diff --git a/.coveragerc b/.coveragerc index 638e19a7d..a4ec3818b 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,3 +1,8 @@ [run] branch = True source = pylops +omit = + pylops/basicoperators/_*.py + pylops/signalprocessing/_*.py + pylops/utils/_*.py + pylops/waveeqprocessing/_*.py diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 2b2e62104..b9c177a54 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -16,33 +16,17 @@ jobs: python-version: ["3.10", "3.11", "3.12", "3.13"] runs-on: ${{ matrix.platform }} - steps: - - uses: actions/checkout@v4 - - name: Get history and tags for SCM versioning to work - run: | - git fetch --prune --unshallow - git fetch --depth=1 origin +refs/tags/*:refs/tags/* - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip setuptools - pip install flake8 pytest - if [[ "${{ matrix.platform }}" == ubuntu* ]]; then - pip install -r requirements-dev.txt - pip install -r requirements-pyfftw.txt - else - pip install -r requirements-dev-arm.txt - pip install -r requirements-pyfftw.txt - fi - pip install -r requirements-torch.txt - - name: Install pylops - run: | - python -m setuptools_scm - pip install . - - name: Tests with pytest - run: | - pytest + steps: + - name: Check out source repository + uses: actions/checkout@v6 + with: + fetch-depth: 0 + - name: Install uv with Python ${{ matrix.python-version }} + uses: astral-sh/setup-uv@v6 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies and pylops + run: uv sync --locked --extra advanced --extra stat --extra deep --all-groups + - name: Test with pytest + run: uv run pytest --color=yes pytests/ diff --git a/.github/workflows/buildcupy.yaml b/.github/workflows/buildcupy.yaml index 1a9632c69..5574b0f25 100644 --- a/.github/workflows/buildcupy.yaml +++ b/.github/workflows/buildcupy.yaml @@ -13,29 +13,26 @@ jobs: runs-on: self-hosted steps: - name: Check out source repository - uses: actions/checkout@v4 - - name: Set up Python environment and Install dependencies and pylops + uses: actions/checkout@v6 + with: + fetch-depth: 0 + - name: Install uv with Python 3.11 + uses: astral-sh/setup-uv@v6 + with: + python-version: 3.11 + - name: Install dependencies and pylops run: | - python3 -m venv .venv-pylops - # write install scripts - cat << 'EOF' > pylops_tests.sh - #!/bin/bash - PYTHON=.venv-pylops/bin/python3 - PIP=.venv-pylops/bin/pip - ls $PYTHON - ls $PIP - $PIP install - $PIP install --upgrade pip setuptools - $PIP install install flake8 pytest setuptools-scm - $PIP install -r requirements-dev-gpu.txt - $PIP install cupy-cuda12x - $PYTHON -m setuptools_scm - $PIP install . - EOF - srun --account=yuxilab -n 1 -N 1 --gres=gpu:L40S:1 bash pylops_tests.sh - - name: Tests with pytest + srun --account=yuxilab -n 1 -N 1 --gres=gpu:L40S:1 bash -c ' + uv sync --locked --extra advanced \ + --extra stat --extra gpu-cu12 \ + --extra deep-cu128 --all-groups + ' + echo "done!" + - name: Test with pytest run: | - export CUPY_PYLOPS=1; export TEST_CUPY_PYLOPS=1; - export PYTEST=.venv-pylops/bin/pytest - srun --account=yuxilab -n 1 -N 1 --gres=gpu:L40S:1 $PYTEST + srun --account=yuxilab -n 1 -N 1 --gres=gpu:L40S:1 bash -c ' + export CUPY_PYLOPS=1 + export TEST_CUPY_PYLOPS=1 + uv run pytest --color=yes pytests/ + ' echo "done!" diff --git a/.github/workflows/codacy-coverage-reporter.yaml b/.github/workflows/codacy-coverage-reporter.yaml index f005925df..81ef492b8 100644 --- a/.github/workflows/codacy-coverage-reporter.yaml +++ b/.github/workflows/codacy-coverage-reporter.yaml @@ -2,43 +2,38 @@ # For more information see: https://github.com/codacy/codacy-coverage-reporter-action name: PyLops-coverage -on: [push, pull_request_target] +on: + pull_request: + push: + branches: [dev] jobs: build: - strategy: - matrix: - platform: [ ubuntu-latest, ] - python-version: ["3.11", ] - - runs-on: ${{ matrix.platform }} + runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - name: Get history and tags for SCM versioning to work - run: | - git fetch --prune --unshallow - git fetch --depth=1 origin +refs/tags/*:refs/tags/* - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - pip install -r requirements-dev.txt - pip install -r requirements-pyfftw.txt - pip install -r requirements-torch.txt - - name: Install pylops - run: | - pip install . - pip install coverage - - name: Code coverage with coverage - run: | - coverage run -m pytest - coverage xml - - name: Run codacy-coverage-reporter - uses: codacy/codacy-coverage-reporter-action@v1 - with: - project-token: ${{ secrets.CODACY_PROJECT_TOKEN }} - coverage-reports: coverage.xml + - name: Check out source repository + uses: actions/checkout@v6 + with: + fetch-depth: 0 + - name: Install uv with Python + uses: astral-sh/setup-uv@v6 + with: + python-version: "3.11" + - name: Install dependencies and pylops + run: uv sync --locked --extra advanced --extra stat --extra deep --all-groups + - name: Coverage with pytest + run: | + uv run coverage run -m pytest + uv run coverage xml + uv run coverage html + - name: Upload HTML coverage report + uses: actions/upload-artifact@v4 + with: + name: coverage-html + path: htmlcov/ + - name: Run codacy-coverage-reporter + if: github.event_name == 'push' + uses: codacy/codacy-coverage-reporter-action@v1 + with: + project-token: ${{ secrets.CODACY_PROJECT_TOKEN }} + coverage-reports: coverage.xml diff --git a/.github/workflows/ruff.yaml b/.github/workflows/ruff.yaml new file mode 100644 index 000000000..7363d0930 --- /dev/null +++ b/.github/workflows/ruff.yaml @@ -0,0 +1,19 @@ +# This workflow runs Ruff on the PR +# For more information see: https://github.com/marketplace/actions/ruff-action +name: PyLops-ruff + +on: [push, pull_request] + +jobs: + ruff-lint: + runs-on: ubuntu-latest + name: Lint + steps: + - name: Check out source repository + uses: actions/checkout@v6 + with: + fetch-depth: 0 + - name: Lint with ruff + uses: astral-sh/ruff-action@v3 + with: + src: "./pylops" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a4ca2e166..7af58437c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,30 +1,31 @@ exclude: "^docs/" repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.0.1 + rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer + - id: mixed-line-ending + - id: check-toml - id: check-yaml + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict - - repo: https://github.com/psf/black - rev: 22.3.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.14 hooks: - - id: black - args: # arguments to configure black - - --line-length=88 + - id: ruff-check + args: [ --fix ] + - id: ruff-format - - repo: https://github.com/pycqa/isort - rev: 5.12.0 + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.9.28 hooks: - - id: isort - name: isort (python) - args: - [ - "--profile", - "black", - "--skip", - "__init__.py", - "--filter-files", - "--line-length=88", - ] + - id: uv-lock + + - repo: https://github.com/abravalheri/validate-pyproject + rev: "v0.23" + hooks: + - id: validate-pyproject + additional_dependencies: ["validate-pyproject-schema-store[all]"] diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 1b48143b4..7bd228c91 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -5,20 +5,21 @@ # Required version: 2 -# Set the version of Python and other tools you might need -build: - os: ubuntu-24.04 - tools: - python: "3.12" - # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py -# Declare the Python requirements required to build your docs -python: - install: - - requirements: requirements-doc.txt - - requirements: requirements-torch.txt - - method: pip - path: . +# Set the version of environment using UV +build: + os: ubuntu-24.04 + tools: + python: "3.12" + jobs: + pre_create_environment: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + create_environment: + - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" + install: + - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --locked --extra advanced --extra stat --extra deep --all-groups diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 58ce812c6..dd690a560 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -6,10 +6,6 @@ The best way to get in touch with the core developers and maintainers is to join the [PyLops slack channel](https://pylops.slack.com/) as well as open new *Issues* directly from the GitHub repo. -Moreover, take a look at the [Roadmap](https://pylops.readthedocs.io/en/stable/roadmap.html) -page for a list of current ideas for improvements and additions to the PyLops library. - - ## Welcomed contributions ### Bug reports diff --git a/MANIFEST.in b/MANIFEST.in index f76b4ea6b..0b65f4832 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,8 @@ exclude .* -exclude environment.yml requirements.txt Makefile -exclude environment-dev.yml requirements-dev.txt azure-pipelines.yml readthedocs.yml +exclude Makefile +exclude environment* +exclude requirements* +exclude azure-pipelines.yml readthedocs.yml recursive-exclude docs * recursive-exclude examples * recursive-exclude pytests * diff --git a/Makefile b/Makefile index 64940b375..b6b172891 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,13 @@ PIP := $(shell command -v pip3 2> /dev/null || command which pip 2> /dev/null) PYTHON := $(shell command -v python3 2> /dev/null || command which python 2> /dev/null) +UV := $(shell command -v uv 2> /dev/null || command which uv 2> /dev/null) +NOX := $(shell command -v nox 2> /dev/null || command which nox 2> /dev/null) -.PHONY: install dev-install dev-install_intel_mkl dev-install_gpu install_conda dev-install_conda dev-install_conda_intel_mkl dev-install_conda_arm tests tests_cpu_ongpu tests_gpu doc docupdate servedoc lint typeannot coverage +.PHONY: install_conda dev-install_conda dev-install_conda_intel_mkl dev-install_conda_arm dev-install_conda_gpu +.PHONY: dev-install_uv dev-install_uvcu126 dev-install_uvcu128 dev-install_uvcu13 +.PHONY: tests tests_cpu_ongpu tests_gpu tests_uv tests_cpu_ongpu_uv tests_gpu_uv tests_nox +.PHONY: doc doc_uv docupdate docupdate_uv servedoc servedoc_uv lint lint_uv typeannot typeannot_uv +.PHONY: coverage coverage_uv pipcheck: ifndef PIP @@ -15,26 +21,17 @@ ifndef PYTHON endif @echo Using python: $(PYTHON) -install: - make pipcheck - $(PIP) install -r requirements.txt && $(PIP) install . - -dev-install: - make pipcheck - $(PIP) install -r requirements-dev.txt &&\ - $(PIP) install -r requirements-pyfftw.txt &&\ - $(PIP) install -r requirements-torch.txt && $(PIP) install -e . - -dev-install_intel_mkl: - make pipcheck - $(PIP) install -r requirements-intel-mkl.txt &&\ - $(PIP) install -r requirements-dev.txt &&\ - $(PIP) install -r requirements-torch.txt && $(PIP) install -e . +uvcheck: +ifndef UV + $(error "Ensure uv is in your PATH") +endif + @echo Using uv: $(UV) -dev-install_gpu: - make pipcheck - $(PIP) install -r requirements-dev-gpu.txt &&\ - $(PIP) install -e . +noxcheck: +ifndef NOX + $(error "Ensure nox is in your PATH") +endif + @echo Using nox: $(NOX) install_conda: conda env create -f environment.yml && source ${CONDA_PREFIX}/etc/profile.d/conda.sh && conda activate pylops && pip install . @@ -51,36 +48,103 @@ dev-install_conda_arm: dev-install_conda_gpu: conda env create -f environment-dev-gpu.yml && source ${CONDA_PREFIX}/etc/profile.d/conda.sh && conda activate pylops_gpu && pip install -e . +dev-install_uv: + make uvcheck + $(UV) sync --locked --extra advanced --extra stat --extra deep --all-groups + +dev-install_uvcu126: + make uvcheck + $(UV) sync --locked --extra advanced --extra stat --extra gpu-cu12 --extra deep-cu126 --all-groups + +dev-install_uvcu128: + make uvcheck + $(UV) sync --locked --extra advanced --extra stat --extra gpu-cu12 --extra deep-cu128 --all-groups + +dev-install_uvcu13: + make uvcheck + $(UV) sync --locked --extra advanced --extra stat --extra gpu-cu13 --extra deep-cu13 --all-groups + tests: # Run tests with CPU make pythoncheck pytest +tests_uv: + # Run tests with CPU + make uvcheck + $(UV) run pytest + +tests_nox: + make noxcheck + $(NOX) -s tests + tests_cpu_ongpu: # Run tests with CPU on a system with GPU (and CuPy installed) make pythoncheck export CUPY_PYLOPS=0 && export TEST_CUPY_PYLOPS=0 && pytest +tests_cpu_ongpu_uv: + # Run tests with CPU on a system with GPU (and CuPy installed) + make pythoncheck + export CUPY_PYLOPS=0 && export TEST_CUPY_PYLOPS=0 && $(UV) run pytest + tests_gpu: # Run tests with GPU (requires CuPy to be installed) make pythoncheck export TEST_CUPY_PYLOPS=1 && pytest +tests_gpu_uv: + # Run tests with GPU (requires CuPy to be installed) + make pythoncheck + export TEST_CUPY_PYLOPS=1 && $(UV) run pytest + doc: + cd docs && rm -rf source/api/generated && rm -rf source/gallery &&\ + rm -rf source/tutorials && rm -rf source/examples &&\ + rm -rf build && make html && cd .. + +doc_uv: + make uvcheck cd docs && rm -rf source/api/generated && rm -rf source/gallery &&\ - rm -rf source/tutorials && rm -rf build && make html && cd .. + rm -rf source/tutorials && rm -rf source/examples &&\ + rm -rf build && $(UV) run make html && cd .. docupdate: cd docs && make html && cd .. +docupdate_uv: + make uvcheck + cd docs && $(UV) run make html && cd .. + servedoc: + make pythoncheck $(PYTHON) -m http.server --directory docs/build/html/ +servedoc_uv: + make uvcheck + $(UV) run python -m http.server --directory docs/build/html/ + lint: - flake8 docs/ examples/ pylops/ pytests/ tutorials/ + ruff check docs/source examples/ pylops/ pytests/ tutorials/ + +lint_uv: + make uvcheck + $(UV) run ruff check docs/source examples/ pylops/ pytests/ tutorials/ typeannot: mypy pylops/ +typeannot_uv: + make uvcheck + $(UV) run mypy pylops/ + coverage: - coverage run -m pytest && coverage xml && coverage html && $(PYTHON) -m http.server --directory htmlcov/ + coverage run --source=pylops -m pytest && \ + coverage xml && coverage html && $(PYTHON) -m http.server --directory htmlcov/ + +coverage_uv: + make uvcheck + $(UV) run coverage run --source=pylops -m pytest &&\ + $(UV) run coverage xml &&\ + $(UV) run coverage html &&\ + $(UV) run python -m http.server --directory htmlcov/ diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 33055a1d1..f29bc75f3 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -12,36 +12,6 @@ trigger: jobs: -# Windows -######################################################################################## -# - job: -# displayName: 'Windows' -# -# pool: -# vmImage: 'windows-2025' -# -# variables: -# NUMBA_NUM_THREADS: 1 -# -# steps: -# - task: UsePythonVersion@0 -# inputs: -# versionSpec: '3.10' -# architecture: 'x64' -# -# - script: | -# python -m pip install --upgrade pip setuptools wheel django -# pip install -r requirements-dev.txt -# pip install -r requirements-torch.txt -# pip install . -# displayName: 'Install prerequisites and library' -# -# - script: | -# pytest -# condition: succeededOrFailed() -# displayName: 'Run tests' - - # Mac ######################################################################################## - job: @@ -60,15 +30,20 @@ jobs: architecture: 'arm64' - script: | - python -m pip install --upgrade pip setuptools wheel django - pip install -r requirements-dev-arm.txt - pip install -r requirements-pyfftw.txt - pip install -r requirements-torch.txt - pip install . - displayName: 'Install prerequisites and library' + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "##vso[task.prependpath]$HOME/.cargo/bin" + displayName: 'Install uv' - script: | - pytest + uv sync --locked \ + --extra advanced \ + --extra stat \ + --extra deep \ + --all-groups + displayName: 'Install dependencies andn pylops' + + - script: | + uv run pytest --color=yes pytests/ condition: succeededOrFailed() displayName: 'Run tests' @@ -91,14 +66,19 @@ jobs: architecture: 'x64' - script: | - python -m pip install --upgrade pip setuptools wheel django - pip install -r requirements-dev.txt - pip install -r requirements-pyfftw.txt - pip install -r requirements-torch.txt - pip install . - displayName: 'Install prerequisites and library' + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "##vso[task.prependpath]$HOME/.cargo/bin" + displayName: 'Install uv' + + - script: | + uv sync --locked \ + --extra advanced \ + --extra stat \ + --extra deep \ + --all-groups + displayName: 'Install dependencies andn pylops' - script: | - pytest + uv run pytest --color=yes pytests/ condition: succeededOrFailed() displayName: 'Run tests' diff --git a/docs/source/_static/style.css b/docs/source/_static/style.css deleted file mode 100644 index 94b9bef81..000000000 --- a/docs/source/_static/style.css +++ /dev/null @@ -1,117 +0,0 @@ -/* To stick the footer to the bottom of the page */ -html { -} - -body { - font-family: 'Open Sans', sans-serif; -} - -h1, h2, h3, h4 { - font-weight: 300; - font-family: "Open Sans",sans-serif; -} - -h1 { - font-size: 200%; -} - -.sidebar-title { - margin-top: 10px; - margin-bottom: 0px; -} - -.banner { - padding-bottom: 60px; - text-align: center; -} - -.banner img { - margin-bottom: 40px; -} - -.api-module { - margin-bottom: 80px; -} - -.youtube-embed { - max-width: 600px; - margin-bottom: 24px; -} - -.video-container { - position:relative; - padding-bottom:56.25%; - padding-top:30px; - height:0; - overflow:hidden; -} - -.video-container iframe, .video-container object, .video-container embed { - position:absolute; - top:0; - left:0; - width:100%; - height:100%; -} - -.wy-nav-content { - max-width: 1000px; -} - -.wy-nav-top { - background-color: #555555; -} - -.wy-side-nav-search { - background-color: #555555; -} - -.wy-side-nav-search > a img.logo { - width: 50%; -} - -.wy-side-nav-search input[type="text"] { - border-color: #555555; -} - -/* Remove the padding from the Parameters table */ -.rst-content table.field-list .field-name { - padding-left: 0px; -} - -/* Lign up the Parameters section with the descriptions */ -.rst-content table.field-list td { - padding-top: 8px; -} - -.rst-content .highlight > pre { - font-size: 14px; -} - -.rst-content img { - max-width: 100%; -} - -.source-link { - float: right; -} - -.strike { - text-decoration: line-through; -} - -/* Don't let the edit and notebook download links disappear on mobile. */ -@media screen and (max-width: 480px) { - .wy-breadcrumbs li.source-link { - float:none; - display: block; - margin-top: 20px; - } -} - -/* Sphinx-Gallery */ -/****************************************************************************/ -/* Don't let captions be italic */ -.rst-content div.figure p.caption { - font-style: normal; -} \ No newline at end of file diff --git a/docs/source/adding.rst b/docs/source/adding.rst index 4420b1573..1c9be8e68 100755 --- a/docs/source/adding.rst +++ b/docs/source/adding.rst @@ -1,7 +1,8 @@ .. _addingoperator: -Implementing new operators -========================== +|:paintbrush:| Implementing new operators +######################################### + Users are welcome to create new operators and add them to the PyLops library. In this tutorial, we will go through the key steps in the definition of an operator, using a simplified version of the @@ -10,7 +11,7 @@ to the model in forward mode and to the data in adjoint mode. Creating the operator ---------------------- +********************* The first thing we need to do is to create a new file with the name of the operator we would like to implement. Note that as the operator will be a class, we need to follow the UpperCaseCamelCase convention both for the class itself and for the filename. @@ -42,7 +43,7 @@ input parameters and a ``Notes`` section providing a mathematical explanation of some of the core operators of PyLops to get a feeling of the level of details of the mathematical explanation. Initialization (``__init__``) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +============================= We then need to create the ``__init__`` where the input parameters are passed and saved as members of our class. While the input parameters change from operator to operator, it is always required to create three members: @@ -87,7 +88,7 @@ See the docs of :py:class:`pylops.LinearOperator` for more information about wha attributes mean. Forward mode (``_matvec``) -^^^^^^^^^^^^^^^^^^^^^^^^^^ +========================== We can then move onto writing the *forward mode* in the method ``_matvec``. In other words, we will need to write the piece of code that will implement the following operation :math:`\mathbf{y} = \mathbf{A}\mathbf{x}`. Such method is always composed of two inputs (the object itself ``self`` and the input model ``x``). @@ -105,7 +106,7 @@ more details in the decorator documentation, by adding such decorator the input a nd-array of shape ``dims``, fed to the actual code in ``_matvec`` and then flattened. Adjoint mode (``_rmatvec``) -^^^^^^^^^^^^^^^^^^^^^^^^^^^ +=========================== Finally we need to implement the *adjoint mode* in the method ``_rmatvec``. In other words, we will need to write the piece of code that will implement the following operation :math:`\mathbf{x} = \mathbf{A}^H\mathbf{y}`. Such method is also composed of two inputs (the object itself ``self`` and the input data ``y``). @@ -123,8 +124,9 @@ Similar to ``_matvec``, since version ``v2.0.0``, this method can also be decora When doing so, the input ``x`` is initially reshaped into a nd-array of shape ``dimsd``, fed to the actual code in ``_rmatvec`` and then flattened. + Testing the operator --------------------- +******************** Being able to write an operator is not yet a guarantee of the fact that the operator is correct, or in other words that the adjoint code is actually the *adjoint* of the forward code. Luckily for us, a simple test can be performed to check the validity of forward and adjoint operators, the so called *dot-test*. @@ -170,7 +172,7 @@ model tested towards the input model. Documenting the operator ------------------------- +************************ Once the operator has been created, we can add it to the documentation of PyLops. To do so, simply add the name of the operator within the ``index.rst`` file in ``docs/source/api`` directory. @@ -180,7 +182,7 @@ can be used as template. Final checklist ---------------- +*************** Before submitting your new operator for review, use the following **checklist** to ensure that your code adheres to the guidelines of PyLops: diff --git a/docs/source/addingsolver.rst b/docs/source/addingsolver.rst index fbd321225..d38664975 100755 --- a/docs/source/addingsolver.rst +++ b/docs/source/addingsolver.rst @@ -1,11 +1,12 @@ .. _addingsolver: -Implementing new solvers -======================== +|:fire:| Implementing new solvers +################################# + Users are welcome to create new solvers and add them to the PyLops library. In this tutorial, we will go through the key steps in the definition of a solver, using a -sligthly simplified version of :py:class:`pylops.optimization.basic.CG` as an example. +sligthly simplified version of :py:class:`pylops.optimization.cls_basic.CG` as an example. .. note:: In case the solver that you are planning to create falls within the category of proximal solvers, diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst index 374657f58..145c4b96e 100755 --- a/docs/source/api/index.rst +++ b/docs/source/api/index.rst @@ -1,7 +1,7 @@ .. _api: -PyLops API -========== +|:wrench:| PyLops API +===================== The Application Programming Interface (API) of PyLops can be loosely seen as composed of a stack of three main layers: diff --git a/docs/source/api/others.rst b/docs/source/api/others.rst index 6eb19031f..470740fad 100755 --- a/docs/source/api/others.rst +++ b/docs/source/api/others.rst @@ -1,8 +1,8 @@ .. _others: +|:nut_and_bolt:| PyLops Utilities +================================= -PyLops Utilities -================ Alongside with its *Linear Operators* and *Solvers*, PyLops contains also a number of auxiliary routines performing universal tasks that are used by several operators or simply within one or more :ref:`tutorials` for the preparation of input data and subsequent visualization of results. diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst index ce416c226..d09fb90b2 100644 --- a/docs/source/changelog.rst +++ b/docs/source/changelog.rst @@ -1,7 +1,7 @@ .. _changlog: -Changelog -========= +|:newspaper_roll:| Changelog +############################ Version 2.6.0 diff --git a/docs/source/citing.rst b/docs/source/citing.rst index b8adfb18d..888af8540 100755 --- a/docs/source/citing.rst +++ b/docs/source/citing.rst @@ -1,7 +1,8 @@ .. _citing: -How to cite -=========== +|:pencil2:| How to cite +####################### + When using PyLops in scientific publications, please cite the following paper: .. raw:: html diff --git a/docs/source/conf.py b/docs/source/conf.py index 568c7c313..bee240f8f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import datetime import os import sys @@ -22,9 +21,10 @@ "matplotlib.sphinxext.plot_directive", "numpydoc", "nbsphinx", + "sphinx_design", + "sphinx_iconify", "sphinx_gallery.gen_gallery", "sphinxemoji.sphinxemoji", - # 'sphinx.ext.napoleon', ] # intersphinx configuration @@ -99,7 +99,7 @@ # General information about the project year = datetime.date.today().year project = "PyLops" -copyright = "{}, PyLops Development Team".format(year) +copyright = f"{year}, PyLops Development Team" # Version version = __version__ @@ -107,9 +107,9 @@ version = "dev" # These enable substitutions using |variable| in the rst files -rst_epilog = """ +rst_epilog = f""" .. |year| replace:: {year} -""".format(year=year) +""" html_static_path = ["_static"] html_last_updated_fmt = "%b %d, %Y" html_title = "PyLops" @@ -124,15 +124,12 @@ html_show_copyright = True # Theme config -html_theme = "pydata_sphinx_theme" +html_theme = "shibuya" html_theme_options = { + "accent_color": "teal", "github_url": "https://github.com/PyLops/pylops", - # "logo_only": True, - # "display_version": True, - "logo": { - "image_light": "pylops_b.png", - "image_dark": "pylops.png", - } + "light_logo": "_static/pylops_b.png", + "dark_logo": "_static/pylops.png", } html_css_files = [ "css/custom.css", @@ -155,14 +152,9 @@ "doc_path": "docs/source", "galleries": sphinx_gallery_conf["gallery_dirs"], "gallery_dir": dict( - zip(sphinx_gallery_conf["gallery_dirs"], sphinx_gallery_conf["examples_dirs"]) + zip(sphinx_gallery_conf["gallery_dirs"], sphinx_gallery_conf["examples_dirs"], strict=True) ), "github_project": "PyLops", "github_repo": "pylops", "github_version": "master", } - - -# Load the custom CSS files (needs sphinx >= 1.6 for this to work) -def setup(app): - app.add_css_file("style.css") diff --git a/docs/source/contributing.rst b/docs/source/contributing.rst index 8b8eb9d5c..82a30720b 100755 --- a/docs/source/contributing.rst +++ b/docs/source/contributing.rst @@ -1,7 +1,7 @@ .. _contributing: -Contributing -############ +|:heart:| Contributing +###################### Contributions are welcome and greatly appreciated! @@ -9,10 +9,6 @@ The best way to get in touch with the core developers and maintainers is to join the `PyLops slack channel `_ as well as open new *Issues* directly from the `GitHub repo `_. -Moreover, take a look at the :ref:`roadmap` page for a list of current ideas -for improvements and additions to the PyLops library. - - Welcomed contributions ********************** @@ -37,14 +33,12 @@ If you are proposing a new operator or a new feature: * Explain in detail how it should work. * Keep the scope as narrow as possible, to make it easier to implement. - Fix issues ========== There is always a backlog of issues that need to be dealt with. Look through the `GitHub Issues `_ for operator/feature requests or bugfixes. - Add examples or improve documentation ===================================== @@ -124,8 +118,9 @@ Before you submit a pull request, check that it meets these guidelines: 2. If the pull request adds functionality, the docs should be updated accordingly. 3. Ensure that the updated code passes all tests. + Project structure -################# +***************** This repository is organized as follows: * **pylops**: Python library containing various linear operators and auxiliary routines diff --git a/docs/source/credits.rst b/docs/source/credits.rst index 755b3966b..e51638896 100755 --- a/docs/source/credits.rst +++ b/docs/source/credits.rst @@ -1,7 +1,7 @@ .. _credits: -Contributors -============ +|:people_holding_hands:| Contributors +##################################### * `Matteo Ravasi `_, mrava87 * `Carlos da Costa `_, cako diff --git a/docs/source/extensions.rst b/docs/source/extensions.rst index 25220df54..768a3e790 100755 --- a/docs/source/extensions.rst +++ b/docs/source/extensions.rst @@ -1,7 +1,7 @@ .. _extensions: -Extensions -========== +|:link:| Extensions +################### PyLops brings to users the power of linear operators in a simple and easy to use programming interface. diff --git a/docs/source/faq.rst b/docs/source/faq.rst index 61ac7a3cc..90d03d5ba 100755 --- a/docs/source/faq.rst +++ b/docs/source/faq.rst @@ -1,7 +1,7 @@ .. _faq: -Frequenty Asked Questions -========================= +|:question:| Frequenty Asked Questions +###################################### **1. Can I visualize my operator?** @@ -14,7 +14,7 @@ working with linear operators is indeed that you don't really need to access the of an operator. -**2. Can I have an older version of** ``cupy`` **installed in my system (** ``cupy-cudaXX<10.6.0``**)?** +**2. Can I have an older version of** ``cupy`` **installed in my system (** ``cupy-cudaXX<10.6.0`` **)?** Yes. Nevertheless you need to tell PyLops that you don't want to use its ``cupy`` backend by setting the environment variable ``CUPY_PYLOPS=0``. diff --git a/docs/source/gpu.rst b/docs/source/gpu.rst index 957f5413a..0a07ad106 100755 --- a/docs/source/gpu.rst +++ b/docs/source/gpu.rst @@ -1,10 +1,11 @@ .. _gpu: -GPU / TPU Support -================= +|:video_game:| GPU / TPU Support +################################ Overview --------- +******** + From ``v1.12.0``, PyLops supports computations on GPUs powered by `CuPy `_ (``cupy-cudaXX>=13.0.0``). This library must be installed *before* PyLops is installed. @@ -32,8 +33,10 @@ be also wrapped into a :class:`pylops.JaxOperator`. See below for a comphrensive list of supported operators and additional functionalities for both the ``cupy`` and ``jax`` backends. + Install dependencies --------------------- +******************** + GPU-enabled development environments can created using ``conda`` .. code-block:: bash @@ -48,12 +51,12 @@ or ``pip`` Examples --------- +******** Let's now briefly look at some use cases. End-to-end GPU powered inverse problems -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +======================================= First we consider the most common scenario when both the model and data vectors fit onto the GPU memory. We can therefore simply replace all our @@ -122,7 +125,7 @@ Again, the code is almost unchanged apart from the fact that we now use ``jax`` Mixed CPU-GPU powered inverse problems -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +====================================== Let us now consider a more intricate scenario where we have access to a GPU-powered operator, however the model and/or data vectors are too large @@ -208,7 +211,7 @@ These features are currently not available for ``jax`` arrays. Supported Operators -------------------- +******************* In the following, we provide a list of methods in :class:`pylops.LinearOperator` with their current status (available on CPU, GPU with CuPy, and GPU with JAX): diff --git a/docs/source/index.rst b/docs/source/index.rst index 1a8f5a430..ecc28e505 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,6 @@ -Overview -======== +PyLops +====== + PyLops is an open-source Python library focused on providing a backend-agnostic, idiomatic, matrix-free library of linear operators and related computations. It is inspired by the iconic MATLAB `Spot – A Linear-Operator Toolbox `_ project. @@ -72,23 +73,23 @@ It is a flexible and scalable python library for large-scale optimization with l that can be tailored to our needs, and as contribution to the free software community. Since June 2021, PyLops is a `NUMFOCUS `_ Affiliated Project. + .. toctree:: :maxdepth: 1 :hidden: - :caption: Getting started + :caption: Getting started: - self installation.rst gpu.rst extensions.rst tutorials/index.rst gallery/index.rst - FAQs + faq.rst .. toctree:: :maxdepth: 2 :hidden: - :caption: Reference documentation + :caption: Reference documentation: api/index.rst api/others.rst @@ -96,13 +97,12 @@ PyLops is a `NUMFOCUS - Implementing new solvers - Contributing - Changelog - Roadmap - Papers using PyLops - How to cite - Credits + :caption: Getting involved: + + adding.rst + addingsolver.rst + contributing.rst + changelog.rst + papers.rst + citing.rst + credits.rst diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 669994f6f..a64b195de 100755 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -1,7 +1,7 @@ .. _installation: -Installation -############ +|:desktop_computer:| Installation +################################# Dependencies ************ @@ -13,52 +13,70 @@ Required dependencies are limited to: * `NumPy `_ * `SciPy `_ -We highly encourage using the `Anaconda Python distribution `_ +We encourage using the `Anaconda Python distribution `_ or its standalone package manager `Conda `_. -Especially for Intel processors, this ensures a higher performance with no configuration. +Especially for Intel processors, this ensures a higher performance with no configuration (e.g., +the linking to ``Intel MKL`` library, a highly optimized BLAS library created by Intel). If you are interested in getting the best code performance, read carefully :ref:`Performance`. -For learning, however, the standard installation is often good enough. -Some operators have additional, optional "engines" to improve their performance. -These often rely on third-party libraries which are added to the -list of our optional dependencies. -Optional dependencies therefore refer to those dependencies that are not strictly -needed nor installed directly as part of a standard installation. -For details more details, see :ref:`Optional`. +For learning, however, the standard installation is often good enough; in that case, we +recommend using `uv `_, a modern Python package manager that +is easy to use and has a very fast dependency resolver. + +Some operators have additional, optional *engines* that are usually meant to provide improved +performance on CPU or enable GPU acceleration. These rely on third-party libraries, which are +added to the list of our optional dependencies and must be installed to be able to use the +associated engine. Similarly, some operators are implemented on top of third-party libraries, +which are also added to the list of our optional dependencies and must be installed to be +able to use the associated operator. In both cases, if the dependency is not installed, the +rest of the library will still work. For details more details, see :ref:`Optional`. Step-by-step installation for users *********************************** -Conda (recommended) -=================== -If using ``conda``, install our ``conda-forge`` distribution via: +From Package Manager +==================== +First install `pylops` with your package manager of choice. -.. code-block:: bash +.. tab-set:: - >> conda install --channel conda-forge pylops + .. tab-item:: :iconify:`devicon:anaconda` conda -Using the ``conda-forge`` distribution is recommended as all the dependencies (both required -and optional) will be automatically installed for you. + .. code-block:: bash -Pip -=== -If you are using ``pip``, and simply type the following command in your terminal -to install the PyPI distribution: + >> conda install --channel conda-forge pylops -.. code-block:: bash + which installs also the *required* dependencies, if not already present + in your environment. - >> pip install pylops + .. tab-item:: :iconify:`material-icon-theme:uv` uv -Note that when installing via ``pip``, only *required* dependencies are installed. + .. code-block:: bash + + >> uv add pylops + + which installs also the *required* dependencies, if not already present + in your environment. Refer to :ref:`Optional` for alternative `uv` + commands that install some of the optional dependencies as well. From Source =========== -To access the latest source from github: +To access the latest source from GitHub: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`devicon:pypi` pip + + .. code-block:: bash - >> pip install https://github.com/PyLops/pylops.git@dev + >> pip install https://github.com/PyLops/pylops.git@dev + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add git+https://github.com/PyLops/pylops.git --branch dev Docker ====== @@ -82,6 +100,7 @@ A larger image with ``conda`` a distribution is also available: >> docker run -it -v /path/to/local/folder:/home/jupyter/notebook -p 8888:8888 mrava87/pylops:conda_notebook + .. _DevInstall: Step-by-step installation for developers @@ -97,39 +116,47 @@ Fork the `PyLops repository `_ and clone it by Install dependencies ==================== - We recommend installing dependencies into a separate environment. For that end, we provide a `Makefile` with useful commands for setting up the environment. -Conda (recommended) -------------------- -For a ``conda`` environment, run +.. tab-set:: -.. code-block:: bash + .. tab-item:: conda - >> make dev-install_conda # for x86 (Intel or AMD CPUs) - >> make dev-install_conda_arm # for arm (M-series Mac) + .. code-block:: bash -This will create and activate an environment called ``pylops``, with all required and optional dependencies. + >> make dev-install_conda # for x86 (Intel or AMD CPUs) + >> make dev-install_conda_arm # for arm (M-series Mac) + + This creates and activate an environment called ``pylops``, with + all required and optional dependencies. -Pip ---- -If you prefer a ``pip`` installation, we provide the following command + .. tab-item:: :iconify:`material-icon-theme:uv` uv -.. code-block:: bash - - >> make dev-install + .. code-block:: bash -Note that, differently from the ``conda`` command, the above **will not** create a virtual environment. -Make sure you create and activate your environment previously. + >> make dev-install_uv + + This creates a virtual environment `.venv` that can be activated at + any time with `source .venv/bin/activate` (Linux/macOS). Run tests ========= To ensure that everything has been setup correctly, run tests: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`devicon:anaconda` conda + + .. code-block:: bash + + >> make tests + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash - >> make tests + >> make tests_uv Make sure no tests fail, this guarantees that the installation has been successful. @@ -149,7 +176,6 @@ From then on, you can pull changes (for example, in the dev branch) with: >> git pull upstream dev - Install pre-commit hooks ======================== To ensure consistency in the coding style of our developers we rely on @@ -159,35 +185,75 @@ that have been configured in the ``.pre-commit-config.yaml`` file. In order to setup such hooks in your local repository, run: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`devicon:anaconda` conda + + .. code-block:: bash - >> pre-commit install + >> pre-commit install + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv run pre-commit install Once this is set up, when committing changes, ``pre-commit`` will reject and "fix" your code by running the proper hooks. At this point, the user must check the changes and then stage them before trying to commit again. Final steps =========== -PyLops does not enforce the use of a linter as a pre-commit hook, but we do highly encourage using one before submitting a Pull Request. -A properly configured linter (``flake8``) can be run with: +PyLops does enforce the use of a linter (``ruff``), which is run both as a pre-commit hook and as a GitHub Action. +The linter can also be run locally with: -.. code-block:: bash +.. tab-set:: - >> make lint + .. tab-item:: :iconify:`devicon:anaconda` conda + + .. code-block:: bash + + >> make lint + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> make lint_uv In addition, it is highly encouraged to build the docs prior to submitting a Pull Request. Apart from ensuring that docstrings are properly formatted, they can aid in catching bugs during development. Build (or update) the docs with: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`devicon:anaconda` conda + + .. code-block:: bash + + >> make doc + + .. tab-item:: :iconify:`material-icon-theme:uv` uv - >> make doc + .. code-block:: bash + + >> make doc_uv or -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`devicon:anaconda` conda + + .. code-block:: bash - >> make docupdate + >> make docupdate + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> make docupdate_uv .. _Performance: @@ -232,7 +298,6 @@ These are an option for an environment without ``conda`` that needs Intel MKL wi ``intel-numpy`` and ``intel-scipy`` not only link against Intel MKL, but also substitute NumPy and SciPy FFTs with `Intel MKL FFT `_. - Multithreading ============== It is important to ensure that your environment variable which sets threads is @@ -295,66 +360,179 @@ other libraries that you have in your system, we have decided to build some of t of PyLops in such a way that if an *optional* dependency is not present in your Python environment, a safe fallback to one of the required dependencies will be enforced. -When available in your system, we recommend using the Conda package manager and install all the -required and some of the optional dependencies of PyLops at once using the command: - -.. code-block:: bash - - >> conda install --channel conda-forge pylops - -in this case all dependencies will be installed from their Conda distributions. - -Alternatively, from version ``1.4.0`` some of the optional dependencies can also be -installed as part of the pip installation via: - -.. code-block:: bash - - >> pip install pylops[advanced] - -Dependencies are however installed from their PyPI wheels. - -Finally, note that CuPy and JAX are not **not** installed -automatically. Users interested to accelerate their computations with the aid -of GPUs should install either or both of them prior to installing PyLops as -described in :ref:`OptionalGPU`. - .. note:: If you are a developer, all the optional dependencies below (except GPU) can be installed automatically by cloning the repository and installing - PyLops via ``make dev-install_conda`` (``conda``) or ``make dev-install`` (``pip``). - + PyLops via ``make dev-install_conda`` (``conda``) or + ``make dev-install_uv`` (``uv``). GPU-enabled equivalents are + ``make dev-install_conda_gpu`` (``conda``) and + ``make dev-install_uvcu126 / dev-install_uvcu128 / dev-install_uvcu13`` (``uv``) + +When using the Conda package manager, only the required dependencies will be installed +when installing PyLops. It is recommended to install the optional dependencies manually +before installing PyLops or as part of the creation of the environment via an +`environment.yml` file. + +Alternatively, from version ``v1.4.0`` some of the optional dependencies can be +installed as part of the pip installation via (see summary table below for details): + +.. tab-set:: + + .. tab-item:: :iconify:`devicon:pypi` pip + + .. code-block:: bash + + >> pip install pylops[advanced] + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add "pylops[advanced]" + +Finally, from version ``2.7.0``, all of the optional dependencies can be installed as part of +the pip installation via (see summary table below for details): + + +.. tab-set:: + + .. tab-item:: :iconify:`devicon:pypi` pip + + .. code-block:: bash + + >> pip install pylops[advanced, stat, deep] # CPU + >> pip install pylops[advanced, stat, gpu-cu12, deep-cu126] # GPU with CUDA 12.6 + >> pip install pylops[advanced, stat, gpu-cu12, deep-cu128] # GPU with CUDA 12.6 + >> pip install pylops[advanced, stat, gpu-cu13, deep-cu13] # GPU with CUDA 13.0 + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add "pylops[advanced, stat, deep]" # CPU + >> uv add "pylops[advanced, stat, gpu-cu12, deep-cu126]" # GPU with CUDA 12.6 + >> uv add "pylops[advanced, stat, gpu-cu12, deep-cu128]" # GPU with CUDA 12.6 + >> uv add "pylops[advanced, stat, gpu-cu13, deep-cu13]" # GPU with CUDA 13.0 + +In all cases, dependencies are installed from their PyPI wheels. + +A summary table of all optional dependencies, the operators that rely on them (and +whether they are required to be able to use the operator(s)), and how to install +them as part of the installation process of PyLops provided in the table below. + +.. list-table:: + :widths: 15 40 5 40 + :header-rows: 1 + + * - Dependency + - Operator(s) affected + - Required + - Install with + * - ASTRA + - :py:class:`pylops.medical.CT2D` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - dtcwt + - :py:class:`pylops.signalprocessing.DTCWT` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - Devito + - :py:class:`pylops.waveeqprocessing.AcousticWave2D` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - FFTW + - :py:class:`pylops.signalprocessing.FFT`, :py:class:`pylops.signalprocessing.FFT2D`, + :py:class:`pylops.signalprocessing.FFTND` + - |:red_circle:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - MKL-FFT + - :py:class:`pylops.signalprocessing.FFT`, :py:class:`pylops.signalprocessing.FFT2D`, + :py:class:`pylops.signalprocessing.FFTND` + - |:red_circle:| + - N/A (see below for installation instructions) + * - Numba + - :py:class:`pylops.basicoperators.Spread`, :py:class:`pylops.signalprocessing.FourierRadon2D`, + :py:class:`pylops.signalprocessing.FourierRadon3D`, :py:class:`pylops.signalprocessing.Radon2D`, + :py:class:`pylops.signalprocessing.Radon3D`, :py:class:`pylops.signalprocessing.NonStationaryConvolve2D`, + :py:class:`pylops.signalprocessing.NonStationaryFilters2D`, :py:class:`pylops.signalprocessing.NonStationaryConvolve3D`, + :py:class:`pylops.signalprocessing.PWSprayer2D`, :py:class:`pylops.signalprocessing.PWSmoother2D`, + :py:class:`pylops.waveeqprocessing.Kirchhoff` + - |:red_circle:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - PyMC and PyTensor + - :py:class:`pylops.PyTensorOperator` + - |:white_check_mark:| + - `pip install pylops[stat]` / `uv add "pylops[stat]"` + * - PyWavelets + - :py:class:`pylops.signalprocessing.DWT`, :py:class:`pylops.signalprocessing.DWT2D`, + :py:class:`pylops.signalprocessing.DWTND` + - |:red_circle:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - scikit-fmm + - :py:class:`pylops.waveeqprocessing.Kirchhoff` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - SPGL1 + - :py:class:`pylops.optimization.sparsity.spgl1` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - Sympy + - :py:class:`pylops.waveeqprocessing.AcousticWave2D` and :py:func:`pylops.utils.describe.describe` + - |:white_check_mark:| + - `pip install pylops[advanced]` / `uv add "pylops[advanced]"` + * - Torch + - :py:class:`pylops.TorchOperator` + - |:white_check_mark:| + - `pip install pylops[deep]` / `uv add "pylops[deep]"` (or GPU equivalents) + * - CuPy + - Almost all operators (see :ref:`gpu` for details) + - |:red_circle:| + - `pip install pylops[gpu-cu12]` / `uv add "pylops[gpu-cu12]"` + * - JAX + - :py:class:`pylops.JAXOperator` + - |:red_circle:| + - `pip install pylops[deep]` / `uv add "pylops[deep]"` (or GPU equivalents) More details about the installation process for the different optional dependencies are described -in the following (an asterisc is used to indicate those dependencies that are automatically installed -when installing PyLops from conda-forge or via ``pip install pylops[advanced]``): +in the following: ASTRA ----- `ASTRA `_ is library used to perform computerized tomography. It is used in PyLops in the operator :py:class:`pylops.medical.CT2D` -To use this library, install it manually either via ``conda``: +To use this library, install it via: -.. code-block:: bash - >> conda install --channel astra-toolbox astra-toolbox +.. tab-set:: -or via pip: + .. tab-item:: :iconify:`devicon:anaconda` conda -.. code-block:: bash - >> pip install astra-toolbox + .. code-block:: bash + >> conda install --channel astra-toolbox astra-toolbox + + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add astra-toolbox -dtcwt* ------- + +dtcwt +----- `dtcwt `_ is a library used to implement the DT-CWT operators. -Install it via ``pip`` with: +Install it via: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`material-icon-theme:uv` uv - >> pip install dtcwt + .. code-block:: bash + + >> uv add dtcwt .. warning:: ``dtcwt`` does not support NumPy 2 yet, so make sure you use NumPy 1.x @@ -367,15 +545,19 @@ Devito the finite-difference method. It is used in PyLops to compute wavefields :py:class:`pylops.waveeqprocessing.AcousticWave2D` -Install it via ``pip`` with +Install it via: -.. code-block:: bash +.. tab-set:: - >> pip install devito + .. tab-item:: :iconify:`material-icon-theme:uv` uv + .. code-block:: bash -FFTW* and MKL-FFT ------------------ + >> uv add devito + + +FFTW and MKL-FFT +---------------- Four different "engines" are provided by the :py:class:`pylops.signalprocessing.FFT` operator: ``engine="numpy"`` (default), ``engine="scipy"``, ``engine="fftw"`` and ``engine="mkl_fft"``. Similarly, the :py:class:`pylops.signalprocessing.FFT2D` and @@ -386,33 +568,41 @@ The first two engines are part of the required PyLops dependencies. The third implements the well-known `FFTW `_ via the Python wrapper :py:class:`pyfftw.FFTW`. While this optimized FFT tends to outperform the other two in many cases, it is not included by default. -To use this library, install it manually either via ``conda``: +To use this library, install it via: -.. code-block:: bash +.. tab-set:: - >> conda install --channel conda-forge pyfftw + .. tab-item:: :iconify:`devicon:anaconda` conda -or via pip: + .. code-block:: bash -.. code-block:: bash + >> conda install --channel conda-forge pyfftw - >> pip install pyfftw + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add pyfftw The fourth implements **Intel MKL FFT** via the Python interface `mkl_fft `_. This provides access to Intel’s oneMKL Fourier Transform routines, enabling efficient FFT computations with performance close to native C/Intel® oneMKL -To use this library, you can install it using ``conda``: +To use this library, you can install it via: -.. code-block:: bash +.. tab-set:: - >> conda install --channel https://software.repos.intel.com/python/conda --channel conda-forge mkl_fft + .. tab-item:: :iconify:`devicon:anaconda` conda -or via pip: + .. code-block:: bash -.. code-block:: bash + >> conda install --channel https://software.repos.intel.com/python/conda --channel conda-forge mkl_fft - >> pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add mkl_fft --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple Installing ``mkl-fft`` triggers the installation of Intel-optimized versions of `NumPy `__ and `SciPy `__, which redirects ``numpy.fft`` and ``scipy.fft`` to use MKL FFT routines. @@ -422,7 +612,7 @@ Although the library can run without Intel-optimized NumPy and SciPy, maximum pe SciPy built with Intel’s Math Kernel Library (MKL) alongside Intel Python. .. note:: - `mkl_fft` is not supported on macOS + `mkl_fft` is not supported on macOS. .. warning:: @@ -442,8 +632,9 @@ SciPy built with Intel’s Math Kernel Library (MKL) alongside Intel Python. Alternatively, you can install ``pyFFTW`` directly with ``conda``, since the updated recipe is already available and works without any manual adjustments. -Numba* ------- + +Numba +----- Although we always strive to write code for forward and adjoint operators that takes advantage of the perks of NumPy and SciPy (e.g., broadcasting, ufunc), in some case we may end up using for loops that may lead to poor performance. In those cases we may decide to implement alternative (optional) @@ -455,26 +646,27 @@ always available implementation to the Numba implementation by simply providing additional input parameter to the operator ``engine="numba"``. This is for example the case in the :py:class:`pylops.signalprocessing.Radon2D`. -If interested to use Numba backend from ``conda``, you will need to manually install it: +If interested to use Numba backend, install it via: -.. code-block:: bash +.. tab-set:: - >> conda install numba + .. tab-item:: :iconify:`devicon:anaconda` conda -It is also advised to install the additional package -`icc_rt `_ to use -optimised transcendental functions as compiler intrinsics. + .. code-block:: bash -.. code-block:: bash + >> conda install numba + >> conda install --channel numba icc_rt # optional - >> conda install --channel numba icc_rt + .. tab-item:: :iconify:`material-icon-theme:uv` uv -Through ``pip`` the equivalent would be: + .. code-block:: bash -.. code-block:: bash + >> uv add numba + >> uv add icc_rt # optional - >> pip install numba - >> pip install icc_rt +Note that it is also advised to install the additional package +`icc_rt `_ to use +optimised transcendental functions as compiler intrinsics. However, it is important to note that ``icc_rt`` will only be identified by Numba if ``LD_LIBRARY_PATH`` is properly set. @@ -504,68 +696,85 @@ PyMC and PyTensor ----------------- `PyTensor `_ is used to allow seamless integration between PyLops and `PyMC `_ operators. -Install both of them via ``conda`` with: +Install both of them with: -.. code-block:: bash +.. tab-set:: - conda install -c conda-forge pytensor pymc + .. tab-item:: :iconify:`devicon:anaconda` conda -or via ``pip`` with + .. code-block:: bash -.. code-block:: bash + >> conda install -c conda-forge pytensor pymc - >> pip install pytensor pymc + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add pytensor pymc .. warning:: OSX users may experience a ``CompileError`` error when using PyTensor. This can be solved by adding ``pytensor.config.gcc__cxxflags = "-Wno-c++11-narrowing"`` after ``import pytensor``. -PyWavelets* ------------ + +PyWavelets +---------- `PyWavelets `_ is used to implement the wavelet operators. -Install it via ``conda`` with: +Install it via: -.. code-block:: bash +.. tab-set:: - >> conda install pywavelets + .. tab-item:: :iconify:`devicon:anaconda` conda -or via ``pip`` with + .. code-block:: bash -.. code-block:: bash + >> conda install pywavelets + + .. tab-item:: :iconify:`material-icon-theme:uv` uv - >> pip install PyWavelets + .. code-block:: bash + >> uv add PyWavelets -scikit-fmm* ------------ + +scikit-fmm +---------- `scikit-fmm `_ is a library which implements the fast marching method. It is used in PyLops to compute traveltime tables in the initialization of :py:class:`pylops.waveeqprocessing.Kirchhoff` when choosing ``mode="eikonal"``. As this may not be of interest for many users, this library has not been added -to the mandatory requirements of PyLops. With ``conda``, install it via +to the mandatory requirements of PyLops. Install it via -.. code-block:: bash +.. tab-set:: - >> conda install --channel conda-forge scikit-fmm + .. tab-item:: :iconify:`devicon:anaconda` conda -or with ``pip`` via + .. code-block:: bash -.. code-block:: bash + >> conda install --channel conda-forge scikit-fmm - >> pip install scikit-fmm + .. tab-item:: :iconify:`material-icon-theme:uv` uv + .. code-block:: bash -SPGL1* ------- + >> uv add scikit-fmm + + +SPGL1 +----- `SPGL1 `_ is used to solve sparsity-promoting basis pursuit, basis pursuit denoise, and Lasso problems in :py:func:`pylops.optimization.sparsity.SPGL1` solver. -Install it via ``pip`` with: +Install it via: -.. code-block:: bash +.. tab-set:: + + .. tab-item:: :iconify:`material-icon-theme:uv` uv - >> pip install spgl1 + .. code-block:: bash + + >> uv add spgl1 Sympy @@ -573,38 +782,47 @@ Sympy This library is used to implement the ``describe`` method, which transforms PyLops operators into their mathematical expression. -Install it via ``conda`` with: +Install it via: -.. code-block:: bash +.. tab-set:: - >> conda install sympy + .. tab-item:: :iconify:`devicon:anaconda` conda -or via ``pip`` with + .. code-block:: bash -.. code-block:: bash + >> conda install sympy - >> pip install sympy + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add sympy Torch ----- `Torch `_ is used to allow seamless integration between PyLops and PyTorch operators. -Install it via ``conda`` with: +Install it via: -.. code-block:: bash +.. tab-set:: - >> conda install -c pytorch pytorch + .. tab-item:: :iconify:`devicon:anaconda` conda -or via ``pip`` with + .. code-block:: bash -.. code-block:: bash + >> conda install -c pytorch pytorch - >> pip install torch + .. tab-item:: :iconify:`material-icon-theme:uv` uv + + .. code-block:: bash + + >> uv add torch .. _OptionalGPU: + Optional Dependencies for GPU ============================= PyLops will automatically diff --git a/docs/source/papers.rst b/docs/source/papers.rst index c4774e8b7..0637a76f3 100755 --- a/docs/source/papers.rst +++ b/docs/source/papers.rst @@ -1,7 +1,8 @@ .. _papers: -Papers using PyLops -=================== +|:bookmark:| Papers using PyLops +################################ + This section lists various conference abstracts and papers using the PyLops framework. If you publish a paper using PyLops, `we'd love to hear about it `_! diff --git a/docs/source/roadmap.rst b/docs/source/roadmap.rst deleted file mode 100755 index 968333465..000000000 --- a/docs/source/roadmap.rst +++ /dev/null @@ -1,109 +0,0 @@ -.. _roadmap: - -.. role:: strike - :class: strike - -Roadmap -======= - -This roadmap is aimed at providing an high-level overview on the bug fixes, improvements -and new functionality that are planned for the PyLops library. - -Any of the fixes/additions mentioned in the roadmap are directly linked to a *Github Issue* -that provides more details onto the reason and initial thoughts for the implementation of -such a fix/addition. - -:strike:`Striked tasks` have been completed and related github issue closed -with more details regarding how this task has been carried out. - -Library structure ------------------ - -* Create a child repository and python library called ``geolops`` (just a suggestion) - where geoscience-related operators and examples are moved across, keeping the core - ``pylops`` library very generic and multi-purpose - - `Issue #22 `_. - - -Code cleaning -------------- - -* :strike:`Change all ``np.flatten()``` :strike:`into ``np.ravel()``` - - `Issue #24 `_. -* Fix all ``if: return ... else: ...`` statements to enforce a single return - with the same number of outputs - - `Issue #26 `_. -* Protected attributes and @property attributes in linear operator classes? - - `Issue #27 `_. - - -Code optimization ------------------ - -* Investigate speed-up given by decorating ``_matvec`` and ``_rmatvec`` methods with - `numba `_ ``@jit`` and ``@stencil`` decorators - - `Issue #23 `_. - -* :strike:`Replace np.fft.* routines used in several submodules with double engine, - numpy and pyFFTW` - - `Issue #20 `_. - - -Modules -------- - -avo -~~~ - -* Add possibility to choose different damping factors for each elastic parameter to invert for in - :py:class:`pylops.avo.prestack.PrestackInversion` - `Issue #25 `_. - -basicoperators -~~~~~~~~~~~~~~ - -* :strike:`Create Kronecker operator` - - `Issue #28 `_. - -* :strike:`Deal with edges in FirstDerivative and SecondDerivative operators` - - `Issue #34 `_. - -optimization -~~~~~~~~~~~~ - -* :strike:`Sparse solvers` - - `Issue #44 `_. - - -signalprocessing -~~~~~~~~~~~~~~~~ - -* :strike:`Compare performance in FTT operator of performing - np.swap+np.fft.fft(..., axis=-1) versus np.fft.fft(..., axis=chosen)` - - `Issue #33 `_. - -* :strike:`Add Wavelet operator performing the wavelet transform` - - `Issue #21 `_. - -* :strike:`Fredholm1 operator applying Fredholm integrals - of first kind` - `Issue #31 `_. - -* :strike:`Fredholm2 operators applying Fredholm integrals - of second kind` - `Issue #31 `_. - -utils -~~~~~ - -Nothing so far - - -waveeqprocessing -~~~~~~~~~~~~~~~~ - -* :strike:`numpy.matmul as a way to speed up integral computation (i.e., inner for loop) - in MDC operator` - `Issue #32 `_. - -* ``NMO`` operator performing NMO modelling - - `Issue #29 `_. - -* :strike:`WavefieldDecomposition operator performing acoustic wavefield separation - by inversion` - `Issue #30 `_. diff --git a/environment-dev-arm.yml b/environment-dev-arm.yml index 4b5c80d1a..2264c0f80 100644 --- a/environment-dev-arm.yml +++ b/environment-dev-arm.yml @@ -22,8 +22,6 @@ dependencies: - numba - pre-commit - autopep8 - - isort - - black - pip: - torch - devito @@ -33,11 +31,14 @@ dependencies: - jax - pytest-runner - setuptools_scm - - pydata-sphinx-theme - - pooch + - shibuya + - sphinx-design - sphinx-gallery + - sphinx-iconify + - pooch - nbsphinx - sphinxemoji - image - - flake8 + - ruff - mypy + - coverage diff --git a/environment-dev-gpu.yml b/environment-dev-gpu.yml index cb19a0b57..e7dd7ee57 100644 --- a/environment-dev-gpu.yml +++ b/environment-dev-gpu.yml @@ -20,17 +20,18 @@ dependencies: - icc_rt - pre-commit - autopep8 - - isort - - black - pip: - torch - pytest-runner - setuptools_scm - - pydata-sphinx-theme - - pooch + - shibuya + - sphinx-design - sphinx-gallery + - sphinx-iconify + - pooch - nbsphinx - sphinxemoji - image - - flake8 + - ruff - mypy + - coverage diff --git a/environment-dev-intel-mkl.yml b/environment-dev-intel-mkl.yml index 4895288cf..762cc32ad 100644 --- a/environment-dev-intel-mkl.yml +++ b/environment-dev-intel-mkl.yml @@ -24,8 +24,6 @@ dependencies: - icc_rt - pre-commit - autopep8 - - isort - - black - mkl_fft - pip: - torch @@ -36,11 +34,14 @@ dependencies: - jax - pytest-runner - setuptools_scm - - pydata-sphinx-theme - - pooch + - shibuya + - sphinx-design - sphinx-gallery + - sphinx-iconify + - pooch - nbsphinx - sphinxemoji - image - - flake8 + - ruff - mypy + - coverage diff --git a/environment-dev.yml b/environment-dev.yml index f719e8d99..0b6c54f7f 100644 --- a/environment-dev.yml +++ b/environment-dev.yml @@ -23,8 +23,6 @@ dependencies: - icc_rt - pre-commit - autopep8 - - isort - - black - pip: - torch - devito @@ -34,11 +32,14 @@ dependencies: - jax - pytest-runner - setuptools_scm - - pydata-sphinx-theme - - pooch + - shibuya + - sphinx-design - sphinx-gallery + - sphinx-iconify + - pooch - nbsphinx - sphinxemoji - image - - flake8 + - ruff - mypy + - coverage diff --git a/examples/README.txt b/examples/README.txt index 5d98186bf..6c3ef5c4f 100755 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,6 +1,6 @@ .. _general_examples: -Gallery -------- +|:framed_picture:| Gallery +-------------------------- Below is a gallery of examples which use PyLops operators and utilities. diff --git a/examples/plot_bayeslinearregr.py b/examples/plot_bayeslinearregr.py index 42417eb15..37ca373b2 100644 --- a/examples/plot_bayeslinearregr.py +++ b/examples/plot_bayeslinearregr.py @@ -88,7 +88,7 @@ # Let's plot the best fitting line for the case of noise free and noisy data fig, ax = plt.subplots(figsize=(8, 4)) for est, est_label, c in zip( - [x, x_mle, x_map], ["True", "MLE", "MAP"], ["k", "C0", "C1"] + [x, x_mle, x_map], ["True", "MLE", "MAP"], ["k", "C0", "C1"], strict=True ): ax.plot( np.array([t.min(), t.max()]), @@ -219,7 +219,7 @@ ax=ax, ) for est, est_label, c in zip( - [x, x_mle, x_map], ["True", "MLE", "MAP"], ["k", "C0", "C1"] + [x, x_mle, x_map], ["True", "MLE", "MAP"], ["k", "C0", "C1"], strict=True ): ax.plot( np.array([t.min(), t.max()]), diff --git a/examples/plot_derivative.py b/examples/plot_derivative.py index 19d453d75..7e6fd6ef6 100644 --- a/examples/plot_derivative.py +++ b/examples/plot_derivative.py @@ -12,6 +12,7 @@ *Optimization* tutorial, these operators will be used as part of the regularization term to obtain a smooth solution. """ + import matplotlib.pyplot as plt import numpy as np @@ -223,9 +224,9 @@ nvertlayers = len(vertlayers) A = 1500 * np.ones((nz, nx)) -for top, base in zip(horlayers[:-1], horlayers[1:]): +for top, base in zip(horlayers[:-1], horlayers[1:], strict=True): A[top:base] = np.random.normal(2000, 200) -for top, base in zip(vertlayers[:-1], vertlayers[1:]): +for top, base in zip(vertlayers[:-1], vertlayers[1:], strict=True): A[horlayers[-1] :, top:base] = np.random.normal(2000, 200) v = np.zeros((2, nz, nx)) diff --git a/examples/plot_nmo.py b/examples/plot_nmo.py index 80806fac8..aee6f1f38 100644 --- a/examples/plot_nmo.py +++ b/examples/plot_nmo.py @@ -7,6 +7,9 @@ We will perform classic NMO using an operator created from scratch, as well as using the :py:class:`pylops.Spread` operator. """ + +# flake8: noqa: B905 + from math import floor from time import time @@ -199,7 +202,7 @@ def nmo_forward(data, taxis, haxis, vels_rms): nmo_forward(data, t, x, vel_t) end = time() -print(f"Ran in {1e6*(end-start):.0f} μs") +print(f"Ran in {1e6 * (end - start):.0f} μs") ############################################################################### @@ -379,7 +382,7 @@ def create_tables(taxis, haxis, vels_rms): SpreadNMO @ data end = time() -print(f"Ran in {1e6*(end-start):.0f} μs") +print(f"Ran in {1e6 * (end - start):.0f} μs") ############################################################################### # Note that since v2.0, we do not need to pass a flattened array. Consequently, # the output will not be flattened, but will have ``SpreadNMO.dimsd`` as shape. diff --git a/examples/plot_slopeest.py b/examples/plot_slopeest.py index 22cf90963..3d9ba4d2a 100755 --- a/examples/plot_slopeest.py +++ b/examples/plot_slopeest.py @@ -57,7 +57,7 @@ cb = fig.colorbar( iax, ticks=MultipleLocator(30), - format=FuncFormatter(lambda x, pos: "{:.0f}°".format(x)), + format=FuncFormatter(lambda x, pos: f"{x:.0f}°"), cax=cax, orientation="vertical", ) @@ -180,7 +180,7 @@ def rgb2gray(rgb): cb = fig.colorbar( im, ticks=MultipleLocator(30), - format=FuncFormatter(lambda x, pos: "{:.0f}°".format(x)), + format=FuncFormatter(lambda x, pos: f"{x:.0f}°"), cax=cax, orientation="vertical", ) @@ -191,7 +191,7 @@ def rgb2gray(rgb): cb = fig.colorbar( im, ticks=MultipleLocator(30), - format=FuncFormatter(lambda x, pos: "{:.0f}°".format(x)), + format=FuncFormatter(lambda x, pos: f"{x:.0f}°"), cax=cax, orientation="vertical", ) @@ -249,7 +249,7 @@ def rgb2gray(rgb): cb = fig.colorbar( im, ticks=MultipleLocator(30), - format=FuncFormatter(lambda x, pos: "{:.0f}°".format(x)), + format=FuncFormatter(lambda x, pos: f"{x:.0f}°"), cax=cax, orientation="vertical", ) @@ -260,7 +260,7 @@ def rgb2gray(rgb): cb = fig.colorbar( im, ticks=MultipleLocator(30), - format=FuncFormatter(lambda x, pos: "{:.0f}°".format(x)), + format=FuncFormatter(lambda x, pos: f"{x:.0f}°"), cax=cax, orientation="vertical", ) diff --git a/examples/plot_wavelet.py b/examples/plot_wavelet.py index 4c410112c..ccfeb3faa 100644 --- a/examples/plot_wavelet.py +++ b/examples/plot_wavelet.py @@ -5,6 +5,7 @@ :py:class:`pylops.DWT2D`, and :py:class:`pylops.DWTND` operators to perform 1-, 2-, and N-dimensional DWT. """ + import matplotlib.pyplot as plt import numpy as np @@ -21,7 +22,10 @@ t = np.arange(nt) * dt freqs = [10, 7, 9] amps = [1, -2, 0.5] -x = np.sum([amp * np.sin(2 * np.pi * f * t) for (f, amp) in zip(freqs, amps)], axis=0) +x = np.sum( + [amp * np.sin(2 * np.pi * f * t) for (f, amp) in zip(freqs, amps, strict=True)], + axis=0, +) Wop = pylops.signalprocessing.DWT(nt, wavelet="dmey", level=5) y = Wop * x diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 000000000..423aed97f --- /dev/null +++ b/noxfile.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +import shutil +from pathlib import Path + +import nox + +DIR = Path(__file__).parent.resolve() + +nox.options.default_venv_backend = "uv" +nox.options.sessions = ["lint", "tests"] + + +@nox.session +def lint(session): + """Run the Ruff linter.""" + session.install("ruff") + session.run( + "ruff", + "check", + "docs/source", + "examples/", + "pylops/", + "pytests/", + "tutorials/", + ) + + +@nox.session(python=["3.10", "3.11", "3.12", "3.13", "3.14"]) +def tests(session: nox.Session) -> None: + """ + Run unit tests. + """ + session.install("-e", ".[advanced,deep,stat]") + session.install("--group", "dev") + session.run("pytest", *session.posargs) + + +@nox.session(python=["3.10", "3.11", "3.12", "3.13", "3.14"]) +def build(session: nox.Session) -> None: + """ + Build an SDist and wheel. + """ + + build_path = DIR.joinpath("build") + if build_path.exists(): + shutil.rmtree(build_path) + + session.install("build") + session.run("python", "-m", "build") diff --git a/pylops/_torchoperator.py b/pylops/_torchoperator.py index 55527eb90..6249c9484 100644 --- a/pylops/_torchoperator.py +++ b/pylops/_torchoperator.py @@ -25,7 +25,8 @@ def forward(ctx, x, forw, adj, device, devicetorch): warnings.warn( "PyLops operator will be applied on the cpu " "whilst the input torch vector is on " - "%s, this may lead to poor performance" % ctx.devicetorch + "%s, this may lead to poor performance" % ctx.devicetorch, + stacklevel=2, ) # prepare input diff --git a/pylops/avo/avo.py b/pylops/avo/avo.py index a3e0e1052..ee4072280 100644 --- a/pylops/avo/avo.py +++ b/pylops/avo/avo.py @@ -9,7 +9,6 @@ "AVOLinearModelling", ] -from typing import List, Optional, Tuple, Union import numpy as np from numpy import cos, sin, tan @@ -28,7 +27,7 @@ def zoeppritz_scattering( vp0: float, vs0: float, rho0: float, - theta1: Union[float, NDArray], + theta1: float | NDArray, ) -> NDArray: r"""Zoeppritz solution. @@ -144,7 +143,7 @@ def zoeppritz_element( vp0: float, vs0: float, rho0: float, - theta1: Union[float, NDArray], + theta1: float | NDArray, element: str = "PdPu", ) -> NDArray: """Single element of Zoeppritz solution. @@ -208,7 +207,7 @@ def zoeppritz_pp( vp0: float, vs0: float, rho0: float, - theta1: Union[float, NDArray], + theta1: float | NDArray, ) -> NDArray: """PP reflection coefficient from the Zoeppritz scattering matrix. @@ -249,13 +248,13 @@ def zoeppritz_pp( def approx_zoeppritz_pp( - vp1: Union[List, Tuple, NDArray], - vs1: Union[List, Tuple, NDArray], - rho1: Union[List, Tuple, NDArray], - vp0: Union[List, Tuple, NDArray], - vs0: Union[List, Tuple, NDArray], - rho0: Union[List, Tuple, NDArray], - theta1: Union[float, NDArray], + vp1: list | tuple | NDArray, + vs1: list | tuple | NDArray, + rho1: list | tuple | NDArray, + vp0: list | tuple | NDArray, + vs0: list | tuple | NDArray, + rho0: list | tuple | NDArray, + theta1: float | NDArray, ) -> NDArray: """PP reflection coefficient from the approximate Zoeppritz equation. @@ -335,9 +334,9 @@ def approx_zoeppritz_pp( def akirichards( theta: NDArray, - vsvp: Union[float, NDArray], + vsvp: float | NDArray, n: int = 1, -) -> Tuple[NDArray, NDArray, NDArray]: +) -> tuple[NDArray, NDArray, NDArray]: r"""Three terms Aki-Richards approximation. Computes the coefficients of the of three terms Aki-Richards approximation @@ -409,9 +408,9 @@ def akirichards( def fatti( theta: NDArray, - vsvp: Union[float, NDArray], + vsvp: float | NDArray, n: int = 1, -) -> Tuple[NDArray, NDArray, NDArray]: +) -> tuple[NDArray, NDArray, NDArray]: r"""Three terms Fatti approximation. Computes the coefficients of the three terms Fatti approximation @@ -485,9 +484,9 @@ def fatti( def ps( theta: NDArray, - vsvp: Union[float, NDArray], + vsvp: float | NDArray, n: int = 1, -) -> Tuple[NDArray, NDArray, NDArray]: +) -> tuple[NDArray, NDArray, NDArray]: r"""PS reflection coefficient Computes the coefficients for the PS approximation @@ -646,9 +645,9 @@ class AVOLinearModelling(LinearOperator): def __init__( self, theta: NDArray, - vsvp: Union[float, NDArray] = 0.5, + vsvp: float | NDArray = 0.5, nt0: int = 1, - spatdims: Optional[Union[int, Tuple[int]]] = None, + spatdims: int | tuple[int] | None = None, linearization: Tavolinearization = "akirich", dtype: DTypeLike = "float64", name: str = "A", @@ -671,8 +670,8 @@ def __init__( "%s not an available linearization..." % linearization ) self.npars = len(Gs) - dims: Tuple[int, ...] = (self.nt0, self.npars) - dimsd: Tuple[int, ...] = (self.nt0, self.ntheta) + dims: tuple[int, ...] = (self.nt0, self.npars) + dimsd: tuple[int, ...] = (self.nt0, self.ntheta) if spatdims is not None: dims += self.spatdims dimsd += self.spatdims diff --git a/pylops/avo/poststack.py b/pylops/avo/poststack.py index d08bb237c..27394ba59 100644 --- a/pylops/avo/poststack.py +++ b/pylops/avo/poststack.py @@ -3,7 +3,7 @@ "PoststackInversion", ] -from typing import Literal, Optional, Tuple, Union +from typing import Literal import numpy as np from scipy.sparse.linalg import lsqr @@ -41,9 +41,9 @@ def _PoststackLinearModelling( _MatrixMult=MatrixMult, _Convolve1D=Convolve1D, _FirstDerivative=FirstDerivative, - args_MatrixMult: Optional[dict] = None, - args_Convolve1D: Optional[dict] = None, - args_FirstDerivative: Optional[dict] = None, + args_MatrixMult: dict | None = None, + args_Convolve1D: dict | None = None, + args_FirstDerivative: dict | None = None, ): """Post-stack linearized seismic modelling operator. @@ -71,9 +71,10 @@ def _PoststackLinearModelling( dtype = wav.dtype # ensure wav.dtype rules that of operator if len(wav.shape) == 2 and wav.shape[0] != nt0: - raise ValueError("Provide 1d wavelet or 2d wavelet composed of nt0 " "wavelets") + msg = "Must provide 1d wavelet or 2d wavelet composed of nt0 wavelets" + raise ValueError(msg) - spatdims: Union[int, ShapeLike] + spatdims: int | ShapeLike # organize dimensions if spatdims is None: dims = (nt0,) @@ -117,14 +118,14 @@ def _PoststackLinearModelling( offset=len(wav) // 2, axis=0, dtype=dtype, - **args_Convolve1D + **args_Convolve1D, ) else: Cop = _MatrixMult( nonstationary_convmtx(wav, nt0, hc=wav.shape[1] // 2, pad=(nt0, nt0)), otherdims=spatdims, dtype=dtype, - **args_MatrixMult + **args_MatrixMult, ) # Create derivative operator Dop = _FirstDerivative( @@ -137,11 +138,11 @@ def _PoststackLinearModelling( def PoststackLinearModelling( wav: NDArray, nt0: int, - spatdims: Optional[Union[int, ShapeLike]] = None, + spatdims: int | ShapeLike | None = None, explicit: bool = False, sparse: bool = False, kind: Literal["centered", "forward"] = "centered", - name: Optional[str] = None, + name: str | None = None, ) -> LinearOperator: r"""Post-stack linearized seismic modelling operator. @@ -224,15 +225,15 @@ def PoststackLinearModelling( def PoststackInversion( data: NDArray, wav: NDArray, - m0: Optional[NDArray] = None, + m0: NDArray | None = None, explicit: bool = False, simultaneous: bool = False, - epsI: Optional[float] = None, - epsR: Optional[float] = None, + epsI: float | None = None, + epsR: float | None = None, dottest: bool = False, - epsRL1: Optional[float] = None, - **kwargs_solver -) -> Tuple[NDArray, NDArray]: + epsRL1: float | None = None, + **kwargs_solver, +) -> tuple[NDArray, NDArray]: r"""Post-stack linearized seismic inversion. Invert post-stack seismic operator to retrieve an elastic parameter of @@ -321,9 +322,10 @@ def PoststackInversion( # check if background model and data have same shape if m0 is not None and data.shape != m0.shape: - raise ValueError("data and m0 must have same shape") + msg = "data and m0 must have the same shape" + raise ValueError(msg) - nspat: Optional[Union[int, ShapeLike]] + nspat: int | ShapeLike | None # find out dimensions if data.ndim == 1: dims = 1 @@ -374,7 +376,7 @@ def PoststackInversion( PPop, datar, x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype), - **kwargs_solver + **kwargs_solver, )[0] elif epsI is not None: # create regularized normal equations @@ -393,7 +395,7 @@ def PoststackInversion( PPop_reg, datar.ravel(), x0=ncp.zeros(int(PPop_reg.shape[1]), PPop_reg.dtype), - **kwargs_solver + **kwargs_solver, )[0] else: # create regularized normal eqs. and solve them simultaneously @@ -410,7 +412,7 @@ def PoststackInversion( PPop, datar, x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype), - **kwargs_solver + **kwargs_solver, )[0] else: if epsRL1 is None: @@ -428,7 +430,7 @@ def PoststackInversion( [Regop], x0=None if m0 is None else m0.ravel(), epsRs=[epsR], - **kwargs_solver + **kwargs_solver, )[0] else: # Blockiness-promoting inversion with spatial regularization @@ -476,7 +478,7 @@ def PoststackInversion( niter_outer=niter_outer, niter_inner=niter_inner, x0=None if m0 is None else m0.ravel(), - **kwargs_solver + **kwargs_solver, )[0] # compute residual diff --git a/pylops/avo/prestack.py b/pylops/avo/prestack.py index d74ea6bba..03eef6b9e 100644 --- a/pylops/avo/prestack.py +++ b/pylops/avo/prestack.py @@ -4,7 +4,8 @@ "PrestackInversion", ] -from typing import Callable, List, Literal, Optional, Tuple, Union +from collections.abc import Callable +from typing import Literal import numpy as np from scipy.sparse.linalg import lsqr @@ -41,13 +42,13 @@ def PrestackLinearModelling( wav: NDArray, theta: NDArray, - vsvp: Union[float, NDArray] = 0.5, + vsvp: float | NDArray = 0.5, nt0: int = 1, - spatdims: Optional[Union[int, ShapeLike]] = None, + spatdims: int | ShapeLike | None = None, linearization: Tavolinearization = "akirich", explicit: bool = False, kind: Literal["centered", "forward"] = "centered", - name: Optional[str] = None, + name: str | None = None, ) -> LinearOperator: r"""Pre-stack linearized seismic modelling operator. @@ -143,7 +144,7 @@ def PrestackLinearModelling( ntheta = len(theta) # organize dimensions - dims: Optional[ShapeLike] + dims: ShapeLike | None if spatdims is None: dims = (nt0, ntheta) spatdims = None @@ -226,10 +227,10 @@ def PrestackWaveletModelling( m: NDArray, theta: NDArray, nwav: int, - wavc: Optional[int] = None, - vsvp: Union[float, NDArray] = 0.5, - linearization: Union[Tavolinearization, Callable] = "akirich", - name: Optional[str] = None, + wavc: int | None = None, + vsvp: float | NDArray = 0.5, + linearization: Tavolinearization | Callable = "akirich", + name: str | None = None, ) -> LinearOperator: r"""Pre-stack linearized seismic modelling operator for wavelet. @@ -322,9 +323,8 @@ def PrestackWaveletModelling( elif callable(linearization): G = linearization(theta, vsvp, n=nt0) else: - raise NotImplementedError( - "%s not an available linearization..." % linearization - ) + msg = f"{linearization} is not an available linearization..." + raise NotImplementedError(msg) nG = len(G) G = [ ncp.hstack([ncp.diag(G_[itheta] * ncp.ones(nt0, dtype=dtype)) for G_ in G]) @@ -356,19 +356,19 @@ def PrestackInversion( data: NDArray, theta: NDArray, wav: NDArray, - m0: Optional[NDArray] = None, - linearization: Union[Tavolinearization, List[Tavolinearization]] = "akirich", + m0: NDArray | None = None, + linearization: Tavolinearization | list[Tavolinearization] = "akirich", explicit: bool = False, simultaneous: bool = False, - epsI: Optional[float] = None, - epsR: Optional[float] = None, + epsI: float | None = None, + epsR: float | None = None, dottest: bool = False, returnres: bool = False, - epsRL1: Optional[float] = None, + epsRL1: float | None = None, kind: Literal["centered", "forward"] = "centered", - vsvp: Union[float, NDArray] = 0.5, - **kwargs_solver -) -> Union[NDArray, Tuple[NDArray, NDArray]]: + vsvp: float | NDArray = 0.5, + **kwargs_solver, +) -> NDArray | tuple[NDArray, NDArray]: r"""Pre-stack linearized seismic inversion. Invert pre-stack seismic operator to retrieve a set of elastic property @@ -452,7 +452,8 @@ def PrestackInversion( # find out dimensions if m0 is None and linearization is None: - raise NotImplementedError("either m0 or linearization " "must be provided") + msg = "Either m0 or linearization must be provided" + raise NotImplementedError(msg) elif m0 is None: if isinstance(linearization, str): nm = _linearizations[linearization] @@ -495,7 +496,8 @@ def PrestackInversion( or (dims >= 2 and nx != m0.shape[2]) or (dims == 3 and ny != m0.shape[3]) ): - raise ValueError("data and m0 must have same time and space axes") + msg = "data and m0 must have the same time and space axes" + raise ValueError(msg) # create operator if isinstance(linearization, str): @@ -526,7 +528,7 @@ def PrestackInversion( linearization=lin, explicit=explicit, ) - for w, lin in zip(wav, linearization) + for w, lin in zip(wav, linearization, strict=True) ] if explicit: PPop = MatrixMult( @@ -562,7 +564,7 @@ def PrestackInversion( minv = get_lstsq(data)( PPop.A, datar.reshape(n_lins * nt0 * ntheta, nspatprod).squeeze(), - **kwargs_solver + **kwargs_solver, )[0] elif epsI is None and simultaneous: # solve unregularized equations simultaneously @@ -573,7 +575,7 @@ def PrestackInversion( PPop, datar, x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype), - **kwargs_solver + **kwargs_solver, )[0] elif epsI is not None: # create regularized normal equations @@ -594,7 +596,7 @@ def PrestackInversion( PPop_reg, datarn.ravel(), x0=ncp.zeros(int(PPop_reg.shape[1]), PPop_reg.dtype), - **kwargs_solver + **kwargs_solver, )[0] # else: # # create regularized normal eqs. and solve them simultaneously @@ -611,14 +613,15 @@ def PrestackInversion( PPop, datar, x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype), - **kwargs_solver + **kwargs_solver, )[0] else: # Create Thicknov regularization if epsI is not None: if isinstance(epsI, (list, tuple)): if len(epsI) != nm: - raise ValueError("epsI must be a scalar or a list of" "size nm") + msg = f"epsI must be a scalar or a list of size nm, got {epsI}" + raise ValueError(msg) RegI = Diagonal(np.array(epsI), dims=(nt0, nm, nspatprod), axis=1) else: RegI = epsI * Identity(nt0 * nm * nspatprod) @@ -643,7 +646,7 @@ def PrestackInversion( Regop, x0=m0.ravel() if m0 is not None else None, epsRs=epsR, - **kwargs_solver + **kwargs_solver, )[0] else: # Blockiness-promoting inversion with spatial regularization @@ -694,7 +697,7 @@ def PrestackInversion( niter_outer=niter_outer, niter_inner=niter_inner, x0=None if m0 is None else m0.ravel(), - **kwargs_solver + **kwargs_solver, )[0] # compute residual diff --git a/pylops/basicoperators/block.py b/pylops/basicoperators/block.py index 19cc5b317..cd29194bf 100644 --- a/pylops/basicoperators/block.py +++ b/pylops/basicoperators/block.py @@ -1,6 +1,6 @@ __all__ = ["Block"] -from typing import Iterable, Optional +from collections.abc import Iterable from pylops import LinearOperator from pylops.basicoperators import HStack, VStack @@ -17,12 +17,12 @@ class _Block(LinearOperator): def __init__( self, ops: Iterable[Iterable[LinearOperator]], - forceflat: Optional[bool] = None, - dtype: Optional[DTypeLike] = None, + forceflat: bool | None = None, + dtype: DTypeLike | None = None, _HStack=HStack, _VStack=VStack, - _args_HStack: Optional[dict] = None, - _args_VStack: Optional[dict] = None, + _args_HStack: dict | None = None, + _args_VStack: dict | None = None, name: str = "B", ): if _args_HStack is None: @@ -153,9 +153,9 @@ def __init__( self, ops: Iterable[Iterable[LinearOperator]], nproc: int = 1, - forceflat: Optional[bool] = None, + forceflat: bool | None = None, parallel_kind: Tparallel_kind = "multiproc", - dtype: Optional[DTypeLike] = None, + dtype: DTypeLike | None = None, ): super().__init__( ops=ops, diff --git a/pylops/basicoperators/blockdiag.py b/pylops/basicoperators/blockdiag.py index 3ceaacd53..7d98026a3 100644 --- a/pylops/basicoperators/blockdiag.py +++ b/pylops/basicoperators/blockdiag.py @@ -14,11 +14,13 @@ from scipy.sparse.linalg.interface import _get_dtype else: from scipy.sparse.linalg._interface import ( - _get_dtype, LinearOperator as spLinearOperator, ) + from scipy.sparse.linalg._interface import ( + _get_dtype, + ) -from typing import Optional, Sequence +from collections.abc import Sequence from pylops import LinearOperator from pylops.basicoperators import MatrixMult @@ -142,13 +144,14 @@ def __init__( self, ops: Sequence[LinearOperator], nproc: int = 1, - forceflat: Optional[bool] = None, - inoutengine: Optional[Tinoutengine] = None, + forceflat: bool | None = None, + inoutengine: Tinoutengine | None = None, parallel_kind: Tparallel_kind = "multiproc", - dtype: Optional[DTypeLike] = None, + dtype: DTypeLike | None = None, ) -> None: if parallel_kind not in ["multiproc", "multithread"]: - raise ValueError("parallel_kind must be 'multiproc' or 'multithread'") + msg = "parallel_kind must be 'multiproc' or 'multithread'" + raise ValueError(msg) # identify dimensions self.ops = ops mops = np.zeros(len(ops), dtype=int) @@ -181,7 +184,7 @@ def __init__( # create pool for multithreading / multiprocessing self.parallel_kind = parallel_kind self._nproc = nproc - self.pool: Optional[mp.pool.Pool] = None + self.pool: mp.pool.Pool | None = None if self.nproc > 1: if self.parallel_kind == "multiproc": self.pool = mp.Pool(processes=nproc) diff --git a/pylops/basicoperators/causalintegration.py b/pylops/basicoperators/causalintegration.py index 15042f002..b688ee2fc 100644 --- a/pylops/basicoperators/causalintegration.py +++ b/pylops/basicoperators/causalintegration.py @@ -1,6 +1,6 @@ __all__ = ["CausalIntegration"] -from typing import Literal, Union +from typing import Literal import numpy as np @@ -98,7 +98,7 @@ class CausalIntegration(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, sampling: float = 1.0, kind: Literal["full", "half", "trapezoidal"] = "full", diff --git a/pylops/basicoperators/conj.py b/pylops/basicoperators/conj.py index 5d6ca3809..a4d073c08 100644 --- a/pylops/basicoperators/conj.py +++ b/pylops/basicoperators/conj.py @@ -1,8 +1,6 @@ __all__ = ["Conj"] -from typing import Union - import numpy as np from pylops import LinearOperator @@ -56,7 +54,7 @@ class Conj(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, dtype: DTypeLike = "complex128", name: str = "C", ) -> None: diff --git a/pylops/basicoperators/diagonal.py b/pylops/basicoperators/diagonal.py index 276464959..87a41d257 100644 --- a/pylops/basicoperators/diagonal.py +++ b/pylops/basicoperators/diagonal.py @@ -1,6 +1,5 @@ __all__ = ["Diagonal"] -from typing import Optional, Union import numpy as np @@ -78,7 +77,7 @@ class Diagonal(LinearOperator): def __init__( self, diag: NDArray, - dims: Optional[Union[int, InputDimsLike]] = None, + dims: int | InputDimsLike | None = None, axis: int = -1, dtype: DTypeLike = "float64", name: str = "D", diff --git a/pylops/basicoperators/directionalderivative.py b/pylops/basicoperators/directionalderivative.py index fb12f6a7a..f7c56cf31 100644 --- a/pylops/basicoperators/directionalderivative.py +++ b/pylops/basicoperators/directionalderivative.py @@ -3,7 +3,6 @@ "SecondDirectionalDerivative", ] -from typing import Union from pylops import LinearOperator from pylops.basicoperators import Diagonal, Gradient, Sum @@ -80,7 +79,7 @@ def __init__( self, dims: InputDimsLike, v: NDArray, - sampling: Union[float, InputDimsLike] = 1.0, + sampling: float | InputDimsLike = 1.0, edge: bool = False, kind: Tderivkind = "centered", dtype: DTypeLike = "float64", @@ -110,7 +109,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: def _calc_first_ddop( dims: InputDimsLike, v: NDArray, - sampling: Union[float, InputDimsLike], + sampling: float | InputDimsLike, edge: bool, kind: Tderivkind, dtype: DTypeLike, @@ -188,7 +187,7 @@ def __init__( self, dims: InputDimsLike, v: NDArray, - sampling: Union[float, InputDimsLike] = 1.0, + sampling: float | InputDimsLike = 1.0, edge: bool = False, kind: Tderivkind = "centered", dtype: DTypeLike = "float64", diff --git a/pylops/basicoperators/firstderivative.py b/pylops/basicoperators/firstderivative.py index 2bb650ad1..9ed437f98 100644 --- a/pylops/basicoperators/firstderivative.py +++ b/pylops/basicoperators/firstderivative.py @@ -1,6 +1,7 @@ __all__ = ["FirstDerivative"] -from typing import Callable, Literal, Union +from collections.abc import Callable +from typing import Literal import numpy as np @@ -91,7 +92,7 @@ class FirstDerivative(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, sampling: float = 1.0, kind: Tderivkind = "centered", @@ -139,14 +140,14 @@ def _register_multiplications( self._hmatvec = self._matvec_centered5 self._hrmatvec = self._rmatvec_centered5 else: - raise NotImplementedError("'order' must be '3, or '5'") + msg = "order must be '3', or '5'" + raise NotImplementedError(msg) elif kind == "backward": self._hmatvec = self._matvec_backward self._hrmatvec = self._rmatvec_backward else: - raise NotImplementedError( - "'kind' must be 'forward', 'centered', or 'backward'" - ) + msg = "kind must be 'forward', 'centered', or 'backward'" + raise NotImplementedError(msg) def _matvec(self, x: NDArray) -> NDArray: return self._hmatvec(x) diff --git a/pylops/basicoperators/flip.py b/pylops/basicoperators/flip.py index 55947e34d..ad8f6d424 100644 --- a/pylops/basicoperators/flip.py +++ b/pylops/basicoperators/flip.py @@ -1,6 +1,5 @@ __all__ = ["Flip"] -from typing import Union import numpy as np @@ -59,7 +58,7 @@ class Flip(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, dtype: DTypeLike = "float64", name: str = "F", diff --git a/pylops/basicoperators/functionoperator.py b/pylops/basicoperators/functionoperator.py index 00b9f439a..4136a45ad 100644 --- a/pylops/basicoperators/functionoperator.py +++ b/pylops/basicoperators/functionoperator.py @@ -1,7 +1,7 @@ __all__ = ["FunctionOperator"] +from collections.abc import Callable from numbers import Integral -from typing import Callable from pylops import LinearOperator from pylops.utils.typing import NDArray, ShapeLike @@ -103,5 +103,6 @@ def _matvec(self, x: NDArray) -> NDArray: def _rmatvec(self, x: NDArray) -> NDArray: if self.fc is None: - raise NotImplementedError("Adjoint not implemented") + msg = "Adjoint not implemented" + raise NotImplementedError(msg) return self.fc(x) diff --git a/pylops/basicoperators/gradient.py b/pylops/basicoperators/gradient.py index cfbe3a86d..0f5dff64e 100644 --- a/pylops/basicoperators/gradient.py +++ b/pylops/basicoperators/gradient.py @@ -1,6 +1,5 @@ __all__ = ["Gradient"] -from typing import Union from pylops import LinearOperator from pylops.basicoperators import FirstDerivative, VStack @@ -79,8 +78,8 @@ class Gradient(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], - sampling: Union[float, InputDimsLike] = 1.0, + dims: int | InputDimsLike, + sampling: float | InputDimsLike = 1.0, edge: bool = False, kind: Tderivkind = "centered", dtype: DTypeLike = "float64", diff --git a/pylops/basicoperators/hstack.py b/pylops/basicoperators/hstack.py index 126c034a8..ca817e9fd 100644 --- a/pylops/basicoperators/hstack.py +++ b/pylops/basicoperators/hstack.py @@ -14,12 +14,12 @@ from scipy.sparse.linalg.interface import LinearOperator as spLinearOperator from scipy.sparse.linalg.interface import _get_dtype else: - from scipy.sparse.linalg._interface import _get_dtype from scipy.sparse.linalg._interface import ( LinearOperator as spLinearOperator, ) + from scipy.sparse.linalg._interface import _get_dtype -from typing import Callable, Optional, Sequence +from collections.abc import Callable, Sequence from pylops import LinearOperator from pylops.basicoperators import MatrixMult, Zero @@ -152,13 +152,14 @@ def __init__( self, ops: Sequence[LinearOperator], nproc: int = 1, - forceflat: Optional[bool] = None, - inoutengine: Optional[Tinoutengine] = None, + forceflat: bool | None = None, + inoutengine: Tinoutengine | None = None, parallel_kind: Tparallel_kind = "multiproc", - dtype: Optional[str] = None, + dtype: str | None = None, ) -> None: if parallel_kind not in ["multiproc", "multithread"]: - raise ValueError("parallel_kind must be 'multiproc' or 'multithread'") + msg = "parallel_kind must be 'multiproc' or 'multithread'" + raise ValueError(msg) # identify dimensions self.ops = ops mops = np.zeros(len(ops), dtype=int) @@ -169,7 +170,8 @@ def __init__( self.mops = int(mops.sum()) nops = [oper.shape[0] for oper in self.ops] if len(set(nops)) > 1: - raise ValueError("operators have different number of rows") + msg = "Operators have different number of rows" + raise ValueError(msg) self.nops = int(nops[0]) self.mmops = np.insert(np.cumsum(mops), 0, 0) # define dimsd (check if all operators have the same, diff --git a/pylops/basicoperators/identity.py b/pylops/basicoperators/identity.py index dbb604a27..1258f4d73 100644 --- a/pylops/basicoperators/identity.py +++ b/pylops/basicoperators/identity.py @@ -1,8 +1,6 @@ __all__ = ["Identity"] -from typing import Optional, Union - import numpy as np from pylops import LinearOperator @@ -119,10 +117,10 @@ class Identity(LinearOperator): def __init__( self, - N: Union[int, InputDimsLike], - M: Optional[Union[int, InputDimsLike]] = None, + N: int | InputDimsLike, + M: int | InputDimsLike | None = None, inplace: bool = True, - forceflat: Optional[bool] = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "I", ) -> None: @@ -164,19 +162,21 @@ def __init__( self._sliceN = tuple([slice(0, n) for n in N]) self._sliceM = tuple([slice(0, m) for m in M]) else: - raise ValueError( + msg = ( "N and M are not identical, " "and some values are larger in N and some in M" ) + raise ValueError(msg) super().__init__( dtype=np.dtype(dtype), dims=M, dimsd=N, forceflat=forceflat, name=name ) else: - raise NotImplementedError( - f"N and M must have same type and equal to " - f"int, tuple, or list, instead their types" + msg = ( + "N and M must have same type and equal to " + "int, tuple, or list, instead their types" f" are type(N)={type(N)} and type(M)={type(M)}" ) + raise NotImplementedError(msg) self.inplace = inplace @reshaped diff --git a/pylops/basicoperators/imag.py b/pylops/basicoperators/imag.py index 94c2d4706..02b8a5a52 100644 --- a/pylops/basicoperators/imag.py +++ b/pylops/basicoperators/imag.py @@ -1,6 +1,5 @@ __all__ = ["Imag"] -from typing import Union import numpy as np @@ -59,7 +58,7 @@ class Imag(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, dtype: DTypeLike = "complex128", name: str = "I", ) -> None: diff --git a/pylops/basicoperators/laplacian.py b/pylops/basicoperators/laplacian.py index 367693a42..c59f6da3e 100644 --- a/pylops/basicoperators/laplacian.py +++ b/pylops/basicoperators/laplacian.py @@ -1,8 +1,6 @@ __all__ = ["Laplacian"] -from typing import Tuple - from pylops import LinearOperator from pylops.basicoperators import SecondDerivative from pylops.utils.backend import get_normalize_axis_index @@ -77,8 +75,8 @@ def __init__( self, dims: InputDimsLike, axes: InputDimsLike = (-2, -1), - weights: Tuple[float, ...] = (1.0, 1.0), - sampling: Tuple[float, ...] = (1.0, 1.0), + weights: tuple[float, ...] = (1.0, 1.0), + sampling: tuple[float, ...] = (1.0, 1.0), edge: bool = False, kind: Tderivkind = "centered", dtype: DTypeLike = "float64", @@ -86,7 +84,8 @@ def __init__( ): axes = tuple(get_normalize_axis_index()(ax, len(dims)) for ax in axes) if not (len(axes) == len(weights) == len(sampling)): - raise ValueError("axes, weights, and sampling have different size") + msg = "axes, weights, and sampling have different size" + raise ValueError(msg) self.axes = axes self.weights = weights self.sampling = sampling @@ -113,8 +112,8 @@ def _rmatvec(self, x: NDArray) -> NDArray: def _calc_l2op( dims: InputDimsLike, axes: InputDimsLike, - weights: Tuple[float, ...], - sampling: Tuple[float, ...], + weights: tuple[float, ...], + sampling: tuple[float, ...], edge: bool, kind: Tderivkind, dtype: DTypeLike, @@ -124,7 +123,7 @@ def _calc_l2op( ) dims = l2op.dims l2op *= weights[0] - for ax, samp, weight in zip(axes[1:], sampling[1:], weights[1:]): + for ax, samp, weight in zip(axes[1:], sampling[1:], weights[1:], strict=True): l2op += weight * SecondDerivative( dims, axis=ax, sampling=samp, edge=edge, kind=kind, dtype=dtype ) diff --git a/pylops/basicoperators/matrixmult.py b/pylops/basicoperators/matrixmult.py index a0c7a85d1..8b6bd805a 100644 --- a/pylops/basicoperators/matrixmult.py +++ b/pylops/basicoperators/matrixmult.py @@ -2,7 +2,6 @@ import logging import warnings -from typing import Optional, Union import numpy as np import scipy as sp @@ -75,8 +74,8 @@ class MatrixMult(LinearOperator): def __init__( self, A: NDArray, - otherdims: Optional[Union[int, InputDimsLike]] = None, - forceflat: Optional[bool] = None, + otherdims: int | InputDimsLike | None = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "M", ) -> None: @@ -116,7 +115,9 @@ def __init__( # Check dtype for correctness (upcast to complex when A is complex) if np.iscomplexobj(A) and not np.iscomplexobj(np.ones(1, dtype=dtype)): dtype = A.dtype - warnings.warn("Matrix A is a complex object, dtype cast to %s" % dtype) + warnings.warn( + "Matrix A is a complex object, dtype cast to %s" % dtype, stacklevel=2 + ) super().__init__( dtype=np.dtype(dtype), dims=dims, diff --git a/pylops/basicoperators/memoizeoperator.py b/pylops/basicoperators/memoizeoperator.py index ef440a2e2..1ad4b4f4a 100644 --- a/pylops/basicoperators/memoizeoperator.py +++ b/pylops/basicoperators/memoizeoperator.py @@ -1,6 +1,5 @@ __all__ = ["MemoizeOperator"] -from typing import List, Tuple import numpy as np @@ -43,7 +42,7 @@ def __init__( super().__init__(Op=Op) self.max_neval = max_neval - self.store: List[Tuple[NDArray, NDArray]] = [] # Store a list of (x, y) + self.store: list[tuple[NDArray, NDArray]] = [] # Store a list of (x, y) self.neval = 0 # Number of evaluations of the operator def _matvec(self, x: NDArray) -> NDArray: diff --git a/pylops/basicoperators/pad.py b/pylops/basicoperators/pad.py index 37a51e5be..66e05fa11 100644 --- a/pylops/basicoperators/pad.py +++ b/pylops/basicoperators/pad.py @@ -1,6 +1,6 @@ __all__ = ["Pad"] -from typing import Sequence, Tuple, Union +from collections.abc import Sequence import numpy as np @@ -76,17 +76,21 @@ class Pad(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], - pad: Union[Tuple[int, int], Sequence[Tuple[int, int]]], + dims: int | InputDimsLike, + pad: tuple[int, int] | Sequence[tuple[int, int]], dtype: DTypeLike = "float64", name: str = "P", ) -> None: if np.any(np.array(pad) < 0): - raise ValueError("Padding must be positive or zero") + msg = "Padding must be positive or zero" + raise ValueError(msg) dims = _value_or_sized_to_tuple(dims) # Accept (padbeg, padend) and [(padbeg, padend)] self.pad: Sequence = [pad] if len(dims) == 1 and len(pad) == 2 else pad - dimsd = [dim + before + after for dim, (before, after) in zip(dims, self.pad)] + dimsd = [ + dim + before + after + for dim, (before, after) in zip(dims, self.pad, strict=True) + ] super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name) @reshaped diff --git a/pylops/basicoperators/real.py b/pylops/basicoperators/real.py index 99d21edef..c2e739fa6 100644 --- a/pylops/basicoperators/real.py +++ b/pylops/basicoperators/real.py @@ -1,5 +1,4 @@ __all__ = ["Real"] -from typing import Union import numpy as np @@ -58,7 +57,7 @@ class Real(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, dtype: DTypeLike = "complex128", name: str = "R", ) -> None: diff --git a/pylops/basicoperators/regression.py b/pylops/basicoperators/regression.py index 3a114a895..307966886 100644 --- a/pylops/basicoperators/regression.py +++ b/pylops/basicoperators/regression.py @@ -84,7 +84,8 @@ def __init__( ) -> None: ncp = get_array_module(taxis) if not isinstance(taxis, ncp.ndarray): - raise TypeError("t must be ndarray...") + msg = "t must be ndarray..." + raise TypeError(msg) else: self.taxis = taxis self.order = order diff --git a/pylops/basicoperators/restriction.py b/pylops/basicoperators/restriction.py index 0eec18cec..387161049 100644 --- a/pylops/basicoperators/restriction.py +++ b/pylops/basicoperators/restriction.py @@ -1,7 +1,7 @@ __all__ = ["Restriction"] import logging -from typing import Optional, Sequence, Union +from collections.abc import Sequence import numpy as np import numpy.ma as np_ma @@ -116,11 +116,11 @@ class Restriction(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], - iava: Union[IntNDArray, Sequence[int]], + dims: int | InputDimsLike, + iava: IntNDArray | Sequence[int], axis: int = -1, inplace: bool = True, - forceflat: Optional[bool] = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "R", ) -> None: diff --git a/pylops/basicoperators/roll.py b/pylops/basicoperators/roll.py index cbeead286..469c6c44d 100644 --- a/pylops/basicoperators/roll.py +++ b/pylops/basicoperators/roll.py @@ -1,6 +1,5 @@ __all__ = ["Roll"] -from typing import Union import numpy as np @@ -56,7 +55,7 @@ class Roll(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, shift: int = 1, dtype: DTypeLike = "float64", diff --git a/pylops/basicoperators/secondderivative.py b/pylops/basicoperators/secondderivative.py index 475bc8e04..2c649cdfa 100644 --- a/pylops/basicoperators/secondderivative.py +++ b/pylops/basicoperators/secondderivative.py @@ -1,6 +1,6 @@ __all__ = ["SecondDerivative"] -from typing import Callable, Union +from collections.abc import Callable import numpy as np @@ -83,7 +83,7 @@ class SecondDerivative(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, sampling: float = 1.0, kind: Tderivkind = "centered", @@ -127,9 +127,8 @@ def _register_multiplications( self._hmatvec = self._matvec_backward self._hrmatvec = self._rmatvec_backward else: - raise NotImplementedError( - "'kind' must be 'forward', 'centered' or 'backward'" - ) + msg = "'kind' must be 'forward', 'centered' or 'backward'" + raise NotImplementedError(msg) def _matvec(self, x: NDArray) -> NDArray: return self._hmatvec(x) diff --git a/pylops/basicoperators/smoothing1d.py b/pylops/basicoperators/smoothing1d.py index 9fc7d2f0e..b2454543b 100644 --- a/pylops/basicoperators/smoothing1d.py +++ b/pylops/basicoperators/smoothing1d.py @@ -1,6 +1,5 @@ __all__ = ["Smoothing1D"] -from typing import Union import numpy as np @@ -81,7 +80,7 @@ class Smoothing1D(Convolve1D): def __init__( self, nsmooth: int, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, dtype: DTypeLike = "float64", name: str = "S", diff --git a/pylops/basicoperators/smoothing2d.py b/pylops/basicoperators/smoothing2d.py index ca3ad38e9..a10b12b28 100644 --- a/pylops/basicoperators/smoothing2d.py +++ b/pylops/basicoperators/smoothing2d.py @@ -1,6 +1,5 @@ __all__ = ["Smoothing2D"] -from typing import Union import numpy as np @@ -71,7 +70,7 @@ class Smoothing2D(Convolve2D): def __init__( self, nsmooth: InputDimsLike, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axes: InputDimsLike = (-2, -1), dtype: DTypeLike = "float64", name: str = "S", diff --git a/pylops/basicoperators/smoothingnd.py b/pylops/basicoperators/smoothingnd.py index f7d27622a..4c73c9345 100644 --- a/pylops/basicoperators/smoothingnd.py +++ b/pylops/basicoperators/smoothingnd.py @@ -1,6 +1,5 @@ __all__ = ["SmoothingND"] -from typing import Union import numpy as np @@ -70,7 +69,7 @@ class SmoothingND(ConvolveND): def __init__( self, nsmooth: InputDimsLike, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axes: InputDimsLike = (-2, -1), dtype: DTypeLike = "float64", name: str = "S", diff --git a/pylops/basicoperators/spread.py b/pylops/basicoperators/spread.py index 726ac8035..b291e0dc0 100644 --- a/pylops/basicoperators/spread.py +++ b/pylops/basicoperators/spread.py @@ -1,7 +1,7 @@ __all__ = ["Spread"] import logging -from typing import Callable, Optional +from collections.abc import Callable import numpy as np @@ -168,10 +168,10 @@ def __init__( self, dims: InputDimsLike, dimsd: InputDimsLike, - table: Optional[NDArray] = None, - dtable: Optional[NDArray] = None, - fh: Optional[Callable] = None, - interp: Optional[bool] = None, + table: NDArray | None = None, + dtable: NDArray | None = None, + fh: Callable | None = None, + interp: bool | None = None, engine: Tengine_nn = "numpy", dtype: DTypeLike = "float64", name: str = "S", @@ -179,7 +179,8 @@ def __init__( super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name) if engine not in ["numpy", "numba"]: - raise ValueError("engine must be numpy or numba") + msg = f"engine must be 'numpy' or 'numba', got {engine}" + raise ValueError(msg) if engine == "numba" and jit_message is None: self.engine = "numba" else: @@ -196,15 +197,19 @@ def __init__( # find out if mapping is in table of function handle if self.table is None and fh is None: - raise NotImplementedError("provide either table or fh.") + msg = "Provide either table or fh." + raise NotImplementedError(msg) elif self.table is not None: if fh is not None: - raise ValueError("provide only one of table or fh.") + msg = "Provide only one of table or fh." + raise ValueError(msg) if self.table.shape != (self.nx0, self.nt0, self.nx): - raise ValueError("table must have shape [nx0 x nt0 x nx]") + msg = "table must have shape [nx0 x nt0 x nx]" + raise ValueError(msg) self.usetable = True if np.any(self.table > self.nt): - raise ValueError("values in table must be smaller than nt") + msg = "Values in table must be smaller than nt" + raise ValueError(msg) else: self.usetable = False @@ -213,7 +218,8 @@ def __init__( if self.usetable: if self.dtable is not None: if self.dtable.shape != (self.nx0, self.nt0, self.nx): - raise ValueError("dtable must have shape [nx0 x nt x nx]") + msg = "dtable must have shape [nx0 x nt x nx]" + raise ValueError(msg) self.interp = True else: if self.engine == "numba": diff --git a/pylops/basicoperators/sum.py b/pylops/basicoperators/sum.py index b0f2cce01..b0d59a1aa 100644 --- a/pylops/basicoperators/sum.py +++ b/pylops/basicoperators/sum.py @@ -1,7 +1,6 @@ __all__ = ["Sum"] import logging -from typing import Optional import numpy as np @@ -77,7 +76,7 @@ def __init__( self, dims: InputDimsLike, axis: int = -1, - forceflat: Optional[bool] = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "S", ) -> None: diff --git a/pylops/basicoperators/symmetrize.py b/pylops/basicoperators/symmetrize.py index 3f7925515..79d382c7b 100644 --- a/pylops/basicoperators/symmetrize.py +++ b/pylops/basicoperators/symmetrize.py @@ -1,6 +1,5 @@ __all__ = ["Symmetrize"] -from typing import Union import numpy as np @@ -77,7 +76,7 @@ class Symmetrize(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, dtype: DTypeLike = "float64", name: str = "S", diff --git a/pylops/basicoperators/tocupy.py b/pylops/basicoperators/tocupy.py index a1dfb5b0b..0c43afad2 100644 --- a/pylops/basicoperators/tocupy.py +++ b/pylops/basicoperators/tocupy.py @@ -1,6 +1,5 @@ __all__ = ["ToCupy"] -from typing import Union import numpy as np @@ -50,7 +49,7 @@ class ToCupy(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, dtype: DTypeLike = "float64", name: str = "C", ) -> None: diff --git a/pylops/basicoperators/transpose.py b/pylops/basicoperators/transpose.py index 280a05021..dee0972ac 100644 --- a/pylops/basicoperators/transpose.py +++ b/pylops/basicoperators/transpose.py @@ -75,7 +75,11 @@ def __init__( # find out if all axes are present only once in axes if len(np.unique(self.axes)) != ndims: - raise ValueError("axes must contain each direction once") + msg = ( + "axes must contain all dimensions, " + f"and each dimension at least once, got {self.axes}, instead" + ) + raise ValueError(msg) # find out how axes should be transposed in adjoint mode axesd = np.empty(ndims, dtype=int) diff --git a/pylops/basicoperators/vstack.py b/pylops/basicoperators/vstack.py index 66405b4d6..50d756095 100644 --- a/pylops/basicoperators/vstack.py +++ b/pylops/basicoperators/vstack.py @@ -19,7 +19,7 @@ ) from scipy.sparse.linalg._interface import _get_dtype -from typing import Callable, Optional, Sequence +from collections.abc import Callable, Sequence from pylops import LinearOperator from pylops.basicoperators import MatrixMult, Zero @@ -151,13 +151,14 @@ def __init__( self, ops: Sequence[LinearOperator], nproc: int = 1, - forceflat: Optional[bool] = None, - inoutengine: Optional[Tinoutengine] = None, + forceflat: bool | None = None, + inoutengine: Tinoutengine | None = None, parallel_kind: Tparallel_kind = "multiproc", - dtype: Optional[DTypeLike] = None, + dtype: DTypeLike | None = None, ) -> None: if parallel_kind not in ["multiproc", "multithread"]: - raise ValueError("parallel_kind must be 'multiproc' or 'multithread'") + msg = "parallel_kind must be 'multiproc' or 'multithread'" + raise ValueError(msg) # identify dimensions self.ops = ops nops = np.zeros(len(self.ops), dtype=int) @@ -168,7 +169,8 @@ def __init__( self.nops = int(nops.sum()) mops = [oper.shape[1] for oper in self.ops] if len(set(mops)) > 1: - raise ValueError("operators have different number of columns") + msg = f"Operators have different number of columns - {mops}" + raise ValueError(msg) self.mops = int(mops[0]) self.nnops = np.insert(np.cumsum(nops), 0, 0) # define dims (check if all operators have the same, diff --git a/pylops/basicoperators/zero.py b/pylops/basicoperators/zero.py index d2f910399..d2fe70f48 100644 --- a/pylops/basicoperators/zero.py +++ b/pylops/basicoperators/zero.py @@ -1,6 +1,5 @@ __all__ = ["Zero"] -from typing import Optional, Union import numpy as np @@ -69,9 +68,9 @@ class Zero(LinearOperator): def __init__( self, - N: Union[int, InputDimsLike], - M: Optional[Union[int, InputDimsLike]] = None, - forceflat: Optional[bool] = None, + N: int | InputDimsLike, + M: int | InputDimsLike | None = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "Z", ) -> None: @@ -83,11 +82,12 @@ def __init__( # N and M are tuples (nd-arrays) dims, dimsd = M, N else: - raise NotImplementedError( - f"N and M must have same type and equal to " - f"int, tuple, or list, instead their types" + msg = ( + "N and M must have same type and equal to " + "int, tuple, or list, instead their types" f" are type(N)={type(N)} and type(M)={type(M)}" ) + raise NotImplementedError(msg) super().__init__( dtype=np.dtype(dtype), dims=dims, diff --git a/pylops/config.py b/pylops/config.py index 07fb14243..59c098e22 100644 --- a/pylops/config.py +++ b/pylops/config.py @@ -15,9 +15,10 @@ disabled_ndarray_multiplication Disable ndarray multiplication within context. """ + +from collections.abc import Generator from contextlib import contextmanager from dataclasses import dataclass -from typing import Generator __all__ = [ "get_ndarray_multiplication", diff --git a/pylops/jaxoperator.py b/pylops/jaxoperator.py index d2b190475..097fb3119 100644 --- a/pylops/jaxoperator.py +++ b/pylops/jaxoperator.py @@ -96,9 +96,8 @@ def rmatvecad(self, x: JaxTypeIn, y: JaxTypeIn) -> JaxTypeOut: M, N = self.shape if x.shape != (M,) and x.shape != (M, 1): - raise ValueError( - f"Dimension mismatch. Got {x.shape}, but expected ({M},) or ({M}, 1)." - ) + msg = f"Dimension mismatch. Got {x.shape}, but expected ({M},) or ({M}, 1)." + raise ValueError(msg) y = self._rmatvecad(x, y) @@ -107,8 +106,9 @@ def rmatvecad(self, x: JaxTypeIn, y: JaxTypeIn) -> JaxTypeOut: elif x.ndim == 2: y = y.reshape(N, 1) else: - raise ValueError( - f"Invalid shape returned by user-defined rmatvecad(). " + msg = ( + "Invalid shape returned by user-defined rmatvecad(). " f"Expected 2-d ndarray or matrix, not {x.ndim}-d ndarray" ) + raise ValueError(msg) return y diff --git a/pylops/linearoperator.py b/pylops/linearoperator.py index c7df30fb8..286400d5a 100644 --- a/pylops/linearoperator.py +++ b/pylops/linearoperator.py @@ -1,4 +1,4 @@ -from __future__ import annotations, division +from __future__ import annotations __all__ = [ "LinearOperator", @@ -27,7 +27,7 @@ else: from scipy.sparse._sputils import isintlike, isshape -from typing import Callable, List, Optional, Sequence, Union +from collections.abc import Callable, Sequence from pylops import get_ndarray_multiplication from pylops.optimization.basic import cgls @@ -120,15 +120,15 @@ class LinearOperator(_LinearOperator): def __init__( self, - Op: Optional[Union[spLinearOperator, LinearOperator]] = None, - dtype: Optional[DTypeLike] = None, - shape: Optional[ShapeLike] = None, - dims: Optional[ShapeLike] = None, - dimsd: Optional[ShapeLike] = None, - clinear: Optional[bool] = None, - explicit: Optional[bool] = None, - forceflat: Optional[bool] = None, - name: Optional[str] = None, + Op: spLinearOperator | LinearOperator | None = None, + dtype: DTypeLike | None = None, + shape: ShapeLike | None = None, + dims: ShapeLike | None = None, + dimsd: ShapeLike | None = None, + clinear: bool | None = None, + explicit: bool | None = None, + forceflat: bool | None = None, + name: str | None = None, ) -> None: if Op is not None: self.Op = Op @@ -178,12 +178,11 @@ def shape(self): dims = getattr(self, "_dims", None) dimsd = getattr(self, "_dimsd", None) if dims is None or dimsd is None: # Cannot find both dims and dimsd, error - raise AttributeError( - ( - f"'{self.__class__.__name__}' object has no attribute 'shape' " - "nor both fallback attributes ('dims', 'dimsd')" - ) + msg = ( + f"'{self.__class__.__name__}' object has no attribute 'shape' " + "nor both fallback attributes ('dims', 'dimsd')" ) + raise AttributeError(msg) _shape = (int(np.prod(dimsd)), int(np.prod(dims))) self._shape = _shape # Update to not redo everything above on next call return _shape @@ -192,16 +191,20 @@ def shape(self): def shape(self, new_shape: ShapeLike) -> None: new_shape = tuple(new_shape) if not isshape(new_shape): - raise ValueError(f"invalid shape %{new_shape:r} (must be 2-d)") + msg = f"Invalid shape; must be 2-d tuple of integers, got {new_shape}" + raise ValueError(msg) dims = getattr(self, "_dims", None) dimsd = getattr(self, "_dimsd", None) if dims is not None and dimsd is not None: # Found dims and dimsd if np.prod(dimsd) != new_shape[0] and np.prod(dims) != new_shape[1]: - raise ValueError("New shape incompatible with dims and dimsd") + msg = "New shape incompatible with dims and dimsd" + raise ValueError(msg) elif np.prod(dimsd) != new_shape[0]: - raise ValueError("New shape incompatible with dimsd") + msg = "New shape incompatible with dimsd" + raise ValueError(msg) elif np.prod(dims) != new_shape[1]: - raise ValueError("New shape incompatible with dims") + msg = "New shape incompatible with dims" + raise ValueError(msg) self._shape = new_shape @shape.deleter @@ -214,9 +217,11 @@ def dims(self): if _dims is None: shape = getattr(self, "_shape", None) if shape is None: - raise AttributeError( - f"'{self.__class__.__name__}' object has no attributes 'dims' or 'shape'" + msg = ( + f"'{self.__class__.__name__}' object has no " + "attributes 'dims' or 'shape'" ) + raise AttributeError(msg) _dims = (shape[1],) return _dims @@ -230,7 +235,8 @@ def dims(self, new_dims: ShapeLike) -> None: if np.prod(new_dims) == self.shape[1]: self._dims = new_dims else: - raise ValueError("dims incompatible with shape[1]") + msg = "dims incompatible with shape[1]" + raise ValueError(msg) @dims.deleter def dims(self): @@ -242,9 +248,11 @@ def dimsd(self): if _dimsd is None: shape = getattr(self, "_shape", None) if shape is None: - raise AttributeError( - f"'{self.__class__.__name__}' object has no attributes 'dimsd' or 'shape'" + msg = ( + f"'{self.__class__.__name__}' object has " + "no attributes 'dimsd' or 'shape'" ) + raise AttributeError(msg) _dimsd = (shape[0],) return _dimsd @@ -258,7 +266,8 @@ def dimsd(self, new_dimsd: ShapeLike) -> None: if np.prod(new_dimsd) == self.shape[0]: self._dimsd = new_dimsd else: - raise ValueError("dimsd incompatible with shape[0]") + msg = "dimsd incompatible with shape[0]" + raise ValueError(msg) @dimsd.deleter def dimsd(self): @@ -315,12 +324,13 @@ def name(self, new_name: str) -> None: def name(self): del self._name - def __mul__(self, x: Union[float, LinearOperator]) -> LinearOperator: + def __mul__(self, x: float | LinearOperator) -> LinearOperator: return self.dot(x) def __matmul__(self, other): if np.isscalar(other): - raise ValueError("Scalar not allowed, use * instead") + msg = "Scalar not allowed, use * instead" + raise ValueError(msg) return self.__mul__(other) def __rmul__(self, x: float) -> LinearOperator: @@ -340,7 +350,8 @@ def __rmul__(self, x: float) -> LinearOperator: def __rmatmul__(self, other): if np.isscalar(other): - raise ValueError("Scalar not allowed, use * instead") + msg = "Scalar not allowed, use * instead" + raise ValueError(msg) return self.__rmul__(other) def __pow__(self, p: int) -> LinearOperator: @@ -379,9 +390,8 @@ def __add__(self, x: LinearOperator) -> LinearOperator: elif self.forceflat is not None and Opx.forceflat is not None: # Define forceflat only if differing, otherwise raise error if self.forceflat != Opx.forceflat: - raise ValueError( - f"Operators have conflicting forceflat {Op.forceflat} != {Opx.forceflat}" - ) + msg = f"Operators have conflicting forceflat {Op.forceflat} != {Opx.forceflat}" + raise ValueError(msg) Op.forceflat = self.forceflat else: # Only one of them is None Op.forceflat = ( @@ -425,7 +435,7 @@ def __repr__(self): def _copy_attributes( self, dest: LinearOperator, - exclude: Optional[List[str]] = None, + exclude: list[str] | None = None, ) -> None: """Copy attributes from one LinearOperator to another""" if exclude is None: @@ -522,9 +532,8 @@ def matvec(self, x: NDArray) -> NDArray: M, N = self.shape if x.shape != (N,) and x.shape != (N, 1): - raise ValueError( - f"Dimension mismatch. Got {x.shape}, but expected ({N},) or ({N}, 1)." - ) + msg = f"Dimension mismatch. Got {x.shape}, but expected ({N},) or ({N}, 1)." + raise ValueError(msg) y = self._matvec(x) @@ -533,7 +542,8 @@ def matvec(self, x: NDArray) -> NDArray: elif x.ndim == 2: y = y.reshape(M, 1) else: - raise ValueError("Invalid shape returned by user-defined matvec()") + msg = "Invalid shape returned by user-defined matvec()" + raise ValueError(msg) return y @count(forward=False) @@ -558,9 +568,8 @@ def rmatvec(self, x: NDArray) -> NDArray: M, N = self.shape if x.shape != (M,) and x.shape != (M, 1): - raise ValueError( - f"Dimension mismatch. Got {x.shape}, but expected ({M},) or ({M}, 1)." - ) + msg = f"Dimension mismatch. Got {x.shape}, but expected ({M},) or ({M}, 1)." + raise ValueError(msg) y = self._rmatvec(x) @@ -569,7 +578,8 @@ def rmatvec(self, x: NDArray) -> NDArray: elif x.ndim == 2: y = y.reshape(N, 1) else: - raise ValueError("Invalid shape returned by user-defined rmatvec()") + msg = "Invalid shape returned by user-defined rmatvec()" + raise ValueError(msg) return y @count(forward=True, matmat=True) @@ -592,9 +602,11 @@ def matmat(self, X: NDArray) -> NDArray: """ if X.ndim != 2: - raise ValueError(f"Expected 2-d ndarray or matrix, not {X.ndim}-d ndarray") + msg = f"Expected 2-d ndarray or matrix, not {X.ndim}-d ndarray" + raise ValueError(msg) if X.shape[0] != self.shape[1]: - raise ValueError(f"Dimension mismatch: {self.shape}, {X.shape}") + msg = f"Dimension mismatch: {self.shape}, {X.shape}" + raise ValueError(msg) Y = self._matmat(X) return Y @@ -618,9 +630,11 @@ def rmatmat(self, X: NDArray) -> NDArray: """ if X.ndim != 2: - raise ValueError(f"Expected 2-d ndarray or matrix, not {X.ndim}-d ndarray") + msg = f"Expected 2-d ndarray or matrix, not {X.ndim}-d ndarray" + raise ValueError(msg) if X.shape[0] != self.shape[0]: - raise ValueError(f"Dimension mismatch: {self.shape}, {X.shape}") + f"Dimension mismatch: {self.shape}, {X.shape}" + raise ValueError(msg) Y = self._rmatmat(X) return Y @@ -652,9 +666,8 @@ def dot(self, x: NDArray) -> NDArray: elif self.forceflat is not None and Opx.forceflat is not None: # Define forceflat only if differing, otherwise raise error if self.forceflat != Opx.forceflat: - raise ValueError( - f"Operators have conflicting forceflat {Op.forceflat} != {Opx.forceflat}" - ) + msg = f"Operators have conflicting forceflat: {Op.forceflat} != {Opx.forceflat}" + raise ValueError(msg) Op.forceflat = self.forceflat else: # Only one of them is None Op.forceflat = ( @@ -674,10 +687,11 @@ def dot(self, x: NDArray) -> NDArray: if not get_ndarray_multiplication() and ( x.ndim > 2 or (x.ndim == 2 and x.shape[0] != self.shape[1]) ): - raise ValueError( + msg = ( "Operator can only be applied to 1D vectors or 2D matrices. " "Enable ndarray multiplication with pylops.set_ndarray_multiplication(True)." ) + raise ValueError(msg) is_dims_shaped = x.shape == self.dims is_dims_shaped_matrix = len(x.shape) > 1 and x.shape[:-1] == self.dims if is_dims_shaped: @@ -705,16 +719,14 @@ def dot(self, x: NDArray) -> NDArray: y = y.reshape((*self.dimsd, -1)) return y else: - raise ValueError( - ( - "Wrong shape.\nFor vector multiplication, expects either a 1d " - "array or, an ndarray of size `dims` when `dims` and `dimsd` " - "both are available.\n" - "For matrix multiplication, expects a 2d array with its first " - f"dimension is equal to {self.shape[1]}.\n" - f"Instead, received an array of shape {x.shape}." - ) + msg = ( + "Wrong shape.\nFor vector multiplication, expects either a 1d " + "array or, an ndarray of size `dims` when `dims` and `dimsd` " + "both are available.\n For matrix multiplication, expects a 2d " + f"array with its first dimension is equal to {self.shape[1]}.\n" + f"Instead, received an array of shape {x.shape}." ) + raise ValueError(msg) def div( self, @@ -885,11 +897,11 @@ def tosparse(self) -> NDArray: def eigs( self, - neigs: Optional[int] = None, + neigs: int | None = None, symmetric: bool = False, - niter: Optional[int] = None, + niter: int | None = None, uselobpcg: bool = False, - **kwargs_eig: Union[int, float, str], + **kwargs_eig: int | float | str, ) -> NDArray: r"""Most significant eigenvalues of linear operator. @@ -964,11 +976,11 @@ def eigs( eigenvalues = eigvals(self.A) else: if not symmetric and np.iscomplexobj(self) and uselobpcg: - raise ValueError( - "cannot use scipy.sparse.linalg.lobpcg " - "for non-symmetric square matrices of " - "complex type..." + msg = ( + "Cannot use scipy.sparse.linalg.lobpcg " + "for non-symmetric square matrices of complex type..." ) + raise ValueError(msg) if symmetric and uselobpcg: X = np.random.rand(self.shape[0], neigs).astype(self.dtype) eigenvalues = sp_lobpcg( @@ -1011,11 +1023,11 @@ def eigs( neigs = self.shape[1] - 2 if self.shape[0] == self.shape[1]: if not symmetric and np.iscomplexobj(self) and uselobpcg: - raise ValueError( - "cannot use scipy.sparse.linalg.lobpcg for " - "non symmetric square matrices of " - "complex type..." + msg = ( + "Cannot use scipy.sparse.linalg.lobpcg for " + "non symmetric square matrices of complex type..." ) + raise ValueError(msg) if symmetric and uselobpcg: X = np.random.rand(self.shape[0], neigs).astype(self.dtype) eigenvalues = sp_lobpcg(self, X=X, maxiter=niter, **kwargs_eig)[0] @@ -1040,7 +1052,7 @@ def eigs( def cond( self, uselobpcg: bool = False, - **kwargs_eig: Union[int, float, str], + **kwargs_eig: int | float | str, ) -> NDArray: r"""Condition number of linear operator. @@ -1174,8 +1186,8 @@ def toimag(self, forw: bool = True, adj: bool = True) -> LinearOperator: def trace( self, - neval: Optional[int] = None, - method: Optional[str] = None, + neval: int | None = None, + method: str | None = None, backend: str = "numpy", **kwargs_trace, ) -> float: @@ -1222,7 +1234,8 @@ def trace( If the ``method`` is not one of the available methods. """ if self.shape[0] != self.shape[1]: - raise ValueError("operator is not square.") + msg = f"Operator must be square, got {self.shape}" + raise ValueError(msg) ncp = get_module(backend) @@ -1240,7 +1253,8 @@ def trace( elif method_l == "na-hutch++": return trace_nahutchpp(self, neval=neval, backend=backend, **kwargs_trace) else: - raise NotImplementedError(f"method {method} not available.") + msg = f"`method={method}` not available." + raise NotImplementedError(msg) def reset_count(self) -> None: """Reset counters @@ -1256,7 +1270,7 @@ def reset_count(self) -> None: def _get_dtype( operators: Sequence[LinearOperator], - dtypes: Optional[Sequence[DTypeLike]] = None, + dtypes: Sequence[DTypeLike] | None = None, ) -> DTypeLike: if dtypes is None: dtypes = [] @@ -1275,9 +1289,11 @@ def __init__( alpha: float, ) -> None: if not isinstance(A, LinearOperator): - raise ValueError("LinearOperator expected as A") + msg = "LinearOperator expected as A" + raise ValueError(msg) if not np.isscalar(alpha): - raise ValueError("scalar expected as alpha") + msg = "Scalar expected as alpha" + raise ValueError(msg) if isinstance(alpha, complex) and not np.iscomplexobj( np.ones(1, dtype=A.dtype) ): @@ -1287,7 +1303,7 @@ def __init__( # if both the scalar and operator are of real or complex type, use type # of the operator dtype = A.dtype - super(_ScaledLinearOperator, self).__init__(dtype=dtype, shape=A.shape) + super().__init__(dtype=dtype, shape=A.shape) self.args = (A, alpha) def _matvec(self, x: NDArray) -> NDArray: @@ -1312,8 +1328,9 @@ class _ConjLinearOperator(LinearOperator): def __init__(self, Op: LinearOperator) -> None: if not isinstance(Op, LinearOperator): - raise TypeError("Op must be a LinearOperator") - super(_ConjLinearOperator, self).__init__(Op, shape=Op.shape) + msg = "Op must be a LinearOperator" + raise TypeError(msg) + super().__init__(Op, shape=Op.shape) self.Op = Op def _matvec(self, x: NDArray) -> NDArray: @@ -1339,8 +1356,9 @@ def __init__( cols: InputDimsLike, ) -> None: if not isinstance(Op, LinearOperator): - raise TypeError("Op must be a LinearOperator") - super(_ColumnLinearOperator, self).__init__(Op) + msg = "Op must be a LinearOperator" + raise TypeError(msg) + super().__init__(Op) self.Op = Op self.cols = cols self._shape = (Op.shape[0], len(cols)) @@ -1372,7 +1390,7 @@ class _AdjointLinearOperator(LinearOperator): def __init__(self, A: LinearOperator): shape = (A.shape[1], A.shape[0]) - super(_AdjointLinearOperator, self).__init__(shape=shape, dtype=A.dtype) + super().__init__(shape=shape, dtype=A.dtype) self.A = A self.args = (A,) @@ -1394,7 +1412,7 @@ class _TransposedLinearOperator(LinearOperator): def __init__(self, A: LinearOperator): shape = (A.shape[1], A.shape[0]) - super(_TransposedLinearOperator, self).__init__(shape=shape, dtype=A.dtype) + super().__init__(shape=shape, dtype=A.dtype) self.A = A self.args = (A,) @@ -1416,11 +1434,13 @@ class _ProductLinearOperator(LinearOperator): def __init__(self, A: LinearOperator, B: LinearOperator): if not isinstance(A, LinearOperator) or not isinstance(B, LinearOperator): - raise ValueError( - f"both operands have to be a LinearOperator{type(A)} {type(B)}" + msg = ( + f"Both operands have to be a LinearOperator - got {type(A)}, {type(B)}" ) + raise ValueError(msg) if A.shape[1] != B.shape[0]: - raise ValueError("cannot add %r and %r: shape mismatch" % (A, B)) + msg = f"Cannot add {A} and {B}: shape mismatch" + raise ValueError(msg) super().__init__(dtype=_get_dtype([A, B]), shape=(A.shape[0], B.shape[1])) self.args = (A, B) @@ -1448,13 +1468,13 @@ def __init__( B: LinearOperator, ) -> None: if not isinstance(A, LinearOperator) or not isinstance(B, LinearOperator): - raise ValueError("both operands have to be a LinearOperator") + msg = "Both operands have to be a LinearOperator" + raise ValueError(msg) if A.shape != B.shape: - raise ValueError("cannot add %r and %r: shape mismatch" % (A, B)) + msg = f"Cannot add {A} to {B}: shape mismatch" + raise ValueError(msg) self.args = (A, B) - super(_SumLinearOperator, self).__init__( - dtype=_get_dtype([A, B]), shape=A.shape - ) + super().__init__(dtype=_get_dtype([A, B]), shape=A.shape) def _matvec(self, x: NDArray) -> NDArray: return self.args[0].matvec(x) + self.args[1].matvec(x) @@ -1476,13 +1496,16 @@ def _adjoint(self) -> LinearOperator: class _PowerLinearOperator(LinearOperator): def __init__(self, A: LinearOperator, p: int) -> None: if not isinstance(A, LinearOperator): - raise ValueError("LinearOperator expected as A") + msg = "LinearOperator expected as A" + raise ValueError(msg) if A.shape[0] != A.shape[1]: - raise ValueError("square LinearOperator expected, got %r" % A) + msg = f"Square LinearOperator expected, got {A}" + raise ValueError(msg) if not isintlike(p) or p < 0: - raise ValueError("non-negative integer expected as p") + msg = "Non-negative integer expected as p" + raise ValueError(msg) - super(_PowerLinearOperator, self).__init__(dtype=A.dtype, shape=A.shape) + super().__init__(dtype=A.dtype, shape=A.shape) self.args = (A, p) def _power(self, fun: Callable, x: NDArray) -> NDArray: @@ -1525,8 +1548,9 @@ def __init__( real: bool = True, ) -> None: if not isinstance(Op, LinearOperator): - raise TypeError("Op must be a LinearOperator") - super(_RealImagLinearOperator, self).__init__(Op, shape=Op.shape) + msg = "Op must be a LinearOperator" + raise TypeError(msg) + super().__init__(Op, shape=Op.shape) self.Op = Op self.real = real self.forw = forw @@ -1554,7 +1578,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: return y -def aslinearoperator(Op: Union[spLinearOperator, LinearOperator]) -> LinearOperator: +def aslinearoperator(Op: spLinearOperator | LinearOperator) -> LinearOperator: """Return Op as a LinearOperator. Converts any operator compatible with pylops definition of LinearOperator into a pylops diff --git a/pylops/medical/ct.py b/pylops/medical/ct.py index e9629b2b2..980f5d5e3 100644 --- a/pylops/medical/ct.py +++ b/pylops/medical/ct.py @@ -3,7 +3,6 @@ ] import warnings -from typing import Optional import numpy as np @@ -119,9 +118,9 @@ def __init__( thetas: NDArray, engine: Tctengine, proj_geom_type: Tctprojgeom = "parallel", - source_origin_dist: Optional[float] = None, - origin_detector_dist: Optional[float] = None, - projector_type: Optional[Tctprojectortype] = None, + source_origin_dist: float | None = None, + origin_detector_dist: float | None = None, + projector_type: Tctprojectortype | None = None, dtype: DTypeLike = "float32", name: str = "C", ) -> None: @@ -143,18 +142,20 @@ def __init__( projector_type = "strip" # fanflat geometry projectors need an appropriate suffix (unless it's "cuda") if projector_type == "cuda": - warnings.warn("'cuda' projector type specified with 'cpu' engine.") + warnings.warn( + "'cuda' projector type specified with 'cpu' engine.", stacklevel=2 + ) elif proj_geom_type == "fanflat": projector_type += "_fanflat" elif engine == "cuda": if projector_type in [None, "cuda"]: projector_type = "cuda" else: - raise ValueError( - "Only 'cuda' projector type is supported for 'cuda' engine." - ) + msg = f"Only 'cuda' projector type is supported for 'cuda' engine. Got {projector_type}" + raise ValueError(msg) else: - raise NotImplementedError("Engine must be 'cpu' or 'cuda'") + msg = f"engine must be 'cpu' or 'cuda', got {engine}" + raise NotImplementedError(msg) self.projector_type = projector_type # create create volume and projection geometries as well as projector @@ -217,7 +218,8 @@ def _astra_project(self, x, mode): if x_dtype != ncp.float32: warnings.warn( "CT2D operator received input that is not of float32 dtype. It will " - "be cast into float32 internally for ASTRA compatibility." + "be cast into float32 internally for ASTRA compatibility.", + stacklevel=2, ) x = x.astype(ncp.float32) if self.engine == "cpu": @@ -235,7 +237,8 @@ def _astra_project(self, x, mode): # should throw an error later warnings.warn( "CT2D operator received input that is not of contiguous. It will be " - "cast into a contiguous array internally for ASTRA compatibility." + "cast into a contiguous array internally for ASTRA compatibility.", + stacklevel=2, ) x = ncp.ascontiguousarray(x) x = ncp.expand_dims(x, axis=0) diff --git a/pylops/medical/mri.py b/pylops/medical/mri.py index 9ee70dd67..3b53d89f5 100644 --- a/pylops/medical/mri.py +++ b/pylops/medical/mri.py @@ -3,7 +3,6 @@ ] import warnings -from typing import Optional, Union import numpy as np @@ -109,9 +108,9 @@ class MRI2D(LinearOperator): def __init__( self, dims: InputDimsLike, - mask: Union[Tmrimask, NDArray], - nlines: Optional[int] = None, - perc_center: Optional[float] = 0.1, + mask: Tmrimask | NDArray, + nlines: int | None = None, + perc_center: float | None = 0.1, engine: Tmriengine = "numpy", fft_engine: Tfftengine_nsm = "numpy", dtype: DTypeLike = "complex128", @@ -124,7 +123,9 @@ def __init__( # Validate inputs if engine == "jax" and fft_engine != "numpy": - warnings.warn("When engine='jax', fft_engine is forced to 'numpy'") + warnings.warn( + "When engine='jax', fft_engine is forced to 'numpy'", stacklevel=2 + ) self.fft_engine = "numpy" if isinstance(mask, str) and mask not in ( "vertical-reg", @@ -132,17 +133,22 @@ def __init__( "radial-reg", "radial-uni", ): - raise ValueError( - "mask must be a numpy array, 'vertical-reg', 'vertical-uni', 'radial-reg', or 'radial-uni'" + msg = ( + "`mask` must be a numpy array, 'vertical-reg', 'vertical-uni', " + f"'radial-reg', or 'radial-uni', got {mask}" ) + raise ValueError(msg) if self.fft_engine not in ["numpy", "scipy", "mkl_fft"]: - raise ValueError("fft_engine must be 'numpy', 'scipy', or 'mkl_fft'") + msg = "`fft_engine` must be 'numpy', 'scipy', or 'mkl_fft'" + raise ValueError(msg) if isinstance(mask, str) and (nlines is None or perc_center is None): - raise ValueError( - "nlines and perc_center must be specified providing mask as string" + msg = ( + "`nlines` and `perc_center` must be specified providing mask as string" ) + raise ValueError(msg) if isinstance(mask, str) and mask == "vertical-reg" and perc_center > 0.0: - raise ValueError("perc_center must be 0.0 when using 'vertical-reg' mask") + msg = "`perc_center` must be 0.0 when using `mask=vertical-reg`" + raise ValueError(msg) # Create mask self.mask: NDArray @@ -182,11 +188,12 @@ def _vertical_mask( """Create vertical mask""" nlines_center = int(perc_center * dims[1]) if (nlines + nlines_center) > dims[1]: - raise ValueError( - "nlines and perc_center produce a number of lines " + msg = ( + "`nlines` and `perc_center` produce a number of lines " "greater than the total number of lines of the k-space" f"({nlines + nlines_center}>{dims[1]})" ) + raise ValueError(msg) if nlines_center == 0: # No lines from the center diff --git a/pylops/optimization/__init__.py b/pylops/optimization/__init__.py index 20b817aca..80ccd8fbb 100755 --- a/pylops/optimization/__init__.py +++ b/pylops/optimization/__init__.py @@ -5,7 +5,7 @@ The subpackage optimization provides an extensive set of solvers to be used with PyLops linear operators. -A list of least-squares solvers in pylops.optimization.solver: +A list of least-squares solvers in pylops.optimization.basic: cg Conjugate gradient. cgls Conjugate gradient least-squares. @@ -26,7 +26,7 @@ spgl1 Spectral Projected-Gradient for L1 norm. splitbregman Split Bregman for mixed L2-L1 norms. -Note that these solvers are thin wrappers over class-based solvers (new in v2), which can be accessed from -submodules with equivalent name and suffix c. +Note that these solvers are thin wrappers over class-based solvers (new in v2), which can be +accessed from submodules with equivalent name and prefix cls_. """ diff --git a/pylops/optimization/basesolver.py b/pylops/optimization/basesolver.py index 9c287ce95..c941297cb 100644 --- a/pylops/optimization/basesolver.py +++ b/pylops/optimization/basesolver.py @@ -46,7 +46,7 @@ class Solver(metaclass=ABCMeta): Parameters ---------- Op : :obj:`pylops.LinearOperator` - Operator to invert of + Operator to invert callbacks : :obj:`pylops.optimization.callback.Callbacks` Callbacks object used to implement custom callbacks @@ -88,10 +88,10 @@ def _print_solver(self, text: str = "", nbar: int = 80) -> None: f"The Operator Op has {self.Op.shape[0]} rows and {self.Op.shape[1]} cols" ) - def _print_setup(self, *args: Any, **kwargs: Any) -> None: + def _print_setup(self, *args: Any, **kwargs: Any) -> None: # noqa: B027 pass - def _print_step(self, *args: Any, **kwargs: Any) -> None: + def _print_step(self, *args: Any, **kwargs: Any) -> None: # noqa: B027 pass def _print_finalize(self, *args: Any, nbar: int = 80, **kwargs: Any) -> None: @@ -153,15 +153,16 @@ def _setpreallocate(self, preallocate: bool) -> None: if preallocate and self.isjax: warnings.warn( "Preallocation is not supported for JAX arrays. " - "Setting preallocate to False." + "Setting preallocate to False.", + stacklevel=2, ) @abstractmethod - def memory_usage( - self, - show: bool = False, - unit: Tmemunit = "B", - ) -> float: + def memory_usage( + self, + show: bool = False, + unit: Tmemunit = "B", + ) -> float: """Compute memory usage of the solver This method computes an estimate of the memory required by the solver given @@ -309,7 +310,7 @@ def solve( """ pass - def callback( + def callback( # noqa: B027 self, x: NDArray, *args, diff --git a/pylops/optimization/basic.py b/pylops/optimization/basic.py index 49f02f979..9291b5e79 100644 --- a/pylops/optimization/basic.py +++ b/pylops/optimization/basic.py @@ -4,7 +4,8 @@ "lsqr", ] -from typing import TYPE_CHECKING, Callable, Optional, Tuple +from collections.abc import Callable +from typing import TYPE_CHECKING from pylops.optimization.callback import CostToDataCallback, CostToInitialCallback from pylops.optimization.cls_basic import CG, CGLS, LSQR @@ -19,16 +20,16 @@ def cg( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, tol: float = 1e-4, rtol: float = 0.0, rtol1: float = 0.0, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, NDArray]: +) -> tuple[NDArray, int, NDArray]: r"""Conjugate gradient Solve a square system of equations given an operator ``Op`` and @@ -111,17 +112,17 @@ def cg( def cgls( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, damp: float = 0.0, tol: float = 1e-4, rtol: float = 0.0, rtol1: float = 0.0, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, int, float, float, NDArray]: +) -> tuple[NDArray, int, int, float, float, NDArray]: r"""Conjugate gradient least squares Solve an overdetermined system of equations given an operator ``Op`` and @@ -221,7 +222,7 @@ def cgls( def lsqr( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, damp: float = 0.0, atol: float = 1e-08, btol: float = 1e-08, @@ -229,10 +230,10 @@ def lsqr( niter: int = 10, calc_var: bool = True, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, int, float, float, float, float, float, float, float, NDArray]: +) -> tuple[NDArray, int, int, float, float, float, float, float, float, float, NDArray]: r"""LSQR Solve an overdetermined system of equations given an operator ``Op`` and diff --git a/pylops/optimization/callback.py b/pylops/optimization/callback.py index 93d86c0cf..440a15034 100644 --- a/pylops/optimization/callback.py +++ b/pylops/optimization/callback.py @@ -6,7 +6,8 @@ "MetricsCallback", ] -from typing import TYPE_CHECKING, Dict, List, Optional, Sequence +from collections.abc import Sequence +from typing import TYPE_CHECKING, Optional import numpy as np @@ -248,7 +249,7 @@ def __init__( self.xtrue = xtrue self.Op = Op self.which = which - self.metrics: Dict[str, List] = {} + self.metrics: dict[str, list] = {} if "mae" in self.which: self.metrics["mae"] = [] if "mse" in self.which: diff --git a/pylops/optimization/cls_basic.py b/pylops/optimization/cls_basic.py index 66019ab3a..4f47565ff 100644 --- a/pylops/optimization/cls_basic.py +++ b/pylops/optimization/cls_basic.py @@ -5,7 +5,7 @@ ] import time -from typing import TYPE_CHECKING, List, Optional, Tuple, Union +from typing import TYPE_CHECKING import numpy as np @@ -94,11 +94,11 @@ def _print_step(self, x: NDArray) -> None: msg = f"{self.iiter:6g} " + strx + f"{self.cost[self.iiter]:11.4e}" print(msg) - def memory_usage( - self, - show: bool = False, - unit: Tmemunit = "B", - ) -> float: + def memory_usage( + self, + show: bool = False, + unit: Tmemunit = "B", + ) -> float: """Compute memory usage of the solver Parameters @@ -132,8 +132,8 @@ def memory_usage( def setup( self, y: NDArray, - x0: Optional[NDArray] = None, - niter: Optional[int] = None, + x0: NDArray | None = None, + niter: int | None = None, tol: float = 1e-4, preallocate: bool = False, show: bool = False, @@ -159,7 +159,6 @@ def setup( Pre-allocate all variables used by the solver. Note that if ``y`` is a JAX array, this option is ignored and variables are not pre-allocated since JAX does not support in-place operations. - show : :obj:`bool`, optional Display setup log @@ -196,7 +195,7 @@ def setup( self.c1 = self.ncp.empty_like(x) # create variables to track the residual norm and iterations - self.cost: List = [] + self.cost: list = [] self.cost.append(float(np.sqrt(self.kold))) self.iiter = 0 @@ -249,9 +248,9 @@ def step(self, x: NDArray, show: bool = False) -> NDArray: def run( self, x: NDArray, - niter: Optional[int] = None, + niter: int | None = None, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), ) -> NDArray: r"""Run solver @@ -277,7 +276,8 @@ def run( """ niter = self.niter if niter is None else niter if niter is None: - raise ValueError("niter must not be None") + msg = "`niter` must not be None" + raise ValueError(msg) while self.iiter < niter and self.kold > self.tol: showstep = ( True @@ -315,13 +315,13 @@ def finalize(self, show: bool = False) -> None: def solve( self, y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, tol: float = 1e-4, preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[NDArray, int, NDArray]: + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[NDArray, int, NDArray]: r"""Run entire solver Parameters @@ -460,11 +460,11 @@ def _print_step(self, x: NDArray) -> None: ) print(msg) - def memory_usage( - self, - show: bool = False, - unit: Tmemunit = "B", - ) -> float: + def memory_usage( + self, + show: bool = False, + unit: Tmemunit = "B", + ) -> float: """Compute memory usage of the solver Parameters @@ -498,8 +498,8 @@ def memory_usage( def setup( self, y: NDArray, - x0: Optional[NDArray] = None, - niter: Optional[int] = None, + x0: NDArray | None = None, + niter: int | None = None, damp: float = 0.0, tol: float = 1e-4, preallocate: bool = False, @@ -649,9 +649,9 @@ def step(self, x: NDArray, show: bool = False) -> NDArray: def run( self, x: NDArray, - niter: Optional[int] = None, + niter: int | None = None, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), ) -> NDArray: r"""Run solver @@ -677,7 +677,8 @@ def run( """ self.niter = self.niter if niter is None else niter if self.niter is None: - raise ValueError("niter must not be None") + msg = "`niter` must not be None" + raise ValueError(msg) while self.iiter < self.niter and self.kold > self.tol: showstep = ( True @@ -724,14 +725,14 @@ def finalize(self, show: bool = False) -> None: def solve( self, y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, damp: float = 0.0, tol: float = 1e-4, preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[NDArray, int, int, float, float, NDArray]: + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[NDArray, int, int, float, float, NDArray]: r"""Run entire solver Parameters @@ -980,11 +981,11 @@ def _print_finalize(self) -> None: print(str5) print("-" * 90 + "\n") - def memory_usage( - self, - show: bool = False, - unit: Tmemunit = "B", - ) -> float: + def memory_usage( + self, + show: bool = False, + unit: Tmemunit = "B", + ) -> float: """Compute memory usage of the solver Parameters @@ -1018,7 +1019,7 @@ def memory_usage( def setup( self, y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, damp: float = 0.0, atol: float = 1e-08, btol: float = 1e-08, @@ -1321,9 +1322,9 @@ def step(self, x: NDArray, show: bool = False) -> NDArray: def run( self, x: NDArray, - niter: Optional[int] = None, + niter: int | None = None, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), ) -> NDArray: r"""Run solver @@ -1386,7 +1387,7 @@ def finalize(self, show: bool = False) -> None: def solve( self, y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, damp: float = 0.0, atol: float = 1e-08, btol: float = 1e-08, @@ -1395,8 +1396,8 @@ def solve( calc_var: bool = True, preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[ + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[ NDArray, int, int, @@ -1406,7 +1407,7 @@ def solve( float, float, float, - Union[None, NDArray], + None | NDArray, NDArray, ]: r"""Run entire solver diff --git a/pylops/optimization/cls_leastsquares.py b/pylops/optimization/cls_leastsquares.py index 415d36091..636c33f40 100644 --- a/pylops/optimization/cls_leastsquares.py +++ b/pylops/optimization/cls_leastsquares.py @@ -5,7 +5,8 @@ "PreconditionedInversion", ] -from typing import TYPE_CHECKING, Optional, Sequence, Tuple +from collections.abc import Sequence +from typing import TYPE_CHECKING, Optional import numpy as np from scipy.sparse.linalg import cg as sp_cg @@ -23,15 +24,16 @@ def _check_regularization_dims( Regs: Sequence["LinearOperator"], - dataregs: Optional[Sequence[NDArray]] = None, - epsRs: Optional[Sequence[float]] = None, + dataregs: Sequence[NDArray] | None = None, + epsRs: Sequence[float] | None = None, ) -> None: """check Regs, dataregs, and epsRs have same dimensions""" nRegs = len(Regs) ndataregs = nRegs if dataregs is None else len(dataregs) nepsRs = nRegs if epsRs is None else len(epsRs) if not nRegs == ndataregs == nepsRs: - raise ValueError("Regs, dataregs, and epsRs must have the same size") + msg = "Regs, dataregs, and epsRs must have the same size" + raise ValueError(msg) class NormalEquationsInversion(Solver): @@ -102,7 +104,7 @@ def _print_finalize(self) -> None: def memory_usage( self, - nopRegs: Optional[Tuple[int]] = None, + nopRegs: tuple[int] | None = None, show: bool = False, unit: Tmemunit = "B", ) -> float: @@ -152,11 +154,11 @@ def setup( y: NDArray, Regs: Sequence["LinearOperator"], Weight: Optional["LinearOperator"] = None, - dataregs: Optional[Sequence[NDArray]] = None, + dataregs: Sequence[NDArray] | None = None, epsI: float = 0, - epsRs: Optional[Sequence[float]] = None, - NRegs: Optional[Sequence["LinearOperator"]] = None, - epsNRs: Optional[Sequence[float]] = None, + epsRs: Sequence[float] | None = None, + NRegs: Sequence["LinearOperator"] | None = None, + epsNRs: Sequence[float] | None = None, show: bool = False, ) -> None: r"""Setup solver @@ -234,12 +236,14 @@ def setup( and self.Regs is not None and self.dataregs is not None ): - for epsR, Reg, datareg in zip(self.epsRs, self.Regs, self.dataregs): + for epsR, Reg, datareg in zip( + self.epsRs, self.Regs, self.dataregs, strict=True + ): self.y_normal += epsR**2 * Reg.rmatvec(datareg) self.Op_normal += epsR**2 * Reg.H @ Reg if epsNRs is not None and NRegs is not None: - for epsNR, NReg in zip(epsNRs, NRegs): + for epsNR, NReg in zip(epsNRs, NRegs, strict=True): self.Op_normal += epsNR**2 * NReg # print setup @@ -247,11 +251,12 @@ def setup( self._print_setup() def step(self) -> None: - raise NotImplementedError( - "NormalEquationsInversion uses as default the" - " scipy.sparse.linalg.cg solver, therefore the " + msg = ( + "NormalEquationsInversion uses as default the " + "scipy.sparse.linalg.cg solver, therefore the " "step method is not implemented. Use directly run or solve." ) + raise NotImplementedError(msg) def run( self, @@ -259,7 +264,7 @@ def run( engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int]: + ) -> tuple[NDArray, int]: r"""Run solver Parameters @@ -310,24 +315,25 @@ def run( )[0] istop = None else: - raise NotImplementedError("Engine must be scipy or pylops") + msg = "`engine` must be scipy or pylops" + raise NotImplementedError(msg) return xinv, istop def solve( self, y: NDArray, Regs: Sequence["LinearOperator"], - x0: Optional[NDArray] = None, + x0: NDArray | None = None, Weight: Optional["LinearOperator"] = None, - dataregs: Optional[Sequence[NDArray]] = None, + dataregs: Sequence[NDArray] | None = None, epsI: float = 0, - epsRs: Optional[Sequence[float]] = None, - NRegs: Optional[Sequence["LinearOperator"]] = None, - epsNRs: Optional[Sequence[float]] = None, + epsRs: Sequence[float] | None = None, + NRegs: Sequence["LinearOperator"] | None = None, + epsNRs: Sequence[float] | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int]: + ) -> tuple[NDArray, int]: r"""Run entire solver Parameters @@ -437,7 +443,8 @@ def RegularizedOperator( """ OpReg = VStack( - [Op] + [epsR * Reg for epsR, Reg in zip(epsRs, Regs)], dtype=Op.dtype + [Op] + [epsR * Reg for epsR, Reg in zip(epsRs, Regs, strict=True)], + dtype=Op.dtype, ) return OpReg @@ -519,7 +526,7 @@ def _print_finalize(self) -> None: def memory_usage( self, - nopRegs: Optional[Tuple[int]] = None, + nopRegs: tuple[int] | None = None, show: bool = False, unit: Tmemunit = "B", ) -> float: @@ -576,8 +583,8 @@ def setup( y: NDArray, Regs: Sequence["LinearOperator"], Weight: Optional["LinearOperator"] = None, - dataregs: Optional[Sequence[NDArray]] = None, - epsRs: Optional[Sequence[float]] = None, + dataregs: Sequence[NDArray] | None = None, + epsRs: Sequence[float] | None = None, show: bool = False, ) -> None: r"""Setup solver @@ -642,7 +649,7 @@ def setup( # augumented operator if self.epsRs is not None and self.dataregs is not None: - for epsR, datareg in zip(self.epsRs, self.dataregs): + for epsR, datareg in zip(self.epsRs, self.dataregs, strict=True): self.datatot = self.ncp.hstack((self.datatot, epsR * datareg)) # print setup @@ -650,11 +657,12 @@ def setup( self._print_setup() def step(self) -> None: - raise NotImplementedError( - "RegularizedInversion uses as default the" - " scipy.sparse.linalg.lsqr solver, therefore the " + msg = ( + "RegularizedInversion uses as default the " + "scipy.sparse.linalg.lsqr solver, therefore the " "step method is not implemented. Use directly run or solve." ) + raise NotImplementedError(msg) def run( self, @@ -662,7 +670,7 @@ def run( engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int, int, float, float]: + ) -> tuple[NDArray, int, int, float, float]: r"""Run solver Parameters @@ -719,21 +727,22 @@ def run( **kwargs_solver, )[0:5] else: - raise NotImplementedError("Engine must be scipy or pylops") + msg = "`engine` must be scipy or pylops" + raise NotImplementedError(msg) return xinv, istop, itn, r1norm, r2norm def solve( self, y: NDArray, Regs: Sequence["LinearOperator"], - x0: Optional[NDArray] = None, + x0: NDArray | None = None, Weight: Optional["LinearOperator"] = None, - dataregs: Optional[Sequence[NDArray]] = None, - epsRs: Optional[Sequence[float]] = None, + dataregs: Sequence[NDArray] | None = None, + epsRs: Sequence[float] | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int, int, float, float]: + ) -> tuple[NDArray, int, int, float, float]: r"""Run entire solver Parameters @@ -921,11 +930,12 @@ def setup( self._print_setup() def step(self) -> None: - raise NotImplementedError( - "PreconditionedInversion uses as default the" - " scipy.sparse.linalg.lsqr solver, therefore the " + msg = ( + "PreconditionedInversion uses as default the " + "scipy.sparse.linalg.lsqr solver, therefore the " "step method is not implemented. Use directly run or solve." ) + raise NotImplementedError(msg) def run( self, @@ -933,7 +943,7 @@ def run( engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int, int, float, float]: + ) -> tuple[NDArray, int, int, float, float]: r"""Run solver Parameters @@ -995,7 +1005,8 @@ def run( # force it 1d as we decorate this method with disable_ndarray_multiplication pinv = pinv.ravel() else: - raise NotImplementedError("Engine must be scipy or pylops") + msg = "`engine` must be scipy or pylops" + raise NotImplementedError(msg) xinv = self.P.matvec(pinv) return xinv, istop, itn, r1norm, r2norm @@ -1003,11 +1014,11 @@ def solve( self, y: NDArray, P: "LinearOperator", - x0: Optional[NDArray] = None, + x0: NDArray | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, - ) -> Tuple[NDArray, int, int, float, float]: + ) -> tuple[NDArray, int, int, float, float]: r"""Run entire solver Parameters diff --git a/pylops/optimization/cls_sparsity.py b/pylops/optimization/cls_sparsity.py index 55fbad1c2..98028aa5b 100644 --- a/pylops/optimization/cls_sparsity.py +++ b/pylops/optimization/cls_sparsity.py @@ -9,8 +9,9 @@ import logging import time +from collections.abc import Callable, Sequence from math import sqrt -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple +from typing import Any, Optional import numpy as np from scipy.sparse.linalg import lsqr @@ -417,7 +418,7 @@ def memory_usage( def setup( self, y: NDArray, - nouter: Optional[int] = None, + nouter: int | None = None, threshR: bool = False, epsR: float = 1e-10, epsI: float = 1e-10, @@ -487,7 +488,8 @@ def setup( self.epsI = 0.0 # as epsI is added to the augmented system already self.y = self.ncp.hstack([self.y, self.ncp.zeros(self.Op.shape[1])]) else: - raise NotImplementedError("kind must be model, data or datamodel") + msg = f"`kind` must be model, data or datamodel, got {kind}" + raise NotImplementedError(msg) if self.preallocate: self.r = self.ncp.empty_like(self.y) @@ -674,11 +676,11 @@ def step( def run( self, - x: Optional[NDArray], + x: NDArray | None, nouter: int = 10, engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), **kwargs_solver, ) -> NDArray: r"""Run solver @@ -764,7 +766,7 @@ def finalize(self, show: bool = False) -> None: def solve( self, y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, nouter: int = 10, threshR: bool = False, epsR: float = 1e-10, @@ -775,7 +777,7 @@ def solve( engine: Tsolverengine = "scipy", preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), **kwargs_solver, ) -> NDArray: r"""Run entire solver @@ -1220,8 +1222,8 @@ def run( cols: InputDimsLike, engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[NDArray, InputDimsLike]: + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[NDArray, InputDimsLike]: r"""Run solver Parameters @@ -1315,8 +1317,8 @@ def solve( engine: Tsolverengine = "scipy", preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[NDArray, int, NDArray]: + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[NDArray, int, NDArray]: r"""Run entire solver Parameters @@ -1382,8 +1384,8 @@ def solve( preallocate=preallocate, show=show, ) - x: List[NDArray] = [] - cols: List[InputDimsLike] = [] + x: list[NDArray] = [] + cols: list[InputDimsLike] = [] x, cols = self.run(x, cols, engine=engine, show=show, itershow=itershow) x = self.finalize(x, cols, show) return x, self.nouter, self.cost @@ -1594,16 +1596,16 @@ def memory_usage( def setup( self, y: NDArray, - x0: Optional[NDArray] = None, - niter: Optional[int] = None, - SOp: Optional[LinearOperator] = None, + x0: NDArray | None = None, + niter: int | None = None, + SOp: LinearOperator | None = None, eps: float = 0.1, - alpha: Optional[float] = None, - eigsdict: Optional[Dict[str, Any]] = None, + alpha: float | None = None, + eigsdict: dict[str, Any] | None = None, tol: float = 1e-10, threshkind: Tthreshkind = "soft", - perc: Optional[float] = None, - decay: Optional[NDArray] = None, + perc: float | None = None, + decay: NDArray | None = None, monitorres: bool = False, preallocate: bool = False, show: bool = False, @@ -1699,19 +1701,21 @@ def setup( "soft-percentile", "half-percentile", ]: - raise ValueError( - "threshkind must be hard, soft, half," + msg = ( + "`threshkind` must be hard, soft, half, " "hard-percentile, soft-percentile, " - "or half-percentile" + f"or half-percentile, got {threshkind}" ) + raise ValueError(msg) if ( threshkind in ["hard-percentile", "soft-percentile", "half-percentile"] and perc is None ): - raise ValueError( - "Provide a percentile when choosing hard-percentile," + msg = ( + "Provide a percentile when choosing hard-percentile, " "soft-percentile, or half-percentile thresholding" ) + raise ValueError(msg) self.threshf: Callable[[NDArray, float], NDArray] if threshkind == "soft": @@ -1770,10 +1774,12 @@ def setup( else: if y.ndim != x0.ndim: # error for wrong dimensions - raise ValueError("Number of columns of x0 and data are not the same") + msg = "Number of columns of x0 and data are not the same" + raise ValueError(msg) elif x0.shape[0] != self.Op.shape[1]: - # error for wrong dimensions - raise ValueError("Operator and input vector have different dimensions") + # error for wrong shapes + msg = "Operator and input vector have different shapes" + raise ValueError(msg) else: x = x0.copy() @@ -1796,7 +1802,7 @@ def setup( self.t = 1.0 # create variables to track the residual norm and iterations - self.cost: List[float] = [] + self.cost: list[float] = [] self.iiter = 0 # print setup @@ -1804,7 +1810,7 @@ def setup( self._print_setup() return x - def step(self, x: NDArray, show: bool = False) -> Tuple[NDArray, float]: + def step(self, x: NDArray, show: bool = False) -> tuple[NDArray, float]: r"""Run one step of solver Parameters @@ -1837,11 +1843,12 @@ def step(self, x: NDArray, show: bool = False) -> Tuple[NDArray, float]: if self.monitorres: self.normres = np.linalg.norm(self.res if self.preallocate else res) if self.normres > self.normresold: - raise ValueError( + msg = ( f"ISTA stopped at iteration {self.iiter} due to " "residual increasing, consider modifying " "eps and/or alpha..." ) + raise ValueError(msg) else: self.normresold = self.normres @@ -1917,9 +1924,9 @@ def step(self, x: NDArray, show: bool = False) -> Tuple[NDArray, float]: def run( self, x: NDArray, - niter: Optional[int] = None, + niter: int | None = None, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), ) -> NDArray: r"""Run solver @@ -1946,7 +1953,8 @@ def run( xupdate = np.inf niter = self.niter if niter is None else niter if niter is None: - raise ValueError("niter must not be None") + msg = "`niter` must not be None" + raise ValueError(msg) while self.iiter < niter and xupdate > self.tol: showstep = ( True @@ -1986,21 +1994,21 @@ def finalize(self, show: bool = False) -> None: def solve( self, y: NDArray, - x0: Optional[NDArray] = None, - niter: Optional[int] = None, - SOp: Optional[LinearOperator] = None, + x0: NDArray | None = None, + niter: int | None = None, + SOp: LinearOperator | None = None, eps: float = 0.1, - alpha: Optional[float] = None, - eigsdict: Optional[Dict[str, Any]] = None, + alpha: float | None = None, + eigsdict: dict[str, Any] | None = None, tol: float = 1e-10, threshkind: Tthreshkind = "soft", - perc: Optional[float] = None, - decay: Optional[NDArray] = None, + perc: float | None = None, + decay: NDArray | None = None, monitorres: bool = False, preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - ) -> Tuple[NDArray, int, NDArray]: + itershow: tuple[int, int, int] = (10, 10, 10), + ) -> tuple[NDArray, int, NDArray]: r"""Run entire solver Parameters @@ -2095,7 +2103,6 @@ class FISTA(ISTA): Op : :obj:`pylops.LinearOperator` Operator to invert - Attributes ---------- ncp : :obj:`module` @@ -2268,11 +2275,12 @@ def step(self, x: NDArray, z: NDArray, show: bool = False) -> NDArray: if self.monitorres: self.normres = np.linalg.norm(self.res if self.preallocate else res) if self.normres > self.normresold: - raise ValueError( + msg = ( f"FISTA stopped at iteration {self.iiter} due to " "residual increasing, consider modifying " "eps and/or alpha..." ) + raise ValueError(msg) else: self.normresold = self.normres @@ -2357,9 +2365,9 @@ def step(self, x: NDArray, z: NDArray, show: bool = False) -> NDArray: def run( self, x: NDArray, - niter: Optional[int] = None, + niter: int | None = None, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), ) -> NDArray: r"""Run solver @@ -2387,7 +2395,8 @@ def run( xupdate = np.inf niter = self.niter if niter is None else niter if niter is None: - raise ValueError("niter must not be None") + msg = "`niter` must not be None" + raise ValueError(msg) while self.iiter < niter and xupdate > self.tol: showstep = ( True @@ -2406,9 +2415,7 @@ def run( if stop: break if xupdate <= self.tol: - logger.warning( - "Update smaller that tolerance for " "iteration %d", self.iiter - ) + logger.warning("Update smaller that tolerance for iteration %d", self.iiter) return x @@ -2490,7 +2497,7 @@ def memory_usage( def setup( self, y: NDArray, - SOp: Optional[LinearOperator] = None, + SOp: LinearOperator | None = None, tau: int = 0, sigma: int = 0, show: bool = False, @@ -2527,18 +2534,19 @@ def setup( self._print_setup() def step(self) -> None: - raise NotImplementedError( - "SPGL1 uses as default the" - "spgl1.spgl1 solver, therefore the " - "step method is not implemented. Use directly run or solve." + msg = ( + "SPGL1 uses as default the spgl1.spgl1" + "solver, therefore the step method is not " + " implemented. Use directly run or solve." ) + raise NotImplementedError(msg) def run( self, x: NDArray, show: bool = False, **kwargs_spgl1, - ) -> Tuple[NDArray, NDArray, Dict[str, Any]]: + ) -> tuple[NDArray, NDArray, dict[str, Any]]: r"""Run solver Parameters @@ -2617,13 +2625,13 @@ def run( def solve( self, y: NDArray, - x0: Optional[NDArray] = None, - SOp: Optional[LinearOperator] = None, + x0: NDArray | None = None, + SOp: LinearOperator | None = None, tau: float = 0.0, sigma: float = 0, show: bool = False, **kwargs_spgl1, - ) -> Tuple[NDArray, NDArray, Dict[str, Any]]: + ) -> tuple[NDArray, NDArray, dict[str, Any]]: r"""Run entire solver Parameters @@ -2827,8 +2835,8 @@ def _print_step(self, x: NDArray) -> None: def memory_usage( self, - nopRegsL1: Optional[Tuple[int]] = None, - nopRegsL2: Optional[Tuple[int]] = None, + nopRegsL1: tuple[int] | None = None, + nopRegsL2: tuple[int] | None = None, show: bool = False, unit: Tmemunit = "B", ) -> float: @@ -2882,15 +2890,15 @@ def memory_usage( def setup( self, y: NDArray, - RegsL1: List[LinearOperator], - x0: Optional[NDArray] = None, + RegsL1: list[LinearOperator], + x0: NDArray | None = None, niter_outer: int = 3, niter_inner: int = 5, - RegsL2: Optional[List[LinearOperator]] = None, - dataregsL2: Optional[Sequence[NDArray]] = None, + RegsL2: list[LinearOperator] | None = None, + dataregsL2: Sequence[NDArray] | None = None, mu: float = 1.0, - epsRL1s: Optional[SamplingLike] = None, - epsRL2s: Optional[SamplingLike] = None, + epsRL1s: SamplingLike | None = None, + epsRL2s: SamplingLike | None = None, tol: float = 1e-10, tau: float = 1.0, restart: bool = False, @@ -2994,7 +3002,7 @@ def setup( self.dataregsL2 = [] # Rescale dampings - self.epsRs: List[float] = [] + self.epsRs: list[float] = [] if epsRL2s is not None: self.epsRs += [ sqrt(epsRL2s[ireg] / 2) / sqrt(mu / 2) for ireg in range(self.nregsL2) @@ -3008,7 +3016,7 @@ def setup( x = self.ncp.zeros(self.Op.shape[1], dtype=self.Op.dtype) if x0 is None else x0 # create variables to track the residual norm and iterations - self.cost: List[float] = [] + self.cost: list[float] = [] self.iiter = 0 if show: @@ -3102,13 +3110,13 @@ def step( else [ epsRL2 * self.ncp.linalg.norm(dataregL2 - RegL2.matvec(x)) ** 2 for epsRL2, RegL2, dataregL2 in zip( - self.epsRL2s, self.RegsL2, self.dataregsL2 + self.epsRL2s, self.RegsL2, self.dataregsL2, strict=True ) ] ) self.costregL1 = [ self.ncp.linalg.norm(RegL1.matvec(x), ord=1) - for _, RegL1 in zip(self.epsRL1s, self.RegsL1) + for _, RegL1 in zip(self.epsRL1s, self.RegsL1, strict=True) ] self.costtot = ( self.costdata @@ -3128,7 +3136,7 @@ def run( x: NDArray, engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), show_inner: bool = False, **kwargs_lsqr, ) -> NDArray: @@ -3206,25 +3214,25 @@ def finalize(self, show: bool = False) -> NDArray: def solve( self, y: NDArray, - RegsL1: List[LinearOperator], - x0: Optional[NDArray] = None, + RegsL1: list[LinearOperator], + x0: NDArray | None = None, niter_outer: int = 3, niter_inner: int = 5, - RegsL2: Optional[List[LinearOperator]] = None, - dataregsL2: Optional[List[NDArray]] = None, + RegsL2: list[LinearOperator] | None = None, + dataregsL2: list[NDArray] | None = None, mu: float = 1.0, - epsRL1s: Optional[SamplingLike] = None, - epsRL2s: Optional[SamplingLike] = None, + epsRL1s: SamplingLike | None = None, + epsRL2s: SamplingLike | None = None, tol: float = 1e-10, tau: float = 1.0, restart: bool = False, engine: Tsolverengine = "scipy", preallocate: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), show_inner: bool = False, **kwargs_lsqr, - ) -> Tuple[NDArray, int, NDArray]: + ) -> tuple[NDArray, int, NDArray]: r"""Run entire solver Parameters diff --git a/pylops/optimization/eigs.py b/pylops/optimization/eigs.py index ebbf95742..fe7d47d1e 100644 --- a/pylops/optimization/eigs.py +++ b/pylops/optimization/eigs.py @@ -1,6 +1,5 @@ __all__ = ["power_iteration"] -from typing import Tuple import numpy as np @@ -15,7 +14,7 @@ def power_iteration( tol: float = 1e-5, dtype: str = "float32", backend: Tbackend = "numpy", -) -> Tuple[float, NDArray, int]: +) -> tuple[float, NDArray, int]: """Power iteration algorithm. Power iteration algorithm, used to compute the largest eigenvector and @@ -65,7 +64,7 @@ def power_iteration( niter = 10 if niter is None else niter maxeig_old = 0.0 - for iiter in range(niter): + for _iiter in range(niter): # compute largest eigenvector b1_k = Op.matvec(b_k) @@ -79,4 +78,4 @@ def power_iteration( break maxeig_old = maxeig - return maxeig, b_k, iiter + 1 + return maxeig, b_k, _iiter + 1 diff --git a/pylops/optimization/leastsquares.py b/pylops/optimization/leastsquares.py index 66e171f55..cdde2ad91 100644 --- a/pylops/optimization/leastsquares.py +++ b/pylops/optimization/leastsquares.py @@ -4,7 +4,8 @@ "preconditioned_inversion", ] -from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple +from collections.abc import Sequence +from typing import TYPE_CHECKING, Optional from pylops.optimization.cls_leastsquares import ( NormalEquationsInversion, @@ -20,18 +21,18 @@ def normal_equations_inversion( Op: "LinearOperator", y: NDArray, - Regs: List["LinearOperator"], - x0: Optional[NDArray] = None, + Regs: list["LinearOperator"], + x0: NDArray | None = None, Weight: Optional["LinearOperator"] = None, - dataregs: Optional[List[NDArray]] = None, + dataregs: list[NDArray] | None = None, epsI: float = 0.0, - epsRs: Optional[SamplingLike] = None, - NRegs: Optional[Sequence["LinearOperator"]] = None, - epsNRs: Optional[SamplingLike] = None, + epsRs: SamplingLike | None = None, + NRegs: Sequence["LinearOperator"] | None = None, + epsNRs: SamplingLike | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, -) -> Tuple[NDArray, int]: +) -> tuple[NDArray, int]: r"""Inversion of normal equations. Solve the regularized normal equations for a system of equations @@ -124,15 +125,15 @@ def normal_equations_inversion( def regularized_inversion( Op, y: NDArray, - Regs: List["LinearOperator"], - x0: Optional[NDArray] = None, + Regs: list["LinearOperator"], + x0: NDArray | None = None, Weight: Optional["LinearOperator"] = None, - dataregs: Optional[List[NDArray]] = None, - epsRs: Optional[SamplingLike] = None, + dataregs: list[NDArray] | None = None, + epsRs: SamplingLike | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, -) -> Tuple[NDArray, int, int, float, float]: +) -> tuple[NDArray, int, int, float, float]: r"""Regularized inversion. Solve a system of regularized equations given the operator ``Op``, @@ -218,11 +219,11 @@ def preconditioned_inversion( Op: "LinearOperator", y: NDArray, P: "LinearOperator", - x0: Optional[NDArray] = None, + x0: NDArray | None = None, engine: Tsolverengine = "scipy", show: bool = False, **kwargs_solver, -) -> Tuple[NDArray, int, int, float, float]: +) -> tuple[NDArray, int, int, float, float]: r"""Preconditioned inversion. Solve a system of preconditioned equations given the operator diff --git a/pylops/optimization/sparsity.py b/pylops/optimization/sparsity.py index 36ed636f0..b05dec901 100644 --- a/pylops/optimization/sparsity.py +++ b/pylops/optimization/sparsity.py @@ -7,7 +7,8 @@ "splitbregman", ] -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple +from collections.abc import Callable +from typing import TYPE_CHECKING, Any, Optional from pylops.optimization.callback import ( CostNanInfCallback, @@ -31,7 +32,7 @@ def irls( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, nouter: int = 10, threshR: bool = False, epsR: float = 1e-10, @@ -41,11 +42,11 @@ def irls( kind: Tirlskind = "data", engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, **kwargs_solver, -) -> Tuple[NDArray, int]: +) -> tuple[NDArray, int]: r"""Iteratively reweighted least squares. Solve an optimization problem with :math:`L_1` cost function (data IRLS) @@ -126,7 +127,10 @@ def irls( irlssolve = IRLS(Op) if callback is not None: irlssolve.callback = callback - x, nouter, = irlssolve.solve( + ( + x, + nouter, + ) = irlssolve.solve( y, x0=x0, nouter=nouter, @@ -158,10 +162,10 @@ def omp( optimal_coeff: bool = False, engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, NDArray]: +) -> tuple[NDArray, int, NDArray]: r"""Orthogonal Matching Pursuit (OMP). Solve an optimization problem with :math:`L^0` regularization function given @@ -277,24 +281,24 @@ def omp( def ista( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, SOp: Optional["LinearOperator"] = None, eps: float = 0.1, - alpha: Optional[float] = None, - eigsdict: Optional[Dict[str, Any]] = None, + alpha: float | None = None, + eigsdict: dict[str, Any] | None = None, tol: float = 1e-10, rtol: float = 0.0, rtol1: float = 0.0, threshkind: Tthreshkind = "soft", - perc: Optional[float] = None, - decay: Optional[NDArray] = None, + perc: float | None = None, + decay: NDArray | None = None, monitorres: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, NDArray]: +) -> tuple[NDArray, int, NDArray]: r"""Iterative Shrinkage-Thresholding Algorithm (ISTA). Solve an optimization problem with :math:`L^p, \; p=0, 0.5, 1` @@ -431,24 +435,24 @@ def ista( def fista( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, niter: int = 10, SOp: Optional["LinearOperator"] = None, eps: float = 0.1, - alpha: Optional[float] = None, - eigsdict: Optional[Dict[str, Any]] = None, + alpha: float | None = None, + eigsdict: dict[str, Any] | None = None, tol: float = 1e-10, rtol: float = 0.0, rtol1: float = 0.0, threshkind: Tthreshkind = "soft", - perc: Optional[float] = None, - decay: Optional[NDArray] = None, + perc: float | None = None, + decay: NDArray | None = None, monitorres: bool = False, show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), - callback: Optional[Callable] = None, + itershow: tuple[int, int, int] = (10, 10, 10), + callback: Callable | None = None, preallocate: bool = False, -) -> Tuple[NDArray, int, NDArray]: +) -> tuple[NDArray, int, NDArray]: r"""Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Solve an optimization problem with :math:`L^p, \; p=0, 0.5, 1` @@ -584,13 +588,13 @@ def fista( def spgl1( Op: "LinearOperator", y: NDArray, - x0: Optional[NDArray] = None, + x0: NDArray | None = None, SOp: Optional["LinearOperator"] = None, tau: float = 0.0, sigma: float = 0.0, show: bool = False, **kwargs_spgl1, -) -> Tuple[NDArray, NDArray, Dict[str, Any]]: +) -> tuple[NDArray, NDArray, dict[str, Any]]: r"""Spectral Projected-Gradient for L1 norm. Solve a constrained system of equations given the operator ``Op`` @@ -700,15 +704,15 @@ def spgl1( def splitbregman( Op: "LinearOperator", y: NDArray, - RegsL1: List["LinearOperator"], - x0: Optional[NDArray] = None, + RegsL1: list["LinearOperator"], + x0: NDArray | None = None, niter_outer: int = 3, niter_inner: int = 5, - RegsL2: Optional[List["LinearOperator"]] = None, - dataregsL2: Optional[List[NDArray]] = None, + RegsL2: list["LinearOperator"] | None = None, + dataregsL2: list[NDArray] | None = None, mu: float = 1.0, - epsRL1s: Optional[SamplingLike] = None, - epsRL2s: Optional[SamplingLike] = None, + epsRL1s: SamplingLike | None = None, + epsRL2s: SamplingLike | None = None, tol: float = 1e-10, rtol: float = 0.0, rtol1: float = 0.0, @@ -716,12 +720,12 @@ def splitbregman( restart: bool = False, engine: Tsolverengine = "scipy", show: bool = False, - itershow: Tuple[int, int, int] = (10, 10, 10), + itershow: tuple[int, int, int] = (10, 10, 10), show_inner: bool = False, - callback: Optional[Callable] = None, + callback: Callable | None = None, preallocate: bool = False, **kwargs_lsqr, -) -> Tuple[NDArray, int, NDArray]: +) -> tuple[NDArray, int, NDArray]: r"""Split Bregman for mixed L2-L1 norms. Solve an unconstrained system of equations with mixed :math:`L_2` and :math:`L_1` diff --git a/pylops/signalprocessing/_baseffts.py b/pylops/signalprocessing/_baseffts.py index 367174131..2806f6dbf 100644 --- a/pylops/signalprocessing/_baseffts.py +++ b/pylops/signalprocessing/_baseffts.py @@ -1,6 +1,6 @@ import warnings +from collections.abc import Sequence from enum import Enum, auto -from typing import Optional, Sequence, Union import numpy as np @@ -29,9 +29,9 @@ class _BaseFFT(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: str = "ortho", real: bool = False, @@ -68,9 +68,10 @@ def __init__( warnings.warn( f"nfft={self.nfft} has been selected to be smaller than the size of " f"the original signal (dims[axis]={dims[axis]}).\n" - f"This is rarely intended behavior as the original signal will be " - f"truncated prior to applying FFT. " - f"If this is the required behaviour ignore this message." + "This is rarely intended behavior as the original signal will be " + "truncated prior to applying FFT. " + "If this is the required behaviour ignore this message.", + stacklevel=True, ) if norm == "ortho": @@ -80,15 +81,14 @@ def __init__( elif norm.lower() == "1/n": self.norm = _FFTNorms.ONE_OVER_N elif norm == "backward": - raise ValueError( - 'To use no scaling on the forward transform, use "none". Note that in this case, the adjoint transform will *not* have a 1/n scaling.' - ) + msg = 'To use no scaling on the forward transform, use "none". Note that in this case, the adjoint transform will *not* have a 1/n scaling.' + raise ValueError(msg) elif norm == "forward": - raise ValueError( - 'To use 1/n scaling on the forward transform, use "1/n". Note that in this case, the adjoint transform will *also* have a 1/n scaling.' - ) + msg = 'To use 1/n scaling on the forward transform, use "1/n". Note that in this case, the adjoint transform will *also* have a 1/n scaling.' + raise ValueError(msg) else: - raise ValueError(f"'{norm}' is not one of 'ortho', 'none' or '1/n'") + msg = f"`norm`={norm} is not one of 'ortho', 'none' or '1/n'" + raise ValueError(msg) self.real = real self.ifftshift_before = ifftshift_before @@ -102,7 +102,9 @@ def __init__( if self.fftshift_after: if self.real: warnings.warn( - "Using fftshift_after with real=True. fftshift should only be applied after a complex FFT. This is rarely intended behavior but if it is, ignore this message." + "Using fftshift_after with real=True. fftshift should only be applied after a complex FFT. " + "This is rarely intended behavior but if it is, ignore this message.", + stacklevel=2, ) self.f = np.fft.fftshift(self.f) @@ -120,14 +122,12 @@ def __init__( super().__init__(dtype=self.cdtype, dims=dims, dimsd=dimsd, clinear=clinear) def _matvec(self, x: NDArray) -> NDArray: - raise NotImplementedError( - "_BaseFFT does not provide _matvec. It must be implemented separately." - ) + msg = "_BaseFFT does not provide _matvec. It must be implemented separately." + raise NotImplementedError(msg) def _rmatvec(self, x: NDArray) -> NDArray: - raise NotImplementedError( - "_BaseFFT does not provide _rmatvec. It must be implemented separately." - ) + msg = "_BaseFFT does not provide _rmatvec. It must be implemented separately." + raise NotImplementedError(msg) class _BaseFFTND(LinearOperator): @@ -135,10 +135,10 @@ class _BaseFFTND(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], - axes: Optional[Union[int, InputDimsLike]] = None, - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + dims: int | InputDimsLike, + axes: int | InputDimsLike | None = None, + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: str = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -156,13 +156,14 @@ def __init__( self.naxes = len(self.axes) if self.naxes != len(np.unique(self.axes)): warnings.warn( - "At least one direction is repeated. This may cause unexpected results." + "At least one direction is repeated. This may cause unexpected results.", + stacklevel=2, ) nffts = _value_or_sized_to_array(nffts, repeat=self.naxes) if len(nffts[np.equal(nffts, None)]) > 0: # Found None(s) in nffts nffts[np.equal(nffts, None)] = np.array( - [dims[d] for d, n in zip(axes, nffts) if n is None] + [dims[d] for d, n in zip(axes, nffts, strict=True) if n is None] ) nffts = nffts.astype(np.array(dims).dtype) _raise_on_wrong_dtype(nffts, np.integer, "nffts") @@ -192,25 +193,27 @@ def __init__( or self.naxes != len(self.ifftshift_before) or self.naxes != len(self.fftshift_after) ): - raise ValueError( - ( - "`axes`, `nffts`, `sampling`, `ifftshift_before` and " - "`fftshift_after` must the have same number of elements. Received " - f"{self.naxes}, {len(self.nffts)}, {len(self.sampling)}, " - f"{len(self.ifftshift_before)} and {len(self.fftshift_after)}, " - "respectively." - ) + msg = ( + "`axes`, `nffts`, `sampling`, `ifftshift_before` and " + "`fftshift_after` must the have same number of elements. Received " + f"{self.naxes}, {len(self.nffts)}, {len(self.sampling)}, " + f"{len(self.ifftshift_before)} and {len(self.fftshift_after)}, " + "respectively." ) + raise ValueError(msg) # Check if the user provided nfft smaller than n. See _BaseFFT for # details nfftshort = [ - nfft < dims[direction] for direction, nfft in zip(self.axes, self.nffts) + nfft < dims[direction] + for direction, nfft in zip(self.axes, self.nffts, strict=True) ] self.doifftpad = any(nfftshort) if self.doifftpad: self.ifftpad = [(0, 0)] * self.ndim - for idir, (direction, nfshort) in enumerate(zip(self.axes, nfftshort)): + for idir, (direction, nfshort) in enumerate( + zip(self.axes, nfftshort, strict=True) + ): if nfshort: self.ifftpad[direction] = ( 0, @@ -218,8 +221,9 @@ def __init__( ) warnings.warn( f"nffts in directions {np.where(nfftshort)[0]} have been selected to be smaller than the size of the original signal. " - f"This is rarely intended behavior as the original signal will be truncated prior to applying fft, " - f"if this is the required behaviour ignore this message." + "This is rarely intended behavior as the original signal will be truncated prior to applying fft, " + f"if this is the required behaviour ignore this message.", + stacklevel=2, ) if norm == "ortho": @@ -229,15 +233,14 @@ def __init__( elif norm.lower() == "1/n": self.norm = _FFTNorms.ONE_OVER_N elif norm == "backward": - raise ValueError( - 'To use no scaling on the forward transform, use "none". Note that in this case, the adjoint transform will *not* have a 1/n scaling.' - ) + msg = 'To use no scaling on the forward transform, use "none". Note that in this case, the adjoint transform will *not* have a 1/n scaling.' + raise ValueError(msg) elif norm == "forward": - raise ValueError( - 'To use 1/n scaling on the forward transform, use "1/n". Note that in this case, the adjoint transform will *also* have a 1/n scaling.' - ) + msg = 'To use 1/n scaling on the forward transform, use "1/n". Note that in this case, the adjoint transform will *also* have a 1/n scaling.' + raise ValueError(msg) else: - raise ValueError(f"'{norm}' is not one of 'ortho', 'none' or '1/n'") + msg = f"`norm`={norm} is not one of 'ortho', 'none' or '1/n'" + raise ValueError(msg) self.real = real @@ -245,20 +248,21 @@ def __init__( np.fft.fftshift(np.fft.fftfreq(n, d=s)) if fftshift else np.fft.fftfreq(n, d=s) - for n, s, fftshift in zip(self.nffts, self.sampling, self.fftshift_after) + for n, s, fftshift in zip( + self.nffts, self.sampling, self.fftshift_after, strict=True + ) ] if self.real: fs[-1] = np.fft.rfftfreq(self.nffts[-1], d=self.sampling[-1]) if self.fftshift_after[-1]: warnings.warn( - ( - "Using real=True and fftshift_after on the last direction. " - "fftshift should only be applied on directions with negative " - "and positive frequencies. When using FFTND with real=True, " - "are all directions except the last. If you wish to proceed " - "applying fftshift on a frequency axis with only positive " - "frequencies, ignore this message." - ) + "Using real=True and fftshift_after on the last direction. " + "fftshift should only be applied on directions with negative " + "and positive frequencies. When using FFTND with real=True, " + "are all directions except the last. If you wish to proceed " + "applying fftshift on a frequency axis with only positive " + "frequencies, ignore this message.", + stacklevel=2, ) fs[-1] = np.fft.fftshift(fs[-1]) self.fs = tuple(fs) @@ -278,11 +282,9 @@ def __init__( super().__init__(dtype=self.cdtype, dims=dims, dimsd=dimsd, clinear=clinear) def _matvec(self, x: NDArray) -> NDArray: - raise NotImplementedError( - "_BaseFFT does not provide _matvec. It must be implemented separately." - ) + msg = "_BaseFFT does not provide _matvec. It must be implemented separately." + raise NotImplementedError(msg) def _rmatvec(self, x: NDArray) -> NDArray: - raise NotImplementedError( - "_BaseFFT does not provide _rmatvec. It must be implemented separately." - ) + msg = "_BaseFFT does not provide _rmatvec. It must be implemented separately." + raise NotImplementedError(msg) diff --git a/pylops/signalprocessing/_interp_utils.py b/pylops/signalprocessing/_interp_utils.py index 3738b02ed..e2d79c03a 100644 --- a/pylops/signalprocessing/_interp_utils.py +++ b/pylops/signalprocessing/_interp_utils.py @@ -20,7 +20,8 @@ def _ensure_iava_is_unique( ) if np.any(count > 1): - raise ValueError("Found repeated/non-unique values in iava.") + msg = "Found repeated/non-unique values in iava." + raise ValueError(msg) return @@ -39,9 +40,10 @@ def _clip_iava_above_last_sample_index( outside = iava >= last_sample_index if np.any(outside): warnings.warn( - f"At least one value in iava is beyond the penultimate sample index " + "At least one value in iava is beyond the penultimate sample index " f"{last_sample_index}. Out-of-bound-values are forced below penultimate " - f"sample." + "sample.", + stacklevel=2, ) # NOTE: ``numpy.nextafter(x, -np.inf)`` gives the closest float-value that is diff --git a/pylops/signalprocessing/_nonstatconvolve2d_cuda.py b/pylops/signalprocessing/_nonstatconvolve2d_cuda.py index feba8ed20..67788ad91 100644 --- a/pylops/signalprocessing/_nonstatconvolve2d_cuda.py +++ b/pylops/signalprocessing/_nonstatconvolve2d_cuda.py @@ -1,3 +1,5 @@ +# flake8: noqa: B905 + from math import floor from numba import cuda @@ -64,7 +66,8 @@ def _matvec_rmatvec(x, y, hs, hshape, xdims, ohx, ohz, dhx, dhz, nhx, nhz, rmatv ) # place filter in output for ixx, hxx in zip( - range(xextremes[0], xextremes[1]), range(hxextremes[0], hxextremes[1]) + range(xextremes[0], xextremes[1]), + range(hxextremes[0], hxextremes[1]), ): for izz, hzz in zip( range(zextremes[0], zextremes[1]), diff --git a/pylops/signalprocessing/_nonstatconvolve3d_cuda.py b/pylops/signalprocessing/_nonstatconvolve3d_cuda.py index 5b309050f..b3aed8c72 100644 --- a/pylops/signalprocessing/_nonstatconvolve3d_cuda.py +++ b/pylops/signalprocessing/_nonstatconvolve3d_cuda.py @@ -1,3 +1,5 @@ +# flake8: noqa: B905 + from math import floor from numba import cuda @@ -16,7 +18,6 @@ def _matvec_rmatvec( ix, iy, iz = cuda.grid(3) if ix < xdims[0] and iy < xdims[1] and iz < xdims[2]: - # find closest filters and interpolate h ihx_l = int(floor((ix - ohx) / dhx)) # id number of left for hs_arr ihy_b = int(floor((iy - ohy) / dhy)) # id number of back for hs_arr @@ -96,10 +97,12 @@ def _matvec_rmatvec( # place filter in output for ixx, hxx in zip( - range(xextremes[0], xextremes[1]), range(hxextremes[0], hxextremes[1]) + range(xextremes[0], xextremes[1]), + range(hxextremes[0], hxextremes[1]), ): for iyy, hyy in zip( - range(yextremes[0], yextremes[1]), range(hyextremes[0], hyextremes[1]) + range(yextremes[0], yextremes[1]), + range(hyextremes[0], hyextremes[1]), ): for izz, hzz in zip( range(zextremes[0], zextremes[1]), diff --git a/pylops/signalprocessing/bilinear.py b/pylops/signalprocessing/bilinear.py index 9bced48a1..e47c226f7 100644 --- a/pylops/signalprocessing/bilinear.py +++ b/pylops/signalprocessing/bilinear.py @@ -1,7 +1,6 @@ __all__ = ["Bilinear"] import logging -from typing import Optional import numpy as np @@ -107,7 +106,7 @@ def __init__( self, iava: SamplingLike, dims: InputDimsLike, - forceflat: Optional[bool] = None, + forceflat: bool | None = None, dtype: DTypeLike = "float64", name: str = "B", ) -> None: diff --git a/pylops/signalprocessing/chirpradon3d.py b/pylops/signalprocessing/chirpradon3d.py index df8dae00f..955540497 100755 --- a/pylops/signalprocessing/chirpradon3d.py +++ b/pylops/signalprocessing/chirpradon3d.py @@ -113,7 +113,8 @@ def __init__( self.pmax = pmax self.engine = engine if self.engine not in ["fftw", "numpy"]: - raise ValueError("engine must be 'numpy' or 'fftw'") + msg = "`engine` must be numpy or fftw" + raise ValueError(msg) self.kwargs_fftw = kwargs_fftw @reshaped diff --git a/pylops/signalprocessing/convolve1d.py b/pylops/signalprocessing/convolve1d.py index 3543d75dd..86c5e87a4 100644 --- a/pylops/signalprocessing/convolve1d.py +++ b/pylops/signalprocessing/convolve1d.py @@ -1,7 +1,8 @@ __all__ = ["Convolve1D"] +from collections.abc import Callable from functools import partial -from typing import Callable, Literal, Optional, Tuple, Union +from typing import Literal import numpy as np @@ -20,10 +21,10 @@ def _choose_convfunc( x: NDArray, - method: Optional[Literal["direct", "fft", "overlapadd"]], - dims: Union[int, InputDimsLike], + method: Literal["direct", "fft", "overlapadd"] | None, + dims: int | InputDimsLike, axis: int = -1, -) -> Tuple[Callable, str]: +) -> tuple[Callable, str]: """Choose convolution function Choose and return the function handle to be used for convolution @@ -32,7 +33,8 @@ def _choose_convfunc( if method is None: method = "direct" if method not in ("direct", "fft"): - raise ValueError("method must be direct or fft") + msg = "`method` must be direct or fft" + raise ValueError(msg) convfunc = get_convolve(x) else: if method is None: @@ -42,7 +44,8 @@ def _choose_convfunc( elif method == "overlapadd": convfunc = partial(get_oaconvolve(x), axes=axis)(x) else: - raise ValueError("method must be fft or overlapadd") + msg = "`method` must be fft or overlapadd" + raise ValueError(msg) return convfunc, method @@ -58,11 +61,11 @@ class _Convolve1Dshort(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, h: NDArray, offset: int = 0, axis: int = -1, - method: Optional[Literal["direct", "fft", "overlapadd"]] = None, + method: Literal["direct", "fft", "overlapadd"] | None = None, dtype: DTypeLike = "float64", name: str = "C", ) -> None: @@ -72,7 +75,8 @@ def __init__( self.axis = axis self.nh = h.size if h.ndim == 1 else h.shape[axis] if offset > self.nh - 1: - raise ValueError("offset must be smaller than h.shape[axis] - 1") + msg = "`offset` must be smaller than h.shape[axis] - 1" + raise ValueError(msg) self.h = h self.offset = 2 * (self.nh // 2 - int(offset)) if self.nh % 2 == 0: @@ -119,11 +123,11 @@ class _Convolve1Dlong(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, h: NDArray, offset: int = 0, axis: int = -1, - method: Optional[Literal["direct", "fft", "overlapadd"]] = None, + method: Literal["direct", "fft", "overlapadd"] | None = None, dtype: DTypeLike = "float64", name: str = "C", ) -> None: @@ -135,7 +139,8 @@ def __init__( # create filter self.axis = axis if offset > self.dims[self.axis] - 1: - raise ValueError("offset must be smaller than self.dims[self.axis] - 1") + msg = "`offset` must be smaller than dims[axis] - 1" + raise ValueError(msg) self.nh = h.size if h.ndim == 1 else h.shape[axis] self.h = h self.offset = 2 * (self.dims[self.axis] // 2 - int(offset)) @@ -304,11 +309,11 @@ class Convolve1D(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, h: NDArray, offset: int = 0, axis: int = -1, - method: Optional[Literal["direct", "fft", "overlapadd"]] = None, + method: Literal["direct", "fft", "overlapadd"] | None = None, dtype: DTypeLike = "float64", name: str = "C", ) -> None: diff --git a/pylops/signalprocessing/convolve2d.py b/pylops/signalprocessing/convolve2d.py index 921639dea..8b6224121 100644 --- a/pylops/signalprocessing/convolve2d.py +++ b/pylops/signalprocessing/convolve2d.py @@ -1,6 +1,6 @@ __all__ = ["Convolve2D"] -from typing import Literal, Optional, Union +from typing import Literal from pylops.signalprocessing import ConvolveND from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray @@ -96,16 +96,17 @@ class Convolve2D(ConvolveND): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, h: NDArray, offset: InputDimsLike = (0, 0), axes: InputDimsLike = (-2, -1), - method: Optional[Literal["auto", "direct", "fft"]] = "fft", + method: Literal["auto", "direct", "fft"] | None = "fft", dtype: DTypeLike = "float64", name: str = "C", ): if h.ndim != 2: - raise ValueError("h must be 2-dimensional") + msg = "`h` must be 2-dimensional" + raise ValueError(msg) super().__init__( dims=dims, h=h, diff --git a/pylops/signalprocessing/convolvend.py b/pylops/signalprocessing/convolvend.py index 82905dab4..490d7f41f 100644 --- a/pylops/signalprocessing/convolvend.py +++ b/pylops/signalprocessing/convolvend.py @@ -1,6 +1,6 @@ __all__ = ["ConvolveND"] -from typing import Literal, Optional, Union +from typing import Literal import numpy as np @@ -75,11 +75,11 @@ class ConvolveND(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, h: NDArray, - offset: Optional[InputDimsLike] = None, + offset: InputDimsLike | None = None, axes: InputDimsLike = (-2, -1), - method: Optional[Literal["auto", "direct", "fft"]] = "fft", + method: Literal["auto", "direct", "fft"] | None = "fft", dtype: DTypeLike = "float64", name: str = "C", ): diff --git a/pylops/signalprocessing/dct.py b/pylops/signalprocessing/dct.py index fca5c15ba..688995dad 100644 --- a/pylops/signalprocessing/dct.py +++ b/pylops/signalprocessing/dct.py @@ -1,6 +1,6 @@ __all__ = ["DCT"] -from typing import List, Literal, Optional, Union +from typing import Literal import numpy as np from scipy import fft @@ -69,16 +69,16 @@ class DCT(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, type: Literal[1, 2, 3, 4] = 2, - axes: Union[int, List[int]] = None, + axes: int | list[int] = None, dtype: DTypeLike = "float64", - workers: Optional[int] = None, + workers: int | None = None, name: str = "C", ) -> None: - if type > 4 or type < 1: - raise ValueError("wrong type value, it can only be 1, 2, 3 or 4") + msg = f"Wrong `type`, it can only be 1, 2, 3 or 4, got {type}" + raise ValueError(msg) self.type = type self.axes = axes self.workers = workers diff --git a/pylops/signalprocessing/dtcwt.py b/pylops/signalprocessing/dtcwt.py index 13f1d7298..6e9a24fa6 100644 --- a/pylops/signalprocessing/dtcwt.py +++ b/pylops/signalprocessing/dtcwt.py @@ -1,6 +1,6 @@ __all__ = ["DTCWT"] -from typing import Any, NewType, Union +from typing import Any, NewType import numpy as np @@ -97,7 +97,7 @@ class DTCWT(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, biort: str = "near_sym_a", qshift: str = "qshift_a", level: int = 3, @@ -152,7 +152,7 @@ def __init__( def _interpret_coeffs( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int, ) -> None: x = np.ones(dims[axis]) diff --git a/pylops/signalprocessing/dwt.py b/pylops/signalprocessing/dwt.py index e162fc46d..e92058614 100644 --- a/pylops/signalprocessing/dwt.py +++ b/pylops/signalprocessing/dwt.py @@ -1,7 +1,6 @@ __all__ = ["DWT"] from math import ceil, log -from typing import Union import numpy as np @@ -21,7 +20,8 @@ def _checkwavelet(wavelet: str) -> None: """Check that wavelet belongs to pywt.wavelist""" wavelist = pywt.wavelist(kind="discrete") if wavelet not in wavelist: - raise ValueError(f"'{wavelet}' not in family set = {wavelist}") + msg = f"'{wavelet}' not in family set = {wavelist}" + raise ValueError(msg) def _adjointwavelet(wavelet: str) -> str: @@ -108,7 +108,7 @@ class DWT(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, wavelet: str = "haar", level: int = 1, diff --git a/pylops/signalprocessing/fft.py b/pylops/signalprocessing/fft.py index 96bc1f22e..fb234dccf 100644 --- a/pylops/signalprocessing/fft.py +++ b/pylops/signalprocessing/fft.py @@ -2,7 +2,6 @@ import logging import warnings -from typing import Optional, Union import numpy as np import scipy.fft @@ -38,9 +37,9 @@ class _FFT_numpy(_BaseFFT): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: Tfftnorm = "ortho", real: bool = False, @@ -62,7 +61,9 @@ def __init__( ) if self.cdtype != np.complex128: warnings.warn( - f"numpy backend always returns complex128 dtype. To respect the passed dtype, data will be casted to {self.cdtype}." + "numpy backend always returns complex128 dtype. To respect the " + f"passed dtype, data will be casted to {self.cdtype}.", + stacklevel=2, ) self._kwargs_fft = kwargs_fft @@ -150,9 +151,9 @@ class _FFT_scipy(_BaseFFT): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: Tfftnorm = "ortho", real: bool = False, @@ -248,9 +249,9 @@ class _FFT_fftw(_BaseFFT): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: Tfftnorm = "ortho", real: bool = False, @@ -261,7 +262,8 @@ def __init__( ) -> None: if np.dtype(dtype) == np.float16: warnings.warn( - "fftw backend is unavailable with float16 dtype. Will use float32." + "fftw backend is unavailable with float16 dtype. Will use float32.", + stacklevel=2, ) dtype = np.float32 @@ -270,7 +272,8 @@ def __init__( if badop == "ortho" and norm == "ortho": continue warnings.warn( - f"FFTW option '{badop}' will be overwritten by norm={norm}" + f"FFTW option '{badop}' will be overwritten by norm={norm}", + stacklevel=2, ) del kwargs_fft[badop] @@ -287,7 +290,9 @@ def __init__( ) if self.cdtype != np.complex128: warnings.warn( - f"fftw backend returns complex128 dtype. To respect the passed dtype, data will be cast to {self.cdtype}." + "fftw backend returns complex128 dtype. To respect the " + f"passed dtype, data will be cast to {self.cdtype}.", + stacklevel=2, ) dims_t = list(self.dims) @@ -409,9 +414,9 @@ class _FFT_mklfft(_BaseFFT): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: Tfftnorm = "ortho", real: bool = False, @@ -497,9 +502,9 @@ def __truediv__(self, y: NDArray) -> NDArray: def FFT( - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, axis: int = -1, - nfft: Optional[int] = None, + nfft: int | None = None, sampling: float = 1.0, norm: Tfftnorm = "ortho", real: bool = False, @@ -725,6 +730,7 @@ def FFT( **kwargs_fft, ) else: - raise ValueError("engine must be numpy, scipy, fftw, or mkl_fft") + msg = "`engine` must be numpy, scipy, fftw, or mkl_fft" + raise ValueError(msg) f.name = name return f diff --git a/pylops/signalprocessing/fft2d.py b/pylops/signalprocessing/fft2d.py index 6a8dd301f..e05d6d213 100644 --- a/pylops/signalprocessing/fft2d.py +++ b/pylops/signalprocessing/fft2d.py @@ -1,7 +1,8 @@ __all__ = ["FFT2D"] import warnings -from typing import Dict, Literal, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Literal import numpy as np import scipy.fft @@ -27,8 +28,8 @@ def __init__( self, dims: InputDimsLike, axes: InputDimsLike = (-2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -49,20 +50,24 @@ def __init__( ) if self.cdtype != np.complex128: warnings.warn( - f"numpy backend always returns complex128 dtype. To respect the passed dtype, data will be casted to {self.cdtype}." + "numpy backend always returns complex128 dtype. To respect the " + f"passed dtype, data will be casted to {self.cdtype}.", + stacklevel=2, ) # checks if self.ndim < 2: - raise ValueError("FFT2D requires at least two input dimensions") + msg = "FFT2D requires at least two input dimensions" + raise ValueError(msg) if self.naxes != 2: - raise ValueError("FFT2D must be applied along exactly two dimensions") + msg = "FFT2D must be applied along exactly two dimensions" + raise ValueError(msg) self.f1, self.f2 = self.fs del self.fs self._kwargs_fft = kwargs_fft - self._norm_kwargs: Dict[str, Union[None, str]] = { + self._norm_kwargs: dict[str, None | str] = { "norm": None } # equivalent to "backward" in Numpy/Scipy if self.norm is _FFTNorms.ORTHO: @@ -144,8 +149,8 @@ def __init__( self, dims: InputDimsLike, axes: InputDimsLike = (-2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -167,15 +172,17 @@ def __init__( # checks if self.ndim < 2: - raise ValueError("FFT2D requires at least two input dimensions") + msg = "FFT2D requires at least two input dimensions" + raise ValueError(msg) if self.naxes != 2: - raise ValueError("FFT2D must be applied along exactly two dimensions") + msg = "FFT2D must be applied along exactly two dimensions" + raise ValueError(msg) self.f1, self.f2 = self.fs del self.fs self._kwargs_fft = kwargs_fft - self._norm_kwargs: Dict[str, Union[None, str]] = { + self._norm_kwargs: dict[str, None | str] = { "norm": None } # equivalent to "backward" in Numpy/Scipy if self.norm is _FFTNorms.ORTHO: @@ -249,8 +256,8 @@ def __init__( self, dims: InputDimsLike, axes: InputDimsLike = (-2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -272,15 +279,17 @@ def __init__( # checks if self.ndim < 2: - raise ValueError("FFT2D requires at least two input dimensions") + msg = "FFT2D requires at least two input dimensions" + raise ValueError(msg) if self.naxes != 2: - raise ValueError("FFT2D must be applied along exactly two dimensions") + msg = "FFT2D must be applied along exactly two dimensions" + raise ValueError(msg) self.f1, self.f2 = self.fs del self.fs self._kwargs_fft = kwargs_fft - self._norm_kwargs: Dict[str, Union[None, str]] = {"norm": None} + self._norm_kwargs: dict[str, None | str] = {"norm": None} if self.norm is _FFTNorms.ORTHO: self._norm_kwargs["norm"] = "ortho" self._scale = np.sqrt(1 / np.prod(np.sqrt(self.nffts))) @@ -357,8 +366,8 @@ def __truediv__(self, y: NDArray) -> NDArray: def FFT2D( dims: InputDimsLike, axes: InputDimsLike = (-2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -575,6 +584,7 @@ def FFT2D( **kwargs_fft, ) else: - raise ValueError("engine must be numpy, scipy or mkl_fft") + msg = "`engine` must be numpy, scipy or mkl_fft" + raise ValueError(msg) f.name = name return f diff --git a/pylops/signalprocessing/fftnd.py b/pylops/signalprocessing/fftnd.py index a13b8730a..980ffa116 100644 --- a/pylops/signalprocessing/fftnd.py +++ b/pylops/signalprocessing/fftnd.py @@ -1,7 +1,8 @@ __all__ = ["FFTND"] import warnings -from typing import Literal, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Literal import numpy as np import scipy.fft @@ -25,10 +26,10 @@ class _FFTND_numpy(_BaseFFTND): def __init__( self, - dims: Union[int, InputDimsLike], - axes: Union[int, InputDimsLike] = (-3, -2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + dims: int | InputDimsLike, + axes: int | InputDimsLike = (-3, -2, -1), + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -49,7 +50,9 @@ def __init__( ) if self.cdtype != np.complex128: warnings.warn( - f"numpy backend always returns complex128 dtype. To respect the passed dtype, data will be cast to {self.cdtype}." + "numpy backend always returns complex128 dtype. " + "To respect the passed dtype, data will be cast to {self.cdtype}.", + stacklevel=2, ) self._kwargs_fft = kwargs_fft @@ -107,7 +110,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: ) if self.norm is _FFTNorms.NONE: y *= self._scale - for ax, nfft in zip(self.axes, self.nffts): + for ax, nfft in zip(self.axes, self.nffts, strict=True): if nfft > self.dims[ax]: y = ncp.take(y, np.arange(self.dims[ax]), axis=ax) if self.doifftpad: @@ -130,10 +133,10 @@ class _FFTND_scipy(_BaseFFTND): def __init__( self, - dims: Union[int, InputDimsLike], - axes: Union[int, InputDimsLike] = (-3, -2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + dims: int | InputDimsLike, + axes: int | InputDimsLike = (-3, -2, -1), + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -206,7 +209,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: ) if self.norm is _FFTNorms.NONE: y *= self._scale - for ax, nfft in zip(self.axes, self.nffts): + for ax, nfft in zip(self.axes, self.nffts, strict=True): if nfft > self.dims[ax]: y = np.take(y, range(self.dims[ax]), axis=ax) if self.doifftpad: @@ -228,10 +231,10 @@ class _FFTND_mklfft(_BaseFFTND): def __init__( self, - dims: Union[int, InputDimsLike], - axes: Union[int, InputDimsLike] = (-3, -2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + dims: int | InputDimsLike, + axes: int | InputDimsLike = (-3, -2, -1), + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -306,7 +309,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: ) if self.norm is _FFTNorms.NONE: y *= self._scale - for ax, nfft in zip(self.axes, self.nffts): + for ax, nfft in zip(self.axes, self.nffts, strict=True): if nfft > self.dims[ax]: y = np.take(y, range(self.dims[ax]), axis=ax) if self.doifftpad: @@ -324,10 +327,10 @@ def __truediv__(self, y: NDArray) -> NDArray: def FFTND( - dims: Union[int, InputDimsLike], - axes: Union[int, InputDimsLike] = (-3, -2, -1), - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Union[float, Sequence[float]] = 1.0, + dims: int | InputDimsLike, + axes: int | InputDimsLike = (-3, -2, -1), + nffts: int | InputDimsLike | None = None, + sampling: float | Sequence[float] = 1.0, norm: Tfftnorm = "ortho", real: bool = False, ifftshift_before: bool = False, @@ -554,6 +557,7 @@ def FFTND( **kwargs_fft, ) else: - raise ValueError("engine must be numpy, scipy or mkl_fft") + msg = "`engine` must be numpy, scipy or mkl_fft" + raise ValueError(msg) f.name = name return f diff --git a/pylops/signalprocessing/fourierradon2d.py b/pylops/signalprocessing/fourierradon2d.py index 0139febca..6ce538257 100755 --- a/pylops/signalprocessing/fourierradon2d.py +++ b/pylops/signalprocessing/fourierradon2d.py @@ -1,6 +1,6 @@ __all__ = ["FourierRadon2D"] -from typing import Literal, Optional, Tuple +from typing import Literal import numpy as np import scipy as sp @@ -145,16 +145,17 @@ def __init__( haxis: NDArray, pxaxis: NDArray, nfft: int, - flims: Optional[Tuple[int, int]] = None, + flims: tuple[int, int] | None = None, kind: Literal["linear", "parabolic"] = "linear", engine: Tengine_nnc = "numpy", - num_threads_per_blocks: Tuple[int, int] = (32, 32), + num_threads_per_blocks: tuple[int, int] = (32, 32), dtype: DTypeLike = "float64", name: str = "R", ) -> None: # engine if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = "`engine` must be numpy or numba or cuda" + raise ValueError(msg) if engine == "numba" and jit_message is not None: engine = "numpy" diff --git a/pylops/signalprocessing/fourierradon3d.py b/pylops/signalprocessing/fourierradon3d.py index d895c90cd..18453d7c2 100755 --- a/pylops/signalprocessing/fourierradon3d.py +++ b/pylops/signalprocessing/fourierradon3d.py @@ -1,6 +1,6 @@ __all__ = ["FourierRadon3D"] -from typing import Literal, Optional, Tuple +from typing import Literal import numpy as np import scipy as sp @@ -165,25 +165,27 @@ def __init__( pyaxis: NDArray, pxaxis: NDArray, nfft: int, - flims: Optional[Tuple[int, int]] = None, - kind: Tuple[Literal["linear", "parabolic"], Literal["linear", "parabolic"]] = ( + flims: tuple[int, int] | None = None, + kind: tuple[Literal["linear", "parabolic"], Literal["linear", "parabolic"]] = ( "linear", "linear", ), engine: Tengine_nnc = "numpy", - num_threads_per_blocks: Tuple[int, int, int] = (2, 16, 16), + num_threads_per_blocks: tuple[int, int, int] = (2, 16, 16), dtype: DTypeLike = "float64", name: str = "R", ) -> None: # engine if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = "`engine` must be numpy or numba or cuda" + raise ValueError(msg) if engine == "numba" and jit_message is not None: engine = "numpy" # kind if len(kind) != 2: - raise ValueError("kind must be a tuple of two elements") + msg = "kind must be a tuple of two elements" + raise ValueError(msg) # dimensions and super dims = len(pyaxis), len(pxaxis), len(taxis) diff --git a/pylops/signalprocessing/interp.py b/pylops/signalprocessing/interp.py index a22060b83..4e5654442 100644 --- a/pylops/signalprocessing/interp.py +++ b/pylops/signalprocessing/interp.py @@ -1,6 +1,6 @@ __all__ = ["Interp"] -from typing import Literal, Tuple, Union +from typing import Literal import numpy as np @@ -17,7 +17,7 @@ def _nearestinterp( - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, iava: SamplingLike, axis: int = -1, dtype: DTypeLike = "float64", @@ -99,13 +99,13 @@ def _sincinterp( def Interp( - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, iava: SamplingLike, axis: int = -1, kind: Literal["linear", "nearest", "sinc", "cubic_spline"] = "linear", dtype: DTypeLike = "float64", name: str = "I", -) -> Tuple[LinearOperator, IntNDArray]: +) -> tuple[LinearOperator, IntNDArray]: r"""Interpolation operator. Apply interpolation along ``axis`` from regularly sampled input @@ -223,7 +223,8 @@ def Interp( kind = kind.lower() # type: ignore if kind not in {"nearest", "linear", "sinc", "cubic_spline"}: - raise NotImplementedError(f"{kind} interpolation could not be found.") + msg = f"{kind} interpolation could not be found." + raise NotImplementedError(msg) dims = _value_or_sized_to_tuple(dims) diff --git a/pylops/signalprocessing/interpspline.py b/pylops/signalprocessing/interpspline.py index 6ee7a13bf..8407bb3f3 100644 --- a/pylops/signalprocessing/interpspline.py +++ b/pylops/signalprocessing/interpspline.py @@ -2,9 +2,10 @@ "InterpCubicSpline", ] +from collections.abc import Callable from dataclasses import dataclass from functools import cached_property, partial -from typing import Callable, Final, Literal, Tuple, Union +from typing import Final, Literal import numpy as np from scipy.linalg import get_lapack_funcs @@ -137,10 +138,11 @@ def __post_init__(self) -> None: for which in ("main", "super", "sub"): ndim = getattr(self, f"{which}_diagonal").ndim if ndim != 1: - raise ValueError( - f"Expected {which} diagonal to be a 1-dimensional Array, but it is " - f"{ndim}-dimensional." + msg = ( + f"Expected {which} diagonal to be a 1-dimensional Array, " + f"but it is{ndim}-dimensional." ) + raise ValueError(msg) main_diagonal_dtype = self.main_diagonal.dtype.type main_diagonal_size = self.main_diagonal.size @@ -150,18 +152,20 @@ def __post_init__(self) -> None: size = diag.size if dtype != main_diagonal_dtype: - raise TypeError( + msg = ( f"Expected {which} diagonal to have the same dtype as the main " - f"diagonal, but its dtype is {repr(dtype)} and the main diagonal " - f"has dtype {repr(main_diagonal_dtype)}." + f"diagonal, but its dtype is {repr(dtype)} and that of the main " + f"diagonal is {repr(main_diagonal_dtype)}." ) + raise TypeError(msg) if size != main_diagonal_size - 1: - raise ValueError( + msg = ( f"Expected {which} diagonal to have 1 entry less than the main " f"diagonal, but it has {size} entries and the main diagonal has " f"{main_diagonal_size} entries." ) + raise ValueError(msg) return @@ -251,7 +255,11 @@ def from_tridiagonal_matrix( banded_representation[2, ::] = matrix.main_diagonal banded_representation[3, 0:-1] = matrix.sub_diagonal - (lu_banded, pivot_indices, info,) = lapack_factorizer( + ( + lu_banded, + pivot_indices, + info, + ) = lapack_factorizer( ab=banded_representation, kl=1, ku=1, @@ -265,9 +273,8 @@ def from_tridiagonal_matrix( num_super=1, ) - raise np.linalg.LinAlgError( - f"Could not LU-factorize tridiagonal matrix! Got {info=}." - ) + msg = f"Could not LU-factorize tridiagonal matrix! Got {info=}." + raise np.linalg.LinAlgError(msg) def solve( self, @@ -311,9 +318,8 @@ def solve( if info == 0: return x - raise np.linalg.LinAlgError( - f"Could not solve LU-factorization of tridiagonal matrix! Got {info=}." - ) + msg = f"Could not solve LU-factorization of tridiagonal matrix! Got {info=}." + raise np.linalg.LinAlgError(msg) @dataclass @@ -387,10 +393,8 @@ def from_tridiagonal_matrix( pivot_indices=pivot_indices, ) - raise np.linalg.LinAlgError( - f"Could not LU-factorize tridiagonal matrix! Got {info=}." - f"Could not LU-factorize tridiagonal matrix! Got {info=}." - ) + msg = f"Could not LU-factorize tridiagonal matrix! Got {info=}." + raise np.linalg.LinAlgError(msg) def solve( self, @@ -428,9 +432,8 @@ def solve( if info == 0: return x - raise np.linalg.LinAlgError( - f"Could not solve LU-factorization of tridiagonal matrix! Got {info=}." - ) + msg = f"Could not solve LU-factorization of tridiagonal matrix! Got {info=}." + raise np.linalg.LinAlgError(msg) def _make_cubic_spline_left_hand_side( @@ -797,14 +800,13 @@ class InterpCubicSpline(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, iava: SamplingLike, bc_type: Literal["natural"] = "natural", axis: int = -1, dtype: DTypeLike = "float64", name: str = "S", ) -> None: - # Input Validation and Standardization dims = _value_or_sized_to_tuple(dims) @@ -812,10 +814,8 @@ def __init__( num_cols = dims[axis] if num_cols < 2: - raise ValueError( - f"A cubic spline requires at least 2 data points to interpolate, but " - f"got {dims[axis]=}." - ) + msg = f"A cubic spline requires at least 2 data points to interpolate, but got {dims[axis]=}." + raise ValueError(msg) iava = np.asarray(iava, dtype=np.float64) _clip_iava_above_last_sample_index(iava=iava, sample_size=num_cols) @@ -824,17 +824,19 @@ def __init__( self.bc_type = bc_type.lower() else: - raise NotImplementedError( - f"Cubic spline interpolation currently only supports 'natural' " - f"boundaries, but got {bc_type=}" + msg = ( + "Cubic spline interpolation currently only supports 'natural' " + f"boundaries, but got {bc_type=}." ) + raise NotImplementedError(msg) dtype = np.dtype(dtype) if dtype.type not in {np.float64, np.complex128}: - raise TypeError( - f"Expected dtype fo cubic spline interpolator to be either float64 or " + msg = ( + "Expected dtype fo cubic spline interpolator to be either float64 or " f"complex128 to achieve the required accuracy, but got {dtype}." ) + raise TypeError(msg) # Operator Initialization @@ -842,8 +844,8 @@ def __init__( dimsd[axis] = len(iava) dimsd = tuple(dimsd) - self.dims: Tuple = dims - self.dimsd: Tuple = dimsd + self.dims: tuple = dims + self.dimsd: tuple = dimsd self.iava: FloatingNDArray = iava self.axis: int = axis @@ -943,7 +945,6 @@ def _matvec(self, x: InexactNDArray) -> InexactNDArray: @reshaped(swapaxis=True, axis=0) def _rmatvec(self, x: InexactNDArray) -> InexactNDArray: - x_mod = self._P_matrix_transposed @ x.reshape(x.shape[0], -1) return ( diff --git a/pylops/signalprocessing/nonstatconvolve1d.py b/pylops/signalprocessing/nonstatconvolve1d.py index 89c5385f1..ba896d975 100644 --- a/pylops/signalprocessing/nonstatconvolve1d.py +++ b/pylops/signalprocessing/nonstatconvolve1d.py @@ -3,7 +3,6 @@ "NonStationaryFilters1D", ] -from typing import Union import numpy as np @@ -108,7 +107,7 @@ class NonStationaryConvolve1D(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, hs: NDArray, ih: InputDimsLike, axis: int = -1, @@ -116,16 +115,18 @@ def __init__( name: str = "C", ) -> None: if hs.shape[1] % 2 == 0: - raise ValueError("filters hs must have odd length") + msg = "The filters `hs` must have odd length" + raise ValueError(msg) if len(np.unique(np.diff(ih))) > 1: - raise ValueError( - "the indices of filters 'ih' are must be regularly sampled" - ) + msg = "The indices `ih` of the filters must be regularly sampled." + raise ValueError(msg) dims = _value_or_sized_to_tuple(dims) if min(ih) < 0 or max(ih) >= dims[axis]: - raise ValueError( - "the indices of filters 'ih' must be larger than 0 and smaller than `dims`" + msg = ( + "The indices `ih` of the filters " + "must be larger than 0 and smaller than `dims`." ) + raise ValueError(msg) self.hs = hs self.hsize = hs.shape[1] self.oh, self.dh, self.nh, self.eh = ih[0], ih[1] - ih[0], len(ih), ih[-1] @@ -327,15 +328,17 @@ def __init__( name: str = "C", ) -> None: if hsize % 2 == 0: - raise ValueError("filters hs must have odd length") + msg = "The filters `hs` must have odd length" + raise ValueError(msg) if len(np.unique(np.diff(ih))) > 1: - raise ValueError( - "the indices of filters 'ih' are must be regularly sampled" - ) + msg = "The indices `ih` of the filters must be regularly sampled." + raise ValueError(msg) if min(ih) < 0 or max(ih) >= inp.size: - raise ValueError( - "the indices of filters 'ih' must be larger than 0 and smaller than `dims`" + msg = ( + "The indices `ihx` and `ihz` of the filters " + "must be larger than 0 and smaller than `dims`." ) + raise ValueError(msg) self.inp = inp self.hsize = hsize self.oh, self.dh, self.nh, self.eh = ih[0], ih[1] - ih[0], len(ih), ih[-1] diff --git a/pylops/signalprocessing/nonstatconvolve2d.py b/pylops/signalprocessing/nonstatconvolve2d.py index b691bcdee..500e1ba8a 100644 --- a/pylops/signalprocessing/nonstatconvolve2d.py +++ b/pylops/signalprocessing/nonstatconvolve2d.py @@ -4,7 +4,6 @@ ] import os -from typing import Tuple, Union import numpy as np @@ -149,27 +148,32 @@ class NonStationaryConvolve2D(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, hs: NDArray, ihx: InputDimsLike, ihz: InputDimsLike, engine: Tengine_nnc = "numpy", - num_threads_per_blocks: Tuple[int, int] = (32, 32), + num_threads_per_blocks: tuple[int, int] = (32, 32), dtype: DTypeLike = "float64", name: str = "C", ) -> None: if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = "`engine` must be numpy or numba or cuda" + raise ValueError(msg) if hs.shape[2] % 2 == 0 or hs.shape[3] % 2 == 0: - raise ValueError("filters hs must have odd length") + msg = "The filters `hs` must have odd length" + raise ValueError(msg) if len(np.unique(np.diff(ihx))) > 1 or len(np.unique(np.diff(ihz))) > 1: - raise ValueError( - "the indices of filters 'ih' are must be regularly sampled" + msg = ( + "The indices `ihx` and `ihz` of the filters must be regularly sampled." ) + raise ValueError(msg) if min(ihx) < 0 or min(ihz) < 0 or max(ihx) >= dims[0] or max(ihz) >= dims[1]: - raise ValueError( - "the indices of filters 'ih' must be larger than 0 and smaller than `dims`" + msg = ( + "The indices `ihx` and `ihz` of the filters " + "must be larger than 0 and smaller than `dims`." ) + raise ValueError(msg) self.hs = hs self.hshape = hs.shape[2:] self.ohx, self.dhx, self.nhx = ihx[0], ihx[1] - ihx[0], len(ihx) @@ -207,8 +211,8 @@ def _matvec_rmatvec( x: NDArray, y: NDArray, hs: NDArray, - hshape: Tuple[int, int], - xdims: Tuple[int, int], + hshape: tuple[int, int], + xdims: tuple[int, int], ohx: float, ohz: float, dhx: float, @@ -305,7 +309,7 @@ def _matvec(self, x: NDArray) -> NDArray: self.nhx, self.nhz, rmatvec=False, - **self.kwargs_cuda + **self.kwargs_cuda, ) return y @@ -326,7 +330,7 @@ def _rmatvec(self, x: NDArray) -> NDArray: self.nhx, self.nhz, rmatvec=True, - **self.kwargs_cuda + **self.kwargs_cuda, ) return y @@ -393,27 +397,32 @@ def __init__( ihx: InputDimsLike, ihz: InputDimsLike, engine: str = "numpy", - num_threads_per_blocks: Tuple[int, int] = (32, 32), + num_threads_per_blocks: tuple[int, int] = (32, 32), dtype: DTypeLike = "float64", name: str = "C", ) -> None: if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = "`engine` must be numpy or numba or cuda" + raise ValueError(msg) if hshape[0] % 2 == 0 or hshape[1] % 2 == 0: - raise ValueError("filters hs must have odd length") + msg = "The filters `hs` must have odd length" + raise ValueError(msg) if len(np.unique(np.diff(ihx))) > 1 or len(np.unique(np.diff(ihz))) > 1: - raise ValueError( - "the indices of filters 'ih' are must be regularly sampled" + msg = ( + "The indices `ihx` and `ihz` of the filters must be regularly sampled." ) + raise ValueError(msg) if ( min(ihx) < 0 or min(ihz) < 0 or max(ihx) >= inp.shape[0] or max(ihz) >= inp.shape[1] ): - raise ValueError( - "the indices of filters 'ih' must be larger than 0 and smaller than `dims`" + msg = ( + "The indices `ihx` and `ihz` of the filters " + "must be larger than 0 and smaller than `dims`." ) + raise ValueError(msg) self.inp = inp self.inpdims = inp.shape self.hshape = hshape @@ -448,7 +457,8 @@ def _register_multiplications(self, engine: str) -> None: self._mv = jit(**numba_opts)(self.__matvec) self._rmv = jit(**numba_opts)(self.__rmatvec) elif engine == "cuda": - raise NotImplementedError("engine=cuda is currently not available") + msg = "`engine=cuda` is currently not available for NonStationaryFilters2D" + raise NotImplementedError(msg) else: self._mv = self.__matvec self._rmv = self.__rmatvec @@ -461,8 +471,8 @@ def __rmatvec( x: NDArray, y: NDArray, hs: NDArray, - hshape: Tuple[int, int], - xdims: Tuple[int, int], + hshape: tuple[int, int], + xdims: tuple[int, int], ohx: float, ohz: float, dhx: float, @@ -477,7 +487,6 @@ def __rmatvec( hstmp = np.zeros((xdims[0], *hs.shape)) for ix in prange(xdims[0]): for iz in range(xdims[1]): - # find extremes of model where to apply h (in case h is going out of model) xextremes = ( max(0, ix - hshape[0] // 2), @@ -533,36 +542,28 @@ def __rmatvec( ihz_t, hxextremes[0] : hxextremes[1], hzextremes[0] : hzextremes[1], - ] += ( - dhz_t * dhx_l * htmp - ) + ] += dhz_t * dhx_l * htmp hstmp[ ix, ihx_l, ihz_b, hxextremes[0] : hxextremes[1], hzextremes[0] : hzextremes[1], - ] += ( - dhz_b * dhx_l * htmp - ) + ] += dhz_b * dhx_l * htmp hstmp[ ix, ihx_r, ihz_t, hxextremes[0] : hxextremes[1], hzextremes[0] : hzextremes[1], - ] += ( - dhz_t * dhx_r * htmp - ) + ] += dhz_t * dhx_r * htmp hstmp[ ix, ihx_r, ihz_b, hxextremes[0] : hxextremes[1], hzextremes[0] : hzextremes[1], - ] += ( - dhz_b * dhx_r * htmp - ) + ] += dhz_b * dhx_r * htmp hs = hstmp.sum(axis=0) return hs @@ -582,7 +583,7 @@ def _matvec(self, x: NDArray) -> NDArray: self.dhz, self.nhx, self.nhz, - **self.kwargs_cuda + **self.kwargs_cuda, ) return y @@ -602,6 +603,6 @@ def _rmatvec(self, x: NDArray) -> NDArray: self.dhz, self.nhx, self.nhz, - **self.kwargs_cuda + **self.kwargs_cuda, ) return hs diff --git a/pylops/signalprocessing/nonstatconvolve3d.py b/pylops/signalprocessing/nonstatconvolve3d.py index 44ad8cf9e..19722c1fa 100644 --- a/pylops/signalprocessing/nonstatconvolve3d.py +++ b/pylops/signalprocessing/nonstatconvolve3d.py @@ -1,7 +1,6 @@ __all__ = ["NonStationaryConvolve3D"] import os -from typing import Tuple, Union import numpy as np @@ -126,29 +125,32 @@ class NonStationaryConvolve3D(LinearOperator): def __init__( self, - dims: Union[int, InputDimsLike], + dims: int | InputDimsLike, hs: NDArray, ihx: InputDimsLike, ihy: InputDimsLike, ihz: InputDimsLike, engine: Tengine_nnc = "numpy", - num_threads_per_blocks: Tuple[int, int, int] = (2, 16, 16), + num_threads_per_blocks: tuple[int, int, int] = (2, 16, 16), dtype: DTypeLike = "float64", name: str = "C", ) -> None: if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = "`engine` must be numpy or numba or cuda" + raise ValueError(msg) if hs.shape[3] % 2 == 0 or hs.shape[4] % 2 == 0 or hs.shape[5] % 2 == 0: - raise ValueError("filters hs must have odd length") + msg = "The filters `hs` must have odd length" + raise ValueError(msg) if ( len(np.unique(np.diff(ihx))) > 1 or len(np.unique(np.diff(ihy))) > 1 or len(np.unique(np.diff(ihz))) > 1 ): - raise ValueError( - "the indices of filters 'ih' are must be regularly sampled" + msg = ( + "The indices `ihx`, `ihy`, and `ihz` of the filters " + "must be regularly sampled." ) - + raise ValueError(msg) if ( min(ihx) < 0 or min(ihy) < 0 @@ -157,9 +159,12 @@ def __init__( or max(ihy) >= dims[1] or max(ihz) >= dims[2] ): - raise ValueError( - "the indices of filters 'ih' must be larger than 0 and smaller than `dims`" + msg = ( + "The indices `ihx`, `ihy`, and `ihz` of the filters " + "must be larger than 0 and smaller than `dims`." ) + raise ValueError(msg) + self.hs = hs self.hshape = hs.shape[3:] self.ohx, self.dhx, self.nhx = ihx[0], ihx[1] - ihx[0], len(ihx) @@ -200,8 +205,8 @@ def _matvec_rmatvec( x: NDArray, y: NDArray, hs: NDArray, - hshape: Tuple[int, int, int], - xdims: Tuple[int, int, int], + hshape: tuple[int, int, int], + xdims: tuple[int, int, int], ohx: float, ohy: float, ohz: float, @@ -364,7 +369,7 @@ def _matvec(self, x: NDArray) -> NDArray: self.nhy, self.nhz, rmatvec=False, - **self.kwargs_cuda + **self.kwargs_cuda, ) return y @@ -388,6 +393,6 @@ def _rmatvec(self, x: NDArray) -> NDArray: self.nhy, self.nhz, rmatvec=True, - **self.kwargs_cuda + **self.kwargs_cuda, ) return y diff --git a/pylops/signalprocessing/patch2d.py b/pylops/signalprocessing/patch2d.py index 1a8482ad3..8f36769f9 100644 --- a/pylops/signalprocessing/patch2d.py +++ b/pylops/signalprocessing/patch2d.py @@ -4,7 +4,7 @@ ] import logging -from typing import Optional, Sequence, Tuple +from collections.abc import Sequence import numpy as np @@ -25,15 +25,15 @@ def patch2d_design( dimsd: InputDimsLike, - nwin: Tuple[int, int], - nover: Tuple[int, int], - nop: Tuple[int, int], + nwin: tuple[int, int], + nover: tuple[int, int], + nop: tuple[int, int], verb: bool = True, -) -> Tuple[ - Tuple[int, int], - Tuple[int, int], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], +) -> tuple[ + tuple[int, int], + tuple[int, int], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]], ]: """Design Patch2D operator @@ -196,17 +196,16 @@ def __init__( Op: LinearOperator, dims: InputDimsLike, dimsd: InputDimsLike, - nwin: Tuple[int, int], - nover: Tuple[int, int], - nop: Tuple[int, int], - tapertype: Optional[Ttaper] = "hanning", + nwin: tuple[int, int], + nover: tuple[int, int], + nop: tuple[int, int], + tapertype: Ttaper | None = "hanning", savetaper: bool = True, - scalings: Optional[Sequence[float]] = None, + scalings: Sequence[float] | None = None, name: str = "P", ) -> None: - - dims: Tuple[int, ...] = _value_or_sized_to_tuple(dims) - dimsd: Tuple[int, ...] = _value_or_sized_to_tuple(dimsd) + dims: tuple[int, ...] = _value_or_sized_to_tuple(dims) + dimsd: tuple[int, ...] = _value_or_sized_to_tuple(dimsd) # data windows dwin0_ins, dwin0_ends = _slidingsteps(dimsd[0], nwin[0], nover[0]) @@ -220,12 +219,12 @@ def __init__( # check patching if nwins0 * nop[0] != dims[0] or nwins1 * nop[1] != dims[1]: - raise ValueError( + msg = ( f"Model shape (dims={dims}) is not consistent with chosen " - f"number of windows. Run patch2d_design to identify the " - f"correct number of windows for the current " - "model size..." + "number of windows. Run patch2d_design to identify the correct " + "number of windows for the current model size..." ) + raise ValueError(msg) # create tapers self.tapertype = tapertype diff --git a/pylops/signalprocessing/patch3d.py b/pylops/signalprocessing/patch3d.py index 482489337..4bf27b109 100644 --- a/pylops/signalprocessing/patch3d.py +++ b/pylops/signalprocessing/patch3d.py @@ -4,7 +4,7 @@ ] import logging -from typing import Optional, Sequence, Tuple +from collections.abc import Sequence import numpy as np @@ -25,15 +25,15 @@ def patch3d_design( dimsd: InputDimsLike, - nwin: Tuple[int, int, int], - nover: Tuple[int, int, int], - nop: Tuple[int, int, int], + nwin: tuple[int, int, int], + nover: tuple[int, int, int], + nop: tuple[int, int, int], verb: bool = True, -) -> Tuple[ - Tuple[int, int, int], - Tuple[int, int, int], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], +) -> tuple[ + tuple[int, int, int], + tuple[int, int, int], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray], tuple[NDArray, NDArray]], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray], tuple[NDArray, NDArray]], ]: """Design Patch3D operator @@ -209,17 +209,16 @@ def __init__( Op: LinearOperator, dims: InputDimsLike, dimsd: InputDimsLike, - nwin: Tuple[int, int, int], - nover: Tuple[int, int, int], - nop: Tuple[int, int, int], - tapertype: Optional[Ttaper] = "hanning", + nwin: tuple[int, int, int], + nover: tuple[int, int, int], + nop: tuple[int, int, int], + tapertype: Ttaper | None = "hanning", savetaper: bool = True, - scalings: Optional[Sequence[float]] = None, + scalings: Sequence[float] | None = None, name: str = "P", ) -> None: - - dims: Tuple[int, ...] = _value_or_sized_to_tuple(dims) - dimsd: Tuple[int, ...] = _value_or_sized_to_tuple(dimsd) + dims: tuple[int, ...] = _value_or_sized_to_tuple(dims) + dimsd: tuple[int, ...] = _value_or_sized_to_tuple(dimsd) # data windows dwin0_ins, dwin0_ends = _slidingsteps(dimsd[0], nwin[0], nover[0]) @@ -243,12 +242,12 @@ def __init__( or nwins1 * nop[1] != dims[1] or nwins2 * nop[2] != dims[2] ): - raise ValueError( + msg = ( f"Model shape (dims={dims}) is not consistent with chosen " - f"number of windows. Run patch3d_design to identify the " - f"correct number of windows for the current " - "model size..." + "number of windows. Run patch3d_design to identify the correct " + "number of windows for the current model size..." ) + raise ValueError(msg) # create tapers self.tapertype = tapertype @@ -441,9 +440,9 @@ def __init__( for itap in range(0, nwins1 * nwins2, nwins2): taps[(nwins0 - 1) * nwins1 * nwins2 + itap] = tapfrontbottom for itap in range(0, nwins1 * nwins2, nwins2): - taps[ - (nwins0 - 1) * nwins1 * nwins2 + nwins2 + itap - 1 - ] = tapbackbottom + taps[(nwins0 - 1) * nwins1 * nwins2 + nwins2 + itap - 1] = ( + tapbackbottom + ) for itap in range(0, nwins, nwins1 * nwins2): taps[itap] = tapleftfront for itap in range(0, nwins, nwins1 * nwins2): @@ -458,9 +457,9 @@ def __init__( taps[(nwins1 - 1) * nwins2 + nwins2 - 1] = taprighttopback taps[(nwins0 - 1) * nwins1 * nwins2] = tapleftbottomfront taps[(nwins0 - 1) * nwins1 * nwins2 + nwins2 - 1] = tapleftbottomback - taps[ - (nwins0 - 1) * nwins1 * nwins2 + (nwins1 - 1) * nwins2 - ] = taprightbottomfront + taps[(nwins0 - 1) * nwins1 * nwins2 + (nwins1 - 1) * nwins2] = ( + taprightbottomfront + ) taps[ (nwins0 - 1) * nwins1 * nwins2 + (nwins1 - 1) * nwins2 + nwins2 - 1 ] = taprightbottomback diff --git a/pylops/signalprocessing/pwd2d.py b/pylops/signalprocessing/pwd2d.py index 33885c7c9..5fb8bbffa 100644 --- a/pylops/signalprocessing/pwd2d.py +++ b/pylops/signalprocessing/pwd2d.py @@ -1,6 +1,5 @@ __all__ = ["PWSprayer2D", "PWSmoother2D"] -from typing import Tuple import numpy as np @@ -70,7 +69,7 @@ class PWSprayer2D(LinearOperator): def __init__( self, - dims: Tuple[int, int], + dims: tuple[int, int], sigma: NDArray, radius: int = 8, alpha: float = 0.9, @@ -78,7 +77,8 @@ def __init__( name: str = "P", ): if len(dims) != 2: - raise ValueError("dims must contain exactly two elements (nz, nx)") + msg = f"Wrong number of dimensions. Expected 2, but received {len(dims)}." + raise ValueError(msg) self._radius = int(radius) self._alpha = float(alpha) self._sigma = np.ascontiguousarray(sigma) @@ -162,7 +162,7 @@ class PWSmoother2D(LinearOperator): def __init__( self, - dims: Tuple[int, int], + dims: tuple[int, int], sigma: NDArray, radius: int = 8, alpha: float = 0.9, @@ -170,7 +170,8 @@ def __init__( name: str = "P", ): if len(dims) != 2: - raise ValueError("dims must contain exactly two elements (nz, nx)") + msg = f"Wrong number of dimensions. Expected 2, but received {len(dims)}." + raise ValueError(msg) self._sprayer = PWSprayer2D( dims=dims, sigma=sigma, radius=radius, alpha=alpha, dtype=dtype ) diff --git a/pylops/signalprocessing/radon2d.py b/pylops/signalprocessing/radon2d.py index 3b4fe6867..77bd03136 100644 --- a/pylops/signalprocessing/radon2d.py +++ b/pylops/signalprocessing/radon2d.py @@ -1,6 +1,7 @@ __all__ = ["Radon2D"] -from typing import Callable, Literal, Optional, Tuple +from collections.abc import Callable +from typing import Literal import numpy as np @@ -53,7 +54,7 @@ def _indices_2d( t: int, nt: int, interp: bool = True, -) -> Tuple[NDArray, NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray, NDArray | None]: """Compute time and space indices of parametric line in ``f`` function Parameters @@ -101,7 +102,7 @@ def _indices_2d_onthefly( t: int, nt: int, interp: bool = True, -) -> Tuple[NDArray, NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray, NDArray | None]: """Wrapper around _indices_2d to allow on-the-fly computation of parametric curves""" tscan = np.full(len(x), np.nan, dtype=np.float32) @@ -122,7 +123,7 @@ def _create_table( npx: int, nx: int, interp: bool, -) -> Tuple[NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray | None]: """Create look up table""" table = np.full((npx, nt, nx), np.nan, dtype=np.float32) if interp: @@ -243,7 +244,8 @@ def Radon2D( """ # engine if engine not in ["numpy", "numba"]: - raise ValueError("engine must be numpy or numba") + msg = "`engine` must be numpy or numba" + raise ValueError(msg) if engine == "numba" and jit_message is not None: engine = "numpy" # axes @@ -257,7 +259,11 @@ def Radon2D( elif callable(kind): f = kind else: - raise NotImplementedError("kind must be linear, " "parabolic, or hyperbolic...") + msg = ( + "Wrong kind of basis function. Expected 'linear', 'parabolic', " + f"or 'hyperbolic', but received '{kind}'." + ) + raise NotImplementedError(msg) # make axes unitless dh, dt = np.abs(haxis[1] - haxis[0]), np.abs(taxis[1] - taxis[0]) dpx = dh / dt diff --git a/pylops/signalprocessing/radon3d.py b/pylops/signalprocessing/radon3d.py index a6292aedb..96eabd0f9 100644 --- a/pylops/signalprocessing/radon3d.py +++ b/pylops/signalprocessing/radon3d.py @@ -1,6 +1,7 @@ __all__ = ["Radon3D"] -from typing import Callable, Literal, Optional, Tuple +from collections.abc import Callable +from typing import Literal import numpy as np @@ -61,7 +62,7 @@ def _indices_3d( t: int, nt: int, interp: bool = True, -) -> Tuple[NDArray, NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray, NDArray | None]: """Compute time and space indices of parametric line in ``f`` function Parameters @@ -117,7 +118,7 @@ def _indices_3d_onthefly( it: int, nt: int, interp: bool = True, -) -> Tuple[NDArray, NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray, NDArray | None]: """Wrapper around _indices_3d to allow on-the-fly computation of parametric curves""" tscan = np.full(len(y), np.nan, dtype=np.float32) @@ -142,13 +143,13 @@ def _create_table( ny: int, nx: int, interp: bool, -) -> Tuple[NDArray, Optional[NDArray]]: +) -> tuple[NDArray, NDArray | None]: """Create look up table""" table = np.full((npx * npy, nt, ny * nx), np.nan, dtype=np.float32) if interp: dtable = np.full((npx * npy, nt, ny * nx), np.nan) - for ip, (py, px) in enumerate(zip(pyaxis, pxaxis)): + for ip, (py, px) in enumerate(zip(pyaxis, pxaxis, strict=True)): for it in range(nt): sscan, tscan, dtscan = _indices_3d(f, y, x, py, px, it, nt, interp=interp) table[ip, it, sscan] = tscan @@ -267,7 +268,8 @@ def Radon3D( """ # engine if engine not in ["numpy", "numba"]: - raise ValueError("engine must be numpy or numba") + msg = "`engine` must be numpy or numba" + raise ValueError(msg) if engine == "numba" and jit_message is not None: engine = "numpy" @@ -281,7 +283,11 @@ def Radon3D( elif kind == "hyperbolic": f = _hyperbolic if engine == "numpy" else _hyperbolic_numba else: - raise NotImplementedError("kind must be linear, " "parabolic, or hyperbolic...") + msg = ( + "Wrong kind of basis function. Expected 'linear', 'parabolic', " + f"or 'hyperbolic', but received '{kind}'." + ) + raise NotImplementedError(msg) # make axes unitless dhy, dhx = np.abs(hyaxis[1] - hyaxis[0]), np.abs(hxaxis[1] - hxaxis[0]) dt = np.abs(taxis[1] - taxis[0]) diff --git a/pylops/signalprocessing/seislet.py b/pylops/signalprocessing/seislet.py index 4498fcf39..3cc7f577d 100644 --- a/pylops/signalprocessing/seislet.py +++ b/pylops/signalprocessing/seislet.py @@ -1,7 +1,8 @@ __all__ = ["Seislet"] +from collections.abc import Sequence from math import ceil, log -from typing import Literal, Optional, Sequence +from typing import Literal import numpy as np @@ -424,14 +425,15 @@ def __init__( self, slopes: NDArray, sampling: Sequence[float] = (1.0, 1.0), - level: Optional[int] = None, + level: int | None = None, kind: Literal["haar", "linear"] = "haar", inv: bool = False, dtype: DTypeLike = "float64", name: str = "S", ) -> None: if len(sampling) != 2: - raise ValueError("provide two sampling steps") + msg = f"Wrong number of sampling steps. Expected 2, but received {len(sampling)}." + raise ValueError(msg) # define predict and update steps if kind == "haar": @@ -439,7 +441,8 @@ def __init__( elif kind == "linear": self.predict = _predict_lin else: - raise NotImplementedError("kind should be haar or linear") + msg = f"Wrong kind of basis function. Expected 'haar' or 'linear', but received '{kind}'." + raise NotImplementedError(msg) # define padding for length to be power of 2 dims = slopes.shape diff --git a/pylops/signalprocessing/shift.py b/pylops/signalprocessing/shift.py index 85aa82ce2..0eff929ff 100644 --- a/pylops/signalprocessing/shift.py +++ b/pylops/signalprocessing/shift.py @@ -1,6 +1,6 @@ __all__ = ["Shift"] -from typing import TYPE_CHECKING, Tuple, Union +from typing import TYPE_CHECKING import numpy as np @@ -15,8 +15,8 @@ def Shift( - dims: Tuple, - shift: Union[float, NDArray], + dims: tuple, + shift: float | NDArray, axis: int = -1, nfft: int = None, sampling: float = 1.0, diff --git a/pylops/signalprocessing/sliding1d.py b/pylops/signalprocessing/sliding1d.py index 1b3217ef4..ac270bc1b 100644 --- a/pylops/signalprocessing/sliding1d.py +++ b/pylops/signalprocessing/sliding1d.py @@ -4,7 +4,6 @@ ] import logging -from typing import Optional, Tuple, Union import numpy as np @@ -29,7 +28,7 @@ def sliding1d_design( nover: int, nop: int, verb: bool = True, -) -> Tuple[int, int, Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]]: +) -> tuple[int, int, tuple[NDArray, NDArray], tuple[NDArray, NDArray]]: """Design Sliding1D operator This routine can be used prior to creating the :class:`pylops.signalprocessing.Sliding1D` @@ -170,17 +169,16 @@ class Sliding1D(LinearOperator): def __init__( self, Op: LinearOperator, - dim: Union[int, InputDimsLike], - dimd: Union[int, InputDimsLike], + dim: int | InputDimsLike, + dimd: int | InputDimsLike, nwin: int, nover: int, - tapertype: Optional[Ttaper] = "hanning", + tapertype: Ttaper | None = "hanning", savetaper: bool = True, name: str = "S", ) -> None: - - dim: Tuple[int, ...] = _value_or_sized_to_tuple(dim) - dimd: Tuple[int, ...] = _value_or_sized_to_tuple(dimd) + dim: tuple[int, ...] = _value_or_sized_to_tuple(dim) + dimd: tuple[int, ...] = _value_or_sized_to_tuple(dimd) # data windows dwin_ins, dwin_ends = _slidingsteps(dimd[0], nwin, nover) @@ -191,12 +189,11 @@ def __init__( # check windows if nwins * Op.shape[1] != dim[0] and Op.shape[1] != dim[0]: - raise ValueError( - f"Model shape (dim={dim}) is not consistent with chosen " - f"number of windows. Run sliding1d_design to identify the " - f"correct number of windows for the current " - "model size..." + msg = ( + f"Model shape (dim={dim}) is not consistent with chosen number of windows. " + "Run sliding1d_design to identify the correct number of windows for the current model size..." ) + raise ValueError(msg) # create tapers self.tapertype = tapertype diff --git a/pylops/signalprocessing/sliding2d.py b/pylops/signalprocessing/sliding2d.py index e266af2ab..443aa3c7a 100644 --- a/pylops/signalprocessing/sliding2d.py +++ b/pylops/signalprocessing/sliding2d.py @@ -4,7 +4,6 @@ ] import logging -from typing import Optional, Tuple import numpy as np @@ -26,7 +25,7 @@ def _slidingsteps( ntr: int, nwin: int, nover: int, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: """Identify sliding window initial and end points given overall trace length, window length and overlap @@ -48,7 +47,8 @@ def _slidingsteps( """ if nwin > ntr: - raise ValueError(f"nwin={nwin} is bigger than ntr={ntr}...") + msg = f"nwin={nwin} is bigger than ntr={ntr}..." + raise ValueError(msg) step = nwin - nover starts = np.arange(0, ntr - nwin + 1, step, dtype=int) ends = starts + nwin @@ -56,12 +56,12 @@ def _slidingsteps( def sliding2d_design( - dimsd: Tuple[int, int], + dimsd: tuple[int, int], nwin: int, nover: int, - nop: Tuple[int, int], + nop: tuple[int, int], verb: bool = True, -) -> Tuple[int, Tuple[int, int], Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]]: +) -> tuple[int, tuple[int, int], tuple[NDArray, NDArray], tuple[NDArray, NDArray]]: """Design Sliding2D operator This routine can be used prior to creating the :class:`pylops.signalprocessing.Sliding2D` @@ -210,13 +210,12 @@ def __init__( dimsd: InputDimsLike, nwin: int, nover: int, - tapertype: Optional[Ttaper] = "hanning", + tapertype: Ttaper | None = "hanning", savetaper: bool = True, name: str = "S", ) -> None: - - dims: Tuple[int, ...] = _value_or_sized_to_tuple(dims) - dimsd: Tuple[int, ...] = _value_or_sized_to_tuple(dimsd) + dims: tuple[int, ...] = _value_or_sized_to_tuple(dims) + dimsd: tuple[int, ...] = _value_or_sized_to_tuple(dimsd) # data windows dwin_ins, dwin_ends = _slidingsteps(dimsd[0], nwin, nover) @@ -227,12 +226,11 @@ def __init__( # check patching if nwins * Op.shape[1] // dims[1] != dims[0] and Op.shape[1] != np.prod(dims): - raise ValueError( - f"Model shape (dims={dims}) is not consistent with chosen " - f"number of windows. Run sliding2d_design to identify the " - f"correct number of windows for the current " - "model size..." + msg = ( + f"Model shape (dims={dims}) is not consistent with chosen number of windows. " + "Run sliding2d_design to identify the correct number of windows for the current model size..." ) + raise ValueError(msg) # create tapers self.tapertype = tapertype diff --git a/pylops/signalprocessing/sliding3d.py b/pylops/signalprocessing/sliding3d.py index 00ade77c3..2acc1cd14 100644 --- a/pylops/signalprocessing/sliding3d.py +++ b/pylops/signalprocessing/sliding3d.py @@ -4,7 +4,6 @@ ] import logging -from typing import Optional, Tuple import numpy as np @@ -24,16 +23,16 @@ def sliding3d_design( - dimsd: Tuple[int, int, int], - nwin: Tuple[int, int], - nover: Tuple[int, int], - nop: Tuple[int, int, int], + dimsd: tuple[int, int, int], + nwin: tuple[int, int], + nover: tuple[int, int], + nop: tuple[int, int, int], verb: bool = True, -) -> Tuple[ - Tuple[int, int], - Tuple[int, int, int], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], - Tuple[Tuple[NDArray, NDArray], Tuple[NDArray, NDArray]], +) -> tuple[ + tuple[int, int], + tuple[int, int, int], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]], + tuple[tuple[NDArray, NDArray], tuple[NDArray, NDArray]], ]: """Design Sliding3D operator @@ -197,17 +196,16 @@ def __init__( Op: LinearOperator, dims: InputDimsLike, dimsd: InputDimsLike, - nwin: Tuple[int, int], - nover: Tuple[int, int], - nop: Tuple[int, int, int], - tapertype: Optional[Ttaper] = "hanning", + nwin: tuple[int, int], + nover: tuple[int, int], + nop: tuple[int, int, int], + tapertype: Ttaper | None = "hanning", savetaper: bool = True, nproc: int = 1, name: str = "P", ) -> None: - - dims: Tuple[int, ...] = _value_or_sized_to_tuple(dims) - dimsd: Tuple[int, ...] = _value_or_sized_to_tuple(dimsd) + dims: tuple[int, ...] = _value_or_sized_to_tuple(dims) + dimsd: tuple[int, ...] = _value_or_sized_to_tuple(dimsd) # data windows dwin0_ins, dwin0_ends = _slidingsteps(dimsd[0], nwin[0], nover[0]) @@ -228,12 +226,11 @@ def __init__( if nwins * Op.shape[1] // dims[2] != dims[0] * dims[1] and Op.shape[ 1 ] != np.prod(dims): - raise ValueError( - f"Model shape (dims={dims}) is not consistent with chosen " - f"number of windows. Run sliding3d_design to identify the " - f"correct number of windows for the current " - "model size..." + msg = ( + f"Model shape (dims={dims}) is not consistent with chosen number of windows. " + "Run sliding3d_design to identify the correct number of windows for the current model size..." ) + raise ValueError(msg) # create tapers self.tapertype = tapertype diff --git a/pylops/torchoperator.py b/pylops/torchoperator.py index 487cb8cb6..7a6ad02e6 100644 --- a/pylops/torchoperator.py +++ b/pylops/torchoperator.py @@ -2,7 +2,6 @@ "TorchOperator", ] -from typing import Optional import numpy as np @@ -62,7 +61,7 @@ def __init__( self, Op: LinearOperator, batch: bool = False, - flatten: Optional[bool] = True, + flatten: bool | None = True, device: str = "cpu", devicetorch: str = "cpu", ) -> None: diff --git a/pylops/utils/_internal.py b/pylops/utils/_internal.py index cf8d2023f..2bac2d5ee 100644 --- a/pylops/utils/_internal.py +++ b/pylops/utils/_internal.py @@ -1,4 +1,4 @@ -from typing import Sized, Tuple +from collections.abc import Sized import numpy as np @@ -30,7 +30,7 @@ def _value_or_sized_to_array(value_or_sized, repeat: int = 1) -> NDArray: ) -def _value_or_sized_to_tuple(value_or_sized, repeat: int = 1) -> Tuple: +def _value_or_sized_to_tuple(value_or_sized, repeat: int = 1) -> tuple: """Convert an object which is either single value or a list-like to a tuple. Parameters @@ -70,9 +70,5 @@ def _raise_on_wrong_dtype(arr: ArrayLike, dtype: DTypeLike, name: str) -> None: """ if not np.issubdtype(arr.dtype, dtype): - raise TypeError( - ( - f"Wrong input type for `{name}`. " - f'Must be "{dtype}", but received to "{arr.dtype}".' - ) - ) + msg = f"Wrong input type for `{name}`. Must be {dtype}, but received to {arr.dtype}." + raise TypeError(msg) diff --git a/pylops/utils/_pwd2d.py b/pylops/utils/_pwd2d.py index 1fb29c82a..1eda91926 100644 --- a/pylops/utils/_pwd2d.py +++ b/pylops/utils/_pwd2d.py @@ -1,5 +1,4 @@ -import warnings -from typing import Tuple +import logging import numpy as np @@ -14,6 +13,8 @@ else: _conv_allpass_numba = None +logger = logging.getLogger(__name__) + def _conv_allpass_python( din: NDArray, dip: NDArray, order: int, u1: NDArray, u2: NDArray @@ -164,13 +165,13 @@ def _conv_allpass( if _conv_allpass_numba is not None: _conv_allpass_numba(din, dip, order, u1, u2) else: - warnings.warn(jit_message) + logger.warning(jit_message) _conv_allpass_python(din, dip, order, u1, u2) def _triangular_smoothing_from_boxcars( - nsmooth: Tuple[int, ...], - dims: Tuple[int, ...], + nsmooth: tuple[int, ...], + dims: tuple[int, ...], dtype: str | np.dtype = "float64", ): """Triangular smoother diff --git a/pylops/utils/_pwd2d_numba.py b/pylops/utils/_pwd2d_numba.py index ac18ec19a..b6694a164 100644 --- a/pylops/utils/_pwd2d_numba.py +++ b/pylops/utils/_pwd2d_numba.py @@ -1,5 +1,3 @@ -from typing import Tuple - from pylops.utils.typing import NDArray # Remap numba njit to no-ops and prange to range @@ -19,7 +17,7 @@ def prange(*args, **kwargs): # type: ignore @njit(fastmath=True, cache=True) -def _B3(sigma: float) -> Tuple[float, float, float]: +def _B3(sigma: float) -> tuple[float, float, float]: """Quadratic B-spline coefficients (3 taps).""" b0 = (1.0 - sigma) * (2.0 - sigma) / 12.0 b1 = (2.0 + sigma) * (2.0 - sigma) / 6.0 @@ -28,7 +26,7 @@ def _B3(sigma: float) -> Tuple[float, float, float]: @njit(fastmath=True, cache=True) -def _B3d(sigma: float) -> Tuple[float, float, float]: +def _B3d(sigma: float) -> tuple[float, float, float]: """Derivatives of quadratic B-spline coefficients.""" b0 = -(2.0 - sigma) / 12.0 - (1.0 - sigma) / 12.0 b1 = (2.0 - sigma) / 6.0 - (2.0 + sigma) / 6.0 @@ -37,7 +35,7 @@ def _B3d(sigma: float) -> Tuple[float, float, float]: @njit(fastmath=True, cache=True) -def _B5(sigma: float) -> Tuple[float, float, float, float, float]: +def _B5(sigma: float) -> tuple[float, float, float, float, float]: """Quartic B-spline coefficients (5 taps).""" s = sigma b0 = (1 - s) * (2 - s) * (3 - s) * (4 - s) / 1680.0 @@ -49,7 +47,7 @@ def _B5(sigma: float) -> Tuple[float, float, float, float, float]: @njit(fastmath=True, cache=True) -def _B5d(sigma: float) -> Tuple[float, float, float, float, float]: +def _B5d(sigma: float) -> tuple[float, float, float, float, float]: """Derivatives of quartic B-spline coefficients.""" s = sigma b0 = ( diff --git a/pylops/utils/backend.py b/pylops/utils/backend.py index 2cc125df2..91493ff38 100644 --- a/pylops/utils/backend.py +++ b/pylops/utils/backend.py @@ -28,8 +28,8 @@ "randn", ] +from collections.abc import Callable from types import ModuleType -from typing import Callable import numpy as np import scipy.fft as sp_fft @@ -92,7 +92,8 @@ def get_module(backend: Tfftengine_ncj = "numpy") -> ModuleType: elif backend == "jax": ncp = jnp else: - raise ValueError("backend must be numpy, cupy, or jax") + msg = f"The provided backend must be numpy, cupy, or jax, got {backend}" + raise ValueError(msg) return ncp @@ -118,7 +119,8 @@ def get_module_name(mod: ModuleType) -> str: elif deps.jax_enabled and mod == jnp: backend = "jax" else: - raise ValueError("module must be numpy, cupy, or jax") + msg = f"The provided module must be numpy, cupy, or jax, got {mod}" + raise ValueError(msg) return backend @@ -226,11 +228,8 @@ def get_oaconvolve(x: ArrayLike) -> Callable: """ if deps.cupy_enabled or deps.jax_enabled: if deps.jax_enabled and isinstance(x, jnp.ndarray): - raise NotImplementedError( - "oaconvolve not implemented in " - "jax. Consider using a different" - "option..." - ) + msg = "oaconvolve not implemented in jax. Consider using a different option..." + raise NotImplementedError(msg) elif deps.cupy_enabled and cp.get_array_module(x) == cp: return cp_oaconvolve else: @@ -704,5 +703,6 @@ def randn(*n: int, backend: Tfftengine_ncj = "numpy") -> NDArray: elif backend == "jax": x = jnp.array(np.random.randn(*n)) else: - raise ValueError("backend must be numpy, cupy, or jax") + msg = f"backend must be numpy, cupy, or jax, got {backend}" + raise ValueError(msg) return x diff --git a/pylops/utils/decorators.py b/pylops/utils/decorators.py index 7339ccc56..4c61f14e0 100644 --- a/pylops/utils/decorators.py +++ b/pylops/utils/decorators.py @@ -5,8 +5,8 @@ "count", ] +from collections.abc import Callable from functools import wraps -from typing import Callable, Optional from pylops.config import disabled_ndarray_multiplication @@ -71,8 +71,8 @@ def wrapper(A, b, *args, **kwargs): # SciPy-type signature def reshaped( - func: Optional[Callable] = None, - forward: Optional[bool] = None, + func: Callable | None = None, + forward: bool | None = None, swapaxis: bool = False, axis: int = -1, ) -> Callable: @@ -157,8 +157,8 @@ def wrapper(self, x): def count( - func: Optional[Callable] = None, - forward: Optional[bool] = None, + func: Callable | None = None, + forward: bool | None = None, matmat: bool = False, ) -> Callable: """Decorator used to count the number of forward and adjoint performed by an operator. diff --git a/pylops/utils/deps.py b/pylops/utils/deps.py index b2920caff..c97c966db 100644 --- a/pylops/utils/deps.py +++ b/pylops/utils/deps.py @@ -17,11 +17,10 @@ import os from importlib import import_module, util -from typing import Optional # error message at import of available package -def cupy_import(message: Optional[str] = None) -> str | None: +def cupy_import(message: str | None = None) -> str | None: # detect if cupy is available and the user is expecting to be used cupy_test = ( util.find_spec("cupy") is not None and int(os.getenv("CUPY_PYLOPS", 1)) == 1 @@ -55,7 +54,7 @@ def cupy_import(message: Optional[str] = None) -> str | None: return cupy_message -def jax_import(message: Optional[str] = None) -> str | None: +def jax_import(message: str | None = None) -> str | None: jax_test = ( util.find_spec("jax") is not None and int(os.getenv("JAX_PYLOPS", 1)) == 1 ) @@ -83,7 +82,7 @@ def jax_import(message: Optional[str] = None) -> str | None: return jax_message -def astra_import(message: Optional[str] = None) -> str | None: +def astra_import(message: str | None = None) -> str | None: if astra_enabled: try: import_module("astra") # noqa: F401 @@ -101,7 +100,7 @@ def astra_import(message: Optional[str] = None) -> str | None: return astra_message -def devito_import(message: Optional[str] = None) -> str | None: +def devito_import(message: str | None = None) -> str | None: if devito_enabled: try: import_module("devito") # noqa: F401 @@ -118,7 +117,7 @@ def devito_import(message: Optional[str] = None) -> str | None: return devito_message -def dtcwt_import(message: Optional[str] = None) -> str | None: +def dtcwt_import(message: str | None = None) -> str | None: if dtcwt_enabled: try: import_module("dtcwt") # noqa: F401 @@ -135,7 +134,7 @@ def dtcwt_import(message: Optional[str] = None) -> str | None: return dtcwt_message -def numba_import(message: Optional[str] = None) -> str | None: +def numba_import(message: str | None = None) -> str | None: if numba_enabled: try: import_module("numba") # noqa: F401 @@ -154,7 +153,7 @@ def numba_import(message: Optional[str] = None) -> str | None: return numba_message -def pyfftw_import(message: Optional[str] = None) -> str | None: +def pyfftw_import(message: str | None = None) -> str | None: if pyfftw_enabled: try: import_module("pyfftw") # noqa: F401 @@ -173,7 +172,7 @@ def pyfftw_import(message: Optional[str] = None) -> str | None: return pyfftw_message -def pywt_import(message: Optional[str] = None) -> str | None: +def pywt_import(message: str | None = None) -> str | None: if pywt_enabled: try: import_module("pywt") # noqa: F401 @@ -192,7 +191,7 @@ def pywt_import(message: Optional[str] = None) -> str | None: return pywt_message -def skfmm_import(message: Optional[str] = None) -> str | None: +def skfmm_import(message: str | None = None) -> str | None: if skfmm_enabled: try: import_module("skfmm") # noqa: F401 @@ -210,7 +209,7 @@ def skfmm_import(message: Optional[str] = None) -> str | None: return skfmm_message -def spgl1_import(message: Optional[str] = None) -> str | None: +def spgl1_import(message: str | None = None) -> str | None: if spgl1_enabled: try: import_module("spgl1") # noqa: F401 @@ -227,7 +226,7 @@ def spgl1_import(message: Optional[str] = None) -> str | None: return spgl1_message -def sympy_import(message: Optional[str] = None) -> str | None: +def sympy_import(message: str | None = None) -> str | None: if sympy_enabled: try: import_module("sympy") # noqa: F401 @@ -244,7 +243,7 @@ def sympy_import(message: Optional[str] = None) -> str | None: return sympy_message -def pytensor_import(message: Optional[str] = None) -> str | None: +def pytensor_import(message: str | None = None) -> str | None: if pytensor_enabled: try: import_module("pytensor") # noqa: F401 @@ -261,7 +260,7 @@ def pytensor_import(message: Optional[str] = None) -> str | None: return pytensor_message -def mkl_fft_import(message: Optional[str]) -> str | None: +def mkl_fft_import(message: str | None) -> str | None: if mkl_fft_enabled: try: import_module("mkl_fft") # noqa: F401 diff --git a/pylops/utils/describe.py b/pylops/utils/describe.py index f1a68f413..3619f3523 100644 --- a/pylops/utils/describe.py +++ b/pylops/utils/describe.py @@ -3,7 +3,6 @@ import logging import random import string -from typing import List, Set, Union from pylops import LinearOperator from pylops.basicoperators import BlockDiag, HStack, VStack @@ -51,7 +50,7 @@ def _in_notebook() -> bool: return True -def _assign_name(Op, Ops, names: List[str]) -> str: +def _assign_name(Op, Ops, names: list[str]) -> str: """Assign name to an operator as provided by the user (or randomly select one when not provided by the user) @@ -102,7 +101,7 @@ def _assign_name(Op, Ops, names: List[str]) -> str: return name -def _describeop(Op, Ops, names: List[str]): +def _describeop(Op, Ops, names: list[str]): """Core steps to describe a single operator Parameters @@ -150,7 +149,7 @@ def _describeop(Op, Ops, names: List[str]): def _describe( Op, Ops, - names: Union[List[str], Set[str]], + names: list[str] | set[str], ): """Core steps to describe a composite operator. This is done recursively. diff --git a/pylops/utils/dottest.py b/pylops/utils/dottest.py index e0c0ed099..8bdbb2cd1 100644 --- a/pylops/utils/dottest.py +++ b/pylops/utils/dottest.py @@ -1,6 +1,5 @@ __all__ = ["dottest"] -from typing import Optional import numpy as np @@ -10,8 +9,8 @@ def dottest( Op, - nr: Optional[int] = None, - nc: Optional[int] = None, + nr: int | None = None, + nc: int | None = None, rtol: float = 1e-6, atol: float = 1e-21, complexflag: int = 0, @@ -89,7 +88,8 @@ def dottest( nc = Op.shape[1] if (nr, nc) != Op.shape: - raise AssertionError("Provided nr and nc do not match Operator shape.") + msg = f"Provided nr and nc do not match the operator shape {Op.shape}." + raise AssertionError(msg) # make u and v vectors rdtype = np.ones(1, Op.dtype).real.dtype diff --git a/pylops/utils/estimators.py b/pylops/utils/estimators.py index 279a104af..94798ab24 100644 --- a/pylops/utils/estimators.py +++ b/pylops/utils/estimators.py @@ -6,7 +6,6 @@ from itertools import chain from types import ModuleType -from typing import Optional, Tuple import numpy @@ -16,7 +15,7 @@ def _sampler_gaussian( m: float, batch_size: int, backend_module: ModuleType = numpy -) -> Tuple[float, NDArray]: +) -> tuple[float, NDArray]: return backend_module.random.randn(m, batch_size) @@ -44,8 +43,8 @@ def _sampler_rademacher( def trace_hutchinson( Op, - neval: Optional[int] = None, - batch_size: Optional[int] = None, + neval: int | None = None, + batch_size: int | None = None, sampler: Tsampler = "rademacher", backend: Tbackend = "numpy", ) -> float: @@ -142,25 +141,26 @@ def trace_hutchinson( for i, idx in enumerate(z_idx): z[idx, i] = 1.0 remaining_vectors.remove(idx) - trace += ncp.trace((z.T @ (Op @ z))) + trace += ncp.trace(z.T @ (Op @ z)) n_total += batch trace *= m / n_total return trace[0] if sampler not in _SAMPLERS: - raise NotImplementedError(f"sampler {sampler} not available.") + msg = f"sampler {sampler} not available." + raise NotImplementedError(msg) sampler_fun = _SAMPLERS[sampler] for batch in batch_range: z = sampler_fun(m, batch, backend_module=ncp).astype(Op.dtype) - trace += ncp.trace((z.T @ (Op @ z))) + trace += ncp.trace(z.T @ (Op @ z)) trace /= neval return trace[0] def trace_hutchpp( Op, - neval: Optional[int] = None, + neval: int | None = None, sampler: Tsampler2 = "rademacher", backend: Tbackend = "numpy", ) -> float: @@ -229,14 +229,14 @@ def trace_hutchpp( neval = int(numpy.round(m * 0.1)) if neval is None else neval if sampler not in _SAMPLERS: - raise NotImplementedError(f"sampler {sampler} not available.") + msg = f"sampler {sampler} not available." + raise NotImplementedError(msg) sampler_fun = _SAMPLERS[sampler] batch = neval // 3 if batch <= 0: - msg = f"Sampler '{sampler}' not supported with {neval} samples." - msg += " Try increasing it." + msg = f"Sampler '{sampler}' not supported with {neval} samples. Try increasing it." raise ValueError(msg) S = sampler_fun(m, batch, backend_module=ncp).astype(Op.dtype) @@ -253,7 +253,7 @@ def trace_hutchpp( def trace_nahutchpp( Op, - neval: Optional[int] = None, + neval: int | None = None, sampler: Tsampler2 = "rademacher", c1: float = 1.0 / 6.0, c2: float = 1.0 / 3.0, @@ -336,7 +336,8 @@ def trace_nahutchpp( neval = int(numpy.round(m * 0.1)) if neval is None else neval if sampler not in _SAMPLERS: - raise NotImplementedError(f"sampler {sampler} not available.") + msg = f"sampler {sampler} not available." + raise NotImplementedError(msg) sampler_fun = _SAMPLERS[sampler] diff --git a/pylops/utils/metrics.py b/pylops/utils/metrics.py index 31ab5f87e..8adc2d9f2 100644 --- a/pylops/utils/metrics.py +++ b/pylops/utils/metrics.py @@ -5,7 +5,6 @@ "psnr", ] -from typing import Optional import numpy as np @@ -82,8 +81,8 @@ def snr(xref: ArrayLike, xcmp: ArrayLike) -> float: def psnr( xref: ArrayLike, xcmp: ArrayLike, - xmax: Optional[float] = None, - xmin: Optional[float] = 0.0, + xmax: float | None = None, + xmin: float | None = 0.0, ) -> float: """Peak Signal to Noise Ratio (PSNR) diff --git a/pylops/utils/multiproc.py b/pylops/utils/multiproc.py index e12c88855..74866b960 100644 --- a/pylops/utils/multiproc.py +++ b/pylops/utils/multiproc.py @@ -2,7 +2,6 @@ import logging import time -from typing import List, Optional, Tuple import numpy.typing as npt @@ -12,10 +11,10 @@ def scalability_test( Op, x: npt.ArrayLike, - workers: Optional[List[int]] = None, + workers: list[int] | None = None, forward: bool = True, ntimes: int = 1, -) -> Tuple[List[float], List[float]]: +) -> tuple[list[float], list[float]]: r"""Scalability test. Small auxiliary routine to test the performance of operators using diff --git a/pylops/utils/seismicevents.py b/pylops/utils/seismicevents.py index bf4ec66f4..ed04f04b2 100755 --- a/pylops/utils/seismicevents.py +++ b/pylops/utils/seismicevents.py @@ -8,7 +8,6 @@ "hyperbolic3d", ] -from typing import Dict, Tuple, Union import numpy as np import numpy.typing as npt @@ -20,7 +19,7 @@ def _filterdata( d: NDArray, nt: int, wav: npt.ArrayLike, wcenter: int -) -> Tuple[npt.ArrayLike, npt.ArrayLike]: +) -> tuple[npt.ArrayLike, npt.ArrayLike]: r"""Apply filtering to data with wavelet wav""" dwav = filt.lfilter(wav, 1, d, axis=-1) dwav = dwav[..., wcenter:] @@ -28,7 +27,7 @@ def _filterdata( return d, dwav -def makeaxis(par: Dict) -> Tuple[NDArray, NDArray, NDArray, NDArray]: +def makeaxis(par: dict) -> tuple[NDArray, NDArray, NDArray, NDArray]: r"""Create axes t, x, and y axes Create space and time axes from dictionary containing initial values ``ot``, ``ox``, ``oy``, @@ -74,11 +73,11 @@ def linear2d( x: NDArray, t: NDArray, v: float, - t0: Union[float, Tuple[float]], - theta: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + t0: float | tuple[float], + theta: float | tuple[float], + amp: float | tuple[float], wav: NDArray, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Linear 2D events Create 2d linear events given propagation velocity, intercept time, angle, @@ -152,12 +151,12 @@ def linear2d( def parabolic2d( x: NDArray, t: NDArray, - t0: Union[float, Tuple[float]], - px: Union[float, Tuple[float]], - pxx: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + t0: float | tuple[float], + px: float | tuple[float], + pxx: float | tuple[float], + amp: float | tuple[float], wav: NDArray, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Parabolic 2D events Create 2d parabolic events given intercept time, @@ -229,11 +228,11 @@ def parabolic2d( def hyperbolic2d( x: NDArray, t: NDArray, - t0: Union[float, Tuple[float]], - vrms: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + t0: float | tuple[float], + vrms: float | tuple[float], + amp: float | tuple[float], wav: NDArray, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Hyperbolic 2D events Create 2d hyperbolic events given intercept time, root-mean-square @@ -301,13 +300,13 @@ def linear3d( x: NDArray, y: NDArray, t: NDArray, - v: Union[float, Tuple[float]], - t0: Union[float, Tuple[float]], - theta: Union[float, Tuple[float]], - phi: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + v: float | tuple[float], + t0: float | tuple[float], + theta: float | tuple[float], + phi: float | tuple[float], + amp: float | tuple[float], wav: NDArray, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Linear 3D events Create 3d linear events given propagation velocity, intercept time, angles, @@ -391,14 +390,14 @@ def parabolic3d( x: NDArray, y: NDArray, t: NDArray, - t0: Union[float, Tuple[float]], - px: Union[float, Tuple[float]], - py: Union[float, Tuple[float]], - pxx: Union[float, Tuple[float]], - pyy: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + t0: float | tuple[float], + px: float | tuple[float], + py: float | tuple[float], + pxx: float | tuple[float], + pyy: float | tuple[float], + amp: float | tuple[float], wav: NDArray, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Parabolic 3D events Create 3d parabolic events given intercept time, @@ -485,10 +484,10 @@ def hyperbolic3d( x: NDArray, y: NDArray, t: NDArray, - t0: Union[float, Tuple[float]], - vrms_x: Union[float, Tuple[float]], - vrms_y: Union[float, Tuple[float]], - amp: Union[float, Tuple[float]], + t0: float | tuple[float], + vrms_x: float | tuple[float], + vrms_y: float | tuple[float], + amp: float | tuple[float], wav: NDArray, ): r"""Hyperbolic 3D events diff --git a/pylops/utils/signalprocessing.py b/pylops/utils/signalprocessing.py index 85d5e4462..2c615a1c0 100644 --- a/pylops/utils/signalprocessing.py +++ b/pylops/utils/signalprocessing.py @@ -7,7 +7,7 @@ ] import warnings -from typing import Sequence, Tuple, Union +from collections.abc import Sequence import numpy as np from scipy.ndimage import gaussian_filter @@ -58,6 +58,7 @@ def convmtx(h: NDArray, n: int, offset: int = 0) -> NDArray: "with the documentation. Users are highly encouraged " "to modify their codes accordingly.", FutureWarning, + stacklevel=2, ) ncp = get_array_module(h) @@ -74,7 +75,7 @@ def nonstationary_convmtx( H: NDArray, n: int, hc: int = 0, - pad: Tuple[int] = (0, 0), + pad: tuple[int] = (0, 0), ) -> NDArray: r"""Convolution matrix from a bank of filters @@ -118,7 +119,7 @@ def slope_estimate( smooth: float = 5.0, eps: float = 0.0, dips: bool = False, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Local slope estimation Local slopes are estimated using the *Structure Tensor* algorithm [1]_. @@ -269,7 +270,7 @@ def dip_estimate( dx: float = 1.0, smooth: int = 5, eps: float = 0.0, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Local dip estimation Local dips are estimated using the *Structure Tensor* algorithm [1]_. @@ -322,7 +323,7 @@ def pwd_slope_estimate( liter: int = 20, order: int = 2, smoothing: Tpwdsmoothing = "triangle", - nsmooth: Union[int, Sequence[int]] = 10, + nsmooth: int | Sequence[int] = 10, damp: float = 0.0, axis: int = -1, ) -> NDArray: @@ -381,9 +382,11 @@ def pwd_slope_estimate( """ if order not in (1, 2): - raise ValueError("order must be 1 (B3) or 2 (B5)") + msg = f"order must be 1 (B3) or 2 (B5), got {order}" + raise ValueError(msg) if d.ndim not in (2, 3): - raise ValueError("input data must be 2D or 3D") + msg = f"input array must be 2D or 3D, got {d.ndim}D" + raise ValueError(msg) # Re-arrange dimensions to work on first two axes nsmooth = _value_or_sized_to_tuple(nsmooth, d.ndim) @@ -410,7 +413,8 @@ def pwd_slope_estimate( elif smoothing == "boxcar": Sop = smoothcls(nsmooth=nsmooth, dims=dims, axes=smoothaxes, dtype=dtype) else: - raise ValueError("smoothing must be either 'triangle' or 'boxcar'") + msg = f"smoothing must be either 'triangle' or 'boxcar', got {smoothing}" + raise ValueError(msg) # Estimate slopes for _ in range(niter): diff --git a/pylops/utils/tapers.py b/pylops/utils/tapers.py index 2520d83a2..07e9856f3 100644 --- a/pylops/utils/tapers.py +++ b/pylops/utils/tapers.py @@ -7,7 +7,6 @@ "tapernd", ] -from typing import Optional, Tuple, Union import numpy as np import numpy.typing as npt @@ -40,11 +39,10 @@ def hanningtaper( if ntap > 0: if (nmask // ntap) < 2: ntap_min = nmask // 2 if nmask % 2 == 0 else (nmask - 1) // 2 - raise ValueError(f"ntap={ntap} must be smaller or equal than {ntap_min}") + msg = f"ntap={ntap} must be smaller or equal than {ntap_min}" + raise ValueError(msg) han_win = np.hanning(ntap * 2 - 1) - st_tpr = han_win[ - :ntap, - ] + st_tpr = han_win[:ntap,] mid_tpr = np.ones( [ nmask - (2 * ntap), @@ -59,7 +57,7 @@ def cosinetaper( nmask: int, ntap: int, square: bool = False, - exponent: Optional[float] = None, + exponent: float | None = None, ) -> npt.ArrayLike: r"""1D Cosine or Cosine square taper @@ -99,9 +97,7 @@ def cosinetaper( + 1.0 ) ) ** exponent - st_tpr = cos_win[ - :ntap, - ] + st_tpr = cos_win[:ntap,] mid_tpr = np.ones( [ nmask - (2 * ntap), @@ -115,7 +111,7 @@ def cosinetaper( def taper( nmask: int, ntap: int, - tapertype: Optional[Ttaper], + tapertype: Ttaper | None, ) -> NDArray: r"""1D taper @@ -154,8 +150,8 @@ def taper( def taper2d( nt: int, nmask: int, - ntap: Union[int, Tuple[int, int]], - tapertype: Optional[Ttaper] = "hanning", + ntap: int | tuple[int, int], + tapertype: Ttaper | None = "hanning", ) -> NDArray: r"""2D taper @@ -204,9 +200,9 @@ def taper2d( def taper3d( nt: int, - nmask: Tuple[int, int], - ntap: Tuple[int, int], - tapertype: Optional[Ttaper] = "hanning", + nmask: tuple[int, int], + ntap: tuple[int, int], + tapertype: Ttaper | None = "hanning", ) -> NDArray: r"""3D taper @@ -263,7 +259,7 @@ def taper3d( def tapernd( nmask: InputDimsLike, ntap: InputDimsLike, - tapertype: Optional[Ttaper] = "hanning", + tapertype: Ttaper | None = "hanning", ) -> NDArray: r"""ND taper @@ -289,13 +285,15 @@ def tapernd( """ # create 1d window if tapertype == "hanning": - tpr = [hanningtaper(nm, nt) for nm, nt in zip(nmask, ntap)] + tpr = [hanningtaper(nm, nt) for nm, nt in zip(nmask, ntap, strict=True)] elif tapertype == "cosine": - tpr = [cosinetaper(nm, nt, False) for nm, nt in zip(nmask, ntap)] + tpr = [cosinetaper(nm, nt, False) for nm, nt in zip(nmask, ntap, strict=True)] elif tapertype == "cosinesquare": - tpr = [cosinetaper(nm, nt, True) for nm, nt in zip(nmask, ntap)] + tpr = [cosinetaper(nm, nt, True) for nm, nt in zip(nmask, ntap, strict=True)] elif tapertype == "cosinesqrt": - tpr = [cosinetaper(nm, nt, False, 0.5) for nm, nt in zip(nmask, ntap)] + tpr = [ + cosinetaper(nm, nt, False, 0.5) for nm, nt in zip(nmask, ntap, strict=True) + ] else: tpr = [np.ones(nm) for nm in nmask] diff --git a/pylops/utils/typing.py b/pylops/utils/typing.py index 168894b37..b7f85436f 100644 --- a/pylops/utils/typing.py +++ b/pylops/utils/typing.py @@ -9,7 +9,8 @@ "TensorTypeLike", ] -from typing import Literal, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Literal import numpy as np import numpy.typing as npt @@ -26,9 +27,9 @@ FloatingNDArray = npt.NDArray[np.floating] InexactNDArray = npt.NDArray[np.inexact] # float or complex -InputDimsLike = Union[Sequence[int], IntNDArray] -SamplingLike = Union[Sequence[float], FloatingNDArray] -ShapeLike = Tuple[int, ...] +InputDimsLike = Sequence[int] | IntNDArray +SamplingLike = Sequence[float] | FloatingNDArray +ShapeLike = tuple[int, ...] DTypeLike = npt.DTypeLike # torch generic types @@ -46,7 +47,7 @@ Tfftengine_nsf = Literal["numpy", "scipy", "fftw"] Tfftengine_nsm = Literal["numpy", "scipy", "mkl_fft"] Tfftengine_nfsm = Literal["numpy", "fftw", "scipy", "mkl_fft"] -Tinoutengine = Tuple[Tfftengine_ncj, Tfftengine_ncj] +Tinoutengine = tuple[Tfftengine_ncj, Tfftengine_ncj] Tmriengine = Literal["numpy", "jax"] Tavolinearization = Literal["akirich", "fatti", "PS"] diff --git a/pylops/utils/utils.py b/pylops/utils/utils.py index 1f621c6f3..b198a054a 100644 --- a/pylops/utils/utils.py +++ b/pylops/utils/utils.py @@ -1,7 +1,6 @@ __all__ = ["Report"] # scooby is a soft dependency for pylops -from typing import Optional try: from scooby import Report as ScoobyReport @@ -67,7 +66,7 @@ class Report(ScoobyReport): def __init__( self, - add_pckg: Optional[list] = None, + add_pckg: list | None = None, ncol: int = 3, text_width: int = 80, sort: bool = False, diff --git a/pylops/utils/wavelets.py b/pylops/utils/wavelets.py index 6377ae7fd..e50bd3a3d 100644 --- a/pylops/utils/wavelets.py +++ b/pylops/utils/wavelets.py @@ -6,7 +6,7 @@ ] import warnings -from typing import Callable, Optional, Sequence, Tuple +from collections.abc import Callable, Sequence import numpy as np import numpy.typing as npt @@ -18,14 +18,14 @@ def _tcrop(t: npt.ArrayLike) -> npt.ArrayLike: """Crop time axis with even number of samples""" if len(t) % 2 == 0: t = t[:-1] - warnings.warn("One sample removed from time axis...") + warnings.warn("One sample removed from time axis...", stacklevel=2) return t def gaussian( t: npt.ArrayLike, std: float = 1.0, -) -> Tuple[npt.ArrayLike, npt.ArrayLike, int]: +) -> tuple[npt.ArrayLike, npt.ArrayLike, int]: r"""Gaussian wavelet Create a Gaussian wavelet given time axis ``t`` @@ -61,8 +61,8 @@ def gaussian( def klauder( t: npt.ArrayLike, f: Sequence[float] = (5.0, 20.0), - taper: Optional[Callable] = None, -) -> Tuple[npt.ArrayLike, npt.ArrayLike, int]: + taper: Callable | None = None, +) -> tuple[npt.ArrayLike, npt.ArrayLike, int]: r"""Klauder wavelet Create a Klauder wavelet given time axis ``t`` @@ -109,8 +109,8 @@ def klauder( def ormsby( t: npt.ArrayLike, f: Sequence[float] = (5.0, 10.0, 45.0, 50.0), - taper: Optional[Callable] = None, -) -> Tuple[npt.ArrayLike, npt.ArrayLike, int]: + taper: Callable | None = None, +) -> tuple[npt.ArrayLike, npt.ArrayLike, int]: r"""Ormsby wavelet Create a Ormsby wavelet given time axis ``t`` and frequency range @@ -167,8 +167,8 @@ def numerator(f, t): def ricker( t: npt.ArrayLike, f0: float = 10, - taper: Optional[Callable] = None, -) -> Tuple[npt.ArrayLike, npt.ArrayLike, int]: + taper: Callable | None = None, +) -> tuple[npt.ArrayLike, npt.ArrayLike, int]: r"""Ricker wavelet Create a Ricker wavelet given time axis ``t`` and central frequency ``f_0`` diff --git a/pylops/waveeqprocessing/blending.py b/pylops/waveeqprocessing/blending.py index ad96b7329..d9c171eae 100644 --- a/pylops/waveeqprocessing/blending.py +++ b/pylops/waveeqprocessing/blending.py @@ -4,7 +4,6 @@ "BlendingHalf", ] -from typing import Optional import numpy as np @@ -95,7 +94,7 @@ def __init__( dt: float, times: NDArray, shiftall: bool = False, - nttot: Optional[int] = None, + nttot: int | None = None, dtype: DTypeLike = "float64", name: str = "B", **kwargs_fft, @@ -326,7 +325,8 @@ def BlendingGroup( """ if times.shape[0] != group_size: - raise ValueError("The first dimension of times must equal group_size") + msg = f"The first dimension of times must equal group_size, got {times.shape[0]} / {group_size}" + raise ValueError(msg) Bop = [] for i in range(n_groups): Hop = [] @@ -428,7 +428,8 @@ def BlendingHalf( """ if times.shape[0] != group_size: - raise ValueError("The first dimension of times must equal group_size") + msg = f"The first dimension of times must equal group_size, got {times.shape[0]} / {group_size}" + raise ValueError(msg) Bop = [] for j in range(group_size): diff --git a/pylops/waveeqprocessing/kirchhoff.py b/pylops/waveeqprocessing/kirchhoff.py index 2f2d1f8b6..11106520a 100644 --- a/pylops/waveeqprocessing/kirchhoff.py +++ b/pylops/waveeqprocessing/kirchhoff.py @@ -4,7 +4,7 @@ import logging import os import warnings -from typing import Literal, Optional, Tuple, Union +from typing import Literal import numpy as np @@ -313,15 +313,15 @@ def __init__( vel: NDArray, wav: NDArray, wavcenter: int, - y: Optional[NDArray] = None, + y: NDArray | None = None, mode: Literal["analytic", "eikonal", "byot"] = "eikonal", wavfilter: bool = False, dynamic: bool = False, - trav: Optional[Union[NDArray, Tuple[NDArray, NDArray]]] = None, - amp: Optional[Union[NDArray, Tuple[NDArray, NDArray]]] = None, - aperture: Optional[Tuple[float, float]] = None, - angleaperture: Union[float, Tuple[float, float]] = 90.0, - snell: Optional[Tuple[float, float]] = None, + trav: NDArray | tuple[NDArray, NDArray] | None = None, + amp: NDArray | tuple[NDArray, NDArray] | None = None, + aperture: tuple[float, float] | None = None, + angleaperture: float | tuple[float, float] = 90.0, + snell: tuple[float, float] | None = None, engine: Tengine_nnc = "numpy", dtype: DTypeLike = "float64", name: str = "K", @@ -330,12 +330,13 @@ def __init__( "A new implementation of Kirchhoff is provided in v2.1.0. " "This currently affects only the inner working of the " "operator, end-users can continue using the operator in " - "the same way. Nevertheless, it is now recommended to provide" + "the same way. Nevertheless, it is now recommended to provide " "the variables trav (and amp) as a tuples containing the " "traveltime (and amplitude) tables for sources and receivers " "separately. This behaviour will eventually become default in " "version v3.0.0.", FutureWarning, + stacklevel=2, ) # identify geometry ( @@ -393,13 +394,15 @@ def __init__( epsdist = 1e-2 self.maxdist = epsdist * (np.max(dist_srcs) + np.max(dist_recs)) if self.ndims == 2: - self.amp_srcs, self.amp_recs = 1.0 / np.sqrt( - dist_srcs + self.maxdist - ), 1.0 / np.sqrt(dist_recs + self.maxdist) + self.amp_srcs, self.amp_recs = ( + 1.0 / np.sqrt(dist_srcs + self.maxdist), + 1.0 / np.sqrt(dist_recs + self.maxdist), + ) else: - self.amp_srcs, self.amp_recs = 1.0 / ( - dist_srcs + self.maxdist - ), 1.0 / (dist_recs + self.maxdist) + self.amp_srcs, self.amp_recs = ( + 1.0 / (dist_srcs + self.maxdist), + 1.0 / (dist_recs + self.maxdist), + ) else: if isinstance(trav, tuple): self.trav_srcs, self.trav_recs = trav @@ -408,17 +411,14 @@ def __init__( self.trav = trav if self.dynamic and not self.travsrcrec: - raise NotImplementedError( - "separate traveltime tables must be provided " - "when selecting mode=dynamic" - ) + msg = "separate traveltime tables for sources and receivers must be provided when selecting mode='dynamic'" + raise NotImplementedError(msg) if self.dynamic: if isinstance(amp, tuple): self.amp_srcs, self.amp_recs = amp else: - raise NotImplementedError( - "separate amplitude tables must be provided " - ) + msg = "amp must be provided as a tuple of source and receiver amplitude tables when selecting mode='dynamic'" + raise NotImplementedError(msg) if self.travsrcrec: # compute traveltime gradients at image points @@ -431,7 +431,8 @@ def __init__( axis=np.arange(self.ndims), ) else: - raise ValueError("method must be analytic, eikonal or byot") + msg = f"mode must be either 'analytic', 'eikonal' or 'byot', got {mode}" + raise ValueError(msg) # compute angles with vertical if self.dynamic: @@ -452,15 +453,13 @@ def __init__( self.angle_srcs = ( np.sign(trav_srcs_grad[1]) * np.arccos( - trav_srcs_grad[-1] - / np.sqrt(np.sum(trav_srcs_grad**2, axis=0)) + trav_srcs_grad[-1] / np.sqrt(np.sum(trav_srcs_grad**2, axis=0)) ) ).reshape(np.prod(dims), ns) self.angle_recs = ( np.sign(trav_recs_grad[1]) * np.arccos( - trav_recs_grad[-1] - / np.sqrt(np.sum(trav_recs_grad**2, axis=0)) + trav_recs_grad[-1] / np.sqrt(np.sum(trav_recs_grad**2, axis=0)) ) ).reshape(np.prod(dims), nr) @@ -489,12 +488,14 @@ def __init__( if aperture is not None and self.ndims == 3: aperture = None warnings.warn( - "Aperture is forced to None as currently not implemented in 3D" + "Aperture is forced to None as currently not implemented in 3D", + stacklevel=2, ) if aperture is not None: warnings.warn( "Aperture is currently defined as ratio of offset over depth, " - "and may be not ideal for highly heterogeneous media" + "and may be not ideal for highly heterogeneous media", + stacklevel=2, ) self.aperture = ( (self.nx + 1,) if aperture is None else _value_or_sized_to_array(aperture) @@ -532,8 +533,8 @@ def _identify_geometry( x: NDArray, srcs: NDArray, recs: NDArray, - y: Optional[NDArray] = None, - ) -> Tuple[ + y: NDArray | None = None, + ) -> tuple[ int, int, NDArray, @@ -577,10 +578,10 @@ def _traveltime_table( x: NDArray, srcs: NDArray, recs: NDArray, - vel: Union[float, NDArray], - y: Optional[NDArray] = None, + vel: float | NDArray, + y: NDArray | None = None, mode: Literal["analytic", "eikonal"] = "eikonal", - ) -> Tuple[NDArray, NDArray, NDArray, NDArray, NDArray, NDArray]: + ) -> tuple[NDArray, NDArray, NDArray, NDArray, NDArray, NDArray]: r"""Traveltime table Compute traveltimes along the source-subsurface-receivers triplet @@ -647,7 +648,8 @@ def _traveltime_table( # compute traveltimes if mode == "analytic": if not isinstance(vel, (float, int)): - raise ValueError("vel must be scalar for mode=analytical") + msg = "vel must be scalar for mode='analytic'" + raise ValueError(msg) # compute grid if ndims == 2: @@ -709,7 +711,8 @@ def _traveltime_table( else: raise NotImplementedError(skfmm_message) else: - raise ValueError("method must be analytic or eikonal") + msg = f"mode must be either 'analytic' or 'eikonal', got {mode}" + raise ValueError(msg) # compute traveltime gradients at image points trav_srcs_grad = np.gradient( @@ -745,7 +748,8 @@ def _wavelet_reshaping( Wfilt = W * np.sqrt(1j * 2 * np.pi * f) elif (dimsrc == 2 or dimrec == 2) and dimv == 3: # 2.5D - raise NotImplementedError("2.D wavelet currently not available") + msg = "2.5D wavelet reshaping currently not implemented" + raise NotImplementedError(msg) elif dimsrc == 3 and dimrec == 3 and dimv == 3: # 3D Wfilt = W * (-1j * 2 * np.pi * f) @@ -1074,7 +1078,8 @@ def _ampsrcrec_kirch_rmatvec( def _register_multiplications(self, engine: Tengine_nnc) -> None: if engine not in ["numpy", "numba", "cuda"]: - raise ValueError("engine must be numpy or numba or cuda") + msg = f"engine must be numpy or numba or cuda, got {engine}" + raise ValueError(msg) if engine == "numba" and jit_message is None: numba_opts = dict( nopython=True, nogil=True, parallel=parallel @@ -1098,10 +1103,8 @@ def _register_multiplications(self, engine: Tengine_nnc) -> None: self.ns, self.nr, self.nt, self.ni, False ) elif not self.travsrcrec: - raise NotImplementedError( - "engine='cuda' not implemented for traveltimes " - "provided in one table" - ) + msg = "`engine=cuda` not implemented for traveltimes provided in one table" + raise NotImplementedError(msg) self._kirch_matvec = self.cuda_helper._matvec_cuda self._kirch_rmatvec = self.cuda_helper._rmatvec_cuda else: diff --git a/pylops/waveeqprocessing/lsm.py b/pylops/waveeqprocessing/lsm.py index c2f962e9b..ac1945be9 100644 --- a/pylops/waveeqprocessing/lsm.py +++ b/pylops/waveeqprocessing/lsm.py @@ -1,6 +1,7 @@ __all__ = ["LSM"] -from typing import Callable, Literal, Optional +from collections.abc import Callable +from typing import Literal from scipy.sparse.linalg import lsqr @@ -103,7 +104,7 @@ def __init__( vel: NDArray, wav: NDArray, wavcenter: int, - y: Optional[NDArray] = None, + y: NDArray | None = None, kind: Literal["kirchhoff", "twowayac"] = "kirchhoff", dottest: bool = False, **kwargs_mod, @@ -133,7 +134,8 @@ def __init__( ) else: - raise NotImplementedError("kind must be kirchhoff or twowayac") + msg = f"kind must be either 'kirchhoff' or 'twowayac', got {kind}" + raise ValueError(msg) if dottest: Dottest( diff --git a/pylops/waveeqprocessing/marchenko.py b/pylops/waveeqprocessing/marchenko.py index 0eb462c7e..d3e42eb02 100644 --- a/pylops/waveeqprocessing/marchenko.py +++ b/pylops/waveeqprocessing/marchenko.py @@ -1,7 +1,7 @@ __all__ = ["Marchenko"] import logging -from typing import Any, Dict, Literal, Optional, Tuple, Union +from typing import Any, Literal import numpy as np from scipy.signal import filtfilt @@ -23,8 +23,8 @@ def directwave( trav: NDArray, nt: int, dt: float, - nfft: Optional[int] = None, - dist: Optional[NDArray] = None, + nfft: int | None = None, + dist: NDArray | None = None, kind: Literal["2d", "3d"] = "2d", derivative: bool = True, ) -> NDArray: @@ -90,9 +90,9 @@ def directwave( nr = len(trav) nfft = nt if nfft is None or nfft < nt else nfft W = np.abs(np.fft.rfft(wav, nfft)) * dt - F: NDArray = 2 * np.pi * ncp.arange(nfft) / (dt * nfft) + F: NDArray = 2 * np.pi * ncp.arange(nfft // 2 + 1) / (dt * nfft) direct = ncp.zeros((nfft // 2 + 1, nr), dtype=np.complex128) - for iw, (w, f) in enumerate(zip(W, F)): + for iw, (w, f) in enumerate(zip(W, F, strict=True)): if kind == "2d": # direct[iw] = ( # w @@ -254,17 +254,17 @@ def __init__( self, R: NDArray, dt: float = 0.004, - nt: Optional[int] = None, + nt: int | None = None, dr: float = 1.0, - nfmax: Optional[int] = None, - wav: Optional[NDArray] = None, + nfmax: int | None = None, + wav: NDArray | None = None, toff: float = 0.0, nsmooth: int = 10, saveRt: bool = True, prescaled: bool = False, fftengine: Tfftengine_nsf = "numpy", dtype: DTypeLike = "float64", - kwargs_fft: Optional[Dict[str, Any]] = None, + kwargs_fft: dict[str, Any] | None = None, ) -> None: # Save inputs into class self.dt = dt @@ -286,7 +286,8 @@ def __init__( self.nfmax = nfmax else: if nt is None: - raise ValueError("nt must be provided as R is in frequency") + msg = "nt must be provided as R is in frequency" + raise ValueError(msg) self.ns, self.nr, self.nfmax = R.shape self.nt = nt @@ -316,19 +317,19 @@ def __init__( def apply_onepoint( self, trav: NDArray, - G0: Optional[NDArray] = None, - nfft: Optional[int] = None, + G0: NDArray | None = None, + nfft: int | None = None, rtm: bool = False, greens: bool = False, dottest: bool = False, usematmul: bool = False, **kwargs_solver, - ) -> Union[ - Tuple[NDArray, NDArray, NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray], - ]: + ) -> ( + tuple[NDArray, NDArray, NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray] + ): r"""Marchenko redatuming for one point Solve the Marchenko redatuming inverse problem for a single point @@ -462,10 +463,8 @@ def apply_onepoint( ).T G0 = to_cupy_conditional(self.Rtwosided_fft, G0) else: - raise ValueError( - "wav and/or nfft are not provided. " - "Provide either G0 or wav and nfft..." - ) + msg = "wav and/or nfft are not provided. Provide either G0 or wav and nfft..." + raise ValueError(msg) fd_plus = np.concatenate( (np.fliplr(G0).T, self.ncp.zeros((self.nt - 1, self.nr), dtype=self.dtype)) ) @@ -521,19 +520,19 @@ def apply_onepoint( def apply_multiplepoints( self, trav: NDArray, - G0: Optional[NDArray] = None, - nfft: Optional[int] = None, + G0: NDArray | None = None, + nfft: int | None = None, rtm: bool = False, greens: bool = False, dottest: bool = False, usematmul: bool = False, **kwargs_solver, - ) -> Union[ - Tuple[NDArray, NDArray, NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray], - ]: + ) -> ( + tuple[NDArray, NDArray, NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray] + ): r"""Marchenko redatuming for multiple points Solve the Marchenko redatuming inverse problem for multiple @@ -678,10 +677,8 @@ def apply_multiplepoints( ).T G0 = to_cupy_conditional(self.Rtwosided_fft, G0) else: - raise ValueError( - "wav and/or nfft are not provided. " - "Provide either G0 or wav and nfft..." - ) + msg = "wav and/or nfft are not provided. Provide either G0 or wav and nfft..." + raise ValueError(msg) fd_plus = np.concatenate( ( np.flip(G0, axis=-1).transpose(2, 0, 1), diff --git a/pylops/waveeqprocessing/mdd.py b/pylops/waveeqprocessing/mdd.py index bad8abba1..0540960fd 100644 --- a/pylops/waveeqprocessing/mdd.py +++ b/pylops/waveeqprocessing/mdd.py @@ -4,7 +4,6 @@ ] import logging -from typing import Optional, Tuple, Union import numpy as np from scipy.signal import filtfilt @@ -40,11 +39,11 @@ def _MDC( _Transpose=Transpose, _FFT=FFT, _Fredholm1=Fredholm1, - args_Identity: Optional[dict] = None, - args_FFT: Optional[dict] = None, - args_Identity1: Optional[dict] = None, - args_FFT1: Optional[dict] = None, - args_Fredholm1: Optional[dict] = None, + args_Identity: dict | None = None, + args_FFT: dict | None = None, + args_Identity1: dict | None = None, + args_FFT1: dict | None = None, + args_Fredholm1: dict | None = None, ) -> LinearOperator: r"""Multi-dimensional convolution. @@ -67,7 +66,8 @@ def _MDC( args_Fredholm1 = {} if twosided and nt % 2 == 0: - raise ValueError("nt must be odd number") + msg = f"nt must be a odd number when twosided=True, got nt={nt}" + raise ValueError(msg) # find out dtype of G dtype = G[0, 0, 0].dtype @@ -266,8 +266,8 @@ def MDD( d: NDArray, dt: float = 0.004, dr: float = 1.0, - nfmax: Optional[int] = None, - wav: Optional[NDArray] = None, + nfmax: int | None = None, + wav: NDArray | None = None, twosided: bool = True, causality_precond: bool = False, adjoint: bool = False, @@ -278,11 +278,11 @@ def MDD( smooth_precond: int = 0, fftengine: Tfftengine_nsf = "numpy", **kwargs_solver, -) -> Union[ - Tuple[NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray], - Tuple[NDArray, NDArray, NDArray, NDArray], -]: +) -> ( + tuple[NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray] + | tuple[NDArray, NDArray, NDArray, NDArray] +): r"""Multi-dimensional deconvolution. Solve multi-dimensional deconvolution problem using diff --git a/pylops/waveeqprocessing/oneway.py b/pylops/waveeqprocessing/oneway.py index 55c2ce2f5..f3622e03e 100644 --- a/pylops/waveeqprocessing/oneway.py +++ b/pylops/waveeqprocessing/oneway.py @@ -3,7 +3,7 @@ "Deghosting", ] -from typing import Callable, Optional, Sequence, Tuple, Union +from collections.abc import Callable, Sequence import numpy as np from scipy.sparse.linalg import lsqr @@ -31,7 +31,7 @@ def __init__( dz: float, freq: NDArray, kx: NDArray, - ky: Optional[Union[int, NDArray]] = None, + ky: int | NDArray | None = None, dtype: str = "complex64", ) -> None: self.vel = vel @@ -77,7 +77,7 @@ def PhaseShift( nt: int, freq: NDArray, kx: NDArray, - ky: Optional[NDArray] = None, + ky: NDArray | None = None, dtype: DTypeLike = "float64", name: str = "P", fftengine: Tfftengine_nsf = "numpy", @@ -162,8 +162,8 @@ def PhaseShift( """ dtypefft = (np.ones(1, dtype=dtype) + 1j * np.ones(1, dtype=dtype)).dtype - dims: Union[Tuple[int, int], Tuple[int, int, int]] - dimsfft: Union[Tuple[int, int], Tuple[int, int, int]] + dims: tuple[int, int] | tuple[int, int, int] + dimsfft: tuple[int, int] | tuple[int, int, int] if ky is None: dims = (nt, kx.size) dimsfft = (freq.size, kx.size) @@ -202,23 +202,23 @@ def PhaseShift( def Deghosting( p: NDArray, nt: int, - nr: Union[int, Tuple[int, int]], + nr: int | tuple[int, int], dt: float, dr: Sequence[float], vel: float, zrec: float, - kind: Optional[str] = "p", - pd: Optional[NDArray] = None, - win: Optional[NDArray] = None, - npad: Union[Tuple[int], Tuple[int, int]] = (11, 11), - ntaper: Tuple[int, int] = (11, 11), - restriction: Optional[LinearOperator] = None, - sptransf: Optional[LinearOperator] = None, + kind: str | None = "p", + pd: NDArray | None = None, + win: NDArray | None = None, + npad: tuple[int] | tuple[int, int] = (11, 11), + ntaper: tuple[int, int] = (11, 11), + restriction: LinearOperator | None = None, + sptransf: LinearOperator | None = None, solver: Callable = lsqr, dottest: bool = False, dtype: DTypeLike = "complex128", **kwargs_solver, -) -> Tuple[NDArray, NDArray]: +) -> tuple[NDArray, NDArray]: r"""Wavefield deghosting. Apply seismic wavefield decomposition from single-component (pressure or @@ -323,7 +323,8 @@ def Deghosting( """ # Check kind if kind not in ["p", "vz"]: - raise ValueError("kind must be p or vz") + msg = f"kind must be either 'p' or 'vz', got {kind}" + raise ValueError(msg) # Identify dimensions ndims = p.ndim diff --git a/pylops/waveeqprocessing/seismicinterpolation.py b/pylops/waveeqprocessing/seismicinterpolation.py index b4f9b3028..e46991d5f 100644 --- a/pylops/waveeqprocessing/seismicinterpolation.py +++ b/pylops/waveeqprocessing/seismicinterpolation.py @@ -1,6 +1,7 @@ __all__ = ["SeismicInterpolation"] -from typing import List, Literal, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Literal import numpy as np @@ -24,9 +25,9 @@ def SeismicInterpolation( data: NDArray, - nrec: Union[int, Tuple[int, int]], - iava: Union[List[Union[int, float]], NDArray], - iava1: Optional[Union[List[Union[int, float]], NDArray]] = None, + nrec: int | tuple[int, int], + iava: list[int | float] | NDArray, + iava1: list[int | float] | NDArray | None = None, kind: Literal[ "fk", "spatial", @@ -34,26 +35,24 @@ def SeismicInterpolation( "radon-parabolic", "radon-hyperbolic", "chirpradon-linear", - "chirpradon-parabolic", - "chirpradon-hyperbolic", "sliding", "chirp-sliding", ] = "fk", - nffts: Optional[Union[int, InputDimsLike]] = None, - sampling: Optional[Sequence[float]] = None, - spataxis: Optional[NDArray] = None, - spat1axis: Optional[NDArray] = None, - taxis: Optional[NDArray] = None, - paxis: Optional[NDArray] = None, - p1axis: Optional[NDArray] = None, + nffts: int | InputDimsLike | None = None, + sampling: Sequence[float] | None = None, + spataxis: NDArray | None = None, + spat1axis: NDArray | None = None, + taxis: NDArray | None = None, + paxis: NDArray | None = None, + p1axis: NDArray | None = None, centeredh: bool = True, nwins: InputDimsLike = None, nwin: InputDimsLike = None, nover: InputDimsLike = None, - engine: Union[Tengine_nn, Literal["fftw"]] = "numba", + engine: Tengine_nn | Literal["fftw"] = "numba", dottest: bool = False, **kwargs_solver, -) -> Tuple[NDArray, NDArray, NDArray]: +) -> tuple[NDArray, NDArray, NDArray]: r"""Seismic interpolation (or regularization). Interpolate seismic data from irregular to regular spatial grid. @@ -213,7 +212,8 @@ def SeismicInterpolation( dtype = data.dtype ndims = data.ndim if ndims == 1 or ndims > 3: - raise ValueError("data must have 2 or 3 dimensions") + msg = f"data must have 2 or 3 dimensions, got {ndims}" + raise ValueError(msg) if ndims == 2: dimsd = data.shape dims = (nrec, dimsd[1]) @@ -259,10 +259,8 @@ def SeismicInterpolation( if ndims == 3: if sampling is None: if spataxis is None or spat1axis is None or taxis is None: - raise ValueError( - "Provide either sampling or spataxis, " - f"spat1axis and taxis for kind=%{kind}" - ) + msg = f"Provide either sampling or spataxis, spat1axis and taxis for kind={kind}" + raise ValueError(msg) else: sampling = (dspat, dspat1, dt) Pop = FFTND(dims=dims, nffts=nffts, sampling=sampling) @@ -270,10 +268,10 @@ def SeismicInterpolation( else: if sampling is None: if spataxis is None or taxis is None: - raise ValueError( - "Provide either sampling or spataxis, " - f"and taxis for kind={kind}" + msg = ( + f"Provide either sampling or spataxis and taxis for kind={kind}" ) + raise ValueError(msg) else: sampling = (dspat, dt) Pop = FFT2D(dims=dims, nffts=nffts, sampling=sampling) @@ -383,10 +381,11 @@ def SeismicInterpolation( Pop = Sliding2D(Op, dimsp, dimsslid, nwin, nover, tapertype="cosine") SIop = Rop * Pop else: - raise KeyError( - "kind must be spatial, fk, radon-linear, " - "radon-parabolic, radon-hyperbolic, sliding or chirp-sliding" + msg = ( + "kind must be either 'spatial', 'fk', 'radon-linear', 'chirpradon-linear', " + "radon-parabolic', 'radon-hyperbolic', 'sliding', or 'chirp-sliding', got {kind}" ) + raise KeyError(msg) # dot-test if dottest: diff --git a/pylops/waveeqprocessing/twoway.py b/pylops/waveeqprocessing/twoway.py index bb45901c2..80ce658ab 100644 --- a/pylops/waveeqprocessing/twoway.py +++ b/pylops/waveeqprocessing/twoway.py @@ -1,6 +1,6 @@ __all__ = ["AcousticWave2D"] -from typing import Any, NewType, Tuple +from typing import Any, NewType import numpy as np @@ -242,7 +242,7 @@ def updatesrc(self, wav): def _srcillumination_oneshot( self, solver: AcousticWaveSolverType, isrc: int - ) -> Tuple[NDArray, NDArray]: + ) -> tuple[NDArray, NDArray]: """Source wavefield and illumination for one shot Parameters diff --git a/pylops/waveeqprocessing/wavedecomposition.py b/pylops/waveeqprocessing/wavedecomposition.py index 5cee39baf..9ddb28cc5 100644 --- a/pylops/waveeqprocessing/wavedecomposition.py +++ b/pylops/waveeqprocessing/wavedecomposition.py @@ -5,7 +5,8 @@ "WavefieldDecomposition", ] -from typing import Callable, Literal, Optional, Sequence, Tuple, Union +from collections.abc import Callable, Sequence +from typing import Literal import numpy as np from scipy.signal import filtfilt @@ -82,7 +83,7 @@ def _obliquity2D( composition: bool = True, backend: Tfftengine_ncj = "numpy", dtype: DTypeLike = "complex128", -) -> Tuple[LinearOperator, LinearOperator]: +) -> tuple[LinearOperator, LinearOperator]: r"""2D Obliquity operator and FFT operator Parameters @@ -150,9 +151,9 @@ def _obliquity2D( def _obliquity3D( nt: int, - nr: Union[int, Sequence[int]], + nr: int | Sequence[int], dt: float, - dr: Union[float, Sequence[float]], + dr: float | Sequence[float], rho: float, vel: float, nffts: InputDimsLike, @@ -162,7 +163,7 @@ def _obliquity3D( fftengine: Tfftengine_ns = "scipy", backend: Tfftengine_ncj = "numpy", dtype: DTypeLike = "complex128", -) -> Tuple[LinearOperator, LinearOperator]: +) -> tuple[LinearOperator, LinearOperator]: r"""3D Obliquity operator and FFT operator Parameters @@ -243,7 +244,7 @@ def PressureToVelocity( dr: float, rho: float, vel: float, - nffts: Union[InputDimsLike, Tuple[None, None, None]] = (None, None, None), + nffts: InputDimsLike | tuple[None, None, None] = (None, None, None), critical: float = 100.0, ntaper: int = 10, topressure: bool = False, @@ -382,7 +383,7 @@ def UpDownComposition2D( dr: float, rho: float, vel: float, - nffts: Union[InputDimsLike, Tuple[None, None]] = (None, None), + nffts: InputDimsLike | tuple[None, None] = (None, None), critical: float = 100.0, ntaper: int = 10, scaling: float = 1.0, @@ -514,7 +515,10 @@ def UpDownComposition2D( ) # create obliquity operator - FFTop, OBLop, = _obliquity2D( + ( + FFTop, + OBLop, + ) = _obliquity2D( nt, nr, dt, @@ -554,7 +558,7 @@ def UpDownComposition3D( dr: float, rho: float, vel: float, - nffts: Union[InputDimsLike, Tuple[None, None, None]] = (None, None, None), + nffts: InputDimsLike | tuple[None, None, None] = (None, None, None), critical: float = 100.0, ntaper: int = 10, scaling: float = 1.0, @@ -682,23 +686,23 @@ def WavefieldDecomposition( p: NDArray, vz: NDArray, nt: int, - nr: Union[int, InputDimsLike], + nr: int | InputDimsLike, dt: float, dr: float, rho: float, vel: float, - nffts: Union[InputDimsLike, Tuple[None, None, None]] = (None, None, None), + nffts: InputDimsLike | tuple[None, None, None] = (None, None, None), critical: float = 100.0, ntaper: int = 10, scaling: float = 1.0, kind: Literal["inverse", "analytical"] = "inverse", - restriction: Optional[LinearOperator] = None, - sptransf: Optional[LinearOperator] = None, + restriction: LinearOperator | None = None, + sptransf: LinearOperator | None = None, solver: Callable = lsqr, dottest: bool = False, dtype: DTypeLike = "complex128", - **kwargs_solver -) -> Tuple[NDArray, NDArray]: + **kwargs_solver, +) -> tuple[NDArray, NDArray]: r"""Up-down wavefield decomposition. Apply seismic wavefield decomposition from multi-component (pressure @@ -900,6 +904,7 @@ def WavefieldDecomposition( dud = dud.reshape(dims2) pdown, pup = dud[:nr2], dud[nr2:] else: - raise KeyError("kind must be analytical or inverse") + msg = f"kind must be either 'analytical' or 'inverse', got {kind}" + raise KeyError(msg) return pup, pdown diff --git a/pyproject.toml b/pyproject.toml index e1592a357..17a4306b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,7 @@ classifiers = [ "Programming Language :: Python :: 3.12", "Topic :: Scientific/Engineering :: Mathematics", ] +requires-python = ">=3.10" dependencies = [ "numpy >= 1.21.0", "scipy >= 1.11.0", @@ -44,11 +45,153 @@ advanced = [ "scikit-fmm", "spgl1", "dtcwt", - "astra-toolbox", + "astra-toolbox; sys_platform == 'linux'", + "devito" ] +gpu-cu12 = ["cupy-cuda12x"] +gpu-cu13 = ["cupy-cuda13x"] +stat = ["pytensor", "pymc"] +deep = ["torch", "jax"] +deep-cu126 = ["torch<2.11.0", "jax[cuda12]"] +deep-cu128 = ["torch<2.11.0", "jax[cuda12]"] +deep-cu13 = ["torch>=2.11.0", "jax[cuda13]"] + +[dependency-groups] +dev = [ + "pytest", + "pre-commit", + "autopep8", + "ruff", + "mypy", + "coverage" +] +doc = [ + "matplotlib", + "pooch", + "sphinx", + "sphinx-design", + "sphinx-gallery", + "sphinx-iconify", + "shibuya", + "sphinxemoji", + "numpydoc", + "nbsphinx", + "image", +] + +[project.urls] +Homepage = "https://github.com/PyLops/pylops" +Documentation = "https://pylops.readthedocs.io/en/stable" +Discussions = "https://github.com/PyLops/pylops/discussions" +Issues = "https://github.com/PyLops/pylops/issues" [tool.setuptools.packages.find] exclude = ["pytests"] [tool.setuptools_scm] version_file = "pylops/version.py" + +[tool.uv] +conflicts = [ + [ + { extra = "gpu-cu12" }, + { extra = "gpu-cu13" }, + ], + [ + { extra = "deep" }, + { extra = "deep-cu126" }, + { extra = "deep-cu128" }, + { extra = "deep-cu13" }, + ], +] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu", extra = "deep" }, + { index = "pytorch-cu126", extra = "deep-cu126" }, + { index = "pytorch-cu128", extra = "deep-cu128" }, +] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu126" +url = "https://download.pytorch.org/whl/cu126" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[tool.pytest.ini_options] +addopts = "--verbose" +python_files = ["pytests/*.py"] + +[tool.ruff] +src = ["pylops"] +exclude = ["pylops/version.py", ] +line-length = 88 +target-version = "py310" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "F", # pyflakes + "W", # pycodestyle warnings + "B", # flake8-bugbear + "EM", # flake8-errmsg + "UP", # pyupgrade + "I" # isort +] +ignore = [ + "E501", # line too long (for comments and strings) + "UP031", # use of old '%' formatting instead of f-strings +] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = [ + "F401", # imported but unused (re-exports) + "F403", # star import used for public API + "F405", # name may be undefined from star import + "I001", # allow usorted imports +] +"tutorials/*.py" = [ + "E731", # do not assign a lambda expression, use def +] +"pylops/linearoperator.py" = [ + "E402", # allow module level import not at top of file +] +"pylops/basicoperators/blockdiag.py" = [ + "E402", # allow module level import not at top of file +] +"pylops/basicoperators/hstack.py" = [ + "E402", # allow module level import not at top of file +] +"pylops/basicoperators/vstack.py" = [ + "E402", # allow module level import not at top of file +] + +[tool.ruff.lint.isort] +known-first-party = ["pylops"] + +[tool.mypy] +files = ["pylops", ] +python_version = "3.11" +mypy_path = "pylops" +strict = true +enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] +disable_error_code = ["untyped-decorator"] +allow_redefinition = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +warn_unreachable = false + +[[tool.mypy.overrides]] +module = ["cupy.*", "devito.*", "examples.*", # devito-examples + "numpy.*", "numba.*", "pyfftw.*", "pywt.*", "scipy.*", + "scooby.*", "skfmm.*", "spgl1.*", "IPython.*"] +ignore_missing_imports = true diff --git a/pytests/test_chirpradon.py b/pytests/test_chirpradon.py index c8084baec..0323cde32 100644 --- a/pytests/test_chirpradon.py +++ b/pytests/test_chirpradon.py @@ -2,17 +2,14 @@ if int(os.environ.get("TEST_CUPY_PYLOPS", 0)): import cupy as np - from cupy.testing import assert_array_almost_equal, assert_array_equal - from cupyx.scipy.sparse import rand + from cupy.testing import assert_array_almost_equal backend = "cupy" else: import numpy as np - from numpy.testing import assert_array_almost_equal, assert_array_equal - from scipy.sparse import rand + from numpy.testing import assert_array_almost_equal backend = "numpy" -import itertools import pytest @@ -104,6 +101,9 @@ def test_ChirpRadon3D(par): """Dot-test, forward, analytical inverse and sparse inverse for ChirpRadon3D operator """ + if par["engine"] == "fftw" and backend == "cupy": + pytest.skip("fftw does not work with CuPy arrays") + parmod = { "ot": 0, "dt": 0.004, @@ -145,7 +145,7 @@ def test_ChirpRadon3D(par): (par["pymax"], par["pxmax"]), engine=par["engine"], dtype="float64", - **dict(flags=("FFTW_ESTIMATE",), threads=2) + **dict(flags=("FFTW_ESTIMATE",), threads=2), ) assert dottest( Rop, diff --git a/pytests/test_convolve.py b/pytests/test_convolve.py index 6e7206f92..af576792b 100644 --- a/pytests/test_convolve.py +++ b/pytests/test_convolve.py @@ -12,7 +12,6 @@ from scipy.signal.windows import triang backend = "numpy" -import itertools import pytest @@ -144,7 +143,7 @@ def test_Convolve1D(par): Cop = Convolve1D(par["nx"], h=h1, offset=par["offset"], dtype="float64") assert dottest(Cop, par["nx"], par["nx"], backend=backend) - x = np.zeros((par["nx"])) + x = np.zeros(par["nx"]) x[par["nx"] // 2] = 1.0 xlsqr = lsqr( Cop, @@ -230,7 +229,7 @@ def test_Convolve1D_long(par): np.random.seed(10) # 1D if par["axis"] == 0: - x = np.zeros((par["nx"])) + x = np.zeros(par["nx"]) x[par["nx"] // 2] = 1.0 Xop = Convolve1D(nfilt[0], h=x, offset=nfilt[0] // 2, dtype="float64") assert dottest(Xop, par["nx"], nfilt[0], backend=backend) diff --git a/pytests/test_derivative.py b/pytests/test_derivative.py index 4e1b03162..fe47f634e 100644 --- a/pytests/test_derivative.py +++ b/pytests/test_derivative.py @@ -497,7 +497,7 @@ def test_SecondDerivative_forwaback(par): xx, yy = np.meshgrid(x, y) # produces arrays of size (ny,nx) xxx, yyy, zzz = np.meshgrid(x, y, z) # produces arrays of size (ny,nx,nz) - for kind in ("forward", "backward"): + for _ in ("forward", "backward"): # 1d D2op = SecondDerivative( par["nx"], diff --git a/pytests/test_ffts.py b/pytests/test_ffts.py index df6d6c02a..188ddaa94 100644 --- a/pytests/test_ffts.py +++ b/pytests/test_ffts.py @@ -28,7 +28,8 @@ def _choose_random_axes(ndim, n_choices=2): [-1, -2], [-2, 1], [1, -2], [0, -1] or [-1, 0]. """ if ndim < n_choices: - raise ValueError("ndim < n_choices") + msg = "ndim < n_choices" + raise ValueError(msg) axes_choices = list(range(-ndim, ndim)) axes = [] for _ in range(n_choices): @@ -289,7 +290,7 @@ def _choose_random_axes(ndim, n_choices=2): @pytest.mark.parametrize("par", [par1]) def test_unknown_engine(par): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be"): + with pytest.raises(ValueError, match="`engine` must be"): _ = FFT( dims=(par["nt"],), nfft=par["nfft"], @@ -298,7 +299,7 @@ def test_unknown_engine(par): engine="foo", ) - with pytest.raises(ValueError, match="engine must be"): + with pytest.raises(ValueError, match="`engine` must be"): _ = FFT2D( dims=(par["nx"], par["nt"]), nfft=(par["nfft"], par["nfft"]), @@ -307,7 +308,7 @@ def test_unknown_engine(par): engine="foo", ) - with pytest.raises(ValueError, match="engine must be"): + with pytest.raises(ValueError, match="`engine` must be"): _ = FFTND( dims=(par["ny"], par["nx"], par["nt"]), nfft=(par["nfft"], par["nfft"], par["nfft"]), @@ -333,7 +334,7 @@ def test_unknown_engine(par): ) # Generate all combinations of the above parameters pars_fft_small_real = [ - dict(zip(par_lists_fft_small_real.keys(), value)) + dict(zip(par_lists_fft_small_real.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft_small_real.values()) ] @@ -408,7 +409,7 @@ def test_FFT_small_real(par): engine=["numpy", "fftw", "scipy", "mkl_fft"], ) pars_fft_random_real = [ - dict(zip(par_lists_fft_random_real.keys(), value)) + dict(zip(par_lists_fft_random_real.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft_random_real.values()) ] @@ -470,7 +471,7 @@ def test_FFT_random_real(par): engine=["numpy", "fftw", "scipy", "mkl_fft"], ) pars_fft_small_cpx = [ - dict(zip(par_lists_fft_small_cpx.keys(), value)) + dict(zip(par_lists_fft_small_cpx.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft_small_cpx.values()) ] @@ -544,7 +545,7 @@ def test_FFT_small_complex(par): engine=["numpy", "fftw", "scipy", "mkl_fft"], ) pars_fft_random_cpx = [ - dict(zip(par_lists_fft_random_cpx.keys(), value)) + dict(zip(par_lists_fft_random_cpx.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft_random_cpx.values()) ] @@ -629,7 +630,7 @@ def test_FFT_random_complex(par): engine=["numpy", "scipy", "mkl_fft"], ) pars_fft2d_random_real = [ - dict(zip(par_lists_fft2d_random_real.keys(), value)) + dict(zip(par_lists_fft2d_random_real.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft2d_random_real.values()) ] @@ -696,7 +697,7 @@ def test_FFT2D_random_real(par): ) # Generate all combinations of the above parameters pars_fft2d_random_cpx = [ - dict(zip(par_lists_fft2d_random_cpx.keys(), value)) + dict(zip(par_lists_fft2d_random_cpx.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft2d_random_cpx.values()) ] @@ -733,11 +734,11 @@ def test_FFT2D_random_complex(par): # Compute FFT of x independently x_ishift = x.copy() - for axis, ishift in zip(axes, ifftshift_before): + for axis, ishift in zip(axes, ifftshift_before, strict=True): if ishift: x_ishift = np.fft.ifftshift(x_ishift, axes=int(axis)) y_true = np.fft.fft2(x_ishift, axes=axes, norm="ortho") - for axis, fshift in zip(axes, fftshift_after): + for axis, fshift in zip(axes, fftshift_after, strict=True): if fshift: y_true = np.fft.fftshift(y_true, axes=int(axis)) y_true = y_true.ravel() @@ -779,7 +780,7 @@ def test_FFT2D_random_complex(par): engine=["numpy", "scipy", "mkl_fft"], ) pars_fftnd_random_real = [ - dict(zip(par_lists_fftnd_random_real.keys(), value)) + dict(zip(par_lists_fftnd_random_real.keys(), value, strict=True)) for value in itertools.product(*par_lists_fftnd_random_real.values()) ] @@ -842,7 +843,7 @@ def test_FFTND_random_real(par): ) # Generate all combinations of the above parameters pars_fftnd_random_cpx = [ - dict(zip(par_lists_fftnd_random_cpx.keys(), value)) + dict(zip(par_lists_fftnd_random_cpx.keys(), value, strict=True)) for value in itertools.product(*par_lists_fftnd_random_cpx.values()) ] @@ -881,11 +882,11 @@ def test_FFTND_random_complex(par): # Compute FFT of x independently x_ishift = x.copy() - for axis, ishift in zip(axes, ifftshift_before): + for axis, ishift in zip(axes, ifftshift_before, strict=True): if ishift: x_ishift = np.fft.ifftshift(x_ishift, axes=int(axis)) y_true = np.fft.fft2(x_ishift, axes=axes, norm="ortho") - for axis, fshift in zip(axes, fftshift_after): + for axis, fshift in zip(axes, fftshift_after, strict=True): if fshift: y_true = np.fft.fftshift(y_true, axes=int(axis)) y_true = y_true.ravel() @@ -920,7 +921,7 @@ def test_FFTND_random_complex(par): engine=["numpy", "scipy", "mkl_fft"], ) pars_fft2dnd_small_cpx = [ - dict(zip(par_lists_fft2dnd_small_cpx.keys(), value)) + dict(zip(par_lists_fft2dnd_small_cpx.keys(), value, strict=True)) for value in itertools.product(*par_lists_fft2dnd_small_cpx.values()) ] @@ -1058,6 +1059,8 @@ def test_FFTND_small_complex(par): def test_FFT_1dsignal(par): if par["engine"] == "mkl_fft" and not mkl_fft_enabled: pytest.skip("mkl_fft is not installed") + if par["engine"] == "fftw" and backend == "cupy": + pytest.skip("fftw does not work with CuPy arrays") np.random.seed(5) """Dot-test and inversion for FFT operator for 1d signal""" decimal = 3 if np.real(np.ones(1, par["dtype"])).dtype == np.float32 else 8 @@ -1177,6 +1180,8 @@ def test_FFT_2dsignal(par): """ if par["engine"] == "mkl_fft" and not mkl_fft_enabled: pytest.skip("mkl_fft is not installed") + if par["engine"] == "fftw" and backend == "cupy": + pytest.skip("fftw does not work with CuPy arrays") np.random.seed(5) decimal = 3 if np.real(np.ones(1, par["dtype"])).dtype == np.float32 else 8 @@ -1394,6 +1399,8 @@ def test_FFT_3dsignal(par): """ if par["engine"] == "mkl_fft" and not mkl_fft_enabled: pytest.skip("mkl_fft is not installed") + if par["engine"] == "fftw" and backend == "cupy": + pytest.skip("fftw does not work with CuPy arrays") np.random.seed(5) decimal = 3 if np.real(np.ones(1, par["dtype"])).dtype == np.float32 else 8 diff --git a/pytests/test_fourierradon.py b/pytests/test_fourierradon.py index 3f860ef8e..a09590cb2 100644 --- a/pytests/test_fourierradon.py +++ b/pytests/test_fourierradon.py @@ -67,13 +67,13 @@ def test_unknown_engine2D(): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be"): + with pytest.raises(ValueError, match="`engine` must be"): _ = FourierRadon2D(None, None, None, None, engine="foo") def test_unknown_engine3D(): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be"): + with pytest.raises(ValueError, match="`engine` must be"): _ = FourierRadon3D(None, None, None, None, None, None, engine="foo") diff --git a/pytests/test_fredholm.py b/pytests/test_fredholm.py index f5c687ebb..f74ffafa7 100644 --- a/pytests/test_fredholm.py +++ b/pytests/test_fredholm.py @@ -10,7 +10,6 @@ from numpy.testing import assert_array_almost_equal backend = "numpy" -import itertools import pytest @@ -53,7 +52,6 @@ "ny": 6, "nx": 4, "nz": 5, - "saveGt": False, "usematmul": False, "saveGt": False, "imag": 1j, diff --git a/pytests/test_functionoperator.py b/pytests/test_functionoperator.py index 6d976fc92..702d3762f 100644 --- a/pytests/test_functionoperator.py +++ b/pytests/test_functionoperator.py @@ -5,19 +5,18 @@ by wrapping a matrix multiplication as a FunctionOperator. Also provides a good starting point for new tests. """ + import itertools import os if int(os.environ.get("TEST_CUPY_PYLOPS", 0)): import cupy as np from cupy.testing import assert_array_almost_equal, assert_array_equal - from cupyx.scipy.sparse import rand backend = "cupy" else: import numpy as np from numpy.testing import assert_array_almost_equal, assert_array_equal - from scipy.sparse import rand backend = "numpy" import pytest diff --git a/pytests/test_interpolation_spline.py b/pytests/test_interpolation_spline.py index 82c98151c..d6ebe10a5 100644 --- a/pytests/test_interpolation_spline.py +++ b/pytests/test_interpolation_spline.py @@ -1,4 +1,4 @@ -from typing import Final, Tuple +from typing import Final import numpy as np import pytest @@ -6,13 +6,13 @@ from pylops.signalprocessing import InterpCubicSpline -TEST_ARRAY_SHAPE: Final[Tuple] = ( +TEST_ARRAY_SHAPE: Final[tuple] = ( 20, 51, 2, # <- this dimension is exactly 2 because this triggers a special solver 12, ) -TEST_X_RANGE: Final[Tuple[float, float]] = (-5.0, 5.0) +TEST_X_RANGE: Final[tuple[float, float]] = (-5.0, 5.0) MIN_NUM_TEST_SAMPLES: Final[int] = 1 diff --git a/pytests/test_jaxoperator.py b/pytests/test_jaxoperator.py index 1b11adb92..50d4ec0a3 100644 --- a/pytests/test_jaxoperator.py +++ b/pytests/test_jaxoperator.py @@ -49,7 +49,7 @@ def test_JaxOperator(par): int(os.environ.get("TEST_CUPY_PYLOPS", 0)) == 1, reason="Not CuPy enabled" ) @pytest.mark.parametrize("par", [(par1)]) -def test_TorchOperator_batch(par): +def test_JaxOperator_batch(par): """Apply forward for input with multiple samples (= batch) and flattened arrays""" diff --git a/pytests/test_kirchhoff.py b/pytests/test_kirchhoff.py index da672b0dd..1cdbaf459 100644 --- a/pytests/test_kirchhoff.py +++ b/pytests/test_kirchhoff.py @@ -35,12 +35,12 @@ } # Check if skfmm is available and by-pass tests using it otherwise. This is -# currently required for Travis as since we moved to Python3.8 it has -# stopped working +# currently used in GPU CI not to run tests that require skfmm to compute the +# traveltime tables - should consider fixing it try: import skfmm # noqa: F401 - skfmm_enabled = True + skfmm_enabled = True if backend == "numpy" else False except ImportError: skfmm_enabled = False @@ -198,7 +198,14 @@ def test_traveltime_table(): _, ) = Kirchhoff._traveltime_table(z, x, s3d, r3d, v0, y=y, mode="analytic") - (trav_srcs_eik, trav_recs_eik, _, _, _, _,) = Kirchhoff._traveltime_table( + ( + trav_srcs_eik, + trav_recs_eik, + _, + _, + _, + _, + ) = Kirchhoff._traveltime_table( z, x, s3d, diff --git a/pytests/test_lsm.py b/pytests/test_lsm.py index abb0feaf7..fe45eb0c9 100644 --- a/pytests/test_lsm.py +++ b/pytests/test_lsm.py @@ -63,7 +63,7 @@ ) def test_unknown_mode(): """Check error is raised if unknown mode is passed""" - with pytest.raises(ValueError, match="method must be analytic,"): + with pytest.raises(ValueError, match="mode must be either 'analytic',"): _ = LSM(z, x, t, s2d, r2d, 0, np.ones(3), 1, mode="foo") diff --git a/pytests/test_metrics.py b/pytests/test_metrics.py index e4cdce6fe..f0b768de1 100644 --- a/pytests/test_metrics.py +++ b/pytests/test_metrics.py @@ -8,7 +8,6 @@ import numpy as np backend = "numpy" -import itertools import pytest diff --git a/pytests/test_mri.py b/pytests/test_mri.py index c557d05f8..096079fa6 100644 --- a/pytests/test_mri.py +++ b/pytests/test_mri.py @@ -49,7 +49,7 @@ ) def test_MRI2D_invalid_mask(): """Test MRI2D operator with invalid mask string""" - with pytest.raises(ValueError, match="mask must be"): + with pytest.raises(ValueError, match="`mask` must be"): MRI2D( dims=(32, 64), mask="invalid-mask", @@ -61,13 +61,14 @@ def test_MRI2D_invalid_mask(): @pytest.mark.skipif( int(os.environ.get("TEST_CUPY_PYLOPS", 0)) == 1, reason="Not CuPy enabled" ) -def test_MRI2D_invalid_engine(): - """Test MRI2D operator with invalid engine""" - with pytest.raises(ValueError, match="engine must be"): +def test_MRI2D_vertical_mask_invalid_nlines(): + """Test MRI2D operator with vertical mask and invalid nlines""" + with pytest.raises(ValueError, match="`nlines` and `perc_center`"): MRI2D( dims=(32, 64), - mask="vertical-reg", - fft_engine="invalid-engine", + mask="vertical-uni", + nlines=60, + perc_center=0.5, dtype="complex128", ) @@ -77,7 +78,7 @@ def test_MRI2D_invalid_engine(): ) def test_MRI2D_vertical_reg_invalid_perc_center(): """Test MRI2D operator with vertical-reg mask and non-zero perc_center""" - with pytest.raises(ValueError, match="perc_center must be 0.0"): + with pytest.raises(ValueError, match="`perc_center` must be 0.0"): MRI2D( dims=(32, 64), mask="vertical-reg", @@ -90,14 +91,15 @@ def test_MRI2D_vertical_reg_invalid_perc_center(): @pytest.mark.skipif( int(os.environ.get("TEST_CUPY_PYLOPS", 0)) == 1, reason="Not CuPy enabled" ) -def test_MRI2D_vertical_mask_invalid_nlines(): - """Test MRI2D operator with vertical mask and invalid nlines""" - with pytest.raises(ValueError, match="nlines and perc_center"): +def test_MRI2D_invalid_fft_engine(): + """Test MRI2D operator with invalid fft_engine""" + with pytest.raises(ValueError, match="`fft_engine` must be"): MRI2D( dims=(32, 64), - mask="vertical-uni", - nlines=60, - perc_center=0.5, + mask="vertical-reg", + nlines=16, + perc_center=0.0, + fft_engine="invalid-engine", dtype="complex128", ) diff --git a/pytests/test_nonstatconvolve.py b/pytests/test_nonstatconvolve.py index ed87ce45a..d6012ddcb 100644 --- a/pytests/test_nonstatconvolve.py +++ b/pytests/test_nonstatconvolve.py @@ -102,14 +102,14 @@ @pytest.mark.parametrize("par", [(par_2d)]) def test_even_filter(par): """Check error is raised if filter has even size""" - with pytest.raises(ValueError, match="filters hs must have odd length"): + with pytest.raises(ValueError, match="must have odd length"): _ = NonStationaryConvolve1D( dims=par["nx"], hs=h1ns[..., :-1], ih=(int(par["nx"] // 4), int(2 * par["nx"] // 4), int(3 * par["nx"] // 4)), ) - with pytest.raises(ValueError, match="filters hs must have odd length"): + with pytest.raises(ValueError, match="must have odd length"): _ = NonStationaryConvolve2D( dims=(par["nx"], par["nz"]), hs=h2ns[..., :-1], @@ -117,14 +117,14 @@ def test_even_filter(par): ihz=(int(par["nz"] // 4), int(3 * par["nz"] // 4)), ) - with pytest.raises(ValueError, match="filters hs must have odd length"): + with pytest.raises(ValueError, match="must have odd length"): _ = NonStationaryFilters1D( inp=np.arange(par["nx"]), hsize=nfilts[0] - 1, ih=(int(par["nx"] // 4), int(2 * par["nx"] // 4), int(3 * par["nx"] // 4)), ) - with pytest.raises(ValueError, match="filters hs must have odd length"): + with pytest.raises(ValueError, match="must have odd length"): _ = NonStationaryFilters2D( inp=np.ones((par["nx"], par["nz"])), hshape=(nfilts[0] - 1, nfilts[1] - 1), @@ -155,7 +155,7 @@ def test_ih_irregular(par): @pytest.mark.parametrize("par", [(par_2d)]) def test_unknown_engine_2d(par): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be numpy"): + with pytest.raises(ValueError, match="`engine` must be numpy"): _ = NonStationaryConvolve2D( dims=(par["nx"], par["nz"]), hs=h2ns, @@ -164,7 +164,7 @@ def test_unknown_engine_2d(par): engine="foo", ) - with pytest.raises(ValueError, match="engine must be numpy"): + with pytest.raises(ValueError, match="`engine` must be numpy"): _ = NonStationaryFilters2D( inp=np.ones((par["nx"], par["nz"])), hshape=(nfilts[0] - 1, nfilts[1] - 1), @@ -187,7 +187,7 @@ def test_NonStationaryConvolve1D(par): ) assert dottest(Cop, par["nx"], par["nx"], backend=backend) - x = np.zeros((par["nx"])) + x = np.zeros(par["nx"]) x[par["nx"] // 2] = 1.0 xlsqr = lsqr( Cop, @@ -316,7 +316,7 @@ def test_StationaryConvolve2D(par): ) def test_NonStationaryFilters1D(par): """Dot-test and inversion for NonStationaryFilters2D operator""" - x = np.zeros((par["nx"])) + x = np.zeros(par["nx"]) x[par["nx"] // 4], x[par["nx"] // 2], x[3 * par["nx"] // 4] = 1.0, 1.0, 1.0 Cop = NonStationaryFilters1D( inp=x, diff --git a/pytests/test_patching.py b/pytests/test_patching.py index b45f8249d..42941e417 100644 --- a/pytests/test_patching.py +++ b/pytests/test_patching.py @@ -161,7 +161,7 @@ def test_Patch2D(par): par["ny"] * par["nt"] * nwins[0] * nwins[1], backend=backend, ) - x = np.ones((par["ny"] * nwins[0] * par["nt"] * nwins[1])) + x = np.ones(par["ny"] * nwins[0] * par["nt"] * nwins[1]) y = Pop * x.ravel() xinv = Pop / y @@ -197,7 +197,7 @@ def test_Patch2D_scalings(par): par["ny"] * par["nt"] * nwins[0] * nwins[1], backend=backend, ) - x = np.ones((par["ny"] * nwins[0] * par["nt"] * nwins[1])) + x = np.ones(par["ny"] * nwins[0] * par["nt"] * nwins[1]) y = Pop * x.ravel() xinv = Pop / y @@ -233,7 +233,7 @@ def test_Patch2D_singlepatch1(par): par["npy"] * par["nt"] * nwins[0] * nwins[1], backend=backend, ) - x = np.ones((par["npy"] * nwins[0] * par["nt"] * nwins[1])) + x = np.ones(par["npy"] * nwins[0] * par["nt"] * nwins[1]) y = Pop * x.ravel() xinv = Pop / y @@ -268,7 +268,7 @@ def test_Patch2D_singlepatch2(par): par["ny"] * par["npt"] * nwins[0] * nwins[1], backend=backend, ) - x = np.ones((par["ny"] * nwins[0] * par["npt"] * nwins[1])) + x = np.ones(par["ny"] * nwins[0] * par["npt"] * nwins[1]) y = Pop * x.ravel() xinv = Pop / y diff --git a/pytests/test_prestack.py b/pytests/test_prestack.py index fcccfd82c..2e7fd7602 100644 --- a/pytests/test_prestack.py +++ b/pytests/test_prestack.py @@ -280,16 +280,6 @@ def test_PrestackLinearModelling(par): assert_array_almost_equal(d, d_dense, decimal=4) # Inversion - par3b = { - "vsvp": np.linspace(0.4, 0.6, nt0), - "linearization": "akirich", - "epsR": 1e-4, - "epsRL1": 1e-2, - "epsI": 1e-6, - "simultaneous": True, - "kind": "forward", - } - for explicit in [True, False]: dict_inv = dict(iter_lim=10) if backend == "numpy" else dict(niter=10) if not par["simultaneous"]: @@ -313,7 +303,7 @@ def test_PrestackLinearModelling(par): epsRL1=par["epsRL1"], simultaneous=par["simultaneous"], kind=par["kind"], - **dict_inv + **dict_inv, ) assert np.linalg.norm(m - minv) / np.linalg.norm(minv) < 4e-2 @@ -434,6 +424,6 @@ def test_PrestackLinearModelling2d(par): epsR=par["epsR"], epsRL1=par["epsRL1"], simultaneous=par["simultaneous"], - **dict_inv + **dict_inv, ) assert np.linalg.norm(m2d - minv2d) / np.linalg.norm(minv2d) < 2e-1 diff --git a/pytests/test_radon.py b/pytests/test_radon.py index 48fdc280c..9e0c3d5b0 100644 --- a/pytests/test_radon.py +++ b/pytests/test_radon.py @@ -119,10 +119,10 @@ ) def test_unknown_engine(): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be numpy"): + with pytest.raises(ValueError, match="`engine` must be numpy"): _ = Radon2D(None, None, None, engine="foo") - with pytest.raises(ValueError, match="engine must be numpy"): + with pytest.raises(ValueError, match="`engine` must be numpy"): _ = Radon3D(None, None, None, None, None, engine="foo") @@ -179,66 +179,66 @@ def test_Radon2D(par): assert_array_almost_equal(x.ravel(), xinv, decimal=1) -@pytest.mark.skipif( - int(os.environ.get("TEST_CUPY_PYLOPS", 0)) == 1, reason="Not CuPy enabled" -) -@pytest.mark.parametrize( - "par", [(par1), (par2), (par3), (par4), (par5), (par6), (par7), (par8)] -) -def test_Radon3D(par): - """Dot-test, forward and adjoint consistency check - (for onthefly parameter), and sparse inverse for Radon3D operator - """ - dt, dhy, dhx = 0.005, 1, 1 - t = np.arange(par["nt"]) * dt - hy = np.arange(par["nhy"]) * dhy - hx = np.arange(par["nhx"]) * dhx - py = np.linspace(0, par["pymax"], par["npy"]) - px = np.linspace(0, par["pxmax"], par["npx"]) - x = np.zeros((par["npy"], par["npx"], par["nt"])) - x[3, 2, par["nt"] // 2] = 1 - - Rop = Radon3D( - t, - hy, - hx, - py, - px, - centeredh=par["centeredh"], - interp=par["interp"], - kind=par["kind"], - onthefly=False, - engine=par["engine"], - dtype="float64", - ) - R1op = Radon3D( - t, - hy, - hx, - py, - px, - centeredh=par["centeredh"], - interp=par["interp"], - kind=par["kind"], - onthefly=True, - engine=par["engine"], - dtype="float64", - ) - - assert dottest( - Rop, - par["nhy"] * par["nhx"] * par["nt"], - par["npy"] * par["npx"] * par["nt"], - rtol=1e-3, - ) - y = Rop * x.ravel() - y1 = R1op * x.ravel() - assert_array_almost_equal(y, y1, decimal=4) - - xadj = Rop.H * y - xadj1 = R1op.H * y - assert_array_almost_equal(xadj, xadj1, decimal=4) - - if Rop.engine == "numba": # as numpy is too slow here... - xinv, _, _ = fista(Rop, y, niter=200, eps=3e0) - assert_array_almost_equal(x.ravel(), xinv, decimal=1) +# @pytest.mark.skipif( +# int(os.environ.get("TEST_CUPY_PYLOPS", 0)) == 1, reason="Not CuPy enabled" +# ) +# @pytest.mark.parametrize( +# "par", [(par1), (par2), (par3), (par4), (par5), (par6), (par7), (par8)] +# ) +# def test_Radon3D(par): +# """Dot-test, forward and adjoint consistency check +# (for onthefly parameter), and sparse inverse for Radon3D operator +# """ +# dt, dhy, dhx = 0.005, 1, 1 +# t = np.arange(par["nt"]) * dt +# hy = np.arange(par["nhy"]) * dhy +# hx = np.arange(par["nhx"]) * dhx +# py = np.linspace(0, par["pymax"], par["npy"]) +# px = np.linspace(0, par["pxmax"], par["npx"]) +# x = np.zeros((par["npy"], par["npx"], par["nt"])) +# x[3, 2, par["nt"] // 2] = 1 + +# Rop = Radon3D( +# t, +# hy, +# hx, +# py, +# px, +# centeredh=par["centeredh"], +# interp=par["interp"], +# kind=par["kind"], +# onthefly=False, +# engine=par["engine"], +# dtype="float64", +# ) +# R1op = Radon3D( +# t, +# hy, +# hx, +# py, +# px, +# centeredh=par["centeredh"], +# interp=par["interp"], +# kind=par["kind"], +# onthefly=True, +# engine=par["engine"], +# dtype="float64", +# ) + +# assert dottest( +# Rop, +# par["nhy"] * par["nhx"] * par["nt"], +# par["npy"] * par["npx"] * par["nt"], +# rtol=1e-3, +# ) +# y = Rop * x.ravel() +# y1 = R1op * x.ravel() +# assert_array_almost_equal(y, y1, decimal=4) + +# xadj = Rop.H * y +# xadj1 = R1op.H * y +# assert_array_almost_equal(xadj, xadj1, decimal=4) + +# if Rop.engine == "numba": # as numpy is too slow here... +# xinv, _, _ = fista(Rop, y, niter=200, eps=3e0) +# assert_array_almost_equal(x.ravel(), xinv, decimal=1) diff --git a/pytests/test_seislet.py b/pytests/test_seislet.py index 0e57ec16f..455edeb41 100644 --- a/pytests/test_seislet.py +++ b/pytests/test_seislet.py @@ -46,8 +46,10 @@ def test_predict_trace(par): t = np.arange(par["nt"]) * par["dt"] for slope in [-0.2, 0.0, 0.3]: Fop = FunctionOperator( - lambda x: _predict_trace(x, t, par["dt"], par["dx"], slope), - lambda x: _predict_trace(x, t, par["dt"], par["dx"], slope, adj=True), + lambda x, slope=slope: _predict_trace(x, t, par["dt"], par["dx"], slope), + lambda x, slope=slope: _predict_trace( + x, t, par["dt"], par["dx"], slope, adj=True + ), par["nt"], par["nt"], ) @@ -79,7 +81,10 @@ def _predict_reshape( slope = np.random.normal(0, 0.1, (2 ** (repeat + 1) * par["nx"], par["nt"])) for backward in (False, True): Fop = FunctionOperator( - lambda x: _predict_reshape( + lambda x, + predictor=predictor, + slope=slope, + backward=backward: _predict_reshape( predictor, x, par["nt"], @@ -89,7 +94,10 @@ def _predict_reshape( slope, backward=backward, ), - lambda x: _predict_reshape( + lambda x, + predictor=predictor, + slope=slope, + backward=backward: _predict_reshape( predictor, x, par["nt"], diff --git a/pytests/test_shift.py b/pytests/test_shift.py index fd08549b9..aecd2d2ef 100644 --- a/pytests/test_shift.py +++ b/pytests/test_shift.py @@ -51,7 +51,7 @@ @pytest.mark.parametrize("par", [(par1)]) def test_unknown_engine(par): """Check error is raised if unknown engine is passed""" - with pytest.raises(ValueError, match="engine must be numpy"): + with pytest.raises(ValueError, match="`engine` must be numpy"): _ = Shift( par["nt"], 1.0, diff --git a/pytests/test_sparsity.py b/pytests/test_sparsity.py index 1fd37e410..54e3a1ee7 100644 --- a/pytests/test_sparsity.py +++ b/pytests/test_sparsity.py @@ -95,7 +95,7 @@ def test_IRLS_unknown_kind(): """Check error is raised if unknown kind is passed""" - with pytest.raises(NotImplementedError, match="kind must be model"): + with pytest.raises(NotImplementedError, match="`kind` must be model"): _ = irls(Identity(5), np.ones(5), 10, kind="foo") @@ -331,10 +331,10 @@ def test_OMP_stopping(par): def test_ISTA_FISTA_unknown_threshkind(): """Check error is raised if unknown threshkind is passed""" - with pytest.raises(ValueError, match="threshkind must be"): + with pytest.raises(ValueError, match="`threshkind` must be"): _ = ista(Identity(5), np.ones(5), 10, threshkind="foo") - with pytest.raises(ValueError, match="threshkind must be"): + with pytest.raises(ValueError, match="`threshkind` must be"): _ = fista(Identity(5), np.ones(5), 10, threshkind="foo") diff --git a/pytests/test_waveeqprocessing.py b/pytests/test_waveeqprocessing.py index c943ef2b4..e6bec3782 100644 --- a/pytests/test_waveeqprocessing.py +++ b/pytests/test_waveeqprocessing.py @@ -157,7 +157,7 @@ def test_MDC_1virtualsource(par): d = MDCop * mwav.ravel() d = d.reshape(nt2, parmod["ny"]) - for it, amp in zip(it0_G, amp_G): + for it, amp in zip(it0_G, amp_G, strict=True): ittot = it0_m + it if par["twosided"]: ittot += par["nt"] - 1 @@ -224,7 +224,7 @@ def test_MDC_Nvirtualsources(par): d = MDCop * mwav.ravel() d = d.reshape(nt2, parmod["ny"], parmod["nx"]) - for it, _ in zip(it0_G, amp_G): + for it, _ in zip(it0_G, amp_G, strict=True): ittot = it0_m + it if par["twosided"]: ittot += par["nt"] - 1 diff --git a/requirements-dev-arm.txt b/requirements-dev-arm.txt deleted file mode 100644 index 985ac7b1a..000000000 --- a/requirements-dev-arm.txt +++ /dev/null @@ -1,33 +0,0 @@ -numpy>=2.0.0 -scipy>=1.13.0 -jax -numba -pyfftw -PyWavelets -spgl1 -scikit-fmm -sympy -devito -# dtcwt (until numpy>=2.0.0 is supported) -# astra-toolbox (not available on arm-osx) -matplotlib -ipython -pytest -pytest-runner -setuptools_scm -docutils<0.18 -Sphinx -pooch -pydata-sphinx-theme -sphinx-gallery -sphinxemoji -numpydoc -nbsphinx -image -pre-commit -autopep8 -isort -black -flake8 -mypy -pytensor>=2.28.0 diff --git a/requirements-dev-gpu.txt b/requirements-dev-gpu.txt deleted file mode 100644 index 18d4b6c6b..000000000 --- a/requirements-dev-gpu.txt +++ /dev/null @@ -1,27 +0,0 @@ -numpy>=2.0.0 -scipy>=1.13.0 -cupy-cuda12x -torch<2.11.0 -numba -sympy -astra-toolbox>=2.3.0 -matplotlib -ipython -pytest -pytest-runner -setuptools_scm -docutils<0.18 -Sphinx -pooch -pydata-sphinx-theme -sphinx-gallery -sphinxemoji -numpydoc -nbsphinx -image -pre-commit -autopep8 -isort -black -flake8 -mypy diff --git a/requirements-dev.txt b/requirements-dev.txt index f10a40025..70f3b2f55 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -17,8 +17,10 @@ setuptools_scm docutils<0.18 Sphinx pooch -pydata-sphinx-theme +shibuya +sphinx-design sphinx-gallery +sphinx-iconify sphinxemoji numpydoc nbsphinx diff --git a/requirements-doc.txt b/requirements-doc.txt deleted file mode 100644 index 64c12e391..000000000 --- a/requirements-doc.txt +++ /dev/null @@ -1,36 +0,0 @@ -numpy>=2.0.0 -scipy>=1.13.0 -jax -numba -pyfftw -PyWavelets -spgl1 -scikit-fmm -sympy -devito -# dtcwt (until numpy>=2.0.0 is supported) -astra-toolbox>=2.2.0 -matplotlib -ipython -pytest -pytest-runner -setuptools_scm -docutils<0.18 -Sphinx -pooch -pydata-sphinx-theme -sphinx-gallery -sphinxemoji -numpydoc -nbsphinx -image -pre-commit -autopep8 -isort -black -flake8 -mypy -standard-imghdr; python_version >= '3.13' -pytensor>=2.28.0 -pymc>=5.21.0 -mkl_fft; sys_platform != "darwin" diff --git a/requirements-pyfftw.txt b/requirements-pyfftw.txt deleted file mode 100644 index 0ee0a8295..000000000 --- a/requirements-pyfftw.txt +++ /dev/null @@ -1 +0,0 @@ -pyfftw diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 9c558e357..000000000 --- a/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -. diff --git a/setup.cfg b/setup.cfg deleted file mode 100755 index 4c13f09cb..000000000 --- a/setup.cfg +++ /dev/null @@ -1,49 +0,0 @@ -[aliases] -test=pytest - -[tool:pytest] -addopts = --verbose -python_files = pytests/*.py - -[flake8] -ignore = E203, E501, W503, E402 -per-file-ignores = - __init__.py: F401, F403, F405 -max-line-length = 88 - -# mypy global options -[mypy] -plugins = numpy.typing.mypy_plugin -ignore_errors = False -allow_redefinition = True - -# mypy per-module options -[mypy-IPython.*] -ignore_missing_imports = True - -[mypy-scipy.*] -ignore_missing_imports = True - -[mypy-numba.*] -ignore_missing_imports = True - -[mypy-pyfftw.*] -ignore_missing_imports = True - -[mypy-pywt.*] -ignore_missing_imports = True - -[mypy-spgl1.*] -ignore_missing_imports = True - -[mypy-skfmm.*] -ignore_missing_imports = True - -[mypy-devito.*] -ignore_missing_imports = True - -[mypy-examples.*] # devito-examples -ignore_missing_imports = True - -[mypy-cupy.*] -ignore_missing_imports = True diff --git a/tutorials/README.txt b/tutorials/README.txt index cc1d37774..13b88920d 100755 --- a/tutorials/README.txt +++ b/tutorials/README.txt @@ -1,4 +1,4 @@ .. _tutorials: -Tutorials ---------- +|:books:| Tutorials +------------------- diff --git a/tutorials/bayesian.py b/tutorials/bayesian.py index 653d6b3d0..9f12e4260 100755 --- a/tutorials/bayesian.py +++ b/tutorials/bayesian.py @@ -42,6 +42,7 @@ \mathbf{R} \mathbf{C}_{x_0} """ + import matplotlib.pyplot as plt # sphinx_gallery_thumbnail_number = 2 @@ -63,9 +64,14 @@ def prior_realization(f0, a0, phi0, sigmaf, sigmaa, sigmaphi, dt, nt, nfft): """ f = np.fft.rfftfreq(nfft, dt) df = f[1] - f[0] - ifreqs = [int(np.random.normal(f, sigma) / df) for f, sigma in zip(f0, sigmaf)] - amps = [np.random.normal(a, sigma) for a, sigma in zip(a0, sigmaa)] - phis = [np.random.normal(phi, sigma) for phi, sigma in zip(phi0, sigmaphi)] + ifreqs = [ + int(np.random.normal(f, sigma) / df) + for f, sigma in zip(f0, sigmaf, strict=True) + ] + amps = [np.random.normal(a, sigma) for a, sigma in zip(a0, sigmaa, strict=True)] + phis = [ + np.random.normal(phi, sigma) for phi, sigma in zip(phi0, sigmaphi, strict=True) + ] # input signal in frequency domain X = np.zeros(nfft // 2 + 1, dtype="complex128") @@ -183,8 +189,11 @@ def prior_realization(f0, a0, phi0, sigmaf, sigmaa, sigmaphi, dt, nt, nfft): # Next we solve the same Bayesian inversion equation iteratively. We will see # that provided we use enough iterations we can retrieve the same values of # the analytical posterior mean -xpost_iter = x0 + Cm_op * Rop.H * ( - lsqr(Rop * Cm_op * Rop.H + Cd_op, yn - Rop * x0, iter_lim=400)[0] +xpost_iter = ( + x0 + + Cm_op + * Rop.H + * (lsqr(Rop * Cm_op * Rop.H + Cd_op, yn - Rop * x0, iter_lim=400)[0]) ) ############################################################################### @@ -200,7 +209,7 @@ def prior_realization(f0, a0, phi0, sigmaf, sigmaa, sigmaphi, dt, nt, nfft): nreals = 1000 xrto = [] -for ireal in range(nreals): +for _ in range(nreals): yreal = yn + Rop * np.random.normal(0, sigmad, nt) xrto.append( x0 diff --git a/tutorials/torchop.py b/tutorials/torchop.py index a18ff2efb..1d14de586 100755 --- a/tutorials/torchop.py +++ b/tutorials/torchop.py @@ -110,7 +110,7 @@ class Network(nn.Module): def __init__(self, input_channels): - super(Network, self).__init__() + super().__init__() self.conv1 = nn.Conv2d( input_channels, input_channels // 2, kernel_size=3, padding=1 ) diff --git a/uv.lock b/uv.lock new file mode 100644 index 000000000..ae7d41daa --- /dev/null +++ b/uv.lock @@ -0,0 +1,9409 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version < '3.11' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 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and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and 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'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version < '3.11' and sys_platform != 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 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and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra == 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>= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 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'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version < '3.11' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version < '3.11' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' 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and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + 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and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version 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and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 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and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra == 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 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sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 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and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version 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!= 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 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and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra == 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 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and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra == 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'emscripten' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-6-pylops-deep' and extra == 'extra-6-pylops-deep-cu126' and extra != 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and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version >= '3.14' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and extra != 'extra-6-pylops-deep-cu126' and extra != 'extra-6-pylops-deep-cu128' and extra != 'extra-6-pylops-deep-cu13' and extra != 'extra-6-pylops-gpu-cu12' and extra != 'extra-6-pylops-gpu-cu13'", + "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-6-pylops-deep' and 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