PreprintarXiv licencearXiv
SGN: A python framework for stream-processing pipelines
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.
Related
- PyExoCross 2.0: A Python Framework for LTE and non-LTE Spectra, Cross Sections, and Spectroscopic Post-processing of Atomic and Molecular Line Lists
- IViS: Interferometric Visibility-domain inversion Software. A GPU-accelerated Python framework for joint deconvolution with ASKAP
- rapidsim: A Python interface and cross-platform distribution framework for the RAPID protoplanetary disc simulation code
- simesh: A Python Framework for Quantitative Analysis of Native AMR Data from MPI-AMRVAC
- specwizard: A Public Python Framework for Creating Synthetic Absorption Spectra from Hydrodynamical Simulations
BibTeX
@misc{huang2026python,
title = {{SGN: A python framework for stream-processing pipelines}},
author = {Yun-Jing Huang and Olivia Godwin and Chad Hanna and James Kennington and Jameson Rollins and Max Melching and Nathanael E Sovitzky and Aaron Viets and Madeline Wade and Zach Yarbrough and Yu-Kuang Chu and William Wyatt Phillips and Surabhi Sachdev and Rhiannon Udall},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.03575},
eprint = {2607.03575},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.03575},
}
RIS
TY - UNPB
TI - SGN: A python framework for stream-processing pipelines
AU - Yun-Jing Huang
AU - Olivia Godwin
AU - Chad Hanna
AU - James Kennington
AU - Jameson Rollins
AU - Max Melching
AU - Nathanael E Sovitzky
AU - Aaron Viets
AU - Madeline Wade
AU - Zach Yarbrough
AU - Yu-Kuang Chu
AU - William Wyatt Phillips
AU - Surabhi Sachdev
AU - Rhiannon Udall
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2607.03575
UR - https://arxiv.org/abs/2607.03575
AB - 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.
ER -