I have a rather large matrix. The data was sampled at 100 Hz. I want to the data to look like it was sampled at 5 Hz

Right now the left column is time and it counts .01, .02, .03 etc.

I want to pull out all of the data at 5 hz intervals such as 0, .2, .4 and delete the rest of the rows.

I will answer any question for clarification too

[Jürgen]

did you ask the same question before ? I already proposed and answer and there was even a more elaborate answer by somebody else

[Jürgen]

by the way , I don't know what the goal is but if you have been oversampling maybe it is better to take the mean of the moving average, depending on your data averaging will probaly decrease noise

[Azzi Abdelmalek]

Jürgen, then you are changing your signal

[Jürgen]

yeah all right all depends on what you are measuring and the first thing to do is to analyse your data in a smart way of course, but let us say that you are doing e.g. loadcell measurements over time to see how a the force changes, you will have noise on the measurement and the average will gie you an idea how the signal moves in time , that is what I meant

Answer 1

[Jürgen]

well If I understand it well you have a vector Sample100 with 100 Hz Samples and you want only the sample @ 5 hz Sample5=Sample100(1:20:end) should do the trick then hope this helps regards,J

Answer 2

[Azzi Abdelmalek]

 fe=100 %sample frequency
 te=1/fe %sample time
 t=0:te:1;y=rand(length(t),1)%
 fd=5  %desired frequency
 td=1/fd %desired sample time
 nt=floor(td/te)
 ind=1:nt:length(t);
 new_t=t(ind);
 new_y=y(ind);
 plot(t,y);hold on;plot(new_t,new_y,'xr','linewidth',14);

Answer 3

[Matt Fig]

Here is another example. You say the first column is time, so it is here.

% Original data with columns: [time,signal]
S = [(0:.01:2*pi).' sin(0:.01:2*pi).']; 
Sd = S(1:20:end,:);  % Downsampled signal.
plot(S(:,1),S(:,2),'b.',Sd(:,1),Sd(:,2),'sr') 
legend('Original','Downsampled')

Answer 4

[Dr. Seis]

Use resample - This is an example of why you may not want to take every Nth amplitude:

Example where a is your input dataset and b is your output dataset:

Fs_in = 100; % samples per second
Fs_out = 5; % samples per second
b = resample(a,Fs_out,Fs_in);