Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9210
Main Title: pypet: A Python Toolkit for Data Management of Parameter Explorations
Author(s): Meyer, Robert
Obermayer, Klaus
Type: Article
Language Code: en
Abstract: pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
URI: https://depositonce.tu-berlin.de/handle/11303/10248
http://dx.doi.org/10.14279/depositonce-9210
Issue Date: 25-Aug-2016
Date Available: 6-Nov-2019
DDC Class: 610 Medizin und Gesundheit
Subject(s): parameter exploration
reproducibility
simulation
python
parallelization
grid computing
Sponsor/Funder: DFG, GRK 1589, Verarbeitung sensorischer Informationen in neuronalen Systemen
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Frontiers in Neuroinformatics
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 10
Article Number: 38
Publisher DOI: 10.3389/fninf.2016.00038
EISSN: 1662-5196
Appears in Collections:FG Neuronale Informationsverarbeitung » Publications



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