Data reduction pipeline for KOALA on the AAT.

Related tags

Deep Learningpythonastronomykoalaaat
Overview

KOALA

Build Status Documentation Status Coverage Status License Supported versions PyPI

KOALA, the Kilofibre Optical AAT Lenslet Array, is a wide-field, high efficiency, integral field unit used by the AAOmega spectrograph on the 3.9m AAT (Anglo-Australian Telescope) at Siding Spring Observatory. PyKOALA is the forthcoming data reduction pipeline for creating science-ready 3D data cubes using Raw Stacked Spectra (RSS) images created with 2dfdr.

Helping to develop PyKOALA

  1. Fork koala into your github account
  2. Clone your fork onto your laptop:
    git clone https://github.com/<your_account>/koala
  1. Add this repository as another remote (to get the latest stuff):
    git remote add upstream https://github.com/pykoala/koala
  1. Create a branch to work on the changes:
    git checkout -b <new_branch>
  1. Add and commit changes
  2. Push up your changes
  3. Create a PR, and wait for someone to review it

Reviewing commits

  1. Look through the changes, and provide comments
  2. Once the PR is ready, type bors r+, then bors will handle the merge (DON'T HIT THE MERGE BUTTON).
  3. If the tests fail, help the proposer fix the failures
  4. Use bors r+ again

You can also use bors try to try running the tests, without merging

Owner
GitHub Repository https://pykoala.readthedocs.io/en/latest/index.html
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