bartels-rausch
091cf00e77
* Transitioned nappNorm, nappDetermineShift, and nappCopyShift to use modern MATLAB arguments blocks, removing their legacy functionSignatures.json entries.
* Refactored nappNorm to rely on prior peak fitting results (e.g., from nappFitGaussXps) for normalization rather than performing the fitting internally.
* Decoupled energy calibration: nappDetermineShift now solely calculates internal binding energy shifts, and nappCopyShift distributes them using a unified matching engine ('earlier', 'after', 'spectrumNumber').
* Implemented a Universal Logging Block to track data provenance, automatically appending execution timestamps and caller script names to processed rows.
* Updated nappCombineSpectra to output a logDataSource cell array and cleanly name combined spectra.
* Created nappSaveDataTable for automatic, script-named .mat file saving, complete with a Live Editor interceptor.
* Added a publish-mode silencer to nappFitGaussXps to prevent optimizer spam in generated PDFs.
* Updated readme.md to document the new arguments structure, the 3-step calibration workflow, the matching engine, and the new table variables.
245 lines
11 KiB
Matlab
245 lines
11 KiB
Matlab
function DataTable = nappGenerateSpectra(DataTable,varargin)
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%NAPPGENERATESPECTRA Generate 1D spectra from image spectra.
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% DataTable = NAPPGENERATESPECTRA(DataTable) processes all data in the
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% input table and generates spectra for the data files in each row. The
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% spectra are written back into the table into new columns (table
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% variables).
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%
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% DataTable = NAPPGENERATESPECTRA(DataTable, tableSelection) restricts
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% processing to the rows where tableSelection is true. The length of
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% tableSelection must match the number of rows in DataTable.
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%
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% DataTable = NAPPGENERATESPECTRA(DataTable, tableSelection, Name, Value)
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% additionally customizes the source variable, reduction mode, and energy
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% or iteration ranges via Name-Value arguments.
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%
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% Required input
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% --------------
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% DataTable
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% Table that contains at least the variables:
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% - name : file name as character vector or string
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% - image or imageRoh : 2D image or image stack stored in a cell
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% - scientaEkin_eV : kinetic energy axis
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%
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% Optional positional input
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% -------------------------
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% tableSelection
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% Logical vector selecting rows to process.
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% Size: [height(DataTable) 1] or [1 height(DataTable)]
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% Default: all rows true
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%
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% Name-Value arguments
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% --------------------
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% 'tableVariable'
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% Name of the variable in DataTable that contains the image data.
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% Type : char or string
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% Values : 'image' (default) | 'imageRoh'
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%
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% 'mode'
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% Reduction mode used to generate the spectra.
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% Type : char or string
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% Values : 'mean' (default) | 'sum' | 'trapz' | 'AugerMeitnerMean'
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% Behaviour:
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% - For '.h5' entries:
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% 'sum' or 'mean' : sum over the selected kinetic energy range
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% (currently identical behaviour; no
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% normalization by time implemented).
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% 'trapz' : trapezoidal integration over kinetic
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% energy for each column.
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% - For '.ibw' entries:
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% 'mean' or 'trapz': mean over the selected iteration range
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% (2nd dimension) and store as a column
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% vector.
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% 'sum' : sum over the selected iteration range.
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%
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% 'iterationRange'
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% Iteration indices for '.ibw' image stacks (2nd dimension).
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% Type : numeric 1x2 vector [startIdx endIdx]
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% Default: [0 0] -> use full range 1 : size(image,2)
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%
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% 'eKinStart'
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% Lower kinetic energy limit for '.h5' data.
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% Type : numeric scalar
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% Default: 0 -> use first element of scientaEkin_eV
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%
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% 'eKinEnd'
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% Upper kinetic energy limit for '.h5' data.
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% Type : numeric scalar
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% Default: 0 -> use last element of scientaEkin_eV
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%
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% Output
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% ------
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% DataTable
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% Same table as input, with the following variables ensured and
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% updated for the selected rows:
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%
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% spectrum_counts
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% Cell array. Each cell contains a spectrum generated from the
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% selected image data, depending on file type and mode
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% ('sum', 'mean', or 'trapz').
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%
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% spectrum_countsPerSecond
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% Cell array. For '.ibw' entries and modes 'mean' or 'trapz',
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% this stores the spectrum obtained by averaging over the chosen
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% iteration range.
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%
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% logGenerateMode
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% Cell array of character vectors specifying the mode used for
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% each processed row.
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%
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% logGenerateRange
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% Numeric matrix with two columns. For '.h5' entries this stores
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% [eKinStart eKinEnd]. For '.ibw' entries this stores
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% [iterationStart iterationEnd].
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%
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% logGenerateDate
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% Cell array of character vectors containing the date and time
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% when the spectra were generated.
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%
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% Notes
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% -----
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% - The function distinguishes between '.h5' and '.ibw' files based on
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% the file name stored in the variable 'name'.
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% Define conditions and defaults of input
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defaultTableVariable = 'image';
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expectedTableVariable = {'image','imageRoh'};
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defaultMode = 'mean';
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expectedMode = {'mean','sum','trapz','AugerMeitnerMean'};
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defaultEkinStart = 0;
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defaultEkinEnd = 0;
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defaultTableSelection = true(size(DataTable,1),1);
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% Parse input
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p = inputParser;
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addRequired(p,'DataTable',@istable);
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addOptional(p,'tableSelection',defaultTableSelection,@islogical);
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addParameter(p,'tableVariable',defaultTableVariable,...
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@(x) any(validatestring(x,expectedTableVariable)));
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addParameter(p,'mode',defaultMode,...
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@(x) any(validatestring(x,expectedMode)));
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addParameter(p,'iterationRange',[0:0]);
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addParameter(p,'eKinStart',defaultEkinStart,@isnumeric)
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addParameter(p,'eKinEnd',defaultEkinEnd,@isnumeric)
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parse(p,DataTable,varargin{:});
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% add empty variable to table
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existColumn = strcmp('spectrum_countsPerSecond',p.Results.DataTable.Properties.VariableNames);
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if ~any(existColumn)
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DataTable.spectrum_countsPerSecond = cell(size(p.Results.DataTable,1),1);
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end
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existColumn = strcmp('spectrum_counts',p.Results.DataTable.Properties.VariableNames);
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if ~any(existColumn)
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DataTable.spectrum_counts = cell(size(p.Results.DataTable,1),1);
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end
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existColumn = strcmp('logGenerateRange',p.Results.DataTable.Properties.VariableNames);
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if ~any(existColumn)
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DataTable.logGenerateRange = NaN(size(p.Results.DataTable,1),2);
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end
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existColumn = strcmp('logGenerateDate',p.Results.DataTable.Properties.VariableNames);
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if ~any(existColumn)
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DataTable.logGenerateDate = cellstr(repmat(' ',size(p.Results.DataTable,1),1));
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end
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existColumn = strcmp('logGenerateMode',p.Results.DataTable.Properties.VariableNames);
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if ~any(existColumn)
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DataTable.logGenerateDate = cellstr(repmat(' ',size(p.Results.DataTable,1),1));
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end
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% check input (all elements of input variable need to have non-zero size)
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if ~all(cell2mat(cellfun(@size,p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable),'UniformOutput',false)),'all')
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disp('The table variable appears to be empty. I quit now')
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error('The table variable appears to be empty.')
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end
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% create empty variables
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tmp_3 = cell(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
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tmp_6 = cell(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
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tmp_4 = NaN(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
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tmp_5 = NaN(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
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% repeat this for all columns in input.
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for ii = 1:size(p.Results.DataTable(p.Results.tableSelection,:),1)
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if endsWith(p.Results.DataTable(p.Results.tableSelection,:).name{ii},'h5') % let's do NEXAFS first.
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% Get index of EkinStart and EkinEnd
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if (p.Results.eKinStart) == 0
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tmp_1 = 1;
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else
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tmp_1 = find(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii} >= p.Results.eKinStart,1);
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end
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if (p.Results.eKinEnd) == 0
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tmp_2 = length(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii});
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else
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tmp_2 = find(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii} >= p.Results.eKinEnd,1);
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end
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if contains(p.Results.mode,'trapz')
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tmp_3(ii,1)= {trapz(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_1:tmp_2),...
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p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(tmp_1:tmp_2,:))'};
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elseif (contains(p.Results.mode,'sum') | contains(p.Results.mode,'mean'))
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tmp_3(ii,1) = {sum(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(tmp_1:tmp_2,:),1)'}; %#ok<*AGROW>
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end
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% log
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tmp_4(ii,1) = p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_1);
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tmp_5(ii,1) = p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_2);
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elseif endsWith(p.Results.DataTable(p.Results.tableSelection,:).name{ii},'ibw') || endsWith(p.Results.DataTable(p.Results.tableSelection,:).name{ii},'xy') % then do Igorfiles or xy files
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if p.Results.iterationRange(1) == 0
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startRange = 1;
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endRange = size(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii},2);
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else
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startRange = p.Results.iterationRange(1);
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endRange = p.Results.iterationRange(2);
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end
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if (contains(p.Results.mode,'mean') | contains(p.Results.mode,'trapz'))
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tmp_6(ii,1)= {mean(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(:,startRange:endRange),2)};
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elseif contains(p.Results.mode,'sum')
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tmp_3(ii,1) = {sum(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(:,startRange:endRange),2)}; %#ok<*AGROW>
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end
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% log
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tmp_4(ii,1) = startRange;
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tmp_5(ii,1) = endRange;
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end
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end
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% write results to table
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DataTable.spectrum_counts(p.Results.tableSelection) = tmp_3;
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DataTable.spectrum_countsPerSecond(p.Results.tableSelection) = tmp_6;
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% write log to table
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DataTable.logGenerateMode(p.Results.tableSelection,:) = cellstr(repmat(p.Results.mode,size(p.Results.DataTable(p.Results.tableSelection,:),1),1));
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DataTable.logGenerateRange(p.Results.tableSelection,:) = [tmp_4 tmp_5];
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% --- Define variables for the Universal Logging Block ---
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numRowsTable = height(DataTable);
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processIdx = find(p.Results.tableSelection);
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% -------------------------------------------------------------------
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% 1. Dynamically get this function's name
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thisFuncName = mfilename;
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colDate = sprintf('log%sDate', thisFuncName);
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colCaller = sprintf('log%sCaller', thisFuncName);
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% 2. Probe for column existence, create empty string arrays if missing
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if ~ismember(colDate, DataTable.Properties.VariableNames)
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DataTable.(colDate) = repmat("", numRowsTable, 1);
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end
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if ~ismember(colCaller, DataTable.Properties.VariableNames)
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DataTable.(colCaller) = repmat("", numRowsTable, 1);
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end
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% 3. Get current time as a string
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currentTimeStr = string(datetime('now', 'Format', 'yyyy-MM-dd HH:mm:ss'));
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% 4. Determine the calling script using the call stack
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stackInfo = dbstack;
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if length(stackInfo) > 1
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callerScript = string(stackInfo(2).file); % The script that called this function
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else
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callerScript = " "; % Called directly from the command window
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end
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% 5. Write the log data only to the rows being processed
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DataTable.(colDate)(processIdx) = currentTimeStr;
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DataTable.(colCaller)(processIdx) = callerScript;
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end |