How do I fill empty Matrix elements with NaNs?

Hello,

I have a matrix that looks something like this:

A =

struct with fields:

CC: 12.4102

CD: [2×1 double]

CO: [4×1 double]

CV: [5×1 double]

LA: [7×1 double]

FB: []

....

The Matrix A contains around 160 vectors that range from length 0 (empty) to length of 7.

Because I need to work with the values, in this case plot them and calculate mean, the vectors within the matrix need to have the same length.

How can I extend every vector to be the length of 7 and fill those new elements with NaNs?

It is important that the vectors keep their original name! Their order is not important.

I tried iterating through each matrix element, but could not get any further than this:

for idx = 1:numel(A)

element = A(i);

%do operation

end

Answer 1

[Bhaskar R]

% assuming a fake structure similor to your case
A.a =  rand(4,1);
A.b = [];
A.c = rand(7,1);
A.d =  rand(10,1);
fields = fieldnames(A); % get fields
for ii =1:length(fields)
    if length(A.(fields{ii})) <=6 % retaining as it is for length 7
        % append nan's
        A.(fields{ii}) = [A.(fields{ii}); nan(7 - length(A.(fields{ii})),1)];
        % plotting
        figure, plot(A.(fields{ii})), title(['Vector : ', fields{ii}]);
    end
end
% mean value of each vector of structure
mean_values =  structfun(@nanmean,A, 'uniform', false);

[JP]

thanks!

Answer 2

[Stephen23]

Fake data:

>> A.a = rand(4,1);
>> A.b = [];
>> A.c = rand(7,1);
>> A.d = rand(10,1);

Then you can simply use structfun.

>> N = max(structfun(@numel,A)); % or 7, as you prefer.
>> F = @(v)[v;nan(N-numel(v),1)];
>> B = structfun(F,A,'UniformOutput',false)
B =
  scalar structure containing the fields:
    a =
       0.061883
       0.186276
       0.093157
       0.819494
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
    b =
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
            NaN
    c =
       0.494865
       0.747194
       0.876034
       0.597573
       0.201666
       0.014505
       0.726006
            NaN
            NaN
            NaN
    d =
       0.916148
       0.136177
       0.749379
       0.973931
       0.035588
       0.379591
       0.640495
       0.961396
       0.673465