// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Mark Borgerding mark a borgerding net
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SPDX-License-Identifier: MPL-2.0

#ifndef EIGEN_FFT_MODULE_H
#define EIGEN_FFT_MODULE_H

#include <complex>
#include <vector>
#include <map>
#include "../../Eigen/Core"

/**
 * \defgroup FFT_Module Fast Fourier Transform module
 *
 * \code
 * #include <contrib/Eigen/FFT>
 * \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 four implementation backend:
 *
 * - kissfft(https://github.com/mborgerding/kissfft) : Simple and not so fast, BSD-3-Clause.
 *   It is a mixed-radix Fast Fourier Transform based up on the principle, "Keep It Simple, Stupid."
 *   Notice that:kissfft fails to handle "atypically-sized" inputs(i.e., sizes with large factors),a workaround is using
 * fftw or pocketfft.
 * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size.
 * - MKL (https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html) : fastest, free -- may be
 * incompatible with Eigen in GPL form.
 * - PocketFFT/DUCC (https://gitlab.mpcdf.mpg.de/mtr/pocketfft, https://gitlab.mpcdf.mpg.de/mtr/ducc) : faster than
 *   kissfft, BSD 3-clause. A heavily modified implementation of FFTPack with the following advantages:
 *   -# strictly C++14 compliant
 *   -# more accurate twiddle factor computation
 *   -# very fast plan generation
 *   -# worst case complexity for transform sizes with large prime factors is N*log(N),
 *      because Bluestein's algorithm is used for these cases.
 *   .
 *   According to the author, DUCC contains the "evolution" of pocketfft, though the interface is very similar.
 *
 * \section FFTThreadSafety Thread safety
 *
 * An FFT object is not thread-safe: it caches a plan, so concurrent transforms need one object per
 * thread. Distinct objects may be used concurrently.
 *
 * That is not by itself enough for the FFTW backend, whose planner keeps process-wide state and
 * whose only concurrent entry points are \c fftw_execute and its new-array variants. %Eigen
 * therefore serializes plan creation and destruction on a mutex of its own, which covers every
 * planner call %Eigen makes from within one process image.
 *
 * Two cases fall outside that. FFTW planner calls made directly, not through %Eigen, share nothing
 * with %Eigen's mutex. And a program that loads several shared libraries which each carry their own
 * copy of %Eigen may end up with one mutex per library instead of one per process: %Eigen gives the
 * mutex default visibility so that the dynamic linker collapses those copies, which covers ELF
 * libraries linked normally or loaded with \c RTLD_GLOBAL, but not separate Windows DLLs, and not a
 * library loaded with \c RTLD_LOCAL unless the compiler emitted a unique symbol for the definition.
 * In either case, call FFTW's own \c fftw_make_planner_thread_safe() once before any transform: it
 * puts the lock inside the planner, where it applies to every caller. It needs FFTW 3.3.5 or later,
 * built with thread support.
 *
 * \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.
 */

#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"

// IWYU pragma: begin_exports

#ifdef EIGEN_FFTW_DEFAULT
// FFTW: faster, GPL -- incompatible with Eigen in LGPL form, bigger code size
#include <fftw3.h>
#include "src/FFT/fftw_impl.h"
namespace Eigen {
template <typename T>
struct default_fft_impl : public internal::fftw_impl<T> {};
}  // namespace Eigen
#elif defined EIGEN_MKL_DEFAULT
// intel Math Kernel Library: fastest, free -- may be incompatible with Eigen in GPL form
#include "src/FFT/imklfft_impl.h"
namespace Eigen {
template <typename T>
struct default_fft_impl : public internal::imklfft::imklfft_impl<T> {};
}  // namespace Eigen
#elif defined EIGEN_POCKETFFT_DEFAULT
// internal::pocketfft_impl: a heavily modified implementation of FFTPack, with many advantages.
#include <pocketfft_hdronly.h>
#include "src/FFT/pocketfft_impl.h"
namespace Eigen {
template <typename T>
struct default_fft_impl : public internal::pocketfft_impl<T> {};
}  // namespace Eigen
#elif defined EIGEN_DUCCFFT_DEFAULT
#include <ducc0/fft/fft.h>
#include <ducc0/infra/string_utils.h>
#include <ducc0/fft/fftnd_impl.h>
#include "src/FFT/duccfft_impl.h"
namespace Eigen {
template <typename T>
struct default_fft_impl : public internal::duccfft_impl<T> {};
}  // namespace Eigen
#else
// internal::kissfft_impl:  small, free, reasonably efficient default, derived from kissfft
#include "src/FFT/kissfft_impl.h"
namespace Eigen {
template <typename T>
struct default_fft_impl : public internal::kissfft_impl<T> {};
}  // namespace Eigen
#endif

// IWYU pragma: end_exports

namespace Eigen {

//
template <typename T_SrcMat, typename T_FftIfc>
struct fft_fwd_proxy;
template <typename T_SrcMat, typename T_FftIfc>
struct fft_inv_proxy;

namespace internal {
template <typename T_SrcMat, typename T_FftIfc>
struct traits<fft_fwd_proxy<T_SrcMat, T_FftIfc> > {
  typedef typename T_SrcMat::PlainObject ReturnType;
};
template <typename T_SrcMat, typename T_FftIfc>
struct traits<fft_inv_proxy<T_SrcMat, T_FftIfc> > {
  typedef typename T_SrcMat::PlainObject ReturnType;
};
}  // namespace internal

template <typename T_SrcMat, typename T_FftIfc>
struct fft_fwd_proxy : public ReturnByValue<fft_fwd_proxy<T_SrcMat, T_FftIfc> > {
  typedef Eigen::Index Index;

  fft_fwd_proxy(const T_SrcMat& src, T_FftIfc& fft, Index nfft) : m_src(src), m_ifc(fft), m_nfft(nfft) {}

  template <typename T_DestMat>
  void evalTo(T_DestMat& dst) const;

  Index rows() const { return m_src.rows(); }
  Index cols() const { return m_src.cols(); }

 protected:
  const T_SrcMat& m_src;
  T_FftIfc& m_ifc;
  Index m_nfft;
};

template <typename T_SrcMat, typename T_FftIfc>
struct fft_inv_proxy : public ReturnByValue<fft_inv_proxy<T_SrcMat, T_FftIfc> > {
  typedef Eigen::Index Index;

  fft_inv_proxy(const T_SrcMat& src, T_FftIfc& fft, Index nfft) : m_src(src), m_ifc(fft), m_nfft(nfft) {}

  template <typename T_DestMat>
  void evalTo(T_DestMat& dst) const;

  Index rows() const { return m_src.rows(); }
  Index cols() const { return m_src.cols(); }

 protected:
  const T_SrcMat& m_src;
  T_FftIfc& m_ifc;
  Index m_nfft;
};

namespace internal {

// The transforms are one-dimensional, but a matrix with a dynamic dimension only knows at run
// time whether that dimension is one, so it cannot be rejected at compile time the way a matrix
// of known two-dimensional shape can. Only the latter is excluded here.
template <typename Derived>
struct fft_accepts_as_vector {
  static constexpr bool value = bool(Derived::IsVectorAtCompileTime) || int(Derived::RowsAtCompileTime) == Dynamic ||
                                int(Derived::ColsAtCompileTime) == Dynamic;
};

// Distance between consecutive elements of a one-dimensional operand. The inner stride does not
// answer this once matrices are accepted: a matrix holding a single row steps along its outer
// stride, and only a unit distance lets the transform run over the operand's own memory.
template <typename Derived>
EIGEN_STRONG_INLINE Index fft_linear_stride(const DenseBase<Derived>& x) {
  return x.rows() == 1 ? x.derived().colStride() : x.derived().rowStride();
}

// Resize a one-dimensional destination. Every orientation a compile-time dimension leaves open
// is settled here: a fixed dimension other than one can only be the long one, and a destination
// with two dynamic dimensions takes the orientation of the source.
template <typename Derived>
EIGEN_STRONG_INLINE void fft_resize_as_vector(DenseBase<Derived>& dst, Index size, bool source_is_row) {
  // A destination already holding the transform's length in one dimension keeps that shape, which
  // is the only one a view such as a Map can be given at all.
  if ((dst.rows() == 1 && dst.cols() == size) || (dst.cols() == 1 && dst.rows() == size)) return;

  constexpr int Rows = int(Derived::RowsAtCompileTime);
  constexpr int Cols = int(Derived::ColsAtCompileTime);
  const bool row = Rows == 1         ? true
                   : Cols == 1       ? false
                   : Cols != Dynamic ? true
                   : Rows != Dynamic ? false
                                     : source_is_row;
  dst.derived().resize(row ? 1 : size, row ? size : 1);
}

}  // namespace internal

#define EIGEN_STATIC_ASSERT_FFT_VECTOR(TYPE) \
  EIGEN_STATIC_ASSERT(internal::fft_accepts_as_vector<TYPE>::value, YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)

template <typename T_Scalar, typename T_Impl = default_fft_impl<T_Scalar> >
class FFT {
 public:
  typedef T_Impl impl_type;
  typedef Eigen::Index Index;
  typedef typename impl_type::Scalar Scalar;
  typedef typename impl_type::Complex Complex;

  using Flag = int;
  static constexpr Flag Default = 0;
  static constexpr Flag Unscaled = 1;
  static constexpr Flag HalfSpectrum = 2;
  static constexpr Flag Speedy = 32767;

  FFT(const impl_type& impl = impl_type(), Flag flags = Default) : m_impl(impl), m_flag(flags) {
    eigen_assert((flags == Default || flags == Unscaled || flags == HalfSpectrum || flags == Speedy) &&
                 "invalid flags argument");
  }

  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, Index nfft) {
    m_impl.fwd(dst, src, static_cast<int>(nfft));
    if (HasFlag(HalfSpectrum) == false) ReflectSpectrum(dst, nfft);
  }

  inline void fwd(Complex* dst, const Complex* src, Index nfft) { m_impl.fwd(dst, src, static_cast<int>(nfft)); }

#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
    defined EIGEN_MKL_DEFAULT
  inline void fwd2(Complex* dst, const Complex* src, int n0, int n1) { m_impl.fwd2(dst, src, n0, n1); }
#endif

  template <typename Input_>
  inline void fwd(std::vector<Complex>& dst, const std::vector<Input_>& src) {
    EIGEN_IF_CONSTEXPR (NumTraits<Input_>::IsComplex == 0) {
      if (HasFlag(HalfSpectrum))
        dst.resize((src.size() >> 1) + 1);  // half the bins + Nyquist bin
      else
        dst.resize(src.size());
    } else {
      dst.resize(src.size());
    }
    fwd(&dst[0], &src[0], src.size());
  }

  template <typename InputDerived, typename ComplexDerived>
  inline void fwd(DenseBase<ComplexDerived>& dst, const DenseBase<InputDerived>& src, Index nfft = -1) {
    typedef typename ComplexDerived::Scalar dst_type;
    typedef typename InputDerived::Scalar src_type;
    EIGEN_STATIC_ASSERT_FFT_VECTOR(InputDerived)
    EIGEN_STATIC_ASSERT_FFT_VECTOR(ComplexDerived)
    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived, InputDerived)  // size at compile-time
    EIGEN_STATIC_ASSERT(
        (std::is_same<dst_type, Complex>::value),
        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)
    eigen_assert((src.rows() == 1 || src.cols() == 1) && "the input of an FFT must be one-dimensional");

    if (nfft < 1) nfft = src.size();

    Index dst_size = nfft;
    EIGEN_IF_CONSTEXPR (NumTraits<src_type>::IsComplex == 0) {
      if (HasFlag(HalfSpectrum)) {
        dst_size = (nfft >> 1) + 1;
      }
    }
    internal::fft_resize_as_vector(dst, dst_size, src.rows() == 1);

    if (internal::fft_linear_stride(src) != 1 || src.size() < nfft) {
      Matrix<src_type, 1, Dynamic> tmp(1, nfft);
      const Index copy_size = (std::min)(src.size(), nfft);
      if (copy_size > 0) {
        tmp.head(copy_size) = src.reshaped().head(copy_size);
      }
      if (copy_size < nfft) tmp.tail(nfft - copy_size).setZero();
      if (internal::fft_linear_stride(dst) != 1) {
        Matrix<dst_type, 1, Dynamic> out(1, dst_size);
        fwd(out.data(), tmp.data(), nfft);
        dst.derived() = out.reshaped(dst.rows(), dst.cols());
      } else {
        fwd(dst.derived().data(), tmp.data(), nfft);
      }
    } else {
      if (internal::fft_linear_stride(dst) != 1) {
        Matrix<dst_type, 1, Dynamic> out(1, dst_size);
        fwd(out.data(), src.derived().data(), nfft);
        dst.derived() = out.reshaped(dst.rows(), dst.cols());
      } else {
        fwd(dst.derived().data(), src.derived().data(), nfft);
      }
    }
  }

  template <typename InputDerived>
  inline fft_fwd_proxy<DenseBase<InputDerived>, FFT<T_Scalar, T_Impl> > fwd(const DenseBase<InputDerived>& src,
                                                                            Index nfft = -1) {
    return fft_fwd_proxy<DenseBase<InputDerived>, FFT<T_Scalar, T_Impl> >(src, *this, nfft);
  }

  template <typename InputDerived>
  inline fft_inv_proxy<DenseBase<InputDerived>, FFT<T_Scalar, T_Impl> > inv(const DenseBase<InputDerived>& src,
                                                                            Index nfft = -1) {
    return fft_inv_proxy<DenseBase<InputDerived>, FFT<T_Scalar, T_Impl> >(src, *this, nfft);
  }

  inline void inv(Complex* dst, const Complex* src, Index nfft) {
    m_impl.inv(dst, src, static_cast<int>(nfft));
    if (HasFlag(Unscaled) == false) scale(dst, Scalar(1. / nfft), nfft);  // scale the time series
  }

  inline void inv(Scalar* dst, const Complex* src, Index nfft) {
    m_impl.inv(dst, src, static_cast<int>(nfft));
    if (HasFlag(Unscaled) == false) scale(dst, Scalar(1. / nfft), nfft);  // scale the time series
  }

  template <typename OutputDerived, typename ComplexDerived>
  inline void inv(DenseBase<OutputDerived>& dst, const DenseBase<ComplexDerived>& src, Index nfft = -1) {
    typedef typename ComplexDerived::Scalar src_type;
    typedef typename ComplexDerived::RealScalar real_type;
    typedef typename OutputDerived::Scalar dst_type;
    const bool realfft = (NumTraits<dst_type>::IsComplex == 0);
    EIGEN_STATIC_ASSERT_FFT_VECTOR(OutputDerived)
    EIGEN_STATIC_ASSERT_FFT_VECTOR(ComplexDerived)
    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived, OutputDerived)  // size at compile-time
    EIGEN_STATIC_ASSERT(
        (std::is_same<src_type, Complex>::value),
        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)
    eigen_assert((src.rows() == 1 || src.cols() == 1) && "the input of an FFT must be one-dimensional");

    if (nfft < 1) {  // automatic FFT size determination
      if (realfft && HasFlag(HalfSpectrum))
        nfft = 2 * (src.size() - 1);  // assume even fft size
      else
        nfft = src.size();
    }
    internal::fft_resize_as_vector(dst, nfft, src.rows() == 1);

    // check for nfft that does not fit the input data size
    Index resize_input = (realfft && HasFlag(HalfSpectrum)) ? ((nfft / 2 + 1) - src.size()) : (nfft - src.size());

    if (internal::fft_linear_stride(src) != 1 || resize_input) {
      // if the vector is strided, then we need to copy it to a packed temporary
      const auto src_lin = src.reshaped();
      Matrix<src_type, 1, Dynamic> 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_lin.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_lin.head(nhead);
          tmp.tail(ntail) = src_lin.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_lin(nfft / 2) + src_lin(src.size() - nfft / 2)) * real_type(.5);
          } else {  // expanding -- split the old Nyquist bin into two halves
            tmp(nhead) = src_lin(nhead) * real_type(.5);
            tmp(tmp.size() - nhead) = tmp(nhead);
          }
        }
      } else {
        tmp = src_lin;
      }

      if (internal::fft_linear_stride(dst) != 1) {
        Matrix<dst_type, 1, Dynamic> out(1, nfft);
        inv(out.data(), tmp.data(), nfft);
        dst.derived() = out.reshaped(dst.rows(), dst.cols());
      } else {
        inv(dst.derived().data(), tmp.data(), nfft);
      }
    } else {
      if (internal::fft_linear_stride(dst) != 1) {
        Matrix<dst_type, 1, Dynamic> out(1, nfft);
        inv(out.data(), src.derived().data(), nfft);
        dst.derived() = out.reshaped(dst.rows(), dst.cols());
      } else {
        inv(dst.derived().data(), src.derived().data(), nfft);
      }
    }
  }

  template <typename Output_>
  inline void inv(std::vector<Output_>& dst, const std::vector<Complex>& src, Index 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);
  }

#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
    defined EIGEN_MKL_DEFAULT
  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, Scalar(1) / (n0 * n1), n0 * n1);
  }
#endif

  inline impl_type& impl() { return m_impl; }

 private:
  template <typename T_Data>
  inline void scale(T_Data* x, Scalar s, Index nx) {
    for (int k = 0; k < nx; ++k) *x++ *= s;
  }

  inline void ReflectSpectrum(Complex* freq, Index nfft) {
    // create the implicit right-half spectrum (conjugate-mirror of the left-half)
    Index nhbins = (nfft >> 1) + 1;
    for (Index k = nhbins; k < nfft; ++k) freq[k] = conj(freq[nfft - k]);
  }

  impl_type m_impl;
  int m_flag;
};

template <typename T_SrcMat, typename T_FftIfc>
template <typename T_DestMat>
inline void fft_fwd_proxy<T_SrcMat, T_FftIfc>::evalTo(T_DestMat& dst) const {
  m_ifc.fwd(dst, m_src, m_nfft);
}

template <typename T_SrcMat, typename T_FftIfc>
template <typename T_DestMat>
inline void fft_inv_proxy<T_SrcMat, T_FftIfc>::evalTo(T_DestMat& dst) const {
  m_ifc.inv(dst, m_src, m_nfft);
}

}  // namespace Eigen

#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"

#endif  // EIGEN_FFT_MODULE_H