hello
see basic demo below
select which option you want
overlap = 0*buffer_size; % overlap expressed in samples (a. For the case of 0% overlap)
% overlap = 0.5*buffer_size; % overlap expressed in samples (b. For the case of 50% overlap)
% dummy data (a noisy sinusoid)
% NB : data must be column oriented
n=5000;
t= (1:n)';
y = sin(2*pi*t./max(t))+0.1*rand(size(t));
% choose the amount of overlap
buffer_size = 1024;
overlap = 0*buffer_size; % overlap expressed in samples (a. For the case of 0% overlap)
% overlap = 0.5*buffer_size; % overlap expressed in samples (b. For the case of 50% overlap)
%%%% main loop %%%%
[tt,yy,start_index,stop_index] = my_movmean(t,y,buffer_size,overlap);
plot(t,y,tt,yy,'*-r');
title('data');
legend('raw data','1024 samples mean');
xlabel('Time(s)');
% display start_index,stop_index in command window
start_index
stop_index
%%%%%%%%%% my functions %%%%%%%%%%%%%%
function [new_time,data_out,start_index,stop_index] = my_movmean(t,data_in,buffer_size,overlap)
% NB : buffer size and overlap are integer numbers (samples)
% data (in , out) are 1D arrays (vectors)
shift = buffer_size-overlap; % nb of samples between 2 contiguous buffers
[samples,~] = size(data_in);
nb_of_loops = fix((samples-buffer_size)/shift +1);
for k=1:nb_of_loops
start_index(k) = 1+(k-1)*shift;
stop_index(k) = min(start_index(k)+ buffer_size-1,samples);
x_index(k) = round((start_index(k)+stop_index(k))/2);
data_out(k,:) = mean(data_in(start_index(k):stop_index(k),:),1,'omitnan'); %
end
new_time = t(x_index); % time values are computed at the center of the buffer
end
