A recent post raised a question about differences in results obtained with numpy.linalg.eigh() and scipy.linalg.eigh(), documented at http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigh.html#numpy.linalg.eigh and http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eigh.html#scipy.linalg.eigh, respectively. It is clear that these functions address different mathematical problems (among other things, the SciPy routine can solve the generalized as well as standard eigenproblems); I am not concerned here with numerical differences in the results for problems both should be able to solve (the author of the original post received useful replies in that thread).
What I would like to ask about is the situation this illustrates, where both NumPy and SciPy provide similar functionality (sometimes identical, to judge by the documentation). Is there some guidance on which is to be preferred? I could argue that using only NumPy when possible avoids unnecessary dependence on SciPy in some code, or that using SciPy consistently makes for a single interface and so is less error prone. Is there a rule of thumb for cases where SciPy names shadow NumPy names? I've used Python for a long time, but have only recently returned to doing serious numerical work with it. The tools are very much improved, but sometimes, like now, I feel I'm missing the obvious. I would appreciate pointers to any relevant documentation, or just a summary of conventional wisdom on the topic. Regards, Michael _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
