I'm working with some researches on about ~40k lines of Python disease modelling code and these are the things that helped us becomes more productive: - tests

I'm working with some researches on about ~40k lines of Python disease modelling code and these are the things that helped us becomes more productive: - tests (unit, smoke, acceptance) (eg. https://mattsegal.dev/alternate-test-styles.html https://mattsegal.dev/alternate-test-styles.html, https://understandlegacycode.com/approval-tests/ https://understandlegacycode.com/approval-tests/) - continuous integration (via GitHub actions eg: https://mattsegal.dev/pytest-on-github-actions.html https://mattsegal.dev/pytest-on-github-actions.html) - 10x performance improvements via cProfile (https://julien.danjou.info/guide-to-python-profiling-cprofile-concrete-case-carbonara/ https://julien.danjou.info/guide-to-python-profiling-cprofil...) - getting fast feedback on code changes by visualizing outputs with Streamlit (https://www.streamlit.io/ https://www.streamlit.io/) - input parameter validation (eg. https://pydantic-docs.helpmanual.io/ https://pydantic-docs.helpmanual.io/, https://mattsegal.dev/cerberus-config-validation.html https://mattsegal.dev/cerberus-config-validation.html) - total automation of data processing job (eg. https://www.prefect.io/ https://www.prefect.io/ - we don't use this but that's the kind of thing we have)