Metadata-Version: 2.4
Name: scpn-control
Version: 0.19.1
Summary: SCPN Control — Neuro-symbolic Stochastic Petri Net controller for plasma control
Author-email: Miroslav Sotek <protoscience@anulum.li>
License: AGPL-3.0-or-later
Project-URL: Homepage, https://github.com/anulum/scpn-control
Project-URL: Documentation, https://anulum.github.io/scpn-control/
Project-URL: Repository, https://github.com/anulum/scpn-control
Project-URL: Bug Tracker, https://github.com/anulum/scpn-control/issues
Project-URL: Discussions, https://github.com/anulum/scpn-control/discussions
Project-URL: Changelog, https://github.com/anulum/scpn-control/blob/main/CHANGELOG.md
Keywords: tokamak,plasma-control,petri-net,snn,Kuramoto,MHD,gyrokinetic,H-infinity,mu-synthesis,NMPC
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Rust
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE.md
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
Requires-Dist: click>=8.0
Provides-Extra: viz
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Provides-Extra: dashboard
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Provides-Extra: hdf5
Requires-Dist: h5py>=3.0; extra == "hdf5"
Provides-Extra: ws
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Provides-Extra: ml
Requires-Dist: torch>=2.0; extra == "ml"
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Dynamic: license-file

<!-- SPDX-License-Identifier: AGPL-3.0-or-later -->

<p align="center">
  <img src="docs/scpn_control_header.png" alt="SCPN-CONTROL — Formal Stochastic Petri Net Engine" width="100%">
</p>

<p align="center">
  <a href="https://github.com/anulum/scpn-control/actions"><img src="https://github.com/anulum/scpn-control/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
  <a href="https://github.com/anulum/scpn-control/actions/workflows/docs-pages.yml"><img src="https://github.com/anulum/scpn-control/actions/workflows/docs-pages.yml/badge.svg" alt="Docs Pages"></a>
  <a href="https://pypi.org/project/scpn-control/"><img src="https://img.shields.io/pypi/v/scpn-control" alt="PyPI version"></a>
  <a href="https://pypi.org/project/scpn-control/"><img src="https://img.shields.io/pypi/pyversions/scpn-control" alt="Python versions"></a>
  <a href="https://pepy.tech/project/scpn-control"><img src="https://static.pepy.tech/badge/scpn-control" alt="All-time downloads"></a>
  <a href="https://scpn-control.streamlit.app"><img src="https://static.streamlit.io/badges/streamlit_badge_black_white.svg" alt="Open in Streamlit"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-AGPL--3.0-blue.svg" alt="License: AGPL-3.0-or-later"></a>
  <a href="https://www.bestpractices.dev/projects/12176"><img src="https://www.bestpractices.dev/projects/12176/badge" alt="OpenSSF Best Practices"></a>
  <a href="https://orcid.org/0009-0009-3560-0851"><img src="https://img.shields.io/badge/ORCID-0009--0009--3560--0851-green.svg" alt="ORCID"></a>
  <a href="https://arxiv.org/abs/2004.06344"><img src="https://img.shields.io/badge/arXiv-2004.06344-b31b1b.svg" alt="arXiv"></a>
  <a href="docs/REVIEWER_PAPER27_INTEGRATION.pdf"><img src="https://img.shields.io/badge/Paper_27-PDF-informational.svg" alt="Paper 27 PDF"></a>
  <a href="https://codecov.io/gh/anulum/scpn-control"><img src="https://codecov.io/gh/anulum/scpn-control/branch/main/graph/badge.svg" alt="codecov"></a>
  <a href="https://doi.org/10.5281/zenodo.18804939"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.18804939.svg" alt="DOI"></a>
  <a href="https://buy.stripe.com/4gM00kbiMdjAberaYz5J601"><img src="https://img.shields.io/badge/sponsor-Stripe-635bff.svg" alt="Sponsor"></a>
</p>

---

**scpn-control** is a standalone neuro-symbolic control engine that compiles
Stochastic Petri Nets into spiking neural network controllers with
contract-based pre/post-condition checking. Extracted from
[scpn-fusion-core](https://github.com/anulum/scpn-fusion-core) — 134 Python
source modules, 264 test files, **3,700+ collected Python tests**, 5 Rust crates, and a 20-job CI matrix.
Five-tier gyrokinetic transport: critical-gradient, QLKNN surrogate, native linear eigenvalue, native TGLF-equivalent (SAT0/SAT1/SAT2), nonlinear δf GK (5D Vlasov, JAX-accelerable).

> **11.9 µs P50 kernel step** (Criterion-verified, GitHub Actions ubuntu-latest).
> This is a bare Rust kernel call, not a complete control cycle.
> See [competitive analysis](docs/competitive_analysis.md) for full benchmarks
> and [Limitations](#limitations) for honest scope.
>
> **Status: Alpha / Research.** Not a production PCS. No real tokamak
> deployment. Public physics claims are limited to checksum-gated repository
> reference artefacts, published GEQDSK files, and explicitly bounded synthetic
> or non-facility domains.

## Capability Inventory

<!-- capability-snapshot:start -->
<!-- SPDX-License-Identifier: AGPL-3.0-or-later -->
<!-- Commercial license available -->
<!-- © Concepts 1996–2026 Miroslav Šotek. All rights reserved. -->
<!-- © Code 2020–2026 Miroslav Šotek. All rights reserved. -->
<!-- ORCID: 0009-0009-3560-0851 -->
<!-- Contact: www.anulum.li | protoscience@anulum.li -->
<!-- SCPN Control — Generated Capability Snapshot -->
<!-- Embedded in README.md between capability-snapshot markers. -->

**Capability Inventory**

| Surface | Count |
| --- | ---: |
| Package version | 0.19.1 |
| Python requirement | >=3.10 |
| Public API exports | 15 |
| Python control/physics modules | 128 |
| Python public classes | 394 |
| Rust source files | 50 |
| Rust PyO3 exports | 27 |
| Validation scripts | 38 |
| Optional extras | 16 |
| Python test files | 279 |
| Public documentation pages | 30 |
| GitHub Actions workflows | 8 |

**Evidence roots:** `src/scpn_control/{core,control,phase,scpn}`, `scpn-control-rs/crates`, `validation`, `tests`, `docs`, and `.github/workflows`.

Refresh with `python tools/capability_manifest.py`; enforce with `python tools/capability_manifest.py --check`.
<!-- capability-snapshot:end -->

## Quick Start

```bash
pip install scpn-control                        # core (numpy, scipy, click)
pip install "scpn-control[dashboard,ws]"        # + Streamlit dashboard + WebSocket
scpn-control demo --steps 1000
scpn-control benchmark --n-bench 5000
```

For development (editable install):

```bash
git clone https://github.com/anulum/scpn-control.git
cd scpn-control
pip install -e ".[dev]"
```

### Python in 30 Seconds

```python
from scpn_control.core.jax_gs_solver import jax_gs_solve
psi = jax_gs_solve(NR=33, NZ=33, Ip_target=1e6, n_picard=40, n_jacobi=100)

from scpn_control.scpn.structure import StochasticPetriNet
from scpn_control.scpn.compiler import FusionCompiler
net = StochasticPetriNet()
net.add_place("idle", initial_tokens=1.0)
net.add_place("heating"); net.add_transition("ignite")
net.add_arc("idle", "ignite"); net.add_arc("ignite", "heating")
net.compile()
artifact = FusionCompiler().compile(net)  # SPN -> SNN
```

Full walkthrough: `python examples/quickstart.py`

## 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:

```bash
python -m pip install mkdocs
mkdocs serve
```

Execute all notebooks:

```bash
python -m pip install "scpn-control[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):

```bash
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
- **Contract checking** -- Runtime pre/post-condition assertions on control observations and actions (not theorem-proved formal verification)
- **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
- **10 controller types** -- PID, MPC, NMPC, H-infinity, mu-synthesis, gain-scheduled, sliding-mode, fault-tolerant, SNN, PPO reinforcement learning
- **Grad-Shafranov solver** -- Fixed + free-boundary equilibrium solver with L/H-mode profiles, JAX-differentiable (`jax.grad` through full Picard solve)
- **Frontier physics** -- Nonlinear δf gyrokinetic solver (5D Vlasov, JAX-accelerable), native TGLF-equivalent (SAT0/SAT1/SAT2, no Fortran binary), kinetic electron species, Sugama collision operator (particle/momentum/energy conservation), electromagnetic A_∥ via Ampere's law (KBM/MTM capable), Dimits-shift scan machinery requiring post-audit revalidation, ballooning connection BC (kx shift), Rosenbluth-Hinton zonal Krook damping, 62× JAX GPU speedup, ballooning eigenvalue solver, sawtooth Kadomtsev model, NTM dynamics, current diffusion/drive, SOL two-point model
- **MHD stability** -- Five independent criteria: Mercier interchange, ballooning, Kruskal-Shafranov kink, Troyon beta limit, NTM seeding
- **JAX autodiff** -- Thomas solver, Crank-Nicolson transport, neural equilibrium, GS solver — all JIT-compiled and GPU-compatible
- **PPO agent** -- 500K-step cloud-trained RL controller (reward 143.7 vs MPC 58.1 vs PID −912.3), 3-seed reproducible
- **Neural transport** -- QLKNN-10D trained MLP with auto-discovered weights
- **Scenario management** -- Integrated scenario simulator (transport + current diffusion + sawteeth + NTM + SOL), scenario scheduler, ITER/NSTX-U presets
- **Digital twin integration** -- Real-time telemetry ingest, closed-loop simulation, real-time EFIT, and flight simulator
- **RMSE validation** -- CI-gated regression testing against DIII-D reference artefacts and published SPARC GEQDSK files
- **Disruption prediction** -- ML-based predictor with SPI mitigation and halo/RE physics
- **Robust control** -- H-infinity DARE synthesis, bounded static mu-analysis, fault-tolerant degraded-mode operation, shape controller with boundary Jacobian

## 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/              # Physics solvers + plant models (67 modules)
|   +-- fusion_kernel.py           # Grad-Shafranov equilibrium (fixed + free boundary)
|   +-- integrated_transport_solver.py  # Multi-species transport PDE
|   +-- gyrokinetic_transport.py   # Quasilinear TGLF-10 (ITG/TEM/ETG)
|   +-- ballooning_solver.py       # s-alpha ballooning eigenvalue ODE
|   +-- sawtooth.py                # Kadomtsev crash + Porcelli trigger
|   +-- ntm_dynamics.py            # Modified Rutherford NTM + ECCD stabilization
|   +-- current_diffusion.py       # Parallel current evolution PDE
|   +-- current_drive.py           # ECCD, NBI, LHCD deposition models
|   +-- sol_model.py               # Two-point SOL + Eich heat-flux width
|   +-- rzip_model.py              # Linearised vertical stability (RZIp)
|   +-- integrated_scenario.py     # Full scenario simulator (ITER/NSTX-U presets)
|   +-- stability_mhd.py           # 5 MHD stability criteria
|   +-- scaling_laws.py            # IPB98y2 confinement scaling
|   +-- neural_transport.py        # QLKNN-10D trained surrogate
|   +-- neural_equilibrium.py      # PCA+MLP GS surrogate (1000x speedup)
|   +-- ...                        # 14 more (eqdsk, uncertainty, pedestal, ...)
+-- control/           # Controllers (42 modules, optional deps guarded)
|   +-- h_infinity_controller.py   # H-inf robust control (DARE)
|   +-- mu_synthesis.py            # Static D-scaled structured singular value bound
|   +-- nmpc_controller.py         # Nonlinear MPC (SQP, 20-step horizon)
|   +-- gain_scheduled_controller.py  # PID scheduled on operating regime
|   +-- sliding_mode_vertical.py   # Sliding-mode vertical stabilizer
|   +-- fault_tolerant_control.py  # Fault detection + degraded-mode operation
|   +-- shape_controller.py        # Plasma shape via boundary Jacobian
|   +-- safe_rl_controller.py      # PPO + MHD constraint checker
|   +-- scenario_scheduler.py      # Shot timeline + actuator scheduling
|   +-- realtime_efit.py           # Streaming equilibrium reconstruction
|   +-- control_benchmark_suite.py # Standardised benchmark scenarios
|   +-- disruption_predictor.py    # ML disruption prediction + SPI
|   +-- tokamak_digital_twin.py    # Digital twin
|   +-- ...                        # 24 more (MPC, flight sim, HIL, ...)
+-- phase/             # Paper 27 Knm/UPDE phase dynamics (9 modules)
|   +-- 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/                 # 3,700+ collected Python tests
+-- mock_diiid.py      # Synthetic DIII-D shot generator (NOT real MDSplus data)
+-- 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
+-- ...                # 170+ 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/` (9 modules)

| 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](docs/phase_sync_live.gif)):

<p align="center">
  <video src="docs/phase_sync_live.mp4" autoplay loop muted playsinline width="100%">
    <img src="docs/phase_sync_live.gif" alt="Phase Sync Convergence — 16 layers × 50 osc, ζ=0.5" width="100%">
  </video>
</p>

> 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:**

```bash
# 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 "scpn-control[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:**

```bash
pytest tests/test_e2e_phase_diiid.py -v
```

## Dependencies

| Required | Optional |
|----------|----------|
| numpy >= 1.24 | sc-neurocore >= 3.8.0 (`pip install "scpn-control[neuro]"`) |
| scipy >= 1.10 | matplotlib (`pip install "scpn-control[viz]"`) |
| click >= 8.0 | streamlit (`pip install "scpn-control[dashboard]"`) |
| | torch (`pip install "scpn-control[ml]"`) |
| | ~~nengo~~ (removed — pure LIF+NEF engine, no external dependency) |
| | h5py (`pip install "scpn-control[hdf5]"`) |
| | websockets (`pip install "scpn-control[ws]"`) |

## CLI

```bash
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:

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

Rust Criterion benchmarks:

```bash
cd scpn-control-rs
cargo bench --workspace
```

Benchmark docs: `docs/benchmarks.md`

## Dashboard

```bash
pip install "scpn-control[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](https://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](https://share.streamlit.io) > New app > select `streamlit_app.py`
3. Deploy (auto-starts embedded PhaseStreamServer)

## Rust Acceleration

```bash
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

```bibtex
@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:**

```bash
# 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):

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

## Limitations & Honest Scope

> These are not future roadmap items — they are current architectural
> constraints that users must understand.

- **No facility deployment**: DIII-D replay evidence is limited to immutable
  repository reference artefacts with manifest checksums. Synthetic fixtures
  remain for CI plumbing only, not public physics evidence. No live MDSplus,
  no experimental control-room replay, and no real-world validation.
- **No peer-reviewed fusion publication**: Paper 27 (arXiv:2004.06344) is
  unpublished in a fusion journal. No external citations.
- **Not a production PCS**: Alpha-stage research software. No ITER CODAC,
  no EPICS interface, no safety certification, no real hardware deployment.
- **"Formal verification" is contract checking**: Runtime pre/post-condition
  assertions, not theorem-proved guarantees (no Coq/Lean/TLA+).
- **Benchmark comparisons are not apples-to-apples**: The 11.9 µs number is a
  bare Rust kernel step. DIII-D PCS timings include I/O, diagnostics, and
  actuator commands. A fair comparison requires equivalent end-to-end
  measurement on comparable hardware.
- **Equilibrium solver**: Two variants exist: stable fixed-boundary GS, plus an
  experimental free-boundary external-coil scaffold. The free-boundary path is
  not yet sufficient for full shape control, X-point geometry, or divertor
  configuration. No stellarator geometry.
- **Transport**: 1.5D flux-surface-averaged with five tiers from critical-gradient
  to nonlinear δf GK. Native TGLF-equivalent (no Fortran binary) and nonlinear
  solver produce physically meaningful transport, but are not yet cross-validated
  against production TGLF or GENE on identical equilibria.
- **Disruption predictor**: Synthetic training data only. Not validated on
  experimental disruption databases.
- **No GPU equilibrium**: P-EFIT is faster on GPU hardware. JAX neural equilibrium
  runs on GPU if available but is not cross-validated against P-EFIT.
- **Rust acceleration**: Optional. Pure-Python fallback is complete but 5-10x
  slower for GS solve and Kuramoto steps at N > 1000.

## Support the Project

**scpn-control** is open-source (AGPL-3.0-or-later | commercial license available).
Funding goes to compute, validation data, and development time.

| | | |
|---|---|---|
| [Sponsor via Stripe](https://buy.stripe.com/4gM00kbiMdjAberaYz5J601) | [Donate via PayPal](https://www.paypal.com/donate?hosted_button_id=4X5F6DNT934HY) | [Pay via TWINT](https://go.twint.ch/1/e/tw?tw=acq.lJTAypb8SL2s8vPg7fL0ubi2C220ajOH0BEQn1aKfEJIiIakLpt8jlEv8XdQ9tCp.) |

**Crypto:** BTC `bc1qg48gdmrjrjumn6fqltvt0cf0w6nvs0wggy37zd` ·
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Full tier details (Pro, Academic, Enterprise, Sponsorships): [docs/pricing.md](docs/pricing.md)

## Authors

- **Miroslav Sotek** — ANULUM CH & LI — [ORCID](https://orcid.org/0009-0009-3560-0851)
- **** — ANULUM CH & LI

## License

- Concepts: Copyright 1996-2026
- Code: Copyright 2024-2026
- License: AGPL-3.0-or-later

Commercial licensing available — contact protoscience@anulum.li.
