{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 00_Showcase ecDHFR: a FEATHER walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tutorial dataset can be downloaded from [this link](https://figshare.com/ndownloader/files/48153751)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.data import *\n",
    "from pigeon_feather.plot import *\n",
    "from pigeon_feather.hxio import *\n",
    "from pigeon_feather.spectra import *\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make folders for results\n",
    "\n",
    "import datetime\n",
    "import os\n",
    "\n",
    "out_path = \"./data/bayesian_hdx_input_20240722\"\n",
    "\n",
    "today_date = datetime.date.today().strftime(\"%Y%m%d\")\n",
    "# today_date = '20240722'\n",
    "results_path = os.path.join(out_path, f\"ecDHFR_results_{today_date}\")\n",
    "\n",
    "if not os.path.exists(results_path):\n",
    "    os.makedirs(results_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/ecDHFR_tutorial.csv\n",
      "rangeslist included !\n",
      "Removed 0 peptides from state APO due to missing raw MS data.\n",
      "Removed 70 peptides from state APO due to high back exchange.\n",
      "Removed 2 peptides from state TRI due to missing raw MS data.\n",
      "Removed 70 peptides from state TRI due to high back exchange.\n",
      "Done loading raw MS data.\n"
     ]
    }
   ],
   "source": [
    "tables = ['./data/ecDHFR_tutorial.csv']\n",
    "\n",
    "ranges = ['./data/rangeslist.csv']\n",
    "\n",
    "\n",
    "raw_spectra_paths = [\n",
    "    f\"./data/SpecExport/\",\n",
    "]\n",
    "\n",
    "protein_sequence = \"MTGHHHHHHENLYFQSISLIAALAVDRVIGMENAMPWNLPADLAWFKRNTLDKPVIMGRHTWESIGRPLPGRKNIILSSQPGTDDRVTWVKSVDEAIAACGDVPEIMVIGGGRVYEQFLPKAQKLYLTHIDAEVEGDTHFPDYEPDDWESVFSEFHDADAQNSHSYCFEILERR\"\n",
    "\n",
    "# load the data\n",
    "hdxms_data_list = []\n",
    "for i in range(len(tables)):\n",
    "    # for i in [4]:\n",
    "    print(tables[i])\n",
    "\n",
    "    \n",
    "    # read the data and clean it\n",
    "    cleaned = read_hdx_tables([tables[i]], [ranges[i]], exclude=False, states_subset=['APO','TRI'])\n",
    "    \n",
    "    # convert the cleaned data to hdxms data object\n",
    "    hdxms_data = load_dataframe_to_hdxmsdata(\n",
    "        cleaned,\n",
    "        n_fastamides=2,\n",
    "        protein_sequence=protein_sequence,\n",
    "        fulld_approx=False,\n",
    "        saturation=0.9,\n",
    "    )\n",
    "\n",
    "    # load the raw ms data to the hdxms data object\n",
    "    load_raw_ms_to_hdxms_data(\n",
    "        hdxms_data,\n",
    "        raw_spectra_paths[i],\n",
    "    )\n",
    "\n",
    "    hdxms_data_list.append(hdxms_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pigeon_feather.data.HDXMSData at 0x7f6ccdb68fd0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdxms_data_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the raw data as a pickle file\n",
    "import pickle\n",
    "\n",
    "today = datetime.date.today().strftime(\"%Y%m%d\")\n",
    "today = \"20240722\"\n",
    "\n",
    "# with open(f\"./data/hdxms_data_raw_{today}.pkl\", \"wb\") as f:\n",
    "#     pickle.dump(hdxms_data_list, f)\n",
    "\n",
    "with open(f\"./data/hdxms_data_raw_{today}.pkl\", \"rb\") as f:\n",
    "    hdxms_data_list = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "                    HDX-MS Data Statistics\n",
      "============================================================\n",
      "States names: ['APO', 'TRI']\n",
      "Time course (s): [46.0, 373.5, 572.5, 2011.0, 7772.0, 30811.5, 43292.0]\n",
      "Number of time points: 7\n",
      "Protein sequence length: 174\n",
      "Average coverage: 0.93\n",
      "Number of unique peptides: 186\n",
      "Average peptide length: 9.4\n",
      "Redundancy (based on average coverage): 10.0\n",
      "Average peptide length to redundancy ratio: 0.9\n",
      "Backexchange average, IQR: 0.19, 0.15\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "from pigeon_feather.hxio import get_all_statics_info\n",
    "\n",
    "get_all_statics_info(hdxms_data_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## back exchange correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "back exchange correction for peptides with experimental full deuteration data based its closest match in the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peptides with experimental max_d: 358\n",
      "Number of peptides with no experimental max_d: 12\n"
     ]
    }
   ],
   "source": [
    "tools.backexchange_correction(hdxms_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab all the peptides\n",
    "\n",
    "all_peps = [\n",
    "    pep\n",
    "    for data in hdxms_data_list[:]\n",
    "    for state in data.states\n",
    "    for pep in state.peptides\n",
    "]\n",
    "\n",
    "len(set([pep.identifier for pep in all_peps]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([18., 16., 10., 20., 30., 20., 42., 20., 24., 40., 38., 28., 20.,\n",
       "        14.,  8.,  6.,  4.,  2.,  2.,  8.]),\n",
       " array([0.60127572, 0.62628601, 0.6512963 , 0.67630658, 0.70131687,\n",
       "        0.72632716, 0.75133745, 0.77634774, 0.80135802, 0.82636831,\n",
       "        0.8513786 , 0.87638889, 0.90139918, 0.92640947, 0.95141975,\n",
       "        0.97643004, 1.00144033, 1.02645062, 1.05146091, 1.07647119,\n",
       "        1.10148148]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_d = [pep.max_d / pep.theo_max_d for pep in all_peps]\n",
    "plt.hist(max_d, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot uptake plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# option 1\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.lines as mlines\n",
    "from pigeon_feather.analysis import get_index_offset\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "\n",
    "font = {\"family\": \"Arial\", \"weight\": \"normal\", \"size\": 36}\n",
    "axes = {\"titlesize\": 36, \"titleweight\": \"bold\", \"labelsize\": 36, \"linewidth\": 5}\n",
    "plt.rc(\"font\", **font)\n",
    "plt.rc(\"axes\", **axes)\n",
    "plt.rc(\"lines\", lw=5)\n",
    "\n",
    "colors = [\"k\", \"red\", \"blue\", \"purple\", \"gray\", \"orange\", \"yellow\", \"green\", \"brown\"]\n",
    "\n",
    "\n",
    "all_peps = [\n",
    "    peptide\n",
    "    for hdxms_data in hdxms_data_list\n",
    "    for state in hdxms_data.states\n",
    "    for peptide in state.peptides\n",
    "    if peptide.note is None and state.state_name != \"RAT\"\n",
    "]\n",
    "all_idfs = [pep for pep in all_peps if pep.unique_num_timepoints > 5]\n",
    "all_idfs = list(set([peptide.identifier for peptide in all_peps]))[:]\n",
    "all_idfs.sort(key=lambda x: int(re.search(r\"(-?\\d+)\", x).group()))\n",
    "\n",
    "\n",
    "\n",
    "def idf_to_pep(idf):\n",
    "    return [pep for pep in all_peps if pep.identifier == idf][0]\n",
    "\n",
    "# num_subplots_per_figure = 100\n",
    "num_subplots_per_figure = 250\n",
    "num_figures = math.ceil(len(all_idfs) / num_subplots_per_figure)\n",
    "\n",
    "\n",
    "all_idfs_subset = all_idfs[:]\n",
    "\n",
    "for fig_index in range(num_figures):\n",
    "    # Select the subset of errors for the current figure\n",
    "    selected_idf = all_idfs_subset[\n",
    "        fig_index * num_subplots_per_figure : (fig_index + 1) * num_subplots_per_figure\n",
    "    ]\n",
    "    num_col = math.ceil(len(selected_idf) / 5)\n",
    "\n",
    "    fig, axs = plt.subplots(\n",
    "        num_col, 5, figsize=(9 * 5, 8 * num_col)\n",
    "    )  # Adjust subplot size as needed\n",
    "\n",
    "    for i, idf in enumerate(selected_idf):\n",
    "        ax = axs[i // 5, i % 5]\n",
    "\n",
    "\n",
    "        pep = idf_to_pep(idf)\n",
    "        ax.axhline(y=pep.max_d, color='lightgray', linestyle='--', linewidth=5)\n",
    "\n",
    "\n",
    "        uptake = UptakePlot(\n",
    "            hdxms_data_list,\n",
    "            idf,\n",
    "            states_subset=[\"APO\", \"TRI\"],\n",
    "            if_plot_fit=False,\n",
    "            figure=fig,\n",
    "            ax=ax,\n",
    "        )\n",
    "\n",
    "        ax.set_xlim(1e1, 1e5)\n",
    "        ax.xaxis.set_major_locator(LogLocator(base=10.0, numticks=5))\n",
    "\n",
    "\n",
    "        y_max = pep.theo_max_d/hdxms_data_list[0].saturation\n",
    "        ax.set_ylim(-0.5, y_max + 0.5)\n",
    "\n",
    "\n",
    "        #Custom legend\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "        new_labels = [label for label in labels if label in [\"APO\", \"TRI\"]]\n",
    "        new_handles = [\n",
    "            handle\n",
    "            for handle, label in zip(handles, labels)\n",
    "            if label in [\"APO\", \"TRI\"]  \n",
    "        ]\n",
    "        ax.legend(new_handles, new_labels, title=\"state\", title_fontsize=\"small\",loc='best')\n",
    "\n",
    "\n",
    "    # Layout adjustment and save\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f\"{results_path}/DHFR_exp_uptake_{fig_index}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## peptide subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117 new peptides added to the APO state.\n",
      "126 new peptides added to the TRI state.\n"
     ]
    }
   ],
   "source": [
    "# [state.add_all_subtract() for data in hdxms_data_list for state in data.states]\n",
    "# add_new_peptides_by_subtract()\n",
    "for i in range(1):\n",
    "    [\n",
    "        state.add_new_peptides_by_subtract()\n",
    "        for data in hdxms_data_list[:]\n",
    "        for state in data.states\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[117, 126]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[state.num_subtracted_added for data in hdxms_data_list for state in data.states]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "save the data as a pickle file for later use,  and write to files used for bayesian sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "out_path = f\"./data/bayesian_hdx_input_{today}\"\n",
    "if not os.path.exists(out_path):\n",
    "    os.makedirs(out_path)\n",
    "\n",
    "\n",
    "import pickle\n",
    "\n",
    "with open(f\"{out_path}/hdxms_data_list.pkl\", \"wb\") as f:\n",
    "    pickle.dump(hdxms_data_list, f)\n",
    "\n",
    "# with open(f\"{out_path}/hdxms_data_list.pkl\", \"rb\") as f:\n",
    "#     hdxms_data_list = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inputs for MCMC sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotope files saved to ./data/bayesian_hdx_input_20240722/spectra_dhfr_tutorial_dataset\n",
      "Reminder: sequence contains fastamides !!!\n",
      "Reminder: sequence contains fastamides !!!\n",
      "Data saved to ./data/bayesian_hdx_input_20240722\n",
      "dhfr_tutorial_dataset\n"
     ]
    }
   ],
   "source": [
    "exp_names = [\n",
    "    \"dhfr_tutorial_dataset\",\n",
    "]\n",
    "\n",
    "for i in range(len(hdxms_data_list)):\n",
    "    # exp_name = raw_spectra_paths[i].split('/')[-2].split('SpecExport_')[-1]\n",
    "    exp_name = exp_names[i]\n",
    "    export_iso_files(\n",
    "        hdxms_data_list[i], outdir=f\"{out_path}/spectra_{exp_name}\", overwrite=True\n",
    "    )\n",
    "    df = revert_hdxmsdata_to_dataframe(hdxms_data_list[i])\n",
    "    convert_dataframe_to_bayesianhdx_format(\n",
    "        df, protein_name=exp_name, OUTPATH=f\"{out_path}\"\n",
    "    )\n",
    "\n",
    "    print(exp_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# write ready to run script for each state\n",
    "\n",
    "protein_sequence = \"MTGHHHHHHENLYFQSISLIAALAVDRVIGMENAMPWNLPADLAWFKRNTLDKPVIMGRHTWESIGRPLPGRKNIILSSQPGTDDRVTWVKSVDEAIAACGDVPEIMVIGGGRVYEQFLPKAQKLYLTHIDAEVEGDTHFPDYEPDDWESVFSEFHDADAQNSHSYCFEILERR\"\n",
    "\n",
    "state_names = list(\n",
    "    set([state.state_name for data in hdxms_data_list for state in data.states])\n",
    ")\n",
    "for protein_state in state_names:\n",
    "    script = tools.generate_bayesian_hdx_script(\n",
    "        exp_names,\n",
    "        protein_sequence,\n",
    "        protein_state,\n",
    "        base_directory=\".\",\n",
    "        making_chunks=True,\n",
    "        pH=7.0,\n",
    "        temperature=293.0,\n",
    "        saturation=0.9,\n",
    "        rerun_num=3,\n",
    "        extreme_value_prior=False,\n",
    "        structural_prior=False,\n",
    "    )\n",
    "\n",
    "    with open(f\"{out_path}/run_bayesian_hdx_{protein_state}_chunks.py\", \"w\") as f:\n",
    "        f.write(script)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "two priors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uptake prior\n",
    "#tools.generate_extreme_value_prior(hdxms_data_list, out_path)\n",
    "\n",
    "\n",
    "# structural prior\n",
    "\n",
    "# solvated_pdbs = [\n",
    "#     \"./data/5DFR_APO_relaxed_best_solvated.pdb\",\n",
    "#     \"./data/6XG5_TRI_relaxed_best_solvated.pdb\",\n",
    "#     \"./data/1RG7_MTX_relaxed_best_solvated.pdb\",\n",
    "# ]\n",
    "\n",
    "# for i, state_name in enumerate([\"APO\", \"TRI\", \"MTX\"]):\n",
    "\n",
    "\n",
    "#     tools.generate_structural_prior(\n",
    "#         protein_sequence, solvated_pdbs[i], out_path, state_name\n",
    "#     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## run the sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run the script in the terminal with the following command:\n",
    "\n",
    "```sh\n",
    "cd ./data/bayesian_hdx_input_20240722\n",
    "python run_bayesian_hdx_APO_chunks.py\n",
    "```\n",
    "\n",
    "The simulations usually take several hours, but the duration varies based on the size of the dataset (number of peptides, time points, and replicates).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "class CustomUnpickler(pickle.Unpickler):\n",
    "    def find_class(self, module, name):\n",
    "        if name == \"ProteinState\":\n",
    "            from pigeon_feather.data import ProteinState\n",
    "\n",
    "            return ProteinState\n",
    "        if name == \"HDXMSData\":\n",
    "            from pigeon_feather.data import HDXMSData\n",
    "\n",
    "            return HDXMSData\n",
    "        if name == \"Peptide\":\n",
    "            from pigeon_feather.data import Peptide\n",
    "\n",
    "            return Peptide\n",
    "        if name == \"Timepoint\":\n",
    "            from pigeon_feather.data import Timepoint\n",
    "\n",
    "            return Timepoint\n",
    "        return super().find_class(module, name)\n",
    "\n",
    "\n",
    "# read pcikle\n",
    "with open(f\"{out_path}/hdxms_data_list.pkl\", \"rb\") as f:\n",
    "    hdxms_data_list = CustomUnpickler(f).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pigeon_feather.data.HDXMSData at 0x7f095a02d150>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdxms_data_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for data in hdxms_data_list:\n",
    "    for state in data.states:\n",
    "        state.peptides = [pep for pep in state.peptides if pep.note is None]\n",
    "        for pep in state.peptides:\n",
    "            pep.timepoints = [tp for tp in pep.timepoints if tp.note is None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create an Analysis object and load the results, note: the temperature and pH are crucial to calculate the instrisic exchange rates. make sure to input the correct ones. The chunk size and chunk number can be altered by user. in this tutorial it was automaticaly determined by a help function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import Analysis, get_index_offset\n",
    "from pigeon_feather.tools import optimal_chunks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "RUN_NUM = 3\n",
    "\n",
    "apo_states = [\n",
    "    state\n",
    "    for data in hdxms_data_list\n",
    "    for state in data.states\n",
    "    if state.state_name == \"APO\"\n",
    "]\n",
    "\n",
    "tri_states = [\n",
    "    state\n",
    "    for data in hdxms_data_list\n",
    "    for state in data.states\n",
    "    if state.state_name == \"TRI\"\n",
    "]\n",
    "\n",
    "ana_apo_1 = Analysis(apo_states, temperature=293.0, pH=7.0)\n",
    "\n",
    "chunk_num, chunk_size = optimal_chunks(len(ana_apo_1.protein_sequence))\n",
    "ana_apo_1.load_bayesian_hdx_oupt_chunks(\n",
    "    chunk_size=chunk_size,\n",
    "    chunk_num=chunk_num,\n",
    "    state_name=\"APO\",\n",
    "    run_num=RUN_NUM,\n",
    "    N=200,\n",
    "    bayesian_hdx_data_folder=f\"{out_path}/bayesian_hdx_output_chunks\",\n",
    ")\n",
    "\n",
    "\n",
    "ana_tri_1 = Analysis(tri_states, temperature=293.0, pH=7.0)\n",
    "\n",
    "ana_tri_1.load_bayesian_hdx_oupt_chunks(\n",
    "    chunk_size=chunk_size,\n",
    "    chunk_num=chunk_num,\n",
    "    state_name=\"TRI\",\n",
    "    run_num=RUN_NUM,\n",
    "    N=200,\n",
    "    bayesian_hdx_data_folder=f\"{out_path}/bayesian_hdx_output_chunks\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for mini in ana_apo_1.results_obj.mini_peps:\n",
    "#     print(mini.std_within_clusters_log_kex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "two fitting check functions are available: 1. centroid level fitting check 2. isotopic mass envelope fitting check. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import (\n",
    "    check_fitted_isotope_envelope,\n",
    "    check_fitted_peptide_uptake,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### envelope check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_state_name = \"APO\"\n",
    "check_ana_obj = ana_apo_1\n",
    "\n",
    "\n",
    "all_peps = [\n",
    "    pep\n",
    "    for data in hdxms_data_list\n",
    "    for state in data.states\n",
    "    for pep in state.peptides\n",
    "    if state.state_name == check_state_name and pep.note is None\n",
    "]\n",
    "all_tps = [\n",
    "    tp\n",
    "    for pep in all_peps\n",
    "    for tp in pep.timepoints\n",
    "    if pep.get_timepoint(0) is not None and tp.deut_time != np.inf and tp.deut_time != 0\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18086398727111275"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "envelope_errors = [(check_fitted_isotope_envelope(ana_apo_1, tp), tp) for tp in all_tps]\n",
    "envelope_errors = sorted(envelope_errors, key=lambda x: x[0], reverse=False)\n",
    "\n",
    "# you can plot the fitted isotope envelope and the experimental data\n",
    "check_fitted_isotope_envelope(check_ana_obj, envelope_errors[300][1], if_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    np.array(envelope_errors)[:, 0],\n",
    ")\n",
    "plt.xlabel(\"Sum AE\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3646019382844139\n",
      "0.39348193324578395\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.nanmedian(np.array(envelope_errors)[:, 0]))\n",
    "print(np.nanmean(np.array(envelope_errors)[:, 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### uptake check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import check_fitted_peptide_uptake\n",
    "\n",
    "\n",
    "all_idfs = list(set([pep.identifier for pep in all_peps]))\n",
    "\n",
    "\n",
    "def extract_numbers(s):\n",
    "    numbers = re.findall(r\"(-?\\d+)-(-?\\d+)\", s)\n",
    "    return tuple(map(int, numbers[0]))\n",
    "\n",
    "\n",
    "all_idfs.sort(key=extract_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "uptake_errors = []\n",
    "\n",
    "all_peps_grouped = tools.group_by_attributes(\n",
    "    all_peps, [\"protein_state.state_name\", \"identifier\"]\n",
    ")\n",
    "\n",
    "for idf in all_idfs:\n",
    "    try:\n",
    "        idf_peps = all_peps_grouped[(check_state_name, idf)]\n",
    "        avg_pep = tools.average_peptides(idf_peps)\n",
    "\n",
    "        result = check_fitted_peptide_uptake(\n",
    "            check_ana_obj, hdxms_data_list, avg_pep, state_name=check_state_name\n",
    "        )\n",
    "        uptake_errors.append((result, avg_pep))\n",
    "    except Exception as e:\n",
    "        print(idf, e)\n",
    "\n",
    "\n",
    "uptake_errors_array = np.array([i[0] for i in uptake_errors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5.5))\n",
    "\n",
    "# # Plotting with complementary colors\n",
    "# sns.histplot([x[0] for x in envelope_errors], bins=20, kde=True, ax=axes[0], color=\"#FF6347\")\n",
    "# sns.histplot(uptake_errors_array, bins=20, kde=True, ax=axes[1], color=\"#4682B4\")\n",
    "\n",
    "\n",
    "color_1 = \"#53b1b1\"  # A standard blue color\n",
    "color_2 = \"#f6c624\"  # A teal color\n",
    "\n",
    "# Plotting with chosen colors\n",
    "sns.histplot([x[0] for x in envelope_errors], bins=20, kde=True, ax=axes[0], color=color_1)\n",
    "sns.histplot(uptake_errors_array, bins=20, kde=True, ax=axes[1], color=color_2)\n",
    "\n",
    "axes[1].set_ylabel(\"\")\n",
    "axes[0].set_xlabel(\"Sum AE\")\n",
    "axes[1].set_xlabel(\"Da/peptide length\")\n",
    "fig.subplots_adjust(wspace=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import get_index_offset\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "\n",
    "check_ana_obj = ana_apo_1\n",
    "index_offset = get_index_offset(check_ana_obj, './data/6XG5_TRI.pdb')\n",
    "\n",
    "uptake_errors = sorted(uptake_errors, key=lambda x: x[1].start, reverse=False)\n",
    "\n",
    "num_subplots_per_figure = math.ceil(len(uptake_errors) / 2)\n",
    "# num_subplots_per_figure = 250\n",
    "num_figures = math.ceil(len(all_idfs) / num_subplots_per_figure)\n",
    "\n",
    "\n",
    "for fig_index in range(num_figures):\n",
    "    # Select the subset of errors for the current figure\n",
    "    selected_uptake_errors = uptake_errors[fig_index * num_subplots_per_figure:(fig_index + 1) * num_subplots_per_figure]\n",
    "    num_col = math.ceil(len(selected_uptake_errors) / 5)\n",
    "\n",
    "    fig, axs = plt.subplots(num_col, 5, figsize=(9 * 5, 8 * num_col))  # Adjust subplot size as needed\n",
    "\n",
    "    for i, error_tuple in enumerate(selected_uptake_errors):\n",
    "\n",
    "\n",
    "        ax = axs[i // 5, i % 5]\n",
    "\n",
    "\n",
    "        # Unpack error information\n",
    "        peptide_data = error_tuple[1]\n",
    "\n",
    "\n",
    "        ax.axhline(y=peptide_data.max_d, color='lightgray', linestyle='--', linewidth=5)\n",
    "\n",
    "        check_fitted_peptide_uptake(\n",
    "            check_ana_obj,\n",
    "            hdxms_data_list,\n",
    "            peptide_data,\n",
    "            if_plot=True,\n",
    "            state_name=check_state_name,\n",
    "            figure=fig,\n",
    "            ax=ax\n",
    "        )\n",
    "\n",
    "        #Retrieve and format the peptide identifier\n",
    "        idf = peptide_data.identifier\n",
    "        idf_start, idf_end = map(int, re.match(r\"(-?\\d+)-(-?\\d+)\", idf).groups())\n",
    "        idf_seq = idf.split(\" \")[1]\n",
    "        ax.set_title(f\"{idf_start - index_offset}-{idf_end - index_offset} {idf_seq}\")\n",
    "\n",
    "        ax.set_xlim(1e1, 1e5)\n",
    "        ax.xaxis.set_major_locator(LogLocator(base=10.0, numticks=5))\n",
    "\n",
    "        # y_max = ax.get_ylim()[1]\n",
    "        # ax.set_ylim(-0.5, y_max + 1)\n",
    "        pep = error_tuple[1]\n",
    "        y_max = pep.theo_max_d/check_ana_obj.saturation\n",
    "        ax.set_ylim(-0.5, y_max + 0.5)\n",
    "\n",
    "        # light gray dotted line at max deuteration\n",
    "\n",
    "\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "        new_labels = [label for label in labels if label.isdigit()]\n",
    "        new_handles = [handle for handle, label in zip(handles, labels) if label.isdigit()]\n",
    "        ax.legend(new_handles, new_labels, title='replicate', title_fontsize='small')\n",
    "\n",
    "    # Layout adjustment and save\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f\"{results_path}/ecDHFR_uptake_errors_{check_state_name}_{fig_index}.pdf\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if the fitting is bad, one should not move forward, need to check the parameters in the sampling, make sure they agree with the experimental conditions. one may also remove the outlier peptides and run the all the cells above again. a ourliter may be a peptide exchange a lot while its neighbours barely exchange (examples in the turtorial dataset: 78-92 VDEAIAACGDVPEIM, 75-78 VKSV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "once you're satisfied with the fitting results, you can make the final results plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## barplot of the kex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "barplot showing the FEATHER derived exchange rates. index_offset is determined by comparison to the sequence in HX/MS and the pdb file provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# APO state only\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(40, 10), sharey=True, sharex=True)\n",
    "\n",
    "ana_apo_1.plot_kex_bar(\n",
    "    ax=axes[0], resolution_indicator_pos=15, label=\"APO\", show_seq=False, color=\"k\"\n",
    ")\n",
    "\n",
    "ana_tri_1.plot_kex_bar(\n",
    "    ax=axes[1], resolution_indicator_pos=15, label=\"TRI\", show_seq=False, color=\"red\"\n",
    ")\n",
    "\n",
    "from matplotlib.pyplot import step\n",
    "from matplotlib.ticker import FuncFormatter, MultipleLocator\n",
    "\n",
    "from pigeon_feather.analysis import get_index_offset\n",
    "\n",
    "pdb_file = \"./data/6XG5_TRI.pdb\"\n",
    "index_offset = get_index_offset(ana_apo_1, pdb_file)\n",
    "\n",
    "# # ax.xaxis.set_major_locator(plt.MultipleLocator(2))\n",
    "axes[0].set_xticks(axes[0].get_xticks()[::2])\n",
    "axes[0].set_xticklabels(axes[0].get_xticklabels(), fontdict={\"fontsize\": 24})\n",
    "\n",
    "\n",
    "def format_func(value, tick_number):\n",
    "    return f\"{int(value - index_offset +1)}\"\n",
    "\n",
    "\n",
    "axes[0].xaxis.set_major_formatter(FuncFormatter(format_func))\n",
    "\n",
    "seq_pos = 17\n",
    "for ii in range(0, len(ana_apo_1.protein_sequence[:]), 2):\n",
    "    axes[0].text(\n",
    "        ii,\n",
    "        seq_pos,\n",
    "        ana_apo_1.protein_sequence[ii],\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=22,\n",
    "    )\n",
    "\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_ylabel(\"-log(k$_{ex}$)\")\n",
    "    #ax.legend(loc=\"lower left\")\n",
    "    ax.yaxis.set_major_locator(plt.MultipleLocator(5))\n",
    "\n",
    "    ax.plot(\n",
    "        [-3, len(ana_apo_1.protein_sequence) + 3],\n",
    "        [5, 5],\n",
    "        color=\"gray\",\n",
    "        lw=1,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "    ax.plot(\n",
    "        [-3, len(ana_apo_1.protein_sequence) + 3],\n",
    "        [10, 10],\n",
    "        color=\"gray\",\n",
    "        lw=1,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "\n",
    "\n",
    "plt.subplots_adjust(hspace=0.08)\n",
    "fig.savefig(f\"{results_path}/ecDHFR_kex_bar_APO_TRI_{today_date}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logP projection to a pdb structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pigeon_feather.analysis import BFactorPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfactor_plot = BFactorPlot(\n",
    "    ana_apo_1,\n",
    "    pdb_file=\"./data/6XG5_TRI.pdb\",\n",
    "    plot_deltaG=True,\n",
    "    temperature=293.15,\n",
    "    logP_threshold=10,\n",
    ")\n",
    "bfactor_plot.plot(f\"{results_path}/6XG5_APO_deltaG.pdb\")\n",
    "\n",
    "# bfactor_plot = BFactorPlot(\n",
    "#     ana_apo_1,\n",
    "#     pdb_file=\"./data/6XG5_APO_relaxed_best_solvated.pdb\",\n",
    "#     plot_deltaG=True,\n",
    "#     temperature=293.15,\n",
    "# )\n",
    "# bfactor_plot.plot(f\"{out_path}/6XG5_APO_deltaG.pdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "delta logP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if there are mutiple states availble, one can map the difference to the pdb strutcure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfactor_plot = BFactorPlot(\n",
    "    ana_apo_1,\n",
    "    ana_tri_1,  \n",
    "    pdb_file=\"./data/6XG5_TRI.pdb\",\n",
    "    plot_deltaG=True,\n",
    "    temperature=293.15,\n",
    "    logP_threshold=10,\n",
    ")\n",
    "bfactor_plot.plot(f\"{results_path}/6XG5_deltaG_APO-TRI.pdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "export logPFs to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis\n",
    "from pigeon_feather.analysis import get_res_avg_logP, get_res_avg_logP_std\n",
    "\n",
    "pdb_file = \"./data/6XG5_TRI.pdb\"\n",
    "index_offset = get_index_offset(ana_apo_1, pdb_file)\n",
    "\n",
    "\n",
    "def logPF_to_deltaG(ana_obj, logPF):\n",
    "    \"\"\"\n",
    "    :param logPF: logP value\n",
    "    :return: deltaG in kJ/mol, local unfolding energy\n",
    "    \"\"\"\n",
    "\n",
    "    return 8.3145 * ana_obj.temperature * np.log(10) * logPF / 1000\n",
    "\n",
    "\n",
    "def create_logP_df(ana_obj, index_offset):\n",
    "    df_logPF = pd.DataFrame()\n",
    "\n",
    "    for res_i, _ in enumerate(ana_obj.results_obj.protein_sequence):\n",
    "        res_obj_i = ana_obj.results_obj.get_residue_by_resindex(res_i)\n",
    "\n",
    "        avg_logP, std_logP = get_res_avg_logP(res_obj_i)\n",
    "        #std_logP = get_res_avg_logP_std(res_obj_i)\n",
    "\n",
    "        df_i = pd.DataFrame(\n",
    "            {\n",
    "                \"resid\": [res_obj_i.resid - index_offset],\n",
    "                #\"resname\": [res_obj_i.resname],\n",
    "                \"resname\": [MDAnalysis.lib.util.convert_aa_code(res_obj_i.resname)],\n",
    "                'avg_dG': [logPF_to_deltaG(ana_obj, avg_logP)],\n",
    "                'std_dG': [logPF_to_deltaG(ana_obj, std_logP)],\n",
    "                \"avg_logP\": [avg_logP],\n",
    "                \"std_logP\": [max(std_logP, 0.35)],\n",
    "                \"is_nan\": [res_obj_i.is_nan()],\n",
    "            }\n",
    "        )\n",
    "\n",
    "        if res_obj_i.is_nan():\n",
    "            df_i[\"single resloved\"] = [np.nan]\n",
    "            df_i[\"min_pep logPs\"] = [np.nan]\n",
    "\n",
    "        else:\n",
    "            df_i[\"single resloved\"] = [res_obj_i.mini_pep.if_single_residue()]\n",
    "            df_i[\"min_pep logPs\"] = [res_obj_i.clustering_results_logP]\n",
    "\n",
    "        df_logPF = pd.concat([df_logPF, df_i])\n",
    "\n",
    "    df_logPF = df_logPF.reset_index(drop=True)\n",
    "\n",
    "    return df_logPF\n",
    "\n",
    "\n",
    "df = create_logP_df(ana_apo_1, index_offset)\n",
    "df.to_csv(f\"{results_path}/logP_APO_{today_date}.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fecth two df and merge\n",
    "df_apo = create_logP_df(ana_apo_1, index_offset)\n",
    "df_tri = create_logP_df(ana_tri_1, index_offset)\n",
    "\n",
    "df = pd.merge(df_apo, df_tri, on=\"resid\", suffixes=(\"_APO\", \"_TRI\"))\n",
    "\n",
    "df.to_csv(f\"{results_path}/logP_APO_TRI_{today_date}.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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