README

FFTW

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A minimal Delphi/FireMonkey demonstration of how to use the fast Fourier transform library FFTW [1] from Delphi. A complex-valued random-walk signal is regenerated every 10 ms, transformed by a double-precision complex-to-complex FFT, and both the time-domain waveform and its raw spectrum are plotted in real time.

利用ライブラリ

• LUX ：Foundational mathematics library for the LUXOPHIA projects.
• LUX.Chart ：Chart-plotting library providing the TChartViewer frame.
• LUX.FFTW ：Delphi binding and object-oriented wrapper for the FFTW 3 library.

1. Overview

• Raw FFTW 3 binding — fftw3.pas translates the FFTW C API to Delphi: plan creation (basic / advanced / guru interfaces), complex-to-complex, real-to-complex/complex-to-real and real-to-real plans, wisdom import/export, threading, and fftw_malloc-family allocators, for both the double-precision (libfftw3-3.dll) and single-precision (libfftw3f-3.dll) libraries.
• Object-oriented wrapper — LUX.FFTW wraps plans in generic classes (TDFT<...>, TDFT1D<...>) whose input/output buffers are grid objects; resizing a grid automatically re-creates the plans.
• Ready-made presets — TSingleDFTcc1D / TDoubleDFTcc1D provide 1-D complex-to-complex transforms out of the box (2-D and 3-D preset units are also included in the library).
• Live demo app — a FireMonkey form animates a Metropolis-type complex random walk, executes the forward transform each timer tick, and displays both domains with TChartViewer; the transform length is adjustable at run time.

2. Mathematical Background

The discrete Fourier transform (DFT) of a length-$N$ complex sequence $x_0,\dots,x_{N-1}$ is [2]

X_k = \sum_{n=0}^{N-1} x_n\, e^{-2\pi i k n / N}, \qquad k = 0,\dots,N-1 \tag{1}

and its inverse is

x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k\, e^{+2\pi i k n / N}. \tag{2}

Evaluating (1) directly costs $O(N^2)$ operations; FFT algorithms such as Cooley–Tukey [3] reduce this to $O(N \log N)$, and FFTW selects among many such algorithms at plan-creation time [1].

FFTW's normalization convention. FFTW_FORWARD computes exactly the unnormalized sum (1), and FFTW_BACKWARD computes the sum in (2) without the $1/N$ factor. Applying forward then backward therefore scales the data by $N$; normalization is left to the caller. This demo executes only the forward transform (TransTF) and plots the unnormalized spectrum $X_k$ as-is.

The demo signal. TDoubleRandWalkC (from the LUX library) maintains $N$ complex samples and advances every sample by one Metropolis random-walk step per timer tick, producing a continuously evolving complex signal $x_n$. No window function is applied — the samples enter the transform with an implicit rectangular window — and since the input is complex, both the real part (blue) and the imaginary part (red) are drawn in each chart.

3. Architecture

Class hierarchy and data flow of the wrapper:

???TForm1 (Main.pas)
  ??????_Wave :IDoubleRandWalkC               ????????? Metropolis complex random walk
  ??????_FFT :IDoubleDFTcc1D                  ????????? 1-D c2c double-precision DFT
  ??????ChartViewerT / ChartViewerF           ????????? time/frequency plots [LUX.Chart]

Data flow (one timer tick)

???_Wave
  ??????_FFT.Times                            ????????? (x[n])
     ??????ChartViewerT                       ????????? time-domain plot
     ??????TransTF                            ????????? fftw_execute_dft via _PlanTF
        ??????_FFT.Freqs                      ????????? (X[k])
           ??????ChartViewerF                 ????????? frequency-domain plot

Class hierarchy

???IDFT
  ??????TDFT<_TItem_,_TTimes_,_TFreqs_>       ????????? LUX.FFTW.pas
     ??????_Times / _Freqs                    ????????? grid buffers
     ??????_PlanTF / _PlanFT                  ????????? plans, run by TransTF/TransFT
     ??????TDFT1D<...>                        ????????? LUX.FFTW.D1.pas
        ??????grid resize
        ???  ??????RecreaPlans                 ????????? destroy + re-create plans
        ??????TSingleDFTcc1D / TDoubleDFTcc1D ????????? LUX.FFTW.D1.Preset.pas

Binding layers (call chain)

???TSingleDFTcc1D / TDoubleDFTcc1D
  ??????fftw(f)_plan_dft_1d( N, in, out, direction, flags )
     ??????fftw3.pas
        ??????cdecl imports
           ??????libfftw3-3.dll / libfftw3f-3.dll

File layout:

???FFTW/
  ??????FFTW.dpr / FFTW.dproj ????????? FireMonkey demo project
  ??????Main.pas / Main.fmx   ????????? main form: charts, 10 ms timer, N scrollbar
  ??????Win64/                ????????? build output with FFTW DLLs (Debug/Release)
  ??????--------/_SCREENSHOT/ ????????? screenshot
  ??????_LIBRARY/LUXOPHIA/    ????????? git-subtree copies of library repositories
     ??????LUX/               ????????? core math & utilities (complex numbers etc.)
     ??????LUX.Chart/         ????????? TChartViewer plotting frame
     ??????LUX.FFTW/          ????????? FFTW binding and wrapper classes
        ??????fftw3.pas       ????????? raw FFTW 3 API translation
        ??????LUX.FFTW.pas    ????????? generic TDFT base classes
        ??????D1/ D2/ D3/     ????????? 1-D/2-D/3-D wrappers and presets

Library repositories: LUX, LUX.Chart, LUX.FFTW.

4. Usage / Controls

The animation starts automatically; a timer advances the random walk and re-runs the FFT every 10 ms.

Changing $N$ resizes the Times/Freqs grids, which automatically re-creates the FFTW plans.

5. Building

• IDE: RAD Studio / Delphi (project format 19.5 = RAD Studio 11 Alexandria; later versions can upgrade the project).
• Framework: FireMonkey (FMX).
• Platforms: Win64 is the working target — fftw3.pas links against the Windows FFTW DLLs.
• Required DLLs: libfftw3-3.dll (double precision) and libfftw3f-3.dll (single precision) must reside next to FFTW.exe. Copies of the FFTW 3.3.11 build from the MSYS2 mingw-w64-fftw package — including the libfftw3_threads-3.dll / libfftw3f_threads-3.dll companions — are already bundled in Win64\Debug|Release.

Open FFTW.dproj, select the Win64 platform, and run. All library units are referenced directly from _LIBRARY\ — no search-path setup is needed.

6. References

1. M. Frigo and S. G. Johnson, "The Design and Implementation of FFTW3", Proceedings of the IEEE, vol. 93, no. 2, pp. 216–231, 2005.
2. Wikipedia: Discrete Fourier transform.
3. J. W. Cooley and J. W. Tukey, "An Algorithm for the Machine Calculation of Complex Fourier Series", Mathematics of Computation, vol. 19, pp. 297–301, 1965.
4. Wikipedia: Fast Fourier transform.

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