Row combination for repeated values

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Maha am 21 Feb. 2020
Kommentiert: Maha am 25 Feb. 2020
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 ?
  1 Kommentar
Jon am 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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Antworten (1)

Spencer Chen
Spencer Chen am 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, 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().
  5 Kommentare
Jon am 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 am 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
2) Find a direct solution to transform A1 into B

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