Plotting power spectrum in python

Question

I have an array with 301 values, which were gathered from a movie clip with 301 frames. This means 1 value from 1 frame. The movie clip is running at 30 fps, so is in fact 10 sec long

Now I would like to get the power spectrum of this "signal" ( with the right Axis). I tried:

 X = fft(S_[:,2]);  pl.plot(abs(X))  pl.show() 

I also tried:

 X = fft(S_[:,2]);  pl.plot(abs(X)**2)  pl.show() 

Though I don't think this is the real spectrum.

the signal:

The spectrum:

The power spectrum :

Can anyone provide some help with this ? I would like to have a plot in Hz.

Answer 1

Numpy has a convenience function, np.fft.fftfreq to compute the frequencies associated with FFT components:

from __future__ import division import numpy as np import matplotlib.pyplot as plt  data = np.random.rand(301) - 0.5 ps = np.abs(np.fft.fft(data))**2  time_step = 1 / 30 freqs = np.fft.fftfreq(data.size, time_step) idx = np.argsort(freqs)  plt.plot(freqs[idx], ps[idx]) 

Note that the largest frequency you see in your case is not 30 Hz, but

In [7]: max(freqs) Out[7]: 14.950166112956811 

You never see the sampling frequency in a power spectrum. If you had had an even number of samples, then you would have reached the Nyquist frequency, 15 Hz in your case (although numpy would have calculated it as -15).

Answer 2

if rate is the sampling rate(Hz), then np.linspace(0, rate/2, n) is the frequency array of every point in fft. You can use rfft to calculate the fft in your data is real values:

import numpy as np import pylab as pl rate = 30.0 t = np.arange(0, 10, 1/rate) x = np.sin(2*np.pi*4*t) + np.sin(2*np.pi*7*t) + np.random.randn(len(t))*0.2 p = 20*np.log10(np.abs(np.fft.rfft(x))) f = np.linspace(0, rate/2, len(p)) plot(f, p) 

signal x contains 4Hz & 7Hz sin wave, so there are two peaks at 4Hz & 7Hz.