Which is the best section of the FFTW tutorial?
Section Real One-dimensional Transforms Tutorialdescribes the one-dimensional transform of real data and its inverse. Finally, Section Real Multi-dimensional Transforms Tutorialdescribes the multi-dimensional transform of real data and its inverse. We recommend that you read these sections in the order that they are presented.
How are input and output arrays in FFTW different?
First of all, the input and output arrays are of different sizes and types: the input is nreal numbers, while the output is n/2+1complex numbers (the non-redundant outputs); this also requires slight “padding” of the input array for in-place transforms.
How to perform a real data DfT in FFTW?
The routines to perform real-data transforms are almost the same as those for complex transforms: you allocate arrays of doubleand/or fftw_complex(preferably using fftw_mallocor fftw_alloc_complex), create an fftw_plan, execute it as many times as you want with fftw_execute(plan), and clean up with fftw_destroy_plan(plan)(and fftw_free).
Can you compute transforms of any dimension in FFTW?
FFTW can also compute transforms of any number of dimensions (rank). The syntax is similar to that for the one-dimensional transforms, with `fftw_’replaced by `fftwnd_'(which stands for “fftwin Ndimensions”). As before, we #include and create a plan for the transforms, this time of type fftwnd_plan:
What is the difference between fftwnd and FFTW?
For two- and three-dimensional transforms, FFTWND provides alternative routines that accept the sizes of each dimension directly, rather than indirectly through a rank and an array of sizes. These are otherwise identical to fftwnd_create_plan, and are sometimes more convenient:
What kind of data structure is FFTW complex?
Note that the transform is out of place: inand outmust point to distinct arrays. It operates on data of type fftw_complex, a data structure with real (in[i].re) and imaginary (in[i].im) floating-point components.