FFTW 3.3.5: Guru Real-data DFTs

cannam@127:

fftw_plan fftw_plan_guru_dft_r2c(
cannam@127:      int rank, const fftw_iodim *dims,
cannam@127:      int howmany_rank, const fftw_iodim *howmany_dims,
cannam@127:      double *in, fftw_complex *out,
cannam@127:      unsigned flags);
cannam@127: 
cannam@127: fftw_plan fftw_plan_guru_split_dft_r2c(
cannam@127:      int rank, const fftw_iodim *dims,
cannam@127:      int howmany_rank, const fftw_iodim *howmany_dims,
cannam@127:      double *in, double *ro, double *io,
cannam@127:      unsigned flags);
cannam@127: 
cannam@127: fftw_plan fftw_plan_guru_dft_c2r(
cannam@127:      int rank, const fftw_iodim *dims,
cannam@127:      int howmany_rank, const fftw_iodim *howmany_dims,
cannam@127:      fftw_complex *in, double *out,
cannam@127:      unsigned flags);
cannam@127: 
cannam@127: fftw_plan fftw_plan_guru_split_dft_c2r(
cannam@127:      int rank, const fftw_iodim *dims,
cannam@127:      int howmany_rank, const fftw_iodim *howmany_dims,
cannam@127:      double *ri, double *ii, double *out,
cannam@127:      unsigned flags);
cannam@127:

Plan a real-input (r2c) or real-output (c2r), multi-dimensional DFT with cannam@127: transform dimensions given by (rank, dims) over a cannam@127: multi-dimensional vector (loop) of dimensions (howmany_rank, cannam@127: howmany_dims). dims and howmany_dims should point cannam@127: to fftw_iodim arrays of length rank and cannam@127: howmany_rank, respectively. As for the basic and advanced cannam@127: interfaces, an r2c transform is FFTW_FORWARD and a c2r transform cannam@127: is FFTW_BACKWARD. cannam@127:

The last dimension of dims is interpreted specially: cannam@127: that dimension of the real array has size dims[rank-1].n, but cannam@127: that dimension of the complex array has size dims[rank-1].n/2+1 cannam@127: (division rounded down). The strides, on the other hand, are taken to cannam@127: be exactly as specified. It is up to the user to specify the strides cannam@127: appropriately for the peculiar dimensions of the data, and we do not cannam@127: guarantee that the planner will succeed (return non-NULL) for cannam@127: any dimensions other than those described in Real-data DFT Array Format and generalized in Advanced Real-data DFTs. (That is, cannam@127: for an in-place transform, each individual dimension should be able to cannam@127: operate in place.) cannam@127: cannam@127:

in and out point to the input and output arrays for r2c cannam@127: and c2r transforms, respectively. For split arrays, ri and cannam@127: ii point to the real and imaginary input arrays for a c2r cannam@127: transform, and ro and io point to the real and imaginary cannam@127: output arrays for an r2c transform. in and ro or cannam@127: ri and out may be the same, indicating an in-place cannam@127: transform. (In-place transforms where in and io or cannam@127: ii and out are the same are not currently supported.) cannam@127:

flags is a bitwise OR (‘|’) of zero or more planner flags, cannam@127: as defined in Planner Flags. cannam@127:

In-place transforms of rank greater than 1 are currently only cannam@127: supported for interleaved arrays. For split arrays, the planner will cannam@127: return NULL. cannam@127: cannam@127:

4.5.4 Guru Real-data DFTs