Skip to main content

SGN

ci ci documentation pypi version

SGN is a lightweight Python library for building streaming data pipelines. Connect sources, transforms, and sinks into clear workflows while SGN handles asynchronous execution under the hood. Zero external dependencies.

Installation

pip install sgn

Example

import functools
from sgn import Pipeline, IterSource, CallableTransform, CollectSink

def scale(frame, factor: float):
    return None if frame.data is None else frame.data * factor

src = IterSource(name="src", source_pad_names=["H1"], iters={"H1": [1, 2, 3]})

transform = CallableTransform.from_callable(
    name="t1",
    sink_pad_names=["H1"],
    callable=functools.partial(scale, factor=10),
    output_pad_name="H1",
)

sink = CollectSink(name="snk", sink_pad_names=["H1"])

p = Pipeline()
p.connect(src, transform)
p.connect(transform, sink)
p.run()

assert list(sink.collects["H1"]) == [10, 20, 30]

Documentation

  • Tutorial — New to SGN? Build your first pipeline step by step.
  • User Guide — Solve specific problems: connecting elements, grouping, parallelism, and more.
  • Background — Understand how SGN works: execution model, core concepts, and design decisions.
  • Reference — Elements and auto-generated API documentation.

Related Libraries

  • sgn-ts: TimeSeries utilities for SGN
  • sgn-ligo: LSC specific utilities for SGN

Instructions for reviewers

  • We will use glreview for this project. Please familiarize yourself with it.

Unreviewed modules

  • A librarian or designated person will generate issues using glreview for all modules
    • The issue will contain an AI review with Claude
    • An AI generated merge request will be made to address issues. The librarian/designee may take liberties to guide that process so that it is high quality
    • The librarian/designee will seek out a human to be assigned to each issue at this point
  • The assigned human will:
    • Verify the pre-review checklist in the git issue (and check boxes if appropriate)
    • Post independent findings as well as notes on the AI review in findings
    • Fill out the overall summary table
    • update the MR (if required) and work with the code authors to approve and merge the MR
    • The issue will be closed when the MR is merged
    • run glreview signoff after the issue is closed

Reviewed modules - updating when code changes

  • FIXME. More to say on this later

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sgn-0.11.1.tar.gz (1.8 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sgn-0.11.1-py3-none-any.whl (125.0 kB view details)

Uploaded Python 3

File details

Details for the file sgn-0.11.1.tar.gz.

File metadata

  • Download URL: sgn-0.11.1.tar.gz
  • Upload date:
  • Size: 1.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Hatch/1.16.5 cpython/3.13.12 HTTPX/0.28.1

File hashes

Hashes for sgn-0.11.1.tar.gz
Algorithm Hash digest
SHA256 0bc682c03082b95a50f1e6d7d835cba0c64e41a2a96b8aecb215c7f1973ded4f
MD5 4321c775e8708996bd99cbcc349d3212
BLAKE2b-256 ed57a66d05c5ea1bd776155a1a88a883c413bb2c1f990c5a34d711e05966ca7e

See more details on using hashes here.

File details

Details for the file sgn-0.11.1-py3-none-any.whl.

File metadata

  • Download URL: sgn-0.11.1-py3-none-any.whl
  • Upload date:
  • Size: 125.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Hatch/1.16.5 cpython/3.13.12 HTTPX/0.28.1

File hashes

Hashes for sgn-0.11.1-py3-none-any.whl
Algorithm Hash digest
SHA256 90018ee891b62bf445f934ef9eac59329e41213574ca84f06516e04eca993228
MD5 9195bdbddd500c088aed78073428eb7e
BLAKE2b-256 7899401b26f739e453ec21225ae0b93c4681383980b36e588ad45bc8d9b144c2

See more details on using hashes here.

Release history Release notifications | RSS feed

0.12.2

2 files

0.12.1

2 files

0.12.0

2 files

This release

0.11.1 This release

2 files

0.11.0

2 files

0.10.0

2 files

0.9.0

2 files

0.8.0

2 files

0.7.0

2 files

0.6.0

2 files

0.5.0

2 files

0.4.0

2 files

0.3.0

2 files

0.2.0

2 files

0.1.0

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page