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Python interface to the Lab Streaming Layer

Project description


This is the Python interface to the Lab Streaming Layer (LSL). LSL is an overlay network for real-time exchange of time series between applications, most often used in research environments. LSL has clients for many other languages and platforms that are compatible with each other.

Pylsl should work with any recent version of the liblsl library (which is also included with this package), on any operating system and with any recent Python version, including 2.7+ and 3.x.

Let us know if you encounter any bugs (ideally using the issue tracker on the GitHub project).


The preferred method of installation is to use pip: pip install pylsl


See the examples in pylsl/examples. Note that these can be run directly from the commandline with (e.g.) python -m pylsl.examples.SendStringMarkers.


If you are a developer or if you are a user that would like to submit a pull request, then the easiest way to modify pylsl is to install it to your python environment in development mode:

  1. Clone this repository.
  2. Download the correct liblsl shared object (*.so on Linux, *.dylib on MacOS, or *.dll on Windows) from the liblsl release page and copy it into this directory's pylsl folder.
    • On platforms with symlinks, be sure to use cp -L to copy the links correctly.
  3. In a conda terminal / command prompt / terminal, make sure your preferred python environment is active.
  4. cd to the root directory for this repository, then do pip install -e .

For pypi maintainers

  1. Manual way:
    1. rm -Rf build dist *.egg-info
    2. python sdist bdist_wheel
    3. twine upload dist/*
  2. TODO: AppVeyor/Travis

Known Issues

  • On Linux one currently cannot call pylsl functions from a thread that is not the main thread.
  • Though


Pylsl was primarily written by Christian Kothe while at Swartz Center for Computational Neuroscience, UCSD. The LSL project was funded by the Army Research Laboratory under Cooperative Agreement Number W911NF-10-2-0022 as well as through NINDS grant 3R01NS047293-06S1. Thanks for contributions, bug reports, and suggestions go to Bastian Venthur, Chadwick Boulay, David Medine, Clemens Brunner, and Matthew Grivich.

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