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.
Let us know if you encounter any bugs (ideally using the issue tracker on the GitHub project).
On all non-Windows platforms and for some Windows-Python combinations, you must first obtain a liblsl shared library:
- On many platforms it can be installed with
conda install -c conda-forge liblsl
- Additionally, on Mac it can be installed with
brew install labstreaminglayer/tap/lsl
- You might be able to find the appropriate liblsl shared object (*.so on Linux, *.dylib on MacOS, or *.dll on Windows) from the liblsl release page.
- Otherwise you might try to clone liblsl and use its
standalone_compilation_linux.sh
script (works on raspberry pi).
Install from pypi
using pip: pip install pylsl
For several distributions, the pip distribution ships with lsl.dll. For every other case, liblsl must be installed somewhere on the PATH (see Prerequisites above) or downloaded and copied somewhere on the search path. We recommend you copy it to the pylsl installed module path's lib
subfolder. i.e. {path/to/env/}site-packages/pylsl/lib
. Use python -m site
to find the "site-packages" path.
(use cp -L
on platforms that use symlinks)
- Download the pylsl source:
git clone https://github.com/labstreaminglayer/liblsl-Python.git && cd liblsl-Python
- Copy the shared object (see Prerequisites above) into
liblsl-Python/pylsl/lib
. - From the
liblsl-Python
working directory, runpip install .
.- Note: You can use
pip install -e .
to install while keeping the files in-place. This is convenient for developing pylsl.
- Note: You can use
See the examples in pylsl/examples. Note that these can be run directly from the commandline with (e.g.) python -m pylsl.examples.{name-of-example}
.
You can get a list of the examples with python -c "import pylsl.examples; help(pylsl.examples)"
pylsl uses continuous integration and distribution.
Whenever a new commit is pushed, AppVeyor prepares several files. First it prepares the source wheels -- this is useful on any platform & Python version that does not have a specific binary distribution. Then it prepares the binary wheels; it downloads liblsl from its releases page, copies it to the package, then builds wheels for distribution. This process is repeated for several variants of Windows and Mac.
In addition, whenever a new git tag
is used on a commit that is pushed to the master branch, the CI systems will deploy the wheels to pypi.
We recently stopped building binary wheels for Linux. In practice, the manylinux
dependencies were often incompatible with real systems.
When we did make manylinux distributions, these relied on special liblsl builds that are not automatically pushed to the liblsl releases page. Special pipelines needed to be run manually on Azure, then the artifacts uploaded to the release page. The Azure pipelines config remains in the liblsl repo in case it is needed again (unlikely).
- Manual way:
rm -Rf build dist *.egg-info
python setup.py sdist bdist_wheel
- Additional steps on Linux:
auditwheel repair dist/*.whl -w dist
rm dist/*-linux_x86_64.whl
twine upload dist/*
- For conda
- build liblsl:
conda build ../liblsl/
conda build .
- build liblsl:
- On Linux one currently cannot call
pylsl
functions from a thread that is not the main thread.- This note has been around for a long time and isn't actually tested/confirmed with more recent liblsl versions. Some users report that it indeed works. Please let us know what your experience is.
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. pylsl is maintained primarily by Chadwick Boulay. Thanks for contributions, bug reports, and suggestions go to Bastian Venthur, David Medine, Clemens Brunner, and Matthew Grivich.