SCPN Phase Orchestrator

Domain-agnostic coherence control compiler built on Kuramoto/UPDE phase dynamics.

Active Development — SCPN Phase Orchestrator is under intensive development. The core UPDE engine, all 12 integration methods, 3-channel oscillator extraction (P/I/S), supervisor with regime management, and Rust FFI acceleration are fully functional and tested (3 945 Python tests passed, 567 Rust tests, zero functional failures). Rust FFI coverage now spans 53 engine modules across UPDE, coupling, monitor, SSGF, and autotune subsystems with Superior-level documentation (567+ lines, 8 mandatory sections) for every Rust-accelerated module. APIs may evolve as this work progresses.

Version: 0.5.0 Status: 142 Python Modules | 12 Engine Variants | 19 Monitors | 24 Domainpacks | 53 Rust Engine Modules | 567 Rust Tests | 3 945 Python Tests Passed

What It Does

Treats Kuramoto phase dynamics as a universal synchrony state-space. Any hierarchical coupled-cycle system — plasma, cloud infrastructure, traffic, power grids, factories, biology — maps onto the same engine.

Core Pipeline

Domain Binder → Oscillator Extractors (P/I/S) → UPDE Engine → Supervisor → Actuation Mapper

3-Channel Oscillator Model

Channel	Source	Phase Extraction
Physical (P)	Continuous waveforms	Hilbert transform, zero-crossing
Informational (I)	Event/decision streams	Event-phase from message timing
Symbolic (S)	Discrete state sequences	Ring-phase θ=2πs/N, graph-walk

4 Universal Control Knobs

Knob	Meaning
K	Coupling strength (Knm matrix)
α	Phase lag (transport/actuator delays)
ζ	Driver strength (external forcing)
Ψ	Reference phase (control target)

Dual Objective

• R_good: Coherence to maintain (actuator ↔ target phase-lock)
• R_bad: Coherence to suppress (harmful mode-locking)

Capabilities

Differentiable Phase Dynamics (nn/ module, JAX)

Module	What it does
KuramotoLayer	Phase-only oscillator layer (equinox), learnable K and ω
StuartLandauLayer	Phase + amplitude layer, bifurcation parameter μ
Simplicial Kuramoto	3-body higher-order coupling (Gambuzza 2023)
BOLD Generator	Balloon-Windkessel hemodynamic model for fMRI
Reservoir Computing	Kuramoto network as nonlinear reservoir + ridge readout
SAF Spectral Loss	Topology optimization via Laplacian eigenstructure
UDE-Kuramoto	Physics backbone sin(Δθ) + learned neural residual
Inverse Pipeline	Infer coupling K and frequencies ω from observed data
OIM Graph Coloring	Oscillator Ising machine for combinatorial optimization

All functions are JIT-compilable, vmap-compatible, and differentiable. Install: pip install scpn-phase-orchestrator[nn]

Advanced Dynamics (upde/ module, NumPy)

Module	What it does
Inertial Kuramoto	Second-order swing equation for power grid stability
Market Kuramoto	Financial regime detection via Hilbert phase + order parameter
Swarmalator	Coupled spatial + phase dynamics (O'Keeffe 2017)
Simplicial Engine	3-body coupling with explosive transitions
Stuart-Landau Engine	Amplitude dynamics with Hopf bifurcation
Stochastic Engine	Euler-Maruyama with optimal noise (D* auto-tuning)
Geometric Engine	Torus-preserving symplectic integrator
Delay Engine	Time-delayed coupling with circular buffer
Ott-Antonsen	Exact mean-field reduction (O(1) prediction)

Closed-Loop Control (unique — no other oscillator library has this)

Module	What it does
MPC Supervisor	Predicts R trajectory 10 steps ahead via OA reduction
Regime Manager	FSM with hysteresis (NOMINAL/DEGRADED/CRITICAL)
Petri Net FSM	Formal state machine with guard conditions
Plasticity	Three-factor Hebbian coupling adaptation
TE Adaptive	Transfer entropy-based causal coupling updates
Audit Trail	SHA256-chained JSONL for deterministic replay

Analysis Toolkit (15 monitors)

Order parameter, PLV, PAC (cross-frequency coupling), chimera detection, EVS (entrainment verification), PID (redundancy/synergy), Lyapunov exponent, entropy production, winding number, ITPC, coupling estimation (including non-sinusoidal harmonics), HCP connectome generation.

Hardware Deployment

Target	Status
Rust FFI	12 PyO3 bindings for native-speed core modules
FPGA	16-oscillator Zynq-7020 kernel, sub-15μs latency
WebAssembly	Browser-based Kuramoto visualization, no server needed
JAX GPU	Transparent GPU acceleration via XLA

Unique Analysis Capabilities

Module	What it does
Hodge Decomposition	Splits coupling K into gradient / curl / harmonic components
Transfer Entropy	Directed causal information flow between oscillators
Coupling Estimation	Infer K from data (least-squares + higher harmonics)

Quickstart

# Install from PyPI
pip install scpn-phase-orchestrator

# Or with optional extras
pip install scpn-phase-orchestrator[queuewaves]  # FastAPI cascade detector
pip install scpn-phase-orchestrator[plot]         # matplotlib visualisation
pip install scpn-phase-orchestrator[otel]         # OpenTelemetry export

# Scaffold a new domainpack
spo scaffold my_domain

# Validate a domain binding spec
spo validate domainpacks/minimal_domain/binding_spec.yaml

# Run a domain simulation
spo run domainpacks/queuewaves/binding_spec.yaml --steps 1000

# Replay from audit log
spo replay audit.jsonl --output report.json

For development, clone the repo and install in editable mode:

git clone https://github.com/anulum/scpn-phase-orchestrator.git
cd scpn-phase-orchestrator
pip install -e ".[dev]"

Platform Support

Platform	Python engine	Rust FFI (optional)
Linux	Full	Full
macOS	Full	Full
Windows	Full	Experimental (requires MSVC toolchain)

The PyPI package is pure Python. Rust FFI provides optional acceleration and is built from source via maturin develop.

Domainpacks

Pack	Domain	Purpose
autonomous_vehicles	Vehicles	Platoon phase-locking, leader-follower sync (3 layers, 8 oscillators)
bio_stub	Biology	Multi-scale biological oscillators (4 layers, 16 oscillators)
cardiac_rhythm	Cardiology	Gap-junction coupling, arrhythmia (4 layers, 10 oscillators)
chemical_reactor	Process control	Hopf bifurcation, Semenov limit (4 layers, 10 oscillators)
circadian_biology	Chronobiology	SCN clock-gene coupled oscillators (4 layers, 10 oscillators)
epidemic_sir	Epidemiology	Epidemic wave synchronisation (3 layers, 8 oscillators)
firefly_swarm	Ecology	Flash synchronisation, Mirollo-Strogatz (2 layers, 8 oscillators)
fusion_equilibrium	Fusion equilibrium	Grad-Shafranov + FusionCoreBridge (6 layers, 12 oscillators)
geometry_walk	Graph systems	Random-walk phase coupling (2 layers, 8 oscillators)
laser_array	Photonics	Semiconductor laser phase-locking (3 layers, 8 oscillators)
manufacturing_spc	Manufacturing	Statistical process control (3 layers, 9 oscillators)
metaphysics_demo	P/I/S showcase	Imprint + geometry ablation (3 layers, 7 oscillators)
minimal_domain	Synthetic	Minimal-but-complete pipeline example (2 layers, 4 oscillators)
network_security	Cybersecurity	Traffic anomaly detection, DDoS suppression (3 layers, 8 oscillators)
neuroscience_eeg	Neuroscience	EEG band->phase, seizure detection (6 layers, 14 oscillators)
plasma_control	Tokamak plasma	MHD/transport multi-scale control (8 layers, 16 oscillators)
pll_clock	Telecommunications	PLL network clock synchronisation (3 layers, 8 oscillators)
power_grid	Power systems	Swing equation = Kuramoto (5 layers, 12 oscillators)
quantum_simulation	Quantum computing	Qubit register phase coupling (3 layers, 8 oscillators)
queuewaves	Cloud/queues	Retry storm desynchronisation (3 layers, 6 oscillators)
rotating_machinery	Vibration	Harmonics, ISO 10816 boundaries (4 layers, 10 oscillators)
satellite_constellation	Aerospace	Orbital slot synchronisation, beam handover (3 layers, 8 oscillators)
swarm_robotics	Robotics	Vicsek collective motion (3 layers, 8 oscillators)
traffic_flow	Transportation	Signal coordination = phase sync (4 layers, 10 oscillators)
financial_markets	Finance	Stock synchronization, crash detection
gene_oscillator	Synthetic biology	Repressilator quorum coupling
vortex_shedding	Fluid dynamics	Wake station Stuart-Landau
robotic_cpg	Robotics	Joint CPG locomotion
sleep_architecture	Sleep medicine	AASM sleep staging from R
musical_acoustics	Acoustics	Consonance = R, groove = alpha
brain_connectome	Neuroscience	HCP-inspired coupling
identity_coherence	Consciousness	SSGF identity model (6 layers, 30 oscillators)

Adding a Domain

1. Create domainpacks/<name>/binding_spec.yaml declaring layers, oscillator families, coupling, drivers, objectives, and boundaries.
2. Optionally add policy.yaml for declarative supervisor rules.
3. Validate: spo validate domainpacks/<name>/binding_spec.yaml
4. Run: spo run domainpacks/<name>/binding_spec.yaml --steps 1000

See metaphysics_demo for a full example exercising all three channels, imprint modulation, geometry projection, and policy-driven control. Spec format reference: binding_spec.schema.json.

Development

pip install -e ".[dev]"
ruff check src/ tests/
ruff format --check src/ tests/
pytest tests/ -v --tb=short
mkdocs build

License

AGPL-3.0-or-later. Commercial licensing available — contact protoscience@anulum.li.

Citation

See CITATION.cff.

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Developed by ANULUM / Fortis Studio