ODE-toolbox 2.5.12

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

• Fixes a bug that could result in the error message Float.__new__() missing 1 required positional argument: 'num' (#98).

Citation

Charl Linssen, Shraddha Jain, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2026) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.18341252.

ODE-toolbox 2.5.11

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 is functionally equivalent to v2.5.10, but adds Python packaging/distribution files to allow for a source distribution on PyPI, rather than a binary distribution. This allows unit tests to be included in the distribution.

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.17169870.

ODE-toolbox 2.5.10

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 singularity detection test for inhomogeneous terms, that, during numerical integration, are equal to zero (#90). In addition, singularity detection can be disabled using a new option disable_singularity_detection (#90).

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.17091854.

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.

ODE-toolbox 2.5.8

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 fixes a bug when computing propagator solvers for a block-diagonal matrix (#85).

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.15090637.

ODE-toolbox 2.5.7

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

• The propagator matrix is now computed by first decomposing the system matrix into its block-diagonal elements (#70). This speeds up computation and makes ODE-toolbox compatible with the latest sympy release (1.13.3 at the time of writing).
• In case of malformed or incorrect input containing division by zero, a more user-friendly error message is provided (#77).

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.15075223.

ODE-toolbox 2.5.6

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.

ODE-toolbox 2.5.5

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 2.5.5 fixes a bug related to the Piecewise function (#74).

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.

ODE-toolbox 2.5.4

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 2.5.4 fixes a bug related to singularity detection (#73).

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.

ODE-toolbox 2.5.3

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 2.5.3 fixes a bug related to analytic solutions for inhomogeneous ODES (#72).

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.