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Updated Fourier analysis script, image plotting script.

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This commit is contained in:
Karthik 2025-04-23 15:33:57 +02:00
parent 88febbd045
commit 4918ee1ec0
2 changed files with 114 additions and 185 deletions
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271
Data-Analyzer/fourierAnalysis.m
View File

@ -4,22 +4,35 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axi
"/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/Data-Analyzer/";
folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/15042025/";
run = '0013';
run = '0035';
folderPath = strcat(folderPath, run);
cam = 5;
angle = 0;
center = [1285, 2105];
center = [1300, 2108];
span = [200, 200];
fraction = [0.1, 0.1];
pixel_size = 5.86e-6;
removeFringes = false;
% scan_parameter = 'rot_mag_fin_pol_angle';
% scan_parameter = 'rot_mag_field';
scan_parameter = 'rot_mag_field_up';
% scan_parameter_text = 'Angle = ';
scan_parameter_text = 'BField = ';
font = 'Bahnschrift';
skipPreprocessing = true;
skipMasking = true;
skipIntensityThresholding = true;
skipBinarization = true;
%% Compute OD image, rotate and extract ROI for analysis
% Get a list of all files in the folder with the desired file name pattern.
@ -63,7 +76,7 @@ else
end
%% Get rotation angles
theta_values = zeros(1, length(files));
scan_parameter_values = zeros(1, length(files));
% Get information about the '/globals' group
for k = 1 : length(files)
@ -71,8 +84,12 @@ for k = 1 : length(files)
fullFileName = fullfile(files(k).folder, baseFileName);
info = h5info(fullFileName, '/globals');
for i = 1:length(info.Attributes)
if strcmp(info.Attributes(i).Name, 'rot_mag_fin_pol_angle')
theta_values(k) = 180 - info.Attributes(i).Value;
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
@ -91,13 +108,12 @@ open(videoFile); % Open the video file to write
% Display the cropped image
for k = 1 : length(od_imgs)
IMG = od_imgs{k};
[IMGFFT, IMGBIN] = computeFourierTransform(IMG);
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
figure(1);
clf
set(gcf,'Position',[500 100 1000 800])
t = tiledlayout(2, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); % 1x4 grid
font = 'Bahnschrift';
% Calculate the x and y limits for the cropped image
y_min = center(1) - span(2) / 2;
@ -109,14 +125,14 @@ for k = 1 : length(od_imgs)
x_range = linspace(x_min, x_max, span(1));
y_range = linspace(y_min, y_max, span(2));
% Display the cropped image
% Display the cropped OD image
ax1 = nexttile;
imagesc(x_range, y_range, IMG)
% Define normalized positions (relative to axis limits)
x_offset = 0.025; % 5% offset from the edges
y_offset = 0.025; % 5% offset from the edges
% Top-right corner (normalized axis coordinates)
hText = text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
hText = text(1 - x_offset, 1 - y_offset, [scan_parameter_text, num2str(scan_parameter_values(k), '%.1f')], ...
'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
axis equal tight;
hcb = colorbar;
@ -134,8 +150,9 @@ for k = 1 : length(od_imgs)
set([hXLabel, hYLabel, hL], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
% Plot the processed image
ax2 = nexttile;
imagesc(x_range, y_range, IMGBIN)
imagesc(x_range, y_range, IMGPR)
axis equal tight;
hcb = colorbar;
colormap(ax2, 'parula');
@ -145,26 +162,23 @@ for k = 1 : length(od_imgs)
set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
hXLabel = xlabel('x (pixels)', 'Interpreter', 'tex');
hYLabel = ylabel('y (pixels)', 'Interpreter', 'tex');
hTitle = title('Denoised - Masked - Binarized', 'Interpreter', 'tex');
hTitle = title('Processed Image', 'Interpreter', 'tex');
set([hXLabel, hYLabel], 'FontName', font)
set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
% Plot the power spectrum
ax3 = nexttile;
[rows, cols] = size(IMGFFT);
zoom_size = 50; % Zoomed-in region around center
mid_x = floor(cols/2);
mid_y = floor(rows/2);
zoomedIMGFFT = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
fft_imgs{k} = zoomedIMGFFT;
imagesc(log(1 + zoomedIMGFFT));
fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
imagesc(log(1 + abs(fft_imgs{k}).^2));
% Define normalized positions (relative to axis limits)
x_offset = 0.025; % 5% offset from the edges
y_offset = 0.025; % 5% offset from the edges
% Top-right corner (normalized axis coordinates)
% hText = text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
% 'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
axis equal tight;
hcb = colorbar;
colormap(ax3, 'jet');
@ -172,31 +186,20 @@ for k = 1 : length(od_imgs)
set(gca,'YDir','normal')
hXLabel = xlabel('k_x', 'Interpreter', 'tex');
hYLabel = ylabel('k_y', 'Interpreter', 'tex');
hTitle = title('Power Spectrum - |S(k_x,k_y)|^2', 'Interpreter', 'tex');
hTitle = title('Power Spectrum - S(k_x,k_y)', 'Interpreter', 'tex');
set([hXLabel, hYLabel, hText], 'FontName', font)
set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
% Plot the angular distribution
%{
nexttile
[theta_vals, angular_intensity] = computeAngularDistribution(zoomedIMGFFT, 10, 20, 100, 75);
polarhistogram('BinEdges', theta_vals, 'BinCounts', angular_intensity, ...
'FaceColor', [0.2 0.6 0.9], 'EdgeColor', 'k');
set(gca, 'FontSize', 14); % For tick labels only
hTitle = title('Angular Distribution', 'Interpreter', 'tex');
set(hTitle, 'FontName', font)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
%}
% Plot the angular structure factor
nexttile
[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(zoomedIMGFFT, 10, 20, 180, 75, 2);
spectral_weight(k) = trapz(theta_vals, sqrt(S_theta));
[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, 180, 75, 2);
spectral_weight(k) = trapz(theta_vals, S_theta);
plot(theta_vals/pi, S_theta,'Linewidth',2);
set(gca, 'FontSize', 14); % For tick labels only
hXLabel = xlabel('\theta (\pi)', 'Interpreter', 'tex');
hXLabel = xlabel('\theta/\pi [rad]', 'Interpreter', 'tex');
hYLabel = ylabel('Normalized magnitude (a.u.)', 'Interpreter', 'tex');
hTitle = title('Angular Spectral Distribution - |S(\theta)|^2', 'Interpreter', 'tex');
hTitle = title('Angular Spectral Distribution - S(\theta)', 'Interpreter', 'tex');
set([hXLabel, hYLabel, hText], 'FontName', font)
set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
@ -215,15 +218,15 @@ close(videoFile);
%% Track spectral weight across the transition
% Assuming theta_values and spectral_weight are column vectors (or row vectors of same length)
[unique_theta, ~, idx] = unique(theta_values);
% Assuming scan_parameter_values and spectral_weight are column vectors (or row vectors of same length)
[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
% Preallocate arrays
mean_sf = zeros(size(unique_theta));
stderr_sf = zeros(size(unique_theta));
mean_sf = zeros(size(unique_scan_parameter_values));
stderr_sf = zeros(size(unique_scan_parameter_values));
% Loop through each unique theta and compute mean and standard error
for i = 1:length(unique_theta)
for i = 1:length(unique_scan_parameter_values)
group_vals = spectral_weight(idx == i);
mean_sf(i) = mean(group_vals);
stderr_sf(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
@ -231,12 +234,13 @@ end
figure(2);
set(gcf,'Position',[100 100 950 750])
errorbar(unique_theta, mean_sf, stderr_sf, 'o--', ...
errorbar(unique_scan_parameter_values, mean_sf, stderr_sf, 'o--', ...
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
set(gca, 'FontSize', 14); % For tick labels only
hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
% hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
hXLabel = xlabel('B_z (G)', 'Interpreter', 'tex');
hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
hTitle = title('Change during rotation', 'Interpreter', 'tex');
hTitle = title('Change across transition', 'Interpreter', 'tex');
set([hXLabel, hYLabel], 'FontName', font)
set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
@ -265,11 +269,11 @@ set(gcf,'Position',[100 100 950 750])
hold on;
% Plot error bars with mean_sf and stderr_sf
errorbar(unique_theta, mean_sf, stderr_sf, 'o--', ...
errorbar(unique_scan_parameter_values, mean_sf, stderr_sf, 'o--', ...
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
% Scatter plot for data points (showing clusters)
scatter(unique_theta, X, 50, idx, 'filled');
scatter(unique_scan_parameter_values, X, 50, idx, 'filled');
% Get the current y-axis limits
current_ylim = ylim;
@ -283,8 +287,8 @@ for i = 1:optimalClusters
clusterIdx = find(idx == i);
% Find the range of x-values for this cluster
x_min = unique_theta(clusterIdx(1)); % Starting x-value for the cluster
x_max = unique_theta(clusterIdx(end)); % Ending x-value for the cluster
x_min = unique_scan_parameter_values(clusterIdx(1)); % Starting x-value for the cluster
x_max = unique_scan_parameter_values(clusterIdx(end)); % Ending x-value for the cluster
% Fill the region corresponding to the cluster
fill([x_min, x_max, x_max, x_min], ...
@ -302,7 +306,7 @@ grid on;
hold off;
%% Helper Functions
function [IMGFFT, IMGBIN] = computeFourierTransform(I)
function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
% computeFourierSpectrum - Computes the 2D Fourier power spectrum
% of binarized and enhanced lattice image features, with optional central mask.
%
@ -312,71 +316,65 @@ function [IMGFFT, IMGBIN] = computeFourierTransform(I)
% Output:
% F_mag - 2D Fourier power spectrum (shifted)
% Preprocessing: Denoise
filtered = imgaussfilt(I, 10);
I_filt = I - filtered; % adjust sigma as needed
if ~skipPreprocessing
% Preprocessing: Denoise
filtered = imgaussfilt(I, 10);
IMGPR = I - filtered; % adjust sigma as needed
else
IMGPR = I;
end
% Elliptical mask parameters
[rows, cols] = size(I_filt);
[X, Y] = meshgrid(1:cols, 1:rows);
cx = cols / 2;
cy = rows / 2;
if ~skipMasking
[rows, cols] = size(IMGPR);
[X, Y] = meshgrid(1:cols, 1:rows);
% Elliptical mask parameters
cx = cols / 2;
cy = rows / 2;
% Shifted coordinates
x = X - cx;
y = Y - cy;
% Shifted coordinates
x = X - cx;
y = Y - cy;
% Ellipse semi-axes
rx = 0.4 * cols;
ry = 0.2 * rows;
% Ellipse semi-axes
rx = 0.4 * cols;
ry = 0.2 * rows;
% Rotation angle in degrees -> radians
theta_deg = 30; % Adjust as needed
theta = deg2rad(theta_deg);
% Rotation angle in degrees -> radians
theta_deg = 30; % Adjust as needed
theta = deg2rad(theta_deg);
% Rotated ellipse equation
cos_t = cos(theta);
sin_t = sin(theta);
% Rotated ellipse equation
cos_t = cos(theta);
sin_t = sin(theta);
x_rot = (x * cos_t + y * sin_t);
y_rot = (-x * sin_t + y * cos_t);
x_rot = (x * cos_t + y * sin_t);
y_rot = (-x * sin_t + y * cos_t);
ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
% Apply cutout mask
I_masked = I_filt .* ellipseMask;
% Apply cutout mask
IMGPR = IMGPR .* ellipseMask;
end
% Apply global intensity threshold mask
intensity_thresh = 0.20;
intensity_mask = I_masked > intensity_thresh;
I_masked = I_masked .* intensity_mask;
% Adaptive binarization and cleanup
IMGBIN = imbinarize(I_masked, 'adaptive', 'Sensitivity', 0.0);
IMGBIN = imdilate(IMGBIN, strel('disk', 2));
IMGBIN = imerode(IMGBIN, strel('disk', 1));
IMGBIN = imfill(IMGBIN, 'holes');
% Compute 2D Fourier Transform
F = fft2(double(I));
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
% Define the radius for the circular region to exclude
region_radius = 4; % Adjust the radius as needed
% Create a circular mask
[~, center_idx] = max(IMGFFT(:));
[cx, cy] = ind2sub(size(IMGFFT), center_idx);
% Equation for a circle (centered at cx, cy)
center_region = (X - cx).^2 + (Y - cy).^2 <= region_radius^2;
% Define a scaling factor for the central region (e.g., reduce amplitude by 90%)
scaling_factor = 0.1; % Scale center region by 10%
% Apply the scaling factor to the center region
IMGFFT(center_region) = IMGFFT(center_region) * scaling_factor;
if ~skipIntensityThresholding
% Apply global intensity threshold mask
intensity_thresh = 0.20;
intensity_mask = IMGPR > intensity_thresh;
IMGPR = IMGPR .* intensity_mask;
end
if ~skipBinarization
% Adaptive binarization and cleanup
IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
IMGPR = imdilate(IMGPR, strel('disk', 2));
IMGPR = imerode(IMGPR, strel('disk', 1));
IMGPR = imfill(IMGPR, 'holes');
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
else
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
end
end
function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma)
@ -610,76 +608,7 @@ for k = 1 : length(od_imgs)
end
%}
%% Averaged FFT
%{
% Assuming od_imgs is a cell array of size 4*n
n = length(fft_imgs) / 4; % Calculate n
fft_imgs_avg = cell(1, n); % Initialize the new cell array to hold the averaged images
for i = 1:n
% Take the 4 corresponding images from od_imgs
img1 = fft_imgs{4*i-3}; % 1st image in the group
img2 = fft_imgs{4*i-2}; % 2nd image in the group
img3 = fft_imgs{4*i-1}; % 3rd image in the group
img4 = fft_imgs{4*i}; % 4th image in the group
% Compute the average of these 4 images
avg_img = (img1 + img2 + img3 + img4) / 4;
% Store the averaged image in the new cell array
fft_imgs_avg{i} = avg_img;
end
% Create VideoWriter object for movie
videoFile = VideoWriter('Averaged_FFT.mp4', 'MPEG-4');
videoFile.Quality = 100; % Set quality to maximum (0–100)
videoFile.FrameRate = 2; % Set the frame rate (frames per second)
open(videoFile); % Open the video file to write
% Display the cropped image
for k = 1 : length(fft_imgs_avg)
figure(3)
clf
set(gcf,'Position',[50 50 1500 550])
set(gca,'FontSize',16,'Box','On','Linewidth',2);
t = tiledlayout(1, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); % 1x2 grid
nexttile
imagesc(log(1 + fft_imgs_avg{k}));
% Define normalized positions (relative to axis limits)
x_offset = 0.025; % 5% offset from the edges
y_offset = 0.025; % 5% offset from the edges
% Top-right corner (normalized axis coordinates)
text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
axis equal tight;
hcb = colorbar;
set(gca,'YDir','normal')
xlabel('X', 'Interpreter', 'tex','FontSize',16);
ylabel('Y', 'Interpreter', 'tex','FontSize',16);
title('Averaged Fourier Power Spectrum','FontSize',16);
% Plot the angular structure factor
nexttile
[theta_vals, angular_intensity] = computeAngularDistribution(fft_imgs_avg{k}, 10, 20, 100, 75);
polarhistogram('BinEdges', theta_vals, 'BinCounts', angular_intensity, ...
'FaceColor', [0.2 0.6 0.9], 'EdgeColor', 'k');
title('Angular Distribution');
drawnow
pause(0.5)
% Capture the current frame and write it to the video
frame = getframe(gcf); % Capture the current figure as a frame
writeVideo(videoFile, frame); % Write the frame to the video
end
% Close the video file
close(videoFile);
%}
%% Angular Distribution
%{

8
Data-Analyzer/plotImages.m
View File

@ -6,15 +6,15 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Ca
folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/";
run = '0013';
run = '0060';
folderPath = strcat(folderPath, run);
cam = 5;
angle = 0;
center = [1285, 2105];
span = [200, 200];
center = [1630, 1700];
span = [500, 500];
fraction = [0.1, 0.1];
pixel_size = 5.86e-6;
@ -104,7 +104,7 @@ for k = 1 : length(od_imgs)
hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
set(hL,'Rotation',-90);
colormap jet;
set(gca,'CLim',[0 3.0]);
set(gca,'CLim',[0 0.4]);
set(gca,'YDir','normal')
set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels