MxlPy
MxlPy (pronounced "em axe el pie") is a Python package for mechanistic learning (Mxl) - the combination of mechanistic modeling and machine learning to deliver explainable, data-informed solutions.
Documentation
You can find extensive documentation directly here on github
Installation
You can install mxlpy using pip: pip install mxlpy.
Due to their sizes, the machine learning packages are optional dependencies. You can install them using
# One of them respectively
pip install mxlpy[torch]
pip install mxlpy[tensorflow]
pip install mxlpy[keras]
pip install mxlpy[jax]
pip install mxlpy[sr]
# together
pip install mxlpy[torch, tensorflow, keras, jax, sr]
If you want access to the sundials solver suite via the assimulo package, we recommend setting up a virtual environment via pixi or mamba / conda using the conda-forge channel.
pixi init
pixi add python assimulo
pixi add --pypi mxlpy
How to cite
If you use this software in your scientific work, please cite this article:
Development setup
You have two choices here, using uv (pypi-only) or using pixi (conda-forge, including assimulo)
uv
- Install
uvas described in the docs. - Run
uv sync --all-extras --all-groupsto install dependencies locally
pixi
- Install
pixias described in the docs - Run
pixi install --frozen
LLMs
We support the llms.txt convention for making documentation available to large language models and the applications that make use of them. It is located at docs/llms.txt
Tool family 🏠
MxlPy is part of a larger family of tools that are designed with a similar set of abstractions. Check them out!
- MxlBricks is built on top of
MxlPyto build mechanistic models composed of pre-defined reactions (bricks) - MxlModels supplies flat, single-file versions of MxlBricks models for easy inspection
- MxlWeb brings simulation of mechanistic models to the browser!
- pysbml simplifies SBML models for import/export with MxlPy
- Parameteriser looks up kinetic parameters from BRENDA and other databases
2 maintainers
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