d@0: d@0: d@0: Complex One-Dimensional DFTs - FFTW 3.2.1 d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0: d@0:
d@0:

d@0: d@0: d@0: Next: , d@0: Previous: Tutorial, d@0: Up: Tutorial d@0:


d@0:
d@0: d@0:

2.1 Complex One-Dimensional DFTs

d@0: d@0:
d@0: Plan: To bother about the best method of accomplishing an accidental result. d@0: [Ambrose Bierce, The Enlarged Devil's Dictionary.] d@0:
d@0: d@0:

The basic usage of FFTW to compute a one-dimensional DFT of size d@0: N is simple, and it typically looks something like this code: d@0: d@0:

     #include <fftw3.h>
d@0:      ...
d@0:      {
d@0:          fftw_complex *in, *out;
d@0:          fftw_plan p;
d@0:          ...
d@0:          in = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * N);
d@0:          out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * N);
d@0:          p = fftw_plan_dft_1d(N, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
d@0:          ...
d@0:          fftw_execute(p); /* repeat as needed */
d@0:          ...
d@0:          fftw_destroy_plan(p);
d@0:          fftw_free(in); fftw_free(out);
d@0:      }
d@0: 
d@0:

(When you compile, you must also link with the fftw3 library, d@0: e.g. -lfftw3 -lm on Unix systems.) d@0: d@0:

First you allocate the input and output arrays. You can allocate them d@0: in any way that you like, but we recommend using fftw_malloc, d@0: which behaves like d@0: malloc except that it properly aligns the array when SIMD d@0: instructions (such as SSE and Altivec) are available (see SIMD alignment and fftw_malloc). d@0: d@0: The data is an array of type fftw_complex, which is by default a d@0: double[2] composed of the real (in[i][0]) and imaginary d@0: (in[i][1]) parts of a complex number. d@0: d@0: The next step is to create a plan, which is an object d@0: that contains all the data that FFTW needs to compute the FFT. d@0: This function creates the plan: d@0: d@0:

     fftw_plan fftw_plan_dft_1d(int n, fftw_complex *in, fftw_complex *out,
d@0:                                 int sign, unsigned flags);
d@0: 
d@0:

d@0: The first argument, n, is the size of the transform you are d@0: trying to compute. The size n can be any positive integer, but d@0: sizes that are products of small factors are transformed most d@0: efficiently (although prime sizes still use an O(n log n) algorithm). d@0: d@0:

The next two arguments are pointers to the input and output arrays of d@0: the transform. These pointers can be equal, indicating an d@0: in-place transform. d@0: d@0: The fourth argument, sign, can be either FFTW_FORWARD d@0: (-1) or FFTW_BACKWARD (+1), d@0: and indicates the direction of the transform you are interested in; d@0: technically, it is the sign of the exponent in the transform. d@0: d@0:

The flags argument is usually either FFTW_MEASURE or d@0: FFTW_ESTIMATE. FFTW_MEASURE instructs FFTW to run d@0: and measure the execution time of several FFTs in order to find the d@0: best way to compute the transform of size n. This process takes d@0: some time (usually a few seconds), depending on your machine and on d@0: the size of the transform. FFTW_ESTIMATE, on the contrary, d@0: does not run any computation and just builds a d@0: reasonable plan that is probably sub-optimal. In short, if your d@0: program performs many transforms of the same size and initialization d@0: time is not important, use FFTW_MEASURE; otherwise use the d@0: estimate. The data in the in/out arrays is d@0: overwritten during FFTW_MEASURE planning, so such d@0: planning should be done before the input is initialized by the d@0: user. d@0: d@0:

Once the plan has been created, you can use it as many times as you d@0: like for transforms on the specified in/out arrays, d@0: computing the actual transforms via fftw_execute(plan): d@0:

     void fftw_execute(const fftw_plan plan);
d@0: 
d@0:

d@0: If you want to transform a different array of the same size, you d@0: can create a new plan with fftw_plan_dft_1d and FFTW d@0: automatically reuses the information from the previous plan, if d@0: possible. (Alternatively, with the “guru” interface you can apply a d@0: given plan to a different array, if you are careful. d@0: See FFTW Reference.) d@0: d@0:

When you are done with the plan, you deallocate it by calling d@0: fftw_destroy_plan(plan): d@0:

     void fftw_destroy_plan(fftw_plan plan);
d@0: 
d@0:

Arrays allocated with fftw_malloc should be deallocated by d@0: fftw_free rather than the ordinary free (or, heaven d@0: forbid, delete). d@0: d@0: The DFT results are stored in-order in the array out, with the d@0: zero-frequency (DC) component in out[0]. d@0: If in != out, the transform is out-of-place and the input d@0: array in is not modified. Otherwise, the input array is d@0: overwritten with the transform. d@0: d@0:

Users should note that FFTW computes an unnormalized DFT. d@0: Thus, computing a forward followed by a backward transform (or vice d@0: versa) results in the original array scaled by n. For the d@0: definition of the DFT, see What FFTW Really Computes. d@0: d@0: If you have a C compiler, such as gcc, that supports the d@0: recent C99 standard, and you #include <complex.h> before d@0: <fftw3.h>, then fftw_complex is the native d@0: double-precision complex type and you can manipulate it with ordinary d@0: arithmetic. Otherwise, FFTW defines its own complex type, which is d@0: bit-compatible with the C99 complex type. See Complex numbers. d@0: (The C++ <complex> template class may also be usable via a d@0: typecast.) d@0: d@0: Single and long-double precision versions of FFTW may be installed; to d@0: use them, replace the fftw_ prefix by fftwf_ or d@0: fftwl_ and link with -lfftw3f or -lfftw3l, but d@0: use the same <fftw3.h> header file. d@0: d@0: Many more flags exist besides FFTW_MEASURE and d@0: FFTW_ESTIMATE. For example, use FFTW_PATIENT if you're d@0: willing to wait even longer for a possibly even faster plan (see FFTW Reference). d@0: You can also save plans for future use, as described by Words of Wisdom-Saving Plans. d@0: d@0: d@0: d@0: