I will defer to you to do the analysis, however getting the data as matrices is not obvious. I can help with that.
Your data are gridded so use reshape to create matrices from them:
A = dlmread('Figure 3(a).txt', '\t', 4, 0);
rs = mean(diff(find(A(:,1) == 0)));
X = reshape(A, rs, rs, []);
figure
meshc(X(:,:,1), X(:,:,2), X(:,:,3))
xlabel('X (Å)')
ylabel('Y (Å)')
zlabel('Z (nm)')
I leave the rest to you.
EDIT — (24 Jan 2024 at 14:21)
Using the ability to run this here that was not available when this originally posted (and adding a few tweaks) —
T1 = readtable('Figure 3(a).txt', 'VariableNamingRule','preserve', 'Delimiter',{' ','\t'}, 'MultipleDelimsAsOne',true, 'HeaderLines',2, 'EmptyLineRule','skip')
VN = T1.Properties.VariableNames;
A = table2array(T1);
rs = mean(diff(find(A(:,1) == 0)));
X = reshape(A, rs, rs, []);
SizeX = size(X)
figure(3)
hmc = meshc(X(:,:,1), X(:,:,2), X(:,:,3));
hmc(1).EdgeColor = 'interp';
hmc(1).FaceColor = 'interp';
colormap(turbo)
xlabel(VN{1})
ylabel(VN{2})
zlabel(VN{3})
title('Figure 3(a)')
The code uses reshape to transform ‘A’ (65536 x 3) to ‘X’ (256 x 256 x 3) and then plots it as a surf plot.
.
