oscillatools 0.1.0

Differentiable, control-oriented toolkit for coupled-phase-oscillator (Kuramoto) dynamics

oscillatools

A differentiable, control-oriented toolkit for coupled-phase-oscillator (Kuramoto) dynamics. It provides the model family, integrators, exact mean-field reductions, stability and continuation analysis, differentiation, and control primitives as a light, standalone distribution whose only hard dependencies are NumPy and SciPy. Every acceleration or interoperability tier (a Rust engine, Julia, JAX, PyTorch, matplotlib, scikit-learn) is an optional extra; the pure-Python NumPy floor always runs.

oscillatools is extracted from scpn-quantum-control, where the toolkit originated, so that the coupled-oscillator surface can be installed without that project's quantum-provider dependency stack.

Installation

pip install oscillatools                 # NumPy + SciPy floor
pip install "oscillatools[rust]"         # optional Rust acceleration engine
pip install "oscillatools[jax]"          # JAX autodiff backend
pip install "oscillatools[torch]"        # PyTorch neural-operator surrogate
pip install "oscillatools[sklearn]"      # scikit-learn estimator interface
pip install "oscillatools[viz]"          # matplotlib renderers
pip install "oscillatools[julia]"        # Julia acceleration tier

Capabilities

• Model family — classical, higher-order (hyperedge/simplicial force), time-delayed, noisy, inertial (second-order), adaptive-coupling, Sakaguchi, Winfree, Stuart–Landau, networked/multiplex, and next-generation QIF neural-mass Kuramoto dynamics.
• Integrators — explicit Euler, RK4, DOPRI adaptive step, symplectic (inertial), method-of-steps (delayed), and Euler–Maruyama (stochastic).
• Exact reductions — Ott–Antonsen and Watanabe–Strogatz manifolds, and the noisy/finite-width mean-field order-parameter theory.
• Analysis — order parameters and Daido harmonics, linear-stability spectra, Lyapunov exponents, pseudo-arclength continuation, saddle-node/fold location, and basin-stability estimation.
• Differentiation — a hand-written reverse-mode adjoint and forward sensitivity over the integrators (NumPy and, optionally, JAX), for gradient-based design.
• Control — optimal coupling design, pinning control, coordinated reset, SDRE/receding-horizon control, and system identification.
• Interoperability — a scikit-learn-style estimator interface and a NumPy-array-first API.

A generated capability handbook and manifest enumerate the full public surface.

Optional acceleration and interoperability

The Rust acceleration engine ships as the separate scpn-quantum-engine wheel and is used automatically when installed ([rust] extra). Without it, the dispatchers fall through to the NumPy implementation. Measured tier selection is reported per call.

Licensing

oscillatools is dual-licensed under AGPL-3.0-or-later with a commercial license available. See NOTICE.md and LICENSES/AGPL-3.0-or-later.txt. For commercial licensing: [e-mail].

Citation

If you use oscillatools, please cite it using the metadata in CITATION.cff.