I have some 9000 points that are plotted on a graph:
[Full resolution]
Actually, the plot is n
A simple (ad hoc) way is to just take a weighted average (tunable by alpha
) at each point with its neighbors:
data(2:n-1) = alpha*data(2:n-1) + (1-alpha)*0.5*(data(1:n-2)+data(3:n))
or some variation thereof. Yes, to be more sophisticated you can Fourier transform your data first, then cut off the high frequencies. Something like:
f = fft(data)
f(n/2+1-20:n/2+20) = zeros(40,1)
smoothed = real(ifft(f))
This cuts out the highest 20 frequencies. Be careful to cut them out symmetrically otherwise the inverse transform is no longer real. You need to carefully choose the cutoff frequency for the right level of smoothing. This is a very simple kind of filtering (box filtering in frequency domain), so you can try gently attenuating high order frequencies if the distortion is unacceptable.