Dear Mohammed Lamine Mekhalfia

Ramadan Kareem!

What i introduce here is a proposal.

If you have a table with two vectors, then one vector , the amplitude vector can be used to represent the amplitudes of the fft components. The time can be used to represent the frequency of the fft components. If you have N samples with a sampling frequency fs, then the frequencies will be kfs/N , where k is the index of the frequencies of the fft solution having the value from k=1 to N. So, the time can be scaled from 1 to N.

Best wishes

Abdelhalim abdelnaby Zekry Thank you, I will go for it . ramadhan kareem

I agreed with Abdelhalim abdelnaby Zekry.

Dear Mohammed:

You can use MATLAB, with plot command and these commands:

1-- Y = fft(X) computes the discrete Fourier transform (DFT) of X using a fast Fourier transform (FFT) algorithm.

If X is a vector, then fft(X) returns the Fourier transform of the vector.

If X is a matrix, then fft(X) treats the columns of X as vectors and returns the Fourier transform of each column.

If X is a multidimensional array, then fft(X) treats the values along the first array dimension whose size does not equal 1 as vectors and returns the Fourier transform of each vector.

2--Y = fft(X,n) returns the n-point DFT. If no value is specified, Y is the same size as X.

If X is a vector and the length of X is less than n, then X is padded with trailing zeros to length n.

If X is a vector and the length of X is greater than n, then X is truncated to length n.

If X is a matrix, then each column is treated as in the vector case.

If X is a multidimensional array, then the first array dimension whose size does not equal 1 is treated as in the vector case.

3-- Y = fft(X,n,dim) returns the Fourier transform along the dimension dim. For example, if X is a matrix, then fft(X,n,2) returns the n-point Fourier transform of each row.

As example:

Fs = 1000; % Sampling frequency

T = 1/Fs; % Sampling period

L = 1500; % Length of signal

t = (0:L-1)*T; % Time vector

Form a signal containing a 50 Hz sinusoid of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1.

S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);

Corrupt the signal with zero-mean white noise with a variance of 4.

X = S + 2*randn(size(t));

Plot the noisy signal in the time domain. It is difficult to identify the frequency components by looking at the signal X(t).

plot(1000*t(1:50),X(1:50))

title('Signal Corrupted with Zero-Mean Random Noise')

xlabel('t (milliseconds)')

ylabel('X(t)')

Hassan Nasser thank you for the insight. for more information, in Matlab type "help fft" and clic "Reference page for fft"