Notebook Gallery

The notebooks demonstrate workflows visually. They are useful for onboarding, teaching, and exploratory analysis. They are not claim-bearing evidence unless their outputs are converted into the matching validation artefact and admitted by the relevant validator.

Suggested first notebook sequence

1. examples/snn_compiler_walkthrough.ipynb for SPN-to-SNN mechanics.
2. examples/q10_breakeven_demo.ipynb for transport and fusion-gain context.
3. examples/h_infinity_controller_demo.ipynb for robust-control intuition.
4. examples/scpn_full_stack_demo_2026.ipynb only after reading the evidence boundary below.

Core notebooks

Notebook	Purpose	Extra dependencies
examples/q10_breakeven_demo.ipynb	Transport and breakeven demonstration	none
examples/snn_compiler_walkthrough.ipynb	Stochastic Petri net to SNN compilation	none
examples/h_infinity_controller_demo.ipynb	DARE-based robust radial controller demo	matplotlib
examples/paper27_phase_dynamics_demo.ipynb	16-layer Kuramoto-Sakaguchi phase dynamics	matplotlib
examples/snn_pac_closed_loop_demo.ipynb	SNN controller coupled to PAC-gated phase dynamics	matplotlib

Extended notebooks

Notebook	Purpose	Extra dependencies
examples/neuro_symbolic_control_demo.ipynb	Full neuro-symbolic stack with optional hardware-simulation backend	sc-neurocore, matplotlib
examples/scpn_full_stack_demo_2026.ipynb	End-to-end control-stack demonstration for the current release line	matplotlib
examples/frontier_physics_demo.ipynb	Gyrokinetic, ballooning, NTM, sawtooth, SOL, and scenario physics surfaces	matplotlib
examples/advanced_control_demo.ipynb	Sliding-mode, gain-scheduled, RWM, mu, FDI, shape-control demonstrations	matplotlib

Execute a notebook

pip install -e ".[viz]" jupyter nbconvert
jupyter nbconvert --to notebook --execute examples/q10_breakeven_demo.ipynb     --output-dir artefacts/notebook-exec

Render as HTML

jupyter nbconvert --to html examples/q10_breakeven_demo.ipynb --output-dir docs/_notebooks
jupyter nbconvert --to html examples/snn_compiler_walkthrough.ipynb --output-dir docs/_notebooks
jupyter nbconvert --to html examples/h_infinity_controller_demo.ipynb --output-dir docs/_notebooks

Notebook to evidence workflow

Use notebooks for exploration, teaching, and communication. Use validators for claims.

1. Run the notebook locally and record the exact environment.
2. Move any claim-bearing computation into a script under validation/ or a module-specific test.
3. Persist JSON and Markdown evidence with schema version, units, source data, checksums, tolerances, and claim boundary.
4. Add or update the matching validator so edited artefacts fail closed.
5. Link the admitted report from validation docs, benchmarks, or release notes.

Interpretation rules

• Notebook plots are explanatory, not facility evidence.
• Timings from notebooks are local observations unless captured by a benchmark artefact with host metadata.
• Physics outputs need the corresponding validator before public claims are admissible.
• Optional dependencies should be installed explicitly so notebook failures are attributable to environment state rather than hidden imports.