/// <summary>
/// Multilevel 1-D Discreete Wavelet Transform
/// </summary>
/// <param name="signal">The signal. Example: new Signal(5, 6, 7, 8, 1, 2, 3, 4)</param>
/// <param name="motherWavelet">The mother wavelet to be used. Example: CommonMotherWavelets.GetWaveletFromName("DB4")</param>
/// <param name="level">The depth-level to perform the DWT</param>
/// <param name="extensionMode">Signal extension mode</param>
/// <param name="convolutionMode">Defines what convolution function should be used</param>
/// <returns></returns>
public static List <DecompositionLevel> ExecuteDWT(Signal signal, MotherWavelet motherWavelet, int level, SignalExtension.ExtensionMode extensionMode = SignalExtension.ExtensionMode.SymmetricHalfPoint, ConvolutionModeEnum convolutionMode = ConvolutionModeEnum.ManagedFFT)
{
var levels = new List <DecompositionLevel>();
var approximation = (double[])signal.Samples.Clone();
var details = (double[])signal.Samples.Clone();
var realLength = signal.Samples.Length;
for (var i = 1; i <= level; i++)
{
var extensionSize = motherWavelet.Filters.DecompositionLowPassFilter.Length - 1;
approximation = SignalExtension.Extend(approximation, extensionMode, extensionSize);
details = SignalExtension.Extend(details, extensionMode, extensionSize);
approximation = WaveMath.Convolve(convolutionMode, approximation, motherWavelet.Filters.DecompositionLowPassFilter);
approximation = WaveMath.DownSample(approximation);
details = WaveMath.Convolve(convolutionMode, details, motherWavelet.Filters.DecompositionHighPassFilter);
details = WaveMath.DownSample(details);
realLength = realLength / 2;
levels.Add(new DecompositionLevel
{
Signal = signal,
Index = i - 1,
Approximation = approximation,
Details = details
});
details = (double[])approximation.Clone();
}
return(levels);
}
/// <summary>
/// Executes the block
/// </summary>
public override void Execute()
{
var inputNode1 = InputNodes[0].ConnectingNode as BlockOutputNode;
var inputNode2 = InputNodes[1].ConnectingNode as BlockOutputNode;
if (inputNode1 == null || inputNode1.Object == null || inputNode2 == null || inputNode2.Object == null)
{
return;
}
var outputs = new List <Signal>();
var signals = inputNode1.Object;
var filters = inputNode2.Object;
for (var i = 0; i < signals.Count; i++)
{
var signal = signals[i];
if (filters.Count == 0)
{
outputs.Add(signal.Clone());
continue;
}
var output = signal.Copy();
var filterIndex = i < filters.Count ? i : 0;
output.Samples = WaveMath.Convolve(ConvolutionMode, signal.Samples, filters[filterIndex].Samples, ReturnOnlyValid, 0, FFTMode);
outputs.Add(output);
}
OutputNodes[0].Object = outputs;
if (Cascade && OutputNodes[0].ConnectingNode != null)
{
OutputNodes[0].ConnectingNode.Root.Execute();
}
}
public void TestConvolve()
{
var signal = new double[] { 1, 2, 3, 4, 5, 6, 7, 8 };
var filter = new double[] { 1, 2, 3 };
var convolved = WaveMath.Convolve(ConvolutionModeEnum.Normal, signal, filter);
var expected = new double[] { 10, 16, 22, 28, 34, 40 };
Assert.IsTrue(convolved.SequenceEqual(expected));
signal = new double[] { 1, 2, 3 };
filter = new double[] { 1, 2, 3, 4, 5 };
convolved = WaveMath.ConvolveNormal(signal, filter);
expected = new double[] { 10, 16, 22 };
Assert.IsTrue(convolved.SequenceEqual(expected));
signal = new double[] { 1, 2, 3, 4, 5, 6, 7, 8 };
filter = new double[] { 1, 2, 3, 4 };
convolved = WaveMath.ConvolveNormal(signal, filter, false);
expected = new double[] { 1, 4, 10, 20, 30, 40, 50, 60, 61, 52, 32 };
Assert.IsTrue(convolved.SequenceEqual(expected));
}
/// <summary>
/// Multilevel inverse discrete 1-D wavelet transform
/// </summary>
/// <param name="decompositionLevels">The decomposition levels of the DWT</param>
/// <param name="motherWavelet">The mother wavelet to be used. Example: CommonMotherWavelets.GetWaveletFromName("DB4") </param>
/// <param name="level">The depth-level to perform the DWT</param>
/// <param name="convolutionMode">Defines what convolution function should be used</param>
/// <returns></returns>
public static double[] ExecuteIDWT(List <DecompositionLevel> decompositionLevels, MotherWavelet motherWavelet, int level = 0, ConvolutionModeEnum convolutionMode = ConvolutionModeEnum.ManagedFFT)
{
if (level == 0 || level > decompositionLevels.Count)
{
level = decompositionLevels.Count;
}
if (level <= 0)
{
return(null);
}
var approximation = (double[])decompositionLevels[level - 1].Approximation.Clone();
var details = (double[])decompositionLevels[level - 1].Details.Clone();
for (var i = level - 1; i >= 0; i--)
{
approximation = WaveMath.UpSample(approximation);
approximation = WaveMath.Convolve(convolutionMode, approximation, motherWavelet.Filters.ReconstructionLowPassFilter, true, -1);
details = WaveMath.UpSample(details);
details = WaveMath.Convolve(convolutionMode, details, motherWavelet.Filters.ReconstructionHighPassFilter, true, -1);
//sum approximation with details
approximation = WaveMath.Add(approximation, details);
if (i <= 0)
{
continue;
}
if (approximation.Length > decompositionLevels[i - 1].Details.Length)
{
approximation = SignalExtension.Deextend(approximation, decompositionLevels[i - 1].Details.Length);
}
details = (double[])decompositionLevels[i - 1].Details.Clone();
}
return(approximation);
}
public void TestConvolveManagedFFT()
{
var signal = new double[] { 1, 2, 3, 4, 5, 6, 7, 8 };
var filter = new double[] { 1, 2, 3 };
var convolved = WaveMath.Convolve(ConvolutionModeEnum.ManagedFFT, signal, filter);
var expected = new double[] { 10, 16, 22, 28, 34, 40 };
Assert.IsTrue(TestUtils.SequenceEquals(convolved, expected));
signal = new double[] { 1, 2, 3 };
filter = new double[] { 1, 2, 3, 4, 5 };
convolved = WaveMath.ConvolveManagedFFT(signal, filter);
expected = new double[] { 10, 16, 22 };
Assert.IsTrue(TestUtils.SequenceEquals(convolved, expected));
signal = new double[] { 1, 2, 3, 4, 5, 6, 7, 8 };
filter = new double[] { 1, 2, 3, 4 };
convolved = WaveMath.ConvolveManagedFFT(signal, filter, false);
expected = new double[] { 1, 4, 10, 20, 30, 40, 50, 60, 61, 52, 32 };
Assert.IsTrue(TestUtils.SequenceEquals(convolved, expected));
Assert.IsNull(WaveMath.ConvolveManagedFFT(null, null, false));
}
