MAJOR update - many changes!
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Data-Analyzer/analyzeFolder.m
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701
Data-Analyzer/analyzeFolder.m
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function results = analyzeFolder(options)
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% Ensure required fields are defined in options
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arguments
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options.scan_parameter (1,:) char
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options.scan_groups (1,:) double
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options.cam (1,1) double
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options.angle (1,1) double
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options.center (1,2) double
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options.span (1,2) double
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options.fraction (1,2) double
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options.ImagingMode (1,:) char
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options.PulseDuration (1,1) double
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options.removeFringes (1,1) logical
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options.skipUnshuffling (1,1) logical
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options.pixel_size (1,1) double
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options.magnification (1,1) double
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options.zoom_size (1,1) double
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options.r_min (1,1) double
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options.r_max (1,1) double
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options.N_angular_bins (1,1) double
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options.Angular_Threshold (1,1) double
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options.Angular_Sigma (1,1) double
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options.Angular_WindowSize (1,1) double
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options.theta_min (1,1) double
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options.theta_max (1,1) double
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options.N_radial_bins (1,1) double
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options.Radial_Sigma (1,1) double
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options.Radial_WindowSize (1,1) double
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options.k_min (1,1) double
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options.k_max (1,1) double
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options.skipPreprocessing (1,1) logical
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options.skipMasking (1,1) logical
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options.skipIntensityThresholding (1,1) logical
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options.skipBinarization (1,1) logical
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options.folderPath (1,:) char
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end
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% Assign variables from options
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scan_parameter = options.scan_parameter;
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scan_groups = options.scan_groups;
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folderPath = options.folderPath;
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center = options.center;
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span = options.span;
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fraction = options.fraction;
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ImagingMode = options.ImagingMode;
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PulseDuration = options.PulseDuration;
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removeFringes = options.removeFringes;
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skipUnshuffling = options.skipUnshuffling;
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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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r_min = options.r_min;
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r_max = options.r_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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Angular_WindowSize = options.Angular_WindowSize;
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theta_min = options.theta_min;
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theta_max = options.theta_max;
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N_radial_bins = options.N_radial_bins;
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Radial_Sigma = options.Radial_Sigma;
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Radial_WindowSize = options.Radial_WindowSize;
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k_min = options.k_min;
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k_max = options.k_max;
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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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cam = options.cam;
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angle = options.angle;
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% Load images and analyze them (keep using the cleaned body of your original function)
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% Fix the incorrect usage of 'cam' and 'angle' not defined locally
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption",
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"/images/ODT_1_Axis_Camera/in_situ_absorption",
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"/images/ODT_2_Axis_Camera/in_situ_absorption",
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"/images/Horizontal_Axis_Camera/in_situ_absorption",
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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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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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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% ===== 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, '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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% ===== Unshuffle if necessary to do so =====
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if ~skipUnshuffling
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n_values = length(scan_groups);
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n_total = length(scan_parameter_values);
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% Infer number of repetitions
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n_reps = n_total / n_values;
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% Preallocate ordered arrays
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ordered_scan_values = zeros(1, n_total);
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ordered_od_imgs = cell(1, n_total);
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counter = 1;
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for rep = 1:n_reps
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for val = scan_groups
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% Find the next unused match for this val
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idx = find(scan_parameter_values == val, 1, 'first');
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% Assign and remove from list to avoid duplicates
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ordered_scan_values(counter) = scan_parameter_values(idx);
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ordered_od_imgs{counter} = od_imgs{idx};
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% Mark as used by removing
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scan_parameter_values(idx) = NaN; % NaN is safe since original values are 0:5:45
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od_imgs{idx} = []; % empty cell so it won't be matched again
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counter = counter + 1;
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end
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end
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% Now assign back
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scan_parameter_values = ordered_scan_values;
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od_imgs = ordered_od_imgs;
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end
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% Extract quantities
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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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radial_spectral_contrast = zeros(1, nimgs);
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angular_spectral_weight = zeros(1, nimgs);
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N_shots = length(od_imgs);
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, ~] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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% Size of original image (in pixels)
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[Ny, Nx] = size(IMG);
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% Real-space pixel size in micrometers after magnification
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dx = pixel_size / magnification;
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dy = dx; % assuming square pixels
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% Real-space axes
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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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% Reciprocal space increments (frequency domain, μm⁻¹)
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dvx = 1 / (Nx * dx);
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dvy = 1 / (Ny * dy);
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% Frequency axes
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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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% Wavenumber axes
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kx_full = 2 * pi * vx * 1E-6; % μm⁻¹
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ky_full = 2 * pi * vy * 1E-6;
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% Crop FFT image 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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% Crop wavenumber axes to match fft_imgs{k}
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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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[k_rho_vals, S_k] = computeRadialSpectralDistribution(fft_imgs{k}, kx, ky, theta_min, theta_max, N_radial_bins);
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S_k_smoothed = movmean(S_k, Radial_WindowSize); % Compute moving average (use convolution) or use conv for more control
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spectral_distribution{k} = S_theta;
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theta_values{k} = theta_vals;
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radial_spectral_contrast(k) = computeRadialSpectralContrast(k_rho_vals, S_k_smoothed, k_min, k_max);
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S_theta_norm = S_theta / max(S_theta); % Normalize to 1
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angular_spectral_weight(k) = trapz(theta_vals, S_theta_norm);
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end
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% Assuming scan_parameter_values and spectral_weight are column vectors (or row vectors of same length)
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[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
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% Preallocate arrays
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mean_rsc = zeros(size(unique_scan_parameter_values));
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stderr_rsc = zeros(size(unique_scan_parameter_values));
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% Loop through each unique theta and compute mean and standard error
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for i = 1:length(unique_scan_parameter_values)
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group_vals = radial_spectral_contrast(idx == i);
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mean_rsc(i) = mean(group_vals);
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stderr_rsc(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
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end
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% Preallocate arrays
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mean_asw = zeros(size(unique_scan_parameter_values));
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stderr_asw = zeros(size(unique_scan_parameter_values));
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% Loop through each unique theta and compute mean and standard error
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for i = 1:length(unique_scan_parameter_values)
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group_vals = angular_spectral_weight(idx == i);
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mean_asw(i) = mean(group_vals);
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stderr_asw(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
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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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mean_max_g2_angle_values = zeros(1, N_params);
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var_max_g2_values = zeros(1, N_params);
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var_max_g2_angle_values = zeros(1, N_params);
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std_error_g2_values = zeros(1, N_params);
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% Also store raw data per group
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g2_all_per_group = cell(1, N_params);
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angle_all_per_group = cell(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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angle_at_max_g2 = 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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angles = 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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angles(k) = mod((peak_idx - 1 + offsets(k)) * angle_per_bin, angle_range);
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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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angle_at_max_g2(j) = angles(max_idx);
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end
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% Store raw values
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g2_all_per_group{i} = g2_values;
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angle_all_per_group{i} = angle_at_max_g2;
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% Final stats
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mean_max_g2_values(i) = mean(g2_values, 'omitnan');
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var_max_g2_values(i) = var(g2_values, 0, 'omitnan');
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mean_max_g2_angle_values(i)= mean(angle_at_max_g2, 'omitnan');
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var_max_g2_angle_values(i) = var(angle_at_max_g2, 0, 'omitnan');
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n_i = numel(g2_all_per_group{i}); % Number of repetitions for this param
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std_error_g2_values(i) = sqrt(var_max_g2_values(i) / n_i);
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end
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results.folderPath = folderPath;
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results.scan_parameter = scan_parameter;
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results.scan_groups = scan_groups;
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results.mean_max_g2_values = mean_max_g2_values;
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results.std_error_g2_values = std_error_g2_values;
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results.mean_max_g2_angle = mean_max_g2_angle_values;
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results.radial_spectral_contrast= mean_rsc;
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results.angular_spectral_weight = mean_asw;
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end
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%% Helper Functions
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function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
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% computeFourierSpectrum - Computes the 2D Fourier power spectrum
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% of binarized and enhanced lattice image features, with optional central mask.
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%
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% Inputs:
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% I - Grayscale or RGB image matrix
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%
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% Output:
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% F_mag - 2D Fourier power spectrum (shifted)
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if ~skipPreprocessing
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% Preprocessing: Denoise
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filtered = imgaussfilt(I, 10);
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IMGPR = I - filtered; % adjust sigma as needed
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else
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IMGPR = I;
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end
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if ~skipMasking
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[rows, cols] = size(IMGPR);
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[X, Y] = meshgrid(1:cols, 1:rows);
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% Elliptical mask parameters
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cx = cols / 2;
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cy = rows / 2;
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% Shifted coordinates
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x = X - cx;
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y = Y - cy;
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% Ellipse semi-axes
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rx = 0.4 * cols;
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ry = 0.2 * rows;
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% Rotation angle in degrees -> radians
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theta_deg = 30; % Adjust as needed
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theta = deg2rad(theta_deg);
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% Rotated ellipse equation
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cos_t = cos(theta);
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sin_t = sin(theta);
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x_rot = (x * cos_t + y * sin_t);
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y_rot = (-x * sin_t + y * cos_t);
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ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
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% Apply cutout mask
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IMGPR = IMGPR .* ellipseMask;
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end
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if ~skipIntensityThresholding
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% Apply global intensity threshold mask
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intensity_thresh = 0.20;
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intensity_mask = IMGPR > intensity_thresh;
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IMGPR = IMGPR .* intensity_mask;
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end
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if ~skipBinarization
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% Adaptive binarization and cleanup
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IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
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IMGPR = imdilate(IMGPR, strel('disk', 2));
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IMGPR = imerode(IMGPR, strel('disk', 1));
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IMGPR = imfill(IMGPR, 'holes');
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F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
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IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
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else
|
||||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||||
end
|
||||
end
|
||||
|
||||
function [k_rho_vals, S_radial] = computeRadialSpectralDistribution(IMGFFT, kx, ky, thetamin, thetamax, num_bins)
|
||||
% IMGFFT : 2D FFT image (fftshifted and cropped)
|
||||
% kx, ky : 1D physical wavenumber axes [μm⁻¹] matching FFT size
|
||||
% thetamin : Minimum angle (in radians)
|
||||
% thetamax : Maximum angle (in radians)
|
||||
% num_bins : Number of radial bins
|
||||
|
||||
[KX, KY] = meshgrid(kx, ky);
|
||||
K_rho = sqrt(KX.^2 + KY.^2);
|
||||
Theta = atan2(KY, KX);
|
||||
|
||||
if thetamin < thetamax
|
||||
angle_mask = (Theta >= thetamin) & (Theta <= thetamax);
|
||||
else
|
||||
angle_mask = (Theta >= thetamin) | (Theta <= thetamax);
|
||||
end
|
||||
|
||||
power_spectrum = abs(IMGFFT).^2;
|
||||
|
||||
r_min = min(K_rho(angle_mask));
|
||||
r_max = max(K_rho(angle_mask));
|
||||
r_edges = linspace(r_min, r_max, num_bins + 1);
|
||||
k_rho_vals = 0.5 * (r_edges(1:end-1) + r_edges(2:end));
|
||||
S_radial = zeros(1, num_bins);
|
||||
|
||||
for i = 1:num_bins
|
||||
r_low = r_edges(i);
|
||||
r_high = r_edges(i + 1);
|
||||
radial_mask = (K_rho >= r_low) & (K_rho < r_high);
|
||||
full_mask = radial_mask & angle_mask;
|
||||
S_radial(i) = sum(power_spectrum(full_mask));
|
||||
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 contrast = computeRadialSpectralContrast(k_rho_vals, S_k_smoothed, k_min, k_max)
|
||||
% Computes the ratio of the peak in S_k_smoothed within [k_min, k_max]
|
||||
% to the value at (or near) k = 0.
|
||||
|
||||
% Ensure inputs are column vectors
|
||||
k_rho_vals = k_rho_vals(:);
|
||||
S_k_smoothed = S_k_smoothed(:);
|
||||
|
||||
% Step 1: Find index of k ≈ 0
|
||||
[~, idx_k0] = min(abs(k_rho_vals)); % Closest to zero
|
||||
S_k0 = S_k_smoothed(idx_k0);
|
||||
|
||||
% Step 2: Find indices in specified k-range
|
||||
in_range = (k_rho_vals >= k_min) & (k_rho_vals <= k_max);
|
||||
|
||||
if ~any(in_range)
|
||||
warning('No values found in the specified k-range. Returning NaN.');
|
||||
contrast = NaN;
|
||||
return;
|
||||
end
|
||||
|
||||
% Step 3: Find peak value in the specified k-range
|
||||
S_k_peak = max(S_k_smoothed(in_range));
|
||||
|
||||
% Step 4: Compute contrast
|
||||
contrast = S_k_peak / S_k0;
|
||||
|
||||
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
|
||||
39
Data-Analyzer/compareAngularCorrelation.m
Normal file
39
Data-Analyzer/compareAngularCorrelation.m
Normal file
@ -0,0 +1,39 @@
|
||||
%% Track spectral weight across the transition
|
||||
|
||||
set(0,'defaulttextInterpreter','latex')
|
||||
set(groot, 'defaultAxesTickLabelInterpreter','latex'); set(groot, 'defaultLegendInterpreter','latex');
|
||||
|
||||
format long
|
||||
|
||||
font = 'Bahnschrift';
|
||||
|
||||
% Load data
|
||||
Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/Comparison/Max_g2_DropletsToStripes.mat', 'unique_scan_parameter_values', 'mean_max_g2_values', 'std_error_g2_values');
|
||||
|
||||
dts_scan_parameter_values = Data.unique_scan_parameter_values;
|
||||
dts_mean_mg2 = Data.mean_max_g2_values;
|
||||
dts_stderr_mg2 = Data.std_error_g2_values;
|
||||
|
||||
Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/Comparison/Max_g2_StripesToDroplets.mat', 'unique_scan_parameter_values', 'mean_max_g2_values', 'std_error_g2_values');
|
||||
|
||||
std_scan_parameter_values = Data.unique_scan_parameter_values;
|
||||
std_mean_mg2 = Data.mean_max_g2_values;
|
||||
std_stderr_mg2 = Data.std_error_g2_values;
|
||||
|
||||
figure(1);
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
errorbar(dts_scan_parameter_values, dts_mean_mg2, dts_stderr_mg2, 'o--', ...
|
||||
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName' , 'Droplets to Stripes');
|
||||
hold on
|
||||
errorbar(std_scan_parameter_values, std_mean_mg2, std_stderr_mg2, 'o--', ...
|
||||
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName', 'Stripes to Droplets');
|
||||
set(gca, 'FontSize', 14, 'YLim', [0, 1]);
|
||||
hXLabel = xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex');
|
||||
hYLabel = ylabel('$\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', 'Interpreter', 'latex');
|
||||
hTitle = title('B = 2.42 G', 'Interpreter', 'tex');
|
||||
legend
|
||||
set([hXLabel, hYLabel], 'FontName', font)
|
||||
set([hXLabel, hYLabel], 'FontSize', 14)
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
grid on
|
||||
%%
|
||||
@ -1,18 +1,18 @@
|
||||
%% ===== Settings =====
|
||||
%% ===== D-S Settings =====
|
||||
groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/Vertical_Axis_Camera/in_situ_absorption"];
|
||||
|
||||
folderPath = "D:/Data - Experiment/2025/07/04/";
|
||||
folderPath = "//DyLabNAS/Data/TwoDGas/2025/06/23/";
|
||||
|
||||
run = '0016';
|
||||
run = '0300';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1430, 2040];
|
||||
center = [1410, 2030];
|
||||
span = [200, 200];
|
||||
fraction = [0.1, 0.1];
|
||||
|
||||
@ -43,29 +43,100 @@ Angular_WindowSize = 5;
|
||||
zoom_size = 50; % Zoomed-in region around center
|
||||
|
||||
% Plotting and saving
|
||||
% scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
scan_parameter = 'rot_mag_field';
|
||||
% scan_parameter_text = 'Angle = ';
|
||||
scan_parameter_text = 'BField = ';
|
||||
scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
% scan_parameter = 'rot_mag_field';
|
||||
|
||||
savefolderPath = 'E:/Results - Experiment/B2.35G/';
|
||||
savefileName = 'Droplets';
|
||||
savefileName = 'DropletsToStripes';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
skipUnshuffling = true;
|
||||
if strcmp(savefileName, 'DropletsToStripes')
|
||||
scan_groups = 0:5:45;
|
||||
scan_groups = 0:5:45;
|
||||
titleString = 'Droplets to Stripes';
|
||||
elseif strcmp(savefileName, 'StripesToDroplets')
|
||||
scan_groups = 45:-5:0;
|
||||
scan_groups = 45:-5:0;
|
||||
titleString = 'Stripes to Droplets';
|
||||
end
|
||||
|
||||
% Flags
|
||||
skipNormalization = false;
|
||||
skipUnshuffling = true;
|
||||
skipPreprocessing = true;
|
||||
skipMasking = true;
|
||||
skipIntensityThresholding = true;
|
||||
skipBinarization = true;
|
||||
skipMovieRender = true;
|
||||
skipSaveFigures = false;
|
||||
skipSaveOD = false;
|
||||
|
||||
%% ===== S-D Settings =====
|
||||
groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/Vertical_Axis_Camera/in_situ_absorption"];
|
||||
|
||||
folderPath = "//DyLabNAS/Data/TwoDGas/2025/06/24/";
|
||||
|
||||
run = '0001';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1410, 2030];
|
||||
span = [200, 200];
|
||||
fraction = [0.1, 0.1];
|
||||
|
||||
pixel_size = 5.86e-6; % in meters
|
||||
magnification = 23.94;
|
||||
removeFringes = false;
|
||||
|
||||
ImagingMode = 'HighIntensity';
|
||||
PulseDuration = 5e-6; % in s
|
||||
|
||||
% Fourier analysis settings
|
||||
|
||||
% Radial Spectral Distribution
|
||||
theta_min = deg2rad(0);
|
||||
theta_max = deg2rad(180);
|
||||
N_radial_bins = 500;
|
||||
Radial_Sigma = 2;
|
||||
Radial_WindowSize = 5; % Choose an odd number for a centered moving average
|
||||
|
||||
% Angular Spectral Distribution
|
||||
r_min = 10;
|
||||
r_max = 20;
|
||||
N_angular_bins = 180;
|
||||
Angular_Threshold = 75;
|
||||
Angular_Sigma = 2;
|
||||
Angular_WindowSize = 5;
|
||||
|
||||
zoom_size = 50; % Zoomed-in region around center
|
||||
|
||||
% Plotting and saving
|
||||
scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
% scan_parameter = 'rot_mag_field';
|
||||
|
||||
savefileName = 'StripesToDroplets';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
if strcmp(savefileName, 'DropletsToStripes')
|
||||
scan_groups = 0:5:45;
|
||||
titleString = 'Droplets to Stripes';
|
||||
elseif strcmp(savefileName, 'StripesToDroplets')
|
||||
scan_groups = 45:-5:0;
|
||||
titleString = 'Stripes to Droplets';
|
||||
end
|
||||
|
||||
% Flags
|
||||
skipNormalization = true;
|
||||
skipUnshuffling = false;
|
||||
skipPreprocessing = true;
|
||||
skipMasking = true;
|
||||
skipIntensityThresholding = true;
|
||||
skipBinarization = true;
|
||||
skipMovieRender = true;
|
||||
skipSaveFigures = false;
|
||||
skipSaveOD = false;
|
||||
|
||||
%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
|
||||
% Get a list of all files in the folder with the desired file name pattern.
|
||||
@ -89,7 +160,7 @@ for k = 1 : length(files)
|
||||
absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img, ImagingMode, PulseDuration), center, span), fraction)';
|
||||
end
|
||||
|
||||
%% ===== Fringe removal =====
|
||||
% ===== Fringe removal =====
|
||||
|
||||
if removeFringes
|
||||
optrefimages = removefringesInImage(absimages, refimages);
|
||||
@ -108,7 +179,7 @@ else
|
||||
end
|
||||
end
|
||||
|
||||
%% ===== Get rotation angles =====
|
||||
% ===== Get rotation angles =====
|
||||
scan_parameter_values = zeros(1, length(files));
|
||||
|
||||
% Get information about the '/globals' group
|
||||
@ -127,7 +198,7 @@ for k = 1 : length(files)
|
||||
end
|
||||
end
|
||||
|
||||
%% ===== Unshuffle if necessary to do so =====
|
||||
% ===== Unshuffle if necessary to do so =====
|
||||
|
||||
if ~skipUnshuffling
|
||||
n_values = length(scan_groups);
|
||||
@ -166,17 +237,10 @@ end
|
||||
|
||||
%% ===== Run Fourier analysis over images =====
|
||||
|
||||
fft_imgs = cell(1, nimgs);
|
||||
spectral_contrast = zeros(1, nimgs);
|
||||
spectral_weight = zeros(1, nimgs);
|
||||
N_shots = length(od_imgs);
|
||||
|
||||
avg_ps_accum = 0;
|
||||
avg_S_k_accum = 0;
|
||||
avg_S_theta_accum = 0;
|
||||
|
||||
% Pre-allocate once sizes are known (after first run)
|
||||
fft_size_known = false;
|
||||
fft_imgs = cell(1, nimgs);
|
||||
radial_spectral_contrast = zeros(1, nimgs);
|
||||
angular_spectral_weight = zeros(1, nimgs);
|
||||
N_shots = length(od_imgs);
|
||||
|
||||
if ~skipMovieRender
|
||||
% Create VideoWriter object for movie
|
||||
@ -194,7 +258,10 @@ if ~skipSaveFigures
|
||||
end
|
||||
end
|
||||
|
||||
% Display the cropped image
|
||||
ps_list = cell(1, N_shots); % 2D power spectrum
|
||||
s_k_list = cell(1, N_shots); % Radial spectrum
|
||||
s_theta_list = cell(1, N_shots); % Angular spectrum
|
||||
|
||||
for k = 1:N_shots
|
||||
IMG = od_imgs{k};
|
||||
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
|
||||
@ -235,8 +302,13 @@ for k = 1:N_shots
|
||||
[k_rho_vals, S_k] = computeRadialSpectralDistribution(fft_imgs{k}, kx, ky, theta_min, theta_max, N_radial_bins);
|
||||
S_k_smoothed = movmean(S_k, Radial_WindowSize); % % Compute moving average (use convolution) or use conv for more control
|
||||
|
||||
spectral_contrast(k) = computeSpectralContrast(fft_imgs{k}, r_min, r_max, Angular_Threshold);
|
||||
spectral_weight(k) = trapz(theta_vals, S_theta);
|
||||
radial_spectral_contrast(k) = computeRadialSpectralContrast(fft_imgs{k}, r_min, r_max, Angular_Threshold);
|
||||
S_theta_norm = S_theta / max(S_theta); % Normalize to 1
|
||||
angular_spectral_weight(k) = trapz(theta_vals, S_theta_norm);
|
||||
|
||||
ps_list{k} = abs(fft_imgs{k}).^2; % store the power spectrum
|
||||
s_k_list{k} = S_k_smoothed; % store smoothed radial spectrum
|
||||
s_theta_list{k} = S_theta; % store angular spectrum
|
||||
|
||||
figure(1);
|
||||
clf
|
||||
@ -268,14 +340,21 @@ for k = 1:N_shots
|
||||
ylabel('y (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
title('OD Image', 'FontSize', 16, 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontName', font);
|
||||
|
||||
text(0.975, 0.975, [scan_parameter_text, num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.1f^\\circ')], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
else
|
||||
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
end
|
||||
|
||||
% ======= FFT POWER SPECTRUM (reciprocal space) =======
|
||||
ax2 = nexttile;
|
||||
imagesc(kx, ky, log(1 + abs(fft_imgs{k}).^2));
|
||||
imagesc(kx, ky, log(1 + ps_list{k}));
|
||||
axis image;
|
||||
set(gca, 'FontSize', 14, 'YDir', 'normal')
|
||||
xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
@ -299,8 +378,13 @@ for k = 1:N_shots
|
||||
|
||||
% ======= ANGULAR DISTRIBUTION (S(θ)) =======
|
||||
nexttile;
|
||||
plot(theta_vals/pi, S_theta, 'LineWidth', 2);
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log', 'YLim', [1E4, 1E7]);
|
||||
if ~skipNormalization
|
||||
plot(theta_vals/pi, S_theta_norm, 'LineWidth', 2);
|
||||
set(gca, 'FontSize', 14, 'YLim', [0, 1]);
|
||||
else
|
||||
plot(theta_vals/pi, S_theta, 'LineWidth', 2);
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log', 'YLim', [1E4, 1E7]);
|
||||
end
|
||||
xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
title('Angular Spectral Distribution - S(\theta)', 'Interpreter', 'tex', ...
|
||||
@ -316,20 +400,6 @@ for k = 1:N_shots
|
||||
|
||||
drawnow;
|
||||
|
||||
if ~fft_size_known
|
||||
fft_sz = size(fft_imgs{k});
|
||||
N_radial_bins_used = length(S_k_smoothed);
|
||||
N_angular_bins_used = length(S_theta);
|
||||
avg_ps_accum = zeros(fft_sz);
|
||||
avg_S_k_accum = zeros(1, N_radial_bins_used);
|
||||
avg_S_theta_accum = zeros(1, N_angular_bins_used);
|
||||
fft_size_known = true;
|
||||
end
|
||||
|
||||
avg_ps_accum = avg_ps_accum + abs(fft_imgs{k}).^2;
|
||||
avg_S_k_accum = avg_S_k_accum + S_k_smoothed;
|
||||
avg_S_theta_accum = avg_S_theta_accum + S_theta;
|
||||
|
||||
if ~skipMovieRender
|
||||
% Capture the current frame and write it to the video
|
||||
frame = getframe(gcf); % Capture the current figure as a frame
|
||||
@ -342,6 +412,14 @@ for k = 1:N_shots
|
||||
% Save current figure as PNG with high resolution
|
||||
print(gcf, fileNamePNG, '-dpng', '-r100'); % 300 dpi for high quality
|
||||
end
|
||||
if ~skipSaveOD
|
||||
odDataStruct = struct();
|
||||
odDataStruct.IMG = IMG;
|
||||
odDataStruct.x = x;
|
||||
odDataStruct.y = y;
|
||||
odDataStruct.scan_parameter_value = scan_parameter_values(k);
|
||||
save(fullfile(saveFolder, sprintf('od_image_%03d.mat', k)), '-struct', 'odDataStruct');
|
||||
end
|
||||
if skipMovieRender & skipSaveFigures
|
||||
pause(0.5);
|
||||
end
|
||||
@ -352,46 +430,398 @@ if ~skipMovieRender
|
||||
close(videoFile);
|
||||
end
|
||||
|
||||
%% ===== Final Averages =====
|
||||
avg_ps = avg_ps_accum / N_shots;
|
||||
avg_S_k = avg_S_k_accum / N_shots;
|
||||
avg_S_theta = avg_S_theta_accum / N_shots;
|
||||
%% Track across the transition
|
||||
|
||||
% Generate figure with 3 subplots
|
||||
figure('Name', 'Average Spectral Analysis', 'Position', [400 200 1200 400]);
|
||||
tavg = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
% Assuming scan_parameter_values and spectral_weight are column vectors (or row vectors of same length)
|
||||
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
|
||||
|
||||
% ==== 1. Average FFT Power Spectrum ====
|
||||
nexttile;
|
||||
imagesc(kx, ky, log(1 + avg_ps));
|
||||
axis image;
|
||||
set(gca, 'FontSize', 14, 'YDir', 'normal')
|
||||
xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
ylabel('k_y [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
title('Average Power Spectrum', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
colorbar;
|
||||
colormap(Colormaps.coolwarm());
|
||||
% Preallocate arrays
|
||||
mean_sc = zeros(size(unique_scan_parameter_values));
|
||||
stderr_sc = zeros(size(unique_scan_parameter_values));
|
||||
|
||||
% ==== 2. Average Radial Spectral Distribution ====
|
||||
nexttile;
|
||||
plot(k_rho_vals, avg_S_k, 'LineWidth', 2);
|
||||
xlabel('k_\rho [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
title('Average S(k_\rho)', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log', 'XLim', [min(k_rho_vals), max(k_rho_vals)]);
|
||||
% Loop through each unique theta and compute mean and standard error
|
||||
for i = 1:length(unique_scan_parameter_values)
|
||||
group_vals = radial_spectral_contrast(idx == i);
|
||||
mean_sc(i) = mean(group_vals);
|
||||
stderr_sc(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
|
||||
end
|
||||
|
||||
figure(2);
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
errorbar(unique_scan_parameter_values, mean_sc, stderr_sc, 'o--', ...
|
||||
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
|
||||
set(gca, 'FontSize', 14); % For tick labels only
|
||||
hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
|
||||
hYLabel = ylabel('Radial Spectral Contrast', 'Interpreter', 'tex');
|
||||
hTitle = title(titleString, 'Interpreter', 'tex');
|
||||
% set([hXLabel, hYLabel], 'FontName', font)
|
||||
set([hXLabel, hYLabel], 'FontSize', 14)
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
grid on
|
||||
|
||||
|
||||
% Preallocate arrays
|
||||
mean_sw = zeros(size(unique_scan_parameter_values));
|
||||
stderr_sw = zeros(size(unique_scan_parameter_values));
|
||||
|
||||
% Loop through each unique theta and compute mean and standard error
|
||||
for i = 1:length(unique_scan_parameter_values)
|
||||
group_vals = angular_spectral_weight(idx == i);
|
||||
mean_sw(i) = mean(group_vals);
|
||||
stderr_sw(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
|
||||
end
|
||||
|
||||
figure(3);
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
errorbar(unique_scan_parameter_values, mean_sw, stderr_sw, 'o--', ...
|
||||
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
|
||||
set(gca, 'FontSize', 14); % For tick labels only
|
||||
hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
|
||||
hYLabel = ylabel('Angular Spectral Weight', 'Interpreter', 'tex');
|
||||
hTitle = title(titleString, 'Interpreter', 'tex');
|
||||
% set([hXLabel, hYLabel], 'FontName', font)
|
||||
set([hXLabel, hYLabel], 'FontSize', 14)
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
grid on;
|
||||
|
||||
% ==== 3. Average Angular Spectral Distribution ====
|
||||
nexttile;
|
||||
plot(theta_vals/pi, avg_S_theta, 'LineWidth', 2);
|
||||
xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
title('Average S(\theta)', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log');
|
||||
grid on;
|
||||
ax = gca;
|
||||
ax.XMinorGrid = 'on';
|
||||
ax.YMinorGrid = 'on';
|
||||
%% Plot Averages
|
||||
|
||||
% Group by scan parameter values
|
||||
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
|
||||
N_params = numel(unique_scan_parameter_values);
|
||||
|
||||
if ~skipSaveFigures
|
||||
% Define folder for saving images
|
||||
saveFolder = [savefileName '_SavedFigures'];
|
||||
if ~exist(saveFolder, 'dir')
|
||||
mkdir(saveFolder);
|
||||
end
|
||||
end
|
||||
|
||||
% Loop over each unique parameter value
|
||||
for p = 1:N_params
|
||||
current_param = unique_scan_parameter_values(p);
|
||||
indices = find(idx == p); % Indices of shots for this parameter
|
||||
N_shots = numel(indices);
|
||||
|
||||
% Initialize accumulators
|
||||
avg_ps = 0;
|
||||
avg_S_k = 0;
|
||||
avg_S_theta = 0;
|
||||
|
||||
% Accumulate values
|
||||
for j = 1:N_shots
|
||||
avg_ps = avg_ps + ps_list{indices(j)};
|
||||
avg_S_k = avg_S_k + s_k_list{indices(j)};
|
||||
avg_S_theta = avg_S_theta + s_theta_list{indices(j)};
|
||||
end
|
||||
|
||||
% Average over repetitions
|
||||
avg_ps = avg_ps / N_shots;
|
||||
avg_S_k = avg_S_k / N_shots;
|
||||
avg_S_theta = avg_S_theta / N_shots;
|
||||
|
||||
% ==== Plot ====
|
||||
figure(3);
|
||||
set(gcf,'Position',[400 200 1200 400])
|
||||
tavg = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
|
||||
% 1. Power Spectrum
|
||||
nexttile;
|
||||
imagesc(kx, ky, log(1 + avg_ps));
|
||||
axis image;
|
||||
set(gca, 'FontSize', 14, 'YDir', 'normal')
|
||||
xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
ylabel('k_y [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
title('Average Power Spectrum', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
colorbar;
|
||||
colormap(Colormaps.coolwarm());
|
||||
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
text(0.975, 0.975, [num2str(current_param, '%.1f^\\circ')], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
else
|
||||
text(0.975, 0.975, [num2str(current_param, '%.2f'), ' G'], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
end
|
||||
|
||||
% 2. Radial Spectrum
|
||||
nexttile;
|
||||
plot(k_rho_vals, avg_S_k, 'LineWidth', 2);
|
||||
xlabel('k_\rho [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
title('Average S(k_\rho)', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log', ...
|
||||
'XLim', [min(k_rho_vals), max(k_rho_vals)]);
|
||||
grid on;
|
||||
|
||||
% 3. Angular Spectrum
|
||||
nexttile;
|
||||
plot(theta_vals/pi, avg_S_theta, 'LineWidth', 2);
|
||||
xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
|
||||
title('Average S(\theta)', 'FontSize', 16, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 14, 'YScale', 'log', ...
|
||||
'YLim', [1E4, 1E7]);
|
||||
grid on;
|
||||
ax = gca;
|
||||
ax.XMinorGrid = 'on';
|
||||
ax.YMinorGrid = 'on';
|
||||
|
||||
drawnow;
|
||||
|
||||
% ==== Save Figure ====
|
||||
if ~skipSaveFigures
|
||||
% Create a filename for each averaged plot
|
||||
fileNamePNG = fullfile(saveFolder, sprintf('fft_avg_analysis_param_%03d.png', p));
|
||||
|
||||
% Save current figure as PNG with high resolution
|
||||
print(gcf, fileNamePNG, '-dpng', '-r300'); % 300 dpi for high quality
|
||||
else
|
||||
pause(0.5)
|
||||
end
|
||||
end
|
||||
|
||||
%% ========= Replot OD images ==========
|
||||
|
||||
% Settings
|
||||
filePattern = fullfile(saveFolder, 'od_image_*.mat');
|
||||
files = dir(filePattern);
|
||||
colormapName = 'inferno';
|
||||
showText = true;
|
||||
showOverlay = true;
|
||||
font = 'Bahnschrift';
|
||||
|
||||
% Load and organize all OD images by parameter and repetition
|
||||
nFiles = length(files);
|
||||
if nFiles == 0
|
||||
error('No .mat OD image files found in folder: %s', saveFolder);
|
||||
end
|
||||
|
||||
% Load all data and extract parameter values
|
||||
scan_values = zeros(1, nFiles);
|
||||
allData = cell(1, nFiles);
|
||||
|
||||
for k = 1:nFiles
|
||||
S = load(fullfile(files(k).folder, files(k).name));
|
||||
scan_values(k) = S.scan_parameter_value;
|
||||
allData{k} = S;
|
||||
end
|
||||
|
||||
% Get unique parameter values
|
||||
unique_params = unique(scan_values);
|
||||
nParams = numel(unique_params);
|
||||
|
||||
% Group images: paramData{i} = [rep1, rep2, ...]
|
||||
|
||||
if strcmp(savefileName, 'StripesToDroplets')
|
||||
unique_params = fliplr(unique_params);
|
||||
end
|
||||
|
||||
paramData = cell(1, nParams);
|
||||
for i = 1:nParams
|
||||
idxs = find(scan_values == unique_params(i));
|
||||
paramData{i} = allData(idxs);
|
||||
end
|
||||
|
||||
% Get number of repetitions (assumes all same)
|
||||
nReps = max(cellfun(@numel, paramData));
|
||||
|
||||
% Initialize figure with one row, nParams columns
|
||||
figure(100); clf;
|
||||
% Set number of columns (e.g., 4 or auto-compute from nParams)
|
||||
nCols = min(4, nParams);
|
||||
nRows = ceil(nParams / nCols);
|
||||
|
||||
% Create tiled layout with multiple rows
|
||||
t = tiledlayout(nRows, nCols, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
|
||||
% Adjust figure size accordingly
|
||||
set(gcf, 'Position', [100 100 300*nCols 300*nRows]);
|
||||
|
||||
% Pre-create image handles
|
||||
axesArray = gobjects(1, nParams);
|
||||
imgArray = gobjects(1, nParams);
|
||||
textArray = gobjects(1, nParams);
|
||||
|
||||
for i = 1:nParams
|
||||
S = paramData{i}{1}; % First repetition to initialize
|
||||
|
||||
ax = nexttile(i);
|
||||
axesArray(i) = ax;
|
||||
|
||||
imgArray(i) = imagesc(S.x, S.y, S.IMG);
|
||||
axis equal tight;
|
||||
set(ax, 'YDir', 'normal');
|
||||
colormap(ax, Colormaps.(colormapName)());
|
||||
colorbar;
|
||||
|
||||
xlabel('x [\mum]', 'Interpreter', 'tex', 'FontSize', 12, 'FontName', font);
|
||||
ylabel('y [\mum]', 'Interpreter', 'tex', 'FontSize', 12, 'FontName', font);
|
||||
|
||||
if showOverlay
|
||||
hold on;
|
||||
drawODOverlays(S.x(1), S.y(1), S.x(end), S.y(end));
|
||||
drawODOverlays(S.x(end), S.y(1), S.x(1), S.y(end));
|
||||
hold off;
|
||||
end
|
||||
|
||||
% Add initial label
|
||||
if showText
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
labelStr = sprintf('%.1f^\\circ', S.scan_parameter_value);
|
||||
else
|
||||
labelStr = sprintf('%.2f G', S.scan_parameter_value);
|
||||
end
|
||||
textArray(i) = text(ax, 0.975, 0.975, labelStr, ...
|
||||
'Color', 'white', 'FontWeight', 'bold', ...
|
||||
'FontSize', 12, 'Interpreter', 'tex', ...
|
||||
'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', ...
|
||||
'VerticalAlignment', 'top');
|
||||
end
|
||||
end
|
||||
|
||||
% 🔁 Loop over repetitions
|
||||
for rep = 1:nReps
|
||||
for i = 1:nParams
|
||||
repsForParam = paramData{i};
|
||||
if rep <= numel(repsForParam)
|
||||
S = repsForParam{rep};
|
||||
imgArray(i).CData = S.IMG;
|
||||
|
||||
% Update text if needed (optional)
|
||||
if showText
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
labelStr = sprintf('%.1f^\\circ', S.scan_parameter_value);
|
||||
else
|
||||
labelStr = sprintf('%.2f G', S.scan_parameter_value);
|
||||
end
|
||||
textArray(i).String = labelStr;
|
||||
end
|
||||
end
|
||||
end
|
||||
drawnow; % Update figure
|
||||
|
||||
% Optional: pause or save frame
|
||||
pause(0.2);
|
||||
end
|
||||
|
||||
%% ========= Replot OD images in chunks by parameter ==========
|
||||
|
||||
% Settings
|
||||
filePattern = fullfile(saveFolder, 'od_image_*.mat');
|
||||
files = dir(filePattern);
|
||||
colormapName = 'inferno';
|
||||
showText = true;
|
||||
showOverlay = true;
|
||||
font = 'Bahnschrift';
|
||||
paramStep = 2; % Show every paramStep-th parameter
|
||||
pauseTime = 0.2; % Seconds between repetitions
|
||||
|
||||
% Load and organize all OD images
|
||||
nFiles = numel(files);
|
||||
scan_values = zeros(1, nFiles);
|
||||
allData = cell(1, nFiles);
|
||||
|
||||
for k = 1:nFiles
|
||||
S = load(fullfile(files(k).folder, files(k).name));
|
||||
scan_values(k) = S.scan_parameter_value;
|
||||
allData{k} = S;
|
||||
end
|
||||
|
||||
% Sort and group by unique parameter values
|
||||
[unique_params, ~, ic] = unique(scan_values);
|
||||
nParams = numel(unique_params);
|
||||
|
||||
paramGroups = cell(1, nParams);
|
||||
for i = 1:nParams
|
||||
paramGroups{i} = allData(ic == i);
|
||||
end
|
||||
|
||||
if strcmp(savefileName, 'StripesToDroplets')
|
||||
unique_params = fliplr(unique_params);
|
||||
paramGroups = fliplr(paramGroups);
|
||||
end
|
||||
|
||||
% Select a subset of parameters
|
||||
selectedIdx = 1:paramStep:nParams;
|
||||
nDisplayParams = numel(selectedIdx);
|
||||
selectedGroups = paramGroups(selectedIdx);
|
||||
|
||||
% Get max number of repetitions
|
||||
nReps = max(cellfun(@numel, selectedGroups));
|
||||
|
||||
% Initialize figure
|
||||
figure(101); clf;
|
||||
tiledlayout(1, nDisplayParams, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
set(gcf, 'Position', [100 100 300*nDisplayParams 300]);
|
||||
|
||||
imgArray = gobjects(1, nDisplayParams);
|
||||
textArray = gobjects(1, nDisplayParams);
|
||||
|
||||
% Initial plot (repetition 1)
|
||||
for j = 1:nDisplayParams
|
||||
ax = nexttile;
|
||||
group = selectedGroups{j};
|
||||
S = group{1};
|
||||
|
||||
imgArray(j) = imagesc(S.x, S.y, S.IMG);
|
||||
axis equal tight;
|
||||
set(ax, 'YDir', 'normal');
|
||||
colormap(ax, Colormaps.(colormapName)());
|
||||
colorbar;
|
||||
|
||||
xlabel('x [\mum]', 'Interpreter', 'tex', 'FontSize', 12, 'FontName', font);
|
||||
ylabel('y [\mum]', 'Interpreter', 'tex', 'FontSize', 12, 'FontName', font);
|
||||
|
||||
if showOverlay
|
||||
hold on;
|
||||
drawODOverlays(S.x(1), S.y(1), S.x(end), S.y(end));
|
||||
drawODOverlays(S.x(end), S.y(1), S.x(1), S.y(end));
|
||||
hold off;
|
||||
end
|
||||
|
||||
if showText
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
labelStr = sprintf('%.1f^\\circ', S.scan_parameter_value);
|
||||
else
|
||||
labelStr = sprintf('%.2f G', S.scan_parameter_value);
|
||||
end
|
||||
textArray(j) = text(0.975, 0.975, labelStr, ...
|
||||
'Color', 'white', 'FontWeight', 'bold', ...
|
||||
'FontSize', 12, 'Interpreter', 'tex', ...
|
||||
'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', ...
|
||||
'VerticalAlignment', 'top');
|
||||
end
|
||||
end
|
||||
|
||||
% Loop through repetitions
|
||||
for rep = 1:nReps
|
||||
for j = 1:nDisplayParams
|
||||
group = selectedGroups{j};
|
||||
if rep <= numel(group)
|
||||
S = group{rep};
|
||||
imgArray(j).CData = S.IMG;
|
||||
|
||||
if showText
|
||||
if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
|
||||
textArray(j).String = sprintf('%.1f^\\circ', S.scan_parameter_value);
|
||||
else
|
||||
textArray(j).String = sprintf('%.2f G', S.scan_parameter_value);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
drawnow;
|
||||
pause(pauseTime);
|
||||
end
|
||||
|
||||
|
||||
%% Helper Functions
|
||||
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
|
||||
@ -548,7 +978,7 @@ function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_mi
|
||||
end
|
||||
end
|
||||
|
||||
function contrast = computeSpectralContrast(IMGFFT, r_min, r_max, threshold)
|
||||
function contrast = computeRadialSpectralContrast(IMGFFT, r_min, r_max, threshold)
|
||||
% Apply threshold to isolate strong peaks
|
||||
IMGFFT(IMGFFT < threshold) = 0;
|
||||
|
||||
|
||||
@ -3,16 +3,16 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1
|
||||
"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/Vertical_Axis_Camera/in_situ_absorption"];
|
||||
|
||||
folderPath = "D:/Data - Experiment/2025/07/04/";
|
||||
folderPath = "//DyLabNAS/Data/TwoDGas/2025/06/23/";
|
||||
|
||||
run = '0016';
|
||||
run = '0300';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1430, 2040];
|
||||
center = [1410, 2030];
|
||||
span = [200, 200];
|
||||
fraction = [0.1, 0.1];
|
||||
|
||||
@ -43,29 +43,30 @@ Angular_WindowSize = 5;
|
||||
zoom_size = 50; % Zoomed-in region around center
|
||||
|
||||
% Plotting and saving
|
||||
% scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
scan_parameter = 'rot_mag_field';
|
||||
% scan_parameter_text = 'Angle = ';
|
||||
scan_parameter_text = 'BField = ';
|
||||
scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
% scan_parameter = 'rot_mag_field';
|
||||
scan_parameter_text = 'Angle = ';
|
||||
% scan_parameter_text = 'BField = ';
|
||||
|
||||
savefolderPath = 'E:/Results - Experiment/B2.35G/';
|
||||
savefileName = 'Droplets';
|
||||
savefileName = 'DropletsToStripes';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
skipUnshuffling = true;
|
||||
if strcmp(savefileName, 'DropletsToStripes')
|
||||
scan_groups = 0:5:45;
|
||||
scan_groups = 0:5:45;
|
||||
titleString = 'Droplets to Stripes';
|
||||
elseif strcmp(savefileName, 'StripesToDroplets')
|
||||
scan_groups = 45:-5:0;
|
||||
scan_groups = 45:-5:0;
|
||||
titleString = 'Stripes to Droplets';
|
||||
end
|
||||
|
||||
% Flags
|
||||
skipUnshuffling = true;
|
||||
skipPreprocessing = true;
|
||||
skipMasking = true;
|
||||
skipIntensityThresholding = true;
|
||||
skipBinarization = true;
|
||||
skipMovieRender = true;
|
||||
skipSaveFigures = false;
|
||||
skipSaveFigures = true;
|
||||
|
||||
%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
|
||||
% Get a list of all files in the folder with the desired file name pattern.
|
||||
@ -172,9 +173,8 @@ for k = 1:N_shots
|
||||
kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
|
||||
ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
|
||||
|
||||
[theta_vals, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, r_min, r_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
|
||||
[theta_values, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, r_min, r_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
|
||||
spectral_distribution{k} = S_theta;
|
||||
theta_values{k} = theta_vals;
|
||||
end
|
||||
|
||||
% Create matrix of shape (N_shots x N_angular_bins)
|
||||
@ -186,15 +186,15 @@ end
|
||||
% Grouping by scan parameter value (e.g., alpha)
|
||||
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
|
||||
|
||||
% Number of unique alpha values
|
||||
N_alpha = length(unique_scan_parameter_values);
|
||||
% Number of unique parameter values
|
||||
N_params = length(unique_scan_parameter_values);
|
||||
|
||||
% Preallocate result arrays
|
||||
g2_all = zeros(N_alpha, N_angular_bins);
|
||||
g2_error_all = zeros(N_alpha, N_angular_bins);
|
||||
g2_all = zeros(N_params, N_angular_bins);
|
||||
g2_error_all = zeros(N_params, N_angular_bins);
|
||||
|
||||
% Compute g2
|
||||
for i = 1:N_alpha
|
||||
for i = 1:N_params
|
||||
group_idx = find(idx == i);
|
||||
group_data = delta_nkr_all(group_idx, :);
|
||||
|
||||
@ -214,23 +214,20 @@ for i = 1:N_alpha
|
||||
end
|
||||
end
|
||||
|
||||
% Reconstruct theta axis from any one of the stored values
|
||||
theta_vals = theta_values{1}; % assuming it's in radians
|
||||
|
||||
% Number of unique alpha values
|
||||
nAlpha = size(g2_all, 1);
|
||||
% Number of unique parameter values
|
||||
nParams = size(g2_all, 1);
|
||||
|
||||
% Generate a colormap with enough unique colors
|
||||
cmap = sky(nAlpha); % You can also try 'jet', 'turbo', 'hot', etc.
|
||||
cmap = sky(nParams); % You can also try 'jet', 'turbo', 'hot', etc.
|
||||
|
||||
figure(1);
|
||||
clf;
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
hold on;
|
||||
legend_entries = cell(nAlpha, 1);
|
||||
legend_entries = cell(nParams, 1);
|
||||
|
||||
for i = 1:nAlpha
|
||||
errorbar(theta_vals/pi, g2_all(i, :), g2_error_all(i, :), ...
|
||||
for i = 1:nParams
|
||||
errorbar(theta_values/pi, g2_all(i, :), g2_error_all(i, :), ...
|
||||
'o', 'Color', cmap(i,:), ...
|
||||
'MarkerSize', 3, 'MarkerFaceColor', cmap(i,:), ...
|
||||
'CapSize', 4);
|
||||
@ -241,15 +238,15 @@ for i = 1:nAlpha
|
||||
end
|
||||
end
|
||||
|
||||
ylim([-1.5 3.0]); % Set y-axis limits here
|
||||
ylim([0.0 1.0]); % Set y-axis limits here
|
||||
set(gca, 'FontSize', 14);
|
||||
hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex');
|
||||
hYLabel = ylabel('$g^{(2)}(\delta\theta)$', 'Interpreter', 'latex');
|
||||
% hTitle = title('Change across transition', 'Interpreter', 'tex');
|
||||
hTitle = title(titleString, 'Interpreter', 'tex');
|
||||
legend(legend_entries, 'Interpreter', 'latex', 'Location', 'bestoutside');
|
||||
set([hXLabel, hYLabel], 'FontName', font)
|
||||
set([hXLabel, hYLabel], 'FontSize', 14)
|
||||
% set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
grid on;
|
||||
|
||||
%% Helper Functions
|
||||
|
||||
@ -3,16 +3,16 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1
|
||||
"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/Vertical_Axis_Camera/in_situ_absorption"];
|
||||
|
||||
folderPath = "D:/Data - Experiment/2025/07/04/";
|
||||
folderPath = "//DyLabNAS/Data/TwoDGas/2025/06/23/";
|
||||
|
||||
run = '0016';
|
||||
run = '0300';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1430, 2040];
|
||||
center = [1410, 2030];
|
||||
span = [200, 200];
|
||||
fraction = [0.1, 0.1];
|
||||
|
||||
@ -43,29 +43,30 @@ Angular_WindowSize = 5;
|
||||
zoom_size = 50; % Zoomed-in region around center
|
||||
|
||||
% Plotting and saving
|
||||
% scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
scan_parameter = 'rot_mag_field';
|
||||
% scan_parameter_text = 'Angle = ';
|
||||
scan_parameter_text = 'BField = ';
|
||||
scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||
% scan_parameter = 'rot_mag_field';
|
||||
scan_parameter_text = 'Angle = ';
|
||||
% scan_parameter_text = 'BField = ';
|
||||
|
||||
savefolderPath = 'E:/Results - Experiment/B2.35G/';
|
||||
savefileName = 'Droplets';
|
||||
savefileName = 'DropletsToStripes';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
skipUnshuffling = true;
|
||||
if strcmp(savefileName, 'DropletsToStripes')
|
||||
scan_groups = 0:5:45;
|
||||
scan_groups = 0:5:45;
|
||||
titleString = 'Droplets to Stripes';
|
||||
elseif strcmp(savefileName, 'StripesToDroplets')
|
||||
scan_groups = 45:-5:0;
|
||||
scan_groups = 45:-5:0;
|
||||
titleString = 'Stripes to Droplets';
|
||||
end
|
||||
|
||||
% Flags
|
||||
skipUnshuffling = true;
|
||||
skipPreprocessing = true;
|
||||
skipMasking = true;
|
||||
skipIntensityThresholding = true;
|
||||
skipBinarization = true;
|
||||
skipMovieRender = true;
|
||||
skipSaveFigures = false;
|
||||
skipSaveFigures = true;
|
||||
|
||||
%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
|
||||
% Get a list of all files in the folder with the desired file name pattern.
|
||||
@ -135,40 +136,30 @@ theta_values = cell(1, nimgs);
|
||||
|
||||
N_shots = length(od_imgs);
|
||||
|
||||
% Compute FFT
|
||||
% Compute FFT for all images
|
||||
for k = 1:N_shots
|
||||
IMG = od_imgs{k};
|
||||
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
|
||||
|
||||
% Size of original image (in pixels)
|
||||
[Ny, Nx] = size(IMG);
|
||||
|
||||
% Real-space pixel size in micrometers after magnification
|
||||
dx = pixel_size / magnification;
|
||||
dy = dx; % assuming square pixels
|
||||
|
||||
% Real-space axes
|
||||
x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6;
|
||||
y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
|
||||
|
||||
% Reciprocal space increments (frequency domain, μm⁻¹)
|
||||
dvx = 1 / (Nx * dx);
|
||||
dvy = 1 / (Ny * dy);
|
||||
|
||||
% Frequency axes
|
||||
vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
|
||||
vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
|
||||
|
||||
% Wavenumber axes
|
||||
kx_full = 2 * pi * vx * 1E-6; % μm⁻¹
|
||||
kx_full = 2 * pi * vx * 1E-6;
|
||||
ky_full = 2 * pi * vy * 1E-6;
|
||||
|
||||
% Crop FFT image around center
|
||||
mid_x = floor(Nx/2);
|
||||
mid_y = floor(Ny/2);
|
||||
fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
|
||||
|
||||
% Crop wavenumber axes to match fft_imgs{k}
|
||||
kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
|
||||
ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
|
||||
|
||||
@ -177,61 +168,62 @@ for k = 1:N_shots
|
||||
theta_values{k} = theta_vals;
|
||||
end
|
||||
|
||||
% Create matrix of shape (N_shots x N_angular_bins)
|
||||
% Convert spectral distribution to matrix (N_shots x N_angular_bins)
|
||||
delta_nkr_all = zeros(N_shots, N_angular_bins);
|
||||
for k = 1:N_shots
|
||||
delta_nkr_all(k, :) = spectral_distribution{k};
|
||||
end
|
||||
|
||||
% Grouping by scan parameter value (e.g., alpha)
|
||||
% Group by scan parameter values (e.g., alpha, angle, etc.)
|
||||
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
|
||||
N_params = length(unique_scan_parameter_values);
|
||||
|
||||
% Number of unique alpha values
|
||||
N_params = length(unique_scan_parameter_values);
|
||||
|
||||
% Define angular range and bins
|
||||
angle_range = 180; % total angular span of the profile
|
||||
% Define angular range and conversion
|
||||
angle_range = 180;
|
||||
angle_per_bin = angle_range / N_angular_bins;
|
||||
|
||||
max_peak_angle = 60;
|
||||
max_peak_angle = 180;
|
||||
max_peak_bin = round(max_peak_angle / angle_per_bin);
|
||||
|
||||
% Parameters for search
|
||||
window_size = 10;
|
||||
angle_threshold = 100;
|
||||
|
||||
ref_peak_angles = [];
|
||||
angle_at_max_g2 = [];
|
||||
g2_values = [];
|
||||
% Initialize containers for final results
|
||||
mean_max_g2_values = zeros(1, N_params);
|
||||
mean_max_g2_angle_values = zeros(1, N_params);
|
||||
var_max_g2_values = zeros(1, N_params);
|
||||
var_max_g2_angle_values = zeros(1, N_params);
|
||||
|
||||
% Also store raw data per group
|
||||
g2_all_per_group = cell(1, N_params);
|
||||
angle_all_per_group = cell(1, N_params);
|
||||
|
||||
for i = 1:N_params
|
||||
group_idx = find(idx == i);
|
||||
group_data = delta_nkr_all(group_idx, :);
|
||||
group_idx = find(idx == i);
|
||||
group_data = delta_nkr_all(group_idx, :);
|
||||
N_reps = size(group_data, 1);
|
||||
|
||||
for j = 1:size(group_data, 1)
|
||||
g2_values = zeros(1, N_reps);
|
||||
angle_at_max_g2 = zeros(1, N_reps);
|
||||
|
||||
for j = 1:N_reps
|
||||
profile = group_data(j, :);
|
||||
|
||||
% Restrict search for peak only in 0° to 90°
|
||||
% Restrict search to 0–60° for highest peak
|
||||
restricted_profile = profile(1:max_peak_bin);
|
||||
[~, peak_idx_rel] = max(restricted_profile);
|
||||
|
||||
% Convert relative peak index to global index in profile
|
||||
peak_idx = peak_idx_rel;
|
||||
peak_angle = (peak_idx - 1) * angle_per_bin; % zero-based bin index to angle
|
||||
peak_angle = (peak_idx - 1) * angle_per_bin;
|
||||
|
||||
% Determine shift direction based on peak angle
|
||||
if peak_angle < angle_threshold
|
||||
offsets = round(50 / angle_per_bin) : round(70 / angle_per_bin);
|
||||
else
|
||||
offsets = -round(70 / angle_per_bin) : -round(50 / angle_per_bin);
|
||||
end
|
||||
|
||||
% Reference window around largest peak
|
||||
ref_window = mod((peak_idx - window_size):(peak_idx + window_size) - 1, N_angular_bins) + 1;
|
||||
ref = profile(ref_window);
|
||||
|
||||
% Store reference peak angle
|
||||
ref_peak_angles(end+1) = peak_angle;
|
||||
|
||||
correlations = zeros(size(offsets));
|
||||
angles = zeros(size(offsets));
|
||||
|
||||
@ -240,25 +232,65 @@ for i = 1:N_params
|
||||
sec_window = mod((shifted_idx - window_size):(shifted_idx + window_size) - 1, N_angular_bins) + 1;
|
||||
sec = profile(sec_window);
|
||||
|
||||
% Calculate g2 correlation
|
||||
num = mean(ref .* sec);
|
||||
denom = mean(ref.^2);
|
||||
g2 = num / denom;
|
||||
|
||||
correlations(k) = g2;
|
||||
|
||||
% Compute angle for this shifted window (map to 0-180 degrees)
|
||||
angle_val = mod((peak_idx - 1 + offsets(k)) * angle_per_bin, angle_range);
|
||||
angles(k) = angle_val;
|
||||
angles(k) = mod((peak_idx - 1 + offsets(k)) * angle_per_bin, angle_range);
|
||||
end
|
||||
|
||||
[max_corr, max_idx] = max(correlations);
|
||||
g2_values(end+1) = max_corr;
|
||||
angle_at_max_g2(end+1) = angles(max_idx);
|
||||
g2_values(j) = max_corr;
|
||||
angle_at_max_g2(j) = angles(max_idx);
|
||||
end
|
||||
|
||||
% Store raw values
|
||||
g2_all_per_group{i} = g2_values;
|
||||
angle_all_per_group{i} = angle_at_max_g2;
|
||||
|
||||
% Final stats
|
||||
mean_max_g2_values(i) = mean(g2_values, 'omitnan');
|
||||
var_max_g2_values(i) = var(g2_values, 0, 'omitnan');
|
||||
mean_max_g2_angle_values(i)= mean(angle_at_max_g2, 'omitnan');
|
||||
var_max_g2_angle_values(i) = var(angle_at_max_g2, 0, 'omitnan');
|
||||
end
|
||||
|
||||
% Plot histograms within 0-180 degrees only
|
||||
%% ── Mean ± Std vs. scan parameter ──────────────────────────────────────
|
||||
% Compute standard error instead of standard deviation
|
||||
std_error_g2_values = zeros(1, N_params);
|
||||
for i = 1:N_params
|
||||
n_i = numel(g2_all_per_group{i}); % Number of repetitions for this param
|
||||
std_error_g2_values(i) = sqrt(var_max_g2_values(i) / n_i);
|
||||
end
|
||||
|
||||
% Plot mean ± SEM
|
||||
figure(1);
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
set(gca, 'FontSize', 14); % For tick labels only
|
||||
errorbar(unique_scan_parameter_values, ... % x-axis
|
||||
mean_max_g2_values, ... % y-axis (mean)
|
||||
std_error_g2_values, ... % ± SEM
|
||||
'--o', 'LineWidth', 1.8, 'MarkerSize', 6 );
|
||||
|
||||
set(gca, 'FontSize', 14, 'YLim', [0, 1]);
|
||||
hXLabel = xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex');
|
||||
hYLabel = ylabel('$\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', 'Interpreter', 'latex');
|
||||
hTitle = title(titleString, 'Interpreter', 'tex');
|
||||
% set([hXLabel, hYLabel], 'FontName', font);
|
||||
set([hXLabel, hYLabel], 'FontSize', 14);
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold');
|
||||
grid on;
|
||||
|
||||
% Define folder for saving images
|
||||
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');
|
||||
|
||||
%{
|
||||
%% Plot histograms within 0-180 degrees only
|
||||
figure(1);
|
||||
hold on;
|
||||
|
||||
@ -324,7 +356,7 @@ text(mode_ref, yl(2)*0.9, sprintf('%.1f°', mode_ref), 'HorizontalAlignment', 'c
|
||||
% Max g2 mode line and label
|
||||
xline(mode_g2, 'r--', 'LineWidth', 1.5, 'DisplayName', sprintf('g_2 Mode: %.1f°', mode_g2));
|
||||
text(mode_g2, yl(2)*0.75, sprintf('%.1f°', mode_g2), 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom', 'FontSize', 12, 'Color', 'r');
|
||||
|
||||
%}
|
||||
|
||||
%% Helper Functions
|
||||
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
|
||||
|
||||
118
Data-Analyzer/extractQuantities.m
Normal file
118
Data-Analyzer/extractQuantities.m
Normal file
@ -0,0 +1,118 @@
|
||||
%% Parameters
|
||||
|
||||
% === Define folders and settings ===
|
||||
|
||||
% === Define folders and settings ===
|
||||
|
||||
baseFolder = '//DyLabNAS/Data/TwoDGas/2025/04/';
|
||||
|
||||
dates = ["01", "02"]; % Example: three folders
|
||||
runs = {
|
||||
["0059", "0060", "0061"],
|
||||
["0007", "0008", "0009", "0010", "0011"]
|
||||
};
|
||||
|
||||
options.scan_parameter = 'rot_mag_fin_pol_angle';
|
||||
options.scan_groups = 0:10:50;
|
||||
options.cam = 5;
|
||||
|
||||
% Image cropping and alignment
|
||||
options.angle = 0;
|
||||
options.center = [1285, 2100];
|
||||
options.span = [200, 200];
|
||||
options.fraction = [0.1, 0.1];
|
||||
|
||||
% Imaging and calibration parameters
|
||||
options.pixel_size = 5.86e-6; % in meters
|
||||
options.magnification = 23.94;
|
||||
options.removeFringes = false;
|
||||
options.ImagingMode = 'HighIntensity';
|
||||
options.PulseDuration = 5e-6;
|
||||
|
||||
% Fourier analysis: Radial
|
||||
options.theta_min = deg2rad(0);
|
||||
options.theta_max = deg2rad(180);
|
||||
options.N_radial_bins = 500;
|
||||
options.Radial_Sigma = 2;
|
||||
options.Radial_WindowSize = 5; % Must be odd
|
||||
|
||||
% Fourier analysis: Angular
|
||||
options.r_min = 10;
|
||||
options.r_max = 20;
|
||||
options.k_min = 1.2; % in μm⁻¹
|
||||
options.k_max = 2.2; % in μm⁻¹
|
||||
options.N_angular_bins = 180;
|
||||
options.Angular_Threshold = 75;
|
||||
options.Angular_Sigma = 2;
|
||||
options.Angular_WindowSize = 5;
|
||||
|
||||
% Optional visualization / zooming
|
||||
options.zoom_size = 50;
|
||||
|
||||
% Optional flags or settings struct
|
||||
options.skipUnshuffling = false;
|
||||
options.skipPreprocessing = true;
|
||||
options.skipMasking = true;
|
||||
options.skipIntensityThresholding = true;
|
||||
options.skipBinarization = true;
|
||||
|
||||
% === Loop through folders and collect results ===
|
||||
|
||||
results_all = [];
|
||||
|
||||
assert(length(dates) == length(runs), ...
|
||||
'Each entry in `dates` must correspond to a cell in `runs`.');
|
||||
|
||||
for i = 1:length(dates)
|
||||
currentDate = dates(i);
|
||||
currentRuns = runs{i};
|
||||
|
||||
for j = 1:length(currentRuns)
|
||||
runID = currentRuns(j);
|
||||
folderPath = fullfile(baseFolder, currentDate, runID);
|
||||
|
||||
if ~endsWith(folderPath, filesep)
|
||||
options.folderPath = [char(folderPath) filesep];
|
||||
else
|
||||
options.folderPath = char(folderPath);
|
||||
end
|
||||
|
||||
try
|
||||
% Unpack options struct into name-value pairs
|
||||
args = [fieldnames(options), struct2cell(options)]';
|
||||
args = args(:)';
|
||||
|
||||
results = analyzeFolder(args{:});
|
||||
results_all = [results_all; results];
|
||||
catch ME
|
||||
warning("Error processing %s/%s: %s", currentDate, runID, ME.message);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% Plotting heatmap of mean_max_g2_values
|
||||
|
||||
N_x = length(options.scan_groups);
|
||||
N_y = length(results_all) / N_x;
|
||||
|
||||
% Preallocate
|
||||
g2_matrix = zeros(N_y, N_x);
|
||||
angle_matrix = zeros(N_y, N_x);
|
||||
|
||||
for i = 1:length(results_all)
|
||||
row = ceil(i / N_x); % outer parameter (e.g., date)
|
||||
col = mod(i-1, N_x) + 1; % inner scan parameter
|
||||
g2_matrix(row, col) = results_all(i).mean_max_g2_values(col);
|
||||
angle_matrix(row, col) = results_all(i).mean_max_g2_angle(col);
|
||||
end
|
||||
|
||||
% Plot heatmap
|
||||
figure;
|
||||
imagesc(options.scan_groups, 1:N_y, g2_matrix);
|
||||
xlabel('Scan Parameter (e.g. Angle)');
|
||||
ylabel('Scan Set Index');
|
||||
title('Mean g_2 Value Heatmap');
|
||||
colorbar;
|
||||
|
||||
|
||||
@ -4,29 +4,42 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axi
|
||||
"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
|
||||
"/images/Vertical_Axis_Camera/in_situ_absorption"];
|
||||
|
||||
folderPath = "D:/Data - Experiment/2025/05/22/";
|
||||
folderPath = "//DyLabNAS/Data/TwoDGas/2025/04/01/";
|
||||
|
||||
run = '0078';
|
||||
run = '0059';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1375, 2020];
|
||||
center = [1285, 2100];
|
||||
span = [200, 200];
|
||||
fraction = [0.1, 0.1];
|
||||
|
||||
pixel_size = 5.86e-6;
|
||||
pixel_size = 5.86e-6; % in meters
|
||||
magnification = 23.94;
|
||||
removeFringes = false;
|
||||
|
||||
%% Compute OD image, rotate and extract ROI for analysis
|
||||
ImagingMode = 'HighIntensity';
|
||||
PulseDuration = 5e-6;
|
||||
|
||||
% Plotting and saving
|
||||
scan_parameter = 'rot_mag_fin_pol_angle';
|
||||
scan_groups = 0:10:50;
|
||||
savefileName = 'DropletsToStripes';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
% Flags
|
||||
skipUnshuffling = false;
|
||||
|
||||
%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
|
||||
% Get a list of all files in the folder with the desired file name pattern.
|
||||
|
||||
filePattern = fullfile(folderPath, '*.h5');
|
||||
files = dir(filePattern);
|
||||
refimages = zeros(span(1) + 1, span(2) + 1, length(files));
|
||||
absimages = zeros(span(1) + 1, span(2) + 1, length(files));
|
||||
filePattern = fullfile(folderPath, '*.h5');
|
||||
files = dir(filePattern);
|
||||
refimages = zeros(span(1) + 1, span(2) + 1, length(files));
|
||||
absimages = zeros(span(1) + 1, span(2) + 1, length(files));
|
||||
|
||||
for k = 1 : length(files)
|
||||
baseFileName = files(k).name;
|
||||
@ -34,16 +47,15 @@ for k = 1 : length(files)
|
||||
|
||||
fprintf(1, 'Now reading %s\n', fullFileName);
|
||||
|
||||
atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle)); % im2double rescales values to between [0, 1], use double instead
|
||||
atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
|
||||
bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
|
||||
dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
|
||||
|
||||
refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
|
||||
absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img), center, span), fraction)';
|
||||
|
||||
absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img, ImagingMode, PulseDuration), center, span), fraction)';
|
||||
end
|
||||
|
||||
% Fringe removal
|
||||
% ===== Fringe removal =====
|
||||
|
||||
if removeFringes
|
||||
optrefimages = removefringesInImage(absimages, refimages);
|
||||
@ -61,69 +73,121 @@ else
|
||||
od_imgs{i} = absimages(:, :, i);
|
||||
end
|
||||
end
|
||||
|
||||
% ===== Get rotation angles =====
|
||||
scan_parameter_values = zeros(1, length(files));
|
||||
|
||||
% Get information about the '/globals' group
|
||||
for k = 1 : length(files)
|
||||
baseFileName = files(k).name;
|
||||
fullFileName = fullfile(files(k).folder, baseFileName);
|
||||
info = h5info(fullFileName, '/globals');
|
||||
for i = 1:length(info.Attributes)
|
||||
if strcmp(info.Attributes(i).Name, scan_parameter)
|
||||
if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
|
||||
scan_parameter_values(k) = 180 - info.Attributes(i).Value;
|
||||
else
|
||||
scan_parameter_values(k) = info.Attributes(i).Value;
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% ===== Unshuffle if necessary to do so =====
|
||||
|
||||
if ~skipUnshuffling
|
||||
n_values = length(scan_groups);
|
||||
n_total = length(scan_parameter_values);
|
||||
|
||||
% Infer number of repetitions
|
||||
n_reps = n_total / n_values;
|
||||
|
||||
% Preallocate ordered arrays
|
||||
ordered_scan_values = zeros(1, n_total);
|
||||
ordered_od_imgs = cell(1, n_total);
|
||||
|
||||
counter = 1;
|
||||
|
||||
for rep = 1:n_reps
|
||||
for val = scan_groups
|
||||
% Find the next unused match for this val
|
||||
idx = find(scan_parameter_values == val, 1, 'first');
|
||||
|
||||
% Assign and remove from list to avoid duplicates
|
||||
ordered_scan_values(counter) = scan_parameter_values(idx);
|
||||
ordered_od_imgs{counter} = od_imgs{idx};
|
||||
|
||||
% Mark as used by removing
|
||||
scan_parameter_values(idx) = NaN; % NaN is safe since original values are 0:5:45
|
||||
od_imgs{idx} = []; % empty cell so it won't be matched again
|
||||
|
||||
counter = counter + 1;
|
||||
end
|
||||
end
|
||||
|
||||
% Now assign back
|
||||
scan_parameter_values = ordered_scan_values;
|
||||
od_imgs = ordered_od_imgs;
|
||||
end
|
||||
|
||||
%% Display Images
|
||||
|
||||
figure(1)
|
||||
clf
|
||||
set(gcf,'Position',[50 50 950 750])
|
||||
|
||||
% Calculate the x and y limits for the cropped image
|
||||
y_min = center(1) - span(2) / 2;
|
||||
y_max = center(1) + span(2) / 2;
|
||||
x_min = center(2) - span(1) / 2;
|
||||
x_max = center(2) + span(1) / 2;
|
||||
% Get image size in pixels
|
||||
[Ny, Nx] = size(od_imgs{1});
|
||||
|
||||
% Generate x and y arrays representing the original coordinates for each pixel
|
||||
x_range = linspace(x_min, x_max, span(1));
|
||||
y_range = linspace(y_min, y_max, span(2));
|
||||
% Define pixel size and magnification (if not already defined earlier)
|
||||
dx = pixel_size / magnification; % e.g. in meters
|
||||
dy = dx; % assuming square pixels
|
||||
|
||||
% Define x and y axes in μm (centered at image center)
|
||||
x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6; % micrometers
|
||||
y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
|
||||
|
||||
% Display the cropped image
|
||||
for k = 1 : length(od_imgs)
|
||||
imagesc(x_range, y_range, od_imgs{k})
|
||||
axis equal tight;
|
||||
hcb = colorbar;
|
||||
hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
|
||||
set(hL,'Rotation',-90);
|
||||
colormap jet;
|
||||
% set(gca,'CLim',[0 0.4]);
|
||||
set(gca,'YDir','normal')
|
||||
set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
|
||||
set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
|
||||
xlabel('Horizontal', 'Interpreter', 'tex','FontSize',16);
|
||||
ylabel('Vertical', 'Interpreter', 'tex','FontSize',16);
|
||||
imagesc(x, y, od_imgs{k});
|
||||
hold on;
|
||||
|
||||
drawnow
|
||||
pause(0.5)
|
||||
% Convert pixel grid to µm (already done: x and y axes)
|
||||
% Draw ↘ diagonal (top-left to bottom-right)
|
||||
drawODOverlays(x(1), y(1), x(end), y(end));
|
||||
|
||||
% Draw ↙ diagonal (top-right to bottom-left)
|
||||
drawODOverlays(x(end), y(1), x(1), y(end));
|
||||
|
||||
hold off;
|
||||
axis equal tight;
|
||||
colormap(Colormaps.inferno());
|
||||
set(gca, 'FontSize', 14, 'YDir', 'normal');
|
||||
|
||||
if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
|
||||
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.1f^\\circ')], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
else
|
||||
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
|
||||
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
|
||||
'Interpreter', 'tex', 'Units', 'normalized', ...
|
||||
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
||||
end
|
||||
|
||||
colorbarHandle = colorbar;
|
||||
ylabel(colorbarHandle, 'Optical Density', 'Rotation', -90, 'FontSize', 14, 'FontName', font);
|
||||
|
||||
xlabel('x (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
ylabel('y (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
||||
title('OD Image', 'FontSize', 16, 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontName', font);
|
||||
|
||||
drawnow;
|
||||
pause(0.5);
|
||||
end
|
||||
|
||||
|
||||
%% Overlay images
|
||||
|
||||
% image_below = ;
|
||||
% image_top = ;
|
||||
|
||||
% Display the first image (opaque)
|
||||
figure (2);
|
||||
clf
|
||||
set(gcf,'Position',[50 50 950 750])
|
||||
|
||||
imagesc(x_range, y_range, image_below);
|
||||
hold on; % Allow overlaying of the second image
|
||||
h = imagesc(x_range, y_range, image_top); % Display the second image (translucent)
|
||||
set(h, 'AlphaData', 0.6); % Adjust transparency: 0 is fully transparent, 1 is fully opaque
|
||||
axis equal tight;
|
||||
hcb = colorbar;
|
||||
hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
|
||||
set(hL,'Rotation',-90);
|
||||
colormap jet;
|
||||
set(gca,'CLim',[0 1.0]);
|
||||
set(gca,'YDir','normal')
|
||||
set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
|
||||
set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
|
||||
xlabel('X', 'Interpreter', 'tex','FontSize',16);
|
||||
ylabel('Y', 'Interpreter', 'tex','FontSize',16);
|
||||
hold off;
|
||||
|
||||
%% Helper Functions
|
||||
|
||||
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
|
||||
@ -174,27 +238,109 @@ function ret = cropODImage(img, center, span)
|
||||
ret = img(y_start:y_end, x_start:x_end);
|
||||
end
|
||||
|
||||
function ret = calculateODImage(imageAtom, imageBackground, imageDark)
|
||||
% Calculate the OD image for absorption imaging.
|
||||
% :param imageAtom: The image with atoms
|
||||
% :type imageAtom: numpy array
|
||||
% :param imageBackground: The image without atoms
|
||||
% :type imageBackground: numpy array
|
||||
% :param imageDark: The image without light
|
||||
% :type imageDark: numpy array
|
||||
% :return: The OD images
|
||||
% :rtype: numpy array
|
||||
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;
|
||||
|
||||
ret = -log(double(abs(denominator ./ numerator)));
|
||||
% 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 drawODOverlays(x1, y1, x2, y2)
|
||||
|
||||
% Parameters
|
||||
tick_spacing = 10; % µm between ticks
|
||||
tick_length = 2; % µm tick mark length
|
||||
line_color = [0.5 0.5 0.5];
|
||||
tick_color = [0.5 0.5 0.5];
|
||||
font_size = 10;
|
||||
|
||||
% Vector from start to end
|
||||
dx = x2 - x1;
|
||||
dy = y2 - y1;
|
||||
L = sqrt(dx^2 + dy^2);
|
||||
|
||||
% Unit direction vector along diagonal
|
||||
ux = dx / L;
|
||||
uy = dy / L;
|
||||
|
||||
% Perpendicular unit vector for ticks
|
||||
perp_ux = -uy;
|
||||
perp_uy = ux;
|
||||
|
||||
% Midpoint (center)
|
||||
xc = (x1 + x2) / 2;
|
||||
yc = (y1 + y2) / 2;
|
||||
|
||||
% Number of positive and negative ticks
|
||||
n_ticks = floor(L / (2 * tick_spacing));
|
||||
|
||||
% Draw main diagonal line
|
||||
plot([x1 x2], [y1 y2], '--', 'Color', line_color, 'LineWidth', 1.2);
|
||||
|
||||
for i = -n_ticks:n_ticks
|
||||
d = i * tick_spacing;
|
||||
xt = xc + d * ux;
|
||||
yt = yc + d * uy;
|
||||
|
||||
% Tick line endpoints
|
||||
xt1 = xt - 0.5 * tick_length * perp_ux;
|
||||
yt1 = yt - 0.5 * tick_length * perp_uy;
|
||||
xt2 = xt + 0.5 * tick_length * perp_ux;
|
||||
yt2 = yt + 0.5 * tick_length * perp_uy;
|
||||
|
||||
% Draw tick
|
||||
plot([xt1 xt2], [yt1 yt2], '--', 'Color', tick_color, 'LineWidth', 1);
|
||||
|
||||
% Label: centered at tick, offset slightly along diagonal
|
||||
if d ~= 0
|
||||
text(xt, yt, sprintf('%+d', d), ...
|
||||
'Color', tick_color, ...
|
||||
'FontSize', font_size, ...
|
||||
'HorizontalAlignment', 'center', ...
|
||||
'VerticalAlignment', 'bottom', ...
|
||||
'Rotation', atan2d(dy, dx));
|
||||
end
|
||||
|
||||
if numel(ret) == 1
|
||||
ret = ret(1);
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user