1. Introduction

This tutorial covers the basics of reducing GMOS (Gemini Multi-Object Spectrographs) data using DRAGONS.

The next two sections explain what are the required software and the data set that we use throughout the tutorial. Chapter 2: Data Reduction contains a quick example on how to reduce data using the DRAGONS command line tools. Chapter 3: Reduction with API shows how we can reduce the data using DRAGONS’ packages from within Python.

1.1. Software Requirements

Before you start, make sure you have DRAGONS properly installed and configured on your machine. You can test that by typing the following commands:

$ conda activate dragons
$ python -c "import astrodata"

Where dragons is the name of the conda environment where DRAGONS should be installed. If you have an error message, make sure:

  • Conda is properly installed;
  • A Conda Virtual Environment is properly created and is active;
  • AstroConda (STScI) is properly installed within the Virtual Environment;
  • DRAGONS was successfully installed within the Conda Virtual Environment;

1.2. Downloading the tutorial datasets

All the data needed to run this tutorial are found in the tutorial’s data package:

Download it and unpack it somewhere convenient.

cd <somewhere convenient>
tar xvf gmosimg_tutorial_datapkg-v1.tar
bunzip2 gmosimg_tutorial/playdata/*.bz2

The datasets are found in the subdirectory gmosimg_tutorial/playdata, and we will work in the subdirectory named gmosimg_tutorial/playground.

Note

All the raw data can also be downloaded from the Gemini Observatory Archive. Using the tutorial data package is probably more convenient but if you really want to learn how to search for and retrieve the data yourself, see the step-by-step instructions in the appendix, Downloading from the Gemini Observatory Archive.

1.3. About the dataset

The data used for this tutorial is a dithered sequence on a starry field.

The table below contains a summary of the dataset downloaded in the previous section:

Science
N20170614S0201-205
10 s, i-band
Bias
N20170613S0180-184
N20170615S0534-538
  
Twilight Flats
N20170702S0178-182
40 to 16 s, i-band