Notebook Examples

Configurable Jupyter notebooks are provided for the main release examples:

• SHARP CEA Cartesian

• Cartesian series

• Spherical HMI

• Analytical benchmark

Colab-ready notebooks are available for the most common remote workflows:

• SHARP CEA tutorial

• SHARP CEA Cartesian

• HMI spherical

Each notebook exposes editable fields near the top for paths, active-region identifiers, time ranges, cadence, and run controls. The notebooks then walk through the full analysis path: data download or file selection, Python API extrapolation, export, metrics, and visualization.

The visualization cells include current maps, free-energy maps, and boundary comparisons. Training cells are disabled by default through RUN_TRAINING = False; set that flag to True once the configuration and input files are ready. GPU runtime is recommended for training; CPU runtime is usually only practical for configuration checks and already-trained outputs.