Installation#

Using PyPI#

Using pip should also work fine:

python -m pip install tanat

Using latest github-hosted version#

If you want to get TanaT’s latest version, you can refer to the official repository hosted at the Inria gitlab:

python -m pip -e install https://gitlab.inria.fr/tanat/core/tanat

Dependencies#

TanaT relies on several foundational libraries from the scientific Python ecosystem, including:

  • pandas for tabular data handling

  • numpy and scipy for numerical and scientific computing

  • matplotlib for basic visualization

  • scikit-learn for machine learning utilities

  • numba for performance optimization through JIT compilation

In addition, TanaT makes use of:

  • scikit-survival for survival analysis

  • sqlalchemy for SQL-based data access

  • tqdm for progress tracking in processing pipelines

  • PyYAML for configuration handling

  • pypassist, tseqmock, and tanat_cli_preset as internal or companion tools for simulation, CLI, and mocking