python_script/JCIM_score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7110ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Study\\AppData\\Roaming\\Python\\Python39\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compile the cython code is highly recommended!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import spectral_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b26bc99e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.jdx\n"
     ]
    }
   ],
   "source": [
    "def msp2list(msp):\n",
    "    with open(msp) as f:\n",
    "        for block in readblocks(f):\n",
    "            x=[]\n",
    "            for line in block:\n",
    "                line = line.strip('\\n')\n",
    "                line = line.split(';')\n",
    "                for pair in line:\n",
    "                    pair = pair.split()\n",
    "                    if len(pair) == 2:\n",
    "                        x.append([float(pair[0]),float(pair[1])])\n",
    "    return x\n",
    "\n",
    "def jdx2list(filename):\n",
    "    '''\n",
    "    read and clean computational mass spectrum result jdx file\n",
    "    Input\n",
    "        filename: str, name and path of the jdx file\n",
    "    Output\n",
    "        x: normalized mass spectrum\n",
    "    '''\n",
    "    with open(filename)as f:\n",
    "        tmp = f.readlines()\n",
    "    x = []\n",
    "    for line in tmp[7:-1]:\n",
    "        pair = line.split()\n",
    "        x.append([float(pair[0]),float(pair[1])])\n",
    "    return x\n",
    "\n",
    "def index2list(massindex, Index):    \n",
    "    spec=Index[Index['Database No.'] == massindex]['MASS']\n",
    "    spec = spec.iloc[0].split()[1:-1]\n",
    "    x = []\n",
    "    for i in range(len(spec)):\n",
    "        if float(spec[i]) != 0:\n",
    "            x.append([i,spec[i]])\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1463e1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './om2/'\n",
    "indexfile = '../QCEIMS_SI.xlsx'\n",
    "Index = pd.read_excel(indexfile,sheet_name='data',header=[0],index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f9b4f02e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "\n",
    "files= os.listdir(path)\n",
    "smi_list = []\n",
    "index = []\n",
    "for file in files:\n",
    "    print(file)\n",
    "    if os.path.splitext(file)[1] == \".jdx\":\n",
    "        query = jdx2list(path+file)\n",
    "        massindex = int(file.split('/')[-1].split('.')[0])\n",
    "        try:\n",
    "            ref = index2list(massindex, Index)\n",
    "            smi = spectral_entropy.similarity(query, ref,method='entropy',ms2_da=0.05)\n",
    "            smi_list.append(smi)\n",
    "            \n",
    "        except:\n",
    "            smi_list.append('NA')\n",
    "        index.append(massindex)\n",
    "            \n",
    "\n",
    "df = pd.DataFrame({'index':index,'entropy':smi_list})\n",
    "df.to_csv('om2_entropy.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4891a749",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [92]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m spec\u001b[38;5;241m=\u001b[39mIndex[Index[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatabase No.\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m massindex][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMASS\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 2\u001b[0m spec \u001b[38;5;241m=\u001b[39m \u001b[43mspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msplit()[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\indexing.py:931\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    928\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    930\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m--> 931\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\indexing.py:1566\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1563\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[1;32m-> 1566\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1568\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\indexing.py:1500\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1498\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[1;32m-> 1500\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "spec=Index[Index['Database No.'] == massindex]['MASS']\n",
    "spec = spec.iloc[0].split()[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "323eadc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Database No.</th>\n",
       "      <th>name</th>\n",
       "      <th>Inchikey</th>\n",
       "      <th>Short</th>\n",
       "      <th>number of atoms</th>\n",
       "      <th>Mass</th>\n",
       "      <th>Match</th>\n",
       "      <th>R.match</th>\n",
       "      <th>Prob</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Time in Seconds</th>\n",
       "      <th>RBN</th>\n",
       "      <th>RBF</th>\n",
       "      <th>PHI</th>\n",
       "      <th>N/O</th>\n",
       "      <th>high/low hinderance</th>\n",
       "      <th>Dot</th>\n",
       "      <th>Cos</th>\n",
       "      <th>MASS</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [#, Database No., name, Inchikey , Short, number of atoms, Mass, Match, R.match, Prob, Rank, Time in Seconds, RBN, RBF, PHI, N/O, high/low hinderance , Dot, Cos, MASS]\n",
       "Index: []"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Index[Index['Database No.'] == massindex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8220ed73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
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     "execution_count": 93,
     "metadata": {},
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   "source": [
    "massindex"
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   "cell_type": "code",
   "execution_count": null,
   "id": "6bbd35d8",
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