/** Test of the Iz periodgram. */
@Test
public void testPeriodgramIzWithZeroData() {
System.out.println("test Iz periodogram with data all zeros");
final int N = 10;
final double Tmin = 0.7;
final double Tmax = 1.2;
final double q = 10;
final double[] y = new double[N];
for (int i = 0; i < y.length; i++) y[i] = 0.0;
QuantisedPeriodogramFFT instance = new QuantisedPeriodogramFFT(N, Tmin, Tmax, q);
instance.estimate(y);
for (double f = 0; f <= q; f += 0.01) assertEquals(instance.Iz(f), N * N, 0.000001);
}
/** Test of the D against B */
@Test
public void testPeriodgramIzAgainstD() {
System.out.println("test slow Iz versus fast FFT computed D");
final int N = 10;
final double Tmin = 0.7;
final double Tmax = 1.2;
final double q = 10;
final double[] y = new double[N];
for (int i = 0; i < y.length; i++) y[i] = 1.0 * i;
QuantisedPeriodogramFFT instance = new QuantisedPeriodogramFFT(N, Tmin, Tmax, q);
instance.estimate(y);
int M = instance.M(); // set M to minimum possible
double W = q / M; // width of the search grid
Complex[] D = instance.computeV(); // compute the vector D by FFT
int K = (int) Math.floor((instance.fmax - instance.fmin) / W);
for (int k = 0; k < K; k++) {
double f = instance.fmin + W * k;
assertEquals(instance.Iz(f), D[k].abs2(), 0.000001);
}
}
/** Test of estimate method, of class Quantised Periodogram FFT. */
@Test
public void testEstimate() {
System.out.println("Quantised Periodogram FFT estimator");
double Tmin = 0.7;
double Tmax = 1.3;
int n = 50;
double T = 1.0;
double phase = 0.4;
QuantisedPeriodogramFFT instance = new QuantisedPeriodogramFFT(n, Tmin, Tmax, 10);
double noisestd = 0.001;
Gaussian noise = new Gaussian(0.0, noisestd * noisestd);
Poisson drv = new Poisson(2);
SparseNoisyPeriodicSignal sig =
new SparseNoisyPeriodicSignal(n, T, phase, new DifferencesIID(n, drv), noise);
double[] y = sig.generate();
instance.estimate(y);
System.out.println(instance.period() + "\t" + instance.phase());
assertEquals(T, instance.period(), 0.001);
assertEquals(phase, instance.phase(), 0.001);
}
