diff --git a/Data-Analyzer/plotImages.m b/Data-Analyzer/plotImages.m new file mode 100644 index 0000000..f835ff9 --- /dev/null +++ b/Data-Analyzer/plotImages.m @@ -0,0 +1,223 @@ +%% 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 diff --git a/Estimations/CavityLaserCalibrationAndTest.m b/Estimations/CavityLaserCalibrationAndTest.m new file mode 100644 index 0000000..5428522 --- /dev/null +++ b/Estimations/CavityLaserCalibrationAndTest.m @@ -0,0 +1,100 @@ +%% Plot cavity signal +% Load the data from the CSV file +ScopeData = readmatrix('.csv'); + +% Extract Time and CavitySignal with offsets +Time = ScopeData(:, 1); +CavitySignal = ScopeData(:, 2); + +% Calculate xoffset and yoffset +xoffset = Time(1); +yoffset = min(CavitySignal); + +% Adjust CavitySignal and Time +CavitySignal = CavitySignal - yoffset; +Time = Time - xoffset; + +% Plot the data +figure; +scatter(Time, CavitySignal, 10, 'b', 'filled', 'MarkerFaceAlpha', 0.5); +grid on; + +% Format the plot +xlabel('\bf Time (s)', 'FontSize', 16); +ylabel('\bf Voltage (V)', 'FontSize', 16); +title('\bf Cavity Signal', 'FontSize', 16); +set(gca, 'FontSize', 12); + +%% Fit cavity signal + +% Extract signal cavity mode +tstartIdx = 1; +tendIdx = 50; +TruncatedScopeData = ScopeData(tstartIdx:tendIdx,:); + +% Fit and plot Airy function, extracting characteristic parameters +fitAndplotCavityMode(TruncatedScopeData); + + +%% Extract distance between consecutive cavity modes and their amplitudes + +% == Add two Airy functions to fit two consecutive cavity modes == % + +function fitAndplotCavityMode(dataset) + % Define the Airy function + AiryFunc = @(a, b, t) a ./ (1 + b * (sin(t / 2)).^2); + + % Perform non-linear fitting to find parameters a and b + t = dataset(:, 1); + CavitySignal = dataset(:, 2); + fitParams = fit(t, CavitySignal, @(a, b, t) AiryFunc(a, b, t), ... + 'StartPoint', [1, 1]); + a = fitParams.a; + b = fitParams.b; % Coefficient of finesse from fit + + % Calculate reflectivity (r) + syms r; + Reflectivity = solve(b == (4 * r) / (1 - r)^2, r); + + % Calculate finesse from reflectivity (r) + Finesse = (pi * sqrt(Reflectivity))/(1 - Reflectivity); + + % Generate fitted data + fitData = [t, AiryFunc(a, b, t)]; + + % Find FWHM + maxSignal = max(fitData(:, 2)); + left = find(fitData(:, 2) >= maxSignal / 2, 1, 'first'); + right = find(fitData(:, 2) >= maxSignal / 2, 1, 'last'); + FWHM = fitData(right, 1) - fitData(left, 1); + + % Calculate FSR from Finesse and FWHM + FSR = Finesse * FWHM; + + % Plot the original data and the fitted curve + figure; + plot(dataset(:, 1), dataset(:, 2), 'o', 'MarkerSize', 5, ... + 'MarkerEdgeColor', 'blue', 'MarkerFaceColor', 'blue', ... + 'DisplayName', 'Cavity Signal', 'MarkerFaceAlpha', 0.5); + hold on; + plot(fitData(:, 1), fitData(:, 2), '--', 'LineWidth', 1.5, ... + 'Color', [1, 0.5, 0], 'DisplayName', 'Best Fit'); + hold off; + + % Customize the plot + grid on; + xlabel('\bf Time (s)', 'FontSize', 16); + ylabel('\bf Voltage (V)', 'FontSize', 16); + title('\bf Airy Function Fit', 'FontSize', 16); + legend('show', 'Location', 'Best'); + + % Annotate the plot with fit results + annotation('textbox', [0.6, 0.7, 0.3, 0.2], ... + 'String', sprintf(['Reflectivity = %.1f\n', ... + 'Finesse = %.1f\n', ... + 'FWHM = %.2f \n', ... + 'FSR = %.2f'], ... + Reflectivity, Finesse, FWHM, FSR), ... + 'EdgeColor', 'none', ... + 'FontSize', 12); +end diff --git a/Estimations/ToQuasi2D.nb b/Estimations/ToQuasi2D.nb new file mode 100644 index 0000000..39adda3 --- /dev/null +++ b/Estimations/ToQuasi2D.nb @@ -0,0 +1,2789 @@ +(* Content-type: application/vnd.wolfram.mathematica *) + +(*** Wolfram Notebook File ***) +(* http://www.wolfram.com/nb *) + +(* CreatedBy='Mathematica 13.2' *) + +(*CacheID: 234*) +(* Internal cache information: +NotebookFileLineBreakTest +NotebookFileLineBreakTest +NotebookDataPosition[ 158, 7] +NotebookDataLength[ 103927, 2781] +NotebookOptionsPosition[ 88773, 2516] +NotebookOutlinePosition[ 89229, 2533] +CellTagsIndexPosition[ 89186, 2530] 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