Added Example Matlab script with most common during beamtime processing
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%% How do I ....
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clc;clear all;close all
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load FileList.mat
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%% ... quick-and-dirty normalize
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%
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% Normalize counts per second to a reference intensity? You need to plot the
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% two spectra and use the cursor to read the intensities from the figures.
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% Then change the spectra intensities directly in the table as shown here:
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%
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% Do not save the FileList. This way you can reload it from the MAT file to
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% start fresh.
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close all;clc
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tmpDataSelection = matches(FileList.name,"0146146_Cl2p_850eV.ibw");
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
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xlim([647 652])
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FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} = FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} .* 22335./39687;
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
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xlim([647 652])
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ylabel("counts [norm to Al]")
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legend
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distFig
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%% ... quick-and-dirty shift spectra
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%
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% If you do not want to generate the binding-energy scale using the
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% nappMATLAB function.
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close all;clc
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% Select your data based on the filename
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tmpDataSelection = startsWith(FileList.name, "0191191_O1s_VB_525eV");
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% List the selected files in the command window
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'}))
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% Generate spectra
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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% Plot in kinetic-energy mode
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne','energyScale','kinetic');
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% Apply the shift to kinetic energy manually and plot
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FileList(tmpDataSelection,:).scientaEkin_eV{:} = FileList(tmpDataSelection,:).scientaEkin_eV{:}-20;
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne','energyScale','kinetic');
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distFig
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%% ... remove a sweep (sequence iteration) from data
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close all; clc
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% Remove sweep 3:
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tmpDataSelection = contains(FileList.name,'0146146_Al2p_850eV.ibw');
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nappPlot(FileList,tmpDataSelection,'tableVariable','image')
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FileList(tmpDataSelection,:).image{:}(:,3)=[];
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nappPlot(FileList,tmpDataSelection,'tableVariable','image')
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distFig
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%% ... remove several sweeps
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close all; clc
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tmpDataSelection = contains(FileList.name,'0146146_Al2p_850eV.ibw');
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% Remove sweeps 5–9
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nappPlot(FileList,tmpDataSelection,'tableVariable','image')
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FileList(tmpDataSelection,:).image{:}(:,5:9)=[];
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nappPlot(FileList,tmpDataSelection,'tableVariable','image')
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distFig
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%% ... correct beamline offset
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% This will correct 1.6 eV on the X-ray energies, which is particularly
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% important for NEXAFS data. One can also do this on selected data only.
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FileList.xRayEkin_eV = cellfun(@(x) x+1.6,FileList.xRayEkin_eV,'UniformOutput',false);
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%% ... on-the-fly shift data in a figure
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
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hax = gca;
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hax.XDir = 'reverse';
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xlabel('Auger-Meitner binding energy [eV]');
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% Shift data (same works in Y direction)
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dataObjs = findobj(gcf,'-property','YData');
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dataObjs(1).XData = dataObjs(1).XData -10;
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dataObjs(2).XData = dataObjs(2).XData +10;
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dataObjs(3).XData = dataObjs(3).XData +20;
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%% ... on-the-fly smooth data in a figure
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clc;close all
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
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hax = gca;
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hax.XDir = 'reverse';
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xlabel('Auger-Meitner binding energy [eV]');
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dataObjs = findobj(gcf,'-property','YData');
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y = dataObjs(1).YData;
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dataObjs(1).YData = smoothdata(y ,'sgolay',10);
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y = dataObjs(2).YData;
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dataObjs(2).YData = smoothdata(y ,'sgolay',10);
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y = dataObjs(3).YData;
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dataObjs(3).YData = smoothdata(y ,'sgolay',10);
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%% ... on-the-fly remove data from a figure
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clc;close all
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
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hax = gca;
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hax.XDir = 'reverse';
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xlabel('Auger-Meitner binding energy [eV]');
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dataObjs = findobj(gcf,'-property','YData');
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delete(dataObjs(1))
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legend('545','550');
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%% ... on-the-fly change colors in a figure
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clc;close all
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
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hax = gca;
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hax.XDir = 'reverse';
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xlabel('Auger-Meitner binding energy [eV]');
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dataObjs = findobj(gcf,'-property','YData');
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dataObjs(2).Color = [184 184 185]/255;
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dataObjs(3).Color = [58 162 190]/255;
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%% Fitting spectra with Gauss
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%
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% The nappFitGaussXps.m allows to set-up your own model reflecting the
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% physics of your peaks. Here is an example. Check the documentation. This
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% needs some test-and try.
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%% SES #0157 Ar2p (gas-phase reference)
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%
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% * Core level: Ar 2p, file 0157157_Ar2p_850eV.
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% * Model: Gaussian spin–orbit doublet, splitting fixed at 2.2 eV, area ratio 0.5.
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% * Peak 1 area: initial 1e5; lower bound 10; upper bound Inf.
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% * Peak 1 width: initial 0.5 eV; bounds 0.5–3 eV.
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% * Peak 1 position: initial 603 eV; bounds 602–604 eV.
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% * Background: linear, 8 eV window below and 5 eV above the peak.
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clc
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close all
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& ~contains(FileList.name,"Survey") ...
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& ~contains(FileList.name,"Resonant") ...
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& startsWith(FileList.name,"0157") ...
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& contains(FileList.name, "Ar2p");
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'}))
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% Defining the mode as 'widthPositionAreaRatio', the first element of the input
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% parameters defines the first peak (initial value, upper and lower bond for
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% fitting) in absolute terms. The second element is then the area ratio
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% relative to the first, and the position relative to the first.
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tmpFitParameters.peakArea = [1e5 0.5 0.1 0.1 0.1] ;
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tmpFitParameters.peakWidth = 0.5;
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tmpFitParameters.peakPos = [603 -2.2 10 10 10];
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tmpFitParameters.peakAreaUpperBond = [Inf 0.5 0.1 0.1 0.1];
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tmpFitParameters.peakWidthUpperBond = [3 NaN NaN NaN NaN];
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tmpFitParameters.peakPosUpperBond = [604 -2.2 10 10 10];
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tmpFitParameters.peakAreaLowerBond = [10 0.5 0.1 0.1 0.1];
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tmpFitParameters.peakWidthLowerBond = [0.5 NaN NaN NaN NaN];
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tmpFitParameters.peakPosLowerBond = [602 -2.2 0.1 0.1 0.1];
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% spectrum range
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tmpFitParameters.backgroundRange = [8 5];
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FileList= nappFitGaussXps(FileList,tmpFitParameters,tmpDataSelection,...
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'energyScale','kinetic','mode','widthPositionAreaRatio','showFigures','on');
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%% RESULTS – SES 0157 Ar2p (gas-phase reference)
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% The gas‑phase Ar2p reference is well described by the doublet: peak 1 at
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% 603.0 eV and peak 2 at 600.8 eV (FWHM ≈ 1.2 eV, width 1.0 eV) match both
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% the height and the base of the experimental lines. The residuals are very small and essentially
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% structureless over the full fit range, indicating that, in contrast to
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% the Cl2p fits, a simple Gaussian doublet provides an excellent and
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% internally consistent description of the Ar2p line shape.
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%% Generate Spectra
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%
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% Here we plot all spectra and all regions to confirm that we get what we
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% need.
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%
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% This file shows some typical out-of-the-box examples.
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%% Start fresh
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clc
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close all
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clearvars
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%% Import Data
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%
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% This was generated by importData.m. You can either force that it runs
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% again before this script runs (preferred, ensures you always have recent
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% data in MATLAB's data table) or load the FileList.
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run('importData.m')
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% load("FileList.mat","FileList")
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%% Plot a Survey
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for ii = 1
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%% Start fresh
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close all
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clc
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%% Select data
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% It is convenient to add date and year of the beamtime. This ensures you
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% can use the code also when you work with data from several beamtimes
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% (indexing of the files starts with 0 for each beamtime by default).
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"Survey") ...
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& (contains(FileList.name,"128"));
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% Always ensure that you select the spectra you intended.
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
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%% Generate spectrum and plot
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
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%% Add annotation to the figure
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%
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% In a survey, we usually want to identify all peaks. Here, based on
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% binding energies from the literature and a shift, we add common regions:
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%
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% **Note that the binding energies are rough estimates – to use this further
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% we need to confirm them and add the source/reference.**
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dataObjs = findobj(gcf,'-property','YData');
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tmpxShift = 7;
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tmpxRayEnergy = 850;
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tmpyPosition = 0.5E5;
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tmpyPositionTwo = 0.8E5;
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% Au peaks
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%nappAnnotateSurvey(tmpxRayEnergy-57-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au5p3/2');
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%nappAnnotateSurvey(tmpxRayEnergy-74-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au5p5/2');
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nappAnnotateSurvey(tmpxRayEnergy-84-tmpxShift,tmpyPosition,dataObjs(1),'Au4f7/2');
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nappAnnotateSurvey(tmpxRayEnergy-88-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au4f5/2');
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%nappAnnotateSurvey(tmpxRayEnergy-107-tmpxShift,tmpyPosition,dataObjs(1),'Au5s');
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%nappAnnotateSurvey(tmpxRayEnergy-335-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4d1/2');
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%nappAnnotateSurvey(tmpxRayEnergy-353-tmpxShift,tmpyPosition,dataObjs(1),'Au4d3/2');
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%nappAnnotateSurvey(tmpxRayEnergy-546-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4p3/2');
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%nappAnnotateSurvey(tmpxRayEnergy-643-tmpxShift,tmpyPosition,dataObjs(1),'Au4p1/2');
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nappAnnotateSurvey(tmpxRayEnergy-762-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4s');
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nappAnnotateSurvey(tmpxRayEnergy-8-tmpxShift,tmpyPosition,dataObjs(1),'AuVB');
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% Usual suspects
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nappAnnotateSurvey(tmpxRayEnergy-285-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'C1s');
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nappAnnotateSurvey(tmpxRayEnergy-534-tmpxShift,tmpyPosition,dataObjs(1),'O1s');
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nappAnnotateSurvey(tmpxRayEnergy-30-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'O2s');
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nappAnnotateSurvey(tmpxRayEnergy-405-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'N1s');
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%nappAnnotateSurvey(tmpxRayEnergy-495-tmpxShift,tmpyPosition,dataObjs(1),'Sn3d');
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%nappAnnotateSurvey(tmpxRayEnergy-691-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'F1s');
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nappAnnotateSurvey(tmpxRayEnergy-295-tmpxShift,tmpyPosition,dataObjs(1),'K2p');
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nappAnnotateSurvey(tmpxRayEnergy-380-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2s');
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nappAnnotateSurvey(tmpxRayEnergy-153-tmpxShift,tmpyPosition,dataObjs(1),'Si2s');
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nappAnnotateSurvey(tmpxRayEnergy-100-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Si2p');
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nappAnnotateSurvey(tmpxRayEnergy-197-tmpxShift,tmpyPosition,dataObjs(1),'Cl2p');
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nappAnnotateSurvey(tmpxRayEnergy-271-tmpxShift,tmpyPosition,dataObjs(1),'Cl2s');
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nappAnnotateSurvey(tmpxRayEnergy-286-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Sn3p');
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% Clay
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%nappAnnotateSurvey(tmpxRayEnergy-351-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2p');
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%nappAnnotateSurvey(tmpxRayEnergy-347-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2p');
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%nappAnnotateSurvey(tmpxRayEnergy-440-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2s');
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%nappAnnotateSurvey(tmpxRayEnergy-34-tmpxShift,tmpyPosition+tmpyPosition,dataObjs(1),'Ca3s');
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%nappAnnotateSurvey(tmpxRayEnergy-26-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca3p');
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%nappAnnotateSurvey(tmpxRayEnergy-297-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2p');
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%nappAnnotateSurvey(tmpxRayEnergy-294-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2p');
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%nappAnnotateSurvey(tmpxRayEnergy-35-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3s');
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%nappAnnotateSurvey(tmpxRayEnergy-19-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3p');
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nappAnnotateSurvey(tmpxRayEnergy-118-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al');
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nappAnnotateSurvey(tmpxRayEnergy-72.5-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al');
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% Ar gas
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nappAnnotateSurvey(tmpxRayEnergy-245-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2p');
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nappAnnotateSurvey(tmpxRayEnergy-323-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2s');
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nappAnnotateSurvey(tmpxRayEnergy-12-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar3p');
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% Some Auger
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nappAnnotateSurvey(363,tmpyPosition-tmpyPositionTwo,dataObjs(1),'N KLL');
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nappAnnotateSurvey(203,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Ar LMN');
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nappAnnotateSurvey(500,tmpyPosition-tmpyPositionTwo,dataObjs(1),'O KLL');
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end
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%% Plot NEXAFS & Resonant Data
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%
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% You can plot images (raw data from SES) without generating the spectra.
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% (You can also first generate the spectra, but to plot images the spectra
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% are not needed.)
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for ii = 1
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%% Start fresh
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clc; close all
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%% Select data
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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% Always ensure that you select the spectra you intended.
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
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%% Plot images
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% This one shows x, y in energy scales (data were interpolated).
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nappPlot(FileList,tmpDataSelection,'tableVariable','image',...
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'mode','OneEach');
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% This one shows sweeps (original data).
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','energyScale','sweep');
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%% Plot selected Auger-Meitner
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%
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% This plots the electron intensity vs. kinetic energy for chosen X-ray
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% energies (directly from the image).
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tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
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& year(FileList.lastModifiedDatenum) == 2025 ...
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& ~contains(FileList.sample,"(bad data)") ...
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& contains(FileList.name,"O_Kedge_Resonant") ...
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& contains(FileList.name,"0025");
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
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% It also shows an example of how the current plot can be edited manually
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% after its generation by nappPlot.
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hax = gca;
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hax.XDir = 'reverse';
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xlabel('Auger-Meitner binding energy [eV]');
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%% Plot image interactive
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%
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||||
% Use the mouse: click once in the image, then the Auger-Meitner and NEXAFS will
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% follow the mouse position.
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% Select data (Nov 2025, O_Kedge resonant, and file number 25):
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& contains(FileList.name,"O_Kedge_Resonant") ...
|
||||
& contains(FileList.name,"0025");
|
||||
% Plot the selected data in an interactive figure:
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','interactive');
|
||||
|
||||
%% Generate spectra and plot
|
||||
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts',...
|
||||
'mode','OneEach');
|
||||
%% arrange figures
|
||||
distFig
|
||||
end
|
||||
|
||||
%% Plot iterations
|
||||
for ii = 1
|
||||
%% Select your data
|
||||
%
|
||||
% You can change the plotStep to show fewer iterations (for example 2 for every second one).
|
||||
% However, the total number of sequence iterations needs to be a multiple of
|
||||
% that factor. This is nice to follow a signal with time, e.g. upon dosing.
|
||||
% It requires that each sequence iteration has long enough dwell time and
|
||||
% iterations to give a signal with decent S/N.
|
||||
|
||||
%% Select data
|
||||
close all; clc
|
||||
tmpDataSelection = startsWith(FileList.name, "0191191_O1s_VB_525eV");
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'}))
|
||||
|
||||
%% Plot the iterations
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','iterations','plotStep',6);
|
||||
|
||||
%% Modify the top figure
|
||||
legend
|
||||
distFig
|
||||
end
|
||||
|
||||
%% Look at all regions
|
||||
%
|
||||
% To confirm you see what you expect or to change the experimental settings
|
||||
% if not.
|
||||
|
||||
for ii = 1
|
||||
%% Start fresh
|
||||
close all
|
||||
clc
|
||||
% Filter data (select all regions of two files).
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& (contains(FileList.name,"146") | contains(FileList.name,"148"));
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'}))
|
||||
|
||||
%% Generate spectra and plot
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
|
||||
distFig
|
||||
%% Discussion
|
||||
%
|
||||
% Your observation and comments could go here
|
||||
end
|
||||
|
||||
%% Look at one region in detail
|
||||
%
|
||||
% To confirm you see what you expect or to change the experimental settings
|
||||
% if not.
|
||||
|
||||
for ii = 1
|
||||
%% Start fresh
|
||||
clc; close all
|
||||
|
||||
%% Select data
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& (contains(FileList.name,"Al2p") | contains(FileList.name,"Cl2p")) ...
|
||||
& (contains(FileList.name,"146") | contains(FileList.name,"148"));
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
|
||||
|
||||
%% Plot
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
|
||||
|
||||
%% Normalize Cl2p intensity to that of Al2p
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& contains(FileList.name,"Cl2p") ...
|
||||
& contains(FileList.name,"146");
|
||||
FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} = FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} .* 22335./39687;
|
||||
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& contains(FileList.name,"Al2p") ...
|
||||
& contains(FileList.name,"146");
|
||||
FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} = FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} .* 22335./39687;
|
||||
|
||||
%% Align to same background
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& contains(FileList.name,"Cl2p") ...
|
||||
& (contains(FileList.name,"146") | contains(FileList.name,"148"));
|
||||
FileList = nappBackground(FileList,647,648,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
||||
'mode','mean','showFigures','off');
|
||||
|
||||
%% Plot again
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne');
|
||||
% Optimize figure
|
||||
xlim([647 652])
|
||||
ylabel("counts [norm to Al]")
|
||||
legend
|
||||
distFig
|
||||
end
|
||||
|
||||
%% Plot several spectra in one figure
|
||||
clc; close all
|
||||
for ii = 1
|
||||
close all
|
||||
clc
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& contains(FileList.name, "Cl2p")...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& contains(FileList.name,"680eV") ...
|
||||
& (contains(FileList.name,"77") | contains(FileList.name,"116") | contains(FileList.name,"130") | contains(FileList.name,"132") | contains(FileList.name,"133"));
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne');
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaRegionIterations','scientaSequenceIterations','scientaDwellTime_ms','scientaPassEnergy_eV'}))
|
||||
end
|
||||
@@ -0,0 +1,51 @@
|
||||
%% 20251119 ISS BeamTime
|
||||
%
|
||||
% We do this for ALL spectra during each beamtime. The code here is just
|
||||
% for illustration and sample names might not make sense.
|
||||
%% Start fresh
|
||||
close all
|
||||
clearvars
|
||||
clc
|
||||
|
||||
%% Import Data
|
||||
FileList = nappFileList('2025/20251119_NappIss','mode','local','importLabView','off');
|
||||
save("FileList.mat","FileList")
|
||||
|
||||
%% Add sample label
|
||||
%
|
||||
% Typical info includes: sample, gas environment, purpose, project.
|
||||
|
||||
FileList = addSampleNotes(FileList,0:3,'test, empty file',11,2025,".h5");
|
||||
FileList = addSampleNotes(FileList,1,'test, empty file',11,2025,"XPS");
|
||||
FileList = addSampleNotes(FileList,128,'this is a sample description',11,2025,"XPS");
|
||||
FileList = addSampleNotes(FileList,[74,78,80,82,83,84,85,86], ...
|
||||
'Al-oxide, water, optimize peak separation, Celia',11,2025,"XPS"); % not ideal E_kin windows
|
||||
% List selected files
|
||||
tmpDataSelection = startsWith(FileList.name, "0074");
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','sample'}))
|
||||
|
||||
%% Identify faulty data and files
|
||||
%
|
||||
% We use the phrase "(bad data)" at the end of the sample description to
|
||||
% filter faulty files during the later analysis.
|
||||
%
|
||||
% Running addSampleNotes again will overwrite the old entry. If you want to
|
||||
% keep notes on the sample and only add (bad data) at the end, you need to
|
||||
% paste the whole text string:
|
||||
FileList = addSampleNotes(FileList,[4,6,7,8,19,43],'(bad data)',11,2025,"XPS"); % data file corrupted
|
||||
FileList = addSampleNotes(FileList,[74,78,80,82,83,84,85,86], ...
|
||||
'Al-oxide, water, optimize peak separation, Celia (bad data)',11,2025,"XPS"); % not ideal E_kin windows
|
||||
% List selected files
|
||||
tmpDataSelection = startsWith(FileList.name, "0074");
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','sample'}))
|
||||
|
||||
%% Add photon energy
|
||||
FileList = addPhotonEnergy(FileList,91:94,750,11,2025);
|
||||
|
||||
%% Save
|
||||
%
|
||||
% The FileList table holds data and metadata for all beamtime data. It is
|
||||
% saved in a mat file for further use.
|
||||
close all
|
||||
clearvars -except FileList
|
||||
save("FileList.mat","FileList")
|
||||
Reference in new issue
Block a user