=module NumRu::FFTW3
Fast Fourier Transforms by using ((<FFTW|URL:http://www.fftw.org>)) Ver.3.
Takeshi Horinouchi
(C) Takeshi Horinouchi / GFD Dennou Club, 2003
NO WARRANTY
==Features
==Features yet to be introduced
==Installation
Install ((<FFTW|URL:http://www.fftw.org>)) Ver.3.
Install ((<NArray|URL:http://www.ruby-lang.org/en/raa-list.rhtml?name=NArray>)).
Then, install this library as follows (replace "version" with the actual version number):
% tar xvzf fftw3-version.tar.gz % cd fftw3-version % ruby extconf.rb % make % make site-install Or % make install (If you are using Ruby 1.8, make install is the same make site-install.)
==How to use
See the following peice of code. (Install this library and copy and paste the following to the interactive shell irb).
require "narray" require "numru/fftw3" include NumRu
na = NArray.float(8,6) # float -> will be corced to complex na[1,1]=1
fc = FFTW3.fft(na, -1)/na.length # forward 2D FFT and normalization nc = FFTW3.fft(fc, 1) # backward 2D FFT (complex) --> nb = nc.real # should be equal to na except round errors
fc = FFTW3.fft(na, -1, 0) / na.shape[0] # forward FFT with the first dim
fc = FFTW3.fft(na, -1, 1) / na.shape[1] # forward FFT with the second dim
==API Reference
===Module methods
---fft(narray, dir [,dim,dim,...])
Complex FFT.
The 3rd, 4th,... arguments are optional.
ARGUMENTS
* narray (NArray or NArray-compatible Array) : array to be
transformed. If real, coerced to complex before transformation.
If narray is single-precision and the single-precision
version of FFTW3 is installed (before installing this module),
this method does a single-precision transform.
Otherwise, a double-precision transform is used.
* dir (-1 or 1) : forward transform if -1; backward transform if 1.
* optional 3rd, 4th,... arguments (Integer) : Specifies dimensions
to apply FFT. For example, if 0, the first dimension is
transformed (1D FFT); If -1, the last dimension is used (1D FFT);
If 0,2,4, the first, third, and fifth dimensions
are transformed (3D FFT); If entirely omitted, ALL DIMENSIONS
ARE SUBJECT TO FFT, so 3D FFT is done with a 3D array.
RETURN VALUE
* a complex NArray
NOTE
* As in FFTW, return value is NOT normalized. Thus, a consecutive
forward and backward transform would multiply the size of
data used for transform. You can normalize, for example,
the forward transform FFTW.fft(narray, -1, 0, 1)
(FFT regarding the first (dim 0) & second (dim 1) dimensions) by
dividing with (narray.shape[0]*narray.shape[1]). Likewise,
the result of FFTW.fft(narray, -1) (FFT for all dimensions)
can be normalized by narray.length.