646 lines
24 KiB
Matlab
646 lines
24 KiB
Matlab
%% ===== 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/2025/06/23/";
|
||
|
||
run = '0300';
|
||
|
||
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';
|
||
scan_parameter_text = 'Angle = ';
|
||
% scan_parameter_text = 'BField = ';
|
||
|
||
savefileName = 'DropletsToStripes';
|
||
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
|
||
skipUnshuffling = true;
|
||
skipPreprocessing = true;
|
||
skipMasking = true;
|
||
skipIntensityThresholding = true;
|
||
skipBinarization = true;
|
||
skipMovieRender = true;
|
||
skipSaveFigures = true;
|
||
|
||
%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
|
||
% Get a list of all files in the folder with the desired file name pattern.
|
||
|
||
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 all images
|
||
for k = 1:N_shots
|
||
IMG = od_imgs{k};
|
||
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
|
||
|
||
[Ny, Nx] = size(IMG);
|
||
dx = pixel_size / magnification;
|
||
dy = dx; % assuming square pixels
|
||
|
||
x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6;
|
||
y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
|
||
|
||
dvx = 1 / (Nx * dx);
|
||
dvy = 1 / (Ny * dy);
|
||
vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
|
||
vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
|
||
|
||
kx_full = 2 * pi * vx * 1E-6;
|
||
ky_full = 2 * pi * vy * 1E-6;
|
||
|
||
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);
|
||
|
||
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
|
||
|
||
% 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);
|
||
|
||
% 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');
|
||
end
|
||
|
||
%% ── Mean ± Std vs. scan parameter ──────────────────────────────────────
|
||
% Compute standard error instead of standard deviation
|
||
std_error_g2_values = zeros(1, N_params);
|
||
for i = 1:N_params
|
||
n_i = numel(g2_all_per_group{i}); % Number of repetitions for this param
|
||
std_error_g2_values(i) = sqrt(var_max_g2_values(i) / n_i);
|
||
end
|
||
|
||
% Plot mean ± SEM
|
||
figure(1);
|
||
set(gcf,'Position',[100 100 950 750])
|
||
set(gca, 'FontSize', 14); % For tick labels only
|
||
errorbar(unique_scan_parameter_values, ... % x-axis
|
||
mean_max_g2_values, ... % y-axis (mean)
|
||
std_error_g2_values, ... % ± SEM
|
||
'--o', 'LineWidth', 1.8, 'MarkerSize', 6 );
|
||
|
||
set(gca, 'FontSize', 14, 'YLim', [0, 1]);
|
||
hXLabel = xlabel('$\alpha$ (degrees)', 'Interpreter', 'latex');
|
||
hYLabel = ylabel('$\mathrm{max}[g^{(2)}_{[50,70]}(\delta\theta)]$', 'Interpreter', 'latex');
|
||
hTitle = title(titleString, 'Interpreter', 'tex');
|
||
% set([hXLabel, hYLabel], 'FontName', font);
|
||
set([hXLabel, hYLabel], 'FontSize', 14);
|
||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold');
|
||
grid on;
|
||
|
||
% Define folder for saving images
|
||
saveFolder = [savefileName '_SavedFigures'];
|
||
if ~exist(saveFolder, 'dir')
|
||
mkdir(saveFolder);
|
||
end
|
||
save([saveFolder savefileName '.mat'], 'unique_scan_parameter_values', 'mean_max_g2_values', 'std_error_g2_values');
|
||
|
||
%{
|
||
%% Plot histograms within 0-180 degrees only
|
||
figure(1);
|
||
hold on;
|
||
|
||
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 gâ‚‚'}, 'FontSize', 12);
|
||
title('Comparison of Reference Peak and Max gâ‚‚ 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 |