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MAJOR update - many changes!

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Karthik 2025-07-10 23:49:03 +02:00
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function results = analyzeFolder(options)
% Ensure required fields are defined in options
arguments
options.scan_parameter (1,:) char
options.scan_groups (1,:) double
options.cam (1,1) double
options.angle (1,1) double
options.center (1,2) double
options.span (1,2) double
options.fraction (1,2) double
options.ImagingMode (1,:) char
options.PulseDuration (1,1) double
options.removeFringes (1,1) logical
options.skipUnshuffling (1,1) logical
options.pixel_size (1,1) double
options.magnification (1,1) double
options.zoom_size (1,1) double
options.r_min (1,1) double
options.r_max (1,1) double
options.N_angular_bins (1,1) double
options.Angular_Threshold (1,1) double
options.Angular_Sigma (1,1) double
options.Angular_WindowSize (1,1) double
options.theta_min (1,1) double
options.theta_max (1,1) double
options.N_radial_bins (1,1) double
options.Radial_Sigma (1,1) double
options.Radial_WindowSize (1,1) double
options.k_min (1,1) double
options.k_max (1,1) double
options.skipPreprocessing (1,1) logical
options.skipMasking (1,1) logical
options.skipIntensityThresholding (1,1) logical
options.skipBinarization (1,1) logical
options.folderPath (1,:) char
end
% Assign variables from options
scan_parameter = options.scan_parameter;
scan_groups = options.scan_groups;
folderPath = options.folderPath;
center = options.center;
span = options.span;
fraction = options.fraction;
ImagingMode = options.ImagingMode;
PulseDuration = options.PulseDuration;
removeFringes = options.removeFringes;
skipUnshuffling = options.skipUnshuffling;
pixel_size = options.pixel_size;
magnification = options.magnification;
zoom_size = options.zoom_size;
r_min = options.r_min;
r_max = options.r_max;
N_angular_bins = options.N_angular_bins;
Angular_Threshold = options.Angular_Threshold;
Angular_Sigma = options.Angular_Sigma;
Angular_WindowSize = options.Angular_WindowSize;
theta_min = options.theta_min;
theta_max = options.theta_max;
N_radial_bins = options.N_radial_bins;
Radial_Sigma = options.Radial_Sigma;
Radial_WindowSize = options.Radial_WindowSize;
k_min = options.k_min;
k_max = options.k_max;
skipPreprocessing = options.skipPreprocessing;
skipMasking = options.skipMasking;
skipIntensityThresholding = options.skipIntensityThresholding;
skipBinarization = options.skipBinarization;
cam = options.cam;
angle = options.angle;
% Load images and analyze them (keep using the cleaned body of your original function)
% Fix the incorrect usage of 'cam' and 'angle' not defined locally
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"];
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, '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
% Extract quantities
fft_imgs = cell(1, nimgs);
spectral_distribution = cell(1, nimgs);
theta_values = cell(1, nimgs);
radial_spectral_contrast = zeros(1, nimgs);
angular_spectral_weight = zeros(1, nimgs);
N_shots = length(od_imgs);
for k = 1:N_shots
IMG = od_imgs{k};
[IMGFFT, ~] = 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, []);
[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_distribution{k} = S_theta;
theta_values{k} = theta_vals;
radial_spectral_contrast(k) = computeRadialSpectralContrast(k_rho_vals, S_k_smoothed, k_min, k_max);
S_theta_norm = S_theta / max(S_theta); % Normalize to 1
angular_spectral_weight(k) = trapz(theta_vals, S_theta_norm);
end
% 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);
% Preallocate arrays
mean_rsc = zeros(size(unique_scan_parameter_values));
stderr_rsc = 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 = radial_spectral_contrast(idx == i);
mean_rsc(i) = mean(group_vals);
stderr_rsc(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
end
% Preallocate arrays
mean_asw = zeros(size(unique_scan_parameter_values));
stderr_asw = 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_asw(i) = mean(group_vals);
stderr_asw(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
end
% 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
% 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);
% Define angular range and conversion
angle_range = 180;
angle_per_bin = angle_range / N_angular_bins;
max_peak_angle = 180;
max_peak_bin = round(max_peak_angle / angle_per_bin);
% Parameters for search
window_size = 10;
angle_threshold = 100;
% 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);
std_error_g2_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, :);
N_reps = 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 to 0–60° for highest peak
restricted_profile = profile(1:max_peak_bin);
[~, peak_idx_rel] = max(restricted_profile);
peak_idx = peak_idx_rel;
peak_angle = (peak_idx - 1) * angle_per_bin;
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
ref_window = mod((peak_idx - window_size):(peak_idx + window_size) - 1, N_angular_bins) + 1;
ref = profile(ref_window);
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);
num = mean(ref .* sec);
denom = mean(ref.^2);
g2 = num / denom;
correlations(k) = g2;
angles(k) = mod((peak_idx - 1 + offsets(k)) * angle_per_bin, angle_range);
end
[max_corr, max_idx] = max(correlations);
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');
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
results.folderPath = folderPath;
results.scan_parameter = scan_parameter;
results.scan_groups = scan_groups;
results.mean_max_g2_values = mean_max_g2_values;
results.std_error_g2_values = std_error_g2_values;
results.mean_max_g2_angle = mean_max_g2_angle_values;
results.radial_spectral_contrast= mean_rsc;
results.angular_spectral_weight = mean_asw;
end
%% 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 [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
View 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
%%

586
Data-Analyzer/conductSpectralAnalysis.m
View File

@ -1,18 +1,18 @@
%% ===== Settings ===== %% ===== D-S Settings =====
groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ... 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/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
"/images/Vertical_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); folderPath = strcat(folderPath, run);
cam = 5; cam = 5;
angle = 0; angle = 0;
center = [1430, 2040]; center = [1410, 2030];
span = [200, 200]; span = [200, 200];
fraction = [0.1, 0.1]; fraction = [0.1, 0.1];
@ -43,29 +43,100 @@ Angular_WindowSize = 5;
zoom_size = 50; % Zoomed-in region around center zoom_size = 50; % Zoomed-in region around center
% Plotting and saving % Plotting and saving
% scan_parameter = 'ps_rot_mag_fin_pol_angle'; scan_parameter = 'ps_rot_mag_fin_pol_angle';
scan_parameter = 'rot_mag_field'; % scan_parameter = 'rot_mag_field';
% scan_parameter_text = 'Angle = ';
scan_parameter_text = 'BField = ';
savefolderPath = 'E:/Results - Experiment/B2.35G/'; savefileName = 'DropletsToStripes';
savefileName = 'Droplets';
font = 'Bahnschrift'; font = 'Bahnschrift';
skipUnshuffling = true;
if strcmp(savefileName, 'DropletsToStripes') if strcmp(savefileName, 'DropletsToStripes')
scan_groups = 0:5:45; scan_groups = 0:5:45;
titleString = 'Droplets to Stripes';
elseif strcmp(savefileName, 'StripesToDroplets') elseif strcmp(savefileName, 'StripesToDroplets')
scan_groups = 45:-5:0; scan_groups = 45:-5:0;
titleString = 'Stripes to Droplets';
end end
% Flags % Flags
skipNormalization = false;
skipUnshuffling = true;
skipPreprocessing = true; skipPreprocessing = true;
skipMasking = true; skipMasking = true;
skipIntensityThresholding = true; skipIntensityThresholding = true;
skipBinarization = true; skipBinarization = true;
skipMovieRender = true; skipMovieRender = true;
skipSaveFigures = false; 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 ===== %% ===== 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. % 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)'; absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img, ImagingMode, PulseDuration), center, span), fraction)';
end end
%% ===== Fringe removal ===== % ===== Fringe removal =====
if removeFringes if removeFringes
optrefimages = removefringesInImage(absimages, refimages); optrefimages = removefringesInImage(absimages, refimages);
@ -108,7 +179,7 @@ else
end end
end end
%% ===== Get rotation angles ===== % ===== Get rotation angles =====
scan_parameter_values = zeros(1, length(files)); scan_parameter_values = zeros(1, length(files));
% Get information about the '/globals' group % Get information about the '/globals' group
@ -127,7 +198,7 @@ for k = 1 : length(files)
end end
end end
%% ===== Unshuffle if necessary to do so ===== % ===== Unshuffle if necessary to do so =====
if ~skipUnshuffling if ~skipUnshuffling
n_values = length(scan_groups); n_values = length(scan_groups);
@ -167,17 +238,10 @@ end
%% ===== Run Fourier analysis over images ===== %% ===== Run Fourier analysis over images =====
fft_imgs = cell(1, nimgs); fft_imgs = cell(1, nimgs);
spectral_contrast = zeros(1, nimgs); radial_spectral_contrast = zeros(1, nimgs);
spectral_weight = zeros(1, nimgs); angular_spectral_weight = zeros(1, nimgs);
N_shots = length(od_imgs); 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;
if ~skipMovieRender if ~skipMovieRender
% Create VideoWriter object for movie % Create VideoWriter object for movie
videoFile = VideoWriter([savefileName '.mp4'], 'MPEG-4'); videoFile = VideoWriter([savefileName '.mp4'], 'MPEG-4');
@ -194,7 +258,10 @@ if ~skipSaveFigures
end end
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 for k = 1:N_shots
IMG = od_imgs{k}; IMG = od_imgs{k};
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization); [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); [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 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); radial_spectral_contrast(k) = computeRadialSpectralContrast(fft_imgs{k}, r_min, r_max, Angular_Threshold);
spectral_weight(k) = trapz(theta_vals, S_theta); 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); figure(1);
clf clf
@ -268,14 +340,21 @@ for k = 1:N_shots
ylabel('y (\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); 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'], ... 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, ... 'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
'Interpreter', 'tex', 'Units', 'normalized', ... 'Interpreter', 'tex', 'Units', 'normalized', ...
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top'); '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) ======= % ======= FFT POWER SPECTRUM (reciprocal space) =======
ax2 = nexttile; ax2 = nexttile;
imagesc(kx, ky, log(1 + abs(fft_imgs{k}).^2)); imagesc(kx, ky, log(1 + ps_list{k}));
axis image; axis image;
set(gca, 'FontSize', 14, 'YDir', 'normal') set(gca, 'FontSize', 14, 'YDir', 'normal')
xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font); xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
@ -299,8 +378,13 @@ for k = 1:N_shots
% ======= ANGULAR DISTRIBUTION (S(θ)) ======= % ======= ANGULAR DISTRIBUTION (S(θ)) =======
nexttile; nexttile;
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); plot(theta_vals/pi, S_theta, 'LineWidth', 2);
set(gca, 'FontSize', 14, 'YScale', 'log', 'YLim', [1E4, 1E7]); set(gca, 'FontSize', 14, 'YScale', 'log', 'YLim', [1E4, 1E7]);
end
xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font); xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font); ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
title('Angular Spectral Distribution - S(\theta)', 'Interpreter', 'tex', ... title('Angular Spectral Distribution - S(\theta)', 'Interpreter', 'tex', ...
@ -316,20 +400,6 @@ for k = 1:N_shots
drawnow; 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 if ~skipMovieRender
% Capture the current frame and write it to the video % Capture the current frame and write it to the video
frame = getframe(gcf); % Capture the current figure as a frame 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 % Save current figure as PNG with high resolution
print(gcf, fileNamePNG, '-dpng', '-r100'); % 300 dpi for high quality print(gcf, fileNamePNG, '-dpng', '-r100'); % 300 dpi for high quality
end 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 if skipMovieRender & skipSaveFigures
pause(0.5); pause(0.5);
end end
@ -352,46 +430,398 @@ if ~skipMovieRender
close(videoFile); close(videoFile);
end end
%% ===== Final Averages ===== %% Track across the transition
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;
% Generate figure with 3 subplots % Assuming scan_parameter_values and spectral_weight are column vectors (or row vectors of same length)
figure('Name', 'Average Spectral Analysis', 'Position', [400 200 1200 400]); [unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
tavg = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
% ==== 1. Average FFT Power Spectrum ==== % Preallocate arrays
nexttile; mean_sc = zeros(size(unique_scan_parameter_values));
imagesc(kx, ky, log(1 + avg_ps)); stderr_sc = zeros(size(unique_scan_parameter_values));
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());
% ==== 2. Average Radial Spectral Distribution ==== % Loop through each unique theta and compute mean and standard error
nexttile; for i = 1:length(unique_scan_parameter_values)
plot(k_rho_vals, avg_S_k, 'LineWidth', 2); group_vals = radial_spectral_contrast(idx == i);
xlabel('k_\rho [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14); mean_sc(i) = mean(group_vals);
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14); stderr_sc(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
title('Average S(k_\rho)', 'FontSize', 16, 'FontWeight', 'bold'); end
set(gca, 'FontSize', 14, 'YScale', 'log', 'XLim', [min(k_rho_vals), max(k_rho_vals)]);
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; grid on;
% ==== 3. Average Angular Spectral Distribution ==== %% Plot Averages
nexttile;
plot(theta_vals/pi, avg_S_theta, 'LineWidth', 2); % Group by scan parameter values
xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14); [unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14); N_params = numel(unique_scan_parameter_values);
title('Average S(\theta)', 'FontSize', 16, 'FontWeight', 'bold');
set(gca, 'FontSize', 14, 'YScale', 'log'); if ~skipSaveFigures
grid on; % Define folder for saving images
ax = gca; saveFolder = [savefileName '_SavedFigures'];
ax.XMinorGrid = 'on'; if ~exist(saveFolder, 'dir')
ax.YMinorGrid = 'on'; 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 %% Helper Functions
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization) function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
@ -548,7 +978,7 @@ function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_mi
end end
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 % Apply threshold to isolate strong peaks
IMGFFT(IMGFFT < threshold) = 0; IMGFFT(IMGFFT < threshold) = 0;

57
Data-Analyzer/extractAutocorrelation.m
View File

@ -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/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
"/images/Vertical_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); folderPath = strcat(folderPath, run);
cam = 5; cam = 5;
angle = 0; angle = 0;
center = [1430, 2040]; center = [1410, 2030];
span = [200, 200]; span = [200, 200];
fraction = [0.1, 0.1]; fraction = [0.1, 0.1];
@ -43,29 +43,30 @@ Angular_WindowSize = 5;
zoom_size = 50; % Zoomed-in region around center zoom_size = 50; % Zoomed-in region around center
% Plotting and saving % Plotting and saving
% scan_parameter = 'ps_rot_mag_fin_pol_angle'; scan_parameter = 'ps_rot_mag_fin_pol_angle';
scan_parameter = 'rot_mag_field'; % scan_parameter = 'rot_mag_field';
% scan_parameter_text = 'Angle = '; scan_parameter_text = 'Angle = ';
scan_parameter_text = 'BField = '; % scan_parameter_text = 'BField = ';
savefolderPath = 'E:/Results - Experiment/B2.35G/'; savefileName = 'DropletsToStripes';
savefileName = 'Droplets';
font = 'Bahnschrift'; font = 'Bahnschrift';
skipUnshuffling = true;
if strcmp(savefileName, 'DropletsToStripes') if strcmp(savefileName, 'DropletsToStripes')
scan_groups = 0:5:45; scan_groups = 0:5:45;
titleString = 'Droplets to Stripes';
elseif strcmp(savefileName, 'StripesToDroplets') elseif strcmp(savefileName, 'StripesToDroplets')
scan_groups = 45:-5:0; scan_groups = 45:-5:0;
titleString = 'Stripes to Droplets';
end end
% Flags % Flags
skipUnshuffling = true;
skipPreprocessing = true; skipPreprocessing = true;
skipMasking = true; skipMasking = true;
skipIntensityThresholding = true; skipIntensityThresholding = true;
skipBinarization = true; skipBinarization = true;
skipMovieRender = true; skipMovieRender = true;
skipSaveFigures = false; skipSaveFigures = true;
%% ===== Load and compute OD image, rotate and extract ROI for analysis ===== %% ===== 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. % 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); kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
ky = ky_full(mid_y - zoom_size : mid_y + 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; spectral_distribution{k} = S_theta;
theta_values{k} = theta_vals;
end end
% Create matrix of shape (N_shots x N_angular_bins) % Create matrix of shape (N_shots x N_angular_bins)
@ -186,15 +186,15 @@ end
% Grouping by scan parameter value (e.g., alpha) % Grouping by scan parameter value (e.g., alpha)
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values); [unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
% Number of unique alpha values % Number of unique parameter values
N_alpha = length(unique_scan_parameter_values); N_params = length(unique_scan_parameter_values);
% Preallocate result arrays % Preallocate result arrays
g2_all = zeros(N_alpha, N_angular_bins); g2_all = zeros(N_params, N_angular_bins);
g2_error_all = zeros(N_alpha, N_angular_bins); g2_error_all = zeros(N_params, N_angular_bins);
% Compute g2 % Compute g2
for i = 1:N_alpha for i = 1:N_params
group_idx = find(idx == i); group_idx = find(idx == i);
group_data = delta_nkr_all(group_idx, :); group_data = delta_nkr_all(group_idx, :);
@ -214,23 +214,20 @@ for i = 1:N_alpha
end end
end end
% Reconstruct theta axis from any one of the stored values % Number of unique parameter values
theta_vals = theta_values{1}; % assuming it's in radians nParams = size(g2_all, 1);
% Number of unique alpha values
nAlpha = size(g2_all, 1);
% Generate a colormap with enough unique colors % 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); figure(1);
clf; clf;
set(gcf,'Position',[100 100 950 750]) set(gcf,'Position',[100 100 950 750])
hold on; hold on;
legend_entries = cell(nAlpha, 1); legend_entries = cell(nParams, 1);
for i = 1:nAlpha for i = 1:nParams
errorbar(theta_vals/pi, g2_all(i, :), g2_error_all(i, :), ... errorbar(theta_values/pi, g2_all(i, :), g2_error_all(i, :), ...
'o', 'Color', cmap(i,:), ... 'o', 'Color', cmap(i,:), ...
'MarkerSize', 3, 'MarkerFaceColor', cmap(i,:), ... 'MarkerSize', 3, 'MarkerFaceColor', cmap(i,:), ...
'CapSize', 4); 'CapSize', 4);
@ -241,15 +238,15 @@ for i = 1:nAlpha
end end
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); set(gca, 'FontSize', 14);
hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex'); hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex');
hYLabel = ylabel('$g^{(2)}(\delta\theta)$', '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'); legend(legend_entries, 'Interpreter', 'latex', 'Location', 'bestoutside');
set([hXLabel, hYLabel], 'FontName', font) set([hXLabel, hYLabel], 'FontName', font)
set([hXLabel, hYLabel], 'FontSize', 14) 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; grid on;
%% Helper Functions %% Helper Functions

138
Data-Analyzer/extractCustomCorrelation.m
View File

@ -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/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
"/images/Vertical_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); folderPath = strcat(folderPath, run);
cam = 5; cam = 5;
angle = 0; angle = 0;
center = [1430, 2040]; center = [1410, 2030];
span = [200, 200]; span = [200, 200];
fraction = [0.1, 0.1]; fraction = [0.1, 0.1];
@ -43,29 +43,30 @@ Angular_WindowSize = 5;
zoom_size = 50; % Zoomed-in region around center zoom_size = 50; % Zoomed-in region around center
% Plotting and saving % Plotting and saving
% scan_parameter = 'ps_rot_mag_fin_pol_angle'; scan_parameter = 'ps_rot_mag_fin_pol_angle';
scan_parameter = 'rot_mag_field'; % scan_parameter = 'rot_mag_field';
% scan_parameter_text = 'Angle = '; scan_parameter_text = 'Angle = ';
scan_parameter_text = 'BField = '; % scan_parameter_text = 'BField = ';
savefolderPath = 'E:/Results - Experiment/B2.35G/'; savefileName = 'DropletsToStripes';
savefileName = 'Droplets';
font = 'Bahnschrift'; font = 'Bahnschrift';
skipUnshuffling = true;
if strcmp(savefileName, 'DropletsToStripes') if strcmp(savefileName, 'DropletsToStripes')
scan_groups = 0:5:45; scan_groups = 0:5:45;
titleString = 'Droplets to Stripes';
elseif strcmp(savefileName, 'StripesToDroplets') elseif strcmp(savefileName, 'StripesToDroplets')
scan_groups = 45:-5:0; scan_groups = 45:-5:0;
titleString = 'Stripes to Droplets';
end end
% Flags % Flags
skipUnshuffling = true;
skipPreprocessing = true; skipPreprocessing = true;
skipMasking = true; skipMasking = true;
skipIntensityThresholding = true; skipIntensityThresholding = true;
skipBinarization = true; skipBinarization = true;
skipMovieRender = true; skipMovieRender = true;
skipSaveFigures = false; skipSaveFigures = true;
%% ===== Load and compute OD image, rotate and extract ROI for analysis ===== %% ===== 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. % 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); N_shots = length(od_imgs);
% Compute FFT % Compute FFT for all images
for k = 1:N_shots for k = 1:N_shots
IMG = od_imgs{k}; IMG = od_imgs{k};
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization); [IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
% Size of original image (in pixels)
[Ny, Nx] = size(IMG); [Ny, Nx] = size(IMG);
% Real-space pixel size in micrometers after magnification
dx = pixel_size / magnification; dx = pixel_size / magnification;
dy = dx; % assuming square pixels dy = dx; % assuming square pixels
% Real-space axes
x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6; x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6;
y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6; y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
% Reciprocal space increments (frequency domain, μm⁻¹)
dvx = 1 / (Nx * dx); dvx = 1 / (Nx * dx);
dvy = 1 / (Ny * dy); dvy = 1 / (Ny * dy);
% Frequency axes
vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx; vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy; vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
% Wavenumber axes kx_full = 2 * pi * vx * 1E-6;
kx_full = 2 * pi * vx * 1E-6; % μm⁻¹
ky_full = 2 * pi * vy * 1E-6; ky_full = 2 * pi * vy * 1E-6;
% Crop FFT image around center
mid_x = floor(Nx/2); mid_x = floor(Nx/2);
mid_y = floor(Ny/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); 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); kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
ky = ky_full(mid_y - zoom_size : mid_y + 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; theta_values{k} = theta_vals;
end 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); delta_nkr_all = zeros(N_shots, N_angular_bins);
for k = 1:N_shots for k = 1:N_shots
delta_nkr_all(k, :) = spectral_distribution{k}; delta_nkr_all(k, :) = spectral_distribution{k};
end 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); [unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
% Number of unique alpha values
N_params = length(unique_scan_parameter_values); N_params = length(unique_scan_parameter_values);
% Define angular range and bins % Define angular range and conversion
angle_range = 180; % total angular span of the profile angle_range = 180;
angle_per_bin = angle_range / N_angular_bins; angle_per_bin = angle_range / N_angular_bins;
max_peak_angle = 180;
max_peak_angle = 60;
max_peak_bin = round(max_peak_angle / angle_per_bin); max_peak_bin = round(max_peak_angle / angle_per_bin);
% Parameters for search
window_size = 10; window_size = 10;
angle_threshold = 100; angle_threshold = 100;
ref_peak_angles = []; % Initialize containers for final results
angle_at_max_g2 = []; mean_max_g2_values = zeros(1, N_params);
g2_values = []; 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 for i = 1:N_params
group_idx = find(idx == i); group_idx = find(idx == i);
group_data = delta_nkr_all(group_idx, :); 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, :); 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); restricted_profile = profile(1:max_peak_bin);
[~, peak_idx_rel] = max(restricted_profile); [~, peak_idx_rel] = max(restricted_profile);
% Convert relative peak index to global index in profile
peak_idx = peak_idx_rel; 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 if peak_angle < angle_threshold
offsets = round(50 / angle_per_bin) : round(70 / angle_per_bin); offsets = round(50 / angle_per_bin) : round(70 / angle_per_bin);
else else
offsets = -round(70 / angle_per_bin) : -round(50 / angle_per_bin); offsets = -round(70 / angle_per_bin) : -round(50 / angle_per_bin);
end end
% Reference window around largest peak
ref_window = mod((peak_idx - window_size):(peak_idx + window_size) - 1, N_angular_bins) + 1; ref_window = mod((peak_idx - window_size):(peak_idx + window_size) - 1, N_angular_bins) + 1;
ref = profile(ref_window); ref = profile(ref_window);
% Store reference peak angle
ref_peak_angles(end+1) = peak_angle;
correlations = zeros(size(offsets)); correlations = zeros(size(offsets));
angles = 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_window = mod((shifted_idx - window_size):(shifted_idx + window_size) - 1, N_angular_bins) + 1;
sec = profile(sec_window); sec = profile(sec_window);
% Calculate g2 correlation
num = mean(ref .* sec); num = mean(ref .* sec);
denom = mean(ref.^2); denom = mean(ref.^2);
g2 = num / denom; g2 = num / denom;
correlations(k) = g2; correlations(k) = g2;
angles(k) = mod((peak_idx - 1 + offsets(k)) * angle_per_bin, angle_range);
% 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 end
[max_corr, max_idx] = max(correlations); [max_corr, max_idx] = max(correlations);
g2_values(end+1) = max_corr; g2_values(j) = max_corr;
angle_at_max_g2(end+1) = angles(max_idx); angle_at_max_g2(j) = angles(max_idx);
end 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 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); figure(1);
hold on; 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 % Max g2 mode line and label
xline(mode_g2, 'r--', 'LineWidth', 1.5, 'DisplayName', sprintf('g_2 Mode: %.1f°', mode_g2)); 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'); text(mode_g2, yl(2)*0.75, sprintf('%.1f°', mode_g2), 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom', 'FontSize', 12, 'Color', 'r');
%}
%% Helper Functions %% Helper Functions
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization) function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)

118
Data-Analyzer/extractQuantities.m Normal file
View 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;

288
Data-Analyzer/plotImages.m
View File

@ -4,23 +4,36 @@ 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/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
"/images/Vertical_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); folderPath = strcat(folderPath, run);
cam = 5; cam = 5;
angle = 0; angle = 0;
center = [1375, 2020]; center = [1285, 2100];
span = [200, 200]; span = [200, 200];
fraction = [0.1, 0.1]; fraction = [0.1, 0.1];
pixel_size = 5.86e-6; pixel_size = 5.86e-6; % in meters
magnification = 23.94;
removeFringes = false; 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. % Get a list of all files in the folder with the desired file name pattern.
filePattern = fullfile(folderPath, '*.h5'); filePattern = fullfile(folderPath, '*.h5');
@ -34,16 +47,15 @@ for k = 1 : length(files)
fprintf(1, 'Now reading %s\n', fullFileName); 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)); bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle)); dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)'; 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 end
% Fringe removal % ===== Fringe removal =====
if removeFringes if removeFringes
optrefimages = removefringesInImage(absimages, refimages); optrefimages = removefringesInImage(absimages, refimages);
@ -61,69 +73,121 @@ else
od_imgs{i} = absimages(:, :, i); od_imgs{i} = absimages(:, :, i);
end end
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 %% Display Images
figure(1) figure(1)
clf clf
set(gcf,'Position',[50 50 950 750]) set(gcf,'Position',[50 50 950 750])
% Calculate the x and y limits for the cropped image % Get image size in pixels
y_min = center(1) - span(2) / 2; [Ny, Nx] = size(od_imgs{1});
y_max = center(1) + span(2) / 2;
x_min = center(2) - span(1) / 2;
x_max = center(2) + span(1) / 2;
% Generate x and y arrays representing the original coordinates for each pixel % Define pixel size and magnification (if not already defined earlier)
x_range = linspace(x_min, x_max, span(1)); dx = pixel_size / magnification; % e.g. in meters
y_range = linspace(y_min, y_max, span(2)); 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 % Display the cropped image
for k = 1 : length(od_imgs) for k = 1 : length(od_imgs)
imagesc(x_range, y_range, od_imgs{k}) imagesc(x, y, od_imgs{k});
axis equal tight; hold on;
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);
drawnow % Convert pixel grid to µm (already done: x and y axes)
pause(0.5) % 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 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 %% Helper Functions
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction) 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); ret = img(y_start:y_end, x_start:x_end);
end end
function ret = calculateODImage(imageAtom, imageBackground, imageDark) function imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
% Calculate the OD image for absorption imaging. %CALCULATEODIMAGE Calculates the optical density (OD) image for absorption imaging.
% :param imageAtom: The image with atoms %
% :type imageAtom: numpy array % imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
% :param imageBackground: The image without atoms %
% :type imageBackground: numpy array % Inputs:
% :param imageDark: The image without light % imageAtom - Image with atoms
% :type imageDark: numpy array % imageBackground - Image without atoms
% :return: The OD images % imageDark - Image without light
% :rtype: numpy array % 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; numerator = imageBackground - imageDark;
denominator = imageAtom - imageDark; denominator = imageAtom - imageDark;
% Avoid division by zero
numerator(numerator == 0) = 1; numerator(numerator == 0) = 1;
denominator(denominator == 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
end end