471 lines
19 KiB
Matlab
471 lines
19 KiB
Matlab
%% ===== BEC-Droplets-Stripes Settings =====
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% Specify data location to run analysis on
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dataSources = {
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struct('sequence', 'TwoDGas', ...
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'date', '2025/06/23', ...
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'runs', [300]) % specify run numbers as a string in "" or just as a numeric value
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};
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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.FullODImagesFolder = 'E:/Data - Experiment/FullODImages/202506';
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options.measurementName = 'DropletsToStripes';
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scriptFullPath = mfilename('fullpath');
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options.saveDirectory = fileparts(scriptFullPath);
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% Camera / imaging settings
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options.cam = 4; % 1 - ODT_1_Axis_Camera; 2 - ODT_2_Axis_Camera; 3 - Horizontal_Axis_Camera;, 4 - Vertical_Axis_Camera;
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options.angle = 0; % angle by which image will be rotated
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options.center = [1410, 2030];
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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 = 23.94;
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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.k_min = 1.2771; % μm⁻¹
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options.k_max = 2.5541; % μm⁻¹
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options.N_angular_bins = 360;
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options.Angular_Threshold = 75;
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options.Angular_Sigma = 2;
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options.Angular_WindowSize = 5;
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options.zoom_size = 50;
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% Flags
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options.skipUnshuffling = false;
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options.skipNormalization = false;
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options.skipFringeRemoval = true;
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options.skipPreprocessing = true;
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options.skipMasking = true;
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options.skipIntensityThresholding = true;
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options.skipBinarization = true;
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options.skipFullODImagesFolderUse = false;
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options.skipSaveData = false;
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options.skipSaveFigures = true;
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options.skipSaveProcessedOD = true;
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options.skipLivePlot = false;
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options.showProgressBar = true;
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% Extras
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options.font = 'Bahnschrift';
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switch options.measurementName
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case 'BECToDroplets'
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options.scan_parameter = 'rot_mag_field';
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options.flipSortOrder = true;
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options.scanParameterUnits = 'gauss';
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options.titleString = 'BEC to Droplets';
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case 'BECToStripes'
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options.scan_parameter = 'rot_mag_field';
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options.flipSortOrder = true;
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options.scanParameterUnits = 'gauss';
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options.titleString = 'BEC to Stripes';
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case 'DropletsToStripes'
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options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
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options.flipSortOrder = false;
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options.scanParameterUnits = 'degrees';
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options.titleString = 'Droplets to Stripes';
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case 'StripesToDroplets'
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options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
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options.flipSortOrder = false;
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options.scanParameterUnits = 'degrees';
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options.titleString = 'Stripes to Droplets';
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end
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%% ===== Collect Images and Launch Viewer =====
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[options.selectedPath, options.folderPath] = Helper.selectDataSourcePath(dataSources, options);
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[od_imgs, scan_parameter_values, scan_reference_values, file_list] = Helper.collectODImages(options);
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%% Conduct spectral analysis
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spectral_analysis_results = Analyzer.extractFullAngularSpectralDistribution(od_imgs, options);
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%% ------------------ 1. Plot of all Angular Spectral Distribution Curves ------------------
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Plotter.plotSpectralCurves( ...
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spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals/pi, ... % correct θ values
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scan_reference_values, ... % correct scan params
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'Title', options.titleString, ...
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'XLabel', '\theta / \pi', ...
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'YLabel', 'S(\theta)', ...
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'HighlightEvery', 10, ... % highlight every 10th repetition
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'FontName', options.font, ...
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'FigNum', 1, ...
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'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
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'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SpectralCurves.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 2. Plot of all Angular Spectral Distribution Curves shifted ------------------
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% --- Recenter curves first ---
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results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals/pi, ...
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scan_reference_values, ...
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'SearchRange', [0 90]); % degrees
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% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
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thetaMin = 0; % in units of pi (since you divided by pi)
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thetaMax = 1; % corresponds to pi/2
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mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
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results.x_values = results.x_values(mask);
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% Apply the same mask to each curve set
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for i = 1:numel(results.curves)
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results.curves{i} = results.curves{i}(:, mask);
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results.curves_mean{i} = results.curves_mean{i}(mask);
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results.curves_error{i}= results.curves_error{i}(mask);
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end
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Plotter.plotSpectralCurvesRecentered( ...
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results, ...
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scan_reference_values, ... % correct scan params
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'Title', options.titleString, ...
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'XLabel', '\theta / \pi', ...
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'YLabel', 'S(\theta)', ...
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'HighlightEvery', 10, ... % highlight every 10th repetition
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'FontName', options.font, ...
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'FigNum', 2, ...
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'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
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'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SpectralCurves.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 3. Plot cumulants from shifted Angular Spectral Distribution Curves ------------------
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Plotter.plotSpectralDistributionCumulants(results, ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'FontName', options.font, ...
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'FontSize', 14, ...
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'FigNum', 3, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SpectralCumulants.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
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[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
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spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals, ...
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'MaxTheta', pi/2, ...
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'ResidualThreshold', 0.15, ...
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'PositionThreshold', pi/15, ...
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'AmplitudeThreshold', 0.15);
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% --- Post-fit diagnostics ---
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pFit_all = vertcat(fitResults.pFit); % Ncurves x Nparams
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isValid_all = [fitResults.isValid]; % logical vector
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% Extract last three parameters (usually secondary peak amplitude, position, width)
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pTail = pFit_all(:, end-2:end);
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% Count curves where *any* of these last three values are NaN or zero
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numWithNaN = sum(any(isnan(pTail), 2));
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numWithZero = sum(any(pTail == 0, 2));
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% Count of valid fits
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numValid = sum(isValid_all);
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% Display summary
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fprintf('\n--- Fit diagnostics (last 3 params) ---\n');
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fprintf('Curves with ≥1 NaN in last three pFit elements: %d\n', numWithNaN);
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fprintf('Curves with ≥1 zero in last three pFit elements: %d\n', numWithZero);
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fprintf('Curves marked as isValid = true: %d\n', numValid);
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fprintf('----------------------------------------\n\n');
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%% ------------------ Plot Fits on Raw ------------------
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options.skipLivePlot = true;
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options.skipSaveFigures = false;
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Plotter.plotTwoModeGaussianFitsOnRawASD(fitResults, rawCurves, 4, 6, ...
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'SkipLivePlot', options.skipLivePlot, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ Interactive Fit Viewer ------------------
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Analyzer.runInteractiveASDTwoGaussianFitGUI(spectral_analysis_results);
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%% ------------------ 5. Plot fit parameters - amplitude ------------------
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Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak amplitude', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 4, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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'PlotType', 'histogram', ...
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'NumberOfBins', 20, ...
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'NormalizeHistogram', true, ...
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'Colormap', @Colormaps.coolwarm, ...
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'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
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'YLim', [0, 1.6]);
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%% ------------------ 6. Plot fit parameters - position ------------------
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Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak position (\theta, rad)', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 5, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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'PlotType', 'histogram', ...
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'NumberOfBins', 20, ...
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'NormalizeHistogram', true, ...
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'Colormap', @Colormaps.coolwarm, ...
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'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
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'YLim', [0, 1.6]);
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%% ------------------ 7. Plot fit parameters - width ------------------
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Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak width (\sigma, rad)', ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'FigNum', 6, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
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'SaveDirectory', options.saveDirectory, ...
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'PlotType', 'histogram', ...
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'NumberOfBins', 20, ...
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'NormalizeHistogram', true, ...
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'Colormap', @Colormaps.coolwarm, ...
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'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
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'YLim', [0, 1.6]);
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%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
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% --- Recenter curves first ---
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results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals/pi, ...
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scan_reference_values, ...
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'SearchRange', [0 90]); % degrees
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% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
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thetaMin = 0; % in units of pi (since you divided by pi)
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thetaMax = 1; % corresponds to pi/2
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mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
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results.x_values = results.x_values(mask);
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% Apply the same mask to each curve set
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for i = 1:numel(results.curves)
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results.curves_mean{i} = results.curves_mean{i}(mask);
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end
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% --- Fit two-Gaussian model to mean curves ---
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[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
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results.curves_mean, ...
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results.x_values*pi, ...
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'MaxTheta', pi/2, ...
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'ResidualThreshold', 0.15, ...
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'PositionThreshold', pi/15, ...
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'AmplitudeThreshold', 0.15, ...
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'RecenterCurves', false);
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% --- Prepare parameter values ---
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N_params = numel(fitResultsMean);
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amp2_vals = nan(1, N_params);
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mu2_vals = nan(1, N_params);
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sigma2_vals = nan(1, N_params);
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for i = 1:N_params
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pFit = fitResultsMean(i).pFit;
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if all(~isnan(pFit))
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% Successful fit → use fitted values
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amp2_vals(i) = pFit(4);
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mu2_vals(i) = pFit(5);
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sigma2_vals(i) = pFit(6);
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else
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% Fit failed → leave as NaN (skipped automatically)
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continue;
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end
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end
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% --- Plot peak amplitude mean with SEM ---
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Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak amplitude', ...
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'FigNum', 7, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakAmplitudeMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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% --- Plot peak position mean with SEM ---
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Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak position (\theta, rad)', ...
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'FigNum', 8, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakPositionMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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% --- Plot peak width mean with SEM ---
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Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
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'Title', options.titleString, ...
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'XLabel', '\alpha (degrees)', ...
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'YLabel', 'Secondary peak width (\sigma, rad)', ...
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'FigNum', 9, ...
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'FontName', options.font, ...
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'FontSize', 16, ...
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'SkipSaveFigures', options.skipSaveFigures, ...
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'SaveFileName', 'SecondaryPeakWidthMeanWithSEM.fig', ...
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'SaveDirectory', options.saveDirectory);
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%% ------------------ 9. Compare mean of individual fits vs fit of mean ------------------
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paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
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paramNames = {'A2', 'mu2', 'sigma2'};
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paramLabels = {'Secondary peak amplitude', 'Secondary peak position (\theta, rad)', 'Secondary peak width (\sigma, rad)'};
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meanOfIndividualFits = struct();
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fitOfMeanCurve = struct();
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for p = 1:numel(paramNames)
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paramName = paramNames{p};
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paramLabel = paramLabels{p};
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paramIdx = paramMap.(paramName);
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% --- Mean of individual fits per scan_reference_values ---
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meanVals = nan(size(scan_reference_values)); % already unique
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for k = 1:numel(scan_reference_values)
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thisAlpha = scan_reference_values(k);
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mask = scan_parameter_values == thisAlpha; % pick all repetitions
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pvals = arrayfun(@(f) f.pFit(paramIdx), fitResults(mask), 'UniformOutput', true);
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meanVals(k) = mean(pvals(~isnan(pvals) & isfinite(pvals)), 'omitnan');
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end
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meanOfIndividualFits.(paramName) = meanVals;
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% --- Fit of mean curve ---
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switch paramName
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case 'A2'
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fitOfMeanCurve.(paramName) = amp2_vals;
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case 'mu2'
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fitOfMeanCurve.(paramName) = mu2_vals;
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case 'sigma2'
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fitOfMeanCurve.(paramName) = sigma2_vals;
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end
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% --- Plot comparison ---
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fig = figure('Name', 'Comparison', 'NumberTitle', 'off');
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set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
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hold on;
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plot(scan_reference_values, meanOfIndividualFits.(paramName), 'o-', 'LineWidth', 1.8, ...
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'DisplayName', 'Mean of Individual Fits');
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plot(scan_reference_values, fitOfMeanCurve.(paramName), 's--', 'LineWidth', 1.8, ...
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'DisplayName', 'Fit of Mean Curve');
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hold off;
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ylim([0, 1.1])
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xlabel('\alpha (degrees)', 'FontName', options.font, 'FontSize', 16);
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ylabel(paramLabel, 'FontName', options.font, 'FontSize', 16);
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title(options.titleString, 'FontName', options.font, 'FontSize', 16);
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legend('Location', 'northeast');
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grid on;
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set(gca, 'FontSize', 14);
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if ~options.skipSaveFigures
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saveas(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.fig']));
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exportgraphics(fig, fullfile(options.saveDirectory, ['Compare_' paramName '.png']), 'Resolution', 300);
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end
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end
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%% ------------------ 10. Save Fit Results ------------------
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outputDir = fullfile(options.saveDirectory, 'DToS_ASDFitData');
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if ~exist(outputDir, 'dir')
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mkdir(outputDir);
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end
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% --- Define filenames for each variable ---
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file_scanParamVals = fullfile(outputDir, 'scan_parameter_values');
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file_scanRefVals = fullfile(outputDir, 'scan_reference_values');
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file_fitResults = fullfile(outputDir, 'fitResults.mat');
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file_rawCurves = fullfile(outputDir, 'rawCurves.mat');
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file_spectralResults = fullfile(outputDir, 'spectral_analysis_results.mat');
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file_meanFits = fullfile(outputDir, 'fitOfMeanCurve.mat');
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file_indivFits = fullfile(outputDir, 'meanOfIndividualFits.mat');
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% --- Save each variable separately ---
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save(file_scanParamVals, 'scan_parameter_values', '-v7.3');
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save(file_scanRefVals, 'scan_reference_values', '-v7.3');
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save(file_fitResults, 'fitResults', '-v7.3');
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save(file_rawCurves, 'rawCurves', '-v7.3');
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save(file_spectralResults, 'spectral_analysis_results', '-v7.3');
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save(file_meanFits, 'fitOfMeanCurve', '-v7.3');
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save(file_indivFits, 'meanOfIndividualFits', '-v7.3');
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fprintf('\n--- Fit data saved successfully ---\n');
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fprintf('scanParamVals: %s\n', file_scanParamVals);
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fprintf('scanRefVals: %s\n', file_scanRefVals);
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fprintf('fitResults: %s\n', file_fitResults);
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fprintf('rawCurves: %s\n', file_rawCurves);
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fprintf('spectral_analysis_results:%s\n', file_spectralResults);
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fprintf('fitOfMeanCurve: %s\n', file_meanFits);
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fprintf('meanOfIndividualFits: %s\n', file_indivFits);
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fprintf('----------------------------------\n\n');
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%% Inspect individual realizations of a single parameter
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% --- Recenter curves first ---
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results = Analyzer.recenterSpectralCurves( ...
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spectral_analysis_results.S_theta_norm_all, ...
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spectral_analysis_results.theta_vals/pi, ...
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scan_reference_values, ...
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'SearchRange', [0 90]); % degrees
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% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
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|
thetaMin = 0; % in units of pi (since you divided by pi)
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thetaMax = 1; % corresponds to pi/2
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|
|
|
mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
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|
results.x_values = results.x_values(mask);
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|
|
|
% --- Apply the same mask to each curve set (1x10 cell, each 60x180) ---
|
|
for i = 1:numel(results.curves)
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|
results.curves{i} = results.curves{i}(:, mask);
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|
end
|
|
|
|
% --- Convert selected curve set (e.g., 5th) into 1x60 cell array of 1xN row vectors ---
|
|
paramIdx = 1; % <-- choose which scan point or curve set to analyze
|
|
curves_matrix = results.curves{paramIdx}; % 60xN numeric
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|
curves_cell = num2cell(curves_matrix, 2); % 1x60 cell array
|
|
curves_cell = cellfun(@(x) x(:).', curves_cell, 'UniformOutput', false); % ensure 1xN row vectors
|
|
|
|
% --- Fit two-Gaussian model to these curves ---
|
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
|
curves_cell, ...
|
|
results.x_values*pi, ...
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|
'MaxTheta', pi/2, ...
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|
'ResidualThreshold', 0.15, ...
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|
'PositionThreshold', pi/15, ...
|
|
'AmplitudeThreshold', 0.15, ...
|
|
'RecenterCurves', false);
|