For testing a Fourier transform I need a more complex signal than an ordinary sinus. I want a mix of three frequenties f, 2f and 5f with an arbitrary phase. In order to keep Engine() simple I added a class Signal:

public class Signal { /** A class to represent an input-signal for the FFT * the input-signal consists of 3 sinus-signals: * a1*sin(2*pi*f*t) + a2*sin(2*pi*2*f*t+phi2)+a5*cos(2*pi*5*f*t) */ private double a1; private double a2; private double a5; private double f; private double phi2; public Signal(double f, double a1, double a2, double a5, double phi2) { this.f = f; this.a1 = a1; this.a2 = a2; this.a5 = a5; this.phi2 = phi2; } public double getSample(double t) { double pi2 = Math.PI * 2; return a1 * Math.cos(pi2 * f * t) + a2 * Math.sin(pi2 * 2 * f * t + phi2) + a5 * Math.sin (pi2 * 5 * f * t); } } |

Signal is used in Engine():

package testFFT; import util.ShowPlot2d; import util.Signal; public class Engine { // a more complicated testsignal public Engine() { } public void plotSignal() { double f = 1.0E3; double a1 = 1.0; double a2 = 0.8; double a5 = 1.2; double phi = Math.PI / 4; int numP = 109; Signal mySignal = new Signal(f, a1, a2, a5, phi); // 3 periods with 36 points per period double[] plotData = new double[numP - 1]; for (int i = 0; i < numP - 1; i++) { double degr = 10 * i; double perT = 1 / f; double t = (degr / 360) * perT; double y = mySignal.getSample(t); plotData[i] = y; } int numberOfPlots = 1; ShowPlot2d myPlot = new ShowPlot2d(numberOfPlots); double scaleFactor = 100; myPlot.add(plotData, scaleFactor, "a complicated sinewave"); myPlot.showFigure(); } } |

with the following output:

What is the sample frequency of the above signal? There are 36 points per period of the base frequency f. So the sample-frequency is 36* f.

The next piece of java-code is fillArray3. This method calculaties the DFT, the Discrete Fourier Transform. For that I needed a dft-program. Because I don't (yet) need real-time respons I simply coded the DFT-formulas directly in java without any attempt for efficiency. The dft-method:

private Complex[] dft(double[] x) { int n = x.length; Complex[] xX = new Complex[n]; // FFT-bins for(int k = 0; k < n; k++) { Complex accum = new Complex(0.0, 0.0); for (int nn=0; nn < n; nn++) { Complex wnkN = Complex.expjphi(-2*Math.PI*nn*k/n); Complex prod = wnkN.multiply(x[nn]); accum = accum.add(prod); } xX[k] = accum; } return xX; } |

Which is called by the following snippet of code:

... for (int i = 0; i < numP - 1; i++) { double degr = 10 * i; double perT = 1 / f; double t = (degr / 360) * perT; double y = mySignal.getSample(t); plotData[i] = y; } double x[] = plotData; Fourier myFourier = new Fourier(); Complex bins[] = myFourier.dft(x); for (int i = 0; i < bins.length; i++) { plotData1[i] = bins[i].getReal(); plotData2[i] = bins[i].getImaginary(); plotData3[i] = bins[i].magnitude(); } int numberOfPlots = 3; ShowPlot2d myPlot = new ShowPlot2d(numberOfPlots, "Three plots"); double scaleFactor = 1; myPlot.add(plotData1, scaleFactor, "real"); scaleFactor = 1; myPlot.add(plotData2, scaleFactor, "imag"); scaleFactor = 1; myPlot.add(plotData3, scaleFactor, "magnitude"); myPlot.showFigure(); ... |

and the output is:

The results are numerically shown in the following table:

Discrete Fourier Transform

Bin-number | Magnitude | Real part | Imaginary part |

3 (f) | 54 | 54 | 0 |

6 (2f) | 43.2 | 30.5 | -30.5 |

15 (5f) | 64.8 | 0 | -64.8 |

93 (5f) | 64.8 | 0 | 64.8 |

102 (2f) | 43.2 | 30.5 | 30.5 |

105 (f) | 54 | 54 | 0 |

The basic frequency f is in bin 3. There are 36 points per period of the base frequency f. So the sample-frequency is 36* f. There are 108 points, so every bin is fs/108= 36*f/108 = f/3. So bin 3 is f, bin 6 is 2f and bin 15 is 5f. Because the dft-transforms of f only have real parts, f must be a cosine. You can find that in the code for "Signal". The dft-transform for 5f is obviously a sine. The signal 2f is complex. There is a 45 degree angle between the real and imaginary part. This can also be found in the java-code for Signal. The amplitude for f is 54+54 = 108. Divided by the number of bins ==> 1. That is correct. The other amplitudes also are in correspondence with the code in Signal.

If you transform a signal into its Fourier components there is also a way back; the Inverse Discrete Fourier Transform. The code is almost the same as for the dft:

private Complex[] idft(Complex[] X) { int n = X.length; Complex[] x = new Complex[n]; for(int k=0; k < n; k++) { Complex accum = new Complex(0.0, 0.0); for(int nn=0; nn < n; nn++) { Complex wnkN = Complex.expjphi(2*Math.PI*nn*k/n); Complex prod = wnkN.multiply(X[nn]); accum = accum.add(prod); } x[k] = accum.multiply( (double) (1.0/n)); } return x; } |

By clearing bins 6, 15, 93 and 102 after the dft and before the idft an ideal lowpass-filter is the result: