{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FTICR-MS Processing and Display\n",
    "\n",
    "a simplified environment for processing 1D Bruker FTICR datasets with `SPIKE`\n",
    "\n",
    "Run each python cell in sequence by using the ⇥Run button above (or typing *shift* Enter).\n",
    "\n",
    "Cells are meant to be used in order, taking you to the complete analysis, but you can go back at any time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization of the environment\n",
    "the following cell should be run only once, at the beginning of the processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "start_time": "2021-09-28T06:54:23.172547Z"
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   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## STARTING Environment..."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    ========================\n",
      "          SPIKE\n",
      "    ========================\n",
      "    Version     : 0.99.29\n",
      "    Date        : 20-09-2021\n",
      "    Revision Id : 529\n",
      "    ========================\n",
      "*** zoom3D not loaded ***\n",
      "plugins loaded:\n",
      "Fitter,  Linear_prediction,  Peaks,  bcorr,  fastclean,  gaussenh,  rem_ridge,  sane,  sg,  test,  urQRd, \n",
      "plugins loaded:\n",
      "msapmin, \n",
      "\n",
      "spike.plugins.report() for a short description of each plugins\n",
      "spike.plugins.report('module_name') for complete documentation on one plugin\n",
      "plugins loaded:\n",
      "Bruker_NMR_FT,  Bucketing,  Integrate,  PALMA,  apmin, \n",
      "plugins loaded:\n",
      "FTMS_calib,  PhaseMS,  diagonal_2DMS, \n",
      "\n",
      "Interactive module version, 1.2.1\n"
     ]
    },
    {
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     "text": [
      "Run date: 2021-09-28T08:54:27.400671\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## ...program is Ready"
      ],
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       "<IPython.core.display.Markdown object>"
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   "source": [
    "# load all python and interactive tools - has to be run only once (but it does not hurt to rerun...)\n",
    "from IPython.display import display, HTML, Markdown, Image\n",
    "display(Markdown('## STARTING Environment...'))\n",
    "import matplotlib as mpl\n",
    "%matplotlib widget\n",
    "import os.path as op\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import spike\n",
    "from spike.Interactive import INTER as I\n",
    "from spike.Interactive import FTICR_INTER as FI\n",
    "from spike.Interactive import MSIsotopic as ISO\n",
    "from spike.Interactive.ipyfilechooser import FileChooser\n",
    "from spike.File import BrukerMS\n",
    "from datetime import datetime\n",
    "I.initialize()\n",
    "print('Run date:', datetime.now().isoformat() )\n",
    "display(Markdown('## ...program is Ready'))\n",
    "from importlib import reload  # this line is debugging help\n",
    "\n",
    "# configurable items - you may change them to fit you preferences\n",
    "verbose = 1                              # chose from 0 (terse) to 3 more verbose\n",
    "mpl.rcParams['figure.figsize'] = (8,4)   # (X,Y) default figure size\n",
    "I.Activate_Wheel = True                  # True/False    scale with wheel control in the graphic cells "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose the file\n",
    "The `FileChooser()` tool creates a dialog box which allows to choose a file on your disk\n",
    "\n",
    "- use the `Select` button, click on the directory to move around `..` is for going back in the directory tree\n",
    "- click on the file to select it\n",
    "- modify the ( *optional* ) `path` argument, to start the exploration on a given location\n",
    "- changing to `path='.'` will start the browsing from the current directory \n",
    "- After the selection, the selected filename is found in `FC.selected`\n",
    "\n",
    "Bruker files are named `fid` and are contained in a `*.d` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T06:54:34.838856Z",
     "start_time": "2021-09-28T06:54:34.745849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16b17276860644f6adb646d79b970fbc",
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       "FileChooser(path='/DATA', filename='', show_hidden='False')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FC = FileChooser(path='/DATA', filetype='fid')\n",
    "display(FC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dataset and display FID\n",
    "\n",
    "This is simply done with the `Import_1D()` tool, which returns a `SPIKE` object.\n",
    "\n",
    "We store the dataset into a variable, here called d1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T06:55:06.962737Z",
     "start_time": "2021-09-28T06:55:05.958619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading file  /DATA/DATA/FT-ICR/M.Witt/Data for Marc Andre Delsuc April 2019/Asphaltene/APPI pos Sample D_000001.d/fid\n"
     ]
    },
    {
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FTICR data-set\n",
       "Bo: 14.00\n",
       "Single Spectrum data-set\n",
       "FT-ICR axis at 1428.571429 kHz,  8388608 real points,  from physical mz =  150.450   to m/z = 2000.000  R max (M=400) = 3155194"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is simply done with the `Import_1D()` tool, which returns a `SPIKE` object.\n",
    "# We store it into a variable, evaluating the variable show a summary of the dataset. \n",
    "print('Reading file ',FC.selected)\n",
    "d1 = BrukerMS.Import_1D(FC.selected)\n",
    "d1.filename = FC.selected\n",
    "d1.set_unit('sec').display(title=FC.selected_path+\" transient\")\n",
    "d1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spectra can be interactively explored with the jupyter tools displayed  on the side of the dataset:\n",
    "\n",
    "- zoom with <button class=\"jupyter-matplotlib-button jupyter-widgets jupyter-button\" href=\"#\" title=\"Zoom to rectangle\" style=\"outline: currentcolor none medium;\"><i class=\"center fa fa-square-o\"></i></button>\n",
    "- shift and resize\n",
    "<button class=\"jupyter-matplotlib-button jupyter-widgets jupyter-button\" href=\"#\" title=\"Pan axes with left mouse, zoom with right\" style=\"outline: currentcolor none medium;\"><i class=\"center fa fa-arrows\"></i></button>\n",
    " (with left and right click)\n",
    "- <button class=\"jupyter-matplotlib-button jupyter-widgets jupyter-button\" href=\"#\" title=\"Back to previous view\" style=\"outline: currentcolor none medium;\"><i class=\"center fa fa-arrow-left\"></i></button>\n",
    "and\n",
    "<button class=\"jupyter-matplotlib-button jupyter-widgets jupyter-button\" href=\"#\" title=\"Forward to next view\" style=\"outline: currentcolor none medium;\"><i class=\"center fa fa-arrow-right\"></i></button>\n",
    "allow to navigate in the zoom history\n",
    "\n",
    "- <button class=\"jupyter-matplotlib-button jupyter-widgets jupyter-button\" href=\"#\" title=\"Download plot\" style=\"outline: currentcolor none medium;\"><i class=\"center fa fa-fw fa-floppy-o\"></i></button> is used to store a `png` graphic file of the current display.\n",
    "\n",
    "The drawing zone can be resized using the little grey triangle on the lower-right corner\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Basic Processing\n",
    "The following cell applies a basic processing, check the documentation for more advanced processing\n",
    "\n",
    "### Compute Spectrum\n",
    "\n",
    "many processing methods are available, they can be either applied one by one, or piped by chaining them.\n",
    "\n",
    "Here we are chaining  apodisation - zerofill - FT - modulus\n",
    "\n",
    "then setting to `m/z` unit (`Hz` and `points` also available) - finally `display()` is used to display the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "FTICR data-set\n",
       "Bo: 14.00\n",
       "Single Spectrum data-set\n",
       "FT-ICR axis at 1428.571429 kHz,  16777216 real points,  from physical mz =  150.450   to m/z = 2000.000  R max (M=400) = 6310389"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "D1 = d1.copy()                               # copy the imported data-set to another object for processing\n",
    "D1.center().kaiser(4).zf(4)   # chaining  centering - apodisation - zerofill\n",
    "                              # kaiser(4) is an apodisation well adapted to FTICR, slightly more resolution than hamming - try varying the argument !\n",
    "if D1.axis1.itype == 0:       # means data is real (common case)\n",
    "    D1.rfft().modulus()           # chaining  real FT - modulus\n",
    "else:     # is 1              data is complex, in Narrow-band acquisition\n",
    "    D1.fft().modulus()            # chaining  complex FT - modulus\n",
    "D1.bcorr(xpoints=50)\n",
    "D1.set_unit('m/z').display(title=FC.selected_path)  # set to ppm unit - and display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peak Detection\n",
    "The following is used to perform an interactive peak picking, and output the result\n",
    "Use the cursor to choose the sensitivity of the picker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "scrolled": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of on-screen peaks is limited to 1000 - zoom in with the top tool to be sure of visualizing them all\n"
     ]
    },
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       "VBox(children=(FloatRangeSlider(value=(308.0000192650493, 313.12848064759), continuous_update=False, descripti…"
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       "Output(layout=Layout(border='1px solid red'))"
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    }
   ],
   "source": [
    "pkname = op.join(op.dirname(FC.selected),\"peak_list.txt\")\n",
    "import spike.plugins.Peaks\n",
    "print(\"number of on-screen peaks is limited to %d - zoom in with the top tool to be sure of visualizing them all\"%(spike.plugins.Peaks.NbMaxDisplayPeaks))\n",
    "FI.MSPeaker(D1, pkname);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Superpose experimental and computed spectra\n",
    "\n",
    "If the spectrum requires recalibration - eventually run the Calibration tool below, and rerun this tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    "ExecuteTime": {
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    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
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       "Box(children=(Box(children=(Label(value='Enter formula:', layout=Layout(flex='3', width='auto')), Dropdown(lay…"
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     },
     "metadata": {},
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   ],
   "source": [
    "from importlib import reload\n",
    "reload(ISO)\n",
    "IS = ISO.Isotope()\n",
    "IS.full(D1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Calibration and reference peaks\n",
    "\n",
    "The calibration used by SPIKE is based on a 2 or 3 parameters equation :\n",
    "$$\n",
    "f = \\frac{A}{m/z} - B + \\frac{C}{(m/z)^2}\n",
    "$$\n",
    "You can change them below.\n",
    "\n",
    "For the moment you can recalibrate on a single reference peak, \n",
    "this modifies the first order $A$ parameter only.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "start_time": "2021-07-27T17:33:11.212812Z"
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   ],
   "source": [
    "reload(FI)\n",
    "FI.Calib(D1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save processed data\n",
    "You can save a dataset, two formats are available:\n",
    "\n",
    "- Native SPIKE format, `*.msh5` where all informations are stored - run the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "msh5name = op.join(op.dirname(FC.selected),\"SpikeProcessed.msh5\")\n",
    "D1.save_msh5(msh5name, compressed=True)\n",
    "print(\"File is stored as %s\"%msh5name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Or `cvs` format, with only the spectrum (for the peak list, see above) - ( *be carefull this file can be very big*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csvname = op.join(op.dirname(FC.selected),\"SpikeProcessed.csv\")\n",
    "D1.save_csv(csvname)\n",
    "print(\"File is stored as %s\"%csvname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### superimpose spectra\n",
    "you can superimpose several spectra stored as `.msh5` files in order to compare them"
   ]
  },
  {
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   "execution_count": 22,
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<td>0.000130</td>\n      <td>2386000.0</td>\n      <td>7.724178e+06</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>309.0950</td>\n      <td>309.095004</td>\n      <td>0.000128</td>\n      <td>2419000.0</td>\n      <td>8.683032e+07</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>309.0910</td>\n      <td>309.091039</td>\n      <td>0.000128</td>\n      <td>2417000.0</td>\n      <td>6.270982e+06</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>309.0887</td>\n      <td>309.088685</td>\n      <td>0.000125</td>\n      <td>2476000.0</td>\n      <td>8.359351e+06</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>308.0929</td>\n      <td>308.092905</td>\n      <td>0.000082</td>\n      <td>3776000.0</td>\n      <td>4.236245e+06</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>308.0928</td>\n      <td>308.092755</td>\n      <td>0.000076</td>\n      <td>4072000.0</td>\n      <td>4.592201e+06</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>308.0926</td>\n      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