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

Feng Yu rainwoodman at gmail.com
Sun Jun 7 16:10:53 EDT 2020


1. The wikipedia pages of CFT and DFT refer to norm='ortho' as 'unitary'.
Since we are in general working with complex numbers, I do suggest unitary
over ortho.
(https://en.wikipedia.org/wiki/Fourier_transform#Other_conventions) and (

2. I share Chris's concern about 'inverse', but I could not come up with a
nice name.

3. Now that we are at this, should we also describe the corresponding
continuum limit of FFT and iFFT in the documentation?

A paragraph doing so could potentially also help people diagnose some of
the normalization factor errors. I assumed it is common that one needs to
translate a CFT into DFT when coding a paper up, and the correct
compensation to the normalization factors will surface if one does the
math. -- I had the impression 1 / N corresponds to 1 / 2pi if the variable
is angular frequency, but it's been a while since I did that last time.

- Yu

On Fri, Jun 5, 2020 at 1:16 PM cvav <cv1038 at wildcats.unh.edu> wrote:

> Ross Barnowski wrote
> > One potential issue that stood out to me was the name of the new keyword
> > option. At face value, "inverse" seems like a poor choice given the
> > established use of the term in Fourier analysis. For example, one might
> > expect `norm="inverse"` to mean that scaling is applied to the ifft, when
> > this is actually the current default.
> Yes that's true, the keyword argument name "inverse" is certainly something
> I don't feel sure about. It'd be nice if everyone interested could suggest
> names that make sense to them and what's their rationale behind them, so
> that we pick something that's as self explanatory as possible.
> My thinking was to indicate that it's the opposite scaling to the default
> option "None", so maybe something like "opposite" or "reversed" could be
> other choices. Otherwise, we can find something that directly describes the
> scaling and not its relationship to the default option.
> Chris
> --
> Sent from: http://numpy-discussion.10968.n7.nabble.com/
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