micki 2.0.0

micki

A modular, extensible, robust object-oriented microkinetic modeling package written in Python.

DEPENDENCIES (installed automatically, see below):

• Python >= 3.12
• sundials4py (the official SUNDIALS Python interface; >= 7.9, beta)
• numpy >= 2.0
• sympy
• ase

No compiler, SUNDIALS build or LAPACK library is needed: sundials4py ships prebuilt wheels that include SUNDIALS.

Earlier versions that generate Fortran and compile it with f2py are kept on branches: fortran-sundials7 (SUNDIALS 7 Fortran 2003 interface) and sundials4 (SUNDIALS 4.X FCMIX interface).

Installation with conda (recommended):

#fetch Micki and create a conda environment with all dependencies
git clone https://github.com/jrschmidt2/micki.git
cd micki
conda env create -f environment.yml
conda activate micki

Python, numpy, sympy and ase come from conda-forge. sundials4py and Micki itself are not on conda-forge, so environment.yml installs them with pip inside the environment (Micki in editable mode, so changes in the clone take effect immediately).

Installation with pip:

#in an environment with Python >= 3.12
pip install micki

Testing:

Run the test suite (about 15 seconds; includes a water-gas shift regression test) from the repository root:
python -m unittest discover -s tests -v

Releasing:

Releases are published to PyPI automatically by GitHub Actions (.github/workflows/publish.yml, PyPI trusted publishing):

1. Set the new version in pyproject.toml and commit it.
2. Create a GitHub release with tag v<version> (e.g. v2.0.0).

Publishing the release runs the tests, builds the package and uploads it to PyPI. The workflow refuses to publish if the tag does not match the version.