pypet: a Python Toolkit for Simulations and Numerical Experiments
-
1
Bernstein Center for Computational Neuroscience Berlin, Germany
-
2
TU-Berlin, Fakultaet IV, Germany
“pypet” (python parameter exploration toolkit [1]) is a new multi-platform python toolkit for management of simulations and storage of numerical data. Exploring or sampling the space of model parameters is one key aspect of simulations or numerical experiments. pypet was especially designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. For instance, pypet could be used to manage the exploration of different neuron models in a python neural network simulation.
Simulation parameters as well as the obtained results are collected by pypet and stored in the widely used HDF5 file format [2]. Furthermore, pypet provides an environment with various features. For example, among these are multiprocessing for fast parallel simulations, dynamic loading of data, integration of Git version control, and merging of results from several simulations. A rich set of data formats is supported encompassing native python types, Numpy and Scipy data, pandas DataFrames [3] as well as data from the BRIAN neural network simulator [4]. Moreover, the toolkit is easily extendible to allow the user to add customized data formats. pypet is a very flexible tool and suited for short python scripts as well as large scale projects in computational neuroscience and other disciplines that involve simulations and numerical experiments.
[1] http://pypet.readthedocs.org/
[2] http://www.hdfgroup.org/HDF5/
[3] http://pandas.pydata.org/
[4] http://briansimulator.org/
Acknowledgements
BCCN Berlin, the Research Training Group GRK 1589/1
Keywords:
python,
parameter exploration,
Toolbox,
numerical experiments,
Simulations
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
General neuroinformatics
Citation:
Meyer
R
(2014). pypet: a Python Toolkit for Simulations and Numerical Experiments.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00051
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
22 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Mr. Robert Meyer, Bernstein Center for Computational Neuroscience Berlin, Berlin, 10115, Germany, robert.meyer@ni.tu-berlin.de