Tutorial ======== A self-contained, minimal walkthrough is available in the ``tutorial/`` directory of the repository. It generates a small synthetic sensor dataset, extracts features, evaluates rule diagnostics, and produces an interval event log — without needing any external data files. Running it ----------- .. code-block:: bash python3 tutorial/toy_walkthrough.py This is also available as a Jupyter notebook, ``tutorial/toy_walkthrough.ipynb``, which mirrors the script and includes a repo-root bootstrap cell for reliable imports when launched from ``tutorial/``. To run it, make sure ``ipykernel`` is installed in your environment: .. code-block:: bash python -m pip install ipykernel What it produces ------------------ Running the walkthrough writes the following to ``tutorial/output/``: * ``toy_sensor_data.csv`` — the synthetic raw sensor dataset. * ``toy_features.csv`` — the engineered feature table. * ``toy_event_log.csv`` — the resulting interval event log. It also prints rule-diagnostic recommendations to the console and (if ``matplotlib`` is installed) renders sensor time-series plots and a simple process graph. Further tutorials ------------------- A tiered set of tutorials — from core concepts through advanced process-mining integration and eventization-quality evaluation — is in progress in the repository's ``tutorials/`` directory. See the `tutorials table in the README `_ for current status.