
This paper introduces Python bindings for the experimental egg-smol library, which aims to bring the benefits of e-graphs to the Python ecosystem. The bindings offer a high-level, Pythonic API providing an accessible and familiar interface for Python users, enabling collaboration and innovation across various domains in the scientific computing and machine learning communities.


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egg-smol-python-a-pythonic-library-for-integrating-e-graphs-into-the-python-ecosystem


Egg-smol Python: A Pythonic Library for Integrating E-graphs into the Python Ecosystem


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GenML introduces a Python library to generate Mittag-Leffler correlated noise, addressing the lack of tools for its direct generation. The software enables precise simulation of M-L noise, validating its effectiveness through quantitative analyses.


genml-python-library-for-mittag-leffler-correlated-noise-generation


GenML: Python Library for Mittag-Leffler Correlated Noise Generation
