On Sun, Jun 28, 2020 at 9:37 PM Neal Becker <ndbecker2@gmail.com> wrote:
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.
If I understand your point correctly and the discussion so far, the intention here is to use the keyword to denote the convention for an FFT-IFFT pair rather than just normalization in a single transformation (either FFT or IFFT). The idea being that calling ifft on the output of fft while using the same `norm` would be more or less identity. This would work for "half", but not for, say, "full". We need to come up with a name that specifies where normalization happens with regards to the forward-inverse pair. Does this make sense, considering your point? András
Thanks, Neal
On Sat, Jun 27, 2020 at 10:40 AM Sebastian Berg <sebastian@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
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