| Name | pyrates JSON |
| Version |
1.0.5
JSON |
| download |
| home_page | None |
| Summary | Dynamical Systems Modeling Framework |
| upload_time | 2024-03-31 21:51:32 |
| maintainer | None |
| docs_url | None |
| author | Richard Gast, Daniel Rose |
| requires_python | >=3.6 |
| license | GPL v3 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
PyRates
=======
[](https://github.com/pyrates-neuroscience/PyRates)
[](https://circleci.com/gh/pyrates-neuroscience/PyRates/tree/master)
[](https://badge.fury.io/py/pyrates)
[](https://pyrates.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/pyrates)
[](https://zenodo.org/badge/latestdoi/162463287)
<img src="https://github.com/pyrates-neuroscience/PyRates/blob/master/PyRates_logo_color.png" width="20%" heigth="20%" align="right">
PyRates is a framework for dynamical systems modeling, developed by Richard Gast and Daniel Rose.
It is an open-source project that everyone is welcome to contribute to.
Basic features
===============
Basic features:
---------------
- Frontend:
- implement models via a frontend of your choice: *YAML* or *Python*
- create basic mathematical building blocks (i.e. differential equations and algebraic equations) and use them to define a networks of nodes connected by edges
- create hierarchical networks by connecting networks via edges
- Backend:
- choose from a number of different backends
- `NumPy` backend for dynamical systems modeling on CPUs via *Python*
- `Tensorflow` and `PyTorch` backends for parameter optimization via gradient descent and dynamical systems modeling on GPUs
- `Julia` backend for dynamical system modeling in *Julia*, via tools such as `DifferentialEquations.jl`
- `Fortran` backend for dynamical systems modeling via *Fortran 90* and interfacing the parameter continuation software *Auto-07p*
- `Matlab` backend for differential equation solving via Matlab
- Other features:
- perform quick numerical simulations via a single function call
- choose between different numerical solvers
- perform parameter sweeps over multiple parameters at once
- generate backend-specific run functions that evaluate the vector field of your dynamical system
- Implement dynamic edge equations that include scalar delays or delay distributions (delay distributions are automatically translated into gamma-kernel convolutions)
- choose from various pre-implemented dynamical systems that can be directly used for simulations or integrated into custom models
Installation
============
Stable release (PyPI)
---------------------
PyRates can be installed via the `pip` command. We recommend to use `Anaconda` to create a new python environment with Python >= 3.6 and then simply run the following line from a terminal with the environment being activated:
```
pip install pyrates
```
You can install optional (non-default) packages by specifying one or more options in brackets, e.g.:
```
pip install pyrates[backends]
```
Available options are `backends`, `dev`, and `all` at the moment.
The latter includes all optional packages.
Furthermore, the option `tests` includes all packages necessary to run tests found in the github repository.
Development version (github)
----------------------------
Alternatively, it is possible to clone this repository and run one of the following lines
from the directory in which the repository was cloned:
```
python setup.py install
```
or
```
pip install '.[<options>]'
```
Documentation
=============
For a full API of PyRates, see https://pyrates.readthedocs.io/en/latest/.
For examplary simulations and model configurations, please have a look at the jupyter notebooks provided in the documenation folder.
References
==========
If you use this framework, please cite:
[Gast, R., Knösche, T. R. & Kennedy, A. (2023). PyRates - A Code-Generation Tool for Dynamical Systems Modeling. PLOS Computational Biology 19 (12), e1011761.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011761)
and
[Gast, R., Rose, D., Salomon, C., Möller, H. E., Weiskopf, N., & Knösche, T. R. (2019). PyRates-A Python framework for rate-based neural simulations. PloS one, 14(12):e0225900.](https://doi.org/10.1371/journal.pone.0225900)
Other work that used PyRates:
[Weise, K., Poßner, L., Müller, E., Gast, R. & Knösche, T. R. (2020) Software X, 11:100450.](https://www.sciencedirect.com/science/article/pii/S2352711020300078)
[Gast, R., Gong, R., Schmidt, H., Meijer, H.G.E., & Knösche, T.R. (2021) On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum. Journal of Neuroscience, 41(31):6673-6683.](https://www.jneurosci.org/content/41/31/6673.abstract)
[Gast, R., Solla, S.A. & Kennedy, A. (2023). Macroscopic dynamics of neural networks with heterogeneous spiking thresholds. Physical Review E, 107(2):024306.](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.024306)
Contact
=======
If you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.richardgast.me).
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"description": "PyRates\n=======\n\n[](https://github.com/pyrates-neuroscience/PyRates) \n[](https://circleci.com/gh/pyrates-neuroscience/PyRates/tree/master)\n[](https://badge.fury.io/py/pyrates)\n[](https://pyrates.readthedocs.io/en/latest/?badge=latest)\n[](https://badge.fury.io/py/pyrates)\n[](https://zenodo.org/badge/latestdoi/162463287)\n\n<img src=\"https://github.com/pyrates-neuroscience/PyRates/blob/master/PyRates_logo_color.png\" width=\"20%\" heigth=\"20%\" align=\"right\">\n\nPyRates is a framework for dynamical systems modeling, developed by Richard Gast and Daniel Rose. \nIt is an open-source project that everyone is welcome to contribute to.\n\nBasic features\n===============\n\nBasic features:\n---------------\n\n- Frontend:\n - implement models via a frontend of your choice: *YAML* or *Python*\n - create basic mathematical building blocks (i.e. differential equations and algebraic equations) and use them to define a networks of nodes connected by edges\n - create hierarchical networks by connecting networks via edges\n- Backend:\n - choose from a number of different backends\n - `NumPy` backend for dynamical systems modeling on CPUs via *Python*\n - `Tensorflow` and `PyTorch` backends for parameter optimization via gradient descent and dynamical systems modeling on GPUs\n - `Julia` backend for dynamical system modeling in *Julia*, via tools such as `DifferentialEquations.jl`\n - `Fortran` backend for dynamical systems modeling via *Fortran 90* and interfacing the parameter continuation software *Auto-07p*\n - `Matlab` backend for differential equation solving via Matlab\n- Other features:\n - perform quick numerical simulations via a single function call\n - choose between different numerical solvers\n - perform parameter sweeps over multiple parameters at once\n - generate backend-specific run functions that evaluate the vector field of your dynamical system\n - Implement dynamic edge equations that include scalar delays or delay distributions (delay distributions are automatically translated into gamma-kernel convolutions)\n - choose from various pre-implemented dynamical systems that can be directly used for simulations or integrated into custom models\n\nInstallation\n============\n\nStable release (PyPI)\n---------------------\n\nPyRates can be installed via the `pip` command. We recommend to use `Anaconda` to create a new python environment with Python >= 3.6 and then simply run the following line from a terminal with the environment being activated:\n```\npip install pyrates\n```\n\nYou can install optional (non-default) packages by specifying one or more options in brackets, e.g.:\n```\npip install pyrates[backends]\n```\n\nAvailable options are `backends`, `dev`, and `all` at the moment. \nThe latter includes all optional packages. \nFurthermore, the option `tests` includes all packages necessary to run tests found in the github repository.\n\nDevelopment version (github)\n----------------------------\n\nAlternatively, it is possible to clone this repository and run one of the following lines \nfrom the directory in which the repository was cloned:\n```\npython setup.py install\n```\nor\n```\npip install '.[<options>]'\n```\n\nDocumentation\n=============\n\nFor a full API of PyRates, see https://pyrates.readthedocs.io/en/latest/.\nFor examplary simulations and model configurations, please have a look at the jupyter notebooks provided in the documenation folder.\n\nReferences\n==========\n\nIf you use this framework, please cite:\n\n[Gast, R., Kn\u00f6sche, T. R. & Kennedy, A. (2023). PyRates - A Code-Generation Tool for Dynamical Systems Modeling. PLOS Computational Biology 19 (12), e1011761.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011761)\n\nand\n\n[Gast, R., Rose, D., Salomon, C., M\u00f6ller, H. E., Weiskopf, N., & Kn\u00f6sche, T. R. (2019). PyRates-A Python framework for rate-based neural simulations. PloS one, 14(12):e0225900.](https://doi.org/10.1371/journal.pone.0225900)\n\nOther work that used PyRates:\n\n[Weise, K., Po\u00dfner, L., M\u00fcller, E., Gast, R. & Kn\u00f6sche, T. R. (2020) Software X, 11:100450.](https://www.sciencedirect.com/science/article/pii/S2352711020300078)\n\n[Gast, R., Gong, R., Schmidt, H., Meijer, H.G.E., & Kn\u00f6sche, T.R. (2021) On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum. Journal of Neuroscience, 41(31):6673-6683.](https://www.jneurosci.org/content/41/31/6673.abstract)\n\n[Gast, R., Solla, S.A. & Kennedy, A. (2023). Macroscopic dynamics of neural networks with heterogeneous spiking thresholds. Physical Review E, 107(2):024306.](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.024306)\n\n\nContact\n=======\n\nIf you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.richardgast.me).\n\n\n",
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