automatic import of python-ptwt

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+/ptwt-0.1.5.tar.gz
diff --git a/python-ptwt.spec b/python-ptwt.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-ptwt
+Version:	0.1.5
+Release:	1
+Summary:	Differentiable and gpu enabled fast wavelet transforms in PyTorch
+License:	EUPL-1.2
+URL:		https://github.com/v0lta/PyTorch-Wavelet-Toolbox
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/24/d6/bfe000cf7ae3b8ae25b7a21d783bc86517674e943134d67b8a4f493d7489/ptwt-0.1.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-PyWavelets
+Requires:	python3-torch
+Requires:	python3-scipy
+Requires:	python3-pooch
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pytest
+Requires:	python3-nox
+
+%description
+********************************
+Pytorch Wavelet Toolbox (`ptwt`)
+********************************
+
+.. image:: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml/badge.svg 
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml
+    :alt: GitHub Actions
+
+.. image:: https://readthedocs.org/projects/pytorch-wavelet-toolbox/badge/?version=latest
+    :target: https://pytorch-wavelet-toolbox.readthedocs.io/en/latest/?badge=latest
+    :alt: Documentation Status
+
+.. image:: https://img.shields.io/pypi/pyversions/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI Versions
+
+.. image:: https://img.shields.io/pypi/v/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI - Project
+
+.. image:: https://img.shields.io/pypi/l/ptwt
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/blob/main/LICENSE
+    :alt: PyPI - License
+
+.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
+    :target: https://github.com/psf/black
+    :alt: Black code style
+
+.. image:: https://static.pepy.tech/personalized-badge/ptwt?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads
+ :target: https://pepy.tech/project/ptwt
+
+
+
+
+Welcome to the PyTorch wavelet toolbox. This package implements:
+
+- the fast wavelet transform (fwt) via ``wavedec`` and its inverse by providing the ``waverec`` function,
+- the two-dimensional fwt is called ``wavedec2`` the synthesis counterpart ``waverec2``,
+- ``wavedec3`` and ``waverec3`` cover the three-dimensional analysis and synthesis case,
+- ``fswavedec2``, ``fswavedec3``, ``fswaverec2`` and ``fswaverec3`` support separable transformations.
+- ``MatrixWavedec`` and ``MatrixWaverec`` implement sparse-matrix-based fast wavelet transforms with boundary filters,
+- 2d sparse-matrix transforms with separable & non-separable boundary filters are available,
+- ``MatrixWavedec3`` and ``MatrixWaverec3`` allow separable 3D-fwt's with boundary filters.
+- ``cwt`` computes a one-dimensional continuous forward transform,
+- single and two-dimensional wavelet packet forward and backward transforms are available via the ``WaveletPacket`` and ``WaveletPacket2D`` objects,
+- finally, this package provides adaptive wavelet support (experimental).
+
+This toolbox extends `PyWavelets <https://pywavelets.readthedocs.io/en/latest/>`_ . We additionally provide GPU and gradient support via a PyTorch backend.
+Complete documentation is available at: https://pytorch-wavelet-toolbox.readthedocs.io/
+
+
+**Installation**
+
+Install the toolbox via pip or clone this repository. In order to use ``pip``, type:
+
+.. code-block:: sh
+
+    $ pip install ptwt
+  
+
+You can remove it later by typing ``pip uninstall ptwt``.
+
+Example usage:
+""""""""""""""
+**Single dimensional transform**
+
+One way to compute fast wavelet transforms is to rely on padding and
+convolution. Consider the following example: 
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  wavelet = pywt.Wavelet('haar')
+  
+  # compare the forward fwt coefficients
+  print(pywt.wavedec(data, wavelet, mode='zero', level=2))
+  print(ptwt.wavedec(data_torch, wavelet, mode='zero', level=2))
+  
+  # invert the fwt.
+  print(ptwt.waverec(ptwt.wavedec(data_torch, wavelet, mode='zero'),
+                     wavelet))
+
+
+The functions ``wavedec`` and ``waverec`` compute the 1d-fwt and its inverse.
+Internally both rely on ``conv1d``, and its transposed counterpart ``conv_transpose1d``
+from the ``torch.nn.functional`` module. This toolbox supports discrete wavelets
+see also ``pywt.wavelist(kind='discrete')``. I have tested
+Daubechies-Wavelets ``db-x`` and symlets ``sym-x``, which are usually a good starting point. 
+
+**Two-dimensional transform**
+
+Analog to the 1d-case ``wavedec2`` and ``waverec2`` rely on 
+``conv2d``, and its transposed counterpart ``conv_transpose2d``.
+To test an example run:
+
+
+.. code-block:: python
+
+  import ptwt, pywt, torch
+  import numpy as np
+  import scipy.misc
+
+  face = np.transpose(scipy.datasets.face(),
+                          [2, 0, 1]).astype(np.float64)
+  pytorch_face = torch.tensor(face)
+  coefficients = ptwt.wavedec2(pytorch_face, pywt.Wavelet("haar"),
+                                  level=2, mode="constant")
+  reconstruction = ptwt.waverec2(coefficients, pywt.Wavelet("haar"))
+  np.max(np.abs(face - reconstruction.squeeze(1).numpy()))
+
+
+
+**Boundary Wavelets with Sparse-Matrices**
+
+In addition to convolution and padding approaches,
+sparse-matrix-based code with boundary wavelet support is available.
+In contrast to padding, boundary wavelets do not add extra pixels at 
+the edges.
+Internally, boundary wavelet support relies on ``torch.sparse.mm``.
+Generate 1d sparse matrix forward and backward transforms with the
+``MatrixWavedec`` and ``MatrixWaverec`` classes.
+Reconsidering the 1d case, try:
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  # forward
+  matrix_wavedec = ptwt.MatrixWavedec(pywt.Wavelet("haar"), level=2)
+  coeff = matrix_wavedec(data_torch)
+  print(coeff)
+  # backward 
+  matrix_waverec = ptwt.MatrixWaverec(pywt.Wavelet("haar"))
+  rec = matrix_waverec(coeff)
+  print(rec)
+
+
+The process for the 2d transforms ``MatrixWavedec2``, ``MatrixWaverec2`` works similarly.
+By default, a separable transformation is used.
+To use a non-separable transformation, pass ``separable=False`` to ``MatrixWavedec2`` and ``MatrixWaverec2``.
+Separable transformations use a 1d transformation along both axes, which might be faster since fewer matrix entries
+have to be orthogonalized.
+
+
+**Adaptive** **Wavelets**
+
+Experimental code to train an adaptive wavelet layer in PyTorch is available in the ``examples`` folder. In addition to static wavelets
+from pywt,
+
+- Adaptive product-filters
+- and optimizable orthogonal-wavelets are supported.
+
+See https://github.com/v0lta/PyTorch-Wavelet-Toolbox/tree/main/examples/network_compression/ for a complete implementation.
+
+
+**Testing**
+
+The ``tests`` folder contains multiple tests to allow independent verification of this toolbox.
+The GitHub workflow executes a subset of all tests for efficiency reasons. 
+After cloning the repository, moving into the main directory, and installing ``nox`` with ``pip install nox`` run
+
+.. code-block:: sh
+
+  $ nox --session test
+
+
+
+to run all existing tests.
+
+Citation
+""""""""
+
+If you use this work in a scientific context please cite:
+
+.. code-block::
+
+  @phdthesis{handle:20.500.11811/9245,
+    urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-63361,
+    author = {{Moritz Wolter}},
+    title = {Frequency Domain Methods in Recurrent Neural Networks for Sequential Data Processing},
+    school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
+    year = 2021,
+    month = jul,
+    url = {https://hdl.handle.net/20.500.11811/9245}
+  }
+
+If you use the boundary wavelet support please additionally cite:
+
+.. code-block::
+
+  @thesis{Blanke2021,
+    author = {Felix Blanke},
+    title = {{Randbehandlung bei Wavelets für Faltungsnetzwerke}},
+    type = {Bachelor's Thesis},
+    annote = {Gbachelor},
+    year = {2021},
+    school = {Institut f\"ur Numerische Simulation, Universit\"at Bonn}
+  }
+
+
+
+%package -n python3-ptwt
+Summary:	Differentiable and gpu enabled fast wavelet transforms in PyTorch
+Provides:	python-ptwt
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ptwt
+********************************
+Pytorch Wavelet Toolbox (`ptwt`)
+********************************
+
+.. image:: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml/badge.svg 
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml
+    :alt: GitHub Actions
+
+.. image:: https://readthedocs.org/projects/pytorch-wavelet-toolbox/badge/?version=latest
+    :target: https://pytorch-wavelet-toolbox.readthedocs.io/en/latest/?badge=latest
+    :alt: Documentation Status
+
+.. image:: https://img.shields.io/pypi/pyversions/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI Versions
+
+.. image:: https://img.shields.io/pypi/v/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI - Project
+
+.. image:: https://img.shields.io/pypi/l/ptwt
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/blob/main/LICENSE
+    :alt: PyPI - License
+
+.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
+    :target: https://github.com/psf/black
+    :alt: Black code style
+
+.. image:: https://static.pepy.tech/personalized-badge/ptwt?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads
+ :target: https://pepy.tech/project/ptwt
+
+
+
+
+Welcome to the PyTorch wavelet toolbox. This package implements:
+
+- the fast wavelet transform (fwt) via ``wavedec`` and its inverse by providing the ``waverec`` function,
+- the two-dimensional fwt is called ``wavedec2`` the synthesis counterpart ``waverec2``,
+- ``wavedec3`` and ``waverec3`` cover the three-dimensional analysis and synthesis case,
+- ``fswavedec2``, ``fswavedec3``, ``fswaverec2`` and ``fswaverec3`` support separable transformations.
+- ``MatrixWavedec`` and ``MatrixWaverec`` implement sparse-matrix-based fast wavelet transforms with boundary filters,
+- 2d sparse-matrix transforms with separable & non-separable boundary filters are available,
+- ``MatrixWavedec3`` and ``MatrixWaverec3`` allow separable 3D-fwt's with boundary filters.
+- ``cwt`` computes a one-dimensional continuous forward transform,
+- single and two-dimensional wavelet packet forward and backward transforms are available via the ``WaveletPacket`` and ``WaveletPacket2D`` objects,
+- finally, this package provides adaptive wavelet support (experimental).
+
+This toolbox extends `PyWavelets <https://pywavelets.readthedocs.io/en/latest/>`_ . We additionally provide GPU and gradient support via a PyTorch backend.
+Complete documentation is available at: https://pytorch-wavelet-toolbox.readthedocs.io/
+
+
+**Installation**
+
+Install the toolbox via pip or clone this repository. In order to use ``pip``, type:
+
+.. code-block:: sh
+
+    $ pip install ptwt
+  
+
+You can remove it later by typing ``pip uninstall ptwt``.
+
+Example usage:
+""""""""""""""
+**Single dimensional transform**
+
+One way to compute fast wavelet transforms is to rely on padding and
+convolution. Consider the following example: 
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  wavelet = pywt.Wavelet('haar')
+  
+  # compare the forward fwt coefficients
+  print(pywt.wavedec(data, wavelet, mode='zero', level=2))
+  print(ptwt.wavedec(data_torch, wavelet, mode='zero', level=2))
+  
+  # invert the fwt.
+  print(ptwt.waverec(ptwt.wavedec(data_torch, wavelet, mode='zero'),
+                     wavelet))
+
+
+The functions ``wavedec`` and ``waverec`` compute the 1d-fwt and its inverse.
+Internally both rely on ``conv1d``, and its transposed counterpart ``conv_transpose1d``
+from the ``torch.nn.functional`` module. This toolbox supports discrete wavelets
+see also ``pywt.wavelist(kind='discrete')``. I have tested
+Daubechies-Wavelets ``db-x`` and symlets ``sym-x``, which are usually a good starting point. 
+
+**Two-dimensional transform**
+
+Analog to the 1d-case ``wavedec2`` and ``waverec2`` rely on 
+``conv2d``, and its transposed counterpart ``conv_transpose2d``.
+To test an example run:
+
+
+.. code-block:: python
+
+  import ptwt, pywt, torch
+  import numpy as np
+  import scipy.misc
+
+  face = np.transpose(scipy.datasets.face(),
+                          [2, 0, 1]).astype(np.float64)
+  pytorch_face = torch.tensor(face)
+  coefficients = ptwt.wavedec2(pytorch_face, pywt.Wavelet("haar"),
+                                  level=2, mode="constant")
+  reconstruction = ptwt.waverec2(coefficients, pywt.Wavelet("haar"))
+  np.max(np.abs(face - reconstruction.squeeze(1).numpy()))
+
+
+
+**Boundary Wavelets with Sparse-Matrices**
+
+In addition to convolution and padding approaches,
+sparse-matrix-based code with boundary wavelet support is available.
+In contrast to padding, boundary wavelets do not add extra pixels at 
+the edges.
+Internally, boundary wavelet support relies on ``torch.sparse.mm``.
+Generate 1d sparse matrix forward and backward transforms with the
+``MatrixWavedec`` and ``MatrixWaverec`` classes.
+Reconsidering the 1d case, try:
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  # forward
+  matrix_wavedec = ptwt.MatrixWavedec(pywt.Wavelet("haar"), level=2)
+  coeff = matrix_wavedec(data_torch)
+  print(coeff)
+  # backward 
+  matrix_waverec = ptwt.MatrixWaverec(pywt.Wavelet("haar"))
+  rec = matrix_waverec(coeff)
+  print(rec)
+
+
+The process for the 2d transforms ``MatrixWavedec2``, ``MatrixWaverec2`` works similarly.
+By default, a separable transformation is used.
+To use a non-separable transformation, pass ``separable=False`` to ``MatrixWavedec2`` and ``MatrixWaverec2``.
+Separable transformations use a 1d transformation along both axes, which might be faster since fewer matrix entries
+have to be orthogonalized.
+
+
+**Adaptive** **Wavelets**
+
+Experimental code to train an adaptive wavelet layer in PyTorch is available in the ``examples`` folder. In addition to static wavelets
+from pywt,
+
+- Adaptive product-filters
+- and optimizable orthogonal-wavelets are supported.
+
+See https://github.com/v0lta/PyTorch-Wavelet-Toolbox/tree/main/examples/network_compression/ for a complete implementation.
+
+
+**Testing**
+
+The ``tests`` folder contains multiple tests to allow independent verification of this toolbox.
+The GitHub workflow executes a subset of all tests for efficiency reasons. 
+After cloning the repository, moving into the main directory, and installing ``nox`` with ``pip install nox`` run
+
+.. code-block:: sh
+
+  $ nox --session test
+
+
+
+to run all existing tests.
+
+Citation
+""""""""
+
+If you use this work in a scientific context please cite:
+
+.. code-block::
+
+  @phdthesis{handle:20.500.11811/9245,
+    urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-63361,
+    author = {{Moritz Wolter}},
+    title = {Frequency Domain Methods in Recurrent Neural Networks for Sequential Data Processing},
+    school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
+    year = 2021,
+    month = jul,
+    url = {https://hdl.handle.net/20.500.11811/9245}
+  }
+
+If you use the boundary wavelet support please additionally cite:
+
+.. code-block::
+
+  @thesis{Blanke2021,
+    author = {Felix Blanke},
+    title = {{Randbehandlung bei Wavelets für Faltungsnetzwerke}},
+    type = {Bachelor's Thesis},
+    annote = {Gbachelor},
+    year = {2021},
+    school = {Institut f\"ur Numerische Simulation, Universit\"at Bonn}
+  }
+
+
+
+%package help
+Summary:	Development documents and examples for ptwt
+Provides:	python3-ptwt-doc
+%description help
+********************************
+Pytorch Wavelet Toolbox (`ptwt`)
+********************************
+
+.. image:: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml/badge.svg 
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/actions/workflows/tests.yml
+    :alt: GitHub Actions
+
+.. image:: https://readthedocs.org/projects/pytorch-wavelet-toolbox/badge/?version=latest
+    :target: https://pytorch-wavelet-toolbox.readthedocs.io/en/latest/?badge=latest
+    :alt: Documentation Status
+
+.. image:: https://img.shields.io/pypi/pyversions/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI Versions
+
+.. image:: https://img.shields.io/pypi/v/ptwt
+    :target: https://pypi.org/project/ptwt/
+    :alt: PyPI - Project
+
+.. image:: https://img.shields.io/pypi/l/ptwt
+    :target: https://github.com/v0lta/PyTorch-Wavelet-Toolbox/blob/main/LICENSE
+    :alt: PyPI - License
+
+.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
+    :target: https://github.com/psf/black
+    :alt: Black code style
+
+.. image:: https://static.pepy.tech/personalized-badge/ptwt?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads
+ :target: https://pepy.tech/project/ptwt
+
+
+
+
+Welcome to the PyTorch wavelet toolbox. This package implements:
+
+- the fast wavelet transform (fwt) via ``wavedec`` and its inverse by providing the ``waverec`` function,
+- the two-dimensional fwt is called ``wavedec2`` the synthesis counterpart ``waverec2``,
+- ``wavedec3`` and ``waverec3`` cover the three-dimensional analysis and synthesis case,
+- ``fswavedec2``, ``fswavedec3``, ``fswaverec2`` and ``fswaverec3`` support separable transformations.
+- ``MatrixWavedec`` and ``MatrixWaverec`` implement sparse-matrix-based fast wavelet transforms with boundary filters,
+- 2d sparse-matrix transforms with separable & non-separable boundary filters are available,
+- ``MatrixWavedec3`` and ``MatrixWaverec3`` allow separable 3D-fwt's with boundary filters.
+- ``cwt`` computes a one-dimensional continuous forward transform,
+- single and two-dimensional wavelet packet forward and backward transforms are available via the ``WaveletPacket`` and ``WaveletPacket2D`` objects,
+- finally, this package provides adaptive wavelet support (experimental).
+
+This toolbox extends `PyWavelets <https://pywavelets.readthedocs.io/en/latest/>`_ . We additionally provide GPU and gradient support via a PyTorch backend.
+Complete documentation is available at: https://pytorch-wavelet-toolbox.readthedocs.io/
+
+
+**Installation**
+
+Install the toolbox via pip or clone this repository. In order to use ``pip``, type:
+
+.. code-block:: sh
+
+    $ pip install ptwt
+  
+
+You can remove it later by typing ``pip uninstall ptwt``.
+
+Example usage:
+""""""""""""""
+**Single dimensional transform**
+
+One way to compute fast wavelet transforms is to rely on padding and
+convolution. Consider the following example: 
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  wavelet = pywt.Wavelet('haar')
+  
+  # compare the forward fwt coefficients
+  print(pywt.wavedec(data, wavelet, mode='zero', level=2))
+  print(ptwt.wavedec(data_torch, wavelet, mode='zero', level=2))
+  
+  # invert the fwt.
+  print(ptwt.waverec(ptwt.wavedec(data_torch, wavelet, mode='zero'),
+                     wavelet))
+
+
+The functions ``wavedec`` and ``waverec`` compute the 1d-fwt and its inverse.
+Internally both rely on ``conv1d``, and its transposed counterpart ``conv_transpose1d``
+from the ``torch.nn.functional`` module. This toolbox supports discrete wavelets
+see also ``pywt.wavelist(kind='discrete')``. I have tested
+Daubechies-Wavelets ``db-x`` and symlets ``sym-x``, which are usually a good starting point. 
+
+**Two-dimensional transform**
+
+Analog to the 1d-case ``wavedec2`` and ``waverec2`` rely on 
+``conv2d``, and its transposed counterpart ``conv_transpose2d``.
+To test an example run:
+
+
+.. code-block:: python
+
+  import ptwt, pywt, torch
+  import numpy as np
+  import scipy.misc
+
+  face = np.transpose(scipy.datasets.face(),
+                          [2, 0, 1]).astype(np.float64)
+  pytorch_face = torch.tensor(face)
+  coefficients = ptwt.wavedec2(pytorch_face, pywt.Wavelet("haar"),
+                                  level=2, mode="constant")
+  reconstruction = ptwt.waverec2(coefficients, pywt.Wavelet("haar"))
+  np.max(np.abs(face - reconstruction.squeeze(1).numpy()))
+
+
+
+**Boundary Wavelets with Sparse-Matrices**
+
+In addition to convolution and padding approaches,
+sparse-matrix-based code with boundary wavelet support is available.
+In contrast to padding, boundary wavelets do not add extra pixels at 
+the edges.
+Internally, boundary wavelet support relies on ``torch.sparse.mm``.
+Generate 1d sparse matrix forward and backward transforms with the
+``MatrixWavedec`` and ``MatrixWaverec`` classes.
+Reconsidering the 1d case, try:
+
+.. code-block:: python
+
+  import torch
+  import numpy as np
+  import pywt
+  import ptwt  # use "from src import ptwt" for a cloned the repo
+  
+  # generate an input of even length.
+  data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
+  data_torch = torch.from_numpy(data.astype(np.float32))
+  # forward
+  matrix_wavedec = ptwt.MatrixWavedec(pywt.Wavelet("haar"), level=2)
+  coeff = matrix_wavedec(data_torch)
+  print(coeff)
+  # backward 
+  matrix_waverec = ptwt.MatrixWaverec(pywt.Wavelet("haar"))
+  rec = matrix_waverec(coeff)
+  print(rec)
+
+
+The process for the 2d transforms ``MatrixWavedec2``, ``MatrixWaverec2`` works similarly.
+By default, a separable transformation is used.
+To use a non-separable transformation, pass ``separable=False`` to ``MatrixWavedec2`` and ``MatrixWaverec2``.
+Separable transformations use a 1d transformation along both axes, which might be faster since fewer matrix entries
+have to be orthogonalized.
+
+
+**Adaptive** **Wavelets**
+
+Experimental code to train an adaptive wavelet layer in PyTorch is available in the ``examples`` folder. In addition to static wavelets
+from pywt,
+
+- Adaptive product-filters
+- and optimizable orthogonal-wavelets are supported.
+
+See https://github.com/v0lta/PyTorch-Wavelet-Toolbox/tree/main/examples/network_compression/ for a complete implementation.
+
+
+**Testing**
+
+The ``tests`` folder contains multiple tests to allow independent verification of this toolbox.
+The GitHub workflow executes a subset of all tests for efficiency reasons. 
+After cloning the repository, moving into the main directory, and installing ``nox`` with ``pip install nox`` run
+
+.. code-block:: sh
+
+  $ nox --session test
+
+
+
+to run all existing tests.
+
+Citation
+""""""""
+
+If you use this work in a scientific context please cite:
+
+.. code-block::
+
+  @phdthesis{handle:20.500.11811/9245,
+    urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-63361,
+    author = {{Moritz Wolter}},
+    title = {Frequency Domain Methods in Recurrent Neural Networks for Sequential Data Processing},
+    school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
+    year = 2021,
+    month = jul,
+    url = {https://hdl.handle.net/20.500.11811/9245}
+  }
+
+If you use the boundary wavelet support please additionally cite:
+
+.. code-block::
+
+  @thesis{Blanke2021,
+    author = {Felix Blanke},
+    title = {{Randbehandlung bei Wavelets für Faltungsnetzwerke}},
+    type = {Bachelor's Thesis},
+    annote = {Gbachelor},
+    year = {2021},
+    school = {Institut f\"ur Numerische Simulation, Universit\"at Bonn}
+  }
+
+
+
+%prep
+%autosetup -n ptwt-0.1.5
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-ptwt -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..fcf6316
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+daa97e11c2ae766b93cc1164cefa3c2b  ptwt-0.1.5.tar.gz