740 lines
27 KiB
Matlab
740 lines
27 KiB
Matlab
%% ===== Settings =====
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "//DyLabNAS/Data/TwoDGas/2025/07/22/";
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run = '0021';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1410, 2030];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6; % in meters
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magnification = 23.94;
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removeFringes = false;
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ImagingMode = 'HighIntensity';
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PulseDuration = 5e-6; % in s
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% Fourier analysis settings
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% Radial Spectral Distribution
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theta_min = deg2rad(0);
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theta_max = deg2rad(180);
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N_radial_bins = 500;
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Radial_Sigma = 2;
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Radial_WindowSize = 5; % Choose an odd number for a centered moving average
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% Angular Spectral Distribution
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r_min = 10;
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r_max = 20;
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N_angular_bins = 180;
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Angular_Threshold = 75;
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Angular_Sigma = 2;
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Angular_WindowSize = 5;
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zoom_size = 50; % Zoomed-in region around center
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% Plotting and saving
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scan_parameter = 'ps_rot_mag_fin_pol_angle';
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% scan_parameter = 'rot_mag_field';
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scan_parameter_text = 'Angle = ';
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% scan_parameter_text = 'BField = ';
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savefileName = 'DropletsToStripes';
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font = 'Bahnschrift';
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if strcmp(savefileName, 'DropletsToStripes')
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scan_groups = 0:5:45;
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titleString = 'Droplets to Stripes';
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elseif strcmp(savefileName, 'StripesToDroplets')
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scan_groups = 45:-5:0;
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titleString = 'Stripes to Droplets';
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end
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% Flags
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skipUnshuffling = true;
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skipPreprocessing = true;
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skipMasking = true;
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skipIntensityThresholding = true;
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skipBinarization = true;
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skipMovieRender = true;
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skipSaveFigures = true;
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%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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files = dir(filePattern);
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refimages = zeros(span(1) + 1, span(2) + 1, length(files));
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absimages = zeros(span(1) + 1, span(2) + 1, length(files));
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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fprintf(1, 'Now reading %s\n', fullFileName);
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atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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if (isempty(atm_img) && isa(atm_img, 'double')) || ...
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(isempty(bkg_img) && isa(bkg_img, 'double')) || ...
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(isempty(dark_img) && isa(dark_img, 'double'))
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refimages = nan(size(refimages)); % fill with NaNs
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absimages = nan(size(absimages));
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else
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refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
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absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img, ImagingMode, PulseDuration), center, span), fraction)';
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end
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end
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%% ===== Fringe removal =====
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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absimages_fringe_removed = absimages(:, :, :) - optrefimages(:, :, :);
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nimgs = size(absimages_fringe_removed,3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages_fringe_removed(:, :, i);
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end
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else
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nimgs = size(absimages(:, :, :),3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages(:, :, i);
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end
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end
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%% ===== Get rotation angles =====
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scan_parameter_values = zeros(1, length(files));
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% Get information about the '/globals' group
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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info = h5info(fullFileName, '/globals');
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for i = 1:length(info.Attributes)
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if strcmp(info.Attributes(i).Name, scan_parameter)
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if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
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scan_parameter_values(k) = 180 - info.Attributes(i).Value;
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else
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scan_parameter_values(k) = info.Attributes(i).Value;
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end
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end
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end
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end
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%% ===== Correlation of a single (highest) peak with a possible peak between 50-70 degrees from experiment data =====
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fft_imgs = cell(1, nimgs);
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spectral_distribution = cell(1, nimgs);
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theta_values = cell(1, nimgs);
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N_shots = length(od_imgs);
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% Compute FFT for all images
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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[Ny, Nx] = size(IMG);
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dx = pixel_size / magnification;
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dy = dx; % assuming square pixels
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x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6;
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y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
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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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kx_full = 2 * pi * vx * 1E-6;
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ky_full = 2 * pi * vy * 1E-6;
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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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[theta_vals, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, r_min, r_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
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spectral_distribution{k} = S_theta;
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theta_values{k} = theta_vals;
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end
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% Convert spectral distribution to matrix (N_shots x N_angular_bins)
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delta_nkr_all = zeros(N_shots, N_angular_bins);
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for k = 1:N_shots
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delta_nkr_all(k, :) = spectral_distribution{k};
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end
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% Group by scan parameter values (e.g., alpha, angle, etc.)
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[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
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N_params = length(unique_scan_parameter_values);
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% Define angular range and conversion
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angle_range = 180;
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angle_per_bin = angle_range / N_angular_bins;
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max_peak_angle = 180;
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max_peak_bin = round(max_peak_angle / angle_per_bin);
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% Parameters for search
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window_size = 10;
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angle_threshold = 100;
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% Initialize containers for final results
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mean_max_g2_values = zeros(1, N_params);
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skew_max_g2_angle_values = zeros(1, N_params);
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var_max_g2_values = zeros(1, N_params);
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kurt_max_g2_angle_values = zeros(1, N_params);
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fifth_order_cumulant_max_g2_angle_values = zeros(1, N_params);
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sixth_order_cumulant_max_g2_angle_values = zeros(1, N_params);
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% Also store raw data per group
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max_g2_all_per_group = cell(1, N_params);
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std_error_g2_values = zeros(1, N_params);
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for i = 1:N_params
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group_idx = find(idx == i);
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group_data = delta_nkr_all(group_idx, :);
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N_reps = size(group_data, 1);
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g2_values = zeros(1, N_reps);
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for j = 1:N_reps
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profile = group_data(j, :);
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% Restrict search to 0–60° for highest peak
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restricted_profile = profile(1:max_peak_bin);
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[~, peak_idx_rel] = max(restricted_profile);
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peak_idx = peak_idx_rel;
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peak_angle = (peak_idx - 1) * angle_per_bin;
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if peak_angle < angle_threshold
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offsets = round(50 / angle_per_bin) : round(70 / angle_per_bin);
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else
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offsets = -round(70 / angle_per_bin) : -round(50 / angle_per_bin);
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end
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ref_window = mod((peak_idx - window_size):(peak_idx + window_size) - 1, N_angular_bins) + 1;
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ref = profile(ref_window);
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correlations = zeros(size(offsets));
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for k = 1:length(offsets)
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shifted_idx = mod(peak_idx + offsets(k) - 1, N_angular_bins) + 1;
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sec_window = mod((shifted_idx - window_size):(shifted_idx + window_size) - 1, N_angular_bins) + 1;
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sec = profile(sec_window);
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num = mean(ref .* sec);
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denom = mean(ref.^2);
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g2 = num / denom;
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correlations(k) = g2;
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end
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[max_corr, max_idx] = max(correlations);
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g2_values(j) = max_corr;
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end
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% Store raw values
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max_g2_all_per_group{i} = g2_values;
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% Compute cumulants
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kappa = computeCumulants(g2_values(:), 6);
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% Final stats
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mean_max_g2_values(i) = kappa(1);
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var_max_g2_values(i) = kappa(2);
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N_eff = sum(~isnan(g2_values));
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std_error_g2_values(i) = sqrt(kappa(2)) / sqrt(N_eff);
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skew_max_g2_angle_values(i) = kappa(3);
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kurt_max_g2_angle_values(i) = kappa(4);
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fifth_order_cumulant_max_g2_angle_values(i) = kappa(5);
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sixth_order_cumulant_max_g2_angle_values(i) = kappa(6);
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end
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%% Plot PDF of order parameter
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if ~skipSaveFigures
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% Define folder for saving images
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saveFolder = [savefileName '_SavedFigures'];
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if ~exist(saveFolder, 'dir')
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mkdir(saveFolder);
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end
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end
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figure(2); % one persistent figure
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set(gcf, 'Color', 'w', 'Position', [100 100 950 750])
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for val = scan_groups
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% Find the index i that matches this scan parameter value
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i = find(unique_scan_parameter_values == val, 1);
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% Skip if not found (sanity check)
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if isempty(i)
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continue;
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end
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g2_vals = g2_all_per_group{i};
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g2_vals = g2_vals(~isnan(g2_vals));
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if isempty(g2_vals)
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continue;
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end
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% KDE estimation
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[f, xi] = ksdensity(g2_vals, 'NumPoints', 200);
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clf;
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histogram(g2_vals, 'Normalization', 'pdf', ...
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'NumBins', 10, ...
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'FaceAlpha', 0.3, ...
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'EdgeColor', 'none', ...
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'FaceColor', [0.3 0.5 0.8]);
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hold on;
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plot(xi, f, 'LineWidth', 2, 'Color', [0 0.2 0.6]);
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set(gca, 'FontSize', 16);
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title(sprintf('%s: \\boldmath$\\alpha = %.1f^{\\circ}$', titleString, val), ...
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'FontSize', 16, 'Interpreter', 'latex');
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xlabel('$\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', 'Interpreter', 'latex', 'FontSize', 14);
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ylabel('PDF', 'FontSize', 14);
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xlim([0.0, 1.5]);
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grid on;
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drawnow;
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% ==== Save Figure ====
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if ~skipSaveFigures
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% Create a filename for each averaged plot
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fileNamePNG = fullfile(saveFolder, sprintf('max_g2_analysis_param_%03d.png', val));
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% Save current figure as PNG with high resolution
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print(gcf, fileNamePNG, '-dpng', '-r300'); % 300 dpi for high quality
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else
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pause(0.5)
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end
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end
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%% Plot all cumulants
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figure(3)
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set(gcf, 'Color', 'w', 'Position', [100 100 950 750])
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scan_vals = unique_scan_parameter_values;
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% Define font style for consistency
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axis_fontsize = 14;
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label_fontsize = 16;
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title_fontsize = 16;
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% 1. Mean with error bars
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subplot(3,2,1);
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errorbar(scan_vals, mean_max_g2_values, std_error_g2_values, 'o-', ...
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'LineWidth', 1.5, 'MarkerSize', 6);
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title('Mean of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_1$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% 2. Variance
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subplot(3,2,2);
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plot(scan_vals, var_max_g2_values, 's-', 'LineWidth', 1.5, 'MarkerSize', 6);
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title('Variance of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_2$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% 3. Skewness
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subplot(3,2,3);
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plot(scan_vals, skew_max_g2_angle_values, 'd-', 'LineWidth', 1.5, 'MarkerSize', 6);
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title('Skewness of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_3$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% 4. Kurtosis
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subplot(3,2,4);
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plot(scan_vals, kurt_max_g2_angle_values, '^-', 'LineWidth', 1.5, 'MarkerSize', 6);
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title('Kurtosis of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_4$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% 5. 5th-order cumulant
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subplot(3,2,5);
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plot(scan_vals, fifth_order_cumulant_max_g2_angle_values, 'v-', 'LineWidth', 1.5, 'MarkerSize', 6);
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title('Fifth-order cumulant of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_5$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% 6. 6th-order cumulant
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subplot(3,2,6);
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plot(scan_vals, sixth_order_cumulant_max_g2_angle_values, '>-', 'LineWidth', 1.5, 'MarkerSize', 6);
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title('Sixth-order cumulant of $\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', ...
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'Interpreter', 'latex', 'FontSize', title_fontsize);
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xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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ylabel('$\kappa_6$', 'Interpreter', 'latex', ...
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'FontSize', label_fontsize);
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set(gca, 'FontSize', axis_fontsize);
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grid on;
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% Super title
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sgtitle(sprintf('Cumulants of Peak Offset Angular Correlation - %s', titleString), ...
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'FontWeight', 'bold', 'FontSize', 16, 'Interpreter', 'latex');
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%% ── Mean ± Std vs. scan parameter ──────────────────────────────────────
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% Plot mean ± SEM
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figure(1);
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set(gcf, 'Color', 'w', 'Position',[100 100 950 750])
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set(gca, 'FontSize', 14); % For tick labels only
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errorbar(unique_scan_parameter_values, ... % x-axis
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mean_max_g2_values, ... % y-axis (mean)
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std_error_g2_values, ... % ± SEM
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'--o', 'LineWidth', 1.8, 'MarkerSize', 6 );
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set(gca, 'FontSize', 14, 'YLim', [0, 1]);
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hXLabel = xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex');
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hYLabel = ylabel('$\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', 'Interpreter', 'latex');
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hTitle = title(titleString, 'Interpreter', 'tex');
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% set([hXLabel, hYLabel], 'FontName', font);
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set([hXLabel, hYLabel], 'FontSize', 14);
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set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold');
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grid on;
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% Define folder for saving images
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saveFolder = [savefileName '_SavedFigures'];
|
||
if ~exist(saveFolder, 'dir')
|
||
mkdir(saveFolder);
|
||
end
|
||
save([saveFolder savefileName '.mat'], 'unique_scan_parameter_values', 'mean_max_g2_values', 'std_error_g2_values');
|
||
|
||
%% Helper Functions
|
||
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
|
||
% computeFourierSpectrum - Computes the 2D Fourier power spectrum
|
||
% of binarized and enhanced lattice image features, with optional central mask.
|
||
%
|
||
% Inputs:
|
||
% I - Grayscale or RGB image matrix
|
||
%
|
||
% Output:
|
||
% F_mag - 2D Fourier power spectrum (shifted)
|
||
|
||
if ~skipPreprocessing
|
||
% Preprocessing: Denoise
|
||
filtered = imgaussfilt(I, 10);
|
||
IMGPR = I - filtered; % adjust sigma as needed
|
||
else
|
||
IMGPR = I;
|
||
end
|
||
|
||
if ~skipMasking
|
||
[rows, cols] = size(IMGPR);
|
||
[X, Y] = meshgrid(1:cols, 1:rows);
|
||
% Elliptical mask parameters
|
||
cx = cols / 2;
|
||
cy = rows / 2;
|
||
|
||
% Shifted coordinates
|
||
x = X - cx;
|
||
y = Y - cy;
|
||
|
||
% Ellipse semi-axes
|
||
rx = 0.4 * cols;
|
||
ry = 0.2 * rows;
|
||
|
||
% Rotation angle in degrees -> radians
|
||
theta_deg = 30; % Adjust as needed
|
||
theta = deg2rad(theta_deg);
|
||
|
||
% Rotated ellipse equation
|
||
cos_t = cos(theta);
|
||
sin_t = sin(theta);
|
||
|
||
x_rot = (x * cos_t + y * sin_t);
|
||
y_rot = (-x * sin_t + y * cos_t);
|
||
|
||
ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
|
||
|
||
% Apply cutout mask
|
||
IMGPR = IMGPR .* ellipseMask;
|
||
end
|
||
|
||
if ~skipIntensityThresholding
|
||
% Apply global intensity threshold mask
|
||
intensity_thresh = 0.20;
|
||
intensity_mask = IMGPR > intensity_thresh;
|
||
IMGPR = IMGPR .* intensity_mask;
|
||
end
|
||
|
||
if ~skipBinarization
|
||
% Adaptive binarization and cleanup
|
||
IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
|
||
IMGPR = imdilate(IMGPR, strel('disk', 2));
|
||
IMGPR = imerode(IMGPR, strel('disk', 1));
|
||
IMGPR = imfill(IMGPR, 'holes');
|
||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||
else
|
||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||
end
|
||
end
|
||
|
||
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma, windowSize)
|
||
% Apply threshold to isolate strong peaks
|
||
IMGFFT(IMGFFT < threshold) = 0;
|
||
|
||
% Prepare polar coordinates
|
||
[ny, nx] = size(IMGFFT);
|
||
[X, Y] = meshgrid(1:nx, 1:ny);
|
||
cx = ceil(nx/2);
|
||
cy = ceil(ny/2);
|
||
R = sqrt((X - cx).^2 + (Y - cy).^2);
|
||
Theta = atan2(Y - cy, X - cx); % range [-pi, pi]
|
||
|
||
% Choose radial band
|
||
radial_mask = (R >= r_min) & (R <= r_max);
|
||
|
||
% Initialize angular structure factor
|
||
S_theta = zeros(1, num_bins);
|
||
theta_vals = linspace(0, pi, num_bins);
|
||
|
||
% Loop through angle bins
|
||
for i = 1:num_bins
|
||
angle_start = (i-1) * pi / num_bins;
|
||
angle_end = i * pi / num_bins;
|
||
angle_mask = (Theta >= angle_start & Theta < angle_end);
|
||
bin_mask = radial_mask & angle_mask;
|
||
fft_angle = IMGFFT .* bin_mask;
|
||
S_theta(i) = sum(sum(abs(fft_angle).^2));
|
||
end
|
||
|
||
% Smooth using either Gaussian or moving average
|
||
if exist('sigma', 'var') && ~isempty(sigma)
|
||
% Gaussian convolution
|
||
half_width = ceil(3 * sigma);
|
||
x = -half_width:half_width;
|
||
gauss_kernel = exp(-x.^2 / (2 * sigma^2));
|
||
gauss_kernel = gauss_kernel / sum(gauss_kernel);
|
||
% Circular convolution
|
||
S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], ...
|
||
gauss_kernel, 'same');
|
||
S_theta = S_theta(half_width+1:end-half_width);
|
||
elseif exist('windowSize', 'var') && ~isempty(windowSize)
|
||
% Moving average via convolution (circular)
|
||
pad = floor(windowSize / 2);
|
||
kernel = ones(1, windowSize) / windowSize;
|
||
S_theta = conv([S_theta(end-pad+1:end), S_theta, S_theta(1:pad)], kernel, 'same');
|
||
S_theta = S_theta(pad+1:end-pad);
|
||
end
|
||
end
|
||
|
||
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
|
||
% image must be a 2D numerical array
|
||
[dim1, dim2] = size(img);
|
||
|
||
s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
|
||
s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
|
||
s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
|
||
s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
|
||
|
||
ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
|
||
end
|
||
|
||
function ret = subtractBackgroundOffset(img, fraction)
|
||
% Remove the background from the image.
|
||
% :param dataArray: The image
|
||
% :type dataArray: xarray DataArray
|
||
% :param x_fraction: The fraction of the pixels used in x axis
|
||
% :type x_fraction: float
|
||
% :param y_fraction: The fraction of the pixels used in y axis
|
||
% :type y_fraction: float
|
||
% :return: The image after removing background
|
||
% :rtype: xarray DataArray
|
||
|
||
x_fraction = fraction(1);
|
||
y_fraction = fraction(2);
|
||
offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
|
||
ret = img - offset;
|
||
end
|
||
|
||
function ret = cropODImage(img, center, span)
|
||
% Crop the image according to the region of interest (ROI).
|
||
% :param dataSet: The images
|
||
% :type dataSet: xarray DataArray or DataSet
|
||
% :param center: The center of region of interest (ROI)
|
||
% :type center: tuple
|
||
% :param span: The span of region of interest (ROI)
|
||
% :type span: tuple
|
||
% :return: The cropped images
|
||
% :rtype: xarray DataArray or DataSet
|
||
|
||
x_start = floor(center(1) - span(1) / 2);
|
||
x_end = floor(center(1) + span(1) / 2);
|
||
y_start = floor(center(2) - span(2) / 2);
|
||
y_end = floor(center(2) + span(2) / 2);
|
||
|
||
ret = img(y_start:y_end, x_start:x_end);
|
||
end
|
||
|
||
function imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||
%CALCULATEODIMAGE Calculates the optical density (OD) image for absorption imaging.
|
||
%
|
||
% imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||
%
|
||
% Inputs:
|
||
% imageAtom - Image with atoms
|
||
% imageBackground - Image without atoms
|
||
% imageDark - Image without light
|
||
% mode - 'LowIntensity' (default) or 'HighIntensity'
|
||
% exposureTime - Required only for 'HighIntensity' [in seconds]
|
||
%
|
||
% Output:
|
||
% imageOD - Computed OD image
|
||
%
|
||
|
||
arguments
|
||
imageAtom (:,:) {mustBeNumeric}
|
||
imageBackground (:,:) {mustBeNumeric}
|
||
imageDark (:,:) {mustBeNumeric}
|
||
mode char {mustBeMember(mode, {'LowIntensity', 'HighIntensity'})} = 'LowIntensity'
|
||
exposureTime double = NaN
|
||
end
|
||
|
||
% Compute numerator and denominator
|
||
numerator = imageBackground - imageDark;
|
||
denominator = imageAtom - imageDark;
|
||
|
||
% Avoid division by zero
|
||
numerator(numerator == 0) = 1;
|
||
denominator(denominator == 0) = 1;
|
||
|
||
% Calculate OD based on mode
|
||
switch mode
|
||
case 'LowIntensity'
|
||
imageOD = -log(abs(denominator ./ numerator));
|
||
|
||
case 'HighIntensity'
|
||
if isnan(exposureTime)
|
||
error('Exposure time must be provided for HighIntensity mode.');
|
||
end
|
||
imageOD = abs(denominator ./ numerator);
|
||
imageOD = -log(imageOD) + (numerator - denominator) ./ (7000 * (exposureTime / 5e-6));
|
||
end
|
||
|
||
end
|
||
|
||
function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
|
||
% removefringesInImage - Fringe removal and noise reduction from absorption images.
|
||
% Creates an optimal reference image for each absorption image in a set as
|
||
% a linear combination of reference images, with coefficients chosen to
|
||
% minimize the least-squares residuals between each absorption image and
|
||
% the optimal reference image. The coefficients are obtained by solving a
|
||
% linear set of equations using matrix inverse by LU decomposition.
|
||
%
|
||
% Application of the algorithm is described in C. F. Ockeloen et al, Improved
|
||
% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
|
||
%
|
||
% Syntax:
|
||
% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
|
||
%
|
||
% Required inputs:
|
||
% absimages - Absorption image data,
|
||
% typically 16 bit grayscale images
|
||
% refimages - Raw reference image data
|
||
% absimages and refimages are both cell arrays containing
|
||
% 2D array data. The number of refimages can differ from the
|
||
% number of absimages.
|
||
%
|
||
% Optional inputs:
|
||
% bgmask - Array specifying background region used,
|
||
% 1=background, 0=data. Defaults to all ones.
|
||
% Outputs:
|
||
% optrefimages - Cell array of optimal reference images,
|
||
% equal in size to absimages.
|
||
%
|
||
|
||
% Dependencies: none
|
||
%
|
||
% Authors: Shannon Whitlock, Caspar Ockeloen
|
||
% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
|
||
% S. Whitlock, Improved detection of small atom numbers through
|
||
% image processing, arXiv:1007.2136
|
||
% Email:
|
||
% May 2009; Last revision: 11 August 2010
|
||
|
||
% Process inputs
|
||
|
||
% Set variables, and flatten absorption and reference images
|
||
nimgs = size(absimages,3);
|
||
nimgsR = size(refimages,3);
|
||
xdim = size(absimages(:,:,1),2);
|
||
ydim = size(absimages(:,:,1),1);
|
||
|
||
R = single(reshape(refimages,xdim*ydim,nimgsR));
|
||
A = single(reshape(absimages,xdim*ydim,nimgs));
|
||
optrefimages=zeros(size(absimages)); % preallocate
|
||
|
||
if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
|
||
k = find(bgmask(:)==1); % Index k specifying background region
|
||
|
||
% Ensure there are no duplicate reference images
|
||
% R=unique(R','rows')'; % comment this line if you run out of memory
|
||
|
||
% Decompose B = R*R' using singular value or LU decomposition
|
||
[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
|
||
|
||
for j=1:nimgs
|
||
b=R(k,:)'*A(k,j);
|
||
% Obtain coefficients c which minimise least-square residuals
|
||
lower.LT = true; upper.UT = true;
|
||
c = linsolve(U,linsolve(L,b(p,:),lower),upper);
|
||
|
||
% Compute optimised reference image
|
||
optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
|
||
end
|
||
end |