Publication Information

Citation Policy

If you use pypet in your research, it would be very kind of you to cite this in your amazing work. A research article about pypet is available at frontiers, the corresponding bibtex entry is:

    author={Meyer, Robert and Obermayer, Klaus},
    title={pypet: A Python Toolkit for Data Management of Parameter Explorations},
    journal={Frontiers in Neuroinformatics},
    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.}

Otherwise you can cite it as:

Brain Days and EuroPython Poster

There is a poster about pypet that was shown at the Berlin Brain Days 2013 and the EuroPython 2014.




  • Thanks to Robert Pröpper and Philipp Meier for answering all my python questions

    You might want to check out their SpykeViewer tool for visualization of MEA recordings and NEO data

  • Thanks to Owen Mackwood for his SNEP toolbox which provided the initial ideas for this project

  • Thanks to Mehmet Nevvaf Timur for his work on the SCOOP integration and the 'NETQUEUE' feature

  • Thanks to Henri Bunting for his work on the BRIAN2 subpackage

  • Thanks to the BCCN Berlin, the Research Training Group GRK 1589/1, and the Neural Information Processing Group for support


Tests can be found in pypet/tests. Note that they involve heavy file IO and you need privileges to write files to a temporary folder. The test suites will make use of the tempfile.gettempdir() function to create such a temporary folder.

Each test module can be run individually, for instance $ python

You can run all tests with $ python which can also be found under pypet/tests. You can pass additional arguments as $ python -k --folder=myfolder/ with -k to keep the HDF5 and log files created by the tests (if you want to inspect them, otherwise they will be deleted after the completed tests), and --folder= to specify a folder where to store the HDF5 files instead of the temporary one. If the folder cannot be created, the program defaults to tempfile.gettempdir().

If you do not want to browse to your installation folder, you can also download the script.

Running all tests can take up to 20 minutes and might temporarily take up to 1 GB of disk space. The test suite encompasses more than 1000 tests and has a code coverage of about 90%!

pypet is constantly tested with Python 3.4, 3.5, and 3.6 for Linux using Travis-CI. Testing for Windows platforms is performed via Appveyor. The source code is available at