I would add one element to the discussion: for some (odd) reasons, SciPy is lacking the functions `rfftn` and `irfftn`, functions using half the memory space compared to their non-real equivalent `fftn` and `ifftn`. However, I haven't (yet) seriously tested `scipy.fftpack.fftn` vs. `np.fft.rfftn` to check if there is a serious performance gain (beside memory usage).
Cheers, Pierre On Tue Oct 28 2014 at 10:54:00 Stefan van der Walt <ste...@sun.ac.za> wrote: > Hi Michael > > On 2014-10-27 15:26:58, D. Michael McFarland <dm...@dmmcf.net> wrote: > > 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'm not sure if you've received an answer to your question so far. My > advice: use the SciPy functions. SciPy is often built on more extensive > Fortran libraries not available during NumPy compilation, and I am not > aware of any cases where a function in NumPy is faster or more extensive > than the equivalent in SciPy. > > If you want code that falls back gracefully when SciPy is not available, > you may use the ``numpy.dual`` library. > > Regards > Stéfan > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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