Moved docu to new training repro
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%% How do I ....
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clc;clearvars; 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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%% ... combine two tables with common and different columns (variables) into one?
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%
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% This example shows how a helper function (outerjoinRowToTable) can
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% append rows from one table to another even if the tables do not start
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% with exactly the same set of variables (columns).[web:59]
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for ii=1
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%% Create simple table 1
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%
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% --- Table 1: one row, six columns of different types --------------------
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%
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% Types requested:
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% 1) character -> stored as cell containing a char vector
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% 2) string -> string scalar
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% 3) cell array -> heterogeneous container
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% 4) cell -> cell holding a scalar
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% 5) array -> numeric vector
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% 6) number -> scalar double
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%
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% MATLAB tables can hold mixed data types column-wise as long as each
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% column has the same number of rows.[web:59]
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%
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% For "character": use a 1x1 cell that contains a char vector
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charCol = {'name1'}; % cell containing a character vector
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stringCol = "hello"; % string scalar
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cellArrayCol = {1 2 3}; % 1x3 cell array
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cellCol = {42}; % 1x1 cell, holding a number
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arrayCol = 1:5; % numeric row vector
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numberCol = 3.14; % scalar double
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Table1 = table(charCol, stringCol, cellArrayCol, cellCol, arrayCol, numberCol, ...
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'VariableNames', {'CharCol','StringCol','CellArrayCol','CellCol','ArrayCol','NumberCol'});
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%% Create Table 2
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%
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% --- Table 2: one row, with a different set of variables -----------------
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%
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% Table2 shares the variable CharCol with Table1, but also has a new
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% variable OneNumber that does not exist in Table1.
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%
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% When we combine the tables, CharCol will become a column with two rows
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% (name1 and name2). The new variable OneNumber will be added to Table1,
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% and the missing value for the first row will be filled with a numeric
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% placeholder (NaN).
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oneNumber = 99; % scalar double
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charCol = {'name2'}; % cell containing a character vector
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Table2 = table(oneNumber, charCol, ...
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'VariableNames', {'OneNumber', 'CharCol'});
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disp(Table1);
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disp(Table2);
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%% Combine both into one
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%
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% outerjoinRowToTable does the following:
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% 1) It compares the VariableNames of both tables.
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% - Columns with the same name (here CharCol) are aligned and will
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% end up as a single column with multiple rows.
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% 2) For variables that only exist in one table:
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% - If a variable is missing in the incoming rows, a new column is
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% added to that smaller table and filled with placeholder values
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% (NaN for numeric types, ' ' or " " for text/cell/string types),
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% so that the column exists for all rows.
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% - This ensures both tables have the same set of columns before
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% concatenation.
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% 3) After the variable sets are harmonized, the rows are concatenated
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% with standard vertical stacking, so the result is one table where:
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% - Shared columns (CharCol) contain data from both input tables
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% in separate rows.
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% - Columns that were unique to one input (e.g. OneNumber or
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% ArrayCol) show data where available and placeholder values where
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% that column did not exist originally.[web:59]
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Table3 = outerjoinRowToTable(Table1,Table2);
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disp(Table3)
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% In a real workflow, this pattern allows you to successively append
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% FileList tables from multiple beamtimes, even if new metadata
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% columns were introduced later. Older rows are back-filled with
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% placeholder values for new variables, while shared columns accumulate
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% additional rows representing more files / experiments.
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%
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% Example:
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% tmpData = load('../beamtimes/20220828BeamTimeIceNappIss/FileList.mat');
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% tmpData2 = load('../beamtimes/20211216BeamTimeLjSim/FileList.mat');
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% FileList = outerjoinRowToTable(tmpData.FileList,tmpData2.FileList);
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end
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@@ -1,464 +0,0 @@
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%[text] # SOP Beamtime analysis
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%[text] This document outlines standard procedures for data analysis during beamtime. Help on how to use the functions can be found here:
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%[text] [https://gitea.psi.ch/5505/functionsMatlab/src/branch/main/beamtime](https://gitea.psi.ch/5505/functionsMatlab/src/branch/main/beamtime)
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%[text] and
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%[text] [https://gitea.psi.ch/5505/functionsMatlab/src/branch/main/beamtime/documentedExampleScripts](https://gitea.psi.ch/5505/functionsMatlab/src/branch/main/beamtime/documentedExampleScripts)
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%%
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%[text] ## Load Data
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%[text] Import raw beamtime data into the Matlab workspace for analysis. Data is organized in a single *matlab data table* where XPS intensities from region iterations are averaged and normalized to counts per second (CPS). This ensures comparison regardless of dwell time or region iteration settings.
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%[text] There are several options to import. The preferred way is to write a importData.m script to call the import function and to add notes to data. Once imported, data is saved to a FileList.mat file, which should be updated regularly to ensure the MATLAB works with the most recent data.
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clc
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close all
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clearvars
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run('importData.m'); % or load("FileList.mat","FileList") %[output:35471fec]
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% add another FileList.mat to the FileList table
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tmpData = load('nappIceHowToData.mat');
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FileList = outerjoinRowToTable(FileList,tmpData.FileList);
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clearvars tmp*
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%%
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%[text] ## Align sample
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%[text] - Approach and take spectra too far away, too close, in working distance.
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%[text] - At least note dwell time and pass energy and area reading for a few distances
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%[text] - also note x,y,z \
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%[text] ## Reference spectra
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%[text] 1. sample prior to dosing.
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%[text] 2. sample prior to adding dosing carrier gas.
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%[text] 3. gas-phase with all settings used for sample.
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%[text] 4. cold and warm sample holder without sample (if contamination is an issue). \
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%[text] ## Survey: Characterize sample
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%[text] Survey spectra are used to characterize new samples and to monitor samples over time. They should show all expected elements and no unexpected ones, and are typically recorded at key times (before/after experiments, when dosing changes, at regular intervals).
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%[text] **If unexpected peaks appear**
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%[text] 1. Identify the element(s) based on peak binding energy, expected additional peaks for that element, and plausible sources in the setup.
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%[text] 2. Decide whether to accept the contamination (and document it) or to remove its source by changing or cleaning the sample or cell, or otherwise eliminating the contamination source. \
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%%
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%[text] ### Plot the survey
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% select the survey to plot using filtering rules
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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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% List the selected spectra to always ensure that you select the spectra you intended.
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disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','sample'})) %[output:4e5ecffd]
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% Generate spectrum
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FileList = nappGenerateSpectra(FileList,tmpDataSelection);
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% Plot spectrum
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nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach'); %[output:04fdda4a]
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%%
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%[text] ### Identify all peaks
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%[text] Use the example peak list as a template to identify and document all relevant features in your own survey. Note that the binding energies are rough estimates – to use this further we need to confirm them and add the source/reference. Select what you need for your data, add what is missing.
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dataObjs = findobj(gcf,'-property','YData');
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% BE shift due to charging and analyzer off-set
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tmpxShift = 7;
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% xray energy of this survey
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tmpxRayEnergy = 850;
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% adjust the height in y-scale units of annotations.
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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'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-88-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au4f5/2'); %[output:8d5664cd]
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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'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-8-tmpxShift,tmpyPosition,dataObjs(1),'AuVB'); %[output:8d5664cd]
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% Usual suspects
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nappAnnotateSurvey(tmpxRayEnergy-285-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'C1s'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-534-tmpxShift,tmpyPosition,dataObjs(1),'O1s'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-30-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'O2s'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-405-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'N1s'); %[output:8d5664cd]
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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'); %[output:8d5664cd]
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nappAnnotateSurvey(tmpxRayEnergy-100-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Si2p'); %[output:8d5664cd]
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||||
nappAnnotateSurvey(tmpxRayEnergy-197-tmpxShift,tmpyPosition,dataObjs(1),'Cl2p'); %[output:8d5664cd]
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||||
nappAnnotateSurvey(tmpxRayEnergy-271-tmpxShift,tmpyPosition,dataObjs(1),'Cl2s'); %[output:8d5664cd]
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||||
nappAnnotateSurvey(tmpxRayEnergy-286-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Sn3p'); %[output:8d5664cd]
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||||
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||||
% Clay
|
||||
%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');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-294-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-35-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-19-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-118-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al'); %[output:8d5664cd]
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||||
nappAnnotateSurvey(tmpxRayEnergy-72.5-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al'); %[output:8d5664cd]
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||||
|
||||
% Ar gas
|
||||
nappAnnotateSurvey(tmpxRayEnergy-245-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2p'); %[output:8d5664cd]
|
||||
nappAnnotateSurvey(tmpxRayEnergy-323-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2s'); %[output:8d5664cd]
|
||||
nappAnnotateSurvey(tmpxRayEnergy-12-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar3p'); %[output:8d5664cd]
|
||||
|
||||
% Some Auger
|
||||
nappAnnotateSurvey(363,tmpyPosition-tmpyPositionTwo,dataObjs(1),'N KLL'); %[output:8d5664cd]
|
||||
nappAnnotateSurvey(203,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Ar LMN'); %[output:8d5664cd]
|
||||
nappAnnotateSurvey(500,tmpyPosition-tmpyPositionTwo,dataObjs(1),'O KLL'); %[output:8d5664cd]
|
||||
%%
|
||||
%[text] ## Optimize settings
|
||||
%[text] Before recording region spectra:
|
||||
%[text] - Make the kinetic energy window wide enough for reliable background and peak fitting (about 1–3 eV margin on each side) and robust against small charging shifts, so peaks stay fully inside the window.
|
||||
%[text] - Adjust region iterations and dwell time to balance signal-to-noise ratio and acquisition time.
|
||||
%[text] - Check that the background is smooth; if not, shift the window slightly.
|
||||
%[text] - Avoid overlaps with other features such as N KLL at ~363 eV or Ar LMN at ~203 eV (kinetic energy).
|
||||
%[text] - Choose pressure to minimize charging, maximize signal intensity, and allow dosing of trace gases
|
||||
%[text] - Is the signal intensity optimal? Consider realigning the beam, the sample - cone distance, find a better sample spot to maximize counts. \
|
||||
%[text] ## Image: Stability and trends
|
||||
%[text] Monitor the stability of kinetic energy across sequence iterations using *images.* If peaks shift continuously, as seen in the August 2021, stop the measurement to troubleshoot sample equilibrium or experimental settings.
|
||||
%[text] 1. Is the sample not yet in equilibrium or a steady-state: Wait or make a new sample with less material
|
||||
%[text] 2. If the sample is in equilibrium or a steady-state: Optimize temperature, humidity, cone distance. \
|
||||
%[text] Short shifts are okay if enough stable iterations remain to average after removing the outliers. Reasons for such shifts could be changes in gas dosing, temperature, opening the beamline shutter.
|
||||
% Filter 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,"146"));
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:3f09c9f5]
|
||||
%%
|
||||
%[text] ### Plot the images
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image'); %[output:340587e5] %[output:7b803654] %[output:7ec3927c] %[output:5e5ac45c] %[output:244f5944]
|
||||
|
||||
%[text] These November 29 data look quite okay. Al2p appears to be deceasing in intensity with time. To look at this in more detail one plot individual iterations. Compare trends of all regions to decide if this is chemical feature, a change in intensity with time, caused by masking (because for example carbon is deposited on the sample, or water adsorbs).
|
||||
%%
|
||||
%[text] ### Plot iterations
|
||||
% Filter data
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& matches(FileList.name,"0146146_Al2p_850eV.ibw");
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:2f867eb6]
|
||||
% plot
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','iterations','plotStep',2); %[output:30898c2d] %[output:0b22a369]
|
||||
legend %[output:0b22a369]
|
||||
%%
|
||||
%[text] ### Plot shifting images
|
||||
%[text] The two O1s images MgO ice in Ar HONO, 21-Aug-2021 and MgO ice AA in N2, 25-Oct-2021 illustrate the difference between unusable and usable data.
|
||||
%[text] - In the 21-Aug-2021 example, the bright band clearly shifts in kinetic energy over the sequence, making the dataset unusable for analysis.
|
||||
%[text] - In the 25-Oct-2021 example, the band is horizontal overall, except for one iteration with a visible shift; here you can keep the dataset after removing that single bad iteration. \
|
||||
%[text] These figures also illustrate that the same file name can appear in different beamtimes, so you must always check the date and time when selecting data.
|
||||
% filter data
|
||||
tmpDataSelection = matches(FileList.name,'0067067_O1s_1000eV.ibw');
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:27ab5aa8]
|
||||
% plot
|
||||
nappPlot(FileList,tmpDataSelection); %[output:3f52d2f2] %[output:2edd5c8e]
|
||||
%[text]
|
||||
%%
|
||||
%[text] #### Remove a single iteration from image
|
||||
% filter data
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 10 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2021 ...
|
||||
& matches(FileList.name,'0067067_O1s_1000eV.ibw');
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:9949b5fb]
|
||||
% plot
|
||||
nappPlot(FileList,tmpDataSelection); %[output:0ab749aa]
|
||||
% remove iteration #9
|
||||
FileList(tmpDataSelection,:).image{:}(:,9)=[];
|
||||
% plot again
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image'); %[output:26c3d5db]
|
||||
%%
|
||||
%[text] ## Binding energy scale
|
||||
%[text] To compare spectra taken at different photon energies or to present your data, convert kinetic energy to binding energy using three steps:
|
||||
%[text] 1. Use *nappDetermineShiftXps* to determine the kinetic energy shift relative to a chosen reference peak (e.g. O 1s at 535 eV binding energy for this ice example).
|
||||
%[text] 2. Use \*nappCopyShift \* to copy this kinetic energy shift to other regions (e.g. C 1s) recorded in the same sequence.
|
||||
%[text] 3. Use \*nappBindingEnergyXps \* to apply the shift and calculate binding energies for all spectra. \
|
||||
%[text] The example shows how a shift determined from selected O1s spectra is applied to C1s spectra with the same file numbers. If the resulting binding energies do not match expectations during beamtime, stop and find the reason (wrong reference, unstable charging, wrong photon energy or the peak is a different species).
|
||||
% generate spectra for C1s and O1s
|
||||
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
||||
& ~contains(FileList.sample,'(bad data)')...
|
||||
& (contains(FileList.name,'O1s') | contains(FileList.name,'C1s'))...
|
||||
& FileList.xRayEkinRange_eV(:,1) == 900 ...
|
||||
& FileList.scientaPassEnergy_eV == 50 ...
|
||||
& (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27);
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
||||
'mode','mean');
|
||||
% determine shift of O1s
|
||||
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
||||
& ~contains(FileList.sample,'(bad data)')...
|
||||
& contains(FileList.name,'O1s')...
|
||||
& FileList.xRayEkinRange_eV(:,1) == 900 ...
|
||||
& FileList.scientaPassEnergy_eV == 50 ...
|
||||
& (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27);
|
||||
FileList = nappDetermineShiftXps(FileList,'tableSelection',tmpDataSelection,...
|
||||
'showFigures','off','tableVariable','spectrum_countsPerSecond','binEnReference',533);
|
||||
% copy shift from O1s to C1s
|
||||
FileList = nappCopyShift(FileList,tmpDataSelection,'energyScale','binding');
|
||||
% calculate binding energy of these C1s
|
||||
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
||||
& ~contains(FileList.sample,'(bad data)')...
|
||||
& contains(FileList.name,'C1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
||||
& FileList.scientaPassEnergy_eV == 50 ...
|
||||
& (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27);
|
||||
FileList = nappBindingEnergyXps(FileList,'tableSelection',tmpDataSelection);
|
||||
% plot C1s spectra
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',... %[output:group:1b93bfe3] %[output:6c799ef9]
|
||||
'mode','AllInOne','energyScale','kinetic'); %[output:group:1b93bfe3] %[output:6c799ef9]
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',... %[output:group:01cb9662] %[output:644d063f]
|
||||
'mode','AllInOne','energyScale','binding'); %[output:group:01cb9662] %[output:644d063f]
|
||||
%[text] ## Normalize spectra
|
||||
%[text] Normalization is done to get an estimate of surface concentration and to identify changes in intensity driven by chemical or physical processes from experimental variations, such as changes in working distance.
|
||||
%%
|
||||
%[text] ### Fit peak area
|
||||
%[text] To obtain O1s peak areas for normalization, fit selected O1s spectra with a peak model and background (here: unconstrained peak with linear background). The example output for 0035035\_O1s\_900eV.ibw , 0056056\_O1s\_900eV.ibw , and 0060060\_O1s\_900eV.ibw shows how the peak position, area, and background parameters are reported for each spectrum.
|
||||
clearvars tmpFitParameters;
|
||||
% initial fit parameter
|
||||
tmpFitParameters.peakArea = 5e5; % <- 1 value => 1 peaks
|
||||
tmpFitParameters.peakWidth = 2;
|
||||
tmpFitParameters.peakPos = 363;
|
||||
% Bounds for areas (lower and upper)
|
||||
tmpFitParameters.peakAreaLowerBound = 1e3;
|
||||
tmpFitParameters.peakAreaUpperBound = Inf;
|
||||
% Bounds for widths
|
||||
tmpFitParameters.peakWidthLowerBound = 1.0;
|
||||
tmpFitParameters.peakWidthUpperBound = 3.0;
|
||||
% Bounds for positions
|
||||
tmpFitParameters.peakPosLowerBound = 361;
|
||||
tmpFitParameters.peakPosUpperBound = 365;
|
||||
tmpFitParameters.backgroundRange = [5 3];
|
||||
|
||||
% filter
|
||||
tmpDataSelection = (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27) ...
|
||||
& matches(FileList.name,"0035035_O1s_900eV.ibw");
|
||||
% show data
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:1cf215b8]
|
||||
%%
|
||||
% fit
|
||||
FileList = nappFitGaussXps(FileList,tmpFitParameters,tmpDataSelection,... %[output:group:67ea982a] %[output:22c700e4] %[output:3b9a689c] %[output:712f3db2] %[output:7541066b]
|
||||
'tableVariable','spectrum_countsPerSecond','energyScale','kinetic',... %[output:22c700e4] %[output:3b9a689c] %[output:712f3db2] %[output:7541066b]
|
||||
'mode','unconstrained','backgroundMode','linear','showFigures','on'); %[output:group:67ea982a] %[output:22c700e4] %[output:3b9a689c] %[output:712f3db2] %[output:7541066b]
|
||||
xlim([360, 366]); %[output:7541066b]
|
||||
%%
|
||||
|
||||
% filter
|
||||
tmpDataSelection = (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27) ...
|
||||
& matches(FileList.name,"0056056_O1s_900eV.ibw");
|
||||
% show data
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:969d4f09]
|
||||
% fit
|
||||
FileList = nappFitGaussXps(FileList,tmpFitParameters,tmpDataSelection,... %[output:group:1a1bf894] %[output:880f99d8] %[output:608e038f] %[output:598292bc] %[output:342429e8]
|
||||
'tableVariable','spectrum_countsPerSecond','energyScale','kinetic',... %[output:880f99d8] %[output:608e038f] %[output:598292bc] %[output:342429e8]
|
||||
'mode','unconstrained','backgroundMode','linear','showFigures','on'); %[output:group:1a1bf894] %[output:880f99d8] %[output:608e038f] %[output:598292bc] %[output:342429e8]
|
||||
xlim([360, 366]); %[output:342429e8]
|
||||
% select
|
||||
tmpDataSelection = (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27) ...
|
||||
& matches(FileList.name,"0060060_O1s_900eV.ibw");
|
||||
% List the selected files in the command window.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'})) %[output:326c5622]
|
||||
%%
|
||||
% fit
|
||||
FileList = nappFitGaussXps(FileList,tmpFitParameters,tmpDataSelection,... %[output:group:9c54ed73] %[output:7f2dd5ee] %[output:5bd5684a] %[output:42116ac3] %[output:31491535]
|
||||
'tableVariable','spectrum_countsPerSecond','energyScale','kinetic',... %[output:7f2dd5ee] %[output:5bd5684a] %[output:42116ac3] %[output:31491535]
|
||||
'mode','unconstrained','backgroundMode','linear','showFigures','on'); %[output:group:9c54ed73] %[output:7f2dd5ee] %[output:5bd5684a] %[output:42116ac3] %[output:31491535]
|
||||
xlim([360, 366]); %[output:31491535]
|
||||
%%
|
||||
%[text] ### Norm the spectra
|
||||
%[text] The example table lists the three C1s spectra ( 0035035\_C1s\_900eV.ibw , 0056056\_C1s\_900eV.ibw , 0060060\_C1s\_900eV.ibw ) and their plots. After scaling, you can compare the C1s spectra directly, since the intensities now reflect a concentration (carbon relative to ice).
|
||||
% filter the data C1s
|
||||
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
||||
& ~contains(FileList.sample,'(bad data)')...
|
||||
& contains(FileList.name,'C1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
||||
& FileList.scientaPassEnergy_eV == 50 ...
|
||||
& (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27);
|
||||
% show data
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','sample'})) %[output:65618880]
|
||||
disp(FileList(tmpDataSelection,{'name','sampleTemp_dC','cellPressure_mbar'})) %[output:9c77f960]
|
||||
% scale
|
||||
FileList = nappScale(FileList,tmpDataSelection,'regionOld','C1s','regionNew','O1s'); % C1s with O1s area
|
||||
% plot scaled data
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsNorm'); %[output:43e6e41e]
|
||||
% plot original data
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond'); %[output:8d96736e]
|
||||
|
||||
% filter the data O1s
|
||||
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
||||
& ~contains(FileList.sample,'(bad data)')...
|
||||
& contains(FileList.name,'O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
||||
& FileList.scientaPassEnergy_eV == 50 ...
|
||||
& (month(FileList.lastModifiedDatenum) == 2) ...
|
||||
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
||||
& (day(FileList.lastModifiedDatenum) >= 27);
|
||||
% show data
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','sample'})) %[output:506ce9c9]
|
||||
disp(FileList(tmpDataSelection,{'name','sampleTemp_dC','cellPressure_mbar'})) %[output:5432d349]
|
||||
% scale
|
||||
FileList = nappScale(FileList,tmpDataSelection,'regionOld','O1s','regionNew','O1s'); % O1s with O1s area
|
||||
|
||||
% plot scaled data
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsNorm'); %[output:15414432]
|
||||
% plot original data
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond'); %[output:93ebe7fc]
|
||||
%%
|
||||
%[text] ## Calculate surface coverage
|
||||
%[text] Ensure that the dosing yields a surface coverage that is low enough to address your science question, but high enough to provide spectra with sufficient intensity for reliable fitting and interpretation (remember, you can always measure longer to improve signal‑to‑noise).
|
||||
%[text]
|
||||
%[text] I think Celia has an example for this
|
||||
|
||||
%[appendix]{"version":"1.0"}
|
||||
%---
|
||||
%[metadata:view]
|
||||
% data: {"layout":"inline","rightPanelPercent":6.5}
|
||||
%---
|
||||
%[output:35471fec]
|
||||
% data: {"dataType":"text","outputData":{"text":"*************************************************\n* Voila: Splitting NEXAFS files done *\n*************************************************\n*************************************************\n* Voila: Done reading & writing XPS data! *\n*************************************************\n*************************************************\n* Voila: Done reading and writing NEXAFS data! *\n*************************************************\n*************************************************\n* Voila: Done creating data list! *\n*************************************************\n*************************************************\n* Voila: nappFileList.m finished ! *\n*************************************************\n","truncated":false}}
|
||||
%---
|
||||
%[output:4e5ecffd]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>sample<\/strong> \n <strong>____________________________<\/strong> <strong>________________________<\/strong> <strong>________________________________<\/strong>\n\n {'0128128_Survey_850eV.ibw'} {'28-Nov-2025 14:08:31'} {'this is a sample description'}\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:04fdda4a]
|
||||
% data: 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||||
%---
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||||
%[output:3f09c9f5]
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||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>__________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0146146_Al2p_850eV.ibw'} {'29-Nov-2025 15:25:25'} 50 \n {'0146146_Ar2p_850eV.ibw'} {'29-Nov-2025 15:25:25'} 50 \n {'0146146_C1s_850eV.ibw' } {'29-Nov-2025 15:25:25'} 50 \n {'0146146_Cl2p_850eV.ibw'} {'29-Nov-2025 15:25:25'} 50 \n {'0146146_O1s_850eV.ibw' } {'29-Nov-2025 15:25:25'} 50 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:340587e5]
|
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% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>__________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0146146_Al2p_850eV.ibw'} {'29-Nov-2025 15:25:25'} 50 \n\n","truncated":false}}
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%---
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% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>__________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0067067_O1s_1000eV.ibw'} {'21-Aug-2021 18:05:35'} 50 \n {'0067067_O1s_1000eV.ibw'} {'25-Okt-2021 09:04:27'} 50 \n\n","truncated":false}}
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%---
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%[output:3f52d2f2]
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% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>__________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0067067_O1s_1000eV.ibw'} {'25-Okt-2021 09:04:27'} 50 \n\n","truncated":false}}
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%---
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%[output:0ab749aa]
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truncated
|
||||
%---
|
||||
%[output:1cf215b8]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>_________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} {'27-Feb-2022 19:46:48'} 50 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:22c700e4]
|
||||
% data: {"dataType":"text","outputData":{"text":"Validating FitParameter structure...\n Number of peaks detected: 1\n Checking core parameters (required for all modes)...\n Checking mode-specific parameters for mode 'unconstrained'...\n Checking background model parameters for 'linear'...\n✓ FitParameter validation successful for 1 peak(s) in 'unconstrained' mode with 'linear' background.\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:3b9a689c]
|
||||
% data: {"dataType":"warning","outputData":{"text":"Warning: Data sizes do not match. Converting all data to column vectors."}}
|
||||
%---
|
||||
%[output:712f3db2]
|
||||
% data: {"dataType":"text","outputData":{"text":"\nGlobalSearch stopped because it analyzed all the trial points.\n\n29 out of 30 local solver runs converged with a positive local solver exit flag.\n <strong>name<\/strong> <strong>peakPosition<\/strong> \n <strong>_________________________<\/strong> <strong>_________________________________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} 363.6 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakArea<\/strong> \n <strong>_________________________<\/strong> <strong>__________________________________________________________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} 5.8028e+05 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakFWHM<\/strong> \n <strong>_________________________<\/strong> <strong>______________________________________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} 1.9598 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakBackground<\/strong> \n <strong>_________________________<\/strong> <strong>__________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} -34.088 17290\n\nFound 1 valid peaks (non-NaN)\n","truncated":false}}
|
||||
%---
|
||||
%[output:7541066b]
|
||||
% data: {"dataType":"image","outputData":{"dataUri":"data:image\/png;base64,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 truncated
|
||||
%---
|
||||
%[output:969d4f09]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>_________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0056056_O1s_900eV.ibw'} {'28-Feb-2022 12:15:48'} 50 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:880f99d8]
|
||||
% data: {"dataType":"text","outputData":{"text":"Validating FitParameter structure...\n Number of peaks detected: 1\n Checking core parameters (required for all modes)...\n Checking mode-specific parameters for mode 'unconstrained'...\n Checking background model parameters for 'linear'...\n✓ FitParameter validation successful for 1 peak(s) in 'unconstrained' mode with 'linear' background.\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:608e038f]
|
||||
% data: {"dataType":"warning","outputData":{"text":"Warning: Data sizes do not match. Converting all data to column vectors."}}
|
||||
%---
|
||||
%[output:598292bc]
|
||||
% data: {"dataType":"text","outputData":{"text":"\nGlobalSearch stopped because it analyzed all the trial points.\n\nAll 18 local solver runs converged with a positive local solver exit flag.\n <strong>name<\/strong> <strong>peakPosition<\/strong> \n <strong>_________________________<\/strong> <strong>_________________________________________<\/strong>\n\n {'0056056_O1s_900eV.ibw'} 363.2 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakArea<\/strong> \n <strong>_________________________<\/strong> <strong>__________________________________________________________________<\/strong>\n\n {'0056056_O1s_900eV.ibw'} 2.6848e+05 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakFWHM<\/strong> \n <strong>_________________________<\/strong> <strong>______________________________________________<\/strong>\n\n {'0056056_O1s_900eV.ibw'} 2.1012 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakBackground<\/strong> \n <strong>_________________________<\/strong> <strong>________________<\/strong>\n\n {'0056056_O1s_900eV.ibw'} 54.186 -15436\n\nFound 1 valid peaks (non-NaN)\n","truncated":false}}
|
||||
%---
|
||||
%[output:342429e8]
|
||||
% data: {"dataType":"image","outputData":{"dataUri":"data:image\/png;base64,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 truncated
|
||||
%---
|
||||
%[output:326c5622]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>scientaPassEnergy_eV<\/strong>\n <strong>_________________________<\/strong> <strong>________________________<\/strong> <strong>____________________<\/strong>\n\n {'0060060_O1s_900eV.ibw'} {'28-Feb-2022 12:47:15'} 50 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:7f2dd5ee]
|
||||
% data: {"dataType":"text","outputData":{"text":"Validating FitParameter structure...\n Number of peaks detected: 1\n Checking core parameters (required for all modes)...\n Checking mode-specific parameters for mode 'unconstrained'...\n Checking background model parameters for 'linear'...\n✓ FitParameter validation successful for 1 peak(s) in 'unconstrained' mode with 'linear' background.\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:5bd5684a]
|
||||
% data: {"dataType":"warning","outputData":{"text":"Warning: Data sizes do not match. Converting all data to column vectors."}}
|
||||
%---
|
||||
%[output:42116ac3]
|
||||
% data: {"dataType":"text","outputData":{"text":"\nGlobalSearch stopped because it analyzed all the trial points.\n\nAll 28 local solver runs converged with a positive local solver exit flag.\n <strong>name<\/strong> <strong>peakPosition<\/strong> \n <strong>_________________________<\/strong> <strong>______________________________________________<\/strong>\n\n {'0060060_O1s_900eV.ibw'} 363.34 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakArea<\/strong> \n <strong>_________________________<\/strong> <strong>__________________________________________________________________<\/strong>\n\n {'0060060_O1s_900eV.ibw'} 2.6879e+05 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakFWHM<\/strong> \n <strong>_________________________<\/strong> <strong>_________________________________________<\/strong>\n\n {'0060060_O1s_900eV.ibw'} 2.119 NaN NaN NaN NaN\n\n <strong>name<\/strong> <strong>peakBackground<\/strong> \n <strong>_________________________<\/strong> <strong>________________<\/strong>\n\n {'0060060_O1s_900eV.ibw'} 201.94 -68423\n\nFound 1 valid peaks (non-NaN)\n","truncated":false}}
|
||||
%---
|
||||
%[output:31491535]
|
||||
% data: {"dataType":"image","outputData":{"dataUri":"data:image\/png;base64,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 truncated
|
||||
%---
|
||||
%[output:65618880]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>sample<\/strong> \n <strong>_________________________<\/strong> <strong>________________________<\/strong> <strong>_______________<\/strong>\n\n {'0035035_C1s_900eV.ibw'} {'27-Feb-2022 19:46:48'} {'Ice with N2'}\n {'0056056_C1s_900eV.ibw'} {'28-Feb-2022 12:15:48'} {'Ice with N2'}\n {'0060060_C1s_900eV.ibw'} {'28-Feb-2022 12:47:15'} {'Ice with N2'}\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:9c77f960]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>sampleTemp_dC<\/strong> <strong>cellPressure_mbar<\/strong>\n <strong>_________________________<\/strong> <strong>_____________<\/strong> <strong>_________________<\/strong>\n\n {'0035035_C1s_900eV.ibw'} -22.031 1.4358 \n {'0056056_C1s_900eV.ibw'} -39.039 1.4357 \n {'0060060_C1s_900eV.ibw'} -39.003 1.4216 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:43e6e41e]
|
||||
% data: 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|
||||
%---
|
||||
%[output:8d96736e]
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||||
% data: 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truncated
|
||||
%---
|
||||
%[output:506ce9c9]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>lastModifiedDatestr<\/strong> <strong>sample<\/strong> \n <strong>_________________________<\/strong> <strong>________________________<\/strong> <strong>_______________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} {'27-Feb-2022 19:46:48'} {'Ice with N2'}\n {'0056056_O1s_900eV.ibw'} {'28-Feb-2022 12:15:48'} {'Ice with N2'}\n {'0060060_O1s_900eV.ibw'} {'28-Feb-2022 12:47:15'} {'Ice with N2'}\n\n","truncated":false}}
|
||||
%---
|
||||
%[output:5432d349]
|
||||
% data: {"dataType":"text","outputData":{"text":" <strong>name<\/strong> <strong>sampleTemp_dC<\/strong> <strong>cellPressure_mbar<\/strong>\n <strong>_________________________<\/strong> <strong>_____________<\/strong> <strong>_________________<\/strong>\n\n {'0035035_O1s_900eV.ibw'} -22.031 1.4358 \n {'0056056_O1s_900eV.ibw'} -39.039 1.4357 \n {'0060060_O1s_900eV.ibw'} -39.003 1.4216 \n\n","truncated":false}}
|
||||
%---
|
||||
%[output:15414432]
|
||||
% data: {"dataType":"image","outputData":{"dataUri":"data:image\/png;base64,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 truncated
|
||||
%---
|
||||
%[output:93ebe7fc]
|
||||
% data: {"dataType":"image","outputData":{"dataUri":"data:image\/png;base64,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 truncated
|
||||
%---
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,58 +0,0 @@
|
||||
%% Fitting spectra with Gauss
|
||||
%
|
||||
% The nappFitGaussXps.m allows to set-up your own model reflecting the
|
||||
% physics of your peaks. Here is an example. Check the documentation. This
|
||||
% needs some test-and try.
|
||||
|
||||
%% SES #0157 Ar2p (gas-phase reference)
|
||||
%
|
||||
% * Core level: Ar 2p, file 0157157_Ar2p_850eV.
|
||||
% * Model: Gaussian spin–orbit doublet, splitting fixed at 2.2 eV, area ratio 0.5.
|
||||
% * Peak 1 area: initial 1e5; lower bound 10; upper bound Inf.
|
||||
% * Peak 1 width: initial 0.5 eV; bounds 0.5–3 eV.
|
||||
% * Peak 1 position: initial 603 eV; bounds 602–604 eV.
|
||||
% * Background: linear, 8 eV window below and 5 eV above the peak.
|
||||
|
||||
clc
|
||||
close all
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& ~contains(FileList.name,"Survey") ...
|
||||
& ~contains(FileList.name,"Resonant") ...
|
||||
& startsWith(FileList.name,"0157") ...
|
||||
& contains(FileList.name, "Ar2p");
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr','scientaPassEnergy_eV'}))
|
||||
|
||||
% Defining the mode as 'widthPositionAreaRatio', the first element of the input
|
||||
% parameters defines the first peak (initial value, upper and lower bond for
|
||||
% fitting) in absolute terms. The second element is then the area ratio
|
||||
% relative to the first, and the position relative to the first.
|
||||
|
||||
tmpFitParameters.peakArea = [1e5 0.5 0.1 0.1 0.1] ;
|
||||
tmpFitParameters.peakWidth = 0.5;
|
||||
tmpFitParameters.peakPos = [603 -2.2 10 10 10];
|
||||
|
||||
tmpFitParameters.peakAreaUpperBond = [Inf 0.5 0.1 0.1 0.1];
|
||||
tmpFitParameters.peakWidthUpperBond = [3 NaN NaN NaN NaN];
|
||||
tmpFitParameters.peakPosUpperBond = [604 -2.2 10 10 10];
|
||||
|
||||
tmpFitParameters.peakAreaLowerBond = [10 0.5 0.1 0.1 0.1];
|
||||
tmpFitParameters.peakWidthLowerBond = [0.5 NaN NaN NaN NaN];
|
||||
tmpFitParameters.peakPosLowerBond = [602 -2.2 0.1 0.1 0.1];
|
||||
% spectrum range
|
||||
tmpFitParameters.backgroundRange = [8 5];
|
||||
|
||||
FileList= nappFitGaussXps(FileList,tmpFitParameters,tmpDataSelection,...
|
||||
'energyScale','kinetic','mode','widthPositionAreaRatio','showFigures','on');
|
||||
|
||||
%% RESULTS – SES 0157 Ar2p (gas-phase reference)
|
||||
% The gas‑phase Ar2p reference is well described by the doublet: peak 1 at
|
||||
% 603.0 eV and peak 2 at 600.8 eV (FWHM ≈ 1.2 eV, width 1.0 eV) match both
|
||||
% the height and the base of the experimental lines. The residuals are very small and essentially
|
||||
% structureless over the full fit range, indicating that, in contrast to
|
||||
% the Cl2p fits, a simple Gaussian doublet provides an excellent and
|
||||
% internally consistent description of the Ar2p line shape.
|
||||
|
||||
|
||||
@@ -1,311 +0,0 @@
|
||||
%% Generate Spectra
|
||||
%
|
||||
% Here we plot all spectra and all regions to confirm that we get what we
|
||||
% need.
|
||||
%
|
||||
% This file shows some typical out-of-the-box examples.
|
||||
|
||||
%% Start fresh
|
||||
clc
|
||||
close all
|
||||
clearvars
|
||||
|
||||
%% Import Data
|
||||
%
|
||||
% This was generated by importData.m. You can either force that it runs
|
||||
% again before this script runs (preferred, ensures you always have recent
|
||||
% data in MATLAB's data table) or load the FileList.
|
||||
run('importData.m')
|
||||
% load("FileList.mat","FileList")
|
||||
|
||||
%% Plot a Survey
|
||||
for ii = 1
|
||||
%% Start fresh
|
||||
close all
|
||||
clc
|
||||
|
||||
%% Select data
|
||||
|
||||
% It is convenient to add date and year of the beamtime. This ensures you
|
||||
% can use the code also when you work with data from several beamtimes
|
||||
% (indexing of the files starts with 0 for each beamtime by default).
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& contains(FileList.name,"Survey") ...
|
||||
& (contains(FileList.name,"128"));
|
||||
% Always ensure that you select the spectra you intended.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
|
||||
|
||||
%% Generate spectrum and plot
|
||||
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
|
||||
|
||||
%% Add annotation to the figure
|
||||
%
|
||||
% In a survey, we usually want to identify all peaks. Here, based on
|
||||
% binding energies from the literature and a shift, we add common regions:
|
||||
%
|
||||
% **Note that the binding energies are rough estimates – to use this further
|
||||
% we need to confirm them and add the source/reference.**
|
||||
dataObjs = findobj(gcf,'-property','YData');
|
||||
tmpxShift = 7;
|
||||
tmpxRayEnergy = 850;
|
||||
tmpyPosition = 0.5E5;
|
||||
tmpyPositionTwo = 0.8E5;
|
||||
|
||||
% Au peaks
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-57-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au5p3/2');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-74-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au5p5/2');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-84-tmpxShift,tmpyPosition,dataObjs(1),'Au4f7/2');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-88-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au4f5/2');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-107-tmpxShift,tmpyPosition,dataObjs(1),'Au5s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-335-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4d1/2');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-353-tmpxShift,tmpyPosition,dataObjs(1),'Au4d3/2');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-546-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4p3/2');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-643-tmpxShift,tmpyPosition,dataObjs(1),'Au4p1/2');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-762-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-8-tmpxShift,tmpyPosition,dataObjs(1),'AuVB');
|
||||
|
||||
% Usual suspects
|
||||
nappAnnotateSurvey(tmpxRayEnergy-285-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'C1s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-534-tmpxShift,tmpyPosition,dataObjs(1),'O1s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-30-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'O2s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-405-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'N1s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-495-tmpxShift,tmpyPosition,dataObjs(1),'Sn3d');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-691-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'F1s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-295-tmpxShift,tmpyPosition,dataObjs(1),'K2p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-380-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-153-tmpxShift,tmpyPosition,dataObjs(1),'Si2s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-100-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Si2p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-197-tmpxShift,tmpyPosition,dataObjs(1),'Cl2p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-271-tmpxShift,tmpyPosition,dataObjs(1),'Cl2s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-286-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Sn3p');
|
||||
|
||||
% Clay
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-351-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-347-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-440-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca2s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-34-tmpxShift,tmpyPosition+tmpyPosition,dataObjs(1),'Ca3s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-26-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ca3p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-297-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-294-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2p');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-35-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3s');
|
||||
%nappAnnotateSurvey(tmpxRayEnergy-19-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K3p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-118-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-72.5-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Al');
|
||||
|
||||
% Ar gas
|
||||
nappAnnotateSurvey(tmpxRayEnergy-245-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2p');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-323-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar2s');
|
||||
nappAnnotateSurvey(tmpxRayEnergy-12-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Ar3p');
|
||||
|
||||
% Some Auger
|
||||
nappAnnotateSurvey(363,tmpyPosition-tmpyPositionTwo,dataObjs(1),'N KLL');
|
||||
nappAnnotateSurvey(203,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Ar LMN');
|
||||
nappAnnotateSurvey(500,tmpyPosition-tmpyPositionTwo,dataObjs(1),'O KLL');
|
||||
end
|
||||
|
||||
%% Plot NEXAFS & Resonant Data
|
||||
%
|
||||
% You can plot images (raw data from SES) without generating the spectra.
|
||||
% (You can also first generate the spectra, but to plot images the spectra
|
||||
% are not needed.)
|
||||
for ii = 1
|
||||
%% Start fresh
|
||||
clc; close all
|
||||
|
||||
%% Select data
|
||||
tmpDataSelection = ...
|
||||
~contains(FileList.sample,"(bad data)") ...
|
||||
& contains(FileList.name,"O_Kedge_Resonant") ...
|
||||
& contains(FileList.name,"0025");
|
||||
% Always ensure that you select the spectra you intended.
|
||||
disp(FileList(tmpDataSelection,{'name','lastModifiedDatestr'}))
|
||||
|
||||
%% Plot images
|
||||
|
||||
% This one shows x, y in energy scales (data were interpolated).
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image',...
|
||||
'mode','OneEach');
|
||||
% This one shows sweeps (original data).
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image','energyScale','sweep');
|
||||
|
||||
%% Plot selected Auger-Meitner
|
||||
%
|
||||
% This plots the electron intensity vs. kinetic energy for chosen X-ray
|
||||
% energies (directly from the image).
|
||||
|
||||
tmpDataSelection = month(FileList.lastModifiedDatenum) == 11 ...
|
||||
& year(FileList.lastModifiedDatenum) == 2025 ...
|
||||
& ~contains(FileList.sample,"(bad data)") ...
|
||||
& contains(FileList.name,"O_Kedge_Resonant") ...
|
||||
& contains(FileList.name,"0025");
|
||||
nappPlot(FileList,tmpDataSelection,'tableVariable','image','mode','AugerMeitner','AugerMeitnerEnergies',[525,545,550]);
|
||||
% It also shows an example of how the current plot can be edited manually
|
||||
% after its generation by nappPlot.
|
||||
hax = gca;
|
||||
hax.XDir = 'reverse';
|
||||
xlabel('Auger-Meitner binding energy [eV]');
|
||||
|
||||
%% Plot image interactive
|
||||
%
|
||||
% Use the mouse: click once in the image, then the Auger-Meitner and NEXAFS will
|
||||
% follow the mouse position.
|
||||
|
||||
% 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','on');
|
||||
|
||||
%% 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
|
||||
|
||||
%% helper functions
|
||||
% Check if the environment is MATLAB or Octave
|
||||
if exist('OCTAVE_VERSION', 'builtin')
|
||||
% Anonymous function mimicking contains
|
||||
contains = @(str, pattern) ~cellfun('isempty', strfind(str, pattern));
|
||||
end
|
||||
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@@ -1,51 +0,0 @@
|
||||
%% 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")
|
||||
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@@ -1,151 +0,0 @@
|
||||
function plotExportedData(dataDirectory)
|
||||
%plotExportedData Plot all exported ASCII data files in one figure
|
||||
%
|
||||
% This function reads all .txt files from the exported data (created by
|
||||
% exportFigureData) and plots them in a single figure for visual comparison.
|
||||
% File names are parsed to extract axis labels and legend entries.
|
||||
%
|
||||
% SYNTAX:
|
||||
% plotExportedData() % Uses current working directory
|
||||
% plotExportedData(dataDirectory) % Uses specified directory path
|
||||
%
|
||||
% INPUTS:
|
||||
% dataDirectory : char or string (optional)
|
||||
% Path to directory containing exported .txt files.
|
||||
% If omitted, uses current working directory (pwd).
|
||||
%
|
||||
% OUTPUT:
|
||||
% A new figure with all data plotted as lines. Axis labels and legend
|
||||
% entries are reconstructed from the file names.
|
||||
%
|
||||
% NOTES:
|
||||
% - Reads all .txt files in the specified directory
|
||||
% - File name format expected: <FigureTitle>_[<TileTitle>_]<YLabel>_vs_<XLabel>_<LineLabel>.txt
|
||||
% - Plots are created with underscores converted back to spaces for display
|
||||
% - X and Y axis labels are set based on parsed filename
|
||||
% - Legend shows line labels from filenames
|
||||
% - All data is plotted in a single axes for direct visual comparison
|
||||
%
|
||||
% EXAMPLE:
|
||||
% % After running exportFigureData():
|
||||
% plotExportedData()
|
||||
% % or
|
||||
% plotExportedData('../exported_data/')
|
||||
|
||||
% Handle input: use current directory if none provided
|
||||
if nargin == 0 || isempty(dataDirectory)
|
||||
dataDirectory = pwd;
|
||||
end
|
||||
|
||||
% Get all .txt files in the directory
|
||||
fileStruct = dir(fullfile(dataDirectory, '*.txt'));
|
||||
if isempty(fileStruct)
|
||||
warning('plotExportedData:NoFiles', 'No .txt files found in %s', dataDirectory);
|
||||
return;
|
||||
end
|
||||
|
||||
% Create a new figure
|
||||
fig = figure('Name', 'Exported Data Comparison', 'NumberTitle', 'off');
|
||||
ax = axes(fig);
|
||||
hold(ax, 'on');
|
||||
grid(ax, 'on');
|
||||
|
||||
% Storage for axis labels (to ensure consistency)
|
||||
xLabelStr = '';
|
||||
yLabelStr = '';
|
||||
legendEntries = {};
|
||||
|
||||
% Loop over all .txt files
|
||||
for ii = 1:length(fileStruct)
|
||||
fileName = fileStruct(ii).name;
|
||||
filePath = fullfile(dataDirectory, fileName);
|
||||
|
||||
% Read the data
|
||||
try
|
||||
data = readmatrix(filePath);
|
||||
catch ME
|
||||
warning('plotExportedData:ReadFailed', 'Could not read file: %s\n%s', fileName, ME.message);
|
||||
continue;
|
||||
end
|
||||
|
||||
% Check if data has at least 2 columns
|
||||
if size(data, 2) < 2 || size(data, 1) < 1
|
||||
warning('plotExportedData:InvalidData', 'File %s does not have valid X,Y columns', fileName);
|
||||
continue;
|
||||
end
|
||||
|
||||
xData = data(:, 1);
|
||||
yData = data(:, 2);
|
||||
|
||||
% Parse filename to extract labels
|
||||
% Expected format: <FigureTitle>_[<TileTitle>_]<YLabel>_vs_<XLabel>_<LineLabel>.txt
|
||||
|
||||
% Remove .txt extension
|
||||
nameNoExt = fileName(1:end-4);
|
||||
|
||||
% Split by '_vs_' to separate y-axis label from the rest
|
||||
parts = strsplit(nameNoExt, '_vs_');
|
||||
|
||||
if length(parts) >= 2
|
||||
% Extract line label (after _vs_<XLabel>_)
|
||||
rightPart = parts{2}; % <XLabel>_<LineLabel>
|
||||
|
||||
% Find the last underscore to separate XLabel from LineLabel
|
||||
underscores = find(rightPart == '_');
|
||||
if ~isempty(underscores)
|
||||
lastUnderscore = underscores(end);
|
||||
xLabelExtracted = rightPart(1:lastUnderscore-1);
|
||||
lineLabel = rightPart(lastUnderscore+1:end);
|
||||
else
|
||||
xLabelExtracted = rightPart;
|
||||
lineLabel = 'data';
|
||||
end
|
||||
|
||||
% Extract y-axis label (before _vs_)
|
||||
yLabelExtracted = parts{1};
|
||||
|
||||
% Remove figure title (first part before first underscore)
|
||||
% This is tricky, so we'll keep what we have and note it's approximate
|
||||
|
||||
% Convert underscores back to spaces for display
|
||||
xLabelDisplay = strrep(xLabelExtracted, '_', ' ');
|
||||
yLabelDisplay = strrep(yLabelExtracted, '_', ' ');
|
||||
lineLabel = strrep(lineLabel, '_', ' ');
|
||||
|
||||
% Store axis labels (use the first valid ones found)
|
||||
if isempty(xLabelStr)
|
||||
xLabelStr = xLabelDisplay;
|
||||
end
|
||||
if isempty(yLabelStr)
|
||||
yLabelStr = yLabelDisplay;
|
||||
end
|
||||
|
||||
else
|
||||
% Fallback if filename parsing fails
|
||||
lineLabel = nameNoExt;
|
||||
end
|
||||
|
||||
% Plot the data
|
||||
plot(ax, xData, yData, 'DisplayName', lineLabel, 'LineWidth', 1.5, 'Marker', 'o', 'MarkerSize', 4);
|
||||
|
||||
% Store legend entry
|
||||
legendEntries{end+1} = lineLabel;
|
||||
end
|
||||
|
||||
% Set axis labels
|
||||
xlabel(ax, xLabelStr, 'Interpreter', 'none');
|
||||
ylabel(ax, yLabelStr, 'Interpreter', 'none');
|
||||
title(ax, 'Exported Data Comparison', 'Interpreter', 'none');
|
||||
|
||||
% Add legend
|
||||
if ~isempty(legendEntries)
|
||||
legend(ax, legendEntries, 'Interpreter', 'none', 'Location', 'best');
|
||||
end
|
||||
|
||||
% Optimize figure appearance
|
||||
grid(ax, 'on');
|
||||
hold(ax, 'off');
|
||||
|
||||
fprintf('Plotted %d data files from: %s\n', length(fileStruct), dataDirectory);
|
||||
|
||||
end
|
||||
@@ -1,22 +0,0 @@
|
||||
%% Start fresh
|
||||
close all
|
||||
clearvars
|
||||
clc
|
||||
|
||||
%% Import Data
|
||||
FileList = nappFileList('2026/testImport','mode','local','importLabView','off');
|
||||
%% Display Data
|
||||
disp(FileList(:,{'name','creationDate','lastModifiedDatestr','image','originalFileName'}))
|
||||
%% Plot images
|
||||
nappPlot(FileList);
|
||||
%% Plot iterations
|
||||
close all
|
||||
nappPlot(FileList,'tableVariable','image','mode','iterations');
|
||||
%% Generate spectra and plot
|
||||
close all
|
||||
FileList = nappGenerateSpectra(FileList);
|
||||
nappPlot(FileList,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
|
||||
%% Generate spectra, remove background and plot
|
||||
close all
|
||||
FileList = nappGenerateSpectra(FileList);
|
||||
nappPlot(FileList,'tableVariable','spectrum_countsPerSecond','mode','OneEach');
|
||||
Reference in new issue
Block a user