4.2. Example 2 - Separate CCDs - Using the “reduce” command line

This is a GMOS-N imaging observation of the galaxy Bootes V obtained in the r-band using the “reduce” command that is operated directly from the unix shell.

Instead of running the default recipe, we will run the recipe to reduce the CCDs separately instead of mosaicing them before the stack. Doing the reduction this way and not mosaicing the CCDs is used when the science objective require very accurate photometry that needs to take into account color-terms and the different color responses of the CCDs.

Just open a terminal and load the DRAGONS conda environment to get started.

4.2.1. 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.

350 s, i-band
For science
For twilights
Twilight Flats

4.2.2. Set up the Calibration Service


Remember to set up the calibration service.

Instructions to configure and use the calibration service are found in Setting up the Calibration Service, specifically the these sections: The Configuration File and Usage from the Command Line.

4.2.3. Check files

For this example, all the raw files we need are in the same directory called ../playdata/example2. 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/example2

directory:  /Users/klabrie/data/tutorials/gmosimg_tutorial/playdata/example2
     N20220613S0138.fits ............... (CAL) (FLAT) (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (TWILIGHT) (UNPREPARED)
     N20220613S0180.fits ............... (BIAS) (CAL) (GEMINI) (GMOS) (NORTH) (RAW) (UNPREPARED)
     N20220627S0115.fits ............... (GEMINI) (GMOS) (IMAGE) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
     N20220627S0222.fits ............... (BIAS) (CAL) (GEMINI) (GMOS) (NORTH) (RAW) (UNPREPARED)
     ................................... (BPM) (CAL) (GEMINI) (GMOS) (NORTH) (OVERSCAN_TRIMMED) (PREPARED) (PROCESSED)
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.

4.2.4. 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.

First, navigate to the playground directory in the unpacked data package:

cd <path>/gmosim_tutorial/playground Lists of Biases

We are going to use two sets of biases, one for the science and one for the twilights. The reason for that is that the twilights and the science were obtained weeks apart and it is always safer to use biases that were obtained close in time with the data we want to use them on. It is also a good example to show you how to specify a date range in the dataselect expression.

Let’s first check the dates for the various observations.

$ showd -d object,ut_date ../playdata/example2/N*.fits

  filename                                                     object      ut_date
  ../playdata/example2/N20220613S0138.fits                   Twilight   2022-06-13
  ../playdata/example2/N20220613S0139.fits                   Twilight   2022-06-13
  ../playdata/example2/N20220613S0140.fits                   Twilight   2022-06-13
  ../playdata/example2/N20220613S0141.fits                   Twilight   2022-06-13
  ../playdata/example2/N20220613S0142.fits                   Twilight   2022-06-13
  ../playdata/example2/N20220613S0180.fits                       Bias   2022-06-13
  ../playdata/example2/N20220613S0181.fits                       Bias   2022-06-13
  ../playdata/example2/N20220613S0182.fits                       Bias   2022-06-13
  ../playdata/example2/N20220613S0183.fits                       Bias   2022-06-13
  ../playdata/example2/N20220613S0184.fits                       Bias   2022-06-13
  ../playdata/example2/N20220627S0115.fits   Disrupting UFD Candidate   2022-06-27
  ../playdata/example2/N20220627S0116.fits   Disrupting UFD Candidate   2022-06-27
  ../playdata/example2/N20220627S0117.fits   Disrupting UFD Candidate   2022-06-27
  ../playdata/example2/N20220627S0118.fits   Disrupting UFD Candidate   2022-06-27
  ../playdata/example2/N20220627S0119.fits   Disrupting UFD Candidate   2022-06-27
  ../playdata/example2/N20220627S0222.fits                       Bias   2022-06-27
  ../playdata/example2/N20220627S0223.fits                       Bias   2022-06-27
  ../playdata/example2/N20220627S0224.fits                       Bias   2022-06-27
  ../playdata/example2/N20220627S0225.fits                       Bias   2022-06-27
  ../playdata/example2/N20220627S0226.fits                       Bias   2022-06-27

The science frames were obtained on 2022-06-27 and the twilights on 2022-06-13. We will create two lists, one of the biases obtained on each of those two days.

The bias files are selected with dataselect:

$ dataselect --tags BIAS ../playdata/example2/*.fits --expr="ut_date=='2022-06-13'" -o biastwi.lis
$ dataselect --tags BIAS ../playdata/example2/*.fits --expr="ut_date=='2022-06-27'" -o biassci.lis List of Flats

Now we build a list for the FLAT files:

$ dataselect --tags FLAT ../playdata/example2/*.fits -o flats.lis

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

$ dataselect --tags FLAT --expr 'filter_name=="r"' ../playdata/example2/*.fits -o flats.lis 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/example2/*.fits -o sci.lis

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/example2/*.fits | showd -d object,exposure_time
filename                                                     object   exposure_time
../playdata/example2/N20220627S0115.fits   Disrupting UFD Candidate           350.0
../playdata/example2/N20220627S0116.fits   Disrupting UFD Candidate           350.0
../playdata/example2/N20220627S0117.fits   Disrupting UFD Candidate           350.0
../playdata/example2/N20220627S0118.fits   Disrupting UFD Candidate           350.0
../playdata/example2/N20220627S0119.fits   Disrupting UFD Candidate           350.0

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

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

$ dataselect --expr '(object=="Disrupting UFD Candidate" and exposure_time==350.)' ../playdata/example2/*.fits -o sci.lis

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.

4.2.5. Bad Pixel Mask

Starting with DRAGONS v3.1, the bad pixel masks (BPMs) are now handled as calibrations. They are downloadable from the archive instead of being packaged with the software. They are automatically associated like any other calibrations. This means that the user now must download the BPMs along with the other calibrations and add the BPMs to the local calibration manager.

See Getting Bad Pixel Masks from the archive in Tips and Tricks to learn about the various ways to get the BPMs from the archive.

To add the static BPM included in the data package to the local calibration database:

caldb add ../playdata/example2/bpm*.fits

4.2.6. Create a Master Bias

We start the data reduction by creating the master biases for the science and the twilight data. Note that the reduction of the biases does not mosaic the biases and it keeps the CCDs separated, always. Because of that, the reduction of the biases for the “Separate CCDs” recipe is exactly the same as for the default recipe.

The biases are created and added to the calibration database using the commands below:

$ reduce @biastwi.lis
$ reduce @biassci.lis

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.

Because the database was given the “store” option in the dragonsrc file, the processed bias will be automatically added to the database at the end of the recipe.

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


The file name of the output processed bias is the file name of the first file in the list with _bias appended as a suffix. This the general naming scheme used by “reduce”.


If you wish to inspect the processed calibrations before adding them to the calibration database, remove the “store” option attached to the database in the dragonsrc configuration file. You will then have to add the calibrations manually following your inspection, eg.

caldb add N20220613S0180_bias.fits


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.

4.2.7. 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. Note that the reduction of the flats does not mosaic the flats and it keeps the CCDs separated, always. Because of that, the reduction of the flats for the “Separate CCDs” recipe is exactly the same as for the default recipe.

$ reduce @flats.lis

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

4.2.8. 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.

4.2.9. Reduce Science Images

Once we have our calibration files processed and added to the database, we can run reduce on our science data. Instead of using the default recipe, we will explicitly call the recipe reduceSeparateCCDs:

$ reduce @sci.lis -r reduceSeparateCCDs

This recipe performs the standardization and corrections needed to convert the raw input science images into a stacked image. To deal with different color terms on the different CCDs, the images are split by CCD midway through the recipe and subsequently reduced separately. The relative WCS is determined from mosaicked versions of the images and then applied to each of the CCDs separately.

The stacked images of each CCD are in separate extension of the file with the _image suffix.

If you reduce -r display the image, you will notice that some sources appear on two CCDs. This is because the each CCD has been stacked individually and because of the dithers some sources ended up moving from to the adjacent CCD.

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.


There is another similar recipe that can be used to reduce the CCDs separately: reduceSeparateCCDsCentral. The difference is that the relative WCS is determined from the central CCD (CCD2) and then applied to CCDs 1 and 3, while in reduceSeparateCCDs the whole image is used to adjust the WCS. The “Central” recipe can be faster than the other but potentially less accurate if you do not have a lot of sources in CCD2.