From 616c9e719f99d9ed1a1a80c9b74b76d71ca2fcf4 Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Wed, 31 Mar 2021 18:39:35 +0200 Subject: [PATCH] ModOpt v1.5 release (#108) * added workflows * test update to deploy test results from fork * fix typo * test commands copied from yaml * removed test deployment * Pyralid style update (#104) * first iteration of pyralid style update * updated progress on pyralid style refactor * further updates * latest update * all tests passing locally * all doctests passing locally * removed autodoc files * updated refactoring * test pull_request_target and add main branch * added logo and fixed link to api docs * added develop branch to CI * updated workflow * test PR review * reverted to standard PR directive * updated readme * test deploy test results from fork * attempt to fix fomatting * removed test deployment * added #97 GPU compatibility in consistent style * additional clean up * more clean up * fixed mistake * Update modopt/interface/errors.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_math.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_math.py Co-authored-by: Zaccharie Ramzi * removed doc requirements from basic test suite * removed contents as they are now automatically generated on GitHub * fix GPU bug in PowerMethod * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * Update modopt/tests/test_opt.py Co-authored-by: Zaccharie Ramzi * renamed dummy functions in test_opt.py Co-authored-by: Zaccharie Ramzi * Release cleanup (#109) * cleaned up readme * cleaned up api docs Co-authored-by: Zaccharie Ramzi --- .github/workflows/cd-build.yml | 87 ++ .github/workflows/ci-build.yml | 126 +++ .gitignore | 149 ++- .pylintrc | 2 + .pyup.yml | 11 + .travis.yml | 43 - 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master + - main + +jobs: + + coverage: + name: Deploy Coverage Results + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Set up Conda with Python 3.8 + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: 3.8 + auto-activate-base: false + + - name: Install dependencies + shell: bash -l {0} + run: | + python -m pip install --upgrade pip + python -m pip install -r develop.txt + python -m pip install twine + python -m pip install . + + - name: Run Tests + shell: bash -l {0} + run: | + python setup.py test + + - name: Check distribution + shell: bash -l {0} + run: | + python setup.py sdist + twine check dist/* + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v1 + with: + token: ${{ secrets.CODECOV_TOKEN }} + file: coverage.xml + flags: unittests + + api: + name: Deploy API Documentation + needs: coverage + runs-on: ubuntu-latest + if: success() + + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Set up Conda with Python 3.8 + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: 3.8 + auto-activate-base: false + + - name: Install dependencies + shell: bash -l {0} + run: | + conda install -c conda-forge pandoc + python -m pip install --upgrade pip + python -m pip install -r docs/requirements.txt + python -m pip install . + + - name: Build API documentation + shell: bash -l {0} + run: | + sphinx-apidoc -t docs/_templates -feTMo docs/source modopt + sphinx-build -E docs/source docs/_build + + - name: Deploy API documentation + uses: peaceiris/actions-gh-pages@v3.5.9 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/_build diff --git a/.github/workflows/ci-build.yml b/.github/workflows/ci-build.yml new file mode 100644 index 00000000..2ed4960d --- /dev/null +++ b/.github/workflows/ci-build.yml @@ -0,0 +1,126 @@ +name: CI + +on: + pull_request: + branches: + - develop + - master + - main + +jobs: + test-full: + name: Full Test Suite + runs-on: ${{ matrix.os }} + + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest] + python-version: [3.8] + + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Report WPS Errors + uses: wemake-services/wemake-python-styleguide@0.14.1 + continue-on-error: true + with: + reporter: 'github-pr-review' + path: './modopt' + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Set up Conda with Python ${{ matrix.python-version }} + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: ${{ matrix.python-version }} + auto-activate-base: false + + - name: Check Conda + shell: bash -l {0} + run: | + conda info + conda list + python --version + + - name: Install Dependencies + shell: bash -l {0} + run: | + python --version + python -m pip install --upgrade pip + python -m pip install -r develop.txt + python -m pip install -r docs/requirements.txt + python -m pip install astropy scikit-image scikit-learn + python -m pip install twine + python -m pip install . + + - name: Run Tests + shell: bash -l {0} + run: | + export PATH=/usr/share/miniconda/bin:$PATH + python setup.py test + + - name: Save Test Results + if: always() + uses: actions/upload-artifact@v2 + with: + name: unit-test-results-${{ matrix.os }}-${{ matrix.python-version }} + path: pytest.xml + + - name: Check Distribution + shell: bash -l {0} + run: | + python setup.py sdist + twine check dist/* + + - name: Check API Documentation build + shell: bash -l {0} + run: | + conda install -c conda-forge pandoc + sphinx-apidoc -t docs/_templates -feTMo docs/source modopt + sphinx-build -b doctest -E docs/source docs/_build + + - name: Upload Coverage to Codecov + uses: codecov/codecov-action@v1 + with: + token: ${{ secrets.CODECOV_TOKEN }} + file: coverage.xml + flags: unittests + + test-basic: + name: Basic Test Suite + runs-on: ${{ matrix.os }} + + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest] + python-version: [3.6, 3.7, 3.9] + + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Set up Conda with Python ${{ matrix.python-version }} + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: ${{ matrix.python-version }} + auto-activate-base: false + + - name: Install Dependencies + shell: bash -l {0} + run: | + python --version + python -m pip install --upgrade pip + python -m pip install -r develop.txt + python -m pip install astropy scikit-image scikit-learn + python -m pip install . + + - name: Run Tests + shell: bash -l {0} + run: | + export PATH=/usr/share/miniconda/bin:$PATH + python setup.py test diff --git a/.gitignore b/.gitignore index f76522d1..06dff8db 100644 --- a/.gitignore +++ b/.gitignore @@ -1,21 +1,142 @@ -# Ignore Python compilation -*.pyc +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class -# Ignore scripts -*.sh +# C extensions +*.so -# Ignore cache -*.cache/ +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec -# Ignore coverage tests +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ .coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ .pytest_cache/ +cover/ +pytest.xml -# Ignore installation -.eggs/ -modopt.egg-info/ -dist/ -build/ +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/source/fortuna.* +docs/source/scripts.* +docs/source/*.nblink + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ -# Ignore pypi config -.pypirc +# Cython debug symbols +cython_debug/ diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 00000000..3ac9aef9 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,2 @@ +[MASTER] +ignore-patterns=**/docs/**/*.py diff --git a/.pyup.yml b/.pyup.yml new file mode 100644 index 00000000..d844b78b --- /dev/null +++ b/.pyup.yml @@ -0,0 +1,11 @@ +# autogenerated pyup.io config file +# see https://pyup.io/docs/configuration/ for all available options + +schedule: '' +update: all +label_prs: update +assignees: sfarrens +requirements: + - requirements.txt + - develop.txt + - docs/requirements.txt diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index b5b770d7..00000000 --- a/.travis.yml +++ /dev/null @@ -1,43 +0,0 @@ -language: python -dist: focal -sudo: required - -# GitHub branch -branches: - only: - - master - -# build jobs -jobs: - include: - - python: 3.6 - - python: 3.7 - - python: 3.8 - after_success: - - coveralls - - stage: deploy - install: - - pip install numpy scipy progressbar2 - - pip install sphinx sphinx-rtd-theme sphinxcontrib-bibtex numpydoc==0.9.2 travis-sphinx - script: - - sphinx-apidoc -feo docs/source modopt - - travis-sphinx -v -o docs/build build -n -s docs/source - after_success: - - travis-sphinx -o docs/build deploy - -# update pre-installed packages -before_install: - - pip list --outdated --format=freeze | grep -v '^\-e' | cut -d = -f 1 | xargs -n1 pip install -U - -# install dependencies -install: - - pip install scikit-learn - - pip install astropy scikit-image - -# run unit tests -script: - - python setup.py test - -# notification settings -notification: - email: false diff --git a/MANIFEST.in b/MANIFEST.in index 916c2c9b..9a2f374e 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,5 @@ include requirements.txt +include develop.txt +include docs/requirements.txt include README.rst include LICENSE.txt diff --git a/README.md b/README.md new file mode 100644 index 00000000..aa72d976 --- /dev/null +++ b/README.md @@ -0,0 +1,79 @@ +# ModOpt + + + +| Usage | Development | Release | +| ----- | ----------- | ------- | +| [![docs](https://img.shields.io/badge/docs-Sphinx-blue)](https://cea-cosmic.github.io/ModOpt/) | [![build](https://github.com/CEA-COSMIC/modopt/workflows/CI/badge.svg)](https://github.com/CEA-COSMIC/modopt/actions?query=workflow%3ACI) | [![release](https://img.shields.io/github/v/release/CEA-COSMIC/modopt)](https://github.com/CEA-COSMIC/modopt/releases/latest) | +| [![license](https://img.shields.io/github/license/CEA-COSMIC/modopt)](https://github.com/CEA-COSMIC/modopt/blob/master/LICENCE.txt) | [![deploy](https://github.com/CEA-COSMIC/modopt/workflows/CD/badge.svg)](https://github.com/CEA-COSMIC/modopt/actions?query=workflow%3ACD) | [![pypi](https://img.shields.io/pypi/v/modopt)](https://pypi.org/project/modopt/) | +| [![wemake-python-styleguide](https://img.shields.io/badge/style-wemake-000000.svg)](https://github.com/wemake-services/wemake-python-styleguide) | [![codecov](https://codecov.io/gh/CEA-COSMIC/modopt/branch/master/graph/badge.svg?token=XHJIQXV7AX)](https://codecov.io/gh/CEA-COSMIC/modopt) | [![python](https://img.shields.io/pypi/pyversions/modopt)](https://www.python.org/downloads/source/) | +| [![contribute](https://img.shields.io/badge/contribute-read-lightgrey)](https://github.com/CEA-COSMIC/modopt/blob/master/CONTRIBUTING.md) | [![CodeFactor](https://www.codefactor.io/repository/github/CEA-COSMIC/modopt/badge)](https://www.codefactor.io/repository/github/CEA-COSMIC/modopt) | | +| [![coc](https://img.shields.io/badge/conduct-read-lightgrey)](https://github.com/CEA-COSMIC/modopt/blob/master/CODE_OF_CONDUCT.md) | [![Updates](https://pyup.io/repos/github/CEA-COSMIC/modopt/shield.svg)](https://pyup.io/repos/github/CEA-COSMIC/ModOpt/) | | + +ModOpt is a series of **Modular Optimisation** tools for solving inverse problems. + +See [documentation](https://CEA-COSMIC.github.io/ModOpt/) for more details. + +## Installation + +To install using `pip` run the following command: + +```bash + $ pip install modopt +``` + +To clone the ModOpt repository from GitHub run the following command: + +```bash + $ git clone https://github.com/CEA-COSMIC/ModOpt.git +``` + +## Dependencies + +All packages required by ModOpt should be installed automatically. Optional packages, however, will need to be installed manually. + +### Required Packages + +In order to run the code in this repository the following packages must be +installed: + +* [Python](https://www.python.org/) [> 3.6] +* [importlib_metadata](https://importlib-metadata.readthedocs.io/en/latest/) [==3.7.0] +* [Numpy](http://www.numpy.org/) [==1.19.5] +* [Scipy](http://www.scipy.org/) [==1.5.4] +* [Progressbar 2](https://progressbar-2.readthedocs.io/) [==3.53.1] + +### Optional Packages + +The following packages can optionally be installed to add extra functionality: + +* [Astropy](http://www.astropy.org/) +* [Matplotlib](http://matplotlib.org/) +* [Scikit-Image](https://scikit-image.org/) +* [Scikit-Learn](https://scikit-learn.org/) +* [Termcolor](https://pypi.python.org/pypi/termcolor) + +For (partial) GPU compliance the following packages can also be installed. +Note that none of these are required for running on a CPU. + +* [CuPy](https://cupy.dev/) +* [Torch](https://pytorch.org/) + +## Citation + +If you use ModOpt in a scientific publication, we would appreciate citations to the following paper: + +[PySAP: Python Sparse Data Analysis Package for multidisciplinary image processing](https://www.sciencedirect.com/science/article/pii/S2213133720300561), S. Farrens et al., Astronomy and Computing 32, 2020 + +The BibTeX citation is the following: +``` +@Article{farrens2020pysap, + title={{PySAP: Python Sparse Data Analysis Package for multidisciplinary image processing}}, + author={Farrens, S and Grigis, A and El Gueddari, L and Ramzi, Z and Chaithya, GR and Starck, S and Sarthou, B and Cherkaoui, H and Ciuciu, P and Starck, J-L}, + journal={Astronomy and Computing}, + volume={32}, + pages={100402}, + year={2020}, + publisher={Elsevier} +} +``` diff --git a/README.rst b/README.rst deleted file mode 100644 index 9690f9d4..00000000 --- a/README.rst +++ /dev/null @@ -1,152 +0,0 @@ -ModOpt -====== - -|travis| |coveralls| |license| |python36| |python37| |python38| |pypi| - -.. |travis| image:: https://travis-ci.org/CEA-COSMIC/ModOpt.svg?branch=master - :target: https://travis-ci.org/CEA-COSMIC/ModOpt - -.. |coveralls| image:: https://coveralls.io/repos/github/CEA-COSMIC/ModOpt/badge.svg - :target: https://coveralls.io/github/CEA-COSMIC/ModOpt - -.. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg - :target: https://github.com/CEA-COSMIC/ModOpt/blob/master/LICENCE.txt - -.. |python36| image:: https://img.shields.io/badge/python-3.6-green.svg - :target: https://www.python.org/ - -.. |python37| image:: https://img.shields.io/badge/python-3.7-green.svg - :target: https://www.python.org/ - -.. |python38| image:: https://img.shields.io/badge/python-3.8-green.svg - :target: https://www.python.org/ - -.. |pypi| image:: https://badge.fury.io/py/modopt.svg - :target: https://badge.fury.io/py/modopt - - -:Author: Samuel Farrens `(samuel.farrens@cea.fr) `_ - -:Version: 1.4.4 - -:Release Date: 16/06/2020 - -:Documentation: |link-to-docs| - -:Repository: |link-to-repo| - -.. |link-to-docs| raw:: html - - https://cea-cosmic.github.io/ModOpt/ - -.. |link-to-repo| raw:: html - - https://github.com/CEA-COSMIC/ModOpt - -ModOpt is a series of Modular Optimisation tools for solving inverse problems. - -Contents -======== - -1. `Dependencies`_ -2. `Installation`_ - -Dependencies -============ - -Required Packages ------------------ - -In order to run the code in this repository the following packages must be -installed: - -* |link-to-python| [Last tested with v3.8.2] - -* |link-to-numpy| [Tested with v1.18.3] - -* |link-to-scipy| [Tested with v1.4.1] - -* |link-to-progressbar| [Tested with v3.51.1] - -.. |link-to-python| raw:: html - - Python - -.. |link-to-numpy| raw:: html - - Numpy - -.. |link-to-scipy| raw:: html - - Scipy - -.. |link-to-progressbar| raw:: html - - Progressbar 2 - -Optional Packages ------------------ - -The following packages can optionally be installed to add extra functionality: - -* |link-to-astropy| [Last tested with v4.0.1] - -* |link-to-matplotlib| [Last tested with v3.2.1] - -* |link-to-skimage| [Requires >=v0.16.2] - -* |link-to-sklearn| [Requires >=v0.22.2] - -* |link-to-termcolor| [Last tested with v1.1.0] - -.. |link-to-astropy| raw:: html - - Astropy - -.. |link-to-matplotlib| raw:: html - - Matplotlib - -.. |link-to-skimage| raw:: html - - Scikit-Image - -.. |link-to-sklearn| raw:: html - - Scikit-Learn - -.. |link-to-termcolor| raw:: html - - Termcolor - -Installation -============ - -To install using `pip` run the following command: - -.. code-block:: bash - - $ pip install modopt - -To install using `easy_install` run the following command: - -.. code-block:: bash - - $ easy_install modopt - -To clone the ModOpt repository from GitHub run the following command: - -.. code-block:: bash - - $ git clone https://github.com/cea-cosmic/ModOpt diff --git a/develop.txt b/develop.txt new file mode 100644 index 00000000..b88396e7 --- /dev/null +++ b/develop.txt @@ -0,0 +1,8 @@ +coverage==5.5 +nose==1.3.7 +pytest==6.2.2 +pytest-cov==2.11.1 +pytest-pep8==1.0.6 +pytest-emoji==0.2.0 +pytest-flake8==1.0.7 +wemake-python-styleguide==0.15.2 diff --git a/docs/_templates/module.rst_t b/docs/_templates/module.rst_t new file mode 100644 index 00000000..73ad73fc --- /dev/null +++ b/docs/_templates/module.rst_t @@ -0,0 +1,8 @@ +{%- if show_headings %} +{{- basename | e | heading(2) }} + +{% endif -%} +.. automodule:: {{ qualname }} +{%- for option in automodule_options %} + :{{ option }}: +{%- endfor %} diff --git a/docs/_templates/package.rst_t b/docs/_templates/package.rst_t new file mode 100644 index 00000000..344f9d16 --- /dev/null +++ b/docs/_templates/package.rst_t @@ -0,0 +1,54 @@ +{%- macro automodule(modname, options) -%} +.. automodule:: {{ modname }} +{%- for option in options %} + :{{ option }}: +{%- endfor %} +{%- endmacro %} + +{%- macro toctree(docnames) -%} +.. toctree:: + :maxdepth: {{ maxdepth }} +{% for docname in docnames %} + {{ docname }} +{%- endfor %} +{%- endmacro %} + +{%- if is_namespace %} +{{- [pkgname, "namespace"] | join(" ") | e | heading }} +{% else %} +{{- pkgname | e | heading }} + +{% endif %} + +{%- if modulefirst and not is_namespace %} +{{ automodule(pkgname, automodule_options) }} +{% endif %} + +{%- if subpackages %} +Subpackages +----------- + +{{ toctree(subpackages) }} +{% endif %} + +{%- if submodules %} +Submodules +---------- +{% if separatemodules %} +{{ toctree(submodules) }} +{% else %} +{%- for submodule in submodules %} +{% if show_headings %} +{{- submodule | e | heading(2) }} +{% endif %} +{{ automodule(submodule, automodule_options) }} +{% endfor %} +{%- endif %} +{%- endif %} + +{%- if not modulefirst and not is_namespace %} +Module contents +--------------- + +{{ automodule(pkgname, automodule_options) }} +{% endif %} diff --git a/docs/_templates/toc.rst_t b/docs/_templates/toc.rst_t new file mode 100644 index 00000000..f0877eeb --- /dev/null +++ b/docs/_templates/toc.rst_t @@ -0,0 +1,8 @@ +{{ header | heading }} + +.. toctree:: + :maxdepth: {{ maxdepth }} +{% for docname in docnames %} + {{ docname }} +{%- endfor %} + diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..e1c873a2 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,7 @@ +jupyter==1.0.0 +nbsphinx==0.8.2 +nbsphinx-link==1.3.0 +numpydoc==1.1.0 +sphinx==3.5.2 +sphinxcontrib-bibtex==2.2.0 +sphinxawesome-theme==1.19.2 diff --git a/docs/source/about.rst b/docs/source/about.rst new file mode 100644 index 00000000..0d412b5d --- /dev/null +++ b/docs/source/about.rst @@ -0,0 +1,48 @@ +About +===== + +ModOpt was developed as part of |link-to-cosmic|, a multi-disciplinary collaboration +between |link-to-neurospin|, experts in biomedical imaging, and |link-to-cosmostat|, +experts in astrophysical image processing. The package was +designed to provide the backend optimisation algorithms for +|link-to-pysap| :cite:`farrens:2020`, but also serves as a stand-alone library +of inverse problem solving tools. While PySAP aims to provide +application-specific tools for solving complex imaging problems, ModOpt can in +principle be applied to any linear inverse problem. + +Contributors +------------ + +You can find a |link-to-contributors|. + +.. |link-to-cosmic| raw:: html + + COSMIC + +.. |link-to-neurospin| raw:: html + + NeuroSpin + +.. |link-to-cosmostat| raw:: html + + CosmoStat + +.. |link-to-pysap| raw:: html + + PySAP + +.. |link-to-contributors| raw:: html + + list of ModOpt contributors here + + +.. image:: cosmostat_logo.jpg + :width: 300 + :alt: CosmoStat Logo + +.. image:: neurospin_logo.png + :width: 300 + :alt: NeuroSpin Logo diff --git a/docs/source/citing.rst b/docs/source/citing.rst new file mode 100644 index 00000000..19471a51 --- /dev/null +++ b/docs/source/citing.rst @@ -0,0 +1,7 @@ +Citing this Package +=================== + +We kindly request that any academic work making use of this package to cite :cite:`farrens:2020`. + +.. bibliography:: my_ref.bib + :style: alpha diff --git a/docs/source/conf.py b/docs/source/conf.py index cca36eb4..95aaba20 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,19 +1,10 @@ # -*- coding: utf-8 -*- -# -# ModOpt documentation build configuration file, created by -# sphinx-quickstart on Mon Oct 24 16:46:22 2016. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. +# Python Template sphinx config +# Import relevant modules import sys import os +from importlib_metadata import metadata # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the @@ -22,8 +13,17 @@ # -- General configuration ------------------------------------------------ +# General information about the project. +project = 'modopt' + +mdata = metadata(project) +author = mdata['Author'] +version = mdata['Version'] +copyright = '2020, {}'.format(author) +gh_user = 'sfarrens' + # If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' +needs_sphinx = '3.3' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom @@ -31,128 +31,79 @@ extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', - 'sphinxcontrib.bibtex', + 'sphinx.ext.coverage', 'sphinx.ext.doctest', + 'sphinx.ext.ifconfig', 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', 'sphinx.ext.mathjax', - 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'numpydoc' + 'sphinx.ext.todo', + 'sphinx.ext.viewcode', + 'sphinxawesome_theme', + 'sphinxcontrib.bibtex', + 'nbsphinx', + 'nbsphinx_link', + 'numpydoc', ] -# Include init in class documentation. -autoclass_content = 'both' +# Include module names for objects +add_module_names = False -# Order docstrings as in the source -autodoc_member_order = 'bysource' +# Set class documentation standard. +autoclass_content = 'class' -# Include private class methods -autodoc_default_flags = ['members', 'private-members'] +# Audodoc options +autodoc_default_options = { + 'member-order': 'bysource', + 'private-members': True, + 'show-inheritance': True +} + +# Generate summaries +autosummary_generate = True # Suppress class members in toctree. numpydoc_show_class_members = False -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -# source_encoding = 'utf-8-sig' +source_suffix = ['.rst', '.md'] # The master toctree document. master_doc = 'index' -# General information about the project. -project = u'modopt' -copyright = u'2017, Samuel Farrens' -author = u'Samuel Farrens' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = u'1.0' -# The full version, including alpha/beta/rc tags. -release = u'1.0' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = [] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. show_authors = True # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False +pygments_style = 'default' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True - # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' -# html_theme = 'bizstyle' +html_theme = 'sphinxawesome_theme' +# html_theme = 'sphinx_book_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} html_theme_options = { - 'collapse_navigation': False, - 'display_version': True, - 'navigation_depth': 3, - 'titles_only': True, - 'includehidden': False, + "nav_include_hidden": True, + "show_nav": True, + "show_breadcrumbs": False, + "breadcrumbs_separator": "/" } +html_collapsible_definitions = True -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -# html_title = None +html_title = '{0} v{1}'.format(project, version) # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None @@ -166,236 +117,111 @@ # pixels large. # html_favicon = None -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] -html_static_path = [] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# html_extra_path = [] - # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' +html_last_updated_fmt = '%d %b, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} -html_sidebars = {'**': ['globaltoc.html', 'relations.html', - 'sourcelink.html', 'searchbox.html'], } - - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True +html_use_smartypants = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True +html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -# html_search_language = 'en' +html_show_copyright = True -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -# html_search_options = {'type': 'default'} +# -- Options for nbshpinx output ------------------------------------------ -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -# html_search_scorer = 'scorer.js' -# Output file base name for HTML help builder. -htmlhelp_basename = 'sftoolsdoc' +# Custom fucntion to find notebooks, create .nblink files and update the +# notebooks.rst file +def add_notebooks(nb_path='../../notebooks'): -# -- Options for LaTeX output --------------------------------------------- + print('Looking for notebooks') + nb_ext = '.ipynb' + nb_rst_file_name = 'notebooks.rst' + nb_link_format = '{{\n "path": "{0}/{1}"\n}}' -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # 'papersize': 'letterpaper', + nbs = sorted([nb for nb in os.listdir(nb_path) if nb.endswith(nb_ext)]) - # The font size ('10pt', '11pt' or '12pt'). - # 'pointsize': '10pt', + for list_pos, nb in enumerate(nbs): - # Additional stuff for the LaTeX preamble. - # 'preamble': '', + nb_name = nb.rstrip(nb_ext) - # Latex figure (float) alignment - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'modopt.tex', u'modopt Documentation', - u'Samuel Farrens', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'modopt', u'modopt Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'modopt', u'modopt Documentation', - author, 'modopt', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -# texinfo_no_detailmenu = False - - -# -- Options for Epub output ---------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project -epub_author = author -epub_publisher = author -epub_copyright = copyright - -# The basename for the epub file. It defaults to the project name. -# epub_basename = project + nb_link_file_name = nb_name + '.nblink' + print('Writing {0}'.format(nb_link_file_name)) + with open(nb_link_file_name, 'w') as nb_link_file: + nb_link_file.write(nb_link_format.format(nb_path, nb)) -# The HTML theme for the epub output. Since the default themes are not -# optimized for small screen space, using the same theme for HTML and epub -# output is usually not wise. This defaults to 'epub', a theme designed to save -# visual space. -# epub_theme = 'epub' + print('Looking for {0} in {1}'.format(nb_name, nb_rst_file_name)) + with open(nb_rst_file_name, 'r') as nb_rst_file: + check_name = nb_name not in nb_rst_file.read() -# The language of the text. It defaults to the language option -# or 'en' if the language is not set. -# epub_language = '' + if check_name: + print('Adding {0} to {1}'.format(nb_name, nb_rst_file_name)) + with open(nb_rst_file_name, 'a') as nb_rst_file: + if list_pos == 0: + nb_rst_file.write('\n') + nb_rst_file.write(' {0}\n'.format(nb_name)) -# The scheme of the identifier. Typical schemes are ISBN or URL. -# epub_scheme = '' + return nbs -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# epub_identifier = '' -# A unique identification for the text. -# epub_uid = '' +# Add notebooks +add_notebooks() -# A tuple containing the cover image and cover page html template filenames. -# epub_cover = () +binder = 'https://mybinder.org/v2/gh' +binder_badge = 'https://mybinder.org/badge_logo.svg' +github = 'https://github.com/' +github_badge = 'https://badgen.net/badge/icon/github?icon=github&label' -# A sequence of (type, uri, title) tuples for the guide element of content.opf. -# epub_guide = () +# Remove promts and add binder badge +nb_header_pt1 = r''' +{% if env.metadata[env.docname]['nbsphinx-link-target'] %} +{% set docpath = env.metadata[env.docname]['nbsphinx-link-target'] %} +{% else %} +{% set docpath = env.doc2path(env.docname, base='docs/source/') %} +{% endif %} -# HTML files that should be inserted before the pages created by sphinx. -# The format is a list of tuples containing the path and title. -# epub_pre_files = [] +.. raw:: html -# HTML files that should be inserted after the pages created by sphinx. -# The format is a list of tuples containing the path and title. -# epub_post_files = [] + -# A list of files that should not be packed into the epub file. -epub_exclude_files = ['search.html'] +''' +nb_header_pt2 = ( + r'''

''' + r'''''' + + r'''Binder badge
''' + r'''
GitHub badge'''.format(github_badge) + + r'''

''' +) -# The depth of the table of contents in toc.ncx. -# epub_tocdepth = 3 +nbsphinx_prolog = nb_header_pt1 + nb_header_pt2 -# Allow duplicate toc entries. -# epub_tocdup = True +# -- Intersphinx Mapping ---------------------------------------------- -# Choose between 'default' and 'includehidden'. -# epub_tocscope = 'default' - -# Fix unsupported image types using the Pillow. -# epub_fix_images = False - -# Scale large images. -# epub_max_image_width = 0 - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# epub_show_urls = 'inline' - -# If false, no index is generated. -# epub_use_index = True - - -# Example configuration for intersphinx: refer to the Python standard library. -# intersphinx_mapping = {'https://docs.python.org/': None} +# Refer to the package libraries for type definitions intersphinx_mapping = { - 'python': ('https://docs.python.org/3/', None), - 'numpy': ('https://docs.scipy.org/doc/numpy', None), - 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), - 'matplotlib': ('http://matplotlib.sourceforge.net/', None), - 'astropy': ('http://docs.astropy.org/en/latest/', None) + 'python': ('http://docs.python.org/3', None), + 'numpy': ('https://numpy.org/doc/stable/', None), + 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), + 'matplotlib': ('https://matplotlib.org', None), + 'astropy': ('http://docs.astropy.org/en/latest/', None), + 'cupy': ('https://docs-cupy.chainer.org/en/stable/', None), + 'torch': ('https://pytorch.org/docs/stable/', None), } + +# -- BibTeX Setting ---------------------------------------------- + +bibtex_bibfiles = ['refs.bib', 'my_ref.bib'] diff --git a/docs/source/contributing.rst b/docs/source/contributing.rst new file mode 100644 index 00000000..f983d030 --- /dev/null +++ b/docs/source/contributing.rst @@ -0,0 +1,9 @@ +Contributing +============ + +Read our `Contribution Guidelines `_ +for details on how to contribute to the development of this package. + +All contributors are kindly asked to adhere to the +`Code of Conduct `_ +at all times to ensure a safe and inclusive environment for everyone. diff --git a/docs/source/cosmostat_logo.jpg b/docs/source/cosmostat_logo.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4945c4684b87ba27a90190c52fa95dbeede8cd9c GIT binary patch literal 169466 zcmeFY30PBC*FQ>YTl?1OwTjje#i<%KBq1RIfj5LCB#?y60}9Pd=8!o-ptf}u6ckVq zs3Qmh0s>G*sS*rROwXOK$UtKWTS%(+yh6L(qr@H`nOGt_45Pk&(D7+?gMy%I-S}X2vlk} zas?`$a3f!(0GhaJAZVil&}NgnNzLWUgt}!sp;)4HweKq^wqGU@xY{$J1P6i|B@C2! zSu{ebg-GLDWPF6c-reo9O(tiPLah+$xXVlmxl-$FaBQ|WcC_V#ZY`t^Eimw^9kJ8Hc~KG}+Z4;0FU3ZYV` zwQ>jo{*|7UonICIlK8(-!r~O-e^wL&B?|H1wMiiSv$8_*mzr7~Mfe+`f7xFx&8!vz zDMGDEui*==;w%2j0<8{hurm2i9`{Eew82W_UqVUba=lik;p$YHzXtrBz@#hxRe#jK z^OHZAWv#F(L4dzHhIQOG3HZ(;l}5qUxk(gUu@ER#3&op&zm@*xA8$l;5}jN)>72j% z%%qnh<+?wXzJY$@W^&0J?{$`QmDV9PZ4d}WT)kXp@8+wKD0Q2FZ$Z=fgPM3oSZ=p00*$MgTp4^pJab$*kqkb 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zJ~(H!##6Rl8^eHDH9i?*1P~4Da93J?d?6-?>@^66>ZeAeJyN3n{%wujRrO}3tzeaX26?cSc{zh!P*@cW0MZtm_5SjL{<+#p(Hgcd|iJNk@jWcglPLCgNf1*`P z%ATR3`oB7A{H&$0)6xKa|7hV}A2?Ki@gB?JZ)lu@QSg186P~tH9EH=}Pmwg;EnC0% zkNn@JU~|@_Rj|pHVod*tn&_S5!nMoN2D*5d^=l(NVt#e|-I%NnL|l=eyP|TmL5bJD2IxkpKVy literal 0 HcmV?d00001 diff --git a/docs/source/dependencies.rst b/docs/source/dependencies.rst new file mode 100644 index 00000000..782807f8 --- /dev/null +++ b/docs/source/dependencies.rst @@ -0,0 +1,98 @@ +Dependencies +============ + +.. note:: + + All packages required by ModOpt should be installed automatically. Optional + packages, however, will need to be installed manually. + +Required Packages +----------------- + +In order to use ModOpt the following packages must be installed: + +* |link-to-python| ``[> 3.6]`` +* |link-to-metadata| ``[==3.7.0]`` +* |link-to-numpy| ``[==1.19.5]`` +* |link-to-scipy| ``[==1.5.4]`` +* |link-to-progressbar| ``[==3.53.1]`` + +.. |link-to-python| raw:: html + + Python + +.. |link-to-metadata| raw:: html + + importlib_metadata + +.. |link-to-numpy| raw:: html + + Numpy + +.. |link-to-scipy| raw:: html + + Scipy + +.. |link-to-progressbar| raw:: html + + Progressbar 2 + +Optional Packages +----------------- + +The following packages can optionally be installed to add extra functionality: + +* |link-to-astropy| +* |link-to-matplotlib| +* |link-to-skimage| +* |link-to-sklearn| +* |link-to-termcolor| + +.. |link-to-astropy| raw:: html + + Astropy + +.. |link-to-matplotlib| raw:: html + + Matplotlib + +.. |link-to-skimage| raw:: html + + Scikit-Image + +.. |link-to-sklearn| raw:: html + + Scikit-Learn + +.. |link-to-termcolor| raw:: html + + Termcolor + +For GPU compliance the following packages can also be installed: + +* |link-to-cupy| +* |link-to-torch| + +.. |link-to-cupy| raw:: html + + CuPy + +.. |link-to-torch| raw:: html + + Torch + +.. note:: + + Note that none of these are required for running on a CPU. diff --git a/docs/source/examples.rst b/docs/source/examples.rst deleted file mode 100644 index ad8e915d..00000000 --- a/docs/source/examples.rst +++ /dev/null @@ -1,6 +0,0 @@ -modopt examples -=============== - -.. toctree:: - - plugin_example diff --git a/docs/source/index.rst b/docs/source/index.rst index 7ed86897..8262d43d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,147 +6,19 @@ ModOpt Documentation ====================== -:Author: Samuel Farrens `(samuel.farrens@cea.fr) `_ - -:Version: 1.4.4 +.. image:: modopt_logo.png -:Release Date: 16/06/2020 - -:Documentation: |link-to-docs| +.. Include table of contents +.. include:: toc.rst +:Author: Samuel Farrens `(samuel.farrens@cea.fr) `_ +:Version: 1.5.0 +:Release Date: 31/03/2021 :Repository: |link-to-repo| -.. |link-to-docs| raw:: html - - https://cea-cosmic.github.io/ModOpt/ - .. |link-to-repo| raw:: html https://github.com/CEA-COSMIC/ModOpt -ModOpt is a series of Modular Optimisation tools for solving inverse problems. - ----- - -Contents -======== - -1. `Dependencies`_ -2. `Installation`_ -3. `Package Contents`_ - ----- - -Dependencies -============ - -Required Packages ------------------ - -In order to run the code in this repository the following packages must be -installed: - -* |link-to-python| [Last tested with v3.8.2] - -* |link-to-numpy| [Tested with v1.18.3] - -* |link-to-scipy| [Tested with v1.4.1] - -* |link-to-progressbar| [Tested with v3.51.1] - -.. |link-to-python| raw:: html - - Python - -.. |link-to-numpy| raw:: html - - Numpy - -.. |link-to-scipy| raw:: html - - Scipy - -.. |link-to-progressbar| raw:: html - - Progressbar 2 - -Optional Packages ------------------ - -The following packages can optionally be installed to add extra functionality: - -* |link-to-astropy| [Last tested with v4.0.1] - -* |link-to-matplotlib| [Last tested with v3.2.1] - -* |link-to-skimage| [Requires >=v0.16.2] - -* |link-to-sklearn| [Requires >=v0.22.2] - -* |link-to-termcolor| [Last tested with v1.1.0] - -.. |link-to-astropy| raw:: html - - Astropy - -.. |link-to-matplotlib| raw:: html - - Matplotlib - -.. |link-to-skimage| raw:: html - - Scikit-Image - -.. |link-to-sklearn| raw:: html - - Scikit-Learn - -.. |link-to-termcolor| raw:: html - - Termcolor - ----- - -Installation -============ - -To install using `pip` run the following command: - -.. code-block:: bash - - $ pip install modopt - -To install using `easy_install` run the following command: - -.. code-block:: bash - - $ easy_install modopt - -To clone the ModOpt repository from GitHub run the following command: - -.. code-block:: bash - - $ git clone https://github.com/cea-cosmic/ModOpt - ----- - -Package Contents -================ - -.. toctree:: - :numbered: - :maxdepth: 3 - - modopt - examples +ModOpt is a series of **Modular Optimisation** tools for solving inverse problems. diff --git a/docs/source/installation.rst b/docs/source/installation.rst new file mode 100644 index 00000000..688f0c18 --- /dev/null +++ b/docs/source/installation.rst @@ -0,0 +1,50 @@ +Installation +============ + +.. note:: + + ModOpt is automatically installed by |link-to-pysap|. The following steps are + intended for those that wish to use ModOpt independently of PySAP or those + aiming to contribute to the development of the package. + + .. |link-to-pysap| raw:: html + + PySAP + +Users +----- + +You can install the latest release of ModOpt from `PyPi `_ +as follows: + +.. code-block:: bash + + pip install modopt + + +Alternatively clone the repository and build the package locally as follows: + +.. code-block:: bash + + pip install . + + +Developers +---------- + +Developers are recommend to clone the repository and build the package locally +in development mode with testing and documentation packages as follows: + +.. code-block:: bash + + pip install -e ".[develop]" + +Troubleshooting +--------------- +If you encounter any difficulties installing ModOpt we recommend that you +open a |link-to-issue| and we will do our best to help you. + +.. |link-to-issue| raw:: html + + new issue diff --git a/docs/source/modopt.base.np_adjust.rst b/docs/source/modopt.base.np_adjust.rst deleted file mode 100644 index ed42e9d2..00000000 --- a/docs/source/modopt.base.np_adjust.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.base.np\_adjust module -============================= - -.. automodule:: modopt.base.np_adjust - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.base.observable.rst b/docs/source/modopt.base.observable.rst deleted file mode 100644 index f41f7b87..00000000 --- a/docs/source/modopt.base.observable.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.base.observable module -============================= - -.. automodule:: modopt.base.observable - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.base.rst b/docs/source/modopt.base.rst deleted file mode 100644 index fc01e712..00000000 --- a/docs/source/modopt.base.rst +++ /dev/null @@ -1,22 +0,0 @@ -modopt.base package -=================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.base.np_adjust - modopt.base.observable - modopt.base.transform - modopt.base.types - modopt.base.wrappers - -Module contents ---------------- - -.. automodule:: modopt.base - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.base.transform.rst b/docs/source/modopt.base.transform.rst deleted file mode 100644 index a44f9056..00000000 --- a/docs/source/modopt.base.transform.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.base.transform module -============================ - -.. automodule:: modopt.base.transform - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.base.types.rst b/docs/source/modopt.base.types.rst deleted file mode 100644 index 1e7bc951..00000000 --- a/docs/source/modopt.base.types.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.base.types module -======================== - -.. automodule:: modopt.base.types - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.base.wrappers.rst b/docs/source/modopt.base.wrappers.rst deleted file mode 100644 index c20b47dc..00000000 --- a/docs/source/modopt.base.wrappers.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.base.wrappers module -=========================== - -.. automodule:: modopt.base.wrappers - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.info.rst b/docs/source/modopt.info.rst deleted file mode 100644 index 2c2cbb77..00000000 --- a/docs/source/modopt.info.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.info module -================== - -.. automodule:: modopt.info - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.interface.errors.rst b/docs/source/modopt.interface.errors.rst deleted file mode 100644 index 9fd217be..00000000 --- a/docs/source/modopt.interface.errors.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.interface.errors module -============================== - -.. automodule:: modopt.interface.errors - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.interface.log.rst b/docs/source/modopt.interface.log.rst deleted file mode 100644 index 0bb6ac91..00000000 --- a/docs/source/modopt.interface.log.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.interface.log module -=========================== - -.. automodule:: modopt.interface.log - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.interface.rst b/docs/source/modopt.interface.rst deleted file mode 100644 index 40217949..00000000 --- a/docs/source/modopt.interface.rst +++ /dev/null @@ -1,19 +0,0 @@ -modopt.interface package -======================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.interface.errors - modopt.interface.log - -Module contents ---------------- - -.. automodule:: modopt.interface - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.math.convolve.rst b/docs/source/modopt.math.convolve.rst deleted file mode 100644 index 10f09fec..00000000 --- a/docs/source/modopt.math.convolve.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.math.convolve module -=========================== - -.. automodule:: modopt.math.convolve - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.math.matrix.rst b/docs/source/modopt.math.matrix.rst deleted file mode 100644 index 149c3b29..00000000 --- a/docs/source/modopt.math.matrix.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.math.matrix module -========================= - -.. automodule:: modopt.math.matrix - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.math.metrics.rst b/docs/source/modopt.math.metrics.rst deleted file mode 100644 index 182c3d4f..00000000 --- a/docs/source/modopt.math.metrics.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.math.metrics module -========================== - -.. automodule:: modopt.math.metrics - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.math.rst b/docs/source/modopt.math.rst deleted file mode 100644 index 23e3a9fd..00000000 --- a/docs/source/modopt.math.rst +++ /dev/null @@ -1,21 +0,0 @@ -modopt.math package -=================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.math.convolve - modopt.math.matrix - modopt.math.metrics - modopt.math.stats - -Module contents ---------------- - -.. automodule:: modopt.math - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.math.stats.rst b/docs/source/modopt.math.stats.rst deleted file mode 100644 index dfc8953e..00000000 --- a/docs/source/modopt.math.stats.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.math.stats module -======================== - -.. automodule:: modopt.math.stats - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.algorithms.rst b/docs/source/modopt.opt.algorithms.rst deleted file mode 100644 index c23dcf1a..00000000 --- a/docs/source/modopt.opt.algorithms.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.algorithms module -============================ - -.. automodule:: modopt.opt.algorithms - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.cost.rst b/docs/source/modopt.opt.cost.rst deleted file mode 100644 index 7d374c71..00000000 --- a/docs/source/modopt.opt.cost.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.cost module -====================== - -.. automodule:: modopt.opt.cost - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.gradient.rst b/docs/source/modopt.opt.gradient.rst deleted file mode 100644 index a97b8bd4..00000000 --- a/docs/source/modopt.opt.gradient.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.gradient module -========================== - -.. automodule:: modopt.opt.gradient - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.linear.rst b/docs/source/modopt.opt.linear.rst deleted file mode 100644 index 0e667748..00000000 --- a/docs/source/modopt.opt.linear.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.linear module -======================== - -.. automodule:: modopt.opt.linear - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.proximity.rst b/docs/source/modopt.opt.proximity.rst deleted file mode 100644 index 3abfb070..00000000 --- a/docs/source/modopt.opt.proximity.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.proximity module -=========================== - -.. automodule:: modopt.opt.proximity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.reweight.rst b/docs/source/modopt.opt.reweight.rst deleted file mode 100644 index 740d1d22..00000000 --- a/docs/source/modopt.opt.reweight.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.opt.reweight module -========================== - -.. automodule:: modopt.opt.reweight - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.opt.rst b/docs/source/modopt.opt.rst deleted file mode 100644 index 4a56c9da..00000000 --- a/docs/source/modopt.opt.rst +++ /dev/null @@ -1,23 +0,0 @@ -modopt.opt package -================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.opt.algorithms - modopt.opt.cost - modopt.opt.gradient - modopt.opt.linear - modopt.opt.proximity - modopt.opt.reweight - -Module contents ---------------- - -.. automodule:: modopt.opt - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.plot.cost_plot.rst b/docs/source/modopt.plot.cost_plot.rst deleted file mode 100644 index 3f31b0af..00000000 --- a/docs/source/modopt.plot.cost_plot.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.plot.cost\_plot module -============================= - -.. automodule:: modopt.plot.cost_plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.plot.rst b/docs/source/modopt.plot.rst deleted file mode 100644 index df2019eb..00000000 --- a/docs/source/modopt.plot.rst +++ /dev/null @@ -1,18 +0,0 @@ -modopt.plot package -=================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.plot.cost_plot - -Module contents ---------------- - -.. automodule:: modopt.plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.rst b/docs/source/modopt.rst deleted file mode 100644 index 5aaff862..00000000 --- a/docs/source/modopt.rst +++ /dev/null @@ -1,31 +0,0 @@ -modopt package -============== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - modopt.base - modopt.interface - modopt.math - modopt.opt - modopt.plot - modopt.signal - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.info - -Module contents ---------------- - -.. automodule:: modopt - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.filter.rst b/docs/source/modopt.signal.filter.rst deleted file mode 100644 index 2ff6f482..00000000 --- a/docs/source/modopt.signal.filter.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.signal.filter module -=========================== - -.. automodule:: modopt.signal.filter - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.noise.rst b/docs/source/modopt.signal.noise.rst deleted file mode 100644 index 497c1ed6..00000000 --- a/docs/source/modopt.signal.noise.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.signal.noise module -========================== - -.. automodule:: modopt.signal.noise - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.positivity.rst b/docs/source/modopt.signal.positivity.rst deleted file mode 100644 index 6c460885..00000000 --- a/docs/source/modopt.signal.positivity.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.signal.positivity module -=============================== - -.. automodule:: modopt.signal.positivity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.rst b/docs/source/modopt.signal.rst deleted file mode 100644 index ea48be16..00000000 --- a/docs/source/modopt.signal.rst +++ /dev/null @@ -1,23 +0,0 @@ -modopt.signal package -===================== - -Submodules ----------- - -.. toctree:: - :maxdepth: 4 - - modopt.signal.filter - modopt.signal.noise - modopt.signal.positivity - modopt.signal.svd - modopt.signal.validation - modopt.signal.wavelet - -Module contents ---------------- - -.. automodule:: modopt.signal - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.svd.rst b/docs/source/modopt.signal.svd.rst deleted file mode 100644 index 2b8adf47..00000000 --- a/docs/source/modopt.signal.svd.rst +++ /dev/null @@ -1,7 +0,0 @@ -modopt.signal.svd module -======================== - -.. automodule:: modopt.signal.svd - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/modopt.signal.validation.rst b/docs/source/modopt.signal.validation.rst deleted file mode 100644 index 4b79c904..00000000 --- a/docs/source/modopt.signal.validation.rst +++ /dev/null @@ -1,7 +0,0 @@ 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Instrumentation and Methods for Astrophysics}, + year = 2020, + month = jul, + volume = {32}, + eid = {100402}, + pages = {100402}, + doi = {10.1016/j.ascom.2020.100402}, +archivePrefix = {arXiv}, + eprint = {1910.08465}, + primaryClass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2020A&C....3200402F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} diff --git a/docs/source/neurospin_logo.png b/docs/source/neurospin_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..669deb0d8cc5db48ba0197ffcbd28c32307daa76 GIT binary patch literal 62657 zcmXV1dpuMB|KHro{ZcM9msIY|rA+RcbRp$7noX3u*$g3f%3Us{p++u6ZjWv098rwqV`A^%5;(^m-y4zX zN*|aKafCek0;4p1!R>2yZ|S}NavZ$vesrh%%f8`*;sx{k`KB zk=ecQ?x}qGF}H6F?pXM5jMtIyGo|`JVBZxlX4P2gpG#repZ}idiuO1b^Nsa5j-ndY zhIcTUH}^$QkB-Dv%5cozeZ4n7K7h{d*{hPj?%Auje%<5!1u5UG6FF7#M|TBQJ3F_( zi3qj;2r?j6@<)AT&;4T?C4?l=MWj#u3N6|bT=B<#?8qG1 z6#fMM%qycS6`j6MA~UXU0sWKM*74W*AYHRjoT>+=9rP5OZN~(NjTP?s&bz39fDC>-RP? 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Exception: # pragma: no cover + _version = 'Unkown' + warn( + 'Could not extract package metadata. Make sure the package is ' + + 'correctly installed.', + ) -from . import * -from .base import * -from .info import __version__, __about__ +__version__ = _version diff --git a/modopt/base/__init__.py b/modopt/base/__init__.py index 97eb692a..1c0c8b2c 100644 --- a/modopt/base/__init__.py +++ b/modopt/base/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""BASE ROUTINES +"""BASE ROUTINES. This module contains submodules for basic operations such as type transformations and adjustments to the default output of Numpy functions. diff --git a/modopt/base/backend.py b/modopt/base/backend.py new file mode 100644 index 00000000..1f5a7a3a --- /dev/null +++ b/modopt/base/backend.py @@ -0,0 +1,187 @@ +# -*- coding: utf-8 -*- + +"""BACKEND MODULE. + +This module contains methods for GPU Compatiblity. + +:Author: Chaithya G R + +""" + +import warnings +from importlib import util + +import numpy as np + +try: + import torch +except ImportError: # pragma: no cover + import_torch = False +else: + import_torch = True + +# Handle the compatibility with variable +gpu_compatibility = { + 'cupy': False, + 'cupy-cudnn': False, +} + +if util.find_spec('cupy') is not None: + try: + import cupy as cp + gpu_compatibility['cupy'] = True + + if util.find_spec('cupy.cuda.cudnn') is not None: + gpu_compatibility['cupy-cudnn'] = True + except ImportError: + pass + + +def get_array_module(input_data): + """Get Array Module. + + This method returns the array module, which tells if the data is residing + on GPU or CPU. + + Parameters + ---------- + input_data : numpy.ndarray or cupy.ndarray + Input data array + + Returns + ------- + module + The numpy or cupy module + + """ + if gpu_compatibility['cupy']: + return cp.get_array_module(input_data) + + return np + + +def move_to_device(input_data): + """Move data to device. + + This method moves data from CPU to GPU if we have the + compatibility to do so. It returns the same data if + it is already on GPU. + + Parameters + ---------- + input_data : numpy.ndarray or cupy.ndarray + Input data array to be moved + + Returns + ------- + cupy.ndarray + The CuPy array residing on GPU + + """ + xp = get_array_module(input_data) + + if xp == cp: + return input_data + + if gpu_compatibility['cupy']: + return cp.array(input_data) + + warnings.warn('Cupy is not installed, cannot move data to GPU') + + return input_data + + +def move_to_cpu(input_data): + """Move data to CPU. + + This method moves data from GPU to CPU.It returns the same data if it is + already on CPU. + + Parameters + ---------- + input_data : cupy.ndarray + Input data array to be moved + + Returns + ------- + numpy.ndarray + The NumPy array residing on CPU + + """ + xp = get_array_module(input_data) + + if xp == np: + return input_data + + return input_data.get() + + +def convert_to_tensor(input_data): + """Convert data to a tensor. + + This method converts input data to a torch tensor. Particularly, this + method is helpful to convert CuPy array to Tensor. + + Parameters + ---------- + input_data : cupy.ndarray + Input data array to be converted + + Returns + ------- + torch.Tensor + The tensor data + + Raises + ------ + ImportError + If Torch package not found + + """ + if not import_torch: + raise ImportError( + 'Required version of Torch package not found' + + 'see documentation for details: https://cea-cosmic.' + + 'github.io/ModOpt/#optional-packages', + ) + + xp = get_array_module(input_data) + + if xp == np: + return torch.Tensor(input_data) + + return torch.utils.dlpack.from_dlpack(input_data.toDlpack()).float() + + +def convert_to_cupy_array(input_data): + """Convert Tensor data to a CuPy Array. + + This method converts input tensor data to a cupy array. + + Parameters + ---------- + input_data : torch.Tensor + Input Tensor to be converted + + Returns + ------- + cupy.ndarray + The tensor data as a CuPy array + + Raises + ------ + ImportError + If Torch package not found + + """ + if not import_torch: + raise ImportError( + 'Required version of Torch package not found' + + 'see documentation for details: https://cea-cosmic.' + + 'github.io/ModOpt/#optional-packages', + ) + + if input_data.is_cuda: + return cp.fromDlpack(torch.utils.dlpack.to_dlpack(input_data)) + + return input_data.detach().numpy() diff --git a/modopt/base/np_adjust.py b/modopt/base/np_adjust.py index b1a4e98e..3df8b411 100644 --- a/modopt/base/np_adjust.py +++ b/modopt/base/np_adjust.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""NUMPY ADJUSTMENT ROUTINES +"""NUMPY ADJUSTMENT ROUTINES. This module contains methods for adjusting the default output for certain Numpy functions. @@ -12,14 +12,14 @@ import numpy as np -def rotate(data): - """Rotate +def rotate(input_data): + """Rotate. This method rotates an input numpy array by 180 degrees. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array (at least 2D) Returns @@ -33,6 +33,7 @@ def rotate(data): Examples -------- + >>> import numpy as np >>> from modopt.base.np_adjust import rotate >>> x = np.arange(9).reshape((3, 3)) >>> x @@ -50,18 +51,17 @@ def rotate(data): numpy.rot90 : base function """ + return np.rot90(input_data, 2) - return np.rot90(data, 2) - -def rotate_stack(data): - """Rotate stack +def rotate_stack(input_data): + """Rotate stack. This method rotates each array in a stack of arrays by 180 degrees. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array (at least 3D) Returns @@ -71,19 +71,22 @@ def rotate_stack(data): Examples -------- + >>> import numpy as np >>> from modopt.base.np_adjust import rotate_stack >>> x = np.arange(18).reshape((2, 3, 3)) >>> x array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8]], + [[ 9, 10, 11], [12, 13, 14], - [15, 16, 17 + [15, 16, 17]]]) >>> rotate_stack(x) array([[[ 8, 7, 6], [ 5, 4, 3], [ 2, 1, 0]], + [[17, 16, 15], [14, 13, 12], [11, 10, 9]]]) @@ -93,18 +96,17 @@ def rotate_stack(data): rotate : looped function """ - - return np.array([rotate(x) for x in data]) + return np.array([rotate(array) for array in input_data]) -def pad2d(data, padding): - """Pad array +def pad2d(input_data, padding): + """Pad array. This method pads an input numpy array with zeros in all directions. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array (at least 2D) padding : int or tuple Amount of padding in x and y directions, respectively @@ -114,12 +116,18 @@ def pad2d(data, padding): numpy.ndarray Padded data + Raises + ------ + ValueError + For + Notes ----- Adjustment to numpy.pad() Examples -------- + >>> import numpy as np >>> from modopt.base.np_adjust import pad2d >>> x = np.arange(9).reshape((3, 3)) >>> x @@ -136,36 +144,36 @@ def pad2d(data, padding): See Also -------- numpy.pad : base function - """ - - data = np.array(data) + input_data = np.array(input_data) if isinstance(padding, int): padding = np.array([padding]) elif isinstance(padding, (tuple, list)): padding = np.array(padding) - elif isinstance(padding, np.ndarray): - pass - else: - raise ValueError('Padding must be an integer or a tuple (or list, ' - 'np.ndarray) of itegers') + elif not isinstance(padding, np.ndarray): + raise ValueError( + 'Padding must be an integer or a tuple (or list, np.ndarray) ' + + 'of itegers', + ) if padding.size == 1: padding = np.repeat(padding, 2) - return np.pad(data, ((padding[0], padding[0]), (padding[1], padding[1])), - 'constant') + pad_x = (padding[0], padding[0]) + pad_y = (padding[1], padding[1]) + return np.pad(input_data, (pad_x, pad_y), 'constant') -def ftr(data): - """Fancy transpose right + +def ftr(input_data): + """Fancy transpose right. Apply fancy_transpose() to data with roll=1. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -178,18 +186,17 @@ def ftr(data): fancy_transpose : base function """ - - return fancy_transpose(data) + return fancy_transpose(input_data) -def ftl(data): - """Fancy transpose left +def ftl(input_data): + """Fancy transpose left. Apply fancy_transpose() to data with roll=-1. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -202,18 +209,17 @@ def ftl(data): fancy_transpose : base function """ + return fancy_transpose(input_data, -1) - return fancy_transpose(data, -1) - -def fancy_transpose(data, roll=1): - """Fancy transpose +def fancy_transpose(input_data, roll=1): + """Fancy transpose. This method transposes a multidimensional matrix. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array roll : int Roll direction and amount (default is ``1``) @@ -229,15 +235,18 @@ def fancy_transpose(data, roll=1): Examples -------- + >>> import numpy as np >>> from modopt.base.np_adjust import fancy_transpose >>> x = np.arange(27).reshape(3, 3, 3) >>> x array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8]], + [[ 9, 10, 11], [12, 13, 14], [15, 16, 17]], + [[18, 19, 20], [21, 22, 23], [24, 25, 26]]]) @@ -245,9 +254,11 @@ def fancy_transpose(data, roll=1): array([[[ 0, 3, 6], [ 9, 12, 15], [18, 21, 24]], + [[ 1, 4, 7], [10, 13, 16], [19, 22, 25]], + [[ 2, 5, 8], [11, 14, 17], [20, 23, 26]]]) @@ -255,9 +266,11 @@ def fancy_transpose(data, roll=1): array([[[ 0, 9, 18], [ 1, 10, 19], [ 2, 11, 20]], + [[ 3, 12, 21], [ 4, 13, 22], [ 5, 14, 23]], + [[ 6, 15, 24], [ 7, 16, 25], [ 8, 17, 26]]]) @@ -267,7 +280,6 @@ def fancy_transpose(data, roll=1): numpy.transpose : base function """ + axis_roll = np.roll(np.arange(input_data.ndim), roll) - axis_roll = np.roll(np.arange(data.ndim), roll) - - return np.transpose(data, axes=axis_roll) + return np.transpose(input_data, axes=axis_roll) diff --git a/modopt/base/observable.py b/modopt/base/observable.py old mode 100755 new mode 100644 index 0162dd25..581d3f7c --- a/modopt/base/observable.py +++ b/modopt/base/observable.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""Observable +"""Observable. This module contains observable classes @@ -9,18 +9,18 @@ """ import time + import numpy as np class SignalObject(object): - """Dummy class for signals. - """ + """Dummy class for signals.""" pass class Observable(object): - """Base class for observable classes + """Base class for observable classes. This class defines a simple interface to add or remove observers on an object. @@ -47,7 +47,7 @@ def __init__(self, signals): self._locked = False def add_observer(self, signal, observer): - """Add an observer to the object + """Add an observer to the object. Raise an exception if the signal is not allowed. @@ -55,16 +55,15 @@ def add_observer(self, signal, observer): ---------- signal : str A valid signal - observer : function + observer : callable A function that will be called when the signal is emitted """ - self._is_allowed_signal(signal) self._add_observer(signal, observer) def remove_observer(self, signal, observer): - """Remove an observer from the object + """Remove an observer from the object. Raise an eception if the signal is not allowed. @@ -72,16 +71,15 @@ def remove_observer(self, signal, observer): ---------- signal : str A valid signal - observer : function + observer : callable An obervation function to be removed """ - self._is_allowed_event(signal) self._remove_observer(signal, observer) def notify_observers(self, signal, **kwargs): - """ Notify observers of a given signal + """Notify observers of a given signal. Parameters ---------- @@ -96,7 +94,6 @@ def notify_observers(self, signal, **kwargs): ``False`` if a notification is in progress, otherwise ``True`` """ - # Check if a notification if in progress if self._locked: return False @@ -105,10 +102,11 @@ def notify_observers(self, signal, **kwargs): # Create a signal object signal_to_be_notified = SignalObject() - setattr(signal_to_be_notified, "object", self) - setattr(signal_to_be_notified, "signal", signal) - for name, value in kwargs.items(): - setattr(signal_to_be_notified, name, value) + signal_to_be_notified.object = self + signal_to_be_notified.signal = signal + + for name, key_value in kwargs.items(): + setattr(signal_to_be_notified, name, key_value) # Notify all the observers for observer in self._observers[signal]: observer(signal_to_be_notified) @@ -116,18 +114,22 @@ def notify_observers(self, signal, **kwargs): self._locked = False def _get_allowed_signals(self): - """Get allowed signals + """Get allowed signals. Events allowed for the current object. - """ + Returns + ------- + list + List of allowed signals + """ return self._allowed_signals allowed_signals = property(_get_allowed_signals) def _is_allowed_signal(self, signal): - """Check if a signal is valid + """Check if a signal is valid. Raise an exception if the signal is not allowed. @@ -136,11 +138,15 @@ def _is_allowed_signal(self, signal): signal: str A signal - """ + Raises + ------ + ValueError + For invalid signal + """ if signal not in self._allowed_signals: - raise Exception("Signal '{0}' is not allowed for '{1}'.".format( - signal, type(self))) + message = 'Signal "{0}" is not allowed for "{1}"' + raise ValueError(message.format(signal, type(self))) def _add_observer(self, signal, observer): """Associate an observer to a valid signal. @@ -149,32 +155,30 @@ def _add_observer(self, signal, observer): ---------- signal : str A valid signal - observer : function + observer : callable An obervation function """ - if observer not in self._observers[signal]: self._observers[signal].append(observer) def _remove_observer(self, signal, observer): - """Remove an observer to a valid signal + """Remove an observer to a valid signal. Parameters ---------- signal : str A valid signal - observer : function + observer : callable An obervation function to be removed """ - if observer in self._observers[signal]: self._observers[signal].remove(observer) class MetricObserver(object): - """Metric observer + """Metric observer. Wrapper of the metric to the observer object notify by the Observable class. @@ -183,31 +187,34 @@ class MetricObserver(object): ---------- name : str The name of the metric - - metric : function + metric : callable Metric function with this precise signature func(test, ref) - mapping : dict Define the mapping between the iterate variable and the metric keyword: {'x_new':'name_var_1', 'y_new':'name_var_2'}. To cancel the need of a variable, the dict value should be None: 'y_new':None. - cst_kwargs : dict Keywords arguments of constant argument for the metric computation - early_stopping : bool If True it will compute the convergence flag (default is ``False``) - wind : int Window on with the convergence criteria is compute (default is ``6``) - eps : float The level of criteria of convergence (default is ``1.0e-3``) """ - def __init__(self, name, metric, mapping, cst_kwargs, early_stopping=False, - wind=6, eps=1.0e-3): + + def __init__( + self, + name, + metric, + mapping, + cst_kwargs, + early_stopping=False, + wind=6, + eps=1.0e-3, + ): self.name = name self.metric = metric @@ -222,7 +229,9 @@ def __init__(self, name, metric, mapping, cst_kwargs, early_stopping=False, self.early_stopping = early_stopping def __call__(self, signal): - """Wrapper the call from the observer signature to the metric + """Call Method. + + Wrapper the call from the observer signature to the metric signature. Parameters @@ -231,11 +240,10 @@ def __call__(self, signal): A valid signal """ - kwargs = {} - for key, value in self.mapping.items(): - if value is not None: - kwargs[value] = getattr(signal, key) + for key, key_value in self.mapping.items(): + if key_value is not None: + kwargs[key_value] = getattr(signal, key) kwargs.update(self.cst_kwargs) self.list_iters.append(signal.idx) self.list_dates.append(time.time()) @@ -245,32 +253,41 @@ def __call__(self, signal): self.is_converge() def is_converge(self): - """Check convergence + """Check convergence. Return ``True`` if the convergence criteria is matched. """ - if len(self.list_cv_values) < self.wind: return start_idx = -self.wind mid_idx = -(self.wind // 2) old_mean = np.array(self.list_cv_values[start_idx:mid_idx]).mean() current_mean = np.array(self.list_cv_values[mid_idx:]).mean() - normalize_residual_metrics = (np.abs(old_mean - current_mean) / - np.abs(old_mean)) + normalize_residual_metrics = ( + np.abs(old_mean - current_mean) / np.abs(old_mean) + ) self.converge_flag = normalize_residual_metrics < self.eps def retrieve_metrics(self): - """Retrieve metrics + """Retrieve metrics. Return the convergence metrics saved with the corresponding iterations. + Returns + ------- + dict + Convergence metrics + """ + time_val = np.array(self.list_dates) + + if time_val.size >= 1: + time_val -= time_val[0] - time = np.array(self.list_dates) - if len(time) >= 1: - time -= time[0] - return {'time': time, 'index': self.list_iters, - 'values': self.list_cv_values} + return { + 'time': time_val, + 'index': self.list_iters, + 'values': self.list_cv_values, + } diff --git a/modopt/base/transform.py b/modopt/base/transform.py index 03d569ff..44bcfa58 100644 --- a/modopt/base/transform.py +++ b/modopt/base/transform.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""DATA TRANSFORM ROUTINES +"""DATA TRANSFORM ROUTINES. This module contains methods for transforming data. @@ -12,7 +12,7 @@ def cube2map(data_cube, layout): - r"""Cube to Map + """Cube to Map. This method transforms the input data from a 3D cube to a 2D map with a specified layout @@ -21,7 +21,7 @@ def cube2map(data_cube, layout): ---------- data_cube : numpy.ndarray Input data cube, 3D array of 2D images - Layout : tuple + layout : tuple 2D layout of 2D images Returns @@ -38,6 +38,7 @@ def cube2map(data_cube, layout): Examples -------- + >>> import numpy as np >>> from modopt.base.transform import cube2map >>> a = np.arange(16).reshape((4, 2, 2)) >>> cube2map(a, (2, 2)) @@ -51,20 +52,25 @@ def cube2map(data_cube, layout): map2cube : complimentary function """ - if data_cube.ndim != 3: raise ValueError('The input data must have 3 dimensions.') if data_cube.shape[0] != np.prod(layout): - raise ValueError('The desired layout must match the number of input ' - 'data layers.') + raise ValueError( + 'The desired layout must match the number of input ' + + 'data layers.', + ) + + res = ([ + np.hstack(data_cube[slice(layout[1] * elem, layout[1] * (elem + 1))]) + for elem in range(layout[0]) + ]) - return np.vstack([np.hstack(data_cube[slice(layout[1] * i, layout[1] * - (i + 1))]) for i in range(layout[0])]) + return np.vstack(res) def map2cube(data_map, layout): - r"""Map to cube + """Map to cube. This method transforms the input data from a 2D map with given layout to a 3D cube @@ -88,16 +94,20 @@ def map2cube(data_map, layout): Examples -------- + >>> import numpy as np >>> from modopt.base.transform import map2cube >>> a = np.array([[0, 1, 4, 5], [2, 3, 6, 7], [8, 9, 12, 13], - [10, 11, 14, 15]]) + ... [10, 11, 14, 15]]) >>> map2cube(a, (2, 2)) array([[[ 0, 1], [ 2, 3]], + [[ 4, 5], [ 6, 7]], + [[ 8, 9], [10, 11]], + [[12, 13], [14, 15]]]) @@ -106,20 +116,26 @@ def map2cube(data_map, layout): cube2map : complimentary function """ - if np.all(np.array(data_map.shape) % np.array(layout)): - raise ValueError('The desired layout must be a multiple of the number ' - 'pixels in the data map.') + raise ValueError( + 'The desired layout must be a multiple of the number ' + + 'pixels in the data map.', + ) d_shape = np.array(data_map.shape) // np.array(layout) - return np.array([data_map[(slice(i * d_shape[0], (i + 1) * d_shape[0]), - slice(j * d_shape[1], (j + 1) * d_shape[1]))] for i in - range(layout[0]) for j in range(layout[1])]) + return np.array([ + data_map[( + slice(i_elem * d_shape[0], (i_elem + 1) * d_shape[0]), + slice(j_elem * d_shape[1], (j_elem + 1) * d_shape[1]), + )] + for i_elem in range(layout[0]) + for j_elem in range(layout[1]) + ]) def map2matrix(data_map, layout): - r"""Map to Matrix + """Map to Matrix. This method transforms a 2D map to a 2D matrix @@ -135,16 +151,12 @@ def map2matrix(data_map, layout): numpy.ndarray 2D matrix - Raises - ------ - ValueError - For invalid layout - Examples -------- + >>> import numpy as np >>> from modopt.base.transform import map2matrix >>> a = np.array([[0, 1, 4, 5], [2, 3, 6, 7], [8, 9, 12, 13], - [10, 11, 14, 15]]) + ... [10, 11, 14, 15]]) >>> map2matrix(a, (2, 2)) array([[ 0, 4, 8, 12], [ 1, 5, 9, 13], @@ -156,31 +168,34 @@ def map2matrix(data_map, layout): matrix2map : complimentary function """ - layout = np.array(layout) - # Select n objects - n_obj = np.prod(layout) - # Get the shape of the images image_shape = (np.array(data_map.shape) // layout)[0] # Stack objects from map data_matrix = [] - for i in range(n_obj): - lower = (image_shape * (i // layout[1]), - image_shape * (i % layout[1])) - upper = (image_shape * (i // layout[1] + 1), - image_shape * (i % layout[1] + 1)) - data_matrix.append((data_map[lower[0]:upper[0], - lower[1]:upper[1]]).reshape(image_shape ** 2)) + for i_elem in range(np.prod(layout)): + lower = ( + image_shape * (i_elem // layout[1]), + image_shape * (i_elem % layout[1]), + ) + upper = ( + image_shape * (i_elem // layout[1] + 1), + image_shape * (i_elem % layout[1] + 1), + ) + data_matrix.append( + ( + data_map[lower[0]:upper[0], lower[1]:upper[1]] + ).reshape(image_shape ** 2), + ) return np.array(data_matrix).T def matrix2map(data_matrix, map_shape): - r"""Matrix to Map + """Matrix to Map. This method transforms a 2D matrix to a 2D map @@ -196,17 +211,13 @@ def matrix2map(data_matrix, map_shape): numpy.ndarray 2D map - Raises - ------ - ValueError - For invalid layout - Examples -------- + >>> import numpy as np >>> from modopt.base.transform import matrix2map >>> a = np.array([[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14], - [3, 7, 11, 15]]) - >>> matrix2map(a, (2, 2)) + ... [3, 7, 11, 15]]) + >>> matrix2map(a, (4, 4)) array([[ 0, 1, 4, 5], [ 2, 3, 6, 7], [ 8, 9, 12, 13], @@ -217,7 +228,6 @@ def matrix2map(data_matrix, map_shape): map2matrix : complimentary function """ - map_shape = np.array(map_shape) # Get the shape and layout of the images @@ -229,18 +239,22 @@ def matrix2map(data_matrix, map_shape): temp = data_matrix.reshape(image_shape, image_shape, data_matrix.shape[1]) - for i in range(data_matrix.shape[1]): - lower = (image_shape * (i // layout[1]), - image_shape * (i % layout[1])) - upper = (image_shape * (i // layout[1] + 1), - image_shape * (i % layout[1] + 1)) - data_map[lower[0]:upper[0], lower[1]:upper[1]] = temp[:, :, i] + for i_elem in range(data_matrix.shape[1]): + lower = ( + image_shape * (i_elem // layout[1]), + image_shape * (i_elem % layout[1]), + ) + upper = ( + image_shape * (i_elem // layout[1] + 1), + image_shape * (i_elem % layout[1] + 1), + ) + data_map[lower[0]:upper[0], lower[1]:upper[1]] = temp[:, :, i_elem] return data_map.astype(int) def cube2matrix(data_cube): - r"""Cube to Matrix + """Cube to Matrix. This method transforms a 3D cube to a 2D matrix @@ -256,6 +270,7 @@ def cube2matrix(data_cube): Examples -------- + >>> import numpy as np >>> from modopt.base.transform import cube2matrix >>> a = np.arange(16).reshape((4, 2, 2)) >>> cube2matrix(a) @@ -269,13 +284,13 @@ def cube2matrix(data_cube): matrix2cube : complimentary function """ - - return data_cube.reshape([data_cube.shape[0]] + - [np.prod(data_cube.shape[1:])]).T + return data_cube.reshape( + [data_cube.shape[0]] + [np.prod(data_cube.shape[1:])], + ).T def matrix2cube(data_matrix, im_shape): - r"""Matrix to Cube + """Matrix to Cube. This method transforms a 2D matrix to a 3D cube @@ -293,16 +308,20 @@ def matrix2cube(data_matrix, im_shape): Examples -------- + >>> import numpy as np >>> from modopt.base.transform import matrix2cube >>> a = np.array([[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14], - [3, 7, 11, 15]]) + ... [3, 7, 11, 15]]) >>> matrix2cube(a, (2, 2)) array([[[ 0, 1], [ 2, 3]], + [[ 4, 5], [ 6, 7]], + [[ 8, 9], [10, 11]], + [[12, 13], [14, 15]]]) @@ -311,5 +330,4 @@ def matrix2cube(data_matrix, im_shape): cube2matrix : complimentary function """ - return data_matrix.T.reshape([data_matrix.shape[1]] + list(im_shape)) diff --git a/modopt/base/types.py b/modopt/base/types.py index 5de2036b..f0eff33f 100644 --- a/modopt/base/types.py +++ b/modopt/base/types.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""TYPE HANDLING ROUTINES +"""TYPE HANDLING ROUTINES. This module contains methods for handing object types. @@ -9,12 +9,13 @@ """ import numpy as np + from modopt.base.wrappers import add_args_kwargs from modopt.interface.errors import warn -def check_callable(val, add_agrs=True): - r""" Check input object is callable +def check_callable(input_obj, add_agrs=True): + """Check input object is callable. This method checks if the input operator is a callable funciton and optionally adds support for arguments and keyword arguments if not already @@ -22,7 +23,7 @@ def check_callable(val, add_agrs=True): Parameters ---------- - val : function + input_obj : callable Callable function add_agrs : bool, optional Option to add support for agrs and kwargs (default is ``True``) @@ -42,23 +43,24 @@ def check_callable(val, add_agrs=True): modopt.base.wrappers.add_args_kwargs : wrapper used """ - - if not callable(val): + if not callable(input_obj): raise TypeError('The input object must be a callable function.') if add_agrs: - val = add_args_kwargs(val) + input_obj = add_args_kwargs(input_obj) - return val + return input_obj -def check_float(val): - r"""Check if input value is a float or a numpy.ndarray of floats, if not +def check_float(input_obj): + """Check Float. + + Check if input object is a float or a numpy.ndarray of floats, if not convert. Parameters ---------- - val : any + input_obj : any Input value Returns @@ -66,40 +68,49 @@ def check_float(val): float or numpy.ndarray Input value as a float + Raises + ------ + TypeError + For invalid input type + Examples -------- + >>> import numpy as np >>> from modopt.base.types import check_float >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> check_float(a) - array([ 0., 1., 2., 3., 4.]) + array([0., 1., 2., 3., 4.]) See Also -------- check_int : related function """ - - if not isinstance(val, (int, float, list, tuple, np.ndarray)): + if not isinstance(input_obj, (int, float, list, tuple, np.ndarray)): raise TypeError('Invalid input type.') - if isinstance(val, int): - val = float(val) - elif isinstance(val, (list, tuple)): - val = np.array(val, dtype=float) - elif isinstance(val, np.ndarray) and (not np.issubdtype(val.dtype, - np.floating)): - val = val.astype(float) + if isinstance(input_obj, int): + input_obj = float(input_obj) + elif isinstance(input_obj, (list, tuple)): + input_obj = np.array(input_obj, dtype=float) + elif ( + isinstance(input_obj, np.ndarray) + and (not np.issubdtype(input_obj.dtype, np.floating)) + ): + input_obj = input_obj.astype(float) + + return input_obj - return val +def check_int(input_obj): + """Check Integer. -def check_int(val): - r"""Check if input value is an int or a np.ndarray of ints, if not convert. + Check if input value is an int or a np.ndarray of ints, if not convert. Parameters ---------- - val : any + input_obj : any Input value Returns @@ -107,13 +118,19 @@ def check_int(val): int or numpy.ndarray Input value as an integer + Raises + ------ + TypeError + For invalid input type + Examples -------- + >>> import numpy as np >>> from modopt.base.types import check_int >>> a = np.arange(5).astype(float) >>> a - array([ 0., 1., 2., 3., 4.]) - >>> check_float(a) + array([0., 1., 2., 3., 4.]) + >>> check_int(a) array([0, 1, 2, 3, 4]) See Also @@ -121,39 +138,59 @@ def check_int(val): check_float : related function """ - - if not isinstance(val, (int, float, list, tuple, np.ndarray)): + if not isinstance(input_obj, (int, float, list, tuple, np.ndarray)): raise TypeError('Invalid input type.') - if isinstance(val, float): - val = int(val) - elif isinstance(val, (list, tuple)): - val = np.array(val, dtype=int) - elif isinstance(val, np.ndarray) and (not np.issubdtype(val.dtype, - np.integer)): - val = val.astype(int) + if isinstance(input_obj, float): + input_obj = int(input_obj) + elif isinstance(input_obj, (list, tuple)): + input_obj = np.array(input_obj, dtype=int) + elif ( + isinstance(input_obj, np.ndarray) + and (not np.issubdtype(input_obj.dtype, np.integer)) + ): + input_obj = input_obj.astype(int) - return val + return input_obj -def check_npndarray(val, dtype=None, writeable=True, verbose=True): - """Check if input object is a numpy array. +def check_npndarray(input_obj, dtype=None, writeable=True, verbose=True): + """Check Numpy ND-Array. + + Check if input object is a numpy array. Parameters ---------- - val : numpy.ndarray + input_obj : numpy.ndarray Input object + dtype : type + Numpy ndarray data type + writeable : bool + Option to make array immutable + verbose : bool + Verbosity option - """ + Raises + ------ + TypeError + For invalid input type + TypeError + For invalid numpy.ndarray dtype - if not isinstance(val, np.ndarray): + """ + if not isinstance(input_obj, np.ndarray): raise TypeError('Input is not a numpy array.') - if ((not isinstance(dtype, type(None))) and - (not np.issubdtype(val.dtype, dtype))): - raise TypeError('The numpy array elements are not of type: {}' - ''.format(dtype)) - - if not writeable and verbose and val.flags.writeable: + if ( + (not isinstance(dtype, type(None))) + and (not np.issubdtype(input_obj.dtype, dtype)) + ): + raise ( + TypeError( + 'The numpy array elements are not of type: {0}'.format(dtype), + ), + ) + + if not writeable and verbose and input_obj.flags.writeable: warn('Making input data immutable.') - val.flags.writeable = writeable + input_obj.flags.writeable = writeable diff --git a/modopt/base/wrappers.py b/modopt/base/wrappers.py index 7b6a1246..c6d4dbf4 100644 --- a/modopt/base/wrappers.py +++ b/modopt/base/wrappers.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""WRAPPERS +"""WRAPPERS. This module contains wrappers for adding additional features to functions @@ -8,23 +8,19 @@ """ -from sys import version_info from functools import wraps -if version_info[0] < 3: # pragma: no cover - from inspect import getargspec as argspec -else: - from inspect import getfullargspec as argspec +from inspect import getfullargspec as argspec def add_args_kwargs(func): - """Add Args and Kwargs + """Add Args and Kwargs. This wrapper adds support for additional arguments and keyword arguments to any callable function. Parameters ---------- - func : function + func : callable Callable function Returns @@ -33,7 +29,6 @@ def add_args_kwargs(func): wrapper """ - @wraps(func) def wrapper(*args, **kwargs): @@ -41,16 +36,14 @@ def wrapper(*args, **kwargs): # if 'args' not in props: if isinstance(props[1], type(None)): - args = args[:len(props[0])] - if ((not isinstance(props[2], type(None))) or - (not isinstance(props[3], type(None)))): - + if ( + (not isinstance(props[2], type(None))) + or (not isinstance(props[3], type(None))) + ): return func(*args, **kwargs) - else: - - return func(*args) + return func(*args) return wrapper diff --git a/modopt/info.py b/modopt/info.py deleted file mode 100644 index 04b5668b..00000000 --- a/modopt/info.py +++ /dev/null @@ -1,25 +0,0 @@ -# -*- coding: utf-8 -*- - -"""MODOPT PACKAGE INFO - -This module provides some basic information about the ModOpt package. - -:Author: Samuel Farrens - -:Version: 1.4.4 - -""" - -# Package Version -version_info = (1, 4, 4) -__version__ = '.'.join(str(c) for c in version_info) - -__about__ = ('ModOpt \n\n ' - 'Author: Samuel Farrens \n ' - 'Year: 2017 \n ' - 'Email: samuel.farrens@cea.fr \n ' - 'Website: https://sfarrens.github.io \n\n ' - 'ModOpt is a series of Modular Optimisation too ls for solving ' - 'inverse problems. \n\n ' - 'Full documentation available here: ' - 'https://cea-cosmic.github.io/ModOpt/') diff --git a/modopt/interface/__init__.py b/modopt/interface/__init__.py index 7820386b..f9439747 100644 --- a/modopt/interface/__init__.py +++ b/modopt/interface/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""INTERFACE ROUTINES +"""INTERFACE ROUTINES. This module contains submodules for error handling, logging and IO interaction. @@ -9,5 +9,3 @@ """ __all__ = ['errors', 'log'] - -from . import * diff --git a/modopt/interface/errors.py b/modopt/interface/errors.py index 9f84e0a6..0fbe7e71 100644 --- a/modopt/interface/errors.py +++ b/modopt/interface/errors.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""ERROR HANDLING ROUTINES +"""ERROR HANDLING ROUTINES. This module contains methods for handing warnings and errors. @@ -8,9 +8,10 @@ """ -import sys import os +import sys import warnings + try: from termcolor import colored except ImportError: @@ -20,7 +21,7 @@ def warn(warn_string, log=None): - """Warning + """Warning. This method creates custom warning messages. @@ -32,14 +33,13 @@ def warn(warn_string, log=None): Logging structure instance (default is ``None``) """ - if import_fail: warn_txt = 'WARNING' else: warn_txt = colored('WARNING', 'yellow') # Print warning to stdout. - sys.stderr.write(warn_txt + ': ' + warn_string + '\n') + sys.stderr.write('{0}: {1}\n'.format(warn_txt, warn_string)) # Check if a logging structure is provided. if not isinstance(log, type(None)): @@ -47,7 +47,7 @@ def warn(warn_string, log=None): def catch_error(exception, log=None): - """Catch error + """Catch error. This method catches errors and prints them to the terminal. It also saves the errors to a log if provided. @@ -60,24 +60,23 @@ def catch_error(exception, log=None): Logging structure instance (default is ``None``) """ - if import_fail: err_txt = 'ERROR' else: err_txt = colored('ERROR', 'red') # Print exception to stdout. - stream_txt = err_txt + ': ' + str(exception) + '\n' + stream_txt = '{0}: {1}\n'.format(err_txt, exception) sys.stderr.write(stream_txt) # Check if a logging structure is provided. if not isinstance(log, type(None)): - log_txt = 'ERROR: ' + str(exception) + '\n' + log_txt = 'ERROR: {0}\n'.format(exception) log.exception(log_txt) def file_name_error(file_name): - """File name error + """File name error. This method checks if the input file name is valid. @@ -92,55 +91,66 @@ def file_name_error(file_name): If file name not specified or file not found """ - if file_name == '' or file_name[0][0] == '-': raise IOError('Input file name not specified.') elif not os.path.isfile(file_name): - raise IOError('Input file name [%s] not found!' % file_name) + raise IOError('Input file name {0} not found!'.format(file_name)) -def is_executable(exe_name): - """Check if Input is Executable +def is_exe(fpath): + """Is Executable. - This methid checks if the input executable exists. + Check if the input file path corresponds to an executable on the system. Parameters ---------- - exe_name : str - Executable name + fpath : str + File path Returns ------- bool - Result of the test + True if file path exists + + """ + return os.path.isfile(fpath) and os.access(fpath, os.X_OK) + + +def is_executable(exe_name): + """Check if Input is Executable. + + This method checks if the input executable exists. + + Parameters + ---------- + exe_name : str + Executable name Raises ------ TypeError For invalid input type + IOError + For invalid system executable """ - if not isinstance(exe_name, str): - raise TypeError('Executable name must be a string.') - def is_exe(fpath): - - return os.path.isfile(fpath) and os.access(fpath, os.X_OK) - fpath, fname = os.path.split(exe_name) - if not fpath: - - res = any([is_exe(os.path.join(path, exe_name)) for path in - os.environ["PATH"].split(os.pathsep)]) + if fpath: + res = is_exe(exe_name) else: - - res = is_exe(exe_name) + res = any( + is_exe(os.path.join(path, exe_name)) + for path in os.environ['PATH'].split(os.pathsep) + ) if not res: - raise IOError('{} does not appear to be a valid executable on this ' - 'system.'.format(exe_name)) + message = ( + '{0} does not appear to be a valid executable on this system.' + ) + raise IOError(message.format(exe_name)) diff --git a/modopt/interface/log.py b/modopt/interface/log.py index fe111466..3b2fa77a 100644 --- a/modopt/interface/log.py +++ b/modopt/interface/log.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""LOGGING ROUTINES +"""LOGGING ROUTINES. This module contains methods for handing logging. @@ -8,12 +8,11 @@ """ -import sys import logging def set_up_log(filename, verbose=True): - """Set up log + """Set up log. This method sets up a basic log. @@ -30,9 +29,8 @@ def set_up_log(filename, verbose=True): Logging instance """ - # Add file extension. - filename += '.log' + filename = '{0}.log'.format(filename) if verbose: print('Preparing log file:', filename) @@ -41,8 +39,10 @@ def set_up_log(filename, verbose=True): logging.captureWarnings(True) # Set output format. - formatter = logging.Formatter(fmt='%(asctime)s %(message)s', - datefmt='%d/%m/%Y %H:%M:%S') + formatter = logging.Formatter( + fmt='%(asctime)s %(message)s', + datefmt='%d/%m/%Y %H:%M:%S', + ) # Create file handler. fh = logging.FileHandler(filename=filename, mode='w') @@ -61,7 +61,7 @@ def set_up_log(filename, verbose=True): def close_log(log, verbose=True): - """Close log + """Close log. This method closes and active logging.Logger instance. @@ -73,7 +73,6 @@ def close_log(log, verbose=True): Option for verbose output (default is ``True``) """ - if verbose: print('Closing log file:', log.name) @@ -81,4 +80,5 @@ def close_log(log, verbose=True): log.info('The log file has been closed.') # Remove all handlers from log. - [log.removeHandler(handler) for handler in log.handlers] + for log_handler in log.handlers: + log.removeHandler(log_handler) diff --git a/modopt/math/__init__.py b/modopt/math/__init__.py index 4341dd48..a22c0c98 100644 --- a/modopt/math/__init__.py +++ b/modopt/math/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""MATHEMATICS ROUTINES +"""MATHEMATICS ROUTINES. This module contains submodules for mathematical applications. @@ -9,5 +9,3 @@ """ __all__ = ['convolve', 'matrix', 'stats', 'metrics'] - -from . import * diff --git a/modopt/math/convolve.py b/modopt/math/convolve.py index a6dbe172..7074e44a 100644 --- a/modopt/math/convolve.py +++ b/modopt/math/convolve.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""CONVOLUTION ROUTINES +"""CONVOLUTION ROUTINES. This module contains methods for convolution. @@ -10,8 +10,10 @@ import numpy as np import scipy.signal + from modopt.base.np_adjust import rotate_stack from modopt.interface.errors import warn + try: from astropy.convolution import convolve_fft except ImportError: # pragma: no cover @@ -28,15 +30,15 @@ warn('Using pyFFTW "monkey patch" for scipy.fftpack') -def convolve(data, kernel, method='scipy'): - r"""Convolve data with kernel +def convolve(input_data, kernel, method='scipy'): + """Convolve data with kernel. This method convolves the input data with a given kernel using FFT and is the default convolution used for all routines Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array, normally a 2D image kernel : numpy.ndarray Input kernel array, normally a 2D kernel @@ -57,19 +59,19 @@ def convolve(data, kernel, method='scipy'): Examples -------- - >>> from modopt.math.convolve import convolve >>> import numpy as np + >>> from modopt.math.convolve import convolve >>> a = np.arange(9).reshape(3, 3) >>> b = a + 10 >>> convolve(a, b) - array([[ 86., 170., 146.], - [ 246., 444., 354.], - [ 290., 494., 374.]]) + array([[ 86., 170., 146.], + [246., 444., 354.], + [290., 494., 374.]]) >>> convolve(a, b, method='astropy') - array([[ 534., 525., 534.], - [ 453., 444., 453.], - [ 534., 525., 534.]]) + array([[534., 525., 534.], + [453., 444., 453.], + [534., 525., 534.]]) See Also -------- @@ -77,33 +79,38 @@ def convolve(data, kernel, method='scipy'): astropy.convolution.convolve_fft : astropy FFT convolution """ - - if data.ndim != kernel.ndim: + if input_data.ndim != kernel.ndim: raise ValueError('Data and kernel must have the same dimensions.') - if method not in ('astropy', 'scipy'): + if method not in {'astropy', 'scipy'}: raise ValueError('Invalid method. Options are "astropy" or "scipy".') if not import_astropy: # pragma: no cover method = 'scipy' if method == 'astropy': - return convolve_fft(data, kernel, boundary='wrap', crop=False, - nan_treatment='fill', normalize_kernel=False) + return convolve_fft( + input_data, + kernel, + boundary='wrap', + crop=False, + nan_treatment='fill', + normalize_kernel=False, + ) elif method == 'scipy': - return scipy.signal.fftconvolve(data, kernel, mode='same') + return scipy.signal.fftconvolve(input_data, kernel, mode='same') -def convolve_stack(data, kernel, rot_kernel=False, method='scipy'): - r"""Convolve stack of data with stack of kernels +def convolve_stack(input_data, kernel, rot_kernel=False, method='scipy'): + """Convolve stack of data with stack of kernels. This method convolves the input data with a given kernel using FFT and is the default convolution used for all routines Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array, normally a 2D image kernel : numpy.ndarray Input kernel array, normally a 2D kernel @@ -119,36 +126,37 @@ def convolve_stack(data, kernel, rot_kernel=False, method='scipy'): Examples -------- - >>> from math.convolve import convolve >>> import numpy as np + >>> from modopt.math.convolve import convolve_stack >>> a = np.arange(18).reshape(2, 3, 3) >>> b = a + 10 - >>> convolve_stack(a, b) - array([[[ 534., 525., 534.], - [ 453., 444., 453.], - [ 534., 525., 534.]], + >>> convolve_stack(a, b, method='astropy') + array([[[ 534., 525., 534.], + [ 453., 444., 453.], + [ 534., 525., 534.]], - [[ 2721., 2712., 2721.], - [ 2640., 2631., 2640.], - [ 2721., 2712., 2721.]]]) - - >>> convolve_stack(a, b, rot_kernel=True) - array([[[ 474., 483., 474.], - [ 555., 564., 555.], - [ 474., 483., 474.]], + [[2721., 2712., 2721.], + [2640., 2631., 2640.], + [2721., 2712., 2721.]]]) + + >>> convolve_stack(a, b, method='astropy', rot_kernel=True) + array([[[ 474., 483., 474.], + [ 555., 564., 555.], + [ 474., 483., 474.]], - [[ 2661., 2670., 2661.], - [ 2742., 2751., 2742.], - [ 2661., 2670., 2661.]]]) + [[2661., 2670., 2661.], + [2742., 2751., 2742.], + [2661., 2670., 2661.]]]) See Also -------- convolve : The convolution function called by convolve_stack """ - if rot_kernel: kernel = rotate_stack(kernel) - return np.array([convolve(data_i, kernel_i, method=method) for data_i, - kernel_i in zip(data, kernel)]) + return np.array([ + convolve(data_i, kernel_i, method=method) + for data_i, kernel_i in zip(input_data, kernel) + ]) diff --git a/modopt/math/matrix.py b/modopt/math/matrix.py index cb60bdf5..cb54cebc 100644 --- a/modopt/math/matrix.py +++ b/modopt/math/matrix.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""MATRIX ROUTINES +"""MATRIX ROUTINES. This module contains methods for matrix operations. @@ -8,12 +8,20 @@ """ -import numpy as np from itertools import product +import numpy as np + +from modopt.base.backend import get_array_module + +try: + import cupy as cp +except ImportError: # pragma: no cover + pass + def gram_schmidt(matrix, return_opt='orthonormal'): - r"""Gram-Schmit + """Gram-Schmit. This method orthonormalizes the row vectors of the input matrix. @@ -29,8 +37,14 @@ def gram_schmidt(matrix, return_opt='orthonormal'): tuple or numpy.ndarray Orthogonal vectors, u, and/or orthonormal vectors, e + Raises + ------ + ValueError + For invalid return option + Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import gram_schmidt >>> a = np.arange(9).reshape(3, 3) >>> gram_schmidt(a) @@ -44,43 +58,44 @@ def gram_schmidt(matrix, return_opt='orthonormal'): https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process """ + if return_opt not in {'orthonormal', 'orthogonal', 'both'}: + raise ValueError( + 'Invalid return_opt, options are: "orthonormal", "orthogonal" or ' + + '"both"', + ) - if return_opt not in ('orthonormal', 'orthogonal', 'both'): - raise ValueError('Invalid return_opt, options are: "orthonormal", ' - '"orthogonal" or "both"') - - u = [] - e = [] + u_vec = [] + e_vec = [] for vector in matrix: - if len(u) == 0: - u_now = vector + if u_vec: + u_now = vector - sum(project(u_i, vector) for u_i in u_vec) else: - u_now = vector - sum([project(u_i, vector) for u_i in u]) + u_now = vector - u.append(u_now) - e.append(u_now / np.linalg.norm(u_now, 2)) + u_vec.append(u_now) + e_vec.append(u_now / np.linalg.norm(u_now, 2)) - u = np.array(u) - e = np.array(e) + u_vec = np.array(u_vec) + e_vec = np.array(e_vec) if return_opt == 'orthonormal': - return e + return e_vec elif return_opt == 'orthogonal': - return u + return u_vec elif return_opt == 'both': - return u, e + return u_vec, e_vec -def nuclear_norm(data): - r"""Nuclear norm +def nuclear_norm(input_data): + r"""Nuclear norm. This method computes the nuclear (or trace) norm of the input data. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -90,6 +105,7 @@ def nuclear_norm(data): Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import nuclear_norm >>> a = np.arange(9).reshape(3, 3) >>> nuclear_norm(a) @@ -103,24 +119,23 @@ def nuclear_norm(data): \|\mathbf{A}\|_* = \sum_{i=1}^{\min\{m,n\}} \sigma_i (\mathbf{A}) """ - # Get SVD of the data. - u, s, v = np.linalg.svd(data) + _, singular_values, _ = np.linalg.svd(input_data) # Return nuclear norm. - return np.sum(s) + return np.sum(singular_values) -def project(u, v): - r"""Project vector +def project(u_vec, v_vec): + r"""Project vector. This method projects vector v onto vector u. Parameters ---------- - u : numpy.ndarray + u_vec : numpy.ndarray Input vector - v : numpy.ndarray + v_vec : numpy.ndarray Input vector Returns @@ -130,11 +145,12 @@ def project(u, v): Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import project >>> a = np.arange(3) >>> b = a + 3 >>> project(a, b) - array([ 0. , 2.8, 5.6]) + array([0. , 2.8, 5.6]) Notes ----- @@ -147,12 +163,11 @@ def project(u, v): (see https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process) """ - - return np.inner(v, u) / np.inner(u, u) * u + return np.inner(v_vec, u_vec) / np.inner(u_vec, u_vec) * u_vec def rot_matrix(angle): - r"""Rotation matrix + r"""Rotation matrix. This method produces a 2x2 rotation matrix for the given input angle. @@ -168,6 +183,7 @@ def rot_matrix(angle): Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import rot_matrix >>> rot_matrix(np.pi / 6) array([[ 0.8660254, -0.5 ], @@ -184,13 +200,17 @@ def rot_matrix(angle): \end{bmatrix} """ - - return np.around(np.array([[np.cos(angle), -np.sin(angle)], - [np.sin(angle), np.cos(angle)]], dtype='float'), 10) + return np.around( + np.array( + [[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]], + dtype='float', + ), + 10, + ) def rotate(matrix, angle): - r"""Rotate + """Rotate. This method rotates an input matrix about the input angle. @@ -213,6 +233,7 @@ def rotate(matrix, angle): Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import rotate >>> a = np.arange(9).reshape(3, 3) >>> rotate(a, np.pi / 2) @@ -221,7 +242,6 @@ def rotate(matrix, angle): [0, 3, 6]]) """ - shape = np.array(matrix.shape) if shape[0] != shape[1]: @@ -229,8 +249,10 @@ def rotate(matrix, angle): shift = (shape - 1) // 2 - index = np.array(list(product(*np.array([np.arange(val) for val in - shape])))) - shift + index = ( + np.array(list(product(*np.array([np.arange(sval) for sval in shape])))) + - shift + ) new_index = np.array(np.dot(index, rot_matrix(angle)), dtype='int') + shift new_index[new_index >= shape[0]] -= shape[0] @@ -239,7 +261,7 @@ def rotate(matrix, angle): class PowerMethod(object): - """Power method class + """Power method class. This method performs implements power method to calculate the spectral radius of the input data @@ -260,14 +282,14 @@ class PowerMethod(object): Examples -------- + >>> import numpy as np >>> from modopt.math.matrix import PowerMethod >>> np.random.seed(1) >>> pm = PowerMethod(lambda x: x.dot(x.T), (3, 3)) - - Power Method converged after 4 iterations! - >>> pm.spec_rad - 0.90429242629600848 - >>> pm.inv_spec_rad - 1.1058369736612865 + >>> np.around(pm.spec_rad, 6) + 0.904292 + >>> np.around(pm.inv_spec_rad, 6) + 1.105837 Notes ----- @@ -275,18 +297,29 @@ class PowerMethod(object): """ - def __init__(self, operator, data_shape, data_type=float, auto_run=True, - verbose=False): + def __init__( + self, + operator, + data_shape, + data_type=float, + auto_run=True, + use_gpu=False, + verbose=False, + ): self._operator = operator self._data_shape = data_shape self._data_type = data_type self._verbose = verbose + if use_gpu: + self.xp = cp + else: + self.xp = np if auto_run: self.get_spec_rad() def _set_initial_x(self): - """Set initial value of x + """Set initial value of x. This method sets the initial value of x to an arrray of random values @@ -296,11 +329,10 @@ def _set_initial_x(self): Random values of the same shape as the input data """ - - return np.random.random(self._data_shape).astype(self._data_type) + return self.xp.random.random(self._data_shape).astype(self._data_type) def get_spec_rad(self, tolerance=1e-6, max_iter=20, extra_factor=1.0): - """Get spectral radius + """Get spectral radius. This method calculates the spectral radius @@ -315,30 +347,35 @@ def get_spec_rad(self, tolerance=1e-6, max_iter=20, extra_factor=1.0): (default is ``1.0``) """ - # Set (or reset) values of x. x_old = self._set_initial_x() # Iterate until the L2 norm of x converges. - for i in range(max_iter): + for i_elem in range(max_iter): + + xp = get_array_module(x_old) - x_old_norm = np.linalg.norm(x_old) + x_old_norm = xp.linalg.norm(x_old) x_new = self._operator(x_old) / x_old_norm - x_new_norm = np.linalg.norm(x_new) + x_new_norm = xp.linalg.norm(x_new) - if(np.abs(x_new_norm - x_old_norm) < tolerance): + if (xp.abs(x_new_norm - x_old_norm) < tolerance): + message = ( + ' - Power Method converged after {0} iterations!' + ) if self._verbose: - print(' - Power Method converged after %d iterations!' % - (i + 1)) + print(message.format(i_elem + 1)) break - elif i == max_iter - 1 and self._verbose: - print(' - Power Method did not converge after %d ' - 'iterations!' % max_iter) + elif i_elem == max_iter - 1 and self._verbose: + message = ( + ' - Power Method did not converge after {0} iterations!' + ) + print(message.format(max_iter)) - np.copyto(x_old, x_new) + xp.copyto(x_old, x_new) self.spec_rad = x_new_norm * extra_factor self.inv_spec_rad = 1.0 / self.spec_rad diff --git a/modopt/math/metrics.py b/modopt/math/metrics.py index 54e303bb..1b870e23 100644 --- a/modopt/math/metrics.py +++ b/modopt/math/metrics.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""METRICS +"""METRICS. This module contains classes of different metric functions for optimization. @@ -9,14 +9,11 @@ """ import numpy as np -from scipy.ndimage import uniform_filter, gaussian_filter -import sys + +from modopt.base.backend import move_to_cpu try: - if sys.version_info.minor == 5: - from skimage.measure import compare_ssim - elif sys.version_info.minor > 5: - from skimage.metrics import structural_similarity as compare_ssim + from skimage.metrics import structural_similarity as compare_ssim except ImportError: # pragma: no cover import_skimage = False else: @@ -24,15 +21,21 @@ def min_max_normalize(img): - """Centre and normalize a given array. + """Min-Max Normalize. + + Centre and normalize a given array. Parameters ---------- img : numpy.ndarray Input image - """ + Returns + ------- + numpy.ndarray + Centred and normalized array + """ min_img = img.min() max_img = img.max() @@ -40,7 +43,9 @@ def min_max_normalize(img): def _preprocess_input(test, ref, mask=None): - """Wrapper to the metric + """Proprocess Input. + + Wrapper to the metric. Parameters ---------- @@ -51,6 +56,11 @@ def _preprocess_input(test, ref, mask=None): mask : numpy.ndarray, optional The mask for the ROI (default is ``None``) + Raises + ------ + ValueError + For invalid mask value + Notes ----- Compute the metric only on magnetude. @@ -61,15 +71,16 @@ def _preprocess_input(test, ref, mask=None): The SNR """ - test = np.abs(np.copy(test)).astype('float64') ref = np.abs(np.copy(ref)).astype('float64') test = min_max_normalize(test) ref = min_max_normalize(ref) if (not isinstance(mask, np.ndarray)) and (mask is not None): - raise ValueError("mask should be None, or a numpy.ndarray," - " got '{0}' instead.".format(mask)) + message = ( + 'Mask should be None, or a numpy.ndarray, got "{0}" instead.' + ) + raise ValueError(message.format(mask)) if mask is None: return test, ref, None @@ -78,7 +89,7 @@ def _preprocess_input(test, ref, mask=None): def ssim(test, ref, mask=None): - """Structural Similarity (SSIM) + """Structural Similarity (SSIM). Calculate the SSIM between a test image and a reference image. @@ -91,6 +102,11 @@ def ssim(test, ref, mask=None): mask : numpy.ndarray, optional The mask for the ROI (default is ``None``) + Raises + ------ + ImportError + If Scikit-Image package not found + Notes ----- Compute the metric only on magnetude. @@ -101,24 +117,25 @@ def ssim(test, ref, mask=None): The SNR """ - if not import_skimage: # pragma: no cover - raise ImportError('Required version of Scikit-Image package not found' - 'see documentation for details: https://cea-cosmic.' - 'github.io/ModOpt/#optional-packages') + raise ImportError( + 'Required version of Scikit-Image package not found' + + 'see documentation for details: https://cea-cosmic.' + + 'github.io/ModOpt/#optional-packages', + ) test, ref, mask = _preprocess_input(test, ref, mask) - assim, ssim = compare_ssim(test, ref, full=True) + test = move_to_cpu(test) + assim, ssim_value = compare_ssim(test, ref, full=True) if mask is None: return assim - else: - return (mask * ssim).sum() / mask.sum() + return (mask * ssim_value).sum() / mask.sum() def snr(test, ref, mask=None): - """Signal-to-Noise Ratio (SNR) + """Signal-to-Noise Ratio (SNR). Calculate the SNR between a test image and a reference image. @@ -141,7 +158,6 @@ def snr(test, ref, mask=None): The SNR """ - test, ref, mask = _preprocess_input(test, ref, mask) if mask is not None: @@ -154,7 +170,7 @@ def snr(test, ref, mask=None): def psnr(test, ref, mask=None): - """Peak Signal-to-Noise Ratio (PSNR) + """Peak Signal-to-Noise Ratio (PSNR). Calculate the PSNR between a test image and a reference image. @@ -177,7 +193,6 @@ def psnr(test, ref, mask=None): The PSNR """ - test, ref, mask = _preprocess_input(test, ref, mask) if mask is not None: @@ -191,7 +206,7 @@ def psnr(test, ref, mask=None): def mse(test, ref, mask=None): - r"""Mean Squared Error (MSE) + r"""Mean Squared Error (MSE). Calculate the MSE between a test image and a reference image. @@ -217,7 +232,6 @@ def mse(test, ref, mask=None): The MSE """ - test, ref, mask = _preprocess_input(test, ref, mask) if mask is not None: @@ -228,7 +242,7 @@ def mse(test, ref, mask=None): def nrmse(test, ref, mask=None): - """Return NRMSE + """Return NRMSE. Parameters ---------- @@ -249,7 +263,6 @@ def nrmse(test, ref, mask=None): The NRMSE """ - test, ref, mask = _preprocess_input(test, ref, mask) if mask is not None: diff --git a/modopt/math/stats.py b/modopt/math/stats.py index 19340754..858e0ade 100644 --- a/modopt/math/stats.py +++ b/modopt/math/stats.py @@ -1,20 +1,15 @@ # -*- coding: utf-8 -*- -"""STATISTICS ROUTINES +"""STATISTICS ROUTINES. This module contains methods for basic statistics. :Author: Samuel Farrens -:References: - -.. bibliography:: refs.bib - :filter: docname in docnames - """ import numpy as np -from scipy.stats import chi2 + try: from astropy.convolution import Gaussian2DKernel except ImportError: # pragma: no cover @@ -24,7 +19,7 @@ def gaussian_kernel(data_shape, sigma, norm='max'): - r"""Gaussian kernel + """Gaussian kernel. This method produces a Gaussian kerenal of a specified size and dispersion @@ -43,29 +38,36 @@ def gaussian_kernel(data_shape, sigma, norm='max'): numpy.ndarray Kernel + Raises + ------ + ImportError + If Astropy package not found + ValueError + For invalid norm + Examples -------- >>> from modopt.math.stats import gaussian_kernel >>> gaussian_kernel((3, 3), 1) - array([[ 0.36787944, 0.60653066, 0.36787944], - [ 0.60653066, 1. , 0.60653066], - [ 0.36787944, 0.60653066, 0.36787944]]) + array([[0.36787944, 0.60653066, 0.36787944], + [0.60653066, 1. , 0.60653066], + [0.36787944, 0.60653066, 0.36787944]]) >>> gaussian_kernel((3, 3), 1, norm='sum') - array([[ 0.07511361, 0.1238414 , 0.07511361], - [ 0.1238414 , 0.20417996, 0.1238414 ], - [ 0.07511361, 0.1238414 , 0.07511361]]) + array([[0.07511361, 0.1238414 , 0.07511361], + [0.1238414 , 0.20417996, 0.1238414 ], + [0.07511361, 0.1238414 , 0.07511361]]) """ - if not import_astropy: # pragma: no cover raise ImportError('Astropy package not found.') - if norm not in ('max', 'sum', 'none'): + if norm not in {'max', 'sum', 'none'}: raise ValueError('Invalid norm, options are "max", "sum" or "none".') - kernel = np.array(Gaussian2DKernel(sigma, x_size=data_shape[1], - y_size=data_shape[0])) + kernel = np.array( + Gaussian2DKernel(sigma, x_size=data_shape[1], y_size=data_shape[0]), + ) if norm == 'max': return kernel / np.max(kernel) @@ -77,14 +79,14 @@ def gaussian_kernel(data_shape, sigma, norm='max'): return kernel -def mad(data): - r"""Median absolute deviation +def mad(input_data): + r"""Median absolute deviation. This method calculates the median absolute deviation of the input data. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -94,6 +96,7 @@ def mad(data): Examples -------- + >>> import numpy as np >>> from modopt.math.stats import mad >>> a = np.arange(9).reshape(3, 3) >>> mad(a) @@ -112,12 +115,11 @@ def mad(data): numpy.median : median function used """ - - return np.median(np.abs(data - np.median(data))) + return np.median(np.abs(input_data - np.median(input_data))) def mse(data1, data2): - """Mean Squared Error + """Mean Squared Error. This method returns the Mean Squared Error (MSE) between two data sets. @@ -128,20 +130,25 @@ def mse(data1, data2): data2 : numpy.ndarray Second data set + Returns + ------- + float + Mean squared error + Examples -------- + >>> import numpy as np >>> from modopt.math.stats import mse >>> a = np.arange(9).reshape(3, 3) >>> mse(a, a + 2) 4.0 """ - return np.mean((data1 - data2) ** 2) def psnr(data1, data2, method='starck', max_pix=255): - r"""Peak Signal-to-Noise Ratio + r"""Peak Signal-to-Noise Ratio. This method calculates the Peak Signal-to-Noise Ratio between an two data sets @@ -162,15 +169,21 @@ def psnr(data1, data2, method='starck', max_pix=255): float PSNR value + Raises + ------ + ValueError + For invalid PSNR method + Examples -------- + >>> import numpy as np >>> from modopt.math.stats import psnr >>> a = np.arange(9).reshape(3, 3) >>> psnr(a, a + 2) 12.041199826559248 >>> psnr(a, a + 2, method='wiki') - 42.110203695399477 + 42.11020369539948 Notes ----- @@ -189,25 +202,24 @@ def psnr(data1, data2, method='starck', max_pix=255): 10\log_{10}(\mathrm{MSE})) """ - if method == 'starck': - - return (20 * np.log10((data1.shape[0] * np.abs(np.max(data1) - - np.min(data1))) / np.linalg.norm(data1 - data2))) + return ( + 20 * np.log10( + (data1.shape[0] * np.abs(np.max(data1) - np.min(data1))) + / np.linalg.norm(data1 - data2), + ) + ) elif method == 'wiki': + return (20 * np.log10(max_pix) - 10 * np.log10(mse(data1, data2))) - return (20 * np.log10(max_pix) - 10 * - np.log10(mse(data1, data2))) - - else: - - raise ValueError('Invalid PSNR method. Options are "starck" and ' - '"wiki"') + raise ValueError( + 'Invalid PSNR method. Options are "starck" and "wiki"', + ) def psnr_stack(data1, data2, metric=np.mean, method='starck'): - r"""Peak Signa-to-Noise for stack of images + """Peak Signa-to-Noise for stack of images. This method calculates the PSNRs for two stacks of 2D arrays. By default the metod returns the mean value of the PSNRs, but any other @@ -237,6 +249,7 @@ def psnr_stack(data1, data2, metric=np.mean, method='starck'): Examples -------- + >>> import numpy as np >>> from modopt.math.stats import psnr_stack >>> a = np.arange(18).reshape(2, 3, 3) >>> psnr_stack(a, a + 2) @@ -247,23 +260,24 @@ def psnr_stack(data1, data2, metric=np.mean, method='starck'): numpy.mean : default metric """ - if data1.ndim != 3 or data2.ndim != 3: raise ValueError('Input data must be a 3D np.ndarray') - return metric([psnr(i, j, method=method) for i, j in - zip(data1, data2)]) + return metric([ + psnr(i_elem, j_elem, method=method) + for i_elem, j_elem in zip(data1, data2) + ]) -def sigma_mad(data): - r"""Standard deviation from MAD +def sigma_mad(input_data): + r"""MAD Standard Deviation. This method calculates the standard deviation of the input data from the MAD. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -273,10 +287,11 @@ def sigma_mad(data): Examples -------- + >>> import numpy as np >>> from modopt.math.stats import sigma_mad >>> a = np.arange(9).reshape(3, 3) >>> sigma_mad(a) - 2.9651999999999998 + 2.9652 Notes ----- @@ -290,5 +305,4 @@ def sigma_mad(data): \sigma = 1.4826 \mathrm{MAD}(X) """ - - return 1.4826 * mad(data) + return 1.4826 * mad(input_data) diff --git a/modopt/opt/__init__.py b/modopt/opt/__init__.py index 6f926099..2fd3d747 100644 --- a/modopt/opt/__init__.py +++ b/modopt/opt/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""OPTIMISATION PROBLEM MODULES +"""OPTIMISATION PROBLEM MODULES. This module contains submodules for solving optimisation problems. @@ -9,5 +9,3 @@ """ __all__ = ['cost', 'gradient', 'linear', 'algorithms', 'proximity', 'reweight'] - -from . import * diff --git a/modopt/opt/algorithms.py b/modopt/opt/algorithms.py index 333f6791..ccd8fe21 100644 --- a/modopt/opt/algorithms.py +++ b/modopt/opt/algorithms.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -r"""OPTIMISATION ALGOTITHMS +r"""OPTIMISATION ALGOTITHMS. This module contains class implementations of various optimisation algoritms. @@ -41,25 +41,27 @@ * x_prox is used in place of :math:`\tilde{x}_{n+1}`. * x_temp is used for intermediate operations. -:References: - -.. bibliography:: refs.bib - :filter: docname in docnames - """ from inspect import getmro -from progressbar import ProgressBar + import numpy as np +from progressbar import ProgressBar + +from modopt.base import backend +from modopt.base.observable import MetricObserver, Observable from modopt.interface.errors import warn from modopt.opt.cost import costObj from modopt.opt.linear import Identity -# Package import -from ..base.observable import Observable, MetricObserver + +try: + import cupy as cp +except ImportError: # pragma: no cover + pass class SetUp(Observable): - """Algorithm Set-Up + r"""Algorithm Set-Up. This class contains methods for checking the set-up of an optimisation algotithm and produces warnings if they do not comply. @@ -69,7 +71,7 @@ class SetUp(Observable): metric_call_period : int, optional Metric call period (default is ``5``) metrics : dict, optional - Metrics to be used (default is ``{}``) + Metrics to be used (default is ``\{\}``) verbose : bool, optional Option for verbose output (default is ``False``) progress : bool, optional @@ -78,50 +80,123 @@ class SetUp(Observable): Generic step size parameter to override default algorithm parameter name (`e.g.` `step_size` will override the value set for `beta_param` in `ForwardBackward`) + use_gpu : bool, optional + Option to use available GPU """ - def __init__(self, metric_call_period=5, metrics={}, verbose=False, - progress=True, step_size=None, **dummy_kwargs): + def __init__( + self, + metric_call_period=5, + metrics=None, + verbose=False, + progress=True, + step_size=None, + use_gpu=False, + **dummy_kwargs, + ): self.converge = False self.verbose = verbose self.progress = progress self.metrics = metrics self.step_size = step_size - self._op_parents = ('GradParent', 'ProximityParent', 'LinearParent', - 'costObj') + self._op_parents = ( + 'GradParent', + 'ProximityParent', + 'LinearParent', + 'costObj', + ) self.metric_call_period = metric_call_period # Declaration of observers for metrics - Observable.__init__(self, ["cv_metrics"]) + super().__init__(['cv_metrics']) + + for name, dic in self.metrics.items(): + observer = MetricObserver( + name, + dic['metric'], + dic['mapping'], + dic['cst_kwargs'], + dic['early_stopping'], + ) + self.add_observer('cv_metrics', observer) + + # Check for GPU + if use_gpu: + if backend.gpu_compatibility['cupy']: + self.xp = cp + else: + warn( + 'CuPy is not installed, cannot run on GPU!' + + 'Running optimization on CPU.', + ) + self.xp = np + use_gpu = False + else: + self.xp = np + self.use_gpu = use_gpu + + @property + def metrics(self): + """Metrics.""" + return self._metrics + + @metrics.setter + def metrics(self, metrics): - for name, dic in metrics.items(): - observer = MetricObserver(name, dic['metric'], - dic['mapping'], - dic['cst_kwargs'], - dic['early_stopping']) - self.add_observer("cv_metrics", observer) + if isinstance(metrics, type(None)): + self._metrics = {} + elif not isinstance(metrics, dict): + raise TypeError( + 'Metrics must be a dictionary, not {0}.'.format(type(metrics)), + ) def any_convergence_flag(self): - """Check convergence flag + """Check convergence flag. Return if any matrices values matched the convergence criteria. + Returns + ------- + bool + True if any convergence criteria met + """ + return any( + obs.converge_flag for obs in self._observers['cv_metrics'] + ) - return any([obs.converge_flag for obs in - self._observers['cv_metrics']]) + def copy_data(self, input_data): + """Copy Data. - def _check_input_data(self, data): - """Check input data type + Set directive for copying data. + + Parameters + ---------- + input_data : numpy.ndarray + Input data + + Returns + ------- + numpy.ndarray + Copy of input data + + """ + if self.use_gpu: + return backend.move_to_device(input_data) + + return self.xp.copy(input_data) + + def _check_input_data(self, input_data): + """Check input data type. This method checks if the input data is a numpy array Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Raises @@ -130,18 +205,17 @@ def _check_input_data(self, data): For invalid input type """ - - if not isinstance(data, np.ndarray): + if not isinstance(input_data, self.xp.ndarray): raise TypeError('Input data must be a numpy array.') - def _check_param(self, param): - """Check algorithm parameters + def _check_param(self, param_val): + """Check algorithm parameters. This method checks if the specified algorithm parameters are floats Parameters ---------- - param : float + param_val : float Parameter value Raises @@ -150,12 +224,11 @@ def _check_param(self, param): For invalid input type """ - - if not isinstance(param, float): + if not isinstance(param_val, float): raise TypeError('Algorithm parameter must be a float value.') def _check_param_update(self, param_update): - """Check algorithm parameter update methods + """Check algorithm parameter update methods. This method checks if the specified algorithm parameters are floats @@ -170,14 +243,18 @@ def _check_param_update(self, param_update): For invalid input type """ + param_conditions = ( + not isinstance(param_update, type(None)) + and not callable(param_update) + ) - if (not isinstance(param_update, type(None)) and - not callable(param_update)): - raise TypeError('Algorithm parameter update must be a callabale ' - 'function.') + if param_conditions: + raise TypeError( + 'Algorithm parameter update must be a callabale function.', + ) def _check_operator(self, operator): - """Check set-Up + """Check set-Up. This method checks algorithm operator against the expected parent classes @@ -188,37 +265,36 @@ def _check_operator(self, operator): Algorithm operator to check """ - if not isinstance(operator, type(None)): - tree = [obj.__name__ for obj in getmro(operator.__class__)] + tree = [op_obj.__name__ for op_obj in getmro(operator.__class__)] - if not any([parent in tree for parent in self._op_parents]): - warn('{0} does not inherit an operator ' - 'parent.'.format(str(operator.__class__))) + if not any(parent in tree for parent in self._op_parents): + message = '{0} does not inherit an operator parent.' + warn(message.format(str(operator.__class__))) def _compute_metrics(self): - """Compute metrics during iteration + """Compute metrics during iteration. This method create the args necessary for metrics computation, then call the observers to compute metrics """ - kwargs = self.get_notify_observers_kwargs() self.notify_observers('cv_metrics', **kwargs) - def _iterations(self, max_iter, bar=None): - """Iterations + def _iterations(self, max_iter, progbar=None): + """Iterate method. + + Iterate the update step of the given algorithm. Parameters ---------- max_iter : int Maximum number of iterations - bar : progressbar.ProgressBar + progbar : progressbar.ProgressBar Progress bar (default is ``None``) """ - for idx in range(max_iter): self.idx = idx self._update() @@ -228,9 +304,14 @@ def _iterations(self, max_iter, bar=None): # We do not call metrics if metrics is empty or metric call # period is None if self.metrics and self.metric_call_period is not None: - if self.idx % self.metric_call_period == 0 or \ - self.idx == (max_iter - 1) or \ - self.converge: + + metric_conditions = ( + self.idx % self.metric_call_period == 0 + or self.idx == (max_iter - 1) + or self.converge, + ) + + if metric_conditions: self._compute_metrics() if self.converge: @@ -238,11 +319,11 @@ def _iterations(self, max_iter, bar=None): print(' - Converged!') break - if not isinstance(bar, type(None)): - bar.update(idx) + if not isinstance(progbar, type(None)): + progbar.update(idx) def _run_alg(self, max_iter): - """Run algorithm + """Run algorithm. Run the update step of a given algorithm up to the maximum number of iterations. @@ -253,16 +334,18 @@ def _run_alg(self, max_iter): Maximum number of iterations """ - if self.progress: - with ProgressBar(redirect_stdout=True, max_value=max_iter) as bar: - self._iterations(max_iter, bar=bar) + with ProgressBar( + redirect_stdout=True, + max_value=max_iter, + ) as progbar: + self._iterations(max_iter, progbar=progbar) else: self._iterations(max_iter) class FISTA(object): - r"""FISTA + """FISTA. This class is inherited by optimisation classes to speed up convergence The parameters for the modified FISTA are as described in :cite:`liang2018` @@ -303,16 +386,28 @@ class FISTA(object): """ - __restarting_strategies__ = [ - 'adaptive', 'adaptive-i', 'adaptive-1', # option 1 in alg 4 - 'adaptive-ii', 'adaptive-2', # option 2 in alg 4 + _restarting_strategies = ( + 'adaptive', # option 1 in alg 4 + 'adaptive-i', + 'adaptive-1', + 'adaptive-ii', # option 2 in alg 4 + 'adaptive-2', 'greedy', # alg 5 None, # no restarting - ] - - def __init__(self, restart_strategy=None, min_beta=None, s_greedy=None, - xi_restart=None, a_cd=None, p_lazy=1, q_lazy=1, r_lazy=4, - **kwargs): + ) + + def __init__( + self, + restart_strategy=None, + min_beta=None, + s_greedy=None, + xi_restart=None, + a_cd=None, + p_lazy=1, + q_lazy=1, + r_lazy=4, + **kwargs, + ): if isinstance(a_cd, type(None)): self.mode = 'regular' @@ -326,29 +421,39 @@ def __init__(self, restart_strategy=None, min_beta=None, s_greedy=None, self._n = 0 else: - raise ValueError('a_cd must either be None (for regular mode) or ' - 'a number > 2') - - if restart_strategy in self.__class__.__restarting_strategies__: - self._check_restart_params(restart_strategy, min_beta, s_greedy, - xi_restart) + raise ValueError( + 'a_cd must either be None (for regular mode) or a number > 2', + ) + + if restart_strategy in self._restarting_strategies: + self._check_restart_params( + restart_strategy, min_beta, s_greedy, xi_restart, + ) self.restart_strategy = restart_strategy self.min_beta = min_beta self.s_greedy = s_greedy self.xi_restart = xi_restart else: - raise ValueError('Restarting strategy must be one of {}.'.format( - ', '.join( - self.__class__.__restarting_strategies__))) + message = 'Restarting strategy must be one of {0}.' + raise ValueError( + message.format( + ', '.join(self._restarting_strategies), + ), + ) self._t_now = 1.0 self._t_prev = 1.0 - self._delta_0 = None + self._delta0 = None self._safeguard = False - def _check_restart_params(self, restart_strategy, min_beta, s_greedy, - xi_restart): - r"""Check restarting parameters + def _check_restart_params( + self, + restart_strategy, + min_beta, + s_greedy, + xi_restart, + ): + """Check restarting parameters. This method checks that the restarting parameters are set and satisfy the correct assumptions. It also checks that the current mode is @@ -384,20 +489,22 @@ def _check_restart_params(self, restart_strategy, min_beta, s_greedy, correct assumptions. """ - if restart_strategy is None: return True if self.mode != 'regular': - raise ValueError('Restarting strategies can only be used with ' - 'regular mode.') + raise ValueError( + 'Restarting strategies can only be used with regular mode.', + ) - greedy_params_check = (min_beta is None or s_greedy is None or - s_greedy <= 1) + greedy_params_check = ( + min_beta is None or s_greedy is None or s_greedy <= 1 + ) if restart_strategy == 'greedy' and greedy_params_check: - raise ValueError('You need a min_beta and an s_greedy > 1 for ' - 'greedy restart.') + raise ValueError( + 'You need a min_beta and an s_greedy > 1 for greedy restart.', + ) if xi_restart is None or xi_restart >= 1: raise ValueError('You need a xi_restart < 1 for restart.') @@ -405,7 +512,7 @@ def _check_restart_params(self, restart_strategy, min_beta, s_greedy, return True def is_restart(self, z_old, x_new, x_old): - r"""Check whether the algorithm needs to restart + """Check whether the algorithm needs to restart. This method implements the checks necessary to tell whether the algorithm needs to restart depending on the restarting strategy. @@ -432,28 +539,30 @@ def is_restart(self, z_old, x_new, x_old): :cite:`liang2018` """ + xp = backend.get_array_module(x_new) + if self.restart_strategy is None: return False - criterion = np.vdot(z_old - x_new, x_new - x_old) >= 0 + criterion = xp.vdot(z_old - x_new, x_new - x_old) >= 0 if criterion: if 'adaptive' in self.restart_strategy: self.r_lazy *= self.xi_restart - if self.restart_strategy in ['adaptive-ii', 'adaptive-2']: + if self.restart_strategy in {'adaptive-ii', 'adaptive-2'}: self._t_now = 1 if self.restart_strategy == 'greedy': - cur_delta = np.linalg.norm(x_new - x_old) - if self._delta_0 is None: - self._delta_0 = self.s_greedy * cur_delta + cur_delta = xp.linalg.norm(x_new - x_old) + if self._delta0 is None: + self._delta0 = self.s_greedy * cur_delta else: - self._safeguard = cur_delta >= self._delta_0 + self._safeguard = cur_delta >= self._delta0 return criterion def update_beta(self, beta): - r"""Update beta + """Update beta. This method updates beta only in the case of safeguarding (should only be done in the greedy restarting strategy). @@ -469,7 +578,6 @@ def update_beta(self, beta): The new value for the beta parameter """ - if self._safeguard: beta *= self.xi_restart beta = max(beta, self.min_beta) @@ -477,10 +585,17 @@ def update_beta(self, beta): return beta def update_lambda(self, *args, **kwargs): - r"""Update lambda + """Update lambda. This method updates the value of lambda + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float @@ -491,7 +606,6 @@ def update_lambda(self, *args, **kwargs): Implements steps 3 and 4 from algoritm 10.7 in :cite:`bauschke2009` """ - if self.restart_strategy == 'greedy': return 2 @@ -499,8 +613,8 @@ def update_lambda(self, *args, **kwargs): self._t_prev = self._t_now if self.mode == 'regular': - self._t_now = (self.p_lazy + np.sqrt(self.r_lazy * - self._t_prev ** 2 + self.q_lazy)) * 0.5 + sqrt_part = self.r_lazy * self._t_prev ** 2 + self.q_lazy + self._t_now = self.p_lazy + np.sqrt(sqrt_part) * 0.5 elif self.mode == 'CD': self._t_now = (self._n + self.a_cd - 1) / self.a_cd @@ -510,7 +624,7 @@ def update_lambda(self, *args, **kwargs): class ForwardBackward(SetUp): - r"""Forward-Backward optimisation + """Forward-Backward optimisation. This class implements standard forward-backward optimisation with an the option to use the FISTA speed-up @@ -550,23 +664,39 @@ class ForwardBackward(SetUp): """ - def __init__(self, x, grad, prox, cost='auto', beta_param=1.0, - lambda_param=1.0, beta_update=None, lambda_update='fista', - auto_iterate=True, metric_call_period=5, metrics={}, - linear=None, **kwargs): + def __init__( + self, + x, + grad, + prox, + cost='auto', + beta_param=1.0, + lambda_param=1.0, + beta_update=None, + lambda_update='fista', + auto_iterate=True, + metric_call_period=5, + metrics=None, + linear=None, + **kwargs, + ): # Set default algorithm properties - super(ForwardBackward, self).__init__( - metric_call_period=metric_call_period, - metrics=metrics, **kwargs) + super().__init__( + metric_call_period=metric_call_period, + metrics=metrics, + **kwargs, + ) # Set the initial variable values self._check_input_data(x) - self._x_old = np.copy(x) - self._z_old = np.copy(x) + self._x_old = self.copy_data(x) + self._z_old = self.copy_data(x) # Set the algorithm operators - (self._check_operator(operator) for operator in (grad, prox, cost)) + for operator in (grad, prox, cost): + self._check_operator(operator) + self._grad = grad self._prox = prox self._linear = linear @@ -577,15 +707,18 @@ def __init__(self, x, grad, prox, cost='auto', beta_param=1.0, self._cost_func = cost # Check if there is a linear op, needed for metrics in the FB algoritm - if metrics != {} and self._linear is None: - raise ValueError('When using metrics, you must pass a linear ' - 'operator') + if metrics and self._linear is None: + raise ValueError( + 'When using metrics, you must pass a linear operator', + ) if self._linear is None: self._linear = Identity() # Set the algorithm parameters - (self._check_param(param) for param in (beta_param, lambda_param)) + for param_val in (beta_param, lambda_param): + self._check_param(param_val) + self._beta = self.step_size or beta_param self._lambda = lambda_param @@ -607,13 +740,12 @@ def __init__(self, x, grad, prox, cost='auto', beta_param=1.0, self.iterate() def _update_param(self): - r"""Update parameters + """Update parameters. This method updates the values of the algorthm parameters with the methods provided """ - # Update the gamma parameter. if not isinstance(self._beta_update, type(None)): self._beta = self._beta_update(self._beta) @@ -623,7 +755,7 @@ def _update_param(self): self._lambda = self._lambda_update(self._lambda) def _update(self): - r"""Update + """Update. This method updates the current reconstruction @@ -632,7 +764,6 @@ def _update(self): Implements algorithm 10.7 (or 10.5) from :cite:`bauschke2009` """ - # Step 1 from alg.10.7. self._grad.get_grad(self._z_old) y_old = self._z_old - self._beta * self._grad.grad @@ -648,19 +779,21 @@ def _update(self): self._z_new = self._x_new # Update old values for next iteration. - np.copyto(self._x_old, self._x_new) - np.copyto(self._z_old, self._z_new) + self.xp.copyto(self._x_old, self._x_new) + self.xp.copyto(self._z_old, self._z_new) # Update parameter values for next iteration. self._update_param() # Test cost function for convergence. if self._cost_func: - self.converge = self.any_convergence_flag() or \ - self._cost_func.get_cost(self._x_new) + self.converge = ( + self.any_convergence_flag() + or self._cost_func.get_cost(self._x_new), + ) def iterate(self, max_iter=150): - r"""Iterate + """Iterate. This method calls update until either convergence criteria is met or the maximum number of iterations is reached @@ -671,7 +804,6 @@ def iterate(self, max_iter=150): Maximum number of iterations (default is ``150``) """ - self._run_alg(max_iter) # retrieve metrics results @@ -680,7 +812,7 @@ def iterate(self, max_iter=150): self.x_final = self._z_new def get_notify_observers_kwargs(self): - """Notify observers + """Notify observers. Return the mapping between the metrics call and the iterated variables. @@ -691,17 +823,19 @@ def get_notify_observers_kwargs(self): The mapping between the iterated variables """ - return {'x_new': self._linear.adj_op(self._x_new), - 'z_new': self._z_new, 'idx': self.idx} + return { + 'x_new': self._linear.adj_op(self._x_new), + 'z_new': self._z_new, + 'idx': self.idx, + } def retrieve_outputs(self): - """Retireve outputs + """Retireve outputs. Declare the outputs of the algorithms as attributes: x_final, y_final, metrics. """ - metrics = {} for obs in self._observers['cv_metrics']: metrics[obs.name] = obs.retrieve_metrics() @@ -709,7 +843,7 @@ def retrieve_outputs(self): class GenForwardBackward(SetUp): - r"""Generalized Forward-Backward Algorithm + """Generalized Forward-Backward Algorithm. This class implements algorithm 1 from :cite:`raguet2011` @@ -749,25 +883,41 @@ class GenForwardBackward(SetUp): """ - def __init__(self, x, grad, prox_list, cost='auto', gamma_param=1.0, - lambda_param=1.0, gamma_update=None, lambda_update=None, - weights=None, auto_iterate=True, metric_call_period=5, - metrics={}, linear=None, **kwargs): + def __init__( + self, + x, + grad, + prox_list, + cost='auto', + gamma_param=1.0, + lambda_param=1.0, + gamma_update=None, + lambda_update=None, + weights=None, + auto_iterate=True, + metric_call_period=5, + metrics=None, + linear=None, + **kwargs, + ): # Set default algorithm properties - super(GenForwardBackward, self).__init__( - metric_call_period=metric_call_period, - metrics=metrics, **kwargs) + super().__init__( + metric_call_period=metric_call_period, + metrics=metrics, + **kwargs, + ) # Set the initial variable values self._check_input_data(x) - self._x_old = np.copy(x) + self._x_old = self.xp.copy(x) # Set the algorithm operators - (self._check_operator(operator) for operator in [grad, cost] + - prox_list) + for operator in [grad, cost] + prox_list: + self._check_operator(operator) + self._grad = grad - self._prox_list = np.array(prox_list) + self._prox_list = self.xp.array(prox_list) self._linear = linear if cost == 'auto': @@ -776,21 +926,25 @@ def __init__(self, x, grad, prox_list, cost='auto', gamma_param=1.0, self._cost_func = cost # Check if there is a linear op, needed for metrics in the FB algoritm - if metrics != {} and self._linear is None: - raise ValueError('When using metrics, you must pass a linear ' - 'operator') + if metrics and self._linear is None: + raise ValueError( + 'When using metrics, you must pass a linear operator', + ) if self._linear is None: self._linear = Identity() # Set the algorithm parameters - (self._check_param(param) for param in (gamma_param, lambda_param)) + for param_val in (gamma_param, lambda_param): + self._check_param(param_val) + self._gamma = self.step_size or gamma_param self._lambda_param = lambda_param # Set the algorithm parameter update methods - (self._check_param_update(param_update) for param_update in - (gamma_update, lambda_update)) + for param_update in (gamma_update, lambda_update): + self._check_param_update(param_update) + self._gamma_update = gamma_update self._lambda_update = lambda_update @@ -798,14 +952,16 @@ def __init__(self, x, grad, prox_list, cost='auto', gamma_param=1.0, self._set_weights(weights) # Set initial z - self._z = np.array([self._x_old for i in range(self._prox_list.size)]) + self._z = self.xp.array([ + self._x_old for i in range(self._prox_list.size) + ]) # Automatically run the algorithm if auto_iterate: self.iterate() def _set_weights(self, weights): - """Set weights + """Set weights. This method sets weights on each of the proximty operators provided @@ -822,37 +978,42 @@ def _set_weights(self, weights): If weights do not sum to one """ - if isinstance(weights, type(None)): - weights = np.repeat(1.0 / self._prox_list.size, - self._prox_list.size) + weights = self.xp.repeat( + 1.0 / self._prox_list.size, + self._prox_list.size, + ) elif not isinstance(weights, (list, tuple, np.ndarray)): raise TypeError('Weights must be provided as a list.') - weights = np.array(weights) + weights = self.xp.array(weights) if not np.issubdtype(weights.dtype, np.floating): raise ValueError('Weights must be list of float values.') if weights.size != self._prox_list.size: - raise ValueError('The number of weights must match the number of ' - 'proximity operators.') + raise ValueError( + 'The number of weights must match the number of proximity ' + + 'operators.', + ) - if np.sum(weights) != 1.0: - raise ValueError('Proximity operator weights must sum to 1.0.' - 'Current sum of weights = ' + - str(np.sum(weights))) + expected_weight_sum = 1.0 + + if self.xp.sum(weights) != expected_weight_sum: + raise ValueError( + 'Proximity operator weights must sum to 1.0. Current sum of ' + + 'weights = {0}'.format(self.xp.sum(weights)), + ) self._weights = weights def _update_param(self): - r"""Update parameters + """Update parameters. This method updates the values of the algorthm parameters with the methods provided """ - # Update the gamma parameter. if not isinstance(self._gamma_update, type(None)): self._gamma = self._gamma_update(self._gamma) @@ -862,7 +1023,7 @@ def _update_param(self): self._lambda_param = self._lambda_update(self._lambda_param) def _update(self): - r"""Update + """Update. This method updates the current reconstruction @@ -871,24 +1032,28 @@ def _update(self): Implements algorithm 1 from :cite:`raguet2011` """ - # Calculate gradient for current iteration. self._grad.get_grad(self._x_old) # Update z values. for i in range(self._prox_list.size): - z_temp = (2 * self._x_old - self._z[i] - self._gamma * - self._grad.grad) - z_prox = self._prox_list[i].op(z_temp, extra_factor=self._gamma / - self._weights[i]) + z_temp = ( + 2 * self._x_old - self._z[i] - self._gamma * self._grad.grad + ) + z_prox = self._prox_list[i].op( + z_temp, + extra_factor=self._gamma / self._weights[i], + ) self._z[i] += self._lambda_param * (z_prox - self._x_old) # Update current reconstruction. - self._x_new = np.sum([z_i * w_i for z_i, w_i in - zip(self._z, self._weights)], axis=0) + self._x_new = self.xp.sum( + [z_i * w_i for z_i, w_i in zip(self._z, self._weights)], + axis=0, + ) # Update old values for next iteration. - np.copyto(self._x_old, self._x_new) + self.xp.copyto(self._x_old, self._x_new) # Update parameter values for next iteration. self._update_param() @@ -898,7 +1063,7 @@ def _update(self): self.converge = self._cost_func.get_cost(self._x_new) def iterate(self, max_iter=150): - r"""Iterate + """Iterate. This method calls update until either convergence criteria is met or the maximum number of iterations is reached. @@ -917,28 +1082,30 @@ def iterate(self, max_iter=150): self.x_final = self._x_new def get_notify_observers_kwargs(self): - """Notify observers + """Notify observers. Return the mapping between the metrics call and the iterated variables. - Return - ---------- + Returns + ------- dict The mapping between the iterated variables """ - return {'x_new': self._linear.adj_op(self._x_new), - 'z_new': self._z, 'idx': self.idx} + return { + 'x_new': self._linear.adj_op(self._x_new), + 'z_new': self._z, + 'idx': self.idx, + } def retrieve_outputs(self): - """Retrieve outputs + """Retrieve outputs. Declare the outputs of the algorithms as attributes: x_final, y_final, metrics. """ - metrics = {} for obs in self._observers['cv_metrics']: metrics[obs.name] = obs.retrieve_metrics() @@ -946,7 +1113,7 @@ def retrieve_outputs(self): class Condat(SetUp): - r"""Condat optimisation + """Condat optimisation. This class implements algorithm 3.1 from :cite:`condat2013` @@ -1000,24 +1167,48 @@ class Condat(SetUp): """ - def __init__(self, x, y, grad, prox, prox_dual, linear=None, cost='auto', - reweight=None, rho=0.5, sigma=1.0, tau=1.0, rho_update=None, - sigma_update=None, tau_update=None, auto_iterate=True, - max_iter=150, n_rewightings=1, metric_call_period=5, - metrics={}, **kwargs): + def __init__( + self, + x, + y, + grad, + prox, + prox_dual, + linear=None, + cost='auto', + reweight=None, + rho=0.5, + sigma=1.0, + tau=1.0, + rho_update=None, + sigma_update=None, + tau_update=None, + auto_iterate=True, + max_iter=150, + n_rewightings=1, + metric_call_period=5, + metrics=None, + **kwargs, + ): # Set default algorithm properties - super(Condat, self).__init__(metric_call_period=metric_call_period, - metrics=metrics, **kwargs) + super().__init__( + metric_call_period=metric_call_period, + metrics=metrics, + **kwargs, + ) # Set the initial variable values - (self._check_input_data(data) for data in (x, y)) - self._x_old = np.copy(x) - self._y_old = np.copy(y) + for input_data in (x, y): + self._check_input_data(input_data) + + self._x_old = self.xp.copy(x) + self._y_old = self.xp.copy(y) # Set the algorithm operators - (self._check_operator(operator) for operator in (grad, prox, prox_dual, - linear, cost)) + for operator in (grad, prox, prox_dual, linear, cost): + self._check_operator(operator) + self._grad = grad self._prox = prox self._prox_dual = prox_dual @@ -1027,20 +1218,26 @@ def __init__(self, x, y, grad, prox, prox_dual, linear=None, cost='auto', else: self._linear = linear if cost == 'auto': - self._cost_func = costObj([self._grad, self._prox, - self._prox_dual]) + self._cost_func = costObj([ + self._grad, + self._prox, + self._prox_dual, + ]) else: self._cost_func = cost # Set the algorithm parameters - (self._check_param(param) for param in (rho, sigma, tau)) + for param_val in (rho, sigma, tau): + self._check_param(param_val) + self._rho = rho self._sigma = sigma self._tau = self.step_size or tau # Set the algorithm parameter update methods - (self._check_param_update(param_update) for param_update in - (rho_update, sigma_update, tau_update)) + for param_update in (rho_update, sigma_update, tau_update): + self._check_param_update(param_update) + self._rho_update = rho_update self._sigma_update = sigma_update self._tau_update = tau_update @@ -1050,13 +1247,12 @@ def __init__(self, x, y, grad, prox, prox_dual, linear=None, cost='auto', self.iterate(max_iter=max_iter, n_rewightings=n_rewightings) def _update_param(self): - r"""Update parameters + """Update parameters. This method updates the values of the algorthm parameters with the methods provided """ - # Update relaxation parameter. if not isinstance(self._rho_update, type(None)): self._rho = self._rho_update(self._rho) @@ -1070,7 +1266,7 @@ def _update_param(self): self._tau = self._tau_update(self._tau) def _update(self): - r"""Update + """Update. This method updates the current reconstruction @@ -1084,15 +1280,24 @@ def _update(self): # Step 1 from eq.9. self._grad.get_grad(self._x_old) - x_prox = self._prox.op(self._x_old - self._tau * self._grad.grad - - self._tau * self._linear.adj_op(self._y_old)) + x_prox = self._prox.op( + self._x_old - self._tau * self._grad.grad - self._tau + * self._linear.adj_op(self._y_old), + ) # Step 2 from eq.9. - y_temp = (self._y_old + self._sigma * - self._linear.op(2 * x_prox - self._x_old)) - - y_prox = (y_temp - self._sigma * self._prox_dual.op(y_temp / - self._sigma, extra_factor=(1.0 / self._sigma))) + y_temp = ( + self._y_old + self._sigma + * self._linear.op(2 * x_prox - self._x_old) + ) + + y_prox = ( + y_temp - self._sigma + * self._prox_dual.op( + y_temp / self._sigma, + extra_factor=(1.0 / self._sigma), + ) + ) # Step 3 from eq.9. self._x_new = self._rho * x_prox + (1 - self._rho) * self._x_old @@ -1101,19 +1306,21 @@ def _update(self): del x_prox, y_prox, y_temp # Update old values for next iteration. - np.copyto(self._x_old, self._x_new) - np.copyto(self._y_old, self._y_new) + self.xp.copyto(self._x_old, self._x_new) + self.xp.copyto(self._y_old, self._y_new) # Update parameter values for next iteration. self._update_param() # Test cost function for convergence. if self._cost_func: - self.converge = self.any_convergence_flag() or\ - self._cost_func.get_cost(self._x_new, self._y_new) + self.converge = ( + self.any_convergence_flag() + or self._cost_func.get_cost(self._x_new, self._y_new) + ) def iterate(self, max_iter=150, n_rewightings=1): - r"""Iterate + """Iterate. This method calls update until either convergence criteria is met or the maximum number of iterations is reached @@ -1126,11 +1333,10 @@ def iterate(self, max_iter=150, n_rewightings=1): Number of reweightings to perform (default is ``1``) """ - self._run_alg(max_iter) if not isinstance(self._reweight, type(None)): - for k in range(n_rewightings): + for _ in range(n_rewightings): self._reweight.reweight(self._linear.op(self._x_new)) self._run_alg(max_iter) @@ -1141,7 +1347,7 @@ def iterate(self, max_iter=150, n_rewightings=1): self.y_final = self._y_new def get_notify_observers_kwargs(self): - """Notify observers + """Notify observers. Return the mapping between the metrics call and the iterated variables. @@ -1155,13 +1361,12 @@ def get_notify_observers_kwargs(self): return {'x_new': self._x_new, 'y_new': self._y_new, 'idx': self.idx} def retrieve_outputs(self): - """Retrieve outputs + """Retrieve outputs. Declare the outputs of the algorithms as attributes: x_final, y_final, metrics. """ - metrics = {} for obs in self._observers['cv_metrics']: metrics[obs.name] = obs.retrieve_metrics() @@ -1169,7 +1374,7 @@ def retrieve_outputs(self): class POGM(SetUp): - r"""Proximal Optimised Gradient Method + """Proximal Optimised Gradient Method. This class implements algorithm 3 from :cite:`kim2017` @@ -1211,23 +1416,46 @@ class POGM(SetUp): SetUp : parent class """ - def __init__(self, u, x, y, z, grad, prox, cost='auto', linear=None, - beta_param=1.0, sigma_bar=1.0, auto_iterate=True, - metric_call_period=5, metrics={}, **kwargs): + + def __init__( + self, + u, + x, + y, + z, + grad, + prox, + cost='auto', + linear=None, + beta_param=1.0, + sigma_bar=1.0, + auto_iterate=True, + metric_call_period=5, + metrics=None, + **kwargs, + ): # Set default algorithm properties - super(POGM, self).__init__(metric_call_period=metric_call_period, - metrics=metrics, linear=linear, **kwargs) + super().__init__( + metric_call_period=metric_call_period, + metrics=metrics, + linear=linear, + **kwargs, + ) # set the initial variable values - (self._check_input_data(data) for data in (u, x, y, z)) - self._u_old = np.copy(u) - self._x_old = np.copy(x) - self._y_old = np.copy(y) - self._z = np.copy(z) + for input_data in (u, x, y, z): + self._check_input_data(input_data) + + self._u_old = self.xp.copy(u) + self._x_old = self.xp.copy(x) + self._y_old = self.xp.copy(y) + self._z = self.xp.copy(z) # Set the algorithm operators - (self._check_operator(operator) for operator in (grad, prox, cost)) + for operator in (grad, prox, cost): + self._check_operator(operator) + self._grad = grad self._prox = prox self._linear = linear @@ -1235,16 +1463,22 @@ def __init__(self, u, x, y, z, grad, prox, cost='auto', linear=None, self._cost_func = costObj([self._grad, self._prox]) else: self._cost_func = cost + # If linear is None, make it Identity for call of metrics if self._linear is None: self._linear = Identity() + # Set the algorithm parameters - (self._check_param(param) for param in (beta_param, sigma_bar)) - if not (0 <= sigma_bar <= 1): + for param_val in (beta_param, sigma_bar): + self._check_param(param_val) + if sigma_bar < 0 or sigma_bar > 1: raise ValueError('The sigma bar parameter needs to be in [0, 1]') + self._beta = self.step_size or beta_param self._sigma_bar = sigma_bar - self._xi = self._sigma = self._t_old = 1.0 + self._xi = 1.0 + self._sigma = 1.0 + self._t_old = 1.0 self._grad.get_grad(self._x_old) self._g_old = self._grad.grad @@ -1253,7 +1487,7 @@ def __init__(self, u, x, y, z, grad, prox, cost='auto', linear=None, self.iterate() def _update(self): - r"""Update + """Update. This method updates the current reconstruction @@ -1267,7 +1501,7 @@ def _update(self): self._u_new = self._x_old - self._beta * self._grad.grad # Step 5 from alg. 3 - self._t_new = 0.5 * (1 + np.sqrt(1 + 4 * self._t_old**2)) + self._t_new = 0.5 * (1 + self.xp.sqrt(1 + 4 * self._t_old ** 2)) # Step 6 from alg. 3 t_shifted_ratio = (self._t_old - 1) / self._t_new @@ -1292,29 +1526,33 @@ def _update(self): self._y_new = self._x_old - self._beta * self._g_new # Step 11 from alg. 3 - restart_crit = np.vdot(- self._g_new, self._y_new - self._y_old) < 0 + restart_crit = ( + self.xp.vdot(-self._g_new, self._y_new - self._y_old) < 0 + ) if restart_crit: self._t_new = 1 self._sigma = 1 # Step 13 from alg. 3 - elif np.vdot(self._g_new, self._g_old) < 0: + elif self.xp.vdot(self._g_new, self._g_old) < 0: self._sigma *= self._sigma_bar # updating variables self._t_old = self._t_new - np.copyto(self._u_old, self._u_new) - np.copyto(self._x_old, self._x_new) - np.copyto(self._g_old, self._g_new) - np.copyto(self._y_old, self._y_new) + self.xp.copyto(self._u_old, self._u_new) + self.xp.copyto(self._x_old, self._x_new) + self.xp.copyto(self._g_old, self._g_new) + self.xp.copyto(self._y_old, self._y_new) # Test cost function for convergence. if self._cost_func: - self.converge = self.any_convergence_flag() or \ - self._cost_func.get_cost(self._x_new) + self.converge = ( + self.any_convergence_flag() + or self._cost_func.get_cost(self._x_new), + ) def iterate(self, max_iter=150): - r"""Iterate + """Iterate. This method calls update until either convergence criteria is met or the maximum number of iterations is reached. @@ -1325,7 +1563,6 @@ def iterate(self, max_iter=150): Maximum number of iterations (default is ``150``) """ - self._run_alg(max_iter) # retrieve metrics results @@ -1334,7 +1571,7 @@ def iterate(self, max_iter=150): self.x_final = self._x_new def get_notify_observers_kwargs(self): - """Notify observers + """Notify observers. Return the mapping between the metrics call and the iterated variables. @@ -1357,13 +1594,12 @@ def get_notify_observers_kwargs(self): } def retrieve_outputs(self): - """Retrieve outputs + """Retrieve outputs. Declare the outputs of the algorithms as attributes: x_final, y_final, metrics. """ - metrics = {} for obs in self._observers['cv_metrics']: metrics[obs.name] = obs.retrieve_metrics() diff --git a/modopt/opt/cost.py b/modopt/opt/cost.py index 703f7946..e5690831 100644 --- a/modopt/opt/cost.py +++ b/modopt/opt/cost.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""COST FUNCTIONS +"""COST FUNCTIONS. This module contains classes of different cost functions for optimization. @@ -9,13 +9,14 @@ """ import numpy as np + +from modopt.base.backend import get_array_module from modopt.base.types import check_callable from modopt.plot.cost_plot import plotCost class costObj(object): - - r"""Generic cost function object + """Generic cost function object. This class updates the cost according to the input cost functio class and tests for convergence @@ -67,20 +68,19 @@ class costObj(object): - COST: 8 False - >>> inst.get_cost(2) - - ITERATION: 4 - - COST: 8 - - - CONVERGENCE TEST - - - CHANGE IN COST: 0.0 - - True """ - def __init__(self, operators, initial_cost=1e6, tolerance=1e-4, - cost_interval=1, test_range=4, verbose=True, - plot_output=None): + def __init__( + self, + operators, + initial_cost=1e6, + tolerance=1e-4, + cost_interval=1, + test_range=4, + verbose=True, + plot_output=None, + ): self._operators = operators if not isinstance(operators, type(None)): @@ -96,22 +96,23 @@ def __init__(self, operators, initial_cost=1e6, tolerance=1e-4, self._verbose = verbose def _check_operators(self): - """Check Operators + """Check Operators. This method checks if the input operators have a `cost` method Raises ------ - ValueError + TypeError For invalid operators type ValueError For operators without `cost` method """ - if not isinstance(self._operators, (list, tuple, np.ndarray)): - raise TypeError(('Input operators must be provided as a list, ' - 'not {}').format(type(self._operators))) + message = ( + 'Input operators must be provided as a list, not {0}' + ) + raise TypeError(message.format(type(self._operators))) for op in self._operators: if not hasattr(op, 'cost'): @@ -119,7 +120,7 @@ def _check_operators(self): op.cost = check_callable(op.cost) def _check_cost(self): - """Check cost function + """Check cost function. This method tests the cost function for convergence in the specified interval of iterations using the last n (test_range) cost values @@ -130,22 +131,30 @@ def _check_cost(self): Result of the convergence test """ - # Add current cost value to the test list self._test_list.append(self.cost) + xp = get_array_module(self.cost) + # Check if enough cost values have been collected if len(self._test_list) == self._test_range: # The mean of the first half of the test list - t1 = np.mean(self._test_list[len(self._test_list) // 2:], axis=0) + t1 = xp.mean( + xp.array(self._test_list[len(self._test_list) // 2:]), + axis=0, + ) # The mean of the second half of the test list - t2 = np.mean(self._test_list[:len(self._test_list) // 2], axis=0) + t2 = xp.mean( + xp.array(self._test_list[:len(self._test_list) // 2]), + axis=0, + ) # Calculate the change across the test list - if not np.around(t1, decimals=16): - cost_diff = 0.0 + if xp.around(t1, decimals=16): + cost_diff = (xp.linalg.norm(t1 - t2) / xp.linalg.norm(t1)) else: - cost_diff = (np.linalg.norm(t1 - t2) / np.linalg.norm(t1)) + cost_diff = 0 + # Reset the test list self._test_list = [] @@ -157,14 +166,19 @@ def _check_cost(self): # Check for convergence return cost_diff <= self._tolerance - else: - - return False + return False def _calc_cost(self, *args, **kwargs): - """Calculate the cost + """Calculate the cost. - This method calculates the cost from each of the input operators + This method calculates the cost from each of the input operators. + + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments Returns ------- @@ -172,13 +186,19 @@ def _calc_cost(self, *args, **kwargs): Cost """ - return np.sum([op.cost(*args, **kwargs) for op in self._operators]) def get_cost(self, *args, **kwargs): - """Get cost function + """Get cost function. - This method calculates the current cost and tests for convergence + This method calculates the current cost and tests for convergence. + + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments Returns ------- @@ -186,15 +206,16 @@ def get_cost(self, *args, **kwargs): Result of the convergence test """ - # Check if the cost should be calculated - if self._cost_interval is None or \ - self._iteration % self._cost_interval: + test_conditions = ( + self._cost_interval is None + or self._iteration % self._cost_interval + ) + if test_conditions: test_result = False else: - if self._verbose: print(' - ITERATION:', self._iteration) @@ -215,10 +236,9 @@ def get_cost(self, *args, **kwargs): return test_result def plot_cost(self): # pragma: no cover - """Plot the cost function + """Plot the cost function. This method plots the cost function as function of iteration number """ - plotCost(self._cost_list, self._plot_output) diff --git a/modopt/opt/gradient.py b/modopt/opt/gradient.py index 98f07891..b949a7eb 100644 --- a/modopt/opt/gradient.py +++ b/modopt/opt/gradient.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""GRADIENT CLASSES +"""GRADIENT CLASSES. This module contains classses for defining algorithm gradients. Based on work by Yinghao Ge and Fred Ngole. @@ -10,18 +10,19 @@ """ import numpy as np + from modopt.base.types import check_callable, check_float, check_npndarray class GradParent(object): - r"""Gradient Parent Class + """Gradient Parent Class. This class defines the basic methods that will be inherited by specific gradient classes Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray The observed data op : function The operator @@ -38,30 +39,39 @@ class GradParent(object): Examples -------- + >>> import numpy as np >>> from modopt.opt.gradient import GradParent >>> y = np.arange(9).reshape(3, 3).astype(float) >>> g = GradParent(y, lambda x: x ** 2, lambda x: x ** 3) >>> g.op(y) - array([[ 0., 1., 4.], - [ 9., 16., 25.], - [ 36., 49., 64.]]) + array([[ 0., 1., 4.], + [ 9., 16., 25.], + [36., 49., 64.]]) >>> g.trans_op(y) - array([[ 0., 1., 8.], - [ 27., 64., 125.], - [ 216., 343., 512.]]) + array([[ 0., 1., 8.], + [ 27., 64., 125.], + [216., 343., 512.]]) >>> g.trans_op_op(y) - array([[ 0.00000000e+00, 1.00000000e+00, 6.40000000e+01], - [ 7.29000000e+02, 4.09600000e+03, 1.56250000e+04], - [ 4.66560000e+04, 1.17649000e+05, 2.62144000e+05]]) + array([[0.00000e+00, 1.00000e+00, 6.40000e+01], + [7.29000e+02, 4.09600e+03, 1.56250e+04], + [4.66560e+04, 1.17649e+05, 2.62144e+05]]) """ - def __init__(self, data, op, trans_op, get_grad=None, cost=None, - data_type=None, verbose=True): + def __init__( + self, + input_data, + op, + trans_op, + get_grad=None, + cost=None, + data_type=None, + verbose=True, + ): self.verbose = verbose self._grad_data_type = data_type - self.obs_data = data + self.obs_data = input_data self.op = op self.trans_op = trans_op @@ -72,35 +82,26 @@ def __init__(self, data, op, trans_op, get_grad=None, cost=None, @property def obs_data(self): - """Observed Data - - Raises - ------ - TypeError - For invalid input type - - """ - + """Observed Data.""" return self._obs_data @obs_data.setter - def obs_data(self, data): + def obs_data(self, input_data): - if self._grad_data_type in (float, np.floating): - data = check_float(data) - check_npndarray(data, dtype=self._grad_data_type, writeable=False, - verbose=self.verbose) + if self._grad_data_type in {float, np.floating}: + input_data = check_float(input_data) + check_npndarray( + input_data, + dtype=self._grad_data_type, + writeable=False, + verbose=self.verbose, + ) - self._obs_data = data + self._obs_data = input_data @property def op(self): - """Operator - - This method defines the operator - - """ - + """Operator.""" return self._op @op.setter @@ -110,12 +111,7 @@ def op(self, operator): @property def trans_op(self): - """Transpose Operator - - This method defines the transpose operator - - """ - + """Transpose operator.""" return self._trans_op @trans_op.setter @@ -125,12 +121,7 @@ def trans_op(self, operator): @property def get_grad(self): - """Get Gradient - - This method defines the calculation of the gradient - - """ - + """Get gradient value.""" return self._get_grad @get_grad.setter @@ -140,30 +131,19 @@ def get_grad(self, method): @property def grad(self): - """Gradient - - The gradient value - - """ - + """Gradient value.""" return self._grad @grad.setter - def grad(self, value): + def grad(self, input_value): - if self._grad_data_type in (float, np.floating): - value = check_float(value) - self._grad = value + if self._grad_data_type in {float, np.floating}: + input_value = check_float(input_value) + self._grad = input_value @property def cost(self): - """Cost Contribution - - This method defines the proximity operator's contribution to the total - cost - - """ - + """Cost contribution.""" return self._cost @cost.setter @@ -171,15 +151,15 @@ def cost(self, method): self._cost = check_callable(method) - def trans_op_op(self, data): - r"""Transpose Operation of the Operator + def trans_op_op(self, input_data): + r"""Transpose Operation of the Operator. This method calculates the action of the transpose operator on the action of the operator on the data Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -195,26 +175,33 @@ def trans_op_op(self, data): \mathbf{H}^T(\mathbf{H}\mathbf{x}) """ - - return self.trans_op(self.op(data)) + return self.trans_op(self.op(input_data)) class GradBasic(GradParent): - r"""Basic Gradient Class + """Basic Gradient Class. This class defines the gradient calculation and costs methods for - common inverse problems + common inverse problems. + + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments Examples -------- + >>> import numpy as np >>> from modopt.opt.gradient import GradBasic >>> y = np.arange(9).reshape(3, 3).astype(float) >>> g = GradBasic(y, lambda x: x ** 2, lambda x: x ** 3) >>> g.get_grad(y) >>> g.grad - array([[ 0.00000000e+00, 0.00000000e+00, 8.00000000e+00], - [ 2.16000000e+02, 1.72800000e+03, 8.00000000e+03], - [ 2.70000000e+04, 7.40880000e+04, 1.75616000e+05]]) + array([[0.00000e+00, 0.00000e+00, 8.00000e+00], + [2.16000e+02, 1.72800e+03, 8.00000e+03], + [2.70000e+04, 7.40880e+04, 1.75616e+05]]) See Also -------- @@ -224,18 +211,18 @@ class GradBasic(GradParent): def __init__(self, *args, **kwargs): - super(GradBasic, self).__init__(*args, **kwargs) + super().__init__(*args, **kwargs) self.get_grad = self._get_grad_method self.cost = self._cost_method - def _get_grad_method(self, data): - r"""Get the gradient + def _get_grad_method(self, input_data): + r"""Get the gradient. This method calculates the gradient step from the input data Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Notes @@ -246,22 +233,27 @@ def _get_grad_method(self, data): \nabla F(x) = \mathbf{H}^T(\mathbf{H}\mathbf{x} - \mathbf{y}) """ - - self.grad = self.trans_op(self.op(data) - self.obs_data) + self.grad = self.trans_op(self.op(input_data) - self.obs_data) def _cost_method(self, *args, **kwargs): - """Calculate gradient component of the cost + """Calculate gradient component of the cost. This method returns the l2 norm error of the difference between the original data and the data obtained after optimisation + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Gradient cost component """ - cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2 if 'verbose' in kwargs and kwargs['verbose']: diff --git a/modopt/opt/linear.py b/modopt/opt/linear.py index 4d0d1e3f..e21ffac9 100644 --- a/modopt/opt/linear.py +++ b/modopt/opt/linear.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""LINEAR OPERATORS +"""LINEAR OPERATORS. This module contains linear operator classes. @@ -8,15 +8,14 @@ """ -from builtins import range, zip import numpy as np + from modopt.base.types import check_callable, check_float -from modopt.math.matrix import rotate -from modopt.signal.wavelet import * +from modopt.signal.wavelet import filter_convolve_stack class LinearParent(object): - r"""Linear Operator Parent Class + """Linear Operator Parent Class. This class sets the structure for defining linear operator instances. @@ -45,12 +44,7 @@ def __init__(self, op, adj_op): @property def op(self): - """Linear Operator - - This method defines the linear operator - - """ - + """Linear Operator.""" return self._op @op.setter @@ -60,12 +54,7 @@ def op(self, operator): @property def adj_op(self): - """Linear Adjoint Operator - - This method defines the linear adjoint operator - - """ - + """Linear Adjoint Operator.""" return self._adj_op @adj_op.setter @@ -75,7 +64,7 @@ def adj_op(self, operator): class Identity(LinearParent): - """Identity Operator Class + """Identity Operator Class. This is a dummy class that can be used in the optimisation classes. @@ -87,12 +76,12 @@ class Identity(LinearParent): def __init__(self): - self.op = lambda x: x + self.op = lambda input_data: input_data self.adj_op = self.op class WaveletConvolve(LinearParent): - """Wavelet Convolution Class + """Wavelet Convolution Class. This class defines the wavelet transform operators via convolution with predefined filters. @@ -113,15 +102,21 @@ class WaveletConvolve(LinearParent): def __init__(self, filters, method='scipy'): self._filters = check_float(filters) - self.op = lambda x: filter_convolve_stack(x, self._filters, - method=method) - self.adj_op = lambda x: filter_convolve_stack(x, self._filters, - filter_rot=True, - method=method) + self.op = lambda input_data: filter_convolve_stack( + input_data, + self._filters, + method=method, + ) + self.adj_op = lambda input_data: filter_convolve_stack( + input_data, + self._filters, + filter_rot=True, + method=method, + ) class LinearCombo(LinearParent): - r"""Linear Combination Class + """Linear Combination Class. This class defines a combination of linear transform operators. @@ -166,7 +161,7 @@ def __init__(self, operators, weights=None): self.adj_op = self._adj_op_method def _check_type(self, input_val): - """ Check input type + """Check input type. This method checks if the input is a list, tuple or a numpy array and converts the input to a numpy array. @@ -185,12 +180,15 @@ def _check_type(self, input_val): ------ TypeError For invalid input type + ValueError + If input list is empty """ - if not isinstance(input_val, (list, tuple, np.ndarray)): - raise TypeError('Invalid input type, input must be a list, tuple ' - 'or numpy array.') + raise TypeError( + 'Invalid input type, input must be a list, tuple or numpy ' + + 'array.', + ) input_val = np.array(input_val) @@ -200,7 +198,7 @@ def _check_type(self, input_val): return input_val def _check_inputs(self, operators, weights): - """ Check inputs + """Check inputs. This method cheks that the input operators and weights are correctly formatted. @@ -225,38 +223,41 @@ def _check_inputs(self, operators, weights): If the individual weight values are not floats """ - operators = self._check_type(operators) for operator in operators: + if not hasattr(operator, 'op'): raise ValueError('Operators must contain "op" method.') + if not hasattr(operator, 'adj_op'): raise ValueError('Operators must contain "adj_op" method.') + operator.op = check_callable(operator.op) operator.cost = check_callable(operator.adj_op) if not isinstance(weights, type(None)): - weights = self._check_type(weights) if weights.size != operators.size: - raise ValueError('The number of weights must match the ' - 'number of operators.') + raise ValueError( + 'The number of weights must match the number of ' + + 'operators.', + ) if not np.issubdtype(weights.dtype, np.floating): raise TypeError('The weights must be a list of float values.') return operators, weights - def _op_method(self, data): - """Operator + def _op_method(self, input_data): + """Operator. This method returns the input data operated on by all of the operators. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -265,16 +266,15 @@ def _op_method(self, data): Linear operation results """ - res = np.empty(len(self.operators), dtype=np.ndarray) - for i in range(len(self.operators)): - res[i] = self.operators[i].op(data) + for index, _ in enumerate(self.operators): + res[index] = self.operators[index].op(input_data) return res - def _adj_op_method(self, data): - """Adjoint operator + def _adj_op_method(self, input_data): + """Adjoint operator. This method returns the combination of the result of all of the adjoint operators. If weights are provided the comibination is the sum @@ -282,7 +282,7 @@ def _adj_op_method(self, data): Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array Returns @@ -291,14 +291,23 @@ def _adj_op_method(self, data): Adjoint operation results """ - if isinstance(self.weights, type(None)): - - return np.mean([operator.adj_op(x) for x, operator in - zip(data, self.operators)], axis=0) - - else: - - return np.sum([weight * operator.adj_op(x) for x, operator, - weight in zip(data, self.operators, self.weights)], - axis=0) + return np.mean( + [ + operator.adj_op(elem) + for elem, operator in zip(input_data, self.operators) + ], + axis=0, + ) + + return np.sum( + [ + weight * operator.adj_op(elem) + for elem, operator, weight in zip( + input_data, + self.operators, + self.weights, + ) + ], + axis=0, + ) diff --git a/modopt/opt/proximity.py b/modopt/opt/proximity.py index f30486ca..474d7395 100644 --- a/modopt/opt/proximity.py +++ b/modopt/opt/proximity.py @@ -1,20 +1,17 @@ # -*- coding: utf-8 -*- -"""PROXIMITY OPERATORS +"""PROXIMITY OPERATORS. This module contains classes of proximity operators for optimisation :Author: Samuel Farrens -:References: - -.. bibliography:: refs.bib - :filter: docname in docnames - """ -import numpy as np import sys + +import numpy as np + try: from sklearn.isotonic import isotonic_regression except ImportError: @@ -22,17 +19,17 @@ else: import_sklearn = True +from modopt.base.transform import cube2matrix, matrix2cube from modopt.base.types import check_callable +from modopt.interface.errors import warn +from modopt.math.matrix import nuclear_norm from modopt.signal.noise import thresh -from modopt.signal.svd import svd_thresh, svd_thresh_coef from modopt.signal.positivity import positive -from modopt.math.matrix import nuclear_norm -from modopt.base.transform import cube2matrix, matrix2cube -from modopt.interface.errors import warn +from modopt.signal.svd import svd_thresh, svd_thresh_coef class ProximityParent(object): - r"""Proximity Operator Parent Class + """Proximity Operator Parent Class. This class sets the structure for defining proximity operator instances. @@ -53,12 +50,7 @@ def __init__(self, op, cost): @property def op(self): - """Linear operator - - This method defines the linear operator. - - """ - + """Linear operator.""" return self._op @op.setter @@ -68,13 +60,17 @@ def op(self, operator): @property def cost(self): - """Cost contribution + """Cost contribution. This method defines the proximity operator's contribution to the total cost. - """ + Returns + ------- + float + Cost + """ return self._cost @cost.setter @@ -84,7 +80,7 @@ def cost(self, method): class IdentityProx(ProximityParent): - """Identity Proxmity Operator + """Identity Proxmity Operator. This is a dummy class that can be used as a proximity operator. @@ -100,12 +96,12 @@ class IdentityProx(ProximityParent): def __init__(self): - self.op = lambda x: x - self.cost = lambda x: 0.0 + self.op = lambda x_val: x_val + self.cost = lambda x_val: 0 class Positivity(ProximityParent): - """Positivity Proximity Operator + """Positivity Proximity Operator. This class defines the positivity proximity operator. @@ -117,30 +113,36 @@ class Positivity(ProximityParent): def __init__(self): - self.op = lambda x: positive(x) + self.op = lambda input_data: positive(input_data) self.cost = self._cost_method def _cost_method(self, *args, **kwargs): - """Calculate positivity component of the cost + """Calculate positivity component of the cost. This method returns 0 as the posivituty does not contribute to the cost. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float ``0.0`` """ - if 'verbose' in kwargs and kwargs['verbose']: print(' - Min (X):', np.min(args[0])) - return 0.0 + return 0 class SparseThreshold(ProximityParent): - """Threshold Proximity Operator + """Threshold Proximity Operator. This class defines the threshold proximity operator. @@ -167,14 +169,14 @@ def __init__(self, linear, weights, thresh_type='soft'): self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the input data thresholded by the weights. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -185,24 +187,29 @@ def _op_method(self, data, extra_factor=1.0): Thresholded data """ - threshold = self.weights * extra_factor - return thresh(data, threshold, self._thresh_type) + return thresh(input_data, threshold, self._thresh_type) def _cost_method(self, *args, **kwargs): - """Calculate sparsity component of the cost + """Calculate sparsity component of the cost. This method returns the l1 norm error of the weighted wavelet coefficients. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Sparsity cost component """ - cost_val = np.sum(np.abs(self.weights * self._linear.op(args[0]))) if 'verbose' in kwargs and kwargs['verbose']: @@ -212,13 +219,13 @@ def _cost_method(self, *args, **kwargs): class LowRankMatrix(ProximityParent): - r"""Low-rank Proximity Operator + """Low-rank Proximity Operator. This class defines the low-rank proximity operator. Parameters ---------- - thresh : float + threshold : float Threshold value treshold_type : {'hard', 'soft'} Threshold type (options are 'hard' or 'soft', default is 'soft') @@ -230,20 +237,17 @@ class LowRankMatrix(ProximityParent): Examples -------- + >>> import numpy as np >>> from modopt.opt.proximity import LowRankMatrix >>> a = np.arange(9).reshape(3, 3).astype(float) >>> inst = LowRankMatrix(10.0, thresh_type='hard') >>> inst.op(a) - array([[[ 2.73843189, 3.14594066, 3.55344943], - [ 3.9609582 , 4.36846698, 4.77597575], - [ 5.18348452, 5.59099329, 5.99850206]], - - [[ 8.07085295, 9.2718846 , 10.47291625], - [ 11.67394789, 12.87497954, 14.07601119], - [ 15.27704284, 16.47807449, 17.67910614]]]) + array([[0.89642146, 1.0976143 , 1.29880715], + [3.29284291, 4.03188864, 4.77093436], + [5.68926437, 6.96616297, 8.24306156]]) >>> inst.cost(a, verbose=True) - - NUCLEAR NORM (X): 469.391329425 - 469.39132942464983 + - NUCLEAR NORM (X): 154.91933384829667 + 154.91933384829667 See Also -------- @@ -251,25 +255,30 @@ class LowRankMatrix(ProximityParent): """ - def __init__(self, thresh, thresh_type='soft', - lowr_type='standard', operator=None): + def __init__( + self, + threshold, + thresh_type='soft', + lowr_type='standard', + operator=None, + ): - self.thresh = thresh + self.thresh = threshold self.thresh_type = thresh_type self.lowr_type = lowr_type self.operator = operator self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the input data after the singular values have been thresholded. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -280,37 +289,46 @@ def _op_method(self, data, extra_factor=1.0): SVD thresholded data """ - # Update threshold with extra factor. threshold = self.thresh * extra_factor if self.lowr_type == 'standard': - data_matrix = svd_thresh(cube2matrix(data), threshold, - thresh_type=self.thresh_type) + data_matrix = svd_thresh( + cube2matrix(input_data), + threshold, + thresh_type=self.thresh_type, + ) elif self.lowr_type == 'ngole': - data_matrix = svd_thresh_coef(cube2matrix(data), self.operator, - threshold, - thresh_type=self.thresh_type) - - new_data = matrix2cube(data_matrix, data.shape[1:]) + data_matrix = svd_thresh_coef( + cube2matrix(input_data), + self.operator, + threshold, + thresh_type=self.thresh_type, + ) # Return updated data. - return new_data + return matrix2cube(data_matrix, input_data.shape[1:]) def _cost_method(self, *args, **kwargs): - """Calculate low-rank component of the cost + """Calculate low-rank component of the cost. This method returns the nuclear norm error of the deconvolved data in matrix form. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Low-rank cost component """ - cost_val = self.thresh * nuclear_norm(cube2matrix(args[0])) if 'verbose' in kwargs and kwargs['verbose']: @@ -320,7 +338,7 @@ def _cost_method(self, *args, **kwargs): class LinearCompositionProx(ProximityParent): - """Proximity Operator of a Linear Composition + """Proximity Operator of a Linear Composition. This class defines the proximity operator of a function given by a composition between an initial function whose proximity operator is known @@ -345,15 +363,15 @@ def __init__(self, linear_op, prox_op): self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - r"""Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the scaled version of the proximity operator as given by Lemma 2.8 of :cite:`combettes2005`. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -364,12 +382,22 @@ def _op_method(self, data, extra_factor=1.0): Result of the scaled proximity operator """ - - return self.linear_op.adj_op(self.prox_op.op(self.linear_op.op(data), - extra_factor=extra_factor)) + return self.linear_op.adj_op( + self.prox_op.op( + self.linear_op.op(input_data), + extra_factor=extra_factor, + ), + ) def _cost_method(self, *args, **kwargs): - """Calculate the cost function associated to the composed function + """Calculate the cost function associated to the composed function. + + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments Returns ------- @@ -377,12 +405,11 @@ def _cost_method(self, *args, **kwargs): The cost of the associated composed function """ - return self.prox_op.cost(self.linear_op.op(args[0]), **kwargs) class ProximityCombo(ProximityParent): - r"""Proximity Combo + """Proximity Combo. This class defines a combined proximity operator. @@ -416,7 +443,7 @@ def __init__(self, operators): self.cost = self._cost_method def _check_operators(self, operators): - """Check operators + """Check operators. This method cheks that the input operators and weights are correctly formatted. @@ -435,12 +462,19 @@ def _check_operators(self, operators): ------ TypeError For invalid input type + ValueError + For empty list + ValueError + For missing op method + ValueError + For missing cost method """ - if not isinstance(operators, (list, tuple, np.ndarray)): - raise TypeError('Invalid input type, operators must be a list, ' - 'tuple or numpy array.') + raise TypeError( + 'Invalid input type, operators must be a list, tuple or ' + + 'numpy array.', + ) operators = np.array(operators) @@ -457,15 +491,15 @@ def _check_operators(self, operators): return operators - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the result of applying all of the proximity operators to the data. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -476,33 +510,43 @@ def _op_method(self, data, extra_factor=1.0): Result """ - res = np.empty(len(self.operators), dtype=np.ndarray) - for i in range(len(self.operators)): - res[i] = self.operators[i].op(data[i], extra_factor=extra_factor) + for index, _ in enumerate(self.operators): + res[index] = self.operators[index].op( + input_data[index], + extra_factor=extra_factor, + ) return res def _cost_method(self, *args, **kwargs): - """Calculate combined proximity operator components of the cost + """Calculate combined proximity operator components of the cost. This method returns the sum of the cost components from each of the proximity operators. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Combinded cost components """ - - return np.sum([operator.cost(data) for operator, data in - zip(self.operators, args[0])]) + return np.sum([ + operator.cost(input_data) + for operator, input_data in zip(self.operators, args[0]) + ]) class OrderedWeightedL1Norm(ProximityParent): - r"""Ordered Weighted L1 norm proximity operator + """Ordered Weighted L1 norm proximity operator. This class defines the OWL proximity operator described in :cite:`figueiredo2014`. @@ -516,16 +560,18 @@ class OrderedWeightedL1Norm(ProximityParent): -------- >>> import numpy as np >>> from modopt.opt.proximity import OrderedWeightedL1Norm - >>> A = np.arange(5)*5 + >>> A = np.arange(5) * 5 + >>> A array([ 0, 5, 10, 15, 20]) - >>> prox_op = OrderedWeightedL1Norm(np.arange(5)) + >>> weights = np.arange(5)[::-1] + >>> prox_op = OrderedWeightedL1Norm(weights) >>> prox_op.weights array([4, 3, 2, 1, 0]) >>> prox_op.op(A) array([ 0., 4., 8., 12., 16.]) >>> prox_op.cost(A, verbose=True) - - OWL NORM (X): 150 - 150 + - OWL NORM (X): 50 + 50 See Also -------- @@ -535,28 +581,30 @@ class OrderedWeightedL1Norm(ProximityParent): def __init__(self, weights): if not import_sklearn: # pragma: no cover - raise ImportError('Required version of Scikit-Learn package not' - ' found see documentation for details: ' - 'https://cea-cosmic.github.io/ModOpt/' - '#optional-packages') + raise ImportError( + 'Required version of Scikit-Learn package not found see ' + + 'documentation for details: ' + + 'https://cea-cosmic.github.io/ModOpt/#optional-packages', + ) if np.max(np.diff(weights)) > 0: raise ValueError('Weights must be non increasing') self.weights = weights.flatten() if (self.weights < 0).any(): - raise ValueError("The weight values must be provided " - "in descending order") + raise ValueError( + 'The weight values must be provided in descending order', + ) self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the input data after the a clustering and a thresholding. Implements (Eq 24) in :cite:`figueiredo2014`. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -567,12 +615,11 @@ def _op_method(self, data, extra_factor=1.0): Thresholded data """ - # Update threshold with extra factor. threshold = self.weights * extra_factor # Squeezing the data - data_squeezed = np.squeeze(data) + data_squeezed = np.squeeze(input_data) # Sorting (non increasing order) input vector's absolute values data_abs = np.abs(data_squeezed) @@ -581,9 +628,10 @@ def _op_method(self, data, extra_factor=1.0): # Projection onto the monotone non-negative cone using # isotonic_regression + data_abs = isotonic_regression( + data_abs - threshold, y_min=0, increasing=False, + ) - data_abs = isotonic_regression(data_abs - threshold, y_min=0, - increasing=False) # Unsorting the data data_abs_unsorted = np.empty_like(data_abs) data_abs_unsorted[data_abs_sort_idx] = data_abs @@ -595,22 +643,29 @@ def _op_method(self, data, extra_factor=1.0): # Removing NAN caused by the sign sign_data[np.isnan(sign_data)] = 0 - return np.reshape(sign_data * data_abs_unsorted, data.shape) + return np.reshape(sign_data * data_abs_unsorted, input_data.shape) def _cost_method(self, *args, **kwargs): - """Calculate OWL component of the cost + """Calculate OWL component of the cost. This method returns the ordered weighted l1 norm of the data. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float OWL cost component """ - - cost_val = np.sum(self.weights * np.sort(np.squeeze( - np.abs(args[0]))[::-1])) + cost_val = np.sum( + self.weights * np.sort(np.squeeze(np.abs(args[0]))[::-1]), + ) if 'verbose' in kwargs and kwargs['verbose']: print(' - OWL NORM (X):', cost_val) @@ -619,7 +674,7 @@ def _cost_method(self, *args, **kwargs): class Ridge(ProximityParent): - r"""L2-norm Proximity Operator (`i.e.` Shrinkage) + r"""L2-norm Proximity Operator (`i.e.` Shrinkage). This class defines the L2-norm proximity operator. @@ -651,14 +706,14 @@ def __init__(self, linear, weights, thresh_type='soft'): self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """Operator Method + def _op_method(self, input_data, extra_factor=1.0): + """Operator Method. This method returns the input data shrinked by the weights Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -669,25 +724,32 @@ def _op_method(self, data, extra_factor=1.0): Thresholded data """ - threshold = self.weights * extra_factor * 2 - return self._linear.op(data) / (1 + threshold) + return self._linear.op(input_data) / (1 + threshold) def _cost_method(self, *args, **kwargs): - """Calculate Ridge component of the cost + """Calculate Ridge component of the cost. This method returns the l2 norm error of the weighted wavelet coefficients. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Sparsity cost component """ - - cost_val = np.sum(np.abs(self.weights * self._linear.op(args[0])**2)) + cost_val = np.sum( + np.abs(self.weights * self._linear.op(args[0]) ** 2), + ) if 'verbose' in kwargs and kwargs['verbose']: print(' - L2 NORM (X):', cost_val) @@ -696,7 +758,7 @@ def _cost_method(self, *args, **kwargs): class ElasticNet(ProximityParent): - r"""Elastic Net + r"""Elastic Net. This class defines the Elastic net proximity operator, which is a linear combination between L2 and L1 norm proximity operators, @@ -731,14 +793,14 @@ def __init__(self, linear, alpha, beta): self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the input data shrinked by the weights. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor @@ -749,26 +811,33 @@ def _op_method(self, data, extra_factor=1.0): Thresholded data """ - soft_threshold = self.beta * extra_factor normalization = (self.alpha * 2 * extra_factor + 1) - return thresh(data, soft_threshold, 'soft') / normalization + return thresh(input_data, soft_threshold, 'soft') / normalization def _cost_method(self, *args, **kwargs): - """Calculate Ridge component of the cost + """Calculate Ridge component of the cost. This method returns the l2 norm error of the weighted wavelet coefficients. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float Sparsity cost component """ - - cost_val = np.sum(np.abs(self.alpha * self._linear.op(args[0])**2) + - np.abs(self.beta * self._linear.op(args[0]))) + cost_val = np.sum( + np.abs(self.alpha * self._linear.op(args[0]) ** 2) + + np.abs(self.beta * self._linear.op(args[0])), + ) if 'verbose' in kwargs and kwargs['verbose']: print(' - ELASTIC NET (X):', cost_val) @@ -777,7 +846,7 @@ def _cost_method(self, *args, **kwargs): class KSupportNorm(ProximityParent): - r"""K-support Norm Proximity Operator + """K-support Norm Proximity Operator. This class defines the squarred K-support norm proximity operator described in :cite:`mcdonald2014`. @@ -804,13 +873,14 @@ class KSupportNorm(ProximityParent): -------- >>> import numpy as np >>> from modopt.opt.proximity import KSupportNorm - >>> A = np.arange(5)*5 + >>> A = np.arange(5) * 5 + >>> A array([ 0, 5, 10, 15, 20]) >>> prox_op = KSupportNorm(beta=3, k_value=1) >>> prox_op.op(A) - array([ 0., 0., 0., 0., 5.]) + array([0., 0., 0., 0., 5.]) >>> prox_op.cost(A, verbose=True) - - OWL NORM (X): 7500.0 + - K-SUPPORT NORM (X): 7500.0 7500.0 See Also @@ -827,20 +897,19 @@ def __init__(self, beta, k_value): @property def k_value(self): - """k value - - """ + """K value.""" return self._k_value @k_value.setter - def k_value(self, k): - if k < 1: - raise ValueError("The k parameter should be greater or " - "equal than 1") - self._k_value = k + def k_value(self, k_val): + if k_val < 1: + raise ValueError( + 'The k parameter should be greater or equal than 1', + ) + self._k_value = k_val def _compute_theta(self, input_data, alpha, extra_factor=1.0): - r""" Compute theta + r"""Compute theta. This method computes theta from Corollary 16: @@ -871,32 +940,33 @@ def _compute_theta(self, input_data, alpha, extra_factor=1.0): Same size as w and each component is equal to theta_i """ - alpha_input = np.dot(np.expand_dims(alpha, -1), - np.expand_dims(np.abs(input_data), -1).T) + alpha_input = np.dot( + np.expand_dims(alpha, -1), + np.expand_dims(np.abs(input_data), -1).T, + ) theta = np.zeros(alpha_input.shape) - theta = (alpha_input - self.beta * extra_factor) * \ - (((alpha_input - self.beta * extra_factor) <= 1) & - ((alpha_input - self.beta * extra_factor) >= 0)) + alpha_beta = alpha_input - self.beta * extra_factor + theta = alpha_beta * ((alpha_beta <= 1) & (alpha_beta >= 0)) theta = np.nan_to_num(theta) - theta += 1.0 * (alpha_input > (self.beta * extra_factor + 1)) + theta += (alpha_input > (self.beta * extra_factor + 1)) return theta - def _interpolate(self, alpha_0, alpha_1, sum_0, sum_1): - """ Linear interpolation of alpha + def _interpolate(self, alpha0, alpha1, sum0, sum1): + """Linear interpolation of alpha. This method estimats alpha* such that sum(theta(alpha*))=k via a linear interpolation. Parameters ----------- - alpha_0: float - A value for wich sum(theta(alpha_0)) <= k - alpha_1: float - A value for which sum(theta(alpha_1)) <= k - sum_0: float - Value of sum(theta(alpha_0)) - sum_1: - Value of sum(theta(alpha_0)) + alpha0: float + A value for wich sum(theta(alpha0)) <= k + alpha1: float + A value for which sum(theta(alpha1)) <= k + sum0: float + Value of sum(theta(alpha0)) + sum1: float + Value of sum(theta(alpha0)) Returns ------- @@ -904,19 +974,19 @@ def _interpolate(self, alpha_0, alpha_1, sum_0, sum_1): An interpolation for which sum(theta(alpha_star)) = k """ + if sum0 == self._k_value: + return alpha0 - if sum_0 == self._k_value: - return alpha_0 - elif sum_1 == self._k_value: - return alpha_1 - else: - slope = (sum_1 - sum_0) / (alpha_1 - alpha_0) - b = sum_0 - slope * alpha_0 - alpha_star = (self._k_value - b) / slope - return alpha_star + elif sum1 == self._k_value: + return alpha1 - def _binary_search(self, data, alpha, extra_factor=1.0): - """ Binary search method + slope = (sum1 - sum0) / (alpha1 - alpha0) + b_val = sum0 - slope * alpha0 + + return (self._k_value - b_val) / slope + + def _binary_search(self, input_data, alpha, extra_factor=1.0): + """Binary search method. This method finds the coordinate of alpha (i) such that sum(theta(alpha[i])) =< k and sum(theta(alpha[i+1])) >= k via binary @@ -924,7 +994,7 @@ def _binary_search(self, data, alpha, extra_factor=1.0): Parameters ---------- - data: numpy.ndarray + input_data: numpy.ndarray absolute value of the input data alpha: numpy.ndarray Array same size as the input data @@ -932,6 +1002,11 @@ def _binary_search(self, data, alpha, extra_factor=1.0): Potential extra factor comming from the optimization process (default is ``1.0``) + Raises + ------ + ValueError + For invalid output alpha value + Returns ------- tuple @@ -942,21 +1017,23 @@ def _binary_search(self, data, alpha, extra_factor=1.0): Value of sum(theta(alpha[index + 1])) """ - first_idx = 0 - data_abs = np.abs(data) + data_abs = np.abs(input_data) last_idx = alpha.shape[0] - 1 found = False prev_midpoint = 0 cnt = 0 # Avoid infinite looops + tolerance = 1e-4 # Checking particular to be sure that the solution is in the array - sum_0 = self._compute_theta(data_abs, alpha[0], extra_factor).sum() - sum_1 = self._compute_theta(data_abs, alpha[-1], extra_factor).sum() - if sum_1 <= self._k_value: + sum0 = self._compute_theta(data_abs, alpha[0], extra_factor).sum() + sum1 = self._compute_theta(data_abs, alpha[-1], extra_factor).sum() + + if sum1 <= self._k_value: midpoint = alpha.shape[0] - 2 found = True - if sum_0 >= self._k_value: + + if sum0 >= self._k_value: found = True midpoint = 0 @@ -968,55 +1045,77 @@ def _binary_search(self, data, alpha, extra_factor=1.0): if prev_midpoint == midpoint: # Particular case - sum_0 = self._compute_theta(data_abs, alpha[first_idx], - extra_factor).sum() - sum_1 = self._compute_theta(data_abs, alpha[last_idx], - extra_factor).sum() - - if (np.abs(sum_0 - self._k_value) <= 1e-4): + sum0 = self._compute_theta( + data_abs, + alpha[first_idx], + extra_factor, + ).sum() + sum1 = self._compute_theta( + data_abs, + alpha[last_idx], + extra_factor, + ).sum() + + if (np.abs(sum0 - self._k_value) <= tolerance): found = True midpoint = first_idx - if (np.abs(sum_1 - self._k_value) <= 1e-4): + if (np.abs(sum1 - self._k_value) <= tolerance): found = True midpoint = last_idx - 1 # -1 because output is index such that - # sum(theta(alpha[index])) <= k - - if (first_idx - last_idx == 2) or (first_idx - last_idx == 1): - sum_0 = self._compute_theta(data_abs, alpha[first_idx], - extra_factor).sum() - sum_1 = self._compute_theta(data_abs, alpha[last_idx], - extra_factor).sum() - if (sum_0 <= self._k_value) or (sum_1 >= self._k_value): + # `sum(theta(alpha[index])) <= k` + + if (first_idx - last_idx) in {1, 2}: + sum0 = self._compute_theta( + data_abs, + alpha[first_idx], + extra_factor, + ).sum() + sum1 = self._compute_theta( + data_abs, + alpha[last_idx], + extra_factor, + ).sum() + + if (sum0 <= self._k_value) & (sum1 >= self._k_value): found = True - sum_0 = self._compute_theta(data_abs, alpha[midpoint], - extra_factor).sum() - sum_1 = self._compute_theta(data_abs, alpha[midpoint + 1], - extra_factor).sum() - - if (sum_0 <= self._k_value) & (sum_1 >= self._k_value): + sum0 = self._compute_theta( + data_abs, + alpha[midpoint], + extra_factor, + ).sum() + sum1 = self._compute_theta( + data_abs, + alpha[midpoint + 1], + extra_factor, + ).sum() + + if sum0 <= self._k_value <= sum1: found = True - elif sum_1 < self._k_value: + elif sum1 < self._k_value: first_idx = midpoint - elif sum_0 > self._k_value: + elif sum0 > self._k_value: last_idx = midpoint prev_midpoint = midpoint if found: - return midpoint, alpha[midpoint], alpha[midpoint + 1], sum_0,\ - sum_1 - else: - raise ValueError("Cannot find the coordinate of alpha (i) such " + - "that sum(theta(alpha[i])) =< k and " + - "sum(theta(alpha[i+1])) >= k ") + return ( + midpoint, alpha[midpoint], alpha[midpoint + 1], sum0, sum1, + ) + + raise ValueError( + 'Cannot find the coordinate of alpha (i) such ' + + 'that sum(theta(alpha[i])) =< k and ' + + 'sum(theta(alpha[i+1])) >= k ', + ) def _find_alpha(self, input_data, extra_factor=1.0): - """ Find alpha value to compute theta + """Find alpha value to compute theta. This method aim at finding alpha such that sum(theta(alpha)) = k. @@ -1033,37 +1132,40 @@ def _find_alpha(self, input_data, extra_factor=1.0): An interpolation of alpha such that sum(theta(alpha)) = k """ - data_size = input_data.shape[0] # Computes the alpha^i points line 1 in Algorithm 1. alpha = np.zeros((data_size * 2)) data_abs = np.abs(input_data) - alpha[:data_size] = (self.beta * extra_factor) / \ - (data_abs + sys.float_info.epsilon) - alpha[data_size:] = (self.beta * extra_factor + 1) / \ - (data_abs + sys.float_info.epsilon) + alpha[:data_size] = ( + (self.beta * extra_factor) + / (data_abs + sys.float_info.epsilon) + ) + alpha[data_size:] = ( + (self.beta * extra_factor + 1) + / (data_abs + sys.float_info.epsilon) + ) alpha = np.sort(np.unique(alpha)) # Identify points alpha^i and alpha^{i+1} line 2. Algorithm 1 - _, alpha_0, alpha_1, sum_0, sum_1 = self._binary_search(input_data, - alpha, - extra_factor) + _, *alpha_sum = self._binary_search( + input_data, + alpha, + extra_factor, + ) # Interpolate alpha^\star such that its sum is equal to k - alpha_star = self._interpolate(alpha_0, alpha_1, sum_0, sum_1) - - return alpha_star + return self._interpolate(*alpha_sum) - def _op_method(self, data, extra_factor=1.0): - """Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the proximity operator of the squared k-support norm. Implements (Alg. 1) in :cite:`mcdonald2014`. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -1074,26 +1176,30 @@ def _op_method(self, data, extra_factor=1.0): Proximal map """ - data_shape = data.shape + data_shape = input_data.shape k_max = np.prod(data_shape) if self._k_value > k_max: - warn("K value of the K-support norm is greater than the input" + - " dimension, its value will be set to " + str(k_max)) + warn( + 'K value of the K-support norm is greater than the input ' + + 'dimension, its value will be set to {0}'.format(k_max), + ) self._k_value = k_max # Computes line 1., 2. and 3. in Algorithm 1 - alpha = self._find_alpha(np.abs(data.flatten()), extra_factor) + alpha = self._find_alpha(np.abs(input_data.flatten()), extra_factor) # Computes line 4. in Algorithm 1 - theta = self._compute_theta(np.abs(data.flatten()), alpha) + theta = self._compute_theta(np.abs(input_data.flatten()), alpha) # Computes line 5. in Algorithm 1. - rslt = np.nan_to_num((data.flatten() * theta) / - (theta + self.beta * extra_factor)) + rslt = np.nan_to_num( + (input_data.flatten() * theta) + / (theta + self.beta * extra_factor), + ) return rslt.reshape(data_shape) def _find_q(self, sorted_data): - """ Find q index value + """Find q index value. This method finds the value of q such that: @@ -1111,55 +1217,74 @@ def _find_q(self, sorted_data): (k - q)>= sorted_data[q+1] """ - first_idx = 0 last_idx = self._k_value - 1 found = False - q = (first_idx + last_idx) // 2 + q_val = (first_idx + last_idx) // 2 cnt = 0 # Particular case - if (sorted_data[0:].sum() / (self._k_value)) >= sorted_data[0]: + if (sorted_data.sum() / (self._k_value)) >= sorted_data[0]: found = True - q = 0 - elif (sorted_data[self._k_value - 1:].sum()) <= sorted_data[ - self._k_value - 1]: + q_val = 0 + + elif ( + (sorted_data[self._k_value - 1:].sum()) + <= sorted_data[self._k_value - 1] + ): found = True - q = self._k_value - 1 + q_val = self._k_value - 1 - while (not found and not cnt == self._k_value and - (first_idx <= last_idx) and last_idx < self._k_value): + while ( + not found and not cnt == self._k_value + and (first_idx <= last_idx < self._k_value) + ): - q = (first_idx + last_idx) // 2 + q_val = (first_idx + last_idx) // 2 cnt += 1 - l1_part = sorted_data[q:].sum() / (self._k_value - q) - if sorted_data[q] >= l1_part and l1_part >= sorted_data[q + 1]: + l1_part = sorted_data[q_val:].sum() / (self._k_value - q_val) + + if ( + sorted_data[q_val + 1] <= l1_part <= sorted_data[q_val] + ): found = True + else: - if sorted_data[q] <= l1_part: - last_idx = q - if l1_part <= sorted_data[q + 1]: - first_idx = q - return q + if sorted_data[q_val] <= l1_part: + last_idx = q_val + if l1_part <= sorted_data[q_val + 1]: + first_idx = q_val + + return q_val def _cost_method(self, *args, **kwargs): - """Calculate OWL component of the cost + """Calculate OWL component of the cost. This method returns the ordered weighted l1 norm of the data. + Parameters + ---------- + args : interable + Positional arguments + kwargs : dict + Keyword arguments + Returns ------- float OWL cost component - """ - data_abs = np.abs(args[0].flatten()) ix = np.argsort(data_abs)[::-1] data_abs = data_abs[ix] # Sorted absolute value of the data - q = self._find_q(data_abs) - cost_val = (np.sum(data_abs[:q]**2) * 0.5 + - np.sum(data_abs[q:])**2 / (self._k_value - q)) * self.beta + q_val = self._find_q(data_abs) + cost_val = ( + ( + np.sum(data_abs[:q_val]**2) * 0.5 + + np.sum(data_abs[q_val:]) ** 2 + / (self._k_value - q_val) + ) * self.beta + ) if 'verbose' in kwargs and kwargs['verbose']: print(' - K-SUPPORT NORM (X):', cost_val) @@ -1168,7 +1293,7 @@ def _cost_method(self, *args, **kwargs): class GroupLASSO(ProximityParent): - """Group LASSO norm proximity + """Group LASSO norm proximity. This class implements the proximity operator of the group-lasso regularization as defined in :cite:`yuan2006`, with groups dimension @@ -1183,7 +1308,8 @@ class GroupLASSO(ProximityParent): -------- >>> import numpy as np >>> from modopt.opt.proximity import GroupLASSO - >>> A = np.arange(15).reshape(3,5) + >>> A = np.arange(15).reshape(3, 5) + >>> A array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) @@ -1193,7 +1319,7 @@ class GroupLASSO(ProximityParent): [ 3.65835921, 4.56799689, 5.50381195, 6.45722157, 7.42264316], [ 7.31671843, 8.37466096, 9.4351062 , 10.49298505, 11.5463338 ]]) >>> prox_op.cost(A, verbose=True) - 211.37821733946427 + 211.37821733946427 See Also -------- @@ -1206,14 +1332,14 @@ def __init__(self, weights): self.op = self._op_method self.cost = self._cost_method - def _op_method(self, data, extra_factor=1.0): - """ Operator + def _op_method(self, input_data, extra_factor=1.0): + """Operator. This method returns the input data thresholded by the weights. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array extra_factor : float Additional multiplication factor (default is ``1.0``) @@ -1224,19 +1350,22 @@ def _op_method(self, data, extra_factor=1.0): With proximal of GroupLASSO regularization """ + norm2 = np.linalg.norm(input_data, axis=0) + denominator = np.maximum(norm2, np.finfo(np.float32).eps) - norm_2 = np.linalg.norm(data, axis=0) - return data * np.maximum(0, 1.0 - self.weights * extra_factor / - np.maximum(norm_2, np.finfo(np.float32).eps)) + return input_data * np.maximum( + 0, + (1.0 - self.weights * extra_factor / denominator), + ) - def _cost_method(self, data): - """Cost function + def _cost_method(self, input_data): + """Cost function. This method calculate the cost function of the proximable part. Parameters ---------- - x: numpy.ndarray + input_data : numpy.ndarray Input array of the sparse code Returns @@ -1245,5 +1374,4 @@ def _cost_method(self, data): The cost of GroupLASSO regularizer """ - - return np.sum(self.weights * np.linalg.norm(data, axis=0)) + return np.sum(self.weights * np.linalg.norm(input_data, axis=0)) diff --git a/modopt/opt/reweight.py b/modopt/opt/reweight.py index fa1ecb71..444b65d5 100644 --- a/modopt/opt/reweight.py +++ b/modopt/opt/reweight.py @@ -1,24 +1,20 @@ # -*- coding: utf-8 -*- -"""REWEIGHTING CLASSES +"""REWEIGHTING CLASSES. This module contains classes for reweighting optimisation implementations :Author: Samuel Farrens -:References: - -.. bibliography:: refs.bib - :filter: docname in docnames - """ import numpy as np + from modopt.base.types import check_float class cwbReweight(object): - r"""Candes, Wakin and Boyd reweighting class + """Candes, Wakin and Boyd reweighting class. This class implements the reweighting scheme described in :cite:`candes2007` @@ -32,39 +28,46 @@ class cwbReweight(object): Examples -------- - >>> from modopt.signal.reweight import cwbReweight + >>> import numpy as np + >>> from modopt.opt.reweight import cwbReweight >>> a = np.arange(9).reshape(3, 3).astype(float) + 1 >>> rw = cwbReweight(a) >>> rw.weights - array([[ 1., 2., 3.], - [ 4., 5., 6.], - [ 7., 8., 9.]]) + array([[1., 2., 3.], + [4., 5., 6.], + [7., 8., 9.]]) >>> rw.reweight(a) >>> rw.weights - array([[ 0.5, 1. , 1.5], - [ 2. , 2.5, 3. ], - [ 3.5, 4. , 4.5]]) + array([[0.5, 1. , 1.5], + [2. , 2.5, 3. ], + [3.5, 4. , 4.5]]) """ - def __init__(self, weights, thresh_factor=1.0): + def __init__(self, weights, thresh_factor=1.0, verbose=False): self.weights = check_float(weights) self.original_weights = np.copy(self.weights) self.thresh_factor = check_float(thresh_factor) self._rw_num = 1 + self.verbose = verbose - def reweight(self, data): - r"""Reweight + def reweight(self, input_data): + r"""Reweight. This method implements the reweighting from section 4 in :cite:`candes2007` Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data + Raises + ------ + ValueError + For invalid input shape + Notes ----- Reweighting implemented as: @@ -74,15 +77,20 @@ def reweight(self, data): w = w \left( \frac{1}{1 + \frac{|x^w|}{n \sigma}} \right) """ + if self.verbose: + print(' - Reweighting: {0}'.format(self._rw_num)) - print(' - Reweighting: {}'.format(self._rw_num)) self._rw_num += 1 - data = check_float(data) + input_data = check_float(input_data) + + if input_data.shape != self.weights.shape: + raise ValueError( + 'Input data must have the same shape as the initial weights.', + ) - if data.shape != self.weights.shape: - raise ValueError('Input data must have the same shape as the ' - 'initial weights.') + thresh_weights = self.thresh_factor * self.original_weights - self.weights *= (1.0 / (1.0 + np.abs(data) / (self.thresh_factor * - self.original_weights))) + self.weights *= np.array( + 1.0 / (1.0 + np.abs(input_data) / (thresh_weights)), + ) diff --git a/modopt/plot/__init__.py b/modopt/plot/__init__.py index 2bb2c486..28d60be6 100644 --- a/modopt/plot/__init__.py +++ b/modopt/plot/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""PLOTTING ROUTINES +"""PLOTTING ROUTINES. This module contains submodules for plotting applications. @@ -9,5 +9,3 @@ """ __all__ = ['cost_plot'] - -from . import * diff --git a/modopt/plot/cost_plot.py b/modopt/plot/cost_plot.py index ff244ac1..aa855eaa 100644 --- a/modopt/plot/cost_plot.py +++ b/modopt/plot/cost_plot.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""PLOTTING ROUTINES +"""PLOTTING ROUTINES. This module contains methods for making plots. @@ -9,7 +9,7 @@ """ import numpy as np -from modopt.interface.errors import warn + try: import matplotlib.pyplot as plt except ImportError: # pragma: no cover @@ -19,7 +19,7 @@ def plotCost(cost_list, output=None): - """Plot cost function + """Plot cost function. Plot the final cost function. @@ -30,14 +30,20 @@ def plotCost(cost_list, output=None): output : str, optional Output file name (default is ``None``) - """ + Raises + ------ + ImportError + If Matplotlib package not found - if not import_fail: + """ + if import_fail: + raise ImportError('Matplotlib package not found') + else: if isinstance(output, type(None)): file_name = 'cost_function.png' else: - file_name = output + '_cost_function.png' + file_name = '{0}_cost_function.png'.format(output) plt.figure() plt.plot(np.log10(cost_list), 'r-') @@ -48,7 +54,3 @@ def plotCost(cost_list, output=None): plt.close() print(' - Saving cost function data to:', file_name) - - else: - - warn('Matplotlib not installed.') diff --git a/modopt/signal/__init__.py b/modopt/signal/__init__.py index 55e846cd..dbc6d053 100644 --- a/modopt/signal/__init__.py +++ b/modopt/signal/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""SIGNAL PROCESSING ROUTINES +"""SIGNAL PROCESSING ROUTINES. This module contains submodules for signal processing. @@ -9,5 +9,3 @@ """ __all__ = ['filter', 'noise', 'positivity', 'svd', 'validation', 'wavelet'] - -from . import * diff --git a/modopt/signal/filter.py b/modopt/signal/filter.py index e50f0bba..8e24768c 100644 --- a/modopt/signal/filter.py +++ b/modopt/signal/filter.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""FILTER ROUTINES +"""FILTER ROUTINES. This module contains methods for distance measurements in cosmology. @@ -9,17 +9,18 @@ """ import numpy as np + from modopt.base.types import check_float -def Gaussian_filter(x, sigma, norm=True): - r"""Gaussian filter +def gaussian_filter(data_point, sigma, norm=True): + """Gaussian filter. This method implements a Gaussian filter. Parameters ---------- - x : float + data_point : float Input data point sigma : float Standard deviation (filter scale) @@ -33,35 +34,33 @@ def Gaussian_filter(x, sigma, norm=True): Examples -------- - >>> from modopt.signal.filter import Gaussian_filter - >>> Gaussian_filter(1, 1) + >>> from modopt.signal.filter import gaussian_filter + >>> gaussian_filter(1, 1) 0.24197072451914337 - >>> Gaussian_filter(1, 1, False) - 0.60653065971263342 + >>> gaussian_filter(1, 1, False) + 0.6065306597126334 """ - - x = check_float(x) + data_point = check_float(data_point) sigma = check_float(sigma) - val = np.exp(-0.5 * (x / sigma) ** 2) + numerator = np.exp(-0.5 * (data_point / sigma) ** 2) if norm: - return val / (np.sqrt(2 * np.pi) * sigma) + return numerator / (np.sqrt(2 * np.pi) * sigma) - else: - return val + return numerator -def mex_hat(x, sigma): - r"""Mexican hat +def mex_hat(data_point, sigma): + """Mexican hat. This method implements a Mexican hat (or Ricker) wavelet. Parameters ---------- - x : float + data_point : float Input data point sigma : float Standard deviation (filter scale) @@ -75,29 +74,28 @@ def mex_hat(x, sigma): -------- >>> from modopt.signal.filter import mex_hat >>> mex_hat(2, 1) - -0.35213905225713371 + -0.3521390522571337 """ - - x = check_float(x) + data_point = check_float(data_point) sigma = check_float(sigma) - xs = (x / sigma) ** 2 - val = 2 * (3 * sigma) ** -0.5 * np.pi ** -0.25 + xs = (data_point / sigma) ** 2 + factor = 2 * (3 * sigma) ** -0.5 * np.pi ** -0.25 - return val * (1 - xs) * np.exp(-0.5 * xs) + return factor * (1 - xs) * np.exp(-0.5 * xs) -def mex_hat_dir(x, y, sigma): - r"""Directional Mexican hat +def mex_hat_dir(data_gauss, data_mex, sigma): + """Directional Mexican hat. This method implements a directional Mexican hat (or Ricker) wavelet. Parameters ---------- - x : float + data_gauss : float Input data point for Gaussian - y : float + data_mex : float Input data point for Mexican hat sigma : float Standard deviation (filter scale) @@ -114,8 +112,7 @@ def mex_hat_dir(x, y, sigma): 0.17606952612856686 """ - - x = check_float(x) + data_gauss = check_float(data_gauss) sigma = check_float(sigma) - return -0.5 * (x / sigma) ** 2 * mex_hat(y, sigma) + return -0.5 * (data_gauss / sigma) ** 2 * mex_hat(data_mex, sigma) diff --git a/modopt/signal/noise.py b/modopt/signal/noise.py index 85775cd2..f67cc066 100644 --- a/modopt/signal/noise.py +++ b/modopt/signal/noise.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""NOISE ROUTINES +"""NOISE ROUTINES. This module contains methods for adding and removing noise from data. @@ -9,17 +9,20 @@ """ from builtins import zip + import numpy as np +from modopt.base.backend import get_array_module + -def add_noise(data, sigma=1.0, noise_type='gauss'): - r"""Add noise to data +def add_noise(input_data, sigma=1.0, noise_type='gauss'): + """Add noise to data. This method adds Gaussian or Poisson noise to the input data Parameters ---------- - data : numpy.ndarray, list or tuple + input_data : numpy.ndarray, list or tuple Input data array sigma : float or list, optional Standard deviation of the noise to be added ('gauss' only, default is @@ -46,58 +49,61 @@ def add_noise(data, sigma=1.0, noise_type='gauss'): >>> from modopt.signal.noise import add_noise >>> x = np.arange(9).reshape(3, 3).astype(float) >>> x - array([[ 0., 1., 2.], - [ 3., 4., 5.], - [ 6., 7., 8.]]) + array([[0., 1., 2.], + [3., 4., 5.], + [6., 7., 8.]]) >>> np.random.seed(1) >>> add_noise(x, noise_type='poisson') - array([[ 0., 2., 2.], - [ 4., 5., 10.], - [ 11., 15., 18.]]) + array([[ 0., 2., 2.], + [ 4., 5., 10.], + [11., 15., 18.]]) >>> import numpy as np >>> from modopt.signal.noise import add_noise >>> x = np.zeros(5) >>> x - array([ 0., 0., 0., 0., 0.]) + array([0., 0., 0., 0., 0.]) >>> np.random.seed(1) >>> add_noise(x, sigma=2.0) array([ 3.24869073, -1.22351283, -1.0563435 , -2.14593724, 1.73081526]) """ + input_data = np.array(input_data) - data = np.array(data) - - if noise_type not in ('gauss', 'poisson'): - raise ValueError('Invalid noise type. Options are "gauss" or' - '"poisson"') + if noise_type not in {'gauss', 'poisson'}: + raise ValueError( + 'Invalid noise type. Options are "gauss" or "poisson"', + ) if isinstance(sigma, (list, tuple, np.ndarray)): - if len(sigma) != data.shape[0]: - raise ValueError('Number of sigma values must match first ' - 'dimension of input data') + if len(sigma) != input_data.shape[0]: + raise ValueError( + 'Number of sigma values must match first dimension of input ' + + 'data', + ) if noise_type == 'gauss': - random = np.random.randn(*data.shape) + random = np.random.randn(*input_data.shape) elif noise_type == 'poisson': - random = np.random.poisson(np.abs(data)) + random = np.random.poisson(np.abs(input_data)) if isinstance(sigma, (int, float)): - return data + sigma * random + return input_data + sigma * random - else: - return data + np.array([s * r for s, r in zip(sigma, random)]) + noise = np.array([sig * rand for sig, rand in zip(sigma, random)]) + return input_data + noise -def thresh(data, threshold, threshold_type='hard'): - r"""Threshold data + +def thresh(input_data, threshold, threshold_type='hard'): + r"""Threshold data. This method perfoms hard or soft thresholding on the input data Parameters ---------- - data : numpy.ndarray, list or tuple + input_data : numpy.ndarray, list or tuple Input data array threshold : float or numpy.ndarray Threshold level(s) @@ -114,7 +120,6 @@ def thresh(data, threshold, threshold_type='hard'): ValueError If `threshold_type` is not 'hard' or 'soft' - Notes ----- Implements one of the following two equations: @@ -151,26 +156,28 @@ def thresh(data, threshold, threshold_type='hard'): >>> np.random.seed(1) >>> x = np.random.ranf((3, 3)) >>> x - array([[ 4.17022005e-01, 7.20324493e-01, 1.14374817e-04], - [ 3.02332573e-01, 1.46755891e-01, 9.23385948e-02], - [ 1.86260211e-01, 3.45560727e-01, 3.96767474e-01]]) + array([[4.17022005e-01, 7.20324493e-01, 1.14374817e-04], + [3.02332573e-01, 1.46755891e-01, 9.23385948e-02], + [1.86260211e-01, 3.45560727e-01, 3.96767474e-01]]) >>> thresh(x, 0.2, threshold_type='soft') - array([[ 0.217022 , 0.52032449, -0. ], - [ 0.10233257, -0. , -0. ], - [-0. , 0.14556073, 0.19676747]]) + array([[0.217022 , 0.52032449, 0. ], + [0.10233257, 0. , 0. ], + [0. , 0.14556073, 0.19676747]]) """ + xp = get_array_module(input_data) - data = np.array(data) + input_data = xp.array(input_data) - if threshold_type not in ('hard', 'soft'): - raise ValueError('Invalid threshold type. Options are "hard" or' - '"soft"') + if threshold_type not in {'hard', 'soft'}: + raise ValueError( + 'Invalid threshold type. Options are "hard" or "soft"', + ) if threshold_type == 'soft': - return np.around(np.maximum((1.0 - threshold / - np.maximum(np.finfo(np.float64).eps, np.abs(data))), - 0.0) * data, decimals=15) + denominator = xp.maximum(xp.finfo(np.float64).eps, xp.abs(input_data)) + max_value = xp.maximum((1.0 - threshold / denominator), 0) + + return xp.around(max_value * input_data, decimals=15) - else: - return data * (np.abs(data) >= threshold) + return input_data * (xp.abs(input_data) >= threshold) diff --git a/modopt/signal/positivity.py b/modopt/signal/positivity.py index 961c8dfc..7b79d0ee 100644 --- a/modopt/signal/positivity.py +++ b/modopt/signal/positivity.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""POSITIVITY +"""POSITIVITY. This module contains a function that retains only positive coefficients in an array @@ -12,21 +12,68 @@ import numpy as np -def positive(data): - r"""Positivity operator +def pos_thresh(input_data): + """Positive Threshold. + + Keep only positive coefficients from input data. + + Parameters + ---------- + input_data : int, float, list, tuple or numpy.ndarray + Input data + + Returns + ------- + int, float, or numpy.ndarray + Positive coefficients + + """ + return input_data * (input_data > 0) + + +def pos_recursive(input_data): + """Positive Recursive. + + Run pos_thresh on input array or recursively for ragged nested arrays. + + Parameters + ---------- + input_data : numpy.ndarray + Input data + + Returns + ------- + numpy.ndarray + Positive coefficients + + """ + if input_data.dtype == 'O': + res = np.array([pos_recursive(elem) for elem in input_data]) + + else: + res = pos_thresh(input_data) + + return res + + +def positive(input_data, ragged=False): + """Positivity operator. This method preserves only the positive coefficients of the input data, all negative coefficients are set to zero Parameters ---------- - data : int, float, list, tuple or numpy.ndarray + input_data : int, float, or numpy.ndarray Input data + ragged : bool, optional + Specify if the input_data is a ragged nested array + (defaul is ``False``) Returns ------- int or float, or numpy.ndarray - Array with only positive coefficients + Positive coefficients Raises ------ @@ -35,6 +82,7 @@ def positive(data): Examples -------- + >>> import numpy as np >>> from modopt.signal.positivity import positive >>> a = np.arange(9).reshape(3, 3) - 5 >>> a @@ -47,33 +95,19 @@ def positive(data): [1, 2, 3]]) """ + if not isinstance(input_data, (int, float, list, tuple, np.ndarray)): + raise TypeError( + 'Invalid data type, input must be `int`, `float`, `list`, ' + + '`tuple` or `np.ndarray`.', + ) - if not isinstance(data, (int, float, list, tuple, np.ndarray)): - raise TypeError('Invalid data type, input must be `int`, `float`, ' - '`list`, `tuple` or `np.ndarray`.') - - def pos_thresh(data): - - return data * (data > 0) - - def pos_recursive(data): - - data = np.array(data) - - if not data.dtype == 'O': - - result = list(pos_thresh(data)) - - else: - - result = [pos_recursive(x) for x in data] - - return result - - if isinstance(data, (int, float)): + if isinstance(input_data, (int, float)): + return pos_thresh(input_data) - return pos_thresh(data) + if ragged: + input_data = np.array(input_data, dtype='object') else: + input_data = np.array(input_data) - return np.array(pos_recursive(data)) + return pos_recursive(input_data) diff --git a/modopt/signal/svd.py b/modopt/signal/svd.py index bc171b93..de3fa453 100644 --- a/modopt/signal/svd.py +++ b/modopt/signal/svd.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""SVD ROUTINES +"""SVD ROUTINES. This module contains methods for thresholding singular values. @@ -10,20 +10,21 @@ import numpy as np from scipy.linalg import svd + +from modopt.base.transform import matrix2cube +from modopt.interface.errors import warn from modopt.math.convolve import convolve from modopt.signal.noise import thresh -from modopt.base.transform import cube2matrix, matrix2cube -from modopt.interface.errors import warn -def find_n_pc(u, factor=0.5): - """Find number of principal components +def find_n_pc(u_vec, factor=0.5): + """Find number of principal components. This method finds the minimum number of principal components required. Parameters ---------- - u : numpy.ndarray + u_vec : numpy.ndarray Left singular vector of the original data factor : float, optional Factor for testing the auto correlation (default is ``0.5``) @@ -33,41 +34,58 @@ def find_n_pc(u, factor=0.5): int Number of principal components + Raises + ------ + ValueError + Invalid left singular vector + Examples -------- + >>> import numpy as np >>> from scipy.linalg import svd >>> from modopt.signal.svd import find_n_pc >>> x = np.arange(18).reshape(9, 2).astype(float) >>> find_n_pc(svd(x)[0]) - array([3]) + 1 """ - - if np.sqrt(u.shape[0]) % 1: - raise ValueError('Invalid left singular value. The size of the first ' - 'dimenion of u must be perfect square.') + if np.sqrt(u_vec.shape[0]) % 1: + raise ValueError( + 'Invalid left singular vector. The size of the first ' + + 'dimenion of ``u_vec`` must be perfect square.', + ) # Get the shape of the array - array_shape = np.repeat(np.int(np.sqrt(u.shape[0])), 2) + array_shape = np.repeat(np.int(np.sqrt(u_vec.shape[0])), 2) # Find the auto correlation of the left singular vector. - u_auto = [convolve(a.reshape(array_shape), - np.rot90(a.reshape(array_shape), 2)) for a in u.T] + u_auto = [ + convolve( + elem.reshape(array_shape), + np.rot90(elem.reshape(array_shape), 2), + ) + for elem in u_vec.T + ] # Return the required number of principal components. - return np.sum([(a[tuple(zip(array_shape // 2))] ** 2 <= factor * - np.sum(a ** 2)) for a in u_auto]) + return np.sum([ + ( + u_val[tuple(zip(array_shape // 2))] ** 2 <= factor + * np.sum(u_val ** 2), + ) + for u_val in u_auto + ]) -def calculate_svd(data): - """Calculate Singular Value Decomposition +def calculate_svd(input_data): + """Calculate Singular Value Decomposition. This method calculates the Singular Value Decomposition (SVD) of the input data using SciPy. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array, 2D matrix Returns @@ -81,29 +99,32 @@ def calculate_svd(data): For invalid data type """ - - if (not isinstance(data, np.ndarray)) or (data.ndim != 2): + if (not isinstance(input_data, np.ndarray)) or (input_data.ndim != 2): raise TypeError('Input data must be a 2D np.ndarray.') - return svd(data, check_finite=False, lapack_driver='gesvd', - full_matrices=False) + return svd( + input_data, + check_finite=False, + lapack_driver='gesvd', + full_matrices=False, + ) -def svd_thresh(data, threshold=None, n_pc=None, thresh_type='hard'): - r"""Threshold the singular values +def svd_thresh(input_data, threshold=None, n_pc=None, thresh_type='hard'): + """Threshold the singular values. This method thresholds the input data using singular value decomposition. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array, 2D matrix threshold : float or numpy.ndarray, optional Threshold value(s) (default is ``None``) n_pc : int or str, optional Number of principal components, specify an integer value or 'all' (default is ``None``) - threshold_type : {'hard', 'soft'}, optional + thresh_type : {'hard', 'soft'}, optional Type of thresholding (default is 'hard') Returns @@ -118,72 +139,81 @@ def svd_thresh(data, threshold=None, n_pc=None, thresh_type='hard'): Examples -------- + >>> import numpy as np >>> from modopt.signal.svd import svd_thresh >>> x = np.arange(18).reshape(9, 2).astype(float) >>> svd_thresh(x, n_pc=1) - array([[ 0.49815487, 0.54291537], - [ 2.40863386, 2.62505584], - [ 4.31911286, 4.70719631], - [ 6.22959185, 6.78933678], - [ 8.14007085, 8.87147725], - [ 10.05054985, 10.95361772], - [ 11.96102884, 13.03575819], - [ 13.87150784, 15.11789866], - [ 15.78198684, 17.20003913]]) + array([[ 0.49815487, 0.54291537], + [ 2.40863386, 2.62505584], + [ 4.31911286, 4.70719631], + [ 6.22959185, 6.78933678], + [ 8.14007085, 8.87147725], + [10.05054985, 10.95361772], + [11.96102884, 13.03575819], + [13.87150784, 15.11789866], + [15.78198684, 17.20003913]]) """ - - if ((not isinstance(n_pc, (int, str, type(None)))) or - (isinstance(n_pc, int) and n_pc <= 0) or - (isinstance(n_pc, str) and n_pc != 'all')): - raise ValueError('Invalid value for "n_pc", specify a positive ' - 'integer value or "all"') + less_than_zero = isinstance(n_pc, int) and n_pc <= 0 + str_not_all = isinstance(n_pc, str) and n_pc != 'all' + + if ( + (not isinstance(n_pc, (int, str, type(None)))) + or less_than_zero + or str_not_all + ): + raise ValueError( + 'Invalid value for "n_pc", specify a positive integer value or ' + + '"all"', + ) # Get SVD of input data. - u, s, v = calculate_svd(data) + u_vec, s_values, v_vec = calculate_svd(input_data) # Find the threshold if not provided. if isinstance(threshold, type(None)): - # Find the required number of principal components if not specified. if isinstance(n_pc, type(None)): - n_pc = find_n_pc(u, factor=0.1) + n_pc = find_n_pc(u_vec, factor=0.1) + print('xxxx', n_pc, u_vec) # If the number of PCs is too large use all of the singular values. - if ((isinstance(n_pc, int) and n_pc >= s.size) or - (isinstance(n_pc, str) and n_pc == 'all')): - n_pc = s.size + if ( + (isinstance(n_pc, int) and n_pc >= s_values.size) + or (isinstance(n_pc, str) and n_pc == 'all') + ): + n_pc = s_values.size warn('Using all singular values.') - threshold = s[n_pc - 1] + threshold = s_values[n_pc - 1] # Threshold the singular values. - s_new = thresh(s, threshold, thresh_type) + s_new = thresh(s_values, threshold, thresh_type) - if np.all(s_new == s): + if np.all(s_new == s_values): warn('No change to singular values.') # Diagonalize the svd s_new = np.diag(s_new) # Return the thresholded data. - return np.dot(u, np.dot(s_new, v)) + return np.dot(u_vec, np.dot(s_new, v_vec)) -def svd_thresh_coef(data, operator, threshold, thresh_type='hard'): - """Threshold the singular values coefficients +def svd_thresh_coef(input_data, operator, threshold, thresh_type='hard'): + """Threshold the singular values coefficients. This method thresholds the input data using singular value decomposition Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data array, 2D matrix operator : class Operator class instance threshold : float or numpy.ndarray Threshold value(s) - threshold_type : {'hard', 'soft'} + thresh_type : {'hard', 'soft'} Type of noise to be added (default is 'hard') Returns @@ -193,33 +223,34 @@ def svd_thresh_coef(data, operator, threshold, thresh_type='hard'): Raises ------ - ValueError - For invalid string entry for n_pc + TypeError + If operator not callable """ - if not callable(operator): raise TypeError('Operator must be a callable function.') # Get SVD of data matrix - u, s, v = calculate_svd(data) + u_vec, s_values, v_vec = calculate_svd(input_data) # Diagnalise s - s = np.diag(s) + s_values = np.diag(s_values) # Compute coefficients - a = np.dot(s, v) + a_matrix = np.dot(s_values, v_vec) # Get the shape of the array - array_shape = np.repeat(np.int(np.sqrt(u.shape[0])), 2) + array_shape = np.repeat(np.int(np.sqrt(u_vec.shape[0])), 2) # Compute threshold matrix. - ti = np.array([np.linalg.norm(x) for x in - operator(matrix2cube(u, array_shape))]) - threshold *= np.repeat(ti, a.shape[1]).reshape(a.shape) + ti = np.array([ + np.linalg.norm(elem) + for elem in operator(matrix2cube(u_vec, array_shape)) + ]) + threshold *= np.repeat(ti, a_matrix.shape[1]).reshape(a_matrix.shape) # Threshold coefficients. - a_new = thresh(a, threshold, thresh_type) + a_new = thresh(a_matrix, threshold, thresh_type) # Return the thresholded image. - return np.dot(u, a_new) + return np.dot(u_vec, a_new) diff --git a/modopt/signal/validation.py b/modopt/signal/validation.py index f54483b8..f39a2eb3 100644 --- a/modopt/signal/validation.py +++ b/modopt/signal/validation.py @@ -1,4 +1,4 @@ -"""VALIDATION ROUTINES +"""VALIDATION ROUTINES. This module contains methods for testing signal and operator properties. @@ -9,9 +9,15 @@ import numpy as np -def transpose_test(operator, operator_t, x_shape, x_args=None, y_shape=None, - y_args=None): - r"""Transpose test +def transpose_test( + operator, + operator_t, + x_shape, + x_args=None, + y_shape=None, + y_args=None, +): + """Transpose test. This method tests two operators to see if they are the transpose of each other. @@ -31,16 +37,22 @@ def transpose_test(operator, operator_t, x_shape, x_args=None, y_shape=None, y_args : tuple, optional Arguments to be passed to transpose operator (default is ``None``) + Raises + ------ + TypeError + If input operators not callable + Examples -------- + >>> import numpy as np >>> from modopt.signal.validation import transpose_test - >>> np.random.seed(2) + >>> np.random.seed(1) + >>> a = np.random.ranf((3, 3)) >>> transpose_test(lambda x, y: x.dot(y), lambda x, y: x.dot(y.T), - (3, 3), x_args=x) + ... a.shape, x_args=a) - | - | = 0.0 """ - if not callable(operator) or not callable(operator_t): raise TypeError('The input operators must be callable functions.') @@ -51,14 +63,14 @@ def transpose_test(operator, operator_t, x_shape, x_args=None, y_shape=None, y_args = x_args # Generate random arrays. - x = np.random.ranf(x_shape) - y = np.random.ranf(y_shape) + x_val = np.random.ranf(x_shape) + y_val = np.random.ranf(y_shape) # Calculate - mx_y = np.sum(np.multiply(operator(x, x_args), y)) + mx_y = np.sum(np.multiply(operator(x_val, x_args), y_val)) # Calculate - x_mty = np.sum(np.multiply(x, operator_t(y, y_args))) + x_mty = np.sum(np.multiply(x_val, operator_t(y_val, y_args))) # Test the difference between the two. print(' - | - | =', np.abs(mx_y - x_mty)) diff --git a/modopt/signal/wavelet.py b/modopt/signal/wavelet.py index 8327a275..0e509cbd 100644 --- a/modopt/signal/wavelet.py +++ b/modopt/signal/wavelet.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""WAVELET MODULE +"""WAVELET MODULE. This module contains methods for performing wavelet transformations using iSAP @@ -9,21 +9,24 @@ Notes ----- This module serves as a wrapper for the wavelet transformation code -`mr_transform`, which is part of the Sparse2D package in iSAP. This executable +`mr_transform`, which is part of the Sparse2D package. This executable should be installed and built before using these methods. Sparse2D Repository: https://github.com/CosmoStat/Sparse2D """ -import numpy as np import subprocess as sp +from datetime import datetime from os import remove from random import getrandbits -from datetime import datetime + +import numpy as np + from modopt.base.np_adjust import rotate_stack from modopt.interface.errors import is_executable from modopt.math.convolve import convolve + try: from astropy.io import fits except ImportError: # pragma: no cover @@ -33,7 +36,7 @@ def execute(command_line): - """ Execute + """Execute. This method executes a given command line. @@ -53,7 +56,6 @@ def execute(command_line): For invalid input type """ - if not isinstance(command_line, str): raise TypeError('Command line must be a string.') @@ -65,15 +67,19 @@ def execute(command_line): return stdout.decode('utf-8'), stderr.decode('utf-8') -def call_mr_transform(data, opt='', path='./', - remove_files=True): # pragma: no cover - r"""Call mr_transform +def call_mr_transform( + input_data, + opt='', + path='./', + remove_files=True, +): # pragma: no cover + """Call mr_transform. This method calls the iSAP module mr_transform Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data, 2D array opt : list or str, optional Options to be passed to mr_transform (default is '') @@ -89,14 +95,19 @@ def call_mr_transform(data, opt='', path='./', Raises ------ + ImportError + If the Astropy package is not found ValueError If the input data is not a 2D numpy array + RuntimeError + For exception encountered in call to mr_transform Examples -------- + >>> import numpy as np >>> from modopt.signal.wavelet import * >>> a = np.arange(9).reshape(3, 3).astype(float) - >>> call_mr_transform(a) + >>> call_mr_transform(a) # doctest: +SKIP array([[[-1.5 , -1.125 , -0.75 ], [-0.375 , 0. , 0.375 ], [ 0.75 , 1.125 , 1.5 ]], @@ -111,14 +122,13 @@ def call_mr_transform(data, opt='', path='./', [[ 3.6484375 , 3.73632812, 3.82421875], [ 3.91210938, 4. , 4.08789062], - [ 4.17578125, 4.26367188, 4.3515625 ]]], dtype=float32) + [ 4.17578125, 4.26367188, 4.3515625 ]]]) """ - if not import_astropy: raise ImportError('Astropy package not found.') - if (not isinstance(data, np.ndarray)) or (data.ndim != 2): + if (not isinstance(input_data, np.ndarray)) or (input_data.ndim != 2): raise ValueError('Input data must be a 2D numpy array.') executable = 'mr_transform' @@ -127,33 +137,36 @@ def call_mr_transform(data, opt='', path='./', is_executable(executable) # Create a unique string using the current date and time. - unique_string = (datetime.now().strftime('%Y.%m.%d_%H.%M.%S') + - str(getrandbits(128))) + unique_string = ( + datetime.now().strftime('%Y.%m.%d_%H.%M.%S') + + str(getrandbits(128)) + ) # Set the ouput file names. - file_name = path + 'mr_temp_' + unique_string - file_fits = file_name + '.fits' - file_mr = file_name + '.mr' + file_name = '{0}mr_temp_{1}'.format(path, unique_string) + file_fits = '{0}.fits'.format(file_name) + file_mr = '{0}.mr'.format(file_name) # Write the input data to a fits file. - fits.writeto(file_fits, data) + fits.writeto(file_fits, input_data) if isinstance(opt, str): opt = opt.split() # Prepare command and execute it command_line = ' '.join([executable] + opt + [file_fits, file_mr]) - stdout, stderr = execute(command_line) + stdout, _ = execute(command_line) # Check for errors if any(word in stdout for word in ('bad', 'Error', 'Sorry')): - remove(file_fits) - raise RuntimeError('{} raised following exception: "{}"' - ''.format(executable, stdout.rstrip('\n'))) + message = '{0} raised following exception: "{1}"' + raise RuntimeError( + message.format(executable, stdout.rstrip('\n')), + ) # Retrieve wavelet transformed data. - result = fits.getdata(file_mr).astype(data.dtype) + data_trans = fits.getdata(file_mr).astype(input_data.dtype) # Remove the temporary files. if remove_files: @@ -161,11 +174,11 @@ def call_mr_transform(data, opt='', path='./', remove(file_mr) # Return the mr_transform results. - return result + return data_trans def trim_filter(filter_array): - """Trim the filters to the minimal size + """Trim the filters to the minimal size. This method will get rid of the extra zero coefficients in the filter. @@ -183,12 +196,17 @@ def trim_filter(filter_array): non_zero_indices = np.array(np.where(filter_array != 0)) min_idx = np.min(non_zero_indices, axis=-1) max_idx = np.max(non_zero_indices, axis=-1) + return filter_array[min_idx[0]:max_idx[0] + 1, min_idx[1]:max_idx[1] + 1] -def get_mr_filters(data_shape, opt='', coarse=False, - trim=False): # pragma: no cover - """Get mr_transform filters +def get_mr_filters( + data_shape, + opt='', + coarse=False, + trim=False, +): # pragma: no cover + """Get mr_transform filters. This method obtains wavelet filters by calling mr_transform. @@ -216,7 +234,6 @@ def get_mr_filters(data_shape, opt='', coarse=False, trim_filter : The function for trimming the wavelet filters """ - # Adjust the shape of the input data. data_shape = np.array(data_shape) data_shape += data_shape % 2 - 1 @@ -229,23 +246,23 @@ def get_mr_filters(data_shape, opt='', coarse=False, mr_filters = call_mr_transform(fake_data.astype(float), opt=opt) if trim: - mr_filters = np.array([trim_filter(f) for f in mr_filters]) + mr_filters = np.array([trim_filter(filt) for filt in mr_filters]) # Return filters if coarse: return mr_filters - else: - return mr_filters[:-1] + return mr_filters[:-1] -def filter_convolve(data, filters, filter_rot=False, method='scipy'): - r"""Filter convolve + +def filter_convolve(input_data, filters, filter_rot=False, method='scipy'): + """Filter convolve. This method convolves the input image with the wavelet filters. Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data, 2D array filters : numpy.ndarray Wavelet filters, 3D array @@ -261,49 +278,60 @@ def filter_convolve(data, filters, filter_rot=False, method='scipy'): Examples -------- + >>> import numpy as np >>> from modopt.signal.wavelet import filter_convolve >>> x = np.arange(9).reshape(3, 3).astype(float) >>> y = np.arange(36).reshape(4, 3, 3).astype(float) - >>> filter_convolve(x, y) - array([[[ 174., 165., 174.], - [ 93., 84., 93.], - [ 174., 165., 174.]], + >>> filter_convolve(x, y, method='astropy') + array([[[ 174., 165., 174.], + [ 93., 84., 93.], + [ 174., 165., 174.]], - [[ 498., 489., 498.], - [ 417., 408., 417.], - [ 498., 489., 498.]], + [[ 498., 489., 498.], + [ 417., 408., 417.], + [ 498., 489., 498.]], - [[ 822., 813., 822.], - [ 741., 732., 741.], - [ 822., 813., 822.]], + [[ 822., 813., 822.], + [ 741., 732., 741.], + [ 822., 813., 822.]], - [[ 1146., 1137., 1146.], - [ 1065., 1056., 1065.], - [ 1146., 1137., 1146.]]]) + [[1146., 1137., 1146.], + [1065., 1056., 1065.], + [1146., 1137., 1146.]]]) - >>> filter_convolve(y, y, filter_rot=True) - array([[ 14550., 14586., 14550.], - [ 14874., 14910., 14874.], - [ 14550., 14586., 14550.]]) + >>> filter_convolve(y, y, method='astropy', filter_rot=True) + array([[14550., 14586., 14550.], + [14874., 14910., 14874.], + [14550., 14586., 14550.]]) """ - if filter_rot: - return np.sum([convolve(coef, f, method=method) for coef, f in - zip(data, rotate_stack(filters))], axis=0) - - else: - return np.array([convolve(data, f, method=method) for f in filters]) - - -def filter_convolve_stack(data, filters, filter_rot=False, method='scipy'): - r"""Filter convolve + return np.sum( + [ + convolve(coef, filt, method=method) + for coef, filt in zip(input_data, rotate_stack(filters)) + ], + axis=0, + ) + + return np.array([ + convolve(input_data, filt, method=method) for filt in filters + ]) + + +def filter_convolve_stack( + input_data, + filters, + filter_rot=False, + method='scipy', +): + """Filter convolve. This method convolves the a stack of input images with the wavelet filters Parameters ---------- - data : numpy.ndarray + input_data : numpy.ndarray Input data, 3D array filters : numpy.ndarray Wavelet filters, 3D array @@ -319,23 +347,25 @@ def filter_convolve_stack(data, filters, filter_rot=False, method='scipy'): Examples -------- + >>> import numpy as np >>> from modopt.signal.wavelet import filter_convolve_stack >>> x = np.arange(9).reshape(3, 3).astype(float) - >>> filter_convolve(x, x) - array([[[ 4., 1., 4.], - [ 13., 10., 13.], - [ 22., 19., 22.]], + >>> filter_convolve_stack(x, x, method='astropy') + array([[[ 4., 1., 4.], + [ 13., 10., 13.], + [ 22., 19., 22.]], - [[ 13., 10., 13.], - [ 49., 46., 49.], - [ 85., 82., 85.]], + [[ 13., 10., 13.], + [ 49., 46., 49.], + [ 85., 82., 85.]], - [[ 22., 19., 22.], - [ 85., 82., 85.], - [ 148., 145., 148.]]]) + [[ 22., 19., 22.], + [ 85., 82., 85.], + [148., 145., 148.]]]) """ - # Return the convolved data cube. - return np.array([filter_convolve(x, filters, filter_rot=filter_rot, - method=method) for x in data]) + return np.array([ + filter_convolve(elem, filters, filter_rot=filter_rot, method=method) + for elem in input_data + ]) diff --git a/modopt/tests/test_base.py b/modopt/tests/test_base.py index 25305062..26e1e4ea 100644 --- a/modopt/tests/test_base.py +++ b/modopt/tests/test_base.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""UNIT TESTS FOR BASE +"""UNIT TESTS FOR BASE. This module contains unit tests for the modopt.base module. @@ -8,189 +8,270 @@ """ +from builtins import range from unittest import TestCase + import numpy as np import numpy.testing as npt -from builtins import range -from modopt.base import * + +from modopt.base import np_adjust, transform, types class NPAdjustTestCase(TestCase): + """Test case for np_adjust module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape((3, 3)) self.data2 = np.arange(18).reshape((2, 3, 3)) - self.data3 = np.array([[0, 0, 0, 0, 0], [0, 0, 1, 2, 0], - [0, 3, 4, 5, 0], [0, 6, 7, 8, 0], - [0, 0, 0, 0, 0]]) + self.data3 = np.array([ + [0, 0, 0, 0, 0], + [0, 0, 1, 2, 0], + [0, 3, 4, 5, 0], + [0, 6, 7, 8, 0], + [0, 0, 0, 0, 0], + ]) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.data3 = None def test_rotate(self): - - npt.assert_array_equal(np_adjust.rotate(self.data1), - np.array([[8, 7, 6], [5, 4, 3], [2, 1, 0]]), - err_msg='Incorrect rotation') + """Test rotate.""" + npt.assert_array_equal( + np_adjust.rotate(self.data1), + np.array([[8, 7, 6], [5, 4, 3], [2, 1, 0]]), + err_msg='Incorrect rotation', + ) def test_rotate_stack(self): - - npt.assert_array_equal(np_adjust.rotate_stack(self.data2), - np.array([[[8, 7, 6], [5, 4, 3], [2, 1, 0]], - [[17, 16, 15], [14, 13, 12], - [11, 10, 9]]]), - err_msg='Incorrect stack rotation') + """Test rotate_stack.""" + npt.assert_array_equal( + np_adjust.rotate_stack(self.data2), + np.array([ + [[8, 7, 6], [5, 4, 3], [2, 1, 0]], + [[17, 16, 15], [14, 13, 12], [11, 10, 9]], + ]), + err_msg='Incorrect stack rotation', + ) def test_pad2d(self): - - npt.assert_array_equal(np_adjust.pad2d(self.data1, (1, 1)), - self.data3, err_msg='Incorrect padding') - - npt.assert_array_equal(np_adjust.pad2d(self.data1, 1), - self.data3, err_msg='Incorrect padding') - - npt.assert_array_equal(np_adjust.pad2d(self.data1, np.array([1, 1])), - self.data3, err_msg='Incorrect padding') + """Test pad2d.""" + npt.assert_array_equal( + np_adjust.pad2d(self.data1, (1, 1)), + self.data3, + err_msg='Incorrect padding', + ) + + npt.assert_array_equal( + np_adjust.pad2d(self.data1, 1), + self.data3, + err_msg='Incorrect padding', + ) + + npt.assert_array_equal( + np_adjust.pad2d(self.data1, np.array([1, 1])), + self.data3, + err_msg='Incorrect padding', + ) npt.assert_raises(ValueError, np_adjust.pad2d, self.data1, '1') def test_fancy_transpose(self): - - npt.assert_array_equal(np_adjust.fancy_transpose(self.data2), - np.array([[[0, 3, 6], [9, 12, 15]], - [[1, 4, 7], [10, 13, 16]], - [[2, 5, 8], [11, 14, 17]]]), - err_msg='Incorrect fancy transpose') + """Test fancy_transpose.""" + npt.assert_array_equal( + np_adjust.fancy_transpose(self.data2), + np.array([ + [[0, 3, 6], [9, 12, 15]], + [[1, 4, 7], [10, 13, 16]], + [[2, 5, 8], [11, 14, 17]], + ]), + err_msg='Incorrect fancy transpose', + ) def test_ftr(self): - - npt.assert_array_equal(np_adjust.ftr(self.data2), - np.array([[[0, 3, 6], [9, 12, 15]], - [[1, 4, 7], [10, 13, 16]], - [[2, 5, 8], [11, 14, 17]]]), - err_msg='Incorrect fancy transpose: ftr') + """Test ftr.""" + npt.assert_array_equal( + np_adjust.ftr(self.data2), + np.array([ + [[0, 3, 6], [9, 12, 15]], + [[1, 4, 7], [10, 13, 16]], + [[2, 5, 8], [11, 14, 17]], + ]), + err_msg='Incorrect fancy transpose: ftr', + ) def test_ftl(self): - - npt.assert_array_equal(np_adjust.ftl(self.data2), - np.array([[[0, 9], [1, 10], [2, 11]], - [[3, 12], [4, 13], [5, 14]], - [[6, 15], [7, 16], [8, 17]]]), - err_msg='Incorrect fancy transpose: ftl') + """Test ftl.""" + npt.assert_array_equal( + np_adjust.ftl(self.data2), + np.array([ + [[0, 9], [1, 10], [2, 11]], + [[3, 12], [4, 13], [5, 14]], + [[6, 15], [7, 16], [8, 17]], + ]), + err_msg='Incorrect fancy transpose: ftl', + ) class TransformTestCase(TestCase): + """Test case for transform module.""" def setUp(self): - + """Set test parameter values.""" self.cube = np.arange(16).reshape((4, 2, 2)) - self.map = np.array([[0, 1, 4, 5], [2, 3, 6, 7], [8, 9, 12, 13], - [10, 11, 14, 15]]) - self.matrix = np.array([[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14], - [3, 7, 11, 15]]) + self.map = np.array( + [[0, 1, 4, 5], [2, 3, 6, 7], [8, 9, 12, 13], [10, 11, 14, 15]], + ) + self.matrix = np.array( + [[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14], [3, 7, 11, 15]], + ) self.layout = (2, 2) def tearDown(self): - + """Unset test parameter values.""" self.cube = None self.map = None self.layout = None def test_cube2map(self): - - npt.assert_array_equal(transform.cube2map(self.cube, self.layout), - self.map, - err_msg='Incorrect transformation: cube2map') - - npt.assert_raises(ValueError, transform.cube2map, self.map, - self.layout) + """Test cube2map.""" + npt.assert_array_equal( + transform.cube2map(self.cube, self.layout), + self.map, + err_msg='Incorrect transformation: cube2map', + ) + + npt.assert_raises( + ValueError, + transform.cube2map, + self.map, + self.layout, + ) npt.assert_raises(ValueError, transform.cube2map, self.cube, (3, 3)) def test_map2cube(self): - - npt.assert_array_equal(transform.map2cube(self.map, self.layout), - self.cube, - err_msg='Incorrect transformation: map2cube') + """Test map2cube.""" + npt.assert_array_equal( + transform.map2cube(self.map, self.layout), + self.cube, + err_msg='Incorrect transformation: map2cube', + ) npt.assert_raises(ValueError, transform.map2cube, self.map, (3, 3)) def test_map2matrix(self): - - npt.assert_array_equal(transform.map2matrix(self.map, self.layout), - self.matrix, - err_msg='Incorrect transformation: map2matrix') + """Test map2matrix.""" + npt.assert_array_equal( + transform.map2matrix(self.map, self.layout), + self.matrix, + err_msg='Incorrect transformation: map2matrix', + ) def test_matrix2map(self): - - npt.assert_array_equal(transform.matrix2map(self.matrix, - self.map.shape), self.map, - err_msg='Incorrect transformation: matrix2map') + """Test matrix2map.""" + npt.assert_array_equal( + transform.matrix2map(self.matrix, self.map.shape), + self.map, + err_msg='Incorrect transformation: matrix2map', + ) def test_cube2matrix(self): - - npt.assert_array_equal(transform.cube2matrix(self.cube), - self.matrix, - err_msg='Incorrect transformation: cube2matrix') + """Test cube2matrix.""" + npt.assert_array_equal( + transform.cube2matrix(self.cube), + self.matrix, + err_msg='Incorrect transformation: cube2matrix', + ) def test_matrix2cube(self): - - npt.assert_array_equal(transform.matrix2cube(self.matrix, - self.cube[0].shape), self.cube, - err_msg='Incorrect transformation: matrix2cube') + """Test matrix2cube.""" + npt.assert_array_equal( + transform.matrix2cube(self.matrix, self.cube[0].shape), + self.cube, + err_msg='Incorrect transformation: matrix2cube', + ) class TypesTestCase(TestCase): + """Test case for types module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = list(range(5)) self.data2 = np.arange(5) self.data3 = np.arange(5).astype(float) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.data3 = None def test_check_float(self): - - npt.assert_array_equal(types.check_float(1.0), 1.0, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_float(1), 1.0, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_float(self.data1), self.data3, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_float(self.data2), self.data3, - err_msg='Float check failed') + """Test check_float.""" + npt.assert_array_equal( + types.check_float(1.0), + 1.0, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_float(1), + 1.0, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_float(self.data1), + self.data3, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_float(self.data2), + self.data3, + err_msg='Float check failed', + ) npt.assert_raises(TypeError, types.check_float, '1') def test_check_int(self): - - npt.assert_array_equal(types.check_int(1), 1, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_int(1.0), 1, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_int(self.data1), self.data2, - err_msg='Float check failed') - - npt.assert_array_equal(types.check_int(self.data3), self.data2, - err_msg='Int check failed') + """Test check_int.""" + npt.assert_array_equal( + types.check_int(1), + 1, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_int(1.0), + 1, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_int(self.data1), + self.data2, + err_msg='Float check failed', + ) + + npt.assert_array_equal( + types.check_int(self.data3), + self.data2, + err_msg='Int check failed', + ) npt.assert_raises(TypeError, types.check_int, '1') def test_check_npndarray(self): - - npt.assert_raises(TypeError, types.check_npndarray, self.data3, - dtype=np.integer) + """Test check_npndarray.""" + npt.assert_raises( + TypeError, + types.check_npndarray, + self.data3, + dtype=np.integer, + ) diff --git a/modopt/tests/test_math.py b/modopt/tests/test_math.py index 9aa76f44..99908e02 100644 --- a/modopt/tests/test_math.py +++ b/modopt/tests/test_math.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""UNIT TESTS FOR MATH +"""UNIT TESTS FOR MATH. This module contains unit tests for the modopt.math module. @@ -9,10 +9,12 @@ """ from unittest import TestCase, skipIf, skipUnless + import numpy as np import numpy.testing as npt -import sys -from modopt.math import * + +from modopt.math import convolve, matrix, metrics, stats + try: import astropy except ImportError: # pragma: no cover @@ -20,10 +22,7 @@ else: # pragma: no cover import_astropy = True try: - if sys.version_info.minor == 5: - from skimage.measure import compare_ssim - elif sys.version_info.minor > 5: - from skimage.metrics import structural_similarity as compare_ssim + from skimage.metrics import structural_similarity as compare_ssim except ImportError: # pragma: no cover import_skimage = False else: @@ -31,178 +30,240 @@ class ConvolveTestCase(TestCase): + """Test case for convolve module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(18).reshape(2, 3, 3) self.data2 = self.data1 + 1 def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None @skipUnless(import_astropy, 'Astropy not installed.') # pragma: no cover def test_convolve_astropy(self): - - npt.assert_allclose(convolve.convolve(self.data1[0], self.data2[0], - method='astropy'), - np.array([[210., 201., 210.], - [129., 120., 129.], - [210., 201., 210.]]), - err_msg='Incorrect convolution: astropy') - - npt.assert_raises(ValueError, convolve.convolve, self.data1[0], - self.data2) - - npt.assert_raises(ValueError, convolve.convolve, self.data1[0], - self.data2[0], method='bla') + """Test convolve using astropy.""" + npt.assert_allclose( + convolve.convolve(self.data1[0], self.data2[0], method='astropy'), + np.array([ + [210.0, 201.0, 210.0], + [129.0, 120.0, 129.0], + [210.0, 201.0, 210.0], + ]), + err_msg='Incorrect convolution: astropy', + ) + + npt.assert_raises( + ValueError, + convolve.convolve, + self.data1[0], + self.data2, + ) + + npt.assert_raises( + ValueError, + convolve.convolve, + self.data1[0], + self.data2[0], + method='bla', + ) def test_convolve_scipy(self): - - npt.assert_allclose(convolve.convolve(self.data1[0], self.data2[0], - method='scipy'), - np.array([[14., 35., 38.], [57., 120., 111.], - [110., 197., 158.]]), - err_msg='Incorrect convolution: scipy') + """Test convolve using scipy.""" + npt.assert_allclose( + convolve.convolve(self.data1[0], self.data2[0], method='scipy'), + np.array([ + [14.0, 35.0, 38.0], + [57.0, 120.0, 111.0], + [110.0, 197.0, 158.0], + ]), + err_msg='Incorrect convolution: scipy', + ) def test_convolve_stack(self): - - npt.assert_allclose(convolve.convolve_stack(self.data1, self.data2), - np.array([[[14., 35., 38.], - [57., 120., 111.], - [110., 197., 158.]], - [[518., 845., 614.], - [975., 1578., 1137.], - [830., 1331., 950.]]]), - err_msg='Incorrect convolution: stack') + """Test convolve_stack.""" + npt.assert_allclose( + convolve.convolve_stack(self.data1, self.data2), + np.array([ + [ + [14.0, 35.0, 38.0], + [57.0, 120.0, 111.0], + [110.0, 197.0, 158.0], + ], + [ + [518.0, 845.0, 614.0], + [975.0, 1578.0, 1137.0], + [830.0, 1331.0, 950.0], + ], + ]), + err_msg='Incorrect convolution: stack', + ) def test_convolve_stack_rot(self): - - npt.assert_allclose(convolve.convolve_stack(self.data1, self.data2, - rot_kernel=True), - np.array([[[66., 115., 82.], - [153., 240., 159.], - [90., 133., 82.]], - [[714., 1087., 730.], - [1125., 1698., 1131.], - [738., 1105., 730.]]]), - err_msg='Incorrect convolution: stack rot') + """Test convolve_stack rotated.""" + npt.assert_allclose( + convolve.convolve_stack(self.data1, self.data2, rot_kernel=True), + np.array([ + [ + [66.0, 115.0, 82.0], + [153.0, 240.0, 159.0], + [90.0, 133.0, 82.0], + ], + [ + [714.0, 1087.0, 730.0], + [1125.0, 1698.0, 1131.0], + [738.0, 1105.0, 730.0], + ], + ]), + err_msg='Incorrect convolution: stack rot', + ) class MatrixTestCase(TestCase): + """Test case for matrix module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3) self.data2 = np.arange(3) self.data3 = np.arange(6).reshape(2, 3) np.random.seed(1) - self.pmInstance1 = matrix.PowerMethod(lambda x: x.dot(x.T), - self.data1.shape, verbose=True) + self.pmInstance1 = matrix.PowerMethod( + lambda x_val: x_val.dot(x_val.T), + self.data1.shape, + verbose=True, + ) np.random.seed(1) - self.pmInstance2 = matrix.PowerMethod(lambda x: x.dot(x.T), - self.data1.shape, auto_run=False, - verbose=True) + self.pmInstance2 = matrix.PowerMethod( + lambda x_val: x_val.dot(x_val.T), + self.data1.shape, + auto_run=False, + verbose=True, + ) self.pmInstance2.get_spec_rad(max_iter=1) + self.gram_schmidt_out = ( + np.array([ + [0, 1.0, 2.0], + [3.0, 1.2, -6e-1], + [-1.77635684e-15, 0, 0], + ]), + np.array([ + [0, 0.4472136, 0.89442719], + [0.91287093, 0.36514837, -0.18257419], + [-1.0, 0, 0], + ]), + ) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None + self.data3 = None + self.pmInstance1 = None + self.pmInstance2 = None + self.gram_schmidt_out = None def test_gram_schmidt_orthonormal(self): - - npt.assert_allclose(matrix.gram_schmidt(self.data1), - np.array([[0., 0.4472136, 0.89442719], - [0.91287093, 0.36514837, -0.18257419], - [-1., 0., 0.]]), - err_msg='Incorrect Gram-Schmidt: orthonormal') - - npt.assert_raises(ValueError, matrix.gram_schmidt, self.data1, - return_opt='bla') + """Test gram_schmidt with orthonormal output.""" + npt.assert_allclose( + matrix.gram_schmidt(self.data1), + self.gram_schmidt_out[1], + err_msg='Incorrect Gram-Schmidt: orthonormal', + ) + + npt.assert_raises( + ValueError, + matrix.gram_schmidt, + self.data1, + return_opt='bla', + ) def test_gram_schmidt_orthogonal(self): - - npt.assert_allclose(matrix.gram_schmidt(self.data1, - return_opt='orthogonal'), - np.array([[0.00000000e+00, 1.00000000e+00, - 2.00000000e+00], - [3.00000000e+00, 1.20000000e+00, - -6.00000000e-01], - [-1.77635684e-15, 0.00000000e+00, - 0.00000000e+00]]), - err_msg='Incorrect Gram-Schmidt: orthogonal') + """Test gram_schmidt with orthogonal output.""" + npt.assert_allclose( + matrix.gram_schmidt(self.data1, return_opt='orthogonal'), + self.gram_schmidt_out[0], + err_msg='Incorrect Gram-Schmidt: orthogonal', + ) def test_gram_schmidt_both(self): - - npt.assert_allclose(matrix.gram_schmidt(self.data1, return_opt='both'), - (np.array([[0.00000000e+00, 1.00000000e+00, - 2.00000000e+00], - [3.00000000e+00, 1.20000000e+00, - -6.00000000e-01], - [-1.77635684e-15, 0.00000000e+00, - 0.00000000e+00]]), - np.array([[0., 0.4472136, 0.89442719], - [0.91287093, 0.36514837, -0.18257419], - [-1., 0., 0.]])), - err_msg='Incorrect Gram-Schmidt: both') + """Test gram_schmidt with both outputs.""" + npt.assert_allclose( + matrix.gram_schmidt(self.data1, return_opt='both'), + self.gram_schmidt_out, + err_msg='Incorrect Gram-Schmidt: both', + ) def test_nuclear_norm(self): - - npt.assert_almost_equal(matrix.nuclear_norm(self.data1), - 15.49193338482967, - err_msg='Incorrect nuclear norm') + """Test nuclear_norm.""" + npt.assert_almost_equal( + matrix.nuclear_norm(self.data1), + 15.49193338482967, + err_msg='Incorrect nuclear norm', + ) def test_project(self): - - npt.assert_array_equal(matrix.project(self.data2, self.data2 + 3), - np.array([0., 2.8, 5.6]), - err_msg='Incorrect projection') + """Test project.""" + npt.assert_array_equal( + matrix.project(self.data2, self.data2 + 3), + np.array([0, 2.8, 5.6]), + err_msg='Incorrect projection', + ) def test_rot_matrix(self): - - npt.assert_allclose(matrix.rot_matrix(np.pi / 6), - np.array([[0.8660254, -0.5], - [0.5, 0.8660254]]), - err_msg='Incorrect rotation matrix') + """Test rot_matrix.""" + npt.assert_allclose( + matrix.rot_matrix(np.pi / 6), + np.array([[0.8660254, -0.5], [0.5, 0.8660254]]), + err_msg='Incorrect rotation matrix', + ) def test_rotate(self): - - npt.assert_array_equal(matrix.rotate(self.data1, np.pi / 2), - np.array([[2, 5, 8], [1, 4, 7], [0, 3, 6]]), - err_msg='Incorrect rotation') + """Test rotate.""" + npt.assert_array_equal( + matrix.rotate(self.data1, np.pi / 2), + np.array([[2, 5, 8], [1, 4, 7], [0, 3, 6]]), + err_msg='Incorrect rotation', + ) npt.assert_raises(ValueError, matrix.rotate, self.data3, np.pi / 2) - def test_PowerMethod_converged(self): - - npt.assert_almost_equal(self.pmInstance1.spec_rad, - 0.90429242629600837, - err_msg='Incorrect spectral radius: converged') - - npt.assert_almost_equal(self.pmInstance1.inv_spec_rad, - 1.1058369736612865, - err_msg='Incorrect inverse spectral radius: ' - 'converged') - - def test_PowerMethod_unconverged(self): - - npt.assert_almost_equal(self.pmInstance2.spec_rad, - 0.92048833577059219, - err_msg='Incorrect spectral radius: ' - 'unconverged') - - npt.assert_almost_equal(self.pmInstance2.inv_spec_rad, - 1.0863798715741946, - err_msg='Incorrect inverse spectral radius: ' - 'unconverged') + def test_powermethod_converged(self): + """Test PowerMethod converged.""" + npt.assert_almost_equal( + self.pmInstance1.spec_rad, + 0.90429242629600837, + err_msg='Incorrect spectral radius: converged', + ) + + npt.assert_almost_equal( + self.pmInstance1.inv_spec_rad, + 1.1058369736612865, + err_msg='Incorrect inverse spectral radius: converged', + ) + + def test_powermethod_unconverged(self): + """Test PowerMethod unconverged.""" + npt.assert_almost_equal( + self.pmInstance2.spec_rad, + 0.92048833577059219, + err_msg='Incorrect spectral radius: unconverged', + ) + + npt.assert_almost_equal( + self.pmInstance2.inv_spec_rad, + 1.0863798715741946, + err_msg='Incorrect inverse spectral radius: unconverged', + ) class MetricsTestCase(TestCase): + """Test case for metrics module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(49).reshape(7, 7) self.mask = np.ones(self.data1.shape) self.ssim_res = 0.8963363560519094 @@ -213,7 +274,7 @@ def setUp(self): self.nrmse_res = 0.31136678840022625 def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.mask = None self.ssim_res = None @@ -224,149 +285,212 @@ def tearDown(self): @skipIf(import_skimage, 'skimage is installed.') # pragma: no cover def test_ssim_skimage_error(self): - + """Test ssim skimage error.""" npt.assert_raises(ImportError, metrics.ssim, self.data1, self.data1) @skipUnless(import_skimage, 'skimage not installed.') # pragma: no cover def test_ssim(self): - - npt.assert_almost_equal(metrics.ssim(self.data1, self.data1 ** 2), - self.ssim_res, - err_msg='Incorrect SSIM result') - - npt.assert_almost_equal(metrics.ssim(self.data1, self.data1 ** 2, - mask=self.mask), self.ssim_mask_res, - err_msg='Incorrect SSIM result') - - npt.assert_raises(ValueError, metrics.ssim, self.data1, self.data1, - mask=1) + """Test ssim.""" + npt.assert_almost_equal( + metrics.ssim(self.data1, self.data1 ** 2), + self.ssim_res, + err_msg='Incorrect SSIM result', + ) + + npt.assert_almost_equal( + metrics.ssim(self.data1, self.data1 ** 2, mask=self.mask), + self.ssim_mask_res, + err_msg='Incorrect SSIM result', + ) + + npt.assert_raises( + ValueError, + metrics.ssim, + self.data1, + self.data1, + mask=1, + ) def test_snr(self): - - npt.assert_almost_equal(metrics.snr(self.data1, self.data1 ** 2), - self.snr_res, - err_msg='Incorrect SNR result') - - npt.assert_almost_equal(metrics.snr(self.data1, self.data1 ** 2, - mask=self.mask), self.snr_res, - err_msg='Incorrect SNR result') + """Test snr.""" + npt.assert_almost_equal( + metrics.snr(self.data1, self.data1 ** 2), + self.snr_res, + err_msg='Incorrect SNR result', + ) + + npt.assert_almost_equal( + metrics.snr(self.data1, self.data1 ** 2, mask=self.mask), + self.snr_res, + err_msg='Incorrect SNR result', + ) def test_psnr(self): - - npt.assert_almost_equal(metrics.psnr(self.data1, self.data1 ** 2), - self.psnr_res, - err_msg='Incorrect PSNR result') - - npt.assert_almost_equal(metrics.psnr(self.data1, self.data1 ** 2, - mask=self.mask), self.psnr_res, - err_msg='Incorrect PSNR result') + """Test psnr.""" + npt.assert_almost_equal( + metrics.psnr(self.data1, self.data1 ** 2), + self.psnr_res, + err_msg='Incorrect PSNR result', + ) + + npt.assert_almost_equal( + metrics.psnr(self.data1, self.data1 ** 2, mask=self.mask), + self.psnr_res, + err_msg='Incorrect PSNR result', + ) def test_mse(self): - - npt.assert_almost_equal(metrics.mse(self.data1, self.data1 ** 2), - self.mse_res, - err_msg='Incorrect MSE result') - - npt.assert_almost_equal(metrics.mse(self.data1, self.data1 ** 2, - mask=self.mask), self.mse_res, - err_msg='Incorrect MSE result') + """Test mse.""" + npt.assert_almost_equal( + metrics.mse(self.data1, self.data1 ** 2), + self.mse_res, + err_msg='Incorrect MSE result', + ) + + npt.assert_almost_equal( + metrics.mse(self.data1, self.data1 ** 2, mask=self.mask), + self.mse_res, + err_msg='Incorrect MSE result', + ) def test_nrmse(self): + """Test nrmse.""" + npt.assert_almost_equal( + metrics.nrmse(self.data1, self.data1 ** 2), + self.nrmse_res, + err_msg='Incorrect NRMSE result', + ) - npt.assert_almost_equal(metrics.nrmse(self.data1, self.data1 ** 2), - self.nrmse_res, - err_msg='Incorrect NRMSE result') - - npt.assert_almost_equal(metrics.nrmse(self.data1, self.data1 ** 2, - mask=self.mask), self.nrmse_res, - err_msg='Incorrect NRMSE result') + npt.assert_almost_equal( + metrics.nrmse(self.data1, self.data1 ** 2, mask=self.mask), + self.nrmse_res, + err_msg='Incorrect NRMSE result', + ) class StatsTestCase(TestCase): + """Test case for stats module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3) self.data2 = np.arange(18).reshape(2, 3, 3) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None @skipIf(import_astropy, 'Astropy is installed.') # pragma: no cover def test_gaussian_kernel_astropy_error(self): - - npt.assert_raises(ImportError, stats.gaussian_kernel, - self.data1.shape, 1) + """Test gaussian_kernel astropy error.""" + npt.assert_raises( + ImportError, + stats.gaussian_kernel, + self.data1.shape, + 1, + ) @skipUnless(import_astropy, 'Astropy not installed.') # pragma: no cover def test_gaussian_kernel_max(self): - - npt.assert_allclose(stats.gaussian_kernel(self.data1.shape, 1), - np.array([[0.36787944, 0.60653066, 0.36787944], - [0.60653066, 1., 0.60653066], - [0.36787944, 0.60653066, 0.36787944]]), - err_msg='Incorrect gaussian kernel: max norm') - - npt.assert_raises(ValueError, stats.gaussian_kernel, - self.data1.shape, 1, norm='bla') + """Test gaussian_kernel with max norm.""" + npt.assert_allclose( + stats.gaussian_kernel(self.data1.shape, 1), + np.array([ + [0.36787944, 0.60653066, 0.36787944], + [0.60653066, 1.0, 0.60653066], + [0.36787944, 0.60653066, 0.36787944], + ]), + err_msg='Incorrect gaussian kernel: max norm', + ) + + npt.assert_raises( + ValueError, + stats.gaussian_kernel, + self.data1.shape, + 1, + norm='bla', + ) @skipUnless(import_astropy, 'Astropy not installed.') # pragma: no cover def test_gaussian_kernel_sum(self): - - npt.assert_allclose(stats.gaussian_kernel(self.data1.shape, 1, - norm='sum'), - np.array([[0.07511361, 0.1238414, 0.07511361], - [0.1238414, 0.20417996, 0.1238414], - [0.07511361, 0.1238414, 0.07511361]]), - err_msg='Incorrect gaussian kernel: sum norm') + """Test gaussian_kernel with sum norm.""" + npt.assert_allclose( + stats.gaussian_kernel(self.data1.shape, 1, norm='sum'), + np.array([ + [0.07511361, 0.1238414, 0.07511361], + [0.1238414, 0.20417996, 0.1238414], + [0.07511361, 0.1238414, 0.07511361], + ]), + err_msg='Incorrect gaussian kernel: sum norm', + ) @skipUnless(import_astropy, 'Astropy not installed.') # pragma: no cover def test_gaussian_kernel_none(self): - - npt.assert_allclose(stats.gaussian_kernel(self.data1.shape, 1, - norm='none'), - np.array([[0.05854983, 0.09653235, 0.05854983], - [0.09653235, 0.15915494, 0.09653235], - [0.05854983, 0.09653235, 0.05854983]]), - err_msg='Incorrect gaussian kernel: sum norm') + """Test gaussian_kernel with no norm.""" + npt.assert_allclose( + stats.gaussian_kernel(self.data1.shape, 1, norm='none'), + np.array([ + [0.05854983, 0.09653235, 0.05854983], + [0.09653235, 0.15915494, 0.09653235], + [0.05854983, 0.09653235, 0.05854983], + ]), + err_msg='Incorrect gaussian kernel: sum norm', + ) def test_mad(self): - - npt.assert_equal(stats.mad(self.data1), 2.0, - err_msg='Incorrect median absolute deviation') + """Test mad.""" + npt.assert_equal( + stats.mad(self.data1), + 2.0, + err_msg='Incorrect median absolute deviation', + ) def test_mse(self): - - npt.assert_equal(stats.mse(self.data1, self.data1 + 2), 4.0, - err_msg='Incorrect mean squared error') + """Test mse.""" + npt.assert_equal( + stats.mse(self.data1, self.data1 + 2), + 4.0, + err_msg='Incorrect mean squared error', + ) def test_psnr_starck(self): - - npt.assert_almost_equal(stats.psnr(self.data1, self.data1 + 2), - 12.041199826559248, - err_msg='Incorrect PSNR: starck') - - npt.assert_raises(ValueError, stats.psnr, self.data1, self.data1, - method='bla') + """Test psnr.""" + npt.assert_almost_equal( + stats.psnr(self.data1, self.data1 + 2), + 12.041199826559248, + err_msg='Incorrect PSNR: starck', + ) + + npt.assert_raises( + ValueError, + stats.psnr, + self.data1, + self.data1, + method='bla', + ) def test_psnr_wiki(self): - - npt.assert_almost_equal(stats.psnr(self.data1, self.data1 + 2, - method='wiki'), - 42.110203695399477, - err_msg='Incorrect PSNR: wiki') + """Test psnr wiki method.""" + npt.assert_almost_equal( + stats.psnr(self.data1, self.data1 + 2, method='wiki'), + 42.110203695399477, + err_msg='Incorrect PSNR: wiki', + ) def test_psnr_stack(self): - - npt.assert_almost_equal(stats.psnr_stack(self.data2, self.data2 + 2), - 12.041199826559248, - err_msg='Incorrect PSNR stack') + """Test psnr stack.""" + npt.assert_almost_equal( + stats.psnr_stack(self.data2, self.data2 + 2), + 12.041199826559248, + err_msg='Incorrect PSNR stack', + ) npt.assert_raises(ValueError, stats.psnr_stack, self.data1, self.data1) def test_sigma_mad(self): - - npt.assert_almost_equal(stats.sigma_mad(self.data1), - 2.9651999999999998, - err_msg='Incorrect sigma from MAD') + """Test sigma_mad.""" + npt.assert_almost_equal( + stats.sigma_mad(self.data1), + 2.9651999999999998, + err_msg='Incorrect sigma from MAD', + ) diff --git a/modopt/tests/test_opt.py b/modopt/tests/test_opt.py index c1e6c808..b6805006 100644 --- a/modopt/tests/test_opt.py +++ b/modopt/tests/test_opt.py @@ -1,18 +1,21 @@ # -*- coding: utf-8 -*- -"""UNIT TESTS FOR SIGNAL +"""UNIT TESTS FOR OPT. -This module contains unit tests for the modopt.signal module. +This module contains unit tests for the modopt.opt module. :Author: Samuel Farrens """ +from builtins import zip from unittest import TestCase, skipIf, skipUnless + import numpy as np import numpy.testing as npt -from builtins import zip -from modopt.opt import * + +from modopt.opt import algorithms, cost, gradient, linear, proximity, reweight + try: import sklearn except ImportError: # pragma: no cover @@ -21,94 +24,148 @@ import_sklearn = True -class dummy(object): +# Basic functions to be used as operators or as dummy functions +func_identity = lambda x_val: x_val +func_double = lambda x_val: x_val * 2 +func_sq = lambda x_val: x_val ** 2 +func_cube = lambda x_val: x_val ** 3 + + +class Dummy(object): + """Dummy class for tests.""" + pass class AlgorithmTestCase(TestCase): + """Test case for algorithms module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) self.data2 = self.data1 + np.random.randn(*self.data1.shape) * 1e-6 self.data3 = np.arange(9).reshape(3, 3).astype(float) + 1 - grad_inst = gradient.GradBasic(self.data1, lambda x: x, lambda x: x) + + grad_inst = gradient.GradBasic( + self.data1, + func_identity, + func_identity, + ) + prox_inst = proximity.Positivity() prox_dual_inst = proximity.IdentityProx() linear_inst = linear.Identity() reweight_inst = reweight.cwbReweight(self.data3) cost_inst = cost.costObj([grad_inst, prox_inst, prox_dual_inst]) self.setup = algorithms.SetUp() - self.fb1 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - beta_update=lambda x: x) - self.fb2 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - cost=cost_inst, - lambda_update=None) - self.fb3 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - beta_update=lambda x: x, - a_cd=3) - self.fb4 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - beta_update=lambda x: x, - r_lazy=3, - p_lazy=0.7, - q_lazy=0.7) - self.fb5 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - restart_strategy='adaptive', - xi_restart=0.9) - self.fb6 = algorithms.ForwardBackward(self.data1, - grad=grad_inst, - prox=prox_inst, - restart_strategy='greedy', - xi_restart=0.9, - min_beta=1.0, - s_greedy=1.1) - self.gfb1 = algorithms.GenForwardBackward(self.data1, - grad=grad_inst, - prox_list=[prox_inst, - prox_dual_inst], - gamma_update=lambda x: x, - lambda_update=lambda x: x) - self.gfb2 = algorithms.GenForwardBackward(self.data1, - grad=grad_inst, - prox_list=[prox_inst, - prox_dual_inst], - cost=cost_inst) - self.gfb3 = algorithms.GenForwardBackward(self.data1, - grad=grad_inst, - prox_list=[prox_inst, - prox_dual_inst], - cost=cost_inst, - step_size=2) - self.condat1 = algorithms.Condat(self.data1, self.data2, - grad=grad_inst, - prox=prox_inst, - prox_dual=prox_dual_inst, - sigma_update=lambda x: x, - tau_update=lambda x: x, - rho_update=lambda x: x) - self.condat2 = algorithms.Condat(self.data1, self.data2, - grad=grad_inst, - prox=prox_inst, - prox_dual=prox_dual_inst, - linear=linear_inst, - cost=cost_inst, - reweight=reweight_inst) - self.condat3 = algorithms.Condat(self.data1, self.data2, - grad=grad_inst, - prox=prox_inst, - prox_dual=prox_dual_inst, - linear=dummy(), - cost=cost_inst, auto_iterate=False) + + self.fb1 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + beta_update=func_identity, + ) + + self.fb2 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + cost=cost_inst, + lambda_update=None, + ) + + self.fb3 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + beta_update=func_identity, + a_cd=3, + ) + + self.fb4 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + beta_update=func_identity, + r_lazy=3, + p_lazy=0.7, + q_lazy=0.7, + ) + + self.fb5 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + restart_strategy='adaptive', + xi_restart=0.9, + ) + + self.fb6 = algorithms.ForwardBackward( + self.data1, + grad=grad_inst, + prox=prox_inst, + restart_strategy='greedy', + xi_restart=0.9, + min_beta=1.0, + s_greedy=1.1, + ) + + self.gfb1 = algorithms.GenForwardBackward( + self.data1, + grad=grad_inst, + prox_list=[prox_inst, prox_dual_inst], + gamma_update=func_identity, + lambda_update=func_identity, + ) + + self.gfb2 = algorithms.GenForwardBackward( + self.data1, + grad=grad_inst, + prox_list=[prox_inst, prox_dual_inst], + cost=cost_inst, + ) + + self.gfb3 = algorithms.GenForwardBackward( + self.data1, + grad=grad_inst, + prox_list=[prox_inst, prox_dual_inst], + cost=cost_inst, + step_size=2, + ) + + self.condat1 = algorithms.Condat( + self.data1, + self.data2, + grad=grad_inst, + prox=prox_inst, + prox_dual=prox_dual_inst, + sigma_update=func_identity, + tau_update=func_identity, + rho_update=func_identity, + ) + + self.condat2 = algorithms.Condat( + self.data1, + self.data2, + grad=grad_inst, + prox=prox_inst, + prox_dual=prox_dual_inst, + linear=linear_inst, + cost=cost_inst, + reweight=reweight_inst, + ) + + self.condat3 = algorithms.Condat( + self.data1, + self.data2, + grad=grad_inst, + prox=prox_inst, + prox_dual=prox_dual_inst, + linear=Dummy(), + cost=cost_inst, + auto_iterate=False, + ) + self.pogm1 = algorithms.POGM( u=self.data1, x=self.data1, @@ -117,12 +174,13 @@ def setUp(self): grad=grad_inst, prox=prox_inst, ) - self.dummy = dummy() - self.dummy.cost = lambda x: x + + self.dummy = Dummy() + self.dummy.cost = func_identity self.setup._check_operator(self.dummy.cost) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.setup = None @@ -136,7 +194,7 @@ def tearDown(self): self.dummy = None def test_set_up(self): - + """Test set_up.""" npt.assert_raises(TypeError, self.setup._check_input_data, 1) npt.assert_raises(TypeError, self.setup._check_param, 1) @@ -144,61 +202,121 @@ def test_set_up(self): npt.assert_raises(TypeError, self.setup._check_param_update, 1) def test_forward_backward(self): + """Test forward_backward.""" + npt.assert_array_equal( + self.fb1.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) - npt.assert_array_equal(self.fb1.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') - - npt.assert_array_equal(self.fb2.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') + npt.assert_array_equal( + self.fb2.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) - npt.assert_array_equal(self.fb3.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') + npt.assert_array_equal( + self.fb3.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) - npt.assert_array_equal(self.fb4.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') + npt.assert_array_equal( + self.fb4.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) - npt.assert_array_equal(self.fb5.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') + npt.assert_array_equal( + self.fb5.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) - npt.assert_array_equal(self.fb6.x_final, self.data1, - err_msg='Incorrect ForwardBackward result.') + npt.assert_array_equal( + self.fb6.x_final, + self.data1, + err_msg='Incorrect ForwardBackward result.', + ) def test_gen_forward_backward(self): + """Test gen_forward_backward.""" + npt.assert_array_equal( + self.gfb1.x_final, + self.data1, + err_msg='Incorrect GenForwardBackward result.', + ) - npt.assert_array_equal(self.gfb1.x_final, self.data1, - err_msg='Incorrect GenForwardBackward result.') - - npt.assert_array_equal(self.gfb2.x_final, self.data1, - err_msg='Incorrect GenForwardBackward result.') + npt.assert_array_equal( + self.gfb2.x_final, + self.data1, + err_msg='Incorrect GenForwardBackward result.', + ) - npt.assert_array_equal(self.gfb3.x_final, self.data1, - err_msg='Incorrect GenForwardBackward result.') + npt.assert_array_equal( + self.gfb3.x_final, + self.data1, + err_msg='Incorrect GenForwardBackward result.', + ) - npt.assert_equal(self.gfb3.step_size, 2, - err_msg='Incorrect step size.') + npt.assert_equal( + self.gfb3.step_size, + 2, + err_msg='Incorrect step size.', + ) - npt.assert_raises(TypeError, algorithms.GenForwardBackward, - self.data1, self.dummy, [self.dummy], weights=1) + npt.assert_raises( + TypeError, + algorithms.GenForwardBackward, + self.data1, + self.dummy, + [self.dummy], + weights=1, + ) - npt.assert_raises(ValueError, algorithms.GenForwardBackward, - self.data1, self.dummy, [self.dummy], weights=[1]) + npt.assert_raises( + ValueError, + algorithms.GenForwardBackward, + self.data1, + self.dummy, + [self.dummy], + weights=[1], + ) - npt.assert_raises(ValueError, algorithms.GenForwardBackward, - self.data1, self.dummy, [self.dummy], - weights=[0.5, 0.5]) + npt.assert_raises( + ValueError, + algorithms.GenForwardBackward, + self.data1, + self.dummy, + [self.dummy], + weights=[0.5, 0.5], + ) - npt.assert_raises(ValueError, algorithms.GenForwardBackward, - self.data1, self.dummy, [self.dummy], weights=[0.5]) + npt.assert_raises( + ValueError, + algorithms.GenForwardBackward, + self.data1, + self.dummy, + [self.dummy], + weights=[0.5], + ) def test_condat(self): + """Test gen_condat.""" + npt.assert_almost_equal( + self.condat1.x_final, + self.data1, + err_msg='Incorrect Condat result.', + ) - npt.assert_almost_equal(self.condat1.x_final, self.data1, - err_msg='Incorrect Condat result.') - - npt.assert_almost_equal(self.condat2.x_final, self.data1, - err_msg='Incorrect Condat result.') + npt.assert_almost_equal( + self.condat2.x_final, + self.data1, + err_msg='Incorrect Condat result.', + ) def test_pogm(self): + """Test pogm.""" npt.assert_almost_equal( self.pogm1.x_final, self.data1, @@ -207,39 +325,50 @@ def test_pogm(self): class CostTestCase(TestCase): + """Test case for cost module.""" def setUp(self): - - class dummy(object): - pass - - dummy_inst1 = dummy() - dummy_inst1.cost = lambda x: x ** 2 - dummy_inst2 = dummy() - dummy_inst2.cost = lambda x: x ** 3 + """Set test parameter values.""" + dummy_inst1 = Dummy() + dummy_inst1.cost = func_sq + dummy_inst2 = Dummy() + dummy_inst2.cost = func_cube self.inst1 = cost.costObj([dummy_inst1, dummy_inst2]) - [self.inst1.get_cost(2) for i in range(2)] self.inst2 = cost.costObj([dummy_inst1, dummy_inst2], cost_interval=2) - [self.inst2.get_cost(2) for i in range(6)] # Test that by default cost of False if interval is None - self.inst_none = cost.costObj([dummy_inst1, dummy_inst2], - cost_interval=None) - [self.inst_none.get_cost(2) for i in range(6)] - self.dummy = dummy() + self.inst_none = cost.costObj( + [dummy_inst1, dummy_inst2], + cost_interval=None, + ) + for _ in range(2): + self.inst1.get_cost(2) + for _ in range(6): + self.inst2.get_cost(2) + self.inst_none.get_cost(2) + self.dummy = Dummy() def tearDown(self): - + """Unset test parameter values.""" self.inst = None def test_cost_object(self): - - npt.assert_equal(self.inst1.get_cost(2), False, - err_msg='Incorrect cost test result.') - npt.assert_equal(self.inst1.get_cost(2), True, - err_msg='Incorrect cost test result.') - npt.assert_equal(self.inst_none.get_cost(2), False, - err_msg='Incorrect cost test result.') + """Test cost_object.""" + npt.assert_equal( + self.inst1.get_cost(2), + False, + err_msg='Incorrect cost test result.', + ) + npt.assert_equal( + self.inst1.get_cost(2), + True, + err_msg='Incorrect cost test result.', + ) + npt.assert_equal( + self.inst_none.get_cost(2), + False, + err_msg='Incorrect cost test result.', + ) npt.assert_equal(self.inst1.cost, 12, err_msg='Incorrect cost value.') @@ -251,84 +380,112 @@ def test_cost_object(self): class GradientTestCase(TestCase): + """Test case for gradient module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) - self.gp = gradient.GradParent(self.data1, lambda x: x ** 2, - lambda x: x ** 3, lambda x: x, - lambda x: 1.0, data_type=np.floating) + self.gp = gradient.GradParent( + self.data1, + func_sq, + func_cube, + func_identity, + lambda input_val: 1.0, + data_type=np.floating, + ) self.gp.grad = self.gp.get_grad(self.data1) - self.gb = gradient.GradBasic(self.data1, lambda x: x ** 2, - lambda x: x ** 3) + self.gb = gradient.GradBasic( + self.data1, + func_sq, + func_cube, + ) self.gb.get_grad(self.data1) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.gp = None self.gb = None def test_grad_parent_operators(self): + """Test GradParent.""" + npt.assert_array_equal( + self.gp.op(self.data1), + np.array([[0, 1.0, 4.0], [9.0, 16.0, 25.0], [36.0, 49.0, 64.0]]), + err_msg='Incorrect gradient operation.', + ) - npt.assert_array_equal(self.gp.op(self.data1), np.array([[0., 1., 4.], - [9., 16., 25.], [36., 49., 64.]]), - err_msg='Incorrect gradient operation.') - - npt.assert_array_equal(self.gp.trans_op(self.data1), - np.array([[0., 1., 8.], [27., 64., 125.], - [216., 343., 512.]]), - err_msg='Incorrect gradient transpose ' - 'operation.') + npt.assert_array_equal( + self.gp.trans_op(self.data1), + np.array( + [[0, 1.0, 8.0], [27.0, 64.0, 125.0], [216.0, 343.0, 512.0]], + ), + err_msg='Incorrect gradient transpose operation.', + ) - npt.assert_array_equal(self.gp.trans_op_op(self.data1), - np.array([[0.00000000e+00, 1.00000000e+00, - 6.40000000e+01], - [7.29000000e+02, 4.09600000e+03, - 1.56250000e+04], - [4.66560000e+04, 1.17649000e+05, - 2.62144000e+05]]), - err_msg='Incorrect gradient transpose ' - 'operation operation.') + npt.assert_array_equal( + self.gp.trans_op_op(self.data1), + np.array([ + [0, 1.0, 6.40000000e1], + [7.29000000e2, 4.09600000e3, 1.56250000e4], + [4.66560000e4, 1.17649000e5, 2.62144000e5], + ]), + err_msg='Incorrect gradient transpose operation operation.', + ) - npt.assert_equal(self.gp.cost(self.data1), 1.0, - err_msg='Incorrect cost.') + npt.assert_equal( + self.gp.cost(self.data1), + 1.0, + err_msg='Incorrect cost.', + ) - npt.assert_raises(TypeError, gradient.GradParent, 1, - lambda x: x ** 2, lambda x: x ** 3) + npt.assert_raises( + TypeError, + gradient.GradParent, + 1, + func_sq, + func_cube, + ) def test_grad_basic_gradient(self): - - npt.assert_array_equal(self.gb.grad, - np.array([[0.00000000e+00, 0.00000000e+00, - 8.00000000e+00], - [2.16000000e+02, 1.72800000e+03, - 8.00000000e+03], - [2.70000000e+04, 7.40880000e+04, - 1.75616000e+05]]), - err_msg='Incorrect gradient.') + """Test GradBasic.""" + npt.assert_array_equal( + self.gb.grad, + np.array([ + [0, 0, 8.0], + [2.16000000e2, 1.72800000e3, 8.0e3], + [2.70000000e4, 7.40880000e4, 1.75616000e5], + ]), + err_msg='Incorrect gradient.', + ) class LinearTestCase(TestCase): + """Test case for linear module.""" def setUp(self): - - self.parent = linear.LinearParent(lambda x: x ** 2, lambda x: x ** 3) + """Set test parameter values.""" + self.parent = linear.LinearParent( + func_sq, + func_cube, + ) self.ident = linear.Identity() filters = np.arange(8).reshape(2, 2, 2).astype(float) self.wave = linear.WaveletConvolve(filters) self.combo = linear.LinearCombo([self.parent, self.parent]) - self.combo_weight = linear.LinearCombo([self.parent, self.parent], - [1.0, 1.0]) + self.combo_weight = linear.LinearCombo( + [self.parent, self.parent], + [1.0, 1.0], + ) self.data1 = np.arange(18).reshape(2, 3, 3).astype(float) self.data2 = np.arange(4).reshape(1, 2, 2).astype(float) self.data3 = np.arange(8).reshape(1, 2, 2, 2).astype(float) - self.data4 = np.array([[[[0., 0.], [0., 4.]], [[0., 4.], [8., 28.]]]]) - self.data5 = np.array([[[28., 62.], [68., 140.]]]) - self.dummy = dummy() + self.data4 = np.array([[[[0, 0], [0, 4.0]], [[0, 4.0], [8.0, 28.0]]]]) + self.data5 = np.array([[[28.0, 62.0], [68.0, 140.0]]]) + self.dummy = Dummy() def tearDown(self): - + """Unset test parameter values.""" self.parent = None self.ident = None self.combo = None @@ -341,41 +498,62 @@ def tearDown(self): self.dummy = None def test_linear_parent(self): + """Test LinearParent.""" + npt.assert_equal( + self.parent.op(2), + 4, + err_msg='Incorrect linear parent operation.', + ) - npt.assert_equal(self.parent.op(2), 4, err_msg='Incorrect linear ' - 'parent operation.') - - npt.assert_equal(self.parent.adj_op(2), 8, err_msg='Incorrect linear ' - 'parent adjoint ' - 'operation.') + npt.assert_equal( + self.parent.adj_op(2), + 8, + err_msg='Incorrect linear parent adjoint operation.', + ) npt.assert_raises(TypeError, linear.LinearParent, 0, 0) def test_identity(self): + """Test Identity.""" + npt.assert_equal( + self.ident.op(1.0), + 1.0, + err_msg='Incorrect identity operation.', + ) - npt.assert_equal(self.ident.op(1.0), 1.0, - err_msg='Incorrect identity operation.') - - npt.assert_equal(self.ident.adj_op(1.0), 1.0, - err_msg='Incorrect identity adjoint operation.') + npt.assert_equal( + self.ident.adj_op(1.0), + 1.0, + err_msg='Incorrect identity adjoint operation.', + ) def test_wavelet_convolve(self): + """Test WaveletConvolve.""" + npt.assert_almost_equal( + self.wave.op(self.data2), + self.data4, + err_msg='Incorrect wavelet convolution operation.', + ) - npt.assert_almost_equal(self.wave.op(self.data2), self.data4, - err_msg='Incorrect wavelet convolution ' - 'operation.') - - npt.assert_almost_equal(self.wave.adj_op(self.data3), self.data5, - err_msg='Incorrect wavelet convolution ' - 'adjoint operation.') + npt.assert_almost_equal( + self.wave.adj_op(self.data3), + self.data5, + err_msg='Incorrect wavelet convolution adjoint operation.', + ) def test_linear_combo(self): + """Test LinearCombo.""" + npt.assert_equal( + self.combo.op(2), + np.array([4, 4]).astype(object), + err_msg='Incorrect combined linear operation', + ) - npt.assert_equal(self.combo.op(2), np.array([4, 4]).astype(object), - err_msg='Incorrect combined linear operation') - - npt.assert_equal(self.combo.adj_op([2, 2]), 8.0, - err_msg='Incorrect combined linear adjoint operation') + npt.assert_equal( + self.combo.adj_op([2, 2]), + 8.0, + err_msg='Incorrect combined linear adjoint operation', + ) npt.assert_raises(TypeError, linear.LinearCombo, self.parent) @@ -383,40 +561,61 @@ def test_linear_combo(self): npt.assert_raises(ValueError, linear.LinearCombo, [self.dummy]) - self.dummy.op = lambda x: x + self.dummy.op = func_identity npt.assert_raises(ValueError, linear.LinearCombo, [self.dummy]) def test_linear_combo_weight(self): + """Test LinearCombo with weight .""" + npt.assert_equal( + self.combo_weight.op(2), + np.array([4, 4]).astype(object), + err_msg='Incorrect combined linear operation', + ) - npt.assert_equal(self.combo_weight.op(2), - np.array([4, 4]).astype(object), - err_msg='Incorrect combined linear operation') - - npt.assert_equal(self.combo_weight.adj_op([2, 2]), 16.0, - err_msg='Incorrect combined linear adjoint operation') + npt.assert_equal( + self.combo_weight.adj_op([2, 2]), + 16.0, + err_msg='Incorrect combined linear adjoint operation', + ) - npt.assert_raises(ValueError, linear.LinearCombo, - [self.parent, self.parent], [1.0]) + npt.assert_raises( + ValueError, + linear.LinearCombo, + [self.parent, self.parent], + [1.0], + ) - npt.assert_raises(TypeError, linear.LinearCombo, - [self.parent, self.parent], ['1', '1']) + npt.assert_raises( + TypeError, + linear.LinearCombo, + [self.parent, self.parent], + ['1', '1'], + ) class ProximityTestCase(TestCase): + """Test case for proximity module.""" def setUp(self): - - self.parent = proximity.ProximityParent(lambda x: x ** 2, - lambda x: x * 2) + """Set test parameter values.""" + self.parent = proximity.ProximityParent( + func_sq, + func_double, + ) self.identity = proximity.IdentityProx() self.positivity = proximity.Positivity() weights = np.ones(9).reshape(3, 3).astype(float) * 3 - self.sparsethresh = proximity.SparseThreshold(linear.Identity(), - weights) + self.sparsethresh = proximity.SparseThreshold( + linear.Identity(), + weights, + ) self.lowrank = proximity.LowRankMatrix(10.0, thresh_type='hard') - self.lowrank_ngole = proximity.LowRankMatrix(10.0, lowr_type='ngole', - operator=lambda x: x * 2) + self.lowrank_ngole = proximity.LowRankMatrix( + 10.0, + lowr_type='ngole', + operator=func_double, + ) self.linear_comp = proximity.LinearCompositionProx( linear_op=linear.Identity(), prox_op=self.sparsethresh, @@ -425,59 +624,76 @@ def setUp(self): if import_sklearn: self.owl = proximity.OrderedWeightedL1Norm(weights.flatten()) self.ridge = proximity.Ridge(linear.Identity(), weights) - self.elasticnet_alpha_0 = proximity.ElasticNet(linear.Identity(), - alpha=0, - beta=weights) - self.elasticnet_beta_0 = proximity.ElasticNet(linear.Identity(), - alpha=weights, - beta=0) + self.elasticnet_alpha0 = proximity.ElasticNet( + linear.Identity(), + alpha=0, + beta=weights, + ) + self.elasticnet_beta0 = proximity.ElasticNet( + linear.Identity(), + alpha=weights, + beta=0, + ) self.one_support = proximity.KSupportNorm(beta=0.2, k_value=1) self.five_support_norm = proximity.KSupportNorm(beta=3, k_value=5) self.d_support = proximity.KSupportNorm(beta=3.0 * 2, k_value=19) - self.group_lasso = proximity.GroupLASSO(weights=np.tile(weights, - (4, 1, 1))) + self.group_lasso = proximity.GroupLASSO( + weights=np.tile(weights, (4, 1, 1)), + ) self.data1 = np.arange(9).reshape(3, 3).astype(float) - self.data2 = np.array([[-0., -0., -0.], [0., 1., 2.], [3., 4., 5.]]) + self.data2 = np.array([[-0, -0, -0], [0, 1.0, 2.0], [3.0, 4.0, 5.0]]) self.data3 = np.arange(18).reshape(2, 3, 3).astype(float) - self.data4 = np.array([[[2.73843189, 3.14594066, 3.55344943], - [3.9609582, 4.36846698, 4.77597575], - [5.18348452, 5.59099329, 5.99850206]], - [[8.07085295, 9.2718846, 10.47291625], - [11.67394789, 12.87497954, 14.07601119], - [15.27704284, 16.47807449, 17.67910614]]]) - self.data5 = np.array([[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], - [[4.00795282, 4.60438026, 5.2008077], - [5.79723515, 6.39366259, 6.99009003], - [7.58651747, 8.18294492, 8.77937236]]]) + self.data4 = np.array([ + [ + [2.73843189, 3.14594066, 3.55344943], + [3.9609582, 4.36846698, 4.77597575], + [5.18348452, 5.59099329, 5.99850206], + ], + [ + [8.07085295, 9.2718846, 10.47291625], + [11.67394789, 12.87497954, 14.07601119], + [15.27704284, 16.47807449, 17.67910614], + ], + ]) + self.data5 = np.array([ + [[0, 0, 0], [0, 0, 0], [0, 0, 0]], + [ + [4.00795282, 4.60438026, 5.2008077], + [5.79723515, 6.39366259, 6.99009003], + [7.58651747, 8.18294492, 8.77937236], + ], + ]) self.data6 = self.data3 * -1 self.data7 = self.combo.op(self.data6) self.data8 = np.empty(2, dtype=np.ndarray) - self.data8[0] = np.array([[-0., -1., -2.], [-3., -4., -5.], - [-6., -7., -8.]]) - self.data8[1] = np.array([[-0., -0., -0.], [-0., -0., -0.], - [-0., -0., -0.]]) + self.data8[0] = np.array( + [[-0, -1.0, -2.0], [-3.0, -4.0, -5.0], [-6.0, -7.0, -8.0]], + ) + self.data8[1] = np.array( + [[-0, -0, -0], [-0, -0, -0], [-0, -0, -0]], + ) self.data9 = self.data1 * (1 + 1j) self.data10 = self.data9 / (2 * 3 + 1) - self.data11 = np.asarray([[0., 0., 0.], - [0., 1., 1.25], - [1.5, 1.75, 2.]]) + self.data11 = np.asarray( + [[0, 0, 0], [0, 1.0, 1.25], [1.5, 1.75, 2.0]], + ) self.random_data = 3 * np.random.random( - self.group_lasso.weights[0].shape) + self.group_lasso.weights[0].shape, + ) self.random_data_tile = np.tile( self.random_data, - (self.group_lasso.weights.shape[0], 1, 1) + (self.group_lasso.weights.shape[0], 1, 1), ) self.gl_result_data = 2 * self.random_data_tile - 3 - self.gl_result_data = (self.gl_result_data * - (self.gl_result_data > 0).astype('int')) / 2 - - class dummy(object): - pass + self.gl_result_data = np.array( + (self.gl_result_data * (self.gl_result_data > 0).astype('int')) + / 2, + ) - self.dummy = dummy() + self.dummy = Dummy() def tearDown(self): - + """Unset test parameter values.""" self.parent = None self.identity = None self.positivity = None @@ -498,66 +714,109 @@ def tearDown(self): self.gl_result_data = None def test_proximity_parent(self): + """Test ProximityParent.""" + npt.assert_equal( + self.parent.op(3), + 9, + err_msg='Inccoret proximity parent operation.', + ) - npt.assert_equal(self.parent.op(3), 9, - err_msg='Inccoret proximity parent operation.') - - npt.assert_equal(self.parent.cost(3), 6, - err_msg='Inccoret proximity parent cost.') + npt.assert_equal( + self.parent.cost(3), + 6, + err_msg='Incorrect proximity parent cost.', + ) def test_identity(self): + """Test IdentityProx.""" + npt.assert_equal( + self.identity.op(3), + 3, + err_msg='Incorrect proximity identity operation.', + ) - npt.assert_equal(self.identity.op(3), 3, - err_msg='Inccoret proximity identity operation.') - - npt.assert_equal(self.identity.cost(3), 0.0, - err_msg='Inccoret proximity identity cost.') + npt.assert_equal( + self.identity.cost(3), + 0, + err_msg='Incorrect proximity identity cost.', + ) def test_positivity(self): + """Test Positivity.""" + npt.assert_equal( + self.positivity.op(-3), + 0, + err_msg='Incorrect proximity positivity operation.', + ) - npt.assert_equal(self.positivity.op(-3), 0, - err_msg='Inccoret proximity positivity operation.') - - npt.assert_equal(self.positivity.cost(-3, verbose=True), 0.0, - err_msg='Inccoret proximity positivity cost.') + npt.assert_equal( + self.positivity.cost(-3, verbose=True), + 0, + err_msg='Incorrect proximity positivity cost.', + ) def test_sparse_threshold(self): + """Test SparseThreshold.""" + npt.assert_array_equal( + self.sparsethresh.op(self.data1), + self.data2, + err_msg='Incorrect sparse threshold operation.', + ) - npt.assert_array_equal(self.sparsethresh.op(self.data1), self.data2, - err_msg='Inccorect sparse threshold operation.') - - npt.assert_equal(self.sparsethresh.cost(self.data1, verbose=True), - 108.0, err_msg='Inccoret sparse threshold cost.') + npt.assert_equal( + self.sparsethresh.cost(self.data1, verbose=True), + 108.0, + err_msg='Incorrect sparse threshold cost.', + ) def test_low_rank_matrix(self): + """Test LowRankMatrix.""" + npt.assert_almost_equal( + self.lowrank.op(self.data3), + self.data4, + err_msg='Incorrect low rank operation: standard', + ) - npt.assert_almost_equal(self.lowrank.op(self.data3), self.data4, - err_msg='Inccorect low rank operation: ' - 'standard') - - npt.assert_almost_equal(self.lowrank_ngole.op(self.data3), self.data5, - err_msg='Inccorect low rank operation: ' - 'ngole') + npt.assert_almost_equal( + self.lowrank_ngole.op(self.data3), + self.data5, + err_msg='Incorrect low rank operation: ngole', + ) - npt.assert_almost_equal(self.lowrank.cost(self.data3, verbose=True), - 469.39132942464983, - err_msg='Inccoret low rank cost.') + npt.assert_almost_equal( + self.lowrank.cost(self.data3, verbose=True), + 469.39132942464983, + err_msg='Incorrect low rank cost.', + ) def test_linear_comp_prox(self): - npt.assert_array_equal(self.linear_comp.op(self.data1), self.data2, - err_msg='Inccorect sparse threshold operation.') + """Test LinearCompositionProx.""" + npt.assert_array_equal( + self.linear_comp.op(self.data1), + self.data2, + err_msg='Incorrect sparse threshold operation.', + ) - npt.assert_equal(self.linear_comp.cost(self.data1, verbose=True), - 108.0, err_msg='Inccoret sparse threshold cost.') + npt.assert_equal( + self.linear_comp.cost(self.data1, verbose=True), + 108.0, + err_msg='Incorrect sparse threshold cost.', + ) def test_proximity_combo(self): - + """Test ProximityCombo.""" for data7, data8 in zip(self.data7, self.data8): - npt.assert_array_equal(data7, data8, - err_msg='Inccorect combined operation') + npt.assert_array_equal( + data7, + data8, + err_msg='Incorrect combined operation', + ) - npt.assert_equal(self.combo.cost(self.data6), 0.0, - err_msg='Inccoret combined cost.') + npt.assert_equal( + self.combo.cost(self.data6), + 0, + err_msg='Incorrect combined cost.', + ) npt.assert_raises(TypeError, proximity.ProximityCombo, 1) @@ -565,132 +824,172 @@ def test_proximity_combo(self): npt.assert_raises(ValueError, proximity.ProximityCombo, [self.dummy]) - self.dummy.op = lambda x: x + self.dummy.op = func_identity npt.assert_raises(ValueError, proximity.ProximityCombo, [self.dummy]) @skipIf(import_sklearn, 'sklearn is installed.') # pragma: no cover def test_owl_sklearn_error(self): - + """Test OrderedWeightedL1Norm with Scikit-Learn.""" npt.assert_raises(ImportError, proximity.OrderedWeightedL1Norm, 1) @skipUnless(import_sklearn, 'sklearn not installed.') # pragma: no cover def test_sparse_owl(self): - + """Test OrderedWeightedL1Norm.""" npt.assert_array_equal( self.owl.op(self.data1.flatten()), self.data2.flatten(), - err_msg='Incorrect sparse threshold operation.') - - npt.assert_equal(self.owl.cost(self.data1.flatten(), verbose=True), - 108.0, err_msg='Incorret sparse threshold cost.') - npt.assert_raises(ValueError, proximity.OrderedWeightedL1Norm, - np.arange(10)) - - def test_ridge(self): + err_msg='Incorrect sparse threshold operation.', + ) - npt.assert_array_equal(self.ridge.op(self.data9), self.data10, - err_msg='Incorect shrinkage operation.') + npt.assert_equal( + self.owl.cost(self.data1.flatten(), verbose=True), + 108.0, + err_msg='Incorrect sparse threshold cost.', + ) - npt.assert_equal(self.ridge.cost(self.data9, verbose=True), - 408.0 * 3.0, err_msg='Incorect shrinkage cost.') + npt.assert_raises( + ValueError, + proximity.OrderedWeightedL1Norm, + np.arange(10), + ) - def test_elastic_net_alpha_0(self): + def test_ridge(self): + """Test Ridge.""" + npt.assert_array_equal( + self.ridge.op(self.data9), + self.data10, + err_msg='Incorect shrinkage operation.', + ) - npt.assert_array_equal(self.elasticnet_alpha_0.op(self.data1), - self.data2, - err_msg='Incorect sparse threshold operation' - ' ElasticNet class.') + npt.assert_equal( + self.ridge.cost(self.data9, verbose=True), + 408.0 * 3.0, + err_msg='Incorect shrinkage cost.', + ) - npt.assert_equal(self.elasticnet_alpha_0.cost(self.data1), - 108.0, err_msg='Incorect shrinkage cost in ElasticNet' - ' class.') + def test_elastic_net_alpha0(self): + """Test ElasticNet.""" + npt.assert_array_equal( + self.elasticnet_alpha0.op(self.data1), + self.data2, + err_msg='Incorect sparse threshold operation ElasticNet class.', + ) - def test_elastic_net_beta_0(self): + npt.assert_equal( + self.elasticnet_alpha0.cost(self.data1), + 108.0, + err_msg='Incorect shrinkage cost in ElasticNet class.', + ) - npt.assert_array_equal(self.elasticnet_beta_0.op(self.data9), - self.data10, - err_msg='Incorect ridge operation' - ' ElasticNet class.') + def test_elastic_net_beta0(self): + """Test ElasticNet with beta=0.""" + npt.assert_array_equal( + self.elasticnet_beta0.op(self.data9), + self.data10, + err_msg='Incorect ridge operation ElasticNet class.', + ) - npt.assert_equal(self.elasticnet_beta_0.cost(self.data9, - verbose=True), - 408.0 * 3.0, err_msg='Incorect shrinkage cost in' - ' ElasticNet class.') + npt.assert_equal( + self.elasticnet_beta0.cost(self.data9, verbose=True), + 408.0 * 3.0, + err_msg='Incorect shrinkage cost in ElasticNet class.', + ) def test_one_support_norm(self): + """Test KSupportNorm with k=1.""" + npt.assert_allclose( + self.one_support.op(self.data1.flatten()), + self.data2.flatten(), + err_msg='Incorect sparse threshold operation for 1-support norm', + rtol=1e-6, + ) - npt.assert_allclose(self.one_support.op(self.data1.flatten()), - self.data2.flatten(), - err_msg='Incorect sparse threshold operation' + - ' for 1-support norm', - rtol=1e-6) - - npt.assert_equal(self.one_support.cost(self.data1.flatten(), - verbose=True), - 259.2, err_msg='Incorect sparse threshold cost.') - - npt.assert_raises(ValueError, proximity.KSupportNorm, 0.0, 0) + npt.assert_equal( + self.one_support.cost(self.data1.flatten(), verbose=True), + 259.2, + err_msg='Incorect sparse threshold cost.', + ) - def test_three_support_norm(self): + npt.assert_raises(ValueError, proximity.KSupportNorm, 0, 0) - npt.assert_allclose(self.five_support_norm.op(self.data1.flatten()), - self.data11.flatten(), - err_msg='Incorect sparse Ksupport norm operation', - rtol=1e-6) + def test_five_support_norm(self): + """Test KSupportNorm with k=5.""" + npt.assert_allclose( + self.five_support_norm.op(self.data1.flatten()), + self.data11.flatten(), + err_msg='Incorect sparse Ksupport norm operation', + rtol=1e-6, + ) - npt.assert_equal(self.five_support_norm.cost(self.data1.flatten(), - verbose=True), - 684.0, err_msg='Inccoret 3-support norm cost.') + npt.assert_equal( + self.five_support_norm.cost(self.data1.flatten(), verbose=True), + 684.0, + err_msg='Incorrect 5-support norm cost.', + ) - npt.assert_raises(ValueError, proximity.KSupportNorm, 0.0, 0) + npt.assert_raises(ValueError, proximity.KSupportNorm, 0, 0) def test_d_support_norm(self): + """Test KSupportNorm with k=19.""" + npt.assert_allclose( + self.d_support.op(self.data9.flatten()), + self.data10.flatten(), + err_msg='Incorect shrinkage operation for d-support norm', + rtol=1e-6, + ) - npt.assert_allclose(self.d_support.op(self.data9.flatten()), - self.data10.flatten(), - err_msg='Incorect shrinkage operation' + - ' for d-support norm', - rtol=1e-6) - - npt.assert_almost_equal(self.d_support.cost(self.data9.flatten(), - verbose=True), - 408.0 * 3.0, - err_msg='Incorect shrinkage cost for' + - ' d-support norm.') + npt.assert_almost_equal( + self.d_support.cost(self.data9.flatten(), verbose=True), + 408.0 * 3.0, + err_msg='Incorrect shrinkage cost for d-support norm.', + ) - npt.assert_raises(ValueError, proximity.KSupportNorm, 0.0, 0) + npt.assert_raises(ValueError, proximity.KSupportNorm, 0, 0) def test_group_lasso(self): + """Test GroupLASSO.""" npt.assert_allclose( - self.group_lasso.op(self.random_data_tile), self.gl_result_data) - npt.assert_equal(self.group_lasso.cost(self.random_data_tile), - np.sum(6 * self.random_data_tile)) + self.group_lasso.op(self.random_data_tile), + self.gl_result_data, + ) + npt.assert_equal( + self.group_lasso.cost(self.random_data_tile), + np.sum(6 * self.random_data_tile), + ) # Check that for 0 weights operator doesnt change result self.group_lasso.weights = np.zeros_like(self.group_lasso.weights) npt.assert_equal( - self.group_lasso.op(self.random_data_tile), self.random_data_tile) + self.group_lasso.op(self.random_data_tile), + self.random_data_tile, + ) npt.assert_equal(self.group_lasso.cost(self.random_data_tile), 0) class ReweightTestCase(TestCase): + """Test case for reweight module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) + 1 - self.data2 = np.array([[0.5, 1., 1.5], [2., 2.5, 3.], [3.5, 4., 4.5]]) + self.data2 = np.array( + [[0.5, 1.0, 1.5], [2.0, 2.5, 3.0], [3.5, 4.0, 4.5]], + ) self.rw = reweight.cwbReweight(self.data1) self.rw.reweight(self.data1) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.rw = None - def test_cwbReweight(self): - - npt.assert_array_equal(self.rw.weights, self.data2, - err_msg='Incorrect CWB re-weighting.') + def test_cwbreweight(self): + """Test cwbReweight.""" + npt.assert_array_equal( + self.rw.weights, + self.data2, + err_msg='Incorrect CWB re-weighting.', + ) npt.assert_raises(ValueError, self.rw.reweight, self.data1[0]) diff --git a/modopt/tests/test_signal.py b/modopt/tests/test_signal.py index 2fa10125..7490b98c 100644 --- a/modopt/tests/test_signal.py +++ b/modopt/tests/test_signal.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""UNIT TESTS FOR SIGNAL +"""UNIT TESTS FOR SIGNAL. This module contains unit tests for the modopt.signal module. @@ -9,53 +9,68 @@ """ from unittest import TestCase + import numpy as np import numpy.testing as npt -from modopt.signal import * + +from modopt.signal import filter, noise, positivity, svd, validation, wavelet class FilterTestCase(TestCase): + """Test case for filter module.""" def test_guassian_filter(self): - - npt.assert_almost_equal(filter.Gaussian_filter(1, 1), - 0.24197072451914337, - err_msg='Incorrect Gaussian filter') - - npt.assert_almost_equal(filter.Gaussian_filter(1, 1, norm=False), - 0.60653065971263342, - err_msg='Incorrect Gaussian filter') + """Test guassian_filter.""" + npt.assert_almost_equal( + filter.gaussian_filter(1, 1), + 0.24197072451914337, + err_msg='Incorrect Gaussian filter', + ) + + npt.assert_almost_equal( + filter.gaussian_filter(1, 1, norm=False), + 0.60653065971263342, + err_msg='Incorrect Gaussian filter', + ) def test_mex_hat(self): - - npt.assert_almost_equal(filter.mex_hat(2, 1), - -0.35213905225713371, - err_msg='Incorrect Mexican hat filter') + """Test mex_hat.""" + npt.assert_almost_equal( + filter.mex_hat(2, 1), + -0.35213905225713371, + err_msg='Incorrect Mexican hat filter', + ) def test_mex_hat_dir(self): - - npt.assert_almost_equal(filter.mex_hat_dir(1, 2, 1), - 0.17606952612856686, - err_msg='Incorrect directional Mexican hat ' - 'filter') + """Test mex_hat_dir.""" + npt.assert_almost_equal( + filter.mex_hat_dir(1, 2, 1), + 0.17606952612856686, + err_msg='Incorrect directional Mexican hat filter', + ) class NoiseTestCase(TestCase): + """Test case for noise module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) - self.data2 = np.array([[0., 2., 2.], [4., 5., 10.], - [11., 15., 18.]]) - self.data3 = np.array([[1.62434536, 0.38824359, 1.47182825], - [1.92703138, 4.86540763, 2.6984613], - [7.74481176, 6.2387931, 8.3190391]]) - self.data4 = np.array([[0., 0., 0.], [0., 0., 5.], [6., 7., 8.]]) - self.data5 = np.array([[0., 0., 0.], [0., 0., 0.], - [1., 2., 3.]]) + self.data2 = np.array( + [[0, 2.0, 2.0], [4.0, 5.0, 10], [11.0, 15.0, 18.0]], + ) + self.data3 = np.array([ + [1.62434536, 0.38824359, 1.47182825], + [1.92703138, 4.86540763, 2.6984613], + [7.74481176, 6.2387931, 8.3190391], + ]) + self.data4 = np.array([[0, 0, 0], [0, 0, 5.0], [6.0, 7.0, 8.0]]) + self.data5 = np.array( + [[0, 0, 0], [0, 0, 0], [1.0, 2.0, 3.0]], + ) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.data3 = None @@ -63,114 +78,164 @@ def tearDown(self): self.data5 = None def test_add_noise_poisson(self): - + """Test add_noise with Poisson noise.""" np.random.seed(1) - npt.assert_array_equal(noise.add_noise(self.data1, - noise_type='poisson'), self.data2, - err_msg='Incorrect noise: Poisson') - - npt.assert_raises(ValueError, noise.add_noise, self.data1, - noise_type='bla') + npt.assert_array_equal( + noise.add_noise(self.data1, noise_type='poisson'), + self.data2, + err_msg='Incorrect noise: Poisson', + ) + + npt.assert_raises( + ValueError, + noise.add_noise, + self.data1, + noise_type='bla', + ) npt.assert_raises(ValueError, noise.add_noise, self.data1, (1, 1)) def test_add_noise_gaussian(self): - + """Test add_noise with Gaussian noise.""" np.random.seed(1) - npt.assert_almost_equal(noise.add_noise(self.data1), self.data3, - err_msg='Incorrect noise: Gaussian') + npt.assert_almost_equal( + noise.add_noise(self.data1), + self.data3, + err_msg='Incorrect noise: Gaussian', + ) np.random.seed(1) - npt.assert_almost_equal(noise.add_noise(self.data1, - sigma=(1, 1, 1)), self.data3, - err_msg='Incorrect noise: Gaussian') + npt.assert_almost_equal( + noise.add_noise(self.data1, sigma=(1, 1, 1)), + self.data3, + err_msg='Incorrect noise: Gaussian', + ) def test_thresh_hard(self): - - npt.assert_array_equal(noise.thresh(self.data1, 5), self.data4, - err_msg='Incorrect threshold: hard') - - npt.assert_raises(ValueError, noise.thresh, self.data1, 5, - threshold_type='bla') + """Test thresh with hard threshold.""" + npt.assert_array_equal( + noise.thresh(self.data1, 5), + self.data4, + err_msg='Incorrect threshold: hard', + ) + + npt.assert_raises( + ValueError, + noise.thresh, + self.data1, + 5, + threshold_type='bla', + ) def test_thresh_soft(self): - - npt.assert_array_equal(noise.thresh(self.data1, 5, - threshold_type='soft'), self.data5, - err_msg='Incorrect threshold: soft') + """Test thresh with soft threshold.""" + npt.assert_array_equal( + noise.thresh(self.data1, 5, threshold_type='soft'), + self.data5, + err_msg='Incorrect threshold: soft', + ) class PositivityTestCase(TestCase): + """Test case for positivity module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3) - 5 self.data2 = np.array([[0, 0, 0], [0, 0, 0], [1, 2, 3]]) - self.data3 = [np.arange(5) - 3, np.arange(4) - 2] - self.data4 = np.array([list([0, 0, 0, 0, 1]), list([0, 0, 0, 1])], - dtype=object) + self.data3 = np.array( + [np.arange(5) - 3, np.arange(4) - 2], + dtype=object, + ) + self.data4 = np.array( + [np.array([0, 0, 0, 0, 1]), np.array([0, 0, 0, 1])], + dtype=object, + ) + self.pos_dtype_obj = positivity.positive(self.data3) self.err = 'Incorrect positivity' def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None def test_positivity(self): - + """Test positivity.""" npt.assert_equal(positivity.positive(-1), 0, err_msg=self.err) - npt.assert_equal(positivity.positive(-1.0), -0.0, err_msg=self.err) + npt.assert_equal( + positivity.positive(-1.0), + -float(0), + err_msg=self.err, + ) - npt.assert_equal(positivity.positive(self.data1), self.data2, - err_msg=self.err) + npt.assert_equal( + positivity.positive(self.data1), + self.data2, + err_msg=self.err, + ) - npt.assert_equal(positivity.positive(self.data3), self.data4, - err_msg=self.err) + for expected, output in zip(self.data4, self.pos_dtype_obj): + print(expected, output) + npt.assert_array_equal(expected, output, err_msg=self.err) npt.assert_raises(TypeError, positivity.positive, '-1') class SVDTestCase(TestCase): + """Test case for svd module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(18).reshape(9, 2).astype(float) self.data2 = np.arange(32).reshape(16, 2).astype(float) - self.data3 = (np.array([[-0.01744594, -0.61438865], - [-0.08435304, -0.50397984], - [-0.15126014, -0.39357102], - [-0.21816724, -0.28316221], - [-0.28507434, -0.17275339], - [-0.35198144, -0.06234457], - [-0.41888854, 0.04806424], - [-0.48579564, 0.15847306], - [-0.55270274, 0.26888188]]), - np.array([42.23492742, 1.10041151]), - np.array([[-0.67608034, -0.73682791], - [0.73682791, -0.67608034]])) - self.data4 = np.array([[-1.05426832e-16, 1.00000000e+00], - [2.00000000e+00, 3.00000000e+00], - [4.00000000e+00, 5.00000000e+00], - [6.00000000e+00, 7.00000000e+00], - [8.00000000e+00, 9.00000000e+00], - [1.00000000e+01, 1.10000000e+01], - [1.20000000e+01, 1.30000000e+01], - [1.40000000e+01, 1.50000000e+01], - [1.60000000e+01, 1.70000000e+01]]) - self.data5 = np.array([[0.49815487, 0.54291537], - [2.40863386, 2.62505584], - [4.31911286, 4.70719631], - [6.22959185, 6.78933678], - [8.14007085, 8.87147725], - [10.05054985, 10.95361772], - [11.96102884, 13.03575819], - [13.87150784, 15.11789866], - [15.78198684, 17.20003913]]) + self.data3 = np.array( + [ + np.array([ + [-0.01744594, -0.61438865], + [-0.08435304, -0.50397984], + [-0.15126014, -0.39357102], + [-0.21816724, -0.28316221], + [-0.28507434, -0.17275339], + [-0.35198144, -0.06234457], + [-0.41888854, 0.04806424], + [-0.48579564, 0.15847306], + [-0.55270274, 0.26888188], + ]), + np.array([42.23492742, 1.10041151]), + np.array([ + [-0.67608034, -0.73682791], + [0.73682791, -0.67608034], + ]), + ], + dtype=object, + ) + self.data4 = np.array([ + [-1.05426832e-16, 1.0], + [2.0, 3.0], + [4.0, 5.0], + [6.0, 7.0], + [8.0, 9.0], + [1.0e1, 1.1e1], + [1.2e1, 1.3e1], + [1.4e1, 1.5e1], + [1.6e1, 1.7e1], + ]) + self.data5 = np.array([ + [0.49815487, 0.54291537], + [2.40863386, 2.62505584], + [4.31911286, 4.70719631], + [6.22959185, 6.78933678], + [8.14007085, 8.87147725], + [10.05054985, 10.95361772], + [11.96102884, 13.03575819], + [13.87150784, 15.11789866], + [15.78198684, 17.20003913], + ]) self.svd = svd.calculate_svd(self.data1) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.data3 = None @@ -178,106 +243,148 @@ def tearDown(self): self.svd = None def test_find_n_pc(self): - - npt.assert_equal(svd.find_n_pc(svd.svd(self.data2)[0]), 2, - err_msg='Incorrect number of principal components.') + """Test find_n_pc.""" + npt.assert_equal( + svd.find_n_pc(svd.svd(self.data2)[0]), + 2, + err_msg='Incorrect number of principal components.', + ) npt.assert_raises(ValueError, svd.find_n_pc, np.arange(3)) def test_calculate_svd(self): - - npt.assert_almost_equal(self.svd[0], np.array(self.data3)[0], - err_msg='Incorrect SVD calculation: U') - - npt.assert_almost_equal(self.svd[1], np.array(self.data3)[1], - err_msg='Incorrect SVD calculation: S') - - npt.assert_almost_equal(self.svd[2], np.array(self.data3)[2], - err_msg='Incorrect SVD calculation: V') + """Test calculate_svd.""" + npt.assert_almost_equal( + self.svd[0], + np.array(self.data3)[0], + err_msg='Incorrect SVD calculation: U', + ) + + npt.assert_almost_equal( + self.svd[1], + np.array(self.data3)[1], + err_msg='Incorrect SVD calculation: S', + ) + + npt.assert_almost_equal( + self.svd[2], + np.array(self.data3)[2], + err_msg='Incorrect SVD calculation: V', + ) def test_svd_thresh(self): - - npt.assert_almost_equal(svd.svd_thresh(self.data1), self.data4, - err_msg='Incorrect SVD tresholding') - - npt.assert_almost_equal(svd.svd_thresh(self.data1, n_pc=1), self.data5, - err_msg='Incorrect SVD tresholding') - - npt.assert_almost_equal(svd.svd_thresh(self.data1, n_pc='all'), - self.data1, - err_msg='Incorrect SVD tresholding') + """Test svd_thresh.""" + npt.assert_almost_equal( + svd.svd_thresh(self.data1), + self.data4, + err_msg='Incorrect SVD tresholding', + ) + + npt.assert_almost_equal( + svd.svd_thresh(self.data1, n_pc=1), + self.data5, + err_msg='Incorrect SVD tresholding', + ) + + npt.assert_almost_equal( + svd.svd_thresh(self.data1, n_pc='all'), + self.data1, + err_msg='Incorrect SVD tresholding', + ) npt.assert_raises(TypeError, svd.svd_thresh, 1) npt.assert_raises(ValueError, svd.svd_thresh, self.data1, n_pc='bla') def test_svd_thresh_coef(self): - - npt.assert_almost_equal(svd.svd_thresh_coef(self.data1, - lambda x: x, 0), - self.data1, - err_msg='Incorrect SVD coefficient ' - 'tresholding') + """Test svd_thresh_coef.""" + npt.assert_almost_equal( + svd.svd_thresh_coef(self.data1, lambda x_val: x_val, 0), + self.data1, + err_msg='Incorrect SVD coefficient tresholding', + ) npt.assert_raises(TypeError, svd.svd_thresh_coef, self.data1, 0, 0) class ValidationTestCase(TestCase): + """Test case for validation module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None def test_transpose_test(self): - + """Test transpose_test.""" np.random.seed(2) - npt.assert_equal(validation.transpose_test(lambda x, y: x.dot(y), - lambda x, y: x.dot(y.T), self.data1.shape, - x_args=self.data1), None) - - npt.assert_raises(TypeError, validation.transpose_test, 0, 0, - self.data1.shape, x_args=self.data1) + npt.assert_equal( + validation.transpose_test( + lambda x_val, y_val: x_val.dot(y_val), + lambda x_val, y_val: x_val.dot(y_val.T), + self.data1.shape, + x_args=self.data1, + ), + None, + ) + + npt.assert_raises( + TypeError, + validation.transpose_test, + 0, + 0, + self.data1.shape, + x_args=self.data1, + ) class WaveletTestCase(TestCase): + """Test case for wavelet module.""" def setUp(self): - + """Set test parameter values.""" self.data1 = np.arange(9).reshape(3, 3).astype(float) self.data2 = np.arange(36).reshape(4, 3, 3).astype(float) - self.data3 = np.array([[[6., 20., 26.], - [36., 84., 84.], - [90., 164., 134.]], - [[78., 155., 134.], - [225., 408., 327.], - [270., 461., 350.]], - [[150., 290., 242.], - [414., 732., 570.], - [450., 758., 566.]], - [[222., 425., 350.], - [603., 1056., 813.], - [630., 1055., 782.]]]) - - self.data4 = np.array([[6496., 9796., 6544.], - [9924., 14910., 9924.], - [6544., 9796., 6496.]]) - - self.data5 = np.array([[[0., 1., 4.], - [3., 10., 13.], - [6., 19., 22.]], - [[3., 10., 13.], - [24., 46., 40.], - [45., 82., 67.]], - [[6., 19., 22.], - [45., 82., 67.], - [84., 145., 112.]]]) + self.data3 = np.array([ + [ + [6.0, 20, 26.0], + [36.0, 84.0, 84.0], + [90, 164.0, 134.0], + ], + [ + [78.0, 155.0, 134.0], + [225.0, 408.0, 327.0], + [270, 461.0, 350], + ], + [ + [150, 290, 242.0], + [414.0, 732.0, 570], + [450, 758.0, 566.0], + ], + [ + [222.0, 425.0, 350], + [603.0, 1056.0, 813.0], + [630, 1055.0, 782.0], + ], + ]) + + self.data4 = np.array([ + [6496.0, 9796.0, 6544.0], + [9924.0, 14910, 9924.0], + [6544.0, 9796.0, 6496.0], + ]) + + self.data5 = np.array([ + [[0, 1.0, 4.0], [3.0, 10, 13.0], [6.0, 19.0, 22.0]], + [[3.0, 10, 13.0], [24.0, 46.0, 40], [45.0, 82.0, 67.0]], + [[6.0, 19.0, 22.0], [45.0, 82.0, 67.0], [84.0, 145.0, 112.0]], + ]) def tearDown(self): - + """Unset test parameter values.""" self.data1 = None self.data2 = None self.data3 = None @@ -285,17 +392,23 @@ def tearDown(self): self.data5 = None def test_filter_convolve(self): - - npt.assert_almost_equal(wavelet.filter_convolve(self.data1, - self.data2), self.data3, - err_msg='Inccorect filter comvolution.') - - npt.assert_almost_equal(wavelet.filter_convolve(self.data2, - self.data2, filter_rot=True), self.data4, - err_msg='Inccorect filter comvolution.') + """Test filter_convolve.""" + npt.assert_almost_equal( + wavelet.filter_convolve(self.data1, self.data2), + self.data3, + err_msg='Inccorect filter comvolution.', + ) + + npt.assert_almost_equal( + wavelet.filter_convolve(self.data2, self.data2, filter_rot=True), + self.data4, + err_msg='Inccorect filter comvolution.', + ) def test_filter_convolve_stack(self): - - npt.assert_almost_equal(wavelet.filter_convolve_stack(self.data1, - self.data1), self.data5, - err_msg='Inccorect filter stack comvolution.') + """Test filter_convolve_stack.""" + npt.assert_almost_equal( + wavelet.filter_convolve_stack(self.data1, self.data1), + self.data5, + err_msg='Inccorect filter stack comvolution.', + ) diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/requirements.txt b/requirements.txt index 0292ca57..3c0e6d4f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ -numpy>=1.16.4 -scipy>=1.3.0 -progressbar2>=3.34.3 +importlib_metadata==3.7.0 +numpy==1.19.5 +scipy==1.5.4 +progressbar2==3.53.1 diff --git a/setup.cfg b/setup.cfg index 1af5f76a..56a40d12 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,17 +1,79 @@ [aliases] test=pytest -[build_sphinx] -all_files=1 -build-dir=docs/build - [metadata] description-file = README.rst -[pycodestyle] -ignore = E121,E126,E402,W504 -statistics = True +[darglint] +docstring_style = numpy +strictness = short + +[flake8] +ignore = + D107, #Justification: Don't need docstring for __init__ in numpydoc style + RST304, #Justification: Need to use :cite: role for citations + W503, #Justification: Have to choose one multiline operator format + WPS202, #Todo: Rethink module size, possibly split large modules + WPS337, #Todo: Consider simplifying multiline conditions. + WPS338, #Todo: Consider changing method order + WPS403, #Todo: Rethink no cover lines + WPS421, #Todo: Review need for print statements + WPS432, #Justification: Mathematical codes require "magic numbers" + WPS433, #Todo: Rethink conditional imports + WPS463, #Todo: Rename get_ methods + WPS615, #Todo: Rename get_ methods +per-file-ignores = + #Justification: Needed for keeping package version and current API + *__init__.py*: F401,F403,WPS347,WPS410,WPS412 + #Todo: Rethink conditional imports + modopt/base/backend.py: WPS229, WPS420 + #Todo: Rethink conditional imports + modopt/base/observable.py: WPS420,WPS604 + #Todo: Check string for log formatting + modopt/interface/log.py: WPS323 + #Todo: Rethink conditional imports + modopt/math/convolve.py: WPS301,WPS420 + #Todo: Rethink conditional imports + modopt/math/matrix.py: WPS420 + #Todo: Check need for del statement, + modopt/opt/algorithms.py: WPS111,WPS420 + #Todo: Consider changing costObj name + modopt/opt/cost.py: N801 + #Todo: + # - Rethink subscript slice assignment + # - Reduce complexity of KSupportNorm + # - Check bitwise operations + modopt/opt/proximity.py: WPS220,WPS231,WPS352,WPS362,WPS465,WPS506,WPS508 + #Todo: Consider changing cwbReweight name + modopt/opt/reweight.py: N801 + #Justification: Needed to import matplotlib.pyplot + modopt/plot/cost_plot.py: N802,WPS301 + #Todo: Investigate possible bug in find_n_pc function + modopt/signal/svd.py: WPS345 + #Todo: Check security of using system executable call + modopt/signal/wavelet.py: S404,S603 + #Todo: Clean up tests + modopt/tests/*.py: E731,F401,WPS301,WPS420,WPS425,WPS437,WPS604 +#WPS Settings +max-arguments = 25 +max-attributes = 40 +max-cognitive-score = 20 +max-function-expressions = 20 +max-line-complexity = 30 +max-local-variables = 10 +max-methods = 20 +max-module-expressions = 20 +max-string-usages = 20 +max-raises = 5 [tool:pytest] -addopts = --verbose --pycodestyle --cov=modopt -testpaths = modopt +testpaths = + modopt +addopts = + --verbose + --emoji + --flake8 + --cov=modopt + --cov-report=term + --cov-report=xml + --junitxml=pytest.xml diff --git a/setup.py b/setup.py index b39f4ed3..92040f6d 100644 --- a/setup.py +++ b/setup.py @@ -4,28 +4,69 @@ from setuptools import setup, find_packages import os -release_info = {} -infopath = os.path.abspath(os.path.join(os.path.dirname(__file__), - "modopt", "info.py")) -with open(infopath) as open_file: - exec(open_file.read(), release_info) +# Set the package release version +major = 1 +minor = 5 +patch = 0 +# Set the package details +name = 'modopt' +version = '.'.join(str(value) for value in (major, minor, patch)) +author = 'Samuel Farrens' +email = 'samuel.farrens@cea.fr' +gh_user = 'cea-cosmic' +url = 'https://github.com/{0}/{1}'.format(gh_user, name) +description = 'Modular Optimisation tools for soliving inverse problems.' +license = 'MIT' + +# Set the package classifiers +python_versions_supported = ['3.6', '3.7', '3.8', '3.9'] +os_platforms_supported = ['Unix', 'MacOS'] + +lc_str = 'License :: OSI Approved :: {0} License' +ln_str = 'Programming Language :: Python' +py_str = 'Programming Language :: Python :: {0}' +os_str = 'Operating System :: {0}' + +classifiers = ( + [lc_str.format(license)] + [ln_str] + + [py_str.format(ver) for ver in python_versions_supported] + + [os_str.format(ops) for ops in os_platforms_supported] +) + +# Source package description from README.md +this_directory = os.path.abspath(os.path.dirname(__file__)) +with open(os.path.join(this_directory, 'README.md'), encoding='utf-8') as f: + long_description = f.read() + +# Source package requirements from requirements.txt with open('requirements.txt') as open_file: install_requires = open_file.read() +# Source test requirements from develop.txt +with open('develop.txt') as open_file: + tests_require = open_file.read() + +# Source doc requirements from docs/requirements.txt +with open('docs/requirements.txt') as open_file: + docs_require = open_file.read() + + setup( - name='modopt', - author='sfarrens', - author_email='samuel.farrens@cea.fr', - version=release_info["__version__"], - url='https://github.com/cea-cosmic/ModOpt', - download_url='https://github.com/cea-cosmic/ModOpt', + name=name, + author=author, + author_email=email, + version=version, + license=license, + url=url, + description=description, + long_description=long_description, + long_description_content_type='text/markdown', packages=find_packages(), install_requires=install_requires, - license='MIT', - description='Modular Optimisation tools for soliving inverse problems.', - long_description=release_info["__about__"], - setup_requires=['pytest-runner', ], - tests_require=['pytest>=6.0.2', 'pytest-cov>=2.10', - 'pytest-pycodestyle>=2.2'], + python_requires='>=3.6', + setup_requires=['pytest-runner'], + tests_require=tests_require, + extras_require={'develop': tests_require + docs_require}, + classifiers=classifiers, )