Metadata-Version: 2.1
Name: odetoolbox
Version: 2.4.1
Summary: ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations
Home-page: https://github.com/nest/ode-toolbox
Author: The NEST Initiative
License: GNU General Public License v2 (GPLv2)
Keywords: computational neuroscience model ordinary differential equation ode dynamical dynamic simulation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires: matplotlib
Requires: numpy
Requires: sympy
License-File: LICENSE.md

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.

