ODE-toolbox 2.5.9

Choosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix.

In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.

Release notes

• This version adds a global configuration parameter that allows the variable name prefix of generated propagators to be customised (#88). It also removes the redundant simplfy_expression parameter to analysis(), as it can and should be passed via the global configuration options in the input dictionary instead.

Citation

Charl Linssen, Shraddha Jain, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2025) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.15474561.