ezmsg

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.

Installation

pip install ezmsg

Dependencies

• Due to reliance on multiprocessing.shared_memory, ezmsg requires minimum Python 3.8.
• typing_extensions

Testing ezmsg requires:

• pytest
• pytest-cov
• pytest-asyncio
• numpy

Setup (Development)

$ 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 its own standalone venv via the pipx cli tool.

Documentation

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.

Extensions

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 a subset of extension packages, 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.

Extensions can be managed manually as well. Here are some of the extensions we manage or are aware of:

• ezmsg-sigproc -- Timeseries signal processing modules
• ezmsg-websocket -- Websocket server and client nodes for ezmsg graphs
• ezmsg-zmq -- ZeroMQ pub and sub nodes for ezmsg graphs
• ezmsg-panel -- Plotting tools for ezmsg that use panel
• ezmsg-blackrock -- Interface for Blackrock Cerebus ecosystem (incl. Neuroport) using pycbsdk
• ezmsg-lsl -- Source unit for LSL Inlet and sink unit for LSL Outlet
• 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.

Publications

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

Financial Support

ezmsg is supported by Johns Hopkins University (JHU), the JHU Applied Physics Laboratory (APL), and by the Wyss Center for Bio and Neuro Engineering.