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
Metadata
2 maintainers
Release files for mxlpy 0.61.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mxlpy-0.61.0.tar.gz | 5.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mxlpy-0.61.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.8 MB
Release files / mxlpy-0.61.0.tar.gz
| Download URL | mxlpy-0.61.0.tar.gz |
|---|---|
| Size | 5.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / mxlpy-0.61.0-py3-none-any.whl
| Download URL | mxlpy-0.61.0-py3-none-any.whl |
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| Size | 315.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 13, 2026.
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