Integrated S-theta, k-means clustering code added.
This commit is contained in:
parent
50722c5140
commit
d4c0dc3f07
@ -80,6 +80,7 @@ end
|
||||
%% Run Fourier analysis over images
|
||||
|
||||
fft_imgs = cell(1, nimgs);
|
||||
integrated_sf = zeros(1, nimgs);
|
||||
|
||||
% Create VideoWriter object for movie
|
||||
videoFile = VideoWriter('Single_Shot_FFT.mp4', 'MPEG-4');
|
||||
@ -92,7 +93,7 @@ for k = 1 : length(od_imgs)
|
||||
IMG = od_imgs{k};
|
||||
[IMGFFT, IMGBIN] = computeFourierTransform(IMG);
|
||||
|
||||
figure(2);
|
||||
figure(1);
|
||||
clf
|
||||
set(gcf,'Position',[500 100 1000 800])
|
||||
t = tiledlayout(2, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); % 1x4 grid
|
||||
@ -146,8 +147,8 @@ for k = 1 : length(od_imgs)
|
||||
hYLabel = ylabel('Y (pixels)', 'Interpreter', 'tex');
|
||||
hTitle = title('Denoised - Masked - Binarized', 'Interpreter', 'tex');
|
||||
|
||||
set([hXLabel, hYLabel, hL], 'FontName', font)
|
||||
set([hXLabel, hYLabel, hL], 'FontSize', 14)
|
||||
set([hXLabel, hYLabel], 'FontName', font)
|
||||
set([hXLabel, hYLabel], 'FontSize', 14)
|
||||
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
|
||||
|
||||
ax3 = nexttile;
|
||||
@ -172,11 +173,11 @@ for k = 1 : length(od_imgs)
|
||||
hXLabel = xlabel('X (pixels)', 'Interpreter', 'tex');
|
||||
hYLabel = ylabel('Y (pixels)', 'Interpreter', 'tex');
|
||||
hTitle = title('Fourier Power Spectrum', 'Interpreter', 'tex');
|
||||
set([hXLabel, hYLabel, hL, hText], 'FontName', font)
|
||||
set([hXLabel, hYLabel, hL], 'FontSize', 14)
|
||||
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 structure factor
|
||||
% Plot the angular distribution
|
||||
%{
|
||||
nexttile
|
||||
[theta_vals, angular_intensity] = computeAngularDistribution(zoomedIMGFFT, 10, 20, 100, 75);
|
||||
@ -190,19 +191,17 @@ for k = 1 : length(od_imgs)
|
||||
% Plot the angular structure factor
|
||||
nexttile
|
||||
[theta_vals, S_theta] = computeAngularStructureFactor(zoomedIMGFFT, 10, 20, 180, 75, 2);
|
||||
|
||||
integrated_sf(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');
|
||||
hYLabel = ylabel('S(\theta)', 'Interpreter', 'tex');
|
||||
hTitle = title('Angular Structure Factor', 'Interpreter', 'tex');
|
||||
set([hXLabel, hYLabel, hL, hText], 'FontName', font)
|
||||
set([hXLabel, hYLabel, hL], 'FontSize', 14)
|
||||
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
|
||||
grid on
|
||||
|
||||
|
||||
|
||||
drawnow
|
||||
pause(0.5)
|
||||
|
||||
@ -214,6 +213,94 @@ end
|
||||
% Close the video file
|
||||
close(videoFile);
|
||||
|
||||
%% Track integrated structure factor across the transition
|
||||
|
||||
% Assuming theta_values and integrated_sf are column vectors (or row vectors of same length)
|
||||
[unique_theta, ~, idx] = unique(theta_values);
|
||||
|
||||
% Preallocate arrays
|
||||
mean_sf = zeros(size(unique_theta));
|
||||
stderr_sf = zeros(size(unique_theta));
|
||||
|
||||
% Loop through each unique theta and compute mean and standard error
|
||||
for i = 1:length(unique_theta)
|
||||
group_vals = integrated_sf(idx == i);
|
||||
mean_sf(i) = mean(group_vals);
|
||||
stderr_sf(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
|
||||
end
|
||||
|
||||
figure(2);
|
||||
set(gcf,'Position',[100 100 950 750])
|
||||
errorbar(unique_theta, 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');
|
||||
hYLabel = ylabel('Integrated S(\theta)', 'Interpreter', 'tex');
|
||||
hTitle = title('Change during rotation', '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
|
||||
grid on
|
||||
|
||||
%% k-means Clustering
|
||||
|
||||
% Reshape to column vector
|
||||
X = mean_sf(:);
|
||||
|
||||
% Determine the number of clusters to try (you can experiment with different values here)
|
||||
optimalClusters = 3; % Based on prior knowledge or experimentation
|
||||
|
||||
% Set the random seed for reproducibility
|
||||
rng(42);
|
||||
|
||||
% Specify initialization method ('plus' is the default)
|
||||
startMethod = 'plus'; % Options: 'uniform', 'plus', 'sample'
|
||||
|
||||
% Apply K-means clustering with controlled initialization
|
||||
[idx, C] = kmeans(X, optimalClusters, 'Start', startMethod);
|
||||
|
||||
% Plot the results
|
||||
figure(3);
|
||||
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--', ...
|
||||
'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
|
||||
|
||||
% Scatter plot for data points (showing clusters)
|
||||
scatter(unique_theta, X, 50, idx, 'filled');
|
||||
|
||||
% Get the current y-axis limits
|
||||
current_ylim = ylim;
|
||||
|
||||
% Generate colors for each cluster
|
||||
colors = lines(optimalClusters); % Create distinct colors for each cluster
|
||||
|
||||
% Loop through each cluster and fill the regions
|
||||
for i = 1:optimalClusters
|
||||
% Find indices of data points that belong to the current cluster
|
||||
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
|
||||
|
||||
% Fill the region corresponding to the cluster
|
||||
fill([x_min, x_max, x_max, x_min], ...
|
||||
[current_ylim(1), current_ylim(1), current_ylim(2), current_ylim(2)], ...
|
||||
colors(i, :), 'EdgeColor', 'none', 'FaceAlpha', 0.3); % Add transparency
|
||||
end
|
||||
|
||||
hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
|
||||
hYLabel = ylabel('Integrated S(\theta)', 'Interpreter', 'tex');
|
||||
hTitle = title('Regimes identified by k-means clustering', '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
|
||||
grid on;
|
||||
hold off;
|
||||
|
||||
%% Helper Functions
|
||||
function [IMGFFT, IMGBIN] = computeFourierTransform(I)
|
||||
% computeFourierSpectrum - Computes the 2D Fourier power spectrum
|
||||
|
||||
Loading…
Reference in New Issue
Block a user