Added routines to analyze radial spectral distributions and corrected other bugs.
This commit is contained in:
parent
44296e216c
commit
e671d12969
50
Data-Analyzer/+Analyzer/computeSpectralAverages.m
Normal file
50
Data-Analyzer/+Analyzer/computeSpectralAverages.m
Normal file
@ -0,0 +1,50 @@
|
|||||||
|
function results = computeSpectralAverages(S_all, scan_reference_values)
|
||||||
|
%% computeSpectralAverages
|
||||||
|
% Compute mean and SEM of spectra grouped by scan parameter.
|
||||||
|
% Handles both 1D curves and 2D matrices.
|
||||||
|
|
||||||
|
[uniqueParams, ~, idx] = unique(scan_reference_values);
|
||||||
|
nParams = numel(uniqueParams);
|
||||||
|
nTotal = numel(S_all);
|
||||||
|
nReps = nTotal / nParams;
|
||||||
|
|
||||||
|
% Determine data dimensionality
|
||||||
|
firstData = S_all{1};
|
||||||
|
dataSize = size(firstData);
|
||||||
|
isMatrix = (numel(size(firstData)) == 2) && all(size(firstData) > 1);
|
||||||
|
|
||||||
|
curves_all = cell(1, nParams);
|
||||||
|
curves_mean = cell(1, nParams);
|
||||||
|
curves_err = cell(1, nParams);
|
||||||
|
|
||||||
|
for i = 1:nParams
|
||||||
|
if isMatrix
|
||||||
|
% --- Case: 2D matrix (e.g., power spectra) ---
|
||||||
|
data_sum = 0;
|
||||||
|
for r = 1:nReps
|
||||||
|
idx_r = (r-1)*nParams + i;
|
||||||
|
data_sum = data_sum + S_all{idx_r};
|
||||||
|
end
|
||||||
|
avg = data_sum / nReps;
|
||||||
|
curves_all{i} = []; % not used for 2D
|
||||||
|
curves_mean{i} = avg;
|
||||||
|
curves_err{i} = [];
|
||||||
|
else
|
||||||
|
% --- Case: 1D curve ---
|
||||||
|
nPoints = numel(S_all{1});
|
||||||
|
curves = zeros(nReps, nPoints);
|
||||||
|
for r = 1:nReps
|
||||||
|
idx_r = (r-1)*nParams + i;
|
||||||
|
curves(r,:) = S_all{idx_r};
|
||||||
|
end
|
||||||
|
curves_all{i} = curves;
|
||||||
|
curves_mean{i} = mean(curves,1);
|
||||||
|
curves_err{i} = std(curves,0,1)/sqrt(nReps);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
results.curves_all = curves_all;
|
||||||
|
results.curves_mean = curves_mean;
|
||||||
|
results.curves_error = curves_err;
|
||||||
|
results.scan_parameters = uniqueParams;
|
||||||
|
end
|
||||||
@ -0,0 +1,95 @@
|
|||||||
|
function results = extractFullRadialSpectralDistribution(od_imgs, options)
|
||||||
|
%% extractFullRadialSpectralDistribution
|
||||||
|
% Author: Karthik
|
||||||
|
% Date: 2025-10-15
|
||||||
|
% Version: 1.0
|
||||||
|
%
|
||||||
|
% Description:
|
||||||
|
% Computes the radial spectral distribution S(k) from OD images
|
||||||
|
%
|
||||||
|
% Inputs:
|
||||||
|
% od_imgs - cell array of OD images
|
||||||
|
% options - struct containing relevant parameters:
|
||||||
|
% pixel_size, magnification, zoom_size,
|
||||||
|
% theta_min, theta_max, N_radial_bins, Radial_WindowSize
|
||||||
|
%
|
||||||
|
% Outputs:
|
||||||
|
% results - struct containing k_rho values and radial spectra
|
||||||
|
%
|
||||||
|
% Notes:
|
||||||
|
% This function is a minimal variant of conductSpectralAnalysis,
|
||||||
|
% stopping right after radial spectral dsitribution extraction.
|
||||||
|
|
||||||
|
%% ===== Unpack options =====
|
||||||
|
pixel_size = options.pixel_size;
|
||||||
|
magnification = options.magnification;
|
||||||
|
zoom_size = options.zoom_size;
|
||||||
|
theta_min = options.theta_min;
|
||||||
|
theta_max = options.theta_max;
|
||||||
|
N_radial_bins = options.N_radial_bins;
|
||||||
|
radial_window_size = options.Radial_WindowSize;
|
||||||
|
skipPreprocessing = options.skipPreprocessing;
|
||||||
|
skipMasking = options.skipMasking;
|
||||||
|
skipIntensityThresholding = options.skipIntensityThresholding;
|
||||||
|
skipBinarization = options.skipBinarization;
|
||||||
|
|
||||||
|
%% ===== Initialization =====
|
||||||
|
N_shots = length(od_imgs);
|
||||||
|
fft_imgs = cell(1, N_shots);
|
||||||
|
S_k_all = cell(1, N_shots);
|
||||||
|
S_k_smoothed_all = cell(1, N_shots);
|
||||||
|
k_rho_vals = [];
|
||||||
|
|
||||||
|
%% ===== Main loop =====
|
||||||
|
for k = 1:N_shots
|
||||||
|
IMG = od_imgs{k};
|
||||||
|
|
||||||
|
% Skip empty or low-intensity images
|
||||||
|
if ~(max(IMG(:)) > 1)
|
||||||
|
IMGFFT = NaN(size(IMG));
|
||||||
|
else
|
||||||
|
% Compute FFT (with same preprocessing pipeline)
|
||||||
|
[IMGFFT, ~] = Calculator.computeFourierTransform(IMG, ...
|
||||||
|
skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
|
||||||
|
end
|
||||||
|
|
||||||
|
% Image dimensions
|
||||||
|
[Ny, Nx] = size(IMG);
|
||||||
|
dx = pixel_size / magnification;
|
||||||
|
dy = dx;
|
||||||
|
|
||||||
|
% Reciprocal-space sampling
|
||||||
|
dvx = 1 / (Nx * dx);
|
||||||
|
dvy = 1 / (Ny * dy);
|
||||||
|
vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
|
||||||
|
vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
|
||||||
|
|
||||||
|
% Convert to wavenumber axes [µm⁻¹]
|
||||||
|
kx_full = 2 * pi * vx * 1E-6;
|
||||||
|
ky_full = 2 * pi * vy * 1E-6;
|
||||||
|
|
||||||
|
% Crop FFT and wavenumber axes around center
|
||||||
|
mid_x = floor(Nx/2);
|
||||||
|
mid_y = floor(Ny/2);
|
||||||
|
fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
|
||||||
|
kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
|
||||||
|
ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
|
||||||
|
|
||||||
|
% ===== Compute radial spectrum =====
|
||||||
|
[k_rho_vals, S_k] = Calculator.computeRadialSpectralDistribution(...
|
||||||
|
fft_imgs{k}, kx, ky, theta_min, theta_max, N_radial_bins);
|
||||||
|
|
||||||
|
% Smooth radial spectrum
|
||||||
|
S_k_smoothed = movmean(S_k, radial_window_size);
|
||||||
|
|
||||||
|
% Store results
|
||||||
|
S_k_all{k} = S_k;
|
||||||
|
S_k_smoothed_all{k} = S_k_smoothed;
|
||||||
|
end
|
||||||
|
|
||||||
|
%% ===== Package results =====
|
||||||
|
results = struct();
|
||||||
|
results.k_rho_vals = k_rho_vals;
|
||||||
|
results.S_k_all = S_k_all;
|
||||||
|
results.S_k_smoothed_all = S_k_smoothed_all;
|
||||||
|
end
|
||||||
@ -1,5 +1,5 @@
|
|||||||
function [fitResults, rawCurves] = fitTwoGaussianCurves(S_theta_all, theta_vals, varargin)
|
function [fitResults, rawCurves] = fitTwoGaussianCurvesToAngularSpectralDistribution(S_theta_all, theta_vals, varargin)
|
||||||
%% fitTwoGaussianCurves
|
%% fitTwoGaussianCurvesToAngularSpectralDistribution
|
||||||
% Fits a two-Gaussian model to multiple spectral curves from θ=0 up to
|
% Fits a two-Gaussian model to multiple spectral curves from θ=0 up to
|
||||||
% the first secondary peak ≥50% of the primary peak, within 0–π/2 radians.
|
% the first secondary peak ≥50% of the primary peak, within 0–π/2 radians.
|
||||||
%
|
%
|
||||||
@ -0,0 +1,146 @@
|
|||||||
|
function [fitResults, rawCurves] = fitTwoGaussianCurvesToRadialSpectralDistribution(S_k_all, k_rho_vals, varargin)
|
||||||
|
%% fitTwoGaussianCurvesToRadialSpectralDistribution
|
||||||
|
% Fits a two-Gaussian model to multiple radial spectral curves (S(k_rho)),
|
||||||
|
% truncating each curve to user-specified k_rho and amplitude ranges.
|
||||||
|
%
|
||||||
|
% Author: Karthik
|
||||||
|
% Date: 2025-10-15
|
||||||
|
% Version: 1.0
|
||||||
|
%
|
||||||
|
% Inputs:
|
||||||
|
% S_k_all - 1xN cell array of radial spectral curves
|
||||||
|
% k_rho_vals - vector of corresponding k_rho values
|
||||||
|
%
|
||||||
|
% Optional name-value pairs:
|
||||||
|
% 'KRhoRange' - [k_min, k_max] (default: [min(k_rho_vals), max(k_rho_vals)])
|
||||||
|
% 'AmplitudeRange' - [y_min, y_max] (default: [0, Inf])
|
||||||
|
% 'ResidualThreshold', 'PositionThreshold', 'AmplitudeThreshold' as before
|
||||||
|
%
|
||||||
|
% Outputs:
|
||||||
|
% fitResults - struct array with fields:
|
||||||
|
% .pFit, .kFit, .xFit, .yFit, .kFine, .isValid, .fitMaxKRho
|
||||||
|
% rawCurves - struct array of truncated raw curves for plotting
|
||||||
|
|
||||||
|
% --- Parse optional inputs ---
|
||||||
|
p = inputParser;
|
||||||
|
addParameter(p, 'KRhoRange', [min(k_rho_vals), max(k_rho_vals)], @(x) isnumeric(x) && numel(x)==2);
|
||||||
|
addParameter(p, 'AmplitudeRange', [0, Inf], @(x) isnumeric(x) && numel(x)==2);
|
||||||
|
addParameter(p, 'ResidualThreshold', 0.3, @(x) isnumeric(x) && isscalar(x));
|
||||||
|
addParameter(p, 'PositionThreshold', 0.1, @(x) isnumeric(x) && isscalar(x));
|
||||||
|
addParameter(p, 'AmplitudeThreshold', 0.5, @(x) isnumeric(x) && isscalar(x));
|
||||||
|
parse(p, varargin{:});
|
||||||
|
opts = p.Results;
|
||||||
|
|
||||||
|
Ncurves = numel(S_k_all);
|
||||||
|
fitResults = struct('pFit',[],'kFit',[],'xFit',[],'yFit',[],...
|
||||||
|
'kFine',[],'isValid',[],'fitMaxKRho',[]);
|
||||||
|
rawCurves = struct('x', cell(1, Ncurves), 'k', cell(1, Ncurves));
|
||||||
|
|
||||||
|
for k = 1:Ncurves
|
||||||
|
% --- Truncate curve to k_rho and amplitude ranges ---
|
||||||
|
xRaw = S_k_all{k};
|
||||||
|
% --- Normalize and smooth ---
|
||||||
|
xNorm = xRaw / max(xRaw);
|
||||||
|
xSmooth = smooth(xNorm, 5, 'moving');
|
||||||
|
|
||||||
|
mask = (k_rho_vals(:) >= opts.KRhoRange(1)) & (k_rho_vals(:) <= opts.KRhoRange(2)) & ...
|
||||||
|
(xSmooth(:) >= opts.AmplitudeRange(1)) & (xSmooth(:) <= opts.AmplitudeRange(2));
|
||||||
|
|
||||||
|
x = xSmooth(mask);
|
||||||
|
kVals = k_rho_vals(mask);
|
||||||
|
|
||||||
|
rawCurves(k).x = x;
|
||||||
|
rawCurves(k).k = kVals;
|
||||||
|
|
||||||
|
if isempty(x)
|
||||||
|
warning('Curve %d is empty after truncation.', k);
|
||||||
|
fitResults(k) = fillZeroStructRadial(kVals, opts.KRhoRange(2));
|
||||||
|
continue;
|
||||||
|
elseif numel(x) < 5
|
||||||
|
warning('Curve %d has too few points after truncation, skipping.', k);
|
||||||
|
fitResults(k) = fillNaNStructRadial();
|
||||||
|
continue;
|
||||||
|
end
|
||||||
|
|
||||||
|
try
|
||||||
|
% --- Detect peaks ---
|
||||||
|
curveAmp = max(x) - min(x);
|
||||||
|
minProm = opts.AmplitudeThreshold * curveAmp;
|
||||||
|
[pk, locIdx] = findpeaks(x, 'MinPeakProminence', minProm);
|
||||||
|
kPeaks = kVals(locIdx);
|
||||||
|
|
||||||
|
%% Generic two-Gaussian
|
||||||
|
twoGauss = @(p,k) ...
|
||||||
|
p(1)*exp(-0.5*((k - p(2))/max(p(3),1e-6)).^2) + ...
|
||||||
|
p(4)*exp(-0.5*((k - p(5))/max(p(6),1e-6)).^2);
|
||||||
|
|
||||||
|
secondaryIdx = find(kPeaks > opts.PositionThreshold, 1, 'first');
|
||||||
|
if ~isempty(secondaryIdx)
|
||||||
|
kSecondary = kPeaks(secondaryIdx);
|
||||||
|
secondaryAmp = pk(secondaryIdx);
|
||||||
|
A1_guess = opts.AmplitudeRange(2);
|
||||||
|
mu1_guess = 0.0;
|
||||||
|
sigma1_guess = 0.1 * kSecondary;
|
||||||
|
A2_guess = secondaryAmp;
|
||||||
|
mu2_guess = kSecondary;
|
||||||
|
sigma2_guess = 0.1 * kSecondary;
|
||||||
|
p0 = [A1_guess, mu1_guess, sigma1_guess, A2_guess, mu2_guess, sigma2_guess];
|
||||||
|
else
|
||||||
|
p0 = [opts.AmplitudeRange(2), 0, 0.1, 0.01, opts.KRhoRange(2)/3, 0.1];
|
||||||
|
end
|
||||||
|
|
||||||
|
lb = [0, 0, 1e-6, 0, opts.PositionThreshold, 1e-6];
|
||||||
|
ub = [2, opts.KRhoRange(2)/2, opts.KRhoRange(2), 2, opts.KRhoRange(2), opts.KRhoRange(2)];
|
||||||
|
optsLSQ = optimoptions('lsqcurvefit','Display','off', ...
|
||||||
|
'MaxFunctionEvaluations',1e4,'MaxIterations',1e4);
|
||||||
|
pFit = lsqcurvefit(twoGauss, p0, kVals(:), x(:), lb, ub, optsLSQ);
|
||||||
|
|
||||||
|
if pFit(4) <= 1e-3
|
||||||
|
pFit(4) = 0;
|
||||||
|
pFit(5:6) = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
fitMaxKRho = max(pFit(5), pFit(2));
|
||||||
|
kFine = linspace(min(kVals), max(kVals), 500);
|
||||||
|
yFit = twoGauss(pFit, kFine);
|
||||||
|
|
||||||
|
fitResults(k).pFit = pFit;
|
||||||
|
fitResults(k).kFit = kVals;
|
||||||
|
fitResults(k).xFit = x;
|
||||||
|
fitResults(k).kFine = kFine;
|
||||||
|
fitResults(k).yFit = yFit;
|
||||||
|
fitResults(k).isValid = true;
|
||||||
|
fitResults(k).fitMaxKRho = fitMaxKRho;
|
||||||
|
|
||||||
|
catch
|
||||||
|
warning('Curve %d fit failed completely.', k);
|
||||||
|
fitResults(k) = fillNaNStructRadial();
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% --- Helper: NaN struct for failed fits ---
|
||||||
|
function s = fillNaNStructRadial()
|
||||||
|
s = struct( ...
|
||||||
|
'pFit', nan(1,6), ...
|
||||||
|
'kFit', nan, ...
|
||||||
|
'xFit', nan, ...
|
||||||
|
'yFit', nan, ...
|
||||||
|
'kFine', nan, ...
|
||||||
|
'isValid', false, ...
|
||||||
|
'fitMaxKRho', nan ...
|
||||||
|
);
|
||||||
|
end
|
||||||
|
|
||||||
|
%% --- Helper: Zero struct for empty curves ---
|
||||||
|
function s = fillZeroStructRadial(kVals, MaxKRho)
|
||||||
|
s = struct( ...
|
||||||
|
'pFit', zeros(1,6), ...
|
||||||
|
'kFit', kVals, ...
|
||||||
|
'xFit', zeros(size(kVals)), ...
|
||||||
|
'yFit', zeros(size(kVals)), ...
|
||||||
|
'kFine', zeros(1,500), ...
|
||||||
|
'isValid', false, ...
|
||||||
|
'fitMaxKRho', MaxKRho ...
|
||||||
|
);
|
||||||
|
end
|
||||||
166
Data-Analyzer/+Plotter/plotSecondaryGaussianRange.m
Normal file
166
Data-Analyzer/+Plotter/plotSecondaryGaussianRange.m
Normal file
@ -0,0 +1,166 @@
|
|||||||
|
function plotSecondaryGaussianRange(fitResults, scanValues, varargin)
|
||||||
|
%% plotSecondaryGaussianRange
|
||||||
|
% Plots secondary Gaussian ranges (mu2 ± sigma2) in a heatmap style
|
||||||
|
% exactly like plotFitParameterPDF, with dashed lines for distribution
|
||||||
|
% edges and solid line for mean mu2.
|
||||||
|
|
||||||
|
% --- Parse optional inputs ---
|
||||||
|
p = inputParser;
|
||||||
|
addParameter(p, 'Title', '', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'XLabel', '', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'YLabel', '', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'FigNum', 1, @(x) isscalar(x));
|
||||||
|
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||||
|
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||||
|
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
||||||
|
addParameter(p, 'SaveFileName', 'SecondaryGaussianRange.fig', @ischar);
|
||||||
|
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||||
|
addParameter(p, 'NumPoints', 200, @(x) isscalar(x));
|
||||||
|
addParameter(p, 'DataRange', [], @(x) isempty(x) || numel(x)==2);
|
||||||
|
addParameter(p, 'XLim', [], @(x) isempty(x) || numel(x)==2);
|
||||||
|
addParameter(p, 'YLim', [], @(x) isempty(x) || numel(x)==2);
|
||||||
|
addParameter(p, 'Colormap', @jet);
|
||||||
|
addParameter(p, 'PlotType', 'histogram', @(x) any(validatestring(x,{'kde','histogram'})));
|
||||||
|
addParameter(p, 'NumberOfBins', 50, @isscalar);
|
||||||
|
addParameter(p, 'NormalizeHistogram', true, @islogical);
|
||||||
|
addParameter(p, 'OverlayMeanSEM', true, @islogical);
|
||||||
|
parse(p, varargin{:});
|
||||||
|
opts = p.Results;
|
||||||
|
|
||||||
|
% --- Determine repetitions and scan parameters ---
|
||||||
|
N_params = numel(scanValues);
|
||||||
|
N_total = numel(fitResults);
|
||||||
|
N_reps = N_total / N_params;
|
||||||
|
|
||||||
|
% --- Extract mu2 and sigma2 ---
|
||||||
|
mu2_values = nan(N_reps, N_params);
|
||||||
|
sigma2_values = nan(N_reps, N_params);
|
||||||
|
for k = 1:N_total
|
||||||
|
paramIdxScan = mod(k-1, N_params) + 1;
|
||||||
|
repIdx = floor((k-1)/N_params) + 1;
|
||||||
|
mu2_values(repIdx, paramIdxScan) = fitResults(k).pFit(5);
|
||||||
|
sigma2_values(repIdx, paramIdxScan) = fitResults(k).pFit(6);
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Mask invalid fits ---
|
||||||
|
for i = 1:N_total
|
||||||
|
paramIdxScan = mod(i-1, N_params) + 1;
|
||||||
|
repIdx = floor((i-1)/N_params) + 1;
|
||||||
|
if ~fitResults(i).isValid && all(fitResults(i).pFit == 0)
|
||||||
|
mu2_values(repIdx, paramIdxScan) = NaN;
|
||||||
|
sigma2_values(repIdx, paramIdxScan) = NaN;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Compute lower and upper bounds ---
|
||||||
|
lowerBound = mu2_values - sigma2_values;
|
||||||
|
upperBound = mu2_values + sigma2_values;
|
||||||
|
|
||||||
|
% Define how much to trim (e.g., 2% on each side)
|
||||||
|
trimFraction = 0.01; % can tune this (0.01–0.05 typical)
|
||||||
|
|
||||||
|
% Compute robust global limits
|
||||||
|
globalLower = quantile(lowerBound(:), trimFraction);
|
||||||
|
globalUpper = quantile(upperBound(:), 1 - trimFraction);
|
||||||
|
|
||||||
|
fprintf('Global lower bound (%.0f%%): %.4f\n', trimFraction*100, globalLower);
|
||||||
|
fprintf('Global upper bound (%.0f%%): %.4f\n', (1-trimFraction)*100, globalUpper);
|
||||||
|
|
||||||
|
% --- Prepare data per scan parameter ---
|
||||||
|
dataCell = cell(N_params,1);
|
||||||
|
for i = 1:N_params
|
||||||
|
combinedData = [];
|
||||||
|
for j = 1:N_reps
|
||||||
|
if ~isnan(mu2_values(j,i)) && ~isnan(sigma2_values(j,i))
|
||||||
|
combinedData = [combinedData; linspace(lowerBound(j,i), upperBound(j,i), 3)']; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
end
|
||||||
|
dataCell{i} = combinedData;
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Determine y-range ---
|
||||||
|
if isempty(opts.DataRange)
|
||||||
|
allData = cell2mat(dataCell(:));
|
||||||
|
y_min = min(allData);
|
||||||
|
y_max = max(allData);
|
||||||
|
else
|
||||||
|
y_min = opts.DataRange(1);
|
||||||
|
y_max = opts.DataRange(2);
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Prepare PDF matrix ---
|
||||||
|
if strcmpi(opts.PlotType,'kde')
|
||||||
|
y_grid = linspace(y_min, y_max, opts.NumPoints);
|
||||||
|
pdf_matrix = zeros(numel(y_grid), N_params);
|
||||||
|
for i = 1:N_params
|
||||||
|
data = dataCell{i};
|
||||||
|
if isempty(data), continue; end
|
||||||
|
pdf_matrix(:,i) = ksdensity(data, y_grid);
|
||||||
|
end
|
||||||
|
else
|
||||||
|
edges = linspace(y_min, y_max, opts.NumberOfBins+1);
|
||||||
|
binCenters = (edges(1:end-1)+edges(2:end))/2;
|
||||||
|
pdf_matrix = zeros(numel(binCenters), N_params);
|
||||||
|
for i = 1:N_params
|
||||||
|
data = dataCell{i};
|
||||||
|
if isempty(data), continue; end
|
||||||
|
counts = histcounts(data, edges);
|
||||||
|
if opts.NormalizeHistogram
|
||||||
|
counts = counts / sum(counts);
|
||||||
|
end
|
||||||
|
pdf_matrix(:,i) = counts(:);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Mask out non-data regions ---
|
||||||
|
maskPerScan = cellfun(@(c) ~isempty(c), dataCell);
|
||||||
|
pdf_matrix(:, ~maskPerScan) = NaN;
|
||||||
|
|
||||||
|
% --- Plot heatmap ---
|
||||||
|
fig = figure(opts.FigNum); clf(fig);
|
||||||
|
set(fig, 'Color', 'w', 'Position', [100 100 950 750]);
|
||||||
|
|
||||||
|
if strcmpi(opts.PlotType,'kde')
|
||||||
|
h = imagesc(scanValues, y_grid, pdf_matrix);
|
||||||
|
set(h, 'AlphaData', ~isnan(pdf_matrix));
|
||||||
|
else
|
||||||
|
h = imagesc(scanValues, binCenters, pdf_matrix);
|
||||||
|
set(h, 'AlphaData', ~isnan(pdf_matrix));
|
||||||
|
end
|
||||||
|
|
||||||
|
colormap([1 1 1; feval(opts.Colormap)]); % white for NaNs
|
||||||
|
c = colorbar;
|
||||||
|
ylabel(c, 'Probability Density', 'Rotation', -90, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
|
|
||||||
|
set(gca, 'YDir','normal', 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
|
xlabel(opts.XLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
|
ylabel(opts.YLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
|
title(opts.Title, 'FontName', opts.FontName, 'FontSize', opts.FontSize+2, 'FontWeight','bold');
|
||||||
|
grid on; hold on;
|
||||||
|
|
||||||
|
% --- Overlay mu2 and distribution edges ---
|
||||||
|
meanMu2 = nanmean(mu2_values,1);
|
||||||
|
minLower = min(lowerBound,[],1);
|
||||||
|
maxUpper = max(upperBound,[],1);
|
||||||
|
|
||||||
|
plot(scanValues, meanMu2, 'k-', 'LineWidth', 2); % solid mu2
|
||||||
|
plot(scanValues, minLower, 'k--', 'LineWidth', 1.5); % dashed lower edge
|
||||||
|
plot(scanValues, maxUpper, 'k--', 'LineWidth', 1.5); % dashed upper edge
|
||||||
|
|
||||||
|
% --- Overlay mean ± SEM if requested ---
|
||||||
|
if opts.OverlayMeanSEM
|
||||||
|
semMu2 = nanstd(mu2_values,0,1) ./ sqrt(sum(~isnan(mu2_values),1));
|
||||||
|
xVec = reshape(scanValues,1,[]);
|
||||||
|
fill([xVec, fliplr(xVec)], [meanMu2 - semMu2, fliplr(meanMu2 + semMu2)], ...
|
||||||
|
[0.2 0.4 0.8], 'FaceAlpha',0.2, 'EdgeColor','none');
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Apply axis limits if specified ---
|
||||||
|
if ~isempty(opts.XLim)
|
||||||
|
xlim(opts.XLim);
|
||||||
|
end
|
||||||
|
if ~isempty(opts.YLim)
|
||||||
|
ylim(opts.YLim);
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
@ -1,5 +1,5 @@
|
|||||||
function plotAverageSpectra(scan_parameter_values, spectral_analysis_results, varargin)
|
function plotSpectralAverages(scan_parameter_values, results_full, varargin)
|
||||||
%% plotAverageSpectra
|
%% plotSpectralAverages
|
||||||
% Author: Karthik
|
% Author: Karthik
|
||||||
% Date: 2025-09-12
|
% Date: 2025-09-12
|
||||||
% Version: 1.0
|
% Version: 1.0
|
||||||
@ -13,7 +13,7 @@ function plotAverageSpectra(scan_parameter_values, spectral_analysis_results, va
|
|||||||
% kx, ky, PS_all, k_rho_vals, S_k_all, theta_vals, S_theta_all
|
% kx, ky, PS_all, k_rho_vals, S_k_all, theta_vals, S_theta_all
|
||||||
%
|
%
|
||||||
% Name-Value Pair Arguments:
|
% Name-Value Pair Arguments:
|
||||||
% 'ScanParameterName', 'FigNum', 'ColormapPS', 'Font',
|
% 'FigNum', 'ColormapPS', 'Font',
|
||||||
% 'SaveFileName', 'SaveDirectory', 'SkipSaveFigures'
|
% 'SaveFileName', 'SaveDirectory', 'SkipSaveFigures'
|
||||||
%
|
%
|
||||||
% Notes:
|
% Notes:
|
||||||
@ -23,78 +23,69 @@ function plotAverageSpectra(scan_parameter_values, spectral_analysis_results, va
|
|||||||
% Dataset colors are distinct
|
% Dataset colors are distinct
|
||||||
|
|
||||||
% --- Extract data from struct ---
|
% --- Extract data from struct ---
|
||||||
kx = spectral_analysis_results.kx;
|
kx = results_full.kx;
|
||||||
ky = spectral_analysis_results.ky;
|
ky = results_full.ky;
|
||||||
ps_list = spectral_analysis_results.PS_all;
|
ps_list = results_full.PS_all;
|
||||||
k_rho_vals = spectral_analysis_results.k_rho_vals;
|
k_rho_vals = results_full.k_rho_vals;
|
||||||
s_k_list = spectral_analysis_results.S_k_all;
|
s_k_list = results_full.S_k_all;
|
||||||
theta_vals = spectral_analysis_results.theta_vals;
|
theta_vals = results_full.theta_vals;
|
||||||
s_theta_list = spectral_analysis_results.S_theta_all;
|
s_theta_list = results_full.S_theta_all;
|
||||||
|
|
||||||
% --- Parse optional parameters ---
|
% --- Parse optional parameters ---
|
||||||
p = inputParser;
|
p = inputParser;
|
||||||
addParameter(p, 'ScanParameterName', 'ScanParameter', @ischar);
|
addParameter(p, 'Title', 'Average Curves', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'AnnotationSuffix', '%.1f^\\circ', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'ColormapPS', [], @(x) isnumeric(x) || ismatrix(x));
|
||||||
|
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||||
|
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||||
addParameter(p, 'FigNum', 1, @(x) isnumeric(x) && isscalar(x));
|
addParameter(p, 'FigNum', 1, @(x) isnumeric(x) && isscalar(x));
|
||||||
addParameter(p, 'ColormapPS', Colormaps.coolwarm(), @(x) isnumeric(x) || ismatrix(x));
|
|
||||||
addParameter(p, 'Font', 'Arial', @ischar);
|
|
||||||
addParameter(p, 'SaveFileName', 'avgspectra.fig', @ischar);
|
addParameter(p, 'SaveFileName', 'avgspectra.fig', @ischar);
|
||||||
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
addParameter(p, 'SaveDirectory', pwd, @ischar);
|
||||||
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
addParameter(p, 'SkipSaveFigures', false, @islogical);
|
||||||
parse(p, varargin{:});
|
parse(p, varargin{:});
|
||||||
opts = p.Results;
|
opts = p.Results;
|
||||||
|
|
||||||
|
axisFontSize = opts.FontSize;
|
||||||
|
titleFontSize = opts.FontSize+2;
|
||||||
|
|
||||||
|
% --- Compute averaged data using shared helper ---
|
||||||
|
PS_data = Analyzer.computeSpectralAverages(ps_list, scan_parameter_values);
|
||||||
|
S_k_data = Analyzer.computeSpectralAverages(s_k_list, scan_parameter_values);
|
||||||
|
S_theta_data = Analyzer.computeSpectralAverages(s_theta_list, scan_parameter_values);
|
||||||
|
|
||||||
% --- Unique scan parameters ---
|
% --- Unique scan parameters ---
|
||||||
[uniqueParams, ~, idx] = unique(scan_parameter_values);
|
uniqueParams = S_k_data.scan_parameters;
|
||||||
nParams = numel(uniqueParams);
|
nParams = numel(uniqueParams);
|
||||||
|
|
||||||
% --- Loop over each unique parameter ---
|
% --- Loop over each unique parameter ---
|
||||||
for pIdx = 1:nParams
|
for pIdx = 1:nParams
|
||||||
currentParam = uniqueParams(pIdx);
|
currentParam = uniqueParams(pIdx);
|
||||||
shotIndices = find(idx == pIdx);
|
|
||||||
nShots = numel(shotIndices);
|
|
||||||
|
|
||||||
% --- Initialize accumulators ---
|
avgPS = PS_data.curves_mean{pIdx};
|
||||||
avgPS = 0;
|
avgS_k = S_k_data.curves_mean{pIdx};
|
||||||
avgS_k = 0;
|
avgS_theta = S_theta_data.curves_mean{pIdx};
|
||||||
avgS_theta = 0;
|
|
||||||
|
|
||||||
% --- Sum over shots ---
|
|
||||||
for j = 1:nShots
|
|
||||||
avgPS = avgPS + ps_list{shotIndices(j)};
|
|
||||||
avgS_k = avgS_k + s_k_list{shotIndices(j)};
|
|
||||||
avgS_theta = avgS_theta + s_theta_list{shotIndices(j)};
|
|
||||||
end
|
|
||||||
|
|
||||||
% --- Average ---
|
|
||||||
avgPS = avgPS / nShots;
|
|
||||||
avgS_k = avgS_k / nShots;
|
|
||||||
avgS_theta = avgS_theta / nShots;
|
|
||||||
|
|
||||||
% ==== Plot ====
|
% ==== Plot ====
|
||||||
fig = figure(opts.FigNum); clf;
|
fig = figure(opts.FigNum); clf;
|
||||||
set(fig, 'Color', 'w', 'Position', [400 200 1200 400]);
|
set(fig, 'Color', 'w', 'Position', [400 200 1200 400]);
|
||||||
tLayout = tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
|
tLayout = tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
|
||||||
|
|
||||||
axisFontSize = 14;
|
|
||||||
titleFontSize = 16;
|
|
||||||
|
|
||||||
% --- 1. Power Spectrum ---
|
% --- 1. Power Spectrum ---
|
||||||
nexttile;
|
nexttile;
|
||||||
imagesc(kx, ky, log(1 + avgPS));
|
imagesc(kx, ky, log(1 + avgPS));
|
||||||
axis image;
|
axis image;
|
||||||
set(gca, 'FontSize', axisFontSize, 'YDir', 'normal');
|
set(gca, 'FontSize', axisFontSize, 'YDir', 'normal');
|
||||||
xlabel('k_x [\mum^{-1}]','Interpreter','tex','FontSize',axisFontSize,'FontName',opts.Font);
|
xlabel('k_x [\mum^{-1}]','Interpreter','tex','FontSize',axisFontSize,'FontName',opts.FontName);
|
||||||
ylabel('k_y [\mum^{-1}]','Interpreter','tex','FontSize',axisFontSize,'FontName',opts.Font);
|
ylabel('k_y [\mum^{-1}]','Interpreter','tex','FontSize',axisFontSize,'FontName',opts.FontName);
|
||||||
title('Average Power Spectrum','FontSize',titleFontSize,'FontWeight','bold');
|
title('Average Power Spectrum','FontSize',titleFontSize,'FontWeight','bold');
|
||||||
|
if isempty(opts.ColormapPS)
|
||||||
|
opts.ColormapPS = Colormaps.coolwarm();
|
||||||
|
end
|
||||||
colormap(opts.ColormapPS);
|
colormap(opts.ColormapPS);
|
||||||
colorbar;
|
colorbar;
|
||||||
|
|
||||||
% --- Annotate scan parameter ---
|
% --- Annotate scan parameter ---
|
||||||
if strcmp(opts.ScanParameterName,'ps_rot_mag_fin_pol_angle')
|
txt = sprintf(opts.AnnotationSuffix, currentParam);
|
||||||
txt = sprintf('%.1f^\\circ', currentParam);
|
|
||||||
else
|
|
||||||
txt = sprintf('%.2f G', currentParam);
|
|
||||||
end
|
|
||||||
text(0.975,0.975,txt,'Color','white','FontWeight','bold','FontSize',axisFontSize, ...
|
text(0.975,0.975,txt,'Color','white','FontWeight','bold','FontSize',axisFontSize, ...
|
||||||
'Interpreter','tex','Units','normalized','HorizontalAlignment','right','VerticalAlignment','top');
|
'Interpreter','tex','Units','normalized','HorizontalAlignment','right','VerticalAlignment','top');
|
||||||
|
|
||||||
@ -119,10 +110,12 @@ function plotAverageSpectra(scan_parameter_values, spectral_analysis_results, va
|
|||||||
ax.YMinorGrid = 'on';
|
ax.YMinorGrid = 'on';
|
||||||
grid on;
|
grid on;
|
||||||
|
|
||||||
|
sgtitle(opts.Title, 'FontName', opts.FontName, 'FontSize', titleFontSize+2);
|
||||||
|
|
||||||
drawnow;
|
drawnow;
|
||||||
|
|
||||||
% --- Save figure ---
|
% --- Save figure ---
|
||||||
saveFigure(fig, ...
|
Plotter.saveFigure(fig, ...
|
||||||
'SaveFileName', opts.SaveFileName, ...
|
'SaveFileName', opts.SaveFileName, ...
|
||||||
'SaveDirectory', opts.SaveDirectory, ...
|
'SaveDirectory', opts.SaveDirectory, ...
|
||||||
'SkipSaveFigures', opts.SkipSaveFigures);
|
'SkipSaveFigures', opts.SkipSaveFigures);
|
||||||
@ -1,4 +1,4 @@
|
|||||||
function results = plotSpectralCurves(S_theta_all, theta_vals, scan_reference_values, varargin)
|
function results = plotSpectralCurves(S_all, x_vals, scan_reference_values, varargin)
|
||||||
%% plotSpectralCurves
|
%% plotSpectralCurves
|
||||||
% Author: Karthik
|
% Author: Karthik
|
||||||
% Date: 2025-10-01
|
% Date: 2025-10-01
|
||||||
@ -33,6 +33,9 @@ function results = plotSpectralCurves(S_theta_all, theta_vals, scan_reference_va
|
|||||||
addParameter(p, 'Title', 'Spectral Curves', @(x) ischar(x) || isstring(x));
|
addParameter(p, 'Title', 'Spectral Curves', @(x) ischar(x) || isstring(x));
|
||||||
addParameter(p, 'XLabel', '\theta / \pi', @(x) ischar(x) || isstring(x));
|
addParameter(p, 'XLabel', '\theta / \pi', @(x) ischar(x) || isstring(x));
|
||||||
addParameter(p, 'YLabel', 'S(\theta)', @(x) ischar(x) || isstring(x));
|
addParameter(p, 'YLabel', 'S(\theta)', @(x) ischar(x) || isstring(x));
|
||||||
|
addParameter(p, 'YScale', 'linear', @(x) ismember(x, {'linear','log'}));
|
||||||
|
addParameter(p, 'XLim', [], @(x) isempty(x) || (isnumeric(x) && numel(x)==2));
|
||||||
|
addParameter(p, 'YLim', [], @(x) isempty(x) || (isnumeric(x) && numel(x)==2));
|
||||||
addParameter(p, 'FontName', 'Arial', @ischar);
|
addParameter(p, 'FontName', 'Arial', @ischar);
|
||||||
addParameter(p, 'FontSize', 14, @isnumeric);
|
addParameter(p, 'FontSize', 14, @isnumeric);
|
||||||
addParameter(p, 'FigNum', [], @(x) isempty(x) || (isnumeric(x) && isscalar(x)));
|
addParameter(p, 'FigNum', [], @(x) isempty(x) || (isnumeric(x) && isscalar(x)));
|
||||||
@ -45,31 +48,19 @@ function results = plotSpectralCurves(S_theta_all, theta_vals, scan_reference_va
|
|||||||
parse(p, varargin{:});
|
parse(p, varargin{:});
|
||||||
opts = p.Results;
|
opts = p.Results;
|
||||||
|
|
||||||
% --- Determine number of scan parameters and repetitions ---
|
% --- Compute averages using computeSpectralAverages ---
|
||||||
N_params = numel(scan_reference_values);
|
avgData = Analyzer.computeSpectralAverages(S_all, scan_reference_values);
|
||||||
Ntotal = numel(S_theta_all);
|
|
||||||
Nreps = Ntotal / N_params;
|
|
||||||
|
|
||||||
% --- Prepare curves, mean, SEM ---
|
% --- Extract averaged curves ---
|
||||||
curves_all = cell(1, N_params);
|
curves_all = avgData.curves_all;
|
||||||
curves_mean = cell(1, N_params);
|
curves_mean = avgData.curves_mean;
|
||||||
curves_err = cell(1, N_params);
|
curves_err = avgData.curves_error;
|
||||||
Npoints = numel(S_theta_all{1});
|
N_params = numel(avgData.scan_parameters);
|
||||||
|
Npoints = numel(S_all{1});
|
||||||
for i = 1:N_params
|
|
||||||
curves = zeros(Nreps, Npoints);
|
|
||||||
for r = 1:Nreps
|
|
||||||
idx = (r-1)*N_params + i; % correct interleaved indexing
|
|
||||||
curves(r,:) = S_theta_all{idx};
|
|
||||||
end
|
|
||||||
curves_all{i} = curves;
|
|
||||||
curves_mean{i} = mean(curves,1);
|
|
||||||
curves_err{i} = std(curves,0,1)/sqrt(Nreps);
|
|
||||||
end
|
|
||||||
|
|
||||||
% --- Create results struct compatible with plotting ---
|
% --- Create results struct compatible with plotting ---
|
||||||
results.curves = curves_all;
|
results.curves = curves_all;
|
||||||
results.x_values = theta_vals; % generic x-axis
|
results.x_values = x_vals; % generic x-axis
|
||||||
results.curves_mean = curves_mean;
|
results.curves_mean = curves_mean;
|
||||||
results.curves_error = curves_err;
|
results.curves_error = curves_err;
|
||||||
results.scan_parameter_values = scan_reference_values;
|
results.scan_parameter_values = scan_reference_values;
|
||||||
@ -111,7 +102,9 @@ function results = plotSpectralCurves(S_theta_all, theta_vals, scan_reference_va
|
|||||||
ylabel(ax, opts.YLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
ylabel(ax, opts.YLabel, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
title(ax, sprintf('%s=%.3g%s', opts.TileTitlePrefix, results.scan_parameter_values(i), opts.TileTitleSuffix), ...
|
title(ax, sprintf('%s=%.3g%s', opts.TileTitlePrefix, results.scan_parameter_values(i), opts.TileTitleSuffix), ...
|
||||||
'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
||||||
set(ax, 'FontName', opts.FontName, 'FontSize', opts.FontSize);
|
set(ax, 'FontName', opts.FontName, 'FontSize', opts.FontSize, 'YScale', opts.YScale);
|
||||||
|
if ~isempty(opts.XLim), xlim(ax, opts.XLim); end
|
||||||
|
if ~isempty(opts.YLim), ylim(ax, opts.YLim); end
|
||||||
end
|
end
|
||||||
|
|
||||||
% --- Save figure ---
|
% --- Save figure ---
|
||||||
|
|||||||
@ -187,16 +187,15 @@ Plotter.plotMultiplePCAResults(compiled_results.pca_results, scan_parameter_valu
|
|||||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
'SaveDirectory', figSaveDir);
|
'SaveDirectory', figSaveDir);
|
||||||
|
|
||||||
%{
|
|
||||||
%% ------------------ 14. Average of Spectra Plots ------------------
|
%% ------------------ 14. Average of Spectra Plots ------------------
|
||||||
|
Plotter.plotSpectralAverages(scan_parameter_values, ...
|
||||||
Plotter.plotAverageSpectra(scan_parameter_values, ...
|
compiled_results.spectral_analysis_results, ...
|
||||||
spectral_analysis_results, ...
|
'Title', options.titleString, ...
|
||||||
'ScanParameterName', scan_parameter, ...
|
'AnnotationSuffix', '%.1f^\\circ', ...
|
||||||
'FigNum', 20, ...
|
'FigNum', 20, ...
|
||||||
'ColormapPS', Colormaps.coolwarm(), ...
|
'ColormapPS', Colormaps.coolwarm(), ...
|
||||||
'Font', 'Bahnschrift', ...
|
'FontName', 'Bahnschrift', ...
|
||||||
'SaveFileName', 'avgSpectra.fig', ...
|
'SaveFileName', 'avgSpectra.fig', ...
|
||||||
'SaveDirectory', figSaveDir, ...
|
'SaveDirectory', pwd, ...
|
||||||
'SkipSaveFigures', options.skipSaveFigures);
|
'SkipSaveFigures', true);
|
||||||
%}
|
|
||||||
|
|||||||
@ -42,13 +42,6 @@ options.Angular_Sigma = 2;
|
|||||||
options.Angular_WindowSize = 5;
|
options.Angular_WindowSize = 5;
|
||||||
options.zoom_size = 50;
|
options.zoom_size = 50;
|
||||||
|
|
||||||
%
|
|
||||||
options.maximumShift = 8;
|
|
||||||
options.Radial_Theta = deg2rad(45);
|
|
||||||
options.Radial_Minimum = 2;
|
|
||||||
options.Radial_Maximum = 6;
|
|
||||||
options.skipLivePlot = false;
|
|
||||||
|
|
||||||
% Flags
|
% Flags
|
||||||
options.skipUnshuffling = false;
|
options.skipUnshuffling = false;
|
||||||
options.skipNormalization = false;
|
options.skipNormalization = false;
|
||||||
@ -165,7 +158,7 @@ Plotter.plotSpectralDistributionCumulants(results, ...
|
|||||||
'SaveFileName', 'SpectralCumulants.fig');
|
'SaveFileName', 'SpectralCumulants.fig');
|
||||||
|
|
||||||
%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
|
%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
|
||||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
spectral_analysis_results.S_theta_norm_all, ...
|
spectral_analysis_results.S_theta_norm_all, ...
|
||||||
spectral_analysis_results.theta_vals, ...
|
spectral_analysis_results.theta_vals, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -182,7 +175,7 @@ function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
|
|||||||
% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
|
% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
|
||||||
%
|
%
|
||||||
% Inputs:
|
% Inputs:
|
||||||
% fitResults - struct array from fitTwoGaussianCurves (may contain fits)
|
% fitResults - struct array from fitTwoGaussianCurvesToAngularSpectralDistribution (may contain fits)
|
||||||
% rawCurves - struct array with fields:
|
% rawCurves - struct array with fields:
|
||||||
% .x - raw curve
|
% .x - raw curve
|
||||||
% .theta - corresponding theta values
|
% .theta - corresponding theta values
|
||||||
@ -311,7 +304,7 @@ for i = 1:numel(results.curves)
|
|||||||
end
|
end
|
||||||
|
|
||||||
% --- Fit two-Gaussian model to mean curves ---
|
% --- Fit two-Gaussian model to mean curves ---
|
||||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurves(...
|
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
results.curves_mean, ...
|
results.curves_mean, ...
|
||||||
results.x_values*pi, ...
|
results.x_values*pi, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -394,7 +387,7 @@ curves_cell = num2cell(curves_matrix, 2); % 1x60 cell array
|
|||||||
curves_cell = cellfun(@(x) x(:).', curves_cell, 'UniformOutput', false); % ensure 1xN row vectors
|
curves_cell = cellfun(@(x) x(:).', curves_cell, 'UniformOutput', false); % ensure 1xN row vectors
|
||||||
|
|
||||||
% --- Fit two-Gaussian model to these curves ---
|
% --- Fit two-Gaussian model to these curves ---
|
||||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
curves_cell, ...
|
curves_cell, ...
|
||||||
results.x_values*pi, ...
|
results.x_values*pi, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -42,12 +42,11 @@ options.Angular_Sigma = 2;
|
|||||||
options.Angular_WindowSize = 5;
|
options.Angular_WindowSize = 5;
|
||||||
options.zoom_size = 50;
|
options.zoom_size = 50;
|
||||||
|
|
||||||
%
|
% Real-Space g2 extraction and analysis
|
||||||
options.maximumShift = 8;
|
options.maximumShift = 8;
|
||||||
options.Radial_Theta = deg2rad(45);
|
options.Radial_Theta = deg2rad(45);
|
||||||
options.Radial_Minimum = 2;
|
options.Radial_Minimum = 2;
|
||||||
options.Radial_Maximum = 6;
|
options.Radial_Maximum = 6;
|
||||||
options.skipLivePlot = false;
|
|
||||||
|
|
||||||
% Flags
|
% Flags
|
||||||
options.skipUnshuffling = false;
|
options.skipUnshuffling = false;
|
||||||
@ -117,7 +116,7 @@ analysis_results = Analyzer.analyzeG2Structures(g2_analysis_results, o
|
|||||||
%% Plot raw OD images and the corresponding real space g2 matrix
|
%% Plot raw OD images and the corresponding real space g2 matrix
|
||||||
|
|
||||||
options.skipLivePlot = true;
|
options.skipLivePlot = true;
|
||||||
options.skipSaveFigures = false;
|
options.skipSaveFigures = true;
|
||||||
saveDirectory = 'C:\Users\Karthik-OfficePC\Documents\GitRepositories\Calculations\Data-Analyzer\+Scripts';
|
saveDirectory = 'C:\Users\Karthik-OfficePC\Documents\GitRepositories\Calculations\Data-Analyzer\+Scripts';
|
||||||
|
|
||||||
Plotter.plotODG2withAnalysis(od_imgs, scan_parameter_values, g2_analysis_results, analysis_results, options, ...
|
Plotter.plotODG2withAnalysis(od_imgs, scan_parameter_values, g2_analysis_results, analysis_results, options, ...
|
||||||
|
|||||||
@ -0,0 +1,257 @@
|
|||||||
|
%% ===== BEC-Droplets-Stripes Settings =====
|
||||||
|
|
||||||
|
% Specify data location to run analysis on
|
||||||
|
dataSources = {
|
||||||
|
struct('sequence', 'TwoDGas', ...
|
||||||
|
'date', '2025/06/23', ...
|
||||||
|
'runs', [300]) % specify run numbers as a string in "" or just as a numeric value
|
||||||
|
};
|
||||||
|
|
||||||
|
options = struct();
|
||||||
|
|
||||||
|
% File paths
|
||||||
|
options.baseDataFolder = '//DyLabNAS/Data';
|
||||||
|
options.FullODImagesFolder = 'E:/Data - Experiment/FullODImages/202506';
|
||||||
|
options.measurementName = 'DropletsToStripes';
|
||||||
|
scriptFullPath = mfilename('fullpath');
|
||||||
|
options.saveDirectory = fileparts(scriptFullPath);
|
||||||
|
|
||||||
|
% Camera / imaging settings
|
||||||
|
options.cam = 4; % 1 - ODT_1_Axis_Camera; 2 - ODT_2_Axis_Camera; 3 - Horizontal_Axis_Camera;, 4 - Vertical_Axis_Camera;
|
||||||
|
options.angle = 0; % angle by which image will be rotated
|
||||||
|
options.center = [1410, 2030];
|
||||||
|
options.span = [200, 200];
|
||||||
|
options.fraction = [0.1, 0.1];
|
||||||
|
options.pixel_size = 5.86e-6; % in meters
|
||||||
|
options.magnification = 23.94;
|
||||||
|
options.ImagingMode = 'HighIntensity';
|
||||||
|
options.PulseDuration = 5e-6; % in s
|
||||||
|
|
||||||
|
% Fourier analysis settings
|
||||||
|
options.theta_min = deg2rad(0);
|
||||||
|
options.theta_max = deg2rad(180);
|
||||||
|
options.N_radial_bins = 150;
|
||||||
|
options.Radial_Sigma = 2;
|
||||||
|
options.Radial_WindowSize = 5; % odd number
|
||||||
|
|
||||||
|
options.k_min = 1.2771; % μm⁻¹
|
||||||
|
options.k_max = 2.5541; % μm⁻¹
|
||||||
|
options.N_angular_bins = 360;
|
||||||
|
options.Angular_Threshold = 75;
|
||||||
|
options.Angular_Sigma = 2;
|
||||||
|
options.Angular_WindowSize = 5;
|
||||||
|
options.zoom_size = 50;
|
||||||
|
|
||||||
|
% Flags
|
||||||
|
options.skipUnshuffling = false;
|
||||||
|
options.skipNormalization = false;
|
||||||
|
|
||||||
|
options.skipFringeRemoval = true;
|
||||||
|
options.skipPreprocessing = true;
|
||||||
|
options.skipMasking = true;
|
||||||
|
options.skipIntensityThresholding = true;
|
||||||
|
options.skipBinarization = true;
|
||||||
|
|
||||||
|
options.skipFullODImagesFolderUse = false;
|
||||||
|
options.skipSaveData = false;
|
||||||
|
options.skipSaveFigures = true;
|
||||||
|
options.skipSaveProcessedOD = true;
|
||||||
|
options.skipLivePlot = false;
|
||||||
|
options.showProgressBar = true;
|
||||||
|
|
||||||
|
% Extras
|
||||||
|
options.font = 'Bahnschrift';
|
||||||
|
switch options.measurementName
|
||||||
|
case 'BECToDroplets'
|
||||||
|
options.scan_parameter = 'rot_mag_field';
|
||||||
|
options.flipSortOrder = true;
|
||||||
|
options.scanParameterUnits = 'gauss';
|
||||||
|
options.titleString = 'BEC to Droplets';
|
||||||
|
case 'BECToStripes'
|
||||||
|
options.scan_parameter = 'rot_mag_field';
|
||||||
|
options.flipSortOrder = true;
|
||||||
|
options.scanParameterUnits = 'gauss';
|
||||||
|
options.titleString = 'BEC to Stripes';
|
||||||
|
case 'DropletsToStripes'
|
||||||
|
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||||
|
options.flipSortOrder = false;
|
||||||
|
options.scanParameterUnits = 'degrees';
|
||||||
|
options.titleString = 'Droplets to Stripes';
|
||||||
|
case 'StripesToDroplets'
|
||||||
|
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||||
|
options.flipSortOrder = false;
|
||||||
|
options.scanParameterUnits = 'degrees';
|
||||||
|
options.titleString = 'Stripes to Droplets';
|
||||||
|
end
|
||||||
|
|
||||||
|
%% ===== Collect Images and Launch Viewer =====
|
||||||
|
|
||||||
|
[options.selectedPath, options.folderPath] = Helper.selectDataSourcePath(dataSources, options);
|
||||||
|
|
||||||
|
[od_imgs, scan_parameter_values, scan_reference_values, file_list] = Helper.collectODImages(options);
|
||||||
|
|
||||||
|
%% Conduct spectral analysis
|
||||||
|
spectral_analysis_results = Analyzer.extractFullRadialSpectralDistribution(od_imgs, options);
|
||||||
|
|
||||||
|
%% ------------------ 1. Plot of all Radial Spectral Distribution Curves ------------------
|
||||||
|
Plotter.plotSpectralCurves( ...
|
||||||
|
spectral_analysis_results.S_k_all, ...
|
||||||
|
spectral_analysis_results.k_rho_vals, ...
|
||||||
|
scan_reference_values, ... % correct scan params
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', 'k_\rho', ...
|
||||||
|
'YLabel', 'S(k_\rho)', ...
|
||||||
|
'YScale', 'log', ...
|
||||||
|
'XLim', [min(spectral_analysis_results.k_rho_vals), max(spectral_analysis_results.k_rho_vals)], ...
|
||||||
|
'HighlightEvery', 10, ... % highlight every 10th repetition
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FigNum', 1, ...
|
||||||
|
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||||
|
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SpectralCurves.fig', ...
|
||||||
|
'SaveDirectory', options.saveDirectory);
|
||||||
|
|
||||||
|
%% ------------------ 2. Fit truncated Radial Spectral Distribution Curves ------------------
|
||||||
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
|
||||||
|
spectral_analysis_results.S_k_all, ... % radial spectral curves
|
||||||
|
spectral_analysis_results.k_rho_vals, ... % corresponding k_rho values
|
||||||
|
'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
|
||||||
|
'AmplitudeRange', [0, 0.5], ... % truncate curves to y >= 0 (all amplitudes)
|
||||||
|
'ResidualThreshold', 0.15, ... % maximum allowed residual
|
||||||
|
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||||
|
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||||
|
%%
|
||||||
|
%{
|
||||||
|
% --- Function call ---
|
||||||
|
plotTwoGaussianFitsOnRaw(fitResults, rawCurves, 8, 12); % 8×12 subplots per page
|
||||||
|
|
||||||
|
% --- Function definition ---
|
||||||
|
function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
|
||||||
|
%% plotTwoGaussianFitsOnRaw
|
||||||
|
% Plots raw radial spectral curves with their two-Gaussian fits.
|
||||||
|
%
|
||||||
|
% Inputs:
|
||||||
|
% fitResults - struct array from fitTwoGaussianCurvesToRadialSpectralDistribution
|
||||||
|
% rawCurves - struct array with fields:
|
||||||
|
% .x - raw normalized amplitudes
|
||||||
|
% .k - corresponding k_rho values
|
||||||
|
% nRows - number of subplot rows per page (default: 8)
|
||||||
|
% nCols - number of subplot columns per page (default: 12)
|
||||||
|
|
||||||
|
if nargin < 3, nRows = 8; end
|
||||||
|
if nargin < 4, nCols = 12; end
|
||||||
|
|
||||||
|
Ncurves = numel(rawCurves);
|
||||||
|
plotsPerPage = nRows * nCols;
|
||||||
|
pageNum = 1;
|
||||||
|
|
||||||
|
for k = 1:Ncurves
|
||||||
|
% --- Create new figure page if needed ---
|
||||||
|
if mod(k-1, plotsPerPage) == 0
|
||||||
|
figure('Name', sprintf('Radial Spectra Page %d', pageNum), ...
|
||||||
|
'NumberTitle', 'off', 'Color', 'w', ...
|
||||||
|
'Position', [50 50 1600 900]);
|
||||||
|
pageNum = pageNum + 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Subplot selection ---
|
||||||
|
subplot(nRows, nCols, mod(k-1, plotsPerPage) + 1);
|
||||||
|
hold on; grid on; box on;
|
||||||
|
|
||||||
|
% --- Plot raw curve ---
|
||||||
|
if isfield(rawCurves, 'x') && isfield(rawCurves, 'k') && ...
|
||||||
|
~isempty(rawCurves(k).x) && all(isfinite(rawCurves(k).x))
|
||||||
|
plot(rawCurves(k).k, rawCurves(k).x, 'k.-', ...
|
||||||
|
'LineWidth', 1, 'DisplayName', 'Raw data');
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Overlay fit if valid ---
|
||||||
|
if k <= numel(fitResults)
|
||||||
|
fit = fitResults(k);
|
||||||
|
if isfield(fit, 'isValid') && fit.isValid && ...
|
||||||
|
isfield(fit, 'kFine') && isfield(fit, 'yFit') && ...
|
||||||
|
~isempty(fit.kFine) && all(isfinite(fit.yFit))
|
||||||
|
plot(fit.kFine, fit.yFit, 'r-', ...
|
||||||
|
'LineWidth', 1.2, 'DisplayName', 'Two-Gaussian fit');
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Labels and title ---
|
||||||
|
xlabel('k_\rho', 'FontSize', 10);
|
||||||
|
ylabel('Normalized amplitude', 'FontSize', 10);
|
||||||
|
title(sprintf('Curve %d', k), 'FontSize', 9, 'Interpreter', 'none');
|
||||||
|
|
||||||
|
% --- Legend on first subplot of each page ---
|
||||||
|
if mod(k-1, plotsPerPage) == 0
|
||||||
|
legend('Location', 'best', 'FontSize', 8);
|
||||||
|
end
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
%}
|
||||||
|
%% ------------------ 3. Plot fit parameters - amplitude ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak amplitude', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 4, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [0, 0.06]);
|
||||||
|
|
||||||
|
%% ------------------ 4. Plot fit parameters - position ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 5, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [1, 2]);
|
||||||
|
%% ------------------ 5. Plot fit parameters - width ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 6, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [0, 1.5]);
|
||||||
|
|
||||||
|
%% ------------------ 6. Plot secondary Gaussian range ------------------
|
||||||
|
Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 8, ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'OverlayMeanSEM', true);
|
||||||
|
|
||||||
|
|
||||||
@ -187,16 +187,14 @@ Plotter.plotMultiplePCAResults(compiled_results.pca_results, scan_parameter_valu
|
|||||||
'SkipSaveFigures', options.skipSaveFigures, ...
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
'SaveDirectory', figSaveDir);
|
'SaveDirectory', figSaveDir);
|
||||||
|
|
||||||
%{
|
|
||||||
%% ------------------ 14. Average of Spectra Plots ------------------
|
%% ------------------ 14. Average of Spectra Plots ------------------
|
||||||
|
Plotter.plotSpectralAverages(scan_parameter_values, ...
|
||||||
Plotter.plotAverageSpectra(scan_parameter_values, ...
|
compiled_results.spectral_analysis_results, ...
|
||||||
spectral_analysis_results, ...
|
'Title', options.titleString, ...
|
||||||
'ScanParameterName', scan_parameter, ...
|
'AnnotationSuffix', '%.1f^\\circ', ...
|
||||||
'FigNum', 20, ...
|
'FigNum', 20, ...
|
||||||
'ColormapPS', Colormaps.coolwarm(), ...
|
'ColormapPS', Colormaps.coolwarm(), ...
|
||||||
'Font', 'Bahnschrift', ...
|
'FontName', 'Bahnschrift', ...
|
||||||
'SaveFileName', 'avgSpectra.fig', ...
|
'SaveFileName', 'avgSpectra.fig', ...
|
||||||
'SaveDirectory', figSaveDir, ...
|
'SaveDirectory', pwd, ...
|
||||||
'SkipSaveFigures', options.skipSaveFigures);
|
'SkipSaveFigures', true);
|
||||||
%}
|
|
||||||
@ -158,7 +158,7 @@ Plotter.plotSpectralDistributionCumulants(results, ...
|
|||||||
'SaveFileName', 'SpectralCumulants.fig');
|
'SaveFileName', 'SpectralCumulants.fig');
|
||||||
|
|
||||||
%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
|
%% ------------------ 4. Fit shifted Angular Spectral Distribution Curves ------------------
|
||||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
spectral_analysis_results.S_theta_norm_all, ...
|
spectral_analysis_results.S_theta_norm_all, ...
|
||||||
spectral_analysis_results.theta_vals, ...
|
spectral_analysis_results.theta_vals, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -175,7 +175,7 @@ function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
|
|||||||
% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
|
% plotTwoGaussianFitsOnRaw - Plots raw curves and overlays valid two-Gaussian fits.
|
||||||
%
|
%
|
||||||
% Inputs:
|
% Inputs:
|
||||||
% fitResults - struct array from fitTwoGaussianCurves (may contain fits)
|
% fitResults - struct array from fitTwoGaussianCurvesToAngularSpectralDistribution (may contain fits)
|
||||||
% rawCurves - struct array with fields:
|
% rawCurves - struct array with fields:
|
||||||
% .x - raw curve
|
% .x - raw curve
|
||||||
% .theta - corresponding theta values
|
% .theta - corresponding theta values
|
||||||
@ -304,7 +304,7 @@ for i = 1:numel(results.curves)
|
|||||||
end
|
end
|
||||||
|
|
||||||
% --- Fit two-Gaussian model to mean curves ---
|
% --- Fit two-Gaussian model to mean curves ---
|
||||||
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurves(...
|
[fitResultsMean, ~] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
results.curves_mean, ...
|
results.curves_mean, ...
|
||||||
results.x_values*pi, ...
|
results.x_values*pi, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -387,7 +387,7 @@ curves_cell = num2cell(curves_matrix, 2); % 1x60 cell array
|
|||||||
curves_cell = cellfun(@(x) x(:).', curves_cell, 'UniformOutput', false); % ensure 1xN row vectors
|
curves_cell = cellfun(@(x) x(:).', curves_cell, 'UniformOutput', false); % ensure 1xN row vectors
|
||||||
|
|
||||||
% --- Fit two-Gaussian model to these curves ---
|
% --- Fit two-Gaussian model to these curves ---
|
||||||
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurves(...
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToAngularSpectralDistribution(...
|
||||||
curves_cell, ...
|
curves_cell, ...
|
||||||
results.x_values*pi, ...
|
results.x_values*pi, ...
|
||||||
'MaxTheta', pi/2, ...
|
'MaxTheta', pi/2, ...
|
||||||
@ -42,12 +42,11 @@ options.Angular_Sigma = 2;
|
|||||||
options.Angular_WindowSize = 5;
|
options.Angular_WindowSize = 5;
|
||||||
options.zoom_size = 50;
|
options.zoom_size = 50;
|
||||||
|
|
||||||
%
|
% Real-Space g2 extraction and analysis
|
||||||
options.maximumShift = 8;
|
options.maximumShift = 8;
|
||||||
options.Radial_Theta = deg2rad(45);
|
options.Radial_Theta = deg2rad(45);
|
||||||
options.Radial_Minimum = 2;
|
options.Radial_Minimum = 2;
|
||||||
options.Radial_Maximum = 6;
|
options.Radial_Maximum = 6;
|
||||||
options.skipLivePlot = false;
|
|
||||||
|
|
||||||
% Flags
|
% Flags
|
||||||
options.skipUnshuffling = false;
|
options.skipUnshuffling = false;
|
||||||
|
|||||||
@ -0,0 +1,257 @@
|
|||||||
|
%% ===== BEC-Droplets-Stripes Settings =====
|
||||||
|
|
||||||
|
% Specify data location to run analysis on
|
||||||
|
dataSources = {
|
||||||
|
struct('sequence', 'TwoDGas', ...
|
||||||
|
'date', '2025/06/23', ...
|
||||||
|
'runs', [300]) % specify run numbers as a string in "" or just as a numeric value
|
||||||
|
};
|
||||||
|
|
||||||
|
options = struct();
|
||||||
|
|
||||||
|
% File paths
|
||||||
|
options.baseDataFolder = '//DyLabNAS/Data';
|
||||||
|
options.FullODImagesFolder = 'E:/Data - Experiment/FullODImages/202506';
|
||||||
|
options.measurementName = 'DropletsToStripes';
|
||||||
|
scriptFullPath = mfilename('fullpath');
|
||||||
|
options.saveDirectory = fileparts(scriptFullPath);
|
||||||
|
|
||||||
|
% Camera / imaging settings
|
||||||
|
options.cam = 4; % 1 - ODT_1_Axis_Camera; 2 - ODT_2_Axis_Camera; 3 - Horizontal_Axis_Camera;, 4 - Vertical_Axis_Camera;
|
||||||
|
options.angle = 0; % angle by which image will be rotated
|
||||||
|
options.center = [1410, 2030];
|
||||||
|
options.span = [200, 200];
|
||||||
|
options.fraction = [0.1, 0.1];
|
||||||
|
options.pixel_size = 5.86e-6; % in meters
|
||||||
|
options.magnification = 23.94;
|
||||||
|
options.ImagingMode = 'HighIntensity';
|
||||||
|
options.PulseDuration = 5e-6; % in s
|
||||||
|
|
||||||
|
% Fourier analysis settings
|
||||||
|
options.theta_min = deg2rad(0);
|
||||||
|
options.theta_max = deg2rad(180);
|
||||||
|
options.N_radial_bins = 500;
|
||||||
|
options.Radial_Sigma = 2;
|
||||||
|
options.Radial_WindowSize = 5; % odd number
|
||||||
|
|
||||||
|
options.k_min = 1.2771; % μm⁻¹
|
||||||
|
options.k_max = 2.5541; % μm⁻¹
|
||||||
|
options.N_angular_bins = 360;
|
||||||
|
options.Angular_Threshold = 75;
|
||||||
|
options.Angular_Sigma = 2;
|
||||||
|
options.Angular_WindowSize = 5;
|
||||||
|
options.zoom_size = 50;
|
||||||
|
|
||||||
|
% Flags
|
||||||
|
options.skipUnshuffling = false;
|
||||||
|
options.skipNormalization = false;
|
||||||
|
|
||||||
|
options.skipFringeRemoval = true;
|
||||||
|
options.skipPreprocessing = true;
|
||||||
|
options.skipMasking = true;
|
||||||
|
options.skipIntensityThresholding = true;
|
||||||
|
options.skipBinarization = true;
|
||||||
|
|
||||||
|
options.skipFullODImagesFolderUse = false;
|
||||||
|
options.skipSaveData = false;
|
||||||
|
options.skipSaveFigures = true;
|
||||||
|
options.skipSaveProcessedOD = true;
|
||||||
|
options.skipLivePlot = false;
|
||||||
|
options.showProgressBar = true;
|
||||||
|
|
||||||
|
% Extras
|
||||||
|
options.font = 'Bahnschrift';
|
||||||
|
switch options.measurementName
|
||||||
|
case 'BECToDroplets'
|
||||||
|
options.scan_parameter = 'rot_mag_field';
|
||||||
|
options.flipSortOrder = true;
|
||||||
|
options.scanParameterUnits = 'gauss';
|
||||||
|
options.titleString = 'BEC to Droplets';
|
||||||
|
case 'BECToStripes'
|
||||||
|
options.scan_parameter = 'rot_mag_field';
|
||||||
|
options.flipSortOrder = true;
|
||||||
|
options.scanParameterUnits = 'gauss';
|
||||||
|
options.titleString = 'BEC to Stripes';
|
||||||
|
case 'DropletsToStripes'
|
||||||
|
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||||
|
options.flipSortOrder = false;
|
||||||
|
options.scanParameterUnits = 'degrees';
|
||||||
|
options.titleString = 'Droplets to Stripes';
|
||||||
|
case 'StripesToDroplets'
|
||||||
|
options.scan_parameter = 'ps_rot_mag_fin_pol_angle';
|
||||||
|
options.flipSortOrder = false;
|
||||||
|
options.scanParameterUnits = 'degrees';
|
||||||
|
options.titleString = 'Stripes to Droplets';
|
||||||
|
end
|
||||||
|
|
||||||
|
%% ===== Collect Images and Launch Viewer =====
|
||||||
|
|
||||||
|
[options.selectedPath, options.folderPath] = Helper.selectDataSourcePath(dataSources, options);
|
||||||
|
|
||||||
|
[od_imgs, scan_parameter_values, scan_reference_values, file_list] = Helper.collectODImages(options);
|
||||||
|
|
||||||
|
%% Conduct spectral analysis
|
||||||
|
spectral_analysis_results = Analyzer.extractFullRadialSpectralDistribution(od_imgs, options);
|
||||||
|
|
||||||
|
%% ------------------ 1. Plot of all Radial Spectral Distribution Curves ------------------
|
||||||
|
Plotter.plotSpectralCurves( ...
|
||||||
|
spectral_analysis_results.S_k_all, ...
|
||||||
|
spectral_analysis_results.k_rho_vals, ...
|
||||||
|
scan_reference_values, ... % correct scan params
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', 'k_\rho', ...
|
||||||
|
'YLabel', 'S(k_\rho)', ...
|
||||||
|
'YScale', 'log', ...
|
||||||
|
'XLim', [min(spectral_analysis_results.k_rho_vals), max(spectral_analysis_results.k_rho_vals)], ...
|
||||||
|
'HighlightEvery', 10, ... % highlight every 10th repetition
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FigNum', 1, ...
|
||||||
|
'TileTitlePrefix', '\alpha', ... % user-defined tile prefix
|
||||||
|
'TileTitleSuffix', '^\circ', ... % user-defined suffix (e.g., degrees symbol)
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SpectralCurves.fig', ...
|
||||||
|
'SaveDirectory', options.saveDirectory);
|
||||||
|
|
||||||
|
%% ------------------ 2. Fit truncated Radial Spectral Distribution Curves ------------------
|
||||||
|
[fitResults, rawCurves] = Analyzer.fitTwoGaussianCurvesToRadialSpectralDistribution(...
|
||||||
|
spectral_analysis_results.S_k_all, ... % radial spectral curves
|
||||||
|
spectral_analysis_results.k_rho_vals, ... % corresponding k_rho values
|
||||||
|
'KRhoRange', [0, 3], ... % truncate curves to 0 <= k_rho <= 3
|
||||||
|
'AmplitudeRange', [0, 0.5], ... % truncate curves to y >= 0 (all amplitudes)
|
||||||
|
'ResidualThreshold', 0.15, ... % maximum allowed residual
|
||||||
|
'PositionThreshold', 0.5, ... % minimum separation between peaks
|
||||||
|
'AmplitudeThreshold', 0.015); % minimum secondary peak fraction
|
||||||
|
%%
|
||||||
|
%{
|
||||||
|
% --- Function call ---
|
||||||
|
plotTwoGaussianFitsOnRaw(fitResults, rawCurves, 8, 12); % 8×12 subplots per page
|
||||||
|
|
||||||
|
% --- Function definition ---
|
||||||
|
function plotTwoGaussianFitsOnRaw(fitResults, rawCurves, nRows, nCols)
|
||||||
|
%% plotTwoGaussianFitsOnRaw
|
||||||
|
% Plots raw radial spectral curves with their two-Gaussian fits.
|
||||||
|
%
|
||||||
|
% Inputs:
|
||||||
|
% fitResults - struct array from fitTwoGaussianCurvesToRadialSpectralDistribution
|
||||||
|
% rawCurves - struct array with fields:
|
||||||
|
% .x - raw normalized amplitudes
|
||||||
|
% .k - corresponding k_rho values
|
||||||
|
% nRows - number of subplot rows per page (default: 8)
|
||||||
|
% nCols - number of subplot columns per page (default: 12)
|
||||||
|
|
||||||
|
if nargin < 3, nRows = 8; end
|
||||||
|
if nargin < 4, nCols = 12; end
|
||||||
|
|
||||||
|
Ncurves = numel(rawCurves);
|
||||||
|
plotsPerPage = nRows * nCols;
|
||||||
|
pageNum = 1;
|
||||||
|
|
||||||
|
for k = 1:Ncurves
|
||||||
|
% --- Create new figure page if needed ---
|
||||||
|
if mod(k-1, plotsPerPage) == 0
|
||||||
|
figure('Name', sprintf('Radial Spectra Page %d', pageNum), ...
|
||||||
|
'NumberTitle', 'off', 'Color', 'w', ...
|
||||||
|
'Position', [50 50 1600 900]);
|
||||||
|
pageNum = pageNum + 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Subplot selection ---
|
||||||
|
subplot(nRows, nCols, mod(k-1, plotsPerPage) + 1);
|
||||||
|
hold on; grid on; box on;
|
||||||
|
|
||||||
|
% --- Plot raw curve ---
|
||||||
|
if isfield(rawCurves, 'x') && isfield(rawCurves, 'k') && ...
|
||||||
|
~isempty(rawCurves(k).x) && all(isfinite(rawCurves(k).x))
|
||||||
|
plot(rawCurves(k).k, rawCurves(k).x, 'k.-', ...
|
||||||
|
'LineWidth', 1, 'DisplayName', 'Raw data');
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Overlay fit if valid ---
|
||||||
|
if k <= numel(fitResults)
|
||||||
|
fit = fitResults(k);
|
||||||
|
if isfield(fit, 'isValid') && fit.isValid && ...
|
||||||
|
isfield(fit, 'kFine') && isfield(fit, 'yFit') && ...
|
||||||
|
~isempty(fit.kFine) && all(isfinite(fit.yFit))
|
||||||
|
plot(fit.kFine, fit.yFit, 'r-', ...
|
||||||
|
'LineWidth', 1.2, 'DisplayName', 'Two-Gaussian fit');
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
% --- Labels and title ---
|
||||||
|
xlabel('k_\rho', 'FontSize', 10);
|
||||||
|
ylabel('Normalized amplitude', 'FontSize', 10);
|
||||||
|
title(sprintf('Curve %d', k), 'FontSize', 9, 'Interpreter', 'none');
|
||||||
|
|
||||||
|
% --- Legend on first subplot of each page ---
|
||||||
|
if mod(k-1, plotsPerPage) == 0
|
||||||
|
legend('Location', 'best', 'FontSize', 8);
|
||||||
|
end
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
%}
|
||||||
|
%% ------------------ 3. Plot fit parameters - amplitude ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'A2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak amplitude', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 4, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakAmplitudePDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [0, 0.06]);
|
||||||
|
|
||||||
|
%% ------------------ 4. Plot fit parameters - position ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'mu2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak position (\theta, rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 5, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakPositionPDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [1, 2]);
|
||||||
|
%% ------------------ 5. Plot fit parameters - width ------------------
|
||||||
|
Plotter.plotFitParameterPDF(fitResults, scan_reference_values, 'sigma2', ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', 'Secondary peak width (\sigma, rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 6, ...
|
||||||
|
'SkipSaveFigures', options.skipSaveFigures, ...
|
||||||
|
'SaveFileName', 'SecondaryPeakWidthPDF.fig', ...
|
||||||
|
'PlotType', 'histogram', ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'XLim', [min(scan_reference_values), max(scan_reference_values)], ...
|
||||||
|
'YLim', [0, 1.5]);
|
||||||
|
|
||||||
|
%% ------------------ 6. Plot secondary Gaussian range ------------------
|
||||||
|
Plotter.plotSecondaryGaussianRange(fitResults, scan_reference_values, ...
|
||||||
|
'Title', options.titleString, ...
|
||||||
|
'XLabel', '\alpha (degrees)', ...
|
||||||
|
'YLabel', '\mu_2 \pm \sigma_2 (rad)', ...
|
||||||
|
'FontName', options.font, ...
|
||||||
|
'FontSize', 16, ...
|
||||||
|
'FigNum', 8, ...
|
||||||
|
'NumberOfBins', 20, ...
|
||||||
|
'NormalizeHistogram', true, ...
|
||||||
|
'Colormap', @Colormaps.coolwarm, ...
|
||||||
|
'OverlayMeanSEM', true);
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in New Issue
Block a user