pypet: A Python Toolkit for Data Management of Parameter Explorations.
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.
Neuroinformatics Group, Department of Software Engineering and Theoretical Computer Science, Technical University BerlinBerlin, Germany; Bernstein Center for Computational NeuroscienceBerlin, Germany.
Robert
R
Meyer
Neuroinformatics Group, Department of Software Engineering and Theoretical Computer Science, Technical University Berlin Berlin, Germany.
Klaus
K
Obermayer
grid computing; parallelization; parameter exploration; python; reproducibility; simulation