dynamodels

Dynamical-system models with pre-allocated history tracking and pluggable time integrators — the modelling core split out of romda so it can be reused on its own. Torch-free; depends only on numpy, scipy, matplotlib, typeguard and multiprocess.

Tutorial: tutorial_dynamodels.ipynb | Interface documentation: model protocol

Install

pip install dynamodels

Quickstart

from dynamodels.physical import Lorenz63

model = Lorenz63(rho=28., dt=0.01)
psi, t = model.time_integrate(Nt=1000)   # (Nt, Nphi, m) states, (Nt,) times
model.update_history(psi, t)
y = model.get_observable_hist()          # (Nt, Nq, m) observables
model.visualize_history()
model.close()                            # release the integrator's pool

A model composes a HistoryTracker (state/time buffer behind hist, hist_t, update_history()) with an Integrator strategy; subclasses supply obs_labels plus either time_derivative(t, psi, **params) (continuous) or time_step(Nt) (discrete map). m-member ensembles are supported natively (init_ensemble). Included physical models (dynamodels.physical): Lorenz63, Lorenz96, VdP, Rijke, Annular, KS — the lorenz63, lorenz96 and rijke modules carry measured dominant-Lyapunov tables (module-level _LAM1_MEASURED; _LAM1_MEASURED_NX10 for Lorenz96) from which instances set t_lyap.

Ecosystem

ntsa — nonlinear time-series analysis for any dynamodels-style model (docs).
romda — bias-aware ensemble data assimilation built on top.

Development

pip install -e ".[dev]"
python -m pytest tests/
ruff check dynamodels/ tests/

Releases: bump version in pyproject.toml, then git tag vX.Y.Z && git push --tags (publishes to PyPI).

License

MIT

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