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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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Karthik 2025-08-24 20:41:17 +02:00
parent c9ed8be2f2
commit cd96f83fb6
10 changed files with 115 additions and 149 deletions
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117
Data-Analyzer/+Analyzer/conductPCA.m
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@ -1,111 +1,26 @@
function conductPCA(od_imgs, scan_reference_values, scan_parameter_values, doPlot, doSave, saveDir) function results = conductPCA(od_imgs)
%% computePCAfromImages: Performs PCA on optical density images and returns results in a struct
%% Performs PCA on optical density images, visualizes and optionally saves results.
% %
% Inputs: % Inputs:
% od_imgs - cell array of OD images % od_imgs - cell array of OD images
% scan_reference_values - array of unique control parameter values
% scan_parameter_values - array mapping each image to a control parameter
% doPlot - logical, true to plot figures
% doSave - logical, true to save figures
% saveDir - directory to save figures if doSave is true
% %
% Requires: % Outputs:
% +Calculator/computeCumulants.m % pcaResults - struct containing PCA outputs:
% .coeff - PCA coefficients (principal components)
% .score - PCA scores for each image
% .explained - variance explained by each PC
% .Nx, .Ny - dimensions of individual images
if nargin < 4, doPlot = true; end
if nargin < 5, doSave = false; end
if nargin < 6, saveDir = pwd; end
%% PCA computation
allImgs3D = cat(3, od_imgs{:}); allImgs3D = cat(3, od_imgs{:});
[Nx, Ny] = size(allImgs3D(:,:,1)); [Nx, Ny] = size(allImgs3D(:,:,1));
Xall = reshape(allImgs3D, [], numel(od_imgs))'; Xall = reshape(allImgs3D, [], numel(od_imgs))';
[coeff, score, ~, ~, explained] = pca(Xall); [coeff, score, ~, ~, explained] = pca(Xall);
figCount = 1;
%% --- Figure 1: PC1 Image ---
if doPlot
pc1_vector = coeff(:,1);
pc1_image = reshape(pc1_vector, Nx, Ny);
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
imagesc(pc1_image); axis image off; colormap(Colormaps.coolwarm()); colorbar;
title(sprintf('First Principal Component (PC1) Image - Explains %.2f%% Variance', explained(1)));
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Image.png')); end
figCount = figCount + 1;
end
%% --- Figure 2: PC1 Scores Scatter ---
if doPlot
numGroups = numel(scan_reference_values);
colors = lines(numGroups);
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]); hold on;
for g = 1:numGroups
idx = scan_parameter_values == scan_reference_values(g);
scatter(repmat(scan_reference_values(g), sum(idx),1), score(idx,1), 36, colors(g,:), 'filled');
end
xlabel('Control Parameter'); ylabel('PC1 Score');
title('Evolution of PC1 Scores'); grid on;
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Scatter.png')); end
figCount = figCount + 1;
end
%% --- Figure 3: PC1 Distributions ---
if doPlot
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
hold on;
for g = 1:numGroups
idx = scan_parameter_values == scan_reference_values(g);
data = score(idx,1);
histogram(data, 'Normalization', 'pdf', 'FaceColor', colors(g,:), 'FaceAlpha', 0.3);
[f, xi] = ksdensity(data);
plot(xi, f, 'Color', colors(g,:), 'LineWidth', 2);
end
xlabel('PC1 Score'); ylabel('Probability Density');
title('PC1 Score Distributions by Group');
legend(arrayfun(@num2str, scan_reference_values, 'UniformOutput', false), 'Location', 'Best');
grid on;
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Distributions.png')); end
figCount = figCount + 1;
end
%% --- Figure 4: Boxplot of PC1 Scores ---
if doPlot
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
boxplot(score(:,1), scan_parameter_values);
xlabel('Control Parameter'); ylabel('PC1 Score');
title('PC1 Score Boxplots by Group'); grid on;
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Boxplot.png')); end
figCount = figCount + 1;
end
%% --- Figure 5: Mean ± SEM of PC1 Scores ---
if doPlot
meanScores = arrayfun(@(g) mean(score(scan_parameter_values == g,1)), scan_reference_values);
semScores = arrayfun(@(g) std(score(scan_parameter_values == g,1))/sqrt(sum(scan_parameter_values == g)), scan_reference_values);
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
errorbar(scan_reference_values, meanScores, semScores, '-o', 'LineWidth', 2);
xlabel('Control Parameter'); ylabel('Mean PC1 Score ± SEM');
title('Mean ± SEM of PC1 Scores'); grid on;
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_MeanSEM.png')); end
figCount = figCount + 1;
end
%% --- Figure 6: Binder Cumulant ---
if doPlot
binderVals = arrayfun(@(g) ...
Calculator.computeCumulants(score(scan_parameter_values == g,1)), ...
scan_reference_values);
figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
plot(scan_reference_values, binderVals, '-o', 'LineWidth', 2);
xlabel('Control Parameter'); ylabel('Binder Cumulant (PC1)');
title('Binder Cumulant of PC1 Scores'); grid on;
if doSave, saveas(gcf, fullfile(saveDir, 'PC1_BinderCumulant.png')); end
end
%% --- ANOVA Test ---
p = anova1(score(:,1), arrayfun(@num2str, scan_parameter_values, 'UniformOutput', false), 'off');
fprintf('[INFO] ANOVA p-value for PC1 score differences between groups: %.4e\n', p);
results = struct( ...
'coeff', coeff, ...
'score', score, ...
'explained', explained, ...
'Nx', Nx, ...
'Ny', Ny ...
);
end end

16
Data-Analyzer/+Analyzer/performAnalysis.m
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@ -52,21 +52,26 @@ function [results, scan_parameter_values] = performAnalysis(options)
spectral_analysis_results = Analyzer.conductSpectralAnalysis(od_imgs, scan_parameter_values, options); spectral_analysis_results = Analyzer.conductSpectralAnalysis(od_imgs, scan_parameter_values, options);
N_shots = length(od_imgs); N_shots = length(od_imgs);
% Extract angular correlations % Extract angular correlations
full_g2_results = Analyzer.extractAutocorrelation(... full_g2_results = Analyzer.extractAutocorrelation(...
spectral_analysis_results.theta_vals, ... spectral_analysis_results.theta_vals, ...
spectral_analysis_results.angular_spectral_distribution, ... spectral_analysis_results.angular_spectral_distribution, ...
scan_parameter_values, N_shots, options.N_angular_bins); scan_parameter_values, N_shots, options.N_angular_bins);
custom_g_results = Analyzer.extractCustomCorrelation(... custom_g_results = Analyzer.extractCustomCorrelation(...
spectral_analysis_results.angular_spectral_distribution, ... spectral_analysis_results.angular_spectral_distribution, ...
scan_parameter_values, N_shots, options.N_angular_bins); scan_parameter_values, N_shots, options.N_angular_bins);
fprintf('\n[INFO] Spectral analysis complete!\n'); fprintf('\n[INFO] Spectral analysis complete!\n');
% PCA % Conduct PCA
fprintf('\n[INFO] Initiating Principal Component Analysis...\n');
pca_results = Analyzer.conductPCA(od_imgs);
fprintf('\n[INFO] Principal Component Analysis complete!\n');
% Lattice Reconstruction % Lattice Reconstruction
@ -75,4 +80,5 @@ function [results, scan_parameter_values] = performAnalysis(options)
results.spectral_analysis_results = spectral_analysis_results; results.spectral_analysis_results = spectral_analysis_results;
results.full_g2_results = full_g2_results; results.full_g2_results = full_g2_results;
results.custom_g_results = custom_g_results; results.custom_g_results = custom_g_results;
results.pca_results = pca_results;
end end

46
Data-Analyzer/+Helper/collectODImages.m
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@ -24,9 +24,15 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
% --- Early exit if processed data already exist AND options match --- % --- Early exit if processed data already exist AND options match ---
reuseVarsExist = evalin('base', ... reuseVarsExist = evalin('base', ...
'exist(''od_imgs'',''var'') && exist(''scan_parameter_values'',''var'') && exist(''file_list'',''var'') && exist(''prior_options'',''var'')'); 'exist(''od_imgs'',''var'') && exist(''scan_parameter_values'',''var'') && exist(''file_list'',''var'') && exist(''prior_options'',''var'')');
if ~isfield(options, 'SAVE_TO_WORKSPACE')
% ===== Estimate dataset memory and get per-run estimates =====
dataSource = makeDataSource(options.folderPath);
[options.SAVE_TO_WORKSPACE, ~] = Helper.estimateDatasetMemory(dataSource, options);
end
% --- Respect SAVE_TO_WORKSPACE flag from batchAnalyze --- % --- Respect SAVE_TO_WORKSPACE flag ---
if isfield(options, 'SAVE_TO_WORKSPACE') && ~options.SAVE_TO_WORKSPACE if ~options.SAVE_TO_WORKSPACE
% Force reprocessing: skip all workspace reuse % Force reprocessing: skip all workspace reuse
reuseVarsExist = false; reuseVarsExist = false;
end end
@ -62,7 +68,7 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
evalin('base', 'exist(''prior_options'',''var'')'); evalin('base', 'exist(''prior_options'',''var'')');
% --- Respect SAVE_TO_WORKSPACE flag --- % --- Respect SAVE_TO_WORKSPACE flag ---
if isfield(options, 'SAVE_TO_WORKSPACE') && ~options.SAVE_TO_WORKSPACE if ~options.SAVE_TO_WORKSPACE
fullDataExists = false; % force recompute even if workspace vars exist fullDataExists = false; % force recompute even if workspace vars exist
end end
@ -208,13 +214,13 @@ function [od_imgs, scan_parameter_values, file_list] = collectODImages(options)
file_list = raw_file_list; file_list = raw_file_list;
end end
% --- Save processed dataset and options for reuse --- % --- Save processed dataset and options to workspace for reuse ---
assignin('base', 'od_imgs', od_imgs); assignin('base', 'od_imgs', od_imgs);
assignin('base', 'scan_parameter_values', scan_parameter_values); assignin('base', 'scan_parameter_values', scan_parameter_values);
assignin('base', 'file_list', file_list); assignin('base', 'file_list', file_list);
assignin('base', 'prior_options', options); assignin('base', 'prior_options', options);
% --- Save OD images as figures if requested --- % --- Save OD images as figures to disk if requested ---
if ~options.skipSaveOD if ~options.skipSaveOD
saveODFigures(od_imgs, options.saveDirectory); saveODFigures(od_imgs, options.saveDirectory);
end end
@ -269,3 +275,33 @@ function saveODFigures(od_imgs, saveDirectory)
end end
fprintf('[INFO] OD figures saved successfully.\n'); fprintf('[INFO] OD figures saved successfully.\n');
end end
function dataSources = makeDataSource(folderPath)
% Split by file separators (handles / or \)
parts = regexp(folderPath, '[\\/]', 'split');
% Remove empty parts caused by leading slashes
parts = parts(~cellfun('isempty', parts));
% Extract sequence, date, and run number
% Now the indices are correct:
% parts = {'DyLabNAS', 'Data', 'StructuralPhaseTransition', '2025', '08', '13', '0062'}
sequence = parts{3}; % "StructuralPhaseTransition"
year = parts{4}; % "2025"
month = parts{5}; % "08"
day = parts{6}; % "13"
runStr = parts{7}; % "0062"
% Build date string
dateStr = sprintf('%s/%s/%s', year, month, day);
% Convert run string to number
runNum = str2double(runStr);
% Construct struct inside a cell array
dataSources = {
struct('sequence', sequence, ...
'date', dateStr, ...
'runs', runNum)
};
end

6
Data-Analyzer/+Helper/estimateDatasetMemory.m
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@ -10,7 +10,7 @@ function [SAVE_TO_WORKSPACE, runMemoryGB] = estimateDatasetMemory(dataSources, o
[~, sys] = memory; [~, sys] = memory;
availableRAM = sys.PhysicalMemory.Available; availableRAM = sys.PhysicalMemory.Available;
else else
availableRAM = 16e9; % fallback: 16 GB if not Windows availableRAM = 8e9; % fallback: 8 GB if not Windows
end end
SAVE_TO_WORKSPACE = true; % default, may change per run SAVE_TO_WORKSPACE = true; % default, may change per run
@ -58,10 +58,10 @@ function [SAVE_TO_WORKSPACE, runMemoryGB] = estimateDatasetMemory(dataSources, o
% Decide workspace flag per run by comparing with 50% of available RAM % Decide workspace flag per run by comparing with 50% of available RAM
if runBytes > 0.75 * availableRAM if runBytes > 0.75 * availableRAM
SAVE_TO_WORKSPACE = false; SAVE_TO_WORKSPACE = false;
fprintf('[INFO] Estimated size on memory of Run %s/%s too large (%.2f GB). Not saving to workspace.\n', ... 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', ...
ds.sequence, runID, runBytes/1e9); ds.sequence, runID, runBytes/1e9);
else else
fprintf('[INFO] Estimated size on memory of Run %s/%s = %.2f GB. Will save to workspace.\n', ... fprintf('\n[INFO] Estimated size on memory of Run %s/%s = %.2f GB. Will save completely to workspace if not done so already.\n', ...
ds.sequence, runID, runBytes/1e9); ds.sequence, runID, runBytes/1e9);
end end
end end

14
Data-Analyzer/+Plotter/plotPDF.m
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@ -4,8 +4,8 @@ function plotPDF(dataCell, referenceValues, varargin)
% Usage: % Usage:
% Plotter.plotPDF(dataCell, referenceValues, ... % Plotter.plotPDF(dataCell, referenceValues, ...
% 'PlotType', 'histogram', ... % 'histogram' (default) or 'kde' % 'PlotType', 'histogram', ... % 'histogram' (default) or 'kde'
% 'NumBins', 50, ... % number of histogram bins % 'NumberOfBins', 50, ... % number of histogram bins
% 'NormalizeHist', true, ... % normalize hist counts to probability density % 'NormalizeHistogram', true, ... % normalize hist counts to probability density
% 'Title', 'My Title', ... % 'Title', 'My Title', ...
% 'XLabel', 'Scan Parameter', ... % 'XLabel', 'Scan Parameter', ...
% 'YLabel', 'Data Values', ... % 'YLabel', 'Data Values', ...
@ -35,8 +35,8 @@ function plotPDF(dataCell, referenceValues, varargin)
addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2); addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2);
addParameter(p, 'Colormap', @jet); addParameter(p, 'Colormap', @jet);
addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'}))); addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'})));
addParameter(p, 'NumBins', 50, @isscalar); addParameter(p, 'NumberOfBins', 50, @isscalar);
addParameter(p, 'NormalizeHist', true, @islogical); addParameter(p, 'NormalizeHistogram', true, @islogical);
parse(p, varargin{:}); parse(p, varargin{:});
opts = p.Results; opts = p.Results;
@ -56,7 +56,7 @@ function plotPDF(dataCell, referenceValues, varargin)
y_grid = linspace(y_min, y_max, opts.NumPoints); y_grid = linspace(y_min, y_max, opts.NumPoints);
pdf_matrix = zeros(numel(y_grid), N_params); pdf_matrix = zeros(numel(y_grid), N_params);
else % Histogram else % Histogram
edges = linspace(y_min, y_max, opts.NumBins+1); edges = linspace(y_min, y_max, opts.NumberOfBins+1);
binCenters = (edges(1:end-1) + edges(2:end)) / 2; binCenters = (edges(1:end-1) + edges(2:end)) / 2;
pdf_matrix = zeros(numel(binCenters), N_params); pdf_matrix = zeros(numel(binCenters), N_params);
end end
@ -72,7 +72,7 @@ function plotPDF(dataCell, referenceValues, varargin)
pdf_matrix(:, i) = f; pdf_matrix(:, i) = f;
else % Histogram else % Histogram
counts = histcounts(data, edges); counts = histcounts(data, edges);
if opts.NormalizeHist if opts.NormalizeHistogram
binWidth = edges(2) - edges(1); binWidth = edges(2) - edges(1);
counts = counts / (sum(counts) * binWidth); % probability density counts = counts / (sum(counts) * binWidth); % probability density
end end
@ -99,7 +99,7 @@ function plotPDF(dataCell, referenceValues, varargin)
if strcmpi(opts.PlotType,'kde') if strcmpi(opts.PlotType,'kde')
ylabel(c, 'PDF', 'Interpreter', 'latex', 'FontSize', opts.FontSize); ylabel(c, 'PDF', 'Interpreter', 'latex', 'FontSize', opts.FontSize);
else else
if opts.NormalizeHist if opts.NormalizeHistogram
ylabel(c, 'Probability Density', 'Interpreter', 'latex', 'FontSize', opts.FontSize); ylabel(c, 'Probability Density', 'Interpreter', 'latex', 'FontSize', opts.FontSize);
else else
ylabel(c, 'Counts', 'Interpreter', 'latex', 'FontSize', opts.FontSize); ylabel(c, 'Counts', 'Interpreter', 'latex', 'FontSize', opts.FontSize);

13
Data-Analyzer/+Scripts/BECToDroplets/plotAnalysisResults.m
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@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
'SkipSaveFigures', options.skipSaveFigures, ... 'SkipSaveFigures', options.skipSaveFigures, ...
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ... 'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
'SaveDirectory', figSaveDir, ... 'SaveDirectory', figSaveDir, ...
'NumBins', 20, ... 'NumberOfBins', 20, ...
'NormalizeHist', true, ... 'NormalizeHistogram', true, ...
'DataRange', [0 1.5], ... 'DataRange', [0 1.5], ...
'Colormap', @Colormaps.coolwarm, ... 'Colormap', @Colormaps.coolwarm, ...
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]); 'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);
@ -108,6 +108,15 @@ Plotter.plotCumulants(options.scan_reference_values, ...
'SkipSaveFigures', options.skipSaveFigures, ... 'SkipSaveFigures', options.skipSaveFigures, ...
'SaveFileName', 'CumulantOfPeakOffsetAngularCorrelation.fig', ... 'SaveFileName', 'CumulantOfPeakOffsetAngularCorrelation.fig', ...
'SaveDirectory', figSaveDir); 'SaveDirectory', figSaveDir);
%% ------------------ 5. PCA ------------------
Plotter.plotPCAResults(compiled_results.pca_results, scan_parameter_values, options.scan_reference_values, ...
'FigNumRange', [7,8,9,10,11,12], ...
'FontName', options.font, ...
'SkipSaveFigures', options.skipSaveFigures, ...
'SaveDirectory', figSaveDir);
%%
%{ %{
%% ------------------ 6. Average of Spectra Plots ------------------ %% ------------------ 6. Average of Spectra Plots ------------------

40
Data-Analyzer/+Scripts/BECToDroplets/runFullAnalysis.m
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@ -10,29 +10,29 @@ dataSources = {
options = struct(); options = struct();
% File / paths % File / paths
options.baseDataFolder = '//DyLabNAS/Data'; options.baseDataFolder = '//DyLabNAS/Data';
options.measurementName = 'BECToDroplets'; options.measurementName = 'BECToDroplets';
scriptFullPath = mfilename('fullpath'); scriptFullPath = mfilename('fullpath');
options.saveDirectory = fileparts(scriptFullPath); options.saveDirectory = fileparts(scriptFullPath);
% Camera / imaging % Camera / imaging
options.cam = 5; options.cam = 5;
options.angle = 0; options.angle = 0;
options.center = [1420, 2050]; options.center = [1420, 2050];
options.span = [200, 200]; options.span = [200, 200];
options.fraction = [0.1, 0.1]; options.fraction = [0.1, 0.1];
options.pixel_size = 5.86e-6; % in meters options.pixel_size = 5.86e-6; % in meters
options.magnification = 24.6; options.magnification = 24.6;
options.removeFringes = false; options.removeFringes = false;
options.ImagingMode = 'HighIntensity'; options.ImagingMode = 'HighIntensity';
options.PulseDuration = 5e-6; % in s options.PulseDuration = 5e-6; % in s
% Fourier analysis settings % Fourier analysis settings
options.theta_min = deg2rad(0); options.theta_min = deg2rad(0);
options.theta_max = deg2rad(180); options.theta_max = deg2rad(180);
options.N_radial_bins = 500; options.N_radial_bins = 500;
options.Radial_Sigma = 2; options.Radial_Sigma = 2;
options.Radial_WindowSize = 5; % odd number options.Radial_WindowSize = 5; % odd number
options.k_min = 1.2771; % μm⁻¹ options.k_min = 1.2771; % μm⁻¹
options.k_max = 2.5541; % μm⁻¹ options.k_max = 2.5541; % μm⁻¹
@ -43,7 +43,7 @@ options.Angular_WindowSize = 5;
options.zoom_size = 50; options.zoom_size = 50;
% Scan parameter % Scan parameter
options.scan_parameter = 'rot_mag_field'; options.scan_parameter = 'rot_mag_field';
switch options.measurementName switch options.measurementName
case 'BECToDroplets' case 'BECToDroplets'

4
Data-Analyzer/+Scripts/BECToDropletsToStripes/plotAnalysisResults.m
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@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
'SkipSaveFigures', options.skipSaveFigures, ... 'SkipSaveFigures', options.skipSaveFigures, ...
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ... 'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
'SaveDirectory', figSaveDir, ... 'SaveDirectory', figSaveDir, ...
'NumBins', 20, ... 'NumberOfBins', 20, ...
'NormalizeHist', true, ... 'NormalizeHistogram', true, ...
'DataRange', [0 1.5], ... 'DataRange', [0 1.5], ...
'Colormap', @Colormaps.coolwarm, ... 'Colormap', @Colormaps.coolwarm, ...
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]); 'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);

4
Data-Analyzer/+Scripts/BECToStripes/plotAnalysisResults.m
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@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
'SkipSaveFigures', options.skipSaveFigures, ... 'SkipSaveFigures', options.skipSaveFigures, ...
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ... 'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
'SaveDirectory', figSaveDir, ... 'SaveDirectory', figSaveDir, ...
'NumBins', 20, ... 'NumberOfBins', 20, ...
'NormalizeHist', true, ... 'NormalizeHistogram', true, ...
'DataRange', [0 1.5], ... 'DataRange', [0 1.5], ...
'Colormap', @Colormaps.coolwarm, ... 'Colormap', @Colormaps.coolwarm, ...
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]); 'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);

4
Data-Analyzer/+Scripts/BECToStripesToDroplets/plotAnalysisResults.m
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@ -89,8 +89,8 @@ Plotter.plotPDF(compiled_results.custom_g_results.max_g2_all_per_scan_parameter_
'SkipSaveFigures', options.skipSaveFigures, ... 'SkipSaveFigures', options.skipSaveFigures, ...
'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ... 'SaveFileName', 'PDF_MaxG2AcrossTransition.fig', ...
'SaveDirectory', figSaveDir, ... 'SaveDirectory', figSaveDir, ...
'NumBins', 20, ... 'NumberOfBins', 20, ...
'NormalizeHist', true, ... 'NormalizeHistogram', true, ...
'DataRange', [0 1.5], ... 'DataRange', [0 1.5], ...
'Colormap', @Colormaps.coolwarm, ... 'Colormap', @Colormaps.coolwarm, ...
'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]); 'XLim', [min(options.scan_reference_values) max(options.scan_reference_values)]);