</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.001217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14858</th>\n",
       "      <td>AANH</td>\n",
       "      <td>3</td>\n",
       "      <td>CSC</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>62.137</td>\n",
       "      <td>0.97920</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>0.001216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14859</th>\n",
       "      <td>AAYQ</td>\n",
       "      <td>5</td>\n",
       "      <td>CC1=CC=C(C(=O)Nc2ccccc2O)NC1</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>230.267</td>\n",
       "      <td>1.76410</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.433333</td>\n",
       "      <td>0.236202</td>\n",
       "      <td>0.255935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14860 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id gen                                              smile source  \\\n",
       "0      ABJY  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "1      AARK  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "2      AAXK   9  Cc1ccc(-c2nnnn2C2CCCCC2c2ccc(-c3nnnn3-c3cccnc3...   LSTM   \n",
       "3      ABXN  10  CC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "4      ACBT  10  OC1=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=CC=...   LSTM   \n",
       "...     ...  ..                                                ...    ...   \n",
       "14855  ACKF   2                                                CNC   LSTM   \n",
       "14856  AACE   5                    CC(=O)NC(=O)C1CCCCC2CCCCC2CCCC1   LSTM   \n",
       "14857  ABOC   7                                                 CC   LSTM   \n",
       "14858  AANH   3                                                CSC   LSTM   \n",
       "14859  AAYQ   5                       CC1=CC=C(C(=O)Nc2ccccc2O)NC1   LSTM   \n",
       "\n",
       "         weight      logp  score  score_best  score_avg  \\\n",
       "0       821.205  17.61030  -13.7       -13.7 -12.577778   \n",
       "1       859.254  18.33260  -13.1       -13.1 -11.488889   \n",
       "2       765.846   6.37382  -12.8       -12.8 -11.377778   \n",
       "3       888.296  18.23730  -12.7       -12.7 -11.911111   \n",
       "4      1005.443  21.02130  -12.7       -12.7 -11.677778   \n",
       "...         ...       ...    ...         ...        ...   \n",
       "14855    45.085  -0.16440   -2.0        -2.0  -1.855556   \n",
       "14856   293.451   4.20610   -1.9        -1.9  -1.744444   \n",
       "14857    30.070   1.02620   -1.8        -1.8  -1.477778   \n",
       "14858    62.137   0.97920   -1.6        -1.6  -1.488889   \n",
       "14859   230.267   1.76410   -1.5        -1.5  -1.433333   \n",
       "\n",
       "       similarity_to_hiv_inhibitors  similarity_to_remdesivir  \n",
       "0                          0.088662                  0.089561  \n",
       "1                          0.093633                  0.094731  \n",
       "2                          0.483449                  0.678427  \n",
       "3                          0.113226                  0.114454  \n",
       "4                          0.039815                  0.035607  \n",
       "...                             ...                       ...  \n",
       "14855                      0.003513                  0.001825  \n",
       "14856                      0.122913                  0.094643  \n",
       "14857                      0.001859                  0.001217  \n",
       "14858                      0.002223                  0.001216  \n",
       "14859                      0.236202                  0.255935  \n",
       "\n",
       "[14860 rows x 11 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>similarity_to_hiv_inhibitors</th>\n",
       "      <th>similarity_to_remdesivir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ACMN</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2=CC=C(C(=O)NC(=O)c3nnnn3-c3cc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>700.675</td>\n",
       "      <td>3.33582</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-12.3</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ABIX</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccccc2C2=CC=C(c3nnnn3-c3cccc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>643.667</td>\n",
       "      <td>4.65752</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-11.566667</td>\n",
       "      <td>0.475743</td>\n",
       "      <td>0.655454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AAHE</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccccc2-c2ccc(-c3nnnn3-c3cccc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>833.793</td>\n",
       "      <td>3.47402</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-11.666667</td>\n",
       "      <td>0.491048</td>\n",
       "      <td>0.693394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ACAF</td>\n",
       "      <td>6</td>\n",
       "      <td>Cc1ccc2ccc3ccc(C(=O)Nc4ccccc4)ccc([nH]c2=O)c2n...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>769.782</td>\n",
       "      <td>4.56982</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-11.088889</td>\n",
       "      <td>0.425541</td>\n",
       "      <td>0.547391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ABYC</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2C2=CC=C(c3ccccc3C3=CC=C(c4nnnn4...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>816.715</td>\n",
       "      <td>1.04002</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>-11.766667</td>\n",
       "      <td>0.509748</td>\n",
       "      <td>0.745409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AAQO</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=C(n2nnnc2-c2ccc(-c3nnnn3-c3ccccc3)cc2C2...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>712.698</td>\n",
       "      <td>3.48020</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>-11.433333</td>\n",
       "      <td>0.494153</td>\n",
       "      <td>0.704020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AAFC</td>\n",
       "      <td>7</td>\n",
       "      <td>Cc1ccc(C(=O)Nc2nnnn2-c2ccc(-c3nnnn3-c3ccc4ccc5...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>664.654</td>\n",
       "      <td>2.76344</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-11.388889</td>\n",
       "      <td>0.443981</td>\n",
       "      <td>0.590933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ABCU</td>\n",
       "      <td>10</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccccc2C2=CC=C(c3nnnn3-c3cccn...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>552.558</td>\n",
       "      <td>3.41342</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.211111</td>\n",
       "      <td>0.475822</td>\n",
       "      <td>0.665149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>AADD</td>\n",
       "      <td>9</td>\n",
       "      <td>CC1=CC=C(c2nnnn2-c2ccnnc2C2=CC=C(c3ccc4c(=O)[n...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>638.652</td>\n",
       "      <td>2.38670</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-10.866667</td>\n",
       "      <td>0.501729</td>\n",
       "      <td>0.730055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>AAKA</td>\n",
       "      <td>9</td>\n",
       "      <td>CC1=CC=C(c2nnnn2C2=CC=C(C(=O)Nc3nnnn3-c3ccnnc3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>721.698</td>\n",
       "      <td>2.32590</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-10.511111</td>\n",
       "      <td>0.505814</td>\n",
       "      <td>0.738454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>AARG</td>\n",
       "      <td>6</td>\n",
       "      <td>CC(C)(C)C(=O)Nc1ccc(C(=O)Nc2nnnn2C2=CC=C(C(=O)...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>674.638</td>\n",
       "      <td>1.62020</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-10.433333</td>\n",
       "      <td>0.466926</td>\n",
       "      <td>0.656953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>AANA</td>\n",
       "      <td>8</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2ccnnc2C2=CC=C3C(=O)Nc4nnnn4C...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>690.648</td>\n",
       "      <td>2.59112</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>-11.266667</td>\n",
       "      <td>0.507365</td>\n",
       "      <td>0.743666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ABDF</td>\n",
       "      <td>6</td>\n",
       "      <td>Cc1ccc(C(=O)Nc2nnnn2-c2cnnn2-c2ccc(-c3ccnnc3-c...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>663.666</td>\n",
       "      <td>3.32082</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-10.566667</td>\n",
       "      <td>0.498047</td>\n",
       "      <td>0.716298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ACPA</td>\n",
       "      <td>10</td>\n",
       "      <td>COc1ccc(-c2nnnn2-c2ccc(-c3nnnn3C3=CC=C(C(=O)Nc...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>772.710</td>\n",
       "      <td>2.38820</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-10.588889</td>\n",
       "      <td>0.470066</td>\n",
       "      <td>0.664278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>AAUV</td>\n",
       "      <td>6</td>\n",
       "      <td>CC1=CC=C(c2nnnn2C2=C(C)C(=O)N2c2ccccc2C2=CC=C(...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>812.799</td>\n",
       "      <td>4.41760</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-10.400000</td>\n",
       "      <td>0.505804</td>\n",
       "      <td>0.728185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ABDE</td>\n",
       "      <td>8</td>\n",
       "      <td>CC1=CC=C(CCc2nnnn2C2=CC=C(c3nnnn3C3=CC=c4c(=O)...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>847.824</td>\n",
       "      <td>1.95559</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.088889</td>\n",
       "      <td>0.504548</td>\n",
       "      <td>0.735235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>AASM</td>\n",
       "      <td>8</td>\n",
       "      <td>CC1=CC=c2[nH]c(c3nnnn23)=CC=C(n2nnnc2N2N=NN=NN...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>727.718</td>\n",
       "      <td>1.01472</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.222222</td>\n",
       "      <td>0.505745</td>\n",
       "      <td>0.737606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AASB</td>\n",
       "      <td>10</td>\n",
       "      <td>CC1=CC=C(N2N=NN=NN2C2=CC=C(c3nnnn3-c3ccccc3C3=...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>651.611</td>\n",
       "      <td>3.02730</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-10.944444</td>\n",
       "      <td>0.474405</td>\n",
       "      <td>0.661624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>AAWP</td>\n",
       "      <td>9</td>\n",
       "      <td>Cc1ccc(-c2nnnn2-c2cnnn2-c2ccc(-c3nnnn3-c3nnnn3...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>840.832</td>\n",
       "      <td>4.69882</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>-11.466667</td>\n",
       "      <td>0.501483</td>\n",
       "      <td>0.718051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AACV</td>\n",
       "      <td>7</td>\n",
       "      <td>Cc1ccc(C(=O)Nc2nnnn2N2N=NN=NN2c2ccc(-c3ccccc3-...</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>532.528</td>\n",
       "      <td>4.27872</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>-10.022222</td>\n",
       "      <td>0.424669</td>\n",
       "      <td>0.553609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gen                                              smile source  \\\n",
       "12  ACMN  10  Cc1ccc(-c2nnnn2C2=CC=C(C(=O)NC(=O)c3nnnn3-c3cc...   LSTM   \n",
       "10  ABIX  10  Cc1ccc(-c2nnnn2-c2ccccc2C2=CC=C(c3nnnn3-c3cccc...   LSTM   \n",
       "18  AAHE  10  Cc1ccc(-c2nnnn2-c2ccccc2-c2ccc(-c3nnnn3-c3cccc...   LSTM   \n",
       "19  ACAF   6  Cc1ccc2ccc3ccc(C(=O)Nc4ccccc4)ccc([nH]c2=O)c2n...   LSTM   \n",
       "14  ABYC   9  Cc1ccc(-c2nnnn2C2=CC=C(c3ccccc3C3=CC=C(c4nnnn4...   LSTM   \n",
       "24  AAQO  10  CC1=CC=C(n2nnnc2-c2ccc(-c3nnnn3-c3ccccc3)cc2C2...   LSTM   \n",
       "26  AAFC   7  Cc1ccc(C(=O)Nc2nnnn2-c2ccc(-c3nnnn3-c3ccc4ccc5...   LSTM   \n",
       "33  ABCU  10  Cc1ccc(-c2nnnn2-c2ccccc2C2=CC=C(c3nnnn3-c3cccn...   LSTM   \n",
       "36  AADD   9  CC1=CC=C(c2nnnn2-c2ccnnc2C2=CC=C(c3ccc4c(=O)[n...   LSTM   \n",
       "37  AAKA   9  CC1=CC=C(c2nnnn2C2=CC=C(C(=O)Nc3nnnn3-c3ccnnc3...   LSTM   \n",
       "39  AARG   6  CC(C)(C)C(=O)Nc1ccc(C(=O)Nc2nnnn2C2=CC=C(C(=O)...   LSTM   \n",
       "40  AANA   8  Cc1ccc(-c2nnnn2-c2ccnnc2C2=CC=C3C(=O)Nc4nnnn4C...   LSTM   \n",
       "51  ABDF   6  Cc1ccc(C(=O)Nc2nnnn2-c2cnnn2-c2ccc(-c3ccnnc3-c...   LSTM   \n",
       "54  ACPA  10  COc1ccc(-c2nnnn2-c2ccc(-c3nnnn3C3=CC=C(C(=O)Nc...   LSTM   \n",
       "47  AAUV   6  CC1=CC=C(c2nnnn2C2=C(C)C(=O)N2c2ccccc2C2=CC=C(...   LSTM   \n",
       "46  ABDE   8  CC1=CC=C(CCc2nnnn2C2=CC=C(c3nnnn3C3=CC=c4c(=O)...   LSTM   \n",
       "45  AASM   8  CC1=CC=c2[nH]c(c3nnnn23)=CC=C(n2nnnc2N2N=NN=NN...   LSTM   \n",
       "55  AASB  10  CC1=CC=C(N2N=NN=NN2C2=CC=C(c3nnnn3-c3ccccc3C3=...   LSTM   \n",
       "41  AAWP   9  Cc1ccc(-c2nnnn2-c2cnnn2-c2ccc(-c3nnnn3-c3nnnn3...   LSTM   \n",
       "57  AACV   7  Cc1ccc(C(=O)Nc2nnnn2N2N=NN=NN2c2ccc(-c3ccccc3-...   LSTM   \n",
       "\n",
       "     weight     logp  score  score_best  score_avg  \\\n",
       "12  700.675  3.33582  -12.3       -12.3 -11.300000   \n",
       "10  643.667  4.65752  -12.3       -12.3 -11.566667   \n",
       "18  833.793  3.47402  -12.2       -12.2 -11.666667   \n",
       "19  769.782  4.56982  -12.2       -12.2 -11.088889   \n",
       "14  816.715  1.04002  -12.2       -12.2 -11.766667   \n",
       "24  712.698  3.48020  -12.1       -12.1 -11.433333   \n",
       "26  664.654  2.76344  -12.0       -12.0 -11.388889   \n",
       "33  552.558  3.41342  -11.9       -11.9 -11.211111   \n",
       "36  638.652  2.38670  -11.9       -11.9 -10.866667   \n",
       "37  721.698  2.32590  -11.9       -11.9 -10.511111   \n",
       "39  674.638  1.62020  -11.9       -11.9 -10.433333   \n",
       "40  690.648  2.59112  -11.9       -11.9 -11.266667   \n",
       "51  663.666  3.32082  -11.8       -11.8 -10.566667   \n",
       "54  772.710  2.38820  -11.8       -11.8 -10.588889   \n",
       "47  812.799  4.41760  -11.8       -11.8 -10.400000   \n",
       "46  847.824  1.95559  -11.8       -11.8 -11.088889   \n",
       "45  727.718  1.01472  -11.8       -11.8 -11.222222   \n",
       "55  651.611  3.02730  -11.8       -11.8 -10.944444   \n",
       "41  840.832  4.69882  -11.8       -11.8 -11.466667   \n",
       "57  532.528  4.27872  -11.7       -11.7 -10.022222   \n",
       "\n",
       "    similarity_to_hiv_inhibitors  similarity_to_remdesivir  \n",
       "12                      0.501429                  0.714787  \n",
       "10                      0.475743                  0.655454  \n",
       "18                      0.491048                  0.693394  \n",
       "19                      0.425541                  0.547391  \n",
       "14                      0.509748                  0.745409  \n",
       "24                      0.494153                  0.704020  \n",
       "26                      0.443981                  0.590933  \n",
       "33                      0.475822                  0.665149  \n",
       "36                      0.501729                  0.730055  \n",
       "37                      0.505814                  0.738454  \n",
       "39                      0.466926                  0.656953  \n",
       "40                      0.507365                  0.743666  \n",
       "51                      0.498047                  0.716298  \n",
       "54                      0.470066                  0.664278  \n",
       "47                      0.505804                  0.728185  \n",
       "46                      0.504548                  0.735235  \n",
       "45                      0.505745                  0.737606  \n",
       "55                      0.474405                  0.661624  \n",
       "41                      0.501483                  0.718051  \n",
       "57                      0.424669                  0.553609  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.sort_values(by=['score_best'])\n",
    "new_table = new_table[(new_table.score < -10) & (new_table.logp < 5) & (new_table.logp > 0)].head(50)\n",
    "new_table.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_table.to_csv(r'./generations/check_smile.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_table.to_csv(r'./generations/master_results_table_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "new_table = new_table.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_table.to_csv(r'./generations/master_results_table_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "mols_for_export = new_table.apply(set_molecule, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = Chem.SDWriter('./generations/genfinal.sdf')\n",
    "for m in mols_for_export:\n",
    "    w.write(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>logp</th>\n",
       "      <th>score</th>\n",
       "      <th>score_best</th>\n",
       "      <th>score_avg</th>\n",
       "      <th>similarity_to_hiv_inhibitors</th>\n",
       "      <th>similarity_to_remdesivir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "      <td>14860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>349.764273</td>\n",
       "      <td>3.487965</td>\n",
       "      <td>-7.177295</td>\n",
       "      <td>-7.177295</td>\n",
       "      <td>-6.678643</td>\n",
       "      <td>0.267964</td>\n",
       "      <td>0.318753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>171.131767</td>\n",
       "      <td>2.463154</td>\n",
       "      <td>1.847241</td>\n",
       "      <td>1.847241</td>\n",
       "      <td>1.739054</td>\n",
       "      <td>0.139600</td>\n",
       "      <td>0.205405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.070000</td>\n",
       "      <td>-9.962380</td>\n",
       "      <td>-13.700000</td>\n",
       "      <td>-13.700000</td>\n",
       "      <td>-12.577778</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.001216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>217.214000</td>\n",
       "      <td>1.877175</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>-8.011111</td>\n",
       "      <td>0.151859</td>\n",
       "      <td>0.140980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>315.088000</td>\n",
       "      <td>3.148800</td>\n",
       "      <td>-7.100000</td>\n",
       "      <td>-7.100000</td>\n",
       "      <td>-6.544444</td>\n",
       "      <td>0.273016</td>\n",
       "      <td>0.288020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>453.580500</td>\n",
       "      <td>4.658655</td>\n",
       "      <td>-5.700000</td>\n",
       "      <td>-5.700000</td>\n",
       "      <td>-5.333333</td>\n",
       "      <td>0.387780</td>\n",
       "      <td>0.489132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1083.557000</td>\n",
       "      <td>23.358620</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-1.433333</td>\n",
       "      <td>0.522304</td>\n",
       "      <td>0.771060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             weight          logp         score    score_best     score_avg  \\\n",
       "count  14860.000000  14860.000000  14860.000000  14860.000000  14860.000000   \n",
       "mean     349.764273      3.487965     -7.177295     -7.177295     -6.678643   \n",
       "std      171.131767      2.463154      1.847241      1.847241      1.739054   \n",
       "min       30.070000     -9.962380    -13.700000    -13.700000    -12.577778   \n",
       "25%      217.214000      1.877175     -8.600000     -8.600000     -8.011111   \n",
       "50%      315.088000      3.148800     -7.100000     -7.100000     -6.544444   \n",
       "75%      453.580500      4.658655     -5.700000     -5.700000     -5.333333   \n",
       "max     1083.557000     23.358620     -1.500000     -1.500000     -1.433333   \n",
       "\n",
       "       similarity_to_hiv_inhibitors  similarity_to_remdesivir  \n",
       "count                  14860.000000              14860.000000  \n",
       "mean                       0.267964                  0.318753  \n",
       "std                        0.139600                  0.205405  \n",
       "min                        0.001859                  0.001216  \n",
       "25%                        0.151859                  0.140980  \n",
       "50%                        0.273016                  0.288020  \n",
       "75%                        0.387780                  0.489132  \n",
       "max                        0.522304                  0.771060  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['smile', 'gen', 'id', 'source', 'weight', 'logp', 'score'], dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_joined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'gen', 'smile', 'source', 'weight', 'logp', 'score', 'score_best',\n",
       "       'score_avg', 'similarity_to_hiv_inhibitors',\n",
       "       'similarity_to_remdesivir'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze_table.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binding Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set()\n",
    "score = sns.relplot(x=\"gen\", y=\"score\", kind=\"line\", data=final_joined)\n",
    "plt.savefig('img/line_plot_score.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binding Score and logP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['smile', 'gen', 'id', 'source', 'weight', 'logp', 'score'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_joined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2t7+lr3/9K63H3Hffz/WRj/wPWWv1pje9Vk996tO0c+e5q/KzSISivtI64ajO+AYFa7x5qmtUlez/pZ8Wt+8XUm1OkhSMblPuic/11aB1WzJ3IoiV40NIe+H6co6k5noRN3NAdvZA+vWg7P5fKXnkPzpfzK8HKY21g1Lza2Hx9SHHF5QGpEJJpjB4UgaldgiqSJXDsvW0EiTzuEOQi2uy0xM++HSEIFedad8pyisYHld4yk4ffNL/dBzbBxgTKBzfoXB8h9yTXqJk/y+U7Lpb8SM/UfzgnTIDIwq3XaBo24VLen/rbul9SMnMAZliKR0XLb0B4EiVSll79uzRZZf9uiTpggsu1MjIiB555GH96Ec/1J/+6Z9Jki677LkqldozDJ7//BdqcND/e/2sZz1bd931I0LRyaq1eWq9LM0dXvPNU1sts5vVoIO7/PSmXFHh5rP8XPzNZy346SvQK8YEMoPrpcH1Cjef1XWba1RlZw/OC0xu/y+711blB/0UvHQqXjMwmcH1C06rOmpQmpmQnV4gKOUH/KagJ1BQak/TrUjVKV8Rcs6Pf5l7mbmk4f8cOqa8uen9rc6UkvyaspFxBeNntIKPGdnkW3Kv4O/OhJGiU85VdMq5fjrc3vsU77pb8S/+TfED35cpbVR02oUKt12goDR29OdKW3pL/rhrt/TepGBgpG8bkwDIltw5ly65mrNarHXzrnNOSpJEQRAseLskhWH7fdRapyha3fdVQtEaay1Erk7Lzq3t5qlSOiXvwEOtIOTKU5IkM7JZ0dmX+E5xG7bxDzpOSCZXVDi6VRrd2nW9/1T/sNzsAdlWYDroq6IP/++OJwh8UGpWl4Y3+nVMwxvnTZFaPCjFPigtVFHqs6DkrJXijulw1TnfudL4pi3H857kbOKnOHau+5mZkJs9JCn9B88E/ve6YZuC7U9uTX0zQ6Nr3rzARAVFp12k6LSL5Gpzih/9uZJdd6vx82+p8fNvKVh/qm/QsPX8YzavaXa5c0lD9uAuWRP4dUe09AYADQ0N6dRTt+rb3/5ma/rcoUMHtWPHmbr44qfp61//il7+8iv1gx98T7Oz7dkC3/nO/6crr7xK1WpV3/ved/S+9928quMkFK0yXw2q+U1T522eWljVzVOb7NxkewPViQf9uo0w56eUnPNfFKxyy2yg14wJZEobpNIGhVvO6brN1cuyMwdbgan19bH7uxtDFEppVak7MJnBda0T+tb0wCi/cFCyVsYsEJSign/Man8o0hWCpv37kVx7Olzx2NPhfMCcnLfmx80e7Ph9GZnSBh94tl3g9/wZ2eSbsfThBy6mMKTcGRcrd8bFspUpJbt/qnj33Wrc/VU17v6qgo2nK9p2ocKtTzxqa24T5mQGcke09B71xwotvQFk2Hvec4Pe//6/0q23fky5XF5/+Zd/rVwup7e+9W264Ybr9YUv3K6zzjqna/pcoVDQH/7h72pubk6ve90bdcYZO1Z1jISiVeCSRrouaKpj81STrg1a3c1TpbRl9sFH0mrQES2zz3iqrwatYctsqbmXUtWfHEr+02iZ9ELH19ZlLXCdoRUuVpzJDyocG5TGTuu63tnEn/x3BqXZA4r3/LS741kQpdPwxlpVpWbjBxMVjhGUDsja5ChBKfe4jvnWHmb1slSelq3NyvjadLomaPFKkHPWd5RL1/u0W14f6Now2Qyu94Gnue5neNxPRVzD95eVFAysU3D2JcqdfYlv7LH7biW771H9J1+U/uNf/NTi0y5UuGXnolWgrpbe1dm0pfegb+ldKPkW5QCQAU95ysV62tOepji2uuWW/2fe7d/61jf01re+TWecsUP33XevfvnLX7Ruu+iiX9Ob3vT7azZWQtEKc0lD1UceVDI5s2abp0rNltm+GpTs/6UUN1tmn67o9Kf4IDS8cZVHccSYbOJbh7tYUqBgaH1Hi17nP7W2VlLiv1orybYvu6TVTtpZ608knVVzKo5xUjtA+aud6ZiX6oy/0pj2ugizQBBbIHw1L/fDFKdec87J/3Jd8wq1pkMtdptTep/2Zeec/7NKz8nljP9DbH5V84+y805qv1bHJaPm3lRBurdRuOKB2QRhqyLUyTnnN+/tCEt25oDs4b1ye37eNV4zMJIGpXZgMqWN/vpjBSVJLgiOKygtGIKc87/isLBgw5auvX46u77NTEhxvf2zFIdlRjb5TZnTNT/B8EY/pj7j9wlL3yuCaNl/j4Phjco/8bly5z5H7vDeVkBKHrvfV9tP2elbfG8+c8FOh8YYKd/Z0vshKYx8eBxcd8IGRwBYKdu2naY/+7M/VRAY5fMFXXvtdT0bi3HOuWPfrf8dPDi76EKtteTiukrlhzVdW91/7JyzspOPKtn3gOxjD8gebrbMHlaw+WyFW85WOL7Dr19YQ81uVc4lPpQNjvqpefnBFft01DmbnndafxLe+i89CUq/d+q43jk/NudagUvOdl92aVDrCGOtk31j0k/YpfZJu5945M/fm6FAx6iCadEgNjo6pMnJufZrHi1sHC2gSOnPnr5Wc3yt8JH+LK4jUDYH7pyc6QgjzecwgV8PEjTHG7RDZnqb/y+9Pej4vvnYBYKn0SK/l3nXKw3Lfv8kl8Q++Md1v5Fx0pCSOP29p+HYpdPZgjQ8pSFqNauNLonl5g7Nqy7ZmQNdAUNhLu2EN9a9bqk01nWi3GphnjTkrJWR//MJCoOtoDS+ab327z2QrgnqDkFHBqiuvX46KkDz9vpJp7u1w8+4TH5g1X5vS+Gaf5dt999d/0+YaRWXXXosmygvmSDdQFatDWQf795Czllfid91t+I9P5MaFSk3kLb4vsBX4Y9yjDkby9XKkjEKShsUDG3oye92fHxYExMzx74j0IHj5sTw2GMPa8uW7b0eRksUBYrj5e9TuFwL/R6CwGhsrLTg/akUnUBcveJbZu97oLtl9oZtyp336wo3n92Tltkuif1JlbNyYaSgtEHhwIiUH1iVE1BjgvRk/ujPvRK/ha4qyQLBqyuEpcGqVQVzaeCyacA6RhXMVp1cvZpOE0zDhIw/mQsC//MG/rr2VyMpTL8GUpBOMTwiWLT+HBaslknzw4hJL/bRNJ9QkgqL/rl274GU+PAUN3x4StLwVKvIpWtoXGdFx4T+951WnVoh7jiZMGo1D+gem5Nqs62qUqvCdGiXkt33dD/H4PpWYGq2EA+GN8oU06luXRUlq0p9n+x01a9naVaCGlXZ6X1dm5za6f2t9wxJ7b1+tp7f3utnZNOad5t0R4Qc2bQibPyHD10fR4SRD41hsTUlMQijjvAb+spQxwcw7eY2M3KVKblKRc44mSDn93w7zmPcmEDhxtMVbjxduSe9WHb/r3wHu13/qfihu2SKw+0W3+tPmXccmaCzpffhtKX3MC29AaDHCEV9rN0y+35fETr4iD/pbrbM3nKOwk1n9qRltksa6afLTi7M+U5LAyNSrnhS/aNuuoLDMe77OF9rcHxYc3wCt2xL3QPJpaHJBycfolxadVJadXJxvV08U/vkfF5wWmLTAGOMVBxWWBxWOH5G93jieru6lHbG85vUPiwljfYdo0JXG/FmdcmW67L7HumqALnKdPtxYU7ByCaFW85pNzwY2bSkpgrL5Y4IOX7qa9KqTqb1PB92Ah90TL4ohTkpzCuIIj8dLeiYImnC5QXVIJKKJYXFkrT+FP/7rlfkmptiW+uHFR1/V0ATRP59eMs5cnFdyWP3Kdl1j+Jf/rviX/xAZmiDwtMuVJQ2m+h6rAmk9L3bt/T+pW++Mzzup9YdY+NhADga11w2kFHLmQjH9LkV9ninz7VbZvsg1GqZvW6z3zdoyzkKRrf2pIOTi+tycc1/bhsVZIbGFBSHTrog1CtMS+gffqpWIiXJEVP2fHBSUpeN65JNp+w1ZzbKpVWL5nS95U/Z85vUzrSn4XXsvdS1wWlTEPoT6mbVZ3jch5+O7niPR+t30qyCphWe5ozO9qRL58cS5nxVJ8r5tVPpdDaTVnMez+9mpTjnfFWtXpbKU+1ufKY51W6Z7+P1spJHf6549z2+46cks26L3wNp6wWLdvt0SUOuUZEUKBjZmLb0Xp0p0LzfYDk4bk4MBw7sVbE4qKGhkb44P1vr6XPOOc3NTataLWvjxlO6bjva9DlC0QpbTiiyc5PtatDEQ+2W2Zt2+AYJW85WMNCbltkursk1av6EJz/g2+wWSmu+VikL+MfmxOOa4cAmUtIMT400PNXk4oZcUpecndc2wk9zbAen46mEuEbN7wk0e0ClkSGVg3UypQ3LC1+t6WvtaWx++loz6HRMX4uaQScvBWnYCaM06DSnIEYrtn5wrTXXRNrqrN9ctlH1NcIw8iFpGR9G2cq0kj0/VbL7btlJv/YzGNuu8LQLFJ163oKVfj+OsmStb+ldGvPrMlfw5Ib3GywHx82JIUliTU5OKO5cy9pDfoPWtV1TFEV5jY6OKzxiHSmhaA0tJRS1W2an1aCZA5Lkp1psOTttmX36414QvBx+X6W6XFKTcU6mWJIGRxUUS2xCuMr4x+bk5dImEZ3T9lyz6hTX5ZK6XNzomFZm1Gyc2Jqyl3498sR8dHRQk5Pl7tebN30tSTs3dsSctNeGgpwPNlHeV3XCvK/ytNbohAu+bha4pJFOtZuWrUzLJHG62XbaOfA4Q6idPahk9z2Kd90tN3tAMoGCTWcq2naBwlPOnfdhk69kVfw0yvygrwQOjKxI6OT9BsvBcYPl6KfjhkYLfcBVZ5Ts+4UPQvt/dUTL7Kf6aXGlsd6MLd1g1sV13xGpUJJZt1lBYZAgBKwA3yzj6Oud2tPT0uCUxOmUvXazCBvX5Wyjo+pkFM81ZKuVdtxxHdPXorwU+rATRPl2uEnX6PhW5r2fWtGvmpuxamBEQfqBka2Xfae/yoyPr8ZIYWFJ75VBaUzBuZcp2vlsuanHFO++x++BdNdnpTBSuGWnwm0XKtx8lg+qXS2967KHHpE1oYKRzQqGaOkNACuJULRKultm3y97eK8kpZ2Jzvfrg3rQMrtzfGrU5GxDklEwMKxg9FSZ/GBPKlRA1vmOg5Ffb6OFg1OojipQWnkqjg5orljprur0U9fAk4QxRsoVFOYK0tCoAmuluCpbLcuVJ2WrM5JzPnTmCkdtlGCMkVl/ivLrT5E7/3myB30XwnjPT5Xs+alvpnPqE/0eSOOny5jAb/4d5eVsLDu1V3Zqr4J0al2v26UDwMmAs98Vluz7pQ7/9MuqPvpLqV6WZHreMrvJOSvVq77jlgkUDI74hbz5ATodAScIYwIpDKT0w4twaFimTAhaayYIpPygwvygNLKxtTWBrc7Ilg9L9bKfaneM1t++xfd2hRu3K3fRi2QnfIvvZM9PlTz8v6VCSdG28xVuuzBtshPJFId9I47ylJLZg+nGuuMLbs4LAL3S/AA+KXduyN6/OBNeYbU7b5eb3K1w01l+fdCms2QKgz0bT2szVZtIQaBgcL3CwfUrupkqAGSdCSMp9K2/w/Wn+CY19apceUq2Oi1Z5/dHOkrrbxOEfhbB5rPlkoafbr3rbsUP3qn4l/8uMzTa2gOpua+R5Ft62/0P+vbr6zbR0htATzRDkJ9mPC1bm5OxVrXqoNzwmX0/XZt3zRU2cPn/pdLcw5pp9K47m7PpZqrWygXhqm+mCgDoZqKCD0CD6xQ0TxRqc349Um02XY+0eOtvE+YUbT1f0dbz/cbde+9VvPtuxff9q+L7viszstm3+N52gYLB9TK5olzSkJ3cIzu5V8HImIKhDavW0hsAXHMacb0iladla7Myzvm938K8X5JhAklxr4e6JISiFWZM0JMKjF+QXfGfRoY5BcMbOzZTJQgBQK8YE0j5AYX5AWl4owKbSPWKD0flKbnqtJwzMmG4YOtvkx9QtP3JirY/Wa46o3jPz5TsuluNn/4vNX76vxRsOM1vErv1PAXFET+1buagkqkJmcF1CoY3rnhLbwDZ0wpBtY5KULoRtwkLrSm8J+o7DaHoBNbc6M84+TnrI5sVFIfZTBUA+pgJQqnop9pp3Ra/MXaj6lt/l6d8C3WZ1p5QnR9smeKwcmc+Xbkzny47d8g3aNh9jxr/8WU1/vMOBeM7FG27UOGp5ziLuAcAACAASURBVEr5glytrLj8C5lc0f8bsUItvQGc/Fp7t9UqUnVKtjqX7mFn/HtToeQbx/R6oCuEUHSCcXFdLvZ7CClXULD+VL+HUK7Y66EBAJah2VlOAyMKRrdKcc1/ElvumGqnwH/41THVLhjaoGDns5Xb+WzZqX2Kd9/tW3z/+HPST0KFW87xLb63nC05l7b0DhSMbJJdn5dzlpkEAFq6QlDlsJ8W55xMYPxayOLwMT90dzaRm5mQPbRHyeQe2cOPKnjCudIzzlyjn2L5CEV9rrWZalzzGznmBhSMpkGIueIAcFLxrb+LCnNFqbTBt/5uVGRrc75pQ2Xaf1JrorT1t59qF6zbrPy6zXLnPU/20G4lu+/2Lb4f/bkUFdIW3xfIjG2XndqnyiPTiqfKkgn9nkhhXooiKchJUc53uQtCKQg69rUKCFHAScTZWKpXZetlP5W3UZEk/yFMRyVo0cc7Jzc3KXt4j+yhPX5N49ReKUnXEOWKCka3Kje6RY21+IEep74MRe973/s0OTmpm266qddD6Yn2ZqoNSc4HoHWbFBSG2EwVADLEBIFUGFJYGJJGNvlNfRsVucqMn2pnG3LOV5uaU+3CsdMUjp2m3IUvlD3wkG/x/ejPlTzyE6kwpGjr+WqcvlMuKcgUS3JBTkoavkGPs5Kzss61OugaSXKSM0pDVC6d2tcMUXmZzo2Bg4AQBfSh1tYBtTm5yvQCIejolSBXnZWd3KPk8KM+AB3aI6XPoSBSsP4URadfrGB0q9/7cmiDjDEqFGJC0XL84Ac/0Gc/+1k95znP6fVQ1lT3ZqpSMDCiYP0pMoVBdi0HAEhSWtUZlorDCkdPlYtr6VSX6bT1t5WMkaKCFOYUbjpT4aYz5X7tJUoee8CvQXroxzr0qx92PqtMsSQzMCxTHJEZGPHTZAZGZAaGpfS6IMq3Ng92yw5R+bQStVCIClkPC6ygdgialStPy8W1dDqc73wZFEcWf2yj5jeKblaADu+RK0+ltxqZkU0KTz1X4ehWv4fayKZ5TWJONH0Vig4fPqybb75Z11xzje69995eD2fV+SBU9QetCXwQGlrvgxB7TAAAjsFEBYVRQRpav0Dr7xlfRQp8V7to63mKtp4nF9dUCsqa3rdftjotV5nxnxpXZ2RnD8odeFBq1Oa/WK6wYGgKmtcNDKdd7nx1yEgdIap+lBBlpLSNb3eIykmh/29eiEqn9BGigDbfgKsqV52Tq0zJpX+P/XtA3jfjWuhxNpab2u/XAKX/uZmJ1u1mcL2C0W0KdjzdV4HWn3JSzlzqqzPv97znPfqTP/kT7d27t9dDWTWtzVRd4oNQazPVgRM+YQMAemd+6+90vUB11p8gVcpp69yccmNbFAajWuxfHRfX06DUHZqa19np/XLVWUnuiEEE3aGpFZ66rwvC+acfSwlRrT5XzRAVRH7905EhKoz874MQhZNYs/mW/7s5488vjZExaQgamF8Jcs7KzR5qhR87+ahfB2QTf4f8oILRrYq2nt+eBpduFL2sMTp37Dv1ib4JRbfddptOOeUUPfOZz9Ttt99+3I8fGyutwqiOn43rqj4sjY4Otq5zNpGtVeRsIhOGioa3KRxap6AwSBBCl/HxhT/FAY6G4wZLYRs1uXpV8dxh2blpjRQ6N1R0aZUm9DMVwqKMWX/U53M2ka3MKJmbVlKeUlKeVjI35S/PTSmZ2adk3/3p+thuQXFI4eA6BUMjCgfXKRxa578OjrQum/yxt5dw1ko2ScNUIuca/muShiyTfvVNzv3PFkUKorwPT5H/Lwhzfg1UWo0yUY71UIvg/aZ3mn+Hk8qMkrkp2biWtscOFWwYlglH5z0mKU+pPrFL9YndahzYpfqBXXL1qiS/FjG3cavy512q3MbTlB8/TWFpdNkfHjibyMUNP722FYaMgqF12rRp8al6/cK4Polwb3zjGzUxMaEwDDU1NaVyuayXvexlete73rWkxx88OCtre/+juLiuUvlhTZWNFFf9J15hTsHQqIKBYV8R4o0WCxgfH9bExEyvh4ETDMcNlmN8fFj79x32XaJs7KfdxHWpXvWfPMc1GWsl064FGRNKQZRWX5ZWdXHO+U+vuypO/rLtqD6pXp7/4DC3aKWpfV3puP5NbYYnpRWpZiXKOScZI5P+tM751w9yRSlfkKJiGqQiKYwyO8Wd95u11d7DzP99MUnsA34QSbn8vOPQNaq+8tM5Da6a/nmZQGbdZoXrT/UVoA1bZYbHl3VO6pyTbCwlDT8DSk7GSS6MFBSGpPyQTL7gp9iFOW3atK5vjpsgMIsWUvrmb/XHP/7x1uXbb79dP/zhD5cciPqOCeVsTcHwuC9dspkqAKDPtNboqLDg5ovOxj40JbFfc1Cv+s6ojapcrSIfl0yrCiMTSmEamNKTNWOM/zAwPyCNbF50LC6Ju6bnHTllzx582E8PcvbInyJtEnGMKXvp+gdjAimcfxJ45M9vlH7qndSl2Yqci2Wb18v46e+5gpQr+n/jo7yfwhdGfkof/+bjODnnpKQuV6/69X2VGZmkkW7kHPpjLD/QOlZd0lByaHd3AJo92Ho+M7RBwfjpCtZvTdcBbVlW4y5nk/R9oCEn2/o7YHJFmYF1CoppZ+QwL7PAtNgTyYk9+j5koryKp52raLLGmyIA4IRlAn+Cr1waGtqzwjs+KU4DU9zwsyMavspk62UZZ+SMb0Nn0vU/rSrTEVPHTRjJDI1KQ/On/7Rf00r1ckeVqbv6dPQmEcUFQ1PQcdk3iWj/u90KjVF3aGqufXJJLFVmpLlJWScZk1aZZGSigt9UPV/0l8MoXe8UMVsEkjr2oWw0Q9C0jI39Rw1B5I+bNAQ5Z+VmDqSNENJK0NRj7Q8JCiXfBe4JT0oD0Kn+g4jjHU9n9cc5/9phpCA/IJXGFOQL6Z5mJ+f00r4MRa94xSv0ile8otfDWLYgV5Ax9V4PAwCAVWGMaTc1WOB252zH1LzYT81r1KS4KtuoyyUNGZNOVnOuoylCuGhwMCaQCiWZQknB+lMWHZuLaz4sHW+TiCD0gWlwnczgqILSBpnBUZmhUQVDo12hyRi/r0trbF0DSNc31ctSdVo2bZPua2r+w1MTFaTcgF83FXQEJtYZn7TaIajiq5+VGRln5ZyVCf0xYYJB30SkMiU78VC7CnT4USlOzyujvG+EcNYz29PgiiPH9UF8q/pjG/711V39MYVBP120GegzIjs/KQAAWBPt0JCfF5pCNRsktD+Vdo16WmmqytYrci5JT9T8/0wQHLGeafFPqU1UkBkuSMMbF72Ps4lcbXbB0OTKU7L7f6HkkdnuB0WFVkAy6X/B4KjfoHJwXSvQtKbopVOVugOTk1z689bKss76n8/4wKQg8tPyoqKfdhjlWqEpSyenJwMfgmqy9YpUmZGtzsi4xLfJD3M+gAShVC93TYFLJvdItTn/JEGoYN1mRa0K0FaZ4bElV2nmr/2RjHPt6k9hVEF+4KSu/hwP/oYBAIA15UNOXorS0NQx08eHpqQ9Pe/IJhD1spxzrcqL1GwC0W4EcaxPzU0QygyskwbWLXofF9flyodl5ybl5g7JzU3Kzk3KTk/IPXZ/u4WxH4D/hH1oVMHQhnZoGhr11aZ0KpMxRjLptMQj1nK11zElUmNKmjskKyfj0v55QZBOqSpK0YBMLl3D0QxNTNnvKdfcJ6xRTTdTblaC0hCUH/AVxMN7O/YDelRu7lDrOczwRoWbz0pbYW9VMLJ5yWG4q/pjXRq006mcGa7+HA9+KwAAoK+01/MssQlEo+a73HU2gWiuaZJkTNRqArHU/YpMlJcZ2aRgZNP813fWV5fmJlthyaXhKX705/O76eUGuitMQ2mFaWjUr2lqbnjban6RNobo/qH9SXVlVrJTsvLrSZprt1rrmHJFmVyho/FDzodQzONcswOhm/fVdXzvkjj9/ce+a6FN/J9F2smwGeJb09DCnBQV5WYPtNYAJZN75Kb3tVpVm4ERH3xOf0p7Q9RccQlj7lzP15DfvcvJBR3Vn5xfy0b15/gQigAAwAllXhOIDv6kMUmnDM1vAuHicjswtZpAdFSZlrCux5igXWnaePq8212jJleenF9lOrxX7tGfd3fRM4HM4PqFq0xDo/7kVkuYlmcTuXpFqs7K2qS12a1z7qRpL75oiLHW1w2tleTSEJOk4aUZYmI/bdMlrf2t1GzF7hspplnadWxw1bFpsAn8nUzzv8B/TY8dV67KHu5oh314r5Ske3TligpGtyo851n+6+hWmeKx93tyzkpxY5Hqz7BMYSit/uSX1VkO3U6cvwkAAADH4JtApCf8C9zebgKR+Kl5SUOqV49oApG23u5sAhFGx1zP1BpDriCzbouCdVvmv75N/Pql8qTc7KRsuR2c4sk9UqPa/YDCkF+7VPJT8TqDU3Ofps6fWVqgW16P2ou3QkwaVjq/Lh5imkGmI8QkSTsEzQsxanaHP0qICdphJohkQn9b58/mW2I3pEZFqld8Q4R6VWpU5OoVuUbzclWuUfHTORvp9fVKO+gGkYL1WxQ1K0CjW31V8Ci/x0WrPyZUkB+Uis3qT9r6msrfqiAUAQCAzOjsHGfU3bZ48SYQFb9epF5Ju3U5ubSa0NU5bwkhwgRhu/34+PzbXb2SVpbSClM5naJ3cJfcrnvU1TUviGSG1vs9aYaaoak9Ta9ZPVip9uLxQCw7N5NOF0uOqMYcZ4hJX8H/mXQEFy0UYvI6MsR0/gytZ0uDjWtUpFq5O9g0qr6StpRgs5hc0bfJzhX9lMjBdenvZkBmcL2vAI1sOmq10YfyRnp8pdUfp7Tz27BMfjBttkH1Z60RigAAAFJLagLR3Myyaz1TTa6WtvpeZD3TUqaqmXSz22D01Hm3ORvLlafmrWOy5UnFBx5qt21uPldxeP46psFRBaVRKT8kY8wx2ov7CkazvXhdU0oOV/xjmqHlcYSYhXQFm2ZomRdsOq5vVnWWHWxGZHIDPtjkiull/9Xk/X38fQvHvT7HtY6TRutnb1V/Btf7zm9Uf/oGoQgAAGCJWlWX3PwmEIuuZ6rX5JLmprZptUTpNK+jbGo7/7UjmdKYVBrTkfd0zvn2zmmFqTM42f2/UlKd6X5AmFu8W97Qev9aHftRSVI4OKigduzqRTPY2EZ1/lS0hYJNZ/WmUe3u7LeQxYJN5/X5gRUJNsf8WW2zKmbT6W+Jn37p0mmUVH9OGIQiAACAFbD09Uydm9qm+zPFNTkbq7XN6/Huz2SMVBhSWBiSNpw2/7WThlz5cEdYSqfnzR6U2/cLP2Ww/Wy+K14rLPngVJkZUnzo8OoEm3WLBRv//WoGm9bvyHZM+2uuhXJWTjZtvpC2SE8bHkjOV8aCyFd8qP6c0AhFAAAAa+DYm9oeZX+m2lyrkUD3/kzp9LxjtBo3YU5meFwaHl+4ylSblZ091NE1z/+XPHa/knQz0UNHPmmu2BVajhpsOqalrWawaf9M7VBz3AEnzEmRb2euMFLQ2WQjCP10wfQr+0OdPAhFAAAAfeCY+zOlVSYlsWzSkBo1X6GJ661W4zK+CcTxtBo3xkjFYYXFYUnb579uXJebm9RwKaeZqtYs2LRen4CDNUAoAgAAOAGYZtvtnBau9iSNY7QaN77K5Jyf2mWW1mrcRHmZdZuVHx1UMFle9H5LsTIBJ/J7Ly0YcIIlb9ALdCIUAQAAnOCMMUtvNZ40m0Ck64DmtRp36dS8jkrTAiHj8QWcKG3i0BFwTOArWq025wQcrB1CEQAAwEluXqvxIzgbS0myQKvxqlytIskpKTfkqhWp2QzChH6tUrrRq6/i5BVEBByceAhFAAAAGWeCNNgs2mo8VnHDgGYHywQcnJQIRQAAAFhUc78i3246PvYDgBMQDdQBAAAAZBqhCAAAAECmEYoAAAAAZBqhCAAAAECmEYoAAAAAZBqhCAAAAECmEYoAAAAAZBqhCAAAAECmEYoAAAAAZBqhCAAAAECmEYoAAAAAZBqhCAAAAECmEYoAAAAAZFrU6wF0uuWWW3THHXdIki677DK94x3v6PGIAAAAAJzs+qZS9P3vf1//+q//qs9+9rP63Oc+p5/+9Kf6+te/3uthAQAAADjJ9U2laHx8XO985zuVz+clSWeeeaYeffTRHo8KAAAAwMmub0LR2Wef3br80EMP6Y477tCnP/3pHo4IAAAAQBYY55zr9SA6PfDAA/r93/99/fEf/7Fe/vKX93o4AAAAAE5yfVMpkqS77rpLb37zm/Wud71LL3nJS47rsQcPzsra/sh34+PDmpiY6fUwcILhuMFycNxgOThusBwcN1iOfjpugsBobKy04G19E4r27t2rP/zDP9TNN9+sZz7zmb0eDgAAAICM6JtQdOutt6pWq+mmm25qXXfVVVfp6quv7uGoAAAAAJzs+iYUXXfddbruuut6PQwAAAAAGdM3+xQBAAAAQC8QigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKb1VSj64he/qMsvv1wveMEL9KlPfarXwwEAAACQAVGvB9C0b98+3Xzzzbr99tuVz+d11VVX6elPf7rOOuusXg8NAAAAwEmsbypF3//+9/WMZzxD69ev1+DgoF74whfqK1/5Sq+HBQAAAOAk1zehaP/+/RofH299v2nTJu3bt6+HIwIAAACQBX0zfc5aK2NM63vnXNf3xzI2VlqNYS3b+Phwr4eAExDHDZaD4wbLwXGD5eC4wXKcCMdN34SiLVu26M4772x9PzExoU2bNi358QcPzspatxpDO27j48OamJjp9TBwguG4wXJw3GA5OG6wHBw3WI5+Om6CwCxaSOmb6XOXXHKJfvCDH+jQoUOqVCr62te+pmc/+9m9HhYAAACAk1zfVIo2b96sP/mTP9HrX/96NRoNXXnllbrooot6PSwAAAAAJ7m+CUWS9NKXvlQvfelLez0MAAAAABnSN9PnAAAAAKAXCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTCEUAAAAAMo1QBAAAACDTlhSKJiYm9OY3v1lPfepT9bSnPU3XXnutpqamVntsAAAAALDqlhSK3vnOd2r79u363Oc+p3/8x3/Uhg0b9O53v3u1xwYAAAAAqy5ayp0ee+wx3Xrrra3vr732Wl1++eWrNigAAAAAWCtLqhSdeuqpeuSRR1rf79+/X5s2bVq1QQEAAADAWllSpSgIAr3sZS/Ts571LIVhqB/84AfasmWLrrnmGknSRz/60VUdJAAAAACsliWFohe96EV60Yte1Pr+2c9+9qoNCAAAAADW0pJC0dOf/vSu740xKhaLGh0dXZVBAQAAAMBaWVIouvrqq7V//36VSiUZYzQzM6MwDDU6OqoPfvCDespTnrLa4wQAAACAVbGkUHTJJZfo6U9/ul72spdJkr761a/qe9/7nq666ipdf/31uu2221Z1kAAAAACwWpbUfe7ee+9tBSJJeuELX6h77rlH5513nhqNxooM5K677tKVV16pK664Qm94wxu0Z8+eFXleAAAAADiaJYWiOI51//33t76///77Za1VrVZTHMcrMpC3v/3teu9736vPf/7zeulLX6r3vve9K/K8AAAAAHA0S5o+97a3vU2ve93rdPbZZ8taq4cfflgf+MAH9KEPfUjPf/7zH/cg6vW63vKWt+jcc8+VJO3cuVP/8A//8LifFwAAAACOZUmh6LLLLtNXv/pV3XnnnYqiSE9+8pO1bt06XXjhhSqVSo97EPl8XldccYUkyVqrW265ZUXCFgAAAAAci3HOuWPdyVqrW2+9Vd/5zncUx7EuvfRSXXPNNYqiJWWqLnfccYduvPHGrut27NihT3ziE6rX63rnO9+pqakpffSjH1Uulzvu5wcAAACA47GkUPT+979f9957r66++mpZa/WZz3xGZ555pt71rnet2EDm5ub0B3/wB1q/fr0+8IEPKJ/PH9fjDx6clbXH/FHWxPj4sCYmZno9DJxgOG6wHBw3WA6OGywHxw2Wo5+OmyAwGhtbeJbbkko93/3ud/XP//zPrcrNc57zHP3mb/7mioait7/97dq+fbv+/M//XEGwpP4PAAAAAPC4LSkUOee6prLl8/kVndr2s5/9TN/4xjd01lln6eUvf7kkadOmTfq7v/u7FXsNAAAAAFjIkkLRueeeq7/6q7/Sa1/7Whlj9MlPflLnnHPOig3ivPPO03333bdizwcAAAAAS7WkeWrXX3+9pqendfXVV+u3fuu3NDk5qfe85z2rPTYAAAAAWHVHrRS99KUv7fp+w4YNkqR7771Xr33ta/XFL35x9UYGAAAAAGvgqKHo3e9+91qNAwAAAAB64qih6GlPe9pajQMAAAAAeoLe1wAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyjVAEAAAAINMIRQAAAAAyre9C0c9+9jNdcMEFvR4GAAAAgIzoq1BUqVR0ww03qNFo9HooAAAAADKir0LRTTfdpDe84Q29HgYAAACADOmbUPSNb3xD1WpVL3rRi3o9FAAAAAAZYpxzbi1f8I477tCNN97Ydd2OHTs0OzurT3ziEyqVStq5c6fuu+++tRwWAAAAgIxa81C0kNtuu00f+9jHNDQ0JEm69957de655+pTn/qUSqXSkp7j4MFZWdvzH0WSND4+rImJmV4PAycYjhssB8cNloPjBsvBcYPl6KfjJgiMxsYWzhbRGo9lQa961av0qle9qvX9zp079fnPf76HIwIAAACQFX2zpggAAAAAeqEvQxHriQAAAACslb4MRQAAAACwVghFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0whFAAAAADKNUAQAAAAg0/omFO3fv1+/93u/p5e97GW66qqrtHv37l4PCQAAAEAG9E0oesc73qHnPve5+tznPqcrrrhCH/jAB3o9JAAAAAAZEPV6AJJ06NAh3Xvvvfr4xz8uSXrlK1+pZz7zmT0eFQAAAIAs6ItK0a5du3Tqqafqpptu0itf+Uq9+c1vVi6X6/WwAAAAAGSAcc65tXzBO+64QzfeeGPXddu3b9ePfvQjfeQjH9Fzn/tc3XbbbfrCF76gT37yk2s5NAAAAAAZtOahaCGPPPKIXv7yl+uuu+6SJFUqFT3jGc/Qf/zHfyz5OQ4enJW1Pf9RJEnj48OamJjp9TBwguG4wXJw3GA5OG6wHBw3WI5+Om6CwGhsrLTwbWs8lgU94QlP0JYtW/Ttb39bkvStb31L559/fo9HBQAAACAL+qLRgiR9+MMf1vXXX6/3v//9KpVKuummm3o9JAAAAAAZ0DehaMeOHawhAgAAALDm+mL6HAAAAAD0CqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkGqEIAAAAQKYRigAAAABkWt+Eot27d+s1r3mNrrjiCr3uda/Tnj17ej0kAAAAABnQN6Hogx/8oF7ykpfo85//vF7wghfo5ptv7vWQAAAAAGRA34Qia61mZ2clSZVKRcVisccjAgAAAJAFUa8H0PSWt7xFV111lT75yU+q0WjoM5/5TK+HBAAAACADjHPOreUL3nHHHbrxxhu7rtuxY4dqtZre9KY36fnPf76++tWv6pZbbtEXvvAFGWPWcngAAAAAMmbNQ9FCDh06pBe/+MX693//99Z1z3jGM/TlL39ZGzZsWNJzHDw4K2t7/qNIksbHhzUxMdPrYaAPOOfknGSdk3NO1knW+uucXOuydU4bNgzp8OGyosAoDAKFoVEYGD4YwFHxfoPl4LjBcnDcYDn66bgJAqOxsdKCt/XF9LnR0VEVCgXdeeeduvjii3XXXXdpaGhoyYEIWCnWdQcV55ysnR9grHVKrFVinZKk+X3zOimxVtZJSeJk5CQjpf/znP+fMUbO+a/T1USHp8rtEOScZIyi0CgfBcpHoXI5/zUK26GpGaACwhMAAMCyVIhhXAAAGxRJREFU9EUoMsbolltu0Q033KBqtaqhoSF9+MMf7vWw0Oc6A4yvwnSGmWZFxocVa51ia314SZqhxSlO/OOa4cYHGB8ujNLsIvmA0rrGyBil//nLgWlfFwRGYRgoMEbWOtVj6/9rxGrEVvVGonps/eXYqhEnqjesRkpFyVkNFiINFtP/8j4AJdapXIuVVFzr59IR4SkMjPK5QLkoUD70ASoXhWlw8uEqDAIFAeEJAACgU1+EIkm66KKLdNttt/V6GCuiWo9VqyfH/8Blnqsu9xR3eYWF5b1a5/n7ggHGOTnbDjBdVZfEV13ixHZUaVw7qBhJcjI+0qQv5Ks7RkYyriO0dIeYIDAKJH8/OdUbLg0pSTuwNANMel290Qw0yfyQ0+i83o/38YrCwAekzrBUmP+1mI9UyIcq5ELNKW6HpyP+AIIwUC4MfPUp16xAEZ4AAEB29U0oOlk04kQ/f/CQJg+XV/y5O09RnZZ6sr22J7bNVzvqUrUFppJ1BhhjjIKOANP8Xk6yzqoRO8VJokbsVO8ILd3BJemoxHRcHydqNOZffzzCwPhqTC5Mp7X56kxpIKfccPv6XNQMHmF6uTn9bYHro0ClUlGP7p9WuRqrXIv912pD5VqsSjXWXHr94Zma5qqxao3Fg3cxH2qwEGmgGGkoDU0Dza+FSAP5UIV8pGI+9OMNA5nAtKpOkhQYKReF3eEpChSGQStAhWmAAgAAOJERilaBdU7Dg7leD2NFuY4paUlarWlWbGzn9+kaHNtcU2Pbt/npas1qSmdwSTqqLJ3Bpfv6ODm+8BKFzZP59gl9PhdosJBrXd950n/My1GgXC5YtRAwWMxpbKSosZGl3T+xVpVqonIt1ly1oUozSLUClb88PVfXYwfLmqvGvsK2gMAYDRTCtBKV8xWoQqjiEQGqkPP3GciHiqJQko+37YAXtNY9hUc0jKBpBAAA6FeEohVmrVOlFiupN9LAoAUDxLww0bX+RUe9X+v7zvsvGE6OFVyO77lXS7MK0aqu5EIV8qGGh3Lzru+svix4fVqNyYX9Of2rGS6dmmuhJKWXq/VYjTiRkZEJ/JS+o/0MYRCoNBioNJiTNLCk127Etjs41drVqM7rJw5XfIWqFmuxP/pcFLSm7zWrT8WCD0/FXKSBQqiBNEAN5P198vmwHVDTP7swNL7jXkcFivAEAADWEqFohX349rv1s4cm1+S1AmMUBOm6mHShffNykJ5cNi933i8XBgqijvt0PG6xxxz9uZvfq+s+Xc+fXtdcy9Luorb2J8CdwURpMGmFlPR2dVz2X/1lI9O6f2Daa5hM83HGtBs0dDZDSDWnBbZ/J1JgQgXGT3mbC0K/dir2a6psc+1Ux5RDo46+D0pf7CjTDjvXUeVzfrrc+uHCkn5X1jpV60k7PM0LVO2vh6arKtdi1RuLV/QG8qEPUIVIxYIPS82v/np/e2kgp/WlgkrFnAp5H6KaOo+XhQ6dBY+mI+648H2OfdXRjtXmsaKuP5uO69Q+5vxl05o62rn0qxWUpdb1R05HbR1fkpIg0NT/397dx0h11X0A/963ufPGsgUWaHzQpn3M49MGSQwa0Ua0mspCSQW1tTZp1BjSaGrbmBiyUfuHUltDhKhpjEnFxLS1llKoDTRtiq3gYnyJKWpjolH61LiBpbSwL/Ny77n3+eOcc19n6bLscmeY7yehM3PnzszZZdjOd3/n/M65RipUzmbabOewmz/Y6bTZ/I6k0xTaWb7kDOd1+B7MYly2ZcC2TLlWTjVAISIiymIommcb3vd2rFo5ACMMYRroGDoM04BlJI+nz0vf1zm46A+9vUp/YJJT8oJU8EDienQ8E2AQquiR7OAQNV3IvZq8L5Qrl3R4MwxZbdGBwbTk9ej7bwBGMtSp44YKPsnGDUamI52pQox+bujL8xgaWoTF5fw/SV2pS+9zpK4npzWKIG5Qkemwl2oRbiS+J/p7p8amg1+2OUW9YmOg6nTotpf/mnwRoNFSa6CafjStLzvFb3Law6k3Gphq+jPuMWaahpyqZ5nxeyB6DyX/hhP3pY4jDibI3h+HkXRwiZ4x/VyJsJJ9/W6T/tkh3+emARkKMj9jdJBK/cJjpkv1b0Y/1tY/zzK/AElet63ka5nRcxgmUudand5PhjFDQsqe0vkk/UuM5M8KxzLglmy4jq44W4ng1J0VZiIiWngMRfPsf94+iIHFFQSeX/RQLkpUTekw3SvIHUuEFGQuk+2tMx9OdPCQH7ANWCZgmFYUEg0zGWDSH+RkKDHOG0zS56SP9RLZIW9+xpzbTDbIbCyb6QIYtzFPdwT0AgEhEh9Fc1UxAzVXrk3K/b1kQpWBEJ6Q004brfyaqOnMWihdNJOXRuo7o4dgJAJy6nw1luxzzXTcyHzf8+dEdcG4jiczZSqgpx+XfZLEqfHTRe9TXT3K5QX133LZwdR0K56qm5gCm13fJ0QAEWb31pJt4wO975beeytM/r2HCz6NFkAqgCWDVnJtWv5+M3XbsuJzXMfComoJ9YqsPtbKDlzHgucLNNt+9HVG75cwhGWZKjDFjUhsy4CtxmBbbCxCRHQ5YijqUecLLbrCEoT50KI/aKUDSuYTnLplJj5kWIaVrmgZBszE5qH5aVsdPvy+RYWBFl709zCPIStZreq0V5T+kJ4PWHqKoNzAVq41sjBQdRO//c+MMzlfUQ5AboCLVJ6Y4eTkNEd9SiaVpKpm6q5EAdIwjdTpyfdyNnDrfhzx8TjkxM+nzoW638jcnwhmep2ZflJ93pIldYyfnoiCjh+EKuAgqh7GVa8wCnu6iqY3ENbHdQXHUF+vmThuAFH4Esk/Isgfi9YqJu4T+fvy54eJ90mQu0//8USAlhc/Z/71gxmncsopmjIo1SsO6lV5uajiqEYjFsq6QhkiUZGWf68lx0JZTUV11Xm6KyXXxBER9SaGogWSDyv50JL8rX1uOlj62ZCuwMShJZ6iYs689seMF68np3clwwtDC82FruABAKyLf76OFawAqmV79KrqtZPj0PekQ46Ruj//uNxjc4/r/n8HQ0trsILzd2ZMfS/DuIoUQleA5HFfBKlwJQMNEuvcwtwUTD31FLYOk9kpmPmfLdlfoiwUEQSYaviYbHiYaHiYaniYmFaXDQ+TDQ//d3ISkw2vY2dG2zJywamuujNWymp9XMlBtWyraXfya9eNYFwVmhzb5LomIqIux1A0zwxDNhM41/Dz64LUbxOTc/qz64Vm+tDQqfJCdLkx1Tw7zlCaX/P5fb2QKZhRpScTtEQA+ELI9XFA4vc98kpc0dNzBzv/4ibbSCT7c9EyTQzUShiold7ya2q2BSZVUJpseJicTgSphofXzzZxYmwCzRk25q6V48pTtWyjVpaXZdeKglSt4kTT8cqODbeUXtdkWd3bOZOI6HLHUDTPbMvE6v9ehtOny0UPhYho3s33FEw9vfK8QStqICKnBvpqauCsGokouoIlpweqEBU1UQFcR1Z+hgbP397eF0EqOKWClPoz/mYDkw2/4xosvdFzTQWnimprXy87qFVkcFpUdTBYK6HsOnBLJlzbgm2bXNdERLSAGIoWAKs4RESzo9cnzodkFUsHrez+a74IommCnh9ABAE8Ea996riULQwTXSSBsgpQK66opprBZB/TaPnRNL10gPIxOd3G6bNNTDY8tLx89ckwEFWcaq6NWkVurFxT4Wmw7mLJgItlAxXUq060rklP0+P/h4iILgxDERERXRZSVaw5rHHTG2jralUuUPkB/KBDoPJmDlSVko2a62DFFYiqUskKlWkaqvrkp8LTRGYq3/jZJqaaXsdW8K5jqsBkyz8VBwPVEgbrLq6ou1iy2MUVA2UM1kqyGQQrTUREOQxFREREkKHKnmPVKhmoZDc8dAxUvpDXs4HKNk0M1l0M1t2orU66QiXXV7U8ofYA8zDVlPuB6fA0Me3h1JtNTI5NwPPzzTdM01Brm2wsrrmqbbkpN1MuxZsnV9UGy7WyjXLZVu31Zbc9uU9VvlEPEVGvYygiIiK6SMlA5VzgY2cbqDw/RFUEGKjpKX9hrkKlA1W77WO6JTDV9FJ7f001ZfMIucGyh1ZboOUJ+OKt96AyDdmOPNldr+TIy3LJhluSoUpPL6yWrWjNVM11UCnLwFVyrLh5UKLJkL5ORFQEhiIiIqICXapAJURcraovKuONN6ajQCWCEJ4nA1LbD9BqC3h+gLavLwO0PYG2J4+1PIGWF6DZFjg72UbTE2i1RcfW5lmWaaBkmyiVLLi2DFbJoFUuWXHAUhWsmmuj7NqoVuJ1Vo5tRWHKgAHTjLsQJqtZRESzwVBERETUo+YaqIaGFuHUqXOpNVSBDlaqrbofxFP9hAjhCbWXlbodb4Ss26gDfhDIypMvLz0/iEKV56tQ5Qm0VMhqqkrVdNPDGXW91RaYRbZS0//ialVJX7cT11XIqrgyXJVLNmpqemC1IitYdmLTXdNM7OMX7fnXueU7EV1eGIqIiIj60MWsoQISFamoIQVynf6EClK6QYWvbsd7VKl9qGBEe1aFodzfyhMB2p4MVfF1GaxafhygWl4QXZ+Y9nC63VSVLNGxMUWWY5uycuVYUcgq2bJa5Saulx0LlbIMV1VXVquqFRtV10HZtaJN1BmsiHoTQxERERFdsKid+hw6/QGIwpNeG6X3pdK3hZBhyBfyutCNKjqspYrSj2Go43LPKhGEaHsBfFWxaqnpgC0viNZTxeFKXjY9gTcnW9HtdoemFVmWaahQFYerbBVLV63KroWqamhRTbRbL5csOLbFYEVUEIYiIiIiuuRM04AJA/Yc26d3qk7JS0Trp4QfwBdyWp9IdP/rVEAyIJ/LgOqqp0JIqKtWevqfmvqng5VeT6WDlb59bipea9VpL6rc90M1ssgHquT0QAuVkiXXV6mKVVW1Yq+WHVTcfLCKQxU7BhKdD0MRERER9RTDMGAZBua65VK8wW9mT6rEeioRBBB+fD0I5PS/aE0VkKhWqSv6QoUrGGHU7MH3A7RF3MSilVxjlVhf1WrH1ycbHppnxaynAxoGULLjIBVXqTLBypVVq7hi5aBWsVAtO6hWHJQsM7GfVlyl8ny5+fFMr507lt246/w3GdaoUAxFRERE1FeiqX8XIVutSjas0OFKqEDlCxnC5NoqyGAVJNqqyyfMrK8KU9MBAVkBa+vmFWptlaeqVm1fdgOMKlZtgVbbR6Pl4Y2JuLIVzGKhVdRyvWTBtU24JRWmyg68ti9Pir59ueiTuj9/n9ExQKXO6PhYI76vw/1Gh4OqD0h6hHLrr/Rzqxc0Muckx5I+PX4GfbdpyjV6jm3CtuQ6NccyYDsWHHXbtuUaNb2OzbZNOLYJxzLh2BYcW3dM1BU9xJVL3dQkd5xBcr4wFBERERFdoIutVmlhGCIMs9MAgRDxdEC9xkoEugNgh8AlQvXBObnYSr+I/E+IECKAbFihpgHqNVa65Xq0ziqxxkq3Xg/QUOu5wvRT576mzl9n7liHJ+n0zJ2fL/9MHQ7NcKzDWDq9RocTZvv1zpVlGrAsA5ZpwLZMdVte2uq6rc/Rxy0TtqUvZciyLQO2Hd92HAOOacF2ZFhzLFlRdBwTjmnCKVkoWZY8z7ZgJTeORjqIAYg2ljYg/y3MdLyXMBQRERERFUR/oDQx96YVmg5V2aqVnCqIVFdAkapaBRABosClq1ZRuFJVq8UDFZw917jYL3levNXH7U4hbCHJvcKg9gLLfG8FEKiuikLEgVYEiKZjxpdh1K0xui0C2SJf/d01PQHRDKN9x4SQr+mr8+eDacjgpYNYLqh1Cm2WAcuMz3UsE5Zt4n3XrcS1qwbnZVwLiaGIiIiI6DKgm1dcrOyaKx20rlhSx+uvX+gWw91pIYoYYRhX/kKoKZYdgqrutKinWIYAgiCAzqOp738y40RTKbOVQHXMiF/TE/l1cEEog1SgAlsQ6BAWVyJToS0RykQQt9TXt9u+iNvui+R58rbWaAn8738t7vrKEUMREREREUVmWnM1UCuhNX15hKJeEgctdRnOcCy6HqpQJv8TqKAVh7B4amYYhBBh/LhsV0cZ2BLr3lQDkWQwM9QYDcOIJm8GQQgRymA1tKx+ib9jc8NQRERERETUpeI1O8VVWmYMZsn71No1naECdXxoWR3tRruwsc8WQxEREREREc3oYoLZ4rqL8R4IRRfZM4WIiIiIiKi3MRQREREREVFfKywU7d69Gz/4wQ+i2+fOncO2bdswPDyM22+/HePj40UNjYiIiIiI+sglD0UTExMYGRnBnj17Usd3796NtWvX4tChQ/j0pz+NHTt2XOqhERERERFRH7rkoeiFF17AVVddhc9//vOp4y+++CI2b94MALjpppvw61//Gp7nXerhERERERFRn7nkoegTn/gEtm3bBstKb9t86tQpDA0NAQBs20a9XseZM2cu9fCIiIiIiKjPLFhL7kOHDuE73/lO6tjVV1+Nn/70p7N6fBiGMM3ZZ7alS7trY6ihoUVFD4F6EN83NBd839Bc8H1Dc8H3Dc1FL7xvFiwUDQ8PY3h4eNbnL1++HKdPn8bKlSvh+z6mpqYwODg468e//vpkvONuwYaGFmF8fKLoYVCP4fuG5oLvG5oLvm9oLvi+obnopveNaRozFlK6piX3+vXrsX//fgDAwYMHsXbtWjiOU/CoiIiIiIjocrdglaILdffdd2P79u3YtGkTFi1ahJ07dxY9JCIiIiIi6gOFhaK77rordXtwcBA/+tGPChoNERERERH1q66pFF0s0zSKHkJKt42HegPfNzQXfN/QXPB9Q3PB9w3NRbe8b843DiMMw+7oTkBERERERFSArmm0QEREREREVASGIiIiIiIi6msMRURERERE1NcYioiIiIiIqK8xFBERERERUV9jKCIiIiIior7GUERERERERH2NoYiIiIiIiPoaQxEREREREfU1hqJ59Mtf/hIbN27EjTfeiEceeaTo4VCP+OEPf4hNmzZh06ZN+O53v1v0cKjHPPjgg9i+fXvRw6AecfjwYWzduhXDw8P49re/XfRwqEccOHAg+v/Ugw8+WPRwqMtNTk7ipptuwr///W8AwOjoKDZv3owbb7wRu3btKnh0M2MomicnT57Erl278Oijj2L//v14/PHH8Y9//KPoYVGXGx0dxdGjR/HUU09h//79+Otf/4rnn3++6GFRjzh27BieeuqpoodBPeK1117Dfffdh4ceeghPP/00XnnlFbz00ktFD4u6XKPRwI4dO/Czn/0MBw4cwB/+8AeMjo4WPSzqUi+//DJuu+02nDhxAgDQbDYxMjKChx56CAcPHsRf/vKXrv25w1A0T0ZHR/H+978fg4ODqFar+PjHP45nn3226GFRlxsaGsL27dtRKpXgOA6uueYa/Oc//yl6WNQD3nzzTezatQt33nln0UOhHvH8889j48aNWLlyJRzHwa5du7BmzZqih0VdTgiBIAjQaDTg+z5834frukUPi7rUL37xC9x3331Yvnw5AOD48eN4xzvegVWrVsG2bWzevLlrPx/bRQ/gcnHq1CkMDQ1Ft5cvX47jx48XOCLqBe985zuj6ydOnMChQ4fw2GOPFTgi6hXf/OY3ce+992JsbKzooVCPePXVV+E4Du68806MjY3hwx/+MO65556ih0Vdrl6v4+6778bw8DAqlQre+9734j3veU/Rw6IutWPHjtTtTp+PT548eamHNSusFM2TIAhgGEZ0OwzD1G2i8/n73/+OL3zhC/ja176Gq666qujhUJd74okncOWVV2LdunVFD4V6iBACx44dw/3334/HH38cx48f5/RLekt/+9vf8OSTT+JXv/oVjhw5AtM08fDDDxc9LOoRvfT5mKFonqxcuRLj4+PR7fHx8ah0SHQ+f/zjH/G5z30OX/3qV7Fly5aih0M94ODBg/jNb36Dm2++Gd///vdx+PBh3H///UUPi7rcsmXLsG7dOixZsgTlchkf+9jHOKOB3tLRo0exbt06LF26FKVSCVu3bsXvfve7oodFPaKXPh8zFM2TD3zgAzh27BjOnDmDRqOB5557Dh/60IeKHhZ1ubGxMXz5y1/Gzp07sWnTpqKHQz1iz549eOaZZ3DgwAF85StfwQ033ICRkZGih0Vd7iMf+QiOHj2Kc+fOQQiBI0eO4Lrrrit6WNTl3vWud2F0dBTT09MIwxCHDx/G6tWrix4W9Yg1a9bgX//6F1599VUIIfDMM8907edjrimaJytWrMC9996LO+64A57n4VOf+hTe/e53Fz0s6nIPP/wwWq0WHnjggejYZz7zGdx2220FjoqILkdr1qzBF7/4RXz2s5+F53n44Ac/iE9+8pNFD4u63PXXX49XXnkFW7duheM4WL16NbZt21b0sKhHuK6LBx54AHfddRdarRbWr1+PDRs2FD2sjowwDMOiB0FERERERFQUTp8jIiIiIqK+xlBERERERER9jaGIiIiIiIj6GkMRERERERH1NYYiIiIiIiLqawxFRERERETU1xiKiIiIiIior3HzViIi6jk//vGPsXfvXtRqNaxduxYvvPACnn32WezcuRO///3vIYTAtddei69//euo1+u44YYbsGXLFhw7dgxjY2O4+eabcc899xT9ZRARUZdgpYiIiHrKkSNHsG/fPuzduxf79u3D1NQUABmULMvCvn378PTTT2P58uXYuXNn9Ljp6Wk8+uij+PnPf46f/OQneO2114r6EoiIqMuwUkRERD3lpZdewoYNGzAwMAAAuP322/Hb3/4WL774IiYmJjA6OgoA8DwPS5cujR730Y9+FACwYsUKLF26FGfPnsWqVasu/RdARERdh6GIiIh6im3bCMMwum1ZFgAgCAKMjIxg/fr1AICpqSm0Wq3oPNd1o+uGYaSeg4iI+hunzxERUU9Zv349nnvuOUxMTAAA9u7dCwC4/vrr8cgjj6DdbiMIAnzjG9/A9773vSKHSkREPYKhiIiIesq6detwyy234NZbb8XWrVsxMTGBSqWCL33pS3jb296GLVu2YOPGjQjDENu3by96uERE1AOMkPMHiIioh/z5z3/Gn/70J9xxxx0AgD179uDll1/G7t27Cx4ZERH1KoYiIiLqKZOTkxgZGcE///lPGIaBK6+8Et/61rewYsWKoodGREQ9iqGIiIiIiIj6GtcUERERERFRX2MoIiIiIiKivsZQREREREREfY2hiIiIiIiI+hpDERERERER9TWGIiIiIiIi6mv/D3HuHZTtozwdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "sns.lineplot(y=final_joined['score'],x=final_joined['gen'], label=\"score\")\n",
    "sns.lineplot(y=final_joined['logp'],x=final_joined['gen'], label=\"logp\")\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"gen\")\n",
    "\n",
    "plt.savefig('img/line_plot_score_logp.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3891, 7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_joined[final_joined[\"logp\"]<5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 426.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "final[\"edible\"] = (final_joined[\"logp\"]<5) & (final_joined[\"logp\"]>0)\n",
    "\n",
    "sns.lmplot(x=\"gen\",y=\"score\", data=final, hue=\"edible\")\n",
    "\n",
    "plt.savefig('img/lm_plot_score_edible.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max, Mean, Min of score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "sns.boxplot(x=\"gen\", y=\"score\", data=final_joined)\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"gen\")\n",
    "\n",
    "sns.set()\n",
    "plt.savefig('img/box_plot_score.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max, Mean and Min of logP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "sns.boxplot(x=\"gen\", y=\"logp\", data=final_joined)\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"gen\")\n",
    "\n",
    "sns.set()\n",
    "plt.savefig('img/box_plot_logp.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box Plot for gen 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 7)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "min() arg is an empty sequence",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-19ebea6d314d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gen\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"logp\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgen15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# Add label for horizontal axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2239\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2240\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2241\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2243\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 444\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdodge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_colors\u001b[1;34m(self, color, palette, saturation)\u001b[0m\n\u001b[0;32m    314\u001b[0m         \u001b[1;31m# Determine the gray color to use for the lines framing the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m         \u001b[0mlight_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcolorsys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrgb_to_hls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrgb_colors\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 316\u001b[1;33m         \u001b[0mlum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlight_vals\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m.6\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    317\u001b[0m         \u001b[0mgray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrgb2hex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: min() arg is an empty sequence"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen15 = final_joined.copy()\n",
    "gen15 = gen15[gen15['gen']==15]\n",
    "print(gen15.shape)\n",
    "\n",
    "# Score\n",
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "sns.boxplot(x=\"gen\", y=\"logp\", data=gen15)\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"gen\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian of the logP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\distributions.py:200: RuntimeWarning: Mean of empty slice.\n",
      "  line, = ax.plot(a.mean(), 0)\n",
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\numpy\\core\\_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\distributions.py:200: RuntimeWarning: Mean of empty slice.\n",
      "  line, = ax.plot(a.mean(), 0)\n",
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\numpy\\core\\_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\seaborn\\distributions.py:200: RuntimeWarning: Mean of empty slice.\n",
      "  line, = ax.plot(a.mean(), 0)\n",
      "C:\\Users\\user\\Anaconda3\\envs\\tfv2\\lib\\site-packages\\numpy\\core\\_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe13c06dd8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the dataframe by target\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "gen0_dist = final_joined.loc[final_joined['gen'] == 0]\n",
    "gen1_dist = final_joined.loc[final_joined['gen'] == 1]\n",
    "gen2_dist = final_joined.loc[final_joined['gen'] == 2]\n",
    "gen3_dist = final_joined.loc[final_joined['gen'] == 3]\n",
    "gen4_dist = final_joined.loc[final_joined['gen'] == 4]\n",
    "gen5_dist = final_joined.loc[final_joined['gen'] == 5]\n",
    "gen6_dist = final_joined.loc[final_joined['gen'] == 6]\n",
    "gen7_dist = final_joined.loc[final_joined['gen'] == 7]\n",
    "gen8_dist = final_joined.loc[final_joined['gen'] == 8]\n",
    "gen9_dist = final_joined.loc[final_joined['gen'] == 9]\n",
    "gen10_dist = final_joined.loc[final_joined['gen'] == 10]\n",
    "gen11_dist = final_joined.loc[final_joined['gen'] == 11]\n",
    "gen12_dist = final_joined.loc[final_joined['gen'] == 12]\n",
    "\n",
    "\n",
    "sns.distplot(gen0_dist[['logp']], hist=False, rug=True, label=\"gen0\")\n",
    "sns.distplot(gen1_dist[['logp']], hist=False, rug=True, label=\"gen1\")\n",
    "sns.distplot(gen2_dist[['logp']], hist=False, rug=True, label=\"gen2\")\n",
    "sns.distplot(gen3_dist[['logp']], hist=False, rug=True, label=\"gen3\")\n",
    "sns.distplot(gen4_dist[['logp']], hist=False, rug=True, label=\"gen4\")\n",
    "sns.distplot(gen5_dist[['logp']], hist=False, rug=True, label=\"gen5\")\n",
    "sns.distplot(gen6_dist[['logp']], hist=False, rug=True, label=\"gen6\")\n",
    "sns.distplot(gen7_dist[['logp']], hist=False, rug=True, label=\"gen7\")\n",
    "sns.distplot(gen8_dist[['logp']], hist=False, rug=True, label=\"gen8\")\n",
    "sns.distplot(gen9_dist[['logp']], hist=False, rug=True, label=\"gen9\")\n",
    "sns.distplot(gen10_dist[['logp']], hist=False, rug=True, label=\"gen10\")\n",
    "sns.distplot(gen11_dist[['logp']], hist=False, rug=True, label=\"gen11\")\n",
    "sns.distplot(gen12_dist[['logp']], hist=False, rug=True, label=\"gen12\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations between attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe13c4b898>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "corr = final_joined.loc[:,final_joined.dtypes == 'float64'].corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns, annot=True, yticklabels=corr.columns, cmap=sns.diverging_palette(220, 10, as_cmap=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dk9AlD+36pZ8fFBdRkJfDjBm3YDAYtA1yAHr77RXUtXXiNuVexJejFsLLj87cffj6+pGUdOXlo5LjvP/+akxmHWPG3ocQAoPBnVMndxIdPYiYmFitwxuwjhw5yNGjh7hj+P00tNWRX5bH/Pl3yGOHhnJzs0hPT+OexJlE+4RxpPIEjZ2tTJt2k9ah9YoQAk9P4xXvlwklBzKZTLz4wnMcPnKQ/0y5gduSUuzSrr+7ByNCI9lTmE/avj2MGDkaPz+/az9RushsNvP55+t5+eUltDbV8f2ROuYm6dH1MJkR66+QGirIqehg6640KisrSE4egru7u4Mi77tsNhuZmYdZsWI5H364htqa84xOhjmTICFKYNA79sCnKILw4K7Ekqcb5BfWsWv3Hg4eTMdgMBIVFY1Op7t2QwNMTU01r73+MuvXf4SXv4Wxs2wE9SJv6uUH4XEqjbU20tOOkJ9/gsGDk/Dx6V4tOenqjh07yrPPL6CsrARmBCFGfvsxQUS5g7eOmqPFpKenyUS4nV2YIbZv3x7E2GEoo5J63khkCHR0kp+eQWdnB6mpI+WFgQPYbFaWLFlEfXMzullzEF8mjoQQEBhE2/GjtLe3MfrLukqSc5w9e5q1a9/DMGYW+oiEi79XPH2xlZ+l6MRxZs2aI4/XTlZbW8OqVSsZOnwWkZFdg+OeXgGcOZ1GU1M9U6dO1zjCgWvDhk9prGnijmH302Hp4EjxfsaOnYC/v/+1nyw5xJYtGyk5V8T3h92GTtFR2VbL4cIsZs+Z3ycHKWRCSSPt7e0sWfIM2dnH+cHI8cwZ/M2tgq+Hv7sHo0MjSS8+y+69uxg2LIXAwCC79tEfqarK8eOZLFmykIMHMxgZCr8YrychsPerP/3cBTdGKwhgT/Y5dmzfgt5gJD4+HkWRJzzt7W3s2LGF5cteYPv2LZg66hg/XOXWiRAb4fhE0uV0iiA8qCux5O8DZRUt7E0/xI4dW+joaCcyMgp3dw+nxuSKzGYzGzZ8wksvPU9FRSmJo1VSJqu4XUeu1GCEiAQwusPpvBq2bd1CZ2cniYlJ6PV97wDrCurqann9jWV8+OEaOj2sqPNDEbGeV32OCHFDHeRBZ2Ej6Tt2cf58KYmJyXh4XP150tWZTCaWLXuBjIx9XcmkG4b2qh0hBAwKgw4Tp9MP0NjYyKhRoy/O1JDs4+OPPyQ9PQ1l6nSUy5Kqwtsb1WTibEY6MTFxREZGaRTlwPPGiuXUNDThPvNBxGU7IQsvf1qy9+Lr60diYi+StVKvbdmykby8HKZN/x/c3Lp2qxRC0NnZSnbWbqZNm4Gn5zd3sZQcy2w28+aKV0kJGcPIiLH4ufmz6+wm/Pz8GTbMPhMZpJ5RVZW33nyVId6DmBLVVdfKoOjYXXKEuLh4oqK6P2vZVciEkgZaWppZtOgJzpzO539uuJEZsY456Pm6uTM2PJqDpUVs37ODwYOTCA0Nc0hf/UFh4VmWL3uha6aFaOcHo/TMTdLjboeEhk4RJAcr3BAhON9kYeeBY+xPT8PfP5DIyKgBObpcXl7Gxx+vZfnyF8nMPIKvZxtTRsKMsV07t+muUPTcWRRFEOwvSElQiQyBphYTGYdOsGXL55SVlREQEEBgYNCAe++6lrels+RfCzly+BAh0TbGzFQJje7eErdrEQL8giFysEpnu8qRA6fYs2cHnp5exMTEyovmbmpubuajj95n2fIXKCsvhXF+cHMwwvubu1N+G+GlRx3mDTrB+SNn2bp1Ex3tHcTFxWM0ymVwPdXW1spzzy0kKysTMTEV5So7unXHxaSSxUphxiFKy0q4Ycw4OSvDTg4dOsC///0GStIQdGPGfev3vAiPhLJSju7bww1jxsmZ4E6Qk5PFRx99gHHcHPSRid+4X/gEYq0o5PSxg9w881aMxitf4Ej2Y7VaefXVFwkITCAlde4l9/n4hJKXuxmDwUhKygiNIhy4srOPsydtJ/OH3Ueodzhuenfya3IpqSxk1qw5Woc3IBUWnmXjxg3cFj+NWN+uwYpAdz92lBzCrFqZMGGSxhH2nEwoOVlDQz0Lnn6M82Ul/GbcNCZGxTm0Py+jGxMjY8gsL2Xr7u0MioklIkKOpH1dWVkpK1e+xurV/6atqZa7h+j4r5E6wr3tf+HqbRSMj1SI81fIr2hl+950jmUeJigohLCw/l+rx2azcfx4Jm+/vYLVq//NuaKzJERZuHk8jB8uCPITLvcaCCHw8xYkxwiSYwBVJftEKTt27uBY5mEMRiMREVED4kLu1KkTvPjic2zZshGDewcjpqgkjOiaWWRvegOExUBwJNRWdrJ/72EOH84gNDRM1rW6iubm5q6ZYy8v4eSJPGxJHjA7BBHvhejhkl2hCESkO2qSF7Y2C6czstm+YwsWs5lBg2JkYqmbamqqeXrBYxSdK0TMGIsyLN4u7QohENGhYDRw/uBR8k7kMvaGcbLu1XU6deoE//rXYggORnfLbMQVvtuFoiAGxWDNP8WRg/sZP36inIHhQBaLhSX/WkwbOtxu+k/Et8zwFkKgBIbTlpWG2Wxm5MjRGkQ68Bw5cpBdu7YzYdJ/4e9/6S7VRjdPamvOkZd7iNmz56LTdW9QQ7KPdevWUFdVx3+MeOjiqgiz1UzGmTTGjh2Pv3+AxhEOPJ9/vp5zhQU8nHoXRt1XS6mr2uo4eCaTOXPm97lZ+TKh5EQ1NdU89eSj1NVU878TZzIyzDmJHXe9gUlRseRUV7B593bCwyMYNCjGKX27svLy86xa9SYrV75OTeV55gzW8fAYPUlBCooDL1aFEIR6CabGKIR4CnJKGti+ew/ZWccICgomNDSs310sd3S0s3PnNpYtW8q2bZtpba7mhiEqt06CoXECb4++8e91dxPERghGJKp4e8K5skb2ph9kx46upVmRkdH9sj5WWVkJr7++jA8+eJd2UwNDJ6gMm6Di6YTyRu5eEJUI3gFQVtTC7p1pnDyVR3T0IAICAh0fQB/R0FDPxx+v5ZVlS8nNzcY6yNiVSErxRbhdX7JTuOkQCV6Q4ImlsZOTGcfZum0T7W1tDBoU0y//5u2loOAMTz39T+qaGhCzJ6HERV77ST0kwgIRAb7UZeZy4GA6o0aOlrXHeqmoqJCFzzyBxcMD3bzbEddIzgmjESKjaM/N5uiRQ0yaOFl+Hhxk06bP2Z+ehttND6ALvHJdN8XTF7WtidOHdjN27AT8/GSdGEdSVZXXX1+G1apn0o0/+NbzVw9Pf/JytxEUFEx8fO92spZ6rrW1lTfeWM6E6Kmkho+5+Psgr1D2FGxBp9cxcuSYq7Qg2ZvVauWN118hxS+eqVGXvvaeBnd2FR8mMjKKmJg4bQLsJZlQcpLKygqefuoftDU38adJNzMk2LlLz4w6PZMiY8mvrWZT2g6CgoKJjbXPKGlfU1FRzqpVK3nrrdeoPF/CzDgdPxqjJzVMh8GJy6yEEET7KkyLVfB3F2QV17F9925yc7MIDg4hJCS0zyeWGhrq+fTTdSx7ZSmHjxzC272NKaNg5jiV6DDn10eyF51OEBbYVWcpIvjCcrg8tmz5gpqaaiIiIvvFBV1DQz2rVq3kzTdfpbaunMEjVUZOVfELts/ytu4SArz9YVCyitEdCk7VsG3rNsrLy4iPH4yX18CdFVBZWcHate/y6msvk59/Emu8O8wKQYz0RXjad9ac8NQhkroSS9Y2M6cPZrN5y0YaGuqJjIzGy8vbrv31dRkZ+3h+ySJMegVx2xREqOMSoCLAFyJDaDtxlrRdO4iPSyAsLNxh/fVH584VsWDh45h0Crrb7kR08+9ZeHoiwiNoyTrGsczDTBgvk0r2Vl1dxYsvPY+ITMY4ds41z410YXFYTh2g8Mwpbrppplwq7UD5+Sf59NN1jBn7HULDvrkMEcDbO5jSkmOcPZPHrFmz5fvhJLt3bycz8wj/MfIH+Ll/lVg16oycbyrl2OnDzJ49f0DMsHcVWVmZ7Ny1nfuSbyHSO+SS+wLcfdlXfpzapvo+t9ubTCg5QVVVJQue+gedra38efItJAQEaxKHQadjYmQsBfW1bN67i+DgEGJj4zSJRQtVVZWsXv1v3nxzOeVlxcyIVfjxDXpGhetw0zCxoQhBrL/C9BgFXzfB8aJatu/aRV5eNqGh4QQHh1y7ERdTU1PN+++v5rXXXubUqRPEhFm4eRxMTO1a1ubIGWDOdGE53JBYQdIgsFptZGYVsWXrJkpKigkPj+yTu2iYTJ1s2PApL774HEVFZ4gZqjLqJpXgKFA0PA8UCviHwKAkFSEgL6uMbVs3YzKZSEhI7JM7Y/RWSUkxq1a9yVtvvU5RcQG2IZ5wawhiuI/dE0mXE546xGAvSPRCNVspOnKSLZs3cv58GZGRUfj6DuxaMqqq8vHHa3n77TdRQwMQ86cgfByf9BTeHhAfhaW4nPQd2/Hx8SUh4dsv8KRLnTtXxNML/kmHEOjm34no4YCA8PZGhIbRlJUpk0p2pqoqL7+8lIrqajzm/hjF7dqbYgi9AeHlR3Xmbry9fWSBbgf697/foKGhmWk3/fSKy9mEEBgMnuTm7CAmJo6oqGgnRznwqKrKG68vw08XxNwhd3/jfk+DF+kFO4mMjCYmJlaDCAem999fTXNtAz8cfgfKZYlVIQTt5g7STh1g2rQZfWqwVCaUHKyurpannnyUzpYW/nzjLOL8ez9Cube4gOLGemL8er/eVa8oTIiM4Ux9DZv37iIiIpLo6P69/K2hoZ733nuHN15/hdKSIqbHKvxojJ4xEfZJJB0otVLWpBLte31X2jpFEOevMD1WwccoOF5Uw9adO8nPP0FkZN9Y3tPY2MAHH7zLa6++RFHRWYbG2pg9CUYkCnw8nZdEOlmkUtMAwf7O69PDTRAXKRieoKIokH3iPFu3bqGsrJSYmFh8fHycFsv1OHbsKIsXP8WRI4cIirIyeoZKZALYo+xB2Vlorgff6/xTVnQQFAERCSod7TaOHDjJ7t3bCA4OISoqus/P7LuaoqJCVq58nVWr3qKssgx1hE9XIinJG+He80SSeqoFak2I4J4XwhLuOkScJ+rQrpkcZVkFbNuyiZLSYiLCIwZkbQaTqZPlr77Itm2bEUkxiFkTEMaeJTpt+cWotY2IoJ4n5oSbARKjoa6RY3v20NLSTGrqKBQtM8EurqiokAULH/sqmXSFhKj19CnUulqUoG8fFBQ+vojQcJqyMsk8eogJEybJpJId7Nu3hy++WI/bxDvQR19azN6cfxhb7Xl0Qd9cSqoEhGOrLiHvUBqTJ02RMygdoLS0hNWrVzJi1B1ERX9VcPt0fhp1tcUEBX2VqPDzj6DgbDolJQXMmHFLvz5Ou4LTp/PZsOET5g25h0H+cRws2UtZUzFRfl3XfAGewRw5n0FlTTnTb5qpcbQDQ2NjI2+9+RozoscyKiQZgL1lxyhuriDGt2tGcYhHAFvOZeDh4cHw4alahtsjMqHkQK2tLSx4+p801tfxp8m3EH8dySSAd7IPca6xjukx17f+WKcojI+I4VRtFVv27mLw4KR+OTW+o6OdTz5Zx8svL6Hg7Gmmxgh+PNbADRE6u+zcdsGHuRZKm1QmDbLPrACdIogL6FoK52WAI2eq2bJ9K+fPl7rs8h6LxcKWLRtZunQxp0+fZGicyrwbYUiswN3N+ScNezKhugGGxTu/b4NeMChMkJqgogg4llvGtm2baW/vIDEx2WVn0TQ2NrJixTLWrn0PnVsHo6arJKSCPesunzwEzXVdNZHswWD8snB3FNSUm0nbvZ/CwrMMGTIMT8/+tcV9ZWU5b771GqtWvUVFXSWM9u1KJMV7IYzXkSzYVws1JsTQ3ic8hVFBDPJAHd61K1x5VhE7tm7h/PlSYmPj8fYeGBdyzc1NLFr8FNlZxxETUhATUxG9SOTY9mdBbSNKcu9GjYVOBwnRYLFwdv9BCgrPMvaGcX2uyKcznDt3IZmkoJt/1xWTSQDW/ftQa2vQJQ+94mMuJJWas47LpJIdNDY28OxzC1EDo3Cbeu83khCd+z/BVluGIXn8N54rhEAXnoApN52S4iKmTpkukxh2tmbNKs6fL2fGzb9Ar//qZOHA/neoqz1HUvL0i78TQkEoOrKzdjJ8eFghz+oAACAASURBVGqfnH3fl6xd+x7VFVU8OPrH6BU9H+WspqyxmIkx04Cuz4fFaib95C4mTJg84GcWO8OOHVvJyj7Gw6l34WvsupZbfeILipvKmRbdVU/J0+DO6YZicgpPMmfOvD6zPPRaCaW+8a9wQRaLhRdeeI6KinJ+O/4mEgKCtA7pEm56Pb+bOINIL19efOE5SktLtA7JblRVZf/+ffzhD7/m00/XMSLYyqM3GfluqgF/975zMmHUCW5J0PPYDD1zE3UcPZzBn/70COvWrcFkMmkd3kWVleU88cTfWL16JaH+HTw4B24e59wZSV+nqiqt7VDfBDlnVFSN1rq6GQWTRgi+P18leZCNL75Yz1/+8jvy809pEs/VnDiRy1/+8v84eHA/g0epTL7NRmAfyjH7B8PEeTaGjFPJzsnk//7v/5GZeUTrsOyitbWVt99ewR//+FsOZx6EsX6o34tCTAjo1YwkRxLuOsSEANT/ioSxfhw8coA//um3vPPOm7S1tWodnkNVVVXyz8f+SkFhAcqsCSijkjW9eBWKQJk0AjFlNFlZx3jyqX/Q2NigWTyuqKyslAULn/jazCT71L1TIqPQzZlPRVUVC595gtbWFru0OxC9s+otOjo7cZv2H726sFK8/TFMmE9ebjb79u1xQIQDV319HenpaSQlT8fdvXsDEknJ0/Hw8OXzzz91cHQDW2trCwcO7Gds1GTc9FdOaE8YNA29omfXrm1OjG5gUlWV3bu2k+g/iCjv0Ks+9qbosdTV15KTk+Wk6BxPJpR6ac2aVeTl5fCjURNJCXHNKzNPg5H/nTQDI/CvJc/0ixP+urpanntuAa+88i981Wb+d7KBh8cYCPHqO4mky3kYBHcM0fOPmwyMCrHxySfr+Ntf/9clEhO5udn87W9/oKy0gDmT4I5pEOCr7WudcxYaW6C9E3Yd7bqtJU93wS0TBPfOBFN7LU8++Xd27dqubVBfs2nT5yxc+BhW0cKk22wkjupaUtbXKArEDYcb77Bh8Ojk+ecXsm7d+5olFO3h8OGD/PFPj7B12yZsQ71QH4zsSiS5ufahWbh9mVh6MBLbEA+2bN3IH//8W44ePaR1aA5RUlLMY4//jerGuq7i2/HO2cG1O5Th8Si3TqS4tITHn/g7NTXVWofkEqqrq1iw8HHarVZ08+yXTLpAiYhEd+tcys6XsWjRU3R0tNu1/YEgKyuTAxnpGEbfjC6g9xvZGIZNRhcWxzurVtLc3GTHCAe27du3YLXaSBkxt9vP0euNDBl2C8eOHaWiotyB0Q1s6el7sVjMTIq5emFnbzcfUsPHsHfvHsxms5OiG5jOnj3D+fIypkXdcM3HjgkdirfRkz17djk+MCdxmbPW0tJSvve973HXXXfx/e9/n7KyMq1DuqIjRw6xefPnzE4YwrTrXJ7maEEeXvx63DRqqqtZsWJ5n774ys4+zt/++ntO5BzjvuE6/jBFT0Kgy/wJX7cAD8F/jzHwqwkGTE1VPPXU39mw4RNsNpsm8eTmZvPs4qfwcjfxwK0qSTHCJaaTF52/+m2tRIYIvnurSky4yooVy9m+fYvWIfH55+tZvXolwdEqk+bZ8OkHJW88fWDCXBtRg1U++eRDPvjgXa1D6jGTycTrr7/C0qWLadZ3wL0RiJuCEJ52KGTlRMJLj7gpGO6JoElpY8mSRbzxxrJ+deJ67lwhTz71KC3mTsTt0xBhrjUbGUDERiDm3Uh1fS2PP/F3qqoqtQ5JU+3tbSx+9mma29tQ5t2O8HPMUg8lKhrdzFspKDzLsuUvanas7ovMZjMr//0mOv9QjKNuvq62hFBwm3ofbW1trF37np0iHNjMZjM7dmwlJnYMvr49S/YNHXYLiqJj69aNDopO2rt3N5F+MQzyj7vmYyfGTKe1tYVjx446PrABbO/eXRh1BiZGpFzzsQZFz6TwERw9cojW1r4/2QNcKKG0dOlSbrvtNj799FNmz57NkiVLtA7pWzU3N7HijVeI8w/kP1OunYV0BUOCQrl36EgOHswgI2Of1uH0yq5d21m8+Cl8RBt/nqrn5nh9v9lJ7HLDQhT+b6qeUWGC999fzRtvLMNqtTo1hubmZl5+eQm+3jbumaHi40IzwMzWq9/WkptRMH8KxEbAO++s4Ny5Is1iSU9PY82adwiPVRk9XUXf85rMLkunh5QbYVCyyoYNn7BlS985cW1oqOfJpx5lz56dXcvb7gtHhNqxkJUGRJgb6n0RMMaP3bt38ORTj/aL5Vfnz5ex8Jkn6BAq3DENEWDfWS72JMKDELdNpaG1mQULH6e+vk7rkDRhs9l4+eWllJefR7llNkqgYxOASlw8uok3cvTIIdate9+hffUnmzd/TnVVBcbJdyPssCOELjACQ8pUdu7aTlFRoR0iHNiOHDlIc3MTQ4fN6vFzPT39iY0fz969ezCZOh0Q3cBWUVFOQcEZxkVN7tbjk4NT8HHzJT1dLgl1FIvFTMb+fYwNHYbHVZYgft3UqNGYLWYOHtzv4Oicw2USSjabjZaWrnXo7e3tLlvk8L333qGttY3/GXMjejuuG1FVlfqONs43N7K9MN/uM4luTxpOQkAwq955q89lQzMy9rFixXKGBgt+f6OeMG/n/dmqqkpDh0pFi0raOavTZnh5GAQ/GqNnfpKOtLRd/Pvfbzil3wt2795Oc3Mzt05QcTe6TjKpL9ApglsngE5R+eKL9ZrE0N7exjvvrMA/BEZMBWfU/FNV6GyD1kYoOYXDN0kQAoZNgJAolfffX0VDQ71jO7SD5uZmFix8jKKSQpgT0rW8Tdc/Pl9CJxCTAmB2CIXFBSxY+DgtLc1ah9VrjY2NLHzmCdqsZpg/BeHrepslXE4E+yPmTqa2oY5FiwfmMqxNmz7n+PGj6CZPRYl0ztblSsoIlOShrF//Ebm52U7psy9rbm7m0/Ufo48Zhj462W7tut1wK4qbB++/v9pubQ5Uu3Ztx9snmKjo3u1CNXTozbS1tXLo0AE7RyZlZKQDcEPUpG49XqfoGBM5kWPHMmlvH3jHBGfIyjpOa1srkyNHdvs5cb6RhHsFs3//XgdG5jwuk1B65JFHWLlyJdOmTePNN9/kJz/5idYhfcO5c4Xs3bubOYOHMsjX365tby86TWVrC02mTlZmHWR70Wm7tq8Ihf8eOYHm5iY2bPjErm07UnV1Fa+//jIJAQo/HavHzY67t3VHWrGN6jZoNsGaHAtpxc6b0i6E4LZkPbMH69i5c5tTZ5fl558kyE8QEuB6F7smM7i7u3P33Xfj7u6OyQVX17i7CWLDVfLzT2jS/9atm2hpaWXoeNVp9ZJK8qGtWWDqEOQdEJTkO75PocCQ8WC2mFy+CKjVauW55xdQXnEedV4IIsH1ExS9IQZ7oc4N4Xx5Kc8veQabzYWmEHaTxWJh6QvP0tDYAHMmI/z6zi52IjQQMWsCpaUlvPbaK316mXtPlZQU88Had1Fi41GGXXvZgb0IIdDdOBXF359lr74oi3RfwxdfrKejvR3j+Pl2bVe4eWAYPYucnOOcPJln17YHkoaGevLyckhMnNrrHajCI4bi7RNMenr/uFh2JYcPHSAuMBF/j+7vLD4qcjwWi5msrEwHRjZwZWTsw8voSUpQQrefI4RgYngqJ0/m9YkB0WtxesGGjRs3smDBgkt+l5CQQGdnJ48//jizZs1i8+bN/OpXv2L9+vXdrtkSFOT4E77lyz/Fw2DkzuTeZeyvJrOi9Bu3Z8Xbb+QGIM4/kEnRcWzdspGHHnoQXzsXqXSEd999E5vVwsNjDBg0GMnPrrR94/b0WOdWNL5jiI68GpWPP1rD7bfPcUodI6NRj00VgOtdjHSaYe7cufzsZz8DYPtW10yQWm2g0wlCQnq/XXtvFRaexidA4BfsvPevuvSbt2OGOL5fL18ICIWzZ/M1ea2764MPPuDsmdNwSzAiykPrcBxKRHug3hTE6R2nSEvbxne+8x2tQ+qRd999l9P5JxEzxyGC7Tt45AwiOgwxfjiHDmaQmbmfOXPmaB2Sw6mqyjOLVqIaDOim3uT0en9Cb0C56RYa13/E5s3r+elPf+rU/vuK1tZWtm3fgj5+BLrACLu3bxg2CfPxHWze/BnTpk20e/sDwb5921FVlYTEG3vdhhAKCQmTycn+HKPRhp+D6pgNNFVVVZwrLuTO4d/t0fPiA5PwcfclJyeT22/v/8cDZzKZTBzLPML40GHolZ6lVSaEp/Lp2V2cOpXF7bff7qAIncPpCaV58+Yxb968S35XV1fHvHnzmDWra63unDlz+Mc//kF9fT2Bgd3LwNbWtmCzOe7iqbq6ivT0dO5ITMHLYP9iJCar5aq37eXOpFT2lxaxdu0n3HnnPQ7pw54y9u9nVKggwEObmTJmq3rV286gCMH0GIV3syvIyjpFZKTjdxgaPHgoBw4coKIWwoNca5aSmwE2bdoEdP3f2wVXx7a2q5RUCSZPTqW62vnLfkpKSvD0cW6B2Mu/shz0FfatPH1VykpLNXmtu6O1tYV3Vq2CeE9Ict7MJFVVodUKJhtqbhMM93HehXayFxS08fY7bzN+/FQ8PfvGjKzz58tYtXo1YnA0SuIgu7ff9Z60g8mCLa8QMSzOIe+JGJkEJZUsW76cwYOH4+fX9xJjPXH48EFysrPR3TgN4aFNwlYJCUVJHsqn69czZcpMwsLsnzDp6zZv/oKO9jY8r7MQ95UIvRF9ylQOH95EVtZJIiJcZ0fGvmL37jQCAqPx94+8rnbi4ieQdfwzduxIY+rUq+9GJnXP9u1ddZBSwkb36HmKUBgWMpJDBw9RUdGATtcHt/p1UcePZ9Le0c7Y0GE9fm6UdwhhXkHs2rmHiRNd+zOiKOKqk3dcYslbQEAAbm5uHD58GIAjR47g5eXV7WSSM6Sl7QJV5eb4JK1DuS7Rvv4MDQpjz+7tLj8VXlVV6hsaCHGhgtBaufAaOKvQ6owZt+Dn68vOw4JOk2v9nRgN0NHRwSeffEJHRwdGg9YRXcpmU9l5GKxWwe23361JDAEBQXS2D5zPTWcbBAS4zvHicnv27MJsMsE4P+fOnMhthkYLtNtgT13XbScRQsA4P0ydJtLSdjut3+v10ccfoCoCMbn7tRB6Qj1RCE2t0NGJuu9Y120HEEIgpo6mo6ODzz/Xppabs6iqyseffIji548ydLimsejGTkBF8NlnrjlzVkuqqrJz13Z0IYPQhTiuvpVhyAQQCrt2bXdYH/1Va2srp06dYFDMmOtuKyg4Di+vAI4ePWyHyCSArKxMgrxCCPXuebJ6eNgoWttaOXvWviVVBrqjRw/hrjcyvAfL3S4QQjA2dBgnTuTS1ta36htfziUSSkIIXnrpJZ555hnuuOMOFi9ezIsvvqh1WJc4eGA/Q4LCCPLoG6OsV3NjdByVVZWUlBRrHcpVCSHw9fGhtt21EhpaqPvyNfD1dc60YQ8PD37+i99S3yz4fB8ul1RyVTabyo7DUFQO3//+w0REXN8IX2/FxsbTXCcYCBusWK3QWKMQG9vzg7mzZGVlIgKNiGAn7+Z2rv3qtx1MhLghAox9pm5DTU01Bw7sh+HxCA/HvFdqccVVb9uT8PdBDI5m2/bN/boYa37+SYrPFSJSRyIUbU9rhacnIimZvfv20NTUqGksrqa0tISy0mL0yeMd2o/i6Ytu0FDS9+9z+YFTV3PyZB42m43o6FHX3ZYQgsioVPLycrDZnDtjuj+yWMycyMtlWMjIXg1MJQenIIQgO/u4A6IbmFRV5VjmUVKCBmPU9W50e3RIMlabtc9v6OASCSWAkSNHsnbtWj777DPWrFnD8OHajjJ9XV1dLWXnSxkT3j+mzl74d/SFL5XUEaPIqYIOy8A+KTh83oa/nx9RUc7ZtQYgJWUEP//5b6isU/hol6CheWC/B9fS3qmyYS+cLIJ7772fWbO0W6c+ffpMrFaVsgEwEFVRCKYOlenTZ2odyhUVFJ5FDbP/UulrstiuftsJ1DAjBYVnnd5vbxw8mAGqihga57hOLNar37YzMSwOs8nEsWNHHdqPlnbv3oEwGlGS7Ft3sreU4alYLZaLuzFJXY4cOQQI9HH2r0N6OUP8CBrq6ygqKnB4X/1JXl42er2RkNDBdmkvIjKF1tYWiovP2aW9gez06Xw6OjtIDundhgOeRi9i/BPIznL9a7++orS0hLr6WkaF9P7Yk+g/CE+De58/RrtMQsmVnTnTtV3R0KBQjSOxD393T8K8fTl9+qTWoVzTnDm30262sflM39spyF5O1tg4UW1jztzbUZw8+jpp0hR+//u/0mH24INtglPnVDni9y3KqlU+2KZwvlrHww//lHvu+Q9N44mJiSUlZQSFOQqd/XdiAhYznDmuEBsbx/Dhjr9I6Q1VVWlrawPPAVqzwENHW1tbn/jeyMnNQgT4Inz7zq5u1xQWhOLuRk5O/7yIMJvNHDp8ABEbj9C7xvpnJTAIJTCI/U7cmbUvyMnNQhccheLp+A1hdIOGAvT5UX9nO306n+CQBHS9nG1xubDwrp05LlxHSb2Xk3McRSgkB/d+wsXQkFQKCs/InSjt5MLEjBHBib1uQ6foSAkaTHbW8T5xnnQlMqHUDSUl51CEINo3QOtQ7CbW15+SPjBikJAwmGnTZrCtwEpB3cCbMttiUlmdbSUsLIzZs+dd+wkOkJo6kieffJaYmES2HoBN+7uKTktgMqukZap8vBPcPYP4+6NPMXPmLK3DAuChh36EzSo4eUjrSBzndCZ0tKr88Ic/cfquTt2lqmpXItiBm0a4NFV12ffmciWlxRDUv3YjEkKgBvhQWlqidSgOceZMPh3t7Shx8VqHcgkRF8+ZM/nywu1LFouZs2dPo4Q7Z2my4uGDzj+UkyfznNJff2A2mykuLiIkpPcXx5fz9g7Gw9NP1u2xg6ys48QFJOJu6P2mA0NCUlFVVSZa7SQ7+ziRPqEEul/feUNqUCL1DXWcP1967Qe7KJlQ6oaqqioCPbww9qOq+GFePtTW1mCzuf7Mn+9974cEB4fwRqaVeicnMtrNKu7u7tx99924u7vTbnZe/1abyluZFpo6BT//+SMYjU6uv/I1wcEh/P3RJ7j//gc5V67j3c2C7DOqQ3dWdGWqqlJQpvLeZsHx03DzzbN5+unnSEiwzzRxe4iMjOLuu/+DiiLB+b6x4qhHqkuh+KTg1lvnkpjoGktdvo2iKPj5+0Oz63/XOkSzBX9//z6RVGppbgZPF9w28np5utPU3KR1FA6Rl5cNQiA0qld3JSIyClSVEydkQgOgrKwMi9mMLtT+OydeiQgZRGGRY4re90dlZSVYrVaCguPs1qYQgqCgOM6dK7JbmwNRY2MjRUUFDA0dcV3txAYMxsPgSVbWMTtFNnCZTCZOncojNfD6k+SpwV3XDtnZWdfdllZkQqkbmpua8HXrXyeZfm4eWG02WlvbtA7lmry8vPjt//sTnaqBZYctTk3qtFtg7ty5/OxnP2Pu3Lm0O2krdFVVWZNj4WSNjR/88CcMHqz97oKKouOOO+7h6QXPMzhxOLuPwtrtgvIa5yaVDLqr33a0+maVDWnwxT7wDYjk739/gv/+75/g7q7NVtVXc+ed95CcPJQTBxRaGrSOxn7aWyAnXSF60CAeeOD7WodzTYkJiSiVpj49nbk3VFVFqTSTONh1E35fZ7PZwPXzXj0nBNY+MHjUG2fPnkEJDEJc54CLqqqoba2oDfVYT+Re92dVhISBolBQcOa62ukvSku7NoFRAnq+O1Vv6QIjaGpsoLmfJlPt7UKdo6CgGLu2GxgYQ1lZKRaL2a7tDiTZ2V0JoGHXmVDSKTqSg1M4fjxzwJ2P2Nvp06cwm82kBF3/QHKwhz9hXkHk5sqEUr/WaerErR/NTgJw03f9e0x9ZBuoQYNi+fVvfk9FC7x6xILJ6pwvQg89bNq0ieXLl7Np0yY89E7pls9OWUkvsXHnnfcyY8Ytzum0myIiIvnzn//BL3/5WyyqH+t2wOYMleZW57wncZFXv+0onSaVvce6ZiVVNrjz4IM/4MknnyU5eahzAugFRdHxy1/+Fg8Pb47vVnDk+ZzFxCWz+Swmx/Rjs8LxPQoCI7/59e8xGjUodt1DqamjsDWbocZBL4qrqjJhazGTmjpS60i6xdvHB9r7xjGxJ9T2TvyctEOosxWXFENA4HW3YzuRC02N0NGOdd+ertvXQeh0KH7+lJS4fmkBZ6isrAAEil+w0/pU/EK+7LvSaX32ZWVlpeh0enx8w+zarn9AFDabVb4P1+HYsaP4uPsR5Rd73W0NDxtJQ0M9xcVF1x/YAJaXl40iFIYExtmlveGBCZw8kYfF4qSZC3YmE0rdIITod5ncC/+cvrAM4YKRI0fz05/9mjN1Nt7MtGB1wnIrD4Ogo6ODTz75hI6ODjwMjn+9thVY2HzWysyZs/jOdx5weH+9IYRg0qQpLFr8AnfffR9F5XpWbxJkZKuYHDyDLHUw+HmDhxvMuKHrtiPZbCrZZ1RWbexa3jZt2kwWL36BefNuR693UobxOgQGBvGrX/2O1ibISf/qs29vZvOls/nMDkpenTwEjTUqP/2fXxPhYstcrmTixBvRGwyQ06x1KM6V24TBaGTixMlaR9ItYaFh0NiqdRh2pzS1Ehpi34tEV2A2m2lsqEfYIVlmu6ym5OW3e0P19aOquvq62+kPqqur0Hn5InTOO2YKn65EY01NldP67MvKy8vw9Q1HUew7gO7v37WzdF+uD6Mli8VCVtYxhoeOQhHXf9k+PHQUAkFm5hE7RDdw5eZkk+AfjYfePuVIUoIS6OjsoLCP7Ip7OZlQ6gYPDw/a+2jG8Eravpyq4O7et5byTZ48lYce+jHZlTZWZ1mw9bNE3/4SKx+fsDJhwmR++MMfu3zCz93dg/vue4BFi15g3PgbOXwCVm9SOFHouN3ghBB4eUCAL6QmCoe+RucqVNZsFew+CrFxQ3n88UX8+Mc/x9+/bxXoHz48lfvv/x6V5wTnTjimD4Ph0tl8BgdsuHT+LJTkC+bPv5Px4yfavwMH8fLyYuaMWXCqFbVhYEz7V+vNkN/KzTNvxcPDU+twuiVxcDLUNKBa+8/yMLW1HVtzK4mJ2i+btrempsauHzzt8Pd1+TmeHc75hIcHjY39aK3xdWhoaAAn7O72dYqn31d9S9dUXl6Or5/9lyT6+nUlsysqyu3e9kBw8mQe7e1tpIaNsUt7Pu5+xAQkcOTwQbu0NxC1tbVSUHiWlED7bQYxLDAegSA3N8dubTqTTCh1g79/AHUdrl9rqCfq29twM7r1mRP9r5s1aw733fddDpTZWH+q/5z451RZeTfbQmrKCH7+81/bfZTIkYKDQ/jlL3/Lo48+SVhEPNsPaVNfyV7qm1U+S1P5bA/ojSE88sjv+ctfHyPOxXYS6onbbruTG8aOJ/+ooKHG/u3rjVwym09v55VoLY2Qd0BhyJCh3H//g/Zt3AnuuutejEYjIq2u3814vZyqqoi0Wtzc3Ljzznu0Dqfbhg9P7UomlfWfWSVqSQXQ9W/rbzo6Orp+cET22h4MRjo7O7SOwiU0NDaAh49zO3XzQCg6GhvrndtvH2SzWamurryY/LEno9ETD08/mVDqpcOHD2LUGxkSar/v8JHhYyk6V0hNTf851jnTyZN5qKrKMDsU5L7A2+hJjG84eX10Bz6ZUOqGiIgomjs7aHbgiUG72XzZbmKOHcUua24kIiLS5WfAXMldd93HzTffytazVvYV9/2kUmmTjTczrcTExPGbR/6AXu+iJ8jXkJQ0hH/842l+9rPfYLZ9VV+ppa1vXECbzCr7jqu8txkq69154IHvs/CZfzFu3MQ++1m5QAjB//zkFwQGBJGd5th6SvZms0J2moKHuye/+MVv0fXBmnZ+fv785wPfRy1th1wnLX0z2S45rmCyOaff7GbUsg6+9+AP8e1DtXtSUkbg5u6BWuDApRkm82XviYM/iAXnCQ4JJSYmzrH9aED9au2+toFcidL/yiX0VnNzM4q7t1P7FEKgeHjR0tLi1H77otraWqxWK752rp90ga9PGFVVsoZST9lsVg4fOsCwkJEYdfYbpRsZMQ6AQ4cy7NbmQJKXl4NRZyAxwL67Vg4LjOf06VN9pr7x18mEUjfEx3dlIM/UO2BY/0ttFtMl9UfaHFXRFrCpNgoaaomLt19m1dmEEDz00I8YMWIka3IsnK1z0oWSA7SaVF49YsXT24/f/e//4eHheruF9YSiKEyZMo1Fi1/krrvuo/C8ntWbBUdPqk6pe9UbqqqSX6yyepPCsXyYNu1mnn32RW677U4Mrjr63QteXt784he/pb0FTh3WOpruO3McmupUfvKTXxEYGKR1OL128823MnLkaNhXj1rhhJkLnbZLjit0Ov57Uq3ogP31jBo1xuU2FLgWg8HAjZOnQOF51E4HHYNN5kvfEwcmlNSmVtSyKqZPm9HnE+LfxmD4sh6Pqy5RtFr7ZPLbEVpbm0GDnVCFmyctLQOsdl0vdBVNx2EJJR/fMDlDqRfy80/R2NTAqMgJdm03xDuMKL8YDhzYb9d2B4q83ByS/AdhUOxbE254UAIWq4X8/FN2bdcZZEKpGwYPTsJgMJBd5bgvQ0+98ZL6I572Xi/yNWfra2kzm/r8FHidTscvf/k7goNDWZFppcXkmsmKq1FVlbePW2jqFDzy2z8SYIfdalyFu7s73/nOAyxcuISUlDGkZ8HabYLKWtd6nxpbVNanwZYMCAmL5R//eJof//jn+Pn5ax2aQyQnD2X+/DspPS2o7QPnd021UJQrmD59JjfcME7rcK6Loij84hePEBwUjNhcg9ro4NkpbsolxxXcHHvIVxvMiE01hASH8POfP9Inkxg33zwb1WJFPVnkmA6MhkvfE6PjEtZq7lkUReGmm252WB9a8vL6csaLqy4r6+zAy9vJxkSz3AAAIABJREFUy7xckMlkwmI2I4w9K7Ggqiq21iZs9VWY8tJ7NdtLNXjQ0tr/Cu3b24Vkj69fuEPa9/ULp6Ghns7OvjfzQksZGfsw6IykhI2ye9tjIidy9uxpueyth5qaGikpLbbrcrcLkgNiUYTCievcZVQLMqHUDUajkZEjR3O4ogSrzTEjvB4Gw2W7iTnuJPNA2Tl0Oh0jR9qnwJuWvLy8+PVvfk+LWbAmu+8VTt9bbCOnysYD//kQgwf3v6KpAGFh4fzud3/mkUd+jxU/PtwBe4+pmC3aJpZsNpVj+SrvbRZUN7jx0EMP89hjC/rt+/B19957PyGhIZw4oGBz0cF9ANXWVTfJx8eHBx/8gdbh2IWXlzd//MNf8RBuiA1VqK0O/N4yKpccVzA67pCvtlgQn1fhqXPnj3/4G15eXg7ry5Hi4uIZNjwFkVOAanHAh8NouOw9ccyxXu3ohFPnmDx5ap+e1Xc1np5e6PR6VBdNGKitrQT498+BiZ5oa+t6f4Rbz2YomU/sR22qQe1ooXPfR5hP9GI2hZuHXPLWDf+fvTcPbuO6832/3dgBYucCghu4i4skarFsS/K+RLIlW7bseIvXXC9xYmcyie2JM/Zk7ksqL7f8bmVm3rvlelVvkqm5k/fmzk3GL5v1Zia248h2vGuXrJUkuJPYd6C7z/sDACVSJAiA6G6AOJ8qldTNxjm/UhNAn+/5/b6/qalJKJUa6PXiNBzJZj7RLKX84TgOH334AQYbNkGjLH0DpU3OdGOTDz44WPKx1zKnTp0AAPTZXCUfW6fUoN3sxMkTlWfMTQWlPNm58zr4YlEcnh6XO5RVkeQ5vDc+jM2br6jYB/7FuFztuPvu+/D5lIBDU2W8Ol6EL0bwxikeAwODuPXW3XKHIyoMw2Dr1ivx4//yN7j++ptx6DTwP/6DwbRXHlEpGCF44w/AwUPAwOAQfvzjv8Ett+yuKCP01aBWq/HYo08hEiQYOSV3NMszcR4IzBE8+OBja+bzCkj78r3wne9BlWDB/EpkUUkCSIQD8+sZqJMsXnzhe3A4St8pSEruvGM/hGhMvCwlCSCHz4BwPPbs2Sd3KKLBsixqa+tAQuVZ0sSEw6ivE6eEqJKIRtNNbQoVlLjREzmP84HR6BCJlqfgWE5MTo7DbHGIllVqsTjn56Hkx7FjRxAKh7C56SpRxrcb6uCydeH996mgVAinTp2AWqGCy9wkyvjrrC6cP3+u4rL5qKCUJ0NDW2C32fHbsycq2mTx3dFzCCfiuOWWXXKHUlJuu20vWppb8IuTApJ8Zdyffz3FgWcUeOKJZyqyNKQYdDo9nnjiabz00iuAwoJfvAV8dopI+p466yb4f/6NgTekwZNPPotvf/u7a3YHPxcbNgxhw4YhXDjKohz9/3gOOHuYRUdHJ7Zvv0bucEpOV1c3Xnzhe1DFUNGiEglzYH41A1WMwYsvvIKOji65Q1o1/f2DWLeuHzh0GkRs02wRIJEYcOI8tl+9A83NpTUNLTcaHY1gggG5w7gMwnEQQkE4HOKUEFUSkUg6Q4hRF+ihtNhLtAhvUUatQyy6tro0i8H4+DjMZqdo45vMabFqfFzEhgdrjPff/yP0agP6GjaINseWpqsxNjaK0dER0eZYa5w6eQLdllYoRdqA7rW5wAs8zp49Lcr4YkEFpTxRKpW4fc8+nPbO4sjMhNzhFEWC4/CrM8fR3dWTflheQyiVSjz8yFfhjQp4+0L5Zyld8An4dELA7bffifr66tvBHBzcgB/96L9iy5Yr8f4R4DcHgYTIHli8QPCHzwgOfAA0t3Tghz/833DttTdUjZi3FPff/xVwKYKRwjd+Rcd9GohHCO6//+E1e496e/vw4gt/eVFUCleWqJQVk9RxBi+9+Jfo6emVO6SSwDAM7r//KyCxOMjhM3KHUzDkkxNgwWD//vvlDkV0mppaQAJ+EJHsCIqFBAIAIWhqWtuCXj5EMiWJBQtKJYDR6BCPRSGUc223zMRiUXg8s7Bam0WbQ6lUw2RqwPi4W7Q51hLxeAyffvoxhhq3QVli4+dL2eTcBpZR4L333hVtjrVEJBLG2LgbvdY20ebotrSCYRh88cVJ0eYQAyooFcANN9yEhvoG/NPxz5Aq164iOfjVmWPwxaK47/6vrMkFWl/fADYNbcG/nyeIpso7S+lXX/AwGY247bY75Q5FNgyGGjz33LfxyCNPwD3N4l9+z8AfEue+xRIEv/oDcPQssGvX7XjllR9UpZC3mJaWNlxxxdUYPckiWUa+tjwHDB9n0dc/gL6+AbnDEZXe3j689OKrUMcZML+uHFEpLSZNQ51g8RcvvYqennVyh1RSOju7cdVVO4CjZ0GClVMyQ2Z9IKdHcestu6viM665uQWE54Eyy1IiPg8AUEEJlwhKmsJMuUtBVsSK0iylZXG70yKP1Sbu76rF2oLR0VFR51grfPLJR0gmE9javF3UeWo0JvTVr8cH7x+komsenDt3BoQQdFtbRZtDr9KiuaYBZ87QDKU1i1KpwsOPfBWToQB+daayDLPcQR9+e/YEduy4Fr29fXKHIxr777kfsZSAt8o4S+mMR8Bpj4C9d+yHTif9jl05wTAMbrllN15++fvgiB7/8y0Gk3Mri0r97ek/+RCMEPzyLQbTPiWeeeZ5PPTQY1AqxdvxqTT27bsHHEcwWkZeSmNngUSM4K5998odiiT09PTipRdfuSgqRcv38wsASJRPeyYlFPiLl15BV1eP3CGJwgMPPAylQgHyp6Nyh5IXhBCQ94/AaDJh3779cocjCc3N6Qd74vXKHMlCiM8LVqFAY6N4ZUSVQjSaMcUu0EOpFGRFrGzZHeVyRkeHAQA2u3iL5Oz4MzNTiMdjos6zFnjvvT/CbqhDu038JjFbm7fD5/fi5MkyTFUvM86c+QIMw6BDJP+kLF2WFpw7expCmWXe5oIKSgWyceMm7Nh+DX51+hjO+eZKNq5aocx5vBqSPI/XP3sfBkMNHnpobXRKWo62Nhe2bt2Gt4eFss1S+t0ZHmaTCTfeeLPcoZQNvb19+P73/1eYzHX41bsMxmZy37t1LgbrXCtn2flCBL98m0GC1+Ev/uJV7Nix9rx4VktLSys2b96K0S9YcGVgFyMIwMgJFl1d3WuuNDcX3d29ePGFv4QiQsD8dgYkWZ4PEiQhgPnNDJRR4MUX/nLNikkAYLPZcfdd94KMTIIMl3+pOzk5DDLjxYMPPAK9fu2Y2OfC6WwCwzAQMhlB5QLxeuFwOOnmBTDfZa1QU+5SkJ0zUqadAMuB0dFhaDQGGAz5eUkSQhCN+OD3TeDUid/n7YFps7WCEEKzlFbA7/fh+PEj2NJ0dd7VJIQQBOI+TIcm8N7wWwX5kg44NkGr0uL99/9YbMhVw7lzZ9Fc0wCtUpPX9YQQ+BJBTERm8dbox3nfly5LM2LxGCYny/+5IwsVlIrg4UeegM1mx//+6UFESuRmu8nRnPN4Nfz82KcYDfjw5FPPwmg0lWzccmXfvnsQT5Gy9FI6601nJ92+Zx/U6vw+kKqFhgZHuhStoQm/PZhfplIugmGC//cPDBhFDb73vf+8pjPzVsuePfuQShCMlYFdzNQwEAsT7N1795oszc1FT886fOvPXgC8KeA/ZkGE8hLFiUCAf58F40vhW3/24prxTMrFrl174GxqBj44WtYG3SQSAz4+jr7+AezYca3c4UiGWq1GbX0DiM8ndygLYAI+tKxxQ/R8iUTCYNUaMHJ0Uc1kKGVFLcrlDA9fgM3uyvv79tTJ3yMYnEY8HsT77/0Up07+Pq/X2WtdAICRkQvFhloVfPTRn0AIKai723vDb2EuMo1wMoh/OfIPeG/4rbxfq1aosd6xFR9//CFSqfL9jpMbQggunD+H9gLM699yf4zpqBfBZAT/cOLXeMv9cV6vc5nSGVAXLpwrKlY5oIJSERgMNfj6N74FXzyG//bpexDI6neSb3J1o8FghEmtwWMbtuEmV2nSHA+Onsfvh09j9+69GBraUpIxy522tnZs2XwF3h5evZeSSsHkPC4EQgh+ezqbnXTrquJaq1gsVnz3u38Fm70evz3IwBcs7v7FkwS/PshAgA7f/e5fobVVPAO9tUB3dy96enoxepKFnBm2hKS9k5xOJ4aGNssXiIxs2LAJjzz8BDASAz72yx3OQj7yAe4YHnvsSaxfv1HuaCRBqVTiyf/0LIRIDOSj43KHsySEEJD3DkFBgCcef7rqhNhmZzOYQPm8VwjHQQgG4XSKZ3JcSUQiEVn8k4BLS95ohtJScBwHt3sU9tr8n5Hco5/nPF4Og8EGrdaI4eHzBcVYbXz44ftoNDXDYcy/rOr49KGcxyuxybkNsVgUx44dKeh11YTHM4dwJAyXKX9B6dDMFzmPl8NZUwuNUk0FpWqgq6sHDz/8BI7MTOCfTxT2xl0KhmFg1ergNJpxU3tPSR4Iz3hn8fdHPkRf3wDuu++hVY9XSdy9/z7EOQH/dm51WUrrG9icx4Vwao5kvJPuhkZDs5OWw2y24MUXX4FKY8Dv3meQLFAUJITg3z8EQhEWf/7nf4GWFiom5cPevXchFiGYlHHzcG4cCPnS2UksW71fTzff/CVce+0NwGcBkLHy8Jsg7hjweRDXX38Tbrihusp1u7q6setLt4GcvAAyPiN3OJdBzrpBRqZwz/774XA0yh2O5DQ1NUEoo05vJJA2CHc6qX8SkM4OkqPDG3BpyRvNUFqKyclxcFwKdrsr79dwXCLn8XIwDAObvQ0jI8MFRFhd+P0+nD59ChsbryjodUk+mfN4JXrqBqBT6/Hxx38q6HXVRNZrrM3oyPs1ST6V83g5WIZFc0093BVUHlq9T+wl4MYbb8FNN96K3509gXdGyqBW5BJmImH85KN3YbXZ8dxz34ZCIUOqsYy0trZh+/Zr8fYFAZ5o8VlK17SyqNMDRjVw/6AS17QW95YRCMEvT/GotdfS7KQ8qK9vwDe+8W34Q8DBAvXaw6eBkUngoa88TsvcCmDjxs1obm7B8DEWBZTflwxCgPNHGdhstnR3rSrn4YefQIOjEcw7XpCUvAtlkhTAvOOFw+nEV77yuKyxyMU99zyA+gYH8O7nZVX6RiIx4IOj6Orqwe7dt8sdjiw0NjalzddCQblDAQCQQLr8jmYopYlEwiCyCUrUlDsXo6MjANL+RlJgs7difNwNvgI7ZUvBoUOfAQA2OKTN0FaySvTXbcShzz+rKCNoKcl6fzUZpeme2lzTgNHRkYL8sOSECkqrgGEYPPzIE9iwfgg/PfwRDk+Pyx0SACCUTOC1D98Gr2DxnRdehtFolDskWfjylx8Eq1TiFyeKb8PNMAwsWgaOGgbXtCmKzhx7d0TARFDAAw8+ApVKVXQ81UR//yBuv/1OnLiAFU26swQjBH86xmBoaDNuuokKd4XAMAzuvHM/wgGC6RHp5/dOA/5ZYM+eu6iRLQCtVounnnwWJJwCPpK5nOcjH0gkhaef/HrVZldqNBp87ZnnQKJxkPcPyx0OgEyp2x8+g5IATz/9DbByeNSUAU5nujSE+Iv3USLJJLRaLfbt2wetVguSLGyHfwF+H8AwcDjy38ley4SjMpa8KZRglCpa8rYMbvcoWFYBs0Wa31WrtQUcx2FqalKS+SqNw4c/g0VvQ6NJev+1/oaNCIWDFVVmJSUTE27U6i3Q5WnIvVqaaxoQiYYRDAYkmW+1UEFplSgUCnzjuT9HS0sb/vaTP+J8CTu/FUOC4/BfP3wHc7EIvvXnL6V37qoUm82OffvuxeFpAYen5NsN8ccJfn1awMDAIK64In+TPQpw1133wm63473DTF4q/YfHAIZV4LHHnqw6H5FSsG3bVXA0NuL8kcKzlBY3piy0UeX5wwzMZjOuu+6Gwl64hunpWYcbrr8ZOBoC8RSxwFWyuY/zgMwlgWMh3HzTl9Z0R7d86OrqwZ133A1yxg3h3FhxgygVuY8LgBw7BzI+g6889FhVlrplmReUVmPMnUxg165deOaZZ7Br1y5gFQ1XiM+H2to62ngjQzQSla3kDQBYtQ6xWFS2+cuZiYkxmC2NYFlpNnGs1ub5eSkLEQQBp06eRI99QJbn157adFfdU6dOSD53JTAxPo5Gfa1k8zXWpOeamCiPZJWVoIJSCdDpdPjOCy/DbLHitQ/fwVRYnrRrXhDw3z49iHO+OTzztedpuQ+A3bv3orWlFf98XEAkKX3aICEEPz/KQYACj1ehWepqUavVuPvu+zDrI3BP5742FCE4PQrcfPMu2O3SfeivJVhWgbv23YuQv/Aspbrm3Me58EymM5T27r2LLsIWce+9D0Kv1wMHvYWnPrfpch+vACEEzEEvDIYa3HPP/YXNvUa588796OjsBg4eAgkVnvXAtDpyHucLmfODfHQcmzZtrTpPq8Xo9QZYbDYQn7f4QdQaHDhwAK+//joOHDgArOZzyOdFS7M0JUSVQCwWBdRa+QJQa2mG0jJMTEzAbJZOjDab0593NEPpciYmxhGOhNBpl2fjxqg1o97YSAWlJSCEYGpqEg6DhIJSZq7JyQnJ5lwNVFAqERaLFS++9AqgUuK//Okt+OPSGqkSQvDTwx/is6kxPPLIV7Ft29WSzl+uKJVKPPnUNxBOAv98jJO8FvV9t4DjMwK+fN9X0NBA09+L4eqrd6KmxoATK5hFnxpO+/DceuttksS1Vrnqqu1wOp04d5hFIQ0sW3oAvZFArSXov5KgJc9nIkKAs4cZWCwW3HDDLcUFvYYxGo348r0PAhNx4GyBi6IBI2BWAjoWuNaWPi6EMxGQyTjuv+8rMBhqCnvtGkWpVOLrz34TalYB8vYnBRtBM33tgMkAaDVgdgyljwuEpDjgrU9gNpnw5JNfoxsVANrbOgBP8RnijFqNeDyON954A/F4HIxaXdQ4JJWC4PfB5eooOpa1hCDwSCbiYOTcKFBpEJf4mbwSEAQBHs8sjBJ5wgCASq2DTmfCzMwKO4RVSLb7XatFvs+OVnM7hi/I2JmlTAkGg0gkE6jX2SSb06Y1QcEqMDtbfo1AloIKSiXE4WjEd174HgKpJF7709uIpaQz7vzlF0fwh9FzuPPO/bj55i9JNm8l4HK14+799+PTSQEfjktnNjcVFvA/T/AYGBjELbfskmzetYZKpcK2bdsxMsWAF5YXBC9MMuju7kFtbZ2E0a09WFaB/fvvRzhAMFHAcwXDABo9YDADLb3p43yYGwf8M8C+ffdCXeQibq1zww03obWtDcwHfpBk/p9hDMMABgVgVYEZMBUkPJCEAOYDP1ztHbj22uuLiHrtUl/fgP/01WdApr0gn5ws6LXpe6IDrEaw/e1FiUHkvUMgwTC+/uyfwWg0Ffz6tUhHRycEvw9kFaVqpYDMzQIA2tupoAQAiUT6fjAqeQWlWDwu3/xlSigUBMdxMNRIt0gGAL3BBq93FdmEaxS3ewRKVoX6GvnKl52mFvgDPoTKpMFBuZAVder0FsnmZBkWdp2ZCkqFcuTIEezfvx979+7F008/jdnZWblDKoqOji48//x34A758XefvAtOArf8d0bO4o0vjuKaa67H/v33iT5fJbJnzx1Y19uH/3Gcx3RY/HuS4gn+/nMeGp0BTz/9XFW3QC8F/f3rkUoRzC3jTZziCGa8BAMDG6QNbI2ydeuVaG1tw/kjLAQR7ccIAc4eYlFbW0u9k3LAsgo88fjTIFEO+GgVPjGF8KEPJMbhicefqlqz51xcddUOXH/9TSCHT4OsVI9bQoTTIyBn3Ni37x709Q1INm+5093dCwAg0/JmPpDpdClPtfuNZckKSlDKuFmgTGefURbi96cfqPQSLpLT81nhX4WB/lplZmYatTV1UMj4fZsVs2gG2UJ8Pg8AwKY1SzqvXWOG1+uRdM5iKYtVLiEEzz//PF544QX8+te/xp133olXXnlF7rCKZuPGTXj88adwdGYS/3j0Y1HLrI7PTuJnhz/C+sENeOIJ6tGzHCyrwNee/SbUWgP+r895JHlxS99+cYLDeFDAU09/A1artLs/a5HW1rQfhW+ZTZPs+dZWlzQBrXFYlsW99z6AaIhgXMSGHzOjQNBLcPfd90GppN0Pc9HZ2Y1bbt6VNuieEndxRCbiwPEQvnTr7Whv7xR1rkrm4YcfR1NzC8g7n4JExC+pIb4g8N4RrOsbwL59+0Wfr5Lo7OwCw7IQpuT1mxCmJtHobKra7rqLSWa65TEyfr4zSvV8HJSLhMMhAIBGI205s0ZTg1AoJOmclcDc3BwsWrusMVh19vlYKBfxZRo+WLXSZgRbNEb4V+MNKCFlISj5fD7E43FcdVW6A9YNN9yAgwcPVvQXwPXX34Q9e+7EW8Nn8O8XvhBljqlwEH/7yR/R6GzCc89/m7baXgGbzY6nnn4O40EBvzzBiTbP55M8/jgq4Lbb9mJoaIto81QTNlvanG65NVsknr2OinelYuPGzejs7MKFo+JkKRECnDvMwuFwYMeOa0o/wRrk3nsfhM1uB/O2F4QTJ9OSpAQw73hhr63FPffQjNdcqNUaPP/ct6EiKMpPqRAIxwG//xgGnR5ff/abNGtsEVqtDp0dXYCMHXEIzwNTkxgcWC9bDOVGKpV5jlfIuGGgVFb0ekIsYrH0A5VarZd0XrVGT7vuLUEwEIBRI20GzGKMmrRgQkveFhII+MAyLGpU0narNGtqEAgEJJ2zWMpCgbBardDr9Th48CB27tyJ3/72t0ilUvD5fGhoyM8szm4vP8PQZ599Gp65afzThx+ixWRFX23pjO/iXAo/+fhdKDUa/OCH/wscDulM9SqZW265DsPDp/GLX/wC6+p4DDlK+1DuiRL801EBPT3d+NrXnoJKRbMuSgEh6fd3ahkdkMsIHg6HDXV1dGe4VHz1q0/g5Zdfxvg55G2ynS/To0DIT/DsS4+ioUHalPvKxYgXvvMdfPe73wU+8gPbRRBQP/KDBJJ48ccvoKWlvvTjrzHq6tbh+eeew2uvvQZ8/gWYLeJ0VyXvHwHxh/DyD3+I7m7aQWwprrpqG87+9/8OEo+D0UrfVYxMT4JwHLZvv5J+D2UIhTL3QUYBlGEVIBDoPVmEVpvOKVBKXI6oVKiRSqXo/VhEPB6DziKtYLEYrSotLjIMR+/PJfB8EjVqHVhG2jycGpUeyVQSJpMaGk15d0CWXFB688038aMf/WjBuY6ODvzt3/4tfvzjH+O1117DnXfeCYvFUtBi3OMJQ8hh2CsXjz/xNQwPj+D/+PQgfnDdbbBoV/9hQQjB3x/+EBOhAF566RUoFAbMztL00XzZs2c/Pvv0M/z86AhcFhYWbWnKBAVC8A+HORBWhaef/ib8/jgAWrdfCoTMzv9yFZ3Z015vGAYDfS+UiubmrnSW0rFzaOoSUCorMEKAC0dZOBwN6O/fRD+/CqC5uQs33HAz3n77P0A69GAcpVs4k8k4cCSIm266FU5nB70vebJx45XYufM6HDz4B5DGWjDO0jYGEM6NgXwxgjvuuAstLd30vixDZ2c/AEAYd0PR2S35/IJ7FAqlEs3NnfQeZfB4Mv8PxZjPJ+PQarXYtWsXDhw4gGSyyOcphgXH8fSeLMLrTf9/MKy0S0FWoQTPc5iZCVKbjktIJBNQKeRtTKJi0+tuvz9E3y+XMDfnhV7i7CQAMGTmHBmZkt0+hWWZnMk7kpe87d69G+++++6CPz/72c+gVCrxj//4j3jjjTdwxx13QBAEWCyVv2ut0+nx/DdfQJTn8H9+/n5J/JT+6D6PD8aGcffd92GAplYXjFKpwtee/SZSRIGfH+VK5nH19gUe57wCHnn0P6G+nmaMlZJEIv0gqV5GY85qz7EYFfBKCcMwuOOO/YiFCaaGSzeuZyLtnbR37920dKcIHnjgYVhtNjB/8IKUyA+O8ATMH7yw2my4776vlGTMauLRR7+K+gYH8M6nIPHSldeQUBQ4eAidXd24664vl2zctUhHRwcMNUYQ96g8AYy50duzDtoSbByuHYoXDEgyhl27duGZZ57Brl27QJJF+pQRQoWLJcg++0qddcFkficIka7rciVACJH8XiyGYRiwDFuWCRpykkgkoZFB7NNksgfnmxuUMWXhoQQAL7/8Mo4cOQIA+OlPf4pdu3atmc5Yzc0tePDBR3F0ZhK/Hz69qrHmohH847FPsK63D3fcsa9EEVYfjY1N+PJ9D+H4jICPJ1b/pTYXJfj1aQGbhrZg587rShAh5VKCwXQNsW6ZjM/s+WBwmTZwlKIZGtoMp9OJkRMsStVfYPgEA7PFjO3bd5ZmwCpDp9PjsUefBPEmgSMl8jo4HADxJfHE409Dp6ML4kLRanX4xtf/DIglQN47VJIxiUBA3vkUalaBZ7/2TeqTuAIsq8DGDRuBcbeozVCWgoRDEHxeDA1tlnTecmf+Ob4I8YBR63DgwAG8/vrrOHDgABh1kZ9LRABLBaXLyIpsBBK/VzLzMTKLJ+UGA0bye7EYQojsMZQjqVQSKkb6zU91Jntw3ouujCmbd/P3v/99/NVf/RV27doFt9uNl156Se6QSspNN92KwYEN+OeTh+CNRYoagxCCfzjyEQjD4smnvk539lfJLbfsRkdHJ355UkAstboP0H85zkGhVOOxx5+kO2Ei4PWmuxwYlnmerMmc91VIN4RKgmVZfOlLexD0EvhnVj9e2A94JoFbb7mNdnZbBZs3b8XQpi1gPguCRFfnmk6iHJjPgukx6YK4aNrbO7F//30g58chnBtb9Xjk2FmQqTk8+shXadZrnmzYsBlCLAYyNyvpvMKYOzP/JknnLXfmrSv4whuhMGot4vE43njjDcTjcTDq4sp7CZeCSi1vKVE5khX7pM4UIkQAwzD0WXkRKpUKKT4laww84UEIgZq+XxbAcRyUMghKiozoynHiNZIqFWUjKG3YsAH/+q//igMHDuBv/uZvUFNTfibbq4GO/1gKAAAgAElEQVRhGDz+xFPgAfzfxz8raozPp8dxaHoc+++5jz5clgCWZfHoo08ilBDwb+eKX5CdmhNwbEbAvrvuhc0mb8vPtYrHk25hWrNMMxKdBlAoGNrqVCR27LgWWq0W7jOrH2vsDKBQsLjuuptWP1iV8+ADDwMcAQ6tsgvI50GABx544JHSBFbF3H77nejo6ALz/hGQWPFp6iQQBj45iU2bttKs1wIYHEzbAJDx1Qt6hSCMu2Gx2uB0Nkk6b7mTzXYkxfoflQIuCZ1GepP2ckehSC+QiRhtXHMgCPz83JSLqNUaJHl5S5uy81NBaSEMI0/2WHbGSsjmK/8I1xD19Q247bY78KfxEZzzFbbw5QUB/3ziczQ6GnHLLbtFirD66OjoxFVX7cA7wwJCicI/LAgh+M1pHjarFbfeepsIEVIAYC6z22xcRlBiGAZGPQOPR9pd6WpBo9Fgx45rMTPCYjWZtwIPTF5gsWXLNpjN8rbHXQs0Njbh6qt2gDkeBokXtyggMR7MiTB27rgWDkdjiSOsPhQKBZ588lkgxYH86WhRYxBCQN47BLVKjcdp1mtBmM0WNLe0QpiQTlAihICZnMD6wQ30Xi1CrzcAAEiiSP+jEkDiURiNtGPVYrIltHwR2WOrQRB4sFRQugydTo84J9/7BADiqfT82fctJY1sghLJlodKPnXBUEFJYm6//U7UGGrwy1NHCnrdn8aHMREK4N4vP0h9FErMXXfdiyRP8IeRwhdkZ70EF3wC9t6xv6CuhJTC8HjmoNeyUCqW/1Q16IT5TCZK6dm583rwPMH0SPFjzE0AyTjBNddcX7K4qp3bbrsDhBOA08WVUuN0GIQTsHv33tIGVsU0N7dgz+13gJx1g0x5Ch/gwgTI+Czuv+8h2Tu7VCL9fQPAzDQIL03mBfF5IcTjWLeuX5L5Kgm1Wg2NVgcSk7FjVDwEk4luYCxGoUivJQSpM5R4Dipa7n4ZhhoDoskiv8dLRDSVnp8KSgtRq9VICtKXnSWFVGb+ZQxkywgqKEmMTqfD7tv24sjMBNxBX16vIYTgt+dOoqmpGVu2bBM5wurD6WzCxo2b8Z6bgC+ws8EfR3gY9Hq6QBYZv98PvTb3vTFoAb+feiiJRWdnF+rr6zE1XPxWyeQFwGAwYHBwYwkjq27a2lxwtXeA+aK4B1Hmiwg6u7rR0tJa4siqm71774bZagX54GhBBtGE54GPjqO5pRU33niziBGuXXp7+0A4DkSiDQYyPTU/L+Vy7PZakHB+z7ulhhABQtgPu53aESxmPkNJkNa3RxA4WvK2BCaTCZGUjMIrgEgiNB8L5SJarQ4JXnpj7ASXnrMSGqVQQUkGbrzxFqhUKvzHhfw6vp32zsId8GHXrj1rpvNduXHDDTcjGBdwcjZ/c8JYiuDwNMGOnddBoyl/9biSCYeD0KpzL8q0GiAclnd3Zy3DMAy2bbsa3mkgVUSZv8ADngkWW7deSbMsS8z2q3eCzCVAgoUtDIg/BeJJ4uqraLe9UqPVavHlex4AmfMBw5N5v46cGoEQiuDBBx6hjTeKpKurBwBAZqYlmY/MTMNgNFJvy2VodDhAgvJkD5OwH4Tn0NBAy3kXkxV1Cs1QSiZj0Gq12LdvH7RaLZLJwsq00oISfQZYjNFoQjhZnKAUTy28J9nStUIJJYPzsVAuotPpEE1J7wMX5dJzarVUUKIsQU2NEdu2XY0PxkeQzCMl+93Rc9Bqtbjqqh0SRFedbNw4BL1Oh8+m8heUjkwL4ASCq6+mizGxicdjUK+QIa1WAolEQvJ20dXEli3bQIR06Vqh+KaBVJJg8+YrSh9YlbN589b0P0YLfIjMXD//ekpJ2bHjWtQ3OEAOfZHX5xLhBTCHT6OnZx0GBzdIEOHaxGazw2SxgsyWoC1lPszNoLuzm/onLUNTUzP4wCyIxF49ACD4pudjoCwku7FTuKAUxa5du/DMM89g165dSCajBb1eEHi6qbQEZrMFwVgAQhFd92LcwnsS4wq7J1mCcT8AwGKxFPX6tYrJZEIoGS3q3qyGYDICtUoNrbb8mwpQQUkmtm/fiVgqiWMzuVdmnCDgkyk3tmzZVhG/UJWKUqnCxqEtOD4LCHkKEkdnBJhNJnR0dIkcHYXnebArPKuzbMbMlgpKotHR0Qm9QY+58cJfOzeR3hHt7x8sfWBVTn29A7baWmCswB20sRjq6utRV1cvTmBVjkKhwN49+0Dm/MDkyhka5MI4hEgMe/feRcWJVdLZ3gFIUPJGUikIPh/a2ztFn6tSaWtrBwQBgjf/TL1Swc+5AYZBa2ub5HOXO8V2eVOr9Thw4ABef/11HDhwAGr1Mt1SloF2eVsai8UKgfCIJsMFv1anXHhPdMrC7kmWYDwAtUoNna64169VTCYzBCIgUmTmV7EEk5GKaShABSWZ6O8fhFarxaHp3Cuzs95ZRJNJ6p0kARs2DCGcEDAWXFmQEAjBaQ+wYeNmWoYoASzLYiV7K0FIl2XRhZh4sKwCgwMb4Z1mUahu551i0dPTS4VxEWAYBoP968FM5p+hRwgBM5XEQP96kaOrbrZvvwZ6gwHk1PDKF58cRn2DAxs2DIke11rH5eqE4PeBpMT1h8n6NLlcHaLOU8lkN934mVV0dCgSfmYUjY1NdIG8BNmS2kIzlNRqHeLxON544w3E43Go1YWV49Aub0tjs6V9vvzxwv3GtKqF90SrKq5EKhD3wWq10efoRWTvjTcelHRebzwAm71W0jmLha6EZUKpVKGvbwAnPblTsk/MTYFhGAwM0F19sRkYSC+sznhWTmkcDxJEkgLNtpAInU6P1ArZ8ikO0GjU9ItQZAYG1iMeIYgWUOqfjANBL0F/Py3jEYve3j6QOA9481xAe1IgCZ4aCYuMWq3Gju3XAMOTIMnl7w0JRkCm5nD9dTfSTYoS4HK5AADEW0SXvQK4KCi1izpPJWO318JmrwU/eU7SeYnAg0xfQN86+hm3FApF+nNG8i5vAg8lFZQuI9vRMxCTx8AeAPxxL6w22ll0MfaMqOOJ+Qt6XYyLL/C2inGFZZF7EkHU1lJBibICPT19mAoHEUykf8Gua+3Cda0Ly6fOeOfQ0txKWzhKgNVqQ31dHc55V97hP+dLX0PbBEuD0WhCLJFbKIolgJqaGokiql6yAoS/AHsS/2z6774++n4Ri3lhaDLPB5bMdVRQEp8rr9wOwvMg7uVNosnI5Py1lNXT1pYWeMjcrKjzEM8cDDXG+cUg5XIYhsHgwHoIE+dABOk8SIRZN4Rkgm78LUPWGFsOQYmWvF1OVjjwxcQVwXPhj3vnxRPKRbK2ALMFin3RVHyBt1Uhxt68wMMb9aO2tjIsCaigJCPt7ekU6dFA+hd0Z2sHdrYuTJseCfrgaqep1FLRu24A5/xYsWzknFeAzWpFbW2dRJFVN1arDeFYbkEpHANsNvpFKDZOZxP0Bj18hQhKM2m/hnb6WSYa9fUNsFitwESeDyzjcdjsduqfJAHd3T3QGwzAWI6uY+5pNDqbaKewEmGz2WGoMc5nEImGZw7trg6aGbsCGzYMQUhEIcyOSjYn5z4FhmWpwf0yqFRZQUlas3RB4Kgp9xKYTGYoFArZBCVe4BGI+WC322WZv5ypqTFCr9NjOuot6HV6lXaBt5Velb/lw1zMD54IcDgqo0MlFZRkJNt1YjK8dE1mOJlAKBFHU1OLlGFVNT096xBOCJiOLC8oEUJwzgf00uwkyaivb0A8ISCRXP6+BCMs6usdEkZVnTAMg+6uXgTm8v/68M8xaGtzQa3WiBhZdcMwDAb614OZSK4oiBOBgJlMUP8kiWBZBdb19oGZXvphlAgCMONFfx/NpCgVDMOgo70DEDFDiXAcBJ+XCuV5MDi4ESyrADdyXLI5+dET6O7qhcFAM5eXQqVSAwB4XlyfscXwfAoq1Qpte6sQlmVht9XCGxW/mcBS+ONeCERAXR3d1FgMwzBoaHBgKlLYvdEptQu8rXTK/AWlqWhaWGxoqIx1DRWUZMRstkCtUmE2urSjf/Y83bGUjmz5x9lLyt6ublHg6paL6bmzUSAQF9DTQ0tFpKKx0QkA8C3j25NMEYSjAhobmySMqnrp7OxG2E/A5fEcSgQg5GHQ2dkjfmBVzuDgBpAYB8wlc184lwSJ8xgYoDv3UtHZ2QMhEF7aRykYAUlx6Orqlj6wNUxnZxcEnzc/Y+7FGRN5ZFAQzxwgCLTTax4YDAb09fWDHz4mSSdWIegB75nA1q1XiD5XpaLRpDd4uFRC0nk5LkGbcyxDXX09PNEC0r9LSFbIolnLS+NsasaEhGLfRDi9GeJ0Nks252qggpKMMAwDq9UGXzy65M998XR7QqvVKmVYVY3D0QiL2bzAmPvKZgWubL4oKGV/1tc3IHl81Uo2S88TWPrn2fPZrD+KuGQXUME8MrPDAYDjCDo6aFttsVm/fmP6H6MrtLYdjQEMg/XrqaAkFc3Nmc+mpVRxXzpLmX5+lZbOzh6AEJDZHKWGGdhFbeUXHy8FmZ4CACoE5snWrVeCD8xC8K18P1YLN3wUAGiH5BxoNGlRJylxK/RUKgattrguZGudhgYH5mQSlOYi6fclTWJYmqamZvhiwYJ8kFbDRHgWJqMJRqNRkvlWCxWUZMZoNCGUXHp3IJxIzF9DkQaGYdDXvx6nvcv7KJ32CDCbTHA6aTaMVNTV1UOjVsOzTIOFrKDU0tIqXVBVTLbEI5CHoJQVndrbqaAkNmazBa1tLsCd+4GHccfhcrXDZDJLExhlvoyAhC/fQCLh9IKOPsiXlu7uXoBhQKamVryW7RsATGZAq4Nix7Xp4xUQpidRW98Ai4Vu+uXD1q3bAIYBd+GI6HNxF46gta2dvqdyoFQqodXqkIgX0LK1BMTjoYpZJEtNQ0MjoskIIsmlK1fEZDY8DaVCST2UliG7sT0elkbwGwvPoKm5cixvqKAkMzq9HnFuaUO8WKaeRKfTSxlS1TM4uAHBuICJ0OWCkkAITnmAgcGN1IRTQliWRUtrG+aWyVCa8wNarZam6kqEyWSG1WbNK0Mp6Em3Tm9srAxjwUpnaONmYCoBkli6cw9J8CDT8fR1FMmYzzSOLiH2ReNQKJW0m2uJMRgMaGlpBZkcX/FahmHA6A1gLFYo+gZW/H4nggBmagr91EsxbywWK3q614HPZA+JhRD2g58ZxZXbrhJ1nrWAyWRGLL60j6sYcFwSqWScbpQvg9OZtneYDk1IPvdUeBwORyNYlnbgW4rshvVYSPwMS4EIGAtPV9QmORWUZEalUiO5TMvOVOa8Wq2WMqSqJ9sR5OTc5e1tx4ME4YRAu4bIQGurC54As2TmmCcAtLa2UZFPQjrauxDyrfwVEvQycLna6UOKRGzYMAQQAowvk6U0FgcIsH79kLSBVTl6fWZjaCkPpWQKOr2efn6JwODABpCZaZB8DN8KgHg9EBJx9FNj+4LYtu0q8N5JCH7xdvmz5W7btl0t2hxrBbvdjkhYuq5i2bloa/qlyWbBTIZWFsFLzVR4vKIyYqSmtrYOOq0O7rD4gtJs1Ickn0JLy8ql1+UCFZRkRqFgISxTWpU9r1DQ2yQlNpsdzkYnTs1efl9OZUQmKihJT0tLGxJJgvCicn9CCDwBBi0tLlniqlZcrg5EArmNuYkAhH0M2tpoFySp6OzshlqjzikoabQadHZSI2EpYVkFFEolwC2xgcTxUKvoxpEYDA6uB+F5kMnS7viTsVEAwMAA7cxXCFdccSUAICVilhI3fBRNza0V025bTmpr6xAOi9cJcTGh0Oz8vJTLsdtrodPqMBEclXTeWCoKb2SuojJipIZhGLS0tmE0tHIJ9WrJztHa6hJ9rlJBlYoyRopOGJSlGVy/EWd9BCl+4T04NUfQ5GyC1WqTKbLqpTmzc+JdVPYWiaW7vDXTnRVJaWtrBwCEfMtfEw2lDbldrnaJoqIolUp0d/WCmVq60xszlUBP9zooFDRjTGpYhgGwxPc6IWBZ+jgmBuvW9UOlUkNwl3aBRtyjaHO1U/+kArHZ7Gjv6AI/fEyU8YVYCPzkBWzLCFeU3DidzYhG/EgkIpLM5/ePZ+alHqRLwbIs2lztcAeGJZ13LDAC4KI/JmVp2tpccIemIJDLK1hKyWhoCizLXmzmUQHQJxiZEQQh85B5OdnzgiDuLy7lcgYG1iPFEwz7Lz78cwLBOR9BP221LQuNjekHEP8i/8hs06TGRqfEEVU3bW0uAEDIu/w1Qe/CaynS0N3dA+JJgKQWfneQlADiTaKrq0emyKobgRCAYcD0tIHpuSSVnWHo97xIqNWadBaRe6Rkm3QkFoMwM41NQ9SHrBi2btkGftYNIbKMKWIWpTr38RLwoycBEGzZckXxAVYR2Y04n9ctyXw+7yiMRjNtCJGD9vZOTATc4ISl/XXFYNR3HkA685yyPK2tLsS5JGajOXZSS8BocBKNDifUao2o85QSKijJDMdxUCwjKCkzO5bcMqbdFPHo7e0DAJzzXnzIdwfSGUt9fdSEUw5MJhM0GjUCizbSAplmGA0NDumDqmKsVhv0Bn3ODKWwP73j5nRWzi7LWqCtrT2dCONdVI/oSWct0Ywx6REEATzHAQoF2J5WsD2XlBYoWKRSpfX4oVxk8+YrIIRCIN7SeMUI7hGAEGzeTEWLYti8eSsAgBs9mfM6ZWt/zuOl4EZPwGqzV1SpiJxku6/Ozp6TZL652fPo6KCiRS66u3vACan5rCEpGPadRUO9gwp9K5DdHBW77G00PI22CntOo4KSzHAcB9UyZrWqTEkCfdCUHoOhBs5GJy5ckqF0wZ8Wl+juvjwwDIPa2jqEFglKoWhatLDZaBmilDAMg9aWNoT9yxsJh3yAo7ERKpVKwsgo2Ww+BBZ9d/hTC39OkYxEIpH+h0p5+Q9VSiSTCWkDqiI2bdoCMAzIyIWSjCcMX4DFaqO7+UXS1NQMq60WvPtUzutUfVeDMdWC0dZAs+NuqPpym2wTnoMwfgabhjZTg/s8MZvNqKtrwMz0GdHnSiQi8Pkm0NnZLfpclUx3dy8A4ILntCTzEUIw7DuL7p5eSearZJqbW6FgFRgJToo2RygZgTcWqDhRnApKMpNMJKBeTlBiqaAkJ+0dnRi7pLxqLEBgNpmof5KM2Gy1iMQXPihGYoDZbKJdxGSgJSMoLVdJEvEr0ErN0iWnrq4+/Y/QouzWEJ/5OTVElZpYLJr+x1KCklqFRDwOYZmOr5TVYbFY02Wgw6sXlEgqBYyPYdsVV1LRokgYhsHGDRshTJ4ByfE7zzAMWIMJrLUe6v7tK/5/8zMjEFIJrF+/sdQhr2n6+voxNXUKRGRfmKnJUwAI+voGRJ2n0rFYrGiob8Q57xeSzDcdnkQoEcS6dbT6YiVUKhWcziZRBaXs2JWWSU4FJZlJpZLzmUiLUWfOJ5NLm6tSxKWpqQX+mIBYKr1anowAzRXUwnEtYjZbEEssfKiMxgGzmRqjykFTUwu4FEE8evnPuBQQDQvzbXAp0qFWq6Ez6IEID6wzpv8AQJRDjdEIpZJmjElNNJp5k6iX+L/PnIvFYpf/jFISrth6FQSvByS4gm/PChD3KAjPYetWavq8GgYHN0JIJiDMls67hx8/A4Zh0NdHO+8VQn//IBLxMDwecUusJieOQ6VS0wylPOjr78c57xeimz8DwNm5dOnpunV9os+1FmhztWMkLF7JW1ZQohlKlIJIpVLLlrwpaYaSrGQ9eeaiaUHJE6U+PXJjMpkQiy9Mh4knGVr3LRPZTi1LeatGg9lrqFm6HBhrjECcB9NbA6a3Jn0yLqDGaJQ3sColEkmbvTHaJYyFNarMNdJ0WqpGtm7dBgAQLpxf1TjC8HnUGI3o7V1XirCqlv7+dJYKN1E67x5+8hxcrg4YDIaSjVkNDA6mG82Mu4+IOs/Y2GH09w/QEvg8WLduALFkFOOB0nanXIqznpOwWe2or6frm3xobXUhEA8hkAiLMv5oaAo2qx3GCntWo4KSzHAcN2++vRjVvCk3FZTkwGazAwACcYIkTxBJCvPnKPJgMNSA4wk4/qKolEgx9AFSJrKd9ZYSlCLB7DXUr0cODIYaILlodzMhwKCn7xU5yApKS2UoMZq0yBQOhy77GaU01NXVo83VsSofJcJxIO4RbN2yjZZYrxKj0QRnUzP4qdUJfFkIl4Iw66ZNU4rAbLbA5eqA231ItDkCgSkEA9PYuHGTaHOsJbKC65m5E6LOQwjBWc8p9PUP0BLePBHbmDttyO0SZWwxoYKSzPA8D3aZNzGbEZR4nrYTlgOj0QQACKeASHLhOYo86HR6AEDikirQZBLQ00WyLJjNFmg0akSXWAdnzzU0NEgbFAUAoNVogdQicyuOQKfVyRNQlRMOZwQlDc1QkottV1wJYWYaJFLczjKZGAdJpXDFFbTcrRSs6+0DmRktiXcPPzcGwnPo6aFlO8WwadMWzM6cRSwWFGV898hnAIChoS2ijL/WsFptaR8lj7g+StPhCYQTIeprVQDzgpIIPkpJPoWp8Nz8HJWEbILST37yE/zd3/3d/HEwGMRTTz2F3bt346GHHsLs7KxcoUkLwbKqMNWK5SUrUsRSBDGOZM7p5Qyp6sn+/yczXsOEECRSwrzQRJEWhmFQX+9YWlAKAhaLBWq1RvrAKFCr1WAW+d0yPGi5gUzMeyhplvJQUmeuoYKSmGzZkil7Gxku6vXCyAVotDr091OPnlLQ1dUDIRmD4JtZ9VjCzMj8mJTC2bx5KwghGBMpS2l09DM0NbVcbBhBWZG+/n6c954W1Ufp7Fy60yI15M4fg6EGNqsd7tB0ycceD89CIELF+ScBMghKoVAIL7/8Mn76058uOP+Tn/wEW7duxZtvvol7770XP/zhD6UOTRYYlln2w0LItE5iWSotyYEms5Oc4tN/0ue0MkZEyQpHyUwVKM8DgkCFPjlpaGhEPHz5V0kszKChoVGGiChAWjhiFn21MDygVC7RZYwiOrm7vCkz11BTbjFxOptQ3+CAMDpc8GsJIcDoCIY2bqKm9iUia84szK3emJufGYWttg5mM/VTLIa2tnZYLDaMjnxe8rET8TCmp05jy5atJR97LdPb24dYKoqp0Lhoc1zwnoHFbEV9Pc0kL4SW1ja4w6UXlNyZMrrm5taSjy02kgtKv//97+FyufD4448vOP/OO+9g7969AIA9e/bg3XffrQozapVShZSwtKCUPU8XAPKQfWhMCQAnZM/ReyEnWa+krKCUyPxNM5Tko66uHrEwQBZVV8UjLN2NlBGFQgEIi26KQOhnmEzE43EwCgWYpTwTM/ckkYhLHFV1wTAMtmzeCkxOgBT4fEnmZiHEoti0iS6KS4XD0QiNVgu+BJ3eiGcMne2dJYiqOmEYBps3b8HE+BHwfGnXXmNjR0CIQN87BdLd3QsgLfqIxQX/GXT39FL/pAJpaWnFZGQOnMCvfHEBjIdnoFapK9IqQvIny3379gHAgnI3AJiZmUFdXV06KKUSNTU18Hq9ef+n2u01pQ1UIgw1eiRDS/TcBpDg0nU9DocNdXWV5fa+lhAIkPWAttuN9F7ISCSSFijiGQ+lrJdSY2MtvS8y4XI1g+cJknGgqSt9TuCBWERAW1szvS8yYTDogMV7FUL6PL0n0qNUAoxyGSPnzHmViqH3RmSuu24n3nzzNyATY2Da2vN+nTA6AoZhcOONO2Ey0XtUKrq7unBydnUZGCQRAx/0YnCwj75/VsG11+7AW2/9O6amvkBT0/JlnUqlJufxYsbch2AymXHllZvmvWEpK1NbWwOT0YQR33nscN2Y81q1Qp3zeClC8QC8kTnsH7qLvm8KpL+/B7/5DY+piAfNxtJtnLpD02hra0VDg6VkY0qFaILSm2++iR/96EcLznV0dOBnP/tZXq8nhBT0wePxhCEs3o2tAFRqLaKpJVokAYim0qvlRILB7Czt/iIHCpaFQC5u9AeDcXovZCSVSn8mxBPp41hGUBIEJb0vMqHVph9EYmGgKbNBnPVU0ulM9L7IBMddooRnEQg4jtB7IgPhcAxY7pkmU9YeDMbovRGZhoZWqDUacGNusAUIShh3w9XeQZ/HSkxTUyuOn/oPEEFYOnsvD3jvBACgtraR3ptV0NTUCaVSiTH34ZyCUkvrJoy5Dy84Xg5CBIyPHcWWLZvg8VCPuEJp7+jC6IWVOyEONAzh5MyRBccrMepPd7xsaGih75sCMZlqAQDj4emSCkrjkVkMrBsqy/vBskzO5B3RBKXdu3dj9+7deV9fX1+Pubk5OBwOcByHSCQCi6XyFLpCMRpNGBkdXfJnoWQcDMOgpqYys6/WAqyChUDIvKBEWwXLS/a9EMsKSpm/TSbafU8u7PZ0Zmk8AiD9T8Qyz421tXXyBEVJl7YtFpR4AoWClrzJASEkR6cNJnMN7egqNkqlCn3r+nF0+ELeryHJBITZGWy4eqeIkVUnra0uEC4JEvSAsRT3fSF4JubHohSPVqtFd3cvJieO57xuXd9NOH70TSSTMWzesh+9fctnz3g9o4jHQxgc3FDqcKuC9vYOHDnyOZJcAuocmWA7XDfinfP/H+KpGHavuwvb225YcWx3YBgMw1RkRzG5aWx0gmEYTIRL10AskorBHw+iubmlZGNKSdnkHl533XV44403AAC/+93vsHXr1qroRmOxWOGLR9MPm4vwxWOoMdSkvTAosqBgFeAJwGcUJeVyJQsUSVAqVTAYDIhmrEaiGQ9bs3nti8/lSm1teqcmfknlbjwjKNlsdhkiogBpQYksytolvEA9lGSCZZfwtMoy34CjbB7J1jQDA+shBPwgkfwyJsjUJEAI7e4mAq2tbQAuZhkVg+CZhMFogsViLVVYVcvg4IZ5EWg5GIaB3mCFxerEuv6bcvrvTE6cAAD63ikSl6sDhBCMB3P7jDEMA7PWigajEztcN+bliTQWGANaPMIAACAASURBVIGjoRFara5U4VYNarUGdbX1GI+UTlCazIhTTmdzycaUkrJ5evnmN7+JQ4cO4fbbb8fPf/5zvPrqq3KHJAl2ux0JjkMkU952KZ5YZH6xRpEHlUqFFE/mTbmrQeQsd6xWKyIZISkSTy/CaIaSfOj1Bmg0asTCF89lBSW7nQpKcqFUqi7LUCI8oZ9hMqFSKUH4ZQw8M+dp9zBp6O1Nt8gWpvITMYTJSSiUSnR1dYsZVlXidDaDYRgI3smixxB8k3BlhCnK6ujt7QMAzEyXxgh6evo06uoa6OZSkWSzh8YDIyUfezw4Ald7AWW/lAU4m5owGZnLeY1aocp5fCkTmbGamppWH5wMyLZV+dxzzy04tlgseP3112WKRj7q6tKm4zOREGrUC9MZZ6IRtLlcMkRFyaJRq5Hko0hm1gFq9cpGdxRxsdlqMelOm3iGo4DFbKaliDLCMAxs9lrEoxeNVeMRwGisgVqd26yTIh5qtQqEEwBCwDBMOltJIFCp6GeYHGg0WpAUB5K5Hwvg+Mw19P0iBW1tLqjUavAz00BnHiLR7DTa2trp55kIqNVqNDicmCtSUCKCAME7hZYttKSqFLS3d4BlFZiZOYPWts2rHm925iyGhtaXILLqxG6vhcFQg7ESC0qRZBi+qAetrVRQKhansxnHjhwGL/BQLLMGGarvxZG5MwuOl2MyMgulQlmx3ZHLJkOpWmlsbAQATIYXppdyAo/ZSAgOR6McYVEyaHU6JDggxmWOtbQ9vdzY7bUIx9ILsnAUsNdW5ofvWqLWXodE5OLXSTwC2Ow0u1JO5he/HFnwt0ZDBSU50OsN6X+kuMt/mGlXaTAYJIyoelEoFGh3dQCzMyteSwQBZG4OXZ1dEkRWnbS1toL4ihSUgnMgfIr6J5UItVqDpqZmeGaHVz1WNOpDNOpHe3vn6gOrUhiGQXt7B8YCwyUdd8yfHs/looJSsTidTeAEHnPxpRtrAcCNLVegQW+DSW3Ao/17cWPLFcteOxGeQ0ODo2I3yKmgJDPpXx4WE+GFv5BT4RAEQuB0Vmbq21pBb6hBNEUQS6UXYwYDFZTkpra2DrG4gBRHEIqxtCy0DLDbaxGPXsy6SERZ1NqpIbecaDTa9D/mBSVh4XmKpBiNmbbM2RaVlxJPC0o1NbR1s1S0t3eAeL0gwgpG6IEACJeii2IRaWlpAx/0giTjBb+Wz2Q2tbS0ljqsqqW9vQNe7+ozYjyedMMhavq8OlyuDkwEx5DiL7dGKZbRQLopQXt7R8nGrDYaG9Pr88kcxtwMw8CqMcFpqMONrVfk9huLzsHZVJn+SQAVlGRHqVTB0eDAWNC/4PxYKH1cqW7va4WaGhMiHINICtBq1NTjogzIdg4LhIFwVEAtzVCSHZvNjkRMgJApDY1HqSG33Gi1GeEolVkwZ0Tx+fMUSZlvHBBdQlDKtKushs625UK6u1gKCAVzXid4PQDSogdFHLL/t3wRZW+CdxIMy1askW050tzcglgsmNOYOx/8vrHMeFTsWw2dnV0QCF/SsrcR3zk4GhphMNAu4sWS9TqaKIExd0rgMBv1VXQSCRWUyoCWVhdGQwsFJXfAD5Z+ScqOyWRCOMkgnCAwGqnxczmQFZSmPIAg0Nb05YDVagMAJGIAlwJSSUIFJZnR6TLZlElhwd/z5ymSkn2PkGxHgUvInrNYbJLGVM1kn62I35fzOuL3AQyDxkanFGFVJdlOb8UYc/OeCTQ0NFJ/yxKSfW/4/cV33gMAv28CRqP5YnYmpSi6u9O+Oxe8pTFKJ4Tggu8sunuW9/OhrIzBUAOT0YTJcG5j7nyYjnggEIEKSpTV0drqwlwkvKDT20jQC2ejk3bkkRmTyYxwgkcgQWCirenLgqyANDG78JgiH5cKSonMepm2cJYXvT4jHCWEBX9Tnx55mC/NDUcv/2E4CrVGg5oaulssFVmBiAT8uS8M+GG311LBQkTs9lpo9XoIniIEDN8kXLSkqqQ4HA4AQDAwtapxgsGpeZ9YSvGYzRY01DfinOeLkow3HZ5AJBFCT8+6koxXzTibmjFeggylbJZTJSeRUEGpDMjWF48GLu6UjQYDaKNmabJjsVggEGAyDJjNdIFcDlgsFrAsi8nMpgBtTS8/2VKdRJQKSuXCvB9PVlCKZwUlKlrIgU6nh95gAEKXC0okGEFtbV1OfwVKaTEYDNAbDCDB3CVvCAXhaHBIE1SVwjAM2lraIBRozE0SMfAh33yGE6U01NbWg2VZBIOrE5TCoRk00PdOSegfGMA57xfgs74Cq+DM3Mn0mP2Dqx6r2mlqasFEZAaEkFWNMx6aAVPhmbBUUCoDFgtKwUQcvliEtnMsA0ym9EI5lCDU36JMYFkFrFbL/LrMZqOm3HJjMpkBAMl4+s+l5yjyMF9mEMsaW/ELz1Mkp77eARIMX3aeDUXRSDu6Sk5dXQNIeAWfmHC4Yts4VxKtrW0g3ikQsoJJ+iVkPZdoh7fSolAoYLfXIRQqPvOC45KIRHyor28oYWTVy8DAesRTMYz6L6x6rC9mj6PWXkc/10pAc3MLYqkEvPEVNiZWYCw8g/q6Bmg0mhJFJj15CUqRSAR//dd/jUcffRR+vx+vvvoqIpGI2LFVDWazBcYaI0aDaUEpKyzRzgjyYzabL/k3FZTKBas1nZWk1Wqg0+lkjoaSFSmSCSAVX3iOIg8mU8bzLZZZoEWzghL1gpMLZ6MTbGDhsxMRCEgwDIejcncmK5W62jqwOZ5lCcdBiEVpWbUEtLa6IKQSIKHcnlaXItAOb6JRX1+P8CoEpexrqWhRGvr714NhGHwxe3RV4/ACh7OeE1i/YSPNiC0B2c8ed2h12Xxj4Rk0V/jnWF6C0g9+8AOYTCZ4PB5oNBqEw2G8+uqrYsdWNTAMg9Y2F9yZTm/ujLBEvyTl51JBiWZclA/Zcioq8pUHSqUKarUKqSSQtYKjXj3yolSqoDPo54UkxHgYjDVQKBTyBlbFNDY6IYSjIBx38WQ4CsILcNAMJcmx2+0QwqHlyxUi4cx1NAtWbLKdwArp9CZ4J6DTG+Y9/Cilo76+YVWCUja7iWYolQaj0YiO9i6cnFmdoHTBexbxVBwbNgyVKLLqphSCUpxLYDriqfgkkrwEpZMnT+Jb3/oWlEoldDodXnvtNZw8eVLs2KqKlpZWjIcCEIiAsVAAJqOJChhlwKW7+XRnv3zIvjeyJYkU+dHpdOCS6S5vLMtCpaImtnJjsVgvCkpRnvpayUxjY6aDi/+Ssjd/uuSqkru7VCo2Wy1IKgUkk0v+nGQEJdqxUnyam9NmtIV0ehN8U2htaaWZFiJQX9+AWCyIZHKJJgJ5EArOzI9DKQ0bNm7CqO88woniy6tOzByGQqHAwMD6Ekb2/7d378Ft1Xfexz/nSLIt+W75Fl8S41DIQiktsLTphmZhaVonCwkplEILSxjaLXTTLcMWmCy90N0SYDOFQtt9hmkJAwtLyzXQAQY2bLtAwhT67BNKaWm5hFxJHNuyJdu6nvP8oUts4iRSYuscS+/XDIN0JIWvUWTpfPT9fX/lKxCoVmtzq947inljO8J7ZMsuj0DJNCffLZVKHXAMR6ezs1vxVFL7xka1Mzyszq5up0uC0r8ssljC4x7Z3ZDYFck9KisrlUpKqWT6Mh/ynRdsDMoYTQdKxqilYCMnxk7q6MjuLLY/ULIzgVIubELRZDd0sCMHzrWaeJxAaeZVVfnV3NIqayi/EzPbtmUP7aGTf4Zkh2lng6FCjYy8r8rKKr4Yn0Yf/ejHZMvWH4+iS+kPe1/TccctkN8fmMbKytu8Y47Re5HCNhSYaOtI+rHz5s3uucl5pUJ/+Zd/qX/7t39TNBrVCy+8oNWrV+vjH//4TNdWVrKT3XdHRrR7dIRvK11iYnDKEh73CATSQZLH43W4EmT5KiplpdKBks/nc7ocKN2hZGRmKBljFh1KDmtv70gHraEJg6BDYVXX1PCFhQOCwcxspNGDDOYmUCqq7q5u2UN78rqvPTosKx5VZydfvs6E1tZ0oDQ8fGSdF8PD76utrZ0vlqZRT0+vamtq9cf+14/o8aHxQe0e2aGTTz5lmisrbz0987V3dFCjifEjevx7I7tVW1M3699n8gqU/umf/kmBQEC1tbW67bbbdPzxx+vaa6+d6drKSnYg51uD+zQWjzOg04UmdivBWU1N6ZkJLS0MS3ULn9cny5JsS/L6CPrcoKGhUdZoUrZlyxpLECg5zOfzqam5+QOBUoQvkBzS3JyejXTwDqWwaurqVVHB8t1i6OzsVmp4r+w8tkbPdjJ1dnbNdFllKTvTbXj4yDovRkZ2z+ot0N3INE2d9JGP6o/9v5NVwG6IWdnOJuYnTa9jjumVlA6GjsTW8C71HHPMrA9f8/rUf8cdd+iaa67R1772tZmup2zV1dWpsqJSr/en/0K2trIzgttUVVU5XQIyPv7xT6qrq5sPLC5imqZsW7JtyWMy+NkN6usbJMuWhhOSLTU0MHPMaZ1zOjW4a2vuujEyqjnHf8S5gspYXV29vF6vrPDUM0nsSFht7FJVNJ2dXZJlyRoZkKfh0P/frdDe/Y/BtKusrFQw2KJQaGfBj00mYwqP7FNHx5kzUFl5O+mkk7Vp0wvaPbJDnfWFLfd8c9/v1VDfqC5Gqkyrnp50oLR1ZJdOCPYW9Nh4KqEd4b366DGfnInSiiqvDqVf/epXM1wGDMNQc3Oz3hraJ2lCKzYc19f3tzp2/rEseXMR0zTV3T1PXi9Lq9zCMAzJzl52thak1dVlNhIYSkhiYwE3aGubI42MpmfAxBOyxqPs8OYQ0zQVbGmVHZ56yZsZCfPlXhFlO/WsPJa9WaG9ClTXMKNnBnV1dSs0tKPgx4VCuyTZuUHrmD4nnPBhSdKf+n9f0ONs29af972hEz/84VnfCeM2tbW1ag626J3hwsPXbeH3ZdlWrstpNsurQ6mrq0uXX365TjnllEkn1atWrZqxwspRU1Ozdu7ambk8u9dSlpKLL/47p0sAgILV1GTm8oQIlNyitbVNdjwhIxaXRqOZY+0OV1W+2lvb1L9j+wHHbSulVDjMc1NE2Y5ja/jwg6Ct0F51slR0RnV3d+t3v9siy0rKNPNfxj40mH49dXUxMH26NTUF1dbarrcH39SZ6sv7cXsiuxWJhbVgwYkzWF35Oqa3V++98eeCH7d1eFf68cfMn+6Sii6vDqWGhga1tbVp586d+tOf/pT7B9OroTE938Lr8bJ7FYBZxbbt3GVrwmU4J/cFUDgpSQoE2NnFaS3ZJVThMSk8mjlGR7JT2traZY8MT/r9JUkKRyTbZtvzIvL7A6qrb5AV6j/8nYf3suR9hnV3z5NlpRQKFTYbZnBwu3w+n9rbCWNnwoK/OEHvDP6poDlKbw+8mX7sghNmqqyydqSDubeO7CqJgdxSnh1Ka9eulSTt3LlTyWRS8+bNm9GiylW2dbeuro6WRACzim3bMszMcjcCJVfIbQ0cSQ+5raryO1gNpP1b1SsyLnssmjnW7GBF5a21tV12IiFFJ58I2CPDkvZvn47i6Ojo1J8HDh0o2bFxpcYjmjOHDqWZ1N2dPtcbGtympqb85+4MDmxTV9dcmcxSnBHHHbdAv/7189obeV/ttfmFqlsH/6y62np+n82Qnp5jJEnbRt7XXwSPyftx28Lvl8RAbinPDqX33ntPy5Yt04oVK7Ry5UqdffbZevvtt2e6trKTXZ7AltsAZpvct2WGZFmF70CC6ZfbSGAsNfk6HJPdac8ei0pj4zJMk6WIDsqeYNnDw5OOEyg5o2NOh+yRQwdK1nD69o4OOpRm0pw5HfJ6fRoc2Jb3Y2zb1tDgNs2dS+PBTPnQh46TJL07mP9KoXdDb+lDxx1XEsGFG2X/vm8PH37+W1bSSmlnZK/mzu2ZoaqKK69A6Xvf+56uuOIKvfLKK/rtb3+rK6+8UjfeeONM11Z2amvTgRLb0wOYbWzLlmFIhljy5hY+X2a781g6UKqoqHSwGkiZQemGIY1HpfGYamtrZZp5fRTDDGhrSy9psz+w05sdHpGvoiK9UyKKpr29Q1Z0TFZ09KD3yQZKbW0Ms59JXq9XnZ1dBQVK42MhRaPhkjlJdqP29g5VB6q1LfROXvcfjUe0L7JHxx573AxXVr7q6xtUW1On7ZH3837MnrEBJa2UurtLY9ZYXp9iBgYGdN555+Wuf+5zn9PQ0NCMFVWuTjvt4/rCF76ky1Z9xelSAODI8AWYa1RUZAKlqDX5Ohxjmh75/X4plpAdi+8fnA5HNDe3SoYhe+QDgdLIiFpa2vhGv8hyg7lDBx/MbYX2yjDNXBiImTNvXo8Gh/IPlAYG0/ctlZNkNzIMQ73zj9V7eQZK7w2lVxT19h47k2WVNcMw1NnVpV2jecx/y9gVSd+3szP/5aRulleglEqlFAqFctcHBwdnrKByVl1drWXLlqu3d/ZPewdQpmhOco3c8umELRmGvN78d+rBzAlUV0vxhBRLqKaaDTic5PP50l1IkfCk40YkrLbWVoeqKl8dmZ3bDhkoDferpaVVXi/jIWZaV9dcjY8Na3x8+PB31v4d3ljyNrPmz/+Qdo/sUCwZO+x9t4XekWEYJbGTmJt1dHRq9+i+Azd4OIhs+FQqmwvk9enyS1/6ki688EL19fXJMAw99dRT+ru/Yyt1AECaYRqyU+l53CzhcQfTNOXxepRKpuT1eum2cAl/lV92IiEjmWJQugu0trRqJDw5ULIjYTU3s/tesTU3N8vr9eWWtU3FHt6rznkM5C6GbKfR4OB2dXbWH/b+Q4Pb1dgYVDVB+Yzq7T1Wtm1rx/C7mh9ccMj7bht6Vx1zutKdsZgx7e0dGo2PazQxrpqKw++o+/7ogJoam1RZWRqjCPL61H/hhRfqxhtvVCKRUDQa1Xe/+11dfPHFM10bAGCW8Jge2VY6UPKwu4trZLuU2OzBPSoqKqVkSkbKUkUFz4vTgsFmGWP7Z/bY8ZjseJzd9xxgmh61trUftEPJtixZw/tK5lt9t8sGSqGhHXndPzS0g+VuRZBdvrZt6N1D3s+2bb03/I5659OdNNNaW9NLcPvH8xsJtG98SK0ltOlDXoHSnj179Mwzz+ib3/ymLrjgAt13333q789/nSAAoLT5fBWyLMlKSV7CC9fwZQZxV5TIt2ClwOf1Kv1iseTxsAzRaU1NQdmjEWXX69qjo7njKL7Ojk7pIDu92ZEh2amkOjq6ilxVeaqrq1d1da2GBg8fKFmWpVBol7q6eG5mWn19vYLBFr0XOvSO64Pj+zQaCzM/qQhaWtJLpPeO5Rco7R0P5R5TCvIKlK677jr19vZKkjo7O3X66adrzZo1M1oYAGD2qKiokG0ZslKSj9kWrlGVCZKqCJRcwzTNdCufZcvjoZvPaY2NTbJTKcnKzL7IBEqNjU0OVlW+5szpUGp4QHYqecBt2c4lOpSKwzAMdXV1KRTaedj7hsN7lUolSmbIsNvNnz9f24YP3aG0bSg9uJtAaeZlO1oHo4efN5a0UhqOhkuqCzavQGloaEiXXnqpJKmyslKXXXbZUXco3X777brzzjsPOP7QQw/p+uuvP6o/GwBQXBUVFUolDaVSUmVlldPlICM7o6eK+QnuwvB618gGR7aVSv97LBsoNTpWUznr6OiUbEvWyMABt2VnK82ZwwylYuns7NJwaOdhhw2HhtKhE91jxdHbe6wGR/cpEhs56H22hd6R1+vT3LksQ5xpgUBAVZVVeQVKoVhYtuzyC5RSqZT27NmTu75vX/5TzD8oHA5rzZo1Wr9+/aTjsVhM69at00033XREfy4AwDmVlVXpQClpqqqKQMktAv70cMhA1eGHRKI4LMuSTEMyJNu2nC6n7DU0NKQvpNKBksbHMsfpUHJCtvtoqsHcVmivAtU1qq2tLXZZZauzs1ux2Nhhd3rLdjF1dhL2FUNujlLo4F1K20Lvat7cHnZELALDMNTY2KTB6MEDvqyhzH1KqQs2r8X7l112mVasWKEzzjhDkrR582Zde+21R/Qf3Lhxo3p6erRq1apJx1955RVZlqVvfvObeu21147ozwYAOKOqqkqphGSaBjtXuYg/Eyhl/w3npVIpyTAkw1QymXK6nLLX0JDpRLLS4Z49NqbKKn/J7L4z27S1zZF0kEBpuJ/lbkXW0ZEOiEJDOxUINBz0fqGhXWpoaOK9pkh6enplGIa2hd7VCW0nH3C7ZVvaPrxVi08504HqylNjU5NCu/LrUJLKMFA6//zz9eEPf1gvv/yyPB6P5s6dq7/+678+ov/gihUrJOmA5W6LFi3SokWL9Oijjx7RnxsMskUlADglGGxQImHL9BhqbKxTSwvfILtBY2OdJKmpqZ7nxC0MSzJNyWPI6zV4XhxWW5v59j61f8lbMNjE8+KQlpZa1dbVKzq878Abw/t0zIl/yXNTRB/5SHpb+lBolzo6Tzzo/YaHd+qYY+bx3BRNrbo6u7T9IB1KeyO7FU/GdPLJH+Y5KZK2thb9buvh541lA6X587vU0FAaz01egdK3v/1tSdKll16qyy67TGeccYbWrFkz5QykrKefflpr166ddKy3t1f33HPPkVd7CAMDEVkWQwkAwAm27ZGVshWL2jIMr/r7w06XBEmmmT5Z5jlxj/HxmOQxZZumRkfHeV5coMrvVzQzQ0ljY6prbOJ5cVBba7ve/cBOb3YyrlRkWPX1zTw3RWTbPvkD1QoNHXynN9u2FBrapVNP+QuemyKaO69Hr//f16e8bXtoqyQpGOzgOSkSv79Gw9GwbNuWYRgHvd9QNCyP6VEsZsya58Y0jUM27+QVKL3++ut6+OGHddddd+m8887TNddco5UrVx7yMX19ferr6yusWgDArJRd5malJD8DoF0jN5SbZYiukUgkZFR4ZJumEomE0+VA6WVv7w8OSpLM8TE1siuSo9rb2/Xu/9si1e5fEmKNpJ+ftrZ2p8oqS4ZhqLOjKzd0eyrh8D4lkzF2eCuyefN69dJLLyg8xSDoHcNbVeGrUEcHS0SLpaGhSQkrqbFkVNW+g3/mGo6FVV/fkN7xtUTk9ZPYti3TNPXSSy/pE5/4hCQpGo3OaGEAgNljYohEeOEeXq8382+GcrpFIpmQPKbkJVByi6bGYDoNly1rdLSkZlvMRi0tbUpFhidtAGSHBzK3tTpVVtnq7u7WUGjHQTdkynYvESgV17x5PZKknSPbDrht18h2dXXPlWl6ilxV+crO4xuKHrrraCgWVkOJ7SKaV6A0d+5cffnLX9aOHTt0+umn65prrtGCBQtmujYAwCxRWVk15WU4K3sCcIjuaxRZMplMz1AyzXS4BMc1NTVJKUuyLNmpVPo6HNPS0iLJllLJ3DErPJS5jUCp2Lq65ioWjWh8LDTl7UOD2yWlgycUz9y58ySlw6OJbNvWzpFtudtRHNn3jaHYoXd6G4qHS+49Jq8lb2vXrtVzzz2nU089VT6fT6eddlpuuPaRWr169ZTHV65cedjldAAAd6msrJhwmd2R3CK7jv9Q6/lRXKlkSvJ4JA8dSm7R2NgkWSnZmcHcdCg5q7m5JX0hlZQy3ZVWZFA+X4Xq6uocrKw8dXfPlSQNDm5XoPrAzorBoe0KBlvY4a3Iampq1VDfqN0jk+dbjcSGNRYfVXc3gVIxZd83hqKHDpRC0bBOaCjDDqVAIKDly5erq6tLknTRRRcxIwMAkOPzVUy4zPIqtznYUgUUXyqVlExDhmEqlR0EDUflAqRkcvJ1OCIYbJYk2RM6lOxISI1NQcJxB3R1pQOloaHtU94eGtxBd5JDuufO1e7I5EDp/ZHsEsQuJ0oqW9n3jcEpZlplRZMxjSXGc7/jSkXpTIMCADgmO6snfZlAyW04CXMPy7LSaxBNQ1bKcrocaP+JgBEITLoOZ+SWg0wMlEZDai6xk7DZora2Vg0NjbmlbROlUgmFQrvohnFIR0eX9kZ2S9r/pdH7kV2SCJSKzefzqa62ToOH6FDK3tbUFCxWWUVBoAQAOGoej3fCZYZAug0dSi5i25KRvcjz4gaN2QGpmSWIDQ0NDlYDr9enmtq6SYGSxkZKbu7IbNLdPXfKDqXh0G7ZtkWg5JCOjk7Fk3ElJ7xW9kZ2y+8PqL6e32PFFgw2a2B86lljknK30aEEAMAHmKYx4TJvLW6xfyg3HUrAwWR357EH9ilQXUOXpQs0NjbJziwJtW1LqbEROscc1NU1V6GhXekOywmGMju8dXWx5M0JbW3tkqSEFc8d64/s0Zz2ObzvO6C5pVX7DrHkbV8mUCq1zQX41A8AOGoTP7jwIQY4OMM0JSsT9Jm8Vtygrq4+fcG2VU93kis0NjTmOpTs6JhkWbngD8XX1dWtVCqhSHjvpONDQztkmqbmzJnjUGXlrb09/f89ntofKO0b26O2dp4PJ7S0tGhgPCTLnno5e//4kDymp+S6YAmUAAAoUdlwj6VV7uHxeCTblm1Zk2aPwTk+n09Vmc1m6rPhEhzV0NAgZTuUxsOSxBIeB3V0pOfxhEI7Jx0fDu1Sa2s7XX0OaWxsktfjVTKVXq6bspIaGh9Qa2ubw5WVp5aWNiWspIZjkSlv7x8Pqbm5WaZZWqMhCJQAAEdtcl5BeOEW+5e88XbvFh6vV0pZUsqS18NJmFtU19RKkmoz/4az6urqJSsl254YKBH2OSXbgTQ8vGfS8ZHh99XR0eFESVB6xEBzc4sSVjpQGhoflG3bJbekarbI/n/vHx+a8vb+8SG1lGDYxydMAMA02B8i0QzjRjwpblHhq5CSKSmZUmVFhdPlIKOmukaSVF1d7XAlkCYuQ7Rkj49OPoaiq66uUU1NrUaGd+eO2balkZE9amtjeZWTmltalMwFSgPpY80tTpZUtlpbM4HS2MEDpVLsHiNQAgActYlLqlheBRxcZWWl7FRKRjKlyspKp8tBRnUgIEkKZP4NVA2AdwAAG6pJREFUZ9XWpjvFvPNOlB2NTDoGZ7S0tCoc7s9dHx8bViqVKMkT5NmkqSl4QKBUatvSzxbNza0yDEN7p+hQGktEFYmPleTrhUAJAHDUCJSA/Pj9fimelJFMpS/DFSoq0uFeZWWVw5VAkmoySw+97b2yY2OSYdA95rCWllaNju7LXY9E0pebm0trC/TZpqmpSSkrJdm2RqLpXcTYEdEZPp9PjQ1N2js2eMBt2WVwpbgckUAJAHDUJgdKU+9ugeJjKLf7+Kv8MpJJKZFUVRWBkltkN6fMBktwVjZQsmOjsqNjqqryl9wg29mmsbFJY6NDuRXUo6PpE+SmJgIlJ9XXp3c/TNlJjURD8lf5VVVFMO6UltZW7ZuiQym7DI4OJQAAAByxQCAgI56UHUvI72d5lVtkw9cK5lq5QrYbyY5HZcfHVV1T43BFaGxsVCIRy22JPpY5QS61LdBnm+z//6SVUjg2oro6ng8ntbS0au946IDjdCgBAIBZK3uyDOcFAtVSLCE7Hld1NYGS23g8dMG4QSCQCZRiY7Jj46oOsNzNafX16aDCslKSpGh0RIZh5LrJ4Iza2jpJUspKKhIfUV19ncMVlbeWljYNR8NKWMlJx/vHhxTwB1RdXXrhOIESAGCaEV64RXapG0ve3CMQqJY1Np67DHchUHKH7HB0Ox6T4tHc0HQ4JxtcWJkT5eh4WDU1tTJNTiedVFeXeV7slCLxcO55gjNaWlpky9bAB7qU9o0Plezue/wGAAAcNcMwJ1wmUHILngv3mRgileI3lbNVNnTlNeMOPp8vHe4lojKSUZaHukBuGWKmQykWi9Cd5ALZ95GUndJYYlQ1LA91VDY06v9goBQdVnMLgRIAAFMyTWPCZd5a3IKTZPeZuFMVHUrukX2N8Fpxj8oqv+xETHYiLr+fIcNOy/7uys5QisfHcp1kcE72fcSyLI0nRtkN0WHBYHpI/WB0OHfMttMdS8EggRIAAFOauPsOgRJwcBNDJE7G3INloe5TWVUlOxGTEjFVVhIoOS3bJZbtUEoQKLmC1+uVYRhK2SnFk3G6+RzW1BSUYRiTlryNJaOKJuO5sKnU8KkfAHDUPJ79bycESu7DybJ7TDwB42TMfXituEdVZZWUjMtKxAmUXCD7+yrXoZQYI7xwCdP0KJlKSOJ9xWler1f1dfUaioZzx4aiI5KkYDDoVFkzik/9AICjNrFDyev1OlgJ4G4TP+xzMuY+lmU5XQIyKisrZCfispNxVVZWOF1O2fP5KmSaHtmZ10giESW8cAnTNJW008PSq6r8DleDpmDzpCVvg5lAqamJDiUAAKbk9e4PlNglyT2YB+M+Ez/s88HffehQco+qyirZsTFJUkVFpcPVwDAMVVVVyc50KCXiUVVV0TnmBqZpKmVlAyWeE6c1NTVpKD6hQymWDpQaGxudKmlGESgBAI6ax7O/K4kOJfchWHIPv98/5WU4K/saoUPJPSoqKqRMoMSSN3fw+wOy7JRsO92hRJelO6QDpfRsK14rzquvb1QoFsldD8XS4VJDA4ESAABTmhgi0aHkPnRduMfED/s+n8/BSjCVbPcFnFdRUSE7Opq7DOf5/X7ZlpV7ndBl6Q6macpW+n2+spJuPqc1NDRqND4mK/OcDMciqg7UlOx7PoESAOCoTQyUvN7SfMMEpsPEWTB0jrlHNnSlQ8k9KioqZcejktLze+A8v98v294fKDFDyR1Mc/97CR1Kzquvr5ekXNfYcCySO1aKCJQAAEdtYojEkjf3IbhwDzot3M2y6OZzi4mvFV437uD3B2RZluzMiTLLdt3BMPaf0vNacV42PEra6dfJSDyi+oYGJ0uaUQRKAICjNrlDiUDJLU4/faGamoI69dTTnS4FGRN3RIR7MEPJfSYu3WEZjzsEAgHZdkpWrkOp2uGKIE3+0ohAyXl1dZM7lEYSY6qrq3OypBnFp34AwFGbODeJJW/uMXfuPP3wh//H6TKAWYQOJbeYuMyNk2R3SAdKEzuUWPLmBqa5v0ekVOf0zCa1tenwKJUJXsPxsdyxUuRYh9Ltt9+uO++8M3f97bff1he/+EUtX75cF154of7whz84VRoAoEATvx3z+fiuAjic7DeYcBc6lNxj4iwYOpTcIRsotbYfJ0mqriZQcoO5c+flLhO+Oq+2tlaSlLRSsmVrLD6umppah6uaOUUPlMLhsNasWaP169dPOn7DDTfoy1/+sjZs2KBvfOMbuu6664pdGgBgGtChBBzaunV36Hvfu9npMjAFNkR0j4kD7CsqCJTcIBColm3bqqtrl0SHklscc0xv7jKfwZzn9wdkmqYs25JlW7Jll3SgVPSvkTdu3Kienh6tWrVq0vELLrhAZ5xxhiTp+OOP1+7du4tdGgBgGkxc/gbgQG1tc5wuAR+Q3eUtu3sVnEeHkvtkZyZFwv2TrsNZE0MkusSdZxiGqgPVSiWs3LK36urSfa0U/W/cihUrJGnScjdJWrlyZe7yHXfcobPPPrugPzcYrDn64gAAR621tXTXiQMoTRUV6Y/EgUCFWlpK95vk2SQY3P9e0tERVH09z4vT5sxpliRFIvvk81WoszPocEWQpIaG/efBbW2lu5vYbFJbW6vwvmGlMsuoOzpaSva9ZcYCpaefflpr166ddKy3t1f33HPPQR9j27ZuvfVWbdmyRffee29B/72BgQhbvQKAC/T3h50uAQAKEo8nJUmjozF+h7lEPL7/cjicUDzO8+K0ZDI9LzES2Se/389rxSVisf2dlTwn7lDlD2jYHsx1KCWT5qx9bkzTOGTzzowFSn19ferr68v7/slkUtddd5327Nmje++9NzfMCgAAACgGmyFKrjF5hhKDht0gOzMpEt6nYLDR4WqQ5fWyzM1tAoGAUpkZSlJpzxtzzd++W265RZFIRHfffTdvGgAAACg68iT3mDiIe+JOonBOIJA+KU4kxhUIdDpcDbKYXek+fn9AluxcoJR97ZQiVwRKg4ODuv/++9XV1aULLrggd3zDhg0OVgUAAADACezs5j4TuyxK+QR5tiFQch+/3y/L3j+Uu5RfL44FSqtXr85dbmpq0htvvOFUKQAAAIAkWpTcYuKSN7jDxECpqsrvYCWYiCVv7uP3B2RNWPI2cdfKUmM6XQAAAADgDiytcgufj0DJbSor93eNVVWV7gnybOPxECi5jd9fJcu2Zdm2qiqrZJqlG7uU7k8GAAAA5IEZPe7j8/mcLgEfYJpmrtPC76dDyS1Y8uY+2Q6+pJUq+fCVQAkAAABlLbu7Wyl/izzbECi5UzZQKuUlPLON10ug5DbZQKnG5y/55aG8awIAAACSaFRyD6+XQMmNsvN6CJTcgyVv7pPtSrJlT1oqWor42wcAmBbXXnuDotGo02UAwBGjQ8k96Lpwp2zoWuonybMJS97cJxu4jsRH1e1vdbiamUWgBACYFieddLLTJQDAEcnOUDIMAiW3oOvC3SoqGJruFgRK7pMNXMeS0ZLv5uNdEwAAAGUtO0OJEzP3YFC6O2WfFzqU3IPw1X0mhkil/lohUAIAAADEkjcgXwxNdw+CcPeZGCIRKAEAAAAlLNOgxIkZcBjZbj6fjyVvbsHvLfepqpoYKLHkDQAAAChZ2dVV2R2sABwaHUru4fFwSu82LHkDAAAAygzf9AP5IXx1D9Pk95bbTOzgK/UB9gRKAAAAgAiUgMPJDuVmELR7MPvNfSaGSARKAAAAQBngJNl9jj/+L5wuAVOgQ8k9CJTcZ+Lro9QDJX4TAAAAAKJDyW1uvvkHqqurd7oMTIHXinsQKLlPtpNPKv0B9gRKAAAAKGvZXd4mngTAeZ2d3U6XgA/I7vLm9RIouQW/t9yt1AfYE2cCAACgrHE+BhSGrhj3MAyeCzfzegmUAAAAgJJHsATkh53F3IPfW+7m85X2ojACJQAAAED7l74BmFp2eRXLrNyD58Ld6FACAAAAShhBElAYQgw34blws1LfEZFACQAAAGWNc2OgMDYpLJAXAiUAAAAAADLoUHIPngp383hKe94YgRIAAADKWvbkmK4LID+8VoD8eDx0KAEAAAAlK3tybJp81Q/kgw4lID8eT2lHLqX90wEAAAB5oukCOLRly5bLNE3V1NQ6XQoyCPfczTRLe8lbafdfAQAAAHnivAw4tLPP/owWLz5LPl9pb4U+u/CLy82YoQQAAACUATqUgMMjTHIXgnB3M83Sjlwc++luv/123Xnnnbnrb731lr7whS/o3HPP1SWXXKKdO3c6VRoAAADKSCBQLan0P/gDKD0seXO3Un9fKfpPFw6HtWbNGq1fv37S8RtvvFFXXXWVnnjiCS1dulQ/+MEPil0aAAAAytDKlZ/XokWLdeKJJzldCgAUiEDJzUo9UCr6DKWNGzeqp6dHq1atmnR8/fr18nq9sixLu3btUl1dXbFLAwAAQBlqbW3T3//9PzhdBgAUjAYldyNQmmYrVqyQpEnL3STJ6/VqZGRES5cuVTQa1X333Vfs0gAAAAAAmDUMo7QDi9mOQOkIPf3001q7du2kY729vbrnnnsO+pi6ujq9+OKL+p//+R9deeWV2rhxY95T0YPBmqMpFwAAAACAWSUWq8hdbmmpdbASTKW5uVaNjaX7vMxYoNTX16e+vr687//UU0+pr69PhmHoU5/6lKLRqIaHh9XU1JTX4wcGIrIstuYAAAAAAJSHRCKRu9zfH3awEkxlaGhMyWTRF4ZNG9M0Dtm845r+q7vvvlvPPfecJOnll19WY2Nj3mESAAAAAADlhhlKblfaT5BrorKbb75Z3/rWt/TjH/9YtbW1uuOOO5wuCQAAAAAA12KGkruVeuDnWKC0evXqSdePPfZY/ed//qdD1QAAAAAAMLuUemAx25V64FfaPx0AAAAAACWLRMnNSj3wI1ACAAAAAGAWMko9sZjl6FACAAAAAACuQ6DkbqX+9BAoAQAAAAAwi/X2zne6BEyptBMl1+zyBgAAAAAACvPlL1+l445b4HQZmEKpd5ARKAEAAAAAMEt96lNnOl0CDqLE8ySWvAEAAAAAAEy/0k6UCJQAAAAAAABQEAIlAAAAAACAaVbqM5QIlAAAAAAAAFAQAiUAAAAAAAAUhEAJAAAAAAAABSF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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "sns.violinplot(x=\"gen\", y=\"score\", data=final_joined)\n",
    "\n",
    "sns.set()\n",
    "plt.savefig('img/violin_plot_score.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KDE jointplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "sns.jointplot(x=\"gen\", y=\"score\", data=final_joined, kind=\"kde\")\n",
    "\n",
    "sns.set()\n",
    "plt.savefig('img/kde_joinplot_score.svg',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}