Discrete Wavelet Transform

Discrete Wavelet Transform

Wraps GSL's gsl_wavelet_transform_forward(), and gsl_wavelet_transform_inverse() and creates plot methods.

AUTHOR:

  • Josh Kantor (2006-10-07) - initial version
  • David Joyner (2006-10-09) - minor changes to docstrings and examples.
sage.gsl.dwt.DWT(n, wavelet_type, wavelet_k)

This function initializes an GSLDoubleArray of length n which can perform a discrete wavelet transform.

INPUT:

  • n – a power of 2
  • T – the data in the GSLDoubleArray must be real
  • wavelet_type – the name of the type of wavelet, valid choices are:
    • 'daubechies'
    • 'daubechies_centered'
    • 'haar'
    • 'haar_centered'
    • 'bspline'
    • 'bspline_centered'

For daubechies wavelets, wavelet_k specifies a daubechie wavelet with \(k/2\) vanishing moments. \(k = 4,6,...,20\) for \(k\) even are the only ones implemented.

For Haar wavelets, wavelet_k must be 2.

For bspline wavelets, wavelet_k of \(103,105,202,204,206,208,301, 305,307,309\) will give biorthogonal B-spline wavelets of order \((i,j)\) where wavelet_k is \(100*i+j\). The wavelet transform uses \(J = \log_2(n)\) levels.

OUTPUT:

An array of the form \((s_{-1,0}, d_{0,0}, d_{1,0}, d_{1,1}, d_{2,0}, \ldots, d_{J-1,2^{J-1}-1})\) for \(d_{j,k}\) the detail coefficients of level \(j\). The centered forms align the coefficients of the sub-bands on edges.

EXAMPLES:

sage: a = WaveletTransform(128,'daubechies',4)
sage: for i in range(1, 11):
...    a[i] = 1
...    a[128-i] = 1
sage: a.plot().show(ymin=0)
sage: a.forward_transform()
sage: a.plot().show()
sage: a = WaveletTransform(128,'haar',2)
sage: for i in range(1, 11): a[i] = 1; a[128-i] = 1
sage: a.forward_transform()
sage: a.plot().show(ymin=0)
sage: a = WaveletTransform(128,'bspline_centered',103)
sage: for i in range(1, 11): a[i] = 1; a[100+i] = 1
sage: a.forward_transform()
sage: a.plot().show(ymin=0)

This example gives a simple example of wavelet compression:

sage: a = DWT(2048,'daubechies',6)
sage: for i in range(2048): a[i]=float(sin((i*5/2048)**2))
sage: a.plot().show()  # long time (7s on sage.math, 2011)
sage: a.forward_transform()
sage: for i in range(1800): a[2048-i-1] = 0
sage: a.backward_transform()
sage: a.plot().show()  # long time (7s on sage.math, 2011)
class sage.gsl.dwt.DiscreteWaveletTransform

Bases: sage.gsl.gsl_array.GSLDoubleArray

Discrete wavelet transform class.

backward_transform()
forward_transform()
plot(xmin=None, xmax=None, **args)
sage.gsl.dwt.WaveletTransform(n, wavelet_type, wavelet_k)

This function initializes an GSLDoubleArray of length n which can perform a discrete wavelet transform.

INPUT:

  • n – a power of 2
  • T – the data in the GSLDoubleArray must be real
  • wavelet_type – the name of the type of wavelet, valid choices are:
    • 'daubechies'
    • 'daubechies_centered'
    • 'haar'
    • 'haar_centered'
    • 'bspline'
    • 'bspline_centered'

For daubechies wavelets, wavelet_k specifies a daubechie wavelet with \(k/2\) vanishing moments. \(k = 4,6,...,20\) for \(k\) even are the only ones implemented.

For Haar wavelets, wavelet_k must be 2.

For bspline wavelets, wavelet_k of \(103,105,202,204,206,208,301, 305,307,309\) will give biorthogonal B-spline wavelets of order \((i,j)\) where wavelet_k is \(100*i+j\). The wavelet transform uses \(J = \log_2(n)\) levels.

OUTPUT:

An array of the form \((s_{-1,0}, d_{0,0}, d_{1,0}, d_{1,1}, d_{2,0}, \ldots, d_{J-1,2^{J-1}-1})\) for \(d_{j,k}\) the detail coefficients of level \(j\). The centered forms align the coefficients of the sub-bands on edges.

EXAMPLES:

sage: a = WaveletTransform(128,'daubechies',4)
sage: for i in range(1, 11):
...    a[i] = 1
...    a[128-i] = 1
sage: a.plot().show(ymin=0)
sage: a.forward_transform()
sage: a.plot().show()
sage: a = WaveletTransform(128,'haar',2)
sage: for i in range(1, 11): a[i] = 1; a[128-i] = 1
sage: a.forward_transform()
sage: a.plot().show(ymin=0)
sage: a = WaveletTransform(128,'bspline_centered',103)
sage: for i in range(1, 11): a[i] = 1; a[100+i] = 1
sage: a.forward_transform()
sage: a.plot().show(ymin=0)

This example gives a simple example of wavelet compression:

sage: a = DWT(2048,'daubechies',6)
sage: for i in range(2048): a[i]=float(sin((i*5/2048)**2))
sage: a.plot().show()  # long time (7s on sage.math, 2011)
sage: a.forward_transform()
sage: for i in range(1800): a[2048-i-1] = 0
sage: a.backward_transform()
sage: a.plot().show()  # long time (7s on sage.math, 2011)
sage.gsl.dwt.is2pow(n)

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