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Use
poetry
for Python dependency management
Poetry is emerging as the best / most updated tool for this purpose, and has achieved enough maturity to adopt. See, for example, https://towardsdatascience.com/packages-part-2-how-to-publish-test-your-package-on-pypi-with-poetry-9fc7295df1a5 1. Remove setup.py in lieu of `pyproject.toml` and `poetry.lock` Most of the configuration is pasted, but I made some updates, e.g. to classifiers to work out-of-the-box for most new synbio projects. 2. Use the `poetry-dynamic-versioning` plugin Enables (as configured) creating a PEP440-compliant release number from tags in the `git` repo. This plugin automates updating multiple files, including `pyproject.toml` and `__init__.py`, which would likely become inconsistent over time using only manual updates. 3. Update Python version to 3.10 Included in pyproject.toml, so also updating in related CI files. At the time of writing, Python 3.11 is the latest, but only recently released. Likely that some libraries won't have added support for 3.11 yet. Earlier `setup.py `and CI configs were inconsistent on the Python version, but poetry will enforce consistency during CI builds. 4. Update CI to use `poetry`
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rev: "v2.6.2" | ||
hooks: | ||
- id: prettier | ||
default_language_version: | ||
python: python3.9 |
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# Expose version number so it's easily checked in client code (e.g. in Jupyter notebooks). | ||
# "0.0.0" is a placeholder -- versions are actually controlled by Git tags. | ||
__version__ = "0.0.0" |
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