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I'm sorry if my question might be simple, but I don't have a strong background on signal processing. I have a csv file of a 8 MHz bandwidth, sampled with a resolution bandwidth of 100 KHz. What I want to do is to compute the PSD over the 8 MHz bandwidth, but I'm not sure if I am doing this correctly.

For a "stable" signal, I multiply the received power over the 100 KHz sample, and then average it at the end over 8 MHz. Is that correct? Does this give me a rough estimation of the PSD?

Are there tools like octave or even python programming that can help in this process? I looked on the internet but didn't had luck.

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  • $\begingroup$ What do you mean by resolution bandwidth? That term is typically used on equipment like spectrum analyzers to refer to the approximate bandwidth for each frequency bin (for digital FFT-based analyzers) or for the analog bandwidth of the front end receiver (for swept-tune analyzers). Specifically, what is the sample rate of your data? $\endgroup$ – Jason R Sep 22 '13 at 23:57
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I don't understand what exactly you are doing, and your Nyquist frequency is only 50KHz if you're sampling at 100KHz (so you won't get 1MHz resolution). Please clarify if you need a better answer.

Regardless, Python has several ways to compute a PSD. One is with matplotlib. For example:

matplotlib.pyplot as pl
# ...load in your data to dataArr...
sampFreq = 100000
Pxx, freqs = pl.psd(dataArr, NFFT=256,Fs=samplFreq,detrend=detrend_mean,window=window_hanning,noverlap=128,sides='onesided',scale_by_freq=True)

and you get a nice plot, along with the PSD data in Pxx and the frequencies in freqs.

Example PSD

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