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Karthik 2025-01-22 16:22:58 +01:00
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Data-Analyzer/plotImages.m Normal file
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%% 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

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%% 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

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Estimations/ToQuasi2D.nb Normal file
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