This can be done without any loops which should be a lot faster. The basic concept is to join all of your data together into one numeric array, and then split it again to get the final vectors.
% Fake data: 1x2 struct, 2x3 numeric arrays, all unique values:
A(1).data = [9,8,7;6,5,NaN];
A(2).data = [0,1,2;3,4,Inf];
% Convert to a simple numeric array:
B = cat(4,A.data); % join along the fourth dimension :)
% Create dimension "indices":
C = arrayfun(@(n)ones(1,n),size(B),'UniformOutput',false);
C{4} = size(B,4);
% Use the "indices" to split the numeric array into a cell:
D = mat2cell(B,C{:});
% Optional: tidy-up by removing superfluous dimensions:
D = cellfun(@squeeze, D, 'UniformOutput',false);
Save this in a script and run it. Then in the command window you can check that it has grouped the data together corresponding to the same location in each of the original separate numeric arrays:
>> D{1,1}
ans =
9
0
>> D{1,3}
ans =
7
2
>> D{2,3}
ans =
NaN
Inf
Because I concatenated along the fourth dimension, it should also work for your 3D arrays.
Important: Do not use i or j as variable names, as these are both names for the inbuilt imaginary unit .
