222 lines
9.3 KiB
Matlab
222 lines
9.3 KiB
Matlab
%% 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 |