elys_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "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.spectra_operations import entropy_similarity_default\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",
    "import toolsets.spectra_plotter as plotter\n",
    "from toolsets.helpers import find_common_items\n",
    "from toolsets.API_gets import name_to_smiles\n",
    "source_dir = '/Users/fanzhoukong/Documents/GitHub/Libgen_data/Elys_low_conc'\n",
    "data_all = pd.read_csv(os.path.join(source_dir, 'elys_data.csv'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    data.reset_index(inplace=True, drop=True)\n",
    "    peaks= []\n",
    "    print('cleaning spectra...')\n",
    "    for index, row in tqdm(data.iterrows(), total = len(data)):\n",
    "        peak_temp = so.convert_scc_to_string(row['peaks'])\n",
    "        peak_temp = so.clean_spectrum(peak_temp, max_mz=row['precursor_mz'], tolerance=0.05)\n",
    "        peaks.append(peak_temp)\n",
    "    data['peaks_cleaned']=peaks\n",
    "    smiles = []\n",
    "    print('fetching smiles...')\n",
    "    for name in tqdm(data['name']):\n",
    "        smiles.append(name_to_smiles(name))\n",
    "    print('finishing up...')\n",
    "    data['reference_smiles']=smiles\n",
    "    data.dropna(subset=['reference_smiles'], inplace=True)\n",
    "    data.reset_index(inplace=True, drop=True)\n",
    "    data['adduct']=data['adduct'].replace({'M-H':'[M-H]-', 'M+H':'[M+H]+'})\n",
    "    data.rename(columns={'adduct':'reference_adduct', 'precursor_mz':'reference_precursor_mz'}, inplace=True)\n",
    "    return(data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectra...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1058/1058 [00:21<00:00, 50.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching smiles...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1058/1058 [20:12<00:00,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finishing up...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data_1 = string_search(data_all, 'concentration', 1)\n",
    "data_1= clean_data(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectra...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 347/347 [00:02<00:00, 160.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching smiles...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 347/347 [05:28<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finishing up...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data_13 = string_search(data_all, 'concentration', 13)\n",
    "data_13= clean_data(data_13)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "data_1.to_csv(os.path.join(source_dir, 'data_1.csv'), index=False)\n",
    "data_13.to_csv(os.path.join(source_dir, 'data_13.csv'), index=False)"
   ],
   "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": 21,
   "outputs": [],
   "source": [
    "def get_formula(data):\n",
    "    from rdkit import Chem\n",
    "    from rdkit.Chem.rdMolDescriptors import CalcMolFormula\n",
    "    formula = []\n",
    "    for index, row in data.iterrows():\n",
    "        mol_temp = Chem.MolFromSmiles(row['reference_smiles'])\n",
    "        try:\n",
    "            formula.append(CalcMolFormula(mol_temp))\n",
    "        except:\n",
    "            formula.append(np.NAN)\n",
    "    data['reference_formula']=formula\n",
    "    data.dropna(subset=['reference_formula'], inplace=True)\n",
    "    data.reset_index(inplace = True, drop = True)\n",
    "    return(data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n",
      "[19:13:59] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "data_1 = get_formula(data_1)\n",
    "data_13=get_formula(data_13)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def denoising(data):\n",
    "    from toolsets.denoising_related_functions import denoise_blacklist\n",
    "    from toolsets.spectra_operations import calculate_explained_intensity\n",
    "    msms_d = []\n",
    "    ei = []\n",
    "    normalized_entropy = []\n",
    "    for index, row in tqdm(data.iterrows(), total=len(data)) :\n",
    "        normalized_entropy.append(so.normalized_entropy(row['peaks_cleaned']))\n",
    "        try:\n",
    "            msms_d_temp =(denoise_blacklist(row, typeofmsms = 'peaks_cleaned', mass_error=0.05, ifppm=False))\n",
    "            msms_d.append(msms_d_temp)\n",
    "            ei.append(calculate_explained_intensity(msms_d_temp, row['peaks_cleaned'],row['reference_precursor_mz'] ))\n",
    "        except:\n",
    "            msms_d.append(np.NAN)\n",
    "            ei.append(np.NAN)\n",
    "    data['peaks_denoised'] = msms_d\n",
    "    data['ei'] = ei\n",
    "    data['normalized_entropy']=normalized_entropy\n",
    "    return(data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 12/744 [00:01<01:56,  6.27it/s][19:15:57] WARNING: not removing hydrogen atom without neighbors\n",
      "  9%|▉         | 67/744 [00:14<03:07,  3.61it/s][19:16:09] WARNING: not removing hydrogen atom without neighbors\n",
      " 27%|██▋       | 199/744 [00:51<01:17,  7.05it/s][19:16:47] WARNING: not removing hydrogen atom without neighbors\n",
      " 36%|███▋      | 270/744 [01:11<01:37,  4.87it/s][19:17:07] WARNING: not removing hydrogen atom without neighbors\n",
      " 44%|████▎     | 324/744 [01:26<02:12,  3.17it/s][19:17:22] WARNING: not removing hydrogen atom without neighbors\n",
      " 61%|██████    | 453/744 [01:58<00:46,  6.25it/s][19:17:54] WARNING: not removing hydrogen atom without neighbors\n",
      " 70%|███████   | 524/744 [02:15<00:29,  7.50it/s][19:18:11] WARNING: not removing hydrogen atom without neighbors\n",
      " 77%|███████▋  | 576/744 [02:26<00:51,  3.28it/s][19:18:22] WARNING: not removing hydrogen atom without neighbors\n",
      " 92%|█████████▏| 685/744 [02:45<00:06,  8.62it/s][19:18:41] WARNING: not removing hydrogen atom without neighbors\n",
      "100%|██████████| 744/744 [03:00<00:00,  4.13it/s]\n"
     ]
    }
   ],
   "source": [
    "data_1_d=denoising(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "   id_spec  id_anno  Formula_fetched  mix charge  reference_precursor_mz  \\\n0  1211164        1              NaN    2    pos                481.2237   \n1  1211055        3              NaN    2    pos                319.2644   \n2  1208852        8              NaN    2    pos                311.1137   \n3  1208861        9              NaN    2    pos                328.1404   \n4  1208840       10              NaN    2    pos                389.1278   \n\n      rt              name reference_adduct  ce  concentration  \\\n0  11.37       Anthothecol           [M+H]+  45              1   \n1  11.17  Allopregnanolone           [M+H]+  45              1   \n2   5.73          Picrotin           [M+H]+  45              1   \n3   5.73          Picrotin         [M+NH4]+  45              1   \n4   5.73          Picrotin      [M+DMSO+H]+  45              1   \n\n                                               peaks     rt_spec  \\\n0  50.23012924194336:94622.4765625;66.43620300292...  673.437960   \n1  51.2735481262207:3498.046875;57.24148178100586...  657.022680   \n2  50.98478698730469:56908.58203125;54.1502189636...  330.448656   \n3  51.43901824951172:41712.60546875;59.0496864318...  331.698402   \n4  61.01113510131836:184118.640625;62.01901626586...  328.698150   \n\n   precursor_mz_spec                                      peaks_cleaned  \\\n0         481.223755  50.230129\\t0.00788695\\n66.436203\\t0.00992827\\n...   \n1         319.263641  51.273548\\t0.14155562\\n57.241482\\t0.1476671\\n7...   \n2         311.113800  50.984787\\t0.00466395\\n54.150219\\t0.00398392\\n...   \n3         328.140472  51.439018\\t0.01170045\\n59.049686\\t0.07699641\\n...   \n4         389.127991  61.011135\\t0.03570441\\n62.019016\\t0.00671662\\n...   \n\n                                    reference_smiles reference_formula  \\\n0  CC(=O)OC1CC2(C)C(CC3OC23C4(C)C1C5(C)C=CC(=O)C(...          C28H32O7   \n1  CC(=O)C1CCC2C3CC[C@H]4C[C@H](O)CC[C@]4(C)C3CC[...          C21H34O2   \n2       CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C          C15H18O7   \n3       CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C          C15H18O7   \n4       CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C          C15H18O7   \n\n                                      peaks_denoised        ei  \\\n0  50.230129\\t0.00788695\\n67.054619\\t0.01371029\\n...  90.41765   \n1  51.273548\\t0.14155562\\n57.241482\\t0.1476671\\n1...  65.36329   \n2  50.984787\\t0.00466395\\n54.150219\\t0.00398392\\n...  97.16105   \n3  59.049686\\t0.07699641\\n73.09111\\t0.01217268\\n9...  85.25774   \n4        79.021515\\t0.87041187\\n82.00885\\t0.00093157  87.13434   \n\n   normalized_entropy  \n0            0.816872  \n1            0.979581  \n2            0.736040  \n3            0.767731  \n4            0.002051  ",
      "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>id_spec</th>\n      <th>id_anno</th>\n      <th>Formula_fetched</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>reference_precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>reference_adduct</th>\n      <th>ce</th>\n      <th>concentration</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>peaks_cleaned</th>\n      <th>reference_smiles</th>\n      <th>reference_formula</th>\n      <th>peaks_denoised</th>\n      <th>ei</th>\n      <th>normalized_entropy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1211164</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>pos</td>\n      <td>481.2237</td>\n      <td>11.37</td>\n      <td>Anthothecol</td>\n      <td>[M+H]+</td>\n      <td>45</td>\n      <td>1</td>\n      <td>50.23012924194336:94622.4765625;66.43620300292...</td>\n      <td>673.437960</td>\n      <td>481.223755</td>\n      <td>50.230129\\t0.00788695\\n66.436203\\t0.00992827\\n...</td>\n      <td>CC(=O)OC1CC2(C)C(CC3OC23C4(C)C1C5(C)C=CC(=O)C(...</td>\n      <td>C28H32O7</td>\n      <td>50.230129\\t0.00788695\\n67.054619\\t0.01371029\\n...</td>\n      <td>90.41765</td>\n      <td>0.816872</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1211055</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>pos</td>\n      <td>319.2644</td>\n      <td>11.17</td>\n      <td>Allopregnanolone</td>\n      <td>[M+H]+</td>\n      <td>45</td>\n      <td>1</td>\n      <td>51.2735481262207:3498.046875;57.24148178100586...</td>\n      <td>657.022680</td>\n      <td>319.263641</td>\n      <td>51.273548\\t0.14155562\\n57.241482\\t0.1476671\\n7...</td>\n      <td>CC(=O)C1CCC2C3CC[C@H]4C[C@H](O)CC[C@]4(C)C3CC[...</td>\n      <td>C21H34O2</td>\n      <td>51.273548\\t0.14155562\\n57.241482\\t0.1476671\\n1...</td>\n      <td>65.36329</td>\n      <td>0.979581</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1208852</td>\n      <td>8</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>pos</td>\n      <td>311.1137</td>\n      <td>5.73</td>\n      <td>Picrotin</td>\n      <td>[M+H]+</td>\n      <td>45</td>\n      <td>1</td>\n      <td>50.98478698730469:56908.58203125;54.1502189636...</td>\n      <td>330.448656</td>\n      <td>311.113800</td>\n      <td>50.984787\\t0.00466395\\n54.150219\\t0.00398392\\n...</td>\n      <td>CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C</td>\n      <td>C15H18O7</td>\n      <td>50.984787\\t0.00466395\\n54.150219\\t0.00398392\\n...</td>\n      <td>97.16105</td>\n      <td>0.736040</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1208861</td>\n      <td>9</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>pos</td>\n      <td>328.1404</td>\n      <td>5.73</td>\n      <td>Picrotin</td>\n      <td>[M+NH4]+</td>\n      <td>45</td>\n      <td>1</td>\n      <td>51.43901824951172:41712.60546875;59.0496864318...</td>\n      <td>331.698402</td>\n      <td>328.140472</td>\n      <td>51.439018\\t0.01170045\\n59.049686\\t0.07699641\\n...</td>\n      <td>CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C</td>\n      <td>C15H18O7</td>\n      <td>59.049686\\t0.07699641\\n73.09111\\t0.01217268\\n9...</td>\n      <td>85.25774</td>\n      <td>0.767731</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1208840</td>\n      <td>10</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>pos</td>\n      <td>389.1278</td>\n      <td>5.73</td>\n      <td>Picrotin</td>\n      <td>[M+DMSO+H]+</td>\n      <td>45</td>\n      <td>1</td>\n      <td>61.01113510131836:184118.640625;62.01901626586...</td>\n      <td>328.698150</td>\n      <td>389.127991</td>\n      <td>61.011135\\t0.03570441\\n62.019016\\t0.00671662\\n...</td>\n      <td>CC(C)(O)C1C2OC(=O)C1C3(O)CC4OC45C(=O)OC2C35C</td>\n      <td>C15H18O7</td>\n      <td>79.021515\\t0.87041187\\n82.00885\\t0.00093157</td>\n      <td>87.13434</td>\n      <td>0.002051</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1_d.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "nn_de=[]\n",
    "for index, row in data_1_d.iterrows():\n",
    "    nn_de.append(so.normalized_entropy(row['peaks_denoised']))\n",
    "data_1_d['nn_de']=nn_de\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "count    744.000000\nmean       0.359228\nstd        0.287651\nmin        0.000009\n25%        0.106219\n50%        0.305343\n75%        0.581505\nmax        0.995173\nName: normalized_entropy, dtype: float64"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1_d['normalized_entropy'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "count    739.000000\nmean       0.351406\nstd        0.302423\nmin       -1.000000\n25%        0.117028\n50%        0.311057\n75%        0.560616\nmax        0.999763\nName: nn_de, dtype: float64"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1_d['nn_de'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 3/258 [00:00<00:09, 27.98it/s][19:18:56] WARNING: not removing hydrogen atom without neighbors\n",
      " 13%|█▎        | 33/258 [00:01<00:08, 27.43it/s][19:18:57] WARNING: not removing hydrogen atom without neighbors\n",
      " 35%|███▌      | 91/258 [00:03<00:05, 28.82it/s][19:19:00] WARNING: not removing hydrogen atom without neighbors\n",
      " 45%|████▌     | 117/258 [00:04<00:04, 31.67it/s][19:19:01] WARNING: not removing hydrogen atom without neighbors\n",
      " 68%|██████▊   | 175/258 [00:06<00:03, 23.66it/s][19:19:03] WARNING: not removing hydrogen atom without neighbors\n",
      " 80%|███████▉  | 206/258 [00:08<00:01, 27.21it/s][19:19:04] WARNING: not removing hydrogen atom without neighbors\n",
      "100%|██████████| 258/258 [00:09<00:00, 25.86it/s]\n"
     ]
    }
   ],
   "source": [
    "data_13_d=denoising(data_13)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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.patches as mpatches\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "\n",
    "ax = sns.scatterplot(x = data_1_d['normalized_entropy'],y = data_1_d['ei'],color = 'grey', alpha = 0.8)\n",
    "ax.set_xlabel(\"(Normalized entropy)^4\", fontsize = 18)\n",
    "ax.set_ylabel(\"Explained intenstiy (%)\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "rect1=mpatches.Rectangle((0.8,0),0.2,100,\n",
    "                        fill=False,\n",
    "                        color=\"purple\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "rect2=mpatches.Rectangle((0,0),1,90,\n",
    "                        fill=False,\n",
    "                        color=\"red\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "rect3=mpatches.Rectangle((0,90),0.8,10,\n",
    "                        fill=False,\n",
    "                        color=\"green\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "                       #facecolor=\"red\")\n",
    "plt.gca().add_patch(rect1)\n",
    "plt.gca().add_patch(rect2)\n",
    "plt.gca().add_patch(rect3)\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",
    "ax.set_facecolor(\"white\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(os.path.join(source_dir,'high_quality_spectra.tiff'), dpi = 300,facecolor = 'none', edgecolor = 'none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "bad_quality = num_search(data_1_d, 'ei', number=10, direction='<', inclusion=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [],
   "source": [
    "bad_quality_good_spec = num_search(bad_quality, 'normalized_entropy', number=0.8, direction='<')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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 ms2_plot"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "text/plain": "     id_spec  id_anno  Formula_fetched  mix charge  reference_precursor_mz  \\\n198  2755056      313              NaN    6    neg                181.0781   \n234  3480383      353              NaN    7    neg                167.1087   \n722  2966488      353              NaN    7    neg                167.1087   \n\n       rt                name reference_adduct  ce  ...  \\\n198  4.87             Harmane           [M-H]-  45  ...   \n234  9.03  Chrysanthemic Acid           [M-H]-  45  ...   \n722  9.03  Chrysanthemic Acid           [M-H]-  35  ...   \n\n                                                 peaks     rt_spec  \\\n198  60.525638580322266:3068.500244140625;71.687942...  293.883216   \n234  138.92958068847656:6940.23388671875;161.215866...  540.700740   \n722  54.634918212890625:2950.27197265625;108.407623...  540.198750   \n\n     precursor_mz_spec                                      peaks_cleaned  \\\n198         181.077469  60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...   \n234         167.107925     138.929581\\t0.69318282\\n161.215866\\t0.30681718   \n722         167.108063  54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...   \n\n                       reference_smiles reference_formula peaks_denoised   ei  \\\n198            CC1=NCCc2c1[nH]c3ccccc23          C12H12N2            NaN  0.0   \n234  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n722  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n\n     normalized_entropy  nn_de  \n198            0.735650    NaN  \n234            0.625916    NaN  \n722            0.432459    NaN  \n\n[3 rows x 21 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>id_spec</th>\n      <th>id_anno</th>\n      <th>Formula_fetched</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>reference_precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>reference_adduct</th>\n      <th>ce</th>\n      <th>...</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>peaks_cleaned</th>\n      <th>reference_smiles</th>\n      <th>reference_formula</th>\n      <th>peaks_denoised</th>\n      <th>ei</th>\n      <th>normalized_entropy</th>\n      <th>nn_de</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>198</th>\n      <td>2755056</td>\n      <td>313</td>\n      <td>NaN</td>\n      <td>6</td>\n      <td>neg</td>\n      <td>181.0781</td>\n      <td>4.87</td>\n      <td>Harmane</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>60.525638580322266:3068.500244140625;71.687942...</td>\n      <td>293.883216</td>\n      <td>181.077469</td>\n      <td>60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...</td>\n      <td>CC1=NCCc2c1[nH]c3ccccc23</td>\n      <td>C12H12N2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.735650</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>3480383</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>138.92958068847656:6940.23388671875;161.215866...</td>\n      <td>540.700740</td>\n      <td>167.107925</td>\n      <td>138.929581\\t0.69318282\\n161.215866\\t0.30681718</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.625916</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>722</th>\n      <td>2966488</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>35</td>\n      <td>...</td>\n      <td>54.634918212890625:2950.27197265625;108.407623...</td>\n      <td>540.198750</td>\n      <td>167.108063</td>\n      <td>54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.432459</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_quality_good_spec"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[5.4634918e+01, 1.0273442e-01],\n       [1.0840762e+02, 1.0795522e-01],\n       [1.3286885e+02, 1.3270830e-01],\n       [1.3892975e+02, 5.5266869e-01],\n       [1.4596822e+02, 1.0393337e-01]], dtype=float32)"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance=bad_quality_good_spec.iloc[2]\n",
    "so.convert_string_to_nist(instance['peaks_cleaned'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from toolsets.spectra_plotter import"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "58.701077"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance['reference_precursor_mz']-108.407623"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [],
   "source": [
    "from rdkit import  Chem"
   ],
   "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": 99,
   "outputs": [
    {
     "data": {
      "text/plain": "id_spec                                                             2966488\nid_anno                                                                 353\nFormula_fetched                                                         NaN\nmix                                                                       7\ncharge                                                                  neg\nreference_precursor_mz                                             167.1087\nrt                                                                     9.03\nname                                                     Chrysanthemic Acid\nreference_adduct                                                     [M-H]-\nce                                                                       35\nconcentration                                                             1\npeaks                     54.634918212890625:2950.27197265625;108.407623...\nrt_spec                                                           540.19875\nprecursor_mz_spec                                                167.108063\npeaks_cleaned             54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...\nreference_smiles                         CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C\nreference_formula                                                  C10H16O2\npeaks_denoised                                                          NaN\nei                                                                      0.0\nnormalized_entropy                                                 0.432459\nnn_de                                                                   NaN\nName: 722, dtype: object"
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [],
   "source": [
    "mol = Chem.MolFromSmiles(instance['reference_smiles'])\n",
    "from rdkit.Chem import Draw\n",
    "Draw.MolToFile(mol,os.path.join(source_dir, 'nn4.png'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "     id_spec  id_anno  Formula_fetched  mix charge  reference_precursor_mz  \\\n198  2755056      313              NaN    6    neg                181.0781   \n234  3480383      353              NaN    7    neg                167.1087   \n722  2966488      353              NaN    7    neg                167.1087   \n\n       rt                name reference_adduct  ce  ...  \\\n198  4.87             Harmane           [M-H]-  45  ...   \n234  9.03  Chrysanthemic Acid           [M-H]-  45  ...   \n722  9.03  Chrysanthemic Acid           [M-H]-  35  ...   \n\n                                                 peaks     rt_spec  \\\n198  60.525638580322266:3068.500244140625;71.687942...  293.883216   \n234  138.92958068847656:6940.23388671875;161.215866...  540.700740   \n722  54.634918212890625:2950.27197265625;108.407623...  540.198750   \n\n     precursor_mz_spec                                      peaks_cleaned  \\\n198         181.077469  60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...   \n234         167.107925     138.929581\\t0.69318282\\n161.215866\\t0.30681718   \n722         167.108063  54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...   \n\n                       reference_smiles reference_formula peaks_denoised   ei  \\\n198            CC1=NCCc2c1[nH]c3ccccc23          C12H12N2            NaN  0.0   \n234  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n722  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n\n     normalized_entropy  nn_de  \n198            0.735650    NaN  \n234            0.625916    NaN  \n722            0.432459    NaN  \n\n[3 rows x 21 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>id_spec</th>\n      <th>id_anno</th>\n      <th>Formula_fetched</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>reference_precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>reference_adduct</th>\n      <th>ce</th>\n      <th>...</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>peaks_cleaned</th>\n      <th>reference_smiles</th>\n      <th>reference_formula</th>\n      <th>peaks_denoised</th>\n      <th>ei</th>\n      <th>normalized_entropy</th>\n      <th>nn_de</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>198</th>\n      <td>2755056</td>\n      <td>313</td>\n      <td>NaN</td>\n      <td>6</td>\n      <td>neg</td>\n      <td>181.0781</td>\n      <td>4.87</td>\n      <td>Harmane</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>60.525638580322266:3068.500244140625;71.687942...</td>\n      <td>293.883216</td>\n      <td>181.077469</td>\n      <td>60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...</td>\n      <td>CC1=NCCc2c1[nH]c3ccccc23</td>\n      <td>C12H12N2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.735650</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>3480383</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>138.92958068847656:6940.23388671875;161.215866...</td>\n      <td>540.700740</td>\n      <td>167.107925</td>\n      <td>138.929581\\t0.69318282\\n161.215866\\t0.30681718</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.625916</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>722</th>\n      <td>2966488</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>35</td>\n      <td>...</td>\n      <td>54.634918212890625:2950.27197265625;108.407623...</td>\n      <td>540.198750</td>\n      <td>167.108063</td>\n      <td>54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.432459</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_quality_good_spec"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "5.892699999999991"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "167.1087-161.216"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [
    "instance = bad_quality_good_spec.iloc[0]\n",
    "mol = Chem.MolFromSmiles(instance['reference_smiles'])\n",
    "from rdkit.Chem import Draw\n",
    "Draw.MolToFile(mol,os.path.join(source_dir, 'nn7.png'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[6.0525639e+01, 8.9415677e-02],\n       [7.1687943e+01, 9.0075672e-02],\n       [7.2460640e+01, 9.4522111e-02],\n       [9.2920219e+01, 3.3919480e-01],\n       [1.3717998e+02, 1.0031385e-01],\n       [1.4349124e+02, 9.5549323e-02],\n       [1.4753958e+02, 9.2118390e-02],\n       [1.5164633e+02, 9.8810174e-02]], dtype=float32)"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so.convert_string_to_nist(instance['peaks_cleaned'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": "     id_spec  id_anno  Formula_fetched  mix charge  reference_precursor_mz  \\\n198  2755056      313              NaN    6    neg                181.0781   \n234  3480383      353              NaN    7    neg                167.1087   \n722  2966488      353              NaN    7    neg                167.1087   \n\n       rt                name reference_adduct  ce  ...  \\\n198  4.87             Harmane           [M-H]-  45  ...   \n234  9.03  Chrysanthemic Acid           [M-H]-  45  ...   \n722  9.03  Chrysanthemic Acid           [M-H]-  35  ...   \n\n                                                 peaks     rt_spec  \\\n198  60.525638580322266:3068.500244140625;71.687942...  293.883216   \n234  138.92958068847656:6940.23388671875;161.215866...  540.700740   \n722  54.634918212890625:2950.27197265625;108.407623...  540.198750   \n\n     precursor_mz_spec                                      peaks_cleaned  \\\n198         181.077469  60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...   \n234         167.107925     138.929581\\t0.69318282\\n161.215866\\t0.30681718   \n722         167.108063  54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...   \n\n                       reference_smiles reference_formula peaks_denoised   ei  \\\n198            CC1=NCCc2c1[nH]c3ccccc23          C12H12N2            NaN  0.0   \n234  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n722  CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C          C10H16O2            NaN  0.0   \n\n     normalized_entropy  nn_de  \n198            0.735650    NaN  \n234            0.625916    NaN  \n722            0.432459    NaN  \n\n[3 rows x 21 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>id_spec</th>\n      <th>id_anno</th>\n      <th>Formula_fetched</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>reference_precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>reference_adduct</th>\n      <th>ce</th>\n      <th>...</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>peaks_cleaned</th>\n      <th>reference_smiles</th>\n      <th>reference_formula</th>\n      <th>peaks_denoised</th>\n      <th>ei</th>\n      <th>normalized_entropy</th>\n      <th>nn_de</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>198</th>\n      <td>2755056</td>\n      <td>313</td>\n      <td>NaN</td>\n      <td>6</td>\n      <td>neg</td>\n      <td>181.0781</td>\n      <td>4.87</td>\n      <td>Harmane</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>60.525638580322266:3068.500244140625;71.687942...</td>\n      <td>293.883216</td>\n      <td>181.077469</td>\n      <td>60.525639\\t0.08941568\\n71.687943\\t0.09007567\\n...</td>\n      <td>CC1=NCCc2c1[nH]c3ccccc23</td>\n      <td>C12H12N2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.735650</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>3480383</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>45</td>\n      <td>...</td>\n      <td>138.92958068847656:6940.23388671875;161.215866...</td>\n      <td>540.700740</td>\n      <td>167.107925</td>\n      <td>138.929581\\t0.69318282\\n161.215866\\t0.30681718</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.625916</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>722</th>\n      <td>2966488</td>\n      <td>353</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>neg</td>\n      <td>167.1087</td>\n      <td>9.03</td>\n      <td>Chrysanthemic Acid</td>\n      <td>[M-H]-</td>\n      <td>35</td>\n      <td>...</td>\n      <td>54.634918212890625:2950.27197265625;108.407623...</td>\n      <td>540.198750</td>\n      <td>167.108063</td>\n      <td>54.634918\\t0.10273442\\n108.407623\\t0.10795522\\...</td>\n      <td>CC(C)=C[C@@H]1[C@@H](C(O)=O)C1(C)C</td>\n      <td>C10H16O2</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.432459</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_quality_good_spec"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": 136,
     "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": [
    "instance = bad_quality_good_spec.iloc[2]\n",
    "ms2_plot(instance['peaks_cleaned'], pmz1=instance['reference_precursor_mz'], savepath=os.path.join(source_dir,'nn4.tiff'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from toolsets.spectra_plotter import ms2_plot\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "instance = bad_quality.iloc[5]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[5.4634918e+01, 1.0273442e-01],\n       [1.0840762e+02, 1.0795522e-01],\n       [1.3286885e+02, 1.3270830e-01],\n       [1.3892975e+02, 5.5266869e-01],\n       [1.4596822e+02, 1.0393337e-01]], dtype=float32)"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so.convert_string_to_nist(instance['peaks_cleaned'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['id_spec', 'id_anno', 'Formula_fetched', 'mix', 'charge',\n       'reference_precursor_mz', 'rt', 'name', 'reference_adduct', 'ce',\n       'concentration', 'peaks', 'rt_spec', 'precursor_mz_spec',\n       'peaks_cleaned', 'reference_smiles', 'reference_formula',\n       'peaks_denoised', 'ei', 'normalized_entropy', 'nn_de'],\n      dtype='object')"
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [
    {
     "data": {
      "text/plain": "0.7701612903225806"
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_search(data_1, 'ei', 90, direction='>', inclusion=True))/len(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [],
   "source": [
    "high_ei = num_search(data_13, 'ei', 90, direction='>', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [
    {
     "data": {
      "text/plain": "13"
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_search(high_ei,'normalized_entropy', 0.8, direction='<', inclusion=False))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "0.050387596899224806"
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_search(high_ei,'normalized_entropy', 0.8, direction='<', inclusion=False))/len(data_13)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [
    {
     "data": {
      "text/plain": "0.44573643410852715"
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-len(num_search(data_13_d, 'normalized_entropy', 0.8, direction='<', inclusion=False))/len(data_13_d)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9457364341085271"
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-len(num_search(data_13_d, 'ei', 90, direction='>', inclusion=True))/len(data_13)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9059139784946236"
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_search(data_1, 'normalized_entropy', 0.8, direction='<', inclusion=False))/len(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9059139784946236"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_search(data_1, 'normalized_entropy', 0.8, direction='<', inclusion=True))/len(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "'C10H16O2'"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance['reference_formula']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "167.108062744141"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance['precursor_mz_spec']-"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "21.139846744140982"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "167.108062744141-145.968216"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "data": {
      "text/plain": "0.3467741935483871"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_13)/len(data_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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.patches as mpatches\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "\n",
    "# ax = sns.scatterplot(x = data_1['normalized_entropy'],y = data_1['ei'],color = 'green')\n",
    "ax = sns.scatterplot(x = data_13['normalized_entropy'],y = data_13['ei'],color = 'grey', alpha = 0.8)\n",
    "ax.set_xlabel(\"(Normalized entropy)^4\", fontsize = 18)\n",
    "ax.set_ylabel(\"Explained intensity (%)\", 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",
    "ax.set_facecolor(\"white\")\n",
    "# plt.axvline(0.8, 0, color = 'red' )\n",
    "# plt.axhline(90, 0, color = 'red' )\n",
    "rect1=mpatches.Rectangle((0.8,0),0.2,100,\n",
    "                        fill=False,\n",
    "                        color=\"purple\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "rect2=mpatches.Rectangle((0,0),1,90,\n",
    "                        fill=False,\n",
    "                        color=\"red\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "rect3=mpatches.Rectangle((0,90),0.8,10,\n",
    "                        fill=False,\n",
    "                        color=\"green\",\n",
    "                       linewidth=2, linestyle='dashed')\n",
    "                       #facecolor=\"red\")\n",
    "plt.gca().add_patch(rect1)\n",
    "plt.gca().add_patch(rect2)\n",
    "plt.gca().add_patch(rect3)\n",
    "fig.tight_layout()\n",
    "plt.savefig(os.path.join(source_dir,'low_quality_spectra.tiff'), dpi = 300,facecolor = 'none', edgecolor = 'none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [],
   "source": [
    "bad_1 = num_search(data_1, 'ei', 90, direction='<', inclusion=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [],
   "source": [
    "bad_1.reset_index(inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "id_spec                                                             2860928\nid_anno                                                                  83\nmix                                                                       3\ncharge                                                                  pos\nreference_precursor_mz                                             283.0587\nrt                                                                     6.51\nname                                2,3,4'-Trihydroxy-4-Methoxybenzophenone\nreference_adduct                                                    [M+Na]+\nce                                                                       45\nconcentration                                                             1\npeaks                     50.73485565185547:9425.169921875;54.1692390441...\nrt_spec                                                          389.376444\nprecursor_mz_spec                                                283.058685\npeaks_cleaned             50.734856\\t0.0659461\\n54.169239\\t0.06321247\\n5...\nreference_smiles                           COc1ccc(c(O)c1O)C(=O)c2ccc(O)cc2\nreference_formula                                                  C14H12O5\npeaks_denoised                                                          NaN\nei                                                                      0.0\nnormalized_entropy                                                 0.985236\nName: 8, dtype: object"
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_1.iloc[8]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[5.0734856e+01, 6.5946102e-02],\n       [5.4169239e+01, 6.3212469e-02],\n       [5.4370113e+01, 6.2046722e-02],\n       [5.4441288e+01, 6.0214650e-02],\n       [5.5466351e+01, 6.2044900e-02],\n       [5.8660717e+01, 6.1258350e-02],\n       [5.8879154e+01, 6.8457693e-02],\n       [5.9806122e+01, 6.9463998e-02],\n       [7.3294762e+01, 7.6273680e-02],\n       [8.1825645e+01, 8.9959912e-02],\n       [9.0677856e+01, 7.0173971e-02],\n       [1.0491871e+02, 7.9836540e-02],\n       [1.3617058e+02, 7.8876831e-02],\n       [2.0564766e+02, 9.2234209e-02]], dtype=float32)"
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so.convert_string_to_nist(bad_1.iloc[8]['peaks_cleaned'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [],
   "source": [
    "from toolsets.denoising_related_functions import denoise_single"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "id_spec                                                             2860928\nid_anno                                                                  83\nmix                                                                       3\ncharge                                                                  pos\nreference_precursor_mz                                             283.0587\nrt                                                                     6.51\nname                                2,3,4'-Trihydroxy-4-Methoxybenzophenone\nreference_adduct                                                    [M+Na]+\nce                                                                       45\nconcentration                                                             1\npeaks                     50.73485565185547:9425.169921875;54.1692390441...\nrt_spec                                                          389.376444\nprecursor_mz_spec                                                283.058685\npeaks_cleaned             50.734856\\t0.0659461\\n54.169239\\t0.06321247\\n5...\nreference_smiles                           COc1ccc(c(O)c1O)C(=O)c2ccc(O)cc2\nreference_formula                                                  C14H12O5\npeaks_denoised                                                          NaN\nei                                                                      0.0\nnormalized_entropy                                                 0.985236\nName: 8, dtype: object"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "prep_formula"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [],
   "source": [
    "msms = so.bin_spectrum(bad_1.iloc[8]['peaks_cleaned'], precursormz= 283.0587)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [],
   "source": [
    "mass, intensity = so.break_spectra(msms)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "normalized_entropy = []\n",
    "peaks_denoised = []\n",
    "for index, row in tqdm(data_1.iterrows(), total = len(data_1)):\n",
    "    normalized_entropy.append(so.normalized_entropy(row['peaks_cleaned']))\n",
    "data_1['normalized_entropy']=normalized_entropy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "data_13 = string_search(data_all, 'concentration',13)\n",
    "data_13.reset_index(inplace=True, drop=True)\n",
    "peaks = []\n",
    "for index, row in data_13.iterrows():\n",
    "    peak_temp = so.convert_scc_to_string(row['peaks'])\n",
    "    peak_temp = so.clean_spectrum(peak_temp, max_mz=row['precursor_mz'], tolerance=0.05)\n",
    "\n",
    "    peaks.append(peak_temp)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 347/347 [03:11<00:00,  1.81it/s]\n"
     ]
    }
   ],
   "source": [
    "smiles = []\n",
    "for name in tqdm(data_13['name']):\n",
    "    smiles.append(name_to_smiles(name))\n",
    "data_13['smiles_fetched']=smiles\n",
    "data_13.dropna(subset=['smiles_fetched'], inplace=True)\n",
    "data_13.reset_index(inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n",
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n",
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n",
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n",
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n",
      "[16:51:22] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.rdMolDescriptors import CalcMolFormula\n",
    "formula = []\n",
    "for index, row in data_13.iterrows():\n",
    "    mol_temp = Chem.MolFromSmiles(row['reference_smiles'])\n",
    "    try:\n",
    "        formula.append(CalcMolFormula(mol_temp))\n",
    "    except:\n",
    "        formula.append(np.NAN)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "data_13['formula_fetched']=formula"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 258/258 [00:00<00:00, 26274.87it/s]\n"
     ]
    }
   ],
   "source": [
    "normalized_entropy = []\n",
    "peaks_denoised = []\n",
    "for index, row in tqdm(data_13.iterrows(), total = len(data_13)):\n",
    "    normalized_entropy.append(so.normalized_entropy(row['peaks_cleaned']))\n",
    "data_13['normalized_entropy']=normalized_entropy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "data_13['adduct']=data_13['adduct'].replace({'M-H':'[M-H]-', 'M+H':'[M+H]+'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['id_spec', 'id_anno', 'mix', 'charge', 'reference_precursor_mz', 'rt',\n       'name', 'reference_adduct', 'ce', 'concentration', 'peaks', 'rt_spec',\n       'precursor_mz_spec', 'peaks_cleaned', 'reference_smiles',\n       'formula_fetched', 'normalized_entropy'],\n      dtype='object')"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_13.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "data_13.rename(columns={'adduct':'reference_adduct', 'precursor_mz':'reference_precursor_mz','smiles_fetched':'reference_smiles'}, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 3/258 [00:00<00:08, 28.43it/s][16:44:58] WARNING: not removing hydrogen atom without neighbors\n",
      " 13%|█▎        | 33/258 [00:01<00:08, 27.64it/s][16:45:00] WARNING: not removing hydrogen atom without neighbors\n",
      " 34%|███▍      | 89/258 [00:03<00:06, 28.16it/s][16:45:02] WARNING: not removing hydrogen atom without neighbors\n",
      " 46%|████▌     | 118/258 [00:04<00:04, 34.43it/s][16:45:03] WARNING: not removing hydrogen atom without neighbors\n",
      " 68%|██████▊   | 175/258 [00:06<00:03, 23.48it/s][16:45:05] WARNING: not removing hydrogen atom without neighbors\n",
      " 79%|███████▉  | 204/258 [00:07<00:02, 26.59it/s][16:45:06] WARNING: not removing hydrogen atom without neighbors\n",
      "100%|██████████| 258/258 [00:09<00:00, 26.08it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.denoising_related_functions import denoise_blacklist\n",
    "from toolsets.spectra_operations import calculate_explained_intensity\n",
    "msms_d = []\n",
    "ei = []\n",
    "for index, row in tqdm(data_13.iterrows(), total=len(data_13)) :\n",
    "    try:\n",
    "        msms_d_temp =(denoise_blacklist(row, typeofmsms = 'peaks_cleaned', mass_error=0.05, ifppm=False))\n",
    "        msms_d.append(msms_d_temp)\n",
    "        ei.append(calculate_explained_intensity(msms_d_temp, row['peaks_cleaned'],row['reference_precursor_mz'] ))\n",
    "    except:\n",
    "        msms_d.append(np.NAN)\n",
    "        ei.append(np.NAN)\n",
    "data_13['peaks_denoised'] = msms_d\n",
    "data_13['ei'] = ei\n",
    "# data_13['c_id']=np.arange(len(for_denoise))\n",
    "# for_denoise.to_csv(os.path.join(source_dir, filename.split('.')[:-1][0]+'_denoised.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "count    258.000000\nmean      56.219046\nstd       23.330363\nmin        0.000000\n25%       41.748380\n50%       57.665125\n75%       74.887455\nmax      100.000000\nName: ei, dtype: float64"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_13['ei'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "(14, 19)"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_search(data_13,column_name='ei', number=90, direction='>', inclusion=False).shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "0.05426356589147287"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14/data_13.shape[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='normalized_entropy', ylabel='ei'>"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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aP4F9WmOzQ5eSgkfmW8M+/9TTC3HXzDH42bOfBHzuVRYz1l9+8rVXKOjHRyI+M7nPiz788ZdcFBnYfPbZZ1i7di2+/PJLZGZm4uKLL8Ztt90GvV6PTz75BPfffz9sNhvy8vJwww034IorrpC7yYpIdSrp4qIEvTMqTywsD/v4WD6jaIIWKU/qUp+o5S72jFd/Av1YAtFgz5+VmdYnqAGAnTa7P9hNth8fSjgv+vDHX/JQXGDj9XrxP//zP1i6dCn+/Oc/o6GhAYsWLUJeXh6uueYaLF26FMuXL8dVV12FPXv24KabbsKoUaMwfvx4WduttF/QyS5YRqX2SDMqLOY+U26B2D+jaIIWqU/qUp2olVDsKYZYA/1YA9Hez3+goT1iYDSiMCupLqRKOy/yx19yUNw6Ni0tLTh+/Di8Xi8E4eTQQWpqKgwGA9544w3k5uZiwYIFSEtLw3nnnYdZs2Zh8+bNMrdam+ukqFmwX98bdx3E4opiVFjMAbf35zOKJmhJxKZ/OUY9RhRmoWxYnigXzWRe7yPeQFQpwy5KwvMiyUFxGZu8vDwsWrQI69evxy9/+Ut4PB5MmTIFixYtwrp16zBy5MiAx1ssFmzdulWm1gZiqlM5gl1kHC4Plm+pxZLKYtw5/XS43N5+f0bR/BJV47i+Uoo95RBvdkFJwy5KwvMiJZriAhuv14vMzEzcdddduPzyy3Ho0CEsW7YM1dXV6OjogMEQmArPzMyEw+GI+XU8Ho8kmzJmZeiQlWEA8H075dz8UUl8/ZCI/hgQYidxh8uDDe/YcFnZEIwqyurTtmhlZehCBi3r5o1HVoYOHo8HRQP0ePjqMtg7XGhzujEgMw1mkx7ZmWkJOy5i6fdIWYdWZ7dmj+doP9NQ8k3pIQOjqpICeAQBBxrakW9Kl3WneznIcV5M5PmGAknV99E+n+K+XW+++SZef/11vPbaawCAkpIS3HTTTVizZg1mzZqFtra2gMc7nU6YTKaYX2f//v2itJdiV1dXJ/lrZJkLUWUx99nVGjg5S8XjaMbeI19F9Vypqakw5hXArTOio8uDrAwddB4HnC0ncOeUIVhxkQUdXR6YMnRI8zhw7OAX+M7rDfpcLf/5nxyi6ffMwuFh79eneLF3715xGqRAqampMX+mPf/2nuklWP2yEHDcVVrMWDhxOC77bQ0cLg+qLGasnjESrf8+BG+E56T4JeJ8Q8HJ1feKC2y+++47uFyB4/hpaWlIT0/HyJEjUVNTE3CfzWZDSUlJzK8zZswY6PVMhSaSx+NBXV0dSktLJVlPpbf1lxeE/PVdNEAPDB0S9u9bnW6ccLgApGD1i5/2WbnX/zwBciM+b6LF0u+tTnfY4Zgh+VnIHlImUUuVKjemz7R6vhX2DhdaO7vh7PZi99d2LN9SC4fr5K/NnTY7fvFKPR6+ukyUzE2r0+3PCGZnpouSEYr2OaV4bbEk+nxD35Oq733PG4kyjsAeKisr8dBDD+F3v/sdrrvuOhw9ehSPPfYYZs2ahalTp+LBBx/Epk2bsGDBAnz00UfYvn07Hn300ZhfR6fT8WCXSaL6Pp6xfd/MoDOG5qL2cFOfmVRq3Dk9mn7PM4Uejlk/bzzyTBlSNxOAuqeb55l0yDNl4EBDO+Y+9m7Qx+yob8SJju64+1OKGWzRPqdaZs/xXC8fufpecYGNxWLB73//e/zmN7/BH//4RwwYMACzZ8/GTTfdBL1ej40bN2LNmjWorq5Gfn4+Vq1ahXPPPVfuZpNC9Wd6Z8+ZQYsmDg+5eaFWi2nlLvZUywUzEqlnSUm1RUg0z6mUrRKIglFcYAMAEydOxMSJE4PeV1paimeeeSbBLaJk0nNmUJc7fA2EVqfwyrXeh5YumFLPkpJiBlu0z5nMs+dI+RQZ2BDJqecv7Yy08Es9xXJxinZ4Rc3DMPHS0gVT6sXppMgIRfucXLOHlIyBDVEvPX9pi7VasdbqFqSipgtmpADUt47Riq2f9Ck8F2MdIykyQtE+J9fsISVjYEPUS89f2ht3HUT1fzY/rOnnxYl1C9FTywUz2gC0aIAeqy48BbrZY9HudItaryRFRija51TaVglEPSluSwUiufVcBt63WrF1WB6evnYCnrvhPLx96/l4ZL4Vg6PMoEQzvBLL47QsEdtQxCvWbSfa7Q0oNhtRXHByva2vGztw4Hh73NtTSLFdQbTPya0SSMmYsSEKQsyZQaxbiJ4atqGItQ4oNTUVx9r6BkNiDDH6jtNmRzc6XG50uDzINaTDqO//FNtoj325Z88RhcLAhigEsWYGabluQYpCZ6VfMGMNQI15BZIOMXa4PFj14qeiBk3RHvvcLZuUiIENqYoaZwxptW5BykJnKS6YYh07sQagbp1RlJlewdoPIOnrshJFjeeeZMXAhlRDrTOGoh1eUcMwjI/aCp3FPHZiDUA7usJv3BfNEGOo9t976Th8dKgp6N+obXq8ksl17mEw1T8MbEgV1HYh7U3uuoUscyEO2h0n9/QR4QSppvVmxD52Yg1ATRnh610iDTGGa/9dL36KJZXFIVfHToa6LKnJde5R6w85JWBgQ6qgpgtpKHLVLRxrc+H+t77FTts+/23xniDVVOgsxbETSwCa5nHENcTY7OjGoonDMf+cYchM1+Hjw03YuOsgHC6Pf9uPUJRYl6U2cpx71P5DTm4MbEgVlHIhVVtq2H+CFHkTTzUVOkt17EQbgDqaGvs9xHi0uROrXqgL+PwqLGZUz7cG7BgejBLrstRIjnOPFn7IyYmBDamCEi6kakwNS3WCVFOhs9zHjtfrRdEAfcxDjKGCUt9Ckb4hqFPzDH0+CyXWZamVHMePUn7IqRUDG1IFuS+kak0NS5mtUEuhs9zHjk+sQ4zhgtIamx1LKooxqaQAg7IzRV/Lhr4nx/EjdzCudgxsSBXkvpCqNTUs5QlS6evN+Mh97PRXpKAUgL/9R5s7RV/Lhk6S4viJNKStlGBcrRjYkGrIeSFVa2pY6hOkWhZoU0sQ1lOkoHRYvhGDcw2qzSaqiZjHTzRD2moNxpWCgQ2pilwXUrWmhqXeYVpN1BKE+RRk6VFVUhA0U1hVUoDCARkA1JNNVFvhfW9iHD+xBKFqDMaVgoENSa7nCc1QOBytTjfyTOoa/1dzaljKHaZJWndMPx0nOlxo6ez2T/Xef7QFSyeN8D9GDdlEJRXeyxlgxRqEqi0YVwoGNiQpJZ3Q4qH21HC7vQFlQ4dAp1NXQJnMmhzduP/l/f5ZUABQZSnAT6eNwrV/2oO/Lj0POUa94rOJShoqk/t8pIYgVAsY2JBklHRCEwNTw+JS+9CElFocLtz1Ql1AUAMAO22N8ELA1ecM818ElZ5NVMpQmRLOR0oIQpPhe8fAhiSjlBOamJgaFkfPX85GvQ5LKosx8Qdm6NNSkWfSa/JkG4vGdlef9Wt8fFO9fRdBpWcTlZKlUML5SO4gVO6MVaIwsCHR+X4R2DtcYR/HtGty6vnL2ajXoXq+FU/WHAzY70iLJ9tYRDPVu+dFUMnZRCVkKQBlBFhyBqFKyFglCgMbElXPXwRPLCwP+1i5x/5JHj1/OS+pLMaTNQf7DLlo8WQbi0jBwKl5hj79otRsotxZCh+lBFhyBaFKyFglCgObJCTVGGvvXwS1R5pRYTH3uWgByhj7T5RkGNOORc9fztahuSF3ptbayTYW4YKBqpICpKSk4MDxdtmOpZ7HdFZGGvS6VDR3upCV2ff4TkSWIth3LKvXrupKCbAAeYJQJWSsEoWBTZKRcoy19y+CjbsOonq+FQACghuljP0nQrKMacei5y/nLrc37GO1dLKNRahgoNJixsKJw3HZb2vgcHlkOZaCHdMVFjMWVxRj/uPvo/y0vD5tkjJLEe47lpqa6r9N6bVIUsvKCH+5N0W4X020804oIqnHWHv/InC4PFi+pRZLKov9xY6mNAFD8rOQZ8ro9+uoRTKNacei5y/njLTUsI9N5uHKnsFAS2c3nN0e7P7aHrCrd6KPpVDHdO+NOYO1SYosRaTv2J1ThgTcruRaJKnpdakhM+gVFjP0uvDfRTXRzjuhiKIZY41HsDFsh8uDDe/Y8N9/+hD5pnQ4G75BdmZ88XSLw4UDDe2oPdyEA8fb0eKIr91Skbq/1cr3y3lSSYF/uDKYZBquDCXHqMeIwizkGNLxoz++jw3v2PxBjU8ij6VIG3Nah+YmtE2RvmNunbHP7b4+LRuWd7JvkyCoAYDmThcWVxT3+b75sm0tndo5HzFjk0SkHmONNIZtNunREtcrqGtoJ5nGtGPl++Vs73BhjvUUrP7bZ30+UyUOD8hVL6WUYylSO3oOLSaiTZHa09HlCXt/MsnKSMf8x9/3Z9C73F5kpKWi9kgzlm+pxfZllXI3UTQMbJKI1LMCIo1hi5GpUdPQjlJmYShVz6GJDSoYHhAjqO5vYKSUYylSO3oOLSaiTZHaY8rgSts+BVl6lJ+WF7RYX2vZUQY2SSQRswLCjWF7PPH9elLbdEUlzcJQOqVOVfYRI6iOJzBSyrEUrh0VFjNqjzQntE2R+iXN4wCQK3k71CCZiqdZY5NEetY29CT2gS3VGLZS0vHRSlR/k/TirZdqdbrDBkaR6sSUciyFaoevTmPjroMJbVO4flk3bzwcTcE/s2Tl++H59q3n44UbJ+LtW8/HI/OtGNzPYXyl1jsyY5Nk1DwrQCnp+Fioub+1Qoy6mHiDantH/NlGpRxLvdth+s86Ni2dLmxfVpnwNoXql6wMHb7zhl9OIBmJlR0Nl4EsGiDv+Y2BTRKK98CWq4BSKen4WCl9mEXLxCo2jzeobnO6I9wfXbZRKcdS8HaYZGkLELw98Q59U2gtDhdqbI34+bRR+Pm0UXC4PEjXpWJn/XHc8+Kn+OXl42VtHwMbiomcs5KSaYyY4idmsXm8QfWACIXzSsw2EoXS7OjGqXkGrH/ti4B1caosBVhcORwnZB6SYmBDUVPCrCSlpONJPtFmDMUsNo83qDab1JltJOqtxeHC7gONeKnuuz6L/e20NQIQcNessfI07j8Y2FDUlDIrSSnpeEq8WDKGYhebxxNUZ2emMduYxLS0X1xjuwuF2ZlBVzAGgJ02O7xeIcGtCsTAhqKmtllJpC6RTv6xZgylKDaPJ6hOpmxjvBfyUH+vxgBBTYuKRqPV2R1xjzeHK3xNmdQY2FDU1DgridQhmpN/rBlDJRabJ0O2Md4Leai/v/+ycbj3pf146/OGfj2vHJQwfC+27Mx0nOgIX0OTbUiPe5X5eHAdG4qa70IRDOsE1OVYqxNffNeKDw6ewBf/bsWxVmfQxyVinYpIJ3/fa8aaMYx37RelrtGhZNF+lv35+zu21WH04Ox+Pa9ctLhfXEGWHg1tXWH3eDObON2bVIKzkrThsL0Dt2+rCxgjr7SYsXZOKYaZv5+ym6gUerSZmP5kDPs7/KO14YNEibcOL9zf77LZsbiiuF/PKxctDt/nGPW4YORAFBecPFcEzIoSafuceDGwoZgkU52AFh1rdfYJaoCTF407ttXhoSvLUJSdmdAUerQn//4OLcU6/KPF4YNEifdCHssmm7E8r1y0Onw/ONcAQRBw5/TT4fYKcLg8SEtNwcHGDrmbBoCBDfVDMtQJaFVThyvkbIZdNjuaOlwoys5M6Ay4aE/+icoYKmX2nxrFeyGPZZPNWJ5XLlLUeSmhgLrF4cKK5+uCfk8mlRTg4avLEtqe3hjYECWR1ggr4PruT2QKPZaTfyIyhlocPhBDNBfUeC/k4f6+sscmm7E+r1zEDsaVMkQaKfi3RygulhoDG6IkEmns23d/IlPosZ78pc4YanX4IB7RXlDjvZCH+/v7LxuH+17aH/B4NdT3iRWMK2mINHLwz+neRJQgeSY9Ki1m7AoyHFVpMSPvP7MZEj1VWkm1W0qcJi6nWC+o0XyW4bI/4f7+f684QxHHSKzECMaVNEQaOfhPk3W6NwMboiRSlJ2JtXNKcce2uoDgxjcrqig7E4A8M+CUUrvF2X+B+nNBDfdZRpP9CfX3SjlG5KCkIdJIwb/ZpGdgQ0SJM8xswkNXlqGpw4VWpxvZmWnIM+n9QY2PkrIoiZbM7703MS+oShpOURslDZFGCv453ZuIEq4oO7NPIBOMWn4hSzFTRC3vXWpiXlCVNJyiNkobIg0X/Hs8noS2pTcGNkSkakqZKaJVYl5Qo8n+KGE6sxIpcYhUqcE/AxsiUi0lD220OFw43t4FT85QfGPvVO1QlpgX1EjZH4Neh2VbahmkhsAh0ugwsCEi1VLq0IaaskjRZEjEuqBGyv58fLhZkUGqkig1S6IkDGyISLWUNFPER8lZpN5iCcDEuKCGy/7ce+k4TK/eGfTvWH+jHGoYKmRgQ0SqpaSZIj5KzSL1JkYA1p+LXKjszzf2DjhcoYtO5VzxWQ0X80RQSyaSgQ0RqZbSZooAyswiBRNvABbPRS5Y9ierPfwy/HKt+KyWi7nU1JSJDL6jGBGRCviGNiaVFATcLudMESVmkYKJJwCLdJFrccS+V5AvSA0mliC1xeHCgYZ27Pu2FYbC4RH3R4v0XGK/T7WKJhBWCmZsiEjVIhW2JnoYQYlZpGDiCcCkGG4TY/aV2NkVtQwrJoJaMpEAAxsiRePYfnRCFbbKMYygxPVGgoknAJPqIhfP7CsphkrUdDGXmloykQADGyLF4th+fOSsCfBdoI+3d+FEayfysw0YmJWhmKAGiC8Ak/Ii19/ZV1JkV9R0MZeaWjKRAAMbIkVSSqGemjNGcg8j5Bj1yMrQoeXIVyguLoNOp5PstfqrvxkSJV7kpMiuKPF9ykUtmUhAoYFNc3Mz1q5di3fffRderxdnn302Vq9ejcLCQnzyySe4//77YbPZkJeXhxtuuAFXXHGF3E0mEpXcF2VA/RkjDiNEpz8ZEiVe5KTIrijxfcpJLSsfKzKwufnmm5GTk4M333wTqampuP3223HXXXfhl7/8JZYuXYrly5fjqquuwp49e3DTTTdh1KhRGD9+vNzNJhJNuIuyUa+DVxBwoKFdskyKUjJG8eAwgrSUdpGTKruitPeZSKEytkp/74oLbD799FN88skn2L17N7KysgAA9913H44fP4433ngDubm5WLBgAQDgvPPOw6xZs7B582YGNqQpoS7KRr0O1fOtuHf7Z9hps/tvFzuTooSMUbw4jBCcmMOLSrrIhcquVJUUYO2c0rjaqaT3KaZwx4KaM7aKC2z27dsHi8WC//u//8OWLVvQ2dmJqqoqrFixAvX19Rg5cmTA4y0WC7Zu3Rrz63g8Htm3Vk82vv5mv0eWb0oPelFeUlmMJ2sOoqZHUAN8n0l5+OoyZGcGfq370++RhnFand2K/xyzMnQhhxHWzRuPrAyd5O9BzGO+1emGvcOFNqcb2ZnpyDel9/msIznW1jcT5+uPogHqv3AXDdDjgbmlOGR3oLmzGxlpqag90oxfbP8Mv7h0nCbeo1jCHQuG9NSwGdtg55mepDrXR/t8igtsWlpa8OWXX2LcuHHYtm0bnE4nbrvtNqxYsQIFBQUwGAIjxczMTDgcjphfZ//+/WI1mWJUV1cn6+unpqbCmFcAt86Iji4PsjJ00HkccDQ1wuv1yto2n9TUVNwzvQSrXxYCMjMTf2DGhndsQf9mR30jjp5ox9cN3wS9P5Z+zywcHvZ+fYoXe/fujfr55JKamoo7pwzBioss6OjywJShQ5rHgWMHv8B3Cfys4znmU1NTkT3oNKx++auAY6HKYsbqGSPR+u9DUR23WeZC3P/WtwHPAZw8blZs/QSrLjwF7faGfrdTCUK9RwBwdkv3HtPS0pBddCqc3jS0Ot3INqQhM8WN1mP/gtvd/wUCpRLpWFg9a0zYjG2480xPcp3rFRfY6PUnI+o777wTGRkZyMrKwk9+8hNceeWVmDt3LpxOZ8DjnU4nTCZTzK8zZswY/2tRYng8HtTV1aG0tFTWGSJq+tVaPd/q/5U+IDMNLZ3hMykuIRVlZWUBt/Wn31ud7rDDOEPys5A9pKzvH6pCLjB0SEJeSYxjvtXpxvJn9va5CO202fGLV+oj/nr2OWh3YKdtX9D7dtrs0M0ei7IE9YtU5HqP3zY7cfu2uoBMaqXFjLVzSnFKbqbor9cfPTN+QroubD91eYSwzxXsPNOTVOd63/NGorjAxmKxwOv1oru7GxkZGQDg/zVy+umn4+mnnw54vM1mQ0lJScyvo9PpFDn9MhnI2fdqK4rNM+mQZ8rw//tAQ3vYx2dnpofs21j6Pc8Uehhn/bzxAW2iyOI55k90dIb99Xyiozuqz6MtwtYC7U636s+JcrzHY619gxoA2GWz445tdXjoyjIUZcsb3PSul3l0wZlhHx9uM1Ig/HmmJ7nO9YrbK2rixIkYOnQo7rjjDnR0dODEiRP49a9/jQsvvBAzZ85EY2MjNm3ahO7ubrz33nvYvn075s2bJ3ezSSXUtN9JMGLtpxMN32yQt289Hy/cOBFv33o+HplvxWARCgd9+/nUHm7CgePtSbXnTqzEmraeDLPE5HiPTR2uPkGNzy6bHU0d8h7bwX7MZaSFv/TnGNITdp6RguICm/T0dPz5z3+GTqfDtGnTMG3aNAwaNAhr165FXl4eNm7ciNdeew0TJkzAqlWrsGrVKpx77rlyN5tUQo61TcS8iCd608ccox4jCrNQNiwPIwqzRHn+o82dWLalFlN+9S7mPLobUx56FzdvqcXR5k4RWqw9Yl2sExkUyxW4JvI9+kTaZDOeTTjFEOzHXO2RZlRYzEEfP6mkAIUDMhS3uWwsFDcUBQBFRUX49a9/HfS+0tJSPPPMMwluEWmF1L/oek6fzDGkQ69Lxe3b6kSdMqnmdTXUNhSoBGJNW0/UYnNyThOO5T2KNe09Un1TrDPXeou3ncF+zG3cdRDV860AEJBt6tlPOUao9jyjyMCGSCpSrm3S+4S+bLIFtYebQk7NjucirtZ1NbSwPk6iiRmQSB0UKyFwjWafLjGDrzyTHpUWM3YFGY6qtJiRZxLvnNKfdgb7MedwebB8Sy2WVBbjrhlj4Oz2BD0W1HqeUdxQFJGUpBrKCXZCtw7NDTn2roZ6Hilwm4P+EbPeSYrhRR+l1LDlGPUoNhuhazmCYrOxT6YmXPAV67BZUXYm1s4pRWWvoR3frKj+Fg6L1c5Qw3MOlwf7jjRjcE6mJMeCnJixoaQjxa/WYCf0Lnf4tUWS8SKeDAWsUlHDr2c1BK5SZA2HmU146MoyNHW4Tq5jk5mGPJM+rtlQYrUz3IrM98weC/t/ipuVfmzFgoENJSWxLxLBTuiRZh4k40Wc2xxomxoCV6mCr6LsTFGndYvZzp4/5po7Xejq9mL313bMemQXHC6ParZKiBaHoohEEOyEHmnmQTJexBM9q4sSS45ZSbFSQ/AFiN/OHKMeBVl6/Oatevzoj+9jwzs2/3o1/R2GUypmbIhEECwT4Zt5kAIEFBYm+0VczbO6KLxEzbwC+j9bSC1ZQynamSzF+wxsiEQQ7ITucHnw1w8OY/288XB2e3kR70EN9SLUP/0NXGMJVOKZLRRN8CXmDuj9JUWQqIYaKDEwsCESCTMRRCfFGrjGEqiIMaU83HdVznV4QrXT3uGCxyvA4xXgcLnh6PagxRF7dkUtw3DxYmBDJCJmIohiE2ugIuZsod6PU8I6PL3lGPXocHlECbbUMgwXLxYPExFRwvTebqGhrSumtW+kHE5Ryjo8PYm57k6yFO8zY0NEcVFCPQKpQ7BhnicWlof9m96BipTDKUqsQYkUbDW0dcX0/UuGIXMGNkTUb0qqRyBlC5V5iKR3oCLlcIoSa1AiBVuHTzjw33/60P/vaL5/YgyZK/kHDYeiiKhfxF6anrQtVOYh1vWepBxOUeI6PJGCrd4S8f072tyJZVtqMeVX72LOo7sx5aF3cfOWWhxt7pTsNWPBjA0R9UuyrIlB4giVefCt95SaktIn8xcqUJFqOCWR6/BEK1yGqsJiRu2R5j63S/n9i6bAOitDJ/rrxoKBDRH1ixLrERJFyWl4pQqVefDtNP3q8iq4vULUgUrv4RRfUbLvM8k3piM1NfZBCaXVoITb62nhxOFYvqU26N+F+v7Fe+xG84MmK0PeYWgGNkQiSqYLnhLrERIhVF3R+nnjYdTrAj7//l5ctShc5qH8tDzkGtP7/V0J9ZncM72kX8+ntGUbggVbaakpuKR6p39bhN6Cff/EqImL7gcNAxsiTUi2QtpkWROjp1Bp+A8PNeHQCQd++44NO23iXFy1RqphnnBDI6tfFlA934o8k7xDI2IIlqEqPy0v6u+fWGv0RPpBY8qQP6zgTwkiESRjIW2yrInRU6g0/JLKYjzyTn1AUAP4Lq5fodXpTlQTFc2XeXj71vPxwo0T8fat5+OR+VYMjiPwDzc0stNmh71De989IPbvn1hr9IQrsK6wmPHhoSYca3PJmqmUP7Qi0oBkLaRVWj2C1EKl4a1Dc7HhHVvQ+3wX1zxThpRNUw2xh3kiD41oN6iM5fsnVk1cqMxbhcWMxRXFJ+ulTsvDnVOGxPZmRMTAhkgEyVxIq7R6BCmFSsN3ub1h/07LF9d4iFGTFrnWS9uXuWi/f2LWxA3JNeC+S8fBdrwdXW4vMtJSUXukGcu31MLh8mBHfSNWXGSJ+vnEpu1PnChBkrWQNtmEqivKSAufdtf6xbU/xKpJC1frVWUxw2xKbNB9rNWJpg4XWp1uZBvSkGfUoyg7M6FtCEbsmrgTDlfAwoC9dXQFL2pOBNbYEIlAiQt7kfhC1TU0tHWhKsTnL8fFVekStf/R6hkjkZ3AoPKwvQO3/t9eXPzwTlz5+3/i4t/sxE//by8O2zsS1oZQxK6JM2XosGyyBU8sLMejC87ExkVnY9lkC4x6nf9+ufBnBJEIlLiwF0kjVF3D+SMHBv3875lektCLqxJEGmISuyYt2GeSb0rHN1/tBwrHx/1+onGs1Ynbt9WhxmYPuH2XzY47ttXhoSvLZM/ciFUT1+Jwwe0RUHu4KaC2rMJiRvV8K/76wWGkeRwAcsV9A1FKrm8bkYSSrZA2mQWra8gxQvaLqxJEM8QkRU1a78/E4/HA6w1f+ySmpg5Xn6DGZ5fNjqYOl6yBTe9gs7jA1O9zU7OjG2te+bzP+62x2ZECYO2cUtgPfwUMlaeAmIENkYiSqZBWCZS2IKLcF1e5hRtiWvHcPtx/2TjkGtKRY9BeTVqkKf1yTvkXe42tDpc7bBDX3uWW9biPOrCZNWsWtm/fjsmTJyMlJSXoY95++23RGkZE8WtxuHC8vQuenKH4xt4ZMoOktAAhGsm2IKIS9T5u0lJS8NGhpqCP3VnfCFtDO/60+xusnVOKqacX4s3PG/o8Tq01aZGGG+UajhRrYb6eOkKsdtzzfjkLeKPu6aVLlwIAli1b5g9s7HY78vPzQwY6RCSfaC/8agwQpDhZU2yCHTdVJQWonm/1T/vtrcvtxY76RtyxrQ4PzC31/9tHzTVpeSY9Ki1m7AqSyai0mJEnUwG5FGts5UbIuOUY0tEW0zOKK6aMje//H3nkEWzevBkejwd/+9vfcMstt+Cxxx6TrJFEFJtoL/xqDRCSdUFEpQh13Oysb4RXELCksjjogoW+afE76hvh7PZqqiatKDsTa+eU4o5tdQHBTaXFjLVzShNaX9Mzk+b2CmEfG009U+/MXLYhHVUlBUG/g1UlBRiYpVdHYOOzYcMGvP/++6iursZPfvITFBQUYNCgQVizZg0efvhhKdpIRDGK9sKv1gAhmRdEVIJwx02NzY4lFcV9bq+wmFF7pNn/7zZnN0YUZiny+OqvYWYTHrqy7Pt1bDLTkGdK7Do2vTNpTywsD/v4SPVMwTJzU08vxJrLxmHVC58GzbjJPQsw5lffvn07tmzZgqKiIqSkpMBoNOKBBx7A1KlTpWgfEfVDtBd+tQYIXBBRXpGOm956Lrfvo9XPqCg7U7bZTz0zaUa9DksqizFwQAaqLGbsDDJEFqmeKVRmzlcb9eAVZ6Dd6e6TcfN45FucD+hHYONwOJCfnw8AEISTKa7MzExZN7wiokDRXvjVGiAk487iShLpuBmWb8Sbt0zC4RMOAAhYbh/gZyQVXybNqNeher4VT9YcxMZdB1E93wovEDCTKZp6pnCZuTc/b8DKS9wYUZgl9tuIW8zRSFlZGTZs2AAA/qLhP//5zygtLRW3ZUTUb9GuhKzWFZOTcWdxJYl03BQOyEBJ0QCcPjgbf9r9DTa8YwsIavgZScOXSVtSWYwnaw6ixmaHw+XB8i21sA7LwxMLy/HEwnK8ecskPDLfCqNehwMN7ag93IQDx9v7rPis1oxuzBmbO++8EwsXLsS2bdvQ0dGB6dOno6OjA08++aQU7SOifoh2JWQ1r5jMBRHlE+1xw88osXyZtN67zTtcnoB/v33r+ehweSLOhlRrRjfmwGbo0KF4+eWX8Y9//APffvstBg0ahAsuuABZWcpLRxElM99F5Xh7F060diI/24CBWRl9LipqvvhwQUT5RHvcqP0zUtMaT75MWqTd5ls6u7F6+2cRZ0Oqdci3X6XLBoMBl1xyidhtISKR5Rj1yMrQoeXIVyguLoNOF3xjOrVffEgechw3iQw01LbGky+T9k1j+E03jXpdVLMh1ZrR5ZYKRESkCokMNNS6xtOQXAN0qSkh15mZVFKA1NTwi+r2rJ1RY0aXU5mIiEjxIgUavQtf4xXNGk9K0OJw9SkALsrOxPowxfVpEQKb3rUzOUY9RhRmoWxYnn8WVLiiY7kxY0NERIqX6MUk1TAjKFQG695Lx6HN6cJ9l42Dy+1FR5c7INPS4nD1u3ZGDcNzzNgQEZHiJTrQUPqMoHAZrDtfqMPr+xtw/oP/wH0v7UdhdmbAKs+hlkuoKinA2jmlIQPERGfN+osZGyIiUrxEBxpKnxHU2O7CR4easGyyBdahuehye5GZrsPHh5uwcddB/7YWoWqChuQa8MDcUhyyO9Dc2Y2MtFTUHmnGL7Z/hl9cOi5o9iVY1sy3wrF1aC6+amhHvkmPfGO6rIv2MrAhIlIwNU03llKiAw2lzwhq7+r2ry7cc42aCosZ1fOtAZtfBhuqa3G4sPL5uqDDe13uk4EQgIBjzyMIMOp1/sUWe65w3LMNk0oKcM/0EtHfc7QY2BARKZQa6hkSRY5AQ6wZQVIEp7kGPX75+pcB2yQA32+bsOLi0QG39x6qC1ez9OGhJjQ5unHXi58GPKaqpADV863+7TF6rnDc0476Rqx+WUD1fCvyTMGXmJASAxsiIgVS63RjKckx9TjetXqkCk5dHm+fgMKnxmaHq9cifb2H6sLVLC2pLMZdL9T12ThzZ30jBEHAkspibHjH1meF44DH2uywd7iQZ8qI5u2IisXDRKQJwaa9qplaphsnWu+px/0JOhJ1rEhZbNve5Q57//H2Lv9/BxuqC1ezZB2aG3Q3cADYZbNj4g/MABBxheM2Z/g2SoUZGyJSPS0O2ahhurEaxXusxDKsJOUU9UjF1HrdybxFqKG6cDVLkWSm6/DOT8/vkxXqbUCmPCEGMzZEpGpqmYIaK6VPN+5JLdmy/hwrvvf2yZEmfNPYgWVP12LKr97FnEd3Y8pD7+LmLbU42twZ9PWkDE7D7bBeVVKAYflGvH3r+XhkvhWDgwRsoaZ8TyopwKl54QO8HEM6MtN1qD3SjAqLOXgbLGaYTfIMlTJjQ0SqluiF2xIlnllA3E8puFiPlZ7vbdlkC2oPNwUtlA1V8yRlcBqpmDpYMNNbz5qlls5uGPU6pKamICMtNeyxl5WZhju31WHcKTlYecloNLR2ISUlxT/VvPy0PNwzvQTZMmVsGNgQkappdcimv7OAuJ/S97LMhThod6DN6Ua2IR0tneEzST2Pld7vLVyhbKgAWuop6ia9DvddOg4dLjccLg9yDOkoHJARU5/nGPXocHkCdvs26nXYuOhsCECf42j9vPFwdLlx9TnD8GTNQfzmrXr//VUWM164sQIZaSk4caQeKBwf1/vrLwY2RKRqahqyiVWss4ASHWgoOVt2rM2F+9/6Fjtt+/y3PX3thLB/0/NY6f3eIhfK9g2gpZyiHi6AzTFG/zzBjhmHy4Mlm/bgrpljcPfMMX22ZPjqWFvQad47bXb84qXPcM+ssfB6w/eXlBjYEJGqKX2F2HjFMt2Y+ymd5L9Y97rw7v7ajkqLGbuCzPjpfaz0fm8ZaeFLUkMF0FJMURczgA11zDhcHtz+fB3evvV8lA3LC7jP6xXCTjX39lgcUA4MbIhI0SLViyh9hdhEUvp+SsdanWjqcKHV6Ua2IQ15Rj2KsjNFbRMQ+mK9cddBVM+3IiUlJegQS89jpfd78xXKBrugRwqg410LpzcxA9j+HDMOV/hp3JHulxoDGyKSTLxFrNHWi8ixcJsSKXk/pcP2Dty+rS4gMKi0mLF2TimGmU2itivUxdrh8mD5llq8eFMFUlNSQh4rLQ4X0lJTUFVS4D/2fEERgID3kMgA2hcYtnR246WbK3Gs1YkVz+3rs6ZRLAFsf46ZHEP495ptSEdL1C0QHwMbIpKEGOuFxJJuF/tXsRopdT+lY63OPkENcHKxtzu21eGhK8tEzdyEu1g7XB6kpqRgRGFW0Pt9x+1Hh5pQPd8Kr3By2MUXFPnqTjpdnoQG0KECw83XnosFf3wvILiJJYDtzzET6W/MJj0DGyLSFjFqAJRcmKpUSt1PqanDFbImY5fNjqYOl6iBTX8DvN7H7fIttVhSWezfKXtYvjHmWUdiCBcY3vfSZ1g/bzz++08fAog9gO3PMRPpb+Sa5u3DwIaIRCdGUKLUwlSlU+J+Sq0RltaPdH9/2rNu3nis2PpJQAFxpACv93HrcHkCpni/fev5CQ1qfEO54faF2mWzY8UlJze87G8A6ztmmh3d6HC50eHyINeQDqM+9AaW4Y4zj8cT0+uLjYENEYlOjKBEy9O4paa0YblIv+Cl+IVfNECPVReeAt3ssWh3uqMK8JQUTPccyt1y3blhH9vp8uDtW8+PK4DtcHmwqtdu3pGGjpV2nPlwSwUiEp0YQUm4JeO1MI07meSZ9KgMsfR+pcWMPImW3m+3N6DYbIx6w8xYjlspt5HoPSSWlRk6cwKcbHd/NwQN9no+at2WRLGBjcfjwY9//GOsXLnSf9snn3yCK664AlarFZMnT8azzz4rYwuJKBQxgpJwe9kk2zRutSvKzsTaOaV9ghvfrCgppnz3R7TH7dHmTizbEv2eUbHqPSTW0NolaWCotZ3kFTsUtWHDBnz44Yc45ZRTAAAtLS1YunQpli9fjquuugp79uzBTTfdhFGjRmH8eHmWbSai4MQqYuU0bu0YZjbhoSvLvl/HJjMNeSZp1rHpr2iO20Ss7tx7SGzFc/uw+dpzcd9LnwUsLihWYKikITgxKDKw+ec//4k33ngDF110kf+2N954A7m5uViwYAEA4LzzzsOsWbOwefNmBjZECiRWUKLUcXyKXVF2pqICmWAiHbeJmK3Xe0issd2FBX98D+vnjceKS0aj0+VBdma6aIGh1urZFBfY2O123HnnnXj00UexadMm/+319fUYOXJkwGMtFgu2bt3ar9fxeDyyV24nG19/s98TS85+z8rQISvDAOD74sNk+vx5zMsnnr4Pd9xGym60Orvj/rzzTel9pqs3trvw33/6EJNKCvDw1WX+gmsxjq1gr+czqaQA+ab0mF5HquM+2udTVGDj9Xrx85//HIsXL8bo0aMD7uvo6IDBEFiZnZmZCYfD0a/X2r9/f7/bSfGpq6uTuwlJif0uH/a9fOLp+7S0NGQXnQqnN82/DUS+8WQGJ1TdiT7Fi7179/b7NQEgNTUV90wvweqXhYDp6lUWM+6ZXoJvvtoPr9eL1NRUGPMK4NYZ0dHlQVaGDjqPA46mxpg2oYz29XyPjfY15TruFRXY/P73v4der8ePf/zjPvcZDAa0tbUF3OZ0OmEy9W8p7jFjxkCvZ3o7kTweD+rq6lBaWgqdLnyVP4mH/S4f9r18xOj7b5v7LoxXaTHjmaXn4eo//LNPcDOppABD8rOQPaQMwMn1eewdLrQ53cjOTEe+KT2mqe3V863+vx+QmQazSX/y7wtPll8ca+tb7+Obol00IPbrW6TXi/Y1pTrufc8biaICmxdffBENDQ0oLy8HcDJwAYC33noLt912G2pqagIeb7PZUFJS0q/X0ul0PNHIhH0vD/a7fNj38ulv34db7Xf13z7Fhh+diav/8J7/dl+BcZ4pA0D8W4oAQJ5J53++3qQoYg72ej33e8s36bFq26fYaYvuNeU67hUV2Lz22msB//ZN9V63bh2amprw4IMPYtOmTViwYAE++ugjbN++HY8++qgcTSUiIg2LtA3Eqhlj8Pat5wctME7EzKlEFDH3Ds6eWFjeJ6gR+zXFoKjAJpy8vDxs3LgRa9asQXV1NfLz87Fq1Sqce274FRmJiIhiFc02EOcU5we9LxFBh9RTtIMFZ13u8HU7SpkWrujAZt26dQH/Li0txTPPPCNTa4iIKFnEsw1EItaFkXqKdrDgLCMt/Jq+SpkWrujAhohICXrWGWQb0lFg4to6WufbBmJXkOGoSKv95hjSsWyyBdahuehye5GZrsPHh5uwcddBOFweUQKA/u5gHq1gwVntkWZUWMxBh+iUtM0JAxsiojDEKAINh0GTMvm2gbhjW13Mq/3qdamoPdwUsDN4hcWM6vlW/PWDw6IEAGKt7h1KsIzQxl0HUT3fioy0VIwZkuMP3PKM6RiWb1TMccvAhogoBKmLQKUOmig+/dkGosXhCjqbqsZmRwog6j5nUm45Eiwj5HB5sPK5fXhqyTl44JXPAwK3qpICPDCnFKfmG+N+7XgpdhNMIiK5Sbk5oNZ2VFaKY61OfPFdKz463ALjEEvcGzgWZWdi9OBsnFOcj9GDsyNuYRDumNlls8PZHf3CedHIMeoxojAr6h3MY3neYJvQ/vSiUXjglS8CFvIDgJ31jVj5/D5829S/RXPFxIwNEVEIUhaBJmLmTLI5bO8IuqDe2jmlGGbu32KusdLShpLBMkJeQcDtzwdfJG+XzY5DdgeMennXbGLGhogoBClnnmjpAqgE4RbUu2NbHY61OhPSDq1tKNk7I9TeFX4afHNnN+wd8mYbGdgQEYXgqzMIJt5ZIFq7AMot0oJ6TQm62Ep5zChBpOM2Iy0VbRHWAJIaAxsiohBC1RmIMfNE6xfARItmQb1EkPKYkVKLw4UDDe2oPdyEA8fbQ9Z4FWTpURXiuK2wmFF7pBkDYtgPSwqssSEiCkOqmSdST9dNNvEsqCc2KWcrSSHW2XmrZpyOZkf3f2ppUvDOl8fwyZFm/GjCafjrB4dhrhiOlgS2vzcGNkREEeQYpbkoqe0CqGTxLKgnBamOGbHFsqTB0eZOrNi6L2C/qAqLGTdPLsGl44fA6fHgntljExpEBsPAhohIRmq5ACpdPAvqJbNgs/OMeh2WVBbDOjQXXzW0I9+kR1ZGGu5+4fudvXs+pqPLjZZ0HRrbu/D0+1/inlljkZYmX3jBwIaIiDSh94J6AzJ0yDfpUZTDxQ5D6T07z6jXoXq+FU/WHOyzAN/CicOx++uTQWPQx1gKsLBiOH6x/TOsunhEYt5AEAxsiIhIM4qyM1GUnQmPx4O9e/diZFmZ3E1StN6znJZUFuPJmoN9ZpjtrG+EVxCwpLIYAII/xtYILwRYh+XB6ZUvvOCsKCIikkW0M3Eosv72Ze/ZedahuSGnzdfY7LAOzY3qMXJO+WbGhoiIEo77ZIknnr7sPTuvyx1+y4dI9/seMySHGRsiIkoSWtwnS67skxh96Zud9/at52O4OfwmlrmGdGSkhQ8dcgzpyExlxoYSrMXhQmO7C63ObmQb0lFg4swMIkoMre2TJWf2Say+9M3Oa3G4+uzq7VNVUgCn24NT8wyoshQETPv2qbSYMSzPgOajXwMDc2N+P2JgYJOEmAImIjlpaZ+sWNaBkYLYfZlj1GPtnFLcvq2uzzXintljMeuRXQBOzoryQgiotakqKcBdM8fg1299hZsmnRbT64qJgU2SkftLSESkpX2y5M4+id2XR5s7sXr7ZzhjaC4WTRyOLrcXuYZ0nGY2wuX2wuHyAACWb6nFI/OtuPECC1o6u5GRloraI8247Lc1cLg8ON7Wher5VuSZEr/TNwObJCP3l5CIyDcTJ9hwh9r2yYqUMbF3uIDj7ZIN94vZlz1/+L71eUOf53rwijP8r+ULcBb88f2gz7XTZoe9w4U8U0YM70YcLB5OMlpKAROROql1o8hgImVM2pzdmPLQu7h5Sy2ONneK/vpi9qXvh69Rr8OyyRY8sbAcjy44ExsXnY3x/1lhuOdrRZohJdeUb2ZskoyWUsBEpF5a2ScrXMbEt9s1IO1wv1h92ersDrnycIXFjDnWUwJeq8vtCft8cu3yzYxNkum9GFNPaksBE5G65Rj1GFGYhbJheRhRmKW6oAYInTGpsJixuKIYG3cd9N/mG+6Xqh3x9mV2ZnrIlYdrbHas/ttnaHG4/K91Sq4h5PWkymKGOcEbj/owY5Nkei/G5KPGFDARkRL0zGLYO05mTWqPNGP5llp/LYqPkof7C7L0mPgDc0Cmpqedveowc4x6PDC3FIfsDjR3diMzXYePDzfhy+9asfKiEbLt8s3AJglpJQVMRKQU/l3aG9px5e//GfJxSh7uzzHqoY+w+F5LZze+Pt4Ot1eA1yvA3uGCy+3F/u9asXHXQZx1Wh7WzinFiSP1QGFOgloeiIFNkvJ/CYmISDRqn/GVF+G60OX24OtGV5/hqgqLGdXzrVi+pRZ3bqvDnVOGSN3UkFhjQ0REJBK1z/gKV4dZaTHD4fKErMF5suYgllQWY0d9I9y68FszSIkZGyIiIhGpebg/VB1mVUkBFk4cDl1KStidvZdUFAMAOrrCz5iSEgMbIiIikal5uD9YYOYRBFz22xr87xVnhP1b39o2pozErzjsw8CGiIiIAvQOzA40tMPh8kTc2TsjLRWTSgqQ5nEAyJW2kSGwxoaIiIjC8tXe1B5pRoXFHPQxFRYzGtq6sG7eeDiagm/dkwgMbIiIiCgsX+3Nl9+1YnFFcZ/gpqqkAGsuK8X0cYNQNEAPrzf8dgtS4lAUERGRhFocLjS2u9Dq7Ea2IV2yDTGlNiTXgP+94gzYO1xYPWssPF4BDpcHOYbA4miPR77CYYCBDRERkWSONnf6d8z2mVRSgHXzxmNIrkHGlvWPGoqiORRFREQkgRaHq09QA3y/IWaLQ5p9oxKpxeHCgYZ21B5uwoHj7Yp4T8zYEBERSaCx3dUnqPHZ0WvfJTUKl41KTZUvb8KMDRERkQRaI2x4qeQNMSOJlI0y5gVfvTgRGNgQEZEiKHFYIx7ZETa8VPKGmJFEykZxSwUiIkpqWiuyBdS/IWY4kbJRHS75ZkYxY0NERLLSapGt2jfE9AmWSRuQGT4vMsAg33tjxoaIiGSl5SJbNW+ICQDfNjlwyO5Ac2c3MtN1ePuLBnz5XStWzRiDyaMH4p0vjvf5mwqLGXqdfHkTBjZERCQrLRfZAupY+yWYf51wYMXz+wJ2866wmLG4ohj3vbwfP7lwJLrc3qD3N7V3YXiBSY5mM7AhIiJ5abnIVq1aHC7c3iuoAeD/t3VYHhrbXbAOy8OSimJ0ub3ISEtF7ZFmLN9Si2evO1uOZgNgYENERDLTcpGtWjW2u7CzV1DjU2OzY0lFMQRBwIZ3bH3u5+7eRESU1LRSZKslkYYHu9xenJpnCPqZyb27NzM2REQkO7UX2WpNpOHBXEM6BmVnBv3MsjJ0+I67exMRUbJTa5GtFoUbHqy0mHGa2ej/rHp/ZnLv7s2hKCIiIgoQaniwqqQA6+aOxyl58q0sHAkzNkRERNSHWocHGdgQERFRUKGGB1scLjS2u9Dq7Ea2IR0FJuUEPAxsiIiIqI9QwYvS9/ViYENEREQBQgUva+eUYvX2z0Lu6/XIfCuyMnSJbm4AFg8TERGRX7hNSW/fVofRg7OD/p1vXy+5MbAhIiIiv3Cbku6sb4R1aG7Iv1XCvl4ciiIiIiK/3qsOG/U6LKkshnVoLrrcXhQOyMCyyRZs3HUQDlfgmjVK2NeLgQ0RERH59Vx12KjXoXq+FU/WHAzYF6rCYkb1fCuWb6n1BzdK2deLQ1FERETk51t1GACWVBbjyZqDQXf5frLmIJZUFgNQ1r5eigxsvvjiCyxevBjnnHMOKioqcNttt+HEiRMAgE8++QRXXHEFrFYrJk+ejGeffVbm1hIREWlHz1WHrUNz+wQ1PjU2O2aWDsY7Pz0fj8y3YrACpnoDCgxsnE4nrr32WlitVuzatQsvvfQSmpubcccdd6ClpQVLly7FZZddhj179mDNmjV44IEHsG/fPrmbTUREpBm+VYezDeFrZr5t7kRmuk4RmRofxQU2R48exejRo3HTTTdBr9cjLy8PV111Ffbs2YM33ngDubm5WLBgAdLS0nDeeedh1qxZ2Lx5s9zNJiIi0pQcox75UQQsK5/bhxaH/NO8fRRXPPyDH/wAf/zjHwNue/311zF27FjU19dj5MiRAfdZLBZs3bo15tfxeDyy70CabHz9zX5PLPa7fNj38mHfiyPflB5yl+8Kixm1R5qxo74Rx9u7/AvzSdX30T6f4gKbngRBwG9+8xv8/e9/x1/+8hc89dRTMBgCx/AyMzPhcDhifu79+/eL1UyKUV1dndxNSErsd/mw7+XDvo9Pamoq7plegtUvAztt3wc3FRYzFlcUY/mWWgDAidZOtBz5KuBv5ep7xQY27e3tuP322/HZZ5/hL3/5C0aNGgWDwYC2traAxzmdTphMppiff8yYMdDrlTMmmAw8Hg/q6upQWloKnU7eJbeTCftdPux7+bDvxXXfZWNx4HgHutxeZKSlovZIc8BU7/xsA4qLywBI1/e+541EkYHN4cOHcd1112HIkCHYunUr8vPzAQAjR45ETU1NwGNtNhtKSkpifg2dTseDXSbse3mw3+XDvpcP+14ceUY9/rT7s6BDUpNKCjAwK6NPP8vV94orHm5pacHChQtx5pln4oknnvAHNQAwdepUNDY2YtOmTeju7sZ7772H7du3Y968eTK2mIiISNt8U8Cr/rO+jU+FxYwbf2jpswKxnBSXsXn++edx9OhRvPrqq3jttdcC7qutrcXGjRuxZs0aVFdXIz8/H6tWrcK5554rU2uJiIiSg0mvw/TSwVg0cXjAkNSSTXtQfloeHplvVcS0b8UFNosXL8bixYtD3l9aWopnnnkmgS0iIiKixnYXbn++LmDvqDGDs7HhR2ei7ttmNHd2o7HdhVZnNwyFw9HqdCPPlPihKMUFNkRERKQ8rc7uoHtHGfU6PLGwHKu21WFnj1WKJ5UUYN288RiS4BWJFVdjQ0RERMqTnZkedO+oJZXF2PB3W0BQAwA76htlWbyPgQ0RERFFVJClx8QfmPvsHRVuP6kd9Y1obGdgQ0RERAqTY9RDn9Y3bOhye8P+XZuzW6omBcXAhoiIiKKSldm3NDcjSLDT04DM8Btpio2BDREREUUlNSUFFRZzwG21R5r73OYzqaQABVmJnQLOwIaIiIiikpaagsUVxQGBzMZdB7HshxZUWQIX75tUUoD188YnfG0bTvcmIiKiqJhNejzwyuewDsvDkopi/0J9H3xzArPLhuDuWWPQ3uWGPsWLIflZyDNlJLyNDGyIiIgoKjlGPX5x6TisfG6ffx0b4GR25sp5QzE41wCPx4O9e/cie0iZLG1kYENERJSEWhwu/0rB2YZ0FJj0UQ0bDck14JH5VjS2u9DR1Y0cgx4ujxf/bnXC0e1BvjEdqanyVbowsCEiIkoyR5s7seK5fdjZY7fuWFYKzjGeDIJCPc8900skaXc0WDxMRESURFocrj7BCBD7SsHhnmf1y1+h1ekWrc2xYGBDRESURBrbXX2CEZ9YVgoO9zw7bXbYOxK74rAPAxsiIqIk0hphJeBoVwqO/DzM2BAREZHEsiOsBBztSsGRn0eeMl4GNkREREmkIEuPSSUFQe+LZaXgcM9TZTHDbErswnw+DGyIiIiSSI5Rj3XzxvcJSmJdKTjc86yeMRLZMmVsON2biIgoyfRci6bN2Y0BmekoyIpuHZtIz5NvSsc3X+0HCsdL1PrwGNgQERElId9aNP3Ve4G/4gITcox6eDweeL1eEVsaGwY2REREFJNwC/wVDZCntsaHNTZEREQUtUgL/Mm1MJ8PAxsiIiKKWqQF/uRamM+HgQ0RERFFLdLCfM2ObmSZCxPUmr4Y2BAREVHUIi3M19LZjfvf+hbH2rilAhERESlcuIX5Kixm1B5pxk6bPaYNNcXEwIaIiIiiFmphvipLAe6eOQZnn5aPZZMt+PBQU9QbaoqJ072JiIgoJr6F+b5t7kSr0w2PV8A/v7ZjzqO74XB5UGExo3q+FR1d0W2oKSYGNkRERBSzHKMezY5urHnlc9TY7AH3+f699rLShLeLQ1FERETULy6Pt09Q41Njs8PlSfwKxAxsiIiIqF/au8IvxtcR4X4pMLAhIiKifok09XtAhPulwMCGiIiI+iXc1O9JJQUoyEr8vlEMbIiIiKhfQk/9NmPdvPFx7R7eX5wVRURERP3mm/rd2O5Cm7MbWZlp8DiaZdvlm4ENERERxSXHqPdnZzweD/Ye+QoYOkSWtnAoioiIiDSDgQ0RERFpBgMbIiIi0gwGNkRERKQZDGyIiIhIMzgrSuFaHC40trvQ6uxGtiEdBSa9LOsCEBERqQEDGwU72tyJFc/tw876Rv9tk0oKsG7eeAzJNcjYMiIiImXiUJRCtThcfYIaANhR34iVz+1Di8MlU8uIiIiUi4GNQjW2u/oENT476hvR2M7AhoiIqDcGNgrV6uwOe39bhPuJiIiSEQMbhVLiVvBERERKx8BGoZS4FTwREZHSMbBRqFBbwU8qKcB6mbaCJyIiUjpO91aw3lvBD8hMR0EW17EhIiIKhYGNwvXcCp6IiIjC41AUERERaQYDGyIiItIMBjZERESkGQxsiIiISDMY2BAREZFmMLAhIiIizWBgQ0RERJqhysDGbrfjxhtvRHl5OSZMmIA1a9bA7XbL3SwiIiKSmSoX6PvJT36CoqIi7Ny5E42NjbjhhhuwadMmXHvttXI3jYiISPVaHC40trvQ6uxGVkYa9LpUtDpdyDHo0dntQZvTDVNGGlIApKYCOQY9UgCc6HCh1enGgCEWNLa7UJRjSHjbVRfYHDp0CB988AF27NgBg8GAoUOH4sYbb8SDDz7IwIaIiChOR5s7seK5fdhZ3+i/bfLogVh5yem4c1sddtrs/tsrLGYsrijGMx98ieuqfoDFm/bA4fIAACotZqydU4phZlNC26+6oaj6+nrk5uaiqKjIf9uIESNw9OhRtLa2ytgyIiIidWtxuPoENQAwZkgOfrH9s4CgBgBqbHY8WXMQowdnY8M79VhSWey/b5fNjju21eFYqzMhbfdRXcamo6MDBkNgasv3b4fDgezs7Kiex+PxwOPxiN4+Cs3X3+z3xGK/y4d9Lx/2ff8cb+/qE9QAgHVoLja8Ywv6NzU2O5ZUFGPDOzYsqigOuG+XzY6mDhcKTOlxty3az1J1gY3RaERnZ2fAbb5/m0zRp7v2798varsoenV1dXI3ISmx3+XDvpcP+z42npyhQW/vcnvD/p3v/mCPa+3sxt69e+NuW7RUF9iUlJSgubkZjY2NKCgoAAAcOHAAgwYNwoABA6J+njFjxkCv567ZieTxeFBXV4fS0lLodDq5m5M02O/yYd/Lh33fPwftjqC3Z6SFr1zx3R/scdmGdJScVhZ323yfaSSqC2yGDx+Os846C2vXrsW9996LpqYmPProo7j88stjeh6dTseDXSbse3mw3+XDvpcP+z42A7MyMKmkADt6DUfVHmlGpcWMXb1qbICTBcS1R5pR9Z//76nSYkaeSZ/Qz0B1xcMAUF1dDbfbjSlTpuDKK69EVVUVbrzxRrmbRUREpGo5Rj3WzRuPSSUFAbfvP9qCu2eNRVWv232zor74rhXLJpdg466D/vt8s6KKsjMT0nYf1WVsAKCgoADV1dVyN4OIiEhzhuQa8Mh8KxrbXWhzdsP0n3Vs2pwurLlsXJ91bFJSgTVzSpEC4PkbJp5cxyZDh3yTnuvYEBERkfxyjHrkGHvXoUaeoFOYnQmPx4O9e/diZFmZJG2LRJVDUURERETBMLAhIiIizWBgQ0RERJrBwIaIiIg0g4ENERERaQYDGyIiItIMBjZERESkGQxsiIiISDMY2BAREZFmJN3Kw4IgADi5S6jH45G5NcnF19/s98Riv8uHfS8f9r18pOp73/P5ruOhpAiRHqExLpcrqm3PiYiISHlKS0uh1/fe7uF7SRfYeL1euN1upKamIiUlRe7mEBERURQEQYDX60VaWhpSU0NX0iRdYENERETaxeJhIiIi0gwGNkRERKQZDGyIiIhIMxjYEBERkWYwsCEiIiLNYGBDREREmsHAhoiIiDRDk4GN3W7HjTfeiPLyckyYMAFr1qyB2+0O+th3330Xs2bNQllZGS655BL8/e9/T3BrtSWWvt+yZQumTZsGq9WKadOmYfPmzQlurXbE0u8+X331Fc444wy8//77CWqlNsXS9x988AGuuOIKWK1WnH/++fj973+f4NZqSyx9/6c//QmTJ0/GmWeeiVmzZuH1119PcGu158SJE5g6dWrYc4gs11hBg6655hrhpz/9qeBwOITDhw8LM2bMEB5//PE+jzt48KBQWloqvPnmm0J3d7fw8ssvC+PHjxf+/e9/y9BqbYi27998802hvLxcqK2tFbxer/Dxxx8L5eXlwmuvvSZDq9Uv2n73cTgcwsyZM4WRI0cK7733XgJbqj3R9r3NZhPOOOMM4fnnnxe8Xq/w+eefC+ecc47w6quvytBqbYi27//xj38I5513nnDgwAFBEAThtddeE0aPHi0cOXIk0U3WjA8//FC48MILw55D5LrGai5jc+jQIXzwwQf4+c9/DoPBgKFDh+LGG28Mmg3Ytm0bysvLceGFFyItLQ3Tp0/H2Wefjb/+9a8ytFz9Yun7Y8eO4brrrkNZWRlSUlJgtVoxYcIE7NmzR4aWq1ss/e7zi1/8AhdeeGECW6lNsfT9008/jSlTpmDOnDlISUnB6NGj8cwzz+Css86SoeXqF0vff/311xAEwf8/nU6H9PR0pKUl3T7Qoti2bRt+9rOf4ZZbbon4ODmusZoLbOrr65Gbm4uioiL/bSNGjMDRo0fR2toa8FibzYaRI0cG3GaxWPDFF18kpK1aE0vfL1iwAEuXLvX/2263Y8+ePRg3blzC2qsVsfQ7ALzwwgs4dOgQli1blshmalIsfb9v3z6ceuqpuPXWWzFhwgRccskl+OCDDzBw4MBEN1sTYun7GTNmoKCgANOnT8fYsWPx//7f/8O6deswaNCgRDdbEyorK/Hmm29i+vTpYR8n1zVWc4FNR0cHDAZDwG2+fzscjoiPzczM7PM4ik4sfd/T8ePHcd1112HcuHGYOXOmpG3Uolj6/cCBA/j1r3+Nhx56CDqdLmFt1KpY+r6lpQVPPfUUZs+ejZqaGtx7771Yv349XnvttYS1V0ti6fvu7m6MHj0azz77LPbu3Yt7770Xd955J7788suEtVdLBg4cGFW2S65rrOYCG6PRiM7OzoDbfP82mUwBtxsMBjidzoDbnE5nn8dRdGLpe5+9e/fi8ssvR3FxMR577DGmhvsh2n7v6urCLbfcgjvuuANDhgxJaBu1KpZjXq/XY8qUKbjggguQlpaGs88+G5deeileffXVhLVXS2Lp+/vuuw8lJSUYP3489Ho95s2bh7KyMmzbti1h7U1Gcl1jNRfYlJSUoLm5GY2Njf7bDhw4gEGDBmHAgAEBjx05ciTq6+sDbrPZbCgpKUlIW7Umlr4HgK1bt2LRokVYuHAhHnroIej1+kQ2VzOi7fe6ujp88803uPPOO1FeXo7y8nIAwPXXX4/Vq1cnutmaEMsxP2LECLhcroDbPB4PBEFISFu1Jpa+P3r0aJ++T0tLQ3p6ekLamqxku8ZKWposk/nz5wu33HKL0NbW5q+Ur66u7vM4m80mlJaWCi+//LK/Yru0tFT4+uuvZWi1NkTb96+99powduxYYceOHTK0Unui7ffeOCsqftH2/e7du4UxY8YIL7zwguD1eoUPPvhAKCsrE9566y0ZWq0N0fb9r3/9a2HChAnCp59+Kng8HuHVV18VSktLhf3798vQam0Jdw6R6xqrycDm+PHjws033yycc845wrnnniusW7dOcLvdgiAIQllZmfDiiy/6H7tjxw5h9uzZQllZmTBjxgzhH//4h1zN1oRo+37mzJnC6NGjhbKysoD/3XXXXXI2X7ViOeZ7YmATv1j6/h//+Icwd+5cwWq1ClOmTBG2bNkiV7M1Idq+7+7uFqqrq4Uf/vCHwplnninMmTOHP6pE0vscooRrbIogMA9KRERE2qC5GhsiIiJKXgxsiIiISDMY2BAREZFmMLAhIiIizWBgQ0RERJrBwIaIiIg0g4ENERERaQYDGyIikRw6dEjuJhAlPQY2RCSLH//4x3jkkUcAAHfffTfuvvtu0V/j/fffx6hRo0R/3mDWr1+Pxx57LCGvRUShcStlIpLdvffeK3cT4tbU1CR3E4gIzNgQadq//vUvjBo1Cs8++ywmT56Ms846C4sXL8a///1vAMBbb72FuXPn4swzz8S0adOwadMmeL1eAMDKlSuxfPlyXHLJJTj33HNx+PBhjBo1Cn/9618xbdo0nHHGGbj++uvx6aef4uqrr4bVasW8efP8wzEulwvr16/HJZdcAqvVivPOOw/33Xdf0N2sV65ciZUrVwIAZs+eDavV6v/fuHHjMGbMGLhcLgiCgKeeegrTpk1DeXk5fvSjH+HTTz/1P09DQwOuv/56nHnmmZgyZQpqampi6q/du3fj8ssvR3l5OWbMmIG//e1vAW28++67cf3118NqtWLKlCl46qmnAAC//e1vsX37dmzfvh2zZ88GAIwaNQr3338/JkyYgOuvvz6q/r7jjjvwX//1XygrK8Mll1yCt956CwDwhz/8AdOmTQto6xNPPIEFCxbE9P6IkoLku1ERkWyOHDkijBw5UrjxxhuFlpYW4fjx48LMmTOFu+66S/jnP/8pjB071r/z7qeffipMmjRJePLJJwVBEIQVK1YIZWVlwpdffim0tLQIgnByw7sFCxYITU1NwrFjx4Ty8nKhqqpKsNlsQkdHh3D11VcLK1euFARBEP7whz8IM2bMEI4dOyYIgiB8/PHHwpgxY4Tdu3cLgiAI11xzjX8n5hUrVggrVqwI2v6KigrhL3/5iyAIgvCXv/xFuOCCC4TPP/9ccLlcwrPPPiuUl5cLx48fFwRBEH70ox8JN910k9DW1iYcPXpUuPTSS4WRI0dG1Veff/65MH78eOH1118X3G638NFHHwkTJkzwb5a4YsUKYezYscKuXbuE7u5uYcuWLcLpp58u/Pvf/w76HkaOHClcd911gsPhEFpaWqLq79GjR/vv37ZtmzB27FjBZrMJx44dE04//XRh7969/uefOXOmsHXr1qjeG1EyYcaGKAlcd911yM7ORkFBASZPnoxvvvkGzz//PKZMmYLp06cjLS0NY8eOxdKlS/HMM8/4/66srAwjR45Edna2/7ZrrrkGubm5KCwsRElJCS666CKMGDECRqMR5557Lr799lsAwJVXXolNmzZh4MCBaGhogNPphMlkwrFjx6Jqc1NTE6699lrMnDnTn5nYvHkz/ud//gejR49Geno6Lr/8cowYMQJ/+9vf8O233+LDDz/Ez372M2RlZWHw4MFYtmxZ1H30zDPPYMqUKbjoooug0+lw5pln4sorr8TmzZv9j5kwYQIqKiqQlpaGefPmwePx4PDhwyGfc+bMmTAYDMjOzo6qvy+44AL//ZdddhnGjRuHV155BYWFhaiqqsKLL74IAPjss8/wr3/9CxdffHHU748oWbDGhigJFBQU+P87LS0NgiDAbrfj9NNPD3jcqaee6g9MAKCwsLDPc+Xm5vr/W6fTIScnx//v1NRU/1BTZ2cn7r33XuzZsweDBg3CmDFjIAiCf+glHKfTiRtuuAElJSW47bbb/Ld/++23WL9+Pf73f//Xf5vb7ca4ceP8AdOQIUP89w0bNizia/V87vfeew/l5eX+2zweT8BzDBw40P/f6enpABD2/fTsv2j6e/jw4QH3Dx48GMePHwcAzJ07F/fccw9uv/12bNu2DRdffDFMJlPU748oWTCwIUpSp5xySp9sw5EjRwIu3ikpKX3+LthtwaxatQo5OTnYtWsXMjIy4PV6cfbZZ0f8O6/Xi5/+9Kfwer148MEHkZr6fWJ50KBBWL58OWbMmOG/7fDhw8jNzUV7e7v/PYwYMQIA/LVE0Rg0aBDmzJkTUMjc0NAQtCYoWj37Kpr+7p3N+te//oXJkycDACZPnox77rkHNTU1ePXVV/Hwww/3u11EWsahKKIkNW/ePLzzzjt49dVX4fF4sH//fjz++OOYN2+eKM/f3t6OjIwMpKamor29Hb/85S/R3t6O7u7usH933333wWaz4Xe/+x0yMzMD7rvyyivx2GOP4cCBAwCAnTt3YsaMGdizZw+GDBmCyspKPPDAA2hpacHx48exYcOGqNt7+eWX46WXXsKuXbvg9XrxzTff4JprrsHGjRuj+nu9Xo+2traQ90fT32+++SZ2794Nt9uNrVu34quvvsLMmTMBnMwQzZ49Gw8//DCysrICMktE9D0GNkRJ6owzzsDDDz+Mxx9/HOXl5Vi2bBnmz5/vn8ETr1WrVuGLL77AOeecg4svvhjt7e2oqqrCV199FfJvjh49iqeffhqNjY2YNm1awOyoDz/8EIsWLcJll12GG2+8EVarFWvWrMHdd9+NKVOmAAAeeughDBgwAD/84Q8xb948TJw4Mer2nnHGGfjVr36FX/3qVzj77LNxzTXXYPLkyfjpT38a1d9Pnz4dH3/8MS644IKQzx+pv8vLy/H444/jnHPOwdNPP40//OEPGDp0qP/+uXPnYv/+/Zg7d27U74so2aQI8eRZiYhIFL7p7uvWrQv5mObmZlRVVeGtt95CUVFRoppGpCqssSEiUjiXy4VDhw7hqaeewvnnn8+ghigMBjZEpHn79u3DwoULQ94/ZMgQvPzyywlsUWxcLheuvvpqDB48GL/73e/kbg6RonEoioiIiDSDxcNERESkGQxsiIiISDMY2BAREZFmMLAhIiIizWBgQ0RERJrBwIaIiIg0g4ENERERaQYDGyIiItIMBjZERESkGf8fPxzt5ip7534AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = data_13['normalized_entropy'],y = data_13['ei'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# EAD_source = '/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD'\n",
    "# UVPD_source = \"/Users/fanzhoukong/Documents/GitHub/Libgen_data/UVPD\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "extra"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 43/9695 [00:24<1:46:27,  1.51it/s]"
     ]
    }
   ],
   "source": [
    "smiles = []\n",
    "for name in tqdm(extracted_wanted['name']):\n",
    "    smiles.append(name_to_smiles(name))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "extracted_wanted['smiles_fetched']=smiles"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(242, 14)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_wanted.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "extracted_wanted.dropna(subset=['smiles_fetched'], inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "extracted_wanted.reset_index(inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "1     48\n2     31\n3     22\n4     21\n5     18\n7     10\n6      7\n8      5\n9      4\n11     3\n10     2\n12     2\n13     2\nName: concentration, dtype: int64"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_wanted['concentration'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "mix_12= string_search(extracted_wanted, 'mix', 12)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "     id_spec  id_anno  mix charge  precursor_mz     rt               name  \\\n10    342594      210   12    pos      146.1657   0.56         SPERMIDINE   \n11    344640      235   12    pos      363.2180   6.86     HYDROCORTISONE   \n20    682538      370   12    neg      283.0609   9.17        Biochanin A   \n21    682532      379   12    neg      407.2791   9.02        Cholic Acid   \n23    822146      230   12    pos      347.1501   5.24   Gibberellic Acid   \n26    838672      379   12    neg      407.2791   9.02        Cholic Acid   \n32    485564      210   12    pos      146.1657   0.56         SPERMIDINE   \n36    476550      379   12    neg      407.2791   9.02        Cholic Acid   \n50    609165      210   12    pos      146.1657   0.56         SPERMIDINE   \n58    393132      370   12    neg      283.0609   9.17        Biochanin A   \n59    393087      379   12    neg      407.2791   9.02        Cholic Acid   \n76    402703      210   12    pos      146.1657   0.56         SPERMIDINE   \n77    405766      223   12    pos      289.2174   9.66         PRASTERONE   \n78    406237      233   12    pos      225.0909  11.35  2-Hydroxychalcone   \n79    404818      235   12    pos      363.2180   6.86     HYDROCORTISONE   \n91    596120      370   12    neg      283.0609   9.17        Biochanin A   \n92    596117      379   12    neg      407.2791   9.02        Cholic Acid   \n97    515912      210   12    pos      146.1657   0.56         SPERMIDINE   \n101   759904      379   12    neg      407.2791   9.02        Cholic Acid   \n127    84224      208   12    pos      285.0769   9.16        Biochanin A   \n128    85055      209   12    pos      301.2385  12.38     Roccellic Acid   \n129    81266      210   12    pos      146.1657   0.56         SPERMIDINE   \n130    84269      223   12    pos      289.2174   9.66         PRASTERONE   \n131    84827      233   12    pos      225.0909  11.35  2-Hydroxychalcone   \n132    83225      235   12    pos      363.2180   6.86     HYDROCORTISONE   \n147   566102      370   12    neg      283.0609   9.17        Biochanin A   \n148   565439      375   12    neg      779.4211   8.22            Gitoxin   \n149   566075      379   12    neg      407.2791   9.02        Cholic Acid   \n157   527616      210   12    pos      146.1657   0.56         SPERMIDINE   \n158   529680      235   12    pos      363.2180   6.86     HYDROCORTISONE   \n169   748350      370   12    neg      283.0609   9.17        Biochanin A   \n170   748332      379   12    neg      407.2791   9.02        Cholic Acid   \n\n     adduct  ce  concentration  \\\n10   [M+H]+   0              3   \n11   [M+H]+   0              3   \n20      M-H   0              3   \n21      M-H   0              3   \n23   [M+H]+   0              8   \n26      M-H   0              8   \n32   [M+H]+   0              7   \n36      M-H   0              7   \n50   [M+H]+   0              5   \n58      M-H   0              5   \n59      M-H   0              5   \n76   [M+H]+   0              2   \n77   [M+H]+   0              2   \n78   [M+H]+   0              2   \n79   [M+H]+   0              2   \n91      M-H   0              2   \n92      M-H   0              2   \n97   [M+H]+   0              6   \n101     M-H   0              6   \n127  [M+H]+   0              1   \n128  [M+H]+   0              1   \n129  [M+H]+   0              1   \n130  [M+H]+   0              1   \n131  [M+H]+   0              1   \n132  [M+H]+   0              1   \n147     M-H   0              1   \n148     M-H   0              1   \n149     M-H   0              1   \n157  [M+H]+   0              4   \n158  [M+H]+   0              4   \n169     M-H   0              4   \n170     M-H   0              4   \n\n                                                 peaks  rt_spec  \\\n10   51.12116622924805:13.024999618530273;54.827529...   36.701   \n11   54.521629333496094:14.125;56.67998504638672:7....  399.056   \n20   52.409542083740234:69.01786041259766;52.412597...  556.748   \n21   51.81279754638672:28.0;54.822967529296875:7.0;...  556.057   \n23   50.65410614013672:17.225000381469727;52.225910...  312.069   \n26   50.628692626953125:5.375;50.69906997680664:7.2...  554.298   \n32   50.16026306152344:27.0;50.267398834228516:22.6...   42.768   \n36   50.28374481201172:9.0;50.36776351928711:8.0;51...  540.230   \n50   50.857200622558594:16.160715103149414;51.08386...   42.686   \n58   50.111263275146484:79.88970947265625;50.115936...  554.502   \n59   51.05177688598633:13.5;52.932254791259766:15.1...  546.749   \n76   50.458011627197266:7.25;50.90478515625:33.2249...   37.601   \n77   50.9100341796875:17.5;53.52819061279297:7.875;...  573.449   \n78   51.845035552978516:16.0;52.93822479248047:148....  684.568   \n79   51.67262268066406:13.024999618530273;51.760982...  400.627   \n91   52.04039001464844:9.125;53.03115463256836:8.12...  551.270   \n92   50.506492614746094:9.0;50.969234466552734:51.8...  551.055   \n97   50.60329818725586:9.0;51.423614501953125:11.0;...   39.504   \n101  50.29555130004883:7.375;50.36285400390625:5.0;...  544.916   \n127  50.0151481628418:386.6391906738281;50.02954483...  556.273   \n128  55.05272674560547:57.0625;55.22163772583008:14...  747.608   \n129  50.55510711669922:7.25;50.771053314208984:14.0...   42.285   \n130  50.16886901855469:11.375;50.487667083740234:10...  560.820   \n131  50.3864860534668:11.375;53.76700973510742:10.0...  687.752   \n132  50.18632888793945:13.024999618530273;51.023300...  397.440   \n147  50.08015823364258:142.27272033691406;50.095813...  555.992   \n148  50.01576232910156:15.125;50.53532028198242:38....  477.537   \n149  50.04560089111328:11.024999618530273;51.503334...  552.831   \n157  50.939796447753906:10.0;50.97162628173828:665....   40.108   \n158  52.11697769165039:16.0;52.386104583740234:8.12...  413.514   \n169  51.62553024291992:7.25;52.673763275146484:9.04...  555.235   \n170  51.05947494506836:8.125;52.84958267211914:34.1...  553.417   \n\n     precursor_mz_spec                                     smiles_fetched  \n10          146.164932                                         NCCCCNCCCN  \n11          363.215070  C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...  \n20          283.062791                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n21          407.282505  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n23          347.167542  CC12[C@@H](O)C=C[C@@]3(OC1=O)[C@@H]4CC[C@]5(O)...  \n26          407.282227  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n32          146.164780                                         NCCCCNCCCN  \n36          407.282111  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n50          146.164932                                         NCCCCNCCCN  \n58          283.062520                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n59          407.282271  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n76          146.164841                                         NCCCCNCCCN  \n77          289.214722  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@]34C...  \n78          225.090027                          Oc1ccccc1C=CC(=O)c2ccccc2  \n79          363.215121  C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...  \n91          283.062991                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n92          407.282825  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n97          146.164780                                         NCCCCNCCCN  \n101         407.282266  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n127         285.074948                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n128         301.214752                    CCCCCCCCCCCCC(C(C)C(O)=O)C(O)=O  \n129         146.164951                                         NCCCCNCCCN  \n130         289.215096  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@]34C...  \n131         225.090485                          Oc1ccccc1C=CC(=O)c2ccccc2  \n132         363.215271  C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...  \n147         283.063055                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n148         779.427002  C[C@H]1O[C@H](C[C@H](O)[C@@H]1O)O[C@H]2[C@@H](...  \n149         407.283337  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n157         146.164680                                         NCCCCNCCCN  \n158         363.215233  C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...  \n169         283.062790                COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O  \n170         407.282550  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  ",
      "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>id_spec</th>\n      <th>id_anno</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>adduct</th>\n      <th>ce</th>\n      <th>concentration</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>smiles_fetched</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10</th>\n      <td>342594</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>3</td>\n      <td>51.12116622924805:13.024999618530273;54.827529...</td>\n      <td>36.701</td>\n      <td>146.164932</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>344640</td>\n      <td>235</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>363.2180</td>\n      <td>6.86</td>\n      <td>HYDROCORTISONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>3</td>\n      <td>54.521629333496094:14.125;56.67998504638672:7....</td>\n      <td>399.056</td>\n      <td>363.215070</td>\n      <td>C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>682538</td>\n      <td>370</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>283.0609</td>\n      <td>9.17</td>\n      <td>Biochanin A</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>3</td>\n      <td>52.409542083740234:69.01786041259766;52.412597...</td>\n      <td>556.748</td>\n      <td>283.062791</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>682532</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>3</td>\n      <td>51.81279754638672:28.0;54.822967529296875:7.0;...</td>\n      <td>556.057</td>\n      <td>407.282505</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>822146</td>\n      <td>230</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>347.1501</td>\n      <td>5.24</td>\n      <td>Gibberellic Acid</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>8</td>\n      <td>50.65410614013672:17.225000381469727;52.225910...</td>\n      <td>312.069</td>\n      <td>347.167542</td>\n      <td>CC12[C@@H](O)C=C[C@@]3(OC1=O)[C@@H]4CC[C@]5(O)...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>838672</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>8</td>\n      <td>50.628692626953125:5.375;50.69906997680664:7.2...</td>\n      <td>554.298</td>\n      <td>407.282227</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>485564</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>7</td>\n      <td>50.16026306152344:27.0;50.267398834228516:22.6...</td>\n      <td>42.768</td>\n      <td>146.164780</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>476550</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>7</td>\n      <td>50.28374481201172:9.0;50.36776351928711:8.0;51...</td>\n      <td>540.230</td>\n      <td>407.282111</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>609165</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>5</td>\n      <td>50.857200622558594:16.160715103149414;51.08386...</td>\n      <td>42.686</td>\n      <td>146.164932</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>393132</td>\n      <td>370</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>283.0609</td>\n      <td>9.17</td>\n      <td>Biochanin A</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>5</td>\n      <td>50.111263275146484:79.88970947265625;50.115936...</td>\n      <td>554.502</td>\n      <td>283.062520</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>393087</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>5</td>\n      <td>51.05177688598633:13.5;52.932254791259766:15.1...</td>\n      <td>546.749</td>\n      <td>407.282271</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>402703</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>2</td>\n      <td>50.458011627197266:7.25;50.90478515625:33.2249...</td>\n      <td>37.601</td>\n      <td>146.164841</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>405766</td>\n      <td>223</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>289.2174</td>\n      <td>9.66</td>\n      <td>PRASTERONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>2</td>\n      <td>50.9100341796875:17.5;53.52819061279297:7.875;...</td>\n      <td>573.449</td>\n      <td>289.214722</td>\n      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@]34C...</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>406237</td>\n      <td>233</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>225.0909</td>\n      <td>11.35</td>\n      <td>2-Hydroxychalcone</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>2</td>\n      <td>51.845035552978516:16.0;52.93822479248047:148....</td>\n      <td>684.568</td>\n      <td>225.090027</td>\n      <td>Oc1ccccc1C=CC(=O)c2ccccc2</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>404818</td>\n      <td>235</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>363.2180</td>\n      <td>6.86</td>\n      <td>HYDROCORTISONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>2</td>\n      <td>51.67262268066406:13.024999618530273;51.760982...</td>\n      <td>400.627</td>\n      <td>363.215121</td>\n      <td>C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>596120</td>\n      <td>370</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>283.0609</td>\n      <td>9.17</td>\n      <td>Biochanin A</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>2</td>\n      <td>52.04039001464844:9.125;53.03115463256836:8.12...</td>\n      <td>551.270</td>\n      <td>283.062991</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>596117</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>2</td>\n      <td>50.506492614746094:9.0;50.969234466552734:51.8...</td>\n      <td>551.055</td>\n      <td>407.282825</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>515912</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>6</td>\n      <td>50.60329818725586:9.0;51.423614501953125:11.0;...</td>\n      <td>39.504</td>\n      <td>146.164780</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>759904</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>6</td>\n      <td>50.29555130004883:7.375;50.36285400390625:5.0;...</td>\n      <td>544.916</td>\n      <td>407.282266</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>84224</td>\n      <td>208</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>285.0769</td>\n      <td>9.16</td>\n      <td>Biochanin A</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.0151481628418:386.6391906738281;50.02954483...</td>\n      <td>556.273</td>\n      <td>285.074948</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>85055</td>\n      <td>209</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>301.2385</td>\n      <td>12.38</td>\n      <td>Roccellic Acid</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>55.05272674560547:57.0625;55.22163772583008:14...</td>\n      <td>747.608</td>\n      <td>301.214752</td>\n      <td>CCCCCCCCCCCCC(C(C)C(O)=O)C(O)=O</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>81266</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.55510711669922:7.25;50.771053314208984:14.0...</td>\n      <td>42.285</td>\n      <td>146.164951</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>84269</td>\n      <td>223</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>289.2174</td>\n      <td>9.66</td>\n      <td>PRASTERONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.16886901855469:11.375;50.487667083740234:10...</td>\n      <td>560.820</td>\n      <td>289.215096</td>\n      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@]34C...</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>84827</td>\n      <td>233</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>225.0909</td>\n      <td>11.35</td>\n      <td>2-Hydroxychalcone</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.3864860534668:11.375;53.76700973510742:10.0...</td>\n      <td>687.752</td>\n      <td>225.090485</td>\n      <td>Oc1ccccc1C=CC(=O)c2ccccc2</td>\n    </tr>\n    <tr>\n      <th>132</th>\n      <td>83225</td>\n      <td>235</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>363.2180</td>\n      <td>6.86</td>\n      <td>HYDROCORTISONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.18632888793945:13.024999618530273;51.023300...</td>\n      <td>397.440</td>\n      <td>363.215271</td>\n      <td>C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>566102</td>\n      <td>370</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>283.0609</td>\n      <td>9.17</td>\n      <td>Biochanin A</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.08015823364258:142.27272033691406;50.095813...</td>\n      <td>555.992</td>\n      <td>283.063055</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>565439</td>\n      <td>375</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>779.4211</td>\n      <td>8.22</td>\n      <td>Gitoxin</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.01576232910156:15.125;50.53532028198242:38....</td>\n      <td>477.537</td>\n      <td>779.427002</td>\n      <td>C[C@H]1O[C@H](C[C@H](O)[C@@H]1O)O[C@H]2[C@@H](...</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>566075</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.04560089111328:11.024999618530273;51.503334...</td>\n      <td>552.831</td>\n      <td>407.283337</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>157</th>\n      <td>527616</td>\n      <td>210</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>146.1657</td>\n      <td>0.56</td>\n      <td>SPERMIDINE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>4</td>\n      <td>50.939796447753906:10.0;50.97162628173828:665....</td>\n      <td>40.108</td>\n      <td>146.164680</td>\n      <td>NCCCCNCCCN</td>\n    </tr>\n    <tr>\n      <th>158</th>\n      <td>529680</td>\n      <td>235</td>\n      <td>12</td>\n      <td>pos</td>\n      <td>363.2180</td>\n      <td>6.86</td>\n      <td>HYDROCORTISONE</td>\n      <td>[M+H]+</td>\n      <td>0</td>\n      <td>4</td>\n      <td>52.11697769165039:16.0;52.386104583740234:8.12...</td>\n      <td>413.514</td>\n      <td>363.215233</td>\n      <td>C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=...</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>748350</td>\n      <td>370</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>283.0609</td>\n      <td>9.17</td>\n      <td>Biochanin A</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>4</td>\n      <td>51.62553024291992:7.25;52.673763275146484:9.04...</td>\n      <td>555.235</td>\n      <td>283.062790</td>\n      <td>COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>748332</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>4</td>\n      <td>51.05947494506836:8.125;52.84958267211914:34.1...</td>\n      <td>553.417</td>\n      <td>407.282550</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix_12"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "     id_spec  id_anno  mix charge  precursor_mz    rt         name adduct  ce  \\\n21    682532      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n26    838672      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n36    476550      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n59    393087      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n92    596117      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n101   759904      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n149   566075      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n170   748332      379   12    neg      407.2791  9.02  Cholic Acid    M-H   0   \n\n     concentration                                              peaks  \\\n21               3  51.81279754638672:28.0;54.822967529296875:7.0;...   \n26               8  50.628692626953125:5.375;50.69906997680664:7.2...   \n36               7  50.28374481201172:9.0;50.36776351928711:8.0;51...   \n59               5  51.05177688598633:13.5;52.932254791259766:15.1...   \n92               2  50.506492614746094:9.0;50.969234466552734:51.8...   \n101              6  50.29555130004883:7.375;50.36285400390625:5.0;...   \n149              1  50.04560089111328:11.024999618530273;51.503334...   \n170              4  51.05947494506836:8.125;52.84958267211914:34.1...   \n\n     rt_spec  precursor_mz_spec  \\\n21   556.057         407.282505   \n26   554.298         407.282227   \n36   540.230         407.282111   \n59   546.749         407.282271   \n92   551.055         407.282825   \n101  544.916         407.282266   \n149  552.831         407.283337   \n170  553.417         407.282550   \n\n                                        smiles_fetched  \n21   C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n26   C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n36   C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n59   C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n92   C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n101  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n149  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  \n170  C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...  ",
      "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>id_spec</th>\n      <th>id_anno</th>\n      <th>mix</th>\n      <th>charge</th>\n      <th>precursor_mz</th>\n      <th>rt</th>\n      <th>name</th>\n      <th>adduct</th>\n      <th>ce</th>\n      <th>concentration</th>\n      <th>peaks</th>\n      <th>rt_spec</th>\n      <th>precursor_mz_spec</th>\n      <th>smiles_fetched</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>21</th>\n      <td>682532</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>3</td>\n      <td>51.81279754638672:28.0;54.822967529296875:7.0;...</td>\n      <td>556.057</td>\n      <td>407.282505</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>838672</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>8</td>\n      <td>50.628692626953125:5.375;50.69906997680664:7.2...</td>\n      <td>554.298</td>\n      <td>407.282227</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>476550</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>7</td>\n      <td>50.28374481201172:9.0;50.36776351928711:8.0;51...</td>\n      <td>540.230</td>\n      <td>407.282111</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>393087</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>5</td>\n      <td>51.05177688598633:13.5;52.932254791259766:15.1...</td>\n      <td>546.749</td>\n      <td>407.282271</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>596117</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>2</td>\n      <td>50.506492614746094:9.0;50.969234466552734:51.8...</td>\n      <td>551.055</td>\n      <td>407.282825</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>759904</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>6</td>\n      <td>50.29555130004883:7.375;50.36285400390625:5.0;...</td>\n      <td>544.916</td>\n      <td>407.282266</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>566075</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50.04560089111328:11.024999618530273;51.503334...</td>\n      <td>552.831</td>\n      <td>407.283337</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>748332</td>\n      <td>379</td>\n      <td>12</td>\n      <td>neg</td>\n      <td>407.2791</td>\n      <td>9.02</td>\n      <td>Cholic Acid</td>\n      <td>M-H</td>\n      <td>0</td>\n      <td>4</td>\n      <td>51.05947494506836:8.125;52.84958267211914:34.1...</td>\n      <td>553.417</td>\n      <td>407.282550</td>\n      <td>C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@@H]3[C@H](O)C...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_search(extracted_wanted, 'name','Cholic Acid' )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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