Dear Frank,
thanks a lot for your reply. I was aware of the need for FFT normalization and
folding, but you're making another good point here, stating that there
should be
examples on this (folding) in the documentation as it will pose quite an
obstacle for newcomers attempting to practically use the FFT.
When writing yesterday's mail, I falsely was under the impression that there
is
such a thing as a "correct" normalization of an FFT. In the meantime,
I have
come to realize that when considering "real", i.e., physical, signals,
there is
no "true" way of normalizing the result of an inverse FFT, since
everything
depends on the sampling characterstics and on the properties of the frequency
domain representation of the signal.
Just imagine two differently sampled versions of the frequency representation of
some physical signal, both attaining the same number of sample points NFFT, but
different sampling frequencies. There is no way to scale these two signals
coherently, based only on the number of sample points. Assuming Fs1 < Fs2 and
assuming the physical signal to be band-limited with Fband < Fs1, the inverse
transform of the signal sampled at Fs1 will naturally be larger (due to the
larger number of sinces/cosines being summed up in the time signal).
When comparing differently sampled versions of the same signal, it might be
desirable to scale the result based on the energy content of the signal, i.e. by
multiplying the signal with the frequency bin width 'fbin =
Fs/(NFFT-1)'. So
this is where your calculation of the attained frequencies comes into play! Note
that this is equivalent to calculating the energy in the frequency domain by
integration of a piecewise constant interpolation of the frequency domain FFT
coefficients.
I want to repeat my question concerning proposals on where to publish such
considerations for the use of as many R users as possible. Might it be a good
idea to create and propose an official vignette to the FFT function?
Kind regards,
Eike
> On February 3, 2015 at 12:39 AM Franklin Bretschneider <bretschr at
xs4all.nl>
> wrote:
>
>
> Dear Eike Petersen,
>
>
> Re:
>
> > Hello everyone,
> >
> > the docpage for the fft function states:
> >
> > ?Description: Performs the Fast Fourier Transform of an array.?
> >
> > and
> >
> > ?Arguments ? inverse: if ?TRUE?, the unnormalized inverse transform is
> > computed
> > (the inverse has a ?+? in the exponent of e, but here, we do _not_
divide by
> > ?1/length(x)?).?
> >
> > Judging from this, I would expect ?fft(x)? to yield the correct FFT of
x,
> > and
> > ?fft(X, inverse = TRUE) / length(X)? to yield the correct inverse FFT
of X.
> >
> > However, it seems to me that actually the result of ?fft(x)? should be
> > scaled by
> > ?1/length(x)?, while ?fft(X, inverse=TRUE)? seems to yield a correct
result
> > by
> > default:
> >
> > t <- seq(0.001, 1, 0.001)
> > y <- 1 + sin(2*pi*20*t) + 1.5 * sin(2*pi*30*t)
> > Y <- fft(y)
> > dev.new()
> > plot(abs(Y)) ## Shows peaks at amplitudes 1000, 500 and 750, while
they
> > should
> > be at amplitude 1, 0.5 and 0.75.
> > y2 <- Re(fft(Y / length(Y), inverse = TRUE))
> > max(abs(y-y2)) ## The IFFT yields a correctly scaled result by
default, if
> > applied to a correctly scaled FFT.
> >
> > Did I get something wrong? If not, having spent quite some time
figuring
> > this
> > out, I would like to see the documentation clearly pointing this out.
I find
> > the
> > current text rather confusing.
> >
> > On another note: I have spent some time working on demo files that
showcase
> > some
> > of the properties of the FFT and their implementation in R. I have
done this
> > primarily for myself, as I keep forgetting how these things work, but
I
> > thought
> > that it might be helpful to others as well. Any hints on where/how I
should
> > publish such a thing?
> >
> > Kind regards and many thanks in advance,
> >
> > Eike
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
>
> As far as I know an FFT must be normalized and folded to obtain a spectrum
in
> the form we like, so this would be my version:
>
> # your time signal:
>
> t <- seq(0.001, 1, 0.001)
> y <- 1 + sin(2*pi*20*t) + 1.5 * sin(2*pi*30*t)
>
> # find time and frequency calibration:
>
> n = length(y)
> dt = t[2]-t[1]
> fNyq = 1/(2*dt)
> tmax = max(t)
> df = 1/tmax
>
> # make frequency vector to display as x-values of the spectrum rather than
the
> index.
>
> f = seq(-fNyq, fNyq-df, by=df)
>
> # make folding mask
>
> mask=rep(c(1, -1),length.out=n)
>
> # fold the spectrum around the Nyquist frequency; so the DC value (f=0) is
in
> the middle; the - and + max frequency at the ends.
>
> yy = y * mask
>
> # # Then do the FFT
>
> YY <- fft(yy)
>
> Plot the amplitude spectrum vector against the freq. vector
>
> plot(f,abs(YY), type='h')
>
>
>
> --------
>
> It would be a good idea to put such an example in the help pages indeed.
> The short example given in the manual isn't of much help for the usual
> (periodic!) time signals.
>
> Hoping this helps, I remain
>
> With best wishes,
>
> Frank
> ----
>
>
>
>
>
>
> Franklin Bretschneider
> Dept of Biology
> Utrecht University
> bretschr at xs4all.nl
>
>
>
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