I'm new to matlab so this might be very simple but I would appreciate the help..
I have a matrix of 32 values across and 2209 down. Each line represents one hour of rainfall so these values represent values of rainfall at 1.875min timesteps. I need to use linear interpolation to make this either 2min or 5 min timesteps (a matrix of 30 values across or 12 values across).
for example, trying to convert the 32x2209 into 30x2209.
The new first value would be 0.9375 * the original first value.
The new second value would be (0.0625 * the original first value) + ((0.9375 - 0.0625) * the original second value)
The new third value would be ((2 * 0.0625) * the original second value) + ((0.9375 - (2 * 0.0625)) * the original third value)
... and so on.
Is there an easy way to do this in matlab?
Many thanks in advance to anyone that can help.
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This is a method that will preserve area.
%Step 0. Just making some random data to work with...
R = rand(2209,32);
%Step 1. Get the cumulative area
cumulativeArea = [zeros(size(R,1),1) cumsum(R,2)];
%Step 2. Interpolate the accumulated area on the new time intervals
t0 = 0:(1/32):1; %Old t1 = 0:(1/30):1; %New cumulativeAreaNew = interp1( t0, cumulativeArea', t1 );
%Step 3. Differentiate the result.
Rnew = diff( cumulativeAreaNew )';
% Just a check:
max(abs(sum(R,2) - sum(Rnew,2))) %Very small
A beginner approach,
%1.875 is the default sample rate n_col_def = (60/1.875);
%row amount n_row_def = 2209;
%create time vector of original data (start from t = 1.875, hop 1.875,until data end) time_1 = (60/n_col_def):(60/n_col_def):(60/n_col_def)*n_row_def*n_col_def;
%your matrix name data_1 = rand(n_col_def,n_row_def);
%transpose the matrix to get correct reshape data_1_tr = data_1'; %reshape matrix to vector (1*n) data_1_res = reshape(data_1_tr,1,n_row_def*n_col_def);
% same row amount with 30 columns require lemgth n_time_2 to be specific n_col_new = (60/2); n_time_new = n_col_new*n_row_def;
%t_start = 2,sample time 2 minutes . [2,4,6...,] time_2 = 2:2:(n_time_new*2); %interpolate the data based on the new time vector (of 5 minutes) data_2 = interp1(time_1,data_1_res,time_2,'linear');
% reshape vector to matrix data_2_res = reshape(data_2,n_col_new,); data_2 = data_2_res';
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