Messaging and Multiprocessing.
ezmsg
is a pure-Python implementation of a directed acyclic graph (DAG) pub/sub messaging pattern based on labgraph
which is optimized and intended for use in constructing real-time software. ezmsg
implements much of the labgraph
API (with a few notable differences), and owes a lot of its design to the labgraph
developers/project.
ezmsg
is very fast and uses Python's multiprocessing.shared_memory
module to facilitate efficient message passing without C++ or any compilation/build tooling.
pip install ezmsg
- Due to reliance on
multiprocessing.shared_memory
,ezmsg
requires minimum Python 3.8. typing_extensions
Testing ezmsg
requires:
pytest
pytest-cov
pytest-asyncio
numpy
$ python3 -m venv env
$ source env/bin/activate
(env) $ pip install --upgrade pip poetry
(env) $ poetry install --with test
(env) $ python -m pytest tests # Optionally, Perform tests
Note that it is generally recommended to install poetry into it's own standalone venv via the pipx
cli tool.
https://ezmsg.readthedocs.io/en/latest/
ezmsg
is very similar to labgraph
, so you might get a primer with their documentation and examples. Additionally, there are many examples provided in the examples/tests directories strewn throughout this repository.
ezmsg
extensions can be installed individually or all at once. To install all the extension packages in one go, you can use the following command:
pip install "ezmsg[all_ext]"
This will install all the available public extension packages for ezmsg
that are listed in pyproject.toml
.
If you prefer to install the extension packages individually, you can use the following command:
pip install "ezmsg[zmq,sigproc,...]"
Please note that the ezmsg
package itself can still be installed without any additional extensions using pip install ezmsg
.
See the extension directory for more details
ezmsg-sigproc
-- Timeseries signal processing modulesezmsg-websocket
-- Websocket server and client nodes forezmsg
graphsezmsg-zmq
-- ZeroMQ pub and sub nodes forezmsg
graphs- ... More to come!
Additionally, the following extensions are contained in external repositories:
- ezmsg-panel -- Plotting tools for
ezmsg
that use panel - ezmsg-blackrock -- Interface for Blackrock Cerebus ecosystem (incl. Neuroport) using
pycbsdk
- ezmsg-unicorn -- g.tec Unicorn Hybrid Black integration for
ezmsg
- ezmsg-gadget -- USB-gadget with HID control integration for Raspberry Pi (Zero/W/2W, 4, CM4)
- ezmsg-openbci -- OpenBCI Cyton serial interface for
ezmsg
- ezmsg-ssvep -- Tools for running SSVEP experiments with
ezmsg
- ezmsg-vispy --
ezmsg
visualization toolkit using PyQt6 and vispy.
A collection of academic papers, journals, and other publications that have cited or utilized ezmsg
in research and development.
These publications provide insights into the practical applications and impact of ezmsg
in various fields.
- A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling
- Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months
ezmsg
is supported by Johns Hopkins University (JHU), the JHU Applied Physics Laboratory (APL), and by the Wyss Center for Bio and Neuro Engineering.