UVPD_library_curation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import os\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import logging\n",
    "# from toolsets.spectra_operations import entropy_similarity_default\n",
    "from toolsets.std_list_prep import complete_adducts, complete_formula, complete_formal_charge, crude_list_cleanup\n",
    "# from toolsets.API_gets import complete_smiles\n",
    "from toolsets.search import string_search, num_search\n",
    "from toolsets.mzml_plot import EIC, TIC\n",
    "import toolsets.spectra_operations as so\n",
    "from tqdm import tqdm\n",
    "from toolsets.features_by_alphapept import find_features_alphapept\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "source_dir = \"/Users/fanzhoukong/Documents/GitHub/Libgen_data/UVPD\""
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# start curating library for UVPD"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [],
   "source": [
    "# std_list_raw_pos = pd.read_csv(os.path.join(source_dir,\"std_list_pos_crude.csv\"))\n",
    "# std_list_raw_neg = pd.read_csv(os.path.join(source_dir,\"std_list_neg_crude.csv\"))\n",
    "# std_list_raw_pos = complete_formula(std_list_raw_pos,'smiles_fetched')\n",
    "# std_list_raw_neg = complete_formula(std_list_raw_neg,'smiles_fetched')\n",
    "# std_list_raw_pos=complete_formal_charge(std_list_raw_pos,'smiles_fetched')\n",
    "# std_list_raw_neg=complete_formal_charge(std_list_raw_neg,'smiles_fetched')\n",
    "# from toolsets.std_list_prep import neutrilize_salt_df\n",
    "# std_list_pos = neutrilize_salt_df(std_list_raw_pos, '[Na+,K+,Cl-,HCl]')\n",
    "# std_list_neg = neutrilize_salt_df(std_list_raw_neg, '[Na+,K+,Cl-,HCl]')\n",
    "# std_list_pos=crude_list_cleanup(std_list_pos, adducts=['[M]+','[M+H]+', '[M+Na]+', '[M+NH4]+', '[M-H2O+H]+'])\n",
    "# std_list_neg=crude_list_cleanup(std_list_neg, adducts=['[M]-','[M-H]-','[M+C2H4O2-H]-','[M-H2O-H]-','[M+Cl]-'])\n",
    "# std_list_pos.to_csv(os.path.join(source_dir, 'std_list_pos_ready.csv'), index =  False)\n",
    "# std_list_neg.to_csv(os.path.join(source_dir, 'std_list_neg_ready.csv'), index =  False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Curating library in UVPD"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4614/4614 [00:30<00:00, 150.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4983/4983 [00:30<00:00, 164.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4513/4513 [00:43<00:00, 102.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning spectrum...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4905/4905 [00:21<00:00, 224.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finding seed...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1619/1619 [00:07<00:00, 229.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1617/1617 [00:03<00:00, 506.38it/s]\n"
     ]
    }
   ],
   "source": [
    "feature_folder = '/Users/fanzhoukong/Documents/GitHub/Libgen_data/UVPD/Features'\n",
    "std_list_pos = pd.read_csv(os.path.join(source_dir, 'std_list_pos_ready.csv'))\n",
    "std_list_neg = pd.read_csv(os.path.join(source_dir, 'std_list_neg_ready.csv'))\n",
    "from toolsets.precursor_matching import precursor_matching_global\n",
    "matched_hcd_pos = precursor_matching_global(std_list_pos, os.path.join(feature_folder, 'HCD_pos_MS2'), adducts = ['[M]+','[M+H]+', '[M+Na]+', '[M+NH4]+', '[M-H2O+H]+'],step=10, ppm = True, comment='HCD_pos')\n",
    "matched_hcd_neg = precursor_matching_global(std_list_neg, os.path.join(feature_folder, 'HCD_neg_MS2'), adducts = ['[M]-','[M-H]-','[M+C2H4O2-H]-','[M-H2O-H]-','[M+Cl]-'],step=10, ppm = True,comment='HCD_neg')\n",
    "matched_uvpd_pos = precursor_matching_global(std_list_pos, os.path.join(feature_folder, 'UVPD_pos_MS2'), adducts = ['[M]+','[M+H]+', '[M+Na]+', '[M+NH4]+', '[M-H2O+H]+'],step=10, ppm = True,comment='UVPD_pos')\n",
    "matched_uvpd_neg = precursor_matching_global(std_list_neg, os.path.join(feature_folder, 'UVPD_neg_MS2'), adducts = ['[M]-','[M-H]-','[M+C2H4O2-H]-','[M-H2O-H]-','[M+Cl]-'],step=10, ppm = True,comment='UVPD_neg')\n",
    "from toolsets.spectra_operations import normalized_entropy, pack_spectra, spectral_entropy\n",
    "from toolsets.deduplication import match_by_rt\n",
    "matched_mapped, matched_unmapped , matched_not_found= match_by_rt([matched_hcd_pos,matched_hcd_neg,matched_uvpd_pos,matched_uvpd_neg])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "HCD_neg     1823\nHCD_pos     1821\nUVPD_pos    1778\nUVPD_neg    1778\nName: comment, dtype: int64"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_mapped['comment'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 58/58 [00:00<00:00, 181.21it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 181.55it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 137.61it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 220.68it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.helpers import save_value_counts\n",
    "vc = save_value_counts(matched_mapped, 'comment')\n",
    "\n",
    "\n",
    "from toolsets.mass_recalibration import data_recalibrate\n",
    "data_all = {}\n",
    "for comment in vc['unique_values'].unique():\n",
    "    data_all[comment]=string_search(matched_mapped, 'comment',comment)\n",
    "    data_all[comment].reset_index(inplace=True, bdrop=True)\n",
    "    data_all[comment]=data_recalibrate(data_all[comment])\n",
    "# for filename in data_all.keys():\n",
    "#     data_all[filename].to_csv(os.path.join(source_dir, filename+'_mapped_raw.csv'), index = False)\n",
    "from toolsets.spectra_operations import weighted_average_spectra\n",
    "\n",
    "# data_all['HCD_neg']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "outputs": [],
   "source": [
    "test = data_all['UVPD_pos']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [],
   "source": [
    "from sklearn import datasets, linear_model\n",
    "regr = linear_model.LinearRegression()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "outputs": [],
   "source": [
    "x_temp = test['precursor_mz'].values\n",
    "y_temp =  test['reference_precursor_mz'].values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [],
   "source": [
    "y_pred = regr.predict(x_temp.reshape(-1,1))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# drawing figures for deduplication"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "from toolsets.spectra_plotter import ms2_plot\n",
    "import toolsets.spectra_operations as so\n",
    "import matplotlib as plt\n",
    "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": 158,
   "outputs": [],
   "source": [
    "matched_mapped_uvpd_pos = string_search(matched_mapped, 'comment','UVPD_pos')\n",
    "key_dup = []\n",
    "for key in matched_mapped_uvpd_pos['key'].unique():\n",
    "    data_temp = string_search(matched_mapped_uvpd_pos,'key', key)\n",
    "    if len(data_temp)>3:\n",
    "        key_dup.append(key)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    }
   ],
   "source": [
    "from toolsets.spectra_plotter import ms_figure_simple"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [],
   "source": [
    "data_temp = string_search(matched_mapped_uvpd_pos, 'key', key_dup[1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [],
   "source": [
    "wa= so.weighted_average_spectra(data_temp)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [],
   "source": [
    "from toolsets.spectra_operations import entropy_similarity_default"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [],
   "source": [
    "msms1 = pack_spectra([100, 105, 171,182, 291],[10, 30, 20, 100, 5])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [],
   "source": [
    "msms2 = pack_spectra([100, 105, 171,182, 291],[0,0 , 15, 100, 3])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAHqCAYAAAD4TK2HAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKG0lEQVR4nO3aQWoiQQCG0eowhxDiHbLx/ifwGAYCdYf0LAIBmZlEZhy6P3kPaiOl/Bvlo3FZ13UdAAAQ87T1AAAA+BtCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAJAlZAACShCwAAElCFkiYc4xluT5zbr0KgC0JWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQ9ZMjOOcayXJ85t14FAMA9PWTIAgDw+IQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkgT+ac4xluT5zbr0KAD4IWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkCRkAQBIErIAACQJWQAAkoQsAABJQhYAgCQhCwBAkpAFACBJyAIAkLTLkJ1zjGW5PnNuvQoAgD3ZZcgCAMB3hCwAAElCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAJAlZAACShCwAAElCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAJAlZAACShCwAAElCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAfJpzjGW5PnNuver3hCwAAElCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAJAlZAACShCwAAElCFgCAJCELAECSkAUAIEnIAgCQJGQBAEgSsgAAJAlZAACShCwAAElCFgCAJCELAECSkAUAIEnIAgCQ9OOWS6fTaby9vf3vLZ/e33997eVljKcbs/tf3w982NN3aU9bAB7ZXn5vD4fDOJ/PX95Z1nVdv/ug4/E4Xl9f7zYMAAC+8vz8PC6Xy5d3bnoiezgc7jIIAABucUt/3vREFgAA9sa/ywAASBKyAAAkCVkAAJKELAAASUIWAIAkIQsAQJKQBQAg6Sd6LTq0dX3lPwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_figure_simple(msms1,\n",
    "                 savepath=os.path.join(figure_dir, 'matching_up_virtual.tiff'),\n",
    "                 color = 'blue')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_figure_simple(msms2,\n",
    "                 savepath=os.path.join(figure_dir, 'matching_down_virtual.tiff'),\n",
    "                 color = 'red')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am ms2 spectra plotter, and I have been sideloaded successfully\n",
      "I have 2 functions, head to tail plot and ms2 plot\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_figure_simple(wa,\n",
    "                 savepath=os.path.join(figure_dir, 'matching_down.tiff'),\n",
    "                 color = 'red')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_figure_simple(data_temp.iloc[1]['peaks'],\n",
    "                 savepath=os.path.join(figure_dir, 'matching_up.tiff'),\n",
    "                 color = 'blue')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_figure_simple(data_temp.iloc[3]['peaks'], savepath=os.path.join(figure_dir, 'denoising4.tiff'), color = sns.color_palette(\"Dark2\")[3])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msms1 = (data_temp.iloc[0]['peaks'])\n",
    "mass, intensity =so.break_spectra(msms1)\n",
    "d = {'m/z':mass, 'intensity':intensity}\n",
    "msms1 = pd.DataFrame(d)\n",
    "max_val = np.max(msms1['intensity'])\n",
    "msms1[\"normalized_intensity\"] = msms1['intensity'] / max_val * 100.0  # normalize intensity to percent\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (7, 5),facecolor=\"white\")\n",
    "# fig.figure()\n",
    "plt.subplots_adjust()\n",
    "ax = fig.subplots()\n",
    "for i in range(len(msms1['m/z'])):\n",
    "    plt.vlines(x = msms1[\"m/z\"][i], ymin = 0, ymax = msms1[\"normalized_intensity\"][i],color = sns.color_palette(\"Dark2\")[1], linewidth = 3)\n",
    "ax.set_xlim(150, 210)\n",
    "ax.set_ylim(0, 100)\n",
    "ax.set(xticklabels=[])\n",
    "ax.set(yticklabels=[])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['bottom'].set(color = 'black', linewidth = 2)\n",
    "ax.spines['left'].set_visible(False)\n",
    "fig.tight_layout()\n",
    "ax.grid(False)\n",
    "plt.savefig(os.path.join(figure_dir, 'denoising_1.tiff'), dpi = 300,transparent=True, edge_color = 'none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# end of deduplication figuring"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 58/58 [00:00<00:00, 192.14it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 195.13it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 191.14it/s]\n",
      "100%|██████████| 59/59 [00:00<00:00, 223.04it/s]\n"
     ]
    }
   ],
   "source": [
    "from toolsets.helpers import save_value_counts\n",
    "vc = save_value_counts(matched_mapped, 'comment')\n",
    "\n",
    "\n",
    "from toolsets.mass_recalibration import data_recalibrate\n",
    "data_all = {}\n",
    "for comment in vc['unique_values'].unique():\n",
    "    data_all[comment]=string_search(matched_mapped, 'comment',comment)\n",
    "    data_all[comment].reset_index(inplace=True, drop=True)\n",
    "    data_all[comment]=data_recalibrate(data_all[comment])\n",
    "for filename in data_all.keys():\n",
    "    data_all[filename].to_csv(os.path.join(source_dir, filename+'_mapped_raw.csv'), index = False)\n",
    "from toolsets.spectra_operations import weighted_average_spectra\n",
    "\n",
    "data_all['HCD_neg']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_all' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [33]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m method \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdata_all\u001B[49m\u001B[38;5;241m.\u001B[39mkeys():\n\u001B[1;32m      2\u001B[0m     wa_spctra \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m      3\u001B[0m     wa_rts \u001B[38;5;241m=\u001B[39m []\n",
      "\u001B[0;31mNameError\u001B[0m: name 'data_all' is not defined"
     ]
    }
   ],
   "source": [
    "for method in data_all.keys():\n",
    "    wa_spctra = []\n",
    "    wa_rts = []\n",
    "    data_temp = data_all[method]\n",
    "    for key in tqdm(data_temp['key'].unique()):\n",
    "        adduct_temp = string_search(data_temp,'key',key)\n",
    "        wa_spctrum = weighted_average_spectra(adduct_temp,typeofmsms = 'peaks_recalibrated')\n",
    "        wa_rt =(adduct_temp['retention_time']*adduct_temp['ms1_precursor_intensity']/adduct_temp['ms1_precursor_intensity'].sum()).sum()\n",
    "        wa_spctra.extend([wa_spctrum]*len(adduct_temp))\n",
    "        wa_rts.extend([wa_rt]*len(adduct_temp))\n",
    "    data_all[method]['peaks_recalibrated_wa']=wa_spctra\n",
    "    data_all[method]['retention_time_wa']=wa_rts\n",
    "    data_all[method].sort_values(by = ['key','ms1_precursor_intensity'], ascending=False, inplace=True)\n",
    "    data_all[method].drop_duplicates('key', keep='first', inplace=True)\n",
    "    data_all[method].reset_index(inplace=True, drop=True)\n",
    "data_enlongated = pd.DataFrame()\n",
    "for method in data_all.keys():\n",
    "    data_temp = data_all[method]\n",
    "    data_enlongated = data_enlongated.append(data_temp)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "from toolsets.helpers import get_df_name"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "def get_df_name(df):\n",
    "    name =[x for x in globals() if globals()[x] is df][0]\n",
    "    return name"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "data_enlongated.to_csv(os.path.join(source_dir, 'data_enlongated_ready_for_denoising_new.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# end for other processing; will run denoising algorithm separately"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [
    "data_enlongated = pd.read_csv(os.path.join(source_dir, 'data_enlongated_ready_for_denoising_new_denoised.csv'))\n",
    "data_enlongated['c_id']=np.arange(len(data_enlongated))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [],
   "source": [
    "from toolsets.denoising_related_functions import post_processing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [],
   "source": [
    "data_all = {}\n",
    "for method in data_enlongated['comment'].unique():\n",
    "    data_all[method]= string_search(data_enlongated, 'comment', method)\n",
    "    # data_all[method]= data_all[method].drop(['Unnamed: 0'], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "data_uvpd = pd.concat([data_all['UVPD_neg'], data_all['UVPD_pos']], axis=0, ignore_index=True)\n",
    "data_hcd = pd.concat([data_all['HCD_neg'], data_all['HCD_pos']], axis=0, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "data_uvpd_good = post_processing(data_uvpd, high_quality=True)\n",
    "data_uvpd_bad = post_processing(data_uvpd, high_quality=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [],
   "source": [
    "data_hcd_good = post_processing(data_hcd, high_quality=True)\n",
    "data_hcd_bad = post_processing(data_hcd, high_quality=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "from toolsets.spectra_operations import normalized_entropy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [],
   "source": [
    "normalized_raw = []\n",
    "for index, row in data_uvpd_bad.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_uvpd_bad['normalized_entropy_raw']=normalized_raw\n",
    "normalized_raw = []\n",
    "for index, row in data_uvpd_good.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_uvpd_good['normalized_entropy_raw']=normalized_raw\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "# ax= sns.histplot(data=data_UVPD, x=\"normalized_entropy\", multiple=\"stack\", kde = True, palette=\"red\")\n",
    "# ax = sns.histplot(data=data_HCD, x=\"normalized_entropy\", multiple=\"stack\", kde = True,color=\"green\")\n",
    "ax=sns.histplot(data=data_uvpd_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[2], alpha = 0.3,stat='probability',kde=True)\n",
    "ax=sns.histplot(data=data_uvpd_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[3], alpha = 0.3,stat='probability',kde=True)\n",
    "plt.axvline(0.8, 0, color = 'red' )\n",
    "# ax=sns.histplot(data=data_uvpd_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[0], stat='probability')\n",
    "# ax=sns.histplot(data=data_uvpd_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[1],stat='probability')\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"Normalized entropy\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.legend(labels=[\"High-quality spectra\",\"Low-quality spectra\"], loc = 'upper left',fontsize=12)\n",
    "plt.savefig(os.path.join(source_dir,'normalized_entropy_UVPD_high_low_quality.tiff'), dpi = 300,transparency = True,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16ac07910>"
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_raw = []\n",
    "for index, row in data_hcd_bad.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_hcd_bad['normalized_entropy_raw']=normalized_raw\n",
    "normalized_raw = []\n",
    "for index, row in data_hcd_good.iterrows():\n",
    "    normalized_raw.append(so.normalized_entropy(row['peaks_recalibrated_wa']))\n",
    "data_hcd_good['normalized_entropy_raw']=normalized_raw\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "# ax= sns.histplot(data=data_UVPD, x=\"normalized_entropy\", multiple=\"stack\", kde = True, palette=\"red\")\n",
    "# ax = sns.histplot(data=data_HCD, x=\"normalized_entropy\", multiple=\"stack\", kde = True,color=\"green\")\n",
    "ax=sns.histplot(data=data_hcd_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[2], alpha = 0.3,stat='probability',kde=True)\n",
    "ax=sns.histplot(data=data_hcd_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette()[3], alpha = 0.3,stat='probability',kde=True)\n",
    "plt.axvline(0.8, 0, color = 'red' )\n",
    "# ax=sns.histplot(data=data_uvpd_good, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[0], stat='probability')\n",
    "# ax=sns.histplot(data=data_uvpd_bad, x='normalized_entropy_raw', multiple=\"stack\",color = sns.color_palette(\"Set2\")[1],stat='probability')\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"Normalized entropy\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.legend(labels=[\"High-quality spectra\",\"Low-quality spectra\"], loc = 'upper left',fontsize=12)\n",
    "# plt.savefig(os.path.join(source_dir,'normalized_entropy_UVPD_high_low_quality.tiff'), dpi = 300,transparency = True,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1012/1012 [00:00<00:00, 5786.82it/s]\n",
      "100%|██████████| 1947/1947 [00:00<00:00, 3637.36it/s]\n"
     ]
    }
   ],
   "source": [
    "data_uvpd_good.to_csv(os.path.join(source_dir, 'UVPD_library.csv'), index = False)\n",
    "data_hcd_good.to_csv(os.path.join(source_dir, 'HCD_library.csv'), index = False)\n",
    "from toolsets.file_io import export_library_msp\n",
    "export_library_msp(data_uvpd_good,os.path.join(source_dir, 'UVPD_library.txt'), typeofmsms='peaks_denoised_normalized')\n",
    "export_library_msp(data_hcd_good,os.path.join(source_dir, 'HCD_library.txt'), typeofmsms='peaks_denoised_normalized')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# getting chemical classes for library"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "outputs": [],
   "source": [
    "from toolsets.API_gets import GNPS_classyfire, get_classyfire_data\n",
    "# GNPS_classyfire(inputt, class_type, input_type ='inchikey')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "outputs": [],
   "source": [
    "data_UVPD=pd.read_csv(os.path.join(source_dir, 'UVPD_library.csv'))\n",
    "data_HCD=pd.read_csv(os.path.join(source_dir, 'HCD_library.csv'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "outputs": [
    {
     "data": {
      "text/plain": "(1012, 36)"
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_UVPD.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1012/1012 [04:50<00:00,  3.48it/s]\n"
     ]
    }
   ],
   "source": [
    "data_UVPD_classyfire= get_classyfire_data(data_UVPD)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1947/1947 [09:26<00:00,  3.44it/s]\n"
     ]
    }
   ],
   "source": [
    "data_HCD_classyfire= get_classyfire_data(data_HCD)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "outputs": [],
   "source": [
    "data_UVPD_classyfire.to_csv(os.path.join(source_dir, 'UVPD_library_cla.csv'), index = False)\n",
    "data_HCD_classyfire.to_csv(os.path.join(source_dir, 'HCD_library_cla.csv'), index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# statistics for UVPD lib"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "from toolsets.spectra_operations import clean_spectrum, entropy_similarity_default"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [],
   "source": [
    "entropy_similiary = []\n",
    "\n",
    "for index, row in data_UVPD.iterrows():\n",
    "    data_temp = string_search(data_HCD,'key',row['key'])\n",
    "    if len(data_temp)==1:\n",
    "        entropy_similiary.append(entropy_similarity_default(row['peaks'], data_temp.iloc[0]['peaks']))\n",
    "    else:\n",
    "        entropy_similiary.append(np.NAN)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "from toolsets.spectra_operations import spectral_entropy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [
    {
     "data": {
      "text/plain": "(1003, 36)"
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_UVPD.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "data": {
      "text/plain": "(833, 36)"
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data_UVPD[~data_UVPD['HCD_similarity'].isnull()]).shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "sns.kdeplot(data=data_UVPD, x='HCD_similarity',hue=\"charge\", multiple=\"stack\",palette=\"tab10\")\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"Entropy similarity\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "\n",
    "plt.axvline(0.481467, 0, color = sns.color_palette(\"tab10\")[0] )\n",
    "plt.axvline(0.478518, 0, color = sns.color_palette(\"tab10\")[1] )\n",
    "plt.legend(labels=['ESI Neg','ESI Pos'], loc = 'upper right',fontsize=12)\n",
    "plt.savefig(os.path.join(source_dir,'entropy_similarity_uvpd_hcd.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.12156862745098039, 0.4666666666666667, 0.7058823529411765)"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"tab10\")[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (9, 5)\n",
    "                      )\n",
    "# ax= sns.histplot(data=data_UVPD, x=\"normalized_entropy\", multiple=\"stack\", kde = True, palette=\"red\")\n",
    "# ax = sns.histplot(data=data_HCD, x=\"normalized_entropy\", multiple=\"stack\", kde = True,color=\"green\")\n",
    "ax=sns.histplot(data=data_UVPD, x='normalized_entropy', multiple=\"stack\",color = sns.color_palette(\"Set2\")[0], stat='probability')\n",
    "ax=sns.histplot(data=data_HCD, x='normalized_entropy', multiple=\"stack\",color = sns.color_palette(\"Set2\")[1],stat='probability')\n",
    "# ax.set_title(\"Peak number difference after denoising\", fontsize = 16)\n",
    "ax.set_xlabel(\"(Normalized entropy)^4\", fontsize = 18)\n",
    "ax.set_ylabel(\"Frequency\", fontsize = 18)\n",
    "ax.grid(False)\n",
    "plt.legend(labels=[\"UVPD\",\"HCD\"], loc = 'upper right',fontsize=12)\n",
    "plt.savefig(os.path.join(source_dir,'normalized_entropy_uvpd_hcd.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_venn import venn3_unweighted, venn3_circles, venn3, venn2_unweighted, venn2\n",
    "set1 = set(string_search(data_UVPD,'charge',1)['reference_inchikey'])\n",
    "set2 = set(string_search(data_UVPD,'charge',-1)['reference_inchikey'])\n",
    "# set2 = set(string_search(data_good_quality,'reference_adduct','[M+Na]+')['reference_InChIKey'])\n",
    "# set3 = set(string_search(data_good_quality,'reference_adduct','[M+NH4]+')['reference_InChIKey'])\n",
    "fig, ax = plt.subplots(\n",
    "    figsize = (7, 5)\n",
    "                      )\n",
    "venn2([set1,set2],('POS','NEG'))\n",
    "# venn3([set1, set2, set3], ('[M+H]+', '[M+Na]+', '[M+NH4]+'))\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig(os.path.join(source_dir,'venn_diagram_UVPD_pos_neg.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [
    {
     "data": {
      "text/plain": "[M+H]+           433\n[M-H2O+H]+       135\n[M+NH4]+         134\n[M-H]-           115\n[M+Na]+          103\n[M+C2H4O2-H]-     72\n[M]+               5\n[M+Cl]-            3\n[M-H2O-H]-         3\nName: reference_adduct, dtype: int64"
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_UVPD['reference_adduct'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [],
   "source": [
    "import toolsets.helpers as helpers\n",
    "adduct_results_pos = helpers.save_value_counts(string_search(data_UVPD,'charge',1),'reference_adduct')\n",
    "adduct_results_neg = helpers.save_value_counts(string_search(data_UVPD,'charge',-1),'reference_adduct')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (12, 5)\n",
    "                      )\n",
    "sns.barplot(x = adduct_results_pos['unique_values'], y = adduct_results_pos['counts'], palette = sns.color_palette(\"pastel\"))\n",
    "ax.set_xlabel(\"Adducts\", fontsize = 18)\n",
    "ax.set_ylabel(\"Counts\", fontsize = 18)\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.grid(False)\n",
    "plt.savefig(os.path.join(source_dir,'adducts_distribution_pos.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [
    {
     "data": {
      "text/plain": "   unique_values  counts\n0         [M-H]-     115\n1  [M+C2H4O2-H]-      72\n2        [M+Cl]-       3\n3     [M-H2O-H]-       3",
      "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>unique_values</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[M-H]-</td>\n      <td>115</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[M+C2H4O2-H]-</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[M+Cl]-</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[M-H2O-H]-</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adduct_results_neg"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [
    {
     "data": {
      "text/plain": "[(0.5490196078431373, 0.3803921568627451, 0.23529411764705882),\n (0.8627450980392157, 0.49411764705882355, 0.7529411764705882),\n (0.4745098039215686, 0.4745098039215686, 0.4745098039215686),\n (0.8352941176470589, 0.7333333333333333, 0.403921568627451),\n (0.5098039215686274, 0.7764705882352941, 0.8862745098039215)]"
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"muted\")[5:]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.5490196078431373, 0.3803921568627451, 0.23529411764705882)"
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"muted\")[5]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize = (12, 5)\n",
    "                      )\n",
    "sns.barplot(x = adduct_results_neg['unique_values'], y = adduct_results_neg['counts'], palette= sns.color_palette(\"pastel\")[5:])\n",
    "ax.set_xlabel(\"Adducts\", fontsize = 18)\n",
    "ax.set_ylabel(\"Counts\", fontsize = 18)\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.grid(False)\n",
    "plt.savefig(os.path.join(source_dir,'adducts_distribution_neg.tiff'), dpi = 300,facecolor = 'white', edgecolor = 'white')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "alphapept_env",
   "language": "python",
   "display_name": "alphapept_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}