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
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