diff --git a/doc/index.rst b/doc/index.rst index 52eed374..c34477a0 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -449,7 +449,7 @@ Initially, individual expressions are read from JSON into Shape instances. Subse Converting direct functions of time ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The aim is to find a representation of the form :math:`a_0 f + a_1 f' + ... + a_{n-1} f^{(n-1)} = f^{(n)}`, with :math:`a_i\in\mathbb{R}\,\forall 0 \leq i < n`. The approach taken here [4]_ works by evaluating the function :math:`f(t)` at times :math:`t = t_0, t_1, \ldots t_n`, which results in :math:`n` equations, that we can use to solve for the coefficients of the potentially :math:`n`-dimensional dynamical system. +The aim is to find a representation of the form :math:`a_0 f + a_1 f' + ... + a_{n-1} f^{(n-1)} = f^{(n)}`, with :math:`a_i\in\mathbb{R}\,\forall 0 \leq i < n`. The approach taken here [5]_ works by evaluating the function :math:`f(t)` at times :math:`t = t_0, t_1, \ldots t_n`, which results in :math:`n` equations, that we can use to solve for the coefficients of the potentially :math:`n`-dimensional dynamical system. 1. Begin by assuming that the dynamical system is of order :math:`n`. 2. Find timepoints :math:`t = t_0, t_1, ..., t_n` such that :math:`f(t_i) \neq 0 \forall 0 \leq i \leq n`. The times can be selected at random. @@ -612,23 +612,27 @@ Citing ODE-toolbox If you use ODE-toolbox in your work, please cite it depending on the version you are using. (It is recommended to use the latest release version whenever possible.) +For version 2.5: + +.. [1] 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 `__. + For version 2.4: -.. [1] Charl Linssen, Pooja N. Babu, 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.5768597 `__. +.. [2] 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 `__. For versions 2.3, 2.2 and 2.1: -.. [2] 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 `__. +.. [3] 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 `__. For version 2.0: -.. [3] Charl Linssen, 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.3822082 `__. +.. [4] Charl Linssen, 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.3822082 `__. References ---------- -.. [4] Inga Blundell, Dimitri Plotnikov, Jochen Martin Eppler and Abigail Morrison (2018) **Automatically selecting a suitable integration scheme for systems of differential equations in neuron models.** Front. Neuroinform. `doi:10.3389/fninf.2018.00050 `__. +.. [5] Inga Blundell, Dimitri Plotnikov, Jochen Martin Eppler and Abigail Morrison (2018) **Automatically selecting a suitable integration scheme for systems of differential equations in neuron models.** Front. Neuroinform. `doi:10.3389/fninf.2018.00050 `__. Acknowledgements diff --git a/setup.py b/setup.py index f64ee85e..11f1fe1e 100755 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ 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.""" setup(name="odetoolbox", - version="2.4.1-post-dev", + version="2.5", author="The NEST Initiative", classifiers=['Development Status :: 4 - Beta', 'Environment :: Console',