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Changes to analyze datasets

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Karthik 2025-07-11 16:33:03 +02:00
parent 25e3bade5e
commit 96c0a50beb
3 changed files with 46 additions and 42 deletions
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64
Data-Analyzer/analyzeFolder.m
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@ -180,7 +180,11 @@ function results = analyzeFolder(options)
for k = 1:N_shots
IMG = od_imgs{k};
[IMGFFT, ~] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
if ~(max(IMG(:)) > 1)
IMGFFT = NaN(size(IMG));
else
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
end
% Size of original image (in pixels)
[Ny, Nx] = size(IMG);
@ -214,7 +218,7 @@ function results = analyzeFolder(options)
kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
[theta_vals, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, r_min, r_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
[theta_vals, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, kx, ky, k_min, k_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
[k_rho_vals, S_k] = computeRadialSpectralDistribution(fft_imgs{k}, kx, ky, theta_min, theta_max, N_radial_bins);
S_k_smoothed = movmean(S_k, Radial_WindowSize); % Compute moving average (use convolution) or use conv for more control
spectral_distribution{k} = S_theta;
@ -234,8 +238,8 @@ function results = analyzeFolder(options)
% Loop through each unique theta and compute mean and standard error
for i = 1:length(unique_scan_parameter_values)
group_vals = radial_spectral_contrast(idx == i);
mean_rsc(i) = mean(group_vals);
stderr_rsc(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
mean_rsc(i) = mean(group_vals, 'omitnan');
stderr_rsc(i) = std(group_vals, 'omitnan') / sqrt(length(group_vals)); % standard error = std / sqrt(N)
end
% Preallocate arrays
@ -245,8 +249,8 @@ function results = analyzeFolder(options)
% Loop through each unique theta and compute mean and standard error
for i = 1:length(unique_scan_parameter_values)
group_vals = angular_spectral_weight(idx == i);
mean_asw(i) = mean(group_vals);
stderr_asw(i) = std(group_vals) / sqrt(length(group_vals)); % standard error = std / sqrt(N)
mean_asw(i) = mean(group_vals, 'omitnan');
stderr_asw(i) = std(group_vals, 'omitnan') / sqrt(length(group_vals)); % standard error = std / sqrt(N)
end
% Convert spectral distribution to matrix (N_shots x N_angular_bins)
@ -314,8 +318,8 @@ function results = analyzeFolder(options)
sec_window = mod((shifted_idx - window_size):(shifted_idx + window_size) - 1, N_angular_bins) + 1;
sec = profile(sec_window);
num = mean(ref .* sec);
denom = mean(ref.^2);
num = mean(ref .* sec, 'omitnan');
denom = mean(ref.^2, 'omitnan');
g2 = num / denom;
correlations(k) = g2;
@ -456,52 +460,50 @@ function [k_rho_vals, S_radial] = computeRadialSpectralDistribution(IMGFFT, kx,
end
end
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma, windowSize)
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, kx, ky, k_min, k_max, num_bins, threshold, sigma, windowSize)
% Apply threshold to isolate strong peaks
IMGFFT(IMGFFT < threshold) = 0;
% Prepare polar coordinates
[ny, nx] = size(IMGFFT);
[X, Y] = meshgrid(1:nx, 1:ny);
cx = ceil(nx/2);
cy = ceil(ny/2);
R = sqrt((X - cx).^2 + (Y - cy).^2);
Theta = atan2(Y - cy, X - cx); % range [-pi, pi]
% Create wavenumber meshgrid
[KX, KY] = meshgrid(kx, ky);
Kmag = sqrt(KX.^2 + KY.^2); % radial wavenumber magnitude
Theta = atan2(KY, KX); % range [-pi, pi]
% Choose radial band
radial_mask = (R >= r_min) & (R <= r_max);
% Restrict to radial band in wavenumber space
radial_mask = (Kmag >= k_min) & (Kmag <= k_max);
% Initialize angular structure factor
S_theta = zeros(1, num_bins);
theta_vals = linspace(0, pi, num_bins);
S_theta = zeros(1, num_bins);
theta_vals = linspace(0, pi, num_bins); % only 0 to pi due to symmetry
% Loop through angle bins
% Loop over angular bins
for i = 1:num_bins
angle_start = (i-1) * pi / num_bins;
angle_start = (i - 1) * pi / num_bins;
angle_end = i * pi / num_bins;
angle_mask = (Theta >= angle_start & Theta < angle_end);
angle_mask = (Theta >= angle_start) & (Theta < angle_end);
bin_mask = radial_mask & angle_mask;
fft_angle = IMGFFT .* bin_mask;
S_theta(i) = sum(sum(abs(fft_angle).^2));
end
% Smooth using either Gaussian or moving average
% Optional smoothing
if exist('sigma', 'var') && ~isempty(sigma)
% Gaussian convolution
% Gaussian smoothing
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);
% Circular convolution
S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], ...
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);
S_theta = S_theta(half_width + 1:end - half_width);
elseif exist('windowSize', 'var') && ~isempty(windowSize)
% Moving average via convolution (circular)
pad = floor(windowSize / 2);
% Moving average smoothing
pad = floor(windowSize / 2);
kernel = ones(1, windowSize) / windowSize;
S_theta = conv([S_theta(end-pad+1:end), S_theta, S_theta(1:pad)], kernel, 'same');
S_theta = S_theta(pad+1:end-pad);
S_theta = conv([S_theta(end - pad + 1:end), S_theta, S_theta(1:pad)], kernel, 'same');
S_theta = S_theta(pad + 1:end - pad);
end
end

8
Data-Analyzer/conductSpectralAnalysis.m
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@ -179,7 +179,7 @@ else
end
end
% ===== Get rotation angles =====
%% ===== Get rotation angles =====
scan_parameter_values = zeros(1, length(files));
% Get information about the '/globals' group
@ -264,7 +264,11 @@ s_theta_list = cell(1, N_shots); % Angular spectrum
for k = 1:N_shots
IMG = od_imgs{k};
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
if ~(max(IMG(:)) > 1)
IMGFFT = NaN(size(IMG));
else
[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
end
% Size of original image (in pixels)
[Ny, Nx] = size(IMG);

14
Data-Analyzer/extractQuantities.m
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@ -2,8 +2,6 @@
% === Define folders and settings ===
% === Define folders and settings ===
baseFolder = '//DyLabNAS/Data/TwoDGas/2025/04/';
dates = ["01", "02"]; % Example: three folders
@ -39,8 +37,8 @@ options.Radial_WindowSize = 5; % Must be odd
% Fourier analysis: Angular
options.r_min = 10;
options.r_max = 20;
options.k_min = 1.2; % in μm⁻¹
options.k_max = 2.2; % in μm⁻¹
options.k_min = 1.2771; % in μm⁻¹
options.k_max = 2.5541; % in μm⁻¹
options.N_angular_bins = 180;
options.Angular_Threshold = 75;
options.Angular_Sigma = 2;
@ -125,7 +123,7 @@ set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
colorbar;
%% HEat map of angular spectral weight
%% Heat map of angular spectral weight
N_x = length(options.scan_groups);
N_y = length(results_all);
@ -158,7 +156,7 @@ set([hXLabel, hYLabel], 'FontSize', 14)
set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
colorbar;
%% HEat map of radial spectral contrast
%% Heat map of radial spectral contrast
N_x = length(options.scan_groups);
N_y = length(results_all);
@ -178,12 +176,12 @@ end
font = 'Bahnschrift';
figure(2)
figure(3)
clf
set(gcf,'Position',[50 50 950 750])
imagesc(options.scan_groups, BFields, radial_spectral_contrast_matrix);
colormap(sky);
clim([0 0.005])
clim([0 0.008])
set(gca, 'FontSize', 14, 'YDir', 'normal');
hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
hYLabel = ylabel('BField (G)', 'Interpreter', 'tex');