Major update - correction to analysis of recentered angular spectral distribution curves, corrections to fitting and plotting of fit results.
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function results = extractFullAngularSpectralDistribution(od_imgs, options)
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%% extractFullAngularSpectralDistribution
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% Author: Karthik
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% Date: 2025-10-09
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% Version: 1.0
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%
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% Description:
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% Computes the angular spectral distribution S(θ) from OD images,
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% extending θ from 0 → 2π using the computeAngularSpectralDistribution function.
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%
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% Inputs:
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% od_imgs - cell array of OD images
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% options - struct containing relevant parameters:
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% pixel_size, magnification, zoom_size,
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% k_min, k_max, N_angular_bins,
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% Angular_Threshold, Angular_Sigma
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%
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% Outputs:
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% results - struct containing θ values and angular spectra
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%
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% Notes:
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% This function is a minimal variant of conductSpectralAnalysis,
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% stopping right after angular spectral extraction.
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%% ===== Unpack options =====
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pixel_size = options.pixel_size;
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magnification = options.magnification;
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zoom_size = options.zoom_size;
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k_min = options.k_min;
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k_max = options.k_max;
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N_angular_bins = options.N_angular_bins;
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Angular_Threshold = options.Angular_Threshold;
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Angular_Sigma = options.Angular_Sigma;
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skipPreprocessing = options.skipPreprocessing;
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skipMasking = options.skipMasking;
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skipIntensityThresholding = options.skipIntensityThresholding;
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skipBinarization = options.skipBinarization;
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%% ===== Initialization =====
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N_shots = length(od_imgs);
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fft_imgs = cell(1, N_shots);
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S_theta_all = cell(1, N_shots);
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S_theta_norm_all = cell(1, N_shots);
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theta_vals = [];
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%% ===== Main loop =====
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for k = 1:N_shots
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IMG = od_imgs{k};
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% Skip empty or low-intensity images
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if ~(max(IMG(:)) > 1)
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IMGFFT = NaN(size(IMG));
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else
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% Compute FFT (with same preprocessing pipeline)
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[IMGFFT, ~] = Calculator.computeFourierTransform(IMG, ...
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skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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end
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% Image dimensions
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[Ny, Nx] = size(IMG);
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dx = pixel_size / magnification;
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dy = dx;
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% Reciprocal-space sampling
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dvx = 1 / (Nx * dx);
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dvy = 1 / (Ny * dy);
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vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
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vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
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% Convert to wavenumber axes [µm⁻¹]
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kx_full = 2 * pi * vx * 1E-6;
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ky_full = 2 * pi * vy * 1E-6;
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% Crop FFT and wavenumber axes around center
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mid_x = floor(Nx/2);
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mid_y = floor(Ny/2);
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fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
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kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
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ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
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% ===== Compute angular spectrum (0 → 2π) =====
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[theta_vals, S_theta] = Calculator.computeAngularSpectralDistribution( ...
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fft_imgs{k}, kx, ky, k_min, k_max, N_angular_bins, ...
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Angular_Threshold, Angular_Sigma, [], 2*pi);
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% Normalize angular spectrum and compute weight
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S_theta_norm = S_theta / max(S_theta);
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% Store results
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S_theta_all{k} = S_theta;
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S_theta_norm_all{k} = S_theta_norm;
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end
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%% ===== Package results =====
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results = struct();
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results.theta_vals = theta_vals;
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results.S_theta_all = S_theta_all;
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results.S_theta_norm_all = S_theta_norm_all;
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end
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@ -5,7 +5,7 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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%
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% Author: Karthik
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% Date: 2025-10-06
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% Version: 1.0
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% Version: 1.1
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%
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% Inputs:
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% S_theta_all - 1xN cell array of spectral curves
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@ -14,21 +14,21 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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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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% '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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%
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% Output:
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% fitResults - struct array with fields:
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% .pFit - fitted parameters
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% .thetaFit - theta values used for fit
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% .xFit - curve values used for fit
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% .yFit - fitted curve evaluated on thetaFine
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% .thetaFine - fine theta for plotting
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% .isValid - whether fit passed deviation threshold
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% .fitMaxTheta - theta used as fit limit
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% .pFit, .thetaFit, .xFit, .yFit, .thetaFine, .isValid, .fitMaxTheta
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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, '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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parse(p, varargin{:});
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opts = p.Results;
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@ -36,12 +36,12 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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fitResults = struct('pFit',[],'thetaFit',[],'xFit',[],'yFit',[],...
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'thetaFine',[],'isValid',[],'fitMaxTheta',[]);
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if opts.RecenterCurves
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% --- Preprocess curves: shift first peak to zero without wrapping ---
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S_theta_all_shifted = cell(size(S_theta_all));
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for k = 1:Ncurves
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curve = S_theta_all{k};
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% --- Find first peak in 0–MaxTheta range ---
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idx_range = find(theta_vals >= 0 & theta_vals <= opts.MaxTheta);
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if isempty(idx_range)
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[~, peak_idx] = max(curve);
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@ -50,11 +50,10 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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peak_idx = idx_range(local_max_idx);
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end
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% Take only the part from the peak to the end
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S_theta_all_shifted{k} = curve(peak_idx:end);
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end
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% --- Pad curves to the same length ---
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% --- Pad curves to same length ---
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Npoints_shifted = max(cellfun(@numel, S_theta_all_shifted));
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for k = 1:Ncurves
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len = numel(S_theta_all_shifted{k});
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@ -63,82 +62,87 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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end
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end
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% --- Define recentered theta values ---
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theta_recentered = theta_vals(1:Npoints_shifted) - theta_vals(1);
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else
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S_theta_all_shifted = S_theta_all;
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theta_recentered = theta_vals;
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end
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for k = 1:Ncurves
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x = S_theta_all_shifted{k}; % use recentered curve
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theta = theta_recentered; % recentered x-values
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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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validIdx = ~isnan(x);
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x = x(validIdx);
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theta = theta(validIdx);
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% --- Restrict to 0–MaxTheta ---
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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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continue;
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end
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% --- Normalize so S(0)=1 ---
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x = x / x(1);
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% --- Smooth for stable peak detection ---
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xSmooth = smooth(x, 5, 'moving');
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curveAmp = max(xSmooth) - min(xSmooth);
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% --- Find local maxima ---
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[pk, locIdx] = findpeaks(xSmooth);
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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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if isempty(pk)
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warning('Curve %d has no significant peaks, restricting to within pi/4.', k);
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fitMaxTheta = pi/4;
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else
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% --- Primary peak ---
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[mainAmp, mainIdx] = max(pk);
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mainTheta = thetaPeaks(mainIdx);
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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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end
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thetaPeaks = thetaPeaks(:); % column vector
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thetaPeaks = thetaPeaks(:);
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pk = pk(:);
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% --- Secondary peak ≥50% of primary after main ---
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secondaryIdx = find((thetaPeaks>mainTheta) & (pk>=0.50*mainAmp), 1, 'first');
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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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fitMaxTheta = opts.MaxTheta;
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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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end
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secondaryAmp = pk(secondaryIdx);
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end
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% --- Extract data up to secondary peak ---
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fitMask = theta>=0 & theta<=fitMaxTheta;
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thetaFit = theta(fitMask);
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xFit = x(fitMask);
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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, skipping.', k);
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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) + ...
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p(3)*exp(-0.5*((theta-p(4))/max(p(5),1e-6)).^2);
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% p = [mu1, sigma1, A2, mu2, sigma2]
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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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% --- Initial guesses ---
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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 = max(xFit)*0.8;
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mu2_guess = fitMaxTheta/2;
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sigma2_guess = fitMaxTheta/5;
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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 ---
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lb = [0, 1e-6, 0, 0, 1e-6];
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ub = [fitMaxTheta/4, fitMaxTheta, 2, fitMaxTheta, fitMaxTheta];
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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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@ -147,21 +151,20 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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try
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pFit = lsqcurvefit(twoGauss, p0, thetaFit, xFit, lb, ub, optsLSQ);
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catch
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warning('Curve %d fit failed, using initial guess.', k);
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pFit = p0;
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warning('Curve %d 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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% --- Compute relative deviation ---
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yFitInterp = twoGauss(pFit, thetaFit);
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relDeviation = abs(yFitInterp - xFit) ./ max(xFit, 1e-6);
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maxRelDev = max(relDeviation);
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% --- Goodness-of-fit ---
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isValid = maxRelDev <= opts.DeviationLimit;
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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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@ -171,5 +174,23 @@ function fitResults = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
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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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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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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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end
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88
Data-Analyzer/+Analyzer/recenterSpectralCurves.m
Normal file
88
Data-Analyzer/+Analyzer/recenterSpectralCurves.m
Normal file
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function results = recenterSpectralCurves(S_theta_all, theta_vals, scan_reference_values, varargin)
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%% recenterSpectralCurves
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% Author: Karthik
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% Date: 2025-10-09
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% Version: 1.0
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%
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% Description:
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% Recenters each curve so its first peak is at zero (no wrap-around),
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% pads shorter curves with NaN, and computes mean and SEM for each
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% scan parameter. Returns a struct ready for plotting.
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%
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% Inputs:
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% S_theta_all - 1x(N_reps*N_params) cell array of curves
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% theta_vals - vector of theta values (radians)
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% scan_reference_values - vector of unique scan parameters
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%
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% Optional name-value pairs:
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% 'SearchRange' - [deg_min deg_max] range for first-peak search (default: [0 90])
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%
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% Output struct fields:
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% results.curves
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% results.x_values
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% results.curves_mean
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% results.curves_error
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% results.scan_parameter_values
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% --- Parse optional inputs ---
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p = inputParser;
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addParameter(p, 'SearchRange', [0 pi/2], @(x) isnumeric(x) && numel(x)==2);
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parse(p, varargin{:});
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opts = p.Results;
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% --- Setup ---
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N_params = numel(scan_reference_values);
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Ntotal = numel(S_theta_all);
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Nreps = Ntotal / N_params;
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theta_min = opts.SearchRange(1);
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theta_max = opts.SearchRange(2);
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% --- Shift each curve so its first peak is at zero ---
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S_theta_all_shifted = cell(size(S_theta_all));
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for k = 1:Ntotal
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curve = S_theta_all{k};
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idx_range = find(theta_vals >= theta_min & theta_vals <= theta_max);
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if isempty(idx_range)
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[~, peak_idx] = max(curve);
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else
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[~, local_max_idx] = max(curve(idx_range));
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peak_idx = idx_range(local_max_idx);
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end
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% From first peak onward
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S_theta_all_shifted{k} = curve(peak_idx:end);
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end
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% --- Pad shorter curves with NaN ---
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Npoints_shifted = max(cellfun(@numel, S_theta_all_shifted));
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for k = 1:Ntotal
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len = numel(S_theta_all_shifted{k});
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if len < Npoints_shifted
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S_theta_all_shifted{k} = [S_theta_all_shifted{k}, nan(1, Npoints_shifted - len)];
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end
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end
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% --- Compute mean and SEM ---
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curves_all = cell(1, N_params);
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curves_mean = cell(1, N_params);
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curves_err = cell(1, N_params);
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for i = 1:N_params
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curves = zeros(Nreps, Npoints_shifted);
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for r = 1:Nreps
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idx = (r-1)*N_params + i; % interleaved indexing
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curves(r,:) = S_theta_all_shifted{idx};
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end
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curves_all{i} = curves;
|
||||
curves_mean{i} = nanmean(curves,1);
|
||||
curves_err{i} = nanstd(curves,0,1)/sqrt(Nreps);
|
||||
end
|
||||
|
||||
% --- Construct results struct ---
|
||||
results.curves = curves_all;
|
||||
results.x_values = theta_vals(1:Npoints_shifted) - theta_vals(1); % shift start to zero
|
||||
results.curves_mean = curves_mean;
|
||||
results.curves_error = curves_err;
|
||||
results.scan_reference_values = scan_reference_values;
|
||||
end
|
||||
@ -1,14 +1,16 @@
|
||||
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, kx, ky, k_min, k_max, num_bins, threshold, sigma, windowSize)
|
||||
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, kx, ky, k_min, k_max, num_bins, threshold, sigma, windowSize, theta_max)
|
||||
%% computeAngularSpectralDistribution
|
||||
% Author: Karthik
|
||||
% Date: 2025-09-12
|
||||
% Version: 1.0
|
||||
% Version: 1.1
|
||||
%
|
||||
% Description:
|
||||
% Brief description of the script functionality.
|
||||
%
|
||||
% Notes:
|
||||
% Optional notes, references.
|
||||
% Computes the angular spectral distribution within a specified radial band.
|
||||
% Supports custom angular range up to 2π.
|
||||
|
||||
if nargin < 10 || isempty(theta_max)
|
||||
theta_max = pi; % default preserves current behavior
|
||||
end
|
||||
|
||||
% Apply threshold to isolate strong peaks
|
||||
IMGFFT(IMGFFT < threshold) = 0;
|
||||
@ -18,17 +20,20 @@ function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, kx,
|
||||
Kmag = sqrt(KX.^2 + KY.^2); % radial wavenumber magnitude
|
||||
Theta = atan2(KY, KX); % range [-pi, pi]
|
||||
|
||||
% Map Theta into [0, 2*pi)
|
||||
Theta(Theta < 0) = Theta(Theta < 0) + 2*pi;
|
||||
|
||||
% Restrict to radial band in wavenumber space
|
||||
radial_mask = (Kmag >= k_min) & (Kmag <= k_max);
|
||||
|
||||
% Initialize angular structure factor
|
||||
S_theta = zeros(1, num_bins);
|
||||
theta_vals = linspace(0, pi, num_bins); % only 0 to pi due to symmetry
|
||||
theta_vals = linspace(0, theta_max, num_bins);
|
||||
|
||||
% Loop over angular bins
|
||||
for i = 1:num_bins
|
||||
angle_start = (i - 1) * pi / num_bins;
|
||||
angle_end = i * pi / num_bins;
|
||||
angle_start = (i - 1) * theta_max / num_bins;
|
||||
angle_end = i * theta_max / num_bins;
|
||||
angle_mask = (Theta >= angle_start) & (Theta < angle_end);
|
||||
bin_mask = radial_mask & angle_mask;
|
||||
fft_angle = IMGFFT .* bin_mask;
|
||||
|
||||
@ -2,7 +2,7 @@ function plotFitParameterPDF(fitResults, scanValues, paramName, varargin)
|
||||
%% plotFitParameterPDF
|
||||
% Author: Karthik
|
||||
% Date: 2025-10-06
|
||||
% Version: 1.0
|
||||
% Version: 1.1
|
||||
%
|
||||
% Description:
|
||||
% Plots 2D PDF (heatmap) of any parameter from two-Gaussian fit results
|
||||
@ -56,8 +56,16 @@ paramValues = nan(N_reps, N_params);
|
||||
for k = 1:N_total
|
||||
paramIdxScan = mod(k-1, N_params) + 1;
|
||||
repIdx = floor((k-1)/N_params) + 1;
|
||||
if fitResults(k).isValid
|
||||
paramValues(repIdx, paramIdxScan) = fitResults(k).pFit(paramIdx);
|
||||
end
|
||||
|
||||
% --- Create mask of true zeros (from fillZeroStruct) ---
|
||||
trueZeroMask = false(size(paramValues));
|
||||
for i = 1:N_total
|
||||
paramIdxScan = mod(i-1, N_params) + 1;
|
||||
repIdx = floor((i-1)/N_params) + 1;
|
||||
if ~fitResults(i).isValid && all(fitResults(i).pFit == 0)
|
||||
trueZeroMask(repIdx, paramIdxScan) = true;
|
||||
end
|
||||
end
|
||||
|
||||
@ -105,13 +113,21 @@ for i = 1:N_params
|
||||
end
|
||||
end
|
||||
|
||||
% --- Mask out non-data regions (make them white) ---
|
||||
dataMask = ~isnan(paramValues); % where valid fits exist
|
||||
maskPerScan = any(dataMask,1); % scans that have any valid data
|
||||
pdf_matrix(:, ~maskPerScan) = NaN; % make empty scans white
|
||||
|
||||
% --- Plot heatmap ---
|
||||
fig = figure(opts.FigNum); clf(fig);
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
if strcmpi(opts.PlotType,'kde')
|
||||
imagesc(scanValues, y_grid, pdf_matrix);
|
||||
h = imagesc(scanValues, y_grid, pdf_matrix);
|
||||
set(h, 'AlphaData', ~isnan(pdf_matrix)); % make NaNs transparent
|
||||
else
|
||||
imagesc(scanValues, binCenters, pdf_matrix);
|
||||
h = imagesc(scanValues, binCenters, pdf_matrix);
|
||||
set(h, 'AlphaData', ~isnan(pdf_matrix)); % make NaNs transparent
|
||||
end
|
||||
|
||||
set(gca, 'YDir', 'normal', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
@ -119,7 +135,9 @@ xlabel(opts.XLabel, 'FontSize', opts.FontSize, 'FontName', opts.FontName);
|
||||
ylabel(opts.YLabel, 'FontSize', opts.FontSize, 'FontName', opts.FontName);
|
||||
title(opts.Title, 'FontSize', opts.FontSize+2, 'FontWeight', 'bold');
|
||||
|
||||
colormap(feval(opts.Colormap));
|
||||
% --- Colormap and colorbar ---
|
||||
cmap = feval(opts.Colormap);
|
||||
colormap([1 1 1; cmap]); % prepend white for NaN regions
|
||||
c = colorbar;
|
||||
if strcmpi(opts.PlotType,'kde') || opts.NormalizeHistogram
|
||||
ylabel(c, 'Probability Density', 'Rotation', -90, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
@ -142,9 +160,4 @@ if opts.OverlayMeanSEM
|
||||
plot(scanValues, meanParam, 'k-', 'LineWidth', 2);
|
||||
end
|
||||
|
||||
% --- Save figure ---
|
||||
if ~opts.SkipSaveFigures
|
||||
if ~exist(opts.SaveDirectory,'dir'), mkdir(opts.SaveDirectory); end
|
||||
savefig(fig, fullfile(opts.SaveDirectory, opts.SaveFileName));
|
||||
end
|
||||
end
|
||||
|
||||
@ -30,7 +30,7 @@ function plotG2Curves(results, varargin)
|
||||
addParameter(p, 'SaveFileName', 'G2Curves.fig', @ischar);
|
||||
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||
addParameter(p, 'TileTitlePrefix', 'Control Parameter', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'TileTitleSuffix', '', @(x) ischar(x) || isstring(x)); % NEW
|
||||
addParameter(p, 'TileTitleSuffix', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'HighlightEvery', 10, @(x) isnumeric(x) && isscalar(x) && x>=1);
|
||||
parse(p, varargin{:});
|
||||
opts = p.Results;
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
function results = plotSpectralCurvesRecentered(S_theta_all, theta_vals, scan_reference_values, varargin)
|
||||
function plotSpectralCurvesRecentered(results, scan_reference_values, varargin)
|
||||
%% plotSpectralCurves
|
||||
% Author: Karthik
|
||||
% Date: 2025-10-01
|
||||
@ -49,64 +49,6 @@ function results = plotSpectralCurvesRecentered(S_theta_all, theta_vals, scan_re
|
||||
|
||||
% --- Determine number of scan parameters and repetitions ---
|
||||
N_params = numel(scan_reference_values);
|
||||
Ntotal = numel(S_theta_all);
|
||||
Nreps = Ntotal / N_params;
|
||||
|
||||
theta_min = deg2rad(0);
|
||||
theta_max = deg2rad(90);
|
||||
|
||||
% --- Preprocess curves: shift first peak to zero without wrapping ---
|
||||
S_theta_all_shifted = cell(size(S_theta_all));
|
||||
for k = 1:Ntotal
|
||||
curve = S_theta_all{k};
|
||||
|
||||
% --- Find first peak only in specified theta range ---
|
||||
|
||||
idx_range = find(theta_vals >= theta_min & theta_vals <= theta_max);
|
||||
if isempty(idx_range)
|
||||
% fallback: search entire curve if range is empty
|
||||
[~, peak_idx] = max(curve);
|
||||
else
|
||||
[~, local_max_idx] = max(curve(idx_range));
|
||||
peak_idx = idx_range(local_max_idx);
|
||||
end
|
||||
|
||||
% Take only the part from peak to end
|
||||
S_theta_all_shifted{k} = curve(peak_idx:end);
|
||||
end
|
||||
|
||||
% --- Adjust Npoints for plotting ---
|
||||
Npoints_shifted = max(cellfun(@numel, S_theta_all_shifted));
|
||||
for k = 1:Ntotal
|
||||
% Pad shorter curves with NaN to keep sizes consistent
|
||||
len = numel(S_theta_all_shifted{k});
|
||||
if len < Npoints_shifted
|
||||
S_theta_all_shifted{k} = [S_theta_all_shifted{k}, nan(1, Npoints_shifted-len)];
|
||||
end
|
||||
end
|
||||
|
||||
% --- Prepare curves, mean, SEM ---
|
||||
curves_all = cell(1, N_params);
|
||||
curves_mean = cell(1, N_params);
|
||||
curves_err = cell(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
curves = zeros(Nreps, Npoints_shifted);
|
||||
for r = 1:Nreps
|
||||
idx = (r-1)*N_params + i; % correct interleaved indexing
|
||||
curves(r,:) = S_theta_all_shifted{idx};
|
||||
end
|
||||
curves_all{i} = curves;
|
||||
curves_mean{i} = nanmean(curves,1);
|
||||
curves_err{i} = nanstd(curves,0,1)/sqrt(Nreps);
|
||||
end
|
||||
|
||||
% --- Create results struct compatible with plotting ---
|
||||
results.curves = curves_all;
|
||||
results.x_values = theta_vals(1:Npoints_shifted) - theta_vals(1); % center first peak at zero
|
||||
results.curves_mean = curves_mean;
|
||||
results.curves_error = curves_err;
|
||||
results.scan_parameter_values = scan_reference_values;
|
||||
|
||||
% --- Create figure ---
|
||||
if isempty(opts.FigNum), fig = figure; else, fig = figure(opts.FigNum); end
|
||||
@ -149,7 +91,7 @@ function results = plotSpectralCurvesRecentered(S_theta_all, theta_vals, scan_re
|
||||
grid(ax,'on');
|
||||
xlabel(ax, opts.XLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel(ax, opts.YLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title(ax, sprintf('%s=%.3g%s', opts.TileTitlePrefix, results.scan_parameter_values(i), opts.TileTitleSuffix), ...
|
||||
title(ax, sprintf('%s=%.3g%s', opts.TileTitlePrefix, results.scan_reference_values(i), opts.TileTitleSuffix), ...
|
||||
'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
set(ax, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
end
|
||||
|
||||
@ -31,7 +31,7 @@ function plotSpectralDistributionCumulants(results, varargin)
|
||||
|
||||
% --- Setup ---
|
||||
N_params = numel(results.curves);
|
||||
xvals = results.scan_parameter_values;
|
||||
xvals = results.scan_reference_values;
|
||||
thetaVals = results.x_values * pi;
|
||||
|
||||
% --- Select specific theta indices (like in g2 case) ---
|
||||
|
||||
@ -187,251 +187,13 @@ Plotter.plotMultiplePCAResults(compiled_results.pca_results, scan_parameter_valu
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
|
||||
%% ------------------ 7. Plot of all Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralCurves( ...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals/pi, ... % correct θ values
|
||||
results_all{1}.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', 20, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
|
||||
%% ------------------ 8. Plot of all Angular Spectral Distribution Curves shifted ------------------
|
||||
results = Plotter.plotSpectralCurvesRecentered( ...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals/pi, ... % correct θ values
|
||||
results_all{1}.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', 21, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
%% ------------------ 9. Plot cumulants from shifted Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralDistributionCumulants(results, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 14, ...
|
||||
'FigNum', 22, ...
|
||||
'SkipSaveFigures', false, ...
|
||||
'SaveFileName', 'SpectralCumulants.fig');
|
||||
|
||||
%% ------------------ 10. Fit shifted Angular Spectral Distribution Curves ------------------
|
||||
fitResults = Analyzer.fitTwoGaussianCurves(...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 1.00);
|
||||
|
||||
%% ------------------ 11. Plot fit parameters - position ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, results_all{1}.scan_reference_values, 'mu2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 23, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(results_all{1}.scan_reference_values), max(results_all{1}.scan_reference_values)]);
|
||||
|
||||
%% ------------------ 12. Plot fit parameters - width ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, results_all{1}.scan_reference_values, 'sigma2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 24, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(results_all{1}.scan_reference_values), max(results_all{1}.scan_reference_values)]);
|
||||
|
||||
%% ------------------ 13. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
S_theta_all = results_all{1}.results.spectral_analysis_results.S_theta_norm_all;
|
||||
theta_vals = results_all{1}.results.spectral_analysis_results.theta_vals;
|
||||
scanValues = results_all{1}.scan_reference_values;
|
||||
|
||||
N_params = numel(scanValues);
|
||||
N_total = numel(S_theta_all);
|
||||
N_reps = N_total / N_params;
|
||||
|
||||
theta_min = deg2rad(0);
|
||||
theta_max = deg2rad(90);
|
||||
|
||||
% --- Shift curves so first peak is at start ---
|
||||
S_theta_all_shifted = cell(size(S_theta_all));
|
||||
for k = 1:N_total
|
||||
curve = S_theta_all{k};
|
||||
idx_range = find(theta_vals >= theta_min & theta_vals <= theta_max);
|
||||
if isempty(idx_range)
|
||||
[~, peak_idx] = max(curve);
|
||||
else
|
||||
[~, local_max_idx] = max(curve(idx_range));
|
||||
peak_idx = idx_range(local_max_idx);
|
||||
end
|
||||
S_theta_all_shifted{k} = curve(peak_idx:end);
|
||||
end
|
||||
|
||||
% --- Pad shorter curves with NaN to match lengths ---
|
||||
Npoints_shifted = max(cellfun(@numel, S_theta_all_shifted));
|
||||
for k = 1:N_total
|
||||
len = numel(S_theta_all_shifted{k});
|
||||
if len < Npoints_shifted
|
||||
S_theta_all_shifted{k} = [S_theta_all_shifted{k}, nan(1, Npoints_shifted-len)];
|
||||
end
|
||||
end
|
||||
|
||||
% --- Compute mean curves per scan parameter ---
|
||||
meanCurves = cell(1, N_params);
|
||||
for i = 1:N_params
|
||||
curves = zeros(N_reps, Npoints_shifted);
|
||||
for r = 1:N_reps
|
||||
idx = (r-1)*N_params + i; % interleaved indexing
|
||||
curves(r,:) = S_theta_all_shifted{idx};
|
||||
end
|
||||
meanCurves{i} = nanmean(curves,1); % mean over repetitions
|
||||
end
|
||||
|
||||
% --- Fit two-Gaussian model to mean curves ---
|
||||
fitResultsMean = Analyzer.fitTwoGaussianCurves(meanCurves, theta_vals(1:Npoints_shifted)-theta_vals(1), ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 1.0);
|
||||
|
||||
% --- Scatter plot of secondary peak position (mu2) vs scan parameter ---
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
if fitResultsMean(i).isValid
|
||||
mu2_vals(i) = fitResultsMean(i).pFit(4); % secondary peak position
|
||||
sigma2_vals(i) = fitResultsMean(i).pFit(5); % secondary peak width
|
||||
end
|
||||
end
|
||||
|
||||
% Secondary peak position
|
||||
plotSecondaryPeakScatter(fitResultsMean, results_all{1}.scan_reference_values, 'mu2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 23, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionScatter.fig');
|
||||
|
||||
% Secondary peak width
|
||||
plotSecondaryPeakScatter(fitResultsMean, results_all{1}.scan_reference_values, 'sigma2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 24, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthScatter.fig');
|
||||
|
||||
|
||||
function plotSecondaryPeakScatter(fitResultsMean, scanValues, parameterName, varargin)
|
||||
%% plotSecondaryPeakScatter
|
||||
% Author: Karthik
|
||||
% Date: 2025-10-06
|
||||
% Version: 1.0
|
||||
%
|
||||
% Description:
|
||||
% Scatter plot of secondary peak fit parameters (mu2 or sigma2) vs scan parameter
|
||||
% in the same style as plotMeanWithSE.
|
||||
|
||||
% --- Parse optional inputs ---
|
||||
p = inputParser;
|
||||
addParameter(p, 'Title', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'XLabel', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'YLabel', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'FigNum', [], @(x) isempty(x) || isscalar(x));
|
||||
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||
addParameter(p, 'YLim', [], @(x) isempty(x) || isnumeric(x));
|
||||
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
||||
addParameter(p, 'SaveFileName', 'secondary_peak_scatter.fig', @ischar);
|
||||
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||
parse(p, varargin{:});
|
||||
opts = p.Results;
|
||||
|
||||
% --- Extract parameter values ---
|
||||
N_params = numel(fitResultsMean);
|
||||
paramVals = nan(1, N_params);
|
||||
for i = 1:N_params
|
||||
if fitResultsMean(i).isValid
|
||||
switch parameterName
|
||||
case 'mu2'
|
||||
paramVals(i) = fitResultsMean(i).pFit(4);
|
||||
case 'sigma2'
|
||||
paramVals(i) = fitResultsMean(i).pFit(5);
|
||||
otherwise
|
||||
error('Unknown parameter name: %s', parameterName);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% --- Prepare figure ---
|
||||
if isempty(opts.FigNum)
|
||||
fig = figure;
|
||||
else
|
||||
fig = figure(opts.FigNum);
|
||||
clf(fig);
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
% --- Plot as mean ± SE style (SE=0 here) ---
|
||||
errorbar(scanValues, paramVals, zeros(size(paramVals)), 'o--', ...
|
||||
'LineWidth', 1.8, 'MarkerSize', 6, 'CapSize', 5);
|
||||
|
||||
% --- Axis formatting ---
|
||||
set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
if ~isempty(opts.YLim)
|
||||
ylim(opts.YLim);
|
||||
end
|
||||
xlabel(opts.XLabel, 'Interpreter', 'tex', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel(opts.YLabel, 'Interpreter', 'tex', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title(opts.Title, 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2, 'FontWeight', 'bold');
|
||||
grid on;
|
||||
|
||||
% --- Save figure ---
|
||||
if ~opts.SkipSaveFigures
|
||||
if ~exist(opts.SaveDirectory, 'dir'), mkdir(opts.SaveDirectory); end
|
||||
savefig(fig, fullfile(opts.SaveDirectory, opts.SaveFileName));
|
||||
end
|
||||
end
|
||||
|
||||
%{
|
||||
%% ------------------ 14. Average of Spectra Plots ------------------
|
||||
|
||||
Plotter.plotAverageSpectra(scan_parameter_values, ...
|
||||
spectral_analysis_results, ...
|
||||
'ScanParameterName', scan_parameter, ...
|
||||
'FigNum', 25, ...
|
||||
'FigNum', 20, ...
|
||||
'ColormapPS', Colormaps.coolwarm(), ...
|
||||
'Font', 'Bahnschrift', ...
|
||||
'SaveFileName', 'avgSpectra.fig', ...
|
||||
|
||||
@ -0,0 +1,342 @@
|
||||
%% ===== BEC-Droplets-Stripes Settings =====
|
||||
|
||||
% Specify data location to run analysis on
|
||||
dataSources = {
|
||||
struct('sequence', 'TwoDGas', ...
|
||||
'date', '2025/06/23', ...
|
||||
'runs', [300]) % 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 = 'DropletsToStripes';
|
||||
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 = 23.94;
|
||||
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;
|
||||
|
||||
%
|
||||
options.maximumShift = 8;
|
||||
options.Radial_Theta = deg2rad(45);
|
||||
options.Radial_Minimum = 2;
|
||||
options.Radial_Maximum = 6;
|
||||
options.skipLivePlot = false;
|
||||
|
||||
% 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 = false;
|
||||
options.showProgressBar = true;
|
||||
|
||||
% Extras
|
||||
options.font = 'Bahnschrift';
|
||||
switch options.measurementName
|
||||
case 'BECToDroplets'
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
options.flipSortOrder = true;
|
||||
options.scanParameterUnits = 'gauss';
|
||||
options.titleString = 'BEC to Droplets';
|
||||
case 'BECToStripes'
|
||||
options.scan_parameter = 'rot_mag_field';
|
||||
options.flipSortOrder = true;
|
||||
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 = Analyzer.fitTwoGaussianCurves(...
|
||||
spectral_analysis_results.S_theta_norm_all, ...
|
||||
spectral_analysis_results.theta_vals, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 0.30, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.02);
|
||||
|
||||
%% ------------------ 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)]);
|
||||
|
||||
%% ------------------ 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)]);
|
||||
|
||||
%% ------------------ 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)]);
|
||||
|
||||
%% ------------------ 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, ...
|
||||
'DeviationLimit', 0.30, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.02, ...
|
||||
'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(pFit == 0)
|
||||
% No secondary peak → plot as zero
|
||||
amp2_vals(i) = 0;
|
||||
mu2_vals(i) = 0;
|
||||
sigma2_vals(i) = 0;
|
||||
|
||||
elseif all(~isnan(pFit))
|
||||
% Successful fit → use fitted values
|
||||
amp2_vals(i) = pFit(3);
|
||||
mu2_vals(i) = pFit(4);
|
||||
sigma2_vals(i) = pFit(5);
|
||||
|
||||
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 = 10; % <-- 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( ...
|
||||
curves_cell, ...
|
||||
results.x_values*pi, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 0.30, ...
|
||||
'PositionThreshold', pi/15, ...
|
||||
'AmplitudeThreshold', 0.02, ...
|
||||
'RecenterCurves', false);
|
||||
@ -106,8 +106,7 @@ Plotter.plotG2Curves(compiled_results.full_g2_results, ...
|
||||
'SaveFileName', 'G2Curves.fig', ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
|
||||
|
||||
Plotter.plotG2PDF(compiled_results.full_g2_results, pi/6, ...
|
||||
Plotter.plotG2PDF(compiled_results.full_g2_results, pi/2, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'g^{(2)}(\delta\theta)', ...
|
||||
@ -188,251 +187,13 @@ Plotter.plotMultiplePCAResults(compiled_results.pca_results, scan_parameter_valu
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
|
||||
%% ------------------ 7. Plot of all Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralCurves( ...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals/pi, ... % correct θ values
|
||||
results_all{1}.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', 20, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
|
||||
%% ------------------ 8. Plot of all Angular Spectral Distribution Curves shifted ------------------
|
||||
results = Plotter.plotSpectralCurvesRecentered( ...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals/pi, ... % correct θ values
|
||||
results_all{1}.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', 21, ...
|
||||
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SpectralCurves.fig', ...
|
||||
'SaveDirectory', figSaveDir);
|
||||
%% ------------------ 9. Plot cumulants from shifted Angular Spectral Distribution Curves ------------------
|
||||
Plotter.plotSpectralDistributionCumulants(results, ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 14, ...
|
||||
'FigNum', 22, ...
|
||||
'SkipSaveFigures', false, ...
|
||||
'SaveFileName', 'SpectralCumulants.fig');
|
||||
|
||||
%% ------------------ 10. Fit shifted Angular Spectral Distribution Curves ------------------
|
||||
fitResults = Analyzer.fitTwoGaussianCurves(...
|
||||
results_all{1}.results.spectral_analysis_results.S_theta_norm_all, ...
|
||||
results_all{1}.results.spectral_analysis_results.theta_vals, ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 1.00);
|
||||
|
||||
%% ------------------ 11. Plot fit parameters - position ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, results_all{1}.scan_reference_values, 'mu2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 23, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(results_all{1}.scan_reference_values), max(results_all{1}.scan_reference_values)]);
|
||||
|
||||
%% ------------------ 12. Plot fit parameters - width ------------------
|
||||
Plotter.plotFitParameterPDF(fitResults, results_all{1}.scan_reference_values, 'sigma2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'FigNum', 24, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||
'PlotType', 'histogram', ...
|
||||
'NumberOfBins', 20, ...
|
||||
'NormalizeHistogram', true, ...
|
||||
'Colormap', @Colormaps.coolwarm, ...
|
||||
'XLim', [min(results_all{1}.scan_reference_values), max(results_all{1}.scan_reference_values)]);
|
||||
|
||||
%% ------------------ 13. Plot fit parameters of mean shifted Angular Spectral Distribution Curves ------------------
|
||||
S_theta_all = results_all{1}.results.spectral_analysis_results.S_theta_norm_all;
|
||||
theta_vals = results_all{1}.results.spectral_analysis_results.theta_vals;
|
||||
scanValues = results_all{1}.scan_reference_values;
|
||||
|
||||
N_params = numel(scanValues);
|
||||
N_total = numel(S_theta_all);
|
||||
N_reps = N_total / N_params;
|
||||
|
||||
theta_min = deg2rad(0);
|
||||
theta_max = deg2rad(90);
|
||||
|
||||
% --- Shift curves so first peak is at start ---
|
||||
S_theta_all_shifted = cell(size(S_theta_all));
|
||||
for k = 1:N_total
|
||||
curve = S_theta_all{k};
|
||||
idx_range = find(theta_vals >= theta_min & theta_vals <= theta_max);
|
||||
if isempty(idx_range)
|
||||
[~, peak_idx] = max(curve);
|
||||
else
|
||||
[~, local_max_idx] = max(curve(idx_range));
|
||||
peak_idx = idx_range(local_max_idx);
|
||||
end
|
||||
S_theta_all_shifted{k} = curve(peak_idx:end);
|
||||
end
|
||||
|
||||
% --- Pad shorter curves with NaN to match lengths ---
|
||||
Npoints_shifted = max(cellfun(@numel, S_theta_all_shifted));
|
||||
for k = 1:N_total
|
||||
len = numel(S_theta_all_shifted{k});
|
||||
if len < Npoints_shifted
|
||||
S_theta_all_shifted{k} = [S_theta_all_shifted{k}, nan(1, Npoints_shifted-len)];
|
||||
end
|
||||
end
|
||||
|
||||
% --- Compute mean curves per scan parameter ---
|
||||
meanCurves = cell(1, N_params);
|
||||
for i = 1:N_params
|
||||
curves = zeros(N_reps, Npoints_shifted);
|
||||
for r = 1:N_reps
|
||||
idx = (r-1)*N_params + i; % interleaved indexing
|
||||
curves(r,:) = S_theta_all_shifted{idx};
|
||||
end
|
||||
meanCurves{i} = nanmean(curves,1); % mean over repetitions
|
||||
end
|
||||
|
||||
% --- Fit two-Gaussian model to mean curves ---
|
||||
fitResultsMean = Analyzer.fitTwoGaussianCurves(meanCurves, theta_vals(1:Npoints_shifted)-theta_vals(1), ...
|
||||
'MaxTheta', pi/2, ...
|
||||
'DeviationLimit', 1.0);
|
||||
|
||||
% --- Scatter plot of secondary peak position (mu2) vs scan parameter ---
|
||||
mu2_vals = nan(1, N_params);
|
||||
sigma2_vals = nan(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
if fitResultsMean(i).isValid
|
||||
mu2_vals(i) = fitResultsMean(i).pFit(4); % secondary peak position
|
||||
sigma2_vals(i) = fitResultsMean(i).pFit(5); % secondary peak width
|
||||
end
|
||||
end
|
||||
|
||||
% Secondary peak position
|
||||
plotSecondaryPeakScatter(fitResultsMean, results_all{1}.scan_reference_values, 'mu2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||
'FigNum', 23, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakPositionScatter.fig');
|
||||
|
||||
% Secondary peak width
|
||||
plotSecondaryPeakScatter(fitResultsMean, results_all{1}.scan_reference_values, 'sigma2', ...
|
||||
'Title', options.titleString, ...
|
||||
'XLabel', '\alpha (degrees)', ...
|
||||
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||
'FigNum', 24, ...
|
||||
'FontName', options.font, ...
|
||||
'FontSize', 16, ...
|
||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||
'SaveFileName', 'SecondaryPeakWidthScatter.fig');
|
||||
|
||||
|
||||
function plotSecondaryPeakScatter(fitResultsMean, scanValues, parameterName, varargin)
|
||||
%% plotSecondaryPeakScatter
|
||||
% Author: Karthik
|
||||
% Date: 2025-10-06
|
||||
% Version: 1.0
|
||||
%
|
||||
% Description:
|
||||
% Scatter plot of secondary peak fit parameters (mu2 or sigma2) vs scan parameter
|
||||
% in the same style as plotMeanWithSE.
|
||||
|
||||
% --- Parse optional inputs ---
|
||||
p = inputParser;
|
||||
addParameter(p, 'Title', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'XLabel', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'YLabel', '', @(x) ischar(x) || isstring(x));
|
||||
addParameter(p, 'FigNum', [], @(x) isempty(x) || isscalar(x));
|
||||
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||
addParameter(p, 'YLim', [], @(x) isempty(x) || isnumeric(x));
|
||||
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
||||
addParameter(p, 'SaveFileName', 'secondary_peak_scatter.fig', @ischar);
|
||||
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||
parse(p, varargin{:});
|
||||
opts = p.Results;
|
||||
|
||||
% --- Extract parameter values ---
|
||||
N_params = numel(fitResultsMean);
|
||||
paramVals = nan(1, N_params);
|
||||
for i = 1:N_params
|
||||
if fitResultsMean(i).isValid
|
||||
switch parameterName
|
||||
case 'mu2'
|
||||
paramVals(i) = fitResultsMean(i).pFit(4);
|
||||
case 'sigma2'
|
||||
paramVals(i) = fitResultsMean(i).pFit(5);
|
||||
otherwise
|
||||
error('Unknown parameter name: %s', parameterName);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% --- Prepare figure ---
|
||||
if isempty(opts.FigNum)
|
||||
fig = figure;
|
||||
else
|
||||
fig = figure(opts.FigNum);
|
||||
clf(fig);
|
||||
end
|
||||
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||
|
||||
% --- Plot as mean ± SE style (SE=0 here) ---
|
||||
errorbar(scanValues, paramVals, zeros(size(paramVals)), 'o--', ...
|
||||
'LineWidth', 1.8, 'MarkerSize', 6, 'CapSize', 5);
|
||||
|
||||
% --- Axis formatting ---
|
||||
set(gca, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
if ~isempty(opts.YLim)
|
||||
ylim(opts.YLim);
|
||||
end
|
||||
xlabel(opts.XLabel, 'Interpreter', 'tex', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
ylabel(opts.YLabel, 'Interpreter', 'tex', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||
title(opts.Title, 'FontName', opts.FontName, 'FontSize', opts.FontSize + 2, 'FontWeight', 'bold');
|
||||
grid on;
|
||||
|
||||
% --- Save figure ---
|
||||
if ~opts.SkipSaveFigures
|
||||
if ~exist(opts.SaveDirectory, 'dir'), mkdir(opts.SaveDirectory); end
|
||||
savefig(fig, fullfile(opts.SaveDirectory, opts.SaveFileName));
|
||||
end
|
||||
end
|
||||
|
||||
%{
|
||||
%% ------------------ 14. Average of Spectra Plots ------------------
|
||||
|
||||
Plotter.plotAverageSpectra(scan_parameter_values, ...
|
||||
spectral_analysis_results, ...
|
||||
'ScanParameterName', scan_parameter, ...
|
||||
'FigNum', 25, ...
|
||||
'FigNum', 20, ...
|
||||
'ColormapPS', Colormaps.coolwarm(), ...
|
||||
'Font', 'Bahnschrift', ...
|
||||
'SaveFileName', 'avgSpectra.fig', ...
|
||||
|
||||
Loading…
Reference in New Issue
Block a user