Difference between scipy periodogram and self implemented power spectral density

Question

I am trying to evaluate the frequency domain of several signals. For this I used the PSD implementation given in this answer. As a comparison I used the signal.periodogram function provided in scipy:

from scipy.signal import tukey
import scipy as sp
f, Pxx_den = sp.signal.periodogram(a_gtrend_orig,12,window=tukey( len(a_gtrend_orig) ))

However when I plot this next to the self-implemented PSD they look significantly different:

As the same window function is used and the periodogram function should also use an FFT where does this difference coming from?

DrM · Accepted Answer · 2018-10-09T14:36:46.717

1

The example that you are comparing this to, is graphing the amplitude at each frequency bin, i.e, abs(fft())

The periodogram produces a power spectral density, that means it is the square of the amplitude at each frequency bin.

The label "windowed psd" is from an early edit, and was corrected later.

edited Oct 09 '18 at 14:36

answered Oct 09 '18 at 14:33

DrM

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Ah that makes sense! – Axel Oct 09 '18 at 14:35
great, you can mark the answer as correct also, if you like. – DrM Oct 09 '18 at 14:40

Difference between scipy periodogram and self implemented power spectral density

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