hello
if your number of periods recorded is not very high and your signal is "clean" then I would prefer to mesure the time difference between succesives zero crossing points (interpolated)
this will be definitively more accurate than a fft with only few samples (frequency resolution df = Fs / samples)
data = readmatrix('data_1945.txt');
t = data(:,1);
fs = 1/mean(diff(t));
y = data(:,2);
% fft method
[fhz,fft_spectrum] = do_fft(t(:),y(:));
[amplitude1, idx] = max(fft_spectrum);
frequency = fhz(idx);
period1 = 1/frequency
amplitude1
% zero crossing method -alternative for clean periodic signals-
zct = find_zc(t,y,0);
period2 = mean(diff(zct))
amplitude2 = 0.5*(max(y) - min(y))
figure(1)
plot(t,y,'b',zct,zeros(1,numel(zct)),'*r','markersize',25)
title('target position: 1.5 radians')
xlabel('Time[s]')
ylabel('Position[radians]')
legend('signal','zc points')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function zct = find_zc(x,y,threshold)
% positive slope "zero" crossing detection, using linear interpolation
y = y - threshold;
zci = @(data) find(diff(sign(data))>0); %define function: returns indices of +ZCs
ix=zci(y); %find indices of + zero crossings of x
ZeroX = @(x0,y0,x1,y1) x0 - (y0.*(x0 - x1))./(y0 - y1); % Interpolated x value for Zero-Crossing
zct = ZeroX(x(ix),y(ix),x(ix+1),y(ix+1));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [freq_vector,fft_spectrum] = do_fft(time,data)
dt = mean(diff(time));
Fs = 1/dt;
nfft = length(data); % maximise freq resolution => nfft equals signal length
% window : hanning
window = hanning(nfft);
cor_coef = length(window)/sum(window);
% fft scaling
% fft_spectrum = abs(fft(data))/nfft;
fft_spectrum = abs(fft(data.*window))*2*cor_coef/nfft;
% one sidded fft spectrum % Select first half
if rem(nfft,2) % nfft odd
select = (1:(nfft+1)/2)';
else
select = (1:nfft/2+1)';
end
fft_spectrum = fft_spectrum(select,:);
freq_vector = (select - 1)*Fs/nfft;
end
