chbrandt/swift_deepsky repository overview

⁠Swift-DeepSky

The Swift-DeepSky pipeline provides an easy, fully automated way to identify and measure objects in Swift/XRT images.

This Docker container is a ready-to-use package of DeepSky, in what follows we show how to run the pipeline through Docker. For more information about Swift-DeepSky, like the goals of the project, please visit https://chbrandt.github.io/swift_deepsky⁠. If you are interested in the source code of the pipeline, check the GitHub repository linked in that page.

For those used to Docker containers and not interested in the details, the following session -- TL;DR -- should give what you want. Those willing to get some further information about Docker and DeepSky may skip the next session for the time being; come back to it after completing the text.

⁠TL;DR

Run the latest version with heasoft_caldb:

$ docker run -d --name caldb chbrandt/heasoft_caldb:swift
$ docker run -it --rm --volumes-from caldb -v $PWD/swift_deepsky_output:/work chbrandt/swift_deepsky    [options]

⁠Image types

There are two types of swift-deepsky containers:

• simple: this is a standalone container that will do a good but not scientifically precise photometric measurement of the objects detected.
• latest: this is the latest stable, science-ready version of the pipeline. This version will take more time to run as extra care is taken during the workflow, and a companion container -- with a calibration data base -- will run in parallel. The act of running this version is still straightforward, the extra complexity is on the house ;)

⁠Run, DeepSky, run!

For starters, to use this docker version of the pipeline, Docker engine must be installed

• Docker for Linux users: https://www.docker.com/products/docker-engine⁠
• Docker for MacOS users: https://www.docker.com/products/docker-desktop⁠ Once you have Docker installed you may download Swift-DeepSky:

$ docker pull chbrandt/swift_deepsky
$ docker pull chbrandt/heasoft_caldb:swift

, where here I'm using the simple container to make for a faster first experience.

We don't expect our users to know the swift_deepsky options by heart. And so, if you simply run the container, without arguments, a help message will tell you about all arguments the pipeline currently accepts. Something like the following :

$ docker run -it --rm chbrandt/swift_deepsky

 Usage: pipeline.sh { --ra <degrees> --dec <degrees> | --object <name> }

 Arguments:
  --ra     VALUE    : Right Ascension (in DEGREES)
  --dec    VALUE    : Declination (in DEGREES)
  --object NAME     : name of object to use as center of the field.
                      If given, CDS/Simbad is queried for the position
                      associated with 'NAME'
  --radius VALUE    : Radius (in ARC-MINUTES) around RA,DEC to search for observations. Default is '12' (arcmin)
(...)

The swift_deepsky will give you reliable photometric results with the latest container version. To run this version we should add one step and one parameter to the command-line.

The latest version needs a companion container: chbrandt/heasoft_caldb:swift. We first run the heasoft_caldb container:

$ docker run --name caldb -d chbrandt/heasoft_caldb:swift

, and then the swift_deepsky container with an extra parameter, like follows:

$ docker run -it --rm -v /tmp/sds:/work --volumes-from caldb chbrandt/swift_deepsky    --object GRB151001A

From the previous example, with the simple version, we only added the argument --volumes-from caldb. Where caldb is the name we gave to the heasoft_caldb container previously ran, and volumes-from is a Docker option to allow the exchange of data between the two containers.

/.\