Tutorials

Quick Start

pip install scpn-control
scpn-control demo --scenario combined --steps 500
scpn-control benchmark --n-bench 5000 --json-out
scpn-control live --port 8765 --zeta 0.5

---

Controller Walkthrough

Four controller tiers in scpn_control.control:

H-infinity (Riccati DARE)

from scpn_control.control.h_infinity_controller import get_radial_robust_controller

ctrl = get_radial_robust_controller(gamma_growth=100.0, damping=10.0)
print(f"gamma={ctrl.gamma:.1f}, GM={ctrl.gain_margin_db:.1f} dB")

for _ in range(500):
    u = ctrl.step(error=0.1, dt=0.001)

MPC (gradient-based)

from scpn_control.control.fusion_sota_mpc import (
    NeuralSurrogate, ModelPredictiveController,
)
import numpy as np

surrogate = NeuralSurrogate(n_coils=3, n_state=4, verbose=False)
target = np.array([1.65, 0.0, 1.4, -1.1])
mpc = ModelPredictiveController(
    surrogate, target, prediction_horizon=10, iterations=20, action_limit=2.0,
)
action = mpc.plan_trajectory(np.array([1.60, 0.05, 1.35, -1.05]))

SNN (Nengo LIF)

Requires pip install scpn-control[nengo].

from scpn_control.control import get_nengo_controller
import numpy as np

NengoSNNController = get_nengo_controller()
ctrl = NengoSNNController()
u = ctrl.step(np.array([0.05, -0.02]))

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Phase Dynamics (Paper 27)

from scpn_control import RealtimeMonitor

mon = RealtimeMonitor.from_paper27(L=16, N_per=50, zeta_uniform=0.5)

for _ in range(200):
    snap = mon.tick()

print(f"R={snap['R_global']:.4f}")
print(f"lambda={snap['lambda_exp']:.6f}")
print(f"latency={snap['latency_us']:.1f} us")

Per-layer coherence:

for i, r in enumerate(snap["R_layer"]):
    print(f"  L{i:02d}: R={r:.3f}")

WebSocket stream:

scpn-control live --port 8765 --layers 16 --n-per 50 --zeta 0.5

---

SCPN Compiler

from scpn_control import StochasticPetriNet, FusionCompiler

net = StochasticPetriNet()
net.add_place("plasma_state", initial_tokens=1.0)
net.add_place("control_signal", initial_tokens=0.0)
net.add_transition("sense", threshold=0.5)
net.add_arc("plasma_state", "sense", weight=1.0)
net.add_arc("sense", "control_signal", weight=1.0)

compiled = FusionCompiler().compile(net)
print(f"Neurons: {compiled.n_neurons}")

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Notebooks

Notebook	Description	Extra deps
q10_breakeven_demo.ipynb	Transport + breakeven analysis	none
snn_compiler_walkthrough.ipynb	Petri net → SNN compilation	none
h_infinity_controller_demo.ipynb	DARE H-inf closed-loop control	matplotlib
neuro_symbolic_control_demo.ipynb	Full closed-loop controller	sc_neurocore

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