diff --git a/Estimations/CavityLaserCalibrationAndTest.m b/Estimations/ExtractParametersFromCavityModeProfile.m similarity index 100% rename from Estimations/CavityLaserCalibrationAndTest.m rename to Estimations/ExtractParametersFromCavityModeProfile.m diff --git a/plotImages.m b/plotImages.m deleted file mode 100644 index f835ff9..0000000 --- a/plotImages.m +++ /dev/null @@ -1,223 +0,0 @@ -%% 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 = "C:/Users/Karthik/Documents/GitRepositories/Calculations/24/"; - -run = '0086'; - -folderPath = strcat(folderPath, run); - -cam = 5; - -angle = 90; -center = [2100, 1150]; -span = [500, 500]; -fraction = [0.1, 0.1]; - -pixel_size = 4.6e-6; - -%% 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 = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle)); - bkg_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle)); - dark_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle)); - - refimages(:,:,k) = subtract_offset(crop_image(bkg_img, center, span), fraction); - absimages(:,:,k) = subtract_offset(crop_image(calculate_OD(atm_img, bkg_img, dark_img), center, span), fraction); - -end -%% Fringe removal - -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 - -%% -figure(1) -clf -r = 120; -x = 250; -y = 250; -for k = 1 : length(od_imgs) - imagesc(xvals, yvals, od_imgs{k}) - hold on - th = 0:pi/50:2*pi; - xunit = r * cos(th) + x; - yunit = r * sin(th) + y; - h = plot(xunit, yunit, Color='yellow'); - xlabel('µm', 'FontSize', 16) - ylabel('µm', 'FontSize', 16) - 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') - title('DMD projection: Circle of radius 200 pixels', 'FontSize', 16); - - drawnow; -end - -%% Helper Functions - -function ret = get_offset_from_corner(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 = subtract_offset(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 = get_offset_from_corner(img, x_fraction, y_fraction); - ret = img - offset; -end - -function ret = crop_image(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 ret = calculate_OD(imageAtom, imageBackground, imageDark) - % Calculate the OD image for absorption imaging. - % :param imageAtom: The image with atoms - % :type imageAtom: numpy array - % :param imageBackground: The image without atoms - % :type imageBackground: numpy array - % :param imageDark: The image without light - % :type imageDark: numpy array - % :return: The OD images - % :rtype: numpy array - - numerator = imageBackground - imageDark; - denominator = imageAtom - imageDark; - - numerator(numerator == 0) = 1; - denominator(denominator == 0) = 1; - - ret = -log(double(abs(denominator ./ numerator))); - - if numel(ret) == 1 - ret = ret(1); - 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 \ No newline at end of file