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In many practical applications, the input data in[i] are purely
cannam@95: real numbers, in which case the DFT output satisfies the “Hermitian”
cannam@95: redundancy: out[i] is the conjugate of out[n-i]. It is
cannam@95: possible to take advantage of these circumstances in order to achieve
cannam@95: roughly a factor of two improvement in both speed and memory usage.
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In exchange for these speed and space advantages, the user sacrifices
cannam@95: some of the simplicity of FFTW's complex transforms. First of all, the
cannam@95: input and output arrays are of different sizes and types: the
cannam@95: input is n real numbers, while the output is n/2+1
cannam@95: complex numbers (the non-redundant outputs); this also requires slight
cannam@95: “padding” of the input array for
cannam@95: in-place transforms. Second, the inverse transform (complex to real)
cannam@95: has the side-effect of overwriting its input array, by default.
cannam@95: Neither of these inconveniences should pose a serious problem for
cannam@95: users, but it is important to be aware of them.
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The routines to perform real-data transforms are almost the same as
cannam@95: those for complex transforms: you allocate arrays of double
cannam@95: and/or fftw_complex (preferably using fftw_malloc or
cannam@95: fftw_alloc_complex), create an fftw_plan, execute it as
cannam@95: many times as you want with fftw_execute(plan), and clean up
cannam@95: with fftw_destroy_plan(plan) (and fftw_free). The only
cannam@95: differences are that the input (or output) is of type double
cannam@95: and there are new routines to create the plan. In one dimension:
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fftw_plan fftw_plan_dft_r2c_1d(int n, double *in, fftw_complex *out, cannam@95: unsigned flags); cannam@95: fftw_plan fftw_plan_dft_c2r_1d(int n, fftw_complex *in, double *out, cannam@95: unsigned flags); cannam@95:cannam@95:
cannam@95: for the real input to complex-Hermitian output (r2c) and
cannam@95: complex-Hermitian input to real output (c2r) transforms.
cannam@95: Unlike the complex DFT planner, there is no sign argument.
cannam@95: Instead, r2c DFTs are always FFTW_FORWARD and c2r DFTs are
cannam@95: always FFTW_BACKWARD.
cannam@95: (For single/long-double precision
cannam@95: fftwf and fftwl, double should be replaced by
cannam@95: float and long double, respectively.)
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Here, n is the “logical” size of the DFT, not necessarily the
cannam@95: physical size of the array. In particular, the real (double)
cannam@95: array has n elements, while the complex (fftw_complex)
cannam@95: array has n/2+1 elements (where the division is rounded down).
cannam@95: For an in-place transform,
cannam@95: in and out are aliased to the same array, which must be
cannam@95: big enough to hold both; so, the real array would actually have
cannam@95: 2*(n/2+1) elements, where the elements beyond the first
cannam@95: n are unused padding. (Note that this is very different from
cannam@95: the concept of “zero-padding” a transform to a larger length, which
cannam@95: changes the logical size of the DFT by actually adding new input
cannam@95: data.) The kth element of the complex array is exactly the
cannam@95: same as the kth element of the corresponding complex DFT. All
cannam@95: positive n are supported; products of small factors are most
cannam@95: efficient, but an O(n log n) algorithm is used even for prime sizes.
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As noted above, the c2r transform destroys its input array even for
cannam@95: out-of-place transforms. This can be prevented, if necessary, by
cannam@95: including FFTW_PRESERVE_INPUT in the flags, with
cannam@95: unfortunately some sacrifice in performance.
cannam@95: This flag is also not currently supported for multi-dimensional real
cannam@95: DFTs (next section).
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Readers familiar with DFTs of real data will recall that the 0th (the
cannam@95: “DC”) and n/2-th (the “Nyquist” frequency, when n is
cannam@95: even) elements of the complex output are purely real. Some
cannam@95: implementations therefore store the Nyquist element where the DC
cannam@95: imaginary part would go, in order to make the input and output arrays
cannam@95: the same size. Such packing, however, does not generalize well to
cannam@95: multi-dimensional transforms, and the space savings are miniscule in
cannam@95: any case; FFTW does not support it.
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An alternative interface for one-dimensional r2c and c2r DFTs can be cannam@95: found in the ‘r2r’ interface (see The Halfcomplex-format DFT), with “halfcomplex”-format output that is the same size cannam@95: (and type) as the input array. cannam@95: That interface, although it is not very useful for multi-dimensional cannam@95: transforms, may sometimes yield better performance. cannam@95: cannam@95: cannam@95: cannam@95: