Skip to main content

Inspiral Search Pipeline using SGN Framework

Project description

SGNL (SGN inspiraL)

DOCUMENTATION

SGNL is a CBC search pipeline implemented using SGN. This page is for the application-specific library sgnl, but there is a family of libraries that extend the functionality of SGN, including:

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

Installation

To install SGNL, simply run:

pip install sgnl

More SGNL-specific documentation coming soon.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

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

sgnl-0.1.0-py3-none-any.whl (258.1 kB view details)

Uploaded Python 3

File details

Details for the file sgnl-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: sgnl-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 258.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.11

File hashes

Hashes for sgnl-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 6087df91be80f89c8926e2a64d41b1f120854ad5e1c2ac0467a252dd337eda41
MD5 bc2e829b3a2e7147b40883f8d042b1da
BLAKE2b-256 78297a96c3431cb574febca9521cf9dda9f6b10ae98e4699c1dc7ab1c9a0d1e7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page