Skip to main content

An automatic differential gain catalog tagger

Project description

A catalog source differential gain tagger based on local noise characteristics

This tool segments regions within residual images that are in need of a differential gain. Preferably the tool is run on stokes V residuals, which typically contain relatively little real flux and mostly residual calibration errors. In principle it can also be run on Stokes I residuals if direction independent calibration was successful.

DS9 region maps containing regions and cluster lead information is output by default as shown as example below. Tigger LSM catalogs can simultaniously be processed and reclustered based on identified dE regions.

https://github.com/bennahugo/catdagger/blob/master/misc/catdagger.png

Project details


Download files

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

Source Distribution

catdagger-0.1.1.tar.gz (11.1 kB view hashes)

Uploaded Source

Built Distribution

catdagger-0.1.1-py2-none-any.whl (12.9 kB view hashes)

Uploaded Python 2

Supported by

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