I am learning numpy/scipy, coming from a MATLAB background. The xcorr function in Matlab has an optional argument "maxlag" that limits the lag range from –maxlag to maxlag. This is very useful if you are looking at the cross-correlation between two very long time series but are only interested in the correlation within a certain time range. The performance increases are enormous considering that cross-correlation is incredibly expensive to compute.

What is troubling me is that numpy.correlate does not have a maxlag feature. This means that even if I only want to see correlations between two time series with lags between -100 and +100 ms, for example, it will still calculate the correlation for every lag between -20000 and +20000 ms (which is the length of the time series). This (theoretically) gives a 200x performance hit! Is it possible that I could contribute this feature?

I have introduced this question as a scipy issue and on the scipy-dev list. It seems the best place to start is with numpy.correlate, so that is what I am requesting. I have done a simple implementation which gives 50x speedup under my conditions.

This is my first experience with contributing to open-source software, so any pointers are appreciated.

Other issues related to correlate functions include ENH: Fold fftconvolve into convolve/correlate functions as a parameter #2651, Use FFT in np.correlate/convolve? (Trac #1260) #1858, and normalized cross-correlation (Trac #1714) #2310.