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I'm trying to use python 3.x to do an fft from some data. But when I plot I get my original data (?) not the data's fft. I'm using matlab so I can compare the results.
I've already tried many examples from this site but nothing seems to work. I'm not used to work with python. How can I get a plot similar to matlab's? I don't care if I get -f/2 to f/2 or 0 to f/2 spectrum.
My data
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
mat = scipy.io.loadmat('sinal2.mat')
sinal2 = mat['sinal2']
Fs = 1000
L = 1997
T = 1.0/1000.0
fsig = np.fft.fft(sinal2)
freq = np.fft.fftfreq(len(sinal2), 1/Fs)
plt.figure()
plt.plot( freq, np.abs(fsig))
plt.figure()
plt.plot(freq, np.angle(fsig))
plt.show()
FFT from python:
FFT from matlab:
The imported signal sinal2 has a size (1997,1). In case of 2 dimensional arrays like this, numpy.fft.fft by default computes the FFT along the last axis. In this case that means computing 1997 FFTs of size 1. As you may know a 1-point FFT is an identity mapping (meaning the FFT of a single value gives the same value), hence the resulting 2D array is identical to the original array.
To avoid this, you can either specify the other axis explicitly:
fsig = np.fft.fft(sinal2, axis=0)
Or otherwise convert the data to a single dimensional array, then compute the FFT of a 1D array:
sinal2 = singal2[:,0]
fsig = np.fft.fft(sinal2)
On a final note, you FFT plot shows a horizontal line connecting the upper and lower halfs of the frequency spectrum. See my answer to another question to address this problem. Since you mention that you really only need half the spectrum, you could also truncate the result to the first N//2+1 points:
plt.plot( freq[0:len(freq)//2+1], np.abs(fsig[0:len(fsig)//2+1]))
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