I usually do something like this —
t = linspace(0, 100, 10000); % Time Vector
s = sum(sin([1 5 10 15 20 25 30 35 40 45].'*2*pi*t)); % Signal (Here A Vector)
figure
plot(t, s)
grid
xlabel('Time')
ylabel('Amplitude')
L = numel(t); % Length Of The Signal Vector
dt = t(2)-t(1); % Sampling Time Increment
Fs = 1/dt; % Sampling Frequency
Fn = Fs/2; % Nyquist Frequency
NFFT = 2^nextpow2(L); % For Efficiency
FTs = fft(s(:).*hamming(L),NFFT)/L; % Fourier Transform (Windowing Not Required, Shown For Information)
Fv = linspace(0, 1, NFFT/2+1)*Fn; % Frequency Vector For One-Sided Fourier Transform Plot
Iv = 1:numel(Fv); % Index Vector (For Convenience)
figure
plot(Fv, abs(FTs(Iv))*2) % Multiply By 2 To Correct For Energy Division Between Positive And Negative Frequencies
grid
xlabel('Frequency (Units)')
ylabel('Magnitude (Units)')
I am not certain what you are doing, however something like this should work.
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