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"
dragons is the name of the conda environment where DRAGONS should
be installed. If you have an error message, make sure:
- Anaconda or MiniConda 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 gsaoiimg_tutorial_datapkg-v1.tar bunzip2 gsaoiimg_tutorial/playdata/*.bz2
The datasets are found in the subdirectory
gsaoiimg_tutorial/playdata, and we
will work in the subdirectory named
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 table below contains a summary of the dataset downloaded in the previous section. Note that for GSAOI, the dark current is low enough that there is no need to correct for it.
Kshort-band, on target, 60 s
Lamp on, Kshort, for science
Lamp off, Kshort, for science
Kshort, standard star, 30 s