Releases: nest/ode-toolbox
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.
ODE-toolbox 2.5.2
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 2.5.2 fixes a bug related to the preserve_expressions flag (#71).
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 2.5.1
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 2.5.1 fixes a bug related to the preserve_expressions flag (#69).
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
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 adds a propagator singularity (division by zero) detection feature.
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 2.4.1
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 2.4.1 fixes the computation of analytic solvers for first-order inhomogeneous ODEs.
Citation
Charl Linssen, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2021) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.5768597.
ODE-toolbox 2.4
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 2.4 adds support to solve first-order inhomogeneous ODEs by exact integration.
Citation
Charl Linssen, Pooja N. Babu, Abigail Morrison and Jochen M. Eppler (2021) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.5768597.
ODE-toolbox 2.3
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 2.3 contains various bug fixes and feature enhancements that allow greater control and flexibility in the use of ODE-toolbox.
Citation
Charl Linssen, Shraddha Jain, Abigail Morrison and Jochen M. Eppler (2020) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.4245012.
ODE-toolbox 2.2
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 2.2 fixes a bug (#43) since the previous release (version 2.1).
Citation
Charl Linssen, Shraddha Jain, Abigail Morrison and Jochen M. Eppler (2020) ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations. Zenodo. doi:10.5281/zenodo.4245012.