{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libray matching with DreaMS\n",
    "\n",
    "## Introduction\n",
    "\n",
    "According to the results presented in our [paper]((https://chemrxiv.org/engage/chemrxiv/article-details/6626775021291e5d1d61967f)), DreaMS embeddings provide a more accurate way to perform library matching than traditional algorithms such as modified cosine similarity or spectral entropy. Library matching is the annotation of a query spectrum with a molecule corresponding to the most similar spectrum in some given spectral library. In this tutorial, we will demonstrate how to perform library matching with DreaMS embeddings using the [MassSpecGym](https://github.com/MassSpecGym/MassSpecGym) dataset as a library, which contains a curated collection of public high-quality MS/MS spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Determination of memory status is not supported on this \n",
      " platform, measuring for memoryleaks will never fail\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from rdkit import Chem\n",
    "import dreams.utils.spectra as su\n",
    "import dreams.utils.io as io\n",
    "from dreams.utils.spectra import PeakListModifiedCosine\n",
    "from dreams.utils.data import MSData\n",
    "from dreams.api import dreams_embeddings\n",
    "from dreams.definitions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load example dataset downloaded from [MSV000086206](https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=11fbd0d4c9144a6c813b196d865567ab) and [MassSpecGym library with pre-computed DreaMS embeddings](https://huggingface.co/datasets/roman-bushuiev/GeMS/blob/main/data/auxiliary/MassSpecGym_DreaMS.hdf5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_pth = Path('../data/S_N1.mzML')  # Example dataset\n",
    "lib_pth = Path('../data/MassSpecGym_DreaMS.hdf5')  # MassSpecGym library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the library embeddings: (231104, 1024)\n"
     ]
    }
   ],
   "source": [
    "msdata_lib = MSData.load(lib_pth)\n",
    "embs_lib = msdata_lib[DREAMS_EMBEDDING]\n",
    "print('Shape of the library embeddings:', embs_lib.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing DreaMS embedding: 100%|██████████| 3809/3809 [01:42<00:00, 37.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the query embeddings: (3809, 1024)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "msdata = MSData.load(in_pth)\n",
    "embs = dreams_embeddings(msdata)\n",
    "print('Shape of the query embeddings:', embs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform library matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute all cosine similarity between the query and library DreaMS embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3809, 231104)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sims = cosine_similarity(embs, embs_lib)\n",
    "sims.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose top-k candidates with the highest similarity per query spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3809, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 5\n",
    "topk_cands = np.argsort(sims, axis=1)[:, -k:][:, ::-1]\n",
    "topk_cands.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Organize the results in a DataFrame with the most important library metadata, such as library SMILES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3809/3809 [03:29<00:00, 18.17it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_id</th>\n",
       "      <th>precursor_mz</th>\n",
       "      <th>RT</th>\n",
       "      <th>topk</th>\n",
       "      <th>library_j</th>\n",
       "      <th>library_SMILES</th>\n",
       "      <th>library_ID</th>\n",
       "      <th>library_precursor_mz</th>\n",
       "      <th>library_adduct</th>\n",
       "      <th>library_collision_energy</th>\n",
       "      <th>DreaMS_similarity</th>\n",
       "      <th>Modified_cosine_similarity</th>\n",
       "      <th>i</th>\n",
       "      <th>j</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>344</td>\n",
       "      <td>565.235133</td>\n",
       "      <td>185.953374</td>\n",
       "      <td>1</td>\n",
       "      <td>217668</td>\n",
       "      <td>CC(=O)N(CCCC[C@@H](C(=O)O)NC(=O)CC(CC(=O)N[C@@...</td>\n",
       "      <td>MassSpecGymID0396609</td>\n",
       "      <td>565.23000</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.979676</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>343</td>\n",
       "      <td>217668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>344</td>\n",
       "      <td>565.235133</td>\n",
       "      <td>185.953374</td>\n",
       "      <td>2</td>\n",
       "      <td>156591</td>\n",
       "      <td>CC1=C2C(=NC=NN2C=C1NC(=O)OC[C@@H]3COCCN3)NC4=C...</td>\n",
       "      <td>MassSpecGymID0225965</td>\n",
       "      <td>531.22629</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.609247</td>\n",
       "      <td>0.643832</td>\n",
       "      <td>343</td>\n",
       "      <td>156591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>344</td>\n",
       "      <td>565.235133</td>\n",
       "      <td>185.953374</td>\n",
       "      <td>3</td>\n",
       "      <td>161476</td>\n",
       "      <td>CN1C2=C(C=CC(=C2)C3=CC=C(C=C3)C[C@@H](C#N)NC(=...</td>\n",
       "      <td>MassSpecGymID0231755</td>\n",
       "      <td>421.18703</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.601104</td>\n",
       "      <td>0.488964</td>\n",
       "      <td>343</td>\n",
       "      <td>161476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>344</td>\n",
       "      <td>565.235133</td>\n",
       "      <td>185.953374</td>\n",
       "      <td>4</td>\n",
       "      <td>135639</td>\n",
       "      <td>CCC1=CC(=C(S1)NC(=O)CCN2CCOCC2)C(=O)OCC</td>\n",
       "      <td>MassSpecGymID0203635</td>\n",
       "      <td>341.15295</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.595223</td>\n",
       "      <td>0.664075</td>\n",
       "      <td>343</td>\n",
       "      <td>135639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>344</td>\n",
       "      <td>565.235133</td>\n",
       "      <td>185.953374</td>\n",
       "      <td>5</td>\n",
       "      <td>155803</td>\n",
       "      <td>CC(=O)NCCCOC1=CC=C(C=C1)C(=O)N2CCC(CC2)N3C(=O)...</td>\n",
       "      <td>MassSpecGymID0225137</td>\n",
       "      <td>450.23873</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.570048</td>\n",
       "      <td>0.725630</td>\n",
       "      <td>343</td>\n",
       "      <td>155803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19040</th>\n",
       "      <td>1789</td>\n",
       "      <td>237.110323</td>\n",
       "      <td>476.531454</td>\n",
       "      <td>1</td>\n",
       "      <td>104889</td>\n",
       "      <td>CC(=CCOC1=C2C=CC(=O)OC2=CC3=C1C=CO3)C</td>\n",
       "      <td>MassSpecGymID0151614</td>\n",
       "      <td>293.08000</td>\n",
       "      <td>[M+Na]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.342242</td>\n",
       "      <td>0.831349</td>\n",
       "      <td>1788</td>\n",
       "      <td>104889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19041</th>\n",
       "      <td>1789</td>\n",
       "      <td>237.110323</td>\n",
       "      <td>476.531454</td>\n",
       "      <td>2</td>\n",
       "      <td>181939</td>\n",
       "      <td>CC1=C(N2C(C(C2=O)[NH3+])SC1)C(=O)[O-]</td>\n",
       "      <td>MassSpecGymID0268406</td>\n",
       "      <td>237.03000</td>\n",
       "      <td>[M+Na]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.328011</td>\n",
       "      <td>0.700030</td>\n",
       "      <td>1788</td>\n",
       "      <td>181939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19042</th>\n",
       "      <td>1789</td>\n",
       "      <td>237.110323</td>\n",
       "      <td>476.531454</td>\n",
       "      <td>3</td>\n",
       "      <td>181941</td>\n",
       "      <td>CC1=C(N2C(C(C2=O)[NH3+])SC1)C(=O)[O-]</td>\n",
       "      <td>MassSpecGymID0268414</td>\n",
       "      <td>237.03000</td>\n",
       "      <td>[M+Na]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.327554</td>\n",
       "      <td>0.809387</td>\n",
       "      <td>1788</td>\n",
       "      <td>181941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19043</th>\n",
       "      <td>1789</td>\n",
       "      <td>237.110323</td>\n",
       "      <td>476.531454</td>\n",
       "      <td>4</td>\n",
       "      <td>104897</td>\n",
       "      <td>CC(=CCOC1=C2C=CC(=O)OC2=CC3=C1C=CO3)C</td>\n",
       "      <td>MassSpecGymID0151624</td>\n",
       "      <td>293.08000</td>\n",
       "      <td>[M+Na]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.327202</td>\n",
       "      <td>0.737573</td>\n",
       "      <td>1788</td>\n",
       "      <td>104897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19044</th>\n",
       "      <td>1789</td>\n",
       "      <td>237.110323</td>\n",
       "      <td>476.531454</td>\n",
       "      <td>5</td>\n",
       "      <td>203873</td>\n",
       "      <td>CCC(C)OC1=CC=C(C=C1)C(=O)O</td>\n",
       "      <td>MassSpecGymID0366734</td>\n",
       "      <td>195.10000</td>\n",
       "      <td>[M+H]+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.323207</td>\n",
       "      <td>0.016541</td>\n",
       "      <td>1788</td>\n",
       "      <td>203873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19045 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature_id  precursor_mz          RT  topk  library_j  \\\n",
       "0             344    565.235133  185.953374     1     217668   \n",
       "1             344    565.235133  185.953374     2     156591   \n",
       "2             344    565.235133  185.953374     3     161476   \n",
       "3             344    565.235133  185.953374     4     135639   \n",
       "4             344    565.235133  185.953374     5     155803   \n",
       "...           ...           ...         ...   ...        ...   \n",
       "19040        1789    237.110323  476.531454     1     104889   \n",
       "19041        1789    237.110323  476.531454     2     181939   \n",
       "19042        1789    237.110323  476.531454     3     181941   \n",
       "19043        1789    237.110323  476.531454     4     104897   \n",
       "19044        1789    237.110323  476.531454     5     203873   \n",
       "\n",
       "                                          library_SMILES  \\\n",
       "0      CC(=O)N(CCCC[C@@H](C(=O)O)NC(=O)CC(CC(=O)N[C@@...   \n",
       "1      CC1=C2C(=NC=NN2C=C1NC(=O)OC[C@@H]3COCCN3)NC4=C...   \n",
       "2      CN1C2=C(C=CC(=C2)C3=CC=C(C=C3)C[C@@H](C#N)NC(=...   \n",
       "3                CCC1=CC(=C(S1)NC(=O)CCN2CCOCC2)C(=O)OCC   \n",
       "4      CC(=O)NCCCOC1=CC=C(C=C1)C(=O)N2CCC(CC2)N3C(=O)...   \n",
       "...                                                  ...   \n",
       "19040              CC(=CCOC1=C2C=CC(=O)OC2=CC3=C1C=CO3)C   \n",
       "19041              CC1=C(N2C(C(C2=O)[NH3+])SC1)C(=O)[O-]   \n",
       "19042              CC1=C(N2C(C(C2=O)[NH3+])SC1)C(=O)[O-]   \n",
       "19043              CC(=CCOC1=C2C=CC(=O)OC2=CC3=C1C=CO3)C   \n",
       "19044                         CCC(C)OC1=CC=C(C=C1)C(=O)O   \n",
       "\n",
       "                 library_ID  library_precursor_mz library_adduct  \\\n",
       "0      MassSpecGymID0396609             565.23000         [M+H]+   \n",
       "1      MassSpecGymID0225965             531.22629         [M+H]+   \n",
       "2      MassSpecGymID0231755             421.18703         [M+H]+   \n",
       "3      MassSpecGymID0203635             341.15295         [M+H]+   \n",
       "4      MassSpecGymID0225137             450.23873         [M+H]+   \n",
       "...                     ...                   ...            ...   \n",
       "19040  MassSpecGymID0151614             293.08000        [M+Na]+   \n",
       "19041  MassSpecGymID0268406             237.03000        [M+Na]+   \n",
       "19042  MassSpecGymID0268414             237.03000        [M+Na]+   \n",
       "19043  MassSpecGymID0151624             293.08000        [M+Na]+   \n",
       "19044  MassSpecGymID0366734             195.10000         [M+H]+   \n",
       "\n",
       "       library_collision_energy  DreaMS_similarity  \\\n",
       "0                           NaN           0.979676   \n",
       "1                          20.0           0.609247   \n",
       "2                          30.0           0.601104   \n",
       "3                          20.0           0.595223   \n",
       "4                          30.0           0.570048   \n",
       "...                         ...                ...   \n",
       "19040                       NaN           0.342242   \n",
       "19041                       NaN           0.328011   \n",
       "19042                       NaN           0.327554   \n",
       "19043                       NaN           0.327202   \n",
       "19044                       NaN           0.323207   \n",
       "\n",
       "       Modified_cosine_similarity     i       j  \n",
       "0                        0.999883   343  217668  \n",
       "1                        0.643832   343  156591  \n",
       "2                        0.488964   343  161476  \n",
       "3                        0.664075   343  135639  \n",
       "4                        0.725630   343  155803  \n",
       "...                           ...   ...     ...  \n",
       "19040                    0.831349  1788  104889  \n",
       "19041                    0.700030  1788  181939  \n",
       "19042                    0.809387  1788  181941  \n",
       "19043                    0.737573  1788  104897  \n",
       "19044                    0.016541  1788  203873  \n",
       "\n",
       "[19045 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a DataFrame with the top-k candidates for each spectrum and their corresponding similarities\n",
    "df = []\n",
    "cos_sim = su.PeakListModifiedCosine()\n",
    "for i, topk in enumerate(tqdm(topk_cands)):\n",
    "    for n, j in enumerate(topk):\n",
    "        df.append({\n",
    "            'feature_id': i + 1,\n",
    "            'precursor_mz': msdata.get_values(PRECURSOR_MZ, i),\n",
    "            'RT': msdata.get_values(RT, i),\n",
    "            'topk': n + 1,\n",
    "            'library_j': j,\n",
    "            'library_SMILES': msdata_lib.get_smiles(j),\n",
    "            'library_ID': msdata_lib.get_values('IDENTIFIER', j),\n",
    "            'library_precursor_mz': msdata_lib.get_values(PRECURSOR_MZ, j),\n",
    "            'library_adduct': msdata_lib.get_values(ADDUCT, j),\n",
    "            'library_collision_energy': msdata_lib.get_values('COLLISION_ENERGY', j),\n",
    "            'DreaMS_similarity': sims[i, j],\n",
    "            'Modified_cosine_similarity': cos_sim(\n",
    "                spec1=msdata.get_spectra(i),\n",
    "                prec_mz1=msdata.get_prec_mzs(i),\n",
    "                spec2=msdata_lib.get_spectra(j),\n",
    "                prec_mz2=msdata_lib.get_prec_mzs(j),\n",
    "            ),\n",
    "            'i': i,\n",
    "            'j': j,\n",
    "        })\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "# Sort hits by DreaMS similarity\n",
    "df_top1 = df[df['topk'] == 1].sort_values('DreaMS_similarity', ascending=False)\n",
    "df = df.set_index('feature_id').loc[df_top1['feature_id'].values].reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store results to CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(io.append_to_stem(in_pth, 'MassSpecGym_hits').with_suffix('.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library matching results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the retrieved library spectra for our query spectra. First, let's look at the confidently matched spectrum with a high DreaMS similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0236163 (top 1 hit)\n",
      "Query precursor m/z: 230.18643445964, Library precursor m/z: 230.1863\n",
      "DreaMS similarity: 0.8856791257858276\n",
      "Modified cosine similarity: 0.9892408193123613\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ebe2dc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0236162 (top 2 hit)\n",
      "Query precursor m/z: 230.18643445964, Library precursor m/z: 230.1863\n",
      "DreaMS similarity: 0.8603854179382324\n",
      "Modified cosine similarity: 0.7990432320842271\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ebe31b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0236161 (top 3 hit)\n",
      "Query precursor m/z: 230.18643445964, Library precursor m/z: 230.1863\n",
      "DreaMS similarity: 0.7787462472915649\n",
      "Modified cosine similarity: 0.716395852717653\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAADVCAYAAADgkwLjAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAJK9JREFUeJzt3Q1wVNXZwPFnk0AohM9gEiJCjNQOIBQVqjGCIgIF5FMhKMiHgK2oUETQgiDVUukMSosVq0aRRhlo2oIMfowoChijwhQUAeVFBCohCUgCRCCQ5L7znGVDgEB2k3tzN5v/b2YnuTd3d88+e3L32XPOPcdjWZYlAAAAVRRW1QcAAABQJBUAAMAWJBUAAMAWJBUAAMAWJBUAAMAWJBUAAMAWJBUAAMAWJBUAAMAWEVJLFBUVyZEjRyQyMlLCwsilAADwV0lJiRQWFkrjxo0lIuLiqUOtSSo0odizZ4/bxQAAoMZKSEiQ6Ojoi/691iQV2kKhWrVqJQ0aNHC7OCGnuLhYdu7cKVdffbWEh4e7XZyQQmydRXydRXxDI74nTpwwX8x9n6VS25MKX5dHvXr1pH79+m4XJyQrttLYcuKwF7F1FvF1FvENrfhWNHzAtcEFp06dkjFjxshHH31ktg8ePChjx46Vvn37ypAhQ2T37t2lxy5btkz69OkjPXv2lIULF7pVZMAVBQUib70VLbNmeWTxYu82AAQjV5KKbdu2yd133y2bN28u3Tdnzhzp3r27vPPOOzJ16lSZMmWK2b9jxw5JTU2V5cuXy9tvvy0bN26UNWvWuFFsoNplZIhcdVWYzJ3bWt54wyPjx2ufpsinn7pdMgAIkqRi6dKlMmnSJOnYsaPZPn36tGzYsMG0UKjk5GQ5duyY7Nq1S9auXWtaKBo1aiR169Y1x6xevdqNYgPVSlskBg4Uad9e5PvvPbJvn0e0Aa9dO5EBA2ixABB8XBlTMXfuXPNTWyBUfn6+SRiioqJKj4mNjZXs7GzJycmRxMTEc/brvqpcFuPrg4J9fDEltvZZtswjeXkeSUvzSKtW3n2tW4ukpYlceaUly5dbMmaM5XYxazzqrrOIb2jE19/HD4qBmvpBf7EBIZZ14UnT4/FU+rm09QPO2bp1q9tFCBmZmfESHx8rrVqdW981sYiPtyQzM0c6dcpyrXyhhrrrLOJbO+IbFEmFXvOqAzePHz9eemVGbm6uaZWIi4szgzh9dL/uq6w2bdqc0yIC+7JYrdQdOnRghLdNkpI88vrr2u2hl0Kf3b93r0hWlkeSkmKlU6cYN4sYEqi7ziK+oRFf/XzWS1drRFKhs3N169ZN0tPTZfTo0ZKZmWn2abfHbbfdJpMnT5YJEyaY+SVWrFghd955Z6WfS1s/qNjO0dgSX3sMHy7y+OMiI0dapgtEWyg0obj3XpFmzTySkuIRQm0f6q6ziG/Njq+/jx0USYWaPXu2zJgxwyQWOr5iwYIFppujbdu2Mm7cOBkxYoSZaluTjP79+7tdXMBx2qC2apV3UGZioiUxMSI5OR5p1sy7nwY3AMHG1aQiTUecnRETE1M6cPN8KSkp5gbUNjfdJPLddyXy858XS1FRHXn1VZGhQ0koAASnoGmpAFA+TSAaNiyWevXqyNixbpcGqN30Uu5//lPM5d1XXUWSfz6SCgAA/JyMTueOycsTufxykf37RaZN83ZHaqsiXJymGwCAmjkZnZirspiM7kIkFQAAVEC7PLSFQocCnj8Z3eHDIunpbpcwOJBUAABQAW2V0C6PsnPG+BIL3V9mDcxajaQCAIAK6GoROoZCuz3K0rljdH+Z1SRqNZIKAAAqMGyYSNOmOhmdN5FQZyej814FAq7+AAAgwMno5MxkdN6EgsnozqKlAgAAP+hlo3v2iOjyU0VFYiaj020uJz2LlgoAAPykLRJNmnh/ZzK6C9FSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbEFSAQAAbBEhQWbSpEny7bffSr169cz24MGDpV+/fjJ9+nTJyckx++fPny+JiYluFxUAAARzUvHll1/KypUrpWnTpqX7HnzwQenevbuMGjVKMjIyZMqUKfLWW2+5Wk4AABDE3R9ZWVly/Phxeeyxx6R///4yd+5cKSgokA0bNsiQIUPMMcnJyXLs2DHZtWuX28UFAADB2lJx6NAhSUpKkjlz5khUVJRMmzZN5s2bJ3Xr1jXbPrGxsZKdnS1t2rQJ+DlKSkqkuLjY5pLDF1Niaz9fTC3LkuLiEreLE3Kou84Kzfh6v48Hw/9jcTXF19/HD6qkomPHjrJw4cLS7fvvv18eeeSRco8NC6tcIwstHM7aunWr20UIUe2ksLBQtmzZ7nZBQhZ111mhFN+TJ9uZn8H0/7g1SOIbVEnFpk2bTNeGjp/wfTOLiIiQU6dOmW6R+vXrm/25ubmmtaIytHWjbKsH7MtitVJ36NBBwsPD3S5OSPF+QyiWyMhI6dSpk9vFCTnUXWeFYnzr1fN+qQ2G/8fiaoqvfgbv3LnTnqQiMzMzoCfXLozKOHnypBlH0blzZ2nQoIEsWbJEevbsaVoX0tPTZfTo0aYsmmhU9uoPbeEIlYodjDS2xNcJxeLxeCQ8PKiGQYUU6q6zQjG+wfR6wh2Or7+P7VdScd9990l8fLxpOaiIXva5bds2qYybb75Zhg8fLikpKVJUVCRdunSRiRMnSn5+vsyYMcMkFjq+YsGCBeYECwAAgodfSYXODfHhhx/69YDXXnttlQo0fvx4cysrJiZGUlNTq/S4AADAWX61pf7jH//w+wEDORYAANSylgodAFLW4cOH5aWXXpLvv/9eWrZsKRMmTJAWLVqUeywAAKgdKjXqS8c3aBKhAyfj4uLkoYcesr9kAAAg9JKKZ5991sxs6aOXdA4YMMDMbqkzX+pMmAAAoHbzq/ujbdu2MnbsWBk0aJC5OuOBBx4wSYVeYqKXgeq02gAAoHbzK6no27ev3H777fLGG2/IPffcY8ZQrFu3TvLy8szCX8F0rS4AAAjyMRU6P4TOV/Hyyy/LF198YS77PHDgAAkFAAAIbEbN5557ziQQ06dPNwM19+7da8Za1KlTx6zPcfnll/vzUAAAoDa3VMycOdNc4aELfOnvqnXr1mbxL+0Oefzxx50uJwAACIWWCl1IROej0Gm6T5w4cc7frr/+eklLS3OqfAAAIJSSCu3yGDFihOnq+P3vf+98qQAAQGgmFV27dpUhQ4b49YAHDx6Uyy67rKrlAgAAoTimonfv3n4/YCDHAgCAWtZSoRNcafdHRXTMRWFhoR3lAgAAoZhUzJ071+8HHDp0aFXKAwAAQjmpGDx4sPMlAQAAtW+VUgAAgPORVAAAAFuQVAAAgOpPKrp16ybz5s2Tr776yp5nBwAAtWugps+iRYvk3Xffld/97ndmcTFdEl1vv/jFL5wrIQBHFRSI/POfIrt3i1x1lV7BJRIV5XapAIR8UnHNNdeY27Rp00xrxZo1a2TKlCkSFhYm/fr1k0GDBkmLFi2cKy0AW2VkiAwcKJKXJ6ILDe/fLzJtmsiqVSI33eR26QDUijEVubm5JqnYunWrmZY7MTFRjhw5IsOGDZPU1FT7SwnAkRYKTSjatxf5/nuRffu8rRXt2okMGOD9OxCstH6+9prIE0+ILF5Mfa2RLRWvv/66vPfee7J9+3ZJSkoy81e88MIL0qBBA/P3Xr16ybhx42T8+PFOlReATbTLQ1sodJHhVq28+1q39m5feaVIerrI2LFulxK4EC1sIdJS8eGHH5pEYv369fLSSy/JwIEDSxMKlZCQIH/84x+dKCcAm2mrhJ6QfQmFjyYWul//DgQbt1vY9PHz87XFnhaSKicVmjSkpKRIkyZNztmv4ypUs2bNzNgKAMEvMdH7DU9PymXt3evdHx/vbPMyzdf2xiUU4unPa7hUC9vhw94WNidbSBISRLKyvImFNsrr9qefOvecIdf9ceDAAfmnvosisnLlSmnevPk5fy8oKJBPqyGiH3zwgfzlL3+RU6dOyY033iizZs2SOnXqOP68qB0CuQKiuq6W8D3Prl0eOXYsXOrWtbcMw4aJTJ8uMnKk94SsJ2ZNKO69V6RhQ5FZs5xrXg7W5uvz49qnj8g771TflTGVjYs/9wv2q3z8fe0ffCASE1P9LWxlW0gOHBDRjx+tG/r/oi0ke/YEVzxde88tP8ybN896/PHHrfbt25ufZW+zZs2yPvnkE8tJubm5VnJyspWVlWWVlJRYU6dOtRYvXhzQY/z000/Wpk2brKNHjzpWztqsqKjIxFd/luvYMct69VXLmjnTsl57zbsdJLT6RkdbVliYZV1xhfenbmdkVO1Y+8pUYomUWOGeIiuj7x+tT946ZFsZ9D6+x4qLsyyPx7KaNbOsJk0sq1s3y9q713vcnj2W1bWr99iqvnV6f30cpx6/snV33bqic+KqsYiIcP69rmpc/LlfddXbSp0bAnjt77/vLbvefMf56PH6nunpxQl6+vI9b7t23lt1PG9l42v3e+77DNWfl+JXUuHz5ptvWm5YsWKF9fDDD5duf/7559add94Z0GOQVLh44vCndruUdARyIq+uD8PynufnP7es+j8rsZqFHbaiw/Osbl2LbSuD3ic+3rKaN/eG/oUXnD1plz05O/H4gdI6u379f63o6JLSmGtMmja1rJtvrr7Ep7Jxqeh+ixa5m8T5k1T489q1nA0aeLf1vdHy6999x910k2U1bOjc69FTk56+VNmkQrVsaVlPPOHM81Ymvk6cq/xNKvy6+uPFF1+UBx54wFxK+te//rXcYyZPnixO0eeNjY0t3dbfs7OzK/VYJSUlUlxcbGPpQo/n6afNT0vbv/3ki+kFsS0okLAzbYYeXyfo3r1inWkztO6/XyQ7WzzaxqntnnFxZlvbPUtWrKhyW7jn9tu9r0XbTMuxbJlH8vI8kpbmKecKCEuWL7dkzBgr4GOrorzn0abWy1t65P/+r4mEhYmkvWFfGX72M5Emp38U8YiMGhUts2Z55PLL9fE95TQvW7JrlyXFxZV/ndqd4+TjB0rr7Jo1Tc/pp9d+/SNHRN58Uxx9r+2IS0X3e/ttq1rqbcDnhgBf+7JlIj/95JErrvCY37XLQccFaVdITo5IRIQld99tyc9+pnGy/3UkJHhk/36P7NvnKWcMkiUJCdVbby8VXyfOVf5+bvqVVPg+wHP0nXOBJgLn0wm3KmPXrl02lCh0hR0/Lu1feEE8RUWy37Ik7/bbpaR+fb/vr3OXlBX91lvSOi/vbEKhWrc229aVV0rRK69IHT1733ijeN5442zSMXKkWAMGyNZVqwJ6/vNdqx21IrJly5Zy/56ZGS/x8bHlnszi4y3JzMyRTp2yzh7bIkZatQq/8NgWJZKZmVt6bFWUV6aUFO/P+fM90rChVW5/8vnlDcSQovXm55Yt3SQiIlr2729tBnCWfR7fAM6IiH2yZcuPlX15jj9+ZezfrzG3SmN+qStjqhJnJ+JS0f0SEo5KfHwjv+q4k84/NwT62jMzI6Vp01jzwd6ypXcMg3430cGZjzziHR+UkOBc3fnFL8KkUaMOMmJEuPTu7RG9XkHLN3KkJY0bF8vVV2+VLVsu/KyqLmXjG8h5zXZWDaDdH5MnTy7d/uKLL6yhQ4dWqukmPz/fNBNxK+e2bp1VEh1tlYSFWSUtWnh/RkdbRevXV3jfwsJCE1/9WXZ/8e9/b5X42gzPU9KypVUSFWWep7x2zxKPxypOTa3SayqJjDS3i/39lVeKrbCwkos0u5ZYqanFZ499/icrzHOJY//2ky3vw6XKpP+xF28mPre8ft/y873vd/PmJt75P+SbroCuXUvOaV7Wbd2fn1+116f3d/LxA71pnZ016/tzYn7p5vhKxtmhuFR0v+ef97+OOxXf8s4Ngb52/b/Q8pbf9VFiNWzofN1Zv95bTo1ny5Ylpjy6rfurs84WVRDfQM5r/t506IDtYyr+97//WTO1Y8myrHXr1lmdO3e2evToYW3dutVy0sGDB81AzR9++MEM1Jw+fbq1SDsKA8CYigpUsRPuov2mFXWWRkVZVosW5T+oHR2VkZHeWwUv+/yTVLljKl5YYkWHHy7/xBd+2Dq26B9VK+v5ZUouvqBMOogyummx1fWmogrL65ey4130fTgz3iXj71+V7ta3Qd8qOwf2lR0g6sTjB6LsmApfPfCNqUhOrrhe2KmycbnU/QKp40646LkhgNegfK/jl7/0vjdlj9MBtWvWWNVCy6FjPPTUFAxjzosuMabCzvfckYGa9913nzV79mzzwd6rVy8rNTXVWr58ecCDJivjgw8+sO644w7zvNOmTTNZWSBIKpwdPXfRE0dFtVsvOXBy1F4FSUVAJ/KZM62MmEHlH3vZQFtHapkyNSu2wjzFVsu4U2e+EVlWxr8PWBkdfmOSGO+3pSp8IFeQSB47cMzRk2ewnJx9ddf7LfTse6utQr6rP6oz8alsXC51PzeTOH+TCn9eu+91+AZr6nukgzerK6EIRkUXia/d77mtAzV9duzYIa+88ors3r3bzF8xYsQIqVevnjzzzDPitB49epgbatj0inpRtA7C9I2q0hmVtJO0WTPv/nHjvJ2i5U2WoMfohdUO07Gg2j+rk+boy9Rilns9d2Ki3HToGdnz9f8k/bMrzh57wz6JumaVSOJAe8u0N0zSH/xYvkvLkETrOxka9rE0vGuPSHi47Hm2i6Q3Gnfp8lZxnu6od9NlrIPzdGt5g2ka8PLqgc5T8e67FdSLIInLpe7ndx13WUWvvaa8jmDgVqwCSip0sqnDhw+b9T+uv/56k1Ds3Lnzghk2UcOnVyxvpJT+3YnarQNuNdHYvr38pKOazhZ+ncjPzBYV9ZsRMlY/eMc6mwCZMi25VYr/9HOx2raV8IIikTFjRP70J4mKi5Mqfx4zT7df9SCYEp9QSuJq++sI1VgFlFSMHj1a+vbtKydPnjSXmepKpbp4mJOXk6KaXGp6RTs+MC9Vu/XqDh25HOxfPypqdXGqvHFxcvqyyyS8Xj3vtY41IZEEUCsFlFTcd999cvvtt5sWipiYGNNq8eqrr0qHDh2cKyFC+wPTyZRa56jVa6u161UXErAjUQml9lenE0kAtU5ASYWKjIyUffv2yV49+ZyxceNG6dKli91lQ3ULpQ9M30ICmlToxGm68o9di0uESvur24kkgNqdVOhy5zqjZtOmTSUi4uxdPR6PfPzxx06UD9UtFD4wy678U2YWz6Be+cctoZRIAqhZScWbb75pEouuXbs6VyKgqiq4qsF8gNb0xMlOoZBIAggKAc11ffr0aUlKSnKuNKhdtEVBP/xzc71jHnTbDvqNW5vyy59juVZe1QAAQZdU6LwU8+fPlx9//NGsx1H2BgQ85iEhwbsSkK6WpWMedPvTT6v+2L6xAXpVQ1naBZKV5f07AMDd7o9ly5bJoUOHZMmSJaX7dFZOHVOhE2MBQTPmQee/KO+qhkouRAcAsDmpWL58eSCHA+6MedDWiOjo8ifU0pv+HQDgblJx+eWXy/Hjx2XdunVmOfThw4ebpcSZpwJBNZOjJhKHDol8/bXIZ5+dvarhhhtErrmGSZ0AwCEBtQVv27ZNevXqJWlpabJw4ULTFTJq1ChZuXKlU+VDKCo7k2NZds3kqJM6NW0q8pvfiNx2m8jTT3t//va3TOoEAMGSVDz11FPy5JNPytKlS808FVdccYWkpqbKokWLnCshQo/vQ1/HPPgmUbN7SnCdvMnX/XHFFd5uFd1mUicACI7uD12d1LdSqA7OVNddd52ZrhsIqpkcmdQJAII7qWjTpo2sXr1aBuiHwRlr1641+4Gg+9BnUicACN6k4oknnpAJEyaYmTV1wOa4cePMpaR///vfnSshQhcf+gBQe5OK9u3by/vvv2+u/jhw4IA0b95cnn32WWnSpIlzJQQAAKGXVGjLhC513q9fv3P2p6SkMIcFAAC1XIVJxf79+83KpOrzzz+X6dOnn/P3goICcwwAAKjdIvyZ8Kpjx46Sn59vtludN2FR3bp1ZfLkyc6VEAAAhE73x0idT0B0MsJr5NZbb3W6TAAAINTHVOicFC+//LLs3bv3gpVJn3nmGbvLBgAAQjWpePTRR81VH7fccouZURMAAMAnoMzgv//9r3z44YfSuHHjQO4GAABqgYDW/oiLi5OTJ086VxoAAFA7Wip69+4tY8eOlUGDBkkzXaehjLvuusvusgEAgFBNKr744guJjo6WDRs2nLNfFxezK6mYNGmSfPvtt1KvXj2zPXjwYDPZls6PkZOTY/bPnz9fEqu6PDYAAHAvqUhLSxOnffnll7Jy5Uppqktjn/Hggw9K9+7dZdSoUZKRkSFTpkyRt956y/GyAAAAm5OKzMzMCo9JSkqSqsrKyjILlT322GPmKpMbb7zRTKylLSN//vOfzTHJycly7Ngx2bVrF6ujAgBQ05KKmTNnXvLv2v2hV4VU1aFDh0xyMmfOHImKipJp06bJvHnzzKyduu0TGxsr2dnZlUoqdH6N4uLiKpcV5/LFlNjazxdTy7KkhPjajrrrLOIbGvH19/H9SirWrl0r1UGnA1+4cGHp9v333y+PPPJIuceGhQV04UopbeGAc7Zu3ep2EUJSOxEpLCyU7Vu2uF2UkEXddRbxrR3xDaoZrDZt2mS6NnT8hO+bmU6yderUKdMtUr9+fbM/NzfXtFZUhrZulG31gH1ZrFbqDh06SHh4uNvFCbnY6neEyMhI6dSpk9vFCTnUXWcR39CIr34G79y5s2YlFToHxty5c6Vz587SoEEDWbJkifTs2dO0LqSnp8vo0aPN+A5NNCp79Ye2cFCxnaOxJb72Kz7TzRhGbB1D3XUW8a3Z8fX3sYMqqbj55ptl+PDhkpKSIkVFRdKlSxeZOHGiWSF1xowZJrHQ8RULFiwwJ1gAABA8giqpUOPHjze3smJiYiQ1NdW1MgEAgIpVbrQjAADAeUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAACALUgqAABAzU4qTp06JWPGjJGPPvrIbB88eFDGjh0rffv2lSFDhsju3btLj122bJn06dNHevbsKQsXLnSryIA7Cgok/OhRkdxckcWLzTYABCNXkopt27bJ3XffLZs3by7dN2fOHOnevbu88847MnXqVJkyZYrZv2PHDklNTZXly5fL22+/LRs3bpQ1a9a4UWyg+mVkSNhVV0mdw4dF6tQRGT9eJCFB5NNP3S4ZAARHUrF06VKZNGmSdOzY0WyfPn1aNmzYYFooVHJyshw7dkx27dola9euNS0UjRo1krp165pjVq9e7UaxgeqlLRIDB4q0by+e778XT1aWiLbgtWsnMmAALRYAgk6EG086d+5c81NbIFR+fr5JGKKiokqPiY2NlezsbMnJyZHExMRz9uu+yiopKZHi4uIqlR8X8sWU2NrHs2yZePLyxJOWJtKqlXdn69YiaWliXXmlWMuXizVmjNvFrPGou84ivqERX38f35WkorwP+vKEhYWJZVkX7Pd4PJV+Lm39gHO2bt3qdhFCRnxmpsTGx4vHl1D4tG4tVny85GRmSlanTm4VL+RQd51FfGtHfIMiqYiOjjYDN48fPy7169c3+3Jzc02rRFxcnBnE6aP7dV9ltWnT5pwWEdiXxWql7tChg4SHh7tdnJDgSUoSz+uvi+zbd7alQu3da7pCYpOSJIakosqou84ivqERX/183rlzZ81IKiIiIqRbt26Snp4uo0ePlszMTLNPuz1uu+02mTx5skyYMEEaNGggK1askDvvvLPSz6WtH1Rs52hsia9Nhg8XefxxsUaO9HaBaNfH3r0i994rnmbNxJOSogF3u5Qhg7rrLOJbs+Pr72MHRVKhZs+eLTNmzDCJhY6vWLBggenmaNu2rYwbN05GjBghRUVFJsno37+/28UFnKctaqtWmUGZlo4r0q6Q/ftFmjXz7qfFDUCQcTWpSNNvX2fExMSUDtw8X0pKirkBtc5NN0nJd9/J/557TloVFYmnTRuRoUNJKAAEpaBpqQBwEVFR8uOAAXKFjp+g+RhAEKs1SYXvCpOTJ0/Sr+fg5UY6mIf42ovYOov4Oov4hkZ8T5w4ccmrNX08VnnXbIagH3/8Ufbs2eN2MQAAqLESEhLMFZtS25MKHeR55MgRiYyMNFeAAAAA/2gLRWFhoTRu3NhcnSm1PakAAADO4is7AACwBUkFAACwBUkFAACwBUkFAACwBUkFAACwBUkFAACwBUkFAACwBUkFAACwBUkFAACwRa1IKj744AO54447pFevXjJ79mw5ffq020Wq8SZNmiS9e/eWgQMHmtvrr78uBw8elLFjx0rfvn1lyJAhsnv3breLWaOcOnVKxowZIx999JHZvlQ8ly1bJn369JGePXvKwoULXSx1zY2v/rzhhhtK6/C9995beuzzzz8vv/71r018ly5d6mKpawb9/+/Xr5/0799fHnroIcnLy6P+OhzfoK2/VojLzc21kpOTraysLKukpMSaOnWqtXjxYreLVeN169bNOnz48Dn7Jk6caC1ZssT8/sknn1gDBgxwqXQ1z9dff20NGTLE6tixo7V27dpLxnP79u1Wjx49rCNHjliFhYXWyJEjrffff9/V8tfE+D733HPWa6+9dsGxa9assYYNG2Zie/ToUatPnz7Wtm3bXCh1zbBx40arX79+VkFBgdmeP3++NXPmTOqvw/EN1vob8i0VGRkZct1110mLFi3E4/HIsGHDZPXq1W4Xq0bLysoyy+w+9thjJnOeO3euFBQUyIYNG8w3EpWcnCzHjh2TXbt2uV3cGkG/TWjrT8eOHc22tqZdLJ5r164130AaNWokdevWNcdQpwOLr9q8ebOsX79eBg8ebL5R79y50+zX+Gq91tg2bNjQfNMmvhfXtGlT0wLcoEEDs92uXTvZu3cv9dfB+O7fvz9o62/IJxW5ubkSGxtbuq2/Z2dnu1qmmu7QoUOSlJQk8+bNk3//+98mxvq7VuKoqKjS44i1/zQxu+WWW0q38/PzLxrPnJycC+q07oP/8VVNmjQxJ+MVK1bI3XffLb/97W/l5MmTJpZxcXGlxxHfS7vqqqvkV7/6lfldv1wsWrRIbr75Zuqvg/HVpCxY629YbViu9XwsfV41+m1P+0GbNWtmThz333+/bNy4sdxjibV99dYXz/IWFtZWOARG63C3bt3M7zreSr8Jbt++nfhWkn5wjRo1Sjp16iSDBg0q9xjqrz3x1SQiWOtvyJ/xNWPTAUM++q26bBaHwG3atKl0sJvSShwREWEGwmm3yMVaieC/6Ojoi8aTOl112hT/0ksvXbBf67HGUmPqc/43P1zom2++MV3LPXr0kKeffpr663B8tcUiWOtvyCcV2gynH4LaB6Uffv/617+ke/fubherRtMmNm1O1hOzfqNesmSJaY7TrDk9Pd0ck5mZaSp4YmKi28WtkTR2F4vnbbfdJmvWrDFdJDr2Qps/qdOB0W91Os7Clxx/8skncuLECWnbtq05ca9atUoKCwvNyfu9996TW2+91e0iBy39ANNm+OnTp8uDDz5o9lF/nY1vMNffCAlxzZs3lz/84Q+mv0kz51/+8pcybtw4t4tV4xO14cOHS0pKihQVFUmXLl1k4sSJ5iQxY8YMcyLRbpEFCxbQrFkFOjirvHjqiUPr8IgRI0z89SStA7PgP22G/9vf/ma+9c2fP1/q169vtuvUqWNOyjt27DADCDW++g2xc+fObhc5aL388sumRUJ/6k1deeWV1F+H4xus9dejl4BU27MBAICQFfLdHwAAoHqQVAAAAFuQVAAAAFuQVAAAAFuQVAAAAFuQVAAAAFuQVAAIOr179zaT9wCoWUgqAAQVnbhHJ/eJjIx0uygAAsTkVwAc98MPP8g999xjZmHVad01YXjqqafMNMO6LHOLFi3MjItXX321vPjii9K4cWOzwuWTTz5Z+hg6pbPu++yzz1x9LQAujpYKANVCFzbS9Ql0HYihQ4eadQyuueYakyS0a9eudIGkdevWmbUKBgwYIJs3bza3jz/+WOLj4+XRRx91+2UAuASSCgDVZsyYMRIeHm7Wi9HWCl2XQNeFuOGGGyQ7O1vy8vJM4qEJhI8uWjd16lS58cYb5a677nK1/ABq+YJiAIKHdmv4FvRq2LBh6X7d1uRhw4YNZsG6sp577jk5evSoLFq0qNrLCyAwJBUAqk1Fq9Zq14eugOvz7rvvmqWx//Of/5gWDQDBje4PAEFBWyq++uorufbaa832N998Y5bP1gGcsbGxbhcPgB9oqQAQFLZs2SJ9+vSRiAjvaUmvEtHxFQ8//LCcOnWq9Li33377nDEXAIIHl5QCAABb0P0BAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAABsQVIBAADEDv8PlRAAkOA96xsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ebe3370>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0391831 (top 4 hit)\n",
      "Query precursor m/z: 230.18643445964, Library precursor m/z: 230.223\n",
      "DreaMS similarity: 0.6461858749389648\n",
      "Modified cosine similarity: 0.1537209674611311\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAADVCAYAAADgkwLjAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAJ95JREFUeJzt3Q10FNX5P/DvJiHQhNfEJhDeX2rLq9FCNQRQRKCAvEUhICiJgKeigoigBUFqy6/UH5WWKlYNIEU5YNqC/EE9RlHAmCqcotKA8otAqAkkIAkkAgnZvf/z3GVDApuwm8xk9uX7OWdPmMkwe/fZm5ln7r1zx6aUUiAiIiKqp5D67oCIiIhIMKkgIiIiQzCpICIiIkMwqSAiIiJDMKkgIiIiQzCpICIiIkMwqSAiIiJDMKkgIiIiQ4QhSFRUVODs2bNo3LgxQkKYSxEREXnK4XCgrKwMLVq0QFhYzalD0CQVklAcO3bM6mIQERH5rU6dOiE6OrrG3wdNUiEtFKJDhw6IjIy0ujgBx2634/Dhw7jxxhsRGhpqdXECCmNrLsbXXIxvYMT3woUL+sLcdS5FsCcVri6PJk2aICIiwuriBGTFFhJbHjiMxdiai/E1F+MbWPG93vABywYXlJeXIyUlBR999JFePnXqFFJTUzFy5EgkJSXhyJEjldtu2rQJI0aMwNChQ7Fq1SqrikxkidJS4O23o7F4sQ3r1jmXiYh8kSVJRXZ2NiZPnoz9+/dXrlu6dCkGDx6Md955B/PmzcPcuXP1+kOHDiEtLQ2bN2/Gjh07sHfvXmRkZFhRbKIGl5kJdO0agmXLOuKNN2yYMUP6NIFPP7W6ZEREPpJUbNy4EbNnz0afPn308qVLl7Bnzx7dQiESExNRUlKCnJwc7Ny5U7dQNG/eHOHh4Xqb7du3W1FsogYlLRJjxwI9ewJHj9pw/LgN0oDXowcwZgxbLIjI91gypmLZsmX6p7RAiOLiYp0wNG3atHKb2NhYnDx5EgUFBejSpUu19bKuPrfFuPqgyDiumDK2xtm0yYaiIhs2bLChQwfnuo4dgQ0bgM6dFTZvVkhJUVYX0++x7pqL8Q2M+Hq6f58YqCkn+poGhCh17UHTZrPV+b2k9YPMc+DAAauLEDCysuIQFxeLDh2q13dJLOLiFLKyChAfn29Z+QIN6665GN/giK9PJBVyz6sM3Dx//nzlnRmFhYW6VaJ169Z6EKeLrJd1ddWtW7dqLSJkXBYrlbp3794c4W2QhAQbXn9duj3kVugr63Nzgfx8GxISYhEfH2NlEQMC6665GN/AiK+cn+XWVb9IKmR2rkGDBiE9PR3Tpk1DVlaWXifdHnfeeSfmzJmDmTNn6vkltmzZgnvuuafO7yWtH6zY5pHYMr7GmDQJePppYOpUpbtApIVCEor77weiomxITraBoTYO6665GF//jq+n+/aJpEIsWbIECxcu1ImFjK9YuXKl7ubo3r07pk+fjilTpuiptiXJGD16tNXFJTKdNKht2+YclNmli0JMDFBQYENUlHM9G9yIyNdYmlRskBFnl8XExFQO3LxacnKyfhEFm/79gW+/deAnP7GjoqIR1qwBJkxgQkFEvslnWiqIyD1JIJo1s6NJk0ZITbW6NETBTW7lfust6Nu7u3Zlkn81JhVEREQeTkYnc8cUFQFt2wJ5ecD8+c7uSGlVJAun6SYiIvLPyeig78riZHTXYlJBRER0HdLlIS0UMhTw6snozpwB0tOtLqFvYFJBRER0HdIqIV0eVeeMcSUWsr7KMzCDGpMKIiKi65CnRcgYCun2qErmjpH1VZ4mEdSYVBAREV3HxIlAq1YyGZ0zkRBXJqNz3gVCvPuDiIjIy8nocHkyOmdCwcnormBLBRERkQfkttFjxwB5/FRFBfRkdLLM20mvYEsFERGRh6RFomVL5785Gd212FJBREREhmBSQURERIZgUkFERESGYFJBREREhmBSQURERIZgUkFERESGYFJBREREhmBSQURERIZgUkFERESGYFJBREREhmBSQURERIZgUkFERESGYFJBREREhmBSQURERIZgUkFERESGYFJBREREhgiDj5k9eza++eYbNGnSRC+PHz8eo0aNwoIFC1BQUKDXr1ixAl26dLG6qEREROTLScWXX36JrVu3olWrVpXrHnnkEQwePBgPPPAAMjMzMXfuXLz99tuWlpOIiIh8uPsjPz8f58+fx1NPPYXRo0dj2bJlKC0txZ49e5CUlKS3SUxMRElJCXJycqwuLhEREflqS8Xp06eRkJCApUuXomnTppg/fz6WL1+O8PBwvewSGxuLkydPolu3bl6/h8PhgN1uN7jk5IopY2s8V0yVUrDbHVYXJ+Cw7porMOPrvB73hb9HewPF19P9+1RS0adPH6xatapy+aGHHsITTzzhdtuQkLo1srCFw1wHDhywuggBqgfKysrwxRcHrS5IwGLdNVcgxffixR76py/9PR7wkfj6VFKxb98+3bUh4ydcV2ZhYWEoLy/X3SIRERF6fWFhoW6tqAtp3aja6kHGZbFSqXv37o3Q0FCrixNQnFcIdjRu3Bjx8fFWFyfgsO6aKxDj26SJ86LWF/4e7Q0UXzkHHz582JikIisry6s3ly6Murh48aIeR9G3b19ERkZi/fr1GDp0qG5dSE9Px7Rp03RZJNGo690f0sIRKBXbF0lsGV8z2GGz2RAa6lPDoAIK6665AjG+vvR5Qk2Or6f79iipePDBBxEXF6dbDq5HbvvMzs5GXQwYMACTJk1CcnIyKioq0K9fP8yaNQvFxcVYuHChTixkfMXKlSv1AZaIiIh8h0dJhcwN8eGHH3q0w5tvvrleBZoxY4Z+VRUTE4O0tLR67ZeIiIjM5VFb6t/+9jePd+jNtkRERBRkLRUyAKSqM2fO4JVXXsHRo0fRrl07zJw5E23atHG7LREREQWHOo36kvENkkTIwMnWrVvj0UcfNb5kREREFHhJxR//+Ec9s6WL3NI5ZswYPbulzHwpM2ESERFRcPOo+6N79+5ITU3FuHHj9N0ZDz/8sE4q5BYTuQ1UptUmIiKi4OZRUjFy5EjcddddeOONN3DffffpMRS7du1CUVGRfvCXL92rS0RERD4+pkLmh5D5Kl599VV8/vnn+rbPEydOMKEgIiIi72bUfOGFF3QCsWDBAj1QMzc3V4+1aNSokX4+R9u2bT3ZFREREQVzS8WiRYv0HR7ygC/5t+jYsaN++Jd0hzz99NNml5OIiIgCoaVCHiQi81HINN0XLlyo9ruf//zn2LBhg1nlIyIiokBKKqTLY8qUKbqr49e//rX5pSIiIqLATCoGDhyIpKQkj3Z46tQp/PjHP65vuYiIiCgQx1QMHz7c4x16sy0REREFWUuFTHAl3R/XI2MuysrKjCgXERERBWJSsWzZMo93OGHChPqUh4iIiAI5qRg/frz5JSEiIqLge0opERER0dWYVBAREZEhmFQQERFRwycVgwYNwvLly/HVV18Z8+5EREQUXAM1XVavXo13330Xjz/+uH64mDwSXV4//elPzSshEZmqtBR46y3gyBGga1e5gwto2tTqUhFRwCcVvXr10q/58+fr1oqMjAzMnTsXISEhGDVqFMaNG4c2bdqYV1oiMlRmJjB2LFBUBMiDhvPygPnzgW3bgP79rS4dEQXFmIrCwkKdVBw4cEBPy92lSxecPXsWEydORFpamvGlJCJTWigkoejZEzh6FDh+3Nla0aMHMGaM8/dEvkrq59q1wDPPAOvWsb76ZUvF66+/jvfeew8HDx5EQkKCnr/ipZdeQmRkpP79sGHDMH36dMyYMcOs8hKRQaTLQ1oo5CHDHTo413Xs6Fzu3BlITwdSU60uJdG12MIWIC0VH374oU4kdu/ejVdeeQVjx46tTChEp06d8Lvf/c6MchKRwaRVQg7IroTCRRILWS+/J/I1Vrewyf6Li6XFni0k9U4qJGlITk5Gy5Ytq62XcRUiKipKj60gIt/XpYvzCk8OylXl5jrXx8WZ27zM5mtj4xII8fTkM9TWwnbmjLOFzcwWkk6dgPx8Z2IhjfKy/Omn5r1nwHV/nDhxAm/Jtwhg69atuOGGG6r9vrS0FJ82QEQ/+OAD/OlPf0J5eTluu+02LF68GI0aNTL9fSk4eHMHREPdLeF6n5wcG0pKQhEebmwZJk4EFiwApk51HpDlwCwJxf33A82aAYsXm9e87HHzdQPfmnL1240YAbzzTg1vb0LZ6tqs78n/c1tcmBjfy29oy8lBtByru3UDWrSo92f/4AMgJkahQwdbg7awVW0hOXECkI8kdUP+XqSF5Ngx37trypI7u5QHli9frp5++mnVs2dP/bPqa/HixeqTTz5RZiosLFSJiYkqPz9fORwONW/ePLVu3Tqv9vHDDz+offv2qXPnzplWzmBWUVGh4ys//Y1U3+hopUJClGrf3vlTljMza9vWodq3rdA/o39UqjLve1GptWuVKikxoUwOBThUaKhDl8mb8l6P/B/Xvlq3VspmUyoqSqmWLZUaNEip3FzndseOKTVwoHPb+n7EkhMlKrrZRTVogL32/Rv5QT2ou7t2VVR7O4lFWFgNb29C2eRzyy5qjPuJEqXWrFFq0aJqde26/6+khuK2vKQym//SnPhWeUNH+/bKIT9r2bcnn0G8/77zb09eru1cZHubzaFDYwYJvYRI3rdHD+fryvs6vxJfOvYaXUVd51D5WRuPkgqXN998U1lhy5Yt6rHHHqtc/uyzz9Q999zj1T6YVJjLX5MKTw9m1bYd6Ki+bf8KFd2oWJXYmhlyUHZXpp/8RKmICIc+4XtaXm/eLy5OqRtucB4YX3rpysGzKkMOnu+/r9Y0eViF2Oy179+bL6aepM7u3v1vFR3tqHw72X2rVkoNGODu7R2qJKqD4WWretJye7KMfNTtGaL2/6fU6tU1hFLqbeNzquTgccM+g1bDd+eoZd/X+wyuHCoy0qFjId+N7E5+79quf3+Hahb2gzP5MoHkchJ6UTWpEO3iKtQzC8qUrxx7zfjz8TSp8Ojuj5dffhkPP/ywvpX0z3/+s9tt5syZA7PI+8bGxlYuy79PnjxZp305HA7Y7XYDS0fCFVN/i+2mTTYUFdmwYYPNzR0QCps3K6SkqOrbvnHVthtD0blzC6T/4QhS/l+Sbgt1fPttndsZ3ZVJmlrbtrXh//5PISQEHpX3emy//S1QXo6I7Gy0PPW/QJMmeCCpBRb/oZl+L/fNywo5OQp2u2fvUU1GBkJGj8a3rdPQ1nal/O7279i0CbaiItjcdJyrzp2hNm+GSkm59vNI66v023hB6mxGRqtq/fTSr3/2LPDmm+7vjHkLd+HBDc96XDZPSDdXjXGPuYRvw38OfHLU+Z65uVCX291zUr9D27aNa/y+duxQ7uu4rrdNkf6vZkjtbsxnELYavjtbLfuu9bNfrhObNgE//GBD+/Y2/W/pcpBxQTExQEEBEBYG3FfxFiJ22GGvY9lr06mTDXl5Nhw/brt2DNKJEHR+6XHY757Y4LefuDv2enNc8/Z9rsejpMJ1Ai+Qb84CkghcTSbcqoucnBwDSkQ1kblL/ElWVhzi4mLdHszi4hSysgoQH59fuW3b1jHo0CHUfV/uuRsqD5z/feEFfC9HPYPKlJzs/LnieYce7+CuDFeX93p6vfYawk+dAioqMBHxQIkC2qxA06TtyMsbrAdwVj3xuwZwhoUdxxdffO/VZwo5fx59kpLkjxldZ9+NvKdtte6/ICsLsXFxsLnJPFRcnP59fnx8tf33fOkl2CoqkKcUiu66C46ICI/Ll5cnMb/ST1/rnTExl3CktJfbX7orm6fCwqKRl9fRfVwKG6Hr86OBDtGV7+Wqay2/eQ95eWNrjGenTucQF9e8hhO2rfoYhHp+BhHn5Xd33c9+uU5kZTVGq5YxyMsLRbt2zjEMklfJ4MwnnnCODxr0o70o3F2KvDqWvTY//WkImjfvjSlTQjF8uA1yv4JrDFJUSwcm/OwbqDFjcGDbNq/qnhnHXm+Oa4ZTfkC6P+bMmVO5/Pnnn6sJEybUqemmuLhYNxPxZeyrrKxMx1d+Wl0Wb16vvWavtX82Lc3u4bZXugUc7dop+8KFppRJxlZ4Wt5aX8XFyhEerhx9+jjb+aXtuU0b3fd9LrSFim5RrgYOdFRrXpZlafovLvb+M9lfe0055HDTvn1l0+zVzdcDE+2V+9fb19Ae7rDZlD0t7cr+d+3S/fW63/7yZ5Dlit27Pa67ixcfrRbX63VFrEGqZ2Xz4iWfWz7/NXF3da+5abKWunb2yaXu/9/l7+svf6m9jlfrzqrnZ6j8rj397q732avUOfm7uF7Xxzk0VY5mzTz+7r197d5doccDSfddu3bSFVOlx9OA2FUYdOz15rjm6UuGDhg+puK///2vWiQdS0qpXbt2qb59+6ohQ4aoAwcOKDOdOnVKD9T87rvv9EDNBQsWqNXSUegFjqkwl7+PqbjmBFfTmIpmF/VBvsZtDRh4UGOZ+leoqJZ2978bYPeur1TOmlJOOUJf3fHav7/KjBiiD+ZybmjXzrlpvYaLyHHDlbzk5lYbIOo8ODt0bCv37+kXY0DncdUxFa63c42pSEy89jvQ4xBatvOs0nipWlziKpxxaXpBZdoSax1wUD2e1b+v2uqT/izZVeImH9ioMRVXvWFtYyqu+exu6pxztw51U6+Kyqrk3M6hwsIcKiPDvHE31T7e/N+otS0eV88842Zsdrt2Sv/C4mOvN8c1SwdqPvjgg2rJkiX6xD5s2DCVlpamNm/e7PWgybr44IMP1N13363fd/78+Tor8waTCnP5a1LhycGs2rYZP6josCJ9FdAutsx50G9VUXmlYtQBze1Jt1GxXn9teR0qOvK8dyd8Ocm3aFHr6LiS1X/TB023B09vVU1iLh/pZH9rnz+lnmm3Tq1t8qtrB9h58sV4MsLPw7qrr0KrvJ00rLju/qiMc+gZlfmPE+7L1qyZIXdO6LhI3BN3qrWtnnDGxd0Z4qoEoPL/ufm+rhS3Sr2teveHwZ+hanykNUWu4Gu7++Oaz15DndO7bXlJ2WBXrZpf0i13kRF2Z0LhYvbtGAbUuYY49npzXLMsqUhISFB2u13l5OSo3r17qwsXLuj18fHxytcxqTCXPycVnhzMqm2bkaXWhs5Qz+A5tTZ0uipBU6ViYw24nHdfpoUL7er50f9U5+TukssHssryPlak1iJVJwBeHxgvd0e4JbeDGHnF5bp0uummKy0WriOdnLmrnRW8+GKqDsm/modXjVXr7tVvd+JEleW7/6FKYru6L5t8pgULlGknLwOSGF3c1RfUMz/6X7X2J//jbKGQlc8/74xhZKTzAxvlcnykK/DokiW6y82w3crn6PqmWtt8jvu/VTNbDOQNw8PdNGOZ20JSl2OvN8c1S5KKQYMG6a6IF198UaWkpOh133zzjbrjjjvqXtIGwqTCXP6eVHjtxhudB/gmTZz3HT7+uKHzVFQlMf337t3OKz2j2jNl+9DQhr3icp0YXS0WktTIiaymhMITBrZUXLfuNvQV6tVt2EYlAEZfwlp1bLCyxaBTJ2di0UAx9IVjrylJxZo1a1S/fv10K4VMePXll1/q5TfeeEP5OiYV5gqqpEJmlZEDihxI2rQxbVKma2K7e7exJ4MOHZyJRf/+1RMVSZLMuuIy8tLJtb96Jlse110rrlDNSgCM/h6sODZY2WIgk1T87GcNFkN/Siq8ekrpgw8+iLvuugtNmjRBTEwMzpw5gzVr1qB3797m3JpC5Gtcc/X+4hdXJjFw3Vdm9ly9cv+77F8ebiD3AcpN+vWZd1f+n9w3Kbeiyb5at3bOPxwV5Zwb2YzPIfs08tGnsj8pq2vSAnlgidyDaMZnkH3J/vfuNf+9zPrOzfoerGDF91GVTGvg7zE0gVdJhWjcuDGOHz+OXDmQXrZ3717069fP6LIR+R7X04xqmhXJ7OeFm3FSzs42/qTVkMw68boj8w/I/mVShIaKVyAkAGax4vsg45IKedy5zKjZqlUrhMn0ZZfZbDZ8/PHH3uyKyD8F4vPCA+Gk1ZCfgVeovoXfh/8mFW+++aZOLAYOHGheiYj85Xnh7qb+k98TEQUpr+a6vnTpEhISEswrDZGvk+eFt2oF3HefM5EQlXP1RjmbXomIgpRXScWUKVOwYsUKfP/99/p5HFVfREHBNTDQNTisfXvnWIqDBxtmcBgRUaB0f2zatAmnT5/G+vXrK9fJbakypuLQoUNmlI/INwcGSkJx7hwgT0Pk4DAiIu+Tis2bN3uzOVFgDw6TxxReftw2ERF52f3Rtm1bfefHV199hffffx9RUVF6rgpZT0REFBRz1cht5YWFwLp1zmWqW1KRnZ2NYcOGYcOGDVi1apXuCnnggQewdetWb3ZDRETkfzIzgU6dgIICoFEjYMYM5/Knn1pdMv9MKp577jk8++yz2Lhxo56non379khLS8Pq1avNKyEREZGvzKbbsydw9CiQn++cl6ZHD+eMrmyx8D6pOHLkCIYMGaL/LYMzxS233KK7QIiIiAJ+Nl2ZPffq2XTlHCgzupJ3SUW3bt2wffv2aut27typ1xMREQWsQJxN1+q7P5555hnMnDlTz6x5/vx5TJ8+Xd9K+te//tWMshH59kCtS5ecA7V4OylR4ONsusa3VPTs2VPf9SGDMx9//HGMHj0a77zzDvr06ePNboj8FwdqEQX3bLpTp3I2XaOSCmmZaNq0KUaNGoUZM2Zg3LhxaNmyJZKTk73ZDZF/4kAtouDlmk1XZs/lbLp17/7Iy8vTTyYVn332GRbII2arKC0t1dsQBfVArYZ47DkRWT+b7rFjzr91Pmq9bkmFTGwl3RvFxcV6ucNVg1TCw8MxZ86c6+2GyP9xoBaR77BqbJO8By8e6jdQc6r0IQHo1asX7rjjDk/+C1Hg4UAtIt8Z2yRdkZJUxMY6xzbNn+/shpDWBPKPuz9kTopXX30Vubm51zyZ9Pe//73RZSPyvYFa0v0nSbZ0eUgLBQdqEVk3tsnVFen6O5SxTdI9we4I/0gqnnzySZw4cQK33367nlGTKCgHasmBS1ol4uKcLRSSUHCgFlHD4Ngmn+ZVZvDvf/8bH374IVq0aGFeiYh8GQdqEVmLY5sCJ6lo3bo1Ll68yKSCghsHahFZh2ObAiepGD58OFJTU/X8FPLY86ruvfdeo8tGRERUHcc2BU5S8fnnnyM6Ohp79uyptl4eLmZUUjF79mx88803aNKkiV4eP368nmxL5scoKCjQ61esWIEuzEaJiIIPxzYFTlKxQbJCk3355ZfYunUrWsl0qJc98sgjGDx4sJ4ePDMzE3PnzsXbb79telmIiMgHcWyTfycVWVlZ190mISGh3oXJz8/XDyp76qmn9F0mt912m55YS1pG/vCHP+htEhMTUVJSgpycHD4dlaiu+FA08ncc2+S/ScWiRYtq/b10f8hdIfV1+vRpnZwsXbpUP2Nk/vz5WL58uZ61U5ZdYmNjcfLkyTolFTK/ht1ur3dZqTpXTBlbP4htZiZCkpKumTjIsWVLUE4c5E18XQ9LcrCee4zHhsCIr6f7tymlFHxUdnY2nnjiCXz//ffYt29f5frJkyfjscceQ38vDoDSAiKPaScKZiHnz6P3mDEI7d0btjfeqJw4SE2dCvt//oMD27bBERFhdTF9Vg8ZJAjgoMyVQBSEunfvjohajhE+NYOVJA7StSHjJ4TkOzLJVnl5uU4KXB+ksLBQt1bUhbRuVG31IOOy2AMHDqB3794IDQ21ujgBxcjY2tatg+3cuSsJhejYUS+Hdu6Mmw4fhkpJQTDxJr4hlweQx8fHN1Dp/B+PDYERXzkHHz58+Lrb+VRSIXNgLFu2DH379kVkZCTWr1+PoUOH6vET6enpmDZtmh7fIYlGXe/+CAkJYcU2kcSW8fXh2MrgthomDrK1bQub/D5Ivz9v4ss67j0eG/w7vp7u26eSigEDBmDSpElITk5GRUUF+vXrh1mzZuknpC5cuFAnFjK+YuXKlXocBxF5iRMHEZGJfCqpEDNmzNCvqmJiYpCWlmZZmYgCBicOIiITuQYzE1EwTRx08KCzVaJ9e+dDmGSZEwcRUaC1VBCRyThxEBGZhEkFUTDixEFEZAJ2fxAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRfycV5eXlSElJwUcffaSXT506hdTUVIwcORJJSUk4cuRI5babNm3CiBEjMHToUKxatcqqIhNRMCstBYqKgMJCYN065zIRWZ9UZGdnY/Lkydi/f3/luqVLl2Lw4MF45513MG/ePMydO1evP3ToENLS0rB582bs2LEDe/fuRUZGhhXFJqJglZkJdOoEFBQAjRoBM2Y4lz/91OqSEfkUS5KKjRs3Yvbs2ejTp49evnTpEvbs2aNbKERiYiJKSkqQk5ODnTt36haK5s2bIzw8XG+zfft2K4pNRMFIWiTGjgV69gSOHgXy8wFpSe3RAxgzhi0WRFWEwQLLli3TP6UFQhQXF+uEoWnTppXbxMbG4uTJkygoKECXLl2qrZd1deVwOGC32+tVfrqWK6aMrfEYW2vja9u0CbaiItg2bAA6dHCu7NgR2LABqnNnqM2boVJSGrLIfoX1NzDi6+n+LUkq3J3o3QkJCYFS6pr1Nputzu8lrR9kngMHDlhdhIDF2FoT37isLMTGxcHmSihcOnaEiotDQVYW8uPjG6aQfoz1Nzji6xNJRXR0tB64ef78eUREROh1hYWFulWidevWehCni6yXdXXVrVu3ai0iZFwWK5W6d+/eCA0Ntbo4AYWxtTa+toQE2F5/HTh+/EpLhcjNhS0/H7EJCYhhUlEj1t/AiK+cnw8fPuwfSUVYWBgGDRqE9PR0TJs2DVlZWXqddHvceeedmDNnDmbOnInIyEhs2bIF99xzT53fS1o/WLHNI7FlfM3B2FoU30mTgKefBqZO1V0euusjNxe4/37YoqJgS06W/2xFkf0K669/x9fTfftEUiGWLFmChQsX6sRCxlesXLlSd3N0794d06dPx5QpU1BRUaGTjNGjR1tdXCIKFtKyuW2bc1CmjO+KiwPy8oCoKOd6tnwS+UZSsUGy/stiYmIqB25eLTk5Wb+IiCzRvz9w7BiQnu6880OSiwkTmFAQ+WpLBRGRT5MEIjXV6lIQ+bSgSSpcd5hcvHiR/Xom3m4kg3kYX2MxtuZifM3F+AZGfC9cuFDr3ZouNuXuns0A9P333+OYNF8SERFRnXTq1EnfsYlgTypkkOfZs2fRuHFjfQcIEREReUZaKMrKytCiRQt9dyaCPakgIiIic/GSnYiIiAzBpIKIiIgMwaSCiIiIDMGkgoiIiAzBpIKIiIgMwaSCiIiIDMGkgoiIiAzBpIKIiIgMwaSCiIiIDBEUScUHH3yAu+++G8OGDcOSJUtw6dIlq4vk92bPno3hw4dj7Nix+vX666/j1KlTSE1NxciRI5GUlIQj8oho8lh5eTlSUlLw0Ucf6eXa4rlp0yaMGDECQ4cOxapVqywstf/GV37eeuutlXX4/vvvr9z2L3/5C375y1/q+G7cuNHCUvsH+fsfNWoURo8ejUcffRRFRUWsvybH12frrwpwhYWFKjExUeXn5yuHw6HmzZun1q1bZ3Wx/N6gQYPUmTNnqq2bNWuWWr9+vf73J598osaMGWNR6fzPf/7zH5WUlKT69Omjdu7cWWs8Dx48qIYMGaLOnj2rysrK1NSpU9X7779vafn9Mb4vvPCCWrt27TXbZmRkqIkTJ+rYnjt3To0YMUJlZ2dbUGr/sHfvXjVq1ChVWlqql1esWKEWLVrE+mtyfH21/gZ8S0VmZiZuueUWtGnTBjabDRMnTsT27dutLpZfy8/P14/Zfeqpp3TmvGzZMpSWlmLPnj36ikQkJiaipKQEOTk5VhfXL8jVhLT+9OnTRy9La1pN8dy5c6e+AmnevDnCw8P1NqzT3sVX7N+/H7t378b48eP1FfXhw4f1eomv1GuJbbNmzfSVNuNbs1atWukW4MjISL3co0cP5Obmsv6aGN+8vDyfrb8Bn1QUFhYiNja2cln+ffLkSUvL5O9Onz6NhIQELF++HP/4xz90jOXfUombNm1auR1j7TlJzG6//fbK5eLi4hrjWVBQcE2dlnXkeXxFy5Yt9cF4y5YtmDx5Mn71q1/h4sWLOpatW7eu3I7xrV3Xrl3xi1/8Qv9bLi5Wr16NAQMGsP6aGF9Jyny1/oYEw+Nar8ZHn9ePXO1JP2hUVJQ+cDz00EPYu3ev220Za+PqrSue7h4sLK1w5B2pw4MGDdL/lvFWciV48OBBxreO5MT1wAMPID4+HuPGjXO7DeuvMfGVJMJX62/AH/ElY5MBQy5yVV01iyPv7du3r3Kwm5BKHBYWpgfCSbdITa1E5Lno6Oga48k6XX/SFP/KK69cs17qscRSYupy9ZUfXevrr7/WXctDhgzBb3/7W9Zfk+MrLRa+Wn8DPqmQZjg5CUoflJz8/v73v2Pw4MFWF8uvSRObNCfLgVmuqNevX6+b4yRrTk9P19tkZWXpCt6lSxeri+uXJHY1xfPOO+9ERkaG7iKRsRfS/Mk67R25qpNxFq7k+JNPPsGFCxfQvXt3feDetm0bysrK9MH7vffewx133GF1kX2WnMCkGX7BggV45JFH9DrWX3Pj68v1NwwB7oYbbsBvfvMb3d8kmfNNN92E6dOnW10sv0/UJk2ahOTkZFRUVKBfv36YNWuWPkgsXLhQH0ikW2TlypVs1qwHGZzlLp5y4JA6PGXKFB1/OUjLwCzynDTDv/jii/qqb8WKFYiIiNDLjRo10gflQ4cO6QGEEl+5Quzbt6/VRfZZr776qm6RkJ/yEp07d2b9NTm+vlp/bXILSIO9GxEREQWsgO/+ICIioobBpIKIiIgMwaSCiIiIDMGkgoiIiAzBpIKIiIgMwaSCiIiIDMGkgoh8zvDhw/XkPUTkX5hUEJFPkYl7ZHKfxo0bW10UIvISJ78iItN99913uO+++/QsrDKtuyQMzz33nJ5mWB7L3KZNGz3j4o033oiXX34ZLVq00E+4fPbZZyv3IVM6y7p//etfln4WIqoZWyqIqEHIg43k+QTyHIgJEybo5xj06tVLJwk9evSofEDSrl279LMKxowZg/379+vXxx9/jLi4ODz55JNWfwwiqgWTCiJqMCkpKQgNDdXPi5HWCnkugTwX4tZbb8XJkydRVFSkEw9JIFzkoXXz5s3DbbfdhnvvvdfS8hNRkD9QjIh8h3RruB7o1axZs8r1sizJw549e/QD66p64YUXcO7cOaxevbrBy0tE3mFSQUQN5npPrZWuD3kCrsu7776rH439z3/+U7doEJFvY/cHEfkEaan46quvcPPNN+vlr7/+Wj8+WwZwxsbGWl08IvIAWyqIyCd88cUXGDFiBMLCnIcluUtExlc89thjKC8vr9xux44d1cZcEJHv4C2lREREZAh2fxAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBERkSGYVBAREZEhmFQQERGRIZhUEBEREYzw/wFqMyRHElSgYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ebe3140>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0391832 (top 5 hit)\n",
      "Query precursor m/z: 230.18643445964, Library precursor m/z: 230.223\n",
      "DreaMS similarity: 0.6406431198120117\n",
      "Modified cosine similarity: 0.09253357021617656\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ebe3680>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = df_top1['i'].iloc[25]\n",
    "df_i = df[df['i'] == i]\n",
    "for _, row in df_i.iterrows():\n",
    "    i, j = row['i'], row['j']\n",
    "    print(f'Library ID: {row[\"library_ID\"]} (top {row[\"topk\"]} hit)')\n",
    "    print(f'Query precursor m/z: {msdata.get_prec_mzs(i)}, Library precursor m/z: {msdata_lib.get_prec_mzs(j)}')\n",
    "    print('DreaMS similarity:', row['DreaMS_similarity'])\n",
    "    print('Modified cosine similarity:', row['Modified_cosine_similarity'])\n",
    "    su.plot_spectrum(spec=msdata.get_spectra(i), mirror_spec=msdata_lib.get_spectra(j))\n",
    "    display(Chem.MolFromSmiles(row['library_SMILES']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top-1 hit demonstrates a strong match, aligning well with the modified cosine similarity. However, annotating a query spectrum with a library spectrum is often impossible due to the limited size of available libraries. In such instances, DreaMS similarity offers an interesting approach, providing insights into the molecular structure even without a definitive match. Let's examine a case with a less confident hit to illustrate this concept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0240559 (top 1 hit)\n",
      "Query precursor m/z: 186.980925613972, Library precursor m/z: 187.123\n",
      "DreaMS similarity: 0.3821600675582886\n",
      "Modified cosine similarity: 0.005611964348858777\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ce2a880>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0015768 (top 2 hit)\n",
      "Query precursor m/z: 186.980925613972, Library precursor m/z: 146.0116\n",
      "DreaMS similarity: 0.3812687397003174\n",
      "Modified cosine similarity: 0.1472003166482408\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ce29460>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0079643 (top 3 hit)\n",
      "Query precursor m/z: 186.980925613972, Library precursor m/z: 146.0712736972\n",
      "DreaMS similarity: 0.37886130809783936\n",
      "Modified cosine similarity: 0.07692617816195739\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ce290e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0117726 (top 4 hit)\n",
      "Query precursor m/z: 186.980925613972, Library precursor m/z: 187.0865894\n",
      "DreaMS similarity: 0.3684600591659546\n",
      "Modified cosine similarity: 0.14840001932798705\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ce2ac70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library ID: MassSpecGymID0117714 (top 5 hit)\n",
      "Query precursor m/z: 186.980925613972, Library precursor m/z: 187.0865894\n",
      "DreaMS similarity: 0.36845511198043823\n",
      "Modified cosine similarity: 0.1485209819270144\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x36ce2b6f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = df_top1['i'].iloc[-5]\n",
    "df_i = df[df['i'] == i]\n",
    "for _, row in df_i.iterrows():\n",
    "    i, j = row['i'], row['j']\n",
    "    print(f'Library ID: {row[\"library_ID\"]} (top {row[\"topk\"]} hit)')\n",
    "    print(f'Query precursor m/z: {msdata.get_prec_mzs(i)}, Library precursor m/z: {msdata_lib.get_prec_mzs(j)}')\n",
    "    print('DreaMS similarity:', row['DreaMS_similarity'])\n",
    "    print('Modified cosine similarity:', row['Modified_cosine_similarity'])\n",
    "    su.plot_spectrum(spec=msdata.get_spectra(i), mirror_spec=msdata_lib.get_spectra(j))\n",
    "    display(Chem.MolFromSmiles(row['library_SMILES']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the query spectrum cannot be confidently matched to any known library spectrum, the top five hits reveal a consistent pattern: all retrieved molecules, though small in size, contain at least two nitrogen atoms. This observation suggests that the unknown compound represented by the query spectrum is nitrogen-rich. Even without a definitive match, we can still formulate a structural hypothesis for the unknown compound."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Even faster search via approximate nearest neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To achieve even faster library matching, we can use the approach of approximate nearest neighbors (ANN). In this example, we'll utilize the [PyNNDescent](https://pynndescent.readthedocs.io/en/latest/index.html) library. However, since DreaMS embeddings are 1024-dimensional vectors, they can be seamlessly integrated with any other vector database of your choice for efficient similarity search or other applications (e.g., clustering or visualization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build an index for the library\n",
    "import pynndescent\n",
    "index = pynndescent.NNDescent(embs_lib, metric='cosine', n_neighbors=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>j</th>\n",
       "      <th>DreaMS_similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168540</td>\n",
       "      <td>0.382160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62809</td>\n",
       "      <td>0.378861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89593</td>\n",
       "      <td>0.368460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89581</td>\n",
       "      <td>0.368455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117184</td>\n",
       "      <td>0.356404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>117183</td>\n",
       "      <td>0.355120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89605</td>\n",
       "      <td>0.354955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41768</td>\n",
       "      <td>0.348629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>84511</td>\n",
       "      <td>0.347867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84520</td>\n",
       "      <td>0.347862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        j  DreaMS_similarity\n",
       "0  168540           0.382160\n",
       "1   62809           0.378861\n",
       "2   89593           0.368460\n",
       "3   89581           0.368455\n",
       "4  117184           0.356404\n",
       "5  117183           0.355120\n",
       "6   89605           0.354955\n",
       "7   41768           0.348629\n",
       "8   84511           0.347867\n",
       "9   84520           0.347862"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Approximate nearest neighbors search for i-th spectrum (note that the first query may be slow)\n",
    "neighbors = index.query(embs[[i]])\n",
    "pd.DataFrame({\n",
    "    'j': neighbors[0][0],\n",
    "    'DreaMS_similarity': 1 - neighbors[1][0]  # Convert distances to similarities\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>j</th>\n",
       "      <th>DreaMS_similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19020</th>\n",
       "      <td>168540</td>\n",
       "      <td>0.382160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19021</th>\n",
       "      <td>12627</td>\n",
       "      <td>0.381269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19022</th>\n",
       "      <td>62809</td>\n",
       "      <td>0.378861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19023</th>\n",
       "      <td>89593</td>\n",
       "      <td>0.368460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19024</th>\n",
       "      <td>89581</td>\n",
       "      <td>0.368455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            j  DreaMS_similarity\n",
       "19020  168540           0.382160\n",
       "19021   12627           0.381269\n",
       "19022   62809           0.378861\n",
       "19023   89593           0.368460\n",
       "19024   89581           0.368455"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exact nearest neighbors search for i-th spectrum\n",
    "df_i[['j', 'DreaMS_similarity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the result of the ANN search is very similar to the exact nearest neighbors search (top 1 hit is identical). However, the ANN search is much faster, especially for large libraries."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dreams",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}