[Numpy-discussion] Choosing between NumPy and SciPy functions

[Pierre Barbier de Reuille]

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 <stefan at sun.ac.za> wrote:

> Hi Michael
>
> On 2014-10-27 15:26:58, D. Michael McFarland <dmmcf at 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
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