2. Data Reduction

This chapter will guide you on reducing GMOS imaging data using command line tools. In this example we reduce a GMOS observation star field. The observation is a simple dither-on-target sequence. Just open a terminal to get started.

While the example cannot possibly cover all situations, it will help you get acquainted with the reduction of GMOS data with DRAGONS. We encourage you to look at the Tips and Tricks and Issues and Limitations chapters to learn more about GMOS data reduction.

DRAGONS installation comes with a set of scripts that are used to reduce astronomical data. The most important script is called “reduce”, which is extensively explained in the Recipe System Users Manual. It is through that command that a DRAGONS reduction is launched.

For this tutorial, we will be also using other Supplemental Tools, like:

2.1. The dataset

If you have not already, download and unpack the tutorial’s data package. Refer to Downloading the tutorial datasets for the links and simple instructions.

The dataset specific to this example is described in:

Here is a copy of the table for quick reference.

10 s, i-band
Twilight Flats
40 to 16 s, i-band

2.2. Set up the Local Calibration Manager

DRAGONS comes with a local calibration manager that uses the same calibration association rules as the Gemini Observatory Archive. This allows reduce to make requests to a local light-weight database for matching processed calibrations when needed to reduce a dataset.

Let’s set up the local calibration manager for this session.

In ~/.geminidr/, create or edit the configuration file rsys.cfg as follow:

standalone = True
database_dir = /path_to_my_data/gmosimg_tutorial/playground

This simply tells the system where to put the calibration database, the database that will keep track of the processed calibrations we are going to send to it.


The tilde (~) in the path above refers to your home directory. Also, mind the dot in .geminidr.

Then initialize the calibration database:

caldb init

That’s it! It is ready to use! You can check the configuration and confirm the setting with caldb config.

You can add processed calibrations with caldb add <filename> (we will later), list the database content with caldb list, and caldb remove <filename> to remove a file only from the database (it will not remove the file on disk). For more the details, check the “caldb” documentation in the Recipe System: User’s Manual.


If you have problems setting up “caldb” or want to bypass it for another reason, you can check the Bypassing automatic calibration association section.

2.3. Check files

For this example, all the raw files we need are in the same directory called ../playdata/. Let us learn a bit about the data we have.

Ensure that you are in the playground directory and that the conda environment that includes DRAGONS has been activated.

Let us call the command tool “typewalk”:

$ typewalk -d ../playdata/

directory:  /data/workspace/gmosimg_tutorial/playdata
 N20170613S0180.fits ............... (AT_ZENITH) (AZEL_TARGET) (BIAS) (CAL) (GEMINI) (GMOS) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
 N20170614S0201.fits ............... (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
 N20170615S0534.fits ............... (AT_ZENITH) (AZEL_TARGET) (BIAS) (CAL) (GEMINI) (GMOS) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
 N20170702S0182.fits ............... (CAL) (FLAT) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (TWILIGHT) (UNPREPARED)
Done DataSpider.typewalk(..)

This command will open every FITS file within the folder passed after the -d flag (recursively) and will print an unsorted table with the file names and the associated tags. For example, calibration files will always have the CAL tag. Flat images will always have the FLAT tag. This means that we can start getting to know a bit more about our data set just by looking the tags. The output above was trimmed for presentation.

2.4. Create File lists

This data set contains science and calibration frames. For some programs, it could have different observed targets and different exposure times depending on how you like to organize your raw data.

The DRAGONS data reduction pipeline does not organize the data for you. You have to do it. DRAGONS provides tools to help you with that.

The first step is to create lists that will be used in the data reduction process. For that, we use “dataselect”. Please, refer to the “dataselect” documentation for details regarding its usage.

2.4.1. List of Biases

The bias files are selected with “dataselect”:

$ dataselect --tags BIAS ../playdata/*.fits -o list_of_bias.txt

2.4.2. List of Flats

Now we can do the same with the FLAT files:

$ dataselect --tags FLAT ../playdata/*.fits -o list_of_flats.txt

If your dataset has flats obtained with more than one filter, you can add the --expr 'filter_name=="i"' expression to get only the flats obtained within the i-band. For example:

$ dataselect --tags FLAT --expr 'filter_name=="i"' ../playdata/*.fits -o list_of_flats.txt

2.4.3. List for science data

The rest is the data with your science target. The simplest way, in this case, of creating a list of science frames is excluding everything that is a calibration:

$ dataselect --xtags CAL ../playdata/*.fits -o list_of_science.txt

This will work for our dataset because we know that a single target was observed with a single filter and with the same exposure time. But what if we don’t know that?

We can check it by passing the “dataselect” output to the “showd” command line using a “pipe” (|):

$ dataselect --expr 'observation_class=="science"' ../playdata/*.fits | showd -d object,exposure_time
filename                             object   exposure_time
../playdata/N20170614S0201.fits   starfield            10.0
../playdata/N20170614S0202.fits   starfield            10.0
../playdata/N20170614S0203.fits   starfield            10.0
../playdata/N20170614S0204.fits   starfield            10.0
../playdata/N20170614S0205.fits   starfield            10.0

The -d flag tells “showd” which “descriptors” will be printed for each input file. As you can see, we have only observed target and only exposure time.

To select on target name and exposure time, specify the criteria in the expr field of “dataselect”:

$ dataselect --expr '(object=="starfield" and exposure_time==10.)' ../playdata/*.fits -o list_of_science.txt

We have our input lists and we have initialized the calibration database, we are ready to reduce the data.

Please make sure that you are still in the playground directory.

2.5. Create a Master Bias

We start the data reduction by creating a master bias for the science data. It can be created and added to the calibration database using the commands below:

$ reduce @list_of_bias.txt
$ caldb add N20170613S0180_bias.fits

The @ character before the name of the input file is the “at-file” syntax. More details can be found in the "at-file" Facility documentation.

To check that the master bias was added to the database, use caldb list.


The master bias will be saved in the same folder where “reduce” was called and inside the ./calibrations/processed_bias folder. The latter location is to cache a copy of the file. This applies to all the processed calibration.

Some people might prefer adding the copy in the calibrations directory as it is safe from a rm *, for example.

$ caldb add ./calibrations/processed_bias/N20170613S0180_bias.fits


reduce” uses the first filename in the input list as basename and adds _bias as a suffix to it. So if your first filename is, for example, N20170613S0180.fits, the output will be N20170613S0180_bias.fits`.

2.6. Create a Master Flat Field

Twilight flats images are used to produce an imaging master flat and the result is added to the calibration database.

$ reduce @list_of_flats.txt
$ caldb add N20170702S0178_flat.fits

Note “reduce” will query the local calibration manager for the master bias and use it in the data reduction.

Once finished you will have the master flat in the current work directory and inside ./calibrations/processed_flat. It will have a _flat suffix.

2.7. Create Master Fringe Frame


The dataset used in this tutorial does not require fringe correction so we skip this step. To find out how to produce a master fringe frame, see Create Master Fringe Frame in the Tips and Tricks chapter.

2.8. Reduce Science Images

Once we have our calibration files processed and added to the database, we can run reduce on our science data:

$ reduce @list_of_science.txt

This command will generate bias and flat corrected files and will stack them. If a fringe frames is needed this command will apply the correction. The stacked image will have the _stack suffix.

The output stack units are in electrons (header keyword BUNIT=electrons). The output stack is stored in a multi-extension FITS (MEF) file. The science signal is in the “SCI” extension, the variance is in the “VAR” extension, and the data quality plane (mask) is in the “DQ” extension.


Depending on your version of Astropy, you might see a lot of Astropy warnings about headers and coordinates system. You can safely ignore them.

Below are one of the raw images and the final stack:


One of the multi-extensions files.


Final stacked image. The light-gray area represents the masked pixels.