scpn-control 0.3.1

SCPN Control — Neuro-symbolic Stochastic Petri Net controller for plasma control

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scpn-control is a standalone neuro-symbolic control engine that compiles Stochastic Petri Nets into spiking neural network controllers with formal verification guarantees. Extracted from scpn-fusion-core — 48 source modules, 50 test files, 701 tests, 5 Rust crates, 13 CI jobs.

11.9 µs P50 control loop — faster than any open-source fusion code, competitive with the DIII-D PCS (4–10 kHz physics loops). With sc-neurocore installed, the SNN compiler uses VectorizedSCLayer + Rust backend for 512× real-time spiking neural network execution. See competitive analysis for full benchmarks.

Quick Start

pip install -e "."
scpn-control demo --steps 1000
scpn-control benchmark --n-bench 5000

Documentation and Tutorials

• Documentation site: https://anulum.github.io/scpn-control/
• Local docs index: docs/index.md
• Benchmark guide: docs/benchmarks.md
• Notebook tutorials:
  • examples/neuro_symbolic_control_demo.ipynb
  • examples/q10_breakeven_demo.ipynb
  • examples/snn_compiler_walkthrough.ipynb
  • examples/paper27_phase_dynamics_demo.ipynb — Knm/UPDE + ζ sin(Ψ−θ)
  • examples/snn_pac_closed_loop_demo.ipynb — SNN-PAC-Kuramoto closed loop
  • examples/streamlit_ws_client.py — live WebSocket phase sync dashboard

Build docs locally:

python -m pip install mkdocs
mkdocs serve

Execute all notebooks:

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

Optional notebook (requires sc_neurocore available in environment):

jupyter nbconvert --to notebook --execute --output-dir artifacts/notebook-exec examples/neuro_symbolic_control_demo.ipynb

Features

• Petri Net to SNN compilation -- Translates Stochastic Petri Nets into spiking neural network controllers with LIF neurons and bitstream encoding
• Formal verification -- Contract-based pre/post-condition checking on all control observations and actions
• Sub-millisecond latency -- <1ms control loop with optional Rust-accelerated kernels
• Rust acceleration -- PyO3 bindings for SCPN activation, marking update, Boris integration, SNN pools, and MPC
• Multiple controller types -- PID, MPC, H-infinity, SNN, neuro-cybernetic dual R+Z
• Grad-Shafranov solver -- Free-boundary equilibrium solver with L-mode/H-mode profile support
• Digital twin integration -- Real-time telemetry ingest, closed-loop simulation, and flight simulator
• RMSE validation -- CI-gated regression testing against DIII-D and SPARC experimental reference data
• Disruption prediction -- ML-based predictor with SPI mitigation and halo/RE physics

Architecture

src/scpn_control/
+-- scpn/              # Petri net -> SNN compiler
|   +-- structure.py   #   StochasticPetriNet graph builder
|   +-- compiler.py    #   FusionCompiler -> CompiledNet (LIF + bitstream)
|   +-- contracts.py   #   ControlObservation, ControlAction, ControlTargets
|   +-- controller.py  #   NeuroSymbolicController (main entry point)
+-- core/              # Solver + plant model (clean init, no import bombs)
|   +-- fusion_kernel.py           # Grad-Shafranov equilibrium solver
|   +-- integrated_transport_solver.py  # Multi-species transport
|   +-- scaling_laws.py            # IPB98y2 confinement scaling
|   +-- eqdsk.py                   # GEQDSK/EQDSK file I/O
|   +-- uncertainty.py             # Monte Carlo UQ
+-- control/           # Controllers (optional deps guarded)
|   +-- h_infinity_controller.py   # H-inf robust control
|   +-- fusion_sota_mpc.py         # Model Predictive Control
|   +-- disruption_predictor.py    # ML disruption prediction
|   +-- tokamak_digital_twin.py    # Digital twin
|   +-- tokamak_flight_sim.py      # IsoFlux flight simulator
|   +-- neuro_cybernetic_controller.py  # Dual R+Z SNN
+-- phase/             # Paper 27 Knm/UPDE phase dynamics (NEW)
|   +-- kuramoto.py    #   Kuramoto-Sakaguchi step + order parameter
|   +-- knm.py         #   Paper 27 Knm coupling matrix builder
|   +-- upde.py        #   UPDE multi-layer solver
|   +-- lyapunov_guard.py          # Sliding-window stability monitor
|   +-- realtime_monitor.py        # Tick-by-tick UPDE + TrajectoryRecorder
|   +-- ws_phase_stream.py         # Async WebSocket live stream server
+-- cli.py             # Click CLI

scpn-control-rs/       # Rust workspace (5 crates)
+-- control-types/     # PlasmaState, EquilibriumConfig, ControlAction
+-- control-math/      # LIF neuron, Boris pusher, Kuramoto, upde_tick
+-- control-core/      # GS solver, transport, confinement scaling
+-- control-control/   # PID, MPC, H-inf, SNN controller
+-- control-python/    # PyO3 bindings (PyRealtimeMonitor, PySnnPool, ...)

tests/                 # 701 tests (50 files)
+-- mock_diiid.py      # Synthetic DIII-D shot generator (14-field NPZ)
+-- test_e2e_phase_diiid.py  # E2E: shot-driven monitor + HDF5/NPZ export
+-- test_phase_kuramoto.py   # 50 Kuramoto/UPDE/Guard/Monitor tests
+-- test_rust_realtime_parity.py  # Rust PyRealtimeMonitor parity
+-- ...                # 44 more test files

Paper 27 Phase Dynamics (Knm/UPDE Engine)

Implements the generalized Kuramoto-Sakaguchi mean-field model with exogenous global field driver ζ sin(Ψ − θ), per arXiv:2004.06344 and SCPN Paper 27.

Modules: src/scpn_control/phase/ (7 files)

Module	Purpose
kuramoto.py	Kuramoto-Sakaguchi step, order parameter R·e^{iΨ}, Lyapunov V/λ
knm.py	Paper 27 16×16 coupling matrix (exponential decay + calibration anchors)
upde.py	UPDE multi-layer solver with PAC gating
lyapunov_guard.py	Sliding-window stability monitor (mirrors DIRECTOR_AI CoherenceScorer)
realtime_monitor.py	Tick-by-tick UPDE + TrajectoryRecorder (HDF5/NPZ export)
ws_phase_stream.py	Async WebSocket server streaming R/V/λ per tick

Rust acceleration: upde_tick() in control-math + PyRealtimeMonitor PyO3 binding.

Live phase sync convergence (GIF fallback):

500 ticks, 16 layers × 50 oscillators, ζ=0.5. R converges to 0.92, V→0, λ settles to −0.47 (stable). Generated by tools/generate_phase_video.py.

WebSocket live stream:

# Terminal 1: start server (CLI)
scpn-control live --port 8765 --zeta 0.5

# Terminal 2: Streamlit WS client (live R/V/λ plots, guard status, control)
pip install -e ".[dashboard,ws]"
streamlit run examples/streamlit_ws_client.py

# Or embedded mode (server + client in one process)
streamlit run examples/streamlit_ws_client.py -- --embedded

E2E test with mock DIII-D shot data:

pytest tests/test_e2e_phase_diiid.py -v

Dependencies

Required	Optional
numpy >= 1.24	matplotlib (pip install -e ".[viz]")
scipy >= 1.10	streamlit (pip install -e ".[dashboard]")
click >= 8.0	torch (pip install -e ".[ml]")
sc-neurocore >= 3.8.0	nengo (pip install -e ".[nengo]")
	h5py (pip install -e ".[hdf5]")
	websockets (pip install -e ".[ws]")

CLI

scpn-control demo --scenario combined --steps 1000   # Closed-loop control demo
scpn-control benchmark --n-bench 5000                 # PID vs SNN timing benchmark
scpn-control validate                                 # RMSE validation dashboard
scpn-control live --port 8765 --zeta 0.5              # Real-time WS phase sync server
scpn-control hil-test --shots-dir ...                 # HIL test campaign

Benchmarks

Python micro-benchmark:

scpn-control benchmark --n-bench 5000 --json-out

Rust Criterion benchmarks:

cd scpn-control-rs
cargo bench --workspace

Benchmark docs: docs/benchmarks.md

Dashboard

pip install -e ".[dashboard]"
streamlit run dashboard/control_dashboard.py

Six tabs: Trajectory Viewer, RMSE Dashboard, Timing Benchmark, Shot Replay, Phase Sync Monitor (live R/V/λ plots), Benchmark Plots (interactive Vega).

Streamlit Cloud

Live dashboard: scpn-control.streamlit.app

The phase sync dashboard runs on Streamlit Cloud with embedded server mode (no external WS server needed). Entry point: streamlit_app.py.

To deploy your own instance:

1. Fork to your GitHub
2. share.streamlit.io > New app > select streamlit_app.py
3. Deploy (auto-starts embedded PhaseStreamServer)

Rust Acceleration

cd scpn-control-rs
cargo test --workspace

# Build Python bindings
cd crates/control-python
pip install maturin
maturin develop --release
cd ../../

# Verify
python -c "import importlib.util; from scpn_control.core._rust_compat import _rust_available; print(bool(importlib.util.find_spec('scpn_control_rs') and _rust_available()))"

The Rust backend provides PyO3 bindings for:

• PyFusionKernel -- Grad-Shafranov solver
• PySnnPool / PySnnController -- Spiking neural network pools
• PyMpcController -- Model Predictive Control
• PyPlasma2D -- Digital twin
• PyTransportSolver -- Chang-Hinton + Sauter bootstrap
• PyRealtimeMonitor -- Multi-layer Kuramoto UPDE tick (phase dynamics)
• SCPN kernels -- dense_activations, marking_update, sample_firing

Citation

@software{sotek2026scpncontrol,
  title   = {SCPN Control: Neuro-Symbolic Stochastic Petri Net Controller},
  author  = {Sotek, Miroslav and Reiprich, Michal},
  year    = {2026},
  url     = {https://github.com/anulum/scpn-control},
  license = {AGPL-3.0-or-later}
}

Release and PyPI

Local publish script:

# Dry run (build + check, no upload)
python tools/publish.py --dry-run

# Publish to TestPyPI
python tools/publish.py --target testpypi

# Bump version + publish to PyPI
python tools/publish.py --bump minor --target pypi --confirm

CI workflow (tag-triggered trusted publishing):

git tag v0.2.0
git push --tags
# → .github/workflows/publish-pypi.yml runs automatically

Limitations

• Equilibrium solver: Fixed-boundary Grad-Shafranov only. Free-boundary with external coil currents is not implemented. No stellarator geometry.
• Transport: 1.5D flux-surface-averaged. No turbulence micro-instability models (TGLF/QuaLiKiz) — uses Chang-Hinton neoclassical + scaling-law anomalous.
• Validation scope: Benchmarked against analytic Solov'ev solutions and published DIII-D/SPARC equilibria (GEQDSK). No real MDSplus shot data yet.
• Disruption predictor: Synthetic training data only. Not validated on experimental disruption databases.
• Rust acceleration: Optional. Pure-Python fallback is complete but 5-10x slower for GS solve and Kuramoto steps at N > 1000.
• Deployment: Research-grade. Not hardened for real-time PCS integration. No ITER CODAC or EPICS interface.

Authors

• Miroslav Sotek — ANULUM CH & LI — ORCID
• Michal Reiprich — ANULUM CH & LI

License

• Concepts: Copyright 1996-2026
• Code: Copyright 2024-2026
• License: GNU AGPL v3

GNU Affero General Public License v3.0 — see LICENSE.

For commercial licensing inquiries, contact: [e-mail]