function DataTable = nappGenerateSpectra(DataTable,varargin)
%NAPPGENERATESPECTRA Generate 1D spectra from image spectra.
%   DataTable = NAPPGENERATESPECTRA(DataTable) processes all data in the
%   input table and generates spectra for the data files in each row. The
%   spectra are written back into the table into new columns (table
%   variables).
%
%   DataTable = NAPPGENERATESPECTRA(DataTable, tableSelection) restricts
%   processing to the rows where tableSelection is true. The length of
%   tableSelection must match the number of rows in DataTable.
%
%   DataTable = NAPPGENERATESPECTRA(DataTable, tableSelection, Name, Value)
%   additionally customizes the source variable, reduction mode, and energy
%   or iteration ranges via Name-Value arguments.
%
%   Required input
%   --------------
%   DataTable
%       Table that contains at least the variables:
%         - name                : file name as character vector or string
%         - image or imageRoh   : 2D image or image stack stored in a cell
%         - scientaEkin_eV      : kinetic energy axis 
%
%   Optional positional input
%   -------------------------
%   tableSelection
%       Logical vector selecting rows to process.
%       Size: [height(DataTable) 1] or [1 height(DataTable)]
%       Default: all rows true
%
%   Name-Value arguments
%   --------------------
%   'tableVariable'
%       Name of the variable in DataTable that contains the image data.
%       Type   : char or string
%       Values : 'image' (default) | 'imageRoh'
%
%   'mode'
%       Reduction mode used to generate the spectra.
%       Type   : char or string
%       Values : 'mean' (default) | 'sum' | 'trapz' | 'AugerMeitnerMean'
%       Behaviour:
%         - For '.h5' entries:
%             'sum' or 'mean'  : sum over the selected kinetic energy range
%                               (currently identical behaviour; no
%                               normalization by time implemented).
%             'trapz'          : trapezoidal integration over kinetic
%                               energy for each column.
%         - For '.ibw' entries:
%             'mean' or 'trapz': mean over the selected iteration range
%                               (2nd dimension) and store as a column
%                               vector.
%             'sum'            : sum over the selected iteration range.
%
%   'iterationRange'
%       Iteration indices for '.ibw' image stacks (2nd dimension).
%       Type   : numeric 1x2 vector [startIdx endIdx]
%       Default: [0 0] -> use full range 1 : size(image,2)
%
%   'eKinStart'
%       Lower kinetic energy limit for '.h5' data.
%       Type   : numeric scalar
%       Default: 0 -> use first element of scientaEkin_eV
%
%   'eKinEnd'
%       Upper kinetic energy limit for '.h5' data.
%       Type   : numeric scalar
%       Default: 0 -> use last element of scientaEkin_eV
%
%   Output
%   ------
%   DataTable
%       Same table as input, with the following variables ensured and
%       updated for the selected rows:
%
%       spectrum_counts
%           Cell array. Each cell contains a spectrum generated from the
%           selected image data, depending on file type and mode
%           ('sum', 'mean', or 'trapz').
%
%       spectrum_countsPerSecond
%           Cell array. For '.ibw' entries and modes 'mean' or 'trapz',
%           this stores the spectrum obtained by averaging over the chosen
%           iteration range. 
%
%       logGenerateMode
%           Cell array of character vectors specifying the mode used for
%           each processed row.
%
%       logGenerateRange
%           Numeric matrix with two columns. For '.h5' entries this stores
%           [eKinStart eKinEnd]. For '.ibw' entries this stores
%           [iterationStart iterationEnd].
%
%       logGenerateDate
%           Cell array of character vectors containing the date and time
%           when the spectra were generated.
%
%   Notes
%   -----
%   - The function distinguishes between '.h5' and '.ibw' files based on
%     the file name stored in the variable 'name'.


% Define conditions and defaults of input
defaultTableVariable        = 'image';
expectedTableVariable       = {'image','imageRoh'};
defaultMode                 = 'mean';
expectedMode                = {'mean','sum','trapz','AugerMeitnerMean'};
defaultEkinStart            = 0;
defaultEkinEnd              = 0;
defaultTableSelection       = true(size(DataTable,1),1);

% Parse input
p = inputParser;
addRequired(p,'DataTable',@istable);
addOptional(p,'tableSelection',defaultTableSelection,@islogical);
addParameter(p,'tableVariable',defaultTableVariable,...
    @(x) any(validatestring(x,expectedTableVariable)));
addParameter(p,'mode',defaultMode,...
    @(x) any(validatestring(x,expectedMode)));
addParameter(p,'iterationRange',[0:0]);
addParameter(p,'eKinStart',defaultEkinStart,@isnumeric)
addParameter(p,'eKinEnd',defaultEkinEnd,@isnumeric)
parse(p,DataTable,varargin{:});

% add empty variable to table
existColumn = strcmp('spectrum_countsPerSecond',p.Results.DataTable.Properties.VariableNames);
if ~any(existColumn)
    DataTable.spectrum_countsPerSecond = cell(size(p.Results.DataTable,1),1);
end
existColumn = strcmp('spectrum_counts',p.Results.DataTable.Properties.VariableNames);
if ~any(existColumn)
    DataTable.spectrum_counts = cell(size(p.Results.DataTable,1),1);
end
existColumn = strcmp('logGenerateRange',p.Results.DataTable.Properties.VariableNames);
if ~any(existColumn)
    DataTable.logGenerateRange = NaN(size(p.Results.DataTable,1),2);
end
existColumn = strcmp('logGenerateDate',p.Results.DataTable.Properties.VariableNames);
if ~any(existColumn)
    DataTable.logGenerateDate = cellstr(repmat(' ',size(p.Results.DataTable,1),1));
end
existColumn = strcmp('logGenerateMode',p.Results.DataTable.Properties.VariableNames);
if ~any(existColumn)
    DataTable.logGenerateDate = cellstr(repmat(' ',size(p.Results.DataTable,1),1));
end

% check input (all elements of input variable need to have non-zero size)
if ~all(cell2mat(cellfun(@size,p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable),'UniformOutput',false)),'all')
    disp('The table variable appears to be empty. I quit now')
    error('The table variable appears to be empty.')
end

% create empty variables
tmp_3 = cell(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
tmp_6 = cell(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
tmp_4 = NaN(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
tmp_5 = NaN(size(p.Results.DataTable(p.Results.tableSelection,:),1),1);
% repeat this for all columns in input.
for ii = 1:size(p.Results.DataTable(p.Results.tableSelection,:),1)
    if endsWith(p.Results.DataTable(p.Results.tableSelection,:).name{ii},'h5') % let's do NEXAFS first.
        % Get index of EkinStart and EkinEnd
        if  (p.Results.eKinStart) == 0
            tmp_1 = 1;
        else
            tmp_1 = find(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii} >= p.Results.eKinStart,1);
        end
        if (p.Results.eKinEnd) == 0
            tmp_2 = length(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii});
        else
            tmp_2 = find(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii} >= p.Results.eKinEnd,1);
        end

        if contains(p.Results.mode,'trapz')
            tmp_3(ii,1)= {trapz(p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_1:tmp_2),...
                p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(tmp_1:tmp_2,:))'};
        elseif (contains(p.Results.mode,'sum') | contains(p.Results.mode,'mean'))

            tmp_3(ii,1) = {sum(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(tmp_1:tmp_2,:),1)'}; %#ok<*AGROW>
        end
        % log
        tmp_4(ii,1) = p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_1);
        tmp_5(ii,1) = p.Results.DataTable(p.Results.tableSelection,:).scientaEkin_eV{ii}(tmp_2);
    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
        if p.Results.iterationRange(1) == 0
            startRange  = 1;
            endRange    = size(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii},2);
        else
            startRange  = p.Results.iterationRange(1);
            endRange    = p.Results.iterationRange(2);
        end

        if (contains(p.Results.mode,'mean') | contains(p.Results.mode,'trapz'))
            tmp_6(ii,1)= {mean(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(:,startRange:endRange),2)};
        elseif contains(p.Results.mode,'sum')
            tmp_3(ii,1) = {sum(p.Results.DataTable(p.Results.tableSelection,:).(p.Results.tableVariable){ii}(:,startRange:endRange),2)}; %#ok<*AGROW>
        end
        % log
        tmp_4(ii,1) = startRange;
        tmp_5(ii,1) = endRange;
    end
end

% write results to table
    DataTable.spectrum_counts(p.Results.tableSelection)                     = tmp_3;
    DataTable.spectrum_countsPerSecond(p.Results.tableSelection)            = tmp_6;
% write log to table    
    DataTable.logGenerateMode(p.Results.tableSelection,:)                   = cellstr(repmat(p.Results.mode,size(p.Results.DataTable(p.Results.tableSelection,:),1),1));
    DataTable.logGenerateRange(p.Results.tableSelection,:)               = [tmp_4 tmp_5];
    DataTable.logGenerateDate(p.Results.tableSelection,:)                   = cellstr(repmat(datestr(now),size(p.Results.DataTable(p.Results.tableSelection,:),1),1));

end