Latest fully functional script - added complete PCA functionality - computes the PCA results and packs in to results struct, included plotting functionality of the PCA results in addition.
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@ -1,111 +1,26 @@
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function conductPCA(od_imgs, scan_reference_values, scan_parameter_values, doPlot, doSave, saveDir)
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%% Performs PCA on optical density images, visualizes and optionally saves results.
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function results = conductPCA(od_imgs)
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%% computePCAfromImages: Performs PCA on optical density images and returns results in a struct
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%
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% Inputs:
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% od_imgs - cell array of OD images
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% scan_reference_values - array of unique control parameter values
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% scan_parameter_values - array mapping each image to a control parameter
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% doPlot - logical, true to plot figures
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% doSave - logical, true to save figures
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% saveDir - directory to save figures if doSave is true
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% od_imgs - cell array of OD images
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%
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% Requires:
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% +Calculator/computeCumulants.m
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% Outputs:
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% pcaResults - struct containing PCA outputs:
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% .coeff - PCA coefficients (principal components)
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% .score - PCA scores for each image
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% .explained - variance explained by each PC
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% .Nx, .Ny - dimensions of individual images
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if nargin < 4, doPlot = true; end
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if nargin < 5, doSave = false; end
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if nargin < 6, saveDir = pwd; end
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%% PCA computation
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allImgs3D = cat(3, od_imgs{:});
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[Nx, Ny] = size(allImgs3D(:,:,1));
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Xall = reshape(allImgs3D, [], numel(od_imgs))';
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[coeff, score, ~, ~, explained] = pca(Xall);
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figCount = 1;
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%% --- Figure 1: PC1 Image ---
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if doPlot
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pc1_vector = coeff(:,1);
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pc1_image = reshape(pc1_vector, Nx, Ny);
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
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imagesc(pc1_image); axis image off; colormap(Colormaps.coolwarm()); colorbar;
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title(sprintf('First Principal Component (PC1) Image - Explains %.2f%% Variance', explained(1)));
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Image.png')); end
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figCount = figCount + 1;
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end
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%% --- Figure 2: PC1 Scores Scatter ---
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if doPlot
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numGroups = numel(scan_reference_values);
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colors = lines(numGroups);
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]); hold on;
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for g = 1:numGroups
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idx = scan_parameter_values == scan_reference_values(g);
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scatter(repmat(scan_reference_values(g), sum(idx),1), score(idx,1), 36, colors(g,:), 'filled');
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end
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xlabel('Control Parameter'); ylabel('PC1 Score');
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title('Evolution of PC1 Scores'); grid on;
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Scatter.png')); end
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figCount = figCount + 1;
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end
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%% --- Figure 3: PC1 Distributions ---
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if doPlot
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
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hold on;
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for g = 1:numGroups
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idx = scan_parameter_values == scan_reference_values(g);
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data = score(idx,1);
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histogram(data, 'Normalization', 'pdf', 'FaceColor', colors(g,:), 'FaceAlpha', 0.3);
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[f, xi] = ksdensity(data);
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plot(xi, f, 'Color', colors(g,:), 'LineWidth', 2);
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end
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xlabel('PC1 Score'); ylabel('Probability Density');
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title('PC1 Score Distributions by Group');
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legend(arrayfun(@num2str, scan_reference_values, 'UniformOutput', false), 'Location', 'Best');
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grid on;
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Distributions.png')); end
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figCount = figCount + 1;
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end
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%% --- Figure 4: Boxplot of PC1 Scores ---
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if doPlot
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
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boxplot(score(:,1), scan_parameter_values);
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xlabel('Control Parameter'); ylabel('PC1 Score');
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title('PC1 Score Boxplots by Group'); grid on;
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Boxplot.png')); end
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figCount = figCount + 1;
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end
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%% --- Figure 5: Mean ± SEM of PC1 Scores ---
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if doPlot
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meanScores = arrayfun(@(g) mean(score(scan_parameter_values == g,1)), scan_reference_values);
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semScores = arrayfun(@(g) std(score(scan_parameter_values == g,1))/sqrt(sum(scan_parameter_values == g)), scan_reference_values);
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
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errorbar(scan_reference_values, meanScores, semScores, '-o', 'LineWidth', 2);
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xlabel('Control Parameter'); ylabel('Mean PC1 Score ± SEM');
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title('Mean ± SEM of PC1 Scores'); grid on;
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_MeanSEM.png')); end
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figCount = figCount + 1;
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end
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%% --- Figure 6: Binder Cumulant ---
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if doPlot
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binderVals = arrayfun(@(g) ...
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Calculator.computeCumulants(score(scan_parameter_values == g,1)), ...
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scan_reference_values);
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figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
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plot(scan_reference_values, binderVals, '-o', 'LineWidth', 2);
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xlabel('Control Parameter'); ylabel('Binder Cumulant (PC1)');
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title('Binder Cumulant of PC1 Scores'); grid on;
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if doSave, saveas(gcf, fullfile(saveDir, 'PC1_BinderCumulant.png')); end
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end
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%% --- ANOVA Test ---
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p = anova1(score(:,1), arrayfun(@num2str, scan_parameter_values, 'UniformOutput', false), 'off');
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fprintf('[INFO] ANOVA p-value for PC1 score differences between groups: %.4e\n', p);
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results = struct( ...
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'coeff', coeff, ...
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'score', score, ...
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'explained', explained, ...
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'Nx', Nx, ...
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'Ny', Ny ...
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);
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end
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@ -52,21 +52,26 @@ function [results, scan_parameter_values] = performAnalysis(options)
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spectral_analysis_results = Analyzer.conductSpectralAnalysis(od_imgs, scan_parameter_values, options);
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N_shots = length(od_imgs);
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N_shots = length(od_imgs);
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% Extract angular correlations
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full_g2_results = Analyzer.extractAutocorrelation(...
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full_g2_results = Analyzer.extractAutocorrelation(...
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spectral_analysis_results.theta_vals, ...
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spectral_analysis_results.angular_spectral_distribution, ...
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scan_parameter_values, N_shots, options.N_angular_bins);
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custom_g_results = Analyzer.extractCustomCorrelation(...
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custom_g_results = Analyzer.extractCustomCorrelation(...
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spectral_analysis_results.angular_spectral_distribution, ...
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scan_parameter_values, N_shots, options.N_angular_bins);
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fprintf('\n[INFO] Spectral analysis complete!\n');
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% PCA
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% Conduct PCA
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fprintf('\n[INFO] Initiating Principal Component Analysis...\n');
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pca_results = Analyzer.conductPCA(od_imgs);
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fprintf('\n[INFO] Principal Component Analysis complete!\n');
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% Lattice Reconstruction
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@ -75,4 +80,5 @@ function [results, scan_parameter_values] = performAnalysis(options)
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results.spectral_analysis_results = spectral_analysis_results;
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results.full_g2_results = full_g2_results;
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results.custom_g_results = custom_g_results;
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results.pca_results = pca_results;
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end
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@ -25,8 +25,14 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
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reuseVarsExist = evalin('base', ...
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'exist(''od_imgs'',''var'') && exist(''scan_parameter_values'',''var'') && exist(''file_list'',''var'') && exist(''prior_options'',''var'')');
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% --- Respect SAVE_TO_WORKSPACE flag from batchAnalyze ---
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if isfield(options, 'SAVE_TO_WORKSPACE') && ~options.SAVE_TO_WORKSPACE
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if ~isfield(options, 'SAVE_TO_WORKSPACE')
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% ===== Estimate dataset memory and get per-run estimates =====
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dataSource = makeDataSource(options.folderPath);
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[options.SAVE_TO_WORKSPACE, ~] = Helper.estimateDatasetMemory(dataSource, options);
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end
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% --- Respect SAVE_TO_WORKSPACE flag ---
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if ~options.SAVE_TO_WORKSPACE
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% Force reprocessing: skip all workspace reuse
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reuseVarsExist = false;
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end
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@ -62,7 +68,7 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
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evalin('base', 'exist(''prior_options'',''var'')');
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% --- Respect SAVE_TO_WORKSPACE flag ---
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if isfield(options, 'SAVE_TO_WORKSPACE') && ~options.SAVE_TO_WORKSPACE
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if ~options.SAVE_TO_WORKSPACE
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fullDataExists = false; % force recompute even if workspace vars exist
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end
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@ -208,13 +214,13 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
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file_list = raw_file_list;
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end
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% --- Save processed dataset and options for reuse ---
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% --- Save processed dataset and options to workspace for reuse ---
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assignin('base', 'od_imgs', od_imgs);
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assignin('base', 'scan_parameter_values', scan_parameter_values);
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assignin('base', 'file_list', file_list);
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assignin('base', 'prior_options', options);
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% --- Save OD images as figures if requested ---
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% --- Save OD images as figures to disk if requested ---
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if ~options.skipSaveOD
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saveODFigures(od_imgs, options.saveDirectory);
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end
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@ -269,3 +275,33 @@ function saveODFigures(od_imgs, saveDirectory)
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end
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fprintf('[INFO] OD figures saved successfully.\n');
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end
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function dataSources = makeDataSource(folderPath)
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% Split by file separators (handles / or \)
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parts = regexp(folderPath, '[\\/]', 'split');
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% Remove empty parts caused by leading slashes
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parts = parts(~cellfun('isempty', parts));
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% Extract sequence, date, and run number
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% Now the indices are correct:
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% parts = {'DyLabNAS', 'Data', 'StructuralPhaseTransition', '2025', '08', '13', '0062'}
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sequence = parts{3}; % "StructuralPhaseTransition"
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year = parts{4}; % "2025"
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month = parts{5}; % "08"
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day = parts{6}; % "13"
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runStr = parts{7}; % "0062"
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% Build date string
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dateStr = sprintf('%s/%s/%s', year, month, day);
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% Convert run string to number
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runNum = str2double(runStr);
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% Construct struct inside a cell array
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dataSources = {
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struct('sequence', sequence, ...
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'date', dateStr, ...
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'runs', runNum)
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};
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end
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@ -10,7 +10,7 @@ function [SAVE_TO_WORKSPACE, runMemoryGB] = estimateDatasetMemory(dataSources, o
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[~, sys] = memory;
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availableRAM = sys.PhysicalMemory.Available;
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else
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availableRAM = 16e9; % fallback: 16 GB if not Windows
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availableRAM = 8e9; % fallback: 8 GB if not Windows
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end
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SAVE_TO_WORKSPACE = true; % default, may change per run
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@ -58,10 +58,10 @@ function [SAVE_TO_WORKSPACE, runMemoryGB] = estimateDatasetMemory(dataSources, o
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% Decide workspace flag per run by comparing with 50% of available RAM
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if runBytes > 0.75 * availableRAM
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SAVE_TO_WORKSPACE = false;
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fprintf('[INFO] Estimated size on memory of Run %s/%s too large (%.2f GB). Not saving to workspace.\n', ...
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fprintf('\n[INFO] Estimated size on memory of Run %s/%s too large (%.2f GB). Will save partially to workspace if not done so already.\n', ...
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ds.sequence, runID, runBytes/1e9);
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else
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fprintf('[INFO] Estimated size on memory of Run %s/%s = %.2f GB. Will save to workspace.\n', ...
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fprintf('\n[INFO] Estimated size on memory of Run %s/%s = %.2f GB. Will save completely to workspace if not done so already.\n', ...
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ds.sequence, runID, runBytes/1e9);
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end
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end
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@ -4,8 +4,8 @@ function plotPDF(dataCell, referenceValues, varargin)
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% Usage:
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% Plotter.plotPDF(dataCell, referenceValues, ...
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% 'PlotType', 'histogram', ... % 'histogram' (default) or 'kde'
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% 'NumBins', 50, ... % number of histogram bins
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% 'NormalizeHist', true, ... % normalize hist counts to probability density
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% 'NumberOfBins', 50, ... % number of histogram bins
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% 'NormalizeHistogram', true, ... % normalize hist counts to probability density
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% 'Title', 'My Title', ...
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% 'XLabel', 'Scan Parameter', ...
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% 'YLabel', 'Data Values', ...
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@ -35,8 +35,8 @@ function plotPDF(dataCell, referenceValues, varargin)
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addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'Colormap', @jet);
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addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'})));
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addParameter(p, 'NumBins', 50, @isscalar);
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addParameter(p, 'NormalizeHist', true, @islogical);
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addParameter(p, 'NumberOfBins', 50, @isscalar);
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addParameter(p, 'NormalizeHistogram', true, @islogical);
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parse(p, varargin{:});
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opts = p.Results;
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@ -56,7 +56,7 @@ function plotPDF(dataCell, referenceValues, varargin)
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y_grid = linspace(y_min, y_max, opts.NumPoints);
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pdf_matrix = zeros(numel(y_grid), N_params);
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else % Histogram
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edges = linspace(y_min, y_max, opts.NumBins+1);
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edges = linspace(y_min, y_max, opts.NumberOfBins+1);
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binCenters = (edges(1:end-1) + edges(2:end)) / 2;
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pdf_matrix = zeros(numel(binCenters), N_params);
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end
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@ -72,7 +72,7 @@ function plotPDF(dataCell, referenceValues, varargin)
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pdf_matrix(:, i) = f;
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else % Histogram
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counts = histcounts(data, edges);
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if opts.NormalizeHist
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if opts.NormalizeHistogram
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binWidth = edges(2) - edges(1);
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counts = counts / (sum(counts) * binWidth); % probability density
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end
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@ -99,7 +99,7 @@ function plotPDF(dataCell, referenceValues, varargin)
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if strcmpi(opts.PlotType,'kde')
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ylabel(c, 'PDF', 'Interpreter', 'latex', 'FontSize', opts.FontSize);
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else
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if opts.NormalizeHist
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if opts.NormalizeHistogram
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ylabel(c, 'Probability Density', 'Interpreter', 'latex', 'FontSize', opts.FontSize);
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else
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ylabel(c, 'Counts', 'Interpreter', 'latex', 'FontSize', opts.FontSize);
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@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
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'SaveDirectory', figSaveDir, ...
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'NumBins', 20, ...
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'NormalizeHist', true, ...
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'NumberOfBins', 20, ...
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'NormalizeHistogram', true, ...
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'DataRange', [0 1.5], ...
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'Colormap', @Colormaps.coolwarm, ...
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'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);
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@ -108,6 +108,15 @@ Plotter.plotCumulants(options.scan_reference_values, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'CumulantOfPeakOffsetAngularCorrelation.fig', ...
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'SaveDirectory', figSaveDir);
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%% ------------------ 5. PCA ------------------
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Plotter.plotPCAResults(compiled_results.pca_results, scan_parameter_values, options.scan_reference_values, ...
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'FigNumRange', [7,8,9,10,11,12], ...
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'FontName', options.font, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveDirectory', figSaveDir);
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%%
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%{
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%% ------------------ 6. Average of Spectra Plots ------------------
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@ -10,29 +10,29 @@ dataSources = {
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options = struct();
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% File / paths
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options.baseDataFolder = '//DyLabNAS/Data';
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options.measurementName = 'BECToDroplets';
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scriptFullPath = mfilename('fullpath');
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options.saveDirectory = fileparts(scriptFullPath);
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options.baseDataFolder = '//DyLabNAS/Data';
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options.measurementName = 'BECToDroplets';
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scriptFullPath = mfilename('fullpath');
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options.saveDirectory = fileparts(scriptFullPath);
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% Camera / imaging
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options.cam = 5;
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options.angle = 0;
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options.center = [1420, 2050];
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options.span = [200, 200];
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options.fraction = [0.1, 0.1];
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options.pixel_size = 5.86e-6; % in meters
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options.magnification = 24.6;
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options.removeFringes = false;
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options.ImagingMode = 'HighIntensity';
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options.PulseDuration = 5e-6; % in s
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options.cam = 5;
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options.angle = 0;
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options.center = [1420, 2050];
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options.span = [200, 200];
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options.fraction = [0.1, 0.1];
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options.pixel_size = 5.86e-6; % in meters
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options.magnification = 24.6;
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options.removeFringes = false;
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options.ImagingMode = 'HighIntensity';
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options.PulseDuration = 5e-6; % in s
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% Fourier analysis settings
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options.theta_min = deg2rad(0);
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options.theta_max = deg2rad(180);
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options.N_radial_bins = 500;
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options.Radial_Sigma = 2;
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options.Radial_WindowSize = 5; % odd number
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options.theta_min = deg2rad(0);
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options.theta_max = deg2rad(180);
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options.N_radial_bins = 500;
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options.Radial_Sigma = 2;
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options.Radial_WindowSize = 5; % odd number
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options.k_min = 1.2771; % μm⁻¹
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options.k_max = 2.5541; % μm⁻¹
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@ -43,7 +43,7 @@ options.Angular_WindowSize = 5;
|
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options.zoom_size = 50;
|
||||
|
||||
% Scan parameter
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
|
||||
switch options.measurementName
|
||||
case 'BECToDroplets'
|
||||
|
||||
@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
|
||||
'SaveDirectory', figSaveDir, ...
|
||||
'NumBins', 20, ...
|
||||
'NormalizeHist', true, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'DataRange', [0 1.5], ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);
|
||||
|
||||
@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
|
||||
'SaveDirectory', figSaveDir, ...
|
||||
'NumBins', 20, ...
|
||||
'NormalizeHist', true, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'DataRange', [0 1.5], ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);
|
||||
|
||||
@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
|
||||
'SaveDirectory', figSaveDir, ...
|
||||
'NumBins', 20, ...
|
||||
'NormalizeHist', true, ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'DataRange', [0 1.5], ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user