Latest scripts to fit the angular spectral distributions with a two-mode Gaussian - fixed several bugs.
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@ -1,4 +1,4 @@
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function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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function [fitResults, rawCurves] = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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%% fitTwoGaussianCurves
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% Fits a two-Gaussian model to multiple spectral curves from θ=0 up to
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% the first secondary peak ≥50% of the primary peak, within 0–π/2 radians.
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@ -13,7 +13,7 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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%
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% Optional name-value pairs:
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% 'MaxTheta' - Maximum theta for search (default: pi/2)
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% 'DeviationLimit' - Max relative deviation allowed (default: 0.3)
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% 'ResidualThreshold' - Max relative deviation allowed (default: 0.3)
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% 'PositionThreshold' - Minimum angular separation between main and
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% secondary peaks (default: 0.1 rad)
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% 'AmplitudeThreshold' - Minimum fraction of primary peak amplitude for secondary peak (default: 0.5)
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@ -25,7 +25,7 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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% --- Parse optional inputs ---
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p = inputParser;
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addParameter(p, 'MaxTheta', pi/2, @(x) isnumeric(x) && isscalar(x));
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addParameter(p, 'DeviationLimit', 0.3, @(x) isnumeric(x) && isscalar(x));
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addParameter(p, 'ResidualThreshold', 0.3, @(x) isnumeric(x) && isscalar(x));
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addParameter(p, 'PositionThreshold', 0.1, @(x) isnumeric(x) && isscalar(x));
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addParameter(p, 'AmplitudeThreshold', 0.5, @(x) isnumeric(x) && isscalar(x));
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addParameter(p, 'RecenterCurves', true, @islogical);
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@ -34,7 +34,9 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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Ncurves = numel(S_theta_all);
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fitResults = struct('pFit',[],'thetaFit',[],'xFit',[],'yFit',[],...
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'thetaFine',[],'isValid',[],'fitMaxTheta',[]);
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'thetaFine',[],'isValid',[],'fitMaxTheta',[]);
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rawCurves = struct('x', cell(1, Ncurves), 'theta', cell(1, Ncurves));
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if opts.RecenterCurves
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% --- Preprocess curves: shift first peak to zero without wrapping ---
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@ -69,128 +71,187 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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end
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for k = 1:Ncurves
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x = S_theta_all_shifted{k};
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theta = theta_recentered;
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validIdx = ~isnan(x);
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x = x(validIdx);
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theta = theta(validIdx);
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mask = theta>=0 & theta<=opts.MaxTheta;
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x = x(mask);
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theta = theta(mask);
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x = x / x(1);
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% --- Store raw data for plotting ---
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rawCurves(k).x = x;
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rawCurves(k).theta = theta;
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try
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x = S_theta_all_shifted{k};
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theta = theta_recentered;
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% --- Filter 1: Ensure curve covers full 0–MaxTheta range ---
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thetaRange = max(theta) - min(theta);
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if thetaRange < 0.9 * opts.MaxTheta
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warning('Curve %d excluded: does not span full 0–MaxTheta range.', k);
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fitResults(k) = fillZeroStruct(theta, opts.MaxTheta);
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continue;
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end
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validIdx = ~isnan(x);
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x = x(validIdx);
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theta = theta(validIdx);
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mask = theta>=0 & theta<=opts.MaxTheta;
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x = x(mask);
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theta = theta(mask);
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if numel(theta) < 8
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warning('Curve %d too short (<8 points), skipping.', k);
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fitResults(k) = fillNaNStruct();
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fitResults(k) = fillNaNStruct();
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continue;
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end
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xSmooth = smooth(x, 5, 'moving');
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curveAmp = max(xSmooth) - min(xSmooth);
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%% 1) Attempt primary detection via findpeaks
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minProm = opts.AmplitudeThreshold * curveAmp;
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[pk, locIdx] = findpeaks(xSmooth, 'MinPeakProminence', minProm);
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thetaPeaks = theta(locIdx);
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foundSecondary = false;
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fitMaxTheta = [];
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secondaryAmp = [];
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secondaryIdx = find(thetaPeaks > opts.PositionThreshold, 1, 'first');
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if ~isempty(secondaryIdx)
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foundSecondary = true;
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fitMaxTheta = thetaPeaks(secondaryIdx);
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secondaryAmp = pk(secondaryIdx);
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end
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%% 2) If secondary found, fit constrained two-Gaussian directly
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if foundSecondary
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twoGauss = @(p,theta) ...
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1.0 * exp(-0.5*((theta - p(1))/max(p(2),1e-6)).^2) + ...
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p(3) * exp(-0.5*((theta - p(4))/max(p(5),1e-6)).^2);
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mu1_guess = 0;
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sigma1_guess = 0.1 * fitMaxTheta;
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A2_guess = secondaryAmp;
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mu2_guess = fitMaxTheta;
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sigma2_guess = fitMaxTheta / 3;
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p0 = [mu1_guess, sigma1_guess, A2_guess, mu2_guess, sigma2_guess];
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lb = [0, 1e-6, 0, max(fitMaxTheta - 0.1, 0), 1e-6];
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ub = [fitMaxTheta/4, fitMaxTheta, 2, min(fitMaxTheta + 0.1, opts.MaxTheta), fitMaxTheta];
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optsLSQ = optimoptions('lsqcurvefit','Display','off', ...
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'MaxFunctionEvaluations',1e4,'MaxIterations',1e4);
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try
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pFit = lsqcurvefit(twoGauss, p0, theta, x, lb, ub, optsLSQ);
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x = x / x(1);
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xSmooth = smooth(x, 5, 'moving');
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curveAmp = max(xSmooth) - min(xSmooth);
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% --- Handle missing or weak secondary peak, discard poor fits ---
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if pFit(3) <= opts.AmplitudeThreshold
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% amplitude too small → discard secondary
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pFit(3) = 0;
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pFit(4:5) = NaN;
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else
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% --- Compute RMS of fit vs raw curve in [0, pi/4] ---
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maskRMS = theta >= 0 & theta <= pi/4;
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if any(maskRMS)
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x_raw_subset = x(maskRMS);
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y_fit_subset = twoGauss(pFit, theta(maskRMS));
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rms_val = sqrt(mean((x_raw_subset - y_fit_subset).^2));
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if rms_val > opts.ResidualThreshold
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% residual too large → discard secondary
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pFit(3) = NaN;
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pFit(4:5) = NaN;
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end
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end
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end
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% --- Map to canonical format with primary amplitude = 1 ---
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pCanonical = [1.0, pFit(1), pFit(2), pFit(3), pFit(4), pFit(5)];
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% --- Robust peak detection ---
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[pk, locIdx] = findpeaks(xSmooth, 'MinPeakProminence', opts.AmplitudeThreshold * curveAmp);
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thetaPeaks = theta(locIdx);
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thetaFine = linspace(0, opts.MaxTheta, 500);
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yFit = 1.0*exp(-0.5*((thetaFine - pCanonical(2))/max(pCanonical(3),1e-6)).^2) + ...
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pCanonical(4)*exp(-0.5*((thetaFine - pCanonical(5))/max(pCanonical(6),1e-6)).^2);
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if isempty(pk)
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warning('Curve %d has no significant peaks.', k);
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fitResults(k) = fillZeroStruct(theta, opts.MaxTheta);
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continue;
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fitResults(k).pFit = pCanonical;
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fitResults(k).thetaFit = theta;
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fitResults(k).xFit = x;
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fitResults(k).thetaFine = thetaFine;
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fitResults(k).yFit = yFit;
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fitResults(k).isValid = true;
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fitResults(k).fitMaxTheta = fitMaxTheta;
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continue; % success → next curve
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catch
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warning('Curve %d constrained fit failed, switching to generic fit.', k);
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end
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end
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thetaPeaks = thetaPeaks(:);
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pk = pk(:);
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% --- Find secondary peak ---
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secondaryIdx = find((thetaPeaks > opts.PositionThreshold) & ...
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(pk >= opts.AmplitudeThreshold * curveAmp), 1, 'first');
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if isempty(secondaryIdx)
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% No secondary peak → bypass fit, fill with zeros
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fitResults(k) = fillZeroStruct(theta, opts.MaxTheta);
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continue;
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else
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fitMaxTheta = thetaPeaks(secondaryIdx);
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secondaryAmp = pk(secondaryIdx);
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end
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% --- Use the full curve for fitting ---
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thetaFit = theta(:);
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xFit = x(:);
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if numel(thetaFit) < 8
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warning('Curve %d has too few points for fitting.', k);
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fitResults(k) = fillNaNStruct();
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continue;
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end
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% --- Two-Gaussian model ---
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twoGauss = @(p,theta) ...
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1.0 * exp(-0.5*((theta - p(1))/max(p(2),1e-6)).^2) + ... % main Gaussian
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p(3) * exp(-0.5*((theta - p(4))/max(p(5),1e-6)).^2); % secondary Gaussian
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% --- Parameter guesses based on secondary peak ---
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mu1_guess = 0;
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sigma1_guess = 0.1 * fitMaxTheta;
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A2_guess = secondaryAmp; % use detected secondary amplitude
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mu2_guess = fitMaxTheta; % center at detected secondary peak
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sigma2_guess = fitMaxTheta / 3; % broad guess
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p0 = [mu1_guess, sigma1_guess, A2_guess, mu2_guess, sigma2_guess];
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% --- Bounds that enforce secondary peak constraints ---
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lb = [0, 1e-6, 0, max(fitMaxTheta - 0.1, 0), 1e-6];
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ub = [fitMaxTheta/4, fitMaxTheta, 2, min(fitMaxTheta + 0.1, opts.MaxTheta), fitMaxTheta];
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optsLSQ = optimoptions('lsqcurvefit','Display','off', ...
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'MaxFunctionEvaluations',1e4,'MaxIterations',1e4);
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% --- Fit ---
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%% 3) Generic two-Gaussian fallback (no or failed secondary detection)
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try
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pFit = lsqcurvefit(twoGauss, p0, thetaFit, xFit, lb, ub, optsLSQ);
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twoGaussFree = @(p,th) ...
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p(1)*exp(-0.5*((th - p(2))/max(p(3),1e-6)).^2) + ...
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p(4)*exp(-0.5*((th - p(5))/max(p(6),1e-6)).^2);
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p0 = [1, 0, 0.1, 0.3, opts.MaxTheta/3, 0.1];
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lb = [0, 0, 1e-6, 0, opts.PositionThreshold, 1e-6];
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ub = [2, opts.MaxTheta/2, opts.MaxTheta, 2, opts.MaxTheta, opts.MaxTheta];
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optsLSQfree = optimoptions('lsqcurvefit', 'Display', 'off', ...
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'MaxFunctionEvaluations', 2e4, 'MaxIterations', 2e4);
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pFree = lsqcurvefit(twoGaussFree, p0, theta(:), xSmooth(:), lb, ub, optsLSQfree);
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% --- Handle missing secondary peak ---
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if pFree(4) <= 1e-3
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pFree(4) = 0; % secondary amplitude = 0
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pFree(5:6) = NaN; % secondary position & width = NaN
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end
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fitMaxTheta = max(pFree(5), pFree(2));
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thetaFine = linspace(0, opts.MaxTheta, 500);
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yFit = twoGaussFree(pFree, thetaFine);
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fitResults(k).pFit = pFree;
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fitResults(k).thetaFit = theta;
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fitResults(k).xFit = x;
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fitResults(k).thetaFine = thetaFine;
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fitResults(k).yFit = yFit;
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fitResults(k).isValid = true;
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fitResults(k).fitMaxTheta = fitMaxTheta;
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catch
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warning('Curve %d fit failed.', k);
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warning('Curve %d: generic two-Gaussian fit failed.', k);
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fitResults(k) = fillNaNStruct();
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continue;
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end
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% --- Evaluate fitted curve ---
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thetaFine = linspace(0, opts.MaxTheta, 500);
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yFit = twoGauss(pFit, thetaFine);
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% --- Assess deviation ---
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relDevAmplitude = abs(pFit(3) - secondaryAmp) / max(secondaryAmp,1e-6);
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relDevPosition = abs(pFit(4) - fitMaxTheta) / max(fitMaxTheta, 1e-6);
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combinedDeviation = sqrt(relDevAmplitude^2 + relDevPosition^2);
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isValid = combinedDeviation <= opts.DeviationLimit;
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% --- Store results ---
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fitResults(k).pFit = pFit;
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fitResults(k).thetaFit = thetaFit;
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fitResults(k).xFit = xFit;
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fitResults(k).thetaFine = thetaFine;
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fitResults(k).yFit = yFit;
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fitResults(k).isValid = isValid;
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fitResults(k).fitMaxTheta = fitMaxTheta;
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catch
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warning('Curve %d fit failed completely.', k);
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fitResults(k) = fillNaNStruct();
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fitResults(k) = fillNaNStruct();
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end
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end
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end
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% --- Helper: return struct with NaNs (fit failed) ---
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function s = fillNaNStruct()
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s = struct('pFit',nan(1,5),'thetaFit',nan,'xFit',nan,'yFit',nan,'thetaFine',nan,...
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'isValid',false,'fitMaxTheta',nan);
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s = struct( ...
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'pFit', nan(1,5), ...
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'thetaFit', nan, ...
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'xFit', nan, ...
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'yFit', nan, ...
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'thetaFine', nan, ...
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'isValid', false, ...
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'fitMaxTheta', nan ...
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);
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end
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% --- Helper: return struct with zeros (no secondary peak found) ---
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function s = fillZeroStruct(theta, fitMaxTheta)
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s = struct('pFit',zeros(1,5),'thetaFit',theta,'xFit',zeros(size(theta)),...
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'yFit',zeros(size(theta)),'thetaFine',zeros(1,500),...
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'isValid',false,'fitMaxTheta',fitMaxTheta);
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s = struct( ...
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'pFit', zeros(1,5), ...
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'thetaFit', theta, ...
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'xFit', zeros(size(theta)), ...
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'yFit', zeros(size(theta)), ...
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'thetaFine', zeros(1,500), ...
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'isValid', false, ...
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'fitMaxTheta', fitMaxTheta ...
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);
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end
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@ -2,7 +2,7 @@ function plotFitParameterPDF(fitResults, scanValues, paramName, varargin)
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%% plotFitParameterPDF
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% Author: Karthik
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% Date: 2025-10-06
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% Version: 1.1
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% Version: 1.2
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%
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% Description:
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% Plots 2D PDF (heatmap) of any parameter from two-Gaussian fit results
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@ -12,7 +12,7 @@ function plotFitParameterPDF(fitResults, scanValues, paramName, varargin)
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% fitResults - struct array from fitTwoGaussianCurves
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% scanValues - vector of scan parameter values
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% paramName - string specifying the parameter to plot:
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% 'mu1', 'sigma1', 'A2', 'mu2', 'sigma2'
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% 'A1', 'mu1', 'sigma1', 'A2', 'mu2', 'sigma2'
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%
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% Optional name-value pairs (same as before, plus OverlayMeanSEM):
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% 'OverlayMeanSEM' - logical, overlay mean ± SEM (default: true)
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@ -31,6 +31,7 @@ addParameter(p, 'SaveDirectory', pwd, @ischar);
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addParameter(p, 'NumPoints', 200, @(x) isscalar(x));
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addParameter(p, 'DataRange', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'YLim', [], @(x) isempty(x) || numel(x)==2);
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addParameter(p, 'Colormap', @jet);
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addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'})));
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addParameter(p, 'NumberOfBins', 50, @isscalar);
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@ -40,9 +41,9 @@ parse(p, varargin{:});
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opts = p.Results;
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% --- Map paramName to index ---
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paramMap = struct('mu1',1,'sigma1',2,'A2',3,'mu2',4,'sigma2',5);
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paramMap = struct('A1',1,'mu1',2,'sigma1',3,'A2',4,'mu2',5,'sigma2',6);
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if ~isfield(paramMap,paramName)
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error('Invalid paramName. Must be one of: mu1, sigma1, A2, mu2, sigma2');
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error('Invalid paramName. Must be one of: A1, mu1, sigma1, A2, mu2, sigma2');
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end
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paramIdx = paramMap.(paramName);
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@ -149,6 +150,10 @@ if ~isempty(opts.XLim)
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xlim(opts.XLim);
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end
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if ~isempty(opts.YLim)
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ylim(opts.YLim);
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end
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% --- Overlay mean ± SEM if requested ---
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if opts.OverlayMeanSEM
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meanParam = nanmean(paramValues,1);
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@ -165,13 +165,79 @@ Plotter.plotSpectralDistributionCumulants(results, ...
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'SaveFileName', 'SpectralCumulants.fig');
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%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
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fitResults = Analyzer.fitTwoGaussianCurves(...
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[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
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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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'DeviationLimit', 0.30, ...
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'ResidualThreshold', 0.15, ...
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'PositionThreshold', pi/15, ...
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'AmplitudeThreshold', 0.02);
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'AmplitudeThreshold', 0.15);
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%{
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% --- Function call ---
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plotTwoGaussianFitsOnRaw(fitResults, rawCurves, 8, 12); % 8×12 subplots per page
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% --- Function definition ---
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function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
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% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
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%
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% Inputs:
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% fitResults - struct array from fitTwoGaussianCurves (may contain fits)
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% rawCurves - struct array with fields:
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% .x - raw curve
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% .theta - corresponding theta values
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% nRows - number of subplot rows per page (default: 4)
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% nCols - number of subplot columns per page (default: 6)
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if nargin < 3, nRows = 4; end
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if nargin < 4, nCols = 6; end
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Ncurves = numel(rawCurves);
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plotsPerPage = nRows * nCols;
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pageNum = 1;
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for k = 1:Ncurves
|
||||
% --- New figure/page if needed ---
|
||||
if mod(k-1, plotsPerPage) == 0
|
||||
figure('Name', sprintf('Curves Page %d', pageNum), ...
|
||||
'NumberTitle', 'off', 'Color', 'w');
|
||||
pageNum = pageNum + 1;
|
||||
end
|
||||
|
||||
% --- Select subplot ---
|
||||
subplot(nRows, nCols, mod(k-1, plotsPerPage)+1);
|
||||
hold on; grid on; box on;
|
||||
|
||||
% --- Plot raw curve ---
|
||||
xRaw = rawCurves(k).x;
|
||||
thetaRaw = rawCurves(k).theta;
|
||||
if ~isempty(xRaw) && ~isempty(thetaRaw) && all(isfinite(xRaw))
|
||||
plot(thetaRaw, xRaw, 'k.-', 'LineWidth', 1, 'DisplayName', 'Raw data');
|
||||
end
|
||||
|
||||
% --- Overlay fit if valid ---
|
||||
if k <= numel(fitResults)
|
||||
fit = fitResults(k);
|
||||
if isfield(fit, 'isValid') && fit.isValid ...
|
||||
&& ~isempty(fit.yFit) && ~isempty(fit.thetaFine)
|
||||
plot(fit.thetaFine, fit.yFit, 'r-', 'LineWidth', 1.2, 'DisplayName', 'Two-Gaussian fit');
|
||||
end
|
||||
end
|
||||
|
||||
% --- Formatting ---
|
||||
xlabel('\theta (rad)');
|
||||
ylabel('Normalized amplitude');
|
||||
title(sprintf('Curve %d', k), 'FontSize', 10);
|
||||
|
||||
% --- Legend once per page ---
|
||||
if mod(k-1, plotsPerPage) == 0
|
||||
legend('Location', 'best', 'FontSize', 8);
|
||||
end
|
||||
|
||||
hold off;
|
||||
end
|
||||
end
|
||||
%}
|
||||
|
||||
%% ------------------ 5. Plot fit parameters - amplitude ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||
@ -187,7 +253,8 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)]);
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 6. Plot fit parameters - position ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
@ -203,7 +270,8 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)]);
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 7. Plot fit parameters - width ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
@ -219,7 +287,8 @@ Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)]);
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
|
||||
@ -242,13 +311,13 @@ for i = 1:numel(results.curves)
|
||||
end
|
||||
|
||||
% --- Fit two-Gaussian model to mean curves ---
|
||||
fitResultsMean = Analyzer.fitTwoGaussianCurves(...
|
||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurves(...
|
||||
results.curves_mean, ...
|
||||
results.x_values*pi, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 0.30, ...
|
||||
'ResidualThreshold', 0.15, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.02, ...
|
||||
'AmplitudeThreshold', 0.15, ...
|
||||
'RecenterCurves', false);
|
||||
|
||||
% --- Prepare parameter values ---
|
||||
@ -259,18 +328,11 @@ sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
pFit = fitResultsMean(i).pFit;
|
||||
|
||||
if all(pFit == 0)
|
||||
% No secondary peak → plot as zero
|
||||
amp2_vals(i) = 0;
|
||||
mu2_vals(i) = 0;
|
||||
sigma2_vals(i) = 0;
|
||||
|
||||
elseif all(~isnan(pFit))
|
||||
if all(~isnan(pFit))
|
||||
% Successful fit → use fitted values
|
||||
amp2_vals(i) = pFit(3);
|
||||
mu2_vals(i) = pFit(4);
|
||||
sigma2_vals(i) = pFit(5);
|
||||
amp2_vals(i) = pFit(4);
|
||||
mu2_vals(i) = pFit(5);
|
||||
sigma2_vals(i) = pFit(6);
|
||||
|
||||
else
|
||||
% Fit failed → leave as NaN (skipped automatically)
|
||||
@ -326,17 +388,17 @@ for i = 1:numel(results.curves)
|
||||
end
|
||||
|
||||
% --- Convert selected curve set (e.g., 5th) into 1x60 cell array of 1xN row vectors ---
|
||||
paramIdx = 10; % <-- choose which scan point or curve set to analyze
|
||||
paramIdx = 1; % <-- choose which scan point or curve set to analyze
|
||||
curves_matrix = results.curves{paramIdx}; % 60xN numeric
|
||||
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 ---
|
||||
fitResultsMean = Analyzer.fitTwoGaussianCurves( ...
|
||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
||||
curves_cell, ...
|
||||
results.x_values*pi, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 0.30, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'ResidualThreshold', 0.15, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.02, ...
|
||||
'AmplitudeThreshold', 0.15, ...
|
||||
'RecenterCurves', false);
|
||||
|
||||
@ -0,0 +1,397 @@
|
||||
%% ===== BEC-Stripes-Droplets Settings =====
|
||||
|
||||
% Specify data location to run analysis on
|
||||
dataSources = {
|
||||
struct('sequence', 'TwoDGas', ...
|
||||
'date', '2025/06/24', ...
|
||||
'runs', [1]) % specify run numbers as a string in "" or just as a numeric value
|
||||
};
|
||||
|
||||
options = struct();
|
||||
|
||||
% File paths
|
||||
options.baseDataFolder = '//DyLabNAS/Data';
|
||||
options.FullODImagesFolder = 'E:/Data - Experiment/FullODImages/202506';
|
||||
options.measurementName = 'StripesToDroplets';
|
||||
scriptFullPath = mfilename('fullpath');
|
||||
options.saveDirectory = fileparts(scriptFullPath);
|
||||
|
||||
% Camera / imaging settings
|
||||
options.cam = 4; % 1 - ODT_1_Axis_Camera; 2 - ODT_2_Axis_Camera; 3 - Horizontal_Axis_Camera;, 4 - Vertical_Axis_Camera;
|
||||
options.angle = 0; % angle by which image will be rotated
|
||||
options.center = [1410, 2030];
|
||||
options.span = [200, 200];
|
||||
options.fraction = [0.1, 0.1];
|
||||
options.pixel_size = 5.86e-6; % in meters
|
||||
options.magnification = 24.6;
|
||||
options.ImagingMode = 'HighIntensity';
|
||||
options.PulseDuration = 5e-6; % in s
|
||||
|
||||
% Fourier analysis settings
|
||||
options.theta_min = deg2rad(0);
|
||||
options.theta_max = deg2rad(180);
|
||||
options.N_radial_bins = 500;
|
||||
options.Radial_Sigma = 2;
|
||||
options.Radial_WindowSize = 5; % odd number
|
||||
|
||||
options.k_min = 1.2771; % μm⁻¹
|
||||
options.k_max = 2.5541; % μm⁻¹
|
||||
options.N_angular_bins = 360;
|
||||
options.Angular_Threshold = 75;
|
||||
options.Angular_Sigma = 2;
|
||||
options.Angular_WindowSize = 5;
|
||||
options.zoom_size = 50;
|
||||
|
||||
% Flags
|
||||
options.skipUnshuffling = false;
|
||||
options.skipNormalization = false;
|
||||
|
||||
options.skipFringeRemoval = true;
|
||||
options.skipPreprocessing = true;
|
||||
options.skipMasking = true;
|
||||
options.skipIntensityThresholding = true;
|
||||
options.skipBinarization = true;
|
||||
|
||||
options.skipFullODImagesFolderUse = false;
|
||||
options.skipSaveData = false;
|
||||
options.skipSaveFigures = true;
|
||||
options.skipSaveProcessedOD = true;
|
||||
options.skipLivePlot = true;
|
||||
options.showProgressBar = true;
|
||||
|
||||
% Extras
|
||||
options.font = 'Bahnschrift';
|
||||
switch options.measurementName
|
||||
case 'BECToDroplets'
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
options.flipSortOrder = false;
|
||||
options.scanParameterUnits = 'gauss';
|
||||
options.titleString = 'BEC to Droplets';
|
||||
case 'BECToStripes'
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
options.flipSortOrder = false;
|
||||
options.scanParameterUnits = 'gauss';
|
||||
options.titleString = 'BEC to Stripes';
|
||||
case 'DropletsToStripes'
|
||||
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
options.flipSortOrder = false;
|
||||
options.scanParameterUnits = 'degrees';
|
||||
options.titleString = 'Droplets to Stripes';
|
||||
case 'StripesToDroplets'
|
||||
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
options.flipSortOrder = false;
|
||||
options.scanParameterUnits = 'degrees';
|
||||
options.titleString = 'Stripes to Droplets';
|
||||
end
|
||||
|
||||
%% ===== Collect Images and Launch Viewer =====
|
||||
|
||||
[options.selectedPath, options.folderPath] = Helper.selectDataSourcePath(dataSources, options);
|
||||
|
||||
[od_imgs, scan_parameter_values, scan_reference_values, file_list] = Helper.collectODImages(options);
|
||||
|
||||
%% Conduct spectral analysis
|
||||
spectral_analysis_results = Analyzer.extractFullAngularSpectralDistribution(od_imgs, options);
|
||||
|
||||
%% ------------------ 1. Plot of all Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralCurves( ...
|
||||
spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ... % correct θ values
|
||||
scan_reference_values, ... % correct scan params
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\theta / \pi', ...
|
||||
'YLabel', 'S(\theta)', ...
|
||||
'HighlightEvery', 10, ... % highlight every 10th repetition
|
||||
'FontName', options.font, ...
|
||||
'FigNum', 1, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 2. Plot of all Angular Spectral Distribution Curves shifted ------------------
|
||||
|
||||
% --- Recenter curves first ---
|
||||
results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ...
|
||||
scan_reference_values, ...
|
||||
'SearchRange', [0 90]); % degrees
|
||||
|
||||
% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
|
||||
thetaMin = 0; % in units of pi (since you divided by pi)
|
||||
thetaMax = 1; % corresponds to pi/2
|
||||
|
||||
mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
|
||||
results.x_values = results.x_values(mask);
|
||||
|
||||
% Apply the same mask to each curve set
|
||||
for i = 1:numel(results.curves)
|
||||
results.curves{i} = results.curves{i}(:, mask);
|
||||
results.curves_mean{i} = results.curves_mean{i}(mask);
|
||||
results.curves_error{i}= results.curves_error{i}(mask);
|
||||
end
|
||||
|
||||
Plotter.plotSpectralCurvesRecentered( ...
|
||||
results, ...
|
||||
scan_reference_values, ... % correct scan params
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\theta / \pi', ...
|
||||
'YLabel', 'S(\theta)', ...
|
||||
'HighlightEvery', 10, ... % highlight every 10th repetition
|
||||
'FontName', options.font, ...
|
||||
'FigNum', 2, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', options.saveDirectory);
|
||||
|
||||
%% ------------------ 3. Plot cumulants from shifted Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralDistributionCumulants(results, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 14, ...
|
||||
'FigNum', 3, ...
|
||||
'SkipSaveFigures', false, ...
|
||||
'SaveFileName', 'SpectralCumulants.fig');
|
||||
|
||||
%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
|
||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
||||
spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'ResidualThreshold', 0.15, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.15);
|
||||
|
||||
%{
|
||||
% --- Function call ---
|
||||
plotTwoGaussianFitsOnRaw(fitResults, rawCurves, 8, 12); % 8×12 subplots per page
|
||||
|
||||
% --- Function definition ---
|
||||
function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
|
||||
% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
|
||||
%
|
||||
% Inputs:
|
||||
% fitResults - struct array from fitTwoGaussianCurves (may contain fits)
|
||||
% rawCurves - struct array with fields:
|
||||
% .x - raw curve
|
||||
% .theta - corresponding theta values
|
||||
% nRows - number of subplot rows per page (default: 4)
|
||||
% nCols - number of subplot columns per page (default: 6)
|
||||
|
||||
if nargin < 3, nRows = 4; end
|
||||
if nargin < 4, nCols = 6; end
|
||||
|
||||
Ncurves = numel(rawCurves);
|
||||
plotsPerPage = nRows * nCols;
|
||||
pageNum = 1;
|
||||
|
||||
for k = 1:Ncurves
|
||||
% --- New figure/page if needed ---
|
||||
if mod(k-1, plotsPerPage) == 0
|
||||
figure('Name', sprintf('Curves Page %d', pageNum), ...
|
||||
'NumberTitle', 'off', 'Color', 'w');
|
||||
pageNum = pageNum + 1;
|
||||
end
|
||||
|
||||
% --- Select subplot ---
|
||||
subplot(nRows, nCols, mod(k-1, plotsPerPage)+1);
|
||||
hold on; grid on; box on;
|
||||
|
||||
% --- Plot raw curve ---
|
||||
xRaw = rawCurves(k).x;
|
||||
thetaRaw = rawCurves(k).theta;
|
||||
if ~isempty(xRaw) && ~isempty(thetaRaw) && all(isfinite(xRaw))
|
||||
plot(thetaRaw, xRaw, 'k.-', 'LineWidth', 1, 'DisplayName', 'Raw data');
|
||||
end
|
||||
|
||||
% --- Overlay fit if valid ---
|
||||
if k <= numel(fitResults)
|
||||
fit = fitResults(k);
|
||||
if isfield(fit, 'isValid') && fit.isValid ...
|
||||
&& ~isempty(fit.yFit) && ~isempty(fit.thetaFine)
|
||||
plot(fit.thetaFine, fit.yFit, 'r-', 'LineWidth', 1.2, 'DisplayName', 'Two-Gaussian fit');
|
||||
end
|
||||
end
|
||||
|
||||
% --- Formatting ---
|
||||
xlabel('\theta (rad)');
|
||||
ylabel('Normalized amplitude');
|
||||
title(sprintf('Curve %d', k), 'FontSize', 10);
|
||||
|
||||
% --- Legend once per page ---
|
||||
if mod(k-1, plotsPerPage) == 0
|
||||
legend('Location', 'best', 'FontSize', 8);
|
||||
end
|
||||
|
||||
hold off;
|
||||
end
|
||||
end
|
||||
%}
|
||||
|
||||
%% ------------------ 5. Plot fit parameters - amplitude ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 4, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 6. Plot fit parameters - position ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 5, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 7. Plot fit parameters - width ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 6, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||
'YLim', [0, 1.6]);
|
||||
|
||||
%% ------------------ 8. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
|
||||
% --- Recenter curves first ---
|
||||
results = Analyzer.recenterSpectralCurves(spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ...
|
||||
scan_reference_values, ...
|
||||
'SearchRange', [0 90]); % degrees
|
||||
|
||||
% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
|
||||
thetaMin = 0; % in units of pi (since you divided by pi)
|
||||
thetaMax = 1; % corresponds to pi/2
|
||||
|
||||
mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
|
||||
results.x_values = results.x_values(mask);
|
||||
|
||||
% Apply the same mask to each curve set
|
||||
for i = 1:numel(results.curves)
|
||||
results.curves_mean{i} = results.curves_mean{i}(mask);
|
||||
end
|
||||
|
||||
% --- Fit two-Gaussian model to mean curves ---
|
||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurves(...
|
||||
results.curves_mean, ...
|
||||
results.x_values*pi, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'ResidualThreshold', 0.15, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.15, ...
|
||||
'RecenterCurves', false);
|
||||
|
||||
% --- Prepare parameter values ---
|
||||
N_params = numel(fitResultsMean);
|
||||
amp2_vals = nan(1, N_params);
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
pFit = fitResultsMean(i).pFit;
|
||||
if all(~isnan(pFit))
|
||||
% Successful fit → use fitted values
|
||||
amp2_vals(i) = pFit(4);
|
||||
mu2_vals(i) = pFit(5);
|
||||
sigma2_vals(i) = pFit(6);
|
||||
|
||||
else
|
||||
% Fit failed → leave as NaN (skipped automatically)
|
||||
continue;
|
||||
end
|
||||
end
|
||||
|
||||
% --- Plot using plotMeanWithSE ---
|
||||
Plotter.plotMeanWithSE(scan_reference_values, amp2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak amplitude', ...
|
||||
'FigNum', 7, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16);
|
||||
|
||||
% --- Plot using plotMeanWithSE ---
|
||||
Plotter.plotMeanWithSE(scan_reference_values, mu2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 8, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16);
|
||||
|
||||
Plotter.plotMeanWithSE(scan_reference_values, sigma2_vals, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 9, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16);
|
||||
|
||||
%% Inspect individual realizations of a single parameter
|
||||
|
||||
% --- Recenter curves first ---
|
||||
results = Analyzer.recenterSpectralCurves( ...
|
||||
spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals/pi, ...
|
||||
scan_reference_values, ...
|
||||
'SearchRange', [0 90]); % degrees
|
||||
|
||||
% --- Restrict to desired theta range (e.g., 0 to 0.5*pi) ---
|
||||
thetaMin = 0; % in units of pi (since you divided by pi)
|
||||
thetaMax = 1; % corresponds to pi/2
|
||||
|
||||
mask = results.x_values >= thetaMin & results.x_values <= thetaMax;
|
||||
results.x_values = results.x_values(mask);
|
||||
|
||||
% --- Apply the same mask to each curve set (1x10 cell, each 60x180) ---
|
||||
for i = 1:numel(results.curves)
|
||||
results.curves{i} = results.curves{i}(:, mask);
|
||||
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
|
||||
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.fitTwoGaussianCurves(...
|
||||
curves_cell, ...
|
||||
results.x_values*pi, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'ResidualThreshold', 0.15, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.15, ...
|
||||
'RecenterCurves', false);
|
||||
Loading…
Reference in New Issue
Block a user