=head1 NAME
PDL::FFTW3 - PDL interface to the Fastest Fourier Transform in the West v3
=head1 SYNOPSIS
use PDL;
use PDL::FFTW3;
use PDL::Graphics::Gnuplot;
use PDL::Complex;
# Basic functionality
my $x = sin( sequence(100) * 2.0 ) + 2.0 * cos( sequence(100) / 3.0 );
my $F = rfft1( $x );
gplot( with => 'lines', inner($F,$F));
=====>
8000 ++------------+-------------+------------+-------------+------------++
+ + + + + +
| |
| * |
7000 ++ * ++
| * |
| * |
| * |
| * |
6000 ++ * ++
| * |
| * |
| * |
5000 ++ * ++
| * |
| * |
| ** |
4000 ++ ** ++
| ** |
| * * |
| * * |
| * * |
3000 ++ * * ++
| * * |
| * * |
| * * * |
2000 ++ * * * ++
| * * * |
| * * ** |
| * * ** |
| * * ** |
1000 ++ * * * * ++
| * * * * |
| ** * * * |
+ * * + + + * * + +
0 ****-------*********************************--************************
0 10 20 30 40 50
# Correlation of two real signals
# two signals offset by 30 units
my $x = sequence(100);
my $y1 = exp( 0.2*($x - 20.5) ** (-2.0) );
my $y2 = exp( 0.2*($x - 50.5) ** (-2.0) );
# compute the correlation
my $F12 = rfft1( cat($y1,$y2) );
my $corr = irfft1( Cmul( $F12(:,:,(1)),
Cconj $F12(:,:,(0)) ) );
# and find the peak
say maximum_ind($corr);
=====> 30
=head1 DESCRIPTION
This is a PDL binding to version 3 of the FFTW library. Supported are complex
<-> complex and real <-> complex FFTs.
=head2 NB to install
wget http://www.fftw.org/fftw-3.3.4.tar.gz
tar xvf fftw-3.3.4.tar.gz
cd fftw-3.3.4/
./configure --prefix=/usr --enable-threads --enable-float --enable-shared --with-pic
make all install install-pkgconfigDATA
make clean
./configure --prefix=/usr --enable-threads --enable-shared --with-pic
make all install install-pkgconfigDATA
This will give you both fftw3f (first chunk) and fftw3 (second).
=head2 Supported operations
This module computes the Discrete Fourier Transform. In its most basic form,
this transform converts a vector of complex numbers in the time domain into
another vector of complex numbers in the frequency domain. These complex <->
complex transforms are supported with C functions for a rank-C
transform. The opposite effect (transform data in the frequency domain back to
the time domain) can be achieved with the C functions.
A common use case is to transform purely-real data. This data has 0 for its
complex component, and FFTW can take advantage of this to compute the FFT faster
and using less memory. Since a Fourier Transform of a real signal has an even
real part and an odd imaginary part, only 1/2 of the spectrum is needed. These
forward real -> complex transforms are supported with the C functions.
The backward version of this transform is complex -> real and is supported with
the C functions.
=head2 Basic usage details
Arbitrary C-dimensional transforms are supported. All functions exported by
this module have the C in their name, so for instance a complex <-> complex
3D forward transform is computed with the C function. The rank I be specified in this way; there is no function called simply C.
In-place operation is supported for complex <-> complex functions, but not the
real ones (real function don't have mathing dimensionality of the input and
output). An in-place transform of C<$x> can be computed with
fft1( $x->inplace );
All the functions in this module support PDL threading. For instance, if we have
4 different image piddles C<$a>, C<$b>, C<$c>, C<$d> and we want to compute
their 2D FFTs at the same time, we can say
my $ABCD_transformed = rfft2( PDL::cat( $a, $b, $c, $d) );
This takes advantage of PDL's automatic parallelization, if appropriate (See
L).
=head2 Data formats
FFTW supports single and double-precision floating point numbers directly. If
possible, the PDL input will be used as-is. If not, a type conversion will be
made to use the lowest-common type. So as an example, the following will perform
a single-precision floating point transform (and return data of that type).
fft1( $x->byte )
This module expects complex numbers to be stored as a (real,imag) pair in the
first dimension of a piddle. Thus in a complex piddle C<$x>, it is expected that
C<$x-Edim(0) == 2> (this module verifies this before proceeding).
Generally, the sizes of the input and the output must match. This is completely
true for the complex <-> complex transforms: the output will have the same size
and the input, and an error will result if this isn't possible for some reason.
This is a little bit more involved for the real <-> complex transforms. If I'm
transforming a real 3D vector of dimensions C, I will get an output of
dimensions C<2,int(K/2)+1,L,M>. The leading 2 is there because the output is
complex; the C is there because the input was real. The first dimension is
always the one that gets the C. This is described in detail in section 2.4
of the FFTW manual.
Note that given a real input, the dimensionality of the complex transformed
output is unambiguous. However, this is I true for the backward transform.
For instance, a 1D inverse transform of a vector of 10 complex numbers can
produce real output of either 18 or 19 elements (because C
and C).
I.
Thus Cdim(0) == 18> is true. If we want the odd-sized output, we have to explicitly pass this into the function like this:
irfft1( sequence(2,10), zeros(19) )
Here I create a new output piddle with the C function; C then
fills in this piddle with the result of the computation. This module validates
all of its input, so only 18 and 19 are valid here. An error will be thrown if
you try to pass in C.
This all means that the following will produce surprising results if
C<$x-Edim(0)> isn't even
irfft1( rfft1( $x ) )
=head2 FFT normalization
Following the widest-used convention for discrete Fourier transforms,
this module normalizes the inverse transform (but not the forward
transform) by dividing by the number of elements in the data set, so
that
ifft1( fft1( $x ) )
is a slow approximate no-op, if C<$x> is well-behaved.
This is different from the behavior of the underlying FFTW3 library itself,
but more consistent with other FFT packages for popular analysis languages
including PDL.
=head1 FUNCTIONS
=head2 fftN (fft1, fft2, fft3, ..., fftn)
The basic complex <-> complex FFT. You can pass in the rank as a
parameter with the C form, or append the rank to the function
name for ranks up to 9. These functions all take one input piddle and
one output piddle. The dimensions of the input and the output are
identical. The output parameter is optional and, if present, must be
the last argument. If the output piddle is passed in, the user I
make sure the dimensions match.
The 0 dim of the input PDL must have size 2 and run over (real,imaginary)
components. The transform is carried out over dims 1 through N.
The fftn form takes a minimum of two arguments: the PDL to transform,
and the number of dimensions to transform as a separate argument.
The following are equivalent:
$X = fftn( $x, 1 );
$X = fft1( $x );
fft1( $x, my $X = $x->zeros );
=head2 ifftN (ifft1, ifft2, ifft3, ..., fftn)
The basic, properly normalized, complex <-> complex backward
FFT. Everything is exactly like in the C functions, except the
inverse transform is computed and normalized, so that (for example)
ifft1( fft1 ( $x ) )
is a good approximation of C<$x> itself.
=head2 rfftN (rfft1, rfft2, rfft3, ..., rfftn)
The real -> complex FFT. You can pass in the rank with the C
form, or append the rank to the function name for ranks up to 9.
These functions all take one input piddle and one output piddle. The
dimensions of the input and the output are not identical, but are
related as described in L. The output can be passed in
as the last argument, if desired. If the output piddle is passed in,
the user I make sure the dimensions match.
In the C form, the rank is the second argument.
The following are equivalent:
$X = rfftn( $x, 1 );
$X = rfft1( $x );
rfft1( $x, my $X = $x->zeroes );
=head2 irfftN (irfft1, irfft2, irfft3, ..., irfftn)
The complex -> real inverse FFT. You can pass in the rank with the
C form, or append the rank to the function name for ranks up
to 9. Argument passing and interpretation is as described in
C above. Please read L for details about dimension
interpretation. There's an ambiguity about the output dimensionality,
which is described in that section.
=head1 AUTHOR
Dima Kogan, C<< >>; contributions from Craig
DeForest, C<< >>.
=head1 HISTORY
=head2 VERSION 0.02
=over
=item Use Alien::FFTW3 for build (if present)
=item add parametric rank functions
=item normalize inverses
=item shorter abbreviation for realfft functions
=back
=head2 VERSION 0.01
Early working version in github
=head1 LICENSE AND COPYRIGHT
Copyright 2013 Dima Kogan and Craig DeForest.
This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License.
=cut