bartels-rausch
091cf00e77
* Transitioned nappNorm, nappDetermineShift, and nappCopyShift to use modern MATLAB arguments blocks, removing their legacy functionSignatures.json entries.
* Refactored nappNorm to rely on prior peak fitting results (e.g., from nappFitGaussXps) for normalization rather than performing the fitting internally.
* Decoupled energy calibration: nappDetermineShift now solely calculates internal binding energy shifts, and nappCopyShift distributes them using a unified matching engine ('earlier', 'after', 'spectrumNumber').
* Implemented a Universal Logging Block to track data provenance, automatically appending execution timestamps and caller script names to processed rows.
* Updated nappCombineSpectra to output a logDataSource cell array and cleanly name combined spectra.
* Created nappSaveDataTable for automatic, script-named .mat file saving, complete with a Live Editor interceptor.
* Added a publish-mode silencer to nappFitGaussXps to prevent optimizer spam in generated PDFs.
* Updated readme.md to document the new arguments structure, the 3-step calibration workflow, the matching engine, and the new table variables.
311 lines
14 KiB
Matlab
311 lines
14 KiB
Matlab
%% 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 = ...
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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):
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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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% Plot the selected data in an interactive figure:
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','interactive');
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%% Generate spectra and plot
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts',...
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'mode','OneEach');
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%% arrange figures
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distFig
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end
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%% Plot iterations
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for ii = 1
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%% Select your data
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%
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% You can change the plotStep to show fewer iterations (for example 2 for every second one).
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% However, the total number of sequence iterations needs to be a multiple of
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% that factor. This is nice to follow a signal with time, e.g. upon dosing.
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% It requires that each sequence iteration has long enough dwell time and
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% iterations to give a signal with decent S/N.
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%% Select data
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close all; clc
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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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%% Plot the iterations
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nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','iterations','plotStep',6);
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%% Modify the top figure
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legend
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distFig
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end
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%% Look at all regions
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%
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% To confirm you see what you expect or to change the experimental settings
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% if not.
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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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% Filter data (select all regions of two files).
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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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& (contains(FileList.name,"146") | contains(FileList.name,"148"));
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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 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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distFig
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%% Discussion
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%
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% Your observation and comments could go here
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end
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%% Look at one region in detail
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%
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% To confirm you see what you expect or to change the experimental settings
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% if not.
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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,"Survey") ...
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& ~contains(FileList.name,"Resonant") ...
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& (contains(FileList.name,"Al2p") | contains(FileList.name,"Cl2p")) ...
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& (contains(FileList.name,"146") | contains(FileList.name,"148"));
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% List the selected files in the command window.
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
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%% 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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%% Normalize Cl2p intensity to that of Al2p
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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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& contains(FileList.name,"Cl2p") ...
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& contains(FileList.name,"146");
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FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} = FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} .* 22335./39687;
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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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& contains(FileList.name,"Al2p") ...
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& contains(FileList.name,"146");
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FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} = FileList(tmpDataSelection,:).spectrum_countsPerSecond{:} .* 22335./39687;
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%% Align to same background
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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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& contains(FileList.name,"Cl2p") ...
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& (contains(FileList.name,"146") | contains(FileList.name,"148"));
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FileList = nappBackground(FileList,647,648,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
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'mode','mean','showFigures','on');
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%% Plot again
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne');
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% Optimize figure
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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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end
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%% Plot several spectra in one figure
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clc; close all
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for ii = 1
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close all
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clc
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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, "Cl2p")...
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& ~contains(FileList.name,"Survey") ...
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& contains(FileList.name,"680eV") ...
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& (contains(FileList.name,"77") | contains(FileList.name,"116") | contains(FileList.name,"130") | contains(FileList.name,"132") | contains(FileList.name,"133"));
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','AllInOne');
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaRegionIterations','scientaSequenceIterations','scientaDwellTime_ms','scientaPassEnergy_eV'}))
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end
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%% helper functions
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% Check if the environment is MATLAB or Octave
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if exist('OCTAVE_VERSION', 'builtin')
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% Anonymous function mimicking contains
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contains = @(str, pattern) ~cellfun('isempty', strfind(str, pattern));
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end |