Wavelets Reference

This is the reference of the wavelets available wiith the pyrwt package. Please refer to the tutorial for further details, as the raw specifications may not be enough to give full guidelines on their uses.

wavelets - A wavelet libray.

A library of functions that create different kinds of wavelets.

rwt.wavelets.waveletCoeffs(name)

Return the wavelet coefficients according to the wavelet name.

Parameters :

name : str

Name of wavelet. Supported values:

• ‘haar‘ : Haar wavelet
• ‘db1‘-‘db20‘ : Daubechie wavelets with different support size
• ‘sym1‘-‘sym20‘ : Symlets wavelets with different support size
• ‘coif1‘-‘coif5‘ : Coiflets wavelets with different support size
• ‘bior1.{1,3,5}‘, ‘bior2.{2,4,6,8}‘, ‘bior3.{1,3,5,7}‘, ‘bior4.4‘, ‘bior5.5‘, ‘bior6.8‘: Biorthogonal wavelets with different support size
• ‘dmey‘ : Discrete Meyer wavelet

Returns :

dec_lo : array

Decoding coefficients of the scaling function

dec_hi : array

Decoding coefficients of the wavelet function

rec_lo : array

Reconstruction coefficients of the scaling function

rec_hi : array

Reconstruction coefficients of the wavelet function

Examples

>>> from rwt.wavelets import waveletCoeffs
>>> dl, dh, rl, rh = waveletCoeffs("db1")

Note

The wavelet coefficients are taken from the PyWavelets - Discrete Wavelet Transform in Python pacakge, copyright (c) 2006-2010 Filip Wasilewski.

rwt.wavelets.waveletlist()

Return the list of supported wavelets.

Returns :

wavelet_list : list of strings

The names of supported wavelets. This names can be used in waveletCoeffs().

.. note:: :

The wavelet coefficients are taken from the PyWavelets - Discrete Wavelet Transform in Python pacakge, copyright (c) 2006-2010 Filip Wasilewski.

rwt.wavelets.daubcqf(N, TYPE='min')

Computes the Daubechies’ scaling and wavelet filters (normalized to sqrt(2)).

Parameters :

N : int

Length of filter (must be even)

TYPE : [‘min’, ‘max’, ‘mid’], optional (default=’min’)

Distinguishes the minimum phase, maximum phase and mid-phase solutions (‘min’, ‘max’, or ‘mid’).

Returns :

h_0 : array-like, shape = [N]

Daubechies’ scaling filter

h_1 : array-like, shape = [N]

Daubechies’ wavelet filter

Examples

>>> from rwt.wavelets import daubcqf
>>> h_0, h_1 = daubcqf(N=4, TYPE='min')
>>> print h_0, h_1
[[0.4830, 0.8365, 0.2241, -0.1294]] [[0.1294, 0.2241, -0.8365, 0.4830]]