Combines most popular python parsers (json, jprops, pickle...) with user-defined parsers and type converters to read objects from files. Supports multifile & multiparser objects, typically useful to organize test data. Leverages PEP484 type hints in order to intelligently use the best parser/converter chain, and to try several combinations if relevant
Project page : https://smarie.github.io/python-parsyfiles/
- Travis and codecov integration
- Doc now generated from markdown using mkdocs
Want to contribute ?
Contributions are welcome ! Simply fork this project on github, commit your contributions, and create_not_able_to_convert pull requests.
Here is a non-exhaustive list of interesting open topics: https://github.com/smarie/python-parsyfiles/issues
Running the tests
This project uses pytest.
pytest -v parsyfiles/tests/
You may need to install requirements for setup beforehand, using
pip install -r ci_tools/requirements-test.txt
Generating the documentation page
This project uses mkdocs to generate its documentation page. Therefore building a local copy of the doc page may be done using:
mkdocs build -f docs/mkdocs.yml
You may need to install requirements for doc beforehand, using
pip install -r ci_tools/requirements-doc.txt
Generating the test reports
The following commands generate the html test report and the associated badge.
pytest --junitxml=junit.xml -v parsyfiles/tests/ ant -f ci_tools/generate-junit-html.xml python ci_tools/generate-junit-badge.py
PyPI Releasing memo
This project is now automatically deployed to PyPI when a tag is created. Anyway, for manual deployment we can use:
twine upload dist/* -r pypitest twine upload dist/*