On 19 May 2013 23:25, <killybear...@gmail.com> wrote:
> How can i at least find a peek in FFT spectrum of a square wave ?
> From there i could easily build formula. Sorry for bothering but i am new to
> Python.
Are you the same person who posted the original question?
You probably want to use numpy for this. I'm not sure if I understand
your question but here goes:
First import numpy (you may need to install this first):
>>> import numpy as np
Create a square wave signal:
>>> x = np.zeros(50)
>>> x[:25] = -1
>>> x[25:] = +1
>>> x
array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,
-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Compute the magnitude spectrum:
>>> spect = abs(np.fft.fft(x)[:25])
>>> spect
array([ 0. , 31.85194222, 0. , 10.67342282,
0. , 6.47213595, 0. , 4.69726931,
0. , 3.73254943, 0. , 3.13762901,
0. , 2.7436023 , 0. , 2.47213595,
0. , 2.28230601, 0. , 2.15105461,
0. , 2.06487174, 0. , 2.01589594, 0. ])
Find the index of the maximum element:
>>> np.argmax(spect)
1
So the peak is the lowest non-zero frequency component of the DFT. In
Hz this corresponds to a frequency of 1/T where T is the duration of
the signal.
Oscar
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