Latest scripts - added g2 correlation analysis
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@ -12,5 +12,6 @@ Time-Series-Analyzer/Time-Series-Data
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*.json
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*.txt
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*.asv
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*.fig
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.ipynb_checkpoints/
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.vscode/
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@ -5,69 +5,46 @@ set(groot, 'defaultAxesTickLabelInterpreter','latex'); set(groot, 'defaultLegend
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format long
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font = 'Bahnschrift';
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% Load data
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/B2.3G/WithoutProcessing/DropletsToStripes.mat', 'unique_theta', 'mean_sf', 'stderr_sf');
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/B2.45G/DropletsToStripes.mat', 'unique_scan_parameter_values', 'mean_sf', 'stderr_sf');
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down_scan_parameter_values = Data.unique_theta;
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dts_mean_sf = Data.mean_sf;
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down_stderr_sf = Data.stderr_sf;
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dts_scan_parameter_values = Data.unique_scan_parameter_values;
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dts_mean_sf = Data.mean_sf;
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dts_stderr_sf = Data.stderr_sf;
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/B2.3G/WithoutProcessing/StripesToDroplets.mat', 'unique_theta', 'mean_sf', 'stderr_sf');
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/B2.45G/StripesToDroplets.mat', 'unique_scan_parameter_values', 'mean_sf', 'stderr_sf');
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std_theta_values = Data.unique_theta;
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std_mean_sf = Data.mean_sf;
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std_stderr_sf = Data.stderr_sf;
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std_scan_parameter_values = Data.unique_scan_parameter_values;
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std_mean_sf = Data.mean_sf;
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std_stderr_sf = Data.stderr_sf;
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% Normalize dts data
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dts_min = min(dts_mean_sf);
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dts_max = max(dts_mean_sf);
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dts_range = dts_max - dts_min;
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dts_mean_sf_norm = (dts_mean_sf - dts_min) / dts_range;
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dts_stderr_sf_norm = dts_stderr_sf / dts_range;
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% Normalize std data
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std_min = min(std_mean_sf);
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std_max = max(std_mean_sf);
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std_range = std_max - std_min;
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std_mean_sf_norm = (std_mean_sf - std_min) / std_range;
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std_stderr_sf_norm = std_stderr_sf / std_range;
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figure(1);
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set(gcf,'Position',[100 100 950 750])
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errorbar(down_scan_parameter_values, dts_mean_sf, down_stderr_sf, 'o--', ...
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errorbar(dts_scan_parameter_values, dts_mean_sf_norm, dts_stderr_sf_norm, 'o--', ...
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'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName' , 'Droplets to Stripes');
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hold on
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errorbar(std_theta_values, std_mean_sf, std_stderr_sf, 'o--', ...
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errorbar(std_scan_parameter_values, std_mean_sf_norm, std_stderr_sf_norm, 'o--', ...
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'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName', 'Stripes to Droplets');
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set(gca, 'FontSize', 14); % For tick labels only
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hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
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hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
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hTitle = title('B = 2.3 G', 'Interpreter', 'tex');
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legend
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set([hXLabel, hYLabel], 'FontName', font)
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set([hXLabel, hYLabel], 'FontSize', 14)
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set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
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grid on
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%%
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%% Track spectral weight across the transition
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set(0,'defaulttextInterpreter','latex')
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set(groot, 'defaultAxesTickLabelInterpreter','latex'); set(groot, 'defaultLegendInterpreter','latex');
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format long
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% Load data
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/RampDownSL.mat', 'unique_scan_parameter_values', 'mean_sf', 'stderr_sf');
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down_scan_parameter_values = Data.unique_scan_parameter_values;
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down_mean_sf = Data.mean_sf;
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down_stderr_sf = Data.stderr_sf;
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Data = load('C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/RampUpSL.mat', 'unique_scan_parameter_values', 'mean_sf', 'stderr_sf');
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up_scan_parameter_values = Data.unique_scan_parameter_values;
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up_mean_sf = Data.mean_sf;
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up_stderr_sf = Data.stderr_sf;
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figure(1);
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set(gcf,'Position',[100 100 950 750])
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errorbar(down_scan_parameter_values, down_mean_sf, down_stderr_sf, 'o--', ...
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'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName' , 'BEC to Droplets');
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hold on
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errorbar(up_scan_parameter_values, up_mean_sf, up_stderr_sf, 'o--', ...
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'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5, 'DisplayName', 'Droplets to BEC');
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set(gca, 'FontSize', 14); % For tick labels only
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hXLabel = xlabel('B_z (G)', 'Interpreter', 'tex');
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hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
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hTitle = title('\alpha = 0', 'Interpreter', 'tex');
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hYLabel = ylabel('Normalized Spectral Weight', 'Interpreter', 'tex');
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hTitle = title('B = 2.45 G', 'Interpreter', 'tex');
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legend
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set([hXLabel, hYLabel], 'FontName', font)
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set([hXLabel, hYLabel], 'FontSize', 14)
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@ -4,27 +4,26 @@ groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axi
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/15042025/";
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folderPath = "D:/Data - Experiment/2025/05/22/";
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run = '0035';
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run = '0078';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1300, 2108];
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center = [1375, 2020];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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removeFringes = false;
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% scan_parameter = 'rot_mag_fin_pol_angle';
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scan_parameter = 'rot_mag_fin_pol_angle';
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% scan_parameter = 'rot_mag_field';
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scan_parameter = 'rot_mag_field_up';
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% scan_parameter_text = 'Angle = ';
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scan_parameter_text = 'BField = ';
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scan_parameter_text = 'Angle = ';
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% scan_parameter_text = 'BField = ';
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font = 'Bahnschrift';
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@ -47,9 +46,9 @@ for k = 1 : length(files)
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fprintf(1, 'Now reading %s\n', fullFileName);
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atm_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
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absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img), center, span), fraction)';
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@ -96,8 +95,13 @@ end
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%% Run Fourier analysis over images
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fft_imgs = cell(1, nimgs);
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spectral_weight = zeros(1, nimgs);
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fft_imgs = cell(1, nimgs);
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spectral_weight = zeros(1, nimgs);
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N_bins = 180;
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Threshold = 75;
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Sigma = 2;
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N_shots = length(od_imgs);
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% Create VideoWriter object for movie
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videoFile = VideoWriter('Single_Shot_FFT.mp4', 'MPEG-4');
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@ -106,8 +110,8 @@ videoFile.FrameRate = 2; % Set the frame rate (frames per second)
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open(videoFile); % Open the video file to write
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% Display the cropped image
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for k = 1 : length(od_imgs)
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IMG = od_imgs{k};
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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figure(1);
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@ -193,7 +197,7 @@ for k = 1 : length(od_imgs)
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% Plot the angular distribution
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nexttile
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[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, 180, 75, 2);
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[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, N_bins, Threshold, Sigma);
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spectral_weight(k) = trapz(theta_vals, S_theta);
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plot(theta_vals/pi, S_theta,'Linewidth',2);
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set(gca, 'FontSize', 14); % For tick labels only
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@ -237,8 +241,8 @@ set(gcf,'Position',[100 100 950 750])
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errorbar(unique_scan_parameter_values, mean_sf, stderr_sf, 'o--', ...
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'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 5);
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set(gca, 'FontSize', 14); % For tick labels only
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% hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
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hXLabel = xlabel('B_z (G)', 'Interpreter', 'tex');
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hXLabel = xlabel('\alpha (degrees)', 'Interpreter', 'tex');
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% hXLabel = xlabel('B_z (G)', 'Interpreter', 'tex');
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hYLabel = ylabel('Spectral Weight', 'Interpreter', 'tex');
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hTitle = title('Change across transition', 'Interpreter', 'tex');
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set([hXLabel, hYLabel], 'FontName', font)
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@ -305,6 +309,112 @@ set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font
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grid on;
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hold off;
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%% Extract g2
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fft_imgs = cell(1, nimgs);
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spectral_distribution = cell(1, nimgs);
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theta_values = cell(1, nimgs);
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N_bins = 32;
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Threshold = 75;
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Sigma = 2;
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N_shots = length(od_imgs);
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% Display the cropped image
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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% Calculate the x and y limits for the cropped image
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y_min = center(1) - span(2) / 2;
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y_max = center(1) + span(2) / 2;
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x_min = center(2) - span(1) / 2;
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x_max = center(2) + span(1) / 2;
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% Generate x and y arrays representing the original coordinates for each pixel
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x_range = linspace(x_min, x_max, span(1));
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y_range = linspace(y_min, y_max, span(2));
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[rows, cols] = size(IMGFFT);
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zoom_size = 50; % Zoomed-in region around center
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mid_x = floor(cols/2);
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mid_y = floor(rows/2);
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fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
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[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, N_bins, Threshold, Sigma);
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spectral_distribution{k} = S_theta;
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theta_values{k} = theta_vals;
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end
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% Create matrix of shape (N_shots x N_bins)
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delta_nkr_all = zeros(N_shots, N_bins);
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for k = 1:N_shots
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delta_nkr_all(k, :) = spectral_distribution{k};
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end
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% Grouping by scan parameter value (e.g., alpha)
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[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
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% Number of unique alpha values
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N_alpha = length(unique_scan_parameter_values);
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% Preallocate result arrays
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g2_all = zeros(N_alpha, N_bins);
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g2_error_all = zeros(N_alpha, N_bins);
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for i = 1:N_alpha
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group_idx = find(idx == i); % Indices of 20 shots for this alpha
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group_data = delta_nkr_all(group_idx, :); % (20 x N_bins) array
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for dtheta = 0:N_bins-1
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temp = zeros(length(group_idx), 1);
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for j = 1:length(group_idx)
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profile = group_data(j, :);
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profile_shifted = circshift(profile, -dtheta, 2);
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num = mean(profile .* profile_shifted);
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denom = mean(profile)^2;
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temp(j) = num / denom - 1;
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end
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g2_all(i, dtheta+1) = mean(temp);
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g2_error_all(i, dtheta+1) = std(temp) / sqrt(length(group_idx)); % Standard error
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end
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end
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% Reconstruct theta axis from any one of the stored values
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theta_vals = theta_values{1}; % assuming it's in radians
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% Number of unique alpha values
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nAlpha = size(g2_all, 1);
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% Generate a colormap with enough unique colors
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cmap = sky(nAlpha); % You can also try 'jet', 'turbo', 'hot', etc.
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figure(4);
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clf;
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set(gcf,'Position',[100 100 950 750])
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hold on;
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legend_entries = cell(nAlpha, 1);
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for i = 1:nAlpha
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errorbar(theta_vals/pi, g2_all(i, :), g2_error_all(i, :), ...
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'o-', 'Color', cmap(i,:), 'LineWidth', 1.2, ...
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'MarkerSize', 5, 'CapSize', 3);
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legend_entries{i} = sprintf('$\\alpha = %g^\\circ$', unique_scan_parameter_values(i));
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end
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ylim([-1.5 3.0]); % Set y-axis limits here
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set(gca, 'FontSize', 14);
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hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex');
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hYLabel = ylabel('$g^{(2)}(\delta\theta)$', 'Interpreter', 'latex');
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hTitle = title('B = 2.45 G - Droplets to Stripes', 'Interpreter', 'tex');
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legend(legend_entries, 'Interpreter', 'latex', 'Location', 'bestoutside');
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set([hXLabel, hYLabel], 'FontName', font)
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set([hXLabel, hYLabel], 'FontSize', 14)
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set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
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grid on;
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%% Helper Functions
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function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
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% computeFourierSpectrum - Computes the 2D Fourier power spectrum
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@ -398,7 +508,7 @@ function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IM
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theta_vals = linspace(0, pi, num_bins);
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% Loop through each angle bin
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for i = 1:180
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for i = 1:num_bins
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angle_start = (i-1) * pi / num_bins;
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angle_end = i * pi / num_bins;
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@ -1,8 +1,8 @@
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%% Parameters
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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"];
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "D:/Data - Experiment/2025/05/22/";
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@ -14,31 +14,12 @@ cam = 5;
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angle = 0;
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center = [1375, 2020];
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span = [150, 150];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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removeFringes = false;
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%{
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folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Imaging-Response-Function-Extractor/";
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run = '0096';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1137, 2023];
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span = [500, 500];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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removeFringes = false;
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%}
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%% Compute OD image, rotate and extract ROI for analysis
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% Get a list of all files in the folder with the desired file name pattern.
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@ -104,7 +85,7 @@ for k = 1 : length(od_imgs)
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hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
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set(hL,'Rotation',-90);
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colormap jet;
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set(gca,'CLim',[0 0.4]);
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% set(gca,'CLim',[0 0.4]);
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set(gca,'YDir','normal')
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set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
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set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
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@ -700,7 +700,7 @@ for j = 1:length(SCATTERING_LENGTH_RANGE)
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aS_string = sprintf('%.6e', aS);
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% Construct base directory for this aS
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baseDir = ['D:/Results - Numerics/Data_Full3D/PhaseDiagram/ImagTimePropagation/Theta0/HighN/aS_' ...
|
||||
baseDir = ['D:/Results - Numerics/Data_Full3D/PhaseDiagram/ImagTimePropagation/Theta0/aS_' ...
|
||||
aS_string '_theta_000_phi_000_N_'];
|
||||
|
||||
% Preallocate results for this curve
|
||||
|
||||
Loading…
Reference in New Issue
Block a user