Azzera filtri
Azzera filtri

Do the Inverse Fourier transform from frequency distribution

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Hello everyone.
I have a set of data : "y_spectrum" shows the electric field amplitude and "t_spectrum" shows the time.
I have done the Fourier transform of those data to get the frequency component according this code:
E = dlmread('y_spectrum.txt',' ',1,0);
t = dlmread('t_spectrum.txt',' ',1,0);
Ts = mean(diff(t));
Fs = 1/Ts;
Tstd = std(diff(t));
Fs = 12E+15; % Sampling Frequency
[Er,tr] = resample(E,t,Fs); % Resample To Regularly-Spaced Intervals
figure
plot(t,E,'.b')
hold on
plot(tr,Er,'-r')
hold off
grid
legend('Original E', 'Resampled E')
Fn = Fs/2; % Nyquist Frequency
L = numel(tr); % Vector Lengths
Erm = Er - mean(Er);
FT_Erm = fft(Er)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;
Iv = 1:numel(Fv);
[pks,locs] = findpeaks(abs(FT_Erm(Iv))*2, 'MinPeakHeight',3E+10);
figure
plot(Fv, abs(FT_Erm(Iv))*2)
hold on
plot(Fv(locs), pks, '.r')
hold off
text(1.5E+15, 7.5E+10, sprintf('Peak %10.3E Frequency %10.3E\n', [pks Fv(locs).'].'))
Is there anyway to do the inverse Fourier transform of the frequency domain obtained from above code?
Thank you very much

Risposte (1)

KALYAN ACHARJYA
KALYAN ACHARJYA il 11 Feb 2020
Is it?
ifft(FT_Erm)
  1 Commento
dang khoa tran
dang khoa tran il 11 Feb 2020
Thank you for your reply.
I also try this but I think it is not since it still shows the variation in frequency domain.

Accedi per commentare.

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