On Tue, Jul 20, 2010 at 6:02 PM, David Goldsmith <d.l.goldsm...@gmail.com>wrote:
> On Thu, Jul 15, 2010 at 9:41 AM, David Goldsmith > <d.l.goldsm...@gmail.com>wrote: > >> On Thu, Jul 15, 2010 at 3:20 AM, Martin Raspaud >> <martin.rasp...@smhi.se>wrote: >> >>> -----BEGIN PGP SIGNED MESSAGE----- >>> Hash: SHA1 >>> >>> David Goldsmith skrev: >>> > >>> > >>> > Interesting comment: it made me run down the fftpack tutorial >>> > <http://docs.scipy.org/scipy/docs/scipy-docs/tutorial/fftpack.rst/ >>> > >>> > josef has alluded to in the past to see if the suggested pointer >>> > could point there without having to write a lot of new content. >>> > What I found was that although the scipy basic fft functions don't >>> > support it (presumably because they're basically just wrappers for >>> > the numpy fft functions), scipy's discrete cosine transforms >>> support >>> > an "norm=ortho" keyword argument/value pair that enables the >>> > function to return the unitary versions that you describe above. >>> > There isn't much narrative explanation of the issue yet, but it got >>> > me wondering: why don't the fft functions support this? If there >>> > isn't a "good" reason, I'll go ahead and submit an enhancement >>> ticket. >>> > >>> > >>> > Having seen no post of a "good reason," I'm going to go ahead and file >>> > enhancement tickets. >>> >>> Hi, >>> >>> I have worked on fourier transforms and I think normalization is >>> generally seen >>> as a whole : fft + ifft should be the identity function, thus the >>> necessity of a >>> normalization, which often done on the ifft. >>> >>> As one of the previous poster mentioned, sqrt(len(x)) is often seen as a >>> good >>> compromise to split the normalization equally between fft and ifft. >>> >>> In the sound community though, the whole normalization often done after >>> the fft, >>> such that looking at the amplitude spectrum gives the correct amplitude >>> values >>> for the different components of the sound (sinusoids). >>> >>> My guess is that normalization requirements are different for every user: >>> that's >>> why I like the no normalization approach of fftw, such that anyone does >>> whatever >>> he/she/it wants. >>> >> >> I get the picture: in the docstring, refer people to fftw. >> >> DG >> > > I can't find this fftw function in either numpy or scipy - where is it? > > It is a GPL package so we don't use it. The fftw homepage is here<http://www.fftw.org/> . Chuck
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