Missed adding the plotting routine for the PCA results in last commit
This commit is contained in:
parent
cd96f83fb6
commit
3c407617a0
189
Data-Analyzer/+Plotter/plotPCAResults.m
Normal file
189
Data-Analyzer/+Plotter/plotPCAResults.m
Normal file
@ -0,0 +1,189 @@
|
||||
function plotPCAResults(pcaResults, scan_parameter_values, scan_reference_values, varargin)
|
||||
%% plotPCAResults: Plots PCA results in a style consistent with plotG2
|
||||
%
|
||||
% Inputs:
|
||||
% pcaResults - struct returned by computePCAfromImages
|
||||
% scan_parameter_values, scan_reference_values
|
||||
% varargin - name-value pairs (same as plotG2 plus 'FigNumRange')
|
||||
|
||||
% --- Parse name-value pairs ---
|
||||
p = inputParser;
|
||||
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||
addParameter(p, 'Colormap', @Colormaps.coolwarm);
|
||||
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
||||
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||
addParameter(p, 'FigNumRange', [], @(x) isnumeric(x) && all(x>0));
|
||||
parse(p, varargin{:});
|
||||
opts = p.Results;
|
||||
|
||||
Nx = pcaResults.Nx;
|
||||
Ny = pcaResults.Ny;
|
||||
coeff = pcaResults.coeff;
|
||||
score = pcaResults.score;
|
||||
explained = pcaResults.explained;
|
||||
|
||||
raw_scan_param_vals = scan_parameter_values;
|
||||
unique_scan_param_vals = scan_reference_values;
|
||||
numGroups = numel(unique_scan_param_vals);
|
||||
colors = lines(numGroups);
|
||||
|
||||
% --- Figure numbering setup ---
|
||||
if isempty(opts.FigNumRange)
|
||||
figCount = 1;
|
||||
figNums = [];
|
||||
else
|
||||
figNums = opts.FigNumRange;
|
||||
figCount = 1;
|
||||
end
|
||||
|
||||
figPos = [100 100 950 750];
|
||||
|
||||
%% --- Figure 1: PC1 Image ---
|
||||
pc1_image = reshape(coeff(:,1), Nx, Ny);
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos);
|
||||
imagesc(pc1_image); axis image off; colormap(opts.Colormap()); colorbar;
|
||||
title(sprintf('First Principal Component (PC1) Image - Explains %.2f%% Variance', explained(1)), ...
|
||||
'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Image.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
figCount = figCount + 1;
|
||||
|
||||
%% --- Figure 2: PC1 Scores Distribution Scatterplot ---
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos); hold on;
|
||||
for g = 1:numGroups
|
||||
idx = raw_scan_param_vals == unique_scan_param_vals(g);
|
||||
scatter(repmat(unique_scan_param_vals(g), sum(idx),1), score(idx,1), 36, colors(g,:), 'filled');
|
||||
end
|
||||
xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel('PC1 Score', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title('Evolution of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
|
||||
grid on;
|
||||
set(gca,'XDir','reverse'); % Ensure decreasing scan_reference_values go left-to-right
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Scatter.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
figCount = figCount + 1;
|
||||
|
||||
%% --- Figure 3: PC1 Scores Distribution Histograms ---
|
||||
numTiles = min(6, numGroups); % show up to 6 groups
|
||||
tileIndices = round(linspace(1, numGroups, numTiles));
|
||||
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos);
|
||||
tLayout = tiledlayout(3,2,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
for t = 1:numTiles
|
||||
g = tileIndices(t);
|
||||
idx = raw_scan_param_vals == unique_scan_param_vals(g);
|
||||
data = score(idx,1);
|
||||
|
||||
nexttile;
|
||||
histogram(data, 'Normalization', 'pdf', 'FaceColor', colors(g,:), 'FaceAlpha', 0.3);
|
||||
hold on;
|
||||
[f, xi] = ksdensity(data);
|
||||
plot(xi, f, 'Color', colors(g,:), 'LineWidth', 2);
|
||||
yl = ylim;
|
||||
plot([median(data) median(data)], yl, 'k--', 'LineWidth', 1);
|
||||
xlabel('PC1 Score');
|
||||
ylabel('Probability');
|
||||
title(sprintf('Control = %g', unique_scan_param_vals(g)));
|
||||
grid on;
|
||||
end
|
||||
sgtitle('PC1 Score Distributions');
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Distributions.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
figCount = figCount + 1;
|
||||
|
||||
%% --- Figure 4: PC1 Scores Distribution Boxplot ---
|
||||
% Construct group labels explicitly
|
||||
groupLabels = arrayfun(@num2str, raw_scan_param_vals, 'UniformOutput', false);
|
||||
|
||||
% Create categorical variable with specified order
|
||||
groupCats = categorical(groupLabels, ...
|
||||
arrayfun(@num2str, unique_scan_param_vals, 'UniformOutput', false), ...
|
||||
'Ordinal', true);
|
||||
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos);
|
||||
|
||||
% Plot boxplot with categorical groups
|
||||
boxplot(score(:,1), groupCats);
|
||||
|
||||
xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel('PC1 Score', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title('PC1 Score Boxplots by Group', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
|
||||
grid on;
|
||||
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_Boxplot.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
figCount = figCount + 1;
|
||||
|
||||
|
||||
%% --- Figure 5: PC1 Scores Distribution Mean ± SEM ---
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos);
|
||||
meanScores = arrayfun(@(g) mean(score(raw_scan_param_vals == g,1)), unique_scan_param_vals);
|
||||
semScores = arrayfun(@(g) std(score(raw_scan_param_vals == g,1))/sqrt(sum(raw_scan_param_vals == g)), unique_scan_param_vals);
|
||||
errorbar(unique_scan_param_vals, meanScores, semScores, '-o', 'LineWidth', 2);
|
||||
xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel('Mean PC1 Score ± SEM', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title('Mean ± SEM of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
|
||||
grid on;
|
||||
set(gca,'XDir','reverse'); % consistent ordering
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_MeanSEM.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
figCount = figCount + 1;
|
||||
|
||||
%% --- Figure 6: PC1 Scores Distribution Binder Cumulant ---
|
||||
cumulantsAll = cell2mat(arrayfun(@(g) {Calculator.computeCumulants(score(raw_scan_param_vals == g,1), 4)}, unique_scan_param_vals));
|
||||
cumulantsAll = reshape(cumulantsAll, 4, numGroups);
|
||||
binderVals = cumulantsAll(4,:);
|
||||
|
||||
if ~isempty(figNums)
|
||||
fig = figure(figNums(figCount)); clf;
|
||||
else
|
||||
fig = figure; clf;
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', figPos);
|
||||
plot(unique_scan_param_vals, binderVals*1E-5, '-o', 'LineWidth', 2); % scale like older code
|
||||
xlabel('Control Parameter', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel('\kappa (\times 10^5)', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title('Binder Cumulant of PC1 Scores', 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2);
|
||||
grid on;
|
||||
set(gca,'XDir','reverse'); % consistent ordering
|
||||
if ~opts.SkipSaveFigures
|
||||
Plotter.saveFigure(fig, 'SaveFileName', 'PC1_BinderCumulant.fig', 'SaveDirectory', opts.SaveDirectory);
|
||||
end
|
||||
|
||||
%% --- ANOVA Test ---
|
||||
p = anova1(score(:,1), groupLabels, 'off');
|
||||
fprintf('[INFO] ANOVA p-value for PC1 score differences between groups: %.4e\n', p);
|
||||
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user