pyrates


Namepyrates JSON
Version 0.9.6 PyPI version JSON
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SummaryNeural Network Modeling Framework
upload_time2021-06-11 15:18:40
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authorRichard Gast, Daniel Rose
requires_python>=3.6
licenseGPL v3
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            PyRates
=======

[![License](https://img.shields.io/github/license/pyrates-neuroscience/PyRates.svg)](https://github.com/pyrates-neuroscience/PyRates) 
[![Build Status](https://travis-ci.com/pyrates-neuroscience/PyRates.svg?branch=master)](https://travis-ci.com/pyrates-neuroscience/PyRates)
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<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 neural modeling and simulations, developed by Richard Gast and Daniel Rose at the Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany. 
It is an open-source project that everyone is welcome to contribute to.

Basic features
===============

- Different backends: `Numpy` for fast simulations of small- to medium-sized networks. `Tensorflow` for efficient parallelization on GPUs/CPUs, `Fortran` for parameter continuations.
- Each model is internally represented by a `networkx` graph of nodes and edges, with the former representing the model units (i.e. single cells, cell populations, ...) and the latter the information transfer between them. In principle, this allows to implement any kind of dynamic neural system that can be expressed as a graph via PyRates.
- Solutions of initial value problems via different numerical solvers (e.g. full interface to `scipy.integrate.solve_ivp`)
- Parameter continuations and bifurcation analysis via PyAuto, an interface to `auto-07p`
- Storage of solutions in `pandas.DataFrame`
- Efficient parameter sweeps on single and multiple machines via the `grid_search` module
- Model optimization via genetic algorithms
- Visualization of results via `seaborn`
- Post-processing of simulation results via `scipy` and `MNE Python`
- The user has full control over the mathematical equations that nodes and edges are defined by. 
- Model configuration and simulation can be done within a few lines of code.  
- Various templates for rate-based population models are provided that can be used for neural network simulations imediatly.

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[tf,plot]
```

Available options are `tf`, `plot`, `proc`, `cluster`, `numba` and `all`. 
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>]'
```

Singularity container
---------------------


Finally, a singularity container of the most recent version of this software can be found [here](https://singularity.gwdg.de/containers/3).
This container provides a stand-alone version of PyRates including all necessary Python tools to be run, independent of local operating systems. 
To be able to use this container, you need to install [Singularity](https://singularity.lbl.gov/) on your local machine first.
Follow these [instructions](https://singularity.lbl.gov/quickstart) to install singularity and run scripts inside the PyRates container.

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.

Reference
=========

If you use this framework, please cite:
[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)

Contact
=======

If you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.cbs.mpg.de/person/59190/376039) or [Daniel Rose](https://www.cbs.mpg.de/person/51141/374227).



            

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    "description": "PyRates\n=======\n\n[![License](https://img.shields.io/github/license/pyrates-neuroscience/PyRates.svg)](https://github.com/pyrates-neuroscience/PyRates) \n[![Build Status](https://travis-ci.com/pyrates-neuroscience/PyRates.svg?branch=master)](https://travis-ci.com/pyrates-neuroscience/PyRates)\n[![PyPI version](https://badge.fury.io/py/pyrates.svg)](https://badge.fury.io/py/pyrates)\n[![Documentation Status](https://readthedocs.org/projects/pyrates/badge/?version=latest)](https://pyrates.readthedocs.io/en/latest/?badge=latest)\n[![Python](https://img.shields.io/pypi/pyversions/pyrates.svg?style=plastic)](https://badge.fury.io/py/pyrates)\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 neural modeling and simulations, developed by Richard Gast and Daniel Rose at the Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany. \nIt is an open-source project that everyone is welcome to contribute to.\n\nBasic features\n===============\n\n- Different backends: `Numpy` for fast simulations of small- to medium-sized networks. `Tensorflow` for efficient parallelization on GPUs/CPUs, `Fortran` for parameter continuations.\n- Each model is internally represented by a `networkx` graph of nodes and edges, with the former representing the model units (i.e. single cells, cell populations, ...) and the latter the information transfer between them. In principle, this allows to implement any kind of dynamic neural system that can be expressed as a graph via PyRates.\n- Solutions of initial value problems via different numerical solvers (e.g. full interface to `scipy.integrate.solve_ivp`)\n- Parameter continuations and bifurcation analysis via PyAuto, an interface to `auto-07p`\n- Storage of solutions in `pandas.DataFrame`\n- Efficient parameter sweeps on single and multiple machines via the `grid_search` module\n- Model optimization via genetic algorithms\n- Visualization of results via `seaborn`\n- Post-processing of simulation results via `scipy` and `MNE Python`\n- The user has full control over the mathematical equations that nodes and edges are defined by. \n- Model configuration and simulation can be done within a few lines of code.  \n- Various templates for rate-based population models are provided that can be used for neural network simulations imediatly.\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[tf,plot]\n```\n\nAvailable options are `tf`, `plot`, `proc`, `cluster`, `numba` and `all`. \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\nSingularity container\n---------------------\n\n\nFinally, a singularity container of the most recent version of this software can be found [here](https://singularity.gwdg.de/containers/3).\nThis container provides a stand-alone version of PyRates including all necessary Python tools to be run, independent of local operating systems. \nTo be able to use this container, you need to install [Singularity](https://singularity.lbl.gov/) on your local machine first.\nFollow these [instructions](https://singularity.lbl.gov/quickstart) to install singularity and run scripts inside the PyRates container.\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\nReference\n=========\n\nIf you use this framework, please cite:\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\nContact\n=======\n\nIf you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.cbs.mpg.de/person/59190/376039) or [Daniel Rose](https://www.cbs.mpg.de/person/51141/374227).\n\n\n",
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