diff --git a/Data-Analyzer/fourierAnalysis.m b/Data-Analyzer/fourierAnalysis.m
index a29b3d3..fc5aefc 100644
--- a/Data-Analyzer/fourierAnalysis.m
+++ b/Data-Analyzer/fourierAnalysis.m
@@ -1,14 +1,14 @@
 %% 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"];
+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/Data-Analyzer/";
 
+run            = '0013';
 
-folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/";
-
-run        = '0013';
-
-folderPath = strcat(folderPath, run);
+folderPath     = strcat(folderPath, run);
 
 cam            = 5; 
 
@@ -23,10 +23,10 @@ removeFringes  = false;
 %% 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));
+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;
@@ -80,7 +80,7 @@ end
 %% Run Fourier analysis over images
 
 fft_imgs                    = cell(1, nimgs);
-integrated_sf               = zeros(1, nimgs);
+spectral_weight             = zeros(1, nimgs);
 
 % Create VideoWriter object for movie
 videoFile = VideoWriter('Single_Shot_FFT.mp4', 'MPEG-4');
@@ -127,8 +127,8 @@ for k = 1 : length(od_imgs)
     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
-    hXLabel = xlabel('X (pixels)', 'Interpreter', 'tex');
-    hYLabel = ylabel('Y (pixels)', 'Interpreter', 'tex');
+    hXLabel = xlabel('x (pixels)', 'Interpreter', 'tex');
+    hYLabel = ylabel('y (pixels)', 'Interpreter', 'tex');
     hTitle = title('OD Image', 'Interpreter', 'tex');
     set([hXLabel, hYLabel, hL, hText], 'FontName', font)
     set([hXLabel, hYLabel, hL], 'FontSize', 14)
@@ -143,8 +143,8 @@ for k = 1 : length(od_imgs)
     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
-    hXLabel = xlabel('X (pixels)', 'Interpreter', 'tex');
-    hYLabel = ylabel('Y (pixels)', 'Interpreter', 'tex');
+    hXLabel = xlabel('x (pixels)', 'Interpreter', 'tex');
+    hYLabel = ylabel('y (pixels)', 'Interpreter', 'tex');
     hTitle = title('Denoised - Masked - Binarized', 'Interpreter', 'tex');
     
     set([hXLabel, hYLabel], 'FontName', font)
@@ -170,9 +170,9 @@ for k = 1 : length(od_imgs)
     colormap(ax3, 'jet');
     set(gca, 'FontSize', 14);   % For tick labels only
     set(gca,'YDir','normal')
-    hXLabel = xlabel('X (pixels)', 'Interpreter', 'tex');
-    hYLabel = ylabel('Y (pixels)', 'Interpreter', 'tex');
-    hTitle = title('Fourier Power Spectrum', 'Interpreter', 'tex');
+    hXLabel = xlabel('k_x', 'Interpreter', 'tex');
+    hYLabel = ylabel('k_y', 'Interpreter', 'tex');
+    hTitle  = title('Power Spectrum - |S(k_x,k_y)|^2', '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
@@ -190,13 +190,13 @@ 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);
+    [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(zoomedIMGFFT, 10, 20, 180, 75, 2);
+    spectral_weight(k)    = trapz(theta_vals, sqrt(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');
+    hYLabel = ylabel('Normalized magnitude (a.u.)', 'Interpreter', 'tex');
+    hTitle = title('Angular Spectral Distribution - |S(\theta)|^2', '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
@@ -213,9 +213,9 @@ end
 % Close the video file
 close(videoFile);
 
-%% Track integrated structure factor across the transition
+%% Track spectral weight across the transition
 
-% Assuming theta_values and integrated_sf are column vectors (or row vectors of same length)
+% Assuming theta_values and spectral_weight are column vectors (or row vectors of same length)
 [unique_theta, ~, idx] = unique(theta_values);
 
 % Preallocate arrays
@@ -224,7 +224,7 @@ 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);
+    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)
 end
@@ -235,7 +235,7 @@ 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');
+hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
 hTitle = title('Change during rotation', 'Interpreter', 'tex');
 set([hXLabel, hYLabel], 'FontName', font)
 set([hXLabel, hYLabel], 'FontSize', 14)
@@ -245,7 +245,7 @@ grid on
 %% k-means Clustering
 
 % Reshape to column vector
-X = mean_sf(:);
+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
@@ -254,10 +254,10 @@ optimalClusters = 3;  % Based on prior knowledge or experimentation
 rng(42);  
 
 % Specify initialization method ('plus' is the default)
-startMethod = 'plus';  % Options: 'uniform', 'plus', 'sample'
+startMethod     = 'plus';  % Options: 'uniform', 'plus', 'sample'
 
 % Apply K-means clustering with controlled initialization
-[idx, C] = kmeans(X, optimalClusters, 'Start', startMethod);
+[idx, C]        = kmeans(X, optimalClusters, 'Start', startMethod);
 
 % Plot the results
 figure(3);
@@ -272,10 +272,10 @@ errorbar(unique_theta, mean_sf, stderr_sf, 'o--', ...
 scatter(unique_theta, X, 50, idx, 'filled');
 
 % Get the current y-axis limits
-current_ylim = ylim;
+current_ylim    = ylim;
 
 % Generate colors for each cluster
-colors = lines(optimalClusters);  % Create distinct 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
@@ -293,7 +293,7 @@ for i = 1:optimalClusters
 end
 
 hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
-hYLabel = ylabel('Integrated S(\theta)', 'Interpreter', 'tex');
+hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
 hTitle = title('Regimes identified by k-means clustering', 'Interpreter', 'tex');
 set([hXLabel, hYLabel], 'FontName', font)
 set([hXLabel, hYLabel], 'FontSize', 14)
@@ -379,7 +379,7 @@ function [IMGFFT, IMGBIN] = computeFourierTransform(I)
 
 end
 
-function [theta_vals, S_theta] = computeAngularStructureFactor(IMGFFT, r_min, r_max, num_bins, threshold, sigma)
+function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma)
     % Apply threshold to isolate strong peaks
     IMGFFT(IMGFFT < threshold) = 0;
 
@@ -402,33 +402,32 @@ function [theta_vals, S_theta] = computeAngularStructureFactor(IMGFFT, r_min, r_
     % Loop through each angle bin
     for i = 1:180
         angle_start = (i-1) * pi / num_bins;
-        angle_end = i * pi / num_bins;
+        angle_end   = i * pi / num_bins;
         
         % Define a mask for the given angle range
-        angle_mask = (Theta >= angle_start & Theta < angle_end);
+        angle_mask  = (Theta >= angle_start & Theta < angle_end);
         
-        bin_mask = radial_mask & angle_mask;
+        bin_mask    = radial_mask & angle_mask;
 
         % Extract the Fourier components for the given angle
-        fft_angle = IMGFFT .* bin_mask;
+        fft_angle   = IMGFFT .* bin_mask;
         
         % Integrate the Fourier components over the radius at the angle
-        S_theta(i) = sum(sum(abs(fft_angle).^2)); % Compute structure factor (sum of squared magnitudes)
+        S_theta(i)  = sum(sum(abs(fft_angle).^2)); % sum of squared magnitudes
     end
     
     % Create a 1D Gaussian kernel
-    half_width = ceil(3 * sigma);
-    x = -half_width:half_width;
+    half_width   = ceil(3 * sigma);
+    x            = -half_width:half_width;
     gauss_kernel = exp(-x.^2 / (2 * sigma^2));
     gauss_kernel = gauss_kernel / sum(gauss_kernel); % normalize
     
     % Apply convolution (circular padding to preserve periodicity)
-    S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], gauss_kernel, 'same');
-    S_theta = S_theta(half_width+1:end-half_width); % crop back to original size
-
-    % Normalize to maximum value of 1
+    S_theta      = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], gauss_kernel, 'same');
+    S_theta      = S_theta(half_width+1:end-half_width); % crop back to original size
+    
+    % Normalize to 1
     S_theta = S_theta / max(S_theta);
-
 end
 
 function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)