How can I create another matrix with the sum of every 30 rows in a 14,400 by 11 matrix?

I have a matrix with 11 columns of daily data for 40 years (14,400 days) and need the sum of every 30 days to be calculated. After looking around a lot I decided I'd ask on here. Thanks in advance for the help!

Answer 1

[Andrei Bobrov]

out = squeeze(sum(reshape(yourdata',11,30,[]),2))';

[Habeeb Alasadi]

thank you for this perfect answer

Answer 2

[Image Analyst]

Did you try the brute force approach? It's pretty simple and intuitive and fast:

m = randi(9, [14400, 11]);
otuputRow = 1;
for row = 1 : 30 : size(m, 1)
  theSums(otuputRow, :) = sum(m(row:row+29,:));
  otuputRow = otuputRow + 1;
end
theSums

Answer 3

[Matt J]

If the 30-day blocks to be summed are tiled, you can also do

 result = downsampn(yourMatrix,[30,1])*30;

where downsampn is given by,

function M=downsampn(M,bindims)
%DOWNSAMPN - simple tool for downsampling n-dimensional nonsparse arrays
%
%  M=downsampn(M,bindims)
%
%in:
%
% M: an array
% bindims: a vector of integer binning dimensions
%
%out:
%
% M: the downsized array

nn=length(bindims);

[sz{1:nn}]=size(M); %M is the original array
sz=[sz{:}];

newdims=sz./bindims;

args=num2cell([bindims;newdims]);

M=reshape(M,args{:});

for ii=1:nn

   M=mean(M,2*ii-1);

end

M=reshape(M,newdims);

[Cedric]

I don't think that we can beat this one. I expected Andrei's solution to be faster, but it seems that dealing with pages makes it slower than iterating over "bindims"..

Answer 4

[Matt J]

 result = conv2(yourMatrix,ones(30,1),'valid');

Answer 5

[Cedric]

Here are a couple additional ways, if you need a sum for each block of 30 days (not a "moving" sum), assuming that your original matrix is named M:

Base on accumarray

 M_aggregated      = zeros( size(M,1)/30, size(M,2) ) ;
 blockId           = zeros( size(M,1), 1 ) ;
 blockId(1:30:end) = 1 ;
 blockId           = cumsum( blockId ) ;
 for cId = 1 : size(M,2)
     M_aggregated(:,cId) = accumarray( blockId, M(:,cId) ) ;
 end

Based on matrix product

 % - Build aggregation matrix.
 aggr = repmat( {ones(1,30)}, size(M,1)/30, 1 ) ;
 aggr = blkdiag( aggr{:} ) ;

 % - Aggregate data.
 M_aggregated = aggr * M ;

Answer 6

[txvmi07]

Thank you everyone for your generous help! All of your suggestions worked great and seeing all the different methodologies was extremely helpful. I've generated a hyperbolic decline curve for an oil and gas well in order to estimate the ultimate volumes that it will produce over 40 years (~14,400 days). While having this data on a daily basis is great it's more function to further analysis on a monthly basis.

I will say that Andrei's response was likely the simplest, but that all examples worked perfectly.

Thanks again!