BaikalETK is an open-source NRPy+-based BSSN thorn, which has been demonstrated to yield excellent agreement with ML_BSSN when evolving BBHs and BNSs, and in certain cases (e.g., ADM mass volume integral over entire grid vs time in a pre-merged BNS simulation) better results than ML_BSSN.

BaikalETK has the following neat features:

1. The core BSSN kernel is generated by NRPy+/SymPy, which is entirely free and open source, just like BaikalETK. According to benchmarks by Erik, BaikalETK runs more than 10% faster than the Kranc/Mathematica -generated ML_BSSN currently part of the Toolkit. Work is ongoing to improve BaikalETK's performance even more.
2. It is documented in a Jupyter notebook, which itself is linked to other Jupyter notebooks that document the BSSN formalism & gauge choices.
3. It can be generated by either running the aforementioned Jupyter notebook or just importing a Python module within NRPy+. The Jupyter notebook contains a self-validation test that will fail if the Python module is updated without a corresponding update to the Jupyter notebook.
4. Its scheduling and general structure are closely based on the very nice Lean code.
5. It supports arbitrary finite-differencing order and a variety of gauge conditions, which can be extended further. It implements Brown’s covariant formulation of the BSSN equations.

Possible complications:

1. Unlike ML_BSSN, which (due to Mathematica licensing constraints and consistency with other monolithic thorns) needed to contain all gauge choices, finite difference orders, etc., BaikalETK will only generate a BSSN thorn with a particular gauge and finite-differencing order chosen at the time of thorn generation. That is to say, choices about finite difference order,

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   Ticket URL: https://bitbucket.org/einsteintoolkit/tickets/issues/2299/proposal-add-baikaletk-to-the-toolkit