Timmie, Matt and I started TimeSeries each on our own, before merging our efforts last Christmas. On my side, I was trying to find something equivalent to pyclimate for my own purpose, but supporting numpy. I poked around this package a bit, wasn't completely happy with it, then figured it would be as easy to redo on top of numpy. It's while I was struggling with subclassing the masked arrays of numpy.core.ma that I decided to reimplement maskedarray, which eventually leads to the current version of timeseries. If I'm not completely mistaken, numpy.core.ma is a translation via numeric of Paul Dubois' s implementation for CDAT. So yeah, I could have followed that path, but I was learning python at the same time and it was a very good exercise to reinvent the wheel, thus adding more noise and confusion. I do agree that there could be some linkage between timeseries, pyclimate and CDAT, at least in terms of converting objects from package to another. However, it's unlikely to happen any time soon. Nevertheless, I may try to implement some of the functions of pyclimate and CDAT in numpy/scipy, when I'll have the need for them. What I would suggest you is to start with timeseries, as it's pretty easy to use. Then, depending on what your needs are, you can start developing your own functions. In any case, don't hesitate to contact Matt or myself if you need some specific help. The TimeSeriesPackage page of the scipy wiki is a good start, Matt did a terrific job. Sincerely P.