SGN: A python framework for stream-processing pipelines

SGN: A python framework for stream-processing pipelines
Yun-Jing Huang, Olivia Godwin, Chad Hanna, James Kennington, Jameson Rollins, Max Melching, Nathanael E Sovitzky, Aaron Viets, Madeline Wade, Zach Yarbrough, Yu-Kuang Chu, William Wyatt Phillips, Surabhi Sachdev, Rhiannon Udall
arXiv:2607.03575v2 Announce Type: replace
Abstract: We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.arXiv:2607.03575v2 Announce Type: replace
Abstract: We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.