[I'm not sure if this is best in 'devel' or 'users']

I'm trying to compute PSDs using matplotlib.mlab.psd and came across the "PSD amplitudes" thread from last year:

http://sourceforge.net/mailarchive/message.php?msg_id=472101A6.9080206%40isla.hawaii.edu

Using the latest version of psd on svn trunk (rev 6429) that added support for pad_to, I can now compute the Matlab pwelch

example using matplotlib. This example is given in the Signal Processing Toolbox User's Guide:

http://www.mathworks.com/access/helpdesk/help/pdf_doc/signal/signal_tb.pdf

(look on pages 2-23 and 2-24). Note I do not have easy access to Matlab itself, so I'm just using this published example.

The Matlab code is as follows:

randn('state',1)

fs = 1000; % Sampling frequency

t = (0:0.3*fs)./fs; % 301 samples

A = [2 8]; % Sinusoid amplitudes (row vector)

f = [150;140]; % Sinusoid frequencies (column vector)

xn = A*sin(2*pi*f*t) + 5*randn(size(t));

Hs = spectrum.welch('rectangular',150,50);

psd(Hs,xn,'Fs',fs,'NFFT',512);

This produces a fairly noisy signal from -20 to -10 dB, with a strong peak of ~6 dB at 150 Hz (see the plot on page 2-24).

While my equivalent (?) python code is:

from scipy import *

from mlabsvn import psd # This is a local copy of svn revision 6429 of matplotlib.mlab

from pylab import *

fs=1000

t=linspace(0,0.3,0.3*fs+1)

A=[2,8]

f=[150,140]

xn=A[0]*sin(2*pi*f[0]*t) + A[1]*sin(2*pi*f[1]*t) + 5*randn(len(t))

Pxx,freqs = psd(xn,Fs=fs,NFFT=150,noverlap=75,pad_to=512)

figure()

plot(freqs, 10*log10(Pxx) )

show()

However, this produces a peak of over 30 dB at 150 Hz. Unless there is a mistake in my code above, there seems to be a

significant difference between the matplotlib and matlab implementations.

I noticed that the values 10*log10(Pxx/len(xn)) produces results that match much better. Without looking more closely at the

code for psd and reviewing Bendat and Piersol I cannot be sure that this is the correct fix.

Does anyone else have any insight? When is the next release planned, and how likely is a fix?

Thanks,

Matt

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