1. Learning Objectives
This material is built for a 1-hr workshop, planning on 45 minutes for the workshop and 15 minutes for questions and helping participants. This is truncated version of the full workshop.
In this workshop, the participants will gain a very basic understanding of the following topic:
What is DRAGONS, what it isn’t.
What is a typical DRAGONS workflow.
What are the principal components of DRAGONS.
What is a recipe, a recipe library, and a primitive.
How to do coarse inspection of the data.
How to use the calibration manager.
How to customize input parameters and recipes.
How to control
What utility tools are available and how to use them.
This workshop tries to cover a lot in a short period of time. The focus will be on some key, often needed elements. The participants should refer to the DRAGONS documentation for more in-depth information.
This workshop uses the command-line interface to DRAGONS. An API is available but will not be covered in this workshop.
1.1. Setting up
The participants who wish to following along and run the examples and exercises are expected to have already installed DRAGONS on their computer. See the installation instructions:
Follow the Python 3 and DRAGONS instructions, IRAF is not needed. Make sure that you do the steps in the “Configure DRAGONS” section.
This workshop is written for DRAGONS v3.0
Also, the participants will need to download the data package to run the examples and the exercises:
Download it and unpack it somewhere convenient.
cd <somewhere convenient> tar xvf niriimg_tutorial_datapkg-v1.tar bunzip2 niriimg_tutorial/playdata/*.bz2
The datasets are found in the subdirectory
we will work in the subdirectory named
Finally, for this short version, additional files that would normally be
created during the workshop but are not due to the time constraint need to
be download and added to the
cd <somewhere convenient>/niriimg_tutorial/playground tar xvzf demo_outputs.tar.gz
The content of the
niriimg_tutorial/playground directory should look like
N20160102S0271_stack.fits cal_manager.db reduce.log N20160102S0296_stack.fits calibrations stdstar.lis N20160102S0373_flat.fits darks20s.lis target.lis N20160102S0423_dark.fits flats.lis