public void HartleyNaiveIsReversible(HartleyOptions options)
{
var dht = new DiscreteHartleyTransform();
VerifyIsReversibleReal(
0x80,
1e-9,
s => dht.NaiveForward(s, options),
s => dht.NaiveInverse(s, options));
}
public void HartleyNaiveIsReversible([Values(HartleyOptions.Default, HartleyOptions.AsymmetricScaling)] HartleyOptions options)
{
var dht = new DiscreteHartleyTransform();
VerifyIsReversibleReal(
0x80,
1e-9,
s => dht.NaiveForward(s, options),
s => dht.NaiveInverse(s, options));
}
public void HartleyNaiveIsReversible(HartleyOptions options)
{
var dht = new DiscreteHartleyTransform();
var samples = SignalGenerator.Random(x => x, GetUniform(1), 0x80);
var work = new double[samples.Length];
samples.CopyTo(work, 0);
work = dht.NaiveForward(work, options);
Assert.IsFalse(work.ListAlmostEqual(samples, 6));
work = dht.NaiveInverse(work, options);
AssertHelpers.ListAlmostEqual(samples, work, 12);
}
public void NaiveMatchesDft([Values(HartleyOptions.Default, HartleyOptions.AsymmetricScaling, HartleyOptions.NoScaling)] HartleyOptions hartleyOptions, [Values(FourierOptions.Default, FourierOptions.AsymmetricScaling, FourierOptions.NoScaling)] FourierOptions fourierOptions)
{
var dht = new DiscreteHartleyTransform();
var samples = SignalGenerator.Random(x => x, _uniform, 0x80);
VerifyMatchesDft(
samples,
1e-5,
false,
s => Transform.FourierForward(s, fourierOptions),
s => dht.NaiveForward(s, hartleyOptions));
VerifyMatchesDft(
samples,
1e-5,
true,
s => Transform.FourierInverse(s, fourierOptions),
s => dht.NaiveInverse(s, hartleyOptions));
}
public void NaiveMatchesDft(HartleyOptions hartleyOptions, FourierOptions fourierOptions)
{
var dht = new DiscreteHartleyTransform();
var samples = SignalGenerator.Random(x => x, GetUniform(1), 0x80);
VerifyMatchesDft(
samples,
1e-5,
false,
s => Transform.FourierForward(s, fourierOptions),
s => dht.NaiveForward(s, hartleyOptions));
VerifyMatchesDft(
samples,
1e-5,
true,
s => Transform.FourierInverse(s, fourierOptions),
s => dht.NaiveInverse(s, hartleyOptions));
}
public void NaiveMatchesDft([Values(HartleyOptions.Default, HartleyOptions.AsymmetricScaling, HartleyOptions.NoScaling)] HartleyOptions hartleyOptions, [Values(FourierOptions.Default, FourierOptions.AsymmetricScaling, FourierOptions.NoScaling)] FourierOptions fourierOptions)
{
var dht = new DiscreteHartleyTransform();
var samples = SignalGenerator.Random(x => x, _uniform, 0x80);
VerifyMatchesDft(
samples,
1e-5,
false,
s => Transform.FourierForward(s, fourierOptions),
s => dht.NaiveForward(s, hartleyOptions));
VerifyMatchesDft(
samples,
1e-5,
true,
s => Transform.FourierInverse(s, fourierOptions),
s => dht.NaiveInverse(s, hartleyOptions));
}
public void NaiveMatchesDft(HartleyOptions hartleyOptions, FourierOptions fourierOptions)
{
var dht = new DiscreteHartleyTransform();
var samples = SignalGenerator.Random(x => x, _uniform, 0x80);
VerifyMatchesDft(
samples,
1e-5,
false,
s => Transform.FourierForward(s, fourierOptions),
s => dht.NaiveForward(s, hartleyOptions));
VerifyMatchesDft(
samples,
1e-5,
true,
s => Transform.FourierInverse(s, fourierOptions),
s => dht.NaiveInverse(s, hartleyOptions));
}
public void HartleyDefaultNaiveSatisfiesParsevalsTheorem(int count)
{
var samples = SignalGenerator.Random(x => x, _uniform, count);
var timeSpaceEnergy = (from s in samples select s * s).Mean();
var work = new double[samples.Length];
samples.CopyTo(work, 0);
// Default -> Symmetric Scaling
var dht = new DiscreteHartleyTransform();
work = dht.NaiveForward(work, HartleyOptions.Default);
var frequencySpaceEnergy = (from s in work select s * s).Mean();
Assert.AreEqual(timeSpaceEnergy, frequencySpaceEnergy, 1e-12);
}
public void NaiveMatchesDFT(HartleyOptions hartleyOptions, FourierOptions fourierOptions)
{
var dht = new DiscreteHartleyTransform();
var samples = Sample.Random(x => x, _uniform, 0x80);
VerifyMatchesDFT(
samples,
1e-10,
false,
s => Transform.FourierForward(s, fourierOptions),
s => dht.NaiveForward(s, hartleyOptions));
VerifyMatchesDFT(
samples,
1e-10,
true,
s => Transform.FourierInverse(s, fourierOptions),
s => dht.NaiveInverse(s, hartleyOptions));
}
public void HartleyDefaultNaiveSatisfiesParsevalsTheorem(int count)
{
var samples = Sample.Random(x => x, _uniform, count);
var timeSpaceEnergy = (from s in samples select s * s).Mean();
var work = new double[samples.Length];
samples.CopyTo(work, 0);
// Default -> Symmetric Scaling
var dht = new DiscreteHartleyTransform();
work = dht.NaiveForward(work, HartleyOptions.Default);
var frequencySpaceEnergy = (from s in work select s * s).Mean();
Assert.AreApproximatelyEqual(timeSpaceEnergy, frequencySpaceEnergy, 1e-12);
}
public void HartleyNaiveIsReversible([Values(HartleyOptions.Default, HartleyOptions.AsymmetricScaling)] HartleyOptions options)
{
var dht = new DiscreteHartleyTransform();
VerifyIsReversibleReal(
0x80,
1e-9,
s => dht.NaiveForward(s, options),
s => dht.NaiveInverse(s, options));
}
public void HartleyNaiveIsReversible(HartleyOptions options)
{
var dht = new DiscreteHartleyTransform();
VerifyIsReversibleReal(
0x80,
1e-9,
s => dht.NaiveForward(s, options),
s => dht.NaiveInverse(s, options));
}