playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# test for psa functionality"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "import collections\n",
    "\n",
    "# Some place to store the pattern defintions\n",
    "Pattern = collections.namedtuple(\"Pattern\", [\"value\", \"subsearch\"])\n",
    "patterns = []\n",
    "\n",
    "# Get the patterns from the tpsa.tab file, ignoring the header line\n",
    "# for line in open(\"tpsa.tab\").readlines()[1:]:\n",
    "#     # Extract the fields\n",
    "#     value, smarts, comment = line.split(\"\\t\")\n",
    "#\n",
    "#     # Use the SMARTS to define a subsearch object\n",
    "#     subsearch = Chem.MolFromSmarts(smarts)\n",
    "#\n",
    "#     # Store for later use\n",
    "#     patterns.append( Pattern(float(value), subsearch) )\n",
    "\n",
    "# Helper function to count how many times a substructure matches\n",
    "def count_matches(subsearch, mol):\n",
    "    return len(mol.GetSubstructMatches(subsearch))\n",
    "\n",
    "def TPSA(mol):\n",
    "    \"Compute the topological polar surface area of a molecule\"\n",
    "    return sum(count_matches(pattern.subsearch, mol)*pattern.value\n",
    "                   for pattern in patterns)\n",
    "\n",
    "# Test it with the reference structure\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'rdkit.Chem' has no attribute 'Descriptors'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Input \u001B[0;32mIn [5]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m mol \u001B[38;5;241m=\u001B[39m Chem\u001B[38;5;241m.\u001B[39mMolFromSmiles(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN2C(=O)N(C)C(=O)C1=C2N=CN1C\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m----> 2\u001B[0m \u001B[43mChem\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mDescriptors\u001B[49m\u001B[38;5;241m.\u001B[39mCalcTPSA(mol)\n",
      "\u001B[0;31mAttributeError\u001B[0m: module 'rdkit.Chem' has no attribute 'Descriptors'"
     ]
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles(\"CN2C(=O)N(C)C(=O)C1=C2N=CN1C\")\n",
    "Chem.Descriptors.CalcTPSA(mol)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "import rdkit"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "61.82"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import rdMolDescriptors\n",
    "rdMolDescriptors.CalcTPSA(mol)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# test for finding MS1 peak"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "from toolsets.ms2_spectra_plotter import ms2_plot, head_to_tail_plot\n",
    "def chop_msms(msms, lowest_allowed, highest_allowed):\n",
    "    import bisect\n",
    "    import toolsets.spectra_operations as so\n",
    "    msms = so.sort_spectra(msms)\n",
    "    mass, intensity = so.break_spectra(msms)\n",
    "    upper_allowed = bisect.bisect_right(mass, highest_allowed)\n",
    "    lower_allowed = bisect.bisect_left(mass, lowest_allowed)\n",
    "    # pep_index = mass.index(parention)\n",
    "    mass_choppped = mass[lower_allowed:upper_allowed]\n",
    "    intensity_chopped = intensity[lower_allowed:upper_allowed]\n",
    "    if len(mass_choppped)>0 and len(intensity_chopped)>0:\n",
    "        return(so.pack_spectra(mass_choppped, intensity_chopped))\n",
    "    else:\n",
    "        return(so.pack_spectra([0],[0]))\n",
    "from toolsets.search import string_search, num_search\n",
    "from mimas.external.features_by_alphapept.load_mzml_data import load_mzml_data\n",
    "def base_peak_finder(msms):\n",
    "    import toolsets.spectra_operations as so\n",
    "    mass, intensity = so.break_spectra(msms)\n",
    "    max_idx = np.argmax(intensity)\n",
    "    return (mass[max_idx], intensity[max_idx])\n",
    "def _extract_MS1(mzml, scan_number):\n",
    "    from toolsets.spectra_operations import pack_spectra\n",
    "    ms1_1 = pack_spectra(mzml['mass_list_ms1'][mzml['indices_ms1'][scan_number]:mzml['indices_ms1'][scan_number+1]],\n",
    "                     mzml['int_list_ms1'][mzml['indices_ms1'][scan_number]:mzml['indices_ms1'][scan_number+1]])\n",
    "    return (ms1_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "test_mzml = load_mzml_data(\"/Volumes/Samsung_T5/EAD/EAD_mzml/PFPpos_P N 3_EAD.mzML\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "from toolsets.file_io import read_in_alphapept\n",
    "test_feature = read_in_alphapept(\"/Volumes/Samsung_T5/EAD/output_with_ms1int/PFPpos_P N 3_EAD.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "test_library = pd.read_csv(\"/Users/fanzhoukong/Documents/GitHub/Libgen_data/EAD/pfp_matched.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "test_library=string_search(test_library, 'reference_Mix',  'PFPpos_P N 3_EAD')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "scan_number                                                                  143\nprecursor_mz                                                          501.158001\ncharge                                                                         1\nms1_intensity_ratio                                                     0.999287\nms1_precursor_intensity                                                  58872.0\nretention_time                                                          0.399333\npeaks                          115.054207\\t12.218229\\n126.032028\\t4.835021\\n1...\nms1_index                                                                     96\nquery_idx                                                                     46\nreference_Name                 (2R,3S,4S,5R,6S)-2-[(3,4,5-trihydroxyoxan-2-yl...\nreference_InChIKey                                   GGIDHIBVLYVTAU-QGPLIYBISA-N\nreference_Mix                                                   PFPpos_P N 3_EAD\nreference_Formula                                                      C20H30O13\nreference_Monoisotopic mass                                           478.168641\nreference_column                                                             PFP\nreference_adduct                                                         [M+Na]+\nreference_precursor_mz                                                  501.1579\nkey                                           GGIDHIBVLYVTAU-QGPLIYBISA-N[M+Na]+\nName: 491, dtype: object"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_library.iloc[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "ms1 = _extract_MS1(test_mzml, 96)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [],
   "source": [
    "from toolsets.alphapept_helper import centroid_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "outputs": [],
   "source": [
    "mass_centroid, intensity_centroid = centroid_data(mass_ms1, intensity_ms1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [
    {
     "data": {
      "text/plain": "20213"
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "3499"
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mass_centroid)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [],
   "source": [
    "import toolsets.spectra_operations as so\n",
    "mass, intensity = so.break_spectra(chop_msms(so.pack_spectra(mass_centroid, intensity_centroid), 501.158001-2, 501.158001+2))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [
    {
     "data": {
      "text/plain": "[499.17302748065254,\n 499.1912606096775,\n 499.208443308014,\n 499.2231895360347,\n 499.5048680517138,\n 499.6504495806763,\n 499.655898553262,\n 499.66822569918526,\n 500.1347974188183,\n 500.1397527302294,\n 500.14648161233015,\n 500.16230745052576,\n 500.17459485296285,\n 500.1823191119079,\n 500.21179346848896,\n 500.64691600106266,\n 501.10293662694494,\n 501.13363241275954,\n 501.157657249956,\n 501.19597218949883,\n 501.63505097980453,\n 502.12105266386476,\n 502.129015371816,\n 502.14141441967166,\n 502.1617908426446,\n 502.1875733617258,\n 502.2087706035046,\n 502.2253234849047,\n 503.07978020845025,\n 503.09584289010553,\n 503.1037847300145,\n 503.11356745505924,\n 503.1224406506439,\n 503.1325330449125,\n 503.13926471870684]"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [],
   "source": [
    "chopped_msms = chop_msms(so.pack_spectra(mass_centroid, intensity_centroid), 501.158001-2, 501.158001+3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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1vHI1qNPp1N13361JkyZp586dGjNmjBYuXKjKykpvHB4AAKDV8uiL3CVp/fr1ys/PV35+vr744gsFBAQoJiZGU6ZMUWxsrLKysnT//fdryZIl6tOnjzdnBgAAaDUsTqfT6ckvDh06VH379lVcXJz69u2rPn36yGq1uu3z5ptvasuWLdqyZYtXhm1ODodDeXl5iouLU2BgoFePbbdLoaHnf66slNq29erhAQBAC+ZxrF2NsrIyDRkyRAUFBc31J7yGWAMAACby+D1rkydP/s73pN1www1avXq1p38CAACg1fM41vbv36+zZ89esr2yslKvvvqqJMlisWjQoEGeTwcAANDKNTrWHnnkEa1YsUIWi0Xl5eWXPF5VVaXMzExvzAYAANDqNfpq0I4dO+r999+X0+nUAw88oPDwcMXGxio2NlYxMTE6ePCgbrrppuaYFQAAoNVpdKzNnTtXknT77bdr3bp1+vrrr1VQUKCCggLt2LFD9fX1evrpp70+KAAAQGvk8ees5eXlKTAwUBaLRSNGjPDmTAAAAPiHRr1n7amnnlJNTY0k6eTJk7JYLM0yFAAAAM5r1CtrISEhqq2tVXBwsIYNG6awsDDFxMQoNjZWPXv2VGxsrCIjI9WmTZvmmhcAAKBV8fhDcUtKSlRYWOh2O3r0qAIDA9WjRw/93//9n7dnbVZ8KC4AADCRx+9Z69y5szp37uz2frXKykoVFhbq4MGDXhkOAACgtfP4Q3HnzJmjTZs2ue4fPXpUe/fudX2ZOwAAAJrO41fWdu3apQcffFCSVFFRoeTkZFVXVyssLEz/8z//o+7du3ttSAAAgNbK41fWzpw5o1tvvVWStHXrVt18883661//qjFjxmjRokVeGxAAAKA18zjWOnTooJKSEknSjh07lJSUJKvVqkmTJmnfvn1eGxAAAKA18zjWxo0bp5/85CdatGiRcnJyXBca1NfXq6qqymsDAgAAtGYev2ctNTVVTqdTu3fv1pNPPqnbbrtNkpSfn68OHTp4bUAAAIDWzONYs1gsmjlzpmbOnOm2vaysTGPGjGnyYAAAAGhCrF3Oww8/7O1DAgAAtFoex9qBAwe0aNEinTp1SrfddpvrK6d69uypjh07enNGAACAVsvjWEtLS1PHjh314IMPqqSkRLm5uVqzZo0qKioUFhamPXv2eHNOAACAVsnjWDt+/LhWrFihrl27um0/evSoCgoKmjwYAAAAmhBrcXFxOn78+CWx1qlTJ3Xq1KnJgwEAAKAJn7M2ffp0LVu2TKdPn/biOAAAAPi2Jn3OmsVi0ejRozVs2DDFxcWpV69eio2NldVq9eaMAAAArZbHsZadna3CwkIVFBTo4MGDWrFihUpLSxUUFKTu3bsrKyvLm3MCAAC0Sh7HWteuXdW1a1eNGjXKta2ystIVbwAAAGg6j2PtzJkzeu2115STk6OgoCCtXr1aN998swYOHKiBAwd6c0YAAIBWy+MLDF588UV9/vnnevrpp1VaWqqzZ89KktLT07V69WqvDQgAANCaeRxrf/rTn/TCCy9o1KhRCgj4/4cZMmSIfve733llOAAAgNbO41iTpJCQkEu2devWTV999VVTDgsAAIB/8DjWvv/972vLli2XbK+qqpLFYmnSUAAAADjP4wsMnnzySY0fP95tW01NjZYuXapevXo1eTAAAAA0IdY6dOig9evX68UXX1R1dbUmTpwou92u0NBQrVixwpszAgAAtFoex5ok3XbbbXr77bdVWlqqwsJCBQUFqW/fvgoLC/PWfAAAAK2ax7E2efJkrVy5UqGhoerYsaM6duzozbkAAACgJlxgsH//ftdnq31bZWWlXn311SYNBQAAgPMaHWuPPPKIVqxYIYvFovLy8kser6qqUmZmpjdmAwAAaPUafRq0Y8eOev/99+V0OvXAAw8oPDxcsbGxio2NVUxMjA4ePKibbrqpOWYFAABodRoda3PnzpUk3X777Vq3bp2+/vprFRQUqKCgQDt27FB9fb2efvpprw8KAADQGnl8gUFeXp6Cgs7/+ogRI7w2EAAAAP4/j2Pts88+06JFi3Tq1Cnddtttio2NVa9evdSzZ0916NDBmzMCAAC0Wh7HWlpamjp27KgHH3xQJSUlys3N1Zo1a1RRUaGwsDDt2bPHm3MCAAC0Sh7H2vHjx7VixQp17drVbfvRo0dVUFDQ5MEux+FwaNq0aerUqZMWLlwo6fzHiLzyyisqLi5WRESEZs6cqYkTJzbbDAAAANeKx5+zFhcXp+PHj1+yvVOnTs36HrYlS5bor3/9q+t+RUWFUlJSlJSUpNzcXKWnp2vBggU6cOBAs80AAABwrXgca9OnT9eyZct0+vRpL45zZX/5y1+UnZ2tUaNGubZlZ2crPDxcU6ZMUVBQkBISEpSYmKi1a9des7kAAACai8enQVNTU2WxWDR69GgNGzZMcXFx6tWrl2JjY2W1Wr05oySpvLxc8+bN07Jly9w+dLeoqEjR0dFu+0ZGRmrjxo1XPF5tba1qa2td9x0Oh1fnBQAA8AaPYy07O1uFhYWu24oVK1RaWqqgoCB1795dWVlZXhvywme3TZ8+XbGxsW6P2e122Ww2t23BwcGqqqq64jGXL1+uJUuWuO7bbDatXLnSazMDAAB4g8ex1rVrV3Xt2tXtlGRlZaUKCgp08OBBrwx3wfLly2W1WjV16tRLHrPZbDpz5ozbtpqaGrVt2/aKx0xNTdX06dNd9x0Oh4qLi70zMAAAgJc0OtbmzZun3r17q3fv3oqNjdV1113neiw0NFQDBw7UwIEDvTrk7373O3399dcaMGCApPMxJkm///3vlZaWpt27d7vtX1xcrKioqCse02q1up2u5TQoAAAwUaNj7fDhw9q2bZvsdrvrlGfv3r3Vq1cv9e7dWz179lRISIhXh9y2bZvb/WeeeUaStHDhQv3973/X66+/rszMTE2ZMkV79+5VVlaWli1b5tUZAAAAfKHRsXbhKsvDhw/r008/dd127typb775RgEBAerWrZvee+89rw/bkIiICK1atUrp6enKyMhQ+/bt9dxzz+nOO++8Jn8fAACgOVmcTqfTWwc7cuSIPvnkExUUFOiJJ57w1mGvCYfDoby8PMXFxSkwMNCrx7bbpdDQ8z9XVkrf8XY6AAAAF48vMGhIly5d1KVLF913333ePCwAAECr5fGH4gIAAKD5EWsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwGLEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAADAYsQYAAGAwYg0AAMBgxBoAAIDBiDUAAACDEWsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwGLEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAADAYsQYAAGAwYg0AAMBgxBoAAIDBiDUAAACDEWsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwGLEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixhiax2yWL5fzNbvf1NAAAtDzEGgAAgMGINQAAAIMRawAAAAYj1gAAAAzmV7FWWFio6dOna9CgQbr77ruVlpamU6dOSZL279+viRMnql+/fho+fLg2bNjg42kBAACazm9iraamRg8//LD69eunXbt2acuWLTp9+rTmzp2riooKpaSkKCkpSbm5uUpPT9eCBQt04MABX48NAADQJH4Ta6WlpYqNjdXs2bNltVoVERGhyZMnKzc3V9nZ2QoPD9eUKVMUFBSkhIQEJSYmau3atb4eGwAAoEn8JtZ69OihX/7ylwoMDHRt2759u3r37q2ioiJFR0e77R8ZGanCwsLLHq+2tlaVlZVuNwAAANP4Tax9m9Pp1OLFi/X+++9r3rx5stvtstlsbvsEBwerqqrqssdYvny54uPjXbehQ4c299gAAACNFuTrARqrsrJSzz77rD799FP96le/UkxMjGw2m86cOeO2X01Njdq2bXvZ46Smpmr69Omu+w6HQ8XFxc02NwAAgCf8Kta++uorzZgxQx07dtTGjRvVvn17SVJ0dLR2797ttm9xcbGioqIueyyr1Sqr1eq673A4mmdoAACAJvCb06AVFRX64Q9/qP79+2vlypWuUJOkkSNHqqysTJmZmaqrq1NOTo6ysrI0fvx4H04MAADQdH7zytpvfvMblZaWauvWrdq2bZvbYx9//LFWrVql9PR0ZWRkqH379nruued05513+mhaAAAA77A4nU6nr4cwgcPhUF5enuLi4tyuOPUGu10KDT3/c2WldIW30vmdlvzcAAAwgd+cBgUAAGiNiDUAAACDEWsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwGLEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAADAYsQYAAGAwYg0AAMBgxBoAAIDBiDUAAACDEWsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwGLEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAADAYsQYAAGAwYg0AAMBgxBoAAIDBiDUAAACDEWsAAAAGI9YAAAAMRqwBl2G3SxbL+Zvd7utpAACtFbEGAABgMGINAADAYMQaAACAwYg1AAAAgxFrAAAABiPWAAAADEasAQAAGIxYAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAzWomKtvLxcs2bN0oABAzR48GClp6fr3Llzvh4LAADAYy0q1h577DGFhIToT3/6kzZu3Ki//OUvyszM9PVYAAAAHgvy9QDe8uWXX+qjjz7Shx9+KJvNpi5dumjWrFl6/fXX9fDDD1/1cex2uwIDA706m93e8M8tAc/NP9nt0i23nP/5xAmpbVvfzgMArVHbq/wf3xYTa0VFRQoPD9ctF/4fSNL3vvc9lZaW6ptvvlG7du3c9q+trVVtba3r/oXTpZGRkaqqqvL6fBfWo0cPrx/a53hu/qklPzcA8AcVFRUKCAiQxWK54n4tJtbsdrtsNpvbtgv3q6qqLom15cuXa8mSJa77ERERWrp0qbZu3dr8wwIAgFYvLy9PcXFx33lGr8XEWkhIiKqrq922Xbjf0MuMqampmj59uut+fX29AgICFBwc/J2F25pUVlZq6NCh+uCDDxQaGurrcXCVWDf/w5r5H9bM/5i4ZgEB3335QIuJtaioKJ0+fVplZWW68cYbJUmHDh3Srbfequuvv/6S/a1Wq6xW67Ue0+8EBgaqurpagYGBXn8vH5oP6+Z/WDP/w5r5H39dsxZzNWi3bt0UHx+vn/70p6qsrNSRI0e0bNkyTZgwwdejAQAAeKzFxJokZWRk6Ny5c7r33ns1adIkDRkyRLNmzfL1WAAAAB5rMadBJenGG29URkaGr8doUaxWqx555BFOGfsZ1s3/sGb+hzXzP/66Zhan0+n09RAAAABoWIs6DQoAANDSEGsAAAAGI9YAAAAMRqwBAAAYjFgDAAAwWIv66A40XX5+vvbt26fbb79d8fHxbo+tWLFCKSkpPpoMjfHWW29pxowZvh4Dl/HJJ5+oa9euateunQ4cOKDs7Gw5nU4lJiYqNjbW1+PhMmprayWd//iH8vJybdu2TW3atNE//dM/6eabb/bxdGjI2bNn9d5776mwsFBVVVVq27atoqKiNHLkyEu+M9xkfHQHXLZv3660tDR973vfU1FRkZKSkvTyyy+7Hu/fv7/27dvnwwlxsdLS0ga3jx07VllZWXI6nerYseM1ngpXsmnTJv3kJz/Rpk2bdPjwYT3xxBNKSEiQw+HQRx99pMWLF+vee+/19Zi4SG5urlJSUhQSEqL/+q//0qOPPqrrr79eQUFBKisr06pVq9S7d29fj4lvycvL08yZMxUREaHIyEgFBwerpqZGxcXFKi8v1y9/+Uv16dPH12NeFWINLg888IAee+wxDRs2TIcOHVJKSopGjx6ttLQ0SVK/fv308ccf+3hKfFuvXr104Z+w0+mUxWJx/SxJFotFBQUFPpsPlxo9erRefvllDRo0SOPGjdOsWbM0cuRISef/g2nJkiXKysry8ZS42IMPPqh//ud/lsViUUZGhh566CHXN+SsXr1av//977VmzRofT4lvmzBhgsaMGaNp06Zd8lhmZqa2bt2qX//619d+ME84gX+Ij493u//FF184Bw0a5Hz33XedTqfTGRcX54uxcAW7du1y3nPPPc6MjAxnSUmJs6SkxHnkyBFn//79Xfdhlm//Oxo0aJDT4XC47tfX1zv79+/vi7HwHQYMGOB0Op3Oc+fOOXv16uU8e/as67G6ujrnoEGDfDUaLiMuLs557ty5Bh+rq6tz9uvX7xpP5DkuMIBLWFiY/va3v7nud+/eXQsWLNDzzz+vwsJC16s2MMfdd9+tzZs3a+/evfr5z3+uiIgIde7cWYGBgerUqZM6derk6xFxkc6dO2vXrl2SpLi4OH3yySeux3JycjhtbaiQkBCdPHlSJ0+eVH19vY4dO+Z67NixYwoODvbhdGjITTfdpPz8/AYf27dvn2655ZZrPJHnuMAALsnJyUpJSdHs2bOVlJQkSRo+fLh+9KMfaerUqa4318IsN954o95++20tXbpU48aN0+uvv+7rkXAFjzzyiB599FH98Ic/VEJCgmbPnq2JEyfq7NmzWrdunV544QVfj4gGJCUl6Uc/+pHq6+vVoUMHvfnmmxoyZIhqa2v1i1/8QqNHj/b1iLjIzJkz9dBDD+n+++9XTEyMbDabqqurVVxcrKysLL/6t8Z71uAmMzNTZ86c0Y9//GO37W+//baWLVum3NxcH02Gq/HRRx/pmWee0d///nfeX2iwnJwcZWZmKj8/XxUVFWrTpo0iIyP1gx/8QMnJyb4eDw1wOp1av369vvjiC/3gBz9QbW2t0tLSdOzYMQ0fPlwvvfSSbDabr8fERfbs2aONGzequLhYdrtdNptNUVFRGj9+vBISEnw93lUj1oAW5tSpU9q1a5fGjh3r61EAAF7AaVC4yc/P17p16y75TJoJEyZo4MCBvh4PDbjcmnXo0IE1MxT/zvwT6wZf4QIDuGzcuFHTpk3Tddddp/Hjx2vGjBlKTk6WzWbTzJkz9dvf/tbXI+IirJn/Yc38E+sGX+I0KFxGjBihV155RXfeeeclj+Xk5Gj+/PnKzs72wWS4HNbM/7Bm/ol18z933HGH6urqrriPv3wOJadB4VJeXq5BgwY1+NiAAQNUXl5+jSfCd2HN/A9r5p9YN/+zevVqPfTQQ3r00UfVq1cvX4/TJJwGhUtUVNRlP8153bp1io6OvsYT4buwZv6HNfNPrJv/6devn+bOnatt27Zp0KBBDd78BadB4bJ//36lpKQoIiJC0dHRCgkJUXV1tYqKilReXs533xmINfM/rJl/Yt3817PPPqvHHnvMrz4E92LEGtx888032r59u+szaaTz5/1HjRql8PBw3w6HBrFm/oc180+sm/+rqqpSmzZt1KZNG1+P0jg++porGCgtLc31c0VFhTMlJcUZGxvr7N27t/P55593+y48mIE18z+smX9i3fzPxWuWmprq7Nmzp1+uGe9Zg8uOHTtcP//sZz9TdXW13nnnHWVmZurzzz/Xz372Mx9Oh4awZv6HNfNPrJv/uXjNqqqq9Otf/9o/18zXtQhzxMXFuX4eOnSo89ixY677R48edd51112+GAtXwJr5H9bMP7Fu/qclrRmvrMHFYrG4fg4ICFBERITr/i233KKamhpfjIUrYM38D2vmn1g3/9OS1oxYg8vZs2c1d+5crVu3TlFRUXr33Xddj2VmZioqKsqH06EhrJn/Yc38E+vmf1rSmhFrcHn99dcVFhamrVu3Kjc3V5s3b5YkLVq0SEuWLNFTTz3l4wlxMdbM/7Bm/ol18z8tac346A40yOl06tSpU7rhhht08OBBhYeH+/Vn1LQGrJn/Yc38E+vmf/x9zYg1AAAAg3EaFAAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAL3rmmWf86wuiARiPWAMAL6mvr9cf//hH3Xvvvb4eBUALQqwBwEVKSkoUExOj7OxsTZkyRXfccYeSk5NVUlKiPXv2aMKECerbt6+mTp2q06dPu35v3759CggIUN++fSVJb775pmJiYi65vf322z56ZgD8UZCvBwAA0xQWFkqS1q1bp8cff1xt27bV7NmzlZaWppCQEM2fP1+SNGPGDG3cuFEPP/ywJOkPf/iDhg0bpoCA8/8d/G//9m8aP36867hLly7Vhx9+qPvuu+8aPyMA/oxYA4CLFBYWKiwsTD//+c/Vvn17SdLgwYOVk5Ojd999VyEhIZKkPn36qKyszPV7O3fu1Jw5c1z3Q0NDFRoaKklasmSJPvzwQ61Zs0a33nrrNXw2APwdp0EB4CIFBQUaPny4K9QkqbS0VPfff78r1CTp2LFj6ty5syTp0KFDOnHihO66665LjrdkyRL95je/0Zo1a9SpU6fmfwIAWhRiDQAuUlhY6Hrf2eW2nT17VocPH1ZsbKyk86dA77rrLgUHB7v9HqEGoKmINQD4lsrKSh09elS9evVybTt69KhOnz6tnj17urYVFRXJ4XC4Ym3nzp0aPny427EINQDeQKwBwLcUFhYqICBAMTExrm0FBQVq166d65Tnhf26dOmi0NBQlZeXKz8/X8OGDXM9vmzZMv3qV7/S4sWLZbVadfLkSZ08eVK1tbXX9PkA8H9cYAAA31JYWKgePXq4nc4sKChwvYL27f0uvNK2c+dO3XHHHbrhhhskSU6nUytXrlRlZaUmTZrk9nvvvPPOJadYAeBKLE6n0+nrIQDAn/3Hf/yH4uPjNWPGDF+PAqAF4jQoADRRfHy8xowZ4+sxALRQvLIGAABgMF5ZAwAAMBixBgAAYDBiDQAAwGDEGgAAgMGINQAAAIMRawAAAAYj1gAAAAxGrAEAABiMWAMAADAYsQYAAGCw/wfzhO8vUXZfRQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms2_plot(so.pack_spectra(mass_the, intensity_the))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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": 165,
     "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": [
    "ms2_plot(chopped_msms)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [
    {
     "data": {
      "text/plain": "502.16028760530634"
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_sorted[-2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3500820276980816\n",
      "3.5637000368667264\n"
     ]
    }
   ],
   "source": [
    "mass_emp, intensity_emp = so.break_spectra(chopped_msms)\n",
    "intensity_sorted,mass_sorted = zip(*sorted(zip(intensity_emp,mass_emp  )))\n",
    "instance_mol = Formula('C20H30O13')\n",
    "mass_the = []\n",
    "intensity_the = []\n",
    "for i in instance_mol.spectrum():\n",
    "    mass_the.append(instance_mol.spectrum()[i][0]+22.989218)\n",
    "    intensity_the.append(instance_mol.spectrum()[i][1])\n",
    "print(intensity_sorted[-1]/intensity_sorted[-2]-intensity_the[0]/intensity_the[1])\n",
    "print(intensity_sorted[-2]/intensity_sorted[-3]-intensity_the[1]/intensity_the[2])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "instance_mol."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "outputs": [
    {
     "data": {
      "text/plain": "(6274.0, 50116.0, 240563.0)"
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity_sorted[-3:]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "outputs": [
    {
     "data": {
      "text/plain": "(503.16188078706125, 502.1617908426446, 501.157657249956)"
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_sorted[-3:]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "outputs": [],
   "source": [
    "instance_mol = Formula('K23I22')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "instance_mol"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "outputs": [],
   "source": [
    "mass_the = []\n",
    "intensity_the = []\n",
    "for i in instance_mol.spectrum():\n",
    "    mass_the.append(instance_mol.spectrum()[i][0])\n",
    "    intensity_the.append(instance_mol.spectrum()[i][1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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": 227,
     "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": [
    "ms2_plot(so.pack_spectra(mass_the, intensity_the))"
   ],
   "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": 219,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.770500516709502\n",
      "2.821217471888498\n",
      "C21H26N4O9\n"
     ]
    }
   ],
   "source": [
    "# this is wrong\n",
    "instance_mol = Formula('C21H26N4O9')\n",
    "mass_the = []\n",
    "intensity_the = []\n",
    "for i in instance_mol.spectrum():\n",
    "    mass_the.append(instance_mol.spectrum()[i][0]+22.989218)\n",
    "    intensity_the.append(instance_mol.spectrum()[i][1])\n",
    "print(intensity_sorted[-1]/intensity_sorted[-2]-intensity_the[0]/intensity_the[1])\n",
    "print(intensity_sorted[-2]/intensity_sorted[-3]-intensity_the[1]/intensity_the[2])\n",
    "print(instance_mol.formula)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "data": {
      "text/plain": "-3.78061982580211"
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [
    {
     "data": {
      "text/plain": "(501.15785902130966, 502.1612777229302)"
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_the[0], mass_the[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "data": {
      "text/plain": "4.424186478912681"
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity_the[1]/intensity_the[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [
    {
     "data": {
      "text/plain": "0.17496921423238215"
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity_the[1]"
   ],
   "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": 116,
   "outputs": [],
   "source": [
    "mass_chopped, intensity_chopped = break_spectra(chopped_ms1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [],
   "source": [
    "from toolsets.mzml_plot import EIC"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['scan_list_ms1', 'rt_list_ms1', 'mass_list_ms1', 'int_list_ms1', 'ms_list_ms1', 'scan_list_ms2', 'rt_list_ms2', 'mass_list_ms2', 'int_list_ms2', 'ms_list_ms2', 'prec_mass_list2', 'mono_mzs2', 'charge2', 'select_windows_ms2', 'indices_ms1'])"
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mzml.keys()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2015/2015 [00:06<00:00, 293.53it/s]\n"
     ]
    }
   ],
   "source": [
    "# ms1s = []\n",
    "EIC_intensity = []\n",
    "for i in tqdm(range(0, len(test_mzml['rt_list_ms1']))):\n",
    "    # ms1s.append(_extract_MS1(test_mzml, i))\n",
    "    chopped_ms1_temp = chop_msms(_extract_MS1(test_mzml, i),143.039474-0.01, 143.039474+0.01)\n",
    "    mass_chopped, intensity_chopped = break_spectra(chopped_ms1_temp)\n",
    "    EIC_intensity.append(np.sum(intensity_chopped) )\n",
    "\n",
    "# for i in tqdm(range(0, len(ms1s))):\n",
    "#     # mass_temp, intensity_temp = break_spectra(ms1s[i])\n",
    "#     chopped_ms1_temp = chop_msms(ms1s[i],143.039474-0.01, 143.039474+0.01)\n",
    "#     mass_chopped, intensity_chopped = break_spectra(chopped_ms1_temp)\n",
    "\n",
    "    # ms1_intensity_sum = chopped_ms1_temp\n",
    "    # break"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = test_mzml['rt_list_ms1'],y = EIC_intensity)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0"
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(intensity_chopped)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [
    {
     "data": {
      "text/plain": 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     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "+_extract_MS1(test_mzml,2014)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "array([7.3312508e-03, 2.0227117e-02, 3.1039618e-02, ..., 1.9979996e+01,\n       1.9989775e+01, 1.9999636e+01])"
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mzml['rt_list_ms1']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [
    {
     "data": {
      "text/plain": "array([      0,     362,     785, ..., 2383971, 2385216, 2386449])"
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mzml['indices_ms1']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10071/10071 [01:40<00:00, 100.16it/s]\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/alphapept_env/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EIC(\"/Volumes/Samsung_T5/nc_mzml/CSH_Mix_1.mzML\", 143.039474)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Guess molecular formula"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from toolsets.isotope import isotope_distribution"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "from molmass import Formula"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "test =Formula('C')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "{12: [12.0, 0.9893], 13: [13.00335483507, 0.0107]}"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.spectrum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "moleclue = molmass.from_string(\"C2H5OH\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "'C2H5OH'"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moleclue."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.00011501, 0.        , 0.        , 0.        ], dtype=float32)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isotope_distribution[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# fitting guassain peaks"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy as scipy\n",
    "from scipy import optimize\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.ticker as ticker\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "figure_dir = '/Users/fanzhoukong/Library/CloudStorage/OneDrive-Personal/Desktop/UCDAVIS_Phd/Libgen/manuscript/figures'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def _2gaussian(x, amp1,cen1,sigma1, amp2,cen2,sigma2):\n",
    "    return amp1*(1/(sigma1*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen1)/sigma1)**2))) + \\\n",
    "            amp2*(1/(sigma2*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen2)/sigma2)**2)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# linearly spaced x-axis of 10 values between 1 and 10\n",
    "x_array = np.linspace(1,100,50)\n",
    "\n",
    "amp1 = 100\n",
    "sigma1 = 10\n",
    "cen1 = 50\n",
    "y_array_gauss = amp1*(1/(sigma1*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen1)/sigma1)**2)))\n",
    "\n",
    "# creating some noise to add the the y-axis data\n",
    "y_noise_gauss = (np.exp((np.random.ranf(50))))/5\n",
    "y_array_gauss += y_noise_gauss"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "\n",
    "def _1gaussian(x, amp1,cen1,sigma1):\n",
    "    return amp1*(1/(sigma1*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen1)/sigma1)**2)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "amp1 = 100\n",
    "sigma1 = 10\n",
    "cen1 = 40\n",
    "\n",
    "amp2 = 75\n",
    "sigma2 = 5\n",
    "cen2 = 65\n",
    "\n",
    "y_array_2gauss = amp1*(1/(sigma1*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen1)/sigma1)**2))) + \\\n",
    "                amp2*(1/(sigma2*(np.sqrt(2*np.pi))))*(np.exp((-1.0/2.0)*(((x_array-cen2)/sigma2)**2)))\n",
    "\n",
    "# creating some noise to add the the y-axis data\n",
    "y_noise_2gauss = (np.exp((np.random.ranf(50))))/5\n",
    "y_array_2gauss += y_noise_2gauss"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 400x300 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "popt_2gauss, pcov_2gauss = scipy.optimize.curve_fit(_2gaussian, x_array, y_array_2gauss, p0=[amp1, cen1, sigma1, \\\n",
    "                                                                                          amp2, cen2, sigma2])\n",
    "\n",
    "perr_2gauss = np.sqrt(np.diag(pcov_2gauss))\n",
    "\n",
    "pars_1 = popt_2gauss[0:3]\n",
    "pars_2 = popt_2gauss[3:6]\n",
    "gauss_peak_1 = _1gaussian(x_array, *pars_1)\n",
    "gauss_peak_2 = _1gaussian(x_array, *pars_2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "gauss_peak_1 = _1gaussian(x_array, *pars_1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 400x300 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3))\n",
    "gs = gridspec.GridSpec(1,1)\n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "ax1.plot(x_array, y_array_gauss, \"ro\")\n",
    "ax1.fill_between(x_array, y_array_gauss, facecolor=\"green\", alpha=0.5)\n",
    "# ax1.plot(x_array, gauss_peak_1, \"g\")\n",
    "ax1.set(xlabel=None, ylabel=None)\n",
    "ax1.set(xticklabels=[], yticklabels=[])\n",
    "# ax1.set_xlim(-5,105)\n",
    "# ax1.set_ylim(-0.5,5)\n",
    "\n",
    "# ax1.set_xlabel(\"x_array\",family=\"serif\",  fontsize=12)\n",
    "# ax1.set_ylabel(\"y_array\",family=\"serif\",  fontsize=12)\n",
    "#\n",
    "# ax1.xaxis.set_major_locator(ticker.MultipleLocator(20))\n",
    "# #ax1.yaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "#\n",
    "# ax1.xaxis.set_minor_locator(AutoMinorLocator(2))\n",
    "# ax1.yaxis.set_minor_locator(AutoMinorLocator(2))\n",
    "\n",
    "# ax1.tick_params(axis='both',which='major', direction=\"out\", top=\"on\", right=\"on\", bottom=\"on\", length=8, labelsize=8)\n",
    "# ax1.tick_params(axis='both',which='minor', direction=\"out\", top=\"on\", right=\"on\", bottom=\"on\", length=5, labelsize=8)\n",
    "ax1.grid(False)\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(figure_dir,'single_peak.png'), format=\"png\",dpi=1000)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 400x300 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3))\n",
    "# gs = gridspec.GridSpec(1,1)\n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "ax1.plot(x_array, y_array_2gauss, \"ro\")\n",
    "\n",
    "# ax1.plot(x_array, _2gaussian(x_array, *popt_2gauss), 'k--')#,\\\n",
    "         #label=\"y= %0.2f$e^{%0.2fx}$ + %0.2f\" % (popt_exponential[0], popt_exponential[1], popt_exponential[2]))\n",
    "\n",
    "# peak 1\n",
    "ax1.plot(x_array, gauss_peak_1, \"g\")\n",
    "ax1.fill_between(x_array, gauss_peak_1.min(), gauss_peak_1, facecolor=\"green\", alpha=0.5)\n",
    "\n",
    "# peak 2\n",
    "ax1.plot(x_array, gauss_peak_2, \"y\")\n",
    "ax1.fill_between(x_array, gauss_peak_2.min(), gauss_peak_2, facecolor=\"yellow\", alpha=0.5)\n",
    "\n",
    "# ax1.set_xlim(-5,105)\n",
    "# ax1.set_ylim(-0.5,8)\n",
    "ax1.set(xlabel=None, ylabel=None)\n",
    "ax1.set(xticklabels=[], yticklabels=[])\n",
    "# ax1.set_xlabel(\"\",family=\"serif\",  fontsize=12)\n",
    "# ax1.set_ylabel(\"y_array\",family=\"serif\",  fontsize=12)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['bottom'].set_visible(False)\n",
    "ax1.spines['left'].set_visible(False)\n",
    "# ax1.spines['left'].set(color = 'grey', linewidth = 2)\n",
    "# ax1.legend(loc=\"best\")\n",
    "\n",
    "# ax1.xaxis.set_major_locator(ticker.MultipleLocator(20))\n",
    "#ax1.yaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "\n",
    "# ax1.xaxis.set_minor_locator(AutoMinorLocator(2))\n",
    "# ax1.yaxis.set_minor_locator(AutoMinorLocator(2))\n",
    "\n",
    "# ax1.tick_params(axis='both',which='major', direction=\"out\", top=\"on\", right=\"on\", bottom=\"on\", length=8, labelsize=8)\n",
    "# ax1.tick_params(axis='both',which='minor', direction=\"out\", top=\"on\", right=\"on\", bottom=\"on\", length=5, labelsize=8)\n",
    "\n",
    "fig.tight_layout()\n",
    "ax1.grid(False)\n",
    "fig.savefig(os.path.join(figure_dir,'feature_finding.png'), format=\"png\",dpi=1000)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# linear regression plot"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [938.23786125]\n",
      "Mean squared error: 2548.07\n",
      "Coefficient of determination: 0.47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sq/fc0hszxn0q1_1cc187d_zjm80000gn/T/ipykernel_29526/3596581769.py:55: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"edge_color\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  plt.savefig(os.path.join(figure_dir,'recalibration.tiff'),dpi = 300,transparent=True, edge_color = 'none')\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGVCAYAAADkPRuSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbDElEQVR4nO3de6xlZXk/8GdgZrhfBihWKIUI2aCgTRXZW5pQW6cRbaFyqGmr9RghRaXQWqxn21BbTUzD3k2nCtIi0lKZ9getVg01AlKqjU2c1dGCct8UItdo6QwzzliYC5zfH6vHwzC3tfZZ6+z17v35JMa45jznPAkO+3ue913vu2R2dnY2AAAStM+oGwAAGJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJCspUW+6Nprr43NmzfHwQcfHBdddFHdPQEAFFIoyGzevDk2bdpUdy8AAKVYWgIAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkKylo24AAEhLlmUxGAyi1WpFu90eaS8mMgBAYd1uNzqdTkxPT0en04lutzvSfgQZAKCQLMui3+/v8Kzf70eWZSPqSJABAAoaDAalni8GQQYAKKTVapV6vhgEGQCgkHa7HTMzMzs863a7I93w660lAKCwXq8XU1NTjXlrSZABAEppt9sjDzBzLC0BAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAoJgHHog46qiIk0+OeOc7I554YtQdxdJRNwAANNjsbMRf/EXEBz84/2zduojBIOK55yL+6Z9G11sIMgDArvz3f0f8yq9ErF27+6/5r/9avH52Q5ABgIRkWRaDwSBarVa02+3qf8CXvhRx3nnFvvYDH6j+55dkjwwAJKLb7Uan04np6enodDrR7Xar+cbPPhvxjndELFlSPMRcd13Ee95Tzc9fAEEGABKQZVn0+/0dnvX7/ciybPhvunZtHl4OPDDixhv3/vU/8RMR99+f75u58MLhf26FBBkASMBgMCj1fLdeeCHij/4oDzBnnFGs5tJLI7Zty/fNnHJKuZ9XM3tkACABrVar1POdPPpoxJveFPHww8V/6Ne+FvHGNxb/+hEwkQGABLTb7ZiZmdnhWbfb3fuG3+uvz6cvJ5xQLMS89a0RGzfmy0cNDzERJjIAkIxerxdTU1N7f2tp48aIt7894vbbi3/zz342Ynq6mkYXkSADAAlpt9u7DzB33BGxcmXxb3byyXnYOe64apobAUtLAJCybdsiLr44Xz4qGmL+5E8inn8+v3Ig4RATYSIDAGm6776IM8/Ml5GK2HffiCyLeN3r6u1rkZnIAEAqZmcjLrggn76cemqxEPOud+UH3m3fPnYhJsJEBgCa75FHIk48sVzNzTdHnHNOPf00iIkMADTVxz+eT1+Khpg3vCE/tG52diJCTISJDAA0y8aNEYcfXq7myisjLrkkDz0TRpABgCb4whcizj+/XM2DD0YUPdl3TAkyADAqzz+fLwetXVu8ptWKuPvuiOXL6+srIfbIAMBiu+uufBlo6dLiIeazn833vjz4oBDzIiYyALBYLr444q/+qlzN009HHHVUPf2MAUEGYIxlWbb3e3mo1/e/H/Hyl5er+dCHIvr9evoZM5aWAMZUt9uNTqcT09PT0el0otvtjrqlyfLpT+fLR2VCzL335stHQkxhggzAGMqyLPov+TDs9/uRZdmIOpoQzz2XB5clSyLe975iNStX5pt+Z2cjXvWqevsbQ4IMwBgaDAalnrNAX/taHl4OOCBfSiriK1/Jw8vtt0fs4+N4WPbIAIyh1m7OFtndc4YwOxvxtrflVwEUdeihEU89FXHQQbW1NWlEQIAx1G63Y2ZmZodn3W7Xht8qPPRQPn3ZZ5/iIWbVqjz4bNwoxFTMRAZgTPV6vZiamvLWUlU+9rGIj360XM1jj0Ucd1wt7ZATZADGWLvdFmAWYsOGiBUrytW8+90R118/kfcejYKlJQB4qc9/Pg8iZULMmjX58tHf/q0Qs4hMZAAgImL79ogzzoi4887iNa98ZcR3vhOxbFl9fbFHJjIATLZvfzufoCxbVjzE/N3f5dOX++4TYkbMRAaAyfTe90Zce225mnXrIo44op5+GIqJDACT46mn8unLkiXFQ8yHP5xPX2ZnhZgGMpEBYPxdfXXEJZeUq7n//ohTTqmnHyojyAAwnp59NuL44yOefrp4zZvfnF8d4MqAZPgnBcB4+Zd/yZeODjyweIi59dZ86ejWW4WYxJjIAJC+2dmIX/7liFtuKV5z5JH5ybsHHlhfX9RO7AQgXYPB/L1HRUPMlVfmwed//keIGQOCDADp+eM/zgPMyScXr3niiTzAXHppfX2x6CwtAZCGZ54p//rzBRdE/PVf19MPjWAiA0Cz3XRTPn0pE2L+4z/y6YsQM/ZMZABoni1bIvbfv1zNz/xMxLe+FbHUR9skMZEBoDluvDGfvpQJMTfdlE9f7rpLiJlA/okDMHpLlpSvWb8+YsWK6nshKSYyAIzGd787f+9RUR/5yPy9R0IMYSIDwGI7++yI224rV/Nv/xZx1ln19EPSBBkA6jfMq9MREc8/78oA9sj/OwCoT79f/tXpVavml4+EGPbCRAaAar3wQsS++5av27Ah4rDDKm+H8SbqAlCNO+7Ipy9lQsy5585PX4QYhmAiA8DCnHBCxKOPlqu5556IU0+tpR0miyADQHmPPpoHmLJmZytvhclmaQmA4t7//nz5qEyI+dzn5pePoGImMgDs2XPPRRxwQPm6LVsili+vvh94ERMZAHZt9ep8+lImxFx22fz0RYhhEZjIADBv2LNbnnwy4phjqu8H9sJEBoCIO+/Mpy9lQsxpp81PX4QYRkSQAZhkK1fmAea1ry1e841v5OHl7rvr6wsKsrQEMGnWr4848sjydS+8UO6malgEJjIAk+JP/zQPImVCzFVXzS8fCTE0kIkMwDh7/vmIpUP8q/6HP4w45JDq+4GKmcgAjKO/+Zt8glImxJx//vz0RYghESYyAONkmOWfBx6IOPnk6nuBRWAiA5C6u+/OA0yZELPffvPTFyGGhAkyAKlqtfLw8prXFK/54hfz8PLcc/X1BYvI0hJASjZtijj00PJ1W7dGLFtWfT8wYiYyACn4wz/Mpy9lQswb3zi/fCTEMKZMZACaath7jx5+OOIVr6i+H2ggExmAprnttvL3HkXMT1+EGCaIiUxNsiyLwWAQrVYr2u32qNsBUjDMq9Nf/GLE295WeSuQCkGmBt1uN/r9/o//98zMTPR6vRF2BDTWE09EHHdc+Tr3HkFEWFqqXJZlO4SYiIh+vx9Zlo2oI6CRfvVX8yBSJsRceql7j+AlTGQqNhgMdvvcEhNMuG3bIpYvL1+3fn3EihXV9wNjwESmYq1Wq9RzYAJ8+tP5BKVMiDn66PnpixADuyXIVKzdbsfMzMwOz7rdrmkMTKK5awPe977iNWvX5uHlBz+ory8YI5aWatDr9WJqaspbSzCJ7rwz4rWvLV83O1t9LzABBJmatNttAQYaqpbjEY4/PuKxx8rVXHVVxCWXVPPzYUIJMsBEqfR4hI0bIw4/vHzdli3DbfoFdmKPDDAxKjse4Q/+IN/7UibEnH32/OZdIQYqYyIDTIwFHY8w7L1H3/tevuwE1MJEBpgYQx2P8OUvL+zeIyEGaiXIABOj1PEIc69On3NO8R/w5S/PBxhgUVhaAibKHo9HeOyx4SYo7j2CkRFkgImz0/EIZ58dcdtt5b7JZZdF/PmfV9sYu1TL6/KMDUEGmExbt0bst1/5ug0bIg47rPJ22LVKX5dnLNkjA0yWD30oXwYqE2KOO25+74sQs2gqe12esWYiA0yGYfaw/Od/Rvzsz1bfC4Us6HV5JoaJDDC+7rhj/u2jMuamL0LMSA31ujwTR5ABxs9ceFm5snjNNdd4dbphSr0uz8SytASMh6efjjj66PJ1W7dGLFtWfT9UYo+vy0MIMkDqVq7Ml5DKOPbYiCeeqKcfKrfT6/LwIoIMkJ5h7z166KGIk06qvh9gZOyRAdJx7bULu/dIiIGxYyIDNN8wr06vXh3xW79VfS9AowgyQDPde2/EaaeVr3PvEUwUS0tAsxx0UB5EyoSY886bXz4SYmCimMgAo/ejH0UcfHD5uvXrI1asqL4fIBkmMsDonHtuPkEpG2Lmpi9CDEw8Exlg8Q2z/PPv/x7xcz9XfS9A0kxkgMXxD/+wsHuPhBhgFwQZoF5z4eU3fqN4zUc/6t4joBBLS0D1Hn884qd/unzdli0Ry5dX3w8wtgQZoDorVkRs2FC+zuQFGJKlJWBh5g6gW7KkXIi5/37LR8CCCTLAcC6/PA8v++5brm4uvJxySj19ARPF0hJQzjCvTl99dcTFF1ffCzDxBBlg7775zYgzzyxf594joGaCDLB7w4SQ00+PWLu2+l4AdkGQAXa0cWPE4YeXr1u3LuKIIypvB2BPBBkgd9JJEQ8/XL7OW0fACHlrCSbd3KvTZULMHXd4dRpoBEGG3cqyLFavXh1Zlo26Far2yU8u7N6jX/zFevoCKEmQYZe63W50Op2Ynp6OTqcT3W531C1Rhbnw8oEPFK+59FLTF6CxBBl2kmVZ9Pv9HZ71+32TmVQ9+OBw05etW/PwcuWV9fQFUAFBhp0MBoNSz2moufBS9gTduenLsmX19AVQIUGGnbRarVLPaZDt24ebvtxzj+UjIEmCDDtpt9sxMzOzw7NutxvtdntEHbFX73xnHl7KTlHmwsupp9bTF0DNnCPDLvV6vZiamorBYBCtVkuIaaphTt7t9SJeElQBUiXIsFvtdluAaaJbb414y1vK11k2AsaQIAOpGGb68vKXRzz1VPW9ADSEPTLQZOvXD7d5d926fAIjxABjzkQGmujYY4cLIZaPgAljIgNNMjd9KRNivvIVr04DE0uQgVHr9xd279EwG38BxoSlJRiVYTbvvuMdEX//99X3ApAoQQYW0733Rpx2Wvm6bdsilvrrCvBS/s0Ii2GY6UuEfS8Ae2GPDNRl27bh9r48+KDNuwAFCTJQtfPPz8PL8uXl6ubCi8s5AQoTZKAqc9OXL3yheM0nP2n6ArAA9sjAQvzrv0a86U3l6wQXgEoIMjCMYTbvnnRSxEMPVd8LwAQTZKCo9esjjjyyfN2GDRGHHVZ5OwDYIwN79/rX5xOYsiFmbu+LEANQG0EGdmV2dn7z7re+Vbzu9ttt3gVYRIIMvNhf/mUeXvYp+VdjLrysXFlPXwDskiADEfPTl9/5neI1l19u+gIwYjb7MrkeeCDila8sX7d9e8S+++7wKMuyGAwG0Wq1ot1uV9QgAHtjIsPkmZu+lAkxhx8+P315SYjpdrvR6XRieno6Op1OdLvdavsFYLcEGSbD1q3D3Xv02GN5eHnmmV3+cZZl0e/3d3jW7/cjy7JhOwWgBEGG8fbbv52Hl/32K1c3N3057rg9ftlgMCj1HIBq2SPDeBrm5N2bbor49V8vVdLazQWPu3sOQLVMZBgft9463PLR3PSlZIiJiGi32zEzM7PDs263a8MvwCIxkSF9w0xfzjkn4uabK/nxvV4vpqamvLUEMAKCDGl6+umIo48uX7d5c8RBB1XeTrvdFmAARsDSEmk57bR8AlM2xMwtH9UQYgAYHUGG5nvxvUf33lu8bu1aJ+8CjDlBhub6xCcWdu/R6afX0hYAzSHI0Dxz05ff//3iNR/7mOkLwASy2ZdmuOeeiFe/unzd88+Xn9gAMDYEGUZrmFenjzkm4sknq+8FgOT4VZbFt2XLcAfXPflkvnQkxADwfwQZFs/0dB5e9t+/XN3c3pdjjqmnLwCSZWmJ+g2zfPT5z0ecf371vQAwVgQZ6vHP/xxx7rnl67x1BEAJlpao1tzelzIh5u1v9+o0AEMRZFi4H/xgqM27/++66/Lw8o//WFNjAIw7QYbhnXhiHl5+8idLlS35v/+ceNpptbQFwOQQZCjnxfcePfJI4bJPvPvdPw4wERHdbtdt0QAsmM2+FNPrRXz4w+Xr/m/fywci4g3vf38MBoNotVpCDACVEGTYs2Fene71ImZmdnrcbrcFGAAqJciws0cfjTjhhPJ1L7wwXPABgCHZI8O8N785DyJlQswrXjH/6rQQA8AiM5GZdFu3Ruy3X/m6738/4mUvq74fACjBRGZSXXVVPkEpG2Lmpi9CDAANYCIzaYZZ/vn61yN+/ucrbwVeKssyb7YBpZjITIK1a4c6effH0xchhkXQ7Xaj0+nE9PR0dDqd6Ha7o24JSIAgM85e9rI8vJxxRvGaa65x7xGLLsuy6Pf7Ozzr9/uRZdmIOgJSYWlp3DzzTMQRR5Sv27o1Ytmy6vuBAgaDwW6fW2IC9sREZlxcemk+fSkTYs45Z376IsQwQq1Wq9RzgDmCTMpefO/Rpz5VvO7xx/Pam2+urzcood1ux8xLToN2HxdQhKWlFH3pSxHnnVe+zr4XGqzX68XU1JS3loBSBJmUDPPq9C23RJx9dvW9QA3cxwWUJcg03SOPRJx4Yvk69x4BMAHskWmqX/iFPIiUCTHdrnuPAJgoJjJNsmVLxP77l6/74Q8jDjmk+n4ozIm0AKNhItMEq1blE5QyIeakk+anL0LMSDmRFmB0BJlRmnt1+oMfLF7zne/k4eWhh+rri8KcSAswWoLMYluzZmH3Hr3mNfX0xVD2dCItAPUTZBbLoYfm4eUNbyhec9117j1qOCfSAoyWIFOndevmpy+bNhWv27YtDy8XXlhfb1TCibQAoyXI1OHP/iwPL0cdVbzm135tfvqy1MtkKen1erFmzZq44YYbYs2aNXHFFVeMuiWAieETsyqzsxH7DJELn3wy4phjqu+HReVEWoDRMJFZqG98I5++lA0xc9MXIQYAhibIDOtVr8oDzFlnFa+5/XabdwGgQpaWytiwIWLFivJ17j0CgFqYyBTxuc/lQaRMiFm92r1HAFAzE5nd2b494vWvj7jrrnJ1zz473H1JAEBpJjIv9e1v5xOUZcuKh5j3vnd++iLEFJJlWaxevdpR/gAsiCAz56KL8gBz+unFa773vTy8XHNNbW2NI5csAlCVyQ4yTz01f/LuZz5TrKbbnZ++HH98vf2NIZcsAlClyQwyV1+dh5djjy1ec999eXhxauuCuGQRgCpNzmbfZ5/NJyhPP1285pd+KeLWW4c7sXfMZFkWg8EgWq3Wgk6wdckiAFUa/0/o22/Ppy8HHlg8xNxySz59+epXhZiodk+LSxYBqNKS2dm9HzO7atWq2LRpUxxyyCFx2WWXLUZfCzM7G/HWt+bTlKKOOCLi8cfzwMOPZVkWnU5np+dr1qxZUPioasIDwGQbr6WlBx+MOOWUcjWf+ETE7/1eLe2Mgz3taVlIAHHJIgBVGI8g85GPRHz84+VqHn884qd+qp5+xog9LQA0WbobQNavn391umiIec975l+dFmIKsacFgCZLbyJz000Rv/mb5WqyLOKMM+rpZwL0er2YmpqypwWAxkkjyGzbFvG610XcfXfxmle/Or9uYNmy+vqaIPa0ANBEzV5aWrs2Xzpavrx4iLnxxnzp6LvfFWIAYMw1cyJzwQUR119frmb9+ogVK+rpBwBopOZMZNatizjrrHwCUzTEXH75/OZdIQYAJk4zJjJnnhnxzW8W//oHHog4+eT6+gEAkjD6icy11xYLMW95S8QLL+TTFyEGAIgmTGS+/vU9//lXv5pf3ggA8BKjn8h86lM7P7vwwojnnsunL0IMALAbo5/IHHFExP/+b8RnPhPR6Ti4DgAobPRBJiLigAMifvd3R90FAJCY0S8tAQAMSZABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASFYzDsRLSJZlMRgMotVqRbvdHnU7ADDRTGRK6Ha70el0Ynp6OjqdTnS73VG3BAATTZApKMuy6Pf7Ozzr9/uRZdmIOgIABJmCBoNBqecAQP0EmYJarVap5wBA/QSZgtrtdszMzOzwrNvt2vALACPkraUSer1eTE1NeWsJABpCkCmp3W4LMLALjiYARsHSEo2SZVmsXr3a22CJcTQBMCqCDI3hwzBNjiYARkmQoRF8GKbL0QTAKAkyNMJifBhatqqHowmAURJkaIS6PwwtW9XH0QTAKAkyNEKdH4aWrerX6/VizZo1ccMNN8SaNWviiiuuGHVLwITw+jWNUdc5PXtatjI1qI6jCYBREGRolDo+DO3hABhflpYYe/ZwAIwvExkmguslAMaTIMPEsIcDYPxYWgIAktXIiYzL5wCAIho3kXFwGQBQVKOCjIPLAIAyGhVkXD4HAJTRqCDj4DIAoIxGBRkHlwEAZTTurSUHlwEARTUuyEQ4uAwAKKZRS0sAAGUIMgBAsgQZACBZjdwjA4yWa0KAVJjIADtwTQiQksYEmSzLYvXq1a4jgBFyTQiQmkYEGb8BQjO4JgRIzciDjN8AoTlcEwKkZuRBxm+A0ByuCQFSM/K3lvwGCM3imhAgJSOfyPgNEJqn3W7Hu971Ln8PgcYb+UQmwm+AAMBwGhFkIlwUCQCUN/KlJQCAYQkyAECyBBkAIFmCDACQLEEGAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFmF7lr60Y9+FBERmzdvjlWrVtXaEADAnIMPPjguuuii3f55oSAzOzv74//etGlTNZ0BACxQoSCzdOnS2L59eyxZsiQOOuigunsCAIiIfCKzJ0tm58YtAACJsdkXAEiWIAMAJEuQAQCSJcgAAMkSZACAZAkyAECyBBkAIFn/HxoQSRolSjuWAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Load the diabetes dataset\n",
    "diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n",
    "\n",
    "# Use only one feature\n",
    "diabetes_X = diabetes_X[:, np.newaxis, 2]\n",
    "\n",
    "# Split the data into training/testing sets\n",
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "\n",
    "# Split the targets into training/testing sets\n",
    "diabetes_y_train = diabetes_y[:-20]\n",
    "diabetes_y_test = diabetes_y[-20:]\n",
    "\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(diabetes_X_train, diabetes_y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = regr.predict(diabetes_X_test)\n",
    "\n",
    "# The coefficients\n",
    "print(\"Coefficients: \\n\", regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print(\"Coefficient of determination: %.2f\" % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "fig = plt.figure(figsize = (7, 5),facecolor=\"white\")\n",
    "ax = fig.subplots()\n",
    "plt.scatter(diabetes_X_test, diabetes_y_test, color=\"black\", s = 10)\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color=\"red\", linewidth=3)\n",
    "# ax.set_ylim(0, 100)\n",
    "ax.set(xticklabels=[])\n",
    "ax.set(yticklabels=[])\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['bottom'].set(color = 'grey', linewidth =2)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['left'].set(color = 'grey', linewidth = 2)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig(os.path.join(figure_dir,'recalibration.tiff'),dpi = 300,transparent=True, edge_color = 'none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# deduplication for GT data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from toolsets.search import string_search, num_search\n",
    "def deduplication_adduct(matched_dup):\n",
    "    output = pd.DataFrame()\n",
    "    for mix in matched_dup['File name'].unique():\n",
    "        data_temp =string_search(matched_dup,'File name', mix)\n",
    "        for index, row in data_temp.iterrows():\n",
    "            name_filtered =find_neighbor_adducts(data_temp, row['Product name'], row['retention_time'])\n",
    "            output = pd.concat([output, name_filtered], axis = 0)\n",
    "    output.drop_duplicates('spectra_id', keep='first', inplace=True)\n",
    "    return (output)\n",
    "def find_neighbor_adducts(data_temp, compound_name, rt):\n",
    "    rt_filtered = num_search(data_temp, 'retention_time',number = rt,  direction='between', step=10/60, inclusion=True)\n",
    "    name_filtered = string_search(rt_filtered, 'Product name', compound_name)\n",
    "    name_filtered.reset_index(inplace=True, drop=True)\n",
    "    name_filtered_copy = name_filtered.copy()\n",
    "    if len(name_filtered)>1:\n",
    "        name_filtered_copy.sort_values(by=['confidence_level_num'], ascending=True, inplace=True, ignore_index=True)\n",
    "        name_filtered_copy.drop_duplicates('Product name', keep='first', inplace=True)\n",
    "        return(name_filtered_copy)\n",
    "    else:\n",
    "        return(name_filtered_copy)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "matched_dup = pd.read_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/GT/msms_matched_deduplicated.csv')\n",
    "unmatched_dup = pd.read_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/GT/msms_unmatched_deduplicated.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "matched_dup.reset_index(inplace=True, drop=True)\n",
    "unmatched_dup.reset_index(inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "unmatched_dup['spectra_id']= np.arange(len(unmatched_dup))\n",
    "matched_dup['spectra_id']= np.arange(len(matched_dup))\n",
    "matched_dup['confidence_level_num']=matched_dup['Confidence Level '].str[-1]\n",
    "unmatched_dup['confidence_level_num']=unmatched_dup['Confidence Level '].str[-1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "matched_deduplicated = deduplication_adduct(matched_dup)\n",
    "unmatched_deduplicated = deduplication_adduct(unmatched_dup)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "  Output type  precursor_mz  ms1_precursor_intensity  retention_time  \\\n0   162 shift      633.4020               12200000.0            9.84   \n0   324 shift      795.4551                4390000.0            8.09   \n0   162 shift      355.1040                2020000.0            4.08   \n0   162 shift      435.1304                5440000.0            5.72   \n0   162 shift      317.1978                 763000.0           10.59   \n\n                              Product name                Aglycone name  \\\n0    18Alpha-Glycyrrhetinic Acid_glycoside  18Alpha-Glycyrrhetinic Acid   \n0  18Alpha-Glycyrrhetinic Acid_diglycoside  18Alpha-Glycyrrhetinic Acid   \n0                     Scopoletin_glycoside                   Scopoletin   \n0                     Naringenin_glycoside                   Naringenin   \n0                   Linalool (+)_glycoside                 Linalool (+)   \n\n  Aglycone RT RT shift (product-substrate)RP or (substrate-product)NP  \\\n0       11.99                                              -2.15        \n0       11.99                                               -3.9        \n0        5.23                                              -1.15        \n0        7.27                                              -1.55        \n0           -                                            #VALUE!        \n\n                                     Aglycone smiles  \\\n0  C[C@]12CC[C@](C[C@@H]1C3=CC(=O)[C@@H]4[C@]5(CC...   \n0  C[C@]12CC[C@](C[C@@H]1C3=CC(=O)[C@@H]4[C@]5(CC...   \n0                      COC1=C(C=C2C(=C1)C=CC(=O)O2)O   \n0      C1[C@H](OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)O   \n0                             CC(=CCC[C@](C)(C=C)O)C   \n\n             Aglycone InChikey  ... Product ion intensity (-90.032 loss)  \\\n0  MPDGHEJMBKOTSU-PMTKVOBESA-N  ...                                  0.0   \n0  MPDGHEJMBKOTSU-PMTKVOBESA-N  ...                                  0.0   \n0  RODXRVNMMDRFIK-UHFFFAOYSA-N  ...                                  0.0   \n0  FTVWIRXFELQLPI-ZDUSSCGKSA-N  ...                                  0.0   \n0  CDOSHBSSFJOMGT-JTQLQIEISA-N  ...                                  0.0   \n\n         inchikey                                             smiles  \\\n0  MPDGHEJMBKOTSU  CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(...   \n0  MPDGHEJMBKOTSU  CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(...   \n0  RODXRVNMMDRFIK                      COC1=C(C=C2C(=C1)C=CC(=O)O2)O   \n0  FTVWIRXFELQLPI          C1C(OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)O   \n0  CDOSHBSSFJOMGT                                CC(=CCCC(C)(C=C)O)C   \n\n   bond_difference rt_match_id hits_rt seed_found         key  spectra_id  \\\n0              0.0          99       0         99    99[M+H]+           0   \n0              0.0          98       0         98    98[M+H]+          11   \n0              0.0        9380       0       9380  9380[M+H]+         578   \n0              0.0        7587       0       7587  7587[M+H]+        2570   \n0              0.0        7357       0       7357  7357[M+H]+        2825   \n\n  confidence_level_num  \n0                    1  \n0                    1  \n0                    1  \n0                    1  \n0                    2  \n\n[5 rows x 37 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>Output type</th>\n      <th>precursor_mz</th>\n      <th>ms1_precursor_intensity</th>\n      <th>retention_time</th>\n      <th>Product name</th>\n      <th>Aglycone name</th>\n      <th>Aglycone RT</th>\n      <th>RT shift (product-substrate)RP or (substrate-product)NP</th>\n      <th>Aglycone smiles</th>\n      <th>Aglycone InChikey</th>\n      <th>...</th>\n      <th>Product ion intensity (-90.032 loss)</th>\n      <th>inchikey</th>\n      <th>smiles</th>\n      <th>bond_difference</th>\n      <th>rt_match_id</th>\n      <th>hits_rt</th>\n      <th>seed_found</th>\n      <th>key</th>\n      <th>spectra_id</th>\n      <th>confidence_level_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>162 shift</td>\n      <td>633.4020</td>\n      <td>12200000.0</td>\n      <td>9.84</td>\n      <td>18Alpha-Glycyrrhetinic Acid_glycoside</td>\n      <td>18Alpha-Glycyrrhetinic Acid</td>\n      <td>11.99</td>\n      <td>-2.15</td>\n      <td>C[C@]12CC[C@](C[C@@H]1C3=CC(=O)[C@@H]4[C@]5(CC...</td>\n      <td>MPDGHEJMBKOTSU-PMTKVOBESA-N</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>MPDGHEJMBKOTSU</td>\n      <td>CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(...</td>\n      <td>0.0</td>\n      <td>99</td>\n      <td>0</td>\n      <td>99</td>\n      <td>99[M+H]+</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>324 shift</td>\n      <td>795.4551</td>\n      <td>4390000.0</td>\n      <td>8.09</td>\n      <td>18Alpha-Glycyrrhetinic Acid_diglycoside</td>\n      <td>18Alpha-Glycyrrhetinic Acid</td>\n      <td>11.99</td>\n      <td>-3.9</td>\n      <td>C[C@]12CC[C@](C[C@@H]1C3=CC(=O)[C@@H]4[C@]5(CC...</td>\n      <td>MPDGHEJMBKOTSU-PMTKVOBESA-N</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>MPDGHEJMBKOTSU</td>\n      <td>CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(...</td>\n      <td>0.0</td>\n      <td>98</td>\n      <td>0</td>\n      <td>98</td>\n      <td>98[M+H]+</td>\n      <td>11</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>162 shift</td>\n      <td>355.1040</td>\n      <td>2020000.0</td>\n      <td>4.08</td>\n      <td>Scopoletin_glycoside</td>\n      <td>Scopoletin</td>\n      <td>5.23</td>\n      <td>-1.15</td>\n      <td>COC1=C(C=C2C(=C1)C=CC(=O)O2)O</td>\n      <td>RODXRVNMMDRFIK-UHFFFAOYSA-N</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>RODXRVNMMDRFIK</td>\n      <td>COC1=C(C=C2C(=C1)C=CC(=O)O2)O</td>\n      <td>0.0</td>\n      <td>9380</td>\n      <td>0</td>\n      <td>9380</td>\n      <td>9380[M+H]+</td>\n      <td>578</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>162 shift</td>\n      <td>435.1304</td>\n      <td>5440000.0</td>\n      <td>5.72</td>\n      <td>Naringenin_glycoside</td>\n      <td>Naringenin</td>\n      <td>7.27</td>\n      <td>-1.55</td>\n      <td>C1[C@H](OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)O</td>\n      <td>FTVWIRXFELQLPI-ZDUSSCGKSA-N</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>FTVWIRXFELQLPI</td>\n      <td>C1C(OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)O</td>\n      <td>0.0</td>\n      <td>7587</td>\n      <td>0</td>\n      <td>7587</td>\n      <td>7587[M+H]+</td>\n      <td>2570</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>162 shift</td>\n      <td>317.1978</td>\n      <td>763000.0</td>\n      <td>10.59</td>\n      <td>Linalool (+)_glycoside</td>\n      <td>Linalool (+)</td>\n      <td>-</td>\n      <td>#VALUE!</td>\n      <td>CC(=CCC[C@](C)(C=C)O)C</td>\n      <td>CDOSHBSSFJOMGT-JTQLQIEISA-N</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>CDOSHBSSFJOMGT</td>\n      <td>CC(=CCCC(C)(C=C)O)C</td>\n      <td>0.0</td>\n      <td>7357</td>\n      <td>0</td>\n      <td>7357</td>\n      <td>7357[M+H]+</td>\n      <td>2825</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 37 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_deduplicated.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "matched_deduplicated.to_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/GT/msms_matched_de_deduplicated.csv', index= False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "unmatched_deduplicated.to_csv('/Users/fanzhoukong/Documents/GitHub/Libgen_data/GT/msms_unmatched_de_deduplicated.csv', index= False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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