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Row combination for repeated values

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Maha
Maha on 21 Feb 2020
Commented: Maha on 25 Feb 2020
Hello,
I have different variables (date, time, lat, lon) and X for different depths, where each column represents n times (number of different depths) the same variable at a different depth, let say 0 m, 100 m, 500 m. Here a more explicit example :
A = [20200101, NaN, NaN, 1200, NaN, NaN, 90, NaN, NaN, 45, NaN, NaN, 2, NaN, NaN; % 0 m
NaN, 20200101, NaN, NaN, 1200, NaN, NaN, 90, NaN, NaN, 45, NaN, NaN, 3, NaN; % 100 m
NaN, NaN, 20200101, NaN, NaN, 1200, NaN, NaN, 90, NaN, NaN, 45, NaN, NaN, 4 ]; % 500 m
How can I keep the repeated band combinations for date, time, latitude and longitude, so my final output would be, for each unique Date / Time / Lat / Lon :
B = [20200101, 1200, 90, 45, 2, 3, 4]; % Date / Time / Lat / Lon / Val 0m / Val100m / Val500m
I was thinking of using a for loop with find
find(A(i,1)==A(:,2) & A(i,1)==A(:,3) etc.)
But I if I have 50 different depths, it will be a lot of combinations. Does anyone has a simpler way in mind ?
Cheers

  1 Comment

Jon
Jon on 21 Feb 2020
I'm not understanding what the columns in your A matrix represent. In particular how come the date appears in a different column in each row. Similarly why does the latitude appear in a different column. It looks like they shift over by 1 column in each succesive row, but I don't know if this is always the case, and what it means.

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Answers (1)

Spencer Chen
Spencer Chen on 21 Feb 2020
Now, you were not very clear on your column structure. I understand it as in:
A = [date.depth1, date.depth2 .. date.depthNtime, lat.depth1, lat.depth2 ..lat.depthN, etc.]
% A = row X (depth and Var)
If that's the case, then something like this code would solve you problem:
nrows = size(A,1);
ndepth = 3;
A2 = reshape(A,nrows,ndepth,[]); % A2 = row X depth X Var
B = squeeze(nansum(A2,2));
The crux of it is to use nansum() to combine and ignore values from other depths. So we restructure the matrix so we can use nansum().
Blessings,
Spencer

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Maha
Maha on 24 Feb 2020
I don't know if I should ask an other question. It turns out that it only works for a consistent matrix A (1 line = 1 depth). Any idea on how to transform A into a consistent matrix ?
The A1 I have :
A1 = [20200101, 1200, 90, 45, 2; % depth 1
20200101, 1200, 90, 45, 3; % depth 2
20200101, 1200, 90 , 45, 4; % depth 3
20200102, 1200, 90 , 45, 5; % depth 1
20200102, 1200, 90 , 45, 6]; % depth 3
A2 is A1 in a consistent version that I don't have yet :
A2 = [20200101, 1200, 90, 45, 2; % depth 1
20200101, 1200, 90, 45, 3; % depth 2
20200101, 1200, 90 , 45, 4; % depth 3
20200102, 1200, 90 , 45, 5; % depth 1
20200102, 1200, 90 , 45, NaN; % depth 2
20200102, 1200, 90 ,45, 6]; % depth 3
I do have the index of each depth available, i.e
ind_depth_1 = [1; 4];
ind_depth_2 = [2];
ind_depth_3 = [3; 5];
The Result that I should have if my solution from the previous message is applyied :
B = [20200101, 1200, 90, 45, 2, 3, 4;
20200102, 1200, 90, 45, 5, NaN, 6];
Jon
Jon on 25 Feb 2020
I'm sorry, but I can not understand the problem description from the above. If you could please provide just one complete example of the input matrix or matrices that you actually would begin with and the final resulting matrix that you are looking for perhaps this would help. I see lots of matrices in the above discussion, but I can't follow what they are trying to illustrate. I think, one, simple, complete example would help.
Maha
Maha on 25 Feb 2020
My actual real matrix looks like A1, from my previous message, with 500k lines, and 10 different depths.
I need the B matrix as a result (every depth information for unique spatial and temporal information).
The solution I've shown just work for a consistent matrix A2, where each line would represent a depth. My A1 / True matrix isn't consistent.
I would like to either
1) switch from A1 to A2, then I could apply my previous solution
or
2) Find a direct solution to transform A1 into B

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