417 lines
14 KiB
Matlab
417 lines
14 KiB
Matlab
%% Parameters
|
|
|
|
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/2025/07/16/";
|
|
|
|
run = '0002';
|
|
|
|
folderPath = strcat(folderPath, run);
|
|
|
|
cam = 5;
|
|
|
|
angle = 0;
|
|
center = [1430, 2025];
|
|
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;
|
|
|
|
% Plotting and saving
|
|
scan_parameter = 'evap_rot_mag_field';
|
|
scan_groups = 0:10:50;
|
|
savefileName = 'Droplets';
|
|
font = 'Bahnschrift';
|
|
|
|
% Flags
|
|
skipUnshuffling = 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, 'rot_mag_fin_pol_angle')
|
|
scan_parameter_values(k) = 180 - info.Attributes(i).Value;
|
|
else
|
|
scan_parameter_values(k) = info.Attributes(i).Value;
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
% ===== Unshuffle if necessary to do so =====
|
|
|
|
if ~skipUnshuffling
|
|
n_values = length(scan_groups);
|
|
n_total = length(scan_parameter_values);
|
|
|
|
% Infer number of repetitions
|
|
n_reps = n_total / n_values;
|
|
|
|
% Preallocate ordered arrays
|
|
ordered_scan_values = zeros(1, n_total);
|
|
ordered_od_imgs = cell(1, n_total);
|
|
|
|
counter = 1;
|
|
|
|
for rep = 1:n_reps
|
|
for val = scan_groups
|
|
% Find the next unused match for this val
|
|
idx = find(scan_parameter_values == val, 1, 'first');
|
|
|
|
% Assign and remove from list to avoid duplicates
|
|
ordered_scan_values(counter) = scan_parameter_values(idx);
|
|
ordered_od_imgs{counter} = od_imgs{idx};
|
|
|
|
% Mark as used by removing
|
|
scan_parameter_values(idx) = NaN; % NaN is safe since original values are 0:5:45
|
|
od_imgs{idx} = []; % empty cell so it won't be matched again
|
|
|
|
counter = counter + 1;
|
|
end
|
|
end
|
|
|
|
% Now assign back
|
|
scan_parameter_values = ordered_scan_values;
|
|
od_imgs = ordered_od_imgs;
|
|
end
|
|
|
|
%% Display Images
|
|
|
|
figure(1)
|
|
clf
|
|
set(gcf,'Position',[50 50 950 750])
|
|
|
|
% Get image size in pixels
|
|
[Ny, Nx] = size(od_imgs{1});
|
|
|
|
% Define pixel size and magnification (if not already defined earlier)
|
|
dx = pixel_size / magnification; % e.g. in meters
|
|
dy = dx; % assuming square pixels
|
|
|
|
% Define x and y axes in μm (centered at image center)
|
|
x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6; % micrometers
|
|
y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
|
|
|
|
% Display the cropped image
|
|
for k = 1 : length(od_imgs)
|
|
imagesc(x, y, od_imgs{k});
|
|
hold on;
|
|
|
|
% Convert pixel grid to µm (already done: x and y axes)
|
|
% Draw ↘ diagonal (top-left to bottom-right)
|
|
drawODOverlays(x(1), y(1), x(end), y(end));
|
|
|
|
% Draw ↙ diagonal (top-right to bottom-left)
|
|
drawODOverlays(x(end), y(1), x(1), y(end));
|
|
|
|
hold off;
|
|
axis equal tight;
|
|
colormap(Colormaps.inferno());
|
|
set(gca, 'FontSize', 14, 'YDir', 'normal');
|
|
|
|
if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
|
|
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.1f^\\circ')], ...
|
|
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
|
|
'Interpreter', 'tex', 'Units', 'normalized', ...
|
|
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
|
else
|
|
text(0.975, 0.975, [num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
|
|
'Color', 'white', 'FontWeight', 'bold', 'FontSize', 24, ...
|
|
'Interpreter', 'tex', 'Units', 'normalized', ...
|
|
'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
|
|
end
|
|
|
|
colorbarHandle = colorbar;
|
|
ylabel(colorbarHandle, 'Optical Density', 'Rotation', -90, 'FontSize', 14, 'FontName', font);
|
|
|
|
xlabel('x (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
|
ylabel('y (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
|
|
title('OD Image', 'FontSize', 16, 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontName', font);
|
|
|
|
drawnow;
|
|
pause(0.5);
|
|
end
|
|
|
|
|
|
%% Helper Functions
|
|
|
|
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 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
|
|
|
|
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
|