190 lines
7.5 KiB
Matlab
190 lines
7.5 KiB
Matlab
function plotPCAResults(pcaResults, scan_parameter_values, scan_reference_values, varargin)
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%% plotPCAResults: Plots PCA results in a style consistent with plotG2
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%
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% Inputs:
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% pcaResults - struct returned by computePCAfromImages
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% scan_parameter_values, scan_reference_values
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% varargin - name-value pairs (same as plotG2 plus 'FigNumRange')
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% --- Parse name-value pairs ---
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p = inputParser;
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addParameter(p, 'FontName', 'Arial', @ischar);
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addParameter(p, 'FontSize', 14, @isnumeric);
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addParameter(p, 'Colormap', @Colormaps.coolwarm);
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addParameter(p, 'SkipSaveFigures', false, @islogical);
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addParameter(p, 'SaveDirectory', pwd, @ischar);
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addParameter(p, 'FigNumRange', [], @(x) isnumeric(x) && all(x>0));
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parse(p, varargin{:});
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opts = p.Results;
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Nx = pcaResults.Nx;
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Ny = pcaResults.Ny;
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coeff = pcaResults.coeff;
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score = pcaResults.score;
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explained = pcaResults.explained;
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raw_scan_param_vals = scan_parameter_values;
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unique_scan_param_vals = scan_reference_values;
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numGroups = numel(unique_scan_param_vals);
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colors = lines(numGroups);
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% --- Figure numbering setup ---
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if isempty(opts.FigNumRange)
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figCount = 1;
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figNums = [];
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else
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figNums = opts.FigNumRange;
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figCount = 1;
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end
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figPos = [100 100 950 750];
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%% --- Figure 1: PC1 Image ---
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pc1_image = reshape(coeff(:,1), Nx, Ny);
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos);
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imagesc(pc1_image); axis image off; colormap(opts.Colormap()); colorbar;
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title(sprintf('First Principal Component (PC1) Image - Explains %.2f%% Variance', explained(1)), ...
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'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Image.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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figCount = figCount + 1;
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%% --- Figure 2: PC1 Scores Distribution Scatterplot ---
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos); hold on;
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for g = 1:numGroups
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idx = raw_scan_param_vals == unique_scan_param_vals(g);
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scatter(repmat(unique_scan_param_vals(g), sum(idx),1), score(idx,1), 36, colors(g,:), 'filled');
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end
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xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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ylabel('PC1 Score', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title('Evolution of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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grid on;
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set(gca,'XDir','reverse'); % Ensure decreasing scan_reference_values go left-to-right
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Scatter.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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figCount = figCount + 1;
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%% --- Figure 3: PC1 Scores Distribution Histograms ---
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numTiles = min(6, numGroups); % show up to 6 groups
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tileIndices = round(linspace(1, numGroups, numTiles));
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos);
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tLayout = tiledlayout(3,2,'TileSpacing','compact','Padding','compact');
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for t = 1:numTiles
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g = tileIndices(t);
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idx = raw_scan_param_vals == unique_scan_param_vals(g);
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data = score(idx,1);
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nexttile;
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histogram(data, 'Normalization', 'pdf', 'FaceColor', colors(g,:), 'FaceAlpha', 0.3);
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hold on;
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[f, xi] = ksdensity(data);
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plot(xi, f, 'Color', colors(g,:), 'LineWidth', 2);
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yl = ylim;
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plot([median(data) median(data)], yl, 'k--', 'LineWidth', 1);
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xlabel('PC1 Score');
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ylabel('Probability');
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title(sprintf('Control = %g', unique_scan_param_vals(g)));
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grid on;
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end
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sgtitle('PC1 Score Distributions');
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Distributions.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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figCount = figCount + 1;
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%% --- Figure 4: PC1 Scores Distribution Boxplot ---
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% Construct group labels explicitly
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groupLabels = arrayfun(@num2str, raw_scan_param_vals, 'UniformOutput', false);
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% Create categorical variable with specified order
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groupCats = categorical(groupLabels, ...
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arrayfun(@num2str, unique_scan_param_vals, 'UniformOutput', false), ...
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'Ordinal', true);
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos);
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% Plot boxplot with categorical groups
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boxplot(score(:,1), groupCats);
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xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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ylabel('PC1 Score', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title('PC1 Score Boxplots by Group', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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grid on;
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Boxplot.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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figCount = figCount + 1;
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%% --- Figure 5: PC1 Scores Distribution Mean ± SEM ---
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos);
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meanScores = arrayfun(@(g) mean(score(raw_scan_param_vals == g,1)), unique_scan_param_vals);
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semScores = arrayfun(@(g) std(score(raw_scan_param_vals == g,1))/sqrt(sum(raw_scan_param_vals == g)), unique_scan_param_vals);
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errorbar(unique_scan_param_vals, meanScores, semScores, '-o', 'LineWidth', 2);
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xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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ylabel('Mean PC1 Score ± SEM', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title('Mean ± SEM of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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grid on;
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set(gca,'XDir','reverse'); % consistent ordering
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_MeanSEM.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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figCount = figCount + 1;
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%% --- Figure 6: PC1 Scores Distribution Binder Cumulant ---
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cumulantsAll = cell2mat(arrayfun(@(g) {Calculator.computeCumulants(score(raw_scan_param_vals == g,1), 4)}, unique_scan_param_vals));
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cumulantsAll = reshape(cumulantsAll, 4, numGroups);
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binderVals = cumulantsAll(4,:);
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if ~isempty(figNums)
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fig = figure(figNums(figCount)); clf;
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else
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fig = figure; clf;
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end
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set(fig, 'Color', 'w', 'Position', figPos);
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plot(unique_scan_param_vals, binderVals*1E-5, '-o', 'LineWidth', 2); % scale like older code
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xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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ylabel('\kappa (\times 10^5)', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title('Binder Cumulant of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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grid on;
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set(gca,'XDir','reverse'); % consistent ordering
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if ~opts.SkipSaveFigures
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Plotter.saveFigure(fig, 'SaveFileName', 'PC1_BinderCumulant.fig', 'SaveDirectory', opts.SaveDirectory);
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end
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%% --- ANOVA Test ---
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p = anova1(score(:,1), groupLabels, 'off');
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fprintf('[INFO] ANOVA p-value for PC1 score differences between groups: %.4e\n', p);
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end
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