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.
Raw data
{
"_id": null,
"home_page": "https://github.com/nest/ode-toolbox",
"name": "odetoolbox",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "computational neuroscience model ordinary differential equation ode dynamical dynamic simulation",
"author": "The NEST Initiative",
"author_email": "",
"download_url": "",
"platform": null,
"description": "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.\n\nIn 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.\n\n",
"bugtrack_url": null,
"license": "GNU General Public License v2 (GPLv2)",
"summary": "ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations",
"version": "2.5.5",
"project_urls": {
"Homepage": "https://github.com/nest/ode-toolbox"
},
"split_keywords": [
"computational",
"neuroscience",
"model",
"ordinary",
"differential",
"equation",
"ode",
"dynamical",
"dynamic",
"simulation"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "429ac3ac2cbb8731976182ff7d752b1b7818c9ed77a78989a3861984faed6166",
"md5": "7b884a0d3a407860c94c0c73786cdfe7",
"sha256": "b8fac31f8bbff47e6c3db281cb2083a9efb4b9b5dea6d788b1ba582d49ac14ad"
},
"downloads": -1,
"filename": "odetoolbox-2.5.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7b884a0d3a407860c94c0c73786cdfe7",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 50951,
"upload_time": "2024-01-09T10:29:58",
"upload_time_iso_8601": "2024-01-09T10:29:58.730456Z",
"url": "https://files.pythonhosted.org/packages/42/9a/c3ac2cbb8731976182ff7d752b1b7818c9ed77a78989a3861984faed6166/odetoolbox-2.5.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-09 10:29:58",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "nest",
"github_project": "ode-toolbox",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "odetoolbox"
}