I have now implemented this functionality in numpy.correlate() and numpy.convolve(). https://github.com/bringingheavendown/numpy. The files that were edited are: numpy/core/src/multiarray/multiarraymodule.c numpy/core/numeric.py numpy/core/tests/test_numeric.py Please look over the code, my design decisions, and the unit tests I have written. This is my first time contributing, so I am not confident about any of these and welcome feedback.
> On Jun 8, 2015, at 9:54 PM, Honi Sanders <h...@brandeis.edu> wrote: > > 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 > https://github.com/scipy/scipy/issues/4940 and on the spicy-dev list > (http://mail.scipy.org/pipermail/scipy-dev/2015-June/020757.html). It seems > the best place to start is with numpy.correlate, so that is what I am > requesting. I have done a simple implementation > (https://gist.github.com/bringingheavendown/b4ce18aa007118e4e084) which gives > 50x speedup under my conditions > (https://github.com/scipy/scipy/issues/4940#issuecomment-110187847). > > This is my first experience with contributing to open-source software, so any > pointers are appreciated. > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
