rushd 0.5.1

Package for maintaining robust, reproducible data management.

rushd

A package for maintaining robust, reproducible data management.

Rationale

Science relies on repeatable results. rushd is a Python package that helps with this, both by making sure that the execution context (e.g. the state of all of the Pip packages) is saved, alongside helper functions that help you cleanly, but repeatedly, separate data from code.

Install

This package is on Pip, so you can just:

pip install rushd

Alternatively, you can get built wheels from the Releases tab on Github.

Quickstart

Simply import rushd!

import rushd as rd

Documentation

See the documentation available at https://gallowaylabmit.github.io/rushd

Developer install and contributing

If you'd like to hack locally on rushd, after cloning this repository:

$ git clone https://github.com/GallowayLabMIT/rushd.git
$ cd rushd

you can create a local virtual environment, and install rushd in "development (editable) mode" with the extra requirements for tests.

$ python -m venv env
$ .\env\Scripts\activate    (on Windows)
$ source env/bin/activate   (on Mac/Linux)
$ pip install -e .[dev]     (on most shells)
$ pip install -e '.[dev]'   (on zsh)

After this 'local install', you can use and import rushd freely without having to re-install after each update.

Pre-commit

We use something called pre-commit to automatically run linters, formatters, and other checks to make sure the code stays high quality.

After doing the developer install and activating the virtual environment, you should run:

$ pre-commit install

to install the git hooks. Now, pre-commit will automatically run whenever you go to commit.

Testing with pytest

We use pytest to test our code. You just type:

$ pytest

to run all tests, though you can add an optional argument to run some subset of the tests:

$ pytest tests/test_file_io.py

Pytest automatically discovers tests put in the tests directory, whose files and functions start with the word test.

Code coverage

On every push, all of the tests are run and the coverage, or which lines are "covered" or executed during all tests, is calculated and uploaded to Codecov. This is a nice way of seeing if you missed any edge cases that need tests added.

Publishing a release

Following the steps described above, the full process for publishing a release is:

1. Test

   • Write new tests as needed
   • Run tests to confirm changes pass

2. Pre-commit

   • Stage changes in git
   • Run pre-commit (requires developer mode)
   • Resolve any errors/warnings from pre-commit (e.g., run ruff --fix)
   • Stage any new fixes and re-run pre-commit

3. Commit changes

   • Commit and sync changes
   • Confirm project builds on github with no errors (see 'Actions' tab)
   • Confirm adequate coverage via codecov (click link on github)

4. Document changes

   • Edit CHANGELOG.md and README.md to reflect changes, then commit
   • Tag the release using git tag -a vX.X.X (updating Xs) with a short changelog summary as the tag message
   • Push changes git push --tags

5. Build the release

   • Build using python -m build
   • Add a release to the github page by copy-pasting the changelog
   • Add the .whl and .tar.gz files (from the build folder) to the github release
   • Upload the package to PyPI using twine upload dist/*

Changelog

See the CHANGELOG for detailed changes.

## [0.5.1] - 2025-05-22
### Modified
- Switched to using `np.nan` instead of `np.NaN` to be compatible with Numpy 2.0
- Removed support for Python 3.7 and added support for 3.13

License

This is licensed by the MIT license. Use freely!

What does the name mean?

The name is a reference to Ibn Rushd, a Muslim scholar born in Córdoba who was responsible for translating and adding scholastic commentary to ancient Greek works, especially Aristotle. His translations spurred further translations into Latin and Hebrew, reigniting interest in ancient Greek works for the first time since the fall of the Roman empire.

His name is pronounced rush-id.

If we take the first and last letter, we also get rd: repeatable data!