process_TITAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from dataclasses import dataclass\n",
    "import sys, os\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>epi</th>\n",
       "      <th>tcr</th>\n",
       "      <th>binding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WTYRPEVRGVW</td>\n",
       "      <td>CASSSGTGPSENEAYF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KFPRGQGVPI</td>\n",
       "      <td>CASSKRALGDTQYF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GPGHKARVL</td>\n",
       "      <td>CASSFGGATGPGELFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TSTLAEQIAW</td>\n",
       "      <td>CASSPGVGNTEAFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SWISDIRAGTAPLCRNHIKSSCSLI</td>\n",
       "      <td>CAVSAGSSGALRTQHF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         epi               tcr  binding\n",
       "0                WTYRPEVRGVW  CASSSGTGPSENEAYF        0\n",
       "1                 KFPRGQGVPI    CASSKRALGDTQYF        1\n",
       "2                  GPGHKARVL  CASSFGGATGPGELFF        1\n",
       "3                 TSTLAEQIAW    CASSPGVGNTEAFF        1\n",
       "4  SWISDIRAGTAPLCRNHIKSSCSLI  CAVSAGSSGALRTQHF        0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contents = pd.read_csv('./data/dat_bencmarking_combined.csv')\n",
    "contents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process tcr and epitope sets\n",
    "tcr_set = contents['tcr'].unique()\n",
    "epi_set = contents['epi'].unique()\n",
    "tcr_rank = np.arange(2, len(tcr_set)+2)\n",
    "epi_rank = np.arange(3, len(epi_set)+3)\n",
    "tcr_pd = pd.DataFrame({\"tcr_set\": tcr_set, \"tcr_rank\": tcr_rank})\n",
    "epi_pd = pd.DataFrame({\"epi_set\": epi_set, \"epi_rank\": epi_rank})\n",
    "tcr_pd.to_csv('./data/tcr.csv', index=False, header=False, sep='\\t')\n",
    "epi_pd.to_csv('./data/epitopes.csv', index=False, header=False, sep='\\t')\n",
    "training_set = pd.read_csv('./data/dat_bencmarking_combined.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_split(x_pep, x_tcr, args):\n",
    "\n",
    "    split_type = args.split\n",
    "    idx_test_remove = None\n",
    "    idx_test = None\n",
    "    idx_train = None\n",
    "\n",
    "    if split_type == 'random':\n",
    "        n_total = len(x_pep)\n",
    "    elif split_type == 'epitope':\n",
    "        unique_peptides = np.unique(x_pep)\n",
    "        n_total = len(unique_peptides)\n",
    "    elif split_type == 'tcr':\n",
    "        unique_tcrs = np.unique(x_tcr)\n",
    "        n_total = len(unique_tcrs)\n",
    "\n",
    "    indexfile = re.sub('.csv', f'_{split_type}_data_shuffle.txt', args.infile)\n",
    "    idx_shuffled = np.loadtxt(indexfile, dtype=np.int32)\n",
    "\n",
    "    # Determine data split from folds\n",
    "    n_test = int(round(n_total / args.n_fold))\n",
    "    n_train = n_total - n_test\n",
    "\n",
    "    # Determine position of current test fold\n",
    "    test_fold_start_index = args.idx_test_fold * n_test\n",
    "    test_fold_end_index = (args.idx_test_fold + 1) * n_test\n",
    "\n",
    "    if split_type == 'random':\n",
    "        # Split data evenly among evenly spaced folds\n",
    "        # Determine if there is an outer testing fold\n",
    "        if args.idx_val_fold < 0:\n",
    "            idx_test = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            idx_train = list(set(idx_shuffled).difference(set(idx_test)))\n",
    "        else:\n",
    "            validation_fold_start_index = args.idx_val_fold * n_test\n",
    "            validation_fold_end_index = (args.idx_val_fold + 1) * n_test\n",
    "            idx_test_remove = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            idx_test = idx_shuffled[validation_fold_start_index:validation_fold_end_index]\n",
    "            idx_train = list(set(idx_shuffled).difference(set(idx_test)).difference(set(idx_test_remove)))\n",
    "    elif split_type == 'epitope':\n",
    "        if args.idx_val_fold < 0:\n",
    "            idx_test_pep = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            test_peptides = unique_peptides[idx_test_pep]\n",
    "            idx_test = [index for index, pep in tqdm(enumerate(x_pep)) if pep in test_peptides]\n",
    "            idx_train = list(set(range(len(x_pep))).difference(set(idx_test)))\n",
    "        else:\n",
    "            validation_fold_start_index = args.idx_val_fold * n_test\n",
    "            validation_fold_end_index = (args.idx_val_fold + 1) * n_test\n",
    "            idx_test_remove_pep = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            test_remove_peptides = unique_peptides[idx_test_remove_pep]\n",
    "            idx_test_pep = idx_shuffled[validation_fold_start_index:validation_fold_end_index]\n",
    "            test_peptides = unique_peptides[idx_test_pep]\n",
    "            idx_test = [index for index, pep in tqdm(enumerate(x_pep)) if pep in test_peptides]\n",
    "            idx_test_remove = [index for index, pep in enumerate(x_pep) if pep in test_remove_peptides]\n",
    "            idx_train = list(set(range(len(x_pep))).difference(set(idx_test)).difference(set(idx_test_remove)))\n",
    "    elif split_type == 'tcr':\n",
    "        if args.idx_val_fold < 0:\n",
    "            idx_test_tcr = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            test_tcrs = unique_tcrs[idx_test_tcr]\n",
    "            idx_test = [index for index, tcr in tqdm(enumerate(x_tcr)) if tcr in test_tcrs]\n",
    "            idx_train = list(set(range(len(x_tcr))).difference(set(idx_test)))\n",
    "        else:\n",
    "            validation_fold_start_index = args.idx_val_fold * n_test\n",
    "            validation_fold_end_index = (args.idx_val_fold + 1) * n_test\n",
    "            idx_test_remove_tcr = idx_shuffled[test_fold_start_index:test_fold_end_index]\n",
    "            test_remove_tcrs = unique_tcrs[idx_test_remove_tcr]\n",
    "            idx_test_tcr = idx_shuffled[validation_fold_start_index:validation_fold_end_index]\n",
    "            test_tcrs = unique_tcrs[idx_test_tcr]\n",
    "            idx_test = [index for index, tcr in tqdm(enumerate(x_tcr)) if tcr in test_tcrs]\n",
    "            idx_test_remove = [index for index, tcr in enumerate(x_tcr) if tcr in test_remove_tcrs]\n",
    "            idx_train = list(set(range(len(x_tcr))).difference(set(idx_test)).difference(set(idx_test_remove)))\n",
    "\n",
    "    return idx_train, idx_test, idx_test_remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81b657d872a94747b99332b13f3f1ffe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('./data/dat_bencmarking_combined_tcr_data_shuffle.txt', 'r') as f:\n",
    "\tlines_tcr = f.readlines()\n",
    "lines_tcr = [int(l.rstrip('\\n')) for l in lines_tcr]\n",
    "train_tcr = np.array(lines_tcr)\n",
    "test_tcr = np.array(set(np.arange(len(training_set))) - set(train_tcr))\n",
    "\n",
    "@dataclass\n",
    "class Args:\n",
    "    split: str = 'tcr'\n",
    "    infile = str = './data/dat_bencmarking_combined.csv'\n",
    "    n_fold: int = 5\n",
    "    idx_test_fold: int = 0\n",
    "    idx_val_fold: int = -1\n",
    "\n",
    "id_train_tcr, id_test_tcr, id_test_rmv_tcr = load_data_split(training_set['epi'], training_set['tcr'], Args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_tcr = training_set.iloc[id_train_tcr]\n",
    "test_dataset_tcr = training_set.iloc[id_test_tcr]\n",
    "tcr_readin = pd.read_csv('./data/tcr.csv', header=None, sep='\\t')\n",
    "tcr_dict = dict(zip(tcr_readin[0], tcr_readin[1]))\n",
    "epi_readin = pd.read_csv('./data/epitopes.csv', header=None, sep='\\t')\n",
    "epi_dict = dict(zip(epi_readin[0], epi_readin[1]))\n",
    "\n",
    "train_dataset_tcr_ready = pd.DataFrame({'ligand_name': train_dataset_tcr['epi'].map(epi_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'sequence_id': train_dataset_tcr['tcr'].map(tcr_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'label': train_dataset_tcr['binding']})\n",
    "train_dataset_tcr_ready.reset_index(drop=True, inplace=True)\n",
    "# train_dataset_tcr.loc[:,'tcr_idx'] = train_dataset_tcr['tcr'].map(tcr_dict).fillna(-1)\n",
    "# train_dataset_tcr.loc[:,'epi_idx'] = train_dataset_tcr['epi'].map(epi_dict).fillna(-1)\n",
    "# train_dataset_tcr.loc[:,'idx'] = np.arange(len(train_dataset_tcr))\n",
    "# train_dataset_tcr.set_index('idx', inplace=True)\n",
    "test_dataset_tcr_ready = pd.DataFrame({'ligand_name': test_dataset_tcr['epi'].map(epi_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'sequence_id': test_dataset_tcr['tcr'].map(tcr_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'label': test_dataset_tcr['binding']})\n",
    "test_dataset_tcr_ready.reset_index(drop=True, inplace=True)\n",
    "# test_dataset_tcr_ready.loc[:,'idx'] = np.arange(len(test_dataset_tcr_ready))\n",
    "# test_dataset_tcr_ready.set_index('idx', inplace=True)\n",
    "train_dataset_tcr_ready.to_csv('./data/tcr_train_split.csv', index=True, sep=',')\n",
    "test_dataset_tcr_ready.to_csv('./data/tcr_test_split.csv', index=True, sep=',')\n",
    "# test_dataset_tcr.loc[:,'tcr_idx'] = test_dataset_tcr['tcr'].map(tcr_dict).fillna(-1)\n",
    "# test_dataset_tcr.loc[:,'epi_idx'] = test_dataset_tcr['epi'].map(epi_dict).fillna(-1)\n",
    "# test_dataset_tcr.loc[:,'idx'] = np.arange(len(test_dataset_tcr))\n",
    "# test_dataset_tcr.set_index('idx', inplace=True)\n",
    "\n",
    "# train_csv_tcr = train_dataset_tcr[['epi_idx', 'tcr_idx', 'binding']]\n",
    "# test_csv_tcr = test_dataset_tcr[['epi_idx', 'tcr_idx', 'binding']]\n",
    "# train_csv_tcr.to_csv('./data/tcr_train_split.csv', index=True, header=['ligand_name','sequence_id','label'], sep=',')\n",
    "# test_csv_tcr.to_csv('./data/tcr_test_split.csv', index=True, header=['ligand_name','sequence_id','label'], sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Epitope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "./data/combined_dataset_repTCRs_epitope_data_shuffle.txt not found.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[63], line 15\u001b[0m\n\u001b[1;32m     12\u001b[0m     idx_test_fold: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     13\u001b[0m     idx_val_fold: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 15\u001b[0m id_train_epi, id_test_epi, id_test_rmv_epi \u001b[38;5;241m=\u001b[39m \u001b[43mload_data_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_set\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mepi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining_set\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtcr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mArgs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[60], line 18\u001b[0m, in \u001b[0;36mload_data_split\u001b[0;34m(x_pep, x_tcr, args)\u001b[0m\n\u001b[1;32m     15\u001b[0m     n_total \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(unique_tcrs)\n\u001b[1;32m     17\u001b[0m indexfile \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.csv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msplit_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_data_shuffle.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, args\u001b[38;5;241m.\u001b[39minfile)\n\u001b[0;32m---> 18\u001b[0m idx_shuffled \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mint32\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Determine data split from folds\u001b[39;00m\n\u001b[1;32m     21\u001b[0m n_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(n_total \u001b[38;5;241m/\u001b[39m args\u001b[38;5;241m.\u001b[39mn_fold))\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/npyio.py:1373\u001b[0m, in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[0m\n\u001b[1;32m   1370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[1;32m   1371\u001b[0m     delimiter \u001b[38;5;241m=\u001b[39m delimiter\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1373\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1374\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconverters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1375\u001b[0m \u001b[43m            \u001b[49m\u001b[43munpack\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1376\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/npyio.py:992\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[0m\n\u001b[1;32m    990\u001b[0m     fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(fname)\n\u001b[1;32m    991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 992\u001b[0m     fh \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    993\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    994\u001b[0m         encoding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/_datasource.py:193\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    189\u001b[0m \n\u001b[1;32m    190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    192\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/_datasource.py:533\u001b[0m, in \u001b[0;36mDataSource.open\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    531\u001b[0m                               encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 533\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: ./data/combined_dataset_repTCRs_epitope_data_shuffle.txt not found."
     ]
    }
   ],
   "source": [
    "with open('./data/dat_bencmarking_combined_tcr_data_shuffle.txt', 'r') as f:\n",
    "\tlines_epi = f.readlines()\n",
    "lines_epi = [int(l.rstrip('\\n')) for l in lines_epi]\n",
    "train_epi = np.array(lines_epi)\n",
    "train_epi = np.array(set(np.arange(len(training_set))) - set(train_epi))\n",
    "\n",
    "@dataclass\n",
    "class Args:\n",
    "    split: str = 'epitope'\n",
    "    infile = str = './data/combined_dataset_repTCRs.csv'\n",
    "    n_fold: int = 5\n",
    "    idx_test_fold: int = 0\n",
    "    idx_val_fold: int = -1\n",
    "\n",
    "id_train_epi, id_test_epi, id_test_rmv_epi = load_data_split(training_set['epi'], training_set['tcr'], Args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_epi = training_set.iloc[id_train_epi]\n",
    "test_dataset_epi = training_set.iloc[id_test_epi]\n",
    "epi_readin = pd.read_csv('./data/tcr.csv', header=None, sep='\\t')\n",
    "epi_dict = dict(zip(epi_readin[0], epi_readin[1]))\n",
    "epi_readin = pd.read_csv('./data/epitopes.csv', header=None, sep='\\t')\n",
    "epi_dict = dict(zip(epi_readin[0], epi_readin[1]))\n",
    "\n",
    "# train_dataset_epi.loc[: ,'tcr_idx'] = train_dataset_epi.iloc['tcr'].map(tcr_dict).fillna(-1)\n",
    "# train_dataset_epi.loc[: ,'epi_idx'] = train_dataset_epi.iloc['epi'].map(epi_dict).fillna(-1)\n",
    "# test_dataset_epi.loc[: ,'tcr_idx'] = test_dataset_epi.iloc['tcr'].map(tcr_dict).fillna(-1)\n",
    "# test_dataset_epi.loc[: ,'epi_idx'] = test_dataset_epi.iloc['epi'].map(epi_dict).fillna(-1)\n",
    "# test_dataset_tcr.loc[: ,'idx'] = np.arange(len(test_dataset_tcr))\n",
    "# test_dataset_tcr.set_index('idx', inplace=True)\n",
    "train_dataset_epi_ready = pd.DataFrame({'ligand_name': train_dataset_epi['epi'].map(epi_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'sequence_id': train_dataset_epi['tcr'].map(tcr_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'label': train_dataset_epi['binding']})\n",
    "train_dataset_epi_ready.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "test_dataset_epi_ready = pd.DataFrame({'ligand_name': test_dataset_epi['epi'].map(epi_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'sequence_id': test_dataset_epi['tcr'].map(tcr_dict).fillna(-1),\n",
    "\t\t\t\t\t\t\t\t\t\t'label': test_dataset_epi['binding']})\n",
    "test_dataset_epi_ready.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "train_dataset_epi_ready.to_csv('./data/epi_train_split.csv', index=True, sep=',')\n",
    "test_dataset_epi_ready.to_csv('./data/epi_test_split.csv', index=True, sep=',')\n",
    "# train_csv_epi = train_dataset_epi[['epi_idx', 'tcr_idx', 'binding']]\n",
    "# test_csv_epi = test_dataset_epi[['epi_idx', 'tcr_idx', 'binding']]\n",
    "# train_csv_epi.to_csv('./data/epi_train_split.csv', index=True, header=['ligand_name','sequence_id','label'], sep=',')\n",
    "# test_csv_epi.to_csv('./data/epi_test_split.csv', index=True, header=['ligand_name','sequence_id','label'], sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_readin = pd.read_csv('./data/tcr.csv', header=None, sep='\\t')\n",
    "tcr_dict = dict(zip(tcr_readin[0], tcr_readin[1]))\n",
    "epi_readin = pd.read_csv('./data/epitopes.csv', header=None, sep='\\t')\n",
    "epi_dict = dict(zip(epi_readin[0], epi_readin[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "epi_dict_rev =  dict(zip(epi_readin[1], epi_readin[0]))\n",
    "tcr_dict_rev =  dict(zip(tcr_readin[1], tcr_readin[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_train_split = pd.read_csv('./data/tcr_train_split.csv', index_col=0)\n",
    "tcr_test_split = pd.read_csv('./data/tcr_test_split.csv', index_col=0)\n",
    "train_pred = {epi_dict_rev[row['ligand_name']]+tcr_dict_rev[row['sequence_id']]: row['label']  for index, row in tcr_train_split.iterrows()}\n",
    "test_pred = {epi_dict_rev[row['ligand_name']]+tcr_dict_rev[row['sequence_id']]: row['label']  for index, row in tcr_test_split.iterrows()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = pd.read_csv('./data/combined_dataset_repTCRs.csv')\n",
    "label = {row['epi']+row['tcr']: row['binding']  for index, row in training_set.iterrows()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300015\n"
     ]
    }
   ],
   "source": [
    "for idx, val in enumerate(label):\n",
    "\ttry: \n",
    "\t\tassert label[val] == train_pred[val]\n",
    "\texcept KeyError:\n",
    "\t\ttry:\n",
    "\t\t\tassert label[val] == test_pred[val]\n",
    "\t\texcept KeyError:\n",
    "\t\t\tprint('wrong')\n",
    "print(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "epi_train_split = pd.read_csv('./data/epi_train_split.csv', index_col=0)\n",
    "epi_test_split = pd.read_csv('./data/epi_test_split.csv', index_col=0)\n",
    "train_pred = {epi_dict_rev[row['ligand_name']]+tcr_dict_rev[row['sequence_id']]: row['label']  for index, row in epi_train_split.iterrows()}\n",
    "test_pred = {epi_dict_rev[row['ligand_name']]+tcr_dict_rev[row['sequence_id']]: row['label']  for index, row in epi_test_split.iterrows()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300015\n"
     ]
    }
   ],
   "source": [
    "for idx, val in enumerate(label):\n",
    "\ttry: \n",
    "\t\tassert label[val] == train_pred[val]\n",
    "\texcept KeyError:\n",
    "\t\ttry:\n",
    "\t\t\tassert label[val] == test_pred[val]\n",
    "\t\texcept KeyError:\n",
    "\t\t\tprint('wrong')\n",
    "print(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr = pd.read_csv('./data/tcr.csv', header=None, sep='\\t')\t\n",
    "tcr[0] = tcr[0].str.upper()\n",
    "tcr.to_csv('./data/tcr.csv', header=None, sep='\\t')\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pd.read_csv('./log/tmp_epis_tcrs_atm-tcratm_tcr-tcr_output.csv')\n",
    "\n",
    "std_list = []\n",
    "mean_list = []\n",
    "arr_list = []\n",
    "for i in range(max(db['Iter'])):\n",
    "\ttest = db[db['Iter'] == i+1]\n",
    "\tepo = test[test['TCRs'] != 'WRONGFORMAT']\n",
    "\t# epo = test\n",
    "\tarr = np.array(epo['yhat'].apply(eval).tolist())\n",
    "\tarr_list.append(arr)\n",
    "\tstd_list.append(arr.std())\n",
    "\tmean_list.append(arr.mean())\n",
    "# Generate example data\n",
    "x = range(1, len(mean_list)+1)\n",
    "y = mean_list\n",
    "y_std = std_list  # Mean (example)\n",
    "\n",
    "# Calculate the mean and standard deviation (for demonstration purposes)\n",
    "x_tcr = x[:40]\n",
    "mean_tcr = np.array(y)[:40]\n",
    "std_tcr = np.array(y_std)[:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = './log/tmp_epis_tcrs_titanbest_ROC-AUC_bimodal_mca_output.csv'\n",
    "db = pd.read_csv(tt)\n",
    "\n",
    "std_list = []\n",
    "mean_list = []\n",
    "arr_list = []\n",
    "for i in range(max(db['Iter'])):\n",
    "\ttest = db[db['Iter'] == i+1]\n",
    "\t# epo = test[test['TCRs'] != 'WRONGFORMAT']\n",
    "\tepo = test\n",
    "\tarr = np.array(epo['yhat'].apply(eval).tolist())\n",
    "\tarr_list.append(arr)\n",
    "\tstd_list.append(arr.std())\n",
    "\tmean_list.append(arr.mean())\n",
    "\t\n",
    "x = range(1, len(mean_list)+1)\n",
    "y = mean_list\n",
    "y_std = std_list  # Mean (example)\n",
    "\n",
    "# Calculate the mean and standard deviation (for demonstration purposes)\n",
    "x_titan = x[:40]\n",
    "mean_titan = np.array(y)[:40]\n",
    "std_titan = np.array(y_std)[:40]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = './log/tmp_epis_tcrs_ergoergo_tcr_lstm_model_output.csv'\n",
    "db = pd.read_csv(tt)\n",
    "\n",
    "std_list = []\n",
    "mean_list = []\n",
    "arr_list = []\n",
    "for i in range(max(db['Iter'])):\n",
    "\ttest = db[db['Iter'] == i+1]\n",
    "\t# epo = test[test['TCRs'] != 'WRONGFORMAT']\n",
    "\tepo = test\n",
    "\tarr = np.array(epo['yhat'].apply(eval).tolist())\n",
    "\tarr_list.append(arr)\n",
    "\tstd_list.append(arr.std())\n",
    "\tmean_list.append(arr.mean())\n",
    "\t\n",
    "x = range(1, len(mean_list)+1)\n",
    "y = mean_list\n",
    "y_std = std_list  # Mean (example)\n",
    "\n",
    "# Calculate the mean and standard deviation (for demonstration purposes)\n",
    "x_ergolstm = x[:40]\n",
    "mean_ergolstm = np.array(y)[:40]\n",
    "std_ergolstm = np.array(y_std)[:40]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = './log/tmp_epis_tcrs_ergoergo_tcr_ae_model_output.csv'\n",
    "db = pd.read_csv(tt)\n",
    "\n",
    "std_list = []\n",
    "mean_list = []\n",
    "arr_list = []\n",
    "for i in range(max(db['Iter'])):\n",
    "\ttest = db[db['Iter'] == i+1]\n",
    "\t# epo = test[test['TCRs'] != 'WRONGFORMAT']\n",
    "\tepo = test\n",
    "\tarr = np.array(epo['yhat'].apply(eval).tolist())\n",
    "\tarr_list.append(arr)\n",
    "\tstd_list.append(arr.std())\n",
    "\tmean_list.append(arr.mean())\n",
    "\t\n",
    "x = range(1, len(mean_list)+1)\n",
    "y = mean_list\n",
    "y_std = std_list  # Mean (example)\n",
    "\n",
    "# Calculate the mean and standard deviation (for demonstration purposes)\n",
    "x_ergoae = x[:40]\n",
    "mean_ergoae = np.array(y)[:40]\n",
    "std_ergoae = np.array(y_std)[:40]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.plot(x_titan, mean_titan, label='Titan', color='b')\n",
    "# Plot the standard deviation as a shaded region\n",
    "plt.fill_between(x_titan, mean_titan - std_titan, mean_titan + std_titan, color='b', alpha=0.1)\n",
    "plt.plot(x_tcr, mean_tcr, label='ATM-TCR', color='r')\n",
    "# Plot the standard deviation as a shaded region\n",
    "plt.fill_between(x_tcr, mean_tcr - std_tcr, mean_tcr + std_tcr, color='r', alpha=0.1)\n",
    "\n",
    "plt.plot(x_ergolstm, mean_ergolstm, label='ERGO-lstm', color='g')\n",
    "# Plot the standard deviation as a shaded region\n",
    "plt.fill_between(x_ergolstm, mean_ergolstm - std_ergolstm, mean_ergolstm + std_ergolstm, color='g', alpha=0.1)\n",
    "\n",
    "plt.plot(x_ergoae, mean_ergoae, label='ERGO-ae', color='c')\n",
    "# Plot the standard deviation as a shaded region\n",
    "plt.fill_between(x_ergoae, mean_ergoae - std_ergoae, mean_ergoae + std_ergoae, color='c', alpha=0.1)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title(\"Score over Epochs\")\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Epochs\")\n",
    "\n",
    "\n",
    "\n",
    "# Show legend\n",
    "plt.legend()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mean_titan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = './metrics/dat_benchmarking_final.csv'\n",
    "db = pd.read_csv(metric)\n",
    "methods = ['yhat_catELMo', 'yhat_pite', 'yhat_titan',\n",
    "       'yhat_atmtcr', 'yhat_ergolstm', 'yhat_ergoae']\n",
    "rate = []\n",
    "for method in methods:\n",
    "\tval = db[method].apply(eval).apply(lambda x: x[0])\n",
    "\tneg = val[val <=0]\n",
    "\trate.append(len(neg) / len(val))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -4.284209\n",
       "1      -1.189537\n",
       "2      -4.511186\n",
       "3      -4.613606\n",
       "4      -0.692551\n",
       "          ...   \n",
       "7675    2.175501\n",
       "7676    1.014653\n",
       "7677    1.240584\n",
       "7678    1.544428\n",
       "7679    1.415443\n",
       "Name: yhat_ergoae, Length: 7680, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[method].apply(eval).apply(lambda x: x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.figure(figsize=(12, 480))\n",
    "# Generate some discrete example data\n",
    "x = np.arange(7680)  # Index values\n",
    "# Generate some example labels (3 different labels: 0, 1, 2)\n",
    "\n",
    "# Define colors and markers for each label\n",
    "colors = {0: 'r', 1: 'g', 2: 'b', 3:'c', 4: 'y', 5: 'm'}\n",
    "\n",
    "# Create the scatter plot\n",
    "for idx, method in enumerate(methods):\n",
    "    plt.barh(x, db[method].apply(eval).apply(lambda x: x[0]), color=colors[idx], label=f'{method}', alpha=0.8)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title(\"Scatter Plot for Multiple Labeled Data\")\n",
    "plt.xlabel(\"Index (x)\")\n",
    "plt.ylabel(\"Value (y)\")\n",
    "\n",
    "# Add grid lines\n",
    "plt.grid(True)\n",
    "\n",
    "# Show legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = './metrics/dat_benchmarking_final.csv'\n",
    "db = pd.read_csv(metric)\n",
    "# Plot for ‘yhat_catELMo’ and ‘yhat_pite’\n",
    "df_downsampled = db\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JL774It555x38/ve/x4svvogRI0bgkksuSTk9gUAA5513Hmpra/HAAw/gmGOOQa9evRAIBPDTn/40pXMtR7zfnew8SGlpaYHP58MLL7wgOQ0IQMLlHImIzIKVfSIiE/j888/h9/vDwadCBfG6uroen62trUVOTg769esHABgxYgQOP/xwLF68GGPGjMGECRPQt29fnHPOObjtttvw448/YuXKlWnPU+3VqxemTZuGadOmoaGhIdzLf/HFF2PHjh3o6OjA/Pnz8dBDD+G+++4Lf8/tdofnmiutvr5e8rWCggL07t1b8jsHHXQQAOCFF16IGwV+8ODB8Hq9sNlscfch15133okrr7wS69atw4svvojDDz8ckyZNkv39SG+//TYmTpyIGTNmRL3e1dWV1vakDBw4EMuWLUMgEIhb4f/4449ht9sxb968cA8vAGzYsEGxdGTq8ssvx+23347HHnsMt9xyS48GrpABAwbA5/OhqakpqsIvCALq6+tx0kknxd2HGtd86J6vr6/H8OHDw6/7fD7ZFd5DDz0UF1xwAV566SVccMEF+PTTTzFt2jTk5uamnJ4tW7Zg48aNmDVrFm644Ybw63v27El5W3LE+91yGzoi9evXD7m5ufjNb37TI1ZByNixY9NOKxGRUXAYPxGRwVVWVuLee+9Fnz59wkHOjjjiCAwfPhxz586NGt5vt9vx4YcfhiP0h5x77rn45ptvsGjRonCF8vDDD8eoUaPw4IMPwuv19gjOl47BgwfjxhtvxNVXX42dO3fC4XDAZrNBEIRwz2DI66+/HndIfabmzZsX1ePf1dWFzz77DGeccUbcis1pp52Gvn37Ytu2bZgwYYLk/woKCtCrVy+cfPLJcfch12WXXYZRo0bhnnvuweLFi3HbbbelHZHeZrP1OL6bNm3qEfQutnc2FRdccAFcLhdmzZqVMB2R+wHEyvFrr73W47OxoxO0UlxcjAcffBAXX3wx/vjHP8b93DnnnANAbEiJ9OGHH8Jut4ffl6LGNR9q6HvnnXeiXv/vf/8Ln88nezt//vOfsWnTJtxwww3Izc3FLbfcklZ6pM41AMnRKZlcdyHxfnfouEiJt9+SkhKcffbZWL9+PY499ljJez2dRgQiIqNhzz4RkYFs2bIlPHe0sbER33//PWbOnInc3Fx89NFH4R7GnJwcTJ8+Hddeey0uuugi3HrrrXC73XjiiSfQ3t6Oxx57LGq755xzDl5++WU0Nzfj2WefjXp95syZ6NevX9Sye6k45ZRTcNFFF+HYY49Fv379sH37dsyZMyeqweHMM8/EE088gYMOOghjxozBkiVL8MYbb6Bv375p7TOZ3NxcTJo0CVOmTEEgEMDjjz+Ozs7OhKMXevfujRdeeAE33HADWltb8atf/QqDBg1CU1MTNm7ciKampnDP+cMPP4xf/OIXmDRpEu655x74/X48/vjj6NWrl+ye29zcXNx+++3429/+hl69eqW0BF6siy66CA8//DAeeughnHXWWdi5cyf+8Y9/YOzYsVEVwdLSUowePRqffPIJzjnnHPTv3z98TpK5+uqrMXPmTPzhD3/Azp07cfbZZyMQCODHH3/EuHHj8Otf/xqTJk1CQUEBrr76avz1r3+Fy+XCjBkzJKPFH3PMMZg3bx5mzJiBn/zkJ8jJyYkatt/Q0CA5b7qsrKzHeuqfffYZSktLe3w23hD9KVOmYMqUKQl/76RJk3D++efjb3/7Gzo7O3Haaadh06ZNeOihh3DCCSfgN7/5TdzvlpWVKX7Njxs3Dtdddx2effZZ5Ofn49xzz8WWLVvw5JNPoqysTPZ2Jk2ahKOOOgrffvstrrvuOgwaNCit9Bx55JE45JBDcN9990EQBPTv3x+fffZZOA5IpND0o+eeew433HAD8vPzccQRR0ies3jmzZuHvLw8TJo0CVu3bsUDDzyA4447DldddVXc7yS63p977jmcfvrpOOOMM/DHP/4RY8aMQVdXF/bs2YPPPvssHLOBiMjUdAwOSEREQaGI3KH/FRQUCIMGDRLOOuss4ZFHHhEaGxslv/fxxx8Lp5xyilBUVCT06tVLOOecc4Tly5f3+FxbW5uQk5Mj9OrVS/B4POHXQxGtL7/88h7fGT16tHDhhRf2eD024vV9990nTJgwQejXr59QWFgoHHzwwcLdd98tNDc3hz9TXV0tXHHFFUK/fv2E0tJS4Re/+IWwZcsWYfTo0VFRxOOtShAv6voNN9wg9OrVK/x3KBr/448/LkybNk0YMWKEUFBQIJxwwgnCV199FfXd2Gj8IUuWLBEuvPBCoX///kJ+fr4wfPhw4cILLxTef//9qM99+umnwrHHHisUFBQIo0aNEh577LFwOuUqLy8XAAh/+MMfJN+Xew7cbrdw7733CsOHDxeKioqEE088Ufj444+FG264oUfE+8WLFwsnnHCCUFhYKCuKeySn0yk8+OCDwmGHHSYUFBQIAwYMEH7+858LK1asCH/ms88+E4477jihqKhIGD58uPCXv/xF+PLLLwUAwrfffhv+XGtrq/CrX/1K6Nu3r2Cz2aKOW+S9EPu/0047Lfy50PGO9z9BiI7Gn4jU6gBOp1P429/+JowePVrIz88Xhg4dKvzxj38U2trakh6rTK/5ULojj5nb7RbuueceYdCgQUJRUZHw05/+VPjhhx96bDOZqVOnCgCElStX9ngv3vEK3VszZ84Mv7Zt2zZh0qRJQmlpqdCvXz/hyiuvFCorKyUj4P/9738Xhg0bJuTk5PT4XYmEzvHatWuFiy++WOjdu7dQWloqXH311UJDQ0PUZ2PvC0FIfL3v379f+N3vficMHz5cyM/PFwYOHCiceuqpwj//+U9ZaSMiMjqbIKS5YC0REZHBlJeXY+zYsXjiiSdw77336p2cpF544QXceeed2LJlC44++mi9k0NZYsKECbDZbFi9erXeSUlq6tSpmDZtGpqamsJxNYiISB4O4yciItLY+vXrsX//fvzjH//AJZdcwoo+qa6zsxNbtmzB/PnzsXbtWnz00Ud6J4mIiFTGyj4REZHGLrvsMtTX1+OMM87Av//9b72TkzTAW05OTsIl98j41q1bh7PPPhsDBgzAQw89hEsvvVTX9AQCAQQCgYSfyctjMZWIKBMcxk9ERJTFQlMfEnnooYcwdepUbRJEWSE0PD+R/fv3ywoeSURE0ljZJyIiymIejwebNm1K+Jlhw4Zh2LBhGqWIskFtbS1qa2sTfubYY49FQUGBRikiIrIeVvaJiIiIiIiILIYT8IiIiIiIiIgshpFPYgQCAdTW1qK0tBQ2m03v5BAREREREZHFCYKArq4uDBs2TLGguKzsx6itrcXIkSP1TgYRERERERFlmaqqKowYMUKRbbGyH6O0tBSAeJDLysp0Tk18Xq8XCxcuxHnnnYf8/Hy9k0My8byZE8+bOfG8mRPPmznxvJkXz5058byZU6Lz1tnZiZEjR4bro0pgZT9GaOh+WVmZ4Sv7JSUlKCsr4w1uIjxv5sTzZk48b+bE82ZOPG/mxXNnTjxv5iTnvCk5lZwB+oiIiIiIiIgshpV9IiIiIiIiIothZZ+IiIiIiIjIYjhnn4iIiIiIFCUIAnw+H/x+v95JsSSv14u8vDy4XC4eY5PIzc2FIAia7pOVfSIiIiIiUozH40FdXR0cDofeSbEsQRAwZMgQVFVVKRrQjdRVVFSEnBztBtezsk9ERERERIoIBALYv38/cnNzMWzYMBQUFLAyqoJAIIDu7m707t1b08ojpUcQBHg8HjQ2NmLgwIEIBAKa7JeVfSIiIiIiUoTH40EgEMDIkSNRUlKid3IsKxAIwOPxaN5TTOkrLi5Gbm4uOjs74fV6UVhYqPo+eWUQEREREZGiWAEl6il0X2g1d593IREREREREZHFsLJPREREREREZDGs7BMRERERESUwdepUHH/88XonAwBQXl6O3NxcbN68We+kkMGxsk9ERERERKSy8vJy2Gw2bNiwQfZ3brzxRlx66aVRr40cORI1NTUYN24cAOC7776DzWZDe3u7coklS2Bln4iIiIiIyCRyc3MxZMgQ5OVxYTVKjJV9IiIiIiJSjSAAHo8+/5Mb9Pytt97CgAED4Ha7o16/4oorcP3114f/njNnDsaMGYM+ffrg17/+Nbq6usLvLViwAKeffjr69u2LAQMG4KKLLsLevXvD748dOxYAcMIJJ8Bms2HixIkJ0zR16lTMnj0bn3zyCWw2G2w2G7777ruoYfzl5eU4++yzAQD9+vWDzWbDjTfeKCs9oZEG8+bNw9lnn42SkhIcd9xx+OGHH+QdNDI8NgcREREREZFqvF7gkUf02ff//i9QUJD8c1deeSXuvPNOfPrpp7jyyisBAM3NzZg/fz4WLFiAJUuWYO/evfj4448xf/58tLW14aqrrsJjjz2Gf/3rXwAAu92OKVOm4JhjjoHdbseDDz6Iyy67DBs2bEBOTg5WrVqFk08+GYsXL8bRRx+NgiQJu/fee7F9+3Z0dnZi5syZAID+/fujtrY2/JmRI0fiww8/xBVXXIGdO3eirKwMxcXFstITcv/99+PJJ5/EYYcdhvvvvx9XX3019uzZw5EDFsAzSEREREREWa24uBjXXHMNZs6cGa7sv/POOxgxYgQmTpyIJUuWIBAIYNasWSgtLQUA/OY3v8HXX38druxfccUVUdt84403MGjQIGzbtg3jx4/HwIEDAQADBgzAkCFDkqapd+/eKC4uhtvtjvv53Nxc9O/fHwAwaNAg9O3bN/xesvSE3HvvvbjwwgsBANOmTcPRRx+NPXv24Mgjj0yaRjI2VvaJiCK0er3Is9lQxtZsIiIiReTniz3seu1brltuuQUnnXQSampqMHz4cMycORM33ngjbDYbAGDMmDHhij4ADB06FI2NjeG/9+7diwceeAArV65Ec3MzAoEAAKCysjKqcq0Vuek59thjw/8eOnQoAKCxsZGVfQtgaZaIKMjl9+P56moAwNTgvDoiIiLKjM0mbyi93k444QQcd9xxeOutt3D++edj8+bN+Oyzz8Lv58e0HNhstnAFGgAuvvhijBw5Eq+99hqGDRuGQCCA8ePHw+PxaPYbIslNT+TvCjVsRP4uMi9W9omIgtp9Pr2TQERERDq6+eab8cwzz6CmpgbnnnsuRo4cKet7LS0t2L59O1555RWcccYZAIBly5ZFfSY0R9/v98tOT0FBQdLPS21XTnrI+hiNn4iIiIiICMC1116LmpoavPbaa/jd734n+3v9+vXDgAED8Oqrr2LPnj345ptvMGXKlKjPDBo0CMXFxViwYAEaGhrQ0dGRdLtjxozBpk2bsHPnTjQ3N8Pr9fb4zOjRo2Gz2TB//nw0NTWhu7tbVnrI+ljZJyIiIiIiAlBWVoYrrrgCvXv3xqWXXir7ezk5OXj33Xexdu1ajB8/HnfffTeeeOKJqM/k5eXh+eefxyuvvIJhw4bhkksuSbrdW265BUcccQQmTJiAgQMHYvny5T0+M3z4cEybNg333XcfBg8ejDvuuENWesj6OIyfiIiIiIgoqK6uDtdeey0KCwvDr02dOhVTp06N+txdd92Fu+66K/z3ueeei23btkV9RhCEqL9vvvlm3HzzzbLTMnDgQCxcuLDH636/H52dneG/H3jgATzwwANRn0mWnjFjxvRIX9++fXu8RubFyj4RWZYvEEBeDgcwERERUXKtra1YuHAhvvnmG7z44ot6J4coY6zsE5ElNXs8eLGmBieUluKSgw7SOzlERERkcCeeeCLa2trw+OOP44gjjtBkn71794773pdffhkOrkeUDlb2iciSlgeHtq3v6mJln4iIiJIqLy/XfJ8bNmyI+97w4cO1SwhZEiv7REREREREOjj00EP1TgJZGCezEhEF2Ww2vZNAOuvy+fCfhgbsdjj0TgoRERFRRljZJyIiCvqytRU7HQ6809Cgd1KIiIiIMsLKPhERUVCnz6d3EoiIiIgUwco+EVlSOgPyua4sEREREVkFK/tEZEmsthOpRxAEzKqrw1xOdyAiIjIsVvaJiIgoJe0+H8pdLuxyOOALBPRODhGR6qZOnYrjjz9el33PmjULffv21WXf2ai8vBw2my3hsohmwco+ERERpYQjZ4iIUqd3JdJms+Hjjz9WdJtjxozBs88+a7htZWLkyJGoq6vD+PHjAQDfffcdbDYb2tvb9U1YGljZJyJL4iJ6RERERNnF4/FkvI3c3FwMGTIEeXl5CqRIX6zsExEF2WxsIiAiIlKaIAjwBAK6/E9u8N233noLAwYMgNvtjnr9iiuuwPXXXx/+e86cORgzZgz69OmDX//61+jq6gq/t2DBApx++uno27cvBgwYgIsuugh79+4Nvz927FgAwAknnACbzYaJEydi6dKlyM/PR319fdR+77nnHpx55plRr3311VcYN24cevfujQsuuCDqO6tXr8akSZNw0EEHoU+fPjjrrLOwbt268PtjxowBAFx22WWw2WzhvxPZu3cvLrnkEgwePBi9e/fGSSedhMWLF4ffnzhxIioqKnD33XfDZrOFy1GhaQfz58/HEUccgZKSEvzqV7+C3W7H7NmzMWbMGPTr1w9/+tOf4Pf7E24LAJYvX46zzjoLJSUl6NevH84//3y0tbWFv3fHHXdgypQpOOiggzBp0qSkv8tms2HGjBm44IILUFxcjLFjx+L9998Pvx85AqO8vBxnn302AKBfv36w2Wy48cYbAYjX9fTp03HwwQejuLgYxx13HD744IOk+9eS+ZsriIiIiIjIsLyCgEcqKnTZ9/+OHo0CGY35V155Je688058+umnuPLKKwEAzc3NmD9/PhYsWIAlS5Zg7969+PjjjzF//ny0tbXhqquuwmOPPYZ//etfAAC73Y4pU6bgmGOOgd1ux4MPPojLLrsMGzZsQE5ODlatWoWTTz4ZixcvxtFHH42CggL0798fBx98MObMmYO//OUvAACfz4e3334bjz32WDh9DocDTz75JObMmYOcnBxcd911eOCBB/Dee+8BALq6unDDDTfg+eefBwA89dRTmDx5Mnbv3o3S0lKsXr0agwYNwsyZM/GLX/wCubm5SY9Jd3c3Jk+ejH/+858oKirC7NmzcfHFF2Pnzp0YNWoU5s2bh+OOOw6///3vccstt0R91+Fw4Pnnn8e7776Lrq4uXH755bj88svRt29ffPHFF9i3bx+uuOIKnH766fif//mfuNvasGEDzjnnHPzud7/D888/j7y8PHz77bfhRgIAmD17Nv74xz9i+fLlsht3HnjgATz22GN47rnnMGfOHFx99dUYP348xo0bF/W5kSNH4sMPP8QVV1yBnTt3oqysDMXFxQCA//u//8O8efMwY8YMHHbYYVi6dCmuu+46DBw4EGeddZasdKiNlX0isiTOKSYiIiK5iouLcc0112DmzJnhyv4777yDESNGYOLEiViyZAkCgQBmzZqF0tJSAMBvfvMbfP311+HK/hVXXBG1zTfeeAODBg3Ctm3bMH78eAwcOBAAMGDAAAwZMiT8uZtuugkzZ84MV/Y///xzOBwOXHXVVeHPeL1e/Pvf/8YhhxwCALj99tvxj3/8I/z+z3/+86h9v/LKK+jXrx+WLFmCiy66KLzvvn37Ru07keOOOw7HHXdc+O9//vOf+Oijj/Dpp5/ijjvuQP/+/ZGbm4vS0tIe2/R6vZgxY0Y4vb/61a8wZ84cNDQ0oHfv3jjqqKNw9tln49tvv8X//M//xN3W9OnTMWHCBLz88svh144++uiofR166KGYPn26rN8UcuWVV+Lmm28GADz88MNYtGgRXnjhhaj9AOKQ/v79+wMABg0aFA6UaLfb8fTTT+Obb77Bz372MwDAwQcfjGXLluGVV15hZZ9ISbscDuTZbDg42NJGRERERMaQb7Phf0eP1m3fct1yyy046aSTUFNTg+HDh2PmzJm48cYbw0PKx4wZE67oA8DQoUPR2NgY/nvv3r144IEHsHLlSjQ3NyMQXK2ksrIyHOxNyo033oj/+7//w8qVK/HTn/4Ub775Jq666ir06tUr/JmSkpJwxRkAhgwZgqampvDfjY2NePDBB/HNN9+goaEBfr8fDocDlZWVsn9/LLvdjmnTpmH+/Pmora2Fz+eD0+mUtc3Y9A4ePBhjxoxB7969o16LPH5SNmzYEG58iWfChAlJ0xMrVEGP/DuVwInbtm2Dy+XqMW3A4/HghBNOSDk9amFln0zP7veH13p+aMwYzrsmAAzQR0REZBQ2m03WUHq9nXDCCTjuuOPw1ltv4fzzz8fmzZvx2Wefhd/Pz8+P+rzNZgtX6AHg4osvxsiRI/Haa69h2LBhCAQCGD9+fNKgcYMGDcLFF1+MmTNn4uCDD8YXX3yB7777LuozUvuOHLJ+4403oqmpCc8++yxGjx6NwsJC/OxnP8soYN1f/vIXfPXVV3jyySdx6KGHori4GL/61a9kbVMqvcmOn5RiGR15kY0imUilDhFK9+eff47hw4dHvVdYWKhIepTAyj6ZnjNizo4AVvKIiIiIKD0333wznnnmGdTU1ODcc8/FyJEjZX2vpaUF27dvxyuvvIIzzjgDALBs2bKozxQUFABA1HzzyP3++te/xogRI3DIIYfgtNNOSynd33//PV5++WVMnjwZAFBVVYXm5uaoz+Tn50vuO9E2b7zxRlx22WUAxDn85eXlUZ8pKChIaZuJSG3r2GOPxddff41p06Ypso+QlStXRgVeXLlyZdweeanzdtRRR6GwsBCVlZWGGbIvhdH4iYiIiIiIAFx77bWoqanBa6+9ht/97neyv9evXz8MGDAAr776Kvbs2YNvvvkGU6ZMifrMoEGDUFxcjAULFqChoQEdHR3h984//3z06dMH//znP/Hb3/425XQfeuihmDNnDrZv344ff/wR1157bY9e8TFjxuDrr79GfX19OJp9sm3OmzcPGzZswMaNG3HNNdf06IkfM2YMli5dipqamh6NC6mS2tbf//53rF69Grfddhs2bdqEHTt2YMaMGRnv6/3338ebb76JXbt24aGHHsKqVatwxx13SH529OjRsNlsmD9/PpqamtDd3Y3S0lLce++9uPvuuzF79mzs3bsX69evx0svvYTZs2dnlDYlsbJPRCRBbjRXIiIiso6ysjJcccUV6N27Ny699FLZ38vJycG7776LtWvXYvz48bj77rvxxBNPRH0mLy8Pzz//PF555RUMGzYMl1xySdT3b7zxRvj9/qgeZ7nefPNNtLW14YQTTsBvfvMb3HnnnRg0aFDUZ5566iksWrQII0eOlDWv/JlnnkG/fv1w6qmn4uKLL8b555+PE088Meoz//jHP1BeXo5DDjkkHAQwXVLbOvzww7Fw4UJs3LgRJ598Mn72s5/hk08+QV5eZgPUp02bhnfffRfHHnssZs+ejXfeeQdHHXWU5GeHDx+OadOm4b777sPgwYPDjQIPP/wwHnzwQTz66KMYN24czj//fHz22WfhJRaNwCawRBuls7MTffr0QUdHB8rKyvROTlxerxdffPEFJk+e3GP+S7Zp9njwYk0NAODBMWOQY+A5YTxv2vm0uRnrgmvfTpWZ6TZ4PJgRvJYi4z/wvJlTOuft9dpaVAfXWI69blZ1dqJXbi6OVmhuoJm1er14vroaAPB/o0cjL0e5vgPeb+bE82ZeSp87l8uF/fv3Y+zYsSgqKlIghdqbNGkSxo0bF17GTiu33HILGhoa8Omnnyb9bCAQQGdnJ8rKypCjYB6cDWw2Gz766KOUGnOU4nA4sH37dhx++OFRwR4BdeqhnLNPRESURIvXiy9aWgAARxuoxd4IGBSViKyitbUVCxcuxDfffIMXX3xRs/12dHRg9erVeOedd/DJJ59otl+yPjYDkemxoElEanMoFHzIijhAkIis4sQTT8Stt96Kxx9/HEcccYRm+73kkkvwy1/+ErfeemuPpdzUdPTRR6N3796S/3vnnXc0S4eS3nnnnbi/6eijj9Y7eZpjzz4REREREWW92EjzWoldZk8rX3zxBbxer+R7gwcP1jg1yvjlL3+JU045RfK90DSVbGqkZmWfiIiIiIgoy4wePVrvJCiutLS0x1z4bMZh/GR62dQ6R0RERGQGLJ8R9RS6L7SahszKPhERERERKSI0VNrhcOicEiLjcTgcCAQCGS8dKBeH8ZPpMUAfEZF+mAcTUaTc3Fz07dsXjY2NAICSkhLmEyoIBALweDxwuVxces8EBEGAw+FAU1MTurq6kJubq8l+WdknIiKitAmCALAgT0QRhgwZAgDhCj8pTxAEOJ1OFBcXszHFRMrKyrB7927N9sfKPhERERERKcZms2Ho0KEYNGhQ3GjvlBmv14ulS5fizDPPDE+dIGPLz89HIBDQdJ+s7BMREemg2+fDuu5uHN+7N8o0mrtHRKSl3NxczYYrZ5vc3Fz4fD4UFRWxsm8iWlf2OcGDLIVxX4nILP7b1IRv2trwdkOD3kkhIiIiCzJVZb+mpgbXXXcdBgwYgJKSEhx//PFYu3Zt+H1BEDB16lQMGzYMxcXFmDhxIrZu3apjiomIKF21bjfeaWhAg8ejd1JUUelyAQAaY35fl8+HFR0dcPj9eiSLiIiILMI0lf22tjacdtppyM/Px5dffolt27bhqaeeQt++fcOfmT59Op5++mm8+OKLWL16NYYMGYJJkyahq6tLv4QTkS4Yqsb8Xqurw26HA2/V1+udFE3NaWjAwtZWzGtq0jspREREZGKmmST4+OOPY+TIkZg5c2b4tTFjxoT/LQgCnn32Wdx///24/PLLAQCzZ8/G4MGDMXfuXNx6661aJ5mIdMQpHeYnCOJZtGdZD3eop3+P06lzSoiIiMjMTFPZ//TTT3H++efjyiuvxJIlSzB8+HDcdtttuOWWWwAA+/fvR319Pc4777zwdwoLC3HWWWdhxYoVcSv7brcbbrc7/HdnZycAMcKlkaOHhtJm5DRqxev1wh8MduH1ehEw8PIjPG/a8fl8UdeFHJHXksfrRU7wWuJ504c/IohNOsc+nfPm9/slrxtvGtdT0n3F+X2Z/m4tROW7Ph8EBfNd3m/mxPNmXjx35sTzZk6Jzpsa59ImhLpODK6oqAgAMGXKFFx55ZVYtWoV7rrrLrzyyiu4/vrrsWLFCpx22mmoqanBsGHDwt/7/e9/j4qKCnz11VeS2506dSqmTZvW4/W5c+eipKREnR9Diuq02fB5QQEA4Cq3G4z5SgCwKi8Pe4MRgK+OaNBLpN1mw5fBa+l/3G7zzHOyqP8UFob/LfccZmpRfj6ac3J67LPJZsPi4LWhVFri/T49fnequmw2zGe+S0REpBiHw4FrrrkGHR0dKCsrU2SbpunZDwQCmDBhAh555BEAwAknnICtW7dixowZuP7668Ofs8X0LgiC0OO1SH//+98xZcqU8N+dnZ0YOXIkzjvvPMUOshq8Xi8WLVqESZMmZf1yGy1eL3bV1QEAJo8ciVyD9+zzvGkj0NqKnO5uAMDkUaNkfafB48Ge4PzwySNHRvXs87xpb01lZfjfcs9hpHTOW319PaqDw+gj91nldqMiGDU/nbRIiff7Mv3dWmj1erEzmO9eMHIk8hTu2ef9Zj48b+bFc2dOPG/mlOi8hUaYK8k0lf2hQ4fiqKOOinpt3Lhx+PDDDwEAQ4YMASAW1IYOHRr+TGNjIwYPHhx3u4WFhSiM6EUJyc/PN8WNY5R0tnm9WNDaitP69MGo4CgMreQDyA32xOXn5xu6sh9ilPNmZXm5uVHXhRz5ghD1nZyYa4nnTVuhcwHIP4dSUjlvuXGum3y/P+XrKem+4vw+pX63mqLy3bw85OUoPw6G95s58byZF8+dOfG8mZPUeVPjPJpmlOppp52GnTt3Rr22a9cujB49GgAwduxYDBkyBIsWLQq/7/F4sGTJEpx66qmapjUbfdDUhJ0OB94M9vQQERERERGRfkzTs3/33Xfj1FNPxSOPPIKrrroKq1atwquvvopXX30VgDh8/6677sIjjzyCww47DIcddhgeeeQRlJSU4JprrtE59dbX7vPpnQQiIiIiUpnT78d+lwuHFxerMqqHiJRjmsr+SSedhI8++gh///vf8Y9//ANjx47Fs88+i2uvvTb8mb/+9a9wOp247bbb0NbWhlNOOQULFy5EaWmpjiknLQmCAJhgGD8RkVWYIsovESlmdn096j0enNanDyb17693cogoAdNU9gHgoosuwkUXXRT3fZvNhqlTp2Lq1KnaJYp0x6o9EamN+QwRkag+GMR0s93Oyj6RwXHsDRERURLsvSYiIiKzYWWfFKFnrxcL4URERERERNFY2SciIiIiTbn8fnQyuC8RkapY2SciIiIiTT1WWYmnq6pg9/v1TgoRkWWxsk9EREREuggFeyMiIuWxsk+mxyjZRERERERE0VjZJyIiIorhDgQgCAwBSxQPO1uIjI+VfSIiIqIIlS4XHq2owKctLXonhUhRgiAwMCJRFmFlnyyFfTCkFF5LRNlrSXs7AGB9V5e+CSFS2OK2NjxdVYVVnZ16J4WINMDKPhFREIckUjy8NojICpZ3dAAAFrS2ZrwtNoon5vT78U5DA7Z0d+udFMpirOwTERERWRBjDhDp57v2dux2OPBBU5PeSVFVQBCwurMTjVxZw5BY2SfD8QYC2G63wxMI6J0UIiIA7MFKpJXzfw2p2ePBU1VVWBnsySUibTmypBy7vrsbn7e04OWaGr2TQhJY2SfDmd/SgvcaGy3fEkrGwwodUeqYVxvTl62t6Pb7FRmurSaOPiAyt1q3W+8kUAKs7JPhbAzObdrlcOicEiIiSqaDPfuGxCo0qY2xTIiMj5V9IiIiIiIiIothZZ+IiIiIiEhBHPlARsDKPhFZEh+yRETGZ7MxtyYxdoPL79c7GUSWw8o+EVlS7HzVnQ4HKl2uhN9hkZNIHs4HJ6UwQB8BwOz6ejxWWYlWr1fvpBBZCiv7ZCksMpCUDp8P/2lowJt1dXonhYiIiGKUBxvjQ0GarYBlUjICVvZJERyGR0bWyWjhlKFUc7gmjwdrOjsRsGivJXN8IiIi48vTOwFERGpgZYTU1u3zocXnw+iioh7vvVRTE/73hLIyLZNFREQGwHIIGQF79olUJAgCVnZ0oNzp1DspRKSwJ6uqMLOuDvsS3N/VbreGKSIiIiI6gJV9IhXtcTqxoLUVs+rr9U6K5fktOlyajC9RZT8b8M4jIiIyJlb2iVTUaoC54u5AAJ5AQO9kqKrW7cbD5eX4uq1N8n25Q+lYaaF4eG1E4/EgIiIyPlb2iSzMLwh4tKICj1RUWHp5o0XBSv737e2KbdPKx4uIiMiIOM+dSFms7JPp8cEQX1fEyAJvFlde5f5yK11LLV4vGjwevZNBFmWle4XICux+P75ta0M716knogis7BMRaUDrkQIvVFdjRk0NnH6/pvslIkpF9jZDK+v9xkYsaW9njCAiisLKPhGRyj5sasKrdXW6rLnexcq+rmw26/WBc4oLkfGUu1wAgHYDxAoiIuNgZZ901+r14oXqaqzt6krr+0KcfxuB3oViK1Y00qH3Udjc3Y06txsVwcIYZQ+98wClbenuxhNVVeGKBVGm9M6fyVislWMS6Y+VfdLdFy0taPF68Vlzs95JsRyrVTTIvDp9PlSbuILIConog6YmOPx+fKpRfi0IApfVJCIiShMr+6Q7X4YFORbCKRsbNTZ1d2Njd7feyZDt6aoqvF5Xp0nQQLvfj6/b2tCqUaCqbB9Bo+avf72uDk9VVcFn8eVDs5lRcu9qlws77Ha9k5H1sjs3JVJent4JICL1ZHslxKo8gQDmNTUBAI4oLkZRbq7OKZKv2u3G4IICVffxcXMzdjsc+BHA0aruSZSNjU2R1Pz1NW43AKDW48GooiIV90TZ7vW6OgDAn0aMwID8fJ1TQ0SkDPbsExGZTORomGxeUjGeyuB0ASd7g4koRQxwR6S+Ze3tWNXZqXcysgJ79kl3Vu59ZjWMKLtZOX8jIiJKVYfPh8VtbQCACaWlyOFzUlXs2SciIkqCDXeUCUEQsn66B1G2YRVWmoej7jTFyj4RkcWwUkFWY+ZCc0AQ8FpdHeY2NuqdFCICUOd249PmZnRxygZlAQ7jJ0WYuSCWLVj9sw7eb+bBhhdq9HhQGww0SGQ0VS4XNnV2Qpu1S4zhldpaAOJw8t8MGaLafpj7kxGwsk9ERJphQwWRvjyBAFq9XgwuKGBMCQnZdkTeqKuDPxBAfm4uLtE7MRpr1Gh5ViI9cRg/WQp70eLLtgIMkVZYYSIzea2uDv+urcVOh0PxbXcDWNHZCZffr/i2tZKtpYjOHFYJlMYnAxkB72yTa/P5WMHVmcvvx5ctLagKLvdlVFa+Sqz62wRBwA67HZ0J5hVa9bcbTbqFNjYEkNE0eTwAgE12u+LbXlRQgMXt7fi8tVXxbRMRUepY2TexXbm5eKG2FvNbWvROSlb7pr0dP3Z24o26Or2ToghvIIA2Dm0zRCV6s92Odxsb8XRVld5JISJKyhVs3NrndOqcEtKC2ZsyzZ5+IjlY2TexzXliyIW1XV06pyS7NSWoGBuhwpiq52tq8Fx1NRqCvT9mZcZjH0tOgdlqhRVfIIAWgzc2OWKGKBtpdJUgCCh3OuE26NJG3kDA1EO808HRHWRVxsn5yEyYI2qLlX1SHNfPNLfQUjRqzOfUEhvBzOmN+nq8UF2NPQa+/t5vatI7CXGt6erCrPp6w440ml5VhccqKw3bGKEGIzUGacXMv5gVESJ1mTl/MCNW9klxOwxcSCeyGqs9NOuCS5Rt6O7WOSXx7U9hiLLWFb3QPOxGg47M8QYr+WYfOUTKsVoelq4fOzuxTYU4CkSU3VjZJyIiorRZsefaer+IjKzR48GXLS34b2Oj4tvu9PnwYnU1VnV2Kr5tIjI+VvbJ9DjkLj4eG7LKNeAJBHQd+p2o8qflnGxBENCdYHUGIiMwc2OJHmm3qxjHYlFbG5q9XnzBYM5EWYmVfdKdVSojRmTmAhcpwyrXwCMVFXi0ogJ+k/UiK90Q8GVrK56sqsImA01zYAC6xHh8KNKGri7Ma2rSLC/zmSzPJCJlsbJPpsfHGGWbbK46qNkDZgahobiL29p0Tom1ZfM9lu3UPvcfNzdjU3c3NmrUYKfm71Fj22wcI1JWnt4JIFISK/7RsuWRyfNuXN+2taEryyvolN2sGNOAMufMohUpiCJFlk3XdnXhpLIy3dKSDdizT6SiREU8QRBYCNSI1XoKzHTVLGlvxzoLLINorStIf2a6hpXEPJ8oMd4j2eVzxpJQHSv7pAg9C8JmLIQLgoCZ9fWYWV+v2YOND1AiUkOyvKXR40FXhkEFzZjPZzM+b4yD9w5ZSUAQMD84FYbkYWWfFMHHemrsgQAqXS5UulwcykcpY+HNWFixia/d68XLNTV4qqpK76Toxmoji4isgnemPjJ5Ym6127EmGOSS5GFl38RsLGASyZJtlbFMf63d78cOux2BLDtulJ5Eldlaj0fDlCiHVz6R9QkAvm9vx16nU5XtszFBedkepDcdrOwTkSSrPqSyreKfznl8rbYW7zY24odg5HdKTI+eWyPdn9l2T1FyvCLMxyjnTMv8tNPnw9dtbZhTX6/ZPrPZfqcTHzU1wcURrZpiNH4TEww0NNA4KSHqycrDaNUooLUH51dvt9txWp8+KuyBMmWUgnky1r3zyKg8gQDybTZL5/tKMEseYmZmPsbdPh8Kc3KQn6Ncv/DsYKNKe4YxXCg17NknMogqlwuv1NaiwuXSOylkIWw/J6Js0eb14pGKCsxtbNQ7KYbBJg9KVYfPhyerqvBsdbUq24+t7DeYdLqXWbCybxGCIGBBSwvWWmCJKz3sdzrxVWsrfAoPLUpleOub9fWoc7sxs65O0TSE06Ly563GKr9/Rk0N3Apd180eD9q8XkW2ZTZWuR5IHlaQMuMOBNChQ+/dumCE7t0Oh2Lb/KGjA9+2tSm2PaPgNa4+sx7jfcEYBmrNj4/NG2bU1KiyHxKxsm8R5S4XVnZ24rPmZr2TYkqz6+vxQ0cHVmo0R1nqAcB5r/oz64M5me12e1rfixwG6/L78WJNDZ6rrua1SkRJPSNzBQYj5yaCIOCr1lYsaW9He5yGTq9F5h9b9flHlO1Y2TexyGj8Wizf5gsEVCnkK/mAyTR1rVk4j2heUxP+29io2Lnt9Png0DhaqpELi1bRpdA57fT5sLarS/FRNGQubDAis/FKXLNftLTgXxUVqHW7U96e0e4BY6WGiJTCyj7F1e3zhYf/uvx+/KuyEq+pNMQ82xjloeoJBLCpuxvb7PYelbl0GmFcfj+erqrC9MpKZRKoMaOcFyv7rr0dnzU347v2dr2TkhKj9XoZLT1Wo1ZewDzGWlYFRwOaLT8zunVdXfiwqQl+gzWIEJkRK/skyen348mqKjxaUQEA2OdyQRCEtFqvybiEOP+WUu504pXaWlQnCCDYptHICKffj3VdXXBxvVXT2ptmIEpWcomIRDVuN1qSxFGJtyqBkavRnzY3Y3N3NzYGYzAYTa3bjTn19ahnmZhMgJV9E1Nz6b16Rsa0nEwf7LOCAQRnGWA92v82NeHT5mZ8mEaMCiMXcIjk4nVsXJu6u/FBYyOnqshk1Aqd0XX4fHitthYvqBQx3QiMuh77a3V12Ot04q2GBr2Togo2qlsLK/sWwYKfMqx2HNXIsH0GGFa3PxgpVsmIy2aix/rRWu8x3lUWNRpFELCiowN7g9cDkRHmQc9rasIWux1ruDqOLNvSDCBqJHpUjpqzdGUUIwjlM+nGJ9rtcMQN+EikNFb2TcxmgEINpUergkGyK4SttySXEXObfS4XFra2Yo6M0Sbft7fjjbo6eAzaU0SZ2+d04smqKuw0SCOgg9da1kgnf1xsweX8KLk9DgfeaWhQbQ37SLVuNxa3tqb83DPi857Sx8o+kYqMnmEqnT42HpBS5FxL7SnEiPi6rQ1VLhfWsrdVE75AAMs7OtCUQe9Vqr30b9XXw+734z8WHVqbTXyBAGrdbt1Haqi9dz1/H5/X+qjUcJ7/q7W1WNbRwQCSWY6VfZKFDwVzssp506pApMbx0ruwqgYz/yIjTEPJBks7OrCotRXfKNR7qcfUFdLPnIYGvFpbi3UpzudX8ypZ3tGhy34zpejyxoKQMEgvGVNDlsXhcvr96MjCpbTjYWXfohgYiKyi0ePBU1VVWB1c4oiimbkhJBFWyRMzcuUCEKOEm5nRj6/VVQQrlKnGPVAi34h37jcwkCDWdHXhdQ2XYOZ9SOl4vLISz1RVwc4VmwCwsm9q8aLxr+jowD8rKrBHw3mLzJCjlTud2Jog6JAeFRmpfaZ73vY4HHi5pkaTpRg/aW5Gt9+Pz1ta4n7Gir3nJE2PvMYXCHB+rUUFBAFdEj1ARspR2rxeQ683vtVux/dxhgmbMW82S4ozzQvT+Z2pjrTIlB7nwhsIIGDC61ZJVinTN2bZiIZ4WNm3oIWtrQCAj1NYlqzJ48HC1ta0I4sahVGy51n19Xi/sRGtGQ4jCgiCIVsm325oQKPHgzkazI01yjnVg96/3SoP/Ewt7+xEOYeuKkrvaztkbkMDnqqqCq/wobZUf/cehwPPVVcbYsnTeN5vbMTXbW2oNvloDrORey0xH5fPHQjgXxUVeLmmRu+kKMoI1wAr3vphZd/EIqPxZ9p6/nJtLVZ0dODTNNYtB4xTcMuU0r0QUj1GgPyMd1Z9PZ6orESdBoWodH67m9NFdKfmXGY972sjFE5CjLjElVXy3Fha9wTvCVbyVxk0cOPaYE9qlQkam4zQMG2kfIPMJ3SfGTHPV1uHz6dq/js/wehMUhcr+wTgQAGrli1vhlIZfPCst+BcQXcggCXt7WjW+JozUyVJTsFV6uEc7zfWut2YUVNjinXp4/2GdM6fGYcSZ6OVHR14oqrKdD1AfkHALoeDjZ862eNwYHuCaXNa2ut0YnlHh+w8hwFDrc0ogUX3OJ34IkFle1VnJ56pqsJXwZHBZC2s7JPuMs0KjZKZGlEqxQitj+NXra34tq0NL2Y4XG5RaytcSQrZcn+ZGsUuIxXl5jQ0oMHjkbUuPWCdXrJtJmjcsJp0rp0FwalkXwTjIxjp3knkm7Y2zG1owDtc8k9xySrNgiDg7YYGvNfYiG6fT/EAfalub059PRa1tspuUP02gyXRrJI/W5mRGppXdXbGjf0Rmv67koGQLYmVfVKEWg+dj5ua2FtiUVUKTU1Y3tGhypA74zyiE3MLguwCRbJGEbmUOjZru7o0Wfe+MsMh0EYsVBsxTdkqNPIq0+vMjIz0fHYaKC3tMuP1bNFxRIIgCIYf5cV8jihzrOyTocRWWjZ0d2NpBi3fWkhU0ZJTKdKqUimnh0TL/WnJyoGMZtTUJFypwKjq3G581tyMz5qbU6owmPEcKSVyTrRx7i5pRk9fLCPlV5GSXe+CIGB+czPW6xRzQO2ewFRHnGVz/hBPomMid5SXXox5V5KeUhqxqloqzIWVfTK8jiQt5EYtpGmh2+fTZPk70ofcKzvVtaiNhvNW5TH7ail6sPv9WNDSYro4AHLtcDiwpqsLn6QZXFcOvyBgaXs7aiSeNV4D9aabEXM+bWRzOTGb8ayLWNknypCeMQOerKrCq7W1mkTrl2KWVtN00il3GKaZmOV8kfkZ5Vr7pLkZKzs7dVlKSwDQ6fOpGqVei6Hrqzo78U1bG16rrVV9X7HMUkkzyvVOPW3p7sZjlZXYp8OUBSvFlGr2ePBpczPaDLRSwTNVVfiho0PvZBgeK/tEEfQoVijxKKhUoLKfzkPJHMWw9MxQoXJg5eOVCesUh7KTka9rpUY+pVPp9AYCeLqqCm+pOFRa6XsnIAg9eusbdB4V0er14v3GRlmN2ka+Fkkepa/pD4Kxn+YwgGZG3qyvx7qurnAgUpffj9dra3WtbHf4fFxBQAZW9i2CDzjKlFl6ULJJvEIPz1V2YqOIPsqdTjxZVZXy8m5dEj36Rr13O3w+1LjdeLW2Fv+qqIArwWiEFq8Xn8ns4VPi177b2IitdjtekRhZoPbRNOr5IkpHJtdzaBpZKCDyD52dqHa7Na9s8zmYujy9E0DK+JHLZQAw3oPZWKlRnxmGrAmCAGiYzmy7BtTG42kNZjqPbzU0ICAIeK+xEVPHjtU7Oap4pqoq6u/9LhfG9eol+dk36+pg9/tR6XbjiOJixdPSHDOSIJXVVoz/BNKeGZ7L8Zg35dbnNVh5m+Jjz75FVDNIm24ybWDQLBq/Rvsh5fCcpcdIy4GpJRuuDS1/Y6J9BViojRKKQdCk0vD+bgaipCSM1rFjFmZu+AnhmU8dK/tkai1er6oNHe1er6kLeuZNORlJm9eL3RHBjfQsaJmxqJJoSLRRzG9uxsy6urTyOzOeE6IQh9+Pj5ua9E6G7mpycrBLhyB2sYQ4/w5Z1dmJp6qqVGlsit1fi9eLVgMFpMuUFs/uegN1PvLZJGJln3SXSUvjC9XV+EDBh3RkNrjNbsez1dV4r7FRse1nEzY0mF/oznyuuppBcNK0vqsLj1VW6h7kLJk1XV2ocLlQ4XLpnRTJvCOdp0SL18te4gS22e34TMUl+8xiYWsrNnR3650M1aRy77ybYnlKj57iL1pa0O33Y35Li6r78QYCeKG6Gs9XV8Nv4k4ftcQ78//WYdWOeHjWRKzskyxqZueRLY1GujFXBCOM7nQ4dE6JcbHV1Bwq3W7MSrPXlpSx2iRxVcx6hcT2Jjn9frxQXa3ItlOp0HyhcgVEaWu7uvROQlJS12RAENAZszxquksctum4zCqHo6dPjSMXeac7IqaD+XieyMQYoI+ygiAIcAQC6JWbq/i2rTAHyigcfj+avV6MLCzkcVXQOhMU6NORanR0UoeexWCn34836up6BHFr1akCtyqiUUeJ45IsF8zWXHJOQwP2O524fsiQ8Guz6utRlMM+LLP7tq1Nt31H3rOR9xYbZcjMmCuS7rSo1H3Y1IQnKiuxx4C99FK/XhAEfKjwHEI1HlVKb/O56mq8WVeHPQaYN6iUTAoJ2VKQT/d3Wuk6ofT80NmZUrT2dCS7h7PlPtVDvCO/P3jvr4lpyHQpHJyTVbzE1K4EW/He2sZGakkOv5+NKiphZZ8U0a7jMDg5tgQz12XBoflyKJHlyNmG1GdqPR5stvAcwnhCUdQjgwTVGijYi1bavF7scThY0KQokQ2jLBSJ4s2l1bKSYP21H7SVTVd2Nv1Ws4nq2Vdwu2o3TqpBiymA0ysrsVDGqI7tBuy0MzoO4yfKkBqF7lTnh1m14N9t8EYktTwXnGtclscsWk/7nE581NzM4Ew60WvpvQaPB86IHmKbzZYwj3XGmSsuOWorzfRl6oXqalw+cCCGFxbqlAJrM/K0M+OmTFtWLSep7aWampSvIUEQ0ronfujowPn9+yf8TGUKQWR57YtM27P/6KOPwmaz4a677gq/JggCpk6dimHDhqG4uBgTJ07E1q1b9UukRXX6fKqtr0sUSY2gOGZ63McGoJJL7UKNmY5hJt6qr0eXzwdHksBfcq5TnyCgzu1mgTPIqEehzu3GjJoa1CkwoshIv7HF68U7DQ2q7kOqcJ/OMTBjAT32vpb7u832W2N/p9qNHHreQ2r17JtB5Hlt8njQmGKZ/9HKStMEpc0Gpqzsr169Gq+++iqOPfbYqNenT5+Op59+Gi+++CJWr16NIUOGYNKkSeiyaHAqvTxdVYWXamoM2euabRlyMnrOp+O5oGzxQ0dHeApKPBu6u/FKbS2+bW/HNrudlX4Zvm5vT/oZpasae00WByKVypZX7UZAhbav9Z2hZ4WbuYBxKdGQYbbGnJBM72VPIIDPTbYyiZWZrrLf3d2Na6+9Fq+99hr69esXfl0QBDz77LO4//77cfnll2P8+PGYPXs2HA4H5s6dq2OKrcuM846SUfrBK5VhNtps+ISZoOUIgpC0B1hNC1tb05pXt8Nuxyu1tVkzWketwrXc9emXtrfjv42N2K1BpbLJ40GLSfNpB4AfkzTUs8Ek+8xtaEh7ib14UrmKAoKA/zQ04OuYucWxFUOzVvKMxijHMd2cRqkcqtPnQ4cBO9iM7uPmZlSlMOzfqkw3IfT222/HhRdeiHPPPRf//Oc/w6/v378f9fX1OO+888KvFRYW4qyzzsKKFStw6623Sm7P7XbDHTFcrzM47MTr9cJr4EJSKG2BBA89v80m+zf4g71Sfr9f/O0+X/i12L/DafD5wtuPfC/ydTl8MfuK3Y8vmKZE6Q7v2+uV7L2I/X3x3vdF/ia/Pypd8Uh9LnJ/UcfR68XXBQU4xG5HTnAZQK/XC28gEH0MY46DL84xDac7zu+KPG8eiWMTuV+vzwdvnGMRua8oMddYZLq9Xi8CCVrGEx230L5jrzup663Hb5a6JmPShTjLM8XbX+x/pbzd2Ih9LhduHjwYwyLmxfY4xjLuDanfF3WcY64XAPi+rQ39bDYc37s35re2JjxGkdt7p74eADCnrg5/GjYsav+h+0XqPot3DOX8Fqm05IX2GXFNxzsOoetY6lxFvh7KHyO3Fe9eCn83zn6l0pEo30u2jXK7PWn6Y1+P3Ge8/D0y/c9XVQEAHhg5MmkvldxrVE4+AEifv8j0S52HqHzTZot6vnm9XngjrrlmrxezGxrgDAQkA+TFvW7j/J5QHiqVbhsOFNoTbSNWvGMg996J3UayPD7ZOcgRhLjHJdHzPTYNUc+miPf8wcpI7HmL9xyIe51H/Ca/3w+/3x8+/tu7u1EoCFHXa7zjL7v8E3OcIn97bF67u7sb27q7sQ3Amb1797ie5eQzkdvzo2cZS266Y78T73gmOn8hoXMWSFDeAoBAzPGIV6ZKh1S6kuUTqT5PE5UhpM5DVPkt8nfHOdZS6Y9Xzkn23fC+BAFPBPPy8/r2xYTSUuQF76lkZZPY3+nxeiFI5D/+QCAcmyZemT9SbF6Z6Hkd3kcKz9YOnw8b7XZM6N0bJTFLZifbT+RnWjwevFpTgwdHjUq6Xy0lOm9q1D1NVdl/9913sW7dOqxevbrHe/XBAuvgwYOjXh88eDAqKiribvPRRx/FtGnTery+cOFClJSUZJhilRUUYO++fXHfLhYEfLFxo6xN7Q5WTnIAfLFxIxpsNuwuKAAAfLFlC6pzcrA7Pz/qO99u3YrBwcxhd0TlZtm2bahOYfmbjfn5qA1mPp9v2RK1LQDw+P0ojtOiGfvZhVu2oDjB5zoCAXwhcSOF3vf4/SgK7mtTfj5ag+n6YsuWhOlvjPlc5P5i30NhYdR5+2rLFhTF/JZvt24NH38AgN8PQeIYhL5T6vOhRaLhpzviMwu3bEGvmPe9Me/3jvi7xOdDZ8Q2Y481ELxeNm8O/90ec93k9vjGAVsLCtARfGDFHjf4/YDPF5V+yc9JkPrMNxHH88stW+JmfB0R6Y/cVsiiRYvi7ver4P5e3bEDJ0ecK19EWr7buhU7ZPRGSv2+yLR44nxmkc+H7YEAvoi8duKQOk5fbNgQ9VrofondV6JjGCvRuQr5assWhFIc+rzb70dlbs8rKPIel7o2Yl8/GcD69euxO7itYp8PXRHXdVVODnbk5uJnXi96A9icn49mGZWxL2LyqiXbtmFPZKUgye8u8PmwOyIAY7z0f75lC9psNpQJAhwR12e8/H2TRPrnJ7kXI/eZ7BqVkw8AYr5YF5OOyGM2a8cOOG02nOr1IrTF0HttgQB+CkTlk99s3Yr+Een6TmL7kb6JzUMhnY+H9mkPBNDH68W23Nyo8wJEV/YTbSOWKxBAVUwaU7l3AGB9Xl74PpDa9/6IZ3Ps+5vy81Efsf88iOcpNr2x1zIALN22DfuD1/PavDzsj0hD5GfzI67j5V4vkJ8fdd4SPQfi3b+R5Y8mQUB7TGOBPRAIlxmkjrHUNhPZFPEMB8R7YHuo4hOTzq+3bYs63qH3XIEAekfklct8PjTE6YyJ3F6RIOCLTZt6lLnkqI/5jtTxFCCevz1xzl+svfv24Yvt2+O+v6WgAK0R56O3IISfG5ECAOpzcjAgEEDyJ4BIKl29fT60xhzH0Ofa45TnEm03URniiy1bUBtxPy2MOFYLYsqW8a7dWGvz8rAv5v6V+90Qd8R3dgMY7/PhmJhjEq9sIvfZvauwEKEtfrFlC/ZJlPkjfbFlCzbk5YWfqwG/H3slnteRv29Tfj6aZD5bPy0ogN1mw/xAABNjznGy8pHUZ+TeU1qTOm8OFVYbME1lv6qqCn/+85+xcOFCFBUVxf1cbO9FsoiQf//73zFlypTw352dnRg5ciTOO+88lJWVZZ5wlXi9XsxbsgSHHHxwuIc4Vu/cXEwePlzW9tZUVob/PXnUKOx3uVDV2Bj+e4fDgfrm5qjv/HzQIIwOnovI758+YACO7dULTV4v1nZ347TSUpQmiCre3tiIXsFhNpNHjsT6YAtmyFElJTisVy8MLShA75jfGrlfADhv2DDJfYU+N7qwEJNjGoQi3x9fUoLJBx0EAKitr0dtcGjz5AStgs2NjSgPpT/4udD2xhQWojw4cmTyqFHwer34z9KlUeft/OHD0Ss3N+q3nD1oECqDxx8ATurdGxdIRCgNfefUfv1wSmlpj/fbfT7sqK0FIB6bPjHHxh0IYGMw8vukYcPQLy8vvM2f9umD0/v06bGvSDkxx6bR48GeYMPb5JEjkZvg3quoq0NTMBOPPW6h3xuZfqnPSZH6zDlDhmBfMF0XjBiB/DgPnCavF7vr6npsy+v1YtGiRZg0aRLy4zwAQ/s7oVcvTB4wIPy6NxDAhuAxnjhoEA5OkH/FbkvqdwFiwXJTcJuRftqnDw4pKsJuGYG4pI7TWTHX4pjg/RKbnkTHUM5vifWLESNQFNxe6PNHlZSgUOKhF3mPx+ZbsfsM+P3A9u044YQTgOCw+Z/FXNf/qKxEPwDuoiJcNWgQ6uvrUS1jSsPkUaOij93AgTi8+EBxMNnvPqWsDJ6IAEZS6QeAYf37Y21rK0YUFOCSAQOwK3h99srJweQRI3pst6GhAVUxweWS3YuR+0x2jUr9rlybDZNHjox6ra2xEb1jhk/GHrNSAMWlpfhFcEpe6L1heXnAxo1R+eTPY0bMtEpsP1LkPR+5/3i/57DiYkweOBB9OjvhiIkVEFnZT7SNWONKSlAUcw0fP3gwRqUQEd/R3By+D6T2vcluR0twWljs+y2NjSiNOEb5wfMUm95RAwbgsJipZWcedBDGBTs8vC0tyAsuXxt7Dk8uK4M3eB2f2rcvlq5eHXXeEl178e7fyPLH4Px8NMQU9g8tKgqXGY4sLkZRnCkxiZ7bkZoaGlARcc9MHDQIY4P3gCfiGQmI93ldU1N4+6HfcGTw+gn9fVq/fpgg8UwGon936D6OLXPJsc/lQnXEd2KP55etrdjrcuHYwkLY4py/kIDfj7379uGQgw/G5LFj4+6zpr4edRH5Y7+8PEwOjgiL9GNXF9a1tcGRl4c7Jd6XIpWuU/v2xU9jyuKhz42KU55LtN1EZYjJo0Zht9OJ2uD5PX/4cGyuqQn/O7L8Ge/ajeVraUFuxLFP5bshDr8/nA4AGFpUhMmDBgFIXjaR++zeUFUVDjQ7edQobLTb0ZpguunkUaNQ0N4OX/DeP7F3b+RILBcdVT5saECljMCnkddobBlT6jfF7kfqM+eNHAlBEGSXW9SW6Lx1qhDY0DSV/bVr16KxsRE/+clPwq/5/X4sXboUL774Inbu3AlA7OEfOnRo+DONjY09evsjFRYWolDiwZufnx+3UG8kObm5yI1z8ebm5Mj+DZHbyM/PR77PF34tPz8f+Xl5PfYTeYyivp+Xh/z8fMysrYU3EECL348bI85JrLyIbefn5/fYz06XCztdLhTl5OC+0aPjpjsyrfF+X25uruQxCb2fF0x76LOR6YonNyenx+ci9ye1jZyY1/NjzmPscYhMl2S64/yu/IjPSB2bQCAQfr8guA+pYxG5r0g5NlvUZ/IFIfpcJqhg5ObmIjfYOt3juAX3HZl+qc9JkfpM7DUWL8OPTH/ktiL/ljrO37e3xz1uiDjG+XHOYyyp3xf5Pb/fL/mZvJhzmIjUcdrt8eCksrIe94vkfSbzoSkrLXl5yA8WpCKv6XjHIXQdxztXsd+L3Fa869obvJZzE+SpsemI/FyBjPslUm5Mnhrvu5tdLuTm5KDO50NBxD7j5mUS6U92L0buM9k1KvW7cmPyAUD6/EldS96I70b+NkAin4zYR7zrI7yvOM+seL8nfK1LbDdy6b1E2+jxusS25jQ1YWqCylSP35HkOZQfk7dFij1GAQA/2O090vRJW1vPYxVxHeTFpCEqb5VIX+x5i3ftxbv+I8sfORHPifA+I35znsR5ltpmIrHnKfK3CxH5d+g9qWMRm/fGe2YD0b87dN1F/ua8vDxZweFiy2mxx3NdsJFom9OZsIwVKSdOvhJ+P6K8E5n+WLvcbuTm5KArEEirHBoidRyT5YGJtpuoDJGfn498r/dAmSj2fEaUnxKVE6K2KXF/yv1u+DMxx1yqvBevbCL32Z2bkxOVx0nln7HbiXquJnheh/eRxrM1towp9Zti9yP1mefr6+H0+/F/o0cjzyAVfkD6vKlR9zTOL07inHPOwebNm7Fhw4bw/yZMmIBrr70WGzZswMEHH4whQ4ZEDYnweDxYsmQJTj31VB1Tbl07HY6ES4OF5jfVJOklkxuAxZXC1ACjKGdgEMtr8Xp7BGvSk1ECGlF2M/K641ox4hH4RsW8yqwBE9U4T3K3GTpi6aTBiNeX2Qlx/m0k+53OpKu/UDRnsMGwJUuDHJqmZ7+0tBTjx4+Peq1Xr14YMGBA+PW77roLjzzyCA477DAcdthheOSRR1BSUoJrrrlGjyRb3oqODqzo6EjaS5EsQrhRM1Qj0eKhrvR6yIIgABYq8Nfl5ODfdXW4ImY4sR6NUHrfM3rvPxNmTruZ6F3xSzfnsU6OZQ16nA899sl8ydiMdn4+b2nBZQcdhHqPB3qFEmdeaR6mqezL8de//hVOpxO33XYb2tracMopp2DhwoUojTNvirRhtEwyEb0KqFuDc7riUSJVehe+jSLdo/Bdfj4O83rxdkMD7hk5EjsdjnDMikTbN9NRV/rhzWuO5GqPmJctdd1oVbC00hW7T4PlHSOpMZpDj/OhyPNWgW0YYR/ZJtEV7AkEsL67G0cUS4WB1tZOhwN7nU7Mrq9HXUEBLpH5PbnXjJLP7oAg4D+NjajkSFfdmLqy/91330X9bbPZMHXqVEydOlWX9JCoMWbYfrKe/VSKB5u7u7Hd4cClBx2Eggzm3exzOvF9RwcuGjAAA2Lmx7R4veifl6fpg/T9iGB86YiX1kx+g9attmquUa/kb3EFAljW0YFv29rQNy8PVwYD5VBPUstg6sEYqaBEBcgXIwJQydpWponRgBnSqCSp86v2CC8lKiXZdp6szBsIYHZ9PQ4pLsbZwcCfqUh0LSxua8Oqzk58m0HZ0ytjJKAgCFjW0YFhhYUYkJeHZR0dkp/bEuwk6jb4CMrVXV3YrUKE+XRkaweEqSv7pJ5MbodlHR04VyJyvBI+DEZJ3Wa34z6JKKZy0/1WMErzB01NuDUiUuwWux1b7HacoPNoEGNn3ep4N6LBw+i/f3vwIdsuMf/L6GnX0oo4hZRYUj2CZnwk73U6kWezSY74UJoZj08iviwthJFor9OJQ4I9pka6ErRKC58bytjY3Y1qtxvVbndalf14BIjXKJDZ1D2/jHxup8MRjgPUNy9PspyhNiVH6XyZIKo/acM0AfpIX0YMtvR6xBJp6eqO05u8vqsr423L1SQRwDDVAoYaZ0frApdZhnjFHpfYY69nQdUoPekhHSqO1kiFFrmXIxDAnPp6zKyrSzqaCUivh8F4uXB8aj8zOOVEZKRrIpNzvkqF5abMROmYOVGfM2D5TS1qPXG0zB/aIir3WlX0s+kayUas7JNpNXtTD0uid3GuTSLjnhWzFrRaBADVLpesYWSpbFNL7Wmcc7XpfU3FssojW+vfken+nBENG2pdE0a71vRkhmMRrzHZrA0L2ULLyPjxroQ1nZ1Yk+UNIMlIHXOrPP+0xjzJ2ljZJ0l6ZZhKZziCIOCdhgZFt5mJhTKXPUr1+Ms5aqu6uvB6XR3mZhgfQAvxfo9UY0msgCDI6llNFx+K1mSVs2q261Ot1CrZU5XulqrijFaa19ycfmJIUdUuFyoMNqrMFQhgfksL5re0mGaJtXq3Ozy9TStq53SR2/84jXtWEASs03CUKCVnrqejcljZJ80j9mrJIwhRgUH0bvWVM19Lyobu7oz3vTrYS7Bf5vmWc6wSVaoFiL9X68rHs9XVeCHFYF+ZSHacrPhwYaRpfeidf2UqlXNq5fO/OSY/3+1w4OWaGtS63TqlKHsZpfE78t6OjF+RbplBa/+urcV7jY2oDjacxAZqNrtylwstKY4s3KFzUDrJgJk6pIP0x8o+hYPVWVFsZpdSYdNAD1mPSq37SvzGujgFVLvfj0cqKvBBMKiiVjp9PrQZcLi/GXDennb2ZdibGO/ONds5FAQBC1tb9U5GQmo/Cd5paECjx4NXa2t7VO7Mdj6NxOH3Y6/TmfA5Z5Q4J2qmQssrqCn47M0kiJ1V2HWOWfO1zJGkViEIArba7Sk3ymQDVvZJkjEef8azsbsbs+rq0l4mzkgFt4AgxJ1Tmoqv4hTU13d3wx/MfKXo1ZiixF4jz+Neg42MUXT4ss2GVg0enGpfC3K2rvWdaaVe3EzO3ha7XfaqDdkgduRVsnvDOE8U4/l3bS3m1NdjnQIj4/SiV1nsh44OfJdllUWribdkn5a0HJmyw+HA+42NeKG6WrN9mgUr+0RxtErMD/+oqQnlLhe+bW9Pa5taF8wSZbOz6utTXttaqX0n/a4gKBpIUC655yeyAK5U67kgCKh3uzOKN6B0pXm/04nndXpwshKT3XSLGyPxmhmvRTXjlmRKq7R1Bp/hkXPJ69xubJFZ+d/tcKg+n1+LRu+UYwAJAr5qbcV37e3oSDEavHGvOmuJG9fI68XC1tbwtS+XWh1RWsbDqJLRgJ6t12ee3gkgY9JyXU81C1KZ3NiJhs5bYYhasqXu1FwKKJk1XV1Y09WFPwwbptAWD3ivsRGT+/fHQQUFim87E993dOCbtjYc37t32tsw0siRdJRH9Gpm60M5ke8N0FOTCSPP2Tfq9ZbuPV2uQ9C57TLmKDd4PHijrg5n9ukja5tKn5dXamsBAKV5eRhdVCT5mQWtrbhu8GBNg/saKeeOPOZqNLoLggCfICA/J7X+xmqXKyp+kdGj8euRp8ysr0enz5fSUsaBNDtX5ORNRs1Xsw179knSp1kSLVhuq7pSDzytH0QNBg6SI+dBsV+FAus+pxNzJApxdr8fSxWoTKXbU7MkOFpEiWCM6dK7oKTVMpRq0KKhhZGdD0h2l+l9LetNq0J25HW/UWLKVmw6vmxpgScQwGKdh4g3JXg21ns8ikxxS0XkcVL62lXzXvALQlTcHjnX3SfNzfhXRUXCcyDl9bo6rInIA1WPxq/h6Bi7369IrKFQj351CtPEVnGJR8tjZZ90p2R2qlbWvMakhez3Mog0nGkBwciFbamhie81NmKTged2bpOxrJHeQSV9gQB8Bhz1osa1mGpBVSt6XAN63OtmWZIsVbFnL3LIuxbxM1KlxvWWbIt653NWl8r93OnzhUdLyBVq0F7BSmbYE5WVeK66OqWgfrENzHKG7kvdO7tUjDtk5HJgNmFln0gGpQqWmWR8X7W2Ym1XFwRBQJXLlXaQwHiWd3QoOmTPbMWxVIa9pUJuwTTZtfFfGQ03eg7jDwgCHqusxGOVlarOyX27oQE7dV7SCABe0nB5RylGqvCokZKaFIf4KsEXCBg63zLyPPx4nqiqilr+NlXZHllbj/vcfFeZdTSneb07/X48XVWlcGqsJ1uvbc7ZJ91FVk9UH5aV5veUqkRlsp0fgkPM++Tm4u2GBhQqXBhe09WFTTJ6j5VipMpKqsw+Nz5TsefO5fdjo90eXh/aGQigV25uRvvYE6e3ocHjwX8aGjB17NiMtq8mLa5tVyCADp8PffKs9xiv93qxPS8Phym83a12OwRBwHiJuBh2vx//rKhQeI+pSzVnMXpe5AkE8E6K92vkL5Jq4Ij8zUZ5ihh5JJzNZgNM/LylnjyBQNRzplFmI4FbEGD3ejEgPz/8Gq8M62PPPpHJ7AxWgqRGG9S43Xi7vh6NaQ4xThSUkIzPLwi6DJv7T2MjvmxpUXSbSl6LVi3MPCPRk2Pkyp/cRhCfVAVPgf2/39iID5qa4JQYFVVvkGkZRrxWjZYmIzYUGy9Fyoj8XfudzqT3id7nJtN8Qs3US+U7UtQMAP1MVRVeqK6OmoYWu9wnWQ8r+1nELwhpz6dNNQPdlwWZhy8QwHa7PaWMWa1ieOgB9VptLfY4nXhbwyjCatK72hKv4KJ3gSYevdYr13J5HTNQssKt9bVmlEpvMhmttGLQ+5dE2XR2hJj/qkGp3Gi2wQKoRv4uh9+P+hSC0knZ7XCo2uHxeGVl0s+s6uwMB+tV0z4+s7OK9cb/kSRBEPBUVRW8goD7Ro1CboqF0VQfRPGG4JqV3e/vMe/w6/b28NB6o0l1jVWjyqZCnxxv1NXh6kGDdG8EMZs6jSuwSlbQU4mqHI8gCEBMnr+0vR1+QcDZ/fpFvb6wtRU/LStDTsTnA4KAnQ4HRhQWSm5fi+sx1SOaKE1GbawzqmzMb5S83jKh95Xa7PViZWcnzpC5VKJepgcr0j8pLU17G0ovtSh17pI1NG5NYSplZKOykg3M1To3BLRneZwONbCynyV8ghAO6Nbh86F/xHwdSm5VZ2eP5Uk2phG5XYnsWGobC1tbFQ/YpzYjDzdWiysQwLquLowrKUFxGnPaq1wuLG1vx5l9+yqfOFi3UF/j8SBX5YBveh67Grcb7T4fju7VS9bnvYEAvgkufXZSaSl6x8z7DwhCVGV/bVcXPm9piRsnJLL42uzxYH5LC6oUaKRIJJPjrXcFKpEAgHlNTRhRWIiTy8pSTut2ux3LOjpwvERcAjog3WtAz+dst8+HxW1tOKmsDMPjNLwlk2pDV2hFnxqZ97OW95bUvpTssVbjt7ysYGBXJZbqk/J1e7uu+cenCk8JjJStDb2s7JMqnJz7LUnNCsEyg44yiCcy091jgOjqqUiloSLy0fJhUxP8goDNxcW4YciQntuU8SBqscioDVLOa8Glr/oOGyarMBOZO8upuuxKECck1vtNTWhQYSRF7B2XSZHNyMW9HQ4HNnV3Y1N3N04uK0v6zIh9P9XKmdaMfOwjxTvuas2nlvNE+aSlBbsdDmzo7k474OH2NJ+1ppneY/DKnJLXT7lFh+InWn5QbtwDisY5+yRLqpXU9QZZl97Y2b61VLtcUdM3Ujn2VokxkIw/WBDJJCBOJstYkbUZYZmy7gSFMaPkx0ZJhxQ1g3OlqtHjgV8QDH281KL1b47cX7zG5HSXZYtktHhKXT4f3qyrw+Y0RkpahVWmXWpBi3gGVsSefQsT4vyb9GPFoeuha+v1ujpd05FN5icY5mbE+dOkDbnnXtVlvlTctl7MuL59pjZ2d2OHw4GxxcUoi5hyZPSeU0pMThkkkzOcaoPEV62tqHS5UKlQL3WytL9eW4ufGSz+gNlGZeop01HD2Zp7sWc/S6wzSE+72rQqiLDAk5xa8Qn03I4cWlwZ21II4hOPnCHZ6bLF/JfISIycfxslbTuCI4j2O51R97ExUndAuukxylTDZOlX4ngr8iyW2VHxY0xso2QyOQ/p/K5qtxvvB6e6GEWqAbO1oFSKvmNPvCGwsp8ldhts6FYkIxcklKZnli61drWarH4uze6z5mbVtp3qclK8Voxlo91umEpnuhJG49csFcqy4siwWN5AIKXpKInm9yYSClCZKrWuHaOfWbPkB0ZKpdy0pB6qN3NanU+lRmxQZljZtzCjPzyMQNPIsYIg+5wYbV5dIlpfZ940egKMVAAwCiM3AJqdntebEhXCz5qbVVmHWcnjUpdBADoj5Aeft7RIBjLMZMk3PSpkSjdAvFpXhy9SiMatdSDCeL+2MiYdqZ4JNc+cURuJbFC+/KDkcdTqbsoz4PmR89uN1lGn9RK7ZsLKPmUVPVun96RQuTLT0CcB6S1DmK5PVOyRVkOyK84bCGBjd3dajRhKM+O0iWSqYpaLi11CUylG+s1KUCOivlEIEJeo26ljsMvVnZ2YkeIyXHo9vxLtVek0NWl43SmZF3T5fKoEWkt7mkKcEQ9GyqcyvXKMUMFUQo4BK/upMsJU4UwCH1sdA/RRD92MDKqKrXa7oR60SvqoqUmzfW1RYL46ALj8fmyy23FUSUmPdca1tKC1FWsN8KBUkpEKYTtyc9Ev4u9E0eIzYYTfnE7+YpbhuUpy+v3hJeqMzqrPDCP4oqUF+TnK9Xm1RZSdtDhvifYRGbVcqTn7meYVux0O1UdiZGN+RvJl69XBnn3q4dnqasW3We50wmeAnksBYiWPsotUYefTlhZ80dKCt3Re9m+rQo0XIUaoHHzV2qp3ErIWC7vJuXmMUmKEPMUotJizH+8eTuU8GGkJx5B3GhqiRi3yujIunhtrYWXfwtJ9KKkRyG1WfT0+SWEenhIEQUBTTKCfGrcbj1VWapqOSGYqYu51uXRvGOlUcf+haNONFh6urJdNwWkd2VRgMPJvTSffUeP3GCX/M3KDSGzKkq2/btxfYg6pXgsJAz9GbEuLOftq3dearWpkkm2agZL5tZzzHxAEVGscL4PSx8p+FtIrM9ys4bxuQBwePZNrv6dtv9OJmfX1uqZhBdefVY07EEi7UJetBaps0+HzpRQZHTBOMLBE6TDL9Turrg61BmiM1OJ47XQ4sEvHGApGonSgMWPckcqT+l1mubeVpuZSulK+a283VSDpbMc5+1nCqpl9PA6/P+X1Xs1Cy3Np5SBdcghI/XibqbBhpeH2nkAArkAAZTHxF5pzcqLm7FuZ0hXtZ6qqAACjiooU3W46lPxtRn4eRuYf5SZYtkpufreluxsHFxcn/PxcnadU6UHqeLwTcRzkNsgaebRKQBAkg9DZkFrgYq1lHEBQw3PyvcblXXbEmAt79rNE1FBA3VKhndcN2qOfDcfeLOQ+hjPpAU9lP3pYaaEGsWeqq/F0VRXaUuyJVkqmS9UFgteYEXtLOhQK2mrkCokZSB0/szxTPmhqwls6jxSTEq8RKdMrNdXzks55TPSdHRGjJPS+RuIFw9ybYV4ndY6yNYdJdSUIIx8nNdNm5N+tJlb2LSxeBm+Eiz0gCFjf1dWjYK7UUiytGhT4HQz0Z3k1bjcerajAZxbqAU9E6Z5hLfOa0FJTmRYg07U9ItBiOr/7XxUV2NzdnXGFKNk57PT5oiJ1x2OE50SkVBsKsqVhQYtfqVSuUJ9FI8X0vvriBejTo+Ifb4lLpRoRI2XLfR8rO381ycVh/Cbm1nBupNJ7+rGzMzyE+MiSEoW3rr6ONCv62ZQhfx9RoXi/sRGn9umD4YWF+iUoDUuDQ9U2KBwxX01az5nO1sKV0vyCgA9VXsKyzevFczJXW0l3NJjevYghc0wyJDyV+8co8RCsJOVGpDTfy2S7meJ1Y318ClMi7NnPEkbL6s0wFzGRdhVapK1sq92O12pr9U4GqSBerw2gT75jvAWntJWo8pJuY4LcysK8pqaUh5MqnYYQNXoN1bArZiSKERvPzH5PdSs4Cs9oZSkyLuPdyWTE/FULrOxbmBDn35luixL7IsESgywokNU06DRHPp5E91+2S3dEklybNF5xRQ+CIOCjpiYsUOg6q1Sw4Vut53TkErZGKAukmgYzTB8w8rRLo1K7PPWByiOtlKT1daLGEt2kHlb2Lczu9+P12tqEPW9GYMYhZl0Jeo1WWSjoGWUfn4wlfOQ85s3Wgu4KBLC8o0P1CrHS/BHHOZ2cNN5ZUuP8GeWKyOSZ0+rzYWN3dzi4Zb2B1pr+uLlZ7yRYXirD+J1+P7waLYnmlXm/yrn2jXKf6q3KRCNQzfa8JW2xsm9x1W43/mPAuYtGqd4ze0xubkMD9isc9Gyf04lmg/UIS8nGB6jcqq7d78fKjo5wYLxYL9fWRlVEjSQgka5WrxeLWlsTNuQZ0fTKyoy+/2Vra8brm0sdT6uKvaYzXTos9lmYyZHMhpEVANBmgnvUFQjg8cpKPBFcvjIeQRAgCAJWd3VltL9vEwTdNEp5Swtm7DzK9iWOSX0M0EeymC/7JKXscjgyrgzEMsISTFpV5M3UYJDKff6fhgZUJ+jVbPJ44JHZq6X1Eap2uw2xdrwS3Bn2HMq5v+VcF3Iq/Ea5E5RqADfTva0WPY6AFqvtZKouWIGTkwfudjqxNcMgsGbqhVaKVe6+TM89YJ1jQepgz75Jadn7xAKNsXSZbJgxWVOiir7RqZ2jGS3HTCc96UbjJ+thGeAANe6FpgSNF0oc+cigmXLSbzdxGSPeSDOr4x0qT7YeJ1b2TerjLFn3Ox0VOrZwb8hwKJ4c20y0DJzStljwtys97JAVs+yV6NwLgpBSI3G2FoqSWa/yUHked2OTO2df6wYSJfdmldWGBEHAkgTTG6zEqvlGQBAwq67OdNPrjIaVfZNKdY6PkTOCuogeQiUekPN0jKDKAEnqWqtBY0qmUr2C2WtGWtiUQUOZUg1IKy0QvPSHjg69k2BqZsnt0knnnDjT0xa3tuLt+vqsiW0xX6NyULJ8aYvdjm/b2jRJixSznm2jpbvc5cICdnBmhHP2TUrL3rvX6uowID9fte0r2YosAMhNoafUkaVDvpRktAeDlFoGwFGU2eeHZuPoh1TjbqhxXy/Nkl42sq508o5lwQaivU4nDispUTZBBrTGII3yZgjkqJSUOxlUSYV6nBqtamFVrOxTUrVuN2pNND+Xw1Uo1qcccaGo92WOnjFqpdpsBR0lqH0usvGYKiF2Go8Rj2PkOvWx87mNmF4lZLruvdT0LKm1ybU+fnrlyWZvIDY6QRAAE65EIJd1f5k2WNk3qVTn+e5TeOm0dC1ubUW3yr3pqfTsU+Yq+RBPKFlhzoxz9q1awDe7ROc+k+vMCMtZ7XY4MCg/Hx81N6NPnjGKLtl4H2zMkuX9Mj23UtOzMg2UaWZv1NVpvs9vNBrCv9PhwBE6j9iwynWitmw9TuwEJU0t6+jAhu7uhNFnM5VjgIIpkRnZoNwya2RuRoslsbitDXUeDzZ1d+N7HacDGKHhgyiWknerse78+IySTqWW8iRSCyv7JpVJccMIhTi/SmlY3dUVFfCPyOiMcD9G0is1qR6H5gziMKg+pN1g5xTI7DcbpXorNQyatJXtZyCjspdiqUh/20a5l0lZSl5bZpwd365iB6IVsLJPlpJOpGQjFsyVYNXfRdYjAHgzTiTreF6sqVEnMQQguvDYaJAAl0asqHQpPC2N+baxZXK20zm3cr+jdieHEe89I6YpklnvZL3jK0gdt2Tnem2WTC9KlzEmvpGmOAwxO/y7tlbvJFACjR5PVkULTqTN69W9gGF12TiaQW3lLhd+tMBygpnItqUIF8lcAkyqnCV1h/gFARUa5n0r0rxejTp1q87txvyWFr2ToTulz47bZNHvWX5IjJV9k8pkOGM2FsqyUYNBeuOyjdz762WD9kyzKZCA1AuPS7Ks0gcAb6U4GkVKbI8sn87WkEqAvlSXxcyEUSvt6XqFnRqWk6gMEi9AaDkr+wmlNYx/3bp12Lx5c/jvTz75BJdeein+93//Fx5WMDRh9jUn440usNZjiMh817SS6W2XOXLBZfL8zCj0HLWlRdA8KzZE7THISjmkrB0qVOC1uv7N9syi1CU7x7wGrCWtyv6tt96KXbt2AQD27duHX//61ygpKcH777+Pv/71r4omkKyJowuI1KFW8Es1vc1oxqoSYM2KMpGeUi3HpJMz13o86Ag2mpovZ8+cmfItl99vmPgmZpaN17na0qrs79q1C8cffzwA4P3338eZZ56JuXPnYtasWfjwww+VTB9ZFOMGkBmk89Apd7nwam0tanVaFWJ5BnOIeVcqwwojFVjgkqZ67AOVt58pNdafzxbp/PZ5TU14pqpKhdSYg5muludqavByTQ2qdRhSns61FRAE08zNV6rOYKbrSUlpVfYFQUAgeIEsXrwYkydPBgCMHDkSzc3NyqWOVOMLBFDpcllu/haRktJ5vHT7/ah1uxWZz5uOTBsZsqXC3+XzYUNXlyrbnmfA52CqhaW9HF4uiU9MShfnFVubM7gyxy6T5J0zamvxaEWF5HvZUg7IFmkF6JswYQL++c9/4txzz8WSJUswY8YMAMD+/fsxePBgRRNI6ni7oQHlLhfO6NtX76TojoU3SpWcB6EVenet7LW6OnSqtBpCpU6F+kTXZaqFN71GphBZ1Ybublw6cGDa34+9hzlC0phCZUotR7GkM1WriVMOskZaPfvPPPMM1q1bhzvuuAP3338/Dj30UADABx98gFNPPVXRBJLyPmtpCbcwr8zyZYO0VM+M1TKs3ECULY0UalX0ST1GqNzon4JoZsuLvCYZTZjN0w2IePVbS1o9+8cdd1xUNP6QJ554Anl5XM3P6LRc0zWeNq9X7yRort7jQZcBCqukLa0fmpnub7VKQ9tJG9XskTc1qxeyuSRsetq8XrTElJvUapCw+jVIlG3S6tk/+OCD0dLS0uN1l8uFww8/PONEEVlVZ3BOF5ER2Ww2xvGwKDYzWhPPqzq6Dfasfq66WrN9zdEp3owUM17fHBWiPDNeB0aSVmW/vLwcfomM0O12o1rDDImMoYPDYYmIdMcCkfkZvaJg9PQp5d3GxqSfyY4joS+PIODzlhbsM0nQO73wWqREUhpz/+mnn4b//dVXX6FPnz7hv/1+P77++muMHTtWudSRKXysYORpZlikB2+ceeqZXo877HYMLCjIcCtEmWNDgDXxmSmfXoEz1WKEGBZq+76jA95AAKsNHl9qaXu73kkgiiulyv6ll14KQMxgbrjhhqj38vPzMWbMGDz11FOKJY7MIRvn35O1/CvO8jPxyC1ivdvYiMNLSlJPEKnK+kVka+J5o0y8WVendxIoRfEa4o3mm7a28L+FmP9S5pj3Zyalyn4geNONHTsWq1evxkEHHaRKooi0xAyZ4lFirfFdDocCKZGP13Ny2XiMrNAL2GWwedRa+7ylBdcMGoS8nLRmYBKRBirdbqzo6NA7GQlly3QcEqUVOn///v1Kp4OIiIgyEK86b5WCXWTvmVUlOlP7nE782NWF0yKmUBKRsVS5XKhyuTC0sFDvpBABSKGy//zzz+P3v/89ioqK8Pzzzyf87J133plxwsg8rFGMJJLPqte8+ft+KR4rnFufARot9D6OegfE1f8MpMeR5aNCSHuxSyWS/qzS8J0q2ZX9Z555Btdeey2KiorwzDPPxP2czWZjZZ+IiAxL7wobpYfnTaxsO/x+lOTm6p0UU9lqt0u+np1Ff9IC8ysyCtmV/cih+xzGT0REZB5WKHhmQ8Us2W9c3dmJ1Z2duG34cAziSh+6yobrkazJZZLAhyFWeH7piVFeiIhSxAcPkfZ43x2wsbtb7ySYSrxrh9cUZaNlBg8gSMpKK0DflClTJF+32WwoKirCoYceiksuuQT9+/fPKHFkDm6TtRASZarW7dY7CXE1eTwZfZ+9VdbESg2RsrJ1/i9ZX5WOZRzeV8pLq7K/fv16rFu3Dn6/H0cccQQEQcDu3buRm5uLI488Ei+//DLuueceLFu2DEcddZTSaSaDUXI4kB63ODMWIrICKyyvZ3R+lZ8Xcreu15nm0/KAdRxdQQmY+V7ZzGvbUtIaxn/JJZfg3HPPRW1tLdauXYt169ahpqYGkyZNwtVXX42amhqceeaZuPvuu5VOLynMy155IorA6qL11Hs8bAggkpBJhawhw1FURCyDkxbSquw/8cQTePjhh1FWVhZ+raysDFOnTsX06dNRUlKCBx98EGvXrlUsoZQduDwOkX5sMHdvhFxNWbYk0qz6er2TQDJxpBnJsYk9r5bwr4oKvZNgCmyszkxalf2Ojg40Njb2eL2pqQmdnZ0AgL59+8LDVk9K0Us1NXongYgs7tPmZr2ToDkrFJWyocBXz3KTKqx27cxratI7CZSEta44a8jWptS0h/H/7ne/w0cffYTq6mrU1NTgo48+wk033YRLL70UALBq1SocfvjhSqaViIhUtM/lMnTwQUosUeFyBaMvE/XAChkRWV1aAfpeeeUV3H333fj1r38Nn88nbigvDzfccAOeeeYZAMCRRx6J119/XbmUEpHpNXk8GMi1oQ1rdXBkFhGRlGztGZOytqtL7ySQgfFeUQ4b5TKTVmW/d+/eeO211/DMM89g3759EAQBhxxyCHr37h3+zPHHH69UGonIIl6qqcHUsWP1TgaRJf3AxhqilLBCRkRWl1ZlP6R3797o378/bDZbVEWfyEz4sCciK9jtcOidBFWxd0d/TgbRJSIylbTm7AcCAfzjH/9Anz59MHr0aIwaNQp9+/bFww8/jACXkSCiBNqzLBI6EZHSbDYbdjoceF8iWLKa1nDoOhGRqaTVs3///ffjjTfewGOPPYbTTjsNgiBg+fLlmDp1KlwuF/71r38pnU4isoiXamv1TgIRken9p6FB7yRQir5sadE7CaQRP5fRVMx+l0vvJJhaWpX92bNn4/XXX8cvf/nL8GvHHXcchg8fjttuu42VfSKKy8vRP0RElIV+ZFyNrMGSjnI4fSgzaQ3jb21txZFHHtnj9SOPPBKtra0ZJ4qIiIiIiIiI0pdWZf+4447Diy++2OP1F198Eccee2zGiSIiIiKKxAB9REREqUlrGP/06dNx4YUXYvHixfjZz34Gm82GFStWoKqqCl988YXSaaQYbg6DJiIiIpJFsOD8aZYFiUiOtHr2zzrrLOzatQuXXXYZ2tvb0draissvvxxbt27FzJkzlU4jxeDcFWVZrwhARGQ9LVzJgyhsLgM0EqUkW8v7afXsA8CwYcN6BOLbuHEjZs+ejTfffDPjhFF8NhsHMxIRUXbxWbB3loisyYqjScic0urZJ32xqk9ERJS9WA4gIiI5WNmnrLepu1vvJGiu2+fTOwlERERERKQiVvZNiC36ylrc1qZ3EjT3Wl2d3kkgIiIiIiIVpTRn//LLL0/4fnt7eyZpISKNdLBnn4iIiIgMhJEOlJdSZb9Pnz5J37/++uszShAlx559ZTHgIRERERERWU1KlX09l9V79NFHMW/ePOzYsQPFxcU49dRT8fjjj+OII44If0YQBEybNg2vvvoq2tracMopp+Cll17C0UcfrVu61cDKKRERUfZiKUAZfkZMJyKLM82c/SVLluD222/HypUrsWjRIvh8Ppx33nmw2+3hz0yfPh1PP/00XnzxRaxevRpDhgzBpEmT0NXVpWPKiYiIiJTT5ffrnQQiIs1w+mn6UurZ19OCBQui/p45cyYGDRqEtWvX4swzz4QgCHj22Wdx//33h2MLzJ49G4MHD8bcuXNx66236pFsVbBFn4iIKHv52COdEh4tInPb7XDonQTTMk1lP1ZHRwcAoH///gCA/fv3o76+Huedd174M4WFhTjrrLOwYsWKuJV9t9sNt9sd/ruzsxMA4PV64fV61Up+Rrx+PwLBVv0AW/czFgDgDwS02RfPmynxvJkTz5s58bwl5/P7NXtuyWXk8+bz+Qx3vIzEyOcuHr8B7wGtmfG8JdMYUSeL5FHgHvb6fIao24XSIJUWNdJnysq+IAiYMmUKTj/9dIwfPx4AUF9fDwAYPHhw1GcHDx6MioqKuNt69NFHMW3atB6vL1y4ECUlJQqmWjluAHsLCwEAe/ft0zcxFlAoCHBrHAeB582ceN7MiefNnHje4vP6/SjPzdU7GZKMeN6W+3zYnWfKIq+mjHju4ukIBNCYY5rZyKoy03lLV5kC9/CSbduwx0ANRIsWLerxmkOFEQymzPnuuOMObNq0CcuWLevxXmzwOkEQEga0+/vf/44pU6aE/+7s7MTIkSNx3nnnoaysTLlEK8gVCGBjRQX27tuHQw4+GDkGfeCbRXFODpwq3vxdnYDfD/TtJ7a+8ryZD8+bOfG8mRPPW3IFNhsOM9hQfiOft9P69UNHW5veyTAsI5+7eEYXFqIiTi9wtjDjeUvXqf36oTPDe/jMgw7CEQboyPV6vVi0aBEmTZqE/Pz8qPdCI8yVZLrK/p/+9Cd8+umnWLp0KUaMGBF+fciQIQDEHv6hQ4eGX29sbOzR2x+psLAQhcFe8kj5+fk9ToBR+P3+8E2dk5uLXLZsZiQvNxdqZpEbNor/PfVnQG7wjuN5MyeeN3PieTMnnrf4/AByDboyjxHPW15enuHSZERGPHfx5JoorWoz03lLlxL3sNHqdlLpUSN9prkyBEHAHXfcgXnz5uGbb77B2LFjo94fO3YshgwZEjUkwuPxYMmSJTj11FO1Tq6quPSeshwazXXyeDTZDREREZGlVbhceieByBRM07N/++23Y+7cufjkk09QWloanqPfp08fFBcXw2az4a677sIjjzyCww47DIcddhgeeeQRlJSU4JprrtE59cpiVZ+IiIjIvHbtBtxuYPx4luuIkhEMNm3JTExT2Z8xYwYAYOLEiVGvz5w5EzfeeCMA4K9//SucTiduu+02tLW14ZRTTsHChQtRWlqqcWpJiiAAsPGhRkRERNmttlb8r70b6N1b37QQGR2r+ukzTWVfTouOzWbD1KlTMXXqVPUTpCMzVpYFAfhxFVBQAJx4gt6pISIiomxh5IoCOyyJSE2mqeyTudkdgMsl/o+IiIiIiIjUZZoAfXSAGXv2iYiIiIiISDus7JuQ2aPxc8QaERERERHJoUTdIVuD/LGyT5owd/MEERERmZVdoyV2iYiMhpV9EzJ9xTk7G9aIiIgy5vYA1TWAz6d3SsxjSXu73kkgItIFA/QRERERmcTGjYDDAXR0AEcfpXdqiIjUx37C9LFn34TM3rOfTTdsNv3WdPh8QMDiB0kQgOZmwOvVOyVEZAUOh/jflhZ900FE1uTzc0lIK2Fl34TMHqCPCBCHoi5bDqxZo3dK1FVVBWzZCqxdB3Tbrd+4QURERObk8QLLlgFr1uqdkmjZGlxPCazsk/Z4vxIO9EqFeqmsqqlZ/K/LJTZsbNmib3qIiIiIpLS2iv+12/VNBymHlX0iIg2FHqRERERESnI4AbvFO1EoNazsExGpiLNuiIjIDLweYNduoLtb75RQOgICsGoVsHo1YLXVJjkoOH2s7JM2Iio8at+w9Q3iw4oZAxERWRWnsKamtg7YvoPHLZGdu4DaWuPN1yZ5AoED//ZabGnODWyBShsr+2Q5O3aID6vmJr1TQiGBABtfiNTg9YoBIJub9U4JkbHt2gU0NACNjXqnxLjsdg5FI2Nq4ZJGaWNln7SnUa3PEPkCa7jw+8Wo++vW6Z0SIuvZt1+s6G/ZqndKiMzBZ7EeTzIGI3RqsKkmMb3Pj15Y2c8yXV3AqtX6r8/r8QDr1otD7uWw2tyjbNLeIT4Eu7r0Tok+OGef1OTx6J0C46mrAyoq9E5Fctla8NQd82RSmMsFLP0e2L5d75QQ9cTKfpbZvEVc6myzjst/CQD27gM6O8Uh98ns2w98vwxoYRRzS2F5i8iatJwT3dUFbN0GOF0HXtu5C9hfbuxlPWtqgRUrgO44y1u1t4uVh/3lWqaKtGLmhh5DjJo0mJpa8b+GmiJi5ouMFMXKfpYxSg95KsPoKivF/+7dq05aSF2s1JPR+fxAUxPgDyT/rN4Cglig9KhU4O62A5VV0YGeUlFdLTbOdmo0kmftOvHcbZFowDbycO3du8VK086d0u9v2Cieg4oKwOnUNm1E8dTWAstXABWVeqeEiORiZZ80EVXhS7O1kRF0SY6AIPb26Xm5RO6bw/iNxe/v2TO1davYO7x7tz5pSkVFBbBtO7B+vTrbX7MG2LcPqKpO7/t79oqV1HiVWLVYuUJslEb6VDQ0BlfFMeBzO1mWLEBsAKSedgXzyP379U0HEcnHyj4Ryebzi8PV3AaeJ7x9u9jbV6nTnF2HE/jhh/QrS6Su75eJPVORhfm2NvG/9fX6pCkVTcFVRtSu3HKVI+MzYkU6ZPt2sRe40YSr4mzYACxbBrjceqekJwOfck35/Zkdi0BAHD3DaTLG4nAA6zcceCaTMljZzzJGLhxYkdUO9549Yu/nhg16pyS+UGVIr8r23r1i0DROOzE2R5y50v4A56QSKcWMASQ7OsT/Gmr+NcQRaytWiAEos5nTJTbabt6c/jaamsS4GKFAng0NQKtEXCinE2jWOaB1Skw+knDLVvH+27hJ75RYCyv7pDmrVYCzSWgtby2HzDY0ANt3iMPzzYANaua2YoXY888Kv/lZ5lY0eQE+VV6fysEVZR7PfftSi+Oh9vW2bXswzsMulXdkcKHGDqnKuVyRMUmcLrGMsUmi8eDHVWI8kEz2lQojxxnRghkbB82AlX0ikk2P+efbd4gVfjV7M3w+9Qpq8Y5ZIACUV2TvkoRGFZof3dmZ2XZaW4GODLehp0wbrbTOK9jIZh3Ll4tLBDsMEIehXuZzx+0GVq5UN3Adr3F1yKlgahFwtKYWWLZcDHKajIDkDVFaXi41teIqX+kGdg1R+7mRrbcQK/ukvWy921QQEJSJIK7qKVEo81arp7W1TXzAah2crbIKKC8X4wuQclwu/QvFbrfYS6RWEL1sZ8aAdVkrg3sxNJxeT3JHlO0vF+972YHrWA4yFS2eKaEyyB4ZUwA3bwa+/1685qLodF3t3g20tAB1EXFvmlvEzhqlVrkJCOLqXOwgSR0r+2QaSmS2Pr86w4T8frHnWa3lsOL58Ucxw88kM92xE1i9OvMWWbWp9bANFc5qa9XZfjxaBkDz+sSHrtWD3rS2ASt/1H++n5EDWJpddY04X7fOBMEUKXvo3cBI2SU0rcBocSX8EdMQtmwRR2VWVUl/1ulMrdxZWwvs288OknSwsm9B/kBqjXs+v7ZzsPV8Ji5bBqz4Qfle4l27xXl0mzSuZIRade1xgo0lEwiIEcgdjgPz8a2qq0usCGr9cIw3LE3LYc779okPXb0rwWoLNdi0t4v/9fmAPXv6xi1sJKJlPhUIiIUYMw/7j8cfULYitGeP+F+tl/aj9BixDpxS1mugH8BlXFVioHNsRT1GHwBo7xDjIaxZK3876ZZziZV9y3E4xZ7eHTvkf2flSvGm686iG6lLRq/qnr1i5UhOQTVUgdRtuao0H1a1WRTVd+s2cYj3tu3pb8PvE4eWmq1s4HKl9z0B4jWt96iP1jYxSm+qPebV1YDLlYt9+2zYvuPASg1GU1UlDk+UM+zfTAV+n098HsUW6LxZHoSKpHV3iw2yYTIzWkEQl1HL9sB1RiAIqU+z8XgYmC3bhFZ0UjUQJ4Wxsm8xocAeDQ3yvxOK/tmq1fIiJqkpVVeLw57b2jPYiEa/Nd3dqBn51ecTgyzt26fM9iIbXdye1KcuxKuwptLruH6DDes3GGs99q4usVdYjXnMDQ1iRW3DRuW3nYpNm8SRJ6nGVYg85w0NYoOPJlK8Ie0WLfC0B+dcR/bI1NSIAdjSGW1BmdP88ZvCDrdtT69hsrNTHM2T7UvSGcGateI0G7mjJwOCONpyxQ/6NyonovU0jYzKnTHaO7QbNWaS4n3WYWXfYszU62MWAb9CGVga50YQxIw6acU2zQRGJknpTLq2Tmy1VXq0g9MJ/PCDGK9Aa6FKS+RUACH4ul5zNteuE3uFyyuU33ao8JxpZPpEnC75Pb1SwwETMVp+yIIQsDs4DH+vQo2AJF99vdjQ0m6AwHdS0q3sxct7M26sp5SFnpFy48MEIhqp5TYQZEN8hI1xGtjb2g9MU0tKEDtdNmwQR42ZZfnieIz2PDcTVvazmMOp3s0fmxkrsRu9MvgtWw9kvIGAGHU3rcpPGukvrxAz6m1JeiTTPTT7y9P8ogxKn6/Q9lqCgWmMMuyvrhZYvSZ+r7ESDyivT7wOmxLEVdBtCkkG3G6x0Wb5cr1TohyvTxxpEVJeId6fgQCwapWGowsgNqTU1WdH4VhtLhfgSrGxSSlKlXF37BQL/1u2KLRBGZS89OL1UErtw+cXp+Ft3CjRiMBKg6YEyAtenI3ZlADx2b5dxtRbn1+8nhONtIs9hpEN6YJOIyfq6ow1GjIbsbKfxVatUvahHypQ1tUDS5aKy3BIfk65XWom1JJaVQVUVADrNFpSq6ZG/G+8YxmWxkGVE7cgXYKgfHkq1KsbuV2/XzwXaq5tnExlcDhyvACH6Va0AoI4x9zjFZfoa24Gtm5Nb1t66OwSh3Qm6oVQc8SAXvbsif7N5eVi5OT2dnFUSjpxA9o70ltz/McfxUB2oXzEanwSU1fi3W5uT+ojQ0L8AXEu+cqV5mw4qahMLRCWXhIdW58/tR7KyKjg8T5fV3eg8VhrJryM0rZvH7BiRZzppWoOL5RJ6d0KEONHyFnhx+0Sn+1ypt76JUbAtbYCfn/6pa26OnGFE7XtYEBVXbGyn2ViH6atCj3o7A5g6fdij1YoSvLmLcB3S8RCfyzZw5Bk8PnF3jOl5r16vfELHUadW5vOw0qt+fo+H/DDyujeTSVIRdGvrRUrjPHWNhagTuE8cpvpBr9LprJS7AVev175UQxyAu+1d2S2zvWGDeI+9J7vn7Y0rxupBgyPG7Cl8bT1eMRh7xs2iI2z6VJ72HZ3t3jP16cQKwYQD/GevenP308a0DB4DgVBnPrzw8r0limNHF6s1JrRSmlsFBsiEq09vX+/8YLHNjWJ5yNRg1/kVyOfV4GA2Pglu8FaIg0dnWKFbPPmlL9KKQrd33LWjzeqVMoRLS1iJXqXjBgzmV5f7e02lJeXpfXdgCDeA3v2KFjGSPMHCUJqadB6mWuzY2Wf4ursFHsE5GRy+/aJn6uU6GFdtw7RGUAa0VoT2btX7D1bvTrzbTmcwPIV8Xvu4w3J1r23J439qzWSsb4+8weH3LmbyT63fTuwbLnySy2mIt1h/KEeYKdT3jZSWT6zvk7s6duUoKAbipabTCAgNkrEBsfSI9iSEeb0yVlqUe7tunFTz155rZYfam6W39izfYfYa57KKjAAYO8WA6GmO38/9lh0dAItEiNsIq9Fr0Gm/yglFNRu7TpxVR2jzVGXutYdDjHPcLvFToF0NrpqFbB2bYJnTZK8QMupDBSfATr2Fadm4GMpTmee5OutrUBbRIdeuFE2OKQ+styqRoDfVGzclHxZ7MhnqJyVa+gAVvYprnXrxR6B2mAL5a7dYovl3n0GqNxGUHIocKj3OF4viZGjxUpxusRKmKzYDDI+o2hlSmJb7e1iJXTp90BjvOHOtjj/ltDYKD7EUlmdQgtuj/LLwLlc8u/Lmpj16DNRVyf+Fr2XvXK7jbu0HgDkRFb2ZeYjalbs/QGxcbbbHr+3ev0GeduKly9G3p5So8jkFjA9Hnl57/r14vzXhAlJkdMlbziuVhL14Dud8QN7qam9Pf6ot/LynrFh1q478O9E57Wy8sBjKV6lUM7Iqths0QZ9G4ABcfi2WgQYbwQKaW/3nujRBeFGWQMOqU+1LJJK50YkA1VdNMXKPiXV2SkWdmprxVb4qqro4Zpen4w55RHSvdmM0MCQamUi0yTLrVzH28+PP4qVsGqZw2R37xFbWNU61s1JrpMNGw8MN922TSxopyJeutUazp+utTLmz8ar6G3fEf+3hArObW1ir71aUwwiyYmkLzcycyoam8RrNTScT405yQ6nWFFRYl34yGH87R3aX4/ddrEXPdTwV1EhTrVZswb4/nv1e6MkR5HIyN9cLrHHZ6UOq28AYh4qNWJNL0YbCu1wivl25Mi69pj7vSJmpRC5jTwuV8S24lwrHZ3R91J5hfi9qI9ncq8pfJ92dYkNX5ENHkrbtEm8p9ONURFLgHLbSrojlfj8YiN3aCRINjaGGKkMFMmo6bISVvYpKale0ciMX07QMDXuZadTzLzTzSjswaGEWg2LVTUCcJJj0NYuVlqSNVbU1BxYrqiiUmwVVvLcpTp0Ump5vUSHMa0hoUn4/EBzExAIJD+Bco9VJtMcGhqSN5ps3CQWKJVswW9uke4Nk3NZb9ykXDpCtm0Tr9V9+8SREmr01K1ZI1ZUdu0SY4/UN4iNUZmOJtq0KXqpRC0CFa5ZI1YUQzFVYnuI483rFwSgoVG/SPStwcqeUVbfSKazM/1eJznkNgA7XfEr1ZmM0GprB1atPjDFwyHx/Ey1kTYRt9R5j8ho9+4VG7FCystlBNDVcbrP2nViw5eajWuhxlWlRrTt2ycO/zZzoM+dO4Hdu8WGwzVrxXnqqUilnBlQa0i8nOtW54pzOrv/MYN4NCQPK/skKXb+bazIjC/locAKZEZOl5hB7N4tzv9LdbeCIA53bGqSP1Q1YxK/u61NrITLfZD4A9LDHpN9va1NrLRELfuV4MEhCMEpHLXmWtJNqYCTkbZuBbZus6Gurlfy/acwwiUTUlF5QyIDIyrZG7Nli3arUKSipUUMvqa00HJ5gFixWbdOnJO+Zq14HFIprNsdPQOBhSoo3d3yR2AocX3HqwDEyw5qa8XYFz/+mFrWnUmFsrYW2LWrX0qVZiP0Djld4rURKrw2t4jTCrQeMm63i+dLjUL0xo3iM1ep56bcXv5Ec7tj7wuPJ/oLyS4Npecrr9/Qs8EjIIgj58xI6QB7FRViA3Ds9EK5t3A693pkR0cmZRpBEL8fm4bIe7w8ZiRLLI839WJwVXX8FX/MTu7zzywNvkbEyr4F1NaKDxElyzpKR1KPpMRwzN0yopzGihw6vXnzgYxD6Rb2VNau37hJfPA1SESajyUIwLJlYsC5Hg+7FE++2xPdG5KIaq3UCpBdn5B5fKR6L0O9JG1theHX2tulC/HpVq4FQSwghgqDyYYYxvs5AlIfctzWFt2A1NUVP9Ktmj2W6cq0IlUpY4qLVMU1lf1WV8cvqKTSq58ooKJaQj3rofxHi8a/3btt8HhyUu59k0PNNoHYXu4tW8QCesbP0xQbTlavEf/r8SjbCBIb/V6AdMTxVKfthDoMJNMqNWlfhlQq8FXBZ6HdITO+TRIdHQeCVfr84v9qa+P3jEfu0u3OLDaQnPMtQGz4j51eobZQnrm/XLxGGhtj7seIP7rt8ip35RUKDMmXOGbxDuPefWKDb2xQ0ch15BOlu61dXIZw27b4n4nV1SWOYEmn3CtXd7cY7FPuqJzOzvSWg/UH0psW5zJ4TB6jkw7hSKYSetgedJB2+0wUJEgtoWj/ZX3Sa4nXqoe6ogIYORLIy5X/HTktm6GCmxBczSAv4u5tagZ69QaKCuN/P1IqPaF2B1BWZoxI5wAUG4IpCD23tXIlcMQRwNAhyb/f3Azk50e/1qMwLMjrjW1rFwuIHR3A8OE9l1mTWnZQKRs3AQUFwPjx4t/rVJxLmkhAiA5ip7bQGu370owEX98AjB2T/v5D91OmQT/lFO7l9AjJqeP4/eLz5sQTZHxYAUpVVO1x8n6HAygqlr7uOjoBV5LCrADx+ikrAwYmeP4q0SNlt4v5fKrPvshKSKZiY410dkj/tqYmG3JiupIkstuw9g6gb98UEiLjuoiaApbk816v2OC+fTvQr18K6UiyzVADGSDm68nY7WJDTXExcMrJqe9TEJKP5mhpPTDKqKkJGD06+n2XCz3OnZIiG6Pj5X0OhzjtCAAmnpV4e+Xl4j1xyMGKJA8BQdy3wwGcNKHn+6FOkupq4NBD5G/XHxCf46EGn1Qqrlr0aIdGfdrtye8Bl+vAKL9k5ydSe3v6S/GqEfcnm7Bn30K0XO4j5eGkCR62PpmFF69X7CGRuySYzN2rQoshpZG7qK8XK6pKbS/Srl0xw/+TkVlh03MJLLtDnLu3SSIY4f4UeuFiH8Kx01/kxpSI/IyceZFKBwzzeMRKvpoP1FAvYEWctMs57qk2MiZqoNqUYRyBigrpERBypxWFZDo0Vu7yeOlKFAyypSW1yuf2mKX51F7+bP0GMW6F1PSThgZx7nm8NKxf3zO9AKIyyqZGcYiznLg1qYiNyeHxiJXA8vLU7wGpnjqlnk9yn92A2FCQyX6VXKLNJ3HfhvLd2DxQgDiKa/2GxPuVGtUVeW8kXFIs+N+mYKNcqiOo3G7xXLjcyTsOYqcTJbJ+Q/S0n1TOn88nnZbIUS6x2wv92ZFiDJN4HTnpXCdVVQfycCVXmNkbjJeSTqeTEqNNYu0vB5wSzyqHI/moz3grbySTbkWfMseefYvzeMSHV99+Yu+dEXk9QF6xett3OJTJZNSsvxthvmmiQkKinkGPVyyA9u8fLLTI/C3pDG31+8XGBzkiG78ERBe2QpGj29qAXjFT8SPPRTqnJbIhTG6PfNRqgjIaS5xO8d6OvadjG1D8fnGIauS5lVyaTAPd3QeWMBs9quf7tbXK9c6EVFSoO1TA7weQn/RjcbVINJr6fOI1GDtqxGh27xEbtwYMkP+d2HgBWsxll+rZDgQOVOQj71enSxyeOmiQvG1LBo9LwG4Xh8qOGZP4c7ENEHosp6e07m7x90vF+KmrA8aM7vm6UmLz8VQb2ELPP4cD6FUi/ZlMruVMHv8ejxg8DwBOOSWDDUkIjTZLR2jE6U9OjP+Z3buBwYNT2+7OXcljSoU+194ODB0KlKRQvlRiOVopmcy3r5K5mtKevfLnv1dUpDaNw+MVR3zk5eoa35LSxMq+xa1bf+DmHzgw8Wf37ReHNA5K8jmz2b1Hx8AeGVbi4xYg4uS2O3cBB49Nr6JQXn7g33Ie8KFh12vWiMf38MOB3NzkkeIz0SO2gQpPnVBlv7wieaEilSUnE6lLY6it1DzF2PmzHo/YoxCpuRkolDndQ0npxn7Q696VU+jL5PKz2aR72ZYtF/97xhkZbFxBsQ2RnZ3idJXQvdHSIg47Vmvfkcc4dp5sOqqqoqdARQoN/c50akU827aLFV6lRwKkKp0pWUq0R1dWSTeAphzrRIXGcTnHJFGjvNT3hSTv99hGzHf37BEbo4cNjf+dqJEesUHjNBrtGYpfc/zxPd9LusRgRJr37wfGH5344/GeyW3tYhpC5dfQ51KdpqXldMXGRqB3afLPyYnrIgjJe+TT7VTy+cU4A4D0sH0l8uV4AgLgdiUe8ShAjJdS0iv5M1kwQs+aDljZt5jYjCqylU/OHKFt24ABpyubJiDxs1nqvapq8eY+/jgF9q3UvZ3KdqQ+m+JDJJ2egro6sbfxqHGpfzeS1DBxlzu64rV0qTjfL/RaTU1myxjKGaoWWwGTfW5TOHfhyn65/O9kKp3AM3v39pwzLntlikQR/lIVE/Va6bJSbEFCANCiQVRivZeZcsvoodm1W7lI4n6f/GHZsXO3lSDn2pHbw5VIaHRJon0nauyMF1BMDqWjvsuh2JQfhQLXpbtbQeK1lDeSjjR37HDIn3Jh6/EPoL3tQB6UqLKfSFWV8qOlYgmIGPmQ4TLGzc3BBoo0zldo9EvpKUBxUWrf1av6t207MGyYQhtT8Uf0OK8xGbUS+bKU6hrxmojX8L5xE1BUBPTvJ041HTwYGHekOmkxO1b2qYc1KhTkUhXqjYzXYqj2PFUpsXmp05lij1aKmXG6EZxTnTMsVyhgTqTIYWCZDsmVMzw/9rynMr9eLrM0/DY39xwaKLdXIl5veaY/fccO5R+2sddVc7NyPaOxARW1lOxcybkOYyuumTS2SM5Pl5kOs+vsAPr0UXabag0H1prXJw7Blzo+qVbu9LqW5IzUiHc/Ro5USzX9qcYFiUyC3F75ZEnq6hJ7j80y9DrTa8TjTr2ynwmXK72ReSFKjQ5UiuTx1+niSbYaSyi+RqhRraFBLH+4XGLQz0GDtA36a2QM0Ec9yA0Os3uPGJFaSa1t0UvXZZLxd3WnHugloZi0JApmlmqyKyp6to7GVsh6bDPJTjLpZZeSLABkpsOtM40aLTX/OR16V25SWYotlt4rJsRbw10Ov//AsW9pFadRSJ0KJRv65PZQxxtm7Per11u7WqJxLZkVK1KfR55MuvdDV1dq+e/GjcGo6Ao/U+QICNG9sAnzIpnHIzJOTEuL+DU9evaleL3isOdEAgGxErN8uXhuGiSOiZrDd0M6O8Vl2Nojn7cpXpPbdyQZXZjgzcjroqk5veXGgMTXdXj3SaIQppO/r12ncoUyIp0b0wh6Gltu2Lo1s8B4AtRZojTeNbJhY/K57w0N6k9H03KFrMjLsEeDsw7lp9h9/rhK7HjQe3SekbCyT2mrqTmwlmxSMjOATZsyW/s1sqeurk6MqCx3lQKnK/HQ7dhKWKYFt9jMP5WC07JlyfefTmVBL0rMkU0UZTilGRg6V/YzGSGRaWVfzd8u53etXiPGIti8WbwXtRiun4nVa4Dvl6UXLVmNhhmvV70hlelIZQh5ezuwfEX8EQZqqqmRMbc4iWSNqytXJr4HBIjPgHQqTOnYuDFxw1lFpRg9PKRJxr0Yb4nDTNTViaPKtm0/8Fqqt1smDaiRKit7LosaosT9HG8T9Q3yevrjHZd9+8T3XBKNllu2KjcCPJ0K7arV0X/HXpM7doqVd7lp7O5OY7WoCJHnMVnZ0eeXFxAvUZ6WcryKONZv0K6lP3K0S2ycoNZW8femslKHkry+A+UYLtd3ACv7FlJern9FJS2CWFhWouVT6uaWW3lKFsglNqL/3n0ppjkmL44svKRDau6pHOks/aK2qiSBZbJJJoVGPXr2Y3cZanCLTUvkKgC790gXXh0O4PvvD/ytVEFIbVJLeelF6WeAKZ8pKUolUna8Kdyr1wCbEywlmOxabmsTG2q0LKCu3xC/RzC2wiSnArV9R+IMKNm1pOq1lmTbeo6KkpqzH2nHjsymLjkcYieK1PK8zc1AR7v4b7c7N6Xtrl0nDpdWU329eO3JbUhKJ0ZFvE6eZFMiV69O/L5VxHbGJOox7+oSRzKsy7DxNF2ZLkFtVazsW4jdnvlQaL2sWyeue64nuSMAIsUdHidVsIh5LdmczmQFn3TXXs20kUEJsb9N7pSDtCt/KRwrvYfC67l/JQrbcobG19Qkn48HpL+er9b27hOXJkqFWudZ6QqTUr2ilFiq87uVouRUN82mKVisQUvO7mPLC+3tqQWzSzRqMRAQyz+7d/eVt7Ggri7tloWUW95Jp9Mo3alhZmmMDknnOvd4gQ0bUv+eWrGjkkmWB2VB27UkVvYtRqmHrdLrHye7weL1NmeyNmkqBKTZo2KTzvBDQx7TyVg6OsQKuVpzvNRaVioVscvz6bY0ogRB0O66k5KjY66sVORuAckLFnIaeNIdvaK1UE9GZwrzJuWuh5wqvSsuVicI0stepqurS99AkUoqLEx88SlVrkhniHCyfev5DAotS5pkyn6UDRulg+amQ4C81T90ZdJ8zUjPsHTuv8aYWBOKxsEizTAav4UZoVIXotjyP8lIPBDktL6m26tit0fPawzZvRsYHrOkSn0DUFaWeHtdXeLQSjmMVEFOVewoCrUjV6daTtii4zrY+fn67Vsp7TIaztLt2TZqZdbl0m/oYiRBCAaDSzJSScnh4kZ61qht1SqxUf0MBZeoTRRvRAsCxJ7fPmVA//7pb0erUUnpLP24dVv89+Kt3S5Xa2tmAdI2bATGj0fSqOexnTmxZRujRXZXkhHy/XRGmsXOaTebPXuj/16/Xp90ZMLtEaPyW6FslS5W9i3MSIGaEvWUGiAPT7vgW53CXPPqaiAQsGHYsPiF41SCRBmpxTgV5RXGiUoNGKMQYTWyp8TIqBxkEmwpGwmCOOxSy+VJzTacNROhvEvJ3ni9G24bGw8Exj3+OKBvX+vli2reD0pEf9+yBTjs0IgXZHZc1ESUA/bu7fm+VWh1OSZqsMqWOfpW0doKfLfkwN8Tz9IvLXrjMH6TcrmAhoYSeBIUstJdIobUFQjYsHy5fhOz1Ro+LFd5ubEaoih98eZRJupFI3U1NGhb0c9aFqoMRz4TYgPRkobSKBZkS8RxIYtGD4VUVmbP+SV1sbJvUps22dDUVGyNQrWFCk2xpOYl2+356Q17tfBxImXIDXSohIpKYOlS9aMxp4srPJhfe7vO0TITEML/Z25GOcJWG0mQDt3OhQmO/cZN6QclVoIea7bv26/dUpzZwgSXuio4jN+knMFe+85Om64BvYxm3369U3BAR4f8+fdK0yxGgkkkalzRK2qs2e0P3msZDbNX8clr5SGtmTLTqi16rdeclEVKjW3tygUkzWTOfk0NK/yRtf1sPxRSmhr1TgGRObGaSKQSpSv6zhSG3xup0cMI2PhBdMAOiaCilJ2kKvp2e/wVctTS1q7t/jKh1pKgRhllYVTbd6i/D72X3qXU8Zwlx8q+hWn9sCZ1abWeLZGRGSm4I2nAoF2cXd3GncKSqdUKLemWCjMV2FevFhvfBcEaEfANeovJxnntJIfZr/NMcBi/hWk5fzcT2XYDmqlQQ9rrzJJ1bLu60ushM0u+Rta2b5/eKTCetjYbiov1ToU2urqApiaFr4OIskFDQ/yPqSHbymFE2YSVfSIiA5G9bJ1OBCg33LS8PPXvZLKeNRGpq6kp/dp+WoFrdWKD8rEv2tsP/FvL3uq6OqCpib0QRFbFYfxERCRbNi6BRETyNDSUpP1dM1X2AeWnFGndmx9ihakIlL1kT1kWsncECyv7REREZEhdjD1DBpX1qwcQkSmwsk/6y7IHJgsIRETyNHK5raxgxlg2Ho/eKSAiSo6VfSKNtbUVqrp9rhtPalr6vXpLTxFRdmpq0jsFqeGIEyJzyeZ+Nlb2SXcer94p0FZnZ4Gq2zfbvEcyn9Wr9U4BEZF+Kiv1TgERkTys7JPuNm3SOwVE6siWZfSIiIjMzmwjTCg1QpbOo2Vln4iIiIiIiKwpO+v5AFjZJyIiIiIiIrIcVvaJiIiIiIiILIaVfSIiIiIiIrKkAIfxExEREREREVlLY4PeKdAPK/tERERERERkSU5X9sboY2WfiIiIiIiILKmqSu8U6IeVfSIiIiIiIiKLYWWfiIiIiIiIyGJY2SciIiIiIiKyGFb2iYiIiIiIyLIYoI+IiIiIiIiILIGVfSIiIiIiIiKLYWXfhLq79U4BERGR+Q3u7q13EoiIiFTDyr4J+f16p4CISFsjOsv0TgJZkE3vBBAREamIlX0TsrF0QhoZ1zRQ7yRkjRJPgd5JMKwjmg9CTsA6j6ve7kK9k0BERERZwDqlpyzCyj5pJVsjl+rhxPoheifBsAY4i/VOgqKsnoWXsjGDiIgMJlvLtKzsE1FcVq+UGEmuhXquFSdY60rM1gJHMgc5SvROAhERkaWwdGlC7NknrfBS046NR5uyXJE3X+8kEBERWQor+ybEyr76bBbrSSQyM96N5pJu/snzTESkD8Zosi5W9k2IlX31lbCHiYgoLTlsLCUiMpWB9l56J4FUwsq+CWVDZb/Y5JXtPM6/JlJUP1eR3kkgmVjZJyIioxGE7IyYwxoJGVKOYNM1onOmRdVeXEaNSFH9XMUo9OXpnQySobfJG2uJtNCfASnTMqq9r95JIJPKzqo+K/umlA09+0B2Ld/ENdaJ4gtleX3Zu28KfXieKInjudQocjkCJi0DLdpIovVqJAMsehwTaW/XOwX6sGRl/+WXX8bYsWNRVFSEn/zkJ/j+++/1TpKisqWyD5uObXAZPoRT+XYvTwFGdpZltD9S3qAU569lU+MUKceKoxVM9YhihUsX+f5cvZOgO155FIV5ker8fr1ToA/LVfbfe+893HXXXbj//vuxfv16nHHGGbjgggtQWVmpd9IUkzWVfRNLJdBJvgHm9/d3Fku+nq1DnoDUR1sc3jJApZSQ5UQ0ZBp55Y90e36yOd8gkosj+iiRAgs2BOstENA7BfrQv5ahsKeffho33XQTbr75ZowbNw7PPvssRo4ciRkzZuidNMVkS2VfzwJjpoc4leF56fxOpQNgmT0gIllcCtd7qiMypIxQeaRNKve8nj2gRm6IIDK7URzRp5mfVo3UOwnJxYxm1XqaRzbk9lkanw+WajbyeDxYu3Yt7rvvvqjXzzvvPKxYsULyO263G263O/x3Z2cnAMDr9cLr9aqX2Ax4vQciSgqCYMmWKiEQQCAQQCCgz52Z6b79funvS523QCCAgMTnB9pL0NTLIbn9Yk8e7AXKXZ/xfq9UurKFP+AP/3Y595vf78/wmknv+4O7e6Ghtz3t/ZqB3++HTRDk3Zc+IaXzJmVESyna813oLHQn/3ACJd48OPJ9PV6P/B2CkPg3nVA1GCtH1WSUjnT1tReisbg75e+lm2+Evqfl803P54zVpHLeMs0vrUCIyKsyNaatD3p5CrB1cFN6aTFRmTKdayffY8v4WBf4c+HJVW8ceGxeJCdvyuS8xW4/G+5Jj8dniLpdKA1SaVEjfZaq7Dc3N8Pv92Pw4MFRrw8ePBj19fWS33n00Ucxbdq0Hq8vXLgQJSXGDF7h99vQ1jYUANDW1qpzatRhd+aiqzsHbQP1uSmd9lw4e6WfqVdW2tGa64r7fuR5c3flwtPc2uPzgZZOtA/o+ftL2/PQWCjAXazgQ6exC60+T4+XKyrtaM2L/zusLK+2C61C9DFJdL/t3+9Ea7F044wcu3d70NqnK+XvCU2dut0nWtmzx43cgA11PpfkPREp0NyJ9piHZar55J49bjTkODPKAwCg25UDT1HPEpjdmQN3sfh6gTsHnsL4pbQ9+1xozaAxpyBOGuToWudG64TUK/vl5Q60FjhT/l5OXRdaAwfuOS2ebwVV3WgdmVmjDkWTc9727XOitST9/NIKYvP83h156O7Ts3FQjv9v776j5KjuvOF/K3TOcbp7pidJM0qjOJLQoIAEQgSRjI0JIsgYbEwGYzDGz8L6MQvHa/vx7j67eJ2wvbZffPYlvMZmbeQ1cYkGBJIAWYASynFG0mhyvX/c7p4O1d3V1VXd1T2/zzkcWz3V3dV1q+69vxvNO44juN+Mw/NLL0PS1UKdUk1Zq7Z8TTfjr05sUpEfKjV8VMQx73j6l5J3J9MtstOCvQrzs+zv27JlEIc9+v2+anIfEdHnG8Gbb34CfotxOkfWrVuX81p/v/b5Yl0F+0lc1jh3SZJyXku69957ceedd6b+3dfXh3g8jlWrVsHtNuYQq9FR4P/8egeOHDkMn8+f97dVimvQjOAJO3Z7jmFQo1ZPx5AJbrcFnKs6GU/U5MSeMr67eTSAk/5DOa9LkpSTbl67FQ12R87xDWYHeJlKfjt82Oc8rmnPfqPoAty5BWHLSBD9/oOafU8taeK8GPEdBSCfbtna2iI44pdvVFSio6MFu/3bS35f1OSs2nNSKR2T4xAkHqOhg7LPRLqIyQGndQj9pmFF6SZn8uQm9Dfsx3FzbgNYKfL17NuHTeg3sefXNiLipJi/gj+pvREH/ep79m3DIk7KnIMSnR0t2KPinmxtDeOYf3/J72vk3Rjz9qlONzVO6W3GDr4Xvn4b1sfUP79EWT4JAJZRAe3tYRz276ng2RlPdp7vt9lw2FZ6IxkAxCUfmjxuVWUIoDztjEBNWau2fE2nNj9UKmC1wWQfT38leXd2urWOBTAkU/eUE7Jkjh7t6GjGMeceHNewbmkUIYsdoqMfs+dMwrnTXdU+HQwPD2PdunU488wzYTJlTqNNjjDXUl0F+8FgEIIg5PTi79+/P6e3P8liscBiyV1F22Qy5SSAUfD8eIMGx3Hg+epmzAInoO24H8fswxg2adMixfM8OJ6v2m/r6A1gn0d96x8vyJ97cphVerrxPCd7PJ/n9ws8n/dvqs83z+fl+x0TgZB2TeTSLed4QSjrWql9v9b3ghEJggBBUnbf8wIP+5gJA/yIonTL932Rk058Yj1SzmnnzcM4Pu355wr/JrHM+6qc+0PtPSmozDcEnn2f2nRTwySImNTHFtes9+dIb0rTzTpsKju/rAfZ14Dn1d/vgsCXdU0r+cyVS83v1OJ+0/uezc6rleTd2ekmlJDfZ3++IAia17s7DwXwt4Cyxgc9JX8rx4mGiu3kYk09zq+uFugzm83o7u7OGRaxbt06nHrqqVU6K+3xBku1ahUL0w6EdPtscYy2BSLKuQYtcFZhZeVJh/0V/85KKHd3iHJnHXIA4r2eMj+FlMo/YK32KVSMkpW221r1+/5qrgRv7FCSEO2ZK/S4OZ2V+R6lYseMNUKatt6rE3feeSd+8pOf4Gc/+xk++OAD3HHHHdixYwduuOGGap+aZsodYbWm212xjEctXuJyVibNpvQyeHWsQGq9Kr7WIhEgXmQR2vnztfu+fDNf2o74ir53xv6w7OvePIFfMZW8x+ftiYKrQhW2lC0etdDZWdGvS6nGU6ZnepbSGJF9bGOjducRM9hq4N4Bdc+6HKuCbL+So5VnzQIEAViwAJg9C3ApqJS3tOh4QkXK13LoXS4u2KXdQ2C04Eg1ja+5UMH+jlKXx6rFJeSmTNH/O2w2YM0a/b/HiDomA15v8eNG1M1oq3l1F+xfeuml+MEPfoBvfetbmDNnDl588UU888wzaNG11Ky8GdPHs7tlS3N7APJVYlwuYHGbFaf2AB0drAJSiLVA70Mkwv5Xyvo+rxfonpd7fGvaOfI8sGB+/sqMmmKrsxPwpHXGLVmc+Ky0QrCpqfBnzJk9/v+j0dy/m83A8tOAPLNCYDYDM2cWP1ezGQiH2TVJXkellizNfU0UgRUduSXm1CnApPbCn+d0sPdn83qBjrTgbsoU9ttP7ck8Jt3cOfLfMbvZXLTyLYzJZ0edWb3X2Wno841fwzl7oqmK4KJFwNIl7Dqni0bZ70jyetlnKNUxOfc1ucCwPU+ve9cM+c/Nnk00daryc9JDdlBpsQABBQMJ2tuBGdOVfUdbG8uDcvKLPBVXDqwSOn1a8c+e0glMas+sFiafBYsl/3Pa0VH8swFlFdT0z8qoPCcCLVEsPXg3azjCr/NwAIsWKM9t5QIjpderXKWMaFt0CnDKQvacO/K0h3V2AsuWAZ/7HHD++YU/rzPPb0wvW047Tf4YgJWPfh/Ljxx2lt8sWQxMV/CcxGLyr8+cydKju5vlaVM6C59DqeTKpY7JuffrsmWlNUrMm5v57KXXC/LJbrh1DMu35JpMLP9JEgRWXi1bBgQCuec5OWTC/O7xfyvNt+REo6x+lc7rZWne0wPMm1d64004nP/+TUre50uX5g+Bk+V0eh40Y0bu/dLVdSj1fdGofEdA9vlMn5ZbdpVqwXz2G5J1rmCQpVm+ulGhHMvjKb3xQI6SwBFg+d+SxexZ5Hn2G1paWP7C86y+smQJIGTlX+Ew0NxcvO7R1pb/b3Pnsu81DbEPlxv9nV7vsqW1pSod2dNepP6YdErchkYNGo9nzGD5bb6RkqE8A3p5HpiWp86Unn9M1J79upqzn3TjjTfixhtvrPZp6CoYAhobj2PyZD94nkNLC2CxAh9+yDKfxhjw2n+LGEhb+CkWYw9R1MLu/MZEJWJSO/DxJ7nf0d4GzPioAU8jd3Gozk4guYYEB+Dv/g74vx8CB8zjlbJly4DBQeD119m/06cBmUys0GhzsIJi3z6gt3f87y4nMJo2XdZiAabERRzsH8Hu3fLXJBZln5H8nGQAm/zaxaey7x0eZt/n87GMZd8+4MgRYPJkwGphjSffvBL4Xx8BexLrBzU2Avv3jwdqHZPZ+4BE73kTYLOP/05XJ7A5sajt5MnARx+Nn2dbWx/mzPFDSFyQhhZgfdrf09ntrBJ36DCwYwd7zWoFFi4Enls/fm16FgF/1xrGOwe24ehR9nqxID/5WQAL5LfvzPzbnNnAyhCwbwe7pskGjvSM0+lkFdbjx5NrSbD/zd4C5pyzgSP7gN4+4IMP2H0BsDSaMxv461vs3wsWANu3s2vt9bJ7+bIlHA7tYZXkoWGW2X/6KTu+s2O8Mrx3L1vszDIqYmZX4n5LBIahILDp/cRvTTRgnLNCwN6jo+OjESTgk63AzsR1OH0F8PMN7P1Hjko4nEjPxkagIQK8/z4rPHclHo85c4Dnj7Jr0GAxyxZWnZ2sIpPN5wPuXQv86efs39Omsuv94Ye5xyZFIkBoAIgvBP7zPfm/u12A2cLSLxgE3nln/O/hMNDSDLz519z3NjayezwWAz7+mB3bEM5s1BMEVpEZGgJ8XnaPAkBzYiRJMAgcPMjyEaeL3WvDQ8A768c/QxRZEASwgH/LR+wzF0SAbSMsLT7ZilTw7/YA7QuAo2l5RZLJlHjmjwPBfjui0ZMYHQO2bWcLHs2ZzSqCjU3sd+SrMMZiQNdsYFPateroYPd4Mj+YOZM1fAwMAINDgMcNnDjB8uCdO9k9DLBn39fCvkuS2G/v62MNpJMmsetsGQQ+Sqxlt2wZO/aFF8e/2+1i+dSmTezf6Q2adjugZOHe2bPZ94sCcPwE8Le/sdcvugh42gGccgrwySfAgQPj1zIaBVokViG12dh/Xg9w4OD4uQCsHAkGgLfeZvfC1INBXHghcGgne++xPmBj4njLiIhBcQSxGGTz8a4u4Pk8a00uXQKAAz7dCRw+wvKhkycBv5+VFxs2jh+bXsFtagQ2/43dr8mKntXKng8u8Z0A8PTT4++Jx1kl98MPAbuN3RMffZybr3XtD2NDwz5Mm8Y+a8li4OX/YX/r7GT5/tgYO8dsHIBwCHg/7ZwHZDY+6ehgwcP27ew+O9rL7hunY7zxLZmnASzd9uRZ965nEcsP9uwGPn1//PVFpwCvvZ55bHs7y6dDIXae4Nj9A7DesWT5x3MsYA8G5POSJJuNnafbDcSCgGsuMBBm/962Tf49U6eyMsbpAJ5/IfNvs/dG8G5kfH2mWTMBn59d10AAMImAaBqvd8xMpLPZDGzZwq7p/E4Oe4fG6wUeL2sceGc9+91797K0a20FXnst8/u9Xva35niiwyPx+ltvA8cSa91OnszuO0EALGb2WyyW8ecvqbWV/a3TATyRSIfOTlanAYDRMeCll3Kvz/zu8QBuyhTA9SnwfB87t8FB9nyEQuONGBLGy6uQTDkEALNmSejt5RAOsbJ8+Wms/Nj0PisTQiH2OaOj7HdxYK+l51lJnR0svxgeZvlnupYWlgdMnjRernR0sLLG7WbpNmUKe43n2T2yfTsrV6IS+02b3s/5SnR1sbTf9P54frb2TBtWccDzz44fN3Mmq/ft2pW79/ppy4Ct25CqS/X0AK++mvtdixez7wKQ0WiUlEw/ADkFTnqD9bHjLP89czGwqQ/YvDnRECAA9gjL89evz/18jxvo7pbwwVgvBkQvpk7lYOd5zJ3L7vEZ04GTA8B777E8Y1I7K692P+tBc68nNZe+uzuzjpo6x+ns2f8kKz6YNhX4IKtu0jUD+HRT5mu2YVPe0Z4Au3dPnkyUkz5g4CR7dgHA2s/hPZl1af3+8XRN50zUdZKS9dDk/S98Cqw/WkcjeUpUl8H+ROHzDWZUIiINLKNMFm7JntL0QoPjOPiymv/icVaZeenlzM83W4DuLh5Pf8AKyClTgI0bWcUzGMgMzjmOPYSH0iqePAfYrKzgOXoUCDcAR46yDDbZ0ACwc4tFWSUmmaHNns0hxov4yRusQFm4APCYJfhHWaZw8CBwzbnAv7w3HvgBLCDZt2+8ZXdmF3BkI4fGmeOtnp0d7FwDflZ5aYxlng/Ps6AhGURzPOtNmzx5PL8WRWC+yYu3B/vQ3i7l9LZ1dwNPJ5aOaGxk57xjBxBvkvDprsyVTm1W1rv0+F7WM/Dee+PB8MIFievLjwf7bjcwzWbCc+szVwrnOA4zZgCHDrGCPN8wvKlTgJFRlukleyJmzgL2eoHde9i1Te9FS1b05fCJa5MM2ABWSKVX3mZ2JYIrjgULPYuAgUEWFDU1ss9eMB/4jBN4VmKFYFsrC5zO9PvQ6gDiWb8lWUj4A+OvdUwGmjng2suAh7ICiWCItZC703pewmYTTrrTmnm58VZsq5VVwJZ4WJpnV/JFgVUuAXbvfv0y9p43tyQqJuuR6r1dshg40c+udfLZTAZps2exRqJE+xu6u1lFMZxoWJk1E3hvQ+515ziWjtetBF48CsxOXN/k83PasszAPBgYf73/JPv+fMHupEnj+YXDPv47k2bPZmnX2ckax1LXM2sExfTpwInjrBBOfZeN9eS/9x5Lw/QRMi4X6/UD2LPHjbJ76WBibZ+77wb+6wSw4Xhugb1kCUuTkyeBoQ+AW7tdGAmL+OWePWho6EdHh5RqXCu09pDbzc71vPOAlijwfKIy2RgDhkfYsxUIjAdZVut4YJns8WqMsUppspKT/DqOY79v336g8aQVI02s9mt3jOc52edmsQA33wx8fycbqXKyP7PHKRZjPUZbtyV6Q1zAgf3sHE/0s3xkVitwOC3LcbvZdRd4YE4b8PQ2lgfNmM4q5YNDLLgCgC+cDmzfmnlOoWBucGixsPPavx+IHHehpbkfHYl7wxJkFfA9e4D2BmDHIdYAtHfv+HM1bSrgcAKfnQH8/HHWIBIKnUQ8LsFm5SCI4/dzPF54alJ2r14kysovlwt4JVFh7+zIfx/wPMsHOGT2FM3vBnZ+yvLV1laWT/q3sYwxeb1EkQVHQ8MsX/R6gL375EeUJRfZnTGD3bfNcdbomQyK0u8fi5mdc4PZjH0Nmfm+iefhE0XsH2KvT57EyjeeY/ms1cqeU44b74Hl+PGh0F1duVMe7HZ2/rPzjPxrb2fnnGxo5cCub2sraxRtb5dgsnCw28cb9xwOIOTnMTw2hjP8PrhXApuTDaWzgb5juUFFJC1/SDYeJsss34At1XjU0zOehwIs38qnMcYaetJ7WhctAsZGx+sIpyxk/9vWltZZsBgYGWb1oqHBzDIx/VaaO5f9XTSNN44k8RzLW48dG2+QSXZCAIDfljnqLCn9XMNh9pwBgCntN3Mch898Bli/dfzePnkyM205sDI3XXs7u+5NTWyfdrM587oDLNhMH9HHIfO3cdx4vg6MPzcNDeweGRkFXk7UL7u6WLnQ3MKeo/Rrx3Os4Tj9e5K/va11/Pg1MeDfd7OGrRMnWMPDiROsrpUMvp3O8aDw85EQ7AIrk3btYv9rMbN8fNIk1qCdXo/MHhlrSYzoxDb2b5uNlYOmEiKo7HshncvJnjW3HQiI49fab2J5t9fDGmSTHWeiyJ7zpIBnEOFOdq2m2O3wuMcbH2w2VgcRRHbtbFYg1G+HaUyABHbNXE42KvP+i4BHdgMfHWTPY7IXfdEi1mg7MsLuuWCA/a8osrIumQ9Mmgzs2s/SPf5ODLZhExafDfz3DpY+cyeb4IwM439eYXn/3LmJsi9xbdKf284pEo4K4/XeSITdq6LAGkMAVjexWFg+l5wWtXgxcOQwEAhmPjeXzHNgxbAZk2xlDkOpURTs15n0Csyy5RK2HR6vhPd4PJiXqCVzHAcprTlTEFjlfXiIVRwB1tq5dIGAmQLgcrPC/7Rl45/f0MACa1eiUuLOE13Ons0KUkFgrX/HjmX2TiV5PWxY2egIEHAClzR48DfrUXDIzHxDQfafw84KY4tlPJjx+9nQM1uigAsEgFPPATanNUIIAmsUUCJ9eFp23fDS80S4DwFjMpXGgIfDpEksY+qw2fARTmLaVNZCLycYACKJ5Jg2DXj3XTbsL7kJi9vFCigOgMcjYbkrgPcaePztYwn+qeNN5iYxt6AGxgs0q5X1SmefssCza+f3s8p+MvPNNzCwvQ3Yf0C+0t2SFuxnV8KSrJbM4fAOR+K8Ex01yYrUUq8XR7L2TAfYCIDRkcxha42NwJpFgEUEuhwObDwxvpsClzivYjhkjohIXqdCcwQdjvEKVbLwT79fRZG1wKfr7ma9eNmVUpczcy6v3896ezdtYsGU08F6v9N78jiwCpIEVjiLYm5lJXUsl78iHI2yRpt8w+SSfN7MClk+PJc7rBVg37H8NKDb5cKuwUHsHcrd3i678nzveYDVDCCRpA47ex7eTwTjycYpmw3oWQa0hTgAxcdyJiu6SckskQO7x7r48YYFk8ju5wJtBQBYZXnpEoDPU7lrCAMXh+2w8k6ETCb8fO9eNuUo7YPnzWPP0KS0Cp3Vktm4khSNsoA2+fbkM1loKGv2kNIkk0l+KGg2q5UF/HLTf+QEA+w/jwg4EvfX3Dls1EZ7+/g9z3Gskjo6JmHLln60tIyfq4XnMZjd6pbm1FOBfXtZpXC204l3E1FzsqcXYM/28ROsBzjbwoXA8/tZb7JcGidHWSX988IWrDvE4X2wBpt0ycZfu53llXKS35Hewzp3LisfT5xI9KZnme5wYF/W87LU48nI6wRh/DMjZrPs8xUOA30edlyy7OxZBBw+zBp85KawpbOYxxvm0rW0ACdPHkZzSyDnIloswN3xOEYlCVZBwIG08/J6WaNn8lnsnseCk3QzZrDrYrcBd6xko0iwHzg6LF/GFJK8p5INLgIv/0yk/wSTOJ6/52v8Bli+V2zKWkOYBfvBIHBQ4bSchgbW2NExmb1/dDT3d3NcZh2w0HkmxePsPCxmVraolczX5YhCYsqAxM4xec+VKvu5TD6Pw8NstE/6sxRPjOBaEXPBnqibhkO5zxUHFjgPDLDrm+SWKbvOPx/461Y2KqDUe87pZOeTPYqgkPTfm169XrgwczpXaGwMy9xueC0W2W0Ts/Ppr3wF4A8Bj+0DBrLqixxYQ2N6j7zVAlizrluygyc5YgYAuudL6P2U1e/511lhFQgAc/yssaA5JKF3lJWjo6OFp6SJAss7W5oTx6Zd76VLM+tP6VNLTGJuxwMATC02H6bOUbBfx0IuASdN44HSWXJjCdMke/OSwb7ZBAgCl6ooZfP7WM9zRyJwP8Pnw1vJ8WtpOIxnVIJQeC4Uh/GMycLzBXvhAFZAx7N6TZxZzzSv0ypMPM9+j1z9U+S4nPNK12Ay4WCeyUNeD5tK0OkC3k0b+pZeSDkEAf/77CCe29uHFweK77F+w+wA3m47jj3Dg0WPVVKINTez/+RwHGsUGh0dT8tyFtTxykQUPAfwMgWFKZHWnw2FMirA5Sp1DmBnB7BvS/5Ks8AX7n1Kx3OZBWpytEdSahtOlD/nVK6Co1Sz1YodcuOQ07TZbNh6kjVhLfZ44BQE/ENyzHsaLuv/y1UKwiFgoI09g+n5xGQlNdzkOcdZT9eLiSGy6VkFxwFzYhZ8Ojj+zCjNSZQsbtWedp7Z89HdrtwRFeycMhtpvZ7SzktLShbAK8Tlyt9zLOfKhgb8NN/4dLB7pNhipOl/94oiZqcNETnnHODlLcrWRBA5Dmaex+rVwJtbix8vRy7NBJ6lqVemMTyfhS5X3rzuS7EYviUzRl7gWePIobR2VIuleJCvhCBk5vZTp7DRDQ8siMLE88h3eS1m1usrCvKNhBzGy/ZkZ4F7AEANLrjl9bJ8/MudwIOfFj0cQGINl0SwnK9OpgYH1oCSryNCy+/RK6MymXI7cHie5e9ehWVaexsbdZochRMIsvI0fRRZdzcwv4wNcBYvZqPv8qVfoXpSejUou0o0a2QEy71exVu22axA0yRgsgv4ICvrKCeJeH58DYKrrmKdgZMmAaZtialMiQ9PbzjLJ9UQJ+SWp6XUnwhTdwv0kXFOQcDppaw8ljBjOgvk/AoKFLt9/EG0CwI+L9ekpoJchqM2YKzGTW5Py53kWlrTyf1dSfsExxVfvCfJbzLhy015VnrSAccp7/VLvSfvZykvfmyJ617oPddEIrghFsPSRI2xy+HIOb47q7Zps7G1FhbMV3YX+v3ADV+u3ur1pXC7WUW/nLlsQZMJgoJ0ujpt3L5TEGAucx/R5ubcBcNmlNiCz/MsnSyW3BWTv6hF9COj3DrvqT3AHUv9mBnSbvX6cpXQxqKK1o22tzU1YUVa+chx2i5+WIxWv8ZaoGVJr4buUkQibJi+3Vb8XIIB5QujaUGs4vWx23NH0TgKpGX2CMeJTIvLEM9qrbTb2XD35GLXHNgoNy3zNVFgI5fybakZKFBp4jmW75/ak9m47RdFGGVgenrtaNIk1qBY6J69VKN4gRRHwX6dm6QipwqFWCunmgx1ugGHyvgVtnZqqbnc5WkVorK/dG02GyIWCybb7bgrHsdnQyFck7W9wjkyo2AcjuGc4bqFmM21kT5z57Ih2cVG0WiB4zh8NR7HHfF4SYF+KadWSuNQUizKhjEnewtSoyV0ql2rbbhM9uqbzcCFkzwwKzi/YkdotY1VczPrFVu7Vr/rpqVqn6OW319qGk53OGTf01LucA2NNelcjsrl89V0porOGaLOuTJpr2eWMN3hgKdID8hpXi8+W2AendmcuzNFOfm3lDWnoJw8qdFiKXou6YNHrmhowLQC8UL2uZHyULBf53yldq8ajC2tpVvtoz/X6cRSrxefKTYZWUPpmaZc9llOmTJRssBZFVg21SmK4DgOrTYbvpLWRaxFRbxW0qnSPUYuUSxa6dE6ENM7H3SJIpqt1oz8Sg/Z10XJVcp3H95c6p5/RQg8m/va2qpNRW1aHe6RlD5SxKPiXtGqAjzP6ZTtCPiCTiNZ5Ci5d4t1VpSbSxitfuQ02PkUMtlmwz355vLpTIvyIaqyISl7REA+1qzG7M+GQkVHkggch5k1ulz8Mo8HsxPBezi7RSJhqMCaK0RfFOzXObsg4JamJtxZbDJjFXXa7TmZQ2qxKQ1a9jmw9QRm12gmWojeAWU1W1cLDWmsBKXViTuq/GwZpQ9Vy3sl+zeVW7nTIh8pxCuKuDYaxVQtNniukGCeClkl3RWPw5UnwOHKuJ/UpvcZOvesxq1WXBOJYKrdjgvk9uCsoFrrRda7l19rUYslJ+CrVfmCN70bN7XWlmg8klsHSKnLFQ49j6m4XytVlutRqxN5Hq02G25pasKXshoNkzuAtZYwcqjaI6/qTX3kRERW8oEOmExw69hiXO4jWWgoXfpnZ2dQxXoHtTZHprFA7rdnD00y1UCmZeF5VYWTUuUGgqUsuqYVpWcsdx9yJby/HmhdMPs1fraLpUWh808GGRENg+PsBQj1VI3cR+n9oFdP5jyXS1UwvdTrxZUNDUWDtHLu9zabDZc1NOhaJitR7noZJL+gyYSLg0HFvcClOkdmhbdCd2RDmXmXXEdJLQZjnwuFcJrXi7WRCAC2bWW5lshtLSWj9q6WOgGTCWLWdf1CJILTfb6qN3BOZJTbk6pTsrAXgNS2gUlxqzUjCPSVMDc/oGIef3bAkB3AXhgMYnUggIsTGdq5gQBCZrPuvUVaWOx2o63K8zUL3QVyvX8zHA4sr+RqThNYsse6rUCjywoN73NnhXuMCjVGXRYO43SfD2uy1nWoBjUVxmINHdVqlLo6UeHOp5zKCc9xmCe3pLsCk+12LFZYgc+WvbCnUVUjUCv1G+UCWiMo1kjiFUXc3NSEkNmMi3QKbtpLLKtDeeo7+Xrss1lqMLBPSm+4cwgCVvh88Caux1di5S9avLKEdR+M1gEgdz565A1uUcQyr7fqozUnMgr2ScmyK/xaVMxzhu3KHCO3s8DCtM1Ar1cw3zA5nL/QMM/kUKNbmjL3zivWKDHX5cICtzvVWrzQ7cZNjY1wy1yfcjLUcgqMfJVRqyBoXhAVCo5EjoNTEMoa9ixwnCYt8/nUbvVGntJGNTlzXS58pbERawoMYdRyca98z8fstFEzX6vQfFFnoqKSb7h5unq6Zzp1npKQb0jnSp8PUbMZU9Lm7Fd6uLDavPB8DYI7PfM0IwubzRnDq09J3+gbKHkkxDyXC/e3thY8Rs3zWsqoI4cgFN3yWA2t1gG6UcG6HZ8LhXQJ0r5axvQ3pb+13WZL7bojR83izUq/mwMwP62+pSZ9ojpPtVKzW0c1d7Ag6k3MUoUUzACLyd4eREnvaqFh2EorVsWCFbuCAknJQn3J1vjs3v8VXm9VVvYvJN8V8ZtMuCIt2E72XJ4fDJY0JL5Y2oQKFEYdBYIFgeNwV3MzLjNAb6leKrXegZKi9+bGRvRkVZ5L1WA25wzP04LcgkRyU2aS55BkpMIrXxpofQfoUc06XcXomPkul+re72KWeL24PhJBes6i9e8u1tharJKtZ3VXzX1diytXp6fBpeEwvlBkpMe5fj+m2u24skCZkd6AdEEwqFkvZXsZ08jmu1yYUoG1PPS6J7t0WutISeOpWxTzlgXF+E0mXB2JFNyaUm8L0srcUu7FGxsbsdjjwWqZ0S0Cx2V8bjlP/ioVo/G0WjiZmgwqy0j1JaKxQhWA5V6vZntcKl2ExsgLliRdFAymhnhlc4kibm1qSs3RrsY8ckBZ5j7ZZsvbM5evZXauikz8RoXD4JSccyWGllZzkcbsla6bNeoJtyl4/iw8X7X7tRi5kUEWntdtj/taclVWAKTkOTLxfElzI5d4PLIr88s9jXOcTlze0ICz/X6cqaLHMmAyocFsRovVmvr8agSqcqOt0k222QpugUW9W9qabLMVHb3hFEVc1tCAyXnKtftbW7FUp2ldK8r4XBPP4/KGBk3Lt1qcL69GzGzGRTLPYTV+vZrdS9TmbWGzGWf6/ZqOaJKbuuozmXBZiXGAVte+9ponaxsF+xMUz3E5Q6j13vLjumhU861atM4wlHzeLY2N+Fpzc95GgWz5CubkMEU1PWv5JIfbZaetkspByGzG3c3NGa3GxaitdJSabnl7TkssTPWaQ5mNQ+5IlOxh7i6NCvJTPZ6CQ65P9XjgEkW0GjTYL0X66B1LnU/hiFksOVuPKTmvCwKBjHut2Hs4jlO8Mr+Z5zHFblc9woMDcEMshrWRSNUCljabrej6C1xiC6zshuzkyBI9hmZXQ/qcbblexFJUe469mvtJ7j1yC66pGe5caeU+T4WeaLlPzh7hWc/8JpOq3UsqvfZMITflaaxQct8oXYSQGNfEeVonoGJz3LIf8sVuNzYcP17y9ygtYniOU1xBV1twLfV68dLRo6req5TI85o8OOcEAljp88HE8zg4PKzqM7JD3VsaG3FkZET1HrJ2QahIoKNmFEE2NbsxVDLAKLZHdCmuiUTwi717Zf9m5nlc0dCAv9+2TbbxY5UGgUmwCtNX5JpxBI7DPc3N4FAbFXAltL4nsz/NaD0oqoIyhce1Wq0Fpw8B7FlS68uxGE6OjRluoakuhwPPl1DuJa/n2kgEOwYG0GG3l7WmR8xiwVS7Hb9X8J1Gt9Lvx8u9vZp/br4dWqba7fiwv7/ge09xu/F6X5/671aQtoVGq6QPuf9iNIqNJ05ouiCr0aX3fpdyHztFEVc2NGi+88U0ux1vlng/qC0vbYKgyzOhV37AcxzGanBak96oZ79ONVosJfcYRywWfCkWKzlTUFKQFDpGy2GcnRXuvSw3w9J6MSarIMgG+mqvsR5ZZshsLnuP53kul+J1Jxa63XAKguwCj3riOQ49GrWIVzOwvSYSKbp7hR5nl+87bYJQ8jzMUu//UisL+X7/pCrtcFHJrf30tDzx/JynoNd4jtOJtdGopmsJZN8FPMdVNdDPd1cu9XpLHo4LsMbdqQ5HWYG+FjQbGizz3GqxpSyH8XVE1O4Aky/tii1s+tV4PGdnCa0W6EtX6B5otFiwyu/HpeEw4lYrzgkEFHfcdGVtRawFJb/VrlHdSuA4xbsWyJlst6em7KmpT93e1IQvRqOp+2S201nWGhJA4e2ga7m8IPlRz34dmmyz4UqVvRcxiwVOQUDfyIjGZ1VYvgymHuamaf0L0is0aj67Gtc0vZCbYrOVtdDbXfF4Sftze0QRX43HS/rd5V4htUPMQ2YzDgwNqXpvOY1mAZMJh/KMLim03Z6eHIKAGxsbYa7C/TqmwWecEwhgrtOJvx47BoANAd9XQtrq1Tcx1+WCR+eANZKnclzqNqTLPB4s8fth4XnsGhzM+fu5gQCeOXQIgD5DZmulf0jgOEzVIahSS64Xu1oluVZpeGEwiJU+H5yiCJ/JhCMqR+OVosVqhUsU0a8g39Az2AfYdDA19Gqovj4Ww49375b9W4vVigtrcE93uSvlNZngNZlweTiMjwcGcjq0Sin3V/p86B0dzVjotlS1kieSTNSzX4cq/TCWkpWXsm2JnEvCYVh5PmfhKr2cnRgCrfXCP7XfhJGfNbEgUamUXpNSAv3UZ6uocDSq6BG6JBxGxGxWXdHIt9ilkrOv1naOegqbzYrXxihH+hZJdkFQvOhoktw+1qe43ZoP31STwtlpe2EwiOU6j3JxCILsNnRqelkLNZzN13lf+2IL+VVarZQbRspPtBo5yHFcquxZreGaDWoaqbS8D3yJvKtR523etFbo2fxCNKrZzknZDZd6dZac4nYX7ASxCgJmOBxljQZd4vVidSBgqE40NQsflkrJTl31jnr2SUn0GMJYSlE8w+HAdLu97Mwq/f2r/H48e/iw7HGLPB5Mdzg0W0xtIrinuVmTwkSzSprK97XZbDg/GMTTBw8qfs8MhwMzFPawpZ9Xvp7QUqRfL4cg4ETaPuV6ONvvxx/zPDelMMK2YT0eD/567BgiZjOuL2EqU5PFgiaLJTW0d5bDgbePHSu4JWWlVataV4lAWe/pLZeFw/i/u3bp+h3ZCv2i6j8p8owTOuTyiiL2qhwtlY+WwdIMhwM7BwfRXOZ0g0JntMDlyrse082NjRiVJPx/+/eX9f35lHLPKi23OI7TvCFVjl0Q8HmVu1aVeodUe4HLfLJ/h9bPupqFD0npKNgnJZGbu1Tpgl7rVsl4WiHrlek1LrTQ4cWhEP6ffftKXgRNridQT/kqxT6TCSsL9PKpqVwaqdUYUP4b5M662+VCs8UCfnQUN23ZouVpKT6HQtIXVeI4LhU4GysFqq/YPRAwmXB3czOsPF9SAHm6z5cxf7LVZsMtTU26BbrzXS68VsZCXaXQY15wpS33erFtYAACx+HjkydLfn96RVSLBjmtqc1r9Wi8zp5eVuyZq9S9tDoQgMBx2HTiRMHjeI7D5xPbvK1paMALR4/iU5mpI1rjOQ7nlhjolXLt1jQ0FNzmVeA4CBynOj2uaGjAb/btU/nuTHGLJWOxwkL3kJ67sSRdGAyqWgi4nmSvr5SdJi5RxLkG352k1sotPdAwflJ1chl6pR/OqyMRnO33o7XEBbWm2O34ZktLwfls6b/lAr8fU0ZHsUrBtIB8C8qVEoCf4najzWbL+7tua2pS3BOtt5wW5PRAtgLfn++6hszmvA0+Wlc45M7hxjzD3E73+XRfLKwSCwNq2ThUzvQeuyAo/r1uUcT1sZjsQkkBk6mkoZYlre6cld5Krp2WvcFKF8WspFM9HjgFQXYxzOU+H9ZGo5osQHeawqlcRslPs8WtVlwVieCiYFDVyJPsaU3p25gauTLtEkVcEg4X3VHk9qam1LoHHXY7rovF8h5b6dFI5Uzj0nv7t067HddFoyW9p9xyS4/7LX2hxMUeD9psNnTUwHa15d6Jxa6lr0hjx51NTZhm0DyPjJvYTVZEkXabDZ8kekX8Mg9+NQv67EJX7bm022yqVzgtZbG5mNmMeSMjsBUp7Fb5/Zq0KMsNDVN7jT4XCuH/PXBA1XuNOvy0XMV6+2waNAaEzWbsGBjIeG2uy4VlGq4jIZc+FwaDaCtzNfmZTmfR7Ty1qjh7RFHT7Q4LsfF8yWs6qH3uhKzRG+kqEXSYeB7DY2zJQjXrZehtld+PM30+3UcUKZ33WWila72t8Pnw3JEjef9ezvPhEgR4RRFHE4v3znG58FTaFKf06y/bgF9oRx7VZ1UeI0whUkPuWhq5wSXp2mgUr/X14SwD9gSnP99nFjg/uev85VgMOwcHU4uF5lOL91uxhhmjjeSUY/wz1B/17JOiLg2H0ePxYLbTiZU+n6YFc7vNBp8o5s0wJupDWtUGlDyvdyW2HqoVyd6raUX23dbTIrcbnXa75isDVyLjnutyqV4cr8VqxReiUVyoYh5iqYvj1TuHIGCZ14vlXm9VdiaoxneWSs8K54XBIE71eMqeU10JhdKq3CtUaMu5Jp2uTTLvztfwbfw7UxvV3O5RS81WKz4fDudPT4XPcaHrIbcwqBLl3EtRiwUL3W58NR5X/B49tiTU2rmBgKHWn5GjpNG9Fhok9Ga8ZnpiOBaeL9gSq/ZBipjNuDrPsNt6ezQtPI/BsTHVK8TGLBbsrsD8Qb2kt2gXG9Ug9542qxVvJbYwU+qGWAwDY2OKK0p63HNmnscVeXYmKKcAKvbOahduPMcV3T86n2qfuxKV7p9JTun5a9Z8fdkevhq4frVkrsIV/5Nrn1Ty3shO6XK21CpkmdeLiEyl+ubGRnzQ349T3G70py2spmTOvhIRiwW3NTXlHYpee/2kzI2NjSXtA+8QBKwOBPCHAj3HRnrqvxqP43s7d5b9OdlrKFl5Hl+IRgtOn5tTxU4JVwkjn0o5tpLS76OFbnfVzmO+y4Wni9R5r2hoQLPFgod37Ch4XKnTc+uRMe82UlOUFDJyC99NFBzH4WvxOMYAcBqskF6rFZwLg0Fs7u/HwjyV50L30QyHAyLHIZpV4Sx0LQSO071HROsKltLPU7vncT7lDi80UkWTZKaHUfKLFRqsQWJkShsxi7HyPAYS0yayFbtWaqeiFZNvzYOg2YyliQaG/iJlm5IpTQ1mc2qaQJJPp8VsKzXlR47cQsfFpKetHvlt+tzsOU4n1heZfpXPHfF4RiAbNZuxQeF7s3/XiqzRJH6TSbcGLa06BSJmM/YODVW10aHWfTkWQ8RsLroTUqeCUZsrfT7dt2itBRM3AiOqqSloztB4n/pSGGFV4OS8/uE8FSKjVni1nGM21+VS3EuWjeO41OJJeql0GoRlKrFKzuGbLS0lrROhRCirkm30oXuktvxda2tFFnusJi7rf9W6LBzGD3fvznk9u6GzVrRYrZjjdOYscip3nc4PBOASBHQrLCfUXuvVgUBNDKPOp5SdMpSWaz0uF54fHcWaUAiHyij3s4foB00mfDEaLXkHCDPPw5r1nlEd5rxfHYng5NgYjgwPl/xeuXU8vhiN4uDwsKF27+io4lRGNcrJ69LvEJsgYEkVYw8jocmRpGxKCtzsTFvLz65Hyd+dHH40UVomK7GdjhJK77tyhkz3uN2p3qVShstpGejf29KCu+JxWNJ+x3yXC2tU7i2sRiVG/WhdRSx1cb6kQKKBJ31YodZ5XDUXiMun3gN9YLxnNN+9pnQtCrnh8gCrrFXyKsrlbYW2oc2XF06z22UbeeWuk1MUcV4wqHvDxgK3GzZByNg1o5S1QtTkJ+cl1i9RuqtDzncWCXTLnb5j4nnMHxnRZcRD3GpVtP5LsZXfC91/arXbbKp30LhSZoqeiecRtVgqNp2q2L24pqEBlyS2lFQiWRfRa5SQnMsqWN+YqKhnvw5FdW5RLKfinLGdWoUyQyXDrqot3zle2dCAg8PDaDCb8bvEkCa9t1qZ7XDgzb4+3YbL5cNxHG5sbMT/0WCun6rvr+B3zXA4IPI8Lg+HU+mbzS4IuEDFAnelsPB8TgPLeRovJljMArcbvaOjeLW3t6zPqcTCfjc1NmLjiRPoKaFxJjkk9jSvF1PsdoxIEswqz7VQnjnZZoPIcZhss6E3ayi0EtkrudPcf2WuiURwYHg41YAzyWbDuzJDoMUSrqdbFNGX2LXlZNposGqPALs4GMQfDh/GqTL3vzutQd/EcRiq5ImpJHAcbmpshASofiaV6na5MM1uV7yrQ6kaigTTSnZGUUuL+/IzoRDyXZm1kQjeOHYMZ2etHXV1JIJf7t2b8Volc61SFy+txvNbaq/+LIcDUbM51TCtl/RG6akOB74YjeKne/bo+p0TGQX7deTyhgbsHRqSLYj1pEXmWmiP9UK0yDy1HhKtJZHnc3p69G6caLJacXtTE1yiiLdLXBSvXFpsN2hkHXY7tvT3p1rP5dI36WvxeOo5KPUZq1SFx28y4fDwMKaXMSRW4Dic5fengv1ytobMJ2Y2Y3PW9oVqhMxmrCjx+bswGMRKny+1bZ1eq9tfmWexU6VTcU7zerHI7cZD27dreVp1r81mQ1taL1i7BotBXd3QgBd6e7HM48G/7tpV9udpxWsyYY1GC47q8RSoqQ9UcsqSVoF++rVb09AAC88jWOR3XBgIoNvpxPNHj2KbBnmh1mY7nXkXIW612dAq09Nc6Q4JoP5Hm3IcV3RNCTPPYyjPuiLFrPT5cGB4WPUCvgBwd3Nz3nVEiLz6rlnXsSazGVsANFss2JWYaxQxmzFFg7k51e49AFBSz1k6uQqHEX5PMUrO8YZYDLuGhjC9AvOv1G65lo+aNIjX4QqqV4TDODE6qmi/cj16Vr8Ui+FHMvOB1fZqXReNYvvAQM5COZXeT/jaaFS20v61xkY8vXFj3tW8K4HjuIrvT5+enqUMoVc7jcbE8xhWWfm7OBTCf+zdm9NrN5EFzWZ8VqbxqpwpL+XkJnpXq2uhjDYqnygWDfQB1rDcarNBKHMElRyt7o9Yja1LMVHvW3MZI3e0mEOv1+iYekbBfo26NBTCsZERXBQI4F+zhjFVmtZByV3xeKpyXOonVzrIqKSIxZK3F7ie3NrUhN2DgxVp1FCj1WpN9YyU3OOuY+CnZLurmMWCz4ZCePzAgYzXT/d6sWdoCN0lriBsFwRMM8BCV/mG8NsEAUZfEznvwlpl5GUOQcCFwSAEjsNLOlTusy12uzEoSZiiYp7nJJsN/6u1tWhPjWeCV/AcgoA5TicODQ/jf8ocBVOv9B6KbxTl1Ln0qCHVb62r/pVcxwbb8u7xAwewihpoa8bEyBnrkEMQ0DE6WpF5qnrJl8lUuheMlOcsjTN8v8mELqez5ApNW4UWlCll7m0lFVvcqBCnKOLLsRjmV3FfXTUuCgZxhs9XdNhhqStBV1L6uX8hGi3pvYWm9Mx1uTBLg+2flCyKJSamYsgNtVVCyZBMpyhialoD4JdiMVXfpTeHIKDRYlG9iGOhz+U5DmdSBTuvZosFc5xOrMyzzaNWKhXcKtlabCKrRueO0dYyqUZDS8xiwS1NTZqMJE6iBiN91W6kSKome5XO7KyvnPm72c7w+WATBCxXOPTHaBmxUmrXLNCbkgy4x+Mpuo6Aks9R84vTP3eir+h6ns6L+WUzQqPHHJcLSxXkDa0WC1b6fLg8bb6xyyCNiulXMX0eo5JgcbHbjRU+H76sY+B7SSiESTYb1uZZE6CSmtOuTylDfiu5QwHHcbguGsV1JTbckPJxHIeLQqGKbreVvbq+VkHLDIcj73TGUu7m7POpfq6tTK2cZ7U5KtzhZ/R0SZ+yV88jfUtFwX4d0eohLPY50+32jN6F7OO13A3AZzLh7ngcy/O01NOjTJIqtW2fUe+59EIuWGDNhXIXeDzD50ObzYY5GvQay9GjMsFxHJYkVsPX83u0cFNjI07zehUNkRR5Hqd5vbpuVeY3mXBVJKK6115LrSrX8Vjl9yNkNlesQYzjOHAcV7QB8suxGKY7HDhD555oI6r086dXvq20I6JU0x2OjPU2tDp/o5ZfWjFqvg5kXvtyz/PqSAStVis+W6Hdc073+eARRd3ud63STW47REJz9mueHi1XxT6R4zh02GxYl+fvp7jd2DowgJ2Dg4oXbSr0oBull1tPRi2AJycq+OVOF4nrFIxU+86o9vfnc1EwiD8fOYKFMvtbh8xmfCEaVb1o3VKvF0uLHCPX67/Y41H1fXpptFjgFISypj/oQc2K/3ow4r0ds1hwfSxW8vx9jyjipsZGnc4qv6lFRrlFLRZ8PhzGphMnNPvORosFuwYHUw0jU+x2bO7vV7XobXLrvw4DNPQYFcdxqV1JgMo8N0Z8Nqshvd50lt8PjyhWtL5YzXpbu82GdpsNw4n7Tm/LvF4s9XiqUh9vsVqxXcEOEo0WS1V2aKgFxqrlkJqR0UKZ9fCbeR5XRyJ49vBhvFJgcSgqsPLTcnREtlKuu99kwh3xOGwqg/17mptxcmwMPgWr+5c7jH+iS2+QcQkCLi6wFV05294UckEwiFd6e3GuTA+q0hV0K7UOCQfg+mi0phoTa+dM9VsnQeu58EbQnPhNpS4wJ3c/XB4O470TJ1Ijby4Nh3FsdLTkbU05ALc3NeH46Kjue26rzcc/GwrhP/btw5kTcGRErahUGT3b6azIKu21lAdrTc+ystB9ErdYFAX72QqNcJxoKNivI5XMhNQGf+mWVXBeXS25vKEhY25qtZVaSUxnEwTYDLw4Wj5KC7VKPXNKvsclivhMKARzYghxNcxzuTBPZjSBElc0NOAvR47gMwUaKbRmhEBf923NqjRvMWKxYHUgUFb+MVG4RBF3xeOaTEVyiiJOTRtFw3Oc6jSw8HzFpkepEbdacW9zc8Wf42rPgy/n+7pdLnxy8iSaCjSazbDbse7wYV2nBxVT/ZyZ1KovxWJ4ta8PZ1CMkUKlcI2rVs+mWxRxYTCoqiKwOhBA78iIpit5JlkUFvpznE68c+xYVQuzfPS4LrXAr6IVdprdjr8cOaLqvfVotk7z6Cuh026n1ad14DOZcLBCQz2zLaix3R2qqR52oYmZzdgCoMtux9v9/QWP1SpAV/o5810u/OnwYVXrPhi3uaN00+123NjYCH+B+81rMuGe5mZDN/QA2s6BL/f7iXEktxgm42q/dJng0jNjLXrbSzG3SA9evoxQzwpg0GzG6T4fzByHPx4+nPe4FqsVtzc1GXJFbr0t93rxQX8/TjFIRfye5maMSpKqikXIbMZXy5hmYDSTrFbsHBiYMPtFk9Kouc8vCATwvSKBl5xmBQ2hIsdhRJIwieZ0EwBrQiEMDw9jpddbNNh3CwKmORwQOa4iQeUitxvxEuf0drtc6BsdLek9Rg8AOY4rulUpgJJG5Bn9N2up3kYc1NvvIfKMEekQ1QSOw53xOAC2MrMWzvT58MSBA4YJBku1zOvF8NhYwWAfYK3XRlHJwjJoNuO+lpaMlX6rqdxh/kZpsNHCUq8XHlHM2d6SEICN+pnncpU0b90lihkLiBVza1MTtg0MKNpt4avxOI6NjioKHoj29Bi+nmzAUbNrh00Q0DI2BpOCugjHcbi0gtulchyHphJ79c+v0ErnShRaJ4nUhonUKEKMpX5qyROYW+NgZ5bTiXarFY4anGtNlDFKoG8kSue16lnpEjiu6IgZog2jPAGnJUbaLFCQ7jzH4QIVAchMhwMvHD2qqIfSbzIpnhZTq2tykPy+HIvhtb4+WlOnTlRizQ653Vc8oojekRFFI4TUqvYwfqKdckZn2gUB/aOjNA2wAAr2iaxamT9ILaXVs7pCe1ZXioXncVtTk2zFpdq0btAjxuFLzI/VswFumdeLmMWia8WbVJ4eeVXIbDZUj7YRZQfQXJ7/rxejlVBRsxmznc6MBvNbm5owonJ6HkCjFyaakNmMM3w+Vbu4fCUWw7aBAUwvstXpREY1SEJqWJfDgT8ePozJFRr23Wq1YtvAAC4IBjNWXa+XRhclWwRWg1sUcWVDQ8W2pSOVpfdIG4HjJuzCn/Xo0nAYfzp8GJ+jRajqjlcUcXRkRNVCgtXCcVzOLioCx0Gow4CdGiH0s1TlaCKXKGJmDS9OXAkU7BNDoOxTHbsg4BvNzRUrVK9saMCB4WFV8znrUSXv28k1HqzRM06INqY5HJhGvViGVG7D9y1NTRgeG4O1QA9nPeelUYsFewYH0aXg/k4fYVGNILxa25pONDRVrHwU7JO6VM+FYTatFmZU+l1G3K6wWuqx50IvVC0iE1E8scOGkgUPSW3SMtAUOA6CTHAzUfLPqxsa8PHJk4pGIrlFEQGTCSLHwVyFsph6+fX1mVAIm/v7sdDlwroiC26TwijYJ7qhVk9Sr1YHAni9rw9n+XzVPhVCiIFd2dCA7QMDtD0h0Uw9h5g2QUCXwoYxnuNwU2MjOFQ28J7hcOD46CjCJU77ozpxaWY7nZhNjaSaoGB/gjvV48Ervb3VPg3NUZZK9LTA7caCGt2akhBSORae12SV6HoO8GoN1S+Moxo7C12iwZaR9DyTSqLVniY4JVsxqZVcNI62eSOEUC5ACCHlo+HjtY8ajEglUc8+0c0kmw1rIxEEq7DCea0VhUbc7o3Io0KaEEImrsYaWreGyitSSK00HJ3u8+EvR45U+zRqFgX7RDccx6FV4TxFtRlOvcyBmuFw4J3jx9FSQ5UIQgghZKJxiyJub2pK7SFfG+ESMRK6Z0rjphX5y0LB/gSXHGofpq3UqkrgOFwTiVT7NFSbZLNh39AQ7QNPCCGk7nmrMGKR1LZ8XVMXBYMVPY9aVB/detVDwf4E5xAE3NvSAlONDOUhxrTC64VfFNFR43vBk/rHcRwkSaLV0QnR0RynE1tOnpwQq2l32mzYPzSk63eoHcVooQZ4w5vjclX7FEido2CfUGFAymbiecyn1elJDbijqQmfDg5iGjVMEaKbi0IhjEnShFigd7nXi5d13tWo1KmOblHEHKeTRm2SuqDnYuITAQX7hBBSgnpZJ2KicosipotU9BGit4kQ6AOAWIEOk7DJhBarFU6Fc5fnu1xY5vXqe1Kk5oVqZDpKzGLBFQ0N8FLZrQpdNUIIIYQQQgyK4zh8IRoFAIxSgzPRiFsU8ZXGxppYb6mTRuOpRsE+MYSJ0f5P6kGtbFVjNHTVCFGvGlvYktK5K9DzmJ6XmmsgSCPGRkPk6x8F+6SmKWnfpjZwoiUaxq8OXTVCSvfFaBQfnTyJU2hNFENbG4ngf/r6cK7fr/t38RyHC4JBDI2N5W1coK3KjIfKQFItFOwTQgghhBhQ3GpF3Gqt9mmQIlptNrRWcIePeXlWcF/T0IBtAwOYNQF2QSCEKEPjf4ghtFBlhhBCCCFEtQ67HWf6/RNmccRaYkubchGgaTmkgqhnnxjCMo8HDp6nfdoJIYQQQkhduTQcxlMHD2KF14tJNhuOjY6ilTq6SAVQsE8MwcTzWOTx6PLZ1L5NSPXRc0gIIWSiCpvN+FIslvr3abQ1IqkQGsZPahot0Ecqje4nQgghhBBSCyjYJ4QQQgghhExIbRVcXJGQSqNh/IQQUgJa+IgQQgipH40WC66PxeChLQtJHaJgn9Q0f549ZgnR2oKREUAUcV4gUO1TIYQQQoiGGi2Wap8CIbqgSInUtCarFZ8JhXKCfi6t91Wknliigcmjozg3FoOJtswhhBBCCCE1gIJ9UvNmO505rwkchzN8PgxJEjzU+08IIYQQQgiZYCgKInVrKW1rQgghhBBCJogZDgc2nTiBHp22sya1h4J9QgghhBBCCKlxnwkGscDlQtxqrfapEIOgYJ8QQgghhBBCapzI82ilrQRJGr7aJ0AIIYQQQgghhBBtUbBPCCGEEEIIIYTUGQr2CSGE6I42wCSEEEIIqSwK9gkhhBBCCCGEkDpDwT4hhBBCCCGEEFJnKNgnhBBCCCGEEELqDAX7hBBCCCGEEEJInaFgnxBCCCGEEEIIqTMU7BNCCCGEEEIIIXWGgn1CCCG6c4titU+BEEKIQYgc25DVZzJV+UwIqW9U+yKEEKKbyxsa8OngIKbZ7dU+FUIIIQZxXTSKl3p7cbrPV+1TIaSuUbBPCCFEN1PsdkyhQJ8QQkiaiMWCS8Lhap8GIXWPhvETQgghhBBCCCF1hoJ9QgghhBBCCCGkzlCwTwghhBBCCCGE1BkK9gkhhBBCCCGEkDpDwT4hhBBCCCGEEFJnKNgnhBBCCCGEEELqTE0E+9u2bcMXv/hFtLW1wWazYdKkSbj//vsxNDSUcdyOHTtw/vnnw+FwIBgM4tZbb805hhBCCCGEEEIIqXditU9AiQ8//BBjY2P493//d0yePBkbN27E9ddfjxMnTuC73/0uAGB0dBSrV69GKBTCyy+/jEOHDuGaa66BJEn4l3/5lyr/AkIIIYQQQgghpHJqItg/++yzcfbZZ6f+3d7ejs2bN+ORRx5JBfvPPvss3n//fezcuROxWAwA8L3vfQ9r167Fgw8+CLfbXZVzJ4QQQgghhBBCKq0mgn05vb298Pv9qX+/+uqr6OrqSgX6AHDWWWdhcHAQb731FlasWCH7OYODgxgcHEz9u6+vDwAwPDyM4eFhnc6+fMlzM/I5klyUbrWJ0q02UbrVJkq32kTpVrso7WoTpVttKpRueqQlJ0mSpPmn6uzjjz/GvHnz8L3vfQ/XXXcdAOBLX/oStm3bhmeffTbjWIvFgp///Oe4/PLLZT/rgQcewN///d/nvP6b3/wGdrtd+5MnhBBCCCGEEELS9Pf344orrkBvb69mo9Kr2rOfL9BO9+abb2L+/Pmpf+/evRtnn302LrnkklSgn8RxXM77JUmSfT3p3nvvxZ133pn6d19fH+LxOFatWmXoof/Dw8NYt24dzjzzTJhMpmqfDlGI0q02UbrVJkq32kTpVpso3WoXpV1tonSrTYXSLTnCXEtVDfZvvvlmXHbZZQWPaW1tTf3/3bt3Y8WKFejp6cGPfvSjjOMikQhef/31jNeOHDmC4eFhNDQ05P18i8UCi8WS87rJZKqJB6dWzpNkonSrTZRutYnSrTZRutUmSrfaRWlXmyjdapNcuumRjlUN9oPBIILBoKJjd+3ahRUrVqC7uxuPPvooeD5z18Cenh48+OCD2LNnD6LRKAC2aJ/FYkF3d7fm504IIYQQQgghhBhVTSzQt3v3bixfvhzNzc347ne/iwMHDqT+FolEAACrVq3C9OnTcdVVV+Ef//EfcfjwYdx11124/vrrDT0cnxBCCCGEEEII0VpNBPvPPvssPvroI3z00UdoamrK+FtyfUFBEPCHP/wBN954IxYvXgybzYYrrrgitTUfIYQQQgghhBAyUdREsL927VqsXbu26HHNzc34/e9/r/8JEUIIIYQQQgghBsYXP4QQQgghhBBCCCG1pCZ69ispOS1Aj60PtDQ8PIz+/n709fXRCpw1hNKtNlG61SZKt9pE6VabKN1qF6VdbaJ0q02F0i0ZfybjUS1QsJ/l2LFjAIB4PF7lMyGEEEIIIYQQMpEcO3YMHo9Hk8/iJC2bDurA2NgYdu/eDZfLBY7jqn06efX19SEej2Pnzp2020ANoXSrTZRutYnSrTZRutUmSrfaRWlXmyjdalOhdJMkCceOHUMsFsvZZl4t6tnPwvN8zor/RuZ2u+kBr0GUbrWJ0q02UbrVJkq32kTpVrso7WoTpVttypduWvXoJ9ECfYQQQgghhBBCSJ2hYJ8QQgghhBBCCKkzFOzXKIvFgvvvvx8Wi6Xap0JKQOlWmyjdahOlW22idKtNlG61i9KuNlG61aZKpxst0EcIIYQQQgghhNQZ6tknhBBCCCGEEELqDAX7hBBCCCGEEEJInaFgnxBCCCGEEEIIqTMU7BNCCCGEEEIIIXWGgv0a9G//9m9oa2uD1WpFd3c3XnrppWqf0oTy4osv4vzzz0csFgPHcXjqqacy/i5JEh544AHEYjHYbDYsX74cmzZtyjhmcHAQt9xyC4LBIBwOBy644AJ8+umnGcccOXIEV111FTweDzweD6666iocPXpU519Xnx566CEsWLAALpcL4XAYF110ETZv3pxxDKWbMT3yyCOYNWsW3G433G43enp68F//9V+pv1O6Gd9DDz0EjuNw++23p16jdDOmBx54ABzHZfwXiURSf6d0M65du3bhyiuvRCAQgN1ux5w5c/DWW2+l/k5pZzytra05zxvHcbjpppsAUJoZ1cjICL75zW+ira0NNpsN7e3t+Na3voWxsbHUMYZKO4nUlMcee0wymUzSj3/8Y+n999+XbrvtNsnhcEjbt2+v9qlNGM8884x03333SY8//rgEQHryyScz/v7www9LLpdLevzxx6UNGzZIl156qRSNRqW+vr7UMTfccIPU2NgorVu3Tnr77belFStWSLNnz5ZGRkZSx5x99tlSV1eX9Morr0ivvPKK1NXVJZ133nmV+pl15ayzzpIeffRRaePGjdL69eul1atXS83NzdLx48dTx1C6GdPvfvc76Q9/+IO0efNmafPmzdI3vvENyWQySRs3bpQkidLN6N544w2ptbVVmjVrlnTbbbelXqd0M6b7779fmjFjhrRnz57Uf/v370/9ndLNmA4fPiy1tLRIa9eulV5//XVp69at0p///Gfpo48+Sh1DaWc8+/fvz3jW1q1bJwGQnnvuOUmSKM2M6tvf/rYUCASk3//+99LWrVul//zP/5ScTqf0gx/8IHWMkdKOgv0as3DhQumGG27IeG3q1KnS17/+9Sqd0cSWHeyPjY1JkUhEevjhh1OvDQwMSB6PR/rhD38oSZIkHT16VDKZTNJjjz2WOmbXrl0Sz/PSH//4R0mSJOn999+XAEivvfZa6phXX31VAiB9+OGHOv+q+rd//34JgPTCCy9IkkTpVmt8Pp/0k5/8hNLN4I4dOyZ1dHRI69atk0477bRUsE/pZlz333+/NHv2bNm/UboZ1z333CMtWbIk798p7WrDbbfdJk2aNEkaGxujNDOw1atXS9dee23GaxdffLF05ZVXSpJkvOeNhvHXkKGhIbz11ltYtWpVxuurVq3CK6+8UqWzIum2bt2KvXv3ZqSRxWLBaaedlkqjt956C8PDwxnHxGIxdHV1pY559dVX4fF4cMopp6SOWbRoETweD6W1Bnp7ewEAfr8fAKVbrRgdHcVjjz2GEydOoKenh9LN4G666SasXr0aK1euzHid0s3YtmzZglgshra2Nlx22WX45JNPAFC6Gdnvfvc7zJ8/H5dccgnC4TDmzp2LH//4x6m/U9oZ39DQEH71q1/h2muvBcdxlGYGtmTJEvz3f/83/va3vwEA3n33Xbz88ss499xzARjveRPV/1RSaQcPHsTo6CgaGhoyXm9oaMDevXurdFYkXTId5NJo+/btqWPMZjN8Pl/OMcn37927F+FwOOfzw+EwpXWZJEnCnXfeiSVLlqCrqwsApZvRbdiwAT09PRgYGIDT6cSTTz6J6dOnpwo7Sjfjeeyxx/D222/jzTffzPkbPW/Gdcopp+CXv/wlOjs7sW/fPnz729/Gqaeeik2bNlG6Gdgnn3yCRx55BHfeeSe+8Y1v4I033sCtt94Ki8WCq6++mtKuBjz11FM4evQo1q5dC4DySSO755570Nvbi6lTp0IQBIyOjuLBBx/E5ZdfDsB4aUfBfg3iOC7j35Ik5bxGqktNGmUfI3c8pXX5br75Zrz33nt4+eWXc/5G6WZMU6ZMwfr163H06FE8/vjjuOaaa/DCCy+k/k7pZiw7d+7EbbfdhmeffRZWqzXvcZRuxnPOOeek/v/MmTPR09ODSZMm4Re/+AUWLVoEgNLNiMbGxjB//nz8wz/8AwBg7ty52LRpEx555BFcffXVqeMo7Yzrpz/9Kc455xzEYrGM1ynNjOe3v/0tfvWrX+E3v/kNZsyYgfXr1+P2229HLBbDNddckzrOKGlHw/hrSDAYhCAIOa05+/fvz2k9ItWRXLW4UBpFIhEMDQ3hyJEjBY/Zt29fzucfOHCA0roMt9xyC373u9/hueeeQ1NTU+p1SjdjM5vNmDx5MubPn4+HHnoIs2fPxj/90z9RuhnUW2+9hf3796O7uxuiKEIURbzwwgv453/+Z4iimLqmlG7G53A4MHPmTGzZsoWeNwOLRqOYPn16xmvTpk3Djh07AFAZZ3Tbt2/Hn//8Z1x33XWp1yjNjOtrX/savv71r+Oyyy7DzJkzcdVVV+GOO+7AQw89BMB4aUfBfg0xm83o7u7GunXrMl5ft24dTj311CqdFUnX1taGSCSSkUZDQ0N44YUXUmnU3d0Nk8mUccyePXuwcePG1DE9PT3o7e3FG2+8kTrm9ddfR29vL6W1CpIk4eabb8YTTzyBv/zlL2hra8v4O6VbbZEkCYODg5RuBnXGGWdgw4YNWL9+feq/+fPnY82aNVi/fj3a29sp3WrE4OAgPvjgA0SjUXreDGzx4sU528n+7W9/Q0tLCwAq44zu0UcfRTgcxurVq1OvUZoZV39/P3g+M4QWBCG19Z7h0k7xUn7EEJJb7/30pz+V3n//fen222+XHA6HtG3btmqf2oRx7Ngx6Z133pHeeecdCYD0/e9/X3rnnXdS2x8+/PDDksfjkZ544glpw4YN0uWXXy673UZTU5P05z//WXr77bel008/XXa7jVmzZkmvvvqq9Oqrr0ozZ86krVJU+spXviJ5PB7p+eefz9jmpr+/P3UMpZsx3XvvvdKLL74obd26VXrvvfekb3zjGxLP89Kzzz4rSRKlW61IX41fkijdjOqrX/2q9Pzzz0uffPKJ9Nprr0nnnXee5HK5UnUMSjdjeuONNyRRFKUHH3xQ2rJli/TrX/9astvt0q9+9avUMZR2xjQ6Oio1NzdL99xzT87fKM2M6ZprrpEaGxtTW+898cQTUjAYlO6+++7UMUZKOwr2a9C//uu/Si0tLZLZbJbmzZuX2j6MVMZzzz0nAcj575prrpEkiW25cf/990uRSESyWCzSsmXLpA0bNmR8xsmTJ6Wbb75Z8vv9ks1mk8477zxpx44dGcccOnRIWrNmjeRyuSSXyyWtWbNGOnLkSIV+ZX2RSy8A0qOPPpo6htLNmK699tpUfhcKhaQzzjgjFehLEqVbrcgO9indjCm5F7TJZJJisZh08cUXS5s2bUr9ndLNuJ5++mmpq6tLslgs0tSpU6Uf/ehHGX+ntDOmP/3pTxIAafPmzTl/ozQzpr6+Pum2226TmpubJavVKrW3t0v33XefNDg4mDrGSGnHSZIkKR8HQAghhBBCCCGEEKOjOfuEEEIIIYQQQkidoWCfEEIIIYQQQgipMxTsE0IIIYQQQgghdYaCfUIIIYQQQgghpM5QsE8IIYQQQgghhNQZCvYJIYQQQgghhJA6Q8E+IYQQQgghhBBSZyjYJ4QQQgghhBBC6gwF+4QQQgjRBMdxeOqpp6p9GoQQQggBBfuEEEIIAbB27VpcdNFF1T4NQgghhGiEgn1CCCGEEEIIIaTOULBPCCGEkAzLly/Hrbfeirvvvht+vx+RSAQPPPBAxjFbtmzBsmXLYLVaMX36dKxbty7nc3bt2oVLL70UPp8PgUAAF154IbZt2wYA+PDDD2G32/Gb3/wmdfwTTzwBq9WKDRs26PnzCCGEkAmBgn1CCCGE5PjFL34Bh8OB119/Hd/5znfwrW99KxXQj42N4eKLL4YgCHjttdfwwx/+EPfcc0/G+/v7+7FixQo4nU68+OKLePnll+F0OnH22WdjaGgIU6dOxXe/+13ceOON2L59O3bv3o3rr78eDz/8MGbOnFmNn0wIIYTUFU6SJKnaJ0EIIYSQ6lq7di2OHj2Kp556CsuXL8fo6Cheeuml1N8XLlyI008/HQ8//DCeffZZnHvuudi2bRuampoAAH/84x9xzjnn4Mknn8RFF12En/3sZ/jOd76DDz74ABzHAQCGhobg9Xrx1FNPYdWqVQCA8847D319fTCbzeB5Hn/6059SxxNCCCFEPbHaJ0AIIYQQ45k1a1bGv6PRKPbv3w8A+OCDD9Dc3JwK9AGgp6cn4/i33noLH330EVwuV8brAwMD+Pjjj1P//tnPfobOzk7wPI+NGzdSoE8IIYRohIJ9QgghhOQwmUwZ/+Y4DmNj7T2ZeQAAAehJREFUYwAAuUGB2UH62NgYuru78etf/zrn2FAolPr/7777Lk6cOAGe57F3717EYjEtTp8QQgiZ8CjYJ4QQQkhJpk+fjh07dmD37t2p4PzVV1/NOGbevHn47W9/i3A4DLfbLfs5hw8fxtq1a3Hfffdh7969WLNmDd5++23YbDbdfwMhhBBS72iBPkIIIYSUZOXKlZgyZQquvvpqvPvuu3jppZdw3333ZRyzZs0aBINBXHjhhXjppZewdetWvPDCC7jtttvw6aefAgBuuOEGxONxfPOb38T3v/99SJKEu+66qxo/iRBCCKk7FOwTQgghpCQ8z+PJJ5/E4OAgFi5ciOuuuw4PPvhgxjF2ux0vvvgimpubcfHFF2PatGm49tprcfLkSbjdbvzyl7/EM888g//4j/+AKIqw2+349a9/jZ/85Cd45plnqvTLCCGEkPpBq/ETQgghhBBCCCF1hnr2CSGEEEIIIYSQOkPBPiGEEEIIIYQQUmco2CeEEEIIIYQQQuoMBfuEEEIIIYQQQkidoWCfEEIIIYQQQgipMxTsE0IIIYQQQgghdYaCfUIIIYQQQgghpM5QsE8IIYQQQgghhNQZCvYJIYQQQgghhJA6Q8E+IYQQQgghhBBSZyjYJ4QQQgghhBBC6sz/Dxlpeq14+sksAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "# plt.plot(df_downsampled['yhat_catELMo'].apply(eval).apply(lambda x: x[0]), label='yhat_catELMo', color='blue', alpha=0.5)\n",
    "# plt.plot(df_downsampled['yhat_pite'].apply(eval).apply(lambda x: x[0]), label='yhat_pite', color='red', alpha=0.5)\n",
    "plt.plot(df_downsampled['yhat_titan'].apply(eval).apply(lambda x: x[0]), label='yhat_titan', color='b', alpha=0.5)\n",
    "plt.plot(df_downsampled['yhat_atmtcr'].apply(eval).apply(lambda x: x[0]), label='yhatyhat_atmtcr_pite', color='c', alpha=0.5)\n",
    "# plt.plot(df_downsampled['yhat_ergolstm'].apply(eval).apply(lambda x: x[0]), label='yhat_ergolstm', color='m', alpha=0.5)\n",
    "# plt.plot(df_downsampled['yhat_ergoae'].apply(eval).apply(lambda x: x[0]), label='yhat_ergoae', color='y', alpha=0.5)\n",
    "\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Logits')\n",
    "plt.title('Downsampled yhat_catELMo and yhat_pite')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.656314\n",
       "1       22.087420\n",
       "2       19.625666\n",
       "3       26.504938\n",
       "4       16.074224\n",
       "          ...    \n",
       "7675    -2.975493\n",
       "7676     3.406330\n",
       "7677     2.813263\n",
       "7678     4.367562\n",
       "7679     2.974669\n",
       "Name: yhat_catELMo, Length: 7680, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_downsampled['yhat_catELMo'].apply(eval).apply(lambda x: x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Generate some discrete example data (current values and previous reference values)\n",
    "benchmark = ['Titian test', 'Titian benchmark', 'ATM-TCR test', 'ATM-TCR benchmark']  # Indices (0, 1, 2, ..., 29)\n",
    "y_reference = np.array([0.793, 0.472, 0.941, 0.762])  # Current values\n",
    "y = np.array([0.607, 0.933, 0.919, 0.881])  # Reference (previous) values\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Provided data\n",
    "# benchmark = ['Titian test', 'Titian benchmark', 'ATM-TCR test', 'ATM-TCR benchmark']  # Benchmark labels\n",
    "# y_reference = np.array([0.793, 0.472, 0.941, 0.762])  # Reference values\n",
    "# y = np.array([0.607, 0.933, 0.919, 0.881])  # Current values\n",
    "\n",
    "# Identify the bars that have dropped or increased\n",
    "dropped_mask = y < y_reference  # Where the values have dropped\n",
    "increased_mask = y > y_reference  # Where the values have increased\n",
    "\n",
    "# Calculate the difference between the reference and current values\n",
    "dropped_values = y_reference - y  # Difference when values dropped\n",
    "\n",
    "# Create a single figure\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot the dropped portion (blank or light color)\n",
    "plt.bar(np.array(benchmark), y_reference, color='lightgray', label='Before fine-tuning')\n",
    "\n",
    "# # Plot the remaining portion (colored) for dropped values\n",
    "plt.bar(np.array(benchmark)[dropped_mask], y[dropped_mask], color='r', label='Decreased after fine-tuning')\n",
    "\n",
    "# # Plot the increased portion for increased values\n",
    "plt.bar(np.array(benchmark)[increased_mask], y[increased_mask]-y_reference[increased_mask], color='g', label='Increased after fine-tuning', bottom=y_reference[increased_mask])\n",
    "\n",
    "plt.title(\"Fine-tuning with 0.01 original learning rate\")\n",
    "plt.xlabel(\"Methods-Dataset\")\n",
    "plt.ylabel(\"AUC\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Generate some discrete example data (current values and previous reference values)\n",
    "benchmark = ['Titian test', 'Titian benchmark', 'ATM-TCR test', 'ATM-TCR benchmark']  # Indices (0, 1, 2, ..., 29)\n",
    "y_reference = np.array([0.708, 0.515, 0.616, 0.476])  # Current values\n",
    "y = np.array([0.534, 0.851, 0.506, 0.881])  # Reference (previous) values\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Provided data\n",
    "# benchmark = ['Titian test', 'Titian benchmark', 'ATM-TCR test', 'ATM-TCR benchmark']  # Benchmark labels\n",
    "# y_reference = np.array([0.793, 0.472, 0.941, 0.762])  # Reference values\n",
    "# y = np.array([0.607, 0.933, 0.919, 0.881])  # Current values\n",
    "\n",
    "# Identify the bars that have dropped or increased\n",
    "dropped_mask = y < y_reference  # Where the values have dropped\n",
    "increased_mask = y > y_reference  # Where the values have increased\n",
    "\n",
    "# Calculate the difference between the reference and current values\n",
    "dropped_values = y_reference - y  # Difference when values dropped\n",
    "\n",
    "# Create a single figure\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot the dropped portion (blank or light color)\n",
    "plt.bar(np.array(benchmark), y_reference, color='lightgray', label='Before fine-tuning')\n",
    "\n",
    "# # Plot the remaining portion (colored) for dropped values\n",
    "plt.bar(np.array(benchmark)[dropped_mask], y[dropped_mask], color='r', label='After fine-tuning')\n",
    "\n",
    "# # Plot the increased portion for increased values\n",
    "plt.bar(np.array(benchmark)[increased_mask], y[increased_mask]-y_reference[increased_mask], color='g', label='Increased', bottom=y_reference[increased_mask])\n",
    "\n",
    "plt.title(\"Fine-tuning with 0.01 original learning rate\")\n",
    "plt.xlabel(\"Methods-Dataset\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare strict/epitope split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "from pathlib import Path\n",
    "parent = str(Path.cwd().parent)\n",
    "sys.path.append(parent)\n",
    "from tcr_utils.pd_filter import count_entriesByKey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "contents = pd.read_csv('data/combined_dataset_repTCRs.csv')\n",
    "positive = contents[contents['binding'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_count = count_entriesByKey(positive, 'TCRs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SHAGLGVEQ</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASTGSYGYTFGSGTRLTVT</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASSVLNTGELFF</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASRGQNTGELFF</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASSLGGYEQYF</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140670</th>\n",
       "      <td>CASIPWGQGGILEAFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140671</th>\n",
       "      <td>CASKSALAGAGSGELFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140672</th>\n",
       "      <td>CASKSDRNTEAFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140673</th>\n",
       "      <td>CASKSGGSTEAFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140674</th>\n",
       "      <td>CASIPTSGFYNEQFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140675 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  count\n",
       "0                  SHAGLGVEQ     35\n",
       "1       CASTGSYGYTFGSGTRLTVT     33\n",
       "2              CASSVLNTGELFF     25\n",
       "3              CASRGQNTGELFF     25\n",
       "4               CASSLGGYEQYF     18\n",
       "...                      ...    ...\n",
       "140670      CASIPWGQGGILEAFF      1\n",
       "140671     CASKSALAGAGSGELFF      1\n",
       "140672         CASKSDRNTEAFF      1\n",
       "140673         CASKSGGSTEAFF      1\n",
       "140674       CASIPTSGFYNEQFF      1\n",
       "\n",
       "[140675 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcr_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "epi_count = count_entriesByKey(positive, 'Epitopes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming your DataFrame is named df and you have loaded it\n",
    "# df = pd.read_csv('path_to_your_data.csv')  # Uncomment if reading from a file\n",
    "\n",
    "# Sort the DataFrame by 'count' in descending order\n",
    "df_sorted = epi_count.sort_values(by='count', ascending=False)\n",
    "\n",
    "# Plot the sorted counts\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.bar(df_sorted['name'], df_sorted['count'])  # Plot only the top 20 for readability\n",
    "plt.title('Count of TCRs per epitope ')\n",
    "# plt.xlabel('Name')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks([])  # Hide x-axis labels\n",
    "# plt.xticks(rotation=90)  # Rotate the x-axis labels for better readability\n",
    "plt.yscale('log')  # Use a logarithmic scale if needed\n",
    "# plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "plt.tight_layout()  # Adjust layout to prevent label cutoff\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "`bins` must increase monotonically, when an array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Plot the distribution of the sorted 'count' column\u001b[39;00m\n\u001b[1;32m     12\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m---> 13\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m932\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_sorted\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcount\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# kde=True adds a smooth curve\u001b[39;00m\n\u001b[1;32m     14\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of Counts (Sorted)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     15\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/matplotlib/pyplot.py:3440\u001b[0m, in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[1;32m   3415\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mhist)\n\u001b[1;32m   3416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhist\u001b[39m(\n\u001b[1;32m   3417\u001b[0m     x: ArrayLike \u001b[38;5;241m|\u001b[39m Sequence[ArrayLike],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3438\u001b[0m     BarContainer \u001b[38;5;241m|\u001b[39m Polygon \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m[BarContainer \u001b[38;5;241m|\u001b[39m Polygon],\n\u001b[1;32m   3439\u001b[0m ]:\n\u001b[0;32m-> 3440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3442\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3443\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3444\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdensity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdensity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3445\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3446\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcumulative\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcumulative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3447\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbottom\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3448\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhisttype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhisttype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3449\u001b[0m \u001b[43m        \u001b[49m\u001b[43malign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3450\u001b[0m \u001b[43m        \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3451\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3453\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3454\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstacked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3456\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3457\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3458\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/matplotlib/__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1472\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1473\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1474\u001b[0m \u001b[43m            \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1475\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1476\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1478\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1479\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1480\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7001\u001b[0m, in \u001b[0;36mAxes.hist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m   6997\u001b[0m \u001b[38;5;66;03m# Loop through datasets\u001b[39;00m\n\u001b[1;32m   6998\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(nx):\n\u001b[1;32m   6999\u001b[0m     \u001b[38;5;66;03m# this will automatically overwrite bins,\u001b[39;00m\n\u001b[1;32m   7000\u001b[0m     \u001b[38;5;66;03m# so that each histogram uses the same bins\u001b[39;00m\n\u001b[0;32m-> 7001\u001b[0m     m, bins \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistogram\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhist_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   7002\u001b[0m     tops\u001b[38;5;241m.\u001b[39mappend(m)\n\u001b[1;32m   7003\u001b[0m tops \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(tops, \u001b[38;5;28mfloat\u001b[39m)  \u001b[38;5;66;03m# causes problems later if it's an int\u001b[39;00m\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/histograms.py:780\u001b[0m, in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, density, weights)\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;124;03mCompute the histogram of a dataset.\u001b[39;00m\n\u001b[1;32m    682\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    776\u001b[0m \n\u001b[1;32m    777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    778\u001b[0m a, weights \u001b[38;5;241m=\u001b[39m _ravel_and_check_weights(a, weights)\n\u001b[0;32m--> 780\u001b[0m bin_edges, uniform_bins \u001b[38;5;241m=\u001b[39m \u001b[43m_get_bin_edges\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    782\u001b[0m \u001b[38;5;66;03m# Histogram is an integer or a float array depending on the weights.\u001b[39;00m\n\u001b[1;32m    783\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/workspace/.conda/envs/bap_attack/lib/python3.10/site-packages/numpy/lib/histograms.py:431\u001b[0m, in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m    429\u001b[0m     bin_edges \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(bins)\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(bin_edges[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m>\u001b[39m bin_edges[\u001b[38;5;241m1\u001b[39m:]):\n\u001b[0;32m--> 431\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    432\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`bins` must increase monotonically, when an array\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    435\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`bins` must be 1d, when an array\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: `bins` must increase monotonically, when an array"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming your DataFrame is named df and you have loaded it\n",
    "# df = pd.read_csv('path_to_your_data.csv')  # Uncomment if reading from a file\n",
    "\n",
    "# Sort the DataFrame by 'count' in descending order\n",
    "df_sorted = epi_count.sort_values(by='count', ascending=False)\n",
    "\n",
    "# Plot the distribution of the sorted 'count' column\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(range(0, 932), df_sorted['count'].values)  # kde=True adds a smooth curve\n",
    "plt.title('Distribution of Counts (Sorted)')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.yscale('log')  # Use a logarithmic scale for better visibility\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12940, 11573,  8511,  7318,  5793,  5401,  5325,  3848,  3424,\n",
       "        2932,  2710,  2568,  2528,  2460,  2358,  2142,  1875,  1794,\n",
       "        1786,  1751,  1712,  1657,  1379,  1319,  1297,  1281,  1063,\n",
       "        1038,   995,   985,   980,   968,   925,   900,   892,   876,\n",
       "         867,   841,   807,   782,   779,   731,   681,   674,   638,\n",
       "         613,   609,   590,   569,   548,   537,   528,   493,   485,\n",
       "         484,   476,   470,   451,   444,   437,   435,   432,   418,\n",
       "         417,   398,   365,   362,   356,   353,   346,   338,   312,\n",
       "         311,   301,   287,   286,   285,   273,   271,   267,   267,\n",
       "         266,   261,   252,   248,   248,   244,   244,   243,   236,\n",
       "         232,   223,   222,   219,   216,   207,   203,   199,   199,\n",
       "         198,   196,   193,   188,   185,   183,   180,   176,   169,\n",
       "         169,   164,   164,   161,   157,   152,   151,   149,   148,\n",
       "         147,   145,   139,   139,   138,   136,   135,   134,   132,\n",
       "         130,   130,   130,   128,   123,   123,   118,   117,   110,\n",
       "         110,   109,   108,   107,   107,   106,   103,   103,   103,\n",
       "         102,   101,    99,    98,    97,    93,    92,    91,    90,\n",
       "          90,    89,    88,    87,    87,    85,    84,    84,    83,\n",
       "          82,    77,    76,    76,    75,    75,    75,    75,    74,\n",
       "          74,    74,    74,    73,    73,    69,    68,    67,    67,\n",
       "          65,    65,    65,    64,    64,    64,    64,    63,    63,\n",
       "          62,    62,    61,    60,    57,    57,    57,    56,    56,\n",
       "          55,    54,    54,    54,    54,    53,    53,    53,    53,\n",
       "          52,    51,    50,    50,    48,    47,    47,    46,    46,\n",
       "          46,    45,    45,    45,    45,    44,    44,    44,    44,\n",
       "          44,    43,    42,    42,    42,    42,    41,    41,    41,\n",
       "          40,    40,    40,    40,    39,    39,    39,    39,    38,\n",
       "          38,    38,    37,    35,    35,    35,    34,    34,    33,\n",
       "          33,    33,    32,    32,    32,    32,    31,    31,    30,\n",
       "          30,    30,    29,    29,    29,    29,    28,    28,    28,\n",
       "          28,    27,    27,    27,    27,    26,    26,    25,    25,\n",
       "          25,    25,    24,    24,    24,    24,    23,    23,    23,\n",
       "          23,    23,    23,    23,    23,    23,    23,    22,    22,\n",
       "          21,    21,    21,    20,    20,    20,    20,    19,    19,\n",
       "          19,    19,    19,    19,    19,    18,    18,    18,    18,\n",
       "          17,    17,    17,    17,    17,    17,    17,    16,    16,\n",
       "          16,    16,    16,    15,    15,    15,    15,    15,    15,\n",
       "          15,    14,    14,    14,    14,    14,    14,    13,    13,\n",
       "          13,    13,    13,    13,    13,    13,    13,    13,    13,\n",
       "          12,    12,    12,    12,    12,    12,    12,    12,    11,\n",
       "          11,    11,    11,    11,    11,    11,    11,    11,    10,\n",
       "          10,    10,    10,    10,    10,    10,    10,    10,    10,\n",
       "          10,    10,    10,    10,    10,     9,     9,     9,     9,\n",
       "           9,     9,     9,     9,     9,     9,     9,     9,     9,\n",
       "           9,     8,     8,     8,     8,     8,     8,     8,     8,\n",
       "           8,     8,     8,     8,     8,     8,     8,     8,     8,\n",
       "           8,     8,     8,     8,     8,     7,     7,     7,     7,\n",
       "           7,     7,     7,     7,     7,     7,     7,     7,     7,\n",
       "           7,     7,     7,     6,     6,     6,     6,     6,     6,\n",
       "           6,     6,     6,     6,     6,     6,     6,     6,     6,\n",
       "           6,     6,     6,     6,     6,     6,     6,     6,     6,\n",
       "           6,     6,     6,     6,     6,     5,     5,     5,     5,\n",
       "           5,     5,     5,     5,     5,     5,     5,     5,     5,\n",
       "           5,     5,     5,     5,     5,     5,     5,     5,     5,\n",
       "           5,     5,     5,     5,     5,     5,     5,     5,     5,\n",
       "           5,     5,     5,     5,     5,     5,     5,     5,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     4,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     4,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     4,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     4,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     4,     4,\n",
       "           4,     4,     4,     4,     4,     4,     4,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     3,     3,     3,     3,     3,     3,     3,     3,\n",
       "           3,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     2,     2,     2,     2,\n",
       "           2,     2,     2,     2,     2,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "           1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted['count'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>SHAGLGVEQ</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASTGSYGYTFGSGTRLTVT</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASSVLNTGELFF</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASRGQNTGELFF</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASSLGGYEQYF</td>\n",
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       "      <th>140670</th>\n",
       "      <td>CASIPWGQGGILEAFF</td>\n",
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       "    <tr>\n",
       "      <th>140671</th>\n",
       "      <td>CASKSALAGAGSGELFF</td>\n",
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       "    <tr>\n",
       "      <th>140672</th>\n",
       "      <td>CASKSDRNTEAFF</td>\n",
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       "    <tr>\n",
       "      <th>140673</th>\n",
       "      <td>CASKSGGSTEAFF</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>140674</th>\n",
       "      <td>CASIPTSGFYNEQFF</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>140675 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  count\n",
       "0                  SHAGLGVEQ     35\n",
       "1       CASTGSYGYTFGSGTRLTVT     33\n",
       "2              CASSVLNTGELFF     25\n",
       "3              CASRGQNTGELFF     25\n",
       "4               CASSLGGYEQYF     18\n",
       "...                      ...    ...\n",
       "140670      CASIPWGQGGILEAFF      1\n",
       "140671     CASKSALAGAGSGELFF      1\n",
       "140672         CASKSDRNTEAFF      1\n",
       "140673         CASKSGGSTEAFF      1\n",
       "140674       CASIPTSGFYNEQFF      1\n",
       "\n",
       "[140675 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcr_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "CASSLGSYEQYF = positive[positive['TCRs'] == 'CASSLGSYEQYF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epitopes</th>\n",
       "      <th>TCRs</th>\n",
       "      <th>binding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>GTSGSPIVNR</td>\n",
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       "    <tr>\n",
       "      <th>26229</th>\n",
       "      <td>KTAYSHLSTSK</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>35629</th>\n",
       "      <td>KLGGALQAK</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>78827</th>\n",
       "      <td>VQPTESIVRFPNITNLCPF</td>\n",
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       "    <tr>\n",
       "      <th>78828</th>\n",
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       "      <td>CASSLGSYEQYF</td>\n",
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       "    <tr>\n",
       "      <th>78829</th>\n",
       "      <td>MIELSLIDFYLCFLAFLLFLVLIML</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
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       "    <tr>\n",
       "      <th>78830</th>\n",
       "      <td>RQIAPGQTGKIADYNYKL</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
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       "      <td>SELVIGAVIL</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>78833</th>\n",
       "      <td>MGYINVFAFPFTIYSL</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>78834</th>\n",
       "      <td>LITLATCELYHYQECV</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78835</th>\n",
       "      <td>YVVDDPCPIHFY</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139484</th>\n",
       "      <td>YVLDHLIVV</td>\n",
       "      <td>CASSLGSYEQYF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Epitopes          TCRs  binding\n",
       "3675                   GTSGSPIVNR  CASSLGSYEQYF        1\n",
       "26229                 KTAYSHLSTSK  CASSLGSYEQYF        1\n",
       "35629                   KLGGALQAK  CASSLGSYEQYF        1\n",
       "78827         VQPTESIVRFPNITNLCPF  CASSLGSYEQYF        1\n",
       "78828                  IMLIIFWFSL  CASSLGSYEQYF        1\n",
       "78829   MIELSLIDFYLCFLAFLLFLVLIML  CASSLGSYEQYF        1\n",
       "78830          RQIAPGQTGKIADYNYKL  CASSLGSYEQYF        1\n",
       "78831                   FLNGSCGSV  CASSLGSYEQYF        1\n",
       "78832                  SELVIGAVIL  CASSLGSYEQYF        1\n",
       "78833            MGYINVFAFPFTIYSL  CASSLGSYEQYF        1\n",
       "78834            LITLATCELYHYQECV  CASSLGSYEQYF        1\n",
       "78835                YVVDDPCPIHFY  CASSLGSYEQYF        1\n",
       "139484                  YVLDHLIVV  CASSLGSYEQYF        1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CASSLGSYEQYF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "CASSLGETQYF = positive[positive['TCRs'] == 'CASSLGETQYF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "CASSPMGLSYEQYV = positive[positive['TCRs'] == 'CASSPMGLSYEQYV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37947</th>\n",
       "      <td>AVFDRKSDAK</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37948</th>\n",
       "      <td>KLGGALQAK</td>\n",
       "      <td>CASSPMGLSYEQYV</td>\n",
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       "         Epitopes            TCRs  binding\n",
       "37947  AVFDRKSDAK  CASSPMGLSYEQYV        1\n",
       "37948   KLGGALQAK  CASSPMGLSYEQYV        1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CASSPMGLSYEQYV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "CASSYEGNTEAFF = positive[positive['TCRs'] == 'CASSYEGNTEAFF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epitopes</th>\n",
       "      <th>TCRs</th>\n",
       "      <th>binding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26853</th>\n",
       "      <td>LVVDFSQFSR</td>\n",
       "      <td>CASSYEGNTEAFF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39478</th>\n",
       "      <td>KLGGALQAK</td>\n",
       "      <td>CASSYEGNTEAFF</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Epitopes           TCRs  binding\n",
       "26853  LVVDFSQFSR  CASSYEGNTEAFF        1\n",
       "39478   KLGGALQAK  CASSYEGNTEAFF        1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CASSYEGNTEAFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "KLGGALQAK = positive[positive['Epitopes'] == 'KLGGALQAK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "KLGGALQAK.to_csv('./KLGGALQAK.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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