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Merge pull request #122 from PyLops/dev
Release v0.6.0
2 parents 97d24fd + fd53fdb commit 30c4c80

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.readthedocs.yaml

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build:
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os: ubuntu-20.04
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tools:
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python: "3.8"
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python: "3.9"
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apt_packages:
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- libopenblas-dev
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CHANGELOG.md

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# 0.6.0
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:vertical_traffic_light: :vertical_traffic_light: This is the first release supporting PyLops v2.
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:vertical_traffic_light: :vertical_traffic_light:
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* Added ``grad`` method to ``pyproximal.utils.bilinear.LowRankFactorizedMatrix`` operator
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* Allow passing optional arguments to solvers in ``pyproximal.proximal.L2``
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* Modified codebase to integrate with pylops's ``cupy`` backend.
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* Modified codebase to integrate with ``pylops`` v2.
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# 0.5.0
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:vertical_traffic_light: :vertical_traffic_light: This is the latest release supporting PyLops v1.
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:vertical_traffic_light: :vertical_traffic_light:
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* Added ``pyproximal.proximal.Log1`` operator
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* Added ``pyproximal.proximal.Log1`` operator
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* Allow ``radius`` parameter of ``pyproximal.optimization.primal.L0`` to be a function
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* Allow ``tau`` parameter of ``pyproximal.optimization.primal.HQS`` to be a vector
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and change over iterations

Makefile

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docupdate:
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cd docs && make html && cd ..
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servedoc:
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$(PYTHON) -m http.server --directory docs/build/html/
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lint:
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flake8 docs/ examples/ pyproximal/ pytests/ tutorials/
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typeannot:
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mypy pyproximal/

docs/source/_templates/breadcrumbs.html

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docs/source/changelog.rst

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Changelog
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=========
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Version 0.6.0
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--------------
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*Released on: 18/03/2023*
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|:vertical_traffic_light:| |:vertical_traffic_light:| This is the first release supporting PyLops v2.
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|:vertical_traffic_light:| |:vertical_traffic_light:|
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* Added ``grad`` method to :py:class:`pyproximal.utils.bilinear.LowRankFactorizedMatrix` operator
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* Allow passing optional arguments to solvers in :py:class:`pyproximal.proximal.L2`
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* Modified codebase to integrate with pylops's ``cupy`` backend.
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* Modified codebase to integrate with ``pylops`` v2.
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Version 0.5.0
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--------------
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*Released on: 20/08/2022*

docs/source/conf.py

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.. |year| replace:: {year}
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""".format(year=year)
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html_static_path = ["_static"]
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html_last_updated_fmt = '%b %d, %Y'
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html_title = 'PyProximal'
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html_short_title = 'PyProximal'
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html_logo = '_static/pyproximal.png'
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html_favicon = '_static/favicon.ico'
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html_static_path = ['_static']
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html_logo = 'pyproximal.png'
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html_favicon = 'favicon.ico'
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html_extra_path = []
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pygments_style = 'default'
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add_function_parentheses = False
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html_show_copyright = True
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# Theme config
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html_theme = "sphinx_rtd_theme"
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html_theme = "pydata_sphinx_theme"
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html_theme_options = {
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'logo_only': True,
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'display_version': True,

docs/source/installation.rst

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To install the PyProximal library you will need **Python 3.8 or greater**.
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.. note::
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Versions prior to v0.3.0 work alsi with Python 3.6 or greater, however they
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require scipy version to be lower than v1.8.0
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Versions prior to v0.3.0 require Python 3.6 or greater, and scipy version
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lower than v1.8.0.
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Dependencies
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------------

environment-dev.yml

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- python>=3.8.12
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- numpy>=1.15.0
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- scipy>=1.8.0
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- pylops<2.0.0
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- pylops>=2.0.0
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- scikit-image
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- matplotlib
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- ipython

environment.yml

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- python>=3.8.12
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- numpy>=1.15.0
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- scipy>=1.8.0
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- pylops<2.0.0
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- pylops>=2.0.0

pyproximal/optimization/pnp.py

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"Plug-and-Play priors for model based reconstruction",
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IEEE. 2013.
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.. [1] Meinhardt, T., Moeller, M, Hazirbas, C., and Cremer, D.
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.. [2] Meinhardt, T., Moeller, M, Hazirbas, C., and Cremer, D.
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"Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems",
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arXiv. 2017.
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