[Numpy-discussion] Numpy FFT normalization options issue (addition of new option)

[Neal Becker]

Honestly, I don't find "forward" very informative.  There isn't any real
convention on whether FFT of IFFT have any normalization.
To the best of my experience, either forward or inverse could be normalized
by 1/N, or each normalized by 1/sqrt(N), or neither
could be normalized.  I will say my expertise is in signal processing and
communications.

Perhaps
norm = {full, half, none} would be clearest to me.

Thanks,
Neal

On Sat, Jun 27, 2020 at 10:40 AM Sebastian Berg <sebastian at sipsolutions.net>
wrote:

> On Fri, 2020-06-26 at 21:53 -0700, leofang wrote:
> > Hi all,
> >
> >
> > Since I brought this issue from CuPy to Numpy, I'd like to see a
> > decision
> > made sooner than later so that downstream libraries like SciPy and
> > CuPy can
> > act accordingly. I think norm='forward' is fine. If there're still
> > people
> > unhappy with it after my reply, I'd suggest norm='reverse'. It has
> > the same
> > meaning, but is less confusing (than 'inverse' or other choices on
> > the
> > table) to me.
> >
>
> I expect "forward" is good (if I misread something please correct me),
> and I think we can go ahead with it, sorry for the delay.  However, I
> have send an email to scipy-dev, since we should give them at least a
> heads-up, and if you do not mind, I would wait a few days to actually
> merge (although we can also simply reverse, as long as CuPy does not
> have a release with it).
>
> It might be nice to expand the kwarg docs slightly with a sentence for
> each normalization mode?  Refering to `np.fft` docs is good, but if we
> can squeeze in a short refresher and refer there for details/formula it
> would be nicer.
> I feel "forward" is very intuitive, but only after pointing out that it
> is related to whether the fft or ifft has the normalization factor.
>
> Cheers,
>
> Sebastian
>
>
> >
> > Best,
> > Leo
> >
> >
> >
> > --
> > Sent from: http://numpy-discussion.10968.n7.nabble.com/
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