Minor modifications to scripts.
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@ -254,7 +254,6 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
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'YLim', [0, 1.6]);
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%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
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% --- Recenter curves first ---
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results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals/pi, ...
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@ -339,6 +338,98 @@ Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
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'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 9. Compare mean of individual fits vs fit of mean ------------------
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paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
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paramNames = {'A2', 'mu2', 'sigma2'};
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paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
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meanOfIndividualFits = struct();
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fitOfMeanCurve = struct();
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for p = 1:numel(paramNames)
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paramName = paramNames{p};
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paramLabel = paramLabels{p};
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paramIdx = paramMap.(paramName);
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% --- Mean of individual fits per scan_reference_values ---
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meanVals = nan(size(scan_reference_values)); % already unique
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for k = 1:numel(scan_reference_values)
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thisAlpha = scan_reference_values(k);
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mask = scan_parameter_values == thisAlpha; % pick all repetitions
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pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
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meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
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end
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meanOfIndividualFits.(paramName) = meanVals;
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% --- Fit of mean curve ---
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switch paramName
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case 'A2'
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fitOfMeanCurve.(paramName) = amp2_vals;
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case 'mu2'
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fitOfMeanCurve.(paramName) = mu2_vals;
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case 'sigma2'
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fitOfMeanCurve.(paramName) = sigma2_vals;
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end
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% --- Plot comparison ---
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fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
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set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
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hold on;
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plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
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'DisplayName', 'Mean of Individual Fits');
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plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
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'DisplayName', 'Fit of Mean Curve');
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hold off;
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ylim([0, 1.1])
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xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
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ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
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title(options.titleString, 'FontName', options.font, 'FontSize', 16);
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legend('Location', 'northeast');
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grid on;
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set(gca, 'FontSize', 14);
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if ~options.skipSaveFigures
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saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
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exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
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end
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end
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%% ------------------ 10. Save Fit Results ------------------
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outputDir = fullfile(options.saveDirectory, 'BECToD_ASDFitData');
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if ~exist(outputDir, 'dir')
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mkdir(outputDir);
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end
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% --- Define filenames for each variable ---
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file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
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file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
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file_fitResults = fullfile(outputDir, 'fitResults.mat');
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file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
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file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
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file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
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file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
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% --- Save each variable separately ---
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save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
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save(file_scanRefVals, 'scan_reference_values', '-v7.3');
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save(file_fitResults, 'fitResults', '-v7.3');
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save(file_rawCurves, 'rawCurves', '-v7.3');
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save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
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save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
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save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
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fprintf('\n--- Fit data saved successfully ---\n');
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fprintf('scanParamVals: %s\n', file_scanParamVals);
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fprintf('scanRefVals: %s\n', file_scanRefVals);
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fprintf('fitResults: %s\n', file_fitResults);
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fprintf('rawCurves: %s\n', file_rawCurves);
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fprintf('spectral_analysis_results:%s\n', file_spectralResults);
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fprintf('fitOfMeanCurve: %s\n', file_meanFits);
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fprintf('meanOfIndividualFits: %s\n', file_indivFits);
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fprintf('----------------------------------\n\n');
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%% Inspect individual realizations of a single parameter
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% --- Recenter curves first ---
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@ -122,10 +122,31 @@ Plotter.plotSpectralCurves( ...
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'PositionThreshold', 0.5, ... % minimum separation between peaks
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'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
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% --- Post-fit diagnostics ---
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pFit_all = vertcat(fitResults.pFit); % Ncurves x Nparams
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isValid_all = [fitResults.isValid]; % logical vector
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% Extract last three parameters (usually secondary peak amplitude, position, width)
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pTail = pFit_all(:, end-2:end);
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% Count curves where *any* of these last three values are NaN or zero
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numWithNaN = sum(any(isnan(pTail), 2));
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numWithZero = sum(any(pTail == 0, 2));
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% Count of valid fits
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numValid = sum(isValid_all);
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% Display summary
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fprintf('\n--- Fit diagnostics (last 3 params) ---\n');
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fprintf('Curves with ≥1 NaN in last three pFit elements: %d\n', numWithNaN);
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fprintf('Curves with ≥1 zero in last three pFit elements: %d\n', numWithZero);
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fprintf('Curves marked as isValid = true: %d\n', numValid);
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fprintf('----------------------------------------\n\n');
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%% ------------------ Plot Fits on Raw ------------------
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options.skipLivePlot = true;
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options.skipSaveFigures = false;
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Plotter.plotTwoModeGaussianFitsOnRaw(fitResults, rawCurves, 4, 6, ...
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Plotter.plotTwoModeGaussianFitsOnRawRSD(fitResults, rawCurves, 4, 6, ...
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'SkipLivePlot', options.skipLivePlot, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveDirectory', options.saveDirectory);
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@ -140,7 +161,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
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'YLabel', 'Secondary peak amplitude', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 4, ...
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'FigNum', 2, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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@ -158,7 +179,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
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'YLabel', 'Secondary peak position (\theta, rad)', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 5, ...
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'FigNum', 3, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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@ -175,7 +196,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
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'YLabel', 'Secondary peak width (\sigma, rad)', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 6, ...
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'FigNum', 4, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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@ -193,7 +214,7 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
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'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 8, ...
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'FigNum', 5, ...
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'NumberOfBins', 20, ...
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'NormalizeHistogram', true, ...
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'Colormap', @Colormaps.coolwarm, ...
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@ -202,4 +223,159 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
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'SaveFileName', 'SecondaryGaussianRange.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 7. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
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avgData = Analyzer.computeSpectralAverages(spectral_analysis_results.S_k_all, scan_reference_values);
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% Extract averaged curves (mean across repetitions)
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curves_mean = avgData.curves_mean; % [N_scanValues × N_k_rho]
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krho_values = spectral_analysis_results.k_rho_vals;
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% ---------- Fit two-Gaussian model to mean curves ----------
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[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
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curves_mean, ...
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krho_values, ...
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'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
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'AmplitudeRange', [0, 0.3], ... % truncate curves to y >= 0 (all amplitudes)
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'ResidualThreshold', 0.15, ... % maximum allowed residual
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'PositionThreshold', 0.5, ... % minimum separation between peaks
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'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
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% ---------- Extract fit parameters ----------
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N_params = numel(fitResultsMean);
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amp2_vals = nan(1, N_params);
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mu2_vals = nan(1, N_params);
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sigma2_vals = nan(1, N_params);
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for i = 1:N_params
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pFit = fitResultsMean(i).pFit;
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if all(~isnan(pFit))
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amp2_vals(i) = pFit(4);
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mu2_vals(i) = pFit(5);
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sigma2_vals(i) = pFit(6);
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end
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end
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% ---------- Plot with SEM ----------
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Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
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'Title', options.titleString, ...
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'XLabel', 'B (G)', ...
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'YLabel', 'Secondary peak amplitude', ...
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'FigNum', 6, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakAmplitudeMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
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'Title', options.titleString, ...
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'XLabel', 'B (G)', ...
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'YLabel', 'Secondary peak position (\theta, rad)', ...
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'FigNum', 7, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakPositionMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
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'Title', options.titleString, ...
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'XLabel', 'B (G)', ...
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'YLabel', 'Secondary peak width (\sigma, rad)', ...
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'FigNum', 8, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 8. Compare mean of individual fits vs fit of mean ------------------
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paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
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paramNames = {'A2', 'mu2', 'sigma2'};
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paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
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meanOfIndividualFits = struct();
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fitOfMeanCurve = struct();
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for p = 1:numel(paramNames)
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paramName = paramNames{p};
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paramLabel = paramLabels{p};
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paramIdx = paramMap.(paramName);
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% --- Mean of individual fits per scan_reference_values ---
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meanVals = nan(size(scan_reference_values)); % already unique
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for k = 1:numel(scan_reference_values)
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thisAlpha = scan_reference_values(k);
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mask = scan_parameter_values == thisAlpha; % pick all repetitions
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pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
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meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
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end
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meanOfIndividualFits.(paramName) = meanVals;
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% --- Fit of mean curve ---
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switch paramName
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case 'A2'
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fitOfMeanCurve.(paramName) = amp2_vals;
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case 'mu2'
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fitOfMeanCurve.(paramName) = mu2_vals;
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case 'sigma2'
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fitOfMeanCurve.(paramName) = sigma2_vals;
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end
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% --- Plot comparison ---
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fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
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set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
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hold on;
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plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
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'DisplayName', 'Mean of Individual Fits');
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plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
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'DisplayName', 'Fit of Mean Curve');
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hold off;
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xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
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ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
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title(options.titleString, 'FontName', options.font, 'FontSize', 16);
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legend('Location', 'northeast');
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grid on;
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set(gca, 'FontSize', 14);
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if ~options.skipSaveFigures
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saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
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exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
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end
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end
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%% ------------------ 9. Save Fit Results ------------------
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outputDir = fullfile(options.saveDirectory, 'BECToD_RSDFitData');
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if ~exist(outputDir, 'dir')
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mkdir(outputDir);
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end
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% --- Define filenames for each variable ---
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file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
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file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
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file_fitResults = fullfile(outputDir, 'fitResults.mat');
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file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
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file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
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file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
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file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
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% --- Save each variable separately ---
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save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
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save(file_scanRefVals, 'scan_reference_values', '-v7.3');
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save(file_fitResults, 'fitResults', '-v7.3');
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save(file_rawCurves, 'rawCurves', '-v7.3');
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save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
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save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
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save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
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fprintf('\n--- Fit data saved successfully ---\n');
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fprintf('scanParamVals: %s\n', file_scanParamVals);
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fprintf('scanRefVals: %s\n', file_scanRefVals);
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fprintf('fitResults: %s\n', file_fitResults);
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fprintf('rawCurves: %s\n', file_rawCurves);
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fprintf('spectral_analysis_results:%s\n', file_spectralResults);
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fprintf('fitOfMeanCurve: %s\n', file_meanFits);
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fprintf('meanOfIndividualFits: %s\n', file_indivFits);
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fprintf('----------------------------------\n\n');
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@ -340,7 +340,6 @@ Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 9. Compare mean of individual fits vs fit of mean ------------------
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paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
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paramNames = {'A2', 'mu2', 'sigma2'};
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paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
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@ -398,8 +397,8 @@ for p = 1:numel(paramNames)
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end
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end
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%% ------------------ 5. Save Fit Results ------------------
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outputDir = fullfile(options.saveDirectory, 'DToS_FitData');
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%% ------------------ 10. Save Fit Results ------------------
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outputDir = fullfile(options.saveDirectory, 'DToS_ASDFitData');
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if ~exist(outputDir, 'dir')
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mkdir(outputDir);
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end
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@ -122,10 +122,31 @@ Plotter.plotSpectralCurves( ...
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'PositionThreshold', 0.5, ... % minimum separation between peaks
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'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
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% --- Post-fit diagnostics ---
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pFit_all = vertcat(fitResults.pFit); % Ncurves x Nparams
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isValid_all = [fitResults.isValid]; % logical vector
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% Extract last three parameters (usually secondary peak amplitude, position, width)
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pTail = pFit_all(:, end-2:end);
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% Count curves where *any* of these last three values are NaN or zero
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numWithNaN = sum(any(isnan(pTail), 2));
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numWithZero = sum(any(pTail == 0, 2));
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% Count of valid fits
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numValid = sum(isValid_all);
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% Display summary
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fprintf('\n--- Fit diagnostics (last 3 params) ---\n');
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fprintf('Curves with ≥1 NaN in last three pFit elements: %d\n', numWithNaN);
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fprintf('Curves with ≥1 zero in last three pFit elements: %d\n', numWithZero);
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fprintf('Curves marked as isValid = true: %d\n', numValid);
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fprintf('----------------------------------------\n\n');
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%% ------------------ Plot Fits on Raw ------------------
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options.skipLivePlot = true;
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options.skipSaveFigures = false;
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Plotter.plotTwoModeGaussianFitsOnRaw(fitResults, rawCurves, 4, 6, ...
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Plotter.plotTwoModeGaussianFitsOnRawRSD(fitResults, rawCurves, 4, 6, ...
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'SkipLivePlot', options.skipLivePlot, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveDirectory', options.saveDirectory);
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@ -140,7 +161,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
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'YLabel', 'Secondary peak amplitude', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 4, ...
|
||||
'FigNum', 2, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -158,7 +179,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 5, ...
|
||||
'FigNum', 3, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -175,7 +196,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 6, ...
|
||||
'FigNum', 4, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -193,7 +214,7 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 8, ...
|
||||
'FigNum', 5, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
@ -202,4 +223,159 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'SaveFileName', 'SecondaryGaussianRange.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 7. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
avgData = Analyzer.computeSpectralAverages(spectral_analysis_results.S_k_all, scan_reference_values);
|
||||
|
||||
% Extract averaged curves (mean across repetitions)
|
||||
curves_mean = avgData.curves_mean; % [N_scanValues × N_k_rho]
|
||||
krho_values = spectral_analysis_results.k_rho_vals;
|
||||
|
||||
% ---------- Fit two-Gaussian model to mean curves ----------
|
||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
|
||||
curves_mean, ...
|
||||
krho_values, ...
|
||||
'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
|
||||
'AmplitudeRange', [0, 0.3], ... % truncate curves to y >= 0 (all amplitudes)
|
||||
'ResidualThreshold', 0.15, ... % maximum allowed residual
|
||||
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||
|
||||
% ---------- Extract fit parameters ----------
|
||||
N_params = numel(fitResultsMean);
|
||||
amp2_vals = nan(1, N_params);
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
pFit = fitResultsMean(i).pFit;
|
||||
if all(~isnan(pFit))
|
||||
amp2_vals(i) = pFit(4);
|
||||
mu2_vals(i) = pFit(5);
|
||||
sigma2_vals(i) = pFit(6);
|
||||
end
|
||||
end
|
||||
|
||||
% ---------- Plot with SEM ----------
|
||||
Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FigNum', 6, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudeMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 7, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 8, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 8. Compare mean of individual fits vs fit of mean ------------------
|
||||
paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
|
||||
paramNames = {'A2', 'mu2', 'sigma2'};
|
||||
paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
|
||||
|
||||
meanOfIndividualFits = struct();
|
||||
fitOfMeanCurve = struct();
|
||||
|
||||
for p = 1:numel(paramNames)
|
||||
paramName = paramNames{p};
|
||||
paramLabel = paramLabels{p};
|
||||
paramIdx = paramMap.(paramName);
|
||||
|
||||
% --- Mean of individual fits per scan_reference_values ---
|
||||
meanVals = nan(size(scan_reference_values)); % already unique
|
||||
for k = 1:numel(scan_reference_values)
|
||||
thisAlpha = scan_reference_values(k);
|
||||
mask = scan_parameter_values == thisAlpha; % pick all repetitions
|
||||
|
||||
pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
|
||||
meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
|
||||
end
|
||||
meanOfIndividualFits.(paramName) = meanVals;
|
||||
|
||||
% --- Fit of mean curve ---
|
||||
switch paramName
|
||||
case 'A2'
|
||||
fitOfMeanCurve.(paramName) = amp2_vals;
|
||||
case 'mu2'
|
||||
fitOfMeanCurve.(paramName) = mu2_vals;
|
||||
case 'sigma2'
|
||||
fitOfMeanCurve.(paramName) = sigma2_vals;
|
||||
end
|
||||
|
||||
% --- Plot comparison ---
|
||||
fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
hold on;
|
||||
plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Mean of Individual Fits');
|
||||
plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Fit of Mean Curve');
|
||||
hold off;
|
||||
xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
|
||||
ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
|
||||
title(options.titleString, 'FontName', options.font, 'FontSize', 16);
|
||||
legend('Location', 'northeast');
|
||||
grid on;
|
||||
set(gca, 'FontSize', 14);
|
||||
|
||||
if ~options.skipSaveFigures
|
||||
saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
|
||||
exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
|
||||
end
|
||||
end
|
||||
|
||||
%% ------------------ 9. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'DToS_RSDFitData');
|
||||
if ~exist(outputDir, 'dir')
|
||||
mkdir(outputDir);
|
||||
end
|
||||
|
||||
% --- Define filenames for each variable ---
|
||||
file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
|
||||
file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
|
||||
file_fitResults = fullfile(outputDir, 'fitResults.mat');
|
||||
file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
|
||||
file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
|
||||
file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
|
||||
file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
|
||||
|
||||
% --- Save each variable separately ---
|
||||
save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
|
||||
save(file_scanRefVals, 'scan_reference_values', '-v7.3');
|
||||
save(file_fitResults, 'fitResults', '-v7.3');
|
||||
save(file_rawCurves, 'rawCurves', '-v7.3');
|
||||
save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
|
||||
save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
|
||||
save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
|
||||
|
||||
fprintf('\n--- Fit data saved successfully ---\n');
|
||||
fprintf('scanParamVals: %s\n', file_scanParamVals);
|
||||
fprintf('scanRefVals: %s\n', file_scanRefVals);
|
||||
fprintf('fitResults: %s\n', file_fitResults);
|
||||
fprintf('rawCurves: %s\n', file_rawCurves);
|
||||
fprintf('spectral_analysis_results:%s\n', file_spectralResults);
|
||||
fprintf('fitOfMeanCurve: %s\n', file_meanFits);
|
||||
fprintf('meanOfIndividualFits: %s\n', file_indivFits);
|
||||
fprintf('----------------------------------\n\n');
|
||||
@ -254,7 +254,6 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
|
||||
% --- Recenter curves first ---
|
||||
results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ...
|
||||
@ -339,6 +338,98 @@ Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 9. Compare mean of individual fits vs fit of mean ------------------
|
||||
paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
|
||||
paramNames = {'A2', 'mu2', 'sigma2'};
|
||||
paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
|
||||
|
||||
meanOfIndividualFits = struct();
|
||||
fitOfMeanCurve = struct();
|
||||
|
||||
for p = 1:numel(paramNames)
|
||||
paramName = paramNames{p};
|
||||
paramLabel = paramLabels{p};
|
||||
paramIdx = paramMap.(paramName);
|
||||
|
||||
% --- Mean of individual fits per scan_reference_values ---
|
||||
meanVals = nan(size(scan_reference_values)); % already unique
|
||||
for k = 1:numel(scan_reference_values)
|
||||
thisAlpha = scan_reference_values(k);
|
||||
mask = scan_parameter_values == thisAlpha; % pick all repetitions
|
||||
|
||||
pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
|
||||
meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
|
||||
end
|
||||
meanOfIndividualFits.(paramName) = meanVals;
|
||||
|
||||
% --- Fit of mean curve ---
|
||||
switch paramName
|
||||
case 'A2'
|
||||
fitOfMeanCurve.(paramName) = amp2_vals;
|
||||
case 'mu2'
|
||||
fitOfMeanCurve.(paramName) = mu2_vals;
|
||||
case 'sigma2'
|
||||
fitOfMeanCurve.(paramName) = sigma2_vals;
|
||||
end
|
||||
|
||||
% --- Plot comparison ---
|
||||
fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
hold on;
|
||||
plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Mean of Individual Fits');
|
||||
plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Fit of Mean Curve');
|
||||
hold off;
|
||||
ylim([0, 1.1])
|
||||
xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
|
||||
ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
|
||||
title(options.titleString, 'FontName', options.font, 'FontSize', 16);
|
||||
legend('Location', 'northeast');
|
||||
grid on;
|
||||
set(gca, 'FontSize', 14);
|
||||
|
||||
if ~options.skipSaveFigures
|
||||
saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
|
||||
exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
|
||||
end
|
||||
end
|
||||
|
||||
%% ------------------ 10. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'BECToS_ASDFitData');
|
||||
if ~exist(outputDir, 'dir')
|
||||
mkdir(outputDir);
|
||||
end
|
||||
|
||||
% --- Define filenames for each variable ---
|
||||
file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
|
||||
file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
|
||||
file_fitResults = fullfile(outputDir, 'fitResults.mat');
|
||||
file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
|
||||
file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
|
||||
file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
|
||||
file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
|
||||
|
||||
% --- Save each variable separately ---
|
||||
save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
|
||||
save(file_scanRefVals, 'scan_reference_values', '-v7.3');
|
||||
save(file_fitResults, 'fitResults', '-v7.3');
|
||||
save(file_rawCurves, 'rawCurves', '-v7.3');
|
||||
save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
|
||||
save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
|
||||
save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
|
||||
|
||||
fprintf('\n--- Fit data saved successfully ---\n');
|
||||
fprintf('scanParamVals: %s\n', file_scanParamVals);
|
||||
fprintf('scanRefVals: %s\n', file_scanRefVals);
|
||||
fprintf('fitResults: %s\n', file_fitResults);
|
||||
fprintf('rawCurves: %s\n', file_rawCurves);
|
||||
fprintf('spectral_analysis_results:%s\n', file_spectralResults);
|
||||
fprintf('fitOfMeanCurve: %s\n', file_meanFits);
|
||||
fprintf('meanOfIndividualFits: %s\n', file_indivFits);
|
||||
fprintf('----------------------------------\n\n');
|
||||
|
||||
%% Inspect individual realizations of a single parameter
|
||||
|
||||
% --- Recenter curves first ---
|
||||
|
||||
@ -122,10 +122,31 @@ Plotter.plotSpectralCurves( ...
|
||||
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||
|
||||
% --- Post-fit diagnostics ---
|
||||
pFit_all = vertcat(fitResults.pFit); % Ncurves x Nparams
|
||||
isValid_all = [fitResults.isValid]; % logical vector
|
||||
|
||||
% Extract last three parameters (usually secondary peak amplitude, position, width)
|
||||
pTail = pFit_all(:, end-2:end);
|
||||
|
||||
% Count curves where *any* of these last three values are NaN or zero
|
||||
numWithNaN = sum(any(isnan(pTail), 2));
|
||||
numWithZero = sum(any(pTail == 0, 2));
|
||||
|
||||
% Count of valid fits
|
||||
numValid = sum(isValid_all);
|
||||
|
||||
% Display summary
|
||||
fprintf('\n--- Fit diagnostics (last 3 params) ---\n');
|
||||
fprintf('Curves with ≥1 NaN in last three pFit elements: %d\n', numWithNaN);
|
||||
fprintf('Curves with ≥1 zero in last three pFit elements: %d\n', numWithZero);
|
||||
fprintf('Curves marked as isValid = true: %d\n', numValid);
|
||||
fprintf('----------------------------------------\n\n');
|
||||
|
||||
%% ------------------ Plot Fits on Raw ------------------
|
||||
options.skipLivePlot = true;
|
||||
options.skipSaveFigures = false;
|
||||
Plotter.plotTwoModeGaussianFitsOnRaw(fitResults, rawCurves, 4, 6, ...
|
||||
Plotter.plotTwoModeGaussianFitsOnRawRSD(fitResults, rawCurves, 4, 6, ...
|
||||
'SkipLivePlot', options.skipLivePlot, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
@ -140,7 +161,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 4, ...
|
||||
'FigNum', 2, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -158,7 +179,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 5, ...
|
||||
'FigNum', 3, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -175,7 +196,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 6, ...
|
||||
'FigNum', 4, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -193,7 +214,7 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 8, ...
|
||||
'FigNum', 5, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
@ -202,4 +223,159 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'SaveFileName', 'SecondaryGaussianRange.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 7. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
avgData = Analyzer.computeSpectralAverages(spectral_analysis_results.S_k_all, scan_reference_values);
|
||||
|
||||
% Extract averaged curves (mean across repetitions)
|
||||
curves_mean = avgData.curves_mean; % [N_scanValues × N_k_rho]
|
||||
krho_values = spectral_analysis_results.k_rho_vals;
|
||||
|
||||
% ---------- Fit two-Gaussian model to mean curves ----------
|
||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
|
||||
curves_mean, ...
|
||||
krho_values, ...
|
||||
'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
|
||||
'AmplitudeRange', [0, 0.3], ... % truncate curves to y >= 0 (all amplitudes)
|
||||
'ResidualThreshold', 0.15, ... % maximum allowed residual
|
||||
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||
|
||||
% ---------- Extract fit parameters ----------
|
||||
N_params = numel(fitResultsMean);
|
||||
amp2_vals = nan(1, N_params);
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
pFit = fitResultsMean(i).pFit;
|
||||
if all(~isnan(pFit))
|
||||
amp2_vals(i) = pFit(4);
|
||||
mu2_vals(i) = pFit(5);
|
||||
sigma2_vals(i) = pFit(6);
|
||||
end
|
||||
end
|
||||
|
||||
% ---------- Plot with SEM ----------
|
||||
Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', 'B (G)', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FigNum', 6, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudeMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', 'B (G)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 7, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', 'B (G)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 8, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 8. Compare mean of individual fits vs fit of mean ------------------
|
||||
paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
|
||||
paramNames = {'A2', 'mu2', 'sigma2'};
|
||||
paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
|
||||
|
||||
meanOfIndividualFits = struct();
|
||||
fitOfMeanCurve = struct();
|
||||
|
||||
for p = 1:numel(paramNames)
|
||||
paramName = paramNames{p};
|
||||
paramLabel = paramLabels{p};
|
||||
paramIdx = paramMap.(paramName);
|
||||
|
||||
% --- Mean of individual fits per scan_reference_values ---
|
||||
meanVals = nan(size(scan_reference_values)); % already unique
|
||||
for k = 1:numel(scan_reference_values)
|
||||
thisAlpha = scan_reference_values(k);
|
||||
mask = scan_parameter_values == thisAlpha; % pick all repetitions
|
||||
|
||||
pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
|
||||
meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
|
||||
end
|
||||
meanOfIndividualFits.(paramName) = meanVals;
|
||||
|
||||
% --- Fit of mean curve ---
|
||||
switch paramName
|
||||
case 'A2'
|
||||
fitOfMeanCurve.(paramName) = amp2_vals;
|
||||
case 'mu2'
|
||||
fitOfMeanCurve.(paramName) = mu2_vals;
|
||||
case 'sigma2'
|
||||
fitOfMeanCurve.(paramName) = sigma2_vals;
|
||||
end
|
||||
|
||||
% --- Plot comparison ---
|
||||
fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
hold on;
|
||||
plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Mean of Individual Fits');
|
||||
plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Fit of Mean Curve');
|
||||
hold off;
|
||||
xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
|
||||
ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
|
||||
title(options.titleString, 'FontName', options.font, 'FontSize', 16);
|
||||
legend('Location', 'northeast');
|
||||
grid on;
|
||||
set(gca, 'FontSize', 14);
|
||||
|
||||
if ~options.skipSaveFigures
|
||||
saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
|
||||
exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
|
||||
end
|
||||
end
|
||||
|
||||
%% ------------------ 9. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'BECToS_RSDFitData');
|
||||
if ~exist(outputDir, 'dir')
|
||||
mkdir(outputDir);
|
||||
end
|
||||
|
||||
% --- Define filenames for each variable ---
|
||||
file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
|
||||
file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
|
||||
file_fitResults = fullfile(outputDir, 'fitResults.mat');
|
||||
file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
|
||||
file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
|
||||
file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
|
||||
file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
|
||||
|
||||
% --- Save each variable separately ---
|
||||
save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
|
||||
save(file_scanRefVals, 'scan_reference_values', '-v7.3');
|
||||
save(file_fitResults, 'fitResults', '-v7.3');
|
||||
save(file_rawCurves, 'rawCurves', '-v7.3');
|
||||
save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
|
||||
save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
|
||||
save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
|
||||
|
||||
fprintf('\n--- Fit data saved successfully ---\n');
|
||||
fprintf('scanParamVals: %s\n', file_scanParamVals);
|
||||
fprintf('scanRefVals: %s\n', file_scanRefVals);
|
||||
fprintf('fitResults: %s\n', file_fitResults);
|
||||
fprintf('rawCurves: %s\n', file_rawCurves);
|
||||
fprintf('spectral_analysis_results:%s\n', file_spectralResults);
|
||||
fprintf('fitOfMeanCurve: %s\n', file_meanFits);
|
||||
fprintf('meanOfIndividualFits: %s\n', file_indivFits);
|
||||
fprintf('----------------------------------\n\n');
|
||||
|
||||
@ -254,7 +254,6 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
|
||||
% --- Recenter curves first ---
|
||||
results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ...
|
||||
@ -340,7 +339,6 @@ Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 9. Compare mean of individual fits vs fit of mean ------------------
|
||||
|
||||
paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
|
||||
paramNames = {'A2', 'mu2', 'sigma2'};
|
||||
paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
|
||||
@ -398,8 +396,8 @@ for p = 1:numel(paramNames)
|
||||
end
|
||||
end
|
||||
|
||||
%% ------------------ 5. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'SToD_FitData');
|
||||
%% ------------------ 10. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'SToD_ASDFitData');
|
||||
if ~exist(outputDir, 'dir')
|
||||
mkdir(outputDir);
|
||||
end
|
||||
|
||||
@ -122,10 +122,31 @@ Plotter.plotSpectralCurves( ...
|
||||
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||
|
||||
% --- Post-fit diagnostics ---
|
||||
pFit_all = vertcat(fitResults.pFit); % Ncurves x Nparams
|
||||
isValid_all = [fitResults.isValid]; % logical vector
|
||||
|
||||
% Extract last three parameters (usually secondary peak amplitude, position, width)
|
||||
pTail = pFit_all(:, end-2:end);
|
||||
|
||||
% Count curves where *any* of these last three values are NaN or zero
|
||||
numWithNaN = sum(any(isnan(pTail), 2));
|
||||
numWithZero = sum(any(pTail == 0, 2));
|
||||
|
||||
% Count of valid fits
|
||||
numValid = sum(isValid_all);
|
||||
|
||||
% Display summary
|
||||
fprintf('\n--- Fit diagnostics (last 3 params) ---\n');
|
||||
fprintf('Curves with ≥1 NaN in last three pFit elements: %d\n', numWithNaN);
|
||||
fprintf('Curves with ≥1 zero in last three pFit elements: %d\n', numWithZero);
|
||||
fprintf('Curves marked as isValid = true: %d\n', numValid);
|
||||
fprintf('----------------------------------------\n\n');
|
||||
|
||||
%% ------------------ Plot Fits on Raw ------------------
|
||||
options.skipLivePlot = true;
|
||||
options.skipSaveFigures = false;
|
||||
Plotter.plotTwoModeGaussianFitsOnRaw(fitResults, rawCurves, 4, 6, ...
|
||||
Plotter.plotTwoModeGaussianFitsOnRawRSD(fitResults, rawCurves, 4, 6, ...
|
||||
'SkipLivePlot', options.skipLivePlot, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
@ -140,7 +161,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 4, ...
|
||||
'FigNum', 2, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -158,7 +179,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 5, ...
|
||||
'FigNum', 3, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -175,7 +196,7 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 6, ...
|
||||
'FigNum', 4, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'SaveDirectory', options.saveDirectory, ...
|
||||
@ -193,7 +214,7 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 8, ...
|
||||
'FigNum', 5, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
@ -202,4 +223,159 @@ Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||
'SaveFileName', 'SecondaryGaussianRange.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 7. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
avgData = Analyzer.computeSpectralAverages(spectral_analysis_results.S_k_all, scan_reference_values);
|
||||
|
||||
% Extract averaged curves (mean across repetitions)
|
||||
curves_mean = avgData.curves_mean; % [N_scanValues × N_k_rho]
|
||||
krho_values = spectral_analysis_results.k_rho_vals;
|
||||
|
||||
% ---------- Fit two-Gaussian model to mean curves ----------
|
||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
|
||||
curves_mean, ...
|
||||
krho_values, ...
|
||||
'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
|
||||
'AmplitudeRange', [0, 0.3], ... % truncate curves to y >= 0 (all amplitudes)
|
||||
'ResidualThreshold', 0.15, ... % maximum allowed residual
|
||||
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||
|
||||
% ---------- Extract fit parameters ----------
|
||||
N_params = numel(fitResultsMean);
|
||||
amp2_vals = nan(1, N_params);
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
pFit = fitResultsMean(i).pFit;
|
||||
if all(~isnan(pFit))
|
||||
amp2_vals(i) = pFit(4);
|
||||
mu2_vals(i) = pFit(5);
|
||||
sigma2_vals(i) = pFit(6);
|
||||
end
|
||||
end
|
||||
|
||||
% ---------- Plot with SEM ----------
|
||||
Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FigNum', 6, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudeMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 7, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 8, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 8. Compare mean of individual fits vs fit of mean ------------------
|
||||
paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
|
||||
paramNames = {'A2', 'mu2', 'sigma2'};
|
||||
paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
|
||||
|
||||
meanOfIndividualFits = struct();
|
||||
fitOfMeanCurve = struct();
|
||||
|
||||
for p = 1:numel(paramNames)
|
||||
paramName = paramNames{p};
|
||||
paramLabel = paramLabels{p};
|
||||
paramIdx = paramMap.(paramName);
|
||||
|
||||
% --- Mean of individual fits per scan_reference_values ---
|
||||
meanVals = nan(size(scan_reference_values)); % already unique
|
||||
for k = 1:numel(scan_reference_values)
|
||||
thisAlpha = scan_reference_values(k);
|
||||
mask = scan_parameter_values == thisAlpha; % pick all repetitions
|
||||
|
||||
pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
|
||||
meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
|
||||
end
|
||||
meanOfIndividualFits.(paramName) = meanVals;
|
||||
|
||||
% --- Fit of mean curve ---
|
||||
switch paramName
|
||||
case 'A2'
|
||||
fitOfMeanCurve.(paramName) = amp2_vals;
|
||||
case 'mu2'
|
||||
fitOfMeanCurve.(paramName) = mu2_vals;
|
||||
case 'sigma2'
|
||||
fitOfMeanCurve.(paramName) = sigma2_vals;
|
||||
end
|
||||
|
||||
% --- Plot comparison ---
|
||||
fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
hold on;
|
||||
plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Mean of Individual Fits');
|
||||
plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
|
||||
'DisplayName', 'Fit of Mean Curve');
|
||||
hold off;
|
||||
xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
|
||||
ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
|
||||
title(options.titleString, 'FontName', options.font, 'FontSize', 16);
|
||||
legend('Location', 'northeast');
|
||||
grid on;
|
||||
set(gca, 'FontSize', 14);
|
||||
|
||||
if ~options.skipSaveFigures
|
||||
saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
|
||||
exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
|
||||
end
|
||||
end
|
||||
|
||||
%% ------------------ 9. Save Fit Results ------------------
|
||||
outputDir = fullfile(options.saveDirectory, 'SToD_RSDFitData');
|
||||
if ~exist(outputDir, 'dir')
|
||||
mkdir(outputDir);
|
||||
end
|
||||
|
||||
% --- Define filenames for each variable ---
|
||||
file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
|
||||
file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
|
||||
file_fitResults = fullfile(outputDir, 'fitResults.mat');
|
||||
file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
|
||||
file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
|
||||
file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
|
||||
file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
|
||||
|
||||
% --- Save each variable separately ---
|
||||
save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
|
||||
save(file_scanRefVals, 'scan_reference_values', '-v7.3');
|
||||
save(file_fitResults, 'fitResults', '-v7.3');
|
||||
save(file_rawCurves, 'rawCurves', '-v7.3');
|
||||
save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
|
||||
save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
|
||||
save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
|
||||
|
||||
fprintf('\n--- Fit data saved successfully ---\n');
|
||||
fprintf('scanParamVals: %s\n', file_scanParamVals);
|
||||
fprintf('scanRefVals: %s\n', file_scanRefVals);
|
||||
fprintf('fitResults: %s\n', file_fitResults);
|
||||
fprintf('rawCurves: %s\n', file_rawCurves);
|
||||
fprintf('spectral_analysis_results:%s\n', file_spectralResults);
|
||||
fprintf('fitOfMeanCurve: %s\n', file_meanFits);
|
||||
fprintf('meanOfIndividualFits: %s\n', file_indivFits);
|
||||
fprintf('----------------------------------\n\n');
|
||||
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Reference in New Issue
Block a user