CHARISMA Round Robin Script.ipynb

{
 "cells": [
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d89db914",
   "metadata": {},
   "source": [
    "![CHARISMA%20Logo.png](attachment:CHARISMA%20Logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2928cece",
   "metadata": {},
   "source": [
    "# CHARISMA: Script for Round Robin Polystyrene analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "746d4460",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ramanchada.classes import RamanChada, SpectrumGroup\n",
    "import os,path\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51f2e7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from ipyfilechooser import FileChooser\n",
    "except:\n",
    "    !pip install ipyfilechooser\n",
    "    from ipyfilechooser import FileChooser"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fe4491",
   "metadata": {},
   "source": [
    "## First, let's do all steps by hand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374e0489",
   "metadata": {},
   "source": [
    "### Determine the Silicon measured 520.45 cm-1 peak positions for a given laser power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab1f0aa",
   "metadata": {},
   "source": [
    "Load Si SOP file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88b038dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = \"\\\\vm-stor-51\\alle\\Projekte Kunststoffe\\RD\\MA\\Öffentliche_Projekte\\2020-01-07_230007_H2020-CHARISMA_barton\\Round Robin 21\\silicon\"\n",
    "filename=\"S0P_WITEcAlpha532_100x_100_10000msx1.spc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "517f4d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = FileChooser('.')\n",
    "fc.reset(path=\"/\")\n",
    "display(fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8be410e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder=fc.selected\n",
    "print(folder)\n",
    "file=os.path.join(folder,filename)\n",
    "print(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3617f9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP = RamanChada(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5bae471",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcf213c",
   "metadata": {},
   "source": [
    "Crop to ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be9063d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.x_crop(200,1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daabbd18",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "297f25bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.normalize('minmax')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63ea8d4",
   "metadata": {},
   "source": [
    "Fit main peak: Voigt model in interval of 2x FWHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f14ca731",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.peaks(fitmethod='voigt', x_min=400, x_max=600, show=True, interval_width=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec789df",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOP.bands"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39994179",
   "metadata": {},
   "source": [
    "Calculate constant x shift from Si peak position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d23a3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sop_shift = 520.45 - SOP.bands['voigt fitted position'][0]\n",
    "sop_shift"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b59af6d",
   "metadata": {},
   "source": [
    "#### The same steps are performed for all Si types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "170db0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"S1N_WITEcAlpha532_100x_100_10000msx1.spc\"\n",
    "file=os.path.join(folder,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a4cab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N = RamanChada(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2c262c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N.x_crop(200,1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4b3edf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d716306",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N.normalize('minmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d1e900e",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N.peaks(fitmethod='voigt', x_min=400, x_max=600, show=True, interval_width=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3049cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1N.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b354890",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1n_shift = 520.45 - S1N.bands['voigt fitted position'][0]\n",
    "s1n_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30a2afdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"SOB_WITEcAlpha532_100x_100_10000msx1.spc\"\n",
    "file=os.path.join(folder,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "741d2f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB = RamanChada(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e91ad55",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB.x_crop(200,1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e70f3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dd67a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB.normalize('minmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b2549a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB.peaks(fitmethod='voigt', x_min=400, x_max=600, show=True, interval_width=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d81408b",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOB.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc8e91d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sob_shift = 520.45 - SOB.bands['voigt fitted position'][0]\n",
    "sob_shift"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cf9a7a",
   "metadata": {},
   "source": [
    "SOB and SOP position are oddly identical. Check if spectra are identical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e5c8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = SpectrumGroup([SOB, SOP])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f894fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eee4bad4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "G.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc91db60",
   "metadata": {},
   "source": [
    "#### Now, we can make a nice table for the current laser power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f2823a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Si = pd.DataFrame({'SOB': SOB.bands['voigt fitted position'][0],\n",
    "                  'S1N': S1N.bands['voigt fitted position'][0],\n",
    "                  'SOP': SOP.bands['voigt fitted position'][0]},\n",
    "                  index=['Silicon measured 520.45 cm-1 peak position'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59483711",
   "metadata": {},
   "outputs": [],
   "source": [
    "Si"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146bbe70",
   "metadata": {},
   "source": [
    "### Determine Polystyrene peak positions  for a given laser power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c2e1b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"27 polystyrene.spc\"\n",
    "file = os.path.join(folder,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad9441b",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS = RamanChada(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5571595a",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58a0eced",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.x_crop(250,3200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0221de97",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.normalize('minmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa837c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d54d60",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "PS.peaks(fitmethod='vg', show=True, interval_width=1.5, sort_by='position')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af74c3e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.bands"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e087ed",
   "metadata": {},
   "source": [
    "Define reference peak positions for PS and sort (descending)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0fd1d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_peak_pos = [221,\n",
    "407,\n",
    "543,\n",
    "620.9,\n",
    "795.8,\n",
    "1001.4,\n",
    "1031.8,\n",
    "1155.3,\n",
    "1450.5,\n",
    "1583.1,\n",
    "1602.3,\n",
    "2852.4,\n",
    "2904.5,\n",
    "3054.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "767330eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_peak_pos.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1451accc",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.bands['PS position'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52259cd8",
   "metadata": {},
   "source": [
    "Assign each peak found in the spectrum to the nearest reference peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1a9be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for pos in ps_peak_pos:\n",
    "    index = np.argmin(np.abs(PS.bands['position']-pos))\n",
    "    PS.bands['PS position'][index] = pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61d61fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "PS.bands"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49a5e96",
   "metadata": {},
   "source": [
    "Put everything together in new DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5404153e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c536e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "RR = pd.DataFrame()\n",
    "RR['Polystyrene peak positions'] = PS.bands['PS position']\n",
    "RR['Measured positions'] = PS.bands['vg fitted position']\n",
    "RR['Deviation'] = RR['Measured positions'] - RR['Polystyrene peak positions']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f082bbdf",
   "metadata": {},
   "source": [
    "Add Si-adjusted deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87904eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "RR['Deviation after S0B adjustment'] = RR['Deviation'] + sob_shift\n",
    "RR['Deviation after S1N adjustment'] = RR['Deviation'] + s1n_shift\n",
    "RR['Deviation after S0P adjustment'] = RR['Deviation'] + sop_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e9684a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "RR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b14895",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec66a37",
   "metadata": {},
   "source": [
    "## Now, let's put together a script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b592e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter.filedialog import askopenfilenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e9413cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "laser_powers = [5, 25, 50, 75, 100]\n",
    "silicons = ['SOB', 'S1N', 'S0P']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c97821",
   "metadata": {},
   "source": [
    "Here, the user chooses first the PS and then the Si files. There are several Si types per power level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e56ba276",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ps_files = askopenfilenames()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c613275d",
   "metadata": {},
   "outputs": [],
   "source": [
    "si_files = askopenfilenames()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ac5bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25ab8730",
   "metadata": {},
   "outputs": [],
   "source": [
    "si_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db890e7",
   "metadata": {},
   "source": [
    "In the script, I use `labels_from_filenames`. This is a handy tool that extract, well, labels from filenames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e13f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ramanchada.utilities import labels_from_filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d608d46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_basenames = [ os.path.basename(s) for s in ps_files ]\n",
    "ps_basenames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc49d16",
   "metadata": {},
   "source": [
    "E.g. get the 2nd non-numeric block from each name (= instrument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a37b20b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#this relies on proper file names ...\n",
    "labels_from_filenames(ps_basenames, numeric=False, pos=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab6c8b2",
   "metadata": {},
   "source": [
    "Or the 3rd-last numeric block (= laser power):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c73a23e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_from_filenames(ps_basenames, numeric=True, pos=-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398b0a53",
   "metadata": {},
   "source": [
    "Or else the 1st number before \"ms\" (=integration time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42a201a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_from_filenames(ps_basenames, pivot_string='ms', numeric=True, pos=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79698fd6",
   "metadata": {},
   "source": [
    "Now, we can write the script that produces tables for all laser powers.\n",
    "\n",
    "The resulting tables can be saved as Excel etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5543200c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d6e308f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Extract basenames & power values from filenames\n",
    "si_basenames = [ os.path.basename(s) for s in si_files ]\n",
    "ps_basenames = [ os.path.basename(s) for s in ps_files ]\n",
    "powers_si = labels_from_filenames(si_basenames, pos=-3)\n",
    "powers_ps = labels_from_filenames(ps_basenames, pos=-3)\n",
    "# Iterate over all laser powers\n",
    "for power in laser_powers:\n",
    "    # First, load Si files for current power & determine Si shifts\n",
    "    right_files = [ f for indx,f in enumerate(si_files) if powers_si[indx] == power ]\n",
    "    # Iterate Si types\n",
    "    for si in silicons:\n",
    "        # Only one file with current power has the current type string\n",
    "        current_si_file = [ r for r in right_files if si in r ][0]\n",
    "        print(f'Determining Si position for power {power}% and type {si} on file {os.path.basename(current_si_file)}')\n",
    "        # Load the data, pre-process, determine peaks & shifts\n",
    "        S = RamanChada(current_si_file)\n",
    "        S.x_crop(200,1200)\n",
    "        S.normalize('minmax')\n",
    "        S.peaks(fitmethod='voigt', x_min=400, x_max=600, show=False, interval_width=2)\n",
    "        si_shifts.update({si: 520.45 - S.bands['voigt fitted position'][0]})\n",
    "    print(f'Shifts for Laser power {power} % [1/cm]: {si_shifts}')\n",
    "\n",
    "    # now load Polystyrene file. There's only one file with current power.\n",
    "    right_file = [ f for indx,f in enumerate(ps_files) if powers_ps[indx] == power ][0]\n",
    "    PS = RamanChada(right_file)\n",
    "    print(f'Fitting peaks on file {os.path.basename(right_file)}')\n",
    "    PS.x_crop(250,3200)\n",
    "    PS.normalize('minmax')\n",
    "    PS.peaks(fitmethod='vg', show=False, interval_width=1.5, sort_by='position')\n",
    "    PS.bands['PS position'] = np.nan\n",
    "    for pos in ps_peak_pos:\n",
    "        index = np.argmin(np.abs(PS.bands['position']-pos))\n",
    "        PS.bands['PS position'][index] = pos\n",
    "    RR = pd.DataFrame()\n",
    "    RR['Polystyrene peak positions'] = PS.bands['PS position']\n",
    "    RR['Measured positions'] = PS.bands['vg fitted position']\n",
    "    RR['Deviation'] = RR['Measured positions'] - RR['Polystyrene peak positions']\n",
    "    for si in silicons:\n",
    "        RR[f'Deviation after {si} adjustment'] = RR['Deviation'] + si_shifts[si]\n",
    "    print(f'Values for Laser power {power} % [1/cm]')\n",
    "    display(RR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73dee10a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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