Four Python packages, each with its own documentation and executed tutorial notebooks. The tutorials are embedded below: open one to work through it without leaving this page, or follow the link to the full documentation.
dynamodels
Dynamical-system models behind one interface. A Model couples a governing equation to a pre-allocated state history and a pluggable time integrator: three low-order oscillators, two spatially extended PDEs, and the Lorenz systems. It depends only on NumPy, SciPy, Matplotlib and typeguard.
Interactive tutorial
ntsa
Nonlinear time-series analysis of a dynamical system from a single long trajectory: delay embedding, Lyapunov exponents, and classification of the dynamical regime, with a diagnostic figure for each case. The same pipeline runs from measured data alone, with no model equations.
Interactive tutorial
romda
Real-time reduced-order modelling and bias-aware data assimilation: ensemble Kalman filters including the regularized bias-aware EnKF, physical and data-driven forecast models, bias estimators, and POD and SPOD decompositions. The tutorials build up from the base classes to full real-data digital twins.
Interactive tutorial
qlroms
Quantized-local reduced-order models. The state space is partitioned into charts by k-means, each chart gets its own local POD basis, and an exact shared atlas stitches them into one global coordinate system. The local dynamics are advanced by an intrusive Galerkin projection, one quadratic reduced system per chart.