Contribute#

General guidelines#

Overall guidance on contributing to a PyAnsys library appears in the Contributing topic in the PyAnsys developer’s guide. Ensure that you are thoroughly familiar with this guide before attempting to contribute to PyGranta JobQueue.

The following contribution information is specific to PyGranta JobQueue.

Developer environment setup#

PyGranta JobQueue uses Poetry for packaging and dependency management. Installation information is available in the Poetry documentation.

Installing PyGranta JobQueue in developer mode allows you to modify and enhance the source.

Clone the source repository#

Run the following commands to clone and install the latest version of PyGranta JobQueue in editable mode, which ensures changes to the code are immediately visible in the environment. Running these commands also installs the required development dependencies to run the tests, build the documentation, and build the package.

git clone https://github.com/ansys/grantami-jobqueue
cd grantami-jobqueue
poetry install --with doc

Additional tools#

Pre-commit#

The style checks take advantage of pre-commit. Developers are not forced but encouraged to install this tool with this command:

python -m pip install pre-commit && pre-commit install

Tox#

Tests can be run using tox. The project defines the tox environments in the tox.ini file. The following tox environments are provided:

  • tox -e style: Checks for coding style quality.

  • tox -e tests: Runs all tests and checks code coverage. (For requirements, see Server access.)

  • tox -e doc: Checks the documentation-building process.

Optionally, add the -- -m "not integration" suffix to the commands above to skip integration tests. For example, this command only runs tests that do not require a Granta MI instance:

tox -e tests -- -m "not integration"

Server access#

As indicated in Software requirements, running integration tests and building the examples requires access to a valid Granta MI instance.

External contributors may not have an instance of Granta MI at their disposal. Prior to creating a pull request with the desired changes, they should make sure that unit tests pass (Tox), static code validation and styling pass (pre-commit), and that the documentation can be generated successfully without the examples (Documenting).

Continuous Integration (CI) on GitHub is configured to run the integration tests and generate the full documentation on creation and updates of pull requests. CI is not configured to run for pull requests from forks. External contributions require approval from a maintainer for checks to run.

Code formatting and styling#

This project adheres to PyAnsys styling and formatting recommendations. The easiest way to validate changes are compliant is to run this command:

pre-commit run --all-files

Documenting#

As per PyAnsys guidelines, the documentation is generated using Sphinx.

For building documentation, use the Sphinx Makefile:

make -C doc/ html && your_browser_name doc/build/html/index.html

If any changes have been made to the documentation, you should run Sphinx directly with the following extra arguments:

sphinx-build -b html source build -W -n --keep-going

The extra arguments ensure that all references are valid and turn warnings into errors. CI uses the same configuration, so you should resolve any warnings and errors locally before pushing changes.

Example notebooks#

Examples are included in the documentation to give you more context around the core capabilities described in API reference. Additional examples are welcomed, especially if they cover a key use case of the package that has not yet been covered.

The example scripts are placed in the examples directory and are included in the documentation build if the environment variable BUILD_EXAMPLES is set to True. Otherwise, a different set of examples is run to validate the process.

Examples are checked in as scripts using the light format. For more information, see the Jupytext documentation. As part of the documentation-building process, the Python files are converted back into Jupyter notebooks and the output cells are populated by running the notebooks against a Granta MI instance.

This conversion between Jupyter notebooks and Python files is performed by nb-convert. Installation information is available in the nb-convert documentation.

Post issues#

Use the PyGranta JobQueue Issues page to report bugs and request new features. When possible, use the issue templates provided. If your issue does not fit into one of these templates, click the link for opening a blank issue.

If you have general questions about the PyAnsys ecosystem, email pyansys.core@ansys.com. If your question is specific to PyGranta JobQueue, ask your question in an issue as described in the previous paragraph.

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