Reshaping a matrix based on the first row

I am trying to make a for loop that creates a new array that groups row data into corresponding columns:

My current arrays are something like this (both same size):

[270 270 270 271 272 272 273 273 273]

[12 2 3 14 5 2 6 8 11]

I want 4 columns (each representing 270, 271, 272, 273 respectively) that will produce something like this:

[12 14 5 6; 2 NAN 2 8; 3 NAN NAN 11]

my for loop is currently creating a 9 by 3 array that has the correct values in each column but is producing a bigger shape (more NANs).

for i = 1:length(data1)
    for j = 1:length(number)
        if data1(i) == number(j) % my dataset == a number array [270, 271, 272, 273]
            newArray(i,j) = data2(i);
        end
    end       
end

Answer 1

[Voss]

Here's one way:

A = [270 270 270 271 272 272 273 273 273];
B = [ 12   2   3  14   5   2   6   8  11];

[~,~,cidx] = unique(A,'stable');
ridx = accumarray(cidx,A,[],@(x){1:numel(x)});
ridx = [ridx{:}].';

NR = max(ridx);
NC = max(cidx);

result = NaN(NR,NC);
result(sub2ind([NR,NC],ridx,cidx)) = B

result = 3x4

12 14 5 6 2 NaN 2 8 3 NaN NaN 11

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[Kendall Galvez]

Hi Voss,

Thank you for the help. My results are producing NaNs columns (randomly spaced???) and is not producing the right number of columns...I am wondering which part could be the culprit of this.

[Voss]

You're welcome!

Please save the relevant variables in a .mat file and upload it here using the paperclip button.

[Kendall Galvez]

A = EventDays

B = Events

[Voss]

A = load('EventDays.mat').doy_e

A = 1x2065

270 270 270 270 270 NaN 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270

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B = load('Events.mat').events

B = 1x2065

15 2 5 4 5 0 0 3 4 9 1 5 5 10 10 11 11 9 7 3 4 6 1 3 6 0 7 0 3 2

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idx = ~isnan(A);
[~,~,cidx] = unique(A(idx),'stable');
ridx = accumarray(cidx,A(idx),[],@(x){1:numel(x)});
ridx = [ridx{:}].';

NR = max(ridx);
NC = max(cidx);

result = NaN(NR,NC);
result(sub2ind([NR,NC],ridx,cidx)) = B(idx)

result = 72x31

15 7 1 4 4 3 9 2 1 6 2 5 4 1 1 1 0 2 1 3 3 2 3 3 1 5 2 1 0 0 2 1 3 0 3 4 2 1 6 13 2 3 2 1 0 0 1 0 5 4 16 14 5 25 0 0 0 0 0 0 5 2 9 1 5 1 0 2 3 7 0 0 3 0 0 2 7 2 1 13 1 1 2 5 2 0 0 0 7 0 4 6 4 1 8 1 0 0 0 1 2 3 3 0 0 4 1 8 3 3 2 8 6 2 9 0 5 0 3 0 5 4 8 6 4 1 4 4 4 4 1 1 3 6 9 1 0 3 12 4 7 2 1 10 0 1 1 0 0 3 0 2 1 2 3 2 8 3 14 5 1 3 2 0 0 0 0 2 3 9 4 6 4 3 0 0 0 0 0 0 3 4 4 3 8 5 6 4 3 5 0 3 0 1 5 3 2 8 10 4 7 6 3 5 1 0 0 0 1 0 4 3 2 6 5 5 1 1 10 0 1 1 1 3 8 1 2 1 3 19 7 2 2 4 0 5 2 1 5 1 9 1 2 7 12 2 2 2 1 0 10 7 6 0 0 0 2 1 16 12 0 5 1 1 0 0 1 0 0 1 1 5 0 1 10 1 3 4 6 4 1 3 23 1 3 0 4 9 12 9 15 11 7 0 0 1 0 0 0 0

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Answer 2

[Stephen23]

A = [270,270,270,271,272,272,273,273,273];
B = [ 12,  2,  3, 14,  5,  2,  6,  8, 11];
X = ~isnan(A);
C = findgroups(A(X));
R = grouptransform(ones(nnz(X),1),C(:),@cumsum);
M = accumarray([R,C(:)],B(X),[],[],NaN)

M = 3x4

12 14 5 6 2 NaN 2 8 3 NaN NaN 11

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[Stephen23]

Using your uploaded data (my code is unchanged). Note that your data also has NaNs in it, which so far you have not explained how you want to handle. I will remove them.

A = load('EventDays.mat').doy_e

A = 1x2065

270 270 270 270 270 NaN 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270

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B = load('Events.mat').events

B = 1x2065

15 2 5 4 5 0 0 3 4 9 1 5 5 10 10 11 11 9 7 3 4 6 1 3 6 0 7 0 3 2

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X = ~isnan(A);
C = findgroups(A(X));
R = grouptransform(ones(nnz(X),1),C(:),@cumsum);
M = accumarray([R,C(:)],B(X),[],[],NaN)

M = 72x31

15 7 1 4 4 3 9 2 1 6 2 5 4 1 1 1 0 2 1 3 3 2 3 3 1 5 2 1 0 0 2 1 3 0 3 4 2 1 6 13 2 3 2 1 0 0 1 0 5 4 16 14 5 25 0 0 0 0 0 0 5 2 9 1 5 1 0 2 3 7 0 0 3 0 0 2 7 2 1 13 1 1 2 5 2 0 0 0 7 0 4 6 4 1 8 1 0 0 0 1 2 3 3 0 0 4 1 8 3 3 2 8 6 2 9 0 5 0 3 0 5 4 8 6 4 1 4 4 4 4 1 1 3 6 9 1 0 3 12 4 7 2 1 10 0 1 1 0 0 3 0 2 1 2 3 2 8 3 14 5 1 3 2 0 0 0 0 2 3 9 4 6 4 3 0 0 0 0 0 0 3 4 4 3 8 5 6 4 3 5 0 3 0 1 5 3 2 8 10 4 7 6 3 5 1 0 0 0 1 0 4 3 2 6 5 5 1 1 10 0 1 1 1 3 8 1 2 1 3 19 7 2 2 4 0 5 2 1 5 1 9 1 2 7 12 2 2 2 1 0 10 7 6 0 0 0 2 1 16 12 0 5 1 1 0 0 1 0 0 1 1 5 0 1 10 1 3 4 6 4 1 3 23 1 3 0 4 9 12 9 15 11 7 0 0 1 0 0 0 0

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