208 lines
8.4 KiB
Matlab
208 lines
8.4 KiB
Matlab
function plotSinglePCAResults(pcaResults, scan_parameter_values, scan_reference_values, varargin)
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%% plotSinglePCAResults
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% Author: Karthik
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% Date: 2025-09-12
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% Version: 1.0
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%
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% Description:
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% Plots PCA results.
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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','MaxPCToPlot')
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%
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% Notes:
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% Optional notes, references.
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% --- Parse name-value pairs ---
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p = inputParser;
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addParameter(p, 'XLabel', '', @(x) ischar(x) || isstring(x));
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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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set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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(opts.XLabel, '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, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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ylabel('Probability', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title(sprintf('Control = %g', unique_scan_param_vals(g)), 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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grid on;
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end
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sgtitle('PC1 Score Distributions', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
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set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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(opts.XLabel, '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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set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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(opts.XLabel, '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, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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 Cumulants ---
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kappas = cell2mat(arrayfun(@(g) {Calculator.computeCumulants(score(raw_scan_param_vals == g,1), 4)}, unique_scan_param_vals));
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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',[100 100 950 750]);
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t = tiledlayout(2,2,'TileSpacing','Compact','Padding','Compact');
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title(t, 'Cumulants of PC1 Scores', ...
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'FontName', opts.FontName, 'FontSize', opts.FontSize+4);
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cumulLabels = {'\kappa_1','\kappa_2','\kappa_3','\kappa_4'};
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cumulTitles = {'Mean','Variance','Skewness','Binder Cumulant'};
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for k = 1:4
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ax = nexttile; hold(ax,'on');
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plot(ax, unique_scan_param_vals, kappas(:, k), '-o', ...
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'Color', [0.2 0.4 0.7], 'LineWidth', 2, 'MarkerSize', 8, ...
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'MarkerFaceColor', [0.2 0.4 0.7]);
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ylabel(ax, cumulLabels{k}, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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xlabel(ax, opts.XLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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title(ax, cumulTitles{k}, 'FontName', opts.FontName, 'FontSize', opts.FontSize+2);
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grid(ax,'on'); set(ax,'FontName',opts.FontName,'FontSize',opts.FontSize);
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end
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set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
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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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end
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