417 lines
14 KiB
Matlab
417 lines
14 KiB
Matlab
%% Parameters
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "//DyLabNAS/Data/TwoDGas/2025/04/01/";
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run = '0059';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1285, 2100];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6; % in meters
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magnification = 23.94;
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removeFringes = false;
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ImagingMode = 'HighIntensity';
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PulseDuration = 5e-6;
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% Plotting and saving
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scan_parameter = 'rot_mag_fin_pol_angle';
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scan_groups = 0:10:50;
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savefileName = 'DropletsToStripes';
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font = 'Bahnschrift';
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% Flags
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skipUnshuffling = false;
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%% ===== Load and compute OD image, rotate and extract ROI for analysis =====
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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files = dir(filePattern);
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refimages = zeros(span(1) + 1, span(2) + 1, length(files));
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absimages = zeros(span(1) + 1, span(2) + 1, length(files));
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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fprintf(1, 'Now reading %s\n', fullFileName);
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atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
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absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img, ImagingMode, PulseDuration), center, span), fraction)';
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end
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% ===== Fringe removal =====
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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absimages_fringe_removed = absimages(:, :, :) - optrefimages(:, :, :);
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nimgs = size(absimages_fringe_removed,3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages_fringe_removed(:, :, i);
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end
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else
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nimgs = size(absimages(:, :, :),3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages(:, :, i);
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end
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end
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% ===== Get rotation angles =====
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scan_parameter_values = zeros(1, length(files));
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% Get information about the '/globals' group
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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info = h5info(fullFileName, '/globals');
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for i = 1:length(info.Attributes)
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if strcmp(info.Attributes(i).Name, scan_parameter)
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if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
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scan_parameter_values(k) = 180 - info.Attributes(i).Value;
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else
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scan_parameter_values(k) = info.Attributes(i).Value;
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end
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end
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end
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end
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% ===== Unshuffle if necessary to do so =====
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if ~skipUnshuffling
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n_values = length(scan_groups);
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n_total = length(scan_parameter_values);
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% Infer number of repetitions
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n_reps = n_total / n_values;
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% Preallocate ordered arrays
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ordered_scan_values = zeros(1, n_total);
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ordered_od_imgs = cell(1, n_total);
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counter = 1;
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for rep = 1:n_reps
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for val = scan_groups
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% Find the next unused match for this val
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idx = find(scan_parameter_values == val, 1, 'first');
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% Assign and remove from list to avoid duplicates
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ordered_scan_values(counter) = scan_parameter_values(idx);
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ordered_od_imgs{counter} = od_imgs{idx};
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% Mark as used by removing
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scan_parameter_values(idx) = NaN; % NaN is safe since original values are 0:5:45
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od_imgs{idx} = []; % empty cell so it won't be matched again
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counter = counter + 1;
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end
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end
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% Now assign back
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scan_parameter_values = ordered_scan_values;
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od_imgs = ordered_od_imgs;
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end
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%% Display Images
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figure(1)
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clf
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set(gcf,'Position',[50 50 950 750])
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% Get image size in pixels
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[Ny, Nx] = size(od_imgs{1});
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% Define pixel size and magnification (if not already defined earlier)
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dx = pixel_size / magnification; % e.g. in meters
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dy = dx; % assuming square pixels
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% Define x and y axes in μm (centered at image center)
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x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6; % micrometers
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y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
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% Display the cropped image
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for k = 1 : length(od_imgs)
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imagesc(x, y, od_imgs{k});
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hold on;
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% Convert pixel grid to µm (already done: x and y axes)
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% Draw ↘ diagonal (top-left to bottom-right)
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drawODOverlays(x(1), y(1), x(end), y(end));
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% Draw ↙ diagonal (top-right to bottom-left)
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drawODOverlays(x(end), y(1), x(1), y(end));
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hold off;
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axis equal tight;
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colormap(Colormaps.inferno());
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set(gca, 'FontSize', 14, 'YDir', 'normal');
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if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
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text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.1f^\\circ')], ...
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'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
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'Interpreter', 'tex', 'Units', 'normalized', ...
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'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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else
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text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
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'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
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'Interpreter', 'tex', 'Units', 'normalized', ...
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'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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end
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colorbarHandle = colorbar;
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ylabel(colorbarHandle, 'Optical Density', 'Rotation', -90, 'FontSize', 14, 'FontName', font);
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xlabel('x (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('y (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('OD Image', 'FontSize', 16, 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontName', font);
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drawnow;
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pause(0.5);
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end
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%% Helper Functions
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function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
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% image must be a 2D numerical array
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[dim1, dim2] = size(img);
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s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
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s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
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s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
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s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
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ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
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end
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function ret = subtractBackgroundOffset(img, fraction)
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% Remove the background from the image.
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% :param dataArray: The image
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% :type dataArray: xarray DataArray
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% :param x_fraction: The fraction of the pixels used in x axis
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% :type x_fraction: float
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% :param y_fraction: The fraction of the pixels used in y axis
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% :type y_fraction: float
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% :return: The image after removing background
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% :rtype: xarray DataArray
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x_fraction = fraction(1);
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y_fraction = fraction(2);
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offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
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ret = img - offset;
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end
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function ret = cropODImage(img, center, span)
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% Crop the image according to the region of interest (ROI).
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% :param dataSet: The images
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% :type dataSet: xarray DataArray or DataSet
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% :param center: The center of region of interest (ROI)
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% :type center: tuple
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% :param span: The span of region of interest (ROI)
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% :type span: tuple
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% :return: The cropped images
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% :rtype: xarray DataArray or DataSet
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x_start = floor(center(1) - span(1) / 2);
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x_end = floor(center(1) + span(1) / 2);
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y_start = floor(center(2) - span(2) / 2);
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y_end = floor(center(2) + span(2) / 2);
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ret = img(y_start:y_end, x_start:x_end);
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end
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function imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
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%CALCULATEODIMAGE Calculates the optical density (OD) image for absorption imaging.
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%
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% imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
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%
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% Inputs:
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% imageAtom - Image with atoms
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% imageBackground - Image without atoms
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% imageDark - Image without light
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% mode - 'LowIntensity' (default) or 'HighIntensity'
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% exposureTime - Required only for 'HighIntensity' [in seconds]
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%
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% Output:
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% imageOD - Computed OD image
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%
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arguments
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imageAtom (:,:) {mustBeNumeric}
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imageBackground (:,:) {mustBeNumeric}
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imageDark (:,:) {mustBeNumeric}
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mode char {mustBeMember(mode, {'LowIntensity', 'HighIntensity'})} = 'LowIntensity'
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exposureTime double = NaN
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end
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% Compute numerator and denominator
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numerator = imageBackground - imageDark;
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denominator = imageAtom - imageDark;
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% Avoid division by zero
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numerator(numerator == 0) = 1;
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denominator(denominator == 0) = 1;
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% Calculate OD based on mode
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switch mode
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case 'LowIntensity'
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imageOD = -log(abs(denominator ./ numerator));
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case 'HighIntensity'
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if isnan(exposureTime)
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error('Exposure time must be provided for HighIntensity mode.');
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end
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imageOD = abs(denominator ./ numerator);
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imageOD = -log(imageOD) + (numerator - denominator) ./ (7000 * (exposureTime / 5e-6));
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end
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end
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function drawODOverlays(x1, y1, x2, y2)
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% Parameters
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tick_spacing = 10; % µm between ticks
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tick_length = 2; % µm tick mark length
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line_color = [0.5 0.5 0.5];
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tick_color = [0.5 0.5 0.5];
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font_size = 10;
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% Vector from start to end
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dx = x2 - x1;
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dy = y2 - y1;
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L = sqrt(dx^2 + dy^2);
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% Unit direction vector along diagonal
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ux = dx / L;
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uy = dy / L;
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% Perpendicular unit vector for ticks
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perp_ux = -uy;
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perp_uy = ux;
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% Midpoint (center)
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xc = (x1 + x2) / 2;
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yc = (y1 + y2) / 2;
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% Number of positive and negative ticks
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n_ticks = floor(L / (2 * tick_spacing));
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% Draw main diagonal line
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plot([x1 x2], [y1 y2], '--', 'Color', line_color, 'LineWidth', 1.2);
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for i = -n_ticks:n_ticks
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d = i * tick_spacing;
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xt = xc + d * ux;
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yt = yc + d * uy;
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% Tick line endpoints
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xt1 = xt - 0.5 * tick_length * perp_ux;
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yt1 = yt - 0.5 * tick_length * perp_uy;
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xt2 = xt + 0.5 * tick_length * perp_ux;
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yt2 = yt + 0.5 * tick_length * perp_uy;
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% Draw tick
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plot([xt1 xt2], [yt1 yt2], '--', 'Color', tick_color, 'LineWidth', 1);
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% Label: centered at tick, offset slightly along diagonal
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if d ~= 0
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text(xt, yt, sprintf('%+d', d), ...
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'Color', tick_color, ...
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'FontSize', font_size, ...
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'HorizontalAlignment', 'center', ...
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'VerticalAlignment', 'bottom', ...
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'Rotation', atan2d(dy, dx));
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end
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end
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end
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function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
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% removefringesInImage - Fringe removal and noise reduction from absorption images.
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% Creates an optimal reference image for each absorption image in a set as
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% a linear combination of reference images, with coefficients chosen to
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% minimize the least-squares residuals between each absorption image and
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% the optimal reference image. The coefficients are obtained by solving a
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% linear set of equations using matrix inverse by LU decomposition.
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%
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% Application of the algorithm is described in C. F. Ockeloen et al, Improved
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% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
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%
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% Syntax:
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% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
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%
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% Required inputs:
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% absimages - Absorption image data,
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% typically 16 bit grayscale images
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% refimages - Raw reference image data
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% absimages and refimages are both cell arrays containing
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% 2D array data. The number of refimages can differ from the
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% number of absimages.
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%
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% Optional inputs:
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% bgmask - Array specifying background region used,
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% 1=background, 0=data. Defaults to all ones.
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% Outputs:
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% optrefimages - Cell array of optimal reference images,
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% equal in size to absimages.
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%
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% Dependencies: none
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%
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% Authors: Shannon Whitlock, Caspar Ockeloen
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% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
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% S. Whitlock, Improved detection of small atom numbers through
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% image processing, arXiv:1007.2136
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% Email:
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% May 2009; Last revision: 11 August 2010
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% Process inputs
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% Set variables, and flatten absorption and reference images
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nimgs = size(absimages,3);
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nimgsR = size(refimages,3);
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xdim = size(absimages(:,:,1),2);
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ydim = size(absimages(:,:,1),1);
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R = single(reshape(refimages,xdim*ydim,nimgsR));
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A = single(reshape(absimages,xdim*ydim,nimgs));
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optrefimages=zeros(size(absimages)); % preallocate
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if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
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k = find(bgmask(:)==1); % Index k specifying background region
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% Ensure there are no duplicate reference images
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% R=unique(R','rows')'; % comment this line if you run out of memory
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% Decompose B = R*R' using singular value or LU decomposition
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[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
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for j=1:nimgs
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b=R(k,:)'*A(k,j);
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% Obtain coefficients c which minimise least-square residuals
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lower.LT = true; upper.UT = true;
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c = linsolve(U,linsolve(L,b(p,:),lower),upper);
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% Compute optimised reference image
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optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
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end
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end
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