// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Mark Borgerding mark a borgerding net // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_FFT_H #define EIGEN_FFT_H #include #include #include #include /** \ingroup Unsupported_modules * \defgroup FFT_Module Fast Fourier Transform module * * \code * #include * \endcode * * This module provides Fast Fourier transformation, with a configurable backend * implementation. * * The default implementation is based on kissfft. It is a small, free, and * reasonably efficient default. * * There are currently two implementation backend: * * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size. * - MKL (http://en.wikipedia.org/wiki/Math_Kernel_Library) : fastest, commercial -- may be incompatible with Eigen in GPL form. * * \section FFTDesign Design * * The following design decisions were made concerning scaling and * half-spectrum for real FFT. * * The intent is to facilitate generic programming and ease migrating code * from Matlab/octave. * We think the default behavior of Eigen/FFT should favor correctness and * generality over speed. Of course, the caller should be able to "opt-out" from this * behavior and get the speed increase if they want it. * * 1) %Scaling: * Other libraries (FFTW,IMKL,KISSFFT) do not perform scaling, so there * is a constant gain incurred after the forward&inverse transforms , so * IFFT(FFT(x)) = Kx; this is done to avoid a vector-by-value multiply. * The downside is that algorithms that worked correctly in Matlab/octave * don't behave the same way once implemented in C++. * * How Eigen/FFT differs: invertible scaling is performed so IFFT( FFT(x) ) = x. * * 2) Real FFT half-spectrum * Other libraries use only half the frequency spectrum (plus one extra * sample for the Nyquist bin) for a real FFT, the other half is the * conjugate-symmetric of the first half. This saves them a copy and some * memory. The downside is the caller needs to have special logic for the * number of bins in complex vs real. * * How Eigen/FFT differs: The full spectrum is returned from the forward * transform. This facilitates generic template programming by obviating * separate specializations for real vs complex. On the inverse * transform, only half the spectrum is actually used if the output type is real. */ #ifdef EIGEN_FFTW_DEFAULT // FFTW: faster, GPL -- incompatible with Eigen in LGPL form, bigger code size # include namespace Eigen { # include "src/FFT/ei_fftw_impl.h" //template typedef struct ei_fftw_impl default_fft_impl; this does not work template struct default_fft_impl : public ei_fftw_impl {}; } #elif defined EIGEN_MKL_DEFAULT // TODO // intel Math Kernel Library: fastest, commercial -- may be incompatible with Eigen in GPL form namespace Eigen { # include "src/FFT/ei_imklfft_impl.h" template struct default_fft_impl : public ei_imklfft_impl {}; } #else // ei_kissfft_impl: small, free, reasonably efficient default, derived from kissfft // namespace Eigen { # include "src/FFT/ei_kissfft_impl.h" template struct default_fft_impl : public ei_kissfft_impl {}; } #endif namespace Eigen { // template struct fft_fwd_proxy; template struct fft_inv_proxy; template struct ei_traits< fft_fwd_proxy > { typedef typename T_SrcMat::PlainObject ReturnType; }; template struct ei_traits< fft_inv_proxy > { typedef typename T_SrcMat::PlainObject ReturnType; }; template struct fft_fwd_proxy : public ReturnByValue > { fft_fwd_proxy(const T_SrcMat& src,T_FftIfc & fft,int nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {} template void evalTo(T_DestMat& dst) const; int rows() const { return m_src.rows(); } int cols() const { return m_src.cols(); } protected: const T_SrcMat & m_src; T_FftIfc & m_ifc; int m_nfft; private: fft_fwd_proxy& operator=(const fft_fwd_proxy&); }; template struct fft_inv_proxy : public ReturnByValue > { fft_inv_proxy(const T_SrcMat& src,T_FftIfc & fft,int nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {} template void evalTo(T_DestMat& dst) const; int rows() const { return m_src.rows(); } int cols() const { return m_src.cols(); } protected: const T_SrcMat & m_src; T_FftIfc & m_ifc; int m_nfft; private: fft_inv_proxy& operator=(const fft_inv_proxy&); }; template > class FFT { public: typedef T_Impl impl_type; typedef typename impl_type::Scalar Scalar; typedef typename impl_type::Complex Complex; enum Flag { Default=0, // goof proof Unscaled=1, HalfSpectrum=2, // SomeOtherSpeedOptimization=4 Speedy=32767 }; FFT( const impl_type & impl=impl_type() , Flag flags=Default ) :m_impl(impl),m_flag(flags) { } inline bool HasFlag(Flag f) const { return (m_flag & (int)f) == f;} inline void SetFlag(Flag f) { m_flag |= (int)f;} inline void ClearFlag(Flag f) { m_flag &= (~(int)f);} inline void fwd( Complex * dst, const Scalar * src, int nfft) { m_impl.fwd(dst,src,nfft); if ( HasFlag(HalfSpectrum) == false) ReflectSpectrum(dst,nfft); } inline void fwd( Complex * dst, const Complex * src, int nfft) { m_impl.fwd(dst,src,nfft); } /* inline void fwd2(Complex * dst, const Complex * src, int n0,int n1) { m_impl.fwd2(dst,src,n0,n1); } */ template inline void fwd( std::vector & dst, const std::vector<_Input> & src) { if ( NumTraits<_Input>::IsComplex == 0 && HasFlag(HalfSpectrum) ) dst.resize( (src.size()>>1)+1); // half the bins + Nyquist bin else dst.resize(src.size()); fwd(&dst[0],&src[0],static_cast(src.size())); } template inline void fwd( MatrixBase & dst, const MatrixBase & src,int nfft=-1) { typedef typename ComplexDerived::Scalar dst_type; typedef typename InputDerived::Scalar src_type; EIGEN_STATIC_ASSERT_VECTOR_ONLY(InputDerived) EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived) EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,InputDerived) // size at compile-time EIGEN_STATIC_ASSERT((ei_is_same_type::ret), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) EIGEN_STATIC_ASSERT(int(InputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit, THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES) if (nfft<1) nfft = src.size(); if ( NumTraits< src_type >::IsComplex == 0 && HasFlag(HalfSpectrum) ) dst.derived().resize( (nfft>>1)+1); else dst.derived().resize(nfft); if ( src.innerStride() != 1 || src.size() < nfft ) { Matrix tmp; if (src.size() inline fft_fwd_proxy< MatrixBase, FFT > fwd( const MatrixBase & src,int nfft=-1) { return fft_fwd_proxy< MatrixBase ,FFT >( src, *this,nfft ); } template inline fft_inv_proxy< MatrixBase, FFT > inv( const MatrixBase & src,int nfft=-1) { return fft_inv_proxy< MatrixBase ,FFT >( src, *this,nfft ); } inline void inv( Complex * dst, const Complex * src, int nfft) { m_impl.inv( dst,src,nfft ); if ( HasFlag( Unscaled ) == false) scale(dst,Scalar(1./nfft),nfft); // scale the time series } inline void inv( Scalar * dst, const Complex * src, int nfft) { m_impl.inv( dst,src,nfft ); if ( HasFlag( Unscaled ) == false) scale(dst,Scalar(1./nfft),nfft); // scale the time series } template inline void inv( MatrixBase & dst, const MatrixBase & src, int nfft=-1) { typedef typename ComplexDerived::Scalar src_type; typedef typename OutputDerived::Scalar dst_type; const bool realfft= (NumTraits::IsComplex == 0); EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived) EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived) EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,OutputDerived) // size at compile-time EIGEN_STATIC_ASSERT((ei_is_same_type::ret), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) EIGEN_STATIC_ASSERT(int(OutputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit, THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES) if (nfft<1) { //automatic FFT size determination if ( realfft && HasFlag(HalfSpectrum) ) nfft = 2*(src.size()-1); //assume even fft size else nfft = src.size(); } dst.derived().resize( nfft ); // check for nfft that does not fit the input data size int resize_input= ( realfft && HasFlag(HalfSpectrum) ) ? ( (nfft/2+1) - src.size() ) : ( nfft - src.size() ); if ( src.innerStride() != 1 || resize_input ) { // if the vector is strided, then we need to copy it to a packed temporary Matrix tmp; if ( resize_input ) { size_t ncopy = std::min(src.size(),src.size() + resize_input); tmp.setZero(src.size() + resize_input); if ( realfft && HasFlag(HalfSpectrum) ) { // pad at the Nyquist bin tmp.head(ncopy) = src.head(ncopy); tmp(ncopy-1) = real(tmp(ncopy-1)); // enforce real-only Nyquist bin }else{ size_t nhead,ntail; nhead = 1+ncopy/2-1; // range [0:pi) ntail = ncopy/2-1; // range (-pi:0) tmp.head(nhead) = src.head(nhead); tmp.tail(ntail) = src.tail(ntail); if (resize_input<0) { //shrinking -- create the Nyquist bin as the average of the two bins that fold into it tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*src_type(.5); }else{ // expanding -- split the old Nyquist bin into two halves tmp(nhead) = src(nhead) * src_type(.5); tmp(tmp.size()-nhead) = tmp(nhead); } } }else{ tmp = src; } inv( &dst[0],&tmp[0], nfft); }else{ inv( &dst[0],&src[0], nfft); } } template inline void inv( std::vector<_Output> & dst, const std::vector & src,int nfft=-1) { if (nfft<1) nfft = ( NumTraits<_Output>::IsComplex == 0 && HasFlag(HalfSpectrum) ) ? 2*(src.size()-1) : src.size(); dst.resize( nfft ); inv( &dst[0],&src[0],nfft); } /* // TODO: multi-dimensional FFTs inline void inv2(Complex * dst, const Complex * src, int n0,int n1) { m_impl.inv2(dst,src,n0,n1); if ( HasFlag( Unscaled ) == false) scale(dst,1./(n0*n1),n0*n1); } */ inline impl_type & impl() {return m_impl;} private: template inline void scale(T_Data * x,Scalar s,int nx) { #if 1 for (int k=0;k::Map(x,nx) *= s; else Matrix::MapAligned(x,nx) *= s; //Matrix::Map(x,nx) * s; #endif } inline void ReflectSpectrum(Complex * freq,int nfft) { // create the implicit right-half spectrum (conjugate-mirror of the left-half) int nhbins=(nfft>>1)+1; for (int k=nhbins;k < nfft; ++k ) freq[k] = conj(freq[nfft-k]); } impl_type m_impl; int m_flag; }; template template inline void fft_fwd_proxy::evalTo(T_DestMat& dst) const { m_ifc.fwd( dst, m_src, m_nfft); } template template inline void fft_inv_proxy::evalTo(T_DestMat& dst) const { m_ifc.inv( dst, m_src, m_nfft); } } #endif /* vim: set filetype=cpp et sw=2 ts=2 ai: */