Added new script to extract correlation only in a window, modified autocorrelation script with new definition of g2 and with unnormalized angular spectral distribution
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@ -1,11 +1,11 @@
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%% Settings
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%% ===== Settings =====
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "D:/Data - Experiment/2025/07/04/";
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run = '0018';
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run = '0016';
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folderPath = strcat(folderPath, run);
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@ -16,7 +16,7 @@ center = [1430, 2040];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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pixel_size = 5.86e-6; % in meters
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magnification = 23.94;
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removeFringes = false;
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@ -42,6 +42,7 @@ Angular_WindowSize = 5;
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zoom_size = 50; % Zoomed-in region around center
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% Plotting and saving
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% scan_parameter = 'ps_rot_mag_fin_pol_angle';
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scan_parameter = 'rot_mag_field';
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% scan_parameter_text = 'Angle = ';
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@ -58,6 +59,7 @@ elseif strcmp(savefileName, 'StripesToDroplets')
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scan_groups = 45:-5:0;
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end
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% Flags
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skipPreprocessing = true;
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skipMasking = true;
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skipIntensityThresholding = true;
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@ -65,7 +67,7 @@ skipBinarization = true;
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skipMovieRender = true;
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skipSaveFigures = false;
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%% Load and compute OD image, rotate and extract ROI for analysis
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%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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@ -87,7 +89,7 @@ for k = 1 : length(files)
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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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%% ===== Fringe removal =====
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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@ -106,7 +108,7 @@ else
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end
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end
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%% Get rotation angles
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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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@ -125,7 +127,7 @@ for k = 1 : length(files)
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end
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end
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%% Unshuffle if necessary to do so
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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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@ -162,7 +164,7 @@ if ~skipUnshuffling
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od_imgs = ordered_od_imgs;
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end
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%% Run Fourier analysis over images
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%% ===== Run Fourier analysis over images =====
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fft_imgs = cell(1, nimgs);
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spectral_contrast = zeros(1, nimgs);
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@ -1,40 +1,73 @@
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%% Parameters
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%% ===== Settings =====
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "//DyLabNAS/Data/TwoDGas/2025/06/23/";
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folderPath = "D:/Data - Experiment/2025/07/04/";
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run = '0300';
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run = '0016';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1410, 2030];
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center = [1430, 2040];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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pixel_size = 5.86e-6; % in meters
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magnification = 23.94;
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removeFringes = false;
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scan_parameter = 'ps_rot_mag_fin_pol_angle';
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% scan_parameter = 'rot_mag_field';
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scan_parameter_text = 'Angle = ';
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% scan_parameter_text = 'BField = ';
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ImagingMode = 'HighIntensity';
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PulseDuration = 5e-6; % in s
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savefolderPath = 'D:/Results - Experiment/B2.42G/';
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savefileName = 'DropletsToStripes.mat';
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% Fourier analysis settings
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% Radial Spectral Distribution
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theta_min = deg2rad(0);
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theta_max = deg2rad(180);
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N_radial_bins = 500;
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Radial_Sigma = 2;
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Radial_WindowSize = 5; % Choose an odd number for a centered moving average
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% Angular Spectral Distribution
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r_min = 10;
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r_max = 20;
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N_angular_bins = 180;
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Angular_Threshold = 75;
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Angular_Sigma = 2;
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Angular_WindowSize = 5;
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zoom_size = 50; % Zoomed-in region around center
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% Plotting and saving
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% scan_parameter = 'ps_rot_mag_fin_pol_angle';
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scan_parameter = 'rot_mag_field';
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% scan_parameter_text = 'Angle = ';
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scan_parameter_text = 'BField = ';
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savefolderPath = 'E:/Results - Experiment/B2.35G/';
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savefileName = 'Droplets';
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font = 'Bahnschrift';
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skipUnshuffling = true;
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if strcmp(savefileName, 'DropletsToStripes')
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scan_groups = 0:5:45;
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elseif strcmp(savefileName, 'StripesToDroplets')
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scan_groups = 45:-5:0;
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end
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% Flags
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skipPreprocessing = true;
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skipMasking = true;
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skipIntensityThresholding = true;
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skipBinarization = true;
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skipMovieRender = true;
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skipSaveFigures = false;
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%% Compute OD image, rotate and extract ROI for analysis
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%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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@ -53,11 +86,10 @@ for k = 1 : length(files)
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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), 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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%% ===== Fringe removal =====
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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@ -76,7 +108,7 @@ else
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end
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end
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%% Get rotation angles
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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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@ -86,7 +118,7 @@ for k = 1 : length(files)
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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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if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
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scan_parameter_values(k) = 180 - info.Attributes(i).Value;
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else
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scan_parameter_values(k) = info.Attributes(i).Value;
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@ -95,45 +127,58 @@ for k = 1 : length(files)
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end
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end
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%% Extract g2 from experiment data
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%% ===== Extract g2 from experiment data =====
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fft_imgs = cell(1, nimgs);
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spectral_distribution = cell(1, nimgs);
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theta_values = cell(1, nimgs);
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N_bins = 32;
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Threshold = 75;
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Sigma = 2;
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N_shots = length(od_imgs);
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% Display the cropped image
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% Compute FFT
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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% Calculate the x and y limits for the cropped image
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y_min = center(1) - span(2) / 2;
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y_max = center(1) + span(2) / 2;
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x_min = center(2) - span(1) / 2;
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x_max = center(2) + span(1) / 2;
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% Size of original image (in pixels)
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[Ny, Nx] = size(IMG);
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% Generate x and y arrays representing the original coordinates for each pixel
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x_range = linspace(x_min, x_max, span(1));
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y_range = linspace(y_min, y_max, span(2));
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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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[rows, cols] = size(IMGFFT);
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zoom_size = 50; % Zoomed-in region around center
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mid_x = floor(cols/2);
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mid_y = floor(rows/2);
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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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[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, N_bins, Threshold, Sigma);
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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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spectral_distribution{k} = S_theta;
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theta_values{k} = theta_vals;
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end
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% Create matrix of shape (N_shots x N_bins)
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delta_nkr_all = zeros(N_shots, N_bins);
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% Create matrix of shape (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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@ -145,23 +190,24 @@ end
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N_alpha = length(unique_scan_parameter_values);
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% Preallocate result arrays
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g2_all = zeros(N_alpha, N_bins);
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g2_error_all = zeros(N_alpha, N_bins);
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g2_all = zeros(N_alpha, N_angular_bins);
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g2_error_all = zeros(N_alpha, N_angular_bins);
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% Compute g2
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for i = 1:N_alpha
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group_idx = find(idx == i); % Indices of 20 shots for this alpha
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group_data = delta_nkr_all(group_idx, :); % (20 x N_bins) array
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group_idx = find(idx == i);
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group_data = delta_nkr_all(group_idx, :);
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for dtheta = 0:N_bins-1
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for dtheta = 0:N_angular_bins-1
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temp = zeros(length(group_idx), 1);
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for j = 1:length(group_idx)
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profile = group_data(j, :);
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profile_shifted = circshift(profile, -dtheta, 2);
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num = mean(profile .* profile_shifted);
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denom = mean(profile)^2;
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denom = mean(profile.^2);
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temp(j) = num / denom - 1;
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temp(j) = num / denom;
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end
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g2_all(i, dtheta+1) = mean(temp);
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g2_error_all(i, dtheta+1) = std(temp) / sqrt(length(group_idx)); % Standard error
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@ -185,19 +231,25 @@ legend_entries = cell(nAlpha, 1);
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for i = 1:nAlpha
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errorbar(theta_vals/pi, g2_all(i, :), g2_error_all(i, :), ...
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'o-', 'Color', cmap(i,:), 'LineWidth', 1.2, ...
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'MarkerSize', 5, 'CapSize', 3);
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'o', 'Color', cmap(i,:), ...
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'MarkerSize', 3, 'MarkerFaceColor', cmap(i,:), ...
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'CapSize', 4);
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if strcmp(scan_parameter, 'ps_rot_mag_fin_pol_angle')
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legend_entries{i} = sprintf('$\\alpha = %g^\\circ$', unique_scan_parameter_values(i));
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elseif strcmp(scan_parameter, 'rot_mag_field')
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legend_entries{i} = sprintf('B = %.2f G', unique_scan_parameter_values(i));
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end
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end
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ylim([-1.5 3.0]); % Set y-axis limits here
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set(gca, 'FontSize', 14);
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hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex');
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hYLabel = ylabel('$g^{(2)}(\delta\theta)$', 'Interpreter', 'latex');
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hTitle = title('Change across transition', 'Interpreter', 'tex');
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% hTitle = title('Change across transition', 'Interpreter', 'tex');
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legend(legend_entries, 'Interpreter', 'latex', 'Location', 'bestoutside');
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set([hXLabel, hYLabel], 'FontName', font)
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set([hXLabel, hYLabel], 'FontSize', 14)
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set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
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% set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
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grid on;
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%% Helper Functions
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@ -272,7 +324,7 @@ function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMas
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end
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end
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function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma)
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function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma, windowSize)
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% Apply threshold to isolate strong peaks
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IMGFFT(IMGFFT < threshold) = 0;
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@ -287,40 +339,38 @@ function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IM
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% Choose radial band
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radial_mask = (R >= r_min) & (R <= r_max);
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% Initialize the angular structure factor array
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S_theta = zeros(1, num_bins); % Pre-allocate for 180 angle bins
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% Define the angle values for the x-axis
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% Initialize angular structure factor
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S_theta = zeros(1, num_bins);
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theta_vals = linspace(0, pi, num_bins);
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% Loop through each angle bin
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% Loop through angle bins
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for i = 1:num_bins
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angle_start = (i-1) * pi / num_bins;
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angle_end = i * pi / num_bins;
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% Define a mask for the given angle range
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angle_mask = (Theta >= angle_start & Theta < angle_end);
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bin_mask = radial_mask & angle_mask;
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% Extract the Fourier components for the given angle
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fft_angle = IMGFFT .* bin_mask;
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% Integrate the Fourier components over the radius at the angle
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S_theta(i) = sum(sum(abs(fft_angle).^2)); % sum of squared magnitudes
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S_theta(i) = sum(sum(abs(fft_angle).^2));
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end
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% Create a 1D Gaussian kernel
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% Smooth using either Gaussian or moving average
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if exist('sigma', 'var') && ~isempty(sigma)
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% Gaussian convolution
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half_width = ceil(3 * sigma);
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x = -half_width:half_width;
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gauss_kernel = exp(-x.^2 / (2 * sigma^2));
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gauss_kernel = gauss_kernel / sum(gauss_kernel); % normalize
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% Apply convolution (circular padding to preserve periodicity)
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S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], gauss_kernel, 'same');
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S_theta = S_theta(half_width+1:end-half_width); % crop back to original size
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% Normalize to 1
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S_theta = S_theta / max(S_theta);
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gauss_kernel = gauss_kernel / sum(gauss_kernel);
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% Circular convolution
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S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], ...
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gauss_kernel, 'same');
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S_theta = S_theta(half_width+1:end-half_width);
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elseif exist('windowSize', 'var') && ~isempty(windowSize)
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% Moving average via convolution (circular)
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pad = floor(windowSize / 2);
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kernel = ones(1, windowSize) / windowSize;
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S_theta = conv([S_theta(end-pad+1:end), S_theta, S_theta(1:pad)], kernel, 'same');
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S_theta = S_theta(pad+1:end-pad);
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end
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end
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function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
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@ -371,28 +421,51 @@ function ret = cropODImage(img, center, span)
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ret = img(y_start:y_end, x_start:x_end);
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end
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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));
|
||||
|
||||
if numel(ret) == 1
|
||||
ret = ret(1);
|
||||
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)
|
||||
@ -465,4 +538,3 @@ function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
|
||||
optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
615
Data-Analyzer/extractCustomCorrelation.m
Normal file
615
Data-Analyzer/extractCustomCorrelation.m
Normal file
@ -0,0 +1,615 @@
|
||||
%% ===== 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/";
|
||||
|
||||
run = '0016';
|
||||
|
||||
folderPath = strcat(folderPath, run);
|
||||
|
||||
cam = 5;
|
||||
|
||||
angle = 0;
|
||||
center = [1430, 2040];
|
||||
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';
|
||||
% scan_parameter_text = 'Angle = ';
|
||||
scan_parameter_text = 'BField = ';
|
||||
|
||||
savefolderPath = 'E:/Results - Experiment/B2.35G/';
|
||||
savefileName = 'Droplets';
|
||||
font = 'Bahnschrift';
|
||||
|
||||
skipUnshuffling = true;
|
||||
if strcmp(savefileName, 'DropletsToStripes')
|
||||
scan_groups = 0:5:45;
|
||||
elseif strcmp(savefileName, 'StripesToDroplets')
|
||||
scan_groups = 45:-5:0;
|
||||
end
|
||||
|
||||
% Flags
|
||||
skipPreprocessing = true;
|
||||
skipMasking = true;
|
||||
skipIntensityThresholding = true;
|
||||
skipBinarization = true;
|
||||
skipMovieRender = true;
|
||||
skipSaveFigures = 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));
|
||||
|
||||
for k = 1 : length(files)
|
||||
baseFileName = files(k).name;
|
||||
fullFileName = fullfile(files(k).folder, baseFileName);
|
||||
|
||||
fprintf(1, 'Now reading %s\n', fullFileName);
|
||||
|
||||
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, ImagingMode, PulseDuration), center, span), fraction)';
|
||||
end
|
||||
|
||||
%% ===== Fringe removal =====
|
||||
|
||||
if removeFringes
|
||||
optrefimages = removefringesInImage(absimages, refimages);
|
||||
absimages_fringe_removed = absimages(:, :, :) - optrefimages(:, :, :);
|
||||
|
||||
nimgs = size(absimages_fringe_removed,3);
|
||||
od_imgs = cell(1, nimgs);
|
||||
for i = 1:nimgs
|
||||
od_imgs{i} = absimages_fringe_removed(:, :, i);
|
||||
end
|
||||
else
|
||||
nimgs = size(absimages(:, :, :),3);
|
||||
od_imgs = cell(1, nimgs);
|
||||
for i = 1:nimgs
|
||||
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, 'ps_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
|
||||
|
||||
%% ===== Correlation of a single (highest) peak with a possible peak between 50-70 degrees from experiment data =====
|
||||
|
||||
fft_imgs = cell(1, nimgs);
|
||||
spectral_distribution = cell(1, nimgs);
|
||||
theta_values = cell(1, nimgs);
|
||||
|
||||
N_shots = length(od_imgs);
|
||||
|
||||
% Compute FFT
|
||||
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
|
||||
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);
|
||||
|
||||
[theta_vals, 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)
|
||||
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)
|
||||
[unique_scan_parameter_values, ~, idx] = 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
|
||||
angle_per_bin = angle_range / N_angular_bins;
|
||||
|
||||
max_peak_angle = 60;
|
||||
max_peak_bin = round(max_peak_angle / angle_per_bin);
|
||||
|
||||
window_size = 10;
|
||||
angle_threshold = 100;
|
||||
|
||||
ref_peak_angles = [];
|
||||
angle_at_max_g2 = [];
|
||||
g2_values = [];
|
||||
|
||||
for i = 1:N_params
|
||||
group_idx = find(idx == i);
|
||||
group_data = delta_nkr_all(group_idx, :);
|
||||
|
||||
for j = 1:size(group_data, 1)
|
||||
profile = group_data(j, :);
|
||||
|
||||
% Restrict search for peak only in 0° to 90°
|
||||
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
|
||||
|
||||
% 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));
|
||||
|
||||
for k = 1:length(offsets)
|
||||
shifted_idx = mod(peak_idx + offsets(k) - 1, N_angular_bins) + 1;
|
||||
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;
|
||||
end
|
||||
|
||||
[max_corr, max_idx] = max(correlations);
|
||||
g2_values(end+1) = max_corr;
|
||||
angle_at_max_g2(end+1) = angles(max_idx);
|
||||
end
|
||||
end
|
||||
|
||||
% Plot histograms within 0-180 degrees only
|
||||
figure(1);
|
||||
hold on;
|
||||
|
||||
bin_edges = 0:10:180;
|
||||
|
||||
h1 = histogram(ref_peak_angles, 'BinEdges', bin_edges, ...
|
||||
'FaceColor', [0.3 0.7 0.9], 'EdgeColor', 'none', 'FaceAlpha', 0.6);
|
||||
|
||||
h2 = histogram(angle_at_max_g2, 'BinEdges', bin_edges, ...
|
||||
'FaceColor', [0.9 0.4 0.4], 'EdgeColor', 'none', 'FaceAlpha', 0.6);
|
||||
|
||||
h1.Normalization = 'probability';
|
||||
h2.Normalization = 'probability';
|
||||
|
||||
xlabel('Angle (degrees)', 'FontSize', 12);
|
||||
ylabel('Probability', 'FontSize', 12);
|
||||
legend({'Reference Peak Angle', 'Angle at Max g₂'}, 'FontSize', 12);
|
||||
title('Comparison of Reference Peak and Max g₂ Angles', 'FontSize', 14);
|
||||
grid on;
|
||||
xlim([0 180]);
|
||||
hold off;
|
||||
|
||||
% Assume ref_peak_angles and angle_at_max_g2 are row or column vectors of angles in [0,180]
|
||||
|
||||
% Define fine angle grid for KDE evaluation
|
||||
angle_grid = linspace(0, 180, 1000);
|
||||
|
||||
% KDE for reference peak angles
|
||||
[f_ref, xi_ref] = ksdensity(ref_peak_angles, angle_grid, 'Bandwidth', 5);
|
||||
|
||||
% KDE for max g2 angles
|
||||
[f_g2, xi_g2] = ksdensity(angle_at_max_g2, angle_grid, 'Bandwidth', 5);
|
||||
|
||||
% Plot KDEs
|
||||
figure(2);
|
||||
plot(xi_ref, f_ref, 'LineWidth', 2, 'DisplayName', 'Reference Peak Angles');
|
||||
hold on;
|
||||
plot(xi_g2, f_g2, 'LineWidth', 2, 'DisplayName', 'Max g_2 Angles');
|
||||
xlabel('Angle (degrees)');
|
||||
ylabel('Probability Density');
|
||||
title('KDE of Angle Distributions');
|
||||
legend;
|
||||
grid on;
|
||||
|
||||
% Find modes (angle at max KDE value)
|
||||
[~, mode_idx_ref] = max(f_ref);
|
||||
mode_ref = xi_ref(mode_idx_ref);
|
||||
|
||||
[~, mode_idx_g2] = max(f_g2);
|
||||
mode_g2 = xi_g2(mode_idx_g2);
|
||||
|
||||
% Calculate difference in mode
|
||||
mode_diff = abs(mode_ref - mode_g2);
|
||||
fprintf('Mode difference between distributions: %.2f degrees\n', mode_diff);
|
||||
|
||||
% Add vertical dashed lines at mode positions
|
||||
yl = ylim; % get y-axis limits for text positioning
|
||||
|
||||
% Reference peak mode line and label
|
||||
xline(mode_ref, 'k--', 'LineWidth', 1.5, 'DisplayName', sprintf('Ref Mode: %.1f°', mode_ref));
|
||||
text(mode_ref, yl(2)*0.9, sprintf('%.1f°', mode_ref), 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom', 'FontSize', 12, 'Color', 'k');
|
||||
|
||||
% 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)
|
||||
% computeFourierSpectrum - Computes the 2D Fourier power spectrum
|
||||
% of binarized and enhanced lattice image features, with optional central mask.
|
||||
%
|
||||
% Inputs:
|
||||
% I - Grayscale or RGB image matrix
|
||||
%
|
||||
% Output:
|
||||
% F_mag - 2D Fourier power spectrum (shifted)
|
||||
|
||||
if ~skipPreprocessing
|
||||
% Preprocessing: Denoise
|
||||
filtered = imgaussfilt(I, 10);
|
||||
IMGPR = I - filtered; % adjust sigma as needed
|
||||
else
|
||||
IMGPR = I;
|
||||
end
|
||||
|
||||
if ~skipMasking
|
||||
[rows, cols] = size(IMGPR);
|
||||
[X, Y] = meshgrid(1:cols, 1:rows);
|
||||
% Elliptical mask parameters
|
||||
cx = cols / 2;
|
||||
cy = rows / 2;
|
||||
|
||||
% Shifted coordinates
|
||||
x = X - cx;
|
||||
y = Y - cy;
|
||||
|
||||
% Ellipse semi-axes
|
||||
rx = 0.4 * cols;
|
||||
ry = 0.2 * rows;
|
||||
|
||||
% Rotation angle in degrees -> radians
|
||||
theta_deg = 30; % Adjust as needed
|
||||
theta = deg2rad(theta_deg);
|
||||
|
||||
% Rotated ellipse equation
|
||||
cos_t = cos(theta);
|
||||
sin_t = sin(theta);
|
||||
|
||||
x_rot = (x * cos_t + y * sin_t);
|
||||
y_rot = (-x * sin_t + y * cos_t);
|
||||
|
||||
ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
|
||||
|
||||
% Apply cutout mask
|
||||
IMGPR = IMGPR .* ellipseMask;
|
||||
end
|
||||
|
||||
if ~skipIntensityThresholding
|
||||
% Apply global intensity threshold mask
|
||||
intensity_thresh = 0.20;
|
||||
intensity_mask = IMGPR > intensity_thresh;
|
||||
IMGPR = IMGPR .* intensity_mask;
|
||||
end
|
||||
|
||||
if ~skipBinarization
|
||||
% Adaptive binarization and cleanup
|
||||
IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
|
||||
IMGPR = imdilate(IMGPR, strel('disk', 2));
|
||||
IMGPR = imerode(IMGPR, strel('disk', 1));
|
||||
IMGPR = imfill(IMGPR, 'holes');
|
||||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||||
else
|
||||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||||
end
|
||||
end
|
||||
|
||||
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma, windowSize)
|
||||
% Apply threshold to isolate strong peaks
|
||||
IMGFFT(IMGFFT < threshold) = 0;
|
||||
|
||||
% Prepare polar coordinates
|
||||
[ny, nx] = size(IMGFFT);
|
||||
[X, Y] = meshgrid(1:nx, 1:ny);
|
||||
cx = ceil(nx/2);
|
||||
cy = ceil(ny/2);
|
||||
R = sqrt((X - cx).^2 + (Y - cy).^2);
|
||||
Theta = atan2(Y - cy, X - cx); % range [-pi, pi]
|
||||
|
||||
% Choose radial band
|
||||
radial_mask = (R >= r_min) & (R <= r_max);
|
||||
|
||||
% Initialize angular structure factor
|
||||
S_theta = zeros(1, num_bins);
|
||||
theta_vals = linspace(0, pi, num_bins);
|
||||
|
||||
% Loop through angle bins
|
||||
for i = 1:num_bins
|
||||
angle_start = (i-1) * pi / num_bins;
|
||||
angle_end = i * pi / num_bins;
|
||||
angle_mask = (Theta >= angle_start & Theta < angle_end);
|
||||
bin_mask = radial_mask & angle_mask;
|
||||
fft_angle = IMGFFT .* bin_mask;
|
||||
S_theta(i) = sum(sum(abs(fft_angle).^2));
|
||||
end
|
||||
|
||||
% Smooth using either Gaussian or moving average
|
||||
if exist('sigma', 'var') && ~isempty(sigma)
|
||||
% Gaussian convolution
|
||||
half_width = ceil(3 * sigma);
|
||||
x = -half_width:half_width;
|
||||
gauss_kernel = exp(-x.^2 / (2 * sigma^2));
|
||||
gauss_kernel = gauss_kernel / sum(gauss_kernel);
|
||||
% Circular convolution
|
||||
S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], ...
|
||||
gauss_kernel, 'same');
|
||||
S_theta = S_theta(half_width+1:end-half_width);
|
||||
elseif exist('windowSize', 'var') && ~isempty(windowSize)
|
||||
% Moving average via convolution (circular)
|
||||
pad = floor(windowSize / 2);
|
||||
kernel = ones(1, windowSize) / windowSize;
|
||||
S_theta = conv([S_theta(end-pad+1:end), S_theta, S_theta(1:pad)], kernel, 'same');
|
||||
S_theta = S_theta(pad+1:end-pad);
|
||||
end
|
||||
end
|
||||
|
||||
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
|
||||
% image must be a 2D numerical array
|
||||
[dim1, dim2] = size(img);
|
||||
|
||||
s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
|
||||
s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
|
||||
s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
|
||||
s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
|
||||
|
||||
ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
|
||||
end
|
||||
|
||||
function ret = subtractBackgroundOffset(img, fraction)
|
||||
% Remove the background from the image.
|
||||
% :param dataArray: The image
|
||||
% :type dataArray: xarray DataArray
|
||||
% :param x_fraction: The fraction of the pixels used in x axis
|
||||
% :type x_fraction: float
|
||||
% :param y_fraction: The fraction of the pixels used in y axis
|
||||
% :type y_fraction: float
|
||||
% :return: The image after removing background
|
||||
% :rtype: xarray DataArray
|
||||
|
||||
x_fraction = fraction(1);
|
||||
y_fraction = fraction(2);
|
||||
offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
|
||||
ret = img - offset;
|
||||
end
|
||||
|
||||
function ret = cropODImage(img, center, span)
|
||||
% Crop the image according to the region of interest (ROI).
|
||||
% :param dataSet: The images
|
||||
% :type dataSet: xarray DataArray or DataSet
|
||||
% :param center: The center of region of interest (ROI)
|
||||
% :type center: tuple
|
||||
% :param span: The span of region of interest (ROI)
|
||||
% :type span: tuple
|
||||
% :return: The cropped images
|
||||
% :rtype: xarray DataArray or DataSet
|
||||
|
||||
x_start = floor(center(1) - span(1) / 2);
|
||||
x_end = floor(center(1) + span(1) / 2);
|
||||
y_start = floor(center(2) - span(2) / 2);
|
||||
y_end = floor(center(2) + span(2) / 2);
|
||||
|
||||
ret = img(y_start:y_end, x_start:x_end);
|
||||
end
|
||||
|
||||
function imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||||
%CALCULATEODIMAGE Calculates the optical density (OD) image for absorption imaging.
|
||||
%
|
||||
% imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||||
%
|
||||
% Inputs:
|
||||
% imageAtom - Image with atoms
|
||||
% imageBackground - Image without atoms
|
||||
% imageDark - Image without light
|
||||
% mode - 'LowIntensity' (default) or 'HighIntensity'
|
||||
% exposureTime - Required only for 'HighIntensity' [in seconds]
|
||||
%
|
||||
% Output:
|
||||
% imageOD - Computed OD image
|
||||
%
|
||||
|
||||
arguments
|
||||
imageAtom (:,:) {mustBeNumeric}
|
||||
imageBackground (:,:) {mustBeNumeric}
|
||||
imageDark (:,:) {mustBeNumeric}
|
||||
mode char {mustBeMember(mode, {'LowIntensity', 'HighIntensity'})} = 'LowIntensity'
|
||||
exposureTime double = NaN
|
||||
end
|
||||
|
||||
% Compute numerator and denominator
|
||||
numerator = imageBackground - imageDark;
|
||||
denominator = imageAtom - imageDark;
|
||||
|
||||
% Avoid division by zero
|
||||
numerator(numerator == 0) = 1;
|
||||
denominator(denominator == 0) = 1;
|
||||
|
||||
% Calculate OD based on mode
|
||||
switch mode
|
||||
case 'LowIntensity'
|
||||
imageOD = -log(abs(denominator ./ numerator));
|
||||
|
||||
case 'HighIntensity'
|
||||
if isnan(exposureTime)
|
||||
error('Exposure time must be provided for HighIntensity mode.');
|
||||
end
|
||||
imageOD = abs(denominator ./ numerator);
|
||||
imageOD = -log(imageOD) + (numerator - denominator) ./ (7000 * (exposureTime / 5e-6));
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
|
||||
% removefringesInImage - Fringe removal and noise reduction from absorption images.
|
||||
% Creates an optimal reference image for each absorption image in a set as
|
||||
% a linear combination of reference images, with coefficients chosen to
|
||||
% minimize the least-squares residuals between each absorption image and
|
||||
% the optimal reference image. The coefficients are obtained by solving a
|
||||
% linear set of equations using matrix inverse by LU decomposition.
|
||||
%
|
||||
% Application of the algorithm is described in C. F. Ockeloen et al, Improved
|
||||
% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
|
||||
%
|
||||
% Syntax:
|
||||
% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
|
||||
%
|
||||
% Required inputs:
|
||||
% absimages - Absorption image data,
|
||||
% typically 16 bit grayscale images
|
||||
% refimages - Raw reference image data
|
||||
% absimages and refimages are both cell arrays containing
|
||||
% 2D array data. The number of refimages can differ from the
|
||||
% number of absimages.
|
||||
%
|
||||
% Optional inputs:
|
||||
% bgmask - Array specifying background region used,
|
||||
% 1=background, 0=data. Defaults to all ones.
|
||||
% Outputs:
|
||||
% optrefimages - Cell array of optimal reference images,
|
||||
% equal in size to absimages.
|
||||
%
|
||||
|
||||
% Dependencies: none
|
||||
%
|
||||
% Authors: Shannon Whitlock, Caspar Ockeloen
|
||||
% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
|
||||
% S. Whitlock, Improved detection of small atom numbers through
|
||||
% image processing, arXiv:1007.2136
|
||||
% Email:
|
||||
% May 2009; Last revision: 11 August 2010
|
||||
|
||||
% Process inputs
|
||||
|
||||
% Set variables, and flatten absorption and reference images
|
||||
nimgs = size(absimages,3);
|
||||
nimgsR = size(refimages,3);
|
||||
xdim = size(absimages(:,:,1),2);
|
||||
ydim = size(absimages(:,:,1),1);
|
||||
|
||||
R = single(reshape(refimages,xdim*ydim,nimgsR));
|
||||
A = single(reshape(absimages,xdim*ydim,nimgs));
|
||||
optrefimages=zeros(size(absimages)); % preallocate
|
||||
|
||||
if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
|
||||
k = find(bgmask(:)==1); % Index k specifying background region
|
||||
|
||||
% Ensure there are no duplicate reference images
|
||||
% R=unique(R','rows')'; % comment this line if you run out of memory
|
||||
|
||||
% Decompose B = R*R' using singular value or LU decomposition
|
||||
[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
|
||||
|
||||
for j=1:nimgs
|
||||
b=R(k,:)'*A(k,j);
|
||||
% Obtain coefficients c which minimise least-square residuals
|
||||
lower.LT = true; upper.UT = true;
|
||||
c = linsolve(U,linsolve(L,b(p,:),lower),upper);
|
||||
|
||||
% Compute optimised reference image
|
||||
optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user