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
Version 2.5.6 fixes a bug related to inhomogeneous ODEs (#75).
Citation
Charl Linssen, Shraddha Jain, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2022) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.7193351.