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nD Forward and Inverse Discrete Wavelet Transform#

_multilevel

Multilevel 1D and 2D Discrete Wavelet Transform and Inverse Discrete Wavelet Transform.

Single level - dwtn#

pywt.dwtn(data, wavelet, mode='symmetric', axes=None)#

Single-level n-dimensional Discrete Wavelet Transform.

Parameters
dataarray_like

n-dimensional array with input data.

waveletWavelet object or name string, or tuple of wavelets

Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in axes.

modestr or tuple of string, optional

Signal extension mode used in the decomposition, see Modes. This can also be a tuple of modes specifying the mode to use on each axis in axes.

axessequence of ints, optional

Axes over which to compute the DWT. Repeated elements mean the DWT will be performed multiple times along these axes. A value of None (the default) selects all axes.

Axes may be repeated, but information about the original size may be lost if it is not divisible by 2 ** nrepeats. The reconstruction will be larger, with additional values derived according to the mode parameter. pywt.wavedecn should be used for multilevel decomposition.

Returns
coeffsdict

Results are arranged in a dictionary, where key specifies the transform type on each dimension and value is a n-dimensional coefficients array.

For example, for a 2D case the result will look something like this:

{'aa': <coeffs>  # A(LL) - approx. on 1st dim, approx. on 2nd dim
 'ad': <coeffs>  # V(LH) - approx. on 1st dim, det. on 2nd dim
 'da': <coeffs>  # H(HL) - det. on 1st dim, approx. on 2nd dim
 'dd': <coeffs>  # D(HH) - det. on 1st dim, det. on 2nd dim
}

For user-specified axes, the order of the characters in the dictionary keys map to the specified axes.

Single level - idwtn#

pywt.idwtn(coeffs, wavelet, mode='symmetric', axes=None)#

Single-level n-dimensional Inverse Discrete Wavelet Transform.

Parameters
coeffs: dict

Dictionary as in output of dwtn. Missing or None items will be treated as zeros.

waveletWavelet object or name string, or tuple of wavelets

Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in axes.

modestr or list of string, optional

Signal extension mode used in the decomposition, see Modes. This can also be a tuple of modes specifying the mode to use on each axis in axes.

axessequence of ints, optional

Axes over which to compute the IDWT. Repeated elements mean the IDWT will be performed multiple times along these axes. A value of None (the default) selects all axes.

For the most accurate reconstruction, the axes should be provided in the same order as they were provided to dwtn.

Returns
data: ndarray

Original signal reconstructed from input data.

Multilevel decomposition - wavedecn#

pywt.wavedecn(data, wavelet, mode='symmetric', level=None, axes=None)#

Multilevel nD Discrete Wavelet Transform.

Parameters
datandarray

nD input data

waveletWavelet object or name string, or tuple of wavelets

Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in axes.

modestr or tuple of str, optional

Signal extension mode, see Modes. This can also be a tuple containing a mode to apply along each axis in axes.

levelint, optional

Decomposition level (must be >= 0). If level is None (default) then it will be calculated using the dwt_max_level function.

axessequence of ints, optional

Axes over which to compute the DWT. Axes may not be repeated. The default is None, which means transform all axes (axes = range(data.ndim)).

Returns
[cAn, {details_level_n}, … {details_level_1}]list

Coefficients list. Coefficients are listed in descending order of decomposition level. cAn are the approximation coefficients at level n. Each details_level_i element is a dictionary containing detail coefficients at level i of the decomposition. As a concrete example, a 3D decomposition would have the following set of keys in each details_level_i dictionary:

{'aad', 'ada', 'daa', 'add', 'dad', 'dda', 'ddd'}

where the order of the characters in each key map to the specified axes.

Examples

>>> import numpy as np
>>> from pywt import wavedecn, waverecn
>>> coeffs = wavedecn(np.ones((4, 4, 4)), 'db1')
>>> # Levels:
>>> len(coeffs)-1
2
>>> waverecn(coeffs, 'db1')  
array([[[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]]])

Multilevel reconstruction - waverecn#

pywt.waverecn(coeffs, wavelet, mode='symmetric', axes=None)#

Multilevel nD Inverse Discrete Wavelet Transform.

coeffsarray_like

Coefficients list [cAn, {details_level_n}, … {details_level_1}]

waveletWavelet object or name string, or tuple of wavelets

Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in axes.

modestr or tuple of str, optional

Signal extension mode, see Modes. This can also be a tuple containing a mode to apply along each axis in axes.

axessequence of ints, optional

Axes over which to compute the IDWT. Axes may not be repeated.

Returns
nD array of reconstructed data.

Notes

It may sometimes be desired to run waverecn with some sets of coefficients omitted. This can best be done by setting the corresponding arrays to zero arrays of matching shape and dtype. Explicitly removing list or dictionary entries or setting them to None is not supported.

Specifically, to ignore all detail coefficients at level 2, one could do:

coeffs[-2] = {k: np.zeros_like(v) for k, v in coeffs[-2].items()}

Examples

>>> import numpy as np
>>> from pywt import wavedecn, waverecn
>>> coeffs = wavedecn(np.ones((4, 4, 4)), 'db1')
>>> # Levels:
>>> len(coeffs)-1
2
>>> waverecn(coeffs, 'db1')  
array([[[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]],
       [[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]]])

Multilevel fully separable decomposition - fswavedecn#

pywt.fswavedecn(data, wavelet, mode='symmetric', levels=None, axes=None)#

Fully Separable Wavelet Decomposition.

This is a variant of the multilevel discrete wavelet transform where all levels of decomposition are performed along a single axis prior to moving onto the next axis. Unlike in wavedecn, the number of levels of decomposition are not required to be the same along each axis which can be a benefit for anisotropic data.

Parameters
data: array_like

Input data

waveletWavelet object or name string, or tuple of wavelets

Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in axes.

modestr or tuple of str, optional

Signal extension mode, see Modes. This can also be a tuple containing a mode to apply along each axis in axes.

levelsint or sequence of ints, optional

Decomposition levels along each axis (must be >= 0). If an integer is provided, the same number of levels are used for all axes. If levels is None (default), dwt_max_level will be used to compute the maximum number of levels possible for each axis.

axessequence of ints, optional

Axes over which to compute the transform. Axes may not be repeated. The default is to transform along all axes.

Returns
fswavedecn_resultFswavedecnResult object

Contains the wavelet coefficients, slice objects to allow obtaining the coefficients per detail or approximation level, and more. See FswavedecnResult for details.

See also

fswaverecn

inverse of fswavedecn

Notes

This transformation has been variously referred to as the (fully) separable wavelet transform (e.g. refs [1], [3]), the tensor-product wavelet ([2]) or the hyperbolic wavelet transform ([4]). It is well suited to data with anisotropic smoothness.

In [2] it was demonstrated that fully separable transform performs at least as well as the DWT for image compression. Computation time is a factor 2 larger than that for the DWT.

References

1

PH Westerink. Subband Coding of Images. Ph.D. dissertation, Dept. Elect. Eng., Inf. Theory Group, Delft Univ. Technol., Delft, The Netherlands, 1989. (see Section 2.3) http://resolver.tudelft.nl/uuid:a4d195c3-1f89-4d66-913d-db9af0969509

2(1,2)

CP Rosiene and TQ Nguyen. Tensor-product wavelet vs. Mallat decomposition: A comparative analysis, in Proc. IEEE Int. Symp. Circuits and Systems, Orlando, FL, Jun. 1999, pp. 431-434.

3

V Velisavljevic, B Beferull-Lozano, M Vetterli and PL Dragotti. Directionlets: Anisotropic Multidirectional Representation With Separable Filtering. IEEE Transactions on Image Processing, Vol. 15, No. 7, July 2006.

4

RA DeVore, SV Konyagin and VN Temlyakov. “Hyperbolic wavelet approximation,” Constr. Approx. 14 (1998), 1-26.

Examples

>>> from pywt import fswavedecn
>>> fs_result = fswavedecn(np.ones((32, 32)), 'sym2', levels=(1, 3))
>>> print(fs_result.detail_keys())
[(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2), (1, 3)]
>>> approx_coeffs = fs_result.approx
>>> detail_1_2 = fs_result[(1, 2)]

Multilevel fully separable reconstruction - fswaverecn#

pywt.fswaverecn(fswavedecn_result)#

Fully Separable Inverse Wavelet Reconstruction.

Parameters
fswavedecn_resultFswavedecnResult object

FswavedecnResult object from fswavedecn.

Returns
reconstructedndarray

Array of reconstructed data.

See also

fswavedecn

inverse of fswaverecn

Notes

This transformation has been variously referred to as the (fully) separable wavelet transform (e.g. refs [1], [3]), the tensor-product wavelet ([2]) or the hyperbolic wavelet transform ([4]). It is well suited to data with anisotropic smoothness.

In [2] it was demonstrated that the fully separable transform performs at least as well as the DWT for image compression. Computation time is a factor 2 larger than that for the DWT.

References

1

PH Westerink. Subband Coding of Images. Ph.D. dissertation, Dept. Elect. Eng., Inf. Theory Group, Delft Univ. Technol., Delft, The Netherlands, 1989. (see Section 2.3) http://resolver.tudelft.nl/uuid:a4d195c3-1f89-4d66-913d-db9af0969509

2(1,2)

CP Rosiene and TQ Nguyen. Tensor-product wavelet vs. Mallat decomposition: A comparative analysis, in Proc. IEEE Int. Symp. Circuits and Systems, Orlando, FL, Jun. 1999, pp. 431-434.

3

V Velisavljevic, B Beferull-Lozano, M Vetterli and PL Dragotti. Directionlets: Anisotropic Multidirectional Representation With Separable Filtering. IEEE Transactions on Image Processing, Vol. 15, No. 7, July 2006.

4

RA DeVore, SV Konyagin and VN Temlyakov. “Hyperbolic wavelet approximation,” Constr. Approx. 14 (1998), 1-26.

Multilevel fully separable reconstruction coeffs - FswavedecnResult#

class pywt.FswavedecnResult(coeffs, coeff_slices, wavelets, mode_enums, axes)#

Object representing fully separable wavelet transform coefficients.

Parameters
coeffsndarray

The coefficient array.

coeff_sliceslist

List of slices corresponding to each detail or approximation coefficient array.

waveletslist of pywt.DiscreteWavelet objects

The wavelets used. Will be a list with length equal to len(axes).

mode_enumslist of int

The border modes used. Will be a list with length equal to len(axes).

axestuple of int

The set of axes over which the transform was performed.

Attributes
approx

ndarray: The approximation coefficients.

axes

List of str: The axes the transform was performed along.

coeff_slices

List: List of coefficient slices.

coeffs

ndarray: All coefficients stacked into a single array.

levels

List of int: Levels of decomposition along each transformed axis.

modes

List of str: The border mode used along each transformed axis.

ndim

int: Number of data dimensions.

ndim_transform

int: Number of axes transformed.

wavelet_names

List of pywt.DiscreteWavelet: wavelet for each transformed axis.

wavelets

List of pywt.DiscreteWavelet: wavelet for each transformed axis.

Methods

detail_keys()

Return a list of all detail coefficient keys.
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