56 lines
2.0 KiB
Matlab
56 lines
2.0 KiB
Matlab
function results = computeSpectralAverages(S_all, scan_reference_values)
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%% computeSpectralAverages
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% Compute mean and SEM of spectra grouped by scan parameter.
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% Handles both 1D curves and 2D matrices.
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[uniqueParams, ~, idx] = unique(scan_reference_values);
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nParams = numel(uniqueParams);
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nTotal = numel(S_all);
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nReps = nTotal / nParams;
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% Determine data dimensionality
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firstData = S_all{1};
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dataSize = size(firstData);
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isMatrix = (numel(size(firstData)) == 2) && all(size(firstData) > 1);
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curves_all = cell(1, nParams);
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curves_mean = cell(1, nParams);
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curves_err = cell(1, nParams);
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for i = 1:nParams
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if isMatrix
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% --- Case: 2D matrix (e.g., power spectra) ---
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data_sum = zeros(dataSize); % initialize
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nValid = zeros(dataSize); % count of non-NaN entries
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for r = 1:nReps
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idx_r = (r-1)*nParams + i;
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data = S_all{idx_r};
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validMask = ~isnan(data);
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data_sum(validMask) = data_sum(validMask) + data(validMask);
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nValid(validMask) = nValid(validMask) + 1;
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end
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avg = data_sum ./ nValid; % mean ignoring NaNs
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curves_all{i} = []; % not used for 2D
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curves_mean{i} = avg;
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curves_err{i} = []; % SEM not computed for 2D
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else
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% --- Case: 1D curve ---
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nPoints = numel(S_all{1});
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curves = zeros(nReps, nPoints);
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for r = 1:nReps
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idx_r = (r-1)*nParams + i;
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curves(r,:) = S_all{idx_r};
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end
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curves_all{i} = curves;
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curves_mean{i} = mean(curves,1,'omitnan');
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nValid = sum(~isnan(curves),1);
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curves_err{i} = std(curves,0,1,'omitnan') ./ sqrt(nValid);
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end
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end
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results.curves_all = curves_all;
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results.curves_mean = curves_mean;
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results.curves_error = curves_err;
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results.scan_parameters = uniqueParams;
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end
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