EAD_library_curation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from mimas.tools.spectral_file.extract_ms1_feature import process_mzml_file, extract_ms2_spectra, find_features\n",
    "import logging\n",
    "from toolsets.std_list_prep import complete_adducts, complete_formula\n",
    "# from toolsets.API_gets import complete_smiles\n",
    "from toolsets.search import string_search, num_search\n",
    "import toolsets.spectra_operations as so\n",
    "from tqdm import tqdm\n",
    "from toolsets.features_by_alphapept import find_features_alphapept\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "source_dir = '/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD'\n",
    "feature_folder = ('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/features')\n",
    "source_dir = ('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# prep std lists"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# from toolsets.std_list_prep import crude_list_cleanup\n",
    "# std_list_crude_pfp = pd.read_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/PFP_std_list_crude.csv')\n",
    "# std_list_crude_hilic = pd.read_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/HILIC_std_list_crude.csv')\n",
    "# std_list_pfp =crude_list_cleanup(std_list_crude_pfp)\n",
    "# std_list_hilic =crude_list_cleanup(std_list_crude_hilic)\n",
    "#\n",
    "# std_list_hilic.to_csv(os.path.join(source_dir, 'std_list_hilic_cleaned.csv'), index = False)\n",
    "# std_list_pfp.to_csv(os.path.join(source_dir, 'std_list_pfp_cleaned.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "old_lib = pd.read_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/legacy/data_r_u.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# creating library for pfp"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "std_list_pfp = pd.read_csv(os.path.join(source_dir, 'std_list_pfp_cleaned.csv'))\n",
    "std_list_hilic = pd.read_csv(os.path.join(source_dir, 'std_list_hilic_cleaned.csv'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [],
   "source": [
    "from toolsets.precursor_matching import precursor_matching_global"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5102/5102 [02:42<00:00, 31.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is either multiple or none feature file for this Bile acids 2\n",
      "there is either multiple or none feature file for this GC LIB 3\n",
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1115/1115 [00:28<00:00, 38.57it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.precursor_matching import precursor_matching_global\n",
    "matched_pfp = precursor_matching_global(std_list_pfp, feature_folder, adducts = ['[M]+','[M+H]+', '[M+Na]+', '[M+NH4]+', '[M-H2O+H]+'],step=10, ppm = True, comment='PFP')\n",
    "matched_hilic = precursor_matching_global(std_list_hilic, feature_folder, adducts = ['[M]+','[M+H]+', '[M+Na]+', '[M+NH4]+', '[M-H2O+H]+'],step=10, ppm = True, comment='HILIC')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finding seed...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1377/1377 [00:02<00:00, 473.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1311/1311 [00:02<00:00, 599.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finding seed...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 627/627 [00:00<00:00, 638.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 601/601 [00:00<00:00, 649.62it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.deduplication import match_by_rt\n",
    "matched_mapped_pfp, matched_unmapped_pfp , matched_not_found_pfp= match_by_rt([matched_pfp])\n",
    "matched_mapped_hilic, matched_unmapped_hilic , matched_not_found_hilic= match_by_rt([matched_hilic])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "from toolsets.spectra_plotter import ms2_plot"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 45/45 [00:01<00:00, 37.64it/s]\n",
      "100%|██████████| 73/73 [00:00<00:00, 210.80it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.mass_recalibration import data_recalibrate\n",
    "matched_mapped_pfp = data_recalibrate(matched_mapped_pfp)\n",
    "matched_mapped_hilic = data_recalibrate(matched_mapped_hilic)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "matched_mapped_all = pd.concat([matched_mapped_hilic, matched_mapped_pfp], axis=0, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "matched_mapped_all.to_csv(os.path.join(source_dir, 'matched_mapped_with_duplicates_all.csv'), index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2726/2726 [01:52<00:00, 24.12it/s]\n",
      "100%|██████████| 783/783 [00:25<00:00, 30.47it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.spectra_operations import weighted_average_spectra\n",
    "for dataset in [matched_mapped_pfp,matched_mapped_hilic]:\n",
    "    wa_spctra = []\n",
    "    wa_rts = []\n",
    "    for key in tqdm(dataset['key'].unique()):\n",
    "        adduct_temp = string_search(dataset,'key',key)\n",
    "        wa_spctrum = weighted_average_spectra(adduct_temp,typeofmsms = 'peaks_recalibrated')\n",
    "        wa_rt =(adduct_temp['retention_time']*adduct_temp['ms1_precursor_intensity']/adduct_temp['ms1_precursor_intensity'].sum()).sum()\n",
    "        wa_spctra.extend([wa_spctrum]*len(adduct_temp))\n",
    "        wa_rts.extend([wa_rt]*len(adduct_temp))\n",
    "    dataset['peaks_recalibrated_wa']=wa_spctra\n",
    "    dataset['retention_time_wa']=wa_rts\n",
    "    dataset.sort_values(by = ['key','ms1_precursor_intensity'], ascending=False, inplace=True)\n",
    "    dataset.drop_duplicates('key', keep='first', inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "for dataset in [matched_mapped_pfp,matched_mapped_hilic]:\n",
    "    dataset.to_csv(os.path.join(source_dir, get_df_name(dataset)+\"_for_denoise_new.csv\"), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [],
   "source": [
    "matched_mapped_pfp= pd.read_csv(os.path.join(source_dir, 'matched_mapped_pfp_for_denoise_new.csv'))\n",
    "matched_mapped_hilic= pd.read_csv(os.path.join(source_dir, 'matched_mapped_hilic_for_denoise_new.csv'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [],
   "source": [
    "matched_mapped_denoise = pd.concat([matched_mapped_pfp, matched_mapped_hilic], axis=0, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "outputs": [],
   "source": [
    "matched_mapped_denoise.to_csv(os.path.join(source_dir, 'matched_mapped_denoise.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# start here is after denoising"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "data_ead = pd.read_csv(os.path.join(source_dir, 'matched_mapped_denoise_denoised.csv'))\n",
    "data_ead['c_id']=np.arange(len(data_ead))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "1924"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_ead['reference_inchikey'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [],
   "source": [
    "from toolsets.denoising_related_functions import post_processing\n",
    "data_ead_good = post_processing(data_ead, high_quality=True)\n",
    "data_ead_bad = post_processing(data_ead, high_quality=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['scan_number', 'precursor_mz', 'charge', 'ms1_intensity_ratio',\n       'ms1_precursor_intensity', 'retention_time', 'peaks', 'ms1_index',\n       'query_idx', 'reference_name', 'reference_inchikey', 'reference_mix',\n       'reference_smiles', 'reference_formula', 'reference_mono_mass',\n       'reference_formal_charges', 'reference_adduct',\n       'reference_precursor_mz', 'key', 'spectrum_entropy', 'comment',\n       'rt_match_id', 'reference_inchikey_14', 'hits_rt', 'seed_found',\n       'matched_rt', 'calibration', 'peaks_recalibrated',\n       'peaks_recalibrated_wa', 'retention_time_wa', 'peaks_denoised', 'ei',\n       'c_id', 'peaks_denoised_normalized', 'normalized_entropy',\n       'normalized_entropy_raw'],\n      dtype='object')"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ead_good.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [],
   "source": [
    "\n",
    "wa_spectrum = []\n",
    "for key in data_ead_good['key'].unique():\n",
    "    data_temp = string_search(data_ead_good,'key',key)\n",
    "    if len(data_temp)>1:\n",
    "        wa_spectrum.append(so.weighted_average_spectra(data_temp,typeofmsms='peaks_denoised_normalized'))\n",
    "    else:\n",
    "        wa_spectrum.append(data_temp.iloc[0]['peaks_denoised_normalized'])\n",
    "data_ead_good.drop_duplicates('key', keep='first', inplace=True)\n",
    "data_ead_good['peaks_denoised_normalized']=wa_spectrum"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "data": {
      "text/plain": "      scan_number  precursor_mz  charge  ms1_intensity_ratio  \\\n0             523    749.299550       1             0.968634   \n1             518    744.344402       1             0.999243   \n2             521    727.317616       1             0.712541   \n3             734   1205.496037       1             0.907086   \n4             500    147.043749       1             0.932898   \n...           ...           ...     ...                  ...   \n3266          602    216.041965       1             0.910186   \n3267           62    880.717334       1             0.912145   \n3269           72    448.199329       1             0.964788   \n3270           71    426.217639       1             0.997481   \n3271          121    318.221455       1             0.999843   \n\n      ms1_precursor_intensity  retention_time  \\\n0                     42586.0        1.578950   \n1                    295804.0        1.568617   \n2                      5892.0        1.575500   \n3                      7732.0        2.293450   \n4                     12429.0        1.552283   \n...                       ...             ...   \n3266                   4307.0        1.844333   \n3267                   1765.0        0.176200   \n3269                  29180.0        0.188150   \n3270                 373440.0        0.187283   \n3271                8282372.0        0.246300   \n\n                                                  peaks  ms1_index  query_idx  \\\n0     127.055244\\t0.00451007\\n128.060577\\t0.01033574...        329        193   \n1     50.014946\\t0.003061\\n51.023922\\t0.01373499\\n53...        328        189   \n2     127.054512\\t0.00603489\\n128.061432\\t0.01494435...        328        191   \n3     127.054962\\t0.01068208\\n128.060287\\t0.04754418...        453        280   \n4     50.015636\\t0.00856899\\n51.022392\\t0.02365398\\n...        274        219   \n...                                                 ...        ...        ...   \n3266  50.015682\\t0.004411\\n51.023666\\t0.01375799\\n52...        574         26   \n3267  50.01495\\t0.00908773\\n51.02256\\t0.02692029\\n52...         59          1   \n3269  140.052399\\t0.00555222\\n141.06131\\t0.01504695\\...         63          8   \n3270  127.054665\\t0.010525\\n128.050476\\t0.008717\\n14...         63          7   \n3271  115.05208\\t0.00662799\\n132.09534\\t0.04321996\\n...         68         51   \n\n                                         reference_name  ...    calibration  \\\n0     (2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...  ...  recalibrated_   \n1     (2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...  ...  recalibrated_   \n2     (2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...  ...  recalibrated_   \n3     [(2R,3R,4R,6S)-6-[[(6S,7S)-6-[(2S,4R,5R,6R)-4-...  ...  recalibrated_   \n4                                              COUMARIN  ...  recalibrated_   \n...                                                 ...  ...            ...   \n3266                                3-Chloro-L-Tyrosine  ...  recalibrated_   \n3267                                       Coenzyme Q10  ...  recalibrated_   \n3269  (2S)-N-methyl-2-[[2-(2-methyl-1H-indol-3-yl)ac...  ...  recalibrated_   \n3270  (2S)-N-methyl-2-[[2-(2-methyl-1H-indol-3-yl)ac...  ...  recalibrated_   \n3271                                         Butenafine  ...  recalibrated_   \n\n                                     peaks_recalibrated  \\\n0     127.055455\\t0.00451007\\n128.060788\\t0.01033574...   \n1     50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...   \n2     127.054723\\t0.00603489\\n128.061643\\t0.01494435...   \n3     127.055025\\t0.01068208\\n128.06035\\t0.04754418\\...   \n4     50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...   \n...                                                 ...   \n3266  50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....   \n3267  50.014976\\t0.00908773\\n51.022587\\t0.02692029\\n...   \n3269  140.052206\\t0.00555222\\n141.061119\\t0.01504695...   \n3270  127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...   \n3271  115.052516\\t0.00662799\\n132.095759\\t0.04321996...   \n\n                                  peaks_recalibrated_wa retention_time_wa  \\\n0     50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...          1.566822   \n1     50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...          1.568617   \n2     127.054973\\t0.00664201\\n128.061881\\t0.01696303...          1.568086   \n3     127.055025\\t0.01068204\\n128.06035\\t0.04754419\\...          2.293450   \n4     50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...          1.552283   \n...                                                 ...               ...   \n3266  50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....          1.844333   \n3267  50.014976\\t0.009088\\n51.022587\\t0.02692\\n52.02...          0.176200   \n3269  140.052206\\t0.00555199\\n141.061119\\t0.01504698...          0.188150   \n3270  127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...          0.187283   \n3271  115.052516\\t0.00627499\\n119.088862\\t0.000256\\n...          0.247482   \n\n                                         peaks_denoised         ei  c_id  \\\n0     50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...  100.00000     0   \n1     50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...  100.00000     1   \n2     127.054973\\t0.00664201\\n128.061881\\t0.01696303...  100.00000     2   \n3     127.055025\\t0.01068204\\n128.06035\\t0.04754419\\...   99.48690     3   \n4     50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...   90.13491     4   \n...                                                 ...        ...   ...   \n3266  50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....  100.00000  3503   \n3267  50.014976\\t0.009088\\n51.022587\\t0.02692\\n52.02...   98.07570  3504   \n3269  140.052206\\t0.00555199\\n141.061119\\t0.01504698...   98.16850  3506   \n3270  127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...   99.01960  3507   \n3271  115.052516\\t0.00627499\\n119.088862\\t0.000256\\n...   93.32461  3508   \n\n                              peaks_denoised_normalized normalized_entropy  \\\n0     50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...           0.474286   \n1     50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...           0.563655   \n2     127.054973\\t0.00664201\\n128.061881\\t0.01696303...           0.449299   \n3     127.055025\\t0.01073713\\n128.06035\\t0.0477894\\n...           0.727409   \n4     50.015401\\t0.00950685\\n51.022159\\t0.02624286\\n...           0.333110   \n...                                                 ...                ...   \n3266  50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....           0.426684   \n3267  50.014976\\t0.00926631\\n51.022587\\t0.02744819\\n...           0.556103   \n3269  140.052206\\t0.00565557\\n141.061119\\t0.01532771...           0.350960   \n3270  127.054449\\t0.01062921\\n128.050261\\t0.00880331...           0.170014   \n3271  115.052516\\t0.00672383\\n119.088862\\t0.00027431...           0.049284   \n\n      normalized_entropy_raw  \n0                   0.474286  \n1                   0.563655  \n2                   0.449299  \n3                   0.726956  \n4                   0.339807  \n...                      ...  \n3266                0.426684  \n3267                0.557658  \n3269                0.358289  \n3270                0.166575  \n3271                0.069902  \n\n[3260 rows x 36 columns]",
      "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>scan_number</th>\n      <th>precursor_mz</th>\n      <th>charge</th>\n      <th>ms1_intensity_ratio</th>\n      <th>ms1_precursor_intensity</th>\n      <th>retention_time</th>\n      <th>peaks</th>\n      <th>ms1_index</th>\n      <th>query_idx</th>\n      <th>reference_name</th>\n      <th>...</th>\n      <th>calibration</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_wa</th>\n      <th>retention_time_wa</th>\n      <th>peaks_denoised</th>\n      <th>ei</th>\n      <th>c_id</th>\n      <th>peaks_denoised_normalized</th>\n      <th>normalized_entropy</th>\n      <th>normalized_entropy_raw</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>523</td>\n      <td>749.299550</td>\n      <td>1</td>\n      <td>0.968634</td>\n      <td>42586.0</td>\n      <td>1.578950</td>\n      <td>127.055244\\t0.00451007\\n128.060577\\t0.01033574...</td>\n      <td>329</td>\n      <td>193</td>\n      <td>(2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>127.055455\\t0.00451007\\n128.060788\\t0.01033574...</td>\n      <td>50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...</td>\n      <td>1.566822</td>\n      <td>50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...</td>\n      <td>100.00000</td>\n      <td>0</td>\n      <td>50.015493\\t0.002077\\n51.023041\\t0.00914998\\n52...</td>\n      <td>0.474286</td>\n      <td>0.474286</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>518</td>\n      <td>744.344402</td>\n      <td>1</td>\n      <td>0.999243</td>\n      <td>295804.0</td>\n      <td>1.568617</td>\n      <td>50.014946\\t0.003061\\n51.023922\\t0.01373499\\n53...</td>\n      <td>328</td>\n      <td>189</td>\n      <td>(2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...</td>\n      <td>50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...</td>\n      <td>1.568617</td>\n      <td>50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...</td>\n      <td>100.00000</td>\n      <td>1</td>\n      <td>50.015203\\t0.003061\\n51.024178\\t0.01373499\\n53...</td>\n      <td>0.563655</td>\n      <td>0.563655</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>521</td>\n      <td>727.317616</td>\n      <td>1</td>\n      <td>0.712541</td>\n      <td>5892.0</td>\n      <td>1.575500</td>\n      <td>127.054512\\t0.00603489\\n128.061432\\t0.01494435...</td>\n      <td>328</td>\n      <td>191</td>\n      <td>(2R,3S,4S,5R,6R)-6-[1,7-bis(4-hydroxyphenyl)he...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>127.054723\\t0.00603489\\n128.061643\\t0.01494435...</td>\n      <td>127.054973\\t0.00664201\\n128.061881\\t0.01696303...</td>\n      <td>1.568086</td>\n      <td>127.054973\\t0.00664201\\n128.061881\\t0.01696303...</td>\n      <td>100.00000</td>\n      <td>2</td>\n      <td>127.054973\\t0.00664201\\n128.061881\\t0.01696303...</td>\n      <td>0.449299</td>\n      <td>0.449299</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>734</td>\n      <td>1205.496037</td>\n      <td>1</td>\n      <td>0.907086</td>\n      <td>7732.0</td>\n      <td>2.293450</td>\n      <td>127.054962\\t0.01068208\\n128.060287\\t0.04754418...</td>\n      <td>453</td>\n      <td>280</td>\n      <td>[(2R,3R,4R,6S)-6-[[(6S,7S)-6-[(2S,4R,5R,6R)-4-...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>127.055025\\t0.01068208\\n128.06035\\t0.04754418\\...</td>\n      <td>127.055025\\t0.01068204\\n128.06035\\t0.04754419\\...</td>\n      <td>2.293450</td>\n      <td>127.055025\\t0.01068204\\n128.06035\\t0.04754419\\...</td>\n      <td>99.48690</td>\n      <td>3</td>\n      <td>127.055025\\t0.01073713\\n128.06035\\t0.0477894\\n...</td>\n      <td>0.727409</td>\n      <td>0.726956</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>500</td>\n      <td>147.043749</td>\n      <td>1</td>\n      <td>0.932898</td>\n      <td>12429.0</td>\n      <td>1.552283</td>\n      <td>50.015636\\t0.00856899\\n51.022392\\t0.02365398\\n...</td>\n      <td>274</td>\n      <td>219</td>\n      <td>COUMARIN</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...</td>\n      <td>50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...</td>\n      <td>1.552283</td>\n      <td>50.015401\\t0.00856899\\n51.022159\\t0.02365398\\n...</td>\n      <td>90.13491</td>\n      <td>4</td>\n      <td>50.015401\\t0.00950685\\n51.022159\\t0.02624286\\n...</td>\n      <td>0.333110</td>\n      <td>0.339807</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3266</th>\n      <td>602</td>\n      <td>216.041965</td>\n      <td>1</td>\n      <td>0.910186</td>\n      <td>4307.0</td>\n      <td>1.844333</td>\n      <td>50.015682\\t0.004411\\n51.023666\\t0.01375799\\n52...</td>\n      <td>574</td>\n      <td>26</td>\n      <td>3-Chloro-L-Tyrosine</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....</td>\n      <td>50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....</td>\n      <td>1.844333</td>\n      <td>50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....</td>\n      <td>100.00000</td>\n      <td>3503</td>\n      <td>50.016057\\t0.004411\\n51.02404\\t0.01375799\\n52....</td>\n      <td>0.426684</td>\n      <td>0.426684</td>\n    </tr>\n    <tr>\n      <th>3267</th>\n      <td>62</td>\n      <td>880.717334</td>\n      <td>1</td>\n      <td>0.912145</td>\n      <td>1765.0</td>\n      <td>0.176200</td>\n      <td>50.01495\\t0.00908773\\n51.02256\\t0.02692029\\n52...</td>\n      <td>59</td>\n      <td>1</td>\n      <td>Coenzyme Q10</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>50.014976\\t0.00908773\\n51.022587\\t0.02692029\\n...</td>\n      <td>50.014976\\t0.009088\\n51.022587\\t0.02692\\n52.02...</td>\n      <td>0.176200</td>\n      <td>50.014976\\t0.009088\\n51.022587\\t0.02692\\n52.02...</td>\n      <td>98.07570</td>\n      <td>3504</td>\n      <td>50.014976\\t0.00926631\\n51.022587\\t0.02744819\\n...</td>\n      <td>0.556103</td>\n      <td>0.557658</td>\n    </tr>\n    <tr>\n      <th>3269</th>\n      <td>72</td>\n      <td>448.199329</td>\n      <td>1</td>\n      <td>0.964788</td>\n      <td>29180.0</td>\n      <td>0.188150</td>\n      <td>140.052399\\t0.00555222\\n141.06131\\t0.01504695\\...</td>\n      <td>63</td>\n      <td>8</td>\n      <td>(2S)-N-methyl-2-[[2-(2-methyl-1H-indol-3-yl)ac...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>140.052206\\t0.00555222\\n141.061119\\t0.01504695...</td>\n      <td>140.052206\\t0.00555199\\n141.061119\\t0.01504698...</td>\n      <td>0.188150</td>\n      <td>140.052206\\t0.00555199\\n141.061119\\t0.01504698...</td>\n      <td>98.16850</td>\n      <td>3506</td>\n      <td>140.052206\\t0.00565557\\n141.061119\\t0.01532771...</td>\n      <td>0.350960</td>\n      <td>0.358289</td>\n    </tr>\n    <tr>\n      <th>3270</th>\n      <td>71</td>\n      <td>426.217639</td>\n      <td>1</td>\n      <td>0.997481</td>\n      <td>373440.0</td>\n      <td>0.187283</td>\n      <td>127.054665\\t0.010525\\n128.050476\\t0.008717\\n14...</td>\n      <td>63</td>\n      <td>7</td>\n      <td>(2S)-N-methyl-2-[[2-(2-methyl-1H-indol-3-yl)ac...</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...</td>\n      <td>127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...</td>\n      <td>0.187283</td>\n      <td>127.054449\\t0.010525\\n128.050261\\t0.008717\\n14...</td>\n      <td>99.01960</td>\n      <td>3507</td>\n      <td>127.054449\\t0.01062921\\n128.050261\\t0.00880331...</td>\n      <td>0.170014</td>\n      <td>0.166575</td>\n    </tr>\n    <tr>\n      <th>3271</th>\n      <td>121</td>\n      <td>318.221455</td>\n      <td>1</td>\n      <td>0.999843</td>\n      <td>8282372.0</td>\n      <td>0.246300</td>\n      <td>115.05208\\t0.00662799\\n132.09534\\t0.04321996\\n...</td>\n      <td>68</td>\n      <td>51</td>\n      <td>Butenafine</td>\n      <td>...</td>\n      <td>recalibrated_</td>\n      <td>115.052516\\t0.00662799\\n132.095759\\t0.04321996...</td>\n      <td>115.052516\\t0.00627499\\n119.088862\\t0.000256\\n...</td>\n      <td>0.247482</td>\n      <td>115.052516\\t0.00627499\\n119.088862\\t0.000256\\n...</td>\n      <td>93.32461</td>\n      <td>3508</td>\n      <td>115.052516\\t0.00672383\\n119.088862\\t0.00027431...</td>\n      <td>0.049284</td>\n      <td>0.069902</td>\n    </tr>\n  </tbody>\n</table>\n<p>3260 rows × 36 columns</p>\n</div>"
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ead_good"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16735ca60>"
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_raw = []\n",
    "for index, row in data_ead_bad.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_ead_bad['normalized_entropy_raw']=normalized_raw\n",
    "normalized_raw = []\n",
    "for index, row in data_ead_good.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_ead_good['normalized_entropy_raw']=normalized_raw\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "# ax= sns.histplot(data=data_UVPD, x=\"normalized_entropy\", multiple=\"stack\", kde = True, palette=\"red\")\n",
    "# ax = sns.histplot(data=data_HCD, x=\"normalized_entropy\", multiple=\"stack\", kde = True,color=\"green\")\n",
    "ax=sns.histplot(data=data_ead_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[2], alpha = 0.3,stat='probability',kde=True)\n",
    "ax=sns.histplot(data=data_ead_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[3], alpha = 0.3,stat='probability',kde=True)\n",
    "plt.axvline(0.8, 0, color = 'red' )\n",
    "# ax=sns.histplot(data=data_uvpd_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[0], stat='probability')\n",
    "# ax=sns.histplot(data=data_uvpd_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[1],stat='probability')\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"Normalized entropy\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.legend(labels=[\"High-quality spectra\",\"Low-quality spectra\"], loc = 'upper left',fontsize=12)\n",
    "# plt.savefig(os.path.join(source_dir,'normalized_entropy_UVPD_high_low_quality.tiff'), dpi = 300,transparency = True,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "outputs": [],
   "source": [
    "from toolsets.file_io import export_library_msp"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3260/3260 [00:22<00:00, 147.68it/s]\n"
     ]
    }
   ],
   "source": [
    "data_ead_good.to_csv(os.path.join(source_dir, 'EAD_library'), index= False)\n",
    "export_library_msp(data_ead_good,os.path.join(source_dir, 'EAD_library_final.txt'), typeofmsms='peaks_denoised_normalized')\n",
    "# export_library_msp(data_hilic,os.path.join(source_dir, 'EAD_hilic_final.txt'), typeofmsms='peaks_denoised_normalized')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3244/3244 [16:27<00:00,  3.29it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.API_gets import GNPS_classyfire, get_classyfire_data\n",
    "data_EAD=pd.read_csv(os.path.join(source_dir, 'EAD_library.csv'))\n",
    "data_EAD_classyfire= get_classyfire_data(data_EAD)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [],
   "source": [
    "data_EAD_classyfire.to_csv(os.path.join(source_dir, 'EAD_library_cla.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# test some denoising"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from toolsets.denoising_related_functions import denoise_single"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "from rdkit import Chem"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "['H5C6NO', 'H8C3N2Cl']"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denoise_single(105.0447, 212.0837, adduct='[M+H]+', formula = 'C11H14ClN3O', mass_error= 20, ifppm=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "test_smiles= 'CC(C)N(C1=CC=CC=C1)C(=O)CCl'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "mol = Chem.MolFromSmiles(test_smiles)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x15b193400>",
      "image/png": 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\n"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [],
   "source": [
    "benzene_pattern = Chem.MolFromSmiles('C1=CC=CC=C1')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [],
   "source": [
    "mol2 = Chem.MolFromSmiles(matched_mapped_pfp.iloc[30]['reference_smiles'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [],
   "source": [
    "from rdkit.Chem.rdMolDescriptors import CalcMolFormula\n",
    "formula = CalcMolFormula(mol2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "[M+H]+        1006\n[M+Na]+        979\n[M+NH4]+       699\n[M-H2O+H]+      41\n[M]+             1\nName: reference_adduct, dtype: int64"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_mapped_pfp['reference_adduct'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol2.HasSubstructMatch(benzene_pattern)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol.HasSubstructMatch(benzene_pattern)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# search biological samples"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "data_pfp = pd.read_csv(os.path.join(source_dir, 'data_pfp_lib_final.csv'))\n",
    "data_hilic = pd.read_csv(os.path.join(source_dir, 'data_hilic_lib_final.csv'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "from toolsets.API_gets import get_classyfire_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2550/2550 [12:35<00:00,  3.38it/s]\n",
      "100%|██████████| 706/706 [03:15<00:00,  3.62it/s]\n"
     ]
    }
   ],
   "source": [
    "data_pfp_classyfire = get_classyfire_data(data_pfp)\n",
    "data_hilic_classyfire = get_classyfire_data(data_hilic)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "data_pfp_classyfire.to_csv(os.path.join(source_dir, 'data_pfp_lib_final.csv_cla'), index= False)\n",
    "data_hilic_classyfire.to_csv(os.path.join(source_dir, 'data_hilic_lib_final_cla.csv'), index= False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "data_ead = pd.concat([data_pfp, data_hilic], axis= 0, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning incoming spectrum\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2431/2431 [02:28<00:00, 16.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning incoming spectrum\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3333/3333 [04:27<00:00, 12.47it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.file_io import read_msp_files\n",
    "pfp_bio =read_msp_files('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/biological_samples/PFP_pos.msp')\n",
    "hilic_bio = read_msp_files('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/biological_samples/HILIC_pos.msp')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "pfp_bio.to_csv(os.path.join(source_dir, 'pfp_bio_cleaned.csv'), index = False)\n",
    "hilic_bio.to_csv(os.path.join(source_dir, 'hilic_bio_cleaned.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "ax= sns.histplot(data=data_r, x=\"diff_raw\", multiple=\"stack\", kde = True, color = 'green')\n",
    "ax = sns.histplot(data=data_r, x=\"diff_recalibrated\", multiple=\"stack\", kde = True,color = 'red')\n",
    "# ax.set_title(\"\", fontsize = 16)\n",
    "ax.set_xlabel(\"Mass deviation of precursor m/z (Da)\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "plt.legend(labels=[\"Prerecalibration\",\"Postrecalibration\"], loc = 'upper right',fontsize=12)\n",
    "plt.savefig(os.path.join('/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD', 'recalibration.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    }
   ],
   "source": [
    "from toolsets.spectra_plotter import fast_EIC"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fast_EIC()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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CQZC/VcJhULgOT94xFACw9ku2ThAREVHPsZggcqO/7D0Hg8mKMQNDMWtkjNzhXGfptERo1AqU1uhx+kqT3OEQERGRl2ExQeQmNc1teP9AGQDg2axkj2qVcAjRqDE1ORoAsK2gSuZoiIiIyNuwmCBykz/vKUWr2Yqx8WGYPtzzWiUcZqfaZ5fKKaiUORIiIiLyNlwBm7qmUAB33dVxm3qkusmIvx20t0r8zMPGSnzbzBExUCoEFFU242KdAYMjdXKHRL3BnCXqwHwgkgyLCeqaVgvs3i13FF5r/Z5StFlsuH1wGKYmR8kdzk2FBwVgQkIEDpyrQ87pSjx55zC5Q6LeYM4SdWA+EEmG5TqRi1U2GrEx9yIA4GdZwz26VcJhdvuUtTkcN0FEREQ9wGKCyMXe3F0Ck8WGCQkRmJIUKXc43eIYN5FfVo/aljaZoyEiIiJvwWKCuqbXA9HR9k2vlzsar1HR0IqP8soBAMs8dAanrgwM02L0wH4QRWBnIVsnvBJzlqgD84FIMiwm6MZqa+0bddsbu0pgstowcVgEJid69liJb5szyt46wSlivRhzlqgD84FIEiwmiFzk0lUDPjlkb5V4dlaKzNH0nKOr09cltWhps8gcDREREXkDFhNELvLGrhKYrSKmJEUic5h3jJW4VkpsMIZE6mCy2LD3bI3c4RAREZEXYDFB5AIX6wzYdOgSAO9slQAAQRAwJ9XR1YkL2BEREdGtsZggcoE/fVUMi03E1JRoZCREyB1Or80eZZ8i9quiapgsNpmjISIiIk/HYoKojy7U6vGPo5cBAM/OSpY5mr5JHxyOqOBANBstOHiuTu5wiIiIyMNxBWzqmkIBZGR03KYbWvdVMaw2EdOHRyN9cLjc4fSJUiEga1QMPswrR87pSkxNiZY7JOou5ixRB+YDkWSYYdQ1rRbIz7dvWq3c0Xis0poW/NPRKpHlnWMlvm12+xSx209XwWYTZY6Guo05S9SB+UAkGRYTRH3wp53FsInArJGxuG1QmNzhuMTkpEgEBShR1dSG45ca5A6HiIiIPBiLCaJeKqluxr+OVwAAlnn5WIlrBaqUmDYiBgCQc5oL2BEREdGNsZigrhkMQEKCfTMY5I7GI722oxiiCMxJjcXogaFyh+NSnCLWCzFniTowH4gkwwHY1DVRBMrKOm5TJ2cqm/HFySsAgGVeuq7EzUwbHg21UsC5Gj1KqluQFBMsd0h0K8xZog7MByLJsGWCqBde33kWogjcMyYOI/v3kzscl+unUWNSYhQAIOc0WyeIiIioaywmiHrodEUTtpyshCAAz8z0vVYJhzmp9gXsthVw3AQRERF1jcUEUQ+9vvMsAODe2wZgeFyIzNG4T9bIWAgCcLy8AZWNRrnDISIiIg/EYoKoB05dbsS2gqr2VokkucNxq5h+GqTHhwEAtrOrExEREXWBxQRRD7y2w94q8cDYAUiK8d1WCYfZ7bM6cYpYIiIi6gqLCeqaIACjRtk3QZA7Go9w4lIDdhRWQyEAP53pO+tK3MzsUfZxEwdK69DYapY5Grop5ixRB+YDkWQ4NSx1TacDCgrkjsKj/HG7vVViXvpADIv2j6lSh0UHIzkmGMXVLdhVVI156QPlDoluhDlL1IH5QCQZtkwQdcORi1ex60wNlAoBP53hH60SDrPbZ3XiFLFERET0bSwmiLrhtR3FAIAHbx+IhKggmaORlmM17N1namA0W2WOhoiIiDwJiwnqmsEApKbaN4NB7mhkdehCPfaerYFKIeAnftYqAQBjBoaif6gGBpMV+0tq5Q6HboQ5S9SB+UAkGRYT1DVRBE6ftm+iKHc0svpj+wxOCzMGIT5CJ3M00hMEwTkQe1sBuzp5LOYsUQfmA5FkWEwQ3UTuuTrsL6mDWing6em+va7EzTimiN1RWA2rjW/MREREZMdigugmHK0SD2XEY1C4/7VKOEwYGoFQrRr1ehMOl12VOxwiIiLyECwmiG7gm9JaHDxXjwClwq9bJQBArVRg5ogYAOzqRERERB1YTBB1QRRFvLbdPoPTIxPiMSBMK3NE8rt2iliRfZCJiIgIPSwmioqK8MQTT2DChAmYMmUKfvGLX6C+vh4AcPz4cSxcuBDp6emYMWMGNm3a1Om5n3/+ObKyspCWlob58+fj6NGjzn1WqxWvvPIKJk+ejPT0dCxduhTV1dXO/XV1dcjOzkZGRgYyMzOxevVqWCwW5/5bnZuop/aX1CHvQj0CVApk+3mrhMPUlGgEqhQor29F4ZVmucMhIiIiD9DtYsJoNOLJJ59Eeno6vv76a/znP/9BQ0MDfvWrX6GxsRFPPfUU5s2bh/z8fKxevRovv/wyTpw4AQDIzc3FqlWrsHbtWuTn5+P+++/H0qVL0draCgBYv3499u/fj88++wz79u2DRqPBihUrnOdetmwZdDod9u3bh08//RQHDhzAhg0bAOCW56ZeEgRgyBD7JghyRyMpURTxh+1nAACLMgcjtp9G5og8gy5AhTuTowFwATuP5Mc5S3Qd5gORZLpdTFRUVGDEiBF4+umnERAQgPDwcDz88MPIz89HTk4OwsLCsGjRIqhUKkyaNAn33XcfNm7cCADYtGkT5s6di3HjxkGtVmPx4sUIDw/Hli1bnPuXLFmC/v37Izg4GM8//zz27t2L8vJylJWVIS8vD8uXL4dWq0V8fDyys7Odx77VuamXdDrgwgX7pvOvgcd7i2tx5GIDNGoFlk5LlDscj+Ls6lRQJXMkdB0/zlmi6zAfiCTT7WJi2LBhePvtt6FUKp33bdu2DampqSguLkZKSkqnxyclJaGoqAgAUFJScsP9zc3NqKys7LQ/KioKoaGhOHPmDIqLixEWFobY2Fjn/sTERFRUVKCpqemW5ybqCXurhH0Gp0czhyAmhK0S15o1MhYKATh9pQnl9VwIioiIyN/1agC2KIr44x//iF27duH555+HXq+HVtt5gKpGo4GhfdXJm+3X6/UAAN23vjnQaDTQ6/VdPtfxu+P5Nzs3UU/sOlON4+UN0KqV+OFdbJX4toigAIxPiAAA5Jxm6wQREZG/63Ex0dLSgp/+9KfYvHkzPvjgAwwfPhxarRZGo7HT44xGI4KCggDgpvsdhYBj/MS39+t0uuv2OX53PP9m56Zeam0Fxo+3b9/6/++rRFHEH9tncPr+5CGIDgmUOSLPNKd9ATtOEeth/DBniW6I+UAkmR4VExcvXsSDDz6IlpYWfPrppxg+fDgAICUlBcXFxZ0eW1JSguTkZABAcnLyDfeHhoYiNjYWJSUlzn01NTVoaGhASkoKkpOT0dDQgNraWuf+0tJSxMXFISQk5Jbnpl6y2YBDh+ybzSZ3NJLYUViNk5cboQtQ4odT2SpxI1mj7F0OD12oR11Lm8zRkJMf5izRDTEfiCTT7WKisbERjz/+OG6//Xb89a9/RUREhHNfVlYWamtrsWHDBpjNZhw8eBCbN2/Ggw8+CABYsGABNm/ejIMHD8JsNmPDhg2oq6tDVlYWAGD+/PlYv349ysvL0dLSgjVr1mDChAkYPHgwEhISMG7cOKxZswYtLS0oLy/Hm2++iQULFnTr3ETdYW+VsI+VWDw5ARFBATJH5LniI3QY1b8fbCKws6j61k8gIiIin6Xq7gP/8Y9/oKKiAl9++SW2bt3aad/Ro0fxzjvvYPXq1Vi3bh0iIiKwYsUKTJw4EQAwadIkvPjii3jppZdQVVWFpKQkvPXWWwgLCwMAPP3007BYLFi0aBH0ej0yMzPx2muvOY+/bt06rFy5EjNnzoRCocC8efOQnZ0NAAgPD7/puYm6Y1tBFU5faUJwoApL7hwmdzgeb05qHE5faUJOQSUeyoiXOxwiIiKSiSByKVtYrVYcO3YMaWlpnWar8mt6PRAcbL/d0gL48BgUm03EPev2oaiyGT+ZkYSfzx4ud0ger/BKE77z+j4EqBQ4+kIWggK7/b0EuYsf5SzRLTEfiJzc/Tm3V7M5EfmSL09VoqiyGSGBKjx5B1slumNEXAgGR+hgstiw92yN3OEQERGRTFhMkF+z2kS8tsM+VuK/7xyKUJ1a5oi8gyAImN0+EJtTxBIREfkvFhN0Y1FR9s2HfXHyCoqrW9BPo8IP7hgqdzheZc5o+xSxOwurYLZythSP4Ac5S9RtzAciSbCjM3UtKAio8e3uK9e2Siy5cxj6adgq0RO3Dw5HZFAA6vQm5J6rxx3JfNOWlR/kLFG3MR+IJMOWCfJb/z5+Gedq9AjTqbF4SoLc4XgdpULArJGOrk5cwI6IiMgfsZggv2Sx2rBup32hxCV3DkMIWyV6Zc7o9mKioAo2m99PDEdEROR3WExQ11pbgWnT7Ftrq9zRuNw/j1XgfK0eEUEBeHxygtzheK3JiVEIClCissmIk5cb5Q7Hv/l4zhL1CPOBSDIcM0Fds9mAPXs6bvsQs9WGdTuLAQA/nDoMwVwjodc0aiWmDY/BFyevIOd0JcbGh8kdkv/y4Zwl6jHmA5Fk2DJBfufzI5dxsd6AqOAAPDZpiNzheL3ZqfauTtsKOEUsERGRv2ExQX7FZLFh3Vf2Vokf3ZUIXQBbJfpq2vAYqBQCSqpbUFrTInc4REREJCEWE+RXPj18CZeutiI6JBCLMtkq4QqhWjUmJUYCsA/EJiIiIv/BYoL8RpvFijd22WdwWnpXIrQBSpkj8h2zU+0L2HGKWCIiIv/CYoL8xieHLuFyQyti+wXie5mD5Q7Hp8weZR83cfRiA6qbjDJHQ0RERFJhMUE3ptPZNx9gNFvxxlf2VomnpydBo2arhCvF9tMgrX0mp5zT7OokGx/KWaI+Yz4QSYLFBHUtKAjQ6+1bUJDc0fTZx/nlqGwyon+oBg+Pj5c7HJ/kmNWJxYRMfCxnifqE+UAkGRYT5POM5o6xEk9PT0Kgiq0S7jCnfdzEgdJaNBnNMkdDREREUmAxQT5vY+5FVDe3YWCYFg9lsFXCXRKjg5EYHQSzVcSuomq5wyEiIiIJsJigrhmNwNy59s3ovQNqW01WrN9dCgD48YwkBKh4ybvTHOesTuzqJDkfyVkil2A+EEmGK3ZR16xWYMuWjtte6oODZahtaUN8hBYLxg2SOxyfNzs1Dm/uLsXuomoYzVYOdJeSj+QskUswH4gkw69pyWcZTBb8vz32VomfzEiGWsnL3d1uGxiKuH4a6E1WHCitkzscIiIicjN+uiKf9f6BMtTpTRgSqcP89IFyh+MXFAoBWe1rTmwr4AJ2REREvo7FBPmkljYL/tzeKvHTGclQsVVCMo4pYncUVsFqE2WOhoiIiNyJn7DIJ733zQVcNZgxLCoID6QNkDscvzJxWCRCNCrUtphw9OJVucMhIiIiN2IxQT6n2WjGX/aeAwA8M4utElJTKxWYOSIGALs6ERER+Tp+yiKf8+7+C2hsNSMpJhj33sZWCTlcO0WsKLKrExERka9iMUFdCwoCRNG+BQXJHU23Nbaa8da+9laJmclQKgSZI/JPU1OiEaBSoKzOgDNVzXKH4x+8NGeJ3IL5QCQZFhPkU975+jyajRakxAZj7pj+cofjt4ICVbgzKQoAkFPABeyIiIh8FYsJ8hkNBhPe+fo8AGDZrBQo2CohK0dXJ46bICIi8l0sJqhrRiOwcKF9MxrljqZb3t53Hs1tFoyIC8Hd7R9kST4zR8ZAIQAFFU24dNUgdzi+zwtzlshtmA9EkmExQV2zWoFPP7VvVqvc0dzSVb0J7+63t0o8m8VWCU8QGRyIjIQIAMD20+zq5HZelrNEbsV8IJIMiwnyCX/Zdw56kxWpA/phdvsKzCS/2VwNm4iIyKexmCCvV9fShve+uQAAeHZWCgSBrRKewjFuIu98Pa7qTTJHQ0RERK7GYoK83l/2noPBZMVtg0Ixc2SM3OHQNeIjdBjZvx9sIrCjkF2diIiIfA2LCfJqNc1teO/ABQBslfBUjq5OORw3QURE5HNYTJBX+397SmE025AWH4Zpw6PlDoe64OjqtK+4Bq0mDoQkIiLyJSwmyGtVNxnxwcEyAMDPstgq4alG9g/BoHAtjGYb9pytkTscIiIiciEWE9Q1nQ5oabFvOp3c0XTpzd2laLPYMG5IOO5MjpI7HLoBQRAwe5S9dSLnNGd1chsvyFkiyTAfiCTDYoK6JghAUJB988Bv/Csbjfh73kUAbJXwBnNS7eMmdhZWw2y1yRyNj/LwnCWSFPOBSDIsJsgrvbm7BCaLDROGRmByYqTc4dAtZCREICIoAI2tZuSfr5c7HCIiInIRFhPUtbY2YPFi+9bWJnc0nVxuaMVHeeUAOIOTt1AqBMxqn7aXszq5iQfnLJHkmA9EkmExQV2zWID33rNvFovc0XTyxq4SmKw2TBoWiUlslfAaznETBZUQRVHmaHyQB+cskeSYD0SSYTFBXqW83oBNh9pbJbJSZI6GeuKO5CjoApSoaDTi1OUmucMhIiIiF2AxQV7ljV0lMFtF3JEUhQlDI+QOh3pAo1birhT7WiDbCjirExERkS9gMUFe42KdAZsOXwIAPJuVLHM01BuzUx2rYbOYICIi8gUsJshr/OmrYlhtIu5Kica4IWyV8EYzhsdCpRBwtqoF52v1codDREREfcRigrzC+Vo9/nH0MgCOlfBmoTo1Jg6zD5rPYVcnIiIir8digrzCn3baWyVmjIhBWnyY3OFQH8xxdnXiFLFERETejsUEdU2nA6qr7ZtOJ2sopTUt+Oex9laJWWyV8HazRtmLiSMXr6K62ShzND7Eg3KWSHbMByLJsJigrgkCEB1t32ReFG7dzmLYRGDWyFiMGRQqayzUd/1DtRg7KBSiCOw4XS13OL7Dg3KWSHbMByLJsJggj1Zc1Yx/H68AACybxRmcfMXs1PYF7DirExERkVdjMUFda2sDnn7avrW1yRbGazuLIYrA3alxGD2QrRK+wjFu4puSOjQbzTJH4yM8JGeJPALzgUgyLCaoaxYL8Oab9s1ikSWEosombDl5BQDwDFslfEpSTAiGRQfBZLVh95kaucPxDR6Qs0Qeg/lAJBkWE+SxXt9hb5WYO6Y/RvbvJ3c45GKzR9m7OnE1bCIiIu/FYoI8UkFFI748VQlBYKuEr3Kshr37TA3aLFaZoyEiIqLeYDFBHun1HcUAgHtvG4CU2BCZoyF3SBsUhpiQQLS0WXCgtE7ucIiIiKgXWEyQxzl5qRE5p6ugEIBnZrJVwlcpFAKy2tec2FbABeyIiIi8EYsJ8jjrvrK3SjyQNhBJMcEyR0PuNKd9itjtp6tgs4kyR0NEREQ9xWKCPEqDwYRdRfaFzLKnJcocDbnbxGGRCNGoUNvShqPlV+UOh4iIiHqIxQR1TasFzp+3b1qtZKfdWVgNi03E8NgQJHOshM8LUCkwY0QMACCHXZ36RqacJfJIzAciybCYoK4pFEBCgn1TSHeZOKYJdSxqRr7v2iliRZFdnXpNppwl8kjMByLJMMPIYxhMFuwtti9gNmd0nMzRkFTuGh6NAJUCF+oMKK5ukTscIiIi6gEWE9Q1kwlYvty+mUySnHLv2RoYzTYMCtdiFBep8xvBgSrckRQFAMjhAna9J0POEnks5gORZFhMUNfMZuB//9e+mc2SnNIxPeic1DgIgiDJOckzzOYUsX0nQ84SeSzmA5FkWEyQRzBZbNhRaP8geTe7OPmdWaNiIQjAycuNqGholTscIiIi6qZeFxP19fXIyspCbm6u874XX3wRo0ePRnp6unP7+OOPnfs///xzZGVlIS0tDfPnz8fRo0ed+6xWK1555RVMnjwZ6enpWLp0Kaqrq5376+rqkJ2djYyMDGRmZmL16tWwWCzO/cePH8fChQuRnp6OGTNmYNOmTb19aSSDg+fq0Gy0ICo4ALcPDpc7HJJYVHAgMobY/923n2brBBERkbfoVTFx+PBhPPzww7h48WKn+0+ePIlVq1bh6NGjzu3hhx8GAOTm5mLVqlVYu3Yt8vPzcf/992Pp0qVobbV/C7l+/Xrs378fn332Gfbt2weNRoMVK1Y4j71s2TLodDrs27cPn376KQ4cOIANGzYAABobG/HUU09h3rx5yM/Px+rVq/Hyyy/jxIkTvXl5JAPHLE5Zo+KgVLCLkz+6dlYnIiIi8g49LiY+//xzPPfcc3j22Wc73W8ymXD27FmMHj26y+dt2rQJc+fOxbhx46BWq7F48WKEh4djy5Ytzv1LlixB//79ERwcjOeffx579+5FeXk5ysrKkJeXh+XLl0Or1SI+Ph7Z2dnYuHEjACAnJwdhYWFYtGgRVCoVJk2ahPvuu8+5nzybzSYi57RjvASnhPVXs9v/7XPP16PBwAGTRERE3qDHxcQdd9yB7du345577ul0f1FRESwWC9atW4fJkydjzpw5+Mtf/gKbzQYAKCkpQUpKSqfnJCUloaioCM3NzaisrOy0PyoqCqGhoThz5gyKi4sRFhaG2NiOD5qJiYmoqKhAU1MTiouLb3hs8nxHy6+iprkNIYEqTE6MkjscksmQyCCMiAuB1SZiZ2H1rZ9AREREsutxMREdHQ2VSnXd/c3NzZgwYQIee+wx7NmzB6+++ir+9re/4Z133gEA6PV6aL+1CqVGo4HBYIBerwcA6HS66/br9foun+v43fH8Gx2bPN/WU/ZuLTNGxiBAxTkB/NnsVHtXp5zT7OpERETkDa6vCnppypQpmDJlivP32267DY8//ji2bNmCJ598ElqtFkajsdNzjEYjwsPDnYWAY/zEtfuDgoIgiuJ1+xy/BwUFQavVorm5ucvnUi9ptcCpUx233UQUxU5TwpJ/mz0qFut2FmPP2Rq0mqzQBijlDsl7SJSzRF6B+UAkGZd9Dbxjxw589NFHne4zmUzQaDQAgOTkZBQXF3faX1JSguTkZISGhiI2NhYlJSXOfTU1NWhoaEBKSgqSk5PR0NCA2tpa5/7S0lLExcUhJCQEKSkpNzw29ZJCAaSm2jeF+1oLCq8042K9AYEqBe5KiXbbecg7pA7oh4FhWhjNNuxrXw2dukminCXyCswHIsm4LMNEUcTLL7+MAwcOQBRFHD16FO+//75zNqcFCxZg8+bNOHjwIMxmMzZs2IC6ujpkZWUBAObPn4/169ejvLwcLS0tWLNmDSZMmIDBgwcjISEB48aNw5o1a9DS0oLy8nK8+eabWLBgAQAgKysLtbW12LBhA8xmMw4ePIjNmzfjwQcfdNXLIzdxzNxzZ3I0ggJd1lBGXkoQBOdA7BxOEUtEROTxXPbpLSsrC7/85S/x0ksvoaqqClFRUfjJT36CBx54AAAwadIkvPjii879SUlJeOuttxAWFgYAePrpp2GxWLBo0SLo9XpkZmbitddecx5/3bp1WLlyJWbOnAmFQoF58+YhOzsbABAeHo533nkHq1evxrp16xAREYEVK1Zg4sSJrnp5/sdkAtassd/+1a+AgAC3nMZRTHChOnKYPSoO7+6/gJ2FVbBYbVAp+a1it0iUs0RegflAJBlBFEVR7iDkZrVacezYMaSlpUGpZB9tAIBeDwQH22+3tABuGH9SVqfHXa/uhlIh4PCKWQjT8Y89ARarDeNX78BVgxkfLpmISYmRcofkHSTIWSKvwXwgcnL351x+5UeycbRKZA6NYCFBTiqlAjNH2rs6cQE7IiIiz8ZigmTjmBKWXZzo22aPshcT209XgY2nREREnovFBMmiusmIIxcbANj7yBNda2pKNLRqJS43tKKgoknucIiIiOgGWEyQLBwz9aTFhyEuVCNzNORpNGolpqbYV0PPYVcnIiIij8VigmTh6AvPheroRuY4V8PmFLFERESeisUESa7RYMaB0joAwJz2NQWIvm3GiBgoFQKKKptRVqeXOxwiIiLqAlcJo65pNEBeXsdtF9pZVAWLTURKbDCGRQe79NjkO8J0AZg4LAL7S+qQU1CFJVOHyR2SZ3NjzhJ5HeYDkWRYTFDXlEpg/Hi3HJpdnKi7Zo+KsxcTpytZTNyKG3OWyOswH4gkw25OJKlWkxV7ztYAYDFBt5bVPkXsobKrqGlukzkaIiIi+jYWE9Q1kwl49VX7ZjK57LB7ztbAaLZhYJgWqQP6uey45JsGhGlx26BQiCKws5ADsW/KTTlL5JWYD0SSYTFBXTObgV/8wr6ZzS47rGOaz7tHx0EQBJcdl3yXYwE7roZ9C27KWSKvxHwgkgyLCZKM2WrDjvZvl9nFibrLca3sL6lDS5tF5miIiIjoWiwmSDIHz9WhyWhBZFAAxg0Jlzsc8hJJMcEYGhUEk9WGPWdq5A6HiIiIrsFigiSz9ZS9m8rs1FgoFeziRN0jCAK7OhEREXkoFhMkCZtNxPb2lYxns4sT9ZDjmtlVVA2TxSZzNEREROTAYoIkcbS8AdXNbQgJVGFyYqTc4ZCXSY8PQ3RIIJrbLDhwrk7ucIiIiKgdiwmShKN7yvQRMQhUKWWOhryNQiE415zIYVcnIiIij8Figrqm0QC7dtk3jaZPhxJFkateU585xk1sP10Fm02UORoP5MKcJfJ6zAciyajkDoA8lFIJTJvmkkMVVTajrM6AAJUC04ZHu+SY5H8mJUYiOFCF6uY2HLvUgNsHc0awTlyYs0Rej/lAJBm2TJDbOVolpiZHISiQ9Sv1TqBKiekjYgAAOQVcDZuIiMgTsJigrpnNwBtv2Lc+rh7qmBKWXZyor2ZfM25CFNnVqRMX5iyR12M+EEmGXxNT10wm4Mc/tt9evBhQq3t1mIt1BhRVNkOpEDBrZKzr4iO/NG14NAKUCpyr1aO0pgVJMSFyh+Q5XJSzRD6B+UAkGbZMkFs5ujhlDo1AeFCAzNGQtwvRqDE5yT618DZ2dSIiIpIdiwlyq62cxYlczHEtcYpYIiIi+bGYILepbjLiyMWrAIDZqeziRK4xc2QMBAE4fqkRVxpb5Q6HiIjIr7GYILfJOV0FUQTGxoehf6hW7nDIR8SEaJzTwm4/za5OREREcmIxQW7TsVAdWyXItRzXFKeIJSIikheLCXKLRoMZB0rrAAB3c7wEudjsUfZr6uC5OjQaOO0jERGRXDg1LHUtMBD4z386bvfQV2eqYLGJSI4JxrDoYBcHR/4uISoIw2NDcKaqGV+dqcJ30wfJHZL8+pizRD6F+UAkGRYT1DWVCpg7t9dP33bK3v3k7tFslSD3mJ0aizNVzcgpYDEBoM85S+RTmA9EkmE3J3K5VpMVu89WA+CUsOQ+jq5Ou8/UwGi2yhwNERGRf2IxQV0zm4ENG+ybuWd90vcW18BotmFgmBapA/q5JTyi0QP7YUCoBq1mK74urpU7HPn1IWeJfA7zgUgyLCaoayYT8MQT9s1k6tFTt53qWKhOEAR3REcEQRAw27GA3WkuYNeXnCXyOcwHIsmwmCCXMltt2FFoHy/BKWHJ3RyLIe4orIbFapM5GiIiIv/DYoJcKvdcPZqMFkQGBSAjIULucMjHTUiIQKhWjXq9CYfLrsodDhERkd9hMUEutbXgCgAga1QslAp2cSL3UikVmDkyBgCwjQvYERERSY7FBLmMzSY6VyTmLE4klTnXjJsQRVHmaIiIiPwLiwlymaPlDahubkNwoAqTkyLlDof8xNTkaGjUCly62orCK81yh0NERORXWEyQy+QU2GfUmT4iBoEqpczRkL/QBigxNTkaALCtgLM6ERERSYkrYFPXAgOBTz7puH0Loihia/sHubvZxYkkNjs1Djmnq5BzugrPZqXIHY48epizRD6N+UAkGRYT1DWVCli4sNsPP1PVjLI6AwJUCkwbHu3GwIiuN3NEDJQKAYVXmlBeb0B8hE7ukKTXw5wl8mnMByLJsJsTucS2U/aB11OToxAUyBqVpBUeFIAJ7VMRs6sTERGRdFhMUNcsFmDTJvtmsdzy4Y4uTrPZxYlk4ljALsdfp4jtYc4S+TTmA5Fk+BUyda2tDXjoIfvtlhZ7k/ENXKwzoPBKExQCMGskV70mecxOjcNvNp/GobJ61La0ISrYz/pJ9yBniXwe84FIMmyZoD5zdCvJHBqJiKAAmaMhfzUwTIvRA/vBJgJfFVbLHQ4REZFfYDFBfeYoJuakslWC5DVnlL2bHcdNEBERSYPFBPVJdbMRhy9eBcDxEiQ/xzW4r6QW+jb2kyYiInI3FhPUJ9tPV0EUgbGDQjEgTCt3OOTnUmKDMSRSB5PFhj1na+QOh4iIyOexmKA+2dY+cw5bJcgTCIKAOe3XYg67OhEREbkdiwnqtcZWM74pqQUA3D2axQR5htmj7GN3dhZVw2y1yRwNERGRb+NcadS1gADg3Xc7bndhV1E1LDYRSTHBSIwOljA4ohtLHxyOqOBA1La04eC5OtyZ7CcrsncjZ4n8BvOBSDIsJqhrajWwePFNH7L1lL0byd3s4kQeRKkQkDUqBh/mlSOnoMp/iolu5CyR32A+EEmG3ZyoV4xmq3OA6xwWE+RhZrdPEZtzuhI2myhzNERERL6LxQR1zWIBvvjCvlmun2Jz79katJqtzoXCiDzJ5KRIBAUoUdXUhhOXG+UORxq3yFkiv8J8IJIMuzlR19ragHvvtd9uaQFUnS+Vre0z5cxOjYUgCFJHR3RTgSolpo2IwRcnriCnoBJp8WFyh+R+t8hZIr/CfCCSDFsmqMfMVht2FlYDYBcn8lyOa5OrYRMREbkPiwnqsdxz9WhsNSMyKADjEyLkDoeoS9OGR0OtFFBao0dJdYvc4RAREfkkFhPUY45vemeNjIVSwS5O5Jn6adSYlBgFwD4Qm4iIiFyPxQT1iM0mOosJLlRHnm5Oqn0Bu5z2ldqJiIjItVhMUI8cu9SA6uY2BAeqMDkpUu5wiG4qa2QsBAE4Vt6Aqiaj3OEQERH5HBYT1COOVolpw6MRqFLKHA3RzcX00yC9fSannNNsnSAiInI1zpVGXQsIAP7v/zpuAxBFEdtOsYsTeZfZqXE4crEBOQWVeGziELnDcZ8ucpbIbzEfiCTDYoK6plYDTz/d6a6zVS24UGdAgEqBacNjZAqMqGfmpMZh7ZdFOFBah8ZWM0K1arlDco8ucpbIbzEfiCTDbk7UbVvbWyXuTIpCcCDrUPIOQ6OCkBwTDItNxO4z1XKHQ0RE5FN6XUzU19cjKysLubm5zvuOHz+OhQsXIj09HTNmzMCmTZs6Pefzzz9HVlYW0tLSMH/+fBw9etS5z2q14pVXXsHkyZORnp6OpUuXorq6442/rq4O2dnZyMjIQGZmJlavXg2LxdLtc1MPWa3A7t32zWoF0DFeggvVkbeZ7Q+zOnWRs0R+i/lAJJleFROHDx/Gww8/jIsXLzrva2xsxFNPPYV58+YhPz8fq1evxssvv4wTJ04AAHJzc7Fq1SqsXbsW+fn5uP/++7F06VK0trYCANavX4/9+/fjs88+w759+6DRaLBixQrn8ZctWwadTod9+/bh008/xYEDB7Bhw4ZunZt6wWgEpk+3b0YjyusNOH2lCQoBmDUqVu7oiHrEUQDvPlMNo9lHP1h8K2eJ/BrzgUgyPS4mPv/8czz33HN49tlnO92fk5ODsLAwLFq0CCqVCpMmTcJ9992HjRs3AgA2bdqEuXPnYty4cVCr1Vi8eDHCw8OxZcsW5/4lS5agf//+CA4OxvPPP4+9e/eivLwcZWVlyMvLw/Lly6HVahEfH4/s7GznsW91buo7R6vEhKERiAjiYDbyLmMGhqJ/qAZ6kxXflNbKHQ4REZHP6HExcccdd2D79u245557Ot1fXFyMlJSUTvclJSWhqKgIAFBSUnLD/c3NzaisrOy0PyoqCqGhoThz5gyKi4sRFhaG2NiOb8QTExNRUVGBpqamW56b+o5dnMibCYKA2e0tattO+XBXJyIiIon1uJiIjo6GSnX94Fu9Xg+tVtvpPo1GA4PBcMv9er0eAKDT6a7br9fru3yu43fH8292buqbmuY2HCq7CoDFBHmv2e3X7o7CKlhtoszREBER+QaXzeak1Wph/Fa/RKPRiKCgoFvudxQCjvET396v0+mu2+f43fH8m52b+uaromqIInDboFAMCNPe+glEHmjC0AiEatWo05tw5OJVucMhIiLyCS4rJlJSUlBcXNzpvpKSEiQnJwMAkpOTb7g/NDQUsbGxKCkpce6rqalBQ0MDUlJSkJycjIaGBtTWdvR1Li0tRVxcHEJCQm55buqb7YX2biFslSBvplYqMHOEfX0Ux+KLRERE1DcuKyaysrJQW1uLDRs2wGw24+DBg9i8eTMefPBBAMCCBQuwefNmHDx4EGazGRs2bEBdXR2ysrIAAPPnz8f69etRXl6OlpYWrFmzBhMmTMDgwYORkJCAcePGYc2aNWhpaUF5eTnefPNNLFiwoFvnpr7JO1cHgMUEeT/nFLGnqyCK7OpERETUVy5beSw8PBzvvPMOVq9ejXXr1iEiIgIrVqzAxIkTAQCTJk3Ciy++iJdeeglVVVVISkrCW2+9hbCwMADA008/DYvFgkWLFkGv1yMzMxOvvfaa8/jr1q3DypUrMXPmTCgUCsybNw/Z2dndOjf1gloN/O53KKhoRCsUSIwOQlJMsNxREfXJ1JRoBKoUuFhvQFFlM0b27yd3SK7TnrPO20T+jPlAJBlB5NdzsFqtOHbsGNLS0qBUKuUOx6Ms/eAwvjxViaenJ2L5nBFyh0PUZ0++dwg7Cqvw7KwUPDOLXSGJiMi3uftzrsu6OZHvMZqt2H2mBgC7OJHv6OjqxHETREREfcVigrpmteLYZ9uRfLEQg0LUGDMwVO6IiFxi1shYKASgoKIJ5fU+NH201Qrk59s3q4+u8k3UXcwHIsmwmKCuGY2Y+Mh38O/3f4bvJIdDEAS5IyJyiYigAIxPiAAAbD/tQwvYGY3AhAn27VtTZRP5HeYDkWRYTFCXzFab83bWqNibPJLI+zi67TlWdiciIqLeYTFBXTp0od55e9yQCBkjIXI9R4Gcf6Ee9XqTzNEQERF5LxYT1KUdhR3dP5QKdnEi3xIfoUPqgH6wicDOQh/q6kRERCQxFhN0HZtN7FRMEPmi2aMcXZ14rRMREfUWiwm6zvFLDahuYtcP8m2OKWL3FdfAYLLIHA0REZF3YjFB19nKQankB0bEhWBwhA5tFhv2nq2ROxwiIiKvpJI7APIsoigip6AKFqUSZ374MwyPCwHUarnDInI5QRAwe1Qs3v76PHIKqnD36P5yh9Q3ajXw4osdt4n8GfOBSDIsJqiT4uoWnK/VIyAgEANeWwto+EeYfNec0XF4++vz2FFYBbPVBrXSixtrAwKAl16SOwoiz8B8IJKMF79zkjtsPWXv4nRHchRCWEiQj7t9cDgigwLQZLQg73z9rZ9AREREnbCYoE4ci3jNGRkNFBTYN5vtFs8i8k5KhYBZI+0DsXO8fayQzcacJXJgPhBJhsUEOZXXG1BQ0QSFAMxK6AeMHm3fWlvlDo3IbeaMbi8mTldBFEWZo+mD1lbmLJED84FIMiwmyMnRKjE+IQKRwYEyR0MkjcmJUQgKUOJKoxEnLzfKHQ4REZFXYTFBTjnti3fdPTpO5kiIpKNRKzFteAyAjoKaiIiIuofFBAEAaprbkF9mH4A6O5XFBPkXxwJ2OVwNm4iIqEdYTBAAYEdhFUQRGDMwFAPDtHKHQySpacNjoFIIKK5uwbmaFrnDISIi8hosJghAx5Sw7OJE/ihUq8akxEgA9oHYRERE1D0sJghNRjO+Ka0FAMxp7+5B5G8c3fu8fopYIiIiCXEFbMKuomqYrSISo4OQFBNiv1OtBp57ruM2kY+bPSoWL/zzFI5cbEB1kxEx/TRyh9QzzFmiDswHIsmwmKCOhequHXgdEAC8+qpMERFJL7afBmnxYThW3oDthVVYlDlE7pB6hjlL1IH5QCQZdnPyc0azFbvP1AD4VjFB5Ic4qxMREVHPsJjwc/uKa2EwWdE/VIPbBoV27LDZgAsX7JvNJld4RJJyFNTflNaiyWiWOZoeYs4SdWA+EEmGxYSfu7aLkyAIHTtaW4GhQ+1ba6tM0RFJKzE6GInRQTBbRWeLnddgzhJ1YD4QSYbFhB+zWG3YUWjvzsEuTkR2jlzgathERES3xmLCj+Wdr0eDwYxwnRrjE8LlDofIIzimiN1dVI02i1XmaIiIiDwbiwk/trX9m9esUbFQKXkpEAHAbQNDEddPA73Jim9K6+QOh4iIyKPxE6SfstlE54w17OJE1EGhEJA1yj6r08aDZbDaRJkjIiIi8lwsJvzUicuNqGwyIihAiSlJUXKHQ+RRFowbBKVCwI7CavzPZydgY0FBRETUJRYTfmrrKXsXp2kjYqBRK2WOhsizjI0Pw7r/SodSIeDTw5fwy3+cZEFBRETUBa6A7YdEUex61etrqVRAdnbHbSI/M/e2/rCKIpZ9dBQfHyqHQiFg9bzRUCiEWz9ZDsxZog7MByLJMMP8UHF1C87X6hGgVGD68OiuHxQYCLzxhrSBEXmY+8cOgM0m4mefHMOHeRehVACrHhjdeU0WT8GcJerAfCCSDLs5+aFt7V2cpiRFIkSjljkaIs82L30g/nfhWAgC8MHBi3jp3wUQRXZ5IiIiAtgy4Ze2nb5FFycAEEWgttZ+OyoK8MRvYokkMv/2QbDaRPzisxN470AZFAoBv753lGe1UDBniTowH4gkw2LCz5TXG3DqchMUAjCrffrLLhkMQEyM/XZLCxAUJE2ARB5qYUY8RBH4xWcn8O7+C1AKAp6fO9JzCgrmLFEH5gORZNjNyc/knLavLZGREIGo4ECZoyHyLg+Nj8fL88cAAN7++jzWflnELk9EROTXWEz4Gcd4ibu5UB1RrzwyYTB+O280AODPe8/hd9vOsKAgIiK/xWLCj9Q0tyG/rB4AMDv1Jl2ciOimHp04BCsfSAUArN9dit/nnGVBQUREfonFhB/ZUVgFUQTGDAzFoHCd3OEQebXvT0rAi/eNAgD8364SvLajWOaIiIiIpMdiwo90LFTHVgkiV3hiylCsmDsSAPD6zmKs28mCgoiI/AuLCT/RZDTjm5I6ALeYEpaIeuTJO4fhV/eMAAD8YftZvLGrROaIiIiIpMOpYf3ErqJqmKw2DIsOQlJM8K2foFIBjz/ecZuIbuipqYmw2oBXthbh1W1noBAELJ2WKG0QzFmiDswHIskww/xEToF9Stg5qXHdmxc/MBDYsMG9QRH5kKXTEmETRby67Qxe2VoEpcJeZEiGOUvUgflAJBl2c/IDRrMVu85UA+CUsETu9PT0JPwsKwUAsGZLEd7ed07miIiIiNyLLRN+4OviWhhMVvQP1eC2QaHde5Io2lcQBQCdDvCUVX6JPNxPZybDahPx+s5i/PaLQigEAT+4Y6j7T8ycJerAfCCSDFsm/EDHLE7d7OIE2P8IBwfbN8cfZCLqlmWzkvHj6UkAgJX/OY33vrng/pMyZ4k6MB+IJMNiwsdZrDbsKLSPl+BCdUTSEAQBP5+d4hyE/eK/C/C3g2UyR0VEROR6LCZ8XN6Felw1mBGuU2NCQoTc4RD5DUEQ8Is5w/HDqcMAAC/88xT+nntR5qiIiIhci8WEj9t2yt7FadbIWKiU/OcmkpIgCPj/vjMCT7aPmfjV5yfxcT4LCiIi8h38dOnDbDYR266ZEpaIpCcIAp6fOxJPTEkAAPx//ziJTYfK5Q2KiIjIRVhM+LATlxtR2WSELkCJO5Kj5A6HyG8JgoBf3zsKiycnQBSBX3x2Ap8dviR3WERERH3GYsKHOWZxmj48Bhq1UuZoiPybIAh48b5ReHTiYIgi8Nynx/HPo5flDouIiKhPuM6EjxJF0TleolezOCmVwIIFHbeJqM8EQcDK+0fDagM+zLuIn31yDIIAPJA2sO8HZ84SdWA+EEmGxYSPKqluwblaPQKUCswYEdPzA2g0wKZNrg+MyM8pFAJWzxsNURTxUX45nv34GJQKAffeNqBvB2bOEnVgPhBJht2cfJSji9PkpEiEaNQyR0NE11IoBKz57hgsHDcINhF45qNj+PLkFbnDIiIi6jEWEz5qa3sxcTdncSLySAqFgLUP3ob5tw+E1SbiJx8exdb2rolERETegsWED7p01YBTl5ugEIBZo3q56rVeDwiCfdPrXRsgEQEAlAoBry4Yi++mD4TFJuLHfz+C7aerencw5ixRB+YDkWRYTPggx9oSGQkRiAoOlDkaIroZpULA/y4ci/vHDoDFJiJ742HsLOxlQUFERCQxFhM+yDFeggvVEXkHpULAHx4ai3tv6w+zVcTSD45gV1G13GERERHdEosJH1Pb0oZDF+oBALN728WJiCSnUirw2sNpuGdMHExWG374wWHsOVsjd1hEREQ3xWLCx+w4XQWbCIwe2A/xETq5wyGiHlApFXj9v9IxJzUWJosNS94/hH3FLCiIiMhzsZjwMc4uTqPYxYnIG6mVCvzpkduRNcpeUDz53iHsL6mVOywiIqIusZjwIc1GM/aX1AEA7h7NYoLIWwWoFHjje7dj5ogYtFls+O/38nGgtE7usIiIiK7j0mJiy5YtGDVqFNLT053b8uXLAQDHjx/HwoULkZ6ejhkzZmDTt1am/Pzzz5GVlYW0tDTMnz8fR48ede6zWq145ZVXMHnyZKSnp2Pp0qWoru4YnFhXV4fs7GxkZGQgMzMTq1evhsViceVL8wq7ztTAZLVhWFQQkmKC+3YwpRK45x77plS6JkAi6rYAlQJvPno7pg+PhtFsww825CP33E0KCuYsUQfmA5FkXFpMnDx5Eg888ACOHj3q3F599VU0Njbiqaeewrx585Cfn4/Vq1fj5ZdfxokTJwAAubm5WLVqFdauXYv8/Hzcf//9WLp0KVpbWwEA69evx/79+/HZZ59h37590Gg0WLFihfO8y5Ytg06nw759+/Dpp5/iwIED2LBhgytfmldwdHGanRoHQRD6djCNBvjiC/um0bggOiLqqUCVEusfHYe7UqLRarbiiQ35yG+fYOE6zFmiDswHIsm4vJgYPXr0dffn5OQgLCwMixYtgkqlwqRJk3Dfffdh48aNAIBNmzZh7ty5GDduHNRqNRYvXozw8HBs2bLFuX/JkiXo378/goOD8fzzz2Pv3r0oLy9HWVkZ8vLysHz5cmi1WsTHxyM7O9t5bH9hNFuxu30qSXZxIvIdGrUSf35sHO5MjoLBZMXid/JwuOwGBQUREZHEXFZM2Gw2FBQUYPfu3Zg+fTqmTp2KF154AY2NjSguLkZKSkqnxyclJaGoqAgAUFJScsP9zc3NqKys7LQ/KioKoaGhOHPmDIqLixEWFobY2I5pUBMTE1FRUYGmpiZXvTyPt7+kFnqTFXH9NLhtYKjc4RCRC2nUSrz1/QxMToyE3mTF4+/k48jFq3KHRURE5Lpior6+HqNGjcKcOXOwZcsWfPTRR7hw4QKWL18OvV4PrVbb6fEajQYGgwEAbrpfr9cDAHQ63XX79Xp9l891/O44vj/YesqxUF0sFIo+dnECAL0eCAqyb+3/BkQkH41aib8+Ph4Th0Wgpc2Cx/+ah2PlDR0PYM4SdWA+EEnGZcVEVFQUNm7ciAULFkCr1WLAgAFYvnw59u7dC1EUYTQaOz3eaDQiKCgIgP3D/432OwoDx/iJb+/X6XTX7XP87ji+r7NYbdhRWAXAxateGwz2jYg8gjZAiXcWj8eEoRFobrPgsb/m4sSlho4HMGeJOjAfiCThsmKiqKgI//u//wtRFJ33mUwmKBQK3HbbbSguLu70+JKSEiQnJwMAkpOTb7g/NDQUsbGxKCkpce6rqalBQ0MDUlJSkJycjIaGBtTWdszDXlpairi4OISEhLjq5Xm0vAv1uGowI0ynxoShEXKHQ0RupAtQ4d3F4zE+IRzNRgsefTsXpy43yh0WERH5KZcVE2FhYdi4cSPefvttWCwWVFRU4NVXX8V3v/tdzJkzB7W1tdiwYQPMZjMOHjyIzZs348EHHwQALFiwAJs3b8bBgwdhNpuxYcMG1NXVISsrCwAwf/58rF+/HuXl5WhpacGaNWswYcIEDB48GAkJCRg3bhzWrFmDlpYWlJeX480338SCBQtc9dI8Xk6BvVVi1shYqJRcOoTI1wUFqvDuExMwbkg4mowWLHo7F6evsKAgIiLpCeK1TQl9lJeXhz/84Q84e/YsAgMDMXfuXCxfvhyBgYE4efIkVq9ejbNnzyIiIgLZ2dmYP3++87n/+te/sH79elRVVSEpKQkrVqzA2LFjAQBmsxmvv/46/v3vf0Ov1yMzMxOrVq1CZGQkAKC2thYrV65Ebm4uFAoF5s2bh+eeew7Kbs4tbbVacezYMaSlpXX7OZ5CFEVMXvsVrjQa8db3M5A1KvbWT+oOvR4Ibl+roqXF3u+UiDxKs9GM77+Th6MXG9BfZcWB1Q/YdzBnyd/xPYzIyd2fc11aTHgrby4mjpc34IE39kMXoMSRF7KgUbsofv4hJvIKTUYzHvtrHs6WVqLwj+0tssxZ8nd8DyNycvfnXPaJ8XKOheqmDY92XSFBRF6jn0aN938wAakD+jnvK6lqljEiIiLyJyq5A6C+2VrgmBLWxQvVKRTAXXd13CYijxWqVePtJybgxJ/TYGiz4OfvHcZ7T9+FpJhguUMjkgffw4gkw2LCi5VUN+NcjR5qpYDpI2Jce3CtFti927XHJCK3CYsMhXgsF997OxeXrzThkbcO4qOnJiIxmgUF+SG+hxFJhuW6F9vWPovT5MQo9NOoZY6GiOQWHhSAjU9mYkRcCGqa2/DIXw7ifC0X7CIiIvdhMeHFHKte3z3axV2ciMhrRbQXFMNjQ1DdXlBcYEFBRERuwmLCS11uaMXJy40QBPv6Ei6n1wPR0fZNzw8iRB7vmpyNFCzYuCQTyTHBqGwy4pG3DuJiHVcCJj/C9zAiybCY8FLb2lslxg+JQHRIoHtOUltr34jIO1yTs1HBgfj7kolIjA7ClUZ7QVFez4KC/Ajfw4gkwWLCSzmmhJ2d6oZWCSLyCdEhgfhwyUQMiwrC5YZWPPLWQVy6yoKCiIhch8WEF6praUP+hXoAbpgSloh8Skw/DT58aiKGRgXh0lV7QVHR0Cp3WERE5CNYTHihHYVVsIlA6oB+iI/QyR0OEXm42H4a/H1JJoZE6lBeby8orjSyoCAior5jMeGFHFPCslWCiLqrf6gWHy6ZiPgILcrqDPjeW7moajLKHRYREXk5FhNeptloxtfF9gFlnBKWiHpiQJi9oBgUrsX5Wj0e+ctBVLOgICKiPmAx4WV2n6mByWrD0KggJMe4cWVbhQLIyLBvCl4mRB6vmzk7KFyHD5dMxMAwLc7V6vHIWwdR3cyCgnwM38OIJMMM8zJb22dxmpMaB0EQ3HcirRbIz7dvWq37zkNErtGDnI2PsBcUA0I1KK3RY9FbuahtaZMoUCIJ8D2MSDIsJryI0WzF7qJqAMAcTglLRH0wOFKHD5+aiLh+GhRXt2DRW7moY0FBREQ9xGLCi3xTWgu9yYrYfoEYOyhM7nCIyMsNiQzCh09NRExIIM5UNWPR27mo15vkDouIiLwIiwkvsvVURxcnhcKNXZwAwGAAEhLsm4GLXBF5vF7m7NAoe0ERHRKIospmPPp2LhoMLCjIy/E9jEgyLCa8hMVqw45CRxcnCWZxEkWgrMy+iaL7z0dEfdOHnE2MDsaHSzIRFRyI01easOjtXDQazG4KlEgCfA8jkgyLCS+Rf+Eq6vUmhOnUmDA0Qu5wiMjHJMWE4MMlmYgMCkBBRRMe/WsuGltZUBAR0c2xmPAS29pncZo5IhZqJf/ZiMj1kmND8PclExERFICTlxvx/b/mosnIgoKIiG6Mn0q9gCiKyGkvJrhQHRG50/C4EGx8MhPhOjWOX2rE4+/koZkFBRER3QCLCS9w8nIjKhqN0AUocWdylNzhEJGPG9m/Hz54MhNhOjWOXmzA4nfz0dJmkTssIiLyQCwmvICji9NdKdHQqJUyR0NE/iB1QCg++O9M9NOocLjsKp54Nw96FhRERPQtLCa8gGNKWEm7OAkCMGqUfXPnSttE5BpuyNnRA0PxwZOZCNGokH/hKp7YkA+DiQUFeQG+hxFJhsWEhyupbkZpjR5qpYDpI2KkO7FOBxQU2DedTrrzElHvuClnbxsUhr/9dyZCAlXIO1+P/95wCK0mq8uOT+QWfA8jkgyLCQ+3raAKADA5MQr9NGqZoyEif5QWH4b3/nsCggNVOHCuDk++nw+jmQUFERGxmPB4jvESkixUR0R0A7cPDsd7PxiPoAAl9pfUYcn7h1hQEBERiwlPdrmhFScuNUIQgKxRsdKe3GAAUlPtm8Eg7bmJqOckyNlxQyKw4QcToAtQYl9xLZ7622EWFOSZ+B5GJBkWEx7MsbZExpBwRIcESntyUQROn7ZvoijtuYmo5yTK2fEJEXh38Xho1UrsPVuDpR8cRpuFBQV5GL6HEUmGxYQHYxcnIvJEmcMi8c7i8dCoFdh1pgbZHxyByWKTOywiIpIBiwkPVdfShrzz9QBYTBCR55mUGIm/Pj4egSoFdhZVY+kHh3Gxjt1JiIj8DYsJD7WzsBo2ERjVvx/iIzitHRF5nilJUXjr+xkIaC8opr66C4/85SD+dewyx1IQEfkJldwBUNe2FsiwUB0RUQ9NTYnG35/MxLqvSrCvuAYHztXhwLk69NOoMC99IB7KiMfogaFyh0lERG7CYsIDtbRZ8HVxLQB2cSIiz5eREIH3fzABlxtaselQOTYduoTLDa14/0AZ3j9QhtQB/fBf4+Nxf9pAhGq5Xg4RkS9hMeGBdp+phslqQ0KkDimxwfIEIQjAkCEdt4nIs3lAzg4M02LZrBT8dEYy9pfW4uP8cuQUVKGgogkv/KsAv/2iEN8ZHYeHxsdj4tBIKBT820Ju4gH5QOQvWEx4oK2n2mdxGh0HQa4/gjodcOGCPOcmop7zoJxVKATcmRyNO5OjcVVvwudHL+OTQ+UoqmzGP49V4J/HKjA4QoeHx8fjwdsHIS5UI3fI5Gs8KB+IfB2LCQ9jNFuxq6gaALs4EZH3Cw8KwA/uGIonpiTgxKVGfJRfjs3HK3Cx3oBXt53B73POYNrwGDyUEY+ZI2OgVnJeECIib8JiwsN8U1oLvcmK2H6BSBsUJnc4REQuIQgCxsaHYWx8GF64dyS2nKzEJ/nlyLtQj6+KqvFVUTWiggPw4O2D8ND4eCRGy9TFk4iIeoTFhIfZdqoKADB7VJy8/YlbW4GpU+239+4FtFr5YiGiW/OinNUFqLBg3CAsGDcIpTUt+ORQOT47fBm1LW34895z+PPec8gYEo6Hxsfj3tv6QxfAtyrqIS/KByJvJ4gi15m3Wq04duwY0tLSoFQqZYvDYrVhwpqdqNeb8MF/Z+KO5CjZYoFeDwS3fzPY0gIEBckXCxHdmpfnrNlqw66ianxyqBxfFdnX2QGA4EAV7hvbHw9lxCMtPky+cWTkXbw8H4hcyd2fc/l1jwc5VHYV9XoTQrVqZA6LkDscIiLJqJUKzE6Nw+zUOFQ1GfHp4Uv45FA5yuoM+DCvHB/mlSMlNhgPjx+M76YPRERQgNwhExERuAK2R9nWvlAdByESkT+L7afB09OTsPu5afjoqYn4bvpABKoUOFvVglX/OY3MNTvw9MYj2HO2Blab3zeuExHJii0THkIUReQU2MdL3M1ZnIiIIAgCJg6LxMRhkXjp/lT8+3gFPskvx8nLjfji5BV8cfIKBoRqsDAjHgszBmFQuE7ukImI/A6LCQ9x6nITLje0QqtWYmpKtNzhEBF5lFCtGo9NHILHJg5BQUUjPskvx+dHL6Oi0YjXdxZj3VfFuCMpCg9lxGN2aiwCVfKNfyMi8icsJjzE1oIrAIBpw6OhUfNNkIjoRlIHhOI3D4Til/eMxLaCSnycX45vSuuwr7gW+4prEaZT47vpA/Hw+HiMiOsnd7hERD6NxYSH2NbexcmjFqqLknE2KSLqOT/LWY1aiQfSBuKBtIG4WGfApsPl2HToEiqbjHh3/wW8u/8Cxg4KxUPj43H/2AEI0ajlDpmk5Gf5QCQXTg0L+aeGLaluwaw/7IFKIeDwC1kI1fINj4ioN6w2EXvP1uDj/HLsKKyCpX2AtkatwNwxA/Dw+HiMTwjnFLNE5Dc4NawfcMziNDkpioUEEVEfKBUCpo+IwfQRMahtacPnRy7j40PlKKluwWdHLuGzI5cQExKIcUPCMW5IOG4fEo7RA0IRoOIMekREvcFiwgPktBcTc1JjZY6EiMh3RAUHYsnUYXjyzqE4cvEqPs4vx39OXEF1cxu+PFWJL0/Z//YGqBQYOygUtw8Jx7jB9gIjKjhQ5uiJiLwDiwmZVTS04vilRggCkDXKg4qJ1lbgO9+x3/7yS0CrlTceIro55uwNCYKAcUMiMG5IBFY+MBonLjXicNlVHC6rx+Gyq7hqMCP/wlXkX7jqfM7QqCDcPjjc2YKRHBMMhYJdo7wG84FIMiwmZOZolRg3OBwxIRqZo7mGzQbs2dNxm4g8G3O2WzRqJSYMjcCEoREAEiGKIs7X6nG47CqOXLyKw2VXcbaqBedr9Thfq8dnRy4BAEI0KqQPtrdcZCSEY2x8GIID+RbqsZgPRJLhX0KZbXV2cfKgWZyIiPyEIAgYFh2MYdHBWJgRDwBoNJhxpPwqjpTZi4tj5Q1oNlqw92wN9p6tAQAoBGBEXD+MGxKOKUmRuDM5GkEsLojID/Evn4zq9Sbkna8HwGKCiMhThOrUmD48BtOHxwAALFYbiiqbceTiVRy6YC8wLje04vSVJpy+0oS/HSxDgFKBSYmRmDUyBjNHxmJAGLvVEJF/YDEhox2FVbCJwMj+/TA4Uid3OERE1AWVUoHRA0MxemAovj8pAQBQ2WjEkYtXkXe+HrvOVKOszoA9Z2uw52wNXvhXAUb274es9sJizMBQjrcgIp/FYkJG29pnErmbrRJERF4lLlSDe8b0xz1j+uNFcRRKa1qwo7AaO05X4cjFqyi80oTCK01Y91UJYkICMXNkDGaOiMWUpChoA6Rfz4iIyF1YTMikpc2CfSW1AIA5oz1oFiciIuoRQRCQFBOCpJgQ/OiuRNTrTdhVVI0dhVXYe7YG1c1t+DCvHB/mlUOjVuCOpCjMHBmLmSNiENPPgybeICLqBRYTMtl9phomiw0JkToMjw2RO5yu6dj1isirMGc9QkRQAB4cNwgPjhuENosVuefqsbOwCjsKq3G5odXeglFYDQAYOyjUXliMjMGo/v24MrcrMR+IJMFiQibbCqoA2Adee+SbR1AQoNfLHQURdRdz1iMFqpSYmhKNqSnReOl+EUWVzdhZWIXthdU4Xt6A45cacfxSI/6w/SwGhGowc2QsJiVGYnhcCBIig6DkWIveYT4QSYbFhAzaLFbsKrJ/KzWb4yWIiPyCIAgY2b8fRvbvhx/PSEZ1sxG7iqqx/XQ1vi6pQUWjEX87WIa/HSwDAASqFEiKCcbwuBCMiAvB8Lh+GBEXgpiQQM/8EoqI/BKLCRl8U1KHljYLYkICkR4fJnc4REQkg5gQDR4ePxgPjx8Mo9mKb0prsbOwGqcuN+JsVQtazVYUVDShoKKp0/PCdGqkxDoKDPvPlNgQhGjUMr0SIvJnLCZksK19obrZqbGeO12g0Qg8+KD99mefARoOEiTyaMxZr6ZRKzFjRCxmjLBPyGGziSi/akBRZTPOtG9FlU04X6tHg8GMvPP1znWKHAaGaTH8mgJjeFwIhkUFI0ClkOMlyYv5QCQZFhMSs9pEbD9tHy9xd2p/maO5CasV2LKl4zYReTbmrE9RKAQMiQzCkMigTouaGs1WlNa0XFNg2H9WNhlxuaEVlxta8VV7N1oAUCkEDArXIlijgk6tgi5QCV2AEroAFYIClNA6fyoRFKj61r7O9+kClAhUKbyjixXzgUgyLCYkduhCPer0JoRq1cgcFiF3OERE5EU0aiVSB4QidUBop/sbDWacqWrGmcqmTq0ZzW0WXKgzuOz8CgEIClBBG2AvSrQBjmJDCa3acZ8SWrXKeVt37WOveYyjQHE8RqNSem5rPRHdEIsJiW1t7+I0c2QM1Eo/bHomIiKXC9WpMWFoBCYM7fiSShRFXGk0orzeAIPZCkObFQaTBQaTtX2zQN9mRavZ/tN5n8mKVuc+K/RtFrRZbAAAmwg0t1nQ3GZxy+voXJB0Ljy0AcpripFrihhnMdNxn85ixKj2Yza2mqHT2PieS+QmLCYkJIoicq6ZEpaIiMhdBEHAgDAtBoRp+3wsq010FiL6NvvPVrO9AGm9pkBp7ep+s/1+g8nS/tN6zfMtMJptzvO0mu33o4+zumpNRhS23564ZidaAzRQK4X2AqVzi8j1LSZdtbiooA1QdCpYrm1d8ZruX0Ru4DPFRF1dHV544QXk5eVBqVTi/vvvx//8z/9ApfKcl1hQ0YTLDa3QqBWYmhwtdzhERETdolQICNGo3TJjlM0mXlOAWGEwWzpumxwFh7VTwWIvRizfuq+jyLG12K47j9kqwmy1oMno+lYVhWBvVXEUISqFANj/gyAI7T8BAQIcNUen+6/ZJ9h3XvMcQCEIzsd0HPea59zs2N/6Hdec50bHuNHz8a04v/38Gx67/RhKhQCFYB8TpBAEKIVv/d7+UyHYrzlBEKC8Zr/9Me3Han+80P5Yx37Ftb+3n0/pvN35+fbH4Jpzo3Mc1zz/hvE7ni90/L/zN57zSbuPli1bhtjYWOzbtw+1tbVYunQpNmzYgCeffFLu0JzO19q/apk5MhbaAKXM0RAREclPoRAQFKhCUKALP5Lo9cBK+82jv85Cq1rT3kJi6dQy4ixMvnW/szAx3bjIaTVZYbJ2dP/Sm6zQmzjY2985CpJOhUz779cWLIoui6FvFzNdFDJdPLfjMV0XnoFK4HtJ7nvNPlFMlJWVIS8vD3v37oVWq0V8fDyys7Px6quvdquYEEURAGB184wPs0dG40//NRaThkW6/Vx9ZrXaVxB13Pb0eIn8HXOWqMM1+aBWAGqNEv00SgABLj2NxWqDwWyF8ZrixGi2wmITIQIQRUCEiPb/nL+Lzt/tj8O197fvg9hxDHR6zvXHQBf327517BvFcO15Op/jxse+NiY4XseNjm8/O2yiCJtob4myiSJsNsd9Iqy2zvus7U+2tu+3ibjuMTbRfl7rNceziiJsEGGz2c/v3Cdef3yx/fxWmwjbNfFZ259rs7U/7pr4esb+Gmyi/fiQ8U+yViXge0kxzn9PV/OJYqK4uBhhYWGIjY113peYmIiKigo0NTWhX79+N32+zWb/ZuHkyZNujRMABgAoK65CmdvP5AJ79th/FhfLGwcRdQ9zlqiDjPngER+uhG/99Ho+94Ik5/i862oecb33lV6vh1bbeYCZ43eDwXDLYkKlUmHMmDFQKDiAioiIiIh8hyiKsNlsbhtH7BPFhE6nQ2tra6f7HL8HOZr9b0KhUCAgwLVNn0REREREvs4nJl1OTk5GQ0MDamtrnfeVlpYiLi4OISEhMkZGREREROS7fKKYSEhIwLhx47BmzRq0tLSgvLwcb775JhYsWCB3aEREREREPksQ3TW0W2K1tbVYuXIlcnNzoVAoMG/ePDz33HNQKjkFKxERERGRO/hMMUFERERERNLyiW5OREREREQkPRYTRERERETUKywmiIiIiIioV1hMEBERERFRr/hsMVFXV4fs7GxkZGQgMzMTq1evhsVi6fKxH374IebMmYP09HTMmTMHGzdu7LT/rbfewtSpU5GWlobHHnsM586dk+IlkIRcdb20tbVh9erVmDp1KsaNG4eFCxfi4MGDUr0MkoAr/7Y4bNq0CcOHD3dn2CQTV14vf//735GVlYX09HTcd9992LVrlxQvgSTiqmvFaDTi17/+NaZMmYLx48fj8ccfR1FRkVQvgyTS3evFZrPhT3/6E+666y7n344tW7Z0ekyfP+eKPurRRx8Vf/7zn4sGg0G8ePGiOHfuXPGtt9667nHbt28XMzIyxKNHj4o2m008cuSImJGRIW7dulUURVH8xz/+Id55553i2bNnRaPRKL788svi3LlzRZvNJvVLIjdy1fXy29/+Vpw/f75YUVEhWiwW8eOPPxbHjh0rXr58WeqXRG7iqmvF4ezZs2JaWpqYkpIi1UsgCbnyvWjy5Mni8ePHRZvNJm7evFlMTU0VKysrpX5J5CauulZ+97vfiY899ph49epVsa2tTVyzZo04c+ZMqV8OuVl3r5f3339fnDFjhlhWViaKoih+9dVX4ogRI5y/u+Jzrk+2TJSVlSEvLw/Lly+HVqtFfHw8srOzu/xWsKqqCkuWLEFaWhoEQUB6ejoyMzORn58PAPjkk0/wve99D8nJyQgMDMTPf/5zVFRUIDc3V+qXRW7iyuulra0NP/3pT9G/f38olUo89NBDCAgIQEFBgdQvi9zAldcKALS2tuJnP/sZvv/970v5Mkgirrxe3nnnHTzzzDO47bbbIAgC7r33Xnz88ccIDg6W+mWRG7jyWiktLYUoihDbZ/5XKBTQarWSvh5yr55cL4sWLcLmzZsxePBgmEwm1NfXQ6vVQqPRAHDN51yfLCaKi4sRFhaG2NhY532JiYmoqKhAU1NTp8cuWrQITz31lPP3uro65OfnY/To0QCAkpISpKSkOPer1WokJCSwydCHuPJ6WblyJe666y7n/gMHDqC5uRkjRoxw86sgKbjyWgHs18u0adMwefJk9wdPknPV9dLa2ori4mIoFAosWrQImZmZ+K//+i+0trYiKChIstdD7uPKvy0/+MEPcPbsWUycOBFpaWn497//jddee02S10HS6Mn1olAooNPp8PXXX2Ps2LF4/vnn8cwzzyAmJgaAaz7n+mQxodfrr6vCHb8bDIYbPq+mpgZLlizB6NGjce+9997wWBqN5qbHIe/iyuvlWseOHcOyZcvw4x//GPHx8a4NmmThymvlX//6F0pLS/HMM8+4L2CSlauul6amJoiiiHfeeQcvvfQS9u3bh3vvvRdLlizBpUuX3PoaSBqu/NtitVoxZ84c7N27F3l5eZg5cyays7PR1tbmvhdAkurN9TJhwgScPHkS7777Ll577TXnuAlXfM71yWJCp9OhtbW1032O32/0Lc6xY8ewYMECDB06FOvXr4dKpQJg/8cxGo2dHms0GvltkA9x5fXisGnTJjzxxBP40Y9+hKeffto9gZPkXHWtnDt3Dr///e/x+9///rprh3yHq64XtVoNAHjiiSeQnJyMgIAAPProoxgwYAD27Nnj3hdBknDVtWI2m/HMM89g/vz5iI2NRXBwMF544QVUVVVh//79bn8dJI3eXC8BAQFQqVSYNGkSHnjgAWzevBmAaz7n+mQxkZycjIaGBtTW1jrvKy0tRVxcHEJCQq57/KefforFixfj8ccfx+9//3sEBAR0OlZxcbHzd7PZjAsXLnRqEiLv5srrxWq14te//jV+//vf44033sATTzwhyWsgabjqWtm2bRuamprw3e9+FxkZGfjRj34EAMjIyHD+gSfv56rrJSIiApGRkTCZTJ0eb7Va3fsCSDKuulYMBgMaGxs7XStKpRKCIDiLUvJ+Pble1q5di7Vr13a6z2QyISwszHmsPn/OdcGAco/0yCOPiM8++6zY3NzsHOW+bt266x63detWMTU1Vdy7d2+Xx/nkk0/EO++8UywsLHSOcs/KyhJNJpO7XwJJyFXXy6pVq8S77rpLvHTpkrtDJpm46lq51sGDBzmbk49y1fXy+uuvi5MmTRJPnz4tms1m8b333hPT0tI4m5MPcdW18sgjj4gLFy4Ua2trRaPRKK5du1acPn26qNfr3f0SSELdvV62b98ujh07VszLyxOtVqu4c+dOcezYseLhw4dFUXTN51xBFNuH+/uY2tparFy5Erm5uVAoFJg3bx6ee+45KJVKpKen4ze/+Q3uv/9+3HfffSgpKXGOane47777sHLlSoiiiHfffRcbN25EfX09xowZg9/85jcYOnSoTK+M3MEV18uyZcswZcoUKJXK674BcjyfvJ+r/rZcKzc3F9///vdx5swZKV8KScBV14vNZsOGDRvw8ccfo7q6GsOGDcMvf/lLZGRkyPTKyNVcda3U1tbid7/7Hfbv3w+LxYKxY8fil7/8JT+3+JjuXi+AvSXrrbfeQm1tLRISErBs2TLceeedAOCSz7k+W0wQEREREZF7+eSYCSIiIiIicj8WE0RERERE1CssJoiIiIiIqFdYTBARERERUa+wmCAiIiIiol5hMUFERERERL3CYoKIiIiIiHqFxQQREREREfUKiwkiIiIiIuoVFhNERERERNQrLCaIiIiIiKhX/n83jsJSTwYkvAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "ax= sns.lineplot(rt, intensity)\n",
    "\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "# ax.set_xlabel(\"Precursor m/z (Da)\", fontsize = 18)\n",
    "# ax.set_ylabel(\"Mass deviation (Da)\", fontsize = 18)\n",
    "ax.set_xlim(0.2, 0.3)\n",
    "ax.set_ylim(0, 260000)\n",
    "for index, row in data_temp.iterrows():\n",
    "    plt.axvline(x = row['retention_time'], color = 'red',  linestyle =\"--\")\n",
    "    # plt.text(row['retention_time'],row['ms1_precursor_intensity'],'RT: '+str(row['retention_time'])+\" MS1 intensity: \"+str(row['ms1_precursor_intensity']),rotation=0)\n",
    "plt.legend()\n",
    "\n",
    "ax.grid(False)\n",
    "# plt.legend(labels=[\"Precalibration\",'Postcalibration'], loc = 'upper right',fontsize=16)\n",
    "plt.savefig(os.path.join(source_dir,'demo_duplicate_xic.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [],
   "source": [
    "from toolsets.ms2_spectra_plotter import ms2_plot"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [],
   "source": [
    "for key in data_r['key'].unique():\n",
    "    data_temp = string_search(data_r,'key',key)\n",
    "    if len(data_temp)>4:\n",
    "        break"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "data": {
      "text/plain": "     scan_number  precursor_mz  charge  ms1_intensity_ratio  \\\n459          167    355.176743       1             0.992603   \n460          194    355.176357       1             0.992090   \n461          222    355.176576       1             0.976580   \n462          261    355.176525       1             0.996105   \n463          158    355.176682       1             0.993988   \n\n     ms1_precursor_intensity  retention_time  \\\n459                 461889.0        0.299050   \n460                  51295.0        0.329883   \n461                  23101.0        0.381650   \n462                  65474.0        0.481267   \n463                 492034.0        0.288950   \n\n                                                 peaks  ms1_index  query_idx  \\\n459  141.072433\\t26.553855\\n142.078049\\t5.173703\\n1...         69         96   \n460  139.05275\\t4.70916\\n141.06987\\t10.28056\\n148.0...         72        120   \n461  139.053894\\t4.19372\\n141.066879\\t11.857371\\n14...         77        143   \n462  51.0223618\\t7.804189\\n54.0340004\\t7.862366\\n60...         87        173   \n463  141.06995\\t12.122736\\n152.0643\\t5.308776\\n153....         68         88   \n\n    reference_Name  ... reference_Formula reference_Monoisotopic mass  \\\n459       Iclaprim  ...        C19H22N4O3                  354.169191   \n460       Iclaprim  ...        C19H22N4O3                  354.169191   \n461       Iclaprim  ...        C19H22N4O3                  354.169191   \n462       Iclaprim  ...        C19H22N4O3                  354.169191   \n463       Iclaprim  ...        C19H22N4O3                  354.169191   \n\n    reference_column  reference_adduct reference_precursor_mz  \\\n459            HILIC            [M+H]+               355.1765   \n460            HILIC            [M+H]+               355.1765   \n461            HILIC            [M+H]+               355.1765   \n462            HILIC            [M+H]+               355.1765   \n463            HILIC            [M+H]+               355.1765   \n\n                                   key       comments  \\\n459  HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+  recalibrated_   \n460  HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+  recalibrated_   \n461  HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+  recalibrated_   \n462  HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+  recalibrated_   \n463  HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+  recalibrated_   \n\n                                    peaks_recalibrated  diff_raw  \\\n459  141.072449\\t26.553855\\n142.078065\\t5.173703\\n1...  0.000243   \n460  139.052767\\t4.70916\\n141.069886\\t10.28056\\n148... -0.000143   \n461  139.053911\\t4.19372\\n141.066895\\t11.857371\\n14...  0.000076   \n462  51.022413\\t7.804189\\n54.034051\\t7.862366\\n60.0...  0.000025   \n463  141.069966\\t12.122736\\n152.064312\\t5.308776\\n1...  0.000182   \n\n    diff_recalibrated  \n459          0.000174  \n460         -0.000211  \n461          0.000008  \n462         -0.000043  \n463          0.000114  \n\n[5 rows x 22 columns]",
      "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>scan_number</th>\n      <th>precursor_mz</th>\n      <th>charge</th>\n      <th>ms1_intensity_ratio</th>\n      <th>ms1_precursor_intensity</th>\n      <th>retention_time</th>\n      <th>peaks</th>\n      <th>ms1_index</th>\n      <th>query_idx</th>\n      <th>reference_Name</th>\n      <th>...</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_column</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>key</th>\n      <th>comments</th>\n      <th>peaks_recalibrated</th>\n      <th>diff_raw</th>\n      <th>diff_recalibrated</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>459</th>\n      <td>167</td>\n      <td>355.176743</td>\n      <td>1</td>\n      <td>0.992603</td>\n      <td>461889.0</td>\n      <td>0.299050</td>\n      <td>141.072433\\t26.553855\\n142.078049\\t5.173703\\n1...</td>\n      <td>69</td>\n      <td>96</td>\n      <td>Iclaprim</td>\n      <td>...</td>\n      <td>C19H22N4O3</td>\n      <td>354.169191</td>\n      <td>HILIC</td>\n      <td>[M+H]+</td>\n      <td>355.1765</td>\n      <td>HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+</td>\n      <td>recalibrated_</td>\n      <td>141.072449\\t26.553855\\n142.078065\\t5.173703\\n1...</td>\n      <td>0.000243</td>\n      <td>0.000174</td>\n    </tr>\n    <tr>\n      <th>460</th>\n      <td>194</td>\n      <td>355.176357</td>\n      <td>1</td>\n      <td>0.992090</td>\n      <td>51295.0</td>\n      <td>0.329883</td>\n      <td>139.05275\\t4.70916\\n141.06987\\t10.28056\\n148.0...</td>\n      <td>72</td>\n      <td>120</td>\n      <td>Iclaprim</td>\n      <td>...</td>\n      <td>C19H22N4O3</td>\n      <td>354.169191</td>\n      <td>HILIC</td>\n      <td>[M+H]+</td>\n      <td>355.1765</td>\n      <td>HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+</td>\n      <td>recalibrated_</td>\n      <td>139.052767\\t4.70916\\n141.069886\\t10.28056\\n148...</td>\n      <td>-0.000143</td>\n      <td>-0.000211</td>\n    </tr>\n    <tr>\n      <th>461</th>\n      <td>222</td>\n      <td>355.176576</td>\n      <td>1</td>\n      <td>0.976580</td>\n      <td>23101.0</td>\n      <td>0.381650</td>\n      <td>139.053894\\t4.19372\\n141.066879\\t11.857371\\n14...</td>\n      <td>77</td>\n      <td>143</td>\n      <td>Iclaprim</td>\n      <td>...</td>\n      <td>C19H22N4O3</td>\n      <td>354.169191</td>\n      <td>HILIC</td>\n      <td>[M+H]+</td>\n      <td>355.1765</td>\n      <td>HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+</td>\n      <td>recalibrated_</td>\n      <td>139.053911\\t4.19372\\n141.066895\\t11.857371\\n14...</td>\n      <td>0.000076</td>\n      <td>0.000008</td>\n    </tr>\n    <tr>\n      <th>462</th>\n      <td>261</td>\n      <td>355.176525</td>\n      <td>1</td>\n      <td>0.996105</td>\n      <td>65474.0</td>\n      <td>0.481267</td>\n      <td>51.0223618\\t7.804189\\n54.0340004\\t7.862366\\n60...</td>\n      <td>87</td>\n      <td>173</td>\n      <td>Iclaprim</td>\n      <td>...</td>\n      <td>C19H22N4O3</td>\n      <td>354.169191</td>\n      <td>HILIC</td>\n      <td>[M+H]+</td>\n      <td>355.1765</td>\n      <td>HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+</td>\n      <td>recalibrated_</td>\n      <td>51.022413\\t7.804189\\n54.034051\\t7.862366\\n60.0...</td>\n      <td>0.000025</td>\n      <td>-0.000043</td>\n    </tr>\n    <tr>\n      <th>463</th>\n      <td>158</td>\n      <td>355.176682</td>\n      <td>1</td>\n      <td>0.993988</td>\n      <td>492034.0</td>\n      <td>0.288950</td>\n      <td>141.06995\\t12.122736\\n152.0643\\t5.308776\\n153....</td>\n      <td>68</td>\n      <td>88</td>\n      <td>Iclaprim</td>\n      <td>...</td>\n      <td>C19H22N4O3</td>\n      <td>354.169191</td>\n      <td>HILIC</td>\n      <td>[M+H]+</td>\n      <td>355.1765</td>\n      <td>HWJPWWYTGBZDEG-UHFFFAOYSA-N[M+H]+</td>\n      <td>recalibrated_</td>\n      <td>141.069966\\t12.122736\\n152.064312\\t5.308776\\n1...</td>\n      <td>0.000182</td>\n      <td>0.000114</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 22 columns</p>\n</div>"
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_temp"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3529/3529 [04:55<00:00, 11.94it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.spectra_operations import weighted_average_spectra\n",
    "spike_cols = [col for col in data_r.columns if 'reference' in col]\n",
    "spike_cols.extend(['retention_time', 'peaks_recalibrated'])\n",
    "data_r_u = {}\n",
    "for item in spike_cols:\n",
    "    data_r_u[str(item)]= []\n",
    "for key in tqdm(data_r['key'].unique()):\n",
    "    data_temp = string_search(data_r, 'key', key)\n",
    "    instance_max = data_temp.loc[data_temp['ms1_precursor_intensity'].idxmax()]\n",
    "    # if(len(data_temp)>3):\n",
    "    #     break\n",
    "    # concensus_spectra_temp = (weighted_average_spectra(data_temp, typeofmsms = 'peaks_recalibrated'))\n",
    "\n",
    "    for item in spike_cols:\n",
    "        if item != 'peaks_recalibrated':\n",
    "            data_r_u[str(item)].append(instance_max[str(item)])\n",
    "        else:\n",
    "            data_r_u[str(item)].append((weighted_average_spectra(data_temp, typeofmsms = 'peaks_recalibrated')))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "data_d.to_csv(os.path.join(source_dir, 'data_d.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Generating figures"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pfp_bio"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am msms_search!!!!!\n"
     ]
    }
   ],
   "source": [
    "from toolsets.msms_search import entropy_search_fast"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3333/3333 [00:05<00:00, 617.20it/s] \n"
     ]
    }
   ],
   "source": [
    "search_result = pd.DataFrame()\n",
    "entropy_similarity_searched = []\n",
    "unknown_precursormz = []\n",
    "unknown_adduct = []\n",
    "unknown_rt = []\n",
    "unknown_peaks = []\n",
    "for index, row in tqdm(hilic_bio.iterrows(),total=len(hilic_bio)):\n",
    "    result_temp = entropy_search_fast(row['peaks'], row['PRECURSORMZ'], library = library)\n",
    "    if pd.isnull(result_temp)==False:\n",
    "        unknown_precursormz.append(row['PRECURSORMZ'])\n",
    "        unknown_adduct.append(row['PRECURSORTYPE'])\n",
    "        unknown_rt.append(row['RETENTIONTIME'])\n",
    "        unknown_peaks.append(row['peaks'])\n",
    "        entropy_similarity_searched.append(result_temp[0])\n",
    "        search_result=search_result.append(result_temp[1])\n",
    "search_result['entropy_similarity']=entropy_similarity_searched\n",
    "search_result.insert(0, 'unknown_precursormz',unknown_precursormz)\n",
    "search_result.insert(1, 'unknown_adduct',unknown_adduct)\n",
    "search_result.insert(2, 'unknown_rt',unknown_rt)\n",
    "search_result.insert(3, 'unknown_peaks',unknown_peaks)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "search_result.to_csv(os.path.join(source_dir, \"pfp_searched_result.csv\"))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "search_result.to_csv(os.path.join(source_dir, \"hilic_searched_result.csv\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "search_result = pd.DataFrame()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "           reference_Name           reference_InChIKey reference_Mix  \\\n654  N,N-Dimethylarginine  YDGMGEXADBMOMJ-LURJTMIESA-N      MSMLS 13   \n654  N,N-Dimethylarginine  YDGMGEXADBMOMJ-LURJTMIESA-N      MSMLS 13   \n\n    reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n654         C8H18N4O2                   202.142976           [M+H]+   \n654         C8H18N4O2                   202.142976           [M+H]+   \n\n     reference_precursor_mz  retention_time  \\\n654                203.1503        2.273317   \n654                203.1503        2.273317   \n\n                                    peaks_recalibrated  \\\n654  55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...   \n654  55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...   \n\n                           peaks_recalibrated_denoised  comments  \n654  55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...  denoised  \n654  55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...  denoised  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>654</th>\n      <td>N,N-Dimethylarginine</td>\n      <td>YDGMGEXADBMOMJ-LURJTMIESA-N</td>\n      <td>MSMLS 13</td>\n      <td>C8H18N4O2</td>\n      <td>202.142976</td>\n      <td>[M+H]+</td>\n      <td>203.1503</td>\n      <td>2.273317</td>\n      <td>55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...</td>\n      <td>55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>654</th>\n      <td>N,N-Dimethylarginine</td>\n      <td>YDGMGEXADBMOMJ-LURJTMIESA-N</td>\n      <td>MSMLS 13</td>\n      <td>C8H18N4O2</td>\n      <td>202.142976</td>\n      <td>[M+H]+</td>\n      <td>203.1503</td>\n      <td>2.273317</td>\n      <td>55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...</td>\n      <td>55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_result"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                                              N,N-Dimethylarginine\nreference_InChIKey                                   YDGMGEXADBMOMJ-LURJTMIESA-N\nreference_Mix                                                           MSMLS 13\nreference_Formula                                                      C8H18N4O2\nreference_Monoisotopic mass                                           202.142976\nreference_adduct                                                          [M+H]+\nreference_precursor_mz                                                  203.1503\nretention_time                                                          2.273317\npeaks_recalibrated             55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...\npeaks_recalibrated_denoised    55.040665\\t1.363483\\n56.042935\\t2.780965\\n57.0...\ncomments                                                                denoised\nName: 654, dtype: object"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "result = entropy_search_fast(row['peaks'], row['PRECURSORMZ'], library = library)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(entropy_search_fast(row['peaks'], row['PRECURSORMZ'], library = library))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hilic_bio' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [1]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mhilic_bio\u001B[49m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'hilic_bio' is not defined"
     ]
    }
   ],
   "source": [
    "hilic_bio"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                                         reference_Name  \\\n0                             Tauroursodeoxycholic acid   \n1                                   Taurodehydrocholate   \n2                            Glycochenodeoxycholic acid   \n3                                      Deoxycholic Acid   \n4                                 Taurodeoxycholic acid   \n...                                                 ...   \n3570     6-methoxy-7-(3-methylbut-2-enoxy)chromen-2-one   \n3571                                       ABIETIC ACID   \n3572                                      Carnosic Acid   \n3573                               Caryophyllene [T(-)]   \n3574  methyl 2-(9-hydroxy-1-methyl-5,10-dioxo-3,4-di...   \n\n               reference_InChIKey      reference_Mix reference_Formula  \\\n0     BHTRKEVKTKCXOH-LBSADWJPSA-N       Bile acids 1        C26H45NO6S   \n1     UBDJSBRKNHQFPD-PYGYYAGESA-N       Bile acids 1        C26H39NO7S   \n2     GHCZAUBVMUEKKP-GYPHWSFCSA-N       Bile acids 1         C26H43NO5   \n3     KXGVEGMKQFWNSR-LLQZFEROSA-N   PFPpos_POS 2_EAD          C24H40O4   \n4     AWDRATDZQPNJFN-VAYUFCLWSA-N       Bile acids 3        C26H45NO6S   \n...                           ...                ...               ...   \n3570  ZLPLFUBVEZVYDX-UHFFFAOYSA-N  PFPpos_P N 35_EAD          C15H16O4   \n3571  RSWGJHLUYNHPMX-ONCXSQPRSA-N  PFPpos_P N 35_EAD          C20H30O2   \n3572  QRYRORQUOLYVBU-VBKZILBWSA-N  PFPpos_P N 35_EAD          C20H28O4   \n3573  INOSMXBKABLUIL-KOOLHJLFSA-N  PFPpos_P N 35_EAD            C14H22   \n3574  FMEUWIKCSICJBO-UHFFFAOYSA-N  PFPpos_P N 35_EAD          C17H16O6   \n\n      reference_Monoisotopic mass reference_adduct  reference_precursor_mz  \\\n0                      499.296759           [M+H]+                500.3040   \n1                      509.244724           [M+H]+                510.2520   \n2                      449.314123           [M+H]+                450.3214   \n3                      392.292660           [M+H]+                393.2999   \n4                      499.296759           [M+H]+                500.3040   \n...                           ...              ...                     ...   \n3570                   260.104859          [M+Na]+                283.0941   \n3571                   302.224580           [M+H]+                303.2319   \n3572                   332.198759           [M+H]+                333.2060   \n3573                   190.172151          [M+Na]+                213.1614   \n3574                   316.094688           [M+H]+                317.1020   \n\n      retention_time                                 peaks_recalibrated  \\\n0           5.751050  51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...   \n1           2.198233  50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...   \n2           2.304017  50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...   \n3           2.302233  50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....   \n4           5.246883  51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...   \n...              ...                                                ...   \n3570        2.219933  115.055009\\t28.831727\\n116.062078\\t7.34446\\n11...   \n3571        2.088933  50.015617\\t11.470487\\n51.02358\\t21.31449\\n53.0...   \n3572        1.898283  50.015086\\t2.798766\\n51.023489\\t9.378789\\n52.0...   \n3573        2.325250  115.053095\\t32.12139\\n116.055219\\t8.072488\\n11...   \n3574        2.283583  115.056062\\t7.972046\\n119.050017\\t1.101087\\n12...   \n\n                            peaks_recalibrated_denoised  comments  \n0     51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...  denoised  \n1     50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...  denoised  \n2     50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...  denoised  \n3     50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....  denoised  \n4     51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...  denoised  \n...                                                 ...       ...  \n3570  115.055009\\t28.831727\\n116.062078\\t7.34446\\n11...  denoised  \n3571  50.015617\\t11.470487\\n51.02358\\t21.31449\\n53.0...  denoised  \n3572  50.015086\\t2.798766\\n51.023489\\t9.378789\\n52.0...  denoised  \n3573  115.053095\\t32.12139\\n116.055219\\t8.072488\\n11...  denoised  \n3574  115.056062\\t7.972046\\n119.050017\\t1.101087\\n12...  denoised  \n\n[3575 rows x 11 columns]",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Tauroursodeoxycholic acid</td>\n      <td>BHTRKEVKTKCXOH-LBSADWJPSA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.3040</td>\n      <td>5.751050</td>\n      <td>51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...</td>\n      <td>51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Taurodehydrocholate</td>\n      <td>UBDJSBRKNHQFPD-PYGYYAGESA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H39NO7S</td>\n      <td>509.244724</td>\n      <td>[M+H]+</td>\n      <td>510.2520</td>\n      <td>2.198233</td>\n      <td>50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...</td>\n      <td>50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Glycochenodeoxycholic acid</td>\n      <td>GHCZAUBVMUEKKP-GYPHWSFCSA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H43NO5</td>\n      <td>449.314123</td>\n      <td>[M+H]+</td>\n      <td>450.3214</td>\n      <td>2.304017</td>\n      <td>50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...</td>\n      <td>50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Deoxycholic Acid</td>\n      <td>KXGVEGMKQFWNSR-LLQZFEROSA-N</td>\n      <td>PFPpos_POS 2_EAD</td>\n      <td>C24H40O4</td>\n      <td>392.292660</td>\n      <td>[M+H]+</td>\n      <td>393.2999</td>\n      <td>2.302233</td>\n      <td>50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....</td>\n      <td>50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-VAYUFCLWSA-N</td>\n      <td>Bile acids 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.3040</td>\n      <td>5.246883</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3570</th>\n      <td>6-methoxy-7-(3-methylbut-2-enoxy)chromen-2-one</td>\n      <td>ZLPLFUBVEZVYDX-UHFFFAOYSA-N</td>\n      <td>PFPpos_P N 35_EAD</td>\n      <td>C15H16O4</td>\n      <td>260.104859</td>\n      <td>[M+Na]+</td>\n      <td>283.0941</td>\n      <td>2.219933</td>\n      <td>115.055009\\t28.831727\\n116.062078\\t7.34446\\n11...</td>\n      <td>115.055009\\t28.831727\\n116.062078\\t7.34446\\n11...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>3571</th>\n      <td>ABIETIC ACID</td>\n      <td>RSWGJHLUYNHPMX-ONCXSQPRSA-N</td>\n      <td>PFPpos_P N 35_EAD</td>\n      <td>C20H30O2</td>\n      <td>302.224580</td>\n      <td>[M+H]+</td>\n      <td>303.2319</td>\n      <td>2.088933</td>\n      <td>50.015617\\t11.470487\\n51.02358\\t21.31449\\n53.0...</td>\n      <td>50.015617\\t11.470487\\n51.02358\\t21.31449\\n53.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>3572</th>\n      <td>Carnosic Acid</td>\n      <td>QRYRORQUOLYVBU-VBKZILBWSA-N</td>\n      <td>PFPpos_P N 35_EAD</td>\n      <td>C20H28O4</td>\n      <td>332.198759</td>\n      <td>[M+H]+</td>\n      <td>333.2060</td>\n      <td>1.898283</td>\n      <td>50.015086\\t2.798766\\n51.023489\\t9.378789\\n52.0...</td>\n      <td>50.015086\\t2.798766\\n51.023489\\t9.378789\\n52.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>3573</th>\n      <td>Caryophyllene [T(-)]</td>\n      <td>INOSMXBKABLUIL-KOOLHJLFSA-N</td>\n      <td>PFPpos_P N 35_EAD</td>\n      <td>C14H22</td>\n      <td>190.172151</td>\n      <td>[M+Na]+</td>\n      <td>213.1614</td>\n      <td>2.325250</td>\n      <td>115.053095\\t32.12139\\n116.055219\\t8.072488\\n11...</td>\n      <td>115.053095\\t32.12139\\n116.055219\\t8.072488\\n11...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>3574</th>\n      <td>methyl 2-(9-hydroxy-1-methyl-5,10-dioxo-3,4-di...</td>\n      <td>FMEUWIKCSICJBO-UHFFFAOYSA-N</td>\n      <td>PFPpos_P N 35_EAD</td>\n      <td>C17H16O6</td>\n      <td>316.094688</td>\n      <td>[M+H]+</td>\n      <td>317.1020</td>\n      <td>2.283583</td>\n      <td>115.056062\\t7.972046\\n119.050017\\t1.101087\\n12...</td>\n      <td>115.056062\\t7.972046\\n119.050017\\t1.101087\\n12...</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n<p>3575 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "                 reference_Name           reference_InChIKey reference_Mix  \\\n577  Taurochenodeoxycholic acid  BHTRKEVKTKCXOH-BJLOMENOSA-N     BACSMLS 3   \n578  Taurochenodeoxycholic acid  BHTRKEVKTKCXOH-BJLOMENOSA-N     BACSMLS 3   \n\n    reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n577        C26H45NO6S                   499.296759           [M+H]+   \n578        C26H45NO6S                   499.296759          [M+Na]+   \n\n     reference_precursor_mz  retention_time  \\\n577                 500.304        1.577833   \n578                 522.286        1.588000   \n\n                                    peaks_recalibrated  \\\n577  50.014911\\t3.154574\\n51.022833\\t12.263406\\n52....   \n578  50.015483\\t1.130718\\n51.023039\\t2.141511\\n55.0...   \n\n                           peaks_recalibrated_denoised  comments  \n577  50.014911\\t3.154574\\n51.022833\\t12.263406\\n52....  denoised  \n578  50.015483\\t1.130718\\n51.023039\\t2.141511\\n55.0...  denoised  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>577</th>\n      <td>Taurochenodeoxycholic acid</td>\n      <td>BHTRKEVKTKCXOH-BJLOMENOSA-N</td>\n      <td>BACSMLS 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.304</td>\n      <td>1.577833</td>\n      <td>50.014911\\t3.154574\\n51.022833\\t12.263406\\n52....</td>\n      <td>50.014911\\t3.154574\\n51.022833\\t12.263406\\n52....</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>578</th>\n      <td>Taurochenodeoxycholic acid</td>\n      <td>BHTRKEVKTKCXOH-BJLOMENOSA-N</td>\n      <td>BACSMLS 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+Na]+</td>\n      <td>522.286</td>\n      <td>1.588000</td>\n      <td>50.015483\\t1.130718\\n51.023039\\t2.141511\\n55.0...</td>\n      <td>50.015483\\t1.130718\\n51.023039\\t2.141511\\n55.0...</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_InChIKey', 'BHTRKEVKTKCXOH-BJLOMENOSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "          reference_Name           reference_InChIKey reference_Mix  \\\n4  Taurodeoxycholic acid  AWDRATDZQPNJFN-VAYUFCLWSA-N  Bile acids 3   \n\n  reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n4        C26H45NO6S                   499.296759           [M+H]+   \n\n   reference_precursor_mz  retention_time  \\\n4                 500.304        5.246883   \n\n                                  peaks_recalibrated  \\\n4  51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...   \n\n                         peaks_recalibrated_denoised  comments  \n4  51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...  denoised  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-VAYUFCLWSA-N</td>\n      <td>Bile acids 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.304</td>\n      <td>5.246883</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_InChIKey', 'AWDRATDZQPNJFN-VAYUFCLWSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "            reference_Name           reference_InChIKey reference_Mix  \\\n4    Taurodeoxycholic acid  AWDRATDZQPNJFN-VAYUFCLWSA-N  Bile acids 3   \n561  Taurodeoxycholic acid  AWDRATDZQPNJFN-GHMNEPPTSA-N     BACSMLS 2   \n562  Taurodeoxycholic acid  AWDRATDZQPNJFN-GHMNEPPTSA-N     BACSMLS 2   \n\n    reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n4          C26H45NO6S                   499.296759           [M+H]+   \n561        C26H45NO6S                   499.296759           [M+H]+   \n562        C26H45NO6S                   499.296759          [M+Na]+   \n\n     reference_precursor_mz  retention_time  \\\n4                   500.304        5.246883   \n561                 500.304        1.466500   \n562                 522.286        1.476567   \n\n                                    peaks_recalibrated  \\\n4    51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...   \n561  51.021836\\t3.595119\\n55.053473\\t1.85079\\n63.02...   \n562  50.014039\\t3.133716\\n51.021469\\t9.335521\\n52.0...   \n\n                           peaks_recalibrated_denoised  comments  \n4    51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...  denoised  \n561  51.021836\\t3.595119\\n55.053473\\t1.85079\\n63.02...  denoised  \n562  50.014039\\t3.133716\\n51.021469\\t9.335521\\n52.0...  denoised  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-VAYUFCLWSA-N</td>\n      <td>Bile acids 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.304</td>\n      <td>5.246883</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>561</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-GHMNEPPTSA-N</td>\n      <td>BACSMLS 2</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.304</td>\n      <td>1.466500</td>\n      <td>51.021836\\t3.595119\\n55.053473\\t1.85079\\n63.02...</td>\n      <td>51.021836\\t3.595119\\n55.053473\\t1.85079\\n63.02...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>562</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-GHMNEPPTSA-N</td>\n      <td>BACSMLS 2</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+Na]+</td>\n      <td>522.286</td>\n      <td>1.476567</td>\n      <td>50.014039\\t3.133716\\n51.021469\\t9.335521\\n52.0...</td>\n      <td>50.014039\\t3.133716\\n51.021469\\t9.335521\\n52.0...</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_Name', 'Taurodeoxycholic acid')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "from toolsets.API_gets import pubchem_get"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "V_compound = pubchem_get(content='AWDRATDZQPNJFN-VAYUFCLWSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "from toolsets.ms2_spectra_plotter import molplot_from_inchikey"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    },
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x179720ca0>",
      "image/png": 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\n"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molplot_from_inchikey('AWDRATDZQPNJFN-VAYUFCLWSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x17987c100>",
      "image/png": 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\n"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molplot_from_inchikey('AWDRATDZQPNJFN-GHMNEPPTSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "       reference_Name           reference_InChIKey     reference_Mix  \\\n703  Glycocholic Acid  RFDAIACWWDREDC-FRVQLJSFSA-N  PFPpos_P N 2_EAD   \n707  Glycocholic Acid  RFDAIACWWDREDC-FRVQLJSFSA-N  PFPpos_P N 2_EAD   \n708  Glycocholic Acid  RFDAIACWWDREDC-FRVQLJSFSA-N  PFPpos_P N 2_EAD   \n\n    reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n703         C26H43NO6                   465.309038         [M+NH4]+   \n707         C26H43NO6                   465.309038           [M+H]+   \n708         C26H43NO6                   465.309038          [M+Na]+   \n\n     reference_precursor_mz  retention_time  \\\n703                483.3429        2.042933   \n707                466.3163        2.010650   \n708                488.2983        1.993433   \n\n                                    peaks_recalibrated  \\\n703  50.014914\\t1.669367\\n51.023315\\t4.832007\\n55.0...   \n707  50.014708\\t1.747089\\n51.022495\\t4.838093\\n55.0...   \n708  50.015287\\t5.211655\\n51.022721\\t10.420709\\n52....   \n\n                           peaks_recalibrated_denoised  comments  \n703  50.014914\\t1.669367\\n51.023315\\t4.832007\\n55.0...  denoised  \n707  50.014708\\t1.747089\\n51.022495\\t4.838093\\n55.0...  denoised  \n708  50.015287\\t5.211655\\n51.022721\\t10.420709\\n52....  denoised  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>703</th>\n      <td>Glycocholic Acid</td>\n      <td>RFDAIACWWDREDC-FRVQLJSFSA-N</td>\n      <td>PFPpos_P N 2_EAD</td>\n      <td>C26H43NO6</td>\n      <td>465.309038</td>\n      <td>[M+NH4]+</td>\n      <td>483.3429</td>\n      <td>2.042933</td>\n      <td>50.014914\\t1.669367\\n51.023315\\t4.832007\\n55.0...</td>\n      <td>50.014914\\t1.669367\\n51.023315\\t4.832007\\n55.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>707</th>\n      <td>Glycocholic Acid</td>\n      <td>RFDAIACWWDREDC-FRVQLJSFSA-N</td>\n      <td>PFPpos_P N 2_EAD</td>\n      <td>C26H43NO6</td>\n      <td>465.309038</td>\n      <td>[M+H]+</td>\n      <td>466.3163</td>\n      <td>2.010650</td>\n      <td>50.014708\\t1.747089\\n51.022495\\t4.838093\\n55.0...</td>\n      <td>50.014708\\t1.747089\\n51.022495\\t4.838093\\n55.0...</td>\n      <td>denoised</td>\n    </tr>\n    <tr>\n      <th>708</th>\n      <td>Glycocholic Acid</td>\n      <td>RFDAIACWWDREDC-FRVQLJSFSA-N</td>\n      <td>PFPpos_P N 2_EAD</td>\n      <td>C26H43NO6</td>\n      <td>465.309038</td>\n      <td>[M+Na]+</td>\n      <td>488.2983</td>\n      <td>1.993433</td>\n      <td>50.015287\\t5.211655\\n51.022721\\t10.420709\\n52....</td>\n      <td>50.015287\\t5.211655\\n51.022721\\t10.420709\\n52....</td>\n      <td>denoised</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_InChIKey', 'RFDAIACWWDREDC-FRVQLJSFSA-N')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "0.30460419533546346"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy_similarity_default(string_search(data, 'reference_InChIKey', 'RFDAIACWWDREDC-FRVQLJSFSA-N').iloc[1]['peaks_recalibrated_denoised'],\n",
    "                           string_search(data, 'reference_InChIKey', 'BHTRKEVKTKCXOH-BJLOMENOSA-N').iloc[0]['peaks_recalibrated_denoised'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "'[M+H]+'"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_InChIKey', 'RFDAIACWWDREDC-FRVQLJSFSA-N').iloc[1]['reference_adduct']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "'[M+H]+'"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(data, 'reference_InChIKey', 'BHTRKEVKTKCXOH-BJLOMENOSA-N').iloc[0]['reference_adduct']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "data['key']= data['reference_InChIKey']+data['reference_adduct']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "1953"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['reference_InChIKey'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "outputs": [],
   "source": [
    "explained_intensity = []\n",
    "for index, row in data.iterrows():\n",
    "    explained_intensity.append(so.calculate_explained_intensity(row['peaks_recalibrated'], row['peaks_recalibrated_denoised'], row['reference_precursor_mz']))\n",
    "data['explained_intensity']=explained_intensity"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "outputs": [],
   "source": [
    "from toolsets.spectra_operations import normalized_entropy\n",
    "normalized_entropy_one = []\n",
    "normalized_entropy_forth = []\n",
    "for index, row in data.iterrows():\n",
    "    normalized_entropy_one.append(normalized_entropy(row['peaks_recalibrated_denoised'], order=1))\n",
    "    normalized_entropy_forth.append(normalized_entropy(row['peaks_recalibrated_denoised'], order=4))\n",
    "data['normalized_entropy_one']=normalized_entropy_one\n",
    "data['normalized_entropy_forth']=normalized_entropy_forth"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "outputs": [],
   "source": [
    "data_good_quality = num_search(data, 'explained_intensity', 90, '>', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "outputs": [],
   "source": [
    "data_noisy = num_search(data, 'normalized_entropy_forth', 0.8, '>', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "outputs": [],
   "source": [
    "data_non_informative = num_search(data, 'normalized_entropy_one', 0.5, '<', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "outputs": [
    {
     "data": {
      "text/plain": "                                         reference_Name  \\\n66                                  N,N-Dimethylglycine   \n181                 4-ethoxy-N-(2-phenylethyl)benzamide   \n307   N-[(4-fluorophenyl)methyl]-4-hydroxy-2-oxo-1H-...   \n497   N-(4-methoxyphenyl)pyrazolo[1,5-a]pyridine-2-c...   \n526                                        Zoxazolamine   \n715                                  Indole-3-Acetamide   \n818                                  PYROCATECHUIC ACID   \n1388  1-(4-hydroxyphenyl)-3-[(2R,3R,4S,5S,6R)-3,4,5-...   \n1389  1-(4-hydroxyphenyl)-3-[(2R,3R,4S,5S,6R)-3,4,5-...   \n1674                                            Helicin   \n2150                                          pencolide   \n2373  2-(2-hydroxypropan-2-yl)-3-[(2R,3R,4S,5S,6R)-3...   \n2492                                            Gedunin   \n2526                                             NIACIN   \n2620  [(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxym...   \n2630                                          Auraptene   \n3236  3-[5-(3,4-dihydroxy-4-methyl-5-oxooxolan-2-yl)...   \n3354  3-[(2S,3R,4S,5S,6R)-6-[[(2R,3R,4R)-3,4-dihydro...   \n\n               reference_InChIKey      reference_Mix reference_Formula  \\\n66    FFDGPVCHZBVARC-UHFFFAOYSA-N           GC LIB 5           C4H9NO2   \n181   GCXWAEJLLFKQFQ-UHFFFAOYSA-N        Pandemic 12         C17H19NO2   \n307   YTQNZGHPUXHBMQ-UHFFFAOYSA-N        Pandemic 18       C16H12FN3O3   \n497   OOQFUDGMLSSZNX-UHFFFAOYSA-N         Pandemic 9        C15H13N3O2   \n526   YGCODSQDUUUKIV-UHFFFAOYSA-N         Pathogen 1         C7H5ClN2O   \n715   ZOAMBXDOGPRZLP-UHFFFAOYSA-N           MSMLS 21         C10H10N2O   \n818   GLDQAMYCGOIJDV-UHFFFAOYSA-N   PFPpos_POS 1_EAD            C7H6O4   \n1388  NPAQHLFPEOMKAL-UXXRCYHCSA-N   PFPpos_POS 8_EAD          C15H20O8   \n1389  NPAQHLFPEOMKAL-UXXRCYHCSA-N   PFPpos_POS 8_EAD          C15H20O8   \n1674  BGOFCVIGEYGEOF-UJPOAAIJSA-N   PFPpos_P N 2_EAD          C13H16O7   \n2150  NFDKQFGUQFNACT-UTCJRWHESA-N  PFPpos_P N 11_EAD           C9H9NO4   \n2373  HGHSJJSYVZPIAW-NWMIEQKASA-N  PFPpos_P N 15_EAD         C20H24O10   \n2492  YJXDGWUNRYLINJ-POZBICLPSA-N  PFPpos_P N 16_EAD          C28H34O7   \n2526  PVNIIMVLHYAWGP-UHFFFAOYSA-N  PFPpos_P N 17_EAD           C6H5NO2   \n2620  WWBNBPSEKLOHJU-QPQZIUDZSA-N  PFPpos_P N 19_EAD      C14H19NO10S2   \n2630  RSDDHGSKLOSQFK-RVDMUPIBSA-N  PFPpos_P N 19_EAD          C19H22O3   \n3236  BNLUQONOBPMVBG-UHFFFAOYSA-N  PFPpos_P N 30_EAD          C15H18O8   \n3354  OXNYNKGAOAFFBD-AHZIVMAWSA-N  PFPpos_P N 32_EAD         C17H24O12   \n\n      reference_Monoisotopic mass reference_adduct  reference_precursor_mz  \\\n66                     103.063328           [M+H]+                104.0706   \n181                    269.141579           [M+H]+                270.1489   \n307                    313.086269           [M+H]+                314.0935   \n497                    267.100777           [M+H]+                268.1081   \n526                    168.009040           [M+H]+                169.0163   \n715                    174.079313           [M+H]+                175.0866   \n818                    154.026609           [M+H]+                155.0339   \n1388                   328.115818           [M+H]+                329.1231   \n1389                   328.115818         [M+NH4]+                346.1496   \n1674                   284.089603         [M+NH4]+                302.1234   \n2150                   195.053158           [M+H]+                196.0604   \n2373                   424.136947          [M+Na]+                447.1262   \n2492                   482.230453          [M+Na]+                505.2197   \n2526                   123.032028           [M+H]+                124.0393   \n2620                   425.045038         [M+NH4]+                443.0789   \n2630                   298.156895         [M+NH4]+                316.1907   \n3236                   326.100167          [M+Na]+                349.0894   \n3354                   420.126776           [M+H]+                421.1341   \n\n      retention_time                                 peaks_recalibrated  \\\n66          1.955650  56.049357\\t8.982489\\n57.056548\\t1.012778\\n57.8...   \n181         0.195617  121.030189\\t16.610691\\n132.046474\\t1.639989\\n1...   \n307         0.244267  133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...   \n497         0.200350  117.04045\\t3.222095\\n144.564671\\t1.77298\\n145....   \n526         0.208667  126.993533\\t10.189719\\n128.060601\\t1.25459\\n13...   \n715         0.239467  128.051565\\t20.896697\\n129.057506\\t1.955\\n129....   \n818         0.617400  127.055021\\t2.237659\\n128.063131\\t5.889224\\n12...   \n1388        0.286833  121.029475\\t10.53818\\n127.048507\\t1.056636\\n12...   \n1389        0.288550  121.029094\\t5.233374\\n128.057977\\t1.392987\\n13...   \n1674        0.288100  115.052732\\t2.361324\\n117.061378\\t1.678248\\n12...   \n2150        0.622467  115.053089\\t1.347364\\n122.066649\\t70.658653\\n1...   \n2373        0.881483  115.05392\\t1.179318\\n128.061328\\t1.469747\\n152...   \n2492        2.412783  128.063715\\t1.713933\\n152.060701\\t1.572097\\n15...   \n2526        0.125100  52.026778\\t1.018964\\n115.053809\\t1.132182\\n116...   \n2620        0.210733  115.052525\\t1.280598\\n127.045442\\t1.954896\\n12...   \n2630        2.517267  115.053128\\t2.028729\\n119.04961\\t16.032405\\n12...   \n3236        0.678033  123.117339\\t1.732529\\n128.06155\\t1.245863\\n151...   \n3354        0.153767  109.030589\\t3.168308\\n115.040877\\t1.180592\\n12...   \n\n                            peaks_recalibrated_denoised  comments  \\\n66    56.049357\\t8.982489\\n57.056548\\t1.012778\\n58.0...  denoised   \n181   121.030189\\t16.610691\\n132.046474\\t1.639989\\n1...  denoised   \n307   133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...  denoised   \n497   117.04045\\t3.222095\\n145.04565\\t100.0\\n207.083...  denoised   \n526   126.993533\\t10.189719\\n133.037348\\t1.22399\\n13...  denoised   \n715   128.051565\\t20.896697\\n129.057506\\t1.955\\n130....  denoised   \n818   127.055021\\t2.237659\\n136.015146\\t3.731481\\n13...  denoised   \n1388  121.029475\\t10.53818\\n127.048507\\t1.056636\\n12...  denoised   \n1389  121.029094\\t5.233374\\n128.057977\\t1.392987\\n13...  denoised   \n1674  115.052732\\t2.361324\\n117.061378\\t1.678248\\n12...  denoised   \n2150  115.053089\\t1.347364\\n122.066649\\t70.658653\\n1...  denoised   \n2373  115.05392\\t1.179318\\n128.061328\\t1.469747\\n152...  denoised   \n2492  128.063715\\t1.713933\\n152.060701\\t1.572097\\n15...  denoised   \n2526  52.026778\\t1.018964\\n122.02389\\t100.0\\n123.634...  denoised   \n2620  115.052525\\t1.280598\\n127.045442\\t1.954896\\n12...  denoised   \n2630  115.053128\\t2.028729\\n119.04961\\t16.032405\\n12...  denoised   \n3236  123.117339\\t1.732529\\n128.06155\\t1.245863\\n151...  denoised   \n3354  109.030589\\t3.168308\\n115.040877\\t1.180592\\n12...  denoised   \n\n      explained_intensity  normalized_entropy_one  normalized_entropy_forth  \n66               98.62514                0.304339                  0.008579  \n181              98.02025                0.496860                  0.060945  \n307              97.30179                0.352178                  0.015383  \n497              98.66184                0.453497                  0.042296  \n526              93.17269                0.494078                  0.059591  \n715              99.11892                0.417162                  0.030285  \n818              84.65354                0.231207                  0.002858  \n1388            100.00000                0.461216                  0.045250  \n1389             99.12622                0.348186                  0.014698  \n1674             98.32239                0.483199                  0.054514  \n2150            100.00000                0.450408                  0.041155  \n2373            100.00000                0.481728                  0.053852  \n2492            100.00000                0.492381                  0.058776  \n2526             97.88810                0.185737                  0.001190  \n2620            100.00000                0.395769                  0.024534  \n2630             99.22553                0.432982                  0.035146  \n3236            100.00000                0.494068                  0.059587  \n3354             99.02645                0.472801                  0.049971  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n      <th>explained_intensity</th>\n      <th>normalized_entropy_one</th>\n      <th>normalized_entropy_forth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>66</th>\n      <td>N,N-Dimethylglycine</td>\n      <td>FFDGPVCHZBVARC-UHFFFAOYSA-N</td>\n      <td>GC LIB 5</td>\n      <td>C4H9NO2</td>\n      <td>103.063328</td>\n      <td>[M+H]+</td>\n      <td>104.0706</td>\n      <td>1.955650</td>\n      <td>56.049357\\t8.982489\\n57.056548\\t1.012778\\n57.8...</td>\n      <td>56.049357\\t8.982489\\n57.056548\\t1.012778\\n58.0...</td>\n      <td>denoised</td>\n      <td>98.62514</td>\n      <td>0.304339</td>\n      <td>0.008579</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>4-ethoxy-N-(2-phenylethyl)benzamide</td>\n      <td>GCXWAEJLLFKQFQ-UHFFFAOYSA-N</td>\n      <td>Pandemic 12</td>\n      <td>C17H19NO2</td>\n      <td>269.141579</td>\n      <td>[M+H]+</td>\n      <td>270.1489</td>\n      <td>0.195617</td>\n      <td>121.030189\\t16.610691\\n132.046474\\t1.639989\\n1...</td>\n      <td>121.030189\\t16.610691\\n132.046474\\t1.639989\\n1...</td>\n      <td>denoised</td>\n      <td>98.02025</td>\n      <td>0.496860</td>\n      <td>0.060945</td>\n    </tr>\n    <tr>\n      <th>307</th>\n      <td>N-[(4-fluorophenyl)methyl]-4-hydroxy-2-oxo-1H-...</td>\n      <td>YTQNZGHPUXHBMQ-UHFFFAOYSA-N</td>\n      <td>Pandemic 18</td>\n      <td>C16H12FN3O3</td>\n      <td>313.086269</td>\n      <td>[M+H]+</td>\n      <td>314.0935</td>\n      <td>0.244267</td>\n      <td>133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...</td>\n      <td>133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...</td>\n      <td>denoised</td>\n      <td>97.30179</td>\n      <td>0.352178</td>\n      <td>0.015383</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>N-(4-methoxyphenyl)pyrazolo[1,5-a]pyridine-2-c...</td>\n      <td>OOQFUDGMLSSZNX-UHFFFAOYSA-N</td>\n      <td>Pandemic 9</td>\n      <td>C15H13N3O2</td>\n      <td>267.100777</td>\n      <td>[M+H]+</td>\n      <td>268.1081</td>\n      <td>0.200350</td>\n      <td>117.04045\\t3.222095\\n144.564671\\t1.77298\\n145....</td>\n      <td>117.04045\\t3.222095\\n145.04565\\t100.0\\n207.083...</td>\n      <td>denoised</td>\n      <td>98.66184</td>\n      <td>0.453497</td>\n      <td>0.042296</td>\n    </tr>\n    <tr>\n      <th>526</th>\n      <td>Zoxazolamine</td>\n      <td>YGCODSQDUUUKIV-UHFFFAOYSA-N</td>\n      <td>Pathogen 1</td>\n      <td>C7H5ClN2O</td>\n      <td>168.009040</td>\n      <td>[M+H]+</td>\n      <td>169.0163</td>\n      <td>0.208667</td>\n      <td>126.993533\\t10.189719\\n128.060601\\t1.25459\\n13...</td>\n      <td>126.993533\\t10.189719\\n133.037348\\t1.22399\\n13...</td>\n      <td>denoised</td>\n      <td>93.17269</td>\n      <td>0.494078</td>\n      <td>0.059591</td>\n    </tr>\n    <tr>\n      <th>715</th>\n      <td>Indole-3-Acetamide</td>\n      <td>ZOAMBXDOGPRZLP-UHFFFAOYSA-N</td>\n      <td>MSMLS 21</td>\n      <td>C10H10N2O</td>\n      <td>174.079313</td>\n      <td>[M+H]+</td>\n      <td>175.0866</td>\n      <td>0.239467</td>\n      <td>128.051565\\t20.896697\\n129.057506\\t1.955\\n129....</td>\n      <td>128.051565\\t20.896697\\n129.057506\\t1.955\\n130....</td>\n      <td>denoised</td>\n      <td>99.11892</td>\n      <td>0.417162</td>\n      <td>0.030285</td>\n    </tr>\n    <tr>\n      <th>818</th>\n      <td>PYROCATECHUIC ACID</td>\n      <td>GLDQAMYCGOIJDV-UHFFFAOYSA-N</td>\n      <td>PFPpos_POS 1_EAD</td>\n      <td>C7H6O4</td>\n      <td>154.026609</td>\n      <td>[M+H]+</td>\n      <td>155.0339</td>\n      <td>0.617400</td>\n      <td>127.055021\\t2.237659\\n128.063131\\t5.889224\\n12...</td>\n      <td>127.055021\\t2.237659\\n136.015146\\t3.731481\\n13...</td>\n      <td>denoised</td>\n      <td>84.65354</td>\n      <td>0.231207</td>\n      <td>0.002858</td>\n    </tr>\n    <tr>\n      <th>1388</th>\n      <td>1-(4-hydroxyphenyl)-3-[(2R,3R,4S,5S,6R)-3,4,5-...</td>\n      <td>NPAQHLFPEOMKAL-UXXRCYHCSA-N</td>\n      <td>PFPpos_POS 8_EAD</td>\n      <td>C15H20O8</td>\n      <td>328.115818</td>\n      <td>[M+H]+</td>\n      <td>329.1231</td>\n      <td>0.286833</td>\n      <td>121.029475\\t10.53818\\n127.048507\\t1.056636\\n12...</td>\n      <td>121.029475\\t10.53818\\n127.048507\\t1.056636\\n12...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.461216</td>\n      <td>0.045250</td>\n    </tr>\n    <tr>\n      <th>1389</th>\n      <td>1-(4-hydroxyphenyl)-3-[(2R,3R,4S,5S,6R)-3,4,5-...</td>\n      <td>NPAQHLFPEOMKAL-UXXRCYHCSA-N</td>\n      <td>PFPpos_POS 8_EAD</td>\n      <td>C15H20O8</td>\n      <td>328.115818</td>\n      <td>[M+NH4]+</td>\n      <td>346.1496</td>\n      <td>0.288550</td>\n      <td>121.029094\\t5.233374\\n128.057977\\t1.392987\\n13...</td>\n      <td>121.029094\\t5.233374\\n128.057977\\t1.392987\\n13...</td>\n      <td>denoised</td>\n      <td>99.12622</td>\n      <td>0.348186</td>\n      <td>0.014698</td>\n    </tr>\n    <tr>\n      <th>1674</th>\n      <td>Helicin</td>\n      <td>BGOFCVIGEYGEOF-UJPOAAIJSA-N</td>\n      <td>PFPpos_P N 2_EAD</td>\n      <td>C13H16O7</td>\n      <td>284.089603</td>\n      <td>[M+NH4]+</td>\n      <td>302.1234</td>\n      <td>0.288100</td>\n      <td>115.052732\\t2.361324\\n117.061378\\t1.678248\\n12...</td>\n      <td>115.052732\\t2.361324\\n117.061378\\t1.678248\\n12...</td>\n      <td>denoised</td>\n      <td>98.32239</td>\n      <td>0.483199</td>\n      <td>0.054514</td>\n    </tr>\n    <tr>\n      <th>2150</th>\n      <td>pencolide</td>\n      <td>NFDKQFGUQFNACT-UTCJRWHESA-N</td>\n      <td>PFPpos_P N 11_EAD</td>\n      <td>C9H9NO4</td>\n      <td>195.053158</td>\n      <td>[M+H]+</td>\n      <td>196.0604</td>\n      <td>0.622467</td>\n      <td>115.053089\\t1.347364\\n122.066649\\t70.658653\\n1...</td>\n      <td>115.053089\\t1.347364\\n122.066649\\t70.658653\\n1...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.450408</td>\n      <td>0.041155</td>\n    </tr>\n    <tr>\n      <th>2373</th>\n      <td>2-(2-hydroxypropan-2-yl)-3-[(2R,3R,4S,5S,6R)-3...</td>\n      <td>HGHSJJSYVZPIAW-NWMIEQKASA-N</td>\n      <td>PFPpos_P N 15_EAD</td>\n      <td>C20H24O10</td>\n      <td>424.136947</td>\n      <td>[M+Na]+</td>\n      <td>447.1262</td>\n      <td>0.881483</td>\n      <td>115.05392\\t1.179318\\n128.061328\\t1.469747\\n152...</td>\n      <td>115.05392\\t1.179318\\n128.061328\\t1.469747\\n152...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.481728</td>\n      <td>0.053852</td>\n    </tr>\n    <tr>\n      <th>2492</th>\n      <td>Gedunin</td>\n      <td>YJXDGWUNRYLINJ-POZBICLPSA-N</td>\n      <td>PFPpos_P N 16_EAD</td>\n      <td>C28H34O7</td>\n      <td>482.230453</td>\n      <td>[M+Na]+</td>\n      <td>505.2197</td>\n      <td>2.412783</td>\n      <td>128.063715\\t1.713933\\n152.060701\\t1.572097\\n15...</td>\n      <td>128.063715\\t1.713933\\n152.060701\\t1.572097\\n15...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.492381</td>\n      <td>0.058776</td>\n    </tr>\n    <tr>\n      <th>2526</th>\n      <td>NIACIN</td>\n      <td>PVNIIMVLHYAWGP-UHFFFAOYSA-N</td>\n      <td>PFPpos_P N 17_EAD</td>\n      <td>C6H5NO2</td>\n      <td>123.032028</td>\n      <td>[M+H]+</td>\n      <td>124.0393</td>\n      <td>0.125100</td>\n      <td>52.026778\\t1.018964\\n115.053809\\t1.132182\\n116...</td>\n      <td>52.026778\\t1.018964\\n122.02389\\t100.0\\n123.634...</td>\n      <td>denoised</td>\n      <td>97.88810</td>\n      <td>0.185737</td>\n      <td>0.001190</td>\n    </tr>\n    <tr>\n      <th>2620</th>\n      <td>[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxym...</td>\n      <td>WWBNBPSEKLOHJU-QPQZIUDZSA-N</td>\n      <td>PFPpos_P N 19_EAD</td>\n      <td>C14H19NO10S2</td>\n      <td>425.045038</td>\n      <td>[M+NH4]+</td>\n      <td>443.0789</td>\n      <td>0.210733</td>\n      <td>115.052525\\t1.280598\\n127.045442\\t1.954896\\n12...</td>\n      <td>115.052525\\t1.280598\\n127.045442\\t1.954896\\n12...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.395769</td>\n      <td>0.024534</td>\n    </tr>\n    <tr>\n      <th>2630</th>\n      <td>Auraptene</td>\n      <td>RSDDHGSKLOSQFK-RVDMUPIBSA-N</td>\n      <td>PFPpos_P N 19_EAD</td>\n      <td>C19H22O3</td>\n      <td>298.156895</td>\n      <td>[M+NH4]+</td>\n      <td>316.1907</td>\n      <td>2.517267</td>\n      <td>115.053128\\t2.028729\\n119.04961\\t16.032405\\n12...</td>\n      <td>115.053128\\t2.028729\\n119.04961\\t16.032405\\n12...</td>\n      <td>denoised</td>\n      <td>99.22553</td>\n      <td>0.432982</td>\n      <td>0.035146</td>\n    </tr>\n    <tr>\n      <th>3236</th>\n      <td>3-[5-(3,4-dihydroxy-4-methyl-5-oxooxolan-2-yl)...</td>\n      <td>BNLUQONOBPMVBG-UHFFFAOYSA-N</td>\n      <td>PFPpos_P N 30_EAD</td>\n      <td>C15H18O8</td>\n      <td>326.100167</td>\n      <td>[M+Na]+</td>\n      <td>349.0894</td>\n      <td>0.678033</td>\n      <td>123.117339\\t1.732529\\n128.06155\\t1.245863\\n151...</td>\n      <td>123.117339\\t1.732529\\n128.06155\\t1.245863\\n151...</td>\n      <td>denoised</td>\n      <td>100.00000</td>\n      <td>0.494068</td>\n      <td>0.059587</td>\n    </tr>\n    <tr>\n      <th>3354</th>\n      <td>3-[(2S,3R,4S,5S,6R)-6-[[(2R,3R,4R)-3,4-dihydro...</td>\n      <td>OXNYNKGAOAFFBD-AHZIVMAWSA-N</td>\n      <td>PFPpos_P N 32_EAD</td>\n      <td>C17H24O12</td>\n      <td>420.126776</td>\n      <td>[M+H]+</td>\n      <td>421.1341</td>\n      <td>0.153767</td>\n      <td>109.030589\\t3.168308\\n115.040877\\t1.180592\\n12...</td>\n      <td>109.030589\\t3.168308\\n115.040877\\t1.180592\\n12...</td>\n      <td>denoised</td>\n      <td>99.02645</td>\n      <td>0.472801</td>\n      <td>0.049971</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_non_informative"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    },
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x17be3ce80>",
      "image/png": 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\n"
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from toolsets.ms2_spectra_plotter import molplot_from_inchikey\n",
    "molplot_from_inchikey(data_non_informative.iloc[2]['reference_InChIKey'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "outputs": [
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/opt/homebrew/Caskroom/miniforge/base/envs/alphapept_env/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_to_tail_plot(data_non_informative.iloc[2]['peaks_recalibrated'],data_non_informative.iloc[2]['peaks_recalibrated_denoised'], identity=True, savepath=os.path.join(source_dir, 'non-informative.tiff'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                 N-[(4-fluorophenyl)methyl]-4-hydroxy-2-oxo-1H-...\nreference_InChIKey                                   YTQNZGHPUXHBMQ-UHFFFAOYSA-N\nreference_Mix                                                        Pandemic 18\nreference_Formula                                                    C16H12FN3O3\nreference_Monoisotopic mass                                           313.086269\nreference_adduct                                                          [M+H]+\nreference_precursor_mz                                                  314.0935\nretention_time                                                          0.244267\npeaks_recalibrated             133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...\npeaks_recalibrated_denoised    133.040703\\t2.640294\\n175.052518\\t2.931664\\n17...\ncomments                                                                denoised\nexplained_intensity                                                     97.30179\nnormalized_entropy_one                                                  0.352178\nnormalized_entropy_forth                                                0.015383\nName: 307, dtype: object"
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_non_informative.iloc[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "outputs": [
    {
     "data": {
      "text/plain": "2.698210000000003"
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100-97.30179"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "outputs": [
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/opt/homebrew/Caskroom/miniforge/base/envs/alphapept_env/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_to_tail_plot(data_noisy.iloc[1]['peaks_recalibrated'],data_noisy.iloc[1]['peaks_recalibrated_denoised'], identity=True, savepath=os.path.join(source_dir, 'noisy.tiff'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                                                          Cortisol\nreference_InChIKey                                   JYGXADMDTFJGBT-VWUMJDOOSA-N\nreference_Mix                                                           MSMLS 18\nreference_Formula                                                       C21H30O5\nreference_Monoisotopic mass                                           362.209324\nreference_adduct                                                          [M+H]+\nreference_precursor_mz                                                  363.2166\nretention_time                                                            0.2499\npeaks_recalibrated             65.036754\\t6.451719\\n67.053157\\t10.96563\\n77.0...\npeaks_recalibrated_denoised    65.036754\\t6.451719\\n67.053157\\t10.96563\\n77.0...\ncomments                                                                denoised\nexplained_intensity                                                        100.0\nnormalized_entropy_one                                                  0.946601\nnormalized_entropy_forth                                                0.802913\nName: 694, dtype: object"
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_noisy.iloc[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                                   D-Pantothenic Acid Calcium Salt\nreference_InChIKey                                   GHOKWGTUZJEAQD-UHFFFAOYSA-N\nreference_Mix                                                           GC LIB 1\nreference_Formula                                                       C9H17NO5\nreference_Monoisotopic mass                                           219.110673\nreference_adduct                                                         [M+Na]+\nreference_precursor_mz                                                  242.0999\nretention_time                                                           0.60105\npeaks_recalibrated             50.015801\\t2.105584\\n51.02369\\t10.558437\\n52.0...\npeaks_recalibrated_denoised    50.015801\\t2.105584\\n51.02369\\t10.558437\\n52.0...\ncomments                                                                denoised\nexplained_intensity                                                     98.94552\nnormalized_entropy_one                                                  0.764517\nnormalized_entropy_forth                                                0.341624\nName: 8, dtype: object"
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_good_quality.iloc[8]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "outputs": [
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/opt/homebrew/Caskroom/miniforge/base/envs/alphapept_env/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_to_tail_plot(data_good_quality.iloc[8]['peaks_recalibrated'],data_good_quality.iloc[8]['peaks_recalibrated_denoised'], identity=True, savepath=os.path.join(source_dir, 'good_quality.tiff'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                                   D-Pantothenic Acid Calcium Salt\nreference_InChIKey                                   GHOKWGTUZJEAQD-UHFFFAOYSA-N\nreference_Mix                                                           GC LIB 1\nreference_Formula                                                       C9H17NO5\nreference_Monoisotopic mass                                           219.110673\nreference_adduct                                                         [M+Na]+\nreference_precursor_mz                                                  242.0999\nretention_time                                                           0.60105\npeaks_recalibrated             50.015801\\t2.105584\\n51.02369\\t10.558437\\n52.0...\npeaks_recalibrated_denoised    50.015801\\t2.105584\\n51.02369\\t10.558437\\n52.0...\ncomments                                                                denoised\nexplained_intensity                                                     98.94552\nnormalized_entropy_one                                                  0.764517\nnormalized_entropy_forth                                                0.341624\nName: 8, dtype: object"
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_good_quality.iloc[8]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "outputs": [
    {
     "data": {
      "text/plain": "1.054479999999998"
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100-98.94552"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x17cd71d60>",
      "image/png": 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\n"
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molplot_from_inchikey(data_good_quality.iloc[8]['reference_InChIKey'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "outputs": [],
   "source": [
    "data_bad_quality=num_search(data, 'explained_intensity', 20, '<', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "outputs": [
    {
     "data": {
      "text/plain": "                      reference_Name           reference_InChIKey  \\\n234   5-Iodo-6-amino-1,2-benzopyrone  WWRAFPGUBABZSD-UHFFFAOYSA-N   \n1646                         Juglone  KQPYUDDGWXQXHS-UHFFFAOYSA-N   \n\n         reference_Mix reference_Formula  reference_Monoisotopic mass  \\\n234        Pandemic 15          C9H6INO2                   286.944325   \n1646  PFPpos_P N 1_EAD           C10H6O3                   174.031694   \n\n     reference_adduct  reference_precursor_mz  retention_time  \\\n234            [M+H]+                287.9516        0.214583   \n1646           [M+H]+                175.0390        2.182167   \n\n                                     peaks_recalibrated  \\\n234   132.045825\\t5.628332\\n133.049764\\t2.688886\\n13...   \n1646  112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...   \n\n                            peaks_recalibrated_denoised  comments  \\\n234   132.045825\\t5.628332\\n133.049764\\t2.688886\\n13...  denoised   \n1646  112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...  denoised   \n\n      explained_intensity  \n234              11.64441  \n1646             17.00706  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n      <th>explained_intensity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>234</th>\n      <td>5-Iodo-6-amino-1,2-benzopyrone</td>\n      <td>WWRAFPGUBABZSD-UHFFFAOYSA-N</td>\n      <td>Pandemic 15</td>\n      <td>C9H6INO2</td>\n      <td>286.944325</td>\n      <td>[M+H]+</td>\n      <td>287.9516</td>\n      <td>0.214583</td>\n      <td>132.045825\\t5.628332\\n133.049764\\t2.688886\\n13...</td>\n      <td>132.045825\\t5.628332\\n133.049764\\t2.688886\\n13...</td>\n      <td>denoised</td>\n      <td>11.64441</td>\n    </tr>\n    <tr>\n      <th>1646</th>\n      <td>Juglone</td>\n      <td>KQPYUDDGWXQXHS-UHFFFAOYSA-N</td>\n      <td>PFPpos_P N 1_EAD</td>\n      <td>C10H6O3</td>\n      <td>174.031694</td>\n      <td>[M+H]+</td>\n      <td>175.0390</td>\n      <td>2.182167</td>\n      <td>112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...</td>\n      <td>112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...</td>\n      <td>denoised</td>\n      <td>17.00706</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bad_quality"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    }
   ],
   "source": [
    "from toolsets.ms2_spectra_plotter import head_to_tail_plot"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "outputs": [
    {
     "data": {
      "text/plain": "reference_Name                                                           Juglone\nreference_InChIKey                                   KQPYUDDGWXQXHS-UHFFFAOYSA-N\nreference_Mix                                                   PFPpos_P N 1_EAD\nreference_Formula                                                        C10H6O3\nreference_Monoisotopic mass                                           174.031694\nreference_adduct                                                          [M+H]+\nreference_precursor_mz                                                   175.039\nretention_time                                                          2.182167\npeaks_recalibrated             112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...\npeaks_recalibrated_denoised    112.999544\\t9.525539\\n115.053797\\t94.479122\\n1...\ncomments                                                                denoised\nexplained_intensity                                                     17.00706\nName: 1646, dtype: object"
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bad_quality.iloc[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "outputs": [
    {
     "data": {
      "text/plain": "0.4380508928497239"
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_entropy(data_bad_quality.iloc[1]['peaks_recalibrated_denoised'], order = 4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "outputs": [
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/opt/homebrew/Caskroom/miniforge/base/envs/alphapept_env/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_to_tail_plot(data_bad_quality.iloc[1]['peaks_recalibrated'],data_bad_quality.iloc[1]['peaks_recalibrated_denoised'], identity=True, savepath=os.path.join(source_dir, 'bad_quality.tiff'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x287952520>",
      "image/png": "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\n"
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molplot_from_inchikey(data_bad_quality.iloc[1]['reference_InChIKey'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "outputs": [
    {
     "data": {
      "text/plain": "-0.08251748251748252"
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3280-3575)/3575"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "1823"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_good_quality['reference_InChIKey'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "               reference_Name           reference_InChIKey     reference_Mix  \\\n0   Tauroursodeoxycholic acid  BHTRKEVKTKCXOH-LBSADWJPSA-N      Bile acids 1   \n1         Taurodehydrocholate  UBDJSBRKNHQFPD-PYGYYAGESA-N      Bile acids 1   \n2  Glycochenodeoxycholic acid  GHCZAUBVMUEKKP-GYPHWSFCSA-N      Bile acids 1   \n3            Deoxycholic Acid  KXGVEGMKQFWNSR-LLQZFEROSA-N  PFPpos_POS 2_EAD   \n4       Taurodeoxycholic acid  AWDRATDZQPNJFN-VAYUFCLWSA-N      Bile acids 3   \n\n  reference_Formula  reference_Monoisotopic mass reference_adduct  \\\n0        C26H45NO6S                   499.296759           [M+H]+   \n1        C26H39NO7S                   509.244724           [M+H]+   \n2         C26H43NO5                   449.314123           [M+H]+   \n3          C24H40O4                   392.292660           [M+H]+   \n4        C26H45NO6S                   499.296759           [M+H]+   \n\n   reference_precursor_mz  retention_time  \\\n0                500.3040        5.751050   \n1                510.2520        2.198233   \n2                450.3214        2.304017   \n3                393.2999        2.302233   \n4                500.3040        5.246883   \n\n                                  peaks_recalibrated  \\\n0  51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...   \n1  50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...   \n2  50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...   \n3  50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....   \n4  51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...   \n\n                         peaks_recalibrated_denoised  comments  \\\n0  51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...  denoised   \n1  50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...  denoised   \n2  50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...  denoised   \n3  50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....  denoised   \n4  51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...  denoised   \n\n                                 key  explained_intensity  \n0  BHTRKEVKTKCXOH-LBSADWJPSA-N[M+H]+            100.00000  \n1  UBDJSBRKNHQFPD-PYGYYAGESA-N[M+H]+            100.00000  \n2  GHCZAUBVMUEKKP-GYPHWSFCSA-N[M+H]+             99.59382  \n3  KXGVEGMKQFWNSR-LLQZFEROSA-N[M+H]+             99.87746  \n4  AWDRATDZQPNJFN-VAYUFCLWSA-N[M+H]+            100.00000  ",
      "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>reference_Name</th>\n      <th>reference_InChIKey</th>\n      <th>reference_Mix</th>\n      <th>reference_Formula</th>\n      <th>reference_Monoisotopic mass</th>\n      <th>reference_adduct</th>\n      <th>reference_precursor_mz</th>\n      <th>retention_time</th>\n      <th>peaks_recalibrated</th>\n      <th>peaks_recalibrated_denoised</th>\n      <th>comments</th>\n      <th>key</th>\n      <th>explained_intensity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Tauroursodeoxycholic acid</td>\n      <td>BHTRKEVKTKCXOH-LBSADWJPSA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.3040</td>\n      <td>5.751050</td>\n      <td>51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...</td>\n      <td>51.024371\\t3.514247\\n55.057286\\t1.316147\\n63.0...</td>\n      <td>denoised</td>\n      <td>BHTRKEVKTKCXOH-LBSADWJPSA-N[M+H]+</td>\n      <td>100.00000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Taurodehydrocholate</td>\n      <td>UBDJSBRKNHQFPD-PYGYYAGESA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H39NO7S</td>\n      <td>509.244724</td>\n      <td>[M+H]+</td>\n      <td>510.2520</td>\n      <td>2.198233</td>\n      <td>50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...</td>\n      <td>50.017549\\t3.52349\\n51.025134\\t11.744967\\n53.0...</td>\n      <td>denoised</td>\n      <td>UBDJSBRKNHQFPD-PYGYYAGESA-N[M+H]+</td>\n      <td>100.00000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Glycochenodeoxycholic acid</td>\n      <td>GHCZAUBVMUEKKP-GYPHWSFCSA-N</td>\n      <td>Bile acids 1</td>\n      <td>C26H43NO5</td>\n      <td>449.314123</td>\n      <td>[M+H]+</td>\n      <td>450.3214</td>\n      <td>2.304017</td>\n      <td>50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...</td>\n      <td>50.016725\\t1.639344\\n51.025122\\t5.071469\\n53.0...</td>\n      <td>denoised</td>\n      <td>GHCZAUBVMUEKKP-GYPHWSFCSA-N[M+H]+</td>\n      <td>99.59382</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Deoxycholic Acid</td>\n      <td>KXGVEGMKQFWNSR-LLQZFEROSA-N</td>\n      <td>PFPpos_POS 2_EAD</td>\n      <td>C24H40O4</td>\n      <td>392.292660</td>\n      <td>[M+H]+</td>\n      <td>393.2999</td>\n      <td>2.302233</td>\n      <td>50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....</td>\n      <td>50.015884\\t7.533414\\n51.023678\\t20.291616\\n52....</td>\n      <td>denoised</td>\n      <td>KXGVEGMKQFWNSR-LLQZFEROSA-N[M+H]+</td>\n      <td>99.87746</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Taurodeoxycholic acid</td>\n      <td>AWDRATDZQPNJFN-VAYUFCLWSA-N</td>\n      <td>Bile acids 3</td>\n      <td>C26H45NO6S</td>\n      <td>499.296759</td>\n      <td>[M+H]+</td>\n      <td>500.3040</td>\n      <td>5.246883</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>51.024091\\t3.276298\\n55.055206\\t1.706543\\n63.0...</td>\n      <td>denoised</td>\n      <td>AWDRATDZQPNJFN-VAYUFCLWSA-N[M+H]+</td>\n      <td>100.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_good_quality.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "adduct_results = data_good_quality['reference_adduct'].value_counts().rename_axis('adduct').reset_index(name='counts')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "     adduct  counts\n0    [M+H]+    1462\n1   [M+Na]+    1064\n2  [M+NH4]+     754",
      "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>adduct</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[M+H]+</td>\n      <td>1462</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[M+Na]+</td>\n      <td>1064</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[M+NH4]+</td>\n      <td>754</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adduct_results"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# adduct distribution"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (7, 5)\n",
    "                      )\n",
    "colors = ['#FF9999','#97C997','#9999FF']\n",
    "sns.barplot(x = adduct_results['adduct'], y = adduct_results['counts'], palette = colors)\n",
    "ax.set_xlabel(\"Adducts\", fontsize = 18)\n",
    "ax.set_ylabel(\"Counts\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "plt.savefig(os.path.join(source_dir,'adducts_distribution.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3280/3280 [16:02<00:00,  3.41it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.API_gets import GNPS_classyfire\n",
    "chemical_classes = []\n",
    "for index, row in tqdm(data_good_quality.iterrows(), total = len(data_good_quality)):\n",
    "    # break\n",
    "    chemical_classes.append(GNPS_classyfire(inputt = row['reference_InChIKey'],class_type='class'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# adduct distribution: Venn diagram"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "# from matplotlib import matplotlib_venn\n",
    "import matplotlib_venn as venn\n",
    "\n",
    "# df_dataset = pd.read_csv('...path...',delimiter=',',decimal=',')\n",
    "M_H = data_good_quality[(data_good_quality['reference_adduct'] == '[M+H]+')]\n",
    "M_Na = data_good_quality[(data_good_quality['reference_adduct'] == '[M+Na]+')]\n",
    "M_NH4 = data_good_quality[(data_good_quality['reference_adduct'] == '[M+NH4]+')]\n",
    "\n",
    "#\n",
    "# plt.figure(figsize=(4,4))\n",
    "# set1 = set(campaign_a['CLI_ID'])\n",
    "# set2 = set(campaign_b['CLI_ID'])\n",
    "#\n",
    "# venn2([set1, set2], ('Set1', 'Set2'))\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_venn import venn3_unweighted, venn3_circles, venn3\n",
    "set1 = set(string_search(data_good_quality,'reference_adduct','[M+H]+')['reference_InChIKey'])\n",
    "set2 = set(string_search(data_good_quality,'reference_adduct','[M+Na]+')['reference_InChIKey'])\n",
    "set3 = set(string_search(data_good_quality,'reference_adduct','[M+NH4]+')['reference_InChIKey'])\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (7, 5)\n",
    "                      )\n",
    "venn3([set1, set2, set3], ('[M+H]+', '[M+Na]+', '[M+NH4]+'))\n",
    "\n",
    "# plt.show()\n",
    "# plt.savefig(os.path.join(source_dir,'venn_diagram.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x900 with 3 Axes>",
      "image/png": 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09HT07NnT26dC5HccZZH8peW4+fh2F1dg+a5ih4817zg4pE9X7w3Sz7hqz69CWzCalaTzm8+ZiIioI/CrIOvNN99Ea2srFi1ahEWLFpluHzhwIN5++20888wzUKvVuP/++1FbW4uEhASsXLkSvXr1AgDk5OSgtbUVkydPRl1dHQYNGoSlS5f66GyI/I+QRfIHjjbxHdKnq190RAwEUtrz+8vnTkRE1BH4PMgy7/z35ptvOn1saGgonnvuOTz33HN27w8JCcHMmTMxc+ZMRcdIRMpytYbIHzoieoKjwFIuBqNERET+yedBFhF1LMIaIusSN/OGFllJOsRpNSivabRbCqdC23oyT3dEVJInmlO012CUiIgo0Pld4wsi8g5ftPx2tYYIaFtDBMDhvlre7oioBE81p/CX9vxERERkSXYmq6qqCjEx/IebKBD5quW3lDVEQsdB63HqAqw1udjmFJ3DQlBR1ySpjNCf2vMTERHRVZKCrNbWVvzjH//AmjVroNfrsXnzZvz5z3/GihUrcM0113hqjESkIDHlep4KYKSuIfL3johiiA0sJ7+z13SblIC3vQSjRERE7YmkIOsf//gH9uzZg9deew0zZsxA165dodPpsGjRIrz22mueGiMRKcTXLb/Frg06f6kJu4srUHFZWmbHH8lpOiE14G0PwSgREVF7IinI2rx5Mz766CN0794dKpUKERERWLx4MbKysjw1PiJSkK9bfgtriBw1tBAs2nrU4ueYyFD8LvVaZCXpvBI8KNkFUE7TCTkBrz+15yciIuroJAVZ9fX1pnVYRmPbFEmj0UCtZv8MokDg65bfztYQOVNV14x3d5/Au7tPeHztmNLr1cQGlta4xxUREVHgkhQdpaamYvny5QAAlartyuoHH3yAfv36KT8yIlKcP7T8FtYQ6bTyXqPMzY58zrjTBdBRt0YhsARsOyWKwT2uiIiIAo+kTNbcuXPx4IMPYsOGDairq8PIkSNRV1eH9957z1PjIyIFucqqeGv/KWEN0erdpcjdctT1E+xQeu2YmPbyCzb9ZHpN85LCExX1+GjfKZRfuhoQxUSG4oXsZIxMiXPYnEIM7nFFREQUeCQFWddddx22bNmCb775BmfOnIFOp8N//dd/oVOnTp4aHxEpyJ9afgepVYjtHCbruZ4opXO1Xg0Ayi81YfYnPyLzxm7I3XLU6eOr6prx+IcH8adf4zFnZJIpsNxTUolHPvgB9c16l2PiHldERESBSVK5YHNzM958800kJydj6tSpqKysxNtvvw2DweCp8RGRwhyV6+m0Grfat8vZ3NjdLI2UUjpX4xN7rE8O/orHPzwkOiP11rel2PrjWQBtgSVUEBVgAdzjioiIKFBJymQtXrwY+fn5mDBhAgDg5ptvxksvvYTm5mY888wzHhkgESlP6ZbfcptFyG0KIYiNFJcJEzM+T5blPb+xAHclxyFIrUJeSaWo59ydrOMeV0RERAFKUibrq6++wjvvvINrr70WAHDLLbfgzTffxKZNmzwyOCLyHKHld3ZqDwzp09WtAEtuswh3m0LkfOi6AYbY8QkBnydU1bVgX2nVlZ/EhZN9ukV6ZCxERETkeZKCrKamJkRERFjc1qlTJ7S2tio6KCIKDGKaRSzcXOi0dNCdboPVDS14zEkgJ2V85gGfJwjliEOujxX1eLGPIyIiIv8jKci65ZZbsHjxYjQ3NwNoC7r++te/YsCAAR4ZHBH5NymbGzszIjkO38/OxEePDMZrE1Ox9o+DMHfkTaLH4SiQkzq+Eclx+PN/J4h+XSmEcsTBfboiKiLE6WOjIkIwmHtjERERBSzJLdynTp2KAQMGIDo6GhcvXkR8fDzefPNNT42PiPyYkpsbC+WLgoq6JtHjcNRpUOr4thWUYd3+06JfVyy1Crh45XyC1Cq8NLYfHltz0OHjXxrbjw0viIiIApjkFu5bt27FgQMHUFFRAZ1Oh5SUFAQHSzoMEbUTntzcWOpz7AVUUsYnrN2S04DDFYMRyPnwEFaoVRiR3LZv1psPDMCCTT+h/NLVYFLXJQwLxtzMhhdEREQBTlR0VF5eDp1Oh7Nn29oQ/+Y3v8FvfvMbAMD58+cBwNQMg4g6Dk9ubiwcW2yrdHsBldjxDewVjTte3uVWgKXVBKGm0XlrdvMNlJXu8EhERET+Q1SQNXLkSBw8eBCZmZlQqSwnAEajESqVCkePHvXIAInIf3lyc2PzYzsLfpwFcmLHd+DkRdHBHNDW/n3eqCRER4aaAiSD0YjJb+91+Bx7Gyhbl0gSERFR+yAqyNqyZQsAYNOmTYiMZFthIrpK6A5ovQ+VTsQ+WWKP/exnR1Bd32Jzv5hATsz4NuafETWe/zekF+5OjrObcRJ7DCkbKBMREVFgEhVkxcW1TZIee+wxbNq0CZ06dVLkxauqqjBhwgS88MILGDRoEADg8OHDeOGFF1BcXIzo6GhMmzYN9913n+k5GzZswBtvvIELFy7g+uuvx7x585CWlgYA0Ov1+Nvf/oaNGzeioaEBgwcPxsKFC3HNNdcoMl4iss+TpW/CsZfvLMJ7u0+guuFqsCU2kHM1PrFrt+5OjnOYefLk+jQ59AYjSxGJiIh8RHLHioaGBkWCrAMHDuDZZ5/FqVOnTLfV1NTg0UcfxZNPPokJEyZg//79yMnJwQ033ICUlBTs3bsXubm5WLVqFVJSUrB27VpMmzYNu3btQnh4OFasWIHdu3fj008/RefOnTFv3jw8//zzWLlypdvjJSLnlC59sw4SpmcmYnpmouzAwdn4lFhb5sn1aVJtKyizydzFKZBZJCIiInEkBVmDBg3Cfffdh2HDhtlkh6ZPny76OBs2bMCyZcswa9YszJgxw3T7V199haioKEyePBkAMGTIEIwePRpr165FSkoK1q9fj1GjRmHgwIEAgIceeggff/wxtm7dinHjxmH9+vWYOXOmKfM2d+5cDB06FKdPn8Z1110n5VSJ2q3mVgM+yDuBk1X16BUTgSlDeiM0WNKWeR7n7SBBibVlnlyfJoWjLonlNY2YtuYgVjwwgIEWERGRh0maWf3666+47rrrUFpair1795r+27dvn6QXHTp0KLZv346RI0da3F5UVIS+ffta3JaQkIBjx44BAIqLix3eX1tbi/Lycov7Y2NjodVq8fPPP0saH1Eg0huMyCupxMb8M8grqbS7Oe/irYW4cd6/kLvlKN7PO4ncLUdx47x/YfHWQh+M2D4hSLBuRCEECdsKyjzyusLaLZ3WspxPp9WIDkyUOIY79AYjFm4utJtJE25ztHEzERERKUdSJuuDDz5Q5EW7detm9/a6ujqEh4db3KbRaFBfX+/y/rq6OgBARESEzf3CfUTm2tOaFTGZn8VbC/HWt6U2zzUYYbp9zsgkya+t5PvoKkhQwbINutKUWFvmy9bs+0qrnHZJtNfhkIiIiJQnOshavnw5fvrpJwwdOtRUzqe08PBw1NbWWtzW2Nho6mgYHh6OxsZGm/ujo6NNwVdDQ4PD5xMJ2tOaFWflYY+tOYgZdybiN9ERWPmdbYBlbtV3pXj6tze6LB00D6pOVNTjo32nUH5JmffRH4IEJdaW+ao1u9jOhexwSERE5Fmigqy//vWv+Pzzz3HLLbdg2bJlqKurw6OPPqr4YPr27Yvdu3db3FZcXIzExEQAQGJiIoqKimzuHzZsGLRaLbp3725RUnjhwgVUV1fblBhSx9ae1qyIKQ97dUeRnXttGYzAB3kn8Mfbr3f4GHvBqTV33kd/ChK8mel09lpSxuFvHQ6JiIg6KlFB1hdffIH/+7//Q2JiIvbu3YsXXnjBI0FWVlYWXn75ZaxevRqTJ0/GgQMHsHnzZrzxxhsAgPHjxyMnJwd33303Bg4ciLVr16KyshJZWVkAgLFjx2LFihXo168foqOj8eKLLyI9PR09e/ZUfKwUmHxdjqY0V5kfqU5W1Tu8z1Fwak3u+6g3GFFR2yTqsZ4OEuRmOuUEZs5eC4CkcfhTh0MiIqKOTFSQVVtba8omDRw4EOfOnfPIYKKjo/Huu+9i0aJFWLZsGWJiYvD8889j8ODBANq6Dc6fPx8LFizAuXPnkJCQgFWrViEqKgoAkJOTg9bWVkyePBl1dXUYNGgQli5d6pGxUmDyh3I0JSmd0ekVE2H3dmfBqT1S30cxGTLAO0GC3EynnMDM0WuVXSn1tMfZOPylwyEREVFHJyrIUquvrtEIDpa8tZZT1p3/+vXrh3Xr1jl8fHZ2NrKzs+3eFxISgpkzZ2LmzJmKjpHaD38qR1OCkhkdtQqYMqS33fvkZszEvI9iM2TeCBLkZjrlBGZSA1cx4wCudji0DvjEbtxMRERE7hMVMRmNbPdL/sHddTLtbc2Kq/IwKR65Pd5h0wu5Qaer91FKoOGNIEFOplNuYOZOqaerTKEvOxwSERGRyCCrtbUVn3/+uennlpYWi58B4He/+52CwyKypURHwPa2ZsVZeZhYalVbgOWsfbvUoFPs+yg20Jg36iY8lBHv8SBBTqZTbglqeU2Dw+eI5Wy8vupwSERERCKDrNjYWCxbtsz0c3R0tMXPKpWKQRZ5lFIdAdvjmhVH5WHOdNEEIzv1WvTuGokpQ3q7bNsuJWMm5X0UG9TEdg7zymciJ9O5o7Bc1HOsz7Wqrln8wESMg4iIiPyHqCBr586dnh4HkUNKdwT01poVb7YAty4PO1FRj6U7jgOwH0j+dXyKpPOUkjGT8j76W/mm1Eyn3mDEhvwzoo5tfQ4xncLcGmtcAGVciYiIOhplu1gQeYAnOgJ6es2KLzY7ti4Pu0HXya1A0jpIzErS2Q9Ou4RhUnpP9I6NlPw+ismQRYWHwGA0Qm8wejybJTXTua+0ClV1LS6P2zUy1CYg0nVxL3CcN+omSe+HN4N+IiKijo5BlodxYuM+T3UE9NSaFX/Z7NidQNJZkPj97EzFvtNiMmTVDS2Y/PZejwepAimZTrHfuezUa23eIyHAlNv8IjpSfCbMF0E/ERFRR8Ygy4M4sVGGv5WUOeNvmx3LCSS9HSSKXVPmzSBVbIAq9juXlaSzuc08wASkNy0RAjxXF3L8JegnIiLqSJyvdifZhImN9aRRmNhsKyjz0cgCj3DF31FIooL/rE+RUtroj1wFiUBbkKg3KLutw4jkOHw/OxNr/zgIUeEhdh/jyde3RwhQs1N7YEifrnaDYlffTcD5d1MIMHVa6RcIrumswbaCMgxdshOTVu3BU+vyMWnVHgxdstP090XK56k3GJFXUomN+WeQV1LplfeYiIiovZKcyWpubsa///1vnDlzBhMmTMDJkydx4403emJsAcvfshmBLpA6Agb6ZseeWP8mVpBaBbVaheoGx2ucHL2+r8pypXw3HY3ROmsW2ykMT/8zH+cuNTltvnGxrhk5HzrPUGnDQ0V9nst3FmHd/tPMuhMRESlEUpB16tQp/OEPf0BLSwsuXbqEO+64A+PGjcPy5csxfPhwT40x4PhyotpeeasjoLsCqbTRHl8HiXJef1tBGRZsKkT5JfNmHBosGOOd74WY76ar0uEgtQrp8TGmQGtSek+8uqPIYeA2b9RNyN3i+kLOMyPEXQB7dUeRzW0sJyQiIpJPUpC1aNEijB07FtOmTUN6ejri4+PxwgsvYNmyZQyyzCgxUWXDDFue7giohEDf7NjXQaLU199WUIbHrqxpMld+qRGPrTmIN90MEMT+Hjr7bopZEwXAJgiLimgrm6yuv5rZEwI3sRmqitom2efOrDsREZF8koKs/Px8/OMf/4BKpYJK1fYPbnZ2NhYtWuSRwQUqdyeqbJjhmKc6AiolkEob7fF1kCjl9fUGI5797IjT48357IisAEFvMGL5zmK8t7vUonzR2e+hve+mmNLhOZ8dwcV62xLJmiu3zbizL3rHRlgEbhtF7s21fGcRoiJCUFPfIrmxhjBGpbLuvHBEREQdiaTGF507d0ZFRYXFbRcuXIBWq1V0UIHOnUYNbJgR+Bw1M9BpNX5feiUEiQBsvr9igkShecKGQ2fwzne/YMPBXyU1UZDy+nt+qbTI8thzsb4Fe36pFPXagm0FZRj4wna8uuO4zfowqb+HYkqH7QVYwn0AsG7/KdyTcq1F8w2xF3JqGltR7SDAkhLeuFse6qpBBxERUXsjKZM1evRoTJ8+HU8//TQMBgN+/PFHvPzyyxg1apSnxheQ5GYz2DDDsUC7Cu4vpY3NrQZ8kHcCJ6vq0SsmAlOG9EZosPNrK3LXv9nLwAqkZGLFvn5eibjgKa+kEhkJsTa32/tObS8st1t+KJD6e+hucOIokyRmE2dz9vYgi4oIwUO3xePVHcddPt+d8lC2kCcioo5IUpD1+OOPo7GxEdOnT0dDQwOmTJmC8ePHY/r06Z4aX8CSM1Flwwz7fFk+KUzEyy81oupyE2IiQ6HThjsMmPwpGFy8tRCrviuFeRJp0dajeOT2eMwZmeT0uVKDREcTaUGZxAm1uNcXWwBn+zh73yldFw0aW/Wijib291CptWvWwZr1Hluu2HunLta3IPGaSOi6aCyahphztzyUF46IiKijkhRkhYSEYPbs2Zg9ezaqqqoQHR1tWptFtqROVH3d2c0f+fIquNTMjD+tpVu8tRBvfVtqc7vBCNPtrgIt8zVGzoJHZxNpc0Y4nlBLDWYBYMj1sVi+q8TFq7Y9zpzD75SDQMMRMb+HUjNOjtgL1oQLOc9+esRp23tHVADmfl4Ao9H+yJRYQ8gLR0RE1FFJCrLq6uqwfv16PPTQQ6iqqsKf/vQnxMTE4C9/+Qu6d+/uqTEGNCmNGnzd2c3f+PIquNTMjD+VRDW3GrDqO9sAy9yq70rx9G9vdFk6CLgOHl1NpM3Zm1DLLTMc3KcroiJCnK7LiooIwWCr/bTEBIRiiPk9FFM6rHXSmMJVJmlEchw6h4Vg8jt7JY7e+XowANCGB+PhjHg0tRqQV1IpKyvLC0dERNRRSWp8kZubiw0bNgAAFixYgGuvvRZarRYLFizwxNg6HHcaZrRHUq6CK0lqZqa51eA0GMSVx4lt/uCuD/JOwNVLGYxtjxMaVWzMP2O3QYWYRixSJ8jWe1zZO76gzEmjiSC1Ci+N7ef0tV4a288iMJASEDoj9vdQbzBCGx6KP2T0RnRkiMV9QiOUF3+X7LQxhatM0uA+XZ3+3ZDrUmMrXt1R5FajCl44IiKijkpSJmvfvn347LPPUFNTg4MHD2LXrl2IiorC0KFDPTW+DiXQ238rzVdXwaVmZj7IOyEqGHx1+3FkJMTKXqdlXbI3sFc0Dpy8aFPCd7KqXtTxvi2qwNvflzrMUDW3GvDchiMuM4l/u6+/pPMQJtRSgtkFm35CZ00IKi43ubXWTYnvigrifg/tZehiIkPxu9RrkZWkMzXayN1y1O7zxW607ezvhjusA3U5WVlfbwlARETkK5LLBaOiorBt2zZcd9116N69O5qbm7kuS0FyO7u1R+5eBZfbhELqRFxsULN8VzGW7yqWtU7L3oRdrbKcCAvH7RUTIeqY/z5+weY2YSL96LB4rD9wBlV1jsvJhOARRohed2SeAZISzJZfasLkt6+WxMVpNZg3Kgm5WwodPsdeOam7GZPI0CD8/ff9XX52jspHL9Y1473dJ0wBlrOS1HmjxH9HHP3dsP6OuENOiS4vHBERUUclKchKTEzEG2+8gW+//RbDhw/H5cuXsXTpUtx8882KDWjTpk2YP3++xW0tLW0TvYKCAsyfPx+ffvopQkKult48++yzmDBhAgBgw4YNeOONN3DhwgVcf/31mDdvHtLS0hQbnze4apjhTx3sPMmdq+Cu1hE5ew+lTsTFBjUCqRkBRxN2R5mGf0xMkz25Fp5ir2mGIxV1TaI63VlngNzJKpXXNOLxD52/nr2mCu42olj5/26x2w7enJi1hAs2/QRA5XAMKgC5WwpxV7L49Yb2/m5crGtCzoeHTK/tLjmNKnjhiIiIOiJJQdaCBQuwcOFCdOrUCdOnT0dhYSH27t2LZcuWKTagMWPGYMyYMaafz507h3HjxmHWrFkAgCNHjiA3Nxf33nuvzXP37t2L3NxcrFq1CikpKVi7di2mTZuGXbt2ITw8XLExeoOjhhn+1MHO0+ReBXfVhOLRYfHYdLjM4XsoZSIep9VgypDeePv7UtETd/N1Wq4yAlIaNQgT+EX/Ooo/Do132fxCKRW1TXgoI97uRFpg7zvqTlZJSsBgHsxJbX0uEAL6wde7DizErCUsv9Tk9Bhyu+7Z+7uxQq1y+LnIJTVAVmrfuI5ygYmIiAKfyuiof68fMBqNePDBB9GzZ0+88MILaG5uxoABA7BhwwYkJibaPH7mzJkIDw9Hbm6u6ba7774bU6dOxbhx41y+nl6vR35+PlJTUxEUFKTouSjBUfAgTDHa66aeUgJLvcGIoUt2Sp5QWr+HrroLCs+xfjwgLQD46JHBTifReSWVmLRqj4QjXj3uNz+fs9knS60Cht/QDV8fsy0VdIfweQgTaTGt2IXPyt325q589MhgpMfH2GR4/vJFoctgB5D2+6U3GPHq9uNYvqtYgZEDr01MRXZqD7ePIwQnu4sviGp774qr760ndKQLTERE5B/ciQ0kZbKWL1/u8D5PbEi8ceNGFBcX44033gAAHDt2DK2trVi2bBkOHDiAzp07Y9y4cZg6dSrUajWKi4ttgqmEhAQcO3ZM8bF5W0fe1FPKVXC53eOs30NHJU4C68mdq8c7sr2w3OlkVW5J3fnaRswZmYSnf3sjPsg7gZNV9egVE4EpQ3rjwMmLigdZcpoiyM0qiSVkny7WNdsE3nFaDf73nptRdP4yXt1x3OlxdFoNJt7a02Urc2et6OVSquuekOFKj4/BpwfPyA5sfdWowp+2SCAiIhJDUpC1d6/lXizV1dUoKSnBiBEjFB0UABgMBqxYsQKPPfYYOnXqBACora1Feno6pkyZgldeeQVHjx5FTk4O1Go1pk6dirq6OpuyQI1Gg/p6cY0JPMm8zCU2MgxQQVKntI6+qafY/cbcWedj/R6aB3diMjPmjxebMdiYfxZzRzle+C93ki08LzRYjT/efr3FfUptkGtObqAvNzi15qicdEz/OOR8aH9ynvNh2+T8TTuvHxMZgntTe6BLeAg+2nfKIhBztBG1q8yn1PPxRDAjphPhI7fH4+0rpab+0KiiI19gIiKiwCUpyPrggw9sbtu4caNN8KWEvXv34vz58xg/frzptoyMDGRkZJh+TklJwYMPPoitW7di6tSpCA8PR2Oj5UStsbER0dHRio9PCldXuMWUvHBTT3GUuPJvvYZHzpqY9PgYfLjvlNPufABQWdfsNDAWAiKxAYiYybmnWn7LDfRHJMfBYDDi8SsNGsQSznXeqJuQu+WoxXsUHRmC7P7XYv2BMy4n59/PzrSbKXXU/c86e6LkBsfCeQGeC2ZGJMfh0WFta/bMi8VVKuAPGfHIvLE79AYjPs8/i6q6ZtP9vmpU0dEvMBERUWCStBmxPdnZ2fj666+VGIuFL7/8EllZWYiIuNq5bceOHVi3bp3F45qbm6HRtE2sExMTUVRUZHF/cXGx3fVb3uJqs1XAcmNXRwJpU09XG9x6kqsNncVQ4j0MUqtwr8i1NM4C4yC1CmP6i5vUSpmcCxkknVb574vUQF9vMDrcK8oR83MdmXItvp+diY8eGYw/ZPRGTGQoqupa8N5/TloECdbMJ+dCcJyd2sM0URe7wbRSGxwLYiJDPVr+tq2gDCu/LbXpPmk0Au98X4pJq/bg3d0nUFXXjJjIEPwxozc+emQwvp+d6ZOSPF5gIiKiQOR2kLVv3z6LQEgpBw4cwK233mpxm9FoxOLFi5GXlwej0YhDhw7h/fffN7VvHz9+PDZv3ow9e/agpaUFq1evRmVlJbKyshQfnxhSNlsF2iZtu4sq7AYnQvDgTJwP1kpY21ZQhqFLdmLSqj14al0+Jq3ag6FLdjoNIJ2RGrAJWRoAkgMtFZR9D+9M0ol6nL2gTjjv3M0/iW6nrtNqJE3ORyTHmYKT6cMTRD1HjPOXmiQF1nKCFOtzDVKrUNPQtgeVs8DK7njtTM6lZE/ETu5/m9Rd1OOeH3WTx4IZqVm3i3UteHf3CdQ0NPusFC+QLjAREREJJJULZmZmWmw83NLSgoqKCkybNk3xgf3666+45pprLG7LysrCnDlzsGDBApw7dw6xsbF44oknkJ2dDQAYMmQI5s+fb7o/ISEBq1atQlRUlOLjE0PK5FGYtE1+52rpZUxkKH6Xei2yknRIj4/BmP5xTifcY/rH+XRNgtKL0+V2E3O0zidOq8GY/nFY+a131pvI3edLSgOFmMgQzLvnZui6OF/b56j1tWVDhF+drtPSdQkDAJy71OR0kr5o61G8/k0xXhrbT9TnLTZImT68DxK7d7a7jtGdkj17k3Mp2ROxk/tB8TH4qvCcy8fptJ7bbkJqQOsPa57c2S+PiIjIVyQFWU888YTFz2q1Gn369EFycrKigwKAQ4fsr8+YOHEiJk6c6PB52dnZpqDL19wtX6mqa8a7u0/g3d0noOsShsZWg9PHbzpchmdG3OSTiZDSi9PdDdicdSRM6xntlY1R5ezzJbWBQlVdC3RdNE7XoogJVsWMdcGYtk3Hxazlqq5vwWNrDuJNEYG12CAlOiIU96Rcq1hXSWeTcynZE1fr5oTXcbWfmjeCBTl/k3y95knufnlERES+JKlc8N5778W9996LYcOGoU+fPkhPT/dIgNVeKFm+Un6pCdX1zpsoCBMhX5BSXuWKq4ANuLoexhnrdTbCJMy8TO61iakeXW/iaO2TvdI+udmY8poGh/c5WhNobx2gmLEKj9GGi7s+I+ZzSo+PQUSo670ncrccRcZLX9stPZUaPAiT9Ym3XocvfjzrsDzX0bTdvLRUzLq5+aOTEBqsNpWyOnucnGBBbFmtO3+TnH3PPE3K7xEREZE/kJTJunz5MmbPno2dO3fCaDRCpVJhyJAhWLp0Kbp06eKpMQYsT7TKdsVXi7+VXJzujW5iUrsGukPsPl9yGyg4WoMkJ7soZqxZSTo8t6FA1NjEfE56gxENLXpRxyu/1GQ3QyY1eNBGhAAAXt1xtVGOeXZPSvZEaCThyKPD4i32UxM6+1lvEv3I7fGyggUpZbXp8TFXGoNIW7cGtAW54aFBPgtopOyXR0RE5GuSMll///vfUVdXhy+++AKHDx/Gxo0bYTAY8PLLL3tqfAHNnSYMcvlq8bfY1y06V+uygUV77CbmKKtmTu75xHQKs3u73Oyiq7HuK62SNEl3dV4f5J2waCUuxrOfHZHcGAYAHs7ojRl39kVNfYtNZtg6uycmeyIm+/jxD79id1EF9Aaj085+K78ttcjSOcpOmd/+2o7jeExkphJo+2xfyJZXfXCxrtlpF1RvdBUV83tERETkDyRlsnbt2oVPP/0UXbu2XZXu27cvXn75ZYwZMwa5ubkeGWCgU2qzVVes13M4anTgKWKzdst3lWD5rhKnDSw6ajcxueej62L/eZ4KVqU+3tV5naySvll4dX0L9pRUIiMxFkDb5HveqJtc7rX1ryNlAFSis3uusidiso/V9S2Y/M5e07pKMa+9vbDcYeOWTYfLXL6ms3WQI1Pi8Kdf40V3rbR3zMwbu+PAyYum9+RiXTNyt0hvUkNERNReSQqyGhoa0LlzZ4vbunTpAoPBeUOGjs56ohYTHopj52px+mI9DAYj1uw95dbx7ZUvyenK5w6pG9w6a2Aht5uYtwNLpckpL3XWct5TwaqUx4tpid8rRt4WEHm/VJiCLACIjrSf0TNXfqnJ6f32SlGdlZaWXxIfcIp97X98fRxLvy62ub+splFSYOSsrHbOyCT0/00UZn36I+qaxJVqmh9z8OIdLjfalttVlIiIqD2QVC7Yv39/vPbaazBeqe0xGo147bXX0K9fP48Mrj0RJmphwWo889mPWLT1KN7POykpwFIBiIoIsclcmJcvSWl0oDQpG9w6a2DhrMzSWVc+6/25bl20A3/Z/JOk0iWlS56kHE9Keanqyn/OGiVIad4ghdjSPAC4O7nt4oKz854ypDfkxcKWT1KyfFTssSpqnQdOcrxmJ8Byh6NzGZlyLVZOuUXWMV0FWIC0JjVERETtjaRM1syZMzFlyhRs2rQJPXr0wJkzZ6BSqfDee+95anztitT23Pa8NLafw/Ilpduoy2GetdtdXIHluxxPGJ1daXdUZmmv1bqj99W8Bb6YTJ7SGUA5x3N03moVLNbxiGk576nW1+bHdfVdFvP+hwar8cjt0svXrL8zSpaPij3WxXrpDSRcUToccXYug6/v6tHmPL5u/05EROQrKqNR2pLz6upq7NixA1VVVejRowfuuOMOdOrUyVPj8yq9Xo/8/HykpqYiKMh1S2nRxzUYsaekEjkfHkR1g+srwPbouoRhwZibnU6q80oqMWnVHpfH+uiRwR6Z8FiX65XXNGDGPw+7fN5rE1ORndpD1DHtbUI7dMlOUevdVIDD0iVHgZrwSnI2UnbneNbnPbBXtMUaGCmlkK6CPblllvaOKwT71sSc9+KthTZd9xyJjgjBD89n2f0uOCsz7d4lDIAK5y45L0X9fnamqI2ddx07j8/zz7oesA+IORdAmYs/rjj7HSciIvJX7sQGkjJZABAVFYXx48dLfVqHZW8iKtWMO/tiemaCy4mvr7ry6Q1GLN9ZhPd2n7AIImMiQ0U939mVdlet1qW2PbeXyVM6A6jE8eydt9zAeERyHDJv7I4P8k7gZFU9esVEYMqQ3ggNVruVvRuRHAeDwWixrsdRWZiY854zMglP//ZGfJB3At8WXcC/j1c4fO3FY/vZHMPdDZXFZPeU+H32BimZSqGtvNRMohQnKqQ3NyEiIgpkooKszMxMqFTO/6H++uuvFRlQe+LuFWJh75yn7kwU9XhfdOXbVlCGZz87Ynej5Isu2nw7amDhinkmoehcrejnCaVLr24/joyEWFPGRul9ubyxz5cU9gKDt78vxZj+cXYn1mIbFmwrKHPZzc+cmPMODVbjj7dfjz/efj22FZRhwaZCi+YScsstrcsrhb2qzPP4Khd7VXky4xMVEeJys3EpxJSTCvQGIzYddr1WU+7+WgCwdMdx3KDrxAYYHhLoTX+IiNojUUHWE088AaCt0cVf/vIXzJ8/36ODag/E7J/jiuHK3jlpPaNFTU7kduWTy9Wk09m5y10TpEQmYfmuYizfVWyasDe1iuuO6SwDKCfw88Y+X44+I2ed6sRknfQGIxZs+knWmMSet9zNZ109T9iryvo9cfb7psTvsz3ThycgIyEWBoMRk9/Zq8gxxWa+gbbzWr27VNTv02sTUvHMpz/KXr/l6fWgHZUvuskSEZFrooKse++91/T/L730ksXPZJ/UMjZHjADmfHZE1OTEU40O7JE66YyJDLHoSCblSrtA6UyCkLH58519RT3eUQZQbuAX2ykMeSWVotZdyblS7U5g4CrrtK+0ymVLckdiRbRaB9y7Ou+ozFTMe2IvGFDq99mcrksYZmT1RZBaha0/KrOuSwVg3f5TmJ6ZYHG7vffS3l5czlTVN0vapsEcG2B4hqO/iWyfT0Tke5LXZJE4SmYpLta3YPnOIjwlIhiQ0pXPHVInnfPuuRm6LhrZ5SyeyCQIGZt1+09B1yUM5y41Sc4Aygn8hFb8T/8z3yJQse4gKFyNBiDqSrX1RNpgMLodGDj6Hrvz/X56/WEsGOPdTo8CuaWcnsg6TkrvaWoUkrvlqCLHtDd+e++lnPLEazprMKRPV7t/X6LCQ0Q19fFG9raj8IduskRE5BiDLA9Rct0TALy7uxTTMxNF/WMpt8xKCqmTJV0XjVtXsD2RSQCuTkpn3JmIpTuKJGUA5QZ+RrQFztase0aU1zTisTUH7R7D+kq13Yl0eIjEkdly9D125/t97pLzq+yevDov9nu7vbDc4vuq9O8zAPTsGgnAM9/t93b/AoPBiJqGFuR8aPteSg2wdF3CTBcZ7P19MRiNmPy263JHJd5Hrj9q429rP4mIyBKDLA9Jj49RdDF7TUMrlu8sFt0Ew1VXPndJmSzFRIZgYK9ot15P7OR4+vAENDS3YkP+GVEbpgp6x0ZKzgDKnRxHhAahvlnv8nHOgjfzK9UGgxE5Hx6ynUjL3C5AEBXe9rmZlzQKE9r0+BjouoTJKhl0dpXd01fnxX5v3919AunxMabP3dV6RzmqLre9d57I7nxVeB5fFZ5X7HiNrQZsLyy3Ca7uSbnWlI3zxnpQrj+6ylfdZImISBxRQdbnn39u+v+WlhaLnwW/+93vFBoSOfLqjuMAjOgdG+nzK7hSJp1VdS244+VdTidCrq5Oi50cZyTEYkifrnhuVNKVDZEvYPmuEpfPE0qhhElk+aVGVF1uQkxkKLThoabW5Bb7gF2SN3kRE2CJIVypfn5jgUc63t2e2BV3vLzL4YR2wZibHWbaXBHGvnp3KWI7h5k+c09fnRe+t66CY/NgDmj73Ecm6/DO7hOSX9ORC5cb8bcvj2FfaZVix/SU6voWPLbmoM2FI/Pvg6fXg3L9kSVfdJMlIiLxRG1GnJmZ6fwgKlW7aOGu5GbEYjcGdoevr+AKkx7A9SJ4Z5vROip1ezijt6lE0tVGs0BbxmzPnDsRGqw23aY3GDHwhe1OM4rWG9s6WsMCWJZZWTfzaE/CgtV2uy5af46OWvhHhgahTmIwGRUegtv6xGBrwTmXj3Vnc9ttBWWig8MZd/bFuv2nLL4L1mvnOjJ73wdPZJpcbTouduPl9kTM5tsd7T0hIlKaO7GBqCCro1AyyNqYfwZPrctXZmAOOAtcvEVKZz17/+i7ahwRFRGCl8b2M03gXAV11hM6MUFWVEQIDlwJsjy5F5IviW1MIOgUFozLTa1277P+HPUGI/aUVCLvlwoAbWWq+0ursPTrImUGb8dHjwx2qxz2j6v34etjF2Q9V2pnvfbO3vfBUVZa7noqsRet3P1eBBpHfxP94d8GIqL2wJ3YgGuyPCS2k7g21e7whw5S5ovgXZXmWZd6iWkcIZQpzbgzEdMzE+2umzJnXTq0r7TK5bq46voWrN5diilDentkLyRPUaFtg9hKERvEvj55ANQqFcprGpC75Sgu1jU7PE9NsNphgAXYL/Ub3KcrMhJjAbRNpJ/+52HpJySCEmt79AYjDp2ulv184fdOxYwWANvfa0frQd3JcnH9kX3e6iZLRN7HJj+B/x4wyPIUL02+5KxRUfpLK0yqpE6EpDSOeHVHET7adxoLxiTh37OGY/Dir1FlJ7iwDjzFjil3y1G8/k2xV8r/HJXiOeNonUtudjJytxQ6LaOM02ow+Pqups84PDTIdPXbnkaRYzNvO24+WRbWtHmKu2t79pVWuf05GwH4ogZAGx4MvQFOg2Bfcfa75u56Kq4/cswb3WSJyLvY5Kd9vAdq1w/xvq1btyIpKQlpaWmm/2bNmgUAOHz4MO677z6kpaUhMzMT69evt3juhg0bkJWVhdTUVIwdOxaHDh3yypj1BiPySiqxMf+MqRubN4l9vW0FZRi6ZCcmrdqDp9blY9KqPRi6ZCe2/njWYvx6GZfoxU5wKmqboDcYJb9H5ULr72+K7QZYAvPAU8qky1vrq5paDQgPdj0BUquAN+5Pw5sPDIBOa3keOq0GKx4YgJEpcRjTP85pTN/Qosf2wnLTzyOS4/DosHi5w7dLmCxvKyjz6Hf/0WHxdv+46g1G7C6uwN++/Bl/+/IYdhdVOPwOB3K2o6ah1S8DLMDx77+rjpFA20URZ39zhIYljn5rVGj7x9fd7oWBSrjQlZ3aw5RNJKLAJFyUsr4Ibf7vbHvXXt4Dv8xkHTlyBNnZ2Vi8eLHF7TU1NXj00Ufx5JNPYsKECdi/fz9ycnJwww03ICUlBXv37kVubi5WrVqFlJQUrF27FtOmTcOuXbsQHh7usfHai7ZjIkM99nr2WE9whGyVeZe8U1X1eHWH7TqZsppGPP6hZTBq3XjCGdNr1TQgJjLUaSka0JYBefv7Uky89TrR52fuPZEd3s7XNuKelGtFdZPztoZW10Hsk5mJuOtKZuiZETeaPkedNtx0pXpbQRlWflvq9Dg19S2YtuYgXr8/DdGRYSi/1Ij1B35V6lQAWGYQ/3Zff0WPbW5j/lk8M+Imi++kveYby3eVWKznM9cRsx2ORIQGQQVIblJizlUJpxIdI4PUKo93LwxEgV5KQ0SWuMl4+3oP/DbIuvvuu21u/+qrrxAVFYXJkycDAIYMGYLRo0dj7dq1SElJwfr16zFq1CgMHDgQAPDQQw/h448/xtatWzFu3DiPjNVRGcxFEetklGBvgiOlGYUj1Q0teHVHEd77zwm8NLafw3IUua9VXtOIV3cUISJEjfoW8aVzRojf/+mazhoEqVUY0z8Ob7kIRPzRpcYWm45qbcFvPNLjY0RvhizcP/2jQx5dQyRMlmGE4ntKCcovNWH2Jz8iI6ErquqacbqqHqvzTtp9rLCe702rUrSLddL39mqv6pv1GJt6LT7LPyvr+WICHKXWUzlafxQdGYIXspMDpnxEKe2hlIaILHGT8fb1HvhdkGUwGPDTTz8hPDwcb7/9NvR6Pe644w7MnDkTRUVF6Nu3r8XjExIS8MknnwAAiouLbYKphIQEHDt2zCNjFVMG4w3zRt1kCoBOVNTZzVbJJUxUteHBqGm4Wqak6xKG7NRrsfLbUlnnKjxHSoBlLio8BDUNLS43Pm1uNWD9gTOyXsPX3rWTsWsLfo/jvf+U4uHb4iUFt95q0pD3SwXmjUpCzoe2WQclfHLwV3xyUHwmbvanP6KzJgSDr2/7Y2y+loyAf/1U7rSbpOCR23vjix/LRTVYMM+wVNSKC2rFZBhHJMfBYACe31hgKhmuqmtB7pajUKtVHSa44J5hRO0Tm/y0r/fA74KsqqoqJCUl4a677sKyZctw8eJFzJ49G7NmzUK3bt1syv40Gg3q6+sBAHV1dU7vV5qUxg2eMiolDn/54qhHGw0AsAiwgLaMgi+zQw9nxGPpjuNOS4e2F5bjuQ1HAnIvK1fBSXV9y5XNqf3P8l0liNNq8OiweGw6XObz35GahlZMfnsv4rQaTLy1p8/H428aWgwAnF/seOT2eMwdlYRn705yWZ5mL8PibG8xKR0jtxWUIefDjh1ctKdSGiKy5OkmP4FQYtyeGh35XZAVGxuLtWvXmn4ODw/HrFmz8Pvf/x5jx45FY6PlBKmxsRGRkZGmx9q7Pzo62iNj9Yco+osfA2Pxn1KECdn0zATcoOuEBZsKLQJM3ZWJ9P7SKrwjcu2WL0RFhKDmyhoiX2dCPaGsphErvy3FPyamYfaGH1HXJH/Nj1LaSlT9MzD1d1/8WIZn777Jbnt283+0T1TUY+mO4zbfX2cBFiBuPRWDizbtqZSGiCwJTX5cbTIup8lPoJQYe/I98Da/6y547Ngx/O1vf4P5HsnNzc1Qq9VISUlBUZFlKVxxcTESExMBAImJiU7vV1ogRNHtif0JmeWvoDCR9nWAFRrkeJKnAvDS2H5YYadrYHtiBPC/mwpEBVjjBvTAaxNTMeNOz/yuCuMheYRJuzXrbqWv2gmwzFnHPkKXTDH/wEsJLtqz9lRKQ0SWhCY/AGy6qbrT5CeQuvV56j3wBb8LsqKiorB27Vq8/fbbaG1txdmzZ/Hyyy/j3nvvxV133YWKigqsXr0aLS0t2LNnDzZv3mxahzV+/Hhs3rwZe/bsQUtLC1avXo3KykpkZWV5ZKyu2gqTsqIigvH6/WkYkRxn+oNRfslyvYe/TKSb9fZH0iksyHQOI5Lj8P3sTDz13wnt9jtU5WIjaMGWI2X47xu7o1NYMPr/pouHR0VyCJN2oV3+tDUH8Jidf7SdMRjb1pC+NjEVHz0yGN/PzrQIsKy3wjBv687gok17KqUhIltCkx9HW7dIzTopsY2Gtyn9HviK35UL6nQ6vPXWW3jllVewYsUKhIWFYdSoUZg1axbCwsLw7rvvYtGiRVi2bBliYmLw/PPPY/DgwQDaug3Onz8fCxYswLlz55CQkIBVq1YhKirKI2N11laYlHexvhXPbSiA3gjM3/RTQL7fl5v0+MsXhaZF+tsLy/Ha18W+HpYk0REhWPS7fig4W4M3vilR5JiNLQYkL/hSkWORZ1zTWWO3Xb5UsZ3DkJ3aw+Z2V6UsDC7atKdSGiKyT8lNxgO1xLg9bLTud0EWAKSnp2PdunV27+vXr5/D+wAgOzsb2dnZnhqaDSHadnfiQeJUN7TgiY+8s8G0VDGRIaKabJRfarqyb9UA5G4p9MLIlHWxvgVqNdDVy3vBke+oVMDOY+ew6jv3m92cqLBtRCSmW15Wko7BBbhnGFFHYW8NrByBXAWg1HvgK35XLhioGGB1TDGRofhDRm989MhgzLvnZknPnbexICA73QkNBqIjxAVZYcGBP9kL/DNwj9EIRQIsAFj9n1KLshRXpSxGtH3fALSbOn13tZdSGiLyPFYB+I5fZrICiTBBoI7pYl0z3tt9AunxMdB1Ef8Hygig0ksbVitNKC0QO/5gtRpN8H53QXdKeK2f29a18jpF96DrqC7Wt2D5ziI8dWfbnoditsIQSlkcbUjsaL+u9qw9lNIQkeexxNh3GGS5yR/2yiLfMW8d/e9Zw53+IWtvlu8sQlREiMssbl2zb9q3R0eGmjaslUr4/P6Y0Rt3JulM//is23+av+8KWPndL+jZNRK6LhqUVTeIeo7wOAYXVwV6KQ0ReR5LjH2H5YJu8scaVlJWTGSI0/uFzM6BkxdN5UwdQU1jq+gyWRWAqPBgdAoL8uygzMwbdZNbLeFVALYWlJsm8MI/VCqwfNBddU16zPg4H5NW7cH8zT+Jes78zT+Z2gwLwUV2ag8M6dOVkwMiIidYYuwbDLLcZG8RNwW+mMhQPP5ffTB9eB9MurWnqOecr228+odMQulgR2AEUN3QihWTB6KzxjsJ9Gu6aLBu/2nZz7e375Kjf6hIvtrGVtGP87f9XIiIAoWwbcxHjwx2uI0GKYvlgm7YVlCGpTuO+3oY7Yq/tMI3GI2S25MLi0aFcqblO4u4hsfKpwd/xdi0Hvi/vJMefR1teDAKz9YoUtpnna02L1f7qrAc7/l44+uOZuHmQmQl6Zi9IiKSiCXG3sVMlkzOOmKRfDqtBm8+MAB/Ghbvk9ePvFLOJrVbpFoFDOwVbXFbenxX/CGjtyIlcpoQNULbwaTy8/yzHg+wAKCmoRWLth5T5FgVtU02mzQGqVVIj4/BxvyzirwGiWMvu0hEROSPmMmSiQ0vlBMdEYz/HZ2MazqHAUagoq4J/3VDd/S7NgrzNhXgoofb4w+4TovBfWIRFR6C17+RtzGwwQh8kHcCsZ3DcKKiDh/tO4XyS02KjXHVlFtQ29iCxz/0zz3C2rPcLUfx9velNt3r9pVWyW6sQe7hWlgiIvJ3DLJkKq8R1xHLk1Sqtv1r/EVGnxjsLpF+hflifStOVdbhr9uOWQSucVoNxg/sgVXfnVBwlFd1jQxFbnYyAOD5jQVuT5hztxxVYlh2zfrkRywYk4RHbo9XbL8iEq+sphGPrTlo0W2QE33f4X4uRETk7xhkyeQPV7CXTUzD1iNn8a+Cc74eCgDICrAE9tYuldU0eiTA6hQWhEdu74PpmQn467ajeOtb/w9azl1qxLQ1BzEqhQtUfemd3Sfwzu4TiNNqcEuvKF8Pp8Phfi5ERBQoGGTJFNMpzNdDQExEKPaWXvT1MALO5SY9lu44jstNzR7LkilNSFhu+dF3ndVi3Nh3qr0pq2nE5h/LfT2MgCS3uQ33cyEiokDCxhcy+UOL7ld3HOek1w2BEmCZ81V1qFoFLBxzM+aNuslHI6D2YMadibLb33M/FyIiCiTMZMmUHh+DOK3Gp80vfjjJLJZcfrSULSAYjMCTHx3C6/en+fx7T4EpTqvB9MxEJF7T6coaSHENbaYPT0BGQqxpU2giIqJAwEyWTEFqFeaPToIKV8tYiNq73C1HMW9Ukq+HQQFo/ugkbC8sR86Hh0QFWCq0BWYzsvpiSJ+uDLCIiCigMMhyw4jkOKx4YIDs8hfyf53CgjB35I2+HoZfEPYo0kaEICqcSXASb8adfZGVpBO9tyDXXxERUaDjTMlNI5LjYDAAj3940NdDIQ/467j+YHGhpbySSlQ3tPp6GBQgdF3CMD0zQdLegjqtxmZfMiIiokDCIMtNeoMRuVsKfT0M8oC066KgjQjB0//M9/VQ/IrewKCTxFEBWDDmZgSpVaL3FZs+PAEzsvoyg0VERAGNQZabpFydpcBy6HQ1Jr+919fD8Dsr/l3i6yFQAIizykaJ3UA4IyGWARYREQU8BlluEnt1loiooxidosPSiQMsgiWhI2t5TaPdAlxuNExERO2JXza+OHbsGB5++GGkp6cjIyMDzzzzDKqqqgAA8+fPR3JyMtLS0kz/ffzxx6bnbtiwAVlZWUhNTcXYsWNx6NAhj45V7NVZIqL2Tq0C/jQsHv+4f6BNNkroyArYdmRlowsiImpv/C7IamxsxNSpU5GWlobvv/8eX3zxBaqrq/Hcc88BAI4cOYLc3FwcOnTI9N+ECRMAAHv37kVubi5eeukl7N+/H2PGjMG0adPQ0NDgsfEKV2c5LSBHIsOCfD0EIo8KC1Jj/IAeOJZ7N+aMdNzi31FHVm40TERE7Y3fBVlnz57FjTfeiJycHISGhiI6OhoTJkzA/v370dzcjOPHjyM5Odnuc9evX49Ro0Zh4MCBCAkJwUMPPYTo6Ghs3brVY+M1vzobCKwvEqsYHXrc3+/rjzcfGICIUAZb1D416Q349OAZ7Dx2zuVjRyTH4fvZmfjokcF4bWIqPnpkML6fnckAi4iI2hW/C7Kuv/56vP322wgKujoh/fLLL3HzzTfj2LFjaG1txbJly3DbbbfhrrvuwsqVK2EwGAAAxcXF6Nu3r8XxEhIScOzYMY+PWxsg+wYJjeH+kNEbc0feBCMbxXmMWgW8cf8AZCXp8HN5Leqb9b4eEpFHLdxcKKr7ZJBahSF9uiI7tQc3GiYionbJryMDo9GIpUuXYteuXVizZg0qKiqQnp6OKVOm4JVXXsHRo0eRk5MDtVqNqVOnoq6uDuHh4RbH0Gg0qK+v99gYtxWU4bE1gbdH1j9/+NUmq0XKWj4pDYARt7ywHRfrW3w9HCKPEjar3ldahSF9uvp6OERERD7lt0HW5cuXMWfOHPz0009Ys2YNbrjhBtxwww3IyMgwPSYlJQUPPvggtm7diqlTpyI8PByNjZbd/hobGxEdHe2RMeoNRjz72RGPHNvTLjdxM1lPEVpXHzp1EW99W+rr4RB5FTuuEhER+WG5IACcOnUK48aNw+XLl/HJJ5/ghhtuAADs2LED69ats3hsc3MzNJq2RdSJiYkoKiqyuL+4uBiJiYkeGeeekkpUM0NBALpGhuIPGb1N60sMBigWYKkAaDV+ez2EyEJFbRM3rCYiog7P72ZuNTU1ePDBBzF48GAsWrQIavXVONBoNGLx4sXo1asXBg8ejPz8fLz//vuYM2cOAGD8+PHIycnB3XffjYEDB2Lt2rWorKxEVlaWR8aa90uFR45L/is6Ihhj036DzJu6A0agoq4J13Ru29tHWFeiNxjx/MYCxV7z9fvTcFdyHF756me8/o1/bwQcEqRCi95/JtihQWo06w2+HkZA6d45FK9MSMPXR8/hnz/8KjnrnbvlKN7+vtRiI2IiIqKOxu+CrM8++wxnz57Fv/71L2zbts3ivkOHDmHOnDlYsGABzp07h9jYWDzxxBPIzs4GAAwZMgTz58833Z+QkIBVq1YhKirKQ6PloiYlREWEYMItv8HHP/zq95nB5fcPREZCrNPH7CutQlVds9uvJZQdjkiOw7aCMny0/5Tbx/SkYDX8KsACgNsTu+LrYxd8PYyAIPw1W5idjIyEWGQkxGLuqCTsKam8ckFJhWA1sPTrYqgAuxsKC8prGjFtzUG2ZSciog5LZTSyv5xAr9cjPz8fqampFt0NHdldXIHJb+/1wsjaN2Fy9/r9aSg6fxnv7i5FTYN/rhmbPjwBM7L6Ou2GtjH/DJ5aly/puH/M6O0wO7atoAzT1hx0OqmVSxOiRmMLMz1ixESGYNyA32Dd/tOobfTP76dYUeHBgEplcVHDPKh3ZltBGRZuLkRZjfO1Vyq07X/1/exMdg8kIqKAJDU2MOd3maxAMvj6roiKCPH77Iu/M6JtQpa75Si+n52JW3rHiA5epw/vg7qmVrz3n5MeHaNg+a5ifHrwV6eT0Ws6a+zebo+uSxj+954kREeG4XxtI67prME9KddalB4u3FzokQALAF65rz+On7uMpV8XuX6wh3XWBCsavKhVV7csUMK8e27GvWk90KI3YLWXvm+eMH5AD2QkxOKaLhqHJa/OjEiOQ1aSDqt3lyJ3y1GHj2O3QSIi6sgYZLkhSK3ChFt+ww5yCjCfkFVcbhL9vIyEbhjSpyu04aFeCxRclUKlx8cgTqtxeaV/VD8dRvW7FrlbLLMC5hmFfaVVLo8DADGRoRh+Qzf860g56lvE7cf1p2HxGJlyLVryz4h6vKcIGY/nRt6EJz46pNhxle69cKqyDtsKygI2wIqOCIERwCcHz+CTg22fua5LGCal9wTQVuYqNtAKUqsQ2zlM1Ouy2yAREXVEDLLcoDcY8fH+X309jHZFyOaIERMZgvT4mCsT3xOeHZgZIfO2cHMhspJ0NpPSILUK80Yl4fEPne+ftrukEluPlNtkqcyDuKZWcaV8er0Bnx4UFyx1jQxFbnYyRqa0BYhSMm9KE945IagsOFPt9kWLqPAQDOkTg38VnHN/gGY+3HsSKpVfNmR1aXSKDpt/LLe5vfxSE17dcfXihNiSQUD898aX3y8iIiJfCcwZg5/YU1KJ6gaWCipJKFuK07qemL2QnYztheWYtuag1z8H88ybPdGRoS6PUV3fYrcMULht4eZCxEaKyxbUuCizi4kMwR+vtJjfN/dOU4AFXM28OcpfqNDWnMQdc0fehHv6xdm8hkoFPDos3jSpnzMyCW/cn4aYSMvX03UJw4w7E/HHjN4298VpNbgnJQ5R4W23Vze0KB5gAcC52maUXwqsrIxaBfxjUhp+OFkt6vFCgL+toMzlY8V8b+K0bb/PREREHQ0zWW5gC3flCCVjQrnSmP5xTjMafxoWj7uS4zB0yU6PrVcSw1EplLslUkIQB1XbRLW8plHWeUZFhOD1SQMwuE9Xh2VgQWoV5o9OwrQ1B226xgnPeGlsP7TojZLL+YTPtUdUOF7cetTmHAxGYOW3pUjrGW0KtEamXIu7rpRKCplN8zK250YlWdx3sa4ZOR96pjFIoFs+KQ3RkWGiSk4B11lac2K+N/NHJ7HpBRERdUjMZLmhlRtuKsJ6QratoAwrnQRYj9wejzkjk0SvV/IkR6VQSpVIVVxuwvzRSQDkbRhQXd8CtVrlcqI7IjkOKx4YAJ1VBlGn1ZjWno3ufy0eub236NcWXnHeqJuQu8V5846FmwstNrANUqswpE9XZKf2wBCrANH8vvT4GJfH7qhm3NkXI1OulRzwu8rSmhPzvSEiIuqImMlyQ+HZGl8PoV3Qma0DEdNN74sfy/Ds3Tf5fEG9s1IooZRKbgZKcE1nDYb06YoVDwywaZsdFR4iqkxS7PskdI1zlEECgLmjboZapcKq70otGkuoVEB4SBDqm6823RA+V214qNNg2FUXOr3B6HBM3gy0u3cOhUqlxrlL9j9TIWv3+1t+g9e+LvbKmBzRdQnD9MwEAPIDfiW/N0RERB0NgyyZ9AYj9p9wfaW3vVG6Zf28UTfhoYx4SZNmYULu6wX1zkqhXJVSGdH2XtY4WJdlXj4J2J/IGoxGUa3upbxPQpbImTkjk/D0b2/EB3kncLKqHr1iIjBlSG8EqVV2J9obRXYvtDept7cnk3lzBm8G2tUNrXjotl5Y+W2pw/K4eaOSkLulUNHXjQoPxsMZ8egdG9m2Rk8F7Dx6Du/sPmHzWGEcC8bcbPpuyg34lf7eEBERdSQMsmTaV1qFhpb2WaQ0fkAP3N63G67prMHAXtE4cPLi1Ym9wYjJ7yizAXOcVmMRYAHir56fr23EPSnXOp08CoHK38b3x/nLTcj94idU1TkOENUqYGRyHL444nrR/4w7+7oshRJKqayDBCHDA0DSehbriazeYBR1/p5oPBAarMYfb7/e5nZ7E225XegcbcJs3n3Rm4F2U6sBb31bij8Ni8emw2V2P1NXWTspoiJC8PBt8ZiemWATzGckxOLW+BiH3y3z76azgN8eT35viIiIOgoGWTL5ulQNALThwQgPCVa045muSxiWjO9vMamTMrGXYkz/OJvJo5QJudiF9xmJscgrqXQaYAFtTRgmpffEDyerUH7J8V5d5qVYrrgqpXIWhLkK4gKl8YCrTIq9Sb2zslHz5gz/njVcse+jWJsOl+Hfs4ZbXHyQmrWzpgLQvUsY/v77VFRcFrc5sJQyPUcBv71xAP7xvSEiIgpkDLJk8nWpGgD8IeN63KDrZPdqvyPRESG4WN/icFJuXmZkj9ir4nFaDRpa9E5LCzcdLsMzI26yeD2pE3JX2SIhUBEbFFfUNWHBmJsxbU3bHldy3iNrzkqp3F3PIvb8fUlOMOiqbFRYx3Xg5EVJWZqYyBCXwbYrwuu6k7UzZ/69ykiIlfRcKWV61t+1ExX1+GjfKYuLNP70vSEiIgpkDLJkSo+Pga5LmNOMhydFRYSYyoicXaHWdQnDpPSe6B0baZrAby8sd2tS7mhi3zUyFNmp1yIrSSeqrNBeswM5E3IxgYqUDJmjRhOemoC6u54lEBoPSA0GpZSNZqf2sHvsOK0G80YlIToy1PS+lF9qxIyP890+H0fjE7P+Sa2CRdMQbwY21t+16ZkJfv29ISIiClQMsmQKUquwYMzNeOxKxsMR60YRXSNDUVnX7PbrvzS2n2kyZD7JLr/UiKrLTYiJDIVOG2530qTEpNzVMdxpdiAnO+MqUJGTIfP3wMVcIDQekPKeSl3HJfbYeSWV7p+Ik/GJuUgg7F3lD9+rQPjeEBERBSIGWW4YkRyHNx8YgGc/O2JTFhcdEYLFY/vZTPwG9orGHS/vkr2GJM5BoCF1sqTE5MrZMeQ2OxAoHeTIyZBxAqo8se+pnHVcYo6tRGt9Z637gcAo4SQiIiLPUhmNxvbZIk8GvV6P/Px8pKamIigoSPzzDEbsKalE3i8VANomeoOv7+owIBC6pgGu15DEaTWYeGtP9I6N8PlVbyn0BiOGLtnpcpL8/exMr56Pq5bg5D8c/Z4I3xa5m906O66YP4ZvinxdZ/t7ERERkf+TGxsADLIsuPNGSuVosj9v1E1+U0rkLk9Nkt3FyW/g8FRQ7Oy4h05dxMpvS20CrsiwIPz9vv4MxomIiDoIBlkK8WaQBXSMyT4zR+QuT/2eODtuc6sB//efUuw/cRGRoUEYO+A3uC0htt39fhIREZFjDLIU4u0gq6PoCMEkEREREbUv7sQGbHxBHscGEkRERETUkah9PQAiIiIiIqL2hJksM0LlpF6v9/FIiIiIiIjIl4SYQM7qKgZZZgwGAwDgyJEjPh4JERERERH5AyFGkIKNL8wYDAa0trZCrVZDpWJjBiIiIiKijspoNMJgMCA4OBhqtbRVVgyyiIiIiIiIFMTGF0RERERERApikEVERERERKQgBllEREREREQKYpBFRERERESkIAZZRERERERECmKQRUREREREpCAGWURERERERApikEVERERERKQgBllXVFZW4vHHH8ctt9yCQYMGYdGiRWhtbfX1sMgHqqqqkJWVhb179/p6KORlx44dw8MPP4z09HRkZGTgmWeeQVVVla+HRV6Wl5eH++67DwMGDEBGRgZyc3PR2Njo62GRl+n1ekyZMgXPPvusr4dCPrB161YkJSUhLS3N9N+sWbN8PSzyourqajzzzDMYNGgQbr31Vjz++OM4f/686OczyLriz3/+MyIiIvDdd9/hk08+QV5eHlavXu3rYZGXHThwABMmTMCpU6d8PRTyssbGRkydOhVpaWn4/vvv8cUXX6C6uhrPPfecr4dGXlRVVYU//elPmDRpEn744Qds2LAB+/btw8qVK309NPKy5cuX44cffvD1MMhHjhw5guzsbBw6dMj038svv+zrYZEXPfHEE6ivr8f27duxa9cuBAUFYd68eaKfzyALwMmTJ7Fv3z7MmjUL4eHhuO666/D4449j7dq1vh4aedGGDRswc+ZMzJgxw9dDIR84e/YsbrzxRuTk5CA0NBTR0dGYMGEC9u/f7+uhkRfFxMTgP//5D8aOHQuVSoXq6mo0NTUhJibG10MjL8rLy8NXX32F3/72t74eCvnIkSNHkJyc7OthkI8UFBTg8OHDeOmll9ClSxd06tQJubm5mDlzpuhjMMgCUFRUhKioKHTv3t10W58+fXD27FlcunTJhyMjbxo6dCi2b9+OkSNH+noo5APXX3893n77bQQFBZlu+/LLL3HzzTf7cFTkC506dQIA3HHHHRg9ejS6deuGsWPH+nhU5C2VlZWYO3cu/v73vyM8PNzXwyEfMBgM+Omnn/DNN99g+PDhGDZsGObNm4eamhpfD4285Mcff0RCQgL++c9/IisrC0OHDsWSJUvQrVs30cdgkAWgrq7O5g+p8HN9fb0vhkQ+0K1bNwQHB/t6GOQHjEYjXn31VezatQtz58719XDIR7766it8++23UKvVePLJJ309HPICg8GAWbNm4eGHH8aNN97o6+GQj1RVVSEpKQl33XUXtm7dinXr1uHEiRNck9WB1NTU4Oeff8aJEyewYcMGfP755zh37hxmz54t+hicUQKIiIhAQ0ODxW3Cz5GRkb4YEhH5yOXLlzFnzhz89NNPWLNmDW644QZfD4l8RKPRQKPRYNasWbjvvvtQU1MDrVbr62GRB7311lsIDQ3FlClTfD0U8qHY2FiLJSPh4eGYNWsWfv/73+Py5cumbDe1X6GhoQCAuXPnIiwsDJ06dcKf//xn/P73v0ddXZ2o+ICZLACJiYmorq5GRUWF6baSkhLodDp07tzZhyMjIm86deoUxo0bh8uXL+OTTz5hgNUBHTx4ECNGjEBzc7PptubmZoSEhLB0rAPYuHEj9u3bh1tuuQW33HILvvjiC3zxxRe45ZZbfD008qJjx47hb3/7G4xGo+m25uZmqNVq0+Sb2reEhAQYDAa0tLSYbjMYDABg8b1whkEWgN69e2PgwIF48cUXcfnyZZw+fRpvvPEGxo8f7+uhEZGX1NTU4MEHH8SAAQPwzjvvsNFBB3XDDTegsbERf//739Hc3IwzZ85gyZIlGD9+PCdXHcC2bdtw8OBB/PDDD/jhhx9wzz334J577mGXwQ4mKioKa9euxdtvv43W1lacPXsWL7/8Mu69917+HeggbrvtNlx33XV47rnnUFdXh6qqKrz66qu48847RWcyGWRdsWzZMrS2tuK///u/8fvf/x633347Hn/8cV8Pi4i85LPPPsPZs2fxr3/9CwMHDrTYG4U6jsjISLz99tsoKipCRkYGpkyZgttuu42t/Ik6EJ1Oh7feegtff/010tPTMW7cOPTr1w//+7//6+uhkZeEhITggw8+QFBQEO666y7cdddd0Ol0ePHFF0UfQ2UUm/MiIiIiIiIil5jJIiIiIiIiUhCDLCIiIiIiIgUxyCIiIiIiIlIQgywiIiIiIiIFMcgiIiIiIiJSEIMsIiIiIiIiBTHIIiIiIiIiUhCDLCIiIiIiIgUxyCIiIiIiIlIQgywiIiIiIiIFMcgiIiIiIiJSEIMsIiIiIiIiBTHIIiIiIiIiUhCDLCIiIiIiIgUxyCIiIiIiIlIQgywiIiIiIiIFMcgiIiIiIiJSkE+DrKqqKmRlZWHv3r0AgP/93/9FWlqaxX833XQT/vjHP5qec/fdd6N///4WjykpKQEA6PV6LFmyBLfddhvS0tIwbdo0nD9/3ifnRkREREREHZPKaDQaffHCBw4cwLPPPotTp07h/fffx6BBg2we8/333+Ppp5/GmjVrkJiYiMuXL+OWW27B119/jR49etg8fvny5fjqq6/w1ltvoXPnzpg3bx7q6uqwcuVKb5wSERERERERgn3xohs2bMCyZcswa9YszJgxw+5jqqqqMHPmTMydOxeJiYkAgIKCAkRFRdkNsABg/fr1mDlzJuLi4gAAc+fOxdChQ3H69Glcd911LsdlMBjQ2toKtVoNlUol8+yIiIiIiCjQGY1GGAwGBAcHQ62WVgDokyBr6NChGD16NIKDgx0GWX/729+QnJyMMWPGmG47cuQIwsPD8cADD6CoqAg9evTAE088geHDh6O2thbl5eXo27ev6fGxsbHQarX4+eefRQVZra2tOHLkiPsnSERERERE7UK/fv0QGhoq6Tk+CbK6devm9P7Tp09j06ZNWL9+vcXtKpUK/fr1w//8z//g2muvxbZt2/DEE09gzZo10Ol0AICIiAiL52g0GtTV1YkalxCh9uvXD0FBQWJPh4iIiIiI2hm9Xo8jR45IzmIBPgqyXPn0009NTS/MTZ061eLnMWPG4IsvvsCXX36Jxx57DADQ0NBg8ZjGxkZERkaKel2hRDAoKIhBFhERERERyVpG5Jct3L/66itkZ2fb3P7OO+8gLy/P4rbm5maEhYVBq9Wie/fuKC4uNt134cIFVFdXW5QQEhEREREReZLfBVkXL15ESUkJbr31Vpv7ysrKsHDhQpw+fRqtra345JNPcOjQIdx7770AgLFjx2LFihU4ffo0Ll++jBdffBHp6eno2bOnt0+DiDo4vcGIvJJKbMw/g7ySSugNPmnkSkRERD7gd+WCv/76KwCge/fuNvc988wzUKvVuP/++1FbW4uEhASsXLkSvXr1AgDk5OSgtbUVkydPRl1dHQYNGoSlS5d6c/hERNhWUIaFmwtRVtNoui1Oq8H80UkYkRznw5ERERGRN/hsnyx/pNfrkZ+fj9TUVK7JIiJZthWUYdqag7D+wypUc694YAADLSIiogDgTmzgd+WCRESBSm8wYuHmQpsAC4DptoWbC1k6SERE1M4xyCIiUsi+0iqLEkFrRgBlNY3YV1rlvUERERGR1/ndmiwiat/0BiP2lVbhfG0jrumsQXp8DILU0luj+qPztY4DLDmPIyIiosDEIIuIvKa9N4S4prNG0ccRERFRYGK5IBF5hdAQwrqcrrymEdPWHMS2gjIfjUw56fExiNNq4Cgvp0JbUJkeH+PNYREREZGXMcgiIo/rKA0hgtQqzB+dBAA2gZbw8/zRSe2mPJKIiIjsY5BFRB7XkRpCjEiOw4oHBkCntSwJ1Gk1bN9ORETUQXBNFhF5XEdrCDEiOQ5ZSbp22+CDiIiInGOQRUQe1xEbQgSpVRjSp6uvh0FEREQ+wHJBIvI4NoQgIiKijoRBFhF5HBtCEBERUUfCIIuIvIINIYiIiKij4JosIvIaNoQgIiKijoBBFhF5FRtCEBERUXvHckEiIiIiIiIFMcgiIiIiIiJSEIMsIiIiIiIiBTHIIiIiIiIiUhCDLCIiIiIiIgUxyCIiIiIiIlKQT4OsqqoqZGVlYe/evabb5s+fj+TkZKSlpZn++/jjj033b9iwAVlZWUhNTcXYsWNx6NAh0316vR5LlizBbbfdhrS0NEybNg3nz5/36jkREREREVHH5rMg68CBA5gwYQJOnTplcfuRI0eQm5uLQ4cOmf6bMGECAGDv3r3Izc3FSy+9hP3792PMmDGYNm0aGhoaAAArVqzA7t278emnn+K7776DRqPB888/7/VzIyIiIiKijssnQdaGDRswc+ZMzJgxw+L25uZmHD9+HMnJyXaft379eowaNQoDBw5ESEgIHnroIURHR2Pr1q2m+x955BHExcWhU6dOmDt3Lr799lucPn3a4+dERCSG3mBEXkklNuafQV5JJfQGo0eeQ0RERL4T7IsXHTp0KEaPHo3g4GCLQOvYsWNobW3FsmXLcODAAXTu3Bnjxo3D1KlToVarUVxcjHHjxlkcKyEhAceOHUNtbS3Ky8vRt29f032xsbHQarX4+eefcd1113nt/IiIgLbgaF9pFc7XNuKazhpcrGtG7pZClNU0mh4Tp9Vg/ugkjEiOs3uMbQVlWLhZ2nOIiIjIt3wSZHXr1s3u7bW1tUhPT8eUKVPwyiuv4OjRo8jJyYFarcbUqVNRV1eH8PBwi+doNBrU19ejrq4OABAREWFzv3AfEZG32AuO7CmvacS0NQex4oEBNkHTtoIyTFtzENZ5K2fPISIiIt/zq+6CGRkZeP/995Geno6QkBCkpKTgwQcfNJUDhoeHo7HRcsLS2NiIyMhIU/AlrM+yvp+IyFuE4MhVgAXAFEAt3FxoUQaoNxixcHOhTYDl7DlERETkH/wqyNqxYwfWrVtncVtzczM0Gg0AIDExEUVFRRb3FxcXIzExEVqtFt27d0dxcbHpvgsXLqC6utqihJCIyJOcBUeOGAGU1TRiX2mV6bZ9pVVOgzR7zyEiIiL/4FdBltFoxOLFi5GXlwej0YhDhw7h/fffN3UXHD9+PDZv3ow9e/agpaUFq1evRmVlJbKysgAAY8eOxYoVK3D69GlcvnwZL774ItLT09GzZ09fnhYRdSCugiNnztc22v1/sc8hIiIi/+CTNVmOZGVlYc6cOViwYAHOnTuH2NhYPPHEE8jOzgYADBkyBPPnzzfdn5CQgFWrViEqKgoAkJOTg9bWVkyePBl1dXUYNGgQli5d6rsTIqIOx52g55rOGrv/L/Y5RERE5B9URqORBf1X6PV65OfnIzU1FUFBQb4eDhEFoLySSkxatUfSc1QAdFoNvp+diSC1CkBb2eHQJTtRXtNot/TQ3nOIiIhIOe7EBn5VLkhEFOjS42MQp9VAbNgjPG7+6CSLYClIrcL80UkWj3H1HCIiIvIPDLKIiBTkLDiyR6fVOGzFPiI5DiseGACdViP6OUREROR7frUmi4jIegPf9PiYgMvWCMGRvU2E5426CdGRYaLPb0RyHLKSdAH/nhAREXUkDLKIyCfsBVPbC8ttAhNdlzBMSu+J3rGRuKazBgN7RePAyYt+H3AoGRwFqVUY0qerB0ZJREREnsAgi4i8bltBmU0wFRURgur6FpvHll9qwqs7ru6Pp1YB5vvvxmk1mD86yS9L5xgcERERdUwMsojIq7YVlGHamoM2HfPsBVj2GKyeWF7TiGlrDuLPd/ZF79gIv85uERERUcfAIIuIvEZvMGLh5kK7LcnlEo716o7jptv8ObtFRERE7R+7CxKR1+wrrbIoEfQUIbu1raDM469FREREZI1BFhF5zflazwdYwNXs1sLNhdBb1xcSEREReRiDLCLymms6a1w/SCFGAGU1jdhXWuW11yQiIiICGGQRkRelx8cgTqsRtUmvUryVPSMiIiISMMgiIq8JUqswf3QSANgEWsLPUREhir6mN7NnRERERAC7CxKRm+xtKuysffqI5DiseGCA7abDVzoCmm/ge6KiHh/tO4XyS1cfZ71PliOqK8dMj49x5/SIiIiIJGOQRUSy2dtUWEz79BHJcRbBlHVwZr6B7/TMBOwpqUTeLxUAVBgUHwO1SoWKuiacqKjDqzuKoAIs2sILId780UncL4uIiIi8jkEWEcniaFNhoX36igcGOA20gtQqi2DKke2F5RaB3PJdVwO5p+7sixt0nR1mxbhPFhEREfkCgywikszZpsJGtGWSFm4uRFaSzq1MkthAzllWjIiIiMjbGGQRkWSuNhU2b58uJltlj9RATu7rEBERESmN3QWJSDKxbdHdaZ8uJZALVHqDEXklldiYfwZ5JZXcOJmIiKidYCaLiCQT2xbdnfbp3gjkfElu0xAiIiLyf8xkEZFkrjYVVqEtYJDbPl1vMKKitknUYwNxHyxhrZl1pk5Ya7atoMxHIyMiIiIl+DTIqqqqQlZWFvbu3Wu67csvv0R2djYGDBiAzMxMLF++HAaDwXT/3Xffjf79+yMtLc30X0lJCQBAr9djyZIluO2225CWloZp06bh/PnzXj8vovZOzKbCctunbysow9AlO5G75ajTx7kbyPmKq7VmQNtaM3dKB1mGSERE5Fs+Kxc8cOAAnn32WZw6dcp0W0FBAZ555hksXboUd9xxB0pLS/HII48gIiICf/jDH3D58mWUlpbi66+/Ro8ePWyOuWLFCuzevRuffvopOnfujHnz5uH555/HypUrvXlqRB2Cq02F5ZS8OeomaC2Q98HydNMQliESERH5nk+CrA0bNmDZsmWYNWsWZsyYYbr9zJkzmDhxIoYPHw4A6NOnD7KysrB//3784Q9/QEFBAaKiouwGWACwfv16zJw5E3FxbROJuXPnYujQoTh9+jSuu+46z58YUQejZPt0Zxkea4G8D5Yn15q5u3cZERERKcMnQdbQoUMxevRoBAcHWwRZd911F+666y7Tz42Njfjmm28wevRoAMCRI0cQHh6OBx54AEVFRejRoweeeOIJDB8+HLW1tSgvL0ffvn1Nz4+NjYVWq8XPP//MIIvIQ5Rqn+4qwyOYN+omPJQRH3AZLIGnmoZ4a+8yIiIics0nQVa3bt1cPuby5ct46qmnoNFo8NBDDwEAVCoV+vXrh//5n//Btddei23btuGJJ57AmjVroNPpAAAREREWx9FoNKirq1P8HIhIPr3BaJP9Epu5ie0cFtBBgtA0pLym0W5ApEJbps56rZm998z8ffDG3mVEREQkjl+2cP/ll1/w5JNPomvXrnj//ffRqVMnAMDUqVMtHjdmzBh88cUX+PLLL/HYY48BABoaGiwe09jYiMjISO8MnIhccrRmaOKt4rLNgdhN0JzQNGTamoNQARaBlqO1ZmLWWbX3lvdERESBxO9auP/73//Gfffdh9tvvx3vvPMOtFqt6b533nkHeXl5Fo9vbm5GWFgYtFotunfvjuLiYtN9Fy5cQHV1tUUJIRH5jrPW5a/uKEJURIjH2sL7A6HrX1OrAX++MxHdu4RZ3K/TamzWTYlt9+6NvcuIiIhIHL/KZOXn5yMnJwcLFizA+PHjbe4vKyvD+vXrsWrVKsTFxeHzzz/HoUOHsHDhQgDA2LFjsWLFCvTr1w/R0dF48cUXkZ6ejp49e3r7VIjIipg1QwKxGZ5AYi8bpeuiwYw7+6J3bITdEkAp66zkliESERGR8vwqk/Xmm2+itbUVixYtstgHSygTfOaZZzBs2DDcf//9uOWWW7Bu3TqsXLkSvXr1AgDk5OTgjjvuwOTJk3HHHXegqakJS5cu9eEZEfkXX+6fJGbNUHV9C/58Z1/otJbZFnsZnkDiKBt17lIjlu44jrBgNYb06WoTQEpZZ+XJvcuIiIhIGpXRaOQulVfo9Xrk5+cjNTUVQUFBvh4OkaJ8vX/SxvwzeGpdvsvHvTYxFfekXKtIW3h/oDcYMXTJTofBkgpATGQonh91E3TacItzlfKeZae2bW3h68+ZiIiovXAnNvCrckEi8gx/2D9JypohpdrC+wMx2ajKumbM+OdhAJYBkZx1VkruXUZERETyMMgiauf8Zf8ksWuGWlsN+NuXxwC0BVqDr7cto/MUV23S5ZDazc888M1K0slaZ9WeglQiIqJAxCCLqJ3zl/2TXLUuNwKoaWjBlPf2mW5fvqsYncKC8ftbfmNq7uCpgMtTZXZSu/lZB75S270TERGR7/lV4wsiUp4/7Z80IjkOKx4YYNPYIioiBABQ36y3ec7lpla8u/sEJq3ag6FLdppalitJbJt0OYQMnpQwyDzwdfSeBXozECIiovaMmSyids7f9k+yXjMU2ykM//PxIVHPLfPAGjIlyykdlRs6yka5IgS+XGdFREQUWBhkEbVz/rh/kvmaobySSpyrbZb0fCXXkMkpp7QXTG0vLHdabrjigQE297tyoqLe9P9cZ0VERBQ4GGQRtXOu1kIBvlvXozcYsbu4QtJzlF5DJrWc0t7araiIEFTXt9g8x7p7o5CNOlvdgJmfHIarDTRe3XEciddEIjoyjBksIiKiAMIgi6gDcJRJ0Ulo7OCs857Urnx6gxHLdxbhvd0nUN1gG5yIsbu4QpGAQ0o5paNW+PYCLMB+ueGQPl2RV1LpMsASTP/oEMz3jOaeV0RERP6PQRZRB+HOuh5nnfcASOrKt62gDM9+dsRhYCLW8l3FWLPnJB7O6I3pmYmygy2x5ZQDe0Xjjpd3SVpTBdjPvElpMmKwekFv7m1GRERE8rC7IFEHImRSslN7YEgfcftPOeu899iag3hMQlc+4VjuBliC6oYWvLqjCANf2C67A6BQTgnApgOgeTnlgZMXJa2nsmYeWLnTZESIuRZuLoTeOgIjIiIiv8Agi4gcctV5zxF7gYCzY7mrur7FrVbrYtqku9vi3jywErJncplnx4iIiMj/sFyQiBxy1XnPGesyOanH6hMbgRKz7npiXs+droOuyinlZp/sdW80b0biTtDpjb3NiIiISDpmsojIISUm8cIxpB7r7n7XSn4td7M7zsop5WwqLLDXvXFEchweHRYve6yA9/Y2IyIiImkYZBGRQ0pM4oVjSDlWnFYjuz27p7I7QvZJSuYpKjzEYYMKvcGITYfllTeq0PYeeXNvMyIiIhKPQRYROeRO9sY6EJByrPmjkzD4+q6yXtuT2Z2sJB2iIkJEP/71yY47AMotxfT13mZERETkGoMsInJITOc9Z/eZBwLOjiWIigjBm1cyP+aPF8Mb2Z19pVWiOyPGaTUYfL3jbJzYjFtUuGVQZ96Mg4iIiPwTG18QkVOuNjIGbPfJcrTJsaNjRUWE4OHb4jE9M8EiO+Po8da8ld2RUoroaixiM26vTx4AtUoleW8zIiIi8h0GWUTkkqvOe1I2OZa6KbL548svNWJ3UQW++qkMl5r0psd07xKGBWNu9nh2R2xgNOPOvi7HInYT5MHXi9vPjIiIiPwHgyyidkhvMNoNYhzdLobQeU/qfUo/PjxEje+LKyyCLMcFiMpyFRgBgK5LGKZnJrg8lnkbdxUs9x3juisiIqLA5tM1WVVVVcjKysLevXtNtx0+fBj33Xcf0tLSkJmZifXr11s8Z8OGDcjKykJqairGjh2LQ4cOme7T6/VYsmQJbrvtNqSlpWHatGk4f/68186HyB9sKyjD0CU7MWnVHjy1Lh+TVu3B0CU7sXhrod3b5W7g6wvbCsowbc1BlF+yLNs7d6nRrc2IxXK1Rk0FYMGYm0UHRmI2QSYiIqLAozIaje7shSnbgQMH8Oyzz+LUqVN4//33MWjQINTU1OC3v/0tnnzySUyYMAH79+9HTk4OVq9ejZSUFOzduxfTpk3DqlWrkJKSgrVr1+LNN9/Erl27EB4ejuXLl+Orr77CW2+9hc6dO2PevHmoq6vDypUrRY1Jr9cjPz8fqampCAoK8sh5u5NJIOUE2ucgdrxCECL2l1o4grsTernvp5Tn6Q1GDF2y0+HaLKG87vvZmR7/LLcVlNmsE4tzsA5NjED7PhIREXUE7sQGPikX3LBhA5YtW4ZZs2ZhxowZptu/+uorREVFYfLkyQCAIUOGYPTo0Vi7di1SUlKwfv16jBo1CgMHDgQAPPTQQ/j444+xdetWjBs3DuvXr8fMmTMRF9c2yZk7dy6GDh2K06dP47rrrvP+iVpRemJG8vjD5yBlUi12vHqDEQs3F0rax8mItuBk4eZCZCXpZE3s5b6fUs5rX2kVdhdfcNr8woirmxHL2WNLymdiva4sNjIMUAEVl5uQV1IpOUiSWj5JRERE/s0nQdbQoUMxevRoBAcHWwRZRUVF6Nu3r8VjExIS8MknnwAAiouLMW7cOJv7jx07htraWpSXl1s8PzY2FlqtFj///LPPgyxHGYbymrYyJ5YGeYc/fA5SghIp45W775LY4MReELK9sFzW+yn2vOy9V67I2YzY0Wcyb9RNiI4Msxt4CYHRtoIyzPzksMPnll9qRNXlJsREhkKnDZcUgDHDRUREFJh8EmR169bN7u11dXUIDw+3uE2j0aC+vt7l/XV1dQCAiIgIm/uF+3zFWYZBiUwCieMPn4PY4EJvMGLPL5V49tMjoscrJ7gw5+z59oIQXZcwNLYaJL+fYj8Hg8GInA8PScrMAdI3I3b0mZTVNOLxDw9Z3GYdDEt5rqNjOBuXrzOuREREJI9fbUYcHh6OxkbLiV5jYyMiIyNd3i8EXw0NDQ6f7yuuMgzmmQTyHF9/Dq6CC6AtuNj641kMXbITk9/ei+oGxxvfWo9XanBhzdHzhUDC+r0rv9TkdGNeR++n2M/h+Y0FkgIsOZsRSy2xFILhbQVlssozgbZzc9Wkw+F7LuK5RERE5Ht+FWT17dsXRUVFFrcVFxcjMTERAJCYmOjwfq1Wi+7du6O4uNh034ULF1BdXW1TguhtYjMM7mYiyDlffw5ig4vHPzwkqzwuPT4Gui7SAy1nwYncQMLe+Bz97EhVneMAzprcludSSyzNg+E9JZWyyjOF4yzcXAi9wfadFRuM23suERER+Qe/CrKysrJQUVGB1atXo6WlBXv27MHmzZtN67DGjx+PzZs3Y8+ePWhpacHq1atRWVmJrKwsAMDYsWOxYsUKnD59GpcvX8aLL76I9PR09OzZ05enJTrD4G4mgpzz9efgqeBNGG+QWoVJ6dK+666CE7nrvOyNz9HPSpDb8lzOZyIEw3m/VEh+rjlHWVNfZ1zt0RuMyCupxMb8M8grqWSAR0RE5IJfbUYcHR2Nd999F4sWLcKyZcsQExOD559/HoMHDwbQ1m1w/vz5WLBgAc6dO4eEhASsWrUKUVFRAICcnBy0trZi8uTJqKurw6BBg7B06VLfndAVrjYwFVpPSylz6ojcbQLg689B6eDC3nh7x0Y4foIdOhdrfNwJDB29n2I29BVr+vAEZCTEym4I4d5n4v66PXvvr68zrta4NoyIiEg6nwdZP//8s8XP/fr1w7p16xw+Pjs7G9nZ2XbvCwkJwcyZMzFz5kxFx+guYQPTaWsOQgVYTCzlljl1NEpM9Hz9OSgZXDgar9igYfrwPshI6OYyOJEbhDh7P4XP4bE1B2UdWzi+TqvBjKy+bn1e7nwmQ/p0xZo9J52um3NFeH/NLyBU1DZJeq4n+UM3TiIiokDkV+WC7dmI5DiseGAAdFrLiZHcMqeORMkmAL78HITgArDNgUgNExyNVwgaHB1PWH81I+sGDOnT1WWAIuZ4UREhNmvBXL2fI5Lj8IeM3k5f2xElA2Jnn4mz14/TajD4+q54WOY5AFfXwW0rKMPQJTsxadUePLUuH7lbjsLVaUlt8CEH14YRERHJ5/NMVkdivYEp971xzRNt1735OViXOGYl6bDigQG27dC1GswblYTcLYVOsypRESF4fdIADHYQIDnL1uHKzyOT287d1TkLY787WYd3d59wmP17aWw/We9nVlLbcV2JiQyxaILhqsRRKiHwFrMfl3WANz0zEe/954TTLouOjjN/dJLDfcZcxS1j+sd5/O+GlLVh3EiZiIjIEoMsLxM2MCVxPDXR88bn4KzE8fvZmXaDErUaTssZXxrbDxmJsaZGBPaCGkdBg1rVNnl/Z/cJvLP7hNNyS3tjV6kAo9mgrIMdqe+nmFK9mMgQPD8yCRfrm2Vt5iuWvcD7Yl0zcrfYBsPm5xykVuGlsf3sBkqOCO97VpIOQ5fslFU6uulwGZ4ZcZOk90HqmkZ/WxtGREQUSBhkkV8L1Ime3LUsjgIk88m9mPVp5kHD9sJyvLv7hE12xNFYHI1deP4fM3rjziSd28GOq6wb0NbG/X/WH7Y4R09lcOwF3nclu87QOfrM4rQaTLy1J3p2jUDV5SabIDHPjRbwUi8syFnT6OtunO2Nu417iIgosDDIIr8WiBM9d0scnZUzSgnegtQqpMfH4H/+mW93nPbG4mpfLBWArQXleG6UMsGOlFI9XzRbEJvxlFOC6u6FAevnO5rEyw34fd2Nsz1hh0Yioo6HQRb5tUCc6ClR4mhvci8neJM6Fl+swzEPUMprGpC75Siq6prtvrajANUfsgSuAjLrMcZGhrn1euYXFhxN4ueNugm5W466/M5k3tgdB05etHn/2BXVfezQSETUMTHIIr8WiBM9T5U4ygmApI7FV+WZQoCSV1JpN8AS2DvHQMgS2BujrosGUREhqKlvkbQuy/rCgrNJ/OMfHnJ6LOH9HLx4h0VzEfP3z1X5KjnmicY9REQUGBhk+SF/uCrvTwJtouepEkc5AZDUsfi6PFPqOToKMMrcyBK48/tn77mOOgieu3Q1O+toTZo16wsLYtqsi2EeYAG2WRZ2RZWHHRqJiDouBll+JhCuyvtCIE30PFXiKCcAkjoWX5dnSjlHV+vHjADmfHZEUpbAnd8/+9mqMDS2GpxmMqIiQhAWrEb5paubEMdpNRjTPw6bDpc5vbDgahIvl70sC4MA6QK1cQ8REbmPQZYfYe2+c4Ey0fNUiaOcAEjqWJQcu5yMkJRzFBNgXKxvwfKdRXjqzr4ux+tOVszh765Z4GSP8coY104dBLVKZfNePTPiJqfvoScn58yyuM/XmWFWRRAR+Q6DLD/B2v3A42wC44kSR7kB0IjkOLx+/wA8v7HAYr2To7G4O3a9wYjlO4vw3u4TqG6wv85HiXMUG2C8t/sEpv1Xgt3GDuZjlpsV0xuMWLDJ8XPFqLjchOzUHja3u7qw4I2umkoFch1xwu/LzDCrIoiIfItBlp9g7b57vD2Bk7pXlVLjchQAxUSGIjc72eHGwrlbCi0CrJjIEMwbdZPDyZbcsW8rKMOznx1BdX2LzX1iM7JigzyxAUZ1Q4tNY4eo8BA8nNEb0zMTEaRWic6Kzf7kMJaM72/xPizfWYTyS+4FInKDJbGT+Am3XIelXxd5dWzmOuqE31eNe1gVQUTkeyqj0ejOBdh2Ra/XIz8/H6mpqQgKCvLqa2/MP4On1uW7fNxrE1PtXvHuyLw9gXM0gREotVmvM1t/PHslM+U8U+RorMKolJxsuXpfhNfVaTX4fnamTSZpT0kl8n6pANCWvbm1d4zL7NPA3O0W2TKptOHBePi23igsu4SvCs+Leo6uiwYLxlzdGPqxNQdlv76z90NsgCu874D9SfyKBwagqdUg6u+LmLFJ5c3voL9S8m+Uq++G3mDE0CU7HV40UOpzJSLqCNyJDRhkmfFlkJVXUolJq/a4fNxHjwxmJsuMtydwriYw5jwV6Ik9Z29OtqS8L4Dl99hR9isqIgQvje3n9P17bUcRXt1xXP7AZVIBeP3+Acjd4noTZWfHAGy/o3Im5K6eI/bvi/nYjABm3JmI3rGRsrOwnPBfpUS2Xcx3g/+WEBEpx53YgOWCfsLXXd0CkS/WsUnp5mavNMdexmbw9V0ltQgXe85iS1D3lFRCrbZtuiCF1C535i3YHWWCqutb8Niag3jTSaA8PTMB7/2n1G55oqfN21iASid7epmLigiBJjjIoqxQKH/MStJhd3EF8koq8cuFWmwtOGfzfFdlXq7KO4W/L2I/I21ECADg1R1XSwzlXDRgGfRV7jbuEVsCyI6GRET+gUGWnwjETXd9zZMTOPOrzrGRYYCqrTnBsbOXRB/DPOjJvLE7VnxTjLe+/QX1zXrTY5bvKhaVsRFIOWexk6icDw9KblBhTeqETWjBvmDTTy4f6yxQDlKr8NLYfm6V7MlhBEQHWADw0th+doOg7YXlGPjCdpdBopiLBs4m8UFqFcb0j8Nb35Y6fA0ha3Wioh5LdxxXZD0PJ/zKkHJxxdcdDYmIqA2DLD8SaJvu+pqnJnD2SnLkEoKeAbnbcbmp1e5jxGRshKDvXwVlol5XmMiLYb2mSc5kWsqELc6sBburFudA2/u3encpYjuH2c20jUiOw5t2fm+6RoZKCoQ8ZcadfU3vo3kQJHU9lzsXDbYVlGGlkwDrT8Pi8dSdfU3lfUplhznhV4aUiyusiiAi8g8MsvxMIG2662tiJ2ZF5y4jr6RSdHc8V80b5HAUYJlzNHmVE/QJ3xtnky1H5EympbyW1BbsAJC75ajp/+1l2uz93gzsFY07Xt7lkc16BTGRIbhY1+LwnHVdwjA9M8HmdqHtuxxSLxq4ak+vArDpcJlpTy4ls8Oc8CtDygUlVkUQEfkHta8HQLaEsp/s1B4Y0ufqeh29wYi8kkpszD+DvJJK6A0du2eJMIFzNVVYvqsYk1btwdAlO7HNSSbI1WTU04TJqzkh6JMSKAiZImGyBcDle2RNmEy/uv1nUd81Ma8VFRFika2Tm70QMm3Wn6X1701osNo0JqWp0PY+v5CdbPrZ+n4VgAVjbrY7mW3L4skL/qS+b54oMRX7OGffC074xZOaERSqInRay+fptJoO0c2RiMgfMMgKENsKyjB0yU5MWrUHT63LFxU0tHdSgwhHk3OB1OYNnmA+eZUb9E28tafNpsjWk62o8BBRx1q+q8Tud81ewO/wtSJCMOPOvjjwfJbF5C49Pga6LmESz+7qlfmFmwtdBn9CKWFUhLjzNRcZar+LkHlwMDLlWsmTWb3BiN3FFZLHIwR2UrM+UgKn2E7iPg+xjwM44VeCqwtK9r4bI5Lj8P3sTHz0yGC8NjEVHz0yGN/PzuT7TUTkJX5XLrhp0ybMnz/f4raWlrY1IwUFBZg/fz4+/fRThIRcnTQ9++yzmDBhAgBgw4YNeOONN3DhwgVcf/31mDdvHtLS0rx3Ah7AjSUdc7SOzR5XZXD+sPje/Iq13KCvd2yExc/2SukMRiMmv71X9DHNv2sAbN5v8819nZW7Wrex/t97kvD4h4ckn6OUsrURyXHIvLE7pv7ffnxbJD64+fvv+9s91+jIENyb2gPa8FBTcCm2xFfuej93sj5SsiAGsdlxiZE/y6DdI7cE0N2OhkREJJ/fBVljxozBmDFjTD+fO3cO48aNw6xZswAAR44cQW5uLu69916b5+7duxe5ublYtWoVUlJSsHbtWkybNg27du1CeHi4185BSb5oUx5ozCdwu4svYPmuEoePdTY59/Xie+sr0XKDPnvnYT3Z0huMktZrCd+1OZ8dwUU7nfCqG1rw6o4ivPefEw47JTra4+dPw+Lx8Q+/ymrDLuY9khrYdAoLxu9v+Q204aFIj48xfbe2F5bj8/yzqKprxju7T+Cd3Scs1oe5msy6s97PWfMbV/sviVkvp1YBlbVNOHauVtR4KupcNyyxxgm/e9gYiYgosPhdkGXOaDRi1qxZ+K//+i9kZ2ejubkZx48fR3Jyst3Hr1+/HqNGjcLAgQMBAA899BA+/vhjbN26FePGjfPm0O0SJkPlNQ2oqmtGTKcw6Lo4v6LLfWbEESZw7qwpkdsowpGo8BCbzn3OWF+Jlhr0SWki4OzKuCNGwG6AZa66vsVudtVZNnblt6V4/f4B0IaHmPYP04aHYNHWo3DF1XskNbCJCFXjclMr3t19Au+aBVEA8N7uE7KzyXJLP/+Q0RtZSTpJmTHrxiDmn7UjBiMwfZ34jKKvL0h0VMwIEhEFDr8OsjZu3Iji4mK88cYbAIBjx46htbUVy5Ytw4EDB9C5c2eMGzcOU6dOhVqtRnFxsU0wlZCQgGPHjvli+BacXU13ti8R95mRxp2W0XICD3vitBpMvPU6i41cnYmOCMFiO9kfKRvIyiknk1JqKYURltlVMdnY3C2F+H52JjISYwG0BSXv7i6V3ZVO2PT52U+PSPoc65sNFj+X1zTisTUHERUR4lY2WWrpZ6ewYPztvhSngZuUMuIRyXF4/f4BmP7RQbjTL4fdAH2PGUEiosDgt0GWwWDAihUr8Nhjj6FTp04AgNraWqSnp2PKlCl45ZVXcPToUeTk5ECtVmPq1Kmoq6uzKQvUaDSor6/3xSmYuLqaXubkanig7TPjqnTJ08dyt2W01MAjTqvBvFFJiI4MtchQlpy/LOIMgZzhffA/WTcAAPJKKm3O1dUGsgK5JUOWpZYVWL6rWNLzHSmracSCTQVQqVQwGo2Ss7HutKFWep8zAE5LGcVkk6VeBMnNvll2ZsxR4BcdGep2gAWwGyAREZEYfhtk7d27F+fPn8f48eNNt2VkZCAjI8P0c0pKCh588EFs3boVU6dORXh4OBobLSczjY2NiI6O9tq4rUkpE7J3NVzseoqLfrDpqpjSJU8fS4k9YqxLcmIjwwAVUHG5qa2rmrFtTYp5MLStoAx//fJnyRP7oQndsL2w3O65zhuVhE2HnXePjAoPweuTB2Dw9V1lT3yFK+Pp8TH49OCvipVLfrDnlKTHWwcictageGqfMzGcBVJSL4LotM7XkMopI3Y32821P0REROL5bZD15ZdfIisrCxERVzul7dixAxUVFZg4caLptubmZmg0bROYxMREFBVZlmgVFxdj2LBh3hm0HWLLhOxNioRMzt3JOry7+4TD5xqMQM6HB7FC7bsug0p2QHT3WEosEJdSkiNnYi9k1C7WNSHnw0N2z/XxDx2voRFUN7RArVIpli2cN+om5Hx4yGGAqo0IkdWgQoyK2iZsOHQGVZebEBMZCp02HFlJOtFrUHy9z5mzTa+lrPcT06ZdThmxO9nu6cP7YEbWDQ7fd64RIiIisuS3QdaBAwfw//7f/7O4zWg0YvHixejVqxcGDx6M/Px8vP/++5gzZw4AYPz48cjJycHdd9+NgQMHYu3ataisrERWVpYvTgGA9KvHwuPllDz5qsugkh0QlTqWtxaIy5nYCyOYNyoJuVscn6tYcjMUjrKFjw6Lx6bDZXYDVAB4zEkDBbnUKiB3i22jC10XDSal90Tv2AiXn6Gv9zlbvqsYy3cV2824imk+AbR9N8SU40ktI9YbjDAYjJKbsQiGXB9r93dJyew1ERFRe+K3Qdavv/6Ka665xuK2rKwszJkzBwsWLMC5c+cQGxuLJ554AtnZ2QCAIUOGYP78+ab7ExISsGrVKkRFRfngDNpIvXpcUduE3M0/4R0nmSt7fNllUMkOiEoeyxsLxOVM7IWARRseqkhQICdDIabbX3RkqN0AdUh8DPJKq9wetzlHa4XKLzXi1R3HTT/bm8ALmZR/eWhjbhXaMng1VzJ4rgJgRxlXV+v9pAQnUtYeKrFG7ZEPfkB9s95irGP6x2Hlt6Xcv4+IiMgOvw2yDh2y30544sSJFuWC1rKzs01Blz9Ij48RffW4rcua67bVzviiy6CSHRADrZui2HFMH94Hid07WwQsG/PPuPXacju9Sen2Z51R0RuMOHymRu6QbahVjgMse6ybxCjZ5MIe4exfGtsPgO3GxPaYZ1wzb+yOAycvmoJV8/LH8kuNFqWRUjKtYtcebi8sV2SNmnmABbQFUo4asnD/PiIiIj8OstqLILUKD2f0FtXOW4m1JNd01nh1jYTeYERFrbiNSWM7hbl8jNisTEVtEzbmn/H5GhCx481I6Kbo5sfudHpzJ1u4r7TKZsIt1ZTBPXFL75i2rK2MiwpCi3iDoW0topTfm6jwEPy/Ib2wbKe4LorW6/iEAOn9vFL8q+Cc0zGW1TRi8OIdqKq7eoFFbimdvd9pV2sPs5J0GLpkp9P3Jyo8BDnDE0TtSWZ9fq7ut/cd4votIiLqKBhkecH0zES8958THmsYAFg2Uhi6ZKdX1khIzSI8/c98LBjjvDW12G6K5pNzX64BcadlvDubH7vT6c2dbKG7GUS1Cph3z80IDVa7lckrq2nE8xsLRL9vpozUuLb9yJKu7eKgo+NNiI4McxgEBKlVqGlodhpgmTMPsAD5jWCcrXtytPYwr6TS5e9mdUMLamSs0RKrvKbBtDXBiYp6fLTvFMovefZvEwM5IiLyBwyyvCBIrcJLY/t5rLW0MH0Y0z/OYac6pddIyOmod+5Sk8txiNkQ2Lq8TDi/f0xMw7naRpysqkevmAhMGdIbocFqCSOUzp2W8WKbIQi6Robi+VE3SS4ts+bO3mvu7sf2yO3xps/E3WNVSdi2wDooldsYRSi1lEtMKZ15kHCios5uFtz6d9re2kPxAbHn+jHmbjnq9HNS+m8TG3EQEZG/8OwMlEyE0p6oiBC3j2U9L9NpNXj9/gHYdLjMaae6hZsLoXdnN9Ir5LbKFjsO4b3SaS0n4Y7mv8Yr/01fdwi5W47i/byTyN1yFDfO+xcWb5U2IdYbjMgrqcTG/DPIK6kU9X45Gq9OqxHVbv71+wdATLhUWdcMnTYcQ/rI3xMLuJpBc3QEFRy3EXf1XAAICVLZ3K9WAX8aFo85I5MkHctd/29IL3z0yGB8PzvT5nMQGqNkp/YQ/Z4q0cHQvJTO2raCMgxdshOTVu3BU+vyHZYZi/ldio10XZ4LAIN6d/XY5+AqEFbyb5Nw4cf68xECuW0eaoxCRERkDzNZXqI3GPFz+WW3SgaFSdDySWk2JU1KduVzxZ2Jpvk4hHHbyyRYZxrkrN8xGGFanG8+uXfEnavg7rSMj44MFR2w2stOSC2PcpZBk5J9s5dpVAH4x6Q0ZN7YHR/knXCaVZSayTPXWROE2kbXa8PudpDlkUvJhivWx5KaHXb5Oy0yalIHuc4ee5ISf5uU3EaCiIhICQyyvGBbQRkWbPoJ5ZfENYhwxNk6HG925VPiGNsLy/E//8x3GtCYt2B3Z/3Oqu9K8fRvb3RaOqjEZspyW8ZLeT+tS+zcCQztbSwcFRGCxWP7ydrsuYsmGGk9o3HmYgMA4I+3X+/yfFy1NXdk8e/6YdG/jslaC+cOMc1bxDL/LN3ZSNnR96fisri/NxWXm5Cd2kPy56AC7O6pFhMZYrMWTQx3/q548yITERGRGAyyPEzO2iVrf8jojawkndMMhTvrbKRS4hjv2tkHzFFAI6WDoT0GI/BB3gmHk36lN1OWms0S+352jQy1CBrkBobOvpMX7WRaHXW2EzJ3b39Xgp3HLuBSYyv+ffwC/n38AhZtPYpHbo8XlUEckRyHzBu723Tic+RPw+JxT2oPBAerZa2Fk0u4WOIuewGgO9lhR98fqX8TzD/T7YXleHf3CYeZrWizYPyZETdZfD++KizHexL3+ZMyXnsCbesHIiJq/xhkeZA7V6cFUREhSI+PcXn11Z0ud1IJryV3UuhobyR7AY1S+yCdrKp3eJ9SV8HlZpUu1jWL2i8qNzvZFDTIDQzFfCef23AEmTd2R2iw2uU5ffPzOXx97ILNMaSWah44eVFUgPXn/07En7P6AnCcBXOn86IjSlwsARwHgHIn/9FX/j7Y4+p7JfxNGNgr2tQBUAiih/TpivT4GJv3Nio8BA9n9Mb0zETT+M0zuHqDETkfSiv/VOJvkzcvMvkTdlIkIvJfDLI8SIlF8jX1LaLK1YT1LY/JWGcjlbPXcka4Ku4smDAPaGoamhXryNgrJsLhfUpcBXc0CbfePNec3mDE8p1FovZQy7yxG6IjQ9HcasCBkxexu7hCVmAo5jtZVdeCwYu/xn0De2Dlt6UOM2X/mJiKVd/Z35BWIKZUExD/GcR3i7T42Z21cGIpcbFE4CgAPFFRJ+t4jsa0raBM1B5iY/rH4Y6Xd0luD+/IvtIqSV0flfrb5M2LTP6CnRSJiPwbgywPUqI0RWq5WpSddTbaiBC85GKdjVRZSTpEhgahTsLGtDqtBiOTdXhHRClReU0D/vrlz4pMbNUqYMqQ3g7vd/cquKtJuLB5rvnn11Z6VmixZ5AzO49dwM5jF0RlvMxZfwfFfier6ppNmShrwndyzudHXI7FYASe+eQwJtza02PlrnLXwomlxMUSoK3c89+zhtsEnNsKykQF2vZU17fY3fBXTFD43zd1cxpEO2sP74jUv3lKZR3d2UohECmxhpSIiDyLQZYHnahwXKImhZhyNWflTDUe2AR5X2mV6ABr+vAEZCTEIj0+Bnt+qRQVZFXVNSsysQWAUf2cB6fuXgUXMwkvq2nEq9uPIyMhFhfrmkVlGeyR2uX6ms4ai5Iid9a2mTMCorr7AcDn+Wfxef5Zp1fZxZagXpSQJVGKUut4KuuaceDkRbsBkTusxyc2KNxx1LbME7gaoCzY9JPkbnxig+Xpw/sgI6GbollHb5aP+hI7KRIRBQYGWR6iNxjx0b5Tih7T0WRPzJXruRsK0NCsd3sjW0F5TYOox4UFqWGEEQajEV8WlOEvXzifUAoBTYyCXdw2/1iO/Se+xqT0nugdG2lT9uTuVXCx78XyXcVYvqsYapV32mSrVcDOY+dsujj6ok034Pwqe5BahXmjbsLjHx5yeozcLYW4K9m7k0cl1/HIDYicEQLpPSWV+E9JBfaWVrp1PEH5pSbM/uRHLBmfYvN+O1oLJPaCxYysGzzyGXqjfNTX2EmRiCgwMMjykH2lVaJLwcSybvm855dK5JVU4teL9S7/0a2sa8aMfx4G4H7d/raCMtF7VjXpDXh9Vwle31Xi8rHmAY02PFTW2Bwpv9RkUZJl/R5kJenw5zv74r3dpahuuJr5s74Kbm9yKWUNCiA9GyWXwQi7a6Z8EWAJr+vsKnvR+csuj+GLyaOrwEEK64DN3SxZnFaDi3XNGPjCdrf24HPkk4O/Ysexc3j4tnj07BqBqstN+PViPTYeLrP43pv/Pvm6bM/T5aO+xk6KRESBgUGWhyj5D5x1udq2gjI8+9kR2ZMqd+r2leqyZo82PAQPZ8Qj88bu2F9ahajwEIuAR0nmDSkAiOqi5mih+Yibu3tkjIFA6ro8R1fZ9Qaj6Lbfu4svKJqdcNWhzdUGzGLFXenkt7u4AnkllQCMbmexxvSPw+MSu/lJVV3fgld3HHf6GOu/KR2hbM9XOmonRSKiQKMyGo2+urDtd/R6PfLz85GamoqgoCC3jpVXUolJq/a4PSZhqidMXrYVlEnu6ufouDqtBt/PzrRoC+5ssqk3GDF0yU7F1ko5IrW5gzsiw4JQ12QbJJi/71lJOoddAH1VeucvhPOX+j68NjEV2ak9TD9L/X1RqoualA5t9h4r5ULAn4bF4+MfflUk46RWAcsmpOKFrUfd3uRcKdZ/U9he3DOEv8OuSjLN/7YTEZE87sQGzGR5SHp8DHRdNG6XDEaZbfqpNxixYJN7i+QF1hkFMZNNpbqsueIqwIqOCEFTqwH1EjIojtgLsICrgcOznx1x2gVQGKpKBXTEyxXC+6TTavCHjN7Yf+Iivio85/J5sZFhFnsziV3XJhCbjXU20XfVoe31+wcgOjLU9NysJJ3Neh+D0YjJb+91Od57UuIcdmuUY/mktrH5S4AF2P5Nae9le77S0TopEhEFKgZZHhKkVmFSek+XZTauhAWrkZWkA+CZdV7naxtFtwP2dY2/NjwYb0weiFt7x2DYX3cpEmQ5YwSuZB1cZx46YoAlECbXSddq8eBt8bhh3r9cvh9Prz9s8V2OiZS2Bk9MFzVnFw6yknROO7QBwPSPDloE/PYyXFt/LHOZedV1CcP+0ipJ5+dInFaDibf2RIvBgN3F9rsD+pqv/050BCzJJCLyfwyyPKh3rOMNcMUqv9SEPSWVyEiM9cjkJTYyDDM/OSyqHbDcDVOVUtPQih9OXIRapVI82OxoVGjbPw2AYg0TctYexEO39RYVcFp/fnJaszvroubqwsGf7+zrMitrHThZX3QQs+GvCsCk9F5uX2wRlNU0KnYsT+FaIO/oCJ0UiYgCmdr1Q0gupYKSnA8PYuuPZZL2OHps2PUID3H88arQdlUcKohqB+xoTZL1MT397/urO47j7e9cdyok54wAhibEKtqRrrqhBUu/lreprjuJQOuLD672EQKA9/4jvXRPeO7CzYVobjW43DZBrQJevz8NPbu6f7ElEAh/UxztJ0fKE0oys1N7mEo0iYjIPzCT5SFK7pNV3dAiqYNYRKgab337i8MJoHnd/tdHXa+fAYC3vv1F1OP+ODTebttwJX19zD/LpAKJCsAXP5b5ehiKiLXaU03MPkJyg0vhosMHeSdEZcKiI8NQeLZG1msFEm+sBWIjDSIiCiR+mcnaunUrkpKSkJaWZvpv1qxZAIDDhw/jvvvuQ1paGjIzM7F+/XqL527YsAFZWVlITU3F2LFjceiQ881NPaVt/ZRvFqXXNxucXmHXaTWm1uXvimybLWb906h+ce1m4t7etaclZE//Mx/bCq5+77yxJuhkVb2ox52vbZS83iwQCX9TPLUWaFtBGYYu2YlJq/bgqXX5mLRqD4Yu2WnxuRMREfkTv8xkHTlyBNnZ2Vi8eLHF7TU1NXj00Ufx5JNPYsKECdi/fz9ycnJwww03ICUlBXv37kVubi5WrVqFlJQUrF27FtOmTcOuXbsQHh7u1XPwxeLviNAgl8GQWgXsfPq/EBqsxtAlOxV9/S+OcMIjVkdv/a6kc5eaLNZKeWNNkNidL9rz+qQummCMG/Ab/PZmnUezSmIb8xAREfkTv8xkHTlyBMnJyTa3f/XVV4iKisLkyZMRHByMIUOGYPTo0Vi7di0AYP369Rg1ahQGDhyIkJAQPPTQQ4iOjsbWrVu9fQpenVxprqy9EpNtMhiBD/ee9Fo7drJvVEocVLhaZkXyma+V0huMGNgr2qPZI7UK+GCP81Jg8/VJbds5hDl9fCAYfkM3dNZc3SPkUmMrtv1UjpqGZo+WCDpbX2cEMHdDAZpbDR55fSIiIrn8LsgyGAz46aef8M0332D48OEYNmwY5s2bh5qaGhQVFaFv374Wj09ISMCxY8cAAMXFxU7v96b0+Ji2xhJe0NgibYJxsqqebZZ9qFNYEF6bmIYVDwyAzkvfEbnUKmmBoApATGSIp4bjkHmDljte3oUqGd0KxXK1j5v1+qQgtQoLxtzssfF4WpxWgz8Ni8c3P19AbaPlhRwhm+Spsj0xF4Mq65oxePEOlg4SEZFf8bsgq6qqCklJSbjrrruwdetWrFu3DidOnMCsWbNQV1dnU/an0WhQX9+2PsLV/d4kbBjpj5mKXjER7bqMyd/9dVwKgtQqjEiOw/ezM/HRI4Px2sRUTB+e4Ouh2fjvm64BIC7QEh5zb2oPRccQExmCP2b0FvXYV3cU+TxD6+n1Sd7026Rr8NdxKdiYf9Zpt0Yhi6g0sReDqupaPBrsERERSeV3QVZsbCzWrl2L8ePHIzw8HNdeey1mzZqFb7/9FkajEY2Nlv/oNjY2IjIyEgAQHh7u9H5vG5Ech39MTPXJazuiAjBlSG9Tps0fg8D2LDIsCHeZTb7NWzBnJMT6cGT2FZy5hNfvt824RUeEICrCMmMlBBd3Xtk8WwldNMHYPfu/FTmmN77r80bdhO9nZ1oEWELJWyD6qvA8pry7z2kTH/P9ypQm9WKQp4I9IiIiqfyu8cWxY8fwxRdf4Omnn4ZK1TYtam5uhlqtRkpKCv7v//7P4vHFxcVITEwEACQmJqKoqMjm/mHDhnl0zI5aC28rKMOzGwo8+tpSZd7YDaHBbbH1/NFJmLZGfGt4cl9dkx7LdxbhqTv72twnBL6+zsSYK6tpRHRkKL6fnWnzHQdg93uvNxgRp9WgvKbR7eYelxpbMeSlr/Hi75LdPqY3pt6xncNM74Hw3lTUNvnVZ+opnihBFn4nxHzuzjanJiIi8ja/C7KioqKwdu1aaLVaPPzwwzh//jxefvll3Hvvvbjrrrvw97//HatXr8bkyZNx4MABbN68GW+88QYAYPz48cjJycHdd9+NgQMHYu3ataisrERWVpbHxrutoAwLNxdaTKLitBqM6R+Ht7717H5RcgyK7wq9wWgqV/vznX3x6o7jvh5Wh/LqjiLcoOtsU04WpFb55ffmfG2jKeNmzd5tQqnstDUHFemiWF3fgsc/PIQ/DYvHym9L/boz44mKOrt/EzqC2Mgw5JVUKrqPlfl3SSyuNyUiIn+gMortRexF+/btwyuvvILjx48jLCwMo0aNwqxZsxAWFoYjR45g0aJFOH78OGJiYvD4449j7Nixpudu3LgRK1aswLlz55CQkIDnn38e/fv3F/W6er0e+fn5SE1NRVBQkMvHO2ot7O/itBrMH52EEclx2Jh/Bk+ty/f1kDqcOK0G38/OtJiE6g1GDF2y0+8m52unDjKVMordEFZvMGL5zmK8t7sU1Q3yNv61FqfVYN6oJORu8d8AJio8GDUNrQH3N8EdKgDaiBBogoNQfsnyYpPwd8Zd2wrK8NyGAlENTT56ZDAzWUREpIj/3969hzdVpmsDv5M2PaRADxRJZRQK5WAttRyGUxG3SAcEgS0gB5VRZ0AFdJRvQE52g7ujwMbZKB8jRx0+BdFBZRCpKAgjwhSQQ7EFK20toNICbWmhKaVtku+PsmKSZiVrJStZaXv/rsvrGpKVlTfJgnmf9bzv88iNDWwFZJClFjlfZKBOiOV469FeiAwPwWNvH1F7KC1S+qi78GRqvHV52aZDRcjY9b3aw2pkyx/7I7VrrGjWdvHoRKQlGqzB17lSI7YevWC3jycqXIe74loh68erXo1l0ci7cNVYi3f+XSS7qqY70XodQoO1duOOidCh3KhMkNgcucoqCqG3UkVAauvNGLB0r+jvoUHDvkDHmxdERESeYpClEDlfZFZhGaZsOOynkfmGVgM8d38CVu0rUHsoLZawtPTTU8UBG7C/OTkFocFap1lbYZIdpdeholo8GAnkJX6CKL0Or/1nT0RHhFgzdX06RuO+FfsV2V/WHBnahKKm3iz62ysd+AirBwD760npgI6IiAjwLsgKuOqCTUVzWPdvtoABlsqKK2uw7kBRwAZYABDbKtRlQ1gALgMs2+MCWWV1HWa9fwKVN2oxNqUDBnZpi5BgLdJH3dUkxu9v6aPuwl8nprj87ZWuPDgiKc5pf7nmVDafiIiah4ArfNFUsM8UOaMB0L5NKCwW4NJ18bLXTUVcZBhgQUAHgUqxoOH3e2XnGaQlGqwVQgNxCWcgiG0ditIqade4kjelRiTF2S1PVarIBhERkZIYZHlITmlhkqcpLC0TYwGwZMzdAOB0WVNT81ByHEqNTT9YlMo281J5o7ZJFrbxFzk3mpS+KSVW7ZKIiChQcLmgh4TSwoB/mpxKEa0PRkI7dRove6N1mH2sb4gMwzND4lUaTYNWocFYOfEevPBAgkevF5Y1tW+j3OQyIiTI79fahm+KcK7U6Od3VV9J5Q0s+dT5EkkCYvQ61JvMKKm8gZiIENHrUoOGbKjQV42IiKilYCbLC8JEev4nOW73pPjD1ep6XK2uV3sYkgmb4r+eez+On7/aaOlPrzujVes3VHWzHre1CcP/fPGDrNfZLjdroNw0XResBWpNip1Pqq1HL8DQJgyXrrWcrG1DdcTmv0TSU+XVdZj6zlGXxwiB1+LRiVzKR0RELQ6DLAVUBkCA1RRZ0DABCwnW2i39MZktyCosw816M15/5B7AApQab+JcqRHvHCxCZY1/AsnXv/hBdoAnLDdbvS8fb+zNVzQoqaiuw4sPdMWqffkw+zHaKbl2Ey8+0BVvfJXvvzdV2dFz3pWap4YbKEr1ySIiImpqGGR5wWS2iFZdI/f+o3s7RIaHYEf2L9YM1p4zJaK9mF4Y1g19O8XgsY3+6et18qcKj1+77sCPPrkuzpcZ/RpgCf5+qAj6kCBUq5BJo6aJ3UGIiKglY58sG3Jr4TeHXlmBRKzXkm0PnLREAwYv38eCI0QBjr2riIioqWOfLJU05V5ZugDcIyG2r00Ipl7ZeQYArAVHiChw2f69NamRfiUiIlIRgywvNOVeWX8Y3AnPDIkPmMqI7tiW1hYKjkTrdX57/6byPREFEqWbERMRETUVDLK80C8+BjER/pvoK+kfx37GPb+JQmR40xq/kD0ckRSHR/vf6Zf3nNC7AwyRTTegJv8LCWJYbmvPmRK1h0BERORXDLK8EKTV4C9jk9QehkeuVtdh5vsnUXGjaVVGtM0e+mMaq9UAr41LxsF5Q7F1+gD8IbWTH96Vmrq0xPZqDyGgvHPoHHbnFqs9DCIiIr9hkOWlkcm3Iy3xNrWH0ew5a2o6sHOsz993+r3xCAnWIkirQb/4GHyeK++OfABufSMf0gBo3zoE/y4sU3soAUXoH8e9WURE1FIwyPKSyWzBt+yp43NCTy3bpqYDurRFlJf7sqL1OuhDGv810AAY2qMd/qN7e5jMFpjMFmw6VCS7b5bZAvS5M8qrMVLTYQFQddOEq+ydZ4d7s4iIqKVhnywvrd6XL1oVj5Tzh9ROjcpAB2k1WDauJ57dfMLj816trsOWaf1hNlnw8cmfUVRqROGVKlTdNGFf3hXsy7tiDeQ8/Z2PX6jweHzU9BjZS0xUU67ISkREJAczWV4wmS34+6Fzag+jRUhLNDh9fERSHNY+3hvRes/vF2w+fB6DusZiRJIB3/1ciaqb9pPkiuo6BtJEt0zo3cHj1zbliqxERERyMMjywtGi8iZXOKIpahsRYrcXC2gIcLMKy7Aj+xdEhocgfdTdHp//89wSpC7bh/mf5LDBcTOxaORd1iIl3BanDGFf5GvjkmUv03W2p5KIiKg543JBLzTVpS8aIGCCiSi9zm2WaOqAjvjsu4u4rXXDJG3PmRK8svOM3f6oVqHyunA7KrnWNH9Lcu5v/yrAsnE90S8+Bgu356LcWKv2kJo0IVBdPDoRx89flZXZtX1tECvBEBFRCxGQQVZeXh6WL1+O06dPQ6fTITU1FfPnz0dMTAwWL16Mjz/+GDrdr3dS58+fj0mTJgEAtm/fjrfeegtXrlxB586dkZ6ejl69evlknLERoT45r68ZIsMwMsmAt1Vc6qgB8LdHe0Gr1WDG5hOiQZ8+JAhvfJVv/bNYUOa4xI9atorqOjy7+QT+mNoJU357B/72r0K1h9SkRIQE2e0ti47Q4S9jkzAiKQ47sn+RdS5DZBgWj05stKeSiIioOQu4IKumpgbTpk3DxIkTsW7dOhiNRsybNw8LFy7E2rVrkZOTg4yMDDz88MONXnvkyBFkZGRgw4YNSE5OxpYtWzBjxgzs378f4eHhyg82AG/KajUNFe0EcZFhSB+ViOiIEJRU3kC5sRYxrUJRXnVTvUECeOGBrhiZfDsAYM3jvZ1mpqpumlBd23h/FJFUat5IaMp0QRrERIRYM4Dlxjpk7PoeWq1G8r6q5+7vgtSEdugXH8MMFhERtTgBF2RdvHgRPXr0wKxZsxAUFISQkBBMmjQJL730Empra3H27FkkJTlvALxt2zaMGjUKffr0AQA8+eST+PDDD5GZmYnx48crPtZSlQMVZ4QA64+pnTAs0WCd4OzOLcb/fPGD7BLkvhLfLsL6v0ckxSEt0YCjReW4fL0Gsa1C8ed/ZDM75UJMhA5TB3TEm18VqD0URIYH43pNPdgCqfmouFHf6LGSyhrM2HwCf3u0N+Iiw1BSWeM0A61BQ/Zqdlp3BldERNRiBVzhi86dO2Pjxo0ICvp1j80XX3yBu+++G3l5eaivr8eqVaswaNAgDB8+HOvXr4fZbAYAFBQUoFu3bnbnS0hIQF5enk/GGqiVsjQAMnNL7AKsGZtPBEyABTT+7oK0Ggzs0hZjUzpAq9Gg5FrgBbCB4PcDO2Lr9AH4dlEa/vRAN0R72SdMCcvHJ2P1lN5On+MUu/kQAqqMXWeQPuouAI1/X+6/IiIiahBwQZYti8WClStXYv/+/Vi0aBGuX7+Ofv36YerUqfj666+xYsUKvPfee3jnnXcAAEajsdGywLCwMFRXV/tkfP3iYxAVrv4k15Ft40+T2YJXdp6RVehCqwHu797OV8ND24gQ9OkYba0OmFVYBpNNGsQXBUX6dYpW/JxqeDApDgO7tLVOYAd2Vrdam9C/TKuF04pzUXodnhkSr8LISMyDSc7bIUgh/NsSHRGKNY/3hiHS/maJITIMax7vzf1XRETU4gXcckFBVVUVFixYgNOnT2Pz5s3o3r07unfvjtTUVOsxycnJeOKJJ5CZmYlp06YhPDwcNTX2E/SamhpER/tmgh2k1eCp1His3HvWJ+f31uXrDYGW3AyW2QI8PaQLJv32Dsz/JEfxfVDjenfAfSv2240rzmZzvC8yhH07RePouaten2fWf3RBTEQIfq64oXiPNNs9MGKuGhsyfLtzixvtY1NDWqLBmil1Fshfra5DrzujsfbxaCz59DQzlCqK1uuwdFxPpCUaMHj5PtHlflJcvl6DsSkd7Jb5CtU/mcEiIiIK0CDrwoULmD59Om6//XZ89NFHiIlpuFu/d+9elJaWYvLkydZja2trERbWMCnv2rUr8vPz7c5VUFCAIUOG+Gyszw1NwN//XRSQBRluax3mcVbIdhK1el8B1h8otKs25qm0xNuw8ZuiRpM7Yb/Hmsd7Y2iP9oqXmQ8J8q7EOwBEhevwf3736z6T/vExjQIdKSXpHQl7WBaOvAvPbz3p8tiMXd8D0GDW++IVGf3F0CYUfTpG474V+0XHogHwys4zODhvqP2+u4hQQNOwrzH/UhVW71d/b1lz8tajvREZrsOhwiu4WFGDDtHhGNQlFgM6N2RBF49OxIzNJzz+eybcCBGW+VJgMpktDIKJiFQScEFWZWUlnnjiCQwYMACvvvoqtNpfVzRaLBYsXboUHTt2xIABA5CdnY13330XCxYsAABMmDABs2bNwoMPPog+ffpgy5YtKCsrQ1pams/GG6TVYNm4nnh28wmfvYcnovQ69IuPwdGico9ebzuJemFYV9zZVo/ZH2Z7PJ5ofTAyxiTh1c/znE7qLPh1Qt46VKdoAGFoE4oPvv3J6/M8ldrJboLiWLDjttZhMJsteOztI5LPabuHJTI8xO3xxZU1eHlHruoBFgAsGXM3jp+/6jKbZrt0dWCXtk4n5FmFZX4LsgxtGtou+CqjJjVo0YcENaqcqQRDm1AsGXM3RiTFYXduMbafvGj9ff62v9AuY+ysqqehTShq6s2orK5zWdSCTYUDn7NsdxzL6RMR+U3ABVmffPIJLl68iM8//xy7d++2e+7kyZNYsGABlixZgkuXLiE2NhbPP/88xo4dCwAYOHAgFi9ebH0+ISEBGzZsQFRUlE/HPCIpDmudTFgcy6n701OD4hGk1aBffIzLSmCOxCZRhjaeL+HrdUcUPpoxyO3SRWFCnvVjqeRz/35gR7SNCMHKvflOn9cAmNLvTtHnpWoVGoTnhnZt9LjjnXyT2SLr+7btISS1/5DUxrq+ajodpddh2biessYsZFSd3VmXe406M6RrWxzIL3N7XK87o9DD0Mbr68GZh1Nux/bsi26Pmz2sG54bmoDDhWXYfOQcDuSXwuhFJc1FI+/CbW1C7TIVYks4hYzxi8O6oVOsHq8/cg9gAUqNN+2afTvLcrGoRdPh7vfnvjkiIt8LuCDrqaeewlNPPSX6/OTJk+2WCzoaO3asNejyJ2dZjavGWsx6vyHD5c9YK0qvw3NDEwBA1tIgV5MoYSLsyR6gC+UNhUekLl2UE5gKhSC6G1qL3rW9WW+WNV5n/md8sqSJpavvW3i1MMF1XL6j5F605+5PQGJca8x6/6Tka0+jASxODo6JCEH/+Bh0aReBgZ1jMcCm8IbUMd/WOszlnXVvl69JCbAA4PPcS/g895K1SIeSy3w7REvrxdcpVo8grQapXWOR2jUWh/JLZWU/HSXe3gapCbHWP7sqdiM8ZruPVPgNhJsFolkuZkGaBHe/v7BiIC3RwGCZiMiHAi7Iasqc7U94+ud4bPimyOnkVQ45y4uWjevZaFmbs0mTY6bN1SRKCB48WRZZZqzF0aJyyRPyaL37ZXNAQ68oIePmLMgVApisQmkTcDHPDIm3Nk6WwtNJqpSMTrReh6sSAoPUhFgM7NIWa7QayQUnxK7Rq8Za7M4twZrHeyO1a6zdc+7GLGRGhRsOru6sO/vOfKXy1nd4z28icernSq/OJXzGgZ1jsXp/odvjHf8elBq9W7ro2K9PbrEbZ9kNV3+fKLBJXTEgLOElIiLfYJDlQ7tzi7H+QOMiD57Y8Pu+0Go0dlmyjF3S19s7mzT16RiN4+evSp5EjUiKw+xh3Tyqpnj5eg0eSr5d0oQ8tnWopHM+nNLBbrxim/ClBAKReh3CgoNQcu3X7zMmIgR/GZuEkcny79x7MkmVknU0WyyI0usk75kRxrF6X4HHVTBd3f2WkrlLH3UXMna5v7N+cN5QDO3RHgOW7kW5UTyQFMu4efKZikqNXp3HNvs7oEtbSde341JcbzOYjq+XW+xG7PdlUYumServ74tWGURE9CsGWT7iSX8qMXGRYdaqYLaGJ8mfxDtOmuROop4bmoCtR8/LLhxwW+swSRNyqQUgAGBYorR+P1Led9mt0tZK3rn3ZJIqZMHESudfu1FvHb/UPTNC8ZLuhlZYuD1X8p4uW67ufrvL3EWGh0i+sw7AZYAFeB9g2b7vtZp6r/auOWYnpVzfYktxPdmTJhS4seVJ0MbsRvMhZwkvERH5TkA3I27KPOlP5YwG4hvNhUn82JQOdg1qfSlIq8GSMXdDg18njq5o0BAk2mZW3DUxFSadYud3PKcUUt5Xje/TmbREA8KCnZecF7IOUXod2rcR/yzOjEiKQ/qou7wam9jd7xFJcTg4byi2Th+ANyenYOv0ATg4byhGJMXJurOuxt31oT08a7ydPuou62cUSLnOHAk3AQBpf6dsOTve3d8fV5jdaPp88e8nERHJx0yWl8T6kCgxWQnUcrtimQtHYnfv3S2lk5rxkhsENZV9JkeLyu2WLTqyoKFgw5Y/9oZWq5H1WQyR0ooziHF191sscxfod9an3dsFCbe1woZviiQXXYmLDMOTqfFOv29PrjOpf6ccXa2ua5R98qYPFrMbTZ+v/v0kIiJ5GGR5wVW1NE8nKzEROqQ/dDcMbQIzABA4TiTPlVZj69ELdsGBu0IarpYl+arCWVPYZyI1QC813sTYlA6yzu3p0jRv+iNJLY4hnNvd+LS39mSJnSsmIgRlEpdECnf0B3Zpiz//rgfeyzqH8+XVqL5Zj49OOC9P7yq7LPB0qWhaogGbDhXdajotjbPrRW7Qxv5XzQsrRBIRqY9Blofc9SH526O9PJrMvvZwzybzf4COE8nnhiYomiVqKpknpfky8+NJlsPbu99y76y7O3b6vfFYf6BI9PmMsUnI2HXG7d89x2ApJFiLP97b2fr8sMT2fm/mGqTV4MnUeGw8WCQ5oyV2HTS+EWLEyr35zG60EC31308iokChsViU2kbe9JlMJmRnZyMlJQVBQc73xAANSwQHL98nOgkS7gqnj0p0WrZazOxhXfHCsG7yB07NinB9ucv8HJw31OMJk1gWdsw9cfj0VLFPAgtXmV/Hc7s7VsrzMzaL96iL1uuwdJz7Gxpiy4F9bXdusdt2CZ5cB3J+AyIiopZOamzgDIMsG1K/yKzCMkzZcNjt+bZOH4DKG7VY8ukZl3tsgIaJjjeTZmpexIIE4epwVeBCKrEAwpeBhZxzuzvW3fPOAoqocB2eSu2E54Z2Dfi/a7tzi0WrTHpzHagVOBIRETU1DLIUIvWL3JH9C174INvt+d6cnIKxKR1gMluwel8+Vu7Nb3SMkpNmal6YdfBeUw8oGv7tKMDfDxWh4savwRavAyIiIt/zJsjiniwPyN0z09CnqBu6G1pzIzJJxj0V3msKhU5cEXqcKb3fkYiIiHyLQZYH5FZLE3DSTHI19SCBlMHrgIiIqGlhkOUBb/qQcLJERERERNS8adUeQFMl9CExRNovHTREhnF/FRERERFRC8ZMlhe4/I+IiIiIiBwxyPISl/8REREREZEtLhckIiIiIiJSEIMsIiIiIiIiBXG5oA2hL7PJZFJ5JEREREREpCYhJhBiBDkYZNkwm80AgJycHJVHQkREREREgUCIEeTQWDwJzZops9mM+vp6aLVaaDSsEEhERERE1FJZLBaYzWYEBwdDq5W3y4pBFhERERERkYJY+IKIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLJuKSsrw8yZM9G3b1/0798fr776Kurr69UeFqmgvLwcaWlpOHLkiNpDIT/Ly8vDU089hX79+iE1NRUvvfQSysvL1R4W+VlWVhYeeeQR9O7dG6mpqcjIyEBNTY3awyI/M5lMmDp1KubPn6/2UEgFmZmZSExMRK9evaz/zZ07V+1hkR9VVFTgpZdeQv/+/fHb3/4WM2fOxOXLlyW/nkHWLS+++CL0ej2++eYbfPTRR8jKysKmTZvUHhb52fHjxzFp0iRcuHBB7aGQn9XU1GDatGno1asXDh48iM8++wwVFRVYuHCh2kMjPyovL8czzzyDKVOm4NixY9i+fTuOHj2K9evXqz008rPVq1fj2LFjag+DVJKTk4OxY8fi5MmT1v9WrFih9rDIj55//nlUV1djz5492L9/P4KCgpCeni759QyyAJw/fx5Hjx7F3LlzER4ejjvuuAMzZ87Eli1b1B4a+dH27dsxZ84czJ49W+2hkAouXryIHj16YNasWQgJCUF0dDQmTZqEb7/9Vu2hkR/FxMTg3//+N8aNGweNRoOKigrcvHkTMTExag+N/CgrKwtffvklfve736k9FFJJTk4OkpKS1B4GqSQ3NxenTp3CsmXL0KZNG7Rq1QoZGRmYM2eO5HMwyAKQn5+PqKgotG/f3vpYly5dcPHiRVy7dk3FkZE/DR48GHv27MHIkSPVHgqpoHPnzti4cSOCgoKsj33xxRe4++67VRwVqaFVq1YAgPvuuw+jR49Gu3btMG7cOJVHRf5SVlaGRYsW4a9//SvCw8PVHg6pwGw24/Tp0/jXv/6F+++/H0OGDEF6ejoqKyvVHhr5yXfffYeEhAT84x//QFpaGgYPHozly5ejXbt2ks/BIAuA0Whs9A+p8Ofq6mo1hkQqaNeuHYKDg9UeBgUAi8WClStXYv/+/Vi0aJHawyGVfPnllzhw4AC0Wi3+9Kc/qT0c8gOz2Yy5c+fiqaeeQo8ePdQeDqmkvLwciYmJGD58ODIzM/HBBx/g3Llz3JPVglRWVuKHH37AuXPnsH37dvzzn//EpUuXMG/ePMnn4IwSgF6vx40bN+weE/4cERGhxpCISCVVVVVYsGABTp8+jc2bN6N79+5qD4lUEhYWhrCwMMydOxePPPIIKisrERkZqfawyIfWrVuHkJAQTJ06Ve2hkIpiY2PttoyEh4dj7ty5mDhxIqqqqqzZbmq+QkJCAACLFi1CaGgoWrVqhRdffBETJ06E0WiUFB8wkwWga9euqKioQGlpqfWxwsJCGAwGtG7dWsWREZE/XbhwAePHj0dVVRU++ugjBlgt0IkTJzBixAjU1tZaH6utrYVOp+PSsRZgx44dOHr0KPr27Yu+ffvis88+w2effYa+ffuqPTTyo7y8PLz++uuwWCzWx2pra6HVaq2Tb2reEhISYDabUVdXZ33MbDYDgN114QqDLACdOnVCnz598Nprr6Gqqgo//fQT3nrrLUyYMEHtoRGRn1RWVuKJJ55A79698fbbb7PQQQvVvXt31NTU4K9//Stqa2vxyy+/YPny5ZgwYQInVy3A7t27ceLECRw7dgzHjh3DQw89hIceeohVBluYqKgobNmyBRs3bkR9fT0uXryIFStW4OGHH+a/Ay3EoEGDcMcdd2DhwoUwGo0oLy/HypUrMWzYMMmZTAZZt6xatQr19fV44IEHMHHiRNx7772YOXOm2sMiIj/55JNPcPHiRXz++efo06ePXW8UajkiIiKwceNG5OfnIzU1FVOnTsWgQYNYyp+oBTEYDFi3bh2++uor9OvXD+PHj0fPnj3xX//1X2oPjfxEp9PhvffeQ1BQEIYPH47hw4fDYDDgtddek3wOjUVqzouIiIiIiIjcYiaLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQcFqDyCQmM1m1NfXQ6vVQqPRqD0cIiIiIiJSicVigdlsRnBwMLRaebkpBlk26uvrkZOTo/YwiIiIiIgoQPTs2RMhISGyXsMgy4YQofbs2RNBQUEqj4aIiIiIiNRiMpmQk5MjO4sFMMiyIywRDAoKYpBFREREREQebSNi4QsiIiIiIiIFMcgiIiIiIiJSEJcLEpFPmMwWHC0qx+XrNbitdRj6xccgSMuqnURERNT8McgiIsXtzi3GKzvPoLiyxvpYXGQYFo9OxIikOBVHRkREROR7XC5IRIowmS3IKizDf+88jWc3n7ALsACgpLIGMzafwO7cYpVGSEREROQfzGQRkdecZa4cWQBoALyy8wzSEg1cOkhERETNFjNZROSV3bnFmOEkc+WMBUBxZQ2OFpX7fmBEREREKmGQRUQeM5kteGXnGVhkvu7ydfcBGREREVFTxSCLiDx2tKhcUgbL0W2tw3wwGiIiIqLAwD1ZROQxuRkpDQBDZEM5dyIiIqLmipksIvKYnIyUUOZi8ehEFr0gIiKiZo1BFhF5rF98DOIiwyAlZDJEhmHN473ZJ4uIiIiaPS4XJCKPBWk1WDw6ETM2n4AGcFoA44+pnTAs0YB+8THMYBEREVGLwCCLiLwyIikOax7v3ahPVlxkGBaPTgyYzJXJbMHRonJcvl6D21qHMegjIiIin2GQRUReG5EUh7REQ8AGMc6aJQdaEEhERETNB4MsIlJEkFaDgV3aqj2MRoRmyY5LGUsqazBj8wmP9okxK0ZERESuMMgiombLVbNkCxoqHr6y8wzSEg2SgyRmxYiIiMgdVhckombLXbNkC4DiyhocLSqXdD4hK+Z4TiErtju32JvhEhERUTPBIIuImi2pzZKlHOcuKwY0ZMVMZmdHEBERUUvCIIuImi2pzZKlHKd0VoyIiIiaLwZZRNRsuWuWrEHDfqp+8TFuz6VkVoyIiIiaN1WDrPLycqSlpeHIkSPWxxYvXoykpCT06tXL+t+HH35ofX779u1IS0tDSkoKxo0bh5MnT1qfM5lMWL58OQYNGoRevXphxowZuHz5sl8/ExEFDqFZMoBGgZbw58WjEyUVvVAyK0ZERETNm2pB1vHjxzFp0iRcuHDB7vGcnBxkZGTg5MmT1v8mTZoEADhy5AgyMjKwbNkyfPvttxgzZgxmzJiBGzduAADWrFmDQ4cO4eOPP8Y333yDsLAwvPzyy37/bEQUOIRmyYZI++DHEBkmq3y7klkxIiIiat5UCbK2b9+OOXPmYPbs2XaP19bW4uzZs0hKSnL6um3btmHUqFHo06cPdDodnnzySURHRyMzM9P6/PTp0xEXF4dWrVph0aJFOHDgAH766SeffyYiClwjkuJwcN5QbJ0+AG9OTsHW6QNwcN5QWSXXlcyKERERUfOmSpA1ePBg7NmzByNHjrR7PC8vD/X19Vi1ahUGDRqE4cOHY/369TCbzQCAgoICdOvWze41CQkJyMvLw/Xr11FSUmL3fGxsLCIjI/HDDz/4/kMRUUATmiWPTemAgV3aehQMKZUVc8ZktiCrsAw7sn9BVmGZtUqh2ONEREQUuFRpRtyuXTunj1+/fh39+vXD1KlT8b//+7/4/vvvMWvWLGi1WkybNg1GoxHh4eF2rwkLC0N1dTWMRiMAQK/XN3peeI6IyFsjkuKQlmjA0aJyXL5eg9taNywRdAzaTGaL22MEYg2Ox9wTh09PFbPxMRERUROjSpAlJjU1FampqdY/Jycn44knnkBmZiamTZuG8PBw1NTYV+6qqalBdHS0NfgS9mfZPh8REeH7wRNRiyFkxcSIBU3OgiOhwbFjfqq4sgbrDhQ1OrfQ+NjbzBkRERH5TkCVcN+7dy8++OADu8dqa2sRFtawNKdr167Iz8+3e76goABdu3ZFZGQk2rdvj4KCAutzV65cQUVFRaMlhkREviIETY49tYTgaHdusfUxVw2OxbDxMRERUeALqCDLYrFg6dKlyMrKgsViwcmTJ/Huu+9aqwtOmDABO3fuxOHDh1FXV4dNmzahrKwMaWlpAIBx48ZhzZo1+Omnn1BVVYXXXnsN/fr1w5133qnmxyKiFsJV0OQsOHLX4FgMGx8TEREFtoBaLpiWloYFCxZgyZIluHTpEmJjY/H8889j7NixAICBAwdi8eLF1ucTEhKwYcMGREVFAQBmzZqF+vp6PPbYYzAajejfvz/eeOMN9T4QEbUo7oIm2+BoYJe2XjcuZuNjIiKiwKR6kOVY+W/y5MmYPHmy6PFjx461Bl2OdDod5syZgzlz5ig6RiIiKaQGPcJx3jYuZuNjIiKiwBRQywWJiJoyqUGPcJy7Bsdi2PiYiIgosDHIIiJSiLugyTE4ctXgWAwbHxMREQU+BllERApxFTSJBUdiDY7jIsPwzJB4xPmg8TERERH5lsZisbAG8C0mkwnZ2dlISUlBUFCQ2sMhatHkNPMNNHL6ZAnEPm9T/h6IiIiaMm9iA9ULXxAROXIXpARy4GEyWxAZHoKXRvRAedVNxESEwBAZ7naMYg2O3TU+JiIiosDDIIuIAorQzNcxxS408316SDw+PVUsK0vkL66Cw0AJAomIiMj3uFzQBpcLEqnLZLZg8PJ9shv0CuHLi8O6oVOsXpXsllhwKIyA+6iIiIiaFi4XJKJmwV0zXzFCYLNy71nrY/7MbtXWm7Fwe26jAEsYmwbAKzvPIC3RwIwWERFRC8DqgkQUMKQ285VCWF64O7dYsXM6szu3GAOW7kW5sVb0GAuA4soaHC0q9+lYiIiIKDAwyCKigCG1ma8UQlbplZ1nYDL7ZlW0sESw3Fgn6Xglg0giIiIKXAyyiChguGvmK5cvM0gmswWv7DzjdImgGCWDSCIiIgpcDLKIKGC4aubrDV9kkOTsH9OgYY9Yv/gYxcdBREREgYdBFhEFlBFJcVjzeG8YIu2zPnGRYXhmSDw0kB+A+SKDJDdw80UZd5PZgqzCMuzI/gVZhWU+WxZJRERE8rC6IBF5zFdNgUckxSEt0eD03L3ujG7Ui0qMBoDBRxkkqYFb24gQvPpwkuJVDt01bCYiIiL1MMgiIo/4epIfpNVgYJe2jR53DMDOlRqxcm8+NIDd/igh1PNVI2Bh/1hJZY3ovqyYCB2yFjyAkGBlFw24a9jMnlxERETq4nJBIpJNmOQ7ZpOULpsuthxOCMDGpnTAC8O6Ya2T5YWGyDCfBhuu9o8JSxpfe7in4gGWq4Ib/qioSERERO4xk0VEsrib5CvVeFdOpszV8kJfEvaPOY7T4MNle+4KbthWVHSWCSQiIiLfY5BFRLL4Y5LvyXI4seWFvubvAE9qwQ325CIiIlIPgywiksXXk3x/ZcqU5M8AT2rBDfbkIiIiUg/3ZBGRLL6e5MvJlLVE7ho2sycXERGR+lQNssrLy5GWloYjR45YH/viiy8wduxY9O7dG0OHDsXq1athNputzz/44IO455570KtXL+t/hYWFAACTyYTly5dj0KBB6NWrF2bMmIHLly/7/XMRNWe+nuRzOZxr7gpuAL6rqEhERETSqBZkHT9+HJMmTcKFCxesj+Xm5uKll17Ciy++iGPHjmHDhg345JNPsGnTJgBAVVUVioqKkJmZiZMnT1r/69KlCwBgzZo1OHToED7++GN88803CAsLw8svv6zGxyNqtnw9yedyOPfEGjb7uqIiERERSaPKnqzt27dj1apVmDt3LmbPnm19/JdffsHkyZNx//33AwC6dOmCtLQ0fPvtt/jDH/6A3NxcREVFoUOHDk7Pu23bNsyZMwdxcQ0TjEWLFmHw4MH46aefcMcdd/j+gxG1EL6sqtcvPgZReh0qquucPu/LBsP+5k0zZ1cFN3zVJJqIiIikUSXIGjx4MEaPHo3g4GC7IGv48OEYPny49c81NTX417/+hdGjRwMAcnJyEB4ejscffxz5+fno0KEDnn/+edx///24fv06SkpK0K1bN+vrY2NjERkZiR9++IFBFpHCfFVVb8+ZEtEAC2jYk9UclsMp0czZWcENXzeJJiIiIvdUWS7Yrl07BAe7ju+qqqowa9YshIWF4cknnwQAaDQa9OzZE3/5y1/wzTff4Mknn8Tzzz+P7OxsGI1GAIBer7c7T1hYmPU5IlKWbVPggV3aeh34CJUFXYnS65CWaPDqfdTmq2bO/moSTURERK4FZHXBH3/8EZMnT0Z9fT3effddtGrVCgAwbdo0rFq1Cp06dUJISAjGjBmDQYMG4YsvvkB4eDgA4MaNG3bnqqmpQUREhN8/AxHJ566yIABUVNc16cqC7krUAw0l6k1mZ0f4/7xEREQkX8AFWV9//TUeeeQR3HvvvXj77bcRGRlpfe7tt99GVlaW3fG1tbUIDQ1FZGQk2rdvj4KCAutzV65cQUVFhd0SQiJSn8lsQVZhGXZk/4KswjLrxL8lVBb0VYl6lr4nIiIKHAHVjDg7OxuzZs3CkiVLMGHChEbPFxcXY9u2bdiwYQPi4uLwz3/+EydPnsQrr7wCABg3bhzWrFmDnj17Ijo6Gq+99hr69euHO++8098fhYhEuNoz1BIqC/oqkGwJASoREVFTEVBB1tq1a1FfX49XX30Vr776qvXxPn36YOPGjXjppZeg1Wrx6KOP4vr160hISMD69evRsWNHAMCsWbNQX1+Pxx57DEajEf3798cbb7yh0qchIkfCniHHBWvCnqG/PdoLcZFhKKmscbrsralXFjSZLSi9flPSsXIDyZYQoBIRETUVGovFwgX6t5hMJmRnZyMlJQVBQUFqD4eoWTGZLRi8fJ/okjYhgEoflYhZ758AALtASyip0VT7QDnL4DkjfA8H5w2VVUhE+H7dBahyz0tERNRSeRMbBNyeLCLyLbH9UL4mdc9QdERIs2u0K1b1T4wnJep93SSaiIiIpAuo5YJE5Ftq9lCSs2dobEoHn/TgUoOrqn/OROp1Hr+XL5tEExERkXQMsohaCHf7oXydJZK7Z8hZo92mSEpZeluV1XVe/R6+ahJNRERE0jHIImoB3PVQ0qChh1JaosFnk/F+8TGSilrU15vx+hd5ADToHx8DrUaDUuNNvwYLJrNFsSBFbjU/JX6P5hKgEhERNVUMsohaADk9lHw1ORf2DM3YfAIaNC5qYQFQeaMOU/9+1Pr46v325/DH0kall1R6Us3PH78HERER+Q4LXxC1AIHSQ0nYM+RY1CLq1j6k6lqTy9cLSxt35xb7ZHxiBSqkvq+zoiJCBs+TPBh7WhERETVNzGQRtQCB1EPJcc9QbKtQ/J8PT0p6rS+XNspZUgmg0XLCPWdKRDNgYhk8d0qv34TJbOF+KiIioiaGQRZRCyB1P5S/mvza7hnKKizDpeu1kl/rq6V0UpdUvrQtG1/lXUHFjTrrc1F6HSqq6xq9xraoiLOqf+5k7PoeGw8WsTIguaTkHkIiIlIGgyyiFsDdfijA+x5Knk70PF0SJ/V1Uscl9Xwfn7zY6DFnARZgnwE7OG+oXQbvxytVePOrArfvV+yn6o/UNKnZloGIiMQxyCJqIcR6KEVH6PBwSgdEhod4vDQt87uLeHlHLsqNvwYbYhM9x6AnNiLUo88jZWmjnAmor5ZKOmbebDN4UoIsga+rP1LTo3ZbBiIiEscgi6gFsd0PtedMCf6ZfRHlxlq8fegc3j50zqM74Eszz2DdgaJGjzvLwDgLegxtwhAVHoyKG/WS3k9Y2tinYzSyCstEM1RyJ6DCkko5y/nkcMyUycngsdogOQqEtgxERCSO1QWJWpggrQaVN2rx90PnUG603wslt3pf5nfFTgMsgQUNEz2T2SJaue/StRpZARYAjLknDvet2I8pGw7jhQ+yMWXDYQxevs86bncTUNiMSxCk1WDMPf5rxuxJ5ozVBkkgpy0DERH5H4MsohbGkwBE7Dwv78h1+37FlTU4XFjm9q57tF6HyHDXyXVDZBieHhKP9QeKXJZZ92QCuju3GOtdBIzeaBsRgj4do+0e86S0uz+qP1LTEChtGYiIyDkuFyRqYZRqTHy0qLxRJkxM1o+lbt/zanUdtkzrD1gajgc06B8fA61Gg1LjTdzWumGJ4H0r9rtdIvXS8O6SxiVMQF0FnkooM9bivhX77ZZiuipG4sjf1R8p8AVSWwYiImqMQRZRC6PUHXB5d8il5WtKq25ibEoHpHaNdfp8VmGZpABRavAnTEDdBZ5SiZVyB5zvBROKkcz/JEf0dUDD5/K2+iM1L4HWloGIiOxxuSBRC6PUHXCp54mJ0Eku1uDunFIDu5hWoS6X4mnQUGVQmIDuPVMi6bxiovU6rH28N44uHIaYCJ3TY8SWYqYlGhAWHOT2/EITZCLg10wo0PgWhlJtGYiIyHMMsohaGHd7gRwDEHfncecvY5MwoHNbRd5TamBnaBMmeQJqMluwPfsXSed1FBWuw+xhXXHs5TSMSIrD8fNX7crYO3K2F+xoUTlKrrkOHq9W17GAATUiZEINDn8PDZFhLN9ORKQyLhckamGUakxsex6x/UTPDInHyOTbref09j3lLJEK0mqc9gUzOJSpb9hbJh4YCVqFBmHNY33s9og5lo33ZCkmCxiQN2zbMshtBE5ERL7DIIuoBRJrTOwYgHh6nrYRIcgYm4SRyXFuj5XznnIDRCkTUKnBy8S+d+Debu1cHuPJUkwWMCBvBWk17J9GRBRgGGQRtVBK3QGXcx6px5rMFtFj5AZr7iagUoMXKXuiPClG4IsCBq6+PyIiIvI9BllEzYy7CbYvJuBy7qS7O3Z3bnGjACrOIYBScomUuyBHeH8pQY4nSzGVWr4pkPL9ERERkW+pWviivLwcaWlpOHLkiPWxU6dO4ZFHHkGvXr0wdOhQbNu2ze4127dvR1paGlJSUjBu3DicPHnS+pzJZMLy5csxaNAg9OrVCzNmzMDly5f99nmI1LY7txiDl+/DlA2H8cIH2Ziy4TAGL9+H3bnFkp5X2+7cYszYfMJlo2GBEKyNTemAgV3aehwouqvSpoG8IMeTYgRKFTCQ8/0RERGR72gsFouv+m+6dPz4ccyfPx8XLlzAu+++i/79+6OyshK/+93v8Kc//QmTJk3Ct99+i1mzZmHTpk1ITk7GkSNHMGPGDGzYsAHJycnYsmUL1q5di/379yM8PByrV6/Gl19+iXXr1qF169ZIT0+H0WjE+vXrJY3JZDIhOzsbKSkpCApyXVKZmofmtKxKmGA7/oUWPs3TQ+Kx/kCR6PPeVCNT4nusrTdjwNKvRHtcCcvmDs4b6pPfSOkMkCffiTffo8lsweDl+0T7ffn6+yMiImpuvIkNVFkuuH37dqxatQpz587F7NmzrY9/+eWXiIqKwmOPPQYAGDhwIEaPHo0tW7YgOTkZ27Ztw6hRo9CnTx8AwJNPPokPP/wQmZmZGD9+PLZt24Y5c+YgLq5hQrRo0SIMHjwYP/30E+644w7/f1AKaGotq/JFYGcyW/DKzjNOl7tZ0DDB3vBN4wDL9vlXdp5BWqJB9liU+B535xZj4fYcSeXPD/9YhtQE582KveHtEkRnv6vcYgTeFDBw11DZtnw8iyQQERH5lipB1uDBgzF69GgEBwfbBVn5+fno1q2b3bEJCQn46KOPAAAFBQUYP358o+fz8vJw/fp1lJSU2L0+NjYWkZGR+OGHHxhkkR2xrI+wrMpXPWZ8FdhJmWC7yllLnYA7BhJXjbWY9b5336PYbyFm1pYTWDa+p09+H2dBjpSgWMrv6uusKUvBExERBQ5Vgqx27ZyXQTYajQgPD7d7LCwsDNXV1W6fNxqNAAC9Xt/oeeE5IkBa1sfTrI4rUgM7TybjSk2cXZ3HWSCh1cCr79HVbyGm4kadTwNhW1KCJ7HftbiyBs9uPoG3Hu0FrVbjUXAt51pgKXgiIqLAEVDVBcPDw3H9+nW7x2pqahAREWF9vqamptHz0dHR1uDrxo0boq8nAtRZViU1sDObLcjY9b3sybhSE2ex84gFEmYvs2PufgtXfBEI25ISFKclGtwGibO2nnSaRXSX7ZOb9fRFKXgiIiLyjKrVBR1169YN+fn5do8VFBSga9euAICuXbuKPh8ZGYn27dujoKDA+tyVK1dQUVHRaAkitWxqLKuSGtjNfP+kR5XhhAm2WLihQUPWSYwG4mXKPck22XL1PXr6HdsGcL7gLigGGoK8w4VlboNEsWWatucxOUSrnlQJdFclEZBXJZGIiIg8F1BBVlpaGkpLS7Fp0ybU1dXh8OHD2Llzp3Uf1oQJE7Bz504cPnwYdXV12LRpE8rKypCWlgYAGDduHNasWYOffvoJVVVVeO2119CvXz/ceeedan4sCjBqLKvyJmBzNRkXSJlgT7833lqS3NnzYhNwb7JNgOvv8Vypd0t5fbW/SGpQnPVjqVfvI5xn06Ei628rNcBzdi3IKQVvMluQVViGHdm/IKuwTPTaknocERER/SqglgtGR0fjnXfewauvvopVq1YhJiYGL7/8MgYMGACgodrg4sWLsWTJEly6dAkJCQnYsGEDoqKiAACzZs1CfX09HnvsMRiNRvTv3x9vvPGGeh+IFKVU4QA1llV5G7BJWXonTLAdl5gZbJaY9boz2uXzzngayLj7Hk1mC7YeveDRuQW+2l8k/TMrkxXK2PU9Nh4swuLRiYgMD/FqOauUKolSlyKysTEREZFnVOuTFYjYJytwKT3ZE5ZjAfaFG5ToGeWM0MNILLCT6s3JKRib0sHte7maYMsNVrMKyzBlw2FZ45TyPXpyXtvz+7Lnk9SxbZnWH3O2nfIq0yfQoOFavK9bO3x99orb46VcC86466cm/GZSjyMiImquvIkNAmq5IJEznuxPccfdsqq0RIOiS6SkLOeTQkrmRihDPjalAwZ2adsoCHH3vCN3+72Axvu9nC1Pc+RNhgzw7f4iKXvc4iLDMKBzW+vv6i3hCpMSYAGeZfGkLkWsrTd7vGSRiIiIAmy5IJEjX5ZbF1tWtedMCQYv3+d11swxY5SWaBBdzpc+KhEZu84EVGU42/FP/u2deGPvWWu2xXZcALB6Si9ER4TKWsopNUiIidDZNSl2t7xRCUJQPGPzCdHPLAR5I5Li8NajvfDc1pMuqy0qSaxIiTtS95q9l3WOjY2JiIi8wCCLApqvy607Np9Vqkmxq+WNB+cNdbpcT6uFpEm9J3vT5L7G2fij9DoAQEW1MgGP1L1xX8+9H8fPX/VZE18xUva4CUYm347V0GDm+yecfg4LGr4/2+/OG+mj7vLoOyi5Ji17eL68WtJxbGxMRETkHIMsP1CqYENL5M9y60plzTwN1KRM6j3Zmyb3NWLjr6yugwXA7GFd0Sk2wutrWWq2KCRYq1q2REoRCcHI5Dis1Yr/fmmJBmw6VISMXd97Pa7oiFDZr9mdW4yMz05LOrZjjN79QWBjYyIiIjEMsnyM1bm8489y60pkzbwN1FxN6j0J3uS+Rsr4P/j2J8UKTsjJFqnFMdvpirug7MnUeGw8WOR1ARTbmwpSbuKIXQeOhOzh1IGdXI6TjY2JiIhcY5DlQ0otPWvJ/FluXYmsmRKBmrNJvSfBmyev8fXyTGfkZIuaAldBmavsnRzCTQUpN3GkNpMWxvNgkgHHz19F+qhEzHrf/fJVIiIiaozVBX3Em4ai9CspVfmUmuwpkTXz1fJGOcGPN6/x5/JMW3IrHjqS2zBXzQa7YpUtpRCqGvaLj5FcdVNyM+lbX/k7h85hyobDyNh1Bk8PiZfU2JiIiIjsMZPlI2pkBJorfy0pUyJr5qvljZ4EP568xp/LM5WyO7cYSz49Y1fUwdAmDEvGiO85U3sJr232bu+ZErx96Jzb19jeVAAgOUsp9Tpw7JhYUlmD9QeK8LdH5VeOJCIiaukYZPmIWhmB5sofS8rklO0W46vljZ4EP568xp/LM5WwO7cYz25uXNGv5FoNnt18AmsdMi5Sl/B6WqxGzuuE7N3ALm3x2/iYRoGfVgO7kvC2NxWyCssk38TxNCAWgrWMXd/7rOkzERFRc8Ugy0eaYkYg0MkpQOApb7NmSgRqzngS/HjyGl+N3xdMZgvmf5Lj8pgFn+RY95xJ3aNmNluQset72ZkubzJkzm4i9OkYLVq6Xs5NnIeSb3d5HbjCjDsREZFnuCfLR4QJrthU1HZvBQWWEUlxODhvKLZOH4A3J6dg6/QBODhvqNOJsrO9PWJ7brzZy+LJ3jRP97N5O35/7Xc6/GOZ275TV6vrcPjHMgDSl/DOfP+k231OjqTuj3LFcV+aULre2T41uTdxJv/2TsWqGRIREZF7zGT5SFPKCDQF/u41JiVr5i5zofTyRk+ybGmJBrw4rBv+fqgIFTekNxH2dPz+3O+UVVgm+bjUhFivAgXbYjWO5feV6q8mh9Qs5VXjTQxevk80uGwbEYIyY63b92PGnYiISB4GWT7UFHoANQVKTdyV3Gez50yJ0709xZUNe4GeGtQRv7s7TvFg0Db4KblWg/Kqm4iJCEFkeAhMZovdezn73qLCdXgqtROeG9rV7bjkLs/0d8uCwivXJR7ZMCIlAgVnS+fUKHIj5SbOmHviMOv9k6IZrNnDumLGfyTgvhX7m8wePCIioqaCQZaPNbceQP6m1MTd00DN2esMbcJQU29yufzq7/8+j7//+7xPsjhBWg0qb9Tif3bniX4ese+t8kYd3tibj+6G1oqOyd/ZnN25xfg895KkYwd2jgXgPvsj1Z4zJXbBklpFblzdxEkflYiMXeK9sYSm0s8N7cqMOxERkQ9wT5YfeNsDqKVSqteYp/tlRF93rcbtXiCBkNl6c28+auvNiuxVcvd5Mr+76PcebZ705PKUcF1IEaXXYcCtgMjVHjU5dmRftPvuYluFSnqd1OPkENs/GB0RIun3OFxY5pM9hERERC0dM1kUsJRYhuVphsXV6zyxcu9ZvPnVWbuS3J4ueXT3eV7ekYtyo3gQaDvB1mo1imRY/ZnNkdxcF8CycT3tPpMQUMz/JEdyoOyozFhrf81JvUhEjjOZLThcWIasH0sBNNyQGdBZ+s0YZ8s6pX7Ps94/gWXjezLjTkREpDAGWRSwlJi4yw3UhP1XhwquSJ7IS+WYOJKy5NFxP5jZYnH7eVwFWLZmvX/CrhiGN0sb/dGyQPguPpdQqQ8A/pjaSbQYyJJPzwDwLMgCvG/6LNidW9wo4Fu9vwBReh2WjevpcRZJ6vdccaPO7hpkmXYiIiJlMMiigKXExF3OBNjZ/itfcrdXSaxwhVJsAyzAuwIVvm5i7MlvMyzR4PRxoWiIN2yvuXIJ1fmcHSfWSBkAKqrrnDZTlkru/jOlqx8SERG1dNyTRQHL215jJrMFpddvSnqvc6VGp/ucfE1sr5LYvivHwEjpsQCe7dfytCeXFGLfhRh314U3SxadnTtG4l4r2+NMZguWfHra7Ws83Ttn+3u4o+R+OSIiImrAIIsCljcT9925xRi8fB8ydn3v8j2ESfPWoxcU23/lCduJv5L7weSGNN5MuH1RQEHudyEloPN0yaLYuQ1tpJ3P9riGbJr7GwDeBD/C7yE1+8mGw0RERMoJuOWCn376KRYvXmz3WF1dw9373NxcLF68GB9//DF0ul8nDvPnz8ekSZMAANu3b8dbb72FK1euoHPnzkhPT0evXr389wFIUZ70GhMrX+5IKFmd2iUWH534WdFxy2U78ZdT2MGd6IgQu2VqUeE6SdkwVxNuV/3GlC6gIPe7ELsubMccGxEKQ5swXLomr5S77bntztcqFIY2oS6DJtvsl8lswaGCUsnv603wMyIpDq3DdHhs4xG3x5Zev9mo1xoRERF5JuCCrDFjxmDMmDHWP1+6dAnjx4/H3LlzAQA5OTnIyMjAww8/3Oi1R44cQUZGBjZs2IDk5GRs2bIFM2bMwP79+xEeHu63z0DKkjNxl5P5iNQ3BOpqBljO9iopmVFIH3UXDJHhdoUzpEy4xbI9zvZGxUTo8HBKBwxLNFh/F6UKKEj9Ln4/sCMeTHLe/Nnp3ja9zronTuxaiYsMw+Tf3olOsXq7a07sfHByPsfslyd7y7xtojygc1tJ+7Mydn2PjQeL2CidiIhIAQEXZNmyWCyYO3cu/uM//gNjx45FbW0tzp49i6SkJKfHb9u2DaNGjUKfPn0AAE8++SQ+/PBDZGZmYvz48f4cOilM6sRdauZjQu8O+PjEL6ouEQQaJuSOy8+8nVTbMkSG231vJrPF4wIVYhnCcmMd3j50Dm8fOqd482Wp38WDIpXxRJsy36rmpw8JgrHWZPecPiQIzwzpjOeGdnUasLk6X6ReZ1cp0CChQbQrrvaWSSUsu3XWcNiRN8VPiIiI6FcBvSdrx44dKCgowPz58wEAeXl5qK+vx6pVqzBo0CAMHz4c69evh9lsBgAUFBSgW7dududISEhAXl6e38dO6pCa+dj7/WXFAizHhJqhTSgiQoIkvTZar0OaQxU8dwU/AKB96xAY2sgvCuLpPjepGUJ3DZ7l8qT4iclsQVZhGbaf+BkLt+e6bMrsGGABwI1aE97Ym489Z0rsHpfSoyxcF4Qtf+xv1xhYWF7oyT47T4uFOBLbL+fIV82qiYiIWpqADbLMZjPWrFmDZ599Fq1atQIAXL9+Hf369cPUqVPx9ddfY8WKFXjvvffwzjvvAACMRmOjZYFhYWGorq72+/hJHXL6A3lLc+u/1VN6Y+v0AVg58R6kj7oL8x68C08P6SLpHFer6xoVNpBSGe76TRPGpsRZx+E4LkB8gu5JgQqpGUKlJ+lyg0Kh4MmUDYcx+x+nJJdXtyX2GaT2XNNqNRib0gEDu/zaUNiTfXazh3VTNJs0IikO6aPucnscqw0SERF5L2CXCx45cgSXL1/GhAkTrI+lpqYiNTXV+ufk5GQ88cQTyMzMxLRp0xAeHo6aGvuJTE1NDaKjo/02bnJdGMHX55HSrylSYvEHR1qNfUNhx6Vg//PFD3YTaX1IEKqdZEocXb5e0+izpiUa8PSQeKw7UOT0NdW1Jqw7UIRnhsTj01PFkouCCOQWqJCzT0yYpG86VITY1qFeF7+QWvzEk+V47j6D0KQa8K7psCf77DrF6mW/xhWT2eK22qYtVhskIiLyXMAGWV988QXS0tKg1/860di7dy9KS0sxefJk62O1tbUIC2u4I9+1a1fk5+fbnaegoABDhgzxz6DJ6cZ+T/bpeHoeV/tPhCn+U6mdsHJvvpNX21s08i4k3t4GpVU3cVvrMPTpGI3j5682CkrEJvdSAiygoUfX4OX77AOINqGoqTe7fe2O7Is48NJQp+MS4xjQPZR8u9sAyJN9YrYTem/3arkLCpUse2/LNtDwpjm2J9+fknvzAPnZtNhWocgqLFOkSiQREVFLE7BB1vHjx/H73//e7jGLxYKlS5eiY8eOGDBgALKzs/Huu+9iwYIFAIAJEyZg1qxZePDBB9GnTx9s2bIFZWVlSEtLU+MjtDhiwYbczfTensdd5iMt0YAPvv3JbfGHPwyObzSpdCyu4M3kXoOGqnTOAj4pPZSE446fvyq5mp+z4NXQJhRT+t2JTrERosGkuwyh23FK+O1MZgsO/1iGrMIyABYM7ByLATZL7lwVP1Gy7L0t20BHyncQFa6D2WJpVApdzvfnqviIN+RkpjQaYOaW46i8UW99TOmiJkRERM1ZwAZZP//8M2677Ta7x9LS0rBgwQIsWbIEly5dQmxsLJ5//nmMHTsWADBw4EAsXrzY+nxCQgI2bNiAqKgoFT5ByyKlKMArO88gLdHgNsuixHncZT7cVVuTWnDA08m9cGYlMi9SJ8+iweu1m3aBXqNlkW3CsGRMovU784S73253bjHmf5JjV5lv9f5CRIXr8FRqfKMy6o7kLm1zV2XPWaAjpUpfxY06PLbxSKOARGqFPynNlMW4W14rJzNmscAuwAJYeZCIiEgOjcViYQmpW0wmE7Kzs5GSkoKgIGnV4ahBVmEZpmw47Pa4rdMHuMy6KHUeKZxN7IGG7NKycT0lTSR3ZP+CFz7IdnucYxNgoQfTyr1nZY/bkZTvwmS2NFqSKNfax3sDAOZ+dArXa6QthXTGcby7c4vxrMTgzdAmFEvG3N3ot5F63QjiIsMw5p44rL+1583ZslKxYEJKryuxc7h7rafZIinLa4VrwNNsJPBr8Hlw3lAuHSQiombPm9ggYDNZ1LRIzSR8fqu0t7cZCXfnkcoxwAIaeh5JvWMvNTvwt0d7Q6vV2GUZPvvuokdjtmVoEyppWZkSy+kWfJKDYy+n4T9TyvDe4fMen8f2NzaZLVjy6RnJry25dhPPbj6BtQ6/Tb/4GMREhEiqJtg6LBjpoxIxMjkOve6MdltQw5GQJT1cWIZZ759wWkTFNnPXOlSHUuNNa0ET2wxrbKtQwALr847Xs5TiL1KX18rplyXGWUEQIiIiaoxBFilCarDxbtZ5vJt1XvSOvVLncUdYluiMnGWJUqoZGiLD7PYWCZQobLBkzN2SgkwlKsVdra7D4R/L0Kmtd1XvbD/30aJylFyTP7b5n+TY/TZBWg3+MjYJM993nxGrqqnHrPdPYI22t+wqi4IgrQZarcZllUohIHns7SPWx+Rcr2LZqfRRdyE6ItQapC359LTk5bVi+xXlYuVBIiIi1wK2TxY1LVIa6NoSa1rr6Xkyv7uIrMIy7Mj+BVmFZW57NEnteeSuV5CnzX0B+Z/VVpRe1yib44pSleqyCsswdWCnRg2YpXDWONjTyXpFdR0OF5bZPTYyOQ7PDIl3+1rHPlhCQQ3H3lbueDJ2qc2aheyU4zVaXFmDme+fxJQNh/HCB9l4bOMRl0VSnF3HI5LicHDeUEk9s8QoXfmQiIiouWGQRYpwFWw4I9bw1ZPzWAA8t/XXieeUDYfRJ2MP3tybLxpsedPzyJEnzX0B+Z9VEBMRgqMLh8nK3nkT0NmzICRYi+n3ug9mbIkFnN5M1rN+LG302IKRiXjr0V5oHeZ63bQSDXc9GbuUZs2+KEfveB0HaTV4MjXeo2siJkKneOVDIiKi5oZBFilGCDbat5E2+RSb6IoFLa44zlcrbtRh5d6z6POXPU6zBlInyLERoZKOE7IDW6cPwJuTU7B1+gAcnDcUaYkGlxk2ud8ZAJQba3H8/FXJxwOeB3SOBnaOBdAQzDwzJL5RRkurAdISb0OcxICzT8doxESEeDga559kZPLt+O+xPSWdwZtlb1eN0srsO3IX4PmiHL2z693Ta+LhlA4sekFEROQG92SRokYkxaF1qM5uH4o7zia6tntlPs8txrtZnhVaqKiuEy2UIKVv0Z+3ncKSMdL20Dj2cZLaUNmT7+xQQansJrHe7seJ0uswwObzLRiZiD//rgfeyzqH8+XV6Bijx9SBnRASrJVcsOGVnWckFatwxlXhBYPEoNXTTJrJbLFrtuwJsQBPyf1O7npueXJNDEs0KDY+IiKi5opBFimuVOYdfrGJrm3Q4mmQJXAsYiG10tqla571BpLbUFnud7Z6f4H1f8sppiAEr6v35ePvh865LNzgaNm4no0CpZBgLf54b2drI+FVX+XDWSNhR2Lfj1TReh0GdBYPsqQUJImJCEFJ5Q1kFZZJDlSF4PFQwRWvs01i171S+52k9tySUi1ROJ8vmiQTERE1R1wuSIqTM0l0LITgTL/4GBjaSFu2J8bVssT2Ls4tZQ+NLZPZgkP5pZj/cY5oxTdn5/NmYi21mIJgz5kSvLE3X3Qy3cZhP1NkeDBmD+uGNJEMxu7cYvT5yx48tvEIVu8vwOr9hXjs7SOiSzWV2HO01EnAZ8vdUjgLgDJjLWb/4xSmbDiMwcv3SSpGMXj5PkzZcBir9xd6PHZnBUBs9YuPQZRe5/H5Be72BNoK0mqQ2jUWy8b3hAbyi7gQERGRPQZZpDg5RRakTNr2nClBTb3Z63GJLUv868QUl6+TWiRBmIQ/9vYRSaW9bc931VjrUcU+4XyAtEBQSoATrgvCiw8kICq8YaJfeaMeK/eedRqICI2EnfUbE5ZqOr7Gmz1HMREhkqsqytnb5y5QFav2J5dYsGIyW6x791bvy3f6fbo7b1xkGLb8sb/dnkAp35Pte0eGh+Bvj8ov4kJERET2uFywCZOy70UNUpbiRel1WDaup9tJm7fLymyJZYtKq6Qt1XO1Vybzu4uY+f5JWeMR9lWdKzVi5d58Wa91JLVJrJQA59L1WrzxVUGjxx2XOkptJLzk09N2SzW92XOUPuouWRN92719JddqkPHZaZQbXTcPduyNpmS1P2eNjp3t3ZPLgobALbVrrOgxwr8XJddqUF51EzERIbhQfgNbj16w61Xm2IsrkP5tISIiaioYZDVRUosqqEVsQ32UXoenBsXjuaEJbidtSk5uXS3PkrpUT+y4zO+K8dxWeQEWYL+vSinuAhhvAhzHQERqI+GSazftgj9vlkYaIsNlv0bY25dVWOY0wBKIBapKVPuL0uvwtym9G+1TU/ImgityArmSyhrMev8k1jzeG2NTOvh4ZERERM0Tg6wmSG5RBbXYZhGEO+J9Okbj+Pmr+Oy7i27vkCtZytrVskQpRRLENvzvzi3GzPdPKDJGJdzWOkw0w2kyW1B63bOy4wLbQEROwGZ7rNTKjs5c9bASoeMY5BynRLW/iuo6aLUan2XIxLJwgPxATjjOMQNJRERE0jHIamJcTcxcLXny1VjEJvOHC8tuNYttyCI8lHw79pwpwX0r9kvOvilVyjoi1HVjWlfLG11t+Bd+i0ARpdfhqrEWg5fvs88ehutwb9e2+PbcVZRc8y7IEgi/uVTnSo3W/y21sqMzGbvOYHiSZ9e2pxlLpar9OV7PSt5EEILfv36Zh2h9KCpu1EIDoH+ntljyqWeBXMm1m1i9rwAvDOuqyBiJiIhaEgZZTUhtvRkZn512OTGTujfHW2LLFcfcE4cPj/1st3F/9f4CRIQEwVhranQeV9k3pSa3xpsmPLv5BP6Y2gnDEg1Os2cjkuLwt0d74+UduXZ9m5ztoRH4ommsN+rqzZj1fuOMRcWNOuz8rkTR9xKCakObMElLBrcevYDnhna1fu+e9uzy5tr2NGPpTebNluP1rGQ/LMFb//rR7s+r4XkVRABYufcsuhtaWa//QN0HSkREFGgYZDURSzPPYMM3RZBQRRyAbyZwArHlR8WVNVh3oMjpa5wFWMCv2beFn+Qg5+dKaDQNma8BndsqNrkVvH3oHN4+dA4xESH4z5TbkWYTcO3OLUbGLvvGuDERukaFFmwnmfmXqiS/d2RYMB4f2BF/86L0tzti37HShP1tQVoNloxJxLOb3S+XdNyXBTQEWkN7tLc2M752ow7/zL7o9lyeXttSMpaTf3tno6Ws3mTehHMbIhuWymYVllkLT1yRWHBFbUJmfM+ZkoDeB0pERBRINBaLxdd7rpsMk8mE7OxspKSkICjI9RIzf1qaeUY0eBGzdfoAn2SyTGZLo+VoviBUHwSAGbcm8b64UIXs2/oDRY3OL0y8//ZoL0RHhGLvmRJsz/7FZfEEMa1CgzFtcDze+Mq7KoKB4Jkh8VgwMtH65//eeRrvHDrn9nW/H9gRDybF2QW2nlTV8/badva+0XodLIBdBtYxgPB0vBoATw+Jx6enigMq8ynH7GHd8Mbes6J/R7zdB8oMGRERBSJvYgMGWTYCMciqrTejR/rnkjNYwl3zg/OG+mSSklVYhikbDit+XjFrH+8NAF6XuPaGVgPJ3787+pAgVPsp4+Qr0Xodjr2cZr2+5F4TMRE69LojCl/lXZH1vrbXNgCvJuW2k3p3JfRnD+tmrYYpvO5QwRVJDYnbRoRgQp8OToP4piQqXCfa+83bf3MCvVIqERG1XN7EBmxGHODeyzonK8ACpDX49ZQvlyE6s3B7Dm7UmfH6hHuwZVp/rJx4D2IiQvw6BqUCLABNPsACgKvVdTj8Y5n1z3KaTwNAubFOdoAlWDw6EXvOlGDw8n2YsuEwXvggG1M2HHbaKNkVoaz7Q8m344Nvf3J57Mq9Z5G67Cvszi22vm52WnfEuWlyHBOhw8F5Q/HhsZ8VCbCiwnV4KDkOhjbK7FWUQ25zbanEmjy7aw5NREQU6BhkBRCT2YKswjLsyP4FWYVlMJktOF9eLfn1hsgwn5dvt60S5w/lxjrM/jAbj719BHO2ncKF8mq7fVP0q3Cd/7KvWYW/BlnCniUAkgMtTzw9JB4AFJ2USy1eUnLtpt35g7QajLnH9d+zR/r8BusPFNotQfRGxY06fPZdMa7X1EIfEnj/dMu9AeOuUirQkME2KXmXg4iIyE9Y+CJAiC2ZSZW492TqgDuxZEySVxksd/siducWu1xW5WsllTWqvn+gu1HnvyxZ4ZXrdn/2tFqgHDuyL2JHdrGi7QvkBgbC+QHgw2M/uzz2H8d+hslslnV+KYy1ypzz9wPvRJuwEMWaYsutBuouwPVXpVQiIiJfCMggKzMzE3PmzEFoaKj1sWHDhmHFihU4deoU/vKXv6CgoADR0dGYMWMGHnnkEetx27dvx1tvvYUrV66gc+fOSE9PR69evdT4GJK5ai788Ylf3FY002qA9IfutptY1tabrVXbOsboMXVgJ4QEi9/9dhbkRYXr8FRqJzw3tKFPjto9oVrq/exWoUGouhlYyww/z72E3bnFdllT2+bTn+cW492s84q+p7seX55MyuUEBrbnN1ssbjNUVxXKYPnKe1kX8MID0npgxUSE4KqxVnazblc8aQ7NAhlERNRUBGSQlZOTg7Fjx2Lp0qV2j1dWVuLpp5/Gn/70J0yaNAnffvstZs2ahe7duyM5ORlHjhxBRkYGNmzYgOTkZGzZsgUzZszA/v37ER4ertKncU1Kc2F9aBCMLibZ0++NtwugnJV7fzXze0y/174qnEAsyKu4UYeVe/Px93+fw1OD4iVlKB5KjsPBglLFlkgR/BpgSS1RLpY1EvYsebI/RylyslNXPVh6eqigFJZmEPJbALzxVb5oDzvg1wAqfVQiZr0vr1m3O3KbQ7NABhERNSWBt7AfDUFWUlJSo8e//PJLREVF4bHHHkNwcDAGDhyI0aNHY8uWLQCAbdu2YdSoUejTpw90Oh2efPJJREdHIzMz098fwSXbvVebDhW5XTJjvGnCwM7Rjfa7aDUN5bRfGnGX9XzPv38c6w407qdltgDrDhRhaaZ9NspVkCeoqK7Dyr1nJX22B3rchtf+syei9TpJx5PvRYRK36tliAzDM7f2PrniqtjB7txivCHxevEFqZN3k9mCjF3ys7Or9xdg0yF5LRUCmav+ahY0BFAjkxuWg7Z3KLrhzT5QdwVTNPi1JxsLZBARUVMTcJkss9mM06dPIzw8HBs3boTJZMJ9992HOXPmID8/H926dbM7PiEhAR999BEAoKCgAOPHj2/0fF5ent/GL0ZY5rLnTAn+mX1RdvGGrB+vWv+3PiQIv+0UjTHJt+PnihqkLtuHkmvS7t5v+KYIf/5dD2vmS+rGf6kWfJKDmnrl96GQ51xlQW0JzZdHJt+OepMFb0vofeWYNZIStPtSlF4nedmaN9e+UvuiAl2UXmfdg9bA/pf1pgOIlObQQkEVd9l+uXvxiIiIfC3gMlnl5eVITEzE8OHDkZmZiQ8++ADnzp3D3LlzYTQaGy37CwsLQ3V1QwU+d8+rZXdusbXk9DuHznldHa+61oSvz5bizx99h5V7z0oOsICGjNZ7Weesf1a6JDsDLNf0IYHRf82Zq8Y6zHr/JHbnFmOY3cRanGPWSImg3ZtpspzX+rodQavQgLuHJVtFdR0OF5ZZM0mO++IuOVRdlEsomGKIFM+QySmQQUREFCgCbhYQGxtrXf4HAOHh4Zg7dy4mTpyIcePGoabG/v9sa2pqEBERYT3W2fPR0dG+H7gIsf1OarItCy+3Ihh5Z2iP2/DZd4G5tMk2K/D13PsRFxmGksoaWcUO9pwp8Xoc0REhdjciYiJ0KDdK2+N3tbpOcuELX1/7/zM+GRm71GuirZSZ75+ARuN8r54SmSTbginOClp4UiCDiIhIbQGXycrLy8Prr79utwyltrYWWq0WycnJyM+3L+FdUFCArl0bKmR17drV5fP+ZjJbsOTT0wEVYAFAxxi99X8L+yKoMV9knf5dWIb2rUN82k/KG0JW4O+HijCoS1vRAAtoXOxgd24x3pGwxNCdRQ/2QPqou/D7gR2RPuouvOykWIsrUifbcpsoSxWj12Ht470xMjnOutytKau8UeeykI0SmSShYMrYlA4Y2KWt3XUlt0AGERFRIAi4ICsqKgpbtmzBxo0bUV9fj4sXL2LFihV4+OGHMXz4cJSWlmLTpk2oq6vD4cOHsXPnTus+rAkTJmDnzp04fPgw6urqsGnTJpSVlSEtLU2Vz7J6X4HbstP+ptUAUwd2sv7ZtpEs2fNF36lyYy2qak3WDICtQAq8ln6eh49P/OL0OWfFDoS9WEp49fM8ZOz6Hu9mnUfGru/xl0x5541tFYpDBaV4/Ysf8PoXeTiUX+q0oa2rJsre/BbpDyUiLdGArMIy3Kw3Y0LvDl6crenwVSZJToEMIiKiQKGxeLNz2UeOHj2K//3f/8XZs2cRGhqKUaNGYe7cuQgNDUVOTg5effVVnD17FjExMZg5cybGjRtnfe2OHTuwZs0aXLp0CQkJCXj55Zdxzz33SHpfk8mE7OxspKSkICjIuyzG7txiPLv5hFfn8IXp98Zj0SjnZdznf5LD0ut+FKXX2X3fWg0aVYUMNKOTDXhjcu9Gy8KyCsswZcNhlUb1K40GCAvW4kad/d7AKL0Oy8b1dFoFT6w0ePqoRGTsOiO6ZFLM7GHd8MG3F5r8MkG5tk4f4LOmwcKya8B5gQxPKxwSERG54k1sEJBBllqUCrJMZgtSl30VcFkswPVEyGS2YPW+Aqw/UOiyrDN5T9jT9PqEe7Av75KkSn6BQKsB8jIebNTYekf2L3jhg2x1BiXDWpHJuFiTW7l7KkODtaitNwfcEmFfM7QJxaH5D/i0uh/7ZBERkb95ExsEXOGL5uBoUXlABlgA8F5WkdPN5UDD8qnuhlYMsLwQFa5D5Y06t5NsYR+L2WJBZq73xSL8xWwBMj47jb6dYuyuIX/th4lw05jbnYWf5GBoj/aNgkRhT5Ajofrdwu05kopvtMQACwCm9LvT5+XT3RXIICIiCiQMsnwgkKtcZeZeQmbuJQCN7wIrua+mOXDs3SP5RTI8u+W4V0GDJ4TP5bhcUar3Dl/Ae4cvAPj1GjKbLQ0V6Fx8YRGhQWgdGuzVDQhvv6vy6joMWLoXrz3csHRQLINla0RSHIb2aI8+f9mD6zX1Ls/fEgMsAOgUG+GX9xELhomIiAINgywfaCpVrkoqazBj8wnJ/WhamlHJcdj1XbGsiXNFdR1mD+uG/5clrR+avwMsoCG4WjquJ+7rdhv+tPUE9nx/2eNzlVTWSN57qAHw0vAe+Cb/CrZnX/T4Pb1VbqzDjM0n8PSQeHx6qlh0+ZnJbMHhwjJk/VgKQINBnWPwxRnPv6vmrKn8m0dEROQvDLIUJNwVL7lWg4gQLYy1/mnMK9x3v79HO+zLuyL5dUKFu4Xbc/Hdz5UouHxd1ntGepgJaQqi9Dq8ObkXHkqOw/yPc1BxQ/rn7BSrx+EFD2DA0r2S+zv509XqOnx84mfM3HLC60Ibcl5eddOE/7PtlHdvqBALgHUHiho9Ltx4eHpIPD489nOzvb6VpNUAZddrkFVYxmV8REREtzDIUoizTdn+Yrh19z0yPERWkAU0TDbLjbV461+Fst/3qUHxWLn3rOzXueLREj0fWDauJ4K0GoxIikNe8XW88VW++xfdclvrMIQEa/Hawz0DrhG1YA8zMk4Jv5WzAIycM1uA5xyKnrAgBRERtXQB1yerKRIqkPk7wPr9wI7YMq0/Xp9wD27Wm3HkxzL44+Zx3K0+Sc8NTVC8kbEhMgxrH++NtY/3RpRe1+j5KL0OzwyJ9/p9WoUGOW02HH2rkaztPrUPvv1J8nmj9Dprvx6haEJUeOPPIVdEaBBCg/nXlZoGISO4O7dY7aEQERGpgpksLwnFItTIVkTrdZiz7ZTfgzuh6r/QzNWbbE1MhA7/d3JvlBpvNlpmlJZowOEfy5BVWAYLLIgK1yFGH4JXP89zec5wXeM+SY6qbpqw5Y/9AQBZP5ai3mxBVU09NBoNfrl6A7X1ZoQEa63LP6WqqK7DF7kliI4IsS6denNiCp74f99KPoczauzdIvKUsBT5lZ1nkJZo4NJBIiJqcRhkeUnNYhFvflWgyvteunbTrmDGmsd7Y/GO07h0XX7VuIdTOiC1a6zdY44V3xLjWiNj1/eSv2d3AZag1HgTDyXfjq3fXmhU4OLVzO8x/d54JN4eKfWjWD231X6vk1LzSw0AfUgQS+xTkyC0KThaVM6KgERE1OIwyPLS3jNNp8eRUhzvUh8/f9WjAAsAhiUa7P7sz71t50qr0XPJF6h2ErSYLQ37ch5Klr+nxLGYhLfFJQQWgAEWNTmB3NKCiIjIVxhkecFktuCTk7/47PyBUgTCGeEu9cR1/8bx8xUenSPaZv8S8OveNn99ZilFOzJzitG+l06jKwAAGrVJREFUdQguX68N2N+CKJCxvDsREbVE3EnvhaNF5biqcInn6PBgjLi7PYDADbBseRpgAQ2lxPfcygSqubfNFbMFuLdrOwCy+wwTtWgaNBTJsb2RQkRE1FIwyPKCL5bBXL1Rj92nLyl+3kAkLDkU9mAFaiNkfWgw1jzeGwaFKynKJUxao8KZgKbAJtyQWDw6kUUviIioRWKQ5QUug/GO7cb4QN630TFGjxFJcTg4byi2Th+AP6Z28vsYhGnqogfvQr1/elwjXKdFRGjjMvdk73eJ7dUeQsAx3GrzwD5ZRETUUjHI8kK/+BgY2jDQ8pZQRTAQaTXA1IGdADSUrB/YpS3SR9+NtY/3VrxHmCuGyDA8PSQe6Z/moupmvV/eM0ofghXj74EGgbNUslUABn39uRzOSq8LwpZp/XFw3lAGWERE1KIxyPJCkFaDJWMS1R5Gkyf0x4qLDAuYybxg+r3xCHHSBHhEUhy+nns/YiK8bzQsZuGDPfDc/Ql47v4umNj3N1h3oEjxPYCuFFfWIDoiJCCWSgrudSj3rzatBni0f8eAvHbV8Mx9nZGaEMslgkRE1OIxyPLSiKQ4rH28N/QhgXeHPdDZbowXGhsLj6tNowGeGRKPBSPFg+jj56+i3Oi7oOeNr/Kxen8BVu8vVLQnWqvQINzXrZ2kYy9fr0FaogGvT7gHz93fBQ8mNZTcV+M3itbr8Hj/Tiq8szizBXj/yHmkj7oLQGBcu2qJ1uvw3NCuag+DiIgoIHAHvUJuBFD/ov87pRdiW4XiUMEVrN5fqPZwRFkAjLknznrXW2hs7NgnKy4yDOmjElFceQMZu7736Zj0uiA82LM9/vOe36D8Ri2yCsusQaBtk+SG77fUp2Nx1r9LCVU3Tfj67BVJx+45cwnLPs+z+z1ahQYhOEiLCj9m1TQAlo7riQFd2iIuMgwllTUBU4kyY9f3iLu1nPPDYz/79XsJFMLvIyeD5dh0XPh7RkRE1BxoLBZLoMxVVGcymZCdnY2UlBQEBUnLTJnMFgxevi8gKuNF63VYOq6ndS+EMDY1J6TReh36dIzC3u+dT+o1QKMN8mKTrx3Zv+CFD7J9Ntbn7k9AYlwbZOxqHOSNuScOn54qDojfWQkaNGTrvGmUHKbToqbO91U44iLDsHh0ovUa2Z1bjGc3n/DoXON7d8DHJ5TvbRfIPe18zfH3kcJZ03FPzkNERORLnsQGAmayvBRIpcdDg7VISzRY/ywswZux+YRqk8Cr1XWiARbQMKYFn+QgLdFgvYstFJhw5OviGLogDWa937gZcnFlDdYdKPLonDERISg31no/OIVZAHh7e8XXAZY+JAgbft8XAzq3bZThiNLrZGWMhAl8WqIBX+VdVjzbpHaApQHw4rBu6BSrR2yrUPz5H9m4dO2m4uPSoKEIy+sT7kGp8aZHGSixpuMllTWYsfkEqxISEVGzwD1ZXgqk0uMl127iaFG53WPCEjzHwgXRet8VbJDranUdVu/Ld3ucL4tjxEWGYevRC4pPSn8/ILCLIgh7rAJRda0Jx85dtZvACxN0OUFSTEQIvp57P0YkxWHPmRK3r+3bMcrTIavmxWHd8MKwrhib0gGpCbFYMuZuAI33iAl/jtLrZF+Ttr2vUrvGYmxKBwzs0jgAdsVV03HhMaF3HhERUVPGIMtLgVZ6vKTyRqPHbHs8vTk5BVunD8Cxl9Mwe1g3FUbo3N8PnXM6sTKZLcgqLMOO7F9wtKgc6aN8U80xtUtblFy7qfh549tFBFRBD0dd2kWoPQSX/v7vIut14WqC7kq5sRbHz1+FyWzBkk/PuD3++PkK+QNVWadYvd2fxW6uGCLDsPbx3lg2ricAedekEr2v3GX+bXvnERERNWUBuVwwLy8Py5cvx+nTp6HT6ZCamor58+cjJiYGixcvxscffwyd7tdMzPz58zFp0iQAwPbt2/HWW2/hypUr6Ny5M9LT09GrVy+fjbVPx2hovdzboiSxpWnOluA5TszUVHGjDkeLyu3GKLZv4+kh8dh2/GdFK/tdqVI+wAIagvCBXdo6Leih9j6euMgwDOwcG9DFUSqqf70uvFmae/l6w8S95Jr71/vjN4kICYJRwcImzm72jEiKQ1qiQbS4hLNr0pX0Ud7vl5Ka+Q+kFQJERESeCLggq6amBtOmTcPEiROxbt06GI1GzJs3DwsXLsTatWuRk5ODjIwMPPzww41ee+TIEWRkZGDDhg1ITk7Gli1bMGPGDOzfvx/h4eE+Ge/x81cDJsACgJhWoZKPVSILp2SAaTuxcrVvY/2BIjwxsCM2ZZ1X5o0BHFH4zrmwd6XfrUa1zia8fTpGY82/CrFy71lF31uqiX3vwOXrNYiJCMFVY63q+4rECNeFNxPv21qHBdTEfcWEZERHhFqvhavGm8jY9b1dwBMRGgSTyYyaete/jFbTcLPHGbH9jYD9NVlyq3Kn2E0aDYCMXWcwPMngVQVAqf/mBNoKASIiIrkCLsi6ePEievTogVmzZiEoKAghISGYNGkSXnrpJdTW1uLs2bNISkpy+tpt27Zh1KhR6NOnDwDgySefxIcffojMzEyMHz/eJ+MNpIkbABjaSJ+cCHucpFQfbBsRgrEpt+OBHu1htlhuBSUW9O/UFtAAz289iYob3mWWhImVu30bGgAfn/zZq/cSaNAwma26qXy59MWjE+0mpM4mvC8M64ruhlaNMgrReh0sgN3+IaGUfXREiNtJsRRvfuV+H5w7UXodwoKDJGWIPCVcF55OvIVebL5cgiYnK9kQsHyPg/OG2l0fw5PiGmWdDv9Yhsc2HnF5PrOl4WaPWDDlinBNZhWWubyWbJfxefI+Anf/5jjenCAiImqqAi7I6ty5MzZu3Gj32BdffIG7774beXl5qK+vx6pVq3D8+HG0bt0a48ePx7Rp06DValFQUNAomEpISEBeXp7PxuvPO67uKtXFyZycSKk++MfUThiWaLAuM8r87iJe3pFrXaq3GoWIiwzDpN/+ButvVeCTmxFxnFhJ2bdxvcb7oEj4zEo3MZBbilpsWRcA0aVe7ibF/qABsGxcT+vYvzxTgk3/PqfY9+l4Xci5KWBLCHb7xcfA0CbMbUAohD1iQUCkXgetRmP3/RtulfmXUoVSLGBxFoSXSlzG6u3NHn8t43P1b45tYQ32yyIioqYuoAtfWCwWrFy5Evv378eiRYtw/fp19OvXD1OnTsXXX3+NFStW4L333sM777wDADAajY2WBYaFhaG6utpnY+wXH4OYiBCfnR9omHzERYbhL2OTGvobOXleA88mJ2Ib5ONubZBPH323tYLY0swzmPn+yUZ7oYpvLeF7ekh8o/NI+WxwGLsvsoPReh0iw+3vKbRvE4rZw7oqsjemTVgw/pDaCVunD8DBeUNdBli2xTyyCstgMlusE2zbim3OHhP4KoPaOkxaD4iYCJ21CIIwzsWj78bfpiiz/9HZdSFM0G2fdyVar8Nam0INQVoNloxxXzjl6SHxTt/DNiiwDbBiInRIH3UXFoxMxNrHeyMyXFrlTim/ob+W1/lzGZ+rohws305ERM1FwGWyBFVVVViwYAFOnz6NzZs3o3v37ujevTtSU1OtxyQnJ+OJJ55AZmYmpk2bhvDwcNTU2E9campqEB3tfL+CEoK0Gvxnyu1459A5n5zfdrI5IikOa7SNN6sb3GROxJr7CtxtkAeAzO+KXd6ltwD49FQxvp57P97LOoeMXd9L+nzOxi51ItcqNBhVN+tFn4+J0CH9obthaBOGq8Za/PdnZ1B5w/Z4DSq9XOIovM/hBcMQEuz+noVSTVh9lUGVmiFMf+hup+MdmXw71mo1Tj+jsNRx75kSbM/+xS5Yd9zbJ1wXaYkGZBWWWa/LtESD04INcZFhmNT3DtSbzQAagj5n/bVGJMVh7eO9Mf+TnEal3KP0Oiy71cy7153Rjd4jSq/D1eq6Rq+7aqzDrPdPYo1WgxFJcWgdqsNjb7te4gdI+w39tbzO38v4pPybQ0RE1JQFZJB14cIFTJ8+Hbfffjs++ugjxMQ0/B/73r17UVpaismTJ1uPra2tRVhYw2Sla9euyM+332dSUFCAIUOG+HS8aYkGnwVZjkGI3MmJ1Em9qw3yJrMFL+/IdTvW4soavJd1DufLpWUOn7u/C2andW809oZlXaFuS6oHB2lElzlqALz2cMOEeXdusdMmwyXXamT9bmLLm157uKfkAEupJqyeLp1Tiqu9f+6u0YFd2mLhqMRGRUCOn79qd/yeMyUYvHyf02v34LyhHk/QhfEd/rEMWYVlACwY2DkWA2yyhY6fQWjw64ywT/CVnWeQlmjAgC5tFQtY/LW8To1lfK7+zSEiImrqAm65YGVlJZ544gn07t0bb7/9tjXAAhqWDy5duhRZWVmwWCw4efIk3n33XWv59gkTJmDnzp04fPgw6urqsGnTJpSVlSEtLc2nYxYmvEppFRqElRPvEV165moZmS1hUu+4v0mY1O/OLQbgfPmaraNF5ZL3/2Ts+h7vSqz6l5rQzunYg7QaTOl3p9vXV1TX4cVh3Rp993E2y46k9FZyN2+M1uvw1qO9vFrepHQTVtulc0qLiQgRXY4nLF11FyC4ukadZVZDgrV2x+85U+Ly2t1zpkTS3wFX40tNiMWc4d0xZ3gPpHaNbXQOYR/Xba3DkFVY6jLot91n5WpZoycBi7+W13EZHxERkXICLpP1ySef4OLFi/j888+xe/duu+dOnjyJBQsWYMmSJbh06RJiY2Px/PPPY+zYsQCAgQMHYvHixdbnExISsGHDBkRFRfl0zLZ3gQHv++xMv7cLHu79G7fL/MSYzBYc/rEM8z/OcVmh75WdZ2A2WxqVjnbMdCm9/0fKnfxOsdKa5HaK1bvMakjprSTENc6yYhoAS28tIXNW/U3qRFlOE1apd/eFSfHC7bmSguBFI+/C2wd/xKVrN11mWNJHJWLW+77JaEjJrEqpLilkjXy5vMzZWN05WHCloSy+PgTjenfA57klqLbZ8+duaa8Yfy2v4zI+IiIiZWgsFqVrqzVdJpMJ2dnZSElJQVCQtAIAtnbnFovu9ZjU9zf48Nuf3ZY5j9LrcPzlNOw5U+LR3h1PJoaOhOmUcPc6q7AMUzYc9vh8rs4tRup7bp0+wGVQsv3Ez5j9j1Nuz/PUoI7YffqS13ulxOzI/gUvfJDt9rg3J6dgbEoHWeeurTdjwNK9os2ZheDp4Lyh1gwR4DyAEn4XpfaO2RJbLunp9ebut/eG2Fg9pQHwULIBb0zuzYCFiIioifAmNgi4TFZT5xhgAUBldR3WHyjC3x7tjY0Hf8SJCxWir182rqd1Iix3745SE0PHbIGwHNKbwE0g9U6+UhvxpS5z/E2064yYt3xZvS0kWIvXHu7pMngSsk9C9std8RSlMxpSlkv++R+nEBESjFKJv5mvKixKWWIqlwXAzu9KcHvU91gw0jfLPImIiChwMMhSiDAxc0YIWuZ+fApGF01vh93VDq1DdZjppEiD7XmcLZVSemLouHxt8ehEPHtrEu+J3w/siAeT4iRP1JXaiB/TKlTS+GJahfp0I76vq7dJDZ6EY6UEUEp+H1KWbRprTZj6zlFEhEq7U+SrCotSxuqpDd8U4c+/6yGpUAoRERE1XQyyFCJlz42rAAsA9n5/BXu/v+LyGLG9O76aGArZghFJcXjr0d54busJSKzNYOfBpDjZE3Y5gYMYV1XwPDnOU/6o3iYn++Tvym5ysk7u/p4oXU7cka8yZEDD/r/3ss7hj/d2dvq8p/swiYiIKLAwyFKILydmUt7PV+9vmy0YmRyH1eiFme+flPx6JTI03ixbk7LUUUq1PCUoETS6E6hlsT3NOvmrnLgtX2XIBGItDnyxD46IiIjUwSBLIb6emLl7P6XfXyw4Ems2K3YOwPsJsTeBg6vKj/6YsDtqqdXbPO3rFR0RYrevTsmAVIyve5B1jNE3ekzJHmpERESkPgZZClGyOIQ7zjIvSk4M3QUfzgKFq8ZaZOzyXYbGG/7IIMkRqNkmX3IMdqUymcyYPawrOsVG+C0glbK0M1KvQ2V1ney/a1oNMHVgJ7vHAqVkPRERESmHQZZCgrQajLknDusOFPn8vZwFP+4mhnImg1KCD2eBwvCkwM3QtNQMUiD5ta9Xjmi5eUeVNfV4Y28+1jze26+BqbvAHIDTv2vuTL83vlHRC1/0UCMiIiJ1MchSiMlswaenil0eExqkwU2Td3mm2cO6iQY/riaGY+6Jw/pbAaCzO/MvDuuGTrF6r4KPQM/QBPr4WoIRSXEY2qM9Biz9SnJ5fUCdTI67wNzZ3zUxWk1DgOWsfLvU/ZT+3vdJREREnmOQpRAp1f28DbAMbULx3NAEl8e4mhj2ujM6YJbMUcvV0NcrSXJLADUzOa4Cc8e/a7ERoYAGKK26iRh9CPJKruOnq9XoGKPH1IGdRMu2+7KHGhEREamDQZZCpN5ljgrXofKG/L0cGgBLxtwtuceUs4khl8xRoBiRFIe1j/fG/E9ynDbwdiYQMzmugrB7u7WTdA5f91AjIiIi/2NHTIVIvcv8VGo8gF+X6UkRFxmmWHUxYVI4NqUDBnZpywCLVDMiKQ7HX07DopF3STq+uWZyhP2UQON/F9SogElERETeYyZLIVLvRj83NAHdDa2c9sNJH5WI6IgQlFTeQLmxFjGtQmFow2wTNV9BWg3+MDge7xwqatGZnECrgElERETeYZClEClln4W70Vy2R/QrOX93mjP+u0BERNR8aCwWiy/6bTZJJpMJ2dnZSElJQVBQkEfn2J1b7DRLxbvRRK7x7w4REREFEm9iA2ayFMa70USe4d8dIiIiai4YZPkA+zEReYZ/d4iIiKg5YHVBIiIiIiIiBTGTZUPYnmYymVQeCRERERERqUmICTwpYcEgy4bZbAYA5OTkqDwSIiIiIiIKBEKMIAerC9owm82or6+HVquFRsPN9kRERERELZXFYoHZbEZwcDC0Wnm7rBhkERERERERKYiFL4iIiIiIiBTEIIuIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLJuKSsrw8yZM9G3b1/0798fr776Kurr69UeFqmgvLwcaWlpOHLkiNpDIT/Ly8vDU089hX79+iE1NRUvvfQSysvL1R4W+VlWVhYeeeQR9O7dG6mpqcjIyEBNTY3awyI/M5lMmDp1KubPn6/2UEgFmZmZSExMRK9evaz/zZ07V+1hkR9VVFTgpZdeQv/+/fHb3/4WM2fOxOXLlyW/nkHWLS+++CL0ej2++eYbfPTRR8jKysKmTZvUHhb52fHjxzFp0iRcuHBB7aGQn9XU1GDatGno1asXDh48iM8++wwVFRVYuHCh2kMjPyovL8czzzyDKVOm4NixY9i+fTuOHj2K9evXqz008rPVq1fj2LFjag+DVJKTk4OxY8fi5MmT1v9WrFih9rDIj55//nlUV1djz5492L9/P4KCgpCeni759QyyAJw/fx5Hjx7F3LlzER4ejjvuuAMzZ87Eli1b1B4a+dH27dsxZ84czJ49W+2hkAouXryIHj16YNasWQgJCUF0dDQmTZqEb7/9Vu2hkR/FxMTg3//+N8aNGweNRoOKigrcvHkTMTExag+N/CgrKwtffvklfve736k9FFJJTk4OkpKS1B4GqSQ3NxenTp3CsmXL0KZNG7Rq1QoZGRmYM2eO5HMwyAKQn5+PqKgotG/f3vpYly5dcPHiRVy7dk3FkZE/DR48GHv27MHIkSPVHgqpoHPnzti4cSOCgoKsj33xxRe4++67VRwVqaFVq1YAgPvuuw+jR49Gu3btMG7cOJVHRf5SVlaGRYsW4a9//SvCw8PVHg6pwGw24/Tp0/jXv/6F+++/H0OGDEF6ejoqKyvVHhr5yXfffYeEhAT84x//QFpaGgYPHozly5ejXbt2ks/BIAuA0Whs9A+p8Ofq6mo1hkQqaNeuHYKDg9UeBgUAi8WClStXYv/+/Vi0aJHawyGVfPnllzhw4AC0Wi3+9Kc/qT0c8gOz2Yy5c+fiqaeeQo8ePdQeDqmkvLwciYmJGD58ODIzM/HBBx/g3Llz3JPVglRWVuKHH37AuXPnsH37dvzzn//EpUuXMG/ePMnn4IwSgF6vx40bN+weE/4cERGhxpCISCVVVVVYsGABTp8+jc2bN6N79+5qD4lUEhYWhrCwMMydOxePPPIIKisrERkZqfawyIfWrVuHkJAQTJ06Ve2hkIpiY2PttoyEh4dj7ty5mDhxIqqqqqzZbmq+QkJCAACLFi1CaGgoWrVqhRdffBETJ06E0WiUFB8wkwWga9euqKioQGlpqfWxwsJCGAwGtG7dWsWREZE/XbhwAePHj0dVVRU++ugjBlgt0IkTJzBixAjU1tZaH6utrYVOp+PSsRZgx44dOHr0KPr27Yu+ffvis88+w2effYa+ffuqPTTyo7y8PLz++uuwWCzWx2pra6HVaq2Tb2reEhISYDabUVdXZ33MbDYDgN114QqDLACdOnVCnz598Nprr6Gqqgo//fQT3nrrLUyYMEHtoRGRn1RWVuKJJ55A79698fbbb7PQQQvVvXt31NTU4K9//Stqa2vxyy+/YPny5ZgwYQInVy3A7t27ceLECRw7dgzHjh3DQw89hIceeohVBluYqKgobNmyBRs3bkR9fT0uXryIFStW4OGHH+a/Ay3EoEGDcMcdd2DhwoUwGo0oLy/HypUrMWzYMMmZTAZZt6xatQr19fV44IEHMHHiRNx7772YOXOm2sMiIj/55JNPcPHiRXz++efo06ePXW8UajkiIiKwceNG5OfnIzU1FVOnTsWgQYNYyp+oBTEYDFi3bh2++uor9OvXD+PHj0fPnj3xX//1X2oPjfxEp9PhvffeQ1BQEIYPH47hw4fDYDDgtddek3wOjUVqzouIiIiIiIjcYiaLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhBDLKIiIiIiIgUxCCLiIiIiIhIQQyyiIiIiIiIFMQgi4iIiIiISEEMsoiIiIiIiBTEIIuIiIiIiEhB/x8D9hdojB5jaQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots(3, figsize=(10, 9))\n",
    "# ax[0].scatter(x = string_search(data_good_quality,'reference_adduct','[M+H]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+H]+')['reference_precursor_mz'])\n",
    "# ax[0].set_xlabel(\"\")\n",
    "# ax[0].set_ylabel(\"House Price\")\n",
    "# ax[0].set_xlim(left = 0, right=6)\n",
    "# ax[1].scatter(x = string_search(data_good_quality,'reference_adduct','[M+Na]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+Na]+')['reference_precursor_mz'])\n",
    "# ax[1].set_xlim(left = 0, right=6)\n",
    "# # ax[1].set_xlabel(\"Overall Quality\")\n",
    "# # ax[1].set_ylabel(\"House Price\")\n",
    "# ax[2].scatter(x = string_search(data_good_quality,'reference_adduct','[M+NH4]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+NH4]+')['reference_precursor_mz'])\n",
    "# ax[2].set_xlim(left = 0, right=6)\n",
    "# ax[0].grid(False)\n",
    "# ax[1].grid(False)\n",
    "# ax[2].grid(False)\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots(\n",
    "#     figsize = (7, 5)\n",
    "#                       )\n",
    "fig = plt.figure(figsize = (7, 5))\n",
    "ax1 = fig.add_subplot()\n",
    "\n",
    "ax1.scatter(x = string_search(data_good_quality,'reference_adduct','[M+H]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+H]+')['reference_precursor_mz'], label = '[M+H]+', color = '#FF9999')\n",
    "ax1.scatter(x = string_search(data_good_quality,'reference_adduct','[M+Na]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+Na]+')['reference_precursor_mz'],label = '[M+Na]+', alpha=0.2,color = '#97C997')\n",
    "ax1.scatter(x = string_search(data_good_quality,'reference_adduct','[M+NH4]+')['retention_time'], y = string_search(data_good_quality,'reference_adduct','[M+NH4]+')['reference_precursor_mz'], label  = '[M+NH4]+',alpha=0.5, color = '#9999FF')\n",
    "plt.legend(loc='upper left')\n",
    "ax1.grid(False)\n",
    "ax1.set_xlabel(\"Retention time (min)\",fontsize = 18)\n",
    "ax1.set_ylabel(\"Precursor m/z\", fontsize = 18)\n",
    "# ax.set_xlabel(\"Retention Time (Min)\", fontsize = 18)\n",
    "# ax.set_ylabel(\"Precursor m/z\", fontsize = 18)\n",
    "# plt.show()\n",
    "plt.savefig(os.path.join(source_dir,'rt_premz.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# rt_mz distribution"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (7, 5)\n",
    "                      )\n",
    "plt.scatter(data_good_quality['retention_time'], data_good_quality['reference_precursor_mz'])\n",
    "# ax= sns.histplot(data=data, x=\"normalized_entroy_denoised\", multiple=\"stack\", kde = True,\n",
    "#                  # hue=\"evaluations\"\n",
    "#                  )\n",
    "# ax.yaxis\n",
    "# plt.vlines(x =0.8, ymin = 0, ymax=250,colors='red')\n",
    "# ax = sns.histplot(data=data_ead_r, x=\"diff_recalibrated\", multiple=\"stack\", kde = True,color = \"green\")\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"Retention Time (Min)\", fontsize = 18)\n",
    "ax.set_ylabel(\"Precursor m/z\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "# plt.legend(labels=[\"Before recalibration\",\"After recalibration\"], loc = 'upper right')\n",
    "plt.savefig(os.path.join(source_dir,'rt_premz.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# pie chart for chemical classes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data_good_quality['chemical_classes']=chemical_classes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [],
   "source": [
    "class_results = data_good_quality.loc[data_good_quality['chemical_classes']!='inchi_not_found']['chemical_classes'].value_counts().rename_axis('chemical_classes').reset_index(name='counts')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['reference_Name', 'reference_InChIKey', 'reference_Mix',\n       'reference_Formula', 'reference_Monoisotopic mass', 'reference_adduct',\n       'reference_precursor_mz', 'retention_time', 'peaks_recalibrated',\n       'peaks_recalibrated_denoised', 'comments', 'key', 'explained_intensity',\n       'chemical_classes'],\n      dtype='object')"
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_good_quality.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [],
   "source": [
    "labels = []\n",
    "for i in range(0,15):\n",
    "    labels.append(class_results.iloc[i]['chemical_classes']+' '+str(np.round(class_results.iloc[i]['counts']/sum(class_results.iloc[0:15]['counts'])*100,1))+ '%')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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JIEKIOk/TdRxOiEuycTDdgfxSFFXB30shprGZZqFGNE1HVSWICCEESAARQtRhmq6jAAfSHexMtmF11PSIhCcK9VPpGWWmQYABXdelR0QIUedJABFC1DmlfR6peU62xltlA0FRLVrUN9IzyozRIGVZQoi6TQKIEKJO0XWdQpvOH/E2WVJXVDsvI/RoYqZFmEl2VRdC1FkSQIQQdYKm66DDzmQ7e1PtaPKbT9SghgEqfZt54e+lSEmWEKLOkQAihPB4uq6TVaix8aiVXIv8yhO1g6pAxwgTnRuZQJGyLCFE3SEBRAjhsXTNiY7K9kQb+9NkdStROwV4K1zV1IvwQGlSF0LUDWpND0AIIS4LXUexF6MoUGTXJXyIWivfovPTfgubj1nR9JJyQSGE8GASQIQQnkXTQHPC/jWwdjYUZNC/mQmj/LYTtdyhDAcr9hSTW6whxQlCCE8mJVhCCM+ha1CQCXHfQ36667bAhuj97yU9X2f1fkvNjk+ISlAV6NbETPtwk5RkCSE8kgQQIcSVT9dBUeD4Vtj3i2sGpKyWA9BbD2LDESvHsmXpXXFlaBRkYEBzL0xGaVAXQngWCSBCiCubprlmPnYth5S9FR+jqOj970HzC2NhnAWHVr1DFOJieRuhf3MvGgUba3ooQghRZSSACCGuXLoGRSdh2yIoyDj3sf5h6APuI6MQ/rdPSrHElaVdQyPdm5hluV4hhEeQtkwhxJUrdT/8/sn5wwdAQQbKwd8I81dpWd9w+ccmRBXal+bgx78sWOy6rJIlhLjiSQARQlxZ9JKSq72r4c/vwGmr/Pce3QR5qfSONmGW337iCpNdpLFyr4XsQlklSwhxZZO3YCHElUNzgq0INn/haji/ULqOsmMpqgLXtPWu+vEJcZlZ7Dr/22fhWJYspiCEuHJJABFCXBl0DfLSYP3HkJN08ecpyEQ5sI5QP5XWYdLYK648mg4bjlrZnmhD13WZDRFCXHEkgAghaj9dh/TDsGU+2Aov/XxHt8DJFHpFGTFLBhFXqL0n7Kw7ZMUpu6cLIa4wEkCEELVfwnbYvgic9io6oY6ycymKojOsjZRiiStX0kknP+4tplia04UQVxAJIEKI2qn0Ymr/Gtiz6tTXVaUwG2XfGkL9DLRtKNMg4sp1slhn5Z5iMgukOV0IcWWQACKEqH1KV7rasRSObLp8j3N8K3p2Ij0aG/GWDCKuYFYH/LzfQkquU0KIEKLWkwAihKhdNA2cDtj6NSTvvuwPp+xchoLOMFkVS1zhnDqsO2QlIUdCiBCidpMAIoSoPTQn2Itg038g63j1PGZRDsq+nwnxNdA+XKZBxJVN02H9YStHMh0SQoQQtZYEECFE7aBprj0+Nn0O+enV+9jx29Cz4unW2IiPZBBxhdOBTcdsHEh31PRQhBCiQhJAhBA1r3TmY/MXUJRTI0NQdi5D0TUpxRIeY2u8jd0ptpoehhBCnEECiBCiZmlOsBe7Zj5qKHwAUJyL8tdPBPsa6BhhqrlxCFGF4pLs/JkoIUQIUbtIABFC1Bx3+Ki5mY9yEv5EzzxGTKQBX3NND0aIqrHnhJ2t8daaHoYQQrhJABFC1Ixy4SO7pkfjpuxcDpqT4bJBofAg+9Mc/CEhRAhRS0gAEUJUP00Du6Wk56P2hA8ALHkoe/9HoI+Bzo2kFEt4jn1pDukJEULUChJAhBDVS9NcMx+bP4fCWhY+SiXtRE8/QpdGBvylFEt4kLgkOwfT7bJErxCiRkkAEUJUH10DzQGxX9Xe8FFC2bUCNIesiiU8TuxxG4myWaEQogZJABFCVI/Si53t30J+Ws2OpTKs+Sh7VhHgbSCmsZRiCc+hA+uPWEnL19AkhAghaoAEECFE9VAU2LUSMo/W9EgqL3k3etpBOoYbCPCq6cEIUXU0HdYetJBTJCFECFH9JIAIIarHwd8gaWdNj+KCKbtXgtMupVjC4zg0+OWAhQKrLiFECFGtJIAIIS4vXYPEnXBofU2P5OJYC1H2/Ii/l4HuTaQUS3gWqwN+3m/B5kBCiBCi2kgAEUJcPpoGWQmwe2VNj+TSpOxFT91P+4ZGgmQiRHiYQpvOukMWAGlMF0JUCwkgQojLQ3O6Vrravsg1C3KFU3b/AA4rw2SDQuGBMgo0Yo/bUBSlpocihKgDJIAIIaqeroHDBlu/AoeH7L5sK0LZ/QO+XgZ6RkkplvA8hzIcHJA9QoQQ1UACiBDiMlDgz+/AklfTA6laqfvQU/bStoGRYB/5pFh4nj/ibWQWyMpYQojLSwKIEKJq6bprxausYzU9kstC2bMKHBaGtZF1eYXn0XRYd9iK1SErYwkhLh8JIEKIqqNpkHkMDv9e0yO5fOzFKLtW4GM20CfaXNOjEaLKWew66w5aQZemdCHE5SEBRAhRNTQNbIUQt6SmR3L5pR1ET95NqzADIb7ya1R4nsxCjS3SlC6EuEzknVMIUUV014pX9uKaHki1UPb+D+zFDGstsyDCMx3OdHAgTZrShRBVTwKIEKJq/PUTnEyp6VFUH7sFZdcKvM0G+jaTECI807YEG/kW6QcRQlQtCSBCiEuja5CyF+K31fRIql/6IfTEnbQINVDfT36dCs/j1GH9EQ9ZSlsIUWvIO6YQ4uLpGljyYdcVvtP5JVD+Wg22QoZKKZbwUNlFGjuSpBRLCFF1JIAIIS6eosLOZeC01fRIao7DirJzOV4mA/2aSwgRnmnvCTuZhbI/iBCiakgAEUJcHF2D439AVnxNj6TmZR5FT/iT5qEGwvzl16rwPDrw+xErmiZL8wohLp28UwohLlxp6dX+NTU9klpD2fczWAsY2kpmQYRnKrDqbI2XpXmFEJdOAogQ4sIpKuxYBk57TY+k9nDYUHYsw2wyMLCFhBDhmQ5nOkjIcUgplhDikkgAEUJcmNLSq2wpvTpD1nH0438QXc9AwwD59So80+ZjVuwOKcUSQlw8eYcUQlSelF6dl7J/DVjyGdJSZkGEZ7I6YPNxq5RiCSEumgQQIUTlSenV+TntKDuWYjIZuLqlV02PRojLIiHHSdJJKcUSQlwcCSBCiMrRNddmg1J6dX7ZCXAsliYhKhGB8mtWeKatx21I/hBCXIxa/c64d+9eHn30Ufr06UNMTAzDhg3jjTfe4OTJkzU9NOFhYmNjadOmzSWdIy4ujk6dOp1x++rVq7n++uvp2rUrw4YNY/HixWc9h9Vq5dVXX6Vfv350796dO++8kyNHjgDgdDp55pln6NatGyNHjmTbtlM7jycmJjJmzBhstsu0H4eug8MGB9ZdnvN7ov1roTiPq1uaa/cvWiEuUoFNZ2eybFAohLhwtfZ9cd26ddx22200a9aMZcuW8eeff/Lhhx+SmJjI2LFjSUtLq+khCgG4GjEXL17M5MmTzwgAW7Zs4bnnnuPpp58mLi6OV155hRkzZrBr164KzzV9+nT27t3L999/z+bNm2nRogWPPfYYABs2bGD79u38+uuv3Hbbbbz55pvu73v11Vd59tlnMZsvY9/BgbVgt1y+83sazYGy43uMBpVBraQUS3imv1Lt5Ft1KcUSQlyQWhlAbDYbL7zwAg888ABTp06lYcOGKIpCixYteP/99wkPD2fmzJkALFmyhHHjxjF58mR69OjBihUrsFgsvPTSS/Tq1YtBgwbx3nvvMWTIEGJjYwH4888/ueOOO+jfvz+dOnVi3Lhx7NixA3B9Ej5kyBDmzp3LgAED6NWrF4888ggFBQXu8X377beMGjWKbt26MXr0aJYvX+6+z2Kx8OabbzJo0CB69uzJpEmT3Beb27dvp0OHDvz2228ApKam0rt3bxYsWMDHH3/MiBEjyr0On376KbfffjsASUlJ3HPPPe5Pvz///PNyn9jv3buXSZMm0bNnT4YPH87nn3/u/lRq9uzZPProozz11FP06NGDgQMH8vbbb5/19c/Ozuapp56iZ8+e9O7dm6lTp5KbmwtAcnIyjz/+OFdddRX9+vXjySefJD09vdxrN2/ePPcn+O+88w5r1qxhxIgRxMTE8Mgjj7gv0idNmsTrr7/OuHHj6Nq1K+PGjSv3qf6BAwe477776NWrFwMHDmT69Onk5+e7/96HDBlSbtyTJk1i9uzZADz33HO8+OKLPPjgg8TExDB06FDmz5/vPjY9PZ0HH3yQbt26MXToUDZu3FjuXLNnz2bQoEH06tWL8ePHs2bN2Zuup02bxrfffsujjz56xn2ff/45d9xxB4MGDUJRFPr06cN3331HVFTUGcdmZWWxbNkyZs6cSYMGDTCbzTz11FO88cYb6LqOwWAATq08o6quf76//PILPj4+9OvX76xjvCSaBoVZkPDn5Tm/J8tJQjm2hcbBKo2DauWvWyEuiaZD7HErqjSkCyEuQK18R4yLiyMzM5OxY8eecZ+qqtx000388ssvOBwOwHXxPXr0aDZt2sSwYcP417/+xe7du1m2bBk//vgjKSkpJCcnA66AMGXKFEaMGMH69euJjY0lKiqq3KfJycnJpKWl8fPPP/Ptt98SFxfH119/DbgufF9//XVeeOEF/vjjD6ZNm8aMGTP4+eefAdcn2Bs2bGD+/Pls3LiRa665hrvuuouUlBS6d+/OlClTeOGFF8jOzuaJJ56gf//+3HrrrYwdO5bExER27tzpHsfSpUsZN24cTqeTBx54gAYNGrBhwwY+/fRTli5d6j4uLS2NO++8k5EjR7Jp0yY++OADvv76axYuXOg+5qeffqJ///7Exsbyyiuv8Mknn7hD1+kee+wxCgoK+Omnn1izZg15eXnMmDEDu93O5MmTMRgM/PTTT6xatQqABx980P13kZycTEZGBr/++ivvvPMOH330EV999RWLFi1i+fLlxMbG8uOPP7ofa+HChTzzzDNs3bqVYcOGMWXKFHJycsjJyeGOO+6gZcuWrF+/nu+++45jx47xzDPPVPbHiCVLljBp0iT++OMP7rvvPl5//XX3zNnUqVMxGo2sX7+eL7/8kvXr17u/b8uWLSxcuJBvv/2W2NhYJkyYwPPPP4/dXnHj9WOPPcbChQtp3779Gfft2rWL4OBg7r//fnr37s0NN9xAQkICwcHBZxy7Z88eAgIC2LFjB6NGjeKqq67imWeeISQkBEVR6Nu3L/369WPEiBF89913TJs2jeLiYmbNmsU//vGPSr8uF0xVYc8qpNj7Ih34FYpOMrCFlGIJz3QiT5O9QYQQF6RWvh+WfqJev379Cu9v0KABdrudnJwcAEwmEzfccANmsxmDwcDy5cuZOnUqERER+Pn58eKLL7o/PTaZTCxcuJDbbrsNm81GcnIywcHBZ5R0/f3vf8fb25vo6Gh69+7NsWPHAPjuu++45ZZbuOqqqzAYDFx11VXccsstLFiwAKvVysqVK3nyySeJjo7GbDZz55130rx5c1auXAnAQw89RNOmTbnpppvIysri5Zdfdj+nAQMGsGzZMsAVqpKSkhg5ciQ7duzg+PHj/POf/8TX15fIyEimTp3qHuvy5ctp0aIFt99+OyaTiZYtW3LPPffw1VdfuY9p2rQpY8eOxWAwMGjQIMLCwjh+/PgZr21ycjJbt27l2WefJSQkBH9/f15//XWmTJnCtm3bSExMZMaMGQQEBBAYGMiMGTPYv38/e/bscZ/jgQcewGQy0b9/fwAmTpxIUFAQTZo0oVWrViQlJbmPHT9+PH369MFsNvPggw/i4+PDunXrWLNmDSaTiaeeegpvb2/CwsL45z//ydq1a8nIyDjfjxAAvXv3pl+/fhiNRsaPH4/T6SQhIYHk5GS2bdvGU089hb+/PxERETz88MPu7/Py8iI3N5dFixbx119/MWHCBDZv3ozJZKrwccLDw886htzcXD799FOmTJnCxo0b+fvf/87UqVPLBc2yx+bn5/PTTz/x3//+l59++gkfHx8efPBBnE4nqqry8ssvs2XLFpYtW0bnzp2ZO3cu48ePJycnh9tvv51x48bxzTffVOr1qRTNCakHIOt41Z2zrikpxTIYVAa3llIs4Zm2JUhDuhCi8mplAAkLCwMgJSWlwvuTkpIwmUyEhIS4jy8tRzl58iTFxcVERka6j/f393cfazAYiI2NZfjw4QwdOpRXX32VxMTEM5roSscArtBSen9mZiZNmjQpd2zjxo1JTk4mNzcXu91O48aNz7i/9KJbVVUmTpxIcnIyo0aNws/Pz33cuHHj+PHHH7Hb7Xz//feMHDkSPz8/UlNTCQkJwdfXt9w5SyUnJ7N371569Ojh/u+NN94gNTW1wudT+pw0TTvjtS29uC/7+oWFhdGqVSuysrLcoaTsaxscHOyeYQLKvdYAgYGB7vtUVS33Wjdt2tT9Z0VRCA8PJyMjg6ysLBo1auQ+R9nnXPaxzuX0v0MATdPcYbNRo0bu+8uWRMXExDB79mzi4uK4/fbb6devHx988EGFr9f5mM1mxo8fT0xMDEajkeHDh3PVVVexevXqCo91Op08++yz1KtXj4CAAP7xj39w4MABdwAu6+jRo2zcuJG//e1vPP/880ycOJEvvviC2bNnc/jw4Qse61n99VPVnauuOpmCcmQTjYIMNAmulb92hbgkBVadvSekIV0IUTm18p2we/fuhIWFVbhakNPpdNf/G41GgHKbIYWGhuLt7V0uvBQVFblnS3bu3Mkrr7zCu+++y8aNG/niiy8uqHa+cePGJCQklLstMTGRsLAw6tevj5eXF4mJieXuT0hIoEGDBoArIL3xxhuMHz+eefPmsW/fPvdxpT0NGzduZNWqVYwfPx5wXShnZ2dTXFzsPrbs8wsPD6d3795s27bN/d+aNWv4/vvvK/28SkVERJxx/sOHD/Pee+8RGRlJTk5OuX6Y/Px8cnJyyl3sX8jmVGVnnjRNIyUlhYiICCIjI0lJScHpdLrvL33dSwPn6Q3fpX/H51M6Y1H276lsWEtJSSE0NJRPP/2UrVu38sYbb/Dhhx+WK9OqrBYtWpwxTqfTWeGbdMuWLQHKHV/6/Cs6/tVXX2XatGkYjUYOHTpEhw4dCAgIICoqikOHDl3wWM+ga3B0MxSfvPRzCTj0GxRmMaCFGbVW/uYV4tLsOWHH5pQd0oUQ51cr3wZNJhMzZ87kyy+/5N133yUtLQ1N0zh8+DAPP/wwqampZ615L+0RmT17NmlpaRQXFzNz5kz3hVx+fj6qquLt7Q3Ajh07mD9/fqWXL73ppptYuHAhmzdvxul0uvsFxo8fj6qqjB8/nnfeeYf4+HhsNhtffPEFhw8fZtSoUQC88MILtGzZkn/9619MnDiRJ554wh0sTCYTY8aMYdasWfj7+9OjRw8AunTpQsuWLXn99dcpLi4mLS2N999/3z2m0aNHs2PHDpYvX47D4XA3WL/++usX/No3bNiQfv368eabb5KXl0dBQQH/93//R2JiIp06daJly5a89NJL5Ofnk5+fz/Tp04mKiqJbt24X/Fjgaujfs2cPNpuNOXPmoOs6gwcPZtCgQQC89dZbWCwWMjIyeO211+jTpw+RkZG0aNGCzMxMtmzZgq7rLFu2zL1c7fk0atSI/v37M3PmTHJzc8nIyODf//63+/7du3dz7733sn//fsxmM6GhocCpmZ0LMXHiRL755hs2bdqEpmmsXr2a2NhYrr/++jOObdmyJT179uTFF18kOzubwsJCXn/9dTp06ECrVq3KHfvjjz8SHh5O9+7dAYiOjiYuLo6cnByOHTtGdHT0BY+1HF0HWzEc3nj+Y0XlaE6UHUsxqApDZVUs4YEcGuxOuUxLgQshPEqtDCAAAwYMYMGCBSQkJDB+/Hi6devGgw8+SFRUFMuXL3d/Ul+RJ598kubNm3PdddcxYsQIwsPDUVUVk8lEv379uO2227j99tvp2bMnM2bMYNKkSWRnZ5OZmXnecV177bX84x//4NVXX6VHjx5Mnz6dZ555xt0w/8wzz9C/f3/uuusuevfuzapVq/j0009p1qwZ33zzDbGxsbz22msAPPHEEyiK4v4aXGVYf/31F+PGjXPfpqoq77//PsePH+eqq67izjvvpGfPnu6yosjISObNm8fChQvp27cvN9xwA82bN7+oAAKui35/f3+uvfZahg4dSr169ZgxYwZGo5GPPvoIh8PBiBEjGDx4MHa7nf/85z/u2agL1atXL15++WX69OlDbGwsn332GQEBAQQEBPCf//yHgwcPMmjQIK6//noiIyOZNWsWAJ06dWLKlCk899xz9OrViy1btpyxiti5vP322wQEBDB48GDGjx9P37593feNGDGCyZMnM2XKFLp27cpjjz3GtGnT6NKlywU/v/Hjx/PSSy8xc+ZMunfvzuzZs3n33Xfp0KEDAB9++KE7nALMnTuXVq1aMXbsWAYMGEBRUREffPBBuXMWFBQwd+5cnnrqKfdtL730Eh9++CEjR45k0qRJFTbEX7AD68ApFxNVKvcEyuGNhAcaiK5nOP/xQlxhDqQ7sDpkFkQIcW6K7oG/Jf744w/atGnj7j0oKCige/furF69ulzPQW108uRJBgwYwC+//ELDhg0B18pdcXFx9OrVy90TsXbtWl566SV+//33mhzuJZk0aZJ7mWNRi+g6WPJg3b9dZViiaikq+oD70HxDWRhnwSEvsfAwbRsa6RllvqByXCFE3VJrZ0AuxWeffcZrr72GxWLBarXy/vvv06xZs1odPmw2G4cOHeLtt99m0KBB7vABrtKsxx9/nEWLFqFpGllZWXz22WcMHjy4BkcsPJaiwMHfJHxcLrqGsmMpqqowtLV3TY9GiCp3MN1BsV2XWRAhxFl5ZAAp3bBu0KBB9OvXj/j4eD7++OOaHtY52Ww2br31VuLi4njuuefK3WcwGJgzZw7ff/89PXv2ZPTo0bRq1eqM44S4ZLoORSchueKd2kUVyUtDObieBgEqzaQUS3gYTYddyRXvmySEEOChJVhCiEuwY5kEkOqgqOj970Xzqy+lWMLjqArc2NkHX7MipVhCiDN45AyIEOIi6DoU50LK7poeSd1QWoqlwDVtpBRLeBZNh53JdgkfQogKSQARQpxyeAOynXE1yk9HOfgbYf4qLepLKZbwLEcyHeRbNekFEUKcQQKIEKJk349CSNpZ0yOpe45ugrxU+kSbMMtvZOFBdFy9IDILIoQ4nbzdCSFcjmwCzXn+40TV0vVTpVhtpRRLeJZjWQ6sDpkBEUKUJwFECAFOOyT8WdOjqLsKMlEOrCPUT6VV2MVt6ilEbaTpcCDNjiZlWEKIMiSACFHXaU5IjHOFEFFzjm6Bkyn0jjJilgwiPMiBdAdShCWEKEsCiBB1nWqA+O01PQqBjrJzKYqiM0xWxRIepNiuE5/tRNNkFkQI4SIBRIi6TNMg6zgUZtX0SARAYTbKvjWE+hlo21CmQYTn2JdmR1VlHkQI4SIBRIi6TFXh+B81PQpR1vGt6NmJ9GhsxFsyiPAQGQUa2YVOWZJXCAFIABGibrMWQNrBmh6FOI2ycxkKUoolPMtfaQ5ZklcIAUgAEaLu0jVX74eu1fRIxOmKclD2/UyIn4H24TINIjzDcVmSVwhRQgKIEHWZLL1be8VvQ8+Kp1tjIz6SQYQHkCV5hRClJIAIURdpTkjd7yrBErWWsnMZiq4xTDYoFB7icKYDVcqwhKjzJIAIURepBpn9uBIU56L89RPBvgY6RphqejRCXLICq05mgTSjC1HXSQARoi6yFbmW3xW1X8Kf6JnHiIk04Guu6cEIcemOZjlqeghCiBomAUSIukZzQspekE8grxjKzuWgORkuq2IJD3A821nTQxBC1DAJIELUNarBFUDElcOSh7L3fwT6GOjcSEqxxJXNYtdJzdOkGV2IOkwCiBB1jbUAchJrehTiQiXtRE8/QpdGBvylFEtc4Y5mOZBWdCHqLgkgQtQlmhOS99T0KMRFUnatAM0hq2KJK15CjgNNJkCEqLMkgAhRl6gGOPFXTY9CXCxrPsqe/xHgbSAmUkqxxJXL7oSkk04pwxKijpIAIkRdUpwHJ5NrehTiUiTvQk87SMcIIwFeNT0YIS7esSzZE0SIukoCiBB1haZBipRfeQJl90pw2qQUS1zRkk46cThlBkSIukgCiBB1harK6leewlqIsudH/L0MdG8ipVjiyqTpcCJPyrCEqIskgAhRV1gLIS+1pkchqkrKXvTU/bRvaCRIJkLEFSo51ymrYQlRB0kAEaIu0JyQcaSmRyGqmLL7B3BauUY2KBRXqJRcJ4r0gQhR50gAEaIuUA2QebSmRyGqmq0IZdcP+HkZ6BklpVjiylNg1SmwajU9DCFENZMAIkRdkXmspkcgLofUfegpe2nbwEiwj3ySLK48SSedaLIpiBB1igQQIeqCgkzXDujCIyl7VoHDwrA2si6vuPKk5DpRVQnPQtQlEkCE8HSaE9IP1/QoxOVkL0bZtRIfs4He0eaaHo0QFyRVVsISos6RACKEp5P+j7oh7QB68m5ahxkI8ZVf7eLK4dAgI19DlxAiRJ0h71JCeDrNCdkJNT0KUQ2Uvf8DezHDWsssiLiyJOc6kfghRN0hAUQIT6brkJMETntNj0RUB7sFZdcKvM0G+jaTECKuHCm5TlRZjleIOkMCiBCeTNdl9au6Jv0QeuJOWoQaqO8nv+LFlSGnWMMpK2EJUWfIu5MQnkxVITelpkchqpny12qwFzFUSrHEFULXIadI9gMRoq6QACKEp8s9UdMjENXNYUXZsQwvk4F+zSWEiCtDRoEm+4EIUUdIABHCk1nywVZU06MQNSHzKHrCnzSvZyDMX37Vi9ovq1CT/UCEqCPkXUkIT6VprgZ0UWcp+34GWwFDW8ksiKj9sgqdNT0EIUQ1kQAihCeT8qu6zWFD2bEcs8nAwBYSQkTtlmvRcTilBEuIukACiBCeSlUlgAjIOoYev43oegYaBsivfFG7ZRXJhoRC1AXybiSEJ5MAIgBl3y9gyWdwS5kFEbVbZoETyR9CeD4JIEJ4Kkse2ItrehSiNnDaUXYsxWwycHVLr5oejRBnJY3oQtQNEkCE8ESaBjnJNT0KUZtkJ8CxWJqEqEQEyq9+UTtlFspeIELUBfIuJIRH0iE/vaYHIWqbA+ugOI+rW5rll7+olQqtOprUYAnh8eQ9SAhPpBqgKKemRyFqG6cdZcf3GA0qg1pJKZaofXRcIUQI4dkkgAjhqSSAiIrkJKEc20LjYJXIIHkLELVPnkVWwhLC08m7jxCeqlACiDiLA79C0UkGtZBSLFH75Ft1NMkfQng0ee8RwhM57WArrOlRiNpKc6DsWIrBoDK4tZRiidol36IhC2EJ4dkkgAjhiYpza3oEorY7mYxyZBONggw0CZa3AlF75Ft1FEUSiBCeTN51hPA0ug4FWTU9CnElOPQbFGYxoLkZVd4NRC2Rb5WleIXwdPKWI4Sn0TVpQBeVozlLSrEUhsiqWKKWKLBIA4gQnk4CiBCeRlElgIjKyz2BcngjEYEGousZano0QuDUodguIUQITyYBRAhPoygSQMSFObQeCjLo38yEUd4VRC2Qb5EyLCE8mbzVCOGJLAU1PQJxJdE1lB1LUVWFIa29a3o0QlBkkx3RhfBkEkCE8ET24poegbjS5KWhHFxPwwCVZlKKJWqY1aG7tkUXQngkCSBCeCIJIOJiHNkI+en0lVIsUcNsTl3yhxAeTN5ihPA0mtO1EaEQF6q0FEuBa9pIKZaoOVaHjmwFIoTnkgAihKdxWGt6BOJKlp+OcvA3wvxVWtSXUixRM2wOUCWBCOGxJIAI4WnslpoegbjSHd0Eean0iTZhlncJUQNsTinAEsKTyVuLEJ7GJv0f4hLp+qlSrLZSiiWqn9UhAUQITyYBRAhPoutgK6zpUQhPUJCJcmAdoX4qrcKMNT0aUcfYHDU9AiHE5SQBRAhPomuyApaoOke3QG4KvaOMmCWDiGpklRIsITyaBBAhPI30gIgqo6PsWIai6AyTVbFENbJJCZYQHk0CiBCexim1C6IKFWah7FtDqJ+Btg1lGkRUD4cGuuyELoTHkgAihBDi3I5vRc9OpEdjI96SQUQ1kfghhOeSACKEx5G3bVH1lJ3LUJBSLFF9ZAJECM8lAUQIIcT5FeWg7PuZED8D7cNlGkQIIcTFk3cRITyNfGwoLpfUA+hR3enWOBSzAfItOrJYkbhc5EdLCM8lAUQIIeoEFXwCwDsIvAPA2w/M/mD2BZMPmL3B6OX6z2BCMxjRVQMoKoqioqCgKAoKoDmddI70quknJIQQ4golAUQIjyOfG3ok1QjegeAT6AoQZn/w8gOzD5h8weQNJi8wmNENJnSDoVyAUJWzV9w6dAdWzY5Nt1Gs2bBoRVjtNqy6Datmw6rZUVHpE9AJo2pEVZRTKxTpOoqigKKg664JOAXAdVOFNKeO3argsIHDBvaS/zusp/6sa1X+CoorTMseoBpqehSXzul0kpKSQpMmTWp6KELUGhJAhPA0kj9qJ6O5ZPYhELz9waskQJh8Sv4rHyA01QCqCooBRVHOGSBsmh2bbseq2bBoNix6gStAaK4QYdFs5e4vGyxsug3tPD80rb2juS6oH4pTR9ecJd2DrnShKwqKrqMDOjqqqrpSiKaBoYKrR6cT1enEbNAx+RlRAtWSsFI+rTgdOg57SSgpCSv2kpBy+p+l6lCcTZs2bRg0aBAfffRRuZ+xJUuW8O9//5u1a9de8mNMmjSJXr168cgjj1R4/9SpU2nVqhWPPPIISUlJDB06lDVr1tC4ceMLfqyioiIeeeQRtm3bRseOHfnqq68udfiXXXFxMXfddRe33HIL48aNO+/x6enpjB07lqeeesp9/MGDB3nqqadITEzkqquu4u2338bHxweADz/8EJvNxqOPPnpZn4eoWhJAhPA4cjV2WRi9wSeozAyE35kBwmgGoxldNaIZjKCoUDL7cPoFdild17HpZQOEFYuWh8VZGhJKA4O9THA4dZtNt6Nfxr/za4P709anGZzMQdm6BX34ta7pCR1QSrJISehQXU8IFAWnqnB6/NCcThRVRTEYXDMn+XkoGRmQkw22kmkPbx/w88fg54fBxwcvLy/0IFcoUwznCCulocSinJpROS2oSFipm3777TfmzZvHfffdVyOPn5OTU2Xn2rdvHxs2bCA2Npbg4OAqO+/lcujQIZ599ln27t3LLbfcct7jNU3jqaeeOuM1mzNnDv369ePhhx/m3nvvZenSpUycOJHk5GR++OEHFi9efLmegrhMJIAIIeoGs39JeAh0BQcvf/DyPTNAGMyn+h/UkgDB2QOEpmuuAFESEIq1Itcsg6PMTENJuLCWmX0onZWw6fZqfiEqx1f1ZmLotQSZAtD370P5Ywv0HQCUZAy1TBAouarXS/pEAAwlAQVcFxWKoqAaDOQ48ihyWtDR8fYxE9i0CaZmzVFU1wyPXlQEmRkoWZmQnQVZWSiFBbhffVWFoCAICoaAQPAvCSvevq6wEmxGNxpdQUc98+/MHVbKzKy4w0qZoCJhpeadY9LvgkyaNIlZs2bRvXt3unXrVuExa9eu5eOPPyY+Pp6ioiI6derEq6++StOmTVmyZAkLFiygbdu2rFy5El9fX2677TamTJni/nmPj49n8uTJ7N69m8DAQJ5++mlGjhzJ888/z7Zt24iLi2Pv3r288MILAKxYsYLly5dz4sQJunbtyhtvvEHDhg0B+OWXX/jggw84fvw4YWFhTJw4kTvuuIO1a9fyxBNPADB48GCmTZvG9u3bAXj99dfdz6VNmzbMnz+f3r17M2TIEPr378+aNWsICwtjyZIlLFmyhK+//prk5GRsNhu9evVi5syZ1KtXj9mzZ3Po0CHMZjO//vorvr6+3HDDDTz55JMA/PHHH8ycOZOEhARCQkK4+uqrefbZZzEaz7yc3Lx5M08++SRTpkypdAibM2cO4eHhRERElLu99Py6rqPrOoaS2dXXXnuNp556Ci8v6Um70kgAEcLTeELRdEXc/Q+BZcqXfEt6IHxc5UulAUI9FSDKNlBXxKlr2EpnGDQbFq3ANftgr3j2obSEyabZsOh2HLrn7Tzf0qsJo4IHoOrAb2tRjh0FQG8cRUE2+AaBLTcd7+CG6JqOopY0p+u6+/+qqqJpOugaqsGAbskHWzHBRi+CfOqjKirFmpWDlkSOWZLQ0WlsDifcVJ+QyIZ4N26MUvKzrNtskJ2FklkSSrIzIf54hQlBgdPCSogrsASWhBXfMjMrvmZ0wwWGlTJ9KmX7V2TisYqdo4foQg0bNgxd13niiSdYunTpGTMHqampPPbYY8yaNYshQ4aQk5PDww8/zJw5c/i///s/AHbu3ElMTAybN2/m4MGD3HvvvYSFhTFhwgQANm7cyLx582jXrh1z587lH//4B0OHDuW1114jISHBXaKVlJQEwN69e1m0aBGapnHXXXcxZ84cXn75ZbZs2cLjjz/Om2++yfDhwzlw4AAPPfQQAHfddReffPIJd9xxB3FxcQDuAHIuu3btYtWqVQDs2bOHV199lfnz59O5c2dSU1O58847mT9/Po8//jgAP/30E6+//jpvvPEGGzZs4IEHHmDo0KF07dqVZ555hkcffZQbb7yRpKQkJk6cSI8ePRgxYsQZj9u2bVvWrVuHl5cX//nPf847zi1btvDDDz/w3XffMXr06HL3PfTQQ+7XtH///owdO5Zff/0Vo9HIoEGDzntuUftIABHC0xjNNT2CM6lqmf6HgFMBomwDdckKTLrB6Pqv0g3UTvfsg1WzUazlYXXYsJR8bSvpgShXwlSmlMmJdDuXNTyoLx18m0NuLsraXyAv13VHWAMULzPZh8AnUMFelIvZrx4U6hBoQlEVd3O6qihoJd3oDk0lK9dJkLcvPgH+4LShHv8DHDZ86jenXWA0HX1b4tQ1Eq2p7C8+yhFrEvnOQhoYQ2jh3YRG5gaE1g/CNywM1VDySajT6SoLy8yA7GxXKMnOBqfz1JPRNMjJcv1XgXJhxWCA4GAIDIbAgFNlYN6VDCt2Hbt7FkU5I6i4Z1jsSFipBLWKdyl79tlniYuL47nnnmPu3Lnl7qtXrx4//PADUVFRFBQUkJqaSkhICGlpae5jgoODeeqppzCZTHTq1IlbbrmF5cuXuwPIddddR4cOHdx/fv/998nKyiI8PLzC8Tz44IMEBAQAMGDAAHbt2gW4elOGDh3KddddB0CHDh24//77+e9//8tdd911Uc99xIgRBAYGAtC6dWtWrlxJ48aNyc3NJT09nXr16pV7rk2bNmXs2LEADBo0iLCwMI4fP07Xrl3x8vJi1apVBAcH07NnT3777TdX31cFQkJCKj3GrKwspk2bxvvvv4+fn98Z97do0YJFixa5v7Zarbzzzjt89NFHfP755yxbtozAwED++c9/0rJly0o/rqg5EkCE8DSGyzAVbTSDV4BrBsIrwNVEbfIt0wNR2v/gdar/QTWAopw3QNg1x6n+B92GRSvEYreeJTCcfpsdTQJElfBWvbgt9FqCTYHoBw+gxG4qfzHfNQbNqZOXoRDZGhTVQFFWAn4Nm6MdL8TQzB+bzYbZbAbdgYKBYk1DUSEiyEBOkZPd8Vaah5qoH93T9fF2xmEM2xaBpmFo2oOo0GiigyIYovQmy36Sw5ZEjloS2VKw293nEqD60tInisbmcMKCQvAPbolBNaKUrsyVn+cKJVlZJbMlWWC1nv8FcDpd35NVibBiNJ5WBhaAwdcXg7c33l7e6H5mdIOhwrCi6zpOR5lAYi3fs3J6cKmrqnoi12w2895773HjjTfy2Weflbs4NplMrFy5kgULFqAoCq1bt6agoKBcWVFkZCQmk8n9dUREBKtXr3Z/XXZWpfQ4h+Pss6OnH+8s+beWlZVFu3btyh3buHFjkpOTL+wJl9GgQQP3n1VVZf78+axYsQJfX1/atGlDQUHBqVXtgLCwsHLfbzKZ0DTX79kvvviC2bNnM2PGDDIyMhgwYADTp08/a9CqDF3XeeaZZ5g0aRIdO3as1Pd89NFHjBkzhoKCAj7++GN++ukn1q1bx7Rp08oFFVF7SQARwpMoasUzIO4G6tLZB39XE7W5bP+Da/8H9xKuStkZiLM3UNt1R7kG6mItr0z50pm9D+Vvu7wN1KJymnk1ZkzwQFfJ1fpfUY4ePuMYvWEjcjNAc4KuKyiqgexDsfg1bA4FDrQCOwYfAwUFBfj7+6Po6XirYdh1neNWK5HeZno39eZopoONx6x0jDDRtF5zjA1aoRdmoxzfirpruevnsGlv6oW3oad/e3oHdMKiWTliSeSoJYnj1hTiCvcTV7jfPTYzRpr7NCHaK4IGPqEERUdhalqmr6S4tK+kTCgpKLj4F8zhuICwYiqZWXGVgSn+/hh9/TB6e+Nt9ioJK0ZXGdt5wkrZMrByyxd7YFi5HJWkUVFRvPLKKzzzzDPlVmNatWoVX375Jd988w3R0dEAvPLKKxw8eNB9THp6OnrpktNAUlISjRo1qvIxRkZGkpCQUO62xMTEM0JBKVVVsZYJ2NnZ2WccU7b89PPPP2fjxo2sWLGC+vXrA67ZmMqwWq0cPnyY6dOnYzQaOXbsGC+88AL/+te/eP/99yt1joqcOHGCrVu3snPnTubMmQNAQUEBM2bMYPXq1Xz00Ufljo+Pj+fXX39l4cKF/Pzzz0RFReHv70/Hjh3L/Z2J2k0CiBCeRFHQGrZCH/FMpRuoSwOERbNh0SxYtNySBmp7uWVcK+qHqK0N1KLyhgb1obNPS9fMwdpfIPfkmQdFN0MxGsg5UXqDK4A4ivNxWotQo3xxbszEOKwhuq6XhJAGqPp+zEoLosxmDlgtBBpUmoaaiQrxYWeynQXHLbSsb6RToyB824+ANkNQknbA8W0o+39BQYWorng37kLbwGg6+LZE0zWSrGkctroCSZ6zABsO9hcfY3/xMfeQVVQamxvS3Luxq68kogHekRX0lWRlnpotyT1Z9Z3nDjtkZrj+q0C5sGIyu8JKUBD4l4YVX1dY8fJC93etsHbWsGIvE1ZKysBO32fFYSspA6vlDJfp6uS6664jNjaWBQsWEBkZCUB+fj6qquLt7Y2u6/z+++8sXbqUVq1aub8vIyODjz/+mMmTJ7Nv3z6+/fZbpk+fXqnHNJvN5OfnV+rY8ePHc/vtt7Nq1Sp3D8gnn3zCzTffXOHxLVq0YP78+aSlpREUFMScOXPO+vsecM/smEwmHA4HP/zwA7///jsjR44879gUReGJJ57gnnvuYfLkyYSFhWE0Gi+o1KoijRo1Yvfu3eVuGzJkCA8//HCFy/a++uqr/OMf/8BkMhEdHc2xY8fIzs4mLi6OqKioSxqLqD4SQITwMHY0DloTKggOZ85I2D2wgVpUjhdmJoZdSz1TEPrhQyhbNro+2a9Ix8447Dr52SV7f2i4L+QLThwiuFlXnCYVbV8+AR2CSEpKQlEU/Pzaouh/AC1o512PFLuNzYX5dPLxpVsTE60bGImNt/HdTgv1fFV6RplpENUdpWkv9IyjKMe3QsKfkPCna0nfkCjUZj1pHNqUJoHhDAnqRbY9l8OWBI5akzhhy3TPqGloJNhOkGA7Ue6phJX0lUSeta/kZElfSUkoyck+++tS1ew2yEh3/VeBcmHFbIbgENfMSkCAK6z4+GL09sHbuySsGAznDCulSxSX22fltBkWZw2FFfUyXp1MmzaNnTt3kpeXB8CNN97I9u3bGTVqFAaDgebNm3PnnXfy1VdfYbO5ppbCwsJISkqif//++Pn58dhjj7n7NM5n7NixTJ8+nT179vDmm2+e89guXbowa9Ys5syZw7Rp0wgJCWHixIlnXUL4lltuYffu3YwZMwaz2cydd955zpmZyZMnc/DgQQYPHoyXlxft27fntttuY8uWLed9Hmazmblz5/LGG2/w0UcfYTAYGDhwIE899dR5v7cio0aNYvTo0ZWegQFXg3y9evXo2bMn4OqRueWWWxg5ciShoaHMnDnzosYiqp+i67LQoBCeJN9ZyCdp39X0MEQtFm1uxA0hgzCgomzeCIfPUbagKOh/m0xWikLyAddNbfuCbk/nxB/LQTXS9Oo70Y4UoP2Vh+HqMAgwsn//fpo2bYqvry/om4BQdFpToGnsKC4kQDXQ3tsHs6qSlONga4KNAquOUYUeTcw0D1UxGg3oRSddQSRxh+vKuJSXPzTrgx7eBt03CFUxlJRqJXHUkkS8NaXSM3T+qi8tvZvQxCuc+qYQAhSfivtKsrNLZksyK9dXUlt4eZ0qAwsIBD9/8PUFb290sxeYyoQV5SxhxVqmwb6iTSGrOKwEhUHTLlV3vktRlZsWCiFcJIAI4WFsmp1/p35T08MQtdTgwJ509W0DBfmukquT51mfv31H6NWHQ39AUcmCWG36gKJnkRL7PQCRvcdhMgXhWJ0KZhXj8IYUWYo5dOgQbdu2wcvLG4UNuOYlBqChsKu4iEyng9ZmL5qYvFCAPal29qTYcZSsK9CyvoFOjcz4eymgOd3lWRScXs6kQpMu0LgLzqBwDAaTq1TLlubuHcl1XljPh6uvpDHRXo1oYAolSPXDpBhP6yvJPLVfyaX2ldQW3t6u5vqg0rDiB75+4OWNbjafN6yU31NFKbe3StnQ4jzPpFJoJES2rbqleC+FBBAhqp6UYAnhYcyqCQVFmrtFOWaM3Bp2LfVNIehHD6Ns2li5ZoA2bbEV6xTlnroS1HVQy3QJ5ybsIazDQJT6XuiZVpx7cvHtHExYWBj79x+gXbt2mM39UViPylIUriXGx5ejNisHbVaO2ax08fGjU4SJlvWNbEuwcTzbyeFMJ4cziwn2UegZZSa8SQxKdA/0rOMox7ZC2kFca9pqkBgHiXElpVpNUJv1onG9aJoE9mRwSanWEUsiR6xJnLBlnPffh6uv5Dj7i4+Xuz3KHEEz70giTPUJiQjDOzLyVF+J3ebaOLHMJoqXpa/kcrJYwJIKaaln3KWc9n+8vSG4XknPSgCKnx8mX19M3t54+3pBoOnsYUXTcdgraLAv+c83CNdfbS0IIEKIqiczIEJ4oLmpiyjWLDU9DFFLNDGHc2PIYFfJVexmOLj//N8Erk+7J04i/bhC6tFTN7fsCWZzPkkbF7pvazr4bvRkK84414yKYUAYSoiJAwcOYLFYaN++HSaTCYVfgRRgKDrhZDkd7CouxoFOiKrSydsPb4NKWr6T2ONWThafeosyqhDT2ETL+gZMRgO6JQ/l2B+u8GEvrvg5mP2heW8Ib4PmG4yqGLBqtnKral3qYgphZfcrMQbhp3iV7yvJPVl+aeDq7CupLXx8y5SBBbhmVnx8wdsH3WxGN5rgtLCiaTpqBXuuCCGufBJAhPBAX6QvI8uRW9PDELXAwIDudPdrB4WFKOt+cV0AV1b3XtCpM/s3gbXo1M0teoC3TyGJv58q9WsYcy0+wY1wrDoBTh2MYBgZgdVm5cABV/NIhw7tMRoNKKwDUoEOaHTFquvsKC4kv2SvgWYmL5qbvVAVOJDuYEeSDVuZLUkAmtUz0CXSRIC34uqKT9qNcvwPyE/j7BRo3AWadMUZ1BCDwYymayTb0t17jlxoqdbZnL+vJL98s3tWFljlQwPA1Z8yaDA0CK8dNVhCiConAUQID/R91hqOWS9+4ypx5TNj5Jb6Iwkz10M/fhRl4+9gv7BP+vUJE7FovhyMLX8R2Lwb+PpbSFj/pfs2r6AGNOo5Bsf2bPSkktmISB+M3V07SqempqKqKu3bt8NgUFFYA6QDYWgMBQzstRRzoqQsTAU6e/tS32jE4YTtiTYOZzjOKJwK9IaeUV5EBCqoqgE9OxHlWCykHXAFk3MJaQxNe6GFNkUx+6IoCjmOPNeqWpYkUipRqnUhzBhp5u3qK2loCiXI4ItJMZXvK8nKdO1XUtrs7gl9JRdj1BgIa3D+44QQVyQJIEJ4GF3X+TXvj3IbtYm6pZGpAePrDcGoGFG2bob9+y78JH7+6DfdQsohhczy+6LRrCv4B9uI/3V+udujB06CXHBuynTfZugbilLfi4MHD1JcXFwmhCgo/AJkAmZ0RqIogSTYrBywWtyX/f6qSldvX3wNBrILncTG28goODNYqEBMExOt6hswmwzo1gLXjEjCn2ArOuP4M5j9oFlvCG+L5neqVOuoJYkjliTirclYL9O+N1HmcJp5NybCVJ9gQwA+qrlMX4ndtV9J6Spc2VmuhQM8/a375omu5vcrnNPpJCUlhSZNmtT0UISoVSSACOFhnLqTnYUH+TXvj5oeiqgB/QJi6OXXAYqKXCVXWZnn/6aK9B+E3qIlf/2unLHLdnQnCApzcnztf8rdXr/9APwjWuP4KRUsJSFBBcO1EdgcNg4cOICu6xgMBtq3b4eq6ij8DJTu3twXnWbkaU52FBdhLfP2FGk00cbLB6OqcCTTzp+JdortFb99RYWoxESaCfRRXBfqKXtdS/nmnqjw+Ao17gKNu+IMDi9XqlXaO3LSWbmN5S7Wqb6SMEKNwWfpK8l0zZJkeVhfiaLApLtdm6legjZt2jBo0CA++uijck3wVbmq1aRJk+jVqxePPPJIhfc/+uijtGrVikceeYSkpCSGDh3KmjVraNy48SU/9pUmOzubW265hVdffZXevXuf9bh7772X2NhYjMZT6yTNmjWLgQMHsnXrVp5//nmys7MZOXIkr7zyCmrJz8k///lPunbtyvjx4y/7cxGXTlbBEsLDqKgEGwNqehiimhkxcHP9EYSb66MnxKNs+A1stvN/41noTaIpyOGM8AEllU3KmReH2Ye34x/RGrWxL9rhktIhDZzbs/HqFUpERAQpKSk4nU727dtPu3ZtUdVrUPgJOAlsQiGVALUPV/n5s7O4iBynq/kj2WEn2WGno7c3zULNRIcY2ZlsZ1+aHe20HJKQo5GQY8HfDL2ivWgU0QGlcWf0kymu8qwTf52/PCtpJyTtdK2qFRSJ2rwXkaHNiAzswdVBPTnpyCvpG0ki2ZZe5avOZThyyCgov0Syn+pDS+8omniFExYYQkBQcwytWp/qKynIh8wMVwlXaW+J5QrsK/HxveTwUeq3335j3rx5Z93I73LLyTnPMtd1xPbt23nuuedISEg477F79uzh008/pVevXmfc9+abb3LHHXdwww03cOONN7JhwwYGDhzIrl27OHr0KC+//PLlGL64DCSACOFhFEUhxBhY08MQ1SjcVJ+b6l2DSTHC1s0of+29tBOGhqF4mck5XPHdmub6OUNRypUCabYiHJYCjFFlAghAqhU91UJYeBi5ubkUFhbicDhKluhtA8owFFYDecBRVDIwMZIePn4ctFqIt59KQXssFg5joauvH92amGjVwMjWeBspuad1qQMFNlh7yIoKdIk00bpBQ7xibkRvPxwlfpurPMtaiR6L3GSI+x4VwOwLzXoTHN6Wbn5t6eHfAZtmd5VqWRM5bknBql988DuXQq2YnUUH2Fl0wH2bGSNNvRvT1KsRDbzqERzVGFN0szJ9JcWQVSaUZGW5gkptFlB1H6BMmjSJWbNm0b17d7p161bhMWvXruXjjz8mPj6eoqIiOnXqxKuvvkrTpk1ZsmQJCxYsoG3btqxcuRJfX19uu+02pkyZ4p5ViY+PZ/LkyezevZvAwECefvppRo4cyfPPP8+2bduIi4tj7969vPDCCwCsWLGC5cuXc+LECbp27cobb7xBw4YNAfjll1/44IMPOH78OGFhYUycOJE77rgDVVV57rnnUFWVpKQkdu3aRUREBE8++STXXHMNH3/8Md999x2rV692P69PP/2UtWvX8tVXX9GmTRsmTZrEihUriImJYe7cuXzyySesWLGCEydOoCgKAwcO5LXXXsPb25vnnnsOs9lMeno6sbGx1KtXjzvvvJM77rgDgNWrV/P++++TmppKgwYNGD16NA899FCFr+/333/P+++/z9NPP83UqVPP+feVmJhIbm4u7du3r/D+0lkRXdfds6mapvHqq6/yyiuvnLHcs6i9quYjBiFErRJouPJrp0Xl9PHvzMTQkZgsdpRVK+BSwwdATAyappObXvHdWsm1vlJmL5BS+ckHUAJMEGQqd7tzaza6QyM6OtpdMmG329m//yC6bkRnOOBfehYUvkMhkzbePnTx9qXsI1mALUWF7CwuwtsM17TxZnArL9eGhRWNF4hLtrMwzsLaA8WcdHqjtxqAPuRR9JgbITiy0i8NtiI4sA5+m4v6479gxzLMOSm0NkcyKmQgU8Jv5ubQEXT3a0+w4fLPRNpwcNBynJ9yN/Fl5kr+nbaQd1O/5NvMn9iWv5cUNZ/i8DD0jp1h8DVw0y3ot9+Jfu310KsPtGgFIfVq12pTAQFV1uMybNgwbrnlFp544glOnjx5xv2pqak89thj3H///WzevJlff/0VXdeZM2eO+5idO3fi4+PD5s2bmTt3Ll988QWLFy92379x40aefPJJYmNjGTduHP/4xz+w2+289tpr9OjRgwceeIAPP/zQffzevXtZtGgRv/32G7m5ue7H2rJlC48//jj33nsvW7du5Z133uE///kP8+ef6rX6/vvvufXWW9m2bRsPPPAAjz/+OEeOHGHs2LEkJiayc+dO97FLly5l3Lhx7q8TEhL49ddfefPNN1m1ahXz589n9uzZbNu2jQULFrBhwwZWrFjhPn7JkiVMmjSJP/74g/vuu4/XX3+dtLQ0LBYLTz/9NC+++CLbt2/n7bff5pNPPmHXrl0V/h3079+fn3/+meuuu+68f1+7d+/Gz8+PqVOn0qdPH66//vpyr/XTTz/NokWLGDlyJEOGDKFv37588803dOvWjTZt2pz3/KL2kBkQITyQQTHgr/pSoFWi+VZckQyoTAgdQSOvMPTEBJTffwObtUrOrTeMJC/jVNA44/6S6iVFNaCftqV1bsIuQpp3Q23ii5Zbfilo59ZsTH3r06hRI5KSkgCw2WwcOHCQNm1agzK8ZCakEFdsWA16VxoYO9DHz5+44iKKtFOlU+lOB+mF+bT28qZJkJkbOvmw94SdPSdO7aZ+uqRcjaRcC75m6BXlReOG7VAadUTPTS0pz9p79idekeRdkLzL9WleUCPUZr1oVL8ZjQK7MSioB7mOfA65V9VKR6umDUITbakk2spvJhhqDKZl6X4loUH4169/qq9E0+BkTklfSZkSrproKwkMck2zGc4MuBfj2WefJS4ujueee465c+eWu69evXr88MMPREVFUVBQQGpqKiEhrpXbSgUHB/PUU09hMpno1KkTt9xyC8uXL2fChAkAXHfddXTo0MH95/fff5+srCzCw8MrHM+DDz5IQMksz4ABA9wX7kuWLGHo0KHuC/UOHTpw//3389///pe77roLgKuvvtp9/9ixY1mwYAE//vgjjzzyCAMGDGDZsmV06dKFvXv3kpSUxMiRI92Pe/311+Pj44OPjw8DBw6kW7duhIeHk52dTU5ODsHBweWed+/evenXrx8A48eP56WXXiIhIYFOnTrh7e3N4sWL0TSNbt26sX37dvcHC6cLCwurxN+Si81mo2vXrkydOpVWrVoRGxvLI488gp+fH9deey3du3cvF5KysrL45ptvWLBgAW+99Ra///47ERERzJgxwz2rJGonCSBCeKggoz8FlVn9R1xxGhhDuDl0OCbVBH/EouzdXXUnj4pGMRnIOUe/trN0BkSp4AJR07AVZGFuUg9tby7lrrczbejJxdRvXJ/c3Fzy812lQFarlUOHDtO6dUt0SsuxSjcW3IFCGj7K1Vzl689uSxHpp10UH7RaOGa10NXHj06NTLQMM/JHgo347LMHiSIb/HrYFdg6NzLRtkEY3l1vQO8wHCV+O8RvA8sFlirlpsCOpadKtZr2IiiiHTG+5Uu1jlqTOG5JxnKZSrXOJstxkqyCk+Vu81N9aOHdhCiviHP0lWSilDa7Z2eD5SybPlaVwKAqnZExm82899573HjjjXz22WeEhIS47zOZTKxcuZIFCxagKAqtW7emoKCgXAN0ZGQkJtOpGb2IiIhypU7BwcHlzgfgOEdwO/14Z8k/qKysLNq1a1fu2MaNG5OcfGpJ9aZNm5a7PyIigoyMDADGjRvHSy+9xD/+8Q++//57Ro4ciZ/fqdnwBg1OLWus6zrvvvsu69ato169erRr1w673U7ZdYnKBofS56VpGt7e3nzzzTd88MEHPPnkkxQUFDBixAheeOEFgoKCzvq8K2Ps2LGMHTvW/XX//v0ZO3Ysq1at4tprrz3j+DfffJO///3vbNu2jd9++43vvvuOzz77jNdff5133333ksYiLi8JIEJ4qGBDAMmcpYZGXLF6+Xekn38XsFpR1v0E6efaeO8idOyM066Tn3X2C8BTMyAVf+J58vguGnYeitLACz2t/KyMc3sONPAiKiqK/fv3uy++iouLOXz4KC1btkCndCaktIH6BCrfo3MtXX38OGazcrjMUr0AduCP4kJCVAOdvH0Z1NKbtDwnsfHld1OvyK4UO7tS7EQEqnRvYiakeV9o0RdSD7hWz8o+f+PsGWxFcPBXOPirq3wsshPmJjG0Do6krW8zNF3nhC3DteeINYkcR96FP0YVKNSK2VV0kF1FB923GTHS3DvStV+JVyhBUZGYo5ue6iuxFLtCSVaZ2ZL8KuwrCQ6psib0UlFRUbzyyis888wz5cqSVq1axZdffsk333xDdHQ0AK+88goHD556PdLT09F13d1fkJSURKNGjap0fOAKOqc3aScmJpYLAmVnKErHMmTIEACGDBnCSy+9xMaNG1m1ahWzZs0qd2zZ/oi33nqLlJQU1q5di7+/q/Rx9OjRlRpnQUEB6enpvP322wDs27ePJ554gg8//JBnn322ks+2YosXL3bPdpSy2Wx4eXmdcey2bdvIzMzk2muv5eOPP6Z169aYzWY6duxYbpZE1E4SQITwQE5dI0hWwvIoKio3hQ6jsVdD9OQklPW/Xpads/XQMHJOKOcswT9XDwhAUfoxNIcDtYkvzrQzy8Kcm7IwDgo744KrsLCQw4ePlISQYSWrY5V+vwWF70EfRFNTY4JUA7ssRdhOG2iO5mR9Ub5rN3V/L0Z39GF/moOdyWfupn66E3kaK/da8DZCr2gzTRq0xhDRDj0/3VWelbwHtIssSUreDcm7XbMjgeGozXrTKKw5EYHdGKT0INdR4N4AMdmWVm2lWhVx4OCgJZ6Dlvhytzcxh9PMK5IIc31CwuvjE9EIxXDafiWloSQrC3JPukqpLoSiVGkTelnXXXcdsbGxLFiwgMhIV99Pfn4+qqri7e2Nruv8/vvvLF26lFatWrm/LyMjg48//pjJkyezb98+vv32W6ZPn16pxzSbze6ZvvMZP348t99+O6tWrWL48OEcOHCATz75hJtvvtl9zM8//8ymTZvo1asXS5cu5eDBg7zzzjuAa5ZizJgxzJo1C39/f3r06HHWxyooKMDLywuDwYDVauWrr77i4MGDDB48+LzjLCws5L777mPmzJlcf/31NGjQAFVVy80sXayCggLeeecdoqOjadu2LevXr2flypV8+umn5Y5zOBzMnDnTHYKaNm3KsmXLKC4uJi4ujqioqEsei7i8JIAI4YEUkKV4PUhYScmVWTXD9j9Qdu88/zddjLbtUQwqJ8+zXYbzPAEEoDgnGd+IKDCeBMdpF9O5drT4Quo1rUdubi65ZXpFCgsLOXr0GM2bN0PnmpJ9QsqWKv2GQhuCDT24ytefHcVF5FbQs3HMbiXebqWLty9tGhppXt/In2fZTf10FgesP+J6zA7hRtqHh+Ld6XpoNwwl4U9XeVZx7nnOcg55qbBzGQqgGL2hWW+CItrR1a8N3f3bY9PsHLMmc9SSyDFLCha9anp7LlXFfSVBtPSOOntfSe5JlIyMMn0l2eA4x4aOAYFV1vtRkWnTprFz507y8lwzTjfeeCPbt29n1KhRGAwGmjdvzp133slXX32FrWQZ67CwMJKSkujfvz9+fn489thjlWqoBldJ0fTp09mzZw9vvvnmOY/t0qULs2bNYs6cOUybNo2QkBAmTpxYbgnhHj168Mknn/Dwww/TtGlTPv7443KbHI4bN44vvviCJ5544pyP9fjjj/OPf/yDvn374uvrS/fu3bnhhhvKzfycTcOGDXn//fd57733ePHFF/H29ua6665z96lcqJiYGGbMmMGYMWO48847KSoq4uGHHyYrK4smTZrwxhtvnBGm/vvf/zJo0CB3Sdo111zDunXrGDBgANHR0bz11lsXNRZRfWQjQiE81ElHPp+lf1/TwxCXqLtfewYGxIDVhvLrGkhLPf83Xayx47F5B7Nvw7nr74MaQNPOkLJ1Gda8jAqPMfnXI7L3jTh3nkSPr7gXyTCiIZoR9u/ff0bNfFBQEE2bRgM5JTumn37RGoLOcHSM7LdaSLKfvZ8iQFXpUmY39S3xNjIr2E39XBoEqPRoYibUt2T54fSDKMe2QtbxCzrPeUV0gKhuaMERqEYvdF0nxZbBEatrz5FsxyUEn2riq3q79ytpYAwhQPXFoBrL9JUUuPYrKQ0lWVmn+kqaNoOrh9bsEyijKjctvFTPPfccAK+//vpZjzl58iQDBgzgl19+kSZsUavJDIgQHirYGICXYr5sexKIy0tFZVy9oTTxCofUEyi/rbu8zb9GI3pgMDnx52/+La1COtcMiL0gG81mdZVhnSWAODdlYby6AY0bN+b48ePl7svNzSU+PoHo6Ch0hpSEkLIzHTkofAeMoL13CEEGA/ssxVQUK/I1jQ1FBTQ2mWjt48N17X04kmlne6Idy1l2Uz9der7Gj39Z8DJCjygzTeu3xNCwDXpBFsqxLa4SK+c5PtmvrBN74cReV6lWQEOU5n1oVL8ZEQExDAzsTl5JqdaRkg0QtQqfcc0q0iwV9pU0846k6Vn7SiyuUGIyu8q2qrgHxNPZbDbi4+OZP38+gwYNkvAhaj0JIEJ4sIbmUBKs56mnEbVOqDGIW0JH4KV6oez4E3btqLJ9Ec6qc1cUVSGnEhMspSVYnCOAABSkHSEoqgNOXwMUVdCAke9AO1JAcKtgQkJCztg1+uTJk6iqSpMmjdEZjMI6yocQBwo/gN6LRsZWBPoa2FFcSPFZXqsku50ku52O3j40CzURHWJkR7KN/WmOM3ZTPxurAzYetbERaNfQSIfwYHw6XgftrkFJiHOVZxVV0e7X+WmnlWr1IjCiPV382tCtpFTruDWZI5YkjlmSa02pVkUcODhkiefQaX0ljc0Nae4VSYQ5jJCI+vgoZsBVRioqz2azceuttxIREVFuzxEhaispwRLCQ2m6xqb8nWwtqMIlWsVlF+PblqsDu4PNjvLbWjiRUi2Pq0+YiEXz5WDs+S/9vP2hTR9I3bGa4szEsx6nGs1EDZyEdiAf7eDZG3EN1zRE91bYv38/dvuZswihoaE0bhwJpKLwK1T4qX8TNAagobCruIhM57mbxb1RiPH1w19VybfqxB63cSLvAvb/KCPMT6V7lJkwv5LyrIzDrvKszKMXdb5KiegAUTFowY3cpVon7JkcKZkduRJKtSoypeHN+Bi8a3oYQojLTAKIEB5K03WOWZJYlrOupociKmlcvaFEezWC9DRXv0fxZd5voZSvL/qEiZw4pJBRiRVnzd7Qrj+k7fqFovTj5zy28VUTMCp+OH4+x3LBPirGaxqSX1DA0aMVX7SHhYWVLH2ahMJ6qLCV3A+dkYA3R2xWjlZiY8YGBiPtvX0wqyqJOQ7+SLBRYL24t0WzCj2izTQNUTEaDeiFOSjHYyFpJzguYylkQENo1gs9rAW6lx+qopLnKHSvqpVkS6uVpVqn81N9eCB8Qk0PQwhRDSSACOHBijULc1MX1fQwxHkEGwKYGDoSb4M3yq4dsOPPy19yVVa/AegtW/PXBgVHJap4DEboeDWk715HYdqRcx4b2KQjoW364FifgZ5z9otwtbU/hnZBJCYmkpWVVeExDRs2LNldOh6FjVQcQgCGohNOltPBruIiKrNwbhsvbxobXeU/e0p2U3dewjV76zAjnRoZ8TWroDlQEnfA8T+gsOLnVmWMXtC0FzRqj9OvHgbViF1znFpVy5pMsVY7S7WaezVmbOiQmh6GEKIaSAARwsN9nLqYAk12RK+tOvu2YkhgLxS7w1VylZJ8/m+qYvqtkygsNnNkeyUr7xXoMhQy/lpPQcr5l+1sOngyWkIR2q5zlwUZBjcAfwP79+93L4F6uoiIiJIdnY+hsOkcZ+uARlesus6O4kLyK7EfhQno6uNHsMFAsV3nj3gb8TkXV5ZVKsRXpVeUmQb+rqZ9PeOoa3PD9MOcPUBVoYh20KQbWkgkisEVsFLtmRy2uFbVynKcvPxjqKR+AV3p4d8RgyIN6EJ4OgkgQni4Fdm/cshyETs5i8tubMgQmnlHQka6q+SqqAaCYmgo+vVjSdqnkH0B7Sadh+pk7d9EfvK+8x4b0eN6vPwa4PjfiYrbN0p5qRiHN6SwqIjDhw+f9bDIyEjq168PHEYh9hwnDENjKGBgr6WYE+faf6KMENVAJx9fvFWV1DwnWyuxm/r5GFXo3sRMi9CS8qziXNfmhok7qNS0U1UICINmvdHDWrpLtfKdhRwuTuSoNZEkaxrOGizVuiV0JI3MYeV27BbiShUfH090dHRND6PWko8ZhPBgTl0j3Fy/pochThOk+vNAgwk092mMsmcXyqqVNRM+ALp2Q9fh5DlaNCqmu5dQPZ+Tx3agmFSUhudpLrZqOPfk4efnR1hY2FkPS05OJjs7G0VpBZx9t2fIQGUJCvl08vGlrZd3pVZXytGcrC/M57DFQpi/yuiOPvSMMmO6hP3xHBrExtv4+k8LG49aKFAC0NsNQ7/mCeh4Hfif/flWmfwM2LUSZc17qD/9HxxYR0BhAZ19WzI+dBgPhd/K6JBBtPdpgY9avY3gBlTCzaGXHD6OHTvGs88+y8CBA4mJieGaa67hrbfeorCw0H1MTEwM27Ztu9QhV6mUlBRiYmJISameRScAXnzxRV588cWz3j9kyBCWLFlSbeOpinHExsbSpk2bi36se++9t0pWEXvjjTeYO3eu++ua/JmLi4ujU6dO5z3u66+/ZtiwYcTExDB69GjWrTvVPzp//nx69+5Nv379+Oabb9y32+12brzxxrP27p2LLMMrhAdTUYgwVcOFjai0Dj4tGRbUG8XhhF9WQ9LZV5GqDnp4JHkZUMFm4uf5xnPvA1JWcVYSmsOOGuWL84Tl3Kc9VojexJeIiAjy8vKwWiueHUhMTERRFOrVawu6E4g7yxltKCwHvS9NTM0IMhjYUVyEtRKT/0ftVo7brXTxObWb+vaS3dQvxZFMJ0cyiwn2UegZZSa8SVeU6O7oWfGuWZH0g5e/B8hhg8Mb4PAGDADhbTFFdadFSCQtvaMASLNnuRvZMy9zqVYDUygG5dJ2QP/zzz+ZPHkykydPZunSpdSrV49jx47x4osvMnnyZL7++msMBgNxcWf7Wak5jRo1qvZxvfzyy9X6eFeCefPmVcl5Tl9SvCZ+5nRd57vvvuO11147a0lrqe+//545c+Ywd+5cOnXqxA8//MAjjzzCmjVr8PPz4/XXX+f7779H13XGjRvHDTfcgK+vL1988QUDBw6kefPmFzw+mQERwoMpikJDcyiqrKpfK4wOuZrhwVehZGWjLPuuxsMHjZugmIzkpF7Mz4de6QACUJSZiNLA27VU1Hk4N2Wg6Jy3fCEhIYGTJ0+C0gHofJ6zbkJhMwGqylV+/oQYKjd2DYgrLiK2qACnotG3mRejOnhT3+/S3z5PFuv8fMDKN39a2JdqwxHUGHrcjD7kUWjeF0w+l/wYlZa6H7Z+hbr6TZT1H6Ek7qCh00zfgK7c0WAM9zUcz5CgXkR7NcJwGS4dGns1RNMvrfzrxRdfZOzYsTz66KPUq1cPgGbNmvHuu+8SGhpKYqLr31ubNm2IjXWV7g0ZMoSPPvqIsWPHEhMTw9ixY9myZQsASUlJtGnThm+//ZYhQ4bQvXt37r77blJTXZvl6LrOxx9/zOjRo+nRowc9e/bkySefxGJxheznnnuOl19+mQceeICYmBiGDRvG5s2beeWVV+jZsyf9+vXj22+/LfdYSUlJgCtgP/jgg3Tv3p2rrrqK6dOnn/UicvHixYwbN47evXsTExPDAw88QHZ2tvv+L774wv3J9rhx49i8ebN7fKW7q+u6zocffkj//v3p0aMHb7zxBk7nqU8l/vjjD8aNG0ePHj0YNmwYr732Gg5HxUH8zz//5I477qB///506tSJcePGsWPHDsA1QzFkyBDmzp3LgAED6NWrF4888ggFBQWVGsfp0tPTefDBB+nWrRtDhw5l48aN5e5PSEjgwQcfpHfv3gwePJh3333X/TouWbKEcePGMXnyZHr06MGKFSuYNGkSs2fPJiEhgbZt25b7ZP/IkSN06NCB9PR0CgoKeOGFFxg+fDhdu3ZlwIAB7pmTOXPmsGLFClasWMGYMWOAUz9zixcvZuDAgWhletK+/vprRo0aBUBBQQEvv/wygwYN4qqrrmLq1KlkZma6j509ezaDBg2iV69ejB8/njVr1pz1tZk2bRrffvstjz766FmPKfXZZ5/x2GOP0blzZxRF4frrr2fhwoX4+/tjKPldWdqxoSgKiqKQmprKsmXLmDJlynnPXxEJIEJ4OJNiJMIssyA1KUD15f4G42nlE4Wydw/KqhVQpiSkxnTqgtOhk595/kNPp1/ADAhAzpFtoIAaWYmLagc4d57Ex8fnvDs6Hz9+nLy8PFA6Ax3Oc+KjqKzEiIMePn5Em8yVHn/pbup/WYoI8lG5roMP/ZqZ8TZderh3aPBHgp1v/rSw/rCFfN0Pve1g9GseR+98PQRW867WBRmweyXK2lmuUq39awgoLKCTT0vGh17DQxG3Mibkajr4tMS3ikq1oszhXMr2gwkJCRw6dIjrr7/+jPvq16/PBx98QNOmTSv83u+++45Zs2axadMm2rZty/Tp08vd/+uvv7J06VJWr15NZmYmH3zwAQCrVq1i/vz5zJ49m23btrFgwQI2bNjAihUryp37vvvuY/v27XTu3Jl77rmHpk2bsnnzZh544AFefvnlM4KFw+HgnnvuISwsjPXr17Ny5Up27NjB7Nmzzxj7rl27ePXVV5k+fTqxsbGsWrWK48ePM3/+fMB1kf3BBx/w5ptvsn37diZOnMiUKVNcwf201+CLL77go48+YtOmTZhMJnfQAnjmmWeYNGkS27Zt4z//+Q//+9//Krz4tVgsTJkyhREjRrB+/XpiY2OJiorizTffdB+TnJxMWloaP//8M99++y1xcXF8/fXXlRrH6aZOnYrRaGT9+vV8+eWXrF+/3n1fUVERd911F61atWL9+vV8/fXXbNq0qdzruHfvXkaPHs2mTZsYNmyY+/aoqCh69+7NsmXL3LctWbKEAQMG0KBBA9566y2SkpJYvHgxcXFxvPDCC7z77rvEx8fz97//ndGjRzN69GiWL19ebrzXXXcdBQUF7hAIrtmHm266CXCFhvj4eJYsWcIvv/yCv78/Dz/8MLqus2XLFhYuXMi3335LbGwsEyZM4Pnnn69w7ySAxx57jIULF9K+ffuzvn4AxcXFHDp0CFVVuf322+nduze33norxcXF+Pn54ePjw4svvsgDDzzAlClTePXVV/Hx8eFf//oXU6dOxdv74n4HSAARwsNpukYzr8iaHkad1c6nOfeEjcVPN8Pan2FbLFRiRabqoNdvwMlU5aKrfSrbAwLgKM7DaS1GifKt3NgSi9GzbISHh+Pjc+7QcvToUdcnqEoM0PY8Z85DZTEKmbTx9qGztw8XUviTZLeztjCPFLuNZvWNjOvsQ7twI1XVN30828nS3RaW7S4mJQ/0yM4w4H70vndBRHuo7hWiHDY4sgl+/xjDqpmwbRGmjHiaGxswPPgqHmg4gdvqX0cv/07UN4Zc1EOoqER6NUS9hBex9BN/1+IEF+amm24iOjoaHx8fRo8ezfHjx8vdf9999xEYGEj9+vUZMmSI+/6BAweyePFimjZtSnZ2Njk5OQQHB5OWdqqhqk+fPvTo0QNVVenTpw++vr5MmjQJo9HI4MGDsdls5T7hBtcMQnJyMtOmTcPPz4/Q0FD+/e9/M2HCmXuktG7dmpUrV9K5c2dyc3NJT0+nXr167jF8//333HLLLcTExKCqKhMmTOCzzz4746Jx2bJl3HzzzXTo0AGz2cxjjz1GSMipv08vLy9WrVrFunXrCA4O5rfffmPEiBFnjMdkMrFw4UJuu+02bDYbycnJZ7wmAH//+9/x9vYmOjqa3r17c+zYsUqNo6zk5GS2bdvGU089hb+/PxERETz88MPu+3/99VdsNhtPPPEEXl5eRERE8Nhjj/HVV1+VG+8NN9yA2Ww+4zWZMGECy5cvR9d1nE4ny5cvdweFRx55hPfeew9/f39SU1Px8vICXDMy5+Lr68v111/P0qVLAdesyr59+7jhhhvIyspi9erVPP/884SGhuLn58e0adPYvXs3e/fuxcvLi9zcXBYtWsRff/3FhAkT2Lx5MyaTqcLHci1Zfn55eXnous5nn33G9OnT+f3337n++uu577773DNyt956K7/99hvr1q3jxhtvZMOGDTgcDrp3786jjz7KDTfcwIwZM84ahioiPSBCeDgFhRbeTdiQX/vqnj3dqOCBtPaJhuwslHW/QEmZQa3Qph2KQSXn7B8untOFzoAAFKQeIrhpF5z+Rig4fx+Fc3MmxmsjiI6O5sCBA5xr0cbDhw/TqlUr/Px6lPSEHDrHmTVgNegxNDS2J8DPQFxxEUUXEAz3WIo5goWuvr70aGKmTQMTscetnMirmnCZZ4E1B62oQNcmJlqHNcLcbTy6tQDl+B+Q8CfYamDhgrQDkHbA9emlXyg070PDsJY0COhC/8AYCpxF7r6RRGtqpVbVCjeHYrzE/o/SRQsyMjIqnOnIzMw8azgpe7vRaDzj5+xs9+u6zrvvvsu6deuoV68e7dq1w263l/v+4OBg958NBgOBgYHur0sb7rXTfu4yMjIICQkpF7wbN25c4dhVVWX+/PmsWLECX19f2rRpQ0FBgXsMGRkZJRt4ntKtW7czzpOenk5ERES5sZb9vi+++ILZs2czY8YMMjIyGDBgANOnTz/jItdgMBAbG8t9991HUVERLVu2rPA1LbvIhMlkct9/vnGUVRpqyt4fFRXl/nPpYhU9e/Z036brOna73b3XUFhYGOpZPkgZPnw4r7zyCrGxsVitVnRd5+qrrwYgKyuL1157jb/++ovGjRvTsWNH4My/y4pMmDCBO+64g8LCQpYsWcKQIUOoV68eu3btAuDmm28ud7zBYCApKYmRI0cye/Zs/vvf/zJv3jy8vb2ZNGkSU6ZMOetzqIzSAHP33XfTqlUrAP72t7/xzTff8Ntvv3H77beXO95ms/F///d/fPDBB3z44YcEBwcza9Ys7r33XhYvXszEiRMr9bgSQITwcIqiEGoKxl/1lf1Aqom/6svE+iMJMPrDX3tr1ayHW9v22K06hScv8lPniwggOUfjCIrqjNrEF21f3vm/QQPH9hy8etUjPDycEydOnPPwQ4cO0bp1a3x9e5eEkPOtzBKHQho+yiCu8vVnt6WI9LPUtVekGJ3NRYU0NBpp5+XDsLY+JOQ42BZvo8BWNU3kGvBnop0/E+1Ehah0jfQhqPUgaDUQUva69hTJPffrctkUZsHuH1AAxWiGqB74N+pAp4CWdPVri0N3cNySwlFrEkctSRRpFS9A0NQrEk3XUC9hdicyMpLWrVvz448/lrvgBNfF4uDBg5k5c2aFJVoX66233iIlJYW1a9fi7+8PwOjRo8sdczGreoWHh5OTk0NxcbE7hGzbto09e/Zw1113lTv2888/Z+PGjaxYscIdlB588EH3/REREWf8u3n33XfdvQllH7O0RwZcF+qln+ZbrVYOHz7M9OnTMRqNHDt2jBdeeIF//etfvP/+++XOs3PnTl555RUWLFjgvij/7LPP3DMclXnuZxtHRceCq1+mRYsWAOXKtcLDw4mKiuJ///uf+7aCggKysrLcPULn+vsxm82MGTOGlStXUlxczNixYzEaXZfNjz32GEOGDOHTTz/FaDSSk5PDokWV2/S3U6dOREdH8/PPP7NixQpeffVVAHe56apVq8oFtMOHD9OkSRNSUlIIDQ3l008/xWazsXnzZh5++GE6dOjgDkYXo169eoSGhp5RCni23pt58+Zx3XXXERkZyaFDhxg2bBiKotChQwcOHjz/vlClpARLiDpA13XXfhPismvtHc09YWPx171g3RrYurn2hQ+jET0omJwTF1/yoqPABQYQNAf2opOolSzDAiDVgp5uoUGDBvj6nv/7Dh48SHFxMTpXAU0r8QApqHyPShFdffxoZa7cUr1lpTkc/FqYT4LNSmSQgRs6+9Al0oShit9hE3I0lu+x8P3OYpJydfSIDtD/XvR+90CjjtVfnlWWwwZHN8GGT1ylWn8sxJhxnObGBgwLuooHw2/m9vqj6O3fibDTSrVaeDdGqYKFMv75z3/y3Xff8e9//5ucnBx0XWffvn08+OCDdOjQocKSoUtRUFCAl5cXBoMBq9XKZ599xsGDBy+oDKUinTt3pmnTprzxxhsUFxeTmZnJzJkzyzWWlx2D0WjEZDLhcDhYtmwZv//+u3sM48aNY+HChezatQtN0/juu+/46quvzihrmjBhAosWLSIuLg673c7cuXPJyMgAXBfpTzzxBJ999hkOh4OwsDCMRmOFpVH5+fmoquouZ9qxYwfz588/7ypMlRnH6Ro1akT//v2ZOXMmubm5ZGRk8O9//9t9/+DBgyksLGTevHnYbDby8vJ49tlnmTp1aqWD4c0338wvv/zC2rVr3eVXpc/T29sbg8FAdna2O0SUvu5ms5n8/PxzPs/3338fVVXp378/4AogV199Na+99ho5OTnu53/TTTeRl5fH7t27uffee9m/fz9ms5nQ0FCAs5aoXYhbb72VOXPmsG/fPhwOB/PnzyctLY1rrrmm3HFJSUn8/PPP3H333QA0bdqUHTt2YLfb2bVr1wXteyIBRIg6QEeXPpBqMDK4H6NCBqLm5aEs/x7iK/epX7Xr2AVFVci5lA/OdVAuomwmN2EPircBpX7lG8CdW7LRHRrR0dGVKjU4cOAANpsVnX5Ak0o8ggWF70FPpKnZTHcfP8wX8cn1fquF3wvzyNOcdG5k4sbOPkSFXFppUUUKbLDukJWvt1vYlWzD5tcQYm5Ev2YqtBoEXv5V/pgXLP0g/PEN6k//h/LbXIj/kwZOI1cFdGFSg9E80PAmhgb1pq1PM8JM9apk88FevXrx5Zdf8tdffzFq1Ci6devGo48+Sp8+fZg3b95Za+Uv1uOPP47FYqFv374MGTKEHTt2cMMNN1zQp8AVMZlMfPjhh6SlpXH11Vdzww030LNnzwpXM5o8eTIREREMHjyYAQMGsHz5cm677Tb3GEaPHs0jjzzC008/TY8ePVi4cCGffPKJewag1PXXX8+jjz7K1KlT6dWrF4mJie79NMxmM3PnzmXNmjX07t2bIUOGEBYWxlNPPXXGePr168dtt93G7bffTs+ePZkxYwaTJk0iOzv7jF6XipxrHBV5++23CQgIYPDgwYwfP56+ffu67/P39+fzzz8nNjaWgQMHcs0116Cqarn9Oc6nVatWNG3alA4dOpQr7Zs5cyY//vgj3bp1Y9y4cTRs2JD27du7X/frrruOP//886wzE6NHjyYrK4tx48aV+5325ptvEhgYyNixY+nTpw+//fYb8+bNIywsjBEjRjB58mSmTJlC165deeyxx5g2bRpdunSp9PMptW3btnL7zjz88MPce++9PP744/Ts2ZNly5bxySefnLEIyKuvvsozzzyD2ez6/X3//feTkJBAnz59CAgI4NZbb630GGQndCHqCLvmYE7qArQa3OnYU/mq3kwMvZYgUwD6/n0of2yBcywdWdP0m27Fih8Htlz8RV/7ATq23HjSd/1ywd/bdPDd6MkWnHEnK/9N9c0Y+9YnMzOT5OTkyo2xfTtMJhMKvwGV+x5og0YP7LrOjuIici94gxSXeqqBjmV2U4+Nt5J7ibupn0tkkEq3JmaCfRTQgdR9rvKsnKTL9pgXRTVCdE+I7IAzIAyD6uoPkN3PhahbJIAIUYd8m/kTibaL7DoWFWrp1YRRwQNQdVA2rodjF74jbLXy8UW/eSInDitkxF/8adr1B0dBImk7Vl/w94Z3uxbvwAgc/0sFZ+Xfggw9QlAjfTl8+LB734Dz6dChPUajAYV1QGV/9uuhMQwwst9qIcleufKRijQ3edHM7IWqwL40BzuTbdgvYzb1NUGvaC8aBymoBgN6Xqprc8OUvRex22Q16HsXBEXCJTTRCiGuPPIvXog6wqlr0gdSxYYHXcXokEGo+fmukqvaHj4AurpWwDl5qTlUB8VwceuY5ByNQzGqKBEXtn68c1sOms1Z6VIsgH379uNwaOgMBhpU8pGyUfkOhZO09/ahg7fPRb9ZHrVbWVeYR6bTQduGRsZ18aVl/cu3/kuRHX49bOXL7RZ2JNmweodBlxtc5VltBoN3wGV77AtmMEFQhIQPIeog+VcvRB1hUFRaeFemHl6cj7fqxeSwsXT0awWHD6GsWAp5uTU9rErRo5tSeBLs1ks8z0WsglXKejIVzW5DbXIBzeglnFuyMBqNREZWLkxrmsa+fftwOjV0hgCV3SfCgcIPoB+kkdFEb19/fC6yTOiM3dSbV91u6ueyK8XOoh0Wft5fTI7NjN68L/qQR9G73QT1os5/gsutfnNXSZYQos6RACJEHRJiDCTYUIs+Ab0CNfNqzANh4wlSfOH3X1E2/V6r+z3KCQkBL69LWv2qlKaBegkXj4UZx1HCvMD7At+GcuxoCUWEhoaW21PhXFwhZD+aBjpDgXrn/Z5TtqLwO36qwlV+AYRe5KwPVLybet9mZrwv8zX4iTyNlXstfLujmPhsJ1qD1nDVnegDH4QmXWsuBDRsUzvLwoQQl50EECHqEE3XaOPTtKaHccUaGtSbsSFXoxYUuGY9jhyu6SFdmK7dQYeT596st1J0nUsqnck+vA0ANfLCZ0G0HSfRLA6ioqIwGCo3C+N0OktCiILONUDwBTxiAirLMGClm48vzc1eFzzmskp3Uz9ht9G8vpEbu/jSrmHV7aZ+NhYHrD9i46vtFrYnWLF41UPvdD36NU9A26HgE3R5B1CWaoCIdhe+lLMQwiNIABGiDlFQ6ODboqaHccXxwsxdYTfQxa8NHD3iCh+5J2t6WBdMb9SY3EzQKr/X3tnPpV18CRaAZivCYSlEjb7wAALg3JSFQTXQpEnlywodDgf79x9A11V0hgGVm0FxKURhMQpptDB7EePje8k7+e62FLOxoAALTnpEmRnT0YeIwOp5W96b6uDbHRZW77eQZTWiN+uDPvgR9B43Q2jTyz+ABq3AWPmlmIUQnkUCiBB1iKIoBBsDaWgKremhXDGizBE80HA8Iao/bFiPsuE3uIDdsmuNRo1RTEZOplbNx+yXGkAAClIOoASYIPAi9mfId6AdLSA4OJjg4OBKf5vdbmf//oPouhGd4cCF7pnxCwq7qG8wcpVfAP6X2EBdjMbmokJ2WYrw9YJhbX24uqUXfubqWZY2PV/jx78sLNpRzNEsB1r9ltBnEvqghyCqu6tR/HJo1FHKr4Sowy5LAImPv4S1Heu4/Pz8Cnc7FWd3/Pjxmh7CFUXTNdr6NKvpYVwRrg7syfh6QzEUFqOsXAaHL22DsRrVuQtOh07e+fcCqxRXALm0t5CT8TvRNQ21ic9Ffb+2Nw+t0EGTJk0wGis/H2Gz2Thw4CC6bioJIX4X+Mi7UfgZL0Wjt68/EcZLv0hPczhYV7qberCBsaW7qVfT9hhWB2w86irP2hpvpdgUjN7xWtfqWe2Gge+l77bsZvSChq0vuvzqxRdfJCYmhpiYGDp16kTbtm3dX8fExLBt27aqG+tpRo0axfLlyy/b+ePj4+nduzdJSWffv0XTNN59910GDhxI9+7dufnmm9m6dav7/h9//JEBAwbQq1cvZs2aVe5777nnHjZv3nzZxi9EZV3Qu0dubi7Tp09n0KBBdO3alf79+/Pss8+SmnpqPcc33njjgnaZvFDn+seflJREmzZtzvkPtzpcyjiGDRvGoUOHLsOoTu18eTazZ89m0qRJlT7fkCFDWLJkyUWNZfny5YwaNeqivresv/76i+uvv9799YsvvsiLL754yee9EGlpaTz66KP07t2b/v37M3PmTKzWsy8x9NVXXzF8+HBiYmIYPnw4X375pfu+gwcPMmbMGGJiYnjooYcoLi523/fhhx/y/vvvX/J4VUWlnU9zFGTjr7MxY+SOsNF0828Hx4+5ltjNubI/GNDDGnIyTUGvon0oNe3idkI//SS2gmzXalgX+ePo3JSJqihERV3Yqk5Wq5VDhw4DXiXlWBcagtJRWYJKAZ18fGnr5V0l/6JKd1PPL9lNfWyXy7Ob+jnHkOZg8U4Lq/6ykFFsRG/aC67+O3rPiRBWBSWcEe1Bufjw+vLLLxMXF0dcXBwzZsygUaNG7q/j4uLo0aPHpY/xLH744QfGjBlzWc69Zs0aJk6cyMmTJ8953IIFC/jll1/49ttv+eOPP7juuut44IEH3O87r7zyCjNmzGDFihV8/vnnHDlyBIBVq1YRHBzMVVdddVnGL8SFuKDfAFOnTiUnJ4fFixezY8cOli5dis1m4+6778ZRUpKQk5NzWQZa6nL+468NLufr16NHD+Li4i7b+S/EmDFj+OGHHy75PPn5+djtdvfXL7/8Mi+//PIln7eyNE3joYcewmaz8b///Y/ly5ezf/9+pk+fXuHxa9euZdasWbzzzjvExcXx1ltv8eabb7JlyxYA5syZQ79+/diwYQM5OTksXboUgOTkZH744QceeOCBKhm3r8Gbpl6NquRcnqaxuSEPNpxAqBoImzagrF8HDvv5v7E2a9UGxaCSU4V7UFbFDAhAbvwuFC+Da0Wsi1HkRDv4/+ydZ1gU1xqA31l2lypSFUTBjrGgIIiKgmLviqKJRmMXe4xGvYmiqDEm11iiibGlmGti7L3XaERsmMTepSkgIAICC7tzf6xMWMFO08z7PPsoM2fO+WZmd+Z852spWFpaYmPzMtmtID09nWvXbgCmjy0hL1eXBDQIbAHxFhVUahqYmWNcAJHkGuBkehpnHqWhVEKzaia0qmFCaZOiXTS4n6Zj96UMfjubzrX4bLQ2laBBb8Tmo6Ci16vHcFSoh75ce+Fw8OBB3n33XRo1akTdunV5//33JUv5xo0bee+995g1axYNGzakUaNGfPrpp9J7ZPLkyQQHBxMUFIS7uzstWrRg1apVUt+5F9769u3LV199RZ8+fXB3d6ddu3bs3LlTavvLL7/QsmVLPD096dSpE+vWrXuqzIsXL2bevHmMGzfuued38+ZNdDodOp1OqiJvYvLPd9fIyIjcNaaNjIxIS0vjm2++YfLkyS92EWVkCpmXenucOXOGVq1aYW9vD4CdnR2ffPIJdevW5eHDh3zzzTds27aNbdu2SUrC/fv3mTBhAj4+PjRp0oTg4GCpgm1YWBh+fn6MHz8eT09Pli1bhk6nY9myZbRs2ZL69evTo0cPjh49KsmQ+8efmprKpEmTqF+/Pk2bNmXLli0G8r7Mj3/9+vUEBATg7e2Nu7s7w4YNk1yhFi1axJgxY5gwYQKenp74+vry1VdfScc+T44nWbRoEX5+fjRo0IDu3btz4MABANq0aQPAkCFDWL58OQDHjx+nR48eeHp65rH+TJ48mTFjxtCuXTsaNmxIREQE0dHRfPjhhzRq1AgfHx/Gjx9PXFycdL1dXV2l48+ePUv37t2pV68e77777jMtNqIo8t1339GkSRM8PT354osv0OZKParRaFi4cCEtWrSgQYMGDBkyxMAVz9XVlVmzZuHt7U1QUBAbN27E398fgD59+jBv3jyD8QIDA1mxYsUz701kZCRDhgwBwN3dnfDwcCZPnszkyZNJTU3F3d2dY8eOSX0+fPgQNzc3/vrrL0CvzHbq1In69esTEBBg0PbUqVMEBATg6elJq1at+OyzzyQlOze3bt3i/PnzBAcHY21tjY2NDR999BHbtm0jJSUlT3t/f38OHjxI7dq1yc7OJikpCUEQpHSiOW4koigiiqKU4eezzz5jwoQJGBu/XvadHHSijtpm1Qqkr7cJ31IeBNq0wuhRBsKOrXD1cnGLVDC8U5OsTJG0Alzf0BVADAhAWuxNdNnZr1QTRJLlSiq6lCzKly+PWv1yk+JHjx5x/fpNwPyxJeRVfmN/IHCCUgoFjcwtsH7BzFzPI1Gn5UhaCtczMihjoaBzHVM8ndWoijhxlEYHobc1/HI2g9BbmTwyskSs2UafPatWWzB/iZgyMxuwLv9aFpBnce/ePcaOHcvQoUMJDQ3l8OHDiKLIN998I7U5e/Ystra2HD16lKVLl7Jz50727t0r7d+4cSN9+/bl1KlTDBkyhDlz5hAbG5vveGvXruXTTz8lLCyM1q1bExwcTGZmJpGRkXz++ecsW7aM06dPM3HiRGbOnCm9j58kMDCQ7du3v5B14t133yUjI4NmzZpRp04dFixYwNdffy29H6ZPn86cOXPo3r07w4cPp2LFiixevJhevXpJ8zcZmeLmpZ4AHTp0YNq0aUyfPp2dO3cSHR2Nvb09c+bMwcbGhpEjR9KpUyc6derE1q1bpdVhhULBnj172LZtG3FxcQYuMvfu3aNy5cqEhobSu3dvvvnmG1avXs3ChQsJCwtj4MCBjBgxQpo05mbGjBncuXOHvXv3snXrVs6cOSPte5kf/19//cWsWbOYPn06YWFh7Nq1i9u3bxuseuzdu5cmTZoQFhbGzJkzWb58OefOnXuuHE9y4sQJfvvtN9atW0dYWBiBgYHS6suePXsAWL58OUOGDOHy5csMHz6coUOHSuPOnj3bQCE7evQoCxcuZO/evTg6OjJw4ECMjIzYu3cvu3btAiAoKCjP5DkpKYlhw4bRpk0bTp06xccff8z+/fufKveGDRv46aefWLp0KcePH0elUhm43s2fP5/Dhw/z448/cvToUerWrcvAgQMNXJEiIiI4fPgwX375pUHfgYGB0vcF4MaNG1y6dImuXbs+895UqFBBUtTCw8MN3MssLCxo164dmzZtkrZt374dFxcX3NzcOHLkCNOmTSM4OJiTJ08yevRoRo8eLbm/TZw4kb59+3L69Gl++OEHdu/eLSmKucmR2dT0H/cNQRDIysoiMjIy32tpYWHBzZs3cXNzY+jQobz33nvUrFkTgBEjRnDmzBlatGiBk5MTXbt25fDhwyiVSvz8/J56f14WhaCgikl5TBUFo9C86ahR8r5dRzxL1YaIOwhbN0JiQnGLVTAoFIhWNgVS+yM3Ys76QwFMJDMexCA4moLy1WXUHr+PAC/tigWQlpbGzZu3gFKPU/S+ysr+DRRsR0k2nqbmuKgKLsNTTjX1hJxq6m5mVCnEaurP4lp8Nhv+zGD7hQxi0xSIzh7QbASi9/v6zFbPc0Qr76bXXgsJGxsbduzYgb+/P6mpqdy7dw9ra2sDBcLExISgoCBUKhVubm64urpy69Ytab+3tzc+Pj4olUq6d++OVqslIiIi3/HatGlDzZo1UavVdOvWjZSUFBISEiQrxJo1azhz5gyNGjXi3LlzlClTJt9+ypYti/CC1rOsrCwaNGjArl27OHv2LIMHD2bMmDHEx8cD0LJlS/bt28exY8cYOnQo165d4/Tp0/To0YOpU6fSuXNnxo8fn+8imYxMUfFSb45Zs2YRHBzM3bt3CQ4Oxt/fn1atWj01JuP8+fNcuHCBadOmYWFhgbW1NZMmTWLHjh0GrkY9evRApVJhYWHBhg0bGDp0KLVq1UKpVNK+fXv8/f1Zv369Qd8ajYZdu3YxevRobG1tsba2ZuLEidL+l/nxV69ene3bt+Pm5kZycjJxcXHY2NgYPLAqVqxI165dMTIyws/PD3t7e27fvv1cOZ7E2NiY5ORk1q5dy8WLFwkMDCQ0NBSVKm8Q45o1a2jRogWtW7fGyMgIDw8PevbsyerVq6U29erVo3r16lhaWnL69GkiIyMJCQmhVKlSWFpaEhISwuXLlzl//rxB34cPH8bU1JQhQ4agUqmoX78+3bt3f6rcW7ZsoWfPntSqVQu1Ws3YsWOxttYHJeZc548++ogKFSpgbGzMyJEjycrK4vDhw1IfHTt2xNTUNE/xsLZt25KWlkZYWBigX33y8/PDzs7uhe7N0wgMDOTAgQOSxW3Tpk306NEDgP/973+89957eHl5YWRkRPPmzfH392fNmjXSfdq1axeHDh3CysqKI0eOSBaq3FSuXJlq1arx+eef8/DhQxITE1m8eDEAGRkZT5WtQoUK/Pnnn6xfv54dO3awbNkyAKpUqcLatWsJCwvjq6++QhRF5s2bx3/+8x9+/PFHunXrxgcffMD1669ff0JA4B3Tyq/dz5tOOVUZhpXtgb3SCk4cRzh8ALLecJer3NSpi6AQCtT9Cv6ZQxaEFSTpxhlQgFDu1YLRAcjQobvwEAsLC+zsXrTa+T+kpKRw+/YdoPTjYoWvElj+EAXrEUjA1cQUNxNTCspYoQPO5lRTV+jweVxN3baQq6k/jaRHOvZezuDXsxlcjtWgtXIGr3cR/UdDpYagzMedTVCAs8dr1Y95HiqViu3bt+Pr60uHDh2YN28eCQkJBi5Jtra2BpN9lUplsD+3lSDn3ax7itKUu22OBVun01GuXDl+/vlnoqOjCQoKokGDBsyePfuZ8YEvysSJE/H19aVy5cqYmJgwcuRISpUqxe7du/NtP3PmTKZMmcLGjRu5e/cuW7ZswczMjKVLl762LDIyr8pLLaEoFAq6dOlCly5dEEWRGzdusGXLFiZOnIi9vX0e02FUVBRarTbPyq1arTZYHc6tFNy/fz9PXvfy5ctz+bKhK0RSUhIajQZHR0dpW+7jcn78K1asICgoCK1WS0BAAB9//HEeNxaFQsGqVavYtm0bZmZmuLq6kpqa+tQHEugfSjqd7rlyPIm7uzuLFi2SZDMxMaFv374MHz4cxRMP5ejoaE6cOGEQUKfVag1W+HJfu4SEBKytrbGw+CetpIWFBVZWVkRHRxu8lGNjY3F0dDR4CDs7O3Pp0qV85Y6LizM4RyMjI8qV08cQJCYm8ujRI8aOHWtwDllZWURHR+cra25MTEzo1KkTmzdvpkGDBmzdupWZM2cCL3Zvnoa7uzvly5dnz5491KtXj8uXL0sWk+joaE6ePMmvv/4qtddqtTRs2BCAn376iUWLFhESEkJ8fDxNmzZl+vTpODg4GIxhZGTEkiVL+Oyzz2jdujX29vYMGDCAI0eOULr004t65bzU6tSpQ79+/di2bRtDhw7N027p0qV07tyZ1NRUli1bxt69ezl06BCffPIJa9eufe41eB5uZtU5m5b/Pf834FPKnQbmtSD9EcLBnZBQQCmiShBi1epkpolkpBasBSQng6qgUPxjDXlFNCkJ6LIyUTiboY149Ooy3UxDqGBGuXLlSElJeenJXnJyMnfuRODi4oyIPwL7gZc9OR2wG0R3yiprUsrciPBHj3hUQNH/OdXUK6hUVDM1pUMtU67HZ3E2UkNGMWSHztbByTtZnLyTRRU7I9zKlcLinZbg2hwh6k+4fQpS9SvzlHUF45fNOPZy7Nq1i//973/8+uuvuLi4APoJ+NWrRZvBLiEhAa1WyzfffINOp+Ps2bOMGTOGSpUq0adPn9fqOyYmBo1GY7BNqVTmu5C5ZcsWKlasSN26ddm4cSM1a9ZEEARq166dr1VfRqaoeGEF5OjRo4wZM0ZaERYEgapVqzJ+/Hj++OMPLl68mEcBcXBwwMTEhLCwMMmXXaPREBkZiYuLi+SqlHsS7OTklMd1JTIyMs/k1draGmNjYyIjI6lcWb+Km9sl6GV+/D/++CN//PEH27ZtkybpQUFBL3RdnifHk8TExGBra8vKlSvRaDSEhoYyatQoatWqRbNmzQzaOjg40K1bN4Og6ri4OIPJ95PXLikpidTUVEkJSUlJISkpCXt7e4PjHBwciI6ORqfTSUrDs+R2cHAwuC+iKErubDnX4Pvvv6devXpSm5s3b1K2bNl8ZX2Snj178t5779GqVSsEQaBp06bA690b0FvXtm/fzp07d2jZsqVUL8DBwYGuXbsaTPpjYmIwMTEhMzOT69evM336dJRKJbdu3WLKlCnMnj07TxYqURRJTk5mwYIFUhDgkSNHMDc3l15+ufnxxx85d+4cCxYskLZpNJp8lZU7d+5w+PBhfvvtN/bt24ezszMWFhbUrl27QF6mgiBgoypNBXVZIjXPtyi9TShR0NOuLQ5qO8SIO/raHk+80N8KTEzAwoKkGwUfvFyQFhCAtNhbWFZ4B62pEaS/ukajPZ6Ask1ZXFxcXul38uDBAxQKBRUqlEekOQKHeHklBCAcgVhMBT8amVvwd8Yj4gqwfkxkVhbRWVnUNjGlsp0KFxsl56I0XI7L5gXWZwqFG/e13LifjpWpgJezGocK9RBc6iMm3EG4fRIqNcjJXlBoMqSkpKBQKDAxMUEURY4ePcrmzZupVq1oY95iYmIYOHAgS5cupVGjRtK7MMdz4HXw9/dnyZIleHp64uDgwC+//EJ8fDzNmzc3aJeSksKKFSv4+eefAb0nx++//05WVhbnzp17JXdFGZmC4oWfAl5eXtja2vKf//yHK1eukJWVRWpqKlu3buX27dvS5FmtVkt+hW5ubri4uDBnzhzS0tLIyMhg9uzZ9O/f3yCAOTeBgYEsW7aMCxcuoNVq2bVrFwcPHqRbt24G7dRqNV27dmXhwoXcu3ePlJQU/vvf/0r7c378oaGhKBSKZ/74U1NTpdWD7OxstmzZwtGjRw2yKz2N58nxJH///TeDBw/m8uXLqNVqbG1tDeTKff1yJs/Hjh1Dp9Nx+/Zt3n//fb7//vt8+65Tpw5Vq1Zl2rRppKSkkJKSwvTp03F2dsbDw8Ogrb+/P6IosmjRIjQaDefPn39mkH5gYCBr164lPDycrKwslixZIvmbKhQKevTowVdffcW9e/fQ6XRs2rSJjh07vnBNmBo1alC5cmVmz55Nt27dJIX1efcmx5r1NF/Wrl27ShnbAgMDpe09e/Zk1apVUmzR33//TUBAANu3b0cQBD766CO+//57srOzsbe3R6lU5vvdEQSBiRMnSgkUbt++zdy5c+nbt2++dQk8PT3Zv38/O3fuRKfTcebMGVatWsV7772Xp+2sWbP4z3/+g0qlwsXFhVu3bpGYmEh4eHiBvTh0og5Pi9oF0tebQlmlLUFle1JWaQMnQxEO7ns7lQ+AevURhIJ3v4J/YkAKSgFJunkGUSe+VjA6AFk6tH89wNTU1GAB5GVITEwkKioaKIOIH69eMisGBZtQkE49U3OqqY0LNPm1DvgrI50/0gyrqTsUUTX1p/EgXWTflUx+OZvBpXsaskuXh/qBYONcqMoHQLdu3WjcuDEdOnSgYcOGLFmyhA8++IBbt27lsRoUJnXq1CE4OJjp06fj7u5Onz596N27N+3atXvpvmJiYgzqm0yfPh1fX1/69OlD48aN2bdvHytXrszzfV+4cCH9+/eXFt569uyJsbExDRs2JCYmhuHDh7/2ecrIvCqC+CK+LI+Ji4tj8eLFHDt2jISEBFQqFfXq1WP06NHUrVsX0AdZjxs3DmNjYw4fPsy9e/f44osvOHnyJJmZmbi5ufHJJ59QtWpVwsLC6NevH1euXJHG0Gq1rFy5kvXr1xMfH4+LiwsjRoygdevWgH7iPGrUKAICAsjMzOTzzz9nx44dKJVK+vXrx4IFCzhw4ADly5dn/fr1LF++nLi4OEqVKkXPnj0ZOXJknpX4Bw8eMHnyZE6ePImxsTE1a9akcuXKnDhxgm3btrFo0SJOnjwprSK8rBxPsnTpUtasWUNSUhK2trYMHjxYmoDOmjWLdevW0b9/f8aNG8fhw4f5+uuvuXPnDqampnTs2JGPPvoItVotpdObM2eO1Pfdu3eZM2cOp06dQqPR0LhxYyZPnky5cuXyXO+cdLGXL1/GxcWFunXrcuvWLYPzzM3PP//MypUrSU5Opm3btly4cIH+/ftL12DRokXs3LmTBw8eUKFCBUaPHk3Lli0BfRasVatW4e3tDejjPBYvXszBgwel/n/77TemTZvGvn37JDe2592bR48eMXjwYC5evMjChQulwPvc12T8+PGEh4dz4MABg3u/ceNGvv/+e2JiYrCysqJnz54MGzYMQRA4e/YsX3zxBdevX8fIyAhfX1+mTZtGqVKl8lyXq1evEhISwqVLl7CwsKBHjx6MGjVKsix16NCBTp06SZabgwcPsmDBAqKionBycmLIkCF5Ukvv3buXAwcO8MUXX0jb5s2bx5o1a7C1teXzzz83sDa9Lj/FbSEhO7nA+iupNLRwo5GFG6SnIxzeD4+V6LcVsdf7PMo05vrpgreA2DhBhXcgKnQ9WWkPCqTP8o0DUYrmZO9/fYucURM7BBs1V69eNain8zLY29s/djWNQuB3Xi91bDNEnEjSavkz4xFZhWCmKKtUUtPYFJVCwZ3EbE5HaEjTFJM55AlaVjfGobQRigJIUywjI/Pm81IKiIyMzNuHTtRxKf0mex4cL25RCg0FCnratqGcsT1iZATC0SOgef1g0BJNaWvErgFEXRZIjH5+85fFygFcakN02CY0KQWTMczSuQ621b3J/j0OMek1EwEYgbKtI5lZGq5cvfJCcWP5UbZs2cexXxEIHOP1lJAa6KhPligSnv6Ih7rXDJ55Cu8Ym1DucY2Ov2OyuHA3C20xvumNldCjnhlGCln5kJGR0VO8dloZGZliJ6cyurniNTIQlWDKKK0ZUTYQR7UtnD6JcGDv2698ALh7gAjJhRTeIwWhF6BLzcOIC4haHcLrumEBaCH7bBLGJsZ5kke8DLGxsY/j3ZwRed0K0pdRsBuVoKWBmTlO+QQNFwSXclVTr+ukoqubKRWKuJp6bqrZq5B1DxkZmdzICoiMjAwA7ubvFLcIBU4Di9r0sWuPSqNF2LUDzuetJ/S2IpYrz8ME0BZSZiRdAceAPO6VzJR4FOXNCubtdDcDMT6TMmXKYGb26krN3bt3uX//PlAJEe/XFCoBBRsQeEAtEzNqmZgWyovYoJq6CpoXUzV1hQDvOBRPzRIZGZmSi6yAyMjIoBAU1DN3RSW8HROFHJcrn1LucPcuwuaNEPcvyvTlWA5BrSrw4oO5KRwFBB7cPoegUiCUzaeOxCugDUtAzBZxcXF54UJv+REdHU1CQgKCUA3wfG77Z5ONwA4Qr1JOqcLbzAKTQoqNyKmmfuNxNfVOdUypX6HoqqlXslViqlK81rWXkZF5+5AVEBkZGQBUgpI6ZkWbqrIwsFNaEVS2B07qMghnTyPs2w2ZTy8K+VZS1x2dVuRhIZY1kSwrBayApN+PRJed9frZsHLQgfZ0Imq1Wqpd9KpERUWRmJgIQg3AvQCEO4nAMcwVAo3MS2FrVHgLADeyMjmS9pBEbTbvOCjpVgTV1AXArZzqleNvZGRk3l5kBURGRkbC06IWigJNFFq0eJjXpK9dB4yzRIQ9O+HvP4tbpGJBtC/Lg1h9yYXCorAsIADpCVF6C4iqgF5RcZmId9Oxt7c3KNT6KkRERPDgwQMQagFuBSDcHRRsQUkmHqZmVFIbP/+QVySbf6qp6x5XU29fs/CqqVe0NaKUiWz9kJGRyYusgMjIyAD6miYWRmZUN61Y3KK8NAoU9LBphZ9lfYiN1btc3btb3GIVD1WrIRgpSLpXuJO+HAtIYSggiTdOgwAKp4JLjKA9lYROo8XZ2VlKkf2q3L59m4cPH4LgBtQqAOnSEFiPQCxV1ca4m5q9eJXgVyCnmvrljEdYmSloX9OERhXVmBTgoAJQ10ktWz9kZGTyRVZAZGRkJHSijkal6iK8QVYQG2Vpgsr2oIKxA8K5swh7d0HGq9V9eCt4pzbZGpHUxMIdRicpIAX/Gsl+lIxOk4HgXEBuWI/RhiWiUqlwcnJ67b5u3rxJamoqCO5AjdcXDoD9CPyFnZGSRualsCiEa5ubiKwsDqc9JDY7iyr2SrrVNaNGWSUFYbBwsTHCUrZ+yMjIPAVZAZGRkZFQCAqslZbUMqtS3KK8EPXMXPnAriPGWegVjz/D4d+84qpQIFrbkFiIwee5EUWxUCwgACn3rqOwVoNFAS7LJ2rQRTzC1tYWS0vL1+7u+vXrpKWlgeAJFFT81N8I7MNY0OFtZoGDsnBS9eaQU039+ONq6l451dRLvfr0QADqlZetHy9KSkqKPrZIRuZfhKyAyMjIGCCKIk1KeaAs4RmxAmxa0Lx0A4iPR9iyAe7GFLdIxU9tNwSFwIN7RTVg4SkgybfCEXUiivIFW59Gd+4Bugy9K5aR0evLfu3aNR49egSCN1D59QUEIA4FG1GQipupGa7GJoVuk3wk6gh9lMbfGY8wM4bW75jiV9UYc/XLj1zZTvna1g9/f3/q1KmDu7s77u7u1KtXjy5durBu3TqDdu7u7pw+ffqVx3kaixYtom/fvgBs3LgRf3//Ah8jh1atWnHt2rVC6z+HmJgY3N3diYnJ/1n5Ouf566+/0qZNG9zd3WnTpg2rV69+7jFarZZRo0axaNEig+1ffvklXl5eNG/enH379knbHz58SPv27WVl7S2hZM8wZGRkihxBEDBVGFPf/B3CUv8ubnHyYGVUivds22KqNNVbPM6d/XdbPXIhVq2O5pFIekoRub2IBVuIMDe6bA1Zjx6gcrZEdzmlQPvWht5H2awM5cuX586dO6/d39WrV3F1dcXEpBECOuD2a/cJGgS2gOiDs6oipY2M+DP9EZmF/F2/l53NvewU3jE2obyVmvJWpi9VTV0hQD0nfear13W/CgkJISAgAACNRsPhw4f5z3/+Q1JSEkOHDgUgPDz8tcYoCSQlJRXJOOXKlSuU67V//37mzZvH8uXLqVu3LufOnWPo0KHY2dnRpk2bfI+JiYlh6tSpHDt2DFdXV2n7jRs3+PXXX9m9ezfnz58nODiYVq1aATB//nz69++PjY1NgZ+DTNEjW0BkZGTyIAgCDSzqYKoomFoMBYWbWTX623fGRKuAfbsh/IysfORgbAKlSpEYU3Q+92IhWkAAHkZeQDA1QrBVF3DH2ehupmFtbY2VlVWBdHnlyhU0mkxEfIAKBdKnnj8QCMNSoaCRmQXWBWC1eRGkaupirmrqVs8fu6q9EjO1UOCxH2q1mtatWzNp0iQWL16sj78BXF1dCQsLA/ST12HDhtGsWTPc3Nxo3749hw4dAvQplF1dXZkzZw5eXl588skneHl5sW3bNmkMjUaDt7c3oaGhecbPzs5m7ty5NGvWDA8PD6ZMmUJ2tj4QShRFVq1aRZs2bfD09KR3796cP39eOtbf35/FixdLFoI+ffpw/fp1AGmCPmTIEJYvX44oiixbtoxOnTrh6emJl5cX48ePJyNDn0p88uTJBAcHExQUhLu7Oy1atGDVqlXSWElJSUydOpUmTZrg7e3NsGHDuH37tsE1iIqKkq5X3759cXd3p1OnTly8eNHgfKdPn46Pjw/e3t707t2bM2fO5HtvYmNjGTJkCPXq1UMQBNzd3fH29ubUqVP5tr916xbdunWjbt26uLsbprPOnSBCFEXp7wsXLnDp0iUCAwPz7VPmzUNWQGRkZPLFSFDQqFRBpBktGLpYN6dF6YYI9+/rXa6io4pbpJJFPXcEoSjdr9BbQApRAUmJvoyo1RVcTZBc6M4no3uUTYUKFVAqC8YZ4NKly2RlaRBpCrx+oPs/XEfBdpRCNp6m5jirClghewoa4OSjNM7mVFOvbkJLV2Msn1JNXaUAd6fCla1Zs2ZkZmZy9uzZPPtGjx5N9erV2bdvH6dPn6ZJkyZMnz7doE1aWhp//PEHkydPpkOHDmzZskXad+jQIczNzWnYsGGevmNjY7G0tGT//v2sXbuW7du3s3v3bgB++eUXfvjhBxYuXEhoaCgBAQEMGDCA+/f/KcTz22+/sWDBAkJDQ6lSpQpBQUFkZWWxZ88eAJYvX86QIUPYtWsXq1atYtGiRZw+fZo1a9Zw7NgxA0Vp48aN9O3bl1OnTjFkyBDmzJlDbKy+0OqYMWOIiIhg06ZNHDlyhMqVK9O/f39JYcshKyuLYcOGUa1aNU6cOMG8efPYv3+/tH/Lli2Eh4eza9cujh8/jpeXFyEhIfnekz59+kgWKYCEhAROnTpF7dq1821vb2/P/v37GTNmDCqVYYxTpUqVGDBgAAEBAfz3v/9l1qxZiKLIzJkzCQ4OlpMavEXICoiMjEy+KAQFbmbVsTIqVaxylFZYMKxMIFVMKyCc/wth13Z49KhYZSqJiBUrk5YsoinKmouFrIAAZDyMRXAyBaOCn3hoj99HIQhUqFBwFouLFy+RnZ2NiC/gUGD9wkMUrEcggRompriZmFJExcxJyKmmnplB2VJGdM6ppv7EDKJOORUqJYU6SbS2tgbQ12J5gqVLlzJ69GhEUSQ6OhpLS0tpYp5D165dUavVWFpa0r17d44fP058fDwAmzZtIiAgIF/5LSwsGDJkCEqlkqpVq1KjRg0iIiIAWL16NcOGDaNGjRqoVCp69OhBlSpV2Lp1q3T8oEGDeOeddzAxMeE///kPd+/ezVeJ8vX1Zf369VSsWJHExESSkpKwsrIyOA9vb298fHxQKpV0794drVZLREQEkZGRnDx5kqlTp2Jvb4+JiQkTJkwgOzubI0eOGIwTHh7O3bt3mThxIsbGxlSrVo0BAwZI+01MTIiKimL9+vXcunWLsWPHGpzP04iPj2fIkCHUrl2bjh075tvGwsKCUqWe/l4ZM2YMf/zxB3v27MHPz4+1a9dSu3ZtrKysGDRoEF27duXrr79+riwyJRtZAZGRkXkmTS09im3sWqZVGFCmC2Y6I9i/B86ckl2u8sOyNJiYkFRE2a9yECl8BSTp5hkEpQLBoRDcAdO06K6lUrp06QL1K7906TLZ2TpEmgNlCqxffc6q3SBepKxSRUMzC8wKKQYnP25onqimXteMyo+rqVsYC9R0UKEo5BXqnABkW1vbPPsuX75M9+7d8fX1ZcqUKVy5ciVPJq4yZf65H3Xq1KFKlSrs2LGDhIQEjh07RteuXfMdt3Tp0gaKiUqlQqvVV+OMjo7miy++wNPTU/pcvnzZINjbxcVF+r+pqSlWVlaS4pMbURSZP38+DRo0oHfv3qxevZqsrCyD87C3tzeQA0Cn00kWl9wKtZGREY6OjkRHRxuMExsbi7W1NSYm//yunJ2dpf936NCBqVOncuDAAbp27Urz5s359ddf8702OZw7d44ePXpQqVIllixZUiCWxaSkJH7++Wc+/PBD5syZQ4MGDVi7di379u3j999/f+3+ZYoPOQhdRkbmqSgEBdVMXXBMteNu1v3nH1CAdLL2o6qJM9y/j3B4P6SlFen4bxTueiXxQexz2hU0olDoCkhm0j10WRoUFczQRhd8fRfd5RQEJ1OcnJxISUkhKyvr9fvU6bh06RI1a76DkZE/AvuBgvz9nEXgHqYKPxqZW/BXxiPiH8cjFDY51dQtFQrqmpjRpLIxNcooydZBUZQPOnjwIGZmZtStW9dge2xsLGPHjmXx4sVSJqc9e/awd+9eg3ZPWje6d+/Ojh07UKlUeHp6Ur58+ZeWycHBgTFjxtChQwdpW0REhEF8UW4LRlpaGklJSTg6Oubpa+7cucTExHDw4EEsLCwA6NSp0wvJkVPfJiIigmrV9GmhtVotMTExBkoLgKOjI4mJiaSlpWFubg7AvXv/+G/eunWLWrVq0bVrVzIyMti9ezeTJk3C09NT6js369evZ9asWYwZM4aBAwe+kLwvwty5cwkKCsLCwoJr167Rs2dP1Go11atX5+rVq/j6+hbYWDJFi2wBkZGReSY6UYdfaa8iG6+UwoyhZbpTzdQF4eJ5hF3bZOXjOYhOzqQkgPb1584vNy6FU4jwSdLu30EoYwzGhTOW9g+9K1buFeDXRa+EXEanExFpARR05p4YFGxCQTrupuZUVRsXcP/P5qFOx9FHqVzOSMfKTIGDpVGhWj80Gg07d+5k3rx5jBs3Tpqc55CWloZWq8XUVJ+2+fr163zzzTfSsU+jc+fOXL58mXXr1kkZt16Wnj17smTJEm7cuAHA0aNH6dChg0EQ9g8//MCdO3dIT0/n888/p3LlylIAtlqtJiVFn+ktNTUVY2NjjIyMyMzM5Pvvv+fq1asvpBiXKVMGPz8/Zs2aRXx8PBkZGcydOxetVkvz5s0N2rq7u1OpUiVmzZpFeno6d+7c4fvvv5f2Hzp0iFGjRhEVFYWJiQlWVlYolcp8Xaf27NnD9OnTWbRoUYEqH+fOnSMmJkZy5XJxcSE8PJz09HQuXbpkYFWSefOQFRAZGZlnohAUlFPb845pQdU4eDo1TCsxyL4r5qIaDu6DU2Gg0xX6uG80Dg4IalWRu18Beg2kkC0gAA9u6LPvKMoXfDA6ABk6dJdSKFWqFHZ2dgXWrVar5dKlK+h0AiItAasC61tPBgIbQYyiktqY+qZmqIo4SDcyS4MGXaEUHZw2bZpUB8TX15f//e9/hISE0K9fvzxtK1euzMSJE/n444+pX78+Y8eOpXv37qhUKq5evfrUMWxsbPDz8yMqKorWrVu/kpz9+/ena9eujBgxAnd3dz777DOCg4Np0aKF1KZ+/fqMHDkSHx8f4uPjWbZsmZThqVevXowfP5758+fz4YcfkpGRQePGjfH39+fcuXN06dLlmeeQmy+//JIKFSrQrVs3GjduzJUrV/jpp5/yZHszMjJi2bJlxMXF0bhxYwYPHmwgb79+/WjWrBnvvvsu9erV47///S/z58/HwSFvXNPixYvRarWMGTNGul/u7u4EBwcDsHXr1jzZrp6HVqtl9uzZTJ06Vdo2YcIEDhw4gK+vL56enrRs2fKl+pQpWQiiXKpURkbmOYiiiEbM4oe4zTzSFU6UcwcrX6qbukBiAsKhA5BasLUf3lpat0NXthznjwiIRayr1WwqonkYQdyf+57f+DWp0OQ9jDRqsg/FFdoYRs3soZSSK1eukJmZWWD9qlQq3nnHFUHQIrAHeFhgff9DDXTURyOKnEt/xEOdthDGyEtFtTHV1MZvdHaizz//nIyMjKdmeXpd/P39GTVq1CtbWGRk3kZkC4iMjMxzEQQBlaDEv3SDAu/bXGHKkDIBuJpVRLh8EWHHVln5eAnEMg48iKXIlQ8AsQhiQHJIibmKYKkCy8ILXdQeTwCRAnXFAn3K08uXryKKSkRaAxbPPebluYyC3agFLQ3MzHF6Ir1pYWAiCFR5g5WPu3fvEhoayubNm3n33XeLWxwZmX8VsgIiIyPzQigEBdVNK1LFpOBSllYzcWGwfTcsRGM4dADCQmWXq5ehclUEpRFJ94pnAiiKoFAUTS6TB7fDEXVi4blhAWh06P5OxszMzCBbUoF0rdFw5cpVRFH1WAkxL9D+9SSgYAMCydQyMaOmsWmhvuRrGJsWRdx5obF27VpGjBhBv379eOedd4pbHBmZfxWyC5aMjMwLoxNF0nUZ/BC3GY34ehHPba189HElD5IQDu6HlMJwS3nL6dSV7FK2XDgq6OMxipgajYHseGJObnlu24KgnHc31GorsvfcK9TzNWpqh2Ct5sqVK1IF6oLC1NSU6tWrAukI7AUKq6aNNyJVSdFpOZf+iIwCftWXVaqoa1qIyqCMjMxbjWwBkZGReWEUgoCpwhhfy/qv3IeZwoRB9t2oaVYFrl5G2L5FVj5eBYUC0dpWb/0opmUkna7w64DkJvnO3wjGRgh2hZvxSRt6H1En4uLiUuDuRenp6Vy7dgMwRaQVUAj1TQAIQ+AYFgoFjcxLYWtUcJYqtSBQ08SkUALPZWRk/h3ICoiMjMxLoRAUuJlXp7y67EsfW8W4AkPsA7AUTOHIQYTQP0BbNMGybx01ayMoBJLuFp8Iog4EoegUkLR71xG1WhQVCnnlPRu04Q8wMTGhbNmX/54/j0ePHnH9+k3A/LESUlgK1R0UbEVJJh6mZlQqoFS9NYxNMEJ4Y2M/ZGRkih9ZAZGRkXlpdKKONlaNUfLik89WpRvR2doPRUoKwtZNcOtmIUr49iNWdyXzkUh6Mcbri2LRWkAA0pNiEMqZgrKQJ7/R6Yj3MylbtixmZgWv8KSlpXHz5i2g1OMUveoCH0NPKgIbEIijmrEJ7qZmr1WBuKxSiYNKXegVz2VkZN5uZAVERkbmpVEICkoZWdCoVN3ntjVRqBlg35U65tXg+jWEbZvhYXLhC/k2o1aDhWXx1P7IhaijSOqA5CbpxllQgOBoWuhjaU8kIGpFnJ2dC2W1PyUlhdu37wClHxcrLKzMVSKwD8S/sDNS0sjcAotXKCCpd70ylV2vZGRkXhtZAZGRkXklFIJAfYtaOKqeXritkrETw+x7YCWYwdHDCMePyi5XBUFdD7371b3iFUMfA1K0rxFNSjy6rEwUzkUQAK0D7elEjI2NcXR0LJQhkpOTuXMnArBGxB9ewqr48vyFwD6MBfA2s8BB+XIKT20TU9n1SkZGpkCQFRAZGZnXQKSjjR/GQt6JTIvSDehq3RxFapre6nHjetGL95YiVqrMo2QRTXoxy1EMCghAWtxtFHbGYFoE1pfYTMR7Gdjb22NuXhipc+HBgwdERkYBtog0p3CVkDgUbEBBKm6mZrgam7xQKt0KKjV2SpXseiUjI1MgyAqIjIzMK6MQFJgrTGll1UjaZoya/vZdqGteA27dQNi2CZIfFJ+QbxulLMHUlMRidr8C0GmLNgg9h6SbZx7XBCl8NywA7clExCwdLi4uKApJ4UpMTCQqKhoog4gfhft61iCwBcTbOKvUeJmaY/wMxcJMUFDd+PWzXvn7+1OnTh3c3d1xd3enXr16NGnShC+++ALd4/o/ffv2ZdGiRa81zqvyqmMnJSUxefJkfHx88PLy4oMPPuDSpUvPPe7OnTt4e3sTFRUlbYuNjeXdd9/F3d2dvn37cv/+fWnftm3bmDRp0kvLJyNTEpEVEBkZmdcip0BhHbNqOKsdGVa2O9YKC/jjd4SjRyA7u7hFfLtw16dATo4tZjkoPguITpNOdkYKCufCsUjkh/ZkIiqVinLlyhXaGAkJCcTE3AUcEWkKhV7m7xgCYVgaKWhkZoGVUV5lUgHUNTVDgAJxvQoJCSE8PJzw8HDOnTvHypUr2bx5M4sXL37tvouLTz/9lKSkJLZv384ff/yBh4cHgwcP5tGjp9d4OXDgAO+99x4PHjww2P7TTz/h6OjI8ePHsbW15ccffwQgNTWV7777jokTJxbimcjIFB2yAiIjI/PaiKKIf+kGdLdpgVFaur62x7WrxS3WW4lY3pmUBMh+vTqQBSNLTtF6oehfJQ+jLyNYKBGsCitw+wkSNIhRj7Czs6NUqVKFNkx8fDz37t0DyiPShMJXQq6jYAcqIRsvU3OcVYbZuGoYm2KhUBSa65WrqyteXl5cvHhR2nbnzh0GDhyIl5cXLVq0YPfu3dK++/fvM2HCBHx8fGjSpAnBwcGkpqYCEBYWhr+/P0uWLKFp06Y0aNCA0aNHS/sB1q1bR4cOHfDw8KBTp05s3bo1X7muXbtGnz598PLyonnz5kyaNMmgnxxEUUQQBMaOHYu1tTVqtZpBgwZx//59bt++nW/fixcvZt68eYwbNy7PPqVSKfULYPRYKVy4cCF9+vTB1tb2WZdTRuaNQVZAZGRkXhtBEBBEAUQRYdd2SEosbpHeTsqURVCr9MUHSwCPvWaKPBUvwMM75xF1OoTCrgmSC+3ZB+gytTg7O0sTw8IgNjaWuLg4wBmRRs9t//okI7AegURqmJjiZmKKEVBOqaK8Wl1oQedZWVmEhYVx4sQJfHx8pO1//PEH48ePJywsjICAAP7zn/+QlZWFTqdjxIgRKBQK9uzZw7Zt24iLiyM4OFg6Njo6mtjYWPbt28e6desIDw/nl19+AWDjxo3MmTOHKVOmcOrUKT755BNCQkLYt29fHtlCQkJo1KgRJ0+eZMOGDVy8eJF169blaScIAt988w01a9aUtu3evRszMzMqVaqU73kHBgayfft2GjXKe2/79etHUlISzZs3JzU1lf79+3P58mX++usv3n333Re/uDIyJZyCK40qIyPzr0ahUOhnpN6N4PCB4hbn7aSuOzqtyMO4EqKAPE5oJiiMELVFbZLRkZlyH+MKdujOJxdZNXht6H2UfmVwcnIiIiKi0Ma5e/cugiBgb18JES0CYYU2lh4dsAtED8oq36GUuRGmgkJa4S8oQkJCmD17tvS3g4MDAwYM4P3335e2tW/fnlq1akn///rrr0lISCAuLo4LFy7www8/SAkBJk2aRNu2bZk6dap0/MiRIzExMcHFxQVvb29u3boFwIYNG+jVq5c08W/UqBG9evVizZo1tGrVykBOY2Njjh49SpUqVWjUqBFbtmx5ofifAwcOMGvWLKZPn46paf4xSs8qbmlnZye5XYHeEjJq1CimTJnCrl27WLlyJWq1mgkTJuDp6flceWRkSiqyAiIjI1NwKBRQsRLUrA0Xzxe3NG8dYllHkuP+sTwUN7kVkOLgwa0/cajXCqGsCeK9jKIZNDkb3e00bCrZkJycTHJy4dW0iYmJQaFQYGdXDUQtcLrQxvqHswgkYiroLRIFbf2YNm0aAQEBz2xjZWUl/V+l0rvYZWdnExUVhVarxc/Pz6C9Wq0mMjJS+tve3t7g+Bx3pvv371OhQgWDY8uXL8/BgwfzyLBgwQIWLVrE/Pnz+eijj/Dw8GD69OlUq1YtX5lFUWTJkiUsX76c2bNn0759+2ee44uyceNGqlevTvny5fnggw/Yvn079+7dY8yYMRw9elROiSzzxiIrIDIyMgWPVwO4Hw9xJSBS+m2hYmUEpVGx1/7ITXErIOn376DLzkZRwQxtUSkggO6vZBQOJlSoUIG0tDSyCzHRQlRUFAqFAhubGo+VkPBCG0uPAFRBgVgssT3PwsHBARMTE8LCwiQXOI1GQ2RkJC4uLpw5c+aZx5cvXz6P1SoyMtJAYQHQ6XRcvHiR0aNH88knn3D37l0+//xzJk+ezIYNG/L0m56ezrhx47h27RqrV682cMd6HZKTk/nxxx9ZvXo1t27dwszMjHLlymFnZ0d8fDxJSUnY2NgUyFgyMkVNyXq6yMjIvB2IgH8rMLcobkneHmrXITtLJKUEhdf8EwNSfK+S9MQoBAcTUBXtSnD28QSMFEZ5VtQLg4iICH22JKEW4FbIo9UFHEqc8gHg5uaGi4sLc+bMIS0tjYyMDGbPnk3//v3RvkCB0x49evDbb78RGhqKVqvlxIkT/Pbbb3Tv3t2gnUKhYNasWSxYsIDMzExsbGwwNjbG2to6337HjRvHvXv32LBhQ4EpHwDz5s1j0KBBWFpa4uTkxMOHD7lz5w7h4eGULl3awFIkI/OmIVtAZGRkCh6FAtRqaN0WdmwFjaa4JXqzUSgQbexIihKKLNbhRdA9XvgvLgsIQNKNM5jZu6BwMkV3++lpTwuc1Gx011MoXb001tbWJCUlFepwt2/fpnLlylhauj22hFwohFGcQahdCP0WDEqlkqVLl/LFF1/QunVrMjMzcXNz44cffsDY2Pi5x7dr147U1FRmzZpFTEwMZcuWZeLEiXTt2jVP2wULFjBz5kyaNGmCTqfDy8uLmTNn5ml34cIFDh06hFqtpnnz5gb7li9fjqenJ4MHD6ZcuXLMmDHjhc/177//5ubNm4SEhAD62JBJkybx7rvvYmJiwpw5cwqtJo2MTFEgiK9bWUhGRkbmaeh0ejesvbtKTuDCm0itOuDlzbWT8OhhcQvzD5Z2UKkexJzaSmZyXLHJ4dy0D8IjI7S/xxf52EYtyiCaKrh8+TJZWYUfiF+lShV9GmDxNHC5AHu2BVoDCpDjCmRkZAoZWX2WkZEpPBQKKOsATXyLW5I3GrF6DTTpYolSPgC0xRwDkkNq7A0U1mowL3o5tMfvoxAEnJ2di2S8GzdukJaWBoInkH9A9MtjAfgDgqx8yMjIFAmyAiIjI1O4CAJUrgrucsrIV0KlhlKWJN4teRPDkuCCBfDg5llEnYiifNHVBJFI16G7lEKpUqWKrEjctWvX9FW2BW+gymv2pgZaAKoSGfchIyPzdiI/bWRkZIqGuvWgumtxS/HmUbcegkIoUdmvcvgnC1bxvkp02Rqy0pNRuBSDAgLorqeiS9bg5OSEWq1+/gEFwNWrV0lPT0ekIVDxFXsxQm/5MJeVDxkZmSJFfuLIyMgUDaIIjZqAU/niluSNQqxclUcPRTRFGF/9omhzLCBC8VpAAFIiLyKYKhFsikYBeBLt8QQEEVxcXIpszCtXrpCZmYmID/Cy2bgEoAlgKysfMjIyRY781JGRkSkacnzLm7eEInJVeeOxKAWmpiSVQPcryJ2Gt/gVkIdRFxG1OhQViscKgkaH9nwyZmZmeepKFCb64HcNIk0Bp5c4siFQXo75kJGRKRZkBURGRqboEAR9YHqrdvrJtcyzcfcA4EEJreeYEwNCCVBAADIexiKUNy22N5t4+xHigyzKlSuHiYlJkY178eIlsrKyEfEDHF/gCHcQqsjKh4yMTLEhKyAyMjJFS06NkPYdoZSshDwLsbwLqYmQXYLLqIiiWOwxIDk8uBmOoFQgOJgWmwza4/GIOrFIXbFAbwnJzs5GpBlQ5hktaz4uaCgjIyNTfJSMt4aMjMy/C4UCTEyhfScoZVnc0pRM7MsgGKtLrPuVhCgiKEpGTduMpBh0WVkonIvJDQsgG3TnHmBiYoKDg0ORDavT6bh06TJarQ4Rf8Aun1Y1QPAoMplkZGRknoasgMjIyBQPCgUYm+iVEMvSxS1NyaOuOzqdSHLR19Z7aUqKBQTg0f0IhDLGYFx8MolR6YgJGsqWLYupadFZY3KUEJ0ORFoANrn2vvO4dsjr4+rqSlhYWL77vvvuOwYPHlwg4xQlffv2ZdGiRcUydkxMDO7u7sTExOS7f+PGjfj7+xf4uJmZmXz22Wf4+vpSv359AgMDOXHixFPbX758mQEDBtCgQQN8fHyYOHEiiYmJAKSlpTFkyBDc3d0JCAjgxo0b0nFnz56lb9++BS6/zJtNyXlryMjI/PtQKMDYWO+OVdqquKUpUYgO5UiO+yfVbUlF74JVMmJAAJJunAZA4VR8blgA2tD7oNW7YglFGGuh1WofKyECIi0BK/RuV/WLZPygoCBWrFhRJGO9LZQrV47w8HDKlStXpOPOnTuXs2fP8ttvv3Hy5EkCAwMJCgrKVxHKyMhg8ODBuLu7c+zYMbZv386DBw/45JNPANi8eTOpqakcO3YMb29vFi9eDOi/j5999hnTpk0r0nOTKfnICoiMjEzxolCA2hjadQQrq+KWpmTgUhFBaVTy3a8eU5IUkOyMFLSZj1A4mxevIDrIPpOIsbExjo4vEhhecGRnZ3P58hVE0QiRtkXqdrVo0SKD1e4dO3bQpk0bPD09GTRoEFOnTmXy5MkATJ48meDgYIKCgnB3d6dFixasWrVKOvbGjRsMGzaMZs2a4ebmRvv27Tl06BAAUVFRuLq6sm7dOvz9/alfvz4DBgzg3r2nF8xZtWoVzZs3x9vbm3HjxjF69Og8Vo/Y2Fhq1qzJ2bNnpW3379+nVq1aREREkJ2dzcKFC/Hz88PDw4M+ffpw+fJlQD9J//LLL/Hz88PLy4u+ffvy119/Sf388ssvtGzZEk9PTzp16sS6desMziUqKko67759++Lu7k6nTp24ePGigYwXLlygb9++eHl50bp1a3788UdEUZTkHzx4MA0aNMDX15dRo0YRFxeX7/XIzMxkzJgxODo6YmRkRM+ePVGr1Vy4cCFP25iYGGrUqMHIkSNRq9VYW1vTq1cvTp06BYBSqXfDzJFD8dgq+vPPP9OwYUOqVq361Psi8+9EVkBkZGSKn5zA9HYdwcq6uKUpfmq7kZ0lkpJY3IK8GCXJBQsg9e41hNIqKFXMsSn3MhFjM7C3t8fcvGgVoqysLBISEhGK8TUfHh7OpEmTmDRpEidOnODdd99l48aNBm02btxI3759OXXqFEOGDGHOnDnExurTvo0ePZrq1auzb98+Tp8+TZMmTZg+fbrB8YcPH2bz5s3s2bOH+/fv8+233+Yry44dO1i8eDFfffUVx44dw9PTk7179+ZpV7ZsWXx8fNiyZYu0bevWrbi7u+Ps7MySJUvYvn07K1eu5NSpUzRo0IBhw4ah1WqZPn06x44dY9WqVfzxxx+0bNmS/v37ExMTQ2RkJJ9//jnLli3j9OnTTJw4kZkzZ+ZRDrKyshg2bBjVqlXjxIkTzJs3j/3790v7Y2Nj+eCDD2jbti3Hjx/n22+/5ZdffuG3334DYN68eTg4OPDHH3+wc+dOHj16xLJly/K9JjNmzMDPz0/6OzQ0lJSUFGrUqJGnbeXKlVmxYgVGRv8sNuzZs4datfQJDTp37oydnR0tWrTg3LlzjB07lri4ODZs2MDIkSPzHV/m303JemvIyMj8e1EoQPVYCbG2eX77txjRxo4H9wQQi1uS5yOKJaMQYW6Sbp1D1InFVxMkF9qwRMRsHS4uLtKqcFHg5OSkr0dSjEUGN2zYQOvWrfH390epVNKqVStatmxp0Mbb2xsfHx+USiXdu3dHq9USEREBwNKlSxk9ejSiKBIdHY2lpaWknOQwZMgQLC0tsbOzw9/fn9u3b+cry/r16+nVqxceHh6oVCr69OlDnTp18m3bvXt3du/ejUajTz+3adMmunfvLv1/8ODBVK1aFSMjI4YPH87ChQvJyMhg+/btjB8/HhcXF9RqNR988AGVK1dm+/btGBkZIYoia9as4cyZMzRq1Ihz585RpoxhxrLw8HDu3r3LxIkTMTY2plq1agwYMEDav3XrVqpUqUKfPn1QqVRUrVqVQYMGsXr1agCMjY05c+YMO3bsIC0tjRUrVjBlypTn3qtz587x4YcfMmrUKCpUeHZRS1EUmT9/PocOHeLTTz8FwNTUlEWLFhEWFsavv/6Ks7MzX3zxBWPGjOHPP/8kMDCQ7t27s2/fvufKIvPvQFZAZGRkSg4KBahUeiXEruiKuZUoatZCMFKQ9HRPkpKFKJQoFywAdNlkpSWWCAUEQHsyEZVKVWQ+/s7OztjZ5ZcFq2i5e/cuTk6GxRGfnNzmLtqoUqkAfTA96IOeu3fvjq+vL1OmTOHKlSuSi08Ouc9TqVTm2f8ysuSQE/B95MgRLly4QHR0NG3atAEgPj7e4D6q1Wrq1atHWloaWVlZlC9f3qCv8uXLExUVRbly5fj555+Jjo4mKCiIBg0aMHv2bDIzMw3ax8bGYm1tbVBHxtnZWfp/dHQ0Fy5cwNPTU/p88cUXkuvZlClTaN++PStXrsTPz4+AgABOnz6d73nmsG7dOgYMGEBQUNBzrRWpqamMGTOGbdu28b///Q9XV9d82504cYKUlBRatGjBhAkT+OSTT/j666+ZPHkyDx48eOYYMv8OZAVERkamZKFQgFKpV0JcKha3NEWP6ztoMkQeJRe3IC+GSMmKAcnhQcR5BBMjBHvj4hYF7msQo9Oxs7OjVCHWvhEEgYoVK2JtbV2kge9Pw8nJKU9A89MyPT1JbGwsY8eOZdy4cZw4cYLVq1fTsWPHIpFFrVbTqVMnduzYwY4dO2jXrh1mZnpl1tHRkbt370pts7KymD17NqIoYmxsTGRkpEFfERERlClThoSEBLRaLd988w1hYWEsW7aMnTt3sn79eoP2jo6OJCYmkpaWJm3LHdfi4OCAt7c3p0+flj4HDhxg06ZNAFy8eJFevXqxbds2jh8/Tv369Rk1alS+56nVagkODuarr77im2++MbC05EdERATdu3cnNTWV9evXP1X5yMrK4ssvv2Tq1KkkJiZKMTROTk6YmppKFi6ZfzeyAiIjI1PyUCj0n+YtoY5bcUtTdKjUiKVKkxRT/JPHF6YkWkCAtLvXELXakmMFOZOELlOLs7OzgR99QWFkZESVKlUoXbp0kSgfiYmJ3Lt3z+CTnZ1t0CYwMJB9+/Zx9OhRtFotR44cyTfuIj/S0tLQarVSGuPr16/zzTffAEiuUS9Dz549Wbt2LX/99RfZ2dls2LCBc+fOPbV9jx49OHr0KPv27SMgIEDaHhAQwMqVK7l16xbZ2dksXbqU/fv3Y2NjQ/fu3Zk3bx537txBo9Hw008/cf36dTp06EBMTAwDBw4kNDQUhUJB2bJlAbC2Nox5c3d3p1KlSsyaNYv09HTu3LnD999/L+3v1KkT586dY+vWrWRnZxMXF0dQUBBz5swB9GmQZ86cSWpqKpaWlpiamuYZI4fPP/+c33//nQ0bNtC4ceNnXr/k5GQ++OADPDw8WLlyJTY2T3eT/eGHH2jRogUVKlTAxsYGS0tLwsPDuXPnDikpKXksUTL/TkpG9SgZGRmZJ8mZRNVvoK8TEvoHPHbNeGtxq4ugEN4c9ytALKEKCED6g7uYlnOCPx+AtvgDarQnElD62uPk5FSgq8DGxsZUrlwZtVpdZJaPDz/8MM+2nTt3Gvxdp04dQkJCmD59OklJSXh6etKoUSPJ1epZVK5cmYkTJ/Lxxx+Tnp6Og4MDPXv25L///S9Xr17F6iUz5rVp04aIiAhGjBiBRqPB19eX2rVrP1WWGjVq4OzszKNHj6hf/58UxoMHDyY7O5tBgwaRnJxMnTp1WL58OSqViokTJ7Jo0SL69+/PgwcPcHV1ZeXKlVSqVAmA4OBgpk+fTlxcHKVKlaJ37960a9eO6OhoqX8jIyOWLVtGcHAwjRs3lgK7cxQ3JycnVqxYwdy5c5k1axZGRkY0a9ZMisWYMWMGISEhtGjRAo1GQ+3atVm4cGGe80tMTGT16tUYGRnlsSyFhITQuXNngoODiYmJYcWKFWzcuJGYmBh27drF7t27DdqHh4dL/7979y47d+5k7dq1gD4b1meffcbHH3+MTqfj008/xdbW9oXvm8zbiyA+zWFSRkZGpqQgihB7Dw7ug1dY/XxTEAPfI0NnxtWwN8cC8k4T0D6K5t7ZXcUtSh6MS5ehnFdnss8mIkamF7c4ACjqWmFU0Zxbt26RnPz6fnYWFhZUqlQJhUJRItyucnPr1i10Oh1VqlSRto0ePZrKlSszbty4IpXl8uXLlCpVymD1PSAggHfffZeePXvme8yoUaNwc3Nj6NChRSWmjMy/BtkFS0ZGpuQjCFCmLHTsAoXoQ1+smJuDmRmJb0jtjxxEsWTGgABkJseh1WSiqFDMNUFyofvzAbqMbCpUqCDVTnhVbG1tqVKlSolUPkDvNvXBBx9I1p6wsDCOHj1qkPq1qDhx4gRBQUHEx8cjiiI7d+7k+vXrNGrUKE/byMhI9u3bx/Hjxw3cr2RkZAoO2QVLRkbmzUChAItS0LErHNgLcbHPPeSNop7ezePBG+R+BSDqSq4CApAWf4tS5VzBRAEZJcOFT3s8AWWzMpQvX/6paWOfhSAIlCtXziCDVEmkVatWXL9+nX79+pGcnIyTkxMzZ87Ew6PoCiPm8P777xMdHU23bt1IS0ujcuXKLFmyJN9MWIsXL+bAgQN88sknJSKbmIzM24jsgiUjI/NmkRMHcux3uHm9eGUpQMT3+pKapubm2ZK3kv0sqjcEI5KIPrGhuEXJF4XaDOem76G7+BDd9dTiFkdCUdMSo2qluHPnDklJSS98nFKppFKlSpiZmZVIq4eMjIzMiyC7YMnIyLxZKBR6lyzfZtDQBwoho1CRY2sLajVJb5j7FZR8C4hO84jsjFQULiUjG1YOuosP0aVlU758+RcKygYwMzPD1dVVVj5kZGTeeGQFREZG5s0jZ/LlWgM6dIZSlsUrz+tSrz6iCMlxxS3Iy6NXQEr2qyQl+gqChQpKv9hEv6jQ/hGPQhCeW3ka9PEe1apVQ6lUysqHjIzMG0/JfmvIyMjIPAtBACtr6NwNXCoVtzSvjOhQjofxoNMWtyQvT0kOQs8hOeIvRJ2uxNQEkUjXobuSgqWl5VNTkyoUCpydnSUlRVY+ZGRk3gZkBURGRubNJqdyevMW0MhH//83iQouCCrlG+l+BY9DcoQS/irR6dCkJOgVkBJ2mXVXU9E9zMLJyQm1Wm2wz8zMjBo1akiF5GTlQ0ZG5m2hhL81ZGRkZF6AnIlZNVe9NcTmDSp0VccNbZZISkJxC/JqiNqSbwEBeHDnTwS1AqGMSXGLkgft8fsAODs7S9scHByoVq0aKpVKVjxkZGTeOmQFREZG5u1BStXbBWrVKW5pXgjR1p6kWIE3NR+hTgdCSbeAAI/ibqPLzkZRwbS4RclLpg7d+YeYm5tTrlw5qlevTtmyZREEocCUD39/f+rUqYO7u7vBZ+DAgQD07duXRYsWFchYhUVwcDDBwcFP3e/v78/GjRtfut+oqChGjRpFw4YN8fb2ZsSIEURGRj5Vhiev4TvvvMOgQYMAuHr1Kp07d8bd3Z0RI0aQnv5PAczvvvuOr7/++qXlk5F5Gyn5bw0ZGRmZl0Gh0H+8vKFN+5JduND1HQQjBQ/uFrcgr86bEISeQ3pSNIKjKahKnkVBvJUG6VrKlCmDqalpoVg9QkJCCA8PN/h8//33BT5OYTFjxgxmzJhR4P2OHDmS0qVLc/DgQQ4ePIiVlRUjRox4qgy5r9+iRYuwtLRk8uTJAHzzzTf4+Phw7NgxkpKS2Lx5MwDR0dHs2LGDYcOGFbj8MjJvIm/GW0NGRkbmVSjrAF17QB23f9y0ShLv1ESTIZKWXNyCvDo5gfNvghtW0o3TIIBQroRZQcyMMGpih2CmRBTFYne5Sk1NZcqUKbRu3Zp69erRtGlTvvvuOwDWr1+Pr68vOt0/RR1/+eUXOnToAEBSUhJTp06lSZMmeHt7M2zYMKnYYlRUFK6urqxbtw5/f3/q16/PgAEDuHfvn+qb+/fvJyAgAA8PD9q0acOPP/4ojTV58mRpoi+KIt999x1NmjTB09OTL774Aq32nywOp06dIiAgAE9PT1q1asVnn31GdnZ2nnNNTk7Gzs6OsWPHYmZmhrm5Of369ePq1askJz/7h5mYmMiECRP49NNPqVatGoBU3V4URURRxOhxmvDPPvuMCRMmYGxs/OI3QkbmLUZWQGRkZN5eFAp9nRAPL+gSAPZliluif1AqES2t3tjg8xxy5qFvggKSlZqELisThXPJyYalqGSO0r8sgrU+AL24lQ+AuXPnEhUVxfr16wkPD2fKlCnMnz+fO3fu0L59e1JTUwkNDZXab9q0iR49egAwZswYIiIi2LRpE0eOHKFy5cr079+f1NR/ikAePnyYzZs3s2fPHu7fv8+3334LwIkTJ/jwww8ZPHgwJ0+eZN68efzwww+sWrUqj4wbNmzgp59+YunSpRw/fhyVSmWgyEycOJG+ffty+vRpfvjhB3bv3s2BAwfy9FO6dGlWrlxJmTL/PBv27NmDk5MTpUuXfu51ql27Np07d5a2jRgxgjNnztCiRQucnJzo2rUrhw8fRqlU4ufn97xLLyPzr+ENSxcjI1Py0Wq1xMTESGkzMzMzSUpKwsHBoZgl+xcjCGBZGtp3giuX4Oxp0GiKV6Y6dREUAkn3nt+0JCPmLDq/AQoIQOq9G5R2roXWzAgeFV/eY8FahcLNCoWVusisHiEhIcyePdtg2++//46ZmaFCNnr0aIyMjLCwsODevXvSqn1cXBwuLi507NiRzZs34+Pjw40bN7h06RJLly4lMjKSkydPsmPHDuzt7QGYMGEC27Zt48iRI9StWxeAIUOGYGmpr93j7+9PeHg4ABs3bqRFixa0b98egFq1ajF06FB+/vln+vfvbyDjli1b6NmzJ7Vq1QJg7NixrF27VtpvbGzMrl27sLKywsvLiyNHjqB4AVfBX3/9le+//54lS5Y8s11kZCRbt25l3bp1BturVKliIEdmZibz5s1j6dKl/Pjjj2zZsgVLS0umTp1K1apVnyuPjMzbimwBkZF5gucFaz6PcePGSX6/AL179+b48eOvLM/q1atxdXXlxx9/fOU+nsTV1ZWwsLCXPu7GjRsMGjQIT09PmjVrxpIlSwxcMXKj0+lYtGgRfn5+uLu706lTJ3bu3CntP3nyJK1ataJ+/fp8+umnBv1MnTqVDRs2vPyJPYucCurVa0BAYLHXDRGrVCM9RSQzrVjFeG0kF6w3IBAd4MHNM4g6sfhqgqgVGNWzQulbBsFSXxixqKwe06ZN4/Tp0wafJ5UPgISEBMaOHSsFZOdYDnJ+o4GBgezfv5+0tDQ2btyIv78/NjY23L+vz+aVu7CikZERjo6OREdHS9vs7Oyk/yuVereznHGfLMpYvnx5g2NziIuLw9HR0WCccuXKSX//9NNPlClThpCQEOk8cltInkSj0RASEsKCBQtYunQpjRs3fmpb0FtgcgLQn8XSpUvp3LkzqampLFu2jJ9//pkePXrwySefPPM4GZm3nTfjjSEjU8S8TrBmUlLSM/9+WVavXs17773HqlWr8vVhLirS0tIYPHgwjo6O/P7776xevZqdO3dK7hNPsnr1ajZv3szPP/9MeHg4H330EePHjyciIgKAL7/8kn79+nHo0CFOnDjBsWPHAPjrr7+4efMmAQEBhXMiCgUYm+jrhrRsA+YWhTPOszAzA3PzN979Ct4sFywAXbaG7PSHRe+GJTx2t2pZFuGx8iMoSub9Hzt2LLVr1yY0NJRNmzbx0UcfGeyvU6cOLi4u7Nu3j23btknuV05OTgDSbxz+sQjnWESehZOTk8GxoLc05Hesg4ODQaYqURSJi4sD9FaH69evM336dA4fPsz27dtJSUnJY/3JITExkb59+3Lu3DnWr19Pw4YNnyvr3r176dKlyzPb3Llzh8OHD/PBBx9w7do1nJ2dsbCwoHbt2ly9evW5Y8jIvM3ICoiMzEsSGxvLhx9+iL+/P3Xr1qVFixasX78egE8//ZTTp0+zdOlSgoKCGDhwIDExMUybNo0ZM2YwaNAgpk6datDfsGHDWLhwYb5jhYaGkpCQwOTJk9HpdOzZs8dgf04QpJeXF97e3owbN47k5GSWLVtGmzZtDNquXLmSPn365Bljz549dOjQgfr169OuXbunKhRnzpwhISGB4OBgzMzMcHJyYvjw4fz666/SCmZu+vTpw7Zt23B2dkaj0ZCYmIipqSkmJvo6DPkFa+p0OmbNmkVwcHDhrgrn9F3OCbr1gLruoFQV3nhPUs8DgKTYohuysHiTgtBzeBh1EcFMiWCjfn7jAkCwUaNsVgZFndKgFEqs4pFDSkoKJiYmGBkZkZiYyKxZswDIysqS2gQGBvL111+jUCho0qQJAGXKlMHPz49Zs2YRHx9PRkYGc+fORavV0rx58+eO2717dw4ePMiuXbvQarVcvHiR5cuX07179zxtAwMDWbt2LeHh4WRlZbFkyRLi4+MBvUXpo48+4vvvvyc7Oxt7e3uUSqVU0DE3WVlZDB48GAsLC3799dc8Fpj8SEpK4saNG3h5eT2z3axZs/jPf/6DSqXCxcWFW7dukZiYSHh4uEHNFxmZfyOyAiIj85JMmTIFlUrFjh07OHv2LO+//z4zZ84kLS2Nzz77DE9PT4YNG8Z3333H999/T7ly5QgJCSE4OJju3buze/duNI/jD+7fv88ff/zx1NX+n3/+mZ49e2JiYkLv3r3zWGHGjh1Lamoqe/fu5cCBAzx8+JCQkBC6du1KZGQkf/75p9R28+bNecbJyMjg448/Jjg4mDNnzvDVV1+xfPly/vrrrzyy6HQ6VCoVKtU/E3VBELh//z4PHz7M016hUGBmZsaxY8eoW7cun376KWPHjpWCPT/++GPWrl1L27Zt8ff3p3Hjxvz66694eHjg6ur6gnfjNcmpol7PAwJ7wTu19NsKGdG5EmkPIDuz0IcqdN5IBSTyAqJWJ1kiCg0TI4zqW6Nsag8WygKt61GYfP755+zcuRMPDw8CAgIoW7YsNWvWNFi179SpEwkJCQQEBBjEVnz55ZdUqFCBbt260bhxY65cucJPP/2ElZXVc8etW7cuCxcuZPny5Xh6ejJq1Cjee+89goKC8rTt2LEjY8aMYdy4cTRo0IDIyEjpuaFWq1myZAkHDhzA29sbf39/7O3tmTBhQp5+Dh06xIULFzh16hSNGjUycLuNiYkBoEOHDlIWMNBn8wIoW7bsU89l79692NjYSEpKrVq16NWrF23btmX58uWFkk5YRuZNQhDzW7qUkfkX4+/vT0JCgsFEG/4J1oyNjcXc3BwTExPu3r1LaGgoU6dO5dChQ5QrV46+ffvSoEEDRo8eLfU3atQoAgIC0Gg0NG3alJCQENq2bcsPP/zAwYMH+fnnn/PIER0dTdu2bdm3bx8ODg48ePAAPz8/li9fToMGDYiOjsbf35/du3dTqZI+niE+Pp4HDx5QrVo1hg0bhpOTE8HBwVy4cIH333+fY8eOYW5ujqurK6tWraJu3br4+vri5+cnpb5UqVT5Bms+fPiQdu3a0a1bN0aOHEliYiJjx47l77//5vfff3/qy1ij0aBQKDh16hQjRozgs88+k4JMc5OQkMAHH3zAmjVr+O677zh69CiOjo6EhIQ880VfYOQ8Ch890gep37xOoVQHtLZF7NyVqEsCiTEF331RU8oOKteDmFPbyEx+c0w6jp4dMTYvQ/buu5B/GNOro1agqFYKRWVzoOS6WsnIyMgUF7IFREYmH54VrBkZGcnAgQNp3Lgx48eP59SpUwBPDcbOjVqtpmPHjmzZsgXQp6/Mz70A9Ln1s7Oz6dKlC97e3rRp04bs7GzJCpLjbpDjdw1gb28v5aMPCAhg586dZGVlsWnTJtq2bYu5ubnBGCYmJvz666/odDrGjx+Pl5cXkyZNyjf/vaWlJcuXL+fPP/+kWbNmfPjhh3Tt2lXa96xzViqVNGrUiC5durBt27Z823355ZeMHDmS06dPc+TIEdatW0e9evWYM2fOU/suUARB/zEzg6Z+0LU7OLsU/DjuHogiPHhz5urP5B8LyJv1OnlwMxxBpUAoa1JwnSoFFK6lULYqi6KyOYKi5LtbycjIyBQHb9YbQ0ammMnKymLYsGF06dKFsLAw1q5dywcffPBSfXTv3p2jR48SHh5OVFRUnlgN0AdRrl+/ns8++4wtW7ZInyVLlnD48GFu3LghZYDJcRMAuH79OgsWLAD0lheAP/74g127duWr6KSmphIXF8dXX33F8ePH+e233zh//ryBu0EOGo2G7OxsVq1aRVhYGOvWrUOhUFC1alVMTfMWdpszZ04e5UGj0eTrinH69Gnu379Pu3btuHr1KtWrV0etVhdPsGaOi0wpS/BvBR27gIPjs495CURHJx7e/2fi/qaje5wX4U1ywQJIT4xGl5VVMMHoSgFFdQuUrR1QuJZCUCpkxUNGRkbmGcgKiIzMS5CVlUVGRgYmJiYIgkBMTAz//e9/pX2gX/FPSUmRjnny75o1a1K1alVmzJhB+/bt8528b9u2DUEQ6NSpEw4ODtLH19eX6tWr8+OPP1K2bFl8fHz48ssvefjwIampqfz3v/+VMsOoVCo6d+7MwoULsbCwwNPTM884aWlpDBkyhG3btiGKImXKlEGhUOQbrAkwaNAg1q9fjyiKkqLyNAXM09OTNWvWcOrUKXQ6HQcPHmTnzp0EBgYatMvOzubzzz+XgvMrVqzI5cuXSU9PL95gzZwVfRtbaNsBWrd7fUWkfAUElfKtyH6Vg/YNjAHJ4VFCBEIZE1C/4qtQKaCo9ljxqGGpt6i8ATEeMjIyMsWNrIDIyLwEZmZmzJ49m2+++QZ3d3f69euHj48PdnZ20kp9165d2bBhA7179wagR48ezJ8/3yAAMiAggIsXLz7T/apTp0554lAAevXqxZYtW0hISGDu3LlYWFjQrl07WrRogY2NDSEhIXnGeVqQe9myZfn6669Zvnw5Hh4edOzYkYYNG+Yp+gV6Rerbb7+VAsU//PBDhgwZQs+ePaU27u7ubN26FYCWLVsyZcoUpkyZgpeXF9988w2LFi3Cw8PDoN+ff/4ZPz8/KlasKB3n5uZG06ZNOXz4MBMnTsxX9iIjRxFxcNQrIp0DoErVVwtWr1MXbbZIyv2CFbE4eVMtIABJN87o0+M65V0EeCamRihqWaJs44DiHVnxkJGRkXlZ5CB0GZli4MCBA8ydO5ddu3YV6jgPHjygadOm7N+/v2gCuf8N6HR65SMjHS5e0FdWz3yxdFZi34Ek3lUQdbmQZSxCFAqo4w/xFw6Tevd6cYvz0lRo0htFhhLtkfjnthVs1CiqWCA4moAoB5fLyMjIvCrK4hZARubfRFJSEvfu3WPJkiW89957hTaORqPhzp07rFq1Cj8/P1n5KEhyLB8mpvr0vXXd4fo1uHgekh88/TjXGghGCpKeXoz5jeRNK0T4JKn3rmFVsS7aUkpIyafQpwBCOVMUVS1QWKkRdaLe2iHrHjIyMjKvjKyAyMgUIefPn2fUqFE0btyYd999t9DG0Wg0vPvuuzg6OuYbUC5TQOQoI9Wqg2sNiI6CC39DTHTetjVqkpUpkvbg7Zu5iqL4xiogSTfDKe3shqK8GbpLuerZqBUoXMxQVLZAMDGSim3KVg8ZGRmZ10dWQGRkipCmTZsaFAcsLCwsLDhz5kyhjyPzmBxFxLEcOJWH1FS4dgVuXIfUFDAyQixtTVLEWzp5FUUQ3kwFBF02WY+SUDmXRnflIUIZExQuZvrg9Fy3S47xkJGRkSk4ZAVERkZGpqDIUUTMzfWuWe71IS4WMjIQFAJJd4tXvMLkTbWAAKTF38G6kjvKdo4ISoXezUq2dMgUE3fu3MHFpRBqEMnIlCDkLFgyMjIyBY0g/KOM2NlDBX0qYYeqYFUW3uC5er7oXbDerNeJyqw0VpU9KO/TE+tK7og6HYJSfw6FrXzcunWLSZMm4evri7u7Oy1btmTu3LmkpaUV6rgFRXBwMMHBwcUtxnPx9/dn48aNL9Q2LCwMV1fXVx5r8ODBBeLu+sUXX7BkyRLpb3d3d06fPv3a/b4Mq1evpnXr1ri7u9O6dWv+97//PbWtRqPhv//9L76+vnh5eTFy5Eju3v1npWXVqlV4e3vj4+PDr7/+Km3PysqiW7du3Lx5s1DPRabkIltAZGRkZAqTXBNzS1soba8P3H4Yr6+G/vA+iLpilK+AeBMsIGoLG0xty2PuUAXjUraIoo4cP6uiUqDOnj3LwIEDGThwIJs3b8bGxoZbt24RHBzMwIED+eWXXzAyKtnXcsaMGcUtQoljxYoVBdJPUlKSwd/h4eEF0u+LcvDgQRYuXMj3339P7dq1+euvv3j//fepWrUqDRs2zNP+q6++4uDBg6xcuRIXFxcWLFjAgAED2Lp1KxqNhjlz5rBp0yZEUSQgIIAuXbpgZmbGTz/9hK+vL5UrVy7S85MpObxZS1YyMjIybzDC4yeuQqFXRCq6QW0/qFgX7CqAiUXxyvfKiCVTAVGoTDAvWwW7mr44+76PU8MArKt6orawAUAQir5+R3BwMF27dmXMmDHY2OjlqFSpEvPnz8fW1lYqJBodHc2HH35Io0aN8PHxYfz48cTFxQH5r9ZPnjyZyZMnA7Bo0SLGjh3LpEmT8PDwwNfXl127dvHNN9/QuHFjGjRowLfffisde/bsWfr160eTJk2oU6cOAQEBnDt3ThrLz8+P8ePH4+npybJly/KMNWbMGCZMmICnpye+vr589dVXUt+nTp0iICAAT09PWrVqxWeffUZ2dj7Zxl5ADn9/f5YsWULTpk1p0KABo0ePJjU1FdBb4b777juaNGmCp6cnX3zxBdqcKpn5EBcXR1BQEB4eHrRo0YI//vjDYH9ERARBQUF4e3vTvHlz5s+fj0ajAWDjxo0EBAQwcOBAPD092bZtG3379mXRokVERERQo0YNg5X9GzduUKtWLeLi4khNTWXKlCm0bt2aevXq0bRpU8ly8s0337Bt2za2bdtG586dAXB1dSUsLIz169fj6+uLTvfPasUvv/xChw4dAEhNTWXGjBn4+fnRqFEjxo0bx/37/xQcWrRoEX5+fjRo0IDu3btz4MCBfK+Lv78/Bw8epHbt2mRnZ5OUlIQgCFhaWubbfvv27YwcOZJq1aqhVqsZP348sbGxhIaGSoq0lMBBEBAEgXv37rFlyxaGDx/+1Psj8/YjKyAyMjIyxYCkjBiBpR2Uqw6uDfUKiYsb2JYHY7PilfFFESkhCoggYGLlgHUVT8p5d8PZtw9l6jTHwqEqRmqTx02Kr2hgREQE165do2PHjnn22dnZ8e2331KxYkWysrIYOHAgRkZG7N27V6oXFBQU9NTJ+5Ps2bOH5s2bc+bMGTp37sz48eNJTU3lyJEjzJ49m4ULFxIdHU1GRgbDhw+nTZs2/P7774SFheHs7MyXX34p9XXv3j0qV65MaGioVGA1N3v37qVJkyaEhYUxc+ZMli9fLikOEydOpG/fvpw+fZoffviB3bt35zv5fRE5oqOjiY2NZd++faxbt47w8HB++eUXADZs2MBPP/3E0qVLOX78OCqVinv3np7zety4cSiVSn7//Xf+97//8fvvv0v7Hj16RP/+/alWrRq///47v/zyC8ePH2fRokVSmwsXLtCpUyeOHz9Oq1atpO3Ozs54e3uzZcsWadvGjRtp2rQpZcqUYe7cuURFRbF+/XrCw8OZMmUK8+fP586dO4wcOZJOnTrRqVMnqaBrDu3btyc1NZXQ0FBp26ZNm+jRowcAn3zyCXfu3GHjxo3s378fCwsLRo0ahSiKnDhxgt9++41169YRFhZGYGAgn376KVlZWfleGwsLC27evImbmxtDhw7lvffeo2bNmvm21Wq1mJr+U8gz57d169YtTE1NCQ4OZtiwYQwfPpxZs2ZhamrK7NmzGTduHCYmJk+9PzJvP7ICIiMjI1PMCIL+A2Ck0ltHnFyhRmOo5QvOtcHGCdQvWbC7yBCFYlNAlGaWlHJ6hzJ1W+HSrB+Onh0p7eKG2sJGmgyVlPiUxMREQK9sPIvTp08TGRlJSEgIpUqVwtLSkpCQEC5fvsz58+dfaKyqVavStm1bBEHAx8cHrVZLUFAQKpUKf39/AGJiYlCpVPz222/07t0bjUZDdHQ0VlZWxMbGGvTXo0cPVCoVFhZ5zXQVK1aka9euGBkZ4efnh729Pbdv3wbA2NiYXbt2cejQIaysrDhy5Aht2rTJ08eLyjFy5EhMTExwcXHB29ubW7duAbBlyxZ69uxJrVq1UKvVjB07Fmtr63yvTXR0NKdPn2bChAlYWFjg6OjIqFGjpP2HDx9Go9Hw0UcfYWxsjKOjI2PHjmX16tUG8nbp0gW1Wp1nIh0YGMjWrVsRRRGtVsvWrVslRWH06NEsWLAACwsL7t27h7GxMYBk3XoaZmZmdOzYkc2bNwN6q8qlS5fo0qULCQkJ7Nmzh08//RRbW1vMzc355JNP+Pvvv7lw4QLGxsYkJyezdu1aLl68SGBgIKGhoahUqqeOV6FCBf7880/Wr1/Pjh07WLZsWb7tWrduzXfffUdERASZmZksXLiQzMxMMjIyAHj33Xc5cuQIhw4dolu3bhw7dozs7Gzq16/PmDFj6NKlCyEhIU9VhmTeXuQYEBkZGZkSRu4FeqUarMrog9cFAbIyISURUhMhPQUy0/RZcIsTUSyaSb7SxAK1pR3GlvbSR6FUPXbxEBGEnCDykqFwPIm9vT0A8fHxVKxYMc/++/fvY2dnR0JCAtbW1gaTfQsLC6ysrIiOjn6uAgNgZWUl/V/x+HqULl3a4G+dToeRkRFhYWEMGTKER48eUbVqVZRKpeQ2k0OZMmWee145qFQqyVXop59+YtGiRYSEhBAfH0/Tpk2ZPn06Dg4OBse8qBy5x1KpVNL+uLg4HB0dDforV65cvvLmKDW59zs7O0v/j46OJjExES8vL2mbKIpkZWWRkJAgyaF4yvesdevWzJw5k7CwMDIzMxFFkWbNmgGQkJDAZ599xsWLFylfvjy1a9cGMHCtehqBgYH069ePtLQ0Nm7ciL+/PzY2Nvz1118A9OzZ06C9kZERUVFRtG3blkWLFvHzzz+zYsUKTExM6Nu3L8OHD3/qOeQoJ3Xq1KFfv35s27aNoUOH5mk3efJk5s6dS58+fVAqlfTo0YPq1avn67KVE7D+7bff8t1332FlZcXChQsZPHgw69evL9TivDIlD1kBkZGRkSnhCLnmCCpjsC4L1g56hUQUIfORXhnJSIOMVP1Hk16kEhaoBURQGKGysEZtYfPPp5QtRir9arGo00IuVyr9vyU/ba6TkxPVq1dn586dBpNb0E9Mmzdvzueff46TkxNJSUmkpqZKSkhKSgpJSUkGE1+NRoNarQb0wcu5V/xf1M3szz//ZObMmaxZs0aaDH///feSZeFl+8tNZmYm169fZ/r06SiVSm7dusWUKVOYPXs2X3/99SvJ8TQcHByk+BnQKwxPsyrkKD+RkZFUqVIFwMBdy8HBAWdnZ3bv3i1tS01NJSEhQYrbedb1UKvVdO7cme3bt5Oenk7Xrl1RKvXTrbFjx+Lv78/KlStRKpUkJSWxdu3aFzrHOnXq4OLiwr59+9i2bRuzZs0CoGzZsgDs2rXLQEG7fv06FSpUICYmBltbW1auXIlGoyE0NJRRo0ZRq1YtSTHK4ccff+TcuXMsWLBA2qbRaCTl9UliY2MZPny4lBUtOTmZpUuXSvcwNytWrKB9+/Y4OTlx7do1WrVqhSAI1KpVi6tXr77QNZB5eyiZy0QyMjIyMk9FUPxjJREEMDHXW0gcKkGluvCOD7j56124KtaFctXA1gksbEBlYqjQFASiKCC8RCFChdoUdSlbzOycKVX+HayreGJXyw+H+h2o4PMuLs3749SgK3bvNMWy/DuYWDtKyof+/I3e2MKAU6dOZcOGDSxevJikpCREUeTSpUsEBQVRq1Yt2rRpQ506dahatSrTpk0jJSWFlJQUpk+fjrOzMx4eHjg7O6NUKtmxYwcAx48f58SJE68kT0pKCgqFQnIjOnfuHKtWrZICrl8HQRD46KOP+P7778nOzsbe3h6lUpmva9TryhEYGMjatWsJDw8nKyuLJUuWEB8fn2/bcuXK0aRJEz7//HOSk5OJj49n8eLF0v7mzZuTlpbGihUr0Gg0PHz4kEmTJjFu3LgX/t717NmT/fv3c/DgQcn9Kuc8TUxMMDIyIjExUVIiclyQ1Go1KSkpzzzPr7/+GoVCQZMmTQC9AtKsWTM+++wzkpKSpPPv0aMHDx8+5O+//2bw4MFcvnwZtVqNra0tQL73wdPTk/3797Nz5050Oh1nzpxh1apVT7VO/Pjjj0yePJm0tDSSk5MJCQmhVq1auLm5GbSLiopi3759DBgwANC77Z07d46srCz++usvue7JvxDZAiIjIyPzFvCkEUBQ6IPY1aaPXaQEQ9cuUQdaLWizQJsN2Rr9vzl/G/z/8b85WWulvnLHrpiYY2bvgmCkQmGkRDBSojBSolCqMTI2R2ligdLEAiO1qYGLlCiKUsdPuk4JgvDmVlh/Cg0aNOB///sf3333HR06dCA9PR07Ozvatm3LsGHDJNeXpUuXMmfOHNq0aYNGo6Fx48b88MMPKJVKypQpwyeffMK3337LzJkzadiwIQEBAaSnv7zZy8fHh969e9OnTx90Oh3ly5enb9++fPXVVwZZlF4FtVrNkiVL+OKLL1i6dClGRkb4+voyYcKEApejY8eOJCUlMW7cOJKTk2nbtu0z63p89dVXhISE0Lx5cywsLAgICODPP/8E9O5uP/74I3PmzGHFihXodDq8vb0N6nM8j2rVqlGxYkWUSqWBu93nn3/O7Nmz+f777yldujTt27enZs2aXL16lSZNmtC+fXvGjRtHs2bNOHz4cJ5+O3XqxJdffsmgQYMM3Ke+/PJLvvrqK7p27UpqairVqlVjxYoV2Nvb06ZNG27fvs3w4cNJSkrC1taWTz75hLp16+bpv3bt2nz99dcsWLCAKVOm4OTkxKeffkr79u0BfXzSkCFD2LFjB+XKlePjjz9m2rRpUlxRkyZNDDKs5TBr1iwmTpwoWeyGDh3KuHHjaNiwIY0bN+bdd9994Wsr83YgiE86WMrIyMjI/CsRxX/iSQRe3VKSE5OBCI9zZJXYuAwZGRkZmaJHVkBkZGRkZGRkZGRkZIoMeUlKRkZGRkZGRkZGRqbIkBUQGRkZGRkZGRkZGZkiQ1ZAZGRkZGRkZGRkZGSKDFkBkZGRkZGRkZGRkZEpMmQFREZGRkZGRqZY0Wq1BoUEX3RffsTFxfHo0aOCEu2NIDMz06CYooxMSUdWQGRkZN46bt++XdwiyBQBrq6uuLm54e7uTr169fDy8mL48OHcvXu3uEUr0YSFhT2zRsarMnnyZCZPnvxKx44bN47NmzcDEBMTg7u7OzExMXn2PY/79+/Tpk0bEhMTX0mOqKgoXF1diYqKeqH2ixYtom/fvq80FoC7uzunT59+5eNz6N27N8ePHwf0tTrc3d1fu8+XpV27dtStWxd3d3fpc+PGjWcek56eTq9evdi4caO0TavVMnHiRDw8PGjbtq3B9YmMjKRz584FUihTpniRFRCZl+LWrVtMmjQJX19f3N3dadmyJXPnziUtLe2V+3ydl9ar8uRLpqBeAi/Kd999x+DBg5+6v2/fvixatKjI5CkIOV72xf0kwcHBBAcHv9KxuVm9ejVTp06V/u7QoQNbt2597X5fhmXLllGrVi2DF/H8+fPzbSuKIsuXL8ff3x8PDw/69+/P1atXpf07d+6kadOmNGjQgIULFxocO2jQIEJDQwv1XEo6y5cvJzw8nHPnznHo0CFEUeTjjz8ubrFkXpKkpCTp/+XKlSM8PJxy5crl2fc8MjIy3ijrR3h4OJ6enq/dT+5r5OnpSXh4+Gv3+TKkpqZy69Ytdu7cSXh4uPSpUqXKU4+5du0affr04dy5cwbbjx07xpkzZzh8+DC9e/fmyy+/lPbNmjWLSZMmSQUNZd5cZAVE5oU5e/Ys3bp1w8nJic2bNxMeHs7y5cv5888/GThwIFqttrhFfGUK6iXwogQFBbFixYoiG+9NYMaMGcyYMeO1+3ly5XPHjh107tz5tft9Gc6fP8/w4cMNXsTjxo3Lt+3PP//MihUrmDt3LidPnqRFixb069dPOo+ZM2cSEhLCtm3b+PHHH6UVxV27dmFlZUWjRo2K7LxKOhYWFvTs2ZPz589L21JTU5kxYwZ+fn40atSIcePGSZW1c5TmdevW4e/vT/369RkwYIDkyhIUFGSgRNauXRtXV1ciIiIA/XerU6dO1K9fn4CAAI4dOyaNm1PFu0+fPri7u9OuXTt27twp7b9//z4TJkzAx8eHJk2aEBwcTGpqar7nJYoiy5Yto1OnTnh6euLl5cX48ePJyMgA9Is4wcHBkrwtWrRg1apV0vFxcXEEBQXh4eFBixYt+OOPP556DbOzs5k+fTo+Pj54e3vTu3dvzpw5A8DGjRulite5zzP3IkViYiLDhw/Hy8uLrl278vvvv0v79uzZQ4cOHahfvz7t2rWTKmZ/+umnnD59mqVLlxIUFGSwmPGsfTnkWCG0Wi0dO3YE9JXRc673s+5TamoqkyZNon79+jRt2pQtW7Y89dqA/j3YvXt36tWrx7vvvptnweX48eP06NEDT0/PPIsfkydPZsyYMbRr146GDRsSERGBq6srYWFhrF+/Hl9fX3Q6ndT+l19+oUOHDgDcuHGDYcOG0axZM9zc3Gjfvj2HDh0CYODAgcTExDBt2jRmzJhhYOGaOHEi48ePN5Dxww8/JCQkBICIiAiCgoLw9vamefPmzJ8/X7IupKamMm7cOLy9vfHx8WHQoEFPtWicP38eKysrnJycnnn9cggNDeWDDz6gW7dukqKZg5GREZBT0BSp4vv+/fsxNTXFx8fnhcaQKeGIMjIvSIcOHcRp06bl2R4fHy8OHz5cvHXrliiKonjmzBmxb9++oo+Pj1i7dm2xW7duYnh4uCiKonjixAnR19dX/Oijj8T69euLS5cuFSdNmiQOGTJEDAoKEj09PcUuXbqIR44ckfpPTEwUp0yZIvr4+IgNGjQQhw4dKo21fft2sVatWuKlS5dEURTFCxcuiHXq1BGPHDkiTp06VRwwYICBrCEhIeLHH38sRkZGitWrVxcjIyNFURTF6tWriydOnBBFURQTEhLE8ePHi56enmKDBg3EDz/8UHzw4EG+1+TAgQNir169xIYNG4pubm5inz59JNlEURS3bt0qdujQQaxXr57Ytm1bcceOHaIoiuLXX38tvv/++1K7tWvXiv7+/mK9evXEiRMnit27dxe//vprURRF8erVq2Lv3r1FT09PsVmzZuLEiRPFlJSUfOW5fv26OHToUNHPz0+sU6eO2K5dO/HgwYOiKIrSOa9du1Zs3ry56OHhIfbv31+8e/fuC8nxJCkpKeLEiRNFDw8PsUmTJuLixYsNrml8fLw4fvx4sXHjxqKPj484depUSe6nfQ8mTZokpqSkiPXq1ROPHj0qjZWcnCzWqVNH/PPPP8XMzExxzpw5Ytu2bcV69eqJDRs2FGfMmCHqdDpx48aNYq1atcQaNWqI9evXF0VRFJs3by5u2LBBPH78uFinTh3x4cOHUr+HDx8Wvby8xMzMTDEzM1NcsGCB6O/vL3p5eYmDBw8Wb9++LbVdvXq12KJFC7F+/fpix44dxbVr1+Z7XURRFJs1ayYeOnToqftzExgYKC5atMhgW9u2bcXVq1eLoiiKPj4+4v79+8V79+6J9erVE2/duiWmpqaKHTp0EOPi4l5ojLeV3L9bURTFBw8eiB999JH48ccfS9tGjx4tDhw4ULx//76YmpoqTpkyRezVq5eo0+mk38SIESPE5ORkMT4+XuzYsaM4derUPGM9ePBAbNeunThnzhxRFPXfnfr164snT54Us7OzxYMHD4r16tUTr169KoqiKL7//vtigwYNxAsXLoiZmZnivHnzxPr164sZGRmiVqsVAwMDxY8//lhMSUkRExMTxWHDhonjxo3L9zx37Ngh+vj4SM+W69eviw0aNJC+g5MmTRJr1aolHjt2TMzKyhJ//fVX8Z133hHv3bsniqIo9u7dWxw5cqSYkpIixsTEiF26dBGrV6+e71jr168XO3fuLCYnJ4vZ2dnivHnzxE6dOomiKIobNmwQmzdvbtD+/fffl54RkyZNEt955x1xz549YlZWlrhp0yaxVq1a4p07d8T09HSxTp060v26cOGCWK9ePfHPP//M08+Tz+dn7RNFw+fpk/ufd58+/vhjsVevXuL9+/fFxMREccCAAXn6zyExMVH09PQUly5dKmo0GvH06dOih4eHNPalS5dENzc3cc+ePWJ2drZ45swZ0dvbW/z999+l61OvXj3xypUrYnJysiiK/3yH09LSRHd3d/HYsWPSeD169BC///57URRFsV27duLcuXNFjUYjZmZmip999pno6+srtc15zomi/vmac39PnTolurm5Sc/enGfp+fPnxbS0NLF58+bi3LlzxYyMDDEmJkbs0aOHOHfuXFEURXHhwoXi4MGDxfT0dDEzM1OcNGmSGBQUlO/3ZtmyZWKzZs3EPn36iA0aNBC7desmvXvyIzExUczIyMgjuyiKolarFadOnSp6e3uLnTt3Fv/880/x0aNHYseOHaXvtMybj2wBkXkhIiIiuHbtmrS6lBs7Ozu+/fZbKlasSEZGBsOHD6dNmzb8/vvvhIWF4ezsbGBCvXfvHpUrVyY0NJTevXsDepNrt27dCA0NpX///owYMUJaZRwzZgwRERFs2rSJI0eOULlyZfr3709qaiodOnSgU6dOTJw4keTkZMaNG0f//v3x9fWlR48ehIaGEhsbC4BGo2HHjh0EBAQ881zHjh1Lamoqe/fu5cCBAzx8+FBaLcrNvXv3GDt2LEOHDiU0NJTDhw8jiiLffPMNoPez/uSTT/j44485c+YM//nPf5g4cSLXr1836Cc0NJQZM2Ywa9YsTp06Rd26dfn777+l/SEhITRq1IiTJ0+yYcMGLl68yLp16/KVffTo0VSvXp19+/Zx+vRpmjRpwvTp0w3aHD58mM2bN7Nnzx7u378vrUI+T44nmTFjBnfu3GHv3r1s3bpVWiUF0Ol0jBgxAoVCwZ49e9i2bRtxcXEGLlb5fQ9Av4rdrl07Nm3aJG3bvn07Li4uuLm58dNPP3H06FF++uknwsPD+fbbb1mzZg0nTpygW7duDBs2DE9PzzwudQ0bNqRs2bLs2rVL2rZp0yY6d+6MWq1m/vz5HD58mB9//JGjR49St25dBg4cSGZmJpGRkXz++ecsW7aM06dPM3HiRGbOnElcXFye65KQkEBMTAxr166lSZMm+Pv78+WXX5KZmZnvddRqtZiZmRlsUygU3Lx5E4Dp06czZ84cunfvzvDhw6lYsSKLFy+mV69e2NvbP/X+/FsICgrC09MTDw8PGjRowJEjR+jVqxegvxd79uzh008/xdbWFnNzcz755BP+/vtvLly4IPUxZMgQLC0tsbOzw9/fP08MUWZmJsOHD6dq1apMnDgRgP/973+89957eHl5YWRkRPPmzfH392fNmjXScW3atKFmzZqo1Wq6detGSkoKCQkJnD9/ngsXLjBt2jQsLCywtrZm0qRJ7NixI193I19fX9avX0/FihVJTEwkKSkJKysr6dkGSKvUSqWS7t27o9VqiYiIIDo6mtOnTzNhwgQsLCxwdHRk1KhRT72eJiYmREVFsX79em7dusXYsWNfyoWxefPmtG7dGqVSSdeuXaldu7ZkiTAxMWH9+vWEhoZSpUoVzpw5g5ub2wv3/So86z5pNBp27drF6NGjsbW1xdraWrq/+XH48GFMTU0ZMmQIKpWK+vXr0717d2n/mjVraNGiBa1bt8bIyAgPDw969uzJ6tWrpTb16tWjevXqWFpaGvRtZmZGx44dpViXGzducOnSJbp06QLA0qVLGT16NKIoEh0djaWlpcH9fxqenp44OjpKz73t27dTuXJlatWqxeHDh9FoNHz00UcYGxvj6OjI2LFjJXlNTEy4fPkymzdvJjY2ltmzZ7NkyZJ8xxEEgTp16jBr1iyOHj1K//79GT16dB73qhysra0xNjbOd59CoWDGjBmcOHGCLVu24ObmxpIlS+jevTtJSUn06dOHgIAAfv311+eev0zJRVncAsi8GeS4g9jZ2T2znUql4rfffsPFxYXMzEyio6OxsrLKM5Ht0aMHKpUKlUoF/PPSAujatStr1qxh586ddOjQgZMnT7Jjxw5psjVhwgS2bdvGkSNH6NChA1OnTiUgIEAy5Y4dOxYANzc3qlSpwvbt2xk0aBCHDx/GwsICb29voqOj85U/OjqakydPsnv3bqytrQGYM2cODx48yNPWxsaGHTt24OzsTGpqKvfu3cPa2lp6KWzevJnWrVvj5+cH6CcRv/zyC2XLljXoZ+vWrbRu3Vpypendu7eBgmFsbMzRo0epUqUKjRo1YsuWLZJJ+kmWLl1K2bJln/mSyplsAfj7+0u+ws+TIzc5L+7vvvsOW1tbQG/qz3lZ5kywfvjhB8zNzQGYNGkSbdu2NYjPePJ7kENgYCADBgwgNTUVCwsLNm3aRI8ePQDo2bMn3bp1w9bWlri4ODIyMjA3N3/uy1gQBHr06MHmzZvp2bMnDx8+5ODBg6xduxZRFFmzZg1ff/01FSpUAGDkyJGsXbuWw4cPU6dOHalNmzZtaNSoEefOncv3PsTHx+Pp6UlAQAALFiwgMjKSDz/8kPT0dKZNm5anfZs2bfj5559p1KgRVatWlSZ+Hh4eALRs2ZKWLVtK7a9du8bp06dZtWoVU6dO5c8//6RatWpMnz6dUqVKPfMavI189913eHt7A3r//9WrV/PBBx/w22+/SW6hPXv2NDjGyMiIqKgorKysAMPnmlKplFw/QK9Mf/zxx+h0Ov773/8iCALwz7Mi9yRIq9XSsGFD6e/cCqJSqZT6i4qKQqvVSs+GHNRqNZGRkdKzJwdRFJk/fz6HDh3CxsaGd955h6ysLAM5c4+V83vS6XTS7yK3m4uzs3M+V1JPhw4dyMrKYt26dcybNw9bW1uCgoJ47733nnpMbsqXL2/wt6OjI7GxsZiYmPDrr7/y7bffMn78eFJTU2nTpg1TpkyhdOnSL9T3q/Cs+5SUlIRGo8HR0VHal/P7z4/Y2FgcHR2l7wDor+WlS5eksU6cOGHgzqvVag2ud5kyZZ7af2BgIP369SMtLU1yd7OxsQHg8uXLjBgxgvj4eKpUqYKNjY3B/X8WgYGBbNmyhcDAQDZt2kRgYKAkb2JiIl5eXlJbURTJysoiISGBIUOGoFarWb9+PTNmzKBChQqMHz9eelfn5smYxs6dO7N9UO+K9QAADpVJREFU+3b27NlDvXr1XkjOp3Hz5k3++OMPfvvtN3r16sWAAQPw8/OjTZs2eHl5UbVq1dfqX6Z4kBUQmRci5+UWHx9PxYoV8+y/f/8+dnZ2GBkZERYWxpAhQ3j06BFVq1bN80KHvA/hp720cny1c78UjIyMcHR0lJQIMzMzunfvzty5cxk5cqTkPwoQEBDA5s2bGTRoEBs3bqRbt24GL48niY+PBzDwY7W3t893pVmlUrF9+3bWrFmDIAhUr16d1NRUaaIRFxdHzZo1DY7Jb7UvNjaWWrVqGWzLfb4LFixg0aJFzJ8/n48++ggPDw+mT59OtWrV8vT1Ii+pp022nidHbp734n7eBCuHp72M3d3dKV++vPTyunz5MsuXLwf0WVNmzJjBqVOncHBwoGbNmoiiaOA7/TQCAgJYtGgRkZGRHD16lGrVqlGjRg0SEhJ49OgRY8eONVAqsrKyiI6OlpSEFStWEBQUhFarJSAggI8//jjPKl6NGjUMVjyrVKnCiBEjmD59er4KyMCBA0lPT2fkyJFoNBratWtHkyZN8qyQ5jBz5kymTJnCxo0buXv3Llu2bCE4OJilS5cyYcKE516DtxkTExMGDRrEsmXLOH78uBT7s2vXLoPf8PXr16lQoYL0e38Ws2fP5tKlS/z2228G99rBwYGuXbsydOhQaVtMTAwmJibP7dPBwQETExPCwsKk55VGoyEyMhIXF5c87efOnUtMTAwHDx7EwsICgE6dOj13nJyxQJ89KCcg+FnpWm/dukWtWrXo2rUrGRkZ7N69m0mTJuHp6YlCociTfehJi82TVsHIyEhq1apFamoqcXFxfPXVVwBcunSJjz76iO+++45Jkya90LnAP/EBWVlZT5UhN8+6TxYWFhgbGxMZGUnlypWBZ18bBwcHoqOj0el00nMid3sHBwe6detmEMsWFxdn8Ax+1vunTp06uLi4sG/fPrZt28asWbMA/bN57NixLF68WIrB2bNnD3v37n1qX7np1q0bCxYs4Pjx41y5ckXyZHBwcMDZ2Zndu3dLbVNTU0lISMDGxoYrV67g7+9P//79SUlJ4ZdffmHcuHGcOHEiz2LHypUrqVmzpkFMmub/7d17UFRlHwfwrxtBNGKNNkI5E07NOFOYLLvrDgEhS4ROgnJZwSk1qHFIp0ZiBnDExhIKB+SiGKggmeXUIATIxSlNh8LUooDoQtLVEmmAYYDdiev+3j+YPa8Ly6V33tk3e7+f/zh7OJxzduF5vud5nh8jI9OOcvwVGRkZ2LVrF5ycnNDR0QEvLy+4ubnh/vvvR0dHBwPILYpTsGhOlixZgmXLltksorTq7e2FwWBAbW0tWltbkZ6ejry8PFy8eBFvvfWW3QVjk/8I22u0lixZogQB63QsYOKJUmdnp9KhuHbtGoqKirBhwwZkZWXZNAjr16/HTz/9hObmZly8eHHW6VfWDrW1/CMw0VnJz8+fsu+ZM2fwzjvv4O2330ZDQwOKi4ttAse9995rcxwAKC0txZdffmmzzcPDY0qNe+s1WCwWfPvtt3jxxRfx4Ycf4vz581i0aJHdqmHWRsraQJw8edLulLnpzHQek1mHz2/ef3JDbO1gNTU1oampCZ9++imqqqps7tFMjbHRaERtbS2qq6sREhKiPK3evXs3XF1d0djYiJqaGmRmZs4pfAATYTIwMBC1tbU4ffq0MqpivZ7S0lLlfJuamlBZWYnY2Fj09vZifHwcb7zxBq5cuYKjR4+ivr4e5eXlU37GZ599hiNHjthsGxkZmbZj+scff8BoNOL8+fNobGxEamoq2tvbsXz58in7VldXY+nSpfD29sbVq1fx8MMPY968eVi+fLlN5az/V2NjY6ioqMDAwAC0Wi3c3d0RFBSE1157DX19fRgdHUVRURGMRiMGBgZmPV5xcTHq6+tRXFysPIm2iomJwYkTJ/DVV18BANra2hAVFYXa2tpZj7tixQp4enpi3759MJvNGBoawuuvv464uDi7xTxMJhNcXFxw2223YXh4GKWlpbh69apNJ3w69913HwICApCZmYn+/n50d3fj0KFD0+5/4cIFvPDCC/j9999xxx134O6774aTkxPc3Nzw4IMPoqenB5cvX4aIoLq6esqi5I8++ggNDQ0YHR1FWVkZfvzxR4SHh8NsNmPr1q2oqamBiGDx4sVQqVTKaI+zszMGBwftntPNry1atAh33XUX6urqICL45ptvbDrQ1g6vdUH/TO+Ts7MzIiIicODAAXR1dWFwcBDZ2dnT3pvg4GCICAoKCjAyMoKvv/7aZpTY+jersbERFosFv/zyCzZt2oTS0tJpjznZhg0bcPDgQahUKgQEBAAAzGYzxsfH4erqCmCiTbJO9bUGwpnu38KFC2EwGLB7926EhoYqI04GgwFmsxklJSUYGRnBwMAAUlNT8dJLL2HevHk4deoUUlJS0Nvbi/nz52P+/Pm488477VagunHjBl599VX89ttvGBsbQ3l5OZqbmxEZGTnna7envr4eHh4e0Gq1AABPT080Nzejr68PP//8s93ATrcGBhCas5dffhkVFRU4dOgQ+vr6ICL47rvv8Pzzz8PLywurV6/G4OAgVCqV0tlqaWnBiRMnZq3ZPV2jtXjxYqxatQoZGRno7u7G0NAQ9u/fj/HxcRgMBoyOjiIpKQlr165FRkYGVq5cqUyXACYaq1WrVmHv3r3Q6XRTqm1M5u7uDn9/f2RlZWFgYAAmkwnZ2dl2/wnWzdcqIvj4449RVVWldAoiIyNx9uxZpTH65JNPUFBQMOXJUXR0NM6dO4cLFy5gbGwMlZWVaG1tBTAxFzYjIwP5+fkYHh7GwoUL4eLiMmWKBjC3RmomM53HZLM13H+1g2VPREQEWlpaUFVVpUwZAP7dGVOpVDCZTMjKyoLJZFLuu4uLC0wm07TTE2JiYlBWVobvv/9eeYqsUqlgNBqRk5ODrq4uWCwWVFZWIiwsDL/++is6Ozvx7LPP4tKlS1CpVMo0Onvvg6urKwoKClBTUwOLxYKOjg4UFhYq6xImq6urw/bt29HX1wez2YycnBw4OztPqTY0ODiIkpISJCUlAQCWLl2KtrY2jI6OoqWlZcZpNf9kW7duVapU6fV6nDx5Erm5ucoUtqysLCxYsAARERHw9fVFQ0MDSkpK5rR+JicnB3/++SdiYmJsqmGdPn0aa9asQVJSEnbt2gWNRoMdO3YgLi5uTv8TwsnJCUeOHEFPTw9CQ0MREBCAa9eu4c0337T7xDgxMRFDQ0Pw8/NDcHAwWlpasH79+jmHzpycHLi5ucFgMCA6Ohp+fn7T7rtlyxYEBQVh48aNUKvVyM7ORl5eHjw8PPDII49g27Zt2LlzJ/R6PS5fvozVq1fbfP/jjz+O4uJi6PV6lJWV4dixY3B3d4e7uzsOHjyI4uJiaDQahIWFwdfXF3FxcQAmft8rKips1oNZ3fyas7Mz0tPTcebMGWg0Guzbt89mit0999yDJ554ArGxsXj33XdnfZ/S0tKwYsUKhIeHIzQ0FN7e3tPemwULFuDYsWO4dOkS9Ho90tLSbK7f29sbubm5yM3NxcqVK7Fp0yYEBwdPqUI1k/DwcPT29iIqKkoZZXnggQeQkpKC5ORkaLVa7NixA9HR0bj99tuVz4DRaEReXt60o6AxMTG4fv268tAFmFhvd/z4cVy5cgWBgYEICQmBSqVS1nkkJSXB09MTa9euhUajwfvvv4/CwkK7n9GUlBQEBgbiqaeegk6nw3vvvYejR48qAeHw4cNKRa+5MplMKCoqsrmmPXv24PDhw1izZg02b948ZZYB3UIcueKdbn2tra2ybds2efTRR0WtVktISIjs379fqbBhsVgkMzNT9Hq96HQ6iYiIkJKSEnnooYeku7vbpjqHVWpqqiQmJsrTTz8tarVaoqOj5YsvvlBe7+vrk7S0NPH39xcfHx+Jj4+X9vZ2ERHJzs6W4OBgMZlMIjJRwcrX11cKCwuV7z937pwsW7ZMampqlG2zVcFKTk4WPz8/0ev1snPnTrtVp4aHhyU5OVm0Wq3o9XrZuHGjHDhwQPR6vQwPD4uISH19vYSFhYmPj4+EhYUpVUEmV8Gqq6uT0NBQ8fb2loSEBImPj1eqvvzwww/yzDPPiE6nE41GIwkJCdLZ2Wn3/SkpKRF/f3/RaDTy5JNPyvHjx8XLy0va2tpmrR4z23lMNjQ0JHv27BGdTqfc85uPf+PGDUlMTBQ/Pz/RarUSHx8vHR0dIiLTfg5SU1NttiUlJYnBYBCLxaJsa2trk8jISFGr1eLv7y9paWny3HPPyd69e0VkompYUFCQ+Pj4SH9//5QKK2NjYxIYGCgpKSlTric7O1sMBoP4+PjIunXr5OzZs8rrp06dktDQUFGr1fLYY49JQUGBzXnd7IMPPpB169bZ7Ds+Pi4iItevXxe1Wi2ff/65iIiMjIzIK6+8Ir6+vqLVaiUhIcFuBZ709HQpLy9XvjaZTJKQkCAajUa2bNkiPT09ds+FiIjo72aeyBxXMRHdotrb27F582Y0Njb+V+ajEhEREdF/jovQ6R/LZDKhs7MT+fn5iIqKYvggIiIi+hvgGhD6x+rq6kJsbCz6+/uxffv2//XpEBEREREATsEiIiIiIiKH4QgIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5DAMIERERERE5zL8An1zbt6K2ylYAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (10, 5)\n",
    "                      )\n",
    "# labels=[f'{class_results.iloc[i]['Class']} {np.round(class_results.iloc[i]['counts']/sum(class_results.iloc[0:15]['counts'])*100,1)}%' for i in range(0,15)]\n",
    "colors = sns.color_palette('pastel')[0:15]\n",
    "plt.pie(class_results.iloc[0:15]['counts'], labels = labels,colors = colors\n",
    "        # ,autopct='%.0f%%'\n",
    "        )\n",
    "# plt.show()\n",
    "plt.savefig(os.path.join(source_dir, 'pie_chart.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# searching biological samples"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "alphapept_env",
   "language": "python",
   "display_name": "alphapept_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}