Notebook Gallery¶
Interactive Jupyter notebooks demonstrating scpn-control capabilities.
Each notebook is self-contained and can be executed locally with jupyter nbconvert.
Q10 Breakeven Demo¶
File: examples/q10_breakeven_demo.ipynb
Extra deps: none
Demonstrates the integrated transport solver running a simulated tokamak discharge toward Q=10 breakeven. Shows temperature/density profile evolution, fusion power buildup, and energy confinement time convergence.
SNN Compiler Walkthrough¶
File: examples/snn_compiler_walkthrough.ipynb
Extra deps: none
Step-by-step compilation of a Stochastic Petri Net into a spiking neural
network. Covers StochasticPetriNet graph construction, FusionCompiler
compilation, CompiledNet inspection (neuron count, weight matrix), and
NeuroSymbolicController closed-loop execution.
H-infinity Controller Demo¶
File: examples/h_infinity_controller_demo.ipynb
Extra deps: matplotlib
DARE-based H-infinity controller for radial position regulation. Demonstrates controller synthesis, gain margin computation, step response, and disturbance rejection with anti-windup.
pip install "scpn-control[viz]"
jupyter nbconvert --to notebook --execute examples/h_infinity_controller_demo.ipynb
Paper 27 Phase Dynamics Demo¶
File: examples/paper27_phase_dynamics_demo.ipynb
Extra deps: matplotlib
Full 16-layer Kuramoto-Sakaguchi simulation using the Paper 27 Knm coupling matrix. Shows order parameter R convergence, Lyapunov exponent λ stabilisation, per-layer coherence, and PAC gating effects.
pip install "scpn-control[viz]"
jupyter nbconvert --to notebook --execute examples/paper27_phase_dynamics_demo.ipynb
SNN-PAC Closed-Loop Demo¶
File: examples/snn_pac_closed_loop_demo.ipynb
Extra deps: matplotlib
Combines the SNN controller with PAC-gated Kuramoto dynamics in a closed loop. The SNN controller adjusts coil currents based on phase coherence feedback, demonstrating the full neuro-symbolic control pipeline.
pip install "scpn-control[viz]"
jupyter nbconvert --to notebook --execute examples/snn_pac_closed_loop_demo.ipynb
Neuro-Symbolic Control Demo¶
File: examples/neuro_symbolic_control_demo.ipynb
Extra deps: sc-neurocore
Full-stack demonstration requiring the sc-neurocore hardware simulation
backend. Shows VectorizedSCLayer compilation, bitstream encoding, and
real-time SNN execution at 512× real-time.
pip install "scpn-control[neuro,viz]"
jupyter nbconvert --to notebook --execute examples/neuro_symbolic_control_demo.ipynb
Note
This notebook requires sc-neurocore >= 3.8.0 which is not available on PyPI.
Contact protoscience@anulum.li for access.
Full Stack Demo (2026)¶
File: examples/scpn_full_stack_demo_2026.ipynb
Extra deps: matplotlib
Comprehensive end-to-end demonstration of the v0.19.0 control stack. Includes equilibrium initialization, transport evolution, SNN controller coupling, and real-time WebSocket telemetry visualization.
Frontier Physics Demo¶
File: examples/frontier_physics_demo.ipynb
Extra deps: matplotlib
Demonstrates all Phase 3 frontier physics modules with matplotlib visualizations: gyrokinetic transport (ITG spectrum + radial chi profile), ballooning stability (s-alpha diagram), current diffusion + current drive (ECCD+NBI profiles), NTM dynamics (Modified Rutherford Equation), sawtooth cycles (Kadomtsev crash), SOL model (two-point), and a coupled ITER 15MA integrated scenario.
pip install "scpn-control[viz]"
jupyter nbconvert --to notebook --execute examples/frontier_physics_demo.ipynb
Advanced Control Demo¶
File: examples/advanced_control_demo.ipynb
Extra deps: matplotlib
Demonstrates all Phase 4 advanced control modules: super-twisting sliding-mode vertical stabilizer, gain-scheduled multi-regime controller, RWM feedback stabilization (open vs closed-loop growth rates), mu-synthesis (structured singular value), fault-tolerant control (sensor dropout FDI), and isoflux shape controller convergence.
pip install "scpn-control[viz]"
jupyter nbconvert --to notebook --execute examples/advanced_control_demo.ipynb
Example Scripts¶
In addition to Jupyter notebooks, the following Python scripts demonstrate high-performance and deployment scenarios.
Digital Twin Performance¶
File: examples/digital_twin_demo.py
Runs the real-time digital twin with 10kHz control loop and simulated diagnostics. Benchmarks the Rust kernel vs Python fallback and reports P50/P99 latencies.
Full Pipeline Benchmark¶
File: examples/full_pipeline_benchmark.py
Stresses the entire 16-layer stack (including Kuramoto and SPN logic) under various CPU/GPU dispatch configurations. Generates performance scaling reports for large-scale ensembles.
Running All Notebooks¶
pip install -e ".[viz]" jupyter nbconvert
# Core notebooks (no extra deps)
jupyter nbconvert --to notebook --execute examples/q10_breakeven_demo.ipynb
jupyter nbconvert --to notebook --execute examples/snn_compiler_walkthrough.ipynb
# Visualization notebooks (matplotlib)
jupyter nbconvert --to notebook --execute examples/h_infinity_controller_demo.ipynb
jupyter nbconvert --to notebook --execute examples/paper27_phase_dynamics_demo.ipynb
jupyter nbconvert --to notebook --execute examples/snn_pac_closed_loop_demo.ipynb