Volume 16 Supplement 1

24th Annual Computational Neuroscience Meeting: CNS*2015

Open Access

pypet: a python toolkit for simulations and numerical experiments

BMC Neuroscience201516(Suppl 1):P184

https://doi.org/10.1186/1471-2202-16-S1-P184

Published: 18 December 2015

"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 and numerical experiments. pypet was especially designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. Moreover, special focus is put on managing different neuron models in python network simulations like BRIAN [2]. Simulation parameters as well as the obtained results are collected by pypet and stored in the widely used HDF5 file format [3]. This allows fast and convenient loading of data for further analyses.

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 supervision of experiments via the electronic lab notebook Sumatra [4]. A rich set of data formats is supported, encompassing native python types, numpy and scipy data, pandas DataFrames [5], and data from BRIAN [2]. 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. Thus, pypet supports reproducible research in computational neuroscience and other disciplines that involve simulations and numerical experiments.

Authors’ Affiliations

(1)
Neuroinformatics Group, Technische Universitaet Berlin
(2)
Bernstein Center for Computational Neuroscience Berlin

References

  1. Meyer R, Obermayer K: pypet. online documentation, 2013-2015, [http://pypet.readthedocs.org/]
  2. Goodman DF, Brette R: The Brian simulator. Front Neurosci. 2009, 3 (2): 192-197. [http://briansimulator.org/]PubMedPubMed CentralView ArticleGoogle Scholar
  3. The HDF Group: Hierarchical Data Format, version 5. 1997, [http://www.hdfgroup.org/HDF5/] -2015Google Scholar
  4. Davison AP: Automated capture of experiment context for easier reproducibility in computational research. Computing in Science and Engineering. 2012, 14 (4): 48-56. [http://neuralensemble.org/sumatra/]View ArticleGoogle Scholar
  5. McKinney W: Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. 2010, 51-56. [http://pandas.pydata.org]Google Scholar

Copyright

© Meyer and Obermayer 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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