I'm implementing an existing MATLAB script in Python and noticing some differences in the behaviour of MATLAB's pwelch function compared to scipy.signal.welch.

From the docs, scipy uses a default window size of 256, whereas MATLAB: "By default, x is divided into the longest possible segments to obtain as close to but not exceed 8 segments with 50% overlap". Both use Hamming windows and overlap by 50%, so I assume the differences I'm seeing are due to window sizes.

However, if I set the same window size for both, I still see different frequency resolution in the results. e.g. for data series of length 6144, sampled every 0.292969 seconds, and set window size as follows:

n = 1000
freqs, power = signal.welch(timeseries, fs=1.0/0.292969, scaling='density', nperseg=n)
print(freqs[2]) # output 0.0034

n = 1500
freqs, power = signal.welch(timeseries, fs=1.0/0.292969, scaling='density', nperseg=n)
print(freqs[2]) # output 0.00227555370371

for MATLAB:

n = 1000;
dt = 0.292969;
[psd,freq] = pwelch(timeseries,n,[],[],1/dt);
freq(2) % output 0.0033

n = 1500;
[psd,freq] = pwelch(timeseries,n,[],[],1/dt);
freq(2) % output 0.0017

For higher window size the difference in output resolution is quite stark, and results in quite different amplitudes for peaks in the spectrum. Am I missing some other parameter which needs adjusting?

I'm implementing an existing MATLAB script in Python and noticing some differences in the behaviour of MATLAB's pwelch function compared to scipy.signal.welch.

From the docs, scipy.signal.welch uses a default window size of 256, whereas MATLAB's pwelch doc says:

By default, x is divided into the longest possible segments to obtain as close to but not exceed 8 segments with 50% overlap".

Both use Hamming windows and overlap by 50%, so I assume the differences I'm seeing are due to window sizes.

However, if I set the same window size for both, I still see different frequency resolution in the results. e.g. for data series of length 6144, sampled every 0.292969 seconds, and set window size as follows:

n = 1000
freqs, power = signal.welch(timeseries, fs=1.0/0.292969, scaling='density', nperseg=n)
print(freqs[2]) # output 0.0034

n = 1500
freqs, power = signal.welch(timeseries, fs=1.0/0.292969, scaling='density', nperseg=n)
print(freqs[2]) # output 0.00227555370371

for MATLAB:

n = 1000;
dt = 0.292969;
[psd,freq] = pwelch(timeseries,n,[],[],1/dt);
freq(2) % output 0.0033

n = 1500;
[psd,freq] = pwelch(timeseries,n,[],[],1/dt);
freq(2) % output 0.0017

For higher window size the difference in output resolution is quite stark, and results in quite different amplitudes for peaks in the spectrum. Am I missing some other parameter which needs adjusting?