`mlpy.wavelet` - Wavelet Transform¶

Padding¶

mlpy.wavelet.pad(x, method='reflection')¶

Pad to bring the total length N up to the next-higher power of two.

Parameters :	x : 1d array_like object data method : string (‘reflection’, ‘periodic’, ‘zeros’) method
Returns :	xp, orig : 1d numpy array, 1d numpy array bool padded version of x and a boolean array with value True where xp contains the original data

Discrete Wavelet Transform¶

Discrete Wavelet Transform based on the GSL DWT [Gsldwt].

For the forward transform, the output is the discrete wavelet transform $f_i \rightarrow w_{j,k}$ in a packed triangular storage layout, where $j$ is the index of the level $j = 0 \dots J-1$ and $k$ is the index of the coefficient within each level, $k = 0 \dots (2^j)-1$ . The total number of levels is $J = \log_2(n)$ . The output data has the following form,

$(s_{-1,0}, d_{0,0}, d_{1,0}, d_{1,1}, d_{2,0}, \dots, d_{j,k}, ..., d_{J-1,2^{J-1}-1})$

where the first element is the smoothing coefficient $s_{-1,0}$ , followed by the detail coefficients $d_{j,k}$ for each level $j$ . The backward transform inverts these coefficients to obtain the original data.

Note

from GSL online manual (http://www.gnu.org/software/gsl/manual/)

mlpy.wavelet.dwt(x, wf, k, centered=False)¶

Discrete Wavelet Tranform

Parameters :

x : 1d array_like object (the length is restricted to powers of two)

data

wf : string (‘d’: daubechies, ‘h’: haar, ‘b’: bspline)

wavelet family

k : integer

member of the wavelet family

daubechies : k = 4, 6, ..., 20 with k even

haar : the only valid choice of k is k = 2

bspline : k = 103, 105, 202, 204, 206, 208, 301, 303, 305 307, 309

centered : bool

align the coefficients of the various sub-bands on edges. Thus the resulting visualization of the coefficients of the wavelet transform in the phase plane is easier to understand.

Returns :

X : 1d numpy array: discrete wavelet transformed data

Example

>>> import numpy as np
>>> import mlpy.wavelet as wave
>>> x = np.array([1,2,3,4,3,2,1,0])
>>> wave.dwt(x=x, wf='d', k=6)
array([ 5.65685425,  3.41458985,  0.29185347, -0.29185347, -0.28310081,
       -0.07045258,  0.28310081,  0.07045258])

mlpy.wavelet.idwt(X, wf, k, centered=False)¶

Inverse Discrete Wavelet Tranform

Parameters :	X : 1d array_like object discrete wavelet transformed data wf : string (‘d’: daubechies, ‘h’: haar, ‘b’: bspline) wavelet type k : integer member of the wavelet family daubechies : k = 4, 6, ..., 20 with k even haar : the only valid choice of k is k = 2 bspline : k = 103, 105, 202, 204, 206, 208, 301, 303, 305 307, 309 centered : bool if the coefficients are aligned
Returns :	x : 1d numpy array data

Example:

>>> import numpy as np
>>> import mlpy.wavelet as wave
>>> X = np.array([ 5.65685425,  3.41458985,  0.29185347, -0.29185347, -0.28310081,
...               -0.07045258,  0.28310081,  0.07045258])
>>> wave.idwt(X=X, wf='d', k=6)
array([  1.00000000e+00,   2.00000000e+00,   3.00000000e+00,
         4.00000000e+00,   3.00000000e+00,   2.00000000e+00,
         1.00000000e+00,  -3.53954610e-09])

Undecimated Wavelet Transform¶

Undecimated Wavelet Transform (also known as stationary wavelet transform, redundant wavelet transform, translation invariant wavelet transform, shift invariant wavelet transform or Maximal overlap wavelet transform) based on the “wavelets” R package.

mlpy.wavelet.uwt(x, wf, k, levels=0)¶

Undecimated Wavelet Tranform

Parameters :

x : 1d array_like object (the length is restricted to powers of two)

data

wf : string (‘d’: daubechies, ‘h’: haar, ‘b’: bspline)

wavelet family

k : int

member of the wavelet family

daubechies: k = 4, 6, ..., 20 with k even

haar: the only valid choice of k is k = 2

bspline: k = 103, 105, 202, 204, 206, 208, 301, 303, 305 307, 309

levels : int

level of the decomposition (J). If levels = 0 this is the value J such that the length of X is at least as great as the length of the level J wavelet filter, but less than the length of the level J+1 wavelet filter. Thus, j <= log_2((n-1)/(l-1)+1), where n is the length of x

Returns :

X : 2d numpy array (2J * len(x))

misaligned scaling and wavelet coefficients:

[[wavelet coefficients W_1]
 [wavelet coefficients W_2]
               :
 [wavelet coefficients W_J]
 [scaling coefficients V_1]
 [scaling coefficients V_2]
              :
 [scaling coefficients V_J]]

mlpy.wavelet.iuwt(X, wf, k)¶

Inverse Undecimated Wavelet Tranform

Parameters :	X : 2d array_like object (the length is restricted to powers of two) misaligned scaling and wavelet coefficients wf : string (‘d’: daubechies, ‘h’: haar, ‘b’: bspline) wavelet family k : int member of the wavelet family daubechies: k = 4, 6, ..., 20 with k even haar: the only valid choice of k is k = 2 bspline: k = 103, 105, 202, 204, 206, 208, 301, 303, 305 307, 309
Returns :	x : 1d numpy array data

mlpy.wavelet.uwt_align_h2(X, inverse=False)¶

UWT h2 coefficients aligment.

If inverse = True performs the misalignment for a correct reconstruction.

mlpy.wavelet.uwt_align_d4(X, inverse=False)¶

UWT d4 coefficients aligment.

If inverse = True performs the misalignment for a correct reconstruction.

Continuous Wavelet Transform¶

Continuous Wavelet Transform based on [Torrence98].

mlpy.wavelet.cwt(x, dt, scales, wf='dog', p=2)¶

Continuous Wavelet Tranform.

Parameters :	x : 1d array_like object data dt : float time step scales : 1d array_like object scales wf : string (‘morlet’, ‘paul’, ‘dog’) wavelet function p : float wavelet function parameter (‘omega0’ for morlet, ‘m’ for paul and dog)
Returns :	X : 2d numpy array transformed data

mlpy.wavelet.icwt(X, dt, scales, wf='dog', p=2)¶

Inverse Continuous Wavelet Tranform. The reconstruction factor is not applied.

Parameters :	X : 2d array_like object transformed data dt : float time step scales : 1d array_like object scales wf : string (‘morlet’, ‘paul’, ‘dog’) wavelet function p : float wavelet function parameter
Returns :	x : 1d numpy array data

mlpy.wavelet.autoscales(N, dt, dj, wf, p)¶

Compute scales as fractional power of two.

Parameters :	N : integer number of data samples dt : float time step dj : float scale resolution (smaller values of dj give finer resolution) wf : string wavelet function (‘morlet’, ‘paul’, ‘dog’) p : float omega0 (‘morlet’) or order (‘paul’, ‘dog’)
Returns :	scales : 1d numpy array scales

mlpy.wavelet.fourier_from_scales(scales, wf, p)¶

Compute the equivalent fourier period from scales.

Parameters :	scales : list or 1d numpy array scales wf : string (‘morlet’, ‘paul’, ‘dog’) wavelet function p : float wavelet function parameter (‘omega0’ for morlet, ‘m’ for paul and dog)
Returns :	fourier wavelengths

mlpy.wavelet.scales_from_fourier(f, wf, p)¶

Compute scales from fourier period.

Parameters :	f : list or 1d numpy array fourier wavelengths wf : string (‘morlet’, ‘paul’, ‘dog’) wavelet function p : float wavelet function parameter (‘omega0’ for morlet, ‘m’ for paul and dog)
Returns :	scales

[Torrence98]

C Torrence and G P Compo. Practical Guide to Wavelet Analysis

[Gsldwt]

Gnu Scientific Library, http://www.gnu.org/software/gsl/

Example (requires matplotlib)

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import mlpy.wavelet as wave
>>> x = np.random.sample(512)
>>> scales = wave.autoscales(N=x.shape[0], dt=1, dj=0.25, wf='dog', p=2)
>>> X = wave.cwt(x=x, dt=1, scales=scales, wf='dog', p=2)
>>> fig = plt.figure(1)
>>> ax1 = plt.subplot(2,1,1)
>>> p1 = ax1.plot(x)
>>> ax1.autoscale_view(tight=True)
>>> ax2 = plt.subplot(2,1,2)
>>> p2 = ax2.imshow(np.abs(X), interpolation='nearest')
>>> plt.show()

`mlpy.wavelet` - Wavelet Transform¶

Padding¶

Discrete Wavelet Transform¶

Undecimated Wavelet Transform¶

Continuous Wavelet Transform¶

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mlpy.wavelet - Wavelet Transform¶

Padding¶

Discrete Wavelet Transform¶

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`mlpy.wavelet` - Wavelet Transform¶